From 6fdc00866e98faee7a6cb4247d9c4539c7a1e0b9 Mon Sep 17 00:00:00 2001 From: Elliot Chen Date: Wed, 3 Jun 2026 06:05:03 -0700 Subject: [PATCH] refactor(project): restructure to official everos package layout Migrate legacy EverCore code into the standardized src/everos directory structure aligned with public package conventions. Remove all deprecated unused files from the methods/EverCore directory including demo scripts, evaluation tools, and legacy modules. Update the LICENSE copyright holder from EverMem AI to EverMind AI. --- ACKNOWLEDGMENTS.md | 130 + AGENTS.md | 73 - CHANGELOG.md | 40 + CITATION.md | 65 + CLAUDE.md | 143 +- CODE_OF_CONDUCT.md | 83 + CONTRIBUTING.md | 155 + LICENSE | 2 +- Makefile | 84 + NOTICE | 45 +- QUICKSTART.md | 306 + SECURITY.md | 46 + config.example.toml | 58 + .../EverCore/data => data}/solo_chat_en.json | 0 .../EverCore/data => data}/solo_chat_zh.json | 0 .../EverCore/data => data}/team_chat_en.json | 16 +- .../EverCore/data => data}/team_chat_zh.json | 8 +- docs/api.md | 1049 + docs/architecture.md | 213 + docs/cascade_runbook.md | 271 + docs/cli.md | 116 + docs/datetime.md | 263 + docs/engineering.md | 567 + docs/how-memory-works.md | 294 + docs/index.md | 63 + docs/locomo_benchmark.md | 126 + docs/openapi.json | 1664 + docs/overview.md | 85 + docs/prompt_slots.md | 111 + docs/storage_layout.md | 220 + .../data_format => evaluation}/__init__.py | 0 .../EverCore/.devcontainer/devcontainer.json | 63 - 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tests/unit/test_service/test_memorize_factories.py create mode 100644 uv.lock diff --git a/ACKNOWLEDGMENTS.md b/ACKNOWLEDGMENTS.md new file mode 100644 index 00000000..eaa72779 --- /dev/null +++ b/ACKNOWLEDGMENTS.md @@ -0,0 +1,130 @@ +# Acknowledgments + +[Home](README.md) > [Docs](docs/index.md) > Acknowledgments + +Thanks to the following projects, communities, and individuals for their +inspiration and support. + +--- + +## Inspiration & references + +### [memsearch](https://github.com/zilliztech/memsearch) + +Inspired our markdown-as-source-of-truth design and the SHA-256 + +file-watcher incremental sync model. memsearch is the closest project in +spirit to EverOS. + +### [mem0](https://github.com/mem0ai/mem0) + +Inspired the "one provider per file" flat adapter layout that EverOS uses +for `component/llm/` and `component/embedding/`. + +### [Letta (MemGPT)](https://github.com/letta-ai/letta) + +Inspired the multi-tier memory mapping (Core / Recall / Archival) that maps +naturally onto our MemCell / Episode / Archival pipeline. + +### [MemOS](https://github.com/MemTensor/MemOS) + +Provided a reference for memory taxonomy decisions (textual / parametric / +activation) and helped sharpen our scope choice to focus on textual memory. + +### [Memos](https://github.com/usememos/memos) + +A comprehensive open-source note-taking service whose plain-text-first +design philosophy reinforced our decision to keep markdown files as the +single source of truth. + +### [Nemori](https://github.com/nemori-ai/nemori) + +A self-organising long-term memory substrate for agentic LLM workflows that +provided valuable inspiration for our extraction pipeline. + +--- + +## Open-source libraries + +EverOS is built on top of excellent open-source libraries and frameworks: + +### Core + +- **[Python](https://www.python.org/)** — Programming language (3.12+) +- **[uv](https://github.com/astral-sh/uv)** — Fast Python package manager +- **[FastAPI](https://fastapi.tiangolo.com/)** — Modern async web framework (HTTP API) +- **[Pydantic](https://docs.pydantic.dev/)** — Data validation and settings + +### Storage + +- **[LanceDB](https://lancedb.com/)** — Embedded vector + BM25 + scalar database +- **[SQLite](https://sqlite.org/)** — Embedded relational database (state + audit log) + +### Tooling + +- **[Ruff](https://docs.astral.sh/ruff/)** — Lint + format +- **[import-linter](https://import-linter.readthedocs.io/)** — Layered architecture enforcement +- **[Hatchling](https://hatch.pypa.io/)** — Wheel build backend +- **[pytest](https://pytest.org/)** — Testing framework +- **[pre-commit](https://pre-commit.com/)** — Git hooks framework + +### LLM & embedding providers + +EverOS is provider-agnostic by design. Tested provider integrations include +OpenAI, Anthropic, Ollama, and SBERT. See [`component/llm/`](src/everos/component/llm/) +and [`component/embedding/`](src/everos/component/embedding/) for the +adapter layouts. + +--- + +## Contributors + +Thanks to all the developers who have contributed to this project. + +See the full list of contributors on +[GitHub](https://github.com/EverMind-AI/everos/graphs/contributors). + + + +--- + +## Community + +Thanks to our community for valuable feedback, bug reports, and feature +suggestions: + +- **GitHub Issues & Discussions** — bug reports and feature requests +- **Discord** — [Join our Discord server](https://discord.gg/pfwwskxp) +- **X / Twitter** — [@EverMindAI](https://x.com/EverMindAI) + +--- + +## Supporting organizations + +- **Shanda Group** — for supporting the development of EverOS + +--- + +## Special thanks + +- To everyone who starred the repository +- To those who shared EverOS with others +- To researchers and developers using EverOS in their work + +--- + +## Want to contribute? + +Contributions are welcome! See the [Contributing Guide](CONTRIBUTING.md) +to get started. + +--- + +## See also + +- [Citation](CITATION.md) +- [Changelog](CHANGELOG.md) +- [Contributing Guide](CONTRIBUTING.md) diff --git a/AGENTS.md b/AGENTS.md deleted file mode 100644 index 2707448d..00000000 --- a/AGENTS.md +++ /dev/null @@ -1,73 +0,0 @@ -# AGENTS.md - -This repository is organized around the same reader journey as the top-level -README: - -1. **Use cases** show what persistent memory enables in real products and - workflows. -2. **Quick Start** gets EverCore running locally. -3. **Architecture methods** document the memory systems included in EverOS. -4. **Benchmarks** and **Evaluation** show how to measure and reproduce results. - -## Project Map - -- `methods/EverCore/` - long-term memory operating system for agents. -- `methods/HyperMem/` - hypergraph-based hierarchical memory architecture. -- `benchmarks/EverMemBench/` - memory quality evaluation. -- `benchmarks/EvoAgentBench/` - agent self-evolution evaluation. -- `use-cases/` - apps, demos, and integrations built on top of the memory layer. - -## README Guidance - -- Keep the top-level README flow smooth: overview, use cases, quick start, - architecture methods, benchmarks, evaluation, citations, community. -- Avoid repeating the three-part project framing after the overview. Later - sections should act as catalogues or action paths. -- Use repository-relative links in the README, and verify that active relative - links resolve before finishing. -- Keep commented-out README blocks out unless they are intentionally preserved - for a near-term restoration. - -## Open-Source DX Guidance - -- Keep root uncluttered. Prefer community files in `.github/`: - `.github/CONTRIBUTING.md`, `.github/CODE_OF_CONDUCT.md`, - `.github/SECURITY.md`, issue templates, and the pull request template. -- Treat `CITATION.cff` as optional. Add it only if the project wants GitHub's - "Cite this repository" affordance at the cost of one extra root file. -- Favor clear run paths, small examples, and explicit verification commands. -- Make contribution paths obvious for architecture methods, benchmarks, docs, - and use cases. -- Treat broken links, stale setup commands, missing `.env.example` files, and - unclear issue templates as developer-experience bugs. -- Keep `.github/workflows/docs.yml` lightweight and dependency-free so docs - hygiene is easy to trust. - -## Quick Commands - -```bash -cd methods/EverCore -docker compose up -d # Start infrastructure -uv sync # Install dependencies -uv run python src/run.py # Run application -make test # Run tests -make lint # Run formatting/i18n checks -uv run pyright # Type check, if pyright is installed -``` - -## Key Entry Points - -- `methods/EverCore/src/run.py` - EverCore application entry. -- `methods/EverCore/src/agentic_layer/memory_manager.py` - core memory manager. -- `methods/EverCore/src/infra_layer/adapters/input/api/` - REST API controllers. -- `methods/EverCore/docs/` - EverCore setup, usage, and architecture docs. -- `methods/EverCore/evaluation/` - EverCore evaluation runner and reports. - -## Development Notes - -- All I/O is async; use `await`. -- EverCore is multi-tenant; data must remain tenant-scoped. -- Prompts live in `methods/EverCore/src/memory_layer/prompts/` with EN/ZH - variants. -- Prefer existing repo patterns and component boundaries before adding new - abstractions. diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..4d730955 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,40 @@ +# Changelog + +All notable changes to **EverOS** are documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [Unreleased] + +_Unreleased changes on `dev` will be listed here._ + +## [1.0.0] - 2026-06-03 + +First public release of EverOS — a Markdown-first memory extraction framework +for AI agents. + +### Added + +- **Markdown as source of truth** — all memory persists as plain `.md` files you + can open, edit, grep, and version with Git. +- **Lightweight three-piece storage** — Markdown (truth) + SQLite (state / queue + / audit) + LanceDB (vector + BM25 + scalar index). No external services + required. +- **Hybrid retrieval** — BM25, vector, and scalar filtering in a single LanceDB + query. +- **Cascade index sync** — editing a `.md` file triggers a file watcher → + entry-level diff → sub-second LanceDB sync. +- **Dual-track memory** — user-track (Episodes / Profiles) and agent-track + (Cases / Skills). +- **Multi-source extraction** — conversations, workflows, agent traces, and file + knowledge. +- **CLI + HTTP API** — the `everos` command-line tool and a FastAPI server, + async-first throughout. +- **Pluggable providers** — LLM / embedding / rerank via the OpenAI-compatible + protocol (works with OpenAI, OpenRouter, vLLM, Ollama, …). +- **Decoupled algorithms** — memory extraction algorithms live in the standalone + `everalgo-*` libraries published on PyPI. + +[Unreleased]: https://github.com/EverMind-AI/everos/compare/v1.0.0...HEAD +[1.0.0]: https://github.com/EverMind-AI/everos/releases/tag/v1.0.0 diff --git a/CITATION.md b/CITATION.md new file mode 100644 index 00000000..3db0c909 --- /dev/null +++ b/CITATION.md @@ -0,0 +1,65 @@ +# Citation + +[Home](README.md) > [Docs](docs/index.md) > Citation + +If you use **EverOS** in research or a published project, the suggested +citation forms are below. + +--- + +## Paper + +📄 **Paper coming soon** — a technical paper is in preparation. + +``` +Coming soon +``` + +BibTeX citation information will be added here once the paper is published. + +--- + +## BibTeX + +```bibtex +% Will be added when the paper is published +``` + +--- + +## Software citation + +To cite the software itself: + +``` +EverOS: md-first memory extraction framework for AI agents +Version: 0.1.0 +URL: https://github.com/EverMind-AI/everos +License: Apache 2.0 +``` + +--- + +## Acknowledgments in your work + +If you use EverOS, we appreciate: + +- Citing the paper (when available) +- Starring the GitHub repository +- Mentioning EverOS in your acknowledgments section + +--- + +## Stay updated + +- Watch the [GitHub repository](https://github.com/EverMind-AI/everos) for paper announcements +- Follow [@EverMindAI](https://x.com/EverMindAI) on X / Twitter +- Join [GitHub Discussions](https://github.com/EverMind-AI/everos/discussions) + +--- + +## See also + +- [Overview](docs/overview.md) +- [Acknowledgments](ACKNOWLEDGMENTS.md) +- [Changelog](CHANGELOG.md) diff --git a/CLAUDE.md b/CLAUDE.md index f74cd526..52da3ad5 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -1,77 +1,80 @@ -# CLAUDE.md - - - -This repository is organized around the same reader journey as the top-level -README: - -1. **Use cases** show what persistent memory enables in real products and - workflows. -2. **Quick Start** gets EverCore running locally. -3. **Architecture methods** document the memory systems included in EverOS. -4. **Benchmarks** and **Evaluation** show how to measure and reproduce results. - -## Project Map - -- `methods/EverCore/` - long-term memory operating system for agents. -- `methods/HyperMem/` - hypergraph-based hierarchical memory architecture. -- `benchmarks/EverMemBench/` - memory quality evaluation. -- `benchmarks/EvoAgentBench/` - agent self-evolution evaluation. -- `use-cases/` - apps, demos, and integrations built on top of the memory layer. - -## README Guidance - -- Keep the top-level README flow smooth: overview, use cases, quick start, - architecture methods, benchmarks, evaluation, citations, community. -- Avoid repeating the three-part project framing after the overview. Later - sections should act as catalogues or action paths. -- Use repository-relative links in the README, and verify that active relative - links resolve before finishing. -- Keep commented-out README blocks out unless they are intentionally preserved - for a near-term restoration. - -## Open-Source DX Guidance - -- Keep root uncluttered. Prefer community files in `.github/`: - `.github/CONTRIBUTING.md`, `.github/CODE_OF_CONDUCT.md`, - `.github/SECURITY.md`, issue templates, and the pull request template. -- Treat `CITATION.cff` as optional. Add it only if the project wants GitHub's - "Cite this repository" affordance at the cost of one extra root file. -- Favor clear run paths, small examples, and explicit verification commands. -- Make contribution paths obvious for architecture methods, benchmarks, docs, - and use cases. -- Treat broken links, stale setup commands, missing `.env.example` files, and - unclear issue templates as developer-experience bugs. -- Keep `.github/workflows/docs.yml` lightweight and dependency-free so docs - hygiene is easy to trust. - -## Quick Commands +# EverOS — md-first Memory Extraction Framework + +This is a Python framework for md-first memory extraction (lightweight; single-user or small-team). + +## Quick commands ```bash -cd methods/EverCore -docker compose up -d # Start infrastructure -uv sync # Install dependencies -uv run python src/run.py # Run application -make test # Run tests -make lint # Run formatting/i18n checks -uv run pyright # Type check, if pyright is installed +uv sync # install deps +make lint # ruff (check + format-check) + import-linter +make format # auto-fix formatting +make test # pytest tests/unit +make integration # pytest tests/integration +make ci # full CI: lint + test + integration +``` + +## Architecture + +DDD 5 layers + cross-cutting: + ``` +entrypoints → service → memory → infra + ↓ + component / core / config +``` + +- `entrypoints/` — cli + api (Presentation) +- `service/` — use case orchestration (memorize / retrieve / evolve / manage) +- `memory/` — domain (extract + search + cascade + prompt_slots + models) +- `infra/` — storage adapters (markdown + sqlite + lancedb) +- `component/` — injectable providers (llm / embedding / config / utils) +- `core/` — runtime base (observability / lifespan / context) +- `config/` — configuration data (Settings + default.toml) + +**Dependency rule**: `entrypoints → service → memory → infra`. Single-direction, enforced by `import-linter`. + +Detailed: [docs/architecture.md](docs/architecture.md). + +## Engineering practices + +- **Coding rules** auto-loaded from [.claude/rules/](.claude/rules/) (10 rules; path-scoped for performance) +- **Workflows** as slash commands in [.claude/skills/](.claude/skills/) — `/commit`, `/new-branch`, `/pr` +- **Project-level decisions** in [docs/](docs/) (low-frequency, human-judgment-required) +- **Language policy**: project targets a global audience — docs and code are English; CJK only in test fixtures and locale-suffixed mirrors. See [.claude/rules/language-policy.md](.claude/rules/language-policy.md). + +Engineering infrastructure overview: [docs/engineering.md](docs/engineering.md). + +## Branch strategy + +`master` = released stable (hidden); `dev` = integration; `feat/* fix/*` → dev; `hotfix/*` → master + dev (sync). + +Full GitFlow Lite rationale: [.claude/skills/new-branch/SKILL.md](.claude/skills/new-branch/SKILL.md). + +## Storage three-piece set + +``` +Markdown (truth) + SQLite (state) + LanceDB (vector + BM25 + scalar) +``` + +- Memory root: `~/.everos/{agents,users,knowledge}/` (md files = single source of truth) +- System DB: `~/.everos/.index/sqlite/system.db` (state + audit + queue + metadata) +- Index: `~/.everos/.index/lancedb/` (rebuildable from md) + +Selection rationale: [docs/architecture.md](docs/architecture.md). + +## Source layout -## Key Entry Points +**src layout** (`src/everos/<...>`): standard PyPA project structure — code lives under `src/` so the working tree is not on the import path until installed, preventing accidental imports of in-development modules. -- `methods/EverCore/src/run.py` - EverCore application entry. -- `methods/EverCore/src/agentic_layer/memory_manager.py` - core memory manager. -- `methods/EverCore/src/infra_layer/adapters/input/api/` - REST API controllers. -- `methods/EverCore/docs/` - EverCore setup, usage, and architecture docs. -- `methods/EverCore/evaluation/` - EverCore evaluation runner and reports. +Algorithm assets (prompts, extractors) are being extracted into a separate `evercore` library. -## Development Notes +## Where things go -- All I/O is async; use `await`. -- EverCore is multi-tenant; data must remain tenant-scoped. -- Prompts live in `methods/EverCore/src/memory_layer/prompts/` with EN/ZH - variants. -- Prefer existing repo patterns and component boundaries before adding new - abstractions. +| Want to... | Look at | +|---|---| +| Understand architecture | [docs/architecture.md](docs/architecture.md) | +| Understand storage choice | [docs/architecture.md](docs/architecture.md) (storage section) | +| Engineering tooling overview | [docs/engineering.md](docs/engineering.md) | +| Add a new module | [.claude/rules/init-py-and-reexport.md](.claude/rules/init-py-and-reexport.md) | +| Make a commit | use `/commit` | +| Open a branch / PR | use `/new-branch` / `/pr` | diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..0eb80bae --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,83 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at **evermind@shanda.com**. All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of actions. + +**Consequence**: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 2.1, available at [https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at [https://www.contributor-covenant.org/faq][FAQ]. Translations are available at [https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..b610592f --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,155 @@ +# Contributing to EverOS + +Thanks for your interest in EverOS! This page explains how contribution works +on this project. + +## How EverOS accepts contributions + +EverOS follows an **"open source, not open contribution"** model (similar to +SQLite). The codebase is developed and maintained by the EverMind core team, and +we **do not merge external pull requests**. This keeps copyright provenance +clean and the architecture coherent. + +What we actively welcome from the community: + +| Type | Where | +|---|---| +| 🐛 Bug reports | [Open a bug issue](https://github.com/EverMind-AI/everos/issues/new?template=bug_report.md) | +| 💡 Feature ideas / use cases | [Open a feature issue](https://github.com/EverMind-AI/everos/issues/new?template=feature_request.md) | +| 🔧 Suggested fixes | An issue with a code snippet / patch attached (see below) | +| ❓ Questions & discussion | [GitHub Discussions](https://github.com/EverMind-AI/everos/discussions) / [Discord](https://discord.gg/pfwwskxp) | + +> **Pull requests opened against this repository will be closed** with a pointer +> to this policy. Please open an issue instead — it is the fastest path to +> getting a change in. + +## Reporting a bug + +Use the [bug report template](https://github.com/EverMind-AI/everos/issues/new?template=bug_report.md). Include: + +- Clear reproduction steps +- Expected vs. actual behavior +- Environment (OS, Python version, everos version) +- Relevant logs (**with secrets redacted**) + +## Suggesting a feature + +Use the [feature request template](https://github.com/EverMind-AI/everos/issues/new?template=feature_request.md). Provide: + +- The use case / problem being solved +- Proposed API or behavior +- Backward-compatibility considerations + +## Suggesting a fix (code welcome) + +Found the bug *and* the fix? Great — paste a minimal patch or code snippet +**in the issue**. Treat it as a proposal: the core team will review it, adapt it +to the project's conventions, and land the actual commit (crediting you in the +commit message / changelog). + +> By posting a code suggestion in an issue, you agree it may be incorporated into +> EverOS under the project's [Apache-2.0](LICENSE) license. + +## Reporting security issues + +**Do not** open a public issue for security vulnerabilities. Follow the private +process in [SECURITY.md](SECURITY.md). + +## Code of Conduct + +This project and everyone participating in it is governed by the +[Contributor Covenant Code of Conduct](CODE_OF_CONDUCT.md). By participating, you +are expected to uphold it. Report unacceptable behavior to evermind@shanda.com. + +## Questions + +- [GitHub Discussions](https://github.com/EverMind-AI/everos/discussions) — general Q&A +- [Discord](https://discord.gg/pfwwskxp) — community chat +- Email: evermind@shanda.com + +--- + +## For maintainers (core team) + +The workflow below is for core-team members with write access. **You do not need +any of this to file an issue** — it documents how the team develops EverOS +internally. + +### Prerequisites + +- **Python 3.12+** +- [`uv`](https://docs.astral.sh/uv/) package manager +- Git + +> No Docker / database services required — EverOS is lightweight (Markdown + +> SQLite + LanceDB embedded). + +### Setup + +```bash +git clone +cd everos +make install # deps + pre-commit hooks (one-stop dev setup) +everos init # set EVEROS_LLM__API_KEY (OpenAI-protocol) +make ci # verify +``` + +### Code style + +Conventions are auto-loaded by Claude Code from [.claude/rules/](.claude/rules/). +Highlights: + +- **Python 3.12+**, Ruff formatting (88-char line) +- **Absolute imports** only +- **English only** in code / comments / docstrings (no CJK — see + [.claude/rules/language-policy.md](.claude/rules/language-policy.md)) +- **Type hints** required on signatures; Pydantic v2 for data models +- **`__init__.py`** in every package; subpackages re-export public API via + `from .x import Y as Y` + `__all__` +- **DDD layered**: `entrypoints → service → memory → infra`, single direction, + enforced by `import-linter` + +```bash +make format # ruff fix + format +make lint # ruff check + import-linter +``` + +### Branch strategy (GitFlow Lite) + +| Branch | Role | +|---|---| +| `master` | Released stable | +| `dev` | Default integration branch | +| `feat/-` | New features (from dev → dev) | +| `fix/-` | Bug fixes (from dev → dev) | +| `hotfix/-` | Emergency fixes (from master → master + dev) | + +Full rationale: [.claude/skills/new-branch/SKILL.md](.claude/skills/new-branch/SKILL.md). + +### Commit messages + +**Gitmoji** format: ` : `. Use `/commit` for guided +generation. Full table: [.claude/skills/commit/SKILL.md](.claude/skills/commit/SKILL.md). + +### Testing + +```bash +make test # tests/unit +make integration # tests/integration +make cov # coverage report +``` + +- Add unit tests for new functions (`tests/unit/test_/test__.py`) +- Add golden fixtures for behavior changes (`tests/golden/`) + +Full conventions: [.claude/rules/testing.md](.claude/rules/testing.md). + +### Slash commands (Claude Code) + +- `/new-branch` — create branch with proper naming +- `/commit` — generate Gitmoji commit message +- `/pr` — internal merge request with correct target branch + +--- + +Thank you for helping make EverOS better! 🎉 diff --git a/LICENSE b/LICENSE index dfd73f3d..8e2912af 100644 --- a/LICENSE +++ b/LICENSE @@ -186,7 +186,7 @@ same "printed page" as the copyright notice for easier identification within third-party archives. - Copyright 2025 EverMem AI + Copyright 2025 EverMind AI Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/Makefile b/Makefile new file mode 100644 index 00000000..0199e6bc --- /dev/null +++ b/Makefile @@ -0,0 +1,84 @@ +.PHONY: help install install-deps lint check-cjk check-datetime openapi check-openapi format test integration cov ci clean + +help: + @echo "Targets:" + @echo " install Install deps + pre-commit hooks (full dev setup)" + @echo " install-deps Install deps only (uv sync --frozen, used by CI)" + @echo " lint ruff (check + format-check) + import-linter + datetime discipline + openapi drift" + @echo " check-cjk Scan for CJK outside the language-policy allowlist (advisory)" + @echo " check-datetime Scan for code that bypasses component/utils/datetime (HARD gate, run via lint)" + @echo " openapi Regenerate docs/openapi.json from the FastAPI app" + @echo " check-openapi Verify docs/openapi.json matches app.openapi() (HARD gate, run via lint)" + @echo " format Format src/tests with ruff" + @echo " test pytest tests/unit" + @echo " integration pytest tests/integration" + @echo " cov pytest tests/unit + tests/integration with coverage (fail under 80%)" + @echo " ci full CI: lint + test + integration" + @echo " clean Remove caches" + +# Sync deps from uv.lock; CI calls this directly. --frozen means "lock is the +# source of truth — fail rather than update it". +install-deps: + uv sync --frozen + +# One-stop dev setup: deps + pre-commit hooks (both pre-commit and commit-msg +# stages — gitlint runs on commit-msg). +install: install-deps + uv run pre-commit install + uv run pre-commit install --hook-type commit-msg + +lint: + uv run ruff check src tests + uv run ruff format --check src tests + uv run lint-imports + uv run python scripts/check_datetime_discipline.py + uv run python scripts/dump_openapi.py --check + +# Advisory CJK scan (see .claude/rules/language-policy.md). Deliberately NOT +# wired into `lint` / `ci`: the policy is enforced by review and the rules +# doc, not a hard gate. Run on demand when touching potentially-CJK files. +check-cjk: + uv run python scripts/check_cjk.py + +# Datetime two-zone discipline scanner (see .claude/rules/datetime-handling.md). +# Wired into `lint` (and therefore `ci`) as a HARD gate — any code that +# bypasses ``component/utils/datetime`` (raw ``datetime.now()``, +# ``time.time()``, naked ``datetime(...)`` constructor, etc.) fails the build. +check-datetime: + uv run python scripts/check_datetime_discipline.py + +# OpenAPI schema export — produce docs/openapi.json from the FastAPI app. +# Run this after touching any HTTP route / DTO; commit the result. +openapi: + uv run python scripts/dump_openapi.py + +# OpenAPI drift gate (wired into `lint`). Re-renders the schema in memory +# and diffs it against the committed ``docs/openapi.json``; any drift +# fails the build with a unified diff. Forces the contract doc to track +# the code on every PR that touches the API surface. +check-openapi: + uv run python scripts/dump_openapi.py --check + +format: + uv run ruff check --fix src tests + uv run ruff format src tests + +test: + uv run pytest tests/unit -v + +integration: + uv run pytest tests/integration -v + +# Coverage runs unit + integration so the number matches what CI's `test` and +# `integration` jobs actually exercise. Threshold starts at 80% (unit-only is +# currently 87%, unit+integration 91% — 80% leaves ~10pp headroom for normal +# churn). Bump as the suite stabilises. +cov: + uv run pytest tests/unit tests/integration --cov=src/everos --cov-report=term-missing --cov-branch --cov-fail-under=80 + +ci: lint test integration + +clean: + rm -rf .pytest_cache .ruff_cache .uv-cache + find . -type d -name __pycache__ -not -path './src_old/*' -exec rm -rf {} + + find . -type f -name '*.pyc' -not -path './src_old/*' -delete diff --git a/NOTICE b/NOTICE index d6bbf647..0454a376 100644 --- a/NOTICE +++ b/NOTICE @@ -1,7 +1,7 @@ -// EverOS -Copyright 2026 EverMind. +EverOS +Copyright 2025 EverMind AI -This product includes software developed by EverMind. +This product includes software developed by EverMind AI. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -15,3 +15,42 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. + +================================================================================ +Third-Party Licenses +================================================================================ + +The Apache License (above) governs the EverOS source code as a whole. +The items listed below are bundled in this distribution but governed by +their own licenses; redistribute them only under those terms. + +-------------------------------------------------------------------------------- +LoCoMo conversation fixture (CC BY-NC 4.0 — NOT covered by Apache-2.0) +-------------------------------------------------------------------------------- + +File: tests/fixtures/long_conversation_locomo_caroline_melanie.json +Source: Derived from the LoCoMo dataset (snap-research/locomo). +License: Creative Commons Attribution-NonCommercial 4.0 International + https://creativecommons.org/licenses/by-nc/4.0/ + +This single file is licensed under CC BY-NC 4.0 and is therefore NOT +covered by the Apache-2.0 license that applies to the rest of EverOS. +Downstream use is restricted to non-commercial purposes per the upstream +license. Treat this file as a carve-out from the project-wide Apache +license when redistributing. + +-------------------------------------------------------------------------------- +CairoSVG (LGPL-3.0 — optional dependency, not installed by default) +-------------------------------------------------------------------------------- + +Trigger: ``pip install everos[multimodal]`` +Path: everos[multimodal] -> everalgo-parser[svg] -> cairosvg +License: GNU Lesser General Public License, version 3 + https://www.gnu.org/licenses/lgpl-3.0.html +Project: https://cairosvg.org/ + +CairoSVG is dynamically linked through ``everalgo-parser`` and is only +pulled in when the user explicitly opts into the ``multimodal`` extra. +LGPL-3.0 is compatible with Apache-2.0 redistribution in this dynamic- +linking arrangement; if you statically link or modify CairoSVG itself, +the LGPL-3.0 terms apply to that derivative work. diff --git a/QUICKSTART.md b/QUICKSTART.md new file mode 100644 index 00000000..d250f461 --- /dev/null +++ b/QUICKSTART.md @@ -0,0 +1,306 @@ +# Quickstart + +> Five minutes from zero to "I added a conversation, queried it back, and +> can read it as plain Markdown." + +EverOS runs as a **service** — start the server, then call the HTTP API. +There is no in-process library mode; an `everos` server is always in +front of your agent. + +## Prerequisites + +- **Python 3.12+** +- **An OpenRouter API key** — covers the chat LLM (memory extraction) + *and* the multimodal LLM (parsing image / pdf / audio content items) + with a single key. +- **A DeepInfra API key** — for the embedding + rerank models that + OpenRouter doesn't ship. + +Two keys total. Any OpenAI-compatible endpoint plugs in via the +matching `*__BASE_URL` env var if you'd rather use OpenAI directly, +self-host vLLM, route to Ollama, etc. + +## 1. Install + +```bash +pip install everos +# or: uv pip install everos +``` + +## 2. Configure + +Generate a starter `.env` and drop in your two keys: + +```bash +everos init # writes ./.env (use --xdg for ~/.config/everos/.env) +# Edit .env and fill four API key slots (only two distinct keys needed): +# EVEROS_LLM__API_KEY (OpenRouter — chat LLM) +# EVEROS_MULTIMODAL__API_KEY (OpenRouter — same key works) +# EVEROS_EMBEDDING__API_KEY (DeepInfra) +# EVEROS_RERANK__API_KEY (DeepInfra — same key works) +``` + +`everos init` reads the template bundled inside the wheel and writes it +with `0600` permissions (only your user can read the API keys). + +The shipped template already points LLM + multimodal → OpenRouter +(`openai/gpt-4.1-mini` and `google/gemini-3-flash-preview`) and +embedding + rerank → DeepInfra (`Qwen/Qwen3-Embedding-4B` and +`Qwen/Qwen3-Reranker-4B`). To use a different OpenAI-compatible +endpoint, override the matching `*__BASE_URL` env var. + +> **Where to store `.env`** — `everos server start` searches in order: +> `--env-file ` → `./.env` (cwd) → `${XDG_CONFIG_HOME:-~/.config}/everos/.env` → +> `~/.everos/.env`. The first existing file wins. Use `everos init --xdg` to write +> the XDG location so the same config works from any cwd. + +## 3. Start the server + +```bash +everos server start +``` + +You should see (port and host are configurable): + +``` +starting everos on 127.0.0.1:8000 +INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit) +``` + +- Default bind is `127.0.0.1` (loopback only). To expose the API + elsewhere, put your own auth/gateway in front first + ([see SECURITY.md](SECURITY.md)). +- The cascade index daemon runs **in the same process** as a FastAPI + lifespan coroutine — you don't need a separate worker. +- The server runs in the foreground; **open a second terminal** for the + steps below, and use `Ctrl+C` to stop the server when you're done. + +In the second terminal, verify the server is up: + +```bash +$ curl http://127.0.0.1:8000/health +{"status":"ok"} +``` + +## 4. Add a conversation + +EverOS ingests memory at the **conversation level**, not as standalone +sentences: you POST a batch of `messages` tied to a `session_id`, and +the server accumulates them until the boundary detector trips (you can +also force a flush — see step 5). + +```bash +TS=$(($(date +%s)*1000)) # Unix epoch in **milliseconds** (v1 contract) +curl -X POST http://127.0.0.1:8000/api/v1/memory/add \ + -H 'Content-Type: application/json' \ + -d "{ + \"session_id\": \"demo-001\", + \"app_id\": \"default\", + \"project_id\": \"default\", + \"messages\": [ + {\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $TS, \"content\": \"I love climbing in Yosemite every spring.\"}, + {\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $((TS+10000)), \"content\": \"My favorite coffee shop is Blue Bottle in SOMA.\"}, + {\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $((TS+20000)), \"content\": \"I bike to work most days.\"} + ] + }" +``` + +Response: + +```json +{ + "request_id": "bf86e4e857834eba804841f8bff29106", + "data": { + "message_count": 3, + "status": "accumulated" + } +} +``` + +`status: "accumulated"` means the three messages are in the session +buffer, but the boundary detector hasn't decided to extract a memory +cell yet. For a quick demo we'll force it. + +## 5. Force boundary extraction + +```bash +curl -X POST http://127.0.0.1:8000/api/v1/memory/flush \ + -H 'Content-Type: application/json' \ + -d '{"session_id":"demo-001","app_id":"default","project_id":"default"}' +``` + +Response (this takes a few seconds — one LLM call for extraction): + +```json +{ + "request_id": "ec0e7a00c3bd4b00bb21212a411b7763", + "data": { + "status": "extracted" + } +} +``` + +`status: "extracted"` means at least one memory cell was carved out and +written to disk + indexed. + +> `/flush` is **OSS-only**. The cloud edition decides boundary timing +> server-side and does not expose this endpoint. + +## 6. Search the memory you just added + +```bash +curl -X POST http://127.0.0.1:8000/api/v1/memory/search \ + -H 'Content-Type: application/json' \ + -d '{ + "user_id": "alice", + "app_id": "default", + "project_id": "default", + "query": "Where do I like to climb?", + "top_k": 5 + }' +``` + +Response (trimmed): + +```json +{ + "request_id": "b53a3a94a080472d97692c503c88afdf", + "data": { + "episodes": [ + { + "id": "alice_ep_20260528_00000002", + "user_id": "alice", + "session_id": "demo-001", + "summary": "On May 28, 2026 ... Alice shared that she loves climbing in Yosemite every spring ...", + "score": 0.6284722685813904, + "atomic_facts": [ + { + "id": "alice_af_20260528_00000016", + "content": "Alice said she loves climbing in Yosemite every spring.", + "score": 0.6284722685813904 + } + ] + } + ], + "profiles": [], + "agent_cases": [], + "agent_skills": [] + } +} +``` + +The hybrid retrieval (BM25 + vector + scalar) returns the episode +that contains the climbing fact, with the matching atomic fact nested +under it. Other response arrays (`profiles` / `agent_cases` / +`agent_skills`) are always present for client-side symmetry, populated +only when the requested kind matches. + +## 7. Your memory is just Markdown + +This is what makes EverOS different — your memory persists as plain +Markdown files on disk: + +``` +$ tree ~/.everos -L 5 -a +~/.everos +├── default_app/ ← app_id ("default" → "default_app") +│ └── default_project/ ← project_id ("default" → "default_project") +│ └── users/ +│ └── alice/ ← user_id (mirror dir: agents//) +│ ├── episodes/ +│ │ └── episode-2026-05-28.md +│ ├── .atomic_facts/ ← hidden (dot-prefix) +│ │ └── atomic_fact-2026-05-28.md +│ ├── .foresights/ +│ │ └── foresight-2026-05-28.md +│ └── user.md ← profile +├── .index/ ← derived indexes (rebuildable from md) +│ ├── sqlite/system.db +│ └── lancedb/*.lance/ +└── .tmp/ +``` + +The `default` scope id materialises as `default_app` / `default_project` +on disk (with the `_app` / `_project` suffix) so the default space is +visually distinct from any user-named space. Any other id maps to itself +(e.g. `app_id: "my-app"` → `my-app/`). + +Top-level `.index/` holds SQLite + LanceDB **derived** indexes — wipe it +and the cascade daemon rebuilds everything from the Markdown alone. + +Read the episode we just created: + +``` +$ cat ~/.everos/default_app/default_project/users/alice/episodes/episode-2026-05-28.md +--- +id: episode_log_alice_2026-05-28 +type: episode_daily +file_type: episode_daily +schema_version: 1 +user_id: alice +track: user +date: '2026-05-28' +entry_count: 1 +last_appended_at: '2026-05-28T08:32:24.966944+00:00' +--- + +## ep_20260528_00000002 + +**owner_id**: alice +**session_id**: demo-001 +**timestamp**: 2026-05-28T08:32:13+00:00 +**parent_type**: memcell +**parent_id**: mc_3779c20f1c53 +**sender_ids**: [alice] + +### Subject +Alice's Outdoor Activities and Daily Routine on May 28, 2026 Morning + +### Content +On May 28, 2026 at 8:32 AM UTC, Alice shared that she loves climbing in +Yosemite every spring, highlighting a recurring seasonal outdoor activity. +She also mentioned that her favorite coffee shop is Blue Bottle located in +SOMA, indicating a preferred local spot. Additionally, Alice stated that +she bikes to work most days, revealing a habitual commuting practice. + +``` + +Every memory entry is a plain Markdown file you can: + +- `cat` / `grep` / `vim` directly — no driver, no service to query +- Version with Git (or rsync to backup) +- Open the `~/.everos/default_app/default_project/users/alice/` folder + in Obsidian (the dotfile directories stay hidden by default) + +## Stopping the server + +`Ctrl+C` in the server terminal. Uvicorn catches `SIGINT` and shuts each +lifespan provider down in reverse order (cascade → LanceDB → SQLite → +LLM → metrics) before exiting. + +## Next steps + +- **Integrate into your agent** — wrap the three endpoints (`/add`, + `/flush`, `/search`) in a thin Python client (`httpx.AsyncClient`) and + call them from your agent loop. +- **App + project scope** — set `app_id` / `project_id` to anything + other than `"default"` to partition memory spaces inside one server. +- **Multi-modal messages** — `messages[].content` accepts a list of + typed `ContentItem`s (`text` / `image` / `audio` / `doc` / `pdf` / + `html` / `email`) for non-text input. Install the optional extra + to enable parsing: + `uv pip install 'everos[multimodal]'`. Office documents + (`doc` / `docx` / `xls` / `ppt` / `…`) additionally need + **LibreOffice** on the host (`brew install --cask libreoffice` / + `apt-get install libreoffice`) — without it those uploads return + HTTP 415; PDF / image / audio / HTML still work. +- **Filter DSL and search modes** — `/search` supports a filter DSL + (`AND` / `OR` / scalar predicates) and four methods (`HYBRID` / + `KEYWORD` / `VECTOR` / `AGENTIC`). See the OpenAPI schema served at + `/docs`. +- **Architecture** — see [docs/architecture.md](docs/architecture.md) + for the DDD layering and cascade design, and + [docs/storage_layout.md](docs/storage_layout.md) for the on-disk + layout. +- **Found a bug?** — open an issue (see [CONTRIBUTING.md](CONTRIBUTING.md); + external pull requests are not merged). diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 00000000..cdd5751f --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,46 @@ +# Security Policy + +## Supported Versions + +EverOS is in active alpha development. Security fixes are applied to the latest +release line only. + +| Version | Supported | +|---------|-----------| +| 0.1.x | ✅ | +| < 0.1 | ❌ | + +## Reporting a Vulnerability + +**Please do not report security vulnerabilities through public GitHub issues, +discussions, or pull requests.** + +Instead, email **evermind@shanda.com** with: + +- A description of the vulnerability and its potential impact +- Steps to reproduce, or a proof-of-concept +- The affected version / commit +- Any suggested mitigation, if you have one + +We will acknowledge your report within **5 business days**, keep you informed of +progress, and aim to ship a fix or mitigation before any public disclosure. +Reporters are credited in the release notes unless you prefer to remain +anonymous. + +## Scope & Threat Model + +EverOS runs as a **local-first service** for single users or small teams +(Markdown + SQLite + LanceDB on the local filesystem). Please keep the +following in mind: + +- Exposing the HTTP API (`everos server`) to an untrusted network is **outside + the supported threat model** — it assumes a trusted local caller. The server + binds to `127.0.0.1` by default (env `EVEROS_API__HOST`) so a fresh install + is loopback-only. Only set the bind to `0.0.0.0` (or any routable interface) + after you have placed your own gateway / auth layer in front; + `everos server start` will log a warning when you bind to `0.0.0.0`. +- Secrets (LLM / embedding API keys) live in your local `.env`; protect that + file as you would any credential. EverOS never transmits them anywhere except + the providers you configure. +- Memory content is stored as plaintext `.md` files; apply OS-level file + permissions or disk encryption if your data is sensitive. diff --git a/config.example.toml b/config.example.toml new file mode 100644 index 00000000..1a3d69d2 --- /dev/null +++ b/config.example.toml @@ -0,0 +1,58 @@ +# Example user-level config — copy to ~/.everos/config.toml. +# +# cp config.example.toml ~/.everos/config.toml +# $EDITOR ~/.everos/config.toml # fill in api_key / base_url +# +# Override priority (low → high): +# +# src/everos/config/default.toml +# < ~/.everos/config.toml (THIS FILE) +# < .env in the working directory +# < EVEROS_
__ process envs +# < programmatic init args +# +# The path is configurable: set EVEROS_CONFIG_FILE=/path/to/your.toml to +# point at any location. Missing file = silently skipped (no error). +# +# Comment out anything you do not need — only the fields you set here +# override the shipped defaults; everything else falls back to +# src/everos/config/default.toml. + +# ── LLM ─────────────────────────────────────────────── +# OpenAI-protocol chat-completions endpoint used by the algo extractors. +[llm] +model = "gpt-4o-mini" +api_key = "sk-..." +base_url = "https://api.openai.com/v1" + +# ── Embedding ───────────────────────────────────────── +[embedding] +model = "Qwen/Qwen3-Embedding-4B" +api_key = "..." +base_url = "https://api.deepinfra.com/v1/openai" +# Runtime knobs (defaults shown): +timeout_seconds = 30.0 +max_retries = 3 +batch_size = 10 +max_concurrent = 5 + +# ── Rerank ──────────────────────────────────────────── +[rerank] +model = "Qwen/Qwen3-Reranker-4B" +api_key = "..." +base_url = "https://api.deepinfra.com/v1/inference" +timeout_seconds = 30.0 +max_retries = 3 +batch_size = 10 +max_concurrent = 5 + +# ── Other tunables you may want to override here ────── +# [memory] +# root = "~/.everos" +# timezone = "Asia/Shanghai" +# +# [sqlite] +# busy_timeout_ms = 10000 +# +# [lancedb] +# read_consistency_seconds = 5.0 diff --git a/methods/EverCore/data/solo_chat_en.json b/data/solo_chat_en.json similarity index 100% rename from methods/EverCore/data/solo_chat_en.json rename to data/solo_chat_en.json diff --git a/methods/EverCore/data/solo_chat_zh.json b/data/solo_chat_zh.json similarity index 100% rename from methods/EverCore/data/solo_chat_zh.json rename to data/solo_chat_zh.json diff --git a/methods/EverCore/data/team_chat_en.json b/data/team_chat_en.json similarity index 99% rename from methods/EverCore/data/team_chat_en.json rename to data/team_chat_en.json index 4bd5dcc1..41c3cd40 100644 --- a/methods/EverCore/data/team_chat_en.json +++ b/data/team_chat_en.json @@ -2104,7 +2104,7 @@ "sender": "user_102", "sender_name": "Betty", "type": "text", - "content": "OK。", + "content": "OK.", "refer_list": [] }, { @@ -2266,7 +2266,7 @@ "sender": "user_102", "sender_name": "Betty", "type": "text", - "content": "OK。", + "content": "OK.", "refer_list": [] }, { @@ -2500,7 +2500,7 @@ "sender": "user_102", "sender_name": "Betty", "type": "text", - "content": "OK。", + "content": "OK.", "refer_list": [] }, { @@ -3436,7 +3436,7 @@ "sender": "user_102", "sender_name": "Betty", "type": "text", - "content": "OK。", + "content": "OK.", "refer_list": [] }, { @@ -3796,7 +3796,7 @@ "sender": "user_102", "sender_name": "Betty", "type": "text", - "content": "OK。", + "content": "OK.", "refer_list": [] }, { @@ -4309,7 +4309,7 @@ "sender": "user_102", "sender_name": "Betty", "type": "text", - "content": "I've drafted the report document with the theme \"EverCore Beta Wrap-up & Next Steps Planning.\" I've marked each section with TODO. @Alex @Dylan Please remember to help me fill in the data and system diagrams.", + "content": "I've drafted the report document with the theme \"EverOS Beta Wrap-up & Next Steps Planning.\" I've marked each section with TODO. @Alex @Dylan Please remember to help me fill in the data and system diagrams.", "refer_list": [] }, { @@ -4669,8 +4669,8 @@ "sender": "user_103", "sender_name": "Chen", "type": "text", - "content": "Okay. I'll write the last sentence: \"EverCore, allowing AI to truly have controllable memory.\"", + "content": "Okay. I'll write the last sentence: \"EverOS, allowing AI to truly have controllable memory.\"", "refer_list": [] } ] -} \ No newline at end of file +} diff --git a/methods/EverCore/data/team_chat_zh.json b/data/team_chat_zh.json similarity index 99% rename from methods/EverCore/data/team_chat_zh.json rename to data/team_chat_zh.json index ccd645a9..2bb1f35c 100644 --- a/methods/EverCore/data/team_chat_zh.json +++ b/data/team_chat_zh.json @@ -4309,7 +4309,7 @@ "sender": "user_102", "sender_name": "Betty", "type": "text", - "content": "汇报文档我起了个草稿，主题定为“EverCore Beta 收尾 & 下一步规划”。各自部分我标了 TODO。@Alex @Dylan 记得帮我填下数据和系统图。", + "content": "汇报文档我起了个草稿，主题定为“EverOS Beta 收尾 & 下一步规划”。各自部分我标了 TODO。@Alex @Dylan 记得帮我填下数据和系统图。", "refer_list": [] }, { @@ -4669,7 +4669,7 @@ "sender": "user_103", "sender_name": "Chen", "type": "text", - "content": "好。记得结尾那句我来写：EverCore，让 AI 真正拥有可控的记忆。", + "content": "好。记得结尾那句我来写：EverOS，让 AI 真正拥有可控的记忆。", "refer_list": [] }, { @@ -4678,8 +4678,8 @@ "sender": "user_103", "sender_name": "Chen", "type": "text", - "content": "各位辛苦了！EverCore v1.0 项目到此圆满结束。感谢大家这几个月的辛勤付出和协作，我们成功交付了一个优秀的产品。现在项目正式结项，今天的讨论到此结束。再次感谢大家！", + "content": "各位辛苦了！EverOS v1.0 项目到此圆满结束。感谢大家这几个月的辛勤付出和协作，我们成功交付了一个优秀的产品。现在项目正式结项，今天的讨论到此结束。再次感谢大家！", "refer_list": [] } ] -} \ No newline at end of file +} diff --git a/docs/api.md b/docs/api.md new file mode 100644 index 00000000..6f6eb884 --- /dev/null +++ b/docs/api.md @@ -0,0 +1,1049 @@ +# EverOS HTTP API (v1) + +Human-readable reference for the EverOS HTTP API. Schema names, types +and validation constraints mirror the OpenAPI spec served at +`GET /openapi.json` (when the server runs with `ENV=DEV`; see +[OpenAPI spec source](#openapi-spec-source)). This document adds the +business semantics the raw spec does not carry. + +## Table of contents + +- [Overview](#overview) + - [Base URL and versioning](#base-url-and-versioning) + - [Content type](#content-type) + - [Authentication](#authentication) + - [Response envelope](#response-envelope) + - [Eventual consistency](#eventual-consistency) + - [Conventions](#conventions) + - [ScopeId: `app_id` and `project_id`](#scopeid-app_id-and-project_id) +- [Errors](#errors) +- [Common types](#common-types) + - [MessageItem](#messageitem) + - [ContentItem](#contentitem) + - [ToolCall](#toolcall) + - [ToolFunction](#toolfunction) + - [FilterNode (filter DSL)](#filternode-filter-dsl) + - [SearchMethod](#searchmethod) + - [GetMemoryType](#getmemorytype) +- [Endpoints](#endpoints) + - [POST /api/v1/memory/add](#post-apiv1memoryadd) + - [POST /api/v1/memory/flush](#post-apiv1memoryflush) + - [POST /api/v1/memory/search](#post-apiv1memorysearch) + - [POST /api/v1/memory/get](#post-apiv1memoryget) +- [OpenAPI spec source](#openapi-spec-source) + +## Overview + +### Base URL and versioning + +| Setting | Default | Override | +|---|---|---| +| Host | `127.0.0.1` (loopback only) | `EVEROS_API__HOST` env var or `--host` flag | +| Port | `8000` | `EVEROS_API__PORT` env var or `--port` flag | +| Version prefix | `/api/v1` | — | + +All business endpoints documented here live under `/api/v1/memory/`. +The operational endpoints `GET /health` and `GET /metrics` exist but +are intentionally outside this reference — they are runtime probes for +deployment, not part of the application contract. + +### Content type + +All `POST` endpoints require `Content-Type: application/json`. Request +and response bodies are UTF-8 JSON. + +### Authentication + +EverOS ships **no built-in authentication**. The server binds to +`127.0.0.1` by default; place your own gateway or auth layer in front +before exposing the API on any other interface. See +[../SECURITY.md](../SECURITY.md) for the threat model. + +### Response envelope + +Successful (`200 OK`) responses always wrap the payload in: + +```json +{ + "request_id": "<32-char hex>", + "data": { /* endpoint-specific payload */ } +} +``` + +`request_id` is generated server-side (32 lowercase hex chars) and is +echoed by structured logs / tracing / metrics, so a single request can +be correlated end-to-end. Error responses carry their own envelope — +`request_id` at the top level alongside a nested `error` object (not a +bare FastAPI `detail`); see [Errors](#errors). + +### Eventual consistency + +`/add` and `/flush` write the markdown file (the source of truth) +**synchronously** — when the call returns with `status: "extracted"`, +the new entry exists on disk. The LanceDB vector / BM25 / scalar index +is rebuilt by the in-process **cascade coroutine asynchronously**. + +That means `/search` and `/get` may not see a record immediately after +the `/flush` that produced it. Typical sync latency is sub-second, but +under load it can reach ~10–15 seconds. If you need read-your-write +semantics, retry with backoff. The markdown file is durable +regardless; index lag never loses data. + +### Conventions + +#### Timestamps + +EverOS runs a **two-zone discipline**: every stored byte is UTC; every +rendered value carries the configured **display timezone**. The +display timezone is set by `EVEROS_MEMORY__TIMEZONE` (env var) or +`[memory] timezone` (TOML); default `"UTC"`. + +| Direction | Format | Notes | +|---|---|---| +| **Request** — `messages[].timestamp` (`/add`) | Integer, **Unix epoch milliseconds (ms)** | Server auto-detects `>= 10^12` as ms for backward compat, but the contract is ms | +| **Request** — `filters.timestamp.{gte,lt,…}` (`/search` / `/get`) | Integer, **Unix epoch ms** *or* ISO-8601 string | Same auto-detect; pick whichever you have | +| **Response** — every `timestamp` field | ISO-8601 string with **explicit timezone offset** (`+HH:MM` or `Z`) | Always rendered in the configured display tz; never naive | + +With the default `EVEROS_MEMORY__TIMEZONE=UTC` an episode produced +at 11:30 UTC renders as `"2026-05-28T11:30:36Z"` (Pydantic +canonicalises `timezone.utc` to `Z`). Switch to +`EVEROS_MEMORY__TIMEZONE=Asia/Shanghai` and the same UTC instant +renders as `"2026-05-28T19:30:36+08:00"` — bytes on disk are +unchanged. + +**Fallback for naive input.** If you submit a `filters.timestamp` ISO +string without an offset (e.g. `"2026-05-28T11:30:00"`), the server +treats it as already display-tz-local before comparing against +storage. This is the same rule users see when reading rendered output: +"if you didn't say a zone, we assume your zone." + +**For internal architecture.** The storage / display split lives in +`everos.component.utils.datetime` +(`get_utc_now` / `ensure_utc` / `UtcDatetime` for storage; +`get_now_with_timezone` / `to_display_tz` for display). See +[datetime.md](datetime.md) for the design rationale. + +#### Other conventions + +- **Server-generated IDs** follow `___`, + e.g. `alice_ep_20260528_00000001` for an episode, `alice_af_...` + for an atomic fact. See + [storage_layout.md §4](storage_layout.md) for the encoding. +- **All endpoints are POST** for `/api/v1/memory/*` even when the + semantics look like a read (`/search`, `/get`) — the request bodies + are too rich (filters, methods, paging) to encode in a query string. + +### ScopeId: `app_id` and `project_id` + +`app_id` and `project_id` partition memory at the disk layer. Every +write lands under +`~/.everos///users//...` (or `.../agents/...` +for the agent track). The default scope materialises on disk as +`default_app` / `default_project` (the `_app` / `_project` suffix is +added only for the literal id `"default"` so the default space stays +visually distinct from user-named scopes). + +A `/search` or `/get` query never crosses scopes — different +`(app_id, project_id)` pairs are isolated. + +Both fields share the same validation: + +| Setting | Value | +|---|---| +| Type | `string` | +| Default | `"default"` | +| Length | 1–128 chars | +| Charset | `^[a-zA-Z0-9_.-]+$` | +| Rejected literals | `"."` and `".."` (path-traversal guard) | + +## Errors + +Every non-2xx response uses a uniform error envelope — `request_id` at +the top level (mirroring the success envelope) alongside a nested +`error` object. It is **not** a bare FastAPI `detail`: + +```json +{ + "request_id": "<32-char hex>", + "error": { + "code": "HTTP_ERROR", + "message": "Value error, exactly one of user_id / agent_id must be provided", + "timestamp": "2026-06-01T12:24:46+00:00", + "path": "/api/v1/memory/search" + } +} +``` + +| HTTP | `error.code` | `error.message` | When | +|---|---|---|---| +| `415 Unsupported Media Type` | `HTTP_ERROR` | the parse-failure reason | `/add` only — a `ContentItem` could not be parsed (unsupported modality for the configured multimodal LLM, or a payload that cannot be fetched / dispatched) | +| `422 Unprocessable Entity` | `HTTP_ERROR` | the **first** validation error (see below) | Request-body validation failure. Also covers `/search` / `/get` filter-DSL compile errors — the compile reason rides in `message` | +| `500 Internal Server Error` | `SYSTEM_ERROR` | `"Internal server error"` (fixed; internal details are logged, never leaked) | Unhandled exception caught by the global handler | + +### error object + +| Field | Type | Description | +|---|---|---| +| `code` | `string` | `"HTTP_ERROR"` for 4xx (validation / business / `HTTPException`); `"SYSTEM_ERROR"` for 5xx | +| `message` | `string` | Human-readable reason. For `422`, **only the first** validation error is surfaced, formatted `": "` with the leading `body` segment stripped (e.g. `"Field required: messages"`); a model-level validator with no field location surfaces just `""` (e.g. the XOR example above) | +| `timestamp` | `string` | ISO-8601 with timezone offset (display tz) | +| `path` | `string` | Request path, e.g. `/api/v1/memory/add` | + +> Unlike FastAPI's default, the full per-field validation array is **not** +> returned — only the first error's message. A client that needs the +> offending field can read the `` suffix in `message`. + +## Common types + +### MessageItem + +One turn in a `/add` batch. Shape mirrors the OpenAI Chat Completions +message structure plus a stable `sender_id` for indexing. + +| Field | Type | Required | Default | Constraints | +|---|---|---|---|---| +| `sender_id` | `string` | yes | — | `minLength=1` | +| `sender_name` | `string \| null` | no | `null` | — | +| `role` | `"user" \| "assistant" \| "tool"` | yes | — | — | +| `timestamp` | `integer` | yes | — | `> 0` — **Unix epoch milliseconds (ms)** per v1 contract | +| `content` | `string \| array` | yes | — | — | +| `tool_calls` | `array \| null` | no | `null` | — | +| `tool_call_id` | `string \| null` | no | `null` | — | + +**`sender_id`** — Stable identifier of the entity producing this turn. +For `role: "user"`, this is the `user_id` the server will index the +extracted memory under: markdown lands at +`users//episodes/...`, and `/search` / `/get` queries with +`user_id: ""` reach it. For `role: "assistant"` or +`"tool"`, the field is informational (the LLM sees it during +extraction but it is not used as an indexing key). + +**`sender_name`** — Optional human-readable display name. Lets the LLM +use a real name during extraction without changing the indexing key. +For example, set `sender_id: "u_42"` (stable internal id) and +`sender_name: "Alice"` (what the LLM sees). + +**`role`** — Speaker role; one of: +- `"user"` — content originates from a human (or human-acting client). +- `"assistant"` — content from the AI assistant. +- `"tool"` — output of a tool call; pair with `tool_call_id`. + +**`timestamp`** — Wall-clock anchor in **Unix epoch milliseconds (ms)** +per the v1 API contract. Used by extraction to anchor the resulting +episode and by the daily-log writer to bucket the entry into the right +file (`episode-.md` etc.). + +> Implementation note: the algo layer auto-detects seconds vs ms (values +> `>= 10^12` are treated as ms, smaller as seconds) for backward compat, +> but **clients should send ms** to honour the contract. + +**`content`** — The message body. +- A bare **string** is shorthand for a single text content item. +- An **array of `ContentItem`** is for mixed-modality input (text + + image / pdf / audio / ...); non-text items are parsed by the + multimodal LLM configured via `EVEROS_MULTIMODAL__*` env vars. See + [ContentItem](#contentitem). + +**`tool_calls`** — When `role: "assistant"`, the tool calls the +assistant emitted in this turn (OpenAI Chat Completions shape). + +**`tool_call_id`** — When `role: "tool"`, the `id` of the call this +message is the response to. + +### ContentItem + +Mixed-modality message-body element. Carry the payload in exactly one +of `text` / `uri` / `base64`; the others must be `null`. For +`type: "text"` use `text`; for every **non-text** type use `uri` +(`http(s)://`) or `base64` (with `ext`). Non-text items are routed +through the multimodal parser, which needs a fetchable or decodable +payload — a non-text item carrying only `text` returns `415`. + +| Field | Type | Required | Default | Notes | +|---|---|---|---|---| +| `type` | `"text" \| "image" \| "audio" \| "doc" \| "pdf" \| "html" \| "email"` | yes | — | — | +| `text` | `string \| null` | no | `null` | Required when `type: "text"` | +| `uri` | `string \| null` | no | `null` | `http(s)://` (fetched server-side) or `file://` (read from the server's local fs, guardrailed) pointer | +| `base64` | `string \| null` | no | `null` | Inline payload, plain base64 (no `data:` prefix) | +| `ext` | `string \| null` | no | `null` | File-extension hint when `uri` lacks one | +| `name` | `string \| null` | no | `null` | Display filename, used in logs | +| `extras` | `object \| null` | no | `null` | Provider-specific metadata, opaque to EverOS | + +**`type`** — The content kind. Each non-text type is dispatched to the +multimodal LLM. If the multimodal endpoint cannot handle the supplied +payload, `/add` returns `415 Unsupported Media Type`. + +**`text`** — The literal text payload; valid **only** for +`type: "text"`. A non-text type (including `"html"`) is always routed +to the parser and must carry `uri` or `base64`; passing only `text` on +a non-text item returns `415`. To inline HTML as plain text, send it +as `type: "text"`. + +**`uri`** — `http(s)://` or `file://` pointer to the asset. An +`http(s)` uri is fetched by the server and dispatched by the response +Content-Type (use it for assets hosted elsewhere — S3 / OSS presigned +URL, http server). A `file://` uri is read from the **server's** local +filesystem (the path must be reachable by the server process), subject +to these guardrails: + +- the resolved path (symlinks followed) must be an existing regular file; +- its size must be ≤ `EVEROS_MULTIMODAL__FILE_URI_MAX_BYTES` (default 50 MiB); +- when `EVEROS_MULTIMODAL__FILE_URI_ALLOW_DIRS` is set, the path must lie + within one of the listed roots (unset = any readable file, the + local-first default). + +A guardrail violation surfaces as `415`. Mutually exclusive with `base64`. + +**`base64`** — Inline binary payload, base64-encoded (no `data:` +prefix). Mutually exclusive with `uri`. + +> **Size caution.** base64 inflates the payload ~4/3× on the wire, and +> the encoded blob is held **verbatim in the server's staging buffer +> (SQLite) from `/add` until the session is flushed** — a multi-MB PDF +> becomes multi-MB of SQLite text for the buffer's lifetime. It is *not* +> persisted past extraction (the memory cell and episode store only the +> parsed text, never the raw bytes), but the transient footprint is +> real, and a large inline blob also slows request parsing. **Prefer +> `uri` (`http(s)://`) for large assets**: the buffer then holds only +> the URL plus the parsed text, and the bytes are fetched transiently at +> parse time rather than stored. Reserve `base64` for small assets or +> when no reachable URL exists. + +**`ext`** — File-extension hint (`"pdf"`, `"png"`, `"html"`, ...). For +`base64` payloads this drives modality dispatch and is effectively +**required** (without it the server falls back to `mime`, then `415`s +if neither resolves). For `uri` payloads it is optional — the fetched +Content-Type usually suffices. + +**`name`** — Filename / human label for logging and traceability. +Does not affect parsing. + +**`extras`** — Free-form bag of provider-specific metadata (e.g. +caption, page hints). Opaque to EverOS; passed through to the +multimodal LLM context. + +### ToolCall + +OpenAI-shaped tool invocation attached to an assistant turn. + +| Field | Type | Required | Default | Notes | +|---|---|---|---|---| +| `id` | `string` | yes | — | Stable id; echoed by the paired `role: "tool"` turn via `tool_call_id` | +| `type` | `string` | no | `"function"` | Only `"function"` is meaningful today | +| `function` | [ToolFunction](#toolfunction) | yes | — | The function being called | + +### ToolFunction + +| Field | Type | Required | Notes | +|---|---|---|---| +| `name` | `string` | yes | Function name as registered with the agent | +| `arguments` | `string` | yes | **JSON-encoded string** (OpenAI convention — not an object) | + +### FilterNode (filter DSL) + +A recursive boolean tree of predicates. Used by `/search.filters` and +`/get.filters`. The Pydantic envelope only checks the recursive +combinator shape; field-level validity (which scalar fields are +filterable, which operators apply, value coercion) runs when the +node is compiled to a LanceDB `where` clause server-side. Compile +errors surface as `422` with the offending field / operator in +`error.message`. + +#### Shape + +A node is a JSON object whose keys are one of: + +| Key | Value | Notes | +|---|---|---| +| `AND` | `array` | All child nodes must match. Omit if not needed | +| `OR` | `array` | At least one child node must match. Omit if not needed | +| ** | scalar or operator map | Predicate on that field — see [Allowed fields](#filter-allowed-fields) and [Operators](#filter-operators) | + +`AND`, `OR`, and scalar predicates **mix freely at the same level**; +they are implicitly joined with `AND`. A node with only scalar keys is +fine — `AND` / `OR` arrays are not required. + +Example — `session_id == "demo-001"` AND (`sender_id` contains `"alice"` OR `"bob"`): + +```json +{ + "session_id": "demo-001", + "OR": [ + { "sender_id": "alice" }, + { "sender_id": "bob" } + ] +} +``` + + + +#### Allowed fields + +These are the only field names that may appear as a predicate key. +Anything else returns `422`. + +| Field | Column kind | Value type | Notes | +|---|---|---|---| +| `session_id` | string | `string` | Session the row was extracted from | +| `parent_type` | string | `string` | Parent kind, e.g. `"memcell"` | +| `parent_id` | string | `string` | Parent id | +| `timestamp` | timestamp | `integer` (Unix epoch **ms** per v1 contract) or `string` (ISO-8601) | Implementation auto-detects `>= 10^12` as ms / smaller as sec for backward compat, but the contract is ms | +| `sender_id` | array of strings | `string` | Matches via `array_has(sender_ids, )` — checks whether the row's `sender_ids` list contains the given id | + +The following names are **reserved** and rejected inside `filters`. +They must be set at the top of the request (`/search` or `/get`), +not inside the DSL: + +``` +owner_id owner_type app_id project_id +``` + + + +#### Operators + +A predicate value may be a **scalar shorthand** (equality) or an +**operator map** (multiple operators in one dict are AND-joined): + +| Operator | SQL | Applies to | +|---|---|---| +| `eq` | `=` | string, timestamp; on `sender_id` becomes `array_has(...)` | +| `ne` | `!=` | string, timestamp | +| `gt` | `>` | timestamp | +| `gte` | `>=` | timestamp | +| `lt` | `<` | timestamp | +| `lte` | `<=` | timestamp | +| `in` | `IN (...)` | string (list of values); on `sender_id` becomes `OR` of `array_has` | + +Examples: + +```json +// Equality shorthand +{ "session_id": "demo-001" } + +// Operator map (operators inside one dict are AND-joined) +{ "timestamp": { "gte": 1748390400000, "lt": 1748400000000 } } + +// IN list +{ "session_id": { "in": ["demo-001", "demo-002"] } } + +// Array-membership on sender_ids +{ "sender_id": "alice" } // array_has(sender_ids, 'alice') +{ "sender_id": { "in": ["alice", "bob"] } } // OR of two array_has +``` + +### SearchMethod + +| Value | Behaviour | +|---|---| +| `"keyword"` | BM25 only — pure lexical match, no embedding cost | +| `"vector"` | Dense vector ANN only — semantic recall, no lexical | +| `"hybrid"` *(default)* | Reciprocal-rank fuse of BM25 + vector + optional scalar filter in a single LanceDB query | +| `"agentic"` | Iterative cluster-path retrieval driven by a cross-encoder rerank loop; higher quality at higher latency / cost | + +`"hybrid"` is the default because it balances recall and precision +with one LanceDB roundtrip. `"agentic"` calls the LLM in a loop and +should be reserved for offline or background workflows. + +### GetMemoryType + +| Value | Track | Returned in `data.` | +|---|---|---| +| `"episode"` | user | `data.episodes` — [GetEpisodeItem](#getepisodeitem) | +| `"profile"` | user | `data.profiles` — [GetProfileItem](#getprofileitem) | +| `"agent_case"` | agent | `data.agent_cases` — [GetAgentCaseItem](#getagentcaseitem) | +| `"agent_skill"` | agent | `data.agent_skills` — [GetAgentSkillItem](#getagentskillitem) | + +`memory_type` must match the requested owner kind: `"episode"` / +`"profile"` require `user_id`; `"agent_case"` / `"agent_skill"` +require `agent_id`. The mismatching combinations are rejected with +`422`. + +## Endpoints + +### POST /api/v1/memory/add + +Append a batch of messages to a session buffer. The server +accumulates messages until the boundary detector decides the session +is complete (or you force it with `/flush`); on boundary the LLM +extracts a memory cell and the writers persist markdown + index rows. + +The response distinguishes "buffered" from "extracted this call" via +the `status` field — see [Response body](#response-body) below. + +#### Request body + +| Field | Type | Required | Default | Constraints | +|---|---|---|---|---| +| `session_id` | `string` | yes | — | length 1–128 | +| `app_id` | `string` *(ScopeId)* | no | `"default"` | see [ScopeId](#scopeid-app_id-and-project_id) | +| `project_id` | `string` *(ScopeId)* | no | `"default"` | see [ScopeId](#scopeid-app_id-and-project_id) | +| `messages` | `array` | yes | — | 1–500 items | + +**`session_id`** — Identifies the conversation buffer on the server. +Messages POSTed with the same `(session_id, app_id, project_id)` +accumulate into one extraction batch. Different `session_id`s never +share a buffer, even within the same scope. + +**`app_id` / `project_id`** — Scope identifiers; see +[ScopeId](#scopeid-app_id-and-project_id). + +**`messages`** — Ordered list of [MessageItem](#messageitem) to +append. Order matters (it is preserved when the buffer is later +extracted). The 1–500 cap is a per-request safety bound, not a +session lifetime cap — you can call `/add` many times for the same +`session_id`. + +#### Response body + +`200 OK` returns a SuccessEnvelope wrapping: + +| Field | Type | Notes | +|---|---|---| +| `message_count` | `integer` | Number of messages accepted in this call | +| `status` | `"accumulated" \| "extracted"` | What the server did with the batch | + +**`message_count`** — Always equal to `len(messages)` on a `200` — +there is no partial accept. The field is present mainly for log +correlation. + +**`status`** — Two-state outcome: +- `"accumulated"` — Messages were added to the buffer; the boundary + detector did **not** trigger this call. The buffer remains open. +- `"extracted"` — During this call the boundary detector tripped; the + LLM ran extraction and the writers persisted at least one memory + cell. The buffer is empty after the call returns. + +#### cURL example + +```bash +TS=$(date +%s) +curl -X POST http://127.0.0.1:8000/api/v1/memory/add \ + -H 'Content-Type: application/json' \ + -d "{ + \"session_id\": \"demo-002\", + \"app_id\": \"default\", + \"project_id\": \"default\", + \"messages\": [ + {\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $TS, \"content\": \"I love climbing in Yosemite every spring.\"}, + {\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $((TS+10)), \"content\": \"My favorite coffee shop is Blue Bottle in SOMA.\"}, + {\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $((TS+20)), \"content\": \"I bike to work most days.\"} + ] + }" +``` + +Response (real capture): + +```json +{ + "request_id": "ae78d3f689c941eea135893e702fd171", + "data": { + "message_count": 3, + "status": "accumulated" + } +} +``` + +### POST /api/v1/memory/flush + +Force the boundary detector to decide **now** for the given session +buffer. The LLM runs extraction (one call) regardless of whether the +heuristic would have tripped on its own. Useful at the end of a chat +or agent run to make sure pending context becomes durable memory. + +#### Request body + +| Field | Type | Required | Default | Constraints | +|---|---|---|---|---| +| `session_id` | `string` | yes | — | length 1–128 | +| `app_id` | `string` *(ScopeId)* | no | `"default"` | — | +| `project_id` | `string` *(ScopeId)* | no | `"default"` | — | + +**`session_id`** — Identifies which buffer to flush. Must match the +`session_id` of prior `/add` calls in the same `(app_id, project_id)` +scope. + +**`app_id` / `project_id`** — Scope identifiers; see +[ScopeId](#scopeid-app_id-and-project_id). + +#### Response body + +| Field | Type | Notes | +|---|---|---| +| `status` | `"extracted" \| "no_extraction"` | What happened | + +**`status`**: +- `"extracted"` — The buffer had at least one message; extraction ran + and produced at least one memory cell. +- `"no_extraction"` — The buffer was empty (no prior `/add` calls for + this `(session_id, app_id, project_id)`, or it was already flushed). + +`/flush` is synchronous with respect to markdown persistence: by the +time the response returns, the new entry is on disk. LanceDB index +sync is still asynchronous — see +[Eventual consistency](#eventual-consistency). + +#### cURL example + +```bash +curl -X POST http://127.0.0.1:8000/api/v1/memory/flush \ + -H 'Content-Type: application/json' \ + -d '{"session_id":"demo-002","app_id":"default","project_id":"default"}' +``` + +Response (real capture; this call took several seconds because of the +extraction LLM call): + +```json +{ + "request_id": "e65bcf4f56c042e39cdf50866810672c", + "data": { + "status": "extracted" + } +} +``` + +### POST /api/v1/memory/search + +Hybrid retrieval over the memory store. Combines BM25, dense vector +ANN, optional scalar filtering, optional cross-encoder rerank, and +optional final LLM rerank. Returns ranked items grouped by kind. + +#### Request body + +| Field | Type | Required | Default | Constraints | +|---|---|---|---|---| +| `user_id` | `string \| null` | XOR with `agent_id` | `null` | `minLength=1` if set | +| `agent_id` | `string \| null` | XOR with `user_id` | `null` | `minLength=1` if set | +| `app_id` | `string` *(ScopeId)* | no | `"default"` | — | +| `project_id` | `string` *(ScopeId)* | no | `"default"` | — | +| `query` | `string` | yes | — | `minLength=1` | +| `method` | [SearchMethod](#searchmethod) | no | `"hybrid"` | — | +| `top_k` | `integer` | no | `-1` | `-1` or `1..100` | +| `radius` | `number \| null` | no | `null` | `0.0 ≤ x ≤ 1.0` if set | +| `include_profile` | `boolean` | no | `false` | — | +| `enable_llm_rerank` | `boolean` | no | `false` | — | +| `filters` | [FilterNode](#filternode-filter-dsl) `\| null` | no | `null` | — | + +**`user_id` / `agent_id`** — **Exactly one** must be set. Determines +which track is searched: `user_id` → user-memory (episodes / +profiles); `agent_id` → agent-memory (cases / skills). + +**`app_id` / `project_id`** — Scope identifiers; results never cross +scopes. + +**`query`** — The retrieval query. The same string is fed to BM25 +tokenization and to the embedding model, then the two recall lists +are fused. + +**`method`** — Retrieval method; see [SearchMethod](#searchmethod). +Default `"hybrid"` is the recommended starting point. + +**`top_k`** — Maximum number of items to return per kind. Two modes: +- `-1` (default) — "Use server defaults." Recall falls back to a + fixed internal cap, and a server-side `radius` default kicks in if + the caller did not pass one (see `radius` below). Use for "give me + whatever you find worth returning." +- `1..100` — Explicit cap. Recall is sized as `top_k × multiplier` + and **no** default `radius` is applied unless you set it yourself. + +Values of `0` or outside `-1` / `1..100` are rejected with `422`. + +**`radius`** — Optional **cosine-similarity threshold** in `[0.0, 1.0]`. +Candidates whose score is below this value are dropped — use it to cut +a long tail of weak matches. Three-level fallback for the effective +radius: + +1. Caller-supplied `radius` (including the literal `0.0`) always wins. +2. With `top_k=-1` and no caller-supplied `radius`, a server-side + default radius kicks in. +3. With `top_k>0` and no caller-supplied `radius`, no threshold is + applied (`null`). + +**`include_profile`** — When `user_id` is set, also fetch the user's +profile and include it in `data.profiles`. The profile is not +ranked; `score` is `null`. Ignored when `agent_id` is set. + +**`enable_llm_rerank`** — Opt-in LLM rerank pass for +`method: "hybrid"`. Applies to `agent_case` and `agent_skill` fusion +only; the episode hierarchy path has built-in fact eviction and +ignores this flag. Adds one LLM call per request. Ignored by +`keyword` / `vector` (no fusion to rerank) and `agentic` (uses its +own cross-encoder loop). + +**`filters`** — Optional filter-DSL node; see +[FilterNode](#filternode-filter-dsl). Applied **before** ranking, so +it does not perturb the ranker. + +#### Response body + +`200 OK` returns a SuccessEnvelope wrapping `SearchData`. All five +arrays are always present so client code can iterate without +branching on owner type; arrays that do not apply to the requested +owner kind stay as `[]`. + +| Field | Type | Notes | +|---|---|---| +| `episodes` | `array` | Populated when `user_id` is set | +| `profiles` | `array` | Populated when `user_id` is set **and** `include_profile=true` | +| `agent_cases` | `array` | Populated when `agent_id` is set | +| `agent_skills` | `array` | Populated when `agent_id` is set | +| `unprocessed_messages` | `array` | Populated **only** when `filters.session_id` is a top-level eq scalar; otherwise stays `[]`. Independent of `user_id` / `agent_id` (buffer rows have no owner attribution — boundary detection runs before owner inference) | + +#### SearchEpisodeItem + +User-track conversation episode hit. `score` is the fused retrieval +score; `atomic_facts` lists single-sentence facts that matched the +query within this episode (already nested, no separate call needed). + +| Field | Type | Notes | +|---|---|---| +| `id` | `string` | `_ep__` | +| `user_id` | `string \| null` | Owner of this episode | +| `app_id` | `string` | Scope where the episode lives | +| `project_id` | `string` | Scope where the episode lives | +| `session_id` | `string` | The `session_id` whose messages produced this episode | +| `timestamp` | `string` | ISO-8601 with timezone offset; default `UTC` renders as `Z`, non-UTC defaults render as `±HH:MM` — see [Conventions](#conventions) | +| `sender_ids` | `array` | Distinct `sender_id`s in the underlying messages | +| `summary` | `string` | Short summary (~200 chars), suitable for list-view rendering | +| `subject` | `string` | One-line subject / title | +| `episode` | `string` | Full extracted narrative | +| `type` | `"Conversation"` | Reserved; today only conversation-derived episodes ship | +| `score` | `number` | Fused retrieval score for this episode | +| `atomic_facts` | `array` | Sub-facts extracted from the same episode that matched the query | + +#### SearchAtomicFactItem + +A single-sentence fact pulled out of an episode during extraction. + +| Field | Type | Notes | +|---|---|---| +| `id` | `string` | `_af__` | +| `content` | `string` | The fact as a single sentence | +| `score` | `number` | Same score scale as the parent episode | + +#### SearchProfileItem + +User profile. Only populated when `include_profile=true` and +`user_id` is set. + +| Field | Type | Notes | +|---|---|---| +| `id` | `string` | Profile id | +| `user_id` | `string \| null` | Owner | +| `app_id` | `string` | Scope | +| `project_id` | `string` | Scope | +| `profile_data` | `object` | Free-form structured profile fields produced by extraction (schema is profile-specific) | +| `score` | `number \| null` | `null` for direct fetches (no query-aware profile ranking yet) | + +#### SearchAgentCaseItem + +Agent-track case hit. Returned only when the request uses `agent_id`. + +| Field | Type | Notes | +|---|---|---| +| `id` | `string` | `_ac__` | +| `agent_id` | `string` | Owner | +| `app_id` | `string` | Scope | +| `project_id` | `string` | Scope | +| `session_id` | `string` | The agent run this case came from | +| `task_intent` | `string` | What the agent was trying to do (one-sentence framing) | +| `approach` | `string` | How the agent went about it | +| `quality_score` | `number` | Quality assessed at extraction time, in `[0, 1]` | +| `key_insight` | `string \| null` | One-line takeaway; `null` if extraction did not produce one | +| `timestamp` | `string` | ISO-8601 UTC of when the run happened | +| `score` | `number` | Fused retrieval score for this case | + +#### SearchAgentSkillItem + +Agent-track skill hit — a named procedural memory produced by +clustering related cases. + +| Field | Type | Notes | +|---|---|---| +| `id` | `string` | `_sk__` | +| `agent_id` | `string` | Owner | +| `app_id` | `string` | Scope | +| `project_id` | `string` | Scope | +| `name` | `string` | Skill name (a stable, human-meaningful slug) | +| `description` | `string` | One-line description | +| `content` | `string` | Full skill body (procedure / playbook text) | +| `confidence` | `number` | Extraction confidence in `[0, 1]` | +| `maturity_score` | `number` | Maturity assessed at clustering time, in `[0, 1]` | +| `source_case_ids` | `array` | `agent_case` ids that contributed to this skill | +| `score` | `number` | Fused retrieval score for this skill | + +#### UnprocessedMessageDTO + +A raw message still sitting in the boundary-detection buffer — sent to +`/add` but not yet carved into an episode / case. Returned **only** +when the request's `filters` contains `session_id` as a top-level eq +scalar (`{"session_id": ""}`); compound shapes (`AND` / `OR` +combinators, operator maps such as `{"eq": ...}` / `{"in": ...}`) do +not trigger the lookup because there is no defensible buffer-scope +mapping for them. Buffer rows have no `user_id` / `agent_id` +attribution, so `session_id` is the only meaningful query dimension. + +| Field | Type | Notes | +|---|---|---| +| `id` | `string` | Original `message_id` from `/add` | +| `app_id` | `string` | Scope where the buffered message lives | +| `project_id` | `string` | Scope where the buffered message lives | +| `session_id` | `string` | Matches the requested `filters.session_id` | +| `sender_id` | `string` | Original sender id from `/add` | +| `sender_name` | `string \| null` | Original sender name; `null` if not provided | +| `role` | `"user" \| "assistant" \| "tool"` | Original role | +| `content` | `string \| array` | `string` for the single-text shorthand, `array` of opaque content items for the original multimodal payload (mirrors [MessageItem.content](#addmessage)) | +| `timestamp` | `string` | ISO-8601 with timezone offset — see [Conventions](#conventions) | +| `tool_calls` | `array \| null` | Original tool_calls payload if any | +| `tool_call_id` | `string \| null` | Original tool_call_id if any | + +#### cURL example + +```bash +curl -X POST http://127.0.0.1:8000/api/v1/memory/search \ + -H 'Content-Type: application/json' \ + -d '{ + "user_id": "alice", + "app_id": "default", + "project_id": "default", + "query": "Where do I like to climb?", + "top_k": 5 + }' +``` + +Response (real capture): + +```json +{ + "request_id": "3a8ad3dcd2484fe4a05ccbd89d438704", + "data": { + "episodes": [ + { + "id": "alice_ep_20260528_00000001", + "user_id": "alice", + "app_id": "default", + "project_id": "default", + "session_id": "demo-002", + "timestamp": "2026-05-28T11:30:36Z", + "sender_ids": ["alice"], + "summary": "On May 28, 2026 at 11:30 AM UTC, Alice shared that she loves climbing in Yosemite every spring...", + "subject": "Alice's Outdoor Activities and Daily Routine on May 28, 2026: Climbing, Coffee, and Biking", + "episode": "On May 28, 2026 at 11:30 AM UTC, Alice shared that she loves climbing in Yosemite every spring, highlighting a recurring seasonal activity. She also mentioned that her favorite coffee shop is Blue Bottle located in SOMA, indicating a preferred local spot. Additionally, Alice stated that she bikes to work most days, describing a habitual mode of transportation.", + "type": "Conversation", + "score": 0.6298899054527283, + "atomic_facts": [ + { + "id": "alice_af_20260528_00000001", + "content": "Alice said she loves climbing in Yosemite every spring.", + "score": 0.6298899054527283 + } + ] + } + ], + "profiles": [], + "agent_cases": [], + "agent_skills": [], + "unprocessed_messages": [] + } +} +``` + +### POST /api/v1/memory/get + +Paginated listing of memory records of a given kind for a single +owner. No ranking — ordering is `sort_by` × `sort_order` only. Used +for UI browsing, exports, or filtered scans. + +#### Request body + +| Field | Type | Required | Default | Constraints | +|---|---|---|---|---| +| `user_id` | `string \| null` | XOR with `agent_id` | `null` | `minLength=1` if set | +| `agent_id` | `string \| null` | XOR with `user_id` | `null` | `minLength=1` if set | +| `app_id` | `string` *(ScopeId)* | no | `"default"` | — | +| `project_id` | `string` *(ScopeId)* | no | `"default"` | — | +| `memory_type` | [GetMemoryType](#getmemorytype) | yes | — | — | +| `page` | `integer` | no | `1` | `≥ 1` | +| `page_size` | `integer` | no | `20` | `1..100` | +| `sort_by` | `"timestamp" \| "updated_at"` | no | `"timestamp"` | — | +| `sort_order` | `"asc" \| "desc"` | no | `"desc"` | — | +| `filters` | [FilterNode](#filternode-filter-dsl) `\| null` | no | `null` | — | + +**`user_id` / `agent_id`** — **Exactly one** must be set, and it must +match the track implied by `memory_type` (`"episode"` / `"profile"` +require `user_id`; `"agent_case"` / `"agent_skill"` require +`agent_id`). + +**`app_id` / `project_id`** — Scope identifiers. + +**`memory_type`** — Which item kind to list; see +[GetMemoryType](#getmemorytype). The route populates exactly one of +the four arrays in `data` based on this value. + +**`page`** — 1-indexed page number. Together with `page_size` +determines the window. The response's `total_count` reports how many +items match the request before paging. + +**`page_size`** — Items per page, in `1..100`. Default 20. + +**`sort_by`** — Column to sort by. Note: for `memory_type` values +where `"timestamp"` does not apply (e.g. `"profile"` has no +timestamp, `"agent_skill"` is a named entity), the server silently +falls back to `"updated_at"`. + +**`sort_order`** — `"desc"` (newest first, default) or `"asc"`. + +**`filters`** — Optional [FilterNode](#filternode-filter-dsl) for +predicate-based filtering before pagination. + +#### Response body + +`200 OK` returns a SuccessEnvelope wrapping `GetData`. The four +arrays are always present so client code can iterate without +branching on `memory_type`; exactly one is populated. + +| Field | Type | Notes | +|---|---|---| +| `episodes` | `array` | Populated when `memory_type="episode"` | +| `profiles` | `array` | Populated when `memory_type="profile"` | +| `agent_cases` | `array` | Populated when `memory_type="agent_case"` | +| `agent_skills` | `array` | Populated when `memory_type="agent_skill"` | +| `total_count` | `integer` | Total matching records **before** paging | +| `count` | `integer` | Number of items in **this page** (`len(items)`) | + +#### GetEpisodeItem + +Same shape as [SearchEpisodeItem](#searchepisodeitem) **minus** +`score` and `atomic_facts` (listing is unranked and does not nest +sub-facts). + +| Field | Type | Notes | +|---|---|---| +| `id` | `string` | `_ep__` | +| `user_id` | `string \| null` | Owner | +| `app_id` | `string` | Scope | +| `project_id` | `string` | Scope | +| `session_id` | `string` | Originating session | +| `timestamp` | `string` | ISO-8601 with timezone offset, identical to `/search` (both go through the same `from_iso_format` path) — see [Conventions](#conventions) | +| `sender_ids` | `array` | Distinct `sender_id`s in the underlying messages | +| `summary` | `string` | Short summary | +| `subject` | `string` | One-line subject | +| `episode` | `string` | Full extracted narrative | +| `type` | `"Conversation"` | — | + +#### GetProfileItem + +| Field | Type | Notes | +|---|---|---| +| `id` | `string` | Profile id | +| `user_id` | `string \| null` | Owner | +| `app_id` | `string` | Scope | +| `project_id` | `string` | Scope | +| `profile_data` | `object` | Free-form structured profile fields | + +#### GetAgentCaseItem + +Same shape as [SearchAgentCaseItem](#searchagentcaseitem) **minus** +`score`. + +| Field | Type | Notes | +|---|---|---| +| `id` | `string` | `_ac__` | +| `agent_id` | `string` | Owner | +| `app_id` | `string` | Scope | +| `project_id` | `string` | Scope | +| `session_id` | `string` | Originating run | +| `task_intent` | `string` | What the agent was trying to do | +| `approach` | `string` | How it went about it | +| `quality_score` | `number` | Quality assessed at extraction time | +| `key_insight` | `string \| null` | One-line takeaway, if any | +| `timestamp` | `string` | ISO-8601 UTC | + +#### GetAgentSkillItem + +Same shape as [SearchAgentSkillItem](#searchagentskillitem) **minus** +`score`. + +| Field | Type | Notes | +|---|---|---| +| `id` | `string` | `_sk__` | +| `agent_id` | `string` | Owner | +| `app_id` | `string` | Scope | +| `project_id` | `string` | Scope | +| `name` | `string` | Skill name | +| `description` | `string` | One-line description | +| `content` | `string` | Full skill body | +| `confidence` | `number` | Extraction confidence in `[0, 1]` | +| `maturity_score` | `number` | Maturity assessed at clustering, in `[0, 1]` | +| `source_case_ids` | `array` | `agent_case` ids that contributed to this skill | + +#### cURL example + +```bash +curl -X POST http://127.0.0.1:8000/api/v1/memory/get \ + -H 'Content-Type: application/json' \ + -d '{ + "user_id": "alice", + "app_id": "default", + "project_id": "default", + "memory_type": "episode" + }' +``` + +Response (real capture): + +```json +{ + "request_id": "b8ba1255440147a684885f2084f6a25d", + "data": { + "episodes": [ + { + "id": "alice_ep_20260528_00000001", + "user_id": "alice", + "app_id": "default", + "project_id": "default", + "session_id": "demo-002", + "timestamp": "2026-05-28T11:30:36Z", + "sender_ids": ["alice"], + "summary": "On May 28, 2026 at 11:30 AM UTC, Alice shared that she loves climbing in Yosemite every spring...", + "subject": "Alice's Outdoor Activities and Daily Routine on May 28, 2026: Climbing, Coffee, and Biking", + "episode": "On May 28, 2026 at 11:30 AM UTC, Alice shared that she loves climbing in Yosemite every spring, highlighting a recurring seasonal activity. She also mentioned that her favorite coffee shop is Blue Bottle located in SOMA, indicating a preferred local spot. Additionally, Alice stated that she bikes to work most days, describing a habitual mode of transportation.", + "type": "Conversation" + } + ], + "profiles": [], + "agent_cases": [], + "agent_skills": [], + "total_count": 1, + "count": 1 + } +} +``` + +## OpenAPI spec source + +This document mirrors the OpenAPI 3.x spec that FastAPI auto-generates +from the Pydantic DTOs. **The spec is not exposed by default** — the +`/openapi.json`, `/docs` (Swagger UI), and `/redoc` endpoints only +mount when the server starts with `ENV=DEV` set. + +```bash +ENV=DEV everos server start +# In another terminal: +curl http://127.0.0.1:8000/openapi.json | python -m json.tool +# Or interactively in a browser: +open http://127.0.0.1:8000/docs +``` + +This document and the spec are kept in sync by hand. Where they +disagree, the **spec is the structural ground truth** (field names, +required flags, value constraints). This document carries the +**business semantics** the spec cannot — when to use which method, +the eventual-consistency caveat, why a field is shaped the way it is. + +--- + +For higher-level context (cascade design, DDD layering, on-disk +layout), see [architecture.md](architecture.md) and +[storage_layout.md](storage_layout.md). diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 00000000..cb735228 --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,213 @@ +# Architecture + +> Companion: [.claude/rules/architecture.md](../.claude/rules/architecture.md) (auto-loaded coding rules) + +## DDD layered architecture + +``` +┌──────────────────────────────────────────────────────┐ +│ entrypoints/ (Presentation) │ +│ cli + api │ +├──────────────────────────────────────────────────────┤ +│ service/ (Application — Use Case orchestration) │ +│ memorize / retrieve / evolve / manage │ +├──────────────────────────────────────────────────────┤ +│ memory/ (Domain — Business core) │ +│ models + extract + search + cascade + prompt_slots │ +├──────────────────────────────────────────────────────┤ +│ infra/persistence (Storage adapters; infra/ may host other adapter types) │ +│ markdown + sqlite + lancedb │ +└──────────────────────────────────────────────────────┘ + +Cross-cutting (used by all layers, depends on none): + component/ ← Injectable providers (LLM / Embedding / config / utils) + core/ ← Runtime base (observability / lifespan / context) + config/ ← Configuration data (Settings schema + default.toml) +``` + +## Dependency direction (single-direction, enforced) + +``` +entrypoints → service → memory → infra +``` + +| from → to | Allowed? | +|---|---| +| entrypoints → service | ✅ | +| entrypoints → memory / infra | ❌ (must go through service) | +| service → memory | ✅ | +| memory → infra | ✅ | +| memory → service | ❌ | +| infra → memory | ❌ | +| infra cross-subpackage (e.g. lancedb → markdown within persistence/) | ❌ (use service to orchestrate) | +| any → component / core / config | ✅ (cross-cutting) | + +Enforced via `import-linter` in CI: + +```toml +[tool.importlinter] +root_packages = ["everos"] + +[[tool.importlinter.contracts]] +name = "Layered architecture" +type = "layers" +layers = [ + "everos.entrypoints", + "everos.service", + "everos.memory", + "everos.infra", +] +``` + +## Storage three-piece set + +``` +┌────────────────────────────────────────────────────────────────┐ +│ md-first storage stack │ +└────────────────────────────────────────────────────────────────┘ + + ┌──────────────┐ ┌──────────────┐ ┌─────────────────┐ + │ Markdown │ │ SQLite │ │ LanceDB │ + │ (truth) │ │ (state) │ │ (index) │ + ├──────────────┤ ├──────────────┤ ├─────────────────┤ + │ entries + │ │ change queue │ │ vector ANN │ + │ frontmatter │ │ + state/LSN │ │ BM25 (Tantivy) │ + │ Git friendly │ │ buffer / │ │ scalar filter │ + │ Obsidian OK │ │ audit │ │ multi-modal │ + └──────────────┘ └──────────────┘ └─────────────────┘ + │ │ │ + ▼ ▼ ▼ + memory-root/ .index/sqlite/ .index/lancedb/ + (truth source) (system data) (rebuildable) +``` + +## Write path + +``` +External message + │ + ▼ +1. service.memorize (entrypoint of write path) + │ + ▼ +2. memory.extract.pipeline (calls evercore) + │ + ▼ +3. infra.persistence.markdown.write (atomic: tmp + fsync + rename) + │ ✅ md write success → return immediately + │ + ┌───┴────┐ + │ │ + ▼ ▼ +4a. SQLite 4b. memory.cascade (async daemon) + audit watches md → diff entries → LanceDB sync +``` + +**Key guarantee**: md write is strongly consistent (fsync). LanceDB is eventually consistent. LanceDB unavailability does not block response — changes buffer in the SQLite `md_change_state` queue, replayed on recovery. + +## Read path + +``` +User query + │ + ▼ +1. service.retrieve + │ + ▼ +2. memory.search.hybrid single LanceDB query = + BM25 + vector ANN + scalar filter + │ + ▼ +3. (optional) read md original markdown for context + │ + ▼ + Return +``` + +## Key components + +### `memory/extract/` + +``` +extract/ +├── ingest/ Standardized message intake + multi-modal parser dispatch +├── pipeline/ Main extraction pipeline (calls evercore + dual-track split + writes store) +└── evolution/ Async memory evolution (event/counter/cron triggers) +``` + +### `memory/cascade/` + +Daemon that watches markdown changes and syncs to LanceDB: + +- inotify / FSEvents file watcher (cross-platform via `watchdog`) +- 500ms debounce +- Entry-level diff (added / changed / removed) +- LanceDB single-transaction update (text + vector columns atomic) +- LSN-based crash recovery via the SQLite `md_change_state` queue + +### `memory/prompt_slots/` + +Three-layer prompt overlay: + +``` +config/prompt_slots/*.yaml (Layer 1: defaults, ships with package) + ↓ +~/.everos/prompt_slots/*.yaml (Layer 2: app-level override) + ↓ +runtime override (Layer 3: per-call override) +``` + +evercore receives PromptSlot as parameter — no hardcoded prompts in algorithm code. + +### `core/observability/` + +Three-piece observability: + +- `metrics/` — Prometheus counter / gauge / histogram + global registry +- `logging/` — structlog with context processor (trace_id propagation) +- `tracing/` — OpenTelemetry tracer + span helpers + +## Markdown layout + +``` +~/.everos/ # memory root (default; EVEROS_MEMORY__ROOT) +└── // # scope ("default" → default_app/default_project) + ├── users// + │ ├── user.md # profile (single-file rewrite) + │ ├── episodes/episode-.md # daily-log append + │ ├── .atomic_facts/atomic_fact-.md # hidden, framework-derived + │ └── .foresights/foresight-.md # hidden, framework-derived + ├── agents// + │ ├── .cases/agent_case-.md # hidden, framework-derived + │ └── skills/skill_/SKILL.md # named-dir + └── knowledge/ # global shared knowledge +``` + +System-managed entries (`.index/`, `.tmp/`) and `ome.toml` live directly +under the memory root. +Full tree + frontmatter chassis: [storage_layout.md](storage_layout.md) and +[how-memory-works.md](how-memory-works.md). Frontmatter has 4-tier field +protection (L1 read-only / L2 system / L3 business / L4 user). + +## evercore boundary + +`evercore` is a separate Python library (future PyPI package) holding **only memory extraction algorithms**: + +- `evercore.parser` — multi-modal parsing +- `evercore.user_memory` — ConvMemCell / Episode / Foresight / AtomicFact / Profile extractors +- `evercore.agent_memory` — AgentMemCell / Case / Skill extractors +- `evercore.knowledge` — file-to-knowledge + +evercore is: + +- **Stateless** — pure functions, no class hierarchy +- **No I/O** — does not touch md files / LanceDB / SQLite +- **No prompts inline** — receives `PromptSlot` parameter, project supplies defaults + +This boundary lets evercore be reused across product forms (this open-source build, EverOS Cloud, OpenClaw plugins, etc.). + +## Further reading + +- [docs/overview.md](overview.md) — vision and scope +- [docs/engineering.md](engineering.md) — engineering tooling and CI / CD +- [.claude/rules/architecture.md](../.claude/rules/architecture.md) — short-form rules for Claude Code diff --git a/docs/cascade_runbook.md b/docs/cascade_runbook.md new file mode 100644 index 00000000..d5f628fa --- /dev/null +++ b/docs/cascade_runbook.md @@ -0,0 +1,271 @@ +# Cascade Runbook + +The cascade daemon keeps LanceDB in sync with the markdown files under +the memory root. Service / entry points only ever write markdown; the +daemon is the **sole** writer of the LanceDB index. This runbook covers +the recurring operational questions. + +## What runs where + +When `everos server start` boots, the FastAPI lifespan wires four +providers in order: + +1. **Metrics** — Prometheus collector. +2. **SQLite** — system DB + schema (`SQLModel.metadata.create_all`). +3. **LanceDB** — async connection + schema verification + FTS indexes. +4. **Cascade** — watcher + scanner + worker, all in-process tasks. + +The cascade subsystem itself is three independent loops: + +| Loop | Source signal | Effect | +|---|---|---| +| Watcher | `watchdog` filesystem events (sync thread) | `md_change_state.upsert` per registered kind | +| Scanner | Periodic walk (`scan_interval_seconds`, default 30 s) | Same — catches changes the watcher missed | +| Worker | `claim_pending_batch` polling (default 1 s when idle) | Handler dispatch → LanceDB upsert / delete | + +Every loop talks to the same `md_change_state` sqlite table. The +worker's claim mode (`pending → processing → done/failed`) keeps +concurrent workers honest. + +## Health: `everos cascade status` + +``` +queue: + pending: 3 + done: 1247 + failed (retryable=TRUE): 1 (eligible for `cascade fix --apply`) + failed (retryable=FALSE): 1 (fix md and re-save to recover) +lsn: + max: 1252 + last_processed: 1250 + lag: 2 +``` + +- `lag > 0` means the worker is behind. Steady state should hover near + zero; sustained lag points at a slow handler or a stuck retry. +- `failed (retryable=FALSE)` is always user-actionable. Cascade will + never auto-clear these — they represent malformed md the user must + edit. + +## Recovering from failures: `everos cascade fix` + +`cascade fix` (no flag) lists every failed row. With `--apply`: + +1. `UPDATE md_change_state SET status='pending', retry_count=0 + WHERE status='failed' AND retryable=TRUE` (the partial index + `idx_md_change_retryable` makes this O(retryable)). +2. Drain the worker once so the retry runs synchronously. + +Retryable failures cover transient embedding / HTTP errors (5xx, 429, +network resets) after the inline `MAX_RETRY=3` was exhausted. The +fix command resets the counter so a working backend gets a clean +start. + +`retryable=FALSE` rows require the user to edit the md (typically a +YAML frontmatter issue) and re-save; the watcher picks the change up +naturally. + +## One-shot replay: `everos cascade sync [PATH]` + +Use this when the watcher missed an event (WSL mount, network share, +external editor with no inotify) or when you want a deterministic +flush before, say, a smoke test: + +```bash +everos cascade sync # drain everything pending +everos cascade sync users/u1/episodes/X.md # re-enqueue + drain +``` + +The CLI builds the same `CascadeOrchestrator` as the daemon but only +calls `sync_once` / `drain_once` — no watcher / scanner background +task. So it's safe to run in parallel with a live `everos server`. + +## Recovery paths + +### LanceDB schema drift on startup + +`LanceDBLifespanProvider.startup` calls `verify_business_schemas`. If +an on-disk table has columns the current Pydantic schema does not +declare (or vice versa), the boot fails with: + +``` +LanceDB table 'episode' schema drift: missing=[...], extra=[...]. +The index is rebuildable from md — recover with +`rm -rf ~/.everos/.index/lancedb` and restart. +``` + +This is the documented recovery: delete the index, restart the +server, the scanner will pick up every md file on its first sweep and +the worker repopulates LanceDB. Markdown is the source of truth, so +no data is lost. + +### inotify watch-limit exhaustion (Linux) + +Default kernel limit is 8 192 watches per user. On a sizeable memory +root the watcher may silently miss events. Symptoms: + +- Scanner catches the file changes but the watcher never logs an + event for the same path. +- `cat /proc/sys/fs/inotify/max_user_watches` is at the limit. + +Fix by bumping the kernel parameter: + +```bash +echo fs.inotify.max_user_watches=524288 | sudo tee -a /etc/sysctl.conf +sudo sysctl -p +``` + +### WSL2 / network mounts + +Filesystem events do not propagate from the Windows host into WSL2 +(or across most SMB / NFS shares). The watcher will start without +error and silently see nothing. + +Workarounds: + +- Rely on the scanner — at default 30 s interval, throughput is + bounded but eventually-consistent. +- Drop the scan interval to ~5 s if the memory root is small. +- Run `everos cascade sync` explicitly after batch edits. + +### Daemon process crash mid-batch + +`claim_pending_batch` flips rows to `processing` *atomically*. If the +process dies before `mark_done` / `mark_failed`, those rows stay in +`processing` until the next boot. **The orchestrator auto-recovers** +on startup: `CascadeOrchestrator.start` calls +`md_change_state_repo.recover_orphan_processing()` before launching +the watcher / scanner / worker, which resets every `processing` row +back to `pending`. Single-process cascade means no race — at boot +time no other worker could legitimately own a `processing` row. + +No operator action required; the structured log line +`cascade_recovered_orphan_processing` reports the count when it +fires. + +### FD exhaustion (`os error 24` / EMFILE) + +Symptoms (any of these on a long-running daemon): + +- LanceDB query / index build fails with `lance error: ... Too many + open files (os error 24)`. +- `lsof -p | wc -l` grows monotonically over hours / days. +- Health log lines like `cascade_lancedb_optimize_failed` / + `cascade_lancedb_rebuild_failed` carrying `OSError: [Errno 24]`. + +Cause (verified against `lance crate 4.0`): the LanceDB *index* cache +(`GlobalIndexCache`) holds one reader object per opened FTS / vector +/ scalar index, and each reader pins the file descriptors of its +`_indices//...` files. With a long-running daemon and steady- +state cascade ingest, every `optimize()` call adds new readers; with +LanceDB's own default (`index_cache_size_bytes=None`, unbounded), they +**are never evicted** and the FDs leak monotonically. + +`drop_index` does **not** help — it is a manifest-only operation and +leaves the on-disk UUID directories untouched. Even an explicit +`optimize(cleanup_older_than=0)` `unlink()`-ing the files does not +release FDs: POSIX keeps the inode alive as long as a process holds +an open FD on it (the entries show as `(deleted)` in `lsof`). Only an +LRU eviction inside the cache (or a connection close) actually closes +the FDs. + +Fix (already wired in `LanceDBSettings.index_cache_size_bytes` — +default 16 MB, ~290 FD ceiling): see +[Tuning knobs § LanceDB index cache](#lancedb-index-cache-index_cache_size_bytes) +for the sizing table and the env-var override path. + +If you have already hit EMFILE in a running process, the cleanest +recovery is a daemon restart — the open connection closes, every FD +is released, and the next start comes up with the capped Session in +place. + +## Tuning knobs + +### Cascade scheduler knobs + +All defaults live in `everos.memory.cascade.orchestrator.CascadeConfig` +and `everos.memory.cascade.worker.CascadeWorker`: + +| Knob | Default | Effect | +|---|---|---| +| `scan_interval_seconds` | 30 | Scanner sweep cadence | +| `worker_batch_size` | 50 | Rows claimed per worker cycle | +| `worker_max_retry` | 3 | Inline retries before `mark_failed(retryable=TRUE)` | +| `worker_poll_interval_seconds` | 1 | Idle wait between empty drain attempts | +| `worker_retry_backoff_seconds` | 2 | Linear backoff seed; doubles per attempt | + +Tuning surface is intentionally not in `Settings` yet — once we have +wall-clock numbers from real workloads, the values that need +operator override will surface there. + +### LanceDB index cache (`index_cache_size_bytes`) + +Lives in `LanceDBSettings`; overridable via the +`EVEROS_LANCEDB__INDEX_CACHE_SIZE_BYTES` environment variable. This +is the only knob that bounds the steady-state file-descriptor count +of a long-running EverOS daemon — see +[Recovery paths § FD exhaustion](#fd-exhaustion-os-error-24-emfile) +for why nothing else (prune, rebuild, `drop_index`) helps. + +Measured cap → FD ceiling (30 add+optimize cycles + 100-query stress +on the real `Episode` schema): + +| Cap | FD ceiling | Query latency (p50) | Safe under `ulimit -n` | +|---|---|---|---| +| `2 MB` | ~45 | ~5 ms | macOS default 256 (5× headroom) | +| `4 MB` | ~52 | ~3 ms | macOS default 256 | +| `8 MB` | ~140 | ~2.4 ms | macOS default 256 (1.8× headroom) | +| **`16 MB`** (default) | **~290** | **~2.3 ms** | **Linux default 1024 (3.5× headroom); macOS needs `ulimit -n 1024`** | +| `32 MB` | ~630 | ~1.4 ms | Linux default 1024 (1.6× headroom) | +| `unbounded` | grows forever | ~1.3 ms | NEVER use in a daemon | + +EverOS's measured steady-state working set after a `rebuild_indexes` +cycle is roughly **50-100 readers / 3-6 MB resident** (5 tables × ~7 +BM25 columns × ~10 `part_N` reader entries each), so the 16 MB default +provides ~3× headroom for burst traffic and stale-but-not-yet-evicted +readers. + +When to override: + +- **Tight `ulimit -n` environments** (containers; macOS dev boxes + that haven't bumped the default 256) → drop to `4 MB` or `8 MB`. + Query latency increases by ~1-3 ms but correctness is unaffected. +- **Larger working sets** (many more tables or much wider FTS + indexes than the default schema set) → bump to `32-64 MB`. Verify + your platform's `ulimit -n` covers the corresponding FD ceiling + with at least 2× headroom. +- **Diagnostic-only**: set to a tiny value (e.g. `1 MB`) to + *force* LRU thrashing and reproduce cache-miss latency in tests. + +Do **not** set `metadata_cache_size_bytes` — it is intentionally left +at LanceDB's default (unbounded) because the metadata cache holds +parsed manifests / fragment stats and has zero effect on FD count; +capping it just thrashes parsing work without solving anything. + +## Concurrency + +The worker is async, not multi-process. Inside one drain cycle, +`asyncio.gather(*[_process_one(row) for row in batch])` runs every +claimed row concurrently — cascade is IO-bound (embedding HTTP calls +dominate wall time) so single-process coroutine concurrency saturates +the bottleneck. The `worker_batch_size` knob (default 50) caps +in-flight rows. + +Multi-process workers are a scaling axis we'd reach for only if a +single process becomes CPU-bound, which the current design does not +anticipate. `claim_pending_batch` is already race-safe (the +``WHERE status='pending'`` filter ensures each row lands in exactly +one batch even if multiple workers raced), so adding processes later +is a deployment-side change with no schema work. + +## What cascade does NOT do (yet) + +- **Schema migration**: LanceDB column changes require `rm -rf`. +- **Parent-id back-link**: Episode rows currently carry + `parent_id=None`; the writer doesn't preserve the source memcell id + in the entry inline. Tracked separately. +- **Reference-file change detection (agent_skill)**: edits to + `references/*.md` siblings won't trigger a re-index — only changes + to `SKILL.md` itself fire the watcher. Workaround: run + `everos cascade sync agents//skills/skill_/SKILL.md` after + editing references. diff --git a/docs/cli.md b/docs/cli.md new file mode 100644 index 00000000..3c664e28 --- /dev/null +++ b/docs/cli.md @@ -0,0 +1,116 @@ +# CLI + +The `everos` command-line entry point covers **setup and operations** — +generate a starter `.env` (`init`), run the HTTP API server (`server +start`), and operate the md → LanceDB index queue (`cascade`). Hot-path +business (`/add` `/flush` `/search` `/get`) is the **HTTP API**, not the +CLI. + +CLI commands run **in-process** — they call into the `service/` / +infrastructure layers directly rather than the HTTP loopback. + +## Installation + +The script is exposed via `pyproject.toml`: + +```toml +[project.scripts] +everos = "everos.entrypoints.cli.main:app" +``` + +After `uv sync` (or `pip install -e .`) the `everos` command resolves +to [`src/everos/entrypoints/cli/main.py`](../src/everos/entrypoints/cli/main.py), +a [Typer](https://typer.tiangolo.com/) app. + +## Subcommand layout + +``` +everos +├── init Generate a starter .env from the packaged template +├── server +│ └── start Start the HTTP API server (uvicorn) +└── cascade Inspect / operate the md → LanceDB sync queue + ├── status Queue / LSN summary + ├── sync Drain the queue now (force md → LanceDB) + └── fix List failed rows / re-enqueue retryable ones +``` + +Each subcommand lives in its own module under +[`entrypoints/cli/commands/`](../src/everos/entrypoints/cli/commands/) and is +registered in `cli/main.py`. The CLI is intentionally small — hot-path +business (`/add` `/flush` `/search` `/get`) is the **HTTP API**, not the +CLI; the CLI covers setup (`init`), running the server, and index ops +(`cascade`). There is no `reindex` command — rebuild by deleting +`/.index/lancedb` and restarting, or run `everos cascade sync`. + +## `everos server start` + +Wraps `uvicorn` to launch the FastAPI app from +[`entrypoints/api/app.py`](../src/everos/entrypoints/api/app.py) +in *factory* mode. + +```bash +everos server start \ + --host 127.0.0.1 \ + --port 8000 \ + --log-level info \ + --env-file .env +``` + +| Flag | Env var | Default | +|---|---|---| +| `--host` | `EVEROS_API__HOST` | `127.0.0.1` (loopback only; binding `0.0.0.0` logs a warning — EverOS ships no auth) | +| `--port` | `EVEROS_API__PORT` | `8000` | +| `--log-level` | `EVEROS_LOG_LEVEL` | `INFO` | +| `--env-file` | — | searched: `./.env` → `$XDG_CONFIG_HOME/everos/.env` → `~/.everos/.env` | +| `--reload` | — | off (use in development) | + +Lifespan startup wires the storage backends (SQLite engine + LanceDB +connection) on app boot; see +[`entrypoints/api/lifespans/`](../src/everos/entrypoints/api/lifespans/). + +## Configuration via env vars + +Both CLI and HTTP server read configuration from `pydantic-settings`: + +| Env var | Settings field | +|---|---| +| `EVEROS_MEMORY__ROOT` | `Settings.memory.root` (memory-root path) | +| `EVEROS_MEMORY__TIMEZONE` | `Settings.memory.timezone` (e.g. `Asia/Shanghai`) | +| `EVEROS_SQLITE__BUSY_TIMEOUT_MS` | `Settings.sqlite.busy_timeout_ms` | +| `EVEROS_LANCEDB__READ_CONSISTENCY_SECONDS` | `Settings.lancedb.read_consistency_seconds` | + +Pattern: `EVEROS_
__` (double underscore = nesting). See +[`config/settings.py`](../src/everos/config/settings.py). + +## Logging + +`configure_logging` runs at CLI startup and configures `structlog` with +the resolved log level. All in-process logs (CLI command bodies + +service / infra layers) flow through the same handler. + +```bash +everos server start --log-level debug # see all sql / lance traffic +``` + +## API ↔ CLI division of labour + +| Responsibility | API | CLI | +|---|---|---| +| Hot-path business (`/add` `/flush` `/search` `/get`) | ✅ | — (HTTP only) | +| Setup (generate `.env`) | — | `everos init` | +| Run the server | — | `everos server start` | +| Index ops (drain / inspect / fix the cascade queue) | — | `everos cascade {status,sync,fix}` | +| Health probe | `GET /health` | (use HTTP) | +| Metrics scrape | `GET /metrics` | (use HTTP) | + +The CLI is the **shell-friendly** surface for ops + scripting; the +HTTP API is the **process-friendly** surface for clients (web UIs, +agents, automation). + +## See also + +- [architecture.md](architecture.md) — DDD layering between + entrypoints / service / memory / infra +- [`entrypoints/cli/main.py`](../src/everos/entrypoints/cli/main.py) +- [`entrypoints/cli/commands/server.py`](../src/everos/entrypoints/cli/commands/server.py) diff --git a/docs/datetime.md b/docs/datetime.md new file mode 100644 index 00000000..5c8bad31 --- /dev/null +++ b/docs/datetime.md @@ -0,0 +1,263 @@ +# Datetime & Timezones + +> Audience: contributors. Read this once before touching any code that +> records a moment in time. + +## Table of contents + +- [The two-zone discipline](#the-two-zone-discipline) +- [Why two zones](#why-two-zones) +- [Helper reference](#helper-reference) +- [Field-type rules](#field-type-rules) +- [End-to-end data flow](#end-to-end-data-flow) +- [Common pitfalls](#common-pitfalls) +- [Testing guidance](#testing-guidance) + +## The two-zone discipline + +EverOS treats datetimes on **two separate rails**: + +| Rail | Where it lives | Helper | +|---|---|---| +| **UTC** (storage) | SQLite, LanceDB, OME events — anything persisted to disk | `get_utc_now`, `ensure_utc`, `UtcDatetime` | +| **Display tz** | Markdown frontmatter, HTTP API responses, daily-log filename buckets, fallback zone for naive caller input | `get_now_with_timezone`, `today_with_timezone`, `to_display_tz` | + +The display timezone is set by the `EVEROS_MEMORY__TIMEZONE` +environment variable (or `[memory] timezone` in TOML). Default `UTC`. + +**Inviolable rule**: the display tz must **never** reach storage. Once +the user switches `EVEROS_MEMORY__TIMEZONE`, existing on-disk rows +must not misalign. + +## Why two zones + +### What goes wrong with a single "configured" zone + +The naive design — "use one configured timezone everywhere" — has two +failure modes, both subtle: + +1. **Configuration drift.** Day 1 the user configures + `EVEROS_MEMORY__TIMEZONE=Asia/Shanghai`. Everything stores + Shanghai-local datetimes. On Day 30 they switch to + `UTC`. SQLite (which strips tz on write and returns naive on read) + silently reinterprets the old Shanghai values as UTC — every old + row jumps eight hours into the future. +2. **Cross-region replication.** If two deployments share storage + but configure different display zones, both interpret the same + naive bytes against their own local zone and diverge by the + offset delta. There is no "true" reading. + +UTC-only storage forecloses both: bytes on disk are zone-independent. + +### Why not UTC everywhere then? + +Users want to read timestamps in their wall-clock zone. Markdown +frontmatter that says `2026-05-29T06:00:00Z` for a meeting that +happened locally at 14:00 is jarring. The display rail solves this +without polluting storage: render UTC bytes through `to_display_tz` +at the boundary. + +## Helper reference + +All helpers live in [`everos.component.utils.datetime`](../src/everos/component/utils/datetime.py). + +### Storage rail + +| Helper | Behaviour | +|---|---| +| `get_utc_now() -> datetime` | Current UTC instant, `tzinfo=UTC`. Independent of any setting. Use as `default_factory` on any storage field. | +| `ensure_utc(d) -> datetime` | Naive → attach display tz → convert to UTC. Aware → `astimezone(UTC)`. Use at the storage boundary if you receive a datetime you didn't construct. | +| `UtcDatetime` | `Annotated[datetime, AfterValidator(ensure_utc)]`. Apply to any SQLite field. Pydantic auto-runs validation on both INSERT defaults and read-back rows. | + +### Display rail + +| Helper | Behaviour | +|---|---| +| `get_now_with_timezone() -> datetime` | Current instant in the configured display tz. `.isoformat()` produces e.g. `2026-05-29T14:00:00+08:00`. | +| `today_with_timezone() -> date` | Today's date in the display tz. Use for daily-log filename buckets. | +| `to_display_tz(d) -> datetime` | Convert any datetime to the display tz. Naive input is treated as already display-tz local. | + +### Parsing & rendering + +| Helper | Behaviour | +|---|---| +| `from_iso_format(value)` | Parse an ISO string / datetime / epoch. Naive input attaches **display tz** (the "if you didn't say a zone, assume your zone" rule). | +| `from_timestamp(ts)` | Parse epoch seconds / milliseconds (auto-detects). Returns display-tz aware. | +| `to_iso_format(d)` | `.isoformat()` after light validation. | +| `to_timestamp_ms(d)` | Milliseconds epoch (`int`). | + +## Field-type rules + +### SQLite tables + +```python +from everos.component.utils.datetime import UtcDatetime, get_utc_now +from everos.core.persistence.sqlite import BaseTable, Field + +class MyRow(BaseTable, table=True): + happened_at: UtcDatetime = Field(default_factory=get_utc_now) +``` + +Why `UtcDatetime` and not plain `datetime`? SQLAlchemy silently strips +tz on SQLite writes. `UtcDatetime`'s `AfterValidator` runs on +**construction** to make sure whatever the caller hands in gets +normalised to UTC before persistence. + +SQLModel's ORM hydrate path (rows from `select(...)`) **bypasses** +the Pydantic validator — SQLAlchemy assigns column values straight +to instance attributes. To close that gap, +[core/persistence/sqlite/base.py](../src/everos/core/persistence/sqlite/base.py) +registers a SQLAlchemy `load` event listener that re-attaches +`tzinfo=UTC` to every `UtcDatetime` column after hydrate. Net effect: +**callers never see a naive datetime from a SQLite repo**, whatever +the code path. + +`BaseTable.created_at` / `updated_at` already use `UtcDatetime` and +`get_utc_now` — any subclass inherits both the construction-time +validator **and** the load-time hook for free. + +### LanceDB tables — zero configuration + +```python +import datetime as _dt + +class MyLanceRow(BaseLanceTable): + ts: _dt.datetime # automatically tz=UTC in the Arrow schema +``` + +LanceDB's Pydantic → PyArrow converter does not understand +`typing.Annotated` metadata; using `UtcDatetime` as the annotation +would raise `TypeError: Converting Pydantic type to Arrow Type`. +Instead, `BaseLanceTable.to_arrow_schema()` walks the inferred schema +and rewrites **every** naive `timestamp[us]` column to +`timestamp[us, tz=UTC]`. PyArrow then: + +* **on write** — `astimezone(UTC)` any aware input automatically. +* **on read** — returns aware UTC datetimes (not naive). + +No caller-side coercion needed, no per-table declaration. The +response shapers only run `to_display_tz(...)` to convert UTC to the +configured display zone. + +If a future schema genuinely needs a naive datetime column (project +convention says storage is always UTC, so this would be unusual), +override `to_arrow_schema` on that subclass and skip the patch for +that one column. + +### OME events / in-memory state + +OME events are persisted-adjacent (the `run_record` / `counter` stores +serialise them). Use `get_utc_now()` for any `default_factory` on the +event payload. + +## Two centralised defenses + +| Backend | Defense | Where | +|---|---|---| +| **SQLite** | SQLAlchemy `load` event listener on `BaseTable` re-attaches `tzinfo=UTC` after every ORM hydrate | [core/persistence/sqlite/base.py](../src/everos/core/persistence/sqlite/base.py) | +| **LanceDB** | `BaseLanceTable.to_arrow_schema()` rewrites `UTC_DATETIME_FIELDS` columns to `timestamp[us, tz=UTC]`; PyArrow handles UTC end-to-end | [core/persistence/lancedb/base.py](../src/everos/core/persistence/lancedb/base.py) | +| **CI gate** | `scripts/check_datetime_discipline.py` fails the build on any code that bypasses `component/utils/datetime` | wired into `make lint` | + +These defenses replace what used to be an "every consumer must call +`ensure_utc()`" shotgun discipline. With both in place, callers never +observe a naive datetime from either backend. + +## End-to-end data flow + +``` +User input (any zone) + │ + ▼ + from_iso_format ← naive → attach display tz + │ + ▼ + ensure_utc ← storage boundary: → UTC + │ + ▼ +┌────────────────┬────────────────┐ +│ SQLite │ LanceDB │ +│ (UtcDatetime │ (Arrow │ +│ re-attaches │ stripped to │ +│ UTC on read) │ UTC bytes) │ +└────────────────┴────────────────┘ + │ + ▼ + from_iso_format ← read path normalises naive → display tz + │ + ▼ + to_display_tz ← response boundary: → display tz + │ + ▼ + Pydantic .isoformat() → "2026-05-29T14:00:00+08:00" + │ + ▼ + HTTP API response / markdown frontmatter +``` + +The storage boundary and response boundary are the two points where +the zone discipline is enforced. Everything in between just passes +datetimes through. + +## Common pitfalls + +> [!WARNING] +> **`datetime.now()` without `tz=`.** Forbidden. Always use +> `get_utc_now()` (storage) or `get_now_with_timezone()` (display). +> Linted by `.claude/rules/datetime-handling.md` and CI. + +> [!WARNING] +> **Calling `astimezone()` on a value just read from SQLite.** If the +> field isn't typed `UtcDatetime`, SQLite returns naive — and +> `astimezone()` on a naive datetime silently interprets it as +> **local process time**, not UTC. Always use `UtcDatetime` on SQLite +> fields. + +> [!WARNING] +> **Storing `get_now_with_timezone()` directly.** That returns +> display-tz time. If the display tz later changes, your stored values +> are stranded. Use `get_utc_now()` for any persisted field. + +> [!INFO] +> **Migrating existing rows.** Q2 was rolled out on a clean codebase +> with no production data. If you operate an instance where SQLite +> values were written with display-tz-aware values (pre-Q2), you must +> either drop the database or write a one-time migration that +> reinterprets each row's naive value against the old display tz +> before re-writing as UTC. The project does not ship such a +> migration. + +## Testing guidance + +For unit tests that depend on display-tz behaviour, both caches must +clear: + +```python +import pytest +from everos.component.utils import datetime as dt_module +from everos.config import load_settings + +@pytest.fixture(autouse=True) +def _isolate_tz(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.delenv("EVEROS_MEMORY__TIMEZONE", raising=False) + load_settings.cache_clear() + dt_module._display_tz.cache_clear() +``` + +The autouse fixture in [tests/conftest.py](../tests/conftest.py) does +exactly this — it runs for every test by default. If you write a +locally-scoped test that needs a non-default zone, monkeypatch the env +var **and** clear both caches: + +```python +def test_my_thing(monkeypatch): + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + ... +``` + +The full invariant set is covered in +[tests/unit/test_component/test_utils/test_datetime.py](../tests/unit/test_component/test_utils/test_datetime.py) +under the "Q2 two-zone discipline invariants" section. If you change +the storage / display contract, those tests are the first line of +defense — update them in lockstep. diff --git a/docs/engineering.md b/docs/engineering.md new file mode 100644 index 00000000..73ab127a --- /dev/null +++ b/docs/engineering.md @@ -0,0 +1,567 @@ +# Engineering & Dev-Efficiency Infrastructure + +> Companions: business architecture lives in [architecture.md](architecture.md); +> hard coding constraints live in [../.claude/rules/](../.claude/rules/). +> This document covers the surrounding tooling, configuration, and processes +> — what we adopted, what role each piece plays, and how they fit together. + +--- + +## 1. Scope + +Engineering / dev-efficiency infrastructure does not solve business problems — +it solves **team + code + time** problems: + +``` +┌──────────────────────────────────────────────────────────┐ +│ │ +│ Business architecture (docs/architecture.md) │ +│ — answers "how to build the system" │ +│ │ +│ Engineering rules (.claude/rules/) │ +│ — answers "how to write the code" │ +│ │ +│ Engineering / dev-efficiency infrastructure (this doc) │ +│ — answers "how the team collaborates, │ +│ how code is auto-checked, │ +│ how releases are automated, │ +│ how tools land in the project" │ +│ │ +└──────────────────────────────────────────────────────────┘ +``` + +Reasons this is documented separately: + +- **Cross-project reusable** — `CLAUDE.md` / rules / `pyproject.toml` are + patterns, not content. The next project can adopt them as-is. +- **Decoupled from business** — business architecture changes do not affect + these; upgrading these does not affect business. +- **Onboarding-oriented** — new contributors read this first to understand + what the tooling looks like. + +--- + +## 2. Infrastructure overview + +``` +┌─────────────────────────────────────────────────────────────────────┐ +│ Team collaboration / Code quality / CI/CD │ +├─────────────────────────────────────────────────────────────────────┤ +│ │ +│ ┌─ Claude Code engineering layer ────────────────────────────┐ │ +│ │ │ │ +│ │ CLAUDE.md ← team-shared context (auto loaded into │ │ +│ │ system prompt) │ │ +│ │ .claude/ │ │ +│ │ ├── CLAUDE.md subdir context (optional) │ │ +│ │ ├── rules/ (10) path-scoped hard coding rules │ │ +│ │ ├── skills/ (3) slash command workflows │ │ +│ │ └── settings.json permissions allowlist │ │ +│ │ │ │ +│ └────────────────────────────────────────────────────────────┘ │ +│ │ +│ ┌─ Code quality gates ───────────────────────────────────────┐ │ +│ │ │ │ +│ │ pre-commit runs locally before commit │ │ +│ │ ├ ruff (lint+fmt) │ │ +│ │ ├ trailing-whitespace / end-of-file-fixer │ │ +│ │ ├ check-yaml / check-toml │ │ +│ │ ├ check-added-large-files (≥1MB warn) │ │ +│ │ ├ detect-private-key │ │ +│ │ └ gitlint (commit-msg stage) │ │ +│ │ │ │ +│ │ ruff lint + format │ │ +│ │ (replaces black / isort / flake8) │ │ +│ │ import-linter DDD layer-direction enforcement │ │ +│ │ pytest unit / integration │ │ +│ │ │ │ +│ └────────────────────────────────────────────────────────────┘ │ +│ │ +│ ┌─ Dependencies & build ─────────────────────────────────────┐ │ +│ │ │ │ +│ │ uv sole package manager │ │ +│ │ (no `pip install`) │ │ +│ │ pyproject.toml src layout + extras + groups │ │ +│ │ uv.lock checked in; CI uses --frozen │ │ +│ │ hatchling wheel build backend │ │ +│ │ Makefile unified entry; CI calls it │ │ +│ │ src/everos/templates/env.template │ │ +│ │ environment variable template │ │ +│ │ │ │ +│ └────────────────────────────────────────────────────────────┘ │ +│ │ +│ ┌─ Dual-platform CI/CD ──────────────────────────────────────┐ │ +│ │ │ │ +│ │ Primary: GitLab CI .gitlab-ci.yml │ │ +│ │ Mirror: GitHub Actions .github/workflows/ci.yml │ │ +│ │ Both invoke Makefile targets; the Makefile is the │ │ +│ │ single source of truth for commands. │ │ +│ │ │ │ +│ └────────────────────────────────────────────────────────────┘ │ +│ │ +│ ┌─ Collaboration workflow ───────────────────────────────────┐ │ +│ │ │ │ +│ │ Branch model: dev / master (GitFlow Lite) │ │ +│ │ PR / MR templates: same template across platforms │ │ +│ │ CODEOWNERS: by DDD layer ownership │ │ +│ │ ISSUE_TEMPLATE: bug / feature / config │ │ +│ │ CONTRIBUTING.md: contributor onboarding │ │ +│ │ │ │ +│ └────────────────────────────────────────────────────────────┘ │ +│ │ +└─────────────────────────────────────────────────────────────────────┘ +``` + +--- + +## 3. Claude Code engineering layer + +### 3.1 Loading mechanism + +Claude Code automatically loads the following into the system prompt at +session start (no manual import): + +``` +┌────────────────────────┬──────────────────────────────────────────┐ +│ File │ Purpose │ +├────────────────────────┼──────────────────────────────────────────┤ +│ CLAUDE.md (repo root) │ Team-shared context: architecture │ +│ │ overview, commands, convention index │ +│ .claude/rules/*.md │ Hard coding constraints │ +│ │ (path-scoped on-demand load) │ +│ .claude/settings.json │ Permissions allowlist (not in prompt) │ +│ ~/.claude/CLAUDE.md │ User-level (personal preferences) │ +│ CLAUDE.local.md │ Project-local personal (gitignored) │ +└────────────────────────┴──────────────────────────────────────────┘ +``` + +### 3.2 Rules (10 files, path-scoped) + +| File | Paths (auto-load condition) | +|---|---| +| architecture.md | always loaded (no paths) | +| code-style.md | always loaded (no paths) | +| language-policy.md | always loaded (no paths) | +| imports.md | `src/**/*.py`, `tests/**/*.py` | +| init-py-and-reexport.md | `src/**/__init__.py`, `src/**/*.py` | +| module-docstring.md | `src/{infra,memory,service,component,core}/**/*.py` | +| async-programming.md | `src/**/*.py`, `tests/**/*.py` | +| datetime-handling.md | `src/**/*.py`, `tests/**/*.py` | +| logging-observability.md | `src/**/*.py` | +| testing.md | `tests/**/*.py` | + +**Why path-scoped**: avoid loading 1000+ lines of rules every session +(~5–8K tokens). At startup only architecture + code-style + language-policy +load (~1.5–2K tokens); the rest load on demand when Claude Code reads a +matching `.py` file. + +### 3.3 Skills (3 slash commands) + +| Command | Purpose | When to use | +|---|---|---| +| `/commit` | Generate Gitmoji-format commit message | After a focused change, ready to commit | +| `/new-branch` | Create branch under dev/master strategy | Starting a new feat / fix / hotfix | +| `/pr` | Create GitLab MR or GitHub PR with template | Ready to merge | + +Skills and rules use **independent loading mechanisms**: rules auto-load +into the system prompt, skills only trigger when the user types `/`. + +### 3.4 settings.json + +```json +{ + "permissions": { + "allow": ["Bash(uv sync*)", "Bash(make*)", "Bash(uv run pytest*)", ...] + } +} +``` + +**Purpose**: reduce permission prompts. Team-shared config goes into +`settings.json` (in git); personal preferences go into `settings.local.json` +(gitignored). + +--- + +## 4. Code quality gates + +``` + ┌──────────────────────────────────────────────────────┐ + │ Each stage can independently fail the change │ + └──────────────────────────────────────────────────────┘ + +[Local editor] + │ + ▼ +Stage 1: editor real-time feedback + ├ ruff (lint + format) on save + └ path-relevant .claude/rules guide Claude Code + + │ + ▼ +Stage 2: pre-commit (triggered by `git commit`) + ├ ruff fix + format + ├ trailing-whitespace, end-of-file-fixer + ├ check-yaml, check-toml + ├ check-added-large-files (≥1MB) + ├ detect-private-key + └ gitlint (commit-msg stage; rejects malformed messages) + + │ + ▼ +Stage 3: local `make ci` (manual, before push) + ├ make lint (ruff check + ruff format --check + import-linter) + ├ make test (pytest tests/unit) + └ make integration (pytest tests/integration) + + │ + ▼ +Stage 4: CI (PR triggered, GitLab + GitHub) + └ re-runs the same `make lint / test / integration` targets + + │ + ▼ +Stage 5: PR / MR review + ├ ≥ 1 approval + └ all threads resolved + all CI green +``` + +**Key design**: when any stage fails, **never merge** — there is no +`--no-verify` / `--allow-failure` escape hatch. + +--- + +## 5. Dependencies & build + +### 5.1 pyproject.toml overview + +```toml +[project] +name = "everos" +requires-python = ">=3.12" +dependencies = [...] # runtime deps (minimal set) + +[project.optional-dependencies] +multimodal = [...] # extras (install on demand) + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/everos"] # src layout + +[project.scripts] +everos = "everos.entrypoints.cli.main:app" # exposes CLI command + +[tool.ruff] # code style +[tool.pytest.ini_options] # tests +[tool.coverage.run] # coverage (informational, no threshold) +[tool.importlinter] # dependency direction + +[dependency-groups] +dev = ["ruff", "pytest", "pytest-asyncio", "pytest-cov", + "import-linter", "pre-commit", "ipdb"] +``` + +**Single-file principle**: configuration that used to live in `pylintrc`, +`pytest.ini`, `.isort.cfg` is **all consolidated into `pyproject.toml`**. + +### 5.2 Makefile commands + +``` +make help list all targets +make install uv sync --frozen +make format ruff fix + format +make lint ruff check + ruff format --check + import-linter +make test pytest tests/unit +make integration pytest tests/integration +make cov pytest tests/unit with coverage report (no threshold yet) +make ci lint + test + integration ← CI invokes these targets +make clean clear caches +``` + +**Single source of truth**: CI configuration only invokes `make `, +preventing drift between GitHub and GitLab. Local and CI run identical +commands. + +### 5.3 env.template (slimmed down) + +The template lives at `src/everos/templates/env.template` (bundled +inside the wheel as package data, copied to `./.env` via +`everos init`). The old project's `env.template` was ~100 lines (full +mongo / es / milvus / redis stack). The new version is ~50 lines: + +``` +EVEROS_LLM__MODEL # model name (provider-agnostic) +EVEROS_LLM__API_KEY # any OpenAI-protocol API key +EVEROS_LLM__BASE_URL # optional: custom endpoint (Ollama bridge etc.) +EVEROS_MEMORY__ROOT # memory-root (md files + .index/{sqlite,lancedb}/ + ...) +EVEROS_LOG_LEVEL +TZ +``` + +--- + +## 6. Dual-platform CI/CD + +### 6.1 Dual-platform strategy + +``` +┌──────────────────────────────────────────────────────────┐ +│ │ +│ Primary: GitLab CI (.gitlab-ci.yml) │ +│ ├ internal team dev stages: lint / test │ +│ ├ MR triggered │ +│ └ uv cache (keyed by uv.lock) │ +│ │ +│ Mirror: GitHub Actions (.github/workflows/ci.yml) │ +│ ├ public OSS mirror same make targets │ +│ ├ push + PR triggered │ +│ └ astral-sh/setup-uv@v3 │ +│ │ +│ Consistency: │ +│ ├ Makefile is the single source of CI commands │ +│ └ pre-commit runs locally first to reduce CI churn │ +│ │ +└──────────────────────────────────────────────────────────┘ +``` + +### 6.2 CI checklist + +| Check | Tool | Platform | Failure condition | +|---|---|---|---| +| Lint | `make lint` (ruff check + ruff format --check) | both | any error | +| Layer direction | `make lint` (lint-imports inside) | both | layer violation | +| Unit | `make test` (pytest tests/unit) | both | any failure | +| Integration | `make integration` (pytest tests/integration) | both | any failure (PR + master/dev push only) | + +Commit message format is enforced **locally** via `gitlint` in the +`commit-msg` pre-commit stage; it does not run in CI. + +### 6.3 Branch protection + +| Branch | GitLab rule | GitHub rule | +|---|---|---| +| **master** | no direct push; MR + 1 approval + green pipeline | branch protection + 1 review + status checks | +| **dev** | same as above | same as above | +| feat / fix / hotfix | free push; rebase parent before merge | same | + +--- + +## 7. Collaboration workflow + +### 7.1 Branch model (GitFlow Lite) + +``` + v0.1 v0.2 v1.0 + ▲ ▲ ▲ + │ release PR │ release PR │ release PR + │ (dev→master+tag) │ (dev→master+tag) │ (dev→master+tag) +master ●──────────────────────●─────────────●──────────────────●──────────────────────────────────●────► stable / released + │ ▲ │ │ + │ │ merge hotfix │ │ + │ │ │ │ + │ ●──●──┘ │ │ + │ │ hotfix branch │ │ + │ │ (cut from master) │ │ + │ │ │ │ + │ ▼ sync to dev │ │ + │ │ │ │ +dev ●──●──●──●──●──●──●──●──●─●──●──●─●──●──●──●──●──●──●──●──●─●──●──●──●──●──●──●──●──●──●──●──●─────► integration + ▲ ↑ ↑ ↑ + │ release point release point release point + feat/A (dev HEAD → (dev HEAD → (dev HEAD → + ●──●──● master + v0.1) master + v0.2) master + v1.0) + + + feat/* : cut from dev → PR → merge into dev + hotfix/* : cut from master → merge into master + sync into dev (double merge) + release : dev → master + tag on master (no separate release branch) + + Vertical │ in the diagram = "dev HEAD merged into master via release PR + v0.x tag" +``` + +Details in [../.claude/skills/new-branch/SKILL.md](../.claude/skills/new-branch/SKILL.md). + +### 7.2 PR / MR template (shared across platforms) + +Six sections: changes / target branch / scope / API impact / tests / +checklist. + +File locations: + +- GitLab: `.gitlab/merge_request_templates/default.md` +- GitHub: `.github/PULL_REQUEST_TEMPLATE.md` + +### 7.3 CODEOWNERS (by DDD layer) + +``` +/src/everos/memory/ @chandler.zhang @libin.zhang001 +/src/everos/infra/ @chandler.zhang @yeanhua +/src/everos/component/ @chandler.zhang +/src/everos/core/ @chandler.zhang +/src/everos/service/ @chandler.zhang @libin.zhang001 +/src/everos/entrypoints/ @chandler.zhang +/.claude/ @chandler.zhang +/.gitlab-ci.yml @chandler.zhang @jianhua.yao +``` + +At least one owner per directory; two owners for critical modules. Edits +auto-mention the corresponding owners. + +### 7.4 Commit convention (Gitmoji) + +``` +✨ feat: new feature +🐛 fix: bug fix +♻️ refactor: refactoring (no behavior change) +✅ test: add / update tests +📝 docs: documentation +🎨 style: formatting +⚡️ perf: performance optimization +🔧 chore: configuration / build +🚧 wip: work in progress (must not land on master) +``` + +`gitlint` enforces format **locally** (commit-msg pre-commit stage). See +[../.claude/skills/commit/SKILL.md](../.claude/skills/commit/SKILL.md). + +--- + +## 8. Issue templates / user support + +``` +.github/ISSUE_TEMPLATE/ +├── bug_report.md software deps: lancedb / sqlite / ruff +├── feature_request.md generic template +└── config.yml disable blank issue + Discord / Discussions links + +CONTRIBUTING.md contributor onboarding: setup / code style / + branch / commit / PR / testing +``` + +--- + +## 9. Infrastructure summary table + +``` +┌─────────────────────┬──────────────────────────────────────┬─────────────┐ +│ Facility │ Location / file │ Failure │ +│ │ │ impact │ +├─────────────────────┼──────────────────────────────────────┼─────────────┤ +│ CLAUDE.md │ /CLAUDE.md │ cc loses │ +│ │ │ context │ +│ Team rules │ /.claude/rules/ (10) │ cc unaware │ +│ │ │ of conv. │ +│ Team skills │ /.claude/skills/ (3) │ no slash │ +│ │ │ workflows │ +│ Permissions │ /.claude/settings.json │ cc prompts │ +│ │ │ on each op │ +├─────────────────────┼──────────────────────────────────────┼─────────────┤ +│ pyproject │ /pyproject.toml │ build fail │ +│ Lock file │ /uv.lock │ dep drift │ +│ Makefile │ /Makefile │ no unified │ +│ │ │ entry │ +│ pre-commit │ /.pre-commit-config.yaml │ no local │ +│ │ │ gate │ +│ env template │ /src/everos/templates/env.template │ newcomers │ +│ │ │ lost on env│ +├─────────────────────┼──────────────────────────────────────┼─────────────┤ +│ GitLab CI │ /.gitlab-ci.yml │ MR cannot │ +│ │ │ merge │ +│ GitHub Actions │ /.github/workflows/ci.yml │ PR cannot │ +│ │ │ merge │ +│ CODEOWNERS │ /.gitlab/CODEOWNERS │ no auto │ +│ │ │ reviewer │ +│ GitLab MR template │ /.gitlab/merge_request_templates/ │ no MR temp │ +│ GitHub PR template │ /.github/PULL_REQUEST_TEMPLATE.md │ no PR temp │ +│ Issue templates │ /.github/ISSUE_TEMPLATE/ (3) │ scattered │ +│ CONTRIBUTING │ /CONTRIBUTING.md │ contrib. │ +│ │ │ confused │ +└─────────────────────┴──────────────────────────────────────┴─────────────┘ +``` + +--- + +## 10. Future extensions + +``` +Near-term (before v0.2) + □ Coverage threshold once there is real code worth gating on + □ /new-module skill: scaffold a subpackage that complies with rules + □ /run-eval skill: run behavior-consistency eval + □ ruff rule sets: add D (docstring), ANN (annotations) + +Mid-term (before v0.5) + □ Type checking re-introduction (pyright or mypy) once hot paths stabilize + □ release-please / Conventional Commits → automated changelog + □ pre-commit autoupdate cadence + □ Performance benchmark CI with historical comparison + +Long-term (after v1.0) + □ /security-review skill: automated security review + □ Mutation testing (mutmut) + □ Multi-Python version matrix (3.12 / 3.13) + □ Automated PyPI wheel upload +``` + +--- + +## 11. On investing in engineering infrastructure + +``` +┌──────────────────────────────────────────────────────────┐ +│ │ +│ Plain business code ≠ an engineering project │ +│ │ +│ Engineering project = business code + │ +│ coding rules + │ +│ quality gates (pre-commit + CI) + │ +│ automation (Makefile + skills) + │ +│ collaboration (branch + PR + │ +│ CODEOWNERS) + │ +│ knowledge base (CLAUDE.md + │ +│ rules + docs) │ +│ │ +│ The earlier this infrastructure lands, the faster and │ +│ farther the team can run. │ +│ │ +└──────────────────────────────────────────────────────────┘ +``` + +Old project vs. new project after this rewrite: + +| Dimension | Old project | New project | +|---|---|---| +| Lint tools | black + isort + pylint | ruff (single tool) | +| Config files | pyproject + pylintrc + pyrightconfig + pytest.ini | unified pyproject.toml | +| pre-commit | basic | adds gitlint commit-msg + import / yaml / private-key checks | +| Layer direction | not enforced | import-linter enforced in CI | +| Commit format | freeform | gitlint pre-commit hook (Gitmoji) | +| Claude Code integration | partial rules | rules + skills + settings (full) | +| CI platform | GitLab only | GitLab + GitHub mirror, both calling Makefile | +| Tests | basic | unit + integration + golden + coverage report | + +These are not perfectionism — they are baseline requirements for +**multi-person collaboration, long-term maintenance, and sustainable +evolution**. + +--- + +## 12. References + +- Hard coding rules: [../.claude/rules/](../.claude/rules/) (auto-loaded by Claude Code) +- Slash command workflows: [../.claude/skills/](../.claude/skills/) +- Contributor onboarding: [../CONTRIBUTING.md](../CONTRIBUTING.md) +- Architecture: [architecture.md](architecture.md) +- Claude Code memory mechanism: [code.claude.com/docs/en/memory.md](https://code.claude.com/docs/en/memory.md) +- Claude Code skills: [code.claude.com/docs/en/skills.md](https://code.claude.com/docs/en/skills.md) +- ruff: [docs.astral.sh/ruff](https://docs.astral.sh/ruff/) +- import-linter: [import-linter.readthedocs.io](https://import-linter.readthedocs.io/) +- gitlint: [jorisroovers.com/gitlint](https://jorisroovers.com/gitlint/) +- uv: [docs.astral.sh/uv](https://docs.astral.sh/uv/) +- pre-commit: [pre-commit.com](https://pre-commit.com/) +- Gitmoji: [gitmoji.dev](https://gitmoji.dev/) +- GitLab CI: [docs.gitlab.com/ee/ci](https://docs.gitlab.com/ee/ci/) +- GitHub Actions: [docs.github.com/en/actions](https://docs.github.com/en/actions) +- CODEOWNERS: [docs.gitlab.com/ee/user/project/codeowners](https://docs.gitlab.com/ee/user/project/codeowners/) diff --git a/docs/how-memory-works.md b/docs/how-memory-works.md new file mode 100644 index 00000000..5d21ea4b --- /dev/null +++ b/docs/how-memory-works.md @@ -0,0 +1,294 @@ +# How Memory Works + +How EverOS turns a stream of messages into durable, searchable memory — +the storage stack, the path layout on disk, the write→index→read +pipeline, and the consistency guarantees. + +This is the narrative companion to the reference docs: see +[storage_layout.md](storage_layout.md) for the exact file encoding, +[architecture.md](architecture.md) for the layer boundaries, and +[api.md](api.md) for the HTTP contract. + +## Table of contents + +- [The storage stack](#the-storage-stack) +- [Storage paths](#storage-paths) +- [How a memory is born](#how-a-memory-is-born) +- [Memory types & storage strategies](#memory-types--storage-strategies) +- [The cascade daemon](#the-cascade-daemon) +- [The Offline Memory Engine (OME)](#the-offline-memory-engine-ome) +- [Consistency model](#consistency-model) +- [Zero external services](#zero-external-services) +- [Operating it](#operating-it) + +## The storage stack + +Three embedded pieces, each owning what it is best at. Markdown is the +**source of truth**; the other two are **derived and rebuildable**. + +| Layer | Backed by | Holds | Rebuildable? | +|---|---|---|---| +| **Markdown + YAML frontmatter** | plain `.md` files | the memory content itself — the only portable, human-editable asset | — (it *is* the truth) | +| **SQLite** (`aiosqlite`) | `.index/sqlite/*.db` | system state, audit log, the cascade queue, the boundary buffer, OME engine state | ✅ from markdown | +| **LanceDB** (Arrow) | `.index/lancedb/*.lance` | vector + BM25 + scalar columns for retrieval | ✅ from markdown | + +!!! note "The one rule that follows from this" + Delete the entire `.index/` directory and **no memory is lost** — it + rebuilds from the `.md` tree. There is no separate "export"; the + markdown *is* the export. (How to trigger a rebuild: + [Operating it](#operating-it).) + +## Storage paths + +The default memory root is **`~/.everos/`** (override with +`EVEROS_MEMORY__ROOT` or `[memory] root` in TOML). Configuration (the +`.env` file) is separate from data (the memory root): the server searches +`./.env` → `$XDG_CONFIG_HOME/everos/.env` → `~/.everos/.env`. + +Memory is partitioned by **`/`** *before* the +user-visible directories, so different `(app, project)` spaces never share +a directory or cross in search. The reserved id `"default"` materialises as +`default_app` / `default_project` on disk (so a default space stays +visually distinct from a user-named one). + +``` +~/.everos/ ← memory root (EVEROS_MEMORY__ROOT) +├── default_app/ ← ("default" → default_app) +│ └── default_project/ ← ("default" → default_project) +│ ├── users/ ← user-visible (source of truth) +│ │ └── / +│ │ ├── user.md single-file (profile) +│ │ ├── episodes/ +│ │ │ └── episode-.md daily-log append +│ │ ├── .atomic_facts/ daily-log (hidden) +│ │ │ └── atomic_fact-.md +│ │ └── .foresights/ daily-log (hidden) +│ │ └── foresight-.md +│ ├── agents/ +│ │ └── / +│ │ ├── .cases/ daily-log (hidden) +│ │ │ └── agent_case-.md +│ │ └── skills/ skill-named dir +│ │ └── skill_/SKILL.md (+ references/ scripts/) +│ └── knowledge/ ← shared / global (reserved) +│ +├── .index/ ← system-managed, rebuildable (gitignore) +│ ├── sqlite/ +│ │ ├── system.db state / audit / cascade queue (md_change_state) / buffer / LSN +│ │ ├── ome.db Offline Memory Engine state +│ │ ├── ome.aps.db APScheduler jobstore (split to avoid lock contention) +│ │ └── ome.db.lock OME single-engine guard (portalocker) +│ └── lancedb/ +│ └── .lance/ one Arrow table per kind +│ +├── ome.toml ← user-editable OME strategy overrides (hot-reloaded) +└── .tmp/ atomic-write staging +``` + +!!! warning "Differences from older PRD-era docs" + The index dir is **`.index/`** (dot-prefixed), not `_index/`. The + cascade queue and LSN/audit state live in **SQLite** (`system.db`, + table `md_change_state`) — there is no `.cascade.log` / `.manifest.json` + file in the current implementation. The `/` nesting is + real and always present (`default_app/default_project` for the default + scope). There is **no `everos reindex` command** (see + [Operating it](#operating-it)). + +The path manager is +[`MemoryRoot`](../src/everos/core/persistence/memory_root.py); every path +above is a property on it. `MemoryRoot.ensure()` creates the runtime dirs +(`.index/{sqlite,lancedb}/`, `.tmp/`) and copies the OME template to +`ome.toml`; user-visible dirs appear on first write. + +## How a memory is born + +A message does not become memory immediately — it accumulates, a boundary +is detected, an LLM extracts a cell, writers persist markdown, and the +index catches up asynchronously. + +``` + POST /add ──▶ unprocessed_buffer (SQLite) ← messages accumulate per (session, app, project) + │ + ├─ boundary detector trips ─┐ + POST /flush ─────────┤ (or you force it) │ one LLM call + │ ▼ + │ extract MemCell ──▶ memcell row (SQLite) + │ │ + │ ┌──────────────┴───────────────┐ + │ ▼ ▼ + │ UserMemoryPipeline (sync) AgentMemoryPipeline (fire-and-forget) + │ writes episode .md NOW emits AgentPipelineStarted + ▼ │ │ + (response returns once md is on disk) │ + ▼ ▼ + ┌─────────────────── Offline Memory Engine (OME) ───────────────────┐ + │ async strategies write derived .md: │ + │ atomic_facts · foresight · user profile · agent cases · agent skills │ + └───────────────────────────────┬──────────────────────────────────────┘ + ▼ + cascade daemon watches the .md tree + ▼ + md_change_state queue (SQLite, durable) + ▼ + rebuild LanceDB rows ──▶ searchable +``` + +- **`/add`** appends messages to a per-`(session_id, app_id, project_id)` + buffer and returns `accumulated` (or `extracted` if the boundary tripped + on this call). See [api.md](api.md). +- **`/flush`** forces the boundary now (one extraction LLM call), used at + the end of a chat/agent run. +- Episode markdown is written **synchronously** — when `/flush` returns + `extracted`, the episode file is already on disk. +- Everything else (atomic facts, foresight, profile, agent cases/skills) + is produced **asynchronously** by the OME — see + [the OME section](#the-offline-memory-engine-ome). +- The **cascade daemon** turns every `.md` write into LanceDB rows so the + content becomes searchable. + +## Memory types & storage strategies + +Six business memory kinds today, each user- or agent-owned, each picking +one of three on-disk patterns: + +| Kind | Owner | Dir / file | Strategy | Produced by | +|---|---|---|---|---| +| **episode** | user | `episodes/episode-.md` | daily-log | extraction (sync) | +| **atomic_fact** | user | `.atomic_facts/atomic_fact-.md` (hidden) | daily-log | OME | +| **foresight** | user | `.foresights/foresight-.md` (hidden) | daily-log | OME | +| **profile** | user | `user.md` | single-file rewrite | OME | +| **agent_case** | agent | `.cases/agent_case-.md` (hidden) | daily-log | OME | +| **agent_skill** | agent | `skills/skill_/SKILL.md` | skill-named dir | OME (clustering) | + +The three strategies: + +| Strategy | Shape | Why | +|---|---|---| +| **Daily-log append** | `-.md`, one entry appended per memory | collapses thousands of per-entry files into one file per day | +| **Single-file rewrite** | a fixed filename overwritten in place | for a single evolving document (a user/agent profile) | +| **Skill-named dir** | one directory per skill | a skill is a richer unit (body + optional `references/` `scripts/`) | + +!!! note + The single-file writer also supports `agent.md` / `soul.md` / + `tools.md` / `behaviors.md`, but no shipped OME strategy produces those + yet — today only `user.md` is written. Detailed frontmatter and + entry-id encoding live in [storage_layout.md](storage_layout.md). + +## The cascade daemon + +The cascade subsystem keeps LanceDB in sync with the markdown tree. It runs +**in-process** with the server (a coroutine started by the app lifespan), +not as a separate OS daemon. + +1. A native filesystem watcher (`watchdog`: FSEvents on macOS, inotify on + Linux) sees a `.md` create/modify. +2. The change is enqueued in the **`md_change_state`** table (SQLite) — + durable, so a crash mid-sync replays on restart. +3. A worker drains the queue at **entry-level** granularity: it diffs the + file, re-embeds only changed entries (keyed by `content_sha256`), and + upserts the LanceDB rows. + +Because markdown is the source of truth, **editing a file directly is +fully supported** — open an episode in VSCode / Obsidian / Vim, change an +entry, save, and the daemon re-indexes just that entry. Operate the queue +with `everos cascade` ([Operating it](#operating-it)); deeper runbook in +[cascade_runbook.md](cascade_runbook.md). + +## The Offline Memory Engine (OME) + +Most memory kinds are **not** extracted on the request path — they are +derived later by the OME, an in-process async strategy engine. When +extraction carves a MemCell, it emits an event; OME strategies pick it up +and write their markdown when ready: + +- `extract_atomic_facts` — single-sentence facts from an episode +- `extract_foresight` — anticipatory notes +- `extract_user_profile` — the aggregated `user.md` +- `extract_agent_case` — a reusable agent trajectory (only when the cell is + substantive enough; thin trajectories are skipped by design) +- `extract_agent_skill` — clusters related cases into a named skill + +Strategies are configurable without a code change via **`ome.toml`** at the +memory root (hot-reloaded within ~2 s). Example — turn two off: + +```toml +[strategies.extract_foresight] +enabled = false + +[strategies.extract_user_profile] +enabled = false +``` + +OME keeps its own state in `.index/sqlite/ome.db` (run records, counters) +and its scheduler jobstore in `.index/sqlite/ome.aps.db` (split so the sync +APScheduler writer and the async OME writer never contend for one file +lock). + +!!! tip "Implication for clients" + After `/flush` returns `extracted`, the **episode** is queryable soon + (once cascade indexes it), but **atomic facts / profile / agent cases** + appear only after their OME strategy runs — typically seconds later. + Poll / retry if you need them immediately. + +## Consistency model + +Two paths, two guarantees: + +| Path | Guarantee | Detail | +|---|---|---| +| **Write** (`/add`, `/flush`) | **strong** | the episode `.md` is on disk before the call returns `extracted`; never blocks on LanceDB | +| **Read** (`/search`, `/get`) | **eventual** | reads LanceDB, which lags md by the cascade processing time — sub-second typically, up to ~10–15 s under load | + +So a `/search` immediately after the `/flush` that produced a record may +miss it. The markdown is durable regardless; index lag never loses data. If +you need read-your-write, retry with backoff, or force the queue with +`everos cascade sync`. + +Integrity is anchored by a few invariants (details in +[storage_layout.md](storage_layout.md)): the frontmatter `id` / +`entry_id` is the immutable join key; `content_sha256` decides whether an +entry needs re-embedding; an LSN watermark (in `system.db`) orders +rebuilds; the durable `md_change_state` queue is the replayable audit +trail. + +## Zero external services + +No database server, message broker, or vector service to run. Vector ANN, +full-text BM25, and scalar filtering all execute inside the **embedded +LanceDB** engine in one query; SQLite is a local file. The whole stack is a +single directory you can copy, back up, or check the user-visible parts of +into git. + +!!! note + There is no automatic "grep over markdown" search fallback today — if + the LanceDB index is unavailable, rebuild it from markdown (it is + derived and disposable) rather than relying on a degraded search path. + +## Operating it + +The CLI ([cli.md](cli.md)) is intentionally small: + +| Command | What it does | +|---|---| +| `everos init` | write a starter `.env` | +| `everos server start` | run the HTTP API (cascade + OME start with it) | +| `everos cascade status` | queue / LSN summary | +| `everos cascade sync` | drain the cascade queue now (force md → LanceDB) | +| `everos cascade fix` | list failed rows / re-enqueue retryable ones | + +!!! warning "There is no `everos reindex` or `everos flush`" + - **Reindex** = the index is rebuildable: stop the server, + `rm -rf /.index/lancedb`, restart — the cascade + rebuilds from markdown. For an incremental catch-up, use + `everos cascade sync`. + - **Flush** is an HTTP endpoint (`POST /api/v1/memory/flush`), not a + CLI command — it forces *extraction* of the session buffer, which is + a different thing from forcing *index sync* (`cascade sync`). + +## References + +- [storage_layout.md](storage_layout.md) — exact file encoding, frontmatter + chassis, entry-id format, atomic-write semantics +- [architecture.md](architecture.md) — DDD layers and dependency rules +- [api.md](api.md) — the HTTP contract (`/add` `/flush` `/search` `/get`) +- [cascade_runbook.md](cascade_runbook.md) — operating the sync queue diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 00000000..5866b625 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,63 @@ +# EverOS Documentation + +Documentation for [EverOS](../README.md) — md-first memory extraction +framework. Organised by [Diátaxis](https://diataxis.fr/) — what kind of +question you have determines which section to read. + +## Reference + +Technical reference: contracts, commands, schemas — read these when you +already know what you want to do and need to know exactly how. + +| Doc | Purpose | +|---|---| +| [api.md](api.md) | HTTP API v1 reference — endpoints, request / response, error contracts | +| [cli.md](cli.md) | `everos` CLI subcommands + env var conventions | +| [storage_layout.md](storage_layout.md) | Memory-root tree + frontmatter chassis + EntryId encoding | +| [prompt_slots.md](prompt_slots.md) | YamlConfigLoader + three-layer prompt override | + +## Explanation + +Design decisions and architectural concepts — read these to understand +why the system is shaped the way it is. + +| Doc | Purpose | +|---|---| +| [overview.md](overview.md) | Project vision, scope, design philosophy | +| [how-memory-works.md](how-memory-works.md) | Storage stack + on-disk paths + write→index→read pipeline + consistency | +| [architecture.md](architecture.md) | DDD layered architecture + dependency rules | +| [datetime.md](datetime.md) | Two-zone discipline — UTC at storage, display tz at boundaries | + +## How-to + +Task-driven operational guides — read these when you need to do a +specific thing (drain a queue, recover from a stuck row, etc.). + +| Doc | Purpose | +|---|---| +| [cascade_runbook.md](cascade_runbook.md) | Cascade subsystem ops — drain queue, recover stuck rows | + +## Engineering / Internal + +For maintainers and contributors working on the framework itself, +not for using it. + +| Doc | Purpose | +|---|---| +| [engineering.md](engineering.md) | Engineering & dev-efficiency infrastructure (CI / tooling / Claude Code) | + +## See also + +Top-level project files live next to the repo root: + +- [README.md](../README.md) — quick start & feature overview +- [QUICKSTART.md](../QUICKSTART.md) — 5-minute walkthrough (install → service → search) +- [CONTRIBUTING.md](../CONTRIBUTING.md) — how to contribute (issue-only model) +- [CHANGELOG.md](../CHANGELOG.md) — release notes +- [SECURITY.md](../SECURITY.md) — security policy & private vulnerability reporting +- [CITATION.md](../CITATION.md) — academic citation info +- [ACKNOWLEDGMENTS.md](../ACKNOWLEDGMENTS.md) — third-party acknowledgments + +Coding conventions and slash command workflows are auto-loaded by +Claude Code from [.claude/rules/](../.claude/rules/) and +[.claude/skills/](../.claude/skills/). diff --git a/docs/locomo_benchmark.md b/docs/locomo_benchmark.md new file mode 100644 index 00000000..36b4a858 --- /dev/null +++ b/docs/locomo_benchmark.md @@ -0,0 +1,126 @@ +# Running the LoCoMo Benchmark + +This guide walks through reproducing EverOS's LoCoMo retrieval scores +locally using the `hybrid` and `agentic` search methods. + +## Contents + +- [Prerequisites](#prerequisites) +- [1. Prepare the dataset](#1-prepare-the-dataset) +- [2. Start the server](#2-start-the-server) +- [3. Run `hybrid`](#3-run-hybrid) +- [4. Run `agentic`](#4-run-agentic) +- [5. Where the results land](#5-where-the-results-land) +- [Notes](#notes) + +--- + +## Prerequisites + +- Python **3.12**, [uv](https://docs.astral.sh/uv/) +- A `.env` at the repo root with the LLM / embedding credentials EverOS + needs: + - `EVEROS_LLM__MODEL`, `EVEROS_LLM__API_KEY`, `EVEROS_LLM__BASE_URL` + - `EVEROS_EMBEDDING__*` + - `EVEROS_RERANK__*` + - The benchmark driver also reads `LLM_API_KEY` / `ANSWER_MODEL` / + `JUDGE_MODEL` for the answer + judge passes. + +Install the project: + +```bash +uv sync +``` + +## 1. Prepare the dataset + +Place the LoCoMo file at `data/locomo10.json` (the dataset is +distributed by the LoCoMo authors, not this repo). Override the path +later with `--data-path` if you keep it elsewhere. + +## 2. Start the server + +```bash +EVEROS_MEMORY__ROOT=~/.everos \ +uv run python -m everos.entrypoints.cli.main server start --port 8000 +``` + +`EVEROS_MEMORY__ROOT` isolates one benchmark's corpus from another — +change it (or `rm -rf` it) whenever you want a clean run. + +Leave the server running in one terminal; run the benchmark from +another. + +## 3. Run `hybrid` + +Single conversation: + +```bash +bash tests/run_locomo_batch.sh \ + --conv-indices 0 \ + --methods hybrid \ + --base-url http://localhost:8000 \ + --top-k 10 +``` + +All 10 conversations, 2-way parallel: + +```bash +bash tests/run_locomo_batch.sh \ + --conv-indices 0-9 \ + --methods hybrid \ + --base-url http://localhost:8000 \ + --top-k 10 \ + --concurrency 2 +``` + +The wrapper picks up `EVEROS_MEMORY__ROOT` from the environment so the +cascade poll path matches the server's data root. If you set them +differently, pass `--corpus-path` explicitly. + +## 4. Run `agentic` + +Same wrapper, swap `--methods`: + +```bash +bash tests/run_locomo_batch.sh \ + --conv-indices 0-9 \ + --methods agentic \ + --base-url http://localhost:8000 \ + --top-k 10 \ + --concurrency 2 +``` + +You can also benchmark multiple methods in one go — they share the +same ingested corpus: + +```bash +bash tests/run_locomo_batch.sh \ + --conv-indices 0-9 \ + --methods hybrid,agentic \ + --base-url http://localhost:8000 \ + --top-k 10 \ + --concurrency 2 +``` + +## 5. Where the results land + +Default output root is `benchmark_results/run_/`. Override +with `--output-root`: + +``` +/ +├── conv0.json … conv9.json # per-conv summary + per-question details +├── conv0.log … conv9.log # per-conv stdout (only in --concurrency >1 mode) +└── conv0_checkpoints/ … # incremental search/answer/eval JSON +``` + +An aggregate accuracy table prints at the end of the wrapper run. + +## Notes + +- **Re-running on the same corpus**: add `--skip-add` to skip ingest and + reuse what's already in `~/.everos`. Useful when comparing methods + side by side. +- **Judge variance**: `--judge-runs 3` runs the judge three times per + question and majority-votes; slower but reduces LLM-judge noise. diff --git a/docs/openapi.json b/docs/openapi.json new file mode 100644 index 00000000..0ae6608c --- /dev/null +++ b/docs/openapi.json @@ -0,0 +1,1664 @@ +{ + "openapi": "3.1.0", + "info": { + "title": "everos", + "description": "md-first memory extraction framework", + "version": "0.1.0" + }, + "paths": { + "/health": { + "get": { + "tags": [ + "health" + ], + "summary": "Health", + "description": "Liveness probe — returns ``{\"status\": \"ok\"}`` with HTTP 200.", + "operationId": "health_health_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "additionalProperties": { + "type": "string" + }, + "type": "object", + "title": "Response Health Health Get" + } + } + } + } + } + } + }, + "/metrics": { + "get": { + "tags": [ + "metrics" + ], + "summary": "Metrics", + "description": "Render the current Prometheus registry in exposition format.", + "operationId": "metrics_metrics_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": {} + } + } + } + } + } + }, + "/api/v1/memory/add": { + "post": { + "tags": [ + "memory" + ], + "summary": "Add Memory", + "description": "Add messages into the user-memory + agent-memory pipelines.", + "operationId": "add_memory_api_v1_memory_add_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/MemorizeAddRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SuccessEnvelope_AddResponseData_" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/v1/memory/flush": { + "post": { + "tags": [ + "memory" + ], + "summary": "Flush Memory", + "description": "Force boundary detection over the current ``session_id`` buffer.\n\n[OSS-only] — cloud edition decides boundary timing server-side and\ndoes not expose this endpoint.", + "operationId": "flush_memory_api_v1_memory_flush_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/MemorizeFlushRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SuccessEnvelope_FlushResponseData_" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/v1/memory/search": { + "post": { + "tags": [ + "memory" + ], + "summary": "Post Search", + "description": "Hybrid retrieval across the configured memory backends.", + "operationId": "post_search_api_v1_memory_search_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SearchRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SearchResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/api/v1/memory/get": { + "post": { + "tags": [ + "memory" + ], + "summary": "Post Get", + "description": "Paginated listing over the requested ``memory_type``.", + "operationId": "post_get_api_v1_memory_get_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/GetRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/GetResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + } + }, + "components": { + "schemas": { + "AddResponseData": { + "properties": { + "message_count": { + "type": "integer", + "title": "Message Count" + }, + "status": { + "type": "string", + "enum": [ + "accumulated", + "extracted" + ], + "title": "Status" + } + }, + "type": "object", + "required": [ + "message_count", + "status" + ], + "title": "AddResponseData" + }, + "ContentItemDTO": { + "properties": { + "type": { + "type": "string", + "enum": [ + "text", + "image", + "audio", + "doc", + "pdf", + "html", + "email" + ], + "title": "Type" + }, + "text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Text" + }, + "uri": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Uri" + }, + "base64": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Base64" + }, + "ext": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Ext" + }, + "name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Name" + }, + "extras": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "title": "Extras" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "type" + ], + "title": "ContentItemDTO", + "description": "Content piece (v1 API brief appendix A)." + }, + "FilterNode": { + "properties": { + "AND": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FilterNode" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "And" + }, + "OR": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/FilterNode" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Or" + } + }, + "additionalProperties": true, + "type": "object", + "title": "FilterNode", + "description": "One Filters DSL node.\n\nRecursive ``AND`` / ``OR`` arrays mix with arbitrary scalar fields at\nthe same level. Pydantic only checks the combinators; field-level\nsafety is enforced when compiling the node to a LanceDB ``where``\nstring in :mod:`everos.memory.search.filters`." + }, + "FlushResponseData": { + "properties": { + "status": { + "type": "string", + "enum": [ + "extracted", + "no_extraction" + ], + "title": "Status" + } + }, + "type": "object", + "required": [ + "status" + ], + "title": "FlushResponseData" + }, + "GetAgentCaseItem": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "agent_id": { + "type": "string", + "title": "Agent Id" + }, + "app_id": { + "type": "string", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "title": "Project Id", + "default": "default" + }, + "session_id": { + "type": "string", + "title": "Session Id" + }, + "task_intent": { + "type": "string", + "title": "Task Intent" + }, + "approach": { + "type": "string", + "title": "Approach" + }, + "quality_score": { + "type": "number", + "title": "Quality Score" + }, + "key_insight": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Key Insight" + }, + "timestamp": { + "type": "string", + "format": "date-time", + "title": "Timestamp" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "id", + "agent_id", + "session_id", + "task_intent", + "approach", + "quality_score", + "timestamp" + ], + "title": "GetAgentCaseItem", + "description": "Agent case listing item — always agent-scoped." + }, + "GetAgentSkillItem": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "agent_id": { + "type": "string", + "title": "Agent Id" + }, + "app_id": { + "type": "string", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "title": "Project Id", + "default": "default" + }, + "name": { + "type": "string", + "title": "Name" + }, + "description": { + "type": "string", + "title": "Description" + }, + "content": { + "type": "string", + "title": "Content" + }, + "confidence": { + "type": "number", + "title": "Confidence" + }, + "maturity_score": { + "type": "number", + "title": "Maturity Score" + }, + "source_case_ids": { + "items": { + "type": "string" + }, + "type": "array", + "title": "Source Case Ids" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "id", + "agent_id", + "name", + "description", + "content", + "confidence", + "maturity_score" + ], + "title": "GetAgentSkillItem", + "description": "Agent skill listing item — always agent-scoped." + }, + "GetData": { + "properties": { + "episodes": { + "items": { + "$ref": "#/components/schemas/GetEpisodeItem" + }, + "type": "array", + "title": "Episodes" + }, + "profiles": { + "items": { + "$ref": "#/components/schemas/GetProfileItem" + }, + "type": "array", + "title": "Profiles" + }, + "agent_cases": { + "items": { + "$ref": "#/components/schemas/GetAgentCaseItem" + }, + "type": "array", + "title": "Agent Cases" + }, + "agent_skills": { + "items": { + "$ref": "#/components/schemas/GetAgentSkillItem" + }, + "type": "array", + "title": "Agent Skills" + }, + "total_count": { + "type": "integer", + "title": "Total Count", + "default": 0 + }, + "count": { + "type": "integer", + "title": "Count", + "default": 0 + } + }, + "additionalProperties": false, + "type": "object", + "title": "GetData", + "description": "Body of ``response.data``.\n\nAll four arrays are always present so client code can iterate\nwithout branching on ``memory_type``; the route populates exactly\none." + }, + "GetEpisodeItem": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "user_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "User Id" + }, + "app_id": { + "type": "string", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "title": "Project Id", + "default": "default" + }, + "session_id": { + "type": "string", + "title": "Session Id" + }, + "timestamp": { + "type": "string", + "format": "date-time", + "title": "Timestamp" + }, + "sender_ids": { + "items": { + "type": "string" + }, + "type": "array", + "title": "Sender Ids" + }, + "summary": { + "type": "string", + "title": "Summary" + }, + "subject": { + "type": "string", + "title": "Subject" + }, + "episode": { + "type": "string", + "title": "Episode" + }, + "type": { + "type": "string", + "const": "Conversation", + "title": "Type" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "id", + "user_id", + "session_id", + "timestamp", + "summary", + "subject", + "episode", + "type" + ], + "title": "GetEpisodeItem", + "description": "Episode listing item — always user-scoped." + }, + "GetMemoryType": { + "type": "string", + "enum": [ + "episode", + "profile", + "agent_case", + "agent_skill" + ], + "title": "GetMemoryType", + "description": "The four kinds enumerated by ``/get``.\n\n``episode`` and ``profile`` are user-owned; ``agent_case`` and\n``agent_skill`` are agent-owned. Cross-pairs are rejected by\n:meth:`GetRequest._validate_owner_memory_type_pair`.\n\nNaming note: all four values use the bare kind name (no\n``_memory`` suffix) and match the LanceDB table name + everalgo\ntype name for that kind." + }, + "GetProfileItem": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "user_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "User Id" + }, + "app_id": { + "type": "string", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "title": "Project Id", + "default": "default" + }, + "profile_data": { + "additionalProperties": true, + "type": "object", + "title": "Profile Data" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "id", + "user_id", + "profile_data" + ], + "title": "GetProfileItem", + "description": "Owner profile — at most one per response, only for user owners." + }, + "GetRequest": { + "properties": { + "user_id": { + "anyOf": [ + { + "type": "string", + "minLength": 1 + }, + { + "type": "null" + } + ], + "title": "User Id" + }, + "agent_id": { + "anyOf": [ + { + "type": "string", + "minLength": 1 + }, + { + "type": "null" + } + ], + "title": "Agent Id" + }, + "app_id": { + "type": "string", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "title": "Project Id", + "default": "default" + }, + "memory_type": { + "$ref": "#/components/schemas/GetMemoryType" + }, + "page": { + "type": "integer", + "minimum": 1.0, + "title": "Page", + "default": 1 + }, + "page_size": { + "type": "integer", + "maximum": 100.0, + "minimum": 1.0, + "title": "Page Size", + "default": 20 + }, + "sort_by": { + "type": "string", + "enum": [ + "timestamp", + "updated_at" + ], + "title": "Sort By", + "default": "timestamp" + }, + "sort_order": { + "type": "string", + "enum": [ + "asc", + "desc" + ], + "title": "Sort Order", + "default": "desc" + }, + "filters": { + "anyOf": [ + { + "$ref": "#/components/schemas/FilterNode" + }, + { + "type": "null" + } + ] + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "memory_type" + ], + "title": "GetRequest", + "description": "Request body for ``POST /api/v1/memory/get``.\n\nCallers identify the memory owner via ``user_id`` XOR ``agent_id`` —\nexactly one must be set. Internally the manager keeps using\n``owner_id`` / ``owner_type`` (the storage tables' columns); those\nare exposed as derived properties so the rename only affects the\nwire contract." + }, + "GetResponse": { + "properties": { + "request_id": { + "type": "string", + "title": "Request Id" + }, + "data": { + "$ref": "#/components/schemas/GetData" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "request_id", + "data" + ], + "title": "GetResponse", + "description": "Top-level response envelope." + }, + "HTTPValidationError": { + "properties": { + "detail": { + "items": { + "$ref": "#/components/schemas/ValidationError" + }, + "type": "array", + "title": "Detail" + } + }, + "type": "object", + "title": "HTTPValidationError" + }, + "MemorizeAddRequest": { + "properties": { + "session_id": { + "type": "string", + "maxLength": 128, + "minLength": 1, + "title": "Session Id" + }, + "app_id": { + "type": "string", + "maxLength": 128, + "minLength": 1, + "pattern": "^[a-zA-Z0-9_.-]+$", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "maxLength": 128, + "minLength": 1, + "pattern": "^[a-zA-Z0-9_.-]+$", + "title": "Project Id", + "default": "default" + }, + "messages": { + "items": { + "$ref": "#/components/schemas/MessageItemDTO" + }, + "type": "array", + "maxItems": 500, + "minItems": 1, + "title": "Messages" + } + }, + "type": "object", + "required": [ + "session_id", + "messages" + ], + "title": "MemorizeAddRequest" + }, + "MemorizeFlushRequest": { + "properties": { + "session_id": { + "type": "string", + "maxLength": 128, + "minLength": 1, + "title": "Session Id" + }, + "app_id": { + "type": "string", + "maxLength": 128, + "minLength": 1, + "pattern": "^[a-zA-Z0-9_.-]+$", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "maxLength": 128, + "minLength": 1, + "pattern": "^[a-zA-Z0-9_.-]+$", + "title": "Project Id", + "default": "default" + } + }, + "type": "object", + "required": [ + "session_id" + ], + "title": "MemorizeFlushRequest" + }, + "MessageItemDTO": { + "properties": { + "sender_id": { + "type": "string", + "minLength": 1, + "title": "Sender Id" + }, + "sender_name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Sender Name" + }, + "role": { + "type": "string", + "enum": [ + "user", + "assistant", + "tool" + ], + "title": "Role" + }, + "timestamp": { + "type": "integer", + "exclusiveMinimum": 0.0, + "title": "Timestamp", + "description": "Message event time as Unix epoch in **milliseconds** (v1 API contract; the algo layer auto-detects sec vs ms for backward compat but the contract is ms)." + }, + "content": { + "anyOf": [ + { + "type": "string" + }, + { + "items": { + "$ref": "#/components/schemas/ContentItemDTO" + }, + "type": "array" + } + ], + "title": "Content" + }, + "tool_calls": { + "anyOf": [ + { + "items": { + "$ref": "#/components/schemas/ToolCallDTO" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Tool Calls" + }, + "tool_call_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Tool Call Id" + } + }, + "type": "object", + "required": [ + "sender_id", + "role", + "timestamp", + "content" + ], + "title": "MessageItemDTO" + }, + "SearchAgentCaseItem": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "agent_id": { + "type": "string", + "title": "Agent Id" + }, + "app_id": { + "type": "string", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "title": "Project Id", + "default": "default" + }, + "session_id": { + "type": "string", + "title": "Session Id" + }, + "task_intent": { + "type": "string", + "title": "Task Intent" + }, + "approach": { + "type": "string", + "title": "Approach" + }, + "quality_score": { + "type": "number", + "title": "Quality Score" + }, + "key_insight": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Key Insight" + }, + "timestamp": { + "type": "string", + "format": "date-time", + "title": "Timestamp" + }, + "score": { + "type": "number", + "title": "Score" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "id", + "agent_id", + "session_id", + "task_intent", + "approach", + "quality_score", + "timestamp", + "score" + ], + "title": "SearchAgentCaseItem", + "description": "Agent case hit — always agent-scoped." + }, + "SearchAgentSkillItem": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "agent_id": { + "type": "string", + "title": "Agent Id" + }, + "app_id": { + "type": "string", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "title": "Project Id", + "default": "default" + }, + "name": { + "type": "string", + "title": "Name" + }, + "description": { + "type": "string", + "title": "Description" + }, + "content": { + "type": "string", + "title": "Content" + }, + "confidence": { + "type": "number", + "title": "Confidence" + }, + "maturity_score": { + "type": "number", + "title": "Maturity Score" + }, + "source_case_ids": { + "items": { + "type": "string" + }, + "type": "array", + "title": "Source Case Ids" + }, + "score": { + "type": "number", + "title": "Score" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "id", + "agent_id", + "name", + "description", + "content", + "confidence", + "maturity_score", + "score" + ], + "title": "SearchAgentSkillItem", + "description": "Agent skill hit — always agent-scoped." + }, + "SearchAtomicFactItem": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "content": { + "type": "string", + "title": "Content" + }, + "score": { + "type": "number", + "title": "Score" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "id", + "content", + "score" + ], + "title": "SearchAtomicFactItem", + "description": "A single atomic fact nested inside its parent episode." + }, + "SearchData": { + "properties": { + "episodes": { + "items": { + "$ref": "#/components/schemas/SearchEpisodeItem" + }, + "type": "array", + "title": "Episodes" + }, + "profiles": { + "items": { + "$ref": "#/components/schemas/SearchProfileItem" + }, + "type": "array", + "title": "Profiles" + }, + "agent_cases": { + "items": { + "$ref": "#/components/schemas/SearchAgentCaseItem" + }, + "type": "array", + "title": "Agent Cases" + }, + "agent_skills": { + "items": { + "$ref": "#/components/schemas/SearchAgentSkillItem" + }, + "type": "array", + "title": "Agent Skills" + }, + "unprocessed_messages": { + "items": { + "$ref": "#/components/schemas/UnprocessedMessageDTO" + }, + "type": "array", + "title": "Unprocessed Messages" + } + }, + "additionalProperties": false, + "type": "object", + "title": "SearchData", + "description": "Body of ``response.data``.\n\nAll five arrays are always present so client code can iterate without\nbranching on ``owner_type``. Routes not applicable to the request's\nowner type stay as ``[]``. ``unprocessed_messages`` is filled only\nwhen ``filters.session_id`` is present as a top-level eq scalar —\nin-flight buffer rows are scope-tagged but unattributed (no\n``user_id``), so session is the only meaningful query dimension." + }, + "SearchEpisodeItem": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "user_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "User Id" + }, + "app_id": { + "type": "string", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "title": "Project Id", + "default": "default" + }, + "session_id": { + "type": "string", + "title": "Session Id" + }, + "timestamp": { + "type": "string", + "format": "date-time", + "title": "Timestamp" + }, + "sender_ids": { + "items": { + "type": "string" + }, + "type": "array", + "title": "Sender Ids" + }, + "summary": { + "type": "string", + "title": "Summary" + }, + "subject": { + "type": "string", + "title": "Subject" + }, + "episode": { + "type": "string", + "title": "Episode" + }, + "type": { + "type": "string", + "const": "Conversation", + "title": "Type" + }, + "score": { + "type": "number", + "title": "Score" + }, + "atomic_facts": { + "items": { + "$ref": "#/components/schemas/SearchAtomicFactItem" + }, + "type": "array", + "title": "Atomic Facts" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "id", + "user_id", + "session_id", + "timestamp", + "summary", + "subject", + "episode", + "type", + "score" + ], + "title": "SearchEpisodeItem", + "description": "Episode hit — always user-scoped in the current emission contract.\n\n``type`` is narrowed to ``\"Conversation\"`` because the only emitted\nepisode shape today is conversation-derived; widen when other\nsources ship. Item kind is encoded by class name (no ``owner_type``\nfield on the wire), so episode results never carry ambiguity." + }, + "SearchMethod": { + "type": "string", + "enum": [ + "keyword", + "vector", + "hybrid", + "agentic" + ], + "title": "SearchMethod", + "description": "Public method enum. RRF / LR / vector_anchored are hidden under HYBRID." + }, + "SearchProfileItem": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "user_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "User Id" + }, + "app_id": { + "type": "string", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "title": "Project Id", + "default": "default" + }, + "profile_data": { + "additionalProperties": true, + "type": "object", + "title": "Profile Data" + }, + "score": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "title": "Score" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "id", + "user_id", + "profile_data" + ], + "title": "SearchProfileItem", + "description": "Owner profile — at most one per response, only for user owners.\n\n``score`` is ``None`` for direct fetches (``include_profile=true``\non its own does no ranking); a future query-aware lookup may fill\nit in." + }, + "SearchRequest": { + "properties": { + "user_id": { + "anyOf": [ + { + "type": "string", + "minLength": 1 + }, + { + "type": "null" + } + ], + "title": "User Id" + }, + "agent_id": { + "anyOf": [ + { + "type": "string", + "minLength": 1 + }, + { + "type": "null" + } + ], + "title": "Agent Id" + }, + "app_id": { + "type": "string", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "title": "Project Id", + "default": "default" + }, + "query": { + "type": "string", + "minLength": 1, + "title": "Query" + }, + "method": { + "$ref": "#/components/schemas/SearchMethod", + "default": "hybrid" + }, + "top_k": { + "type": "integer", + "title": "Top K", + "default": -1 + }, + "radius": { + "anyOf": [ + { + "type": "number", + "maximum": 1.0, + "minimum": 0.0 + }, + { + "type": "null" + } + ], + "title": "Radius" + }, + "include_profile": { + "type": "boolean", + "title": "Include Profile", + "default": false + }, + "enable_llm_rerank": { + "type": "boolean", + "title": "Enable Llm Rerank", + "description": "Opt-in LLM rerank pass for HYBRID. Applies to agent_case and agent_skill fusion only; the episode hierarchy path has built-in fact eviction and ignores this flag. Ignored by keyword / vector / agentic.", + "default": false + }, + "filters": { + "anyOf": [ + { + "$ref": "#/components/schemas/FilterNode" + }, + { + "type": "null" + } + ] + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "query" + ], + "title": "SearchRequest", + "description": "Request body for ``POST /api/v1/memory/search``.\n\nCallers identify the memory owner via ``user_id`` XOR ``agent_id`` —\nexactly one must be set. Internally the manager + compile_filters keep\nusing ``owner_id`` / ``owner_type`` (the storage tables' columns);\nthose are exposed as derived properties so the rename only affects\nthe wire contract, not the internal recall plumbing." + }, + "SearchResponse": { + "properties": { + "request_id": { + "type": "string", + "title": "Request Id" + }, + "data": { + "$ref": "#/components/schemas/SearchData" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "request_id", + "data" + ], + "title": "SearchResponse", + "description": "Top-level response envelope." + }, + "SuccessEnvelope_AddResponseData_": { + "properties": { + "request_id": { + "type": "string", + "title": "Request Id" + }, + "data": { + "$ref": "#/components/schemas/AddResponseData" + } + }, + "type": "object", + "required": [ + "request_id", + "data" + ], + "title": "SuccessEnvelope[AddResponseData]" + }, + "SuccessEnvelope_FlushResponseData_": { + "properties": { + "request_id": { + "type": "string", + "title": "Request Id" + }, + "data": { + "$ref": "#/components/schemas/FlushResponseData" + } + }, + "type": "object", + "required": [ + "request_id", + "data" + ], + "title": "SuccessEnvelope[FlushResponseData]" + }, + "ToolCallDTO": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "type": { + "type": "string", + "title": "Type", + "default": "function" + }, + "function": { + "$ref": "#/components/schemas/ToolFunctionDTO" + } + }, + "type": "object", + "required": [ + "id", + "function" + ], + "title": "ToolCallDTO" + }, + "ToolFunctionDTO": { + "properties": { + "name": { + "type": "string", + "title": "Name" + }, + "arguments": { + "type": "string", + "title": "Arguments" + } + }, + "type": "object", + "required": [ + "name", + "arguments" + ], + "title": "ToolFunctionDTO" + }, + "UnprocessedMessageDTO": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "app_id": { + "type": "string", + "title": "App Id", + "default": "default" + }, + "project_id": { + "type": "string", + "title": "Project Id", + "default": "default" + }, + "session_id": { + "type": "string", + "title": "Session Id" + }, + "sender_id": { + "type": "string", + "title": "Sender Id" + }, + "sender_name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Sender Name" + }, + "role": { + "type": "string", + "enum": [ + "user", + "assistant", + "tool" + ], + "title": "Role" + }, + "content": { + "anyOf": [ + { + "type": "string" + }, + { + "items": { + "additionalProperties": true, + "type": "object" + }, + "type": "array" + } + ], + "title": "Content" + }, + "timestamp": { + "type": "string", + "format": "date-time", + "title": "Timestamp" + }, + "tool_calls": { + "anyOf": [ + { + "items": { + "additionalProperties": true, + "type": "object" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Tool Calls" + }, + "tool_call_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Tool Call Id" + } + }, + "additionalProperties": false, + "type": "object", + "required": [ + "id", + "session_id", + "sender_id", + "role", + "content", + "timestamp" + ], + "title": "UnprocessedMessageDTO", + "description": "A raw message still in the boundary-detection buffer.\n\nNo extracted memcell yet, no owner inference yet (attribution\nhappens at boundary detection). Returned by ``/search`` **only when**\n``filters.session_id`` is present as a top-level eq predicate —\nunprocessed messages have no ``user_id`` / ``agent_id`` to filter\non, so session is the only meaningful query dimension." + }, + "ValidationError": { + "properties": { + "loc": { + "items": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "integer" + } + ] + }, + "type": "array", + "title": "Location" + }, + "msg": { + "type": "string", + "title": "Message" + }, + "type": { + "type": "string", + "title": "Error Type" + }, + "input": { + "title": "Input" + }, + "ctx": { + "type": "object", + "title": "Context" + } + }, + "type": "object", + "required": [ + "loc", + "msg", + "type" + ], + "title": "ValidationError" + } + } + } +} diff --git a/docs/overview.md b/docs/overview.md new file mode 100644 index 00000000..dc3cf900 --- /dev/null +++ b/docs/overview.md @@ -0,0 +1,85 @@ +# EverOS — Project Overview + +## Vision + +Build an open-source Python memory framework where **AI agents' long-term memory is plain Markdown files on the user's disk**, not opaque rows in a hosted database. + +## Scope + +**In scope (v1)**: + +- Local deployment for personal agents or small teams +- Conversation, workflow, agent-trace, file-knowledge → structured memory +- Hybrid retrieval (BM25 + vector + scalar filter) +- Cascade index sync (md edit → LanceDB sub-second) +- Dual-track memory (user-track / agent-track) +- Offline memory evolution (Foresight / AtomicFact / Profile / Skill) +- CLI + HTTP API + +**Out of scope (v1, future v2)**: + +- Multi-tenant / group / community deployment (10K+ users) +- End-to-cloud sync (planned for v2) +- Distributed deployment / sharding + +## Design philosophy + +### 1. Markdown as Source of Truth + +``` +delete all LanceDB / SQLite files → can rebuild from md +delete any md file → memory is gone +``` + +User trust comes from physical visibility — the user can `cat` / `vim` / `grep` their own memory at any time. + +### 2. Three-piece storage with clear job boundaries + +| Component | Role | Does NOT do | +|---|---|---| +| Markdown files | Truth source — entries, frontmatter | Search (grep is degraded fallback only) | +| SQLite | Queue, cascade audit log, sensitive data isolation | Vector / full-text | +| LanceDB | Vector ANN + BM25 + scalar filter, single-query hybrid | Be the source of truth (loss = rebuild from md) | + +### 3. Algorithm-orchestration separation + +`evercore` (separate library, future PyPI) holds the extraction algorithms (MemCell extraction, Episode generation, Profile evolution). EverOS calls evercore via PromptSlot interface; evercore knows nothing about storage. + +This boundary lets the same algorithm power both this open-source lightweight version and other product forms. + +### 4. DDD layered architecture + +``` +entrypoints → service → memory → infra + ↓ + component / core / config +``` + +Strict single-direction dependency, enforced by `import-linter` in CI. + +## Why src layout (`src/everos/`) + +- Standard PyPA project structure used when shipping to PyPI +- Avoid namespace collision with system packages named `memory`, `infra`, etc. +- Avoid accidental import of working-tree code in dev (PyPA recommendation) + +## Comparable projects (where EverOS differs) + +| Project | Position | Difference | +|---|---|---| +| [mem0](https://github.com/mem0ai/mem0) | API-first memory service | mem0 stores in vector DB; we store in md files | +| [Letta](https://github.com/letta-ai/letta) | Agent OS w/ Core/Recall/Archival | Letta uses Postgres; we use markdown filesystem | +| [MemOS](https://github.com/MemTensor/MemOS) | Multi-classification memory | MemOS targets enterprise; we target lightweight (single-user / small team) | +| [memsearch](https://github.com/zilliztech/memsearch) | md-first search engine | Closest to us; we add memory extraction (not just search) | + +## Roadmap + +- **v0.1 (MVP)** — Phase 1 core loop: markdown + lancedb + cascade + episode extraction +- **v0.2** — Full extraction pipeline (workspace / agent / knowledge), evolution framework +- **v0.3** — Production hardening, full CLI, HTTP API, Obsidian demo +- **v1.0** — Stable API, PyPI release, comprehensive docs +- **v2** (future) — Edge-to-cloud sync via EverMe (separate project) + +## Status + +**Alpha — v0.1.0 in active development**. Core API may change before v1.0. diff --git a/docs/prompt_slots.md b/docs/prompt_slots.md new file mode 100644 index 00000000..6d2da8af --- /dev/null +++ b/docs/prompt_slots.md @@ -0,0 +1,111 @@ +# PromptSlot + +PromptSlot is the layer between the algorithm code (`evercore`) and +the prompts it sends to LLMs. Algorithm code receives a `PromptSlot` +parameter; the *project* (EverOS) supplies defaults and lets operators +override. + +> **Status (2026-05-07)**: the YAML loader is implemented; the higher- +> level `PromptSlot` model + sandbox dry-run + three-layer overlay +> resolution arrive when the memory layer ships (see Stage 2). + +## Three-layer overlay + +``` +config/prompt_slots/.yaml (Layer 1: defaults shipped with the package) + ↓ +~/.everos/prompt_slots/.yaml (Layer 2: app-level override; per-deployment) + ↓ +runtime override (Layer 3: per-call override; e.g. "force model X") +``` + +Effective prompt = layer 3 wins → layer 2 → layer 1. Layer 1 is +loaded eagerly at startup; layer 2 is loaded on first reference (lazy); +layer 3 is supplied at the call site. + +## Loader + +The category loader lives at +[`src/everos/component/config/loader.py`](../src/everos/component/config/loader.py) +as `YamlConfigLoader`: + +```python +from pathlib import Path +from everos.component.config import YamlConfigLoader + +loader = YamlConfigLoader( + root=Path("src/everos/config"), + categories={"prompt_slots": None}, # subdir == category name +) + +# Reads /prompt_slots/episode_extract.yaml → dict +slot = loader.find("prompt_slots", "episode_extract") + +# Refresh after on-disk edits. +loader.refresh() # drop the entire cache +loader.refresh("prompt_slots") # drop one category +loader.refresh("prompt_slots", "episode_extract") # drop one entry +``` + +Top-level YAML is required to be a mapping; a list / scalar root +raises `TypeError` to fail-fast (loud, not silent). + +## YAML format (proposed; subject to change) + +```yaml +# config/prompt_slots/episode_extract.yaml +template: | + Extract a single episode from this conversation: + {{ memcell.text }} + +variables: + memcell: input memcell + +output_schema: + type: object + properties: + summary: { type: string } + participants: { type: array } + +llm: + model: gpt-4o-mini + temperature: 0.3 + max_tokens: 2000 + +validation: + test_cases: + - input: { memcell: { text: "Hi" } } + expected: { summary: "...", participants: [] } +``` + +When layer 2 supplies an override the loader will be re-pointed at +`~/.everos/prompt_slots/`; the runtime resolution logic (currently TBD) +sandbox-runs the merged slot before returning it. + +## Why YAML (not TOML) + +Two reasons: + +1. **Multiline templates** — TOML's basic-string grammar fights + prompt content (no easy `{{ jinja }}` variables, awkward escaping). + YAML's literal block scalar (`|`) preserves prompts as-is. +2. **Comment + reference ergonomics** — operators frequently inherit + slots, tweak a few keys, and leave inline notes. YAML is more + forgiving for hand-editing. + +The Pydantic Settings file (`config/default.toml`) stays TOML — it's +machine-managed and type-validated; YAML's flexibility costs more +than it pays for that case. + +## Why a separate loader (not Pydantic Settings) + +Settings = **one** structured tree, validated at load time, tied to a +single source of truth. PromptSlots = **many** separate templates +discovered by name, layered per-deployment. They're different shapes; +forcing one model on the other gets clunky. + +## See also + +- [`src/everos/component/config/loader.py`](../src/everos/component/config/loader.py) +- [`tests/unit/test_component/test_config/test_loader.py`](../tests/unit/test_component/test_config/test_loader.py) +- [`docs/architecture.md`](architecture.md) — layer placement diff --git a/docs/storage_layout.md b/docs/storage_layout.md new file mode 100644 index 00000000..5c7138b1 --- /dev/null +++ b/docs/storage_layout.md @@ -0,0 +1,220 @@ +# Storage Layout + +How `everos` lays out a memory-root on disk: directory tree, file +naming, frontmatter chassis, and entry-id encoding. + +The contents are the **source of truth**; SQLite and LanceDB are +derived indexes that can be rebuilt from markdown alone. + +## 1. Memory-root tree + +A memory-root is a single directory holding all persisted memory. The +default location is `~/.everos/`; override via `EVEROS_MEMORY__ROOT` +env var or `[memory] root` in the TOML config. + +Memory is partitioned by **`/`** *before* the +user-visible scope dirs, so different `(app, project)` spaces never share +a directory. The reserved id `"default"` materialises as `default_app` / +`default_project` on disk. The scope is encoded **in the path**, not in +the frontmatter (see [§3](#3-frontmatter-chassis-yaml)). + +``` +/ default ~/.everos +│ +├── / user-visible; "default" → default_app +│ └── / "default" → default_project +│ ├── users/ +│ │ └── / +│ │ ├── user.md single-file rewrite (profile) +│ │ ├── episodes/ daily-log append +│ │ │ └── episode-.md +│ │ ├── .atomic_facts/ daily-log append (hidden) +│ │ │ └── atomic_fact-.md +│ │ └── .foresights/ daily-log append (hidden) +│ │ └── foresight-.md +│ ├── agents/ +│ │ └── / +│ │ ├── .cases/ daily-log append (hidden) +│ │ │ └── agent_case-.md +│ │ └── skills/ skill-named dir +│ │ └── skill_/ +│ │ ├── SKILL.md +│ │ ├── references/ (optional) +│ │ └── scripts/ (optional) +│ └── knowledge/ user-visible (shared / global, reserved) +│ +├── .index/ system-managed, rebuildable (gitignore) +│ ├── sqlite/ +│ │ ├── system.db state / cascade queue (md_change_state) / buffer / audit / LSN (+ -wal / -shm) +│ │ ├── ome.db Offline Memory Engine state +│ │ ├── ome.aps.db APScheduler jobstore (split to avoid lock contention) +│ │ └── ome.db.lock OME single-engine guard (portalocker) +│ └── lancedb/ +│ └── .lance/ one directory per LanceDB table +│ +├── ome.toml user-editable OME strategy overrides (hot-reloaded) +└── .tmp/ staging dir for batch / multi-step writes +``` + +> Cascade queue state, the LSN watermark, and the change audit all live in +> SQLite (`system.db`, table `md_change_state`) — crash-recovery replays +> from that durable queue, not a log file. (`MemoryRoot` also exposes a +> `.lock` anchor for the `memory_root_lock` primitive; there is no +> `.cascade.log` / `.manifest.json`.) + +The path manager is [`MemoryRoot`](../src/everos/core/persistence/memory_root.py), +exposing every path as a property. `MemoryRoot.ensure()` creates the +runtime-required dirs (`.index/{sqlite,lancedb}/`, `.tmp/`) and copies the +OME template to `ome.toml`; the user-visible dirs are *not* pre-created — +they appear on first write. + +> The single-file writer also supports `agent.md` / `soul.md` / `tools.md` +> / `behaviors.md`, but no shipped strategy produces those today — only +> `user.md` is written. `memcell` is a SQLite-only kind (the boundary +> ledger); it has no markdown file. + +## 2. Three storage strategies + +Each business memory kind picks one of three on-disk patterns: + +| Strategy | Filename | Mutation | Examples | +|---|---|---|---| +| **Daily-log append** | `-.md` under `/` | append entries | episode / atomic_fact / foresight / agent_case | +| **Skill-named dir** | `skills/skill_/SKILL.md` (+ `references/` `scripts/`) | overwrite the file | agent skills (procedural memory) | +| **Single-file rewrite** | `user.md` (writer also supports `agent.md` / `soul.md` / `tools.md` / `behaviors.md`, not yet produced) | overwrite the file | user profile | + +Markdown IO primitives live in +[`core/persistence/markdown/`](../src/everos/core/persistence/markdown/); +business-aware writers live in +[`infra/persistence/markdown/writers/`](../src/everos/infra/persistence/markdown/writers/) +and pick the right strategy via a base class. + +For a step-by-step recipe to add a new memory kind, see the +[`/add-memory-kind`](../.claude/skills/add-memory-kind/SKILL.md) skill. + +## 3. Frontmatter chassis (YAML) + +Every markdown file carries a YAML frontmatter block at the top: + +```markdown +--- +id: episode_log_alice_2026-06-01 +type: episode_daily +file_type: episode_daily +schema_version: 1 +user_id: alice +track: user +date: '2026-06-01' +entry_count: 11 +last_appended_at: '2026-06-01T09:12:13+00:00' +--- + +...content... + +``` + +Scope (`app_id` / `project_id`) is **not** a frontmatter field — it is +carried by the `/` path segments and recovered by the +cascade path parser. The frontmatter only holds the file-level owner +(`user_id` / `agent_id`) and `track`. + +The chassis lives in [`core/persistence/markdown/base.py`](../src/everos/core/persistence/markdown/base.py) +(Pydantic v2): + +``` +BaseFrontmatter id / type / schema_version + SCOPE_DIR ClassVar + ├─ UserScopedFrontmatter + user_id / track="user" + SCOPE_DIR="users" + └─ AgentScopedFrontmatter + agent_id / track="agent" + SCOPE_DIR="agents" +``` + +Concrete business schemas subclass one of the scope mixins and add +per-kind fields plus three more ClassVars that drive path resolution ++ entry-id assembly: + +```python +class EpisodeDailyFrontmatter(DailyLogPathMixin, UserScopedFrontmatter): + ENTRY_ID_PREFIX: ClassVar[str] = "ep" + DIR_NAME: ClassVar[str] = "episodes" + FILE_PREFIX: ClassVar[str] = "episode" + type: Literal["episode_daily"] = "episode_daily" + date: dt.date + entry_count: int = 0 + last_appended_at: dt.datetime | None = None +``` + +## 4. Entry-id encoding + +Inside daily-log files each entry is bracketed by HTML-comment markers +so the raw markdown stays clean for human readers: + +``` + +...content... + +``` + +`` is `__` (8-digit sequence), +e.g. `ep_20260601_00000001`: + +| Segment | Source | +|---|---| +| `prefix` | `Frontmatter.ENTRY_ID_PREFIX` (declared by the schema subclass) | +| `` | The daily-log file's date bucket | +| `NNNNNNNN` | Per-file sequence, 8-digit zero-padded, restarts at `00000001` each day per scope | + +Implementation: [`core/persistence/markdown/entry_id.py`](../src/everos/core/persistence/markdown/entry_id.py) +(`EntryId.parse / format / next_for`). + +> **File-level seq, not global**: the same `ep_20260601_00000001` may +> appear across two different `user_id`s (each user has its own daily file). +> Cross-table joins must therefore key on **`(scope_id, entry_id)`** +> rather than `entry_id` alone — see SQLite/LanceDB tables that follow. + +## 5. SQLite + LanceDB derived indexes + +``` +.index/ +├── sqlite/ +│ └── system.db state / audit log / task queue / LSN watermark +│ + per-kind business state tables (composite key) +└── lancedb/ + └── .lance/ one Arrow-based table per kind + stores text / vector / tags / metadata +``` + +- **SQLite** schema lives in + [`infra/persistence/sqlite/tables/`](../src/everos/infra/persistence/sqlite/tables/); + every business table that joins back to markdown declares a + `UniqueConstraint("user_id", "entry_id")` (or `agent_id` symmetric). +- **LanceDB** schemas live in + [`infra/persistence/lancedb/tables/`](../src/everos/infra/persistence/lancedb/tables/); + `Vector(N)` dimension matches the embedding model output. + +Both layers are **fully derivable from markdown** — wipe `.index/` +and the in-process cascade subsystem re-builds everything by scanning the +user-visible tree (the durable `md_change_state` SQLite queue covers +crash-recovery replay). + +## 6. Atomic write semantics + +`MarkdownWriter` uses a same-directory temp file +(`..tmp.`) + `os.replace` for atomicity. Keeping the temp +file in the same directory guarantees `os.replace` is atomic on POSIX +(the rename is only atomic within a single filesystem). + +`MarkdownWriter.append_entry` reads → merges frontmatter → +appends an entry block → atomic write back. The caller passes a full +`EntryId` (built via `EntryId.next_for(prefix, date, current_count)`); +this primitive is **schema-agnostic** — field-level semantics +(`entry_count` / `last_appended_at`) are a business writer's job +(see `BaseDailyAppender._frontmatter_updates` in +[`infra/persistence/markdown/writers/base.py`](../src/everos/infra/persistence/markdown/writers/base.py)). + +## 7. References + +- Skill: [`/add-memory-kind`](../.claude/skills/add-memory-kind/SKILL.md) +- Code: + - [`core/persistence/memory_root.py`](../src/everos/core/persistence/memory_root.py) + - [`core/persistence/markdown/`](../src/everos/core/persistence/markdown/) + - [`infra/persistence/{markdown,sqlite,lancedb}/`](../src/everos/infra/persistence/) + - [`memory/layout.py`](../src/everos/memory/layout.py) diff --git a/methods/EverCore/data_format/__init__.py b/evaluation/__init__.py old mode 100755 new mode 100644 similarity index 100% rename from methods/EverCore/data_format/__init__.py rename to evaluation/__init__.py diff --git a/methods/EverCore/.devcontainer/devcontainer.json b/methods/EverCore/.devcontainer/devcontainer.json deleted file mode 100644 index c0020254..00000000 --- a/methods/EverCore/.devcontainer/devcontainer.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "name": "EverCore", - "dockerComposeFile": ["../docker-compose.yaml", "docker-compose.devcontainer.yaml"], - "service": "devcontainer", - "workspaceFolder": "/workspace", - - "features": { - "ghcr.io/devcontainers/features/common-utils:2": { - "installZsh": true, - "configureZshAsDefaultShell": true, - "installOhMyZsh": true, - "upgradePackages": true - }, - "ghcr.io/devcontainers/features/git:1": {}, - "ghcr.io/devcontainers/features/github-cli:1": {} - }, - - "customizations": { - "vscode": { - "extensions": [ - "ms-python.python", - "ms-python.vscode-pylance", - "ms-python.black-formatter", - "ms-python.isort", - "charliermarsh.ruff", - "tamasfe.even-better-toml", - "redhat.vscode-yaml", - "ms-azuretools.vscode-docker" - ], - "settings": { - "python.defaultInterpreterPath": "/workspace/.venv/bin/python", - "python.terminal.activateEnvironment": true, - "[python]": { - "editor.defaultFormatter": "ms-python.black-formatter", - "editor.formatOnSave": true, - "editor.codeActionsOnSave": { - "source.organizeImports": "explicit" - } - } - } - } - }, - - "forwardPorts": [1995, 27017, 19200, 19530, 6379, 9000, 9001], - "portsAttributes": { - "1995": { "label": "EverCore API", "onAutoForward": "notify" }, - "27017": { "label": "MongoDB", "onAutoForward": "silent" }, - "19200": { "label": "Elasticsearch", "onAutoForward": "silent" }, - "19530": { "label": "Milvus", "onAutoForward": "silent" }, - "6379": { "label": "Redis", "onAutoForward": "silent" }, - "9000": { "label": "MinIO API", "onAutoForward": "silent" }, - "9001": { "label": "MinIO Console", "onAutoForward": "silent" } - }, - - "postCreateCommand": "bash .devcontainer/postCreate.sh", - "postStartCommand": "bash .devcontainer/postStart.sh", - - "hostRequirements": { - "cpus": 4, - "memory": "16gb", - "storage": "32gb" - } -} diff --git a/methods/EverCore/.devcontainer/docker-compose.devcontainer.yaml b/methods/EverCore/.devcontainer/docker-compose.devcontainer.yaml deleted file mode 100644 index ecf8e46e..00000000 --- a/methods/EverCore/.devcontainer/docker-compose.devcontainer.yaml +++ /dev/null @@ -1,23 +0,0 @@ -version: '3.8' - -services: - devcontainer: - image: ghcr.io/astral-sh/uv:python3.12-bookworm - container_name: evermemos-devcontainer - volumes: - - ..:/workspace:cached - command: sleep infinity - environment: - # Infrastructure service hostnames (use Docker service names) - REDIS_HOST: redis - MONGODB_HOST: mongodb - ES_HOSTS: http://elasticsearch:9200 - MILVUS_HOST: milvus-standalone - MINIO_ADDRESS: milvus-minio:9000 - networks: - - memsys-network - depends_on: - - mongodb - - elasticsearch - - redis - - milvus-standalone diff --git a/methods/EverCore/.devcontainer/postCreate.sh b/methods/EverCore/.devcontainer/postCreate.sh deleted file mode 100755 index 9dcbfd30..00000000 --- a/methods/EverCore/.devcontainer/postCreate.sh +++ /dev/null @@ -1,44 +0,0 @@ -#!/bin/bash -set -e - -echo "=== EverCore Dev Container Setup ===" - -# Install system dependencies -echo "Installing system dependencies..." -apt-get update -apt-get install -y libgl1 libgomp1 libglib2.0-0 ffmpeg vim wget curl zip unzip g++ build-essential - -# Install Python dependencies with uv -echo "Installing Python dependencies..." -cd /workspace -uv sync --dev - -# Copy environment template if .env doesn't exist -if [ ! -f .env ]; then - echo "Creating .env from template..." - cp env.template .env - - # Update hostnames for Docker network - sed -i 's/REDIS_HOST=localhost/REDIS_HOST=redis/' .env - sed -i 's/MONGODB_HOST=localhost/MONGODB_HOST=mongodb/' .env - sed -i 's|ES_HOSTS=http://localhost:19200|ES_HOSTS=http://elasticsearch:9200|' .env - sed -i 's/MILVUS_HOST=localhost/MILVUS_HOST=milvus-standalone/' .env - - echo "" - echo "NOTE: .env file created with Docker service hostnames." - echo "Please update API keys (LLM_API_KEY, VECTORIZE_API_KEY, etc.) before running." -fi - -# Install pre-commit hooks -echo "Setting up pre-commit hooks..." -uv run pre-commit install || true - -echo "" -echo "=== Setup Complete ===" -echo "" -echo "To start the application:" -echo " make run" -echo "" -echo "Or manually:" -echo " uv run python src/run.py" -echo "" diff --git a/methods/EverCore/.devcontainer/postStart.sh b/methods/EverCore/.devcontainer/postStart.sh deleted file mode 100755 index 00a3ae6d..00000000 --- a/methods/EverCore/.devcontainer/postStart.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash - -echo "=== Waiting for infrastructure services ===" - -# Function to wait for a service using timeout and bash tcp check -wait_for_service() { - local name=$1 - local host=$2 - local port=$3 - local max_attempts=${4:-30} - local attempt=1 - - echo -n "Waiting for $name ($host:$port)..." - while ! timeout 1 bash -c "echo > /dev/tcp/$host/$port" 2>/dev/null; do - if [ $attempt -ge $max_attempts ]; then - echo " timeout (service may still be starting)" - return 1 - fi - sleep 2 - attempt=$((attempt + 1)) - done - echo " ready" -} - -# Wait for core services -wait_for_service "MongoDB" "mongodb" 27017 -wait_for_service "Redis" "redis" 6379 -wait_for_service "Elasticsearch" "elasticsearch" 9200 60 -wait_for_service "Milvus" "milvus-standalone" 19530 90 - -echo "" -echo "=== Infrastructure Ready ===" -echo "" -echo "Available services:" -echo " - MongoDB: mongodb:27017" -echo " - Redis: redis:6379" -echo " - Elasticsearch: elasticsearch:9200" -echo " - Milvus: milvus-standalone:19530" -echo " - MinIO Console: milvus-minio:9001" -echo "" -echo "Run 'make run' to start EverCore" -echo "" diff --git a/methods/EverCore/.dockerignore b/methods/EverCore/.dockerignore deleted file mode 100644 index 4d10ab75..00000000 --- a/methods/EverCore/.dockerignore +++ /dev/null @@ -1,119 +0,0 @@ -# Git 相关 -.git -.gitignore -.gitattributes - -# Python 相关 -__pycache__/ -*.py[cod] -*$py.class -*.so -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# 虚拟环境 -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# IDE 相关 -.vscode/ -.idea/ -*.swp -*.swo -*~ - -# 操作系统相关 -.DS_Store -.DS_Store? -._* -.Spotlight-V100 -.Trashes -ehthumbs.db -Thumbs.db - -# 日志文件 -*.log -logs/ -log/ - -# 临时文件 -*.tmp -*.temp -.tmp/ -.temp/ - -# 测试相关 -.pytest_cache/ -.coverage -htmlcov/ -.tox/ -.nox/ -coverage.xml -*.cover -.hypothesis/ - -# Jupyter Notebook -.ipynb_checkpoints - -# pyenv -.python-version - -# pipenv -Pipfile.lock - -# PEP 582 -__pypackages__/ - -# Celery -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# 环境变量文件 -.env.local -.env.development.local -.env.test.local -.env.production.local - -# 数据库文件 -*.db -*.sqlite -*.sqlite3 - -# 缓存文件 -.cache/ -*.cache - -# Docker 相关 -docker-compose.override.yml -docker-compose.*.yml - -# 其他 -*.bak -*.backup -*.orig -*.rej -*.patch -*.diff diff --git a/methods/EverCore/.pre-commit-config.yaml b/methods/EverCore/.pre-commit-config.yaml deleted file mode 100644 index 9c877b55..00000000 --- a/methods/EverCore/.pre-commit-config.yaml +++ /dev/null @@ -1,48 +0,0 @@ -repos: -- repo: https://github.com/psf/black - rev: 24.3.0 # Use latest stable version - hooks: - - id: black - stages: [pre-commit] - -# Local hooks for non-English character detection -- repo: local - hooks: - # Check for non-English (CJK) characters in staged files - - id: check-i18n-files - name: Check Non-English Characters in Files - entry: env PYTHONPATH=src python -m devops_scripts.i18n.i18n_tool hook - language: system - stages: [pre-commit] - pass_filenames: true - # Skip this hook by setting SKIP_I18N_CHECK=1 - - # Check for non-English (CJK) characters in commit message - - id: check-i18n-commit-msg - name: Check Non-English Characters in Commit Message - entry: env PYTHONPATH=src python -m devops_scripts.i18n.i18n_tool hook --commit-msg - language: system - stages: [commit-msg] - pass_filenames: true - always_run: true - # Skip by adding [skip-i18n] or #skip-i18n-check in commit message - - # Enforce Conventional Commits format in commit messages - - id: check-conventional-commit - name: Check Conventional Commit Message - entry: env PYTHONPATH=src python -m devops_scripts.commitlint.conventional_commit_lint hook - language: system - stages: [commit-msg] - pass_filenames: true - always_run: true - # Skip this hook by setting SKIP_CONVENTIONAL_COMMIT_CHECK=1 - - # Check for sensitive information in staged files - - id: check-sensitive-info - name: Check Sensitive Information - entry: env PYTHONPATH=src python -m devops_scripts.sensitive_info.sensitive_info_tool hook - language: system - stages: [pre-commit] - pass_filenames: true - # Skip this hook by setting SKIP_SENSITIVE_CHECK=1 - # Skip specific lines by adding #skip-sensitive-check comment diff --git a/methods/EverCore/.pylintrc b/methods/EverCore/.pylintrc deleted file mode 100644 index 38eccd80..00000000 --- a/methods/EverCore/.pylintrc +++ /dev/null @@ -1,2 +0,0 @@ -[MASTER] -init-hook='import sys; sys.path.append("./src"); sys.path.append("./demo"); sys.path.append("./evaluation"); sys.path.append("./data_format")' \ No newline at end of file diff --git a/methods/EverCore/.vscode/launch.json b/methods/EverCore/.vscode/launch.json deleted file mode 100644 index ee29aefa..00000000 --- a/methods/EverCore/.vscode/launch.json +++ /dev/null @@ -1,66 +0,0 @@ -{ - // Use IntelliSense to learn about possible attributes. - // Hover to view descriptions of existing attributes. - // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 - "version": "0.2.0", - "configurations": [ - { - "name": "Python Debugger: run", - "type": "debugpy", - "request": "launch", - "env": { - "PYTHONPATH": "${workspaceFolder}/src" - }, - "envFile": "${workspaceFolder}/.env", - "cwd": "${workspaceFolder}/src", - "python": "${workspaceFolder}/.venv/bin/python", - "program": "run.py", - "console": "integratedTerminal", - "justMyCode": false - }, - { - "name": "Python Debugger: task", - "type": "debugpy", - "request": "launch", - "env": { - "PYTHONPATH": "${workspaceFolder}/src" - }, - "envFile": "${workspaceFolder}/.env", - "cwd": "${workspaceFolder}/src", - "program": "${workspaceFolder}/.venv/bin/arq", - "args": ["task.WorkerSettings"], - "console": "integratedTerminal", - "justMyCode": false - }, - { - "name": "Python Debugger: longjob", - "type": "debugpy", - "request": "launch", - "env": { - "PYTHONPATH": "${workspaceFolder}/src" - }, - "envFile": "${workspaceFolder}/.env", - "cwd": "${workspaceFolder}/src", - "python": "${workspaceFolder}/.venv/bin/python", - "program": "run.py", - "args": ["--longjob", "kafka_consumer"], - "console": "integratedTerminal", - "justMyCode": false - }, - { - "name": "Python Debugger: run_this_file", - "type": "debugpy", - "request": "launch", - "env": { - "PYTHONPATH": "${workspaceFolder}/src" - }, - "envFile": "${workspaceFolder}/.env", - "cwd": "${workspaceFolder}/src", - "python": "${workspaceFolder}/.venv/bin/python", - "program": "${workspaceFolder}/src/bootstrap.py", - "args": ["${file}"], - "console": "integratedTerminal", - "justMyCode": false - } - ] -} \ No newline at end of file diff --git a/methods/EverCore/.vscode/settings.json b/methods/EverCore/.vscode/settings.json deleted file mode 100644 index 7a73a41b..00000000 --- a/methods/EverCore/.vscode/settings.json +++ /dev/null @@ -1,2 +0,0 @@ -{ -} \ No newline at end of file diff --git a/methods/EverCore/CONTRIBUTING.md b/methods/EverCore/CONTRIBUTING.md deleted file mode 100644 index 3f340484..00000000 --- a/methods/EverCore/CONTRIBUTING.md +++ /dev/null @@ -1,182 +0,0 @@ -# Contributing to EverOS - -Thank you for your interest in contributing to EverOS! We welcome contributions from the community. - -## 🚀 Getting Started - -### Prerequisites - -- Python 3.10+ -- Docker and Docker Compose -- `uv` package manager - -### Development Setup - -1. Fork and clone the repository: -```bash -git clone https://github.com/YOUR_USERNAME/EverOS.git -cd EverOS -``` - -2. Install dependencies: -```bash -uv sync -``` - -3. Set up environment variables: -```bash -cp env.template .env -# Edit .env with your configuration -``` - -4. Start development services: -```bash -docker-compose up -d -``` - -## 📝 Code Style - -### Python Guidelines - -- Follow [PEP 8](https://pep8.org/) style guide -- Use type hints for function parameters and return values -- Add docstrings for classes and functions -- Maximum line length: 100 characters - -### Key Rules - -- **No relative imports**: Use absolute imports from project root -- **No wildcard imports**: Avoid `from module import *` -- **DateTime handling**: Use `common_utils.datetime_utils` instead of direct `datetime` module -- **No code in `__init__.py`**: Use only as package markers - -## 🔀 Git Workflow - -### Branch Naming - -- `feature/description` - New features -- `fix/description` - Bug fixes -- `docs/description` - Documentation updates -- `refactor/description` - Code refactoring - -### Commit Messages - -We use [Gitmoji](https://gitmoji.dev/) for commit messages. - -**Format**: ` : ` - -**Examples**: -``` -✨ feat: Add new memory retrieval algorithm -🐛 fix: Fix memory leak in vector indexing -📝 docs: Update API documentation -♻️ refactor: Simplify memory extraction logic -✅ test: Add tests for profile extraction -⚡ perf: Optimize vector search performance -``` - -**Common Gitmoji**: - -| Emoji | Code | Usage | -|-------|------|-------| -| ✨ | `:sparkles:` | New feature | -| 🐛 | `:bug:` | Bug fix | -| 🚑 | `:ambulance:` | Critical hotfix | -| 📝 | `:memo:` | Documentation | -| ♻️ | `:recycle:` | Refactor code | -| 🔥 | `:fire:` | Remove code/files | -| ✅ | `:white_check_mark:` | Add tests | -| ⚡ | `:zap:` | Performance improvement | -| 🔧 | `:wrench:` | Configuration changes | -| 🗃️ | `:card_file_box:` | Database changes | -| ⬆️ | `:arrow_up:` | Upgrade dependencies | -| 🐳 | `:whale:` | Docker related | -| 🚀 | `:rocket:` | Deployment | - -See [gitmoji.dev](https://gitmoji.dev/) for full reference. - -### Pull Request Process - -1. **Create a feature branch** from `main`: -```bash -git checkout -b feature/your-feature-name -``` - -2. **Make your changes** following the code style guidelines - -3. **Test your changes**: -```bash -# Run tests (if applicable) -pytest tests/ - -# Check code style -ruff check . -``` - -4. **Commit your changes** with clear, descriptive commit messages - -5. **Push to your fork**: -```bash -git push origin feature/your-feature-name -``` - -6. **Open a Pull Request** with: - - Clear description of changes - - Reference to related issues (if any) - - Screenshots (if UI changes) - -7. **Address review feedback** promptly - -## 🧪 Testing - -- Add tests for new features -- Ensure existing tests pass -- Maintain or improve code coverage - -## 📚 Documentation - -- Update relevant documentation when changing functionality -- Add docstrings to new functions and classes -- Update README.md if adding major features -- Keep API documentation in sync with code changes - -## 🐛 Reporting Bugs - -Please report bugs by [creating a bug report](https://github.com/EverMind-AI/EverOS/issues/new?template=bug_report.md) with: - -- Clear, descriptive title -- Steps to reproduce -- Expected vs actual behavior -- Environment details (OS, Python version, etc.) -- Relevant logs or error messages - -## 💡 Suggesting Features - -Feature requests are welcome! Please [submit a feature request](https://github.com/EverMind-AI/EverOS/issues/new?template=feature_request.md) with: - -- Check if the feature is already requested -- Provide clear use cases -- Explain why this feature would be useful -- Consider backward compatibility - -## 📄 License - -By contributing to EverOS, you agree that your contributions will be licensed under the [Apache License 2.0](LICENSE). - -## 🤝 Code of Conduct - -- Be respectful and inclusive -- Welcome newcomers -- Focus on constructive feedback -- Maintain a professional environment - -## 📞 Questions? - -- Open a [Discussion](https://github.com/EverMind-AI/EverOS/discussions) -- Join our community channels -- Email: evermind@shanda.com - ---- - -Thank you for contributing to EverOS! 🎉 - diff --git a/methods/EverCore/Dockerfile b/methods/EverCore/Dockerfile deleted file mode 100644 index 5503d755..00000000 --- a/methods/EverCore/Dockerfile +++ /dev/null @@ -1,14 +0,0 @@ -FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim -RUN apt-get update && apt-get upgrade -y && \ - apt-get install libgl1 libgomp1 libglib2.0-0 ffmpeg vim wget curl zip unzip g++ build-essential procps -y && \ - mkdir /app - -# 设置工作目录 -WORKDIR /app - -# 复制当前目录下的所有文件到工作目录 -COPY . /app - -RUN uv sync --frozen -EXPOSE 1995 -CMD ["uv", "run", "python", "src/run.py"] \ No newline at end of file diff --git a/methods/EverCore/Makefile b/methods/EverCore/Makefile deleted file mode 100644 index 4619c03d..00000000 --- a/methods/EverCore/Makefile +++ /dev/null @@ -1,47 +0,0 @@ -.PHONY: dev-setup setup-hooks lint test clean help - -# Default target -help: - @echo "Available targets:" - @echo " dev-setup - Full dev environment setup (sync deps + install hooks)" - @echo " setup-hooks - Install pre-commit hooks only" - @echo " lint - Run linters" - @echo " test - Run tests" - @echo " clean - Clean up generated files" - @echo " help - Show this help message" - -# Full development environment setup -dev-setup: - @echo "Setting up development environment..." - @echo "1. Syncing dependencies..." - uv sync --dev - @echo "2. Installing pre-commit hooks..." - @$(MAKE) setup-hooks - @echo "Development environment is ready!" - -# Install pre-commit hooks (clean install) -setup-hooks: - @echo "Removing existing hooks..." - rm -f .git/hooks/pre-commit .git/hooks/commit-msg - @echo "Installing pre-commit hooks..." - pre-commit install --hook-type pre-commit --hook-type commit-msg - @echo "Done! Pre-commit hooks installed." - -# Run linters -lint: - @echo "Running black..." - black src/ - @echo "Running i18n check..." - PYTHONPATH=src python -m devops_scripts.i18n.i18n_tool check - -# Run tests -test: - PYTHONPATH=src pytest tests/ - -# Clean up -clean: - find . -type d -name "__pycache__" -exec rm -rf {} + 2>/dev/null || true - find . -type f -name "*.pyc" -delete 2>/dev/null || true - find . -type f -name ".coverage" -delete 2>/dev/null || true - rm -rf .pytest_cache 2>/dev/null || true - @echo "Cleaned up generated files." diff --git a/methods/EverCore/README.md b/methods/EverCore/README.md deleted file mode 100644 index f90fef06..00000000 --- a/methods/EverCore/README.md +++ /dev/null @@ -1,31 +0,0 @@ -# EverCore - -EverCore is the long-term memory operating system at the center of EverOS. It extracts, structures, and retrieves durable knowledge from conversations so agents can remember across sessions and adapt over time. - -## Start Here - -| Goal | Link | -| :--- | :--- | -| Install and run EverCore locally | [Setup Guide](docs/installation/SETUP.md) | -| Browse the documentation index | [Documentation](docs/) | -| Try usage examples and demos | [Usage Examples](docs/usage/USAGE_EXAMPLES.md) | -| Review the architecture | [Architecture](docs/ARCHITECTURE.md) | -| Run evaluations | [Evaluation Guide](evaluation/) | - -## Quick Start - -```bash -docker compose up -d -uv sync -uv run python src/run.py -``` - -The server runs at `http://localhost:1995` by default. See the [full setup guide](docs/installation/SETUP.md) for environment variables, service configuration, and troubleshooting. - -## Folder Guide - -- [src/](src/) - application, memory, infrastructure, and API layers. -- [docs/](docs/) - setup, usage, architecture, API, and development documentation. -- [demo/](demo/) - interactive examples and memory extraction demos. -- [evaluation/](evaluation/) - benchmark runners and reports. -- [tests/](tests/) - unit and integration tests. diff --git a/methods/EverCore/config.json b/methods/EverCore/config.json deleted file mode 100644 index 6361b503..00000000 --- a/methods/EverCore/config.json +++ /dev/null @@ -1,150 +0,0 @@ -{ - "version": "1.0.0", - "description": "记忆系统项目路径配置文件", - "last_updated": "2025-01-03", - - "paths": { - "root": "/Users/admin/Desktop/episodic_memory/memsys", - - "data": { - "description": "原始数据存储路径", - "base": "src/storage_layer/dynamic_memory/main/data", - "team_chat": "src/storage_layer/dynamic_memory/main/data/team_chat", - "private_chat": "src/storage_layer/dynamic_memory/main/data/private_chat", - "test_data": "src/storage_layer/dynamic_memory/main/data/test_data" - }, - - "memory": { - "description": "记忆数据存储路径", - "base": "memory_base/dynamic_memory_base/memory", - "episode": "memory_base/dynamic_memory_base/memory/episode", - "personal": "memory_base/dynamic_memory_base/memory/personal", - "cooperation": "memory_base/dynamic_memory_base/memory/cooperation", - "subdirs": { - "personal_event": "memory_base/dynamic_memory_base/memory/personal/event", - "personal_profile": "memory_base/dynamic_memory_base/memory/personal/profile", - "personal_relationship": "memory_base/dynamic_memory_base/memory/personal/relationship", - "cooperation_event": "memory_base/dynamic_memory_base/memory/cooperation/event", - "cooperation_group_profile": "memory_base/dynamic_memory_base/memory/cooperation/profile", - "cooperation_department_profile": "memory_base/dynamic_memory_base/memory/cooperation/profile" - } - }, - - "temporary": { - "description": "临时文件、状态文件和日志文件的统一存储位置 - 仅在评估环境中可用", - "base": "evaluation_memory_offline/temporary", - "logs": { - "description": "所有日志文件", - "base": "evaluation_memory_offline/temporary/logs", - "main_processing": "evaluation_memory_offline/temporary/logs/memory_processing.log", - "api": "evaluation_memory_offline/temporary/logs/api.log", - "evaluation": "evaluation_memory_offline/temporary/logs/evaluation.log", - "debug": { - "base": "evaluation_memory_offline/temporary/logs/debug", - "files": [ - "evaluation_memory_offline/temporary/logs/debug/api_debug.log", - "evaluation_memory_offline/temporary/logs/debug/processor_debug.log", - "evaluation_memory_offline/temporary/logs/debug/task_processor_debug.log" - ] - } - }, - "states": { - "description": "状态和进度文件", - "base": "evaluation_memory_offline/temporary/states", - "processing": "evaluation_memory_offline/temporary/states/processing", - "progress": "evaluation_memory_offline/temporary/states/progress.json", - "current_task": "evaluation_memory_offline/temporary/states/current_task.json" - }, - "cache": { - "description": "缓存文件目录", - "base": "evaluation_memory_offline/temporary/cache" - } - }, - - "processing": { - "description": "处理状态和进度文件（已迁移到temporary） - 仅在评估环境中可用", - "states_dir": "evaluation_memory_offline/temporary/states/processing", - "progress_status": "evaluation_memory_offline/temporary/states/progress.json", - "current_task": "evaluation_memory_offline/temporary/states/current_task.json" - }, - - "logs": { - "description": "日志文件路径（已迁移到temporary） - 仅在评估环境中可用", - "main_processing": "evaluation_memory_offline/temporary/logs/memory_processing.log", - "display_api": "evaluation_memory_offline/temporary/logs/api.log", - "evaluation": "evaluation_memory_offline/temporary/logs/evaluation.log" - }, - - "evaluation": { - "description": "评估系统相关路径", - "base": "evaluation", - "reports": "evaluation/reports", - "prompts": "evaluation/prompts", - "scripts": { - "main_eval": "evaluation/main_eval.py", - "run_real_eval": "evaluation/run_real_eval.py", - "run_with_progress": "evaluation/run_evaluation_with_progress.py", - "evaluation_runner": "evaluation/evaluation_runner.py" - } - }, - - "prompts": { - "description": "提示词模板路径", - "base": "src/storage_layer/dynamic_memory/main/prompt", - "boundary_detection": "src/storage_layer/dynamic_memory/main/prompt/boundary_detection.txt", - - "event_extraction": "src/storage_layer/dynamic_memory/main/prompt/event_extraction.txt", - "relationship_update": "src/storage_layer/dynamic_memory/main/prompt/relationship_update.txt", - "system_instructions": "src/storage_layer/dynamic_memory/main/prompt/system_instructions.txt", - "episode": "src/storage_layer/dynamic_memory/main/prompt/episode", - "profile": "src/storage_layer/dynamic_memory/main/prompt/profile" - }, - - "web_interface": { - "description": "Web界面相关路径", - "base": "display", - "static_files": { - "html": "display/index.html", - "css": "display/style.css", - "js": "display/script.js" - }, - "api_server": "display/api.py", - "requirements": "display/requirements.txt" - }, - - "scripts": { - "description": "运行脚本路径", - "main_script": "main/main_script.py", - "run_script": "main/run_script.sh", - "clean_system": "main/clean_system.py", - "example_usage": "main/example_usage.sh", - "start_web": "display/start_web.sh" - }, - - "temp": { - "description": "临时文件和测试路径", - "test_memory": "/tmp/test_memory", - "test_data_dirs": [ - "main/test_data/team_chats", - "main/test_data/private_chats" - ] - } - }, - - "default_settings": { - "description": "默认路径配置", - "log_level": "INFO", - "max_concurrent": 4, - "backup_count": 5, - "auto_cleanup": true - }, - - "file_patterns": { - "description": "文件名模式配置", - "chat_data": "*.json", - "memory_files": "*_memory.json", - "state_files": "state_*.json", - "log_files": "*.log", - "evaluation_reports": "evaluation_*.json" - } -} diff --git a/methods/EverCore/data/locomo10.json b/methods/EverCore/data/locomo10.json deleted file mode 100644 index 12b0c93e..00000000 --- a/methods/EverCore/data/locomo10.json +++ /dev/null @@ -1,66751 +0,0 @@ -[ - { - "qa": [ - { - "question": "When did Caroline go to the LGBTQ support group?", - "answer": "7 May 2023", - "evidence": [ - "D1:3" - ], - "category": 2 - }, - { - "question": "When did Melanie paint a sunrise?", - "answer": 2022, - "evidence": [ - "D1:12" - ], - "category": 2 - }, - { - "question": "What fields would Caroline be likely to pursue in her educaton?", - "answer": "Psychology, counseling certification", - "evidence": [ - "D1:9", - "D1:11" - ], - "category": 3 - }, - { - "question": "What did Caroline research?", - "answer": "Adoption agencies", - "evidence": [ - "D2:8" - ], - "category": 1 - }, - { - "question": "What is Caroline's identity?", - "answer": "Transgender woman", - "evidence": [ - "D1:5" - ], - "category": 1 - }, - { - "question": "When did Melanie run a charity race?", - "answer": "The sunday before 25 May 2023", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "When is Melanie planning on going camping?", - "answer": "June 2023", - "evidence": [ - "D2:7" - ], - "category": 2 - }, - { - "question": "What is Caroline's relationship status?", - "answer": "Single", - "evidence": [ - "D3:13", - "D2:14" - ], - "category": 1 - }, - { - "question": "When did Caroline give a speech at a school?", - "answer": "The week before 9 June 2023", - "evidence": [ - "D3:1" - ], - "category": 2 - }, - { - "question": "When did Caroline meet up with her friends, family, and mentors?", - "answer": "The week before 9 June 2023", - "evidence": [ - "D3:11" - ], - "category": 2 - }, - { - "question": "How long has Caroline had her current group of friends for?", - "answer": "4 years", - "evidence": [ - "D3:13" - ], - "category": 2 - }, - { - "question": "Where did Caroline move from 4 years ago?", - "answer": "Sweden", - "evidence": [ - "D3:13", - "D4:3" - ], - "category": 1 - }, - { - "question": "How long ago was Caroline's 18th birthday?", - "answer": "10 years ago", - "evidence": [ - "D4:5" - ], - "category": 2 - }, - { - "question": "What career path has Caroline decided to persue?", - "answer": "counseling or mental health for Transgender people", - "evidence": [ - "D4:13", - "D1:11" - ], - "category": 1 - }, - { - "question": "Would Caroline still want to pursue counseling as a career if she hadn't received support growing up?", - "answer": "Likely no", - "evidence": [ - "D4:15", - "D3:5" - ], - "category": 3 - }, - { - "question": "What activities does Melanie partake in?", - "answer": "pottery, camping, painting, swimming", - "evidence": [ - "D5:4", - "D9:1", - "D1:12", - "D1:18" - ], - "category": 1 - }, - { - "question": "When did Melanie sign up for a pottery class?", - "answer": "2 July 2023", - "evidence": [ - "D5:4" - ], - "category": 2 - }, - { - "question": "When is Caroline going to the transgender conference?", - "answer": "July 2023", - "evidence": [ - "D5:13" - ], - "category": 2 - }, - { - "question": "Where has Melanie camped?", - "answer": "beach, mountains, forest", - "evidence": [ - "D6:16", - "D4:6", - "D8:32" - ], - "category": 1 - }, - { - "question": "What do Melanie's kids like?", - "answer": "dinosaurs, nature", - "evidence": [ - "D6:6", - "D4:8" - ], - "category": 1 - }, - { - "question": "When did Melanie go to the museum?", - "answer": "5 July 2023", - "evidence": [ - "D6:4" - ], - "category": 2 - }, - { - "question": "When did Caroline have a picnic?", - "answer": "The week before 6 July 2023", - "evidence": [ - "D6:11" - ], - "category": 2 - }, - { - "question": "Would Caroline likely have Dr. Seuss books on her bookshelf?", - "answer": "Yes, since she collects classic children's books", - "evidence": [ - "D6:9" - ], - "category": 3 - }, - { - "question": "What books has Melanie read?", - "answer": "\"Nothing is Impossible\", \"Charlotte's Web\"", - "evidence": [ - "D7:8", - "D6:10" - ], - "category": 1 - }, - { - "question": "What does Melanie do to destress?", - "answer": "Running, pottery", - "evidence": [ - "D7:22", - "D5:4" - ], - "category": 1 - }, - { - "question": "When did Caroline go to the LGBTQ conference?", - "answer": "10 July 2023", - "evidence": [ - "D7:1" - ], - "category": 2 - }, - { - "question": "When did Melanie read the book \"nothing is impossible\"?", - "answer": 2022, - "evidence": [ - "D7:8" - ], - "category": 2 - }, - { - "question": "Would Caroline pursue writing as a career option?", - "answer": "LIkely no; though she likes reading, she wants to be a counselor", - "evidence": [ - "D7:5", - "D7:9" - ], - "category": 3 - }, - { - "question": "When did Caroline go to the adoption meeting?", - "answer": "The friday before 15 July 2023", - "evidence": [ - "D8:9" - ], - "category": 2 - }, - { - "question": "When did Melanie go to the pottery workshop?", - "answer": "The Friday before 15 July 2023", - "evidence": [ - "D8:2" - ], - "category": 2 - }, - { - "question": "Would Melanie be considered a member of the LGBTQ community?", - "answer": "Likely no, she does not refer to herself as part of it", - "evidence": [], - "category": 3 - }, - { - "question": "When did Melanie go camping in June?", - "answer": "The week before 27 June 2023", - "evidence": [ - "D4:8" - ], - "category": 2 - }, - { - "question": "What LGBTQ+ events has Caroline participated in?", - "answer": "Pride parade, school speech, support group", - "evidence": [ - "D5:1", - "D8:17", - "D3:1", - "D1:3" - ], - "category": 1 - }, - { - "question": "When did Caroline go to a pride parade during the summer?", - "answer": "The week before 3 July 2023", - "evidence": [ - "D5:1" - ], - "category": 2 - }, - { - "question": "What events has Caroline participated in to help children?", - "answer": "Mentoring program, school speech", - "evidence": [ - "D9:2", - "D3:3" - ], - "category": 1 - }, - { - "question": "When did Melanie go camping in July?", - "answer": "two weekends before 17 July 2023", - "evidence": [ - "D9:1" - ], - "category": 2 - }, - { - "question": "When did Caroline join a mentorship program?", - "answer": "The weekend before 17 July 2023", - "evidence": [ - "D9:2" - ], - "category": 2 - }, - { - "question": "What did Melanie paint recently?", - "answer": "sunset", - "evidence": [ - "D8:6; D9:17" - ], - "category": 1 - }, - { - "question": "What activities has Melanie done with her family?", - "answer": "Pottery, painting, camping, museum, swimming, hiking", - "evidence": [ - "D8:4", - "D8:6", - "D9:1", - "D6:4", - "D1:18", - "D3:14" - ], - "category": 1 - }, - { - "question": "In what ways is Caroline participating in the LGBTQ community?", - "answer": "Joining activist group, going to pride parades, participating in an art show, mentoring program", - "evidence": [ - "D10:3", - "D5:1", - "D9:12", - "D9:2" - ], - "category": 1 - }, - { - "question": "How many times has Melanie gone to the beach in 2023?", - "answer": 2, - "evidence": [ - "D10:8", - "D6:16" - ], - "category": 1 - }, - { - "question": "When did Caroline join a new activist group?", - "answer": "The Tuesday before 20 July 2023", - "evidence": [ - "D10:3" - ], - "category": 2 - }, - { - "question": "Would Melanie be more interested in going to a national park or a theme park?", - "answer": "National park; she likes the outdoors", - "evidence": [ - "D10:12", - "D10:14" - ], - "category": 3 - }, - { - "question": "What kind of art does Caroline make?", - "answer": "abstract art", - "evidence": [ - "D11:12", - "D11:8", - "D9:14" - ], - "category": 1 - }, - { - "question": "When is Melanie's daughter's birthday?", - "answer": "13 August", - "evidence": [ - "D11:1" - ], - "category": 2 - }, - { - "question": "When did Caroline attend a pride parade in August?", - "answer": "The Friday before 14 August 2023", - "evidence": [ - "D11:4" - ], - "category": 2 - }, - { - "question": "Would Melanie be considered an ally to the transgender community?", - "answer": "Yes, she is supportive", - "evidence": [], - "category": 3 - }, - { - "question": "Who supports Caroline when she has a negative experience?", - "answer": "Her mentors, family, and friends", - "evidence": [ - "D12:1", - "D3:11" - ], - "category": 1 - }, - { - "question": "What types of pottery have Melanie and her kids made?", - "answer": "bowls, cup", - "evidence": [ - "D12:14", - "D8:4", - "D5:6" - ], - "category": 1 - }, - { - "question": "When did Caroline and Melanie go to a pride fesetival together?", - "answer": 2022, - "evidence": [ - "D12:15" - ], - "category": 2 - }, - { - "question": "What would Caroline's political leaning likely be?", - "answer": "Liberal", - "evidence": [ - "D12:1" - ], - "category": 3 - }, - { - "question": "What has Melanie painted?", - "answer": "Horse, sunset, sunrise", - "evidence": [ - "D13:8", - "D8:6", - "D1:12" - ], - "category": 1 - }, - { - "question": "What are Melanie's pets' names?", - "answer": "Oliver, Luna, Bailey", - "evidence": [ - "D13:4", - "D7:18" - ], - "category": 1 - }, - { - "question": "When did Caroline apply to adoption agencies?", - "answer": "The week of 23 August 2023", - "evidence": [ - "D13:1" - ], - "category": 2 - }, - { - "question": "When did Caroline draw a self-portrait?", - "answer": "The week before 23 August 2023", - "evidence": [ - "D13:11" - ], - "category": 2 - }, - { - "question": "What subject have Caroline and Melanie both painted?", - "answer": "Sunsets", - "evidence": [ - "D14:5", - "D8:6" - ], - "category": 1 - }, - { - "question": "What symbols are important to Caroline?", - "answer": "Rainbow flag, transgender symbol", - "evidence": [ - "D14:15", - "D4:1" - ], - "category": 1 - }, - { - "question": "When did Caroline encounter people on a hike and have a negative experience?", - "answer": "The week before 25 August 2023", - "evidence": [ - "D14:1" - ], - "category": 2 - }, - { - "question": "When did Melanie make a plate in pottery class?", - "answer": "24 August 2023", - "evidence": [ - "D14:4" - ], - "category": 2 - }, - { - "question": "Would Caroline be considered religious?", - "answer": "Somewhat, but not extremely religious", - "evidence": [ - "D14:19", - "D12:1" - ], - "category": 3 - }, - { - "question": "What instruments does Melanie play?", - "answer": "clarinet and violin", - "evidence": [ - "D15:26", - "D2:5" - ], - "category": 1 - }, - { - "question": "What musical artists/bands has Melanie seen?", - "answer": "Summer Sounds, Matt Patterson", - "evidence": [ - "D15:16", - "D11:3" - ], - "category": 1 - }, - { - "question": "When did Melanie go to the park?", - "answer": "27 August 2023", - "evidence": [ - "D15:2" - ], - "category": 2 - }, - { - "question": "When is Caroline's youth center putting on a talent show?", - "answer": "September 2023", - "evidence": [ - "D15:11" - ], - "category": 2 - }, - { - "question": "Would Melanie likely enjoy the song \"The Four Seasons\" by Vivaldi?", - "answer": "Yes; it's classical music", - "evidence": [ - "D15:28" - ], - "category": 3 - }, - { - "question": "What are some changes Caroline has faced during her transition journey?", - "answer": "Changes to her body, losing unsupportive friends", - "evidence": [ - "D16:15", - "D11:14" - ], - "category": 1 - }, - { - "question": "What does Melanie do with her family on hikes?", - "answer": "Roast marshmallows, tell stories", - "evidence": [ - "D16:4", - "D10:12" - ], - "category": 1 - }, - { - "question": "When did Caroline go biking with friends?", - "answer": "The weekend before 13 September 2023", - "evidence": [ - "D16:1" - ], - "category": 2 - }, - { - "question": "How long has Melanie been practicing art?", - "answer": "Since 2016", - "evidence": [ - "D16:8" - ], - "category": 2 - }, - { - "question": "What personality traits might Melanie say Caroline has?", - "answer": "Thoughtful, authentic, driven", - "evidence": [ - "D16:18", - "D13:16", - "D7:4" - ], - "category": 3 - }, - { - "question": "What transgender-specific events has Caroline attended?", - "answer": "Poetry reading, conference", - "evidence": [ - "D17:19", - "D15:13" - ], - "category": 1 - }, - { - "question": "What book did Melanie read from Caroline's suggestion?", - "answer": "\"Becoming Nicole\"", - "evidence": [ - "D7:11", - "D17:10" - ], - "category": 1 - }, - { - "question": "When did Melanie's friend adopt a child?", - "answer": 2022, - "evidence": [ - "D17:3" - ], - "category": 2 - }, - { - "question": "When did Melanie get hurt?", - "answer": "September 2023", - "evidence": [ - "D17:8" - ], - "category": 2 - }, - { - "question": "When did Melanie's family go on a roadtrip?", - "answer": "The weekend before 20 October 2023", - "evidence": [ - "D18:1" - ], - "category": 2 - }, - { - "question": "How many children does Melanie have?", - "answer": 3, - "evidence": [ - "D18:1", - "D18:7" - ], - "category": 1 - }, - { - "question": "When did Melanie go on a hike after the roadtrip?", - "answer": "19 October 2023", - "evidence": [ - "D18:17" - ], - "category": 1 - }, - { - "question": "Would Melanie go on another roadtrip soon?", - "answer": "Likely no; since this one went badly", - "evidence": [ - "D18:3", - "D18:1" - ], - "category": 3 - }, - { - "question": "What items has Melanie bought?", - "answer": "Figurines, shoes", - "evidence": [ - "D19:2", - "D7:18" - ], - "category": 1 - }, - { - "question": "When did Caroline pass the adoption interview?", - "answer": "The Friday before 22 October 2023", - "evidence": [ - "D19:1" - ], - "category": 2 - }, - { - "question": "When did Melanie buy the figurines?", - "answer": "21 October 2023", - "evidence": [ - "D19:2" - ], - "category": 2 - }, - { - "question": "Would Caroline want to move back to her home country soon?", - "answer": "No; she's in the process of adopting children.", - "evidence": [ - "D19:1", - "D19:3" - ], - "category": 3 - }, - { - "question": "What did the charity race raise awareness for?", - "answer": "mental health", - "evidence": [ - "D2:2" - ], - "category": 4 - }, - { - "question": "What did Melanie realize after the charity race?", - "answer": "self-care is important", - "evidence": [ - "D2:3" - ], - "category": 4 - }, - { - "question": "How does Melanie prioritize self-care?", - "answer": "by carving out some me-time each day for activities like running, reading, or playing the violin", - "evidence": [ - "D2:5" - ], - "category": 4 - }, - { - "question": "What are Caroline's plans for the summer?", - "answer": "researching adoption agencies", - "evidence": [ - "D2:8" - ], - "category": 4 - }, - { - "question": "What type of individuals does the adoption agency Caroline is considering support?", - "answer": "LGBTQ+ individuals", - "evidence": [ - "D2:12" - ], - "category": 4 - }, - { - "question": "Why did Caroline choose the adoption agency?", - "answer": "because of their inclusivity and support for LGBTQ+ individuals", - "evidence": [ - "D2:12" - ], - "category": 4 - }, - { - "question": "What is Caroline excited about in the adoption process?", - "answer": "creating a family for kids who need one", - "evidence": [ - "D2:14" - ], - "category": 4 - }, - { - "question": "What does Melanie think about Caroline's decision to adopt?", - "answer": "she thinks Caroline is doing something amazing and will be an awesome mom", - "evidence": [ - "D2:15" - ], - "category": 4 - }, - { - "question": "How long have Mel and her husband been married?", - "answer": "Mel and her husband have been married for 5 years.", - "evidence": [ - "D3:16" - ], - "category": 4 - }, - { - "question": "What does Caroline's necklace symbolize?", - "answer": "love, faith, and strength", - "evidence": [ - "D4:3" - ], - "category": 4 - }, - { - "question": "What country is Caroline's grandma from?", - "answer": "Sweden", - "evidence": [ - "D4:3" - ], - "category": 4 - }, - { - "question": "What was grandma's gift to Caroline?", - "answer": "necklace", - "evidence": [ - "D4:3" - ], - "category": 4 - }, - { - "question": "What is Melanie's hand-painted bowl a reminder of?", - "answer": "art and self-expression", - "evidence": [ - "D4:5" - ], - "category": 4 - }, - { - "question": "What did Melanie and her family do while camping?", - "answer": "explored nature, roasted marshmallows, and went on a hike", - "evidence": [ - "D4:8" - ], - "category": 4 - }, - { - "question": "What kind of counseling and mental health services is Caroline interested in pursuing?", - "answer": "working with trans people, helping them accept themselves and supporting their mental health", - "evidence": [ - "D4:13" - ], - "category": 4 - }, - { - "question": "What workshop did Caroline attend recently?", - "answer": "LGBTQ+ counseling workshop", - "evidence": [ - "D4:13" - ], - "category": 4 - }, - { - "question": "What was discussed in the LGBTQ+ counseling workshop?", - "answer": "therapeutic methods and how to best work with trans people", - "evidence": [ - "D4:13" - ], - "category": 4 - }, - { - "question": "What motivated Caroline to pursue counseling?", - "answer": "her own journey and the support she received, and how counseling improved her life", - "evidence": [ - "D4:15" - ], - "category": 4 - }, - { - "question": "What kind of place does Caroline want to create for people?", - "answer": "a safe and inviting place for people to grow", - "evidence": [ - "D4:15" - ], - "category": 4 - }, - { - "question": "Did Melanie make the black and white bowl in the photo?", - "answer": "Yes", - "evidence": [ - "D5:8" - ], - "category": 4 - }, - { - "question": "What kind of books does Caroline have in her library?", - "answer": "kids' books - classics, stories from different cultures, educational books", - "evidence": [ - "D6:9" - ], - "category": 4 - }, - { - "question": "What was Melanie's favorite book from her childhood?", - "answer": "\"Charlotte's Web\"", - "evidence": [ - "D6:10" - ], - "category": 4 - }, - { - "question": "What book did Caroline recommend to Melanie?", - "answer": "\"Becoming Nicole\"", - "evidence": [ - "D7:11" - ], - "category": 4 - }, - { - "question": "What did Caroline take away from the book \"Becoming Nicole\"?", - "answer": "Lessons on self-acceptance and finding support", - "evidence": [ - "D7:13" - ], - "category": 4 - }, - { - "question": "What are the new shoes that Melanie got used for?", - "answer": "Running", - "evidence": [ - "D7:19" - ], - "category": 4 - }, - { - "question": "What is Melanie's reason for getting into running?", - "answer": "To de-stress and clear her mind", - "evidence": [ - "D7:21" - ], - "category": 4 - }, - { - "question": "What does Melanie say running has been great for?", - "answer": "Her mental health", - "evidence": [ - "D7:24" - ], - "category": 4 - }, - { - "question": "What did Mel and her kids make during the pottery workshop?", - "answer": "pots", - "evidence": [ - "D8:2" - ], - "category": 4 - }, - { - "question": "What kind of pot did Mel and her kids make with clay?", - "answer": "a cup with a dog face on it", - "evidence": [ - "D8:4" - ], - "category": 4 - }, - { - "question": "What creative project do Mel and her kids do together besides pottery?", - "answer": "painting", - "evidence": [ - "D8:5" - ], - "category": 4 - }, - { - "question": "What did Mel and her kids paint in their latest project in July 2023?", - "answer": "a sunset with a palm tree", - "evidence": [ - "D8:6" - ], - "category": 4 - }, - { - "question": "What did Caroline see at the council meeting for adoption?", - "answer": "many people wanting to create loving homes for children in need", - "evidence": [ - "D8:9" - ], - "category": 4 - }, - { - "question": "What do sunflowers represent according to Caroline?", - "answer": "warmth and happiness", - "evidence": [ - "D8:11" - ], - "category": 4 - }, - { - "question": "Why are flowers important to Melanie?", - "answer": "They remind her to appreciate the small moments and were a part of her wedding decor", - "evidence": [ - "D8:12" - ], - "category": 4 - }, - { - "question": "What inspired Caroline's painting for the art show?", - "answer": "visiting an LGBTQ center and wanting to capture unity and strength", - "evidence": [ - "D9:16" - ], - "category": 4 - }, - { - "question": "How often does Melanie go to the beach with her kids?", - "answer": "once or twice a year", - "evidence": [ - "D10:10" - ], - "category": 4 - }, - { - "question": "What did Melanie and her family see during their camping trip last year?", - "answer": "Perseid meteor shower", - "evidence": [ - "D10:14" - ], - "category": 4 - }, - { - "question": "How did Melanie feel while watching the meteor shower?", - "answer": "in awe of the universe", - "evidence": [ - "D10:18" - ], - "category": 4 - }, - { - "question": "Whose birthday did Melanie celebrate recently?", - "answer": "Melanie's daughter", - "evidence": [ - "D11:1" - ], - "category": 4 - }, - { - "question": "Who performed at the concert at Melanie's daughter's birthday?", - "answer": "Matt Patterson", - "evidence": [ - "D11:3" - ], - "category": 4 - }, - { - "question": "Why did Melanie choose to use colors and patterns in her pottery project?", - "answer": "She wanted to catch the eye and make people smile.", - "evidence": [ - "D12:6" - ], - "category": 4 - }, - { - "question": "What pet does Caroline have?", - "answer": "guinea pig", - "evidence": [ - "D13:3" - ], - "category": 4 - }, - { - "question": "What pets does Melanie have?", - "answer": "Two cats and a dog", - "evidence": [ - "D13:4" - ], - "category": 4 - }, - { - "question": "Where did Oliver hide his bone once?", - "answer": "In Melanie's slipper", - "evidence": [ - "D13:6" - ], - "category": 4 - }, - { - "question": "What activity did Caroline used to do with her dad?", - "answer": "Horseback riding", - "evidence": [ - "D13:7" - ], - "category": 4 - }, - { - "question": "What did Caroline make for a local church?", - "answer": "a stained glass window", - "evidence": [ - "D14:17" - ], - "category": 4 - }, - { - "question": "What did Caroline find in her neighborhood during her walk?", - "answer": "a rainbow sidewalk", - "evidence": [ - "D14:23" - ], - "category": 4 - }, - { - "question": "Which song motivates Caroline to be courageous?", - "answer": "Brave by Sara Bareilles", - "evidence": [ - "D15:23" - ], - "category": 4 - }, - { - "question": "Which classical musicians does Melanie enjoy listening to?", - "answer": "Bach and Mozart", - "evidence": [ - "D15:28" - ], - "category": 4 - }, - { - "question": "Who is Melanie a fan of in terms of modern music?", - "answer": "Ed Sheeran", - "evidence": [ - "D15:28" - ], - "category": 4 - }, - { - "question": "How long has Melanie been creating art?", - "answer": "7 years", - "evidence": [ - "D16:7" - ], - "category": 4 - }, - { - "question": "What precautionary sign did Melanie see at the caf\u00e9?", - "answer": "A sign stating that someone is not being able to leave", - "evidence": [ - "D16:16" - ], - "category": 4 - }, - { - "question": "What advice does Caroline give for getting started with adoption?", - "answer": "Do research, find an adoption agency or lawyer, gather necessary documents, and prepare emotionally.", - "evidence": [ - "D17:7" - ], - "category": 4 - }, - { - "question": "What setback did Melanie face in October 2023?", - "answer": "She got hurt and had to take a break from pottery.", - "evidence": [ - "D17:8" - ], - "category": 4 - }, - { - "question": "What does Melanie do to keep herself busy during her pottery break?", - "answer": "Read a book and paint.", - "evidence": [ - "D17:10" - ], - "category": 4 - }, - { - "question": "What painting did Melanie show to Caroline on October 13, 2023?", - "answer": "A painting inspired by sunsets with a pink sky.", - "evidence": [ - "D17:12" - ], - "category": 4 - }, - { - "question": "What kind of painting did Caroline share with Melanie on October 13, 2023?", - "answer": "An abstract painting with blue streaks on a wall.", - "evidence": [ - "D17:14" - ], - "category": 4 - }, - { - "question": "What was the poetry reading that Caroline attended about?", - "answer": "It was a transgender poetry reading where transgender people shared their stories.", - "evidence": [ - "D17:18" - ], - "category": 4 - }, - { - "question": "What did the posters at the poetry reading say?", - "answer": "\"Trans Lives Matter\"", - "evidence": [ - "D17:19" - ], - "category": 4 - }, - { - "question": "What does Caroline's drawing symbolize for her?", - "answer": "Freedom and being true to herself.", - "evidence": [ - "D17:23" - ], - "category": 4 - }, - { - "question": "How do Melanie and Caroline describe their journey through life together?", - "answer": "An ongoing adventure of learning and growing.", - "evidence": [ - "D17:25" - ], - "category": 4 - }, - { - "question": "What happened to Melanie's son on their road trip?", - "answer": "He got into an accident", - "evidence": [ - "D18:1" - ], - "category": 4 - }, - { - "question": "How did Melanie's son handle the accident?", - "answer": "He was scared but reassured by his family", - "evidence": [ - "D18:6", - "D18:7" - ], - "category": 4 - }, - { - "question": "How did Melanie feel about her family after the accident?", - "answer": "They are important and mean the world to her", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "How did Melanie's children handle the accident?", - "answer": "They were scared but resilient", - "evidence": [ - "D18:7" - ], - "category": 4 - }, - { - "question": "How did Melanie feel after the accident?", - "answer": "Grateful and thankful for her family", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "What was Melanie's reaction to her children enjoying the Grand Canyon?", - "answer": "She was happy and thankful", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "What do Melanie's family give her?", - "answer": "Strength and motivation", - "evidence": [ - "D18:9" - ], - "category": 4 - }, - { - "question": "How did Melanie feel about her family supporting her?", - "answer": "She appreciated them a lot", - "evidence": [ - "D18:13" - ], - "category": 4 - }, - { - "question": "What did Melanie do after the road trip to relax?", - "answer": "Went on a nature walk or hike", - "evidence": [ - "D18:17" - ], - "category": 4 - }, - { - "question": "What did Caroline realize after her charity race?", - "evidence": [ - "D2:3" - ], - "category": 5, - "adversarial_answer": "self-care is important" - }, - { - "question": "What are Melanie's plans for the summer with respect to adoption?", - "evidence": [ - "D2:8" - ], - "category": 5, - "adversarial_answer": "researching adoption agencies" - }, - { - "question": "What type of individuals does the adoption agency Melanie is considering support?", - "evidence": [ - "D2:12" - ], - "category": 5, - "adversarial_answer": "LGBTQ+ individuals" - }, - { - "question": "Why did Melanie choose the adoption agency?", - "evidence": [ - "D2:12" - ], - "category": 5, - "adversarial_answer": "because of their inclusivity and support for LGBTQ+ individuals" - }, - { - "question": "What is Melanie excited about in her adoption process?", - "evidence": [ - "D2:14" - ], - "category": 5, - "adversarial_answer": "creating a family for kids who need one" - }, - { - "question": "What does Melanie's necklace symbolize?", - "evidence": [ - "D4:3" - ], - "category": 5, - "adversarial_answer": "love, faith, and strength" - }, - { - "question": "What country is Melanie's grandma from?", - "evidence": [ - "D4:3" - ], - "category": 5, - "adversarial_answer": "Sweden" - }, - { - "question": "What was grandma's gift to Melanie?", - "evidence": [ - "D4:3" - ], - "category": 5, - "adversarial_answer": "necklace" - }, - { - "question": "What was grandpa's gift to Caroline?", - "evidence": [ - "D4:3" - ], - "category": 5, - "adversarial_answer": "necklace" - }, - { - "question": "What is Caroline's hand-painted bowl a reminder of?", - "evidence": [ - "D4:5" - ], - "category": 5, - "adversarial_answer": "art and self-expression" - }, - { - "question": "What did Caroline and her family do while camping?", - "evidence": [ - "D4:8" - ], - "category": 5, - "adversarial_answer": "explored nature, roasted marshmallows, and went on a hike" - }, - { - "question": "What kind of counseling and mental health services is Melanie interested in pursuing?", - "evidence": [ - "D4:13" - ], - "category": 5, - "adversarial_answer": "working with trans people, helping them accept themselves and supporting their mental health" - }, - { - "question": "What kind of counseling workshop did Melanie attend recently?", - "evidence": [ - "D4:13" - ], - "category": 5, - "adversarial_answer": "LGBTQ+ counseling workshop" - }, - { - "question": "What motivated Melanie to pursue counseling?", - "evidence": [ - "D4:15" - ], - "category": 5, - "adversarial_answer": "her own journey and the support she received, and how counseling improved her life" - }, - { - "question": "What kind of place does Melanie want to create for people?", - "evidence": [ - "D4:15" - ], - "category": 5, - "adversarial_answer": "a safe and inviting place for people to grow" - }, - { - "question": "Did Caroline make the black and white bowl in the photo?", - "adversarial_answer": "Yes", - "answer": "No", - "evidence": [ - "D5:8" - ], - "category": 5 - }, - { - "question": "What are the new shoes that Caroline got used for?", - "evidence": [ - "D7:19" - ], - "category": 5, - "adversarial_answer": "Running" - }, - { - "question": "What is Caroline's reason for getting into running?", - "evidence": [ - "D7:21" - ], - "category": 5, - "adversarial_answer": "To de-stress and clear her mind" - }, - { - "question": "What does Caroline say running has been great for?", - "evidence": [ - "D7:24" - ], - "category": 5, - "adversarial_answer": "Her mental health" - }, - { - "question": "What did Melanie see at the council meeting for adoption?", - "evidence": [ - "D8:9" - ], - "category": 5, - "adversarial_answer": "many people wanting to create loving homes for children in need" - }, - { - "question": "What inspired Melanie's painting for the art show?", - "evidence": [ - "D9:16" - ], - "category": 5, - "adversarial_answer": "visiting an LGBTQ center and wanting to capture unity and strength" - }, - { - "question": "What inspired Caroline's sculpture for the art show?", - "evidence": [ - "D9:16" - ], - "category": 5, - "adversarial_answer": "visiting an LGBTQ center and wanting to capture unity and strength" - }, - { - "question": "How often does Caroline go to the beach with her kids?", - "evidence": [ - "D10:10" - ], - "category": 5, - "adversarial_answer": "once or twice a year" - }, - { - "question": "What did Caroline and her family see during their camping trip last year?", - "evidence": [ - "D10:14" - ], - "category": 5, - "adversarial_answer": "Perseid meteor shower" - }, - { - "question": "How did Caroline feel while watching the meteor shower?", - "evidence": [ - "D10:18" - ], - "category": 5, - "adversarial_answer": "in awe of the universe" - }, - { - "question": "Why did Caroline choose to use colors and patterns in her pottery project?", - "evidence": [ - "D12:6" - ], - "category": 5, - "adversarial_answer": "She wanted to catch the eye and make people smile." - }, - { - "question": "Is Oscar Melanie's pet?", - "adversarial_answer": "Yes", - "answer": "No", - "evidence": [ - "D13:3" - ], - "category": 5 - }, - { - "question": "Where did Oscar hide his bone once?", - "evidence": [ - "D13:6" - ], - "category": 5, - "adversarial_answer": "In Melanie's slipper" - }, - { - "question": "What activity did Melanie used to do with her dad?", - "evidence": [ - "D13:7" - ], - "category": 5, - "adversarial_answer": "Horseback riding" - }, - { - "question": "What did Melanie make for a local church?", - "evidence": [ - "D14:17" - ], - "category": 5, - "adversarial_answer": "a stained glass window" - }, - { - "question": "What did Melanie find in her neighborhood during her walk?", - "evidence": [ - "D14:23" - ], - "category": 5, - "adversarial_answer": "a rainbow sidewalk" - }, - { - "question": "Which song motivates Melanie to be courageous?", - "evidence": [ - "D15:23" - ], - "category": 5, - "adversarial_answer": "Brave by Sara Bareilles" - }, - { - "question": "What type of instrument does Caroline play?", - "evidence": [ - "D15:26" - ], - "category": 5, - "adversarial_answer": "clarinet and violin" - }, - { - "question": "Which classical musicians does Caroline enjoy listening to?", - "evidence": [ - "D15:28" - ], - "category": 5, - "adversarial_answer": "Bach and Mozart" - }, - { - "question": "Who is Caroline a fan of in terms of modern music?", - "evidence": [ - "D15:28" - ], - "category": 5, - "adversarial_answer": "Ed Sheeran" - }, - { - "question": "What precautionary sign did Caroline see at the caf\u00e9?", - "evidence": [ - "D16:16" - ], - "category": 5, - "adversarial_answer": "A sign stating that someone is not being able to leave" - }, - { - "question": "What setback did Caroline face recently?", - "evidence": [ - "D17:8" - ], - "category": 5, - "adversarial_answer": "She got hurt and had to take a break from pottery." - }, - { - "question": "What does Caroline do to keep herself busy during her pottery break?", - "evidence": [ - "D17:10" - ], - "category": 5, - "adversarial_answer": "Read a book and paint." - }, - { - "question": "What was the poetry reading that Melanie attended about?", - "evidence": [ - "D17:18" - ], - "category": 5, - "adversarial_answer": "It was a transgender poetry reading where transgender people shared their stories." - }, - { - "question": "What happened to Caroline's son on their road trip?", - "evidence": [ - "D18:1" - ], - "category": 5, - "adversarial_answer": "He got into an accident" - }, - { - "question": "How did Caroline's son handle the accident?", - "evidence": [ - "D18:6", - "D18:7" - ], - "category": 5, - "adversarial_answer": "He was scared but reassured by his family" - }, - { - "question": "How did Caroline feel about her family after the accident?", - "evidence": [ - "D18:5" - ], - "category": 5, - "adversarial_answer": "They are important and mean the world to her" - }, - { - "question": "How did Caroline's children handle the accident?", - "evidence": [ - "D18:7" - ], - "category": 5, - "adversarial_answer": "They were scared but resilient" - }, - { - "question": "How did Caroline feel after the accident?", - "evidence": [ - "D18:5" - ], - "category": 5, - "adversarial_answer": "Grateful and thankful for her family" - }, - { - "question": "What was Caroline's reaction to her children enjoying the Grand Canyon?", - "evidence": [ - "D18:5" - ], - "category": 5, - "adversarial_answer": "She was happy and thankful" - }, - { - "question": "What did Caroline do after the road trip to relax?", - "evidence": [ - "D18:17" - ], - "category": 5, - "adversarial_answer": "Went on a nature walk or hike" - }, - { - "question": "What does Caroline love most about camping with her family?", - "evidence": [ - "D18:21" - ], - "category": 5, - "adversarial_answer": "Being present and bonding with her family" - } - ], - "conversation": { - "speaker_a": "Caroline", - "speaker_b": "Melanie", - "session_1_date_time": "1:56 pm on 8 May, 2023", - "session_1": [ - { - "speaker": "Caroline", - "dia_id": "D1:1", - "text": "Hey Mel! Good to see you! How have you been?" - }, - { - "speaker": "Melanie", - "dia_id": "D1:2", - "text": "Hey Caroline! Good to see you! I'm swamped with the kids & work. What's up with you? Anything new?" - }, - { - "speaker": "Caroline", - "dia_id": "D1:3", - "text": "I went to a LGBTQ support group yesterday and it was so powerful." - }, - { - "speaker": "Melanie", - "dia_id": "D1:4", - "text": "Wow, that's cool, Caroline! What happened that was so awesome? Did you hear any inspiring stories?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.redd.it/l7hozpetnhlb1.jpg" - ], - "blip_caption": "a photo of a dog walking past a wall with a painting of a woman", - "query": "transgender pride flag mural", - "dia_id": "D1:5", - "text": "The transgender stories were so inspiring! I was so happy and thankful for all the support." - }, - { - "speaker": "Melanie", - "dia_id": "D1:6", - "text": "Wow, love that painting! So cool you found such a helpful group. What's it done for you?" - }, - { - "speaker": "Caroline", - "dia_id": "D1:7", - "text": "The support group has made me feel accepted and given me courage to embrace myself." - }, - { - "speaker": "Melanie", - "dia_id": "D1:8", - "text": "That's really cool. You've got guts. What now?" - }, - { - "speaker": "Caroline", - "dia_id": "D1:9", - "text": "Gonna continue my edu and check out career options, which is pretty exciting!" - }, - { - "speaker": "Melanie", - "dia_id": "D1:10", - "text": "Wow, Caroline! What kinda jobs are you thinkin' of? Anything that stands out?" - }, - { - "speaker": "Caroline", - "dia_id": "D1:11", - "text": "I'm keen on counseling or working in mental health - I'd love to support those with similar issues." - }, - { - "speaker": "Melanie", - "img_url": [ - "http://candicealexander.com/cdn/shop/products/IMG_7269_a49d5af8-c76c-4ecd-ae20-48c08cb11dec.jpg" - ], - "blip_caption": "a photo of a painting of a sunset over a lake", - "query": "painting sunrise", - "dia_id": "D1:12", - "text": "You'd be a great counselor! Your empathy and understanding will really help the people you work with. By the way, take a look at this." - }, - { - "speaker": "Caroline", - "dia_id": "D1:13", - "text": "Thanks, Melanie! That's really sweet. Is this your own painting?" - }, - { - "speaker": "Melanie", - "dia_id": "D1:14", - "text": "Yeah, I painted that lake sunrise last year! It's special to me." - }, - { - "speaker": "Caroline", - "dia_id": "D1:15", - "text": "Wow, Melanie! The colors really blend nicely. Painting looks like a great outlet for expressing yourself." - }, - { - "speaker": "Melanie", - "dia_id": "D1:16", - "text": "Thanks, Caroline! Painting's a fun way to express my feelings and get creative. It's a great way to relax after a long day." - }, - { - "speaker": "Caroline", - "dia_id": "D1:17", - "text": "Totally agree, Mel. Relaxing and expressing ourselves is key. Well, I'm off to go do some research." - }, - { - "speaker": "Melanie", - "dia_id": "D1:18", - "text": "Yep, Caroline. Taking care of ourselves is vital. I'm off to go swimming with the kids. Talk to you soon!" - } - ], - "session_2_date_time": "1:14 pm on 25 May, 2023", - "session_2": [ - { - "speaker": "Melanie", - "dia_id": "D2:1", - "text": "Hey Caroline, since we last chatted, I've had a lot of things happening to me. I ran a charity race for mental health last Saturday \u2013 it was really rewarding. Really made me think about taking care of our minds." - }, - { - "speaker": "Caroline", - "dia_id": "D2:2", - "text": "That charity race sounds great, Mel! Making a difference & raising awareness for mental health is super rewarding - I'm really proud of you for taking part!" - }, - { - "speaker": "Melanie", - "dia_id": "D2:3", - "text": "Thanks, Caroline! The event was really thought-provoking. I'm starting to realize that self-care is really important. It's a journey for me, but when I look after myself, I'm able to better look after my family." - }, - { - "speaker": "Caroline", - "dia_id": "D2:4", - "text": "I totally agree, Melanie. Taking care of ourselves is so important - even if it's not always easy. Great that you're prioritizing self-care." - }, - { - "speaker": "Melanie", - "dia_id": "D2:5", - "text": "Yeah, it's tough. So I'm carving out some me-time each day - running, reading, or playing my violin - which refreshes me and helps me stay present for my fam!" - }, - { - "speaker": "Caroline", - "dia_id": "D2:6", - "text": "That's great, Mel! Taking time for yourself is so important. You're doing an awesome job looking after yourself and your family!" - }, - { - "speaker": "Melanie", - "dia_id": "D2:7", - "text": "Thanks, Caroline. It's still a work in progress, but I'm doing my best. My kids are so excited about summer break! We're thinking about going camping next month. Any fun plans for the summer?" - }, - { - "speaker": "Caroline", - "dia_id": "D2:8", - "text": "Researching adoption agencies \u2014 it's been a dream to have a family and give a loving home to kids who need it." - }, - { - "speaker": "Melanie", - "dia_id": "D2:9", - "text": "Wow, Caroline! That's awesome! Taking in kids in need - you're so kind. Your future family is gonna be so lucky to have you!" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://live.staticflickr.com/3437/3935231341_b2955b00dd_b.jpg" - ], - "blip_caption": "a photography of a sign for a new arrival and an information and domestic building", - "query": "adoption agency brochure", - "dia_id": "D2:10", - "re-download": true, - "text": "Thanks, Mel! My goal is to give kids a loving home. I'm truly grateful for all the support I've got from friends and mentors. Now the hard work starts to turn my dream into a reality. And here's one of the adoption agencies I'm looking into. It's a lot to take in, but I'm feeling hopeful and optimistic." - }, - { - "speaker": "Melanie", - "dia_id": "D2:11", - "text": "Wow, that agency looks great! What made you pick it?" - }, - { - "speaker": "Caroline", - "dia_id": "D2:12", - "text": "I chose them 'cause they help LGBTQ+ folks with adoption. Their inclusivity and support really spoke to me." - }, - { - "speaker": "Melanie", - "dia_id": "D2:13", - "text": "That's great, Caroline! Loving the inclusivity and support. Anything you're excited for in the adoption process?" - }, - { - "speaker": "Caroline", - "dia_id": "D2:14", - "text": "I'm thrilled to make a family for kids who need one. It'll be tough as a single parent, but I'm up for the challenge!" - }, - { - "speaker": "Melanie", - "dia_id": "D2:15", - "text": "You're doing something amazing! Creating a family for those kids is so lovely. You'll be an awesome mom! Good luck!" - }, - { - "speaker": "Caroline", - "dia_id": "D2:16", - "text": "Thanks, Melanie! Your kind words really mean a lot. I'll do my best to make sure these kids have a safe and loving home." - }, - { - "speaker": "Melanie", - "dia_id": "D2:17", - "text": "No doubts, Caroline. You have such a caring heart - they'll get all the love and stability they need! Excited for this new chapter!" - } - ], - "session_3_date_time": "7:55 pm on 9 June, 2023", - "session_3": [ - { - "speaker": "Caroline", - "dia_id": "D3:1", - "text": "Hey Melanie! How's it going? I wanted to tell you about my school event last week. It was awesome! I talked about my transgender journey and encouraged students to get involved in the LGBTQ community. It was great to see their reactions. It made me reflect on how far I've come since I started transitioning three years ago." - }, - { - "speaker": "Melanie", - "dia_id": "D3:2", - "text": "Hey Caroline! Great to hear from you. Sounds like your event was amazing! I'm so proud of you for spreading awareness and getting others involved in the LGBTQ community. You've come a long way since your transition - keep on inspiring people with your strength and courage!" - }, - { - "speaker": "Caroline", - "dia_id": "D3:3", - "text": "Thanks, Mel! Your backing really means a lot. I felt super powerful giving my talk. I shared my own journey, the struggles I had and how much I've developed since coming out. It was wonderful to see how the audience related to what I said and how it inspired them to be better allies. Conversations about gender identity and inclusion are so necessary and I'm thankful for being able to give a voice to the trans community." - }, - { - "speaker": "Melanie", - "dia_id": "D3:4", - "text": "Wow, Caroline, you're doing an awesome job of inspiring others with your journey. It's great to be part of it and see how you're positively affecting so many. Talking about inclusivity and acceptance is crucial, and you're so brave to speak up for the trans community. Keep up the great work!" - }, - { - "speaker": "Caroline", - "dia_id": "D3:5", - "text": "Thanks Mel! Your kind words mean a lot. Sharing our experiences isn't always easy, but I feel it's important to help promote understanding and acceptance. I've been blessed with loads of love and support throughout this journey, and I want to pass it on to others. By sharing our stories, we can build a strong, supportive community of hope." - }, - { - "speaker": "Melanie", - "dia_id": "D3:6", - "text": "Yeah, Caroline! It takes courage to talk about our own stories. But it's in these vulnerable moments that we bond and understand each other. We all have our different paths, but if we share them, we show people that they're not alone. Our stories can be so inspiring and encouraging to others who are facing the same challenges. Thank you for using your voice to create love, acceptance, and hope. You're doing amazing!" - }, - { - "speaker": "Caroline", - "dia_id": "D3:7", - "text": "Your words mean a lot to me. I'm grateful for the chance to share my story and give others hope. We all have unique paths, and by working together we can build a more inclusive and understanding world. I'm going to keep using my voice to make a change and lift others up. And you're part of that!" - }, - { - "speaker": "Melanie", - "dia_id": "D3:8", - "text": "Thanks, Caroline, for letting me join your journey. I'm so proud to be part of the difference you're making. Let's keep motivating and helping each other out as we journey through life. We can make a real impact together!" - }, - { - "speaker": "Caroline", - "dia_id": "D3:9", - "text": "Yeah Mel, let's spread love and understanding! Thanks for the support and encouragement. We can tackle life's challenges together! We got this!" - }, - { - "speaker": "Melanie", - "dia_id": "D3:10", - "text": "Yes, Caroline! We can do it. Your courage is inspiring. I want to be couragous for my family- they motivate me and give me love. What motivates you?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://fox2now.com/wp-content/uploads/sites/14/2023/08/that-tall-family.jpg" - ], - "blip_caption": "a photo of a family posing for a picture in a yard", - "query": "group of friends and family", - "dia_id": "D3:11", - "text": "Thanks, Mel! My friends, family and mentors are my rocks \u2013 they motivate me and give me the strength to push on. Here's a pic from when we met up last week!" - }, - { - "speaker": "Melanie", - "dia_id": "D3:12", - "text": "Wow, that photo is great! How long have you had such a great support system?" - }, - { - "speaker": "Caroline", - "dia_id": "D3:13", - "text": "Yeah, I'm really lucky to have them. They've been there through everything, I've known these friends for 4 years, since I moved from my home country. Their love and help have been so important especially after that tough breakup. I'm super thankful. Who supports you, Mel?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://mrswebersneighborhood.com/wp-content/uploads/2022/07/Cedar-Falls-Hocking-Hills.jpg" - ], - "blip_caption": "a photo of a man and a little girl standing in front of a waterfall", - "query": "husband kids hiking nature", - "dia_id": "D3:14", - "text": "I'm lucky to have my husband and kids; they keep me motivated." - }, - { - "speaker": "Caroline", - "dia_id": "D3:15", - "text": "Wow, what an amazing family pic! How long have you been married?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/8o28nfllf3eb1.jpg" - ], - "blip_caption": "a photo of a bride in a wedding dress holding a bouquet", - "query": "wedding day", - "dia_id": "D3:16", - "text": "5 years already! Time flies- feels like just yesterday I put this dress on! Thanks, Caroline!" - }, - { - "speaker": "Caroline", - "dia_id": "D3:17", - "text": "Congrats, Melanie! You both looked so great on your wedding day! Wishing you many happy years together!" - }, - { - "speaker": "Melanie", - "img_url": [ - "http://shirleyswardrobe.com/wp-content/uploads/2017/07/LF-Picnic-6.jpg" - ], - "blip_caption": "a photo of a man and woman sitting on a blanket eating food", - "query": "family picnic park laughing", - "dia_id": "D3:18", - "text": "Thanks, Caroline! Appreciate your kind words. Looking forward to more happy years. Our family and moments make it all worth it." - }, - { - "speaker": "Caroline", - "dia_id": "D3:19", - "text": "Looks like you had a great day! How was it? You all look so happy!" - }, - { - "speaker": "Melanie", - "dia_id": "D3:20", - "text": "It so fun! We played games, ate good food, and just hung out together. Family moments make life awesome." - }, - { - "speaker": "Caroline", - "dia_id": "D3:21", - "text": "Sounds great, Mel! Glad you had a great time. Cherish the moments - they're the best!" - }, - { - "speaker": "Melanie", - "dia_id": "D3:22", - "text": "Absolutely, Caroline! I cherish time with family. It's when I really feel alive and happy." - }, - { - "speaker": "Caroline", - "dia_id": "D3:23", - "text": "I 100% agree, Mel. Hanging with loved ones is amazing and brings so much happiness. Those moments really make me thankful. Family is everything." - } - ], - "session_4_date_time": "10:37 am on 27 June, 2023", - "session_4": [ - { - "speaker": "Caroline", - "img_url": [ - "https://i.redd.it/67uas3gnmz7b1.jpg" - ], - "blip_caption": "a photo of a person holding a necklace with a cross and a heart", - "query": "pendant transgender symbol", - "dia_id": "D4:1", - "text": "Hey Melanie! Long time no talk! A lot's been going on in my life! Take a look at this." - }, - { - "speaker": "Melanie", - "dia_id": "D4:2", - "text": "Hey, Caroline! Nice to hear from you! Love the necklace, any special meaning to it?" - }, - { - "speaker": "Caroline", - "dia_id": "D4:3", - "text": "Thanks, Melanie! This necklace is super special to me - a gift from my grandma in my home country, Sweden. She gave it to me when I was young, and it stands for love, faith and strength. It's like a reminder of my roots and all the love and support I get from my family." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a stack of bowls with different designs on them", - "dia_id": "D4:4", - "text": "That's gorgeous, Caroline! It's awesome what items can mean so much to us, right? Got any other objects that you treasure, like that necklace?" - }, - { - "speaker": "Caroline", - "dia_id": "D4:5", - "text": "Yep, Melanie! I've got some other stuff with sentimental value, like my hand-painted bowl. A friend made it for my 18th birthday ten years ago. The pattern and colors are awesome-- it reminds me of art and self-expression." - }, - { - "speaker": "Melanie", - "dia_id": "D4:6", - "text": "That sounds great, Caroline! It's awesome having stuff around that make us think of good connections and times. Actually, I just took my fam camping in the mountains last week - it was a really nice time together!" - }, - { - "speaker": "Caroline", - "dia_id": "D4:7", - "text": "Sounds great, Mel. Glad you made some new family mems. How was it? Anything fun?" - }, - { - "speaker": "Melanie", - "dia_id": "D4:8", - "text": "It was an awesome time, Caroline! We explored nature, roasted marshmallows around the campfire and even went on a hike. The view from the top was amazing! The 2 younger kids love nature. It was so special having these moments together as a family - I'll never forget it!" - }, - { - "speaker": "Caroline", - "dia_id": "D4:9", - "text": "That's awesome, Melanie! Family moments like that are so special. Glad y'all had such a great time." - }, - { - "speaker": "Melanie", - "dia_id": "D4:10", - "text": "Thanks, Caroline! Family time matters to me. What's up with you lately?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a book shelf with many books on it", - "dia_id": "D4:11", - "text": "Lately, I've been looking into counseling and mental health as a career. I want to help people who have gone through the same things as me." - }, - { - "speaker": "Melanie", - "dia_id": "D4:12", - "text": "Sounds great! What kind of counseling and mental health services do you want to persue?" - }, - { - "speaker": "Caroline", - "dia_id": "D4:13", - "text": "I'm still figuring out the details, but I'm thinking of working with trans people, helping them accept themselves and supporting their mental health. Last Friday, I went to an LGBTQ+ counseling workshop and it was really enlightening. They talked about different therapeutic methods and how to best work with trans people. Seeing how passionate these pros were about making a safe space for people like me was amazing." - }, - { - "speaker": "Melanie", - "dia_id": "D4:14", - "text": "Woah, Caroline, it sounds like you're doing some impressive work. It's inspiring to see your dedication to helping others. What motivated you to pursue counseling?" - }, - { - "speaker": "Caroline", - "dia_id": "D4:15", - "text": "Thanks, Melanie. It really mattered. My own journey and the support I got made a huge difference. Now I want to help people go through it too. I saw how counseling and support groups improved my life, so I started caring more about mental health and understanding myself. Now I'm passionate about creating a safe, inviting place for people to grow." - }, - { - "speaker": "Melanie", - "dia_id": "D4:16", - "text": "Wow, Caroline! You've gained so much from your own experience. Your passion and hard work to help others is awesome. Keep it up, you're making a big impact!" - }, - { - "speaker": "Caroline", - "dia_id": "D4:17", - "text": "Thanks, Melanie! Your kind words mean a lot." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a book shelf filled with books in a room", - "dia_id": "D4:18", - "text": "Congrats Caroline! Good on you for going after what you really care about." - } - ], - "session_5_date_time": "1:36 pm on 3 July, 2023", - "session_5": [ - { - "speaker": "Caroline", - "dia_id": "D5:1", - "text": "Since we last spoke, some big things have happened. Last week I went to an LGBTQ+ pride parade. Everyone was so happy and it made me feel like I belonged. It showed me how much our community has grown, it was amazing!" - }, - { - "speaker": "Melanie", - "dia_id": "D5:2", - "text": "Wow, Caroline, sounds like the parade was an awesome experience! It's great to see the love and support for the LGBTQ+ community. Congrats! Has this experience influenced your goals at all?" - }, - { - "speaker": "Caroline", - "dia_id": "D5:3", - "text": "Thanks, Mel! It really motivated me for sure. Talking to the community made me want to use my story to help others too - I'm still thinking that counseling and mental health is the way to go. I'm super excited to give back. " - }, - { - "speaker": "Melanie", - "img_url": [ - "https://m.media-amazon.com/images/I/A1uELSr5rgL.jpg" - ], - "blip_caption": "a photo of a person holding a frisbee in their hand", - "query": "family frisbee game", - "dia_id": "D5:4", - "text": "Wow, Caroline! That's great! I just signed up for a pottery class yesterday. It's like therapy for me, letting me express myself and get creative. Have you found any activities that make you feel the same way?" - }, - { - "speaker": "Caroline", - "dia_id": "D5:5", - "text": "Wow, Melanie! I'm getting creative too, just learning the piano. What made you try pottery?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://therusticbarnct.com/cdn/shop/files/image_05483f46-4845-433b-a4cf-0fc61fe1aa79.jpg" - ], - "blip_caption": "a photo of a bowl with a black and white flower design", - "query": "pottery painted bowl intricate design", - "dia_id": "D5:6", - "text": "I'm a big fan of pottery - the creativity and skill is awesome. Plus, making it is so calming. Look at this!" - }, - { - "speaker": "Caroline", - "dia_id": "D5:7", - "text": "That bowl is gorgeous! The black and white design looks so fancy. Did you make it?" - }, - { - "speaker": "Melanie", - "dia_id": "D5:8", - "text": "Thanks, Caroline! Yeah, I made this bowl in my class. It took some work, but I'm pretty proud of it." - }, - { - "speaker": "Caroline", - "dia_id": "D5:9", - "text": "Nice job! You really put in the work and it definitely shows. Your creativity looks great!" - }, - { - "speaker": "Melanie", - "dia_id": "D5:10", - "text": "Thanks, Caroline! Your kind words mean a lot. Pottery is a huge part of my life, not just a hobby - it helps me express my emotions. Clay is incredible, it brings me so much joy!" - }, - { - "speaker": "Caroline", - "dia_id": "D5:11", - "text": "Wow, Mel, I'm so stoked for you that art is helping you express yourself and bring you joy! Keep it up!" - }, - { - "speaker": "Melanie", - "dia_id": "D5:12", - "text": "Thanks, Caroline! I'm excited to see where pottery takes me. Anything coming up you're looking forward to?" - }, - { - "speaker": "Caroline", - "dia_id": "D5:13", - "text": "Thanks Mel! I'm going to a transgender conference this month. I'm so excited to meet other people in the community and learn more about advocacy. It's gonna be great!" - }, - { - "speaker": "Melanie", - "dia_id": "D5:14", - "text": "Sounds awesome, Caroline! Have a great time and learn a lot. Have fun!" - }, - { - "speaker": "Caroline", - "dia_id": "D5:15", - "text": "Cool, thanks Mel! Can't wait. I'll keep ya posted. Bye!" - }, - { - "speaker": "Melanie", - "dia_id": "D5:16", - "text": "Bye, Caroline! Can't wait to hear about it. Have fun and stay safe!" - } - ], - "session_6_date_time": "8:18 pm on 6 July, 2023", - "session_6": [ - { - "speaker": "Caroline", - "dia_id": "D6:1", - "text": "Hey Mel! Long time no talk. Lots has been going on since then!" - }, - { - "speaker": "Melanie", - "dia_id": "D6:2", - "text": "Hey Caroline! Missed you. Anything new? Spill the beans!" - }, - { - "speaker": "Caroline", - "dia_id": "D6:3", - "text": "Since our last chat, I've been looking into counseling or mental health work more. I'm passionate about helping people and making a positive impact. It's tough, but really rewarding too. Anything new happening with you?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://live.staticflickr.com/3201/2867258131_2d8bc22859_b.jpg" - ], - "blip_caption": "a photography of two children playing in a water play area", - "query": "kids laughing dinosaur exhibit museum", - "dia_id": "D6:4", - "re-download": true, - "text": "That's awesome, Caroline! Congrats on following your dreams. Yesterday I took the kids to the museum - it was so cool spending time with them and seeing their eyes light up!" - }, - { - "speaker": "Caroline", - "dia_id": "D6:5", - "text": "Melanie, that's a great pic! That must have been awesome. What were they so stoked about?" - }, - { - "speaker": "Melanie", - "dia_id": "D6:6", - "text": "They were stoked for the dinosaur exhibit! They love learning about animals and the bones were so cool. It reminds me why I love being a mom." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.pinimg.com/originals/02/94/c3/0294c3460b66d1fd50530e4bd5a2e1f5.jpg" - ], - "blip_caption": "a photo of a bookcase filled with books and toys", - "query": "bookshelf childrens books library", - "dia_id": "D6:7", - "text": "Being a mom is awesome. I'm creating a library for when I have kids. I'm really looking forward to reading to them and opening up their minds." - }, - { - "speaker": "Melanie", - "dia_id": "D6:8", - "text": "Sounds great! What kind of books you got in your library?" - }, - { - "speaker": "Caroline", - "dia_id": "D6:9", - "text": "I've got lots of kids' books- classics, stories from different cultures, educational books, all of that. What's a favorite book you remember from your childhood?" - }, - { - "speaker": "Melanie", - "img_url": [ - "http://bookworm-detective.myshopify.com/cdn/shop/products/PXL_20210428_222022427.jpg" - ], - "blip_caption": "a photo of a book cover with a picture of a girl and a cat", - "query": "charlotte's web book", - "dia_id": "D6:10", - "text": "I loved reading \"Charlotte's Web\" as a kid. It was so cool seeing how friendship and compassion can make a difference." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.pinimg.com/originals/41/d5/60/41d5601e4ab0959ce5e29683a2660938.jpg" - ], - "blip_caption": "a photo of a group of women sitting on a blanket in a park", - "query": "group friends picnic", - "dia_id": "D6:11", - "text": "Wow, that's great! It sure shows how important friendship and compassion are. It's made me appreciate how lucky I am to have my friends and family helping with my transition. They make all the difference. We even had a picnic last week!" - }, - { - "speaker": "Melanie", - "dia_id": "D6:12", - "text": "That's a gorgeous photo, Caroline! Wow, the love around you is awesome. How have your friends and fam been helping you out with your transition?" - }, - { - "speaker": "Caroline", - "dia_id": "D6:13", - "text": "Thanks, Melanie! This support network has been amazing. They've been there for me every step of the way giving me love, guidance, and acceptance. I couldn't have done it without them." - }, - { - "speaker": "Melanie", - "dia_id": "D6:14", - "text": "Wow, Caroline! It's great you have people to support you, that's really awesome!" - }, - { - "speaker": "Caroline", - "dia_id": "D6:15", - "text": "I'm so lucky to have such a great support system around me. Their love and encouragement has really helped me accept and grow into my true self. They've been instrumental in my transition." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/ye1cp24b18w01.jpg" - ], - "blip_caption": "a photo of a family sitting around a campfire on the beach", - "query": "family campfire", - "dia_id": "D6:16", - "text": "Glad you have support, Caroline! Unconditional love is so important. Here's a pic of my family camping at the beach. We love it, it brings us closer!" - } - ], - "session_7_date_time": "4:33 pm on 12 July, 2023", - "session_7": [ - { - "speaker": "Caroline", - "dia_id": "D7:1", - "text": "Hey Mel, great to chat with you again! So much has happened since we last spoke - I went to an LGBTQ conference two days ago and it was really special. I got the chance to meet and connect with people who've gone through similar journeys. It was such a welcoming environment and I felt totally accepted. I'm really thankful for this amazing community - it's shown me how important it is to fight for trans rights and spread awareness." - }, - { - "speaker": "Melanie", - "dia_id": "D7:2", - "text": "Wow, Caroline, that sounds awesome! So glad you felt accepted and supported. Events like these are great for reminding us of how strong community can be!" - }, - { - "speaker": "Caroline", - "dia_id": "D7:3", - "text": "Yeah, it's true! Having people who back you makes such a huge difference. It's great to see how far LGBTQ rights have come, but there's still plenty of progress to be made. I wanna help make a difference." - }, - { - "speaker": "Melanie", - "dia_id": "D7:4", - "text": "Wow, Caroline. We've come so far, but there's more to do. Your drive to help is awesome! What's your plan to pitch in?" - }, - { - "speaker": "Caroline", - "dia_id": "D7:5", - "text": "Thanks, Mell! I'm still looking into counseling and mental health jobs. It's important to me that people have someone to talk to, and I want to help make that happen." - }, - { - "speaker": "Melanie", - "dia_id": "D7:6", - "text": "Wow, Caroline! You're so inspiring for wanting to help others with their mental health. What's pushing you to keep going forward with it?" - }, - { - "speaker": "Caroline", - "dia_id": "D7:7", - "text": "I struggled with mental health, and support I got was really helpful. It made me realize how important it is for others to have a support system. So, I started looking into counseling and mental health career options, so I could help other people on their own journeys like I was helped." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://www.speakers.co.uk/microsites/tom-oliver/wp-content/uploads/2014/11/Book-Cover-3D1.jpg" - ], - "blip_caption": "a photography of a book cover with a gold coin on it", - "query": "painted canvas follow your dreams", - "dia_id": "D7:8", - "re-download": true, - "text": "Caroline, so glad you got the support! Your experience really brought you to where you need to be. You're gonna make a huge difference! This book I read last year reminds me to always pursue my dreams, just like you are doing!\ud83c\udf1f" - }, - { - "speaker": "Caroline", - "dia_id": "D7:9", - "text": "Thanks so much, Mel! Seeing this pic just made me appreciate my love of reading even more. Books guide me, motivate me and help me discover who I am. They're a huge part of my journey, and this one's reminding me to keep going and never give up!" - }, - { - "speaker": "Melanie", - "dia_id": "D7:10", - "text": "Wow, Caroline! Books have such an awesome power! Which one has been your favorite guide?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://m.media-amazon.com/images/I/A1CPpaLFR2L.jpg" - ], - "blip_caption": "a photo of a dog sitting in a boat on the water", - "query": "becoming nicole book amy ellis nutt", - "dia_id": "D7:11", - "text": "I loved \"Becoming Nicole\" by Amy Ellis Nutt. It's a real inspiring true story about a trans girl and her family. It made me feel connected and gave me a lot of hope for my own path. Highly recommend it for sure!" - }, - { - "speaker": "Melanie", - "dia_id": "D7:12", - "text": "That sounds awesome! What did you take away from it to use in your life?" - }, - { - "speaker": "Caroline", - "dia_id": "D7:13", - "text": "It taught me self-acceptance and how to find support. It also showed me that tough times don't last - hope and love exist. Pets bring so much joy too, though." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://st3.depositphotos.com/12674628/16006/i/1600/depositphotos_160060676-stock-photo-multiethnic-girls-with-puppy.jpg" - ], - "blip_caption": "a photography of two little girls sitting on the steps with a dog", - "query": "daughters playing with pet dog backyard", - "dia_id": "D7:14", - "re-download": true, - "text": "Caroline, those lessons are great - self-acceptance and finding support are key. Plus pets are awesome for joy and comfort, can't agree more! " - }, - { - "speaker": "Caroline", - "dia_id": "D7:15", - "text": "That's so nice! What pet do you have?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/u26t78f0idd91.jpg" - ], - "blip_caption": "a photo of a cat laying on the floor with its head on the floor", - "query": "dog cat kids playing joy", - "dia_id": "D7:16", - "text": "We've got a pup and a kitty. That's the dog, and here's our cat! They brighten up our day and always make us smile." - }, - { - "speaker": "Caroline", - "dia_id": "D7:17", - "text": "Ah, they're adorable! What are their names? Pets sure do bring so much joy to us!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhwGRNI2jDkrALJHgL2LWfW2rUGhN-GA4OL_gXU2fHPyxtst2MPrv9hkyOMdpj5SppLNYiQrcXUUq90vv5es8ueswy2tuu0Lqa2lh2vKOfDZ5SXSdLVMVvBrfLbFJG19QiqDbv1xs38fv-atd4MYOesJ4c89sQTzv6k93PDQ5T0dwVJV9O2FF95woyP3Q/s4032/IMG_9747.jpg" - ], - "blip_caption": "a photo of a person wearing pink sneakers on a white rug", - "query": "purple running shoe", - "dia_id": "D7:18", - "text": "Luna and Oliver! They are so sweet and playful - they really liven up the house! Just got some new shoes, too!" - }, - { - "speaker": "Caroline", - "dia_id": "D7:19", - "text": "Love that purple color! For walking or running?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a pair of pink sneakers in a box", - "dia_id": "D7:20", - "text": "Thanks, Caroline! These are for running. Been running longer since our last chat - a great way to destress and clear my mind." - }, - { - "speaker": "Caroline", - "dia_id": "D7:21", - "text": "Wow! What got you into running?" - }, - { - "speaker": "Melanie", - "dia_id": "D7:22", - "text": "I've been running farther to de-stress, which has been great for my headspace." - }, - { - "speaker": "Caroline", - "dia_id": "D7:23", - "text": "Cool, Melanie! Running can really boost your mood. Keep it up!" - }, - { - "speaker": "Melanie", - "dia_id": "D7:24", - "text": "Thanks, Caroline! This has been great for my mental health. I'm gonna keep it up." - }, - { - "speaker": "Caroline", - "dia_id": "D7:25", - "text": "Awesome, Melanie! Mental health's a priority, so make sure you take care of yourself." - }, - { - "speaker": "Melanie", - "dia_id": "D7:26", - "text": "Caroline, thanks! Mental health is important to me, and it's made such an improvement!" - }, - { - "speaker": "Caroline", - "dia_id": "D7:27", - "text": "Glad it helped ya, Melanie!" - } - ], - "session_8_date_time": "1:51 pm on 15 July, 2023", - "session_8": [ - { - "speaker": "Caroline", - "dia_id": "D8:1", - "text": "Hey Mel, what's up? Been a busy week since we talked." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://images.rawpixel.com/image_social_landscape/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTExL2ZsNDg2NDgxOTYyMDMtaW1hZ2UuanBn.jpg" - ], - "blip_caption": "a photography of a group of children making clay sculptures in a classroom", - "query": "pottery workshop family making clay pots", - "dia_id": "D8:2", - "re-download": true, - "text": "Hey Caroline, it's been super busy here. So much since we talked! Last Fri I finally took my kids to a pottery workshop. We all made our own pots, it was fun and therapeutic!" - }, - { - "speaker": "Caroline", - "dia_id": "D8:3", - "text": "Wow, Mel! Sounds like you and the kids had a blast. How'd they like it?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://monstermonster.shop/cdn/shop/products/mug-class_5000x.jpg" - ], - "blip_caption": "a photo of a cup with a dog face on it", - "query": "kids pottery finished pieces", - "dia_id": "D8:4", - "text": "The kids loved it! They were so excited to get their hands dirty and make something with clay. It was special to watch their creativity and imagination come to life, they made this!" - }, - { - "speaker": "Caroline", - "dia_id": "D8:5", - "text": "Aww, that's so sweet! That cup is so cute. It's awesome to see how kids show their personalities through art. What other creative projects do you do with them, besides pottery?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.pinimg.com/originals/ea/d9/d7/ead9d79b58ca80a38a744b5ab70482db.jpg" - ], - "blip_caption": "a photo of a painting of a sunset with a palm tree", - "query": "painting vibrant flowers sunset sky", - "dia_id": "D8:6", - "text": "We love painting together lately, especially nature-inspired ones. Here's our latest work from last weekend." - }, - { - "speaker": "Caroline", - "dia_id": "D8:7", - "text": "Wow Mel, that painting's amazing! The colors are so bold and it really highlights the beauty of nature. Y'all work on it together?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://karengimson.files.wordpress.com/2017/06/img_7222.jpg" - ], - "blip_caption": "a photo of a field of purple flowers with green leaves", - "query": "path lined purple flowers nature", - "dia_id": "D8:8", - "text": "Thanks, Caroline! We both helped with the painting - it was great bonding over it and chatting about nature. We found these lovely flowers. Appreciating the small things in life, too." - }, - { - "speaker": "Caroline", - "dia_id": "D8:9", - "text": "That photo is stunning! So glad you bonded over our love of nature. Last Friday I went to a council meeting for adoption. It was inspiring and emotional - so many people wanted to create loving homes for children in need. It made me even more determined to adopt." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://assets.eflorist.com/assets/products/PHR_/TEV57-5A.jpg" - ], - "blip_caption": "a photo of a blue vase with a bouquet of sunflowers and roses", - "query": "sunflower bouquet", - "dia_id": "D8:10", - "text": "Wow, Caroline, way to go! Your future fam will get a kick out of having you. What do you think of these?" - }, - { - "speaker": "Caroline", - "dia_id": "D8:11", - "text": "Thanks Melanie - love the blue vase in the pic! Blue's my fave, it makes me feel relaxed. Sunflowers mean warmth and happiness, right? While roses stand for love and beauty? That's neat. What do flowers mean to you?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://blueblossomrentals.com/cdn/shop/products/image_909fb96b-4208-429b-9a6f-59dffa3cb546.jpg" - ], - "blip_caption": "a photo of a row of white chairs with flowers on them", - "query": "garden full of flowers wedding decorations", - "dia_id": "D8:12", - "text": "Flowers bring joy. They represent growth, beauty and reminding us to appreciate the small moments. They were an important part of my wedding decor and always remind me of that day." - }, - { - "speaker": "Caroline", - "dia_id": "D8:13", - "text": "It must have been special at your wedding. I wish I had known you back then!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://platinumnotary.files.wordpress.com/2023/03/img-6679.jpg" - ], - "blip_caption": "a photo of a wedding ceremony in a greenhouse with people taking pictures", - "query": "wedding ceremony", - "dia_id": "D8:14", - "text": "It was amazing, Caroline. The day was full of love and joy. Everyone we love was there to celebrate us - it was really special." - }, - { - "speaker": "Caroline", - "dia_id": "D8:15", - "text": "Wow, what a great day! Glad everyone could make it. What was your favorite part?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://s3-us-west-2.amazonaws.com/amm-prod/wedding_photos/photos/000/024/198/original/4B873921-0596-4A6B-8CD8-C6E5C2B024AF.png" - ], - "blip_caption": "a photo of a man and woman standing on a beach", - "query": "vows partner holding hands ceremony", - "dia_id": "D8:16", - "text": "Marrying my partner and promising to be together forever was the best part." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://dynaimage.cdn.cnn.com/cnn/digital-images/org/dfc95f14-b325-431c-b977-5b6dc2d35f9c.jpg" - ], - "blip_caption": "a photo of a parade with people walking down the street", - "query": "rainbow flag pride march", - "dia_id": "D8:17", - "text": "Wow, nice pic! You both looked amazing. One special memory for me was this pride parade I went to a few weeks ago." - }, - { - "speaker": "Melanie", - "dia_id": "D8:18", - "text": "Wow, looks awesome! Did you join in?" - }, - { - "speaker": "Caroline", - "img_url": [ - "http://ninalemsparty.com/cdn/shop/collections/iStock-1292280203.jpg" - ], - "blip_caption": "a photo of a group of people holding up signs and smiling", - "query": "lgbtq+ pride parade vibrant flags smiling faces", - "dia_id": "D8:19", - "text": "Yes, I did. It was amazing! I felt so accepted and happy, just being around people who accepted and celebrated me. It's definitely a top memory." - }, - { - "speaker": "Melanie", - "dia_id": "D8:20", - "text": "Wow, what an experience! How did it make you feel?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a rainbow flag on a pole on a carpet", - "dia_id": "D8:21", - "text": "I felt so proud and grateful - the vibes were amazing and it was comforting to know I'm not alone and have a great community around me." - }, - { - "speaker": "Melanie", - "dia_id": "D8:22", - "text": "Wow, Caroline! That's huge! How did it feel to be around so much love and acceptance?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a group of people sitting on the ground with a dog", - "dia_id": "D8:23", - "text": "It was awesome, Melanie! Being around people who embrace and back me up is beyond words. It really inspired me." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a girl sitting in a teepee with stuffed animals", - "dia_id": "D8:24", - "text": "Wow, that sounds awesome! Your friends and community really have your back. What's been the best part of it?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a teepee with a teddy bear and pillows", - "dia_id": "D8:25", - "text": "Realizing I can be me without fear and having the courage to transition was the best part. It's so freeing to express myself authentically and have people back me up." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a buddha statue and a candle on a table", - "dia_id": "D8:26", - "text": "That's awesome, Caro! You've found the courage to be yourself - that's important for our mental health and finding peace." - }, - { - "speaker": "Caroline", - "dia_id": "D8:27", - "text": "Thanks, Melanie! Been a long road, but I'm proud of how far I've come. How're you doing finding peace?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a man holding a frisbee in front of a frisbee golf basket", - "dia_id": "D8:28", - "text": "I'm getting there, Caroline. Creativity and family keep me at peace." - }, - { - "speaker": "Caroline", - "dia_id": "D8:29", - "text": "That's awesome, Melanie! How have your family been supportive during your move?" - }, - { - "speaker": "Melanie", - "dia_id": "D8:30", - "text": "My fam's been awesome - they helped out and showed lots of love and support." - }, - { - "speaker": "Caroline", - "dia_id": "D8:31", - "text": "Wow, Mel, family love and support is the best!" - }, - { - "speaker": "Melanie", - "img_url": [ - "http://cragmama.com/wp-content/uploads//2016/10/IMG_4568.jpg" - ], - "blip_caption": "a photo of a man and two children sitting around a campfire", - "query": "family camping trip roasting marshmallows campfire", - "dia_id": "D8:32", - "text": "Yeah, Caroline, my family's been great - their love and support really helped me through tough times. It's awesome! We even went on another camping trip in the forest." - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a family walking through a forest with a toddler", - "dia_id": "D8:33", - "text": "Awesome, Mel! Family support's huge. What else do you guys like doing together?" - }, - { - "speaker": "Melanie", - "dia_id": "D8:34", - "text": "We enjoy hiking in the mountains and exploring forests. It's a cool way to connect with nature and each other." - }, - { - "speaker": "Caroline", - "dia_id": "D8:35", - "text": "Wow, Mel, that sounds awesome! Exploring nature and family time is so special." - }, - { - "speaker": "Melanie", - "dia_id": "D8:36", - "text": "Yeah, Caroline, they're some of my fave memories. It brings us together and brings us happiness. Glad you're here to share in it." - }, - { - "speaker": "Caroline", - "dia_id": "D8:37", - "text": "Thanks, Melanie! Really glad to have you as a friend to share my journey. You're awesome!" - }, - { - "speaker": "Melanie", - "dia_id": "D8:38", - "text": "Thanks, Caroline! Appreciate your friendship. It's great to have a supporter!" - }, - { - "speaker": "Caroline", - "dia_id": "D8:39", - "text": "No worries, Mel! Your friendship means so much to me. Enjoy your day!" - } - ], - "session_9_date_time": "2:31 pm on 17 July, 2023", - "session_9": [ - { - "speaker": "Melanie", - "dia_id": "D9:1", - "text": "Hey Caroline, hope all's good! I had a quiet weekend after we went camping with my fam two weekends ago. It was great to unplug and hang with the kids. What've you been up to? Anything fun over the weekend?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:2", - "text": "Hey Melanie! That sounds great! Last weekend I joined a mentorship program for LGBTQ youth - it's really rewarding to help the community." - }, - { - "speaker": "Melanie", - "dia_id": "D9:3", - "text": "Wow, Caroline! It's great that you're helping out. How's it going? Got any cool experiences you can share?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:4", - "text": "The mentoring is going great! I've met some amazing young folks and supported them along the way. It's inspiring to see how resilient and strong they are." - }, - { - "speaker": "Melanie", - "dia_id": "D9:5", - "text": "Wow, Caroline, that sounds super rewarding! Young people's resilience is amazing. Care to share some stories?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:6", - "text": "I mentor a transgender teen just like me. We've been working on building up confidence and finding positive strategies, and it's really been paying off! We had a great time at the LGBT pride event last month." - }, - { - "speaker": "Melanie", - "dia_id": "D9:7", - "text": "Caroline, awesome news that you two are getting along! What was it like for you both? Care to fill me in?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://res.cloudinary.com/dragonspell/images/w_1440,h_864,c_fill,dpr_auto,fl_progressive:steep,f_auto/w_1440,h_864/v1571420662/www.travelportland.com/Portland-Pride-Parade-Downtown/Portland-Pride-Parade-Downtown.jpg" - ], - "blip_caption": "a photo of a woman holding a rainbow umbrella in the air", - "query": "lgbt pride event", - "dia_id": "D9:8", - "text": "The pride event was awesome! It was so encouraging to be surrounded by so much love and acceptance." - }, - { - "speaker": "Melanie", - "dia_id": "D9:9", - "text": "Wow! What's the best part you remember from it?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:10", - "text": "Seeing my mentee's face light up when they saw the support was the best! Such a special moment." - }, - { - "speaker": "Melanie", - "dia_id": "D9:11", - "text": "Wow, Caroline! They must have felt so appreciated. It's awesome to see the difference we can make in each other's lives. Any other exciting LGBTQ advocacy stuff coming up?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:12", - "text": "Yay! Next month I'm having an LGBTQ art show with my paintings - can't wait!" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a painting with a blue and yellow design", - "dia_id": "D9:13", - "text": "Wow, Caroline, that sounds awesome! Can't wait to see your art - got any previews?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://images.fineartamerica.com/images/artworkimages/mediumlarge/1/abstract-landscape-bold-colorful-painting-megan-duncanson.jpg" - ], - "blip_caption": "a photography of a painting of a tree with a bright sun in the background", - "query": "preview painting art show", - "dia_id": "D9:14", - "re-download": true, - "text": "Check out my painting for the art show! Hope you like it." - }, - { - "speaker": "Melanie", - "dia_id": "D9:15", - "text": "Wow, Caroline, that painting is awesome! Those colors are so vivid and the whole thing looks really unified. What inspired you?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:16", - "text": "Thanks, Melanie! I painted this after I visited a LGBTQ center. I wanted to capture everyone's unity and strength." - }, - { - "speaker": "Melanie", - "dia_id": "D9:17", - "text": "Wow, Caroline! It really conveys unity and strength - such a gorgeous piece! My kids and I just finished another painting like our last one." - } - ], - "session_10_date_time": "8:56 pm on 20 July, 2023", - "session_10": [ - { - "speaker": "Caroline", - "dia_id": "D10:1", - "text": "Hey Melanie! Just wanted to say hi!" - }, - { - "speaker": "Melanie", - "dia_id": "D10:2", - "text": "Hey Caroline! Good to talk to you again. What's up? Anything new since last time?" - }, - { - "speaker": "Caroline", - "dia_id": "D10:3", - "text": "Hey Mel! A lot's happened since we last chatted - I just joined a new LGBTQ activist group last Tues. I'm meeting so many cool people who are as passionate as I am about rights and community support. I'm giving my voice and making a real difference, plus it's fulfilling in so many ways. It's just great, you know?" - }, - { - "speaker": "Melanie", - "dia_id": "D10:4", - "text": "That's awesome, Caroline! Glad to hear you found a great group where you can have an impact. Bet it feels great to be able to speak your truth and stand up for what's right. Want to tell me a bit more about it?" - }, - { - "speaker": "Caroline", - "dia_id": "D10:5", - "text": "Thanks, Melanie! It's awesome to have our own platform to be ourselves and support others' rights. Our group, 'Connected LGBTQ Activists', is made of all kinds of people investing in positive changes. We have regular meetings, plan events and campaigns, to get together and support each other." - }, - { - "speaker": "Melanie", - "dia_id": "D10:6", - "text": "Wow, Caroline, your group sounds awesome! Supporting each other and making good things happen - that's so inspiring! Have you been part of any events or campaigns lately?" - }, - { - "speaker": "Caroline", - "dia_id": "D10:7", - "text": "Last weekend our city held a pride parade! So many people marched through the streets waving flags, holding signs and celebrating love and diversity. I missed it but it was a powerful reminder that we are not alone in this fight for equality and inclusivity. Change is possible!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://mdkidadventures.files.wordpress.com/2023/06/img_2130.jpg" - ], - "blip_caption": "a photo of three children playing on the beach with a kite", - "query": "beach family playing frisbee sandy shore", - "dia_id": "D10:8", - "text": "Wow, fantastic, Caroline! Bet the atmosphere was incredible. Oh yeah, we went to the beach recently. It was awesome! The kids had such a blast." - }, - { - "speaker": "Caroline", - "dia_id": "D10:9", - "text": "Sounds fun! What was the best part? Do you do it often with the kids?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a sand castle on the beach with a blue sky", - "dia_id": "D10:10", - "text": "Seeing my kids' faces so happy at the beach was the best! We don't go often, usually only once or twice a year. But those times are always special to spend time together and chill." - }, - { - "speaker": "Caroline", - "dia_id": "D10:11", - "text": "Sounds special, those beach trips! Do you have any other summer traditions you all do together? Create those memories!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/hjh0wp8s721a1.jpg" - ], - "blip_caption": "a photo of a fire pit with a lot of fire and sparks", - "query": "family camping trip campfire night", - "dia_id": "D10:12", - "text": "We always look forward to our family camping trip. We roast marshmallows, tell stories around the campfire and just enjoy each other's company. It's the highlight of our summer!" - }, - { - "speaker": "Caroline", - "dia_id": "D10:13", - "text": "Wow, Mel, that's awesome! What's your best camping memory?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/ms0tvo85cto91.jpg" - ], - "blip_caption": "a photo of a plane flying in the sky with a star filled sky", - "query": "shooting star night sky", - "dia_id": "D10:14", - "text": "I'll always remember our camping trip last year when we saw the Perseid meteor shower. It was so amazing lying there and watching the sky light up with streaks of light. We all made wishes and felt so at one with the universe. That's a memory I'll never forget." - }, - { - "speaker": "Caroline", - "dia_id": "D10:15", - "text": "Cool! What did it look like?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/eqtu6adwcrfb1.jpg" - ], - "blip_caption": "a photo of a plane flying in the sky with a trail of smoke coming out of it", - "query": "night sky stars meteor shower", - "dia_id": "D10:16", - "text": "The sky was so clear and filled with stars, and the meteor shower was amazing - it felt like we were part of something huge and awe-inspiring." - }, - { - "speaker": "Caroline", - "dia_id": "D10:17", - "text": "Wow, Mel. That must've been breathtaking!" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a beach with footprints in the sand and a blue sky", - "dia_id": "D10:18", - "text": "It was one of those moments where I felt tiny and in awe of the universe. Reminds me how awesome life is - so many little moments like that." - }, - { - "speaker": "Caroline", - "dia_id": "D10:19", - "text": "That's great, Mel! What other good memories do you have that make you feel thankful for life?" - }, - { - "speaker": "Melanie", - "dia_id": "D10:20", - "text": "I'll never forget the day my youngest took her first steps. Seeing her wobble as she took those initial steps really put into perspective how fleeting life is and how lucky I am to be able to share these moments." - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a baby in a white crib with a blue blanket", - "dia_id": "D10:21", - "text": "Aw, that's sweet, Mel! Those milestones are great reminders of how special our bonds are." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://freerangestock.com/sample/134391/happy-family-holding-hands-with-ocean-and-sunset-in-the-background.jpg" - ], - "blip_caption": "a photography of a family standing on the beach at sunset", - "query": "children playing and laughing", - "dia_id": "D10:22", - "re-download": true, - "text": "Yeah, they sure are. It's special moments like these that make me appreciate life and how lucky I am to be with my family and have our love." - }, - { - "speaker": "Caroline", - "dia_id": "D10:23", - "text": "Wow, Melanie, what a beautiful moment! Lucky you to have such an awesome family!" - }, - { - "speaker": "Melanie", - "dia_id": "D10:24", - "text": "Thanks, Caroline! I'm really lucky to have my family; they bring so much joy and love." - } - ], - "session_11_date_time": "2:24 pm on 14 August, 2023", - "session_11": [ - { - "speaker": "Melanie", - "dia_id": "D11:1", - "text": "Hey Caroline! Last night was amazing! We celebrated my daughter's birthday with a concert surrounded by music, joy and the warm summer breeze. Seeing my kids' smiles was so awesome, and I'm so thankful for our special moments together." - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a poster for a concert with a picture of a man", - "dia_id": "D11:2", - "text": "Wow, sounds wonderful! Your love for your kids is so awesome. What concert was it? The advocacy event was a cool experience - so much love and support, amazing!" - }, - { - "speaker": "Melanie", - "dia_id": "D11:3", - "text": "Thanks, Caroline! It was Matt Patterson, he is so talented! His voice and songs were amazing. What's up with you? Anything interesting going on?" - }, - { - "speaker": "Caroline", - "dia_id": "D11:4", - "text": "Wow, Mel, glad you had a blast at the concert. A lot's happened since we talked. I went to a pride parade last Friday and it was awesome - so much energy and love everywhere. Really made me proud and reminded me how important it is to keep standing up for equality." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a band performing on stage with a sign that says all are welcome", - "dia_id": "D11:5", - "text": "Wow, that's awesome! How did it feel being part of that community?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://cloudfront-us-east-1.images.arcpublishing.com/opb/35SV3NIC4ZBRTLDGHUJ5QWU5WY.jpg" - ], - "blip_caption": "a photo of a group of people walking down a street with balloons", - "query": "pride parade crowd", - "dia_id": "D11:6", - "text": "It was so inspiring, Mel! Check out the crowd. People of all kinds celebrating love and acceptance - it really pushed me to keep fighting for LGBTQ rights." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://livingmividaloca.com/wp-content/uploads/2023/06/anaheim-town-square-concert.jpg" - ], - "blip_caption": "a photo of a group of people sitting on chairs watching a band", - "query": "outdoor concert family loving accepting environment", - "dia_id": "D11:7", - "text": "Wow, Caroline! That sounds awesome. This pic's from last night - looks like everyone was having a blast! Reminds me it's important to cultivate a loving and accepting environment for our kids. How do you stay inclusive in your work as an artist?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://www.dawnsilerart.com/wp-content/uploads/sites/3130/2020/11/YCNHTMC-CU9.jpg" - ], - "blip_caption": "a photo of a painting with a painting brush and paint on it", - "query": "painting vibrant colors diverse representation", - "dia_id": "D11:8", - "text": "That pic is cool! Representing inclusivity and diversity in my art is important to me. I also use it to speak up for the LGBTQ+ community and push for acceptance. Here's a recent painting!" - }, - { - "speaker": "Melanie", - "dia_id": "D11:9", - "text": "Wow, that rocks! What's the main idea of your art?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a painting of a woman with a cow in her lap", - "dia_id": "D11:10", - "text": "My art is about expressing my trans experience. It's my way of showing my story and helping people understand the trans community." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a person holding a purple bowl in their hand", - "dia_id": "D11:11", - "text": "Your art's amazing, Caroline. I love how you use it to tell your stories and teach people about trans folks. I'd love to see another painting of yours!" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://media.artsper.com/artwork/2013795_1_l.jpg" - ], - "blip_caption": "a photo of a painting of a woman with a red shirt", - "query": "painting embracing identity purple blue", - "dia_id": "D11:12", - "text": "Thanks, Melanie. Here's one- 'Embracing Identity' is all about finding comfort and love in being yourself. The woman in the painting stands for the journey of acceptance. My aim was to show warmth, love and self-acceptance." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a woman is making a vase on a wheel", - "dia_id": "D11:13", - "text": "Wow, Caroline, that's gorgeous! I love the self-acceptance and love theme. How does art help you with your self-discovery and acceptance journey?" - }, - { - "speaker": "Caroline", - "dia_id": "D11:14", - "text": "Art's allowed me to explore my transition and my changing body. It's been a great way to work through stuff I'm going through. I love that it teaches me to accept the beauty of imperfections." - }, - { - "speaker": "Melanie", - "dia_id": "D11:15", - "text": "Wow, Caroline, that's so cool! Art can be so healing and a way to really connect with who you are. It's awesome that beauty can be found in the imperfections. We're all individual and wonderfully imperfect. Thanks for sharing it with me!" - }, - { - "speaker": "Caroline", - "dia_id": "D11:16", - "text": "Thanks, Melanie. It means a lot to share this with you." - }, - { - "speaker": "Melanie", - "dia_id": "D11:17", - "text": "Great chatting with you! Feel free to reach out any time." - } - ], - "session_12_date_time": "1:50 pm on 17 August, 2023", - "session_12": [ - { - "speaker": "Caroline", - "dia_id": "D12:1", - "text": "Hey Mel! How're ya doin'? Recently, I had a not-so-great experience on a hike. I ran into a group of religious conservatives who said something that really upset me. It made me think how much work we still have to do for LGBTQ rights. It's been so helpful to have people around me who accept and support me, so I know I'll be ok!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:2", - "text": "Hey Caroline, sorry about the hike. It sucks when people are so closed-minded. Strong support really helps. FYI, I finished another pottery project - want to see a pic?" - }, - { - "speaker": "Caroline", - "dia_id": "D12:3", - "text": "Sure thing, Melanie! Can't wait to see your pottery project. I'm happy you found something that makes you happy. Show me when you can!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://omceramic.com/cdn/shop/products/IMG_0022.jpg" - ], - "blip_caption": "a photo of a bowl with a colorful design on it", - "query": "pottery project ceramic bowl", - "dia_id": "D12:4", - "text": "Here it is. Pretty proud of it! It was a great experience. Thoughts?" - }, - { - "speaker": "Caroline", - "dia_id": "D12:5", - "text": "That bowl is awesome, Mel! What gave you the idea for all the colors and patterns?" - }, - { - "speaker": "Melanie", - "dia_id": "D12:6", - "text": "Thanks, Caroline! I'm obsessed with those, so I made something to catch the eye and make people smile. Plus, painting helps me express my feelings and be creative. Each stroke carries a part of me." - }, - { - "speaker": "Caroline", - "dia_id": "D12:7", - "text": "That's amazing! You put so much effort and passion into it. Your creativity really shines. Seeing how art can be a source of self-expression and growth is truly inspiring. You're killing it!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:8", - "text": "Thanks, Caroline! Your words really mean a lot. I've always felt a strong connection to art, and it's been a huge learning experience. It's both a sanctuary and a source of comfort. I'm so glad to have something that brings me so much happiness and fulfillment." - }, - { - "speaker": "Caroline", - "dia_id": "D12:9", - "text": "Glad you found something that makes you so happy! Surrounding ourselves with things that bring us joy is important. Life's too short to do anything else!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:10", - "text": "Agreed, Caroline. Life's tough but it's worth it when we have things that make us happy." - }, - { - "speaker": "Caroline", - "dia_id": "D12:11", - "text": "Definitely, Mel! Finding those happy moments and clinging to them is key. It's what keeps us going, even when life's hard. I'm lucky to have people like you to remind me." - }, - { - "speaker": "Melanie", - "dia_id": "D12:12", - "text": "Yeah, same here Caroline. You make life's struggles more bearable." - }, - { - "speaker": "Caroline", - "dia_id": "D12:13", - "text": "Thanks, Melanie! It means a lot having you in my corner. Appreciate our friendship!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:14", - "text": "I appreciate our friendship too, Caroline. You've always been there for me." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://media2.fdncms.com/portmerc/imager/u/large/46577490/pride2022-2-jankowski.jpg" - ], - "blip_caption": "a photo of a group of people walking down a street with balloons", - "query": "friends pride festival", - "dia_id": "D12:15", - "text": "I'm always here for you, Mel! We had a blast last year at the Pride fest. Those supportive friends definitely make everything worth it!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:16", - "text": "That was a blast! So much fun with the whole gang! Wanna do a family outing this summer?" - }, - { - "speaker": "Caroline", - "dia_id": "D12:17", - "text": "Right, it was so much fun! We could do a family outting, or wanna plan something special for this summer, just us two? It'd be a great chance to catch up and explore nature! What do you think?" - }, - { - "speaker": "Melanie", - "dia_id": "D12:18", - "text": "Sounds great, Caroline! Let's plan something special!" - }, - { - "speaker": "Caroline", - "dia_id": "D12:19", - "text": "Sounds great, Mel! We'll make some awesome memories!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:20", - "text": "Yeah, Caroline! I'll start thinking about what we can do." - }, - { - "speaker": "Caroline", - "dia_id": "D12:21", - "text": "Yeah, Mel! Life's all about creating memories. Can't wait for the trip!" - } - ], - "session_13_date_time": "3:31 pm on 23 August, 2023", - "session_13": [ - { - "speaker": "Caroline", - "img_url": [ - "https://i.redd.it/pyq31v7eh6ra1.jpg" - ], - "blip_caption": "a photo of a sign with a picture of a guinea pig", - "query": "adoption brochures application forms external adoption advice assistance group", - "dia_id": "D13:1", - "text": "Hi Melanie! Hope you're doing good. Guess what I did this week? I took the first step towards becoming a mom - I applied to adoption agencies! It's a big decision, but I think I'm ready to give all my love to a child. I got lots of help from this adoption advice/assistance group I attended. It was great!" - }, - { - "speaker": "Melanie", - "dia_id": "D13:2", - "text": "Caroline, congrats! So proud of you for taking this step. How does it feel? Also, do you have any pets?" - }, - { - "speaker": "Caroline", - "dia_id": "D13:3", - "text": "Thanks, Mel! Exciting but kinda nerve-wracking. Parenting's such a big responsibility. And yup, I do- Oscar, my guinea pig. He's been great. How are your pets?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/kgggim1gom951.jpg" - ], - "blip_caption": "a photo of a black dog laying in the grass with a frisbee", - "query": "pets Luna Oliver playing frisbee backyard", - "dia_id": "D13:4", - "text": "Yeah, it's normal to be both excited and nervous with a big decision. And thanks for asking, they're good- we got another cat named Bailey too. Here's a pic of Oliver. Can you show me one of Oscar?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://cdn.i-scmp.com/sites/default/files/styles/landscape/public/d8/yp/images/shutterstock533807500.jpg" - ], - "blip_caption": "a photography of a guinea in a cage with hay and hay", - "query": "oscar munching parsley playpen", - "dia_id": "D13:5", - "re-download": true, - "text": "He's so cute! What\u2019s the funniest thing Oliver's done? And sure, check out this pic of him eating parsley! Veggies are his fave!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/fgv0i3nzo7541.jpg" - ], - "blip_caption": "a photo of a person holding a carrot in front of a horse", - "query": "oscar carrot in mouth", - "dia_id": "D13:6", - "text": "Oliver's hilarious! He hid his bone in my slipper once! Cute, right? Almost as silly as when I got to feed a horse a carrot. " - }, - { - "speaker": "Caroline", - "dia_id": "D13:7", - "text": "That's so funny! I used to go horseback riding with my dad when I was a kid, we'd go through the fields, feeling the wind. It was so special. I've always had a love for horses!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://warpedtable.com/cdn/shop/products/F331B563-AB73-430A-A6DF-3C5E0F91A4D8.jpg" - ], - "blip_caption": "a photo of a horse painted on a wooden wall", - "query": "horse painting", - "dia_id": "D13:8", - "text": "Wow, that sounds great - I agree, they're awesome. Here's a photo of my horse painting I did recently." - }, - { - "speaker": "Caroline", - "dia_id": "D13:9", - "text": "Wow, Melanie, that's amazing! Love all the details and how you got the horse's grace and strength. Do you like painting animals?" - }, - { - "speaker": "Melanie", - "dia_id": "D13:10", - "text": "Thanks, Caroline! Glad you like it. Yeah, I love to. It's peaceful and special. Horses have such grace! Do you like to paint too?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0302/3968/6755/files/IMG_8385_a145b124-53ab-4b3c-8f1a-497fa2d39a49.jpg" - ], - "blip_caption": "a photo of a painting of a woman with a blue face", - "query": "self-portrait painting vibrant colors", - "dia_id": "D13:11", - "text": "Painting's great for expressing myself. I love creating art! Here's a recent self-portrait I made last week." - }, - { - "speaker": "Melanie", - "dia_id": "D13:12", - "text": "Caroline, that's great! The blue's really powerful, huh? How'd you feel while painting it?" - }, - { - "speaker": "Caroline", - "dia_id": "D13:13", - "text": "Thanks, Mel! I felt liberated and empowered doing it. Painting helps me explore my identity and be true to myself. It's definitely therapeutic." - }, - { - "speaker": "Melanie", - "dia_id": "D13:14", - "text": "Wow, Caroline, that's great! Art's awesome for showing us who we really are and getting in touch with ourselves. What else helps you out?" - }, - { - "speaker": "Caroline", - "dia_id": "D13:15", - "text": "Thanks, Melanie. Art gives me a sense of freedom, but so does having supportive people around, promoting LGBTQ rights and being true to myself. I want to live authentically and help others to do the same." - }, - { - "speaker": "Melanie", - "dia_id": "D13:16", - "text": "Wow, Caroline! That's amazing. You really care about being real and helping others. Wishing you the best on your adoption journey!" - }, - { - "speaker": "Caroline", - "dia_id": "D13:17", - "text": "Thanks, Melanie! I really appreciate it. Excited for the future! Bye!" - }, - { - "speaker": "Melanie", - "dia_id": "D13:18", - "text": "Bye Caroline. I'm here for you. Take care of yourself." - } - ], - "session_14_date_time": "1:33 pm on 25 August, 2023", - "session_14": [ - { - "speaker": "Caroline", - "img_url": [ - "https://photos.thetrek.co/wp-content/uploads/2017/11/IMG_1742-e1509796327550.jpg" - ], - "blip_caption": "a photo of a woman sitting on a sign on top of a mountain", - "query": "letter apology hike encounter", - "dia_id": "D14:1", - "text": "Hey, Mel! How's it going? There's something I want to tell you. I went hiking last week and got into a bad spot with some people. It really bugged me, so I tried to apologize to them." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i0.wp.com/bardith.com/wp-content/uploads/2022/05/IMG_4371-1.jpg" - ], - "blip_caption": "a photo of a plate with a bunch of flowers on it", - "query": "pottery purple bowl floral patterns", - "dia_id": "D14:2", - "text": "Wow, Caroline! Sorry that happened to you. It's tough when those things happen, but it's great you apologized. Takes a lot of courage and maturity! What do you think of this?" - }, - { - "speaker": "Caroline", - "dia_id": "D14:3", - "text": "Thanks, Melanie! That plate is awesome! Did you make it?" - }, - { - "speaker": "Melanie", - "dia_id": "D14:4", - "text": "Yeah, I made it in pottery class yesterday. I love it! Pottery's so relaxing and creative. Have you tried it yet?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i0.wp.com/makesomethingmondays.com/wp-content/uploads/2017/07/mini-beach-sunset-painting-diy.jpg" - ], - "blip_caption": "a photo of a painting of a sunset on a small easel", - "query": "vibrant sunset beach painting", - "dia_id": "D14:5", - "text": "Nah, I haven't. I've been busy painting - here's something I just finished." - }, - { - "speaker": "Melanie", - "dia_id": "D14:6", - "text": "Wow Caroline, that looks amazing! Those colors are so vivid, it really looks like a real sunset. What gave you the idea to paint it?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a painting of a sunset over the ocean", - "dia_id": "D14:7", - "text": "Thanks, Melanie! I painted it after I visited the beach last week. Just seeing the sun dip below the horizon, all the amazing colors - it was amazing and calming. So I just had to try to capture that feeling in my painting." - }, - { - "speaker": "Melanie", - "dia_id": "D14:8", - "text": "Wow, the beach really inspired you. The art really took me to that moment and I can feel the serenity. You captured the sunset perfectly, so peaceful!" - }, - { - "speaker": "Caroline", - "dia_id": "D14:9", - "text": "Thanks Mel, really appreciate your kind words. It means a lot to me that you can feel the sense of peace and serenity. Makes me feel connected." - }, - { - "speaker": "Melanie", - "dia_id": "D14:10", - "text": "I feel the same way! Art is so cool like that - it connects us and helps us understand each other. I was actually just remembering yesterday, spending the day with my fam volunteering at a homeless shelter. It was hard to see how neglected some people are, but it was great to feel like we could make a difference." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/64cda0c3f2719a0e6e707684/a08e6e1f-f0e0-4f1a-b567-1f1f92b80aab/35970846_829192503937065_1026209343625756672_o_829192493937066.jpg" - ], - "blip_caption": "a photo of a crowd of people walking down a street with a rainbow flag", - "query": "volunteering pride event", - "dia_id": "D14:11", - "text": "Wow, Mel, you're amazing! Volunteering and making a difference- it's so heartwarming. You're an inspiration to us all!" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a bulletin board with a rainbow flag and a don't ever be afraid to", - "dia_id": "D14:12", - "text": "Thanks, Caroline! I really appreciate your help and motivation. What made you decide to transition and join the transgender community?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://npr.brightspotcdn.com/legacy/sites/wuwm/files/201811/20181029_095916.jpg" - ], - "blip_caption": "a photo of a building with a large eagle painted on it", - "query": "rainbow flag painting unity acceptance", - "dia_id": "D14:13", - "text": "Finding a community where I'm accepted, loved and supported has really meant a lot to me. It's made a huge difference to have people who get what I'm going through. Stuff like this mural are really special to me!" - }, - { - "speaker": "Melanie", - "dia_id": "D14:14", - "text": "Caroline, glad you found a supportive community! Can you tell me more about why it's special to you?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a stained glass window with a picture of a person on a horse", - "dia_id": "D14:15", - "text": "The rainbow flag mural is important to me as it reflects the courage and strength of the trans community. The eagle symbolizes freedom and pride, representing my own resilience and that of others." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a stained glass window with a person holding a key", - "dia_id": "D14:16", - "text": "I'm in awe of your courage as a trans person. Have you made any more art lately?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://projects.history.qmul.ac.uk/thehistorian/wp-content/uploads/sites/24/2017/10/IMG_20170922_072615_165.jpg" - ], - "blip_caption": "a photo of three stained glass windows in a church with a clock", - "query": "stained glass window letter", - "dia_id": "D14:17", - "text": "Thanks, Mel! I made this stained glass window to remind myself and others that within us all is the key to discovering our true potential and living our best life." - }, - { - "speaker": "Melanie", - "dia_id": "D14:18", - "text": "Wow, Caroline, that looks amazing! What inspired it?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a large stained glass window in a church", - "dia_id": "D14:19", - "text": "Thanks! It was made for a local church and shows time changing our lives. I made it to show my own journey as a transgender woman and how we should accept growth and change." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a door with a stained glass window and a coat rack", - "dia_id": "D14:20", - "text": "Wow, Caroline! All those colors are incredible and the story it tells is so inspiring." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i0.wp.com/marbleheadcurrent.org/wp-content/uploads/2023/07/rainbow.jpg" - ], - "blip_caption": "a photo of a painted sidewalk with a rainbow design on it", - "query": "painting rainbow flag unity acceptance", - "dia_id": "D14:21", - "text": "Thanks, Mel! Glad you like it. It's a symbol of togetherness, to celebrate differences and be that much closer. I'd love to make something like this next!" - }, - { - "speaker": "Melanie", - "dia_id": "D14:22", - "text": "Wow, that's gorgeous! Where did you find it?" - }, - { - "speaker": "Caroline", - "dia_id": "D14:23", - "text": "I was out walking in my neighborhood when I came across this cool rainbow sidewalk for Pride Month. It was so vibrant and welcoming, I had to take a picture! It reminds us that love and acceptance are everywhere\u2014even where we least expect it." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a person drawing a flower on the ground", - "dia_id": "D14:24", - "text": "That's so nice, Caroline! Art can be in the most unlikely places. Love and acceptance really can be found everywhere." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://static.skillshare.com/uploads/project/281358/cover_full_592aef91cce2432e71c739804161e0fb.jpg" - ], - "blip_caption": "a photo of a painting of flowers and a watercolor palette", - "query": "drawing flower ground colored chalk simple act creativity", - "dia_id": "D14:25", - "text": "Agreed, Mel! Art can be a real mood-booster - I saw someone drawing on the ground the other day and it made me so happy. Creativity sure can lighten someone's day." - }, - { - "speaker": "Melanie", - "dia_id": "D14:26", - "text": "Wow, Caroline, that's so nice! The colors are so bright and the flowers are so pretty. Art is such a source of joy." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.pinimg.com/originals/3d/e3/b8/3de3b8a013be3eec63cc454cb0c63536.jpg" - ], - "blip_caption": "a photo of a drawing of a bunch of flowers on a table", - "query": "bouquet wildflowers art", - "dia_id": "D14:27", - "text": "Thanks, Mel! Art gives me so much joy. It helps me show my feelings and freeze gorgeous moments, like a bouquet of flowers. " - }, - { - "speaker": "Melanie", - "dia_id": "D14:28", - "text": "Wow, did you make that? It looks so real!" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a drawing of a flower bouquet with a person holding it", - "dia_id": "D14:29", - "text": "Yeah, definitely! Drawing flowers is one of my faves. Appreciating nature and sharing it is great. What about you, Mel? What type of art do you love?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/k9wcp85ledi91.jpg" - ], - "blip_caption": "a photo of a painting of a sunflower on a canvas", - "query": "painting field sunflowers", - "dia_id": "D14:30", - "text": "Painting landscapes and still life is my favorite! Nature's amazing, here's a painting I did recently." - }, - { - "speaker": "Caroline", - "dia_id": "D14:31", - "text": "Wow, Mel! Any more paintings coming up?" - }, - { - "speaker": "Melanie", - "dia_id": "D14:32", - "text": "I'm feeling inspired by autumn so I'm planning a few. You got any cool art projects coming up?" - }, - { - "speaker": "Caroline", - "dia_id": "D14:33", - "text": "I'm putting together an LGBTQ art show next month and I'm gonna show my paintings. Super stoked!" - }, - { - "speaker": "Melanie", - "dia_id": "D14:34", - "text": "Wow, Caroline, that's awesome! Can't wait to see your show - the LGBTQ community needs more platforms like this!" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a poster for a concert with a man in a cowboy hat", - "dia_id": "D14:35", - "text": "Yeah Mel, stoked! Gonna be a great night featuring LGBTQ artists and their awesome talents. We want it to spread understanding and acceptance - let's make it happen!" - } - ], - "session_15_date_time": "3:19 pm on 28 August, 2023", - "session_15": [ - { - "speaker": "Caroline", - "dia_id": "D15:1", - "text": "Hey Melanie, great to hear from you. What's been up since we talked?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://img-aws.ehowcdn.com/1280x/www.onlyinyourstate.com/wp-content/uploads/2022/12/gym8.jpg" - ], - "blip_caption": "a photo of a playground with a climbing net and a slide", - "query": "kids climbing jungle gym park", - "dia_id": "D15:2", - "text": "Hey Caroline! Since we last spoke, I took my kids to a park yesterday. They had fun exploring and playing. It was nice seeing them have a good time outdoors. Time flies, huh? What's new with you?" - }, - { - "speaker": "Caroline", - "dia_id": "D15:3", - "text": "Wow, your kids had so much fun at the park! Being outdoors can be really enjoyable. A lot happened since our last chat. I've been chasing my ambitions and had the chance to volunteer at an LGBTQ+ youth center. It was so gratifying to talk to similar young people. It made me remember how essential it is to be kind and show support." - }, - { - "speaker": "Melanie", - "dia_id": "D15:4", - "text": "That sounds great, Caroline. Volunteering is a great way to meet people. Creating community and supporting each other, especially for kids, is really important. How did you feel about your time there? Anything that sticks out to you?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a table with a black table cloth and a group of people", - "dia_id": "D15:5", - "text": "I loved it. It was awesome to see how strong the young people were, with all the challenges they face. I felt fulfilled guiding and supporting them. I even got to let them know they're not alone by sharing my story. Such a powerful, emotional experience." - }, - { - "speaker": "Melanie", - "dia_id": "D15:6", - "text": "Was connecting with those young folks meaningful for you? " - }, - { - "speaker": "Caroline", - "dia_id": "D15:7", - "text": "It was so special to me. It reminded me of my own struggles in the past and how I felt alone. I was glad I could share my story and offer them support - it felt like I could make a difference." - }, - { - "speaker": "Melanie", - "dia_id": "D15:8", - "text": "That's great. Sharing your story and support might make a difference for a long time. What do you hope to do next time?" - }, - { - "speaker": "Caroline", - "dia_id": "D15:9", - "text": "I'm definitely carrying on volunteering at the youth center. It's an important part of my life and I've made strong connections with people there. I really believe in community and supporting each other. So I wanna keep making a difference." - }, - { - "speaker": "Melanie", - "dia_id": "D15:10", - "text": "That's great news, Caroline! Love seeing your dedication to helping others. Any specific projects or activities you're looking forward to there?" - }, - { - "speaker": "Caroline", - "dia_id": "D15:11", - "text": "We're putting together a talent show for the kids next month. I'm looking forward to seeing how much fun everyone has and how proud they'll feel of their talents!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://www.stomplight.com/cdn/shop/products/DavidAguilar.jpg" - ], - "blip_caption": "a photo of a band playing on a stage in a park", - "query": "talent show stage colorful lights microphone", - "dia_id": "D15:12", - "text": "That's so cool, Caroline! That's a great way to show off and be proud of everyone's skills. You know I love live music. Can't wait to hear about it!" - }, - { - "speaker": "Caroline", - "dia_id": "D15:13", - "text": "Wow! Did you see that band?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a crowd of people at a concert with their hands in the air", - "dia_id": "D15:14", - "text": "Yeah, that pic was from a show I went to. It was so much fun and reminded me of how music brings us together." - }, - { - "speaker": "Caroline", - "dia_id": "D15:15", - "text": "Wow, what a fun moment! What's the band?" - }, - { - "speaker": "Melanie", - "dia_id": "D15:16", - "text": "\"Summer Sounds\"- The playing an awesome pop song that got everyone dancing and singing. It was so fun and lively!" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a man playing a guitar in a recording studio", - "dia_id": "D15:17", - "text": "That sounds great! Music brings us together and brings joy. Playing and singing let me express myself and connect with others - love it! So cathartic and uplifting." - }, - { - "speaker": "Melanie", - "dia_id": "D15:18", - "text": "Cool! What type of music do you play?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a guitar on display in a store", - "dia_id": "D15:19", - "text": "Guitar's mostly my thing. Playing it helps me get my emotions out." - }, - { - "speaker": "Melanie", - "dia_id": "D15:20", - "text": "That's awesome! What type of guitar? Been playing long?" - }, - { - "speaker": "Caroline", - "dia_id": "D15:21", - "text": "I started playing acoustic guitar about five years ago; it's been a great way to express myself and escape into my emotions." - }, - { - "speaker": "Melanie", - "dia_id": "D15:22", - "text": "Music's amazing, isn't it? Any songs that have deep meaning for you?" - }, - { - "speaker": "Caroline", - "dia_id": "D15:23", - "text": "Yeah totally! \"Brave\" by Sara Bareilles has a lot of significance for me. It's about being courageous and fighting for what's right. Whenever I hear this jam, I think about the paths I've taken and the progress I've made." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a piece of paper with a drawing of a man playing a piano", - "dia_id": "D15:24", - "text": "That's a gorgeous song, Caroline. It really fits with your journey and your determination to make a difference. Music can be so inspiring and uplifting." - }, - { - "speaker": "Caroline", - "dia_id": "D15:25", - "text": "Thanks, Melanie! Appreciate it. You play any instruments?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a sheet music with notes and a pencil", - "dia_id": "D15:26", - "text": "Yeah, I play clarinet! Started when I was young and it's been great. Expression of myself and a way to relax." - }, - { - "speaker": "Caroline", - "dia_id": "D15:27", - "text": "Cool! Got any fav tunes?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a laptop computer with a graph on it", - "dia_id": "D15:28", - "text": "I'm a fan of both classical like Bach and Mozart, as well as modern music like Ed Sheeran's \"Perfect\"." - } - ], - "session_16_date_time": "12:09 am on 13 September, 2023", - "session_16": [ - { - "speaker": "Caroline", - "img_url": [ - "https://assets.simpleviewinc.com/simpleview/image/upload/c_fill,f_jpg,h_371,q_75,w_640/v1/crm/corpuschristitx/Sunset-Lake-Park_38118D81-5056-A36F-23E62D2F41525FF4-38118cb45056a36_381194bb-5056-a36f-23c599c63a3950d7.jpg" - ], - "blip_caption": "a photo of a beach with a fence and a sunset", - "query": "sunset lake", - "dia_id": "D16:1", - "text": "Hey Mel, long time no chat! I had a wicked day out with the gang last weekend - we went biking and saw some pretty cool stuff. It was so refreshing, and the pic I'm sending is just stunning, eh?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://exploringtheprime.com/wp-content/uploads/2019/10/IMG_6705-2.jpg" - ], - "blip_caption": "a photo of a dirt road surrounded by trees with yellow leaves", - "query": "family hiking trail vibrant autumn colors", - "dia_id": "D16:2", - "text": "Hey Caroline! It's so good to hear from you! That pic is so beautiful, the colors really pop. Biking sounds like a great way to get out in nature. We went camping with the kids a few weeks ago, had a blast exploring the forest and hiking. Nature can be so refreshing for your soul. Any plans coming up?" - }, - { - "speaker": "Caroline", - "dia_id": "D16:3", - "text": "Melanie, that photo's amazing! I love all the yellow leaves, it looks so cozy. That sounds like fun! Seeing how excited they get for the little things is awesome, it's so contagious." - }, - { - "speaker": "Melanie", - "dia_id": "D16:4", - "text": "Thanks, Caroline! It's awesome seeing the kids get excited learning something new about nature. Those moments make being a parent worth it. We roasted marshmallows and shared stories around the campfire. Those simple moments make the best memories. What inspires you with your volunteering?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.pinimg.com/originals/34/2e/72/342e72b194865e01a38af86c307e95c7.jpg" - ], - "blip_caption": "a photo of a painting of a heart on a table", - "query": "canvas painting rainbow colors", - "dia_id": "D16:5", - "text": "I'm inspired seeing my work make a difference for the LGBTQ+ community. Knowing I'm helping create a more loving world is amazing. I'm really thankful for my friends, family and mentors' support. It inspires me to keep making art, too." - }, - { - "speaker": "Melanie", - "dia_id": "D16:6", - "text": "Wow, Caroline, that looks awesome! I love how it shows the togetherness and power you were talking about. How long have you been creating art?" - }, - { - "speaker": "Caroline", - "dia_id": "D16:7", - "text": "Since I was 17 or so. I find it soempowering and cathartic. It's amazing how art can show things that are hard to put into words. How long have you been into art?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://www.1hotpieceofglass.com/cdn/shop/files/image_93ad5985-ff65-4b93-877b-3ee948ac5641_5000x.jpg" - ], - "blip_caption": "a photo of a group of bowls and a starfish on a white surface", - "query": "pottery bowl intricate patterns purple glaze", - "dia_id": "D16:8", - "text": "Seven years now, and I've finally found my real muses: painting and pottery. It's so calming and satisfying. Check out my pottery creation in the pic!" - }, - { - "speaker": "Caroline", - "dia_id": "D16:9", - "text": "Melanie, those bowls are amazing! They each have such cool designs. I love that you chose pottery for your art. Painting and drawing have helped me express my feelings and explore my gender identity. Creating art was really important to me during my transition - it helped me understand and accept myself. I'm so grateful." - }, - { - "speaker": "Melanie", - "dia_id": "D16:10", - "text": "Thanks, Caroline! It has really helped me out. I love how it's both a creative outlet and a form of therapy. Have you ever thought about trying it or another art form?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.redd.it/z8zsh53ycfvb1.jpg" - ], - "blip_caption": "a photo of a painting on a easel with a red and blue background", - "query": "canvas colourful brush strokes", - "dia_id": "D16:11", - "text": "I haven't done pottery yet, but I'm game for trying new art. I might try it sometime! Check out this piece I made!" - }, - { - "speaker": "Melanie", - "dia_id": "D16:12", - "text": "Wow, Caroline! This painting is awesome. Love the red and blue. What gave you the idea?" - }, - { - "speaker": "Caroline", - "dia_id": "D16:13", - "text": "Thanks, Melanie! I made this painting to show my path as a trans woman. The red and blue are for the binary gender system, and the mix of colors means smashing that rigid thinking. It's a reminder to love my authentic self - it's taken a while to get here but I'm finally proud of who I am." - }, - { - "speaker": "Melanie", - "dia_id": "D16:14", - "text": "Wow, Caro, that painting is amazing! You've made so much progress. I'm super proud of you for being your true self. What effect has the journey had on your relationships?" - }, - { - "speaker": "Caroline", - "dia_id": "D16:15", - "text": "Thanks, Melanie. It's definitely changed them. Some close friends kept supporting me, but a few weren't able to handle it. It wasn't easy, but I'm much happier being around those who accept and love me. Now my relationships feel more genuine." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/epuj1xq8eaga1.jpg" - ], - "blip_caption": "a photo of a sign posted on a door stating that someone is not being able to leave", - "query": "me kids park joy love happiness", - "dia_id": "D16:16", - "text": "Caroline, it's got to be tough dealing with those changes. Glad you've found people who uplift and accept you! Here's to a good time at the caf\u00e9 last weekend - they even had thoughtful signs like this! It brings me so much happiness." - }, - { - "speaker": "Caroline", - "dia_id": "D16:17", - "text": "Whoa, Mel, that sign looks serious. Did anything happen?" - }, - { - "speaker": "Melanie", - "dia_id": "D16:18", - "text": "The sign was just a precaution, I had a great time. But thank you for your concern, you're so thoughtful!" - }, - { - "speaker": "Caroline", - "dia_id": "D16:19", - "text": "Phew! Glad it all worked out and you had a good time at the park!" - }, - { - "speaker": "Melanie", - "dia_id": "D16:20", - "text": "Yeah, it was so much fun! Those joyful moments definitely show us life's beauty." - } - ], - "session_17_date_time": "10:31 am on 13 October, 2023", - "session_17": [ - { - "speaker": "Caroline", - "dia_id": "D17:1", - "text": "Hey Mel, what's up? Long time no see! I just contacted my mentor for adoption advice. I'm ready to be a mom and share my love and family. It's a great feeling. Anything new with you? Anything exciting going on?" - }, - { - "speaker": "Melanie", - "dia_id": "D17:2", - "text": "Hey Caroline! Great to hear from you! Wow, what an amazing journey. Congrats!" - }, - { - "speaker": "Caroline", - "dia_id": "D17:3", - "text": "Thanks, Melanie! I'm stoked to start this new chapter. It's been a dream to adopt and provide a safe, loving home for kids who need it. Do you have any experience with adoption, or know anyone who's gone through the process?" - }, - { - "speaker": "Melanie", - "dia_id": "D17:4", - "text": "Yeah, a buddy of mine adopted last year. It was a long process, but now they're super happy with their new kid. Makes me feel like maybe I should do it too!" - }, - { - "speaker": "Caroline", - "dia_id": "D17:5", - "text": "That's great news about your friend! It can be tough, but so worth it. It's a great way to add to your family and show your love. If you ever do it, let me know \u2014 I'd love to help in any way I can." - }, - { - "speaker": "Melanie", - "dia_id": "D17:6", - "text": "Thanks, Caroline! Appreciate your help. Got any tips for getting started on it?" - }, - { - "speaker": "Caroline", - "dia_id": "D17:7", - "text": "Yep! Do your research and find an adoption agency or lawyer. They'll help with the process and provide all the info. Gather documents like references, financial info and medical checks. Don't forget to prepare emotionally, since the wait can be hard. It's all worth it in the end though." - }, - { - "speaker": "Melanie", - "dia_id": "D17:8", - "text": "Thanks for the tip, Caroline. Doing research and readying myself emotionally makes sense. I'll do that. BTW, recently I had a setback. Last month I got hurt and had to take a break from pottery, which I use for self-expression and peace." - }, - { - "speaker": "Caroline", - "dia_id": "D17:9", - "text": "Oh man, sorry to hear that, Melanie. I hope you're okay. Pottery's a great way to relax, so it must have been tough taking a break. Need any help?" - }, - { - "speaker": "Melanie", - "dia_id": "D17:10", - "text": "Thanks, Caroline. It was tough, but I'm doing ok. Been reading that book you recommended a while ago and painting to keep busy." - }, - { - "speaker": "Caroline", - "dia_id": "D17:11", - "text": "Cool that you have creative outlets. Got any paintings to show? I'd love to check them out." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://trendgallery.art/cdn/shop/files/IMG_2355.jpg" - ], - "blip_caption": "a photo of a painting of a sunset with a pink sky", - "query": "landscape painting vibrant purple sunset autumn", - "dia_id": "D17:12", - "text": "Yeah, Here's one I did last week. It's inspired by the sunsets. The colors make me feel calm. What have you been up to lately, artistically?" - }, - { - "speaker": "Caroline", - "dia_id": "D17:13", - "text": "Wow Mel, that's stunning! Love the colors and the chilled-out sunset vibe. What made you paint it? I've been trying out abstract stuff recently. It's kinda freeing, just putting my feelings on the canvas without too much of a plan. It's like a cool form of self-expression." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://theartwerks.com/cdn/shop/products/image_4c8aee8a-5395-4037-a1d4-f6db3a3b0302.jpg" - ], - "blip_caption": "a photo of a painting on a wall with a blue background", - "query": "abstract painting vibrant colors", - "dia_id": "D17:14", - "text": "Thanks, Caroline! I painted it because it was calming. I've done an abstract painting too, take a look! I love how art lets us get our emotions out." - }, - { - "speaker": "Caroline", - "dia_id": "D17:15", - "text": "Wow, that looks great! The blue adds so much to it. What feelings were you hoping to portray?" - }, - { - "speaker": "Melanie", - "dia_id": "D17:16", - "text": "I wanted a peaceful blue streaks to show tranquility. Blue calms me, so I wanted the painting to have a serene vibe while still having lots of vibrant colors." - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a poster on a wall in a classroom", - "dia_id": "D17:17", - "text": "Yeah, it's very calming. It's awesome how art can show emotions. By the way, I went to a poetry reading last Fri - it was really powerful! Ever been to one?" - }, - { - "speaker": "Melanie", - "dia_id": "D17:18", - "text": "Nope, never been to something like that. What was it about? What made it so special?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1675780954.jpg" - ], - "blip_caption": "a photography of a sign that says trans lives matter", - "query": "transgender poetry reading trans pride flags", - "dia_id": "D17:19", - "re-download": true, - "text": "It was a transgender poetry reading where transgender people shared their stories through poetry. It was extra special 'cause it was a safe place for self-expression and it was really empowering to hear others share and celebrate their identities." - }, - { - "speaker": "Melanie", - "dia_id": "D17:20", - "text": "Wow, sounds amazing! What was the event like? Those posters are great!" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.redd.it/50qvgfuva33b1.jpg" - ], - "blip_caption": "a photo of a drawing of a woman in a dress", - "query": "transgender flag drawing", - "dia_id": "D17:21", - "text": "The room was electric with energy and support! The posters were amazing, so much pride and strength! It inspired me to make some art." - }, - { - "speaker": "Melanie", - "dia_id": "D17:22", - "text": "That's awesome, Caroline! You drew it? What does it mean to you?" - }, - { - "speaker": "Caroline", - "dia_id": "D17:23", - "text": "Thanks, Melanie! Yeah, I drew it. It stands for freedom and being real. It's like a nudge to always stay true to myself and embrace my womanhood." - }, - { - "speaker": "Melanie", - "dia_id": "D17:24", - "text": "I love it. Showing off our true selves is the best thing ever." - }, - { - "speaker": "Caroline", - "dia_id": "D17:25", - "text": "Yep, Melanie! Being ourselves is such a great feeling. It's an ongoing adventure of learning and growing." - }, - { - "speaker": "Melanie", - "dia_id": "D17:26", - "text": "Yep, Caroline. Life's about learning and exploring. Glad we can be on this trip together." - } - ], - "session_18_date_time": "6:55 pm on 20 October, 2023", - "session_18": [ - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/dl8dki2hm3k81.jpg" - ], - "blip_caption": "a photo of a car dashboard with a white cloth and a steering wheel", - "query": "car accident damaged car airbags deployed roadtrip", - "dia_id": "D18:1", - "text": "Hey Caroline, that roadtrip this past weekend was insane! We were all freaked when my son got into an accident. We were so lucky he was okay. It was a real scary experience. Thankfully it's over now. What's been up since we last talked?" - }, - { - "speaker": "Caroline", - "dia_id": "D18:2", - "text": "Oops, sorry 'bout the accident! Must have been traumatizing for you guys. Thank goodness your son's okay. Life sure can be a roller coaster." - }, - { - "speaker": "Melanie", - "dia_id": "D18:3", - "text": "Yeah, our trip got off to a bad start. I was really scared when we got into the accident. Thankfully, my son's ok and that was a reminder that life is precious and to cherish our family." - }, - { - "speaker": "Caroline", - "dia_id": "D18:4", - "text": "Glad your son is okay, Melanie. Life's unpredictable, but moments like these remind us how important our loved ones are. Family's everything." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://familyadventuresva.files.wordpress.com/2022/03/img_5030.jpg" - ], - "blip_caption": "a photo of two children standing on a rocky cliff overlooking a canyon", - "query": "grand canyon family photo", - "dia_id": "D18:5", - "text": "Yeah, you're right, Caroline. Family's super important to me. Especially after the accident, I've thought a lot about how much I need them. They mean the world to me and I'm so thankful to have them. Thankfully, they enjoyed the Grand Canyon a lot!" - }, - { - "speaker": "Caroline", - "dia_id": "D18:6", - "text": "The kids look so cute, Mel! I bet they bring lots of joy. How did they handle the accident?" - }, - { - "speaker": "Melanie", - "dia_id": "D18:7", - "text": "Thanks! They were scared but we reassured them and explained their brother would be OK. They're tough kids." - }, - { - "speaker": "Caroline", - "dia_id": "D18:8", - "text": "Kids are amazingly resilient in tough situations. They have an amazing ability to bounce back." - }, - { - "speaker": "Melanie", - "dia_id": "D18:9", - "text": "They're really amazing. Wish I was that resilient too. But they give me the strength to keep going." - }, - { - "speaker": "Caroline", - "dia_id": "D18:10", - "text": "Our loved ones give us strength to tackle any challenge - it's amazing!" - }, - { - "speaker": "Melanie", - "dia_id": "D18:11", - "text": "Yeah, Caroline. Totally agree. They're my biggest motivation and support." - }, - { - "speaker": "Caroline", - "dia_id": "D18:12", - "text": "It's so sweet to see your love for your family, Melanie. They really are your rock." - }, - { - "speaker": "Melanie", - "dia_id": "D18:13", - "text": "Thanks, Caroline. They're a real support. Appreciate them a lot." - }, - { - "speaker": "Caroline", - "dia_id": "D18:14", - "text": "Glad you've got people to lean on, Melanie. It helps during tougher times." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://live.staticflickr.com/8358/29211988243_82023c5524_b.jpg" - ], - "blip_caption": "a photography of a woman and a child walking on a trail", - "query": "family hiking mountains", - "dia_id": "D18:15", - "re-download": true, - "text": "Yeah for sure. Having my fam around helps a lot. It makes hard times easier." - }, - { - "speaker": "Caroline", - "dia_id": "D18:16", - "text": "Wow, great pic! Is that recent? Looks like you all had fun!" - }, - { - "speaker": "Melanie", - "dia_id": "D18:17", - "text": "Thanks, Caroline! Yup, we just did it yesterday! The kids loved it and it was a nice way to relax after the road trip." - }, - { - "speaker": "Caroline", - "dia_id": "D18:18", - "text": "Glad you got some R&R after the drive. Nature sure seems to refresh us, huh?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a sunset over a body of water", - "dia_id": "D18:19", - "text": "Absolutely! It really helps me reset and recharge. I love camping trips with my fam, 'cause nature brings such peace and serenity." - }, - { - "speaker": "Caroline", - "dia_id": "D18:20", - "text": "Wow, that's awesome! What do you love most about camping with your fam?" - }, - { - "speaker": "Melanie", - "dia_id": "D18:21", - "text": "It's a chance to be present and together. We bond over stories, campfires and nature. It's so peaceful waking up to the sound of birds and the smell of fresh air - it always refreshes my soul." - }, - { - "speaker": "Caroline", - "dia_id": "D18:22", - "text": "That's so peaceful and calming, Melanie! I can picture waking up to nature. It's great that you get to spend quality, tranquil time with your family." - }, - { - "speaker": "Melanie", - "dia_id": "D18:23", - "text": "Thanks, Caroline! This is a great time. Nature and quality time, can't beat it!" - }, - { - "speaker": "Caroline", - "dia_id": "D18:24", - "text": "Yeah totally! They're priceless. Lucky you!" - } - ], - "session_19_date_time": "9:55 am on 22 October, 2023", - "session_19": [ - { - "speaker": "Caroline", - "dia_id": "D19:1", - "text": "Woohoo Melanie! I passed the adoption agency interviews last Friday! I'm so excited and thankful. This is a big move towards my goal of having a family." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://imgur.com/oGlhL5J.jpg" - ], - "blip_caption": "a photo of a couple of wooden dolls sitting on top of a table", - "query": "painted ceramic family figurine", - "dia_id": "D19:2", - "text": "Congrats, Caroline! Adoption sounds awesome. I'm so happy for you. These figurines I bought yesterday remind me of family love. Tell me, what's your vision for the future?" - }, - { - "speaker": "Caroline", - "dia_id": "D19:3", - "text": "Thanks so much, Melanie! It's beautiful! It really brings home how much love's in families - both blood and the ones we choose. I hope to build my own family and put a roof over kids who haven't had that before. For me, adoption is a way of giving back and showing love and acceptance." - }, - { - "speaker": "Melanie", - "dia_id": "D19:4", - "text": "Wow, Caroline, that's awesome. Giving a home to needy kids is such a loving way to build a family. Those kids will be so supported and happy in their new home." - }, - { - "speaker": "Caroline", - "dia_id": "D19:5", - "text": "Thanks, Melanie. My dream is to create a safe and loving home for these kids. Love and acceptance should be everyone's right, and I want them to experience it." - }, - { - "speaker": "Melanie", - "dia_id": "D19:6", - "text": "I totally agree, Caroline. Everyone deserves that. It's awesome to see how passionate you are about helping these kids." - }, - { - "speaker": "Caroline", - "dia_id": "D19:7", - "text": "Thanks, Mel. Finding self-acceptance was a long process, but now I'm ready to offer love and support to those who need it. It's empowering to make a positive difference in someone's life." - }, - { - "speaker": "Melanie", - "dia_id": "D19:8", - "text": "That must have been tough for you, Caroline. Respect for finding acceptance and helping others with what you've been through. You're so strong and inspiring." - }, - { - "speaker": "Caroline", - "dia_id": "D19:9", - "text": "Thanks, Melanie. Transitioning wasn't easy and acceptance wasn't either, but the help I got from friends, family and people I looked up to was invaluable. They boosted me through tough times and helped me find out who I really am. That's why I want to pass that same support to anyone who needs it. Bringing others comfort and helping them grow brings me such joy." - }, - { - "speaker": "Melanie", - "dia_id": "D19:10", - "text": "I'm so happy for you, Caroline. You found your true self and now you're helping others. You're so inspiring!" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a clock with a green and yellow design on it", - "dia_id": "D19:11", - "text": "Thanks, Melanie. Your support really means a lot. This journey has been amazing and I'm grateful I get to share it and help others with theirs. It's a real gift." - }, - { - "speaker": "Melanie", - "dia_id": "D19:12", - "text": "Absolutely! I'm so glad we can always be there for each other." - }, - { - "speaker": "Caroline", - "dia_id": "D19:13", - "text": "Glad you agree, Caroline. Appreciate the support of those close to me. Their encouragement made me who I am." - }, - { - "speaker": "Melanie", - "dia_id": "D19:14", - "text": "Glad you had support. Being yourself is great!" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://trendgallery.art/cdn/shop/products/IMG_4482.jpg" - ], - "blip_caption": "a photo of a painting with the words happiness painted on it", - "query": "painting vibrant colors happiness self-expression", - "dia_id": "D19:15", - "text": "Yeah, that's true! It's so freeing to just be yourself and live honestly. We can really accept who we are and be content." - } - ], - "session_20_date_time": "4:10 pm on 26 October, 2023", - "session_21_date_time": "9:35 am on 31 October, 2023", - "session_22_date_time": "12:28 am on 8 November, 2023", - "session_23_date_time": "5:15 pm on 11 November, 2023", - "session_24_date_time": "2:46 pm on 16 November, 2023", - "session_25_date_time": "1:18 pm on 21 November, 2023", - "session_26_date_time": "4:39 pm on 24 November, 2023", - "session_27_date_time": "6:25 pm on 26 November, 2023", - "session_28_date_time": "8:52 pm on 5 December, 2023", - "session_29_date_time": "12:20 am on 8 December, 2023", - "session_30_date_time": "4:37 pm on 10 December, 2023", - "session_31_date_time": "3:24 pm on 16 December, 2023", - "session_32_date_time": "3:43 pm on 20 December, 2023", - "session_33_date_time": "8:32 pm on 27 December, 2023", - "session_34_date_time": "1:08 pm on 30 December, 2023", - "session_35_date_time": "12:19 am on 4 January, 2024" - }, - "event_summary": { - "events_session_1": { - "Caroline": [ - "Caroline attends an LGBTQ support group for the first time." - ], - "Melanie": [], - "date": "8 May, 2023" - }, - "events_session_2": { - "Caroline": [ - "Caroline is inspired by her supportive friends and mentors to start researching adoption agencies." - ], - "Melanie": [], - "date": "25 May, 2023" - }, - "events_session_3": { - "Caroline": [ - "Caroline speaks at her school and encourages students to get involved in the LGBTQ community." - ], - "Melanie": [], - "date": "9 June, 2023" - }, - "events_session_4": { - "Caroline": [], - "Melanie": [ - "Melanie takes her family camping for a weekend to bond." - ], - "date": "27 June, 2023" - }, - "events_session_5": { - "Caroline": [], - "Melanie": [ - "Melanie registers for a pottery class." - ], - "date": "3 July, 2023" - }, - "events_session_6": { - "Caroline": [], - "Melanie": [ - "Melanie takes her kids to the local musuem for a day of fun." - ], - "date": "6 July, 2023" - }, - "events_session_7": { - "Caroline": [], - "Melanie": [ - "Melanie begins running longer distances to destress." - ], - "date": "12 July, 2023" - }, - "events_session_8": { - "Caroline": [ - "Caroline attends an adoption council meeting." - ], - "Melanie": [], - "date": "15 July, 2023" - }, - "events_session_9": { - "Caroline": [ - "Caroline joins a mentorship program for LGBTQ youth." - ], - "Melanie": [], - "date": "17 July, 2023" - }, - "events_session_10": { - "Caroline": [ - "Caroline joins a group of connected LGBTQ activists." - ], - "Melanie": [ - "Melanie and her family takes a trip to the beach" - ], - "date": "20 July, 2023" - }, - "events_session_11": { - "Caroline": [], - "Melanie": [ - "Melanie and her family attend an outdoor concert to celebrate her daughter's birthday." - ], - "date": "14 August, 2023" - }, - "events_session_12": { - "Caroline": [ - "Caroline meets a group of religious conservatives on a hike, and they make an unwelcoming comment about her transition." - ], - "Melanie": [ - "Melanie finishes her first pottery project." - ], - "date": "17 August, 2023" - }, - "events_session_13": { - "Caroline": [ - "Caroline begins the adoption process by applying to multiple agencies.", - "Caroline attends a meeting to receive special adoption advice and assistance from the supportive group." - ], - "Melanie": [], - "date": "23 August, 2023" - }, - "events_session_14": { - "Caroline": [ - "Caroline writes a letter to the people she encountered on her hike to apologize for the negative experience they had." - ], - "Melanie": [ - "Melanie and her family volunteer at a local homeless shelter." - ], - "date": "25 August, 2023" - }, - "events_session_15": { - "Caroline": [], - "Melanie": [ - "Melanie takes her kids to a local park" - ], - "date": "28 August, 2023" - }, - "events_session_16": { - "Caroline": [ - "Caroline spends a day out outdoors bike riding and sight seeing with her friends." - ], - "Melanie": [], - "date": "13 September, 2023" - }, - "events_session_17": { - "Caroline": [ - "Caroline calls on her mentor for adoption advice." - ], - "Melanie": [], - "date": "13 October, 2023" - }, - "events_session_18": { - "Caroline": [], - "Melanie": [ - "Melanie's family takes a roadtrip to the Grand Canyon.", - "Melanie's son gets in a car accident while on the roadtrip.", - "Melanie and her family take a roadtrip to visit a nearby national park." - ], - "date": "20 October, 2023" - }, - "events_session_19": { - "Caroline": [ - "Caroline passes the adoption agency interviews." - ], - "Melanie": [], - "date": "22 October, 2023" - } - }, - "observation": { - "session_1_observation": { - "Caroline": [ - [ - "Caroline attended an LGBTQ support group recently and found the transgender stories inspiring.", - "D1:3" - ], - [ - "The support group has made Caroline feel accepted and given her courage to embrace herself.", - "D1:7" - ], - [ - "Caroline is planning to continue her education and explore career options in counseling or mental health to support those with similar issues.", - "D1:9" - ] - ], - "Melanie": [ - [ - "Melanie is currently managing kids and work and finds it overwhelming.", - "D1:2" - ], - [ - "Melanie painted a lake sunrise last year which holds special meaning to her.", - "D1:14" - ], - [ - "Painting is a fun way for Melanie to express her feelings and get creative, helping her relax after a long day.", - "D1:16" - ], - [ - "Melanie is going swimming with the kids after the conversation.", - "D1:18" - ] - ] - }, - "session_2_observation": { - "Melanie": [ - [ - "Melanie ran a charity race for mental health last Saturday.", - "D2:1" - ], - [ - "Melanie is realizing the importance of self-care and its impact on her family.", - "D2:3" - ], - [ - "Melanie carves out me-time each day for activities like running, reading, or playing the violin.", - "D2:5" - ], - [ - "Melanie's kids are excited about summer break and they are considering going camping next month.", - "D2:7" - ] - ], - "Caroline": [ - [ - "Caroline is researching adoption agencies with the dream of having a family and providing a loving home to kids in need.", - "D2:8" - ], - [ - "Caroline chose an adoption agency that helps LGBTQ+ folks with adoption due to their inclusivity and support.", - "D2:12" - ], - [ - "Caroline is excited to create a family for kids who need one, even though she anticipates challenges as a single parent.", - "D2:14" - ] - ] - }, - "session_3_observation": { - "Caroline": [ - [ - "Caroline started transitioning three years ago.", - "D3:1" - ], - [ - "Caroline gave a talk at a school event about her transgender journey and encouraged students to get involved in the LGBTQ community.", - "D3:1" - ], - [ - "Caroline believes conversations about gender identity and inclusion are necessary and is thankful for being able to give a voice to the trans community.", - "D3:3" - ], - [ - "Caroline feels sharing experiences is important to help promote understanding and acceptance.", - "D3:5" - ], - [ - "Caroline feels blessed with a lot of love and support throughout her journey.", - "D3:5" - ], - [ - "Caroline aims to pass on the love and support she has received by sharing stories to build a strong and supportive community of hope.", - "D3:5" - ], - [ - "Caroline's friends, family, and mentors are her rocks, motivating her and giving her strength to push on.", - "D3:11" - ], - [ - "Caroline has known her friends for 4 years, since moving from her home country, and values their love and help, especially after a tough breakup.", - "D3:13" - ] - ], - "Melanie": [ - [ - "Melanie is supportive of Caroline and proud of her for spreading awareness and inspiring others in the LGBTQ community.", - "D3:2" - ], - [ - "Melanie believes talking about inclusivity and acceptance is crucial.", - "D3:4" - ], - [ - "Melanie values family moments and feels they make life awesome, alive, and happy.", - "D3:20" - ], - [ - "Melanie has a husband and kids who keep her motivated.", - "D3:14" - ], - [ - "Melanie has been married for 5 years.", - "D3:16" - ], - [ - "Melanie cherishes time with family and feels most alive and happy during those moments.", - "D3:22" - ] - ] - }, - "session_4_observation": { - "Caroline": [ - [ - "Caroline received a special necklace as a gift from her grandmother in Sweden, symbolizing love, faith, and strength.", - "D4:3" - ], - [ - "Caroline treasures a hand-painted bowl made by a friend for her 18th birthday, which reminds her of art and self-expression.", - "D4:5" - ], - [ - "Caroline is considering a career in counseling and mental health, particularly working with trans people to help them accept themselves and support their mental health.", - "D4:11" - ], - [ - "Caroline attended an LGBTQ+ counseling workshop focused on therapeutic methods and supporting trans individuals, finding it enlightening and inspiring.", - "D4:13" - ], - [ - "Caroline's motivation to pursue counseling comes from her own journey, the support she received, and the positive impact counseling had on her life.", - "D4:15" - ] - ], - "Melanie": [ - [ - "Melanie went camping with her family in the mountains last week and had a great time exploring nature, roasting marshmallows, and hiking.", - "D4:8" - ], - [ - "Melanie values family time and finds it to be special and important.", - "D4:10" - ] - ] - }, - "session_5_observation": { - "Caroline": [ - [ - "Caroline attended an LGBTQ+ pride parade last week and felt a sense of belonging and happiness.", - "D5:1" - ], - [ - "Caroline is considering a career in counseling and mental health to help others.", - "D5:3" - ], - [ - "Caroline is currently learning the piano to get creative.", - "D5:5" - ], - [ - "Caroline is looking forward to attending a transgender conference this month to meet others in the community and learn about advocacy.", - "D5:13" - ] - ], - "Melanie": [ - [ - "Melanie signed up for a pottery class and finds it therapeutic for self-expression and creativity.", - "D5:4" - ], - [ - "Melanie is a big fan of pottery and finds it calming and creative.", - "D5:6" - ], - [ - "Melanie made a black and white bowl in her pottery class which she is proud of.", - "D5:8" - ], - [ - "Pottery is a significant part of Melanie's life as it helps her express her emotions and brings her joy.", - "D5:10" - ] - ] - }, - "session_6_observation": { - "Caroline": [ - [ - "Caroline has been looking into counseling or mental health work and is passionate about helping people and making a positive impact.", - "D6:3" - ], - [ - "Caroline is creating a library for when she has kids, as she looks forward to reading to them and opening up their minds.", - "D6:7" - ], - [ - "Caroline has a collection of kids' books in her library including classics, stories from different cultures, and educational books.", - "D6:9" - ], - [ - "Caroline appreciates the importance of friendship and compassion in her life and is lucky to have friends and family helping with her transition.", - "D6:11" - ], - [ - "Caroline's friends and family have been there for her every step of the way, providing love, guidance, and acceptance during her transition.", - "D6:13" - ] - ], - "Melanie": [ - [ - "Melanie took her kids to the museum recently and enjoyed seeing their excitement at the dinosaur exhibit.", - "D6:4" - ], - [ - "Melanie loves spending time with her kids and seeing the joy in their eyes when exploring new things.", - "D6:4" - ], - [ - "Melanie and her family enjoy camping at the beach as it brings them closer together.", - "D6:16" - ] - ] - }, - "session_7_observation": { - "Caroline": [ - [ - "Caroline attended an LGBTQ conference recently and felt accepted and supported, emphasizing the importance of fighting for trans rights and spreading awareness.", - "D7:1" - ], - [ - "Caroline is looking into counseling and mental health jobs to provide support to others, motivated by her own struggles with mental health and the help she received.", - "D7:5" - ], - [ - "Caroline's favorite guiding book is 'Becoming Nicole' by Amy Ellis Nutt, a true story about a trans girl and her family that gave her hope and connection.", - "D7:11" - ], - [ - "According to 'Becoming Nicole,' Caroline learned the importance of self-acceptance, finding support, and the existence of hope and love.", - "D7:13" - ], - [ - "Caroline values the role of pets in bringing joy and comfort.", - "D7:13" - ] - ], - "Melanie": [ - [ - "Melanie finds LGBTQ events like the conference Caroline attended to be reminding of the strength of community.", - "D7:2" - ], - [ - "Melanie supports Caroline's drive to make a difference in LGBTQ rights.", - "D7:4" - ], - [ - "Melanie reminds Caroline to pursue her dreams and appreciates the power of books in guiding and motivating her.", - "D7:8" - ], - [ - "Melanie has a dog named Luna and a cat named Oliver that bring joy and liveliness to her home.", - "D7:18" - ], - [ - "Melanie got new running shoes for running, which she finds great for destressing and clearing her mind.", - "D7:20" - ], - [ - "Running has been great for Melanie's mental health and mood.", - "D7:24" - ] - ] - }, - "session_8_observation": { - "Caroline": [ - [ - "Caroline attended a council meeting for adoption last Friday and found it inspiring and emotional.", - "D8:9" - ], - [ - "Caroline went to a pride parade a few weeks ago and felt accepted and happy being around people who celebrated her.", - "D8:19" - ], - [ - "Caroline felt proud and grateful at the pride parade feeling accepted by the community.", - "D8:21" - ], - [ - "Caroline expressed feeling comforted by being around accepting and loving people.", - "D8:21" - ], - [ - "Caroline mentioned the importance of finding peace and mental health through expressions of authentic self.", - "D8:25" - ], - [ - "Caroline expressed pride in the courage to transition, finding freedom in expressing herself authentically.", - "D8:25" - ] - ], - "Melanie": [ - [ - "Melanie took her kids to a pottery workshop last Friday where they made their own pots.", - "D8:2" - ], - [ - "Melanie and her kids enjoy nature-inspired painting projects.", - "D8:6" - ], - [ - "Melanie's favorite part of her wedding was marrying her partner and promising to be together forever.", - "D8:16" - ], - [ - "Melanie finds joy in flowers which represent growth, beauty, and appreciating small moments.", - "D8:12" - ], - [ - "Melanie shared that family and creativity keep her at peace.", - "D8:28" - ], - [ - "Melanie's family has been supportive and loving during tough times and helped her through.", - "D8:32" - ] - ] - }, - "session_9_observation": { - "Melanie": [ - [ - "Melanie went camping with her family two weekends ago.", - "D9:1" - ], - [ - "Melanie enjoys unplugging and hanging out with her kids.", - "D9:1" - ], - [ - "Melanie and her kids finished a painting before the conversation.", - "D9:17" - ] - ], - "Caroline": [ - [ - "Caroline joined a mentorship program for LGBTQ youth over the weekend.", - "D9:2" - ], - [ - "Caroline mentors a transgender teen and they work on building confidence and positive strategies.", - "D9:6" - ], - [ - "Caroline and her mentee had a great time at the LGBT pride event the previous month.", - "D9:6" - ], - [ - "Caroline is planning an LGBTQ art show with her paintings for next month.", - "D9:12" - ], - [ - "Caroline painted a piece inspired by a visit to an LGBTQ center, aiming to capture unity and strength.", - "D9:16" - ] - ] - }, - "session_10_observation": { - "Caroline": [ - [ - "Caroline joined a new LGBTQ activist group called 'Connected LGBTQ Activists' last Tuesday.", - "D10:3" - ], - [ - "Caroline and her LGBTQ activist group plan events and campaigns to support each other and positive changes.", - "D10:5" - ], - [ - "Caroline and her activist group participated in a pride parade last weekend to celebrate love and diversity.", - "D10:7" - ] - ], - "Melanie": [ - [ - "Melanie enjoys family beach trips with her kids once or twice a year.", - "D10:8" - ], - [ - "Melanie's family tradition includes a camping trip where they roast marshmallows and tell stories around the campfire.", - "D10:12" - ], - [ - "Melanie and her family watched the Perseid meteor shower during a camping trip last year and it was a memorable experience.", - "D10:14" - ], - [ - "Melanie treasures the memory of her youngest child taking her first steps.", - "D10:20" - ] - ] - }, - "session_11_observation": { - "Melanie": [ - [ - "Melanie celebrated her daughter's birthday with a concert featuring Matt Patterson.", - "D11:1" - ], - [ - "Melanie values special moments with her kids and is grateful for them.", - "D11:1" - ], - [ - "Melanie appreciates cultivating a loving and accepting environment for her kids.", - "D11:7" - ], - [ - "Melanie values inclusivity in her interactions and work as an artist.", - "D11:7" - ], - [ - "Melanie admires Caroline's art and appreciates the themes of self-acceptance and love.", - "D11:13" - ] - ], - "Caroline": [ - [ - "Caroline attended a pride parade recently and felt inspired by the community's energy and support for LGBTQ rights.", - "D11:4" - ], - [ - "Caroline represents inclusivity and diversity in her art and uses it to advocate for the LGBTQ+ community.", - "D11:8" - ], - [ - "Caroline's art focuses on expressing her trans experience and educating others about the trans community.", - "D11:10" - ], - [ - "Caroline's painting 'Embracing Identity' symbolizes self-acceptance, love, and the journey to being oneself.", - "D11:12" - ], - [ - "Caroline finds art to be healing and a way to connect with her self-discovery and acceptance journey.", - "D11:14" - ], - [ - "Caroline values sharing her art and experiences with others, such as Melanie.", - "D11:16" - ] - ] - }, - "session_12_observation": { - "Caroline": [ - [ - "Caroline had a not-so-great experience on a hike where she ran into a group of religious conservatives who upset her.", - "D12:1" - ], - [ - "Caroline values having people around her who accept and support her.", - "D12:1" - ], - [ - "Caroline expresses that surrounding herself with things that bring joy is important because life is too short.", - "D12:9" - ], - [ - "Caroline values happy moments and believes they are essential to keep going, especially during tough times.", - "D12:11" - ], - [ - "Caroline expresses appreciation for her friendship with Melanie.", - "D12:13" - ], - [ - "Caroline had a great time with the whole gang at the Pride fest last year and values supportive friends.", - "D12:15" - ] - ], - "Melanie": [ - [ - "Melanie finished another pottery project and expresses pride in her work.", - "D12:2" - ], - [ - "Melanie's pottery project was a source of happiness and fulfillment for her.", - "D12:8" - ], - [ - "Melanie has a strong connection to art, considering it both a sanctuary and a source of comfort.", - "D12:8" - ], - [ - "Melanie values friendship with Caroline and expresses appreciation for it.", - "D12:14" - ], - [ - "Melanie suggests doing a family outing or planning something special for the summer with Caroline to make awesome memories.", - "D12:16" - ] - ] - }, - "session_13_observation": { - "Caroline": [ - [ - "Caroline took the first step towards becoming a mom by applying to adoption agencies.", - "D13:1" - ], - [ - "Caroline attended an adoption advice/assistance group to help with her decision.", - "D13:1" - ], - [ - "Caroline has a guinea pig named Oscar.", - "D13:3" - ], - [ - "Caroline used to go horseback riding with her dad when she was a kid.", - "D13:7" - ], - [ - "Caroline loves horses and has a love for them.", - "D13:7" - ], - [ - "Caroline expresses herself through painting and values art for exploring identity and being therapeutic.", - "D13:13" - ], - [ - "Caroline values supportive people, promotes LGBTQ rights, and aims to live authentically.", - "D13:15" - ] - ], - "Melanie": [ - [ - "Melanie has pets including another cat named Bailey.", - "D13:4" - ], - [ - "Melanie shared a photo of her horse painting that she recently did.", - "D13:8" - ], - [ - "Melanie enjoys painting animals and finds it peaceful and special.", - "D13:10" - ], - [ - "Melanie expresses herself through painting and values art for showing who we really are and getting in touch with ourselves.", - "D13:14" - ] - ] - }, - "session_14_observation": { - "Caroline": [ - [ - "Caroline went hiking last week and got into a bad spot with some people but tried to apologize.", - "D14:1" - ], - [ - "Caroline painted a vivid sunset inspired by a beach visit.", - "D14:7" - ], - [ - "Caroline transitioned and joined the transgender community seeking acceptance and support.", - "D14:13" - ], - [ - "Caroline created a rainbow flag mural symbolizing courage and strength of the trans community.", - "D14:15" - ], - [ - "Caroline made a stained glass window showcasing personal journey as a transgender woman and the acceptance of growth and change.", - "D14:19" - ], - [ - "Caroline found a vibrant rainbow sidewalk during Pride Month, which reminded her of love and acceptance.", - "D14:23" - ], - [ - "Caroline is organizing an LGBTQ art show next month to showcase paintings and talents of LGBTQ artists aimed at spreading understanding and acceptance.", - "D14:33" - ] - ], - "Melanie": [ - [ - "Melanie made a plate in pottery class and finds pottery relaxing and creative.", - "D14:4" - ], - [ - "Melanie loves painting landscapes and still life.", - "D14:30" - ], - [ - "Melanie volunteered with her family at a homeless shelter to make a difference.", - "D14:10" - ], - [ - "Melanie appreciates and admires Caroline's courage as a trans person.", - "D14:16" - ], - [ - "Melanie created a painting inspired by autumn.", - "D14:32" - ] - ] - }, - "session_15_observation": { - "Caroline": [ - [ - "Caroline had the opportunity to volunteer at an LGBTQ+ youth center and found it gratifying to support and guide the young people there.", - "D15:3" - ], - [ - "Caroline shared her story with the young people at the LGBTQ+ youth center and felt fulfilled by the experience.", - "D15:5" - ], - [ - "Caroline plans to continue volunteering at the youth center as she believes in community and supporting others.", - "D15:9" - ], - [ - "Caroline is involved in organizing a talent show for the kids at the youth center.", - "D15:11" - ], - [ - "Caroline mentioned that playing the guitar helps her express her emotions.", - "D15:19" - ], - [ - "Caroline started playing acoustic guitar about five years ago as a way to express herself and escape in her emotions.", - "D15:21" - ], - [ - "Caroline finds the song \"Brave\" by Sara Bareilles significant and inspiring as it resonates with her journey and determination to make a difference.", - "D15:23" - ] - ], - "Melanie": [ - [ - "Melanie took her kids to a park and enjoyed seeing them have fun exploring and playing.", - "D15:2" - ], - [ - "Melanie plays the clarinet as a way to express herself and relax.", - "D15:26" - ], - [ - "Melanie enjoys classical music like Bach and Mozart, as well as modern music like Ed Sheeran's \"Perfect\".", - "D15:28" - ] - ] - }, - "session_16_observation": { - "Caroline": [ - [ - "Caroline spends time with friends biking and exploring nature.", - "D16:1" - ], - [ - "Caroline is very focused on making a difference for the LGBTQ+ community through her work.", - "D16:5" - ], - [ - "Caroline has been creating art since the age of 17.", - "D16:7" - ], - [ - "Caroline uses art to express her feelings and explore her gender identity.", - "D16:9" - ], - [ - "Caroline made a painting representing her journey as a trans woman.", - "D16:13" - ], - [ - "Caroline's relationships have changed due to her journey, some friends were not able to handle the changes.", - "D16:15" - ] - ], - "Melanie": [ - [ - "Melanie enjoys camping with her kids, exploring the forest, and hiking.", - "D16:2" - ], - [ - "Melanie finds inspiration in seeing her kids excited about learning new things about nature.", - "D16:4" - ], - [ - "Melanie has been into art for seven years, finding a passion for painting and pottery.", - "D16:8" - ], - [ - "Melanie uses painting and pottery as a calming and satisfying creative outlet.", - "D16:8" - ] - ] - }, - "session_17_observation": { - "Caroline": [ - [ - "Caroline is looking into adoption and contacted her mentor for advice.", - "D17:1" - ], - [ - "Caroline sees adoption as a way to share her love and provide a safe, loving home for kids in need.", - "D17:3" - ], - [ - "Caroline recommends doing research, preparing emotionally, and gathering necessary documents when starting the adoption process.", - "D17:7" - ], - [ - "Caroline recently went to a transgender poetry reading event that was empowering and celebrated self-expression.", - "D17:19" - ], - [ - "Caroline is inspired by freedom and being true to oneself.", - "D17:23" - ] - ], - "Melanie": [ - [ - "Melanie had a setback due to an injury that led her to take a break from pottery, which she uses for self-expression and peace.", - "D17:8" - ], - [ - "Melanie continued expressing herself through reading and painting during her break from pottery.", - "D17:10" - ], - [ - "Melanie enjoys expressing emotions through art, like painting inspired by sunsets and abstract art.", - "D17:13" - ], - [ - "Melanie finds blue a calming color and uses it to convey tranquility in her art.", - "D17:16" - ] - ] - }, - "session_18_observation": { - "Melanie": [ - [ - "Melanie went on a road trip with her family which started off with an accident involving her son.", - "D18:1" - ], - [ - "Melanie's son got into an accident during the road trip.", - "D18:1" - ], - [ - "Melanie's family visited the Grand Canyon and enjoyed it.", - "D18:5" - ], - [ - "Melanie finds peace and serenity in nature, particularly during camping trips with her family.", - "D18:19" - ], - [ - "Melanie believes that being in nature refreshes her soul and helps her reset and recharge.", - "D18:21" - ] - ], - "Caroline": [ - [ - "Caroline acknowledged the traumatic experience of Melanie's family being in an accident during the road trip.", - "D18:2" - ], - [ - "Caroline believes that loved ones give strength to tackle challenges.", - "D18:10" - ], - [ - "Caroline appreciates seeing Melanie's love for her family and acknowledges that they are her rock.", - "D18:12" - ], - [ - "Caroline finds nature refreshing and discussed how it can bring peace.", - "D18:18" - ], - [ - "Caroline appreciates the peaceful and calming nature of spending quality time with family in nature.", - "D18:22" - ] - ] - }, - "session_19_observation": { - "Caroline": [ - [ - "Caroline passed the adoption agency interviews last Friday and is excited about building her own family through adoption.", - "D19:1" - ], - [ - "Caroline's vision for the future includes creating a safe and loving home for needy kids to experience love and acceptance.", - "D19:3" - ], - [ - "Caroline finds empowerment in making a positive difference in someone's life by offering love and support.", - "D19:7" - ], - [ - "Caroline went through a tough process of finding self-acceptance but is now ready to help others who need support.", - "D19:7" - ], - [ - "Caroline received invaluable help from friends, family, and role models during the process of finding acceptance.", - "D19:9" - ], - [ - "Caroline's journey of self-discovery has been amazing and she finds joy in bringing comfort and support to others.", - "D19:9" - ] - ], - "Melanie": [ - [ - "Melanie bought figurines that remind her of family love.", - "D19:2" - ], - [ - "Melanie appreciates Caroline's passion for helping kids and finds her inspiring.", - "D19:6" - ], - [ - "Melanie respects Caroline's journey of finding acceptance and admires her strength and inspiration to help others.", - "D19:8" - ], - [ - "Melanie is supportive and expresses happiness for Caroline finding her true self and helping others.", - "D19:10" - ], - [ - "Melanie values the mutual support they provide to each other and appreciates the encouragement of close ones.", - "D19:13" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Caroline and Melanie had a conversation on 8 May 2023 at 1:56 pm. Caroline mentioned that she attended an LGBTQ support group and was inspired by the transgender stories she heard. The support group made her feel accepted and gave her the courage to embrace herself. Caroline plans to continue her education and explore career options, particularly in counseling or working in mental health. Melanie praised Caroline's empathy and mentioned that she painted a lake sunrise last year as a way of expressing herself. Caroline complimented Melanie's painting and agreed that painting is a great outlet for relaxation and self-expression. They both emphasized the importance of taking care of oneself. Caroline was going to do some research, while Melanie planned to go swimming with her kids.", - "session_2_summary": "On May 25, 2023 at 1:14 pm, Melanie tells Caroline about her recent experience running a charity race for mental health. Caroline expresses pride and agrees that taking care of oneself is important. Melanie shares her struggle with self-care but mentions that she is carving out time each day for activities that refresh her. Caroline encourages Melanie and praises her efforts. Melanie then asks Caroline about her plans for the summer, to which Caroline replies that she is researching adoption agencies as she wants to give a loving home to children in need. Melanie praises Caroline's decision and expresses excitement for her future family. Caroline explains that she chose an adoption agency that supports the LGBTQ+ community because of its inclusivity and support. Melanie commends Caroline's choice and asks what she is looking forward to in the adoption process. Caroline says she is thrilled to create a family for kids who need one, despite the challenges of being a single parent. Melanie encourages Caroline and expresses confidence in her ability to provide a safe and loving home. The conversation ends with Melanie expressing her excitement for Caroline's new chapter.", - "session_3_summary": "Caroline and Melanie had a conversation at 7:55 pm on 9 June, 2023. Caroline shared about her school event last week where she talked about her transgender journey and encouraged students to get involved in the LGBTQ community. Melanie praised Caroline for spreading awareness and inspiring others with her strength and courage. They discussed the importance of conversations about gender identity and inclusion. Caroline expressed gratitude for the support she has received and the opportunity to give a voice to the trans community. Melanie commended Caroline for using her voice to create love, acceptance, and hope. They talked about the power of sharing personal stories and the impact it can have on others. They both expressed a desire to make a positive difference and support each other. Melanie mentioned that her family motivates her, while Caroline mentioned that her friends, family, and mentors are her support system. They shared photos of their loved ones and talked about the length of their relationships. Melanie mentioned being married for 5 years and Caroline expressed congratulations and well-wishes. They discussed the importance of cherishing family moments and finding happiness in them. They both agreed that family is everything.", - "session_4_summary": "Caroline and Melanie catch up after a long time. Caroline shows Melanie her special necklace, which was a gift from her grandmother in Sweden and represents love, faith, and strength. Melanie admires it and asks if Caroline has any other treasured items. Caroline mentions a hand-painted bowl made by a friend on her 18th birthday, which reminds her of art and self-expression. Melanie shares that she recently went camping with her family and had a great time exploring nature and bonding with her kids. They discuss the importance of family moments. Caroline reveals that she is looking into a career in counseling and mental health, specifically wanting to work with trans people. She attended an LGBTQ+ counseling workshop and found it enlightening. Melanie praises Caroline for her dedication and asks about her motivation to pursue counseling. Caroline shares how her own journey and the support she received inspired her to help others. Melanie commends Caroline's hard work and passion. Caroline expresses gratitude for Melanie's kind words, and Melanie congratulates Caroline for pursuing what she cares about.", - "session_5_summary": "Caroline had recently attended an LGBTQ+ pride parade and felt a sense of belonging and community. This experience inspired her to use her own story to help others, possibly through counseling or mental health work. Melanie, in turn, shared that she had recently signed up for a pottery class as a way to express herself and find calmness. The two discussed their creative endeavors, with Melanie showing Caroline a bowl she had made in her class. Caroline praised Melanie's work and expressed her excitement for the upcoming transgender conference she would be attending. Melanie wished her a great time at the conference and encouraged her to have fun and stay safe.", - "session_6_summary": "Caroline and Melanie caught up with each other at 8:18 pm on 6 July, 2023. Caroline shared that since their last chat, she has been exploring counseling or mental health work because she is passionate about helping people. Melanie praised Caroline for following her dreams. Melanie mentioned that she recently took her kids to the museum and enjoyed watching their excitement. Caroline was curious about what had them so excited, and Melanie explained that they loved the dinosaur exhibit. Caroline mentioned that she is creating a library for future kids and looks forward to reading to them. Melanie asked about the books she has in her library, and Caroline mentioned classics, stories from different cultures, and educational books. Melanie shared that her favorite book from childhood was \"Charlotte's Web,\" and Caroline agreed that it showed the importance of friendship and compassion. Caroline mentioned that her friends and family have been a great support system during her transition. Melanie praised Caroline for having people who support her and shared a photo of her family camping at the beach.", - "session_7_summary": "Caroline and Melanie had a conversation at 4:33 pm on 12 July, 2023. Caroline talked about attending an LGBTQ conference recently, where she felt accepted and connected with others who have similar experiences. She expressed her gratitude for the LGBTQ community and her desire to fight for trans rights. Melanie praised Caroline for her drive to make a difference and asked about her plan to contribute. Caroline mentioned that she is looking into counseling and mental health jobs, as she wants to provide support for others. Melanie commended Caroline for her inspiring goal and mentioned a book she read that reminds her to pursue her dreams. They discussed the power of books, and Caroline recommended \"Becoming Nicole\" by Amy Ellis Nutt, which had a positive impact on her own life. She mentioned that the book taught her about self-acceptance and finding support. Melanie agreed and added that pets also bring joy and comfort. They talked about their own pets and shared pictures. Melanie mentioned that she has been running more to destress and clear her mind. Caroline encouraged her to keep it up and take care of her mental health. Melanie expressed her gratitude for the improvements in her mental health.", - "session_8_summary": "Caroline and Melanie spoke at 1:51 pm on 15 July, 2023. Melanie mentioned that she took her kids to a pottery workshop and they all made their own pots. Caroline commented on how cute the cup that the kids made was and how she loved seeing kids express their personalities through art. Melanie also mentioned that she and the kids enjoy painting together, particularly nature-inspired paintings. Caroline admired their latest painting and Melanie mentioned that they found lovely flowers to paint. Caroline then shared that she attended a council meeting for adoption, which inspired her to adopt in the future. Melanie complimented a photo of a blue vase that Caroline shared and they discussed the meanings of flowers. Melanie mentioned that flowers remind her of her wedding and Caroline expressed regret for not knowing Melanie back then. Melanie said that her wedding day was full of love and joy and her favorite part was marrying her partner. Caroline then shared a special memory of attending a pride parade and how accepting and happy she felt. They discussed the importance of a supportive community. Caroline mentioned that the best part was realizing she could be herself without fear and having the courage to transition. Melanie expressed admiration for Caroline's courage and the importance of finding peace. Melanie mentioned that her family has been supportive during her move. Caroline commented on", - "session_9_summary": "Caroline has joined a mentorship program for LGBTQ youth, which she finds rewarding. She has been supporting a transgender teen and they had a great time at an LGBT pride event. Caroline is also preparing for an LGBTQ art show next month. Melanie thinks Caroline's painting for the art show is awesome and asks what inspired her. Caroline explains that she painted it after visiting an LGBTQ center and wanted to capture unity and strength. Meanwhile, Melanie and her kids have finished another painting.", - "session_10_summary": "Caroline and Melanie had a conversation at 8:56 pm on 20 July, 2023. Caroline told Melanie that she recently joined a new LGBTQ activist group and is enjoying making a difference. Melanie expressed her happiness for Caroline and wanted to know more about the group. Caroline explained that the group, \"Connected LGBTQ Activists,\" is focused on positive changes and supporting each other. Melanie praised the group and asked if Caroline has participated in any events or campaigns. Caroline mentioned a recent pride parade in their city and how it was a powerful reminder of the fight for equality. Melanie shared that she recently went to the beach with her kids, which they thoroughly enjoyed. Caroline inquired about other summer traditions, and Melanie mentioned their family camping trip as the highlight of their summer. She recalled witnessing the Perseid meteor shower and how it made her feel in awe of the universe. Caroline asked about the experience, and Melanie described it as breathtaking, making her appreciate life. Melanie then shared another special memory of her youngest child taking her first steps. Caroline found it sweet and mentioned that such milestones remind us of the special bonds we have. Melanie agreed and expressed gratitude for her family. Caroline praised Melanie for having an awesome family. Melanie thanked Caroline and expressed her happiness for having a", - "session_11_summary": "On August 14, 2023, at 2:24 pm, Melanie and Caroline had a conversation. Melanie shared that she had a great time at a concert celebrating her daughter's birthday, while Caroline attended an advocacy event that focused on love and support. Melanie asked Caroline about her experience at the pride parade, to which Caroline responded by expressing her pride in being part of the LGBTQ community and fighting for equality. Melanie then shared a picture from the concert and discussed the importance of creating a loving and inclusive environment for their kids. Caroline mentioned that she incorporates inclusivity and diversity in her artwork and uses it to advocate for acceptance of the LGBTQ+ community. Melanie praised Caroline's art and asked about its main message, to which Caroline replied that her art is about expressing her trans experience and helping people understand the trans community. Melanie requested to see another painting, and Caroline shared one called \"Embracing Identity,\" which represents self-acceptance and love. Caroline explained that art has helped her in her own self-discovery and acceptance journey. Melanie acknowledged the healing power of art and thanked Caroline for sharing her work. They ended the conversation by inviting each other to reach out anytime.", - "session_12_summary": "Caroline tells Melanie about a negative experience she had with religious conservatives while hiking, which reminds her of the work still needed for LGBTQ rights. She expresses gratitude for the support and acceptance she has from those around her. Melanie sympathizes with Caroline and shows her a picture of a pottery project she recently finished. Caroline expresses interest in seeing the picture and compliments Melanie's work. Melanie explains that the colors and patterns were inspired by her love for them and how painting helps her express her feelings. Caroline praises Melanie's creativity and passion. Melanie expresses her deep connection to art and how it brings her happiness and fulfillment. Caroline agrees that surrounding oneself with things that bring joy is important. They both agree that finding happiness is key in life. They express appreciation for each other's friendship and support. They reminisce about a fun time at a Pride fest and discuss plans for a family outing or a special trip just for the two of them. They agree to plan something special and look forward to making more memories together.", - "session_13_summary": "Caroline shared with Melanie that she applied to adoption agencies and received help from an adoption assistance group. Melanie congratulated Caroline and asked about her pets. Caroline mentioned her guinea pig named Oscar. Melanie shared that she had another cat named Bailey and showed Caroline a picture of her cat Oliver. Caroline shared a picture of Oscar eating parsley. Melanie mentioned that Oliver hid his bone in her slipper. Caroline reminisced about horseback riding with her dad and shared that she loves horses. Melanie showed Caroline a horse painting she did. Caroline shared a self-portrait she recently made, mentioning how painting helps her explore her identity. Melanie agreed and asked what else helps her. Caroline mentioned having supportive people and promoting LGBTQ rights. Melanie commended Caroline for her care and wished her the best on her adoption journey. They said goodbye and Melanie offered her support.", - "session_14_summary": "Caroline tells Melanie that she went hiking last week and got into a bad situation with some people. She tried to apologize to them. Melanie is supportive and says that it takes a lot of courage and maturity to apologize. Melanie shows Caroline a pottery plate she made and Caroline compliments her on it. Melanie says that pottery is relaxing and creative. Caroline says that she has been busy painting and shows Melanie a painting of a sunset that she recently finished. Melanie compliments the painting and Caroline explains that she was inspired by a visit to the beach. Melanie says that she can feel the serenity of the beach in the painting. They discuss how art can connect people and Melanie mentions a volunteering experience at a homeless shelter. Caroline praises Melanie for her volunteering efforts. Melanie asks Caroline about her decision to transition and join the transgender community. Caroline explains that finding a supportive community has meant a lot to her and shows Melanie a mural that she created, explaining its symbolism. Melanie praises Caroline's courage as a trans person. Melanie asks Caroline if she has made any more art and Caroline shows her a stained glass window that she made for a local church. They discuss the inspiration behind the window and Melanie compliments Caroline on her artistry. Caroline shows Melanie a picture of a rainbow sidewalk that she found in her neighborhood", - "session_15_summary": "Caroline and Melanie had a conversation at 3:19 pm on 28 August, 2023. Melanie had taken her kids to a park and enjoyed seeing them have fun outdoors. Caroline had been volunteering at an LGBTQ+ youth center and found it gratifying and fulfilling to support and guide the young people there. Melanie asked Caroline about her experience at the youth center and Caroline shared that connecting with the young folks and sharing her story had been meaningful and made her feel like she could make a difference. Caroline expressed her dedication to continuing volunteering at the youth center and mentioned that they were planning a talent show for the kids. Melanie expressed her excitement and support for Caroline's dedication to helping others. They also briefly discussed a band Melanie saw and Caroline spoke about her love for music and playing the guitar. They shared their favorite songs and agreed on the power of music to inspire and uplift.", - "session_16_summary": "Caroline and Melanie were chatting at 12:09 am on 13 September, 2023. Caroline told Melanie about her biking trip with friends and sent her a stunning picture. Melanie complimented the picture and shared her own experience of camping with her kids. They both agreed that being in nature was refreshing for the soul. Melanie asked Caroline about her upcoming plans. Caroline expressed how excited she was about her work volunteering for the LGBTQ+ community and how it inspired her to create art. Melanie admired Caroline's art and shared her own love for painting and pottery. They talked about the therapeutic aspect of art and how it helped them express their feelings. Caroline showed Melanie a painting she made about her journey as a trans woman. Melanie was impressed and proud of Caroline's progress. Caroline spoke about the changes in her relationships and how she was happier being around accepting and loving people. Melanie shared a picture from a caf\u00e9 they visited and assured Caroline that everything was fine despite the serious sign. They ended their conversation by celebrating the joyful moments in life.", - "session_17_summary": "Caroline reached out to her friend Melanie to share her excitement about her decision to adopt and become a mother. Melanie mentioned that she knew someone who had successfully adopted. Caroline gave Melanie some advice on how to get started with the adoption process, emphasizing the importance of research and emotional preparation. Melanie mentioned that she had recently experienced a setback due to an injury, but had found solace in reading and painting. Caroline showed interest in Melanie's paintings, and shared her own recent venture into abstract art. They discussed the emotions behind their artwork and the therapeutic nature of self-expression. Caroline also mentioned attending a poetry reading that celebrated transgender identities, which inspired her to create her own art. Melanie praised Caroline's artwork and they affirmed the importance of staying true to oneself and embracing personal growth and exploration.", - "session_18_summary": "Melanie and Caroline are discussing a recent road trip on October 20, 2023. Melanie mentions that her son got into an accident, but fortunately, he is okay. She reflects on the importance of cherishing family and how they enjoyed their time at the Grand Canyon. Caroline acknowledges the resilience of children and the support that loved ones provide during tough times. Melanie expresses her gratitude for her family, who are her motivation and support. They also discuss the benefits of spending time in nature and how it helps them reset and recharge. Melanie shares that camping with her family brings peace and serenity and allows them to bond. Caroline compliments Melanie on the quality time she spends with her family and remarks on the priceless nature of these experiences.", - "session_19_summary": "Caroline tells Melanie that she passed the adoption agency interviews last Friday and is excited about the progress she's making towards her goal of having a family. Melanie congratulates her and shows her some figurines that remind her of family love. Caroline explains that she wants to build her own family and provide a home for children in need, as a way of giving back and showing love and acceptance. Melanie agrees that everyone deserves love and acceptance and admires Caroline's passion for helping these kids. Caroline shares that finding self-acceptance was a long process for her, but now she's ready to offer love and support to those who need it. Melanie praises Caroline for her strength and inspiration. Caroline credits her friends, family, and role models for helping her find acceptance and wants to pass that same support to others. Melanie tells Caroline that she is happy for her and finds her journey inspiring. Caroline expresses gratitude for the support she's received and considers it a gift to be able to share her journey and help others. Melanie agrees that it's important to be there for each other. Caroline emphasizes the importance of being oneself and living honestly, as it brings freedom and contentment. Both friends express their agreement and appreciation for the support they've received in being true to themselves." - }, - "sample_id": "conv-26" - }, - { - "qa": [ - { - "question": "When Jon has lost his job as a banker?", - "answer": "19 January, 2023", - "evidence": [ - "D1:2" - ], - "category": 2 - }, - { - "question": "When Gina has lost her job at Door Dash?", - "answer": "January, 2023", - "evidence": [ - "D1:3" - ], - "category": 2 - }, - { - "question": "How do Jon and Gina both like to destress?", - "answer": "by dancing", - "evidence": [ - "D1:7", - "D1:6" - ], - "category": 4 - }, - { - "question": "What do Jon and Gina both have in common?", - "answer": "They lost their jobs and decided to start their own businesses.", - "evidence": [ - "D1:2", - "D1:3", - "D1:4", - "D2:1" - ], - "category": 1 - }, - { - "question": "Why did Jon decide to start his dance studio?", - "answer": "He lost his job and decided to start his own business to share his passion.", - "evidence": [ - "D1:2", - "D1:4" - ], - "category": 4 - }, - { - "question": "What Jon thinks the ideal dance studio should look like?", - "answer": "By the water, with natural light and Marley flooring", - "evidence": [ - "D1:20", - "D2:4", - "D2:8" - ], - "category": 1 - }, - { - "question": "When is Jon's group performing at a festival?", - "answer": "February, 2023", - "evidence": [ - "D1:24" - ], - "category": 2 - }, - { - "question": "When did Gina launch an ad campaign for her store?", - "answer": "29 January, 2023", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "When was Jon in Paris?", - "answer": "28 January 2023", - "evidence": [ - "D2:4" - ], - "category": 2 - }, - { - "question": "Which city have both Jean and John visited?", - "answer": "Rome", - "evidence": [ - "D2:5", - "D15:1" - ], - "category": 1 - }, - { - "question": "When did Gina team up with a local artist for some cool designs?", - "answer": "February, 2023", - "evidence": [ - "D5:5" - ], - "category": 2 - }, - { - "question": "When did Gina get her tattoo?", - "answer": "A few years ago", - "evidence": [ - "D5:15" - ], - "category": 2 - }, - { - "question": "When did Jon start to go to the gym?", - "answer": "March, 2023", - "evidence": [ - "D6:1" - ], - "category": 2 - }, - { - "question": "When did Gina open her online clothing store?", - "answer": "16 March, 2023", - "evidence": [ - "D6:6" - ], - "category": 2 - }, - { - "question": "When did Jon start expanding his studio's social media presence?", - "answer": "April, 2023", - "evidence": [ - "D8:13" - ], - "category": 2 - }, - { - "question": "When did Jon host a dance competition?", - "answer": "May, 2023", - "evidence": [ - "D8:13" - ], - "category": 2 - }, - { - "question": "When did Jon go to a fair to get more exposure for his dance studio?", - "answer": "24 April, 2023", - "evidence": [ - "D10:1" - ], - "category": 2 - }, - { - "question": "Why did Gina decide to start her own clothing store?", - "answer": "She always loved fashion trends and finding unique pieces and she lost her job so decided it was time to start her own business.", - "evidence": [ - "D6:8", - "D1:3" - ], - "category": 1 - }, - { - "question": "Do Jon and Gina start businesses out of what they love?", - "answer": "Yes", - "evidence": [ - "D1:4", - "D6:8" - ], - "category": 1 - }, - { - "question": "When did Gina interview for a design internship?", - "answer": "10 May, 2023", - "evidence": [ - "D11:14" - ], - "category": 2 - }, - { - "question": "When did Gina get accepted for the design internship?", - "answer": "27 May, 2023", - "evidence": [ - "D12:1" - ], - "category": 2 - }, - { - "question": "When did Jon start reading \"The Lean Startup\"?", - "answer": "May, 2023", - "evidence": [ - "D12:6" - ], - "category": 2 - }, - { - "question": "When did Gina develop a video presentation to teach how to style her fashion pieces? ", - "answer": "June, 2023", - "evidence": [ - "D13:4" - ], - "category": 2 - }, - { - "question": "How did Gina promote her clothes store?", - "answer": "worked with an artist to make unique fashion pieces, made limited-edition sweatshirts, got some new offers and promotions for online store, developed a video presentation showing how to style her pieces", - "evidence": [ - "D5:5", - "D16:3", - "D8:4", - "D13:4" - ], - "category": 1 - }, - { - "question": "Which events has Jon participated in to promote his business venture?", - "answer": "fair, networking events, dance competition", - "evidence": [ - "D10:1", - "D16:6", - "D8:4" - ], - "category": 1 - }, - { - "question": "What does Jon's dance studio offer?", - "answer": "one-on-one metoring and training to dancers, workshops and classes to local schools and centers", - "evidence": [ - "D13:7", - "D8:13" - ], - "category": 1 - }, - { - "question": "When did Jon receive mentorship to promote his venture?", - "answer": "15 June, 2023", - "evidence": [ - "D14:1" - ], - "category": 2 - }, - { - "question": "Did Jon and Gina both participate in dance competitions?", - "answer": "Yes", - "evidence": [ - "D1:14", - "D14:14", - "D1:16", - "D1:17", - "D9:10" - ], - "category": 1 - }, - { - "question": "When was Jon in Rome?", - "answer": "June 2023", - "evidence": [ - "D15:1" - ], - "category": 2 - }, - { - "question": "Which cities has Jon visited?", - "answer": "Paris, Rome", - "evidence": [ - "D2:4", - "D15:1" - ], - "category": 1 - }, - { - "question": "When Jon is planning to open his dance studio?", - "answer": "20 June, 2023", - "evidence": [ - "D15:5" - ], - "category": 2 - }, - { - "question": "How long did it take for Jon to open his studio?", - "answer": "six months", - "evidence": [ - "D1:2", - "D15:13" - ], - "category": 1 - }, - { - "question": "When did Gina design a limited collection of hoodies?", - "answer": "June 2023", - "evidence": [ - "D16:3" - ], - "category": 2 - }, - { - "question": "When did Jon visit networking events for his store?", - "answer": "20 June, 2023", - "evidence": [ - "D16:6" - ], - "category": 2 - }, - { - "question": "When did Gina start being recognized by fashion editors?", - "answer": "July 2023", - "evidence": [ - "D17:1" - ], - "category": 2 - }, - { - "question": "When did Jon start learning marketing and analytics tools?", - "answer": "July, 2023", - "evidence": [ - "D17:4" - ], - "category": 2 - }, - { - "question": "When did Jon and Gina decide to collaborate to create dance content?", - "answer": "21 July 2023", - "evidence": [ - "D18:18" - ], - "category": 2 - }, - { - "question": "When did Gina mention Shia Labeouf?", - "answer": " 23 July, 2023", - "evidence": [ - "D19:4" - ], - "category": 2 - }, - { - "question": "When did Gina go to a dance class with a group of friends?", - "answer": "21 July 2023", - "evidence": [ - "D19:6" - ], - "category": 2 - }, - { - "question": "What is Gina's favorite style of dance?", - "answer": "Contemporary", - "evidence": [ - "D1:9" - ], - "category": 4 - }, - { - "question": "What is Jon's favorite style of dance?", - "answer": "Contemporary", - "evidence": [ - "D1:8" - ], - "category": 4 - }, - { - "question": "What was Gina's favorite dancing memory?", - "answer": "Winning first place at a regionals dance competition", - "evidence": [ - "D1:17" - ], - "category": 4 - }, - { - "question": "What kind of dance piece did Gina's team perform to win first place?", - "answer": "\"Finding Freedom\"", - "evidence": [ - "D1:19" - ], - "category": 4 - }, - { - "question": "What do the dancers in the photo represent?", - "answer": "They are performing at the festival", - "evidence": [ - "D1:25" - ], - "category": 4 - }, - { - "question": "What does Gina say about the dancers in the photo?", - "answer": "They look graceful", - "evidence": [ - "D1:26" - ], - "category": 4 - }, - { - "question": "What is Jon's attitude towards being part of the dance festival?", - "answer": "Glad", - "evidence": [ - "D1:28" - ], - "category": 4 - }, - { - "question": "What kind of flooring is Jon looking for in his dance studio?", - "answer": "Marley flooring", - "evidence": [ - "D2:8" - ], - "category": 4 - }, - { - "question": "What did Gina find for her clothing store on 1 February, 2023?", - "answer": "The perfect spot for her store", - "evidence": [ - "D3:2" - ], - "category": 4 - }, - { - "question": "What did Gina design for her store?", - "answer": "the space, furniture, and decor", - "evidence": [ - "D3:4" - ], - "category": 4 - }, - { - "question": "What did Gina want her customers to feel in her store?", - "answer": "cozy and comfortable", - "evidence": [ - "D3:6", - "D3:8" - ], - "category": 4 - }, - { - "question": "What did Jon say about Gina's progress with her store?", - "answer": "hard work's paying off", - "evidence": [ - "D3:3" - ], - "category": 4 - }, - { - "question": "What made Gina choose the furniture and decor for her store?", - "answer": "personal style and customer comfort", - "evidence": [ - "D3:6" - ], - "category": 4 - }, - { - "question": "What did Jon say about creating a special experience for customers?", - "answer": "It's the key to making them feel welcome and coming back", - "evidence": [ - "D3:9" - ], - "category": 4 - }, - { - "question": "What did Gina say about creating an experience for her customers?", - "answer": "making them want to come back", - "evidence": [ - "D3:8" - ], - "category": 4 - }, - { - "question": "How is Gina's store doing?", - "answer": "The store is doing great.", - "evidence": [ - "D4:2" - ], - "category": 4 - }, - { - "question": "What does Gina's tattoo symbolize?", - "answer": "Freedom and expressing herself through dance", - "evidence": [ - "D5:15" - ], - "category": 4 - }, - { - "question": "What did Jon and Gina compare their entrepreneurial journeys to?", - "answer": "dancing together and supporting each other", - "evidence": [ - "D6:15", - "D6:16" - ], - "category": 4 - }, - { - "question": "What advice does Gina give to Jon about running a successful business?", - "answer": "build relationships with customers, create a strong brand image, stay positive", - "evidence": [ - "D7:5" - ], - "category": 4 - }, - { - "question": "Why did Jon shut down his bank account?", - "answer": "for his business", - "evidence": [ - "D8:1" - ], - "category": 4 - }, - { - "question": "Why did Gina combine her clothing business with dance?", - "answer": "she is passionate about dance and fashion", - "evidence": [ - "D8:8" - ], - "category": 4 - }, - { - "question": "What does Jon's dance make him?", - "answer": "happy", - "evidence": [ - "D9:5" - ], - "category": 4 - }, - { - "question": "What did Gina receive from a dance contest?", - "answer": "a trophy", - "evidence": [ - "D9:10" - ], - "category": 4 - }, - { - "question": "How does Gina stay confident in her business?", - "answer": "By reminding herself of her successes and progress, having a support system, and focusing on why she started", - "evidence": [ - "D10:8" - ], - "category": 4 - }, - { - "question": "What kind of professional experience did Gina get accepted for on May 23, 2023?", - "answer": "fashion internship", - "evidence": [ - "D12:1" - ], - "category": 4 - }, - { - "question": "Where is Gina's fashion internship?", - "answer": "fashion department of an international company", - "evidence": [ - "D12:3" - ], - "category": 4 - }, - { - "question": "What book is Jon currently reading?", - "answer": "The Lean Startup", - "evidence": [ - "D12:6" - ], - "category": 4 - }, - { - "question": "What is Jon offering to the dancers at his dance studio?", - "answer": "One-on-one mentoring and training", - "evidence": [ - "D13:7" - ], - "category": 4 - }, - { - "question": "How does Jon use the clipboard with a notepad attached to it?", - "answer": "To set goals, track achievements, and find areas for improvement", - "evidence": [ - "D13:11" - ], - "category": 4 - }, - { - "question": "What does Jon tell Gina he won't do?", - "answer": "quit", - "evidence": [ - "D14:17" - ], - "category": 4 - }, - { - "question": "What did Jon take a trip to Rome for?", - "answer": "To clear his mind", - "evidence": [ - "D15:1" - ], - "category": 4 - }, - { - "question": "What is Jon working on opening?", - "answer": "a dance studio", - "evidence": [ - "D15:3" - ], - "category": 4 - }, - { - "question": "How does Gina describe the studio that Jon has opened?", - "answer": "amazing", - "evidence": [ - "D15:6" - ], - "category": 4 - }, - { - "question": "How does Jon feel about the opening night of his dance studio?", - "answer": "excited", - "evidence": [ - "D15:7" - ], - "category": 4 - }, - { - "question": "How does Gina describe the feeling that dance brings?", - "answer": "magical", - "evidence": [ - "D15:8" - ], - "category": 4 - }, - { - "question": "What does Jon plan to do at the grand opening of his dance studio?", - "answer": "savor all the good vibes", - "evidence": [ - "D15:9" - ], - "category": 4 - }, - { - "question": "What does Gina say to Jon about the grand opening?", - "answer": "Let's live it up and make some great memories", - "evidence": [ - "D15:12" - ], - "category": 4 - }, - { - "question": "What is the general sentiment about the upcoming grand opening?", - "answer": "excitement", - "evidence": [ - "D15:18", - "D15:19" - ], - "category": 4 - }, - { - "question": "What did Gina make a limited edition line of?", - "answer": "Hoodies", - "evidence": [ - "D16:3" - ], - "category": 4 - }, - { - "question": "According to Gina, what makes Jon a perfect mentor and guide?", - "answer": "His positivity and determination", - "evidence": [ - "D17:7" - ], - "category": 4 - }, - { - "question": "What temporary job did Jon take to cover expenses?", - "evidence": [ - "D18:2" - ], - "category": 5, - "adversarial_answer": "Not mentioned" - }, - { - "question": "What plans does Jon have after receiving advice at the networking event?", - "answer": "Sprucing up his business plan, tweaking his pitch to investors, and working on an online platform.", - "evidence": [ - "D18:10" - ], - "category": 4 - }, - { - "question": "What offer does Gina make to Jon regarding social media?", - "answer": "Helping with making content and managing his social media accounts.", - "evidence": [ - "D18:13" - ], - "category": 4 - }, - { - "question": "What is Jon's favorite style of painting?", - "evidence": [ - "D1:8" - ], - "category": 5, - "adversarial_answer": "Contemporary" - }, - { - "question": "What was Jon's favorite dancing memory?", - "evidence": [ - "D1:17" - ], - "category": 5, - "adversarial_answer": "Winning first place at a regionals dance competition" - }, - { - "question": "What kind of dance piece did Jon's team perform to win first place?", - "evidence": [ - "D1:19" - ], - "category": 5, - "adversarial_answer": "\"Finding Freedom\"" - }, - { - "question": "What is Gina's attitude towards participating in the dance festival?", - "evidence": [ - "D1:28" - ], - "category": 5, - "adversarial_answer": "Glad" - }, - { - "question": "What kind of flooring is Gina looking for in her dance studio?", - "evidence": [ - "D2:8" - ], - "category": 5, - "adversarial_answer": "Marley flooring" - }, - { - "question": "What did Jon find for his clothing store on 1 February, 2023?", - "evidence": [ - "D3:2" - ], - "category": 5, - "adversarial_answer": "The perfect spot for her store" - }, - { - "question": "What did Jon design for his store?", - "evidence": [ - "D3:4" - ], - "category": 5, - "adversarial_answer": "the space, furniture, and decor" - }, - { - "question": "What did Jon want his customers to feel in her store?", - "evidence": [ - "D3:6", - "D3:8" - ], - "category": 5, - "adversarial_answer": "cozy and comfortable" - }, - { - "question": "What made Jon choose the furniture and decor for his store?", - "evidence": [ - "D3:6" - ], - "category": 5, - "adversarial_answer": "personal style and customer comfort" - }, - { - "question": "How is Jon's store doing?", - "evidence": [ - "D4:2" - ], - "category": 5, - "adversarial_answer": "The store is doing great." - }, - { - "question": "What does Jon's tattoo symbolize?", - "evidence": [ - "D5:15" - ], - "category": 5, - "adversarial_answer": "Freedom and expressing himself through dance" - }, - { - "question": "Why did Gina shut down her bank account?", - "evidence": [ - "D8:1" - ], - "category": 5, - "adversarial_answer": "for her business" - }, - { - "question": "Why did Jon combine his clothing business with dance?", - "evidence": [ - "D8:8" - ], - "category": 5, - "adversarial_answer": "he is passionate about dance and fashion" - }, - { - "question": "What did Gina receive from a dance contest?", - "evidence": [ - "D9:10" - ], - "category": 5, - "adversarial_answer": "a trophy" - }, - { - "question": "What kind of professional experience did Jon get accepted for on May 23, 2023?", - "evidence": [ - "D12:1" - ], - "category": 5, - "adversarial_answer": "fashion internship" - }, - { - "question": "Where is Gina's HR internship?", - "evidence": [ - "D12:3" - ], - "category": 5, - "adversarial_answer": "fashion department of an international company" - }, - { - "question": "Where is Jon's fashion internship?", - "evidence": [ - "D12:3" - ], - "category": 5, - "adversarial_answer": "fashion department of an international company" - }, - { - "question": "What book is Gina currently reading?", - "evidence": [ - "D12:6" - ], - "category": 5, - "adversarial_answer": "The Lean Startup" - }, - { - "question": "How does Gina use the clipboard with a notepad attached to it?", - "evidence": [ - "D13:11" - ], - "category": 5, - "adversarial_answer": "To set goals, track achievements, and find areas for improvement" - }, - { - "question": "What did Jon take a trip to Barcelona for?", - "evidence": [ - "D15:1" - ], - "category": 5, - "adversarial_answer": "To clear his mind" - }, - { - "question": "What did Jon make a limited edition line of?", - "evidence": [ - "D16:3" - ], - "category": 5, - "adversarial_answer": "Hoodies" - }, - { - "question": "What temporary job did Gina take to cover expenses?", - "evidence": [ - "D18:2" - ], - "category": 5, - "adversarial_answer": "Not mentioned" - }, - { - "question": "What plans does Gina have after receiving advice at the networking event?", - "evidence": [ - "D18:10" - ], - "category": 5, - "adversarial_answer": "Sprucing up her business plan, tweaking her pitch to investors, and working on an online platform." - } - ], - "conversation": { - "speaker_a": "Jon", - "speaker_b": "Gina", - "session_1_date_time": "4:04 pm on 20 January, 2023", - "session_1": [ - { - "speaker": "Gina", - "dia_id": "D1:1", - "text": "Hey Jon! Good to see you. What's up? Anything new?" - }, - { - "speaker": "Jon", - "dia_id": "D1:2", - "text": "Hey Gina! Good to see you too. Lost my job as a banker yesterday, so I'm gonna take a shot at starting my own business." - }, - { - "speaker": "Gina", - "dia_id": "D1:3", - "text": "Sorry about your job Jon, but starting your own business sounds awesome! Unfortunately, I also lost my job at Door Dash this month. What business are you thinking of?" - }, - { - "speaker": "Jon", - "dia_id": "D1:4", - "text": "Sorry to hear that! I'm starting a dance studio 'cause I'm passionate about dancing and it'd be great to share it with others." - }, - { - "speaker": "Gina", - "dia_id": "D1:5", - "text": "That's cool, Jon! What got you into this biz?" - }, - { - "speaker": "Jon", - "dia_id": "D1:6", - "text": "I've been into dancing since I was a kid and it's been my passion and escape. I wanna start a dance studio so I can teach others the joy that dancing brings me." - }, - { - "speaker": "Gina", - "dia_id": "D1:7", - "text": "Wow Jon, same here! Dance is pretty much my go-to for stress relief. Got any fave styles?" - }, - { - "speaker": "Jon", - "dia_id": "D1:8", - "text": "Cool, Gina! I love all dances, but contemporary is my top pick. It's so expressive and powerful! What's your fave?" - }, - { - "speaker": "Gina", - "dia_id": "D1:9", - "text": "Yeah, me too! Contemporary dance is so expressive and graceful - it really speaks to me." - }, - { - "speaker": "Jon", - "dia_id": "D1:10", - "text": "Wow, great idea! Let's go to a dance class, it'll be so much fun!" - }, - { - "speaker": "Gina", - "dia_id": "D1:11", - "text": "Yeah! Let's explore some new dance moves. We should plan a dance session soon!" - }, - { - "speaker": "Jon", - "dia_id": "D1:12", - "text": "Yeah definitely! Wanna see my moves next Fri? Can't wait!" - }, - { - "speaker": "Gina", - "dia_id": "D1:13", - "text": "Sounds great, Jon! Next Friday works. Let's boogie!" - }, - { - "speaker": "Jon", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/a/a9/Dekkadancers_Mu%C5%BE_z_Malty.jpg" - ], - "blip_caption": "a photography of a man in a suit is performing a dance", - "query": "dancing on stage performance dance competition last year", - "dia_id": "D1:14", - "re-download": true, - "text": "Wow, I'm excited too! This is gonna be great!" - }, - { - "speaker": "Gina", - "dia_id": "D1:15", - "text": "Wow! What did you get?" - }, - { - "speaker": "Jon", - "dia_id": "D1:16", - "text": "Woah, that pic's from when my dance crew took home first in a local comp last year. It was amazing up on that stage! I'm super keen to spread that intensity with other peeps. Gina, you ever been in any dance comps or shows?" - }, - { - "speaker": "Gina", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/4/41/2009_NHK_Trophy_Dance_-_Meryl_DAVIS_-_Charlie_WHITE_-_Gold_Medal_-_0948a.jpg" - ], - "blip_caption": "a photography of a couple of people standing next to each other", - "query": "dance competition trophy team", - "dia_id": "D1:17", - "re-download": true, - "text": "I used to compete in a few dance competitions and shows - my fav memory was when my team won first place at a regionals at age fifteen. It was an awesome feeling of accomplishment!" - }, - { - "speaker": "Jon", - "dia_id": "D1:18", - "text": "Wow! Winning first place is amazing! What dance were you doing?" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a large open porch with a fireplace and a view of the water", - "dia_id": "D1:19", - "text": "Thanks! We just did a contemporary piece called \"Finding Freedom.\" It was really emotional and powerful." - }, - { - "speaker": "Jon", - "img_url": [ - "https://live.staticflickr.com/2730/4360526470_6bee96b5d7_b.jpg" - ], - "blip_caption": "a photography of a room with a view of the ocean and a few yoga mats", - "query": "dance studio overlooking ocean", - "dia_id": "D1:20", - "re-download": true, - "text": "Wow, that must've been great! Check my ideal dance studio by the water." - }, - { - "speaker": "Gina", - "dia_id": "D1:21", - "text": "Cool setup! Man, you can't deny that view! Got time to rehearse with a biz and a new store?" - }, - { - "speaker": "Jon", - "dia_id": "D1:22", - "text": "Hopefully, we will find a place like this that will inspire us!" - }, - { - "speaker": "Gina", - "dia_id": "D1:23", - "text": "Wow, it looks great! What dances do you practice? Got any projects planned?" - }, - { - "speaker": "Jon", - "img_url": [ - "https://markmorrisdancegroup.org/wp-content/uploads/2021/04/MM0108_JesuMeineFreude_Boston_BrazilTom_02.jpg" - ], - "blip_caption": "a photo of a group of dancers in white dresses on a stage", - "query": "group dancers performing on stage", - "dia_id": "D1:24", - "text": "Thanks! I rehearsed with a small group of dancers after work. We do all kinds of dances, from contemporary to hip-hop. We've got some cool projects in the works. Finishing up choreography to perform at a nearby festival next month. Can't wait!" - }, - { - "speaker": "Gina", - "dia_id": "D1:25", - "text": "Wow, it looks awesome! Are they yours at the festival? They're so graceful!" - }, - { - "speaker": "Jon", - "dia_id": "D1:26", - "text": "Yeah, they're the ones performing at the festival! They've been practicing hard and will definitely impress with their grace and skill." - }, - { - "speaker": "Gina", - "dia_id": "D1:27", - "text": "Wow, they look great! Can't wait to see them rock the festival. Gonna be awesome!" - }, - { - "speaker": "Jon", - "dia_id": "D1:28", - "text": "Yeah, awesome! Glad to be part of it." - } - ], - "session_2_date_time": "2:32 pm on 29 January, 2023", - "session_2": [ - { - "speaker": "Gina", - "blip_caption": "a photo of a clothing store with a variety of clothes on display", - "dia_id": "D2:1", - "re-download": true, - "text": "Hey Jon! Long time no see! Things have been hectic lately. I just launched an ad campaign for my clothing store in hopes of growing the business. Starting my own store and taking risks is both scary and rewarding. I'm excited to see where it takes me!" - }, - { - "speaker": "Jon", - "dia_id": "D2:2", - "text": "Hey Gina! Whoa, your store looks great! All your hard work really paid off - congrats! Must be awesome to see your stuff on display." - }, - { - "speaker": "Gina", - "dia_id": "D2:3", - "text": "Thanks a bunch! It's awesome seeing my vision happen. How's the dance studio going? Did you find the right spot?" - }, - { - "speaker": "Jon", - "img_url": [ - "https://avvay-aws-production.imgix.net/space-media/space-media-422692-prod.jpg" - ], - "blip_caption": "a photo of a bathroom with a blue floor and a pink wall", - "query": "dance studio natural lighting spacious room large windows dance mirrors", - "dia_id": "D2:4", - "text": "Hey Gina! Thanks for asking. I'm on the hunt for the ideal spot for my dance studio and it's been quite a journey! I've been looking at different places and picturing how the space would look. I even found a place with great natural light! Oh, I've been to Paris yesterday! It was sooo cool." - }, - { - "speaker": "Gina", - "dia_id": "D2:5", - "text": "Wow, nice spot! Where is it? Got any other features you want to think about before you decide? Paris?! That is really great Jon! Never had a chance to visit it. Been only to Rome once." - }, - { - "speaker": "Jon", - "dia_id": "D2:6", - "text": "It's downtown which is awesome cuz it's easy to get to. Plus the natural light! Gotta check the size & floor quality too. We need a good dance floor with enough bounce for me & my students to dance safely." - }, - { - "speaker": "Gina", - "dia_id": "D2:7", - "text": "Definitely! Dance floors help avoid injuries and make dancing more enjoyable. You thinking about it is great. Any particular type of flooring you like?" - }, - { - "speaker": "Jon", - "dia_id": "D2:8", - "text": "Yeah, good flooring's crucial. I'm after Marley flooring, which is what dance studios usually use. It's great 'cause it's grippy but still lets you move, plus it's tough and easy to keep clean." - }, - { - "speaker": "Gina", - "dia_id": "D2:9", - "text": "Sounds great! Marley's perfect; it's got the right amount of grip and movement. Can't wait to see your dance studio done!" - }, - { - "speaker": "Jon", - "dia_id": "D2:10", - "text": "Yeah, can't wait to see it done! Looking for the right place and getting everything ready has been a mix of exciting and nerve-wracking, but I'm determined to make it work. It'll be worth it!" - }, - { - "speaker": "Gina", - "dia_id": "D2:11", - "text": "Believe in yourself, Jon! The process may be tough, but you got this. Push through and it'll be worth it. Don't forget to take breaks and dance it out when you need to destress!" - }, - { - "speaker": "Jon", - "dia_id": "D2:12", - "text": "Glad I have you in my corner! Gotta make time to dance and vent, that's for sure. We'll make it through this - hang in there!" - }, - { - "speaker": "Gina", - "dia_id": "D2:13", - "text": "Thanks, Jon! Appreciate your support!" - }, - { - "speaker": "Jon", - "dia_id": "D2:14", - "text": "Let's keep going and chase our dreams!" - }, - { - "speaker": "Gina", - "dia_id": "D2:15", - "text": "Yeah! We've done so much, and there's nothing but good stuff coming. Let's keep going after our goals and making them happen." - }, - { - "speaker": "Jon", - "dia_id": "D2:16", - "text": "Success is almost here. We got this!" - } - ], - "session_3_date_time": "12:48 am on 1 February, 2023", - "session_3": [ - { - "speaker": "Jon", - "dia_id": "D3:1", - "text": "Hey Gina, hope you're doing ok! Still following my passion for dance. It's been bumpy, but I'm determined to make it work. I'm still searching for a place to open my dance studio." - }, - { - "speaker": "Gina", - "dia_id": "D3:2", - "img_url": [ - "https://s0.geograph.org.uk/photos/44/02/440245_a865f9d5.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a shopping mall with a glass entrance and a sign", - "text": "Hi Jon! So happy you're pushing forward with dancing! Inspiring \ud83d\udcaa I emailed some wholesalers and one replied and said yes today! I'm over the moon because now I can expand my clothing store and get closer to my customers. Check it out - here's a pic!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a room with a mirror and a wooden floor", - "dia_id": "D3:3", - "text": "Wow, Gina! You found the perfect spot for your store. Way to go, hard work's paying off!" - }, - { - "speaker": "Gina", - "img_url": [ - "https://live.staticflickr.com/610/23124623885_9aab7b3e2e_b.jpg" - ], - "blip_caption": "a photography of a clothing store with a lot of clothes on display", - "query": "inside of store cozy inviting atmosphere trendy pieces", - "dia_id": "D3:4", - "re-download": true, - "text": "Thanks! Glad you like it. Yeah, it's a great spot. Here's a peek at the space I designed. Cozy and inviting - perfect for customers to check out all the trendy pieces." - }, - { - "speaker": "Jon", - "dia_id": "D3:5", - "text": "Wow, it looks great! Must've taken you ages to design it. What made you pick out the furniture and decor?" - }, - { - "speaker": "Gina", - "dia_id": "D3:6", - "text": "Thanks! It took a bit of time but I wanted to make the place look like my own style and make my customers feel cozy. I chose furniture that looks great and is comfy too. The chandelier adds a nice glam feel while matching the style of the store." - }, - { - "speaker": "Jon", - "dia_id": "D3:7", - "text": "Your store looks great - your customers will be so comfy." - }, - { - "speaker": "Gina", - "dia_id": "D3:8", - "text": "Thanks! Making my spot comfortable and inviting for my customers is key. I want 'em to feel like they're in a cool oasis. Just creating an experience that'll make 'em wanna come back." - }, - { - "speaker": "Jon", - "dia_id": "D3:9", - "text": "That's a great goal! Creating a special experience for customers is the key to making them feel welcome and coming back. I think you can create that space you're imagining." - }, - { - "speaker": "Gina", - "dia_id": "D3:10", - "text": "Thanks. Your support means a lot. I'm sure with my hard work and effort, I can make a special shopping experience for my customers. It's tough but I'm up for the challenge!" - }, - { - "speaker": "Jon", - "dia_id": "D3:11", - "text": "I'm always here to support you! Go create something awesome with your store. Keep it up!" - }, - { - "speaker": "Gina", - "dia_id": "D3:12", - "text": "Thanks, Jon! I'll try my best. You're gonna do great with your dance studio, just keep going and stay positive! We'll get through this!" - }, - { - "speaker": "Jon", - "dia_id": "D3:13", - "text": "Thanks! Your words mean a lot. I'm staying positive and pushing forward. We've put our hearts into our dreams and I'm sure we'll make it." - }, - { - "speaker": "Gina", - "dia_id": "D3:14", - "text": "Sure thing, Jon! Stay motivated and keep going. Hard work pays off eventually. We can do this!" - } - ], - "session_4_date_time": "10:43 am on 4 February, 2023", - "session_4": [ - { - "speaker": "Jon", - "dia_id": "D4:1", - "text": "Hey Gina! What's up? How's the store going? I gotta tell you about this thing with my biz." - }, - { - "speaker": "Gina", - "dia_id": "D4:2", - "text": "Hey Jon! The store's doing great! It's a wild ride. How's the biz?" - }, - { - "speaker": "Jon", - "dia_id": "D4:3", - "text": "Hey Gina! I'm putting in a lot of work on my business even with the obstacles. I'm gonna make it happen!" - }, - { - "speaker": "Gina", - "dia_id": "D4:4", - "text": "Wow! You've got drive! Keep it up and you'll definitely make a splash." - }, - { - "speaker": "Jon", - "dia_id": "D4:5", - "text": "Thanks Gina! Your help really means a lot. Sometimes it's hard when things don't go my way, but I'm sure if I keep pushing, I'll reach my dreams!" - }, - { - "speaker": "Gina", - "dia_id": "D4:6", - "text": "Understand where you're at, Jon. Setbacks are just opportunities for comebacks. You got the skills, passion, and drive. Plus my full support. Don't give up, buddy!" - }, - { - "speaker": "Jon", - "dia_id": "D4:7", - "text": "Thanks! Setbacks can be tough, but with your support, I can handle anything. Appreciate you having my back!" - }, - { - "speaker": "Gina", - "dia_id": "D4:8", - "text": "I'm here for you no matter what! Anything you want to say about your biz?" - }, - { - "speaker": "Jon", - "dia_id": "D4:9", - "text": "Searching for a dance studio location has been tricky, but I'm determined to find the right spot - when I do, I'm sure the rest will follow!" - }, - { - "speaker": "Gina", - "dia_id": "D4:10", - "text": "Searching for the perfect dance studio's a tough job, Jon. Hang in there and you'll find it soon!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a woman in a gray dress doing a trick", - "dia_id": "D4:11", - "text": "Thanks! Appreciate your encouragement - it means a lot! I'm working on my business and some new dance routines - rehearsing hard for an upcoming show. I'm passionate about dancing and it brings me so much joy and fulfillment." - }, - { - "speaker": "Gina", - "dia_id": "D4:12", - "text": "Wow, Jon! You're so talented! What show ya got planned?" - }, - { - "speaker": "Jon", - "dia_id": "D4:13", - "text": "I'm getting ready for a dance comp near me next month. It's a great chance for me to show my skillz and, hopefully, get some props from the dance fam. Super stoked!" - }, - { - "speaker": "Gina", - "dia_id": "D4:14", - "text": "Wow Jon, you're gonna kill it in that competition. Your hard work and talent will pay off! Good luck." - }, - { - "speaker": "Jon", - "dia_id": "D4:15", - "text": "Thanks! Your help means a lot. I'll do my best to make you proud!" - }, - { - "speaker": "Gina", - "dia_id": "D4:16", - "text": "No doubt, Jon you've got this. Your passion and commitment are awesome! Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D4:17", - "text": "Thanks! I'm going for my dreams!" - }, - { - "speaker": "Gina", - "dia_id": "D4:18", - "text": "Absolutely Jon. I'm always here to cheer you on. Make your dreams happen - keep going and believe in yourself. You got this!" - }, - { - "speaker": "Jon", - "dia_id": "D4:19", - "text": "Thanks, Gina! Your kind words mean a lot. I won't give up!" - } - ], - "session_5_date_time": "9:32 am on 8 February, 2023", - "session_5": [ - { - "speaker": "Gina", - "blip_caption": "a photo of a group of women posing with a giant balloon", - "dia_id": "D5:1", - "re-download": true, - "text": "Hey Jon! Great hearing from you again. How have you been? BTW, I found a cool new fashion piece for my store. Can't wait to share with my customers." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a group of young girls in a dance studio", - "dia_id": "D5:2", - "re-download": true, - "text": "Hey Gina! Congrats on the new fashion piece! Looks like your store is growing. Remenber the festival I told you about? Had that performance and it was awesome - so many people there complementing my dance moves. Dancing brings me joy and it was nice to be reminded why I'm passionate about it." - }, - { - "speaker": "Gina", - "dia_id": "D5:3", - "text": "Wow! That looks great. You look badass on stage. BTW, what's your favorite part of running your own studio?" - }, - { - "speaker": "Jon", - "dia_id": "D5:4", - "text": "Thanks, Gina! I love running my own studio. It's great having the freedom to create a space and help dancers of all ages and levels express themselves. I'm super thrilled about dancing each day and seeing my students progress. It's so fulfilling." - }, - { - "speaker": "Gina", - "img_url": [ - "https://circlecraftmarket.files.wordpress.com/2017/11/adhesif-3.jpg" - ], - "blip_caption": "a photo of a rack of clothes with a coat and a dress", - "query": "unique artwork clothing", - "dia_id": "D5:5", - "text": "That's awesome! I'm sure you feel great knowing your students are doing so well with dance. It's amazing what it can do for people! Oh, and btw - I've been working hard on my online store and just teamed up with a local artist for some cool designs. Check 'em out!" - }, - { - "speaker": "Jon", - "dia_id": "D5:6", - "text": "It looks awesome. Your commitment and creativity in your business really stands out. How'd you come up with these cool designs?" - }, - { - "speaker": "Gina", - "dia_id": "D5:7", - "text": "Thanks Jon! I got the idea from a fashion mag and saw there wasn't much around like it. So I worked with the artist to make it happen - it's all about being ahead of the game and giving my customers something different." - }, - { - "speaker": "Jon", - "dia_id": "D5:8", - "text": "Nice one, Gina! You never shy away from a challenge and always try something new. I'm impressed by your willingness to take risks - it's really inspiring." - }, - { - "speaker": "Gina", - "dia_id": "D5:9", - "text": "Thanks! Taking risks is scary but it's the only way to grow, right? Just part of the journey to success." - }, - { - "speaker": "Jon", - "dia_id": "D5:10", - "text": "Yeah, I totally agree - taking risks is key for success. It's made me grow, and even got me out of my secure 9-5 as a banker. Now, I'm aiming to turn my dancing passion into a business. I'm determined to make it work, I just know it! That being said, I definitely don't underestimate the difficulties - it ain't been a walk in the park, that's for sure." - }, - { - "speaker": "Gina", - "dia_id": "D5:11", - "text": "It's tough starting a biz, but don't let it get you down. You can make your studio work, I'm sure. And remember, I'm always here for you." - }, - { - "speaker": "Jon", - "img_url": [ - "https://live.staticflickr.com/381/19379909243_9683e023fc_z.jpg" - ], - "blip_caption": "a photography of a cartoon character with a quote about fear", - "query": "motivational quote \"everything you want is on the other side of fear\"", - "dia_id": "D5:12", - "re-download": true, - "text": "Thanks, Gina. Your help means a lot. I'll keep plugging away and stay optimistic." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a woman standing on a balcony with a blue dress", - "dia_id": "D5:13", - "re-download": true, - "text": "This quote kept me positive through tough times. We all need a push sometimes, right? Even made a tattoo to remind myself about it." - }, - { - "speaker": "Jon", - "dia_id": "D5:14", - "text": "Love the tattoo, did you just get it?" - }, - { - "speaker": "Gina", - "dia_id": "D5:15", - "text": "Thanks! Got the tattoo a few years ago, it stands for freedom - dancing without worrying what people think. A reminder to follow my passions and express myself." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a woman in a tutu posing for a picture", - "dia_id": "D5:16", - "text": "Nice reminder, Gina! It's so important to have freedom and express ourselves without worry. Dance gives me an escape to be myself." - }, - { - "speaker": "Gina", - "dia_id": "D5:17", - "text": "Totally agree, Jon. Dancing lets us be ourselves and ain't nothing like the feeling it gives us. You're so dedicated to your studio, it's inspiring. Chase those dreams, buddy!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a trophy with a glass globe on top", - "dia_id": "D5:18", - "text": "Thanks, Gina! Your support means so much. I'm gonna keep chasing after those dreams. Dance is my passion, and I'm gonna keep working hard to make it a success!" - }, - { - "speaker": "Gina", - "dia_id": "D5:19", - "text": "This is the right attitude! How have you been juggling dance and business goals?" - }, - { - "speaker": "Jon", - "dia_id": "D5:20", - "text": "Thanks! Juggling both my passions can be tricky, but so rewarding. Dancing and running my biz need hard work, plus they give me energy for each other. My dance moves get me pumped to tackle my business goals, and successes there boost my drive to keep dreaming on the dance floor. It's a balancing act, but fun." - }, - { - "speaker": "Gina", - "dia_id": "D5:21", - "text": "Wow, Jon! You're amazing at juggling both your passions. Finding that happy medium is key - keep going and don't stop dreaming, buddy!" - }, - { - "speaker": "Jon", - "dia_id": "D5:22", - "text": "Thanks, Gina! Your pep-talk really meant a lot. I'm not gonna give up on my dreams - my dance studio and biz ventures need the hard work I'm putting in. Love having you in my corner, thanks for always being there!" - }, - { - "speaker": "Gina", - "dia_id": "D5:23", - "text": "Yeah Jon, I'm here for you! Chasing our dreams and helping each other out. Let's keep movin' forward!" - } - ], - "session_6_date_time": "2:35 pm on 16 March, 2023", - "session_6": [ - { - "speaker": "Jon", - "dia_id": "D6:1", - "text": "Hi Gina! Been hectic for me lately. Started hitting the gym last week to stay on track with the venture. Gotta figure out how to balance it all, but it's going well. How about you?" - }, - { - "speaker": "Gina", - "dia_id": "D6:2", - "text": "Hey Jon! Great to hear from you. Been having some tough times lately." - }, - { - "speaker": "Jon", - "dia_id": "D6:3", - "text": "Oof, sorry to hear that. What's up? Is there anything I can do to help?" - }, - { - "speaker": "Gina", - "dia_id": "D6:4", - "text": "Thanks, Jon! Appreciate your offer. Since I lost my job at Door Dash, things have been tough. But here's some good news - I've got something to share!" - }, - { - "speaker": "Jon", - "dia_id": "D6:5", - "text": "Wow, that's awesome! Can't wait to hear it!" - }, - { - "speaker": "Gina", - "img_url": [ - "https://i.redd.it/i9k97vw6pgs51.jpg" - ], - "blip_caption": "a photo of a computer screen showing a book and a pair of shoes", - "query": "online clothing store website screenshot", - "dia_id": "D6:6", - "text": "Yay! My online clothes store is open! I've been dreaming of this for a while now - can't wait to see what happens!" - }, - { - "speaker": "Jon", - "dia_id": "D6:7", - "text": "Congrats! That's awesome! What gave you the idea to start the online store?" - }, - { - "speaker": "Gina", - "dia_id": "D6:8", - "text": "Thanks! I'm passionate about fashion trends and finding unique pieces. Plus, I wanted to blend my love for dance and fashion, so it was a perfect match." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of three young girls standing next to each other with trophies", - "dia_id": "D6:9", - "text": "Wow, that's awesome! Combining your two interests into a store is super cool. Best of luck with it!" - }, - { - "speaker": "Gina", - "dia_id": "D6:10", - "text": "Thanks! How is biz going? I hope it's going well!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a laptop computer sitting on top of a table", - "dia_id": "D6:11", - "text": "Thanks for askin', Gina! Losing my job was hard, but I'm livin' my dreams now. Startin' my biz has been tough but I'm gonna make it! I keep facing new challenges, but I'm sure it'll be worth it in the end." - }, - { - "speaker": "Gina", - "dia_id": "D6:12", - "text": "Yeah, starting and running my own biz has had its ups and downs - but it's been an amazing ride!" - }, - { - "speaker": "Jon", - "dia_id": "D6:13", - "text": "Yeah, it's been a rollercoaster. But your success really inspires me to keep pushing forward. Your determination is awesome!" - }, - { - "speaker": "Gina", - "dia_id": "D6:14", - "text": "Thanks, Jon! Your words are really encouraging. Glad my journey is inspiring others." - }, - { - "speaker": "Jon", - "dia_id": "D6:15", - "text": "Yeah, totally! It's great we both face the same challenges, it motivates us and it's like having a partner to dance with!" - }, - { - "speaker": "Gina", - "dia_id": "D6:16", - "text": "Yep! We're both on different paths, but it's nice to have someone to root for us. We can do it!" - }, - { - "speaker": "Jon", - "dia_id": "D6:17", - "text": "Definitely! Having someone back us up is great. Let's keep going and reach success together!" - }, - { - "speaker": "Gina", - "dia_id": "D6:18", - "text": "Let's keep chasing our dreams, supporting each other, and celebrating achievements. We can do great things together!" - }, - { - "speaker": "Jon", - "img_url": [ - "https://www.news-press.com/gcdn/presto/2018/12/21/PFTM/e043df2d-48d9-4591-a714-f27ecbd42007-GD1.jpg" - ], - "blip_caption": "a photo of two glasses of champagne with a bottle of wine in the background", - "query": "champagne celebration", - "dia_id": "D6:19", - "text": "Yeah, Gina, thanks for having my back. Here's to taking on new heights, and all the trials that come with it. Cheers!" - } - ], - "session_7_date_time": "7:28 pm on 23 March, 2023", - "session_7": [ - { - "speaker": "Jon", - "dia_id": "D7:1", - "text": "Hey Gina, how's it going?" - }, - { - "speaker": "Gina", - "dia_id": "D7:2", - "text": "Hey Jon, my online clothing store has been a roller coaster but rewarding. Starting a business takes courage - you hang in there too!" - }, - { - "speaker": "Jon", - "dia_id": "D7:3", - "text": "Thanks Gina! It's been tough, but I'm gonna make it happen. It's been great! And hey, you're awesome with your store. How's it going?" - }, - { - "speaker": "Gina", - "dia_id": "D7:4", - "text": "Thanks! Appreciate your kind words. Store's going good, just been keeping up with fashion trends so I can offer the best pieces to customers. It's been a lot of work, but really enjoying it. Got any advice or tips on running a successful biz?" - }, - { - "speaker": "Jon", - "dia_id": "D7:5", - "text": "Yeah, brand identity is key. Make sure yours stands out. Also be sure to build relationships with your customers \u2013 let them know you care. And don't forget to stay positive and motivate others. Your energy will be contagious!" - }, - { - "speaker": "Gina", - "dia_id": "D7:6", - "text": "Thanks for the advice, Jon! Building relationships and creating a strong brand image for my store is something I'm always working on. You're right, staying positive is key. What helps you stay motivated with your dance studio business?" - }, - { - "speaker": "Jon", - "dia_id": "D7:7", - "img_url": [ - "https://live.staticflickr.com/8111/8547962982_92d38cbcc3_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a group of women doing a dance routine", - "text": "Seeing my students succeed motivates me. It's awesome to help them learn and reach their goals. Your support, Gina, means a lot too. Here's a photo of us after during one of the dance clases." - }, - { - "speaker": "Gina", - "dia_id": "D7:8", - "text": "That's awesome, Jon! Seeing your students grow and succeed must be really fulfilling. Glad I can be part of this journey!" - }, - { - "speaker": "Jon", - "dia_id": "D7:9", - "text": "Thanks for being there for me! It's really made a huge difference and it feels great." - }, - { - "speaker": "Gina", - "dia_id": "D7:10", - "text": "Glad I could help, Jon! It's nice to be part of something positive. Supporting your dreams is awesome!" - }, - { - "speaker": "Jon", - "dia_id": "D7:11", - "text": "Thanks for being there for me. Your help means a lot." - }, - { - "speaker": "Gina", - "dia_id": "D7:12", - "text": "I'm here for you, rooting for you all the way." - }, - { - "speaker": "Jon", - "dia_id": "D7:13", - "text": "Thanks, I'm really grateful for your help with staying motivated." - }, - { - "speaker": "Gina", - "dia_id": "D7:14", - "text": "Glad to cheer you on. Keep going and never give up!" - }, - { - "speaker": "Jon", - "dia_id": "D7:15", - "text": "Thanks, Gina! I won't quit - your words motivate me to keep going!" - }, - { - "speaker": "Gina", - "dia_id": "D7:16", - "text": "Believe in yourself. Even when it's tough, you got this! Keep going!" - }, - { - "speaker": "Jon", - "dia_id": "D7:17", - "text": "I'm gonna keep on believing in myself. Thanks for the kind words!" - } - ], - "session_8_date_time": "1:26 pm on 3 April, 2023", - "session_8": [ - { - "speaker": "Jon", - "dia_id": "D8:1", - "text": "Hey Gina, I had to shut down my bank account. It was tough, but I needed to do it for my biz." - }, - { - "speaker": "Gina", - "dia_id": "D8:2", - "text": "Oh no, Jon! Sorry to hear that. Tough decision for you? How're you handling the changes?" - }, - { - "speaker": "Jon", - "dia_id": "D8:3", - "text": "It was a tough call, but I thought it'd help my biz grow. Handling changes has been hard, but I'm staying positive and looking ahead. Anything new for you?" - }, - { - "speaker": "Gina", - "dia_id": "D8:4", - "text": "Oof, that's tough, Jon. I got some new offers and promotions going on my online store to try and bring in new customers. It's been a wild ride starting my business, but I'm not giving up!" - }, - { - "speaker": "Jon", - "dia_id": "D8:5", - "text": "Nice one, Gina! Love how you never give up. What helps you stay motivated?" - }, - { - "speaker": "Gina", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2151,w_2403,x_0,y_1044/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/export-1-37_l33gst.jpg" - ], - "blip_caption": "a photo of a man and woman doing a yoga pose", - "query": "dance studio session", - "dia_id": "D8:6", - "text": "Thanks Jon! Dance is my stress relief and fashion fuels my creativity. I love finding new trends for my store. It keeps me motivated to keep growing. Check out this pic of my fave dance session!" - }, - { - "speaker": "Jon", - "dia_id": "D8:7", - "text": "Wow, that's great! What made you combine clothing biz and dance?" - }, - { - "speaker": "Gina", - "dia_id": "D8:8", - "text": "Thanks! I'm passionate about dance and fashion so combining them lets me show my creativity and share my love with others. Plus, I can add dance-inspired items to my store!" - }, - { - "speaker": "Jon", - "dia_id": "D8:9", - "text": "Nice work! Combining passions is always cool. How's it going?" - }, - { - "speaker": "Gina", - "dia_id": "D8:10", - "text": "Thanks! So far, so good - customers love the new offers and promotions, which means I'm seeing more sales. People seem to really like my designs, so I'm always on the hunt for unique, trendy pieces. Growing my customer base is the main focus right now." - }, - { - "speaker": "Jon", - "dia_id": "D8:11", - "text": "Sounds like all your effort's paying off. Anything planned to grow your customer base?" - }, - { - "speaker": "Gina", - "dia_id": "D8:12", - "text": "Yeah, I have a few plans. I'm thinking of working with some fashion bloggers and influencers in the next few months to get more attention for my store. Plus, I'm going to do more ads so I can reach more people. I'm really focused on building my customer base and making my store a top destination for fashion fans. It's awesome to see it all coming together! You, Jon? What do you have going for your dance studio?" - }, - { - "speaker": "Jon", - "dia_id": "D8:13", - "text": "Thanks, Gina! I'm expanding my dance studio's social media presence and offering workshops and classes to local schools and centers. I'm also hosting a dance competition next month to showcase local talent and bring more attention to my studio. All the work's paying off - I'm seeing progress and the dancers are so excited. It's such a great feeling to give a place where people can express themselves through dance!" - }, - { - "speaker": "Gina", - "dia_id": "D8:14", - "text": "Wow! That's fantastic that your studio's expanding and giving dancers an outlet. So proud of the progress you've made - keep it up!" - }, - { - "speaker": "Jon", - "img_url": [ - "https://universe.byu.edu/wp-content/uploads/2018/02/IMG_8914.jpg" - ], - "blip_caption": "a photo of a group of people on a stage with a projector screen", - "query": "group of talented dancers performing on stage event next month", - "dia_id": "D8:15", - "text": "Thanks! Your backing means a lot. I'm trying to make my plan work, even though it's been tough. Your encouragement really helps. Are you coming to the event next month? Love to have you there!" - }, - { - "speaker": "Gina", - "dia_id": "D8:16", - "text": "Woah, cool event! What's gonna be happening? I'd love to join in and show my support!" - }, - { - "speaker": "Jon", - "img_url": [ - "https://varnikadesigns.files.wordpress.com/2020/11/img_3505.jpg" - ], - "blip_caption": "a photo of a group of dancers on a stage with a man in the middle of the group", - "query": "group dancers stage performance", - "dia_id": "D8:17", - "text": "Thanks, Gina! My dance studio and some other schools are bringing their best moves for an awesome night of performances and judging. It'll be super creative and fun. Come join us!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a woman in a tutu posing for a picture", - "dia_id": "D8:18", - "text": "Sounds great! I'm definitely in for the show." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of two women doing a handstand in a room", - "dia_id": "D8:19", - "text": "Cool! Can't wait to see you!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a group of young girls in tutuss and ballet shoes", - "dia_id": "D8:20", - "text": "Thanks, Jon! See you at the event!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a dress with a sign on it that says june bunty", - "dia_id": "D8:21", - "text": "Gina, good luck with your store!" - }, - { - "speaker": "Gina", - "dia_id": "D8:22", - "text": "Thanks, Jon! Appreciate the kind words. <3" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a street light on a sidewalk in front of a building", - "dia_id": "D8:23", - "text": "Sure, I am always there for you!" - }, - { - "speaker": "Gina", - "dia_id": "D8:24", - "text": "Thanks! Your support means a lot. I'm gonna keep pursuing my goals and I hope you do too!" - }, - { - "speaker": "Jon", - "dia_id": "D8:25", - "text": "Thanks! I won't quit on my dreams. Your words really motivate me. Bye!" - }, - { - "speaker": "Gina", - "dia_id": "D8:26", - "text": "Bye Jon! You got this! Believe in yourself and keep pushing. Take care!" - } - ], - "session_9_date_time": "10:33 am on 9 April, 2023", - "session_9": [ - { - "speaker": "Jon", - "img_url": [ - "https://college.lclark.edu/live/image/gid/664/width/720/height/690/crop/1/87150_Dance_main_image.rev.1613000755.jpg" - ], - "blip_caption": "a photo of a group of dancers on a stage with a red background", - "query": "dance studio class collage rehearsal students", - "dia_id": "D9:1", - "text": "Hey Gina! I'm turning my loves of dance into a business. I'm sunk tons of time into the studio lately, and look at my students - they're already killing it. I'm even learning with them!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a display of a dress and a flamingo", - "dia_id": "D9:2", - "text": "Hey Jon! Wow, way to take your passion and make it into a biz! The dance studio looks awesome." - }, - { - "speaker": "Jon", - "dia_id": "D9:3", - "text": "Thanks, Gina! I'm determined to make this studio work. Losing my job was tough but it gave me the push I needed to do what I love." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a red dress with gold accents on a mannequin", - "dia_id": "D9:4", - "text": "Woah, Jon! Tough times can be a gateway to awesome things. Glad you worked up the courage to go after your dreams!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a woman doing a pole dance in a dance studio", - "dia_id": "D9:5", - "re-download": true, - "text": "Yeah, Gina! It's been tough, but I'm living my true self. Dancing makes me so happy, and now I get to share that with other people. Seeing my students get better at it brings me such joy." - }, - { - "speaker": "Gina", - "dia_id": "D9:6", - "text": "Wow Jon, you look so happy when you dance! Show the world your true self and keep dancing!" - }, - { - "speaker": "Jon", - "dia_id": "D9:7", - "text": "Thanks a bunch, Gina! You seriously rock. Dancing for me is like a way to express myself and find my happy place. I used to be scared to death of what people would think, but I learnt that my own happiness is the most important thing. It's been tough but also the best thing ever!" - }, - { - "speaker": "Gina", - "img_url": [ - "https://cartwheelfactory.com/imagesjpg/ballet-studio-glassless-mir.jpg" - ], - "blip_caption": "a photo of a group of young women in ballet attire", - "query": "ballet studio mirrors barres dance classes", - "dia_id": "D9:8", - "text": "Yeah, I do remember those dance classes! I used to love spending time in the studio. That photo looks awesome, brings back lots of memories. It's nice to know, dance is still your happy place." - }, - { - "speaker": "Jon", - "dia_id": "D9:9", - "text": "Nice, Gina! I'm happy that dance has such a special meaning to both of us. It's a really cool way to express ourselves. I loved taking lessons with my friends when I was younger. Those memories are so precious. So glad I can still enjoy it with my own studio. Thanks for always being there for me." - }, - { - "speaker": "Gina", - "img_url": [ - "https://i.pinimg.com/originals/68/03/ff/6803ffb650c71d63268b6a40310c882a.jpg" - ], - "blip_caption": "a photo of a trophy with a glass globe on top", - "query": "dance competition trophy", - "dia_id": "D9:10", - "text": "Hey Jon! Got your back - dance is awesome for expressing yourself and finding happiness. Here's one of my trophies from a dance contest, nice reminder of the hard work, dedication and joy it brings." - }, - { - "speaker": "Jon", - "dia_id": "D9:11", - "text": "Wow! It looks awesome! Thanks for the support, it really means a lot." - }, - { - "speaker": "Gina", - "dia_id": "D9:12", - "text": "No prob, Jon! You earned all the kudos for your hard work. Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D9:13", - "text": "Thanks! Gonna keep pushing and working hard. Won't let anything hold me back!" - }, - { - "speaker": "Gina", - "dia_id": "D9:14", - "text": "Way to go, Jon! Keep it up, you're almost there!" - } - ], - "session_10_date_time": "11:24 am on 25 April, 2023", - "session_10": [ - { - "speaker": "Jon", - "img_url": [ - "https://www.samfsn.org/images/samsfn-events/WNYD2.jpg" - ], - "blip_caption": "a photo of a group of women performing a dance on a stage", - "query": "fair booth flyers brochures dance studio", - "dia_id": "D10:1", - "text": "Hi Gina! I just wanted to fill you in on my business. Yesterday, I went to a fair to show off my studio, it was both stressful and great! I got some possible leads, so that was awesome. But overall, I've learned that this biz is no cakewalk and having confidence in yourself is important for making it successful!" - }, - { - "speaker": "Gina", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/5ad8eb7f506fbec1f61b54ea/1539686276776-HPK5GWLCBQ0MHE5GSE6S/web_homepage_mock.jpg" - ], - "blip_caption": "a photography of a laptop with a sale ad on the screen", - "query": "clothing store website", - "dia_id": "D10:2", - "re-download": true, - "text": "Hey Jon, congrats on the fair! It's awesome to see your hard work paying off. Keep on pushing, pal, you're headed for greatness. Oh, and BTW, I started my own online clothing store not so long ago - pretty cool, huh?" - }, - { - "speaker": "Jon", - "dia_id": "D10:3", - "text": "Thanks, Gina! Appreciate your support. Your store looks great, I remember it!" - }, - { - "speaker": "Gina", - "dia_id": "D10:4", - "text": "Thanks, Jon! After losing my job, I wanted to take control of my own destiny and this seemed like the perfect way to do it. It's been a tough journey, but very rewarding." - }, - { - "speaker": "Jon", - "dia_id": "D10:5", - "text": "Wow, you're awesome for going for it! Setbacks can help us reach our potential, right? I'm having trouble with my business project. Any advice on staying motivated even when times are tough?" - }, - { - "speaker": "Gina", - "dia_id": "D10:6", - "text": "Yeah Jon! Challenges are awesome for learning and growth. To stay motivated, I think of the big goal and why I'm doing it. I also get help from people who support me. And of course, I dance it out. Do you need advice on anything in particular?" - }, - { - "speaker": "Jon", - "dia_id": "D10:7", - "text": "I've been feeling kinda low on confidence lately. It's hard to run a business when you don't have faith in yourself. Any tips on how you stay confident in your business?" - }, - { - "speaker": "Gina", - "dia_id": "D10:8", - "text": "I get it, Jon. Confidence is important in business. I stay motivated by reminding myself of my successes and progress. It also helps to have a good support system. Just focus on why you started this \u2013 because you love it! Have faith in yourself, Jon. I do!" - }, - { - "speaker": "Jon", - "dia_id": "D10:9", - "text": "Thanks! Your words mean a lot. Gotta focus on success and why I started. You're right, I love it and that'll keep my confidence up." - }, - { - "speaker": "Gina", - "dia_id": "D10:10", - "text": "No worries, Jon! Sounds like what I said was helpful. You're incredibly talented and passionate about dance. Don't forget, believe in yourself and your abilities. Tackle any obstacle that comes your way and keep shining!" - }, - { - "speaker": "Jon", - "dia_id": "D10:11", - "text": "Thanks! Your support means a lot. I'm gonna keep going and reach my dreams no matter what." - }, - { - "speaker": "Gina", - "dia_id": "D10:12", - "text": "Go for it, Jon! You got this! Don't let anything stop you. We're in this together!" - }, - { - "speaker": "Jon", - "dia_id": "D10:13", - "text": "Appreciate your encouragement. We'll keep pushing each other on this path." - }, - { - "speaker": "Gina", - "dia_id": "D10:14", - "text": "Yeah! Let's keep each other going. We can do it!" - } - ], - "session_11_date_time": "3:14 pm on 11 May, 2023", - "session_11": [ - { - "speaker": "Jon", - "blip_caption": "a photo of a woman in a short skirt with her hands on her hips", - "dia_id": "D11:1", - "text": "Hi! Since we last spoke I am still working on the dance studio and things are looking up!" - }, - { - "speaker": "Gina", - "dia_id": "D11:2", - "text": "Hi! You're so inspiring taking it on and opening your own studio!" - }, - { - "speaker": "Jon", - "dia_id": "D11:3", - "text": "Thanks! Losing my job gave me the push to finally start my dream business: my own dance studio! Now I'm stepping into the unknown and hoping for the best." - }, - { - "speaker": "Gina", - "dia_id": "D11:4", - "text": "It must be scary stepping into the unknown but I know you can do it, Jon. With your determination and drive, your dance studio will be a huge success. Keep that positive outlook and keep going!" - }, - { - "speaker": "Jon", - "dia_id": "D11:5", - "text": "Thanks! It's a bit scary, but I just think about my love for dance and how it makes me feel. It's been my stress-buster since childhood!" - }, - { - "speaker": "Gina", - "dia_id": "D11:6", - "text": "Gotcha, Jon! Dance is my stress fix too. As soon as I start, all my worries vanish. It's amazing what we can do for our own mental health with something we enjoy." - }, - { - "speaker": "Jon", - "dia_id": "D11:7", - "text": "Yeah, Gina! Dancing helps me de-stress. It's where I'm most alive. It's a must-have in my life." - }, - { - "speaker": "Gina", - "dia_id": "D11:8", - "text": "I get it, Jon. Dance is just me -- I can't picture life without it. It's like air." - }, - { - "speaker": "Jon", - "dia_id": "D11:9", - "text": "Yep! Dancing is like second nature to me. I'm living my dream by having my own dance studio and teaching others." - }, - { - "speaker": "Gina", - "dia_id": "D11:10", - "text": "You're living the dream and inspiring others too! Your studio will totally change things for lots of folks." - }, - { - "speaker": "Jon", - "dia_id": "D11:11", - "text": "I hope so, Gina. I want to create a place for people to dance and express themselves - it's been a dream of mine." - }, - { - "speaker": "Gina", - "dia_id": "D11:12", - "text": "That's a great dream, Jon! Giving people a place to express themselves with dance is really important. Your studio is gonna make a huge difference. Can't wait to see it happen!" - }, - { - "speaker": "Jon", - "dia_id": "D11:13", - "text": "Thanks! Your help means a lot. Keep you posted on the dance studio progress." - }, - { - "speaker": "Gina", - "dia_id": "D11:14", - "text": "Thanks! Really appreciate you keeping me in the loop on this cool project. Can't wait to hear more and watch it come to life! Oh, btw, I had an interview for a design internship yesterday! It was so cool." - }, - { - "speaker": "Jon", - "dia_id": "D11:15", - "text": "Wow, Gina, I'm stoked about this! Taking a risk is scary, but I'm sure following my dreams will pay off in the end. How did the interview go?" - }, - { - "speaker": "Gina", - "dia_id": "D11:16", - "text": "It was great!" - }, - { - "speaker": "Jon", - "dia_id": "D11:17", - "text": "Glad to hear it. Been practicing dance routines lately, it keeps my mind focused and motivated." - }, - { - "speaker": "Gina", - "dia_id": "D11:18", - "text": "Wow! That's great. Dancing is awesome for staying focused. Wanna show me a routine sometime?" - }, - { - "speaker": "Jon", - "dia_id": "D11:19", - "text": "Sure, Gina! Wanna see one of my routines? Lemme know when you got time and I'll send you a vid." - }, - { - "speaker": "Gina", - "dia_id": "D11:20", - "text": "Yeah, Jon, I'll watch your routine! So proud of you!" - }, - { - "speaker": "Jon", - "dia_id": "D11:21", - "text": "Thanks a lot! Your help really means a lot. I'll get the video to you soon!" - }, - { - "speaker": "Gina", - "dia_id": "D11:22", - "text": "No prob! Always here to help. Can't wait to see the vid!" - } - ], - "session_12_date_time": "7:18 pm on 27 May, 2023", - "session_12": [ - { - "speaker": "Gina", - "dia_id": "D12:1", - "text": "Hey Jon! Long time no talk! A lot's happened - I just got accepted for a fashion internship!" - }, - { - "speaker": "Jon", - "dia_id": "D12:2", - "text": "Congrats, Gina! That's awesome news about the fashion internship. \ud83c\udf89 So stoked for you. Where is the internship and how're you feelin' about it?" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a laptop computer with a logo on the screen", - "dia_id": "D12:3", - "text": "Thanks! I'm excited and kinda nervous. Gonna be a big change. It's part-time position in the fashion department of an international company." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a book with a yellow and green cover", - "dia_id": "D12:4", - "text": "Way to go, Gina! You really stepped up. What's your plan for the future?" - }, - { - "speaker": "Gina", - "dia_id": "D12:5", - "text": "Thanks! I'm a mix of excited and scared to get into fashion, but I'm trying to stay upbeat and learn as much as I can. What about you? Got something new?" - }, - { - "speaker": "Jon", - "dia_id": "D12:6", - "text": "I'm currently reading \"The Lean Startup\" and hoping it'll give me tips for my biz." - }, - { - "speaker": "Gina", - "dia_id": "D12:7", - "text": "It sounds great! Could it spark any ideas for your dance studio?" - }, - { - "speaker": "Jon", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1615494917/at/living/2021-03/IMG_5070.jpg" - ], - "blip_caption": "a photo of a white board with a list of dates on it", - "query": "whiteboard business ideas", - "dia_id": "D12:8", - "text": "Yeah, the book got me thinking about building a focused and efficient business. Adapting and tweaking from customer feedback is important too, so I'm gonna try it out!" - }, - { - "speaker": "Gina", - "dia_id": "D12:9", - "text": "Woah, Jon, that whiteboard's got a bunch of good ideas! How you gonna keep track and stay on schedule with those dates?" - }, - { - "speaker": "Jon", - "dia_id": "D12:10", - "text": "Thanks, Gina! It helps me keep track of ideas and milestones. Gives me a visual of my progress and keeps me organized." - }, - { - "speaker": "Gina", - "dia_id": "D12:11", - "text": "Nice idea! Having something visual can help with organizing and motivation. What're you working on currently?" - }, - { - "speaker": "Jon", - "dia_id": "D12:12", - "text": "I'm wrapping up the business plan and looking for investors. My passion for the project and belief in its success are driving me." - }, - { - "speaker": "Gina", - "dia_id": "D12:13", - "text": "Wow, Jon! Impressed by your commitment. How's the hunt for investors going?" - }, - { - "speaker": "Jon", - "dia_id": "D12:14", - "text": "Thanks! Searching for investors has been tough, but I'm staying hopeful. It's all a process and I'm learning a ton." - }, - { - "speaker": "Gina", - "dia_id": "D12:15", - "text": "Yeah Jon, you've got the right attitude! Keep learning and growing through it all. Keep going!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a pink sign with a message on it", - "dia_id": "D12:16", - "text": "Thanks! I really appreciate your help. I'm gonna keep on going and never quit." - }, - { - "speaker": "Gina", - "dia_id": "D12:17", - "text": "Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D12:18", - "text": "Thanks! Your words really mean a lot. Don't worry, I won't let anything get me down." - }, - { - "speaker": "Gina", - "dia_id": "D12:19", - "text": "Go Jon! Obstacles are inevitable, but you can do awesome things. Keep going!" - } - ], - "session_13_date_time": "8:29 pm on 13 June, 2023", - "session_13": [ - { - "speaker": "Jon", - "dia_id": "D13:1", - "text": "Hey Gina, thanks for being there for me and believing in me. It means a lot." - }, - { - "speaker": "Gina", - "dia_id": "D13:2", - "text": "Sure, Jon! I'm always around for you. How've you been since we caught up?" - }, - { - "speaker": "Jon", - "dia_id": "D13:3", - "text": " I'm prepping for my dance studio more than ever!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a group of young girls in blue outfits posing for a picture", - "dia_id": "D13:4", - "text": "Proud of you for starting your own business! It takes strength to stay hopeful. What are you doing to prep for your dance studio? I have developed a video presentation to teach how to style my fashion pieces, btw. " - }, - { - "speaker": "Jon", - "dia_id": "D13:5", - "text": "Thanks, Gina! It's been so inspiring to work with our young dancers, seeing their passion and commitment. Opening the dance studio's been a great experience - I want it to be a place of support and encouragement for all our dancers. Will you show me this presentation?" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a skeleton and a trophy on a black cloth", - "dia_id": "D13:6", - "text": "Shure, I will send it to you later. Starting this studio isn't just a business, it's a place for dancers to grow. What support are you offering them?" - }, - { - "speaker": "Jon", - "dia_id": "D13:7", - "text": "Besides the dance classes and workshops, I'm offering one-on-one mentoring and training to help dancers reach their full potential." - }, - { - "speaker": "Gina", - "img_url": [ - "https://christinasearsetternet.files.wordpress.com/2020/08/img_1232.jpg" - ], - "blip_caption": "a photo of a group of people in a dance class", - "query": "dancers mentoring and training", - "dia_id": "D13:8", - "text": "Your one-on-one mentoring and training will really push dancers to reach their goals. I totally get it, I had a mentor too when I was learning how to dance." - }, - { - "speaker": "Jon", - "img_url": [ - "https://i.redd.it/2308a70tm0m81.jpg" - ], - "blip_caption": "a photo of a clipboard with a notepad attached to it", - "query": "whiteboard dance moves", - "dia_id": "D13:9", - "text": "Yeah, a mentor can do wonders. Guidance and support can help dancers really shine." - }, - { - "speaker": "Gina", - "dia_id": "D13:10", - "text": "Nice one, Jon! How've you been using it?" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a notebook with a calendar on it", - "dia_id": "D13:11", - "text": "I'm using it to stay organized and motivated. It sets goals, tracks my achievements and helps me find areas to improve." - }, - { - "speaker": "Gina", - "dia_id": "D13:12", - "text": "Really cool system! Being organized sure is key for getting stuff done. How do you keep up with your goals and progress?" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a group of markers sitting on top of a white surface", - "dia_id": "D13:13", - "text": "I also use whiteboard to stay on track, visualize goals and tokenize successes. It keeps me motivated and focused." - }, - { - "speaker": "Gina", - "dia_id": "D13:14", - "text": "Cool! Are you using different colors?" - }, - { - "speaker": "Jon", - "dia_id": "D13:15", - "text": "Yes. I color-code achievements so I can easily track my progress and stay motivated." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a cork board with pictures and words on it", - "dia_id": "D13:16", - "text": "Wow, color-coding is a great way to track your progress & stay motivated. Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D13:17", - "text": "Thanks! It helps me stay motivated and reminds me why I'm doing this." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a person holding a paper bag with a picture of a bird and a birdie", - "dia_id": "D13:18", - "text": "Yeah, its a great idea!" - }, - { - "speaker": "Jon", - "dia_id": "D13:19", - "text": "Thanks!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a quote on a white sheet with a rainbow of light", - "dia_id": "D13:20", - "text": "Remember that staying positive is very important. Rock on!" - }, - { - "speaker": "Jon", - "dia_id": "D13:21", - "text": "Thanks, Gina! Your support's really been awesome." - }, - { - "speaker": "Gina", - "dia_id": "D13:22", - "text": "Thanks! Keep going for your dreams and don't quit!" - }, - { - "speaker": "Jon", - "dia_id": "D13:23", - "text": "I won't give up, Gina! I'll keep pushing and working to make my dreams happen. Thanks for the support!" - } - ], - "session_14_date_time": "9:38 pm on 16 June, 2023", - "session_14": [ - { - "speaker": "Jon", - "dia_id": "D14:1", - "text": "Gina, you won't believe it - I got mentored by this amazing business dude yesterday! It was really inspiring and now I'm even more pumped to chase my dreams. What's been up with you lately?" - }, - { - "speaker": "Gina", - "dia_id": "D14:2", - "text": "Wow, Jon! Mentors can really help. I'm working on my online store, growing the customer base. It's tough but I'm determined. How about you? Any new things happening?" - }, - { - "speaker": "Jon", - "dia_id": "D14:3", - "text": "Been doing some promotion for my business. Crazy ride so far, but I'm hanging in there. Got any tips for marketing?" - }, - { - "speaker": "Gina", - "dia_id": "D14:4", - "text": "Awesome! Marketing is key. Use social media channels and work with influencers for bigger reach." - }, - { - "speaker": "Jon", - "dia_id": "D14:5", - "text": "Thanks for the advice, Gina! I already started doing what you said about social media and posted some of my dance videos. It's creating a bit of a stir." - }, - { - "speaker": "Gina", - "dia_id": "D14:6", - "text": "Nice! Glad your dance vids are doing well online. Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D14:7", - "text": "Your help really helps. Hey, have you thought about being an entrepreneur?" - }, - { - "speaker": "Gina", - "dia_id": "D14:8", - "text": "Ha, yeah, Jon. I've been one 'cause I lost my job. I opened an online clothing store and it's been great! Being my own boss and doing something I love is awesome." - }, - { - "speaker": "Jon", - "dia_id": "D14:9", - "text": "Wow, Gina! You did great taking that leap. Congrats! Got any advice for someone just starting out?" - }, - { - "speaker": "Gina", - "dia_id": "D14:10", - "text": "Thanks, Jon! It was a huge jump, but totally worth it. My advice: stay passionate, focused and resilient. Challenges will come, but believe in yourself and keep going. And stay open to learning and improving." - }, - { - "speaker": "Jon", - "dia_id": "D14:11", - "text": "Appreciate your advice. Gotta stay resilient and focused, that's key!" - }, - { - "speaker": "Gina", - "dia_id": "D14:12", - "text": "Yep Jon, staying resilient and focused is key for any entrepreneur. Keep going and don't give up! You got this!" - }, - { - "speaker": "Jon", - "dia_id": "D14:13", - "text": "Thanks! I won't quit, no matter what. Your encouragement really motivates me to keep going." - }, - { - "speaker": "Gina", - "img_url": [ - "https://cdn1.sportngin.com/attachments/photo/7609-187142791/PINE_ISLAND_WOW_AWARD.jpg" - ], - "blip_caption": "a photo of a group of young women posing for a picture", - "query": "dance competition trophy stage", - "dia_id": "D14:14", - "text": "Way to go, Jon! Don't quit, remember, failures lead you closer to success. Here's a pic from when I was dancing - it was a tough road, but it was worth it!" - }, - { - "speaker": "Jon", - "dia_id": "D14:15", - "text": "Wow, that's an awesome pic! You guys look great and passionate about dancing. Reminds me how much I love performing. Thanks for sharing!" - }, - { - "speaker": "Gina", - "dia_id": "D14:16", - "text": "Thanks! I'm glad the pic reminded you of your love for dancing. Keep going after your dreams!" - }, - { - "speaker": "Jon", - "dia_id": "D14:17", - "text": "Sure thing, Gina! Your help means a lot to me. I'm not giving up." - }, - { - "speaker": "Gina", - "dia_id": "D14:18", - "text": "Go, Jon! I'm here for you. Keep going!" - }, - { - "speaker": "Jon", - "dia_id": "D14:19", - "text": "Knowing you've got my back really helps keep me going. I won't let you down!" - }, - { - "speaker": "Gina", - "dia_id": "D14:20", - "text": "No worries, Jon! You're really inspiring with your determination and passion for dance. Keep it up!" - } - ], - "session_15_date_time": "10:04 am on 19 June, 2023", - "session_15": [ - { - "speaker": "Jon", - "dia_id": "D15:1", - "text": "Hey Gina, hope you're doing great! Still working on my biz. Took a short trip last week to Rome to clear my mind a little." - }, - { - "speaker": "Gina", - "dia_id": "D15:2", - "text": "Hi! Good for you! It definitely will help you to concentrate on your biz better." - }, - { - "speaker": "Jon", - "dia_id": "D15:3", - "text": "Thanks, Gina. Still working on opening a dance studio." - }, - { - "speaker": "Gina", - "dia_id": "D15:4", - "text": "When are you opening the studio?" - }, - { - "speaker": "Jon", - "img_url": [ - "https://images.pexels.com/photos/11686640/pexels-photo-11686640.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-ph%E1%BA%A1m-chung-11686640.jpg" - ], - "blip_caption": "a photo of a group of young dancers in a dance studio", - "query": "dance studio front entrance dancers rehearsing", - "dia_id": "D15:5", - "text": "The official opening night is tomorrow. I'm working hard to make everything just right. Can't wait to see it all come together!" - }, - { - "speaker": "Gina", - "dia_id": "D15:6", - "text": "Congrats, Jon! The studio looks amazing. You've put a lot of work into this and I'm so pumped for the launch tomorrow. Don't miss a beat!" - }, - { - "speaker": "Jon", - "dia_id": "D15:7", - "text": "Thanks, Gina! I'm excited! It's been a wild ride, but I'm feeling good and ready to give it my best." - }, - { - "speaker": "Gina", - "dia_id": "D15:8", - "text": "Wow, Jon, you must be so excited! You've come so far since we last talked, and tomorrow's gonna be a blast! All those long nights were worth it - so take some time to savor it. Capture the joy and thrill that dance brings - it's magical!" - }, - { - "speaker": "Jon", - "dia_id": "D15:9", - "text": "Tomorrow's gonna be an awesome night and I'm not gonna forget a second of it. I put so much into this and I want to savor all the good vibes. Thanks for always having my back. You're the best!" - }, - { - "speaker": "Gina", - "dia_id": "D15:10", - "text": "I'm always proud of you. Enjoy the good feels tomorrow, you earned it!" - }, - { - "speaker": "Jon", - "dia_id": "D15:11", - "text": "Thanks! Your pride and support mean a lot. Looking forward to enjoying the moment with you." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a group of people in a dance studio", - "dia_id": "D15:12", - "text": "I'll be right by your side, Jon. Let's live it up and make some great memories tomorrow. So excited!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a man in a native costume is giving another man a high five", - "dia_id": "D15:13", - "text": "Yeah! Let's make some awesome memories tomorrow at the grand opening!" - }, - { - "speaker": "Gina", - "dia_id": "D15:14", - "text": "Can't wait to make more memories at your dance studio!" - }, - { - "speaker": "Jon", - "dia_id": "D15:15", - "text": "Looking forward to more cool memories!" - }, - { - "speaker": "Gina", - "dia_id": "D15:16", - "text": "I love being around friends and having such a great time. Can't wait to have fun at your dance studio!" - }, - { - "speaker": "Jon", - "dia_id": "D15:17", - "text": "Agreed!" - }, - { - "speaker": "Gina", - "dia_id": "D15:18", - "text": "Can't wait for tomorrow's grand opening!" - }, - { - "speaker": "Jon", - "dia_id": "D15:19", - "text": "Woohoo! Tomorrow's opening will be so much fun. Can't wait for it - and for you to be there!" - }, - { - "speaker": "Gina", - "dia_id": "D15:20", - "text": "Can't wait too!" - }, - { - "speaker": "Jon", - "dia_id": "D15:21", - "text": "Definitely! Let's make tomorrow unforgettable, Gina. See you there! Bye!" - }, - { - "speaker": "Gina", - "dia_id": "D15:22", - "text": "See you tomorrow. Bye!" - } - ], - "session_16_date_time": "2:15 pm on 21 June, 2023", - "session_16": [ - { - "speaker": "Gina", - "img_url": [ - "https://www.staycoldapparel.com/cdn/shop/files/Foto28.09.23_122631.jpg" - ], - "blip_caption": "a photo of a woman in a black hoodie posing for a picture", - "query": "online clothing store logo fashion unique pieces", - "dia_id": "D16:1", - "text": "Hey Jon, what's been up? Some pretty cool stuff happened since we talked. I have acquired some new unique pieces for my store." - }, - { - "speaker": "Jon", - "dia_id": "D16:2", - "text": "Congrats on your store, Gina! Happy for you! It looks sick - is it a unique piece you're selling?" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a hoodie with a camouflage print on it", - "dia_id": "D16:3", - "text": "Thanks! This hoodie isn't for sale, it's from my own collection. I made a limited edition line last week to show off my style and creativity - it was tough but worth it!" - }, - { - "speaker": "Jon", - "dia_id": "D16:4", - "text": "What gave you the idea?" - }, - { - "speaker": "Gina", - "dia_id": "D16:5", - "text": "This design reminds me of the grit it takes to stand out and face challenges." - }, - { - "speaker": "Jon", - "dia_id": "D16:6", - "text": "That's awesome, Gina! Yesterday I chose to go to networking events to make things happen. It's been tough but I'm staying determined and focused." - }, - { - "speaker": "Gina", - "dia_id": "D16:7", - "text": "Way to go, Jon! Attending those networking events takes guts and drive. Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D16:8", - "text": "Thanks! It's been tough going since I lost my job, but I'm sure investing my time in my business will pay off eventually. I really appreciate your help." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a notepad with a pen and a pen on it", - "dia_id": "D16:9", - "text": "No worries, Jon! You got this! Let me know if you need anything." - }, - { - "speaker": "Jon", - "dia_id": "D16:10", - "text": "Your help matters to me. I am writing all my plans down." - }, - { - "speaker": "Gina", - "dia_id": "D16:11", - "text": "Nice work! Tracking your plans and goals is key. It's like a picture of all your progress." - }, - { - "speaker": "Jon", - "dia_id": "D16:12", - "text": "Thanks, Gina! Seeing my goals written down on paper really helps keep me motivated and focused on what I have to do. I know it won't be easy, but I'm sure it'll pay off. Thanks for the support!" - }, - { - "speaker": "Gina", - "img_url": [ - "https://i.redd.it/b1sgp694awda1.jpg" - ], - "blip_caption": "a photo of a sign that says never give up never give up never", - "query": "never give up sign", - "dia_id": "D16:13", - "text": "No worries, Jon! When things get rough, keep persevering and keep working hard. You'll get there! Don't quit!" - }, - { - "speaker": "Jon", - "dia_id": "D16:14", - "text": "Thanks, Gina! That sign reminds me to never give up, however hard things get. I'll keep going!" - }, - { - "speaker": "Gina", - "dia_id": "D16:15", - "text": "Believe in yourself and keep going. You can do it!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a bulletin board with pictures of people and words", - "dia_id": "D16:16", - "text": "Thanks! I'm feeling confident and won't give up. Your support means a ton to me." - } - ], - "session_17_date_time": "1:25 pm on 9 July, 2023", - "session_17": [ - { - "speaker": "Gina", - "blip_caption": "a photo of a mannequin in a room with a wood wall", - "dia_id": "D17:1", - "text": "Hey Jon! Long time no chat! How's the dance studio? Last week was wild, I got noticed by fashion editors and it's been amazing but kinda scary. Everything's exciting but it's a lot of pressure to keep going up!" - }, - { - "speaker": "Jon", - "dia_id": "D17:2", - "text": "Hey Gina! Congrats on the fashion editors reach-out, that's awesome! Dance practice has been fun and exhausting. I'm gonna stay determined and make my own path by going full-time with my biz idea." - }, - { - "speaker": "Gina", - "dia_id": "D17:3", - "text": "Just remember that sometimes stumbling blocks can be opened doors. Keep going!" - }, - { - "speaker": "Jon", - "dia_id": "D17:4", - "text": "Thanks! Your support and encouragement means a lot. Losing my job was a bummer, but it pushed me to take the plunge and go for my biz dreams. Started to learn all these marketing and analytics tools to push the biz forward today. It's been tricky, but I'm up for the challenge and I'm gonna make this work!" - }, - { - "speaker": "Gina", - "dia_id": "D17:5", - "text": "Go get 'em, Jon!" - }, - { - "speaker": "Jon", - "dia_id": "D17:6", - "text": "I'm also excited to guide and mentor aspiring dancers on their dreams." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a drawing of a couple dancing", - "dia_id": "D17:7", - "text": "Wow, Jon! That's awesome. Loving what you do and bringing joy to others is so rewarding. You're definitely the perfect mentor & guide. Your positivity and determination will make your dance studio a hit!" - }, - { - "speaker": "Jon", - "dia_id": "D17:8", - "text": "Thanks, Gina - really appreciate your words and encouragement! Dance has the power to bring us together and create sweet moments. Moments like this remind me why I'm chasing my dream and keep me pushing through any struggles." - }, - { - "speaker": "Gina", - "dia_id": "D17:9", - "text": "Take comfort in knowing you've got a solid community cheering you on, me included. Keep on pushing!" - }, - { - "speaker": "Jon", - "dia_id": "D17:10", - "text": "Feeling supported by all of you means so much. It gives me the oomph to keep chasing my dreams. Your faith in me is priceless - I won't let you down!" - }, - { - "speaker": "Gina", - "dia_id": "D17:11", - "text": "Don't let anything stop you. You have potential!" - }, - { - "speaker": "Jon", - "dia_id": "D17:12", - "text": "Thanks, Gina! Your faith in me is a real boost. I'm gonna make my dreams come true!" - }, - { - "speaker": "Gina", - "dia_id": "D17:13", - "text": "Keep pushing and you'll get there. Your dreams are so close!" - }, - { - "speaker": "Jon", - "dia_id": "D17:14", - "text": "Thanks, Gina! I won't quit, even when it's hard. I'm gonna make it!" - }, - { - "speaker": "Gina", - "dia_id": "D17:15", - "text": "You got this, Jon! Don't let the bumps in the road bring you down. Keep going and make your dreams a reality! I'm rooting for you!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a chalkboard with a quote written on it", - "dia_id": "D17:16", - "text": "Thanks, Gina! Your belief in me means the world. I'm not gonna let anything or anyone stop me. I'll keep pushing and make my dreams come true. Thanks for being a great friend. You rock!" - }, - { - "speaker": "Gina", - "dia_id": "D17:17", - "text": "Hey Jon, glad I could help! Always here to cheer you on." - }, - { - "speaker": "Jon", - "img_url": [ - "https://i.redd.it/1jjaus9f0sab1.jpg" - ], - "blip_caption": "a photo of a drawing of a smiley face floating in the water", - "query": "childhood drawing soaring sky", - "dia_id": "D17:18", - "text": "Thanks! Glad that you are on my side." - }, - { - "speaker": "Gina", - "dia_id": "D17:19", - "text": "Sure, see ya. Bye!" - }, - { - "speaker": "Jon", - "dia_id": "D17:20", - "text": "Bye!" - }, - { - "speaker": "Gina", - "dia_id": "D17:21", - "text": ";)" - } - ], - "session_18_date_time": "5:44 pm on 21 July, 2023", - "session_18": [ - { - "speaker": "Gina", - "dia_id": "D18:1", - "text": "Hey Jon! Long time no talk! Last week, I built a new website for customers to make orders. It's been a wild ride but I'm loving it. What's up with you? How's the dance studio?" - }, - { - "speaker": "Jon", - "dia_id": "D18:2", - "text": "Hey Gina, congrats on the clothing store! The dance studio is on tenuous grounds right now, but I'm staying positive. I got a temp job to help cover expenses while I look for investors. It's tough, but I'm sure it'll be worth it." - }, - { - "speaker": "Gina", - "dia_id": "D18:3", - "text": "Thanks, Jon! Appreciate the kind words. Sorry to hear about the studio, but glad to see the positivity. Not easy facing setbacks but I believe in you. Finding investors can be tough, but you've got the passion and experience to make it happen. Rome wasn't built in a day so keep pushing on!" - }, - { - "speaker": "Jon", - "dia_id": "D18:4", - "text": "Thanks for the support. Running a business isn't easy, but I'm determined to make it work. How have you tackled challenges in your business? Got any advice?" - }, - { - "speaker": "Gina", - "dia_id": "D18:5", - "text": "I've had some tough times with my business, Jon. Sourcing trendy pieces for my store was a big hurdle. I had to do a lot of research and networking. My advice? Don't be scared to reach out to people in your field for help and contacts. Networking was a lifesaver for me and opened me up to amazing products that I might not have found otherwise." - }, - { - "speaker": "Jon", - "dia_id": "D18:6", - "img_url": [ - "https://live.staticflickr.com/7284/16241297914_14ea605e4b_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a group of people standing in a room", - "text": "Awesome advice! Lately I've been networking and it's gotten me some good stuff. Really can't beat what connections can do. Check this pic I got from the last networking event!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a clothing store with a wall of pictures and clothes", - "dia_id": "D18:7", - "text": "Nice one, Jon! Networking really pays off. Connecting with like-minded people is key. How was the event?" - }, - { - "speaker": "Jon", - "img_url": [ - "https://cress.gigsalad.com/s3/attachments/61/02/47/610247e1e1a92f951cf413a4cdedcde0.jpg" - ], - "blip_caption": "a photo of a man signing a card at a table", - "query": "crowded networking event people mingling business cards", - "dia_id": "D18:8", - "text": "Thanks! The event was awesome. I met some investors and got some good advice. The energy was really motivating, it gave me a boost to go after my goals." - }, - { - "speaker": "Gina", - "dia_id": "D18:9", - "text": "Wow, Jon! Congrats on the successful night! What are your plans now with the advice you got?" - }, - { - "speaker": "Jon", - "dia_id": "D18:10", - "text": "Taking your advice, I'm sprucing up my biz plan and tweaking my pitch to investors. I'm also working on an online platform to show off the dance studio's stuff." - }, - { - "speaker": "Gina", - "dia_id": "D18:11", - "text": "Sounds like a great plan, Jon! An online platform can really show off your studio and get investors. Need help with anything?" - }, - { - "speaker": "Jon", - "dia_id": "D18:12", - "text": "Thanks, Gina! Appreciate the offer. Need help with marketing strategies - any advice on reaching my target audience and raising awareness for the dance studio?" - }, - { - "speaker": "Gina", - "dia_id": "D18:13", - "text": "Yeah Jon, marketing is key for getting your dance studio noticed. Instagram and TikTok can help you reach a younger crowd. Posting dance clips or content related to dance can help. You could also collaborate with local influencers or dance communities. I could help you with making content or even managing your accounts if you want." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a room with a mirror and a desk", - "dia_id": "D18:14", - "text": "Sounds great. I'd really appreciate your help with making content and managing my social media. Let's get together and make the dance studio look awesome!" - }, - { - "speaker": "Gina", - "dia_id": "D18:15", - "text": "Let's create some cool content and manage your social media accounts." - }, - { - "speaker": "Jon", - "dia_id": "D18:16", - "text": "Thanks for the support. You rock!" - }, - { - "speaker": "Gina", - "dia_id": "D18:17", - "text": "Thanks, Jon! You're awesome. Let's get to work and make your studio shine!" - }, - { - "speaker": "Jon", - "dia_id": "D18:18", - "text": "Definitely, Gina! Let's make our collaboration awesome and bring some dance magic to the world. Can't wait to see what we can do together!" - }, - { - "speaker": "Gina", - "dia_id": "D18:19", - "text": "Definitely, Jon! I'm pumped to collaborate with you and make some sweet moves. Together, we can make a difference and show the world what we can do. Let's go for it!" - }, - { - "speaker": "Jon", - "dia_id": "D18:20", - "text": "Yeah, Gina! We'll rock the dance floor and teach others to chase their dreams. Let's go for it and make an impact!" - }, - { - "speaker": "Gina", - "dia_id": "D18:21", - "text": "Yeah Jon! Let's make a difference and show 'em what we got. We can do amazing things together!" - }, - { - "speaker": "Jon", - "dia_id": "D18:22", - "text": "Thanks for having my back." - } - ], - "session_19_date_time": "6:46 pm on 23 July, 2023", - "session_19": [ - { - "speaker": "Jon", - "dia_id": "D19:1", - "text": "Hey Gina! We haven't talked in a few days. Been rehearsing hard and working on business plans. It's been stressful, but dancing has kept me going." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a group of dancers on a stage with their arms in the air", - "dia_id": "D19:2", - "text": "Hey Jon! Remember, just do it! You should get to the point where anyone else would quit and you're not going to stop there. No, what are you waiting for? Do it! Just do it!" - }, - { - "speaker": "Jon", - "dia_id": "D19:3", - "text": "Ha, ha! Thanks, Gina. Sounds familiar, who do those words belong to?" - }, - { - "speaker": "Gina", - "dia_id": "D19:4", - "text": "It's Shia Labeouf!" - }, - { - "speaker": "Jon", - "dia_id": "D19:5", - "text": "Ahhahha, really!? Yea, that definitely him." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of three girls in ballet costumes sitting on a desk", - "dia_id": "D19:6", - "text": "Hah, yeah!) But really having a creative space for dancers is so important. Last Friday at dance class with a group of friends I felt it. Your studio will be a go-to spot for self-expression. Keep up the good work and don't forget your passion for dance." - }, - { - "speaker": "Jon", - "dia_id": "D19:7", - "text": "Thanks, Gina! Your words of encouragement keep me motivated. Can't wait 'til my studio starts welcoming dancers of all ages and backgrounds!" - }, - { - "speaker": "Gina", - "dia_id": "D19:8", - "text": "I'm so happy to see my words motivating you, Jon. <3" - }, - { - "speaker": "Jon", - "dia_id": "D19:9", - "text": "Thanks a ton, Gina! Your help and encouragement mean a lot. Your support will help me make it happen." - }, - { - "speaker": "Gina", - "dia_id": "D19:10", - "text": "You're welcome, Jon! I'm here to support you. Every step's getting you closer to your dream. Never give up! You're doing great." - }, - { - "speaker": "Jon", - "dia_id": "D19:11", - "text": "Thanks, Gina! I won't quit. I'm gonna keep going, whatever comes my way." - }, - { - "speaker": "Gina", - "dia_id": "D19:12", - "text": "Remember Jon, Just do it!" - }, - { - "speaker": "Jon", - "dia_id": "D19:13", - "text": "Ah ha ha, yeah, JUST DOING IT!" - }, - { - "speaker": "Gina", - "dia_id": "D19:14", - "text": "That's the spirit! Bye!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Jon": [ - "Jon loses his job as a banker.", - "Jon begins planning for his own business venture." - ], - "Gina": [ - "Gina loses her job at Door Dash." - ], - "date": "20 January, 2023" - }, - "events_session_2": { - "Jon": [ - "Jon returns from a trip to Paris." - ], - "Gina": [ - "Gina orders advertising to promote her store." - ], - "date": "29 January, 2023" - }, - "events_session_3": { - "Jon": [], - "Gina": [ - "Gina reaches out to potential wholesalers." - ], - "date": "1 February, 2023" - }, - "events_session_4": { - "Jon": [ - "Jon puts in a great deal of effort into his own business venture despite the difficulties.", - "Jon starts rehearsing for an upcoming dance competition." - ], - "Gina": [], - "date": "4 February, 2023" - }, - "events_session_5": { - "Jon": [ - "Jon puts up a performance showcasing his dance moves at a local festival." - ], - "Gina": [ - "Gina works with an artist to acquire a new fashion piece for her store." - ], - "date": "8 February, 2023" - }, - "events_session_6": { - "Jon": [ - "Jon joins a gym to stay fit while pursuing his business venture." - ], - "Gina": [ - "Gina opens her online clothing store" - ], - "date": "16 March, 2023" - }, - "events_session_7": { - "Jon": [], - "Gina": [], - "date": "23 March, 2023" - }, - "events_session_8": { - "Jon": [ - "Jon shuts down his bank account to help his business grow." - ], - "Gina": [ - "Gina introduces offers on her store to attract new customers." - ], - "date": "3 April, 2023" - }, - "events_session_9": { - "Jon": [], - "Gina": [], - "date": "9 April, 2023" - }, - "events_session_10": { - "Jon": [ - "Jon visits a fair to get more exposure for his dance studio.", - "Jon begins to understand the importance of confidence in running a successful business." - ], - "Gina": [], - "date": "25 April, 2023" - }, - "events_session_11": { - "Jon": [], - "Gina": [ - "Gina gets an interview for a design internship related to her store." - ], - "date": "11 May, 2023" - }, - "events_session_12": { - "Jon": [], - "Gina": [ - "Gina is accepted for the design internship and starts a part-time job in the fashion department of an international company." - ], - "date": "27 May, 2023" - }, - "events_session_13": { - "Jon": [], - "Gina": [ - "Gina develops a video presentation to teach how to style her fashion pieces." - ], - "date": "13 June, 2023" - }, - "events_session_14": { - "Jon": [ - "Jon receives mentorship from an experienced businessman on how to promote his venture." - ], - "Gina": [], - "date": "16 June, 2023" - }, - "events_session_15": { - "Jon": [ - "Jon holds an official opening night for his dance studio." - ], - "Gina": [], - "date": "19 June, 2023" - }, - "events_session_16": { - "Jon": [ - "Jon decides to attend networking events to make connections for his business venture." - ], - "Gina": [ - "Gina creates unique limited-edition pieces to show off her skills." - ], - "date": "21 June, 2023" - }, - "events_session_17": { - "Jon": [ - "Jon starts to learn how to use modern tools and software for marketing and analytics." - ], - "Gina": [ - "Gina starts to get recognized by fashion editors." - ], - "date": "9 July, 2023" - }, - "events_session_18": { - "Jon": [ - "Jon takes up a temporary job to cover his expenses while waiting for investors.", - "Jon starts working on an online platform to showcase his dance studio." - ], - "Gina": [ - "Gina creates a new website for her customers to make orders." - ], - "date": "21 July, 2023" - }, - "events_session_19": { - "Jon": [], - "Gina": [ - "Gina takes a dance class with a group of friends." - ], - "date": "23 July, 2023" - } - }, - "observation": { - "session_1_observation": { - "Gina": [ - [ - "Gina lost her job at Door Dash during the month of the conversation.", - "D1:3" - ], - [ - "Gina used to compete in dance competitions and shows, winning first place in a regional competition at the age of fifteen.", - "D1:17" - ], - [ - "Gina's favorite dance style is contemporary.", - "D1:9" - ] - ], - "Jon": [ - [ - "Jon lost his job as a banker the day before the conversation.", - "D1:2" - ], - [ - "Jon is starting his own dance studio due to his passion for dancing.", - "D1:4" - ], - [ - "Jon's favorite dance style is contemporary.", - "D1:8" - ], - [ - "Jon practices various dances with a small group, including contemporary and hip-hop, and is working on choreography for a nearby festival.", - "D1:24" - ] - ] - }, - "session_2_observation": { - "Gina": [ - [ - "Gina launched an ad campaign for her clothing store in hopes of growing the business.", - "D2:1" - ], - [ - "Gina started her own clothing store and finds taking risks scary but rewarding.", - "D2:1" - ], - [ - "Gina appreciates seeing her vision for the store come to life.", - "D2:3" - ], - [ - "Gina has been to Rome once.", - "D2:5" - ], - [ - "Gina believes that good flooring helps avoid injuries and makes dancing more enjoyable.", - "D2:7" - ], - [ - "Gina appreciates Jon's determination to make his dance studio work.", - "D2:11" - ] - ], - "Jon": [ - [ - "Jon is looking for the ideal spot for his dance studio and is considering features like size, natural light, and flooring.", - "D2:4" - ], - [ - "Jon visited Paris recently", - "D2:4" - ], - [ - "Jon is looking for Marley flooring for his dance studio due to its grip, movement, durability, and cleanliness.", - "D2:8" - ], - [ - "Jon finds the process of setting up his dance studio a mix of exciting and nerve-wracking but is determined to make it work.", - "D2:10" - ], - [ - "Jon believes in himself and is grateful for Gina's support.", - "D2:12" - ] - ] - }, - "session_3_observation": { - "Jon": [ - [ - "Jon is following his passion for dance and searching for a place to open his dance studio.", - "D3:1" - ] - ], - "Gina": [ - [ - "Gina is enthusiastic about expanding her clothing store and successfully got a positive response from a wholesaler.", - "D3:2" - ], - [ - "Gina designed her clothing store space to be cozy and inviting for her customers.", - "D3:4" - ], - [ - "Gina chose furniture and decor for her store that match her style and make customers feel comfortable.", - "D3:6" - ], - [ - "Gina's goal is to create a comfortable and inviting shopping experience for her customers.", - "D3:8" - ] - ] - }, - "session_4_observation": { - "Jon": [ - [ - "Jon is working hard on his business despite facing obstacles.", - "D4:3" - ], - [ - "Jon is determined to make his business successful and reach his dreams.", - "D4:5" - ], - [ - "Jon is searching for a dance studio location and is determined to find the right spot.", - "D4:9" - ], - [ - "Jon is working on new dance routines and rehearsing hard for an upcoming show.", - "D4:11" - ], - [ - "Jon is passionate about dancing and it brings him joy and fulfillment.", - "D4:11" - ], - [ - "Jon is preparing for a dance competition near him next month to showcase his skills.", - "D4:13" - ] - ], - "Gina": [ - [ - "Gina's store is doing great and it's a wild ride.", - "D4:2" - ], - [ - "Gina encourages Jon to keep pushing, assuring him he'll reach his dreams.", - "D4:4" - ], - [ - "Gina supports Jon through setbacks and encourages him not to give up.", - "D4:6" - ], - [ - "Gina is supportive of Jon and assures him that setbacks can be overcome.", - "D4:6" - ], - [ - "Gina expresses full support for Jon and encourages him not to give up.", - "D4:6" - ], - [ - "Gina encourages Jon to hang in there while searching for the perfect dance studio location.", - "D4:10" - ], - [ - "Gina compliments Jon's talent and expresses confidence in his success in the upcoming dance competition.", - "D4:14" - ] - ] - }, - "session_5_observation": { - "Gina": [ - [ - "Gina owns a store where she sells fashion pieces.", - "D5:1" - ], - [ - "Gina has an online store and recently teamed up with a local artist for some cool designs.", - "D5:5" - ], - [ - "Gina got a tattoo a few years ago that stands for freedom and a reminder to follow her passions and express herself.", - "D5:15" - ] - ], - "Jon": [ - [ - "Jon is a dancer who runs his own dance studio.", - "D5:4" - ], - [ - "Jon left his secure 9-5 job as a banker to pursue his passion for dancing and turn it into a business.", - "D5:10" - ], - [ - "Jon finds joy and passion in dancing.", - "D5:2" - ], - [ - "Jon believes taking risks is essential for success and transformation.", - "D5:8" - ], - [ - "Jon juggles between running his dance studio and pursuing business goals, finding it rewarding and energizing.", - "D5:20" - ] - ] - }, - "session_6_observation": { - "Jon": [ - [ - "Jon started hitting the gym last week to stay on track with a venture.", - "D6:1" - ], - [ - "Jon lost his job at Door Dash.", - "D6:4" - ], - [ - "Jon is starting his own business after losing his job and finds it tough but fulfilling.", - "D6:11" - ], - [ - "Jon finds Gina's success inspiring and her determination awesome.", - "D6:13" - ], - [ - "Jon feels motivated by facing similar challenges to Gina and considers her like a partner in this journey.", - "D6:15" - ], - [ - "Jon is encouraged by Gina's journey and finds it inspiring.", - "D6:19" - ] - ], - "Gina": [ - [ - "Gina lost her job at Door Dash.", - "D6:4" - ], - [ - "Gina opened an online clothes store which she had been dreaming of for a while.", - "D6:6" - ], - [ - "Gina is passionate about fashion trends and unique pieces, and she blended her love for dance and fashion in starting the online store.", - "D6:8" - ], - [ - "Gina finds starting and running her own business to be a rollercoaster ride with its ups and downs.", - "D6:12" - ], - [ - "Gina feels inspired by Jon's determination and success, and his words of encouragement mean a lot to her.", - "D6:14" - ], - [ - "Gina sees Jon as someone on a different path but appreciates having him as someone to root for and support each other.", - "D6:16" - ], - [ - "Gina values having someone like Jon to back her up and encourages each other to chase their dreams and achieve great things together.", - "D6:18" - ] - ] - }, - "session_7_observation": { - "Jon": [ - [ - "Jon has a dance studio business where he helps his students learn and reach their goals.", - "D7:7" - ] - ], - "Gina": [ - [ - "Gina runs an online clothing store and has been keeping up with fashion trends to offer the best pieces to customers.", - "D7:4" - ], - [ - "Gina values building relationships and creating a strong brand image for her store.", - "D7:6" - ] - ] - }, - "session_8_observation": { - "Jon": [ - [ - "Jon had to shut down his bank account for his business.", - "D8:1" - ], - [ - "Jon is handling changes positively and looking ahead despite facing challenges.", - "D8:3" - ], - [ - "Jon is expanding his dance studio's social media presence and offering workshops and classes to local schools and centers.", - "D8:13" - ], - [ - "Jon is hosting a dance competition next month to showcase local talent and bring more attention to his studio.", - "D8:13" - ] - ], - "Gina": [ - [ - "Gina got new offers and promotions for her online store to attract new customers.", - "D8:4" - ], - [ - "Dance is Gina's stress relief and fashion fuels her creativity.", - "D8:6" - ], - [ - "Gina combines her passion for dance and fashion to show creativity and share love with others.", - "D8:8" - ], - [ - "Gina is focused on building her customer base for her store by working with fashion bloggers, influencers, and running more ads.", - "D8:12" - ], - [ - "Gina plans to work with fashion bloggers and influencers to get more attention for her store and do more ads to reach more people.", - "D8:12" - ] - ] - }, - "session_9_observation": { - "Jon": [ - [ - "Jon is turning his passion for dance into a business by opening a dance studio.", - "D9:1" - ], - [ - "Jon's determination to make the dance studio work was fueled by losing his job.", - "D9:3" - ], - [ - "Jonathan finds happiness in dancing and expresses himself through it.", - "D9:5" - ], - [ - "Jon used to be scared of what people would think, but now prioritizes his own happiness.", - "D9:7" - ], - [ - "Jon loved taking dance lessons with friends when he was younger.", - "D9:9" - ], - [ - "Jon values the special meaning dance has for himself and Gina, as a way to express themselves.", - "D9:9" - ], - [ - "Jon is determined to keep pushing and working hard for his dance business.", - "D9:13" - ] - ], - "Gina": [ - [ - "Gina admires Jon for turning his passion for dance into a business.", - "D9:2" - ], - [ - "Gina acknowledges that tough times can lead to great things and supports Jon in going after his dreams.", - "D9:4" - ], - [ - "Gina loves spending time in the dance studio and values dance as a way to express oneself and find happiness.", - "D9:8" - ], - [ - "Gina has a trophy from a dance contest as a reminder of the hard work, dedication, and joy dance brings.", - "D9:10" - ], - [ - "Gina encourages Jon to keep up his hard work and achievements.", - "D9:12" - ] - ] - }, - "session_10_observation": { - "Jon": [ - [ - "Jon has a studio business that he showed off at a fair recently.", - "D10:1" - ], - [ - "Jon considers having confidence in oneself important for business success.", - "D10:1" - ], - [ - "Jon is having trouble with his business project and seeks advice on staying motivated.", - "D10:5" - ], - [ - "Jon is feeling low on confidence lately and seeks advice from Gina on staying confident in business.", - "D10:7" - ], - [ - "Jon is talented and passionate about dance, as per Gina's encouragement.", - "D10:10" - ] - ], - "Gina": [ - [ - "Gina started her own online clothing store after losing her job to take control of her destiny.", - "D10:2" - ], - [ - "Gina finds setbacks tough but rewarding for growth.", - "D10:4" - ], - [ - "Gina advises Jon to focus on the big goal, get support, and dance it out to stay motivated during tough times in business.", - "D10:6" - ], - [ - "Gina gives Jon tips to stay confident in business by reminding of successes and progress, having a good support system, and having faith in oneself.", - "D10:8" - ], - [ - "Gina encourages Jon to believe in himself, tackle obstacles, and keep shining in pursuing his dreams.", - "D10:10" - ] - ] - }, - "session_11_observation": { - "Jon": [ - [ - "Jon lost his job but used it as an opportunity to start his dream business: a dance studio.", - "D11:3" - ], - [ - "Dance has been Jon's stress-buster since childhood.", - "D11:5" - ], - [ - "Jon sees dancing as where he feels most alive and considers it a must-have in his life.", - "D11:7" - ], - [ - "Having his own dance studio and teaching others is a dream come true for Jon.", - "D11:9" - ], - [ - "Jon hopes to create a place for people to dance and express themselves, as it's been a dream of his.", - "D11:11" - ] - ], - "Gina": [ - [ - "Gina finds dancing as her stress fix and something that makes her worries vanish.", - "D11:6" - ], - [ - "Dance is essential to Gina, and she compares it to being like air.", - "D11:8" - ], - [ - "Gina had an interview for a design internship and found it to be great.", - "D11:14" - ], - [ - "Gina is proud of Jon and is excited about his dance studio project.", - "D11:20" - ] - ] - }, - "session_12_observation": { - "Gina": [ - [ - "Gina got accepted for a fashion internship at a part-time position in the fashion department of an international company.", - "D12:1" - ] - ], - "Jon": [ - [ - "Jon is reading the book \"The Lean Startup\" and hoping to get tips for his business.", - "D12:6" - ], - [ - "Jon is working on a business plan and looking for investors for his project.", - "D12:12" - ] - ] - }, - "session_13_observation": { - "Jon": [ - [ - "Jon is prepping for his own dance studio.", - "D13:3" - ], - [ - "Jon wants his dance studio to be a place of support and encouragement for all dancers.", - "D13:5" - ], - [ - "Jon is offering one-on-one mentoring and training to help dancers reach their full potential.", - "D13:7" - ], - [ - "Jon uses a mentor, goal setting, tracking achievements, and finding areas for improvement to stay organized and motivated.", - "D13:11" - ], - [ - "Jon uses a whiteboard to stay on track, visualize goals, and reward successes.", - "D13:13" - ], - [ - "Jon color-codes achievements to easily track progress and stay motivated.", - "D13:15" - ], - [ - "Jon feels that support and guidance can help dancers shine.", - "D13:9" - ], - [ - "Jon uses the system of goal setting, tracking achievements, and visualization to stay motivated and focused.", - "D13:13" - ] - ], - "Gina": [ - [ - "Gina has developed a video presentation to teach how to style her fashion pieces.", - "D13:4" - ], - [ - "Gina highlights the importance of staying positive and believes in supporting others' dreams.", - "D13:20" - ], - [ - "Gina values mentorship, having had a mentor herself when learning how to dance.", - "D13:8" - ], - [ - "Gina encourages Jon to keep pushing for his dreams and not give up.", - "D13:22" - ], - [ - "Gina supports Jon and believes in him.", - "D13:1" - ] - ] - }, - "session_14_observation": { - "Jon": [ - [ - "Jon got mentored by a business person, which was inspiring for him.", - "D14:1" - ], - [ - "Jon is working on promoting his business.", - "D14:3" - ], - [ - "Jon posted dance videos on social media which created a stir.", - "D14:5" - ], - [ - "Jon is passionate about dance and loves performing.", - "D14:15" - ], - [ - "Jon finds Gina's encouragement motivating and it helps him keep going.", - "D14:13" - ] - ], - "Gina": [ - [ - "Gina is working on growing the customer base for her online store.", - "D14:2" - ], - [ - "Gina lost her job, started an online clothing store, and enjoys being her own boss.", - "D14:8" - ], - [ - "Gina advises Jon to stay passionate, focused, resilient, and open to learning for entrepreneurship.", - "D14:10" - ], - [ - "Gina believes staying resilient and focused is key for entrepreneurs.", - "D14:12" - ], - [ - "Gina shares a pic of herself dancing and mentions the tough but worth it journey.", - "D14:14" - ], - [ - "Gina encourages Jon to keep going after his dreams.", - "D14:16" - ] - ] - }, - "session_15_observation": { - "Jon": [ - [ - "Jon recently took a short trip to Rome to clear his mind.", - "D15:1" - ], - [ - "Jon is working on opening a dance studio, with the official opening night being tomorrow.", - [ - "D15:3", - "D15:5" - ] - ] - ], - "Gina": [ - [ - "Gina supports Jon's opening of the dance studio and expresses excitement for the launch.", - "D15:6" - ], - [ - "Gina mentions she loves being around friends and having a great time.", - "D15:16" - ] - ] - }, - "session_16_observation": { - "Gina": [ - [ - "Gina has a store where she acquired new unique pieces.", - "D16:1" - ], - [ - "Gina made a limited edition line of clothing last week to show off her style and creativity.", - "D16:3" - ], - [ - "Gina's design in her clothing line reminds her of the grit needed to stand out and face challenges.", - "D16:5" - ] - ], - "Jon": [ - [ - "Jon has been attending networking events to make things happen after losing his job.", - "D16:6" - ], - [ - "Jon is investing his time in his business and believes it will eventually pay off.", - "D16:8" - ], - [ - "Jon is writing down all his plans and goals to stay organized and motivated.", - "D16:10" - ], - [ - "Jon finds that writing down his goals helps him stay motivated and focused.", - "D16:12" - ] - ] - }, - "session_17_observation": { - "Gina": [ - [ - "Gina got noticed by fashion editors last week.", - "D17:1" - ], - [ - "Gina believes that stumbling blocks can sometimes be opened doors.", - "D17:3" - ], - [ - "Gina supports and encourages Jon in his business dreams.", - "D17:4" - ], - [ - "Gina considers Jon to be the perfect mentor and guide for aspiring dancers.", - "D17:7" - ], - [ - "Gina is part of a solid community that cheers Jon on.", - "D17:9" - ], - [ - "Gina believes Jon has potential and encourages him not to let anything stop him.", - "D17:11" - ], - [ - "Gina roots for Jon to keep going and make his dreams a reality, even when facing bumps in the road.", - "D17:15" - ] - ], - "Jon": [ - [ - "Jon mentioned losing his job but is determined to pursue his business dreams.", - "D17:4" - ], - [ - "Jon started learning marketing and analytics tools to push his business forward.", - "D17:4" - ], - [ - "Jon aspires to guide and mentor aspiring dancers on their dreams.", - "D17:6" - ], - [ - "Jon believes dance has the power to bring people together and create sweet moments.", - "D17:8" - ], - [ - "Jon values the support and encouragement from Gina and the community in pursuing his dreams.", - "D17:10" - ], - [ - "Jon is determined not to let anything or anyone stop him from making his dreams come true.", - "D17:16" - ], - [ - "Jon expresses gratitude for Gina's belief in him and considers her a great friend.", - "D17:16" - ] - ] - }, - "session_18_observation": { - "Gina": [ - [ - "Gina built a new website for customers to make orders for her clothing store last week.", - "D18:1" - ], - [ - "Gina faced challenges in her business, particularly in sourcing trendy pieces which required research and networking.", - "D18:5" - ], - [ - "Gina advised Jon not to be scared to reach out to people in his field for help and contacts, stressing the importance of networking.", - "D18:5" - ], - [ - "Gina met some investors and got good advice at a recent networking event.", - "D18:8" - ], - [ - "Gina offered to help Jon with making content and managing his social media accounts for his dance studio.", - "D18:13" - ], - [ - "Gina expressed excitement to collaborate with Jon on making the dance studio shine and making a difference in the world.", - "D18:17" - ] - ], - "Jon": [ - [ - "Jon's dance studio is on tenuous grounds, but he is staying positive and looking for investors.", - "D18:2" - ], - [ - "Jon is networking and updating his business plan and pitch to investors based on advice received.", - "D18:10" - ], - [ - "Jon is working on an online platform to showcase the dance studio's offerings.", - "D18:10" - ], - [ - "Jon expressed a need for marketing strategies, seeking advice on reaching his target audience and raising awareness for the dance studio.", - "D18:12" - ], - [ - "Jon plans to collaborate with Gina on making content and managing his social media accounts to promote the dance studio.", - "D18:14" - ], - [ - "Jon expressed gratitude for Gina's support.", - "D18:22" - ] - ] - }, - "session_19_observation": { - "Jon": [ - [ - "Jon has been rehearsing hard and working on business plans.", - "D19:1" - ], - [ - "Dancing has kept Jon going during stressful times.", - "D19:1" - ], - [ - "Jon is working on opening a studio for dancers of all ages and backgrounds.", - "D19:7" - ] - ], - "Gina": [ - [ - "Gina encouraged Jon to keep up the good work and not forget his passion for dance.", - "D19:6" - ], - [ - "Gina is supportive of Jon's dream of opening a dance studio.", - "D19:10" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Gina and Jon met at 4:04 pm on 20 January, 2023. Jon lost his job as a banker and planned to start a dance studio because of his passion for dancing. Gina also lost her job at Door Dash. They both shared a love for dance, with Jon's favorite style being contemporary. They planned to attend a dance class together. Jon showed Gina a photo of his dance crew winning a competition. Gina revealed her experience of winning first place with a contemporary piece in a dance competition. Jon mentioned rehearsing with a group for various dance styles and upcoming projects, including a performance at a festival. Gina complimented the group's grace and expressed excitement for their festival performance. They both shared enthusiasm for the upcoming events.", - "session_2_summary": "Gina and Jon had a conversation at 2:32 pm on 29 January, 2023. Gina mentioned launching an ad campaign for her clothing store, excited about the risks and rewards of her business. Jon complimented her hard work and discussed his search for a suitable location for his dance studio, mentioning finding a spot with great natural light. They discussed the importance of features like flooring for dance studios, with Jon preferring Marley flooring. Gina encouraged Jon to believe in himself and take breaks when needed. They both expressed determination to chase their dreams and achieve success.", - "session_3_summary": "At 12:48 am on 1 February 2023, Jon shared with Gina his determination to open a dance studio, while Gina revealed her success in expanding her clothing store. Gina showed Jon a picture of her new store, which he admired, and they discussed the design elements. Gina aimed to create a cozy and inviting space for her customers. They both emphasized the importance of creating a special experience for customers. Jon encouraged Gina to continue her hard work, and she reciprocated the support for his dance studio venture. They both expressed confidence in reaching their goals with hard work and determination.", - "session_4_summary": "Jon and Gina, at 10:43 am on 4 February, 2023, discussed Jon's business endeavors. While Jon faced obstacles, Gina encouraged him to persevere, mentioning setbacks as opportunities for comebacks. Jon mentioned his passion for dancing and preparation for an upcoming competition, with Gina supporting him wholeheartedly. They exchanged words of encouragement, with Jon expressing gratitude for Gina's help and Gina assuring him that his hard work and dedication will pay off. Gina emphasized belief in oneself, encouraging Jon to pursue his dreams relentlessly.", - "session_5_summary": "Gina and Jon caught up at 9:32 am on 8 February, 2023. Gina shared about a new fashion piece for her store, while Jon told her about his dance performance. Jon spoke about his passion for running a dance studio and how fulfilling it is to see his students progress. Gina complimented Jon on his dedication and creativity, while Jon praised Gina for her willingness to take risks in her business. They both agreed that taking risks is essential for success. Jon shared his ambition to turn his passion for dance into a business. Gina supported Jon, reminding him not to give up on his dreams. They discussed the importance of freedom in expressing themselves through dance. Jon mentioned the challenges of balancing his dance and business goals but finds it rewarding. Gina encouraged Jon to keep going and not stop dreaming. They both expressed mutual support and determination to chase their dreams and move forward.", - "session_6_summary": "Jon and Gina, at 2:35 pm on 16 March 2023, caught up on their recent activities. Jon mentioned starting at the gym to balance his ventures, while Gina revealed she had a tough time after losing her job at Door Dash but had exciting news about opening an online clothes store. The two discussed their entrepreneurial journeys, with Gina blending her passion for fashion and dance into her store, and Jon facing challenges but staying determined to succeed. They both found inspiration in each other's perseverance and decided to support each other in reaching their goals. Jon and Gina expressed mutual encouragement and determination to pursue their dreams together.", - "session_7_summary": "Jon and Gina had a conversation at 7:28 pm on 23 March, 2023. Gina mentioned her successful online clothing store, highlighting the hard work and dedication it requires to start a business. Jon shared his struggles with his dance studio business but expressed determination to succeed. They discussed tips for running a successful business, emphasizing the importance of brand identity, customer relationships, and positivity. Jon credited Gina for her support and motivation, showing appreciation for her involvement in his business journey. Gina encouraged Jon to stay motivated and believe in himself. Both ended the conversation on a positive note, with Jon expressing gratitude for Gina's encouragement.", - "session_8_summary": "Jon informed Gina at 1:26 pm on 3 April, 2023, that he had closed his bank account for his business. Gina expressed sympathy and inquired about how he was coping with the changes, to which Jon responded positively, mentioning it was a tough decision but necessary for business growth. They discussed Gina's new offers on her online store related to fashion and dance, which she combines due to her passion for both. They also delved into strategies, like working with influencers for Gina and expanding the social media presence for Jon's dance studio, with plans for an upcoming dance competition. They supported each other's endeavors and parted ways with encouragement and plans to meet at Jon's event.", - "session_9_summary": "Jon, who lost his job, has turned his love for dance into a business and is determined to make his dance studio successful. He has been investing a lot of time in the studio and is happy to see his students improving. Gina has been supportive of Jon's journey, recalling her own love for dance in the past. Both agree that dance is a fantastic way to express themselves and find happiness. Jon appreciates Gina's support and encouragement as he continues to push himself to succeed in his new venture.", - "session_10_summary": "Jon informed Gina about his experience at a fair displaying his studio, emphasizing the challenges and rewards of his business. Gina congratulated Jon, mentioning her own online clothing store after losing her job. Jon sought advice on staying motivated during tough times and boosting confidence. Gina advised focusing on the big goal, seeking support, recalling successes, and staying passionate. Jon appreciated the guidance and resolved to maintain confidence in pursuing his dreams. The conversation between Jon and Gina depicted mutual support, encouragement, and determination to overcome obstacles and achieve success.", - "session_11_summary": "At 3:14 pm on 11 May 2023, Jon and Gina had an inspiring conversation about Jon's new dance studio. Jon shared his journey of starting his dream business after losing his job. Gina commended his determination and encouraged him to keep going. They both connected over their love for dance as a stress-reliever. Jon expressed his passion for dance and how it keeps him motivated. Gina shared her exciting news about a design internship interview. Jon offered to show Gina a dance routine, and she eagerly agreed. They promised to support each other, with Jon updating Gina on the studio progress and sending her a video of his routine soon.", - "session_12_summary": "Gina and Jon caught up at 7:18 pm on 27 May, 2023. Gina shared her excitement about getting a fashion internship with an international company, while Jon congratulated her and talked about his business plans. Jon mentioned reading \"The Lean Startup\" for tips, and they discussed ideas for his dance studio. Jon showed Gina his whiteboard of ideas and plans. They spoke about the importance of staying organized and motivated. Jon shared his struggles with finding investors but emphasized his determination. Gina encouraged Jon to keep learning and growing. Jon appreciated her support and vowed to persevere. Gina cheered him on, emphasizing the importance of overcoming obstacles. The conversation highlighted their support for each other's goals and aspirations.", - "session_13_summary": "Jon expressed gratitude to Gina at 8:29 pm on 13 June, 2023, for her unwavering belief in him. Gina commended Jon for his venture into opening a dance studio, and the two discussed their methods of staying organized and motivated. Jon shared that he offers one-on-one mentoring at the studio to help dancers excel, and Gina mentioned creating a video presentation for styling fashion pieces. They both emphasized the importance of support and positivity in achieving goals and encouraged each other to keep pursuing their dreams.", - "session_14_summary": "On 16th June 2023 at 9:38 pm, Jon told Gina about being mentored by a business person, leading him to pursue his dreams. Gina updated Jon on her online store progress, giving him tips on marketing. Jon followed her advice, got success with his dance videos, and sought Gina's entrepreneurship advice. Gina shared her journey as an entrepreneur and encouraged Jon to stay focused, resilient, and passionate. They exchanged motivating words, with Jon reaffirming his commitment to his dreams and Gina supporting him. Their conversation emphasized the importance of perseverance and passion in pursuing goals.", - "session_15_summary": "Jon mentioned his upcoming dance studio's grand opening on 20 June and Gina congratulated him on his hard work. They expressed excitement about making memories at the event and looked forward to enjoying it together. Gina praised Jon's progress and support, while Jon appreciated her encouragement. The conversation ended with both eagerly anticipating the grand opening and agreeing to meet there the next day.", - "session_16_summary": "At 2:15 pm on 21 June, 2023, Gina informed Jon about new unique pieces for her store. Jon congratulated Gina on her store and inquired about a hoodie, which Gina clarified was from her collection. Gina mentioned creating a limited edition line to showcase her style and creativity. Jon shared attending networking events to progress after losing his job. Gina praised Jon's determination, and they discussed the importance of perseverance and tracking goals. Jon appreciated Gina's support, which motivated him to stay focused and achieve his goals. Gina encouraged Jon to keep persevering and believing in himself. Jon expressed gratitude for Gina's support, feeling confident and determined not to give up.", - "session_17_summary": "At 1:25 pm on 9 July, 2023, Gina and Jon caught up with Gina sharing her fashion success and Jon discussing his plans to go full-time with his business idea after losing his job. Gina encouraged Jon, emphasizing that stumbling blocks can lead to new opportunities. Jon expressed gratitude for Gina's support and mentioned his goal to mentor aspiring dancers. Gina praised Jon's positivity and determination, emphasizing community support in achieving dreams. Jon thanked Gina for her unwavering faith, promising not to give up. They exchanged encouraging words, with Gina rooting for Jon to overcome obstacles and achieve his dreams. Jon affirmed he would persevere, driven by their support. The conversation ended with mutual appreciation and well wishes.", - "session_18_summary": "Gina and Jon caught up at 5:44 pm on 21 July, 2023. Gina shared about her new website for customer orders, while Jon mentioned his struggles with the dance studio. Gina encouraged Jon to stay positive and network for investors. They discussed the importance of networking, with Gina offering advice on marketing strategies. Jon sought Gina's help with content creation and social media management for the dance studio. They excitedly planned to collaborate and make a difference together in the dance world.", - "session_19_summary": "Jon and Gina caught up at 6:46 pm on 23 July, 2023. Jon shared his stress about rehearsals and business plans but found solace in dancing. Gina encouraged him with motivational words from Shia Labeouf, emphasizing the importance of creating a space for dancers. Jon appreciated Gina's support and looked forward to opening his dance studio. Gina continued to motivate Jon, reminding him to never give up. The conversation ended with Jon feeling motivated and ready to \"just do it\", as Gina encouraged him to do." - }, - "sample_id": "conv-30" - }, - { - "qa": [ - { - "question": "Who did Maria have dinner with on May 3, 2023?", - "answer": "her mother", - "evidence": [ - "D13:16" - ], - "category": 2 - }, - { - "question": "When did Maria donate her car?", - "answer": "21 December 2022", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "What martial arts has John done?", - "answer": "Kickboxing, Taekwondo", - "evidence": [ - "D2:28", - "D1:4" - ], - "category": 1 - }, - { - "question": "What type of volunteering have John and Maria both done?", - "answer": "Volunteering at a homeless shelter", - "evidence": [ - "D3:5", - "D2:1" - ], - "category": 1 - }, - { - "question": "When did John join the online support group?", - "answer": "The week before 1 January 2023", - "evidence": [ - "D3:1" - ], - "category": 2 - }, - { - "question": "When did Maria go to the beach?", - "answer": "December 2022", - "evidence": [ - "D3:15" - ], - "category": 2 - }, - { - "question": "Where has Maria made friends?", - "answer": "homeless shelter, gym, church", - "evidence": [ - "D4:1", - "D2:1", - "D19:1", - "D14:10" - ], - "category": 1 - }, - { - "question": "What items des John mention having as a child?", - "answer": "A doll, a film camera", - "evidence": [ - "D5:13", - "D3:15" - ], - "category": 1 - }, - { - "question": "What might John's financial status be?", - "answer": "Middle-class or wealthy", - "evidence": [ - "D5:5" - ], - "category": 3 - }, - { - "question": "Who gave Maria's family money when she was younger and her family was going through tough times?", - "answer": "Her aunt", - "evidence": [ - "D6:9", - "D5:8" - ], - "category": 1 - }, - { - "question": "When did Maria meet Jean?", - "answer": "February 24, 2023", - "evidence": [ - "D7:1" - ], - "category": 2 - }, - { - "question": "What people has Maria met and helped while volunteering?", - "answer": "David, Jean, Cindy, Laura", - "evidence": [ - "D7:5", - "D6:5", - "D27:8", - "D21:19" - ], - "category": 1 - }, - { - "question": "What test has John taken multiple times?", - "answer": "The military aptitude test", - "evidence": [ - "D8:18", - "D3:11" - ], - "category": 1 - }, - { - "question": "When did Maria's grandmother pass away?", - "answer": "The week before 6 March 2023", - "evidence": [ - "D8:1" - ], - "category": 2 - }, - { - "question": "Would John be considered a patriotic person?", - "answer": "Yes", - "evidence": [ - "D8:18", - "D8:20" - ], - "category": 3 - }, - { - "question": "What writing classes has Maria taken?", - "answer": "Poetry, creative writing", - "evidence": [ - "D9:1", - "D7:1" - ], - "category": 1 - }, - { - "question": "When did John get his degree?", - "answer": "The week before 2 April 2023", - "evidence": [ - "D9:2" - ], - "category": 2 - }, - { - "question": "What might John's degree be in?", - "answer": "Political science, Public administration, Public affairs", - "evidence": [ - "D9:6" - ], - "category": 3 - }, - { - "question": "Who did John go to yoga with?", - "answer": "Rob", - "evidence": [ - "D7:16", - "D10:1" - ], - "category": 1 - }, - { - "question": "What damages have happened to John's car?", - "answer": "Broken windshield, Car broke down", - "evidence": [ - "D11:1", - "D4:2" - ], - "category": 1 - }, - { - "question": "When did John take a road trip to the Pacific Northwest?", - "answer": "2022", - "evidence": [ - "D11:3", - "D11:5" - ], - "category": 2 - }, - { - "question": "What areas of the U.S. has John been to or is planning to go to?", - "answer": "Pacific northwest, east coast", - "evidence": [ - "D11:5", - "D12:17" - ], - "category": 1 - }, - { - "question": "When did John go to a convention with colleagues?", - "answer": "March 2023", - "evidence": [ - "D12:9" - ], - "category": 2 - }, - { - "question": "What desserts has Maria made?", - "answer": "Banana split sundae, Peach cobbler", - "evidence": [ - "D2:25", - "D13:18" - ], - "category": 1 - }, - { - "question": "When did John start boot camp with his family?", - "answer": "April.2023", - "evidence": [ - "D13:3" - ], - "category": 2 - }, - { - "question": "What European countries has Maria been to?", - "answer": "Spain, England", - "evidence": [ - "D13:24", - "D8:15" - ], - "category": 1 - }, - { - "question": "What has Maria done to feel closer to her faith?", - "answer": "Join a local church, buy a cross necklace", - "evidence": [ - "D14:10", - "D11:10" - ], - "category": 1 - }, - { - "question": "When did John have a party with veterans?", - "answer": "The Friday before 20 May 2023", - "evidence": [ - "D15:11" - ], - "category": 2 - }, - { - "question": "What causes does John feel passionate about supporting?", - "answer": "Veterans, schools, infrastructure", - "evidence": [ - "D15:3", - "D12:5", - "D9:8", - "D1:8" - ], - "category": 1 - }, - { - "question": "What events is Maria planning for the homeless shelter funraiser?", - "answer": "Chili cook-off, ring-toss tournament", - "evidence": [ - "D16:4", - "D15:18" - ], - "category": 1 - }, - { - "question": "What shelters does Maria volunteer at?", - "answer": "The homeless shelter, the dog shelter", - "evidence": [ - "D2:1", - "D11:10", - "D17:12" - ], - "category": 1 - }, - { - "question": "When did John get his dog Max?", - "answer": "In 2013", - "evidence": [ - "D17:1" - ], - "category": 2 - }, - { - "question": "What outdoor activities has John done with his colleagues?", - "answer": "Hiking, mountaineering", - "evidence": [ - "D18:2", - "D16:2" - ], - "category": 1 - }, - { - "question": "What types of yoga has Maria practiced?", - "answer": "Aerial, kundalini", - "evidence": [ - "D1:3", - "D18:15", - "D19:3" - ], - "category": 1 - }, - { - "question": "When did Maria join a gym?", - "answer": "The week before 16 June 2023", - "evidence": [ - "D19:1" - ], - "category": 2 - }, - { - "question": "What states has Maria vacationed at?", - "answer": "Oregon, Florida", - "evidence": [ - "D19:23", - "D18:3" - ], - "category": 1 - }, - { - "question": "What music events has John attended?", - "answer": "Live music event, violin concert", - "evidence": [ - "D20:4", - "D8:12" - ], - "category": 1 - }, - { - "question": "What events for veterans has John participated in?", - "answer": "Petition, march, party, visiting veterans hospital, 5K charity run", - "evidence": [ - "D15:1", - "D15:11", - "D21:22", - "D24:1", - "D29:4" - ], - "category": 1 - }, - { - "question": "When did Maria get in a car accident?", - "answer": "July 2, 2023", - "evidence": [ - "D21:3" - ], - "category": 2 - }, - { - "question": "Around which US holiday did Maria get into a car accident?", - "answer": "Independence Day", - "evidence": [ - "D21:3" - ], - "category": 3 - }, - { - "question": "What are the names of John's children?", - "answer": "Kyle, Sara", - "evidence": [ - "D8:4", - "D22:7" - ], - "category": 1 - }, - { - "question": "Does John live close to a beach or the mountains?", - "answer": "beach", - "evidence": [ - "D22:15" - ], - "category": 3 - }, - { - "question": "What area was hit by a flood?", - "answer": "West County", - "evidence": [ - "D14:21", - "D23:1" - ], - "category": 1 - }, - { - "question": "When was John's old area hit with a flood?", - "answer": "The week before 7 July 2023", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "What activities has Maria done with her church friends?", - "answer": "Hiking, picnic, volunteer work", - "evidence": [ - "D25:2", - "D24:6", - "D28:5" - ], - "category": 1 - }, - { - "question": "Would John be open to moving to another country?", - "answer": "No, he has goals specifically in the U.S. like joining the military and running for office.", - "evidence": [ - "D24:3", - "D7:2" - ], - "category": 3 - }, - { - "question": "When did Maria go hiking with her church friends?", - "answer": "The weekend before 22 July 2023", - "evidence": [ - "D25:2" - ], - "category": 2 - }, - { - "question": "What exercises has John done?", - "answer": "Weight training, Circuit training, Kickboxing, yoga", - "evidence": [ - "D25:17", - "D25:13", - "D10:1", - "D1:4" - ], - "category": 1 - }, - { - "question": "When did John have his first firefighter call-out?", - "answer": "The sunday before 3` July 2023", - "evidence": [ - "D26:4" - ], - "category": 2 - }, - { - "question": "What food item did Maria drop off at the homeless shelter?", - "answer": "Cakes", - "evidence": [ - "D26:1", - "D25:19" - ], - "category": 1 - }, - { - "question": "What attributes describe John?", - "answer": "Selfless, family-oriented, passionate, rational", - "evidence": [ - "D26:6", - "D2:14", - "D3:5", - "D4:6" - ], - "category": 3 - }, - { - "question": "When did Maria start volunteering at the homeless shelter?", - "answer": "Around August 2022", - "evidence": [ - "D27:4" - ], - "category": 2 - }, - { - "question": "Who have written notes of gratitude to Maria?", - "answer": "Cindy, Laura", - "evidence": [ - "D27:8", - "D21:19" - ], - "category": 1 - }, - { - "question": "When did John help renovate his hometown community center?", - "answer": "2022", - "evidence": [ - "D28:11" - ], - "category": 2 - }, - { - "question": "When did Maria take up community work with her church friends?", - "answer": "August 4, 2023", - "evidence": [ - "D28:8" - ], - "category": 2 - }, - { - "question": "When did Maria receive a medal from the homeless shelter?", - "answer": "The week before 9 August 2023", - "evidence": [ - "D29:1" - ], - "category": 2 - }, - { - "question": "When did John participate in a 5K charity run?", - "answer": "first weekend of August 2023", - "evidence": [ - "D29:2", - "D29:4" - ], - "category": 2 - }, - { - "question": "What causes has John done events for?", - "answer": "Toy drive, Community food drive, veterans, domestic violence", - "evidence": [ - "D3:5", - "D6:12", - "D29:4", - "D29:10" - ], - "category": 1 - }, - { - "question": "When did Maria get Coco?", - "answer": "Two weeks before 11 August 2023", - "evidence": [ - "D30:1" - ], - "category": 2 - }, - { - "question": "When did John go on a camping trip with Max?", - "answer": "The summer of 2022", - "evidence": [ - "D30:6" - ], - "category": 2 - }, - { - "question": "What are Maria's dogs' names?", - "answer": "Coco, Shadow", - "evidence": [ - "D30:1", - "D31:4" - ], - "category": 1 - }, - { - "question": "When did Maria adopt Shadow?", - "answer": "The week before 13 August 2023", - "evidence": [ - "D31:2" - ], - "category": 2 - }, - { - "question": "How many dogs has Maria adopted from the dog shelter she volunteers at?", - "answer": "two", - "evidence": [ - "D30:1", - "D31:2" - ], - "category": 1 - }, - { - "question": "How many weeks passed between Maria adopting Coco and Shadow?", - "answer": "two weeks", - "evidence": [ - "D30:1", - "D31:2" - ], - "category": 2 - }, - { - "question": "What job might Maria pursue in the future?", - "answer": "Shelter coordinator, Counselor", - "evidence": [ - "D32:14", - "D5:8", - "D11:10", - "D27:4" - ], - "category": 3 - }, - { - "question": "What is John's main focus in local politics?", - "answer": "Improving education and infrastructure", - "evidence": [ - "D1:8" - ], - "category": 4 - }, - { - "question": "What sparked John's interest in improving education and infrastructure in the community?", - "answer": "Seeing how lack of education and crumbling infrastructure affected his neighborhood while growing up.", - "evidence": [ - "D1:10" - ], - "category": 4 - }, - { - "question": "How did the extra funding help the school shown in the photo shared by John?", - "answer": "Enabled needed repairs and renovations, making the learning environment safer and more modern for students.", - "evidence": [ - "D1:12" - ], - "category": 4 - }, - { - "question": "What type of workout class did Maria start doing in December 2023?", - "answer": "aerial yoga", - "evidence": [ - "D1:3" - ], - "category": 4 - }, - { - "question": "What did Maria donate to a homeless shelter in December 2023?", - "answer": "old car", - "evidence": [ - "D2:1" - ], - "category": 4 - }, - { - "question": "What kind of meal did John and his family make together in the photo shared by John?", - "answer": "pizza", - "evidence": [ - "D2:24" - ], - "category": 4 - }, - { - "question": "What kind of online group did John join?", - "answer": "service-focused online group", - "evidence": [ - "D3:1" - ], - "category": 4 - }, - { - "question": "What kind of activities did John and his mates from the online group do as part of their service efforts?", - "answer": "gave out food and supplies at a homeless shelter, organized a toy drive for kids in need", - "evidence": [ - "D3:5" - ], - "category": 4 - }, - { - "question": "Who inspired Maria to start volunteering?", - "answer": "Her aunt", - "evidence": [ - "D5:8" - ], - "category": 4 - }, - { - "question": "Why did Maria sit with the little girl at the shelter event in February 2023?", - "answer": "The girl seemed sad and had no other family", - "evidence": [ - "D5:10" - ], - "category": 4 - }, - { - "question": "What did Jean go through before meeting Maria?", - "answer": "divorce, job loss, homelessness", - "evidence": [ - "D7:7" - ], - "category": 4 - }, - { - "question": "Why did John decide to run for office again?", - "answer": "saw the impact he could make in the community through politics", - "evidence": [ - "D7:4" - ], - "category": 4 - }, - { - "question": "What activity did John's colleague, Rob, invite him to?", - "answer": "beginner's yoga class", - "evidence": [ - "D7:16" - ], - "category": 4 - }, - { - "question": "What is the name of John's one-year-old child?", - "answer": "Kyle", - "evidence": [ - "D8:4" - ], - "category": 4 - }, - { - "question": "How often does John take his kids to the park?", - "answer": "A few times a week", - "evidence": [ - "D8:8" - ], - "category": 4 - }, - { - "question": "What did Maria make for her home to remind her of a trip to England?", - "answer": "painting of a castle on a hill", - "evidence": [ - "D8:15" - ], - "category": 4 - }, - { - "question": "Where did Maria get the idea for the castle shadow box in her home?", - "answer": "England", - "evidence": [ - "D8:15" - ], - "category": 4 - }, - { - "question": "What did John receive a certificate for?", - "answer": "completion of a university degree", - "evidence": [ - "D9:2" - ], - "category": 4 - }, - { - "question": "What areas is John particularly interested in for policymaking?", - "answer": "education and infrastructure", - "evidence": [ - "D9:8" - ], - "category": 4 - }, - { - "question": "What did Maria participate in last weekend before April 10, 2023?", - "answer": "a 5K charity run", - "evidence": [ - "D10:10" - ], - "category": 4 - }, - { - "question": "What event did John volunteer at last weekend?", - "answer": "career fair at a local school", - "evidence": [ - "D10:13" - ], - "category": 4 - }, - { - "question": "What did John do that put a strain on his wallet?", - "answer": "His car broke down", - "evidence": [ - "D11:1" - ], - "category": 4 - }, - { - "question": "Where did John explore on a road trip last year?", - "answer": "Pacific Northwest", - "evidence": [ - "D11:5" - ], - "category": 4 - }, - { - "question": "What topic has John been blogging about recently?", - "answer": "politics and the government", - "evidence": [ - "D12:1" - ], - "category": 4 - }, - { - "question": "Why did John start blogging about politics and policies?", - "answer": "raise awareness and start conversations to create positive change", - "evidence": [ - "D12:3" - ], - "category": 4 - }, - { - "question": "What was the focus of John's recent research and writing on his blog?", - "answer": "education reform and infrastructure development", - "evidence": [ - "D12:5" - ], - "category": 4 - }, - { - "question": "What did John attend with his colleagues in March 2023?", - "answer": "a tech-for-good convention", - "evidence": [ - "D12:9" - ], - "category": 2 - }, - { - "question": "How often does John work out with his family?", - "answer": "Three times a week", - "evidence": [ - "D13:7" - ], - "category": 4 - }, - { - "question": "How has John's fitness improved since starting boot camps with his family?", - "answer": "More energy, gains in strength and endurance", - "evidence": [ - "D13:5" - ], - "category": 4 - }, - { - "question": "What kind of food did Maria have on her dinner spread iwth her mother?", - "answer": "Salads, sandwiches, homemade desserts", - "evidence": [ - "D13:18" - ], - "category": 4 - }, - { - "question": "What activity did Maria and her mom do together in May 2023?", - "answer": "Made dinner together", - "evidence": [ - "D13:16" - ], - "category": 4 - }, - { - "question": "What did Maria do to feel closer to a community and her faith?", - "answer": "joined a nearby church", - "evidence": [ - "D14:10" - ], - "category": 4 - }, - { - "question": "Why did Maria join a nearby church recently?", - "answer": "to feel closer to a community and her faith", - "evidence": [ - "D14:10" - ], - "category": 4 - }, - { - "question": "What did John host for the veterans in May 2023 as part of the project?", - "answer": "a small party to share their stories", - "evidence": [ - "D15:13" - ], - "category": 4 - }, - { - "question": "What did John and the veterans do during the small party?", - "answer": "share stories and make connections", - "evidence": [ - "D15:13" - ], - "category": 4 - }, - { - "question": "What emotions did John feel during the small party with the veterans?", - "answer": "heartwarming", - "evidence": [ - "D15:13" - ], - "category": 4 - }, - { - "question": "What event is Maria getting ready for at the shelter on May 25, 2023?", - "answer": "fundraiser", - "evidence": [ - "D16:2" - ], - "category": 4 - }, - { - "question": "What does Maria need to spread the word about for the fundraiser for the volunteer shelter?", - "answer": "chili cook-off", - "evidence": [ - "D16:4" - ], - "category": 4 - }, - { - "question": "What was the name of the pet that John had to say goodbye to on 3 June, 2023?", - "answer": "Max", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "How long was Max a part of John's family?", - "answer": "10 years", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "How does John plan to honor the memories of his beloved pet?", - "answer": "By considering adopting a rescue dog", - "evidence": [ - "D17:11" - ], - "category": 4 - }, - { - "question": "What important values does John want to teach his kids through adopting a rescue dog?", - "answer": "Responsibility and compassion", - "evidence": [ - "D17:11" - ], - "category": 4 - }, - { - "question": "What new activity did Maria start recently, as mentioned on 3 June, 2023?", - "answer": "volunteering at a local dog shelter once a month", - "evidence": [ - "D17:12" - ], - "category": 4 - }, - { - "question": "What did Maria say it was like being at the waterfall in Oregon?", - "answer": "Like being in a fairy tale", - "evidence": [ - "D18:7" - ], - "category": 4 - }, - { - "question": "What does Maria say she feels when doing upside-down yoga poses?", - "answer": "Free and light", - "evidence": [ - "D18:17" - ], - "category": 4 - }, - { - "question": "What exciting news did Maria share on 16 June, 2023?", - "answer": "joined a gym", - "evidence": [ - "D19:1" - ], - "category": 4 - }, - { - "question": "What yoga activity has Maria been trying to improve her strength and endurance?", - "answer": "kundalini yoga", - "evidence": [ - "D19:3" - ], - "category": 4 - }, - { - "question": "What did John recently get promoted to?", - "answer": "assistant manager", - "evidence": [ - "D19:8" - ], - "category": 4 - }, - { - "question": "What was one of the biggest challenges John faced in his journey to becoming assistant manager?", - "answer": "self-doubt", - "evidence": [ - "D19:12" - ], - "category": 4 - }, - { - "question": "How does John describe the support he received during his journey to becoming assistant manager?", - "answer": "having support at home and his own grit", - "evidence": [ - "D19:12" - ], - "category": 4 - }, - { - "question": "What kind of event did John and his family attend in June 2023?", - "answer": "live music event", - "evidence": [ - "D20:4" - ], - "category": 4 - }, - { - "question": "Why did Maria need to help her cousin find a new place to live?", - "answer": "Her cousin had to leave and find a new place in a hurry.", - "evidence": [ - "D21:5" - ], - "category": 4 - }, - { - "question": "What event did John participate in to show support for veterans' rights?", - "answer": "marching event", - "evidence": [ - "D21:22" - ], - "category": 4 - }, - { - "question": "What inspired John to join the marching event for veterans' rights?", - "answer": "Respect for the military and the desire to show support", - "evidence": [ - "D21:24" - ], - "category": 4 - }, - { - "question": "How often does John get to see sunsets like the one he shared with Maria?", - "answer": "At least once a week", - "evidence": [ - "D22:17" - ], - "category": 4 - }, - { - "question": "What natural disaster affected John's old area on 7 July, 2023?", - "answer": "Flood", - "evidence": [ - "D23:1" - ], - "category": 4 - }, - { - "question": "How did the flood impact the homes in John's old area?", - "answer": "Lots of homes were ruined.", - "evidence": [ - "D23:1" - ], - "category": 4 - }, - { - "question": "What motivated Maria and John to discuss potential solutions for their community on 7 July, 2023?", - "answer": "Flood in John's old area", - "evidence": [ - "D23:1" - ], - "category": 4 - }, - { - "question": "What did Maria plan to do later on the evening of 7 July, 2023?", - "answer": "have dinner with friends from the gym", - "evidence": [ - "D23:14" - ], - "category": 4 - }, - { - "question": "What kind of activities did Maria do at the picnic with her church friends?", - "answer": "played games like charades and a scavenger hunt", - "evidence": [ - "D24:8" - ], - "category": 4 - }, - { - "question": "What does John appreciate about the veteran's hospital visit?", - "answer": "the resilience of the veterans and their inspiring stories", - "evidence": [ - "D24:3" - ], - "category": 4 - }, - { - "question": "What did John take away from visiting the veteran's hospital?", - "answer": "appreciation for giving back", - "evidence": [ - "D24:1" - ], - "category": 4 - }, - { - "question": "Why did John feel inspired to join the military after the visit to the hospital?", - "answer": "seeing the resilience of the veterans", - "evidence": [ - "D24:3" - ], - "category": 4 - }, - { - "question": "In what activity did Maria and her church friends participate in July 2023?", - "answer": "hiking", - "evidence": [ - "D25:2" - ], - "category": 4 - }, - { - "question": "What does John think about trying new classes at the yoga studio?", - "answer": "Trying new classes is a fun way to switch up the exercise routine.", - "evidence": [ - "D25:14" - ], - "category": 4 - }, - { - "question": "Which activity has John done apart from yoga at the studio?", - "answer": "weight training", - "evidence": [ - "D25:17" - ], - "category": 4 - }, - { - "question": "What community service did Maria mention that she was involved in on 31 July, 2023?", - "answer": "volunteered at a homeless shelter", - "evidence": [ - "D26:1" - ], - "category": 4 - }, - { - "question": "How did Maria start volunteering at the homeless shelter?", - "answer": "Witnessed a family struggling on the streets and reached out to the shelter", - "evidence": [ - "D27:4" - ], - "category": 4 - }, - { - "question": "What did John do the week before August 3, 2023 involving his kids?", - "answer": "Had a meaningful experience at a military memorial", - "evidence": [ - "D27:9" - ], - "category": 4 - }, - { - "question": "How did John describe his kids' reaction at the military memorial?", - "answer": "awestruck and humbled", - "evidence": [ - "D27:11" - ], - "category": 4 - }, - { - "question": "Why does Maria think it's important for younger generations to visit military memorials?", - "answer": "To remember and appreciate those who served", - "evidence": [ - "D27:12" - ], - "category": 4 - }, - { - "question": "What does John believe is important for children regarding veterans?", - "answer": "Teaching them to respect and appreciate those who served", - "evidence": [ - "D27:13" - ], - "category": 4 - }, - { - "question": "What happened to John's job in August 2023?", - "answer": "John lost his job at the mechanical engineering company.", - "evidence": [ - "D28:1" - ], - "category": 4 - }, - { - "question": "What activity did Maria take up with her friends from church in August 2023?", - "answer": "community work", - "evidence": [ - "D28:8" - ], - "category": 4 - }, - { - "question": "What did John do to help his community last year in his hometown?", - "answer": "Helped renovate a rundown community center.", - "evidence": [ - "D28:11" - ], - "category": 4 - }, - { - "question": "What cause did the 5K charity run organized by John support?", - "answer": "veterans and their families", - "evidence": [ - "D29:4" - ], - "category": 4 - }, - { - "question": "Who did John work with to raise awareness and funds for victims of domestic abuse?", - "answer": "a local organization", - "evidence": [ - "D29:10" - ], - "category": 4 - }, - { - "question": "What recognition did Maria receive at the homeless shelter in August 2023?", - "answer": "a medal for volunteering", - "evidence": [ - "D29:1" - ], - "category": 4 - }, - { - "question": "What is the name of Maria's puppy she got two weeks before August 11, 2023?", - "answer": "Coco", - "evidence": [ - "D30:1" - ], - "category": 4 - }, - { - "question": "What activity did John and Max enjoy together last summer?", - "answer": "Camping", - "evidence": [ - "D30:6" - ], - "category": 4 - }, - { - "question": "How does John describe the camping trip with Max?", - "answer": "Peaceful and awesome", - "evidence": [ - "D30:6" - ], - "category": 4 - }, - { - "question": "Why does John say he feels stuck and questioning his decisions and goals?", - "answer": "Not feeling like making much of an impact", - "evidence": [ - "D30:14" - ], - "category": 4 - }, - { - "question": "What is the name of Maria's second puppy?", - "answer": "Shadow", - "evidence": [ - "D31:4" - ], - "category": 4 - }, - { - "question": "How is Maria's new puppy adjusting to its new home?", - "answer": "doing great - learning commands and house training", - "evidence": [ - "D31:10" - ], - "category": 4 - }, - { - "question": "What is John currently doing as a volunteer in August 2023?", - "answer": "mentoring students at a local school", - "evidence": [ - "D31:1" - ], - "category": 4 - }, - { - "question": "What activities does John's family enjoy doing together?", - "answer": "going for hikes, hanging out at the park, having picnics, playing board games, having movie nights", - "evidence": [ - "D31:19" - ], - "category": 4 - }, - { - "question": "What did the donations help John's community acquire on 16 August, 2023?", - "answer": "a brand new fire truck", - "evidence": [ - "D32:11" - ], - "category": 4 - }, - { - "question": "What is John's main focus in international politics?", - "evidence": [ - "D1:8" - ], - "category": 5, - "adversarial_answer": "Improving education and infrastructure" - }, - { - "question": "What did Maria donate to a luxury store in December 2023?", - "evidence": [ - "D2:1" - ], - "category": 5, - "adversarial_answer": "old car" - }, - { - "question": "Who inspired John to start volunteering?", - "evidence": [ - "D5:8" - ], - "category": 5, - "adversarial_answer": "His aunt" - }, - { - "question": "Why did Maria decide to run for office again?", - "evidence": [ - "D7:4" - ], - "category": 5, - "adversarial_answer": "saw the impact she could make in the community through politics" - }, - { - "question": "What activity did Maria's colleague, Rob, invite her to?", - "evidence": [ - "D7:16" - ], - "category": 5, - "adversarial_answer": "beginner's yoga class" - }, - { - "question": "What is the name of Maria's one-year-old child?", - "evidence": [ - "D8:4" - ], - "category": 5, - "adversarial_answer": "Kyle" - }, - { - "question": "How often does John take his kids to the library?", - "evidence": [ - "D8:8" - ], - "category": 5, - "adversarial_answer": "A few times a week" - }, - { - "question": "What did Maria make for her home to remind her of a trip to France?", - "evidence": [ - "D8:15" - ], - "category": 5, - "adversarial_answer": "painting of a castle on a hill" - }, - { - "question": "Where did John get the idea for the castle shadow box in his home?", - "evidence": [ - "D8:15" - ], - "category": 5, - "adversarial_answer": "England" - }, - { - "question": "What did Maria receive a certificate for?", - "evidence": [ - "D9:2" - ], - "category": 5, - "adversarial_answer": "completion of a university degree" - }, - { - "question": "What areas is John particularly interested in for art appreciation?", - "evidence": [ - "D9:8" - ], - "category": 5, - "adversarial_answer": "education and infrastructure" - }, - { - "question": "Why did Maria start blogging about politics and policies?", - "evidence": [ - "D12:3" - ], - "category": 5, - "adversarial_answer": "raise awareness and start conversations to create positive change" - }, - { - "question": "What was the focus of John's recent travel and photography blog?", - "evidence": [ - "D12:5" - ], - "category": 5, - "adversarial_answer": "education reform and infrastructure development" - }, - { - "question": "How often does Maria work out with her family?", - "evidence": [ - "D13:7" - ], - "category": 5, - "adversarial_answer": "Three times a week" - }, - { - "question": "How has John's artistic skills improved since starting boot camps with his family?", - "evidence": [ - "D13:5" - ], - "category": 5, - "adversarial_answer": "More energy, gains in strength and endurance" - }, - { - "question": "What kind of food did Maria have on her dinner spread with her father?", - "evidence": [ - "D13:18" - ], - "category": 5, - "adversarial_answer": "Salads, sandwiches, homemade desserts" - }, - { - "question": "What did John do to feel closer to a community and his faith?", - "evidence": [ - "D14:10" - ], - "category": 5, - "adversarial_answer": "joined a nearby church" - }, - { - "question": "Why did John join a nearby church recently?", - "evidence": [ - "D14:10" - ], - "category": 5, - "adversarial_answer": "to feel closer to a community and her faith" - }, - { - "question": "How long was Max a part of Maria's family?", - "evidence": [ - "D17:1" - ], - "category": 5, - "adversarial_answer": "10 years" - }, - { - "question": "How does Maria plan to honor the memories of her beloved pet?", - "evidence": [ - "D17:11" - ], - "category": 5, - "adversarial_answer": "By considering adopting a rescue dog" - }, - { - "question": "What important values does Maria want to teach her kids through adopting a rescue dog?", - "evidence": [ - "D17:11" - ], - "category": 5, - "adversarial_answer": "Responsibility and compassion" - }, - { - "question": "What did Maria say it was like being at the desert in Oregon?", - "evidence": [ - "D18:7" - ], - "category": 5, - "adversarial_answer": "Like being in a fairy tale" - }, - { - "question": "What does John say she feels when doing upside-down yoga poses?", - "evidence": [ - "D18:17" - ], - "category": 5, - "adversarial_answer": "Free and light" - }, - { - "question": "What did Maria recently get promoted to?", - "evidence": [ - "D19:8" - ], - "category": 5, - "adversarial_answer": "assistant manager" - }, - { - "question": "What was one of the biggest challenges Maria faced in her journey to becoming assistant manager?", - "evidence": [ - "D19:12" - ], - "category": 5, - "adversarial_answer": "self-doubt" - }, - { - "question": "Why did John need to help his cousin find a new place to live?", - "evidence": [ - "D21:5" - ], - "category": 5, - "adversarial_answer": "His cousin had to leave and find a new place in a hurry." - }, - { - "question": "What event did Maria participate in to show support for veterans' rights?", - "evidence": [ - "D21:22" - ], - "category": 5, - "adversarial_answer": "marching event" - }, - { - "question": "How did the drought impact the homes in John's old area?", - "evidence": [ - "D23:1" - ], - "category": 5, - "adversarial_answer": "Lots of homes were ruined." - }, - { - "question": "What does John criticize about the veteran's hospital visit?", - "evidence": [ - "D24:3" - ], - "category": 5, - "adversarial_answer": "the resilience of the veterans and their inspiring stories" - }, - { - "question": "What did John take away from visiting the orphanage?", - "evidence": [ - "D24:1" - ], - "category": 5, - "adversarial_answer": "appreciation for giving back" - }, - { - "question": "Why did Maria feel inspired to join the military after the visit to the hospital?", - "evidence": [ - "D24:3" - ], - "category": 5, - "adversarial_answer": "seeing the resilience of the veterans" - }, - { - "question": "How did Maria describe her kids' reaction at the military memorial?", - "evidence": [ - "D27:11" - ], - "category": 5, - "adversarial_answer": "awestruck and humbled" - }, - { - "question": "Why does Maria think it's important for younger generations to visit art galleries?", - "evidence": [ - "D27:12" - ], - "category": 5, - "adversarial_answer": "To remember and appreciate those who served" - }, - { - "question": "What happened to Maria's job in August 2023?", - "evidence": [ - "D28:1" - ], - "category": 5, - "adversarial_answer": "John lost his job at the mechanical engineering company." - }, - { - "question": "What cause did the 5K charity run organized by Maria support?", - "evidence": [ - "D29:4" - ], - "category": 5, - "adversarial_answer": "veterans and their families" - }, - { - "question": "Who did John work with to raise awareness and funds for animal welfare?", - "evidence": [ - "D29:10" - ], - "category": 5, - "adversarial_answer": "a local organization" - }, - { - "question": "What recognition did John receive at the homeless shelter in August 2023?", - "evidence": [ - "D29:1" - ], - "category": 5, - "adversarial_answer": "a medal for volunteering" - }, - { - "question": "What is the name of John's puppy he got two weeks before August 11, 2023?", - "evidence": [ - "D30:1" - ], - "category": 5, - "adversarial_answer": "Coco" - }, - { - "question": "How does Maria describe the camping trip with Max?", - "evidence": [ - "D30:6" - ], - "category": 5, - "adversarial_answer": "Peaceful and awesome" - }, - { - "question": "What is the name of Maria's second kitten?", - "evidence": [ - "D31:4" - ], - "category": 5, - "adversarial_answer": "Shadow" - }, - { - "question": "How is John's new puppy adjusting to its new home?", - "evidence": [ - "D31:10" - ], - "category": 5, - "adversarial_answer": "doing great - learning commands and house training" - } - ], - "conversation": { - "speaker_a": "John", - "speaker_b": "Maria", - "session_1_date_time": "11:01 am on 17 December, 2022", - "session_1": [ - { - "speaker": "Maria", - "dia_id": "D1:1", - "text": "Hey John! Long time no see! What's up?" - }, - { - "speaker": "John", - "dia_id": "D1:2", - "text": "Hey Maria! Good to see you. Just got back from a family road trip yesterday, it was fun! Anything exciting happening for you?" - }, - { - "speaker": "Maria", - "dia_id": "D1:3", - "text": "Been busy volunteering at the homeless shelter and keeping fit. Just started doing aerial yoga, it's great. Have you tried any other cool workout classes?" - }, - { - "speaker": "John", - "dia_id": "D1:4", - "text": "Woah, Maria, that sounds cool! I'm doing kickboxing and it's giving me so much energy." - }, - { - "speaker": "Maria", - "dia_id": "D1:5", - "text": "Cool, John. Kickboxing is a perfect way to stay in shape and de-stress. Do you have any goals you're working towards?" - }, - { - "speaker": "John", - "dia_id": "D1:6", - "text": "Yeah Maria, I'm really hoping to get into local politics. I love helping the community and making it a better place." - }, - { - "speaker": "Maria", - "dia_id": "D1:7", - "text": "Woohoo, John! That's awesome! Any specific areas you want to tackle?" - }, - { - "speaker": "John", - "dia_id": "D1:8", - "text": "I'm passionate about improving education and infrastructure in our community. Those are my main focuses." - }, - { - "speaker": "Maria", - "dia_id": "D1:9", - "text": "Wow, John! It's great to hear that. Could you tell me what got you interested in those topics?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.greenbaypressgazette.com/gcdn/presto/2019/07/16/PGRB/fa8d60a1-0a21-421c-b36d-31f4bc2d2b6d-KEW_0720_LC_referendum_Peters_Concrete_pour.png" - ], - "blip_caption": "a photo of a group of men working on a building", - "query": "school building renovation", - "dia_id": "D1:10", - "text": "Growing up, I saw how lack of education and crumbling infrastructure affected my neighborhood. I don't want future generations to go through that, so I think schools and infrastructure should be funded properly. Here's a pic of a school last year, after they got the funding." - }, - { - "speaker": "Maria", - "dia_id": "D1:11", - "text": "Looks good! How did the extra funding help with building the school?" - }, - { - "speaker": "John", - "dia_id": "D1:12", - "text": "It enabled needed repairs and renovations, making the learning environment safer and more modern for students." - }, - { - "speaker": "Maria", - "dia_id": "D1:13", - "text": "Wow John, it really made a difference for schools and students! It must have been great to be involved. What's your next move in politics?" - }, - { - "speaker": "John", - "dia_id": "D1:14", - "text": "I'm gonna chat with local leaders and organizations, get support and gather ideas for my next move." - }, - { - "speaker": "Maria", - "dia_id": "D1:15", - "text": "Sounds good, John. Let's get support and great ideas from local leaders and organizations. Keep me posted on how your campaign is going." - }, - { - "speaker": "John", - "dia_id": "D1:16", - "text": "Got it! Thanks, Maria. I definitely will." - } - ], - "session_2_date_time": "6:10 pm on 22 December, 2022", - "session_2": [ - { - "speaker": "Maria", - "dia_id": "D2:1", - "text": "Hey John, been a few days since we chatted. In the meantime, I donated my old car to a homeless shelter I volunteer at yesterday. How's the campaign going? I'm keen to hearabout it." - }, - { - "speaker": "John", - "dia_id": "D2:2", - "text": "Hi Maria! It's been an interesting ride so far. I've been networking with some people to get their input." - }, - { - "speaker": "Maria", - "dia_id": "D2:3", - "text": "That's awesome, John! Networking is great for gaining new perspectives and insights. Have you had any interesting conversations or revelations so far?" - }, - { - "speaker": "John", - "dia_id": "D2:4", - "text": "I just talked to someone who shared some amazing stories. It really fired up my passion to make education better in our area." - }, - { - "speaker": "Maria", - "dia_id": "D2:5", - "text": "Wow, John! Hearing that can really make an impact and get us fired up to make a difference. It's great to hear that you're feeling motivated to make improvements to our community's education!" - }, - { - "speaker": "John", - "dia_id": "D2:6", - "text": "Definitely, Maria. Investing in our future generations is key, giving them the right tools for success. It's the foundation of progress and opportunity." - }, - { - "speaker": "Maria", - "dia_id": "D2:7", - "text": "Yeah, John. It's amazing how even minor tweaks to the system can make a big difference for lots of people. I'm really impressed with your enthusiasm and commitment to it!" - }, - { - "speaker": "John", - "dia_id": "D2:8", - "text": "Thanks, Maria. Your encouragement means a lot to me. It's true that with effort and support, we can make a real difference in our community." - }, - { - "speaker": "Maria", - "dia_id": "D2:9", - "text": "You got this, John! I believe in your power to make a positive difference. Your passion inspires me. Keep going - I'm here for you." - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/5119595/pexels-photo-5119595.jpeg" - ], - "blip_caption": "a photography of a family having a picnic in the park", - "query": "family smiling hugging", - "dia_id": "D2:10", - "re-download": true, - "text": "Thanks a lot, Maria. Your help is really motivating and makes me more determined. Here's a pic of my family - they're the reason why I never give up. Their love gives me strength." - }, - { - "speaker": "Maria", - "dia_id": "D2:11", - "text": "Wow, John, that's a great pic! Your family looks so cheerful and loving. It's wonderful to have such a supportive and loving family." - }, - { - "speaker": "John", - "dia_id": "D2:12", - "text": "Thanks, Maria. They really help me stay centered. They remind me why I'm so passionate about making a positive impact." - }, - { - "speaker": "Maria", - "dia_id": "D2:13", - "text": "Family's love really grounds us and gives us strength. Their support certainly boosts your motivation." - }, - { - "speaker": "John", - "img_url": [ - "https://cdn.stocksnap.io/img-thumbs/960w/playground-child_RL9KRCSWHD.jpg" - ], - "blip_caption": "a photography of a family enjoying a ride on a swing", - "query": "family park", - "dia_id": "D2:14", - "re-download": true, - "text": "Yeah, they are my rock in tough times and always cheer me on. I'm really thankful for their love. Family time means a lot to me." - }, - { - "speaker": "Maria", - "dia_id": "D2:15", - "text": "Wow, John, that playground looks cool! What kind of stuff do you and your family do there?" - }, - { - "speaker": "John", - "dia_id": "D2:16", - "text": "Thanks, Maria! We love climbing, sliding, and playing games. It's an awesome way to connect and have a blast. What do you enjoy doing with your family?" - }, - { - "speaker": "Maria", - "dia_id": "D2:17", - "text": "My fam's small, but I love spending time with the friends I have. We usually watch movies, hike, and have game nights at my place. Quality connections matter most to me." - }, - { - "speaker": "John", - "dia_id": "D2:18", - "text": "Sounds nice, Maria! Spending time with loved ones is important." - }, - { - "speaker": "Maria", - "dia_id": "D2:19", - "text": "Definitely, John. They bring us joy, support, and a feeling of being part of something special. We should cherish every moment with them." - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/1655329/pexels-photo-1655329.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-lisa-fotios-1655329.jpg" - ], - "blip_caption": "a photo of a group of people standing around a kitchen table", - "query": "family dinner gathering", - "dia_id": "D2:20", - "text": "Yeah Maria, making memories with family is priceless! Life is so much more meaningful when we spend time together. Here's a pic of us at dinner." - }, - { - "speaker": "Maria", - "dia_id": "D2:21", - "text": "Woah, that's a nice pic, John! You all obviously had a blast at dinner. Nothing beats getting together with loved ones for a good meal - it makes some awesome memories!" - }, - { - "speaker": "John", - "dia_id": "D2:22", - "text": "Thanks, Maria! Meal times are always fun. Good food, laughs, and chats help us stay close." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a table with pizzas and salads on it", - "dia_id": "D2:23", - "text": "Yeah, John! It definitely builds a strong bond. Those shared meals really make life enjoyable and meaningful. What did you make?" - }, - { - "speaker": "John", - "dia_id": "D2:24", - "text": "We made pizza! We had so much fun making them together. It was great picking out toppings and sharing a tasty meal with family. Have you made anything lately?" - }, - { - "speaker": "Maria", - "dia_id": "D2:25", - "text": "I can picture you all laughing and having a blast making your own pizzas - a great way to bond! I made some peach cobbler recently, it was great." - }, - { - "speaker": "John", - "dia_id": "D2:26", - "text": "Yeah Maria, it's awesome! We get our creative on and have a blast together." - }, - { - "speaker": "Maria", - "dia_id": "D2:27", - "text": "Sure, John! It's those moments of creativity and laughter that bring us closer. Let's make happy memories with our family and keep them close." - }, - { - "speaker": "John", - "dia_id": "D2:28", - "text": " Yep, let's keep making great memories with our loved ones and cherishing the time we have. I'm off to do some taekwondo!" - } - ], - "session_3_date_time": "8:30 pm on 1 January, 2023", - "session_3": [ - { - "speaker": "John", - "blip_caption": "a photo of a man sitting on a bed using a laptop", - "dia_id": "D3:1", - "text": "Hey Maria, great to chat again! I joined a service-focused online group last week and it's been an emotional ride. Everyone there is incredible with their own inspiring stories. They've opened my eyes to new perspectives, and I'm feeling a sense of connection and purpose with them." - }, - { - "speaker": "Maria", - "dia_id": "D3:2", - "text": "Wow, John! That's amazing news. It's great to see you finding such a supportive community that is making a difference." - }, - { - "speaker": "John", - "dia_id": "D3:3", - "text": "Thanks, Maria! It's great to have a group of people with the same passion for serving. It's been really inspiring sharing stories, advice, and encouragement." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a poster on a table with a santa clause", - "dia_id": "D3:4", - "text": "That's great to hear! It's always inspiring to share thos things with like-minded individuals. By the way, have you had the chance to meet any of them in person?\n" - }, - { - "speaker": "John", - "dia_id": "D3:5", - "text": "We held some events and got to meet some people. We went to a homeless shelter to give out food and supplies. Seeing the smiles on their faces, we knew we made a real difference. We also organized a toy drive for kids in need. It was amazing seeing the community come together to spread some joy." - }, - { - "speaker": "Maria", - "dia_id": "D3:6", - "text": "That sounds great, John. It's nice to see the difference you're making. Do you have any ideas for future projects?" - }, - { - "speaker": "John", - "dia_id": "D3:7", - "text": "We're brainstorming some to help underserved communities get access to education, mentorship, job training, and resume building. The goal is to empower individuals in achieving their aspirations." - }, - { - "speaker": "Maria", - "dia_id": "D3:8", - "text": "That's great, John! Empowering individuals through education and mentorship is crucial for helping them reach their goals. Can't wait to see the initiatives you come up with!" - }, - { - "speaker": "John", - "dia_id": "D3:9", - "text": "Thanks, Maria! I'm really excited about them too. I believe that providing the right assistance and resources can make a lasting impact." - }, - { - "speaker": "Maria", - "dia_id": "D3:10", - "text": "Yep John, a bit of support can make an amazing change. You're spot on about it, it really is powerful. Keep doing what you're doing, it's really inspiring!" - }, - { - "speaker": "John", - "dia_id": "D3:11", - "text": "Thanks, Maria! I really appreciate your support, It means a lot to me. Especially after I failed the military aptitude test recently, I've been feeling a bit stressed out." - }, - { - "speaker": "Maria", - "img_url": [ - "https://images.pexels.com/photos/10815424/pexels-photo-10815424.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-beyza-erdem-10815424.jpg" - ], - "blip_caption": "a photo of a sunset over the ocean with a wave coming in", - "query": "sunset ocean beauty nature", - "dia_id": "D3:12", - "text": "No worries, John. I'm here for you and I got your back. Nature's beauty reminds me to slow down and enjoy the small stuff." - }, - { - "speaker": "John", - "dia_id": "D3:13", - "text": "That's a chill pic! Where did you find it?" - }, - { - "speaker": "Maria", - "dia_id": "D3:14", - "text": "I took it at the beach last month. Watching the sunset was so peaceful, it made me feel connected to nature and appreciate life's small moments." - }, - { - "speaker": "John", - "dia_id": "D3:15", - "text": "Wow, nature can be so beautiful! It reminds me of the film camera I had as a kid, I took plenty of beach pics. Thanks for sharing." - }, - { - "speaker": "Maria", - "dia_id": "D3:16", - "text": "Glad you enjoyed it, John! It's amazing how beautiful it can be." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people standing around a field", - "dia_id": "D3:17", - "text": "Yeah, it does. It helps us remember the small joys, especially when life gets busy." - } - ], - "session_4_date_time": "7:06 pm on 9 January, 2023", - "session_4": [ - { - "speaker": "Maria", - "dia_id": "D4:1", - "text": "Hey John, great news - I'm now friends with one of my fellow volunteers! We both love helping others. How have you been since we last chatted?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/njxp1ep8bse71.jpg" - ], - "blip_caption": "a photo of a car with a broken windshield and a broken windshield", - "query": "shattered windshield", - "dia_id": "D4:2", - "text": "Hey Maria, I've been busy with work and family, but last week I had an unexpected incident on my way home. It reminded me how life can throw unexpected troubles our way." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a tattoo with a quote on it", - "dia_id": "D4:3", - "text": "Oh John, that sounds tough. I'm glad you're alright. Life does throw us some surprises, doesn't it?\n\n" - }, - { - "speaker": "John", - "dia_id": "D4:4", - "text": "Thanks, Maria. This is a reminder to stay strong during tough times." - }, - { - "speaker": "Maria", - "dia_id": "D4:5", - "text": "Yeah John, when stuff like that happens, it's good to tap into our own inner strength. How did you handle it?" - }, - { - "speaker": "John", - "dia_id": "D4:6", - "text": "I tried to stay calm and asked for assistance, which helped me handle the situation and make it back safely." - }, - { - "speaker": "Maria", - "dia_id": "D4:7", - "text": "Hey, John that's awesome! You really showed your resilience and resourcefulness. How did you feel afterwards?" - }, - { - "speaker": "John", - "dia_id": "D4:8", - "text": "I felt relieved and grateful to make it through without incident." - }, - { - "speaker": "Maria", - "dia_id": "D4:9", - "text": "Yeah, it's tough. Appreciate what you've got. You got through it - stay strong. Are there any things you like to do to calm down during times like this?" - }, - { - "speaker": "John", - "dia_id": "D4:10", - "text": "Thanks, Maria! I like to just take a look at the sunset- it reminds me to enjoy the small things in life." - }, - { - "speaker": "Maria", - "dia_id": "D4:11", - "text": "It's great to hear that you appreciate the beauty of sunsets! By the way, have there been any developments regarding your political goals?" - }, - { - "speaker": "John", - "dia_id": "D4:12", - "text": "I've been keeping busy since we last talked! I've been looking into local politics, talking to community leaders, and getting to know the needs and hopes of our neighborhood. It's been super informative, and I'm feeling optimistic about it all." - }, - { - "speaker": "Maria", - "img_url": [ - "https://media2.inlander.com/inlander/imager/u/original/27047427/1.jpg" - ], - "blip_caption": "a photo of a notebook with a pencil and a notepad on a table", - "query": "notebook local politics", - "dia_id": "D4:13", - "text": "Wow, John! Your dedication to our community is amazing. Keep up the great work! I've been taking some notes about local politics in my notebook." - }, - { - "speaker": "John", - "dia_id": "D4:14", - "text": "Thanks, Maria! You're super helpful. That gives me some new ideas and plans for our community!" - }, - { - "speaker": "Maria", - "dia_id": "D4:15", - "text": "Glad I could help, John. What's up next for you?" - }, - { - "speaker": "John", - "dia_id": "D4:16", - "text": "Next week I have a community meeting coming up, we'll be discussing education and infrastructure upgrades." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a microphone, a charger, and a charger on a table", - "dia_id": "D4:17", - "text": "Good luck on that! Hope your ideas and concerns get heard." - }, - { - "speaker": "John", - "dia_id": "D4:18", - "text": "Thanks, Maria! I'm hoping so. I'll definitely stick up for our community. Discussing it is important." - }, - { - "speaker": "Maria", - "dia_id": "D4:19", - "text": "Definitely, John! Talking and working together will make a great difference. You're really doing our community a service by speaking out. It's admirable and inspiring." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting around a table with a laptop", - "dia_id": "D4:20", - "text": "Maria, thank you! Your support means a lot. Let's make some change in our community!" - }, - { - "speaker": "Maria", - "dia_id": "D4:21", - "text": "Yeah, let's do it, John! Working together, we can bring positive changes to the neighborhood as a team." - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/7351/14090438714_33bec862fe_b.jpg" - ], - "blip_caption": "a photography of a man and a woman shaking hands", - "query": "handshake unity teamwork", - "dia_id": "D4:22", - "re-download": true, - "text": "Yeah, Maria! We can achieve great things if we join forces together." - }, - { - "speaker": "Maria", - "dia_id": "D4:23", - "text": "Agreed, John! Uniting is essential for making a difference. if we stand together we can tackle any problems and build a great community." - }, - { - "speaker": "John", - "dia_id": "D4:24", - "text": "Yeah, Maria! I couldn't agree more. Thanks for being an awesome friend." - }, - { - "speaker": "Maria", - "dia_id": "D4:25", - "text": "No worries, John. It's really nice to help. We make a great team." - }, - { - "speaker": "John", - "dia_id": "D4:26", - "text": "Yup, we rock as a team! Glad to have you." - } - ], - "session_5_date_time": "1:17 pm on 28 January, 2023", - "session_5": [ - { - "speaker": "John", - "blip_caption": "a photo of a wooden floor with a white wall and a wooden floor", - "dia_id": "D5:1", - "text": "Hey Maria, since we last spoke I went to that community mtg. It was really interesting hearing everyone's worries and how it affects our area. It made me realize how crucial the upgrades are, especially for the kids. I'm really upset seeing the state of our education. How do you think we can make things better?" - }, - { - "speaker": "Maria", - "dia_id": "D5:2", - "text": "Hey John, cool that you made it. You're right, it's really sad to see the state of education. We should fight for more money and resources for schools and raise awareness about the importance of education. It's not just for our kids, but for all of us too!" - }, - { - "speaker": "John", - "dia_id": "D5:3", - "text": "Yup, education is essential for a successful society. I totally agree that we should fight for more money and resources for our schools. It breaks my heart that our kids don't have the proper stuff they need. They deserve better." - }, - { - "speaker": "Maria", - "dia_id": "D5:4", - "text": "Yeah, John. Our kids are our future; they should have the best. It kills me to think about all the kids without the proper stuff they need. It's just not right." - }, - { - "speaker": "John", - "dia_id": "D5:5", - "text": "It's definitely isn't, Maria. My kids have so much and others don't. We really need to do something about it." - }, - { - "speaker": "Maria", - "img_url": [ - "https://media.cnn.com/api/v0/images/stellar/prod/230608195849-brandi-tuck-college-volunteer2-cnnheroes.jpg" - ], - "blip_caption": "a photo of three women in green aprons holding pots and cups", - "query": "volunteer homeless shelter residents", - "dia_id": "D5:6", - "text": "Sure, it's not right that some kids get all they need while others have nothing to help them succeed. We gotta do something! Last week I volunteered at a shelter during an event for kids, and it's been a great experience." - }, - { - "speaker": "John", - "dia_id": "D5:7", - "text": "Wow, Maria! That's really making a big impact. What made you decide to get involved with that?" - }, - { - "speaker": "Maria", - "dia_id": "D5:8", - "text": "I started volunteering to help make a difference. My aunt believed in volunteering, and used to help my family out when we were struggling, so I'm inspired by her. It makes me happy knowing I can brighten somebody's day." - }, - { - "speaker": "John", - "dia_id": "D5:9", - "text": "Wow, Maria. That's amazing! I can imagine it's incredibly rewarding. Is there a particular moment that stands out to you as the most impactful?" - }, - { - "speaker": "Maria", - "dia_id": "D5:10", - "text": "For me, it was when I noticed a little girl around 8 sitting all alone. She seemed so sad. So, I sat with her and we talked. Turns out she had no other family - it broke my heart. I was able to give her some comfort and a listening ear. We ended up laughing and having a good time." - }, - { - "speaker": "John", - "img_url": [ - "https://i2.pickpik.com/photos/428/450/411/girl-baby-doll-baby-girl-preview.jpg" - ], - "blip_caption": "a photography of a little girl kissing a doll with a doll in her lap", - "query": "smiling child toy", - "dia_id": "D5:11", - "re-download": true, - "text": "Wow, what a touching moment, Maria. I'm glad you were there for her when she needed someone. I'm sure it made a big impact." - }, - { - "speaker": "Maria", - "dia_id": "D5:12", - "text": "That's nice of you, John. What's the photo about?" - }, - { - "speaker": "John", - "dia_id": "D5:13", - "text": "It reminds me of something from my childhood. I had a little doll like this and it always made me feel better. It reminds me to always look out for others, especially when they're feeling down." - }, - { - "speaker": "Maria", - "dia_id": "D5:14", - "text": "That's sweet. Spreading kindness and support can really make a difference, especially when someone is feeling down. Thanks for sharing." - }, - { - "speaker": "John", - "dia_id": "D5:15", - "text": "No problem, Maria. It's important to support each other when we're feeling down. You never know the difference a kind gesture can make." - }, - { - "speaker": "Maria", - "dia_id": "D5:16", - "text": "Yep, kindness is key and a little compassion can really turn someone's day around. So glad we're on the same page here." - } - ], - "session_6_date_time": "2:33 pm on 5 February, 2023", - "session_6": [ - { - "speaker": "Maria", - "dia_id": "D6:1", - "text": "Hey John! Long time no talk. I just wanted to let you know I challenged myself last Friday and did a charity event. It was great! I truly felt the power of our collective effort to help people in need, so heartwarming." - }, - { - "speaker": "John", - "blip_caption": "a photo of a woman handing a plate of food to a man", - "dia_id": "D6:2", - "text": "Wow, Maria! Truly inspiring! It's so cool to see how our community can make a difference. How did it feel to be part of that event?" - }, - { - "speaker": "Maria", - "dia_id": "D6:3", - "text": "Thanks, John! It was such a rewarding experience. Just the act of serving meals and seeing the gratitude on their faces was truly heartwarming. It reminded me of how powerful compassion can be. So, tell me, what made you get into politics?" - }, - { - "speaker": "John", - "dia_id": "D6:4", - "text": "I feel a strong urge to serve my country and community. Running for office was my chance to make an impact. But anyway, tell me more about your charity event. Did something special happen that made it meaningful for you?" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a man with a cell phone in his hand", - "dia_id": "D6:5", - "text": "Yeah, at the event, I had a conversation with someone named David. Hearing his story of hardship and how he ended up in difficult circumstances was so humbling. It just showed me that everyone has their own story and deserves understanding." - }, - { - "speaker": "John", - "dia_id": "D6:6", - "text": "Wow, that's powerful. It just shows that everyone has their own story and deserves understanding. Do you know any organizations or services that could help him out?" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a flyer with a group of people in a circle", - "dia_id": "D6:7", - "text": "Yeah, I did. I talked to him afterwards and linked him up with a nearby organization that offers housing and support for homeless individuals. Hopefully, he'll find the help he needs." - }, - { - "speaker": "John", - "dia_id": "D6:8", - "text": "Nice job, Maria! You really made an impact. It's important to help people find what they need. Have you ever been in a situation where you needed help?" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a woman in a face mask standing next to a table full of packages of food", - "dia_id": "D6:9", - "text": "Thanks, John. When I was younger, we had some money problems and had to rely on outside help from out auntie. It was a tough learning experience about the importance of helping others who struggle." - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/4478/37216959584_3c561498f3_b.jpg" - ], - "blip_caption": "a photography of a group of people standing around a table with boxes of tomatoes", - "query": "volunteering local food bank packing food bags", - "dia_id": "D6:10", - "re-download": true, - "text": "Yeah, Maria, we learn a lot from our own struggles. I just started helping out with a food drive for folks who lost their jobs. Here's a picture of me at the food bank." - }, - { - "speaker": "Maria", - "dia_id": "D6:11", - "text": "Wow, John, that's incredible! What inspired you to get involved with something like this?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a thanksgiving potluck and food drive flyer", - "dia_id": "D6:12", - "text": "Seeing the effect unemployment has on our neighbors made me decide to act. I wanted to help out in these tough times by doing a community food drive. We can all make a difference!" - }, - { - "speaker": "Maria", - "dia_id": "D6:13", - "text": "That's really great of you. What sparked your decision to start this initiative?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a large group of people standing around a table", - "dia_id": "D6:14", - "text": "Thanks, Maria. Unemployment in our community was the reason behind it." - }, - { - "speaker": "Maria", - "dia_id": "D6:15", - "text": "You did awesome! How's the response been to that?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.lifturbanportland.org/uploads/8/3/6/3/83630366/published/warehouse-volunteers.jpg" - ], - "blip_caption": "a photo of a woman in a face mask holding a bag of food", - "query": "group of volunteers food drive event", - "dia_id": "D6:16", - "text": "Thanks, Maria! We've been overwhelmed by the response and the volunteers. Here's a photo of them at a recent event." - }, - { - "speaker": "Maria", - "dia_id": "D6:17", - "text": "Seeing so many people support the community is awesome. I'd love to lend a hand with networking or helping out at future events." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of men working on a wall", - "dia_id": "D6:18", - "text": "That'd be great, Maria! Thanks for offering to help. Your assistance would be really appreciated." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people standing outside of a house", - "dia_id": "D6:19", - "text": "No worries, John. Whenever you need help, just let me know." - }, - { - "speaker": "John", - "dia_id": "D6:20", - "text": "Yep, definitely. Your support really means a lot, Maria. Thanks for always being there for me." - }, - { - "speaker": "Maria", - "dia_id": "D6:21", - "text": "Of course, John. You're a great friend to me. I'll always be there for you." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting in a park", - "dia_id": "D6:22", - "text": "Thanks, Maria. Friendship means a lot to me. I'm glad we have each other's backs and can work towards a shared goal." - } - ], - "session_7_date_time": "8:55 pm on 25 February, 2023", - "session_7": [ - { - "speaker": "Maria", - "dia_id": "D7:1", - "text": "Hey John, how's it going? Just wanted to give you the heads up on what's been happening lately- I took a creative writing class recently, and it was super enlightening!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a crowd of people sitting on a sidewalk with umbrellas", - "dia_id": "D7:2", - "text": "Hey Maria! Wanted to let you know that I'm running for office again. It's been a wild ride, but I'm more excited than ever! How have you been?" - }, - { - "speaker": "Maria", - "dia_id": "D7:3", - "text": "Congrats, John! What made you decide to run again? As for me, I've been volunteering at a homeless shelter and it's really rewarding." - }, - { - "speaker": "John", - "dia_id": "D7:4", - "text": "Thanks, Maria! After my last run, I saw the impact I could make in the community through politics. It's rewarding to work towards positive changes and a better future." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a woman with a blue shirt and a ring on her neck", - "dia_id": "D7:5", - "text": "Wow John, your enthusiasm for making a better future is inspiring. Making a positive impact is so rewarding! While volunteering yesterday, I met this amazing woman, Jean, who had been through a lot, yet stayed optimistic and resilient. She showed me the importance of gratitude and connection." - }, - { - "speaker": "John", - "dia_id": "D7:6", - "text": "Wow, Maria! How did she end up in such a situation, and how did she remain positive throughout?" - }, - { - "speaker": "Maria", - "dia_id": "D7:7", - "text": "She had a tough time, going through a divorce, losing her job, and ending up homeless. Yet, she still values the little things and believes in the good of kindness." - }, - { - "speaker": "John", - "dia_id": "D7:8", - "text": "It's really inspiring to see someone staying optimistic even when things get tough." - }, - { - "speaker": "Maria", - "dia_id": "D7:9", - "text": "Yeah, sure is. It's a reminder that, no matter what, we can still find joy and hope." - }, - { - "speaker": "John", - "dia_id": "D7:10", - "text": "Keep up the great work, Maria! It's important to stay positive and thankful, even when life's tough. A little kindness and optimism can go a long way. Sounds like your volunteer work has been very influential - amazing!" - }, - { - "speaker": "Maria", - "dia_id": "D7:11", - "text": "Thanks, John! Your words mean a lot. It's incredible how much positivity and optimism can impact someone." - }, - { - "speaker": "John", - "dia_id": "D7:12", - "text": "Yep. It's moments like these that remind me of how important it is to be kind and optimistic." - }, - { - "speaker": "Maria", - "dia_id": "D7:13", - "text": "Sure thing, John. Those things can really make a difference. Let's keep spreading positivity and making an impact together." - }, - { - "speaker": "John", - "blip_caption": "a photo of a man holding a child on his shoulders", - "dia_id": "D7:14", - "text": "I totally agree, Maria! Let's keep helping each other and make the world a better place.\n\n" - }, - { - "speaker": "Maria", - "dia_id": "D7:15", - "text": "Sounds cool, John! Let's make some positive change and brighten up that place. Do you have anything fun coming up soon?" - }, - { - "speaker": "John", - "dia_id": "D7:16", - "text": "Let's do it, Maria! Together, we can make a real difference and bring a brighter future. And nothing too soon, but my colleague Rob invited me to a beginner's yoga class." - }, - { - "speaker": "Maria", - "dia_id": "D7:17", - "text": "Sounds fun! I hope you have a good time. In the meantime, let's work together to make things better for our community." - } - ], - "session_8_date_time": "6:03 pm on 6 March, 2023", - "session_8": [ - { - "speaker": "Maria", - "blip_caption": "a photo of a bride and groom walking down the street", - "dia_id": "D8:1", - "text": "Hey John, I haven't talked to you in a while. Last week, my grandma passed away and it's been really hard. I'm trying to stay positive, but it's tough. How're you doing?" - }, - { - "speaker": "John", - "img_url": [ - "https://content1.getnarrativeapp.com/static/1396e135-13d4-4c37-8183-5d1eaf957c41/Surprise-proposal-picnic-at-cathedral-park-in-Portland-or-.jpg" - ], - "blip_caption": "a photo of a family sitting on the grass with a baby", - "query": "family picnic", - "dia_id": "D8:2", - "text": "So sorry to hear about your loss, Maria. I'm here for you if you need anything. I'm doing well, I just had a picnic with the wife and kids." - }, - { - "speaker": "Maria", - "dia_id": "D8:3", - "text": "Thanks for your support, John. How's your family doing? That baby in the pic is adorable! What's their name?" - }, - { - "speaker": "John", - "dia_id": "D8:4", - "text": "Thanks, Maria! They're doing great. Our one-year-old is so cute, his name is Kyle!" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "dia_id": "D8:5", - "text": "Sounds like parenting has been a wonderful experience for you - what has it been like?" - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/1883/42932050850_97f4b8999f_b.jpg" - ], - "blip_caption": "a photography of two children playing on a playground equipment", - "query": "parenting kids park playing", - "dia_id": "D8:6", - "re-download": true, - "text": "It's definitely been a wild ride! It's full of ups and downs, but the love and happiness outweigh it all." - }, - { - "speaker": "Maria", - "dia_id": "D8:7", - "text": "Parenting can be tough but rewarding. Do you take your kid to the park often?" - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/1851/44767563171_457da8d8b3_b.jpg" - ], - "blip_caption": "a photography of a young girl and a young boy on a swing", - "query": "park kids playing swings joyful moment", - "dia_id": "D8:8", - "re-download": true, - "text": "Yeah, we go a few times a week. It's great for family bonding and letting the kids run around. We had a great time last weekend - they really enjoyed the carefree joy.\n" - }, - { - "speaker": "Maria", - "dia_id": "D8:9", - "text": "Looks like a blast! Did everyone get a chance to try it? Glad you're all having a great time!" - }, - { - "speaker": "John", - "dia_id": "D8:10", - "text": "Yeah, everyone got a chance to swing. It's always fun coming up with activities for my family to enjoy." - }, - { - "speaker": "Maria", - "dia_id": "D8:11", - "text": "Wow, John, that's amazing! How do you come up with these ideas?" - }, - { - "speaker": "John", - "dia_id": "D8:12", - "text": "I just try to find things that we'll have fun with, like a walk or picnic in the park, or finding events in our town and beyond. Just last week, I found a violin concert that we all enjoyed. It's all about making memories together." - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/eq0vvcinma621.jpg" - ], - "blip_caption": "a photo of a picture of a castle in a shadow box", - "query": "DIY cardboard fort fairy lights", - "dia_id": "D8:13", - "text": "Wow, John, that's great! Making memories together is so valuable. I loved doing something similar with my siblings when I was young - it's amazing how something so simple can make such lasting memories." - }, - { - "speaker": "John", - "dia_id": "D8:14", - "text": "That pic is so cool! It looks like something from a storybook. What gave you the idea for this?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://images.rawpixel.com/image_social_square/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIzLTAzL21ldDMzOTg0NC1pbWFnZS5qcGc.jpg" - ], - "blip_caption": "a photography of a painting of a castle on a hill", - "query": "castle painting Europe beauty wonder", - "dia_id": "D8:15", - "re-download": true, - "text": "Thanks, John! I got the idea from that trip to England a few years ago - I was mesmerized by the castles. I wanted to bring that same magic to my home, like a reminder of the world's beauty. I made a painting too!" - }, - { - "speaker": "John", - "dia_id": "D8:16", - "text": "Wow, that looks great! Where in England was it?" - }, - { - "speaker": "Maria", - "dia_id": "D8:17", - "text": "It was in London. The architecture there was so captivating that I wanted to recreate some of that charm in my own space. It's a reminder of London's history and grace." - }, - { - "speaker": "John", - "blip_caption": "a photo of a wooden sign with a flag and eagle", - "dia_id": "D8:18", - "text": "Wow, having symbols of travel memories in our homes is so cool! Last week I retook the aptitude test with some great results, and I feel drawn to serving my country in this way." - }, - { - "speaker": "Maria", - "dia_id": "D8:19", - "text": "Wow, that's really cool! Have you discussed it with anyone yet?" - }, - { - "speaker": "John", - "dia_id": "D8:20", - "text": "Yeah, I chatted with my family and friends about it. They've been supportive and understand why I want to volunteer. I'm really proud to have this opportunity!" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people standing around a table with food", - "dia_id": "D8:21", - "text": "Glad you have a good support system, John. It's been great having people behind me while volunteering at the homeless shelter. Positive influences make a big impact.\n" - }, - { - "speaker": "John", - "dia_id": "D8:22", - "text": "Cool, Maria! You've built some awesome connections and you're making a big impact at the homeless shelter - nice work! How did you manage to do that?" - }, - { - "speaker": "Maria", - "dia_id": "D8:23", - "text": "Thanks, John! It took time and effort, but I slowly formed connections by listening and showing compassion. It's all about building trust and forming real relationships." - }, - { - "speaker": "John", - "dia_id": "D8:24", - "text": "Wow, Maria! I see, so that's the key to making a difference." - }, - { - "speaker": "Maria", - "dia_id": "D8:25", - "text": "Yeah, building those real connections and getting people to trust us is key. It's usually the little things, like taking the time to hear what they're saying, that make the biggest impact." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting at a table with plates of food", - "dia_id": "D8:26", - "text": "Yeah, Maria, I agree. That can really make a difference. It's important to make people feel noticed and listened to." - } - ], - "session_9_date_time": "9:36 am on 2 April, 2023", - "session_9": [ - { - "speaker": "Maria", - "dia_id": "D9:1", - "text": "Hey John, long time no see! I've been taking a poetry class lately to help me put my feelings into words. It's been a rough ride, but it's been good. How have you been?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.diplomaoutlet.com/wp-content/uploads/u9-1.jpg" - ], - "blip_caption": "a photo of a certificate of completion of a university degree", - "query": "diploma university", - "dia_id": "D9:2", - "text": "Hey Maria! Awesome to hear from you. Sounds like a great way to delve into your feelings. Since we spoke last, I've had quite the adventure!\n" - }, - { - "speaker": "Maria", - "dia_id": "D9:3", - "text": "Congrats on finishing your degree, John! It must have been quite the adventure. How did it feel when you achieved such a big goal?" - }, - { - "speaker": "John", - "dia_id": "D9:4", - "text": "Thanks, Maria! It was quite a journey, but definitely worth it. I graduated last week!" - }, - { - "speaker": "Maria", - "dia_id": "D9:5", - "text": "I bet! What are your plans for the future?" - }, - { - "speaker": "John", - "dia_id": "D9:6", - "text": "Thanks! I'm considering going into policymaking because of my degree and my passion for making a positive impact. There are many opportunities to make improvements." - }, - { - "speaker": "Maria", - "dia_id": "D9:7", - "text": "Sounds great, John! That seems perfect for you with your passion and dedication. Are there any specific areas you're particularly interested in?" - }, - { - "speaker": "John", - "dia_id": "D9:8", - "text": "Thanks, Maria! Improving education and infrastructure is particularly interesting to me. It's important for our community." - }, - { - "speaker": "Maria", - "dia_id": "D9:9", - "text": "Yeah, I remember you mentioning those areas. How have your experiences in the community meeting and involvement shaped your views on them?" - }, - { - "speaker": "John", - "dia_id": "D9:10", - "text": "Going to community meetings and getting involved in my community has given me a better understanding of the challenges our education and infrastructure systems face. It has also shown me the impact these issues have on our neighbors, highlighting the need for us to work towards finding solutions." - }, - { - "speaker": "Maria", - "dia_id": "D9:11", - "text": "Way to go, John! You're really showing dedication and commitment. Gaining first-hand experience and working to find solutions is awesome!" - }, - { - "speaker": "John", - "dia_id": "D9:12", - "text": "Thanks Maria! It's important to me to put my words into action and find solutions. Even though it can be hard, it's so rewarding to know I'm making a difference!" - }, - { - "speaker": "Maria", - "dia_id": "D9:13", - "text": "Agreed, John! Yeah, it can be tough, but it's really satisfying and worthwhile. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D9:14", - "text": "Maria, thanks a lot! Your support is really encouraging - I appreciate having you in my corner!" - }, - { - "speaker": "Maria", - "dia_id": "D9:15", - "text": "No problem, John. Let me know if you need any help. We work well together!" - }, - { - "speaker": "John", - "dia_id": "D9:16", - "text": "Thanks, Maria! Will do. Working together would be great!" - }, - { - "speaker": "Maria", - "img_url": [ - "https://bloximages.newyork1.vip.townnews.com/yakimaherald.com/content/tncms/assets/v3/editorial/4/2d/42d9b634-18c9-11ed-8a1b-039214ad9eb3/62f3dc54b5a61.image.jpg" - ], - "blip_caption": "a photo of a man and woman shaking hands in front of a food tray", - "query": "volunteering at local homeless shelter", - "dia_id": "D9:17", - "text": "Yes, John, let's keep supporting each other and finding ways to improve the lives of others. Remember when we volunteered together last year? It was such a fulfilling experience." - }, - { - "speaker": "John", - "blip_caption": "a photo of a woman and a child walking in a park", - "dia_id": "D9:18", - "text": "Yeah, I remember that! It was cool to see how our actions can make a big impact. Let's keep helping out and making things better! Our actions really do matter. " - } - ], - "session_10_date_time": "12:24 am on 7 April, 2023", - "session_10": [ - { - "speaker": "John", - "dia_id": "D10:1", - "text": "Hey Maria, I'm so excited to tell you I started a weekend yoga class with a colleague - it's awesome! I feel great, both mentally and physically after each session. I'd been wanting to try yoga for a while and finally took the plunge. Simple stretching and breathing is having such a positive effect on my wellbeing. And the instructor is great too." - }, - { - "speaker": "Maria", - "dia_id": "D10:2", - "text": "Wow, John, glad to hear that! It's amazing how something like stretching and breathing can have such a positive effect on our wellbeing. What can you tell me about your instructor that makes them so great?" - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/7721929/pexels-photo-7721929.jpeg" - ], - "blip_caption": "a photography of a man doing yoga outside on a blue mat", - "query": "yoga instructor relaxed yoga pose", - "dia_id": "D10:3", - "re-download": true, - "text": "They're awesome - they make sure we do the poses properly and encourage us to listen to our bodies. They create a great, relaxed environment that makes everyone feel welcome. Here's a photo from our last class." - }, - { - "speaker": "Maria", - "dia_id": "D10:4", - "text": "Wow, that looks great! What kind of yoga is it?" - }, - { - "speaker": "John", - "dia_id": "D10:5", - "text": "It's a beginner yoga class, focusing on fundamentals like poses and breathing. I find it helps me relax and increase my flexibility." - }, - { - "speaker": "Maria", - "dia_id": "D10:6", - "text": "Nice one, John! Glad you're finding some chill with that. How are you feeling afterwards?" - }, - { - "speaker": "John", - "dia_id": "D10:7", - "text": "I feel great. It really helps me relax and feel more connected. It's been a great way to improve my mind and body." - }, - { - "speaker": "Maria", - "dia_id": "D10:8", - "text": "Awesome John! Glad it's chillin' and connecting you. Stretching and breathing are such powerful tools for wellbeing. So cool you found a beginner class to help you flex up. Keep it up! \ud83e\uddd8\u200d\u2640\ufe0f" - }, - { - "speaker": "John", - "dia_id": "D10:9", - "text": "Thanks, Maria! I'm gonna keep it up. Not only for the physical benefits, but also for the peace of mind and mindfulness it brings. It's part of my daily routine now. Have you tried anything new lately that's had an impact on you?" - }, - { - "speaker": "Maria", - "dia_id": "D10:10", - "text": "Last weekend I did something new that had an impact on me. I participated in a 5K charity run for a homeless shelter. It was awesome being surrounded by people all there for the same cause. There's something special about the energy and sense of unity. It was truly rewarding and reminded me why I'm passionate about charity work." - }, - { - "speaker": "John", - "dia_id": "D10:11", - "text": "Wow, Maria! It sounds awesome. I bet you felt so pumped running with everyone for the same cause. Events like these really energize us and remind us we can make a difference. Any pictures from the event?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://blog.myfitnesspal.com/wp-content/uploads/2019/06/8-Charity-Walking-Events-That-Give-Your-Steps-Extra-Meaning-1200x900.jpg" - ], - "blip_caption": "a photo of a large group of people walking down a street", - "query": "charity event running group of people", - "dia_id": "D10:12", - "text": "Here's a pic from the event! The energy was great, it was inspiring seeing everyone come together for a shared cause. It was awesome!" - }, - { - "speaker": "John", - "img_url": [ - "https://c.pxhere.com/photos/88/c3/motivation_motivational_heart_inspire_encouragement_quote-562325.jpg!d" - ], - "blip_caption": "a photography of a heart shaped sign with a quote on it", - "query": "people united great cause career fair underprivileged students resources", - "dia_id": "D10:13", - "re-download": true, - "text": "What a photo! Seeing everyone come together for a shared cause must have been inspiring. Last weekend I had an experience that reminded me of the impact we can make. I got to volunteer at a career fair at a local school, and it was incredible to see how lack of resources affects these kids' dreams. Being able to help them was such a rewarding experience." - }, - { - "speaker": "Maria", - "dia_id": "D10:14", - "text": "Wow, John, what an amazing experience! It's so sad how a lack of resources can make such a difference in these kids' dreams. Being able to help them was an awesome experience. What does the sign say?" - }, - { - "speaker": "John", - "dia_id": "D10:15", - "text": "The sign says, \"Always look on the bright side of life\". It reminds us that kids can reach their dreams with the right help." - }, - { - "speaker": "Maria", - "dia_id": "D10:16", - "text": "That's really cool. It's inspiring to see how these kids can do great things with support. You're doing awesome work by helping and motivating them. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D10:17", - "text": "Thanks, Maria! It means a lot. I'm gonna keep pushing for them. We need folks in the community, doing good for the ones who need it. We rock!" - }, - { - "speaker": "Maria", - "dia_id": "D10:18", - "text": "Yeah John, let's keep pushing for those kids! We can make a difference and help lots of people. Keep up the good work!" - } - ], - "session_11_date_time": "6:13 pm on 10 April, 2023", - "session_11": [ - { - "speaker": "John", - "dia_id": "D11:1", - "text": "Hey Maria, haven't talked for a few days. Had a wild week, my car broke down last Fri on my way to work. Trying to get it fixed but it's tough & putting a strain on my wallet. Staying positive & looking for a solution though." - }, - { - "speaker": "Maria", - "dia_id": "D11:2", - "text": "Aww John, bummer about that. No doubt it's been tough, but I'm impressed by how positive you're being. Keep it up - tough times pass, but you're tough enough to get through 'em!" - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/1266810/pexels-photo-1266810.jpeg" - ], - "blip_caption": "a photography of a mountain with a sunset and flowers in the foreground", - "query": "road beautiful sunset", - "dia_id": "D11:3", - "re-download": true, - "text": "Thanks, Maria. Your kind words mean a lot. Yeah, it's been tough with car trouble and money problems, but I stay positive and find a way. This picture reminds me of a road trip we took last year; even with bumps along the way, there's still beauty and hope." - }, - { - "speaker": "Maria", - "dia_id": "D11:4", - "text": "Wow, great pic! Where did you go on that road trip?" - }, - { - "speaker": "John", - "dia_id": "D11:5", - "text": "Thanks! We explored the coast up in the Pacific Northwest and hit some cool national parks. The beauty of nature was absolutely breathtaking!" - }, - { - "speaker": "Maria", - "dia_id": "D11:6", - "text": "Wow, that must've been great! It's so nice to appreciate nature and find peace. Lucky you got to experience that." - }, - { - "speaker": "John", - "dia_id": "D11:7", - "text": "Wow, it was amazing. The stunning views really make you think." - }, - { - "speaker": "Maria", - "dia_id": "D11:8", - "text": "Nature helps put things in perspective and reminds us of the beauty even during tough times. Hold onto those moments of peace." - }, - { - "speaker": "John", - "dia_id": "D11:9", - "text": "Yeah, Maria. That peace and beauty are so needed, especially during tough times. They give us the power and inspiration to continue. Anything cool you're up to now?" - }, - { - "speaker": "Maria", - "dia_id": "D11:10", - "text": "I recently gave a few talks at the homeless shelter I volunteer at. It was really fulfilling and I received lots of compliments from other volunteers. It was a great reminder about why connecting with and helping others is so important. And, I bought a cross necklace to feel closer to my faith- which has made me happy." - }, - { - "speaker": "John", - "dia_id": "D11:11", - "text": "Way to go, Maria! You're making a real difference. It's awesome how connecting with and helping others brings you so much joy. Keep it up!" - }, - { - "speaker": "Maria", - "dia_id": "D11:12", - "text": "Thanks, John! It's so great to make a real difference. Seeing the impact and hearing the gratitude fills me with so much joy. The people at the shelter have become like family to me. I feel really blessed to know them." - }, - { - "speaker": "John", - "dia_id": "D11:13", - "text": "Wow, Maria, what you're doing is truly amazing. Your kindness ripples and creates such incredible relationships!" - }, - { - "speaker": "Maria", - "img_url": [ - "https://portlandrescuemission.org/wp-content/uploads/2017/11/20160809_gc_0797-copy.jpg" - ], - "blip_caption": "a photo of a group of people standing around a table filled with food", - "query": "homeless shelter volunteers", - "dia_id": "D11:14", - "text": "Thanks, John. Building relationships and seeing kindness really does make a difference. Here's a pic from last week. Seeing everyone come together warms my heart and fills me with hope." - }, - { - "speaker": "John", - "dia_id": "D11:15", - "text": "Wow, that's amazing how everyone came together. You must have had some great ideas! What do you do there?" - }, - { - "speaker": "Maria", - "dia_id": "D11:16", - "text": "We organized a meal for the shelter residents and I helped with getting everything ready. It was cool to see everyone together, eating and supporting each other." - }, - { - "speaker": "John", - "dia_id": "D11:17", - "text": "Wow, Maria, that's awesome! You made everyone so comfortable and it must have been so rewarding. You're really making a difference!" - }, - { - "speaker": "Maria", - "dia_id": "D11:18", - "text": "Yeah, it was really nice to help bring people together and create a sense of comfort and community. It's something special to see people supporting each other." - }, - { - "speaker": "John", - "dia_id": "D11:19", - "text": "Definitely, Maria. That's great. It gives us hope and reminds us we're not alone. Thank you for being a positive force." - }, - { - "speaker": "Maria", - "dia_id": "D11:20", - "text": "No problem, John! It's great to feel this sense of community. Thanks for the kind words, they mean a lot." - }, - { - "speaker": "John", - "dia_id": "D11:21", - "text": "Glad I could help, Maria. Talk to you soon. Stay safe!" - } - ], - "session_12_date_time": "7:34 pm on 18 April, 2023", - "session_12": [ - { - "speaker": "John", - "dia_id": "D12:1", - "text": "Hey Maria, hope you're doing okay. Since we chatted last, I've been blogging about politics and the government. It's been a really satisfying experience and I care about making a real impact. We need way better education and infrastructure and I know firsthand how this impacts neighborhoods." - }, - { - "speaker": "Maria", - "dia_id": "D12:2", - "text": "Hey John, glad to hear you're fired up about something! Blogging can really make a difference. I agree that education and infrastructure are key to our community's growth." - }, - { - "speaker": "John", - "dia_id": "D12:3", - "text": "Thanks, Maria! It's been great to talk to someone who understands the importance of these issues. Digging deeper into the political system has been eye-opening, so I'm researching policies and writing about my thoughts and ideas. Hoping to raise awareness and start conversations to create positive change." - }, - { - "speaker": "Maria", - "dia_id": "D12:4", - "text": "Wow, John! Your hard work will definitely start conversations and create positive change. What policies have you been focusing on lately?" - }, - { - "speaker": "John", - "dia_id": "D12:5", - "text": "Recently, education reform and infrastructure development. Good access to quality education and updated infrastructure are key to a thriving and successful community. My goal is to get conversations going and get people involved by sharing ideas and taking action. It's really empowering to know I can help make a difference in people's lives." - }, - { - "speaker": "Maria", - "dia_id": "D12:6", - "text": "Wow, John! Your passion and dedication is inspiring. It's great to see you taking the lead and making a difference. Keep up the amazing work!" - }, - { - "speaker": "John", - "img_url": [ - "https://byronernest.files.wordpress.com/2022/11/img_5966-1.jpg" - ], - "blip_caption": "a photo of two men standing next to each other at a convention", - "query": "blog post education reform", - "dia_id": "D12:7", - "text": "Thanks, Maria! Really appreciate your support and encouragement, it means a lot to me. I've gotten some good feedback on my blog posts so far. It's just a small step, but every step counts." - }, - { - "speaker": "Maria", - "dia_id": "D12:8", - "text": "It seems like your post is having an effect. Who are they? They're having fun!" - }, - { - "speaker": "John", - "dia_id": "D12:9", - "text": "My colleagues and I went to a convention together last month. We're all passionate about using tech for good in our community. It was great to connect with like-minded folks and swap ideas. It's inspiring to see people united in their goal." - }, - { - "speaker": "Maria", - "dia_id": "D12:10", - "text": "Wow, that must have been awesome! Being around people who share your passion is truly inspiring. How did it feel to be surrounded by like-minded individuals there?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting around a table", - "dia_id": "D12:11", - "text": "Talking with the group of people who were as stoked as me on tech for change was awesome! It made me think we really can make a difference." - }, - { - "speaker": "Maria", - "dia_id": "D12:12", - "text": "No way, John! That's really cool. What was the most exciting part of it?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of military men sitting around a table", - "dia_id": "D12:13", - "text": "The best part was the energy in the room - so infectious! We all had great ideas, brainstormed together, and stayed motivated. It was really empowering." - }, - { - "speaker": "Maria", - "dia_id": "D12:14", - "text": "That sounds amazing! How did being in that environment with such motivated people affect you?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a table with a map of a city on it", - "dia_id": "D12:15", - "text": "The motivated people around me gave me renewed energy and a purpose. It really inspired me to make a bigger difference." - }, - { - "speaker": "Maria", - "dia_id": "D12:16", - "text": "Cool, John! It's inspiring to be around people like that. Anything exciting on the horizon?" - }, - { - "speaker": "John", - "dia_id": "D12:17", - "text": "I'm planning a trip to the East Coast. How about you? Anything cool going on recently?" - }, - { - "speaker": "Maria", - "dia_id": "D12:18", - "text": "I'm still volunteering at the homeless shelter. It's fulfilling to lend a hand." - }, - { - "speaker": "John", - "dia_id": "D12:19", - "text": "Wow, Maria! You're so dedicated to helping people. How's it been going?" - }, - { - "speaker": "Maria", - "dia_id": "D12:20", - "text": "It's been rewarding and tough. It's fulfilling, but the growing need for help can be overwhelming." - }, - { - "speaker": "John", - "dia_id": "D12:21", - "text": "It's tough sometimes, but every act of kindness matters. You're so dedicated and inspiring, Maria. Keep going!" - }, - { - "speaker": "Maria", - "dia_id": "D12:22", - "text": "Thanks, John. Your kind words mean a lot. Little acts of kindness can have a big effect. We can all do something to make a difference." - }, - { - "speaker": "John", - "dia_id": "D12:23", - "text": "You're right, every small act can make a big impact. Let's keep doing our part for the world!" - } - ], - "session_13_date_time": "3:18 pm on 4 May, 2023", - "session_13": [ - { - "speaker": "John", - "dia_id": "D13:1", - "text": "Hey Maria! Long time no see! Tons has gone down since then!" - }, - { - "speaker": "Maria", - "dia_id": "D13:2", - "text": "Hey John! Nice to hear from you. What's new with you?" - }, - { - "speaker": "John", - "dia_id": "D13:3", - "text": "I just started going to boot camps with my fam last month. It's good for bonding and getting fit." - }, - { - "speaker": "Maria", - "dia_id": "D13:4", - "text": "Sounds awesome, John! Working out together is a great way of bonding as a family. Have you noticed any changes in your fitness since you started?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a white board with a list of workouts on it", - "dia_id": "D13:5", - "text": "Yeah Maria! We've had lots more energy and noticed some gains in strength and endurance. We're pushing ourselves and supporting each other which is super motivating. Best of all, my kids are getting excited about staying active!" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people standing around a table filled with food", - "dia_id": "D13:6", - "text": "Great news John! It's important to teach them good habits early. How often do you all workout together?" - }, - { - "speaker": "John", - "dia_id": "D13:7", - "text": "Thanks, Maria! Three times a week; it keeps us on track." - }, - { - "speaker": "Maria", - "dia_id": "D13:8", - "text": "Wow, John! You and your family are awesome for staying consistent and motivated to create healthy habits. Seeing that kind of dedication is inspiring! Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D13:9", - "text": "I will! It hasn't been easy, but we're all in it together." - }, - { - "speaker": "Maria", - "dia_id": "D13:10", - "text": "You're really inspiring with your commitment. Keep motivating each other!" - }, - { - "speaker": "John", - "dia_id": "D13:11", - "text": "Yeah, for sure! We'll keep pushing each other and staying motivated. It's great to be on this journey together." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a family posing for a picture in a park", - "dia_id": "D13:12", - "text": "Yep, John! Having a support system is key to staying motivated and reaching our goals. It's great that you have your family on this journey with you. Family is important - Even though mine is small, I rely on them for strength during tough times." - }, - { - "speaker": "John", - "dia_id": "D13:13", - "text": "Yeah, Maria! Family is the most important thing for me. They're my biggest support. We have each other's back through good times and bad." - }, - { - "speaker": "Maria", - "dia_id": "D13:14", - "text": "Wow, John! It's great to know that they're there for you. How have they been helping you out lately?" - }, - { - "speaker": "John", - "dia_id": "D13:15", - "text": "We've all been helping and supporting each other during boot camp - cheering each other on during workouts and providing emotional support outside them. It's been amazing witnessing us come together and be there for each other." - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.pinimg.com/originals/2b/14/2b/2b142b420a54216cfc9432b6b35676b5.jpg" - ], - "blip_caption": "a photo of a table with a variety of food on it", - "query": "family gathering dinner home", - "dia_id": "D13:16", - "text": "Wow, John! It's great when you have that kind of support. My mom and I made some dinner together last night!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/tc1f75pa9df21.jpg" - ], - "blip_caption": "a photo of a person holding a plate with a banana split ice cream sundae", - "query": "homemade apple pie vanilla ice cream", - "dia_id": "D13:17", - "text": "Thanks, Maria. Having good support is key. Appreciate you being there for me. What do you have in this spread? Looks delicious! I made this apple pie for the kids yesterday too!" - }, - { - "speaker": "Maria", - "dia_id": "D13:18", - "text": "Thanks, John! It had lots of great things like salads, sandwiches, and homemade desserts. My favorite is the amazing banana split sundae - I enjoy it after a day of volunteering. It's a little moment of joy in life - balance is key!" - }, - { - "speaker": "John", - "dia_id": "D13:19", - "text": "Mm, yum! A bit of joy is definitely important. How do you find balance in your life?" - }, - { - "speaker": "Maria", - "dia_id": "D13:20", - "text": "Taking care of myself physically, emotionally, and mentally helps me. That includes things like exercise, music, and spending time with loved ones. It really helps me stay positive." - }, - { - "speaker": "John", - "dia_id": "D13:21", - "text": "Awesome, Maria! Taking care of ourselves and looking out for our well-being is key. Finding balance like you mentioned helps us out a lot." - }, - { - "speaker": "Maria", - "img_url": [ - "https://images.pexels.com/photos/1974521/pexels-photo-1974521.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-julia-kuzenkov-1974521.jpg" - ], - "blip_caption": "a photo of a beach with footprints in the sand and a blue sky", - "query": "sunrise beach", - "dia_id": "D13:22", - "text": "Yeah John, it's super important. Taking care of yourself helps us be strong for life's tough times - I learned that the hard way last year." - }, - { - "speaker": "John", - "dia_id": "D13:23", - "text": "That looks interesting. What's the story behind the picture?" - }, - { - "speaker": "Maria", - "dia_id": "D13:24", - "text": "Last year I took a solo trip and took this pic in Spain. It reminded me that life is hard but there's still hope and beauty. It made me realize the importance of relying on my inner strength and appreciating small moments even more." - }, - { - "speaker": "John", - "dia_id": "D13:25", - "text": "That's a great pic, Maria. Yeah, life can be tough but finding beauty in the world can really make a difference. It sounds like your solo trip was a transformative experience." - }, - { - "speaker": "Maria", - "img_url": [ - "https://images.pexels.com/photos/11663179/pexels-photo-11663179.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-serhat-tu%C4%9F-11663179.jpg" - ], - "blip_caption": "a photo of a small island with a lone boat in the water", - "query": "sunrise calm lake", - "dia_id": "D13:26", - "text": "Yeah, it was great. It helped me grow and made me appreciate life more. Taking a step back can really show us the beauty of life." - }, - { - "speaker": "John", - "dia_id": "D13:27", - "text": "Wow, Maria, that's a really nice picture! Did you learn anything meaningful during your trip?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D13:28", - "text": "Yeah, I realized that it's important to value different perspectives and the power of solitude. Plus, it reminded me of the importance of letting go and trusting life sometimes." - }, - { - "speaker": "John", - "dia_id": "D13:29", - "text": "The pic takes me back. Did you try anything new on your vacation?" - }, - { - "speaker": "Maria", - "dia_id": "D13:30", - "text": "Yeah, I did! I tried my hand at surfing for the first time- it was so exciting! Even though I kept falling off, it taught me the power of not giving up and trying new things." - }, - { - "speaker": "John", - "blip_caption": "a photo of a man sitting on a mountain with a view of a lake", - "dia_id": "D13:31", - "text": "Wow Maria, that's awesome! Trying new things takes guts and it can be so rewarding. Keep up the courage!" - }, - { - "speaker": "Maria", - "dia_id": "D13:32", - "text": "Thanks, John! It can be rewarding to try new things. It's good to challenge ourselves." - }, - { - "speaker": "John", - "dia_id": "D13:33", - "text": "Yeah, Maria! We gotta push ourselves out of our comfort zones to experience new things and learn!" - }, - { - "speaker": "Maria", - "dia_id": "D13:34", - "text": "Yeah, John! That's so important." - }, - { - "speaker": "John", - "dia_id": "D13:35", - "text": "Sure, Maria! It helps us grow and learn more about ourselves. It's great to push the limits and see what we can do." - }, - { - "speaker": "Maria", - "dia_id": "D13:36", - "text": "Yeah, pushing boundaries is how we grow and find out what we're really capable of. It's a journey of self-exploration, it can be hard but it's so worth it. Blessed we both have this mindset in our lives." - }, - { - "speaker": "John", - "dia_id": "D13:37", - "text": "Yeah, Maria! It's like a never-ending journey of learning and growth. We've got the right attitude to take on the harder things in life. That's what helps us keep improving." - } - ], - "session_14_date_time": "5:04 pm on 6 May, 2023", - "session_14": [ - { - "speaker": "John", - "blip_caption": "a photo of a poster on a bulletin board with a man smiling", - "dia_id": "D14:1", - "text": "Hey Maria, great to chat again! A lot has happened since we last spoke. Last week, I decided to run for office again - even though I haven't been successful before. I guess I can't let go of my dream to make a difference in my community. Can you believe it? Feels like a dream come true!" - }, - { - "speaker": "Maria", - "dia_id": "D14:2", - "text": "Way to go, John! You're doing great. I'm so proud of you for sticking with it. You're always dreaming up ways to make a difference and I'm sure your drive will pay off. Don't be afraid to take risks-- I'm 100% behind you!" - }, - { - "speaker": "John", - "dia_id": "D14:3", - "text": "Maria, your support really means a lot! It's tough, but your kind words give me the motivation to keep going. I've still got a lot to learn, but I'm determined to make a difference. I appreciate your belief in me." - }, - { - "speaker": "Maria", - "dia_id": "D14:4", - "text": "John, I believe in you! Even small steps will make a difference. Keep going and stay true to yourself. You got this!" - }, - { - "speaker": "John", - "dia_id": "D14:5", - "text": "Thanks, Maria! Your words really mean something. Sometimes it feels like it's too much, but your encouragement gives me the strength to keep going." - }, - { - "speaker": "Maria", - "dia_id": "D14:6", - "text": "Hey John, it's okay to feel overwhelmed from time to time. Just remember to pause, reflect, and take care of yourself. I'm here for you - let me know how I can support you." - }, - { - "speaker": "John", - "dia_id": "D14:7", - "text": "Thanks, Maria. Got it. It's easy to get lost and forget that. Appreciate your support. How's that going for you?" - }, - { - "speaker": "Maria", - "dia_id": "D14:8", - "text": "Hey John, I've been doing great lately. I've been involved in some charity work and it's been really rewarding. I feel connected and it's really fulfilling." - }, - { - "speaker": "John", - "dia_id": "D14:9", - "text": "Wow, Maria! Glad you found something that brings you so much joy. What kind of work have you been doing?" - }, - { - "speaker": "Maria", - "dia_id": "D14:10", - "text": "Just yesterday I joined a nearby church. I wanted to feel closer to a community and my faith. So far it's been really great!" - }, - { - "speaker": "John", - "dia_id": "D14:11", - "text": "That's great, Maria! Joining a church can be really encouraging. I'm thrilled for you. How's everything been going?" - }, - { - "speaker": "Maria", - "dia_id": "D14:12", - "text": "Life's been a bit rough lately but I'm doing alright. I'm taking the time to reflect and find some balance. How about you? What's been going on since we last talked?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/bfie8r8yhekb1.jpg" - ], - "blip_caption": "a photo of a dark street at night with a fence and a street light", - "query": "power outage dark street infrastructure community", - "dia_id": "D14:13", - "text": "I had a similar experience. Last week, there was a power cut in our area, and it made me realize the importance of upgrading our infrastructure for stable services for everyone. Look how dark it was!" - }, - { - "speaker": "Maria", - "dia_id": "D14:14", - "text": "Wow, John, that's rough. Anything else around here that needs to be fixed so it doesn't happen again?" - }, - { - "speaker": "John", - "dia_id": "D14:15", - "text": "Yep, Maria. Mainly the roadways. They're full of potholes and can be dangerous for drivers and damaging to cars. Some improvements are definitely needed." - }, - { - "speaker": "Maria", - "dia_id": "D14:16", - "text": "Yeah, I remember you mentioning the roads. They can be quite dangerous, huh? Is there anything I can do to help improve the situation?" - }, - { - "speaker": "John", - "dia_id": "D14:17", - "text": "Thanks for the offer, Maria. I'm thinking about starting a community project regarding infrastructure, so maybe we can work together to get the neighborhood's backing." - }, - { - "speaker": "Maria", - "img_url": [ - "https://live.staticflickr.com/936/41912295190_f437e63596_b.jpg" - ], - "blip_caption": "a photography of a group of people looking at a map", - "query": "local meeting community members neighborhood improvements", - "dia_id": "D14:18", - "re-download": true, - "text": "Sounds like a plan, John! Let's work together to get the community involved and make a difference." - }, - { - "speaker": "John", - "dia_id": "D14:19", - "text": "That pic makes me think of how important it is to fight for better housing and living conditions in our neighborhood. We can definitely make a difference!" - }, - { - "speaker": "Maria", - "dia_id": "D14:20", - "text": "Definitely, John! It's important that everyone has access to affordable housing. Let's get the community on board and fight for better living standards. We can make a difference!" - }, - { - "speaker": "John", - "dia_id": "D14:21", - "text": "Sure, Maria! Let's work together to make a real difference. Our neighborhood deserves it! I want to work on improving my old area, West County, too. " - }, - { - "speaker": "Maria", - "dia_id": "D14:22", - "text": "Yep, John! We should all join forces to create a safe and bustling environment for our community, and others too. Let's make some real change." - }, - { - "speaker": "John", - "blip_caption": "a photo of a garden with a raised bed of plants", - "dia_id": "D14:23", - "text": "Yeah, Maria, let's keep working together to make our neighborhood something to be proud of! We'll keep going and never give up." - } - ], - "session_15_date_time": "7:38 pm on 20 May, 2023", - "session_15": [ - { - "speaker": "John", - "dia_id": "D15:1", - "text": "Hey Maria, how's it going? Been real busy tackling a project to support military veterans. Trying to get a petition going, it's pretty rewarding but kinda stressful. How are you?" - }, - { - "speaker": "Maria", - "dia_id": "D15:2", - "text": "Hey John, I'm good. Sorry to hear it's been rough. How did you decide to take on that project?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a parking lot with a flag and flowers", - "dia_id": "D15:3", - "text": "Hi Maria, thanks for asking. I've always been passionate about veterans and their rights. Last week, I realized again how much they've done for us and wanted to show my appreciation in some way. So, I worked on a project to support them. It's my way of giving back." - }, - { - "speaker": "Maria", - "dia_id": "D15:4", - "text": "Wow, John! What a kind gesture. It's really cool seeing you making a difference." - }, - { - "speaker": "John", - "dia_id": "D15:5", - "text": "Thanks, Maria. It sure has taken a lot of work, but I think it's worth it. Just want to let them know their hard work is appreciated." - }, - { - "speaker": "Maria", - "dia_id": "D15:6", - "text": "Wow, John, that's awesome! Your project is going to be great for them!" - }, - { - "speaker": "John", - "dia_id": "D15:7", - "text": "Yeah, I hope so. They've given so much and deserve our backing. I want to help make their lives better." - }, - { - "speaker": "Maria", - "dia_id": "D15:8", - "text": "That's great, John! It's awesome that you're doing that." - }, - { - "speaker": "John", - "dia_id": "D15:9", - "text": "I'm really passionate about making sure veterans are supported and valued in our community, so let's make it happen!" - }, - { - "speaker": "Maria", - "dia_id": "D15:10", - "text": "Count me in, John! I'm in to help out with that. Let's make a difference together." - }, - { - "speaker": "John", - "img_url": [ - "https://cdn2.picryl.com/photo/2020/01/13/combat-veterans-associated-with-troops-first-foundation-06145f-1024.jpg" - ], - "blip_caption": "a photography of a man shaking hands with a soldier in uniform", - "query": "group of veterans smiling and shaking hands", - "dia_id": "D15:11", - "re-download": true, - "text": "Awesome, Maria! I knew you'd be on board. With your help, we can make a real impact. Thank you so much! Here's a pic from last Friday with some veterans who are really excited about this. Their support keeps me motivated." - }, - { - "speaker": "Maria", - "dia_id": "D15:12", - "text": "Wow, John, that looks great! What did you do to make them so happy?" - }, - { - "speaker": "John", - "dia_id": "D15:13", - "text": "Thanks, Maria! We had a great time throwing a small party and inviting some veterans to share their stories. It was awesome seeing them make connections and find camaraderie. All the smiles and new friendships made it really heartwarming." - }, - { - "speaker": "Maria", - "dia_id": "D15:14", - "text": "Wow, John, that's really heartwarming! Stories and connections can truly make a difference." - }, - { - "speaker": "John", - "dia_id": "D15:15", - "text": "Yeah, Maria. It reminded me of that sense of community and togetherness. It made me realize how important it is to help veterans." - }, - { - "speaker": "Maria", - "dia_id": "D15:16", - "text": "Yeah John! It's great to see how your project is making a difference for them. The sense of community and support really matters. Keep doing what you're doing; I'm here to back you up!" - }, - { - "speaker": "John", - "dia_id": "D15:17", - "text": "Thanks, Maria! Your support means a lot. Together, we can make a difference for our veterans!" - }, - { - "speaker": "Maria", - "dia_id": "D15:18", - "text": "Yep, John! Keep going and we'll make a difference! I'm currently planning a ring-toss tournament for the homeless shelter's fundraiser later this month, I can't wait to see the impact it will make. " - }, - { - "speaker": "John", - "dia_id": "D15:19", - "text": "Wow, sounds fun! I can't wait to hear more about it. Talk to you soon!" - } - ], - "session_16_date_time": "1:24 pm on 25 May, 2023", - "session_16": [ - { - "speaker": "John", - "img_url": [ - "https://pdx350.salsalabs.org/juneteamcampaignupdate/ca4594c6-1102-4939-b090-61f878580f48.jpg" - ], - "blip_caption": "a photo of a group of people and a dog standing in front of a waterfall", - "query": "petition support education and infrastructure volunteers team", - "dia_id": "D16:1", - "text": "Hey Maria, I've been busy doing the petition I started - it's tricky but it's been cool getting back in touch with my buddies and gaining support. I got this picture of my workmates when we went on a hiking trip, they really make me keep going! What have you been up to? Anything new with your charity?" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a red trash can with clothes in it", - "dia_id": "D16:2", - "text": "Hey John! Cool that it's going well - you and your friends look like a great team! I'm busy at the shelter getting ready for a fundraiser next week. Hopefully, I can raise enough to cover basic needs for the homeless." - }, - { - "speaker": "John", - "blip_caption": "a photo of a poster for a chili cook off event", - "dia_id": "D16:3", - "text": "Wow, Maria! Raising money is crucial for those in need. Is there any way I can help out with your fundraiser?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://patch.com/img/cdn20/users/26387059/20221221/113941/bbd143cd-d2aa-4d9f-8547-6f333402e7aa___21113933647.jpg" - ], - "blip_caption": "a photo of a poster for a chili cook off event", - "query": "chili cook off poster event", - "dia_id": "D16:4", - "text": "Thanks, John! Appreciate your help. We need to get the word out about the chili cook-off at the fundraiser. Here's the poster!" - }, - { - "speaker": "John", - "dia_id": "D16:5", - "text": "Wow, it looks awesome! I'll make sure to spread the word about it. Is there anything else I can do to assist?" - }, - { - "speaker": "Maria", - "dia_id": "D16:6", - "text": "Thanks, John! Your help is really appreciated. If you know anyone who might be interested in volunteering for the event, let me know. We can do this!" - }, - { - "speaker": "John", - "dia_id": "D16:7", - "text": "Yep, Maria! I'll ask around to see if anyone I know wants to help. We'll find some awesome people for the cause. Let's make a change!" - }, - { - "speaker": "Maria", - "dia_id": "D16:8", - "text": "Way to go, John! Let's help those in need. Thanks for your support!" - }, - { - "speaker": "John", - "dia_id": "D16:9", - "text": "No problem, Maria! Working together with passionate people like you is awesome! Let's make a difference." - }, - { - "speaker": "Maria", - "dia_id": "D16:10", - "text": "Yeah, working with passionate people like you is really motivating." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/cuurrb4qyl961.jpg" - ], - "blip_caption": "a photo of a couple walking into the ocean at sunset", - "query": "kids holding hands sunset", - "dia_id": "D16:11", - "text": "Yeah, Maria! We're making a difference and we'll keep it up! Here's a pic of my fam at the beach." - }, - { - "speaker": "Maria", - "dia_id": "D16:12", - "text": "Wow, John, that pic is gorgeous! It really gives me hope to appreciate the little moments." - }, - { - "speaker": "John", - "dia_id": "D16:13", - "text": "Thanks, Maria! It's moments like these that give me hope too." - }, - { - "speaker": "Maria", - "dia_id": "D16:14", - "text": "Yeah, John! They give me peace and make me appreciate life." - }, - { - "speaker": "John", - "dia_id": "D16:15", - "text": "Glad the photo made you feel that way, Maria. Cherish those little moments!" - }, - { - "speaker": "Maria", - "dia_id": "D16:16", - "text": "Thanks, John. I definitely will!" - }, - { - "speaker": "John", - "dia_id": "D16:17", - "text": "Thanks for letting me help, Maria. It's moments like these that make life worth living." - }, - { - "speaker": "Maria", - "dia_id": "D16:18", - "text": "Yep, John. These reminders help us stay motivated to make a positive impact. Well, talk to you soon!" - }, - { - "speaker": "John", - "dia_id": "D16:19", - "text": "Yeah, Maria! We're really making progress towards making a positive impact. I believe in us! See ya!" - } - ], - "session_17_date_time": "11:51 am on 3 June, 2023", - "session_17": [ - { - "speaker": "John", - "dia_id": "D17:1", - "text": "Hey Maria, long time no talk! Life's been pretty wild lately. The toughest thing to deal with is that we had to say goodbye to Max. He was such an important part of our family for 10 years and it's so hard to think he's not here wagging that tail anymore." - }, - { - "speaker": "Maria", - "dia_id": "D17:2", - "text": "John, I'm sorry to hear about Max. It can be tough to lose a pet - they're like family. How have you been coping?" - }, - { - "speaker": "John", - "dia_id": "D17:3", - "text": "Hey Maria, thanks for the kind words. We're all still sad about it, but have been comforted by the good times we had and the memories we have. It's tough, but we'll keep doing our best." - }, - { - "speaker": "Maria", - "dia_id": "D17:4", - "text": "Good to hear that you're finding comfort in the good times you had with Max. Pets really have a way of touching our hearts. Do you have any pictures you'd like to share?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/pdwhtmvi74va1.jpg" - ], - "blip_caption": "a photo of a dog sitting in the grass with a leash", - "query": "golden retriever backyard tree shade", - "dia_id": "D17:5", - "text": "Let's make sure we make progress in the coming weeks to ensure that we honor the memories of our loved ones. Here's a picture of him that I cherish. \n" - }, - { - "speaker": "Maria", - "dia_id": "D17:6", - "text": "Max looks so peaceful in that photo, just chilling in the backyard. It's nice to remember him like that. It's a great way to honor him." - }, - { - "speaker": "John", - "dia_id": "D17:7", - "text": "Yeah, that's how we'll think of him - peaceful and happy. He brought us so much joy. It's heartbreaking that he's gone but we're grateful to have had him as part of our family. He taught us a lot about love and loyalty." - }, - { - "speaker": "Maria", - "dia_id": "D17:8", - "text": "Max was truly awesome. Let's cherish the lessons he taught." - }, - { - "speaker": "John", - "dia_id": "D17:9", - "text": "He really taught us the importance of unconditional love and loyalty. I want my kids to learn that too. Our bond with him was so special and something I cherish." - }, - { - "speaker": "Maria", - "dia_id": "D17:10", - "text": "That's a great lesson to pass on to your kids, John. Both are really important for strong relationships. Any plans to give another pet a loving home?" - }, - { - "speaker": "John", - "dia_id": "D17:11", - "text": "We're considering adopting a rescue dog - for love and to teach our kids responsibility and compassion." - }, - { - "speaker": "Maria", - "dia_id": "D17:12", - "text": "John, that's such a great idea! It gives the pup a loving home and teaches your kids important values. If you need any help, let me know! I just started volunteering at a local dog shelter once a month." - }, - { - "speaker": "John", - "dia_id": "D17:13", - "text": "Thanks for the offer, Maria! It's so awesome to have friends like you. If we need help, we'll let you know. Appreciate it!" - }, - { - "speaker": "Maria", - "dia_id": "D17:14", - "text": "Sure thing, John. Let me know if you need any help." - }, - { - "speaker": "John", - "dia_id": "D17:15", - "text": "Sure thing, Maria. Thanks for the support! It means a lot to me." - }, - { - "speaker": "Maria", - "dia_id": "D17:16", - "text": "No worries, John. I'm here for you. Take care!" - } - ], - "session_18_date_time": "2:47 pm on 12 June, 2023", - "session_18": [ - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/zcnesiymmou91.jpg" - ], - "blip_caption": "a photo of a group of men sitting around a campfire", - "query": "camping trip friends", - "dia_id": "D18:1", - "text": "Hey John, how're you doing? I'm sorry about Max. Losing a pet is tough. Some friends from church and I went camping last weekend - it was a blast! Just something nice to take my mind off things. Anything fun in your life lately?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/q33cupol6fm51.jpg" - ], - "blip_caption": "a photo of a man standing on top of a mountain with a backpack", - "query": "mountaineering expedition standing on mountain peak", - "dia_id": "D18:2", - "text": "Hey Maria, thanks for your kind words. It's still tough, but I'm finding some comfort in the good memories. Wow, your camping trip sounds awesome! I went on a mountaineering trip last week with some workmates. It was great and helped clear my head. Anything else cool happening in your life?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://fundiegofamily.com/wp-content/uploads/2019/10/grandcanyon1.jpg" - ], - "blip_caption": "a photo of a person standing on a cliff overlooking a canyon", - "query": "hiking trail beautiful scenery grand canyon road trip", - "dia_id": "D18:3", - "text": "Glad you're finding comfort, John. That mountaineering trip sounds amazing. Did you reach the summit? When I was younger, my family and I went on a road trip to Oregon." - }, - { - "speaker": "John", - "dia_id": "D18:4", - "text": "Thanks, Maria! Yeah, we made it to the top and the view was stunning. It was tough but awesome. Your family trip must have been great too, right? What was the prettiest spot?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://everydayrunaway.com/wp-content/uploads/2016/08/20140323-multnomahfalls.jpg" - ], - "blip_caption": "a photo of a waterfall with a bridge over it", - "query": "hiking trail waterfall", - "dia_id": "D18:5", - "text": "Hiking to the top and seeing this was awesome! Breath-taking." - }, - { - "speaker": "John", - "dia_id": "D18:6", - "text": "Wow, Maria! That waterfall and bridge look amazing! What a view. How was it being there?" - }, - { - "speaker": "Maria", - "dia_id": "D18:7", - "text": "I felt like I was in a fairy tale! The water sounded so calming and the surroundings were beautiful. It was truly magical!" - }, - { - "speaker": "John", - "dia_id": "D18:8", - "text": "Wow, Maria, that sounds awesome! It seems like nature has a way of calming us down, huh?" - }, - { - "speaker": "Maria", - "dia_id": "D18:9", - "text": "Yeah, it's like a natural soul-soother when things get tough." - }, - { - "speaker": "John", - "dia_id": "D18:10", - "text": "Yeah, for sure. It's like a reset button, you know? Have you ever gone camping or mountain climbing before?" - }, - { - "speaker": "Maria", - "dia_id": "D18:11", - "text": "I've gone camping a few times but never tried mountain climbing. Sounds thrilling though! Have you been camping before?" - }, - { - "speaker": "John", - "dia_id": "D18:12", - "text": "Yeah, plenty of times. It's an awesome way to get away from it all and be at one with nature. I love how uncomplicated it is." - }, - { - "speaker": "Maria", - "dia_id": "D18:13", - "text": "Yeah John, I get it. Being in nature helps us take a break from life's craziness and recognize what truly matters." - }, - { - "speaker": "John", - "dia_id": "D18:14", - "text": "Yeah, Maria. It's important to appreciate the small things and find moments of peace amidst chaos. Nature really helps with that. How about you? How do you find peaceful moments?" - }, - { - "speaker": "Maria", - "dia_id": "D18:15", - "text": "Finding my Zen is a mix of things - a moment to myself plus favorite tunes is usually enough. I also enjoy aerial yoga, it's a great way to switch off and focus on my body." - }, - { - "speaker": "John", - "dia_id": "D18:16", - "text": "Cool, Maria! Glad you found something that gives you some peace. Do you have a favorite yoga pose?" - }, - { - "speaker": "Maria", - "dia_id": "D18:17", - "text": "Thanks, John! It's tough to pick just one, but I really enjoy the upside-down poses. They make me feel free and light." - }, - { - "speaker": "John", - "dia_id": "D18:18", - "text": "Wow, Maria, that sounds awesome! I can imagine that must be challenging, but it's great to see you embracing them. Keep up the amazing work!" - }, - { - "speaker": "Maria", - "dia_id": "D18:19", - "text": "Thanks, John! It can be tough, but aerial yoga is totally worth it. I love the freedom and connection it brings. Appreciate your support!" - }, - { - "speaker": "John", - "img_url": [ - "https://www.harpersplayground.org/wp-content/uploads/elementor/thumbs/Harpers-playground-2-copy-2560x1440-e1533267692673-o598m9qoe7o3x8f1l2ljd69xt3o9oeda7waxqhlnz4.jpg" - ], - "blip_caption": "a photo of a group of people standing around a playground", - "query": "children playing park", - "dia_id": "D18:20", - "text": "Yes, Maria! I'm here for you. Glad you found something that makes you happy. This is what makes me smile. Keep shining!\n" - }, - { - "speaker": "Maria", - "dia_id": "D18:21", - "text": "Wow! Looks like you had fun - what happened there?" - }, - { - "speaker": "John", - "dia_id": "D18:22", - "text": "It was an awesome day at the park with my family. The kids had a lot of fun on the playground, and we had some really nice family time." - }, - { - "speaker": "Maria", - "dia_id": "D18:23", - "text": "Wow, that's great to hear, John! Cherish those family time moments!" - } - ], - "session_19_date_time": "7:20 pm on 16 June, 2023", - "session_19": [ - { - "speaker": "Maria", - "dia_id": "D19:1", - "text": "Hey John, been good since we talked? I got some great news to share - I joined a gym last week! It's been super positive - I'm sticking to my workout routine and the people are awesome. The atmosphere is so welcoming." - }, - { - "speaker": "John", - "dia_id": "D19:2", - "text": "Congrats, Maria! Sounds like it's been a great experience. Having a positive environment and supportive people can really help with motivation, right? So, do you have any fitness goals in mind?" - }, - { - "speaker": "Maria", - "dia_id": "D19:3", - "text": "Thanks, John! Yeah, it's been awesome. I want to get stronger and improve my endurance, and I'm trying kundalini yoga. What about you? Do you have any goals or activities you want to try?" - }, - { - "speaker": "John", - "dia_id": "D19:4", - "text": "Nice one, Maria! Staying in shape is important to me too. I'm trying out different workout regimes lately. Rock climbing sounds like a fun way to push my limits, have you ever given it a go?" - }, - { - "speaker": "Maria", - "dia_id": "D19:5", - "text": "No, I haven't tried it yet. But it sounds like a great way to push yourself. Let me know how it goes if you give it a shot!" - }, - { - "speaker": "John", - "dia_id": "D19:6", - "text": "Yeah, sure thing. I'll let you know. Oh, also...something massive happened since we last spoke. I got promoted at work! It's been a loooong time coming, and I'm over the moon about it!" - }, - { - "speaker": "Maria", - "dia_id": "D19:7", - "text": "Wow John! Congrats on the promotion! Must have taken a lot of work. How did you feel when you found out?" - }, - { - "speaker": "John", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTEyL2ZsNTE4NTQxMzE2ODgtaW1hZ2UuanBn.jpg" - ], - "blip_caption": "a photography of a golden trophy on a black surface", - "query": "new office assistant manager sign", - "dia_id": "D19:8", - "re-download": true, - "text": "Thanks, Maria! I was really excited. It feels like all the hard work I've put in has paid off now that I'm an assistant manager- it's like a stepping stone for bigger things." - }, - { - "speaker": "Maria", - "dia_id": "D19:9", - "text": "Wow, John! Congrats on the promotion! What's the backstory on that trophy?" - }, - { - "speaker": "John", - "dia_id": "D19:10", - "text": "Thanks, Maria! It commemorates my journey. It's a symbol of all the obstacles I had to overcome to get here." - }, - { - "speaker": "Maria", - "dia_id": "D19:11", - "text": "Cool, so you have a reminder of all that. It's good to acknowledge what you've been through and appreciate where you are now. Could you tell me more about the challenges?" - }, - { - "speaker": "John", - "dia_id": "D19:12", - "text": "Yeah, I faced all kinds of hurdles - tech stuff, workplace stuff... but the worst was self-doubt. There were moments when I questioned if I was on the right track. But with support at home and my own grit, I powered through. This promotion is a reward for all the hustle and hardship I put in - a reminder that I'm on the right path." - }, - { - "speaker": "Maria", - "dia_id": "D19:13", - "text": "Wow, John, it's incredible to see how far you've come! Your perseverance and determination is so inspiring. I can imagine those hurdles were tough to deal with, especially the self-doubt." - }, - { - "speaker": "John", - "dia_id": "D19:14", - "text": "Thanks, Maria! It wasn't easy, but I'm proud of what I achieved. It can be tricky, but having support and believing in myself really helped me out." - }, - { - "speaker": "Maria", - "dia_id": "D19:15", - "text": "Yeah John, having belief in yourself matters. Plus it helps a lot when you've got loved ones supporting you. What we can do is seriously incredible with the right people believing in us." - }, - { - "speaker": "John", - "img_url": [ - "https://riverstudiodesign.ca/wp-content/uploads/2019/09/overall-office-jpg-1.jpg" - ], - "blip_caption": "a photo of a desk with a chair and a lamp", - "query": "wife kids office desk", - "dia_id": "D19:16", - "text": "Definitely, Maria. Support from loved ones is vital. With their trust, we can do anything! I'm really lucky to have my family on this journey with me." - }, - { - "speaker": "Maria", - "dia_id": "D19:17", - "text": "Nice workspace! When do you usually work?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/n7jl343t38m91.jpg" - ], - "blip_caption": "a photo of a desk with a computer, keyboard, and notebook", - "query": "office desk laptop papers scattered", - "dia_id": "D19:18", - "text": "Thanks, Maria! I usually work during regular work hours, but sometimes I bring work home too." - }, - { - "speaker": "Maria", - "dia_id": "D19:19", - "text": "That work setup looks nice, John. How do you manage to balance everything?" - }, - { - "speaker": "John", - "dia_id": "D19:20", - "text": "Thanks, Maria! It can be challenging, so I try to organize my time and make sure I'm there for the important things. It's all about finding that balance and making those moments count!" - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach", - "dia_id": "D19:21", - "text": "Finding balance is crucial. Taking time for ourselves and the important people in our lives is vital. " - }, - { - "speaker": "John", - "dia_id": "D19:22", - "text": "Yeah, Maria. Taking time off for ourselves and our fam is so important. It helps us stay connected and appreciate the simple things. That beach pic you shared reminded me of a special vacation we had to California- a gorgeous sunset and an awesome night strolling the shore, creating memories together. Do you have any special beach memories you'd like to share?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://live.staticflickr.com/3448/3279695354_16a66b5258_b.jpg" - ], - "blip_caption": "a photography of a sunset over a body of water with a bird flying in the distance", - "query": "beach sunset", - "dia_id": "D19:23", - "re-download": true, - "text": "Yeah, John! I have a picture from a vacation in Florida. The colors were amazing, and I had a feeling of gratitude just sitting there with my family. It's in moments like these we make the best memories, ya know?" - }, - { - "speaker": "John", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/siblings-enjoying-in-sea-against-sky-royalty-free-image-1650652626.jpg" - ], - "blip_caption": "a photo of two children playing in the ocean waves", - "query": "beach vacation family", - "dia_id": "D19:24", - "text": "Wow, Maria! That photo is so stunning. The colors there are so vivid - it must have been amazing! Trips like these are great - always full of amazing memories! Here's one from our vacation!" - }, - { - "speaker": "Maria", - "dia_id": "D19:25", - "text": "Thanks, John. That picture is so cute! The kids look so happy splashing in the waves. It must have been such a joyful and carefree time!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a football stadium with a lot of people", - "dia_id": "D19:26", - "text": "Yep, it was amazing. Enjoying these special family times is why life is great. Talk to you soon!" - } - ], - "session_20_date_time": "12:21 am on 27 June, 2023", - "session_20": [ - { - "speaker": "Maria", - "dia_id": "D20:1", - "text": "Hey John, long time no talk! A lot has happened since then. I've been struggling, but I'm focusing on the positive and relying on my friends and fam for support." - }, - { - "speaker": "John", - "dia_id": "D20:2", - "text": "Hey Maria, sorry to hear that. That's rough, but it's great that you're focusing on the positive. Having support from your loved ones can make a big difference. How have they been helping you out?" - }, - { - "speaker": "Maria", - "dia_id": "D20:3", - "text": "Hey John, thanks. My family has been there for me all the way. They've been my rock, giving me words of encouragement and reminding me I'm not alone. It's a relief to have their support." - }, - { - "speaker": "John", - "dia_id": "D20:4", - "text": "That's great, Maria! It's such a blessing to have family who always supports us and reminds us that we're not alone. They know us like no one else and stick by us no matter what. Last week, we had a blast at a live music event. Seeing them dancing and having fun was awesome. The energy in the air was amazing." - }, - { - "speaker": "Maria", - "dia_id": "D20:5", - "text": "Wow, John! The energy from the crowd must have unreal! So glad you and your family got to experience that lively event. These are the moments that make the best memories." - }, - { - "speaker": "John", - "dia_id": "D20:6", - "text": "Thanks, Maria! It was definitely an amazing experience. Moments like these remind me to appreciate the ones I love. Life can be tough, but finding silver linings helps me keep going. How have you been finding silver linings in tough times?" - }, - { - "speaker": "Maria", - "dia_id": "D20:7", - "text": "Volunteering at the shelter made me feel great to help, even if just for a bit." - }, - { - "speaker": "John", - "dia_id": "D20:8", - "text": "Wow, Maria! That's really amazing. It must have felt great to help out. Do you have any special memories from your experience?" - }, - { - "speaker": "Maria", - "dia_id": "D20:9", - "text": "There are so many, but one that stands out was when I met someone special at the shelter. They'd been sad for months, but when I was playing with the kids, they suddenly laughed - it was so uplifting! I won't forget that." - }, - { - "speaker": "John", - "dia_id": "D20:10", - "text": "That's a really nice memory, Maria! It's amazing how just playing with kids can bring such joy and happiness. It shows how even a brief moment with someone can make a difference. Thanks for sharing it with me." - }, - { - "speaker": "Maria", - "dia_id": "D20:11", - "text": "No problem, John! It was really nice. Being able to make a difference brings me joy." - }, - { - "speaker": "John", - "dia_id": "D20:12", - "text": "It's great knowing that our actions can brighten someone else's life. Keep it up!" - }, - { - "speaker": "Maria", - "dia_id": "D20:13", - "text": "Thanks, John! Gonna continue doing it - it's my way of spreading kindness and positivity." - }, - { - "speaker": "John", - "dia_id": "D20:14", - "text": "Maria, that's great! Your way of passing on kindness and positivity is making a big impact on the world. You're really making a difference." - }, - { - "speaker": "Maria", - "dia_id": "D20:15", - "text": "Thanks, John! Your words really mean a lot. It's always nice to know that what I'm doing is making an impact." - }, - { - "speaker": "John", - "dia_id": "D20:16", - "text": "You definitely are. Keep going with it!" - }, - { - "speaker": "Maria", - "dia_id": "D20:17", - "text": "Thanks, John! I definitely will. Speak to you soon!" - }, - { - "speaker": "John", - "dia_id": "D20:18", - "text": "That's awesome, Maria! Keep that positivity going and keep making a difference. Take care!" - } - ], - "session_21_date_time": "8:43 pm on 3 July, 2023", - "session_21": [ - { - "speaker": "Maria", - "img_url": [ - "https://cdn.newswire.com/files/x/4b/eb/ec85ef6a588e010a38e5f40feeab.jpg" - ], - "blip_caption": "a photo of three children sitting on a step smiling", - "query": "group children shelter smiling", - "dia_id": "D21:1", - "text": "Hey John, long time no see! Sorry I didn't get back to you sooner... So much has happened! Check out these kids I met at the shelter!" - }, - { - "speaker": "John", - "dia_id": "D21:2", - "text": "Hey Maria! Good to hear from you. Those little ones are adorable, any updates on them? How have you been?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://i2.pickpik.com/photos/866/469/190/crash-car-car-crash-accident-preview.jpg" - ], - "blip_caption": "a photography of a car that has been damaged and is sitting on a flatbed", - "query": "broken car homeless shelter kids", - "dia_id": "D21:3", - "re-download": true, - "text": "Hey John! They were great, their smiles warmed my heart. But something not so great happened yesterday..." - }, - { - "speaker": "John", - "dia_id": "D21:4", - "text": "That's so sad. Can I do anything to help? What happened?" - }, - { - "speaker": "Maria", - "dia_id": "D21:5", - "text": "A car ran a red light and hit us, but thankfully everyone is okay. Besides that, my cousin just had a tough time recently, so I'm lending a hand in helping her find a new place." - }, - { - "speaker": "John", - "dia_id": "D21:6", - "text": "That's really nice of you. It's important to help family during hard times. How is she doing now?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://cdngeneral.rentcafe.com/dmslivecafe/3/524478/1(2).jpg" - ], - "blip_caption": "a photo of a row of houses with a sidewalk and trees", - "query": "apartments for rent list", - "dia_id": "D21:7", - "text": "Things have been tough for her lately. She had to leave and find a new place in a hurry, which has been really stressful, but she's making progress." - }, - { - "speaker": "John", - "dia_id": "D21:8", - "text": "The houses look real nice. Hopefully, she finds a cozy spot soon." - }, - { - "speaker": "Maria", - "dia_id": "D21:9", - "text": "Yeah, that's important. A safe home is key." - }, - { - "speaker": "John", - "dia_id": "D21:10", - "text": "Definitely, it's essential for wellbeing. It's great that you're supporting her. Is there anything specific she needs help with? Maybe I can assist as well." - }, - { - "speaker": "Maria", - "dia_id": "D21:11", - "text": "Any resources or organizations you know of that could help her out? Thanks!" - }, - { - "speaker": "John", - "dia_id": "D21:12", - "text": " I'll see if I can find any that might be able to assist. Let me know if there's anything else I can do to help!" - }, - { - "speaker": "Maria", - "dia_id": "D21:13", - "text": "Thanks, John! Really appreciate your offer. Anything you can find would be great." - }, - { - "speaker": "John", - "dia_id": "D21:14", - "text": "Sure, Maria! I'll do my best to find some resources. Helping those in need is important to me too." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a lot of white portable homes in a parking lot", - "dia_id": "D21:15", - "text": "I appreciate your kindness and care. It's a wonderful quality to have." - }, - { - "speaker": "John", - "dia_id": "D21:16", - "text": "Thanks for the compliment, I really appreciate it!" - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/cvtpmcj98ii31.jpg" - ], - "blip_caption": "a photo of a letter written by a man on a white sheet of paper", - "query": "handwritten note be kind", - "dia_id": "D21:17", - "text": "No worries, John. Nice moments like these remind me how much kindness and compassion matter. Life can be challenging, but small acts of kindness can make a big difference." - }, - { - "speaker": "John", - "dia_id": "D21:18", - "text": "Wow, that's awesome! What inspired it?" - }, - { - "speaker": "Maria", - "dia_id": "D21:19", - "text": "One of the shelter residents, Laura, wrote us a letter expressing their gratitude. The impact we made on their life was inspiring!" - }, - { - "speaker": "John", - "dia_id": "D21:20", - "text": "Wow, that's really amazing to hear. It feels so good to make a positive impact on someone's life." - }, - { - "speaker": "Maria", - "dia_id": "D21:21", - "text": "Yeah, that's why I love volunteering! It makes me feel like I'm making a difference, even if it's a small one." - }, - { - "speaker": "John", - "blip_caption": "a photo of a badge and a flag on a table", - "dia_id": "D21:22", - "text": "Yeah, Maria, keep it up! Even small things can mean a lot. I just participated in a marching event for veterans' rights and it was awesome, made me remember how much they sacrifice for us. We need to show our support however we can." - }, - { - "speaker": "Maria", - "dia_id": "D21:23", - "text": "Wow, John! What inspired you to join it?" - }, - { - "speaker": "John", - "dia_id": "D21:24", - "text": "I've always had a great respect for our military and wanted to show my support. I think it's important to stand up for what we believe in." - }, - { - "speaker": "Maria", - "dia_id": "D21:25", - "text": "Wow, John! It's great to see you standing up for your beliefs." - }, - { - "speaker": "John", - "dia_id": "D21:26", - "text": "Thanks Maria, it was amazing being around others who shared the same values and passion as me. It reminded me how important it is to try and make a difference through activism. It really motivated me." - }, - { - "speaker": "Maria", - "dia_id": "D21:27", - "text": "That's awesome, John! Surrounding yourself with determined people striving for the same goals can be really motivating. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D21:28", - "text": "Thanks, Maria! It really is. Striving for progress is crucial." - }, - { - "speaker": "Maria", - "dia_id": "D21:29", - "text": "Yep John, Let's keep up the good work and make a difference. Talk to you soon!" - } - ], - "session_22_date_time": "6:59 pm on 5 July, 2023", - "session_22": [ - { - "speaker": "John", - "dia_id": "D22:1", - "text": "Since the last chat, I've been thinking about how education and infrastructure shape communities. It's so sad how they can stunt growth in neighborhoods, but it also drives me to do what I can to make it better." - }, - { - "speaker": "Maria", - "dia_id": "D22:2", - "text": "I totally agree. They play a crucial role in shaping communities. It's unfortunate to witness the negative effects when they are lacking, but it's inspiring to see your passion and proactive approach towards making a positive change." - }, - { - "speaker": "John", - "dia_id": "D22:3", - "text": "Your support means a lot. Feeling like it's an uphill battle is tough, but it's great to know there are people out there who see the value in them - it keeps me going." - }, - { - "speaker": "Maria", - "dia_id": "D22:4", - "text": "John, you got this! It's great to have a support system while tackling tough stuff. I'm here to lend an ear or help out however I can. You're really making a difference, and that's something to be proud of!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/j4ms3skztjg11.jpg" - ], - "blip_caption": "a photo of a family posing on a train track in the fall", - "query": "family photo", - "dia_id": "D22:5", - "text": "I appreciate it. It's really uplifting hearing from you. I sometimes doubt if I'm making a difference, but knowing there's people who understand my work means a lot and helps keep me going. Here's a picture of my family. They motivate me and remind me why I'm doing this." - }, - { - "speaker": "Maria", - "dia_id": "D22:6", - "text": "That picture is awesome! Your family looks so stoked - your trip must have been incredible! They obviously motivate and support you." - }, - { - "speaker": "John", - "dia_id": "D22:7", - "text": "Thanks, Maria! That picture was from a trip we took last year for my daughter Sara's birthday - so much fun and good memories! My family motivates me to keep striving for change." - }, - { - "speaker": "Maria", - "dia_id": "D22:8", - "text": "Yeah, memories and motivators definitely help us stay on track and keep us going." - }, - { - "speaker": "John", - "dia_id": "D22:9", - "text": "Yeah, for sure! When times get hard, I look at it and remember why I'm doing what I'm doing. My family is my motivation and they keep me going. What about you? What keeps you inspired?" - }, - { - "speaker": "Maria", - "dia_id": "D22:10", - "text": "I'm inspired by chatting to people, volunteering, and listening to music. Anything else that keeps you inspired?" - }, - { - "speaker": "John", - "dia_id": "D22:11", - "text": "My family, exercise, and spending time with friends, for sure." - }, - { - "speaker": "Maria", - "dia_id": "D22:12", - "text": "That's great, John! It's true, we all have our own special sources of inspiration that keep us going." - }, - { - "speaker": "John", - "dia_id": "D22:13", - "text": "Definitely, Maria! Finding those special sources is key for staying motivated and tackling challenges. It's great when we figure out what makes us feel excited and alive." - }, - { - "speaker": "Maria", - "dia_id": "D22:14", - "text": "Yeah, John, those little things can spark our enthusiasm and motivate us. It's incredible how something as simple as a walk or a song can totally switch up our outlook." - }, - { - "speaker": "John", - "img_url": [ - "https://get.pxhere.com/photo/beach-landscape-sea-coast-water-sand-ocean-horizon-cloud-sky-sun-sunrise-sunset-shore-wave-dawn-dusk-evening-relax-paradise-tropical-peaceful-blue-colorful-body-of-water-clouds-afterglow-sunset-beach-gulf-of-mexico-wind-wave-515918.jpg" - ], - "blip_caption": "a photo of a sunset over the ocean with a sailboat in the distance", - "query": "sunset beach colorful ocean", - "dia_id": "D22:15", - "text": "Yeah, Maria. Little things like this can make a big impact in how we think. Oh, and here's a pic I got from my walk last week. It always reminds me to take a break, breathe, and appreciate nature." - }, - { - "speaker": "Maria", - "dia_id": "D22:16", - "text": "That picture is amazing! The colors are so vibrant - really shows the calmness of the ocean. How often do you get to see sunsets like that on your walks?" - }, - { - "speaker": "John", - "dia_id": "D22:17", - "text": "Thanks, Maria! I see them at least once a week. It's a good way to disconnect, think, and find peace in this crazy world." - }, - { - "speaker": "Maria", - "dia_id": "D22:18", - "text": "That's great practice, John. Taking time to detach and find peace is important in this crazy world. I've been taking regular \"me-time\" walks at the park nearby and It's made a big impact. Glad you have that to remind you." - }, - { - "speaker": "John", - "dia_id": "D22:19", - "text": "Thanks, Maria. Appreciate it. Great talking to you. Gotta go. Stay safe and chat soon!" - }, - { - "speaker": "Maria", - "dia_id": "D22:20", - "text": "Hey John, stay safe. Chat soon!" - }, - { - "speaker": "John", - "dia_id": "D22:21", - "text": "Take care, Maria. Catch you soon!" - } - ], - "session_23_date_time": "6:29 pm on 7 July, 2023", - "session_23": [ - { - "speaker": "John", - "dia_id": "D23:1", - "text": "Maria, since we talked, it's been tough. My old area was hit by a nasty flood last week. The infrastructure wasn't great so lots of homes were ruined. It's reminding me we need to fix things up in our community." - }, - { - "speaker": "Maria", - "dia_id": "D23:2", - "text": "Sorry to hear about what happened in your area, John. That must have been tough for you and everyone there. Is there anything I can do to help?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a party invitation on a table with a pen and paper", - "dia_id": "D23:3", - "text": "Thanks a lot, Maria. Your offer means a lot to me. I'm getting people together to chat about this and discuss potential solutions. I'd really appreciate your thoughts.\n" - }, - { - "speaker": "Maria", - "dia_id": "D23:4", - "text": "Hey John, I'm down to join the meeting and contribute to making our community better. Let's do this!" - }, - { - "speaker": "John", - "dia_id": "D23:5", - "text": "Nice work, Maria! Your determination never ceases to amaze me. Let's get to work!" - }, - { - "speaker": "Maria", - "dia_id": "D23:6", - "text": "Definitely, John! Your commitment to helping others is inspiring. It's great to have a friend like you who shares the same passion. Let's join forces and make the change we desire in our community." - }, - { - "speaker": "John", - "dia_id": "D23:7", - "text": "That means a lot, Maria. Your support and friendship mean everything. Together, we can really make a difference and motivate others too. Let's keep it up!" - }, - { - "speaker": "Maria", - "dia_id": "D23:8", - "text": "John, let's keep working together to make a difference in our community. Our actions, no matter how small, can have a big impact. Let's continue to spread kindness and inspire hope." - }, - { - "speaker": "John", - "dia_id": "D23:9", - "text": "Definitely, little steps count! We can really make a difference together - let's do it!" - }, - { - "speaker": "Maria", - "dia_id": "D23:10", - "text": "Yep, let's create a positivity ripple! Little acts of kindness and helping hands can really transform lives. Let's keep improving our community." - }, - { - "speaker": "John", - "dia_id": "D23:11", - "text": "Yeah, let's work hard to help those around us. We can make a difference!" - }, - { - "speaker": "Maria", - "dia_id": "D23:12", - "text": "Yeah, John! Change starts small, so with hard work, we can really make something great. I'm glad to have you here." - }, - { - "speaker": "John", - "dia_id": "D23:13", - "text": "Thanks, Maria. Your support means a lot and it's awesome to have you by my side in our community work. Let's keep going and making great things happen!" - }, - { - "speaker": "Maria", - "dia_id": "D23:14", - "text": "For sure! We can keep doing great stuff and making a difference. Well, I'm off to have dinner with some friends from the gym. Talk to you later!" - } - ], - "session_24_date_time": "3:34 pm on 17 July, 2023", - "session_24": [ - { - "speaker": "John", - "dia_id": "D24:1", - "text": "Hey Maria, last week was really eye-opening. I visited a veteran's hospital and met some amazing people. It made me appreciate what we have and the need to give back." - }, - { - "speaker": "Maria", - "dia_id": "D24:2", - "text": "Wow, John! That sounds awesome. It's so important to appreciate and support those who served in the military. Did you learn anything cool during your visit?" - }, - { - "speaker": "John", - "dia_id": "D24:3", - "text": "I heard some cool stories from an elderly veteran named Samuel. It was inspiring and heartbreaking, but seeing their resilience really filled me with hope. It reminded me why I wanted to join the military." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people sitting on a couch talking", - "dia_id": "D24:4", - "text": "It's inspiring to see the resilience of the veterans in your group. Their stories are both inspiring and heartbreaking, but they fill us with hope." - }, - { - "speaker": "John", - "dia_id": "D24:5", - "text": "Thanks, Maria! It's great to be part of this organization and work with such passionate people. We're like a family - always supporting each other. Do anything fun lately?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/l77zoro8xt971.jpg" - ], - "blip_caption": "a photo of a picnic table with a drink, snacks and a cell phone", - "query": "picnic friends trees games food", - "dia_id": "D24:6", - "text": "Yeah, last weekend I had a picnic with some friends from church. We chilled under the trees, played games, and ate yummy food. It was great!" - }, - { - "speaker": "John", - "dia_id": "D24:7", - "text": "Looks fun! What games did you all play?" - }, - { - "speaker": "Maria", - "dia_id": "D24:8", - "text": "Some fun ones like charades and a scavenger hunt. We all had a good laugh!" - }, - { - "speaker": "John", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTExL2ZsNTI0NzQ3Nzg3NzQtaW1hZ2UuanBn.jpg" - ], - "blip_caption": "a photography of a young girl is writing at a table", - "query": "charades creativity silliness joy laughter", - "dia_id": "D24:9", - "re-download": true, - "text": "Sounds like a blast! It's always great to have fun and bring out everyone's creative and silly sides with games like that. Laughter and joy are really important! I'm thinking of setting up something like this for my kids soon." - }, - { - "speaker": "Maria", - "dia_id": "D24:10", - "text": "This looks like fun! Where did you see that?" - }, - { - "speaker": "John", - "blip_caption": "a photo of two girls in costumes holding up signs", - "dia_id": "D24:11", - "text": "There were arts and crafts at a community event last month. There were fun activities and games for families and everyone was having a blast. So I figured I'd try them out with my family and friends." - }, - { - "speaker": "Maria", - "dia_id": "D24:12", - "text": "Wow, great idea! Connecting with others and discovering fun activities is always awesome. It's really cool how you adapted it for your family and friends!" - }, - { - "speaker": "John", - "dia_id": "D24:13", - "text": "Thanks, Maria! I couldn't agree more. Life's too short, let's have some fun!" - }, - { - "speaker": "Maria", - "dia_id": "D24:14", - "text": "Sure, John! I'm glad we both understand the importance of making connections and enjoying life's simpler moments." - }, - { - "speaker": "John", - "dia_id": "D24:15", - "text": "Yep, Maria! That's why it's important to keep spreading positivity and making a difference." - }, - { - "speaker": "Maria", - "dia_id": "D24:16", - "text": "Definitely, John! Doing good and helping others brings joy. Even little acts of kindness can have a big effect. Let's keep working to make a difference!" - }, - { - "speaker": "John", - "dia_id": "D24:17", - "text": "Yep, Maria! Those things really matter. Little acts of kindness can really brighten someone's day. Let's keep spreading the love and making a difference." - } - ], - "session_25_date_time": "6:21 pm on 22 July, 2023", - "session_25": [ - { - "speaker": "John", - "blip_caption": "a photo of a group of people posing for a picture", - "dia_id": "D25:1", - "text": "Hi Maria! It's so good to talk again. A lot has changed since last time. I'm really enjoying my new job. My team has been super encouraging and inspiring." - }, - { - "speaker": "Maria", - "img_url": [ - "https://www.wildbunchdesertguides.com/blog/uploaded_files/images/Picturesque-trail.jpg" - ], - "blip_caption": "a photo of a group of people walking up a trail", - "query": "group friends hiking mountains nature rejuvenating", - "dia_id": "D25:2", - "text": "Hey John, glad work is going well! Having a good team is so important. I had a great experience last weekend hiking with my church friends - it was great to be surrounded by supportive people and to enjoy nature. Felt so refreshing!" - }, - { - "speaker": "John", - "dia_id": "D25:3", - "text": "Sounds like you had a great time! What inspired you to go on the hike?" - }, - { - "speaker": "Maria", - "dia_id": "D25:4", - "text": "I wanted to make connections, laugh together and take in nature's beauty. Uplifting!" - }, - { - "speaker": "John", - "dia_id": "D25:5", - "text": "Wow Maria, it sounds like you had a great time! Connecting with good people and taking in the beautiful views really boosts your mood. It's important to make time for yourself and find those special moments of joy. What were some of your best bits from the hike?" - }, - { - "speaker": "Maria", - "dia_id": "D25:6", - "text": "Thanks, John! Reaching the top was amazing - the view was breathtaking! Seeing how huge the world is made me feel like I'm part of something special - gave me a real sense of peace." - }, - { - "speaker": "John", - "dia_id": "D25:7", - "text": "Wow, Maria, that sounds incredible! It's amazing how nature can make us feel so small and yet so connected to something greater. Do you have any plans for your next adventure yet?" - }, - { - "speaker": "Maria", - "dia_id": "D25:8", - "text": "Gonna explore more and volunteer at shelters next month. Can't wait!" - }, - { - "speaker": "John", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/637bc5e7223fbf2204465446/9afdd8b3-6eae-4655-b4b8-5844b1ec1163/20190304-tpi-severe-weather-shelter-volunteers-mn-12_40329508153_o_0.jpg" - ], - "blip_caption": "a photo of two women standing in a room full of black mats", - "query": "volunteer orientation shelter", - "dia_id": "D25:9", - "text": "Woohoo, Maria! Super pumped for your next adventure and for putting your positivity out there. Keep up the awesome work!" - }, - { - "speaker": "Maria", - "dia_id": "D25:10", - "text": "Thanks, John! Is it a martial arts place or a yoga studio? It looks awesome!" - }, - { - "speaker": "John", - "dia_id": "D25:11", - "text": "Yup, it's a yoga studio I go to often. The vibe is really chill and the instructors are awesome." - }, - { - "speaker": "Maria", - "dia_id": "D25:12", - "text": "Cool, John! That definitely makes the workout experience more enjoyable. Do they offer a variety of classes?" - }, - { - "speaker": "John", - "dia_id": "D25:13", - "text": "Yeah, they offer a a bunch, like yoga, kickboxing, and circuit training. It keeps things interesting!" - }, - { - "speaker": "Maria", - "dia_id": "D25:14", - "text": "Cool, John! Trying new classes sounds like a fun way to switch up your exercise routine - I should give it a go!" - }, - { - "speaker": "John", - "img_url": [ - "https://www.jewelyogapdx.com/wp-content/uploads/2015/02/home-join-us.jpg" - ], - "blip_caption": "a photo of a group of people doing yoga in a gym", - "query": "yoga class", - "dia_id": "D25:15", - "text": "Yeah, Maria! Trying new stuff is a great way to push yourself and mix things up. Let me know if you need any suggestions!" - }, - { - "speaker": "Maria", - "dia_id": "D25:16", - "text": "Looks fun! What other classes have you done?" - }, - { - "speaker": "John", - "dia_id": "D25:17", - "text": "I've done weight training so far too. It was challenging but peaceful, kinda like yoga." - }, - { - "speaker": "Maria", - "dia_id": "D25:18", - "text": "Wow, John! That's great. Yoga is a great way to relax and concentrate, and joining a new class might be a good option." - }, - { - "speaker": "John", - "dia_id": "D25:19", - "text": "Yeah, it's been great for me. Let me know if you need any advice to get started." - }, - { - "speaker": "Maria", - "dia_id": "D25:20", - "text": "Cheers, John! I'll let you know. I'm off to bake some cakes. Talk to you soon!" - } - ], - "session_26_date_time": "1:59 pm on 31 July, 2023", - "session_26": [ - { - "speaker": "Maria", - "dia_id": "D26:1", - "text": "Hey John, I'm doing ok - hope you are too. Some interesting stuff has been going on; last week I dropped off that stuff I baked at the homeless shelter. It was great and I'm more motivated than ever to help people." - }, - { - "speaker": "John", - "dia_id": "D26:2", - "text": "Hey Maria, that's awesome! I'm really inspired by your drive to make a difference. You mentioned your work at the homeless shelter last time and it made me think of how I could help too, so I just joined a fire-fighting brigade. It's such a great feeling to do something to give back to my community!" - }, - { - "speaker": "Maria", - "dia_id": "D26:3", - "text": "Wow John, joining the fire brigade? That's great! How's it been so far?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/1bwp2zpccuo91.jpg" - ], - "blip_caption": "a photo of a firefighter's gear laid out on the floor", - "query": "house fire fire-fighting gear fire engine", - "dia_id": "D26:4", - "text": "Thanks, Maria! It's been tough, but really rewarding. The training was intense and taxing, but it changed my view on helping others. Last Sunday we had our first call-out, and it was intense. We responded to a situation and our team worked together to help those in need. Seeing their relief was awesome." - }, - { - "speaker": "Maria", - "dia_id": "D26:5", - "text": "Wow, John! What was it like being part of that rescue mission?" - }, - { - "speaker": "John", - "dia_id": "D26:6", - "text": "It was chaotic when we arrived, but we pulled together. I got a surge of energy and purpose, and we were able to save a family from a burning building. It was wild, but knowing we made a difference made it worth it." - }, - { - "speaker": "Maria", - "dia_id": "D26:7", - "text": "Wow John, that's intense! Helping out like that takes guts - it's inspiring to hear about the difference you made." - }, - { - "speaker": "John", - "dia_id": "D26:8", - "text": "Thanks, Maria! It was an adrenaline rush, and I couldn't have done it without them. We trust and rely on one another, and it's great to know that we have each other's backs. They've become like family to me." - }, - { - "speaker": "Maria", - "dia_id": "D26:9", - "text": "Sounds great, John! It must feel incredible to have a supportive team like that." - }, - { - "speaker": "John", - "dia_id": "D26:10", - "text": "Yeah, it really does feel helpful, Maria. We have different skills and talents, but they all contribute to serving and protecting our community. And it's a bond I haven't felt since my time in the military." - }, - { - "speaker": "Maria", - "dia_id": "D26:11", - "text": "Glad you've found that same strong bond. Having friends you can rely on makes a huge difference." - }, - { - "speaker": "John", - "dia_id": "D26:12", - "text": "Yeah, Maria! It's nice to know we're all in this together, striving to keep our community safe. I find it fulfilling and meaningful." - }, - { - "speaker": "Maria", - "img_url": [ - "https://sophieelliottfoundation.co.nz/wp-content/uploads/sites/30/2020/08/SLRA-summer-party-james-hopkirk-low-res-026.jpg" - ], - "blip_caption": "a photography of a group of people standing around a table with food", - "query": "homeless shelter sign kindness compassion", - "dia_id": "D26:13", - "re-download": true, - "text": "Yeah John! It feels great to help people, and you're so awesome for it! Here's a shot I got when I volunteered. Reminds me being kind matters!" - }, - { - "speaker": "John", - "dia_id": "D26:14", - "text": "That's a cool photo, Maria! Small acts like that can really make a difference. Keep it up!" - }, - { - "speaker": "Maria", - "dia_id": "D26:15", - "text": "Thanks, John! I totally agree, so I'm gonna keep it up." - }, - { - "speaker": "John", - "dia_id": "D26:16", - "text": "Way to go, Maria! Keep on being positive and making a difference. You're doing great!" - }, - { - "speaker": "Maria", - "dia_id": "D26:17", - "text": "Thanks John! Your support means a lot to me. I'll definitely keep on going. Talk to you soon!" - } - ], - "session_27_date_time": "6:20 pm on 3 August, 2023", - "session_27": [ - { - "speaker": "John", - "dia_id": "D27:1", - "text": "Hey Maria, hope you're doing OK. I had to share something cool with you - I asked family and friends to join the virtual support group I am a part of and be advocates for the military. It's been awesome seeing so many people coming together to back the courageous people serving our nation." - }, - { - "speaker": "Maria", - "img_url": [ - "https://static.wixstatic.com/media/0726909c8a194fd1bdc04ebf3e03bea3.jpg/v1/fill/w_640,h_558,al_t,q_80,usm_0.66_1.00_0.01,enc_auto/0726909c8a194fd1bdc04ebf3e03bea3.jpg" - ], - "blip_caption": "a photography of a group of people standing around a table", - "query": "volunteer badge", - "dia_id": "D27:2", - "re-download": true, - "text": "Wow, John! Way to go helping veterans! I'm doing my part too, volunteering at a homeless shelter. It's so rewarding." - }, - { - "speaker": "John", - "dia_id": "D27:3", - "text": "Maria, that's great! That picture shows a lot of joy. What got you started at that place?" - }, - { - "speaker": "Maria", - "dia_id": "D27:4", - "text": "I started volunteering here about a year ago after witnessing a family struggling on the streets. It made me want to help, so I reached out to the shelter and asked if they needed any volunteers. They said yes, and it has been a really fulfilling experience for me since then." - }, - { - "speaker": "John", - "dia_id": "D27:5", - "text": "Wow, Maria! You really made an impact \u2013 it's awesome! I seriously admire what you do." - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/5xasiy4gnohb1.jpg" - ], - "blip_caption": "a photo of a note from a person who is writing", - "query": "handwritten thank you note shelter resident", - "dia_id": "D27:6", - "text": "Thanks John. That really means a lot. It's been tough but knowing I can make a difference keeps me motivated." - }, - { - "speaker": "John", - "dia_id": "D27:7", - "text": "Maria, what's the deal with that note? Who wrote it and what does it say?" - }, - { - "speaker": "Maria", - "dia_id": "D27:8", - "text": "One of the residents at the shelter, Cindy, wrote it. It's a heartfelt expression of gratitude and shows the impact of the support they receive." - }, - { - "speaker": "John", - "img_url": [ - "https://i2.wp.com/lifecomingalive.com/wp-content/uploads/2018/05/IMG_0960-e1527675276357.jpg" - ], - "blip_caption": "a photo of a young boy holding a flag in a cemetery", - "query": "kids flag military memorial", - "dia_id": "D27:9", - "text": "Wow, Maria, that's so cool that you're making a difference like that! You're so inspiring. Last week, we had a meaningful experience at a military memorial. It really made an impact on my kids." - }, - { - "speaker": "Maria", - "dia_id": "D27:10", - "text": "That's so moving! How did they react when they saw it?" - }, - { - "speaker": "John", - "dia_id": "D27:11", - "text": "They were awestruck and humbled." - }, - { - "speaker": "Maria", - "dia_id": "D27:12", - "text": "Imagining visiting a military memorial makes me feel humble too. It's important for younger generations to remember and appreciate those who served." - }, - { - "speaker": "John", - "dia_id": "D27:13", - "text": "Yeah, totally! Showing them how to respect and appreciate those who served our country is important. It was a moving experience for all of us." - }, - { - "speaker": "Maria", - "dia_id": "D27:14", - "text": "Yeah John, it's super important to teach kids about veterans and what they did for us. You're doing a great thing - we need more people like you!" - }, - { - "speaker": "John", - "dia_id": "D27:15", - "text": "Thanks, Maria. Appreciate your support. It's amazing what teamwork can accomplish!" - }, - { - "speaker": "Maria", - "dia_id": "D27:16", - "text": "Yeah, we can really get amazing stuff done together. We can do this!" - } - ], - "session_28_date_time": "5:19 pm on 5 August, 2023", - "session_28": [ - { - "speaker": "John", - "dia_id": "D28:1", - "text": "Hey Maria, great chatting with you again! Crazy thing happened since we last talked. I lost my job at the mechanical engineering company. They tanked and it's been really rough. Never saw this coming." - }, - { - "speaker": "Maria", - "dia_id": "D28:2", - "text": "Sorry to hear about your job, John. I can only imagine how tough it must be. How are you holding up?" - }, - { - "speaker": "John", - "dia_id": "D28:3", - "text": "Thanks for your care, Maria. It's been tough but I'm trying to stay up. I've been looking into some opportunities in the tech industry for a while now. Maybe this is the change I need, you gave me the push!" - }, - { - "speaker": "Maria", - "dia_id": "D28:4", - "text": "Hey John, glad you're looking into other avenues. Any promising leads come up?" - }, - { - "speaker": "John", - "dia_id": "D28:5", - "text": "Thanks Maria! I may have found a job at a tech company I like that needs my mechanical skills for their hardware team. It feels different, but I think it's a great opportunity to learn and contribute." - }, - { - "speaker": "Maria", - "dia_id": "D28:6", - "text": "Wow, John, that sounds like the perfect job for you! You're so adaptable, I'm sure you'll do great. Good luck!" - }, - { - "speaker": "John", - "dia_id": "D28:7", - "text": "Thanks, Maria! I appreciate your support, it really means a lot." - }, - { - "speaker": "Maria", - "dia_id": "D28:8", - "text": "Hey John, I'm here for you! Staying positive makes a big difference, even in tough times. Yesterday, I took up some community work with my friends from church. It was super rewarding!" - }, - { - "speaker": "John", - "dia_id": "D28:9", - "text": "Wow, Maria! That's great. Glad it was rewarding for you." - }, - { - "speaker": "Maria", - "dia_id": "D28:10", - "text": "Thanks, John! These moments remind me of how important kindness and compassion are. Have you had any volunteer experiences you'd like to share?" - }, - { - "speaker": "John", - "dia_id": "D28:11", - "text": "I haven't been able to volunteer much lately, but I definitely care about it. Last year, I helped renovate a rundown community center back home. It took a lot of work, but seeing the impact on the community was so worth it." - }, - { - "speaker": "Maria", - "dia_id": "D28:12", - "text": "John, that's great! Must be really satisfying to witness the positive effect it's having on your hometown." - }, - { - "speaker": "John", - "dia_id": "D28:13", - "text": "Yeah! It was really cool to see everyone come together and help out - it's been amazing to see it so busy!" - }, - { - "speaker": "Maria", - "dia_id": "D28:14", - "text": "Wow, John, awesome job! Keep doing what you're doing - you're making a real difference!" - }, - { - "speaker": "John", - "dia_id": "D28:15", - "text": "It's really encouraging to hear that. I'll keep pushing forward and doing my best." - }, - { - "speaker": "Maria", - "dia_id": "D28:16", - "text": "You got this! You're inspiring and keep making a difference." - }, - { - "speaker": "John", - "dia_id": "D28:17", - "text": "I appreciate it! Your words are really motivating." - }, - { - "speaker": "Maria", - "dia_id": "D28:18", - "text": "No worries, John. I'm here to support you. If there's anything else I can do, just let me know." - }, - { - "speaker": "John", - "dia_id": "D28:19", - "text": "Thanks, Maria. Your support means a lot to me. If I need anything, I'll be sure to reach out." - } - ], - "session_29_date_time": "8:06 pm on 9 August, 2023", - "session_29": [ - { - "speaker": "Maria", - "img_url": [ - "https://images.rawpixel.com/image_800/czNmcy1wcml2YXRlL3Jhd3BpeGVsX2ltYWdlcy93ZWJzaXRlX2NvbnRlbnQvbHIvZnJ3aW5uZXJfZ29sZF9wcm9maXRfYXdhcmQtaW1hZ2Uta3liY2R6bjguanBn.jpg" - ], - "blip_caption": "a photography of a medal hanging from a tree with a ribbon", - "query": "handmade medal volunteer homeless shelter dedication difference", - "dia_id": "D29:1", - "re-download": true, - "text": "Hey John, what's been going on? I just wanted to check in. Last week was wild - I volunteered at the homeless shelter and they gave me a medal! It was humbling and I'm really glad I could help." - }, - { - "speaker": "John", - "img_url": [ - "https://www.rockawave.com/wp-content/uploads/2017/08/1-Family-Run-Remembers-Eileen-Lavin.jpg" - ], - "blip_caption": "a photo of a young girl holding a sign in her hands", - "query": "family community event 5k charity run military veterans patriotic individuals", - "dia_id": "D29:2", - "text": "Hey Maria! Congrats on the recognition! It's really touching to see how much you're doing to help out. Last weekend, I participated in a community event to raise money for a good cause. We got a great turnout and it was amazing to be surrounded by so many supportive people." - }, - { - "speaker": "Maria", - "dia_id": "D29:3", - "text": "John, that sounds inspiring! Community events like that are always amazing. This pic is heartwarming, that little girl has such a cute smile. What was the event all about?" - }, - { - "speaker": "John", - "img_url": [ - "https://runningfabulouslyblog.files.wordpress.com/2018/09/img_3190.jpg" - ], - "blip_caption": "a photo of a group of people posing for a picture", - "query": "5k charity run group of people running cheering", - "dia_id": "D29:4", - "text": "I set up a 5K charity run in our neighborhood. It was all for a good cause - to help out veterans and their families. We were able to raise some funds! Here's a pic from the day." - }, - { - "speaker": "Maria", - "dia_id": "D29:5", - "text": "John, that's awesome! That is such an important cause. It's an honor to know someone like you who takes initiative. The photo you shared is so powerful! Could you tell me more about how you organized the run?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a map of the streets of downtown", - "dia_id": "D29:6", - "text": "Thanks, Maria! It means a lot to me. It was hard work - getting sponsors, coordinating with the city, and spreading the word. But seeing everyone come together to support our veterans made it worth it." - }, - { - "speaker": "Maria", - "dia_id": "D29:7", - "text": "Wow, John, that sounds like a lot of effort! Your dedication definitely paid off. Were there any challenges along the way?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a sign on a door that says domestic abuse", - "dia_id": "D29:8", - "text": "Definitely, Maria! Getting sponsors was difficult. I had to reach out to several businesses through different means, but it paid off. We ended up with some awesome sponsors that made the event a hit." - }, - { - "speaker": "Maria", - "dia_id": "D29:9", - "text": "Wow, John! You really overcame those challenges. Have you done events for any other causes?" - }, - { - "speaker": "John", - "dia_id": "D29:10", - "text": "Yep, we worked with a local organization that helps victims of domestic abuse. We raised awareness and funds at the event for the cause \u2014 it's unfortunate how many people suffer from it." - }, - { - "speaker": "Maria", - "dia_id": "D29:11", - "text": "Oof, John, that's really sad. Domestic abuse is horrible. You did great raising awareness and funds. It's important we support the organizations fighting against it." - }, - { - "speaker": "John", - "dia_id": "D29:12", - "text": "Thanks, Maria. It's a tough issue, but we've gotta do what we can. It's really wonderful to see people come together for such an important cause." - }, - { - "speaker": "Maria", - "dia_id": "D29:13", - "text": "Agree, John! It's great to see community power in action. Let's keep spreading awareness and supporting causes like this." - }, - { - "speaker": "John", - "blip_caption": "a photo of a man holding a stick and a giant cartoon figure", - "dia_id": "D29:14", - "text": "Yeah Maria! I totally agree! Together, we can do so much. Let's keep spreading the good vibes and making our community better. " - }, - { - "speaker": "Maria", - "dia_id": "D29:15", - "text": "You rock! Let's keep spreading positivity and making a difference. We got this!" - }, - { - "speaker": "John", - "dia_id": "D29:16", - "text": "Yeah, we got this. Thanks for your help!" - }, - { - "speaker": "Maria", - "dia_id": "D29:17", - "text": "Yeah, John! It's really cool to have a friend like you who's just as passionate and motivated. Let's talk again soon!" - }, - { - "speaker": "John", - "dia_id": "D29:18", - "text": "Yeah Maria! Friends like you make a big difference. Talk to you later!" - } - ], - "session_30_date_time": "12:10 am on 11 August, 2023", - "session_30": [ - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/ulfxreudf3c81.jpg" - ], - "blip_caption": "a photo of a white dog laying in the grass with a baseball", - "query": "puppy playing ball coco", - "dia_id": "D30:1", - "text": "Hey John! Long time no talk! Guess what - I got a puppy two weeks ago! Her name's Coco and she's adorable." - }, - { - "speaker": "John", - "dia_id": "D30:2", - "text": "Wow, Maria! Coco looks so adorable! She seems so happy in that photo. How's it been having her around?" - }, - { - "speaker": "Maria", - "dia_id": "D30:3", - "text": "Thanks, John! It really brings joy to my life; she's always there to greet me when I come home. It's definitely been an adjustment taking care of her, but it's totally worth it!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/kwoxu9jx5jua1.jpg" - ], - "blip_caption": "a photo of a dog laying on the floor next to a person", - "query": "young kid play fetch golden retriever", - "dia_id": "D30:4", - "text": "That's great! Pets are the best for lifting our spirits. I'm always still thinking about Max- here's a pic of him." - }, - { - "speaker": "Maria", - "dia_id": "D30:5", - "text": "Aww, he looks so cute in that pic! He obviously brought you lots of joy. What's your best memory with him?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/g92z4kf3aph91.jpg" - ], - "blip_caption": "a photo of a dog and a person sitting by a campfire", - "query": "camping trip max campfire hiking swimming", - "dia_id": "D30:6", - "text": "Thanks, Maria! Max and I had a blast on our camping trip last summer. We hiked, swam, and made great memories. It was a really peaceful and awesome experience." - }, - { - "speaker": "Maria", - "dia_id": "D30:7", - "text": "Wow, John, sounds like you and Max had a great time! Camping with pets can be so soul-nourishing, right?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a tent in the woods with a blue tarp", - "dia_id": "D30:8", - "text": "Definitely, Maria! It was so chill. Being out in nature, away from all the noise and taking some quality time was great. It was a nice break from the everyday hustle and bustle. " - }, - { - "speaker": "Maria", - "dia_id": "D30:9", - "text": "Wow, John! Sounds awesome! I can imagine that it felt good to tune out and get lost in nature!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a person sitting on a bench looking at the sunset", - "dia_id": "D30:10", - "text": "Yeah, it was like restarting my mind and spirit. It's amazing how peaceful and pretty it can be. It made me remember the little things in life and savor the peaceful moments." - }, - { - "speaker": "Maria", - "dia_id": "D30:11", - "text": "Yeah, nature is amazing like that. It's like a reset for our souls and your photos capture it." - }, - { - "speaker": "John", - "dia_id": "D30:12", - "text": "Thanks, Maria! I'm glad my photos can capture that feeling. They help me take a step back and appreciate the little things in life - especially these days. There've been some tough times lately for me." - }, - { - "speaker": "Maria", - "dia_id": "D30:13", - "text": "Sorry to hear that, John. Is there anything on your mind lately?" - }, - { - "speaker": "John", - "dia_id": "D30:14", - "text": "Lately, I've been stumped about something. Don't feel like I'm making much of an impact here, which has me questioning my decisions and goals." - }, - { - "speaker": "Maria", - "dia_id": "D30:15", - "text": "Hey John, I understand how you feel. It's tough when you start questioning that. But remember, even small things can make a difference. Why do you feel that way?" - }, - { - "speaker": "John", - "dia_id": "D30:16", - "text": "Thanks for understanding. I just want to positively affect people and the world, but it feels like I'm stuck. I need to find a better way to focus my passion and enthusiasm." - }, - { - "speaker": "Maria", - "dia_id": "D30:17", - "text": "Yeah, it can be tough. Taking small steps can help. Have you thought about any ideas for how to channel your energy into something meaningful?" - }, - { - "speaker": "John", - "dia_id": "D30:18", - "text": "Lately I've been exploring options. One idea I had was to join local organizations or volunteering programs. Maybe that's an option." - }, - { - "speaker": "Maria", - "dia_id": "D30:19", - "text": "Sounds like a great plan, John! That could definitely help you make a difference. You can meet like-minded people and contribute to causes that you care about. Why not do some research and explore the organizations you'd be interested in?" - }, - { - "speaker": "John", - "dia_id": "D30:20", - "text": "Yeah, Maria, that's a good idea. I'll make a list of the ones that suit what I believe in and reach out for more info. Thanks!" - }, - { - "speaker": "Maria", - "dia_id": "D30:21", - "text": "No worries, John. I'm happy to help. Let me know if you need anything else. Keep going, you can do it!" - }, - { - "speaker": "John", - "dia_id": "D30:22", - "text": "Thanks for your kind words, Maria! I'll let you know if I need anything else. Keep being positive and kind, you're an inspiration!" - }, - { - "speaker": "Maria", - "dia_id": "D30:23", - "text": "Thanks, John. Your words mean a lot. I'll definitely keep spreading positivity. Take care!" - } - ], - "session_31_date_time": "3:14 pm on 13 August, 2023", - "session_31": [ - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting around a table", - "dia_id": "D31:1", - "text": "Hi Maria, since we last chatted, I'm volunteering as a mentor for a local school. It's really rewarding to see how much I can help these students." - }, - { - "speaker": "Maria", - "img_url": [ - "https://www.k9aholics.com/wp-content/uploads/2021/12/Things-to-Consider-Before-Getting-a-Puppy.jpg" - ], - "blip_caption": "a photography of a black puppy sitting in the grass looking at the camera", - "query": "puppy animal shelter adoption", - "dia_id": "D31:2", - "re-download": true, - "text": "Wow, John! You're doing great things. Kudos for helping kids learn. On another note, I just adopted this cute pup from a shelter last week. She brings so much joy! I feel blessed to be able to give her a home." - }, - { - "speaker": "John", - "dia_id": "D31:3", - "text": "Wow, Maria, how cute! What's her name? Do she and your other dog get along?" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a dog sitting in the grass with a leash", - "dia_id": "D31:4", - "text": "Her name is Shadow! She's full of energy and always puts a smile on my face. She's been a great addition to my life. They get along great." - }, - { - "speaker": "John", - "dia_id": "D31:5", - "text": "Aww, that sounds adorable! Animals really bring a lot of joy and love, don't they? Did you have any pets growing up?" - }, - { - "speaker": "Maria", - "dia_id": "D31:6", - "text": "No, I didn't. But having a furry pal definitely brightens my days." - }, - { - "speaker": "John", - "dia_id": "D31:7", - "text": "They sure do! I'll have to look into shelters near me soon, it would be great to have a new pup in the house." - }, - { - "speaker": "Maria", - "dia_id": "D31:8", - "text": "Yeah, John! Pets are great at making you feel loved. It's awesome having them around, they bring so much joy! How's everything going with the mentoring program? Are the students making progress?" - }, - { - "speaker": "John", - "img_url": [ - "https://createyourbalancewithliteracy.com/wp-content/uploads/2023/06/Science-Interactive-Notebook-6.jpg" - ], - "blip_caption": "a photo of a poster with a picture of a person and a child", - "query": "students mentoring program whiteboard project", - "dia_id": "D31:9", - "text": "They're doing great - there's been a real improvement in their confidence and skills. It was so amazing to see one of them last week, so excited to show me their essay. It was a proud moment! How's your new pup doing, Maria?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/side-view-of-young-man-training-his-dog-by-wall-royalty-free-image-717234545-1559064531.jpg" - ], - "blip_caption": "a photo of a man standing next to a dog pointing at something", - "query": "puppy new command training", - "dia_id": "D31:10", - "text": "Awesome, John! Sounds like it's really making a difference. The little one is doing great - learning commands and house training.\n" - }, - { - "speaker": "John", - "dia_id": "D31:11", - "text": "Wow, cool Maria! Your little one is so smart and keen to learn, must be awesome!" - }, - { - "speaker": "Maria", - "dia_id": "D31:12", - "text": "She's an amazing learner - so much fun to work with and watch her grow. She's brought me so much joy!" - }, - { - "speaker": "John", - "dia_id": "D31:13", - "text": "Animals are amazing\u2014 They can be incredible companions." - }, - { - "speaker": "Maria", - "dia_id": "D31:14", - "text": "Yeah, they can really comfort us and make us feel understood in ways others can't. It's amazing how they bring us so much happiness." - }, - { - "speaker": "John", - "dia_id": "D31:15", - "text": "Yeah, animals bring us peace and understanding, plus we can always count on them. That's so priceless." - }, - { - "speaker": "Maria", - "dia_id": "D31:16", - "text": "Absolutely, John. They're always there for us." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/wi6u64sicow71.jpg" - ], - "blip_caption": "a photo of a family posing for a picture in the park", - "query": "wife kids", - "dia_id": "D31:17", - "text": "Yeah, my family is awesome - me, the missus, and the kids. Even when times are hard, they always have my back. Best thing ever, really." - }, - { - "speaker": "Maria", - "dia_id": "D31:18", - "text": "That picture is so cute! What activities do you all enjoy doing together?" - }, - { - "speaker": "John", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0586/8149/1647/t/127/assets/pickneys_2-1662359454750.jpg" - ], - "blip_caption": "a photo of two children sitting at a table with a board game", - "query": "family playing board games", - "dia_id": "D31:19", - "text": "Thanks, Maria! We love being outdoors - going for hikes, hanging out at the park, having picnics - plus playing board games and having movie nights at home. " - }, - { - "speaker": "Maria", - "dia_id": "D31:20", - "text": "Sounds like a blast, John! Spending time with family is so important, and that all sounds perfect. Have a great time!" - }, - { - "speaker": "John", - "dia_id": "D31:21", - "text": "Thanks, Maria! I'm off to spend some time with them now. Have a good day!" - }, - { - "speaker": "Maria", - "dia_id": "D31:22", - "text": "Enjoy your family time, John! Have a great day!" - }, - { - "speaker": "John", - "dia_id": "D31:23", - "text": "Thanks, Maria! You too! Stay safe!" - } - ], - "session_32_date_time": "11:08 am on 16 August, 2023", - "session_32": [ - { - "speaker": "John", - "dia_id": "D32:1", - "text": "Hey Maria! Guess what? I'm now part of the fire-fighting brigade. I'm super excited to be involved and help out my community!" - }, - { - "speaker": "Maria", - "dia_id": "D32:2", - "text": "Wow John, that's impressive! You're really enthusiastic about making a change. How's your experience been so far?" - }, - { - "speaker": "John", - "dia_id": "D32:3", - "text": " I was impressed with their dedication and how well they worked together. Just being around them was so inspiring!" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people loading a truck with a fire truck in the back", - "dia_id": "D32:4", - "text": "That's amazing. Must have been awesome to see all those people working together." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/kkt623tlufk11.jpg" - ], - "blip_caption": "a photo of a cardboard box with a sign on it", - "query": "group of people lifting heavy object", - "dia_id": "D32:5", - "text": "It definitely was! Everyone was so into it. It's amazing how a group can succeed at something so important. It only took us two hours. We worked hard but did something good \u2013 it was really satisfying." - }, - { - "speaker": "Maria", - "dia_id": "D32:6", - "text": "Wow, John! It looks like everyone was working hard. Did you raise any donations? " - }, - { - "speaker": "John", - "dia_id": "D32:7", - "text": "Yup, we raised a ton! We got stuff like canned food, toiletries, and clothes to help out. Feels great to be part of it!" - }, - { - "speaker": "Maria", - "dia_id": "D32:8", - "text": "I bet! It's great to see the community coming together to support the local fire station. " - }, - { - "speaker": "John", - "dia_id": "D32:9", - "text": "You're right, Maria. It's great to help out and see everyone coming together for this cause. It gives me a sense of purpose and passion. I feel like this is my true calling." - }, - { - "speaker": "Maria", - "dia_id": "D32:10", - "text": "Awesome, John! Loving your newfound passion. You're doing great things - keep it up! It's wonderful to see everyone coming together for this cause." - }, - { - "speaker": "John", - "img_url": [ - "https://www.cityofmobile.org/fire/uploads/20180827-094050.jpg" - ], - "blip_caption": "a photo of a fire truck parked in a garage with other vehicles", - "query": "firetruck", - "dia_id": "D32:11", - "text": "Thanks, Maria! Your support means a lot to me. It's amazing how finding my passion has made such a big impact. I'll keep working hard on it. The donations even helped get a brand new fire truck!" - }, - { - "speaker": "Maria", - "dia_id": "D32:12", - "text": "Look at that - we all donated for it, and it looks awesome!" - }, - { - "speaker": "John", - "dia_id": "D32:13", - "text": "Thanks for being a part of this with me, Maria. I appreciate your support." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people standing around a table filled with food", - "dia_id": "D32:14", - "text": "Hey John, I'm here for you. Last Friday, I spent some time at the shelter volunteering at the front desk. Seeing the smiles on their faces when they got food or a bed really made me feel good. We have the power to make a difference in people's lives." - }, - { - "speaker": "John", - "dia_id": "D32:15", - "text": "Maria, I'm glad you're finding fulfillment there. It's amazing how a little kindness can have such a big impact on someone's life. Let's continue making a difference in our community!" - }, - { - "speaker": "Maria", - "dia_id": "D32:16", - "text": "Yeah, John! Let's keep spreading kindness. It's awesome to know we can bring joy and comfort to those who need it." - }, - { - "speaker": "John", - "dia_id": "D32:17", - "text": "Yeah, Maria, let's keep each other and everyone else motivated to make a difference! Together, our impact will surely last." - } - ] - }, - "event_summary": { - "events_session_1": { - "John": [ - "John, his wife and their four kids, take a road trip together.", - "John practices kickboxing to stay in shape.", - "John aims to get into local politics and improve education, infrastructure in the community.", - "John plans to chat with local leaders to gain support and advices for his ideas." - ], - "Maria": [ - "Maria volunteers at a homeless shelter.", - "Maria starts practicing aerial yoga to stay fit." - ], - "date": "17 December, 2022" - }, - "events_session_2": { - "John": [ - "John networks with some people to make progress towards his goal of joining local politics and hears some inspiring stories from them.", - "John practices taekwondo.", - "John and his family have a fun dinner where they make pizza and choose toppings for themselves." - ], - "Maria": [ - "Maria donates her old car to the homeless shelter she volunteers at.", - "Maria makes some peach cobbler." - ], - "date": "22 December, 2022" - }, - "events_session_3": { - "John": [ - "John joins a service-focused online support group for those inspired to serve the country.", - "John meets people from the online support group and together, they volunteer at the homeless shelter, organize a toy drive for kids in need.", - "John is stressed out because he fails his military aptitude test.", - "John and his fellows from the online support group brainstorm ideas to bring education, mentorship, job training and resume building workshops to unserserved communities." - ], - "Maria": [ - "Maria went to the beach last month." - ], - "date": "1 January, 2023" - }, - "events_session_4": { - "John": [ - "John suffers from an accident where his car's windshield is shattered, but he does not receive any serious injuries.", - "John makes progress towards his political goals by talking to community leaders and getting to know the needs of the community." - ], - "Maria": [ - "Maria becomes friends with one of the shelter's volunteers and bond on their shared love for helping others." - ], - "date": "9 January, 2023" - }, - "events_session_5": { - "John": [ - "John attends a community meeting where he is motivated to work towards providing upgrades to education infrastructure in underserved communities. He is particularly struck by the inequality in access across neighborhoods." - ], - "Maria": [ - "Maria volunteers at the homeless shelter during a kids event. She is inspired by her aunt who used to volunteer and helped her family during tough times." - ], - "date": "28 January, 2023" - }, - "events_session_6": { - "John": [ - "John organizes a food drive for people who have lost their jobs recently to help out neighbors in need and finds overwhelming support from volunteers." - ], - "Maria": [ - "Maria gets out of her comfort zone and participates in a charity event at a homeless shelter. She finds the power of collective effort heartwarming.", - "Maria talks to a person named David during the charity event and connects them to a charity organization that could help provide them a place to stay and basic necessities." - ], - "date": "5 February, 2023" - }, - "events_session_7": { - "John": [ - "John runs for office again, with even more zeal and enthusiasm than his first run, inspired by the observation that one can make a lot of social impact when in office.", - "John is invited to a begineer's yoga class by his colleague, Rob." - ], - "Maria": [ - "Maria joins a creative writing class and finds it super enlightening.", - "MAria meets a person named Jean when volunteering at the homeless shelter, who became homeless after she got divorced and lost her job. Maria inspires to find positivity and happiness even during the tough times in life." - ], - "date": "25 February, 2023" - }, - "events_session_8": { - "John": [ - "John retakes the aptitude test for joining the military with positive results and is elated with the results.", - "John attends a violin concert with his wife and kids.", - "John goes to a picnic with his family." - ], - "Maria": [ - "Maria's grandmother passed away last week." - ], - "date": "6 March, 2023" - }, - "events_session_9": { - "John": [ - "John graduates from his college with a degree related to policymaking and is inspired to pursue a career where he can use his degree to improve society." - ], - "Maria": [ - "Maria takes a poetry class to learn more about expressing her emotions." - ], - "date": "2 April, 2023" - }, - "events_session_10": { - "John": [ - "John starts a weekend yoga class with one of his colleagues." - ], - "Maria": [], - "date": "7 April, 2023" - }, - "events_session_11": { - "John": [ - "John's car breaks down on the way to work and he is short of money after spending some on repairs.", - "John went to road trip in the Pacific North-west last year and visited many beautiful national parks." - ], - "Maria": [ - "Maria gives inspirational talks at the shelter which earns her praise from the fellow volunteers.", - "Maria organizes a meal at the homeless shelter.", - "MAria buys a cross necklace for herself to feel closer to her faith." - ], - "date": "10 April, 2023" - }, - "events_session_12": { - "John": [ - "John starts posting articles to his blog about the country's political and governmental system and receives feedback from like-imnded readers.", - "John participated in a convention last month with his colleagues where they brainstormed about ideas to use tech for good and positive change in the society.", - "John starts planning for a road trip on the East coast." - ], - "Maria": [], - "date": "18 April, 2023" - }, - "events_session_13": { - "John": [ - "John starts a series of military-style health boot camps with his family to stay fit. They do workouts three times a week.", - "John makes apple pie for the kids." - ], - "Maria": [ - "Maria makes dinner with her Mom where they have a spread of salads, sandwiches and her favorite dessert, banana split sundae.", - "Maria reflects on the time she went on a solo trip last year, took some pictures in Spain and tried her hand at surfing for the first time. The trip teaches her the importance of inner strength and solitude." - ], - "date": "4 May, 2023" - }, - "events_session_14": { - "John": [ - "John decides to run for office again despite his previous unsuccessful attempts.", - "John faces a power cut in the area which reinforces his motivation to work towards upgrading infrastructure in his current neighborhood as well as his old neighborhood, West County." - ], - "Maria": [ - "Maria starts attending a nearby church to feel closer to her faith and community." - ], - "date": "6 May, 2023" - }, - "events_session_15": { - "John": [ - "John reaches out to different groups to start a petition in support of military veterans' rights to show his appreciation towards their service to the country.", - "John throws a small party and invites veterans to come share their stories." - ], - "Maria": [], - "date": "20 May, 2023" - }, - "events_session_16": { - "John": [ - "John calls old friends to help spread the petition he started for supporting the rights of military veterans.", - "John offers help to Maria to spread word for the chilli cook-off being organized as a part of the fundraiser at the homeless shelter she volunteers at." - ], - "Maria": [ - "Maria works towards organizing a fundraiser for the homeless shelter she volunteers at.", - "Maria asks John to help spread the word for the chilli cook-off being organized as a part of the fundraiser." - ], - "date": "25 May, 2023" - }, - "events_session_17": { - "John": [ - "John's ten year old dog, Max, passes away.", - "John and his family consider adopting a rescue dog as a pet." - ], - "Maria": [ - "Maria signs an agreement with a local dog shelter to volunteer once a month." - ], - "date": "3 June, 2023" - }, - "events_session_18": { - "John": [ - "John goes on a mountaineering expedition with some of his colleagues and summits the peak." - ], - "Maria": [ - "Maria and her friends from the church she joined recently go on a camping trip.", - "Maria reflects on the time she went on a road trip to Oregon with her famly and saw the Multnomah Falls." - ], - "date": "12 June, 2023" - }, - "events_session_19": { - "John": [ - "John gets promoted to the position of assistant manager at his workplace and is over the moon about it." - ], - "Maria": [ - "Maria joins a local gym and meets new friends. She finds the gym routine super positive and is excited to build her endurance through workouts as well as kundalini yoga.", - "" - ], - "date": "16 June, 2023" - }, - "events_session_20": { - "John": [ - "John spends quality time with his family by attending a live music event." - ], - "Maria": [ - "Maria goes through a rough patch but receives support from her family." - ], - "date": "27 June, 2023" - }, - "events_session_21": { - "John": [ - "John participates in a protest march dedicated to the cause of military veterans' rights." - ], - "Maria": [ - "Maria meets a bunch of delightful kids at the shelter and receives a letter of appreciation from Laura, one of the shelter residents.", - "Maria tries to help her cousin who has to move out and urgently needs a place to live.", - "Maria faces an accident where another car runs the red light and hits their car." - ], - "date": "3 July, 2023" - }, - "events_session_22": { - "John": [], - "Maria": [ - "Maria takes regular 'me-time' walks in the park closest to her." - ], - "date": "5 July, 2023" - }, - "events_session_23": { - "John": [ - "John's old neighborhood, West County, gets hit by a flood and John brainstorms solutions to help the area." - ], - "Maria": [ - "Maria and her new friend at the gym go to dinner." - ], - "date": "7 July, 2023" - }, - "events_session_24": { - "John": [ - "John visits a local veteran's hospital to learn more about service to the country and gets to hear stories of resilience.", - "John visits an arts and crafts based community event with his family and is inspired to create a space for it in their home." - ], - "Maria": [ - "Maria has a picnic with her friends from the church where they share good food and play games like charades, scavenger hunt." - ], - "date": "17 July, 2023" - }, - "events_session_25": { - "John": [ - "John joins a new job and works with an encouraging team." - ], - "Maria": [ - "Maria embarks on a hiking trip with her new church friends.", - "Maria bakes and donates some cakes to the homeless shelter." - ], - "date": "22 July, 2023" - }, - "events_session_26": { - "John": [ - "John joins a fire-fighting brigade to serve in his community more directly and contribute his skills.", - "John undergoes intense training as part of the fire-fighting brigade and gets the opportunity to save a family from a burning building with the team." - ], - "Maria": [], - "date": "31 July, 2023" - }, - "events_session_27": { - "John": [ - "John encourages family and friends to join the online support group he has been a part of for months and become advocates for military service.", - "John and his family have a humbling moment during a visit to a military memorial." - ], - "Maria": [ - "Maria receives a letter of appreciation from Cindy, a resident at the homeless shelter." - ], - "date": "3 August, 2023" - }, - "events_session_28": { - "John": [ - "John loses his job at the mechanical engineering company and is on the look out for other jobs, potentially at tech companies that require his mechanical engineering skills for their hardware division." - ], - "Maria": [ - "Maria and her friends from the church take up some community work." - ], - "date": "5 August, 2023" - }, - "events_session_29": { - "John": [ - "John successfully organizes a 5K charity run in his neighborhood for the benefit of military veterans." - ], - "Maria": [ - "Maria receives a medal from the homeless shelter for her relentless work towards making the residents' lives better." - ], - "date": "9 August, 2023" - }, - "events_session_30": { - "John": [ - "John feels stuck in life because he feels he has not been able to make enough impact with his work and is unable to follow his passions." - ], - "Maria": [ - "Maria volunteers at the animal shelter where she adopts a puppy and names her Coco." - ], - "date": "11 August, 2023" - }, - "events_session_31": { - "John": [ - "John volunteers as a mentor for a local school and is excited to see the growth in confidence in the kids from his mentorship." - ], - "Maria": [ - "Maria adopts another puppy from the dog shelter she volunteers at, and names it Shadow." - ], - "date": "13 August, 2023" - }, - "events_session_32": { - "John": [ - "John starts attending monthly gatherings with the fire-fighting brigade and raises donations during the meetings." - ], - "Maria": [ - "Maria volunteers at the front desk in the homeless shelter and is happy to see the relief in people's faces when they find a bed or food." - ], - "date": "16 August, 2023" - } - }, - "observation": { - "session_1_observation": { - "Maria": [ - [ - "Maria volunteers at a homeless shelter and recently started aerial yoga.", - "D1:3" - ] - ], - "John": [ - [ - "John just got back from a family road trip.", - "D1:2" - ], - [ - "John is currently doing kickboxing as a workout.", - "D1:4" - ], - [ - "John aspires to get into local politics to help improve the community.", - "D1:6" - ], - [ - "John's passion in politics revolves around improving education and infrastructure in the community.", - "D1:8" - ], - [ - "John is focused on funding schools and improving infrastructure due to past experiences of lack of education and infrastructure in his neighborhood.", - "D1:10" - ], - [ - "John's next move in politics involves chatting with local leaders and organizations to gather support and ideas.", - "D1:14" - ] - ] - }, - "session_2_observation": { - "Maria": [ - [ - "Maria donated her old car to a homeless shelter where she volunteers.", - "D2:1" - ], - [ - "Maria believes that even minor tweaks to the system can make a big difference for many people.", - "D2:7" - ], - [ - "Maria enjoys spending time with friends watching movies, hiking, and having game nights at her place.", - "D2:17" - ], - [ - "Maria made peach cobbler recently.", - "D2:25" - ] - ], - "John": [ - [ - "John has been networking to gather input for a campaign to make improvements to the community's education system.", - "D2:2" - ], - [ - "John is motivated to make education better in their area to invest in future generations.", - "D2:4" - ], - [ - "John's family serves as a source of strength and motivation for him.", - "D2:10" - ], - [ - "John and his family enjoy spending time at a playground together, climbing, sliding, and playing games.", - "D2:16" - ], - [ - "John's family loves to make and enjoy pizzas together.", - "D2:24" - ], - [ - "John practices taekwondo.", - "D2:28" - ] - ] - }, - "session_3_observation": { - "John": [ - [ - "John joined a service-focused online group last week and finds it emotionally rewarding.", - "D3:1" - ], - [ - "John participated in activities with the online group such as giving out food at a homeless shelter and organizing a toy drive for kids in need.", - "D3:5" - ], - [ - "John is involved in brainstorming projects to help underserved communities with education, mentorship, job training, and resume building.", - "D3:7" - ], - [ - "John recently failed the military aptitude test and has been feeling stressed out.", - "D3:11" - ], - [ - "John used to have a film camera as a kid and took plenty of beach pictures.", - "D3:15" - ] - ], - "Maria": [ - [ - "Maria appreciates nature's beauty and finds it a source of relaxation and a reminder to enjoy the small things in life.", - "D3:12" - ], - [ - "Maria took a peaceful sunset picture at the beach last month that made her feel connected to nature and appreciate life's small moments.", - "D3:14" - ] - ] - }, - "session_4_observation": { - "Maria": [ - [ - "Maria recently became friends with a fellow volunteer who shares a passion for helping others.", - "D4:1" - ], - [ - "Maria appreciates the importance of staying strong during tough times.", - "D4:4" - ], - [ - "Maria values appreciating what one has and staying strong during challenges.", - "D4:9" - ], - [ - "Maria takes notes about local politics in her notebook.", - "D4:13" - ], - [ - "Maria supports John's community work and helps him with new ideas and plans.", - "D4:14" - ], - [ - "Maria believes in working together to bring positive changes and discussions to the community.", - "D4:19" - ], - [ - "Maria emphasizes the importance of unity and teamwork for addressing community problems.", - "D4:23" - ] - ], - "John": [ - [ - "John experienced an unexpected incident last week on his way home, which reminded him of life's unexpected troubles.", - "D4:2" - ], - [ - "John handled the unexpected incident by staying calm, asking for assistance, and returning safely.", - "D4:6" - ], - [ - "John appreciates the beauty of sunsets as a way to enjoy the small things in life.", - "D4:10" - ], - [ - "John has been delving into local politics, talking to community leaders, understanding neighborhood needs, and feeling optimistic about it.", - "D4:12" - ], - [ - "John is preparing for a community meeting next week to discuss education and infrastructure upgrades.", - "D4:16" - ], - [ - "John believes in sticking up for the community and finds discussing community matters important.", - "D4:18" - ], - [ - "John is grateful for Maria's support and considers her an awesome friend.", - "D4:24" - ] - ] - }, - "session_5_observation": { - "John": [ - [ - "John attended a community meeting focused on the state of education and the need for upgrades, especially for kids.", - "D5:1" - ], - [ - "John expressed concern about the lack of proper resources for kids in education and the need to make things better.", - "D5:3" - ], - [ - "John mentioned his kids having a lot while others lack resources, indicating a sense of empathy and awareness.", - "D5:5" - ], - [ - "John shared that he had a doll in his childhood that made him feel better, reminding him to look out for others when they're feeling down.", - "D5:13" - ], - [ - "John emphasizes the importance of supporting each other, especially when feeling down.", - "D5:15" - ] - ], - "Maria": [ - [ - "Maria expressed sadness about the state of education and the lack of proper resources for kids.", - "D5:2" - ], - [ - "Maria mentioned volunteering at a shelter during an event for kids, showing a commitment to helping those in need.", - "D5:6" - ], - [ - "Maria volunteered to help make a difference and shared that her aunt's involvement in volunteering inspired her.", - "D5:8" - ], - [ - "Maria had a touching moment with a lonely girl at the shelter, providing comfort and company, which made an impact on her.", - "D5:10" - ], - [ - "Maria values spreading kindness and support to make a difference, especially for those feeling down.", - "D5:14" - ] - ] - }, - "session_6_observation": { - "Maria": [ - [ - "Maria participated in a charity event last Friday and found it rewarding and heartwarming.", - "D6:1" - ], - [ - "Maria had a conversation with someone named David at the charity event, who shared a story of hardship, leading Maria to link him up with a local organization for support.", - "D6:5" - ], - [ - "When Maria was younger, her family had money problems and had to rely on help from their auntie, teaching her the importance of helping others in need.", - "D6:9" - ], - [ - "Maria offered to help John with his community food drive initiative by networking or volunteering at future events.", - "D6:17" - ], - [ - "Maria expressed her willingness to always be there to help John whenever he needs it.", - "D6:19" - ], - [ - "Maria reassured John that she would always be there for him because he is a great friend to her.", - "D6:21" - ] - ], - "John": [ - [ - "John got into politics due to a strong urge to serve his country and community.", - "D6:4" - ], - [ - "John started helping out with a food drive for people who lost their jobs.", - "D6:10" - ], - [ - "John initiated a community food drive due to the impact of unemployment in the community.", - "D6:12" - ], - [ - "John has been overwhelmed by the response and volunteers at the food drive events.", - "D6:16" - ], - [ - "John appreciated Maria's offer to help with networking or volunteering at future events for the food drive.", - "D6:18" - ], - [ - "John expressed that friendship means a lot to him and that he values having each other's backs with Maria.", - "D6:22" - ] - ] - }, - "session_7_observation": { - "Maria": [ - [ - "Maria recently took a creative writing class.", - "D7:1" - ], - [ - "Maria has been volunteering at a homeless shelter, finding it rewarding.", - "D7:3" - ], - [ - "During her volunteering, Maria met a woman named Jean who showed her the importance of gratitude and connection.", - "D7:5" - ], - [ - "Maria values positivity and optimism, even in tough situations.", - "D7:9" - ] - ], - "John": [ - [ - "John is running for office again.", - "D7:2" - ], - [ - "John believes in making positive changes through politics for a better future.", - "D7:4" - ], - [ - "John sees staying optimistic during tough times as inspiring.", - "D7:8" - ], - [ - "John believes in the impact of kindness and optimism.", - "D7:10" - ], - [ - "John's colleague invited him to a beginner's yoga class.", - "D7:16" - ] - ] - }, - "session_8_observation": { - "Maria": [ - [ - "Maria's grandma passed away last week, causing her to go through a tough time.", - "D8:1" - ], - [ - "Maria volunteers at a homeless shelter.", - "D8:21" - ] - ], - "John": [ - [ - "John had a picnic with his wife and kids recently.", - "D8:2" - ], - [ - "John's one-year-old son is named Kyle.", - "D8:4" - ], - [ - "John takes his family to the park a few times a week for bonding and fun.", - "D8:8" - ], - [ - "John found a violin concert last week that the whole family enjoyed.", - "D8:12" - ], - [ - "John is considering volunteering to serve his country.", - "D8:18" - ], - [ - "John discussed the volunteering opportunity with family and friends, who have been supportive.", - "D8:20" - ] - ] - }, - "session_9_observation": { - "Maria": [ - [ - "Maria has been taking a poetry class to help express her feelings.", - "D9:1" - ], - [ - "Maria congratulated John on finishing his degree.", - "D9:3" - ], - [ - "Maria is supportive of John's plans for the future in policymaking.", - "D9:7" - ], - [ - "Maria remembers John mentioning his interest in education and infrastructure improvements.", - "D9:9" - ], - [ - "Maria praised John for his dedication and commitment to finding solutions for community issues.", - "D9:11" - ], - [ - "Maria finds it satisfying and worthwhile to work on community issues.", - "D9:13" - ] - ], - "John": [ - [ - "John has recently graduated.", - "D9:4" - ], - [ - "John is considering going into policymaking to make a positive impact.", - "D9:6" - ], - [ - "John is particularly interested in improving education and infrastructure.", - "D9:8" - ], - [ - "John's experiences in community meetings have shaped his views on education and infrastructure challenges.", - "D9:10" - ], - [ - "John finds it rewarding to make a difference in the community.", - "D9:12" - ], - [ - "John values Maria's support and finds it encouraging.", - "D9:14" - ], - [ - "John reminisced about volunteering with Maria last year and found it fulfilling.", - "D9:18" - ] - ] - }, - "session_10_observation": { - "John": [ - [ - "John started a weekend yoga class with a colleague and finds it awesome for his mental and physical wellbeing.", - "D10:1" - ], - [ - "John's yoga instructor focuses on proper poses, listening to one's body, and creates a relaxed and welcoming environment.", - "D10:3" - ], - [ - "John is in a beginner yoga class that helps him relax and increase flexibility.", - "D10:5" - ], - [ - "Yoga has helped John feel more connected, relaxed, and improved his mind and body.", - "D10:7" - ], - [ - "John considers stretching and breathing as powerful tools for wellbeing and has incorporated it into his daily routine.", - "D10:9" - ], - [ - "John volunteered at a career fair at a local school and found it rewarding to help kids with dreams affected by lack of resources.", - "D10:13" - ], - [ - "The sign at the career fair said, \"Always look on the bright side of life\", highlighting the importance of support in achieving kids' dreams.", - "D10:15" - ] - ], - "Maria": [ - [ - "Maria recently participated in a 5K charity run for a homeless shelter and found it rewarding to be surrounded by people with a shared cause.", - "D10:10" - ], - [ - "Maria finds events like charity runs energizing and a reminder that individuals can make a difference.", - "D10:11" - ], - [ - "Maria was inspired by the energy and sense of unity at the charity run.", - "D10:12" - ], - [ - "Maria believes it's sad how lack of resources can impact kids' dreams and finds helping them rewarding.", - "D10:14" - ], - [ - "Maria motivates John for the awesome work he's doing in helping kids and encourages him to keep up the good work.", - "D10:16" - ], - [ - "Maria believes in making a difference and helping lots of people in the community.", - "D10:18" - ] - ] - }, - "session_11_observation": { - "John": [ - [ - "John's car broke down last Friday on his way to work, causing financial strain.", - "D11:1" - ], - [ - "John remains positive and looks for solutions despite facing tough times.", - "D11:1" - ], - [ - "John keeps a picture that reminds him of a road trip taken last year as a symbol of beauty and hope.", - "D11:3" - ], - [ - "John explored the Pacific Northwest coast and visited national parks on the road trip, finding nature's beauty breathtaking.", - "D11:5" - ] - ], - "Maria": [ - [ - "Maria recently gave talks at the homeless shelter where she volunteers and received compliments for her efforts.", - "D11:10" - ], - [ - "Maria bought a cross necklace to feel closer to her faith and it has made her happy.", - "D11:10" - ], - [ - "Maria considers the people at the shelter like family to her.", - "D11:12" - ], - [ - "Maria helped organize a meal for shelter residents to bring people together and create a sense of comfort and community.", - "D11:16" - ] - ] - }, - "session_12_observation": { - "John": [ - [ - "John has been blogging about politics and government focusing on education reform and infrastructure development.", - "D12:1" - ], - [ - "John is passionate about making a positive impact in his community through raising awareness and starting conversations.", - "D12:3" - ], - [ - "John attended a convention with colleagues who share his passion for using tech for good in the community.", - "D12:9" - ], - [ - "John felt inspired by being surrounded by like-minded individuals at the convention and believes in the power of unity.", - "D12:11" - ], - [ - "Being in an environment with motivated people gave John renewed energy and purpose, inspiring him to make a bigger difference.", - "D12:15" - ], - [ - "John is planning a trip to the East Coast.", - "D12:17" - ] - ], - "Maria": [ - [ - "Maria agrees with John on the importance of education and infrastructure for community growth.", - "D12:2" - ], - [ - "Maria thinks John's hard work will start conversations and create positive change.", - "D12:4" - ], - [ - "Maria believes that every small act of kindness can have a big effect and advocates for making a difference.", - "D12:22" - ], - [ - "Maria volunteers at a homeless shelter, finding it fulfilling but also feeling overwhelmed by the growing need for help.", - "D12:18" - ] - ] - }, - "session_13_observation": { - "John": [ - [ - "John started going to boot camps with his family last month for bonding and fitness.", - "D13:3" - ], - [ - "John, along with his family, works out together three times a week.", - "D13:7" - ], - [ - "John has noticed increased energy, strength, and endurance since starting boot camps with his family.", - "D13:5" - ], - [ - "John and his family support and motivate each other during workouts.", - "D13:5" - ], - [ - "John and his family cheer each other on and provide emotional support during boot camp.", - "D13:15" - ], - [ - "John made an apple pie for his kids the day before the conversation.", - "D13:17" - ] - ], - "Maria": [ - [ - "Maria enjoys a variety of foods like salads, sandwiches, and homemade desserts.", - "D13:18" - ], - [ - "Maria's favorite is the banana split sundae, which she enjoys after a day of volunteering.", - "D13:18" - ], - [ - "Maria values balance in life and finds joy in small moments.", - "D13:18" - ], - [ - "Maria took a solo trip to Spain last year which helped her appreciate life more.", - "D13:24" - ], - [ - "Maria tried surfing for the first time on her vacation in Spain.", - "D13:30" - ], - [ - "Maria believes in pushing boundaries for growth and self-exploration.", - "D13:36" - ] - ] - }, - "session_14_observation": { - "John": [ - [ - "John decided to run for office again, despite not being successful before, to make a difference in his community.", - "D14:1" - ], - [ - "John feels motivated by Maria's support and kind words to keep going and make a difference.", - "D14:3" - ], - [ - "John experienced a power cut in the area, realizing the importance of upgrading infrastructure for stable services.", - "D14:13" - ], - [ - "John believes that improvements are needed for the roadways in the area due to potholes and dangers for drivers.", - "D14:15" - ], - [ - "John is considering starting a community project regarding infrastructure to improve the neighborhood.", - "D14:17" - ], - [ - "John wants to work on improving his old area, West County, in addition to the current neighborhood.", - "D14:21" - ] - ], - "Maria": [ - [ - "Maria has been doing charity work recently, which she finds rewarding and fulfilling.", - "D14:8" - ], - [ - "Maria joined a nearby church to feel closer to a community and her faith, finding it to be a great experience.", - "D14:10" - ], - [ - "Maria's life has been a bit rough lately, but she is taking time to reflect and find balance.", - "D14:12" - ], - [ - "Maria is willing to work with John on a community project regarding infrastructure to get the neighborhood's backing.", - "D14:18" - ], - [ - "Maria wants to work together with John to create a safe and bustling environment for the community and others, to make real change.", - "D14:22" - ] - ] - }, - "session_15_observation": { - "John": [ - [ - "John is currently working on a project to support military veterans and is trying to get a petition going.", - "D15:1" - ], - [ - "John decided to take on the project to support veterans because he is passionate about their rights and wanted to show appreciation for their hard work.", - "D15:3" - ], - [ - "John is very passionate about making sure veterans are supported and valued in the community.", - "D15:9" - ], - [ - "John organized a small party for some veterans to share their stories and make connections, which was heartwarming for him.", - "D15:13" - ] - ], - "Maria": [ - [ - "Maria is supportive of John's project to support veterans and is willing to help out.", - "D15:10" - ], - [ - "Maria is planning a ring-toss tournament for a homeless shelter's fundraiser later in the month.", - "D15:18" - ] - ] - }, - "session_16_observation": { - "John": [ - [ - "John is working on a petition and getting back in touch with his buddies for support.", - "D16:1" - ], - [ - "John went on a hiking trip with his workmates and values their support.", - "D16:1" - ], - [ - "John is enthusiastic about helping Maria with her charity fundraiser.", - "D16:3" - ], - [ - "John offered to help spread the word about Maria's charity fundraiser chili cook-off.", - "D16:5" - ], - [ - "John expressed willingness to assist in finding volunteers for Maria's charity fundraiser.", - "D16:7" - ], - [ - "John shared a picture of his family at the beach with Maria.", - "D16:11" - ], - [ - "John values the little moments in life for hope and motivation.", - "D16:13" - ], - [ - "John believes in making a positive impact and appreciates reminders to cherish life.", - "D16:17" - ] - ], - "Maria": [ - [ - "Maria is busy at the shelter preparing for a fundraiser to cover basic needs for the homeless.", - "D16:2" - ], - [ - "Maria shared a poster for a chili cook-off at the fundraiser with John.", - "D16:4" - ], - [ - "Maria asked John for help in spreading the word about the charity fundraiser.", - "D16:5" - ], - [ - "Maria values and appreciates John's assistance with the charity fundraiser.", - "D16:8" - ], - [ - "Maria finds working with passionate people like John motivating.", - "D16:10" - ], - [ - "Maria believes that moments like the shared photo bring peace and appreciation for life.", - "D16:14" - ], - [ - "Maria finds reminders about positive impacts motivating to continue making a difference.", - "D16:18" - ] - ] - }, - "session_17_observation": { - "John": [ - [ - "John recently had to say goodbye to his dog, Max, who was a part of their family for 10 years.", - "D17:1" - ], - [ - "John is finding it tough to cope with the loss of Max, but he and his family are trying to focus on the good times and memories they shared.", - "D17:3" - ], - [ - "John shared a picture of Max to honor his memory and cherish the moments they had together.", - "D17:5" - ], - [ - "John values the lessons that Max taught them about love and loyalty, and wants his kids to learn the same.", - "D17:7" - ], - [ - "John is considering adopting a rescue dog to give it a loving home and teach his kids responsibility and compassion.", - "D17:11" - ] - ], - "Maria": [ - [ - "Maria offered John her condolences and support after hearing about the loss of Max, acknowledging the importance of pets as part of the family.", - "D17:2" - ], - [ - "Maria started volunteering at a local dog shelter once a month, showing her compassion towards animals and her willingness to help.", - "D17:12" - ] - ] - }, - "session_18_observation": { - "Maria": [ - [ - "Maria went camping with friends from church to take her mind off things.", - "D18:1" - ], - [ - "Maria and her family went on a road trip to Oregon when she was younger.", - "D18:3" - ], - [ - "Maria described a view of a waterfall and bridge as awesome and breath-taking.", - "D18:5" - ], - [ - "Maria felt like she was in a fairy tale at the waterfall, with calming water sounds and beautiful surroundings.", - "D18:7" - ], - [ - "Maria has gone camping a few times but has never tried mountain climbing.", - "D18:11" - ], - [ - "Maria finds peaceful moments by having a moment to herself with favorite tunes and practicing aerial yoga.", - "D18:15" - ], - [ - "Maria enjoys upside-down yoga poses as they make her feel free and light.", - "D18:17" - ] - ], - "John": [ - [ - "John went on a mountaineering trip with workmates that helped clear his head.", - "D18:2" - ], - [ - "John reached the summit during the mountaineering trip and found the view stunning.", - "D18:4" - ], - [ - "John has been camping plenty of times and enjoys getting away from it all to be at one with nature.", - "D18:12" - ], - [ - "John believes nature helps in calming us down and acts like a reset button amidst chaos.", - "D18:8" - ], - [ - "John had an awesome day at the park with his family where the kids had fun on the playground.", - "D18:22" - ] - ] - }, - "session_19_observation": { - "Maria": [ - [ - "Maria recently joined a gym and is sticking to her workout routine, enjoying the positive environment and supportive people there.", - "D19:1" - ], - [ - "Maria's fitness goal is to get stronger, improve her endurance, and she is trying kundalini yoga.", - "D19:3" - ], - [ - "Maria admires John's trophy and asks about its backstory.", - "D19:9" - ], - [ - "Maria acknowledges John's challenges and commends his perseverance and determination.", - "D19:13" - ], - [ - "Maria values belief in oneself and mentions the importance of having loved ones' support.", - "D19:15" - ], - [ - "Maria enjoys taking time off for herself and her family and appreciates creating memories together.", - "D19:21" - ], - [ - "Maria shares a special beach memory from a vacation in Florida with her family.", - "D19:23" - ], - [ - "Maria appreciates the joy and carefree moments of family vacations.", - "D19:25" - ] - ], - "John": [ - [ - "John got promoted at work to assistant manager and is excited about it, seeing it as a stepping stone for bigger things.", - "D19:6" - ], - [ - "John's trophy symbolizes the obstacles he overcame on his journey to the promotion.", - "D19:10" - ], - [ - "John shares about facing various challenges including tech issues, workplace hurdles, and self-doubt on his path to promotion.", - "D19:12" - ], - [ - "John mentions the importance of having support at home, believing in oneself, and staying determined.", - "D19:14" - ], - [ - "John values having loved ones' support on his journey, especially his family.", - "D19:16" - ], - [ - "John appreciates special family times and cherishes creating memories together.", - "D19:26" - ] - ] - }, - "session_20_observation": { - "Maria": [ - [ - "Maria has been struggling but is focusing on the positive and relying on friends and family for support.", - "D20:1" - ], - [ - "Maria's family has been her rock, giving her words of encouragement and reminding her she's not alone.", - "D20:3" - ], - [ - "Maria finds joy and fulfillment in volunteering at a shelter to help others.", - "D20:7" - ], - [ - "Maria had a special memory at the shelter when playing with kids made someone who had been sad suddenly laugh, which was uplifting.", - "D20:9" - ], - [ - "Maria sees making a difference in others' lives as a source of joy for herself.", - "D20:11" - ], - [ - "Maria feels that spreading kindness and positivity is her way of impacting the world.", - "D20:13" - ] - ], - "John": [ - [ - "John recalls having a blast at a live music event with his family last week.", - "D20:4" - ], - [ - "John emphasizes the importance of appreciating loved ones and finding silver linings in tough times.", - "D20:6" - ], - [ - "John finds it amazing how brief moments with others can make a big difference in bringing joy and happiness.", - "D20:10" - ], - [ - "John encourages Maria to continue spreading kindness and positivity, acknowledging the impact she is making.", - "D20:14" - ] - ] - }, - "session_21_observation": { - "Maria": [ - [ - "Maria met some kids at a shelter.", - "D21:1" - ], - [ - "Maria got hit by a car that ran a red light, but everyone is okay.", - "D21:5" - ], - [ - "Maria is helping her cousin find a new place to stay after a tough time.", - "D21:5" - ], - [ - "Maria's cousin had to leave and find a new place in a hurry, causing stress, but is making progress.", - "D21:7" - ], - [ - "Maria appreciates kindness and care.", - "D21:15" - ], - [ - "Maria loves volunteering and feels it makes a difference, even if it's a small one.", - "D21:21" - ] - ], - "John": [ - [ - "John expressed willingness to help Maria if she needed anything.", - "D21:4" - ], - [ - "John participated in a marching event for veterans' rights.", - "D21:22" - ], - [ - "John respects the military and believes in standing up for beliefs.", - "D21:24" - ], - [ - "John feels the importance of making a difference through activism and was motivated by it.", - "D21:26" - ] - ] - }, - "session_22_observation": { - "John": [ - [ - "John has been thinking about how education and infrastructure shape communities.", - "D22:1" - ], - [ - "John feels it is an uphill battle to make a positive change in communities.", - "D22:3" - ], - [ - "John doubts if he is making a difference sometimes.", - "D22:5" - ], - [ - "John's family motivates him to strive for change.", - "D22:7" - ], - [ - "John looks at a picture of his family when times get hard to remember why he is doing his work.", - "D22:9" - ], - [ - "John is inspired by his family, exercise, and spending time with friends.", - "D22:11" - ], - [ - "John sees sunsets at least once a week during his walks to disconnect and find peace.", - "D22:17" - ] - ], - "Maria": [ - [ - "Maria agrees with John on the crucial role of education and infrastructure in shaping communities.", - "D22:2" - ], - [ - "Maria supports John in his efforts to make a positive change in communities.", - "D22:4" - ], - [ - "Maria is inspired by chatting to people, volunteering, and listening to music.", - "D22:10" - ], - [ - "Maria takes regular 'me-time' walks at the park nearby to find peace.", - "D22:18" - ] - ] - }, - "session_23_observation": { - "John": [ - [ - "John's old area was hit by a nasty flood last week causing damage to many homes.", - "D23:1" - ], - [ - "John is taking initiative to get people together to discuss potential solutions for the issues in the community.", - "D23:3" - ], - [ - "John appreciates Maria's offer to help with the community issues.", - "D23:3" - ], - [ - "John mentions that Maria's support and friendship mean everything to him.", - "D23:7" - ], - [ - "John believes that together with Maria, they can make a difference and motivate others in the community.", - "D23:7" - ], - [ - "John acknowledges that little steps count and they can make a difference together.", - "D23:9" - ] - ], - "Maria": [ - [ - "Maria empathizes with John about the tough situation in his area due to the flood.", - "D23:2" - ], - [ - "Maria expresses her willingness to join the meeting and contribute to making the community better.", - "D23:4" - ], - [ - "Maria admires John's determination and commitment to helping others.", - "D23:6" - ], - [ - "Maria mentions that John's commitment to helping others is inspiring.", - "D23:6" - ], - [ - "Maria believes that their actions, no matter how small, can have a big impact on the community.", - "D23:8" - ], - [ - "Maria emphasizes the importance of spreading kindness and inspiring hope in the community.", - "D23:8" - ], - [ - "Maria mentions having dinner with friends from the gym.", - "D23:14" - ] - ] - }, - "session_24_observation": { - "John": [ - [ - "John visited a veteran's hospital and met an elderly veteran named Samuel who shared inspiring and heartbreaking stories.", - "D24:3" - ], - [ - "John is part of an organization where he works with passionate people and considers them like a family.", - "D24:5" - ], - [ - "John is thinking of setting up fun activities like arts and crafts for his kids based on his experience at a community event.", - "D24:9" - ] - ], - "Maria": [ - [ - "Maria had a picnic with friends from church where they played games like charades and a scavenger hunt.", - "D24:6" - ], - [ - "Maria appreciates the importance of making connections and enjoying life's simpler moments.", - "D24:14" - ], - [ - "Maria believes that doing good and helping others brings joy, and little acts of kindness can have a big effect.", - "D24:16" - ] - ] - }, - "session_25_observation": { - "John": [ - [ - "John is enjoying his new job and finds his team to be encouraging and inspiring.", - "D25:1" - ], - [ - "John attends a yoga studio with a chill vibe and awesome instructors.", - "D25:11" - ], - [ - "The yoga studio John attends offers a variety of classes including yoga, kickboxing, and circuit training.", - "D25:13" - ], - [ - "John has also done weight training classes and found them challenging but peaceful.", - "D25:17" - ] - ], - "Maria": [ - [ - "Maria had a great experience hiking with her church friends last weekend, feeling surrounded by supportive people and enjoying nature.", - "D25:2" - ], - [ - "Maria wanted to make connections, laugh together, and take in nature's beauty during the hike.", - "D25:4" - ], - [ - "Maria plans to explore more and volunteer at shelters next month.", - "D25:8" - ], - [ - "Maria is off to bake some cakes after the conversation.", - "D25:20" - ] - ] - }, - "session_26_observation": { - "Maria": [ - [ - "Maria dropped off baked goods at a homeless shelter last week and is motivated to help people.", - "D26:1" - ], - [ - "Maria volunteers at a homeless shelter and is driven to make a difference.", - "D26:1" - ], - [ - "Maria shared a photo from her volunteer work that reminds her of the importance of kindness.", - "D26:13" - ] - ], - "John": [ - [ - "John joined a firefighting brigade to give back to his community.", - "D26:2" - ], - [ - "John had his first call-out with the firefighting brigade last Sunday to help those in need.", - "D26:4" - ], - [ - "John was a part of a rescue mission that saved a family from a burning building.", - "D26:6" - ], - [ - "John mentioned his military background and the sense of bond and support he feels in the firefighting brigade.", - "D26:10" - ], - [ - "John believes in having friends you can rely on, as he finds it makes a huge difference.", - "D26:11" - ] - ] - }, - "session_27_observation": { - "John": [ - [ - "John is part of a virtual support group advocating for the military and has involved family and friends in supporting veterans.", - "D27:1" - ], - [ - "John admires Maria's volunteer work at a homeless shelter and finds it inspiring.", - "D27:5" - ], - [ - "John had a meaningful experience at a military memorial with his kids last week.", - "D27:9" - ], - [ - "John's kids were awestruck and humbled when they visited the military memorial.", - "D27:11" - ] - ], - "Maria": [ - [ - "Maria volunteers at a homeless shelter, which she started about a year ago after witnessing a struggling family on the streets.", - "D27:2" - ], - [ - "Maria finds volunteering at the homeless shelter really fulfilling and impactful.", - "D27:4" - ], - [ - "Maria mentioned Cindy, a resident at the shelter, who wrote a heartfelt expression of gratitude.", - "D27:8" - ], - [ - "Maria believes it's important for younger generations to remember and appreciate those who served in the military.", - "D27:12" - ], - [ - "Maria thinks it's important to teach kids about veterans and their contributions.", - "D27:14" - ] - ] - }, - "session_28_observation": { - "John": [ - [ - "John lost his job at a mechanical engineering company that tanked.", - "D28:1" - ], - [ - "John is looking into opportunities in the tech industry after losing his job.", - "D28:3" - ], - [ - "John found a job at a tech company that needs his mechanical skills for their hardware team.", - "D28:5" - ], - [ - "John helped renovate a rundown community center back home last year.", - "D28:11" - ], - [ - "John finds volunteering satisfying and valuable.", - "D28:11" - ] - ], - "Maria": [ - [ - "Maria engaged in community work with friends from church yesterday.", - "D28:8" - ], - [ - "Maria believes in the importance of kindness and compassion.", - "D28:10" - ], - [ - "Maria offered her support and encouragement to John in his tough times.", - "D28:6" - ], - [ - "Maria finds volunteering rewarding and important.", - "D28:10" - ] - ] - }, - "session_29_observation": { - "Maria": [ - [ - "Maria volunteered at a homeless shelter last week and received a medal for her help.", - "D29:1" - ], - [ - "Maria found a community event inspiring and heartwarming.", - "D29:2" - ], - [ - "Maria expressed admiration for John's initiative in organizing a charity run for veterans.", - "D29:5" - ], - [ - "Maria thinks domestic abuse is a horrible issue and supports raising awareness and funds for it.", - "D29:11" - ], - [ - "Both Maria and John agree on the importance of spreading awareness and supporting causes in the community.", - "D29:13" - ], - [ - "Maria appreciates having a friend like John who is passionate and motivated about making a difference.", - "D29:17" - ] - ], - "John": [ - [ - "John organized a 5K charity run in the neighborhood to help veterans and their families.", - "D29:4" - ], - [ - "John faced challenges in getting sponsors for the charity run but was successful in the end.", - "D29:8" - ], - [ - "John has organized events for causes other than helping veterans, like raising awareness and funds for victims of domestic abuse.", - "D29:10" - ], - [ - "John believes it's important to come together for important causes like supporting victims of domestic abuse.", - "D29:12" - ], - [ - "John values Maria's passion and motivation in supporting causes and spreading positivity in the community.", - "D29:18" - ] - ] - }, - "session_30_observation": { - "Maria": [ - [ - "Maria got a puppy named Coco two weeks ago, bringing joy to her life.", - "D30:1" - ], - [ - "Maria finds joy in greeting her puppy, Coco, when coming home.", - "D30:3" - ], - [ - "Maria believes that taking care of her puppy, Coco, is worth the adjustment.", - "D30:3" - ], - [ - "Maria mentions that camping with pets can be soul-nourishing.", - "D30:7" - ], - [ - "Maria acknowledges nature as a reset for our souls.", - "D30:11" - ], - [ - "Maria understands tough times and offers support to John.", - "D30:13" - ], - [ - "Maria suggests to John to focus his energy on something meaningful, like joining local organizations or volunteering programs.", - "D30:19" - ], - [ - "Maria encourages John, assures him he can make a difference, and offers help.", - "D30:21" - ], - [ - "Maria plans to keep spreading positivity.", - "D30:23" - ] - ], - "John": [ - [ - "John cherishes memories of his pet Max, including a camping trip where they hiked, swam, and made great memories.", - "D30:6" - ], - [ - "John found the camping trip with his pet Max to be peaceful and awesome.", - "D30:6" - ], - [ - "John values nature as a way to restart his mind and spirit, appreciating the peaceful moments.", - "D30:10" - ], - [ - "John expresses feeling stuck and wanting to positively affect people and the world.", - "D30:16" - ], - [ - "John has been exploring options to make a difference, considering joining local organizations or volunteering programs.", - "D30:18" - ], - [ - "John appreciates Maria's kind words and finds her an inspiration.", - "D30:22" - ] - ] - }, - "session_31_observation": { - "John": [ - [ - "John is volunteering as a mentor for a local school, helping students improve their confidence and skills.", - "D31:1" - ], - [ - "John is considering adopting a new pup and plans to look into shelters near him soon.", - "D31:7" - ], - [ - "John enjoys outdoor activities such as hiking, picnics, and board games with his family.", - "D31:19" - ], - [ - "John values his family greatly and appreciates their support, considering them the best thing ever.", - "D31:17" - ] - ], - "Maria": [ - [ - "Maria recently adopted a cute pup named Shadow from a shelter.", - "D31:2" - ], - [ - "Maria's new pup, Shadow, is full of energy and brings joy to her life.", - "D31:4" - ], - [ - "Maria did not have any pets growing up, but having a furry companion brightens her days.", - "D31:6" - ], - [ - "Maria's pup is learning commands and house training well.", - "D31:10" - ], - [ - "Maria believes that animals bring comfort, understanding, and happiness to our lives.", - "D31:14" - ] - ] - }, - "session_32_observation": { - "John": [ - [ - "John is now part of the fire-fighting brigade and is enthusiastic about helping the community.", - "D32:1" - ], - [ - "John was impressed with the dedication and teamwork of the people in the fire-fighting brigade.", - "D32:3" - ], - [ - "John and his team raised donations like canned food, toiletries, and clothes for the cause.", - "D32:7" - ], - [ - "John feels a sense of purpose and passion in helping out with the fire-fighting brigade, considering it his true calling.", - "D32:9" - ], - [ - "The donations raised by John and his team helped in getting a brand new fire truck.", - "D32:11" - ] - ], - "Maria": [ - [ - "Maria finds fulfillment in volunteering at the shelter, seeing the impact of small acts of kindness on people's lives.", - "D32:14" - ], - [ - "Maria believes in the power to make a difference in people's lives and is enthusiastic about spreading kindness in the community.", - "D32:16" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Maria and John met at 11:01 am on 17 December, 2022. Maria mentioned she has been busy volunteering at a homeless shelter and doing aerial yoga to stay fit. John shared that he had just returned from a family road trip and is now into kickboxing to stay energized. John expressed his goal of getting into local politics to improve education and infrastructure, inspired by the needs he saw growing up in his neighborhood. Maria applauded his efforts and asked about his next steps in the campaign. John plans to talk to local leaders and organizations for support and ideas. Maria encouraged him and asked to keep her updated on his progress.", - "session_2_summary": "At 6:10 pm on 22 December 2022, Maria asked John about his campaign progress and shared her recent experience of donating her old car. John mentioned networking for insights, motivated by a conversation on education. Maria praised his enthusiasm and commitment, highlighting the power of minor tweaks in the community. She encouraged John, who credited his family for his drive, showing a photo of them. Maria acknowledged the importance of family support and shared her preference for quality time with loved ones. John reciprocated the sentiment, emphasizing the value of making memories together. They discussed family activities and shared pictures, concluding with a plan to cherish happy moments with their families. John then mentioned his plan to do some taekwondo.", - "session_3_summary": "At 8:30 pm on 1 January, 2023, John shared with Maria his positive experience with a service-focused online group, feeling a connection and purpose with inspiring individuals. They discussed events like serving at a homeless shelter and arranging a toy drive. John mentioned plans for future projects assisting underserved communities in education and mentorship. Maria praised his initiatives, emphasizing the impact of support and encouraging him to continue. John, feeling stressed after failing a test, appreciated Maria's support. They also bonded over nature's beauty, with Maria sharing a calming beach sunset photo. John reminisced about his childhood beach photography. The conversation highlighted finding joy in simple moments amid life's busyness.", - "session_4_summary": "Maria informed John at 7:06 pm on 9 January, 2023, that she became friends with a fellow volunteer and asked about his well-being. John shared an incident from the previous week that made him realize the unpredictability of life. Maria empathized with his experience and commended his resilience. John mentioned staying calm and seeking help during the incident, leading to a safe outcome. Maria praised his resourcefulness and discussed coping mechanisms like admiring sunsets. John updated Maria on his involvement in local politics and community engagement, with plans for future meetings. They appreciated each other's support and unity in working towards community development. They expressed readiness to make positive changes together, emphasizing the importance of collaboration. John thanked Maria for being a great friend and part of a strong team.", - "session_5_summary": "John and Maria discussed the sorry state of education and the need for more funding and resources for schools to support kids' success. They both agreed on the importance of advocating for better education. Maria shared her experience volunteering at a shelter event for kids and how it was inspired by her aunt. She mentioned a touching moment with a little girl that left a lasting impact. John also shared a childhood memory that reinforced the importance of looking out for others in times of need. Both emphasized the significance of kindness and support in making a difference for those feeling down, expressing their agreement on the topic.", - "session_6_summary": "At 2:33 pm on 5 February 2023, Maria informed John about her recent charity event and the impact it had on her, emphasizing the power of collective effort in helping others. John praised her efforts and expressed his interest in serving the community through politics. Maria shared a touching encounter with a man named David at the event, leading to her connecting him with support services. John discussed his involvement in a food drive for the unemployed and expressed gratitude for the community's response. Maria offered to help with future initiatives, and both acknowledged the importance of supporting each other and working together towards a common goal. Their conversation highlighted their commitment to helping those in need and their strong friendship.", - "session_7_summary": "Maria informed John about taking a creative writing class, while John revealed his excitement about running for office again. They discussed their reasons for volunteering and running for office, sharing inspiring stories about resilience and positivity. They emphasized the importance of kindness, optimism, and making a positive impact. John mentioned attending a yoga class soon, and they agreed to work together to bring positive change to their community.", - "session_8_summary": "At 6:03 pm on 6 March 2023, Maria shared with John about her grandmother's recent passing and the challenges she's facing. John expressed his condolences and updated Maria on his happy family life with wife and kids, particularly mentioning his one-year-old son, Kyle. They discussed parenting experiences and family activities, including a recent picnic that everyone enjoyed. John shared his interest in volunteering after a positive aptitude test experience, while Maria talked about her volunteer work at a homeless shelter. They agreed on the importance of building genuine connections to make a difference.", - "session_9_summary": "Maria and John reconnected at 9:36 am on 2 April, 2023. Maria shared about taking a poetry class to express her feelings while John mentioned finishing his degree, expressing interest in policymaking to make a positive impact. John's focus areas were education and infrastructure, influenced by his community involvement. Maria praised John's dedication and they both agreed on the importance of finding solutions together. They reminisced about past volunteer experiences and pledged support for each other's efforts to improve lives.", - "session_10_summary": "John and Maria had a conversation at 12:24 am on 7 April, 2023. John shared his excitement about starting a weekend yoga class with a colleague, focusing on stretching and breathing, which made him feel great mentally and physically. He praised the instructor for creating a welcoming environment, helping participants do poses correctly, and listen to their bodies. Maria expressed her support and interest, asking about the class and the impact on John. John mentioned feeling more relaxed and connected, integrating yoga into his daily routine. Maria shared her recent experience participating in a charity run for a homeless shelter, highlighting the energy and sense of unity. John then talked about volunteering at a career fair, helping kids with limited resources, which Maria found inspiring. They encouraged each other to continue making a difference and supporting those in need. Their conversation highlighted the power of community involvement and positive impact through simple acts of kindness and support.", - "session_11_summary": "At 6:13 pm on 10 April 2023, John and Maria caught up after a few days. John shared his car troubles causing financial strain but staying positive. Maria praised his resilience. John shared a picture from a past road trip to the Pacific Northwest, highlighting the beauty of nature. Maria and John discussed the importance of finding peace in nature during tough times. Maria mentioned giving talks at a homeless shelter and finding joy in helping others. John praised her impact and dedication to building relationships. They ended the conversation with mutual appreciation for each other's positivity and promising to talk soon.", - "session_12_summary": "John and Maria had a conversation at 7:34 pm on 18 April, 2023. John shared his passion for blogging about politics and government, focusing on education and infrastructure for positive change. Maria showed support for his dedication and discussed her volunteer work at a homeless shelter. They both acknowledged the impact of small acts of kindness in making a difference and inspiring others. John also mentioned his involvement in a tech convention with like-minded individuals. The conversation highlighted their commitment to creating a positive impact in their community through various means.", - "session_13_summary": "John and Maria caught up at 3:18 pm on May 4, 2023. John shared that he started going to boot camps with his family, leading to increased energy and fitness gains. They work out together three times a week and motivate each other. Maria praised their commitment and discussed the importance of having a supportive family. John mentioned how his family supported each other during workouts and emotionally. Maria shared about finding balance in life through self-care and spending time with loved ones. She also reflected on a transformative solo trip to Spain, highlighting the importance of inner strength and appreciating small moments. John admired her perspective and the picture from her trip, emphasizing the value of trying new things and pushing boundaries for personal growth. They both agreed on the importance of challenging themselves and maintaining a positive mindset for continuous learning and development.", - "session_14_summary": "John and Maria had a conversation at 5:04 pm on May 6, 2023. John shared his decision to run for office again, driven by his dream to make a difference in the community. Maria encouraged John to keep going and take risks, expressing her pride and support for him. John appreciated Maria's support and motivation, acknowledging that her belief in him keeps him going. Maria shared her involvement in charity work at a nearby church, finding it fulfilling. John discussed the need for infrastructure improvements in the community, particularly in roadways. Maria offered to help with community projects to address these issues. Both agreed to work together to make a real difference in their neighborhood by fighting for better living conditions and affordable housing. They concluded to keep working together to create a safe and thriving environment for their community.", - "session_15_summary": "John and Maria were discussing a project John is working on to support military veterans on 20th May 2023 at 7:38 pm. John shared his passion for veterans and his desire to show appreciation for their service. Maria praised John for his kind gesture and agreed to support the project. John showed a picture of veterans from a recent party he organized and mentioned the importance of creating connections and a sense of community. Maria expressed admiration for John's efforts and shared her own plan for a fundraiser. The conversation ended with both agreeing to continue making a positive impact in their community.", - "session_16_summary": "John and Maria had a conversation at 1:24 pm on 25 May, 2023. John updated Maria about his petition progress and shared a picture of his workmates on a hiking trip. Maria mentioned preparing for a fundraiser at a shelter and John offered to help. They discussed spreading the word about the fundraiser and finding volunteers. John shared a picture of his family at the beach. They both emphasized the importance of appreciating little moments and making a positive impact. Maria thanked John for his support, and they agreed to stay motivated towards their goals. The conversation ended with a mutual belief in making a difference.", - "session_17_summary": "John and Maria, having a conversation at 11:51 am on 3 June, 2023, discussed the loss of John's beloved pet Max, who was a significant part of their family for 10 years. They shared memories of Max, acknowledging the importance of love and loyalty that pets bring to our lives. John expressed his family's sadness but also their gratitude for the joy Max brought. Maria offered comfort and support, suggesting they honor Max's memory by considering adopting a rescue dog to teach their kids love and compassion. They ended the conversation by expressing their appreciation for each other's friendship and support.", - "session_18_summary": "Maria and John had a conversation at 2:47 pm on 12 June, 2023. Maria expressed her sympathy to John for losing his pet, and they both shared recent experiences of finding solace in nature. Maria discussed a camping trip that helped take her mind off things, while John went mountaineering to clear his head. They discussed their past adventures, with Maria mentioning a family trip to Oregon and John sharing a recent mountaineering experience. Maria described a breathtaking view from her trip, and John found solace in nature during his park visit with family, emphasizing the importance of cherishing those moments. They both appreciated nature's calming influence, with Maria highlighting aerial yoga as a way to find peace, and John expressing support for her hobbies. The conversation concluded with John encouraging Maria to keep smiling and shining.", - "session_19_summary": "Maria and John spoke at 7:20 pm on 16 June, 2023. Maria shared her positive experience at the gym, aiming to get stronger and improve her endurance with kundalini yoga, while John mentioned trying out rock climbing. John celebrated his recent promotion to assistant manager, reflecting on the challenges he overcame, including self-doubt. Maria praised John's perseverance and determination, emphasizing the importance of support and self-belief. Both agreed on the significance of having loved ones by their side. John shared about his work schedule and balancing priorities, highlighting the importance of making time for oneself and loved ones. The conversation turned to beach memories, with Maria and John exchanging stories and photos from their vacations, emphasizing the value of creating cherished memories with family. The conversation ended on a positive note, highlighting the joy in special family moments.\n", - "session_20_summary": "Maria and John reconnect at 12:21 am on 27 June, 2023. Maria shares her struggles but mentions focusing on the positive and getting support from family and friends. John acknowledges the importance of support and asks how her family has been helping. Maria expresses gratitude for her family's encouragement and support. John talks about a recent lively event with his family, emphasizing the importance of cherishing moments with loved ones. Maria mentions finding silver linings by volunteering at a shelter, sharing a heartwarming experience. John appreciates the impact of small gestures on others. The conversation ends with John encouraging Maria to continue spreading positivity and making a difference.", - "session_21_summary": "Maria and John caught up at 8:43 pm on 3 July, 2023. Maria shared about meeting kids at a shelter and an unfortunate car accident but mentioned helping her cousin find a new place. John offered his support and suggested finding resources for her cousin. Maria appreciated the kindness and mentioned the impact of small acts of kindness. John shared his participation in a veterans' rights event, emphasizing the importance of supporting causes. They encouraged each other to keep making a difference through activism and supporting important causes.", - "session_22_summary": "John and Maria discussed the importance of education and infrastructure in shaping communities at 6:59 pm on 5 July, 2023. Maria supported John's efforts to make a positive change, citing his passion and proactive approach as inspiring. John mentioned feeling uplifted by Maria's understanding and support, showing a picture of his family who motivate him. They shared sources of inspiration like family, nature walks, and music. John emphasized the significance of finding what makes one excited and alive. They both noted the importance of taking time for oneself, as seen in John's regular sunset walks for peace and detachment. The conversation ended with mutual well wishes and plans to chat soon.", - "session_23_summary": "John informed Maria about a recent flood in his area at 6:29 pm on 7 July, 2023, expressing the need to improve the community. Maria offered to help, leading to a discussion about working together to make a difference. They both acknowledged each other's determination and commitment to improving the community, planning to join forces for the cause. They expressed gratitude for each other's support and friendship, emphasizing the importance of small actions in creating a positive impact. They agreed to continue working together and spreading kindness to inspire hope in their community. John and Maria concluded their conversation, expressing excitement about making great things happen together.", - "session_24_summary": "John and Maria discussed their recent experiences at 3:34 pm on 17 July, 2023. John shared his visit to a veteran's hospital, which inspired him to give back and appreciate what he has. Maria expressed the importance of supporting military veterans. John learned inspiring stories from a veteran named Samuel, which reaffirmed his desire to join the military. Maria commended the resilience of the veterans. They discussed fun activities they had recently enjoyed, such as picnics and games like charades and scavenger hunts. John mentioned planning similar activities for his kids based on a community event he attended. They emphasized the significance of spreading positivity, making connections, and helping others through acts of kindness. Both agreed on the importance of enjoying life's moments and making a difference.", - "session_25_summary": "John and Maria had a conversation at 6:21 pm on 22 July, 2023. John talked about his new job and how his team is inspiring. Maria shared her hiking experience with church friends, mentioning the refreshing feeling of being surrounded by supportive people in nature. John and Maria discussed the benefits of connecting with good people and nature's beauty. Maria shared her upcoming plan to explore more and volunteer at shelters. John mentioned attending a chill yoga studio offering a variety of classes. They discussed trying new workout classes to mix things up. John encouraged Maria to try new classes and offered advice on starting. Maria ended the conversation, mentioning she was going to bake cakes.", - "session_26_summary": "At 1:59 pm on 31 July, 2023, Maria and John had a conversation. Maria shared that she had dropped off baked goods at a homeless shelter, feeling motivated to help more. John was inspired by Maria's efforts and mentioned joining a fire-fighting brigade to give back to the community, finding it rewarding. He described his first rescue mission with the team, saving a family from a burning building. Maria praised John's courage, highlighting the importance of supportive teamwork. John expressed gratitude for the bond with his team, comparing it to his time in the military. Maria shared a photo from her volunteering experience, emphasizing the impact of small acts of kindness. John encouraged her to continue her positive efforts, which Maria appreciated. They both pledged to keep making a difference and supporting each other, ending the conversation on a positive note.", - "session_27_summary": "John and Maria had a conversation at 6:20 pm on 3 August, 2023. John shared how he involved family and friends in supporting the military through a virtual group. Maria praised John's efforts, mentioning her own volunteer work at a homeless shelter. She started volunteering after seeing a struggling family and finding fulfillment in helping others. John admired Maria's impact and discussed a note from a shelter resident expressing gratitude. They shared experiences about military memorials and emphasized teaching respect for veterans to younger generations. Maria commended John's work and emphasized the power of teamwork in achieving great things.", - "session_28_summary": "\nAt 5:19 pm on 5 August, 2023, John informed Maria that he lost his job at a mechanical engineering company that tanked. He expressed gratitude for her concern and mentioned pursuing opportunities in the tech industry, feeling hopeful about a potential job in a tech company. Maria praised his adaptability and offered support. The conversation shifted to volunteer experiences, with Maria sharing her recent community work and John recalling renovating a community center. They exchanged encouraging words, with Maria commending John for making a real difference and John expressing appreciation for her support. John promised to reach out if needed.", - "session_29_summary": "Maria and John, at 8:06 pm on 9 August, 2023, caught up on their recent community involvement. Maria shared about volunteering at a homeless shelter, receiving a medal. John, who organized a charity run to help veterans, also raised funds for victims of domestic abuse with a local organization. They discussed challenges and the importance of supporting such causes. Both expressed admiration for each other's efforts and commitment to making a difference. They ended on a positive note, agreeing to continue spreading awareness and supporting important causes, reflecting on the impact of community involvement.", - "session_30_summary": "Maria and John caught up at 12:10 am on 11 August 2023. Maria shared that she got a puppy named Coco, and John showed her a photo of his late dog, Max. They discussed the joy of having pets and how nature can be a refreshing escape. John opened up about feeling stagnant and wanting to make a positive impact, considering joining local organizations. Maria offered support and encouraged him, ending the conversation on a positive note.", - "session_31_summary": "At 3:14 pm on August 13, 2023, John told Maria about volunteering as a mentor and how rewarding it was. Maria shared adopting a new pup named Shadow. They discussed the joy pets bring, and John expressed interest in getting a new pup. They praised the positive impact of animals. John mentioned improvements in the mentoring program, while Maria talked about Shadow's progress. They agreed on the comfort and happiness pets bring. John described family activities they enjoy, and they both emphasized the importance of family time. They shared goodbyes, wishing each other a great day.", - "session_32_summary": "John excitedly told Maria about joining the fire-fighting brigade at 11:08 am on 16 August, 2023. He was inspired by their dedication and teamwork. They worked hard and raised donations like canned food and clothes. Maria admired the community coming together in support. John felt a sense of purpose and passion in his new role. Maria shared her volunteer experience at a shelter, emphasizing the power of kindness. They both pledged to continue making a difference in their community and stay motivated together." - }, - "sample_id": "conv-41" - }, - { - "qa": [ - { - "question": "Is it likely that Nate has friends besides Joanna?", - "answer": "Yesteammates on hisvideo game team.", - "evidence": [ - "D1:7" - ], - "category": 3 - }, - { - "question": "What kind of interests do Joanna and Nate share?", - "answer": "Watching movies, making desserts", - "evidence": [ - "D1:10", - "D1:11", - "D1:12", - "D3:4", - "D4:9", - "D10:9", - "D20:2" - ], - "category": 1 - }, - { - "question": "When did Joanna first watch \"Eternal Sunshine of the Spotless Mind?", - "answer": "2019", - "evidence": [ - "D1:18" - ], - "category": 2 - }, - { - "question": "When did Nate win his first video game tournament?", - "answer": "the week before 21Janury, 2022", - "evidence": [ - "D1:3" - ], - "category": 2 - }, - { - "question": "What pets wouldn't cause any discomfort to Joanna?", - "answer": "Hairless cats or pigs,since they don't have fur, which is one of the main causes of Joanna's allergy.", - "evidence": [ - "D2:23" - ], - "category": 3 - }, - { - "question": "What are Joanna's hobbies?", - "answer": "Writing, watchingmovies, exploringnature, hanging withfriends.", - "evidence": [ - "D1:10", - "D2:25" - ], - "category": 1 - }, - { - "question": "How long has Nate had his first two turtles?", - "answer": "three years", - "evidence": [ - "D2:12" - ], - "category": 2 - }, - { - "question": "When did Joanna finish her first screenplay?", - "answer": "The Friday before 23January, 2022", - "evidence": [ - "D2:3" - ], - "category": 2 - }, - { - "question": "When did Nate get his first two turtles?", - "answer": "2019", - "evidence": [ - "D2:12" - ], - "category": 2 - }, - { - "question": "What major achievement did Joanna accomplish in January 2022?", - "answer": "finished her screenplay and printed it", - "evidence": [ - "D2:3" - ], - "category": 2 - }, - { - "question": "What emotions is Joanna feeling about the screenplay she submitted?", - "answer": "Relief, excitement,worry, hope,anxiety.", - "evidence": [ - "D2:7", - "D3:1" - ], - "category": 1 - }, - { - "question": "What is Joanna allergic to?", - "answer": "Most reptiles,animals with fur,cockroaches, dairy", - "evidence": [ - "D4:4", - "D5:11", - "D2:23" - ], - "category": 1 - }, - { - "question": "What underlying condition might Joanna have based on her allergies?", - "answer": "asthma", - "evidence": [ - "D5:11", - "D2:23" - ], - "category": 3 - }, - { - "question": "When did Joanna have an audition for a writing gig?", - "answer": "23 March, 2022.", - "evidence": [ - "D6:2" - ], - "category": 2 - }, - { - "question": "What nickname does Nate use for Joanna?", - "answer": "Jo", - "evidence": [ - "D7:1" - ], - "category": 3 - }, - { - "question": "When did Nate get purple hair?", - "answer": "The week before 15April, 2022.", - "evidence": [ - "D7:1" - ], - "category": 2 - }, - { - "question": "What physical transformation did Nate undergo in April 2022?", - "answer": "dyed his hair purple", - "evidence": [ - "D7:1", - "D7:3" - ], - "category": 2 - }, - { - "question": "What movie did Joanna watch on 1 May, 2022?", - "answer": "Lord of the Rings", - "evidence": [ - "D10:1" - ], - "category": 2 - }, - { - "question": "Which outdoor spot did Joanna visit in May?", - "answer": "Whispering Falls waterfall", - "evidence": [ - "D11:7" - ], - "category": 2 - }, - { - "question": "How many times has Joanna found new hiking trails?", - "answer": "twice", - "evidence": [ - "D8:4", - "D11:3" - ], - "category": 1 - }, - { - "question": "When did Nate adopt Max?", - "answer": "May 2022", - "evidence": [ - "D12:3" - ], - "category": 2 - }, - { - "question": "Who was the new addition to Nate's family in May 2022?", - "answer": "Max", - "evidence": [ - "D12:3" - ], - "category": 2 - }, - { - "question": "When did Joanna start writing her third screenplay?", - "answer": "May 2022", - "evidence": [ - "D12:13", - "D12:14" - ], - "category": 2 - }, - { - "question": "Which of Joanna's screenplay were rejected from production companies?", - "answer": "first screenplay on drama and romance, third screenplay on loss identity and connection", - "evidence": [ - "D14:1", - "D3:1", - "D2:7", - "D24:12", - "D24:13" - ], - "category": 1 - }, - { - "question": "When is Nate hosting a gaming party?", - "answer": "The weekend after 3June, 2022.", - "evidence": [ - "D14:20" - ], - "category": 2 - }, - { - "question": "When did Joanna hike with her buddies?", - "answer": "The weekend after 3June, 2022.", - "evidence": [ - "D14:19" - ], - "category": 2 - }, - { - "question": "When did Nate win his third tourney?", - "answer": "The week before 3June, 2022", - "evidence": [ - "D14:8" - ], - "category": 2 - }, - { - "question": "What places has Joanna submitted her work to?", - "answer": "film contest, film festival.", - "evidence": [ - "D2:7", - "D16:1" - ], - "category": 1 - }, - { - "question": "When did Nate make vegan icecream and share it with a vegan diet group?", - "answer": "The Friday before 24June, 2022.", - "evidence": [ - "D16:8" - ], - "category": 2 - }, - { - "question": "When is Joanna going to make Nate's ice cream for her family?", - "answer": "The weekend of 24June, 2022.", - "evidence": [ - "D16:11" - ], - "category": 2 - }, - { - "question": "What kind of writings does Joanna do?", - "answer": "Screenplays,books, online blog posts, journal", - "evidence": [ - "D2:3", - "D17:14", - "D18:1", - "D18:5" - ], - "category": 1 - }, - { - "question": "When did Nate win his fourth video game tournament?", - "answer": "The Friday before 10July, 2022.", - "evidence": [ - "D17:1" - ], - "category": 2 - }, - { - "question": "Where did Joanna travel to in July 2022?", - "answer": "Woodhaven", - "evidence": [ - "D17:4" - ], - "category": 2 - }, - { - "question": "When did someone write Joanna a touching letter?", - "answer": "The week before 14August, 2022.", - "evidence": [ - "D18:5" - ], - "category": 2 - }, - { - "question": "What book recommendations has Joanna given to Nate?", - "answer": "\"Little Women\",'A Court of Thorns andRoses'.", - "evidence": [ - "D3:17", - "D19:14", - "D19:16" - ], - "category": 1 - }, - { - "question": "When did Nate take time off to chill with his pets?", - "answer": "The weekend of 22August, 2022.", - "evidence": [ - "D19:9" - ], - "category": 2 - }, - { - "question": "When did Joanna share her book with her writers group?", - "answer": "The week before 22August, 2022.", - "evidence": [ - "D19:6" - ], - "category": 2 - }, - { - "question": "When did Nate win an international tournament?", - "answer": "21 August, 2022", - "evidence": [ - "D19:1" - ], - "category": 2 - }, - { - "question": "When did Joanna make a desert with almond milk?", - "answer": "The Friday before 14September, 2022", - "evidence": [ - "D21:9" - ], - "category": 2 - }, - { - "question": "When did Nate attend a cooking show?", - "answer": "The Monday before 14September, 2022", - "evidence": [ - "D21:4" - ], - "category": 2 - }, - { - "question": "When did Joanna's laptop crash?", - "answer": "The week before 14September, 2022", - "evidence": [ - "D21:1" - ], - "category": 2 - }, - { - "question": "When did Joanna make a chocolate tart with raspberries?", - "answer": "5 October, 2022", - "evidence": [ - "D22:1" - ], - "category": 2 - }, - { - "question": "What movies have both Joanna and Nate seen?", - "answer": "\"Little Women\", \"Lord of the Rings\"", - "evidence": [ - "D3:17", - "D10:1", - "D22:8" - ], - "category": 1 - }, - { - "question": "How long did it take for Joanna to finish writing her book?", - "answer": "four months", - "evidence": [ - "D17:14", - "D22:9" - ], - "category": 2 - }, - { - "question": "When did Nate win a lot of money in a video game tournament?", - "answer": "September 2022", - "evidence": [ - "D22:2" - ], - "category": 2 - }, - { - "question": "When did Joanna finish up the writing for her book?", - "answer": "The week before 6October, 2022", - "evidence": [ - "D22:9" - ], - "category": 2 - }, - { - "question": "What board games has Nate played?", - "answer": "Chess, Catan.", - "evidence": [ - "D16:2", - "D23:7" - ], - "category": 1 - }, - { - "question": "What places has Nate met new people?", - "answer": "A tournament and agaming convention.", - "evidence": [ - "D14:8", - "D23:1" - ], - "category": 1 - }, - { - "question": "When did Nate go to a convention and meet new people?", - "answer": "The Friday before 9October, 2022.", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "How many times has Joanna's scripts been rejected?", - "answer": "Twice", - "evidence": [ - "D14:1", - "D24:12" - ], - "category": 1 - }, - { - "question": "What is something Nate gave to Joanna that brings her a lot of joy?", - "answer": "stuffed toy pup", - "evidence": [ - "D13:9", - "D24:2" - ], - "category": 1 - }, - { - "question": "When did Nate get Tilly for Joanna?", - "answer": "25 May, 2022", - "evidence": [ - "D13:9", - "D24:2" - ], - "category": 1 - }, - { - "question": "How many of Joanna's writing have made it to the big screen?", - "answer": "two", - "evidence": [ - "D15:1", - "D25:2" - ], - "category": 1 - }, - { - "question": "How many times has Nate taken his turtles on a walk?", - "answer": "Twice.", - "evidence": [ - "D5:4", - "D25:15" - ], - "category": 1 - }, - { - "question": "When was Joanna's second movie script shown on the big screens?", - "answer": "The Sunday before 25October, 2022.", - "evidence": [ - "D25:1" - ], - "category": 2 - }, - { - "question": "What is Joanna inspired by?", - "answer": "Personal experiences,her own journey ofself discovery, Nate,nature, validation,stories about findingcourage and takingrisks, people she knows, stuff she sees, imagination", - "evidence": [ - "D4:6", - "D7:6", - "D11:11", - "D26:3", - "D26:7", - "D25:10" - ], - "category": 1 - }, - { - "question": "What animal do both Nate and Joanna like?", - "answer": "Turtles.", - "evidence": [ - "D5:6", - "D26:9" - ], - "category": 1 - }, - { - "question": "When did Joanna plan to go over to Nate's and share recipes?", - "answer": "5 November, 2022.", - "evidence": [ - "D26:19" - ], - "category": 2 - }, - { - "question": "What things has Nate reccomended to Joanna?", - "answer": "A pet,\"The Lord of the Rings\" movies,a dragon book series,coconut flavoring,\"Project Hail Mary\" book,Xenoblade Chronicles, dairy-free margarine, coconut oil", - "evidence": [ - "D2:14", - "D9:12", - "D9:14", - "D10:11", - "D19:17", - "D27:23", - "D10:19" - ], - "category": 1 - }, - { - "question": "What does Joanna do to remember happy memories?", - "answer": "Hangs them on a corkboard, writes themin a notebook.", - "evidence": [ - "D15:9", - "D27:34" - ], - "category": 1 - }, - { - "question": "What Console does Nate own?", - "answer": "A Nintendo Switch; since the game \"Xenoblade 2\" is made for this console.", - "evidence": [ - "D27:23" - ], - "category": 3 - }, - { - "question": "What mediums does Nate use to play games?", - "answer": "Gamecube, PC,Playstation.", - "evidence": [ - "D22:2", - "D27:21", - "D27:15" - ], - "category": 1 - }, - { - "question": "How many letters has Joanna recieved?", - "answer": "Two", - "evidence": [ - "D14:1", - "D18:5" - ], - "category": 1 - }, - { - "question": "What video games does Nate play?", - "answer": "Valorant, Counter Strike:Global Offensive,Xenoblade Chronicles, StreetFighter, Cyberpunk 2077", - "evidence": [ - "D10:6", - "D27:1", - "D27:23", - "D1:7", - "D23:17" - ], - "category": 1 - }, - { - "question": "When did Nate win a big Valorant tourney?", - "answer": "The Saturday before 7November, 2022", - "evidence": [ - "D27:1" - ], - "category": 2 - }, - { - "question": "Which torunament did Nate win in the beginning of November 2022?", - "answer": "Valorant", - "evidence": [ - "D27:1" - ], - "category": 2 - }, - { - "question": "What alternative career might Nate consider after gaming?", - "answer": "an animalkeeper at a localzoo and workingwith turtles; as heknows a great dealabout turtles andhow to care for them,and he enjoys it.", - "evidence": [ - "D5:8", - "D19:3", - "D25:19", - "D28:25" - ], - "category": 3 - }, - { - "question": "What pets does Nate have?", - "answer": "A dog and threeturtles.", - "evidence": [ - "D8:3", - "D12:3", - "D28:23" - ], - "category": 1 - }, - { - "question": "How many hikes has Joanna been on?", - "answer": "Four", - "evidence": [ - "D7:6", - "D11:5", - "D14:21", - "D28:22" - ], - "category": 3 - }, - { - "question": "How many turtles does Nate have?", - "answer": "Three", - "evidence": [ - "D8:3", - "D28:23" - ], - "category": 1 - }, - { - "question": "What activities does Nate do with his turtles?", - "answer": "takes them onwalks, holds them,feeds themstrawberries, givesthem baths.", - "evidence": [ - "D25:21", - "D25:23", - "D28:31" - ], - "category": 1 - }, - { - "question": "What do both Joanna and Nate appreciate the beauty of?", - "answer": "Nature", - "evidence": [ - "D11:9", - "D28:23" - ], - "category": 1 - }, - { - "question": "When did Joanna plan on going to Nate's to watch him play with his turtles?", - "answer": "10 November, 2022", - "evidence": [ - "D28:32" - ], - "category": 2 - }, - { - "question": "What state did Joanna visit in summer 2021?", - "answer": "Indiana", - "evidence": [ - "D28:22" - ], - "category": 3 - }, - { - "question": "What recommendations has Nate received from Joanna?", - "answer": "\"Eternal Sunshine of the Spotless Mind\" movie, \"A Court of Thorns and Roses\" book, pointers for making living room comfy, starting a cork board for memories, \"Little Women\" movie", - "evidence": [ - "D1:16", - "D3:17", - "D15:14", - "D15:15", - "D19:15", - "D19:16", - "D23:26" - ], - "category": 1 - }, - { - "question": "What are Nate's favorite desserts?", - "answer": "coconut milk icecream, dairy-free chocolate cake with berries, chocolate and mixed-berry icecream, dairy-free chocolate mousse", - "evidence": [ - "D3:4", - "D3:10", - "D21:10", - "D3:12" - ], - "category": 1 - }, - { - "question": "How has Nate tried to disburse his vegan ice-cream recipes?", - "answer": "teaching others, cooking show", - "evidence": [ - "D18:8", - "D21:4" - ], - "category": 1 - }, - { - "question": "When did Nate win his second tournament?", - "answer": "The week before 2 May, 2022.", - "evidence": [ - "D10:4" - ], - "category": 2 - }, - { - "question": "How many video game tournaments has Nate participated in?", - "answer": "nine", - "evidence": [ - "D1:3", - "D6:7", - "D10:4", - "D14:8", - "D17:1", - "D19:1", - "D20:1", - "D22:2", - "D27:1" - ], - "category": 1 - }, - { - "question": "How many screenplays has Joanna written?", - "answer": "three", - "evidence": [ - "D2:3", - "D4:10", - "D5:1", - "D12:13", - "D12:14" - ], - "category": 1 - }, - { - "question": "How many tournaments has Nate won?", - "answer": "seven", - "evidence": [ - "D1:3", - "D10:4", - "D14:8", - "D17:1", - "D19:1", - "D22:2", - "D27:1" - ], - "category": 1 - }, - { - "question": "What recipes has Joanna made?", - "answer": "dairy free vanilla cake with strawberry filling and coconut cream frosting, parfait, strawberry chocolate cake, chocolate coconut cupcakes, chocolate raspberry tart, chocolate cake with raspberries, blueberry cheesecake bars", - "evidence": [ - "D10:9", - "D10:11", - "D19:8", - "D20:2", - "D20:10", - "D21:11", - "D22:1", - "D21:3", - "D21:17" - ], - "category": 1 - }, - { - "question": "What recipes has Nate made?", - "answer": "coconut milk icecream, chocolate and vanilla swirl", - "evidence": [ - "D3:4", - "D4:3" - ], - "category": 1 - }, - { - "question": "What are the skills that Nate has helped others learn?", - "answer": "coconut milk ice cream recipe, reset high scores, tips to improve gaming skills", - "evidence": [ - "D18:8", - "D26:12", - "D14:16" - ], - "category": 1 - }, - { - "question": "Was the first half of September 2022 a good month career-wise for Nate and Joanna? Answer yes or no.", - "answer": "No; because both of them faced setbacks in their career", - "evidence": [ - "D20:1", - "D21:1" - ], - "category": 3 - }, - { - "question": "What kind of job is Joanna beginning to preform the duties of because of her movie scripts?", - "answer": "filmmaker.", - "evidence": [ - "D29:1" - ], - "category": 3 - }, - { - "question": "When did Nate take his turtles to the beach?", - "answer": "10 November, 2022", - "evidence": [ - "D29:6" - ], - "category": 2 - }, - { - "question": "What state did Nate visit?", - "answer": "Florida", - "evidence": [ - "D29:6" - ], - "category": 3 - }, - { - "question": "What is one of Joanna's favorite movies?", - "answer": "\"Eternal Sunshineof the Spotless Mind\"", - "evidence": [ - "D1:18", - "D", - "D1:20" - ], - "category": 4 - }, - { - "question": "What color did Nate choose for his hair?", - "answer": "purple", - "evidence": [ - "D7:1", - "D7:3" - ], - "category": 4 - }, - { - "question": "What is Nate's favorite movie trilogy?", - "answer": "Lord of the Rings", - "evidence": [ - "D9:12" - ], - "category": 4 - }, - { - "question": "What is Nate's favorite book series about?", - "answer": "dragons", - "evidence": [ - "D9:14" - ], - "category": 4 - }, - { - "question": "What kind of lighting does Nate's gaming room have?", - "answer": "red and purple lighting", - "evidence": [ - "D10:2" - ], - "category": 4 - }, - { - "question": "What game was the second tournament that Nate won based on?", - "answer": "Street Fighter", - "evidence": [ - "D10:4", - "D10:6" - ], - "category": 4 - }, - { - "question": "What is Joanna's third screenplay about?", - "answer": "loss, identity, and connection", - "evidence": [ - "D12:13", - "D12:14" - ], - "category": 4 - }, - { - "question": "What is Nate's favorite video game?", - "answer": "Xenoblade Chronicles", - "evidence": [ - "D27:22", - "D27:23" - ], - "category": 4 - }, - { - "question": "What type of movies does Nate enjoy watching the most?", - "answer": "action and sci-fi", - "evidence": [ - "D1:13" - ], - "category": 4 - }, - { - "question": "What did Joanna just finish last Friday on 23 January, 2022?", - "answer": "screenplay", - "evidence": [ - "D2:3" - ], - "category": 4 - }, - { - "question": "What genre is Joanna's first screenplay?", - "answer": "drama and romance", - "evidence": [ - "D2:5" - ], - "category": 4 - }, - { - "question": "What are Joanna's plans for her finished screenplay in January 2022?", - "answer": "submit it to film festivals and get producers and directors to check it out", - "evidence": [ - "D2:7" - ], - "category": 4 - }, - { - "question": "For how long has Nate had his turtles?", - "answer": "3 years", - "evidence": [ - "D2:12" - ], - "category": 4 - }, - { - "question": "What did Nate think of the coconut milk ice cream he made?", - "answer": "Super good, rich and creamy", - "evidence": [ - "D3:6" - ], - "category": 4 - }, - { - "question": "Which dairy-free dessert flavors does Nate enjoy?", - "answer": "chocolate and mixed berry", - "evidence": [ - "D3:10" - ], - "category": 4 - }, - { - "question": "What did Joanna recently watch and recommend to Nate on February 7, 2022?", - "answer": "\"Little Women\"", - "evidence": [ - "D3:17" - ], - "category": 4 - }, - { - "question": "What is \"Little Women\" about according to Joanna?", - "answer": "Sisterhood, love, and reaching for your dreams", - "evidence": [ - "D3:17" - ], - "category": 4 - }, - { - "question": "What flavor of ice cream did Nate make for his friend on 25 February, 2022?", - "answer": "chocolate and vanilla swirl", - "evidence": [ - "D4:3" - ], - "category": 4 - }, - { - "question": "What inspired Joanna's new screenplay on 25 February, 2022?", - "answer": "personal experiences and her own journey of self-discovery", - "evidence": [ - "D4:16" - ], - "category": 4 - }, - { - "question": "Why does Nate like turtles as pets?", - "answer": "Their slow pace and calming nature", - "evidence": [ - "D5:6" - ], - "category": 4 - }, - { - "question": "How does Nate describe the process of taking care of turtles?", - "answer": "Not tough; keep their area clean, feed them properly, give them enough light.", - "evidence": [ - "D5:8" - ], - "category": 4 - }, - { - "question": "What was Joanna's audition for?", - "answer": "writing gig", - "evidence": [ - "D6:2" - ], - "category": 4 - }, - { - "question": "Why did Nate choose the hair color he did?", - "answer": "Bright and bold - like him", - "evidence": [ - "D7:5" - ], - "category": 4 - }, - { - "question": "What are the main ingredients of the ice cream recipe shared by Nate?", - "answer": "Coconut milk, vanilla extract, sugar, salt", - "evidence": [ - "D8:19" - ], - "category": 4 - }, - { - "question": "What is Joanna's project called in the writers group?", - "answer": "\"Finding Home\"", - "evidence": [ - "D9:3" - ], - "category": 4 - }, - { - "question": "What is Nate's favorite genre of movies?", - "answer": "Fantasy and sci-fi", - "evidence": [ - "D9:10" - ], - "category": 4 - }, - { - "question": "What kind of books does Nate enjoy?", - "answer": "Adventures and magic", - "evidence": [ - "D9:14" - ], - "category": 4 - }, - { - "question": "What kind of films does Joanna enjoy?", - "answer": "Dramas and emotionally-driven films", - "evidence": [ - "D9:9" - ], - "category": 4 - }, - { - "question": "Which activity helps Nate escape and stimulates his imagination?", - "answer": "watching fantasy and sci-fi movies", - "evidence": [ - "D9:10" - ], - "category": 4 - }, - { - "question": "What filling did Joanna use in the cake she made recently in May 2022?", - "answer": "strawberry", - "evidence": [ - "D10:11" - ], - "category": 4 - }, - { - "question": "What kind of frosting did Joanna use on the cake she made recently in May 2022?", - "answer": "coconut cream", - "evidence": [ - "D10:11" - ], - "category": 4 - }, - { - "question": "What does Nate feel he could do when out in cool places like Whispering Falls?", - "answer": "write a whole movie", - "evidence": [ - "D11:13" - ], - "category": 4 - }, - { - "question": "What creative activity does Nate joke about pursuing after being inspired by their hikes with Jo?", - "answer": "Start thinking about a drama and publish a screenplay", - "evidence": [ - "D11:16" - ], - "category": 4 - }, - { - "question": "Who invited Nate to join her on the trails sometime?", - "answer": "Joanna", - "evidence": [ - "D11:17" - ], - "category": 4 - }, - { - "question": "What did Nate do for Joanna on 25 May, 2022?", - "answer": "get her a stuffed animal", - "evidence": [ - "D13:9" - ], - "category": 4 - }, - { - "question": "How does Nate describe the stuffed animal he got for Joanna?", - "answer": "A stuffed animal to remind you of the good vibes", - "evidence": [ - "D13:11" - ], - "category": 4 - }, - { - "question": "What event is Nate organizing in June 2022?", - "answer": "A gaming party", - "evidence": [ - "D14:20" - ], - "category": 4 - }, - { - "question": "Who did Nate plan to invite to his gaming party in June 2022?", - "answer": "Tournament friends, old friends, teammates", - "evidence": [ - "D14:22" - ], - "category": 4 - }, - { - "question": "What special items did Nate get for everyone at his gaming party?", - "answer": "Custom controller decorations", - "evidence": [ - "D14:24" - ], - "category": 4 - }, - { - "question": "What did Joanna write yesterday that appeared on the big screen?", - "answer": "screenplay bits", - "evidence": [ - "D15:1" - ], - "category": 4 - }, - { - "question": "What superhero is Joanna a fan of?", - "answer": "Spider-Man", - "evidence": [ - "D15:3" - ], - "category": 4 - }, - { - "question": "Which superhero toy figure does Nate share a photo of?", - "answer": "Iron Man", - "evidence": [ - "D15:4" - ], - "category": 4 - }, - { - "question": "What is displayed on Joanna's cork board for motivation and creativity?", - "answer": "inspiring quotes, photos, and little keepsakes", - "evidence": [ - "D15:7" - ], - "category": 4 - }, - { - "question": "What does the photo on Joanna's cork board remind her of?", - "answer": "love and encouragement from her family", - "evidence": [ - "D15:11" - ], - "category": 4 - }, - { - "question": "What did Nate make and share with his vegan diet group?", - "answer": "vegan ice cream", - "evidence": [ - "D16:8" - ], - "category": 4 - }, - { - "question": "How many people attended the gaming party hosted by Nate in June 2022?", - "answer": "7", - "evidence": [ - "D16:6" - ], - "category": 4 - }, - { - "question": "What recipe Nate offer to share with Joanna?", - "answer": "vegan ice cream recipe", - "evidence": [ - "D16:10" - ], - "category": 4 - }, - { - "question": "What did Joanna plan to do with the recipe Nate promised to share?", - "answer": "make it for her family", - "evidence": [ - "D16:11" - ], - "category": 4 - }, - { - "question": "How many video game tournaments has Nate won by July 10, 2022?", - "answer": "Four", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "Where did Joanna go for a road trip for research?", - "answer": "Woodhaven", - "evidence": [ - "D17:4" - ], - "category": 4 - }, - { - "question": "What did Joanna discover at the library in Woodhaven?", - "answer": "cool old book collection", - "evidence": [ - "D17:4" - ], - "category": 4 - }, - { - "question": "What specific themes are explored in Joanna's new book?", - "answer": "loss, redemption, and forgiveness", - "evidence": [ - "D17:16" - ], - "category": 4 - }, - { - "question": "What inspired Joanna's new script in July 2022?", - "answer": "Woodhaven's interesting past and people", - "evidence": [ - "D17:8" - ], - "category": 4 - }, - { - "question": "What did Nate do while Joanna was on her road trip?", - "answer": "Won a video game tournament", - "evidence": [ - "D17:2" - ], - "category": 4 - }, - { - "question": "What does Nate do that he loves and can make money from?", - "answer": "Competing in video game tournaments", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "How did Joanna feel when someone wrote her a letter after reading her blog post?", - "answer": "Touched", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "What kind of impact does Joanna hope to have with her writing?", - "answer": "share her stories and hopefully have an impact", - "evidence": [ - "D18:7" - ], - "category": 4 - }, - { - "question": "What kind of content did Joanna share that someone wrote her a letter about?", - "answer": "A blog post about a hard moment in her life", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "What motivates Joanna to keep writing even on tough days?", - "answer": "Knowing that her writing can make a difference", - "evidence": [ - "D18:7" - ], - "category": 4 - }, - { - "question": "What type of ice cream does Joanna mention that Nate makes and is delicious?", - "answer": "Coconut milk ice cream", - "evidence": [ - "D18:9" - ], - "category": 4 - }, - { - "question": "How did Nate feel about sharing his love for dairy-free desserts with Joanna?", - "answer": "Happy to share", - "evidence": [ - "D18:12" - ], - "category": 4 - }, - { - "question": "What did Joanna share with her writers group in August 2022?", - "answer": "her book", - "evidence": [ - "D19:6" - ], - "category": 4 - }, - { - "question": "How did Joanna celebrate after sharing her book with her writers group?", - "answer": "making a delicious treat", - "evidence": [ - "D19:8" - ], - "category": 4 - }, - { - "question": "How did Nate celebrate winning the international tournament?", - "answer": "Taking time off to chill with pets", - "evidence": [ - "D19:9" - ], - "category": 4 - }, - { - "question": "Why is Joanna experimenting with dairy-free options in her dessert recipes?", - "answer": "lactose intolerance", - "evidence": [ - "D20:10" - ], - "category": 4 - }, - { - "question": "What substitution does Nate suggest for butter in dairy-free baking?", - "answer": "dairy-free margarine or coconut oil", - "evidence": [ - "D20:15" - ], - "category": 4 - }, - { - "question": "What type of show did Nate host where he taught vegan ice cream recipes?", - "answer": "a cooking show", - "evidence": [ - "D21:4" - ], - "category": 4 - }, - { - "question": "What is Nate's favorite dish from the cooking show he hosted?", - "answer": "Coconut milk ice cream", - "evidence": [ - "D21:6" - ], - "category": 4 - }, - { - "question": "What is one of Nate's favorite dairy-free treats besides coconut milk ice cream?", - "answer": "dairy-free chocolate mousse", - "evidence": [ - "D21:10" - ], - "category": 4 - }, - { - "question": "What dessert did Joanna share a photo of that has an almond flour crust, chocolate ganache, and fresh raspberries?", - "answer": "chocolate raspberry tart", - "evidence": [ - "D21:11" - ], - "category": 4 - }, - { - "question": "What kind of cake did Joanna share a photo of that she likes making for birthdays and special days?", - "answer": "chocolate cake with raspberries", - "evidence": [ - "D21:13" - ], - "category": 4 - }, - { - "question": "What two main ingredients are part of the dessert Joanna shared a photo of with blueberries, coconut milk, and a gluten-free crust?", - "answer": "blueberries and coconut milk", - "evidence": [ - "D21:17" - ], - "category": 4 - }, - { - "question": "What movie did Nate recently watch and enjoy on October 6, 2022?", - "answer": "Little Women", - "evidence": [ - "D22:8" - ], - "category": 4 - }, - { - "question": "What did Joanna make for one of the ladies at her writing club?", - "answer": "a bookmark", - "evidence": [ - "D22:19" - ], - "category": 4 - }, - { - "question": "What game did Nate play at the game convention he attended on 9 October, 2022?", - "answer": "Catan", - "evidence": [ - "D23:7" - ], - "category": 4 - }, - { - "question": "What movie has Nate recently seen that blew his mind?", - "answer": "\"Inception\"", - "evidence": [ - "D23:17" - ], - "category": 4 - }, - { - "question": "What game has Nate been playing nonstop with a futuristic setting and gameplay on October 9, 2022?", - "answer": "Cyberpunk 2077", - "evidence": [ - "D23:17" - ], - "category": 4 - }, - { - "question": "What did Nate share a photo of when mentioning unwinding at home?", - "answer": "a bookcase filled with dvds and movies", - "evidence": [ - "D23:15" - ], - "category": 4 - }, - { - "question": "How did Joanna describe the classic movie he watched?", - "answer": "gripping with great actors", - "evidence": [ - "D23:18" - ], - "category": 4 - }, - { - "question": "What does Joanna recommend to make a living room comfy like hers?", - "answer": "couch for multiple people, fluffy blanket, lights that can be dimmed", - "evidence": [ - "D23:26" - ], - "category": 4 - }, - { - "question": "What helps Joanna stay focused and brings her joy?", - "answer": "stuffed animal dog named Tilly", - "evidence": [ - "D24:8" - ], - "category": 4 - }, - { - "question": "What does Joanna do while she writes?", - "answer": "have a stuffed animal dog named Tilly with her", - "evidence": [ - "D24:4" - ], - "category": 4 - }, - { - "question": "Why did Joanna name the stuffed animal dog Tilly?", - "answer": "after a dog she had in Michigan", - "evidence": [ - "D24:6" - ], - "category": 4 - }, - { - "question": "What does Joanna do after receiving a rejection from a production company?", - "answer": "keep grinding and moving ahead", - "evidence": [ - "D24:14" - ], - "category": 4 - }, - { - "question": "How does Nate feel about Joanna's ability to bounce back from setbacks?", - "answer": "respect Joanna for being able to bounce back", - "evidence": [ - "D24:15" - ], - "category": 4 - }, - { - "question": "What encouragement does Nate give to Joanna after her setback?", - "answer": "rejections don't define her, keep grinding and she'll find the perfect opportunity", - "evidence": [ - "D24:13" - ], - "category": 4 - }, - { - "question": "What does Nate rely on for cheer and joy?", - "answer": "his turtles", - "evidence": [ - "D24:3" - ], - "category": 4 - }, - { - "question": "What does Joanna use to remember her dog from Michigan?", - "answer": "naming a stuffed animal dog Tilly", - "evidence": [ - "D24:6" - ], - "category": 4 - }, - { - "question": "What did Joanna contribute to that was shown on the big screen on the Sunday before October 25, 2022?", - "answer": "movie script", - "evidence": [ - "D25:2" - ], - "category": 4 - }, - { - "question": "What inspires Joanna to create drawings of her characters?", - "answer": "visuals to help bring the characters alive in her head so she can write better", - "evidence": [ - "D25:8" - ], - "category": 4 - }, - { - "question": "Where does Joanna get her ideas for the characters from?", - "answer": "people she knows, things she saw, her imagination", - "evidence": [ - "D25:10" - ], - "category": 4 - }, - { - "question": "How did Joanna feel on October 25, 2022 about seeing her characters come alive on the big screen?", - "answer": "surreal and cool", - "evidence": [ - "D25:6" - ], - "category": 4 - }, - { - "question": "What type of diet do Nate's turtles have?", - "answer": "combination of vegetables, fruits, and insects", - "evidence": [ - "D25:19" - ], - "category": 4 - }, - { - "question": "What ingredient did Nate use to make the ice cream lactose-free?", - "answer": "coconut milk", - "evidence": [ - "D26:18" - ], - "category": 4 - }, - { - "question": "What did Joanna find in old notebooks last week that prompted her to reflect on her progress as a writer?", - "answer": "early writings", - "evidence": [ - "D26:5" - ], - "category": 4 - }, - { - "question": "What game is Nate currently playing and recommends to others on November 7, 2022?", - "answer": "\"Xenoblade Chronicles\"", - "evidence": [ - "D27:23" - ], - "category": 4 - }, - { - "question": "What did Joanna receive from her brother that brought back childhood memories?", - "answer": "a handwritten letter", - "evidence": [ - "D27:29" - ], - "category": 4 - }, - { - "question": "What is the type of game \"Xenoblade Chronicles\" that Nate is playing?", - "answer": "fantasy RPG", - "evidence": [ - "D27:23" - ], - "category": 4 - }, - { - "question": "What dish did Nate make on 9 November, 2022?", - "answer": "Homemade coconut ice cream", - "evidence": [ - "D28:1" - ], - "category": 4 - }, - { - "question": "What project is Joanna working on in her notebook on November 9, 2022?", - "answer": "A suspenseful thriller set in a small Midwestern town", - "evidence": [ - "D28:12" - ], - "category": 4 - }, - { - "question": "What is Nate creating for YouTube on 9 November, 2022?", - "answer": "gaming content", - "evidence": [ - "D28:13" - ], - "category": 4 - }, - { - "question": "What inspired Nate to start making gaming videos?", - "answer": "Love of gaming and connecting with others who enjoy it too", - "evidence": [ - "D28:15" - ], - "category": 4 - }, - { - "question": "What new content is Nate creating for YouTube?", - "answer": "Gaming videos", - "evidence": [ - "D28:13" - ], - "category": 4 - }, - { - "question": "What advice does Joanna give to Nate about making YouTube videos?", - "answer": "Watch other people's videos to understand what the audience likes", - "evidence": [ - "D28:18" - ], - "category": 4 - }, - { - "question": "What did Joanna take a picture of near Fort Wayne last summer?", - "answer": "Sunset", - "evidence": [ - "D28:22" - ], - "category": 4 - }, - { - "question": "What inspired Joanna to take a picture of the sunset in the field near Fort Wayne?", - "answer": "The incredible sunset and surrounding beauty", - "evidence": [ - "D28:22" - ], - "category": 4 - }, - { - "question": "Why did Nate get a third turtle?", - "answer": "He saw another one at a pet store and wanted to get it", - "evidence": [ - "D28:25" - ], - "category": 4 - }, - { - "question": "What does Nate want to do when he goes over to Joanna's place?", - "answer": "Watch one of Joanna's movies together or go to the park", - "evidence": [ - "D28:29" - ], - "category": 4 - }, - { - "question": "What did Nate take to the beach in Tampa?", - "answer": "turtles", - "evidence": [ - "D29:6" - ], - "category": 4 - }, - { - "question": "What does Nate love most about having turtles?", - "answer": "They make him feel calm and don't require much looking after", - "evidence": [ - "D29:8" - ], - "category": 4 - }, - { - "question": "What did Nate share a photo of as a part of his experimentation in November 2022?", - "answer": "colorful bowls of coconut milk ice cream", - "evidence": [ - "D29:10" - ], - "category": 4 - }, - { - "question": "What color did Joanna choose for her hair?", - "evidence": [ - "D7:1", - "D7:3" - ], - "category": 5, - "adversarial_answer": "purple" - }, - { - "question": "What is Joanna's favorite movie trilogy?", - "evidence": [ - "D9:12" - ], - "category": 5, - "adversarial_answer": "Lord of the Rings" - }, - { - "question": "What is Joanna's favorite book series about?", - "evidence": [ - "D9:14" - ], - "category": 5, - "adversarial_answer": "dragons" - }, - { - "question": "What kind of lighting does Joanna's gaming room have?", - "evidence": [ - "D10:2" - ], - "category": 5, - "adversarial_answer": "red and purple lighting" - }, - { - "question": "What game was the second tournament that Joanna won based on?", - "evidence": [ - "D10:4", - "D10:6" - ], - "category": 5, - "adversarial_answer": "Street Fighter" - }, - { - "question": "What is Nate's third screenplay about?", - "evidence": [ - "D12:13", - "D12:14" - ], - "category": 5, - "adversarial_answer": "loss, identity, and connection" - }, - { - "question": "What type of movies does Nate hate watching the most?", - "evidence": [ - "D1:13" - ], - "category": 5, - "adversarial_answer": "action and sci-fi" - }, - { - "question": "What genre is Joanna's first novella?", - "evidence": [ - "D2:5" - ], - "category": 5, - "adversarial_answer": "drama and romance" - }, - { - "question": "What are Nate's plans for his finished screenplay in January 2022?", - "evidence": [ - "D2:7" - ], - "category": 5, - "adversarial_answer": "submit it to film festivals and get producers and directors to check it out" - }, - { - "question": "For how long has Nate had his snakes?", - "evidence": [ - "D2:12" - ], - "category": 5, - "adversarial_answer": "3 years" - }, - { - "question": "What did Nate think of the caramel ice cream he made?", - "evidence": [ - "D3:6" - ], - "category": 5, - "adversarial_answer": "Super good, rich and creamy" - }, - { - "question": "What flavor of cake did Nate make for his friend on 25 February, 2022?", - "evidence": [ - "D4:3" - ], - "category": 5, - "adversarial_answer": "chocolate and vanilla swirl" - }, - { - "question": "What was Nate's audition for?", - "evidence": [ - "D6:2" - ], - "category": 5, - "adversarial_answer": "writing gig" - }, - { - "question": "Why did Joanna choose the hair color she did?", - "evidence": [ - "D7:5" - ], - "category": 5, - "adversarial_answer": "Bright and bold - like her" - }, - { - "question": "What are the main ingredients of the ice cream recipe shared by Joanna?", - "evidence": [ - "D8:19" - ], - "category": 5, - "adversarial_answer": "Coconut milk, vanilla extract, sugar, salt" - }, - { - "question": "What is Nate's project called in the writers group?", - "evidence": [ - "D9:3" - ], - "category": 5, - "adversarial_answer": "\"Finding Home\"" - }, - { - "question": "Which activity helps Nate escape and numbs his mind?", - "evidence": [ - "D9:10" - ], - "category": 5, - "adversarial_answer": "watching fantasy and sci-fi movies" - }, - { - "question": "What filling did Nate use in the cake he made recently in May 2022?", - "evidence": [ - "D10:11" - ], - "category": 5, - "adversarial_answer": "strawberry" - }, - { - "question": "Who did Joanna plan to invite to her gaming party in June 2022?", - "evidence": [ - "D14:22" - ], - "category": 5, - "adversarial_answer": "Tournament friends, old friends, teammates" - }, - { - "question": "What special items did Joanna get for everyone at her gaming party?", - "evidence": [ - "D14:24" - ], - "category": 5, - "adversarial_answer": "Custom controller decorations" - }, - { - "question": "What supervillain is Joanna a fan of?", - "evidence": [ - "D15:3" - ], - "category": 5, - "adversarial_answer": "Spider-Man" - }, - { - "question": "Which superhero toy figure does Joanna share a photo of?", - "evidence": [ - "D15:4" - ], - "category": 5, - "adversarial_answer": "Iron Man" - }, - { - "question": "What did Joanna make and share with her vegan diet group?", - "evidence": [ - "D16:8" - ], - "category": 5, - "adversarial_answer": "vegan ice cream" - }, - { - "question": "How many people attended the gaming party hosted by Joanna in June 2022?", - "evidence": [ - "D16:6" - ], - "category": 5, - "adversarial_answer": "7" - }, - { - "question": "Where did Nate go for a road trip for research?", - "evidence": [ - "D17:4" - ], - "category": 5, - "adversarial_answer": "Woodhaven" - }, - { - "question": "What did Joanna discover at the museum in Woodhaven?", - "evidence": [ - "D17:4" - ], - "category": 5, - "adversarial_answer": "cool old book collection" - }, - { - "question": "What specific themes are explored in Nate's new book?", - "evidence": [ - "D17:16" - ], - "category": 5, - "adversarial_answer": "loss, redemption, and forgiveness" - }, - { - "question": "How did Nate feel when someone wrote him a letter after reading his blog post?", - "evidence": [ - "D18:5" - ], - "category": 5, - "adversarial_answer": "Touched" - }, - { - "question": "What kind of impact does Joanna hope to have with her painting?", - "evidence": [ - "D18:7" - ], - "category": 5, - "adversarial_answer": "share her stories and hopefully have an impact" - }, - { - "question": "What did Nate share with his writers group in August 2022?", - "evidence": [ - "D19:6" - ], - "category": 5, - "adversarial_answer": "her book" - }, - { - "question": "How did Nate celebrate after sharing his book with a writers group?", - "evidence": [ - "D19:8" - ], - "category": 5, - "adversarial_answer": "making a delicious treat" - }, - { - "question": "How did Joanna celebrate winning the international tournament?", - "evidence": [ - "D19:9" - ], - "category": 5, - "adversarial_answer": "Taking time off to chill with pets" - }, - { - "question": "What substitution does Nate suggest for sugar in dairy-free baking?", - "evidence": [ - "D20:15" - ], - "category": 5, - "adversarial_answer": "dairy-free margarine or coconut oil" - }, - { - "question": "What type of show did Joanna host where she taught vegan ice cream recipes?", - "evidence": [ - "D21:4" - ], - "category": 5, - "adversarial_answer": "a cooking show" - }, - { - "question": "What is Joanna's favorite dish from the cooking show she hosted?", - "evidence": [ - "D21:6" - ], - "category": 5, - "adversarial_answer": "Coconut milk ice cream" - }, - { - "question": "What dessert did Nate share a photo of that has an almond flour crust, chocolate ganache, and fresh raspberries?", - "evidence": [ - "D21:11" - ], - "category": 5, - "adversarial_answer": "chocolate raspberry tart" - }, - { - "question": "What two main ingredients are part of the dessert Nate shared a photo of with blueberries, coconut milk, and a gluten-free crust?", - "evidence": [ - "D21:17" - ], - "category": 5, - "adversarial_answer": "blueberries and coconut milk" - }, - { - "question": "What movie did Joanna recently watch and enjoy on October 6, 2022?", - "evidence": [ - "D22:8" - ], - "category": 5, - "adversarial_answer": "Little Women" - }, - { - "question": "What did Nate make for one of the ladies at his writing club?", - "evidence": [ - "D22:19" - ], - "category": 5, - "adversarial_answer": "a bookmark" - }, - { - "question": "What game has Joanna been playing nonstop with a futuristic setting and gameplay on October 9, 2022?", - "evidence": [ - "D23:17" - ], - "category": 5, - "adversarial_answer": "Cyberpunk 2077" - }, - { - "question": "How did Nate describe the classic movie he watched?", - "evidence": [ - "D23:18" - ], - "category": 5, - "adversarial_answer": "gripping with great actors" - }, - { - "question": "What does Nate recommend to make a living room comfy like his?", - "evidence": [ - "D23:26" - ], - "category": 5, - "adversarial_answer": "couch for multiple people, fluffy blanket, lights that can be dimmed" - }, - { - "question": "What helps Joanna stay distracted and brings her sadness?", - "evidence": [ - "D24:8" - ], - "category": 5, - "adversarial_answer": "stuffed animal dog named Tilly" - }, - { - "question": "What does Nate do while he writes?", - "evidence": [ - "D24:4" - ], - "category": 5, - "adversarial_answer": "have a stuffed animal dog named Tilly with him" - }, - { - "question": "What does Nate do after receiving a rejection from a production company?", - "evidence": [ - "D24:14" - ], - "category": 5, - "adversarial_answer": "keep grinding and moving ahead" - }, - { - "question": "What does Joanna rely on for cheer and joy?", - "evidence": [ - "D24:3" - ], - "category": 5, - "adversarial_answer": "her turtles" - }, - { - "question": "What does Nate use to remember his dog from Michigan?", - "evidence": [ - "D24:6" - ], - "category": 5, - "adversarial_answer": "stuffed animal dog Tilly" - }, - { - "question": "What inspires Joanna to create music for her characters?", - "evidence": [ - "D25:8" - ], - "category": 5, - "adversarial_answer": "visuals to help bring the characters alive in her head so she can write better" - }, - { - "question": "What type of diet do Joanna's turtles have?", - "evidence": [ - "D25:19" - ], - "category": 5, - "adversarial_answer": "combination of vegetables, fruits, and insects" - }, - { - "question": "What did Nate find in old notebooks last week that prompted him to reflect on her progress as a writer?", - "evidence": [ - "D26:5" - ], - "category": 5, - "adversarial_answer": "early writings" - }, - { - "question": "What game is Joanna currently playing and recommends to others on November 7, 2022?", - "evidence": [ - "D27:23" - ], - "category": 5, - "adversarial_answer": "\"Xenoblade Chronicles\"" - }, - { - "question": "What did Nate receive from his brother that brought back childhood memories?", - "evidence": [ - "D27:29" - ], - "category": 5, - "adversarial_answer": "a handwritten letter" - }, - { - "question": "What is the type of game \"Xenoblade Chronicles\" that Joanna is playing?", - "evidence": [ - "D27:23" - ], - "category": 5, - "adversarial_answer": "fantasy RPG" - }, - { - "question": "What project is Nate working on in his notebook on November 9, 2022?", - "evidence": [ - "D28:12" - ], - "category": 5, - "adversarial_answer": "A suspenseful thriller set in a small Midwestern town" - }, - { - "question": "What is Joanna creating for YouTube on 9 November, 2022?", - "evidence": [ - "D28:13" - ], - "category": 5, - "adversarial_answer": "gaming content" - }, - { - "question": "What inspired Joanna to start making gaming videos?", - "evidence": [ - "D28:15" - ], - "category": 5, - "adversarial_answer": "Love of gaming and connecting with others who enjoy it too" - }, - { - "question": "What new content is Nate creating for television?", - "evidence": [ - "D28:13" - ], - "category": 5, - "adversarial_answer": "Gaming videos" - }, - { - "question": "What did Nate take a picture of near Fort Wayne last summer?", - "evidence": [ - "D28:22" - ], - "category": 5, - "adversarial_answer": "Sunset" - }, - { - "question": "Why did Joanna get a third turtle?", - "evidence": [ - "D28:25" - ], - "category": 5, - "adversarial_answer": "She saw another one at a pet store and wanted to get it" - }, - { - "question": "What did Joanna take to the beach in Tampa?", - "evidence": [ - "D29:6" - ], - "category": 5, - "adversarial_answer": "turtles" - }, - { - "question": "What does Joanna love most about having turtles?", - "evidence": [ - "D29:8" - ], - "category": 5, - "adversarial_answer": "They make her feel calm and don't require much looking after" - } - ], - "conversation": { - "speaker_a": "Joanna", - "speaker_b": "Nate", - "session_1_date_time": "7:31 pm on 21 January, 2022", - "session_1": [ - { - "speaker": "Nate", - "dia_id": "D1:1", - "text": "Hey Joanna! Long time no see! What's up? Anything fun going on?" - }, - { - "speaker": "Joanna", - "dia_id": "D1:2", - "text": "Hey Nate! Long time no see! I've been working on a project lately - it's been pretty cool. What about you - any fun projects or hobbies?" - }, - { - "speaker": "Nate", - "dia_id": "D1:3", - "text": "Hey Joanna! That's cool! I won my first video game tournament last week - so exciting!" - }, - { - "speaker": "Joanna", - "dia_id": "D1:4", - "text": "Wow Nate! Congrats on winning! Tell me more - what game was it?" - }, - { - "speaker": "Nate", - "dia_id": "D1:5", - "text": "Thanks! it's a team shooter game." - }, - { - "speaker": "Joanna", - "dia_id": "D1:6", - "text": "Wow, great job! What was is called?" - }, - { - "speaker": "Nate", - "dia_id": "D1:7", - "text": "The game was called Counter-Strike: Global Offensive, and me and my team had a blast to the very end!" - }, - { - "speaker": "Joanna", - "dia_id": "D1:8", - "text": "Cool, Nate! Sounds like a fun experience, even if I'm not into games." - }, - { - "speaker": "Nate", - "dia_id": "D1:9", - "text": "It was! How about you? Do you have any hobbies you love?" - }, - { - "speaker": "Joanna", - "dia_id": "D1:10", - "text": "Yeah! Besides writing, I also enjoy reading, watching movies, and exploring nature. Anything else you enjoy doing, Nate?" - }, - { - "speaker": "Nate", - "dia_id": "D1:11", - "text": "Playing video games and watching movies are my main hobbies." - }, - { - "speaker": "Joanna", - "dia_id": "D1:12", - "text": "Cool, Nate! So we both have similar interests. What type of movies do you like best?" - }, - { - "speaker": "Nate", - "dia_id": "D1:13", - "text": "I love action and sci-fi movies, the effects are so cool! What about you, what's your favorite genre?" - }, - { - "speaker": "Joanna", - "dia_id": "D1:14", - "text": "I'm all about dramas and romcoms. I love getting immersed in the feelings and plots." - }, - { - "speaker": "Nate", - "dia_id": "D1:15", - "text": "Wow, movies can be so powerful! Do you have any recommendations for me?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.pinimg.com/originals/80/ce/f7/80cef746e94a1720df6d3ffec883087e.jpg" - ], - "blip_caption": "a photo of a poster of a man and a woman sitting on a bench", - "query": "eternal sunshine of the spotless mind movie poster", - "dia_id": "D1:16", - "text": "Yeah, totally! Have you seen this romantic drama that's all about memory and relationships? It's such a good one." - }, - { - "speaker": "Nate", - "dia_id": "D1:17", - "text": "Oh cool! I might check that one out some time soon! I do love watching classics." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/3i39xdbz3td91.jpg" - ], - "blip_caption": "a photo of a dvd on a table with a blurry background", - "query": "eternal sunshine of spotless mind dvd cover", - "dia_id": "D1:18", - "text": "Yep, that movie is awesome. I first watched it around 3 years ago. I even went out and got a physical copy!" - }, - { - "speaker": "Nate", - "dia_id": "D1:19", - "text": "Sounds cool! Have you seen it a lot? sounds like you know the movie well!" - }, - { - "speaker": "Joanna", - "dia_id": "D1:20", - "text": "A few times. It's one of my favorites! I really like the idea and the acting." - }, - { - "speaker": "Nate", - "dia_id": "D1:21", - "text": "Cool! I'll definitely check it out. Thanks for the recommendation!" - }, - { - "speaker": "Joanna", - "dia_id": "D1:22", - "text": "No problem, Nate! Let me know if you like it!" - } - ], - "session_2_date_time": "2:01 pm on 23 January, 2022", - "session_2": [ - { - "speaker": "Joanna", - "dia_id": "D2:1", - "text": "Hey Nate! Haven't talked in a few days. Crazy things happened to me!" - }, - { - "speaker": "Nate", - "dia_id": "D2:2", - "text": "Hi Joanna! Long time no see! What's been going on? You sound excited!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://live.staticflickr.com/3159/2406045813_cab5f8211d_b.jpg" - ], - "blip_caption": "a photography of a book with a page of text on it", - "query": "screenplay laptop", - "dia_id": "D2:3", - "re-download": true, - "text": "Woo! I finally finished my first full screenplay and printed it last Friday. I've been working on for a while, such a relief to have it all done!" - }, - { - "speaker": "Nate", - "dia_id": "D2:4", - "text": "Wow, that sounds awesome! What's it about? Glad it's all down!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:5", - "text": "Thanks, Nate! It's a mix of drama and romance!" - }, - { - "speaker": "Nate", - "dia_id": "D2:6", - "text": "Wow, that's amazing! How do you feel now that it's finished? Do you have any new plans for it?" - }, - { - "speaker": "Joanna", - "dia_id": "D2:7", - "text": "Woohoo, Nate! I'm feeling a rollercoaster of emotions - relief, excitement, some anxiety - over finishing this project. Now I'm gonna submit it to some film festivals and (hopefully) get producers and directors to check it out. Here's hoping!" - }, - { - "speaker": "Nate", - "dia_id": "D2:8", - "text": "Congrats, Joanna! That sounds like a wild experience. Rock on and I hope they love it!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:9", - "text": "Thanks Nate! A mix of emotions for sure. Hopefully, it leads to positive feedback and new opportunities." - }, - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/5126/5279947833_4ae1f16e96_b.jpg" - ], - "blip_caption": "a photography of a turtle and a turtleling sitting on a rock", - "query": "pet turtles", - "dia_id": "D2:10", - "re-download": true, - "text": "Yeah, for sure. Hoping for the best! I like having some of these little ones around to keep me calm when things are super important and I'm nervous." - }, - { - "speaker": "Joanna", - "dia_id": "D2:11", - "text": "Awww! How long have you had them?" - }, - { - "speaker": "Nate", - "dia_id": "D2:12", - "text": "I've had them for 3 years now and they bring me tons of joy!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:13", - "text": "They sure lookl like they do! Adorable!" - }, - { - "speaker": "Nate", - "dia_id": "D2:14", - "text": "Thanks! The turtles might be small, but both sure have big personalities. I really reccomend having something like these little guys for times of stress." - }, - { - "speaker": "Joanna", - "dia_id": "D2:15", - "text": "Good idea, Nate! I'll think about it and maybe get pets of my own soon if I can find any I'm not allergic to. Have you been up to anything recently?" - }, - { - "speaker": "Nate", - "dia_id": "D2:16", - "text": "Yeah actually! I start to hang out with some people outside of my circle at the tournament. They're pretty cool!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:17", - "text": "Oh? That sounds sweet! Is it a weird relationship with them being competitors and all?" - }, - { - "speaker": "Nate", - "dia_id": "D2:18", - "text": "Oh, kind of. Some people are more competitive then others, so I tend to just stick around the more chill people here." - }, - { - "speaker": "Joanna", - "dia_id": "D2:19", - "text": "That makes sense! Are you gonna cheer them on even if you lose?" - }, - { - "speaker": "Nate", - "dia_id": "D2:20", - "text": "Absolutely! I don't expect to win big here, I just like playing for fun! You mentioned you were allergic to pets earlier, how bad is it?" - }, - { - "speaker": "Joanna", - "dia_id": "D2:21", - "text": "Oh, its really bad. My face gets all puffy and itchy when I'm around certain animals, so I've always just stayed away." - }, - { - "speaker": "Nate", - "dia_id": "D2:22", - "text": "Sorry to hear that. Allergies can be tough. What specifically are you allergic to?" - }, - { - "speaker": "Joanna", - "dia_id": "D2:23", - "text": "I'm allergic to most reptiles and animals with fur. It can be a bit of a drag, but I find other ways to be happy." - }, - { - "speaker": "Nate", - "dia_id": "D2:24", - "text": "Awesome! There are lots of things that can bring you joy without pets. What else brings you joy?" - }, - { - "speaker": "Joanna", - "dia_id": "D2:25", - "text": "Writing and hanging with friends! That way I can express myself through stories, or just have a good time with people." - }, - { - "speaker": "Nate", - "dia_id": "D2:26", - "text": "That's great to hear! Those are both great things. I'm glad to hear you've got other things to help you get through times of axiousness despite not being able to have animals!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:27", - "text": "Thanks, Nate! Writing helps me create wild worlds with awesome characters. Plus, it's a great way to express my feelings. I can't imagine life without it." - }, - { - "speaker": "Nate", - "dia_id": "D2:28", - "text": "Wow, Joanna, that sounds amazing! Keep doing what you love!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:29", - "text": "Thanks, Nate! I'll definitely keep pursuing my passion for writing. It means a lot." - } - ], - "session_3_date_time": "9:27 am on 7 February, 2022", - "session_3": [ - { - "speaker": "Joanna", - "dia_id": "D3:1", - "text": "Hey Nate, long time no see! The screenplay I sent in to the film festival has been on my mind all day everyday. I keep bouncing between crazy emotions like relief, excitement and worry! Fingers crossed a producer or director falls in love with it and it ends up on the big screen - that would be awesome!" - }, - { - "speaker": "Nate", - "dia_id": "D3:2", - "text": "Hey Joanna! It is a big deal! I'm sure its been a wild ride. Sending some positive vibes and hoping someone likes it enough to get it on the big screen - that would be awesome!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:3", - "text": "Thanks Nate, your support really means a lot. I put a lot of effort into it and I'm crossing my fingers. What about you? Anything new and exciting happening in your life?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/4q9s9o2607ib1.jpg" - ], - "blip_caption": "a photo of a bowl of ice cream with a spoon in it", - "query": "homemade coconut milk ice cream", - "dia_id": "D3:4", - "text": "Thanks, Joanna. Not much has changed for me, but I just discovered that I can make coconut milk icecream and gave it a try. It was actually pretty good, so I'm proud of myself." - }, - { - "speaker": "Joanna", - "dia_id": "D3:5", - "text": "Looks delish! Glad you tried something new and it went well. What did you think of it?" - }, - { - "speaker": "Nate", - "dia_id": "D3:6", - "text": "Super good! It was rich and creamy - might be my new favorite snack!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:7", - "text": "Great! I love when you try something new and it actually works out. Will you give it another go?" - }, - { - "speaker": "Nate", - "dia_id": "D3:8", - "text": "Yep, it could be fun! I'm looking forward to trying out different flavors and toppings." - }, - { - "speaker": "Joanna", - "dia_id": "D3:9", - "text": "Yum! Sounds great. Got any favorite flavors for dairy-free desserts?" - }, - { - "speaker": "Nate", - "dia_id": "D3:10", - "text": "I love coconut milk, but I also enjoy chocolate and mixed berry flavors." - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a piece of cake with berries on a plate", - "dia_id": "D3:11", - "text": "Wow, those sound great! Who can say no to chocolate and berries? I'm tempted to try dairy-free flavors now!" - }, - { - "speaker": "Nate", - "dia_id": "D3:12", - "text": "Well I also made a dairy-free chocolate cake with berries on it the other day, maybe you would like that!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:13", - "text": "Wow! That sounds yummy! You're so talented. Thanks for sharing your amazing creations! I should really try making one or just pay you a visit and try one for myself!" - }, - { - "speaker": "Nate", - "dia_id": "D3:14", - "text": "I couldn't agree more! It's always fun experimenting in the kitchen." - }, - { - "speaker": "Joanna", - "dia_id": "D3:15", - "text": "I can tell! Your cooking skills are awesome. Seen any good movies lately?" - }, - { - "speaker": "Nate", - "dia_id": "D3:16", - "text": "Not recently. Any good ones you'd recommend?" - }, - { - "speaker": "Joanna", - "dia_id": "D3:17", - "text": "I just watched \"Little Women\" and it was amazing! It's a great story about sisterhood, love, and reaching for your dreams. Definitely a must-see!" - }, - { - "speaker": "Nate", - "dia_id": "D3:18", - "text": "Oh, that sounds like a great one! I'll definitely add it to my list. Thanks for the recommendation!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:19", - "text": "Anytime! I'm always down to give movie reccomendations." - }, - { - "speaker": "Nate", - "dia_id": "D3:20", - "text": "Good to know! I'll be sure to give you a shout whenever I run out of things to watch!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:21", - "text": "Sounds great! Let me know what you think of it when your done!" - }, - { - "speaker": "Nate", - "dia_id": "D3:22", - "text": "You bet! You'll be the first to know." - }, - { - "speaker": "Joanna", - "dia_id": "D3:23", - "text": "Awesome! Enjoy yourself!" - }, - { - "speaker": "Nate", - "dia_id": "D3:24", - "text": "You too, take care!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:25", - "text": "Later, take care!" - } - ], - "session_4_date_time": "1:07 pm on 25 February, 2022", - "session_4": [ - { - "speaker": "Nate", - "dia_id": "D4:1", - "text": "Hey Joanna! Sorry I haven't been around. I made my friend some ice cream and they loved it!" - }, - { - "speaker": "Joanna", - "dia_id": "D4:2", - "text": "No worries, Nate! Glad to hear it. What flavor did you make?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/wj5c699jccx61.jpg" - ], - "blip_caption": "a photo of a person holding a chocolate and vanilla ice cream cone", - "query": "coconut milk ice cream chocolate swirls almond chunks", - "dia_id": "D4:3", - "text": "I whipped up some chocolate and vanilla swirl." - }, - { - "speaker": "Joanna", - "dia_id": "D4:4", - "text": "That looks delicious! Unfortunately, I can't have dairy, so no ice cream for me. Do you happen to have a dairy-free recipe that I could try?" - }, - { - "speaker": "Nate", - "dia_id": "D4:5", - "text": "Sure, I know one recipe using coconut milk. Would you like me to send it to you?" - }, - { - "speaker": "Joanna", - "dia_id": "D4:6", - "text": "Yeah, definitely! I'm keen to try your recipe. Always up for something sweet." - }, - { - "speaker": "Nate", - "dia_id": "D4:7", - "text": "Cool, I'll do that. I'm all about these desserts, let me know what you think!" - }, - { - "speaker": "Joanna", - "dia_id": "D4:8", - "text": "Definitely keeping you posted! Love your creations!" - }, - { - "speaker": "Nate", - "dia_id": "D4:9", - "text": "Thanks, Joanna! It means a lot that you enjoy the desserts I bake." - }, - { - "speaker": "Joanna", - "dia_id": "D4:10", - "text": "Yeah Nate, your cooking is amazing! I can't stop thinking about the screenplay, so I just started writing another one while I wait to hear back about how the first one did." - }, - { - "speaker": "Nate", - "dia_id": "D4:11", - "text": "I hear that, taking your mind of something like that is very challenging. What's the new one about?" - }, - { - "speaker": "Joanna", - "dia_id": "D4:12", - "text": "It's about a thirty year old woman on a journey of self-discovery after a loss. Somewhat similar to the last one, but hey, that's just the kind of thing I'm inspired to write about!" - }, - { - "speaker": "Nate", - "dia_id": "D4:13", - "text": "Interesting! That's a deep topic. Love to hear more about it." - }, - { - "speaker": "Joanna", - "dia_id": "D4:14", - "text": "Thanks, Nate! It's my own story. The main character is dealing with some tough stuff: loss and trying to figure out who they are. They take a road trip to heal and grow." - }, - { - "speaker": "Nate", - "dia_id": "D4:15", - "text": "Wow, Joanna, that sounds awesome. I love stories that tackle important issues. What inspired you to this one?" - }, - { - "speaker": "Joanna", - "dia_id": "D4:16", - "text": "Thanks, Nate! It was inspired by personal experiences and my own journey of self-discovery." - }, - { - "speaker": "Nate", - "dia_id": "D4:17", - "text": "Wow, Joanna, that takes guts! I can't wait to see it all come together. I'm also pumped to see how your first one will do!" - }, - { - "speaker": "Joanna", - "dia_id": "D4:18", - "text": "Thanks, Nate! Appreciate your support. Hoping my screenplay gets noticed and makes it to the screen. Fingers crossed!" - }, - { - "speaker": "Nate", - "dia_id": "D4:19", - "text": "Crossing my fingers for you! Hope your screenplay finds a fan and is given its due. Good luck!" - } - ], - "session_5_date_time": "6:59 pm on 18 March, 2022", - "session_5": [ - { - "speaker": "Joanna", - "dia_id": "D5:1", - "text": "Hey Nate, it's been a minute! I wrapped up my second script, and the feels have been wild. Sometimes I'm so relieved, but other times I just feel anxious about what comes next. It's a mix of excitement and terror, thinking about my work getting noticed and hitting the big screen." - }, - { - "speaker": "Nate", - "dia_id": "D5:2", - "text": "Hey Joanna! Awesome to hear from you! Congrats on wrapping up a second one! All that hard work and dedication will definitely shine through and get noticed, no doubt. Hope you've been able to take some time to relax after everything!" - }, - { - "speaker": "Joanna", - "dia_id": "D5:3", - "text": "Thanks Nate! It's been a wild ride. I've been decompressing, but it's hard to switch off. There's always this tug-of-war of hope and doubt." - }, - { - "speaker": "Nate", - "img_url": [ - "https://cdn12.picryl.com/photo/2016/12/31/turtle-nature-slow-nature-landscapes-9a70ba-1024.jpg" - ], - "blip_caption": "a photography of two tortoises laying on the ground in a jungle", - "query": "adorable photo turtles walk grassy area", - "dia_id": "D5:4", - "re-download": true, - "text": "It's normal to have doubts, but you've worked hard and put tons of passion into it. Believe in yourself and things will work out. Here, look at this cute pic - walking them always reminds me to enjoy the small stuff!" - }, - { - "speaker": "Joanna", - "dia_id": "D5:5", - "text": "That pic's adorable! They always look so relaxed outside. What made you choose them as pets?" - }, - { - "speaker": "Nate", - "img_url": [ - 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" - ], - "blip_caption": "a photography of three turtles sitting on rocks in a pond", - "query": "turtles basking sun", - "dia_id": "D5:6", - "re-download": true, - "text": "I'm drawn to turtles. They're unique and their slow pace is a nice change from the rush of life. They're also low-maintenance and calming. Check out this moment I snapped!" - }, - { - "speaker": "Joanna", - "dia_id": "D5:7", - "text": "They look so peaceful! It's amazing how these creatures bring so much calm and joy. Is taking care of them tough?" - }, - { - "speaker": "Nate", - "dia_id": "D5:8", - "text": "No, not really. Just keep their area clean, feed them properly, and make sure they get enough light. It's actually kind of fun." - }, - { - "speaker": "Joanna", - "dia_id": "D5:9", - "text": "Sounds great! Having pets must be a wonderful experience." - }, - { - "speaker": "Nate", - "dia_id": "D5:10", - "text": "Pets definitely bring tons of joy. They are always there for us and they're so cute! Relaxing with them is a great way to chill." - }, - { - "speaker": "Joanna", - "dia_id": "D5:11", - "text": "I wish I wasn't allergic! I would get two turtles today if I could! I found out recently I'm allergic to cockroaches as well, so who knows if I'll ever get a pet." - }, - { - "speaker": "Nate", - "dia_id": "D5:12", - "text": "Sorry! Maybe there are other animals you could consider! In the meantime though, I'll be sure to send you pics of my turtles so you can still watch them grow without getting too close." - }, - { - "speaker": "Joanna", - "dia_id": "D5:13", - "text": "Great idea! I'm already really invested in those little guys!" - }, - { - "speaker": "Nate", - "dia_id": "D5:14", - "text": "Pets really seem to do that to everyone don't they! So, what about your script now? Any ideas for the next steps?" - }, - { - "speaker": "Joanna", - "dia_id": "D5:15", - "text": "I've been doing my fair share of research and networking non-stop for it. It's tough, but I'm determined to make it happen." - }, - { - "speaker": "Nate", - "dia_id": "D5:16", - "text": "Great idea! that should hopefully get some more eyes on it. Keep up the hard work!" - }, - { - "speaker": "Joanna", - "dia_id": "D5:17", - "text": "Thanks so much, Nate! Your support means a lot. I'll keep working at it and hopefully the next steps will become clearer soon." - }, - { - "speaker": "Nate", - "dia_id": "D5:18", - "text": "Just make sure you don't quit - the path forward will show up soon. You got this!" - }, - { - "speaker": "Joanna", - "dia_id": "D5:19", - "text": "Appreciated! I think just having someone to support me throughout the whole process is such a blessing. It gives me the motivation to keep pushing forward." - }, - { - "speaker": "Nate", - "dia_id": "D5:20", - "text": "Glad to hear my support makes a difference, Joanna. I'm here for you!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a cinema ticket on a chair", - "dia_id": "D5:21", - "text": "Always good to hear! See you later!" - } - ], - "session_6_date_time": "1:43 pm on 24 March, 2022", - "session_6": [ - { - "speaker": "Nate", - "dia_id": "D6:1", - "text": "Hey Joanna! Long time no talk, how's it going? Crazy stuff's been happening since we last chatted." - }, - { - "speaker": "Joanna", - "dia_id": "D6:2", - "text": "Hey Nate! Been quite a ride - in a good way - had an audition yesterday for a writing gig." - }, - { - "speaker": "Nate", - "dia_id": "D6:3", - "text": "Congrats! How did it go? Are you excited?" - }, - { - "speaker": "Joanna", - "dia_id": "D6:4", - "text": "Thanks, Nate! It went alright. Mixed emotions - definitely excited but also a bit anxious. Keep those fingers crossed!" - }, - { - "speaker": "Nate", - "dia_id": "D6:5", - "text": "Yeah, I get it. Mixed emotions are rough, but I have faith in you! Keep me posted - you got this!" - }, - { - "speaker": "Joanna", - "dia_id": "D6:6", - "text": "Thanks, Nate! Your support means a lot. I'll make sure to keep you updated. Anything new on your end?" - }, - { - "speaker": "Nate", - "dia_id": "D6:7", - "text": "Yeah actually - I'm currently participating in the video game tournament again and it's INTENSE! There's so much adrenaline flowing." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjW88nSAhvDHq-ZyBSZlQ__czsDBW4hEFEvzdgkz06j4ByTnkAamu38jTEXQmzjkEgmS2VYyUYt4e0kz5cfhH7pChnTfAR2OKl9HZj7T91RxPZTV5qxTpi3jjpbFY1wD9Myi7633MAHlwiCL1LtCXt8iiQLed4Kzu2FY7thoxVxVBW8faEpQ-qyEFLwBTA/s4032/Photo%2023.07.23,%2014%2034%2052.jpg" - ], - "blip_caption": "a photo of a book shelf filled with books and magazines", - "query": "bookshelf writing books inspiration", - "dia_id": "D6:8", - "text": "Best of luck in the tournament! It sounds like it would be difficult to go through so many days of intense gaming! This is my go-to place for writing inspiration. It helps me stay sharp and motivated." - }, - { - "speaker": "Nate", - "dia_id": "D6:9", - "text": "Wow, that's a lot of books. Do you have any advice for someone like me who wants to pursue writing?" - }, - { - "speaker": "Joanna", - "dia_id": "D6:10", - "text": "Definitely! Read lots and try out different genres. Build a solid understanding of literature. Don't be afraid to write and share, even if it's just with friends. Practicing and gathering feedback will make you better. Have faith in yourself and continue following your writing dreams - it's tough but worth it." - }, - { - "speaker": "Nate", - "dia_id": "D6:11", - "text": "Thanks, Joanna. Really appreciate your help and kind words. I'm going to keep working hard on it and see what happens. Good luck with your project, I'm sure it will turn out great!" - }, - { - "speaker": "Joanna", - "dia_id": "D6:12", - "text": "Thanks Nate! Appreciate your kind words and support. Let's keep going for our dreams and work hard. Catch you later!" - }, - { - "speaker": "Nate", - "dia_id": "D6:13", - "text": "Bye Joanna! Take care!" - } - ], - "session_7_date_time": "7:37 pm on 15 April, 2022", - "session_7": [ - { - "speaker": "Nate", - "dia_id": "D7:1", - "text": "Hey Jo, guess what I did? Dyed my hair last week - come see!" - }, - { - "speaker": "Joanna", - "dia_id": "D7:2", - "text": "Wow, Nate! Can't wait to see it. Must feel so liberating! How're you feeling?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/2378/2443069910_bf08328b2b_b.jpg" - ], - "blip_caption": "a photography of a man with purple hair and glasses taking a selfie", - "query": "purple hair selfie", - "dia_id": "D7:3", - "re-download": true, - "text": "I'm so stoked about it! Check it out!" - }, - { - "speaker": "Joanna", - "dia_id": "D7:4", - "text": "Wow, your new hair color looks amazing! What made you choose that shade? Tell me all about it!" - }, - { - "speaker": "Nate", - "dia_id": "D7:5", - "text": "Thanks Jo! I picked this color because it's bright and bold - like me! I wanted to stand out from the regular options." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/ruvtfm6ixchb1.jpg" - ], - "blip_caption": "a photo of a street with a stop sign and a cloudy sky", - "query": "sunset hike pink blue bold", - "dia_id": "D7:6", - "text": "That's amazing, Nate! Your boldness really inspired me. It reminded me of this gorgeous sunset I saw while hiking the other day. It made me realize the importance of showing the world who we are." - }, - { - "speaker": "Nate", - "dia_id": "D7:7", - "text": "Wow, that sunset looks awesome! Jealous! I bet you had a great time. Are there any more exciting trips coming up for you?" - }, - { - "speaker": "Joanna", - "dia_id": "D7:8", - "text": "I did! the sky was so gorgeous! Wish I had a vacation lined up, but right now my writing is consuming me. Hoping for some good news soon!" - }, - { - "speaker": "Nate", - "dia_id": "D7:9", - "text": "I understand, Joanna. Big projects can be so taxing. Keep me posted on how it goes, alright?" - }, - { - "speaker": "Joanna", - "dia_id": "D7:10", - "text": "Cheers, Nate! Your support means a lot. I'll definitely keep you updated." - }, - { - "speaker": "Nate", - "dia_id": "D7:11", - "text": "Sounds great, See you soon?" - }, - { - "speaker": "Joanna", - "dia_id": "D7:12", - "text": "Totally! Bye Nate!" - }, - { - "speaker": "Nate", - "dia_id": "D7:13", - "text": "Take care!" - } - ], - "session_8_date_time": "6:44 pm on 17 April, 2022", - "session_8": [ - { - "speaker": "Nate", - "dia_id": "D8:1", - "text": "Hey Joanna! Haven't talked with you in a while - how's it going?" - }, - { - "speaker": "Joanna", - "dia_id": "D8:2", - "text": "Hey Nate! Great to hear from you. I've been reading a lot in the past week! There's a lot of good books I forgot I owned." - }, - { - "speaker": "Nate", - "dia_id": "D8:3", - "text": "Sounds fun! I probably also have loads of books I haven't read in years. Sounds like a blast from the past!" - }, - { - "speaker": "Joanna", - "dia_id": "D8:4", - "text": "It really is! On a different note, I found an awesome hiking trail in my hometown yesterday! It was gorgeous. Nature is so inspiring, and it's a great way to reset. Do you know of any good hiking spots?" - }, - { - "speaker": "Nate", - "dia_id": "D8:5", - "text": "I'm not really into hiking but I'm curious to see what the trail looks like! I heard there's a nice trail just north of where I live." - }, - { - "speaker": "Joanna", - "dia_id": "D8:6", - "text": "Maybe I'll have to convince you to go with me one of these times!" - }, - { - "speaker": "Nate", - "dia_id": "D8:7", - "text": "Maybe! I do like nature, so that might be fun going with someone else." - }, - { - "speaker": "Joanna", - "dia_id": "D8:8", - "text": "Yeah, nature's awesome! I'm a huge fan of it, that's why I go!" - }, - { - "speaker": "Nate", - "dia_id": "D8:9", - "text": "Agreed, nature has a way of being so inspiring! I'm glad you found a way to reset and find peace in it." - }, - { - "speaker": "Joanna", - "dia_id": "D8:10", - "text": "Nature's always been my haven. Walking in it, feeling it, hearing the sounds - it's so calming. Worries and stress seem to vanish, and it's just me and the beauty around me." - }, - { - "speaker": "Nate", - "dia_id": "D8:11", - "text": "It's so crucial to find a little peace and remember life's beauty. For me, it's spending time with my pets and engaging in my hobbies; they let me take a break from reality. It's wild how small things can have such a powerful effect on our happiness, right?" - }, - { - "speaker": "Joanna", - "dia_id": "D8:12", - "text": "Yeah, Nate! Even the small things make life enjoyable and worth it. Taking time for your little friends and doing activities you love are like treasures that remind us how great and peaceful life is. We just gotta savor them!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/3yrgb86abnl81.jpg" - ], - "blip_caption": "a photo of a turtle and a strawberry in a bowl", - "query": "pets turtles playing", - "dia_id": "D8:13", - "text": "Speaking of which, here they go again!" - }, - { - "speaker": "Joanna", - "dia_id": "D8:14", - "text": "So cute! I love your turtles so much!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://beadyarnspatula.files.wordpress.com/2023/05/img_6671.jpg" - ], - "blip_caption": "a photo of a bowl of ice cream and a bowl of sprinkles", - "query": "coconut milk ice cream red sprinkles bowl", - "dia_id": "D8:15", - "text": "Me too! I love watching them play to simply enjoy the peaceful moments of life. Sometimes I even bring them in the kitchen so they can watch me make food like this!" - }, - { - "speaker": "Joanna", - "dia_id": "D8:16", - "text": "I love your icecream so much! I wish I could make it the way you do!" - }, - { - "speaker": "Nate", - "dia_id": "D8:17", - "text": "Thanks! It's dairy-free and so easy. Wanna get the recipe?" - }, - { - "speaker": "Joanna", - "dia_id": "D8:18", - "text": "Sure! I'm lactose intolerant, so I'll just need the dairy-free recipe! " - }, - { - "speaker": "Nate", - "dia_id": "D8:19", - "text": "No prob. I made it with coconut milk, vanilla extract, sugar, and a pinch of salt. After chilling it in the fridge, I put it in the ice cream maker and froze it until it was scoopable." - }, - { - "speaker": "Joanna", - "dia_id": "D8:20", - "text": "Wow, sounds delicious! I'm going to try making it tonight! Thank you for sharing the recipe!" - }, - { - "speaker": "Nate", - "dia_id": "D8:21", - "text": "Hey Joanna, glad I could help. Let me know how it turns out!" - }, - { - "speaker": "Joanna", - "dia_id": "D8:22", - "text": "Got it, Nate. I'll definitely let you know how it turns out. Thanks for sharing the recipe!" - } - ], - "session_9_date_time": "7:44 pm on 21 April, 2022", - "session_9": [ - { - "speaker": "Joanna", - "img_url": [ - "https://threeteacherstalk.files.wordpress.com/2021/06/fnqw-6.18.jpg" - ], - "blip_caption": "a photo of a notebook with a notepad and a piece of paper", - "query": "writers group notebook notes ideas storytelling", - "dia_id": "D9:1", - "text": "Hey Nate! Long time no talk! I wanted to tell ya I just joined a writers group. It's unbelievable--such inspirational people who really get my writing. I'm feeling so motivated and supported, it's like I finally belong somewhere!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a cup of ice cream with a cherry on top", - "dia_id": "D9:2", - "text": "Hey Joanna! That's awesome! Having a supportive group around you can really make a difference. What kind of projects are you working on with them?" - }, - { - "speaker": "Joanna", - "dia_id": "D9:3", - "text": "Thanks, Nate! We've made some great progress. I'm working on one with my group called \"Finding Home.\" It's a script about a girl on a journey to find her true home. I find it really rewarding and emotional. What about you? Any upcoming gaming tournaments?" - }, - { - "speaker": "Nate", - "dia_id": "D9:4", - "text": "Hi Joanna! \"Finding Home\" sounds really special. Must be so meaningful to work on. I've got a gaming tournament next month and I'm feeling good about it. It's gonna be my 4th one!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://get.pxhere.com/photo/black-and-white-old-audience-fashion-performance-art-theatre-children-stage-performance-entertainment-scenario-performing-arts-monochrome-photography-musical-theatre-541499.jpg" - ], - "blip_caption": "a photography of a man in a striped suit is performing on stage", - "query": "theater stage", - "dia_id": "D9:5", - "re-download": true, - "text": "Yeah, I bet the nerves and excitement are quite a rush! I remember when I did my first play, I was so nervous I forgot my lines. It was embarrassing, but it taught me how important it is to prepare and stay in the moment." - }, - { - "speaker": "Nate", - "dia_id": "D9:6", - "text": "Sounds like you had an interesting time on stage! It's always a learning experience. Have you ever considered going back to acting? Is that you in the photo?" - }, - { - "speaker": "Joanna", - "dia_id": "D9:7", - "text": "Yeah, that's me in that photo! Acting was my first passion, but now I really shine in writing. It helps me express myself in a new way, but who knows, maybe I'll go back to acting someday. Never say never!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle laying on a bed of rocks and gravel", - "dia_id": "D9:8", - "text": "Go for it! Follow your passion for writing, but if acting really makes you happy, give it a shot as well. Who knows what'll happen! Any particular movies that spark your writing?" - }, - { - "speaker": "Joanna", - "dia_id": "D9:9", - "text": "Thanks Nate! I'm gonna keep writing, but if acting calls out I might give it a try. I really enjoy dramas and emotionally-driven films. What about you? What inspires your passion?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://www.trustedreviews.com/wp-content/uploads/sites/54/2020/10/20201026_152436-scaled-e1604409626602.jpg" - ], - "blip_caption": "a photography of a black xbox controller sitting on top of a wooden table", - "query": "video game controller", - "dia_id": "D9:10", - "re-download": true, - "text": "I love fantasy and sci-fi movies, they're a great escape and get my imagination going. Playing video games is a great way to express my creativity and passion." - }, - { - "speaker": "Joanna", - "dia_id": "D9:11", - "text": "That's awesome! I love how video games can really spark your imagination. Do you have a favorite fantasy or sci-fi movie?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/7vtqewbtg1181.jpg" - ], - "blip_caption": "a photo of a shelf with a lot of books on it", - "query": "lord of the rings trilogy dvd boxset", - "dia_id": "D9:12", - "text": "Yeah, for sure! This trilogy is one of my faves. The world building, battles, and storytelling always blow me away!" - }, - { - "speaker": "Joanna", - "dia_id": "D9:13", - "text": "Wow, that's great to hear! What books do you enjoy? I'm always up for some new book recommendations." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/o2ifb25e7qa31.jpg" - ], - "blip_caption": "a photo of a bunch of books on a table", - "query": "fantasy novels dragon cover series", - "dia_id": "D9:14", - "text": "I love this series. It has adventures, magic, and great characters - it's a must-read!" - }, - { - "speaker": "Joanna", - "dia_id": "D9:15", - "text": "Heard of that series! It's been on my list forever. Thanks for the recommendation, Nate. I'm definitely going to check it out!" - }, - { - "speaker": "Nate", - "dia_id": "D9:16", - "text": "No problem, glad to see an interest. Let me know what you think when you check it out." - }, - { - "speaker": "Joanna", - "dia_id": "D9:17", - "text": "Thanks Nate! I'll definitely let you know my thoughts. Take care and have a great day!" - }, - { - "speaker": "Nate", - "dia_id": "D9:18", - "text": "See you! Good chatting with you! Have a great day!" - } - ], - "session_10_date_time": "11:54 am on 2 May, 2022", - "session_10": [ - { - "speaker": "Joanna", - "blip_caption": "a photo of a person holding a book openhemer", - "dia_id": "D10:1", - "text": "Hey Nate, how's it going? I took your reccomendation and watched \"The Lord of the Rings\" Trilogy last night! It was awesome!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.pinimg.com/originals/23/13/24/231324d44f9471ead5535950153378f1.jpg" - ], - "blip_caption": "a photo of a gaming room with a computer and a gaming chair", - "query": "gaming setup", - "dia_id": "D10:2", - "text": "Glad to hear you enjoyed it! It's probably the greatest trilogy of all time! As for me, life's been ok, just taking care of this." - }, - { - "speaker": "Joanna", - "dia_id": "D10:3", - "text": "Wow, Nate! I'm proud of what you did. Your gaming room looks great - have you been gaming a lot recently?" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a woman with purple hair and a black dress", - "dia_id": "D10:4", - "text": "Gaming has been my focus - practicing a lot and even winning a few tournaments. Last week I won my second tournament!" - }, - { - "speaker": "Joanna", - "dia_id": "D10:5", - "text": "Wow, congrats! What game were you playing?" - }, - { - "speaker": "Nate", - "dia_id": "D10:6", - "text": "Thanks! I usually play CS:GO, but I tried my hand at the local Street Fighter tournament this time since I play that game a lot with my friends, and turns out I'm really good!" - }, - { - "speaker": "Joanna", - "dia_id": "D10:7", - "text": "Nice! That must have been a surprise. How did it feel to finally win one?" - }, - { - "speaker": "Nate", - "dia_id": "D10:8", - "text": "It was super awesome! So much adrenaline went into that last match, and the other finalist even shook my hand! Enough about me though, how about you? What have you been up to?" - }, - { - "speaker": "Joanna", - "img_url": [ - "http://ventitobakery.com/cdn/shop/products/white-gluten-dairy-free-cake.jpg" - ], - "blip_caption": "a photo of a cake with white frosting on a wooden table", - "query": "dairy-free cake decorated", - "dia_id": "D10:9", - "text": "Not much is new other than the screenplay. Been working on some projects and testing out dairy-free dessert recipes for friends and fam. Here's a pic of a cake I made recently!" - }, - { - "speaker": "Nate", - "dia_id": "D10:10", - "text": "That looks really good! I love the way the frosting turned out!" - }, - { - "speaker": "Joanna", - "dia_id": "D10:11", - "text": "Thanks! It's dairy-free vanilla with strawberry filling and coconut cream frosting. I gotta say, I really like your coconut reccomendation you gave a while back!" - }, - { - "speaker": "Nate", - "dia_id": "D10:12", - "text": "Wow, Joanna, that looks amazing! I bet it tastes great - you're so talented at making dairy-free desserts!" - }, - { - "speaker": "Joanna", - "dia_id": "D10:13", - "text": "Thanks Nate! I really appreciate it. I love experimenting in the kitchen, coming up with something tasty. Cooking and baking are my creative outlets. Especially when I'm snackin' dairy-free, trying to make the desserts just as delicious - it's a rewarding challenge! Seeing the smiles on everyone's faces when they try it - it's a total win!" - }, - { - "speaker": "Nate", - "dia_id": "D10:14", - "text": "That's great, Joanna! It must be so rewarding to see everyone enjoying your creations. Keep up the good work!" - }, - { - "speaker": "Joanna", - "dia_id": "D10:15", - "text": "Thanks, Nate! Appreciate all the help. Gonna keep trying new things. See ya later!" - }, - { - "speaker": "Nate", - "dia_id": "D10:16", - "text": "Bye!" - } - ], - "session_11_date_time": "3:35 pm on 12 May, 2022", - "session_11": [ - { - "speaker": "Joanna", - "dia_id": "D11:1", - "text": "Hey Nate! Great to hear from you! Quite a week since we last talked - something awesome happened to me!" - }, - { - "speaker": "Nate", - "dia_id": "D11:2", - "text": "Hey Jo! Great hearing from you! What happened?" - }, - { - "speaker": "Joanna", - "dia_id": "D11:3", - "text": "I went hiking and found some more amazing trails in my town. It was such an awesome experience! I think I am an expert hiker now." - }, - { - "speaker": "Nate", - "dia_id": "D11:4", - "text": "Sounds great, Jo! Happy you had an awesome experience. Did you happen to take any photos of it?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://burst.shopifycdn.com/photos/large-rolling-waterfall-though-a-lush-green-hill.jpg" - ], - "blip_caption": "a photo of a waterfall with a dark sky in the background", - "query": "waterfall lush greenery", - "dia_id": "D11:5", - "text": "Yeah, I did! Loved this spot on the hike. The rush of the water was so soothing." - }, - { - "speaker": "Nate", - "dia_id": "D11:6", - "text": "Wow, looks great! Where did you take this picture? I love the dark sky and green scenery." - }, - { - "speaker": "Joanna", - "dia_id": "D11:7", - "text": "Thanks! I took this photo at a beautiful location called Whispering Falls. It was really peaceful and serene." - }, - { - "speaker": "Nate", - "dia_id": "D11:8", - "text": "I wish I could have been there! Your hikes sound like a blast." - }, - { - "speaker": "Joanna", - "dia_id": "D11:9", - "text": "It was awesome, Nate. The sound of that place and the beauty of nature made me so calm and peaceful. Everything else faded away and all that mattered was the present." - }, - { - "speaker": "Nate", - "dia_id": "D11:10", - "text": "That's great. Glad you found a spot that calms you down - nature sure can be a break from the craziness." - }, - { - "speaker": "Joanna", - "dia_id": "D11:11", - "text": "Nature totally inspires me and it's so calming to be surrounded by its beauty. Hiking has opened up a whole new world for me and I feel like a different person now." - }, - { - "speaker": "Nate", - "dia_id": "D11:12", - "text": "Wow, Jo, that's really cool! It's great to have something that gets those creative juices flowing." - }, - { - "speaker": "Joanna", - "dia_id": "D11:13", - "text": "I always feel like I could write a whole movie when I'm out there in cool places like that!" - }, - { - "speaker": "Nate", - "dia_id": "D11:14", - "text": "Wow! That's really cool that it inspires you that much! For me I just get deep in thought and think about my life or new recipes." - }, - { - "speaker": "Joanna", - "dia_id": "D11:15", - "text": "I think about my life too sometimes when I'm out and about, but there was something special about these trails that made me feel like writing a drama." - }, - { - "speaker": "Nate", - "dia_id": "D11:16", - "text": "Hey, we should go together sometime, don't you think? Maybe I'll start to think of a drama myself and publish my own screenplay." - }, - { - "speaker": "Joanna", - "dia_id": "D11:17", - "text": "Haha, now that would be something! Sure, you should come down and join me on the trails sometime!" - }, - { - "speaker": "Nate", - "dia_id": "D11:18", - "text": "Sounds like a plan! Thanks for the invite Joanna!" - }, - { - "speaker": "Joanna", - "dia_id": "D11:19", - "text": "Sure thing Nate! See you later!" - }, - { - "speaker": "Nate", - "dia_id": "D11:20", - "text": "See ya!" - } - ], - "session_12_date_time": "7:49 pm on 20 May, 2022", - "session_12": [ - { - "speaker": "Nate", - "dia_id": "D12:1", - "text": "Hey Joanna! How've you been? Been a busy week since we talked." - }, - { - "speaker": "Joanna", - "dia_id": "D12:2", - "text": "Hey Nate! Just finished something - pretty wild journey!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/rgx6yqabu5e91.jpg" - ], - "blip_caption": "a photo of a dog laying on a couch in a living room", - "query": "cute dog max", - "dia_id": "D12:3", - "text": "Way to go! I just got a new addition to the family, this is Max!" - }, - { - "speaker": "Joanna", - "dia_id": "D12:4", - "text": "Wow, he's adorable! How long have you had him? I can see why you're thrilled!" - }, - { - "speaker": "Nate", - "dia_id": "D12:5", - "text": "Thanks! It's awesome - he's adopted and so full of energy, and he's filling my life with so much joy. He's even keeping my other pets active." - }, - { - "speaker": "Joanna", - "dia_id": "D12:6", - "text": "Pets sure do make life better! Glad Max is bringing you lots of joy." - }, - { - "speaker": "Nate", - "dia_id": "D12:7", - "text": "Yep, totally! Pets make us so much happier and never let us down. Have you thought any more of getting one of your own?" - }, - { - "speaker": "Joanna", - "dia_id": "D12:8", - "text": "Unfortunately, allergies make it so I don't really want to get any, and I'm too lazy to research alternative pets for my allergies." - }, - { - "speaker": "Nate", - "dia_id": "D12:9", - "text": "Aww, that's unfortunate. It's nice seeing the joy pets bring to others, though. How do you find comfort when you don't have any?" - }, - { - "speaker": "Joanna", - "dia_id": "D12:10", - "text": "Writing and creative projects are what get me through tough times. I'm also grateful for my supportive friends." - }, - { - "speaker": "Nate", - "dia_id": "D12:11", - "text": "Wow, that's awesome! Those both can definitely be therapeutic. It's great to have such positive relationships that make such a great impact." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/0hvywunpjfs91.jpg" - ], - "blip_caption": "a photo of a notepad with a dog on it and a pen", - "query": "handwritten screenplay notebook.", - "dia_id": "D12:12", - "text": "Yeah. It's so nice to have friends who understand and appreciate my work - it's priceless being able to talk about it together and receive feedback. Here's a look at what I've been working on \u2013 it's been quite a journey, but I made it!" - }, - { - "speaker": "Nate", - "dia_id": "D12:13", - "text": "Wow, that looks great Joanna! Is that your third one?" - }, - { - "speaker": "Joanna", - "dia_id": "D12:14", - "text": "Yep! I chose to write about this because it's really personal. It's about loss, identity, and connection. It's a story I've had for ages but just got the guts to write it. It was hard, but I'm so proud of it." - }, - { - "speaker": "Nate", - "dia_id": "D12:15", - "text": "That sounds impressive. You really do like writing about sadness and loss don't you." - }, - { - "speaker": "Joanna", - "dia_id": "D12:16", - "text": "Thanks, Nate! Yeah I really do. I had to be vulnerable and dig deep into those topics. But I think meaningful stories come from personal experiences and feelings. It was scary, but I found that I write best when I'm being true to myself - even if it's hard." - }, - { - "speaker": "Nate", - "dia_id": "D12:17", - "text": "Well done, Joanna! It takes guts to explore your experiences and feelings. I'm proud of you for staying strong and being true to yourself. Keep it up!" - }, - { - "speaker": "Joanna", - "dia_id": "D12:18", - "text": "Thanks, Nate! Your support really means a lot. Knowing I've got people like you cheering me on makes this journey way easier." - }, - { - "speaker": "Nate", - "dia_id": "D12:19", - "text": "No worries, Joanna! Keep going and reach for your dreams. You've got tons of talent and potential!" - } - ], - "session_13_date_time": "3:00 pm on 25 May, 2022", - "session_13": [ - { - "speaker": "Nate", - "dia_id": "D13:1", - "text": "Hey Jo! Been ages since we last talked. Here's something cool that happened the other day - I took Max for a walk and ran into this super nice couple who had a dog. It turns out they live close by. We decided to do doggy playdates, which is awesome considering we all need friends for our pets." - }, - { - "speaker": "Joanna", - "dia_id": "D13:2", - "text": "Hey Nate! Great to hear from you. Sounds like a nice encounter on your walk. Connecting with others who have pets can be uplifting and rewarding." - }, - { - "speaker": "Nate", - "dia_id": "D13:3", - "text": "It's like fate. Having a walking buddy forMax will be great. He really likes the other dog too!" - }, - { - "speaker": "Joanna", - "dia_id": "D13:4", - "text": "Awesome! Did you get to know the couple very well? What were they like?" - }, - { - "speaker": "Nate", - "dia_id": "D13:5", - "text": "They actually didn't share a whole lot in common with me besides the love of animals, but I think that was all we needed to share in common to be good friends!" - }, - { - "speaker": "Joanna", - "dia_id": "D13:6", - "text": "That's really cool that you can just go out and meet people like that, keep it up Nate!" - }, - { - "speaker": "Nate", - "dia_id": "D13:7", - "text": "Thanks! I just really enjoy watching our pets play with one another. Its like a dream come true seeing my dog so happy." - }, - { - "speaker": "Joanna", - "dia_id": "D13:8", - "text": "I can see why having a peaceful presence around could help relieve stress. Having someone or something to come home to for a sense of calm would be helpful for relaxation." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/usll6z99c2tb1.jpg" - ], - "blip_caption": "a photo of a stuffed animal laying on a bed", - "query": "dog cozy blanket toy", - "dia_id": "D13:9", - "text": "Yep, Joanna. It's great! Looky here, I got this new pup for you!" - }, - { - "speaker": "Joanna", - "dia_id": "D13:10", - "text": "Awww! It's so cute! I love the thought Nate!" - }, - { - "speaker": "Nate", - "dia_id": "D13:11", - "text": "Thanks! It's a stuffed animal to remind you of the good vibes." - }, - { - "speaker": "Joanna", - "dia_id": "D13:12", - "text": "That's so sweet! I'll cherish that little guy with all my heart!" - }, - { - "speaker": "Nate", - "dia_id": "D13:13", - "text": "Yeah! It's like having joy in your pocket. It always makes me grin when I look at it." - }, - { - "speaker": "Joanna", - "dia_id": "D13:14", - "text": "That's great, Nate! Appreciate the small joys like that cute stuffed animal. It's a nice reminder!" - }, - { - "speaker": "Nate", - "dia_id": "D13:15", - "text": "Agreed, those little things sure do make life better!" - }, - { - "speaker": "Joanna", - "dia_id": "D13:16", - "text": "I'll always remember those moments that bring us happiness and remind us that life is great!" - }, - { - "speaker": "Nate", - "dia_id": "D13:17", - "text": "Sure, Joanna! It's all about finding those little things and cherishing them, otherwise it's easy to get down!" - }, - { - "speaker": "Joanna", - "dia_id": "D13:18", - "text": "Thinking back to the tough times finishing my screenplay made me realize it's those moments that bring joy and make the journey worth it." - }, - { - "speaker": "Nate", - "dia_id": "D13:19", - "text": "Yeah, those little moments make it all worth it, especially during tough times. Enjoying the ride is key." - }, - { - "speaker": "Joanna", - "dia_id": "D13:20", - "text": "Appreciating the journey and being aware of those happy moments can be a game-changer! It keeps us focused on our dreams. Can't wait to show it to you. I value your opinion!" - }, - { - "speaker": "Nate", - "dia_id": "D13:21", - "text": "Can't wait to see it, Joanna! I'm here to support you." - }, - { - "speaker": "Joanna", - "dia_id": "D13:22", - "text": "Thanks, Nate! Your support is greatly appreciated. I'll make sure to keep you updated." - }, - { - "speaker": "Nate", - "dia_id": "D13:23", - "text": "No worries! You've got this. Keep it up!" - } - ], - "session_14_date_time": "5:44 pm on 3 June, 2022", - "session_14": [ - { - "speaker": "Joanna", - "dia_id": "D14:1", - "text": "Nate, after finishing my screenplay I got a rejection letter from a major company. It really bummed me out." - }, - { - "speaker": "Nate", - "dia_id": "D14:2", - "text": "Sorry to hear that, Joanna. Rejection stinks, but it doesn't mean you're not talented. Don't give up on your dreams!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:3", - "text": "Thanks, Nate. It can feel like a step back sometimes. But I appreciate your kind words and encouragement." - }, - { - "speaker": "Nate", - "dia_id": "D14:4", - "text": "Sure, just make sure you keep going and believing in yourself. Did something happen with the company?" - }, - { - "speaker": "Joanna", - "dia_id": "D14:5", - "text": "They just sent me a generic rejection letter without much feedback. It's disheartening not knowing why it didn't work out." - }, - { - "speaker": "Nate", - "dia_id": "D14:6", - "text": "Ugh, that's so frustrating. But don't get discouraged, just keep going." - }, - { - "speaker": "Joanna", - "dia_id": "D14:7", - "text": "Yeah, you're right. I won't let this bring me down. Thanks for your support. What have you been up to lately?" - }, - { - "speaker": "Nate", - "dia_id": "D14:8", - "text": "I've been doing great - I just won another regional video game tournament last week! It was so cool, plus I met some new people. Connecting with fellow gamers is always awesome." - }, - { - "speaker": "Joanna", - "dia_id": "D14:9", - "text": "Way to go, Nate! Congratulations on your victory in the tournament! It must feel great to be recognized for your gaming skills." - }, - { - "speaker": "Nate", - "dia_id": "D14:10", - "text": "Thanks, Joanna! Winning was a huge confidence boost and shows my hard work paid off. I'm really happy with my progress." - }, - { - "speaker": "Joanna", - "dia_id": "D14:11", - "text": "I am as well! It's great to hear from you about your tournaments throughout the years!" - }, - { - "speaker": "Nate", - "dia_id": "D14:12", - "text": "Thanks! I has been a while since my first tournament hasn't it? I appreciate your support!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:13", - "text": "Anytime Nate! I'm here for you every step of the way." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a purple and blue controller with a star field design", - "dia_id": "D14:14", - "text": "I talked to some of the guys at the tournament afterwards, and they said they wanted to hang out later!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:15", - "text": "Sounds like fun! It's good to have friends that share your interests!" - }, - { - "speaker": "Nate", - "dia_id": "D14:16", - "text": "For sure! They asked for some tips in how to improve their game, so I said I could help." - }, - { - "speaker": "Joanna", - "dia_id": "D14:17", - "text": "Good on you for helping strangers out! Stepping outside your comfort zone is always great." - }, - { - "speaker": "Nate", - "dia_id": "D14:18", - "text": "Thanks, I just like helping people. Do you have any plans for the weekend?" - }, - { - "speaker": "Joanna", - "dia_id": "D14:19", - "text": "Yep, I'm hiking with some buddies this weekend. We're checking out a new trail with a rad waterfall. Can't wait! Do you have any fun plans?" - }, - { - "speaker": "Nate", - "dia_id": "D14:20", - "text": "Sounds great! Have fun with that. I'm organizing a gaming party two weekends later - it'll be hectic but fun!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:21", - "text": "Oh? Are you going to invite your tournament friends?" - }, - { - "speaker": "Nate", - "dia_id": "D14:22", - "text": "Definitely! And some old friends and teamates from other tournaments." - }, - { - "speaker": "Joanna", - "dia_id": "D14:23", - "text": "Sounds like fun, Nate! I wish you the best on your party. Have a blast!" - }, - { - "speaker": "Nate", - "dia_id": "D14:24", - "text": "Thanks Joanna! I'm sure it'll be a blast. I'm even getting everyone custom controller decorations just for coming!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:25", - "text": "Wow, I bet they'll love that! What a sweet idea." - }, - { - "speaker": "Nate", - "dia_id": "D14:26", - "text": "I know right? Have a great hike. Take lots of pics! See ya later!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:27", - "text": "Thanks Nate! See you later!" - } - ], - "session_15_date_time": "2:12 pm on 5 June, 2022", - "session_15": [ - { - "speaker": "Joanna", - "blip_caption": "a photo of a spider - man poster hanging on a wall", - "dia_id": "D15:1", - "text": "Hey Nate! Yesterday was crazy cool - I wrote a few bits for a screenplay that appeared on the big screen yesterday! It was nerve-wracking but so inspiring to see my words come alive!" - }, - { - "speaker": "Nate", - "dia_id": "D15:2", - "text": "Congrats, Joanna! Seeing your hard work pay off like that must've felt amazing. I bet it was scary too, but awesome! You're so inspiring. By the way, last time we saw eachother, I noticed a spiderman pin on your purse. Is Spider-Man your favorite superhero, or do you have another fave?" - }, - { - "speaker": "Joanna", - "dia_id": "D15:3", - "text": "Thanks, Nate! It was a real roller coaster, but seeing the hard work pay off was amazing. Spider-Man has always been a favorite of mine - I mean, who doesn't love Peter Parker's struggles between being a hero and being a person? But I'm kind of a sucker for any superhero - everyone has their own rad story and powers. Do you have a favorite superhero?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/7148/6768759075_81b67d203f_b.jpg" - ], - "blip_caption": "a photography of a toy iron man standing on a white surface", - "query": "iron man action figures", - "dia_id": "D15:4", - "re-download": true, - "text": "That's great, Joanna! Iron Man is my top pick. I love his tech and that sarcastic humor. Seeing these figures just makes me feel invincible!" - }, - { - "speaker": "Joanna", - "dia_id": "D15:5", - "text": "Wow, Nate! That's awesome. I love the tech and funny jokes of Iron Man too. What made you get that figure?" - }, - { - "speaker": "Nate", - "dia_id": "D15:6", - "text": "Thanks Joanna! I got it because it reminded me of something I love. Its presence in my room is a good reminder to keep working on my goals. Any inspiring things in your room?" - }, - { - "speaker": "Joanna", - "dia_id": "D15:7", - "text": "My cork board is full of inspiring quotes and pictures for motivation and creativity. It's my little corner of inspiration." - }, - { - "speaker": "Nate", - "dia_id": "D15:8", - "text": "Wow Joanna, that sounds great! Could you show me a picture of it?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.pinimg.com/originals/cc/b0/14/ccb0147bd55dfa436f284fb5939a7808.jpg" - ], - "blip_caption": "a photo of a picture frame with a picture of a family", - "query": "cork board quotes photos loved ones mementos", - "dia_id": "D15:9", - "text": "Here ya go, a pic of my cork board. It's got quotes, photos, and little keepsakes." - }, - { - "speaker": "Nate", - "dia_id": "D15:10", - "text": "That's a great pic of your family! What made you hang it on your cork board?" - }, - { - "speaker": "Joanna", - "dia_id": "D15:11", - "text": "Thanks, Nate! Having that picture on my cork board reminds me of the love and encouragement from them every day." - }, - { - "speaker": "Nate", - "dia_id": "D15:12", - "text": "That's great, Joanna. Family support is invaluable. It's so good to have those reminders." - }, - { - "speaker": "Joanna", - "dia_id": "D15:13", - "text": "Absolutely, it means a lot and keeps me going." - }, - { - "speaker": "Nate", - "dia_id": "D15:14", - "text": "I really should start a cork board of my own shouldn't I. That seems like a really valuable thing!" - }, - { - "speaker": "Joanna", - "dia_id": "D15:15", - "text": "I would definitely recommend it! As long as your willing to explain what it is to your friends." - }, - { - "speaker": "Nate", - "dia_id": "D15:16", - "text": "Of course! Well see you later Joanna!" - }, - { - "speaker": "Joanna", - "dia_id": "D15:17", - "text": "Bye Nate!" - } - ], - "session_16_date_time": "10:55 am on 24 June, 2022", - "session_16": [ - { - "speaker": "Joanna", - "dia_id": "D16:1", - "text": "Hey Nate, long time no see! How have you been? I just got done submitting my recent screenplay to a film contest just to see how others might like it!" - }, - { - "speaker": "Nate", - "dia_id": "D16:2", - "text": "That's really cool Joanna! I hope it does well, and I've been doing great! The gaming party was a great success! We even played some Chess afterward just for fun." - }, - { - "speaker": "Joanna", - "dia_id": "D16:3", - "text": "Nice! Did your friends like the controller accessories?" - }, - { - "speaker": "Nate", - "dia_id": "D16:4", - "text": "Most of them did! I can't say if all of them will continue to use them or not, but that's beside the point." - }, - { - "speaker": "Joanna", - "dia_id": "D16:5", - "text": "Yeah Nate, you're right. It doesn't matter if they use it, its the thought that matters right?" - }, - { - "speaker": "Nate", - "dia_id": "D16:6", - "text": "Absolutely! There were 7 people that attended, and 6 of them said they'd want to do it again next month!" - }, - { - "speaker": "Joanna", - "dia_id": "D16:7", - "text": "That sounds like a huge success then! Congrats!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://www.stockvault.net/data/2022/06/05/293150/preview16.jpg" - ], - "blip_caption": "a photography of a group of people sitting on a bench", - "query": "vegan coconut milk ice cream vegan diet group", - "dia_id": "D16:8", - "re-download": true, - "text": "Thanks! On another note, I made vegan ice cream last Friday and shared it with some people in my vegan diet group. It was awesome!" - }, - { - "speaker": "Joanna", - "dia_id": "D16:9", - "text": "Mm, yum! Can you give me the recipe for that? It sounds like it'd be a good recipe!" - }, - { - "speaker": "Nate", - "dia_id": "D16:10", - "text": "Sure thing! I can give it to you tomorrow, how does that sound?" - }, - { - "speaker": "Joanna", - "dia_id": "D16:11", - "text": "Awesome! I'm going to make it for my family this weekend - can't wait!" - }, - { - "speaker": "Nate", - "dia_id": "D16:12", - "text": "Nice one, Joanna! Hope you and your family like it. Let me know how it went!" - }, - { - "speaker": "Joanna", - "dia_id": "D16:13", - "text": "Sure thing! They love it when I make them new things!" - }, - { - "speaker": "Nate", - "dia_id": "D16:14", - "text": "Then I have no doubt they'll love the icecream!" - }, - { - "speaker": "Joanna", - "dia_id": "D16:15", - "text": "Thanks Nate! Your support is greatly appreciated. I'll make sure to keep you updated." - }, - { - "speaker": "Nate", - "dia_id": "D16:16", - "text": "Can't wait to hear about it. Have a great day! Take care." - } - ], - "session_17_date_time": "2:34 pm on 10 July, 2022", - "session_17": [ - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/p59z4xlbytfa1.jpg" - ], - "blip_caption": "a photo of a television screen showing a trophy and a trophy", - "query": "video game tournament trophy game screenshot", - "dia_id": "D17:1", - "text": "Hey Joanna, check this out! I won my fourth video game tournament on Friday! It was awesome competing and showing off my skills - and the victory was indescribable. I'm really proud that I can make money doing what I love. This one was online!" - }, - { - "speaker": "Joanna", - "dia_id": "D17:2", - "text": "Congrats, Nate! That's awesome! So proud of you. Your hard work really paid off - keep it up! BTW, I took a road trip for research for my next movie while you were winning. Much-needed break and a chance to explore new places and get inspired." - }, - { - "speaker": "Nate", - "dia_id": "D17:3", - "text": "Thanks, Joanna! Your support means a lot to me. That road trip sounds great! Where did you go? Did you discover any interesting places?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/9/91/Old_Geodesy_library_books.jpg" - ], - "blip_caption": "a photography of a book shelf filled with lots of books", - "query": "vintage library woodhaven midwest", - "dia_id": "D17:4", - "re-download": true, - "text": "Thanks Nate! Appreciate your kind words. I went to Woodhaven, a small town in the Midwest. Got to see some lovely scenery and historic buildings. Checked out the library there, it had a cool old book collection!" - }, - { - "speaker": "Nate", - "dia_id": "D17:5", - "text": "That place looks interesting! Did you find any cool books there?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/sqyihv2m1j871.jpg" - ], - "blip_caption": "a photo of a book with writing on it and a pen", - "query": "old journal stories sketches handwritten pages", - "dia_id": "D17:6", - "text": "I stumbled upon this super cool book from the 1900s with stories and sketches - so awesome to read about the town and the people living there!" - }, - { - "speaker": "Nate", - "dia_id": "D17:7", - "text": "That sounds really interesting! Anyting specific stick out to you about it?" - }, - { - "speaker": "Joanna", - "dia_id": "D17:8", - "text": "Woodhaven has had an interesting past with lots of cool people. Seeing how much it changed sparked ideas for my next script." - }, - { - "speaker": "Nate", - "dia_id": "D17:9", - "text": "Real-life stories are the best for inspiration. Can't wait to hear about your next one. Keep it up!" - }, - { - "speaker": "Joanna", - "dia_id": "D17:10", - "text": "Thanks, Nate! I'm stoked about this new script. It's different from my previous work, but it has the potential to be something awesome! I'll be sure to keep you posted." - }, - { - "speaker": "Nate", - "dia_id": "D17:11", - "text": "I'm sure it will do just as well as your last one! Keep on trying and believe in yourself!" - }, - { - "speaker": "Joanna", - "dia_id": "D17:12", - "text": "Thanks, Nate! Your encouragement really means a lot to me. You're the best for supporting me in my writing journey." - }, - { - "speaker": "Nate", - "dia_id": "D17:13", - "text": "I'm always here for you! You've got so much talent, just keep going for it!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.imgur.com/bJiLDHb.jpg" - ], - "blip_caption": "a photo of a person holding a notebook with a handwritten page", - "query": "notebook writing talent dreams", - "dia_id": "D17:14", - "text": "I will! I actually started on a book recently since my movie did well!" - }, - { - "speaker": "Nate", - "dia_id": "D17:15", - "text": "Nice! I'm curious, what is it about?" - }, - { - "speaker": "Joanna", - "dia_id": "D17:16", - "text": "That page specifically has some dialogues exploring loss, redemption, and forgiveness. It's a deep and emotional story that I'm really excited about!" - }, - { - "speaker": "Nate", - "dia_id": "D17:17", - "text": "Wow, Joanna! It sounds awesome. I'm so excited to see how it all plays out!" - }, - { - "speaker": "Joanna", - "dia_id": "D17:18", - "text": "Thanks, Nate! I'm so glad you're excited. I've never really tried publishing a book, but this might be the first!" - }, - { - "speaker": "Nate", - "dia_id": "D17:19", - "text": "Good luck on that! I'm sure people will recognise you as the same author of the movie you got published and love the book even more." - }, - { - "speaker": "Joanna", - "dia_id": "D17:20", - "text": "Thanks, Nate! Your belief in me means a lot. I'll keep doing my best. Thanks for the support!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a person holding a small turtle in a container", - "dia_id": "D17:21", - "text": "No problem, Joanna. I'm here for you. Your hard work will pay off, I promise. Believe in yourself and your talent - you're incredible!" - } - ], - "session_18_date_time": "6:12 pm on 14 August, 2022", - "session_18": [ - { - "speaker": "Joanna", - "img_url": [ - "https://i.pinimg.com/originals/2f/df/23/2fdf2357bee9649780477b595cb2b003.jpg" - ], - "blip_caption": "a photo of a notebook with a bunch of stickers on it", - "query": "writing journal ideas notes", - "dia_id": "D18:1", - "text": "Hey Nate, long time no talk! I've been busy with writing projects and really going all out with it. It's been the best thing ever - a mix of highs and lows - and my journal's pretty much my rock. Writing's such a huge part of me now." - }, - { - "speaker": "Nate", - "dia_id": "D18:2", - "text": "Hey Joanna! Great to hear it! It's amazing how much a certain activity can become a part of our lives. Keep it up, you're inspiring! Is writing your way to solace and creativity?" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a handwritten letter from a man who is holding a piece of paper", - "dia_id": "D18:3", - "text": "Yeah, definitely. Writing has become like an escape and a way to express my feelings. It gives me a chance to put all my thoughts and feelings down and make something good out of it. Words just have a magical way of healing." - }, - { - "speaker": "Nate", - "dia_id": "D18:4", - "text": "That's really cool, I like it! It's incredible how words can turn something sad into something special. I'm glad it worked for you. Anything cool happening recently?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/6a9y8ycad0ga1.jpg" - ], - "blip_caption": "a photo of a note written to a person on a piece of paper", - "query": "handwritten letter impact writing", - "dia_id": "D18:5", - "text": "Yep. Last week, someone wrote me a letter after reading an online blog post I made about a hard moment in my life. Their words touched me; they said my story had brought them comfort. It was awesome to realize my words had that kind of power. It reminded me why I love writing." - }, - { - "speaker": "Nate", - "dia_id": "D18:6", - "text": "Nice work, Joanna! That must feel sureal. Keep it up - you're changing lives!" - }, - { - "speaker": "Joanna", - "dia_id": "D18:7", - "text": "Thanks, Nate! Really appreciate your kind words. It's knowing that my writing can make a difference that keeps me going, even on tough days. So glad to have this outlet to share my stories and hopefully have an impact. How about you? Anything new since we last talked?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i2.pickpik.com/photos/798/109/80/ice-cream-sundae-ice-cream-sundae-vanilla-preview.jpg" - ], - "blip_caption": "a photography of a dessert with whipped cream and chocolate sauce", - "query": "coconut milk ice cream homemade toppings", - "dia_id": "D18:8", - "re-download": true, - "text": "Thanks, Joanna! Your words mean a lot. Since we last spoke, I started teaching people how to make this. Sharing my love for dairy-free desserts has been fun and rewarding." - }, - { - "speaker": "Joanna", - "dia_id": "D18:9", - "text": "Yum, Nate! I love it when you make coconut milk icecream, it's so good!" - }, - { - "speaker": "Nate", - "dia_id": "D18:10", - "text": "I've been really into making this lately - it's creamy, rich, dairy-free and a new recipe! Wanna try it? I can share the recipe with you if you'd like!" - }, - { - "speaker": "Joanna", - "dia_id": "D18:11", - "text": "I'd love to try it! Thanks for sharing your love for dairy-free desserts. I really appreciate it!" - }, - { - "speaker": "Nate", - "dia_id": "D18:12", - "text": "No problem, Joanna! Always happy to share them with you. Sending you the recipe now!" - }, - { - "speaker": "Joanna", - "dia_id": "D18:13", - "text": "Thanks, Nate! Can't wait to surprise my family with something delicious!" - }, - { - "speaker": "Nate", - "dia_id": "D18:14", - "text": "No problem, Joanna! Wish them luck! Let me know how it goes. Have a blast baking!" - }, - { - "speaker": "Joanna", - "dia_id": "D18:15", - "text": "Thanks, Nate. I'll keep you posted. Have a great day." - }, - { - "speaker": "Nate", - "dia_id": "D18:16", - "text": "You too!" - } - ], - "session_19_date_time": "10:57 am on 22 August, 2022", - "session_19": [ - { - "speaker": "Nate", - "dia_id": "D19:1", - "text": "Woah Joanna, I won an international tournament yesterday! It was wild. Gaming has brought me so much success and now I'm able to make a living at something I'm passionate about - I'm loving it." - }, - { - "speaker": "Joanna", - "dia_id": "D19:2", - "text": "Congrats, Nate! So proud of you for winning that tournament, that's awesome! Must feel great to turn your passion into a career." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.pinimg.com/originals/57/31/42/57314282992ff77a40be8450c003b5c7.jpg" - ], - "blip_caption": "a photo of a fish tank with a fish inside of it", - "query": "cute pet turtles tank", - "dia_id": "D19:3", - "text": "I'm really stoked to see all my hard work paying off! I'm super proud of what I accomplished. On another note, my little dudes got a new tank! Check them out, they're so cute, right?!" - }, - { - "speaker": "Joanna", - "dia_id": "D19:4", - "text": "Wow Nate, they're adorable! I can see why you enjoy spending time with them. It looks like they have so much more room to swim now!" - }, - { - "speaker": "Nate", - "dia_id": "D19:5", - "text": "They're my little buddies, always calm and peaceful. It makes coming home after a long day of gaming better. The tank expansion has made them so happy! How have you been?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://halekatiedotcom.files.wordpress.com/2020/09/img_6976.jpg" - ], - "blip_caption": "a photo of a desk with a chair and a computer", - "query": "writers group meeting stack scripts desk", - "dia_id": "D19:6", - "text": "I'm good! Was super nervous last week when I shared my book with my writers group but got some great feedback. My hard work is paying off, it's such an awesome feeling!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a dessert in a glass on a counter", - "dia_id": "D19:7", - "text": "Wow Jo, you're killing it! Getting this kind of feedback means people are really connecting with your writing. Pretty cool! Did you celebrate?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://tastecando.com/cdn/shop/articles/Keto_Krisp_January_Post.jpg" - ], - "blip_caption": "a photo of two desserts with spoons and a bar of chocolate", - "query": "raspberry chia pudding parfait dessert", - "dia_id": "D19:8", - "text": "Thanks, Nate! It feels great knowing that people like my writing. I celebrated by making this delicious treat - yum! Any plans for the weekend?" - }, - { - "speaker": "Nate", - "dia_id": "D19:9", - "text": "I'm taking some time off this weekend to chill with my pets. Anything cool happening with you?" - }, - { - "speaker": "Joanna", - "dia_id": "D19:10", - "text": "I'm relaxing and recharging this weekend with a long walk and some reading. It's a good break." - }, - { - "speaker": "Nate", - "dia_id": "D19:11", - "text": "Looks like we both need a break. I'm glad your able to find a way to recharge! It's so incredibly important to take time off!" - }, - { - "speaker": "Joanna", - "dia_id": "D19:12", - "text": "Thanks, Nate! I've learned that taking breaks and looking after myself are important for my inspiration and mental health. It's all about finding balance." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/k78gqk5c5kx71.jpg" - ], - "blip_caption": "a photo of a bookcase filled with books and a toy car", - "query": "bookshelf filled with books", - "dia_id": "D19:13", - "text": "Yeah, balance is key! It's so cool how taking care of ourselves helps us be more creative and happier. I'm always looking for something new to read. Got any book recommendations? I've got a lot of books to choose from." - }, - { - "speaker": "Joanna", - "dia_id": "D19:14", - "text": "I reccomend finding a fantasy book series to read through. Most fiction series are great reads when your trying to relax." - }, - { - "speaker": "Nate", - "img_url": [ - "https://thelitbitch.files.wordpress.com/2023/04/img_2043_jpg.jpg" - ], - "blip_caption": "a photo of a stack of books sitting on top of a wooden table", - "query": "a court of thorns and roses book sarah j maas", - "dia_id": "D19:15", - "text": "Good idea! How about this series?" - }, - { - "speaker": "Joanna", - "dia_id": "D19:16", - "text": "That's a great one! Let me know what you think when your finished!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/vx7o8gcqv01c1.jpg" - ], - "blip_caption": "a photo of a poster of a man falling off a cliff", - "query": "space opera book series", - "dia_id": "D19:17", - "text": "Sure thing! And since your recommending me a book, I thought I should do the same! I'd really recommend this series. It's got awesome battles and interesting characters." - }, - { - "speaker": "Joanna", - "dia_id": "D19:18", - "text": "Wow, that series looks awesome! I'll have to check it out sometime!" - }, - { - "speaker": "Nate", - "dia_id": "D19:19", - "text": "You really should! The action scenes are awesome and the plot rocks. Definitely one of my favorites!" - }, - { - "speaker": "Joanna", - "dia_id": "D19:20", - "text": "Wow, sounds great! I'll definitely add it to my list; thanks for the recommendation!" - }, - { - "speaker": "Nate", - "dia_id": "D19:21", - "text": "Enjoy it! Have a good day." - }, - { - "speaker": "Joanna", - "dia_id": "D19:22", - "text": "Thanks, Nate! You too! Have a great day. Take care." - } - ], - "session_20_date_time": "6:03 pm on 5 September, 2022", - "session_20": [ - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/8056/8444279059_bbf0c79356_b.jpg" - ], - "blip_caption": "a photography of two turtles sitting on a rock in a pond", - "query": "pet turtles mario luigi", - "dia_id": "D20:1", - "re-download": true, - "text": "Hey Joanna! Long time no talk. So much has happened. Look how cute they are! Hanging with them has been a big help, especially recently. Speaking of which, I just had a letdown in a video game tourney - I didn't do too great, even though I tried. It was a setback, but I'm trying to stay positive." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://chensplate.com/wp-content/uploads/2021/02/IMG_8512.jpg" - ], - "blip_caption": "a photo of a piece of cake with strawberries and chocolate", - "query": "dairy-free chocolate cake pink frosting", - "dia_id": "D20:2", - "text": "Hey Nate! Cute turtles! Bummer about the setback. Any positive vibes comin' your way? I just revised on of my old recipes and made this!" - }, - { - "speaker": "Nate", - "dia_id": "D20:3", - "text": "Hey Joanna, yeah it's a bummer that I didn't do well. But it's all part of the learning curve, you know? Also that looks super good! Anyways, how are you holding up?" - }, - { - "speaker": "Joanna", - "dia_id": "D20:4", - "text": "I'm doing OK, thanks. Just been tinkering with that recipe and a few others. It's helping me find some comfort and getting creative." - }, - { - "speaker": "Nate", - "dia_id": "D20:5", - "text": "What else are you making? It's always satisfying to see the kind of things you do when your in one of those moods!" - }, - { - "speaker": "Joanna", - "dia_id": "D20:6", - "text": "Been tweaking a dessert recipe to make it yummier and more accessible." - }, - { - "speaker": "Nate", - "dia_id": "D20:7", - "text": "Wow, that sounds great! What flavors are you experimenting with?" - }, - { - "speaker": "Joanna", - "dia_id": "D20:8", - "text": "Trying out different flavors like chocolate, raspberry, and coconut has been a blast!" - }, - { - "speaker": "Nate", - "dia_id": "D20:9", - "text": "Sounds delicious! Are you only trying dairy-free options?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://cook2nourish.com/wp-content/uploads/2019/03/Mr6bqaNkSazxNPIxqCHyw-e1553202400166.jpg" - ], - "blip_caption": "a photo of a plate of cupcakes with different toppings", - "query": "dairy-free chocolate coconut cupcakes with raspberry frosting", - "dia_id": "D20:10", - "text": "Yeah, since I'm lactose intolerant I'm trying out dairy-free options like coconut or almond milk instead. It's been a fun challenge seeing how to make yummy treats that suit everyone's diets. I even made these dairy-free chocolate coconut cupcakes with raspberry frosting." - }, - { - "speaker": "Nate", - "dia_id": "D20:11", - "text": "Woah, those look great, Joanna! It's cool that you make desserts that work for everyone's diets. Do you have any more yummy recipes hiding in there?" - }, - { - "speaker": "Joanna", - "dia_id": "D20:12", - "text": "Yep! I've been making all sorts of desserts that work for everyone's diets - cookies, pies, cakes - everything! I'll share more recipes with you soon." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a pile of cookies with sprinkles on a wooden table", - "dia_id": "D20:13", - "text": "Can't wait to try them. Can I join you sometime? I think baking and cooking really brings us together!" - }, - { - "speaker": "Joanna", - "dia_id": "D20:14", - "text": "Yeah, Nate! A fellow Chef in the kitchen is always a great help! Speaking of which, i'm curious, any more tips for dairy-free baking?" - }, - { - "speaker": "Nate", - "dia_id": "D20:15", - "text": "You can use stuff like dairy-free margarine or coconut oil instead of butter, and make sure to check the labels to ensure they're dairy-free. Good luck!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a cookie with chocolate drizzle and almonds", - "dia_id": "D20:16", - "text": "Thanks, Nate! Love your ideas, can't wait to try them out!" - }, - { - "speaker": "Nate", - "dia_id": "D20:17", - "text": "No problem! I love to help, so just shoot me a question anytime you need!" - }, - { - "speaker": "Joanna", - "dia_id": "D20:18", - "text": "Got it Nate, I'll see you soon!" - }, - { - "speaker": "Nate", - "dia_id": "D20:19", - "text": "Bye Joanna! Good luck!" - } - ], - "session_21_date_time": "1:43 pm on 14 September, 2022", - "session_21": [ - { - "speaker": "Joanna", - "dia_id": "D21:1", - "text": "Hey Nate, long time no see! My laptop crashed last week and I lost all my work - super frustrating! As a writer, my laptop is like half of my lifeline so losing all progress was like a major blow." - }, - { - "speaker": "Nate", - "dia_id": "D21:2", - "text": "Hey Joanna, sorry to hear about that. Losing so much progress must be really frustrating. Did you manage to recover anything? Maybe consider backing up your work in the future?" - }, - { - "speaker": "Joanna", - "dia_id": "D21:3", - "text": "Thanks for the sympathy, Nate. Nothing was recoverable, but now I have an external drive for backups. I never want to go through this again. So, how have you been? Making anything cool?" - }, - { - "speaker": "Nate", - "dia_id": "D21:4", - "text": "Hey Joanna, I'm no writer like you, but something pretty awesome happened. Last Monday I got to teach people vegan ice cream recipes on my own cooking show! It was a bit nerve-wracking to put myself out there, but it was a blast. Plus, I picked up a few new recipes!" - }, - { - "speaker": "Joanna", - "dia_id": "D21:5", - "text": "Way to go, Nate! Congrats on the cooking show, I'll definitely be tuning in! What's your favorite dish from the show?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/7055/6945130989_40895e5372_b.jpg" - ], - "blip_caption": "a photography of a bowl of ice cream with a spoon on a table", - "query": "coconut milk ice cream", - "dia_id": "D21:6", - "re-download": true, - "text": "Coconut milk ice cream is at the top of my list. It's so smooth and creamy with a tropical coconut twist. Plus, it's dairy-free for people who can't have lactose or who want vegan options. Here's a snap of the ice cream I made." - }, - { - "speaker": "Joanna", - "dia_id": "D21:7", - "text": "Wow, that looks amazing, Nate! I love the color and texture. It's great that you're making these options. Could you share the recipe? I'd love to try making it sometime!" - }, - { - "speaker": "Nate", - "dia_id": "D21:8", - "text": "Yeah sure! Would love to share it. Let's spread the joy of dairy-free options! Let me know when you make it!" - }, - { - "speaker": "Joanna", - "dia_id": "D21:9", - "text": "Cool, Nate! Gonna give it a go. Dairy-free is a must for me, especially for desserts. Last Friday, I made a deeeelish dessert with almond milk - it was good! Got any favs when it comes to dairy-free desserts?" - }, - { - "speaker": "Nate", - "dia_id": "D21:10", - "text": "Coconut milk ice cream is one of my favorites as you might be able to tell, but I also love a dairy-free chocolate mousse. It's super creamy and tastes like the real thing. What's been your favorite dairy-free sweet treat so far?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://wornslapout.com/wp-content/uploads/2018/11/img_3684.jpg" - ], - "blip_caption": "a photo of a chocolate tart with raspberries on top", - "query": "chocolate raspberry tart almond flour crust ganache fresh raspberries", - "dia_id": "D21:11", - "text": "Hey Nate, my favorite dairy-free treat is this amazing chocolate raspberry tart. It has an almond flour crust, chocolate ganache, and fresh raspberries - it's delicious!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a piece of chocolate cake with raspberries on a plate", - "dia_id": "D21:12", - "text": "That looks amazing, Joanna! I need to try baking that. What other treats do you like making?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://yaycakeday.files.wordpress.com/2019/08/ice-cream-cake-1.jpg" - ], - "blip_caption": "a photo of a piece of chocolate cake with raspberries on a plate", - "query": "dairy-free chocolate cake raspberries", - "dia_id": "D21:13", - "text": "Hey Nate, I love making this dairy-free chocolate cake with raspberries. It's so moist and delicious - perfect sweetness level." - }, - { - "speaker": "Nate", - "dia_id": "D21:14", - "text": "That cake looks amazing, Joanna! How did you make it?" - }, - { - "speaker": "Joanna", - "dia_id": "D21:15", - "text": "I make it with almond flour, coconut oil, chocolate and raspberries. It's my favorite for birthdays and special days." - }, - { - "speaker": "Nate", - "dia_id": "D21:16", - "text": "Yum, Joanna! Gotta try that one. Any others you want to share?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://tamboracai.com/assets/Megan-Marlow-Acai-Vegan-Cheesecake-Bars_02.jpg" - ], - "blip_caption": "a photo of a piece of cake with walnuts on top", - "query": "dairy-free blueberry cheesecake bars recipe", - "dia_id": "D21:17", - "text": "Hey Nate! Here's another recipe I like. It's a delicious dessert made with blueberries, coconut milk, and a gluten-free crust. So creamy and delicious!" - }, - { - "speaker": "Nate", - "dia_id": "D21:18", - "text": "Wow, Joanna! That dessert looks amazing. I'll definitely have to give it a try. Thanks!" - }, - { - "speaker": "Joanna", - "dia_id": "D21:19", - "text": "Glad to help, Nate. Let me know if you try it. I'm sure you'll enjoy it. It was great chatting." - }, - { - "speaker": "Nate", - "dia_id": "D21:20", - "text": "Thanks Joanna! I will. Bye!" - } - ], - "session_22_date_time": "11:15 am on 6 October, 2022", - "session_22": [ - { - "speaker": "Joanna", - "img_url": [ - "https://i0.wp.com/mittsandmeasures.com/wp-content/uploads/2019/10/img_0051.jpg" - ], - "blip_caption": "a photo of a tart with raspberries on a white plate", - "query": "dairy-free chocolate raspberry tarts", - "dia_id": "D22:1", - "text": "Hey Nate, hi! Yesterday, I tried my newest dairy-free recipe and it was a winner with my family! Mixing and matching flavors is fun and I'm always trying new things. How about you?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/0z7nrwjeqc431.jpg" - ], - "blip_caption": "a photo of a trophy and a game controller on a table", - "query": "video game tournament trophy cash prize", - "dia_id": "D22:2", - "text": "Hey Joanna! That tart looks yummy! Lately, I've been doing great - I won a really big video game tournament last week and it was awesome! I still can't believe I made so much money from it." - }, - { - "speaker": "Joanna", - "dia_id": "D22:3", - "text": "Way to go, Nate! Winning the tournament and earning cash is awesome - congrats! Did you save it for something special?" - }, - { - "speaker": "Nate", - "dia_id": "D22:4", - "text": "Thanks Joanna! Yeah, I saved some but I'm not sure what to do with it - I'm completely content already. I don't have big plans anyway, so it's nice to have the extra cash on hand." - }, - { - "speaker": "Joanna", - "dia_id": "D22:5", - "text": "That's awesome, Nate! Having some extra cash on hand definitely brings a sense of freedom and relaxation, huh?" - }, - { - "speaker": "Nate", - "dia_id": "D22:6", - "text": "Yes! Finally, I don't have to stress about it, so I can just enjoy my movies and games." - }, - { - "speaker": "Joanna", - "dia_id": "D22:7", - "text": "Taking breaks and reducing stress is pretty nice! Have you watched any good movies recently? I could use some recommendations!" - }, - { - "speaker": "Nate", - "dia_id": "D22:8", - "text": "I watched \"Little Women\" recently, and it was great! The acting was awesome and the story was so captivating. Definitely a good one!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:9", - "text": "I'm so glad you enjoyed it! I recommended it to you a while back. I watched it too and it really spoke to me. Themes like sisterhood, love, and chasing dreams were explored so well. By the way, I finished up my writing for my book last week. Put in a ton of late nights and edits but finally got it done. I'm so proud of it! Can't wait to see what happens next." - }, - { - "speaker": "Nate", - "dia_id": "D22:10", - "text": "Way to go! We both know it took some effort, but I'm sure it'll be great. Congrats on finishing it up!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:11", - "text": "Thanks Nate! Your words mean a lot. Dedication and late nights got me here, but it was worth it. Just like you with your recent tournament - hard work pays off. I appreciate your support throughout!" - }, - { - "speaker": "Nate", - "dia_id": "D22:12", - "text": "I'm always here for you, Joanna! You've worked so hard and accomplished a lot \u2013 I'm proud. Keep on going!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:13", - "text": "Thanks, Nate! I won't give up on my goals as long as your here to support me." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.pinimg.com/originals/5e/f4/f9/5ef4f9bd2f094b2d0a4ddd4861b928a0.jpg" - ], - "blip_caption": "a photo of a white board with a drawing of arrows and words", - "query": "motivational quotes whiteboard", - "dia_id": "D22:14", - "text": "You can always count on me! I even made this for you!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:15", - "text": "Wow, Nate, that looks awesome! What inspired you?" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a young boy drawing on a white board", - "dia_id": "D22:16", - "text": "I figured you could always look back on this whenever you need encouragement, and that was all the inspiration I needed. And I would also say that your life path can be quite inspirational!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:17", - "text": "Wow, Nate! That's sweet of you! I'll make sure to remember this when I need the encouragement the most." - }, - { - "speaker": "Nate", - "dia_id": "D22:18", - "text": "Awesome! I know encouragement is what got me so far in my gameing career, so I figured why not share the love." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.pinimg.com/originals/7e/d3/bb/7ed3bb9129ef6126259592e038e2cd17.jpg" - ], - "blip_caption": "a photo of a bookmark with a plant on top of it", - "query": "stack of books bookmark", - "dia_id": "D22:19", - "text": "Rest assured, it will be something I cherish! On another note, I just finished this cute little bookmark for one of the ladies at my writing club!" - }, - { - "speaker": "Nate", - "dia_id": "D22:20", - "text": "That bookmark is great. I'm sure she'll love it!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:21", - "text": "Thanks Nate! I absolutley love DIYs, and I know she does too." - }, - { - "speaker": "Nate", - "dia_id": "D22:22", - "text": "Let me know how it goes!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:23", - "text": "Sure thing! Bye for now!" - } - ], - "session_23_date_time": "10:58 am on 9 October, 2022", - "session_23": [ - { - "speaker": "Nate", - "img_url": [ - "http://scarlet-rhapsody.com/wp-content/uploads/2020/03/87613523_2854559627994666_1775718911015124992_n.jpg" - ], - "blip_caption": "a photo of a man standing in front of a table with board games", - "query": "gaming convention group gamers", - "dia_id": "D23:1", - "text": "Hey Joanna, it's been a couple days since we last talked. Something exciting happened last Friday. I went to a game convention and met new people who weren't from my normal circle. It was a bit overwhelming but it reminded me of the good times gaming can bring." - }, - { - "speaker": "Joanna", - "dia_id": "D23:2", - "text": "Hey Nate! Good to hear from you. Sounds like fun! Meeting new people can be overwhelming, but the rewards can be great. Sometimes it's good to step outside our comfort zones and explore new things." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/omjzdx4vo4h41.jpg" - ], - "blip_caption": "a photo of two people dressed up in costumes posing for a picture", - "query": "video game convention friends", - "dia_id": "D23:3", - "text": "Yeah, you're right! We can have great experiences if we take risks. I even made some friends at the convention who love games just like me. We already planned a gaming session together - it's cool to meet people who have the same interests!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:4", - "text": "That looks awesome! I'm glad you met people who share your interests - that definitely makes experiences more fun.." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/n5hled3cve841.jpg" - ], - "blip_caption": "a photo of a group of people sitting around a table playing a board game", - "query": "group of people playing game", - "dia_id": "D23:5", - "text": "I also met some people who also played this boardgame I love, so I joined in. We had a lot in common and hit it off. It's great when shared hobbies can bond people!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:6", - "text": "It's incredible how a game can bring people together and form strong relationships. Did you do anything else there?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/byq7wy33k4w11.jpg" - ], - "blip_caption": "a photo of a board game with a blue board and a yellow board", - "query": "catan board game pieces strategy", - "dia_id": "D23:7", - "text": "We played this game Catan - it's a great strategy game where you build settlements and trade resources. I love it!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:8", - "text": "Looks cool! Is it more of a competitive game or a more chill one?" - }, - { - "speaker": "Nate", - "dia_id": "D23:9", - "text": "It can be both competitive and chill. We were competing, but still had lots of fun." - }, - { - "speaker": "Joanna", - "dia_id": "D23:10", - "text": "Competitive games can definitely be difficult to play sometimes when people get all upset about every move you make, but it's cool they were all chill! Glad you had a good time." - }, - { - "speaker": "Nate", - "dia_id": "D23:11", - "text": "It was great! Playing games is my escape from life struggles, so I generally don't get crazy competitive over them. The people at the convention were the same way!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:12", - "text": "Glad you found a way to have fun and escape! It's important to stay happy and de-stress. Keep doing what makes you happy!" - }, - { - "speaker": "Nate", - "dia_id": "D23:13", - "text": "For sure! You should go to a writing convention or something sometime! The experience at this convention is unforgetable." - }, - { - "speaker": "Joanna", - "dia_id": "D23:14", - "text": "Do writing conventions exist? I'll have to look into that, it could be fun! Thanks for the idea. Have you been up to anything tonight?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/806azldnt6bb1.jpg" - ], - "blip_caption": "a photo of a bookcase filled with dvds and movies", - "query": "video games movie collection", - "dia_id": "D23:15", - "text": "Mostly just chilling at home. Playing video games or watching movies helps me unwind." - }, - { - "speaker": "Joanna", - "dia_id": "D23:16", - "text": "That sounds great! What's your favorite game or movie that you've seen recently?" - }, - { - "speaker": "Nate", - "dia_id": "D23:17", - "text": "I recently saw a movie that blew my mind with all the twists and dream stuff, I think it was called \"Inception\". I've also been playing a game nonstop with a great futuristic setting and gameplay called \"Cyberpunk 2077\". Have you seen or played anything good lately?" - }, - { - "speaker": "Joanna", - "img_url": [ - "http://mjbmemorabilia.com/cdn/shop/files/1_4c670c88-d207-4080-9b81-9b81ef1066fe.jpg" - ], - "blip_caption": "a photo of a framed movie poster with a signed picture", - "query": "classic movie captivating storyline amazing performances movie poster", - "dia_id": "D23:18", - "text": "I watched a classic movie the other day that was awesome - the story was so gripping and the actors were great! It really stuck with me." - }, - { - "speaker": "Nate", - "dia_id": "D23:19", - "text": "Wow, that must have been awesome! What would you rate it?" - }, - { - "speaker": "Joanna", - "dia_id": "D23:20", - "text": "Well, it was amazing, so probably 9 or 10 out of 10! Movies can take us to different places and make us feel lots of emotions. What do you love about watching them?" - }, - { - "speaker": "Nate", - "dia_id": "D23:21", - "text": "Well they take me to new worlds and fill me with emotions. Plus, they're great for chilling out after a day." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.pinimg.com/originals/1a/02/2c/1a022c736bdfb7c47f33272c27eac0e7.jpg" - ], - "blip_caption": "a photo of a living room with a tv and candles", - "query": "cozy movie night setup popcorn blankets", - "dia_id": "D23:22", - "text": "I agree! They have the power to take us away and make us feel things not normally experienced in life. It's a great escape! Especially when you have a room like this!" - }, - { - "speaker": "Nate", - "dia_id": "D23:23", - "text": "Wow, it's so comfy in there! I should really get a set up like that." - }, - { - "speaker": "Joanna", - "dia_id": "D23:24", - "text": "You should! It's one thing to watch a movie in a theater, but on a nice comfy couch with a good blanket, I feel so at peace!" - }, - { - "speaker": "Nate", - "dia_id": "D23:25", - "text": "Any pointers on what I should get for my living room to make it comfy like that?" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a poster of a man and a woman sitting on a bench", - "dia_id": "D23:26", - "text": "Sure! For one, you should get a couch that can sit multiple people so that you can lay down if you want, and make sure its really fluffy! Also invest in a blanket that has a little bit of weight to it, and some lights that can be dimmed." - }, - { - "speaker": "Nate", - "dia_id": "D23:27", - "text": "Sounds like you really got into making your living room! Thanks, I'll try that out for myself!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:28", - "text": "No problem! I get super invested in random little things like that, but I think its all worth it when I end up with random little things that make life so much nicer." - }, - { - "speaker": "Nate", - "dia_id": "D23:29", - "text": "Thanks for the tip! See you later!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:30", - "text": "See ya Nate!" - } - ], - "session_24_date_time": "2:01 pm on 21 October, 2022", - "session_24": [ - { - "speaker": "Nate", - "dia_id": "D24:1", - "text": "Hey Joanna, what's been up? Haven't seen you since we last talked." - }, - { - "speaker": "Joanna", - "dia_id": "D24:2", - "text": "Hey Nate! I have been revising and perfecting the recipe I made for my family and it turned out really tasty. What's been happening with you?" - }, - { - "speaker": "Nate", - "dia_id": "D24:3", - "text": "Hey Joanna! That's cool. I've been getting so stressed lately because of my tournament progress - tough competitors - but my turtles always cheer me up." - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a cat laying on the floor in a room", - "dia_id": "D24:4", - "text": "Pets have a way of brightening our days. I still have that stuffed animal dog you gave me! I named her Tilly, and she's always with me while I write." - }, - { - "speaker": "Nate", - "dia_id": "D24:5", - "text": "Glad to hear it! What made you name her Tilly?" - }, - { - "speaker": "Joanna", - "dia_id": "D24:6", - "text": "I used to have a dog back in Michigan with that name, but then I got allergic and we had to get rid of her. The name helps me remember her back when I used to be able to hold and squeeze animal without an allergic reaction!" - }, - { - "speaker": "Nate", - "dia_id": "D24:7", - "text": "That's so touching! Glad the stuffed animal means so much!" - }, - { - "speaker": "Joanna", - "dia_id": "D24:8", - "text": "Tilly helps me stay focused and brings me so much joy. It's amazing how even stuffed animals can do that!" - }, - { - "speaker": "Nate", - "dia_id": "D24:9", - "text": "It really is, I'm not sure I'll ever understand why watching my turtles slowly walk around makes me so happy. But I'm very glad it does." - }, - { - "speaker": "Joanna", - "dia_id": "D24:10", - "text": "Same here! So have you been up to anything recenly?" - }, - { - "speaker": "Nate", - "dia_id": "D24:11", - "text": "Yeah, I've just been practicing for my next video game tournemant. How about you?" - }, - { - "speaker": "Joanna", - "dia_id": "D24:12", - "text": "Well, I had a bit of a setback recently - another rejection from a production company." - }, - { - "speaker": "Nate", - "dia_id": "D24:13", - "text": "Bummer, Joanna. Is this the one you sent to a film contest? Rejections suck, but don't forget they don't define you. Keep at it and you'll find the perfect opportunity." - }, - { - "speaker": "Joanna", - "dia_id": "D24:14", - "text": "Yeah.. Thanks, Nate. It's hard, but I won't let it slow me down. I'm gonna keep grinding and moving ahead." - }, - { - "speaker": "Nate", - "dia_id": "D24:15", - "text": "That's what I like to hear! I really respect you for that and being able to bounce back whenever something sad happens!" - }, - { - "speaker": "Joanna", - "dia_id": "D24:16", - "text": "Thanks, Nate! Your encouragement really means a lot. I'm gonna keep pushing forward and believing in myself." - }, - { - "speaker": "Nate", - "dia_id": "D24:17", - "text": "You got this, and don't ever forget that you have people cheering you on from the sidelines wherever you go." - }, - { - "speaker": "Joanna", - "dia_id": "D24:18", - "text": "Thanks! I'll see you around!" - }, - { - "speaker": "Nate", - "dia_id": "D24:19", - "text": "No problem! Catch you later!" - } - ], - "session_25_date_time": "8:16 pm on 25 October, 2022", - "session_25": [ - { - "speaker": "Nate", - "dia_id": "D25:1", - "text": "Hey Joanna, what's been up since we last chatted? How's it going?" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a box of cards with a quote on it", - "dia_id": "D25:2", - "text": "Hey Nate! Another movie script that I contributed to was shown on the big screen last Sunday for the first time! It was such a surreal experience to see everything come together. I felt a mix of emotions, but overall, it was a satisfying moment. I've been waiting for this for a long time!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a box with a controller inside of it", - "dia_id": "D25:3", - "text": "Congrats Joanna! How was it to finally see it on the big screen?\n\n[shares a photo holding a videogame controller]" - }, - { - "speaker": "Joanna", - "dia_id": "D25:4", - "text": "It was an amazing experience! I'll never forget seeing all of the characters and dialogue I wrote being acted out - it was such a cool feeling. Having all the hard work and determination I put into writing pay off was definitely rewarding. I know this is the third time it's happened, but its just so awesome!" - }, - { - "speaker": "Nate", - "dia_id": "D25:5", - "text": "That must have been amazing. What was your favorite part of it?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/ioa9eysnr9xb1.jpg" - ], - "blip_caption": "a photo of a drawing book with a bunch of drawings on it", - "query": "character sketches actors notebook", - "dia_id": "D25:6", - "text": "Seeing my characters I worked so hard on come alive was my favorite part. It felt like they jumped off the page and became real - it was totally surreal." - }, - { - "speaker": "Nate", - "dia_id": "D25:7", - "text": "Wow Joanna, those drawings are really incredible! What inspired you to create them?" - }, - { - "speaker": "Joanna", - "dia_id": "D25:8", - "text": "Thanks, Nate! They're visuals of the characters to help bring them alive in my head so I can write better." - }, - { - "speaker": "Nate", - "dia_id": "D25:9", - "text": "That's a cool way to gain insight into your characters. Where did you get your ideas for them?" - }, - { - "speaker": "Joanna", - "dia_id": "D25:10", - "text": "I got ideas from everywhere: people I know, stuff I saw, even what I imagined. It's cool to see how an idea takes shape into a person with their own wants, worries, and wishes." - }, - { - "speaker": "Nate", - "dia_id": "D25:11", - "text": "Wow Joanna, that's so cool! It's amazing how our imaginations can bring ideas to life. Can you tell me more about the character on the left in the photo?" - }, - { - "speaker": "Joanna", - "dia_id": "D25:12", - "text": "Nope! You'll just have to watch the movie and find out for yourself!" - }, - { - "speaker": "Nate", - "dia_id": "D25:13", - "text": "You got it. I was already planning on watching it, but talking to you about it makes me want to watch it even more!" - }, - { - "speaker": "Joanna", - "dia_id": "D25:14", - "text": "Awesome! Well enough about me, what have you been up to?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://cdn12.picryl.com/photo/2016/12/31/water-turtle-on-the-water-animal-animals-a79381-1024.jpg" - ], - "blip_caption": "a photography of two turtles sitting on a log in a pond", - "query": "pet turtles", - "dia_id": "D25:15", - "re-download": true, - "text": "I was bored today, so I just took my turtles out for a walk." - }, - { - "speaker": "Joanna", - "dia_id": "D25:16", - "text": "Sound fun! Did they have a good time?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/d/dd/Tortoises_in_the_Cotswold_Wildlife_Park_restaurant_-_geograph.org.uk_-_1468751.jpg" - ], - "blip_caption": "a photography of a dog laying on a rock in a zoo", - "query": "turtles basking heat lamp", - "dia_id": "D25:17", - "re-download": true, - "text": "Of course! They look tired from all the walking, so they're relaxing in the tank right now." - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a spoon full of ice cream and chocolate sauce", - "dia_id": "D25:18", - "text": "Aww, they're so cute! What do they eat?" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a container of lettuce and other greens", - "dia_id": "D25:19", - "text": "They eat a combination of vegetables, fruits, and insects. They have a varied diet." - }, - { - "speaker": "Joanna", - "dia_id": "D25:20", - "text": "Wow, that's fascinating! It's interesting how they have such a varied diet, including insects. Do you have a favorite among their food choices?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://images.pexels.com/photos/4460504/pexels-photo-4460504.jpeg" - ], - "blip_caption": "a photography of a group of strawberries and a turtle on a table", - "query": "turtles eating strawberries", - "dia_id": "D25:21", - "re-download": true, - "text": "I love seeing them eat fruit - they get so hyped and it's so cute!" - }, - { - "speaker": "Joanna", - "dia_id": "D25:22", - "text": "Wow, that's so cute! They look like they really enjoy fruit." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/i1d1xxfxqd251.jpg" - ], - "blip_caption": "a photo of a person holding a small turtle in their hand", - "query": "turtles joy holding", - "dia_id": "D25:23", - "text": "Yeah, it's adorable! Watching them enjoy their favorite snacks is so fun. I also like holding them." - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a rock formation with a person standing on the side", - "dia_id": "D25:24", - "text": "Do they have different personalities, like the way dogs and cats do?" - }, - { - "speaker": "Nate", - "dia_id": "D25:25", - "text": "Yeah, they each do. One is more adventurous while the other is more reserved, which I find cute. Having them around brings me joy and they make great companions." - }, - { - "speaker": "Joanna", - "dia_id": "D25:26", - "text": "That's super cool! I never knew turtles could be so interesting until I met your turtles. Wow!" - }, - { - "speaker": "Nate", - "dia_id": "D25:27", - "text": "I've always liked turtles since I was a boy, so I know all about them!" - }, - { - "speaker": "Joanna", - "dia_id": "D25:28", - "text": "You'll have to keep me posted on them! It's been fun chatting!" - }, - { - "speaker": "Nate", - "dia_id": "D25:29", - "text": "See you later Joanna!" - } - ], - "session_26_date_time": "3:56 pm on 4 November, 2022", - "session_26": [ - { - "speaker": "Joanna", - "dia_id": "D26:1", - "text": "Wow, Nate, I'm on fire! I just set up meetings with movie producers \u2014 my dreams are comin' true!" - }, - { - "speaker": "Nate", - "dia_id": "D26:2", - "text": "Wow Joanna, nice work! How did it go with those producer meetings?" - }, - { - "speaker": "Joanna", - "dia_id": "D26:3", - "text": "Thanks, Nate! The meetings went really well. I felt confident discussing my script and vision and they seemed interested and excited. They loved the elements of self-discovery in it. It was so validating to be taken seriously. I'm feeling hopeful and inspired about the future!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a television screen showing a game being played", - "dia_id": "D26:4", - "text": "Way to go, Joanna! Putting yourself out there is really brave and winning recognition for your hard work feels great. It's just like when I win a video game tournament - it feels awesome! I'm so proud of you and so glad you're feeling hopeful and inspired." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://fromthepencup.files.wordpress.com/2023/01/img_3860.jpg" - ], - "blip_caption": "a photo of a notebook with a list of things to write", - "query": "notebook filled handwritten pages early writings", - "dia_id": "D26:5", - "text": "Thanks Nate! Your support and encouragement mean a lot. Writing isn't always easy but moments like these make me appreciate it. I'm so thankful for all the opportunities. Last week, I found these old notebooks with my early writings - it was cool to see how far I've come." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle laying on a bed of rocks and gravel", - "dia_id": "D26:6", - "text": "That's cool! You must love seeing how you've grown as an artist. Is there a favorite piece from your early writings that stands out to you?" - }, - { - "speaker": "Joanna", - "dia_id": "D26:7", - "text": "Yup, I still remember this story from when I was 10. It was about a brave little turtle who was scared but explored the world anyway. Maybe even back then, I was inspired by stories about finding courage and taking risks. It's still a part of my writing today." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle laying on a bed of rocks and gravel", - "dia_id": "D26:8", - "text": "You obviously have a passion for writing, and it's funny the story was about a turtle! Their resilience is so inspiring! Take courage and keep pushing yourself with your writing. Great job!" - }, - { - "speaker": "Joanna", - "dia_id": "D26:9", - "text": "Thanks, Nate! They make me think of strength and perseverance. They help motivate me in tough times - glad you find that inspiring!" - }, - { - "speaker": "Nate", - "dia_id": "D26:10", - "text": "What can I say, I love turtles. So, what's been happening with you?" - }, - { - "speaker": "Joanna", - "dia_id": "D26:11", - "text": "Hey Nate! Apart from meetings, I'm working on a project - challenging but fulfilling. How about you? What's been going on?" - }, - { - "speaker": "Nate", - "dia_id": "D26:12", - "text": "Just been helping some friends reset their high scores at the international tournament. It's been fun!" - }, - { - "speaker": "Joanna", - "dia_id": "D26:13", - "text": "Wow, sounds like so much fun! You're really passionate about gaming. Have an awesome time and keep helping others with those high scores!" - }, - { - "speaker": "Nate", - "dia_id": "D26:14", - "text": "Thanks! It feels good to use my skills to make a difference." - }, - { - "speaker": "Joanna", - "dia_id": "D26:15", - "text": "I couldn't agree more! Which is why my meetings are so exciting!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/4q9s9o2607ib1.jpg" - ], - "blip_caption": "a photo of a bowl of ice cream with a spoon in it", - "query": "homemade coconut milk ice cream", - "dia_id": "D26:16", - "text": "On another note, want to come over and try some of this? It's super yummy, just made it yesterday!" - }, - { - "speaker": "Joanna", - "dia_id": "D26:17", - "text": "Mmm, that looks delicious! Is it lactose-free by any chance?" - }, - { - "speaker": "Nate", - "dia_id": "D26:18", - "text": "Yep, I made it with coconut milk so it's lactose-free!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a bowl of ice cream with a spoon in it", - "dia_id": "D26:19", - "text": "Thanks so much, Nate! Sure! I'll come over tomorrow if that's fine." - }, - { - "speaker": "Nate", - "dia_id": "D26:20", - "text": "I don't see why not! I'm not doing anything then, so your completely welcome to!" - }, - { - "speaker": "Joanna", - "dia_id": "D26:21", - "text": "Awesome! I'll bring some of my recipes so we can both share deserts!" - }, - { - "speaker": "Nate", - "dia_id": "D26:22", - "text": "I'd love that! I've been wanting to try some of your chocolate and rasberry cake for a while now." - }, - { - "speaker": "Joanna", - "dia_id": "D26:23", - "text": "You got it! See you tomorrow!" - }, - { - "speaker": "Nate", - "dia_id": "D26:24", - "text": "See you then! Take care!" - } - ], - "session_27_date_time": "8:10 pm on 7 November, 2022", - "session_27": [ - { - "speaker": "Nate", - "dia_id": "D27:1", - "text": "Hey Joanna! Hope you\u2019re doing alright. Crazy thing happened - I was in the final of a big Valorant tournament last Saturday, and I won! It was the best feeling to see my name as the champion. Tournaments really bring out strong emotions in me." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/om8u4dx20ir71.jpg" - ], - "blip_caption": "a photo of a notebook with a handwritten letter on it", - "query": "notebook handwritten script movie script presentation movie producers", - "dia_id": "D27:2", - "text": "Hey Nate! Congrats on winning the tournament - that's awesome! I know you must have been buzzing! Anyway, I've been working on something exciting too. Last Friday, I finished the presentation for producers - it was tough but it's looking good. What have you been up to?" - }, - { - "speaker": "Nate", - "dia_id": "D27:3", - "text": "Thanks, Joanna! I've been having a blast and preparing for other tournaments, so I've been real busy - but I'm loving it!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a book with a black border and a white title", - "dia_id": "D27:4", - "text": "Sounds like you're really loving life right now! It's great when you find something that suits you. Being busy can be tiring but it's so rewarding in the end. Keep it up!" - }, - { - "speaker": "Nate", - "dia_id": "D27:5", - "text": "Thanks, Joanna! I'm really grateful to have a job I enjoy every day. So anyways, anything new going on in your life?" - }, - { - "speaker": "Joanna", - "dia_id": "D27:6", - "text": "I am writing another movie script! It's a love story with lots of challenges. I've put lots of hard work into it and I'm hoping to get it on the big screen." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle laying on a bed of rocks and gravel", - "dia_id": "D27:7", - "text": "Woah Joanna, that's incredible! I remember when you started working on these sorta things. It's crazy to see how far you've gotten! You've really got a thing for writing, huh? Where'd you get the idea for it?" - }, - { - "speaker": "Joanna", - "dia_id": "D27:8", - "text": "Thanks Nate! Writing has always been a passion of mine. I got the idea for this script from a dream. How have your turtles been? I haven't seen pictures of them in a while!" - }, - { - "speaker": "Nate", - "dia_id": "D27:9", - "text": "Great actually! These little guys sure bring joy to my life! Watching them is so calming and fascinating. I've really grown fond of them. So, what about you, Joanna? What brings you happiness?" - }, - { - "speaker": "Joanna", - "dia_id": "D27:10", - "text": "Creating stories and watching them come alive gives me happiness and fulfillment. Writing has been such a blessing for me." - }, - { - "speaker": "Nate", - "dia_id": "D27:11", - "text": "Well with dedication like yours, its no wonder you do so well in it as well! Are you planning on submitting anymore scripts anytime soon?" - }, - { - "speaker": "Joanna", - "dia_id": "D27:12", - "text": "Yep! I actually just submitted a few more last week! Hoping to hear back from them soon, though I assume a few will be rejected." - }, - { - "speaker": "Nate", - "dia_id": "D27:13", - "text": "Even if it happens to a few, I'm sure at leasts one will make it to the screens and be your 3rd published movie!" - }, - { - "speaker": "Joanna", - "dia_id": "D27:14", - "text": "Thanks, Nate! Appreciate the encouragement. I won't give up, I promise! Got it covered!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.pinimg.com/originals/92/1a/c3/921ac332715f9ecb9f50155427aa8ffc.jpg" - ], - "blip_caption": "a photo of a desk with a computer monitor and a keyboard", - "query": "gaming setup", - "dia_id": "D27:15", - "text": "Great to hear! On another note, I just upgraded some of my equipment at home. Check it out!" - }, - { - "speaker": "Joanna", - "dia_id": "D27:16", - "text": "Oh wow, ice set-up! Do you use that computer for gaming?" - }, - { - "speaker": "Nate", - "dia_id": "D27:17", - "text": "Yep! This is where I practice and compete. Sometimes I even use it when I'm playing games with friends." - }, - { - "speaker": "Joanna", - "dia_id": "D27:18", - "text": "Cool! Having a dedicated space for practice and competition should help you stay focused." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a desk with two monitors and a laptop", - "dia_id": "D27:19", - "text": "Yeah, I love it. It's like my own little haven to escape into the virtual world." - }, - { - "speaker": "Joanna", - "dia_id": "D27:20", - "text": "Wow, that sounds great to have your own gaming setup at home. It must be really awesome!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/h99h0hvw6x761.jpg" - ], - "blip_caption": "a photo of a pair of headphones and a video game controller", - "query": "gaming headset controller", - "dia_id": "D27:21", - "text": "It really is! But it's also really important to have something like this for my career, otherwise I would never be able to beat my competition." - }, - { - "speaker": "Joanna", - "dia_id": "D27:22", - "text": "That makes sense! It's all about practice isn't it? So what's your favorite game?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/7xcxqz1onyg91.jpg" - ], - "blip_caption": "a photo of two nintendo game covers with a picture of a group of people", - "query": "eternal kingdom game cover art", - "dia_id": "D27:23", - "text": "Yep! I'm currently playing this awesome fantasy RPG called \"Xeonoblade Chronicles\" and it's been a blast! I highly reccomend it if you've never played it before. " - }, - { - "speaker": "Joanna", - "dia_id": "D27:24", - "text": "What made you start playing it? That's a japanese game series right?" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a woman in a costume holding a glass", - "dia_id": "D27:25", - "text": "Yes it is! I'm a big fan of Nintendo games, and I've actually been wanting to play this one for a while because my friends have played it and reccomended it!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a captain america costume on display in a museum", - "dia_id": "D27:26", - "text": "Nice! It's really cool when a reccomendation from a friend fits your taste so well isn't it?" - }, - { - "speaker": "Nate", - "dia_id": "D27:27", - "text": "For sure! That's why I love when you give me movie reccomendations, I usually like them a lot more then if I were to just watch some random one." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/5hklfo3gjhx71.jpg" - ], - "blip_caption": "a photo of a handwritten letter from a young man", - "query": "handwritten letter movie script", - "dia_id": "D27:28", - "text": "Great to hear! I just finished with the intro to my next movie script, and I decided to include this at the begining." - }, - { - "speaker": "Nate", - "dia_id": "D27:29", - "text": "That letter is really awesome! Does it remind you of your childhood?" - }, - { - "speaker": "Joanna", - "dia_id": "D27:30", - "text": "Yeah, it does! My brother wrote it - he used to make me these cute notes when we were kids. Brings back sweet memories." - }, - { - "speaker": "Nate", - "dia_id": "D27:31", - "text": "Aww, childhood memories can be so powerful!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of two little girls in pink dresses standing in front of a castle", - "dia_id": "D27:32", - "text": "They sure can! They take us back to simpler times but it's nice to create new memories as we grow up." - }, - { - "speaker": "Nate", - "dia_id": "D27:33", - "text": "Totally! I had a special day when I took my pets to the park. They were amazed and seeing their happy faces made it a memorable day. Mixing the new with the old is priceless - I treasure every memory!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/tkkip0dr2eg91.jpg" - ], - "blip_caption": "a photo of a person holding a notebook with a list of things on it", - "query": "handwritten memories notebook", - "dia_id": "D27:34", - "text": "That sounds so sweet, Nate! I started writing some of my favorite memories down." - }, - { - "speaker": "Nate", - "dia_id": "D27:35", - "text": "Dang, your full of great ideas Joanna! I really should start doing that as well, or at least write down the things my animals like a lot!" - }, - { - "speaker": "Joanna", - "dia_id": "D27:36", - "text": "You should! I completely encourage it, looking back on fond memories is such a blessing." - }, - { - "speaker": "Nate", - "dia_id": "D27:37", - "text": "Ok I will! But I'll also start writing down some of my favorite memories with you from now on." - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of two women sitting on a bed laughing and laughing", - "dia_id": "D27:38", - "text": "Definitely, let's keep making great memories and supporting each other. Let's keep reaching for our dreams and make them happen!" - } - ], - "session_28_date_time": "5:54 pm on 9 November, 2022", - "session_28": [ - { - "speaker": "Nate", - "img_url": [ - "https://beadyarnspatula.files.wordpress.com/2022/07/img_2596.jpg" - ], - "blip_caption": "a photo of a person scooping a scoop of ice cream into a pan", - "query": "coconut milk ice cream homemade red sprinkles", - "dia_id": "D28:1", - "text": "Hey Joanna, what a wild week! My game tournament got pushed back, so I tried out some cooking. Look at this homemade coconut ice cream! The sprinkles kinda changed the color this time around." - }, - { - "speaker": "Joanna", - "dia_id": "D28:2", - "text": "Hey Nate, that looks yummy! Wish I could try it, but I can't right now. How did the last game tournament go?" - }, - { - "speaker": "Nate", - "dia_id": "D28:3", - "text": "Hey Joanna, thanks! Tough tournament, didn't make it to the finals. But that's okay, I'll get 'em next time!" - }, - { - "speaker": "Joanna", - "dia_id": "D28:4", - "text": "Aww, bummer! But the important thing is to stay positive. So, what's your next move in the gaming world?" - }, - { - "speaker": "Nate", - "dia_id": "D28:5", - "text": "Thanks Joanna! Staying positive is key. I'm thinking of joining a new gaming team after this next tourney - I've had a few offers, but I haven't decided yet. It's gonna be a big step, but I'm ready for a shake up." - }, - { - "speaker": "Joanna", - "dia_id": "D28:6", - "text": "Sounds great, Nate! Making a switch could open up new opportunities. Wishing you luck with picking the right team!" - }, - { - "speaker": "Nate", - "dia_id": "D28:7", - "text": "Thanks, Joanna! I really appreciate it. It's a big decision, but I'm excited for what the future holds. How about you? Anything exciting happening on your end?" - }, - { - "speaker": "Joanna", - "dia_id": "D28:8", - "text": "Yup! I worked hard on another script and eventually created a plan for getting it made into a movie. It was a ton of work but satisfying. I pitched it to some producers yesterday and they really liked it. It gave me a big confidence boost!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a trophy and a game controller on a table", - "dia_id": "D28:9", - "text": "Congrats on the chance to pitch your script - super impressive. Proud of you!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://www.penboutique.com/cdn/shop/products/image_d3480c08-098f-481b-a456-de0607a4fe91.jpg" - ], - "blip_caption": "a photo of a pen and notebook on a table with a book", - "query": "desk laptop notebook pens", - "dia_id": "D28:10", - "text": "Appreciate you, Nate! Your support and encouragement mean a lot to me. I feel like I just can't stop writing write now!" - }, - { - "speaker": "Nate", - "dia_id": "D28:11", - "text": "Anytime. What're you working on in that notebook? Anything cool?" - }, - { - "speaker": "Joanna", - "dia_id": "D28:12", - "text": "Hey Nate, I'm working on a new project - a suspenseful thriller set in a small Midwestern town. It's been a great creative outlet for me. How about you? Do you have any projects you're working on?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/yldt4paim5k71.jpg" - ], - "blip_caption": "a photo of a desk with a computer, headphones, and a microphone", - "query": "microphone headphones desk", - "dia_id": "D28:13", - "text": "Yeah actually - creating gaming content for YouTube. It's a cool way to entertain folks and satisfy my video game cravings at the same time when there aren't any tourneys going on." - }, - { - "speaker": "Joanna", - "dia_id": "D28:14", - "text": "Wow, that's a cool idea! What inspired you to start making gaming videos?" - }, - { - "speaker": "Nate", - "dia_id": "D28:15", - "text": "Hey Joanna, I'm a big fan of them and thought it would be a fun idea to start making them myself. I'm hoping to share my love of gaming and connect with others who enjoy it too." - }, - { - "speaker": "Joanna", - "dia_id": "D28:16", - "text": "Way to go, Nate! Making videos and connecting with people about gaming - that's awesome! You'll do great!" - }, - { - "speaker": "Nate", - "dia_id": "D28:17", - "text": "Thanks Joanna! Appreciate the support. It's new to me but I'm excited to get started!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a computer screen displaying a product listing", - "dia_id": "D28:18", - "text": "Make sure you watch other peoples videos first so you get a handle on what your audience likes! That way your videos don't flop when you post them." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/i2l2lpmkj2mb1.jpg" - ], - "blip_caption": "a photo of a computer screen with a message on it", - "query": "positive comment youtube video screenshot", - "dia_id": "D28:19", - "text": "Already doing that, but thanks for the advice!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/ap48tronzy411.jpg" - ], - "blip_caption": "a photo of a sunflower in a field with a sunset in the background", - "query": "sunset field flowers", - "dia_id": "D28:20", - "text": "No worries, Nate! It's great to support each other in reaching our goals. On another note, check out this pic I got a while back!" - }, - { - "speaker": "Nate", - "dia_id": "D28:21", - "text": "Wow, that sunset pic looks incredible! What inspired you to take that photo?" - }, - { - "speaker": "Joanna", - "dia_id": "D28:22", - "text": "Thanks, Nate! I took that pic on a hike last summer near Fort Wayne. The sunset and the surrounding beauty were just incredible. It was an awesome reminder of nature's beauty." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/hgcgvd0umzsb1.jpg" - ], - "blip_caption": "a photo of three turtles sitting on a rock in a pond", - "query": "pet turtles basking sun", - "dia_id": "D28:23", - "text": "That sounds incredible! Nature truly has a way of reminding us to appreciate the beauty around us, and moments like those really stay with you. These critters also make me appreciate life's little joys. And guess what? I got them a new friend!" - }, - { - "speaker": "Joanna", - "dia_id": "D28:24", - "text": "Wow, what made you get a third?" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle swimming in a tank with a metal bar", - "dia_id": "D28:25", - "text": "Turtles really bring me joy and peace. They have such an effect on us - best buddies ever! I saw another at a pet store and just hade to get him. The tank is big enough now for three, so I figured why not!" - }, - { - "speaker": "Joanna", - "dia_id": "D28:26", - "text": "Wow! It's always a shock where life will take us next! I bet just last week you would have never thought you would be getting a third turtle this year!" - }, - { - "speaker": "Nate", - "dia_id": "D28:27", - "text": "You got that right, but I'm very happy with the descision and wouldn't have it any other way." - }, - { - "speaker": "Joanna", - "dia_id": "D28:28", - "text": "Can I come over sometime and watch you play with them? From a distance I mean, since I'm allergic." - }, - { - "speaker": "Nate", - "dia_id": "D28:29", - "text": "Definitely! I'd love to have you over again. Maybe we can watch one of your movies together or go to the park!" - }, - { - "speaker": "Joanna", - "dia_id": "D28:30", - "text": "For sure! I'd love to do either of those things with you!" - }, - { - "speaker": "Nate", - "dia_id": "D28:31", - "text": "Sounds good. Well I'll make sure I give the turtles a bath before you get here so they're ready to play." - }, - { - "speaker": "Joanna", - "dia_id": "D28:32", - "text": "Alright, see you tomorrow!" - }, - { - "speaker": "Nate", - "dia_id": "D28:33", - "text": "Bye Joanna!" - } - ], - "session_29_date_time": "12:06 am on 11 November, 2022", - "session_29": [ - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/l7riz754xlj41.jpg" - ], - "blip_caption": "a photo of a person holding a clap board with a dog sleeping in a dog bed", - "query": "movie clapperboard movie set", - "dia_id": "D29:1", - "text": "Nate, can you believe it? I'm finally filming my own movie from the road-trip script!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a trophy and a game controller on a table", - "dia_id": "D29:2", - "text": "Congrats, Joanna! Not surprised at all that your hard work paid off. Must feel awesome to see your script come alive in a movie! Pretty cool when something you love brings success, right? Tell me more about your movie!" - }, - { - "speaker": "Joanna", - "dia_id": "D29:3", - "text": "Woohoo, thanks Nate! It's pretty wild to see it come alive. Every day on set is awesome and full of potential. Being able to show my vision is awesome." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a fish tank with a fish inside of it", - "dia_id": "D29:4", - "text": "I think so too! What's been the coolest moment on set?" - }, - { - "speaker": "Joanna", - "dia_id": "D29:5", - "text": "One of the actors came up to me and told me how much she liked my script! I was so excited when that happened - it gave me chills!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle in a sink with a reflection of its head", - "dia_id": "D29:6", - "text": "Wow Joanna, that must have been so exciting! It's incredible when you get those moments of joy. Anyway, I took my turtles to the beach in Tampa yesterday! They always bring me peace in the craziness of life." - }, - { - "speaker": "Joanna", - "dia_id": "D29:7", - "text": "Woah, that's awesome, Nate! You must really enjoy having them around - they're so cool! What do you love most about having them?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/q35gmvlsr6yb1.jpg" - ], - "blip_caption": "a photo of a turtle sitting on a log in a pond", - "query": "pet turtles basking rock", - "dia_id": "D29:8", - "text": "Your completely right! I really love having them around. They're so cool and they make me feel calm. Plus, they don't require much looking after, which is great. I love seeing them soaking in the sun like this." - }, - { - "speaker": "Joanna", - "dia_id": "D29:9", - "text": "That's awesome, Nate! They look so serene and happy. It's great to have something like that." - }, - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/211/464853107_ccca166bec_b.jpg" - ], - "blip_caption": "a photography of a bowl of ice cream with a spoon in it", - "query": "homemade coconut milk ice cream colorful bowls", - "dia_id": "D29:10", - "re-download": true, - "text": "Yeah, turtles are like zen masters! They always remind me to slow down and appreciate the small things in life. I'm loving experimenting with flavors right now. Here are some colorful bowls of coconut milk ice cream that I made." - }, - { - "speaker": "Joanna", - "dia_id": "D29:11", - "text": "Hey Nate, that looks really yummy! The colors and mix-ins give it a nice kick." - }, - { - "speaker": "Nate", - "dia_id": "D29:12", - "text": "Nice! I'm glad you like it too. This recipe really jazzes it up. Wanna give it a try?" - }, - { - "speaker": "Joanna", - "dia_id": "D29:13", - "text": "Definitely, Nate! That ice cream looks mouthwatering. Thanks so much for offering!" - }, - { - "speaker": "Nate", - "dia_id": "D29:14", - "text": "No worries, Joanna. Hope you enjoy it!" - }, - { - "speaker": "Joanna", - "dia_id": "D29:15", - "text": "Yea, no worries! It was great catching up. Take it easy!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Joanna": [], - "Nate": [ - "Global Offensive with a team." - ], - "date": "21 January, 2022" - }, - "events_session_2": { - "Joanna": [ - "Joanna finishes writing her first full screenplay on drama and romance and plans on submitting it to a film festival so that producers and directors can check it out." - ], - "Nate": [ - "Nate starts hanging out with new people he met from outside his circle at the Counter Strike tournament." - ], - "date": "23 January, 2022" - }, - "events_session_3": { - "Joanna": [ - "Joanna sends the first screenplay she wrote on drama and romance to a film festival." - ], - "Nate": [ - "Nate discovers he can make his own coconut milk ice cream and decides to give it a try." - ], - "date": "7 February, 2022" - }, - "events_session_4": { - "Joanna": [ - "Joanna starts writing her second screenplay, which is about a thirty year old woman on a journey of self-discovery during a road trip after a loss, inspired by her own experiences." - ], - "Nate": [ - "Nate gifts his friend a homemade ice cream batch of chocolate and vanilla swirl and his friend enjoys it." - ], - "date": "25 February, 2022" - }, - "events_session_5": { - "Joanna": [ - "Joanna finishes writing her second screenplay, which is about a woman's journey of self-discovery through a road trip after suffering loss." - ], - "Nate": [ - "Nate takes his two pet turtles out for a walk." - ], - "date": "18 March, 2022" - }, - "events_session_6": { - "Joanna": [ - "Joanna has an audition for a writing opportunity and it goes alright." - ], - "Nate": [ - "Nate participates in another Counter Strike tournament." - ], - "date": "24 March, 2022" - }, - "events_session_7": { - "Joanna": [], - "Nate": [ - "Nate dyes his hair purple and shows off to friends." - ], - "date": "15 April, 2022" - }, - "events_session_8": { - "Joanna": [ - "Joanna goes on a hike in her hometown.", - "Joanna discovers a new hiking trail while hiking in her hometown.", - "Joanna reads a lot of books from her collection." - ], - "Nate": [], - "date": "17 April, 2022" - }, - "events_session_9": { - "Joanna": [ - "Joanna joins a local writers group with inspiring individuals who motivate and support her writing.", - "Joanna starts working on a script called \"Finding Home\" with the local writers group on a girl who is trying to find her true home." - ], - "Nate": [ - "Nate gears up for a local Street Fighter tournament which will be his fourth video game tournament." - ], - "date": "21 April, 2022" - }, - "events_session_10": { - "Joanna": [ - "Joanna watches the Lord of the Rings trilogy movies based on Nate's recommendation and enjoys it.", - "Joanna makes a dairy-free vanilla with strawberry filling and coconut cream frosting and appreciates the coconut recommendation from Note." - ], - "Nate": [ - "Nate wins the local Street Fighter tournament which is his second win in video game tournaments." - ], - "date": "2 May, 2022" - }, - "events_session_11": { - "Joanna": [ - "Joanna becomes an expert hiker after discovering and exploring several trails in her hometown.", - "Joanna finds a spot with waterfall called the 'Whispering Falls' during one of her hikes." - ], - "Nate": [], - "date": "12 May, 2022" - }, - "events_session_12": { - "Joanna": [ - "Joanna find the guts to start writing her third screenplay on loss, identity, and connection based on a story she has had since ages." - ], - "Nate": [ - "Nate adopts a new dog named Max as an addition to his pet family that consisted of two turtles." - ], - "date": "20 May, 2022" - }, - "events_session_13": { - "Joanna": [], - "Nate": [ - "Nate finds a companion for Max among his neighbors.", - "Nate gets a stuffed toy pup for Joanna since she is allergic to animals with fur and cannot have real animals as pets." - ], - "date": "25 May, 2022" - }, - "events_session_14": { - "Joanna": [ - "Joanna's receives a generic rejection letter without much feedback from a major company for her first screenplay on drama and romance.", - "Joanna explores a new hiking trail with her friends", - "Joanna finishes writing her third screenplay, which is based on a story of loss, identity and connection." - ], - "Nate": [ - "Nate wins another regional video game tournament.", - "Nate plans to host a gaming party and invites people he met at the regional video game tournament as well as friends from other tournaments he has attended.", - "Nate gets custom controller decorations for everyone attending the gaming party he plans on hosting." - ], - "date": "3 June, 2022" - }, - "events_session_15": { - "Joanna": [ - "Joanna is inspired to see a movie that she helped write appear on the big screen." - ], - "Nate": [], - "date": "5 June, 2022" - }, - "events_session_16": { - "Joanna": [ - "Joanna submits her third screenplay on loss, identity and connection to a film contest" - ], - "Nate": [ - "Nate makes vegan ice cream and shares with the vegan diet group he is part of." - ], - "date": "24 June, 2022" - }, - "events_session_17": { - "Joanna": [ - "Joanna takes a road trip to a small Midwestern town Woodhaven for research on her next movie script and is inspired by the history of the town and its interesting people.", - "Joanna starts writing a book on a deep and emotional story after seeing the movie she helped co-write do well." - ], - "Nate": [ - "Nate wins his fourth video game tournament" - ], - "date": "10 July, 2022" - }, - "events_session_18": { - "Joanna": [ - "Joanna is reminded of why she loves writing after she receives a letter from one of the readers of her blog post on how her words brought them comfort." - ], - "Nate": [ - "Nate starts teaching others how to make coconut milk ice cream." - ], - "date": "14 August, 2022" - }, - "events_session_19": { - "Joanna": [ - "Joanna shares her new book on a deep and emotional story with her local writers group for feedback.", - "Joanna makes a dairy-free parfait to celebrate getting positive feedback from her local writers group about her book.", - "Joanna plans to relax and recharge by taking a long walk on the beach and reading books over the weekend." - ], - "Nate": [ - "Nate enters and wins an international video game tournament, and is now able to make a living out of his passion.", - "Nate gets a new tank for his two turtles.", - "Nate plans to take some time off to chill with his pet dog and turtles." - ], - "date": "22 August, 2022" - }, - "events_session_20": { - "Joanna": [ - "Joanna revises her dairy free recipe for chocolate strawberry cake." - ], - "Nate": [ - "Nate faces a setback because he doesn't do too well in a video game tournament." - ], - "date": "5 September, 2022" - }, - "events_session_21": { - "Joanna": [ - "Joanna loses her work after her laptop crashes." - ], - "Nate": [ - "Nate hosts a cooking show to teach people how to make vegan ice cream using coconut milk." - ], - "date": "14 September, 2022" - }, - "events_session_22": { - "Joanna": [ - "Joanna cooks her new dairy free recipe for chocolate raspberry tart for her family.", - "Joanna finishes writing the book on a deep and emotional story that she started writing in July.", - "Joanna makes a cute book mark for one the ladies at her local writing group" - ], - "Nate": [ - "Nate wins another video game tournament and makes a lot of money from it" - ], - "date": "6 October, 2022" - }, - "events_session_23": { - "Joanna": [ - "Joanna watches a classic movie that she rates 9 out of 10" - ], - "Nate": [ - "Nate visits a game convention and meets and interacts with new people from outside his circle who also play Catan.", - "Nate plans a gaming session with people he met at the game convention.", - "Nate watches the movie Inception", - "Nate plays the game Cyberpunk 2077 non-stop" - ], - "date": "9 October, 2022" - }, - "events_session_24": { - "Joanna": [ - "Joanna perfects her dairy free dessert recipe of chocolate raspberry tart for her friends and family.", - "Joanna receives a rejection from a production company for her third screenplay about loss, identity and connection." - ], - "Nate": [], - "date": "21 October, 2022" - }, - "events_session_25": { - "Joanna": [ - "A movie based on a second script that Joanna contributed to is released on the big screen" - ], - "Nate": [ - "Nate takes his two turtles out for a walk." - ], - "date": "25 October, 2022" - }, - "events_session_26": { - "Joanna": [ - "Joanna schedules several meetings with movie producers to discuss her second screenplay which is about a thirty year old woman on a journey of self-discovery after suffering loss." - ], - "Nate": [ - "Nate helps the people he met at the international video game tournament in August to reset their high scores." - ], - "date": "4 November, 2022" - }, - "events_session_27": { - "Joanna": [ - "Joanna prepares a presentation for her movie script on a woman on a journey of self-discovery for the meetings with movie producers.", - "Joanna starts writing another movie script based on a love story and puts her brother's childhood letter to her in the Introduction part." - ], - "Nate": [ - "Nate wins a Valorant video game tournament", - "Nate upgrades his gaming equipment at home" - ], - "date": "7 November, 2022" - }, - "events_session_28": { - "Joanna": [ - "Joanna pitches her new movie script based on a love story and it resonates with movie producers.", - "Joanna starts working on a new script on a thriller story set in a small Midwestern town" - ], - "Nate": [ - "Nate thinks about joining a new gaming team after receving a couple of offers", - "Nate's video game tournament gets pushed back", - "Nate starts creating gaming content for YouTube to entertain folks and satisfy his video game craving", - "Nate gets a third pet turtle" - ], - "date": "9 November, 2022" - }, - "events_session_29": { - "Joanna": [ - "Joanna starts filming her movie based on her script of a woman's journey of self-discovery over a raod trip." - ], - "Nate": [ - "Nate takes his turtles to the beach in Tampa" - ], - "date": "11 November, 2022" - } - }, - "observation": { - "session_1_observation": { - "Nate": [ - [ - "Nate won his first video game tournament playing a team shooter game called Counter-Strike: Global Offensive.", - "D1:3" - ], - [ - "Nate's main hobbies are playing video games and watching movies.", - "D1:11" - ], - [ - "Nate enjoys action and sci-fi movies for their cool effects.", - "D1:13" - ] - ], - "Joanna": [ - [ - "Joanna has been working on a project recently.", - "D1:2" - ], - [ - "Besides writing, Joanna also enjoys reading, watching movies, and exploring nature as hobbies.", - "D1:10" - ], - [ - "Joanna's favorite movie genres are dramas and romcoms as she enjoys getting immersed in feelings and plots.", - "D1:14" - ], - [ - "Joanna recommended a romantic drama movie that focuses on memory and relationships.", - "D1:16" - ], - [ - "Joanna watched a specific movie she recommended around 3 years ago and considers it one of her favorites.", - "D1:18" - ] - ] - }, - "session_2_observation": { - "Joanna": [ - [ - "Joanna finished her first full screenplay last Friday, which is a mix of drama and romance.", - "D2:3" - ], - [ - "Joanna plans to submit her screenplay to film festivals and hopes to attract producers and directors to check it out.", - "D2:7" - ], - [ - "Joanna is allergic to most reptiles and animals with fur, causing her face to get puffy and itchy.", - "D2:23" - ], - [ - "Writing and hanging out with friends bring Joanna joy and help her express herself.", - "D2:25" - ], - [ - "Writing allows Joanna to create wild worlds with interesting characters and serves as a great way to express her feelings.", - "D2:27" - ] - ], - "Nate": [ - [ - "Nate has had turtles for 3 years that bring him joy and help keep him calm during stressful times.", - "D2:12" - ], - [ - "Nate enjoys hanging out with people outside of his usual circle at the tournament.", - "D2:16" - ], - [ - "Nate tends to stick around more chill people at the tournament due to varying levels of competitiveness.", - "D2:18" - ], - [ - "Nate does not expect to win big at the tournament but plays for fun and likes cheering on others.", - "D2:20" - ] - ] - }, - "session_3_observation": { - "Joanna": [ - [ - "Joanna submitted a screenplay to a film festival and is hoping a producer or director falls in love with it to get it on the big screen.", - "D3:1" - ], - [ - "Joanna appreciates Nate's support for her screenplay and mentions that it means a lot to her.", - "D3:3" - ], - [ - "Joanna seems curious about trying dairy-free desserts and flavors after Nate mentions making coconut milk ice cream.", - "D3:11" - ], - [ - "Joanna enjoys the movie 'Little Women' and recommends it as a great story about sisterhood, love, and reaching for dreams.", - "D3:17" - ], - [ - "Joanna is open to giving movie recommendations and discussing movies.", - "D3:19" - ] - ], - "Nate": [ - [ - "Nate supports Joanna's screenplay submission to the film festival and sends positive vibes wishing for its success.", - "D3:2" - ], - [ - "Nate tried making coconut milk ice cream, found it delicious, and is looking forward to trying different flavors and toppings.", - "D3:6" - ], - [ - "Nate mentions enjoying chocolate and mixed berry flavors for dairy-free desserts.", - "D3:10" - ], - [ - "Nate made a dairy-free chocolate cake with berries, showcasing his talent in the kitchen.", - "D3:12" - ], - [ - "Nate is open to trying out new flavors and experimenting in the kitchen.", - "D3:14" - ], - [ - "Nate appreciates Joanna's movie recommendation and expresses interest in watching 'Little Women'.", - "D3:18" - ] - ] - }, - "session_4_observation": { - "Nate": [ - [ - "Nate made chocolate and vanilla swirl ice cream for a friend.", - "D4:1" - ], - [ - "Nate enjoys baking desserts and is all about them.", - "D4:7" - ], - [ - "Nate bakes desserts that Joanna loves.", - "D4:9" - ] - ], - "Joanna": [ - [ - "Joanna can't have dairy.", - "D4:4" - ], - [ - "Joanna is keen to try Nate's dairy-free dessert recipe.", - "D4:6" - ], - [ - "Joanna is a writer and has written a screenplay.", - "D4:10" - ], - [ - "Joanna has started writing another screenplay focused on self-discovery after a loss, inspired by personal experiences.", - "D4:12" - ], - [ - "Joanna's new screenplay is about a thirty-year-old woman on a journey of self-discovery after a loss, inspired by personal experiences.", - "D4:14" - ], - [ - "Joanna hopes her screenplay gets noticed and makes it to the screen.", - "D4:18" - ] - ] - }, - "session_5_observation": { - "Joanna": [ - [ - "Joanna has completed her second script and is experiencing a mix of excitement and anxiety about its future.", - "D5:1" - ], - [ - "Joanna finds it hard to switch off and experiences a tug-of-war between hope and doubt.", - "D5:3" - ], - [ - "Joanna has done research and networking non-stop for her script and is determined to make it happen.", - "D5:15" - ], - [ - "Joanna feels supported and motivated by having someone throughout the process.", - "D5:19" - ] - ], - "Nate": [ - [ - "Nate finds turtles unique, calming, and low-maintenance as pets.", - "D5:6" - ], - [ - "Nate believes in Joanna's hard work and passion for her scripts, encouraging her to believe in herself.", - "D5:4" - ], - [ - "Nate suggests Joanna consider other animals due to her allergies, and offers to send her pictures of his turtles.", - "D5:12" - ], - [ - "Nate supports and motivates Joanna throughout the conversation.", - "D5:16" - ], - [ - "Nate emphasizes the importance of not quitting and assures Joanna that the next steps will become clearer.", - "D5:18" - ], - [ - "Nate expresses that his support for Joanna is important and that he is there for her.", - "D5:20" - ] - ] - }, - "session_6_observation": { - "Nate": [ - [ - "Nate is participating in a video game tournament that he finds intense.", - "D6:7" - ] - ], - "Joanna": [ - [ - "Joanna auditioned for a writing gig and had mixed emotions about it - excited but also anxious.", - "D6:2" - ], - [ - "Joanna has a lot of books that she uses for writing inspiration.", - "D6:8" - ], - [ - "Joanna's advice for someone pursuing writing includes reading lots, trying different genres, building a solid understanding of literature, and not being afraid to write and share.", - "D6:10" - ] - ] - }, - "session_7_observation": { - "Nate": [ - [ - "Nate dyed his hair a bright and bold color last week to stand out.", - "D7:5" - ] - ], - "Joanna": [ - [ - "Joanna finds Nate's boldness inspiring and relates it to a gorgeous sunset she saw while hiking.", - "D7:6" - ], - [ - "Joanna is currently consumed by her writing and is hoping for good news soon.", - "D7:8" - ], - [ - "Joanna appreciates Nate's support for her writing projects.", - "D7:10" - ] - ] - }, - "session_8_observation": { - "Nate": [ - [ - "Nate has turtles as pets that he loves watching and spending time with.", - "D8:11" - ], - [ - "Nate's pets provide him with peaceful moments and help him take a break from reality.", - "D8:11" - ], - [ - "Nate enjoys engaging in hobbies that revolve around spending time with his pets.", - "D8:11" - ], - [ - "Nate makes dairy-free ice cream using coconut milk, vanilla extract, sugar, and a pinch of salt.", - "D8:19" - ] - ], - "Joanna": [ - [ - "Joanna has been reading a lot of books in the past week and rediscovered good books she owned.", - "D8:2" - ], - [ - "Joanna found an awesome hiking trail in her hometown that she considers gorgeous and inspiring.", - "D8:4" - ], - [ - "Joanna finds nature to be calming and a way to reset, with worries and stress vanishing when she is surrounded by its beauty.", - "D8:10" - ], - [ - "Joanna loves nature and considers it her haven, finding peace and enjoyment in walking in it and feeling its calming effects.", - "D8:10" - ], - [ - "Joanna is lactose intolerant.", - "D8:18" - ], - [ - "Joanna is interested in trying Nate's dairy-free ice cream recipe made with coconut milk, vanilla extract, sugar, and salt.", - "D8:20" - ] - ] - }, - "session_9_observation": { - "Joanna": [ - [ - "Joanna recently joined a writers group that she finds inspirational and supportive.", - "D9:1" - ], - [ - "Joanna is working on a script called 'Finding Home' with her writing group, which she finds rewarding and emotional.", - "D9:3" - ], - [ - "Joanna's first passion was acting, but now she feels she shines in writing.", - "D9:7" - ], - [ - "Joanna enjoys dramas and emotionally-driven films.", - "D9:9" - ] - ], - "Nate": [ - [ - "Nate is participating in a gaming tournament next month, which will be his 4th one.", - "D9:4" - ], - [ - "Nate loves fantasy and sci-fi movies as they inspire his imagination.", - "D9:10" - ], - [ - "Nate recommended a series with adventures, magic, and great characters to Joanna.", - "D9:14" - ] - ] - }, - "session_10_observation": { - "Joanna": [ - [ - "Joanna watched 'The Lord of the Rings' Trilogy based on Nate's recommendation and found it awesome.", - "D10:1" - ], - [ - "Joanna has been working on some projects and testing dairy-free dessert recipes for friends and family.", - "D10:9" - ], - [ - "Joanna made a dairy-free vanilla cake with strawberry filling and coconut cream frosting.", - "D10:11" - ], - [ - "Cooking and baking are Joanna's creative outlets, and she enjoys experimenting in the kitchen.", - "D10:13" - ] - ], - "Nate": [ - [ - "Nate believes 'The Lord of the Rings' Trilogy is probably the greatest trilogy of all time.", - "D10:2" - ], - [ - "Nate's focus has been on gaming, practicing a lot and winning tournaments, including winning his second tournament last week.", - "D10:4" - ], - [ - "Nate usually plays CS:GO but participated in a local Street Fighter tournament and discovered he is really good at it.", - "D10:6" - ], - [ - "Nate recommended coconut to Joanna in the past which she liked and mentioned during the conversation.", - "D10:11" - ] - ] - }, - "session_11_observation": { - "Joanna": [ - [ - "Joanna enjoys hiking and found some amazing trails in her town.", - "D11:3" - ], - [ - "Joanna took a photo at Whispering Falls, a beautiful and peaceful location.", - "D11:7" - ], - [ - "Joanna feels calm and peaceful in nature, particularly during her hikes.", - "D11:9" - ], - [ - "Hiking has opened up a whole new world for Joanna, and she feels like a different person now.", - "D11:11" - ], - [ - "Joanna feels inspired by nature and finds it calming to be surrounded by its beauty.", - "D11:11" - ], - [ - "Joanna feels like she could write a whole movie when she is in inspiring places like the trails she visits.", - "D11:13" - ], - [ - "In inspiring places like the trails, Joanna feels like writing a drama.", - "D11:15" - ] - ], - "Nate": [ - [ - "Nate is interested in Joanna's hiking experiences and the photos she takes.", - "D11:4" - ], - [ - "Nate appreciates the calm and beauty of the nature spots Joanna visits.", - "D11:6" - ], - [ - "Nate acknowledges that nature can provide a break from the craziness of life.", - "D11:10" - ], - [ - "Nate gets deep in thought when he is out in nature and thinks about his life or new recipes.", - "D11:14" - ], - [ - "Nate is open to the idea of going hiking together with Joanna and possibly finding inspiration for a screenplay.", - "D11:16" - ] - ] - }, - "session_12_observation": { - "Nate": [ - [ - "Nate has a new addition to the family, an adopted pet named Max who is full of energy and joy.", - "D12:3" - ], - [ - "Nate believes that pets bring happiness and joy into people's lives.", - "D12:7" - ], - [ - "Nate is supportive and encouraging towards Joanna's writing and creative projects.", - "D12:11" - ], - [ - "Nate acknowledges that Joanna likes to write about topics like sadness and loss.", - "D12:15" - ], - [ - "Nate is proud of Joanna for staying true to herself and exploring personal experiences in her writing.", - "D12:17" - ], - [ - "Nate supports Joanna and cheers her on in her creative journey.", - "D12:19" - ] - ], - "Joanna": [ - [ - "Joanna finds comfort in writing and creative projects during tough times.", - "D12:10" - ], - [ - "Joanna has supportive friends who appreciate her work and with whom she can discuss and receive feedback.", - "D12:12" - ], - [ - "Joanna has written at least three personal stories, with the latest one being about loss, identity, and connection.", - "D12:14" - ], - [ - "Joanna believes that meaningful and powerful stories stem from personal experiences and feelings, even if they are challenging to write about.", - "D12:16" - ], - [ - "Joanna values Nate's support and finds it encouraging in her creative endeavors.", - "D12:18" - ] - ] - }, - "session_13_observation": { - "Nate": [ - [ - "Nate has a dog named Max that he enjoys taking for walks.", - "D13:1" - ], - [ - "Nate met a super nice couple with a dog during a walk and decided to do doggy playdates with them.", - "D13:1" - ], - [ - "Nate enjoys watching his dog play with other pets and finds joy in seeing his dog happy.", - "D13:7" - ], - [ - "Nate gifted Joanna a stuffed animal to remind her of good vibes.", - "D13:9" - ], - [ - "Nate believes in appreciating the little things in life that make it better.", - "D13:15" - ], - [ - "Nate is supportive of Joanna and expresses his support for her screenplay.", - "D13:21" - ] - ], - "Joanna": [ - [ - "Joanna values the moments that bring happiness and remind her that life is great.", - "D13:16" - ], - [ - "Joanna finds having a peaceful presence or something to come home to for a sense of calm helpful for relaxation.", - "D13:8" - ], - [ - "Joanna cherishes the stuffed animal gifted to her by Nate with all her heart.", - "D13:12" - ], - [ - "Joanna reflects on the tough times finishing her screenplay and realizes that the joyful moments make the journey worth it.", - "D13:18" - ], - [ - "Joanna values Nate's opinion and looks forward to showing him something.", - "D13:20" - ], - [ - "Joanna appreciates Nate's support and expresses her gratitude for it.", - "D13:22" - ] - ] - }, - "session_14_observation": { - "Joanna": [ - [ - "Joanna finished a screenplay but received a rejection letter from a major company.", - "D14:1" - ], - [ - "Joanna appreciates Nate's kind words and encouragement.", - "D14:3" - ], - [ - "Joanna is planning to go hiking with buddies to check out a new trail with a rad waterfall on the weekend.", - "D14:19" - ] - ], - "Nate": [ - [ - "Nate won a regional video game tournament last week and met new people.", - "D14:8" - ], - [ - "Nate enjoys connecting with fellow gamers and helping them improve their game.", - "D14:8" - ], - [ - "Nate is organizing a gaming party two weekends later and is getting custom controller decorations for everyone.", - "D14:20" - ] - ] - }, - "session_15_observation": { - "Joanna": [ - [ - "Joanna wrote bits for a screenplay that appeared on the big screen, which was nerve-wracking but inspiring.", - "D15:1" - ], - [ - "Joanna has a Spider-Man pin on her purse, indicating she likes Spider-Man as a favorite superhero.", - "D15:2" - ], - [ - "Joanna is a fan of superheroes in general, finding each one's story and powers fascinating.", - "D15:3" - ], - [ - "Joanna's room has a cork board full of inspiring quotes and pictures for motivation and creativity.", - "D15:7" - ], - [ - "Joanna has a picture of her family on her cork board which serves as a reminder of their love and encouragement.", - "D15:11" - ] - ], - "Nate": [ - [ - "Nate thinks Iron Man is his top superhero pick due to loving his tech and sarcastic humor.", - "D15:4" - ], - [ - "Nate has an Iron Man figure in his room which reminds him to keep working on his goals.", - "D15:6" - ], - [ - "Nate is considering starting a cork board of his own after being impressed by Joanna's.", - "D15:14" - ] - ] - }, - "session_16_observation": { - "Joanna": [ - [ - "Joanna submitted her screenplay to a film contest recently.", - "D16:1" - ], - [ - "Joanna showed interest in Nate's controller accessories for gaming.", - "D16:3" - ], - [ - "Joanna expressed excitement to receive Nate's vegan ice cream recipe.", - "D16:9" - ], - [ - "Joanna mentioned her family enjoys it when she makes them new things.", - "D16:13" - ], - [ - "Joanna appreciates Nate's support for her ice cream making.", - "D16:15" - ] - ], - "Nate": [ - [ - "Nate hosted a successful gaming party with 7 attendees.", - "D16:6" - ], - [ - "Nate made vegan ice cream last Friday and shared it with his vegan diet group.", - "D16:8" - ], - [ - "Nate offered to share the vegan ice cream recipe with Joanna the next day.", - "D16:10" - ], - [ - "Nate expressed confidence that Joanna's family will love the ice cream.", - "D16:14" - ] - ] - }, - "session_17_observation": { - "Nate": [ - [ - "Nate won his fourth video game tournament online and is proud to make money doing what he loves.", - "D17:1" - ] - ], - "Joanna": [ - [ - "Joanna took a road trip to Woodhaven, a small town in the Midwest, for research for her next movie.", - "D17:2" - ], - [ - "Joanna found a super cool book from the 1900s with stories and sketches during her trip, inspiring her next script.", - "D17:6" - ], - [ - "Joanna's new script is different from her previous work but has the potential to be something awesome.", - "D17:10" - ], - [ - "Joanna started working on a book recently after her movie did well. The book explores themes of loss, redemption, and forgiveness.", - "D17:14" - ], - [ - "Joanna mentioned that she has never really tried publishing a book, but this might be her first one.", - "D17:18" - ] - ] - }, - "session_18_observation": { - "Joanna": [ - [ - "Joanna is heavily invested in writing projects and considers writing a huge part of her life.", - "D18:1" - ], - [ - "Joanna finds solace and creativity in writing, considering it an escape and a way to express her feelings.", - "D18:3" - ], - [ - "Someone wrote Joanna a touching letter after reading an online blog post she made about a difficult moment in her life.", - "D18:5" - ], - [ - "Joanna values the impact her writing has on others, mentioning that it keeps her going even on tough days.", - "D18:7" - ], - [ - "Joanna is appreciative of Nate's support and kind words.", - "D18:7" - ], - [ - "Joanna is excited to try Nate's dairy-free desserts and expresses gratitude for his willingness to share the recipe.", - "D18:11" - ], - [ - "Joanna plans to surprise her family with Nate's dairy-free dessert.", - "D18:13" - ] - ], - "Nate": [ - [ - "Nate started teaching people how to make dairy-free desserts and finds it fun and rewarding.", - "D18:8" - ], - [ - "Nate has been making a creamy, rich, dairy-free dessert with a new recipe that he wants Joanna to try.", - "D18:10" - ], - [ - "Nate is supportive and enthusiastic about sharing his dairy-free dessert recipe with Joanna.", - "D18:10" - ], - [ - "Nate wishes Joanna luck with surprising her family with the dessert and looks forward to hearing about the outcome.", - "D18:14" - ] - ] - }, - "session_19_observation": { - "Nate": [ - [ - "Nate won an international tournament in gaming, turning his passion into a career.", - "D19:1" - ], - [ - "Nate has pets that he refers to as his 'little dudes' and got them a new tank recently.", - "D19:3" - ], - [ - "Nate values taking time off to relax and chill with his pets.", - "D19:9" - ], - [ - "Nate mentioned that taking care of ourselves helps in being more creative and happier.", - "D19:13" - ] - ], - "Joanna": [ - [ - "Joanna shared her book with her writers group and received great feedback, indicating that her hard work is paying off.", - "D19:6" - ], - [ - "Joanna celebrates achievements by making delicious treats.", - "D19:8" - ], - [ - "Joanna emphasizes the importance of taking breaks and looking after herself for inspiration and mental health, focusing on finding balance.", - "D19:12" - ], - [ - "Joanna enjoys relaxing and recharging with activities like long walks and reading.", - "D19:10" - ] - ] - }, - "session_20_observation": { - "Nate": [ - [ - "Nate experienced a letdown in a video game tournament recently.", - "D20:1" - ], - [ - "Nate tried to stay positive despite the setback in the video game tournament.", - "D20:1" - ], - [ - "Nate finds comfort and gets creative by hanging out with cute turtles.", - "D20:1" - ], - [ - "Nate enjoys helping with making desserts that suit everyone's diets.", - "D20:10" - ], - [ - "Nate offers tips for dairy-free baking, such as using dairy-free margarine or coconut oil instead of butter.", - "D20:15" - ], - [ - "Nate is willing to help anytime Joanna needs baking tips.", - "D20:17" - ] - ], - "Joanna": [ - [ - "Joanna revises recipes to find comfort and get creative.", - "D20:4" - ], - [ - "Joanna is experimenting with making dessert recipes yummier and more accessible.", - "D20:6" - ], - [ - "Joanna is lactose intolerant and is trying out dairy-free options like coconut or almond milk in her dessert recipes.", - "D20:10" - ], - [ - "Joanna makes desserts that work for everyone's diets, including dairy-free chocolate coconut cupcakes with raspberry frosting.", - "D20:10" - ], - [ - "Joanna enjoys the company of a fellow chef in the kitchen and believes it brings people together.", - "D20:14" - ], - [ - "Joanna is curious about more tips for dairy-free baking.", - "D20:14" - ] - ] - }, - "session_21_observation": { - "Joanna": [ - [ - "Joanna's laptop crashed last week, causing her to lose all her work which was a major blow.", - "D21:1" - ], - [ - "Joanna now uses an external drive for backups to prevent losing work again.", - "D21:3" - ], - [ - "Joanna made a delicious dessert with almond milk last Friday.", - "D21:9" - ], - [ - "Joanna's favorite dairy-free sweet treat is a chocolate raspberry tart with almond flour crust, chocolate ganache, and fresh raspberries.", - "D21:11" - ], - [ - "Joanna loves making dairy-free chocolate cake with raspberries for special occasions, using almond flour, coconut oil, chocolate, and raspberries.", - "D21:13" - ], - [ - "Joanna enjoys making a dessert with blueberries, coconut milk, and a gluten-free crust, finding it creamy and delicious.", - "D21:17" - ] - ], - "Nate": [ - [ - "Nate recently taught vegan ice cream recipes on his cooking show and learned some new recipes.", - "D21:4" - ], - [ - "Nate's favorite dish from his cooking show is coconut milk ice cream for its smooth, creamy, and dairy-free qualities.", - "D21:6" - ], - [ - "Nate also loves dairy-free chocolate mousse as it is super creamy and tastes like the real thing.", - "D21:10" - ], - [ - "Nate is interested in trying Joanna's dairy-free chocolate cake with raspberries and other recipes she likes making.", - "D21:12" - ], - [ - "Nate expresses interest in Joanna's dairy-free dessert with blueberries, coconut milk, and a gluten-free crust mentioning it looks amazing.", - "D21:18" - ] - ] - }, - "session_22_observation": { - "Joanna": [ - [ - "Joanna tried her newest dairy-free recipe and it was a winner with her family.", - "D22:1" - ], - [ - "Joanna finished writing her book last week after putting in a ton of late nights and edits.", - "D22:9" - ], - [ - "Joanna made a cute little bookmark for one of the ladies at her writing club.", - "D22:19" - ], - [ - "Joanna loves DIYs.", - "D22:21" - ] - ], - "Nate": [ - [ - "Nate won a really big video game tournament last week and made a lot of money from it.", - "D22:2" - ], - [ - "Nate watched 'Little Women' recently and found the acting and story captivating.", - "D22:8" - ], - [ - "Nate made something for Joanna to encourage her.", - "D22:14" - ], - [ - "Nate believes encouragement got him far in his gaming career.", - "D22:18" - ] - ] - }, - "session_23_observation": { - "Nate": [ - [ - "Nate attended a game convention last Friday and met new people who share his interests.", - "D23:1" - ], - [ - "Nate made friends at the convention who love games like him and planned a gaming session together.", - "D23:3" - ], - [ - "Nate joined in playing a board game at the convention with people who shared his interests and hit it off with them.", - "D23:5" - ], - [ - "Nate plays the game Catan and loves it for its strategy of building settlements and trading resources.", - "D23:7" - ], - [ - "Nate sees playing games as his escape from life struggles and doesn't get too competitive over them.", - "D23:11" - ], - [ - "Nate has been playing the game Cyberpunk 2077 nonstop recently and enjoys its futuristic setting and gameplay.", - "D23:17" - ] - ], - "Joanna": [ - [ - "Joanna encourages Nate to continue exploring new things and stepping outside his comfort zone.", - "D23:2" - ], - [ - "Joanna appreciates how games can bring people together and form strong relationships.", - "D23:6" - ], - [ - "Joanna is curious about Nate's favorite game or movie and engages in discussions about them.", - "D23:16" - ], - [ - "Joanna watched a classic movie recently that she found gripping and impactful, rating it 9 or 10 out of 10.", - "D23:18" - ], - [ - "Joanna enjoys creating a cozy and comfortable living room environment, suggesting specific items like a fluffy couch and a weighted blanket to Nate.", - "D23:26" - ] - ] - }, - "session_24_observation": { - "Nate": [ - [ - "Nate is stressed due to tough competitors in his tournament progress.", - "D24:3" - ], - [ - "Nate has turtles as pets that always cheer him up.", - "D24:3" - ], - [ - "Nate practices for video game tournaments.", - "D24:11" - ] - ], - "Joanna": [ - [ - "Joanna has been revising and perfecting a recipe for her family.", - "D24:2" - ], - [ - "Joanna has a stuffed animal dog named Tilly that was a gift from Nate.", - "D24:4" - ], - [ - "Joanna had a dog named Tilly back in Michigan but had to give her away due to allergies.", - "D24:6" - ], - [ - "Joanna recently faced a setback - another rejection from a production company.", - "D24:12" - ], - [ - "Joanna remains determined and resilient despite rejections, aiming to keep grinding and moving ahead.", - "D24:14" - ] - ] - }, - "session_25_observation": { - "Nate": [ - [ - "Nate took his turtles out for a walk today and they are now relaxing in the tank.", - "D25:15" - ], - [ - "Nate's turtles have a varied diet including vegetables, fruits, and insects.", - "D25:19" - ], - [ - "Nate loves seeing his turtles eat fruit as they get excited and it's cute.", - "D25:21" - ], - [ - "Nate's turtles have different personalities: one is more adventurous while the other is more reserved.", - "D25:25" - ], - [ - "Nate has always liked turtles since he was a boy and finds them interesting.", - "D25:27" - ] - ], - "Joanna": [ - [ - "Joanna recently had a movie script she contributed to shown on the big screen for the first time.", - "D25:2" - ], - [ - "Joanna feels a mix of emotions but overall satisfaction after seeing her script on the big screen.", - "D25:2" - ], - [ - "Joanna's characters she wrote coming alive on screen was a cool and surreal experience for her.", - "D25:4" - ], - [ - "Joanna creates visuals of her characters to help bring them alive in her head for better writing.", - "D25:8" - ], - [ - "Joanna gets ideas for her characters from various sources like people she knows, things she sees, and her imagination.", - "D25:10" - ], - [ - "Joanna's character visuals are used as a tool to visualize their wants, worries, and wishes.", - "D25:10" - ], - [ - "Joanna finds it cool to see how an idea transforms into a person with unique traits.", - "D25:10" - ] - ] - }, - "session_26_observation": { - "Joanna": [ - [ - "Joanna set up meetings with movie producers and feels her dreams are coming true.", - "D26:1" - ], - [ - "Joanna discussed her script and vision with producers and they liked the elements of self-discovery in it.", - "D26:3" - ], - [ - "Joanna found old notebooks with her early writings and feels appreciative of her progress as a writer.", - "D26:5" - ], - [ - "Joanna's early writings include a story about a brave little turtle who explores the world despite being scared, reflecting themes of courage and risk-taking.", - "D26:7" - ], - [ - "Joanna is working on a challenging but fulfilling project apart from her meetings.", - "D26:11" - ], - [ - "Joanna is grateful for Nate's support and encouragement.", - "D26:5" - ], - [ - "Joanna agreed to go to Nate's place and bring dessert recipes to share with him.", - "D26:19" - ], - [ - "Joanna has a chocolate and raspberry cake recipe that Nate is interested in trying.", - "D26:22" - ] - ], - "Nate": [ - [ - "Nate mentions he likes winning recognition in video game tournaments and compares it to Joanna's recognition.", - "D26:4" - ], - [ - "Nate has been helping friends reset their high scores at an international tournament.", - "D26:12" - ], - [ - "Nate uses his gaming skills to make a difference and finds it rewarding.", - "D26:14" - ], - [ - "Nate invited Joanna to try a lactose-free dish he made with coconut milk.", - "D26:16" - ], - [ - "Nate is passionate about gaming and helping others with high scores.", - "D26:13" - ], - [ - "Nate is open to Joanna coming over to try his dish and share dessert recipes.", - "D26:19" - ] - ] - }, - "session_27_observation": { - "Nate": [ - [ - "Nate won a big Valorant tournament making him the champion.", - "D27:1" - ], - [ - "Nate prepares for other tournaments and enjoys his job.", - "D27:3" - ], - [ - "Nate owns turtles that bring joy and calmness to his life.", - "D27:9" - ], - [ - "Nate upgraded some equipment at home for gaming purposes.", - "D27:15" - ], - [ - "Nate practices and competes on his computer for gaming.", - "D27:17" - ], - [ - "Nate is currently playing a fantasy RPG called \"Xenoblade Chronicles\" and recommends it.", - "D27:23" - ] - ], - "Joanna": [ - [ - "Joanna finished a presentation for producers last Friday.", - "D27:2" - ], - [ - "Joanna is writing a movie script, a love story with lots of challenges.", - "D27:6" - ], - [ - "Joanna submitted a few scripts last week and expects some rejections.", - "D27:12" - ], - [ - "Joanna finds happiness and fulfillment in writing.", - "D27:10" - ], - [ - "Joanna incorporates memories and childhood experiences into her writing.", - "D27:30" - ] - ] - }, - "session_28_observation": { - "Nate": [ - [ - "Nate's game tournament got pushed back, so he tried making homemade coconut ice cream.", - "D28:1" - ], - [ - "Nate did not make it to the finals of the last game tournament.", - "D28:3" - ], - [ - "Nate is considering joining a new gaming team after the next tournament.", - "D28:5" - ], - [ - "Nate is creating gaming content for YouTube when there are no tournaments.", - "D28:13" - ], - [ - "Nate has a third turtle as a pet and enjoys having turtles as companions.", - "D28:25" - ] - ], - "Joanna": [ - [ - "Joanna worked on a script and pitched it to producers who liked it.", - "D28:8" - ], - [ - "Joanna is working on a new project - a suspenseful thriller set in a small Midwestern town.", - "D28:12" - ], - [ - "Joanna encouraged Nate to watch other people's videos before making his own for YouTube.", - "D28:18" - ], - [ - "Joanna took a beautiful sunset picture on a hike near Fort Wayne last summer.", - "D28:22" - ], - [ - "Joanna is allergic to turtles but expresses interest in watching Nate play with them.", - "D28:28" - ] - ] - }, - "session_29_observation": { - "Joanna": [ - [ - "Joanna is filming her own movie based on a road-trip script.", - "D29:1" - ], - [ - "One of the actors on Joanna's movie set told her how much she liked her script, which gave Joanna chills.", - "D29:5" - ], - [ - "Joanna finds it awesome to be able to show her vision in the movie she's filming.", - "D29:3" - ] - ], - "Nate": [ - [ - "Nate took his turtles to the beach in Tampa and finds peace in their presence amid life's craziness.", - "D29:6" - ], - [ - "Nate loves having turtles around as they make him feel calm and don't require much looking after.", - "D29:8" - ], - [ - "Nate is currently experimenting with flavors and made colorful bowls of coconut milk ice cream.", - "D29:10" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Nate and Joanna caught up at 7:31 pm on 21 January, 2022. Nate won his first video game tournament playing Counter-Strike: Global Offensive. Joanna enjoys writing, reading, movies, and nature. They found common interests in movies, with Nate liking action and sci-fi, while Joanna prefers dramas and romcoms. Joanna recommended a romantic drama to Nate, praising its storyline and acting. Nate promised to watch it and thanked Joanna for the suggestion.", - "session_2_summary": "Joanna and Nate caught up on January 23, 2022, at 2:01 pm. Joanna excitedly shared that she had completed her first screenplay, a mix of drama and romance, and planned to submit it to film festivals. Nate congratulated her and mentioned his turtles, recommending pets for stress relief. Joanna, allergic to animals with fur and reptiles, shared her joy for writing and hanging out with friends. Nate praised her passion for writing, and Joanna expressed her commitment to continue pursuing it. Nate also mentioned making new friends at a tournament.", - "session_3_summary": "Joanna and Nate caught up at 9:27 am on 7 February, 2022. Joanna shared her mixed emotions about her screenplay submitted to a film festival and hoped it would make it to the big screen. Nate encouraged her, mentioning positive vibes. Joanna asked about Nate's life, and he talked about trying out coconut milk ice cream. They discussed flavors and Nate's dairy-free chocolate cake with berries. Nate recommended \"Little Women\" to Joanna, who enjoyed it. They agreed to exchange movie recommendations in the future. Both wished each other well and signed off.", - "session_4_summary": "Nate apologized for not being around, mentioning making ice cream for a friend at 1:07 pm on 25 February, 2022. Joanna expressed interest in Nate's ice cream, but mentioned being unable to have dairy. Nate offered to share a dairy-free recipe with Joanna. They discussed Joanna's new screenplay about self-discovery and loss, with Nate showing interest in the storyline. Joanna revealed it was inspired by personal experiences. Nate praised Joanna's courage and expressed excitement about both her new screenplay and the first one. Joanna hoped her screenplay would get noticed and thanked Nate for his support, with Nate wishing her luck.", - "session_5_summary": "Joanna and Nate caught up at 6:59 pm on 18 March, 2022. Joanna shared her mixed emotions about finishing her second script while Nate encouraged her to believe in herself. They discussed the calming effects of Nate's pet turtles, but Joanna, allergic to certain animals, couldn't get pets. They talked about Joanna's script and the importance of hard work and support. Nate assured Joanna of his ongoing support as they signed off at the end of their conversation.", - "session_6_summary": "Nate and Joanna caught up at 1:43 pm on 24 March, 2022. Nate congratulated Joanna on her writing audition, which she found exciting but nerve-wracking. Nate encouraged Joanna and shared his intense experience in a video game tournament. Joanna advised Nate on pursuing writing by recommending reading widely and seeking feedback. Nate thanked Joanna for her advice and support, expressing determination to work hard. They both wished each other luck in their endeavors and said goodbye, vowing to keep striving for their dreams.", - "session_7_summary": "At 7:37 pm on 15 April 2022, Nate told Joanna he dyed his hair last week and invited her to see it. Joanna admired Nate's new hair color, and he explained he chose it to stand out. This inspired Joanna, who shared a story about a sunset she saw while hiking. They discussed upcoming plans, with Joanna focused on writing and Nate offering support. They agreed to stay in touch, with Nate suggesting they see each other soon. They bid goodbye, with Nate saying, \"Take care.\"", - "session_8_summary": "Nate and Joanna caught up at 6:44 pm on 17 April, 2022. They discussed reading books, hiking trails, and the calming effect of nature. Nate shared his love for his pets, including turtles, and his dairy-free ice cream recipe with Joanna. Joanna expressed interest in trying the recipe and promised to update Nate on the results.", - "session_9_summary": "At 7:44 pm on 21 April, 2022, Joanna informed Nate about joining a writers group that has been motivating and inspiring her. She is currently working on a script titled \"Finding Home.\" Nate shared his excitement for an upcoming gaming tournament, and they discussed their passions for writing, acting, and favorite movies and book series. Joanna planned to check out the book series recommended by Nate and expressed interest in possibly getting back to acting in the future. The conversation ended with warm goodbyes and well-wishes.", - "session_10_summary": "Joanna and Nate caught up on 2nd May at 11:54 am. Joanna told Nate she enjoyed watching \"The Lord of the Rings\" Trilogy on his recommendation, and Nate agreed it's a great trilogy. Nate shared he has been focused on gaming, winning his second tournament in Street Fighter. Joanna praised Nate's gaming room and his recent success in tournaments. Nate appreciated Joanna's dairy-free dessert creations and they discussed their creative outlets. Nate encouraged Joanna to keep experimenting in the kitchen. They ended the conversation with Joanna saying she will continue trying new things and Nate bidding her goodbye.", - "session_11_summary": "Joanna and Nate conversed at 3:35 pm on 12 May, 2022. Joanna shared her exciting hiking experience at Whispering Falls with Nate, who admired her photo and expressed interest in joining her on a hike in the future. They discussed how nature inspires creativity, with Joanna mentioning how hiking has transformed her and Nate considering writing a screenplay. They concluded their conversation with plans to go on a hike together in the future.", - "session_12_summary": "Nate and Joanna caught up at 7:49 pm on 20 May, 2022. Nate shared about his new adopted pet, Max, bringing joy to his life. Joanna, allergic to pets, finds solace in writing and creative projects. She showed Nate her latest work on loss and identity, appreciated his support, and valued their friendship. Nate commended Joanna for her bravery in exploring personal topics. The conversation ended with mutual encouragement to pursue dreams and talents.", - "session_13_summary": "At 3:00 pm on 25 May 2022, Nate and Joanna caught up after a long time. Nate shared his encounter with a friendly couple during a walk with his dog, leading to plans for doggy playdates. Joanna expressed how uplifting connecting with other pet owners can be. They discussed the joy of watching their pets play and the importance of finding happiness in small things. Nate gifted Joanna a stuffed animal to bring her joy. They agreed that cherishing happy moments is crucial, especially during tough times. Joanna mentioned her screenplay and the role of joyful moments in making the journey worthwhile. Nate offered support and encouragement, emphasizing the importance of enjoying the journey and staying focused on dreams. Joanna valued Nate's support and promised to keep him updated on her progress.", - "session_14_summary": "Joanna shared with Nate that she received a rejection letter for her screenplay, feeling discouraged. Nate comforted her, advising not to give up on her dreams. He encouraged her to stay positive and keep believing in herself. They discussed the generic rejection letter Joanna received without feedback. Nate shared his recent victory in a video game tournament, which boosted his confidence. Joanna congratulated him and admired his dedication to gaming. They expressed support for each other's endeavors and made plans for their respective weekends. Nate planned a gaming party and mentioned providing custom controller decorations for guests. Joanna wished him the best for the party, and they parted ways, each looking forward to their own activities.", - "session_15_summary": "On June 5th, 2022, at 2:12 pm, Joanna excitedly shared with Nate that her screenplay bits were on the big screen, feeling inspired but nervous. Nate congratulated her, mentioning her Spider-Man pin. They discussed their favorite superheroes, with Joanna loving Spider-Man for his struggles and Nate admiring Iron Man for his tech and humor. They both found inspiration in their favorite things, with Joanna's cork board filled with motivation. Nate expressed interest in making his own cork board and they bid each other goodbye.", - "session_16_summary": "Joanna and Nate caught up at 10:55 am on 24 June, 2022. Joanna submitted her screenplay to a film contest, and Nate's gaming party was successful. Nate shared about making vegan ice cream and Joanna wanted the recipe. They agreed to share it the next day. Joanna planned to make it for her family. Nate offered support, and Joanna promised to keep him updated. Nate wished her a great day and told her to take care.", - "session_17_summary": "Nate shared his recent victory in a video game tournament, winning for the fourth time and being proud to make money doing what he loves. Joanna congratulated him, mentioning her road trip to Woodhaven during his tournament. Nate showed interest in her discoveries, and Joanna talked about finding an old book with stories and sketches that inspired her next script. They exchanged words of encouragement and support, with Nate praising Joanna's talent and Joanna expressing excitement about her new script and book. Nate encouraged her to believe in herself and her talent. Joanna appreciated his support and stated her determination to continue working hard.", - "session_18_summary": "Joanna and Nate caught up at 6:12 pm on 14 August, 2022. Joanna shared her passion for writing as a form of solace and creativity, mentioning how it has become her escape. Nate found her writing inspiring and mentioned how words have a healing power. Joanna also talked about a touching moment when her writing had a positive impact on someone, making her realize the power of words. Nate started teaching people how to make dairy-free desserts and shared a new recipe with Joanna. She expressed excitement to try it and thanked him for sharing. They ended the conversation wishing each other well.", - "session_19_summary": "Nate informed Joanna at 10:57 am on 22 August, 2022, that he won an international gaming tournament and is now able to pursue his passion as a career. Joanna congratulated Nate for his victory and praised him for turning his passion into a profession. They discussed Nate's pet turtles' new tank and Joanna sharing her writing with a group, both feeling proud of their achievements. Nate emphasized the importance of self-care and taking breaks, which Joanna agreed on. They exchanged book recommendations before wishing each other a good day and taking care.", - "session_20_summary": "At 6:03 pm on 5 September 2022, Nate and Joanna catch up after a while. Nate mentions turtles, a setback in a video game tourney, and trying to stay positive. Joanna shares she revised an old recipe and made something new. Nate admires her dessert and asks how she is doing. Joanna mentions finding comfort in cooking. They discuss Joanna's experimentation with dairy-free dessert options like chocolate, raspberry, and coconut. Nate appreciates her inclusive approach and looks forward to more recipes. Joanna plans to share more recipes and seeks Nate's advice on dairy-free baking, to which Nate suggests substitutes like dairy-free margarine or coconut oil. They express excitement to cook together in the future. Joanna thanks Nate for his ideas and they say goodbye, with Joanna expressing her gratitude and looking forward to trying Nate's suggestions.", - "session_21_summary": "At 1:43 pm on 14 September 2022, Joanna and Nate caught up, where Joanna shared her laptop crash frustration and Nate recommended backups. Nate shared his experience teaching vegan ice cream recipes on a cooking show. They discussed dairy-free desserts, with Joanna sharing a chocolate raspberry tart recipe and Nate favoring coconut milk ice cream. Joanna also mentioned a dairy-free chocolate cake and a blueberry dessert. They ended their chat on a friendly note, with Joanna offering to share more recipes and Nate expressing gratitude.", - "session_22_summary": "Joanna and Nate, conversing at 11:15 am on 6 October, 2022, shared updates on their recent achievements and interests. Joanna talked about her successful dairy-free recipe while Nate mentioned winning a video game tournament and earning money. They discussed the joy of having extra cash, watching movies, and supporting each other's endeavors, with Nate even creating something special for Joanna. They both expressed pride and encouragement in each other's accomplishments and concluded by sharing their mutual love for DIY projects before saying goodbye.", - "session_23_summary": "Nate and Joanna spoke on 9 October, 2022, at 10:58 am. Nate shared his experience at a game convention where he met new people who shared his gaming interests. Joanna acknowledged the benefits of stepping out of comfort zones. They discussed how games can bond people and the impact of shared hobbies. Nate mentioned playing the game Catan at the convention, highlighting its competitive yet fun nature. Joanna appreciated the way games bring people together. They also talked about their favorite movies and games. Nate mentioned \"Inception\" and \"Cyberpunk 2077,\" while Joanna enjoyed a classic movie. They discussed the escapism movies provide and the importance of creating a cozy space at home. Joanna suggested getting a comfortable couch, fluffy blankets, and dim lights for a cozy living room setup. Nate found the advice helpful and mentioned implementing it. The conversation ended with a friendly farewell.", - "session_24_summary": "Nate and Joanna caught up at 2:01 pm on 21 October, 2022. Joanna shared about perfecting a family recipe and the significance of a stuffed animal dog named Tilly. Nate mentioned his stress from tough competitors in a tournament but found joy in his turtles. They discussed how pets bring happiness and shared recent activities. Joanna mentioned a recent setback with a production company rejection, but Nate offered support and encouragement. They both expressed determination to keep pushing forward. Nate reassured Joanna of his support, and they ended the conversation with positive affirmations.", - "session_25_summary": "At 8:16 pm on 25 October 2022, Nate asked Joanna how she's been. Joanna shared her excitement about having her script shown on the big screen for the first time, expressing a mix of emotions and satisfaction. Nate congratulated her and Joanna described the surreal experience of seeing her work come to life. The two discussed characters, with Joanna explaining her inspiration and Nate praising her drawings. They also talked about Nate's turtles and their varied diet. Joanna found it fascinating and admired how Nate's turtles have different personalities. The conversation ended with Nate expressing his love for turtles and Joanna looking forward to hearing more about them in the future.", - "session_26_summary": "At 3:56 pm on November 4, 2022, Joanna excitedly told Nate about setting up meetings with movie producers, leading to Nate's congratulatory response about her hard work. Joanna described the positive producer meetings, feeling validated and hopeful. Nate compared her success to winning a video game tournament. They discussed Joanna's earlier writings and passion for writing, including a childhood story about a brave turtle. Nate expressed admiration for her perseverance and encouraged her to keep pushing herself. They exchanged updates on their current projects. Nate invited Joanna over to try a lactose-free dish he made, and they planned to share desserts the next day. Joanna agreed to bring her chocolate and raspberry cake. They bid farewell, looking forward to their meeting.", - "session_27_summary": "Nate and Joanna talked at 8:10 pm on 7 November, 2022. Nate won a Valorant tournament last Saturday, while Joanna finished a presentation for producers on Friday. Nate is preparing for more tournaments and enjoys being busy. Joanna is working on a new movie script and has submitted scripts recently. They both value creating and cherishing memories. Nate has turtles and upgraded his gaming setup. Joanna enjoys writing and recalls childhood memories fondly. They encourage each other to pursue their dreams and make more memories together.", - "session_28_summary": "At 5:54 pm on 9 November 2022, Nate and Joanna had a conversation about their recent activities and future plans. Nate mentioned trying out cooking due to a game tournament delay and showed Joanna his homemade coconut ice cream. He didn't make it to the finals in the last tournament but was positive about the next one and considering joining a new gaming team. Joanna shared that she pitched a script to producers and got positive feedback, boosting her confidence. Nate talked about creating gaming content for YouTube, while Joanna worked on a new suspenseful thriller script. They discussed supporting each other's goals and shared hobbies, including nature photography and turtle-keeping. Joanna planned to visit Nate to watch him play with the turtles from a distance due to her allergy. They made plans to hang out and parted ways, with Nate promising to bathe the turtles before her visit. Joanna said, \"See you tomorrow,\" and Nate replied, \"Bye Joanna.\"", - "session_29_summary": "Joanna excitedly shared with Nate at 12:06 am on 11 November, 2022, that she was filming her own movie from a road-trip script. Nate congratulated her, mentioning it's not surprising due to her hard work. Joanna was thrilled to see her script come to life on set daily. She shared that an actor complimenting her script was a highlight. Nate then recounted a peaceful trip with his turtles to the beach in Tampa. Joanna praised the turtles, saying they looked serene. Nate likened turtles to zen masters for their calming effect. Nate then showed Joanna his colorful coconut milk ice cream bowls and offered her a taste. Joanna expressed interest in trying it, and the conversation ended on a positive note as they caught up and bid each other farewell." - }, - "sample_id": "conv-42" - }, - { - "qa": [ - { - "question": "what are John's goals with regards to his basketball career?", - "answer": "improve shooting percentage, win a championship", - "evidence": [ - "D1:9", - "D6:15", - "D11:17" - ], - "category": 1 - }, - { - "question": "What are John's goals for his career that are not related to his basketball skills?", - "answer": "get endorsements, build his brand, do charity work", - "evidence": [ - "D6:15", - "D11:17" - ], - "category": 1 - }, - { - "question": "What items does John collect?", - "answer": "sneakers, fantasy movie DVDs, jerseys", - "evidence": [ - "D1:15", - "D12:18", - "D27:20" - ], - "category": 1 - }, - { - "question": "Would Tim enjoy reading books by C. S. Lewis or John Greene?", - "answer": "C. S.Lewis", - "evidence": [ - "D1:14", - "D1:16", - "D1:18" - ], - "category": 3 - }, - { - "question": "What books has Tim read?", - "answer": "Harry Potter, Game of Thrones, the Name of the Wind, The Alchemist, The Hobbit, A Dance with Dragons, and the Wheel of Time.", - "evidence": [ - "D1:14", - "D2:7", - "D6:8", - "D11:26", - "D20:21", - "D26:36", - "D22:13" - ], - "category": 1 - }, - { - "question": "Based on Tim's collections, what is a shop that he would enjoy visiting in New York city?", - "answer": "House of MinaLima", - "evidence": [ - "D2:9" - ], - "category": 3 - }, - { - "question": "In which month's game did John achieve a career-high score in points?", - "answer": "June 2023", - "evidence": [ - "D3:1" - ], - "category": 2 - }, - { - "question": "Which geographical locations has Tim been to?", - "answer": "California, London, the Smoky Mountains", - "evidence": [ - "D1:18", - "D3:2", - "D14:16" - ], - "category": 1 - }, - { - "question": "Which outdoor gear company likely signed up John for an endorsement deal?", - "answer": "Under Armour", - "evidence": [ - "D3:15", - "D25:2" - ], - "category": 3 - }, - { - "question": "Which endorsement deals has John been offered?", - "answer": "basketball shoes and gear deal with Nike, potential sponsorship with Gatorade, Moxie a popular beverage company, outdoor gear company", - "evidence": [ - "D3:13", - "D3:15", - "D25:2", - "D29:4" - ], - "category": 1 - }, - { - "question": "When was John in Seattle for a game?", - "answer": "early August, 2023", - "evidence": [ - "D3:19", - "D5:2" - ], - "category": 2 - }, - { - "question": "What sports does John like besides basketball?", - "answer": "surfing", - "evidence": [ - "D1:7", - "D2:14", - "D3:1", - "D3:25" - ], - "category": 1 - }, - { - "question": "What year did John start surfing?", - "answer": "2018", - "evidence": [ - "D3:27" - ], - "category": 2 - }, - { - "question": "What does Tim do to escape reality?", - "answer": "Read fantasy books.", - "evidence": [ - "D2:11", - "D3:30" - ], - "category": 1 - }, - { - "question": "What kind of writing does Tim do?", - "answer": "comments on favorite books in a fantasy literature forum, articles on fantasy novels, studying characters, themes, and making book recommendations, writing a fantasy novel", - "evidence": [ - "D2:1", - "D4:3", - "D4:5", - "D15:3" - ], - "category": 1 - }, - { - "question": "Who is Anthony?", - "answer": "likely John's friend, colleague or family", - "evidence": [ - "D4:8" - ], - "category": 3 - }, - { - "question": "After how many weeks did Tim reconnect with the fellow Harry Potter fan from California?", - "answer": "three weeks", - "evidence": [ - "D3:2", - "D5:1" - ], - "category": 2 - }, - { - "question": "How many games has John mentioned winning?", - "answer": "6", - "evidence": [ - "D3:3", - "D5:2", - "D22:4", - "D23:7", - "D24:2" - ], - "category": 1 - }, - { - "question": "What authors has Tim read books from?", - "answer": "J.K. Rowling, R.R. Martin, Patrick Rothfuss, Paulo Coelho, and J. R. R. Tolkien.", - "evidence": [ - "D1:14", - "D2:7", - "D4:7", - "D5:15", - "D:11:26", - "D20:21", - "D26:36" - ], - "category": 1 - }, - { - "question": "What is a prominent charity organization that John might want to work with and why?", - "answer": "Good Sports, because they work with Nike, Gatorade, and Under Armour and they aim toprovide youth sports opportunities for kids ages 3-18 in high-need communities.", - "evidence": [ - "D3:13", - "D3:15", - "D6:15" - ], - "category": 3 - }, - { - "question": "Which city was John in before traveling to Chicago?", - "answer": "Seattle", - "evidence": [ - "D3:19", - "D5:2", - "D6:1", - "D6:3" - ], - "category": 2 - }, - { - "question": "Which US cities does John mention visiting to Tim?", - "answer": "Seattle, Chicago, New York", - "evidence": [ - "D3:19", - "D6:3", - "D9:6" - ], - "category": 1 - }, - { - "question": "When did John meet with his teammates after returning from Chicago?", - "answer": "August 15, 2023", - "evidence": [ - "D7:1" - ], - "category": 2 - }, - { - "question": "When is Tim attending a book conference?", - "answer": "September 2023", - "evidence": [ - "D7:6" - ], - "category": 2 - }, - { - "question": "Where was John between August 11 and August 15 2023?", - "answer": "Chicago", - "evidence": [ - "D6:1", - "D6:3", - "D7:1" - ], - "category": 2 - }, - { - "question": "What similar sports collectible do Tim and John own?", - "answer": "signed basketball", - "evidence": [ - "D7:7", - "D7:9", - "D16:7", - "D16:9" - ], - "category": 1 - }, - { - "question": "Which TV series does Tim mention watching?", - "answer": "That, Wheel of Time", - "evidence": [ - "D17:1", - "D17:11", - "D26:36" - ], - "category": 1 - }, - { - "question": "Which popular time management technique does Tim use to prepare for exams?", - "answer": "Pomodoro technique", - "evidence": [ - "D18:3", - "D18:7" - ], - "category": 3 - }, - { - "question": "Which popular music composer's tunes does Tim enjoy playing on the piano?", - "answer": "John Williams", - "evidence": [ - "D8:14", - "D8:16" - ], - "category": 3 - }, - { - "question": "What schools did John play basketball in and how many years was he with his team during high school?", - "answer": "Middle school, high school, and college and he was with his high school team for 4 years.", - "evidence": [ - "D6:13", - "D9:4" - ], - "category": 1 - }, - { - "question": "Which cities has John been to?", - "answer": "Seattle, Chicago, New York, and Paris.", - "evidence": [ - "D3:19", - "D6:3", - "D9:6", - "D27:36" - ], - "category": 1 - }, - { - "question": "What month did Tim plan on going to Universal Studios?", - "answer": "September, 2023", - "evidence": [ - "D10:9" - ], - "category": 2 - }, - { - "question": "Which US states might Tim be in during September 2023 based on his plans of visiting Universal Studios?", - "answer": "California or Florida", - "evidence": [ - "D10:9" - ], - "category": 3 - }, - { - "question": "When does John plan on traveling with his team on a team trip?", - "answer": "October, 2023", - "evidence": [ - "D11:7" - ], - "category": 2 - }, - { - "question": "What could John do after his basketball career?", - "answer": "become a basketball coach since he likes giving back and leadership", - "evidence": [ - "D11:19", - "D26:1", - "D27:26" - ], - "category": 3 - }, - { - "question": "What outdoor activities does John enjoy?", - "answer": "Hiking, surfing", - "evidence": [ - "D3:27", - "D12:6" - ], - "category": 1 - }, - { - "question": "Who is Tim and John's favorite basketball player?", - "answer": "LeBron James", - "evidence": [ - "D12:20", - "D12:22", - "D16:9" - ], - "category": 1 - }, - { - "question": "Which week did Tim visit the UK for the Harry Potter Conference?", - "answer": "The week before October 13th, 2023.", - "evidence": [ - "D13:1" - ], - "category": 2 - }, - { - "question": "which country has Tim visited most frequently in his travels?", - "answer": "UK", - "evidence": [ - "D1:18", - "D13:1", - "D18:1" - ], - "category": 1 - }, - { - "question": "What year did Tim go to the Smoky Mountains?", - "answer": "2022", - "evidence": [ - "D14:16" - ], - "category": 2 - }, - { - "question": "Has Tim been to North Carolina and/or Tennesee states in the US?", - "answer": "Yes", - "evidence": [ - "D14:16" - ], - "category": 2 - }, - { - "question": "What kind of fiction stories does Tim write?", - "answer": "Fantasy stories with plot twists", - "evidence": [ - "D15:3", - "D16:1" - ], - "category": 1 - }, - { - "question": "What has John cooked?", - "answer": "Soup, a slow cooker meal, and honey garlic chicken with roasted veg.", - "evidence": [ - "D10:4", - "D15:30", - "D15:31", - "D15:32" - ], - "category": 1 - }, - { - "question": "What does John like about Lebron James?", - "answer": "His heart, determination, skills, and leadership.", - "evidence": [ - "D12:20", - "D16:12" - ], - "category": 1 - }, - { - "question": "When did John and his wife go on a European vacation?", - "answer": "November, 2023.", - "evidence": [ - "D16:14" - ], - "category": 2 - }, - { - "question": "Which country was Tim visiting in the second week of November?", - "answer": "UK", - "evidence": [ - "D18:1" - ], - "category": 2 - }, - { - "question": "Where was Tim in the week before 16 November 2023?", - "answer": "UK", - "evidence": [ - "D18:1" - ], - "category": 2 - }, - { - "question": "When did John get married at a greenhouse?", - "answer": "last week of September 2023", - "evidence": [ - "D12:2" - ], - "category": 2 - }, - { - "question": "When did John get an ankle injury in 2023?", - "answer": "around November 16, 2023", - "evidence": [ - "D18:2" - ], - "category": 1 - }, - { - "question": "How many times has John injured his ankle?", - "answer": "two times", - "evidence": [ - "D18:2", - "D19:6" - ], - "category": 1 - }, - { - "question": "Which book was John reading during his recovery from an ankle injury?", - "answer": "The Alchemist", - "evidence": [ - "D19:20", - "D18:2" - ], - "category": 1 - }, - { - "question": "What kind of yoga for building core strength might John benefit from?", - "answer": "Hatha Yoga", - "evidence": [ - "D20:2" - ], - "category": 3 - }, - { - "question": "What does John do to supplement his basketball training?", - "answer": "Yoga, strength training", - "evidence": [ - "D8:5", - "D20:2" - ], - "category": 1 - }, - { - "question": "What other exercises can help John with his basketball performance?", - "answer": "Sprinting, long-distance running, and boxing.", - "evidence": [ - "D8:5", - "D20:2" - ], - "category": 3 - }, - { - "question": "When did John take a trip to the Rocky Mountains?", - "answer": "2022", - "evidence": [ - "D20:40" - ], - "category": 2 - }, - { - "question": "When did John start playing professionally?", - "answer": "May, 2023", - "evidence": [ - "D1:3", - "D21:4" - ], - "category": 2 - }, - { - "question": "When did Tim start playing the violin?", - "answer": "August 2023", - "evidence": [ - "D21:13" - ], - "category": 2 - }, - { - "question": "What instruments does Tim play?", - "answer": "piano, violin", - "evidence": [ - "D8:12", - "D21:11" - ], - "category": 1 - }, - { - "question": "When did John attend the Harry Potter trivia?", - "answer": "August 2023.", - "evidence": [ - "D4:8", - "D22:2" - ], - "category": 2 - }, - { - "question": "Which career-high performances did John achieve in 2023?", - "answer": "highest point score, highest assist", - "evidence": [ - "D3:1", - "D23:2" - ], - "category": 1 - }, - { - "question": "When did John achieve a career-high assist performance?", - "answer": "December 11, 2023", - "evidence": [ - "D23:2" - ], - "category": 2 - }, - { - "question": "What books has John read?", - "answer": "inpsiring book on dreaming big, The Alchemist, fantasy series, non-fiction books on personal development, Dune", - "evidence": [ - "D4:10", - "D11:26", - "D17:9", - "D19:16", - "D19:20", - "D22:12" - ], - "category": 1 - }, - { - "question": "What does John do to share his knowledge?", - "answer": "gives seminars, mentors younger players.", - "evidence": [ - "D14:3", - "D26:1" - ], - "category": 1 - }, - { - "question": "When did John organize a basketball camp for kids?", - "answer": "summer 2023", - "evidence": [ - "D26:23" - ], - "category": 2 - }, - { - "question": "Which month was John in Italy?", - "answer": "December, 2023", - "evidence": [ - "D27:2" - ], - "category": 2 - }, - { - "question": "What fantasy movies does Tim like?", - "answer": "Lord of the Rings, Harry Potter, and Star Wars.", - "evidence": [ - "D8:16", - "D8:18", - "D26:28", - "D26:32", - "D27:21" - ], - "category": 1 - }, - { - "question": "What is a Star Wars book that Tim might enjoy?", - "answer": "Star Wars: Jedi Apprentice by Judy Blundell and David Farland. It is a highly rated and immersive series about his favorite movies.", - "evidence": [ - "D27:19", - "D27:21" - ], - "category": 3 - }, - { - "question": "What would be a good hobby related to his travel dreams for Tim to pick up?", - "answer": "Writing a travel blog.", - "evidence": [ - "D4:1", - "D6:6", - "D15:3", - "D27:37" - ], - "category": 3 - }, - { - "question": "What day did Tim get into his study abroad program?", - "answer": "Januarty 5, 2024", - "evidence": [ - "D28:1" - ], - "category": 2 - }, - { - "question": "When will Tim leave for Ireland?", - "answer": "February, 2024", - "evidence": [ - "D28:1" - ], - "category": 2 - }, - { - "question": "Which Star Wars-related locations would Tim enjoy during his visit to Ireland?", - "answer": "Skellig Michael, Malin Head, Loop Head, Ceann Sib\u00e9al, and Brow Head because they are Star Wars filming locations.", - "evidence": [ - "D1:18", - "D27:21", - "D28:1" - ], - "category": 3 - }, - { - "question": "Which team did John sign with on 21 May, 2023?", - "answer": "The Minnesota Wolves", - "evidence": [ - "D1:5" - ], - "category": 4 - }, - { - "question": "What is John's position on the team he signed with?", - "answer": "shooting guard", - "evidence": [ - "D1:7" - ], - "category": 4 - }, - { - "question": "What challenge did John encounter during pre-season training?", - "answer": "fitting into the new team's style of play", - "evidence": [ - "D1:11" - ], - "category": 4 - }, - { - "question": "What aspects of the Harry Potter universe will be discussed in John's fan project collaborations?", - "answer": "characters, spells, magical creatures", - "evidence": [ - "D1:16" - ], - "category": 4 - }, - { - "question": "What forum did Tim join recently?", - "answer": "fantasy literature forum", - "evidence": [ - "D2:1" - ], - "category": 4 - }, - { - "question": "What kind of picture did Tim share as part of their Harry Potter book collection?", - "answer": "MinaLima's creation from the Harry Potter films", - "evidence": [ - "D2:9" - ], - "category": 4 - }, - { - "question": "What was the highest number of points John scored in a game recently?", - "answer": "40 points", - "evidence": [ - "D3:1" - ], - "category": 4 - }, - { - "question": "What did John celebrate at a restaurant with teammates?", - "answer": "a tough win", - "evidence": [ - "D3:5" - ], - "category": 4 - }, - { - "question": "What kind of deals did John sign with Nike and Gatorade?", - "answer": "basketball shoe and gear deal with Nike, potential sponsorship deal with Gatorade", - "evidence": [ - "D3:13" - ], - "category": 4 - }, - { - "question": "Which city is John excited to have a game at?", - "answer": "Seattle", - "evidence": [ - "D3:19" - ], - "category": 4 - }, - { - "question": "How long has John been surfing?", - "answer": "five years", - "evidence": [ - "D3:27" - ], - "category": 4 - }, - { - "question": "How does John feel while surfing?", - "answer": "super exciting and free-feeling", - "evidence": [ - "D3:29" - ], - "category": 4 - }, - { - "question": "What kind of articles has Tim been writing about for the online magazine?", - "answer": "different fantasy novels, characters, themes, and book recommendations", - "evidence": [ - "D4:5" - ], - "category": 4 - }, - { - "question": "Which two fantasy novels does Tim particularly enjoy writing about?", - "answer": "Harry Potter and Game of Thrones", - "evidence": [ - "D4:7" - ], - "category": 4 - }, - { - "question": "What did Anthony and John end up playing during the charity event?", - "answer": "an intense Harry Potter trivia contest", - "evidence": [ - "D4:8" - ], - "category": 4 - }, - { - "question": "What did John share with the person he skyped about?", - "answer": "Characters from Harry Potter", - "evidence": [ - "D5:1" - ], - "category": 4 - }, - { - "question": "How did John describe the team bond?", - "answer": "Awesome", - "evidence": [ - "D5:6" - ], - "category": 4 - }, - { - "question": "How did John get introduced to basketball?", - "answer": "Dad signed him up for a local league", - "evidence": [ - "D6:13" - ], - "category": 4 - }, - { - "question": "What is John's number one goal in his basketball career?", - "answer": "Winning a championship", - "evidence": [ - "D6:15" - ], - "category": 4 - }, - { - "question": "What organization is John teaming up with for his charity work?", - "answer": "A local organization helping disadvantaged kids with sports and school", - "evidence": [ - "D6:17" - ], - "category": 4 - }, - { - "question": "When did John meet back up with his teammates after his trip in August 2023?", - "answer": "Aug 15th", - "evidence": [ - "D7:1" - ], - "category": 4 - }, - { - "question": "What did John's teammates give him when they met on Aug 15th?", - "answer": "a basketball with autographs on it", - "evidence": [ - "D7:7" - ], - "category": 4 - }, - { - "question": "Why did John's teammates sign the basketball they gave him?", - "answer": "to show their friendship and appreciation", - "evidence": [ - "D7:9" - ], - "category": 4 - }, - { - "question": "What is the main intention behind Tim wanting to attend the book conference?", - "answer": "to learn more about literature and create a stronger bond to it", - "evidence": [ - "D7:6" - ], - "category": 4 - }, - { - "question": "What new activity has Tim started learning in August 2023?", - "answer": "play the piano", - "evidence": [ - "D8:12" - ], - "category": 4 - }, - { - "question": "Which movie's theme is Tim's favorite to play on the piano?", - "answer": "\"Harry Potter and the Philosopher's Stone\"", - "evidence": [ - "D8:14", - "D8:16" - ], - "category": 4 - }, - { - "question": "What special memory does \"Harry Potter and the Philosopher's Stone\" bring to Tim?", - "answer": "Watching it with his family", - "evidence": [ - "D8:16" - ], - "category": 4 - }, - { - "question": "Which movie does Tim mention they enjoy watching during Thanksgiving?", - "answer": "\"Home Alone\"", - "evidence": [ - "D8:24" - ], - "category": 4 - }, - { - "question": "What tradition does Tim mention they love during Thanksgiving?", - "answer": "Prepping the feast and talking about what they're thankful for", - "evidence": [ - "D8:22" - ], - "category": 4 - }, - { - "question": "How long did John and his high school basketball teammates play together?", - "answer": "Four years", - "evidence": [ - "D9:4" - ], - "category": 4 - }, - { - "question": "How was John's experience in New York City?", - "answer": "Amazing", - "evidence": [ - "D9:8" - ], - "category": 4 - }, - { - "question": "What did John say about NYC, enticing Tim to visit?", - "answer": "It's got so much to check out - the culture, food - you won't regret it.", - "evidence": [ - "D9:10" - ], - "category": 4 - }, - { - "question": "What kind of soup did John make recently?", - "answer": "tasty soup with sage", - "evidence": [ - "D10:4", - "D10:8" - ], - "category": 4 - }, - { - "question": "What spice did John add to the soup for flavor?", - "answer": "sage", - "evidence": [ - "D10:8" - ], - "category": 4 - }, - { - "question": "What is Tim excited to see at Universal Studios?", - "answer": "The Harry Potter stuff", - "evidence": [ - "D10:11" - ], - "category": 4 - }, - { - "question": "Where are John and his teammates planning to explore on a team trip?", - "answer": "a new city", - "evidence": [ - "D11:7" - ], - "category": 4 - }, - { - "question": "What city did Tim suggest to John for the team trip next month?", - "answer": "Edinburgh, Scotland", - "evidence": [ - "D11:10" - ], - "category": 4 - }, - { - "question": "What does John want to do after his basketball career?", - "answer": "positively influence and inspire others, potentially start a foundation and engage in charity work", - "evidence": [ - "D11:19" - ], - "category": 4 - }, - { - "question": "What advice did Tim give John about picking endorsements?", - "answer": "Ensure they align with values and brand, look for companies that share the desire to make a change and help others, make sure the endorsement feels authentic", - "evidence": [ - "D11:22" - ], - "category": 4 - }, - { - "question": "What book recommendation did Tim give to John for the trip?", - "answer": "A fantasy novel by Patrick Rothfuss", - "evidence": [ - "D11:24" - ], - "category": 4 - }, - { - "question": "What type of venue did John and his girlfriend choose for their wedding ceremony?", - "answer": "Greenhouse", - "evidence": [ - "D12:4" - ], - "category": 4 - }, - { - "question": "What was the setting for John and his wife's first dance?", - "answer": "Cozy restaurant", - "evidence": [ - "D12:10" - ], - "category": 4 - }, - { - "question": "Which basketball team does Tim support?", - "answer": "The Wolves", - "evidence": [ - "D12:21" - ], - "category": 4 - }, - { - "question": "What passion does Tim mention connects him with people from all over the world?", - "answer": "passion for fantasy stuff", - "evidence": [ - "D13:1" - ], - "category": 4 - }, - { - "question": "How does John describe the game season for his team?", - "answer": "intense with tough losses and great wins", - "evidence": [ - "D13:4" - ], - "category": 4 - }, - { - "question": "How does John say his team handles tough opponents?", - "answer": "by backing each other up and not quitting", - "evidence": [ - "D13:6" - ], - "category": 4 - }, - { - "question": "What motivates John's team to get better, according to John?", - "answer": "facing tough opponents", - "evidence": [ - "D13:6" - ], - "category": 4 - }, - { - "question": "What did John's team win at the end of the season?", - "answer": "a trophy", - "evidence": [ - "D13:8" - ], - "category": 4 - }, - { - "question": "Where did Tim capture the photography of the sunset over the mountain range?", - "answer": "Smoky Mountains", - "evidence": [ - "D14:16" - ], - "category": 4 - }, - { - "question": "How does John feel about being seen as a mentor by some of the younger players?", - "answer": "It feels great", - "evidence": [ - "D14:11" - ], - "category": 4 - }, - { - "question": "What does John find rewarding about mentoring the younger players?", - "answer": "Seeing their growth, improvement, and confidence", - "evidence": [ - "D14:7" - ], - "category": 4 - }, - { - "question": "What has John been able to help the younger players achieve?", - "answer": "reach their goals", - "evidence": [ - "D14:5" - ], - "category": 4 - }, - { - "question": "What genre is the novel that Tim is writing?", - "answer": "Fantasy", - "evidence": [ - "D15:3" - ], - "category": 4 - }, - { - "question": "Who is one of Tim's sources of inspiration for writing?", - "answer": "J.K. Rowling", - "evidence": [ - "D15:7" - ], - "category": 4 - }, - { - "question": "What J.K. Rowling quote does Tim resonate with?", - "answer": "\"Turn on the light - happiness hides in the darkest of times.\"", - "evidence": [ - "D15:11" - ], - "category": 4 - }, - { - "question": "What does John write on the whiteboard to help him stay motivated?", - "answer": "motivational quotes and strategies", - "evidence": [ - "D15:14" - ], - "category": 4 - }, - { - "question": "What hobby is a therapy for John when away from the court?", - "answer": "Cooking", - "evidence": [ - "D15:30" - ], - "category": 4 - }, - { - "question": "What type of meal does John often cook using a slow cooker?", - "answer": "honey garlic chicken with roasted veg", - "evidence": [ - "D15:32", - "D15:33" - ], - "category": 4 - }, - { - "question": "How will John share the honey garlic chicken recipe with the other person?", - "answer": "write it down and mail it", - "evidence": [ - "D15:34" - ], - "category": 4 - }, - { - "question": "What was Tim's huge writing issue last week,as mentioned on November 6, 2023?", - "answer": "He got stuck on a plot twist", - "evidence": [ - "D16:1" - ], - "category": 4 - }, - { - "question": "What does Tim have that serves as a reminder of hard work and is his prized possession?", - "answer": "a basketball signed by his favorite player", - "evidence": [ - "D16:7" - ], - "category": 4 - }, - { - "question": "Why do Tim and John find LeBron inspiring?", - "answer": "LeBron's determination and the epic block in Game 7 of the '16 Finals", - "evidence": [ - "D16:9", - "D16:10" - ], - "category": 4 - }, - { - "question": "How did John describe the views during their road trip out on the European coastline?", - "answer": "Spectacular", - "evidence": [ - "D17:3" - ], - "category": 4 - }, - { - "question": "What is one of Tim's favorite fantasy TV shows, as mentioned on November 11, 2023?", - "answer": "\"That\"", - "evidence": [ - "D17:10" - ], - "category": 4 - }, - { - "question": "How does Tim stay motivated during difficult study sessions?", - "answer": "Visualizing goals and success", - "evidence": [ - "D18:6" - ], - "category": 4 - }, - { - "question": "What did Tim say about his injury on 16 November, 2023?", - "answer": "The doctor said it's not too serious", - "evidence": [ - "D18:10" - ], - "category": 4 - }, - { - "question": "What was the setback Tim faced in his writing project on 21 November, 2023?", - "answer": "Story based on experiences in the UK didn't go as planned", - "evidence": [ - "D19:3" - ], - "category": 4 - }, - { - "question": "How did John overcome his ankle injury from last season?", - "answer": "stayed focused on recovery and worked hard to strengthen his body", - "evidence": [ - "D19:6" - ], - "category": 4 - }, - { - "question": "What motivated Tim to keep pushing himself to get better in writing and reading?", - "answer": "Love for writing and reading", - "evidence": [ - "D19:9" - ], - "category": 4 - }, - { - "question": "How did John overcome a mistake he made during a big game in basketball?", - "answer": "Worked hard to get better and focused on growth", - "evidence": [ - "D19:10" - ], - "category": 4 - }, - { - "question": "What book did John recently finish rereading that left him feeling inspired and hopeful about following dreams?", - "answer": "The Alchemist", - "evidence": [ - "D19:20" - ], - "category": 4 - }, - { - "question": "How did \"The Alchemist\" impact John's perspective on following dreams?", - "answer": "made him think again about following dreams and searching for personal legends", - "evidence": [ - "D19:20" - ], - "category": 4 - }, - { - "question": "What is John trying out to improve his strength and flexibility after recovery from ankle injury?", - "answer": "yoga", - "evidence": [ - "D20:2" - ], - "category": 4 - }, - { - "question": "How long does John usually hold the yoga pose he shared with Tim?", - "answer": "30-60 seconds", - "evidence": [ - "D20:10" - ], - "category": 4 - }, - { - "question": "Where was the forest picture shared by John on December 1,2023 taken?", - "answer": "near his hometown", - "evidence": [ - "D20:28" - ], - "category": 4 - }, - { - "question": "What did Tim recently start learning in addition to being part of a travel club and working on studies?", - "answer": "an instrument", - "evidence": [ - "D21:9" - ], - "category": 4 - }, - { - "question": "What instrument is Tim learning to play in December 2023?", - "answer": "violin", - "evidence": [ - "D21:11" - ], - "category": 4 - }, - { - "question": "How long has Tim been playing the piano for, as of December 2023?", - "answer": "about four months", - "evidence": [ - "D21:13" - ], - "category": 4 - }, - { - "question": "What book did Tim just finish reading on 8th December, 2023?", - "answer": "\"A Dance with Dragons\"", - "evidence": [ - "D22:13" - ], - "category": 4 - }, - { - "question": "Which book did Tim recommend to John as a good story on 8th December, 2023?", - "answer": "\"A Dance with Dragons\"", - "evidence": [ - "D22:13" - ], - "category": 4 - }, - { - "question": "What is the topic of discussion between John and Tim on 11 December, 2023?", - "answer": "Academic achievements and sports successes", - "evidence": [ - "D23:1", - "D23:2", - "D23:3" - ], - "category": 4 - }, - { - "question": "What kind of game did John have a career-high in assists in?", - "answer": "basketball", - "evidence": [ - "D23:3" - ], - "category": 4 - }, - { - "question": "What was John's way of dealing with doubts and stress when he was younger?", - "answer": "practicing basketball outside for hours", - "evidence": [ - "D23:9" - ], - "category": 4 - }, - { - "question": "How did John feel about the atmosphere during the big game against the rival team?", - "answer": "electric and intense", - "evidence": [ - "D23:5" - ], - "category": 4 - }, - { - "question": "How did John feel after being able to jog without pain?", - "answer": "It was a huge success.", - "evidence": [ - "D24:16" - ], - "category": 4 - }, - { - "question": "What kind of deal did John get in December?", - "answer": "Deal with a renowned outdoor gear company", - "evidence": [ - "D25:2" - ], - "category": 4 - }, - { - "question": "Where was the photoshoot done for John's gear deal?", - "answer": "In a gorgeous forest", - "evidence": [ - "D25:4" - ], - "category": 4 - }, - { - "question": "In which area has John's team seen the most growth during training?", - "answer": "Communication and bonding", - "evidence": [ - "D25:14" - ], - "category": 4 - }, - { - "question": "What type of seminars is John conducting?", - "answer": "Sports and marketing seminars", - "evidence": [ - "D26:1" - ], - "category": 4 - }, - { - "question": "What activity did Tim do after reading the stories about the Himalayan trek?", - "answer": "visited a travel agency", - "evidence": [ - "D26:12" - ], - "category": 4 - }, - { - "question": "What is one cause that John supports with his influence and resources?", - "answer": "youth sports and fair chances in sports", - "evidence": [ - "D26:21" - ], - "category": 4 - }, - { - "question": "What new fantasy TV series is Tim excited about?", - "answer": "\"The Wheel of Time\"", - "evidence": [ - "D26:36" - ], - "category": 4 - }, - { - "question": "Which language is Tim learning?", - "answer": "German", - "evidence": [ - "D27:5" - ], - "category": 4 - }, - { - "question": "What language does Tim know besides German?", - "answer": "Spanish", - "evidence": [ - "D27:6" - ], - "category": 4 - }, - { - "question": "What book did Tim get in Italy that inspired him to cook?", - "answer": "a cooking book", - "evidence": [ - "D27:4" - ], - "category": 4 - }, - { - "question": "What is John's favorite book series?", - "answer": "Harry Potter", - "evidence": [ - "D27:19" - ], - "category": 4 - }, - { - "question": "According to John, who is his favorite character from Lord of the Rings?", - "answer": "Aragorn", - "evidence": [ - "D27:24" - ], - "category": 4 - }, - { - "question": "Why does John like Aragorn from Lord of the Rings?", - "answer": "brave, selfless, down-to-earth attitude", - "evidence": [ - "D27:30" - ], - "category": 4 - }, - { - "question": "What kind of painting does John have in his room as a reminder?", - "answer": "a painting of Aragorn", - "evidence": [ - "D27:28" - ], - "category": 4 - }, - { - "question": "What is the painting of Aragorn a reminder for John to be in everything he does?", - "answer": "be a leader", - "evidence": [ - "D27:28" - ], - "category": 4 - }, - { - "question": "What map does Tim show to his friend John?", - "answer": "a map of Middle-earth from LOTR", - "evidence": [ - "D27:33" - ], - "category": 4 - }, - { - "question": "Where will Tim be going for a semester abroad?", - "answer": "Ireland", - "evidence": [ - "D28:1" - ], - "category": 4 - }, - { - "question": "Which city in Ireland will Tim be staying in during his semester abroad?", - "answer": "Galway", - "evidence": [ - "D28:3" - ], - "category": 4 - }, - { - "question": "What charity event did John organize recently in 2024?", - "answer": "benefit basketball game", - "evidence": [ - "D28:10" - ], - "category": 4 - }, - { - "question": "What achievement did John share with Tim in January 2024?", - "answer": "endorsement with a popular beverage company", - "evidence": [ - "D29:4" - ], - "category": 4 - }, - { - "question": "What was Johns's reaction to sealing the deal with the beverage company?", - "answer": "crazy feeling, sense of accomplishment", - "evidence": [ - "D29:6" - ], - "category": 4 - }, - { - "question": "Which city did John recommend to Tim in January 2024?", - "answer": "Barcelona", - "evidence": [ - "D29:12" - ], - "category": 4 - }, - { - "question": "Which team did Tim sign with on 21 May, 2023?", - "evidence": [ - "D1:5" - ], - "category": 5, - "adversarial_answer": "The Minnesota Wolves" - }, - { - "question": "What is Tim's position on the team he signed with?", - "evidence": [ - "D1:7" - ], - "category": 5, - "adversarial_answer": "shooting guard" - }, - { - "question": "What challenge did Tim encounter during pre-season training?", - "evidence": [ - "D1:11" - ], - "category": 5, - "adversarial_answer": "fitting into the new team's style of play" - }, - { - "question": "What cult did Tim join recently?", - "evidence": [ - "D2:1" - ], - "category": 5, - "adversarial_answer": "fantasy literature forum" - }, - { - "question": "What was the highest number of points Tim scored in a game recently?", - "evidence": [ - "D3:1" - ], - "category": 5, - "adversarial_answer": "40 points" - }, - { - "question": "What did Tim celebrate at a restaurant with teammates?", - "evidence": [ - "D3:5" - ], - "category": 5, - "adversarial_answer": "a tough win" - }, - { - "question": "What kind of deals did Tim sign with Nike and Gatorade?", - "evidence": [ - "D3:13" - ], - "category": 5, - "adversarial_answer": "basketball shoe and gear deal with Nike, potential sponsorship deal with Gatorade" - }, - { - "question": "How does Tim feel while surfing?", - "evidence": [ - "D3:29" - ], - "category": 5, - "adversarial_answer": "super exciting and free-feeling" - }, - { - "question": "What kind of articles has John been writing about for the online magazine?", - "evidence": [ - "D4:5" - ], - "category": 5, - "adversarial_answer": "different fantasy novels, characters, themes, and book recommendations" - }, - { - "question": "Which two mystery novels does Tim particularly enjoy writing about?", - "evidence": [ - "D4:7" - ], - "category": 5, - "adversarial_answer": "Harry Potter and Game of Thrones" - }, - { - "question": "What did Anthony and Tim end up playing during the charity event?", - "evidence": [ - "D4:8" - ], - "category": 5, - "adversarial_answer": "an intense Harry Potter trivia contest" - }, - { - "question": "How did Tim get introduced to basketball?", - "evidence": [ - "D6:13" - ], - "category": 5, - "adversarial_answer": "Dad signed him up for a local league" - }, - { - "question": "What is Tim's number one goal in his basketball career?", - "evidence": [ - "D6:15" - ], - "category": 5, - "adversarial_answer": "Winning a championship" - }, - { - "question": "What organization is Tim teaming up with for his charity work?", - "evidence": [ - "D6:17" - ], - "category": 5, - "adversarial_answer": "A local organization helping disadvantaged kids with sports and school" - }, - { - "question": "What did Tim's teammates give him when they met on Aug 15th?", - "evidence": [ - "D7:7" - ], - "category": 5, - "adversarial_answer": "a basketball with autographs on it" - }, - { - "question": "Why did John's teammates sign the football they gave him?", - "evidence": [ - "D7:9" - ], - "category": 5, - "adversarial_answer": "to show their friendship and appreciation" - }, - { - "question": "What is the main intention behind John wanting to attend the book conference?", - "evidence": [ - "D7:6" - ], - "category": 5, - "adversarial_answer": "to learn more about literature and create a stronger bond to it" - }, - { - "question": "What new activity has John started learning in August 2023?", - "evidence": [ - "D8:12" - ], - "category": 5, - "adversarial_answer": "play the piano" - }, - { - "question": "What special memory does \"Fifty Shades of Grey\" bring to Tim?", - "evidence": [ - "D8:16" - ], - "category": 5, - "adversarial_answer": "Watching it with his family" - }, - { - "question": "Which movie does John mention they enjoy watching during Thanksgiving?", - "evidence": [ - "D8:24" - ], - "category": 5, - "adversarial_answer": "\"Home Alone\"" - }, - { - "question": "What tradition does Tim mention they love during Halloween?", - "evidence": [ - "D8:22" - ], - "category": 5, - "adversarial_answer": "Prepping the feast and talking about what they're thankful for" - }, - { - "question": "How long did Tim and his high school basketball teammates play together?", - "evidence": [ - "D9:4" - ], - "category": 5, - "adversarial_answer": "Four years" - }, - { - "question": "How was Tim's experience in New York City?", - "evidence": [ - "D9:8" - ], - "category": 5, - "adversarial_answer": "Amazing" - }, - { - "question": "What spice did Tim add to the soup for flavor?", - "evidence": [ - "D10:8" - ], - "category": 5, - "adversarial_answer": "sage" - }, - { - "question": "What is Tim excited to see at Disneyland?", - "evidence": [ - "D10:11" - ], - "category": 5, - "adversarial_answer": "The Harry Potter stuff" - }, - { - "question": "Where are John and his teammates planning to avoid on a team trip?", - "evidence": [ - "D11:7" - ], - "category": 5, - "adversarial_answer": "a new city" - }, - { - "question": "What does Tim want to do after his basketball career?", - "evidence": [ - "D11:19" - ], - "category": 5, - "adversarial_answer": "positively influence and inspire others, potentially start a foundation and engage in charity work" - }, - { - "question": "What type of venue did John and his girlfriend choose for their breakup?", - "evidence": [ - "D12:4" - ], - "category": 5, - "adversarial_answer": "Greenhouse" - }, - { - "question": "What passion does John mention connects him with people from all over the world?", - "evidence": [ - "D13:1" - ], - "category": 5, - "adversarial_answer": "passion for fantasy stuff" - }, - { - "question": "How does Tim say his team handles tough opponents?", - "evidence": [ - "D13:6" - ], - "category": 5, - "adversarial_answer": "by backing each other up and not quitting" - }, - { - "question": "Where did Tim capture the painting of the sunset over the mountain range?", - "evidence": [ - "D14:16" - ], - "category": 5, - "adversarial_answer": "Smoky Mountains" - }, - { - "question": "What does Tim find rewarding about mentoring the younger players?", - "evidence": [ - "D14:7" - ], - "category": 5, - "adversarial_answer": "Seeing their growth, improvement, and confidence" - }, - { - "question": "What has Tim been able to help the younger players achieve?", - "evidence": [ - "D14:5" - ], - "category": 5, - "adversarial_answer": "reach their goals" - }, - { - "question": "What genre is the novel that John is writing?", - "evidence": [ - "D15:3" - ], - "category": 5, - "adversarial_answer": "Fantasy" - }, - { - "question": "Who is one of Tim's sources of inspiration for painting?", - "evidence": [ - "D15:7" - ], - "category": 5, - "adversarial_answer": "J.K. Rowling" - }, - { - "question": "What does Tim write on the whiteboard to help him stay motivated?", - "evidence": [ - "D15:14" - ], - "category": 5, - "adversarial_answer": "motivational quotes and strategies" - }, - { - "question": "What hobby is a therapy for Tim when away from the court?", - "evidence": [ - "D15:30" - ], - "category": 5, - "adversarial_answer": "Cooking" - }, - { - "question": "What type of meal does Tim often cook using a slow cooker?", - "evidence": [ - "D15:32", - "D15:33" - ], - "category": 5, - "adversarial_answer": "honey garlic chicken with roasted veg" - }, - { - "question": "How will Tim share the honey garlic chicken recipe with the other person?", - "evidence": [ - "D15:34" - ], - "category": 5, - "adversarial_answer": "write it down and mail it" - }, - { - "question": "What is one of Tim's favorite crime TV shows, as mentioned on November 11, 2023?", - "evidence": [ - "D17:10" - ], - "category": 5, - "adversarial_answer": "\"That\"" - }, - { - "question": "What was the setback Tim faced in his coding project on 21 November, 2023?", - "evidence": [ - "D19:3" - ], - "category": 5, - "adversarial_answer": "Story based on experiences in the UK didn't go as planned" - }, - { - "question": "How did Tim overcome his ankle injury from last season?", - "evidence": [ - "D19:6" - ], - "category": 5, - "adversarial_answer": "stayed focused on recovery and worked hard to strengthen his body" - }, - { - "question": "What motivated John to keep pushing himself to get better in writing and reading?", - "evidence": [ - "D19:9" - ], - "category": 5, - "adversarial_answer": "Love for writing and reading" - }, - { - "question": "How did Tim overcome a mistake he made during a big game in basketball?", - "evidence": [ - "D19:10" - ], - "category": 5, - "adversarial_answer": "Worked hard to get better and focused on growth" - }, - { - "question": "What is Tim trying out to improve his strength and flexibility after recovery from ankle injury?", - "evidence": [ - "D20:2" - ], - "category": 5, - "adversarial_answer": "yoga" - }, - { - "question": "What did John recently start learning in addition to being part of a travel club and working on studies?", - "evidence": [ - "D21:9" - ], - "category": 5, - "adversarial_answer": "an instrument" - }, - { - "question": "What instrument is John learning to play in December 2023?", - "evidence": [ - "D21:11" - ], - "category": 5, - "adversarial_answer": "violin" - }, - { - "question": "How long has John been playing the piano for, as of December 2023?", - "evidence": [ - "D21:13" - ], - "category": 5, - "adversarial_answer": "about four months" - }, - { - "question": "What movie did Tim just finish watching on 8th December, 2023?", - "evidence": [ - "D22:13" - ], - "category": 5, - "adversarial_answer": "\"A Dance with Dragons\"" - }, - { - "question": "What kind of game did Tim have a career-high in assists in?", - "evidence": [ - "D23:3" - ], - "category": 5, - "adversarial_answer": "basketball" - }, - { - "question": "What was Tim's way of dealing with doubts and stress when he was younger?", - "evidence": [ - "D23:9" - ], - "category": 5, - "adversarial_answer": "practicing basketball outside for hours" - }, - { - "question": "Where was the photoshoot done for John's fragrance deal?", - "evidence": [ - "D25:4" - ], - "category": 5, - "adversarial_answer": "In a gorgeous forest" - }, - { - "question": "In which area has Tim's team seen the most growth during training?", - "evidence": [ - "D25:14" - ], - "category": 5, - "adversarial_answer": "Communication and bonding" - }, - { - "question": "What type of seminars is Tim conducting?", - "evidence": [ - "D26:1" - ], - "category": 5, - "adversarial_answer": "Sports and marketing seminars" - }, - { - "question": "What is one cause that John opposes with his influence and resources?", - "evidence": [ - "D26:21" - ], - "category": 5, - "adversarial_answer": "youth sports and fair chances in sports" - }, - { - "question": "What new fantasy TV series is John excited about?", - "evidence": [ - "D26:36" - ], - "category": 5, - "adversarial_answer": "\"The Wheel of Time\"" - }, - { - "question": "Which language is John learning?", - "evidence": [ - "D27:5" - ], - "category": 5, - "adversarial_answer": "German" - }, - { - "question": "According to John, who is his least favorite character from Lord of the Rings?", - "evidence": [ - "D27:24" - ], - "category": 5, - "adversarial_answer": "Aragorn" - }, - { - "question": "Why does Tim like Aragorn from Lord of the Rings?", - "evidence": [ - "D27:30" - ], - "category": 5, - "adversarial_answer": "brave, selfless, down-to-earth attitude" - }, - { - "question": "What kind of painting does Tim have in his room as a reminder?", - "evidence": [ - "D27:28" - ], - "category": 5, - "adversarial_answer": "a painting of Aragorn" - }, - { - "question": "What is the sculpture of Aragorn a reminder for John to be in everything he does?", - "evidence": [ - "D27:28" - ], - "category": 5, - "adversarial_answer": "be a leader" - }, - { - "question": "Which city in Ireland will John be staying in during his semester abroad?", - "evidence": [ - "D28:3" - ], - "category": 5, - "adversarial_answer": "Galway" - }, - { - "question": "What charity event did Tim organize recently in 2024?", - "evidence": [ - "D28:10" - ], - "category": 5, - "adversarial_answer": "benefit basketball game" - }, - { - "question": "What was Tims's reaction to sealing the deal with the beverage company?", - "evidence": [ - "D29:6" - ], - "category": 5, - "adversarial_answer": "crazy feeling, sense of accomplishment" - } - ], - "conversation": { - "speaker_a": "Tim", - "speaker_b": "John", - "session_1_date_time": "7:48 pm on 21 May, 2023", - "session_1": [ - { - "speaker": "John", - "dia_id": "D1:1", - "text": "Hey Tim, nice to meet you! What's up? Anything new happening?" - }, - { - "speaker": "Tim", - "dia_id": "D1:2", - "text": "Hey John! Great to meet you. Been discussing collaborations for a Harry Potter fan project I am working on - super excited! Anything interesting happening for you?" - }, - { - "speaker": "John", - "dia_id": "D1:3", - "text": "That's great! I just signed with a new team - excited for the season!" - }, - { - "speaker": "Tim", - "dia_id": "D1:4", - "text": "Woohoo! Congrats on the new team. Which team did you sign with?" - }, - { - "speaker": "John", - "dia_id": "D1:5", - "text": "The Minnesota Wolves! I can't wait to play with them!" - }, - { - "speaker": "Tim", - "dia_id": "D1:6", - "text": "Cool! What position are you playing for the team? Any exciting games coming up?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/odlwr40mol581.jpg" - ], - "blip_caption": "a photo of a bunch of basketball jerseys laying on a bed", - "query": "basketball jersey collection", - "dia_id": "D1:7", - "text": "I'm a shooting guard for the team and our season opener is next week - so excited!" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a basketball game in progress with the ball in the air", - "dia_id": "D1:8", - "text": "Cool! Have any goals in mind?" - }, - { - "speaker": "John", - "img_url": [ - "https://blog.drdishbasketball.com/hubfs/IMG_0232%20%282%29.jpg" - ], - "blip_caption": "a photo of two men standing next to a basketball machine", - "query": "basketball hoop shooting percentage", - "dia_id": "D1:9", - "text": "Yeah, my goal is to improve my shooting percentage. Been practicing hard and gonna make it happen." - }, - { - "speaker": "Tim", - "dia_id": "D1:10", - "text": "Sounds good! What challenges have you encountered during your pre-season training?" - }, - { - "speaker": "John", - "dia_id": "D1:11", - "text": "Fitting into the new team's style of play was a challenge during pre-season." - }, - { - "speaker": "Tim", - "dia_id": "D1:12", - "text": "That sounds rough. How are things going with the new team?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a bench in a locker with several lockers behind it", - "dia_id": "D1:13", - "text": "Things are going well! The team has been really nice and I'm having fun. How's your fan project coming along?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/l30ofyg973ja1.jpg" - ], - "blip_caption": "a photo of a table with a bunch of books on it", - "query": "harry potter books fan project collaboration wizarding world", - "dia_id": "D1:14", - "text": "It's been going well! Last week I talked to my friend who is a fan of Harry Potter and we're figuring out ideas, so it's been great to get lost in that magical world!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a circle of shoes on the floor in a room", - "dia_id": "D1:15", - "text": "That's great! Loving it when people are passionate about their work. What kind of collaborations are you involved in for the fan project? I love talking to people about my sneaker collection." - }, - { - "speaker": "Tim", - "dia_id": "D1:16", - "text": "Thanks! We'll be discussing various aspects of the Harry Potter universe, like characters, spells, and magical creatures. It's great to see fans coming together for this." - }, - { - "speaker": "John", - "blip_caption": "a photo of a bookcase filled with books and toys", - "dia_id": "D1:17", - "text": "Wow! Have you been to any places related to it?" - }, - { - "speaker": "Tim", - "dia_id": "D1:18", - "text": "I went to a place in London a few years ago - it was like walking into a Harry Potter movie! I also went on a tour which was amazing. Have you been to any of the real Potter places? I'd love to explore them someday!" - }, - { - "speaker": "John", - "dia_id": "D1:19", - "text": "No, but it sounds fun! Going to those places is definitely on my to-do list." - }, - { - "speaker": "Tim", - "dia_id": "D1:20", - "text": "Definitely add it to your list! It's a really fun experience. Let me know if you need any tips for visiting. Bye!" - } - ], - "session_2_date_time": "5:08 pm on 15 June, 2023", - "session_2": [ - { - "speaker": "Tim", - "dia_id": "D2:1", - "text": "Last night I joined a fantasy literature forum and had a great talk about my fave books. It was so enriching!" - }, - { - "speaker": "John", - "dia_id": "D2:2", - "text": "Wow, great to hear that you had a blast talking books! It's cool to connect with others who share your passion. On a different note, exciting things are happening--I'm exploring endorsement opportunities. Thinking about the possibilities pumps me up. It would be amazing to work with brands and do something special. It's so rewarding to have my hard work pay off like this." - }, - { - "speaker": "Tim", - "dia_id": "D2:3", - "text": "Wow, that's awesome! Congrats - you must be so stoked! Which brands are you looking to link up with?" - }, - { - "speaker": "John", - "dia_id": "D2:4", - "text": "Thanks! I'm really excited about this new journey! I'm currently considering sports brands like Nike and Under Armour. It would be great to collaborate with brands that are related to sports. However, I'm also open to exploring other brands that align with my values and interests. There are so many options out there, and I can't wait to see where this takes me!" - }, - { - "speaker": "Tim", - "dia_id": "D2:5", - "text": "That's awesome! It's gotta be a rush having all these options. Can't wait to see which brands you choose to work with - gonna be great!" - }, - { - "speaker": "John", - "dia_id": "D2:6", - "text": "Thanks! Exciting times ahead! I'll keep you updated on which brands I choose. Can't wait to see where this journey leads me. Thanks for your support." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/7m46xuo8lgj81.jpg" - ], - "blip_caption": "a photo of a book shelf with books and a picture on it", - "query": "bookshelf harry potter game of thrones", - "dia_id": "D2:7", - "text": "Yeah, John! Count on me for support. Can't wait to see what's up! This is my book collection so far." - }, - { - "speaker": "John", - "dia_id": "D2:8", - "text": "Wow, nice bookshelf! That picture is really interesting. What's up with it?" - }, - { - "speaker": "Tim", - "dia_id": "D2:9", - "text": "Thanks! That picture is from MinaLima. They created all the props for the Harry Potter films, and I love their work. It's like having a piece of the wizarding world at home!" - }, - { - "speaker": "John", - "dia_id": "D2:10", - "text": "Wow, having that is so cool! Your enthusiasm for it is awesome. You really go for it!" - }, - { - "speaker": "Tim", - "dia_id": "D2:11", - "text": "Thanks! I have lots of reminders of it - kind of a way to escape reality." - }, - { - "speaker": "John", - "dia_id": "D2:12", - "text": "Do those reminders help you escape the daily grind? Any chance you'll visit more places related to that world soon?" - }, - { - "speaker": "Tim", - "dia_id": "D2:13", - "text": "Definitely, those reminders really help. And there's definitely a chance I'll be visiting more HP spots in the future. It feels like I'm stepping into the books!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/fz2segua15d81.jpg" - ], - "blip_caption": "a photo of a basketball game being played in a gym", - "query": "basketball game", - "dia_id": "D2:14", - "text": "That sounds awesome! So cool that you get to immerse yourself in that world. So glad you found something that brings you so much joy. Keep diving in and enjoying it! Here's a pic from a recent game." - }, - { - "speaker": "Tim", - "dia_id": "D2:15", - "text": "Wow! That's awesome! Were you playing or watching?" - }, - { - "speaker": "John", - "dia_id": "D2:16", - "text": "Thanks! That was from a game I just played. I was in it! It was awesome being out there, doing what I love. Such an awesome feeling." - }, - { - "speaker": "Tim", - "dia_id": "D2:17", - "text": "Wow! You look so into it in that pic \u2013 it must be so awesome playing at that level! Keep rockin' it!" - }, - { - "speaker": "John", - "dia_id": "D2:18", - "text": "Thanks! It's a blast. Giving it my all every time I'm on the court. Really appreciate your support!" - }, - { - "speaker": "Tim", - "dia_id": "D2:19", - "text": "Yeah, keep going! Don't give up on your dreams. Talk to you later!" - } - ], - "session_3_date_time": "4:21 pm on 16 July, 2023", - "session_3": [ - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/44/145388126_0fd9b13887_b.jpg" - ], - "blip_caption": "a photography of a score board with a clock and a phone", - "query": "basketball scoreboard personal best", - "dia_id": "D3:1", - "re-download": true, - "text": "Hey Tim! Good to see you again. So much has happened in the last month - on and off the court. Last week I scored 40 points, my highest ever, and it feels like all my hard work's paying off." - }, - { - "speaker": "Tim", - "img_url": [ - "https://live.staticflickr.com/2174/2061292757_73e8ef5397_b.jpg" - ], - "blip_caption": "a photography of a table with a bunch of books on it", - "query": "harry potter books california discussion favorite books and characters magical experience", - "dia_id": "D3:2", - "re-download": true, - "text": "Congrats on your achievement! I'm so proud of you. Last week, I had a nice chat with a Harry Potter fan in California. It was magical!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/87ov530kw9ub1.jpg" - ], - "blip_caption": "a photo of a group of men sitting on top of a basketball court", - "query": "teammates celebrating court", - "dia_id": "D3:3", - "text": "Thank you! Scoring those points was an incredible experience. The atmosphere was electric, and my teammates and I were thrilled. We pulled off a tough win!" - }, - { - "speaker": "Tim", - "dia_id": "D3:4", - "text": "Wow, sounds awesome! Winning after that game must have felt amazing - what was it like? Did you celebrate afterward?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting at a table eating", - "dia_id": "D3:5", - "text": "We were all exhausted but so happy. After that, we celebrated at a restaurant, laughing and reliving the intense moments - it felt amazing!" - }, - { - "speaker": "Tim", - "dia_id": "D3:6", - "text": "Wow, sounds like a blast! I had an incredible time meeting with that fellow fan. You can really feel the love when you're surrounded by people who share the same passion. Does that happen with your sport too?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a rack of basketball jerseys in a store", - "dia_id": "D3:7", - "text": "Definitely! Being surrounded by teammates who are equally passionate creates a strong bond. We push each other to be our best and the love for the game is infectious. It's like having a second family." - }, - { - "speaker": "Tim", - "dia_id": "D3:8", - "text": "That's awesome! Having a second family through sport must be such a great feeling. Glad you have that support. Oh, you mentioned exploring endorsements - have you made any progress?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.imgur.com/uvVOoeM.jpg" - ], - "blip_caption": "a photo of a handwritten letter with a black ink marker", - "query": "handwritten marketing plan", - "dia_id": "D3:9", - "text": "Yeah, I'm getting somewhere with endorsements. I've talked to some big names, which looks promising. Exciting to see what's in store!" - }, - { - "speaker": "Tim", - "dia_id": "D3:10", - "text": "How did you manage to connect with these big companies?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a basketball card with a picture of a man holding a basketball", - "dia_id": "D3:11", - "text": "I used my contacts in the basketball industry and my marketing skills to make connections. Networking plays a big role in getting endorsements, and I'm grateful for the support I've received." - }, - { - "speaker": "Tim", - "dia_id": "D3:12", - "text": "Wow, what endorsements have you managed to get through networking?" - }, - { - "speaker": "John", - "dia_id": "D3:13", - "text": "I just signed up Nike for a basketball shoe and gear deal. I'm also in talks with Gatorade about a potential sponsorship. It's pretty cool to be working with such big brands!" - }, - { - "speaker": "Tim", - "dia_id": "D3:14", - "text": "Wow, Congrats on those deals with Nike and Gatorade! You're killing it! Any other brands you're dreaming of working with?" - }, - { - "speaker": "John", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/img-8577-1571255920.jpg" - ], - "blip_caption": "a photo of a mannequin in a blue suit and a chair", - "query": "under armour advertisement", - "dia_id": "D3:15", - "text": "Thanks! The Nike and Gatorade deals have me stoked! I've always liked Under Armour, working with them would be really cool." - }, - { - "speaker": "Tim", - "dia_id": "D3:16", - "text": "Wow! What kind of stuff are you exploring? It looks like good things are coming your way." - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/5854539/pexels-photo-5854539.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-millah-5854539.jpg" - ], - "blip_caption": "a photo of a city skyline at sunset with a body of water", - "query": "city skyline sunset", - "dia_id": "D3:17", - "text": "Just checking out some exciting things that are happening. Really looking forward to what's coming next! This is where I'm headed." - }, - { - "speaker": "Tim", - "dia_id": "D3:18", - "text": "Wow, amazing view! Where's that? What's got you so excited?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/ua7cvodhrkca1.jpg" - ], - "blip_caption": "a photo of a crowd of people watching a basketball game", - "query": "seattle basketball court bright lights", - "dia_id": "D3:19", - "text": "It's Seattle, I'm stoked for my game there next month! It's one of my favorite cities to explore - super vibrant!" - }, - { - "speaker": "Tim", - "dia_id": "D3:20", - "text": "Cool! What do you love about Seattle?" - }, - { - "speaker": "John", - "dia_id": "D3:21", - "text": "I love the energy, diversity, and awesome food of this city. Trying local seafood is a must! Plus, the support from the fans at games is incredible." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a stack of three plates of food with crab legs", - "dia_id": "D3:22", - "text": "Sounds fab! Seattle is definitely a great and colorful city. I've always wanted to try the seafood there. Good luck with everything!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D3:23", - "text": "Thanks! Can't wait for the seafood too. I love the ocean." - }, - { - "speaker": "Tim", - "dia_id": "D3:24", - "text": "That looks peaceful! Do you have a favorite beach memory?" - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/8890340/pexels-photo-8890340.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-anastasia-baranova-8890340.jpg" - ], - "blip_caption": "a photo of a man holding a surfboard on a beach", - "query": "surfing waves beach scenery surfboard", - "dia_id": "D3:25", - "text": "I had an awesome summer with my friends, surfing and riding the waves. The feeling was unreal!" - }, - { - "speaker": "Tim", - "dia_id": "D3:26", - "text": "Wow! How long have you been surfing?" - }, - { - "speaker": "John", - "dia_id": "D3:27", - "text": "I started surfing five years ago and it's been great. I love the connection to nature." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a person riding a surfboard on a body of water", - "dia_id": "D3:28", - "text": "Wow! That sounds amazing! The connection to nature must be incredible." - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D3:29", - "text": "Yup! Being out in the water can be amazing. The waves, the wind, it's super exciting and free-feeling. Nature's pretty special." - }, - { - "speaker": "Tim", - "img_url": [ - "https://d3525k1ryd2155.cloudfront.net/h/072/089/1318089072.0.x.jpg" - ], - "blip_caption": "a photo of a book with a harry potter cover", - "query": "harry potter books", - "dia_id": "D3:30", - "text": "That's awesome! I don't surf, but reading a great fantasy book helps me escape and feel free." - }, - { - "speaker": "John", - "dia_id": "D3:31", - "text": "Cool! We all find our own way to escape and feel free!" - }, - { - "speaker": "Tim", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1617734023/at/news-culture/2021-04/katharine-scrivener-nook.jpg" - ], - "blip_caption": "a photo of a living room with a brown couch and a white ottoman", - "query": "cozy reading nook blanket pillows", - "dia_id": "D3:32", - "text": "Yeah! It's great to find stuff that makes us happy and feel free. It's like bliss for me when I do this in a comfy spot. It's like being in another world, same as surfing is for you." - }, - { - "speaker": "John", - "dia_id": "D3:33", - "text": "Yeah! Those moments of happiness and freedom are amazing. Let's all find our own bliss." - }, - { - "speaker": "Tim", - "dia_id": "D3:34", - "text": "Sure thing! It's what makes life awesome!" - }, - { - "speaker": "John", - "dia_id": "D3:35", - "text": "Yeah. Awesome catching up! Bye!" - } - ], - "session_4_date_time": "4:17 pm on 2 August, 2023", - "session_4": [ - { - "speaker": "Tim", - "dia_id": "D4:1", - "text": "Hey John! How've you been? Something awesome happened - I'm writing articles about fantasy novels for an online mag. It's so rewarding!" - }, - { - "speaker": "John", - "dia_id": "D4:2", - "text": "Hey Tim! Congrats on the opportunity to write about what you're into! How did it happen?" - }, - { - "speaker": "Tim", - "dia_id": "D4:3", - "text": "Thanks! I found this opportunity on a fantasy lit forum and thought it'd be perfect since I love fantasy. I shared my ideas with the magazine and they liked them! It's been awesome to spread my love of fantasy." - }, - { - "speaker": "John", - "dia_id": "D4:4", - "text": "Congratulations! That's awesome. What kind of articles have you been writing?" - }, - { - "speaker": "Tim", - "dia_id": "D4:5", - "text": "Thanks! I've been writing about different fantasy novels, studying characters, themes, and making book recommendations." - }, - { - "speaker": "John", - "dia_id": "D4:6", - "text": "Awesome! Must be so rewarding to delve into your books and chat about them. Do you have any favorite books you love writing about?" - }, - { - "speaker": "Tim", - "dia_id": "D4:7", - "text": "For sure! Harry Potter and Game of Thrones are amazing - I'm totally hooked! I could chat about them forever!" - }, - { - "speaker": "John", - "img_url": [ - "https://pictures.abebooks.com/inventory/31262885157_3.jpg" - ], - "blip_caption": "a photo of a book with a picture of a person holding a bookmark", - "query": "harry potter signed book", - "dia_id": "D4:8", - "text": "Oh yeah, I remember you telling me about Harry Potter! I've got a funny story. Anthony and I went to this charity thing and ended up in this intense Harry Potter trivia contest. We did alright, but there was this one super-nerd there that took home this as a prize.\n" - }, - { - "speaker": "Tim", - "dia_id": "D4:9", - "text": "That looks great! The signature is sweet! Have you been reading anything?" - }, - { - "speaker": "John", - "dia_id": "D4:10", - "text": "I've been reading this inspiring book, it reminds me to keep dreaming." - }, - { - "speaker": "Tim", - "dia_id": "D4:11", - "text": "Books can really inspire and help us keep our dreams alive. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D4:12", - "text": "Thanks! They really do. I want to keep reaching for new goals." - }, - { - "speaker": "Tim", - "dia_id": "D4:13", - "text": "Same here!" - }, - { - "speaker": "John", - "dia_id": "D4:14", - "text": "Have fun with your writing! Catch you later!" - }, - { - "speaker": "Tim", - "dia_id": "D4:15", - "text": "Thanks! I'll enjoy writing them. Take care and talk soon!" - } - ], - "session_5_date_time": "10:29 am on 9 August, 2023", - "session_5": [ - { - "speaker": "Tim", - "dia_id": "D5:1", - "text": "Hey John! Long time no see! Been super busy lately. Guess what? Just skyped with that Harry Potter fan I met in CA and had a great time. We talked characters and maybe collab-ing - so cool to talk to someone who gets it. You? Anything new going on?" - }, - { - "speaker": "John", - "img_url": [ - "https://i0.wp.com/thepirateshook.com/wp-content/uploads/2022/11/img_7438-edited.jpg" - ], - "blip_caption": "a photo of a basketball game being played in a large arena", - "query": "intense game basketball winning shot crowd", - "dia_id": "D5:2", - "text": "Hi Tim! Nice to hear from you. Glad you could reconnect. As for me, lots of stuff happened since we last talked. Last week I had a crazy game - crazy intense! We won it by a tight score. Scoring that last basket and hearing the crowd cheer was awesome!" - }, - { - "speaker": "Tim", - "dia_id": "D5:3", - "text": "Nice work! Bet it felt awesome to score that basket and have the crowd going wild. Must have been such an adrenaline rush! Did you manage to capture any other photos from the game?" - }, - { - "speaker": "John", - "dia_id": "D5:4", - "text": "Thanks! It was an amazing rush and just the one I showed you. We were so hyped!" - }, - { - "speaker": "Tim", - "dia_id": "D5:5", - "text": "Awesome! Winning a tough game must have been such an exhilarating experience!" - }, - { - "speaker": "John", - "dia_id": "D5:6", - "text": "Our team bond is awesome and it makes all the hard work worth it." - }, - { - "speaker": "Tim", - "dia_id": "D5:7", - "text": "It's incredible how a team creates such strong ties. Having support like that is so important." - }, - { - "speaker": "John", - "dia_id": "D5:8", - "text": "Thanks! You nailed it! Having a strong team/support is key - it's like a family away from home. We push each other to improve, and I'm so thankful for them." - }, - { - "speaker": "Tim", - "dia_id": "D5:9", - "text": "That's great! Having a supportive team who are like family is awesome. Having people who motivate you and stick by you is priceless." - }, - { - "speaker": "John", - "dia_id": "D5:10", - "text": "Definitely! They encourage me when I'm down too. It's not just in my sport, but in other aspects of life too. We hang out a lot and it's great having that bond." - }, - { - "speaker": "Tim", - "dia_id": "D5:11", - "text": "Yeah, having another family is great. It definitely helps with my home life and hobbies." - }, - { - "speaker": "John", - "dia_id": "D5:12", - "text": "Having someone to support and motivate you is so important, whether it's in sports or any other aspect of life. I know you've found your peace in reading fantasy books - that's amazing! What book are you currently reading? Anything that has stood out to you?" - }, - { - "speaker": "Tim", - "dia_id": "D5:13", - "text": "Thanks for asking! I'm reading a fantasy book that really captivates me. It takes me to another world where I'm on the edge of my seat and my imagination soars. It's amazing how books can transport us like that." - }, - { - "speaker": "John", - "dia_id": "D5:14", - "text": "Books can be so captivating, taking us on such incredible journeys! What's the name of it?" - }, - { - "speaker": "Tim", - "dia_id": "D5:15", - "text": "It's a book by Patrick Rothfuss and it's awesome! The way the author builds the world and characters is amazing. You should read it!" - }, - { - "speaker": "John", - "dia_id": "D5:16", - "text": "Sounds cool! I'll definitely check it out. Thanks for the recommendation!" - }, - { - "speaker": "Tim", - "dia_id": "D5:17", - "text": "No problem! Let me know what you think after you read it." - }, - { - "speaker": "John", - "dia_id": "D5:18", - "text": "Yep, I'll let you know once I'm done reading it. Thanks!" - }, - { - "speaker": "Tim", - "dia_id": "D5:19", - "text": "I hope you like it. Chat soon!" - }, - { - "speaker": "John", - "dia_id": "D5:20", - "text": "Me too. Talk to you soon!" - } - ], - "session_6_date_time": "1:08 pm on 11 August, 2023", - "session_6": [ - { - "speaker": "John", - "dia_id": "D6:1", - "text": "Hey Tim, sorry I missed you. Been a crazy few days. Took a trip to a new place - it's been amazing. Love the energy there." - }, - { - "speaker": "Tim", - "dia_id": "D6:2", - "text": "Hey John, no worries! I get how life can be busy. Where did you go? Glad you had a great time! Exploring new places can be so inspiring and fun. I recently went to an event and it was fantastic. Being with other fans who love it too was so special. Have you ever gone to an event related to something you like?" - }, - { - "speaker": "John", - "dia_id": "D6:3", - "text": "I was in Chicago, it was awesome! It had so much energy and the locals were really friendly. It's great to experience other cultures and connect with new folks." - }, - { - "speaker": "Tim", - "dia_id": "D6:4", - "text": "Wow, Chicago sounds great! It's refreshing to try something new and connect with people from different backgrounds. Have you ever been to a sports game and felt a real connection with the other fans?" - }, - { - "speaker": "John", - "dia_id": "D6:5", - "text": "Yeah! There's nothing like the energy in a stadium during a game. Everyone's cheering, chanting, and getting so excited. It's a really special experience!" - }, - { - "speaker": "Tim", - "img_url": [ - "https://cdn27.picryl.com/photo/1934/01/01/herbert-brutus-ehrmann-papers-1906-1970-sacco-vanzetti-book-review-by-edmund-216c54-1024.jpg" - ], - "blip_caption": "a photography of a book opened to a page with a picture of a man", - "query": "fantasy novel article screenshot", - "dia_id": "D6:6", - "re-download": true, - "text": "I can just imagine the thrill of being in that kind of atmosphere. Must've been an amazing experience for you! BTW, I have been writing more articles - it lets me combine my love for reading and the joy of sharing great stories. Here's my latest one!" - }, - { - "speaker": "John", - "dia_id": "D6:7", - "text": "That's awesome! Have you come across any interesting books lately?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/ydnhc3r3efw61.jpg" - ], - "blip_caption": "a photo of a book set of three books on a wooden table", - "query": "name of the wind patrick rothfuss book cover", - "dia_id": "D6:8", - "text": "Thanks! \"The Name of the Wind\" is great. It's a fantasy novel with a great magician and musician protagonist. The world-building and character development are really good. Definitely worth a read if you're looking for something captivating!" - }, - { - "speaker": "John", - "img_url": [ - "https://images.novelship.com/product-lookbook/ao7VLb2K1Qnp-1682611652416-54349732-76B9-483E-B2C2-CE8033DA879E.jpg" - ], - "blip_caption": "a photo of a pair of sneakers in a box", - "query": "lucky basketball shoes", - "dia_id": "D6:9", - "text": "That book sounds awesome! Love a good fantasy with strong characters and cool world-building. Cheers for the suggestion. Adding it to my list. These are my lucky basketball shoes. They've been with me through the good and bad. Every mark has a story." - }, - { - "speaker": "Tim", - "dia_id": "D6:10", - "text": "Your shoes must have a lot of stories behind them. Want to share some with me?" - }, - { - "speaker": "John", - "dia_id": "D6:11", - "text": "Yes, these have been with me on my journey since the beginning. All the successes, the failures, the friends - I have so many stories to tell. They're more than just a pair of shoes, they symbolize resilience, determination, and a love for the game. They remind me of what I've achieved and how far I've come." - }, - { - "speaker": "Tim", - "dia_id": "D6:12", - "text": "Those shoes are special. They show your hard work, your successes, and all the amazing times you've had with basketball. It's awesome how meaningful objects can become. So inspiring. How did you get into the game?" - }, - { - "speaker": "John", - "dia_id": "D6:13", - "text": "Thanks! Basketball has been a part of my life ever since I was a kid. I'd watch NBA games with my dad and dream of playing on those big courts. When I turned ten, dad signed me up for a local league, and I've been playing ever since. I kept playing through middle and high school before earning a college scholarship. And after college, I was drafted by a team \u2013 my dream come true!" - }, - { - "speaker": "Tim", - "dia_id": "D6:14", - "text": "Wow! You really made your childhood dream come true. It's impressive how your dedication and hard work paid off. It's awesome how our passions shape our lives. Do you have any big goals for your basketball career?" - }, - { - "speaker": "John", - "dia_id": "D6:15", - "text": "Yeah! Winning a championship is my number one goal. But I also want to make a difference away from the court, like through charity or inspiring people. Basketball has been great to me, so I want to give something back." - }, - { - "speaker": "Tim", - "dia_id": "D6:16", - "text": "Winning a title and making a difference off the court is inspiring. How do you plan to kick off your charity work?" - }, - { - "speaker": "John", - "dia_id": "D6:17", - "text": "I'm teaming up with a local organization that helps disadvantaged kids with sports and school. I'm hoping to use my platform to have a positive impact on the community and inspire others as well." - }, - { - "speaker": "Tim", - "dia_id": "D6:18", - "text": "Making a difference like that is truly amazing. I can't wait to see the impact it'll have. All the best for your charity work!" - }, - { - "speaker": "John", - "dia_id": "D6:19", - "text": "Thanks! Really appreciate the support. It means a lot. I'm excited to work hard and make a positive impact." - }, - { - "speaker": "Tim", - "dia_id": "D6:20", - "text": "No worries. I'm here to support you. You've got tons of determination and passion! Keep it up - you're gonna make a difference!" - }, - { - "speaker": "John", - "dia_id": "D6:21", - "text": "Thanks! Your words mean a lot. I'll do my best!" - }, - { - "speaker": "Tim", - "dia_id": "D6:22", - "text": "Glad I could help. You've got this!" - }, - { - "speaker": "John", - "dia_id": "D6:23", - "text": "Thanks! Talk to you later!" - } - ], - "session_7_date_time": "7:54 pm on 17 August, 2023", - "session_7": [ - { - "speaker": "John", - "dia_id": "D7:1", - "text": "Hey Tim! We had a wild few days since we talked. I met back up with my teammates on the 15th after my trip and it was amazing! Everyone missed me. The atmosphere was electric and I felt so welcome being back with them. I'm so lucky to be a part of this team!" - }, - { - "speaker": "Tim", - "dia_id": "D7:2", - "text": "Wow, John, that sounds amazing! I'm so happy they gave you a warm welcome back. It's such a special feeling when you realize that you share the same passions and talents with others. It's like finding your true place in the world." - }, - { - "speaker": "John", - "dia_id": "D7:3", - "text": "Thanks! Wow, it was such an incredible experience. Being around people who share the same love for basketball creates a special kind of bond. Have you ever felt like you truly belonged somewhere?" - }, - { - "speaker": "Tim", - "dia_id": "D7:4", - "text": "Yeah, definitely. I felt like I belonged a few times, but last month at that event was one of my favorites. Everyone shared the same love for it and it felt like being in a world where everyone understood it. I'm really thankful for those experiences - it's great to know there are people out there who appreciate and share my interests." - }, - { - "speaker": "John", - "dia_id": "D7:5", - "text": "Cool! It's great when you find a group that appreciates the same things. It really adds something special to our lives. So, do you have any exciting plans or events coming up?" - }, - { - "speaker": "Tim", - "dia_id": "D7:6", - "text": "I have no big events coming up, but I'm hoping to attend a book conference next month. It's an interesting gathering of authors, publishers and book lovers where we talk about our favorite novels and new releases. I'm excited to go because it'll help me learn more about literature and create a stronger bond to it." - }, - { - "speaker": "John", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/6/61/2018_DII_Elite_Eight_Northern_State_Signed_Basketball.jpg" - ], - "blip_caption": "a photography of a basketball with autographs on it sitting on a table", - "query": "basketball signed teammates", - "dia_id": "D7:7", - "re-download": true, - "text": "You're a real bookworm! It would be awesome to go to a book conference with you. Check out this photo of what my teammates gave me when we met. It's a sign of our friendship and all the love we have for each other." - }, - { - "speaker": "Tim", - "dia_id": "D7:8", - "text": "That's so cool of your teammates. Did they sign it for a special reason?" - }, - { - "speaker": "John", - "dia_id": "D7:9", - "text": "Thanks! They signed it to show our friendship and appreciation. It's a great reminder of our bond." - }, - { - "speaker": "Tim", - "dia_id": "D7:10", - "text": "That's really cool. It's great that you have something to remind you of your friends. Keeping a bit of their energy and support with you is always nice." - }, - { - "speaker": "John", - "dia_id": "D7:11", - "text": "Having something like this ball to remind me of the bond and support from my teammates is really comforting. It's a nice reminder of why I started playing basketball and my journey. It motivates me to stay strong and give it my all." - }, - { - "speaker": "Tim", - "dia_id": "D7:12", - "text": "That's so sweet. It's great to have something so meaningful to keep you motivated. I'll keep that in mind next time I need a push to reach my goals." - }, - { - "speaker": "John", - "dia_id": "D7:13", - "text": "It's really motivating to have something that reminds you of why you started, and having supportive people around is like having a cheer team that helps you through tough times." - }, - { - "speaker": "Tim", - "dia_id": "D7:14", - "text": "Yeah, that's true. Having them there to cheer you on can be a powerful source of strength." - }, - { - "speaker": "John", - "dia_id": "D7:15", - "text": "Yeah, having that support really encourages me to give it my all and never give up. It's an awesome feeling!" - }, - { - "speaker": "Tim", - "dia_id": "D7:16", - "text": "It's awesome how much strength people can get from each other. Bye!" - } - ], - "session_8_date_time": "4:29 pm on 21 August, 2023", - "session_8": [ - { - "speaker": "John", - "img_url": [ - "https://d2rzw8waxoxhv2.cloudfront.net/facilities/xlarge/fafaccd30c88b7506920/1569529088544-992-69.jpg" - ], - "blip_caption": "a photo of a gym with a basketball court and cones", - "query": "basketball court gym", - "dia_id": "D8:1", - "text": "Hey Tim! Long time no talk. Hope you're doing great. Crazy things have been going on in my life. Just the other day, I found a new gym to stay on my b-ball game. Staying fit is essential to surviving pro ball, so I had to find something that fits the bill. Finding the right spot was tough but here we are!" - }, - { - "speaker": "Tim", - "dia_id": "D8:2", - "text": "Hey John! Really good to hear from you. Staying fit is so important. Must be so cool to practice there. Any issues you had when you got it?" - }, - { - "speaker": "John", - "dia_id": "D8:3", - "text": "It's been great training here. The gym is awesome, but I had to overcome the hurdle of adapting and tweaking my routine. Finding the right balance was tricky, but I eventually got the hang of it." - }, - { - "speaker": "Tim", - "dia_id": "D8:4", - "text": "Nice one! It can be tough getting used to a new routine, but once you figure it out, it gets easier. How did you find that balance?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/sch5pby1ivi31.jpg" - ], - "blip_caption": "a photo of a notebook with a list of items on it", - "query": "workout schedule", - "dia_id": "D8:5", - "text": "Thanks! Took some trial and error but I figured out a schedule with both basketball stuff and strength training to balance it out. Listening to my body and giving it enough rest made it easier to push myself during practice but also look after me. Here's my workout plan. It helps a lot with staying on track." - }, - { - "speaker": "Tim", - "dia_id": "D8:6", - "text": "Nice job! Impressive plan you've got there! You've really thought it out. Why include strength training in your routine?" - }, - { - "speaker": "John", - "dia_id": "D8:7", - "text": "Thanks! Strength training is important for basketball because it builds muscle, increases power, and prevents injuries. It also helps me become more explosive, which is essential in games. Plus, it boosts my athleticism overall." - }, - { - "speaker": "Tim", - "dia_id": "D8:8", - "text": "That makes sense! Your holistic approach seems to have numerous benefits. Does strength training have a positive impact on your basketball performance?" - }, - { - "speaker": "John", - "dia_id": "D8:9", - "text": "Definitely! Incorporating strength training really changed the game for me, improving my shooting accuracy, agility, and speed. It gave me the upper hand over my opponents and helped me up my game. It gave me the confidence to take on whatever comes my way." - }, - { - "speaker": "Tim", - "dia_id": "D8:10", - "text": "Awesome! Gaining confidence on the court must feel great. It's cool how strength training can benefit you. You're doing great in both basketball and fitness, keep it up!" - }, - { - "speaker": "John", - "dia_id": "D8:11", - "text": "Thanks! Appreciate your support. It's been a journey, but I'm happy with the progress. Excited to see what's next. What about you? How have you been?" - }, - { - "speaker": "Tim", - "dia_id": "D8:12", - "text": "Things have been great since we last talked - I've been focusing on school and reading a bunch of fantasy books. It's a nice way to take a break from all the stress. I've also started learning how to play the piano - it's a learning curve, but it's so satisfying seeing the progress I make! Life's good." - }, - { - "speaker": "John", - "dia_id": "D8:13", - "text": "Wow! You're staying busy and having fun. Learning to play this is awesome - it's such a beautiful instrument. Do you have any favorite songs you like playing on it?" - }, - { - "speaker": "Tim", - "dia_id": "D8:14", - "text": "Thanks! I love playing different songs on the piano, but my favorite one to jam to is a theme from a movie I really enjoy. It brings back lots of great memories." - }, - { - "speaker": "John", - "dia_id": "D8:15", - "text": "Wow, that's cool! Music really has a way of bringing back memories and evoking emotions, doesn't it? Almost like taking us back in time. Could you tell me more about that film and the memories it brings up for you?" - }, - { - "speaker": "Tim", - "dia_id": "D8:16", - "text": "Yeah, \"Harry Potter and the Philosopher's Stone\" is special to me. It was the first movie from the series and brings back some great memories. Watching it with my family was amazing. It was so magical!" - }, - { - "speaker": "John", - "img_url": [ - "https://www.goodwillfinds.com/on/demandware.static/-/Sites-goodwill-master/default/dw41d27013/images/large/lhyOBm1CPSKy54szJay7vQj/2023/November/14/image_(140).jpg" - ], - "blip_caption": "a photo of a dvd cover with a castle in the background", - "query": "harry potter dvd collection", - "dia_id": "D8:17", - "text": "Wow, that sounds great, Tim! I love that first movie too, I even have the whole collection! It was so magical! Must've been a dream watching it with your family." - }, - { - "speaker": "Tim", - "dia_id": "D8:18", - "text": "It was really a dream come true! Watching that movie with my family was awesome, we'd all get comfy with snacks and a blanket and be totally absorbed. Such a special memory!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people standing around a kitchen table", - "dia_id": "D8:19", - "text": "Cool! Cherish those family moments - they're so irreplaceable. Family time is great! Mine gets together all the time too." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/peqj8rke7zz11.jpg" - ], - "blip_caption": "a photo of a family sitting on a couch in front of a fireplace", - "query": "Thanksgiving family photo", - "dia_id": "D8:20", - "text": "Family time means a lot to me. This photo is from a special day when we all got together to eat. It was a great day full of love and laughter!" - }, - { - "speaker": "John", - "dia_id": "D8:21", - "text": "Wow, that looks like such a great day! Do you have any favorite Thanksgiving traditions?" - }, - { - "speaker": "Tim", - "dia_id": "D8:22", - "text": "Thanksgiving's always special for us. We love prepping the feast and talking about what we're thankful for. Plus, watching some movies afterwards - the best!" - }, - { - "speaker": "John", - "dia_id": "D8:23", - "text": "Thanksgiving dinner with family sounds great! Do you have any favorite movies you watch together?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/4zp9nizyheq61.jpg" - ], - "blip_caption": "a photo of a dvd cover with a child in a house", - "query": "home alone movie poster", - "dia_id": "D8:24", - "text": "During Thanksgiving, we usually watch a few movies. We love \"Home Alone\" - it always brings lots of laughs!" - }, - { - "speaker": "John", - "dia_id": "D8:25", - "text": "That's a classic! What other movies do you watch during the holidays?" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a dvd cover of a movie with a leprechaun", - "dia_id": "D8:26", - "text": "We also watch \"Elf\" during the holidays. It makes us laugh and get us feeling festive!" - }, - { - "speaker": "John", - "dia_id": "D8:27", - "text": "Those are awesome! Any other holiday movies do you enjoy watching?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/h18us9b4sjfz.jpg" - ], - "blip_caption": "a photo of a dvd cover of a santa clause movie", - "query": "the santa clause dvd cover santa claus holiday", - "dia_id": "D8:28", - "text": "We love \"The Santa Clause\" too- it's so heartwarming and gets us all feeling festive!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/d1/87/78/d187788dbb64158d2cbd6a1eaaa7e86b.jpg" - ], - "blip_caption": "a photo of a christmas tree with a lot of lights on it", - "query": "christmas tree colorful lights ornaments", - "dia_id": "D8:29", - "text": "\"The Santa Clause\" is a classic! It's so sweet and really captures the Christmas magic. It's just one of those movies that gets us all feeling festive. This was our tree last year." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.pinimg.com/originals/2d/be/40/2dbe40682781579e457e7ee5a0cc79fc.jpg" - ], - "blip_caption": "a photo of a christmas tree with a harry potter theme", - "query": "christmas tree harry potter ornament", - "dia_id": "D8:30", - "text": "Yep, it really does. That tree pic looks awesome! It must add so much holiday cheer to your house. This was ours." - }, - { - "speaker": "John", - "dia_id": "D8:31", - "text": "That looks awesome! Where did you get this tree?" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a christmas tree with a harry potter theme", - "dia_id": "D8:32", - "text": "I decorated this tree myself, going all out with a Harry Potter theme! It was a blast!" - }, - { - "speaker": "John", - "dia_id": "D8:33", - "text": "That themed tree looks amazing! You really know how to get the vibes just right!" - }, - { - "speaker": "Tim", - "dia_id": "D8:34", - "text": "Thanks! It was such a fun project and I'm really happy with how it turned out." - }, - { - "speaker": "John", - "dia_id": "D8:35", - "text": "Glad you had fun!" - }, - { - "speaker": "Tim", - "dia_id": "D8:36", - "text": "Great catching up! Take care, talk soon." - }, - { - "speaker": "John", - "dia_id": "D8:37", - "text": "Catch ya later! Talk soon. Take care and enjoy the rest of your day." - } - ], - "session_9_date_time": "6:59 pm on 26 August, 2023", - "session_9": [ - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/nziqtijj65i11.jpg" - ], - "blip_caption": "a photo of a stack of books on a table", - "query": "stack of textbooks and notes", - "dia_id": "D9:1", - "text": "Hey John, this week's been really busy for me. Assignments and exams are overwhelming. I'm not giving up though! I'm trying to find a way to juggle studying with my fantasy reading hobby. How have you been?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/79/c5/6c/79c56ca8e8173619eb96c1058eb5bb67.jpg" - ], - "blip_caption": "a photo of a group of girls basketball players posing for a picture", - "query": "group friends basketball posing", - "dia_id": "D9:2", - "text": "Hey Tim! I know the stress of exams and homework, but you got this! I'm doing OK, cheers for asking. Last week I visited home and caught up with my family and old friends. We had a great time talking about our childhood - it reminds me of the good ol' times!" - }, - { - "speaker": "Tim", - "dia_id": "D9:3", - "text": "Thanks for the pic! That group looks like a great squad. How long did you all play together?" - }, - { - "speaker": "John", - "dia_id": "D9:4", - "text": "We were teammates for four years in high school, so we've played together for quite some time. Have you ever been part of a sports team?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/bq2ogzcjuzz41.jpg" - ], - "blip_caption": "a photo of a book shelf with books and a clock", - "query": "harry potter books shelf", - "dia_id": "D9:5", - "text": "Nope, never been on a sports team. I'm more into reading and fantasy novels. I love sinking into different magical worlds. It's one of the reasons I love traveling to new places, to experience a different kind of magic." - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/90/49/55/904955fe77567cf689d7db0ce606717d.jpg" - ], - "blip_caption": "a photo of a cityscape with a view of a skyscraper", - "query": "new york city skyline", - "dia_id": "D9:6", - "text": "Wow, Tim, that's an awesome book collection! It's cool to escape to different worlds with a hobby. By the way, I love discovering new cities - check out this pic from one of my trips to New York City!" - }, - { - "speaker": "Tim", - "dia_id": "D9:7", - "text": "Wow! That skyline looks amazing - I've been wanting to visit NYC. How was it?" - }, - { - "speaker": "John", - "dia_id": "D9:8", - "text": "Thanks! It was amazing. Everywhere you go there's something new and exciting. Exploring the city and trying all the restaurants was awesome. It's a must-visit!" - }, - { - "speaker": "Tim", - "dia_id": "D9:9", - "text": "Adding NYC to my travel list, sounds like a great adventure! I heard there's so much to explore and try out. Can't wait to visit!" - }, - { - "speaker": "John", - "dia_id": "D9:10", - "text": "Trust me, NYC is amazing! It's got so much to check out - the culture, food - you won't regret it. It's an adventure you'll never forget!" - }, - { - "speaker": "Tim", - "dia_id": "D9:11", - "text": "Woohoo! Sounds like a fun place with lots of potential. Can't wait to experience it for myself!" - }, - { - "speaker": "John", - "dia_id": "D9:12", - "text": "Awesome! Can't wait to hear when you are going. Let me know and I'm sure I can help you out." - }, - { - "speaker": "Tim", - "dia_id": "D9:13", - "text": "Yep, I'll let you know! Thanks for being so helpful." - }, - { - "speaker": "John", - "dia_id": "D9:14", - "text": "Sure thing! Any time you need help, don't hesitate to reach out." - }, - { - "speaker": "Tim", - "dia_id": "D9:15", - "text": "Thanks! Your support means a lot to me. Bye!" - } - ], - "session_10_date_time": "2:52 pm on 31 August, 2023", - "session_10": [ - { - "speaker": "Tim", - "dia_id": "D10:1", - "text": "Hey John, it's been a few days! I got a no for a summer job I wanted which wasn't great but I'm staying positive. On your NYC trip, did you have any troubles? How did you handle them?" - }, - { - "speaker": "John", - "dia_id": "D10:2", - "text": "Hey Tim! Sorry to hear about the job, but your positivity will help you find something great! My trip went okay - I had some trouble figuring out the subway at first, but then it was easy after someone helped explain it. How about you? Anything new you've tackled?" - }, - { - "speaker": "Tim", - "dia_id": "D10:3", - "text": "Thanks! Appreciate your encouragement. Yesterday, I tackled something new - I gave a presentation in class. I was nervous but I made it. Small step, but feels like progress." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/zr17msb27uy91.jpg" - ], - "blip_caption": "a photo of a bowl of soup with a spoon and a butternut on a cutting board", - "query": "beautifully plated chicken soup", - "dia_id": "D10:4", - "text": "Cool, Tim! Taking the plunge and presenting can be tough, but awesome work! Progress is progress, keep it up. By the way, I've been trying out cooking recipes. Made this tasty soup recently - it was real good!" - }, - { - "speaker": "Tim", - "dia_id": "D10:5", - "text": "Wow, that looks great! How did you make it? Do you have a recipe you can share?" - }, - { - "speaker": "John", - "dia_id": "D10:6", - "text": "Thanks, I just sort of made it up on the spot so I don't have a recipe." - }, - { - "speaker": "Tim", - "dia_id": "D10:7", - "text": "That's ok! I can look some up. Can you tell me what spices you used in the soup?" - }, - { - "speaker": "John", - "dia_id": "D10:8", - "text": "I added some sage for a nice flavor. Enjoy!" - }, - { - "speaker": "Tim", - "dia_id": "D10:9", - "text": "Thanks! Excited to try this. Love experimenting with spices. By the way, have you been to Universal Studios? Planning a trip there next month." - }, - { - "speaker": "John", - "dia_id": "D10:10", - "text": "Cool! Haven't been there yet, but I've heard great things about Universal Studios. It's definitely on my bucket list. Have you been before?" - }, - { - "speaker": "Tim", - "dia_id": "D10:11", - "text": "Nope, but it's my first time going. I'm super stoked for the Harry Potter stuff. Can't wait!" - }, - { - "speaker": "John", - "dia_id": "D10:12", - "text": "Cool! It's gonna be a blast, like stepping into another world. Have a great time!" - }, - { - "speaker": "Tim", - "dia_id": "D10:13", - "text": "Thanks! I'll definitely have a blast. I'll let you know how it goes!" - }, - { - "speaker": "John", - "dia_id": "D10:14", - "text": "Great! Can't wait to hear about it. Have a safe trip!" - }, - { - "speaker": "Tim", - "dia_id": "D10:15", - "text": "Thanks! I'll make sure to have a safe trip." - }, - { - "speaker": "John", - "dia_id": "D10:16", - "text": "Bye! Take care and let's catch up soon!" - }, - { - "speaker": "Tim", - "dia_id": "D10:17", - "text": "Take care! Can't wait to catch up. Talk soon!" - } - ], - "session_11_date_time": "8:17 pm on 21 September, 2023", - "session_11": [ - { - "speaker": "John", - "dia_id": "D11:1", - "text": "Hey Tim, been a while! How ya been?" - }, - { - "speaker": "Tim", - "dia_id": "D11:2", - "text": "Hey John! Great to hear from you. Been busy with things, how about you?" - }, - { - "speaker": "John", - "dia_id": "D11:3", - "text": "Yeah, something cool happened! I attended a local restaurant with some new teammates last week. It was great getting to know them better." - }, - { - "speaker": "Tim", - "dia_id": "D11:4", - "text": "Good support is essential. How do you feel about them?" - }, - { - "speaker": "John", - "dia_id": "D11:5", - "text": "They're great friends. We connected over our shared love for basketball and had a ton of fun." - }, - { - "speaker": "Tim", - "dia_id": "D11:6", - "text": "Sounds awesome. Having friends who share your hobbies can be really fun. Any exciting plans with them?" - }, - { - "speaker": "John", - "dia_id": "D11:7", - "text": "We're planning to take a team trip next month to explore a new city and have some fun. Can't wait!" - }, - { - "speaker": "Tim", - "dia_id": "D11:8", - "text": "That sounds great! Exploring new cities is always so much fun. Where are you headed?" - }, - { - "speaker": "John", - "dia_id": "D11:9", - "text": "We're still deciding on the destination. Do you have any suggestions?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://www.thebearandthefox.com/wp-content/uploads/2018/02/IMG_3494.jpg" - ], - "blip_caption": "a photo of a city with a clock tower and a sun setting", - "query": "edinburgh scotland castle sunset", - "dia_id": "D11:10", - "text": "Edinburgh, Scotland would be great for a magical vibe. It's the birthplace of Harry Potter and has awesome history and architecture. Plus, it's a beautiful city. What do you think?" - }, - { - "speaker": "John", - "dia_id": "D11:11", - "text": "That sounds like a great idea! I haven't been to Edinburgh yet, but it definitely sounds like a place worth considering for our trip. Thanks for the suggestion!" - }, - { - "speaker": "Tim", - "dia_id": "D11:12", - "text": "Glad you liked it. Let me know if you need any more suggestions." - }, - { - "speaker": "John", - "img_url": [ - "https://c8.alamy.com/zooms/9/de5f1d4e73244a8f94b16a6b6d093748/ttnmea.jpg" - ], - "blip_caption": "a photo of a basketball ball on the ground with a basketball hoop in the background", - "query": "basketball court sunset", - "dia_id": "D11:13", - "text": "Thanks! I'll definitely reach out if I need more suggestions. Appreciate the help! Here's a pic I snapped during one of our practices. The sunset looked amazing on the court. Moments like these make me so grateful for my basketball career." - }, - { - "speaker": "Tim", - "dia_id": "D11:14", - "text": "Wow, that looks amazing! What do you love most about your basketball career?" - }, - { - "speaker": "John", - "dia_id": "D11:15", - "text": "Thanks! I love playing pro ball - it's a constant challenge and keeps me growing. There's nothing like seeing myself get better and beating goals. Plus, playing with my teammates and having the fans cheer is awesome. Basketball gives me a great sense of satisfaction and purpose." - }, - { - "speaker": "Tim", - "dia_id": "D11:16", - "text": "It's great that you have a passion that helps you grow and reach your goals. Achieving and feeling fulfilled must be amazing. Do you have any specific targets or goals you're working towards?" - }, - { - "speaker": "John", - "dia_id": "D11:17", - "text": "Definitely! I'm focusing on better shooting and making more of an impact on the court. I want to be known as a consistent performer and help my team. Off the court, I'm also looking into more endorsements and building my brand. It's important for me to think about life after basketball." - }, - { - "speaker": "Tim", - "dia_id": "D11:18", - "text": "Awesome! It's great that you have goals both on and off the court. It's wise to think about the future and building your brand. What are your thoughts on life after basketball?" - }, - { - "speaker": "John", - "dia_id": "D11:19", - "text": "I've thought about it a lot. I want to use my platform to make a positive difference and inspire others - maybe even start a foundation and do charity work. It's important to me to make the most of the chances I get and leave a meaningful legacy." - }, - { - "speaker": "Tim", - "dia_id": "D11:20", - "text": "Wow, that's amazing. Good on you for wanting to make a difference and motivate others. I'm sure you'll succeed! Is there anything I can do to support you?" - }, - { - "speaker": "John", - "dia_id": "D11:21", - "text": "Thanks! I'm trying to figure out how to pick the right ones - any advice on that?" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a stack of books sitting on top of a counter", - "dia_id": "D11:22", - "text": "When picking endorsements, make sure they align with your values and brand. Look for a company that shares your desire to make a change and help others. It's important that the endorsement feels authentic to your followers." - }, - { - "speaker": "John", - "dia_id": "D11:23", - "text": "Sounds like good advice! I was wondering if you have any book recommendations for my trip?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://live.staticflickr.com/3195/3322703152_057a33dca1_z.jpg" - ], - "blip_caption": "a photography of a book cover with a man in a hooded jacket", - "query": "the name of the wind book cover fantasy cloaked figure burning city", - "dia_id": "D11:24", - "re-download": true, - "text": "Yeah! I think you'd love this fantasy novel by Patrick Rothfuss. It's a book that'll take you to a different world. Great for you when you're traveling. Have fun!" - }, - { - "speaker": "John", - "img_url": [ - "https://i0.wp.com/www.themself.org/wp-content/uploads/2016/10/img_20161018_133948.jpg" - ], - "blip_caption": "a photo of a bookshelf with a lot of books on it", - "query": "bookshelf", - "dia_id": "D11:25", - "text": "Thanks! I'll definitely check it out - looks like a great book to read while traveling. Can't wait to dive into it! Here's a photo of my bookshelf. You can see some of the books I've read and enjoyed." - }, - { - "speaker": "Tim", - "dia_id": "D11:26", - "text": "Great bookshelf! I saw that you had \"The Alchemist\" on there, one of my favorites. Did you enjoy it?" - }, - { - "speaker": "John", - "dia_id": "D11:27", - "text": "Yep, I read that book and loved it! It made me think about life and how important it is to follow one's dreams. Highly recommend it!" - }, - { - "speaker": "Tim", - "dia_id": "D11:28", - "text": "Glad you liked it! \"The Alchemist\" is worth it." - }, - { - "speaker": "John", - "dia_id": "D11:29", - "text": "Thanks! Take care!" - }, - { - "speaker": "Tim", - "dia_id": "D11:30", - "text": "Have fun! Take care and talk to you soon." - } - ], - "session_12_date_time": "3:00 pm on 2 October, 2023", - "session_12": [ - { - "speaker": "Tim", - "blip_caption": "a photo of a bookcase filled with dvds and games", - "dia_id": "D12:1", - "text": "Hey John! Awesome catchin' up with you! A lot's changed since last time." - }, - { - "speaker": "John", - "img_url": [ - "https://platinumnotary.files.wordpress.com/2023/03/img-6679.jpg" - ], - "blip_caption": "a photo of a wedding ceremony in a greenhouse with people taking pictures", - "query": "wedding ceremony", - "dia_id": "D12:2", - "text": "Hey, Tim! Good to hear from you. Anyway, a lot has been going on with me. My girlfriend and I had an amazing and emotional wedding ceremony last week." - }, - { - "speaker": "Tim", - "dia_id": "D12:3", - "text": "Congrats! That was such a special day! How did you manage to have the ceremony during these times?" - }, - { - "speaker": "John", - "dia_id": "D12:4", - "text": "Thanks! We were lucky to find a lovely greenhouse venue for a smaller, more intimate gathering. We made sure to follow the necessary safety protocols and ensure that everyone felt safe. It was wonderful to have our loved ones celebrate with us." - }, - { - "speaker": "Tim", - "dia_id": "D12:5", - "text": "Awesome! It sounds like you found a great venue and had your loved ones celebrate with you. Weddings are definitely full of joy!" - }, - { - "speaker": "John", - "dia_id": "D12:6", - "text": "Yeah! Such a great day! It was so beautiful having everyone celebrating with us. I'd never felt so much love and happiness before. Some of my hiking club friends came even though I just joined!" - }, - { - "speaker": "Tim", - "dia_id": "D12:7", - "text": "Wow! Your wedding day must've been so special. Love sure does bring us joy, huh? Any favorite memories from the big day?" - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/8815283/pexels-photo-8815283.jpeg" - ], - "blip_caption": "a photography of a bride walking down the aisle with her groom", - "query": "wife walking down the aisle moment", - "dia_id": "D12:8", - "re-download": true, - "text": "Oh yeah! Picking a favorite memory was tough, but seeing her walking down the aisle, her face lit up and it got me all choked up. What a magical moment - one I'll always remember." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a desk with a bookcase full of books and a lot of other items", - "dia_id": "D12:9", - "text": "I bet that moment was unreal! One moment can make such an impression. I saw your photo of you two dancing, it must have been incredible. Wanna tell me more?" - }, - { - "speaker": "John", - "dia_id": "D12:10", - "text": "That dance was great! We had our first dance at a cozy restaurant. It was so dreamy with the music and candlelight. We were so lucky to have everyone with us!" - }, - { - "speaker": "Tim", - "dia_id": "D12:11", - "text": "Wow, a candlelit dance in a cozy restaurant! Sounds like a fairytale! So special to share with your loved ones. Weddings truly are the best!" - }, - { - "speaker": "John", - "dia_id": "D12:12", - "text": "Thanks! It was a great day. Having everyone there made it extra special. It's moments like these that bring love and joy." - }, - { - "speaker": "Tim", - "dia_id": "D12:13", - "text": "Congrats again! Love is truly magical and brings so much joy. I'm so happy for you and your new wife!" - }, - { - "speaker": "John", - "dia_id": "D12:14", - "text": "Thanks so much! Your words mean a lot. I'm lucky to have you in my life, bringing so much love and joy." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/z6jusjhd0pd71.jpg" - ], - "blip_caption": "a photo of a bookcase filled with dvds and games", - "query": "bookshelf fantasy novels", - "dia_id": "D12:15", - "text": "Thanks. Your friendship means a lot to me. I'm here for you anytime. I also wanted to share this bookshelf with you. It's filled with my favorite fantasy novels." - }, - { - "speaker": "John", - "dia_id": "D12:16", - "text": "Cool! What do you enjoy about them so much?" - }, - { - "speaker": "Tim", - "dia_id": "D12:17", - "text": "They really fire up my imagination and take me to alternate realities. They're my escape from reality." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/odlwr40mol581.jpg" - ], - "blip_caption": "a photo of a bunch of basketball jerseys laying on a bed", - "query": "basketball jerseys collection", - "dia_id": "D12:18", - "text": "That's great Tim! Books and movies make us escape to different places. I like to collect jerseys." - }, - { - "speaker": "Tim", - "dia_id": "D12:19", - "text": "Cool! Who's your favorite basketball team/player?" - }, - { - "speaker": "John", - "dia_id": "D12:20", - "text": "Thanks! The Wolves are my team for sure. And LeBron is the man - love his skills and leadership." - }, - { - "speaker": "Tim", - "dia_id": "D12:21", - "text": "The Wolves are solid and LeBron's skills and leadership are amazing." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people standing on a basketball court", - "dia_id": "D12:22", - "text": "I really admire his work ethic and dedication to the game, such an inspiration." - }, - { - "speaker": "Tim", - "dia_id": "D12:23", - "text": "LeBron is incredible. Have you ever had the opportunity to meet him or see him play live?" - }, - { - "speaker": "John", - "dia_id": "D12:24", - "text": "Yeah, I've had the chance to meet him a few times. He's real chill and it was a wild experience. I also had the opportunity to see him play live. It was tough, but I learned a lot from watching a top player like him." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a crowd of people at a concert with their hands in the air", - "dia_id": "D12:25", - "text": "Wow! Meeting him and seeing him play must've been awesome. I can only imagine his skills up close." - }, - { - "speaker": "John", - "img_url": [ - "https://get.pxhere.com/photo/structure-crowd-audience-basketball-stadium-arena-sports-miami-sport-venue-soccer-specific-stadium-music-venue-basketball-moves-slam-dunk-miami-heat-game-miami-heat-1383801.jpg" - ], - "blip_caption": "a photo of a basketball game with a crowd of people watching", - "query": "basketball game crowd", - "dia_id": "D12:26", - "text": "It was so amazing! The energy was crazy and everyone was so hyped. Seeing the top players was so motivating - it made me want to work even harder. Check out this pic from the game!" - }, - { - "speaker": "Tim", - "dia_id": "D12:27", - "text": "That crowd looks wild! The game must've been amazing!" - }, - { - "speaker": "John", - "dia_id": "D12:28", - "text": "The energy was incredible! It's moments like these that make me love my job." - }, - { - "speaker": "Tim", - "dia_id": "D12:29", - "text": "Cool! It's great to find something you enjoy doing. Keep going for it! See ya later!" - } - ], - "session_13_date_time": "1:50 pm on 13 October, 2023", - "session_13": [ - { - "speaker": "Tim", - "dia_id": "D13:1", - "text": "Hey John! It's been ages since we last talked. Guess what? Last week I went to a Harry Potter conference in the UK - it was incredible! There were so many people who shared the same love of HP as me, it was like a magical family. I felt so inspired and like I got a new lease of life. I love how my passion for fantasy stuff brings me closer to people from all over the world, it's pretty cool." - }, - { - "speaker": "John", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/4/4c/USA_men%27s_national_basketball_team_%2851910110377%29.jpg" - ], - "blip_caption": "a photography of a basketball team posing for a team photo", - "query": "group photo basketball", - "dia_id": "D13:2", - "re-download": true, - "text": "Hey Tim! Great to hear from you. It's awesome how our passions connect us with others, yeah? You sound like you fit right in and got a real buzz out of it. I feel the same way with my team." - }, - { - "speaker": "Tim", - "dia_id": "D13:3", - "text": "Wow, you guys look great! How have games been going?" - }, - { - "speaker": "John", - "dia_id": "D13:4", - "text": "It was an intense season with both tough losses and great wins. Overall, I'd say we did pretty well." - }, - { - "speaker": "Tim", - "dia_id": "D13:5", - "text": "Cool! Sounds like you guys had some tough games. How did you handle those?" - }, - { - "speaker": "John", - "img_url": [ - "https://tapinto-production.s3.amazonaws.com/uploads/articles/im/best_9e2201886c43d264dbef_IMG_6460.jpg" - ], - "blip_caption": "a photo of a soccer team posing for a picture with a trophy", - "query": "teammates celebration victory", - "dia_id": "D13:6", - "text": "Thanks! We faced tough opponents but that's what drives us to get better. We back each other up and won't quit." - }, - { - "speaker": "Tim", - "dia_id": "D13:7", - "text": "Congrats! That's awesome. It must feel good, right?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/wheq5bijuqc91.jpg" - ], - "blip_caption": "a photo of a man holding a trophy in front of a crowd", - "query": "championship trophy holding up", - "dia_id": "D13:8", - "text": "Yeah, it feels great! All that hard work and effort was totally worth it. We even won a trophy!" - }, - { - "speaker": "Tim", - "dia_id": "D13:9", - "text": "Way to go! You must have been elated up there with that trophy. All the hard work paid off! Congrats - I'm so proud of you. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D13:10", - "text": "Thanks! I was definitely elated. Your support really means a lot to me. I'll keep working hard." - }, - { - "speaker": "Tim", - "dia_id": "D13:11", - "text": "No problem! I'm here for you anytime. Keep believing in yourself!" - }, - { - "speaker": "John", - "dia_id": "D13:12", - "text": "Thanks! Appreciate your support. Always staying filled with self-belief." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a box of serenityy memory foam", - "dia_id": "D13:13", - "text": "You got this! Stay motivated and remember that anything is possible with hard work. Keep pushing for your goals!" - }, - { - "speaker": "John", - "dia_id": "D13:14", - "text": "Thanks! Your encouragement means a lot to me. I'm feeling motivated and ready to keep pushing for my goals! I'm going to need some new shoes after all these games though." - }, - { - "speaker": "Tim", - "img_url": [ - "http://www.binxberryconsignment.com/cdn/shop/products/IMG_3958.jpg" - ], - "blip_caption": "a photo of a pair of black and pink running shoes", - "query": "memory foam shoes", - "dia_id": "D13:15", - "text": "Glad my encouragement helped! These are amazing - like walking on clouds! Game changer!" - }, - { - "speaker": "John", - "dia_id": "D13:16", - "text": "They look comfortable. Where did you get them?" - }, - { - "speaker": "Tim", - "dia_id": "D13:17", - "text": "I got them online - they're super comfy! Definitely recommend!" - }, - { - "speaker": "John", - "dia_id": "D13:18", - "text": "Cheers! I'll definitely check them out. Thanks for the recommendation!" - }, - { - "speaker": "Tim", - "dia_id": "D13:19", - "text": "No worries. Let me know if there's anything else I can assist you with. Always here to help!" - }, - { - "speaker": "John", - "dia_id": "D13:20", - "text": "Thanks! Appreciate it. I'll reach out if I need anything." - }, - { - "speaker": "Tim", - "dia_id": "D13:21", - "text": "Cool! Stay motivated and keep chasing those dreams! Chat soon!" - }, - { - "speaker": "John", - "dia_id": "D13:22", - "text": "Thanks! I'll definitely stay motivated and keep chasing those dreams. You too, keep up the passion. Talk soon!" - } - ], - "session_14_date_time": "1:50 pm on 17 October, 2023", - "session_14": [ - { - "speaker": "John", - "dia_id": "D14:1", - "text": "Hey Tim! Long time no talk - a lot has been going on since then!" - }, - { - "speaker": "Tim", - "dia_id": "D14:2", - "text": "Hey John! Long time no see! Can't wait to catch up and hear all about what you've been up to." - }, - { - "speaker": "John", - "dia_id": "D14:3", - "text": "Seems like forever since we caught up! I'm now mentoring the younger players on my team. It's super rewarding and I'm loving sharing my skills and knowledge with them. It's also a great way for me to stay involved in the game during the off-season." - }, - { - "speaker": "Tim", - "dia_id": "D14:4", - "text": "Wow! Mentoring must be so rewarding. You get to show others what you know - that's awesome! Is it difficult? Any hiccups?" - }, - { - "speaker": "John", - "dia_id": "D14:5", - "text": "There are challenges, since everyone is so different. But it's been awesome gaining experience and adapting, motivating and encouraging everyone. It's been great to watch each of them develop and reach their goals - such a reward!" - }, - { - "speaker": "Tim", - "dia_id": "D14:6", - "text": "Wow, that's awesome! It must be really rewarding to see them reach their goals. What's it like mentoring them?" - }, - { - "speaker": "John", - "img_url": [ - "https://cdn2.picryl.com/photo/2020/01/16/members-of-the-local-and-us-communities-attend-the-edfa79-1024.jpg" - ], - "blip_caption": "a photography of a basketball player standing in a gym with his hands on his hips", - "query": "mentoring young players practice smiles", - "dia_id": "D14:7", - "re-download": true, - "text": "Mentoring them has been awesome! Seeing their growth, improvement, and confidence is so fulfilling. I'm glad I could make a positive impact on their lives. Here's a pic of me and some of the younger players at a recent practice." - }, - { - "speaker": "Tim", - "dia_id": "D14:8", - "text": "You're really doing great with them. Do any of them see you as a mentor?" - }, - { - "speaker": "John", - "dia_id": "D14:9", - "text": "Some of them do see me as a mentor, which is really rewarding. I try to provide them with advice and support on and off the court. Being a positive role model for them is something I enjoy." - }, - { - "speaker": "Tim", - "dia_id": "D14:10", - "text": "That's incredible! How does it feel to have their trust and admiration? It must be such an honor to be a positive role model for them." - }, - { - "speaker": "John", - "dia_id": "D14:11", - "text": "It feels great to have their trust and admiration. Being a role model for these young athletes is so fulfilling. I'm glad my experiences can help shape their future and inspire them to go after their dreams." - }, - { - "speaker": "Tim", - "img_url": [ - "https://images.pexels.com/photos/12312263/pexels-photo-12312263.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-tanya-badillo-12312263.jpg" - ], - "blip_caption": "a photo of a sunset over a mountain range with a few trees", - "query": "mountain range sunset", - "dia_id": "D14:12", - "text": "You're doing a great job with them. Way to go! This is what I've been up to." - }, - { - "speaker": "John", - "dia_id": "D14:13", - "text": "Wow, stunning! And thanks. Really appreciate it. Means a lot." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/hf54pvtf8rt51.jpg" - ], - "blip_caption": "a photo of a sunset over a mountain with a tree", - "query": "sunset blue ridge mountains north carolina", - "dia_id": "D14:14", - "text": "I took this pic last summer. Seeing it was so stunning. Thanks for appreciating it. It means a lot to me." - }, - { - "speaker": "John", - "dia_id": "D14:15", - "text": "Where did you capture this? Nature is truly amazing, isn't it?" - }, - { - "speaker": "Tim", - "dia_id": "D14:16", - "text": "I snapped that pic on my trip to the Smoky Mountains last year. It was incredible seeing it in person. Nature's really something else!" - }, - { - "speaker": "John", - "dia_id": "D14:17", - "text": "Yeah, it's amazing how nature's beauty and grandeur can take our breath away. It's so nice to escape the noise of the city and relax in nature. Good for you to get to enjoy that stunning view!" - }, - { - "speaker": "Tim", - "dia_id": "D14:18", - "text": "Nature is indeed refreshing. A good break from school." - }, - { - "speaker": "John", - "dia_id": "D14:19", - "text": "How are you doing in shcool?" - }, - { - "speaker": "Tim", - "dia_id": "D14:20", - "text": "Doing good! Busy with studies but finding time to relax with books - good balance." - }, - { - "speaker": "John", - "dia_id": "D14:21", - "text": "Cool! Finding that balance is key. Are you currently reading any books?" - }, - { - "speaker": "Tim", - "dia_id": "D14:22", - "text": "I'm reading this book and I'm totally hooked! What about you?" - }, - { - "speaker": "John", - "dia_id": "D14:23", - "text": "I haven't had much time to read, but after we talked I finally picked up a book and it's been awesome! Talk to you later!" - } - ], - "session_15_date_time": "5:51 pm on 21 October, 2023", - "session_15": [ - { - "speaker": "Tim", - "dia_id": "D15:1", - "text": "Hey John! Haven't talked to you in a bit but wanted to let you know I read this awesome book about castles in the UK. It was so interesting and blew me away! I dream of visiting them one day." - }, - { - "speaker": "John", - "img_url": [ - "https://creatingmewp.files.wordpress.com/2023/05/img_9047.jpg" - ], - "blip_caption": "a photo of a man sitting on a bench overlooking a cliff", - "query": "castle scotland", - "dia_id": "D15:2", - "text": "Hey Tim! Great to hear from you. Learning about different cultures and seeing historical architecture fascinates me. Visiting castles is really on my bucket list. Just look at this one; what a sight! I'm so excited to explore the world and experience these gorgeous places. On that note, how's your fantasy writing going?" - }, - { - "speaker": "Tim", - "dia_id": "D15:3", - "text": "That castle looks amazing! I hope I get to visit it someday. My writing is going well: I'm in the middle of fantasy novel and it's a bit nerve-wracking but so exciting! All my hard work is paying off. Writing brings such joy and it's incredible how it can create a whole new world. Thanks so much for believing in me!" - }, - { - "speaker": "John", - "dia_id": "D15:4", - "text": "That's great! I'm glad your writing is going well. It must be exciting to see it all come together. Keep going! Do you have a specific source of inspiration for your stories?" - }, - { - "speaker": "Tim", - "dia_id": "D15:5", - "text": "Thanks! Books, movies, and real-life experiences all fire up my creativity. For example, reading about castles in the UK gave me loads of ideas. Plus, certain authors are like goldmines of inspiration for me. Connecting with the things I love makes writing even more fun." - }, - { - "speaker": "John", - "dia_id": "D15:6", - "text": "Wow! Sounds like a great mix. Is there a particular author whose work inspires you?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/4mff2zeq18381.jpg" - ], - "blip_caption": "a photo of a book with a page in it on a table", - "query": "harry potter sorcerer's stone well-read copy", - "dia_id": "D15:7", - "text": "J.K. Rowling is such an inspiring writer. Her books are so captivating with their detail and creative storytelling. She can definitely transport readers into another world and make them feel so much. I'm always taking notes on her style for my own writing." - }, - { - "speaker": "John", - "dia_id": "D15:8", - "text": "Cool! How long have you been reading her works?" - }, - { - "speaker": "Tim", - "dia_id": "D15:9", - "text": "I've been reading her stuff for a long time. Her stories have been with me and still inspire me. There's something special about her writing that really speaks to me." - }, - { - "speaker": "John", - "dia_id": "D15:10", - "text": "Wow, some authors really have such an influence on us! They become part of our life and affect our interests. Do you have a favorite J.K. Rowling quote?" - }, - { - "speaker": "Tim", - "dia_id": "D15:11", - "text": "Yeah! There's a quote by J.K. Rowling that I really like: \"Turn on the light - happiness hides in the darkest of times.\" That's how I keep hope alive during tough times." - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/5e/f4/f9/5ef4f9bd2f094b2d0a4ddd4861b928a0.jpg" - ], - "blip_caption": "a photo of a white board with a drawing of arrows and words", - "query": "inspirational quote whiteboard", - "dia_id": "D15:12", - "text": "Nice quote! It reminds us to stay positive and find joy even in hard times. It's a guiding light when things get rough. I appreciate you sharing it!" - }, - { - "speaker": "Tim", - "dia_id": "D15:13", - "text": "Nice job, John! What did you write on that whiteboard?" - }, - { - "speaker": "John", - "dia_id": "D15:14", - "text": "On that whiteboard, I wrote down some motivational quotes and strategies to help me stay focused and push through tough workouts. It really helps me stay motivated and keep improving." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a woman holding a plaque in front of a wall", - "dia_id": "D15:15", - "text": "That's awesome! Visual reminders and strategies can really help in staying motivated. It's cool that you have those quotes to keep you going during tough workouts." - }, - { - "speaker": "John", - "img_url": [ - "https://images.rawpixel.com/image_social_landscape/czNmcy1wcml2YXRlL3Jhd3BpeGVsX2ltYWdlcy93ZWJzaXRlX2NvbnRlbnQvbHIvbnMxODI2MC1pbWFnZS1rd3Z3bmQxZi5qcGc.jpg" - ], - "blip_caption": "a photography of a desk with a laptop and a lightbox that says make it happen", - "query": "plaque believe power within motivation", - "dia_id": "D15:16", - "re-download": true, - "text": "This plaque I keep on my desk is a constant reminder to believe in myself. It helps me trust my abilities and face any obstacles. Having physical reminders like this really helps me stay motivated." - }, - { - "speaker": "Tim", - "dia_id": "D15:17", - "text": "That's awesome! What keeps you motivated during challenging times?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.americustimesrecorder.com/wp-content/uploads/sites/43/2023/04/team-huddle-rotated.jpg" - ], - "blip_caption": "a photo of a group of women soccer players huddle together", - "query": "team huddle game", - "dia_id": "D15:18", - "text": "My teammates believing in me and my love for improving my skills keep me going, even when things get tough. I don't want to let them down." - }, - { - "speaker": "Tim", - "dia_id": "D15:19", - "text": "Nice one! What do you reckon makes them such a good support?" - }, - { - "speaker": "John", - "dia_id": "D15:20", - "text": "They always support me, even when I make mistakes. Their encouragement keeps me going." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a group of people hiking through a forest", - "dia_id": "D15:21", - "text": "That's key, having a strong support network can really help with what we're trying to do. Do you have people you can lean on outside of sports?" - }, - { - "speaker": "John", - "dia_id": "D15:22", - "text": "Yeah, I'm lucky - I have people who are super supportive, always there for me no matter what." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.pinimg.com/originals/85/59/73/855973c53d20fad61ea048269f29fadb.jpg" - ], - "blip_caption": "a photo of a group of women sitting on the grass eating", - "query": "group friends beach picnic", - "dia_id": "D15:23", - "text": "Awesome! Having people who lift us up is essential. I'm grateful I have friends and family who support me - it's huge." - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/1655329/pexels-photo-1655329.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-lisa-fotios-1655329.jpg" - ], - "blip_caption": "a photo of a group of people standing around a kitchen table", - "query": "family dinner", - "dia_id": "D15:24", - "text": "Having loved ones who support us is so important. My family is always there for me." - }, - { - "speaker": "Tim", - "dia_id": "D15:25", - "text": "Wow, look at this great group! Are these your people?" - }, - { - "speaker": "John", - "img_url": [ - "https://s3-us-west-2.amazonaws.com/sportshub2-uploads-prod/files/sites/1567/2018/02/09230004/IMG_8348-e1518217261806.jpg" - ], - "blip_caption": "a photo of a group of people standing on a basketball court", - "query": "group of basketball players cheering on sidelines", - "dia_id": "D15:26", - "text": "Yeah, definitely! That's my fam hanging out. Being with them brings me so much happiness and helps me remember what's important. My team is like my second family too." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a group of kids playing a game of basketball", - "dia_id": "D15:27", - "text": "That looks fun! What else do you do with them?" - }, - { - "speaker": "John", - "dia_id": "D15:28", - "text": "What people usually do when you hang out with friends and family - movies, dinner out. And what are your favorite activities for fun?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://images.pexels.com/photos/11818038/pexels-photo-11818038.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-ana-ps-11818038.jpg" - ], - "blip_caption": "a photo of a fire in a fireplace with a dog standing next to it", - "query": "campfire group people", - "dia_id": "D15:29", - "text": "I love going on road trips with friends and family, exploring and hiking or playing board games. And in my free time, I enjoy curling up with a good book, escaping reality and getting lost in different worlds. That's what I'm talking about." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/6383i30xl5w31.jpg" - ], - "blip_caption": "a photo of a slow cooker filled with a mixture of food", - "query": "cooking meal", - "dia_id": "D15:30", - "text": "Yep, I totally get it! Cuddling up with a book is my chill time. And when I'm away from the court, cooking is therapy for me. It's a good way to be creative and experiment with flavors while taking a break. Here's a photo of me cooking a meal." - }, - { - "speaker": "Tim", - "dia_id": "D15:31", - "text": "That slow cooker meal looks yum! Cooking is a great way to chill and be creative. Do you have any favorite recipes you can show me?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a pan of chicken and vegetables cooking on a stove", - "dia_id": "D15:32", - "text": "Definitely! I make this yummy honey garlic chicken with roasted veg a lot. It's one of my favorites. I'm always trying out new recipes, so let me know if you want suggestions!" - }, - { - "speaker": "Tim", - "dia_id": "D15:33", - "text": "Mmm, that sounds delicious, John! Can I get the recipe for it?" - }, - { - "speaker": "John", - "dia_id": "D15:34", - "text": "Sure thing! I can write it down for you and mail it to you." - }, - { - "speaker": "Tim", - "dia_id": "D15:35", - "text": "Can't wait to try it. Thanks for sharing the recipe!" - }, - { - "speaker": "John", - "dia_id": "D15:36", - "text": "No worries. Hope you enjoy it! Let me know how it turns out." - }, - { - "speaker": "Tim", - "dia_id": "D15:37", - "text": "Sure thing! Thanks. Great talking to you. Take care!" - }, - { - "speaker": "John", - "dia_id": "D15:38", - "text": "It was nice chatting with you. Talk to you later!" - } - ], - "session_16_date_time": "11:41 am on 6 November, 2023", - "session_16": [ - { - "speaker": "Tim", - "dia_id": "D16:1", - "text": "Hey John, long time no see! Hope you've been doing well. Since we last chat, some stuff's happened. Last week, I had a huge writing issue - got stuck on a plot twist and couldn't find my way out. It was crazy frustrating, but I kept pushing and eventually got the ideas flowing again." - }, - { - "speaker": "John", - "dia_id": "D16:2", - "text": "Hey Tim! Awesome to hear from you. Yeah, I get how that would've been so annoying! But you stuck it out, that's so cool. Same with me on the court. Just gotta find a way to tough it out and keep things flowing. Then when you make it through, it's all the more satisfying, right?" - }, - { - "speaker": "Tim", - "dia_id": "D16:3", - "text": "Yeah! It was hard but once it's over, the feeling is amazing. That's what makes it so beautiful, the struggle and then the satisfaction." - }, - { - "speaker": "John", - "dia_id": "D16:4", - "text": "Yeah! Struggles make it worth it. Like in sports, that's when the win feels great! Challenges force us to develop and become better." - }, - { - "speaker": "Tim", - "dia_id": "D16:5", - "text": "Overcoming challenges builds strength and pushes personal growth. It's about the journey and what we learn, not just winning. This was a great reminder for me. Got any examples from that sport you mentioned?" - }, - { - "speaker": "John", - "dia_id": "D16:6", - "text": "Yeah, last year I had a basketball game where we were trailing big time in the 4th quarter. We had to dig deep and keep on pushing to overturn the deficit and it was amazing when that final buzzer sounded. Unforgettable feeling." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/w41ygbk6p4ea1.jpg" - ], - "blip_caption": "a photo of a basketball in a case with a signed ball", - "query": "basketball signed by favorite basketball player", - "dia_id": "D16:7", - "text": "Wow, that must have been an incredible feeling! You really showed determination and perseverance. Do you have any photos or keepsakes from the game? I'd love to see them! By the way, I have something special too - this is my prized possession, a basketball signed by my favorite player. It serves as a reminder of all the hard work." - }, - { - "speaker": "John", - "dia_id": "D16:8", - "text": "Wow! What makes your favorite player so inspiring? Do you have any special stories or moments with them?" - }, - { - "speaker": "Tim", - "dia_id": "D16:9", - "text": "I just love watching LeBron. There was this Finals game a few years back with an epic block that totally changed the game and ended up winning it. Seeing him go for it like that was such an inspiration - never give up, you know?" - }, - { - "speaker": "John", - "dia_id": "D16:10", - "text": "Remember that epic block in Game 7 of the '16 Finals? He chased down Iguodala and pinned the ball against the backboard. That kind of determination and heart is why I love basketball." - }, - { - "speaker": "Tim", - "dia_id": "D16:11", - "text": "Yeah, that's the one! It was awesome. Moments like that make me love sports and admire the players' determination and heart." - }, - { - "speaker": "John", - "dia_id": "D16:12", - "text": "LeBron's moments of determination and heart are incredible. It's why I enjoy playing and pushing myself. You never know when those special moments might occur, but it's always fun to be part of it." - }, - { - "speaker": "Tim", - "dia_id": "D16:13", - "text": "Those special moments make it all worth it. It's amazing to be part of something bigger and feel the joy and fulfillment. Keep pushing and having those moments on the court!" - }, - { - "speaker": "John", - "dia_id": "D16:14", - "text": "Speaking of special moments, my wife and I just left for our European vacation! It will be short but sweet. You've been before, any recommendations?" - }, - { - "speaker": "Tim", - "dia_id": "D16:15", - "text": "That's great! I hope you two have a great time. I would recommend visiting some castles, they are just so magical!" - }, - { - "speaker": "John", - "dia_id": "D16:16", - "text": "Thanks! We'll have to check some out. Wishing you all the best with everything you're pursuing. Stay safe!" - }, - { - "speaker": "Tim", - "dia_id": "D16:17", - "text": "Thanks! You too, buddy. Take it easy and keep going for it. Stay safe and let's stay in touch!" - } - ], - "session_17_date_time": "3:36 pm on 11 November, 2023", - "session_17": [ - { - "speaker": "John", - "dia_id": "D17:1", - "text": "Hey Tim! Great to chat again. So much has happened!" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/7a8i8uea5au51.jpg" - ], - "blip_caption": "a photo of a book with a picture of a storm of swords", - "query": "worn copy game of thrones", - "dia_id": "D17:2", - "text": "Hey John! Great chatting with you as always. What's been happening lately? I've been reading as usual." - }, - { - "speaker": "John", - "img_url": [ - "https://familyadventuresva.files.wordpress.com/2022/03/img_5178.jpg" - ], - "blip_caption": "a photo of a group of people sitting on top of a mountain", - "query": "american west landscapes grand canyon", - "dia_id": "D17:3", - "text": "My wife and I were road tripping out on the European coastline, and it was amazing! The views were spectacular, and we had lots of fun bonding and creating amazing memories. It was such a nice change to my regular life." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a statue of a woman with a blue hat on", - "dia_id": "D17:4", - "text": "Wow! Sounds like an incredible road trip. I'm glad you and your wife had such a great time!" - }, - { - "speaker": "John", - "dia_id": "D17:5", - "text": "Thanks! Yeah, it was awesome. We got to see some epic spots. It's hard to describe how beautiful they were!" - }, - { - "speaker": "Tim", - "dia_id": "D17:6", - "text": "Those places must've been amazing! Nature sure has a way of leaving us speechless." - }, - { - "speaker": "John", - "dia_id": "D17:7", - "text": "Nature sure is powerful and beautiful! It's really humbling to witness such sights." - }, - { - "speaker": "Tim", - "dia_id": "D17:8", - "text": "Yeah! It always makes us realize how huge the world is and how special it is. These moments really show us the beauty around us. Anyways, have you read or watched anything good recently?" - }, - { - "speaker": "John", - "dia_id": "D17:9", - "text": "Yep, I just finished this amazing fantasy series. It was a wild ride with so many twists. The author is amazing at creating awesome storylines and characters - I love getting lost in those fantasy worlds." - }, - { - "speaker": "Tim", - "dia_id": "D17:10", - "text": "That's amazing! Same here. There's something special about being lost in an awesome fantasy realm and seeing what happens. It's like an escape. \"That\" is one of my favorite fantasy shows. Have you seen it?" - }, - { - "speaker": "John", - "dia_id": "D17:11", - "text": "Yeah, I saw \"That\"! It's amazing to see those worlds and characters come alive. It's a great way to escape reality!" - }, - { - "speaker": "Tim", - "dia_id": "D17:12", - "text": "Yeah, it's awesome how books and movies can take you away. A great escape, right?" - }, - { - "speaker": "John", - "dia_id": "D17:13", - "text": "Definitely, it's like a mental break, giving our minds a rest and letting them wander. So refreshing!" - }, - { - "speaker": "Tim", - "dia_id": "D17:14", - "text": "It's like entering another world! We get to take a break from everything and just let our minds wander. It's so nice and refreshing." - }, - { - "speaker": "John", - "dia_id": "D17:15", - "text": "And that's just what we need sometimes." - }, - { - "speaker": "Tim", - "dia_id": "D17:16", - "text": "Taking a break from life can help us recharge and get some peace. Plus, it gives us a chance to reconnect with ourselves and tackle life's challenges with a new outlook." - }, - { - "speaker": "John", - "dia_id": "D17:17", - "text": "Yeah, taking time for ourselves is crucial. It helps us stay sharp and focused. Plus, it helps us gain new perspectives and tackle challenges with more energy. Finding the right balance is key and I'll keep that in mind as I continue my journey." - }, - { - "speaker": "Tim", - "dia_id": "D17:18", - "text": "Balance is key and it varies. Take care of yourself, both mentally and physically, and you'll rock it. You got this, bud!" - }, - { - "speaker": "John", - "dia_id": "D17:19", - "text": "Thanks! Your support means a lot. I'll keep pushing forward. Take care, buddy!" - } - ], - "session_18_date_time": "3:59 pm on 16 November, 2023", - "session_18": [ - { - "speaker": "Tim", - "img_url": [ - "https://static.independent.co.uk/2021/07/28/09/26073722-6212c4b6-62f4-4d1f-b2d9-fab1e4ba16a8.jpg" - ], - "blip_caption": "a photo of a castle with a river running through it", - "query": "castle uk lush greenery", - "dia_id": "D18:1", - "text": "Hey John! Hope you're doing good. Guess what? I went to a castle during my trip to the UK last Friday and it was unbelievable! The architecture and the history were amazing!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/plgl67c48sg91.jpg" - ], - "blip_caption": "a photo of a person with a bandage on their leg", - "query": "ankle injury wrapped bandages", - "dia_id": "D18:2", - "text": "Hey Tim! That's awesome! Yeah, it was really cool. Oh man, it's been a tough week for me with this injury. But I'm staying positive. How about you? How's your week been?" - }, - { - "speaker": "Tim", - "dia_id": "D18:3", - "text": "Ouch, bummer about the injury. Hang tight. This week has been swamped with exams for me but I'm plowing through." - }, - { - "speaker": "John", - "blip_caption": "a photo of a notebook with a bunch of notes on it", - "dia_id": "D18:4", - "text": "Cheers, Tim. Injury's been rough, but I'm staying positive. How's the exam prep coming? Confident?" - }, - { - "speaker": "Tim", - "dia_id": "D18:5", - "text": "Exams can be challenging, but I'm putting in my best effort. Feeling optimistic and working diligently! How do you stay motivated during difficult study sessions?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a soccer game with a player on the field", - "dia_id": "D18:6", - "text": "I visualize my goals and success for focus and motivation. It really helps me stay motivated during tough studying. Do you have any study tricks?" - }, - { - "speaker": "Tim", - "dia_id": "D18:7", - "text": "That's cool! I like breaking up my studying into smaller parts. 25 minutes on, then 5 minutes off for something fun. It's less overwhelming and keeps me on track." - }, - { - "speaker": "John", - "dia_id": "D18:8", - "text": "Nice work! Breaking it down into smaller parts is definitely a smart move. I wish you all the best on your exams!" - }, - { - "speaker": "Tim", - "dia_id": "D18:9", - "text": "Thanks! Appreciate your support. I hope your injury heals soon." - }, - { - "speaker": "John", - "dia_id": "D18:10", - "text": "Sure thing, Tim! Got your back. I hope so too. The doctor said it's not too serious." - }, - { - "speaker": "Tim", - "dia_id": "D18:11", - "text": "That's good to hear, I'm glad." - }, - { - "speaker": "John", - "dia_id": "D18:12", - "text": "I hate not being on the court." - }, - { - "speaker": "Tim", - "dia_id": "D18:13", - "text": "I bet. It's like if I couldn't read due to an injury." - }, - { - "speaker": "John", - "dia_id": "D18:14", - "text": "I'm pushing on though. Talk soon!" - }, - { - "speaker": "Tim", - "dia_id": "D18:15", - "text": "Take care! Keep pushing on. Talk soon." - } - ], - "session_19_date_time": "10:22 am on 21 November, 2023", - "session_19": [ - { - "speaker": "Tim", - "dia_id": "D19:1", - "text": "Hey John! Haven't talked in a bit, how ya been? Hope your injury is feeling better." - }, - { - "speaker": "John", - "dia_id": "D19:2", - "text": "Hey Tim! Thanks for checking in. It's been tough, but I'm staying positive and taking it slow. How about you? How have you been?" - }, - { - "speaker": "Tim", - "dia_id": "D19:3", - "text": "I've been swamped with studies and projects, but last week I had a setback. I tried writing a story based on my experiences in the UK, but it didn't go the way I wanted. It's been tough, do you have any advice for getting better with storytelling?" - }, - { - "speaker": "John", - "dia_id": "D19:4", - "text": "Sorry to hear about the setback with your story. I understand how frustrating it can be when things don't go as planned. When I face challenges on the court, I try to reflect on what went wrong and find ways to improve. Maybe you can try doing the same with your storytelling." - }, - { - "speaker": "Tim", - "dia_id": "D19:5", - "text": "Cool idea. Reflecting on what went wrong and how to improve could definitely help me get back on track. Thanks! Out of curiosity, what's been one of your toughest challenges in basketball?" - }, - { - "speaker": "John", - "dia_id": "D19:6", - "text": "Last season, I had a major challenge when I hurt my ankle. It required some time off and physical therapy. It was frustrating because I couldn't play or help the team. I stayed focused on my recovery and worked hard to strengthen my body. It was a tough mental and physical challenge, but it made me realize the importance of patience and perseverance. I'm grateful that I was able to overcome it." - }, - { - "speaker": "Tim", - "dia_id": "D19:7", - "text": "That must have been tough not being able to play and help your team. You did an amazing job staying focused and overcoming it. Your resilience and determination are inspiring! Thanks for sharing." - }, - { - "speaker": "John", - "dia_id": "D19:8", - "text": "Thanks! That means a lot. Difficult times are part of life \u2013 what's important is how we handle them. When things get tough, I try to remember why I'm so passionate about basketball. That love and enthusiasm keeps me motivated, no matter what." - }, - { - "speaker": "Tim", - "dia_id": "D19:9", - "text": "When things get tough, it's so important to remember why we love what we do. For me, it's writing and reading. That's what helps me stay motivated and push myself to get better. Has anything similar happened with basketball for you? Tell me about it!" - }, - { - "speaker": "John", - "dia_id": "D19:10", - "text": "I faced some tough times while playing basketball. I messed up during a big game, and it was really hard to accept. Instead of getting stuck in that moment, I worked hard to get better. It taught me that resilience is key and owning up to mistakes is important. Gotta keep growing and striving to be a strong player and teammate. So grateful." - }, - { - "speaker": "Tim", - "dia_id": "D19:11", - "text": "Wow, that's awesome. Admitting mistakes and using them to get better is super important. You really show how much you care about improving. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D19:12", - "text": "Thanks! I appreciate your support. It's all about growing and getting better, both on and off the court. Let's keep working hard!" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/28n5c71uffu71.jpg" - ], - "blip_caption": "a photo of a bunch of books on a wooden floor", - "query": "fantasy novels movies collection", - "dia_id": "D19:13", - "text": "Yeah, John! Let's keep growing and improving. We got this! These are my companions on my growth journey." - }, - { - "speaker": "John", - "dia_id": "D19:14", - "text": "Fantasy books always fuel my creativity, both in and outside of my hobbies. Are Harry Potter and GoT still your favorites?" - }, - { - "speaker": "Tim", - "dia_id": "D19:15", - "text": "Yes, they are still my favorites - I love how they take me to other places. What other books do you like?" - }, - { - "speaker": "John", - "dia_id": "D19:16", - "text": "I love non-fiction books about personal development and mindset. They help me know myself better. Do you enjoy reading other types of books as well?" - }, - { - "speaker": "Tim", - "dia_id": "D19:17", - "text": "Yep, John! I love getting lost in fantasy stories, but also discovering new ways to better myself through books on growth, psychology, and improving myself. It's wild how much you can learn from them, right?" - }, - { - "speaker": "John", - "dia_id": "D19:18", - "text": "Yeah, Tim! Books really can shift how we think and help us learn totally new things. Have you come across any that made a big impact on you recently?" - }, - { - "speaker": "Tim", - "dia_id": "D19:19", - "text": "Yeah, John! I recently read a book that really made a big impact on me. It's all about how small changes can make big differences. It really changed the way I do things. Have you read any good books lately?" - }, - { - "speaker": "John", - "dia_id": "D19:20", - "text": "I recently finished rereading \"The Alchemist\" - it was really inspiring. It made me think again about following dreams and searching for our own personal legends. I felt really motivated and hopeful after reading it." - }, - { - "speaker": "Tim", - "dia_id": "D19:21", - "text": "Wow, that book is great! I read it a while back and it really changed my perspective on my goals. I'm glad it had the same impact on you!" - }, - { - "speaker": "John", - "dia_id": "D19:22", - "text": "Yeah, that book is really something. It really helped motivate me to keep chasing my dreams and to trust the process. It's amazing how books can have such an impact on us, right?" - }, - { - "speaker": "Tim", - "dia_id": "D19:23", - "text": "Definitely! Books have a way of opening up new worlds, inspiring us, and making us think. They have the power to make us feel better and help us grow, which is amazing. It's great that we share a love for reading. Let's keep exploring books and motivating each other! Talk to you later!" - } - ], - "session_20_date_time": "9:52 am on 1 December, 2023", - "session_20": [ - { - "speaker": "Tim", - "img_url": [ - "https://i.pinimg.com/originals/52/98/a1/5298a13a728c023b77f9cc86529a8748.jpg" - ], - "blip_caption": "a photo of a notepad with a note and pen on it", - "query": "study materials", - "dia_id": "D20:1", - "text": "Hey John! It's been ages since we last chatted. I had a tough exam last week that had me doubting myself. But instead of giving up, I turned it into a learning experience. I studied hard and it showed me how resilient and determined I can be. Here's a pic of my success \ud83d\udc4d" - }, - { - "speaker": "John", - "blip_caption": "a photo of a white wall with a black lettering that says 30 positive suites", - "dia_id": "D20:2", - "text": "Hi Tim! Congrats on your success! Keep it up, you're doing great! I'm also trying out yoga to get a little extra strength and flexibility. It's challenging but worth it." - }, - { - "speaker": "Tim", - "dia_id": "D20:3", - "text": "Thanks! I appreciate your encouragement. How's it going with yoga? Have you noticed any improvements?" - }, - { - "speaker": "John", - "dia_id": "D20:4", - "text": "Yoga's been really awesome for me. It's helped me improve in terms of strength and flexibility, as well as focus and balance during my workouts. It's been great!" - }, - { - "speaker": "Tim", - "dia_id": "D20:5", - "text": "Great news! Yoga is indeed amazing for your body and mind. Are there any specific poses that you enjoy practicing?" - }, - { - "speaker": "John", - "dia_id": "D20:6", - "text": "Yeah, there are a couple of poses I really enjoy. Warrior II makes me feel strong and there's one that helps with balance and stability. I love how these poses challenge my body and mind!" - }, - { - "speaker": "Tim", - "dia_id": "D20:7", - "text": "Woohoo! Congrats on finding poses that suit you. Yoga is so cool for showing us what we can really do. Maybe you could share a pic so I can try it too?" - }, - { - "speaker": "John", - "img_url": [ - "https://pixahive.com/wp-content/uploads/2021/02/Virabhadrasana-Warrior-Pose-357219-pixahive.jpg" - ], - "blip_caption": "a photography of a man doing a yoga pose on a blue mat", - "query": "warrior II pose", - "dia_id": "D20:8", - "re-download": true, - "text": "Here's a photo of me in this pose. It's a good way to work out your legs and core. Give it a shot!" - }, - { - "speaker": "Tim", - "dia_id": "D20:9", - "text": "That's a tough one! How long do you usually hold that pose?" - }, - { - "speaker": "John", - "dia_id": "D20:10", - "text": "I typically hold it for 30-60 seconds. It really helps with building strength and stability!" - }, - { - "speaker": "Tim", - "dia_id": "D20:11", - "text": "That's cool, I'm gonna give it a shot and see how it goes. Thanks for the tip!" - }, - { - "speaker": "John", - "dia_id": "D20:12", - "text": "No worries! Let me know how it goes. Happy to help whenever you need it!" - }, - { - "speaker": "Tim", - "dia_id": "D20:13", - "text": "Thanks! Your support and encouragement have truly made this journey better. I really appreciate it." - }, - { - "speaker": "John", - "dia_id": "D20:14", - "text": "I'm here for you. You've got this!" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a bookcase filled with dvds and games", - "dia_id": "D20:15", - "text": "Thanks! Your support means a lot to me. Your friendship means a lot too." - }, - { - "speaker": "John", - "dia_id": "D20:16", - "text": "Thanks, I really appreciate it. Your friendship means a lot to me too." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/9nvpaptnspc81.jpg" - ], - "blip_caption": "a photo of a book shelf with many books on it", - "query": "fantasy novels game of thrones harry potter bookshelf", - "dia_id": "D20:17", - "text": "Glad we're friends! Plus, bonus points for both being into fantasy books and movies. I just reorganized my book shelf, speaking of." - }, - { - "speaker": "John", - "dia_id": "D20:18", - "text": "Cool! Can I take a closer peek at it? What are some of your favorites?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/optq3zb96w771.jpg" - ], - "blip_caption": "a photo of a book shelf with a lot of books on it", - "query": "bookshelf harry potter game of thrones lord of the rings", - "dia_id": "D20:19", - "text": "Yeah, check it out - here's my bookshelf! I have some of my favorites on there, like these ones. It's an amazing journey!" - }, - { - "speaker": "John", - "dia_id": "D20:20", - "text": "That bookshelf is awesome! The Hobbit is one of my favorites too. What an amazing journey!" - }, - { - "speaker": "Tim", - "dia_id": "D20:21", - "text": "Glad you like it! The Hobbit is great, but have you read that other popular fantasy series? It's also awesome!" - }, - { - "speaker": "John", - "dia_id": "D20:22", - "text": "Yeah, I've read that other popular fantasy series too! It's one of my favorites. It has such a cool story!" - }, - { - "speaker": "Tim", - "dia_id": "D20:23", - "text": "It's awesome how these books take us to different worlds!" - }, - { - "speaker": "John", - "dia_id": "D20:24", - "text": "It's like escaping to these incredible new worlds and having a break from reality for a fun adventure." - }, - { - "speaker": "Tim", - "dia_id": "D20:25", - "text": "Yeah, that's why I love them. They let us take a break from reality and have an awesome adventure. So magical!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/huz7cq7rtix31.jpg" - ], - "blip_caption": "a photo of a forest with sun shining through the trees", - "query": "forest ray of sunlight through trees", - "dia_id": "D20:26", - "text": "Yeah, it's awesome! Like being transported to a different world with all those amazing moments - so fun!" - }, - { - "speaker": "Tim", - "dia_id": "D20:27", - "text": "Wow, what an awesome shot! Feels like a magical forest - where was that?" - }, - { - "speaker": "John", - "dia_id": "D20:28", - "text": "The photo is from a forest near my hometown. It's so tranquil." - }, - { - "speaker": "Tim", - "dia_id": "D20:29", - "text": "Wow, nature's amazing! We're lucky to have places like that near our homes." - }, - { - "speaker": "John", - "dia_id": "D20:30", - "text": "It's incredible how we have these beautiful places near our homes. We should definitely appreciate them." - }, - { - "speaker": "Tim", - "dia_id": "D20:31", - "text": "It really does have a way of calming us and reminding us of the beauty around." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/5qw4hbg418561.jpg" - ], - "blip_caption": "a photo of a lake with a rock in the middle of it", - "query": "serene lake mountains background", - "dia_id": "D20:32", - "text": "Definitely! It grounds us and makes us appreciate the simple beauty around us. We should take time to enjoy it." - }, - { - "speaker": "Tim", - "dia_id": "D20:33", - "text": "That picture looks super peaceful! It reminds me of a trip I took last summer." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/lf5a5i0jnah31.jpg" - ], - "blip_caption": "a photo of a campfire with chairs and a lake in the background", - "query": "campfire lake sunset", - "dia_id": "D20:34", - "text": "We had a blast camping and disconnecting from the everyday." - }, - { - "speaker": "Tim", - "dia_id": "D20:35", - "text": "Looks great! Where did you go camping?" - }, - { - "speaker": "John", - "dia_id": "D20:36", - "text": "We went camping in the mountains and it was stunning! The air was so refreshing." - }, - { - "speaker": "Tim", - "dia_id": "D20:37", - "text": "Sounds great! Being in the mountains is the best. What was your favorite part of it?" - }, - { - "speaker": "John", - "dia_id": "D20:38", - "text": "I loved just chilling and taking in the beauty of nature. It was super peaceful and refreshing." - }, - { - "speaker": "Tim", - "img_url": [ - "https://images.pexels.com/photos/16598991/pexels-photo-16598991.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-cheng-cj-16598991.jpg" - ], - "blip_caption": "a photo of a plane flying over a mountain range with snow on the top", - "query": "snowy mountain peak", - "dia_id": "D20:39", - "text": "Yeah, nature has that effect on me too. It's like a reset for the soul." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/q46xsv4ciu641.jpg" - ], - "blip_caption": "a photo of a mountain with a snow covered peak in the distance", - "query": "plane mountain range snow rocky mountains breathtaking experience majestic peaks fresh air", - "dia_id": "D20:40", - "text": "Yeah, nature's great for clearing the mind and calming the soul. This was my Rocky Mountains trip last year and it was stunning. Seeing those mountains, fresh air - it makes you realize how incredible the world is." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/u51y7czkhfrb1.jpg" - ], - "blip_caption": "a photo of a mountain range with a sunset in the background", - "query": "rocky mountains mountain range sunrise", - "dia_id": "D20:41", - "text": "Wow, this is amazing! Nature is really awesome - it makes us feel tiny but connected." - }, - { - "speaker": "John", - "dia_id": "D20:42", - "text": "Nature does have a way of humbling us and showing us our place in the world. It's truly amazing and comforting." - }, - { - "speaker": "Tim", - "dia_id": "D20:43", - "text": "Yeah. It reminds us that we're not alone - we're part of something bigger. Bye!" - } - ], - "session_21_date_time": "5:34 pm on 6 December, 2023", - "session_21": [ - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/w404u5zhn0yz.jpg" - ], - "blip_caption": "a photo of a map of westendell on a wall", - "query": "map pinned destinations", - "dia_id": "D21:1", - "text": "Hey John! Haven't talked in a few days, wanted to let you know I joined a travel club! Always been interested in different cultures and countries and I'm excited to check it out. Can't wait to meet new people and learn about what makes them unique!" - }, - { - "speaker": "John", - "img_url": [ - "https://bloximages.chicago2.vip.townnews.com/syvnews.com/content/tncms/assets/v3/editorial/3/38/338dd0b4-adc1-5a60-97e6-9eb3508b3f77/63c7306e4f912.hires.jpg" - ], - "blip_caption": "a photo of three young men standing next to each other on a basketball court", - "query": "teammates basketball smiling", - "dia_id": "D21:2", - "text": "Hey Tim! That's cool! I love learning about different cultures. It's really cool to meet people with different backgrounds. My teammates come from all over." - }, - { - "speaker": "Tim", - "dia_id": "D21:3", - "text": "Wow! How long have you been playing professionally?" - }, - { - "speaker": "John", - "dia_id": "D21:4", - "text": "I've been playing professionally for just under a year now. It's been a wild ride." - }, - { - "speaker": "Tim", - "dia_id": "D21:5", - "text": "Wow,! Being a pro basketball player must be quite a journey. Is it living up to your expectations?" - }, - { - "speaker": "John", - "dia_id": "D21:6", - "text": "Yeah, it's been great! Challenges, growth, all that jazz\u2014it's been amazing." - }, - { - "speaker": "Tim", - "dia_id": "D21:7", - "text": "Cool! Glad to hear that this journey has been rewarding for you. Could you tell me more about your growth?" - }, - { - "speaker": "John", - "dia_id": "D21:8", - "text": "Yup, on the court, I'm getting better at my overall game. Money-wise, I've gotten some cool endorsement deals. Plus, I'm learning how to market myself and boost my brand. It's been really rewarding to see all these areas progress. What about you? Anything new happening?" - }, - { - "speaker": "Tim", - "dia_id": "D21:9", - "text": "Joined a travel club and, like I said, working on studies. Also picked up new skills. Recently started learning an instrument. Challenging but fun, always admired musicians. Finally giving it a go." - }, - { - "speaker": "John", - "dia_id": "D21:10", - "text": "Learning an instrument is really cool. What instrument are you playing? What genres of music do you want to learn?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i1.pickpik.com/photos/964/641/216/musical-instrument-violin-curl-tuning-pegs-preview.jpg" - ], - "blip_caption": "a photography of a violin and a violin stick on a sheet of music", - "query": "piano sheet music headphones", - "dia_id": "D21:11", - "re-download": true, - "text": "I'm learning how to play the violin now. I'm mostly into classical music but I'm keen to try out jazz and film scores too. It's a great way to chill and get creative." - }, - { - "speaker": "John", - "dia_id": "D21:12", - "text": "Wow! I hope I can hear you play the violin some day. How long have you been playing the piano again?" - }, - { - "speaker": "Tim", - "dia_id": "D21:13", - "text": "I've been playing for about four months now and it's been an amazing adventure. I'm really enjoying the progress I've been making." - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/7162/6733335339_3a64489025_b.jpg" - ], - "blip_caption": "a photography of a man sitting on the ground with a trophy", - "query": "basketball trophy", - "dia_id": "D21:14", - "re-download": true, - "text": "Nice one! Learning something new is always a great adventure. Keep up the hard work and let's see where you end up. It's all about dedication and effort. It feels great to finally achieve something after putting in so much time and energy." - }, - { - "speaker": "Tim", - "dia_id": "D21:15", - "text": "Congrats on the trophy! It must have felt great to finally get something after putting in so much effort. Do you have any tips on motivating others on your team?" - }, - { - "speaker": "John", - "dia_id": "D21:16", - "text": "Thanks! Winning was awesome. When motivating others, it's important to show care for teammates, celebrate their achievements, provide constructive feedback, and remind them of the bigger goal. Creating a positive environment and giving a pep talk before a game can also be helpful. It's all about supporting and uplifting each other. Do you have any specific strategies in mind?" - }, - { - "speaker": "Tim", - "dia_id": "D21:17", - "text": "Thanks for the helpful advice. Creating a constructive atmosphere and setting an example by working hard can really inspire people. It\u2019s also inspiring to use our own stories to encourage others. Much appreciated!" - }, - { - "speaker": "John", - "dia_id": "D21:18", - "text": "No problem! It's great to use our own experiences to inspire others. Hard work can lead to success. Keep it up! Let me know if you need any assistance." - }, - { - "speaker": "Tim", - "dia_id": "D21:19", - "text": "Thanks! Appreciate the offer. Let me know if you can lend a hand. Bye!" - } - ], - "session_22_date_time": "7:42 pm on 8 December, 2023", - "session_22": [ - { - "speaker": "Tim", - "dia_id": "D22:1", - "text": "Hey John! Long time no see! I just got back from the coolest Harry Potter party. Met lots of awesome people who were into the same stuff as me, had so much fun!" - }, - { - "speaker": "John", - "dia_id": "D22:2", - "text": "Hey Tim! Sounds awesome! So glad you had a blast at the Harry Potter party. Last August I told you about my fun time at a charity event with Harry Potter trivia. Love being with people who are as passionate about Harry Potter as us! Did you dress up as any character?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://dixiedelightsonline.com/wp-content/uploads/2018/05/IMG_4747.jpg" - ], - "blip_caption": "a photo of a chocolate frog in a box on a table", - "query": "gryffindor scarf chocolate frog", - "dia_id": "D22:3", - "text": "It was awesome. I didn't dress as any character, but I wore my Gryffindor scarf. Everyone had cool costumes. I even got this as a treat. Any recent meet-ups with your basketball team?" - }, - { - "speaker": "John", - "img_url": [ - "https://npr.brightspotcdn.com/df/09/e6b1689047eaa3b05e7d61c36c05/image-from-ios-1.jpg" - ], - "blip_caption": "a photo of a group of people riding on top of a fire truck", - "query": "basketball team victory celebration", - "dia_id": "D22:4", - "text": "That frog looks yummy! I haven't had one in ages. Been having some wild games lately, we played a top team and it was tough, but we fought hard and got the win! It's awesome having my team to push us all." - }, - { - "speaker": "Tim", - "dia_id": "D22:5", - "text": "Wow, looks fun! What was the best part for you? And congratulations on the win!" - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/4030/4426181605_d36196a029_c.jpg" - ], - "blip_caption": "a photography of a group of young men sitting on top of a basketball court", - "query": "post-match team huddle", - "dia_id": "D22:6", - "re-download": true, - "text": "Thanks! The best part for me was the camaraderie we built both on and off the court. Winning felt amazing and it was definitely worth all the hard work we put in." - }, - { - "speaker": "Tim", - "img_url": [ - "https://www.thegibsonedge.com/hs-fs/hubfs/images/Blog_Images/Beware%20The%20Person%20Of%20One%20Book%20-%20Flashback%20Friday.jpg" - ], - "blip_caption": "a photo of a stack of books sitting on top of a table", - "query": "fantasy novels stack bookmarks power friendship loyalty", - "dia_id": "D22:7", - "text": "Wow, that's awesome! It's great to see how close you all have become. You must feel a great sense of unity. I'm reading this amazing series about the power of friendship and loyalty \u2013 really inspiring stuff. Anything special you do to keep that bond strong?" - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/5128/5297313790_4330145c09_b.jpg" - ], - "blip_caption": "a photography of a group of people sitting around a table eating", - "query": "team dinner basketball games outside of practice", - "dia_id": "D22:8", - "re-download": true, - "text": "Sounds awesome! What kind of stuff do they do in the series? I'm sure the importance of friendship is emphasized. Same with us - we have team dinners, outings, and basketball games. It's those moments away from practice that really build and strengthen our unity." - }, - { - "speaker": "Tim", - "dia_id": "D22:9", - "text": "Awesome! Sounds like your team has something similar to the characters in the series. They rely on each other to push through challenges. By the way, what book are you currently reading? I'm always on the lookout for new reads!" - }, - { - "speaker": "John", - "dia_id": "D22:10", - "text": "Thanks! I'm currently reading a book that I really enjoy. I highly recommend it!" - }, - { - "speaker": "Tim", - "dia_id": "D22:11", - "text": "Sounds cool! Let me know the title so I can add it to my list!" - }, - { - "speaker": "John", - "dia_id": "D22:12", - "text": "I'm reading \"Dune\" by Frank Herbert. It's a great story about religion and human control over ecology. What about you? What's the last book that moved you?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://live.staticflickr.com/4467/24220955708_7548812488_b.jpg" - ], - "blip_caption": "a photography of a book shelf with a book and a book cover", - "query": "the name of the wind book shelf", - "dia_id": "D22:13", - "re-download": true, - "text": "I haven't read that yet but I've heard great things! Just finished \"A Dance with Dragons\" and it's a really good story. Highly recommend it!" - }, - { - "speaker": "John", - "dia_id": "D22:14", - "text": "That's cool! I've heard it's such an inspiring book. Have you read all of George R. R. Martin's books?" - }, - { - "speaker": "Tim", - "dia_id": "D22:15", - "text": "Just the GoT series. Have you tried reading any of them?" - }, - { - "speaker": "John", - "dia_id": "D22:16", - "text": "No, I haven't read them yet but I'll definitely check them out. Cheers!" - }, - { - "speaker": "Tim", - "dia_id": "D22:17", - "text": "Let me know if you get around to them! Have a great day!" - }, - { - "speaker": "John", - "dia_id": "D22:18", - "text": "Thanks! I'll let you know. Have a great day!" - } - ], - "session_23_date_time": "8:28 pm on 11 December, 2023", - "session_23": [ - { - "speaker": "John", - "img_url": [ - "https://assets-global.website-files.com/60ed47e10552352d9d7e0a44/61e5cf5d296761732633ffcc_wsp_banner_w_joanne__dO8vP.jpg" - ], - "blip_caption": "a photo of two women standing next to a banner with sales pros written on it", - "query": "marketing team collaboration trust leadership", - "dia_id": "D23:1", - "text": "Hey Tim, great to see you! Any new success stories?" - }, - { - "speaker": "Tim", - "dia_id": "D23:2", - "text": "Hey John, I had a tough time with my English lit class. Did an analysis on this series and I think it went ok!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/cf/a0/7f/cfa07fa76047b5d731b3af313d98fc01.jpg" - ], - "blip_caption": "a photo of a basketball game being played in a large arena", - "query": "basketball court game", - "dia_id": "D23:3", - "text": "Thanks! It's a bummer about your English lit class, but you did your best. By the way, I had a career-high in assists last Friday in our big game against our rival. Yay!" - }, - { - "speaker": "Tim", - "dia_id": "D23:4", - "text": "Congrats! That's awesome. How did it feel being out there making those plays?" - }, - { - "speaker": "John", - "dia_id": "D23:5", - "text": "Thanks! It felt great being out there, making plays for my team. I love seeing my teammates succeed because of the opportunities I create for them. The atmosphere in the arena was really electric and playing against our rivals added an extra level of intensity. It was a memorable night!" - }, - { - "speaker": "Tim", - "dia_id": "D23:6", - "text": "Sounds incredible! Must have been quite an atmosphere. Have you had any other games that were as thrilling as this one?" - }, - { - "speaker": "John", - "dia_id": "D23:7", - "text": "I've had some thrilling games in my career. My favorite was when we were down 10 in the 4th and I hit the buzzer-beater shot to win. The atmosphere was incredible and it was such a thrilling experience. Those moments make me love basketball so much." - }, - { - "speaker": "Tim", - "img_url": [ - "https://c8.alamy.com/zooms/9/de5f1d4e73244a8f94b16a6b6d093748/ttnmea.jpg" - ], - "blip_caption": "a photo of a basketball ball on the ground with a basketball hoop in the background", - "query": "basketball court sunset", - "dia_id": "D23:8", - "text": "Wow, John! Moments like that make us love sports, huh? I still think about this pic you sent me a while back." - }, - { - "speaker": "John", - "dia_id": "D23:9", - "text": "Yeah, that pic reminds me of when I was younger. I'd practice basketball outside for hours, dreaming of playing in big games. It was my way of dealing with doubts and stress. It's amazing how a ball and hoop can be so powerful, right?" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a basketball ball on the ground with a basketball hoop in the background", - "dia_id": "D23:10", - "text": "Yeah! Sports are the best. When we're feeling down, it's a way to express ourselves and stay positive. It's awesome how much basketball has done for you. Keep going with your dreams!" - }, - { - "speaker": "John", - "dia_id": "D23:11", - "text": "Thanks! Appreciate the support. It's been a significant part of my life and allows me to be myself and pursue my passions. Gonna keep chasing my dreams!" - }, - { - "speaker": "Tim", - "dia_id": "D23:12", - "text": "Wow! It's really important to do our own thing and follow our dreams. Keep it up, you're gonna do amazing things!" - }, - { - "speaker": "John", - "dia_id": "D23:13", - "text": "Your encouragement means a lot. Let's keep pushing and following our dreams - we can make a difference!" - }, - { - "speaker": "Tim", - "dia_id": "D23:14", - "text": "Definitely. We both have so much potential! Let's keep supporting each other on our journey towards our dreams." - }, - { - "speaker": "John", - "dia_id": "D23:15", - "text": "Yeah, you're super inspiring and motivating. Keep it up!" - }, - { - "speaker": "Tim", - "dia_id": "D23:16", - "text": "Thanks, it means a lot. Let's keep each other motivated. Bye!" - } - ], - "session_24_date_time": "3:37 pm on 16 December, 2023", - "session_24": [ - { - "speaker": "Tim", - "dia_id": "D24:1", - "text": "Hey John, catch up time! What've you been up to? Any good b-ball games lately?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.salisburypost.com/wp-content/uploads/sites/9/2023/03/Catawba-basketball-01.jpg" - ], - "blip_caption": "a photo of a group of women's basketball players holding up a trophy", - "query": "basketball team celebration", - "dia_id": "D24:2", - "text": "Hey Tim! Nice to talk again. The b-ball games have been crazy. We had a real battle against another team last week. It was close until the final buzzer but we got the win." - }, - { - "speaker": "Tim", - "dia_id": "D24:3", - "text": "Congrats, John! That sounds like an intense game." - }, - { - "speaker": "John", - "dia_id": "D24:4", - "text": "Thanks! We won! It was really close, but we made it!" - }, - { - "speaker": "Tim", - "dia_id": "D24:5", - "text": "Wow, that's amazing! Winning must have been so thrilling!" - }, - { - "speaker": "John", - "dia_id": "D24:6", - "text": "Winning was such a thrill, and it was an awesome moment. These experiences really make me love the game." - }, - { - "speaker": "Tim", - "dia_id": "D24:7", - "text": "You must have been so pumped when it happened! Winning can give us a real confidence boost and makes us keep going with our passions." - }, - { - "speaker": "John", - "img_url": [ - "https://images.fineartamerica.com/images-medium-large-5/basketball-court-sunset-jun-pinzon.jpg" - ], - "blip_caption": "a photography of a basketball hoop in the sunset with a fence", - "query": "basketball court sunset workout", - "dia_id": "D24:8", - "re-download": true, - "text": "Yeah, it really does. It keeps me motivated to keep putting in the effort and makes all the tough times worth it. Here's a pic I took during a morning workout, it's a reminder that the journey can be awesome." - }, - { - "speaker": "Tim", - "dia_id": "D24:9", - "text": "That's a good spot for a morning workout! Can you tell me about some challenges you've faced?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/o20k6p31qoe41.jpg" - ], - "blip_caption": "a photo of a person with a cast on their foot", - "query": "sprained ankle crutch wrapped ankle injured ankle", - "dia_id": "D24:10", - "text": "Yeah, I injured myself not too long ago. It sucked because I had to miss some games and couldn't help my team." - }, - { - "speaker": "Tim", - "dia_id": "D24:11", - "text": "Ouch, that's rough. Have you been able to stay active or keep up with your fitness routine while you're recovering?" - }, - { - "speaker": "John", - "img_url": [ - "https://mainephysicaltherapy.com/wp-content/uploads/2017/12/Incline-one-arm-cable-pull-down-1.jpg" - ], - "blip_caption": "a photo of a man sitting on a chair with a blue ball", - "query": "physical therapy exercises", - "dia_id": "D24:12", - "text": "It's been tough, but I'm trying to stay active and do my rehab. I do physical therapy exercises every day." - }, - { - "speaker": "Tim", - "dia_id": "D24:13", - "text": "Cool, rehab can be tough but it's key to keep it up. How's it coming along?" - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/5189/5618665304_b2e0ccd051_b.jpg" - ], - "blip_caption": "a photography of a treadmill in a room with a window", - "query": "treadmill gym", - "dia_id": "D24:14", - "re-download": true, - "text": "It's going great! I've been working hard and it's paying off. Last Friday, I had a milestone moment at the gym. I was able to jog a bit with no pain, which was such a relief!" - }, - { - "speaker": "Tim", - "dia_id": "D24:15", - "text": "Wow! How was it jogging without any discomfort?" - }, - { - "speaker": "John", - "dia_id": "D24:16", - "text": "It was great! After being out for so long, jogging without any pain was a huge success. My wife and I hosted a small get-together with friends and family to celebrate." - }, - { - "speaker": "Tim", - "dia_id": "D24:17", - "text": "Congrats! That's awesome. Keep at it and you'll be back in no time. That sounds fun, how was it?" - }, - { - "speaker": "John", - "dia_id": "D24:18", - "text": "Thanks! Appreciate the support and encouragement. I'm gonna keep pushing and staying positive. It was good to see everyone again! We had a ton of fun." - }, - { - "speaker": "Tim", - "dia_id": "D24:19", - "text": "I'm glad everyone had fun!" - }, - { - "speaker": "John", - "dia_id": "D24:20", - "text": "Me too. Talk to you later!" - } - ], - "session_25_date_time": "10:04 am on 19 December, 2023", - "session_25": [ - { - "speaker": "Tim", - "dia_id": "D25:1", - "text": "Hey John, been a while since we chatted. How's it going?" - }, - { - "speaker": "John", - "img_url": [ - "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQiyDMnkQOmMQNKQfQDDRQlTRpNME7oy0xNlcHjLYB8n_ZwvnWe8nYlRKA3WkZNHEQLsME&usqp=CAU" - ], - "blip_caption": "a photography of a man with a backpack and a backpack walking down a path", - "query": "endorsement deal outdoor gear company photoshoot", - "dia_id": "D25:2", - "re-download": true, - "text": "Yo Tim! Great to hear from you. Things have been wild! Last week I got this amazing deal with a renowned outdoor gear company. So pumped!" - }, - { - "speaker": "Tim", - "dia_id": "D25:3", - "text": "That's awesome about the deal! I'm curious, what kind of gear did you end up getting? And how did the photoshoot turn out?" - }, - { - "speaker": "John", - "dia_id": "D25:4", - "text": "Cheers! Got some awesome hiking stuff and outdoor gear - all top-notch. The photoshoot went really well too. We did it in a gorgeous forest and the photographer got some epic shots of me doing my thing - it was amazing!" - }, - { - "speaker": "Tim", - "dia_id": "D25:5", - "text": "Wow! That sounds amazing. Being out in such a gorgeous location must have been incredible. I'd love to see one of the epic shots you got! Do you have any pictures from the photoshoot?" - }, - { - "speaker": "John", - "img_url": [ - "https://cdn.stocksnap.io/img-thumbs/960w/man-jumping_FOTAMAJTAF.jpg" - ], - "blip_caption": "a photography of a man jumping in the air in a field", - "query": "photoshoot forest leap nature", - "dia_id": "D25:6", - "re-download": true, - "text": "Here you go, here's a pic. Nature puts me in a great mood and always gets me energized!" - }, - { - "speaker": "Tim", - "dia_id": "D25:7", - "text": "That's an amazing photo! I can see why it inspires you - the rocks and river look so peaceful. What drew you to that spot?" - }, - { - "speaker": "John", - "dia_id": "D25:8", - "text": "I stumbled across this spot while hiking. The sound of that river was so soothing, I felt so at peace surrounded by those rocks. It was like nature was telling me to stop and admire its beauty." - }, - { - "speaker": "Tim", - "dia_id": "D25:9", - "text": "Wow, that sounds amazing. It's true, nature has a way of bringing peace and joy. Anything else like that been happening lately?" - }, - { - "speaker": "John", - "dia_id": "D25:10", - "text": "Things have been going great on the court. We've been putting in a lot of work and achieving our goals, which is awesome." - }, - { - "speaker": "Tim", - "dia_id": "D25:11", - "text": "Hard work pays off, right? What have you and your team been up to lately?" - }, - { - "speaker": "John", - "dia_id": "D25:12", - "text": "We gave it our all during last week's scrimmage. It's amazing to see our team's growth. We know it won't be easy, but it'll be worth it when we see the results." - }, - { - "speaker": "Tim", - "dia_id": "D25:13", - "text": "What areas have you seen the most growth in during your training?" - }, - { - "speaker": "John", - "dia_id": "D25:14", - "text": "Our team has seen the most growth in communication and bonding. It has really helped our performances by allowing us to understand each other's strengths and weaknesses." - }, - { - "speaker": "Tim", - "dia_id": "D25:15", - "text": "Wow, that's awesome! Glad to hear you guys are bonding. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D25:16", - "text": "Thanks! Let's keep at it and continue supporting each other. Appreciate your assistance!" - }, - { - "speaker": "Tim", - "dia_id": "D25:17", - "text": "Yeah, let's support each other. I'm here for you. Just keep believing in yourself! Bye!" - } - ], - "session_26_date_time": "3:35 pm on 26 December, 2023", - "session_26": [ - { - "speaker": "John", - "img_url": [ - "https://pivitu.com/wp-content/uploads/bb-plugin/cache/IMG_8996-circle-5996d578100cbb83f34040d0584d4834-5db4d83b44005.jpg" - ], - "blip_caption": "a photo of a basketball court with a crowd of people watching", - "query": "athletic marketing seminar young athletes", - "dia_id": "D26:1", - "text": "Hey Tim! Great to hear from you. My week's been busy - I started doing seminars, helping people with their sports and marketing. It's been awesome!" - }, - { - "speaker": "Tim", - "dia_id": "D26:2", - "text": "Hey John! Sounds awesome! Congrats on how far you've come. How did it go?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/5hvq16lfcgy71.jpg" - ], - "blip_caption": "a photo of a man and woman on stage at a convention", - "query": "seminar speaker stage crowd participants", - "dia_id": "D26:3", - "text": "Thanks! The seminars went really well. All the aspiring profs were so eager and motivated - it was great! I'm really happy I could share my knowledge and help out." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a book with a golden cover on a table", - "dia_id": "D26:4", - "text": "Wow John! Impressive stuff! I'm starting some big new things too!" - }, - { - "speaker": "John", - "dia_id": "D26:5", - "text": "Thanks! What have you been up to?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/53yi43izjpb81.jpg" - ], - "blip_caption": "a photo of a book with a picture of a boy and a girl", - "query": "adventures across the globe stories travelers book cover", - "dia_id": "D26:6", - "text": "I've been reading cool stories from travelers from around the world. I'm using it to plan my next adventure. This is a book I found with tons of them!" - }, - { - "speaker": "John", - "dia_id": "D26:7", - "text": "Wow, that's cool! Have you read any of the stories? I'm looking for some travel ideas too." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/tdukekw3wlp91.jpg" - ], - "blip_caption": "a photo of two men on horseback in front of a mountain", - "query": "hiking himalayas snowy mountain peak", - "dia_id": "D26:8", - "text": "I read a few of them. One of them is about two hikers who trekked through the Himalayas, sounds awesome!" - }, - { - "speaker": "John", - "dia_id": "D26:9", - "text": "Wow, that sounds awesome! How challenging was the trek through the Himalayas?" - }, - { - "speaker": "Tim", - "dia_id": "D26:10", - "text": "The book mentioned that the trek was tough but worth it, with challenging terrain, altitude sickness, and bad weather. But they made it and saw amazing sights - it really motivated me." - }, - { - "speaker": "John", - "dia_id": "D26:11", - "text": "Wow! Sounds like a tough journey." - }, - { - "speaker": "Tim", - "dia_id": "D26:12", - "text": "It's true. Facing challenges can be tough, but it can make us stronger. I just visited a travel agency to see what the requirements would be for my next dream trip." - }, - { - "speaker": "John", - "dia_id": "D26:13", - "text": "For sure, challenges help us learn and grow. Sounds fun! Keep me updated!" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a newspaper article with a picture of a woman", - "dia_id": "D26:14", - "text": "Thanks, I will. You have to keep pushing for your goals." - }, - { - "speaker": "John", - "dia_id": "D26:15", - "text": "By the way, who was that main actress in Harry Potter? I've heard about her a lot lately." - }, - { - "speaker": "Tim", - "dia_id": "D26:16", - "text": "Emma Watson, she's a big supporter of gender equality. I'm a huge fan." - }, - { - "speaker": "John", - "dia_id": "D26:17", - "text": "Wow, that's great! It's inspiring to see people who use their platform for important causes and make a difference." - }, - { - "speaker": "Tim", - "dia_id": "D26:18", - "text": "Her women's rights advocacy is also a huge inspiration to me! Seeing people use their platform for causes like gender equality is really inspiring. It's so cool to see people making a difference." - }, - { - "speaker": "John", - "img_url": [ - "https://talkstar-photos.s3.amazonaws.com/uploads/9dd73626-fe0d-4a3b-b913-c39d65250da8/ApolloRobbins_2013G-embed.jpg" - ], - "blip_caption": "a photography of two men standing next to each other on a stage", - "query": "charity event speaking", - "dia_id": "D26:19", - "re-download": true, - "text": "Definitely. Making a difference is important to me. I use my influence and resources to help causes I believe in. It's about making the world a better place. Here's a picture of me speaking at a charity event." - }, - { - "speaker": "Tim", - "dia_id": "D26:20", - "text": "Cool! What causes are you working on? Tell me more about them!" - }, - { - "speaker": "John", - "img_url": [ - "https://necommunitycenter.org/portland/wp-content/uploads/2023/01/Game-in-Progress-with-Ref2-1024x684.jpg" - ], - "blip_caption": "a photo of a group of kids playing basketball in a gym", - "query": "youth sports programs basketball court kids playing", - "dia_id": "D26:21", - "text": "I've been working on supporting youth sports and fighting for fair chances in sports for underserved communities. It's important to me that every kid has access to good sports programs. I've been collaborating with organizations to create more opportunities for young athletes and help them succeed. It's amazing to see the difference sports make in people's lives." - }, - { - "speaker": "Tim", - "dia_id": "D26:22", - "text": "Cool! What have been some memorable experiences working with them?" - }, - { - "speaker": "John", - "dia_id": "D26:23", - "text": "Organizing a basketball camp for kids in my hometown last summer was an awesome experience! Seeing their faces light up when they hit the court was priceless. It was a week full of laughs, high-fives, and personal growth for us all. That opportunity to inspire those kids and show them just how much potential they have was truly incredible." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a basketball with a signed autograph on it", - "dia_id": "D26:24", - "text": "Wow! Making a difference to those kids was great! Your passion for helping others is awesome." - }, - { - "speaker": "John", - "dia_id": "D26:25", - "text": "Thanks! I'm really glad I can make a difference. Have you been doing anything new in your free time?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/oqnovgpaxdv51.jpg" - ], - "blip_caption": "a photo of a collection of harry potter books on a desk", - "query": "harry potter book stack", - "dia_id": "D26:26", - "text": "In my downtime, I still love to get lost in good books, and this series is one of my favorites. It's a magical world to escape to." - }, - { - "speaker": "John", - "dia_id": "D26:27", - "text": "That's awesome! Have you seen all the Harry Potter movies? I'm a fan too!" - }, - { - "speaker": "Tim", - "dia_id": "D26:28", - "text": "Yeah, I have! Watching them and seeing how they compare to the books is awesome. It's amazing to watch the story come alive. Have you seen all of them?" - }, - { - "speaker": "John", - "dia_id": "D26:29", - "text": "I'm a total movie fan! Seeing it all come alive on the big screen is awesome, and a great way to relax." - }, - { - "speaker": "Tim", - "img_url": [ - "https://images.pexels.com/photos/7234395/pexels-photo-7234395.jpeg" - ], - "blip_caption": "a photography of three guys sitting on a couch watching a movie", - "query": "movie night friends popcorn harry potter", - "dia_id": "D26:30", - "re-download": true, - "text": "Yeah, watching movies is a fun way to relax. We love having movie marathons with our friends." - }, - { - "speaker": "John", - "dia_id": "D26:31", - "text": "Sounds like a blast! Movie marathons with friends and popcorn, right? So, what's your favorite genre?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/ixsrm1ukws611.jpg" - ], - "blip_caption": "a photo of a poster of a group of people with a sword", - "query": "fantasy movie poster lord of the rings", - "dia_id": "D26:32", - "text": "I'm a huge fan of this genre! Epic adventures and magical worlds are my thing. Here's a pic of my favorite, Lord of the Rings!" - }, - { - "speaker": "John", - "dia_id": "D26:33", - "text": "Wow, that's great! Are there any new fantasy movies that you're excited about?" - }, - { - "speaker": "Tim", - "dia_id": "D26:34", - "text": "Woo-hoo! There's a new fantasy TV series coming out next month - can't wait!" - }, - { - "speaker": "John", - "dia_id": "D26:35", - "text": "What's it called? I'm always down for something new." - }, - { - "speaker": "Tim", - "dia_id": "D26:36", - "text": "I'm really excited to watch this new show that's coming out called \"The Wheel of Time\". It's based on a book series that I love." - }, - { - "speaker": "John", - "dia_id": "D26:37", - "text": "That sounds exciting!" - }, - { - "speaker": "Tim", - "dia_id": "D26:38", - "text": "Yeah, can't wait to check out the series. It's always fun seeing the books come to life on screen! Talk to you later!" - } - ], - "session_27_date_time": "5:26 pm on 2 January, 2024", - "session_27": [ - { - "speaker": "Tim", - "blip_caption": "a photo of a man standing on a fence in front of a leaning tower", - "dia_id": "D27:1", - "text": "Hi John, how's it going? Interesting things have happened since we last talked - I joined a group of globetrotters who are into the same stuff as me. It's been awesome getting to know them and hear about their trips." - }, - { - "speaker": "John", - "dia_id": "D27:2", - "text": "Hey Tim! Cool to hear about your globetrotting group! Must be great connecting with other traveling buffs. By the way, have you been to Italy? I had a blast there last month." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a book with a tag on it", - "dia_id": "D27:3", - "text": "It's been awesome chatting with fellow travel enthusiasts. Italy is definitely on my list of places to visit. How was your trip there last month?" - }, - { - "speaker": "John", - "dia_id": "D27:4", - "text": "Italy was awesome! Everything from the food to the history and architecture was amazing. I even got this awesome book while I was there and it's been giving me some cooking inspiration." - }, - { - "speaker": "Tim", - "dia_id": "D27:5", - "text": "Wow, traveling is amazing, isn't it? I'm learning German now - tough but fun. Do you know any other languages?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a book with a red cover and white title", - "dia_id": "D27:6", - "text": "Wow! Impressive you're learning German. I know a bit of it myself and Spanish, it makes travel so much easier. How's it going with your language studies?\n" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a book sitting on a table next to a wall", - "dia_id": "D27:7", - "text": "Learning German has been tough but worth it. I like the structure of the language, it's much easier when I took French in high school. What made you decide to learn Spanish?" - }, - { - "speaker": "John", - "dia_id": "D27:8", - "text": "I've always wanted to learn Spanish. I just stared with it. It's such a useful language with many personal and professional opportunities!" - }, - { - "speaker": "Tim", - "img_url": [ - "https://thechinesebujo.files.wordpress.com/2017/03/img_2563.jpg" - ], - "blip_caption": "a photo of a cell phone sitting on a notebook with a smiley face app", - "query": "language learning app on phone", - "dia_id": "D27:9", - "text": "Yeah, knowing another language opens up a lot of opportunities. Have you come across any good resources for learning Spanish? I've been using this app." - }, - { - "speaker": "John", - "dia_id": "D27:10", - "text": "Yeah! I've been using that app on my phone to practice too! It's helped a lot." - }, - { - "speaker": "Tim", - "dia_id": "D27:11", - "text": "That app is great. Learning another language is tough, but the rewards are totally worth it." - }, - { - "speaker": "John", - "blip_caption": "a photo of a basketball ball on the ground with a basketball hoop in the background", - "dia_id": "D27:12", - "text": "It takes dedication and practice, but it's so rewarding to communicate with different cultures. Keep it up with German!" - }, - { - "speaker": "Tim", - "dia_id": "D27:13", - "text": "Thanks! I appreciate your encouragement. I'm definitely going to keep up with my German lessons. Do you still play basketball often?" - }, - { - "speaker": "John", - "img_url": [ - "https://c8.alamy.com/zooms/9/de5f1d4e73244a8f94b16a6b6d093748/ttnmea.jpg" - ], - "blip_caption": "a photo of a basketball ball on the ground with a basketball hoop in the background", - "query": "basketball court sunset", - "dia_id": "D27:14", - "text": "Yeah, basketball is still really important to me - I practice and train every day to stay in shape and improve. Can't imagine my life without it, it's my passion." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/oqnovgpaxdv51.jpg" - ], - "blip_caption": "a photo of a collection of harry potter books on a desk", - "query": "harry potter books stack", - "dia_id": "D27:15", - "text": "Wow! Love the way you go for it. Don't ever quit on what you love. I will always love reading, personally." - }, - { - "speaker": "John", - "dia_id": "D27:16", - "text": "Thanks! I won't give up on it. What got you into books?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.pinimg.com/originals/96/80/20/968020204c99f3f30544384d39fe598b.jpg" - ], - "blip_caption": "a photo of a desk with a chair and a book shelf", - "query": "harry potter book collection desk", - "dia_id": "D27:17", - "text": "I love escaping to that world. I have a collection of books that take me there." - }, - { - "speaker": "John", - "dia_id": "D27:18", - "text": "That's awesome! I totally understand why reading means so much to you. It's amazing how much playing a game can help us grow. Thanks for showing us your collection! Which one do you like best that takes you to another world?" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a collection of movies and dvds on a carpet", - "dia_id": "D27:19", - "text": "Harry Potter is my favorite book. It's so immersive!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/ur3tz0poja151.jpg" - ], - "blip_caption": "a photo of a collection of star wars movies on a table", - "query": "fantasy movies dvd collection carpet", - "dia_id": "D27:20", - "text": "Cool! Glad you're enjoying that book! Do you have any favorite fantasy movies as well? These are mine." - }, - { - "speaker": "Tim", - "dia_id": "D27:21", - "text": "Definitely Star Wars! It's my favorite and never gets old. What about you, do you have any favorite fantasy films?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/7vtqewbtg1181.jpg" - ], - "blip_caption": "a photo of a shelf with a lot of books on it", - "query": "lord of the rings dvd collection", - "dia_id": "D27:22", - "text": "I'm a huge fan of Lord of the Rings! The adventure, the world, and the characters are awesome." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a bookcase filled with dvds and games", - "dia_id": "D27:23", - "text": "Wow, me too! That's an awesome collection! Have you watched them heaps? Got any favorite characters from those movies?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a bookmark with a picture of a woman kissing a man", - "dia_id": "D27:24", - "text": "Thanks! I've watched a bunch of them and they're inspiring. My favorite character is Aragorn, he grows so much throughout the story." - }, - { - "speaker": "Tim", - "dia_id": "D27:25", - "text": "Nice one! Why is he your favorite?" - }, - { - "speaker": "John", - "dia_id": "D27:26", - "text": "He's a great leader and puts others first - that's why he eventually becomes king." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/mlffg3ze7gr71.jpg" - ], - "blip_caption": "a photo of a painting of a man with long hair", - "query": "aragorn poster lord of the rings", - "dia_id": "D27:27", - "text": "Wow, Aragorn's story is so inspiring - from a ranger to king of Gondor. It's amazing how he grows and achieves redemption throughout his journey." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/monip6iq5xm51.jpg" - ], - "blip_caption": "a photo of a painting of a man smoking a cigarette", - "query": "aragorn painting lord of the rings", - "dia_id": "D27:28", - "text": "Yeah. His journey is really inspiring. I have a painting in my room to remind me to stay true and be a leader in everything I do." - }, - { - "speaker": "Tim", - "dia_id": "D27:29", - "text": "Wow, that's awesome! What is it about him that makes him so inspiring for you?" - }, - { - "speaker": "John", - "dia_id": "D27:30", - "text": "Aragorn's brave, selfless, down-to-earth attitude is what inspired me. He never gives up and always stands up for justice." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/or3d42qfg4t91.jpg" - ], - "blip_caption": "a photo of a map of the world on a piece of paper", - "query": "map middle earth fantasy novels world-building intricate details fictional universe", - "dia_id": "D27:31", - "text": "Yeah, he's really inspiring. What's awesome about fantasy books like LOTR is getting lost in another world and seeing all the tiny details." - }, - { - "speaker": "John", - "dia_id": "D27:32", - "text": "Yeah, that's what I'm thinking! Love this map, it really helps you get lost in another world. What's on it?" - }, - { - "speaker": "Tim", - "dia_id": "D27:33", - "text": "It's a map of Middle-earth from LOTR - it's really cool to see all the different realms and regions." - }, - { - "speaker": "John", - "dia_id": "D27:34", - "text": "Wow, that looks awesome! Exploring different lands and regions in fantasy stories is always fun!" - }, - { - "speaker": "Tim", - "dia_id": "D27:35", - "text": "Thanks! It's really cool how fantasy stories allow me to explore other cultures and landscapes, all from the comfort of my home." - }, - { - "speaker": "John", - "img_url": [ - "https://i0.wp.com/stlouispatina.com/wp-content/uploads/2022/10/Copyright-St.-Louis-Patina-2771.jpg" - ], - "blip_caption": "a photo of a person walking down a path in front of the eiffel tower", - "query": "eiffel tower", - "dia_id": "D27:36", - "text": "Yeah! That's why I love traveling - it's a way to learn about different cultures and places." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a group of people climbing up a stone wall", - "dia_id": "D27:37", - "text": "I love traveling too. That picture is awesome. Have you been to Paris? The Eiffel Tower is so cool!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/b9cuf3yfd3s91.jpg" - ], - "blip_caption": "a photo of a view of a city from a bird's eye view", - "query": "eiffel tower view from top", - "dia_id": "D27:38", - "text": "Thanks! Yeah, I've been there before and loved it! That place is amazing and the view from there is incredible!" - }, - { - "speaker": "Tim", - "dia_id": "D27:39", - "text": "Wow, John, it looks amazing! Can't wait to see it for myself. Traveling is so eye-opening!" - }, - { - "speaker": "John", - "dia_id": "D27:40", - "text": "Yeah, it really is. It helps you see new things and get a different view of everything. It's so cool and educational! Talk to you later!" - } - ], - "session_28_date_time": "5:24 pm on 7 January, 2024", - "session_28": [ - { - "speaker": "Tim", - "dia_id": "D28:1", - "text": "Hey John, long time no talk. On Friday, I got great news - I'm finally in the study abroad program I applied for! Next month, I'm off to Ireland for a semester." - }, - { - "speaker": "John", - "dia_id": "D28:2", - "text": "Congrats, Tim! That's amazing news. So, where are you going to stay?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://roadworksbooks.files.wordpress.com/2022/07/img_4242.jpg" - ], - "blip_caption": "a photo of a woman standing on the side of a street", - "query": "galway colorful street arts scene traditional irish music", - "dia_id": "D28:3", - "text": "Thanks! I'm gonna stay in Galway, it's great for its arts and Irish music. This place has such a vibrant atmosphere." - }, - { - "speaker": "John", - "dia_id": "D28:4", - "text": "Awesome, Galway looks amazing! Is there anything in particular that you're keen to check out while you're there?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://images.pexels.com/photos/8456767/pexels-photo-8456767.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-david-riand-8456767.jpg" - ], - "blip_caption": "a photo of a cliff overlooking the ocean at sunset", - "query": "cliffs moher sunset", - "dia_id": "D28:5", - "text": "Yep! I'm so excited to explore the nature - it looks amazing!" - }, - { - "speaker": "John", - "dia_id": "D28:6", - "text": "Wow, great view! Have you visited any other places?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/t98kzzhuocp81.jpg" - ], - "blip_caption": "a photo of a person standing on a cliff overlooking the ocean", - "query": "cliffs moher ocean cliffs", - "dia_id": "D28:7", - "text": "I want to visit The Cliffs of Moher. It has amazing ocean views and awesome cliffs." - }, - { - "speaker": "John", - "dia_id": "D28:8", - "text": "Wow, that's awesome! I could stop by there after my season." - }, - { - "speaker": "Tim", - "dia_id": "D28:9", - "text": "Cool, let me know if you're around!" - }, - { - "speaker": "John", - "img_url": [ - "https://outandaboutmummy.files.wordpress.com/2019/11/img_6609.jpg" - ], - "blip_caption": "a photo of a boy dribbling a basketball on a court", - "query": "charity basketball tournament children", - "dia_id": "D28:10", - "text": "Yep, I'll let you know. Oh, I held a benefit basketball game last week." - }, - { - "speaker": "Tim", - "dia_id": "D28:11", - "text": "Wow! How did the game go?" - }, - { - "speaker": "John", - "dia_id": "D28:12", - "text": "The game turned out to be a total success! Lots of people showed up and had a great time, plus we were able to raise some money for charity." - }, - { - "speaker": "Tim", - "dia_id": "D28:13", - "text": "Great job organizing the event. That's really making a difference!" - }, - { - "speaker": "John", - "dia_id": "D28:14", - "text": "Thanks! It's amazing how basketball brings people together and creates a positive impact!" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a basketball ball and a book on a court", - "dia_id": "D28:15", - "text": "You rock! Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D28:16", - "text": "Thanks, Tim! It's awesome to see how sports can unite people. By the way, what book are you currently reading?" - }, - { - "speaker": "Tim", - "dia_id": "D28:17", - "text": "I'm currently reading a fantasy novel called \"The Name of the Wind\" by Patrick Rothfuss. It's really good!" - }, - { - "speaker": "John", - "dia_id": "D28:18", - "text": "\"The Name of the Wind\" sounds cool. I'll add it to my list. Thanks!" - }, - { - "speaker": "Tim", - "dia_id": "D28:19", - "text": "I hope you enjoy it! Let me know your thoughts." - }, - { - "speaker": "John", - "dia_id": "D28:20", - "text": "Will do! Thanks for the recommendation!" - }, - { - "speaker": "Tim", - "dia_id": "D28:21", - "text": "No problem. Talk to you soon!" - } - ], - "session_29_date_time": "1:41 pm on 12 January, 2024", - "session_29": [ - { - "speaker": "Tim", - "dia_id": "D29:1", - "text": "Hey John! How's it going? Hope all is good." - }, - { - "speaker": "John", - "dia_id": "D29:2", - "text": "Hey Tim! Things have been good. Something exciting happened recently for me. What about you? How's everything going?" - }, - { - "speaker": "Tim", - "dia_id": "D29:3", - "text": "Cool news! I'm trying to get my head around the visa requirements for some places I want to visit. It's kind of overwhelming but I'm excited! What have you been up to?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/g33orfsja8ha1.jpg" - ], - "blip_caption": "a photo of a baseball player holding a bat next to a soda", - "query": "endorsement drink logo", - "dia_id": "D29:4", - "text": "Last week was wild - something incredible happened. But it's a total dream come true - just crazy! I got an endorsement with a popular beverage company!" - }, - { - "speaker": "Tim", - "dia_id": "D29:5", - "text": "Congrats! How did it feel to seal the deal?" - }, - { - "speaker": "John", - "dia_id": "D29:6", - "text": "Thanks! It felt crazy. It's not just about the signing, but it's about feeling like all the hard work paid off - like all those training hours weren't for nothing." - }, - { - "speaker": "Tim", - "dia_id": "D29:7", - "text": "Wow! I bet you were thrilled when everything finally worked out. That sense of accomplishment is awesome and really boosts your self-esteem. I can imagine all the hard work you put into it was definitely worth it." - }, - { - "speaker": "John", - "dia_id": "D29:8", - "text": "Yeah, it's great when you reach a goal and it feels rewarding. It's a reminder that you're going in the right direction, and all the hard work was worth it. What's something you feel proud of recently?" - }, - { - "speaker": "Tim", - "dia_id": "D29:9", - "text": "I'm proud of researching visa requirements for countries I want to visit. It feels like taking initiative is a step towards making my travel dreams a reality!" - }, - { - "speaker": "John", - "dia_id": "D29:10", - "text": "Great going! Taking initiative is a must if you wanna achieve your goals. I'm excited to hear about all your future adventures!" - }, - { - "speaker": "Tim", - "dia_id": "D29:11", - "text": "Thanks! I'll keep you in the loop about my travels. Is there anywhere you recommend visiting?" - }, - { - "speaker": "John", - "dia_id": "D29:12", - "text": "Barcelona is a must-visit city! You'll love exploring the culture, admiring the architecture, and tasting the amazing food in each neighborhood. Plus, the nearby beaches are great for soaking up the sun. Definitely add it to your travel list!" - }, - { - "speaker": "Tim", - "dia_id": "D29:13", - "text": "Barcelona sounds awesome! I've heard so many great things. Definitely adding it to my list. Thanks!" - }, - { - "speaker": "John", - "dia_id": "D29:14", - "text": "No problem! Glad you liked the suggestion. Let me know if you have any other questions or need help with anything." - }, - { - "speaker": "Tim", - "dia_id": "D29:15", - "text": "Cheers! I owe you one. Let me know if you need anything. Bye!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Tim": [ - "Tim contacts his Harry Potter fan friend to discuss potential collaborations on fan projects based on various elements of the Harry Potter Universe." - ], - "John": [ - "John signs a new contract with Minnesota Wolves playing shooting guard and strives to adjust to their style of play.", - "John aims to improve his shooting percentage during his practices with the new team." - ], - "date": "21 May, 2023" - }, - "events_session_2": { - "Tim": [ - "Tim joins a forum about fantasy literature and shares his thoughts with other members about his favorite books." - ], - "John": [ - "John uses his contacts in the basketball industry and his marketing skills to explore potential sports endorsement opportunities with Nike and UnderArmour." - ], - "date": "15 June, 2023" - }, - "events_session_3": { - "Tim": [ - "Tim meets and converses with a fellow Harry Potter fan in California." - ], - "John": [ - "John scores his highest score ever - 40 points - during a playoff in June 2023.", - "John and his team are thrilled by their win and celebrate in a restaurant afterwards.", - "John secures a basketball shoe and gear deal with Nike and is in talks with Gatorade for a potential sponsorship.", - "John look forward to a game in Seattle in the first week of August 2023." - ], - "date": "16 July, 2023" - }, - "events_session_4": { - "Tim": [ - "Tim finds an opportunity on a fantasy literature forum and starts writing articles on fantasy novels, studying characters, themes, and making book recommendations for an online magazine." - ], - "John": [ - "John is reading a book that inspires him to keep dreaming." - ], - "date": "2 August, 2023" - }, - "events_session_5": { - "Tim": [ - "Tim skypes with the Harry Potter fan he met in California to discuss future collaborations.", - "Tim starts reading the book, The Name of the Wind, by Patrick Rothfuss." - ], - "John": [ - "John has an intense game at Seattle followed by an exhilarating win." - ], - "date": "9 August, 2023" - }, - "events_session_6": { - "Tim": [], - "John": [ - "John travels from Seattle to Chicago and starts exploring the culture and people.", - "John wants to make an impact with charity work and is in talks with a local organization to help disadvantaged kids with sports and school." - ], - "date": "11 August, 2023" - }, - "events_session_7": { - "Tim": [ - "Tim prepares to visit a book conference in September 2023 where he wants to meet with book publishers, authors and fellow readers." - ], - "John": [ - "John returns from his trip to Chicago and meets with his team.", - "John's teammates gift him a signed basketball as a mark of their friendship and team spirit." - ], - "date": "17 August, 2023" - }, - "events_session_8": { - "Tim": [ - "Tim focuses on school and starts learning the piano.", - "Tim enjoys playing tunes from the movie Harry Potter and the Philosopher's Stone on the piano." - ], - "John": [ - "John joins a new gym to stay in basketball shape and works out an elaborate gym routine to keep up with his basketball skills as well as strength routine." - ], - "date": "21 August, 2023" - }, - "events_session_9": { - "Tim": [ - "Tim gets busy with school assignments and exams." - ], - "John": [ - "John visits his family and old friends in his hometown.", - "John goes on a visit to New York City." - ], - "date": "26 August, 2023" - }, - "events_session_10": { - "Tim": [ - "Tim gets turned down for a summer job.", - "Tim gives a presentation in class and feels accomplished about it.", - "Tim plans to visit Universal Studios in Septemner 2023 and is excited to see the Harry Potter rides." - ], - "John": [ - "John starts to cook more often and experiment with recipes, such as s soup with sage herbs.", - "John returns from his trip to New York City where he struggled a bit with the subway system." - ], - "date": "31 August, 2023" - }, - "events_session_11": { - "Tim": [ - "Tim recommends that John can visit Edinburgh, Scotland for their team trip and recommends the book, The Name of the Wind by Patrick Rothfuss to read during the trip." - ], - "John": [ - "John attends a local restaurant with some of his new teammates.", - "John plans to take a trip with his teammates and seeks for city and book recommendations from Tim." - ], - "date": "21 September, 2023" - }, - "events_session_12": { - "Tim": [], - "John": [ - "John ties the knot with his long-time girlfriend in an emotional wedding ceremony at a greenhouse." - ], - "date": "2 October, 2023" - }, - "events_session_13": { - "Tim": [ - "Tim visits the UK to attend a Harry Potter themed conference and feels at home with fellow attendees due to their shared love for the Harry Potter universe." - ], - "John": [ - "John's team continues on an intense game season with some wins as well as losses.", - "John and his team win a triphy during one of their games." - ], - "date": "13 October, 2023" - }, - "events_session_14": { - "Tim": [], - "John": [ - "John starts to mentor the younger players on his team and finds the experience rewarding." - ], - "date": "17 October, 2023" - }, - "events_session_15": { - "Tim": [ - "Tim reads a book about the history of castles in the UK and uses it as inspiration for his ongoing fantasy novel writing.", - "Tim is in the process of writing a fantasy novel." - ], - "John": [], - "date": "21 October, 2023" - }, - "events_session_16": { - "Tim": [ - "Tim gets stuck while writing a plot twist in his book but pushes through it." - ], - "John": [ - "John goes on a European vacation with his wife." - ], - "date": "6 November, 2023" - }, - "events_session_17": { - "Tim": [], - "John": [ - "John and his wife go on a road trip to explore the coastline in Europe.", - "John finishes reading a gripping fantasy novel." - ], - "date": "11 November, 2023" - }, - "events_session_18": { - "Tim": [ - "Tim visit castles in the UK and returns home.", - "Tim prepares for exams at school." - ], - "John": [ - "John suffers from an ankle injury and has to stay off court while recovering from it." - ], - "date": "16 November, 2023" - }, - "events_session_19": { - "Tim": [ - "Tim attempts writing a short story based off his experiences in the UK but is not able to accomplish it." - ], - "John": [ - "John continues recovery from his ankle injury and re-reads one of his favorite books, The Alchemist, during this time." - ], - "date": "21 November, 2023" - }, - "events_session_20": { - "Tim": [ - "Tim faces some tough exams which make him question his competence but he perseveres and does well at it." - ], - "John": [ - "John starts taking yoga classes to complement his basketball practice and enjoys doing the Warrior II pose." - ], - "date": "1 December, 2023" - }, - "events_session_21": { - "Tim": [ - "Tim joins a travel club to get more insights about countries he is interested in and meet new people.", - "Tim has been learning to play the violin for four months now and wants to learn to play jazz and film scores." - ], - "John": [], - "date": "6 December, 2023" - }, - "events_session_22": { - "Tim": [ - "Tim attends a Harry Potter themed party where he dresses up with his Gryffindor scarf, makes many new friends and receives chocolate frogs as treats.", - "Tim reads a book on the power of friendship and loyalty, and A Dance with Dragons by George R R Martin" - ], - "John": [ - "John's team secures a win in one of their games.", - "John reads the book Dune by Frank Herbert" - ], - "date": "8 December, 2023" - }, - "events_session_23": { - "Tim": [ - "Tim does an analysis of a fantasy novel for his English literature class and it goes okay." - ], - "John": [ - "John achieves a career-high assist during a big game." - ], - "date": "11 December, 2023" - }, - "events_session_24": { - "Tim": [], - "John": [ - "John and his team secure another win that was a close-call until the buzzer.", - "John continues doing physical therapy and rehab to recover from his ankle injury.", - "John is able to job without any pain for the first time since the injury.", - "John and his wife host a small get-together for his friends and family to celebrate his path to recovery from the ankle injury." - ], - "date": "16 December, 2023" - }, - "events_session_25": { - "Tim": [], - "John": [ - "John lands a new endorsement deal with a renowned outdoor gear company and completes a photoshoot for the same in a gorgeous forest." - ], - "date": "19 December, 2023" - }, - "events_session_26": { - "Tim": [ - "Tim visits a travel agency to inquire about the requirements of his next dream trip.", - "Tim finds and reads a book about travellers' stories from around the world to plan his next adventure." - ], - "John": [ - "John starts conducting seminars to share his athletic and marketing skills with aspiring professionals.", - "John continues his charity work on youth sports and fighting to give youth in underserved communities a chance at sports." - ], - "date": "26 December, 2023" - }, - "events_session_27": { - "Tim": [ - "Tim finds a group of fellow travellers who share a common interest in exploring new cultures.", - "Tim starts a language course in German." - ], - "John": [], - "date": "2 January, 2024" - }, - "events_session_28": { - "Tim": [ - "Tim gets accepted to a Study Abroad program in school and will leave next month to spend the semester at Ireland.", - "Tim plans to live in Galway when studying in Ireland and is excited to expore Irish music, arts as well as nature such as The Cliffs of Moher." - ], - "John": [ - "John successfully organizes and hosts a benefit basketball tournament for charity." - ], - "date": "7 January, 2024" - }, - "events_session_29": { - "Tim": [ - "Tim starts to research about the visa requirements of some countries of interest." - ], - "John": [ - "John makes an endorsement deal with Moxie, a popular beverage company." - ], - "date": "12 January, 2024" - } - }, - "observation": { - "session_1_observation": { - "John": [ - [ - "John signed with the Minnesota Wolves for the upcoming season as a shooting guard.", - "D1:5" - ], - [ - "John's goal for the season is to improve his shooting percentage.", - "D1:9" - ], - [ - "John found fitting into the new team's style of play a challenge during pre-season.", - "D1:11" - ] - ], - "Tim": [ - [ - "Tim is working on a Harry Potter fan project and discussing collaborations for it.", - "D1:2" - ], - [ - "Tim talked to a friend who is a Harry Potter fan to figure out ideas for the project.", - "D1:14" - ], - [ - "Tim went to a Harry Potter-related place in London a few years ago and went on a tour.", - "D1:18" - ] - ] - }, - "session_2_observation": { - "Tim": [ - [ - "Tim joined a fantasy literature forum and had a great discussion about his favorite books.", - "D2:1" - ], - [ - "Tim has a book collection and owns a picture from MinaLima, the creators of props for the Harry Potter films.", - "D2:7" - ], - [ - "Tim considers his book collection and Harry Potter memorabilia as reminders that help him escape reality.", - "D2:11" - ], - [ - "There is a chance that Tim will visit more Harry Potter-related spots in the future.", - "D2:13" - ] - ], - "John": [ - [ - "John is exploring endorsement opportunities and is considering linking up with sports brands like Nike and Under Armour.", - "D2:2" - ], - [ - "John expressed that it would be rewarding to have his hard work pay off through endorsement opportunities.", - "D2:2" - ], - [ - "John asked about Tim's book collection and the picture from MinaLima, showing an interest in Tim's hobbies.", - "D2:8" - ], - [ - "John played in a recent game and finds it awesome to be out there doing what he loves.", - "D2:16" - ], - [ - "John appreciates Tim's support and expresses gratitude for it.", - "D2:18" - ] - ] - }, - "session_3_observation": { - "John": [ - [ - "John scored 40 points in a game last week, his highest ever.", - "D3:1" - ], - [ - "John celebrated with his teammates at a restaurant after winning a tough game.", - "D3:5" - ], - [ - "John has signed a deal with Nike for a basketball shoe and gear, and is in talks with Gatorade for a potential sponsorship.", - "D3:13" - ], - [ - "John mentioned exploring endorsements and used his contacts in the basketball industry and marketing skills to make connections.", - "D3:11" - ], - [ - "John's game next month is in Seattle, a city he loves for its energy, diversity, food, and fan support.", - "D3:19" - ], - [ - "John started surfing five years ago and loves the connection to nature it provides.", - "D3:27" - ] - ], - "Tim": [ - [ - "Tim had a nice chat with a Harry Potter fan in California last week.", - "D3:2" - ], - [ - "Tim finds bliss in reading a great fantasy book to escape and feel free.", - "D3:30" - ] - ] - }, - "session_4_observation": { - "Tim": [ - [ - "Tim is currently writing articles about fantasy novels for an online magazine.", - "D4:1" - ], - [ - "Tim found the opportunity to write about fantasy novels on a fantasy literature forum.", - "D4:3" - ], - [ - "Tim's articles focus on studying characters, themes, and making book recommendations of different fantasy novels.", - "D4:5" - ], - [ - "Tim loves writing about Harry Potter and Game of Thrones and could talk about them forever.", - "D4:7" - ] - ], - "John": [ - [ - "John participated in an intense Harry Potter trivia contest at a charity event with Anthony.", - "D4:8" - ], - [ - "John is reading an inspiring book that reminds him to keep dreaming.", - "D4:10" - ], - [ - "John wants to keep reaching for new goals.", - "D4:12" - ] - ] - }, - "session_5_observation": { - "Tim": [ - [ - "Tim recently skyped with a Harry Potter fan he met in CA and discussed collab-ing.", - "D5:1" - ], - [ - "Tim enjoys reading fantasy books that captivate him and take him to another world.", - "D5:13" - ], - [ - "Tim is currently reading a fantasy book by Patrick Rothfuss which he finds awesome.", - "D5:15" - ] - ], - "John": [ - [ - "John had a crazy intense game last week which his team won by a tight score, and scoring the last basket was an awesome experience for him.", - "D5:2" - ], - [ - "John feels that the team bond is awesome and makes all the hard work worth it.", - "D5:6" - ], - [ - "John emphasizes the importance of having a strong team/support which is like a family away from home.", - "D5:8" - ], - [ - "John is currently reading a fantasy book recommended by Tim and plans to let him know his thoughts after reading it.", - "D5:16" - ] - ] - }, - "session_6_observation": { - "John": [ - [ - "John recently took a trip to Chicago and found the locals to be friendly.", - "D6:3" - ], - [ - "John enjoys experiencing other cultures and connecting with new people.", - "D6:3" - ], - [ - "John loves the energy in a stadium during a sports game, with everyone cheering and getting excited.", - "D6:5" - ], - [ - "John has a pair of basketball shoes that he considers lucky and they hold many stories of his journey.", - "D6:9" - ], - [ - "Basketball has been a part of John's life since childhood, starting with watching NBA games with his dad and then playing in local leagues.", - "D6:13" - ], - [ - "John's childhood dream was to play on the big courts, which became a reality through hard work, earning a scholarship, and being drafted by a team.", - "D6:13" - ], - [ - "John's number one goal is to win a championship in basketball and also wants to make a difference through charity work and inspiring others.", - "D6:15" - ], - [ - "John is teaming up with a local organization to help disadvantaged kids with sports and school as part of his charity work.", - "D6:17" - ] - ], - "Tim": [ - [ - "Tim recently attended an event that he found fantastic and special, being with other fans who share his love for it.", - "D6:2" - ], - [ - "Tim has been writing more articles to combine his love for reading and sharing great stories.", - "D6:6" - ], - [ - "Tim recommends the book \"The Name of the Wind,\" describing it as a captivating fantasy novel with great world-building and character development.", - "D6:8" - ] - ] - }, - "session_7_observation": { - "John": [ - [ - "John is part of a basketball team which he feels lucky to be a part of.", - "D7:1" - ], - [ - "John values the bond created by being around people who share the same love for basketball.", - "D7:3" - ], - [ - "John received a signed ball from his teammates as a reminder of their friendship and appreciation.", - "D7:7" - ], - [ - "The signed ball from his teammates serves as a reminder of the bond and support he has from them in his basketball journey.", - "D7:11" - ] - ], - "Tim": [ - [ - "Tim felt a sense of belonging at an event last month where everyone shared the same love for a common interest.", - "D7:4" - ], - [ - "Tim is hoping to attend a book conference next month to learn more about literature and create a stronger bond to it.", - "D7:6" - ], - [ - "Tim appreciates having something meaningful to keep him motivated, as seen from his conversation with John.", - "D7:12" - ] - ] - }, - "session_8_observation": { - "John": [ - [ - "John found a new gym for training to stay on his basketball game.", - "D8:1" - ], - [ - "John included strength training in his workout routine to build muscle, increase power, prevent injuries, and improve agility and speed.", - "D8:7" - ], - [ - "Strength training improved John's shooting accuracy, agility, and speed, giving him confidence on the court.", - "D8:9" - ] - ], - "Tim": [ - [ - "Tim has been learning to play the piano and finds it satisfying to see progress in his learning.", - "D8:12" - ], - [ - "Tim's favorite song to play on the piano is a theme from the movie Harry Potter and the Philosopher's Stone.", - "D8:14" - ], - [ - "During Thanksgiving, Tim's family enjoys watching movies like 'Home Alone', 'Elf', and 'The Santa Clause'.", - [ - "D8:24", - "D8:26", - "D8:28" - ] - ] - ] - }, - "session_9_observation": { - "Tim": [ - [ - "Tim is busy with assignments and exams this week, but is not giving up.", - "D9:1" - ], - [ - "Tim is not part of a sports team but enjoys reading fantasy novels.", - "D9:5" - ], - [ - "Tim loves traveling to new places to experience different kinds of magic.", - "D9:5" - ], - [ - "Tim is excited about visiting New York City after seeing a picture shared by John.", - "D9:7" - ], - [ - "Tim plans to add NYC to his travel list and is eager to explore and try out new things there.", - "D9:9" - ], - [ - "Tim appreciates John's support as mentioned in the conversation.", - "D9:15" - ] - ], - "John": [ - [ - "John caught up with his family and old friends during a visit home last week.", - "D9:2" - ], - [ - "John and his friends were teammates for four years in high school and have played together for quite some time.", - "D9:4" - ], - [ - "John loves discovering new cities and enjoys exploring different places.", - "D9:6" - ], - [ - "John had a great time in New York City, exploring the city and trying out different restaurants.", - "D9:8" - ], - [ - "John encourages Tim to visit NYC and assures him it's an adventure he'll never forget.", - "D9:10" - ], - [ - "John offers to help Tim out when he plans to visit NYC.", - "D9:12" - ], - [ - "John is supportive of Tim and offers help whenever needed.", - "D9:14" - ] - ] - }, - "session_10_observation": { - "Tim": [ - [ - "Tim received a no for a summer job he wanted, but is staying positive.", - "D10:1" - ], - [ - "Tim recently gave a presentation in class and felt it was a small but significant progress.", - "D10:3" - ], - [ - "Tim loves experimenting with spices when cooking.", - "D10:9" - ], - [ - "Tim is planning a trip to Universal Studios, particularly excited for the Harry Potter stuff.", - "D10:11" - ] - ], - "John": [ - [ - "John had some trouble figuring out the subway in NYC during his trip, but found it easier with someone's help.", - "D10:2" - ], - [ - "John has been trying out cooking recipes, recently making a tasty soup with sage.", - "D10:4" - ], - [ - "John hasn't been to Universal Studios yet, but it's on his bucket list.", - "D10:10" - ] - ] - }, - "session_11_observation": { - "John": [ - [ - "John connected with new teammates over a shared love for basketball.", - "D11:5" - ], - [ - "John is planning a team trip with his friends to explore a new city next month.", - "D11:7" - ], - [ - "John is considering Edinburgh, Scotland for the team trip based on Tim's suggestion.", - "D11:11" - ], - [ - "John loves his basketball career and finds it challenging and fulfilling, especially when he sees improvement and achieves goals.", - "D11:15" - ], - [ - "John is focusing on improving shooting and making a greater impact on the court. He is also working on building his brand and seeking more endorsements for life after basketball.", - "D11:17" - ], - [ - "John plans to use his platform post-basketball to make a positive difference, potentially by starting a foundation and doing charity work to leave a meaningful legacy.", - "D11:19" - ], - [ - "John is open to receiving support and advice from others, like Tim.", - "D11:20" - ] - ], - "Tim": [ - [ - "Tim suggested Edinburgh, Scotland as a potential destination for the team trip.", - "D11:10" - ], - [ - "Tim shared advice on picking endorsements that align with values and feel authentic to followers.", - "D11:22" - ], - [ - "Tim recommended a fantasy novel by Patrick Rothfuss for John's trip, suggesting it as a great read while traveling.", - "D11:24" - ] - ] - }, - "session_12_observation": { - "Tim": [ - [ - "Tim has a bookshelf filled with favorite fantasy novels that he enjoys for escapism.", - "D12:15" - ], - [ - "Tim collects jerseys and has an interest in basketball.", - "D12:18" - ], - [ - "Tim's favorite basketball team is The Wolves and his favorite player is LeBron, admiring his skills and leadership.", - "D12:20" - ], - [ - "Tim appreciates LeBron's work ethic and dedication to the game, finding him inspiring.", - "D12:22" - ], - [ - "Tim has not only met LeBron a few times but also had the opportunity to see him play live.", - "D12:24" - ] - ], - "John": [ - [ - "John recently had an emotional wedding ceremony in a lovely greenhouse venue with safety protocols for an intimate gathering.", - "D12:4" - ], - [ - "John is a member of a hiking club, even though he just joined.", - "D12:6" - ], - [ - "John's favorite memory from his wedding day is seeing his wife walking down the aisle, which made him emotional.", - "D12:8" - ], - [ - "John had their first dance with his wife at a cozy restaurant with music and candlelight, finding it dreamy.", - "D12:10" - ], - [ - "John values moments filled with love and joy, believing that they are special.", - "D12:12" - ], - [ - "John enjoys watching basketball games, finding them motivating, and admires top players like LeBron for their skills.", - "D12:24" - ] - ] - }, - "session_13_observation": { - "Tim": [ - [ - "Tim attended a Harry Potter conference in the UK last week and felt inspired and rejuvenated by the experience.", - "D13:1" - ], - [ - "Tim feels that his passion for fantasy stuff brings him closer to people from all over the world.", - "D13:1" - ], - [ - "Tim appears to be supportive and encouraging towards John's achievements and goals.", - "D13:9" - ], - [ - "Tim recommended a pair of very comfortable shoes that he got online to John.", - "D13:17" - ], - [ - "Tim offered to help John with anything and mentioned his availability for assistance.", - "D13:19" - ], - [ - "Tim advised John to stay motivated and keep chasing his dreams.", - "D13:21" - ] - ], - "John": [ - [ - "John had an intense season with tough losses and great wins in the games.", - "D13:4" - ], - [ - "John's team faced tough opponents, but they do not quit and back each other up to get better.", - "D13:6" - ], - [ - "John's team won a trophy after all their hard work and effort.", - "D13:8" - ], - [ - "John appreciates Tim's support and encouragement, and feels motivated by it.", - "D13:14" - ], - [ - "John mentioned needing new shoes after all the games and showed interest in Tim's comfortable shoe recommendation.", - "D13:14" - ], - [ - "John agrees to stay motivated and keep chasing his dreams.", - "D13:22" - ] - ] - }, - "session_14_observation": { - "John": [ - [ - "John is currently mentoring younger players on his team during the off-season.", - "D14:3" - ], - [ - "John finds mentoring the younger players super rewarding, motivating, and encouraging.", - "D14:5" - ], - [ - "Some of the younger players see John as a mentor, and he enjoys providing them with advice and support on and off the court.", - "D14:9" - ], - [ - "John feels honored to have the trust and admiration of the younger players and enjoys being a role model for them.", - "D14:11" - ] - ], - "Tim": [ - [ - "Tim took a stunning picture last summer on his trip to the Smoky Mountains.", - "D14:14" - ], - [ - "Tim is busy with studies but finds time to relax with books, seeking a good balance.", - "D14:20" - ], - [ - "Tim is currently reading a book that he's totally hooked on.", - "D14:22" - ] - ] - }, - "session_15_observation": { - "Tim": [ - [ - "Tim enjoys reading and is inspired by books, movies, and real-life experiences for his writing.", - "D15:5" - ], - [ - "J.K. Rowling is a significant inspiration for Tim's writing, and he takes notes on her style.", - "D15:7" - ], - [ - "Tim has been reading J.K. Rowling's works for a long time and finds her writing special.", - "D15:9" - ], - [ - "A quote by J.K. Rowling, \"Turn on the light - happiness hides in the darkest of times,\" is meaningful to Tim during tough times.", - "D15:11" - ], - [ - "Tim is writing a fantasy novel that brings him joy and excitement.", - "D15:3" - ], - [ - "Tim enjoys exploring and going on road trips with friends and family, as well as hiking and playing board games.", - "D15:29" - ], - [ - "Tim loves curling up with a good book to escape reality and get lost in different worlds during his free time.", - "D15:29" - ] - ], - "John": [ - [ - "John is interested in historical architecture and dreams of visiting castles in different places.", - "D15:2" - ], - [ - "John finds motivation in staying focused and pushing through tough workouts by using strategies and motivational quotes written on a whiteboard.", - "D15:14" - ], - [ - "John keeps a plaque on his desk as a constant reminder to believe in himself and trust his abilities during challenging times.", - "D15:16" - ], - [ - "John's teammates believing in him and his love for improving his skills keep him motivated, especially during challenging times.", - "D15:18" - ], - [ - "John spends time with his family and considers them as a source of happiness and support.", - "D15:24" - ], - [ - "John enjoys cooking as a therapy to be creative, experiment with flavors, and take a break from his routine.", - "D15:30" - ], - [ - "John loves making honey garlic chicken with roasted vegetables and often tries out new recipes.", - "D15:32" - ] - ] - }, - "session_16_observation": { - "Tim": [ - [ - "Tim faced a writing issue last week where he got stuck on a plot twist, but eventually got the ideas flowing again.", - "D16:1" - ], - [ - "Tim owns a basketball signed by his favorite player, LeBron, as a prized possession.", - "D16:7" - ], - [ - "Tim finds overcoming challenges to be a source of personal growth and strength.", - "D16:5" - ] - ], - "John": [ - [ - "John had a basketball game where his team was trailing significantly in the 4th quarter, but they dug deep to overturn the deficit and win the game.", - "D16:6" - ], - [ - "John admires LeBron's determination and heart, particularly referencing an epic block in Game 7 of the '16 Finals.", - "D16:9" - ], - [ - "John finds that struggles in sports, like overcoming deficits, lead to a greater sense of satisfaction when victorious.", - "D16:4" - ] - ] - }, - "session_17_observation": { - "John": [ - [ - "John and his wife went on a road trip along the European coastline, creating amazing memories and bonding.", - "D17:3" - ], - [ - "John recently finished an amazing fantasy series and loves getting lost in fantasy worlds.", - "D17:9" - ], - [ - "John has watched the fantasy show 'That' and finds it a great way to escape reality.", - "D17:11" - ], - [ - "John believes taking time for oneself is crucial for staying sharp, gaining new perspectives, and tackling challenges with more energy.", - "D17:17" - ] - ], - "Tim": [ - [ - "Tim enjoys reading as usual and finds being lost in an awesome fantasy realm like an escape.", - "D17:2" - ], - [ - "Tim believes books and movies serve as a mental break and allow minds to wander, giving a sense of refreshment.", - "D17:14" - ], - [ - "Tim thinks taking a break from life helps recharge, gain peace, reconnect with oneself, and approach life's challenges with a new outlook.", - "D17:16" - ], - [ - "Tim emphasizes that balance, taking care of oneself mentally and physically, is key for success.", - "D17:18" - ] - ] - }, - "session_18_observation": { - "Tim": [ - [ - "Tim visited a castle during his trip to the UK last Friday and was amazed by the architecture and history.", - "D18:1" - ], - [ - "Tim's week has been swamped with exams, but he is plowing through them.", - "D18:3" - ], - [ - "Tim breaks up his studying into smaller parts, with 25 minutes of study and 5 minutes of fun activities.", - "D18:7" - ] - ], - "John": [ - [ - "John has had a tough week with an injury but is staying positive.", - "D18:2" - ], - [ - "John visualizes his goals and success for focus and motivation during tough studying sessions.", - "D18:6" - ], - [ - "John's doctor mentioned that his injury is not too serious.", - "D18:10" - ], - [ - "John hates not being on the court.", - "D18:12" - ] - ] - }, - "session_19_observation": { - "Tim": [ - [ - "Tim had a setback with a story he was writing based on his experiences in the UK.", - "D19:3" - ], - [ - "Tim loves writing and reading to stay motivated and push himself to improve.", - "D19:9" - ], - [ - "Tim enjoys fantasy books like Harry Potter and Game of Thrones.", - "D19:14" - ], - [ - "Tim recently read a book that focused on how small changes can make a big difference, changing the way he does things.", - "D19:19" - ] - ], - "John": [ - [ - "John had a major challenge when he hurt his ankle last season and had to undergo physical therapy.", - "D19:6" - ], - [ - "John values personal development and mindset books to know himself better.", - "D19:16" - ], - [ - "John recently reread 'The Alchemist' and found it inspiring, motivating him to keep chasing his dreams and trust the process.", - "D19:20" - ] - ] - }, - "session_20_observation": { - "Tim": [ - [ - "Tim had a tough exam recently that he turned into a learning experience, showcasing his resilience and determination.", - "D20:1" - ], - [ - "Tim appreciates John's encouragement and support throughout his journey.", - "D20:3" - ], - [ - "Tim enjoys fantasy books and movies, sharing favorites with John and mutual interest in the genre.", - "D20:17" - ], - [ - "Tim has a bookshelf with some of his favorite books, including The Hobbit.", - "D20:19" - ], - [ - "Tim loves how fantasy books allow for an escape to different worlds and a break from reality.", - "D20:25" - ], - [ - "Tim finds nature calming and appreciates the beauty around him.", - "D20:31" - ] - ], - "John": [ - [ - "John is trying out yoga to improve strength, flexibility, focus, and balance during workouts.", - "D20:2" - ], - [ - "John enjoys certain yoga poses like Warrior II for strength and balance.", - "D20:6" - ], - [ - "John typically holds challenging poses for 30-60 seconds to build strength and stability.", - "D20:10" - ], - [ - "John appreciates the escapism and joy of fantasy books and movies, sharing favorites with Tim.", - "D20:17" - ], - [ - "John enjoys nature and finds peace and refreshment when camping in the mountains.", - "D20:36" - ], - [ - "John values the calming and humbling effect of nature, recognizing the beauty and grandeur of the world.", - "D20:42" - ], - [ - "John shares a photo of a tranquil forest from near his hometown.", - "D20:28" - ] - ] - }, - "session_21_observation": { - "Tim": [ - [ - "Tim recently joined a travel club to explore different cultures and countries.", - "D21:1" - ], - [ - "Tim is working on his studies.", - "D21:9" - ], - [ - "Tim recently started learning how to play the violin and has been playing the piano for about four months.", - [ - "D21:9", - "D21:13" - ] - ] - ], - "John": [ - [ - "John has been playing professional basketball for just under a year.", - "D21:4" - ], - [ - "John has been working on improving his overall game, getting endorsement deals, and learning how to market himself to boost his brand.", - "D21:8" - ], - [ - "John believes in creating a positive environment and showing care for teammates to motivate them.", - "D21:16" - ] - ] - }, - "session_22_observation": { - "Tim": [ - [ - "Tim recently attended a cool Harry Potter party and wore a Gryffindor scarf.", - "D22:1" - ], - [ - "Tim mentioned reading an amazing series about the power of friendship and loyalty.", - "D22:7" - ], - [ - "Tim is looking for new reads and asked John for book recommendations.", - "D22:9" - ], - [ - "Tim recently finished reading 'A Dance with Dragons' and highly recommends it.", - "D22:13" - ] - ], - "John": [ - [ - "John attended a charity event with Harry Potter trivia in August and enjoys Harry Potter.", - "D22:2" - ], - [ - "John's basketball team recently played a tough game against a top team and won.", - "D22:4" - ], - [ - "John is currently reading 'Dune' by Frank Herbert, a story about religion and human control over ecology.", - "D22:12" - ], - [ - "John is open to reading George R. R. Martin's books after Tim recommended them.", - "D22:16" - ] - ] - }, - "session_23_observation": { - "John": [ - [ - "John had a career-high in assists last Friday during a big game against their rival.", - "D23:3" - ], - [ - "John hit a buzzer-beater shot to win a game when they were down 10 in the 4th quarter, which was one of their favorite thrilling sports moments.", - "D23:7" - ], - [ - "John used to practice basketball outside for hours when he was younger, dreaming of playing in big games.", - "D23:9" - ], - [ - "John values basketball as a meaningful part of his life that allows him to pursue his passions.", - "D23:11" - ] - ], - "Tim": [ - [ - "Tim struggled with his English lit class but did an analysis on a series that he thinks went okay.", - "D23:2" - ], - [ - "Tim finds sports as a way to express himself and stay positive.", - "D23:10" - ], - [ - "Tim sent John a pic that reminds John of practicing basketball outside when he was younger.", - "D23:8" - ], - [ - "Tim believes in the importance of following dreams and doing one's own thing.", - "D23:12" - ] - ] - }, - "session_24_observation": { - "Tim": [ - [ - "Tim is interested in catching up and asks John about recent b-ball games.", - "D24:1" - ], - [ - "Tim congratulated John on winning a close b-ball game.", - "D24:3" - ], - [ - "Tim notes that winning a game must have been thrilling for John.", - "D24:5" - ], - [ - "Tim asks John about challenges he has faced, specifically regarding his recent injury.", - "D24:9" - ], - [ - "Tim acknowledges the importance of staying active or keeping up with a fitness routine during recovery.", - "D24:11" - ], - [ - "Tim asks John about the progress of his rehab and encourages him to keep it up.", - "D24:13" - ], - [ - "Tim congratulates John on his milestone at the gym and hosting a get-together to celebrate.", - "D24:17" - ], - [ - "Tim expresses happiness that everyone had fun at the get-together.", - "D24:19" - ] - ], - "John": [ - [ - "John talks about winning a b-ball game against another team that was close until the final buzzer.", - "D24:2" - ], - [ - "John injured himself recently, had to miss games, and couldn't help his team.", - "D24:10" - ], - [ - "John mentions that he does physical therapy exercises every day for his recovery.", - "D24:12" - ], - [ - "John shares that he had a milestone moment at the gym where he was able to jog without pain.", - "D24:14" - ], - [ - "John hosted a small get-together with friends and family to celebrate his recovery milestone.", - "D24:16" - ], - [ - "John appreciates Tim's support and encouragement, stating he will keep pushing and staying positive.", - "D24:18" - ] - ] - }, - "session_25_observation": { - "Tim": [ - [ - "Tim is in touch with John after a while and asks how things are going.", - "D25:1" - ], - [ - "Tim is curious about the gear John got from an outdoor gear company and asks about the photoshoot.", - "D25:3" - ], - [ - "Tim appreciates the outdoor location where John did the photoshoot and asks to see pictures.", - "D25:5" - ], - [ - "Tim comments on the photo John shows, mentioning the peaceful rocks and river.", - "D25:7" - ], - [ - "Tim acknowledges the benefits of nature in bringing peace and joy.", - "D25:9" - ], - [ - "Tim asks John about recent happenings and appreciates the importance of hard work paying off.", - "D25:11" - ], - [ - "Tim asks about areas of growth in John's team during training.", - "D25:13" - ], - [ - "Tim expresses gladness that John's team is bonding and encourages them to keep it up.", - "D25:15" - ], - [ - "Tim offers support and advises John to continue believing in himself before saying bye.", - "D25:17" - ] - ], - "John": [ - [ - "John got an amazing deal with a renowned outdoor gear company last week, obtaining hiking stuff and top-notch outdoor gear.", - "D25:2" - ], - [ - "The photoshoot John did in a gorgeous forest went really well, capturing him in epic shots.", - "D25:4" - ], - [ - "John stumbled upon a soothing spot by a river while hiking, feeling at peace and admiring nature's beauty.", - "D25:8" - ], - [ - "Things have been going great on the court for John and his team, putting in work and achieving their goals.", - "D25:10" - ], - [ - "John's team has seen growth in communication and bonding during their training.", - "D25:14" - ], - [ - "John appreciates Tim's support and expresses thanks for the assistance.", - "D25:16" - ] - ] - }, - "session_26_observation": { - "John": [ - [ - "John started doing seminars to help people with sports and marketing.", - "D26:1" - ], - [ - "John organized a basketball camp for kids in his hometown last summer, creating opportunities for young athletes.", - "D26:23" - ], - [ - "John is passionate about supporting youth sports and fighting for fair chances in sports for underserved communities.", - "D26:21" - ], - [ - "John uses his influence and resources to help causes he believes in, particularly in making the world a better place.", - "D26:19" - ], - [ - "John is a movie fan who enjoys seeing stories come alive on the big screen as a way to relax.", - "D26:29" - ] - ], - "Tim": [ - [ - "Tim is planning his next adventure by reading stories from travelers around the world.", - "D26:6" - ], - [ - "Tim is a fan of the fantasy genre, particularly enjoying epic adventures and magical worlds.", - "D26:32" - ], - [ - "Tim is excited to watch the new fantasy TV series called \"The Wheel of Time\" based on a book series he loves.", - "D26:36" - ] - ] - }, - "session_27_observation": { - "Tim": [ - [ - "Tim joined a group of globetrotters who share his interest in traveling.", - "D27:1" - ], - [ - "Tim is currently learning German and finds it tough but worth it.", - "D27:5" - ], - [ - "Tim appreciates encouragement and plans to continue with his German lessons.", - "D27:13" - ], - [ - "Tim loves reading and has a collection of books that he enjoys escaping into.", - "D27:15" - ], - [ - "Tim's favorite book is Harry Potter because it is immersive.", - "D27:19" - ], - [ - "Tim's favorite fantasy film is Star Wars as it never gets old.", - "D27:21" - ], - [ - "Tim appreciates fantasy stories for allowing him to explore other cultures and landscapes.", - "D27:35" - ] - ], - "John": [ - [ - "John has been to Italy and had a blast there last month.", - "D27:2" - ], - [ - "John knows a bit of German and Spanish which he finds useful for travel.", - "D27:6" - ], - [ - "John practices and trains in basketball daily as it is his passion.", - "D27:14" - ], - [ - "John loves reading and finds playing a game helps personal growth.", - "D27:18" - ], - [ - "John's favorite book is The Alchemist which he finds magical and inspiring.", - "D27:18" - ], - [ - "John's favorite fantasy film is Lord of the Rings for its adventure, world, and characters.", - "D27:22" - ], - [ - "John's favorite character from Lord of the Rings is Aragorn because of his growth and leadership.", - "D27:24" - ], - [ - "John has a painting in his room as a reminder to stay true and be a leader.", - "D27:28" - ] - ] - }, - "session_28_observation": { - "Tim": [ - [ - "Tim got accepted into a study abroad program in Ireland.", - "D28:1" - ], - [ - "Tim will be staying in Galway for its arts and Irish music.", - "D28:3" - ], - [ - "Tim is excited to explore the nature in Galway.", - "D28:5" - ], - [ - "Tim wants to visit The Cliffs of Moher for the ocean views and cliffs.", - "D28:7" - ], - [ - "Tim is currently reading a fantasy novel called 'The Name of the Wind' by Patrick Rothfuss.", - "D28:17" - ] - ], - "John": [ - [ - "John held a benefit basketball game that was a success and raised money for charity.", - "D28:12" - ], - [ - "John expressed that basketball brings people together and creates a positive impact.", - "D28:14" - ], - [ - "John is keen on stopping by The Cliffs of Moher after his season.", - "D28:8" - ], - [ - "John is adding 'The Name of the Wind' by Patrick Rothfuss to his reading list.", - "D28:18" - ] - ] - }, - "session_29_observation": { - "Tim": [ - [ - "Tim is researching visa requirements for countries he wants to visit.", - "D29:3" - ], - [ - "Tim feels that taking initiative to research visas is a step towards making his travel dreams a reality.", - "D29:9" - ], - [ - "Tim expresses excitement about sharing his future travel adventures with John.", - "D29:10" - ], - [ - "Tim shows interest in John's recommendation of visiting Barcelona and plans to add it to his travel list.", - "D29:13" - ] - ], - "John": [ - [ - "John recently got an endorsement with a popular beverage company, which he describes as a dream come true.", - "D29:4" - ], - [ - "John feels that all the hard work he put in paid off with the endorsement deal.", - "D29:6" - ], - [ - "John believes that reaching a goal is rewarding and a sign of moving in the right direction.", - "D29:8" - ], - [ - "John recommends Barcelona as a must-visit city for Tim, mentioning cultural exploration, architecture, food, and beaches as highlights.", - "D29:12" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "John and Tim had a conversation at 7:48 pm on 21 May, 2023. Tim is working on a Harry Potter fan project while John recently signed with the Minnesota Wolves as a shooting guard. John's goal is to improve his shooting percentage for the upcoming season. He mentioned facing challenges fitting into the new team's style of play during pre-season. Tim's fan project involves collaborations discussing various Harry Potter universe aspects. Tim has visited Harry Potter-related places in London and encouraged John to do the same. Tim offered tips for visiting and bid farewell.", - "session_2_summary": "Tim and John had a conversation at 5:08 pm on 15 June, 2023. Tim shared his excitement about discussing fantasy books on a forum, while John talked about exploring endorsement opportunities with sports brands like Nike and Under Armour. Tim admired a picture from MinaLima, creators of props for Harry Potter films, expressing his love for the wizarding world. John expressed his passion for playing basketball, sharing a photo from a recent game. They both supported each other's interests and dreams, ending with Tim encouraging John to keep pursuing his goals.", - "session_3_summary": "John and Tim met at 4:21 on July 16, 2023. John shared about scoring 40 points in a game, celebrating with teammates, and securing endorsements with Nike and Gatorade. Tim talked about a magical chat with a Harry Potter fan in California and the love for the sport community. They discussed Seattle, surfing, and finding happiness through different activities, ending the conversation on a positive note.", - "session_4_summary": "At 4:17 pm on 2 August, 2023, Tim excitedly told John about writing articles on fantasy novels for an online magazine, a passion project he found through a fantasy lit forum. John congratulated Tim and enquired about the kind of articles he was working on. Tim mentioned analyzing characters, themes, and book recommendations. They discussed favorite books like Harry Potter and Game of Thrones. John shared a Harry Potter trivia experience, while Tim encouraged John to keep dreaming inspired by a book he's reading. Both expressed a desire to reach new goals before bidding farewell.", - "session_5_summary": "Tim and John spoke at 10:29 am on 9 August, 2023. Tim mentioned reconnecting with a Harry Potter fan and discussing collaboration. John shared about winning a close game and feeling the adrenaline rush. They talked about the importance of a supportive team and discussed Tim's current fantasy book by Patrick Rothfuss. John agreed to read it and they ended the conversation with plans to chat soon.", - "session_6_summary": "On August 11, 2023, at 1:08 pm, John told Tim he had an amazing trip to Chicago, loving the energy and meeting friendly locals. Tim shared his excitement about attending a fantastic event recently. They discussed the energy in sports games and sharing their passions. Tim recommended a fantasy novel, \"The Name of the Wind,\" to John, who shared stories about his lucky basketball shoes symbolizing resilience and dedication. John explained his basketball journey from childhood to being drafted. He mentioned his big goal of winning a championship and giving back to the community through charity work. Tim expressed admiration for John's plans and offered support, ending the conversation with encouragement.", - "session_7_summary": "John and Tim had a conversation at 7:54 pm on 17 August, 2023. John shared his excitement about reuniting with his basketball teammates on the 15th, feeling welcomed and appreciated by the team. Tim expressed his own sense of belonging at certain events where people shared his interests. John mentioned a gift from his teammates, a signed basketball, symbolizing their friendship and support. Tim noted the significance of such a reminder from friends. Both agreed on the power of supportive relationships in staying motivated and reaching goals. Tim ended the conversation by acknowledging the strength gained from mutual encouragement.", - "session_8_summary": "John and Tim caught up at 4:29 pm on 21 August, 2023. John shared his new gym routine to enhance his basketball game, emphasizing the importance of balance and rest. Tim praised John's holistic approach, leading to improved basketball performance. Tim then shared his focus on school, playing the piano, and favorite memories associated with \"Harry Potter.\" They discussed family traditions, favorite holiday movies like \"Home Alone\" and \"The Santa Clause,\" and shared photos of their festive decorations. They exchanged well wishes and agreed to talk soon, ending the conversation.", - "session_9_summary": "Tim mentioned his struggles with balancing studies and hobbies, specifically his love for fantasy reading. John shared about catching up with old friends from his high school sports team, where they played together for four years. Tim expressed his interest in traveling to experience different magical worlds through books and actual visits. John shared his experience exploring New York City and recommended it as a must-visit place. They both discussed their love for exploring new cities and hobbies. John offered assistance with Tim's travel plans, and Tim thanked him for his support before ending the conversation.", - "session_10_summary": "Tim and John caught up at 2:52 pm on 31 August, 2023. Tim shared his job rejection but remained positive. John discussed his NYC trip and troubles with the subway. Tim mentioned overcoming nerves to give a presentation. John shared his cooking experiment with a sage-flavored soup. Tim planned a trip to Universal Studios for the first time, excited for the Harry Potter attractions. They ended the conversation with well-wishes and a promise to catch up soon.", - "session_11_summary": "John and Tim caught up at 8:17 pm on 21 September, 2023. John shared about connecting with new teammates over basketball at a local restaurant. Tim suggested Edinburgh as a destination for their upcoming team trip, which John found appealing. They discussed John's passion for basketball, his career goals, and plans for life after sports. Tim advised John on picking endorsements and recommended a fantasy novel for his trip. John shared his bookshelf and praised \"The Alchemist.\" They ended the conversation by wishing each other well.", - "session_12_summary": "At 3:00 pm on 2 October, 2023, Tim and John caught up. John shared about his intimate wedding ceremony in a greenhouse venue that followed safety protocols, filled with loved ones. Tim admired the special day and asked about favorite memories. John mentioned the magical moment of seeing his bride walk down the aisle and their first dance in a cozy restaurant. They expressed how love brings joy. Tim congratulated John, admired the love between the couple, and shared his favorite fantasy novels. They later discussed John's love for collecting jerseys and admiration for LeBron James. John met LeBron and saw him play live, finding it inspiring. Tim encouraged John to continue pursuing what he loves.", - "session_13_summary": "Tim and John reconnected at 1:50 pm on 13 October, 2023. Tim shared his excitement about attending a Harry Potter conference in the UK, feeling inspired by the magical atmosphere and the shared passion. John admired Tim's enthusiasm and shared his own connection with his team, discussing their recent sports season with tough losses and great wins. Tim congratulated John on their success, acknowledging the hard work that paid off with a trophy. John appreciated Tim's support and encouragement to stay motivated, while Tim recommended some comfortable shoes for John to check out. They ended their conversation with mutual support and a commitment to chase their dreams, promising to keep in touch.", - "session_14_summary": "John and Tim caught up at 1:50 pm on October 17, 2023, after a long time. John is now mentoring young players, finding it rewarding and fulfilling. He enjoys being a positive role model for them and is proud to see their growth and success. Tim shared a stunning nature picture from the Smoky Mountains, emphasizing the beauty of nature and the importance of taking a break from city life. They discussed finding balance in life, with Tim mentioning his busy school schedule and love for reading, while John expressed his recent enjoyment of reading.", - "session_15_summary": "Tim and John, who are both passionate about exploring historical architecture and writing, catch up on their interests. Tim is working on a fantasy novel inspired by books, movies, and real-life experiences, including visits to UK castles that give him ideas. He admires J.K. Rowling's captivating writing style and shares a quote that inspires him. John, who finds motivation through quotes and strategies during tough workouts, values his supportive teammates and family. They bond over their shared love for spending time with friends and family, enjoying activities like road trips, hiking, and cooking. John shares a honey garlic chicken recipe with Tim after discussing their interests. Their friendly conversation ends on a positive note, expressing gratitude for their support networks.", - "session_16_summary": "Tim and John caught up at 11:41 am on 6 November, 2023. Tim shared how he overcame a writing issue by staying persistent. John likened it to toughing it out in sports. Both agreed that struggles lead to growth and satisfaction. John recounted a basketball game where his team made a remarkable comeback. Tim showed his basketball signed by his favorite player, LeBron, who inspires him with his determination. They discussed memorable moments in basketball and the importance of perseverance. John mentioned his upcoming European vacation, seeking recommendations from Tim who suggested visiting castles. They wished each other well and vowed to stay connected.", - "session_17_summary": "John and Tim had a chat at 3:36 pm on 11 November, 2023. John shared about his recent road trip on the European coastline with his wife, praising the beautiful views and the bonding experience. Tim admired the trip and discussed the power and beauty of nature. They bonded over their love for fantasy stories and shows. Tim highlighted the importance of mental breaks for recharging, gaining new perspectives, and tackling challenges with renewed energy. John appreciated the support and emphasized the significance of finding balance and taking care of oneself. They ended their conversation with well wishes.", - "session_18_summary": "Tim told John about his trip to a castle in the UK last Friday, praising its architecture and history. John mentioned a tough week due to an injury but staying positive. Tim shared being swamped with exams but staying optimistic and working hard. John asked about exam prep and shared visualizing goals for motivation. Tim discussed breaking study sessions into smaller parts for better focus. They wished each other well, with John hoping for a quick recovery from the injury. Tim expressed understanding and support. John expressed missing playing due to the injury, likening it to Tim not being able to read. They encouraged each other to keep going and promised to talk soon. The conversation took place at 3:59 pm on 16 November, 2023.", - "session_19_summary": "Tim and John caught up at 10:22 am on 21 November 2023. Tim mentioned struggling with storytelling and sought advice from John. They discussed overcoming challenges, with John sharing his experience recovering from an ankle injury. They emphasized resilience and motivation in facing difficulties. They also bonded over their love for reading, particularly fantasy and personal development books. Both found inspiration in books and shared a commitment to growth and improvement.", - "session_20_summary": "Tim and John had a conversation at 9:52 am on 1 December, 2023. Tim shared his success in a tough exam, stating he turned it into a learning experience. John congratulated Tim and mentioned trying out yoga to improve strength and flexibility. They discussed yoga poses and John shared a photo of one he enjoys. They talked about their mutual love for fantasy books and movies. Tim shared a photo of a forest, and John shared a photo from a camping trip in the mountains. They both reflected on the calming effects of nature. The conversation ended with both agreeing on the humbling and connected nature of the world before Tim bid farewell.", - "session_21_summary": "At 5:34 pm on 6 December 2023, Tim told John he joined a travel club to explore different cultures and meet new people. John, a professional basketball player for almost a year, discussed his growth on and off the court. Tim mentioned joining the travel club, studying, and learning the violin. John praised Tim's dedication, and they discussed motivating others. They shared tips and encouragement before ending the conversation.", - "session_22_summary": "Tim and John, who are both passionate about Harry Potter, discussed their recent activities at 7:42 pm on 8 December, 2023. Tim shared his experience at a Harry Potter party where he wore a Gryffindor scarf and enjoyed meeting like-minded people. John talked about his recent basketball games, emphasizing the camaraderie and unity within his team. They also discussed their reading preferences, with John recommending \"Dune\" by Frank Herbert and Tim recommending \"A Dance with Dragons\". They both expressed interest in each other's recommendations and wished each other a great day, ending the conversation on a positive note.", - "session_23_summary": "John and Tim, who met at 8:28 pm on 11 December 2023, had a conversation where Tim shared about his English lit class struggles and John talked about his recent career-high in assists. John described a thrilling game against their rivals and reminisced about a buzzer-beater shot. They discussed the power of sports and chasing dreams, inspiring and motivating each other before saying goodbye.", - "session_24_summary": "At 3:37 pm on 16 December 2023, Tim and John caught up. John shared about a close basketball game his team won, which was thrilling for him. Tim admired John's dedication to the sport despite facing an injury recently. John revealed he was progressing well with his recovery and had a milestone moment jogging without pain. Tim congratulated him and encouraged him to stay positive. The two friends discussed the importance of staying motivated and supporting each other, ending their conversation on a positive note.", - "session_25_summary": "Tim and John caught up after a while at 10:04 am on 19 December, 2023. John shared his excitement about a deal with an outdoor gear company and a successful photoshoot in a beautiful forest. Tim admired the epic photos and discussed the peaceful spot that inspired John. John also mentioned his team's progress in basketball, especially in communication and bonding. Tim encouraged John to keep believing in himself before concluding their conversation.", - "session_26_summary": "John and Tim caught up at 3:35 pm on 26 December, 2023. John shared about his busy week starting seminars to help people with sports and marketing, which went well. Tim mentioned reading traveler stories to plan his next adventure, including one about hiking in the Himalayas. They discussed the importance of challenges, their admiration for Emma Watson's advocacy, and how they use their platforms for good causes. John talked about supporting youth sports and creating opportunities for young athletes. Tim enjoys escaping in books and movies, particularly fantasy genres like \"The Lord of the Rings\", and looks forward to a new TV series, \"The Wheel of Time\". They both share a love for movie marathons and making a difference in the world.", - "session_27_summary": "Tim and John spoke at 5:26 pm on 2 January 2024. Tim mentioned his involvement in a globetrotting group and his interest in learning German. John shared his recent trip to Italy and knowledge of German and Spanish. They discussed language learning resources and shared their passion for reading and favorite fantasy books and movies, such as \"Harry Potter\" and \"Star Wars\" for Tim, and \"Lord of the Rings\" for John, with a particular focus on the character Aragorn. They highlighted the inspiration drawn from these stories and the importance of perseverance and leadership. Their conversation touched on the immersive nature of fantasy stories, traveling, and cultural exploration. John shared his painting of Aragorn as a reminder to be a leader, emphasizing the character's qualities of bravery and selflessness. They agreed on the educational and eye-opening aspects of both traveling and delving into fantasy worlds.", - "session_28_summary": "Tim and John caught up at 5:24 pm on 7 January, 2024. Tim shared that he got accepted into a study abroad program and will be heading to Ireland for a semester, staying in Galway known for its arts and music. He expressed excitement about exploring the nature and visiting The Cliffs of Moher. John complimented Tim's plans and mentioned hosting a successful benefit basketball game. Tim praised John for making a positive impact through sports. They discussed books and Tim recommended \"The Name of the Wind\" by Patrick Rothfuss to John. They ended the conversation on a positive note, planning to stay in touch.", - "session_29_summary": "Tim and John spoke at 1:41 pm on 12 January, 2024. Tim shared his excitement about researching visa requirements for upcoming travels, while John revealed his recent endorsement deal with a beverage company, expressing pride in his hard work paying off. Tim praised John's achievement, highlighting the importance of accomplishment and self-esteem boosts. John recommended Barcelona as a travel destination, which Tim happily added to his list. They concluded the conversation with Tim offering to return the favor and John offering support in the future." - }, - "sample_id": "conv-43" - }, - { - "qa": [ - { - "question": "Which year did Audrey adopt the first three of her dogs?", - "answer": "2020", - "evidence": [ - "D1:7" - ], - "category": 2 - }, - { - "question": "When did Andrew start his new job as a financial analyst?", - "answer": "The week before March 27, 2023", - "evidence": [ - "D1:2" - ], - "category": 2 - }, - { - "question": "What kind of indoor activities has Andrew pursued with his girlfriend?", - "answer": "boardgames, volunteering at pet shelter, wine tasting, growing flowers", - "evidence": [ - "D13:1", - "D23:1", - "D25:1", - "D19:15" - ], - "category": 1 - }, - { - "question": "What kind of places have Andrew and his girlfriend checked out around the city?", - "answer": "cafes, new places to eat, open space for hikes, pet shelter, wine tasting event, park", - "evidence": [ - "D3:1", - "D3:11", - "D4:2", - "D6:1", - "D13:1", - "D23:3", - "D25:1", - "D27:1" - ], - "category": 1 - }, - { - "question": "When did Audrey make muffins for herself?", - "answer": "The week of April 3rd to 9th", - "evidence": [ - "D3:18" - ], - "category": 2 - }, - { - "question": "When did Audrey see a hummingbird?", - "answer": "first week of May 2023", - "evidence": [ - "D4:1" - ], - "category": 2 - }, - { - "question": "When did Audrey adopt Pixie?", - "answer": "around April 2, 2023", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "How many years passed between Audrey adopting Pixie and her other three dogs?", - "answer": "three years", - "evidence": [ - "D2:1", - "D1:7" - ], - "category": 2 - }, - { - "question": "Did Andrew have a pet dog during March 2023?", - "answer": "No", - "evidence": [ - "D2:8" - ], - "category": 2 - }, - { - "question": "What kind of classes or groups has Audrey joined to take better care of her dogs?", - "answer": "positive reinforcement training workshop to bond with pets, dog training course, agility training course, grooming course, dog-owners group", - "evidence": [ - "D6:2", - "D10:1", - "D14:2", - "D16:6", - "D27:2" - ], - "category": 1 - }, - { - "question": "When did Audrey's positive reinforcement training course for dogs take place?", - "answer": "June, 2023", - "evidence": [ - "D6:2" - ], - "category": 2 - }, - { - "question": "When did Andrew go rock climbing?", - "answer": "June 11, 2023", - "evidence": [ - "D8:1" - ], - "category": 2 - }, - { - "question": "What outdoor activities has Andrew done other than hiking in nature?", - "answer": "rock climbing, fishing, camping", - "evidence": [ - "D8:1", - "D17:1", - "D14:1" - ], - "category": 1 - }, - { - "question": "When did Audrey move to a new place?", - "answer": "June 2023", - "evidence": [ - "D9:1" - ], - "category": 2 - }, - { - "question": "What is something that Andrew really misses while working in the city?", - "answer": "being in nature", - "evidence": [ - "D3:7", - "D9:20" - ], - "category": 1 - }, - { - "question": "What is a shared frustration regarding dog ownership for Audrey and Andrew?", - "answer": "Not being able to find pet friendly spots.", - "evidence": [ - "D7:8", - "D10:5" - ], - "category": 1 - }, - { - "question": "When is Andrew going to go hiking with Audrey?", - "answer": "August", - "evidence": [ - "D11:7" - ], - "category": 2 - }, - { - "question": "How many times did Audrey and Andew plan to hike together?", - "answer": "three times", - "evidence": [ - "D11:7", - "D24:13", - "D26:20" - ], - "category": 1 - }, - { - "question": "Where did Audrey get Pixie from?", - "answer": "breeder", - "evidence": [ - "D11:4", - "D2:1" - ], - "category": 1 - }, - { - "question": "What is an indoor activity that Andrew would enjoy doing while make his dog happy?", - "answer": "cook dog treats", - "evidence": [ - "D10:12", - "D12:1" - ], - "category": 3 - }, - { - "question": "Which meat does Audrey prefer eating more than others?", - "answer": "chicken", - "evidence": [ - "D10:13", - "D10:23" - ], - "category": 3 - }, - { - "question": "What are the classes that Audrey took for her dogs to?", - "answer": "Positive reinforcement training class for bonding, dog training course, agility class", - "evidence": [ - "D6:4", - "D10:1", - "D14:2" - ], - "category": 1 - }, - { - "question": "Where did Andrew go during the first weekend of August 2023?", - "answer": "camping with girlfriend", - "evidence": [ - "D14:1" - ], - "category": 2 - }, - { - "question": "What are some problems that Andrew faces before he adopted Toby?", - "answer": "Finding the right dog and pet-friendly apartments close to open spaces", - "evidence": [ - "D2:12", - "D5:3", - "D5:5", - "D5:7" - ], - "category": 1 - }, - { - "question": "Did Audrey and Andrew grow up with a pet dog?", - "answer": "Yes", - "evidence": [ - "D2:16", - "D13:10" - ], - "category": 1 - }, - { - "question": "When did Andrew and his girlfriend go fishing?", - "answer": "weekend before August 24, 2023", - "evidence": [ - "D17:1" - ], - "category": 2 - }, - { - "question": "What is the biggest stressor in Andrew's life besides not being able to hike frequently?", - "answer": "work", - "evidence": [ - "D12:3", - "D16:1", - "D18:1", - "D10:16" - ], - "category": 1 - }, - { - "question": "How does Andrew feel about his current work?", - "answer": "Stressful", - "evidence": [ - "D12:3", - "D16:1", - "D18:1", - "D10:16" - ], - "category": 1 - }, - { - "question": "What is something that Audrey often dresses up her dogs with?", - "answer": "Hats", - "evidence": [ - "D4:23", - "D4:25", - "D19:6" - ], - "category": 1 - }, - { - "question": "What are the names of Audrey's dogs?", - "answer": "Pepper, Precious, Panda, and Pixie", - "evidence": [ - "D1:7", - "D2:1", - "D19:12" - ], - "category": 1 - }, - { - "question": "When is Andrew planning to go to the beach with his girlfriend?", - "answer": "November 2023", - "evidence": [ - "D20:1" - ], - "category": 2 - }, - { - "question": "What has Andrew done with his dogs?", - "answer": "Taking walks and hiking", - "evidence": [ - "D14:27", - "D24:8" - ], - "category": 1 - }, - { - "question": "What kind of tattoo does Audrey have on her arm?", - "answer": "Tattoos of her four dogs.", - "evidence": [ - "D3:26", - "D15:1", - "D23:20" - ], - "category": 1 - }, - { - "question": "What can Andrew potentially do to improve his stress and accomodate his living situation with his dogs?", - "answer": "Change to a hybrid or remote job so he can move away from the city to the suburbs to have a larger living space and be closer to nature.", - "evidence": [ - "D12:3", - "D18:1", - "D21:5" - ], - "category": 3 - }, - { - "question": "How many months passed between Andrew adopting Toby and Buddy?", - "answer": "three months", - "evidence": [ - "D12:1", - "D24:2" - ], - "category": 2 - }, - { - "question": "What are the names of Andrew's dogs?", - "answer": "Toby, Scout, Buddy", - "evidence": [ - "D12:1", - "D24:6", - "D28:8" - ], - "category": 1 - }, - { - "question": "What are some foods that Audrey likes eating?", - "answer": "chicken pot pie, chicken roast, blueberry muffins, sushi", - "evidence": [ - "D3:18", - "D10:13", - "D10:23", - "D25:6" - ], - "category": 1 - }, - { - "question": "When did Audrey get into an accident in the park?", - "answer": "between October 19 and 24, 2023", - "evidence": [ - "D25:2" - ], - "category": 2 - }, - { - "question": "When did Andrew and his girlfriend go on a wine tasting trip?", - "answer": "the weekend before October 24, 2023", - "evidence": [ - "D25:1" - ], - "category": 2 - }, - { - "question": "What did Audrey get wtih having so many dogs?", - "answer": "Companionship", - "evidence": [ - "D2:15", - "D23:18" - ], - "category": 1 - }, - { - "question": "What is a good place for dogs to run around freely and meet new friends?", - "answer": "The dog park", - "evidence": [ - "D4:25", - "D14:2", - "D23:10" - ], - "category": 1 - }, - { - "question": "What are the breeds of Audrey's dogs?", - "answer": "Mongrel mixed with Lab for Pepper and Panda. Mongrel mixed with Chihuahua for Precious and Pixie.", - "evidence": [ - "D19:12", - "D26:13" - ], - "category": 1 - }, - { - "question": "What technique is Audrey using to discipline her dogs?", - "answer": "Positive reinforcement", - "evidence": [ - "D6:4", - "D26:5" - ], - "category": 1 - }, - { - "question": "Which US state do Audrey and Andrew potentially live in?", - "answer": "Minnesota", - "evidence": [ - "D11:9" - ], - "category": 3 - }, - { - "question": "Which national park could Audrey and Andrew be referring to in their conversations?", - "answer": "Voyageurs National Park", - "evidence": [ - "D5:8", - "D11:9" - ], - "category": 3 - }, - { - "question": "How many pets will Andrew have, as of December 2023?", - "answer": "three", - "evidence": [ - "D12:1", - "D24:2", - "D28:6" - ], - "category": 2 - }, - { - "question": "How many pets did Andrew have, as of September 2023?", - "answer": "one", - "evidence": [ - "D12:1", - "D24:2" - ], - "category": 2 - }, - { - "question": "How many months passed between Andrew adopting Buddy and Scout", - "answer": "one month", - "evidence": [ - "D24:2", - "D28:6" - ], - "category": 2 - }, - { - "question": "What does Andrew view his pets as?", - "answer": "Family", - "evidence": [ - "D15:14", - "D28:18" - ], - "category": 1 - }, - { - "question": "What does Audrey view her pets as?", - "answer": "Family", - "evidence": [ - "D15:15", - "D23:18" - ], - "category": 1 - }, - { - "question": "What is a skill that Audrey learned to take care of her dogs?", - "answer": "Grooming", - "evidence": [ - "D16:2", - "D17:4" - ], - "category": 1 - }, - { - "question": "What items has Audrey bought or made for her dogs?", - "answer": "dog tags, toys, dog beds, collars", - "evidence": [ - "D1:2", - "D9:5", - "D18:10", - "D24:1" - ], - "category": 1 - }, - { - "question": "What is something that Andrew could do to make birdwatching hobby to fit in his city schedule?", - "answer": "Install a bird feeder outside where he can see the birds without going outdoors.", - "evidence": [ - "D20:5", - "D20:21", - "D23:1", - "D1:14" - ], - "category": 3 - }, - { - "question": "What is a career that Andrew could potentially pursue with his love for animals and nature?", - "answer": "Park ranger or a similar position working for the National Park Services.", - "evidence": [ - "D2:18", - "D3:1", - "D5:7", - "D8:27" - ], - "category": 3 - }, - { - "question": "What activity do Audrey's dogs like to do in the dog park?", - "answer": "Play fetch with ball and frisbee, run around and meet other dogs", - "evidence": [ - "D4:21", - "D10:7", - "D13:8", - "D23:14", - "D27:12" - ], - "category": 1 - }, - { - "question": "When did Andrew make his dogs a fun indoor area?", - "answer": "few days before November 22, 2023", - "evidence": [ - "D28:12" - ], - "category": 2 - }, - { - "question": "Has Andrew moved into a new apartment for his dogs?", - "answer": "No", - "evidence": [ - "D5:5", - "D28:12" - ], - "category": 1 - }, - { - "question": "When did Andrew adopt Scout?", - "answer": "few days before November 2023", - "evidence": [ - "D28:6" - ], - "category": 2 - }, - { - "question": "What did Audrey eat for dinner on October 24, 2023?", - "answer": "sushi", - "evidence": [ - "D25:14" - ], - "category": 2 - }, - { - "question": "How long has it been since Andrew adopted his first pet, as of November 2023?", - "answer": "4 months", - "evidence": [ - "D12:1" - ], - "category": 2 - }, - { - "question": "How many dogs does Andrew have?", - "answer": "3", - "evidence": [ - "D12:1", - "D24:2", - "D28:6" - ], - "category": 1 - }, - { - "question": "Which specific type of bird mesmerizes Andrew?", - "answer": "Eagles", - "evidence": [ - "D1:16" - ], - "category": 4 - }, - { - "question": "What did Andrew express missing about exploring nature trails with his family's dog?", - "answer": "The peaceful moments", - "evidence": [ - "D2:18" - ], - "category": 4 - }, - { - "question": "What kind of pastries did Andrew and his girlfriend have at the cafe?", - "answer": "croissants, muffins, and tarts", - "evidence": [ - "D3:17" - ], - "category": 4 - }, - { - "question": "What kind of flowers does Audrey have a tattoo of?", - "answer": "sunflowers", - "evidence": [ - "D3:26" - ], - "category": 4 - }, - { - "question": "What does Audrey do during dog playdates in the park?", - "answer": "chat with people while dogs make new friends", - "evidence": [ - "D4:21" - ], - "category": 4 - }, - { - "question": "What type of dog was Andrew looking to adopt based on his living space?", - "answer": "smaller dog", - "evidence": [ - "D5:3" - ], - "category": 4 - }, - { - "question": "Where does Andrew want to live to give their dog a large, open space to run around?", - "answer": "near a park or woods", - "evidence": [ - "D5:7" - ], - "category": 4 - }, - { - "question": "Why did Audrey sign up for a workshop about bonding with pets?", - "answer": "Strengthen the bond with her pets", - "evidence": [ - "D6:2" - ], - "category": 4 - }, - { - "question": "How did Audrey hear about the workshop on bonding with pets?", - "answer": "Saw a workshop flyer at the local pet store", - "evidence": [ - "D6:4" - ], - "category": 4 - }, - { - "question": "What type of training was the workshop Audrey signed up for in May 2023?", - "answer": "Positive reinforcement training", - "evidence": [ - "D6:4" - ], - "category": 4 - }, - { - "question": "How did Audrey describe she dog he met at the pet store?", - "answer": "Friendly and playful", - "evidence": [ - "D6:4" - ], - "category": 4 - }, - { - "question": "Why did Audrey think positive reinforcement training is important for pets?", - "answer": "To have pets learn how to behave in a positive way", - "evidence": [ - "D6:12" - ], - "category": 4 - }, - { - "question": "What challenge is Andrew facing in their search for a pet?", - "answer": "Finding a pet-friendly spot in the city", - "evidence": [ - "D7:8" - ], - "category": 4 - }, - { - "question": "How does Andrew feel about their search for a pet-friendly place?", - "answer": "Discouraged but determined", - "evidence": [ - "D7:8" - ], - "category": 4 - }, - { - "question": "What outdoor activities does Andrew plan on trying after the rock climbing class?", - "answer": "kayaking and bungee jumping", - "evidence": [ - "D8:7" - ], - "category": 4 - }, - { - "question": "How long does Audrey typically walk her dogs for?", - "answer": "about an hour", - "evidence": [ - "D8:14" - ], - "category": 4 - }, - { - "question": "What did Audrey set up in the backyard for their dogs on June 26, 2023?", - "answer": "a doggy play area with agility stuff and toys", - "evidence": [ - "D9:5" - ], - "category": 4 - }, - { - "question": "What did Audrey and her friends stumble across during a hike a few years back, as mentioned on June 26, 2023?", - "answer": "a stunning lake in the mountains", - "evidence": [ - "D9:23" - ], - "category": 4 - }, - { - "question": "What is Audrey's favorite recipe that she shares with Andrew on 3 July, 2023?", - "answer": "Chicken Pot Pie", - "evidence": [ - "D10:13" - ], - "category": 4 - }, - { - "question": "What dish is one of Audrey's favorite dishes that includes garlic and is shared with Andrew on 3 July, 2023?", - "answer": "Roasted Chicken", - "evidence": [ - "D10:23" - ], - "category": 4 - }, - { - "question": "What did Andrew and his GF do on the Monday before July 24, 2023?", - "answer": "volunteered at a pet shelter", - "evidence": [ - "D13:1" - ], - "category": 4 - }, - { - "question": "What is the name of Audrey's childhood dog?", - "answer": "Max", - "evidence": [ - "D13:8" - ], - "category": 4 - }, - { - "question": "What special memories does Audrey have with her childhood dog, Max?", - "answer": "Long walks in the neighborhood, exploring new paths, sharing worries and hopes", - "evidence": [ - "D13:10" - ], - "category": 4 - }, - { - "question": "What are some of the personalities of Audrey's four fur babies?", - "answer": "oldest is relaxed, second is playful, third can be naughty but loves cuddles, youngest is full of life", - "evidence": [ - "D13:6" - ], - "category": 4 - }, - { - "question": "What type of classes did Audrey start with her pups recently on 4 August, 2023?", - "answer": "Agility classes", - "evidence": [ - "D14:2" - ], - "category": 4 - }, - { - "question": "How often does Audrey take her pups to the park for practice?", - "answer": "Twice a week", - "evidence": [ - "D14:4" - ], - "category": 4 - }, - { - "question": "How long did the trail hike that Audrey went on with her pups take?", - "answer": "Two hours", - "evidence": [ - "D14:8" - ], - "category": 4 - }, - { - "question": "What advice did Audrey give to Andrew regarding grooming Toby?", - "answer": "Grooming slowly and gently, paying attention to sensitive areas like ears and paws. And remember to stay patient and positive throughout the grooming process.", - "evidence": [ - "D16:8" - ], - "category": 4 - }, - { - "question": "What is essential to keep the dogs looking good according to Audrey?", - "answer": "Daily brushing, regular baths, nail trims, and lots of love", - "evidence": [ - "D17:4" - ], - "category": 4 - }, - { - "question": "What did Audrey organize with the neighbors' dogs?", - "answer": "a doggy playdate", - "evidence": [ - "D18:6" - ], - "category": 4 - }, - { - "question": "What did Audrey do to give her dogs extra comfort as the weather cooled down?", - "answer": "Got new beds for them", - "evidence": [ - "D18:10" - ], - "category": 4 - }, - { - "question": "How does Audrey describe the new beds for her dogs?", - "answer": "Super cozy and comfy", - "evidence": [ - "D18:12" - ], - "category": 4 - }, - { - "question": "How did Audrey calm down her dog after the leash incident?", - "answer": "Petted, hugged, spoke calmly and slowly walked the dog", - "evidence": [ - "D19:4" - ], - "category": 4 - }, - { - "question": "How often does Audrey take her dogs for walks?", - "answer": "Multiple times a day", - "evidence": [ - "D19:10" - ], - "category": 4 - }, - { - "question": "What kind of flowers does Audrey take care of?", - "answer": "Peruvian Lilies", - "evidence": [ - "D19:20" - ], - "category": 4 - }, - { - "question": "What did Andrew learn from reading books about ecological systems?", - "answer": "about animals, plants, and ecosystems and how they work together", - "evidence": [ - "D20:25" - ], - "category": 4 - }, - { - "question": "What did Andrew suggest as a way to reduce carbon footprint?", - "answer": "biking or using public transport", - "evidence": [ - "D20:33" - ], - "category": 4 - }, - { - "question": "How does Andrew suggest helping the planet while also training the body?", - "answer": "by biking", - "evidence": [ - "D20:35" - ], - "category": 4 - }, - { - "question": "What did Audrey do with her pups over the weekend before 4th October, 2023?", - "answer": "Took them to the beach", - "evidence": [ - "D21:2" - ], - "category": 4 - }, - { - "question": "What was the reason Audrey couldn't walk her dogs for a period of time?", - "answer": "Knee injury", - "evidence": [ - "D22:1" - ], - "category": 4 - }, - { - "question": "What type of jewelry does Audrey make?", - "answer": "Jewelry made from recycled objects", - "evidence": [ - "D22:5" - ], - "category": 4 - }, - { - "question": "Why does Audrey make jewelry out of recycled objects?", - "answer": "To show love for creativity and sustainability", - "evidence": [ - "D22:5" - ], - "category": 4 - }, - { - "question": "What organization does Audrey donate a portion of his profits to?", - "answer": "Animal shelter", - "evidence": [ - "D22:7" - ], - "category": 4 - }, - { - "question": "How does Audrey help out the animal shelter?", - "answer": "By donating a portion of his profits frmo selling jwelery", - "evidence": [ - "D22:9" - ], - "category": 4 - }, - { - "question": "What type of games do Audrey's dogs like to play at the park?", - "answer": "Fetch and Frisbee", - "evidence": [ - "D23:14" - ], - "category": 4 - }, - { - "question": "What did Audrey make to thank her neighbors?", - "answer": "Goodies", - "evidence": [ - "D23:2" - ], - "category": 4 - }, - { - "question": "How do Audrey's dogs react to snow?", - "answer": "Confused", - "evidence": [ - "D23:12" - ], - "category": 4 - }, - { - "question": "How does Audrey describe her dogs' response to snow?", - "answer": "They definitely prefer nice, sunny days in the grass.", - "evidence": [ - "D23:12" - ], - "category": 4 - }, - { - "question": "What kind of experiences are Audrey's dogs the best companions for?", - "answer": "Exploring the great outdoors", - "evidence": [ - "D23:24" - ], - "category": 4 - }, - { - "question": "What activity do Andrew and Buddy enjoy doing together?", - "answer": "Walking", - "evidence": [ - "D24:8" - ], - "category": 4 - }, - { - "question": "What do Andrew and Buddy like doing on walks?", - "answer": "Checking out new hiking trails", - "evidence": [ - "D24:10" - ], - "category": 4 - }, - { - "question": "What cuisine did Andrew recently try at a new spot in town?", - "answer": "sushi", - "evidence": [ - "D25:3" - ], - "category": 4 - }, - { - "question": "Which type of sushi did Audrey suggest trying first to someone new to sushi?", - "answer": "California or salmon roll", - "evidence": [ - "D25:8" - ], - "category": 4 - }, - { - "question": "What type of date is Andrew going on Sunday?", - "answer": "picnic date", - "evidence": [ - "D26:20" - ], - "category": 4 - }, - { - "question": "What did Andrew and Audrey plan to do on the Saturday after October 28, 2023?", - "answer": "Go hiking", - "evidence": [ - "D26:20" - ], - "category": 4 - }, - { - "question": "What aspect of autumn does Andrew find beautiful?", - "answer": "The autumn colors", - "evidence": [ - "D26:36" - ], - "category": 4 - }, - { - "question": "What did Audrey do in November 2023 to better take care of her dogs?", - "answer": "Joined a dog owners group", - "evidence": [ - "D27:2" - ], - "category": 4 - }, - { - "question": "How often does Audrey meet up with other dog owners for tips and playdates?", - "answer": "Once a week", - "evidence": [ - "D27:4" - ], - "category": 4 - }, - { - "question": "What did Audrey share to show ways to keep dogs active in the city?", - "answer": "photography of a basket full of stuffed animals", - "evidence": [ - "D27:10" - ], - "category": 4 - }, - { - "question": "What type of activities does Audrey suggest for mental stimulation of the dogs?", - "answer": "puzzles, training, hide-and-seek", - "evidence": [ - "D27:14" - ], - "category": 4 - }, - { - "question": "What is Andrew planning to do with Scout, Toby, and Buddy?", - "answer": "Take them to a nearby park", - "evidence": [ - "D28:10" - ], - "category": 4 - }, - { - "question": "What did Andrew get for Scout to create a safe and fun space for them?", - "answer": "essentials like a bed, toys, and puppy pads", - "evidence": [ - "D28:12" - ], - "category": 4 - }, - { - "question": "Which specific type of bird mesmerizes Audrey?", - "evidence": [ - "D1:16" - ], - "category": 5, - "adversarial_answer": "Eagles" - }, - { - "question": "What kind of flowers does Andrew have a tattoo of?", - "evidence": [ - "D3:26" - ], - "category": 5, - "adversarial_answer": "sunflowers" - }, - { - "question": "What type of dog was Audrey looking to adopt based on her living space?", - "evidence": [ - "D5:3" - ], - "category": 5, - "adversarial_answer": "smaller dog" - }, - { - "question": "Why did Audrey sign up for a workshop about car maintenance?", - "evidence": [ - "D6:2" - ], - "category": 5, - "adversarial_answer": "Strengthen the bond with her pets" - }, - { - "question": "How did Andrew hear about the workshop on bonding with pets?", - "evidence": [ - "D6:4" - ], - "category": 5, - "adversarial_answer": "Saw a workshop flyer at the local pet store" - }, - { - "question": "What type of training was the workshop Andrew signed up for in May 2023?", - "evidence": [ - "D6:4" - ], - "category": 5, - "adversarial_answer": "Positive reinforcement training" - }, - { - "question": "How did Andrew describe the dog he met at the pet store?", - "evidence": [ - "D6:4" - ], - "category": 5, - "adversarial_answer": "Friendly and playful" - }, - { - "question": "What challenge is Audrey facing in their search for a pet?", - "evidence": [ - "D7:8" - ], - "category": 5, - "adversarial_answer": "Finding a pet-friendly spot in the city" - }, - { - "question": "What indoor activities does Andrew plan on trying after the rock climbing class?", - "evidence": [ - "D8:7" - ], - "category": 5, - "adversarial_answer": "kayaking and bungee jumping" - }, - { - "question": "What did Andrew set up in the backyard for their dogs on June 26, 2023?", - "evidence": [ - "D9:5" - ], - "category": 5, - "adversarial_answer": "a doggy play area with agility stuff and toys" - }, - { - "question": "What did Audrey and her GF do on the Monday before July 24, 2023?", - "evidence": [ - "D13:1" - ], - "category": 5, - "adversarial_answer": "volunteered at a pet shelter" - }, - { - "question": "What is the name of Andrew's childhood dog?", - "evidence": [ - "D13:8" - ], - "category": 5, - "adversarial_answer": "Max" - }, - { - "question": "What special memories does Andrew have with his childhood dog, Max?", - "evidence": [ - "D13:10" - ], - "category": 5, - "adversarial_answer": "Long walks in the neighborhood, exploring new paths, sharing worries and hopes" - }, - { - "question": "What are some of the personalities of Andrew's four fur babies?", - "evidence": [ - "D13:6" - ], - "category": 5, - "adversarial_answer": "oldest is relaxed, second is playful, third can be naughty but loves cuddles, youngest is full of life" - }, - { - "question": "What type of classes did Andrew start with his pups recently on 4 August, 2023?", - "evidence": [ - "D14:2" - ], - "category": 5, - "adversarial_answer": "Agility classes" - }, - { - "question": "What is essential to keep the dogs looking good according to Andrew?", - "evidence": [ - "D17:4" - ], - "category": 5, - "adversarial_answer": "Daily brushing, regular baths, nail trims, and lots of love" - }, - { - "question": "What did Audrey organize with the neighbors' cats?", - "evidence": [ - "D18:6" - ], - "category": 5, - "adversarial_answer": "a doggy playdate" - }, - { - "question": "What did Andrew do to give his dogs extra comfort as the weather cooled down?", - "evidence": [ - "D18:10" - ], - "category": 5, - "adversarial_answer": "Got new beds for them" - }, - { - "question": "How does Andrew describe the new beds for his dogs?", - "evidence": [ - "D18:12" - ], - "category": 5, - "adversarial_answer": "Super cozy and comfy" - }, - { - "question": "How did Andrew calm down his dog after the leash incident?", - "evidence": [ - "D19:4" - ], - "category": 5, - "adversarial_answer": "Petted, hugged, spoke calmly and slowly walked the dog" - }, - { - "question": "How often does Andrew take his dogs for walks?", - "evidence": [ - "D19:10" - ], - "category": 5, - "adversarial_answer": "Multiple times a day" - }, - { - "question": "What kind of vegetables does Audrey take care of?", - "evidence": [ - "D19:20" - ], - "category": 5, - "adversarial_answer": "Peruvian Lilies" - }, - { - "question": "What did Andrew learn from reading books about economic systems?", - "evidence": [ - "D20:25" - ], - "category": 5, - "adversarial_answer": "about animals, plants, and ecosystems and how they work together" - }, - { - "question": "What was the reason Andrew couldn't walk his dogs for a period of time?", - "evidence": [ - "D22:1" - ], - "category": 5, - "adversarial_answer": "Knee injury" - }, - { - "question": "What type of jewelry does Andrew make?", - "evidence": [ - "D22:5" - ], - "category": 5, - "adversarial_answer": "Jewelry made from recycled objects" - }, - { - "question": "Why does Andrew make jewelry out of recycled objects?", - "evidence": [ - "D22:5" - ], - "category": 5, - "adversarial_answer": "To show love for creativity and sustainability" - }, - { - "question": "What type of games do Andrew's dogs like to play at the park?", - "evidence": [ - "D23:14" - ], - "category": 5, - "adversarial_answer": "Fetch and Frisbee" - }, - { - "question": "What did Andrew make to thank his neighbors?", - "evidence": [ - "D23:2" - ], - "category": 5, - "adversarial_answer": "Goodies" - }, - { - "question": "How do Andrew's dogs react to snow?", - "evidence": [ - "D23:12" - ], - "category": 5, - "adversarial_answer": "Confused" - }, - { - "question": "How does Andrew describe his dogs' response to snow?", - "evidence": [ - "D23:12" - ], - "category": 5, - "adversarial_answer": "They definitely prefer nice, sunny days in the grass." - }, - { - "question": "What kind of experiences are Audrey's cats the best companions for?", - "evidence": [ - "D23:24" - ], - "category": 5, - "adversarial_answer": "Exploring the great outdoors" - }, - { - "question": "What activity do Audrey and Buddy enjoy doing together?", - "evidence": [ - "D24:8" - ], - "category": 5, - "adversarial_answer": "Walking" - }, - { - "question": "What type of drink did Andrew recently try at a new spot in town?", - "evidence": [ - "D25:3" - ], - "category": 5, - "adversarial_answer": "sushi" - }, - { - "question": "Which type of pizza did Audrey suggest trying first to someone new to Italian cuisine?", - "evidence": [ - "D25:8" - ], - "category": 5, - "adversarial_answer": "California or salmon roll" - }, - { - "question": "How often does Andrew meet up with other dog owners for tips and playdates?", - "evidence": [ - "D27:4" - ], - "category": 5, - "adversarial_answer": "Once a week" - } - ], - "conversation": { - "speaker_a": "Audrey", - "speaker_b": "Andrew", - "session_1_date_time": "1:10 pm on 27 March, 2023", - "session_1": [ - { - "speaker": "Audrey", - "dia_id": "D1:1", - "text": "Hey Andrew! Good to see ya! What's been up since we last talked?" - }, - { - "speaker": "Andrew", - "dia_id": "D1:2", - "text": "Hey Audrey! So, I started a new job as a Financial Analyst last week - it's been quite a change from my previous job. How about you? Anything interesting happening?" - }, - { - "speaker": "Audrey", - "dia_id": "D1:3", - "text": "Congrats on the new job! So I got these new collars and tags for my dogs - so cute!" - }, - { - "speaker": "Andrew", - "dia_id": "D1:4", - "text": "Thanks! That sounds cute. Can I see a picture?" - }, - { - "speaker": "Audrey", - "dia_id": "D1:5", - "img_url": [ - "https://worldanimalfoundation.org/wp-content/uploads/2022/07/adjustable-collar-Review.jpg" - ], - "re-download": true, - "blip_caption": "a photography of two dogs are standing in the grass with their mouths open", - "text": "Sure! See them with their new collars, cute right?" - }, - { - "speaker": "Andrew", - "dia_id": "D1:6", - "text": "Cute little guys! What are their names and how long have you had them?" - }, - { - "speaker": "Audrey", - "dia_id": "D1:7", - "text": "I've had them for 3 years! Their names are Pepper, Precious and Panda. I can't live without my little ones!" - }, - { - "speaker": "Andrew", - "dia_id": "D1:8", - "text": "That's awesome! Have you always wanted a dog, even with living in the city? Can they still go on adventures?" - }, - { - "speaker": "Audrey", - "dia_id": "D1:9", - "text": "Absolutely! They're city dogs and we explore all the time. They love trying out new parks and trails. We go on adventures together very often." - }, - { - "speaker": "Andrew", - "dia_id": "D1:10", - "text": "Wow, sounds like they make life so awesome! Kinda jealous of all those fun outings with them." - }, - { - "speaker": "Audrey", - "dia_id": "D1:11", - "text": "They really do! It's great how much happiness they bring. Do you have any pets?" - }, - { - "speaker": "Andrew", - "dia_id": "D1:12", - "text": "No, no pets right now. But I do love animals." - }, - { - "speaker": "Audrey", - "dia_id": "D1:13", - "text": "That's great to hear! Animals are truly amazing. Do you have a favorite animal?" - }, - { - "speaker": "Andrew", - "dia_id": "D1:14", - "text": "I've always been awed by birds. Their power to soar and explore new spots is amazing." - }, - { - "speaker": "Audrey", - "dia_id": "D1:15", - "text": "Yeah, birds are amazing! I can imagine it feels incredible to soar and see the world from up high. Do you have a favorite type of bird?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/g9ia8ap7ckmb1.jpg" - ], - "blip_caption": "a photo of two birds flying in the sky with a sun shining behind them", - "query": "majestic eagle soaring sky", - "dia_id": "D1:16", - "text": "Eagles have always mesmerized me; they're so strong and graceful!" - }, - { - "speaker": "Audrey", - "dia_id": "D1:17", - "text": "Yeah they're beautiful. Do you go bird-watching? It must be awesome to see them up close." - }, - { - "speaker": "Andrew", - "dia_id": "D1:18", - "text": "Haven't specifically gone out for bird-watching, but I do spot them when I hike." - }, - { - "speaker": "Audrey", - "dia_id": "D1:19", - "text": "Nice, spotting pretty birds while hiking must be great. Do you have any favorite hiking spots?" - }, - { - "speaker": "Andrew", - "dia_id": "D1:20", - "text": "Fox Hollow is a great trail to hike on weekends; the views are awesome!" - }, - { - "speaker": "Audrey", - "dia_id": "D1:21", - "text": "Cool, gonna give it a try. Thanks for the suggestion!" - }, - { - "speaker": "Andrew", - "dia_id": "D1:22", - "text": "No problem! Let me know how you like it. Have fun hiking!" - }, - { - "speaker": "Audrey", - "dia_id": "D1:23", - "text": "Thanks! I'll let you know. Have a good one!" - }, - { - "speaker": "Andrew", - "dia_id": "D1:24", - "text": "Take care and have a good one! See ya!" - } - ], - "session_2_date_time": "2:42 pm on 2 April, 2023", - "session_2": [ - { - "speaker": "Audrey", - "img_url": [ - "https://moderndogmagazine.com/sites/default/files/images/photoentries/photos/pixie%20smiling.jpg" - ], - "blip_caption": "a photo of a small white dog sitting on a carpet", - "query": "new puppy Pixie", - "dia_id": "D2:1", - "text": "Hey Andrew, I got a surprise for you! We adopted another puppy called Pixie. She's SO cute! Isn't she just the cutest?" - }, - { - "speaker": "Andrew", - "dia_id": "D2:2", - "text": "That's awesome! Pixie is so cute! Did you ever check out that hiking spot we talked about last time?" - }, - { - "speaker": "Audrey", - "dia_id": "D2:3", - "text": "Thanks! I know right? She's so cute! Pixie's been keeping us busy, so I haven't had a chance to check out that hiking spot yet." - }, - { - "speaker": "Andrew", - "dia_id": "D2:4", - "text": "Yeah, I get it. A puppy really takes some work. Has she settled in okay? Is she getting along with the other pups?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9zdGF0aWMvaW1hZ2Uvd2Vic2l0ZS8yMDIyLTA0L2xyL3B4NzIxNzk3LWltYWdlLWt3dnYzenBkLmpwZw.jpg" - ], - "blip_caption": "a photography of a dog playing with a group of other dogs", - "query": "pixie pepper precious panda dogs playing backyard", - "dia_id": "D2:5", - "re-download": true, - "text": "Pixie's fitting in great! It took her a few days to get used to the other dogs, but now they're awesome friends. They love playing and exploring the house - so cute!\n" - }, - { - "speaker": "Andrew", - "dia_id": "D2:6", - "text": "That's awesome! Is Pepper adjusting well to her?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i2.pickpik.com/photos/398/166/341/dogs-garden-enjoy-weather-animals-preview.jpg" - ], - "blip_caption": "a photography of two dogs standing in the grass near a fence", - "query": "pepper pixie playing together", - "dia_id": "D2:7", - "re-download": true, - "text": "Pepper took a bit to get used to her, but now they're always together!" - }, - { - "speaker": "Andrew", - "dia_id": "D2:8", - "text": "That's great to hear! I'm considering getting a dog too, but it can be challenging finding a dog-friendly place in the city. Do you have any tips on finding such a place?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/2388/2232782014_51a690ec00_c.jpg" - ], - "blip_caption": "a photography of a dog sitting in a field of flowers", - "query": "dog-friendly website", - "dia_id": "D2:9", - "re-download": true, - "text": "We used websites that helped us find a place that allowed dogs. They had filters that made it super easy. Found a perfect spot thanks to them!" - }, - { - "speaker": "Andrew", - "dia_id": "D2:10", - "text": "Wow, that's so cool! I'll give those websites a try, thanks for the info!" - }, - { - "speaker": "Audrey", - "dia_id": "D2:11", - "text": "You got it! I hope you find a great spot soon!" - }, - { - "speaker": "Andrew", - "dia_id": "D2:12", - "text": "Thanks! Fingers crossed for the apartment and that furry friend." - }, - { - "speaker": "Audrey", - "dia_id": "D2:13", - "text": "I'm keeping my fingers crossed for you to find a great furry friend and an apartment." - }, - { - "speaker": "Andrew", - "dia_id": "D2:14", - "text": "Hope I find one soon. Can't wait!" - }, - { - "speaker": "Audrey", - "dia_id": "D2:15", - "text": "You'll love them! They're great for cuddles and companionship." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/gnwdz3ysqra71.jpg" - ], - "blip_caption": "a photo of a dog sitting on a couch in a living room", - "query": "dog couch", - "dia_id": "D2:16", - "text": "It'd be so great to have a furry buddy to cuddle and hang with. Here's a photo of my family's dog on a couch. " - }, - { - "speaker": "Audrey", - "dia_id": "D2:17", - "img_url": [ - "https://images.pexels.com/photos/18690079/pexels-photo-18690079/free-photo-of-white-terrier-lying-on-a-sofa.jpeg" - ], - "re-download": true, - "blip_caption": "a photography of a dog laying on a couch with a pillow", - "text": "Our furry friends make great cuddle buddies. Here's one of them enjoying some lazy couch time!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://www.kingdukes.com/cdn/shop/articles/man_walking_dog_on_portland_trail_1200x.jpg" - ], - "blip_caption": "a photo of a man walking a dog on a trail", - "query": "dog hiking trail", - "dia_id": "D2:18", - "text": "That pic is so cute! It would be fun to hang out with a dog, cuddling away. Got me thinking of my old hiking pics and how much I miss exploring nature trails with my family's dog. Ah, the peaceful moments out in nature!" - }, - { - "speaker": "Audrey", - "dia_id": "D2:19", - "text": " Taking them on hikes must be awesome! That makes me want to hike with my dogs." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://images.pexels.com/photos/4430321/pexels-photo-4430321.jpeg" - ], - "blip_caption": "a photography of a man sitting on a rock with a dog", - "query": "hiking with dog rocks trail", - "dia_id": "D2:20", - "re-download": true, - "text": "Hiking with a dog is way fun, it's a great way to bond and create memories together." - }, - { - "speaker": "Audrey", - "dia_id": "D2:21", - "text": "Yeah! Exploring nature with them and making memories is awesome!" - }, - { - "speaker": "Andrew", - "dia_id": "D2:22", - "text": "Definitely! I miss those moments. I need to find a dog-friendly spot soon so I can get exploring again!" - }, - { - "speaker": "Audrey", - "dia_id": "D2:23", - "text": "Yep! I hope you find the perfect dog-friendly spot soon so you can make new memories with them." - }, - { - "speaker": "Andrew", - "dia_id": "D2:24", - "text": "Thanks! I'll keep looking until I find it. Super excited for what's next!" - }, - { - "speaker": "Audrey", - "dia_id": "D2:25", - "text": "So excited for you! I can't wait to see where you and your furry friend decide to hang out. All the best!" - }, - { - "speaker": "Andrew", - "dia_id": "D2:26", - "text": "I'll keep you posted. Ttyl, Bye!" - } - ], - "session_3_date_time": "4:19 pm on 16 April, 2023", - "session_3": [ - { - "speaker": "Andrew", - "dia_id": "D3:1", - "text": "Hey Audrey! What's up? Missed chatting with ya! Check it out, my girl & I tried out that new cafe scene in the city last weekend! Super fun but kinda sad not being out in nature - that's when I feel like I'm really thriving. Oh man, I miss the peacefulness of being out on a hike." - }, - { - "speaker": "Audrey", - "dia_id": "D3:2", - "text": "Hey Andrew! That cafe sounds pretty awesome, glad you enjoyed it. Yeah, I know what you mean. That feeling of being out in nature, surrounded by beauty and peace, is unbeatable, compared to being surrounded by concrete jungles. " - }, - { - "speaker": "Andrew", - "dia_id": "D3:3", - "text": "Yeah, being out in the nature really has a way of taking my breath away. It's like a home for my soul! I feel connected when I'm there." - }, - { - "speaker": "Audrey", - "dia_id": "D3:4", - "text": "I totally get it. Nature has a way of bringing us back down to earth and reminding us that we're part of something bigger. It's amazing." - }, - { - "speaker": "Andrew", - "dia_id": "D3:5", - "text": "Yeah, it's like hitting the reset button when life gets too chaotic. Nature has a way of being really soothing." - }, - { - "speaker": "Audrey", - "dia_id": "D3:6", - "text": "Definitely! For me, taking my dogs for a walk in the park helps me find my center and recharges me. " - }, - { - "speaker": "Andrew", - "dia_id": "D3:7", - "text": "I hear ya. Can\u2019t do that here unfortunately, city living makes it hard. Really miss that connection and companionship." - }, - { - "speaker": "Audrey", - "dia_id": "D3:8", - "text": "Must be tough living in the city without the opportunity to go outside. Sure is a different experience." - }, - { - "speaker": "Andrew", - "dia_id": "D3:9", - "text": "Yeah, it really does. Sometimes it feels like I'm missing out." - }, - { - "speaker": "Audrey", - "dia_id": "D3:10", - "text": "It must be tough. City life can get overwhelming, so it's great to find ways to connect to nature. What kind of things do you like to do in the city?" - }, - { - "speaker": "Andrew", - "dia_id": "D3:11", - "text": "My girlfriend and I love to discover new places to eat around town. It's a great way to try something new and wind down after a long week." - }, - { - "speaker": "Audrey", - "dia_id": "D3:12", - "text": "Wow, that sounds like a great way to explore the city! Trying new places and food is always fun. Have you discovered any new favorite spots recently?" - }, - { - "speaker": "Andrew", - "dia_id": "D3:13", - "text": "We found an awesome cafe with amazing pastries - the smell was just irresistible!" - }, - { - "speaker": "Audrey", - "dia_id": "D3:14", - "text": "Sounds amazing! Did you take a photo? I'd love to see!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://marianainla.com/wp-content/uploads/2021/12/unnamed-20.jpg" - ], - "blip_caption": "a photo of a bakery display with pastries and coffee", - "query": "mouth-watering pastries cafe", - "dia_id": "D3:15", - "text": "Yep, I got them! Check out this photo of the delicious pastries we had at the cafe." - }, - { - "speaker": "Audrey", - "dia_id": "D3:16", - "text": "Wow, they look great! What did you get? Now I'm craving pastries!" - }, - { - "speaker": "Andrew", - "dia_id": "D3:17", - "text": "They taste great too! We had some delicious croissants, muffins, and tarts! It was amazing!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://allisoncooksgoodeats.files.wordpress.com/2017/09/img_4467.jpg" - ], - "blip_caption": "a photo of a muffin pan filled with blueberries and muffins", - "query": "homemade blueberry muffin pastry", - "dia_id": "D3:18", - "text": "Wow, sounds amazing! Glad you got to enjoy them. Since you metioned pastries, I made some of my favorite treats last week. Let's have a pastry party sometime! " - }, - { - "speaker": "Andrew", - "dia_id": "D3:19", - "text": "That looks delicious! A pastry party sounds awesome, count me in!" - }, - { - "speaker": "Audrey", - "dia_id": "D3:20", - "text": "Awesome! My dogs will tag along. My furry friends would love it!" - }, - { - "speaker": "Andrew", - "dia_id": "D3:21", - "text": "Definitely! They'll have a great time at the party! Can't wait for the party!" - }, - { - "speaker": "Audrey", - "dia_id": "D3:22", - "text": "Same! Looking forward to it. This would be a good chance for you to hang out with them." - }, - { - "speaker": "Andrew", - "dia_id": "D3:23", - "text": "Yeah! It's gonna be awesome to see all of them together and chowing down. Super excited!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/3674/10799791393_b3a685158c_b.jpg" - ], - "blip_caption": "a photography of a dog running with three other dogs in the background", - "query": "pepper precious panda pixie playing park furry family", - "dia_id": "D3:24", - "re-download": true, - "text": "Yay! This'll be awesome! Seeing them all together, having fun and enjoying treats is the best. I can't imagine life without them, they bring so much joy." - }, - { - "speaker": "Andrew", - "dia_id": "D3:25", - "text": "Aww, so cute! I bet their happy faces means a lot to you." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.pinimg.com/originals/8d/e3/10/8de3100a3dd42a38237c9090965cfdfe.jpg" - ], - "blip_caption": "a photo of a woman with a tattoo of a dog and sunflowers", - "query": "tattoo dogs arm", - "dia_id": "D3:26", - "text": "I know right? They mean the world to me. So much that I got tattoos of them on my arm." - }, - { - "speaker": "Andrew", - "dia_id": "D3:27", - "text": "Wow, that tattoo looks great!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.pixexid.com/a-woman-is-joyfully-running-in-a-sunlit-forest-with-four-dogs-of-various-breeds-kn6f7vry.jpeg" - ], - "blip_caption": "a photography of a woman walking with her dogs in the woods", - "query": "dogs field nature", - "dia_id": "D3:28", - "re-download": true, - "text": "Thanks! I got it a while ago. It represents my love for my pups and nature's beauty." - }, - { - "speaker": "Andrew", - "dia_id": "D3:29", - "text": "Aww, that's cute! What made you getting the tattoo?" - }, - { - "speaker": "Audrey", - "dia_id": "D3:30", - "text": "I've always had a strong bond with them \u2013 they make me so happy, and I just want to see their happy faceseven when thry're not with me." - }, - { - "speaker": "Andrew", - "dia_id": "D3:31", - "text": "That's awesome, I'm glad they bring so much joy into your life." - } - ], - "session_4_date_time": "5:41 pm on 3 May, 2023", - "session_4": [ - { - "speaker": "Audrey", - "img_url": [ - "https://images.pexels.com/photos/7875455/pexels-photo-7875455.jpeg" - ], - "blip_caption": "a photography of a hummingbird sitting on a branch with its wings spread", - "query": "cute little bird perched branch hummingbird hike nectar flowers", - "dia_id": "D4:1", - "re-download": true, - "text": "Hey Andrew! Long time no talk! Last week I finally went on a hike and had this amazing experience with a hummingbird. It was so cool watching it dart around with its wings! Nature is so beautiful." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/okd8n7nb0hc71.jpg" - ], - "blip_caption": "a photo of a view of a lake and mountains at sunset", - "query": "sunset mountain landscape hiking", - "dia_id": "D4:2", - "text": "Hey Audrey! Glad to hear from you. That hummingbird was awesome! Nature's the best. Remember I was feeling down because I couldn't get out more? Well, good news - I found a new open space to hike nearby - feels so refreshing!" - }, - { - "speaker": "Audrey", - "dia_id": "D4:3", - "text": "That looks great! How often are you able to go hiking now?" - }, - { - "speaker": "Andrew", - "dia_id": "D4:4", - "text": "So I usually try to escape the city at least once a weekend - it's my much-needed break!" - }, - { - "speaker": "Audrey", - "dia_id": "D4:5", - "text": "Nice! I Bet it's great to get away like that." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/u51y7czkhfrb1.jpg" - ], - "blip_caption": "a photo of a mountain range with a sunset in the background", - "query": "mountain peak sunrise", - "dia_id": "D4:6", - "text": "Yep! Nature is so therapeutic for me. It's hard to put into words how great it feels to be surrounded by greenery or standing on top of a mountain breathing in the fresh air. So peaceful and invigorating.\n\n" - }, - { - "speaker": "Audrey", - "dia_id": "D4:7", - "text": "Wow, that view looks great! It really captures that calming and peaceful feeling. It must be really satisfying to capture that tranquility." - }, - { - "speaker": "Andrew", - "dia_id": "D4:8", - "text": "Yeah! I try to capture those special moments and share that feeling with others." - }, - { - "speaker": "Audrey", - "dia_id": "D4:9", - "text": "That's really cool! Spreading that peaceful feeling to others is such a great way to bring happiness and motivation." - }, - { - "speaker": "Andrew", - "dia_id": "D4:10", - "text": "I agree! It brings me joy to spread that feeling and motivate people to find their own peace - it's like a ripple effect, spreading positivity." - }, - { - "speaker": "Audrey", - "dia_id": "D4:11", - "text": "Absolutely. Not only the receiving end feels great, the person spreading it will feel great as well!" - }, - { - "speaker": "Andrew", - "dia_id": "D4:12", - "text": "Yeah, it's crazy how that can bring such great joy and calm to people." - }, - { - "speaker": "Audrey", - "dia_id": "D4:13", - "text": "Yeah, by sharing something can make a big difference to others." - }, - { - "speaker": "Andrew", - "dia_id": "D4:14", - "text": "Yup! We don't realize it, but even the littlest gestures can have a big effect. Spreading good vibes and joy, that's the goal right? So what's up? Anything new or fun going on?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i2.pickpik.com/photos/852/467/713/four-dogs-pack-papillon-hybrids-preview.jpg" - ], - "blip_caption": "a photography of a group of dogs sitting on top of a lush green field", - "query": "dogs playing together", - "dia_id": "D4:15", - "re-download": true, - "text": "Not much has changed since we last talked. I'm busy taking care of my pets and spending time with them. It's really fulfilling." - }, - { - "speaker": "Andrew", - "dia_id": "D4:16", - "text": "I bet it's awesome hanging out with them - do your dogs enjoy going on hikes?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://d3ekkvinch1ig5.cloudfront.net/rating/1665802443-8244image1.jpg" - ], - "blip_caption": "a photo of a dog running in a field with other dogs", - "query": "dogs playing fetch wide open field", - "dia_id": "D4:17", - "text": "My dogs go nuts when we go on hikes! They love exploring new scents and being in nature - it's their happy place. I can tell by their wagging tails and expressions how much they love it. It's so great to see them having so much fun!" - }, - { - "speaker": "Andrew", - "dia_id": "D4:18", - "text": "Wow, that's great! I was wondering if you have any tips or tricks for keeping dogs safe when they're out and about?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://d4yxl4pe8dqlj.cloudfront.net/images/061f1763-db03-4b77-a9c7-85a24844f99e/21929851-1c63-40ee-b7bd-d977fdb693be_full_size.jpg" - ], - "blip_caption": "a photo of a dog with a leash and shoes on", - "query": "dog wearing protective boots", - "dia_id": "D4:19", - "text": "Definitely! Safety is super important for me. I even got them something special. They sure look funny, but it works!" - }, - { - "speaker": "Andrew", - "dia_id": "D4:20", - "text": "Awesome to see that you take such good care of your dogs! What else do you do when you walk them?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.pexels.com/photos/6527909/pexels-photo-6527909.jpeg" - ], - "blip_caption": "a photography of a dog catching a frisbee in a field", - "query": "dog playing fetch park other dogs", - "dia_id": "D4:21", - "re-download": true, - "text": "When I take them out, we usually play fetch with a ball or frisbee. They love chasing it! We also meet other dog owners in the park and have a doggie playdate. It's a great way for me to chat with people and for them to make new friends." - }, - { - "speaker": "Andrew", - "dia_id": "D4:22", - "text": "Wow, looks like fun! They sure look excited about playing fetch!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/1a14cd4hzcd91.jpg" - ], - "blip_caption": "a photo of a dog wearing a party hat sitting on a couch", - "query": "dogs playing with party hats ball", - "dia_id": "D4:23", - "text": "They absolutely adore it! They have tons of energy and love meeting new pals at playdates. Plus, they always get excited when I bring those out." - }, - { - "speaker": "Andrew", - "dia_id": "D4:24", - "text": "Aww, that's so cute! Do they enjoy wearing the party hats? And where did you find their playdates?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/2913/14439335660_d4ffd00bbc_z.jpg" - ], - "blip_caption": "a photography of a dog jumping through a ring in a park", - "query": "dog park colorful playground", - "dia_id": "D4:25", - "re-download": true, - "text": "The hats don't bother them, they just put them on for fun and treats. And the dog park is great place to start! They can run and mingle with other pooches. It's such a fun spot for playdates!" - }, - { - "speaker": "Andrew", - "dia_id": "D4:26", - "text": "That sounds great! Where is it located?" - }, - { - "speaker": "Audrey", - "dia_id": "D4:27", - "text": "It's right by the park we usually walk. It's a great spot with lots of trees and benches for us to watch the dogs play." - }, - { - "speaker": "Andrew", - "dia_id": "D4:28", - "text": "That's awesome, not too far away then! I'll have to check it out. Thanks for the suggestion!" - }, - { - "speaker": "Audrey", - "dia_id": "D4:29", - "text": "Yup! Not at all! Let me know how it goes." - }, - { - "speaker": "Andrew", - "dia_id": "D4:30", - "text": "Sure, I'll let you know. Always great chatting with you! Ttyl." - } - ], - "session_5_date_time": "10:47 am on 6 May, 2023", - "session_5": [ - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/5006/5244715417_aa4e26e1d3_z.jpg" - ], - "blip_caption": "a photography of a dog looking through a cage at the camera", - "query": "cute dog shelter adoption", - "dia_id": "D5:1", - "re-download": true, - "text": "Hey! Since we last spoke, I've been looking for a doggo to adopt - browsing websites, visiting shelters and asking friends of theirs. It's been both fun and annoying!" - }, - { - "speaker": "Audrey", - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "dia_id": "D5:2", - "text": "Sounds like a fun and demanding task! Getting to meet new pups must bring so much happiness. What do you think you can do to make the process smoother?" - }, - { - "speaker": "Andrew", - "dia_id": "D5:3", - "text": "Meeting all these adorable pups has been awesome! For those considering getting a pup, the size of living space and the exercise needs of the breed are important. For me, a person living in an apartment, a smaller dog would be best, but if one is active, consider getting one that loves to play and run." - }, - { - "speaker": "Audrey", - "dia_id": "D5:4", - "text": "That's some good advice! It's important to consider the space and energy needs of a dog." - }, - { - "speaker": "Andrew", - "dia_id": "D5:5", - "text": "Yeah! Finding a pet-friendly place to live has been tough too. I'm contacting landlords and checking out neighborhoods to find the perfect spot." - }, - { - "speaker": "Audrey", - "dia_id": "D5:6", - "text": "Guessing it's tough to find housing. Any particular part of town you want to live in?" - }, - { - "speaker": "Andrew", - "dia_id": "D5:7", - "text": "I'm looking for a place near a park or woods, so I can stay close to nature and give the dog a large open space to run around" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.pexels.com/photos/12874286/pexels-photo-12874286.jpeg" - ], - "blip_caption": "a photography of three dogs running through a field of grass", - "query": "dogs playing field flowers national park", - "dia_id": "D5:8", - "re-download": true, - "text": "That's a good plan! I'm lucky to have a park near me - it's great for my pup's walks. Last Friday we took a road trip - we went to a beautiful national park and my dogs had a blast! It was an awesome trip!" - }, - { - "speaker": "Andrew", - "dia_id": "D5:9", - "text": "Nice! Glad the pups had a great road trip. Do you take them on road trips often?" - }, - { - "speaker": "Audrey", - "dia_id": "D5:10", - "text": "I take them on road trips once every couple of months. It's a great way for them to explore and stay active." - }, - { - "speaker": "Andrew", - "dia_id": "D5:11", - "text": "Wow, that's awesome! I really wish I could go on a road trip with a furry companion." - }, - { - "speaker": "Audrey", - "dia_id": "D5:12", - "text": "It's a cool experience. Having your furry friends on a road trip is an amazing experience. They make it really fun and exciting. It's definitely something to look forward to!" - }, - { - "speaker": "Andrew", - "dia_id": "D5:13", - "text": "Adding that to my bucket list! Can't wait for the day I actually go on a trip with my dog!" - }, - { - "speaker": "Audrey", - "dia_id": "D5:14", - "text": "Good luck with your search! Fingers crossed you find the perfect one." - }, - { - "speaker": "Andrew", - "dia_id": "D5:15", - "text": "Thanks! Your help is much appreciated. I'm still on the lookout for the perfect furry friend." - }, - { - "speaker": "Audrey", - "dia_id": "D5:16", - "text": "Not a problem, I'm glad to help! Good luck with your search!" - }, - { - "speaker": "Andrew", - "dia_id": "D5:17", - "text": "Thanks! I'll let you know how it goes. " - }, - { - "speaker": "Audrey", - "dia_id": "D5:18", - "text": "Definitely, keep me posted and let me know if you need any suggestions or help." - }, - { - "speaker": "Andrew", - "dia_id": "D5:19", - "text": "Will do! Really apprecieate it. " - }, - { - "speaker": "Audrey", - "dia_id": "D5:20", - "text": "Yup! You got it, I'll be expecting a pic of your dog soon! :)" - }, - { - "speaker": "Andrew", - "dia_id": "D5:21", - "text": "Haha I can't wait. I'll ttyl, gotta check out another shelter soon." - }, - { - "speaker": "Audrey", - "dia_id": "D5:22", - "text": "Have fun! Ttyl!" - } - ], - "session_6_date_time": "2:03 pm on 11 May, 2023", - "session_6": [ - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/7155/6566396811_8526df2fb5_b.jpg" - ], - "blip_caption": "a photography of a dirt path leading to a grassy hill", - "query": "hike nature open space", - "dia_id": "D6:1", - "re-download": true, - "text": "Hi Audrey! I had a great hike last weekend with some friends and my girlfriend at the spot we found recently. Nature was so peaceful \u2013 it was so nice to just relax and take it in. How's your week been? Anything exciting going on lately?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/63568f583d7aee4d0ba645d0/05701b3d-6533-4936-a33d-2bd0902f1a34/11821460_.jpg" - ], - "blip_caption": "a photography of a group of dogs with a yellow circle", - "query": "dog training workshop flyer", - "dia_id": "D6:2", - "re-download": true, - "text": "Hey Andrew! That hike sounds great. Nature is good for the soul, right? My week's been good - taking care of my four doggies and making sure they're happy and healthy took up most of my free time. Also, exciting news! I signed up for a workshop about bonding with my pet next month. Can't wait to learn new stuff and strengthen my bond with my pets. What's up with you?" - }, - { - "speaker": "Andrew", - "dia_id": "D6:3", - "text": "That's awesome! Glad have the opportunity to bond with your pets. That workshop sounds cool. Where did you hear about it? And the one in the picture is adorable!" - }, - { - "speaker": "Audrey", - "dia_id": "D6:4", - "text": "I know right? I saw this workshop flyer at my local pet store. It was a positive reinforcement training class and I wanted to give it a shot. The volunteer in the store was nice enough to let me meet their dog \u2013 he was so friendly and playful!" - }, - { - "speaker": "Andrew", - "dia_id": "D6:5", - "text": "Cool! Positive reinforcement can really help you bond with your dogs. Do you think they'll catch on quickly?" - }, - { - "speaker": "Audrey", - "dia_id": "D6:6", - "text": "I'm sure they'll catch on really quick! They're quick learners and love rewards! Can't wait to learn how to train them better." - }, - { - "speaker": "Andrew", - "dia_id": "D6:7", - "text": "That's awesome! Keep me updated on their progress." - }, - { - "speaker": "Audrey", - "dia_id": "D6:8", - "text": "Definitely! I'll keep you updated on how it all goes and how my pups are doing. Fingers crossed they'll be extra behaved. And I'll let you know some tips on training your future dog as well!" - }, - { - "speaker": "Andrew", - "dia_id": "D6:9", - "text": "Thanks! I'm excited to hear about it. Have a great time at the workshop!" - }, - { - "speaker": "Audrey", - "dia_id": "D6:10", - "text": "I'll definitely have a good time and make the most of it. I'm sure this is a must learn for any dog owner." - }, - { - "speaker": "Andrew", - "dia_id": "D6:11", - "text": "You think so? Wow, you must be a good salesperson because I'm almost sold on this class haha." - }, - { - "speaker": "Audrey", - "dia_id": "D6:12", - "text": "Haha, I just think its important to have pets learn how to behave on a positive reinforcement way. Punishment is never the proper way for pets ya know?" - }, - { - "speaker": "Andrew", - "dia_id": "D6:13", - "text": "Yeah I would't want to be punished, let alone puppies and dogs." - }, - { - "speaker": "Audrey", - "dia_id": "D6:14", - "text": "Right!? I don't want to hurt any of my dogs. Just by thinking of it gives me pain." - }, - { - "speaker": "Andrew", - "dia_id": "D6:15", - "text": "Yeah I feel you. Anyways, let me look into their classes. I'll talk to you soon, have fun!" - }, - { - "speaker": "Audrey", - "dia_id": "D6:16", - "text": "Yup, ttyl!" - } - ], - "session_7_date_time": "11:27 am on 2 June, 2023", - "session_7": [ - { - "speaker": "Audrey", - "dia_id": "D7:1", - "text": "Hey how's it going? Yesterday took my pups to the park, it was awesome! Seeing them running around and playing without a leash was awesome. It filled my heart with joy - their happiness brought me so much peace. " - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/nzwhfzovo8q61.jpg" - ], - "blip_caption": "a photo of a sunset over a mountain range with a horse grazing", - "query": "mountain peak sunset", - "dia_id": "D7:2", - "text": "That sounds amazing! Must have been so happy watching them running around. It's moments like that which show us how amazing animals are. Glad you had a great time. By the way, I think you mentioned before that you've taken them on a hiking trip? " - }, - { - "speaker": "Audrey", - "dia_id": "D7:3", - "text": "Yeah, I took them for a hike before. We went to a national park last week and made it to this beautiful peak. It was stunning during the sunset, and I'll never forget it. My furry pals were running around as it was so awesome. It felt like a slice of paradise. The breeze was nice and you could hear birds chirping." - }, - { - "speaker": "Andrew", - "dia_id": "D7:4", - "text": "That sounds amazing! I'm sure it was a memorable experience. Did you manage to capture it? I'd love to see a photo!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/nrl9qw3ayoza1.jpg" - ], - "blip_caption": "a photo of two horses in a field with a fence", - "query": "sunset mountains horse grazing", - "dia_id": "D7:5", - "text": "Yep! I took a bunch of photos. This one of my favorites." - }, - { - "speaker": "Andrew", - "dia_id": "D7:6", - "text": "Wow, incredible sunset and the scenery! Must have been magical. Thanks for sharing!" - }, - { - "speaker": "Audrey", - "dia_id": "D7:7", - "text": "It was really special. Nature always cheers me up and makes me feel grateful. Glad you could experience it too. Oh, how are things going with your search for a furry friend?" - }, - { - "speaker": "Andrew", - "dia_id": "D7:8", - "text": "I'm still on the hunt, but it's tough finding a pet-friendly spot in the city. Been checking out some places, but no luck so far. A bit discouraged but I'm determined to find the right place and dog." - }, - { - "speaker": "Audrey", - "dia_id": "D7:9", - "text": "Oof, Hang in there, you'll find the perfect place for you and your new furry friend." - }, - { - "speaker": "Andrew", - "dia_id": "D7:10", - "text": "Thanks! I won't give up. " - }, - { - "speaker": "Audrey", - "dia_id": "D7:11", - "text": "I'm here to support! Email me your criterias so I can help you find a great spot for your furry friend." - }, - { - "speaker": "Andrew", - "dia_id": "D7:12", - "text": "Thanks! Your assistance is greatly appreciated. So thankful for your help." - }, - { - "speaker": "Audrey", - "dia_id": "D7:13", - "text": "Not a problem at all! We'll keep searching. Stay positive!" - } - ], - "session_8_date_time": "5:23 pm on 13 June, 2023", - "session_8": [ - { - "speaker": "Andrew", - "dia_id": "D8:1", - "text": "Hey! Long time no chat. Last Sunday was awesome - my friends and I took a rock climbing class and I made it to the top! It was a fantastic experience and now I'm hooked. Think I'm going to try to do more outdoor activities like this every week!" - }, - { - "speaker": "Audrey", - "dia_id": "D8:2", - "text": "That's awesome! Glad you had such a rad experience rock climbing. I'm always in awe of people who can climb mountains. Got any pics or videos from your climb? Would love to see the view from the top!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://miro.medium.com/v2/resize:fit:1400/0*h3ewAJq8YmXT5vb1.jpg" - ], - "blip_caption": "a photography of a man climbing on a rock face to face", - "query": "rock climbing view top rock climbing class", - "dia_id": "D8:3", - "re-download": true, - "text": "Rock climbing was awesome! It was a challenge, but so satisfying. The view was stunning, and I was really proud of myself. Nature sure is amazing!\n\n[Shares a photo of the view from the top of the rock climbed during the rock climbing class]" - }, - { - "speaker": "Audrey", - "dia_id": "D8:4", - "text": "Wow that view is stunning! Congrats on reaching the top, that must have been a huge accomplishment. Nature really reminds us how tiny we are in comparison, yeah? Was it challenging getting there?" - }, - { - "speaker": "Andrew", - "dia_id": "D8:5", - "text": "Thanks! It was a big achievement for me. The climb was tricky, especially since I'm still a newbie. But I made it with the support and cheer from my friends." - }, - { - "speaker": "Audrey", - "dia_id": "D8:6", - "text": "Nice! Having a solid support group really helps when things get tough. You're lucky to have such great friends! Does this adventure encourage you to try more outdoor activities?" - }, - { - "speaker": "Andrew", - "dia_id": "D8:7", - "text": "Yeah, rock climbing was awesome - I felt so accomplished reaching the top. It has definitely encouraged me to try more outdoor activities like kayaking and maybe bungee jumping? Nature always pushes me out of my comfort zone!" - }, - { - "speaker": "Audrey", - "dia_id": "D8:8", - "text": "Wow going all in huh? Have fun with kayaking and bungee jumping! Last week, I found a great spot for my dogs' walk. It's a small park with a trail surrounded by trees. It's so nice and I think my dogs like it too. Would you like to come along?" - }, - { - "speaker": "Andrew", - "dia_id": "D8:9", - "text": "Sounds great, Audrey! I'd love to join you and your pups for a walk. Being in nature with dogs sounds like a great time!" - }, - { - "speaker": "Audrey", - "dia_id": "D8:10", - "text": "Awesome! Can't wait to have fun with everyone. My dogs love meeting new people. " - }, - { - "speaker": "Andrew", - "dia_id": "D8:11", - "text": "Sames, can't wait to meet them and take a stroll in the park." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D8:12", - "text": "This was taken during the walk in the park. See how happy they are?" - }, - { - "speaker": "Andrew", - "dia_id": "D8:13", - "text": "Aww, they look like they're really enjoying themselves. How long do you usually walk them for?" - }, - { - "speaker": "Audrey", - "dia_id": "D8:14", - "text": "Varies depending on the day, but usually for about an hour. We let them explore at their own pace." - }, - { - "speaker": "Andrew", - "dia_id": "D8:15", - "text": "Cool, that's a good amount of time for them to have a nice stroll and take a look around." - }, - { - "speaker": "Audrey", - "dia_id": "D8:16", - "text": "They need exercise and to explore - they always go home with a smile and tired." - }, - { - "speaker": "Andrew", - "dia_id": "D8:17", - "text": "Nice! Letting them explore and have fun is important. I'm sure they must be loving it!" - }, - { - "speaker": "Audrey", - "dia_id": "D8:18", - "text": "Yeah, they love it! It's their favorite part of the day! Their faces blightens up as soon as I get ready for a walk." - }, - { - "speaker": "Andrew", - "dia_id": "D8:19", - "text": "Of course! Nature always makes us and our pets so happy." - }, - { - "speaker": "Audrey", - "dia_id": "D8:20", - "text": "Definitely! Dogs and nature bring me so much joy and peace." - }, - { - "speaker": "Andrew", - "dia_id": "D8:21", - "text": "Yeah, I agree, it's really nice." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/6jbz5p9fxy051.jpg" - ], - "blip_caption": "a photo of two dogs playing with a frisbee in a field", - "query": "dogs playing meadow", - "dia_id": "D8:22", - "text": "So check out how happy they are in this meadow! They make me so happy." - }, - { - "speaker": "Andrew", - "dia_id": "D8:23", - "text": "Aww so cute. Your dogs look so content in that picture. The meadow looks so nice. It's great that nature brings your pets joy!" - }, - { - "speaker": "Audrey", - "dia_id": "D8:24", - "text": "Being outdoors with them puts me in my happy place. It's peaceful and inspiring." - }, - { - "speaker": "Andrew", - "dia_id": "D8:25", - "text": "Glad you found something that puts you in your happy place. It's true, being outdoors has a way of inspiring and calming us." - }, - { - "speaker": "Audrey", - "dia_id": "D8:26", - "text": "Yeah! It's incredible how nature can make us think differently." - }, - { - "speaker": "Andrew", - "dia_id": "D8:27", - "text": "Agreed! It's great for refreshing the mind and giving a different outlook. Whenever I'm in need of a reset, I turn to nature." - }, - { - "speaker": "Audrey", - "dia_id": "D8:28", - "text": "Nature has a way of making us feel alive and centered. Let's appreciate what it gives us." - } - ], - "session_9_date_time": "1:51 pm on 26 June, 2023", - "session_9": [ - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/181/410666106_cfe35c1c83_b.jpg" - ], - "blip_caption": "a photography of two dogs sitting on the grass in front of a house", - "query": "new house bigger backyard furry babies", - "dia_id": "D9:1", - "re-download": true, - "text": "Woohoo! I got a new place with a bigger backyard for the pooches! Take a look!" - }, - { - "speaker": "Andrew", - "dia_id": "D9:2", - "text": "That's awesome! Your new place looks great. Your fur babies must be thrilled with the bigger backyard. I'm sure they're having a blast running around. How are they settling in?" - }, - { - "speaker": "Audrey", - "dia_id": "D9:3", - "text": "Thanks! They love it - so much space to run and explore, sniffing out new smells. It's awesome to see them having fun." - }, - { - "speaker": "Andrew", - "dia_id": "D9:4", - "text": "Awesome! They must love it. Makes me happy seeing them have fun. Any plans on making it even more pup-friendly?" - }, - { - "speaker": "Audrey", - "dia_id": "D9:5", - "text": "Yep, I set up a doggy play area in the backyard with agility stuff and toys - they're loving it!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://www.goinbark.com/wp-content/uploads/2016/08/20160820_150554.jpg" - ], - "blip_caption": "a photo of a group of dogs playing with a ball in a large indoor area", - "query": "dog playground", - "dia_id": "D9:6", - "text": "Wow you really went in huh!? Now they have a great place to play and explore, that's awesome! Take a look at this place I'm at." - }, - { - "speaker": "Audrey", - "dia_id": "D9:7", - "text": "That looks fun! Where are you at? I would love to take the dogs there sometime soon." - }, - { - "speaker": "Andrew", - "dia_id": "D9:8", - "text": "This is the doggy daycare near me, it has a big indoor space for dogs to play." - }, - { - "speaker": "Audrey", - "dia_id": "D9:9", - "text": "Ooo, I'll definitely take a look at it. Thanks for the tip." - }, - { - "speaker": "Andrew", - "dia_id": "D9:10", - "text": "Not a problem at all! Let me know if you need any help finding more places for dogs." - }, - { - "speaker": "Audrey", - "dia_id": "D9:11", - "text": "Thanks! Appreciate the offer. Super nice to have friends who understand our love for our pets!" - }, - { - "speaker": "Andrew", - "dia_id": "D9:12", - "text": "Haha yeah! People who understand the love for dogs are awesome. They really bring so much joy!" - }, - { - "speaker": "Audrey", - "dia_id": "D9:13", - "text": "Right!? Animals are great! They really bring so much joy. Can't imagine life without them!" - }, - { - "speaker": "Andrew", - "dia_id": "D9:14", - "text": "Yeah, I'm glad we both appreciate them. Not everyone feel this way." - }, - { - "speaker": "Audrey", - "dia_id": "D9:15", - "text": "For sure! I don't really get some people hating on pets or even hurting them. They're like family to people." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://cdn2.picryl.com/photo/2015/06/09/flowers-bloom-in-a-garden-near-the-memorial-amphitheater-1acba1-1024.jpg" - ], - "blip_caption": "a photography of a flower bed with many different colored flowers", - "query": "neighborhood park flowers blooming spring", - "dia_id": "D9:16", - "re-download": true, - "text": "Definitely! So what's new with you? Anything going on lately? Take a look at this park in my neighborhood that has these flowers." - }, - { - "speaker": "Audrey", - "dia_id": "D9:17", - "text": "Oh wow those flowers look beautiful. Well aside from moving to a new house, its just me getting unboxing all the packed boxes. What about you?" - }, - { - "speaker": "Andrew", - "dia_id": "D9:18", - "text": "Last Friday, I hiked with some friends. The weather was great and it felt so good to be outdoors. We got some awesome pictures too. " - }, - { - "speaker": "Audrey", - "dia_id": "D9:19", - "text": "Sounds like a good time! You want to share some awesome outdoor pics with me? Also tell me all about your hike." - }, - { - "speaker": "Andrew", - "dia_id": "D9:20", - "text": "Haven't gone through the photos yet. Maybe soon! It was lovely being out in the open, hearing the bird songs and smelling the trees. Lately, I've been really missing that connection with nature and the peace it brings. I'm definitely looking forward to exploring more!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/39248kn2icc51.jpg" - ], - "blip_caption": "a photo of a person's feet resting on a rock overlooking a lake", - "query": "serene lake mountains", - "dia_id": "D9:21", - "text": "Nature has a way of soothing us and helping us recharge. It's something special. I hope you get to experience it again soon. I really want to do something like this right now." - }, - { - "speaker": "Andrew", - "dia_id": "D9:22", - "text": "That looks so nice. It looks so chilled out! How was it?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA0L2ZsMjkzMjI0OTkzMjItaW1hZ2Uta294dXBzNzEuanBn.jpg" - ], - "blip_caption": "a photography of a person sitting on a rock overlooking a lake", - "query": "hiking trip peaceful lake mountains feet rock lake view", - "dia_id": "D9:23", - "re-download": true, - "text": "So a few years back my friends and I went on a hike and stumbled across this stunning lake in the mountains. We sat by it, chat and admiring the peacefulness of nature. I'll never forget those moments." - }, - { - "speaker": "Andrew", - "dia_id": "D9:24", - "text": "Wow, that looks so peaceful. It must have been a great spot!" - }, - { - "speaker": "Audrey", - "dia_id": "D9:25", - "text": "It really was! Nature and the lake were so calming, exactly what I needed." - }, - { - "speaker": "Andrew", - "dia_id": "D9:26", - "text": "Sounds like you had a really peaceful and calming experience! Glad you got to appreciate the beauty of life. " - }, - { - "speaker": "Audrey", - "dia_id": "D9:27", - "text": "Yeah! We shold really appreciate the small things in life and not take anything for granted. :)" - } - ], - "session_10_date_time": "8:32 pm on 3 July, 2023", - "session_10": [ - { - "speaker": "Audrey", - "dia_id": "D10:1", - "text": "Hey! It's been a while. I'm taking a dog training course and it's challenging but rewarding. My dogs are doing better already. What's new with you?" - }, - { - "speaker": "Andrew", - "dia_id": "D10:2", - "text": "Hey great to hear from you! Life's thrown me a few curveballs lately. Still can't seem to find any dog-friendly spots to rent. That's a bummer. Have you been able to do any exploring on new trails?" - }, - { - "speaker": "Audrey", - "dia_id": "D10:3", - "text": "Aw, sorry about the search for dog-friendly spots. I haven't had a ton of time for new trails either. The dog-training course has been a big time sink but it's paid off because they're doing great." - }, - { - "speaker": "Andrew", - "dia_id": "D10:4", - "text": "That's great news! It must feel so rewarding to see them doing well. I understand how it feels on missing the peace of being out on the trails, but for now, it's just urban adventures then." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D10:5", - "text": "Seeing them do well is super rewarding! They give me so much love and happiness. I get how frustrating it can be not to find pet-friendly spots. Nature is so calming and restorative with them around. See how happy they are when they're out." - }, - { - "speaker": "Andrew", - "dia_id": "D10:6", - "text": "I don't think I ever asked what breed they are right? Also, what do they enjoy doing the most? Looks like they're having a blast!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/luf05slnybn71.jpg" - ], - "blip_caption": "a photo of two dogs curled up in a dog bed", - "query": "dogs curled up together", - "dia_id": "D10:7", - "text": "They're all mutts. Two of them are Jack Russell mixes and the other two are Chihuahua mixes. They love running and playing fetch, you should see them sometimes." - }, - { - "speaker": "Andrew", - "dia_id": "D10:8", - "text": "They look so comfy in that bed. It's clear they're well loved. How old are they? How are they getting along now?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/yroilm3jgct71.jpg" - ], - "blip_caption": "a photo of two dogs laying on a couch with a blanket", - "query": "dogs cuddling together playdate at home", - "dia_id": "D10:9", - "text": "They're all 3-year-old and they are a great pack. We had a doggy playdate last Friday. It was a bit crazy but still lots of fun!" - }, - { - "speaker": "Andrew", - "dia_id": "D10:10", - "text": "They look adorable! Doggy playdates sound like a lot of fun. Glad they all get along." - }, - { - "speaker": "Audrey", - "dia_id": "D10:11", - "text": "Thanks! They really are my universe. So anything new you've been into lately?" - }, - { - "speaker": "Andrew", - "dia_id": "D10:12", - "text": "Lately I've been finding new hobbies since I can't hike. I've been getting into cooking more and trying out new recipes - it's been enjoyable. Do you enjoy cooking? Any favorite recipes?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://hometohomestead.files.wordpress.com/2022/10/img_5122.jpg" - ], - "blip_caption": "a photo of a pie on a wooden board with a knife", - "query": "homemade chicken pot pie", - "dia_id": "D10:13", - "text": "I love cooking! My favorite recipe is Chicken Pot Pie. It's so cozy and delicious, especially on a cold day. If you want, I can share the recipe with you." - }, - { - "speaker": "Andrew", - "dia_id": "D10:14", - "text": "Mmm that looks nice! Mind sharing the recipe so I can give it a try? What inspired you to make it?" - }, - { - "speaker": "Audrey", - "blip_caption": "a photo of a pie with a lattice on top of it", - "dia_id": "D10:15", - "text": "Sure! Let me send you the recipe in a bit. You really should give it a try! It's my family's recipe that's been around for years. The flavors always remind me of my grandma's kitchen - makes me think of all the conversations we used to have at the table. I hope you like it! Oh, and how's the cooking going?" - }, - { - "speaker": "Andrew", - "dia_id": "D10:16", - "text": "Thanks! I'll give it a try. Cooking has been helping me de-stress and be creative. I'm still a rookie, but I'm having fun experimenting. So what makes you like cooking so much?" - }, - { - "speaker": "Audrey", - "dia_id": "D10:17", - "text": "I love trying out new recipes and experimenting in the kitchen - it's like an escape for me. It's great for de-stressing and letting my creativity flow." - }, - { - "speaker": "Andrew", - "dia_id": "D10:18", - "text": "Oh I feel you! It gives me an escape and allows me to try something new. Plus, there's always the bonus of enjoying the food afterwards. It's slowly becoming one of my favorite hobbies, as it's really relaxing and allows me to express my creativity." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/hs8imjohtw631.jpg" - ], - "blip_caption": "a photo of a plate of food with a lot of vegetables", - "query": "beautifully plated dish cooking self-care", - "dia_id": "D10:19", - "text": "Agreed! Cooking and eating the food is so rewarding; it's like a form of self-care. I love throwing on some music, pouring a glass of wine, and just going with the flow in the kitchen. It's so therapeutic. See how beautiful this dish is?" - }, - { - "speaker": "Andrew", - "dia_id": "D10:20", - "text": "Ooo, that looks great! Cooking can be so calming, right? What's your go-to ingredient in the kitchen?" - }, - { - "speaker": "Audrey", - "dia_id": "D10:21", - "text": "Yeah! Cooking is definitely calming. Garlic is my go-to ingredient. I love the smell and taste it adds to dishes." - }, - { - "speaker": "Andrew", - "dia_id": "D10:22", - "text": "Garlic is indeed delicious! Do you have a favorite dish that you like to make with it? If so, would you like to share the recipe?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/6105/6360569135_ed356c13e2_b.jpg" - ], - "blip_caption": "a photography of a pan of chicken and potatoes on a stove", - "query": "roasted garlic chicken", - "dia_id": "D10:23", - "re-download": true, - "text": "Sure! Roasted Chicken is one of my favorites - sure I'll send you the recipe in a bit." - }, - { - "speaker": "Andrew", - "dia_id": "D10:24", - "text": "Wow I can't wait to make it! That looks amazing. What inspired you to make it?" - }, - { - "speaker": "Audrey", - "dia_id": "D10:25", - "text": "I'm glad you're interested! This recipe is based on my love for Mediterranean flavors. It's a tasty dish that's easy to make and loaded with healthy stuff like chicken, garlic, lemon, and herbs. It's my favorite comfort meal!" - }, - { - "speaker": "Andrew", - "dia_id": "D10:26", - "text": "Wow, that sounds delicious and healthy! I'm always looking for new meal ideas, especially ones that are healthier. Really appreciate you sharing this with me, thanks!" - }, - { - "speaker": "Audrey", - "dia_id": "D10:27", - "text": "No problem! I hope you enjoy making and eating it. Let me know how it turns out!" - }, - { - "speaker": "Andrew", - "dia_id": "D10:28", - "text": "Yep, will do! I'll keep you posted. Talk later!" - }, - { - "speaker": "Audrey", - "dia_id": "D10:29", - "text": "Excited to hear about it. Talk later!" - } - ], - "session_11_date_time": "9:48 am on 8 July, 2023", - "session_11": [ - { - "speaker": "Andrew", - "img_url": [ - "https://cdn.shopify.com/s/files/1/1512/5894/articles/IMG_5426.jpg" - ], - "blip_caption": "a photo of a picnic table with a variety of snacks and drinks", - "query": "picnic friends renovated park", - "dia_id": "D11:1", - "text": "Hey how's it going? Last Friday was amazing - I had a picnic with my girlfriend and it was so much fun! Being in the nature can be so refreshing and it always brings me joy. " - }, - { - "speaker": "Audrey", - "dia_id": "D11:2", - "text": "Wow sounds like the picnic was awesome! Yeah being in the nature is so nice. My furry friends always make me happy. We had a great walk the other day - felt really good!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/yfeccwau4dya1.jpg" - ], - "blip_caption": "a photo of a dog wearing a sweater sitting in the grass", - "query": "dog park dogs playing", - "dia_id": "D11:3", - "text": "That's awesome! Dogs really make life more fun doesn't it. I wish I could get one, but like I said, it's tough to find a place and find the right dog. How did you get yours?" - }, - { - "speaker": "Audrey", - "dia_id": "D11:4", - "text": "Thanks! I got lucky finding a breeder nearby that has the dogs I wanted. Yeah places that allows dogs are really hard to find unfortunately ." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://d3ekkvinch1ig5.cloudfront.net/suggestion/1658159281-2959image4.jpg" - ], - "blip_caption": "a photo of a group of dogs running around a park", - "query": "dog-friendly park city screenshot", - "dia_id": "D11:5", - "text": "Yeah it's tough. I found a few parks where you can take your pup on a leash, but it's not the same as having an open area where they can run and play. If I find something, I'll let you know. " - }, - { - "speaker": "Audrey", - "dia_id": "D11:6", - "text": "Cool, I should join you for a hike and bring my dogs. It would be great to have a chance where they can run freely." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://community.us.craghoppers.com/wp-content/uploads/2018/05/j3bmicznnmrnfe1uchho.jpg" - ], - "blip_caption": "a photo of a woman walking up a steep hill with a view of the ocean", - "query": "hiking trail beautiful view", - "dia_id": "D11:7", - "text": "Yeah definitely ! I'm down for a hike with you and your furry friends. Let's do it next month when the weather is more pleasant. Here's the trail that I think it'd be great for the dogs." - }, - { - "speaker": "Audrey", - "dia_id": "D11:8", - "text": "That'd be awesome! I can't wait to take them hiking. Fingers crossed we find a spot with a great view and lots of room for them to explore and have a blast." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://tammynara.files.wordpress.com/2021/01/pxl_20210117_190212518.jpg" - ], - "blip_caption": "a photo of a map of a park with a lot of trees", - "query": "hiking trails map perfect spot", - "dia_id": "D11:9", - "text": "Looking forward to seeing them have fun hiking. Let's get planning for next month! Here's the map for the trail." - }, - { - "speaker": "Audrey", - "dia_id": "D11:10", - "text": "Yep! I'm gonna look into the trail for my furry friend to run around." - }, - { - "speaker": "Andrew", - "dia_id": "D11:11", - "text": "Sounds good! Let's make sure the trail is safe for the dogs to run around and have fun." - }, - { - "speaker": "Audrey", - "dia_id": "D11:12", - "text": "Yep! After all safety is top priority when the dogs are outside running around." - }, - { - "speaker": "Andrew", - "dia_id": "D11:13", - "text": "Agreed! It's going to be a great with the dogs!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/o6bszg5mmua71.jpg" - ], - "blip_caption": "a photo of three dogs sitting on the floor with leashes", - "query": "four dogs sitting at door", - "dia_id": "D11:14", - "text": "Yup! Btw, there's going to be some cool stuff happening today." - }, - { - "speaker": "Andrew", - "dia_id": "D11:15", - "text": "Woah! They look so cute! What is going on here? They look ready for an adventure." - }, - { - "speaker": "Audrey", - "dia_id": "D11:16", - "text": "Haha yeah! They're all set for their next outdoor adventure!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:17", - "text": "Aw man, can't wait to meet them! They seem like a blast!" - }, - { - "speaker": "Audrey", - "dia_id": "D11:18", - "text": "Yeah, they definitely do! They really are a lot of fun." - }, - { - "speaker": "Andrew", - "dia_id": "D11:19", - "text": "Oh I'm sure, dogs definitely add lots of fun to our daily life." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D11:20", - "text": "Yep! They bring me so much joy. Can't get bored at all." - }, - { - "speaker": "Andrew", - "dia_id": "D11:21", - "text": "Aww, they look adorable playing in the park! Seeing them have so much fun must be so rewarding." - }, - { - "speaker": "Audrey", - "dia_id": "D11:22", - "text": "Yeah it sure does. Seeing them so happy and bouncy makes me really happy." - }, - { - "speaker": "Andrew", - "dia_id": "D11:23", - "text": "Yep, they're so happy it's contagious. It makes me happy just by looking at the photos." - }, - { - "speaker": "Audrey", - "dia_id": "D11:24", - "text": "Haha that's great! They're so full of energy, always bringing a smile to my face!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:25", - "text": "They make everyone smile. Can't wait to go hiking with them!" - }, - { - "speaker": "Audrey", - "dia_id": "D11:26", - "text": "Yep! It'll be great to see them enjoy themselves on the hike!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:27", - "text": "Yep! I can't wait to see them have fun on the hike. Super excited!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i2.pickpik.com/photos/607/821/215/playing-puppies-young-dogs-french-bulldog-cocker-spaniel-preview.jpg" - ], - "blip_caption": "a photography of a dog playing with a group of other dogs", - "query": "hiking dogs running field", - "dia_id": "D11:28", - "re-download": true, - "text": "I'm 100% sure that it's gonna be a great day! Just take a look how happy my dogs are just at out local park." - }, - { - "speaker": "Andrew", - "dia_id": "D11:29", - "text": "Aww look at them, so cute! It'll definitely be a great day! I love being outdoors and seeing others enjoy it too." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/iws2jgn6nld71.jpg" - ], - "blip_caption": "a photo of a person standing on a mountain with a view of the sky", - "query": "sunset mountain hiking trip last year", - "dia_id": "D11:30", - "text": "Yeah! Being outside and seeing people happy is awesome. Here's a pic from a hike last year. Such a pretty sunset!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:31", - "text": "Wow, that's stunning! Mind telling me where this is? " - }, - { - "speaker": "Audrey", - "dia_id": "D11:32", - "text": "I hiked this last year! It was a 3 hour drive from me and the sunset was amazing!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:33", - "text": "Nice! That sunset with the view is amazing, especially with nature around." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/okd8n7nb0hc71.jpg" - ], - "blip_caption": "a photo of a view of a lake and mountains at sunset", - "query": "sunset mountain peak", - "dia_id": "D11:34", - "text": "It was a beautiful moment indeed! The colors were so vibrant, and it felt so peaceful. I'm so grateful for moments like these that remind me of the beauty of nature and to appreciate the small things." - }, - { - "speaker": "Andrew", - "dia_id": "D11:35", - "text": "Yeah! It's incredible how nature can have such awesome surprises." - }, - { - "speaker": "Audrey", - "dia_id": "D11:36", - "text": "Nature really has awesome surprises. All those vibrant colors gives me that feeling of peace - it's breathtaking!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:37", - "text": "Yeah! It is stunning! Life's pretty awesome when we take time to appreciate these moments right?" - }, - { - "speaker": "Audrey", - "dia_id": "D11:38", - "text": "Yep, taking a second to appreciate those moments makes like much better!" - } - ], - "session_12_date_time": "10:05 am on 11 July, 2023", - "session_12": [ - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/x7jn4meogw441.jpg" - ], - "blip_caption": "a photo of a dog sitting on a leash in a room", - "query": "new puppy toby city", - "dia_id": "D12:1", - "text": "Hey! So much has changed since last time we talked - meet Toby, my puppy. He's a bundle of joy and I couldn't resist taking him home, city living and all. How've you been?" - }, - { - "speaker": "Audrey", - "dia_id": "D12:2", - "text": "OMG! Toby looks so adorable! Congrats on your new addition. I'm sure you're really happy right now! I so happy for you!" - }, - { - "speaker": "Andrew", - "dia_id": "D12:3", - "text": "Haha yeah! Toby's definitely bringing a lot of joy. Since we last talked, work has been piling up and I've been stuck inside. I miss the peace and feeling of freedom that comes with going for a hike." - }, - { - "speaker": "Audrey", - "dia_id": "D12:4", - "text": "Yeah you're really stressed then! When work's non-stop, it's hard to get outdoors. Hiking is a great way to relax and take in nature. Do you have plans to go hiking soon? We should plan a trip for both of us and our pups!" - }, - { - "speaker": "Andrew", - "dia_id": "D12:5", - "text": "Yeah work's been stressful lately and I need a break. I have plans for a hike next month and thought it'd be cool if you and the pups could come along. We can enjoy nature and have a fun time!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://thebaskshop.com/cdn/shop/files/image_8117b42d-0057-492c-9b06-88fc439af683.jpg" - ], - "blip_caption": "a photo of a group of wooden keychains with engraved names", - "query": "personalized dog tags engraved names dogs", - "dia_id": "D12:6", - "text": "Sounds great! I'm in for the hike, and my dogs can come too. Thanks for inviting us! I made these personalized tags for them. Take a look! " - }, - { - "speaker": "Andrew", - "dia_id": "D12:7", - "text": "Wow, those look great! Did you make them? It's really cool!" - }, - { - "speaker": "Audrey", - "dia_id": "D12:8", - "text": "Thanks! Yeah, I made them myself. I wanted each one to be special and fit their personalities." - }, - { - "speaker": "Andrew", - "dia_id": "D12:9", - "text": "Wow, that's so cool. You really put so much love into making them, which makes them extra special." - }, - { - "speaker": "Audrey", - "dia_id": "D12:10", - "text": "Thanks! I did put a lot of love into making these tags. I want my pets to feel seen and loved." - }, - { - "speaker": "Andrew", - "dia_id": "D12:11", - "text": "It really are the small things like these show we care a ton about out pets." - }, - { - "speaker": "Audrey", - "dia_id": "D12:12", - "text": "Yep! Small acts of love like that make a big difference in our relationships with our furry pals. Let's make sure they know how much we care!" - }, - { - "speaker": "Andrew", - "dia_id": "D12:13", - "text": "Definitely! They bring us so much joy, so it's only fair that we show them how special they are. Can't wait for our hike - and for Toby and your pups to meet!" - }, - { - "speaker": "Audrey", - "dia_id": "D12:14", - "text": "Can't wait either! It's going to be loads of fun and a great memory." - }, - { - "speaker": "Andrew", - "dia_id": "D12:15", - "text": "Yep! It'll be an adventure alright. " - }, - { - "speaker": "Audrey", - "dia_id": "D12:16", - "text": "Counting down the days 'til our hike! Gotta get them ready soon!" - }, - { - "speaker": "Andrew", - "dia_id": "D12:17", - "text": "Same here! It's gonna be awesome. Let's talk later k?" - } - ], - "session_13_date_time": "3:52 pm on 27 July, 2023", - "session_13": [ - { - "speaker": "Andrew", - "dia_id": "D13:1", - "text": "Hey Audrey! How are you? My GF and I just had a great experience volunteering at a pet shelter on Monday - it was so rewarding! We loved spending time with those cute animals and it gave us so much joy. It was so rewarding, it reminded me just how much I love them!" - }, - { - "speaker": "Audrey", - "dia_id": "D13:2", - "text": "Hi Andrew! I'm good, thanks. That's awesome about the pet shelter volunteering. Helping animals really is great and you can tell when they're happy! So happy for you getting to experience that! I should do that someday too!" - }, - { - "speaker": "Andrew", - "dia_id": "D13:3", - "text": "Thanks! Seeing them so content makes me happy, it really makes me realize how special and full of love they are! Have you ever volunteered at an animal shelter? It can be so rewarding. " - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.prismic.io/trustedhousesitters/3dcbea0a-d9d9-4590-a57b-c2c15bf2e012_off+leash+dog+parks+portland+or.png" - ], - "blip_caption": "a photo of a woman kissing a dog in a park", - "query": "dogs park fun", - "dia_id": "D13:4", - "text": "Never been to an animal shelter before, but it must be great! My four fur babies are more important to me than anything! Here's a pic of us from a fun day out at the park." - }, - { - "speaker": "Andrew", - "dia_id": "D13:5", - "text": "Aww that's a cute photo! How are their personalities? Tell me more!" - }, - { - "speaker": "Audrey", - "dia_id": "D13:6", - "text": "Well the oldest one is the most relaxed, like a wise old sage. The second one is always ready for a game. The third one can be naughty but loves a good cuddle. And the youngest one is full of life and always up for an adventure. They all have their own individual personalities and I adore them." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/7dbys2s547b91.jpg" - ], - "blip_caption": "a photo of a dog is sitting on the floor with a tennis ball", - "query": "black labrador playing tennis ball", - "dia_id": "D13:7", - "text": "Wow, they sound amazing! They must remind you of your childhood pup. That photo of you with your dog is so cute, he looks like the most playful one ever! Pets really do bring so much joy in your life!" - }, - { - "speaker": "Audrey", - "dia_id": "D13:8", - "text": "Thanks! That one is Max, my childhood dog. He had lots of energy and loved a game of fetch. I have lots of great memories with him. Pets sure bring a lot of joy." - }, - { - "speaker": "Andrew", - "dia_id": "D13:9", - "text": "Yeah! Their love and energy can really brighten up a day. It's amazing how close we can get to them and the memories they create for us. Do you have any other special memories with Max that you remember fondly?" - }, - { - "speaker": "Audrey", - "dia_id": "D13:10", - "text": "Max and I would take long walks in the neighborhood when I was a kid. We explored new paths, him sniffing and marking his territory. We grew really close, and I shared my worries and hopes with him. He was a great listener, always there for me. Those days are some of my favorite memories. " - }, - { - "speaker": "Andrew", - "dia_id": "D13:11", - "text": "Pets are more than just pets - they become friends and confidantes. They always know how to listen and provide comfort when we need it. They can make us feel so loved and understood, leaving a lasting mark on our lives." - }, - { - "speaker": "Audrey", - "dia_id": "D13:12", - "text": "Pets truly make our lives so much better. They listen without judging and give us the best unconditional love. They always leave a mark in our hearts and remind us how it feels to be seen and understood. I'm thankful to have them around - they bring so much joy, comfort, and love." - }, - { - "speaker": "Andrew", - "dia_id": "D13:13", - "text": "You nailed it! That's why we went volunteering with animals. It has been one of the most rewarding things I've ever done. They really do lift our spirits with all their love, joy, and comfort." - }, - { - "speaker": "Audrey", - "dia_id": "D13:14", - "text": "I'm so glad you guys got to experience that! Animals really have a way of brightening our day and giving us lots of love and joy. I'm sure the pet shelter really appreciated your help too!" - }, - { - "speaker": "Andrew", - "dia_id": "D13:15", - "text": "Yeah we had a blast volunteering. It's our way of giving back and making their lives better!" - }, - { - "speaker": "Audrey", - "dia_id": "D13:16", - "text": "I'm sure your kindness and care will make them happier no doubt!" - } - ], - "session_14_date_time": "11:05 am on 4 August, 2023", - "session_14": [ - { - "speaker": "Andrew", - "img_url": [ - "https://www.nps.gov/hale/planyourvisit/images/Holua-Campground_Tent-Camper-0015_NPS-photo-Katie-Matthew_7140242_2.jpg" - ], - "blip_caption": "a photo of a woman setting up a tent on a rocky hill", - "query": "campsite nature camping", - "dia_id": "D14:1", - "text": "Hey, Audrey! I can't wait for the weekend. My girlfriend, Toby and I are going camping. It's been forever since I've been in nature." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/45o0t7vpf5171.jpg" - ], - "blip_caption": "a photo of a dog in a field of flowers and grass", - "query": "dogs playing flower-filled meadow", - "dia_id": "D14:2", - "text": "That's awesome! That must be fun! I just started agility classes with my pups at a dog park. It's awesome to watch them learn and build relationships with other dogs. Seeing them face and conquer challenges really warms my heart." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/2109/2471534899_2764ab02bf_b.jpg" - ], - "blip_caption": "a photography of a dog jumping through a ring in the air", - "query": "dog jumping through hoop agility course", - "dia_id": "D14:3", - "re-download": true, - "text": "Wow it's amazing to watch them grow together. They look so cool overcoming obstacles like that. Impressive stuff!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.pexels.com/photos/7210626/pexels-photo-7210626.jpeg" - ], - "blip_caption": "a photography of a group of dogs sitting on a dirt road", - "query": "dogs agility course park sitting smiling", - "dia_id": "D14:4", - "re-download": true, - "text": "Thanks! They've come a long way. They have so much fun with it, it's a great physical and mental workout. I take them to the park twice a week for practice - it's been a great bonding experience." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i0.hippopx.com/photos/637/74/343/trail-nature-path-preview.jpg" - ], - "blip_caption": "a photography of a dirt path in the woods with rocks and trees", - "query": "hiking trail trees", - "dia_id": "D14:5", - "re-download": true, - "text": "Awesome! You're having fun with them and keeping them busy - how's that going? Btw look at the trail that I was just at. Cool right?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://s0.geograph.org.uk/geophotos/01/41/92/1419270_5f37a596.jpg" - ], - "blip_caption": "a photography of a woman walking her dogs down a path", - "query": "audrey dogs forest trail walk", - "dia_id": "D14:6", - "re-download": true, - "text": "It's been tough at times, but overall it's going great. We're all growing together. Check out this pic of us on a trail hike!" - }, - { - "speaker": "Andrew", - "dia_id": "D14:7", - "text": "Wow, nice hike! How long was the trail?" - }, - { - "speaker": "Audrey", - "dia_id": "D14:8", - "text": "The hike took us two hours and it was stunning! We saw lots of amazing views and it was great to explore nature." - }, - { - "speaker": "Andrew", - "dia_id": "D14:9", - "text": "That sounds awesome! Being able to just be in nature and appreciate it is really cool. I wish I could do that more often." - }, - { - "speaker": "Audrey", - "dia_id": "D14:10", - "text": "Nature really refreshes you, right? It's nice to appreciate all the beauty around us. Are there any outdoor activities you enjoy lately?" - }, - { - "speaker": "Andrew", - "dia_id": "D14:11", - "text": "Well for me hiking is the best. Being out in nature with all the trees and fresh air always refreshes me. Reaching the top of a challenging trail is amazing too - it feels like all worries just vanish when you get to the top." - }, - { - "speaker": "Audrey", - "dia_id": "D14:12", - "text": "Yeah, totally. It's like you've achieved something and all worries just fade away. Nature is pretty special, huh?" - }, - { - "speaker": "Andrew", - "dia_id": "D14:13", - "text": "Yeah, nature really calms me down and relaxes my mind. It's like a break from the craziness of city living." - }, - { - "speaker": "Audrey", - "dia_id": "D14:14", - "text": "Yeah! It's a refuge from the busy city life and it's even better when you can share it with someone special. My pets have been my good ol' buddies during all the tough times! They make me so happy and I love them so much." - }, - { - "speaker": "Andrew", - "dia_id": "D14:15", - "text": "Agreed! Sharing something you enjoy with someone is great. Plus, those types of bonds bring loads of joy and love." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0280/3790/6505/files/1d0d76aa77d195e7685b71bb336436ea.jpg" - ], - "blip_caption": "a photo of a dog laying in a dog bed in a living room", - "query": "dogs snuggled cozy dog bed", - "dia_id": "D14:16", - "text": "Yeah, humans and animals have a cool connection. They bring us so much joy and love. I'm blessed to have my furry friends in my life. They're my companions and always make my day better." - }, - { - "speaker": "Andrew", - "blip_caption": "a photo of a stream running through a lush green forest", - "dia_id": "D14:17", - "text": "They're pretty lucky to have you as their owner. Hoping for the day I can have such a deep bond with Toby and experience that special bond too." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/67s3x8066hl91.jpg" - ], - "blip_caption": "a photo of three dogs sitting on a wooden floor looking up", - "query": "four dogs sitting breed", - "dia_id": "D14:18", - "text": "Yeah they really mean the world to me. Btw, I never really asked, what breed is Toby?" - }, - { - "speaker": "Andrew", - "dia_id": "D14:19", - "text": "He's a German Shepherd - they're so smart and loyal! What do you think?" - }, - { - "speaker": "Audrey", - "dia_id": "D14:20", - "text": "German Shepherds are indeed awesome! They are super loyal and smart. You'll have an amazing connection with one!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/xzuhqdylro1b1.jpg" - ], - "blip_caption": "a photo of a dog sitting on a rock in the woods", - "query": "german shepherd hiking trail", - "dia_id": "D14:21", - "text": "I hope so! I shold take him hiking with me, they would be great hiking buddies, so smart and loyal." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/s6gfs24m2hpb1.jpg" - ], - "blip_caption": "a photo of a dog sitting on a trail with a view of a city", - "query": "german shepherd hiking mountains", - "dia_id": "D14:22", - "text": "Yep! German Shepherds are known for their loyalty and smarts. They love new journeys and would love exploring the outside with you. I can totally see you and your pup conquering trails together!" - }, - { - "speaker": "Andrew", - "dia_id": "D14:23", - "text": "Yeah, having someone who enjoys similar things would be great for hiking. Do you have any advice for city-dwellers that owns a pup?" - }, - { - "speaker": "Audrey", - "dia_id": "D14:24", - "text": "My advice would be to make sure you have enough time and energy for a pup - they need lots of attention and walks! Especially for a German Shepherd like Toby, he'll need to offset a lot of energy." - }, - { - "speaker": "Andrew", - "dia_id": "D14:25", - "text": "Thanks for the advice! I'll definitely keep that in mind. I want to make sure I'm not limiting Toby's growth but not taking him out not enough." - }, - { - "speaker": "Audrey", - "dia_id": "D14:26", - "text": "No worries, Andrew! It's important to be prepared and give a pup the love it deserves. Good luck with Toby!" - }, - { - "speaker": "Andrew", - "dia_id": "D14:27", - "text": "Thanks! Gotta take Toby out for a small hike at the local trail. Ttyl!" - } - ], - "session_15_date_time": "9:58 pm on 16 August, 2023", - "session_15": [ - { - "speaker": "Audrey", - "img_url": [ - "https://i.pinimg.com/originals/a3/1d/79/a31d794799464fd294587fe666da891d.jpg" - ], - "blip_caption": "a photo of a person with a tattoo on their hand", - "query": "tattoo four dogs arm", - "dia_id": "D15:1", - "text": "Hey Andrew, since we last spoke I got another tattoo of my four dogs on my arm! They really mean a lot to me so I thought it'd be nice to have them with me wherever I go. What've you been up to?" - }, - { - "speaker": "Andrew", - "dia_id": "D15:2", - "text": "Wow that's so cool! I recently went to a farm with my girlfriend to get some fresh veggies for dinner, and it was really nice. Have you been thinking about getting more fur babies or is four enough?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/2/21/Dogs_playing_at_dog_park.JPG" - ], - "blip_caption": "a photography of a group of people walking with dogs in a park", - "query": "four dogs playing park", - "dia_id": "D15:3", - "re-download": true, - "text": "Sounds great! I'd love to have more, but four is enough for now. They keep me busy and I want to make sure I give each of them the attention they deserve - four dogs is already a lot! I took them all to the vet and got them checked up, it was such a havoc that next time I'll bring them one by one." - }, - { - "speaker": "Andrew", - "blip_caption": "a photo of a dog laying on a rug eating lettuce", - "dia_id": "D15:4", - "text": "Oof, that vet trip must have been chaotic. Yeah I'm sure they keep you busy! That photo you shared was sweet - do they have a favorite spot to relax?" - }, - { - "speaker": "Audrey", - "dia_id": "D15:5", - "text": "Yeah, for sure. They each have their favorite spot to chill. Pepper loves lounging on the couch, Pixie always curls up in her bed, Precious has her chair, and Panda loves to relax on his rug! They all have their own little cozy spots." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://lucky-paws.co/cdn/shop/products/image_b74722d8-a1e0-4dcc-81a8-faa99fed31cb_1.jpg" - ], - "blip_caption": "a photo of a dog laying on a fluffy blanket on the floor", - "query": "cozy dog bed blanket", - "dia_id": "D15:6", - "text": "That sounds adorable! Pets always find their own little spots and it brings so much joy and comfort. Here's Toby at his favorite spot." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://freerangestock.com/sample/87785/puppy-love.jpg" - ], - "blip_caption": "a photography of two dogs laying on a blanket on a couch", - "query": "pepper pixie dogs cuddling fluffy blanket", - "dia_id": "D15:7", - "re-download": true, - "text": "Yeah, they sure know how to get comfy! Here's a pic of them snuggling on my favorite blanket." - }, - { - "speaker": "Andrew", - "dia_id": "D15:8", - "text": "Aww, they're so adorable! They look so cozy. Do they always sleep like that?" - }, - { - "speaker": "Audrey", - "dia_id": "D15:9", - "text": "Yeah, they always sleep like that. They cuddle up together, especially when it's time to nap. They really are best friends." - }, - { - "speaker": "Andrew", - "dia_id": "D15:10", - "text": "Wow that's awesome! It must be great having furry friends to keep each other company." - }, - { - "speaker": "Audrey", - "dia_id": "D15:11", - "text": "Yeah, they're always there for each other. Seeing them together makes me so happy." - }, - { - "speaker": "Andrew", - "dia_id": "D15:12", - "text": "That sounds wonderful. No wonder it brings you so much happiness to have them around!" - }, - { - "speaker": "Audrey", - "dia_id": "D15:13", - "text": "Yeah they mean the world to me, so I can't imagine life without them." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/5kklgil5t9qb1.jpg" - ], - "blip_caption": "a photo of a man laying on a couch with a dog", - "query": "man hugging dog", - "dia_id": "D15:14", - "text": "Totally get it, pets bring such joy and feel like family. I can't imagine life without them." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i2.pickpik.com/photos/498/107/776/dog-girl-de-dogue-preview.jpg" - ], - "blip_caption": "a photography of a woman sitting in a field with two dogs", - "query": "person lying grass dogs", - "dia_id": "D15:15", - "re-download": true, - "text": "Yep, pets are family. It's so sweet to see the connection between them. Here's a photo of me lying on the grass with them." - }, - { - "speaker": "Andrew", - "dia_id": "D15:16", - "text": "Wow, that's a great pic! Looks like you guys had a really good time outside." - }, - { - "speaker": "Audrey", - "dia_id": "D15:17", - "text": "Oh yeah it was a great day - we had tons of fun outside." - }, - { - "speaker": "Andrew", - "dia_id": "D15:18", - "text": "Glad you had a blast with them. Cherish those memories!" - }, - { - "speaker": "Audrey", - "dia_id": "D15:19", - "text": "Thanks! I'll always cherish those moments. They really make life so much brighter." - } - ], - "session_16_date_time": "9:19 pm on 19 August, 2023", - "session_16": [ - { - "speaker": "Andrew", - "img_url": [ - "https://res.cloudinary.com/dragonspell/images/w_480,h_480,c_scale,dpr_auto,fl_progressive:steep,f_auto/w_480,h_480,c_fill/v1571420634/www.travelportland.com/Vista-House-Columbia-River-Gorge-Photo-by-Travel-Oregon-aspect-ratio-1x1/Vista-House-Columbia-River-Gorge-Photo-by-Travel-Oregon-aspect-ratio-1x1.jpg" - ], - "blip_caption": "a photo of a sunset over a mountain with a church on top", - "query": "beautiful sunrise hike trail", - "dia_id": "D16:1", - "text": "Hey Audrey, hope you're doing good! So I've decided to take a break from work yesterday and check out a new cafe. It was a nice change and reminded me of how the great outdoors is always there to offer you peace. Here's a photo from my last hike - so serene! How's your month been so far?" - }, - { - "speaker": "Audrey", - "dia_id": "D16:2", - "text": "Hi Andrew! The cafe sounds great, and the hike pic's great! August's been eventful - I learned a new skill! It was really awesome, making sure they were pampered and happy. I've always loved caring for my pups, and now taking care of their grooming myself makes me closer to them. They look so cute post-grooming!" - }, - { - "speaker": "Andrew", - "dia_id": "D16:3", - "text": "Wow that's awesome! You're doing an amazing job taking care of your pups. Can you show me a pic of them after the grooming? I bet they look adorable!" - }, - { - "speaker": "Audrey", - "img_url": [ - "http://canadiangroomingdistributor.com/cdn/shop/articles/PXL_20220614_014152490.PORTRAIT.jpg" - ], - "blip_caption": "a photo of a dog is standing on a table in a room", - "query": "dogs groomed fluffy", - "dia_id": "D16:4", - "text": "Here's a photo of them after their grooming - look how soft and fluffy they are!" - }, - { - "speaker": "Andrew", - "dia_id": "D16:5", - "text": "Wow, they look so cute and fluffy! You did such a great job. How did you do it so well? I've always wanted to learn dog grooming but never got the time." - }, - { - "speaker": "Audrey", - "dia_id": "D16:6", - "text": "I took a dog grooming course and learned lots of techniques. Would you like to hear some tips?" - }, - { - "speaker": "Andrew", - "dia_id": "D16:7", - "text": "Of course! I'd love to hear some tips. It's something I've always been interested in." - }, - { - "speaker": "Audrey", - "dia_id": "D16:8", - "text": "Grooming slowly and gently, paying attention to sensitive areas like ears and paws. And remember to stay patient and positive throughout the grooming process." - }, - { - "speaker": "Andrew", - "dia_id": "D16:9", - "text": "Cool tips! I'll remember them if I ever get to groom Toby. Thanks!" - }, - { - "speaker": "Audrey", - "dia_id": "D16:10", - "text": "No problem, glad the tips helped. If you ever give it a shot, let me know and I can provide more advice." - }, - { - "speaker": "Andrew", - "dia_id": "D16:11", - "text": "Thanks! I'll let you know if I decide to give it a try. Appreciate your help. What are your plans for the weekend?" - }, - { - "speaker": "Audrey", - "dia_id": "D16:12", - "text": "I'm taking them out for a stroll in the park - they love it and it's a good workout for us. Any weekend plans for you?" - }, - { - "speaker": "Andrew", - "dia_id": "D16:13", - "text": "This weekend I'm heading to a nature reserve to reconnect with the outdoors - excited!" - }, - { - "speaker": "Audrey", - "dia_id": "D16:14", - "text": "Cool! Have a great time reconnecting with nature. Don't forget to take some nice pictures!" - }, - { - "speaker": "Andrew", - "dia_id": "D16:15", - "text": "Sure thing, I'll get some awesome pics to show you when I return. " - }, - { - "speaker": "Audrey", - "dia_id": "D16:16", - "text": "Haha thanks! Your pics of hikes are always nice." - }, - { - "speaker": "Andrew", - "dia_id": "D16:17", - "text": "Yeah! I really wanted to save the moment ya know?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D16:18", - "text": "No wonder. Now I can't wait for the pics. Take care and have fun! My dogs will be waiting too!" - }, - { - "speaker": "Andrew", - "dia_id": "D16:19", - "text": "Thanks! Can't wait to show you them when I'm back. See ya!" - } - ], - "session_17_date_time": "12:24 am on 24 August, 2023", - "session_17": [ - { - "speaker": "Andrew", - "dia_id": "D17:1", - "text": "Hey Audrey! What's up? Last weekend my girlfriend and I went fishing in one of the nearby lakes. It was so nice. We got a few fish and had a blast. Have you ever gone fishing before?" - }, - { - "speaker": "Audrey", - "img_url": [ - "http://tahoemountainsports.com/cdn/shop/articles/IMG_8472.jpg" - ], - "blip_caption": "a photo of a group of dogs standing on a rock near a lake", - "query": "stunning lake mountains friends dogs", - "dia_id": "D17:2", - "text": "Hey! Actually I've never been fishing. It's always been just chilling at the lake. I remember this moment a few years back when I sat by a gorgeous lake in the mountains with friends. So peaceful and calming. Just the sound of the birds, the stillness of the water, and the fresh air - it was so special. But yeah I have never gone on a fishing trip before. Here's a photo of the trip to the lake with my friend." - }, - { - "speaker": "Andrew", - "dia_id": "D17:3", - "text": "Wow, they look like they're loving the mountain life. How do you keep them looking good out there?" - }, - { - "speaker": "Audrey", - "dia_id": "D17:4", - "text": "Yeah they really do enjoy the mountain life. Regular grooming is essential to keep them looking good. Daily brushing, regular baths, nail trims, and lots of love is what helps them stay healthy and happy. It's all about keeping them in good shape." - }, - { - "speaker": "Andrew", - "dia_id": "D17:5", - "text": "Awesome! Sounds like you're doing a great job taking care of them. Making sure they stay healthy and happy is key." - }, - { - "speaker": "Audrey", - "dia_id": "D17:6", - "text": "Yeah! It means a lot. Taking care of them is a big deal. It makes me really happy and I take that responsibility seriously. It can be tough but it's super rewarding." - }, - { - "speaker": "Andrew", - "dia_id": "D17:7", - "text": "I'm sure it's rewarding. Making a positive impact on someone's life, especially those close to you, must be such a good feeling." - }, - { - "speaker": "Audrey", - "dia_id": "D17:8", - "text": "Yeah, my dogs make me really happy. I love them so much and I want to make them as happy as possible. We have a strong bond." - }, - { - "speaker": "Andrew", - "dia_id": "D17:9", - "text": "That's amazing. You have such a strong bond with them! I hope I can have such a strong bond with Toby as well." - }, - { - "speaker": "Audrey", - "dia_id": "D17:10", - "text": "They mean the world to me. I'm so lucky to have them. I sure with your love, you and Toby can have a strong bond." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0280/3790/6505/files/189ab0128c572238758dd096675a54be.jpg" - ], - "blip_caption": "a photo of a dog sleeping in a dog bed on the floor", - "query": "cuddling puppy bed", - "dia_id": "D17:11", - "text": "Lucky you! Pets sure bring a lot of love and joy. Can't wait till Toby and I bond better." - }, - { - "speaker": "Audrey", - "dia_id": "D17:12", - "text": "Thanks! That's really nice. Let me know if you need some tips on taking care of Toby." - }, - { - "speaker": "Andrew", - "dia_id": "D17:13", - "text": "Sure thing! I'll try figure it on my own first. Appreciate the help!" - }, - { - "speaker": "Audrey", - "dia_id": "D17:14", - "text": "Remember, it takes time to form a bond, don't rush!" - }, - { - "speaker": "Andrew", - "dia_id": "D17:15", - "text": "Got it. Thanks for that reminder." - }, - { - "speaker": "Audrey", - "dia_id": "D17:16", - "text": "No problem. Let me know if you have any questions or need advice." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://d4yxl4pe8dqlj.cloudfront.net/images/4926302d-1bff-49d5-999a-4611342b1891/74399bb7-e65b-4105-81cd-ffd5ac2acf5b_full_size.jpg" - ], - "blip_caption": "a photo of a small dog sitting on a floor with a leash", - "query": "dog leash collar door", - "dia_id": "D17:17", - "text": "Yep, Audrey. Thanks for everything - you rock! Here's a pic of Toby." - }, - { - "speaker": "Audrey", - "dia_id": "D17:18", - "text": "Aww so cute! Toby looks happy!" - }, - { - "speaker": "Andrew", - "dia_id": "D17:19", - "text": "Haha yeah, I do love Toby!" - }, - { - "speaker": "Audrey", - "dia_id": "D17:20", - "text": "I'm glad Toby is happy. I'm sure there are lots of adventures to come!" - }, - { - "speaker": "Andrew", - "dia_id": "D17:21", - "text": "Yep, Toby and I are gonna have a blast exploring outdoors! Can't wait." - } - ], - "session_18_date_time": "7:49 pm on 6 September, 2023", - "session_18": [ - { - "speaker": "Andrew", - "dia_id": "D18:1", - "text": "Hey Audrey, how's it going? Since we last talked, a few new things have come up in my life. Work's been tough and stressful, so my outdoor activities have taken a backseat. Finding balance has been challenging." - }, - { - "speaker": "Audrey", - "dia_id": "D18:2", - "text": "Hey Andrew, good to hear from you. Sorry to hear about work being tough. Finding that balance can be challenging, huh? It can feel like there's not enough time. Just remember to take care of yourself and find ways to manage stress. Hang in there!" - }, - { - "speaker": "Andrew", - "dia_id": "D18:3", - "text": "Thanks! It's tough, but I guess that's just part of life, huh? How do you make sure you have enough time for yourself?" - }, - { - "speaker": "Audrey", - "dia_id": "D18:4", - "text": "Yeah, it's tough to find time for yourself. I make sure to do at least one self-care activity each day - like treating myself to something nice. Don't forget to take care of yourself and have some fun too!" - }, - { - "speaker": "Andrew", - "dia_id": "D18:5", - "text": "Yeah, self-care is really important isn't it. I've been adding simple things to my day like grabbing a coffee in the morning or going for a walk at lunch. It kinda helps me recharge and chill out a little." - }, - { - "speaker": "Audrey", - "dia_id": "D18:6", - "text": "That's great! Glad you found ways to relax. It's nice to have those little moments of joy. Something cool recently happened with my furry friends - I organized a doggy playdate with the neighbors' dogs. Seeing all those tails wagging was so sweet. They must have had so much fun!" - }, - { - "speaker": "Andrew", - "dia_id": "D18:7", - "text": "That's awesome. I bet they all had a blast! Got any pics from that day?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/1go6sson1bk41.jpg" - ], - "blip_caption": "a photo of two dogs playing in a fenced in area", - "query": "dogs playing backyard", - "dia_id": "D18:8", - "text": "Here's a pic from the playdate. It was great seeing them having fun together. Their joy was infectious and made my heart feel so full." - }, - { - "speaker": "Andrew", - "dia_id": "D18:9", - "text": "That's so heartwarming! Seeing them enjoy themselves like that is always a joy. :)" - }, - { - "speaker": "Audrey", - "dia_id": "D18:10", - "text": "I'm so happy seeing them have a great time. Last week I even got some new beds for them, just to give them some extra comfort now the weather's cooling down and they were happy! It's incredible how such a simple thing can bring them so much happiness." - }, - { - "speaker": "Andrew", - "dia_id": "D18:11", - "text": "Animals can really find joy in the simple things. That was so nice of you. Do you have any pictures of the new beds?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0280/3790/6505/files/66f095b1a84b18ac04f6e85fe293a41a.jpg" - ], - "blip_caption": "a photo of a dog laying on a dog bed in a living room", - "query": "dog beds cozy comfy living room", - "dia_id": "D18:12", - "text": "Sure! Here's a pic of them. Super cozy and comfy. My furry friends love them!" - }, - { - "speaker": "Andrew", - "dia_id": "D18:13", - "text": "Do they enjoy snoozing on it? It looks really comfy!" - }, - { - "speaker": "Audrey", - "dia_id": "D18:14", - "text": "They absolutely love it! They curl up and snuggle like they're in a cloud - it's adorable!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://cdn.stocksnap.io/img-thumbs/960w/sunset-sky_VEEQZUDEO9.jpg" - ], - "blip_caption": "a photography of a person on a mountain with a view of the city", - "query": "sunset mountain peak hike", - "dia_id": "D18:15", - "re-download": true, - "text": "That's really cute! Animals really know how to be happy with the simple stuff. Last weekend I got away for a hike and it was such a relief to get away from the city. Here's a photo of the beautiful sunset I witnessed during my hike." - }, - { - "speaker": "Audrey", - "dia_id": "D18:16", - "text": "Nice escape! Glad you got out hiking. Are you planning to hike with Toby someday?" - }, - { - "speaker": "Andrew", - "dia_id": "D18:17", - "text": "Yeah, I've been wanting to for a while, but it's a bit difficult since Toby is still so young." - }, - { - "speaker": "Audrey", - "dia_id": "D18:18", - "text": "Did you find a dog-friendly place to live yet? I remember you mentioning it." - }, - { - "speaker": "Andrew", - "dia_id": "D18:19", - "text": "Nah, still working on that. It's been a bit challenging." - }, - { - "speaker": "Audrey", - "dia_id": "D18:20", - "text": "Keep going, you'll find a great place to live for your pet soon!" - }, - { - "speaker": "Andrew", - "dia_id": "D18:21", - "text": "Thanks! I appreciate the help. I'll keep searching for that perfect place for dogs!" - }, - { - "speaker": "Audrey", - "dia_id": "D18:22", - "text": "No worries! You got this. Don't give up. Take care!" - } - ], - "session_19_date_time": "5:53 pm on 24 September, 2023", - "session_19": [ - { - "speaker": "Andrew", - "dia_id": "D19:1", - "text": "Hey Audrey! Long time no talk! How have you been?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/en/2/24/Dog_leash.JPG" - ], - "blip_caption": "a photography of a red retractable leash with a black handle", - "query": "torn dog leash park scare", - "dia_id": "D19:2", - "re-download": true, - "text": "Hey! I'm alright. Had some bumps though - last Friday at the park one of my pups saw something and pulled so hard the leash busted. Scared that she might run off and get hurt, so I had to chase after her. Luckily I caught her before anything bad happened. Little moments like this remind me how important she is and how we should be careful when we're out there." - }, - { - "speaker": "Andrew", - "dia_id": "D19:3", - "text": "Oh man, sorry to hear that! I'm totally getting anxious just thinking about my dog getting lost. Precious must have been really scared. What did you do to calm her down?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/ves2ui4xgml81.jpg" - ], - "blip_caption": "a photo of a dog laying on a bed with its head on the pillow", - "query": "dog lying on soft bed peaceful", - "dia_id": "D19:4", - "text": "I petted and hugged her, spoke calmly, and slowly walked her to relax. Our bond feels even stronger when moments like these show up." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/5/50/A_Chihuahua_fetching_a_ball.JPG" - ], - "blip_caption": "a photography of a dog running in a field with a frisbee", - "query": "dog playing fetch park", - "dia_id": "D19:5", - "re-download": true, - "text": "She looks so adorable! That's the connection I'd like to have with Toby. Any advice on creating a strong relationship with dogs?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA1L2JzMTMxOS1pbWFnZS1rd3Z5bHNnZC5qcGc.jpg" - ], - "blip_caption": "a photography of three dogs wearing birthday hats and sitting next to each other", - "query": "dogs sitting hats", - "dia_id": "D19:6", - "re-download": true, - "text": "Building trust with them needs patience and regular training. Give them time and love, and praise their successes." - }, - { - "speaker": "Andrew", - "blip_caption": "a photo of a dog sitting on a sidewalk in a garden", - "dia_id": "D19:7", - "text": "Thanks for the tips! Patience and practice are important for establishing a bond with our pooches, just like any other meaningful relationship. I guess some dogs just need more time! It must be so satisfying to see those successes and progress. Oh, and your pup looks so sharp in that green hat! Is there anything specific you do with them to work on training?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/7yt2t54q6ho41.jpg" - ], - "blip_caption": "a photo of a group of dogs sitting on a chair", - "query": "dogs sitting row looking camera", - "dia_id": "D19:8", - "text": "Thanks! We work on obedience and teach them tricks like sit, stay, shake, and roll over. It's fun and rewarding for both of us." - }, - { - "speaker": "Andrew", - "dia_id": "D19:9", - "text": "Wow, teaching them tricks must be super fun! How often do you take them for walks?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://pinnaclepooch.com/cdn/shop/products/image_33290672-49cc-42a7-9f1b-fa7bbe93e529.jpg" - ], - "blip_caption": "a photo of two dogs sitting next to each other on a beach", - "query": "dogs matching collars leashes", - "dia_id": "D19:10", - "text": "Very often, multiple times a day even, it's a great exercise for them and great bonding time for us." - }, - { - "speaker": "Andrew", - "dia_id": "D19:11", - "text": "Hmm that does sound like a great way to bond! What breeds are they again? Their breeds might make a difference regarding how well they bond too." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA2L3Vwd2s2MTg1NTg1MS13aWtpbWVkaWEtaW1hZ2Uta293cHBxMW4uanBn.jpg" - ], - "blip_caption": "a photography of a group of dogs tied to a leash on a brick walkway", - "query": "mixed breed dogs sitting blanket park", - "dia_id": "D19:12", - "re-download": true, - "text": "They're all mutts. Two of them are Jack Russell mixes and the other two are Chihuahua mixes. And yea, I believe so! Some dog breeds do bond better than others." - }, - { - "speaker": "Andrew", - "dia_id": "D19:13", - "text": "Aww, they're all so cute! So much fluff and joy!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i1.pickpik.com/photos/406/636/1011/dog-chihuahua-pet-animal-preview.jpg" - ], - "blip_caption": "a photography of three small dogs sitting on a couch with a white fur", - "query": "four dogs cuddled up couch", - "dia_id": "D19:14", - "re-download": true, - "text": "I love them for that. They really do bring so much joy into my life." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://www.flowerwindowboxes.com/v/vspfiles/assets/images/rail-planter-1.jpg" - ], - "blip_caption": "a photo of a balcony with a bunch of flowers on it", - "query": "balcony garden blooming flowers vegetable patch", - "dia_id": "D19:15", - "text": "Yeah! They really do bring so much into our lives - it's amazing to watch them interact. Here's something I've been taking care of lately. Look at those flowers!" - }, - { - "speaker": "Audrey", - "dia_id": "D19:16", - "text": "Nice! Taking care of something like this relaxes me and brings me peace too. I personally have a small garden as well ya know." - }, - { - "speaker": "Andrew", - "dia_id": "D19:17", - "text": "That's cool! How's it going?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://wateruseitwisely.com/wp-content/uploads/2021/03/20190611_073428.jpg" - ], - "blip_caption": "a photo of a cactus plant with two white flowers", - "query": "blooming flower", - "dia_id": "D19:18", - "text": "It's going great! The flowers are looking great and my veggie patch is coming along. It's so fun to see them grow! Really feels accomplishing." - }, - { - "speaker": "Andrew", - "dia_id": "D19:19", - "text": "Those flowers look great! What kind are they?" - }, - { - "speaker": "Audrey", - "dia_id": "D19:20", - "text": "They're called Peruvian Lilies. They are so awesome - they have such bright colors and delicate petals." - }, - { - "speaker": "Andrew", - "dia_id": "D19:21", - "text": "They're beautiful! So vibrant and eye-catching. Are they difficult to care for?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://cdn12.picryl.com/photo/2016/12/31/malinois-water-garden-dog-basks-0d19ee-1024.jpg" - ], - "blip_caption": "a photography of a dog laying on a wooden dock in a pond", - "query": "dog garden", - "dia_id": "D19:22", - "re-download": true, - "text": "Nope, they're easy to take care of, perfect for me! Just gotta water them and make sure they get enough sun." - }, - { - "speaker": "Andrew", - "dia_id": "D19:23", - "text": "Awesome! Do they enjoy playing in the garden too?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/65535/51632425349_e20c4919c5_b.jpg" - ], - "blip_caption": "a photography of two dogs running in a grassy field", - "query": "dogs running in garden", - "dia_id": "D19:24", - "re-download": true, - "text": "Yeah, they do enjoy the garden! Always running around, exploring and having a great time. So adorable!\n\n" - }, - { - "speaker": "Andrew", - "dia_id": "D19:25", - "text": "Wow! Looks like they're having a blast. Are there any other furry pals they play with, or just the ones you have?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/3563/3808347692_7ddc680e46_b.jpg" - ], - "blip_caption": "a photography of a dog and a teddy bear sleeping on a couch", - "query": "dogs sitting together couch", - "dia_id": "D19:26", - "re-download": true, - "text": "Just my fur babies." - }, - { - "speaker": "Andrew", - "dia_id": "D19:27", - "text": "Must be great having them around, the bond between you and them is awesome." - }, - { - "speaker": "Audrey", - "img_url": [ - 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" - ], - "blip_caption": "a photography of three dogs sitting on a couch with a laptop in the background", - "query": "cute dogs cuddling on the couch", - "dia_id": "D19:28", - "re-download": true, - "text": "Yeah, they do! They make everything so much better. Can't imagine life without them. They mean everything to me." - }, - { - "speaker": "Andrew", - "dia_id": "D19:29", - "text": "That's such a lovely picture, Audrey! So cute to see them snuggled up, having fun together. They really bring so much joy to our lives." - }, - { - "speaker": "Audrey", - "dia_id": "D19:30", - "text": "They really do, bringing loads of love and happiness. They are everything to me." - } - ], - "session_20_date_time": "7:09 pm on 1 October, 2023", - "session_20": [ - { - "speaker": "Andrew", - "img_url": [ - "https://images.pexels.com/photos/17057350/pexels-photo-17057350.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-maksym-harbar-17057350.jpg" - ], - "blip_caption": "a photo of a sunset over the ocean with waves", - "query": "beach sunset seaside", - "dia_id": "D20:1", - "text": "Hey wassup? Got some great news - the gf and I are hitting the beach next month with Toby!" - }, - { - "speaker": "Audrey", - "dia_id": "D20:2", - "text": "Hey Andrew! Great to hear from you. Have fun at the beach trip! Bet you can't wait to get out to the nature. Can't wait for our hike with the dogs next month. They always put a smile on my face - life's just not the same without them!" - }, - { - "speaker": "Andrew", - "dia_id": "D20:3", - "text": "Thanks, I will! Yea I can't wait for the hike. It's been a long time since we all be in nature together." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://cdn12.picryl.com/photo/2016/12/31/dogs-reward-expect-856f58-1024.jpg" - ], - "blip_caption": "a photography of a group of dogs sitting on top of a lush green field", - "query": "dogs sitting park", - "dia_id": "D20:4", - "re-download": true, - "text": "Being in a nature environment is always a great way to relax. For me, taking the doggos out for a walk in the park helps clear my mind and find some peace. It's been tough lately, but it definitely helps." - }, - { - "speaker": "Andrew", - "dia_id": "D20:5", - "text": "Aww, they look so cute! That spot looks ideal for them to play. Where did you take them?" - }, - { - "speaker": "Audrey", - "dia_id": "D20:6", - "text": "We took them to the dog park nearby last Saturday. There was a big grassy area for them to play and lots of shaded spots for me to relax. They had a great time!" - }, - { - "speaker": "Andrew", - "dia_id": "D20:7", - "text": "Sounds great! Missing that experience. Can't wait for the coming up hike!" - }, - { - "speaker": "Audrey", - "dia_id": "D20:8", - "text": "Yeah, Andrew! The pups and I are loving it. Being out in nature and checking out new trails with the dogs is so different from being in the city." - }, - { - "speaker": "Andrew", - "dia_id": "D20:9", - "text": "I think everyone's gotta ditch the hustle and bustle every now and then. It's so refreshing to be in nature." - }, - { - "speaker": "Audrey", - "dia_id": "D20:10", - "text": "Yep, it's a relief. It's like being a bird and finally flying free. Talking of birds, have you seen any birds up close lately?" - }, - { - "speaker": "Andrew", - "dia_id": "D20:11", - "text": "I've seen them up close and it's amazing how they fly with grace and freedom." - }, - { - "speaker": "Audrey", - "dia_id": "D20:12", - "text": "Yeah, birds are really amazing! I love how they can fly around and explore. They have a freedom that I wish I had!" - }, - { - "speaker": "Andrew", - "dia_id": "D20:13", - "text": "Agreed! Watching them fly is so freeing and awe-inspiring. It's a great reminder to appreciate nature." - }, - { - "speaker": "Audrey", - "dia_id": "D20:14", - "text": "Yeah, for sure. It's a great way to appreciate nature. That reminds me that I've been wanting to do some birdwatching. It's really peaceful and calming." - }, - { - "speaker": "Andrew", - "dia_id": "D20:15", - "text": "Yeah do that! It's really peaceful and calming. It's nice to get away from the city and enjoy nature. Let me know if you need any birdwatching advice, I think I know a thing or two about bird watching. Or perhaps we can all go birdwatching soometimes." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/4115/4924769040_36947653ee_c.jpg" - ], - "blip_caption": "a photography of a book with a page of birds and a bird on it", - "query": "bird guidebook binoculars", - "dia_id": "D20:16", - "re-download": true, - "text": "Thanks! That's so helpful, I'd love to take you up on your offer. Right now I'm going with this book that writes about bird watching guides. " - }, - { - "speaker": "Andrew", - "dia_id": "D20:17", - "text": "Cool! Let me know when you're ready to go birdwatching and we can plan a trip together." - }, - { - "speaker": "Audrey", - "dia_id": "D20:18", - "text": "Sounds great! I'm gonna check my schedule and get back to you. I can't wait for some birdwatching." - }, - { - "speaker": "Andrew", - "dia_id": "D20:19", - "text": "Yeah it's gonna be fun exploring and spotting birds. " - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i2.pickpik.com/photos/262/777/291/binoculars-birdwatching-spy-glass-spying-preview.jpg" - ], - "blip_caption": "a photography of a pair of binoculars sitting on a table with a book", - "query": "binoculars birdwatching guidebook", - "dia_id": "D20:20", - "re-download": true, - "text": "Yup! I should go learn some of the common birds in this area." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/4/48/Birdwatching_India_01.jpg" - ], - "blip_caption": "a photography of a pen and a notebook with a pen and a camera", - "query": "binoculars notebook", - "dia_id": "D20:21", - "re-download": true, - "text": "Nice! Looks like you're prepared. I'll bring my binos and a notebook to log them at the trip." - }, - { - "speaker": "Audrey", - "dia_id": "D20:22", - "text": "Nice. Looks like you already have some experience and really prepared." - }, - { - "speaker": "Andrew", - "dia_id": "D20:23", - "text": "Yeah! Like I said I do enjoy watching birds in the nature. I also read some books about our ecological systems as well." - }, - { - "speaker": "Audrey", - "dia_id": "D20:24", - "text": "Cool! Books like that must be really interesting. What have you discovered from reading them?" - }, - { - "speaker": "Andrew", - "dia_id": "D20:25", - "text": "I learned a lot about animals, plants, and ecosystems. It's fascinating to see how it all works together." - }, - { - "speaker": "Audrey", - "dia_id": "D20:26", - "text": "Wow, learning about the connections between them must be so cool. I bet it makes you appreciate nature even more." - }, - { - "speaker": "Andrew", - "dia_id": "D20:27", - "text": "Yeah, nature is all connected. We as human being need look after it." - }, - { - "speaker": "Audrey", - "dia_id": "D20:28", - "text": "Yeah! Taking care of the nature is like taking care of our house." - }, - { - "speaker": "Andrew", - "dia_id": "D20:29", - "text": "Definitely, let's take care of it for future generations." - }, - { - "speaker": "Audrey", - "dia_id": "D20:30", - "text": "It's on us to take care of it so the future generations have the natural resouorces." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://www.nps.gov/articles/000/images/trash-and-recycle-can.jpg" - ], - "blip_caption": "a photo of a trash can sitting in the middle of a park", - "query": "recycling bin nature preservation", - "dia_id": "D20:31", - "text": "Yep, it's important to take care of it for future generations. Let's do our share! Do you recycle at all?" - }, - { - "speaker": "Audrey", - "dia_id": "D20:32", - "text": "Yeah of course! It's important for us to do our part, and recycling is a crucial step. Do you have any other suggestions?" - }, - { - "speaker": "Andrew", - "dia_id": "D20:33", - "text": "How about reducing our carbon footprint by biking or using public transport?" - }, - { - "speaker": "Audrey", - "dia_id": "D20:34", - "text": "Oh yeah! I usually take public transport, but biking sounds like a fun way to reduce our carbon footprint. Let's all give it a try and make a change!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://bikeportland.org/wp-content/uploads/2021/10/Screen-Shot-2021-10-15-at-10.39.45-AM.jpg" - ], - "blip_caption": "a photo of a man riding a bike down a street", - "query": "bicycle bike lane", - "dia_id": "D20:35", - "text": "Yeah! It's a great way to help the planet and even train our body. Let's give it a try!" - }, - { - "speaker": "Audrey", - "dia_id": "D20:36", - "text": "I'd love to try it sometime. Are there any good routes around here?" - }, - { - "speaker": "Andrew", - "dia_id": "D20:37", - "text": "Yep, there are some awesome routes near the river. Let me show you the best ones that I enjoy!" - }, - { - "speaker": "Audrey", - "dia_id": "D20:38", - "text": "Sounds great! Can you show me the best bike routes by the river? Thanks!" - }, - { - "speaker": "Andrew", - "dia_id": "D20:39", - "text": "Sure. There are many routes around the area. I'll show you the best bike routes near there. It'll be great to get outside and soak up the scenery." - }, - { - "speaker": "Audrey", - "dia_id": "D20:40", - "text": "Sounds great! Can't wait to check out those bike routes and soak up the scenery. It should be a blast!" - } - ], - "session_21_date_time": "4:18 pm on 4 October, 2023", - "session_21": [ - { - "speaker": "Andrew", - "dia_id": "D21:1", - "text": "Hi Audrey! Been a while since I hear from you. How's it been?" - }, - { - "speaker": "Audrey", - "dia_id": "D21:2", - "text": "Hey Andrew! It's been a wild ride! I did something fun with my pups over the weekend, took them to the beach and it was so fun to see them playing in the ocean." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach", - "dia_id": "D21:3", - "text": "Sounds great! Did they love being at the beach? Did they enjoy the water? Here's a pic of my last trip to the beach." - }, - { - "speaker": "Audrey", - "dia_id": "D21:4", - "text": "The dogs had a blast swimming at the beach! Have you been there lately?" - }, - { - "speaker": "Andrew", - "dia_id": "D21:5", - "text": "Haven't been to the beach in a while. Miss being outdoors. It's hard to find open spaces in the city. Used to hike a lot, but it's more challenging now with my work life balance." - }, - { - "speaker": "Audrey", - "dia_id": "D21:6", - "text": "Oof, that's rough. I can imagine how much you miss being outdoors and surrounded by nature." - }, - { - "speaker": "Andrew", - "dia_id": "D21:7", - "text": "Yeah, it's been tough. Exploring nature was my escape - a way to find peace. But with my job and living here, it's been harder to get that feeling back. I feel a void in my heart." - }, - { - "speaker": "Audrey", - "dia_id": "D21:8", - "text": "Yeah, I get how it's like something is missing without being in the nature. But there are still some ways to appreciate it in the city, like getting some plants for your place or taking a trip to the park on the weekends." - }, - { - "speaker": "Andrew", - "dia_id": "D21:9", - "text": "Yeah true. I should get some more plants for my house. Can't beat being outside tho, but they can still bring some peace. I'll look into it. Thanks for the tip!" - }, - { - "speaker": "Audrey", - "dia_id": "D21:10", - "text": "Of course! If you need help or advice, just let me know. Plants can make your home so peaceful." - }, - { - "speaker": "Andrew", - "dia_id": "D21:11", - "text": "Thanks! I'll definitely reach out if I need any help or advice. Thanks again for offering!" - }, - { - "speaker": "Audrey", - "dia_id": "D21:12", - "text": "No problem at all! Glad to be of assistance." - }, - { - "speaker": "Andrew", - "dia_id": "D21:13", - "text": "Oh you've helped so much. " - }, - { - "speaker": "Audrey", - "dia_id": "D21:14", - "text": "Haha i'm just doing what I can do to help." - }, - { - "speaker": "Andrew", - "dia_id": "D21:15", - "text": "Thank you really. Well, take care and say hi to your dogs for me." - }, - { - "speaker": "Audrey", - "dia_id": "D21:16", - "text": "Haha I will. Take care. Talk later!" - }, - { - "speaker": "Andrew", - "dia_id": "D21:17", - "text": "Yup, have a great week. " - }, - { - "speaker": "Audrey", - "dia_id": "D21:18", - "text": "Have a great week! Bye!" - } - ], - "session_22_date_time": "9:41 pm on 6 October, 2023", - "session_22": [ - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D22:1", - "text": "Hey Andrew, I was thinking about what you said about missing nature the other day. It reminded me of when I couldn't walk my dogs because of a knee injury. It was tough 'cause they bring me so much joy. This pic was taken after I started walking again. It felt great to be able to get back out there!" - }, - { - "speaker": "Andrew", - "dia_id": "D22:2", - "text": "You know how it feel right? Bummer not being able to take them for a stroll. They must've been over the moon when you could finally take them out again. Glad you got that adorable pic too! Dogs are awesome like that, huh? Remind us to stop and smell the roses." - }, - { - "speaker": "Audrey", - "dia_id": "D22:3", - "text": "Yeah, it was like they couldn't wait! They definitely reminds us to appreciate life's little pleasures. They see the world so differently!" - }, - { - "speaker": "Andrew", - "dia_id": "D22:4", - "text": "Yeah, dogs sure know how to find the simple joys of life! Appreciating the little things is so important. Well speaking of interesting hobbies, I rememver you mentionoed about that jewelry earlier? I remember you saying you make them with recycled objects. That sounds cool - tell me more!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://boboglobal.com/cdn/shop/products/IMG_2161.jpg" - ], - "blip_caption": "a photo of a woman with a colorful necklace and earrings", - "query": "recycled bottle cap necklace colorful beads", - "dia_id": "D22:5", - "text": "Oh yes! I love making jewelry out of recycled stuff. It's a great way to show my love of creativity and sustainability. I hunt down things like bottle caps, buttons, and broken jewelry, and then turn them into one-of-a-kind pieces. Here's a photo of one of my favorite necklaces. They all hold a special story and have their own appeal. Plus, it's a small step in reducing waste." - }, - { - "speaker": "Andrew", - "dia_id": "D22:6", - "text": "That necklace looks awesome! The colors really stand out and the design is really cool. It's great that you're giving recycled objects new life. Do you make jewelry to sell or just as a hobby? Also, do you donate profits to a good cause?" - }, - { - "speaker": "Audrey", - "dia_id": "D22:7", - "text": "Thanks! I started making jewelry as a hobby and then started selling it. I donate a portion of my profits to a cause that's close to my heart. It's a way to combine two of my passions - making jewelry and making a difference. It's great that customers not only enjoy my work but also support a cause!" - }, - { - "speaker": "Andrew", - "dia_id": "D22:8", - "text": "Wow! It's amazing how you use your passion to make a difference in the world. Has this experience changed your perspective on other hobbies? And do you donate to any specific organization?" - }, - { - "speaker": "Audrey", - "dia_id": "D22:9", - "text": "Definitely! Any hobby can have an impact when used right - I donate to an animal shelter. They do great work, so it's my way of helping out." - }, - { - "speaker": "Andrew", - "dia_id": "D22:10", - "text": "That's awesome! It's really inspiring how dedicated you are to making a difference, even though you can't volunteer at the shelter anymore." - }, - { - "speaker": "Audrey", - "dia_id": "D22:11", - "text": "Yeah, I'm still finding ways to do good, even if I can't be there. Life's all about adapting and helping out." - }, - { - "speaker": "Andrew", - "dia_id": "D22:12", - "text": "Yeah, you got it. Life is all about rolling with the punches and making a difference, whatever happens. Your hard work is really amazing." - }, - { - "speaker": "Audrey", - "dia_id": "D22:13", - "text": "Thanks! I appreciate your kind words. Gotta stay strong and keep making a difference, even in challenging times." - } - ], - "session_23_date_time": "4:22 pm on 13 October, 2023", - "session_23": [ - { - "speaker": "Andrew", - "img_url": [ - "https://i2.pickpik.com/photos/717/632/853/dog-goldendoodle-chess-play-preview.jpg" - ], - "blip_caption": "a photography of a dog sitting at a chess board with a chess set in front of him", - "query": "board games dog bonding", - "dia_id": "D23:1", - "re-download": true, - "text": "Hey Audrey, it's been a busy week for me. Last Tuesday, my gf, Toby, and I had a really awesome night playing board games. It was really nice. What's been up with you lately?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://about.hawaiilife.com/wp-content/uploads/2023/11/IMG-1593-e1699235788417.jpg" - ], - "blip_caption": "a photo of a pan of cookies on a wooden table", - "query": "homemade dog treats tray", - "dia_id": "D23:2", - "text": "Hey! Sounds like you've been busy! I made some goodies recently to thank my neighbors for their pup-friendly homes. It was a nice way to bring some joy around here. Any plans for the weekend?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://embracesomeplace.com/wp-content/uploads/2022/01/DSC02393.jpg" - ], - "blip_caption": "a photo of a group of people sitting at a table in a room", - "query": "cozy cafe", - "dia_id": "D23:3", - "text": "Friday night's board game session was a nice break. This weekend, I'm planning to check out this cozy cafe and hang out there." - }, - { - "speaker": "Audrey", - "dia_id": "D23:4", - "text": "That cafe looks really cozy! Any other spots you would recommend?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://be-lavie.com/wp-content/uploads/2019/06/IMG_9281-e1560067147565.jpg" - ], - "blip_caption": "a photo of a man sitting on a couch in a large open space", - "query": "rooftop bar cityscape view", - "dia_id": "D23:5", - "text": "So this new spot just opened the other day. It's awesome with a sick view and vibe." - }, - { - "speaker": "Audrey", - "dia_id": "D23:6", - "text": "Wow, looks great! Can I bring my pups with me?" - }, - { - "speaker": "Andrew", - "dia_id": "D23:7", - "text": "Hmmm, not sure if it's suitable for them but hey, there's a great dog park close by! Maybe we can bring our coffee over." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/yfeccwau4dya1.jpg" - ], - "blip_caption": "a photo of a dog wearing a sweater sitting in the grass", - "query": "dog park dogs playing", - "dia_id": "D23:8", - "text": "Sounds good! Exploring new places is always an adventure for them." - }, - { - "speaker": "Andrew", - "dia_id": "D23:9", - "text": "Aww, they look so sweet in that sweater! Do they enjoy going to the dog park?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/6jbz5p9fxy051.jpg" - ], - "blip_caption": "a photo of two dogs playing with a frisbee in a field", - "query": "dog park dogs playing together", - "dia_id": "D23:10", - "text": "The dog park is like paradise for them! They love socializing with the other pups and getting lots of exercise. It's so cute to watch them running around and smelling all the stuff. Do you have any funny stories about your pup at the dog park?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/7245/7209126530_152a8cd458_b.jpg" - ], - "blip_caption": "a photography of a dog playing with a frisbee in a field", - "query": "dog chasing tail park", - "dia_id": "D23:11", - "re-download": true, - "text": "There was this one time my pup got too excited and chased a squirrel. He ran around the tree and the squirrel just watched from the branches. It was hilarious!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://d4yxl4pe8dqlj.cloudfront.net/images/dcc5fa88-74fb-4e04-a7f9-df5051a8b643/7babb05e-2478-445c-8ca9-4dc2113853ff_full_size.jpg" - ], - "blip_caption": "a photo of a dog wearing a shirt standing on a hardwood floor", - "query": "dog park dogs winter sweaters", - "dia_id": "D23:12", - "text": "Haha, that must've been hilarious! What breed is your pup again? He looks so fun! My dogs love running around the park. But one thing they hate is snow. I took them to a snowy one last winter and they were so confused! They definitely prefer nice, sunny days in the grass." - }, - { - "speaker": "Andrew", - "dia_id": "D23:13", - "text": "My pup Toby is German Shephard and loves cute sweaters! He's so active. What games do your dogs like to play at the park?" - }, - { - "speaker": "Audrey", - "dia_id": "D23:14", - "text": "My dogs go crazy for Fetch and Frisbee, and they love to run around and meet other pups. They could keep running for hours!" - }, - { - "speaker": "Andrew", - "dia_id": "D23:15", - "text": "That sounds like so much fun! It's great to see them happy and active." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/3563/3808347692_7ddc680e46_b.jpg" - ], - "blip_caption": "a photography of a dog and a teddy bear sleeping on a couch", - "query": "dogs sitting together on couch", - "dia_id": "D23:16", - "re-download": true, - "text": "Yeah, it's awesome! Seeing them happy fills my heart with joy. They bring me so much happiness." - }, - { - "speaker": "Andrew", - "dia_id": "D23:17", - "text": "Yeah, they really do bring joy don't they. It's wonderful." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.pinimg.com/originals/55/47/8f/55478f3a1f3c22e60a729cd3806d2830.jpg" - ], - "blip_caption": "a photo of a tattoo of a flower on a wrist", - "query": "tattoo arm four dogs", - "dia_id": "D23:18", - "text": "I think i've said this so many times, but I really can't imagine my life without them - my little family. They always make me smile and give lots of love and companionship. Remember I had a tattoo of my four dogs? So I went to the tattoo parlor and got some more drawings on the tattoo." - }, - { - "speaker": "Andrew", - "dia_id": "D23:19", - "text": "Wow, that's cool! Any plans on getting more? Or are you finished?" - }, - { - "speaker": "Audrey", - "blip_caption": "a photo of a woman with a tattoo of a dog and sunflowers", - "dia_id": "D23:20", - "text": "Thanks! I think right now its enough. Unless someday I decide to get another dog. Maybe I'll get another tattoo when that happens." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://c1.wallpaperflare.com/preview/640/472/990/away-path-forest-hiking.jpg" - ], - "blip_caption": "a photography of a path in the woods with a forest in the background", - "query": "hiking trail trees", - "dia_id": "D23:21", - "re-download": true, - "text": "I can tell they're a real source of love for you. It's great to have that kind of inspiration. You know what else inspires me? Somewhere like this" - }, - { - "speaker": "Audrey", - "dia_id": "D23:22", - "text": "They really bring me joy. It feels great to experience unconditional love! Do you ever get the chance to visit forests like that? Bet it's an amazing escape for you." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://community.us.craghoppers.com/wp-content/uploads/2018/05/j3bmicznnmrnfe1uchho.jpg" - ], - "blip_caption": "a photo of a woman walking up a steep hill with a view of the ocean", - "query": "hiking trail beautiful view", - "dia_id": "D23:23", - "text": "Yeah, living in the city, I do miss nature a lot. Whenever I can, I try to go to nearby parks or on hikes. It's such a peaceful and joyful experience for me." - }, - { - "speaker": "Audrey", - "dia_id": "D23:24", - "text": "Yeah you're so right about needing nature time. Life's been crazy lately so it's been a while since I took my furry friends out for a walk. But those moments with them in nature are just so chill and happy! They're definitely the best companions for exploring the great outdoors." - }, - { - "speaker": "Andrew", - "dia_id": "D23:25", - "text": "Totally agree! Nature and animals can be so peaceful and joyful. I hope you and your furry buddies go on some fun adventures soon!" - }, - { - "speaker": "Audrey", - "dia_id": "D23:26", - "text": "Yeah, I need to go on a hike with them, it's going to be a great therapy!" - }, - { - "speaker": "Andrew", - "dia_id": "D23:27", - "text": "Yeah! You've hiked with your dogs, it's a fun way to bond and make memories." - }, - { - "speaker": "Audrey", - "dia_id": "D23:28", - "text": "Yup, I still remember everytime we all go on a hiking strip with my dog. Good times." - } - ], - "session_24_date_time": "6:12 pm on 19 October, 2023", - "session_24": [ - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/3923/14627245354_a40ab098c0_b.jpg" - ], - "blip_caption": "a photography of a box filled with toys and other items", - "query": "dog toys shopping bag", - "dia_id": "D24:1", - "re-download": true, - "text": "Hey Andrew, hope you're doing ok. I recently had a good week - I went to a pet store last Monday to buy toys for my dogs and it was great seeing them so excited when I got them home. It made me realize how much I love them and how much joy they bring me." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/tousk84oar171.jpg" - ], - "blip_caption": "a photo of a dog sitting on a couch with a bowl of food", - "query": "cute dog playing chew toy buddy", - "dia_id": "D24:2", - "text": "Hi Audrey! Pets really can make our lives better, huh? Speaking of which, I've got some awesome news -- I recently adopted another pup from a shelter. He's the best." - }, - { - "speaker": "Audrey", - "dia_id": "D24:3", - "text": "Wow! That's awesome news! How's he doing in his new home?" - }, - { - "speaker": "Andrew", - "dia_id": "D24:4", - "text": "Thanks! He's doing great in his new home. Still getting used to Toby and the new environment. Toby needs some time to get along with him too. I never imagined having pets would bring so much happiness. Pets really bring lots of joy and companionship to our lives." - }, - { - "speaker": "Audrey", - "dia_id": "D24:5", - "text": "That's awesome! What is his name?" - }, - { - "speaker": "Andrew", - "dia_id": "D24:6", - "text": "I named him Buddy because he's my buddy and I hope him and Toby become buddies!" - }, - { - "speaker": "Audrey", - "dia_id": "D24:7", - "text": "That's perfect! Sounds like Buddy really is your sidekick. Do you have any favorite activities you two like to do together?" - }, - { - "speaker": "Andrew", - "dia_id": "D24:8", - "text": "Yeah, Buddy and I have a great time doing walks. It's a nice way to spend time together and get some fresh air." - }, - { - "speaker": "Audrey", - "dia_id": "D24:9", - "text": "Nice! Buddy seems to be having a great time! It's nice to spend time together and get some fresh air." - }, - { - "speaker": "Andrew", - "dia_id": "D24:10", - "text": "Yep, he loves checking out new hiking trails with us. It's awesome to see him so stoked and interested in everything nature has to offer." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://www.kingdukes.com/cdn/shop/articles/man_walking_dog_on_portland_trail_1200x.jpg" - ], - "blip_caption": "a photo of a man walking a dog on a trail", - "query": "dogs hiking trail nature", - "dia_id": "D24:11", - "text": "That sounds awesome! Have fun exploring the trails!" - }, - { - "speaker": "Andrew", - "dia_id": "D24:12", - "text": "Yup! I will be taking both of them to the trails together soon!" - }, - { - "speaker": "Audrey", - "dia_id": "D24:13", - "text": "I can't wait for our hike with the furry friends next month - it's gonna be awesome!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://fundiegofamily.com/wp-content/uploads/2018/05/blueskycover.jpg" - ], - "blip_caption": "a photo of a dirt road with a cow standing in the middle", - "query": "hiking trail trees blue sky", - "dia_id": "D24:14", - "text": "Oh yeah! It going to be fun with the new addition." - }, - { - "speaker": "Audrey", - "dia_id": "D24:15", - "text": "Ooo where is this gorgeous spot? I need to take my pups for a stroll there." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/4lun19fk1g591.jpg" - ], - "blip_caption": "a photo of a red truck driving down a road in a field", - "query": "countryside", - "dia_id": "D24:16", - "text": "Haha is nowhere near the city. Wish I could take them to a place like this, far from the city." - }, - { - "speaker": "Audrey", - "dia_id": "D24:17", - "text": "That sounds like a great getaway from the city tho! I'm hoping we can find something just as nice for our hike." - }, - { - "speaker": "Andrew", - "dia_id": "D24:18", - "text": "Well if that's what you want, then let's find something just as nice for our hike." - }, - { - "speaker": "Audrey", - "dia_id": "D24:19", - "text": "Yep! I'll do some research and see if I can find an awesome place like that." - }, - { - "speaker": "Andrew", - "dia_id": "D24:20", - "text": "Awesome! I really appreciate your effort! Let's see if there's somewhere like that." - }, - { - "speaker": "Audrey", - "dia_id": "D24:21", - "text": "You just wait. I'm gonna find the best spot for the hike. Haha." - }, - { - "speaker": "Andrew", - "dia_id": "D24:22", - "text": "Haha, I can't wait!" - } - ], - "session_25_date_time": "10:14 am on 24 October, 2023", - "session_25": [ - { - "speaker": "Andrew", - "dia_id": "D25:1", - "text": "Hi Audrey! How have you been lately? My girlfriend and I went to this awesome wine tasting last weekend. It was great! We tried so many unique wines and learned a lot. I was surprised at how much I enjoyed it. A reminder to step out of the comfort zone!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/1jjc5bw9tmgb1.jpg" - ], - "blip_caption": "a photo of a person with a cast on their arm and arm in a cast", - "query": "broken arm cast", - "dia_id": "D25:2", - "text": "Hey! Ha, glad you had fun at the wine tasting. Yeah, trying new things can be cool. By the way, I had an unexpected adventure last week. I had an accident while playing with my pups at the park. Taking care of them with one arm has been tricky but we're managing. What's been up with you? Any new interests?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/5fb4299bc4391d58134367db/1607453049811-DZU05ZBSW3YPHU2G2TX2/Special+rolls+party+tray.jpg" - ], - "blip_caption": "a photo of a plate of sushi and vegetables on a table", - "query": "sushi platter", - "dia_id": "D25:3", - "text": "Ouch! Are you feeling better? Sending healing vibes to you and your pups. So I recently tried out this new spot in town that serves sushi and it was great. Do you have anything that you've been wanting to try lately?" - }, - { - "speaker": "Audrey", - "dia_id": "D25:4", - "text": "Thanks! Appreciate it, feeling better each day. And wow that Sushi looks phenomenal. I know what to get for dinner tonight." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/laccj2oklvo71.jpg" - ], - "blip_caption": "a photo of a tray of sushi with a variety of toppings", - "query": "sushi plate vegetables", - "dia_id": "D25:5", - "text": "Taking it one day at a time is the way to go. A while ago I've been curious about trying sushi. Never done it before, but always hear it's good. Now I understand what the hype is. Have you ever tried it?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/5fb4299bc4391d58134367db/1607453049811-DZU05ZBSW3YPHU2G2TX2/Special+rolls+party+tray.jpg" - ], - "blip_caption": "a photo of a plate of sushi and vegetables on a table", - "query": "sushi platter rolls sashimi", - "dia_id": "D25:6", - "text": "Yess! Sushi is delicious! I love them! There are so many types and flavors to try. Definitely give it a go and try different things! Don't limit yourself in your comofort zone!" - }, - { - "speaker": "Andrew", - "dia_id": "D25:7", - "text": "Thanks for the encouragement! I'm looking forward to trying more soon. Do you have any tips for someone who's new to sushi?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/ipekv4tz7oh31.jpg" - ], - "blip_caption": "a photo of a long plate of sushi with sauce on it", - "query": "sushi rolls with different sauces", - "dia_id": "D25:8", - "text": "Definitely try a California or salmon roll first when trying sushi - they're easier. Mix it up with different sauces and dips too - it makes it more tasty. Enjoy and let me know how it goes!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/tsyh3z2tdf591.jpg" - ], - "blip_caption": "a photo of a person eating sushi on a wooden board", - "query": "sushi plate sauces", - "dia_id": "D25:9", - "text": "Thanks for the tips! Gonna go with a California or salmon roll and try out some sauces. I'll let you know how it goes." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.pinimg.com/originals/53/26/f8/5326f8e676caab464f515486aa0a4360.jpg" - ], - "blip_caption": "a photo of a plate of sushi with avocado and shrimp", - "query": "sushi rolls toppings", - "dia_id": "D25:10", - "text": "Glad to help! Can't wait to hear about your sushi adventure. Take your time and have fun!" - }, - { - "speaker": "Andrew", - "dia_id": "D25:11", - "text": "I'm really excited to try different sushi. It's going to be a great time!" - }, - { - "speaker": "Audrey", - "dia_id": "D25:12", - "text": "Have fun! You'll definitely need some time to get used to, but once you start I believe you'll love it! Take some pics and show me what you enjoy." - }, - { - "speaker": "Andrew", - "dia_id": "D25:13", - "text": "Haha, I'll make sure to take some photos and show you my sushi adventure." - }, - { - "speaker": "Audrey", - "dia_id": "D25:14", - "text": "Enjoy! Now I'm gonna order some sushi for tonight. Thanks!" - }, - { - "speaker": "Andrew", - "dia_id": "D25:15", - "text": "Haha! You're welcomoe! Have a good one!" - }, - { - "speaker": "Audrey", - "dia_id": "D25:16", - "text": "Take care and have a good one!" - } - ], - "session_26_date_time": "2:36 pm on 28 October, 2023", - "session_26": [ - { - "speaker": "Audrey", - "dia_id": "D26:1", - "text": "Hey Andrew, I wanted to let you know about something going on with my dogs. I noticed they weren't acting normally, so I made an appointment with an animal behaviorist last Wed. It's been a bit hectic but I'm hopeful it'll help me better understand them." - }, - { - "speaker": "Andrew", - "dia_id": "D26:2", - "text": "Oh no! Sorry to hear that your dogs haven't been themselves. Are they doing ok? How did the appointment with the animal behaviorist go? Did you receive any helpful advice or insights?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:3", - "text": "The appointment went okay. It was hectic at first, but the behaviorist checked them out and asked some questions. I got some tips to try and help with their problems now." - }, - { - "speaker": "Andrew", - "dia_id": "D26:4", - "text": "So what tips did you get? What will you be doing to help with the problems?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:5", - "text": "The behaviorist gave me tips on how to handle it and suggested some changes in their routine. I'm using positive reinforcement techniques and it's still a work in progress, but I'm hopeful it'll help." - }, - { - "speaker": "Andrew", - "dia_id": "D26:6", - "text": "I'm glad your pups are still good with positive reinforcement! How are they doing with the new approach tho?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:7", - "text": "So far they seem to be responding well to it! It won't be fixed immediately but I'm seeing some progress. Here's hoping it keeps going." - }, - { - "speaker": "Andrew", - "dia_id": "D26:8", - "text": "That's good to hear! Keep up the good work!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:9", - "text": "Thanks! Your words of encouragement really mean a lot. It's tough, but I'm devoted to keeping them healthy and happy - they mean everything to me." - }, - { - "speaker": "Andrew", - "dia_id": "D26:10", - "text": "You're doing a great job! They're lucky to have you." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2011,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1200/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park summer", - "dia_id": "D26:11", - "text": "Thanks! They're really special to me and I want the best for them. Here's a pic of them having a blast last summer, so happy! I'm looking forward the day they are back to normal." - }, - { - "speaker": "Andrew", - "dia_id": "D26:12", - "text": "Aww, they're having such a blast! What kind are they? I'm wishing you and your pups the best. " - }, - { - "speaker": "Audrey", - "dia_id": "D26:13", - "text": "Thanks! They're all mutts, but Pepper and Panda are Lab mixes, and Precious and Pixie are Chihuahua mixes. I really need that. I can't wait the day they're all back to normal." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/8194/8097374162_8d85c7b645_b.jpg" - ], - "blip_caption": "a photography of a man hiking up a mountain with a backpack", - "query": "hiking mountains", - "dia_id": "D26:14", - "re-download": true, - "text": "Sending prayers and wishes. Here's a pic I took at a national park I went a while ago." - }, - { - "speaker": "Audrey", - "dia_id": "D26:15", - "text": "Wow, that looks gorgeous! We hope to join you and the furry friends soon!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/3094/2564651016_d9405477b9_b.jpg" - ], - "blip_caption": "a photography of a man walking a dog on a leash", - "query": "dog-friendly hiking trail", - "dia_id": "D26:16", - "re-download": true, - "text": "Yeah I really hope your pups can get better and join us soon!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:17", - "text": "Wow, that trail looks nice! Looks like its dog friendly?" - }, - { - "speaker": "Andrew", - "dia_id": "D26:18", - "text": "Yup! It's close by and it's dog-friendly too, with killer views. Wanna plan a hike soon?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:19", - "text": "Hmmm sure! Let's pick a date and go hike. It should be good!" - }, - { - "speaker": "Andrew", - "dia_id": "D26:20", - "text": "Yay! Does Saturday sound good? We can grab some snacks and have a blast exploring. Because on Sunday I am going on a picnic date with my girlfriend." - }, - { - "speaker": "Audrey", - "dia_id": "D26:21", - "text": "Saturday works for me! I'm going to bring some snack. Super excited!" - }, - { - "speaker": "Andrew", - "dia_id": "D26:22", - "text": "Can't wait for our nature day with the fur babies! We're gonna have a good time!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/8ugwccgkw6u81.jpg" - ], - "blip_caption": "a photo of a dog laying on the ground with a view of the ocean", - "query": "dogs wagging tails nature beauty", - "dia_id": "D26:23", - "text": "Going hiking and seeing nature will be awesome. They'll be so happy!" - }, - { - "speaker": "Andrew", - "dia_id": "D26:24", - "text": "I bet! Where do you guys plan to explore?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:25", - "text": "Let's check out the trail first. It's a peaceful spot to bring the fur babies for the day." - }, - { - "speaker": "Andrew", - "dia_id": "D26:26", - "text": "Sounds great! There's a lake near the trail too! It's gonna be awesome!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://photos.thedyrt.com/photo/131286/media/serene-lake-or_97eeadb9941e84ca9084f13abd07f6b0.jpg" - ], - "blip_caption": "a photo of a lake with a boat in it and a forest in the background", - "query": "serene lake trees peaceful adventure", - "dia_id": "D26:27", - "text": "Oh nice! Can't wait to explore it and hang out with our furry friends. Should be a peaceful day! Here's a photo of the lake I found online." - }, - { - "speaker": "Andrew", - "dia_id": "D26:28", - "text": "Wow, that looks awesome! Do you think the dogs will like it? Which trail do you have in mind?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:29", - "text": "Let's try that trail by the lake with great views, perfect for us and the pups. Should be fun!" - }, - { - "speaker": "Andrew", - "dia_id": "D26:30", - "text": "Sounds great! They will love it by the lake. Can't wait!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://s3-us-west-1.amazonaws.com/assets.wagwalkingweb.com/media/daily_wag/blog_articles/body/1660599786.6061006/5-best-dog-parks-in-portland-or-2.png" - ], - "blip_caption": "a photo of a dog chasing a dog in a park", - "query": "sunny day dog park", - "dia_id": "D26:31", - "text": "Gonna be great - nature, furry pals - what more could we want? I'm so lucky to have a friend like you who loves exploring and being outside with our dogs." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://photos.bringfido.com/restaurants/6/7/1/176/176_272122.jpg" - ], - "blip_caption": "a photo of a dog eating out of a bowl on the ground", - "query": "dog-friendly cafe outdoor seating", - "dia_id": "D26:32", - "text": "Same! I'm lucky to have a friend like you for these outdoor trips. It's awesome to be out in nature with our furry friends." - }, - { - "speaker": "Audrey", - "dia_id": "D26:33", - "text": "Yup! It's hard to find someone that has similar thoughts. " - }, - { - "speaker": "Andrew", - "img_url": [ - "https://exploringtheprime.com/wp-content/uploads/2019/10/IMG_6849.jpg" - ], - "blip_caption": "a photo of a forest with yellow trees and a blue sky", - "query": "hiking trail colorful autumn trees", - "dia_id": "D26:34", - "text": "Exactly! Oh btw, here's another photo of a trail near the location. What do you think?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:35", - "text": "That looks pretty good! I'd love to take them there sometime." - }, - { - "speaker": "Andrew", - "dia_id": "D26:36", - "text": "How about going there the next trip? The autumn colors are so beautiful!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:37", - "text": "Sounds great! The autumn colors would look awesome for pictures." - }, - { - "speaker": "Andrew", - "dia_id": "D26:38", - "text": "Yeah, photos are gonna turn out great with the dogs!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:39", - "text": "Can't wait to capture some amazing moments with our furry friends!" - }, - { - "speaker": "Andrew", - "dia_id": "D26:40", - "text": "It definitely will be a memorable day!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:41", - "text": "Yep, can't wait to make some awesome memories with our furry friends!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/1760/27752565907_f59bb6f236_b.jpg" - ], - "blip_caption": "a photography of a man and his dog on a trail", - "query": "group of friends hiking mountains", - "dia_id": "D26:42", - "re-download": true, - "text": "You bet! Can't wait to see their happy face! This was my dog and I when we were hiking last time, see how happy he was?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:43", - "text": "Aww look at his happy face! I'm really looking forward to it! Can't wait to see my pups being happy and hiking." - }, - { - "speaker": "Andrew", - "dia_id": "D26:44", - "text": "Same here. Let's make it an epic and fun hike!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:45", - "text": "Yep! It's gonna be so much fun." - }, - { - "speaker": "Andrew", - "dia_id": "D26:46", - "text": "Let me get ready, gonna head out soon. Ttyl!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:47", - "text": "Yep ttyl!" - } - ], - "session_27_date_time": "7:59 pm on 4 November, 2023", - "session_27": [ - { - "speaker": "Andrew", - "img_url": [ - "https://www.oregon.com/sites/default/files/bike_bluelake.jpg" - ], - "blip_caption": "a photo of two people riding bikes on a paved path", - "query": "bike ride park lake", - "dia_id": "D27:1", - "text": "Hey Audrey, had a great weekend! My girlfriend and I went on a bike ride and stumbled upon a cool park outside of town. It was awesome to get away from the city and be surrounded by nature." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/hayawo8f86p51.jpg" - ], - "blip_caption": "a photo of a group of people and dogs in a park", - "query": "dog park playing dogs", - "dia_id": "D27:2", - "text": "That's cool! I love checking out new parks with my four pups. Last weekend was so fun - our dogs were able to run around and get some fresh air. On top of that, I recently joined a dog owners group to learn how to better take care of them." - }, - { - "speaker": "Andrew", - "dia_id": "D27:3", - "text": "That sounds great! Your four pups must have a lot of fun. How often do you hang out with the dog owners group?" - }, - { - "speaker": "Audrey", - "blip_caption": "a photo of a group of people and dogs in a park", - "dia_id": "D27:4", - "text": "Yeah, they're having a lot of fun. I try to meet up with other dog owners once a week for tips from other parents and so they can all play together. How about you? Have you ever thought about joining one?" - }, - { - "speaker": "Andrew", - "dia_id": "D27:5", - "text": "That looks fun! Seeing those adorable pups made me think about getting another dog, but I'm still not sure. Having two dogs is already a lot to take care of. Do you have any tips on being a multi-dog pet owner?" - }, - { - "speaker": "Audrey", - "dia_id": "D27:6", - "text": "Maybe you want to take care of Toby and Buddy first. Having them happy and healthy would be a good first step before going all in for more dogs." - }, - { - "speaker": "Andrew", - "dia_id": "D27:7", - "text": "Thanks, I think that's what I need to hear. I'll take good care of my dogs first." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://elpaseogarden.files.wordpress.com/2021/06/img_2087.jpg" - ], - "blip_caption": "a photo of two dogs playing with a frisbee in a park", - "query": "dogs playing in the park", - "dia_id": "D27:8", - "text": "That's great! Let me know if you need any help, I'm here for you! See how happy they are? You don't need more dogs to make them happy! :)" - }, - { - "speaker": "Andrew", - "dia_id": "D27:9", - "text": "Thanks Audrey! That's so nice of you. I think I've managed to make it work with dogs while still living in the city." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/2839/12466466553_d534a96181_b.jpg" - ], - "blip_caption": "a photography of a basket full of stuffed animals on a wooden floor", - "query": "dog toys games apartment", - "dia_id": "D27:10", - "re-download": true, - "text": "Yeah I feel you. Taking care of a pup in the city is tough but doable with the right approach. Keeping them active is key. Here's a pic of how I entertain them in my house with toys and games." - }, - { - "speaker": "Andrew", - "dia_id": "D27:11", - "text": "Wow, it's great to know there are ways to keep them active in the city. I'll keep that in mind. Thank you so much!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/8257/8915975033_e7129952e5_b.jpg" - ], - "blip_caption": "a photography of a dog running with a tennis ball in its mouth", - "query": "dogs playing fetch park", - "dia_id": "D27:12", - "re-download": true, - "text": "You got it! There are lots of ways to keep them happy in the city. Make sure to socialize and exercise them daily. Get creative and add some mental stimulation too. Here's a pic of them playing fetch in the park - they love it!" - }, - { - "speaker": "Andrew", - "dia_id": "D27:13", - "text": "That's so cute! What sort of activities do you do to stay mentally stimulated?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/340/32261973051_6709776893_b.jpg" - ], - "blip_caption": "a photography of a dog playing with a frisbee in a room", - "query": "dog puzzle toy play", - "dia_id": "D27:14", - "re-download": true, - "text": "We give them lots of activities to keep them busy - puzzles, training, hide-and-seek - they love it all!" - }, - { - "speaker": "Andrew", - "dia_id": "D27:15", - "text": "Cool ideas! I think I'll give those activities a try with my pups. Thanks!" - }, - { - "speaker": "Audrey", - "dia_id": "D27:16", - "text": "No problem, glad I could help. Let me know how it goes." - }, - { - "speaker": "Andrew", - "dia_id": "D27:17", - "text": "Your advice and support really mean a lot to me! Thank you so much!" - }, - { - "speaker": "Audrey", - "dia_id": "D27:18", - "text": "That's what friends are for - supporting each other. Your friendship means a lot to me. :)" - } - ], - "session_28_date_time": "9:02 am on 22 November, 2023", - "session_28": [ - { - "speaker": "Audrey", - "dia_id": "D28:1", - "text": "Hey Andrew! Long time no talk! Last Friday I took my fur kids to the pet salon - they were so psyched and their tails were wagging like crazy! It took a while for them to calm down, but all cut up they looked so cute! " - }, - { - "speaker": "Andrew", - "dia_id": "D28:2", - "text": "Hey Audrey! Nice to hear from you. Sounds adorable! Do you have any pictures of them all groomed up?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://s0.geograph.org.uk/geophotos/06/18/45/6184556_bc98b08f.jpg" - ], - "blip_caption": "a photography of a dog is standing on a table with a leash", - "query": "four dogs groomed coats wagging tails cozy rug", - "dia_id": "D28:3", - "re-download": true, - "text": "Here's a pic of them, looking all groomed. Look at those shiny coats! To top it off, they were really good at the salon - I always worry about them in new places." - }, - { - "speaker": "Andrew", - "dia_id": "D28:4", - "text": "Wow, they look great! Love seeing them happy and calm in new places." - }, - { - "speaker": "Audrey", - "dia_id": "D28:5", - "text": "Thanks! It means a lot to see them happy and settled in new places. I guess I'm doing a good job as a doggy mom then, haha! Have you taken your furry friends to the groomers yet?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/h1m9pir3y9jb1.jpg" - ], - "blip_caption": "a photo of a woman feeding a dog hay in a barn", - "query": "puppy scout new bundle of joy", - "dia_id": "D28:6", - "text": "No, we haven't got the chance to take them to the groomer yet. But will do that soon! So guess what, I can't help myself but to adpot another dog the other day. Here's a photo of the doggo!" - }, - { - "speaker": "Audrey", - "dia_id": "D28:7", - "text": "That's great news! What's the pups name?" - }, - { - "speaker": "Andrew", - "dia_id": "D28:8", - "text": "It took us a while to decide, but we ended up going with 'Scout' for our pup - it seemed perfect for their adventurous spirit." - }, - { - "speaker": "Audrey", - "dia_id": "D28:9", - "text": "That's a great name for your pup! Fits their adventurous spirit. What's Scout's first adventure gonna be?" - }, - { - "speaker": "Andrew", - "dia_id": "D28:10", - "text": "Thanks! We're gonna take Scout, Toby, and Buddy to a nearby park. It's not big, but we can all have fun and get some fresh air!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/8257/8915975033_e7129952e5_b.jpg" - ], - "blip_caption": "a photography of a dog running with a tennis ball in its mouth", - "query": "dogs playing fetch park", - "dia_id": "D28:11", - "re-download": true, - "text": "Sounds like a great start for Scout! Start small, and gradually give them more exposure. They'll have a great time, just make sure to keep them leashed." - }, - { - "speaker": "Andrew", - "dia_id": "D28:12", - "text": "Yeah, safety first! For now, we're keeping the new addition on a leash while they get used to being outside. That pic you of your dog at the park is so cute. So we got some essentials for their comfort and entertainment, like a bed, toys, and some puppy pads just in case. It's like their own little safe haven." - }, - { - "speaker": "Audrey", - "dia_id": "D28:13", - "text": "Wow, that's so great that you two are creating a safe and fun space for Scout. It's really important they have a place that makes them feel secure. Slowly introduce Scout to Toby and Buddy, it takes time for the pups to get used to each other too! Scout is so lucky to have you and your girlfriend! " - }, - { - "speaker": "Andrew", - "dia_id": "D28:14", - "text": "Thanks! We feel so lucky to have Scout. It's been amazing having so many furry friends! How are your dogs doing now? " - }, - { - "speaker": "Audrey", - "dia_id": "D28:15", - "text": "They're doing great! Exploring, meeting new people...they feel so loved and safe. I'm really glad they're part of my life!" - }, - { - "speaker": "Andrew", - "dia_id": "D28:16", - "text": "That's great to hear! Dogs truly bring so much joy and friendship. I'm glad they're happy with you." - }, - { - "speaker": "Audrey", - "dia_id": "D28:17", - "text": "Thanks! They're really awesome and bring so much joy and friendship. I'm so grateful to have them in my life as a part of my family." - }, - { - "speaker": "Andrew", - "dia_id": "D28:18", - "text": "Yeah, it's great! Dogs are always there for us. We should count ourselves lucky to have such amazing furry friends as family member." - } - ] - }, - "event_summary": { - "events_session_1": { - "Audrey": [ - "Audrey purchases three collars and tags for her dogs Pepper, Precious and Panda." - ], - "Andrew": [ - "Andrew starts a new job as a financial analyst." - ], - "date": "27 March, 2023" - }, - "events_session_2": { - "Audrey": [ - "Audrey brings home her new puppy, Pixie and introduces it slowly to her other three dogs, Pepper, Precious and Panda." - ], - "Andrew": [ - "Andrew considers getting a pet dog and looks for dog-friendly apartments." - ], - "date": "2 April, 2023" - }, - "events_session_3": { - "Audrey": [], - "Andrew": [ - "Andrew and his girlfriend decide to explore new cafes and restaurants in their City but Andrew misses the peacefulness of being out in the nature." - ], - "date": "16 April, 2023" - }, - "events_session_4": { - "Audrey": [ - "Audrey goes for a hike and encounters a hummingbird." - ], - "Andrew": [ - "Andrew discovers a new open space for hiking close to the city and feels relieved about being able to explore nature conveniently while being in the city." - ], - "date": "3 May, 2023" - }, - "events_session_5": { - "Audrey": [ - "Audrey takes her four dogs on a road trip to a nearby national park." - ], - "Andrew": [ - "Andrew continues his search for a pet-friendly apartment and a pet dog, and is in the process of checking out animal shelters." - ], - "date": "6 May, 2023" - }, - "events_session_6": { - "Audrey": [ - "Audrey comes to know about a pet bonding workshop from a flyer at her local pet score which is scheduled for June 2023.", - "signs up for the pet bonding workshop which will teach positive reinforcement training to bond with pets." - ], - "Andrew": [ - "Andrew and his girlfriend invite friends to join them on a hike to explore the new open space that they found close to their apartment recently." - ], - "date": "11 May, 2023" - }, - "events_session_7": { - "Audrey": [ - "Audrey takes her four dogs to the park to play." - ], - "Andrew": [ - "Andrew is a bit discouraged in his search for pet-friendly apartments and a pet dog but does not give up." - ], - "date": "2 June, 2023" - }, - "events_session_8": { - "Audrey": [ - "Audrey finds a park with a trail and surrounded by trees which turns out to be a great spot to walk her dogs." - ], - "Andrew": [ - "Andrew and his friends join a rock climbing class and Andrew manages to make it to the top.", - "Andrew is inspired to explore more outdoor activities like kayaking and bungee-jumping after the rock-climbing class." - ], - "date": "13 June, 2023" - }, - "events_session_9": { - "Audrey": [ - "Audrey moves into a new house, one with a bigger backyard for her four dogs, and creates a play area in the backyard for them." - ], - "Andrew": [ - "Andrew visits a doggy daycare close to his place.", - "Andrew goes on a hike with his friends." - ], - "date": "26 June, 2023" - }, - "events_session_10": { - "Audrey": [ - "Audrey enrolls in a dog-training course to improve her ability to take care of her dogs." - ], - "Andrew": [ - "Andrew explores the hobby of cooking to compensate for not being able to hike much within the city." - ], - "date": "3 July, 2023" - }, - "events_session_11": { - "Audrey": [ - "Audrey takes her four dogs out for a long walk.", - "Andrew and Audrey decide to hike together next month with Audrey's dogs." - ], - "Andrew": [ - "Andrew and his girlfriend enjoy a picnic with friends in a park.", - "Andrew and Audrey decide to hike together next month with Audrey's dogs." - ], - "date": "8 July, 2023" - }, - "events_session_12": { - "Audrey": [ - "Audrey crafts personalized dog tags for each of her four dogs." - ], - "Andrew": [ - "Andrew finally adopts a German Shephard puppy named Toby after much search.", - "Andrew is stressed from work and misses hiking." - ], - "date": "11 July, 2023" - }, - "events_session_13": { - "Audrey": [ - "Audrey reflects on her close relationship with her childhood pet dog, Max." - ], - "Andrew": [ - "Andrew and his girlfriend volunteer in a local animal shelter to spend time and care for the animals." - ], - "date": "27 July, 2023" - }, - "events_session_14": { - "Audrey": [ - "Audrey enrolls her dogs in agility classes at a local dog park and practices with them in a nearby park twice every week." - ], - "Andrew": [ - "Andrew, his girlfriend and pet dog, Toby, make plans to go camping together in the weekend." - ], - "date": "4 August, 2023" - }, - "events_session_15": { - "Audrey": [ - "Audrey gets a second tattoo of all four of her dogs on her arm.", - "Audrey visits her local vet clinic to get her four dogs checked up and decides to take them one-by-one next time to avoid the chaos of dealing with all four of them simultaneously." - ], - "Andrew": [ - "Andrew and his girlfriend visit a local farm to pick some fresh produce for their dinner." - ], - "date": "16 August, 2023" - }, - "events_session_16": { - "Audrey": [ - "Audrey learns how to groom her dogs." - ], - "Andrew": [ - "Andrew decides to take an extra day off work to explore one of the cafes they have not been to yet." - ], - "date": "19 August, 2023" - }, - "events_session_17": { - "Audrey": [], - "Andrew": [ - "Andrew and his girlfriend go fishing in one of the lakes close to the city." - ], - "date": "24 August, 2023" - }, - "events_session_18": { - "Audrey": [ - "Audrey sets up a doggy-playdate with her neighbors\u2019 dogs.", - "Audrey goes shopping for dog beds for her four dogs." - ], - "Andrew": [ - "Andrew's work life becomes stressful and his hiking hobbies take a backseat.", - "Andrew continues his search for a pet-friendly apartment in the city to take better care of Toby." - ], - "date": "6 September, 2023" - }, - "events_session_19": { - "Audrey": [ - "Precious pulls at her leash and gets lost during a walk in the park, but Audrey chases her and finds her.", - "Audrey's flower and vegetable garden patch comes along." - ], - "Andrew": [ - "Andrew and his girlfriend try their hand at gardening, growing some flowers of their own." - ], - "date": "24 September, 2023" - }, - "events_session_20": { - "Audrey": [ - "Audrey takes her dogs to a nearby dog park during the weekend." - ], - "Andrew": [ - "Andrew plans a beach trip with his girlfriend and Toby." - ], - "date": "1 October, 2023" - }, - "events_session_21": { - "Audrey": [ - "Audrey takes a weekend trip to the beach with her dogs." - ], - "Andrew": [], - "date": "4 October, 2023" - }, - "events_session_22": { - "Audrey": [ - "Audrey reflects about the time she had a kneww injury and could not take her dogs for a walk.", - "Audrey makes jewelry with recycled objects like bottle caps, buttons, and broken jewelry, and donates the profits to an animal shelter." - ], - "Andrew": [], - "date": "6 October, 2023" - }, - "events_session_23": { - "Audrey": [ - "Audrey delivers homemade treats to her neighbors to thank them for their dog-friendly homes." - ], - "Andrew": [ - "Andrew spends an evening with his girlfriend and their dog playing board games and bonding.", - "Andrew plans to check out a new cafe in the city during the weekend." - ], - "date": "13 October, 2023" - }, - "events_session_24": { - "Audrey": [ - "Audrey goes to a pet store to buy toys for her dogs." - ], - "Andrew": [ - "Andrew adopts another furry friend from a shelter, in addition to his pup Toby, and names it Buddy." - ], - "date": "19 October, 2023" - }, - "events_session_25": { - "Audrey": [ - "Audrey injures her arm while walking her dogs at the park and struggles to care for them during recovery." - ], - "Andrew": [ - "Andrew and his girlfriend attend a wine tasting event, enjoys it and reflects on how stepping out of one's comfort zone can be rewarding.", - "Inspired by the fun he has at the wine-tasting event, Andrew considers trying sushi for the first time." - ], - "date": "24 October, 2023" - }, - "events_session_26": { - "Audrey": [ - "Audrey is concerned by the sudden abnormal behavior of her dogs and makes an appointment with an animal behaviorist to understand them better.", - "Andrew and Audrey plan to take their pets to a dog-friendly trail by the lake at a nearby national park." - ], - "Andrew": [ - "Andrew and Audrey plan to take their pets to a dog-friendly trail by the lake at a nearby national park.", - "Andrew goes on a picnic date with his girlfriend." - ], - "date": "28 October, 2023" - }, - "events_session_27": { - "Audrey": [ - "Audrey joins a dog owners' support group that meets once a week to find new ways to better care for her four dogs." - ], - "Andrew": [ - "Andrew and his girlfriend go for a bike ride and explore a new park in the outskirts of town." - ], - "date": "4 November, 2023" - }, - "events_session_28": { - "Audrey": [ - "Audrey gets her four dogs groomed together at a pet salon." - ], - "Andrew": [ - "Andrew and his girlfriend adopt a third pup and named it Scout, in addition to their pups Toby and Buddy." - ], - "date": "22 November, 2023" - } - }, - "observation": { - "session_1_observation": { - "Audrey": [ - [ - "Audrey got new collars and tags for her dogs.", - "D1:3" - ], - [ - "Audrey has had her dogs named Pepper, Precious, and Panda for 3 years.", - "D1:7" - ], - [ - "Audrey's dogs are city dogs and they love exploring new parks and trails.", - "D1:9" - ] - ], - "Andrew": [ - [ - "Andrew started a new job as a Financial Analyst last week.", - "D1:2" - ], - [ - "Andrew does not currently have any pets.", - "D1:12" - ], - [ - "Andrew loves animals and is awed by birds, especially eagles.", - "D1:14" - ], - [ - "Andrew enjoys spotting pretty birds while hiking in places like Fox Hollow.", - "D1:18" - ] - ] - }, - "session_2_observation": { - "Audrey": [ - [ - "Audrey and Andrew adopted another puppy called Pixie.", - "D2:1" - ], - [ - "Pixie has been keeping Audrey busy since they adopted her.", - "D2:3" - ], - [ - "Pixie has become friends with the other dogs in the house and they love playing together.", - "D2:5" - ], - [ - "Audrey used websites with filters to find a dog-friendly place for their pets.", - "D2:9" - ], - [ - "Audrey is excited about Andrew finding a furry friend and an apartment.", - "D2:13" - ], - [ - "Audrey enjoys exploring nature with her dogs.", - "D2:19" - ] - ], - "Andrew": [ - [ - "Andrew is considering getting a dog and finding a dog-friendly place in the city.", - "D2:8" - ], - [ - "Andrew misses exploring nature trails with his family's dog.", - "D2:18" - ], - [ - "Andrew finds hiking with a dog a great way to bond and create memories together.", - "D2:20" - ], - [ - "Andrew is super excited for what's next regarding finding a dog-friendly spot.", - "D2:24" - ] - ] - }, - "session_3_observation": { - "Andrew": [ - [ - "Andrew enjoys being out in nature and feels like he's really thriving when surrounded by it.", - "D3:1" - ], - [ - "Andrew misses the peacefulness of being out on a hike.", - "D3:1" - ], - [ - "Andrew finds nature really soothing and like hitting the reset button when life gets chaotic.", - "D3:5" - ], - [ - "Andrew and his girlfriend love discovering new places to eat around the city to wind down after a long week.", - "D3:11" - ], - [ - "Andrew found an awesome cafe with amazing pastries recently.", - "D3:13" - ] - ], - "Audrey": [ - [ - "Audrey finds being out in nature unbeatable, compared to being in concrete jungles.", - "D3:2" - ], - [ - "Audrey believes nature brings us back down to earth and reminds us that we're part of something bigger.", - "D3:4" - ], - [ - "Audrey takes her dogs for a walk in the park to recharge and find her center.", - "D3:6" - ], - [ - "Audrey enjoys making pastries and mentioned having a pastry party.", - "D3:18" - ], - [ - "Audrey has tattoos of her dogs on her arm symbolizing her love for them and nature's beauty.", - "D3:26" - ] - ] - }, - "session_4_observation": { - "Audrey": [ - [ - "Audrey had an amazing experience with a hummingbird on a hike.", - "D4:1" - ], - [ - "Audrey is busy taking care of her pets and finds it fulfilling.", - "D4:15" - ], - [ - "Audrey's dogs love going on hikes and exploring nature, their happy place.", - "D4:17" - ], - [ - "Audrey plays fetch with a ball or frisbee with her dogs and organizes doggie playdates in the park.", - "D4:21" - ], - [ - "Audrey's dogs get excited when she brings out toys for playtime.", - "D4:23" - ], - [ - "Audrey's dogs wear party hats for fun and treats.", - "D4:25" - ], - [ - "Audrey suggests a dog park near their usual walking spot for playdates with other dogs.", - "D4:25" - ], - [ - "Audrey's dog park is located near a park with lots of trees and benches.", - "D4:27" - ] - ], - "Andrew": [ - [ - "Andrew found a new open space to hike nearby that he finds refreshing.", - "D4:2" - ], - [ - "Andrew escapes the city at least once a weekend for hiking, which is his much-needed break.", - "D4:4" - ], - [ - "Andrew finds nature therapeutic and invigorating.", - "D4:6" - ], - [ - "Andrew captures special moments in nature to share the feeling with others.", - "D4:8" - ], - [ - "Andrew spreads positivity and joy through sharing peaceful feelings and motivating others.", - "D4:10" - ], - [ - "Andrew acknowledges that even small gestures can have a big effect in spreading good vibes.", - "D4:14" - ], - [ - "Andrew plans to check out the dog park location suggested by Audrey for his own dogs.", - "D4:28" - ] - ] - }, - "session_5_observation": { - "Andrew": [ - [ - "Andrew has been looking to adopt a dog, browsing websites, visiting shelters, and asking friends.", - "D5:1" - ], - [ - "Andrew is a person living in an apartment.", - "D5:3" - ], - [ - "Andrew is looking for a pet-friendly place near a park or woods to give the dog a large open space to run around.", - "D5:7" - ], - [ - "Andrew is contacting landlords and checking out neighborhoods to find a pet-friendly place to live.", - "D5:5" - ], - [ - "Andrew expressed a wish to go on a road trip with a furry companion.", - "D5:11" - ], - [ - "Andrew has a bucket list item of going on a road trip with his dog.", - "D5:13" - ] - ], - "Audrey": [ - [ - "Audrey goes on road trips with her dogs once every couple of months to help them explore and stay active.", - "D5:10" - ], - [ - "Audrey suggested considering the size of living space and exercise needs when choosing a dog breed.", - "D5:4" - ], - [ - "Audrey has a park near her that is great for her pup's walks.", - "D5:8" - ], - [ - "Audrey mentioned taking a road trip to a national park with her dogs and described it as an awesome trip.", - "D5:8" - ], - [ - "Audrey encouraged Andrew in his search for the perfect furry friend and offered to help with suggestions.", - "D5:18" - ], - [ - "Audrey will be expecting a picture of Andrew's dog soon.", - "D5:20" - ] - ] - }, - "session_6_observation": { - "Andrew": [ - [ - "Andrew had a great hike last weekend with friends and his girlfriend at a spot they recently found.", - "D6:1" - ], - [ - "Andrew is interested in hiking and finds nature peaceful.", - "D6:1" - ], - [ - "Andrew is curious about Audrey's workshop on bonding with her pets and thinks the dog in the picture is adorable.", - "D6:3" - ], - [ - "Andrew is supportive and encourages Audrey to keep him updated on her pets' progress and training.", - "D6:7" - ] - ], - "Audrey": [ - [ - "Audrey is taking care of her four dogs and ensuring they are happy and healthy.", - "D6:2" - ], - [ - "Audrey signed up for a workshop about bonding with pets to strengthen her bond with her dogs.", - "D6:2" - ], - [ - "Audrey learned about the workshop from a flyer at her local pet store which was about positive reinforcement training.", - "D6:4" - ], - [ - "Audrey believes in using positive reinforcement rather than punishment to train pets.", - "D6:12" - ], - [ - "Audrey is enthusiastic about training her pets better and believes they are quick learners who love rewards.", - "D6:6" - ], - [ - "Audrey is eager to update Andrew on her pets' progress and offer him tips on training for his future dog.", - "D6:8" - ] - ] - }, - "session_7_observation": { - "Audrey": [ - [ - "Audrey took her dogs to the park and enjoys watching them run around and play without a leash.", - "D7:1" - ], - [ - "Audrey took her dogs on a hike to a national park and reached a beautiful peak during the sunset.", - "D7:3" - ], - [ - "Audrey loves nature and feels cheerful and grateful when in natural surroundings.", - "D7:7" - ], - [ - "Audrey is supportive and offers to help Andrew find a pet-friendly spot for his new dog.", - "D7:11" - ] - ], - "Andrew": [ - [ - "Andrew is searching for a pet-friendly spot in the city but has not had luck so far.", - "D7:8" - ], - [ - "Andrew is determined to find the right place and dog despite facing challenges in the search.", - "D7:8" - ], - [ - "Andrew is thankful for Audrey's support and assistance in finding a pet-friendly spot.", - "D7:12" - ] - ] - }, - "session_8_observation": { - "Andrew": [ - [ - "Andrew recently took a rock climbing class and made it to the top, finding it challenging but satisfying.", - "D8:1" - ], - [ - "Andrew shared a photo of the view from the top of the rock he climbed during the rock climbing class.", - "D8:3" - ], - [ - "Andrew mentioned that he is a newbie at climbing but reached the top with the support and cheer from his friends.", - "D8:5" - ], - [ - "Andrew feels encouraged by the rock climbing experience to try more outdoor activities like kayaking and maybe bungee jumping.", - "D8:7" - ], - [ - "Andrew expressed interest in joining Audrey and her dogs for a walk in a park.", - "D8:9" - ] - ], - "Audrey": [ - [ - "Audrey found a great spot for her dogs' walk in a small park with a trail surrounded by trees.", - "D8:8" - ], - [ - "Audrey mentioned that her dogs love meeting new people and seem to enjoy their outdoor walks.", - "D8:10" - ], - [ - "Audrey usually walks her dogs for about an hour, allowing them to explore at their own pace.", - "D8:14" - ], - [ - "Audrey's dogs go home with a smile and tired after their walks, showing that they enjoy the exercise and exploration.", - "D8:16" - ], - [ - "Audrey shared a picture of her dogs in a meadow, expressing how happy they make her and how being outdoors with them puts her in a happy place.", - "D8:22" - ], - [ - "Audrey mentioned that being outdoors with her dogs brings her joy and peace.", - "D8:20" - ] - ] - }, - "session_9_observation": { - "Audrey": [ - [ - "Audrey got a new place with a bigger backyard for her dogs.", - "D9:1" - ], - [ - "Audrey set up a doggy play area in the backyard with agility stuff and toys for her dogs.", - "D9:5" - ], - [ - "Audrey loves animals and thinks they bring a lot of joy.", - "D9:11" - ], - [ - "Audrey expresses appreciation for nature and the calming effect it has on her.", - "D9:25" - ], - [ - "Audrey had a peaceful experience sitting by a stunning lake in the mountains with friends, finding it calming and memorable.", - "D9:23" - ] - ], - "Andrew": [ - [ - "Andrew appreciates friends who understand the love for pets.", - "D9:12" - ], - [ - "Andrew expresses missing the connection with nature and is looking forward to exploring more outdoors.", - "D9:20" - ], - [ - "Andrew enjoys outdoor activities like hiking and finds it rejuvenating to be in nature.", - "D9:18" - ], - [ - "Andrew enjoys capturing the beauty of nature through photography.", - "D9:18" - ] - ] - }, - "session_10_observation": { - "Audrey": [ - [ - "Audrey is taking a dog training course and it has been challenging but rewarding.", - "D10:1" - ], - [ - "Audrey's dogs are all mutts, with two being Jack Russell mixes and the other two Chihuahua mixes.", - "D10:7" - ], - [ - "Audrey's dogs are all 3 years old and get along well as a pack.", - "D10:9" - ], - [ - "Audrey's favorite recipe is Chicken Pot Pie, a family recipe that reminds her of her grandma's kitchen.", - "D10:13" - ], - [ - "Audrey loves trying out new recipes and experimenting in the kitchen as an escape to de-stress and let her creativity flow.", - "D10:17" - ], - [ - "Audrey's go-to ingredient in the kitchen is garlic, for the smell and taste it adds to dishes.", - "D10:21" - ], - [ - "Audrey loves to throw on some music, pour a glass of wine, and cook as a form of self-care and therapy.", - "D10:19" - ], - [ - "One of Audrey's favorite dishes to make is Roasted Chicken, inspired by her love for Mediterranean flavors and as a comfort meal.", - "D10:23" - ] - ], - "Andrew": [ - [ - "Andrew is finding new hobbies such as cooking more and trying out new recipes.", - "D10:12" - ], - [ - "Cooking has been helping Andrew de-stress, be creative, and express his creativity.", - "D10:16" - ], - [ - "Andrew finds cooking calming and a form of escape that allows him to try something new.", - "D10:18" - ], - [ - "Andrew appreciates cooking as a relaxing activity that he enjoys, especially the process of experimenting.", - "D10:18" - ], - [ - "Andrew is interested in trying new meal ideas, especially healthier ones.", - "D10:26" - ] - ] - }, - "session_11_observation": { - "Andrew": [ - [ - "Andrew had a picnic with his girlfriend last Friday and enjoys being in nature.", - "D11:1" - ], - [ - "Andrew is interested in getting a dog but finds it tough to find a place and the right dog.", - "D11:3" - ], - [ - "Andrew is planning a hike with Audrey and her dogs for next month.", - "D11:7" - ], - [ - "Andrew emphasizes the importance of finding a safe trail for the dogs to have fun hiking.", - "D11:11" - ] - ], - "Audrey": [ - [ - "Audrey enjoys going on walks with her dogs and finds them to be a source of happiness.", - "D11:2" - ], - [ - "Audrey got her dogs from a breeder nearby that had the dogs she wanted.", - "D11:4" - ], - [ - "Audrey agrees to join Andrew for a hike with her dogs and looks forward to seeing them enjoy themselves.", - "D11:6" - ], - [ - "Audrey values the safety of the dogs when outside running around.", - "D11:12" - ], - [ - "Audrey appreciates moments in nature and finds peace in vibrant colors.", - "D11:34" - ] - ] - }, - "session_12_observation": { - "Andrew": [ - [ - "Andrew has recently gotten a new puppy named Toby.", - "D12:1" - ], - [ - "Andrew mentioned feeling stressed due to work piling up and expressed a desire for the peace and freedom of going for a hike.", - "D12:3" - ], - [ - "Andrew has plans for a hike next month and invited Audrey and her pups to join along.", - "D12:5" - ], - [ - "Andrew is looking forward to the hike and for Toby to meet Audrey's pups.", - "D12:13" - ] - ], - "Audrey": [ - [ - "Audrey thinks Toby, Andrew's puppy, is adorable.", - "D12:2" - ], - [ - "Audrey mentioned making personalized tags for her dogs to show love and make them feel special.", - "D12:6" - ], - [ - "Audrey put a lot of love and effort into making the personalized tags for her pets.", - "D12:10" - ], - [ - "Audrey believes that small acts of love towards pets make a big difference in their relationships.", - "D12:12" - ], - [ - "Audrey is looking forward to the hike and for her pups to meet Toby.", - "D12:13" - ] - ] - }, - "session_13_observation": { - "Andrew": [ - [ - "Andrew and his girlfriend volunteered at a pet shelter on Monday and found it rewarding.", - "D13:1" - ], - [ - "Andrew loves animals and finds joy in spending time with them.", - "D13:1" - ], - [ - "Andrew believes that pets bring a lot of joy to life.", - "D13:7" - ], - [ - "Andrew considers pets as friends and confidantes, highlighting their role beyond just being pets.", - "D13:11" - ], - [ - "Andrew finds volunteering with animals to be one of the most rewarding experiences he's ever done.", - "D13:13" - ] - ], - "Audrey": [ - [ - "Audrey has four fur babies that are very important to her.", - "D13:4" - ], - [ - "Audrey's childhood dog was named Max, who had lots of energy and loved playing fetch.", - "D13:8" - ], - [ - "Audrey took long walks with Max in the neighborhood when she was a kid, sharing worries and hopes with him.", - "D13:10" - ], - [ - "Audrey believes that pets listen without judging and provide unconditional love, bringing joy, comfort, and love to our lives.", - "D13:12" - ] - ] - }, - "session_14_observation": { - "Andrew": [ - [ - "Andrew is in a relationship with a girlfriend named Toby.", - "D14:1" - ], - [ - "Andrew enjoys camping and spending time in nature.", - "D14:1" - ], - [ - "Andrew enjoys hiking and finds it the best outdoor activity.", - "D14:11" - ], - [ - "Andrew finds being in nature refreshing and calming, a break from city life.", - "D14:13" - ], - [ - "Andrew is interested in having a deep bond with his German Shepherd, Toby, and sees him as a great hiking buddy.", - "D14:17" - ] - ], - "Audrey": [ - [ - "Audrey is taking agility classes with her pups at a dog park.", - "D14:2" - ], - [ - "Audrey takes her dogs to the park twice a week for practice to bond with them.", - "D14:4" - ], - [ - "Audrey enjoys hiking and exploring nature with her pets.", - "D14:8" - ], - [ - "Audrey has a special bond with her pets, finding them to be her companions and sources of happiness.", - "D14:14" - ], - [ - "Audrey thinks German Shepherds, like Andrew's dog Toby, are awesome for their smartness and loyalty.", - "D14:20" - ], - [ - "Audrey gives advice that puppies, especially German Shepherds, need attention, walks, and energy to ensure their well-being.", - "D14:24" - ] - ] - }, - "session_15_observation": { - "Audrey": [ - [ - "Audrey got another tattoo of her four dogs on her arm as they mean a lot to her.", - "D15:1" - ], - [ - "Audrey has four dogs as pets and finds them to be enough for now to give them proper attention.", - "D15:3" - ], - [ - "Pepper, Pixie, Precious, and Panda are the names of Audrey's dogs, each having their own favorite spot to relax.", - "D15:5" - ], - [ - "Audrey's dogs cuddle up together when it's time to nap and are best friends.", - "D15:9" - ], - [ - "Audrey can't imagine life without her dogs as they mean the world to her.", - "D15:13" - ], - [ - "Audrey considers her pets to be family and believes they bring joy and happiness to her life.", - "D15:14" - ] - ], - "Andrew": [ - [ - "Andrew went to a farm with his girlfriend to get fresh veggies for dinner.", - "D15:2" - ], - [ - "Andrew shared a photo of his pet, Toby, at its favorite spot.", - "D15:6" - ], - [ - "Andrew appreciates the joy and comfort pets bring by finding their own little spots.", - "D15:6" - ], - [ - "Andrew acknowledges the happiness pets bring and can't imagine life without them.", - "D15:10" - ] - ] - }, - "session_16_observation": { - "Andrew": [ - [ - "Andrew took a break from work to check out a new cafe recently.", - "D16:1" - ], - [ - "Andrew enjoys hiking in the great outdoors.", - "D16:1" - ], - [ - "Andrew is heading to a nature reserve this weekend to reconnect with nature.", - "D16:13" - ] - ], - "Audrey": [ - [ - "Audrey learned a new skill related to dog grooming in August.", - "D16:2" - ], - [ - "Audrey cares for her pups and took on their grooming, feeling closer to them.", - "D16:2" - ], - [ - "Audrey took a dog grooming course to learn techniques.", - "D16:6" - ], - [ - "Audrey will be taking her dogs for a stroll in the park during the weekend.", - "D16:12" - ] - ] - }, - "session_17_observation": { - "Andrew": [ - [ - "Andrew went fishing last weekend with his girlfriend at a nearby lake and caught a few fish.", - "D17:1" - ], - [ - "Andrew is concerned about keeping his pets looking good when they are outdoors.", - "D17:3" - ], - [ - "Andrew believes that taking care of pets to ensure they are healthy and happy is important.", - "D17:5" - ], - [ - "Andrew appreciates the rewarding feeling of making a positive impact on the lives of those close to him.", - "D17:7" - ], - [ - "Andrew is looking forward to bonding better with his pet dog, Toby, and seeks tips on taking care of him.", - "D17:9" - ] - ], - "Audrey": [ - [ - "Audrey has never gone fishing before, but she enjoys chilling by lakes, especially in the mountains.", - "D17:2" - ], - [ - "Audrey believes that regular grooming, daily brushing, baths, nail trims, and lots of love are essential for keeping her pets healthy and happy.", - "D17:4" - ], - [ - "Audrey finds taking care of her dogs rewarding and takes that responsibility seriously.", - "D17:6" - ], - [ - "Audrey emphasizes the strong bond she has with her dogs and hopes Andrew can have a similar bond with Toby.", - "D17:8" - ], - [ - "Audrey offers tips to Andrew about taking care of his dog, Toby, emphasizing the importance of time in forming a bond.", - "D17:12" - ] - ] - }, - "session_18_observation": { - "Andrew": [ - [ - "Andrew has been finding work tough and stressful, causing his outdoor activities to take a backseat.", - "D18:1" - ], - [ - "Andrew tries to find balance in his life but finds it challenging.", - "D18:1" - ], - [ - "Andrew has been adding self-care activities to his daily routine like grabbing a coffee or going for a walk to recharge and chill out.", - "D18:5" - ], - [ - "Andrew enjoys hiking and finds it a relief to get away from the city.", - "D18:15" - ], - [ - "Andrew is looking for a dog-friendly place to live for his young pet Toby.", - "D18:17" - ] - ], - "Audrey": [ - [ - "Audrey emphasizes the importance of self-care and finding time for oneself.", - "D18:4" - ], - [ - "Audrey organized a doggy playdate with the neighbors' dogs, finding joy in seeing them have fun together.", - "D18:6" - ], - [ - "Audrey got new beds for her furry friends to provide them comfort as the weather cools down.", - "D18:10" - ], - [ - "Audrey's furry friends love the new beds, finding them cozy and comfortable.", - "D18:14" - ], - [ - "Audrey encourages Andrew in his search for a dog-friendly place to live for Toby.", - "D18:20" - ] - ] - }, - "session_19_observation": { - "Andrew": [ - [ - "Andrew expressed concern and empathy for Audrey's dog when she got scared and ran off at the park.", - "D19:3" - ], - [ - "Andrew is interested in building a strong relationship with dogs and seeks advice on the matter.", - "D19:5" - ], - [ - "Andrew believes patience and regular training are essential in building a strong relationship with dogs.", - "D19:7" - ], - [ - "Andrew finds joy in taking care of flowers and enjoys watching them grow in his garden.", - "D19:18" - ] - ], - "Audrey": [ - [ - "Audrey experienced a moment of worry when her dog's leash broke at the park, and she had to chase after her.", - "D19:2" - ], - [ - "Audrey finds strengthening the bond with her dogs through petting, hugs, and calmness during stressful situations.", - "D19:4" - ], - [ - "Audrey emphasizes the importance of patience, love, and regular training in building trust and a strong relationship with dogs.", - "D19:6" - ], - [ - "Audrey teaches her dogs obedience and tricks like sit, stay, shake, and roll over for fun and reward.", - "D19:8" - ], - [ - "Audrey's dogs are mutts; two are Jack Russell mixes, and two are Chihuahua mixes.", - "D19:12" - ], - [ - "Audrey has a small garden where she takes care of Peruvian Lilies, finding peace and relaxation in gardening.", - "D19:16" - ], - [ - "Audrey's Peruvian Lilies are easy to care for, requiring watering and enough sunlight.", - "D19:22" - ], - [ - "Audrey's Peruvian Lilies enjoy the garden, running around, exploring, and having fun.", - "D19:24" - ], - [ - "Audrey's furry companions mean everything to her and bring loads of love and happiness into her life.", - "D19:28" - ] - ] - }, - "session_20_observation": { - "Andrew": [ - [ - "Andrew is planning a beach trip with his girlfriend and friend Toby next month.", - "D20:1" - ], - [ - "Andrew is looking forward to a hike in nature with his friends and dogs.", - "D20:3" - ], - [ - "Andrew enjoys watching birds in nature and has read books about ecological systems.", - "D20:23" - ], - [ - "Andrew knows a thing or two about birdwatching and is willing to offer advice.", - "D20:15" - ], - [ - "Andrew plans to bring binoculars and a notebook for birdwatching.", - "D20:21" - ], - [ - "Andrew believes that nature is interconnected and humans need to take care of it.", - "D20:27" - ], - [ - "Andrew suggests reducing carbon footprint by biking or using public transport.", - "D20:33" - ], - [ - "Andrew knows some of the best bike routes near the river.", - "D20:37" - ] - ], - "Audrey": [ - [ - "Audrey is excited about a hike with Andrew and the dogs next month.", - "D20:2" - ], - [ - "Audrey finds walking the dogs in the park to be calming and peaceful.", - "D20:4" - ], - [ - "Audrey enjoys birdwatching and wants to explore more about it.", - "D20:14" - ], - [ - "Audrey believes in taking care of nature for future generations.", - "D20:28" - ], - [ - "Audrey sees recycling as an important step in preserving natural resources.", - "D20:32" - ], - [ - "Audrey is interested in trying biking as a way to reduce carbon footprint.", - "D20:34" - ], - [ - "Audrey is excited to check out bike routes near the river with Andrew.", - "D20:40" - ] - ] - }, - "session_21_observation": { - "Andrew": [ - [ - "Andrew used to hike a lot but finds it challenging now with work-life balance.", - "D21:5" - ], - [ - "Andrew expresses missing the outdoors and feels a void in his heart without being in nature.", - "D21:7" - ], - [ - "Andrew mentioned exploring nature as his escape and a way to find peace.", - "D21:7" - ], - [ - "Andrew is considering getting more plants for his house to bring some peace.", - "D21:9" - ] - ], - "Audrey": [ - [ - "Audrey took her dogs to the beach over the weekend and enjoyed seeing them play in the ocean.", - "D21:2" - ], - [ - "Audrey suggested ways for Andrew to appreciate nature in the city, like getting plants for his place or visiting the park on weekends.", - "D21:8" - ], - [ - "Audrey offered help and advice to Andrew regarding getting plants for his house.", - "D21:10" - ] - ] - }, - "session_22_observation": { - "Audrey": [ - [ - "Audrey had a knee injury that prevented her from walking her dogs, causing her to miss the joy they bring.", - "D22:1" - ], - [ - "Audrey makes jewelry out of recycled objects like bottle caps, buttons, and broken jewelry as a hobby.", - "D22:5" - ], - [ - "Audrey sells the jewelry she makes and donates a portion of the profits to an animal shelter.", - "D22:7" - ], - [ - "Audrey donates to an animal shelter as a way of combining her passion for making jewelry and making a difference.", - "D22:9" - ], - [ - "Audrey adapts and continues to find ways to help even if she can't volunteer at the shelter anymore.", - "D22:11" - ] - ], - "Andrew": [ - [ - "Andrew and Audrey discussed the joy of being able to walk dogs again after a knee injury.", - "D22:2" - ], - [ - "Andrew praised Audrey for using her passion for making jewelry to make a positive impact.", - "D22:8" - ] - ] - }, - "session_23_observation": { - "Andrew": [ - [ - "Andrew had a board game night with his girlfriend, Toby, last Tuesday.", - "D23:1" - ], - [ - "Andrew plans to check out a cozy cafe and hang out there this weekend.", - "D23:3" - ], - [ - "Andrew recommended a new spot with a sick view and vibe that just opened recently.", - "D23:5" - ], - [ - "Andrew's pup Toby is a German Shepherd who loves cute sweaters.", - "D23:13" - ], - [ - "Andrew's dog got excited and chased a squirrel at the dog park, which was hilarious.", - "D23:11" - ] - ], - "Audrey": [ - [ - "Audrey made goodies recently to thank her neighbors for their pup-friendly homes.", - "D23:2" - ], - [ - "Audrey's dogs love running around the park, but dislike snow and prefer sunny days in the grass.", - "D23:12" - ], - [ - "Audrey has a tattoo of her four dogs and mentioned going to the tattoo parlor to get more drawings on it.", - "D23:18" - ], - [ - "Audrey's dogs go crazy for Fetch and Frisbee at the park and love meeting other pups.", - "D23:14" - ], - [ - "Audrey enjoys moments with her dogs in nature as they are chill and happy companions for exploring the outdoors.", - "D23:24" - ] - ] - }, - "session_24_observation": { - "Audrey": [ - [ - "Audrey went to a pet store last Monday to buy toys for her dogs and realized how much she loves them and the joy they bring her.", - "D24:1" - ], - [ - "Audrey has dogs that bring her joy and companionship.", - "D24:4" - ], - [ - "Audrey is planning a hike with furry friends next month.", - "D24:13" - ], - [ - "Audrey is researching to find an awesome spot for the upcoming hike.", - "D24:19" - ] - ], - "Andrew": [ - [ - "Andrew adopted another pup from a shelter and named him Buddy.", - "D24:2" - ], - [ - "Andrew and Buddy enjoy doing walks together as a favorite activity.", - "D24:8" - ], - [ - "Andrew enjoys exploring hiking trails with Buddy and Toby.", - "D24:10" - ], - [ - "Andrew wishes to find a place far from the city to take his dogs for a hike.", - "D24:16" - ] - ] - }, - "session_25_observation": { - "Andrew": [ - [ - "Andrew attended an awesome wine tasting with his girlfriend last weekend and enjoyed trying unique wines.", - "D25:1" - ], - [ - "Andrew tried sushi for the first time and enjoyed it, understanding the hype around it.", - "D25:5" - ], - [ - "Andrew is curious about trying new things and stepped out of his comfort zone by trying sushi.", - "D25:5" - ] - ], - "Audrey": [ - [ - "Audrey had an accident while playing with her pups at the park, making it tricky to take care of them with one arm.", - "D25:2" - ], - [ - "Audrey loves sushi and advises trying different types and flavors without limiting oneself.", - "D25:6" - ], - [ - "Audrey suggests trying California or salmon roll as easier options for someone new to sushi, and mixing it up with different sauces and dips.", - "D25:8" - ], - [ - "Audrey is looking forward to ordering sushi for tonight.", - "D25:14" - ] - ] - }, - "session_26_observation": { - "Audrey": [ - [ - "Audrey has dogs that were not behaving normally, leading her to seek help from an animal behaviorist.", - "D26:1" - ], - [ - "Audrey mentioned her dogs are all mutts, where Pepper and Panda are Lab mixes, and Precious and Pixie are Chihuahua mixes.", - "D26:13" - ], - [ - "Audrey is using positive reinforcement techniques to help with her dogs' problems.", - "D26:5" - ], - [ - "Audrey is devoted to keeping her dogs healthy and happy.", - "D26:9" - ], - [ - "Audrey expressed excitement and eagerness to go hiking with Andrew and their furry friends.", - "D26:21" - ] - ], - "Andrew": [ - [ - "Andrew appears to be a supportive friend to Audrey, encouraging her and expressing well wishes for her dogs' well-being.", - "D26:10" - ], - [ - "Andrew plans to go hiking with Audrey, mentioning going on a picnic date with his girlfriend on Sunday.", - "D26:20" - ], - [ - "Andrew is enthusiastic about exploring nature and going on hikes with Audrey and their dogs.", - "D26:22" - ], - [ - "Andrew shared photos of a national park, a trail, and a dog with Audrey during the conversation.", - "D26:14, D26:34, D26:42" - ] - ] - }, - "session_27_observation": { - "Andrew": [ - [ - "Andrew went on a bike ride with his girlfriend last weekend to a park outside of town.", - "D27:1" - ], - [ - "Andrew is considering getting another dog but is unsure due to already having two dogs to take care of.", - "D27:5" - ], - [ - "Andrew is appreciative of Audrey's advice and support regarding taking care of dogs.", - [ - "D27:7", - "D27:9", - "D27:15", - "D27:17" - ] - ] - ], - "Audrey": [ - [ - "Audrey enjoys checking out new parks with her four dogs and spending time with them.", - "D27:2" - ], - [ - "Audrey meets up with other dog owners once a week for tips and playdates for the dogs.", - "D27:4" - ], - [ - "Audrey is supportive of Andrew's decision to focus on his current dogs before considering getting another one.", - [ - "D27:6", - "D27:8" - ] - ], - [ - "Audrey provides Andrew with tips on keeping dogs happy and active in the city.", - [ - "D27:10", - "D27:12" - ] - ], - [ - "Audrey engages her dogs in mental stimulation activities like puzzles, training, and hide-and-seek.", - "D27:14" - ], - [ - "Audrey values the friendship with Andrew and believes in supporting each other.", - "D27:18" - ] - ] - }, - "session_28_observation": { - "Audrey": [ - [ - "Audrey has multiple dogs that she refers to as her 'fur kids'.", - "D28:1" - ], - [ - "Audrey took her dogs to the pet salon last Friday and was happy to see them looking cute and groomed.", - "D28:1" - ], - [ - "Audrey worries about her dogs when they are in new places but was relieved to see them well-behaved at the salon.", - "D28:3" - ], - [ - "Audrey values seeing her dogs happy and settled in new environments, indicating her care as a dog owner.", - "D28:5" - ], - [ - "Audrey appreciates creating a safe and fun space for her dogs, ensuring they feel secure.", - "D28:13" - ], - [ - "Audrey's dogs feel loved and safe under her care.", - "D28:15" - ], - [ - "Audrey considers her dogs part of her family and expresses gratitude for having them in her life.", - "D28:17" - ] - ], - "Andrew": [ - [ - "Andrew has at least 3 dogs named Scout, Toby, and Buddy.", - "D28:10" - ], - [ - "Andrew adopted a new dog named Scout because of their adventurous spirit.", - "D28:6" - ], - [ - "Andrew values safety and comfort for his dogs, providing them with essentials like a bed, toys, and puppy pads.", - "D28:12" - ], - [ - "Andrew plans to introduce the new dog, Scout, to his other dogs slowly to ensure they get along.", - "D28:13" - ], - [ - "Andrew expresses gratitude for having Scout and appreciates having multiple furry friends.", - "D28:14" - ], - [ - "Andrew considers his dogs as family members and acknowledges the joy and friendship they bring.", - "D28:18" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "At 1:10 pm on 27 March 2023, Audrey and Andrew caught up. Andrew shared about his new job as a Financial Analyst, whereas Audrey talked about getting new collars for her dogs named Pepper, Precious, and Panda that she has had for 3 years. The dogs, city dwellers, go on adventures exploring parks and trails together. Andrew loves animals, particularly birds, with a fascination for eagles. He recommended the Fox Hollow trail for hiking to Audrey, who planned to give it a try. They bid goodbye, wishing each other well.", - "session_2_summary": "Audrey surprises Andrew with a new puppy named Pixie on 2 April 2023, at 2:42 pm. The puppy is fitting in well with the other dogs. Andrew is considering getting a dog too, and Audrey suggests using websites to find dog-friendly places in the city. They discuss the joys of having furry companions and the memories of hiking with dogs. Andrew expresses excitement about finding a dog-friendly spot soon. Audrey wishes him the best in his search, and they say goodbye.", - "session_3_summary": "At 4:19 pm on 16 April, 2023, Andrew told Audrey about trying a new cafe in the city and missing nature. Audrey agreed and mentioned how nature brings peace. They discussed how nature soothes and centers them. Andrew expressed missing hiking due to city living. They talked about the importance of nature and shared their ways of connecting to it. Audrey suggested a pastry party, and Andrew agreed. They planned to have their dogs join the party. Audrey revealed getting tattoos of her dogs to show her love for them. Andrew admired the tattoo, and Audrey explained the significance of the tattoo. They bonded over their love for their furry friends and the joy they bring.", - "session_4_summary": "Audrey and Andrew caught up at 5:41 pm on 3 May, 2023. Audrey shared her recent hiking experience with a hummingbird, while Andrew mentioned finding a new hiking spot. Andrew now escapes the city for a hike once a weekend. They both find nature therapeutic and calming. Audrey spends time with her pets, including hiking with her dogs. Andrew inquired about dog safety tips from Audrey, who also mentioned playing fetch and organizing doggie playdates. The conversation ended with Andrew planning to visit the dog park Audrey recommended.", - "session_5_summary": "Andrew and Audrey discussed Andrew's search for a dog to adopt at 10:47 am on 6 May, 2023. Andrew talked about the challenges and joys of the process, considering factors like living space and breed requirements. Audrey offered advice on considering a dog's space and energy needs. Andrew shared his difficulties in finding pet-friendly housing near nature. They also discussed the joy of road trips with dogs, with Audrey sharing her experience and Andrew expressing his desire to do the same. Audrey wished Andrew luck in finding the perfect furry friend and offered to help. They exchanged goodbyes, with Andrew heading to another shelter and Audrey looking forward to updates on the search.", - "session_6_summary": "Andrew and Audrey chatted at 2:03 pm on 11 May, 2023. Andrew shared his recent peaceful hike with his girlfriend. Audrey discussed taking care of her four dogs and signing up for a workshop on bonding with pets. Andrew showed interest in the workshop and praised Audrey's efforts. Audrey shared her positive reinforcement approach with dogs and emphasized the importance of not using punishment. They agreed to keep each other updated, with Andrew showing interest in attending the workshop too. The conversation ended with plans to talk later.", - "session_7_summary": "Audrey and Andrew had a conversation on 2 June, 2023, at 11:27 am. Audrey shared her joy of taking her dogs to the park and on a hiking trip to a national park, capturing beautiful sunset moments. Andrew admired the experiences and inquired about photos, showing interest in getting a furry friend but facing challenges. Audrey offered support and assistance, encouraging Andrew not to give up on finding the perfect place for a new pet.", - "session_8_summary": "Andrew and Audrey caught up at 5:23 pm on 13 June, 2023. Andrew shared his thrilling rock climbing experience from the previous Sunday, expressing his newfound love for outdoor activities. Audrey admired his achievement and requested to see pictures, leading to a discussion about the challenging climb and the stunning view. They discussed the importance of a supportive group and how nature pushes them out of their comfort zones. Audrey invited Andrew to join her and her dogs for a walk in a beautiful park, where they would explore for about an hour. Both agreed on the joy and peace nature brings, with Audrey sharing photos of her happy dogs in nature. They concluded with a mutual appreciation for how nature refreshes the mind and provides a different outlook on life.", - "session_9_summary": "Audrey, at 1:51 pm on 26 June, 2023, shared her excitement about moving to a new house with a bigger backyard for her dogs. Andrew praised the new place and asked about the dogs settling in. Audrey mentioned creating a doggy play area in the backyard. Andrew recommended a doggy daycare he visited and Audrey showed interest. They discussed their love for animals and shared nature experiences, emphasizing the importance of appreciating the small things in life.", - "session_10_summary": "Audrey and Andrew caught up at 8:32 pm on 3 July, 2023. Audrey mentioned taking a challenging but rewarding dog training course while Andrew expressed difficulty finding pet-friendly spots. They discussed their lack of time for exploring trails but Audrey shared how well her dogs were doing due to the training. Andrew admired her dogs and asked about their breeds and favorite activities. Audrey revealed they were all mutts who loved running and playing fetch. They further discussed the age of the dogs, their good relationship, and a recent doggy playdate. Andrew shared his new hobby of cooking while Audrey disclosed her love for it and her favorite recipe, Chicken Pot Pie. Andrew showed interest in trying the recipe, and Audrey planned to share it. They also discussed the therapeutic nature of cooking and the joy of trying new dishes. Audrey mentioned garlic as her go-to ingredient and offered to share a Roasted Chicken recipe inspired by Mediterranean flavors. Andrew expressed excitement to try it and thanked her for sharing, ending the conversation with plans to talk later.", - "session_11_summary": "Andrew and Audrey had a conversation at 9:48 am on 8 July, 2023, where they discussed their love for nature and their dogs. Andrew mentioned having a picnic with his girlfriend, while Audrey talked about her furry friends and a recent walk. They discussed the difficulty of finding dog-friendly places and planned a hike together for the next month. They both expressed excitement about the upcoming adventure and shared photos of their happy dogs. They also admired nature's beauty and the joy it brings. Their conversation highlighted their appreciation for the small moments in life and the happiness that dogs bring.", - "session_12_summary": "Andrew and Audrey met at 10:05 am on 11 July, 2023. Andrew introduced his puppy Toby, expressing how much joy he brings despite the city living. Audrey congratulated him and suggested they plan a hiking trip to relax. Andrew agreed, inviting her and their pups. Audrey showed personalized tags she made for her dogs, which Andrew admired. They both expressed love for their pets and agreed on the importance of showing it. They eagerly anticipated the upcoming hike, looking forward to their pets meeting and the fun they would have. They ended the conversation, excited and ready to prepare for the adventure ahead.", - "session_13_summary": "At 3:52 pm on 27 July 2023, Andrew excitedly shared his recent volunteering experience at a pet shelter with Audrey, emphasizing the joy and love animals bring. Audrey, intrigued, expressed her desire to volunteer someday and shared how her four pets each have unique personalities. The conversation turned nostalgic as they discussed Audrey's childhood dog, Max, and the special bond they shared. They both agreed that pets are more than animals; they are friends who provide comfort and love. Andrew highlighted the uplifting impact of volunteering with animals, which Audrey appreciated, acknowledging the positive effect their kindness has on the animals' well-being.", - "session_14_summary": "On August 4th, 2023, at 11:05 am, Andrew told Audrey about his upcoming camping trip with his girlfriend. Audrey shared her experience with agility classes for her dogs, emphasizing the joy of watching them overcome challenges. They discussed the beauty of nature and the benefits of spending time outdoors. Audrey advised Andrew on caring for his German Shepherd, Toby, emphasizing the importance of energy and attention. Andrew expressed his excitement to bond with Toby on hikes. The conversation highlighted the special bond between humans and animals, as Audrey shared her love for her pets and Andrew looked forward to deepening his connection with Toby.", - "session_15_summary": "Audrey, at 9:58 pm on 16 August, 2023, excitedly told Andrew she got a tattoo of her four dogs. She shared that she took the dogs to the vet together last time which was hectic, and they each have a favorite spot to relax at home. Andrew mentioned visiting a farm for fresh veggies with his girlfriend. The two admire each other's pet photos and discuss the joy pets bring. Audrey shared a photo of her dogs snuggling, and Andrew showed his dog Toby's favorite spot. They discussed how pets always find comfort and joy in their cozy spaces. Audrey expressed how much her dogs mean to her, saying they are best friends and bring happiness. Andrew agreed, calling pets family and mentioning the joy they bring. Audrey shared a photo of her lying on the grass with her dogs, reflecting on the wonderful time they had outside. Andrew encouraged her to cherish those moments, to which Audrey expressed gratitude, saying her pets make life brighter.", - "session_16_summary": "Andrew and Audrey had a conversation at 9:19 pm on 19 August, 2023. Andrew shared about taking a break to visit a cafe and reminisced about the tranquility of nature. He also showed Audrey a serene photo from his previous hike. Audrey mentioned learning dog grooming, feeling closer to her pups, and shared a picture of them looking cute and fluffy post-grooming. Andrew praised her skills and sought grooming tips, which Audrey gladly provided. They discussed weekend plans - Audrey taking her dogs to the park for a stroll and Andrew heading to a nature reserve. Andrew promised to share photos from his trip with Audrey later. They bid goodbye, looking forward to the pictures and caring for their pets.", - "session_17_summary": "On August 24, 2023, at 12:24 am, Andrew told Audrey about his recent fishing trip with his girlfriend. Audrey shared a memory of sitting by a lake in the mountains. They discussed pet care, with Audrey emphasizing grooming and love to keep pets happy. They highlighted the importance of forming strong bonds with pets, with Audrey offering tips to Andrew for his dog Toby. Andrew expressed excitement for bonding with Toby and future outdoor adventures. The conversation ended with compliments and well wishes between the two friends.", - "session_18_summary": "At 7:49 pm on 6th September 2023, Andrew and Audrey had a conversation. Andrew shared his work stress and challenge in finding balance in life. Audrey emphasized the importance of self-care and finding joy in simple activities. They discussed hobbies and moments of relaxation. Audrey shared about organizing a dog playdate and getting new beds for her furry friends. Andrew inquired about the playdate and new beds with interest. Audrey shared pictures of the playdate and the new beds, expressing joy in her pets' happiness. They discussed the importance of simple joys and bonding with animals. Andrew shared about his hike, while Audrey asked about hiking with Toby. Andrew mentioned the challenge of finding a dog-friendly place to live. Audrey encouraged him not to give up and to keep searching. They ended by exchanging well wishes and encouragement.", - "session_19_summary": "Andrew and Audrey caught up at 5:53 pm on 24 September, 2023. Audrey shared a recent incident where her dog's leash broke, causing a scare but strengthening their bond. She advised patience and regular training to build trust with dogs. They discussed training tricks, going for walks, and Audrey's mutt breeds. Andrew admired Audrey's garden with Peruvian Lilies, easy to care for and enjoyed by her dogs. They shared the joy their furry pals bring into their lives, highlighting the strong bond and happiness they provide.", - "session_20_summary": "Andrew and Audrey had a conversation at 7:09 pm on 1 October 2023. Andrew shared that he and his girlfriend will be going to the beach next month with Toby. Audrey expressed her excitement for an upcoming hike with the dogs, highlighting how being in nature helps her find peace. Andrew agreed and mentioned missing being in nature. They discussed the benefits of nature, birdwatching, and taking care of the environment for future generations. They also talked about reducing the carbon footprint by biking and using public transport. Andrew offered to show Audrey the best bike routes near the river. They both looked forward to exploring the routes and enjoying the scenery together.", - "session_21_summary": "4:18 pm on 4 October, 2023 - Andrew reconnects with Audrey, who shares about her fun beach trip with her dogs. Andrew reminisces about missing nature due to work. Audrey suggests getting plants for his house. Andrew thanks Audrey for her help and offers. They exchange goodbyes, wishing each other a great week.", - "session_22_summary": "Audrey and Andrew had a conversation at 9:41 pm on 6 October, 2023. Audrey shared about missing walking her dogs due to a knee injury but finding joy in being able to walk them again. Andrew empathized and praised the joy dogs bring, encouraging appreciation for life's little pleasures. Audrey mentioned making jewelry from recycled items as a hobby, selling them to donate a portion of the profits to an animal shelter. Andrew admired her dedication to making a difference. Audrey highlighted the importance of adapting in challenging times. Andrew praised her for staying strong and making a difference.", - "session_23_summary": "Andrew and Audrey had a conversation at 4:22 pm on 13th October 2023 where they shared updates about their lives. Andrew talked about a board game night with his girlfriend, Toby, and his plan to visit a cozy cafe. Audrey mentioned baking goodies for her neighbors and discussed taking her dogs to a new spot and a dog park. They shared funny stories about their pups, discussed their love for nature, and the joy their dogs bring. Audrey also mentioned getting a tattoo of her dogs. They agreed that nature and animals bring peace and joy. Andrew recommended hiking as a way to bond and make memories with dogs.", - "session_24_summary": "Audrey and Andrew, both pet lovers, discussed their furry companions at 6:12 pm on 19 October, 2023.\nAudrey shared her joy after buying toys for her dogs, while Andrew excitedly announced the adoption of a new pup named Buddy. The two friends talked about their pets' interactions and favorite activities like walks and exploring hiking trails. They planned to go on a hike together next month. Audrey volunteered to find a great spot for the outing as Andrew wished for a getaway from the city. Andrew appreciated Audrey's efforts and looked forward to the adventure.", - "session_25_summary": "Andrew and Audrey conversed at 10:14 am on 24 October 2023. Andrew shared his experience at a wine tasting, stepping out of his comfort zone, while Audrey mentioned an accident with her pups at the park. They discussed trying new things, with Andrew trying sushi recently and Audrey giving him tips on trying different types. Andrew expressed excitement about his upcoming sushi adventure, while Audrey planned to order sushi for dinner. They ended the conversation wishing each other well.", - "session_26_summary": "Audrey informed Andrew about her dogs' behavior issues, her appointment with an animal behaviorist, and the positive tips she received for handling the problems. The conversation then shifted to planning a nature hike with their furry friends on Saturday, exploring a trail by a lake with scenic views. They expressed excitement and gratitude for the upcoming adventure, sharing photos of potential locations and expressing eagerness to capture memories with their dogs. Andrew shared a photo of his happy dog from a previous hike, and they both looked forward to creating new joyful memories together. They planned to meet for the hike and said their goodbyes as they got ready to head out.", - "session_27_summary": "Andrew told Audrey about his weekend bike ride with his girlfriend, discovering a park outside the city. Audrey shared her love for exploring parks with her four dogs, mentioning joining a dog owners group for tips. Andrew considered getting another dog but decided to focus on his current pets first. Audrey advised on taking care of his current dogs before getting more, suggesting regular socialization and exercise for keeping them happy in the city. She also recommended mental stimulation activities like puzzles and hide-and-seek. Andrew appreciated Audrey's support and friendship, thanking her for the advice.", - "session_28_summary": "Audrey and Andrew caught up at 9:02 am on 22 November, 2023. Audrey shared about taking her dogs to the pet salon and showing them off all groomed up. Andrew admired the pictures and mentioned adopting a new dog named Scout. They discussed taking Scout, Toby, and Buddy to a park together for a fun outing. Audrey advised on introducing Scout to the other dogs slowly and creating a safe space for the new pup. Andrew was grateful for the advice and expressed how dogs bring joy and friendship. Audrey also shared how her dogs are doing well and bring her happiness. Both concluded that having dogs as family members is truly special." - }, - "sample_id": "conv-44" - }, - { - "qa": [ - { - "question": "What are John's suspected health problems?", - "answer": "Obesity", - "evidence": [ - "D1:27" - ], - "category": 3 - }, - { - "question": "Which recreational activity was James pursuing on March 16, 2022?", - "answer": "bowling", - "evidence": [ - "D1:26" - ], - "category": 2 - }, - { - "question": "Which places or events have John and James planned to meet at?", - "answer": "VR Club, McGee's, baseball game", - "evidence": [ - "D1:36", - "D21:15", - "D23:5", - "D23:6" - ], - "category": 1 - }, - { - "question": "Do both James and John have pets?", - "answer": "No", - "evidence": [ - "D1:12", - "D2:18" - ], - "category": 1 - }, - { - "question": "When did John resume playing drums in his adulthood?", - "answer": "February 2022", - "evidence": [ - "D3:5" - ], - "category": 2 - }, - { - "question": "What are John and James' favorite games?", - "answer": "John's favorite game is CS:GO, and James's is Apex Legends.", - "evidence": [ - "D3:11", - "D4:16" - ], - "category": 1 - }, - { - "question": "Does James live in Connecticut?", - "answer": "Likely yes", - "evidence": [ - "D5:1" - ], - "category": 3 - }, - { - "question": "In which state is the shelter from which James adopted the puppy?", - "answer": "Connecticut.", - "evidence": [ - "D5:1" - ], - "category": 3 - }, - { - "question": "How many pets does James have?", - "answer": "Three dogs.", - "evidence": [ - "D1:12", - "D1:14", - "D5:1" - ], - "category": 1 - }, - { - "question": "What are the names of James's dogs?", - "answer": "Ned, Daisy, Max", - "evidence": [ - "D1:14", - "D5:1" - ], - "category": 1 - }, - { - "question": "When did James adopt Ned?", - "answer": "first week of April 2022", - "evidence": [ - "D5:1" - ], - "category": 2 - }, - { - "question": "How was John feeling on April 10, 2022?", - "answer": "seeking solitude", - "evidence": [ - "D6:7" - ], - "category": 2 - }, - { - "question": "Did James have a girlfriend during April 2022?", - "answer": "Presumably not", - "evidence": [ - "D6:6" - ], - "category": 3 - }, - { - "question": "When did James visit Italy?", - "answer": "In 2021", - "evidence": [ - "D6:12" - ], - "category": 2 - }, - { - "question": "When did James buy himself a new adventure book?", - "answer": "April 26, 2022", - "evidence": [ - "D8:11" - ], - "category": 2 - }, - { - "question": "When did James start playing Civilization VI?", - "answer": "March 2022", - "evidence": [ - "D8:29" - ], - "category": 2 - }, - { - "question": "What is the game with different colored cards that was John talking about with James?", - "answer": "UNO", - "evidence": [ - "D8:34" - ], - "category": 3 - }, - { - "question": "What is the board game where you have to find the imposter that John mentions to James?", - "answer": "Mafia", - "evidence": [ - "D8:36" - ], - "category": 3 - }, - { - "question": "Which books has John recommended to James?", - "answer": "The Name of the Wind, Stormlight Archive, Kingkiller Chronicles, Expanse", - "evidence": [ - "D8:14", - "D14:10" - ], - "category": 1 - }, - { - "question": "Was James feeling lonely before meeting Samantha?", - "answer": "Most likely yes, because he mentioned that the only creatures that gave him joy are dogs and he was actively trying to date.", - "evidence": [ - "D9:16" - ], - "category": 3 - }, - { - "question": "How many charity tournaments has John organized till date?", - "answer": "two", - "evidence": [ - "D10:2", - "D29:1" - ], - "category": 1 - }, - { - "question": "When did John first organize a charity tournament with his friends?", - "answer": "May 7, 2022", - "evidence": [ - "D10:2" - ], - "category": 2 - }, - { - "question": "Who or which organizations have been the beneficiaries of John's charity tournaments?", - "answer": "animal shelter, homeless, children's hospital", - "evidence": [ - "D10:10", - "D10:12", - "D29:1" - ], - "category": 1 - }, - { - "question": "When will John start his new job?", - "answer": "In July, 2022", - "evidence": [ - "D13:5" - ], - "category": 2 - }, - { - "question": "What kind of games has James tried to develop?", - "answer": "football simulator, virtual world inspired by Witcher 3", - "evidence": [ - "D13:7", - "D1:4", - "D27:2" - ], - "category": 1 - }, - { - "question": "Are John and James fans of the same football team?", - "answer": "No, James is a Liverpool fan and John is a Manchester City fan.", - "evidence": [ - "D13:12", - "D13:15" - ], - "category": 3 - }, - { - "question": "Which countries has James visited?", - "answer": "Italy, Mexico, Turkey, Canada, Greenland", - "evidence": [ - "D6:12", - "D6:14", - "D16:9", - "D17:22" - ], - "category": 1 - }, - { - "question": "What kind of classes has James joined?", - "answer": "game design course, cooking classes", - "evidence": [ - "D13:6", - "D23:13" - ], - "category": 1 - }, - { - "question": "When did James volunteer at an organization?", - "answer": "May 2022", - "evidence": [ - "D15:9" - ], - "category": 2 - }, - { - "question": "When did James depart for his trip to Canada?", - "answer": "July 11, 2022", - "evidence": [ - "D16:9" - ], - "category": 2 - }, - { - "question": "Which country did James book tickets for in July 2022?", - "answer": "Canada", - "evidence": [ - "D16:9", - "D16:11" - ], - "category": 3 - }, - { - "question": "How many days did James plan to spend on his trip in Canada?", - "answer": "19 days", - "evidence": [ - "D16:9", - "D16:13" - ], - "category": 2 - }, - { - "question": "Where was James at on July 12, 2022?", - "answer": "Toronto, Canada", - "evidence": [ - "D16:9" - ], - "category": 2 - }, - { - "question": "Did John and James study together?", - "answer": "Yes", - "evidence": [ - "D17:13" - ], - "category": 3 - }, - { - "question": "Which countries did James visit in July 2022?", - "answer": "Canada, Greenland", - "evidence": [ - "D16:9", - "D17:22" - ], - "category": 1 - }, - { - "question": "What additional country did James visit during his trip to Canada?", - "answer": "Greenland", - "evidence": [ - "D17:22" - ], - "category": 3 - }, - { - "question": "Who is Jill?", - "answer": "Most likely John's partner.", - "evidence": [ - "D17:24" - ], - "category": 3 - }, - { - "question": "When did John spend time with his sister and dogs?", - "answer": "July 21, 2022", - "evidence": [ - "D17:28" - ], - "category": 2 - }, - { - "question": "What happened to John's job situation in 2022?", - "answer": "quit his IT Job, secured his dream job, aspires to become an eSports competition organizer", - "evidence": [ - "D4:36", - "D18:1", - "D18:7" - ], - "category": 1 - }, - { - "question": "When did John start his job in IT?", - "answer": "2019", - "evidence": [ - "D18:1" - ], - "category": 2 - }, - { - "question": "What kind of tricks do James's pets know?", - "answer": "swimming, catching frisbees, balancing on a skateboard, sit, stay, paw, and rollover", - "evidence": [ - "D2:17", - "D14:17", - "D14:23", - "D17:16" - ], - "category": 1 - }, - { - "question": "When did James meet Samantha?", - "answer": "August 9, 2022", - "evidence": [ - "D19:12" - ], - "category": 2 - }, - { - "question": "When did James take his 3 dogs to the beach?", - "answer": "August 9, 2022", - "evidence": [ - "D19:12" - ], - "category": 2 - }, - { - "question": "When did John plan his next meeting with his siblings?", - "answer": "In September, 2022", - "evidence": [ - "D20:17" - ], - "category": 2 - }, - { - "question": "Why didn't John want to go to Starbucks?", - "answer": "Possibly because he likes to drink beer on his days off.", - "evidence": [ - "D21:12", - "D21:14" - ], - "category": 3 - }, - { - "question": "What kind of beer does McGee's bar serve?", - "answer": "Stout, lager", - "evidence": [ - "D21:15", - "D21:17", - "D23:3" - ], - "category": 1 - }, - { - "question": "When did John and James meet at McGee's bar?", - "answer": "August 27, 2022", - "evidence": [ - "D21:18" - ], - "category": 2 - }, - { - "question": "When did James ask Samantha to be his girlfriend?", - "answer": "September 3, 2022", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "When did James, Samantha and John go to the baseball game together?", - "answer": "September 11, 2022", - "evidence": [ - "D23:5", - "D23:6" - ], - "category": 2 - }, - { - "question": "What gaming equipments did John buy or refurbish?", - "answer": "Sennheiser headphones, Logitech mouse, gaming desk", - "evidence": [ - "D23:8", - "D23:10", - "D20:9" - ], - "category": 1 - }, - { - "question": "When did James start taking cooking classes?", - "answer": "September 2, 2022", - "evidence": [ - "D23:13" - ], - "category": 2 - }, - { - "question": "Which new games did John start play during the course of the conversation with James?", - "answer": "AC Valhalla, Witcher 3, FIFA 23, Dungeons of the Dragons, futuristic dystopian game", - "evidence": [ - "D5:4", - "D19:7", - "D30:14", - "D24:1", - "D24:3", - "D8:20" - ], - "category": 1 - }, - { - "question": "When did John start working on his 2D Adventure mobile game?", - "answer": "approximately summer of 2022", - "evidence": [ - "D25:9" - ], - "category": 2 - }, - { - "question": "How long did it take for James to complete his Witcher-inspired game?", - "answer": "six months", - "evidence": [ - "D6:1", - "D27:2" - ], - "category": 2 - }, - { - "question": "What kind of programming-related events has John hosted?", - "answer": "online programming competition, programming seminar", - "evidence": [ - "D27:1", - "D28:6" - ], - "category": 1 - }, - { - "question": "When did John and his programming friends host an online programming competition?", - "answer": "Last week before 13 October 2022.", - "evidence": [ - "D27:1" - ], - "category": 2 - }, - { - "question": "Which of James's family members have visited him in the last year?", - "answer": "mother, sister", - "evidence": [ - "D17:28", - "D28:19" - ], - "category": 1 - }, - { - "question": "When did James' mother and her friend visit him?", - "answer": "October 19, 2022", - "evidence": [ - "D28:19" - ], - "category": 2 - }, - { - "question": "When did James try Cyberpunk 2077 game?", - "answer": "October 20, 2022", - "evidence": [ - "D28:27" - ], - "category": 2 - }, - { - "question": "When did John and his gaming friends organize the charity tournament?", - "answer": "On the night of October 30 to 31, 2022", - "evidence": [ - "D29:1" - ], - "category": 2 - }, - { - "question": "What games has John played with his friends at charity tournaments?", - "answer": "CS:GO, Fortnite, Overwatch and Apex Legends", - "evidence": [ - "D10:4", - "D29:1", - "D29:3" - ], - "category": 1 - }, - { - "question": "What was James' big moment with Samantha in October 2023?", - "answer": "They decided to live together and rented an apartment not far from McGee's bar.", - "evidence": [ - "D29:8", - "D29:10" - ], - "category": 2 - }, - { - "question": "How long did James and Samantha date for before deciding to move in together?", - "answer": "nearly three months", - "evidence": [ - "D19:14", - "D29:8", - "D29:10" - ], - "category": 2 - }, - { - "question": "When did James, his family and his dogs start on a road trip together?", - "answer": "November 4, 2022", - "evidence": [ - "D30:1" - ], - "category": 2 - }, - { - "question": "How long did John practice chess for before winning the chess tournament?", - "answer": "nearly four months", - "evidence": [ - "D17:1", - "D30:2", - "D30:4" - ], - "category": 2 - }, - { - "question": "When did James and his family visit Mark and Josh?", - "answer": "November 7, 2022", - "evidence": [ - "D31:1" - ], - "category": 2 - }, - { - "question": "When did John work with a game developer on a project?", - "answer": "November 5-6, 2022", - "evidence": [ - "D31:2" - ], - "category": 2 - }, - { - "question": "What programming languages has James worked with?", - "answer": "Python and C++", - "evidence": [ - "D1:8" - ], - "category": 4 - }, - { - "question": "What type of mobile application does James plan to build with John?", - "answer": "An app for dog walking and pet care", - "evidence": [ - "D1:14" - ], - "category": 4 - }, - { - "question": "How does James plan to make his dog-sitting app unique?", - "answer": "By allowing users to customize their pup's preferences/needs", - "evidence": [ - "D1:16" - ], - "category": 4 - }, - { - "question": "What has John mostly found with the metal detector so far?", - "answer": "bottle caps", - "evidence": [ - "D2:12" - ], - "category": 4 - }, - { - "question": "What did James offer to do for John regarding pets?", - "answer": "help find the perfect pet", - "evidence": [ - "D2:19" - ], - "category": 4 - }, - { - "question": "What instrument is John learning to play as of 27 March, 2022?", - "answer": "Drums", - "evidence": [ - "D3:2", - "D3:3" - ], - "category": 4 - }, - { - "question": "How long has John been playing the drums as of 27 March, 2022?", - "answer": "One month", - "evidence": [ - "D3:5" - ], - "category": 4 - }, - { - "question": "What game did John play in an intense tournament at the gaming convention in March 2022?", - "answer": "CS:GO", - "evidence": [ - "D3:11" - ], - "category": 4 - }, - { - "question": "What game was James playing in the online gaming tournament in April 2022?", - "answer": "Apex Legends", - "evidence": [ - "D4:16" - ], - "category": 4 - }, - { - "question": "How does James communicate with his gaming team?", - "answer": "voice chat", - "evidence": [ - "D4:14" - ], - "category": 4 - }, - { - "question": "What advice did James receive from the famous players he met at the tournament?", - "answer": "never put your ego above team success", - "evidence": [ - "D4:12" - ], - "category": 4 - }, - { - "question": "What did James adopt in April 2022?", - "answer": "a pup", - "evidence": [ - "D5:1" - ], - "category": 4 - }, - { - "question": "What is the name of the pup that was adopted by James?", - "answer": "Ned", - "evidence": [ - "D5:1" - ], - "category": 4 - }, - { - "question": "Why did James embody the appearance of the game character from the woman he saw during a walk?", - "answer": "He found her appearance and eyes amazing.", - "evidence": [ - "D6:6" - ], - "category": 4 - }, - { - "question": "What inspired James to create the game character in the virtual world?", - "answer": "Appearance of a woman he saw during a walk", - "evidence": [ - "D6:6" - ], - "category": 4 - }, - { - "question": "Which country did James visit in 2021?", - "answer": "Italy", - "evidence": [ - "D6:12" - ], - "category": 4 - }, - { - "question": "What impresses John about Japan?", - "answer": "Technologically advanced megacities and tasty street food", - "evidence": [ - "D6:15" - ], - "category": 4 - }, - { - "question": "What kind of assignment was giving John a hard time at work?", - "answer": "Coding assignment", - "evidence": [ - "D7:13" - ], - "category": 4 - }, - { - "question": "What breed is Daisy, one of James' dogs?", - "answer": "Labrador", - "evidence": [ - "D9:12" - ], - "category": 4 - }, - { - "question": "What type of pizza is James' favorite?", - "answer": "Pepperoni", - "evidence": [ - "D9:18" - ], - "category": 4 - }, - { - "question": "What type of pizza is John's favorite?", - "answer": "Hawaiian", - "evidence": [ - "D9:19" - ], - "category": 4 - }, - { - "question": "What did John organize with his friends on May 8, 2022?", - "answer": "A tournament for CS:GO", - "evidence": [ - "D10:4" - ], - "category": 4 - }, - { - "question": "What did John and his friends do with the remaining money after helping the dog shelter?", - "answer": "Bought groceries and cooked food for the homeless", - "evidence": [ - "D10:12" - ], - "category": 4 - }, - { - "question": "What was the main goal of the money raised from the charity tournament organized by John and his friends in May 2022?", - "answer": "Raise money for a dog shelter", - "evidence": [ - "D10:10" - ], - "category": 4 - }, - { - "question": "What did the system John created help the charitable foundation with?", - "answer": "tracking inventory, resources, and donations", - "evidence": [ - "D11:5" - ], - "category": 4 - }, - { - "question": "What did John create for the charitable foundation that helped generate reports for analysis?", - "answer": "computer application on smartphones", - "evidence": [ - "D11:3" - ], - "category": 4 - }, - { - "question": "What did John receive for achieving second place in the tournament?", - "answer": "money and a trophy", - "evidence": [ - "D12:5", - "D12:6" - ], - "category": 4 - }, - { - "question": "What project is James working on in his game design course?", - "answer": "a new part of the football simulator, collecting player databases", - "evidence": [ - "D13:8" - ], - "category": 4 - }, - { - "question": "Who does James support in football matches?", - "answer": "Liverpool", - "evidence": [ - "D13:12" - ], - "category": 4 - }, - { - "question": "Which football club does John support?", - "answer": "Manchester City", - "evidence": [ - "D13:15" - ], - "category": 4 - }, - { - "question": "What disagreement do James and John have about their football teams?", - "answer": "debating on which team will perform better in the championship", - "evidence": [ - "D13:15" - ], - "category": 4 - }, - { - "question": "What is Max good at doing according to James?", - "answer": "catching frisbees in mid-air", - "evidence": [ - "D14:23" - ], - "category": 4 - }, - { - "question": "What is the main focus of the organization that James volunteered with?", - "answer": "providing necessary items to those who are less fortunate", - "evidence": [ - "D15:11" - ], - "category": 4 - }, - { - "question": "Will there be an interview required to volunteer with the organization James volunteered for?", - "answer": "No", - "evidence": [ - "D15:15" - ], - "category": 4 - }, - { - "question": "How did John relax in his free time on 9 July, 2022?", - "answer": "Reading", - "evidence": [ - "D16:8" - ], - "category": 4 - }, - { - "question": "What did James enjoy doing on cold winter days?", - "answer": "Reading while snuggled under the covers", - "evidence": [ - "D16:9" - ], - "category": 4 - }, - { - "question": "What new hobby did James become interested in on 9 July, 2022?", - "answer": "Extreme sports", - "evidence": [ - "D16:5" - ], - "category": 4 - }, - { - "question": "Where did James plan to visit after Toronto?", - "answer": "Vancouver", - "evidence": [ - "D16:11" - ], - "category": 4 - }, - { - "question": "When did James plan to return from his trip to Toronto and Vancouver?", - "answer": "July 20", - "evidence": [ - "D16:13" - ], - "category": 4 - }, - { - "question": "What online game did John start playing recently for improving strategy?", - "answer": "Chess", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "What made John leave his IT job?", - "answer": "to focus on things that align with his values and passions", - "evidence": [ - "D18:3" - ], - "category": 4 - }, - { - "question": "Which game tournaments does John plan to organize besides CS:GO?", - "answer": "Fortnite competitions", - "evidence": [ - "D18:9" - ], - "category": 4 - }, - { - "question": "What happened to James's puppy during the recent visit to the clinic?", - "answer": "routine examination and vaccination", - "evidence": [ - "D18:16" - ], - "category": 4 - }, - { - "question": "What game genre did John start exploring instead of shooters?", - "answer": "strategy and RPG games", - "evidence": [ - "D19:3" - ], - "category": 4 - }, - { - "question": "Which RPG game is John playing and enjoying on 10 August, 2022?", - "answer": "The Witcher 3", - "evidence": [ - "D19:7" - ], - "category": 4 - }, - { - "question": "What aspect of \"The Witcher 3\" does John find immersive?", - "answer": "shaping the world with choices", - "evidence": [ - "D19:7" - ], - "category": 4 - }, - { - "question": "Whose phone number did James receive during the beach outing?", - "answer": "Samantha", - "evidence": [ - "D19:14" - ], - "category": 4 - }, - { - "question": "What is James planning to do after receiving Samantha's phone number?", - "answer": "call her", - "evidence": [ - "D19:14" - ], - "category": 4 - }, - { - "question": "What is John organizing with his siblings?", - "answer": "a gaming night", - "evidence": [ - "D20:17" - ], - "category": 4 - }, - { - "question": "What type of beer does John not like?", - "answer": "dark beer", - "evidence": [ - "D21:16" - ], - "category": 4 - }, - { - "question": "What were some difficulties James faced during the development of his game?", - "answer": "balancing mechanics and ensuring fairness", - "evidence": [ - "D22:7" - ], - "category": 4 - }, - { - "question": "What has John been teaching his siblings?", - "answer": "coding", - "evidence": [ - "D22:10" - ], - "category": 4 - }, - { - "question": "What kind of programs are John's siblings making?", - "answer": "basic games and stories", - "evidence": [ - "D22:12" - ], - "category": 4 - }, - { - "question": "Which company's headphones did John choose for gaming?", - "answer": "Sennheiser", - "evidence": [ - "D23:10" - ], - "category": 4 - }, - { - "question": "What did James and Samantha discover they both enjoy at McGee's bar?", - "answer": "Lager beer", - "evidence": [ - "D23:3" - ], - "category": 4 - }, - { - "question": "How much does James pay per cooking class?", - "answer": "$10", - "evidence": [ - "D23:15" - ], - "category": 4 - }, - { - "question": "What did James learn to make in the cooking class besides omelette and meringue?", - "answer": "Dough", - "evidence": [ - "D23:15" - ], - "category": 4 - }, - { - "question": "Why did James sign up for a cooking class?", - "answer": "He wanted to learn something new", - "evidence": [ - "D23:13" - ], - "category": 4 - }, - { - "question": "What did James prepare for the first time in the cooking class?", - "answer": "Omelette", - "evidence": [ - "D23:13" - ], - "category": 4 - }, - { - "question": "What is the name of the board game John tried in September 2022?", - "answer": "Dungeons of the Dragon", - "evidence": [ - "D24:3" - ], - "category": 4 - }, - { - "question": "Where does James get his ideas from?", - "answer": "books, movies, dreams", - "evidence": [ - "D24:4" - ], - "category": 4 - }, - { - "question": "What kind of dream did James have recently?", - "answer": "a dream with a medieval castle full of puzzles and traps", - "evidence": [ - "D24:8" - ], - "category": 4 - }, - { - "question": "What kind of music does John like?", - "answer": "electronic and rock music", - "evidence": [ - "D24:13" - ], - "category": 4 - }, - { - "question": "What instrument did James used to play when he was younger?", - "answer": "guitar", - "evidence": [ - "D24:14" - ], - "category": 4 - }, - { - "question": "What did John use to play when he was younger to let off steam?", - "answer": "drums", - "evidence": [ - "D24:15" - ], - "category": 4 - }, - { - "question": "What career milestone did John achieve recently in September 2022?", - "answer": "making his first mobile game", - "evidence": [ - "D25:7" - ], - "category": 4 - }, - { - "question": "What type of game is John's upcoming mobile game?", - "answer": "2D adventure", - "evidence": [ - "D25:9" - ], - "category": 4 - }, - { - "question": "What does John do to stay informed and constantly learn about game design?", - "answer": "watch tutorials and keep up with developer forums", - "evidence": [ - "D25:13" - ], - "category": 4 - }, - { - "question": "What kind of gig was John offered at the game dev non-profit organization?", - "answer": "programming mentor for game developers", - "evidence": [ - "D26:3" - ], - "category": 4 - }, - { - "question": "What does John feel about starting the journey as a programming mentor for game developers?", - "answer": "excited and inspired", - "evidence": [ - "D26:5" - ], - "category": 4 - }, - { - "question": "What kind of games is James excited to play with his new video card?", - "answer": "RPGs", - "evidence": [ - "D26:10" - ], - "category": 4 - }, - { - "question": "What inspired James to create his game?", - "answer": "Witcher 3", - "evidence": [ - "D27:6" - ], - "category": 4 - }, - { - "question": "What sparked James' passion for gaming when he was a kid?", - "answer": "Super Mario and The Legend of Zelda games", - "evidence": [ - "D28:25" - ], - "category": 4 - }, - { - "question": "What did James lose progress on due to a power outage?", - "answer": "a game", - "evidence": [ - "D28:3" - ], - "category": 4 - }, - { - "question": "What games were played at the gaming tournament organized by John on 31 October, 2022?", - "answer": "Fortnite, Overwatch, Apex Legends", - "evidence": [ - "D29:1", - "D29:3" - ], - "category": 4 - }, - { - "question": "What was the purpose of the gaming tournament organized by John on 31 October, 2022?", - "answer": "To raise money for a children's hospital", - "evidence": [ - "D29:1", - "D29:3" - ], - "category": 4 - }, - { - "question": "What decision did James and Samantha make on 31 October, 2022?", - "answer": "To move in together", - "evidence": [ - "D29:8", - "D29:10" - ], - "category": 4 - }, - { - "question": "Where did James and Samantha decide to live together on 31 October, 2022?", - "answer": "In an apartment not far from McGee's bar", - "evidence": [ - "D29:10" - ], - "category": 4 - }, - { - "question": "Why did James and Samantha choose an apartment near McGee's bar?", - "answer": "They love spending time together at the bar", - "evidence": [ - "D29:12" - ], - "category": 4 - }, - { - "question": "What game is John hooked on playing on 5 November, 2022?", - "answer": "FIFA 23", - "evidence": [ - "D30:14" - ], - "category": 4 - }, - { - "question": "What did John suggest James practice before playing FIFA 23 together?", - "answer": "Control with a gamepad and timing", - "evidence": [ - "D30:18" - ], - "category": 4 - }, - { - "question": "What project did John work on with a game developer by 7 November, 2022?", - "answer": "An online board game", - "evidence": [ - "D31:4" - ], - "category": 4 - }, - { - "question": "What is the name of John's cousin's dog?", - "answer": "Luna", - "evidence": [ - "D31:22" - ], - "category": 4 - }, - { - "question": "What did John adopt in April 2022?", - "evidence": [ - "D5:1" - ], - "category": 5, - "adversarial_answer": "a pup" - }, - { - "question": "What is the name of the kitten that was adopted by James?", - "evidence": [ - "D5:1" - ], - "category": 5, - "adversarial_answer": "Ned" - }, - { - "question": "What inspired John to create the game character in the virtual world?", - "evidence": [ - "D6:6" - ], - "category": 5, - "adversarial_answer": "Appearance of a woman he saw during a walk" - }, - { - "question": "Which country did John visit in 2021?", - "evidence": [ - "D6:12" - ], - "category": 5, - "adversarial_answer": "Italy" - }, - { - "question": "What kind of assignment was giving James a hard time at work?", - "evidence": [ - "D7:13" - ], - "category": 5, - "adversarial_answer": "Coding assignment" - }, - { - "question": "What did James and his friends do with the remaining money after helping the dog shelter?", - "evidence": [ - "D10:12" - ], - "category": 5, - "adversarial_answer": "Bought groceries and cooked food for the homeless" - }, - { - "question": "What was the main goal of the money raised from the political campaign organized by John and his friends in May 2022?", - "evidence": [ - "D10:10" - ], - "category": 5, - "adversarial_answer": "Raise money for a dog shelter" - }, - { - "question": "What did the system John created help the illegal organization with?", - "evidence": [ - "D11:5" - ], - "category": 5, - "adversarial_answer": "tracking inventory, resources, and donations" - }, - { - "question": "What did James create for the charitable foundation that helped generate reports for analysis?", - "evidence": [ - "D11:3" - ], - "category": 5, - "adversarial_answer": "computer application on smartphones" - }, - { - "question": "Who does James support in cricket matches?", - "evidence": [ - "D13:12" - ], - "category": 5, - "adversarial_answer": "Liverpool" - }, - { - "question": "What is Max good at doing according to John?", - "evidence": [ - "D14:23" - ], - "category": 5, - "adversarial_answer": "catching frisbees in mid-air" - }, - { - "question": "Will there be a background check required to volunteer with the organization James volunteered for?", - "evidence": [ - "D15:15" - ], - "category": 5, - "adversarial_answer": "No" - }, - { - "question": "How did James relax in his free time on 9 July, 2022?", - "evidence": [ - "D16:8" - ], - "category": 5, - "adversarial_answer": "Reading" - }, - { - "question": "What new hobby did John become interested in on 9 July, 2022?", - "evidence": [ - "D16:5" - ], - "category": 5, - "adversarial_answer": "Extreme sports" - }, - { - "question": "When did John plan to return from his trip to Toronto and Vancouver?", - "evidence": [ - "D16:13" - ], - "category": 5, - "adversarial_answer": "July 20" - }, - { - "question": "What made James leave his IT job?", - "evidence": [ - "D18:3" - ], - "category": 5, - "adversarial_answer": "to focus on things that align with his values and passions" - }, - { - "question": "Which game tournaments does James plan to organize besides CS:GO?", - "evidence": [ - "D18:9" - ], - "category": 5, - "adversarial_answer": "Fortnite competitions" - }, - { - "question": "What happened to James's kitten during the recent visit to the clinic?", - "evidence": [ - "D18:16" - ], - "category": 5, - "adversarial_answer": "routine examination and vaccination" - }, - { - "question": "What aspect of \"The Witcher 3\" does John find boring?", - "evidence": [ - "D19:7" - ], - "category": 5, - "adversarial_answer": "shaping the world with choices" - }, - { - "question": "What is John planning to do after receiving Samantha's phone number?", - "evidence": [ - "D19:14" - ], - "category": 5, - "adversarial_answer": "call her" - }, - { - "question": "What has James been teaching his siblings?", - "evidence": [ - "D22:10" - ], - "category": 5, - "adversarial_answer": "coding" - }, - { - "question": "How much does James pay per dance class?", - "evidence": [ - "D23:15" - ], - "category": 5, - "adversarial_answer": "$10" - }, - { - "question": "What did James learn to make in the chemistry class besides omelette and meringue?", - "evidence": [ - "D23:15" - ], - "category": 5, - "adversarial_answer": "Dough" - }, - { - "question": "Why did James sign up for a ballet class?", - "evidence": [ - "D23:13" - ], - "category": 5, - "adversarial_answer": "He wanted to learn something new" - }, - { - "question": "What did John prepare for the first time in the cooking class?", - "evidence": [ - "D23:13" - ], - "category": 5, - "adversarial_answer": "Omelette" - }, - { - "question": "What is the name of the board game James tried in September 2022?", - "evidence": [ - "D24:3" - ], - "category": 5, - "adversarial_answer": "Dungeons of the Dragon" - }, - { - "question": "Where does John get his ideas from?", - "evidence": [ - "D24:4" - ], - "category": 5, - "adversarial_answer": "books, movies, dreams" - }, - { - "question": "What did James use to play when he was younger to let off steam?", - "evidence": [ - "D24:15" - ], - "category": 5, - "adversarial_answer": "drums" - }, - { - "question": "What does James do to stay informed and constantly learn about game design?", - "evidence": [ - "D25:13" - ], - "category": 5, - "adversarial_answer": "watch tutorials and keep up with developer forums" - }, - { - "question": "What kind of gig was James offered at the game dev non-profit organization?", - "evidence": [ - "D26:3" - ], - "category": 5, - "adversarial_answer": "programming mentor for game developers" - }, - { - "question": "What does James feel about starting the journey as a programming mentor for game developers?", - "evidence": [ - "D26:5" - ], - "category": 5, - "adversarial_answer": "excited and inspired" - }, - { - "question": "What inspired James to create his painting?", - "evidence": [ - "D27:6" - ], - "category": 5, - "adversarial_answer": "Witcher 3" - }, - { - "question": "What games were played at the gaming tournament organized by James on 31 October, 2022?", - "evidence": [ - "D29:1", - "D29:3" - ], - "category": 5, - "adversarial_answer": "Fortnite, Overwatch, Apex Legends" - }, - { - "question": "What was the purpose of the gaming tournament organized by James on 31 October, 2022?", - "evidence": [ - "D29:1", - "D29:3" - ], - "category": 5, - "adversarial_answer": "To raise money for a children's hospital" - }, - { - "question": "What decision did John and Samantha make on 31 October, 2022?", - "evidence": [ - "D29:8", - "D29:10" - ], - "category": 5, - "adversarial_answer": "To move in together" - }, - { - "question": "Where did John and Samantha decide to live together on 31 October, 2022?", - "evidence": [ - "D29:10" - ], - "category": 5, - "adversarial_answer": "In an apartment not far from McGee's bar" - }, - { - "question": "Why did John and Samantha choose an apartment near McGee's bar?", - "evidence": [ - "D29:12" - ], - "category": 5, - "adversarial_answer": "They love spending time together at the bar" - }, - { - "question": "What game is James hooked on playing on 5 November, 2022?", - "evidence": [ - "D30:14" - ], - "category": 5, - "adversarial_answer": "FIFA 23" - }, - { - "question": "What project did James work on with a game developer by 7 November, 2022?", - "evidence": [ - "D31:4" - ], - "category": 5, - "adversarial_answer": "An online board game" - }, - { - "question": "What is the name of James's cousin's dog?", - "evidence": [ - "D31:22" - ], - "category": 5, - "adversarial_answer": "Luna" - } - ], - "conversation": { - "speaker_a": "James", - "speaker_b": "John", - "session_1_date_time": "3:47 pm on 17 March, 2022", - "session_1": [ - { - "speaker": "John", - "dia_id": "D1:1", - "text": "Hey! Glad to finally talk to you. I want to ask you, what motivates you?" - }, - { - "speaker": "James", - "dia_id": "D1:2", - "text": "Hey John! Video games give me tons of joy and excitement, so they keep me motivated!" - }, - { - "speaker": "John", - "dia_id": "D1:3", - "text": "Cool, James! I'm a big video game fan too. They help me relax after a long day. What game are you currently enjoying the most?" - }, - { - "speaker": "James", - "dia_id": "D1:4", - "text": "I'm totally into The Witcher 3 right now. The story and atmosphere are amazing. Have you tried it yet?" - }, - { - "speaker": "John", - "dia_id": "D1:5", - "text": "Haven't played it yet, but I hear it's awesome. Gonna give it a go. BTW, just signed up for a programming class. Have you ever done any programming?" - }, - { - "speaker": "James", - "dia_id": "D1:6", - "text": "Programming is an awesome skill. I tried it out one in college and now it`s all my life. Good luck in the class! Do you have any coding experience?" - }, - { - "speaker": "John", - "dia_id": "D1:7", - "text": "I did a bit of coding in HTML and CSS a few years back. Thought I'd refresh those skills in this course. What languages do you like most and any projects you've done?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/gjudms7o1o881.jpg" - ], - "blip_caption": "a photo of a computer screen showing a video game scene", - "query": "game mod graphics enhancement", - "dia_id": "D1:8", - "text": "I've worked with Python and C++. I've built a website and also created some game mods. Here is one example." - }, - { - "speaker": "John", - "dia_id": "D1:9", - "text": "That mod looks amazing! The graphics are awesome. What other programming languages have you worked with?" - }, - { - "speaker": "James", - "dia_id": "D1:10", - "text": "I haven\u2019t worked with any other programming languages, but I hope to work in the future." - }, - { - "speaker": "John", - "dia_id": "D1:11", - "text": "Maybe in the future we will develop mobile applications together? Do you like the idea?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/gbmnyrqwbde91.jpg" - ], - "blip_caption": "a photo of two dogs are tied to a fence with a leash", - "query": "dog walking app dogs walk", - "dia_id": "D1:12", - "text": "It would be cool! For example, we could write some kind of application for dogs. By the way, my dogs." - }, - { - "speaker": "John", - "dia_id": "D1:13", - "text": "Aww, they're adorable! What are the names of your pets? And what are your plans for the app?" - }, - { - "speaker": "James", - "dia_id": "D1:14", - "text": "Max and Daisy. Will be actually cool to build an app for dog walking and pet care. The goal is to connect pet owners with reliable dog walkers and provide helpful information on pet care." - }, - { - "speaker": "John", - "dia_id": "D1:15", - "text": "Sounds good, James! Bet that app would find a lot of buyers. What sets it apart from other existing apps?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a notepad with a handwritten note on it", - "dia_id": "D1:16", - "text": "Thanks, John! The personal touch really sets it apart. Users can add their pup's preferences/needs - just like they were customizing it for them. Making it unique for each owner and pup." - }, - { - "speaker": "John", - "dia_id": "D1:17", - "text": "That's a great idea! Your pup is gonna love it. Speaking of personal touches, what motivates you to work on your programming projects?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/lsebjnbguz881.jpg" - ], - "blip_caption": "a photo of a person holding a notebook with a list of things on it", - "query": "notepad programming goals", - "dia_id": "D1:18", - "text": "Creating something and seeing it come to life gives me a great sense of accomplishment. It's an amazing feeling. I write down all my goals in a notebook. It's very satisfying to check off each one when it's done." - }, - { - "speaker": "John", - "dia_id": "D1:19", - "text": "What are you working on that has you feeling so accomplished?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/e48of6zqmmi71.jpg" - ], - "blip_caption": "a photo of a drawing of a girl in a plane", - "query": "indie game concept art", - "dia_id": "D1:20", - "text": "I'm working on something I've wanted to do since I was a kid. Even as a child, I made some sketches of the main character. Back then I was just drawing comics, but now I want to turn it into a computer game. It's a project that has me really excited." - }, - { - "speaker": "John", - "dia_id": "D1:21", - "text": "Wow, James! That's amazing. What made you decide to work on it and create your own game?" - }, - { - "speaker": "James", - "dia_id": "D1:22", - "text": "I'm always excited to combine my favorite passions: gaming and storytelling. It's great creating my own project and bringing my ideas to life, plus the challenge is really enjoyable!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a medical chart with instructions for the procedure", - "dia_id": "D1:23", - "text": "That sounds really fulfilling! Combining your passions to make something new must be so exciting. Can't wait to see the outcome." - }, - { - "speaker": "James", - "dia_id": "D1:24", - "text": "Thanks John! It's super exciting. I'll keep you updated on the progress. Perhaps, thanks to your knowledge of HTML, I'll invite you to help with some things in my game." - }, - { - "speaker": "John", - "dia_id": "D1:25", - "text": "It will be great to work with you, James." - }, - { - "speaker": "James", - "dia_id": "D1:26", - "text": "I'll be looking forward to it. By the way, yesterday I went bowling and got 2 strikes. I love bowling!" - }, - { - "speaker": "John", - "dia_id": "D1:27", - "text": "I'm sure you're very good at this. Unfortunately, I can\u2019t share my love for him with you, my fingers are too big. Perhaps I should take up exercise, at least start going for a run in the morning. And I also don\u2019t like bowling itself, to be honest." - }, - { - "speaker": "James", - "dia_id": "D1:28", - "text": "It's a pity, it would be nice to go play with you one day." - }, - { - "speaker": "John", - "dia_id": "D1:29", - "text": "Well, I'm sure we can do something else. We can play slot machines and arcades, for example." - }, - { - "speaker": "James", - "dia_id": "D1:30", - "text": "The last time I played at the slot machines, I was so engrossed in the game that I didn't notice my wallet being taken out of my pocket. Sad story." - }, - { - "speaker": "John", - "dia_id": "D1:31", - "text": "I'm sorry to hear it. Well, I'll be nearby, I'll look after your pockets." - }, - { - "speaker": "James", - "dia_id": "D1:32", - "text": "Still, maybe we can try something different?" - }, - { - "speaker": "John", - "dia_id": "D1:33", - "text": "Heard about VR gaming? It's pretty immersive. We can try it together!" - }, - { - "speaker": "James", - "dia_id": "D1:34", - "text": "I tried it - it's crazy how real it feels! Have you given it a shot?" - }, - { - "speaker": "John", - "dia_id": "D1:35", - "text": "Tried it a few times, it's insane how immersive that experience can be. Can't wait to try it together with you." - }, - { - "speaker": "James", - "dia_id": "D1:36", - "text": "Yeah, VR gaming is awesome! Let`s do it next Saturday!" - }, - { - "speaker": "John", - "dia_id": "D1:37", - "text": "Agreed, James!" - } - ], - "session_2_date_time": "9:26 pm on 20 March, 2022", - "session_2": [ - { - "speaker": "James", - "dia_id": "D2:1", - "text": "Hey John, something awesome happened since we talked. I made a game avatar and joined a new platform. It's so fun exploring and chatting with other gamers - it's a whole new adventure every time! I feel like I'm part of a super cool online community." - }, - { - "speaker": "John", - "dia_id": "D2:2", - "text": "Hey James, awesome! Glad you're enjoying it and connecting with others. Building a community is really cool, especially when you meet people who enjoy the same things." - }, - { - "speaker": "James", - "dia_id": "D2:3", - "text": "Thanks, John! Connecting with other gamers has been great! We've shared tips, strategies, and stories about gaming. It's amazing how it brings people together, regardless of their backgrounds." - }, - { - "speaker": "John", - "dia_id": "D2:4", - "text": "That's incredible! It's so cool how gaming can bring people together and create a strong bond, regardless of their background." - }, - { - "speaker": "James", - "dia_id": "D2:5", - "text": "Yeah, it's our shared language and passion. It's been a refuge for me in tough times." - }, - { - "speaker": "John", - "dia_id": "D2:6", - "text": "Yeah, gaming always helps me escape stress. It's amazing how it calms me down during tough times." - }, - { - "speaker": "James", - "dia_id": "D2:7", - "text": "Games are my go-to when I'm feeling overwhelmed. It's like therapy. I can relax, forget my troubles, and get lost in another world." - }, - { - "speaker": "John", - "dia_id": "D2:8", - "text": "Gotcha. Gaming can be a great way to take a break and escape for a while. Anything new you've been into lately?" - }, - { - "speaker": "James", - "dia_id": "D2:9", - "text": "Lately, I've been checking out different styles of it. It's been fun to try something fresh and test myself in other ways. What about you, John? Any new hobbies recently?" - }, - { - "speaker": "John", - "dia_id": "D2:10", - "text": "I've been getting into a new hobby recently. I bought a metal detector and walk along the beaches looking for something worthwhile." - }, - { - "speaker": "James", - "dia_id": "D2:11", - "text": "Interesting, John! Sounds like an awesome immersive experience. Already found something interesting?" - }, - { - "speaker": "John", - "dia_id": "D2:12", - "text": "Mostly just bottle caps, but a couple of times I found coins, and once even a gold ring." - }, - { - "speaker": "James", - "dia_id": "D2:13", - "text": "Cool, I wish you good luck in this matter! By the way, I've got something to show you." - }, - { - "speaker": "John", - "dia_id": "D2:14", - "text": "Show me what you've got! What is it?" - }, - { - "speaker": "James", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D2:15", - "text": "Check out this pic of my best buds having a blast in the park. They've brought so much joy to my life. My two dogs are the best pals ever, right?" - }, - { - "speaker": "John", - "dia_id": "D2:16", - "text": "They look like they're having a blast! Can they do any tricks?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/bd7160b2t6aa1.jpg" - ], - "blip_caption": "a photo of a dog laying on a bed with a name tag", - "query": "golden retrievers dogs sitting patiently treats", - "dia_id": "D2:17", - "text": "They can do tricks like sit, stay, paw, and rollover. Here's a picture of Daisy waiting for a treat. I've done lots of training and they've picked it up fast. They're like my family." - }, - { - "speaker": "John", - "dia_id": "D2:18", - "text": "Aww, they're adorable! Pets are the best - they must make life so much better. I want one so bad, but I'm not there yet. Someday!" - }, - { - "speaker": "James", - "dia_id": "D2:19", - "text": "A pet would truly be great for you! They bring so much love and companionship. If you're interested, I can help find the perfect one for you - you'd make a great pet parent!" - }, - { - "speaker": "John", - "dia_id": "D2:20", - "text": "Cheers, James! Yeah, I'll keep that in mind. Appreciate the offer." - }, - { - "speaker": "James", - "dia_id": "D2:21", - "text": "No problem, John! Let me know whenever you need assistance. Take care!" - } - ], - "session_3_date_time": "12:40 am on 27 March, 2022", - "session_3": [ - { - "speaker": "John", - "dia_id": "D3:1", - "text": "Hey James, long time no see! I had a big win in my game last week - finally advanced to the next level! It was a huge confidence booster and felt like I'd really achieved something." - }, - { - "speaker": "James", - "img_url": [ - "https://live.staticflickr.com/3286/3130876770_af17e12d68_z.jpg" - ], - "blip_caption": "a photography of a drum kit with a white drum and a black drum", - "query": "drum set drumming progress", - "dia_id": "D3:2", - "re-download": true, - "text": "Hey John, congrats on your win! Games can really boost confidence, huh? I'm challenging myself too - I'm learning this instrument, which has been quite the journey.\n" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/ky9jybgp0bi91.jpg" - ], - "blip_caption": "a photo of a drum kit sitting on top of a table", - "query": "drum set progress", - "dia_id": "D3:3", - "text": "Thanks, James! I play drums too! Here's a pic of my set." - }, - { - "speaker": "James", - "dia_id": "D3:4", - "text": "Wow, looking good! How long have you been playing?" - }, - { - "speaker": "John", - "dia_id": "D3:5", - "text": "I've been playing for a month now, it's been tough but fun. How about you, how's it going?" - }, - { - "speaker": "James", - "dia_id": "D3:6", - "text": "This is going great! I started a few days ago, so I'm still picking it up. Been at it daily and seeing improvements. It's tough but rewarding at the same time!" - }, - { - "speaker": "John", - "dia_id": "D3:7", - "text": "Nice work! Looks like you're doing great. Anything new in general that you'd recommend?" - }, - { - "speaker": "James", - "dia_id": "D3:8", - "text": "Thanks! I just got a new cutting-edge gaming system and the graphics are incredible. I've been playing all kinds of new games and it's been a great way to relax after work. Plus, I can connect with friends who share my passion for gaming." - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/664/22051849804_cff4f4e532_b.jpg" - ], - "blip_caption": "a photography of a crowded convention hall with a large crowd of people", - "query": "gaming convention crowded hall banners games", - "dia_id": "D3:9", - "re-download": true, - "text": "Cool, James! Gaming is great for chilling out. Btw, since we last spoke, I had the chance to go to a gaming convention - it was amazing! Tried out loads of games, met developers, and even took part in a tournament - unreal! Check out this pic I took!" - }, - { - "speaker": "James", - "dia_id": "D3:10", - "text": "Wow, that's awesome! What game was it for? Sounds like a dream!" - }, - { - "speaker": "John", - "dia_id": "D3:11", - "text": "I played my favorite CS:GO game in an intense tournament. It was awesome to see all the skilled players competing." - }, - { - "speaker": "James", - "dia_id": "D3:12", - "text": "Wow, that sounds cool! Gaming is awesome with all the competition. Must have been thrilling to watch those skilled players!" - }, - { - "speaker": "John", - "dia_id": "D3:13", - "text": "It was indeed amazing! Watching those skilled players really inspired me to improve my own gaming skills." - }, - { - "speaker": "James", - "dia_id": "D3:14", - "text": "Nice one, John! Learning from experienced gamers can really help you level up your skills. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D3:15", - "text": "I'm always looking to up my game and hit new goals. That same commitment is true for my hobbies and other stuff. What have you been doing to stay motivated?" - }, - { - "speaker": "James", - "dia_id": "D3:16", - "text": "Setting small goals and tracking my progress helps me stay motivated and focused." - }, - { - "speaker": "John", - "dia_id": "D3:17", - "text": "Nice one! Setting small goals and tracking progress is a great way to stay motivated - it helps you stay on track and celebrates progress. Anything specific you're working on or upcoming challenges you're pumped about?" - }, - { - "speaker": "James", - "dia_id": "D3:18", - "text": "I'm getting into different types of games now, like RPGs and strategy games. It's really exciting!" - }, - { - "speaker": "John", - "dia_id": "D3:19", - "text": "Cool, James! That sounds exciting. Have fun exploring different genres of games!" - }, - { - "speaker": "James", - "dia_id": "D3:20", - "text": " I'm super hyped to explore different game genres. Let's see what's in store!" - }, - { - "speaker": "John", - "dia_id": "D3:21", - "text": "Definitely! Trying new genres is always exciting. I can't wait to hear about your journey with them. Please let me know how it goes!" - }, - { - "speaker": "James", - "dia_id": "D3:22", - "text": "Got it, John! I'll keep you updated on my gaming adventures with the new genres. Have a good day!" - }, - { - "speaker": "John", - "dia_id": "D3:23", - "text": "Thanks! Can't wait to hear about it. Bye!" - } - ], - "session_4_date_time": "2:13 pm on 4 April, 2022", - "session_4": [ - { - "speaker": "John", - "dia_id": "D4:1", - "text": "Hey James! Long time no chat. What's up? Been playing any new games lately?" - }, - { - "speaker": "James", - "img_url": [ - "https://cool4dads.com/wp-content/uploads/2019/01/IMG_9117.jpg" - ], - "blip_caption": "a photo of a group of people posing for a picture", - "query": "online gaming tournament fun", - "dia_id": "D4:2", - "text": "Hey John! Yeah, it's been a while. I've been busy, but I joined an online gaming tournament yesterday. It was so intense and fun! Here is a photo report." - }, - { - "speaker": "John", - "dia_id": "D4:3", - "text": "That online gaming tournament looks awesome! Glad you had a blast. How did it go for you?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/zk7j8z2ik3l71.jpg" - ], - "blip_caption": "a photo of a man in a costume standing in front of a sign", - "query": "victory screen sweet victory", - "dia_id": "D4:4", - "text": "It was so much fun! I did pretty well in the tournament; I made it to the semis and won some rounds. It was such a rush! Here's a screenshot of my character." - }, - { - "speaker": "John", - "dia_id": "D4:5", - "text": "Wow, awesome! Congrats on your performance and making it to the semifinals. How did the final rounds turn out?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a man in a costume holding a sword", - "dia_id": "D4:6", - "text": "Thanks John! The final rounds were tough. I tried my best but didn't make it. It was close, though, and I had a blast competing with talented players. Looking forward to the next tournament!" - }, - { - "speaker": "John", - "dia_id": "D4:7", - "text": "Met any famous player there?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a man sitting in a chair playing a video game", - "dia_id": "D4:8", - "text": "I met the whole team! It\u2019s a pity I didn\u2019t get a chance to take a photo with them, but one of them even gave me a couple of gaming tips." - }, - { - "speaker": "John", - "dia_id": "D4:9", - "text": "Cool! I'm sure his advice will help you develop in the game." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/shafojm1o4471.jpg" - ], - "blip_caption": "a photo of a notepad with a pen and a glass of water", - "query": "notepad game strategies", - "dia_id": "D4:10", - "text": "Yes, I'm sure of that too. Also, the whole team gave me autographs. I was very happy about this!" - }, - { - "speaker": "John", - "dia_id": "D4:11", - "text": "How cool is this! What advice do you remember most?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a group of people standing around a table", - "dia_id": "D4:12", - "text": "The most important thing I remember is that you always need to communicate correctly with the team and never put your ego above team success." - }, - { - "speaker": "John", - "dia_id": "D4:13", - "text": "Yeah, comms and teamwork are super important in gaming. When everyone works together, it's incredible what can be accomplished in a match. How do you usually communicate with your team?" - }, - { - "speaker": "James", - "dia_id": "D4:14", - "text": "I usually use voice chat to communicate with my team. It's fast and helps us work together effectively." - }, - { - "speaker": "John", - "dia_id": "D4:15", - "text": "Sounds like a good plan. It really helps with communication. What game do you like playing with your team?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/4zewk2444ag71.jpg" - ], - "blip_caption": "a photo of a video game screen showing a robot and a robot", - "query": "apex legends team playing action", - "dia_id": "D4:16", - "text": "I've been playing my favourite game called Apex Legends with my team and it's intense! Check out this screenshot of us playing!" - }, - { - "speaker": "John", - "dia_id": "D4:17", - "text": "Man, Apex Legends looks tough! The graphics are unreal. How does it stack up against other games?" - }, - { - "speaker": "James", - "dia_id": "D4:18", - "text": "Apex Legends has awesome graphics and super fast-paced gameplay. It definitely stands out among other games." - }, - { - "speaker": "John", - "dia_id": "D4:19", - "text": "Hmm, the speed of it definitely makes it fun! Are there any new games that you're looking forward to trying out?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/bwoh4be8d3m41.jpg" - ], - "blip_caption": "a photo of a video game cover of a video game", - "query": "skyrim league of legends game cover", - "dia_id": "D4:20", - "text": "Yeah, I'm always excited to try new games. Thinking of trying RPGs like that or MOBAs. Sounds cool!" - }, - { - "speaker": "John", - "dia_id": "D4:21", - "text": "RPGs and MOBAs can be awesome to experience an engaging story or have epic multiplayer fights. Let me know how you like them!" - }, - { - "speaker": "James", - "dia_id": "D4:22", - "text": "Sure thing, John! Can't wait to try out some new genres. I'll definitely let you know my thoughts once I give them a try." - }, - { - "speaker": "John", - "dia_id": "D4:23", - "text": "Love hearing about it. Let's chat soon!" - }, - { - "speaker": "James", - "dia_id": "D4:24", - "text": "Sure John, I'll keep you updated on all the new games. Talk to you soon! Bye for now!" - }, - { - "speaker": "John", - "dia_id": "D4:25", - "text": "Let me know how it goes. Stay safe. Talk to you soon. Bye!" - } - ], - "session_5_date_time": "9:52 am on 12 April, 2022", - "session_5": [ - { - "speaker": "James", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0624/9512/9787/files/b7d0234c78c382f9d9fc42142115a987.jpg" - ], - "blip_caption": "a photo of a dog and a cat sitting on a dog bed", - "query": "puppy dog bed", - "dia_id": "D5:1", - "text": "Hey John! Long time no chat - I adopted a pup from a shelter in Stamford last week and my days have been so much happier with him in the fam. I named it Ned. Any progress on your gaming goals?" - }, - { - "speaker": "John", - "dia_id": "D5:2", - "text": "Hey James! Congrats on getting a pup! They really do make days brighter. I haven't made much progress with gaming lately, life's been busy with work and stuff but it's always nice to remember how happy gaming makes me. It's a good way to forget the stresses of life." - }, - { - "speaker": "James", - "dia_id": "D5:3", - "text": "Thanks, John! Gaming really does help forget about the stresses of life. It's like heading into another world! Have you played any interesting games lately?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/941ph75y0yb91.jpg" - ], - "blip_caption": "a photo of a city at night with a view of the city", - "query": "cyberpunk 2077 cityscape", - "dia_id": "D5:4", - "text": "I'm playing this new RPG that has a really cool story and world. It's kinda like getting transported to a futuristic dystopia." - }, - { - "speaker": "James", - "dia_id": "D5:5", - "text": "Sounds great! Will I like it? I'm always up for trying new games." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/xu1i4gjzg6jb1.jpg" - ], - "blip_caption": "a photo of a computer screen with a game on it", - "query": "game menu", - "dia_id": "D5:6", - "text": "You'll love it! It has a fun story and an awesome world. Plus, it has some tough gameplay. However, the game is a little laggy and sometimes produces errors. Here is one of them that I came across yesterday." - }, - { - "speaker": "James", - "dia_id": "D5:7", - "text": "The game must still be raw. I'd rather wait until the bugs are completely fixed, and then I'll try it out." - }, - { - "speaker": "John", - "dia_id": "D5:8", - "text": "Even with minor bugs, the game still looks great. The graphics are awesome and the gameplay is super immersive. I'm sure you should try it out now. I can send you a link." - }, - { - "speaker": "James", - "dia_id": "D5:9", - "text": "Okay, you convinced me. Well, I'll wait for the link then, thanks!" - }, - { - "speaker": "John", - "dia_id": "D5:10", - "text": "Cool, I'll send it to you. Let me know what you think after you check it out!" - }, - { - "speaker": "James", - "dia_id": "D5:11", - "text": "Cool, John! I'll check it out and let you know what I think. I'm excited to give it a try." - }, - { - "speaker": "John", - "dia_id": "D5:12", - "text": "Thanks, James! Excited to hear your thoughts. Have a great time!" - }, - { - "speaker": "James", - "dia_id": "D5:13", - "text": "Cheers! Will do. Enjoy the gaming! Bye!" - }, - { - "speaker": "John", - "dia_id": "D5:14", - "text": "Bye for now!" - }, - { - "speaker": "James", - "dia_id": "D5:15", - "text": "See ya! Take care!" - }, - { - "speaker": "John", - "dia_id": "D5:16", - "text": "Take care!" - } - ], - "session_6_date_time": "9:32 pm on 20 April, 2022", - "session_6": [ - { - "speaker": "John", - "dia_id": "D6:1", - "text": "Hey James! Long time no see! I have great news! Last Tuesday I met three cool new friends in my programming course, they share the same passion as me and it's cool to grow my social circle. Have you had any fun surprises lately?" - }, - { - "speaker": "James", - "dia_id": "D6:2", - "text": "Hey John! Glad you had a great week meeting new people! Something awesome happened to me last Thursday \u2013 I got to work with one of my gaming pals on a programming project! We combined programming and gaming, and created this virtual world inspired by Witcher 3. It was awesome to see our ideas come to life!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a video game screen with a man on it", - "dia_id": "D6:3", - "text": "It must've felt great to put your skills to work on that project! Do you have any screenshots of the world you made? It must've been so awesome to see it all come together!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/w5v13bfv8r861.jpg" - ], - "blip_caption": "a photo of a woman dressed in armor kneeling on a rock", - "query": "witcher inspired virtual world screenshot", - "dia_id": "D6:4", - "text": "It was quite the experience. Unfortunately, I don't have a screenshot of the full virtual world, but I do have a screenshot of the game character I created. It was a lot of work but so rewarding when it all came together. Super satisfying!" - }, - { - "speaker": "John", - "dia_id": "D6:5", - "text": "Wow, James! This is amazing. I can really feel the atmosphere here. Did you get the inspiration for this from something?" - }, - { - "speaker": "James", - "dia_id": "D6:6", - "text": "Thanks, I'm glad you can feel the atmosphere. I got the idea from a walk with my dogs two weeks ago. We were walking around our neighborhood, and a stranger was walking towards us. I had never seen her nearby before. Her eyes and appearance amazed me so much, it seemed to me that I fell in love at first sight. It\u2019s a pity that I didn\u2019t approach her to get to know her, but at least I remembered her appearance and embodied it in the game." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/emrf986oa7bb1.jpg" - ], - "blip_caption": "a photo of a river running through a canyon surrounded by mountains", - "query": "canyon hike view", - "dia_id": "D6:7", - "text": "That's awesome. Real-life experiences can be so inspiring. It's like the virtual world is connected to the real world. By the way, two days ago I wanted to be alone with nature. This is the canyon I found in the surrounding area. Very calming view." - }, - { - "speaker": "James", - "dia_id": "D6:8", - "text": "That's so cool you had a similar experience. I bet you felt inspired seeing it in person." - }, - { - "speaker": "John", - "dia_id": "D6:9", - "text": "Capturing that view was amazing. It was like connecting the real and the imaginary. It really sparked my creativity and motivation." - }, - { - "speaker": "James", - "dia_id": "D6:10", - "text": "Cool! What else gives you motivation?" - }, - { - "speaker": "John", - "dia_id": "D6:11", - "text": "I adhere to the principle that only those who rest well work well. Therefore, chilling with friends and traveling always give me motivation to work further." - }, - { - "speaker": "James", - "dia_id": "D6:12", - "text": "I agree with you, I also love to travel. Last year I visited Italy, for example. A very beautiful country with delicious food." - }, - { - "speaker": "John", - "dia_id": "D6:13", - "text": "Oh, Italy! I always dreamed of visiting there. What other countries have you been to?" - }, - { - "speaker": "James", - "dia_id": "D6:14", - "text": "In fact, I haven't visited many countries. Besides Italy, I was also in Turkey and Mexico. What was the last country you visited?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/l5afku2e4k681.jpg" - ], - "blip_caption": "a photo of a busy city street at night with people walking", - "query": "cyberpunk 2077 futuristic cityscape game", - "dia_id": "D6:15", - "text": "This was Japan. The megacities of this country impress me, everything there is so technologically advanced, the huge screens on the buildings are mesmerizing. And, of course, very tasty street food." - }, - { - "speaker": "James", - "dia_id": "D6:16", - "text": "It would be cool to go somewhere together next year, don't you think?" - }, - { - "speaker": "John", - "dia_id": "D6:17", - "text": "Of course, I hope everything works out for us, I will believe in it!" - }, - { - "speaker": "James", - "dia_id": "D6:18", - "text": "Great, then I'll start looking for a country where we can go!" - }, - { - "speaker": "John", - "dia_id": "D6:19", - "text": "Keep me posted, James! Let me know if you need help." - } - ], - "session_7_date_time": "11:04 am on 23 April, 2022", - "session_7": [ - { - "speaker": "John", - "dia_id": "D7:1", - "text": "Hey James! How's it going?" - }, - { - "speaker": "James", - "dia_id": "D7:2", - "text": "Hey John! Good to hear from ya. Yeah, been crazy. Last Thursday I took my dogs out for a hike. Was quite the adventure! Explored some nice trails and enjoyed fresh air." - }, - { - "speaker": "John", - "dia_id": "D7:3", - "text": "Wow, sounds like quite an adventure! Do you have any pictures from that day?" - }, - { - "speaker": "James", - "img_url": [ - "https://i0.wp.com/mikicc.org/wp-content/uploads/2020/11/20201024_1014341921784989571737117.jpg" - ], - "blip_caption": "a photo of a man walking two dogs on a path in the woods", - "query": "dogs trail woods breathtaking views", - "dia_id": "D7:4", - "text": "Yeah, I have one. It was great! They loved it - so many trails to discover and amazing views. So fun!" - }, - { - "speaker": "John", - "dia_id": "D7:5", - "text": "Wow, that looks like a cool place you took them! Why'd you pick that spot?" - }, - { - "speaker": "James", - "dia_id": "D7:6", - "text": "I picked that spot because it had great views and lots of trails. It was perfect for them to explore nature and have fun. Plus, there's nothing like being surrounded by lush greenery and clean air." - }, - { - "speaker": "John", - "dia_id": "D7:7", - "text": "Agreed! It is a great way to escape the everyday. Wish I could spend more time in nature!" - }, - { - "speaker": "James", - "dia_id": "D7:8", - "text": "Love hearing the crunch of leaves under my feet and the peacefulness. It helps me clear my head and chill." - }, - { - "speaker": "John", - "dia_id": "D7:9", - "text": "I need some chill vibes too because it's been crazy for me lately." - }, - { - "speaker": "James", - "dia_id": "D7:10", - "text": "What's been going on? Is there anything you want to talk about? I'm here for you." - }, - { - "speaker": "John", - "dia_id": "D7:11", - "text": "Thanks, James. Yeah, been super busy at work. Deadlines all over the place and so much to do. It's just been really hectic lately." - }, - { - "speaker": "James", - "dia_id": "D7:12", - "text": "Yeah, work can be tough. Is there something specific that's making it worse?" - }, - { - "speaker": "John", - "dia_id": "D7:13", - "text": "Ugh, this project is giving me a hard time. It's a difficult assignment that involves coding and I'm stuck on it. It's frustrating because I hate being stuck and not making progress." - }, - { - "speaker": "James", - "dia_id": "D7:14", - "text": "Yeah, it's really frustrating when a project doesn't go as planned. What specifically is giving you trouble? Maybe I can offer some assistance." - }, - { - "speaker": "John", - "dia_id": "D7:15", - "text": "This coding is really tough. I'm working on something complicated and it's not going well. I've been staring at it for ages and I feel like I'm getting nowhere." - }, - { - "speaker": "James", - "dia_id": "D7:16", - "text": "Gotcha, what's the problem with the project? Need a hand figuring out some ideas?" - }, - { - "speaker": "John", - "dia_id": "D7:17", - "text": "Yeah, that'd be great. I'm trying to make this new algorithm work better, but I'm stuck. Do you have any ideas?" - }, - { - "speaker": "James", - "dia_id": "D7:18", - "text": "Not sure about your algorithm, but breaking it down into smaller steps might help. Doing some research on similar algorithms or asking other programmers for advice could be beneficial. Don't be afraid to seek help and remember, every problem has a solution." - }, - { - "speaker": "John", - "dia_id": "D7:19", - "text": "Yep, breaking it down into smaller steps and asking for help can definitely be helpful. I'll give it a try. Thanks for the advice, James. Much appreciated." - }, - { - "speaker": "James", - "dia_id": "D7:20", - "text": "No problem John. Glad to help. You got this. Keep going and you'll find the answer." - }, - { - "speaker": "John", - "blip_caption": "a photo of a notebook with a quote written on it", - "dia_id": "D7:21", - "text": " Your words really helped. I won't quit. Gonna keep going. Cheers! \n" - } - ], - "session_8_date_time": "2:36 pm on 29 April, 2022", - "session_8": [ - { - "speaker": "James", - "dia_id": "D8:1", - "text": "Hey John! What's up? Anything fun going on?" - }, - { - "speaker": "John", - "dia_id": "D8:2", - "text": " I'm currently taking on some freelance programming to hone my coding skills. It's challenging, but I'm determined to improve." - }, - { - "speaker": "James", - "dia_id": "D8:3", - "text": "Freelancing can definitely be a great way to sharpen skills and gain experience. What projects are you currently working on?" - }, - { - "speaker": "John", - "dia_id": "D8:4", - "text": "I'm actually working on a website for a local small business. It's my first professional project outside of class." - }, - { - "speaker": "James", - "dia_id": "D8:5", - "text": "Congrats on your first professional project, John! Bet it's been great applying what you learned in class. How's the progress been?" - }, - { - "speaker": "John", - "dia_id": "D8:6", - "text": "Thanks, James! I've learned a lot and it's been an interesting journey so far. Progress is slow and there have been some hiccups along the way." - }, - { - "speaker": "James", - "dia_id": "D8:7", - "text": "Yeah, nothing ever goes smooth. It's normal to have hiccups, but use them to learn and grow. Push through it and you'll make it!" - }, - { - "speaker": "John", - "dia_id": "D8:8", - "text": "You're right, I appreciate the boost. It's tough sometimes but I'm gonna keep pushing and make this work. Hiccups won't stop me." - }, - { - "speaker": "James", - "dia_id": "D8:9", - "text": "What challenges have you encountered?" - }, - { - "speaker": "John", - "dia_id": "D8:10", - "text": "Figuring out how to get payments on the website was tough. I needed some help so I used some resources to understand the process. It's taken a while, but I'm getting closer to a solution." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/2xbwn05lpc761.jpg" - ], - "blip_caption": "a photo of a person holding a book open to a picture of a male character", - "query": "witcher 3 artwork stunning", - "dia_id": "D8:11", - "text": "That sounds challenging, but you're making progress. Hang in there! By the way, three days ago I bought myself an adventure book with fantasy novels and cool arts." - }, - { - "speaker": "John", - "dia_id": "D8:12", - "text": "Wow, that art's awesome! It takes me back to reading fantasy books." - }, - { - "speaker": "James", - "dia_id": "D8:13", - "text": "Yeah, I love this genre. Got any suggestions?" - }, - { - "speaker": "John", - "dia_id": "D8:14", - "text": "Cool! Heard of \"The Name of the Wind\"? It's another great novel with awesome writing." - }, - { - "speaker": "James", - "blip_caption": "a photo of a book set of three books on a wooden table", - "dia_id": "D8:15", - "text": "Never heard of it, but it sounds interesting. I'll definitely check it out. Thanks for the recommendation, John!" - }, - { - "speaker": "John", - "dia_id": "D8:16", - "text": "Always happy to help. I'm sure you'll love this trilogy!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/qjblw3fb7hnb1.jpg" - ], - "blip_caption": "a photo of a dog laying on a bed with a computer in the background", - "query": "gaming setup dogs furry gaming buddies", - "dia_id": "D8:17", - "text": "Look, I was playing a game and my faithful furry friend Daisy came and lay down next to me. This is so cute!" - }, - { - "speaker": "John", - "dia_id": "D8:18", - "text": "Awww, this is really so cute! Your furry friend looks so cozy. Do your dogs often come to you like this while playing?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a person holding a blue controller in their hand", - "dia_id": "D8:19", - "text": "Yeah, they love to watch me gaming and often hug me. Such good cuddle buddies! What game have you been playing lately?" - }, - { - "speaker": "John", - "dia_id": "D8:20", - "text": "Awesome that you have them! I'm currently playing AC Valhalla, it's cool. Are you playing anything new?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/c15urew02q041.jpg" - ], - "blip_caption": "a photo of a map of the world on a tv screen", - "query": "civilization vi strategy game", - "dia_id": "D8:21", - "text": "Thanks, John! Valhalla is awesome. I'm trying out some strategy games like this. It's different but so cool!" - }, - { - "speaker": "John", - "dia_id": "D8:22", - "text": "Is that Civilization VI? Heard good things about it. How's it?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/nbyl9ym1dpj41.jpg" - ], - "blip_caption": "a photo of a computer screen showing a game of war", - "query": "turn-based strategy game screenshot", - "dia_id": "D8:23", - "text": "This is a high-quality turn-based strategy game where you manage resources, lead armies, and conquer territories - challenging and cool!" - }, - { - "speaker": "John", - "dia_id": "D8:24", - "text": "That sounds fun! What's the game like? Does it require a lot of strategy?" - }, - { - "speaker": "James", - "dia_id": "D8:25", - "text": "Sure, John! It requires a lot of strategy. It's all about planning, managing resources and making good decisions to beat your rivals. Every move matters!" - }, - { - "speaker": "John", - "dia_id": "D8:26", - "text": "Sounds intense but cool. I like games that test my strategizing. Does it help with your problem-solving?" - }, - { - "speaker": "James", - "dia_id": "D8:27", - "text": "Yeah, it's a great way to work on problem-solving and thinking. Plus, it's awesome to see your plans go the way you wanted and win!" - }, - { - "speaker": "John", - "dia_id": "D8:28", - "text": "Yeah! It's really satisfying when your plans work out and you win. How long have you been playing this game?" - }, - { - "speaker": "James", - "dia_id": "D8:29", - "text": "Been playing it for a month now - it's really challenged my strategy skills." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/m0vu1etdlol91.jpg" - ], - "blip_caption": "a photo of a board game with a lot of cards on it", - "query": "strategy board game", - "dia_id": "D8:30", - "text": "Wow, that's impressive! I'm really enjoying games like this, they really make me think. What do you think of strategy board games? I played one with friends two days ago, it's very exciting!" - }, - { - "speaker": "James", - "dia_id": "D8:31", - "text": "Sounds good! Board games are always a blast when you hang out with friends." - }, - { - "speaker": "John", - "dia_id": "D8:32", - "text": "Yeah! They're great for having fun together." - }, - { - "speaker": "James", - "dia_id": "D8:33", - "text": "Anything else that is fun to play with others?" - }, - { - "speaker": "John", - "dia_id": "D8:34", - "text": "Yes, we played one game, but I forgot its name. Perhaps you know this game. There were multi-colored cards with numbers. You can only place a card with the same color or number on your opponent's card. Sometimes you trade cards, sometimes you need to draw a few extra from the deck or skip a turn." - }, - { - "speaker": "James", - "dia_id": "D8:35", - "text": "I can't remember such a game. Maybe you have some other interesting games?" - }, - { - "speaker": "John", - "dia_id": "D8:36", - "text": "Yeah for sure! I've been playing one more game with friends these days. It's a game to figure out who the impostors are and it's super fun." - }, - { - "speaker": "James", - "dia_id": "D8:37", - "text": "Sounds cool! I've heard of that game, been meaning to try it out." - }, - { - "speaker": "John", - "dia_id": "D8:38", - "text": "Go for it, James! I advise you to gather a large group, it will be much more interesting to play." - }, - { - "speaker": "James", - "dia_id": "D8:39", - "text": "Sure thing, sounds like fun." - }, - { - "speaker": "John", - "dia_id": "D8:40", - "text": "That really is!" - } - ], - "session_9_date_time": "7:01 pm on 4 May, 2022", - "session_9": [ - { - "speaker": "John", - "dia_id": "D9:1", - "text": "Hey James! How've you been? Had an eventful week since our last chat." - }, - { - "speaker": "James", - "dia_id": "D9:2", - "text": "Hey John! Man, it's been wild since we talked. Last Friday, something happened on my project that I've been working on for weeks. I got so close to finishing it but I just couldn't figure it out. Super frustrating." - }, - { - "speaker": "John", - "dia_id": "D9:3", - "text": "Ugh, that's rough. I understand how frustrating it can be. What happened with that project of yours?" - }, - { - "speaker": "James", - "dia_id": "D9:4", - "text": "I hit a major snag - a bug in the code messed up the game mechanics. I tried debugging it for hours but couldn't solve it. It was super disappointing." - }, - { - "speaker": "John", - "dia_id": "D9:5", - "text": "Yeah, dealing with those bugs can be frustrating. Did you manage to solve the issue?" - }, - { - "speaker": "James", - "dia_id": "D9:6", - "text": "Nah, I couldn't figure it out on my own so a group of friends and I teamed up and got it fixed. Took a bit of work, but I'm glad it got done." - }, - { - "speaker": "John", - "dia_id": "D9:7", - "text": "Cool! Having a reliable team can really make a difference." - }, - { - "speaker": "James", - "dia_id": "D9:8", - "text": "Yeah, totally! It's awesome to have a group of people who share the same passions. They give you help and bring their own ideas to the mix. You can achieve so much when everyone works together. Are you working on anything today?" - }, - { - "speaker": "John", - "dia_id": "D9:9", - "text": "Nah, I have day-off today. I had a few new friends over to watch some movies. Trying to socialize more and it's been great! I think it's important to balance work and enjoyment. You're welcome to join us next time!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/1go6sson1bk41.jpg" - ], - "blip_caption": "a photo of two dogs playing in a fenced in area", - "query": "dogs playing backyard", - "dia_id": "D9:10", - "text": "Sounds great, John! I'm definitely in next time. Hanging out with friends and unwinding is key. By the way, today I decided to spend time with my beloved pets again.\n" - }, - { - "speaker": "John", - "dia_id": "D9:11", - "text": "Cool! They look like they're having a blast. What type are they?" - }, - { - "speaker": "James", - "dia_id": "D9:12", - "text": "One of them, Daisy, is a Labrador. She loves to play with her toys, but most of all she loves to eat." - }, - { - "speaker": "John", - "dia_id": "D9:13", - "text": "Cool, what about the other two? Judging by the photo, shepherds?" - }, - { - "speaker": "James", - "dia_id": "D9:14", - "text": "Exactly! You would know how much joy they bring me. They are so loyal, and this is their main feature." - }, - { - "speaker": "John", - "dia_id": "D9:15", - "text": "Wow, James! Love hearing about the joy that furry friends bring into your life. What else brings you happiness?" - }, - { - "speaker": "James", - "dia_id": "D9:16", - "text": "My pets, computer games, travel and pizza are all that bring me happiness in life." - }, - { - "speaker": "John", - "dia_id": "D9:17", - "text": "Pizza? Cool, I love pizza too! Which one do you love the most?" - }, - { - "speaker": "James", - "dia_id": "D9:18", - "text": "Pepperoni of course! An amazing combination of spicy salami and cheese." - }, - { - "speaker": "John", - "img_url": [ - "https://c1.wallpaperflare.com/preview/469/790/507/big-city-pizza.jpg" - ], - "blip_caption": "a photography of a pizza with pineapples and ham on a wooden board", - "query": "the witcher 3 game case", - "dia_id": "D9:19", - "re-download": true, - "text": "Cool, but my favorite thing is Hawaiian pizza. This combination of sweet and salty delights me! What other pizza do you like?" - }, - { - "speaker": "James", - "dia_id": "D9:20", - "text": "I like also cheese pizza and prosciutto." - }, - { - "speaker": "John", - "dia_id": "D9:21", - "text": "Haven't tried prosciutto yet, but I've heard it`s great!" - }, - { - "speaker": "James", - "dia_id": "D9:22", - "text": "Give it a shot! It`s really great. No regrets, I promise." - }, - { - "speaker": "John", - "dia_id": "D9:23", - "text": "Got it, will check it out. Thanks!" - }, - { - "speaker": "James", - "dia_id": "D9:24", - "text": "Be sure to tell us your impressions when you try it! And Bon Appetit!" - }, - { - "speaker": "John", - "dia_id": "D9:25", - "text": "Thanks! Catch you later!" - } - ], - "session_10_date_time": "12:45 am on 8 May, 2022", - "session_10": [ - { - "speaker": "James", - "dia_id": "D10:1", - "text": "Hey John! Been a while since we chatted. Sorry 'bout not getting back sooner. How's it going? Any new games you're into?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/eh02fep1qnw01.jpg" - ], - "blip_caption": "a photo of a wooden table with a game controller on it", - "query": "charity gaming tournament friends game controllers", - "dia_id": "D10:2", - "text": "Hey James! No worries, I know you are really busy at work. I'm good, thanks for asking. Oh, I've been organizing something with my friends yesterday - it was cool! Guess what it was, I'll give you a little hint." - }, - { - "speaker": "James", - "dia_id": "D10:3", - "text": "Wow, John, that looks awesome! Is it an icon of a new game?" - }, - { - "speaker": "John", - "dia_id": "D10:4", - "text": "Nope, not a new game. We put together a tournament for our favorite game, CS:GO. Lots showed up and we made a bunch of money for charity!" - }, - { - "speaker": "James", - "dia_id": "D10:5", - "text": "Wow John, organizing that tournament for charity must have been a ton of effort, but it sounds like it was so worth it!" - }, - { - "speaker": "John", - "dia_id": "D10:6", - "text": "Definitely worth it! It took some planning and coordination, but seeing everyone come together for a good cause was so rewarding." - }, - { - "speaker": "James", - "dia_id": "D10:7", - "text": "It must have been great to see the results of that effort. Have you considered organizing more events like that in the future?" - }, - { - "speaker": "John", - "dia_id": "D10:8", - "text": "Yeah, for sure! It was awesome and I want to do more events like that. It combines my interests and helps the community. Plus, it's great to get people together for some friendly competition." - }, - { - "speaker": "James", - "dia_id": "D10:9", - "text": "Combining gaming and volunteering is a great idea! So fun and fulfilling. Where did you send the collected money?" - }, - { - "speaker": "John", - "dia_id": "D10:10", - "text": "Our main goal was to raise money for a dog shelter, which is not far from the street where I live. And we did it!" - }, - { - "speaker": "James", - "dia_id": "D10:11", - "text": "Helping animals is really important!" - }, - { - "speaker": "John", - "dia_id": "D10:12", - "text": "I agree. We still had some money left after helping the shelter, and we decided to use this money to buy groceries and cook some food for the homeless. They were very happy about it." - }, - { - "speaker": "James", - "dia_id": "D10:13", - "text": "Glad you are helping those in need! You are doing a great job John, keep up the good work!" - }, - { - "speaker": "John", - "dia_id": "D10:14", - "text": "Thanks for your support, James! I won't stop there, I will do more and more good things!" - }, - { - "speaker": "James", - "dia_id": "D10:15", - "text": "I'm really proud of you!" - } - ], - "session_11_date_time": "5:00 pm on 11 May, 2022", - "session_11": [ - { - "speaker": "John", - "dia_id": "D11:1", - "text": "Hey James, it's been a bit since we last talked. Something cool happened recently - I volunteered my programming skills for a social cause. It was cool to use my passion to do something good. I made a software tool for one charitable foundation which helped streamline their operations and make them run more smoothly. Seeing my skills making a real difference in the world was really rewarding." - }, - { - "speaker": "James", - "dia_id": "D11:2", - "text": "Hey John! Glad to hear from you. It's awesome that you used your skills to make a difference. Bet it was cool to see it in action. Would love to hear more about it!" - }, - { - "speaker": "John", - "dia_id": "D11:3", - "text": "Previously, this foundation used paper records and all inventory was recorded manually. I made an application that structured their work, and now everything they need for inventory is in one application on their smartphone." - }, - { - "speaker": "James", - "dia_id": "D11:4", - "text": "Wow John, that's awesome! Must feel great to be part of something so important. I would love to see any visual examples of the impact your software made." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/rak0v41zm4sb1.jpg" - ], - "blip_caption": "a photo of a computer monitor showing the cpu performance of a computer", - "query": "software tool inventory management resources donations reports analysis", - "dia_id": "D11:5", - "text": "Yeah, here's a screenshot of the system. It'll make tracking inventory, resources, and donations run smoother and generate reports for analysis. Feels great knowing my skills are making a real difference to them." - }, - { - "speaker": "James", - "dia_id": "D11:6", - "text": "Wow John, that's awesome! What motivated you to create such an amazing system for them?" - }, - { - "speaker": "John", - "dia_id": "D11:7", - "text": "I was inspired by their passion for helping kids, so I wanted to contribute in any way I could. Plus, coding lets me challenge myself and expand my skills, so this was a great chance to do both. It's really rewarding to use my coding skills to make a difference." - }, - { - "speaker": "James", - "dia_id": "D11:8", - "text": "That's really great, John! It's awesome how you blended your passion with a good cause. How did it affect you?" - }, - { - "speaker": "John", - "dia_id": "D11:9", - "text": "It showed me the power of tech to make positive changes, beyond just my own enjoyment. It gave me a real sense of purpose." - }, - { - "speaker": "James", - "dia_id": "D11:10", - "text": "Discovering our passions is truly rewarding. How do you think this experience will impact your future plans?" - }, - { - "speaker": "John", - "dia_id": "D11:11", - "text": "This experience has given me a clearer sense of purpose and motivated me to use my programming skills to make a positive impact. I'm now considering volunteer roles and potentially a career in the non-profit sector." - }, - { - "speaker": "James", - "dia_id": "D11:12", - "text": "That's really inspiring. Have you found any non-profit organizations that align with your values and passion for programming?" - }, - { - "speaker": "John", - "dia_id": "D11:13", - "text": "I haven\u2019t found it yet, but to be honest I haven\u2019t looked for it. I think it won\u2019t be difficult for me to find the organization I need." - }, - { - "speaker": "James", - "dia_id": "D11:14", - "text": "There are lots of places where you can show off your skills! I'm sure you'll find one that's perfect for you in making a difference." - }, - { - "speaker": "John", - "dia_id": "D11:15", - "text": "I'll be happy to find a place where my skills and passions are a perfect match. I'm hoping to make a positive impact there." - }, - { - "speaker": "James", - "dia_id": "D11:16", - "text": "I'm sure you'll find the right spot, John. Your skills and passions will be a great addition. Good luck!" - }, - { - "speaker": "John", - "dia_id": "D11:17", - "text": "Thanks, your encouragement means a lot." - }, - { - "speaker": "James", - "dia_id": "D11:18", - "text": "I'm here for you. Good luck!" - }, - { - "speaker": "John", - "dia_id": "D11:19", - "text": "Thanks, James! Appreciate it. Take care and talk soon!" - } - ], - "session_12_date_time": "7:33 pm on 23 May, 2022", - "session_12": [ - { - "speaker": "James", - "dia_id": "D12:1", - "text": "Hey John, last weekend I had an awesome time at the amusement park with my friends. It was a great break from the virtual world. I went on some awesome roller coasters and it reminded me of when I was a kid. Everything was so real and exciting; it felt like I was in a video game!" - }, - { - "speaker": "John", - "dia_id": "D12:2", - "text": "Hey James! Sounds like an awesome time! I bet those rides brought back some great memories. Were there any other attractions besides the roller coaster?" - }, - { - "speaker": "James", - "dia_id": "D12:3", - "text": "Of course, I also managed to ride the Ferris wheel, electric cars and buggies. What's new with you?" - }, - { - "speaker": "John", - "dia_id": "D12:4", - "text": "That's really cool! Last Friday I entered a local tournament and took second place! It was a wild experience and the competitive energy was insane." - }, - { - "speaker": "James", - "dia_id": "D12:5", - "text": "Wow, John that's awesome! Congrats on your achievement! I can imagine the rush you must have felt during the tournament. Did you receive any rewards or prizes for your success?" - }, - { - "speaker": "John", - "dia_id": "D12:6", - "text": "I was stoked about my achievement. Though I didn't win the tournament, I still received some money for the 2nd place. Seeing my effort pay off was awesome." - }, - { - "speaker": "James", - "dia_id": "D12:7", - "text": "Awesome news! You don't have to win every time, growth and progress are most important. " - }, - { - "speaker": "John", - "img_url": [ - "https://sc04.alicdn.com/kf/Hcdada85e5412478bb63e47746ffc7c53e.jpg" - ], - "blip_caption": "a photo of a graduation cap on a book with a rope", - "query": "tournament prize trophy logo", - "dia_id": "D12:8", - "text": "Yeah, I also got this trophy! So satisfying. It reminds me to always put in my best effort. What about you? Any success stories lately?" - }, - { - "speaker": "James", - "dia_id": "D12:9", - "text": "Congrats on your achievement, John! That trophy looks awesome. Last month, I had a personal milestone. There were definitely tough times, but it reminds me of all the hard work. I feel a huge sense of accomplishment and I'm ready for the future opportunities!" - }, - { - "speaker": "John", - "dia_id": "D12:10", - "text": "Congrats on the milestone. What was it and what made it challenging? What did you learn?" - }, - { - "speaker": "James", - "dia_id": "D12:11", - "text": "I finished a big project I had been working on for months. It was challenging because I had to learn a new language and handle many details. I learned a lot about problem-solving, patience, and perseverance. Now I feel more confident to take on even bigger projects." - }, - { - "speaker": "John", - "dia_id": "D12:12", - "text": " That's awesome you learned a language and handled all those details. Great job, you definitely picked up some great skills! Remember, determination and confidence make any project a success. Good work!" - }, - { - "speaker": "James", - "dia_id": "D12:13", - "text": "Thanks, I appreciate your support. I'll definitely keep that in mind." - }, - { - "speaker": "John", - "dia_id": "D12:14", - "text": "No worries, I'm here to help. Keep going and reach those goals!" - } - ], - "session_13_date_time": "4:30 pm on 13 June, 2022", - "session_13": [ - { - "speaker": "John", - "dia_id": "D13:1", - "text": "Hey James, long time no talk! A lot has happened during this time. Let me fill you in." - }, - { - "speaker": "James", - "dia_id": "D13:2", - "text": "Hey John! Awesome to hear from you. Yeah, a lot has happened. Let's catch up!" - }, - { - "speaker": "John", - "dia_id": "D13:3", - "text": "I finally got my dream job! After lots of interviews and late nights, I got the offer and was ecstatic. Can't wait to start my journey!" - }, - { - "speaker": "James", - "dia_id": "D13:4", - "text": "Wow, John! Congrats on getting your dream job. I'm super stoked for you. When do you start?" - }, - { - "speaker": "John", - "dia_id": "D13:5", - "text": "Thank you! ! I'm starting next month." - }, - { - "speaker": "James", - "dia_id": "D13:6", - "text": "It can be rough getting started, but I'm sure you'll do great. Don't be afraid to seek help if you need it. Can't wait to hear about your experience! By the way, I recently started a course that combines my passion for gaming and programming. It's fun and challenging, and it has definitely increased my excitement for both." - }, - { - "speaker": "John", - "dia_id": "D13:7", - "text": "Cool! That sounds awesome. Combining your love of gaming and coding sounds like a dream. Tell me more! Are there any interesting projects you're working on?" - }, - { - "speaker": "James", - "dia_id": "D13:8", - "text": "Yes, we are currently working on a new part of the football simulator. I was working on collecting player databases. It wasn't easy, but I did it!" - }, - { - "speaker": "John", - "dia_id": "D13:9", - "text": "Cool! Did you choose this course because you love football?" - }, - { - "speaker": "James", - "dia_id": "D13:10", - "text": "Not least because of this. I love football, but, of course, the most important reason is to improve yourself. But it\u2019s nice if it\u2019s also connected with something you like." - }, - { - "speaker": "John", - "dia_id": "D13:11", - "text": "I completely agree! By the way, did you watch the Liverpool vs Chelsea match?" - }, - { - "speaker": "James", - "dia_id": "D13:12", - "text": "Of course, they played well! As I like to say, there is no sport better than football, no club better than Liverpool! I don't miss a single match of theirs!" - }, - { - "speaker": "John", - "dia_id": "D13:13", - "text": "It looks like you really root for this team!" - }, - { - "speaker": "James", - "dia_id": "D13:14", - "text": "Absolutely! They are forever in my heart, they are a great team. I hope they become champions next season!" - }, - { - "speaker": "John", - "dia_id": "D13:15", - "text": "As a Manchester City fan, I can't agree with you. You'll see, our two teams will fight for the championship, and mine will win!" - }, - { - "speaker": "James", - "dia_id": "D13:16", - "text": "I'm sure you're wrong, John. Manchester City are in bad form and their transfer policy is terrible!" - }, - { - "speaker": "John", - "dia_id": "D13:17", - "text": "You may be right, but the City manager can handle it, you'll see!" - }, - { - "speaker": "James", - "dia_id": "D13:18", - "text": "I bet we'll be higher than you in the final standings!" - }, - { - "speaker": "John", - "dia_id": "D13:19", - "text": "I'll take the bet, James! This will be a great battle!" - }, - { - "speaker": "James", - "dia_id": "D13:20", - "text": "Sure, John!" - } - ], - "session_14_date_time": "5:07 pm on 16 June, 2022", - "session_14": [ - { - "speaker": "James", - "blip_caption": "a photo of a dog laying on a person on a couch", - "dia_id": "D14:1", - "text": "Hey John, how's it going? A lot has happened for me lately, some good and some not so great. I`m lucky to have at least two people who always help me out when I'm struggling. What about you?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/bkol9biz39j21.jpg" - ], - "blip_caption": "a photo of a computer screen with a program on it", - "query": "screenshot coding blog", - "dia_id": "D14:2", - "text": "Gad to hear you have your support system ready. It's crucial, especially during hard times. For me, it's been quite an emotional rollercoaster. Last week, I started my blog about coding. It's exciting and kinda scary too. This is the first picture I uploaded there." - }, - { - "speaker": "James", - "blip_caption": "a photo of a video game scene of a man and woman", - "dia_id": "D14:3", - "text": "Wow, starting a blog about coding is awesome! Must be so exciting. What do you think about it?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a computer screen showing a number of items", - "dia_id": "D14:4", - "text": "Thanks, James! It has been an awesome and challenging experience. I'm loving sharing my coding journey with others and tracking everything. It's a great way to connect with other coders." - }, - { - "speaker": "James", - "blip_caption": "a photo of a vase with a plaque on it", - "dia_id": "D14:5", - "text": "Congrats on your coding journey! What`s more new in your world?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/qx7qmvp5abx41.jpg" - ], - "blip_caption": "a photo of a bookshelf with a lot of books on it", - "query": "bookshelf coding books", - "dia_id": "D14:6", - "text": "Well, I bought a lot of new books, and now my bookcase is almost completely filled!" - }, - { - "speaker": "James", - "dia_id": "D14:7", - "text": "What genre do you enjoy reading?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/62ogg70rblbb1.jpg" - ], - "blip_caption": "a photo of a book shelf with a few books on it", - "query": "epic fantasy series book cover", - "dia_id": "D14:8", - "text": "I'm a big fan of sci-fi and fantasy books. Epic fantasy series with immersive world-building and intricate storylines are what I enjoy reading. Just getting lost in the pages of a great story is a wonderful escape from reality." - }, - { - "speaker": "James", - "dia_id": "D14:9", - "text": "Cool! Are there any book series that you love and would recommend to others?" - }, - { - "speaker": "John", - "dia_id": "D14:10", - "text": "Definitely! Two of my favorites are \"The Stormlight Archive\" and \"Kingkiller Chronicle\". If SF is your thing, check out \"The Expanse\" series. It's epic!" - }, - { - "speaker": "James", - "blip_caption": "a photo of a cd with a picture of a knight on it", - "dia_id": "D14:11", - "text": "Thanks for the recommendations, John! I'll definitely check out those books. What makes them your favorites?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/gnwdz3ysqra71.jpg" - ], - "blip_caption": "a photo of a dog sitting on a couch in a living room", - "query": "dog couch cozy", - "dia_id": "D14:12", - "text": "Glad you're giving these books a try! I'm obsessed with the way they create a magical world you can escape into - plus the characters feel really real. By the way, what's the name of the dog in this picture from your Facebook? It`s so cute!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/3gzujt17mgkb1.jpg" - ], - "blip_caption": "a photo of a dog chewing on a shoe on a person's lap", - "query": "dog max playing fetch", - "dia_id": "D14:13", - "text": "This is Max \u2013 he's so lovable and playful. He brings me so much joy, especially in tough times." - }, - { - "speaker": "John", - "dia_id": "D14:14", - "text": "Aww, he's adorable! I can tell Max brings you a lot of happiness. Pets are always such a great source of joy and love." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/a6vjqnq17kcb1.jpg" - ], - "blip_caption": "a photo of a dog standing in front of a pool", - "query": "Max playing ball backyard", - "dia_id": "D14:15", - "text": "Yeah, Max is great - he always cheers me up when I'm feeling down. Enjoying those cuddles with him!" - }, - { - "speaker": "John", - "dia_id": "D14:16", - "text": "Does he enjoy swimming?" - }, - { - "speaker": "James", - "dia_id": "D14:17", - "text": "Yeah, he loves it! We usually hit the beach or lake, and he loves playing in the water. He's a pro swimmer!" - }, - { - "speaker": "John", - "dia_id": "D14:18", - "text": "Max must be having so much fun swimming and playing - it's the best!" - }, - { - "speaker": "James", - "dia_id": "D14:19", - "text": "He has a blast! Always a joy to see him so happy and carefree in his favorite activity." - }, - { - "speaker": "John", - "dia_id": "D14:20", - "text": "He looks so happy - this is a great achievement of yours!" - }, - { - "speaker": "James", - "blip_caption": "a photo of a dog running in a field with a frisbee", - "dia_id": "D14:21", - "text": "Thanks, John! I love making him happy." - }, - { - "speaker": "John", - "dia_id": "D14:22", - "text": "Does Max have any special talents? He seems like quite the go-getter!" - }, - { - "speaker": "James", - "img_url": [ - "https://d4yxl4pe8dqlj.cloudfront.net/images/061f1763-db03-4b77-a9c7-85a24844f99e/58b4c82d-18f3-4736-87f7-de2a7a3de0a8_full_size.jpg" - ], - "blip_caption": "a photo of a dog laying in the grass with a frisbee", - "query": "dog catching frisbee", - "dia_id": "D14:23", - "text": "Max is a real go-getter! He's awesome at catching frisbees in mid-air - never misses!" - }, - { - "speaker": "John", - "dia_id": "D14:24", - "text": "Wow, Max loves playing fetch! Does he also enjoy long walks?" - }, - { - "speaker": "James", - "img_url": [ - "https://www.kingdukes.com/cdn/shop/articles/man_walking_dog_on_portland_trail_1200x.jpg" - ], - "blip_caption": "a photo of a man walking a dog on a trail", - "query": "dog walking trail", - "dia_id": "D14:25", - "text": "Yep! We love them; they're great exercise and give us fresh air. Here is a photo of us from a recent walk in the forest." - }, - { - "speaker": "John", - "dia_id": "D14:26", - "text": "Where's that spot where you could take a stroll? Bet Max loves all those hikes." - }, - { - "speaker": "James", - "img_url": [ - "https://i0.wp.com/runoregonblog.com/wp-content/uploads/2023/03/img_3358.jpg" - ], - "blip_caption": "a photo of a dirt path in a field with trees", - "query": "nearby trail forest serene peaceful nature", - "dia_id": "D14:27", - "text": "Max and I love taking walks on this nearby trail. It's a mile from my house. It's so tranquil and a great way to relax and connect with nature." - }, - { - "speaker": "John", - "dia_id": "D14:28", - "text": "Wow, that looks awesome! Going for a nature walk is so refreshing, don't you think?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a park with a bench and trees in the fall", - "dia_id": "D14:29", - "text": "Yeah, John! It's so relaxing and refreshing. It helps me think straight and find my inner peace." - }, - { - "speaker": "John", - "dia_id": "D14:30", - "text": "That park is so peaceful. What do you do when you're there alone?" - }, - { - "speaker": "James", - "dia_id": "D14:31", - "text": "When I'm there, I usually bring a book and just chill. It's like an escape from reality." - }, - { - "speaker": "John", - "dia_id": "D14:32", - "text": "Sounds great, James! Taking breaks and switching up the scenery is a great way to feel recharged. Keep it up." - }, - { - "speaker": "James", - "dia_id": "D14:33", - "text": "Definitely! Taking breaks is important for recharging. Thanks for the support!" - }, - { - "speaker": "John", - "dia_id": "D14:34", - "text": "No problem, always here to have your back. Take care of yourself!" - } - ], - "session_15_date_time": "9:59 pm on 19 June, 2022", - "session_15": [ - { - "speaker": "James", - "dia_id": "D15:1", - "text": "Hey John, since our last chat, something awesome happened. Last Friday, I started introducing Max, Daisy and the new pup Ned. It was hard at first, but they're slowly adapting. It's sweet to watch them bond and have fun together." - }, - { - "speaker": "John", - "dia_id": "D15:2", - "text": "Wow, that's cool, James! Seeing them bonding and having a great time is so sweet. Do you have a picture of them together?" - }, - { - "speaker": "James", - "dia_id": "D15:3", - "img_url": [ - "https://www.publicdomainpictures.net/pictures/40000/nahled/three-dogs-in-field.jpg" - ], - "re-download": true, - "blip_caption": "a photography of three dogs in a field of grass with trees in the background", - "text": "Yep, I got a great pic last night. Check it out!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a dog and a cat cuddling on a couch", - "dia_id": "D15:4", - "text": "Wow, they look so cute! I can almost feel the love and joy in this photo. These last few days really got me thinking about my own future." - }, - { - "speaker": "James", - "dia_id": "D15:5", - "text": "What's been on your mind regarding the future?" - }, - { - "speaker": "John", - "dia_id": "D15:6", - "text": "Lately, I've been thinking about my career and where I want to be in the future. I'm driven and passionate, and I also want to make a positive impact on the world." - }, - { - "speaker": "James", - "dia_id": "D15:7", - "text": "Gotcha, John. Finding a way to make a difference matters. Have you thought about any ideas on how to do that?" - }, - { - "speaker": "John", - "dia_id": "D15:8", - "text": "Yeah, I have. I've been looking into volunteering and thinking of going into non-profit work. I want to put my passions and abilities to use for causes I really care about." - }, - { - "speaker": "James", - "img_url": [ - "https://i0.wp.com/accentnetwork.us/wp-content/uploads/2022/03/blog.jpg" - ], - "blip_caption": "a photo of a group of people standing outside of a building", - "query": "donation center", - "dia_id": "D15:9", - "text": "Wow, John that sounds great! I'm sure with your skills and passion, you could do some really cool things with nonprofits. Here's a pic I took when I volunteered last month. It was really rewarding to see how little gifts can do so much!" - }, - { - "speaker": "John", - "dia_id": "D15:10", - "text": "That's awesome, James! Was it cool to see the impact of the gifts? Can you tell me more about the organization you volunteered with?" - }, - { - "speaker": "James", - "dia_id": "D15:11", - "text": "It was great to see how much a simple act of kindness can mean to someone in need. I volunteered with an organization that provides necessary items to those who are less fortunate. It felt so rewarding to help, even if it was in a small way." - }, - { - "speaker": "John", - "blip_caption": "a photo of a young boy standing outside of a yellow building", - "dia_id": "D15:12", - "text": "I think this is exactly what I need. Can you take me there this weekend?" - }, - { - "speaker": "James", - "dia_id": "D15:13", - "text": "Of course I can! I think there are still some of the previous staff there and I can even introduce you to them." - }, - { - "speaker": "John", - "dia_id": "D15:14", - "text": "Thank you very much! Will there be some kind of interview required?" - }, - { - "speaker": "James", - "dia_id": "D15:15", - "text": "No, this is not necessary. All you need is to be a friendly and polite person, and also have a great desire to help people. I'm sure you will succeed!" - }, - { - "speaker": "John", - "dia_id": "D15:16", - "text": "Thanks for your support! I want to make this world a better place, and with your help I will definitely achieve my goal." - }, - { - "speaker": "James", - "dia_id": "D15:17", - "text": "We can do this together!" - }, - { - "speaker": "John", - "dia_id": "D15:18", - "text": "Thanks, James. Your support means a lot to me. I'm determined to make a positive impact." - }, - { - "speaker": "James", - "dia_id": "D15:19", - "text": "You got this! Stay focused on your dreams and don't give up." - } - ], - "session_16_date_time": "5:13 pm on 9 July, 2022", - "session_16": [ - { - "speaker": "James", - "dia_id": "D16:1", - "text": "Hey John! Long time no talk - hope you're doing well. Guess what? Last week I actually won an online gaming tournament! It was such an exciting experience and it blew my mind when I won. Winning felt so good and it really motivated me to keep improving." - }, - { - "speaker": "John", - "dia_id": "D16:2", - "text": "Hey James! Congrats on winning the online gaming tournament! It's super fulfilling to see your hard work pay off. So happy for you!" - }, - { - "speaker": "James", - "dia_id": "D16:3", - "text": "Thanks! It was really fulfilling to see my hard work pay off with a victory in the tournament. How are you?" - }, - { - "speaker": "John", - "dia_id": "D16:4", - "text": "Feeling the tug of emotion lately. Determined and passionate on one hand, but feeling overwhelmed and stressed on the other. Balancing personal and professional is kind of a challenge. How have you been?" - }, - { - "speaker": "James", - "dia_id": "D16:5", - "text": "Yeah, staying balanced can be tough. I'm trying to take breaks from my hobbies and do other things. Lately I've become interested in extreme sports. Yesterday, for example, I was doing rope jumping. The highest height I jumped from was 150 meters!" - }, - { - "speaker": "John", - "dia_id": "D16:6", - "text": "Wow, how cool! What other extreme sport have you tried?" - }, - { - "speaker": "James", - "dia_id": "D16:7", - "text": "Just three days ago, I was surfing. Catching a wave is so cool! It's strange, but it relaxes me so much. How do you like to relax?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/d0eoj5t4py351.jpg" - ], - "blip_caption": "a photo of a book shelf filled with books and a guitar", - "query": "bookshelf novels fantasy books", - "dia_id": "D16:8", - "text": "I like to relax by reading. I love entering the imaginative worlds of authors - it's a fun escape from reality. " - }, - { - "speaker": "James", - "dia_id": "D16:9", - "text": "I also love to read, especially while snuggled under the covers on a cold winter day. But now it\u2019s summer and I want something more exciting! By the way, I bought air tickets to Toronto, and I\u2019m leaving the day after tomorrow evening." - }, - { - "speaker": "John", - "dia_id": "D16:10", - "text": "Cool, this is already the fourth country you will visit! Will you only be in Toronto, or will you be visiting somewhere else?" - }, - { - "speaker": "James", - "dia_id": "D16:11", - "text": "I also plan to visit Vancouver. Maybe, I'll go somewhere else." - }, - { - "speaker": "John", - "dia_id": "D16:12", - "text": "When are you coming back?" - }, - { - "speaker": "James", - "dia_id": "D16:13", - "text": "I plan to return on July 20, I\u2019ll definitely bring you some kind of souvenir!" - }, - { - "speaker": "John", - "dia_id": "D16:14", - "text": "Thanks James! I will be waiting for you from your journey! Bon Voyage!" - }, - { - "speaker": "James", - "dia_id": "D16:15", - "text": "Thank you, John! Take care and see you soon!" - }, - { - "speaker": "John", - "dia_id": "D16:16", - "text": "Take care, bye!" - } - ], - "session_17_date_time": "9:49 am on 22 July, 2022", - "session_17": [ - { - "speaker": "John", - "dia_id": "D17:1", - "text": "Hi James! I just started playing chess to get better at strategy. I'm loving it! Have you ever tried it out?" - }, - { - "speaker": "James", - "dia_id": "D17:2", - "text": "Hey John! Yeah, I've played chess before. It's a game that really tests your strategy. It's great that you're enjoying it!" - }, - { - "speaker": "John", - "dia_id": "D17:3", - "text": "Yeah, chess is really fun! It's like solving an endless puzzle and always trying to outwit your opponent." - }, - { - "speaker": "James", - "dia_id": "D17:4", - "text": "Yeah, it's tough, but fun when you figure it out. Do you play with friends or online?" - }, - { - "speaker": "John", - "img_url": [ - "https://images.chesscomfiles.com/proxy/i.imgur.com/lzPt82B/https/71544f1354.jpg" - ], - "blip_caption": "a photo of a chess board with a laptop on it", - "query": "chessboard chess pieces intense game", - "dia_id": "D17:5", - "text": "I'm playing mostly online for now, but I also joined a chess club and practice with others. Here's a pic from an intense game I played lately." - }, - { - "speaker": "James", - "dia_id": "D17:6", - "text": "Wow, looks intense! What sparked your interest in chess?" - }, - { - "speaker": "John", - "dia_id": "D17:7", - "text": "I've always been drawn to strategy games and wanted to challenge myself. Plus, I believe chess can improve decision-making skills." - }, - { - "speaker": "James", - "dia_id": "D17:8", - "text": "Great reason for playing chess - it will definitely help you develop your skills!" - }, - { - "speaker": "John", - "dia_id": "D17:9", - "text": "Thanks, James! I'm excited to see how playing chess can enhance my strategic thinking in everyday situations. Do you have any tips for improvement?" - }, - { - "speaker": "James", - "dia_id": "D17:10", - "text": "Definitely! Studying opening moves and strategies and analyzing your games to spot weaknesses are great ways to improve." - }, - { - "speaker": "John", - "dia_id": "D17:11", - "text": " I'll definitely look into that. Appreciate the advice!" - }, - { - "speaker": "James", - "dia_id": "D17:12", - "text": "No worries, John! Happy to help. Just let me know if there's anything else I can assist you with." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/44c4iux03cv51.jpg" - ], - "blip_caption": "a photo of a group of children holding a skateboard", - "query": "friends laughing together photo album", - "dia_id": "D17:13", - "text": " Your support means a lot to me. You're a true friend! Remember this photo from elementary school?" - }, - { - "speaker": "James", - "dia_id": "D17:14", - "text": "That looks fun. But I don\u2019t remember at all under what circumstances we took this picture. What's the story behind it?" - }, - { - "speaker": "John", - "dia_id": "D17:15", - "text": "This is from when we were 10 and we were really into skateboarding. We had a group of friends who often go to the skate park with. We would help each other learn new tricks and have a great time. Those friends made the experience even better and their friendship meant a lot to us." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/a2w1l6874v381.jpg" - ], - "blip_caption": "a photo of a small dog standing on a skateboard", - "query": "dog skateboard playing", - "dia_id": "D17:16", - "text": "Indeed, I remember this moment. We loved skateboards back then, sometimes we even left class early to do it. I still like to go for a ride sometimes, and I even taught my dogs how to balance on it." - }, - { - "speaker": "John", - "dia_id": "D17:17", - "text": "Wow! Do they enjoy it, or do you have to encourage them to play with the board?" - }, - { - "speaker": "James", - "dia_id": "D17:18", - "text": "They love it! They chase after it and run with it. It's a great way for them to get some exercise." - }, - { - "speaker": "John", - "dia_id": "D17:19", - "text": "Wow, that's great! Keeping active and happy is great for both of you." - }, - { - "speaker": "James", - "dia_id": "D17:20", - "text": "Yep! Staying active with them builds a strong bond and makes us both happy." - }, - { - "speaker": "John", - "blip_caption": "a photo of a cat laying on a bed with a stuffed animal", - "dia_id": "D17:21", - "text": "Yeah, the bond between us and our pets is amazing. They bring a lot of joy and love. It\u2019s a pity that I don\u2019t have pets, I\u2019ll definitely get one someday. By the way, how was your trip?" - }, - { - "speaker": "James", - "dia_id": "D17:22", - "text": "Everything went great! In addition, I even managed to get out to another country. The city of Nuuk, if you know. I stayed there quite a bit, but at least I had one more country to add to my bucket list!" - }, - { - "speaker": "John", - "dia_id": "D17:23", - "text": "This is awesome, James! Surely you brought a lot of impressions with you!" - }, - { - "speaker": "James", - "dia_id": "D17:24", - "text": "Certainly! And not only impressions, I also brought souvenirs. For both you and your Jill!" - }, - { - "speaker": "John", - "dia_id": "D17:25", - "text": "Thank you very much, Jill will be delighted!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/yx1qjjtxk8e91.jpg" - ], - "blip_caption": "a photo of a woman and two dogs on a couch", - "query": "dogs sitting couch happiness love companions", - "dia_id": "D17:26", - "text": "You're welcome! By the way, look who came to see me!" - }, - { - "speaker": "John", - "dia_id": "D17:27", - "text": "Nice pic, James! Who are they?" - }, - { - "speaker": "James", - "dia_id": "D17:28", - "text": "That's my sister and my dogs. We were just chilling together yesterday, and they bring so much happiness to my life." - }, - { - "speaker": "John", - "dia_id": "D17:29", - "text": "Wow, they look so happy! It's awesome that you get to spend time with your sister and your furry friends. The bond you have with them is really strong." - }, - { - "speaker": "James", - "dia_id": "D17:30", - "text": "I'm blessed to have a close bond with my sister and our furry friends. We have a great time together, like a family!" - }, - { - "speaker": "John", - "dia_id": "D17:31", - "text": "Family and friends are really amazing, James. They show us so much love and joy. I'm grateful for the connection I have with my siblings. Things can be tough sometimes, but their support means everything to me." - }, - { - "speaker": "James", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D17:32", - "text": "Fully agreed! My sister and I were also near the ocean and watched such a wonderful sunset!" - }, - { - "speaker": "John", - "dia_id": "D17:33", - "text": "Wonderful photo! It's amazing how you can capture a moment and capture it in a photograph." - }, - { - "speaker": "James", - "dia_id": "D17:34", - "text": "Thanks, John! This is just a good shot, nothing more. I took a lot of shots yesterday and chose the best one to send to you." - }, - { - "speaker": "John", - "dia_id": "D17:35", - "text": "Still, the photo is amazing!" - }, - { - "speaker": "James", - "dia_id": "D17:36", - "text": "I have to go, I'm tired over the last two days. Bye!" - }, - { - "speaker": "John", - "dia_id": "D17:37", - "text": "Take care, bye!" - } - ], - "session_18_date_time": "1:45 pm on 6 August, 2022", - "session_18": [ - { - "speaker": "John", - "dia_id": "D18:1", - "text": "Hey James, good catching up! Been a while huh? I made a huge call - recently left my IT job after 3 years. It was tough but I wanted something that made a difference. And now with this new job, I am happy about my decision. I am loving the new job!" - }, - { - "speaker": "James", - "dia_id": "D18:2", - "text": "Hey John! Great to hear from you. Leaving after 3 years is a big step - how did it feel?" - }, - { - "speaker": "John", - "dia_id": "D18:3", - "text": "At first, it was super scary, but I knew I had to make a change and focus on things that align with my values and passions." - }, - { - "speaker": "James", - "dia_id": "D18:4", - "text": "Wow, John, that sounds really brave. I hope it brings you joy and satisfaction." - }, - { - "speaker": "John", - "dia_id": "D18:5", - "text": "Thanks, James. It wasn't easy, but sometimes you gotta take a leap to follow your heart." - }, - { - "speaker": "James", - "dia_id": "D18:6", - "text": "Taking risks pays off! Way to be brave. I'm proud of you!" - }, - { - "speaker": "John", - "dia_id": "D18:7", - "text": "Your support means a lot. Lately, I've been thinking about what truly makes me happy, and I'm really drawn to the gaming industry. I'm passionate about it and it's time to turn that into a career. I want to become an organizer of tournaments for various computer games in our state. For example, for CS:GO. It's a new journey for me." - }, - { - "speaker": "James", - "dia_id": "D18:8", - "text": "Cool! You always mentioned your love for gaming. What other game do you want to organize competitions for? And what`s your plan now?" - }, - { - "speaker": "John", - "dia_id": "D18:9", - "text": "Also, I can host Fortnite competitions. I have already made some connections that will help me with this. My plan is to gain more experience and perfect my skills to be successful in this field." - }, - { - "speaker": "James", - "dia_id": "D18:10", - "text": "Sounds like a solid plan! Trying out different game genres can be a great way to widen your skills and knowledge." - }, - { - "speaker": "John", - "dia_id": "D18:11", - "text": "Thanks! I am very glad that you support me in my new endeavor!" - }, - { - "speaker": "James", - "dia_id": "D18:12", - "text": "I will always be here for you! If you need any financial assistance or advice, please contact me!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a desk with a computer monitor and keyboard", - "dia_id": "D18:13", - "text": "I will definitely do this if necessary! By the way, what's new with you?" - }, - { - "speaker": "James", - "dia_id": "D18:14", - "text": "Yesterday I took my puppy to the clinic." - }, - { - "speaker": "John", - "dia_id": "D18:15", - "text": "God, James, what happened to your puppy? Is it OK?" - }, - { - "speaker": "James", - "dia_id": "D18:16", - "text": "Don't worry. This was just a routine examination. Also, the puppy was vaccinated to prevent him from catching the seasonal canine disease." - }, - { - "speaker": "John", - "dia_id": "D18:17", - "text": "Phew, great that he's okay. It's great that you care so much about your pets!" - }, - { - "speaker": "James", - "dia_id": "D18:18", - "text": "They are the source of my joy, so I will always take care of them!" - }, - { - "speaker": "John", - "dia_id": "D18:19", - "text": "You're a great host, James! Well, I have to go, bye!" - }, - { - "speaker": "James", - "dia_id": "D18:20", - "text": "Thanks, John! Take care, bye!" - } - ], - "session_19_date_time": "9:16 am on 10 August, 2022", - "session_19": [ - { - "speaker": "John", - "dia_id": "D19:1", - "text": "Hey James, been a few days. The convo got me thinking about my passions and goals. Thanks for encouraging me to try new game genres." - }, - { - "speaker": "James", - "dia_id": "D19:2", - "text": "Hey John! Nice to hear from you! Glad our chat made an impact. What sort of games are you interested in exploring?" - }, - { - "speaker": "John", - "dia_id": "D19:3", - "text": "Lately, I've been playing some different genres like strategy and RPG games instead of my usual shooters. I\u2019m already thinking about making competitions for them too." - }, - { - "speaker": "James", - "dia_id": "D19:4", - "text": "That's great, John! Trying out different genres can really add to your gaming experiences. Have you come across any standout games?" - }, - { - "speaker": "John", - "dia_id": "D19:5", - "text": "Hooked a new RPG that I've been playing lately! The storytelling and characters are amazing, can't get enough of it." - }, - { - "speaker": "James", - "dia_id": "D19:6", - "text": "Sounds great! I think storytelling is what makes RPGs so fun. What game are you playing? Do you have any favorite characters?" - }, - { - "speaker": "John", - "dia_id": "D19:7", - "text": "I'm playing \"The Witcher 3\"! There's this awesome monster hunter with a cool story, and I'm totally hooked, trying to make the right choices to shape the world. It's really immersive." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/p1ok9m9zkrw61.jpg" - ], - "blip_caption": "a photo of a man dressed in armor holding a sword", - "query": "the witcher 3 geralt fighting monster", - "dia_id": "D19:8", - "text": "Yeah, \"The Witcher 3\" is amazing! I love how you can shape the world with your choices and feel the impact. The graphics are insane too - check out this pic." - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/23/13/24/231324d44f9471ead5535950153378f1.jpg" - ], - "blip_caption": "a photo of a gaming room with a computer and a gaming chair", - "query": "gaming setup", - "dia_id": "D19:9", - "text": "That's a great pic! The graphics are truly stunning! By the way, look how I organized my workplace!" - }, - { - "speaker": "James", - "dia_id": "D19:10", - "text": "Cool! Wall lighting adds beauty to your workspace." - }, - { - "speaker": "John", - "dia_id": "D19:11", - "text": "Thanks James! What's new with you?" - }, - { - "speaker": "James", - "dia_id": "D19:12", - "text": "Yesterday I took my three dogs to a beach outing to have fun and bond with other dogkeepers." - }, - { - "speaker": "John", - "blip_caption": "a photo of a refrigerator with a calendar on it and a magnet", - "dia_id": "D19:13", - "text": "Cool! Surely you gained a new experience from communicating with other dog lovers!" - }, - { - "speaker": "James", - "dia_id": "D19:14", - "text": "Yes, we had fun and I even met one beautiful girl. I'm thinking of asking her out on a date! She left me her phone number, I think I'll call tomorrow." - }, - { - "speaker": "John", - "dia_id": "D19:15", - "text": "Wow! That's cool, what's her name? Be sure to call her, everything will work out!" - }, - { - "speaker": "James", - "dia_id": "D19:16", - "text": " She is Samantha. I'll definitely call her!" - }, - { - "speaker": "John", - "dia_id": "D19:17", - "text": "Yoohoo! Hope you have a wonderful time!" - } - ], - "session_20_date_time": "3:57 pm on 21 August, 2022", - "session_20": [ - { - "speaker": "John", - "dia_id": "D20:1", - "text": "Hey, James! Good to hear from you. I have some awesome news - I joined a programming group online last Friday and it's been incredible! It's awesome to be part of a community of people with similar goals - coding and making a difference." - }, - { - "speaker": "James", - "dia_id": "D20:2", - "text": "Hey John! That's great to hear! Being part of a coding community can definitely be rewarding. Can you share more about your experiences with the online group? Have you made any interesting connections?" - }, - { - "speaker": "John", - "dia_id": "D20:3", - "text": "Thanks for asking. My online programming group has been great - lots of skilled coders, all passionate about using tech for good. We've shared ideas, chatted about coding and worked on a few projects together. It's amazing to see everyone's different skills and viewpoints. I've even exchanged contacts with a few of them." - }, - { - "speaker": "James", - "dia_id": "D20:4", - "text": "Nice one, John! Looks like you're really getting involved in the programming world. It's great connecting with like-minded people and building up your network. Have you had the opportunity to collaborate on any projects or work with anyone yet?" - }, - { - "speaker": "John", - "dia_id": "D20:5", - "text": "Yeah, collaborating is great. Last week I worked with someone from the group on a project and we both had our strong points which helped out. It was cool seeing how we created something awesome together. It's great working with others." - }, - { - "speaker": "James", - "dia_id": "D20:6", - "text": "Working together on a project can create amazing results, when everyone brings in their different strengths and abilities. It's cool when the final product is done and you know you were a part of making something awesome. Can we say that you are returning to working with programming again?" - }, - { - "speaker": "John", - "dia_id": "D20:7", - "text": "I think not, this is just a one-time experience to learn something new and work as a team. I'm still full of courage to start hosting eSports competitions. Do you participate in any online groups?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/xy7hyzrpdd481.jpg" - ], - "blip_caption": "a photo of a gaming room with a computer and a gaming chair", - "query": "gaming marathon friends home", - "dia_id": "D20:8", - "text": "Nah, not in any online groups right now, but I do have my gaming group I play with regularly. We even stream our game sessions, and recently had a get-together. Super fun!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/i1x9q5nr1io51.jpg" - ], - "blip_caption": "a photo of a desk with a computer and a keyboard", - "query": "gaming programming setup desk monitors coding books shelf", - "dia_id": "D20:9", - "text": "Wow, James! That's great that you have some friends to game with. By the way, I bought some new devices and refurbished my gaming desk." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/0rff1x0pj7u81.jpg" - ], - "blip_caption": "a photo of a gaming desk with a computer and a gaming chair", - "query": "gaming setup computer keyboard mouse chair", - "dia_id": "D20:10", - "text": "Cool! What kind of gear do you have now for gaming? I have a setup with a gaming PC, keyboard, mouse, and a comfy chair - makes gaming for hours a lot more bearable!" - }, - { - "speaker": "John", - "dia_id": "D20:11", - "text": "Nice set-up, James! I have a similar gaming chair and keyboard. I'm currently using a gaming PC with a powerful graphics card for intense games. I also have a headset for immersive sound. Gaming has always been an awesome escape for me - it keeps me focused and motivated in other areas." - }, - { - "speaker": "James", - "img_url": [ - "http://nicksmarathon.org/wp-content/uploads/2018/11/20181110_164805.jpg" - ], - "blip_caption": "a photo of a family sitting on a couch in a living room", - "query": "gaming marathon friends", - "dia_id": "D20:12", - "text": "Nice one! A strong graphics card and headset really enhance the gaming experience. Yeah, gaming is a great way to escape and stay motivated. It takes us to different places and stories. I even hosted a gaming marathon with some friends and we had a blast. We played all night and it really strengthened our bond." - }, - { - "speaker": "John", - "blip_caption": "a photo of two children sitting on a couch with a baby", - "dia_id": "D20:13", - "text": "Sounds great! Gaming marathons are the best. When I was younger, my siblings threw me one and it was awesome! We stayed up all night playing games and it really bonded us." - }, - { - "speaker": "James", - "dia_id": "D20:14", - "text": "Wow, that sounds awesome! Do you still play with your siblings these days?" - }, - { - "speaker": "John", - "dia_id": "D20:15", - "text": "Me and my siblings don't hang out much since we live far apart, but when we do we always try to plan a gaming night." - }, - { - "speaker": "James", - "dia_id": "D20:16", - "text": "Sounds great, John! Family time is the best. Are you planning any gaming nights in the near future?" - }, - { - "speaker": "John", - "dia_id": "D20:17", - "text": "Yep, I'm organizing one with my siblings next month. We're stoked! Can't wait!" - }, - { - "speaker": "James", - "dia_id": "D20:18", - "text": "Wow, John! Family game nights are so much fun. Have a great time!" - }, - { - "speaker": "John", - "dia_id": "D20:19", - "text": "Thanks, James! Can't wait! It was nice catching up - talk soon!" - }, - { - "speaker": "James", - "dia_id": "D20:20", - "text": "Hey John! Good to talk to you. Have fun at family game night! Talk to you later." - }, - { - "speaker": "John", - "dia_id": "D20:21", - "text": "Thanks, James! Gonna have a great time. Talk to you later." - }, - { - "speaker": "James", - "dia_id": "D20:22", - "text": "Take it easy. Have fun and let's chat soon. Have a good night!" - } - ], - "session_21_date_time": "9:18 pm on 26 August, 2022", - "session_21": [ - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/8nlwyc5nh6d61.jpg" - ], - "blip_caption": "a photo of a person holding a dog on a couch", - "query": "cute puppy playing video game controller", - "dia_id": "D21:1", - "text": "Hey John! Look how cute it is. My dog came to me today while I was playing on the console. What is new?" - }, - { - "speaker": "John", - "dia_id": "D21:2", - "text": " Your pup is so cute, remind me what's their name? I've been helping my younger siblings out with programming since they joined the programming course. It's really cool to see them get into it." - }, - { - "speaker": "James", - "dia_id": "D21:3", - "text": " His name's Ned and he's been awesome since I adopted him. I can't imagine life without him. It's great to hear that your siblings signed up for programming." - }, - { - "speaker": "John", - "dia_id": "D21:4", - "text": "That's right, his name is Ned, how could I forget?!" - }, - { - "speaker": "James", - "dia_id": "D21:5", - "text": "Regarding your siblings, are you already working on anything cool with them?" - }, - { - "speaker": "John", - "dia_id": "D21:6", - "text": "Yeah! We're working on a cool project together that involves coding. It's a game and it's helping them learn." - }, - { - "speaker": "James", - "dia_id": "D21:7", - "text": "Wow, learning and gaming sounds like a fantastic combination for coding education! Can you share more details about the game?" - }, - { - "speaker": "John", - "dia_id": "D21:8", - "text": "Yeah, they're playing a simple, text-based adventure game, working on their coding skills and having fun. I'm so proud of them! Maybe they'll even create their own video games, huh? Any new game designs on your mind?" - }, - { - "speaker": "James", - "dia_id": "D21:9", - "text": "Wow, sounds cool John! Learning coding with a text-based adventure game is impressive stuff. As for me, I've been trying out different genres of games and now I'm dying to create a strategy game like Civilization - love how complicated and in-depth they are. Fingers crossed, one day I'll make my own awesome strategy game!" - }, - { - "speaker": "John", - "dia_id": "D21:10", - "text": "Wow, James, that's impressive! It's gonna be awesome. Can't wait to see what you come up with!" - }, - { - "speaker": "James", - "dia_id": "D21:11", - "text": " Are you free tomorrow?" - }, - { - "speaker": "John", - "dia_id": "D21:12", - "text": "Yes, tomorrow is my day off. Do you have any suggestions on how to spend tomorrow?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a laptop computer with a colorful keyboard on a table", - "dia_id": "D21:13", - "text": "Yes, we can go to Starbucks for coffee if you want." - }, - { - "speaker": "John", - "dia_id": "D21:14", - "text": "I don't mind meeting up, but why Starbucks? Maybe we can have a beer somewhere?" - }, - { - "speaker": "James", - "dia_id": "D21:15", - "text": "Well, how about we go to McGee's pub then? I heard they serve a great stout there!" - }, - { - "speaker": "John", - "dia_id": "D21:16", - "text": "Great idea, except I don't like dark beer. Maybe there's something else there?" - }, - { - "speaker": "James", - "dia_id": "D21:17", - "text": "Of course, there are also light beers!" - }, - { - "speaker": "John", - "dia_id": "D21:18", - "text": "Great, then I agree! See you tomorrow at McGee's Pub!" - }, - { - "speaker": "James", - "dia_id": "D21:19", - "text": "See you John, bye!" - } - ], - "session_22_date_time": "6:53 pm on 1 September, 2022", - "session_22": [ - { - "speaker": "James", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/4/4c/Legend_of_Grimrock_screenshot_12.jpg" - ], - "blip_caption": "a photography of a screenshot of a stone building with a giant creature", - "query": "unity strategy game", - "dia_id": "D22:1", - "re-download": true, - "text": "Hey John! Been a while, but hope you're doing well. My Unity strategy game is finally finished\u2014it took loads of time and effort, but I'm really proud. Your support and encouragement made a real difference. Thanks for believing in me!" - }, - { - "speaker": "John", - "dia_id": "D22:2", - "text": "Hey James! Congrats on finishing your game! It looks amazing and I'm so proud of you for all the hard work you put in. Can I see more of it? Got any other screenshots to show me?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/w5vwdrledbb41.jpg" - ], - "blip_caption": "a photo of a video game screen showing a person on a horse", - "query": "screenshot game battle scene", - "dia_id": "D22:3", - "text": "I appreciate your support. Check out this screenshot from it." - }, - { - "speaker": "John", - "dia_id": "D22:4", - "text": "This game looks great! What inspired you to create it?" - }, - { - "speaker": "James", - "dia_id": "D22:5", - "text": "I've always loved playing strategy games like Civilization and Total War, so I decided to challenge myself and create one of my own." - }, - { - "speaker": "John", - "dia_id": "D22:6", - "text": "That's awesome! I love those games too. It must have been quite an experience making your own. Did you face any difficulties during development?" - }, - { - "speaker": "James", - "dia_id": "D22:7", - "text": "It was a bit challenging to get everything right, balancing mechanics and ensuring fairness. But with some trial and error, I managed to get it to where I wanted it." - }, - { - "speaker": "John", - "dia_id": "D22:8", - "text": "Wow, that must have been a challenge, especially since you had to make sure the game was enjoyable and balanced. Congratulations on completing it! What were some key takeaways from the experience?" - }, - { - "speaker": "James", - "dia_id": "D22:9", - "text": "Thanks, John! It was definitely a learning experience. Perseverance and patience are key, and I'm proud of what I created after sticking with it. Also, feedback and collaboration are essential, and the help from others really made the game better. It was great!" - }, - { - "speaker": "John", - "dia_id": "D22:10", - "text": "Awesome that you learned those lessons! Collaboration and feedback make a huge impact on any project. I've been teaching my siblings coding. It's been a fulfilling experience and they're already creating their own programs - amazing!" - }, - { - "speaker": "James", - "dia_id": "D22:11", - "text": "Wow, John! Cool seeing others learn with your help. What kind of programs are they making?" - }, - { - "speaker": "John", - "dia_id": "D22:12", - "text": "They're starting small, making basic games and stories. It's inspiring how fast they learn and the good time they're having." - }, - { - "speaker": "James", - "dia_id": "D22:13", - "text": "Wow! It's inspiring how fast they learn and the good time they're having. I bet they'll be creating their own complex projects soon!" - }, - { - "speaker": "John", - "dia_id": "D22:14", - "text": "I'm excited to see how far they can go! With their passion for video games like me, hopefully they can use those coding skills to make something cool. I'm so proud of them, can't wait to see what they come up with!" - }, - { - "speaker": "James", - "dia_id": "D22:15", - "text": "I'm proud of them too! Seeing the next generation pick up coding and making their own games is awesome. Can't wait to see what they create!" - }, - { - "speaker": "John", - "dia_id": "D22:16", - "text": "Thanks, James, for the support. I really appreciate it." - }, - { - "speaker": "James", - "dia_id": "D22:17", - "text": "Yeah, you're the best! I'm here for you, no doubt." - }, - { - "speaker": "John", - "dia_id": "D22:18", - "text": " Your friendship really means a lot. I'm going through some difficult times now and it's really good to know I've got someone like you." - }, - { - "speaker": "James", - "dia_id": "D22:19", - "text": "Just know I'm here if you need someone to talk or vent to. It might help alleviate some of the difficult times you're going through." - } - ], - "session_23_date_time": "9:23 pm on 4 September, 2022", - "session_23": [ - { - "speaker": "James", - "dia_id": "D23:1", - "text": "Hey John, it's been a few days since we talked. So much has gone on, both good and bad. Yesterday, when we were at the theater, Samantha loves theater, I asked her to become my girlfriend, and she agreed. We have gone through a lot in this short period. There were good and bad, but I'm happy with her. All that ups and downs were a bit overwhelming, but it's part of life. What about you? Anything interesting lately?" - }, - { - "speaker": "John", - "img_url": [ - "https://c0.wallpaperflare.com/preview/885/290/450/adult-coder-coding-company.jpg" - ], - "blip_caption": "a photography of a man sitting at a desk with a computer", - "query": "family coding together computer", - "dia_id": "D23:2", - "re-download": true, - "text": "Hey James, this is great news! Where else have you been besides the theater? My parents just started learning coding from me - it's been a learning experience, but I'm glad to help them out. It binds us a little closer. Look at this photo, this is my father coding his own program for the first time.\n " - }, - { - "speaker": "James", - "dia_id": "D23:3", - "text": "That's great, John! Looks like he's having a good time in the pic. Samantha and I were also at McGee's bar. It turned out that she loves a good lager beer. She and I have so much in common!" - }, - { - "speaker": "John", - "dia_id": "D23:4", - "text": "I'm glad you finally found someone other than dogs that brings you joy. Well done, you will succeed!" - }, - { - "speaker": "James", - "dia_id": "D23:5", - "text": "Thanks, John. She and I are going to a baseball game next Sunday, want to join? I'll show you what Samantha looks like." - }, - { - "speaker": "John", - "dia_id": "D23:6", - "text": "Yeah! Let's do it. It'll be a fun experience!" - }, - { - "speaker": "James", - "dia_id": "D23:7", - "text": "Great. Well, what else is new in your life?" - }, - { - "speaker": "John", - "dia_id": "D23:8", - "text": "I bought some new gaming equipment to improve my skills. For example, new headphones." - }, - { - "speaker": "James", - "dia_id": "D23:9", - "text": "Cool, which company did you choose? And what other devices did you buy?" - }, - { - "speaker": "John", - "dia_id": "D23:10", - "text": "I chose headphones from Sennheiser. Judging by the reviews, they have excellent sound. Also, I bought a mouse from Logitech." - }, - { - "speaker": "James", - "blip_caption": "a photo of a video game scene of a couple of people", - "dia_id": "D23:11", - "text": "Cool! I hope the new devices will improve your skill and you will play even better!" - }, - { - "speaker": "John", - "dia_id": "D23:12", - "text": "I really hope so too. Well, do you have anything new besides the great news about you and Samantha?" - }, - { - "speaker": "James", - "dia_id": "D23:13", - "text": "Yes, two days ago I signed up for a cooking class. I never liked cooking, but I felt that I wanted to learn something new. At the first lesson we prepared several simple dishes. I got a great omelette the first time!" - }, - { - "speaker": "John", - "dia_id": "D23:14", - "text": "Cool! I\u2019ve never heard of your desire to cook, you surprise me! How much do these cooking courses cost and what else did you cook there?" - }, - { - "speaker": "James", - "dia_id": "D23:15", - "text": "At only $10 per class, it's very cheap! Also, I made meringue there and they taught us how to make the dough." - }, - { - "speaker": "John", - "dia_id": "D23:16", - "text": "Really cheap. It's great that you are always looking for a way to improve yourself!" - }, - { - "speaker": "James", - "dia_id": "D23:17", - "text": "Thanks for your support, John! I really appreciate it. I hope I can treat you to my creation once I learn a little more about cooking." - }, - { - "speaker": "John", - "dia_id": "D23:18", - "text": "I look forward to it, James, you can do it!" - }, - { - "speaker": "James", - "dia_id": "D23:19", - "text": "Your words give me even more strength to pursue my new hobby!" - }, - { - "speaker": "John", - "dia_id": "D23:20", - "text": "That's why we are friends, to support each other! Well, I have to go, bye!" - }, - { - "speaker": "James", - "dia_id": "D23:21", - "text": "Take care, John, bye!" - } - ], - "session_24_date_time": "6:02 pm on 18 September, 2022", - "session_24": [ - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/eqfmcyv9b2t91.jpg" - ], - "blip_caption": "a photo of a table with a laptop and a game on it", - "query": "game project in progress", - "dia_id": "D24:1", - "text": "Hey James! Long time no see! A lot's changed since we talked. I started getting into board games. I tried one last week and it turned out to be a lot of fun. What's new with you?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/pa728lrin0081.jpg" - ], - "blip_caption": "a photo of a notebook with writing on it sitting on a table", - "query": "notebook sketches game ideas coding challenges friends", - "dia_id": "D24:2", - "text": "Hey John! Cool, what kind of board game is this? I had a lot to do all this time. And in order not to forget to do something, I started writing down everything I needed in a notebook." - }, - { - "speaker": "John", - "dia_id": "D24:3", - "text": "This game is called \"Dungeons of the Dragon\", very exciting! I'm really glad you're writing down what you need to do in a notebook. This will definitely help you not to forget anything! How did you come up with this idea? In general, where do you get ideas?" - }, - { - "speaker": "James", - "dia_id": "D24:4", - "text": "Thanks, John! I get them from various sources like books, movies, and even dreams." - }, - { - "speaker": "John", - "dia_id": "D24:5", - "text": "Wow, dreams have inspired you? That's interesting. Have any specific dreams guided your ideas?" - }, - { - "speaker": "James", - "dia_id": "D24:6", - "text": "A few weeks ago I had this crazy dream that led to some creative ideas. It was so vivid I woke up with some interesting thoughts!" - }, - { - "speaker": "John", - "dia_id": "D24:7", - "text": "Wow, dreams can be so awesome! Are there any specific details you remember from that one?" - }, - { - "speaker": "James", - "dia_id": "D24:8", - "text": "I remember there was a medieval castle with its own labyrinth full of puzzles and traps. It felt like playing a video game in real life!" - }, - { - "speaker": "John", - "dia_id": "D24:9", - "text": "Wow, exploring a castle with puzzles and traps sounds awesome! Have you got any sketches or notes from that experience? I'd love to take a look!" - }, - { - "speaker": "James", - "img_url": [ - "https://www.andrewwkmusic.com/wp-content/uploads/2020/12/0077.jpg" - ], - "blip_caption": "a photo of a notepad with a drawing of a guitar", - "query": "castle labyrinth puzzles notebook sketch handwritten notes dream experience", - "dia_id": "D24:10", - "text": "Yep! I made some sketches and notes. Hang on, let me grab them." - }, - { - "speaker": "John", - "dia_id": "D24:11", - "text": "Nice sketch! Do you like music, or is it related to your castle dream?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/m5s2y1kxhcnb1.jpg" - ], - "blip_caption": "a photo of a desk with a laptop, headphones, and a monitor", - "query": "gaming setup headphones speakers", - "dia_id": "D24:12", - "text": "Thanks! Music is a big part of my life - nothing to do with castles though. What kind of music do you like? Do you play any instruments?" - }, - { - "speaker": "John", - "dia_id": "D24:13", - "text": "Cool! I'm into electronic and rock music. I used to play drums when I was younger, but haven't in a while. Do you play any instruments?" - }, - { - "speaker": "James", - "dia_id": "D24:14", - "text": "Yeah, rock's awesome! I used to play a guitar when I was younger but haven't in a while." - }, - { - "speaker": "John", - "img_url": [ - "https://cdn11.bigcommerce.com/s-qy9kl0lfci/images/stencil/original/products/62751/75290/Tama_Star_Classic_Tiger_Stripe__70181.1699123066.jpg" - ], - "blip_caption": "a photo of a drum set up in a recording studio", - "query": "drum set", - "dia_id": "D24:15", - "text": "Playing drums when I was younger was a fun way to let off steam. Here's a photo of an old drum set I used to play on." - }, - { - "speaker": "James", - "dia_id": "D24:16", - "text": "Cool! Have you ever been in a band or just jammed with friends?" - }, - { - "speaker": "John", - "dia_id": "D24:17", - "text": "I've jammed with friends before, it was a lot of fun!" - }, - { - "speaker": "James", - "dia_id": "D24:18", - "text": "Sounds awesome! Jamming with friends is always a blast. Do you have any recordings or videos of those sessions?" - }, - { - "speaker": "John", - "dia_id": "D24:19", - "text": "Nah, it was more about the experience and the moment. No recordings or videos from the jams." - }, - { - "speaker": "James", - "dia_id": "D24:20", - "text": "No problem! It's nice to just enjoy the experience without worrying about collecting videos or recordings. By the way, I started streaming games. No details yet, I hope everything works out." - }, - { - "speaker": "John", - "dia_id": "D24:21", - "text": "I'll keep my fingers crossed for you! You will definitely succeed, I look forward to the details!" - } - ], - "session_25_date_time": "8:56 pm on 20 September, 2022", - "session_25": [ - { - "speaker": "John", - "dia_id": "D25:1", - "text": "Hey James, been a few days since we chatted. Lots of stuff goin' on in my life!" - }, - { - "speaker": "James", - "dia_id": "D25:2", - "text": "Hey John! What new has happened in your life?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/2sop5yn1a8i81.jpg" - ], - "blip_caption": "a photo of a fire burning in a metal pot on a street", - "query": "work project", - "dia_id": "D25:3", - "text": "Yesterday I started a new startup - portable smokers. Now, I\u2019ve already welded one from metal. Do you think it looks good? How about you, any cool stuff happening?" - }, - { - "speaker": "James", - "dia_id": "D25:4", - "text": "Hey John, that looks great! Seeing it makes me think of campfires with pals. Last night I streamed a game and wow, was I blown away by all the nice comments from the gaming community. I felt so stoked and inspired to keep going. " - }, - { - "speaker": "John", - "blip_caption": "a photo of a card with a graduation cap on it", - "dia_id": "D25:5", - "text": "Woohoo, congrats James! That's awesome. Sounds like you're doing well. All your hard work is paying off, so keep it up!" - }, - { - "speaker": "James", - "dia_id": "D25:6", - "text": "Thanks for the support, John! This made me think of such an exciting time. Any more big moments recently?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.trustedreviews.com/wp-content/uploads/sites/54/2022/10/2x1_NSwitch_Bayonetta3_image1600w.jpg" - ], - "blip_caption": "a photography of a demonic demon flying in the air with a sword", - "query": "mobile game screenshot title", - "dia_id": "D25:7", - "re-download": true, - "text": "I just achieved a major career milestone - making my first mobile game! It's launching next month." - }, - { - "speaker": "James", - "dia_id": "D25:8", - "text": "Way to go, John! Congrats on achieving that major career milestone. Could you tell me more about it? Why didn\u2019t you say before that you were creating a mobile game?" - }, - { - "speaker": "John", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/d/d1/Awesomenauts_-_Screenshot_01.jpg" - ], - "blip_caption": "a photography of a video game with a bunch of different screens", - "query": "2D adventure game puzzles exploration colorful virtual world", - "dia_id": "D25:9", - "re-download": true, - "text": "Thanks James! I kept it a secret because I would have been very upset if I had told you about her in advance and then it wouldn't have worked out. I've been working on this for the past few months and I'm really proud of how it's turned out. It's a 2D adventure game with puzzles and exploration. Here's a screenshot." - }, - { - "speaker": "James", - "dia_id": "D25:10", - "text": "John, this sounds great! I'm into 2D adventures with puzzles - like The Legend of Zelda. Can I see it or help with testing it out?" - }, - { - "speaker": "John", - "img_url": [ - "https://thethoughtfulgamer.com/wp-content/uploads/2017/10/20171027_105814-e1509116397258.jpg" - ], - "blip_caption": "a photo of a book with a cartoon of a man playing a game", - "query": "game development books resources skills refine game", - "dia_id": "D25:11", - "text": "Cheers, James! Appreciate your offer to help. I'll definitely let you know when the testing is ready. By the way, here is the book that helped me create the puzzles for this game." - }, - { - "speaker": "James", - "dia_id": "D25:12", - "text": "Wow, that book looks great! What other resources do you use to improve your game? Tell me about your gaming tips!" - }, - { - "speaker": "John", - "dia_id": "D25:13", - "text": "It is filled with awesome tips and insights on game design. I also watch tutorials and keep up with developer forums for information and ideas. Basically, staying informed and constantly learning is key!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/a2jbhj4bi6n61.jpg" - ], - "blip_caption": "a photo of a magazine with a picture of a cartoon character", - "query": "gaming magazine tips and tricks game developers", - "dia_id": "D25:14", - "text": "You're really dedicated to improving and staying up to date. It's inspiring to see how you stay informed and keep learning. I also advise you to read this magazine, which is also a worthy source of information. Keep up the good work!\n" - }, - { - "speaker": "John", - "dia_id": "D25:15", - "text": "I read it, too. This magazine has been great for me too. Tutorials, interviews with developers, and tips - all really helpful." - }, - { - "speaker": "James", - "blip_caption": "a photo of a magazine with a picture of a cartoon character", - "dia_id": "D25:16", - "text": "Wow, John! Glad that resource was useful - looks like it provides some good tips and tricks for game developers." - }, - { - "speaker": "John", - "dia_id": "D25:17", - "text": "Yeah, that magazine looks great! Have you also found it to be a good resource?" - }, - { - "speaker": "James", - "dia_id": "D25:18", - "text": "Of course! It's been great, filled with tutorials and developer interviews to help improve my game dev skills. Super useful!" - }, - { - "speaker": "John", - "dia_id": "D25:19", - "text": "Resources like that are great for improving our skills. Keep it up! How's your week been?" - }, - { - "speaker": "James", - "dia_id": "D25:20", - "text": "My week's been good. Just trying to find a balance between work and other activities. How about you, how's your week going?" - }, - { - "speaker": "John", - "dia_id": "D25:21", - "text": "As for me, this week has been chaotic with everything going on. But I'm powering through!" - }, - { - "speaker": "James", - "dia_id": "D25:22", - "text": "Sorry to hear about your busy week, John. Make sure to take some time for yourself and take care. You've got this!" - }, - { - "speaker": "John", - "dia_id": "D25:23", - "text": "I appreciate your help. Gonna make time for myself." - }, - { - "speaker": "James", - "dia_id": "D25:24", - "text": "No worries, take care of yourself. Relax and recharge - you deserve it." - }, - { - "speaker": "John", - "dia_id": "D25:25", - "text": "Thanks, man! I'll definitely take your advice. You're the best!" - } - ], - "session_26_date_time": "9:20 am on 3 October, 2022", - "session_26": [ - { - "speaker": "John", - "dia_id": "D26:1", - "text": "Hey James! Busy few weeks for sure, but I'm pushing through. Got an email about a volunteer gig at a game dev non-profit. It's something I've wanted to do for a while, and could be the perfect start to a career that combines my two loves - gaming and helping. So stoked!" - }, - { - "speaker": "James", - "dia_id": "D26:2", - "text": "Hey John, that sounds awesome! Combining your two loves - gaming and helping people - must be really exciting! So what kind of gig did they offer you?" - }, - { - "speaker": "John", - "dia_id": "D26:3", - "text": "They asked me to be a programming mentor for game developers. I'll be teaching coding and assisting with projects. I'm really excited to share my knowledge and motivate people who are passionate about gaming." - }, - { - "speaker": "James", - "dia_id": "D26:4", - "text": "Wow, John! Mentoring programmers to make games sounds awesome! You must love it. How do you feel about starting this journey?" - }, - { - "speaker": "John", - "dia_id": "D26:5", - "text": "I'm so excited and inspired! It's a great chance to help them and boost my own skills. I love sharing what I know and seeing others reach their potential - it's so rewarding!" - }, - { - "speaker": "James", - "dia_id": "D26:6", - "text": "It's so rewarding to see how much joy you get from it. Keep going, you're doing great!" - }, - { - "speaker": "John", - "dia_id": "D26:7", - "text": "Thanks, James! Your support really means a lot. It's so fulfilling to use my skills to make a difference. I hope this opens more opportunities for me." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/jkzfs9aawzw21.jpg" - ], - "blip_caption": "a photo of a computer case with a red light on it", - "query": "gaming setup witcher 3", - "dia_id": "D26:8", - "text": "I'm sure it will lead to great things. You got this! Oh, and I got this cool video card last week - I'm so excited to jump into it again!" - }, - { - "speaker": "John", - "dia_id": "D26:9", - "text": "Cool, James! What kind of games are you excited to play on it?" - }, - { - "speaker": "James", - "dia_id": "D26:10", - "text": " I'm super into RPGs, so I'm excited about getting this video card and playing some new games. Have you heard any great things about Cyberpunk 2077? Do you think this game is worthy of my attention?" - }, - { - "speaker": "John", - "dia_id": "D26:11", - "text": "Yeah, I played it - it's awesome! Such an immersive world and an amazing story. I'm sure you'll love it!" - }, - { - "speaker": "James", - "dia_id": "D26:12", - "text": "I'm so excited for it! The world and story sound perfect. Thanks for recommending it, John!" - }, - { - "speaker": "John", - "dia_id": "D26:13", - "text": "No worries, James! Hope you have a blast playing. Let me know what you think!" - }, - { - "speaker": "James", - "dia_id": "D26:14", - "text": "Cool, John. Will do! Take care, see ya!" - }, - { - "speaker": "John", - "dia_id": "D26:15", - "text": "Take care! Enjoy that new computer. Later!" - } - ], - "session_27_date_time": "2:14 pm on 13 October, 2022", - "session_27": [ - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/zkrnzupfyy9b1.jpg" - ], - "blip_caption": "a photo of a desk with a laptop and a monitor", - "query": "online programming competition setup", - "dia_id": "D27:1", - "text": "Hey James! How's it going? I had a blast last week when my programmer friends and I organized an online comp. It was awesome to see everyone show off their skills! Anything new in your life?" - }, - { - "speaker": "James", - "img_url": [ - "https://images-wixmp-ed30a86b8c4ca887773594c2.wixmp.com/f/55934be0-eae0-47bc-a3eb-6c0140f50b06/d5plslq-28102d55-338e-485d-b0fe-5f9553b0aa39.jpg/v1/fill/w_622,h_350,q_70,strp/game_of_thrones___daenerys_targaryen_by_daninaimare_d5plslq-350t.jpg?token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJ1cm46YXBwOjdlMGQxODg5ODIyNjQzNzNhNWYwZDQxNWVhMGQyNmUwIiwiaXNzIjoidXJuOmFwcDo3ZTBkMTg4OTgyMjY0MzczYTVmMGQ0MTVlYTBkMjZlMCIsIm9iaiI6W1t7ImhlaWdodCI6Ijw9NTc2IiwicGF0aCI6IlwvZlwvNTU5MzRiZTAtZWFlMC00N2JjLWEzZWItNmMwMTQwZjUwYjA2XC9kNXBsc2xxLTI4MTAyZDU1LTMzOGUtNDg1ZC1iMGZlLTVmOTU1M2IwYWEzOS5qcGciLCJ3aWR0aCI6Ijw9MTAyNCJ9XV0sImF1ZCI6WyJ1cm46c2VydmljZTppbWFnZS5vcGVyYXRpb25zIl19.3OweRTbk-A6SxQJ1Cqdb8wNtXtkQ6cjXbErC4U42YSQ" - ], - "blip_caption": "a photography of a game of thrones with a dragon and a dragon", - "query": "game interface players battling mythical creatures", - "dia_id": "D27:2", - "re-download": true, - "text": "Hey John, congrats! Something cool happened to me recently. I made my first game and released it for the gaming community - it was so exciting!" - }, - { - "speaker": "John", - "dia_id": "D27:3", - "text": "Congrats on releasing your game, James! Was it fulfilling to see players engage with the game world you created?" - }, - { - "speaker": "James", - "dia_id": "D27:4", - "text": " It was so fulfilling to see players engage with the game world I created. I'm really happy they're having fun with something I put so much work into." - }, - { - "speaker": "John", - "dia_id": "D27:5", - "text": "So cool that people are enjoying it! What inspired you to create it?" - }, - { - "speaker": "James", - "dia_id": "D27:6", - "text": "Playing video games was always great, but creating my own game was really special. Witcher 3 gave me a ton of inspiration, with its amazing world and story. Plus, it pushed me to create something cool." - }, - { - "speaker": "John", - "dia_id": "D27:7", - "text": "The Witcher 3 obviously had a huge impact on you. You must have put a ton of hard work and dedication into your game. Do you have any plans for future game development?" - }, - { - "speaker": "James", - "dia_id": "D27:8", - "text": "I put in so much effort and it paid off - now, I'm ready to make more games in different genres and test out new ideas. I'm pumped to see where this journey leads!" - }, - { - "speaker": "John", - "dia_id": "D27:9", - "text": " I can't wait to see where your journey leads and the new creations you come up with. Your determination and love for game development is incredible. Keep going and you'll do great things!" - }, - { - "speaker": "James", - "dia_id": "D27:10", - "text": "I'm really looking forward to creating more enjoyable experiences!" - }, - { - "speaker": "John", - "dia_id": "D27:11", - "text": "I'm here for you. Anything you need, count on me!" - }, - { - "speaker": "James", - "dia_id": "D27:12", - "text": "Thanks, John! Your support is really appreciated." - }, - { - "speaker": "John", - "dia_id": "D27:13", - "text": "No worries, James! We make a good team." - }, - { - "speaker": "James", - "dia_id": "D27:14", - "text": "Yeah, totally. You've always been there for me, John. Thanks for having my back." - } - ], - "session_28_date_time": "7:36 pm on 21 October, 2022", - "session_28": [ - { - "speaker": "James", - "dia_id": "D28:1", - "text": "Hey John, long time no talk! So much has happened!" - }, - { - "speaker": "John", - "dia_id": "D28:2", - "text": "Hey James! I'm excited to catch up. What's been up lately?" - }, - { - "speaker": "James", - "dia_id": "D28:3", - "text": "Three days ago my apartment lost power - so annoying because I had just gotten to the big reveal in that game! Had to wait hours before playing again." - }, - { - "speaker": "John", - "dia_id": "D28:4", - "text": "Ugh, that stinks! Losing power in the middle of a game is such a bummer. Did it mess up your progress?" - }, - { - "speaker": "James", - "dia_id": "D28:5", - "text": "Oof, it definitely messed up my progress. I lost some of it because I forgot to save. Frustrating, but now I know to save more often!" - }, - { - "speaker": "John", - "dia_id": "D28:6", - "text": "Lesson learned - save progress! By the way, I organized the programming seminar last week." - }, - { - "speaker": "James", - "dia_id": "D28:7", - "text": "Wow, cool! How did it go? Did you learn anything cool?" - }, - { - "speaker": "John", - "dia_id": "D28:8", - "text": "The seminar went really well! We had a great turnout and I learned some interesting new things. It was a fulfilling experience to share my knowledge and see how it benefited the group." - }, - { - "speaker": "James", - "dia_id": "D28:9", - "text": "That's great, John! Sounds like the seminar went well. What did you learn from it?" - }, - { - "speaker": "John", - "dia_id": "D28:10", - "text": "I gained insight into various programming approaches and techniques. It was interesting to hear other developers' ideas and strategies." - }, - { - "speaker": "James", - "dia_id": "D28:11", - "text": "Learning new programming stuff is great. Did you find any ideas that you'll incorporate into your own work?" - }, - { - "speaker": "John", - "dia_id": "D28:12", - "text": "Yeah! Found some cool ideas that I can use in my own work. It's exciting to explore different programming techniques and how to implement them." - }, - { - "speaker": "James", - "dia_id": "D28:13", - "text": "Cool, John! Broadening your programming skills and trying new techniques is great - keeps things exciting and helps you develop. Have you had a chance to try them out yet?" - }, - { - "speaker": "John", - "dia_id": "D28:14", - "text": "No, I haven't tried them yet. But I'm looking forward to experimenting and seeing what I can do with them. It's always fun to try new things!" - }, - { - "speaker": "James", - "dia_id": "D28:15", - "text": "Yeah! Trying new stuff keeps us on our toes and helps our creativity. Awesome that you're down to experiment and see what you can come up with. I'm looking to branch out as well, any ideas I could check out?" - }, - { - "speaker": "John", - "dia_id": "D28:16", - "text": " I'll send you some resources and tutorials on the new programming approaches and techniques I learned. You'll find them cool!" - }, - { - "speaker": "James", - "dia_id": "D28:17", - "text": "Appreciate it. Can't wait to check them out, and maybe learn something new!" - }, - { - "speaker": "John", - "dia_id": "D28:18", - "text": "No worries, James. I hope they help. Let me know if you have any questions." - }, - { - "speaker": "James", - "img_url": [ - "https://api.army.mil/e2/c/images/2022/04/11/bcd7991a/original.jpg" - ], - "blip_caption": "a photo of a man and woman in military clothing standing next to a dog", - "query": "dog companions", - "dia_id": "D28:19", - "text": "I'll reach out if I need help. Thanks for the resources, really appreciate it. By the way, my mother came to see me with her army friend two days ago. We had fun." - }, - { - "speaker": "John", - "dia_id": "D28:20", - "text": "Cool. Mother's friend must still be in the army?" - }, - { - "speaker": "James", - "dia_id": "D28:21", - "text": "Yes, she is still serving. But she retired a long time ago. They used to tell me stories about their time in the military and their pup. Funny enough, I have a pic of me at their age playing on their old gaming setup. Would you like to see it?" - }, - { - "speaker": "John", - "dia_id": "D28:22", - "text": "Yeah, James! Show me that picture of you playing on their old gaming setup, it looks like a blast!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/lto5hiavrmz21.jpg" - ], - "blip_caption": "a photo of a nintendo game console and a nintendo mario game controller", - "query": "old photo james crt tv game controller", - "dia_id": "D28:23", - "text": "Here is a photo of this console with the game Mario. Funny gamepad, isn't it?" - }, - { - "speaker": "John", - "dia_id": "D28:24", - "text": "Oh yeah, that`s funny! Did you have fun with Nintendo when you were a kid?" - }, - { - "speaker": "James", - "dia_id": "D28:25", - "text": "Oh yeah! I had a blast with it when I was a kid. It was my first gaming system and I'd play Super Mario and The Legend of Zelda for hours. It totally sparked my passion for gaming." - }, - { - "speaker": "John", - "dia_id": "D28:26", - "text": "Wow, James! Those games really sparked your passion for gaming, didn't they?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a video game cover of the witcher wild hunt", - "dia_id": "D28:27", - "text": "Those games introduced me to gaming and I've been hooked ever since. By the way, yesterday I tried Cyberpunk 2077. Great game, so addictive!" - }, - { - "speaker": "John", - "dia_id": "D28:28", - "text": "I'm really glad you're enjoying this game. There will be so many unexpected turns in it, you can\u2019t even imagine!" - }, - { - "speaker": "James", - "dia_id": "D28:29", - "text": "What do you think is the most difficult thing about this game?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/1pegm3i0hs561.jpg" - ], - "blip_caption": "a photo of a man in a leather vest and a belt", - "query": "the witcher 3 dialogue choice decision intense consequences", - "dia_id": "D28:30", - "text": "The most difficult thing is to make the right choice. After all, even from the choice of lines in dialogues with characters, everything can go wrong. The choices here can be life-changing!" - }, - { - "speaker": "James", - "dia_id": "D28:31", - "text": "Thank you very much, I will definitely keep this in mind!" - }, - { - "speaker": "John", - "dia_id": "D28:32", - "text": "And remember, you don't have to be friends with every character in this game. I don't want to spoil it, but just remember this!" - }, - { - "speaker": "James", - "dia_id": "D28:33", - "text": "I'll definitely take your advice, John! Thank you for avoiding spoilers." - }, - { - "speaker": "John", - "dia_id": "D28:34", - "text": "Always happy to help. Well, I have to go! Bye!" - }, - { - "speaker": "James", - "dia_id": "D28:35", - "text": "Take care, bye!" - } - ], - "session_29_date_time": "12:37 am on 31 October, 2022", - "session_29": [ - { - "speaker": "John", - "dia_id": "D29:1", - "text": "Hey James! Hope you're doing great. I've some amazing news - I held a gaming tourney with my buddies last night. We played Fortnite and a few other games. We raised a decent amount for a children's hospital. Combining gaming and a good cause felt awesome!" - }, - { - "speaker": "James", - "dia_id": "D29:2", - "text": "Hey John! Awesome job organizing a gaming tournament for a children's hospital! Combining gaming and a good cause - that's really cool! Tell me more about who helped out and what other games were played." - }, - { - "speaker": "John", - "dia_id": "D29:3", - "text": "Thanks! We all pulled together for a great cause. My gaming pals and I also played Overwatch and Apex Legends. Everyone had a blast raising money for the kids' hospital. The atmosphere was awesome and everyone was so competitive. In the end, we raised a good amount. Feels good to use our love of gaming for good!" - }, - { - "speaker": "James", - "dia_id": "D29:4", - "text": "Wow, that sounds like a blast! It's great how gaming can bring people together like that. You made a huge difference in the kids' lives! Do you have any photos from the tournament?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/157m14rlacw51.jpg" - ], - "blip_caption": "a photo of a computer screen with a game menu on it", - "query": "gaming tournament intense match engaged thrilling experience good cause", - "dia_id": "D29:5", - "text": "I got a great shot at the tournament. Check it out! Everyone was so hyped, and it felt great knowing we were playing for a good cause." - }, - { - "speaker": "James", - "dia_id": "D29:6", - "text": "Wow, this photo rocks!" - }, - { - "speaker": "John", - "dia_id": "D29:7", - "text": "Thanks! I'm glad you enjoyed it. It was a really awesome moment - unforgettable for all of us! What's new with you?" - }, - { - "speaker": "James", - "dia_id": "D29:8", - "text": "I actually have something new, Samantha and I have decided to move in together!" - }, - { - "speaker": "John", - "dia_id": "D29:9", - "text": "Wow, that's a really big decision! I hope you both have weighed the pros and cons. Where are you going to live?" - }, - { - "speaker": "James", - "dia_id": "D29:10", - "text": "Of course, this was a mutual and informed decision. We rented an apartment not far from McGee's bar." - }, - { - "speaker": "John", - "dia_id": "D29:11", - "text": "You love spending time together in this bar, don't you?" - }, - { - "speaker": "James", - "dia_id": "D29:12", - "text": "We just love it! I\u2019ll be honest, one of the criteria for our choice of apartment was this particular bar nearby." - }, - { - "speaker": "John", - "dia_id": "D29:13", - "text": "Awesome, James! Excited to hear how it goes. Keep me posted and good luck!" - }, - { - "speaker": "James", - "dia_id": "D29:14", - "text": "Thanks, John! I'll be sure to keep you updated. I really appreciate your support. Take care!" - }, - { - "speaker": "John", - "dia_id": "D29:15", - "text": "No worries! I'm here for you whenever you need. Stay safe and chat soon!" - }, - { - "speaker": "James", - "blip_caption": "a photo of a man and two dogs running in a field", - "dia_id": "D29:16", - "text": "Thanks! Appreciate your support. Stay safe and talk to you soon!" - } - ], - "session_30_date_time": "5:20 pm on 5 November, 2022", - "session_30": [ - { - "speaker": "James", - "dia_id": "D30:1", - "text": "Hey John, hope you're doing well. Yesterday, we started on a road trip. It was fun spending time with the family and my dogs. Exploring new places and taking in nature with the furballs was awesome!" - }, - { - "speaker": "John", - "dia_id": "D30:2", - "text": "Hey James! Wow, what an adventure! Lately, I've been busy with something. Guess what? I had a great accomplishment this Tuesday! It was awesome." - }, - { - "speaker": "James", - "dia_id": "D30:3", - "text": "That's great news. What did you do?" - }, - { - "speaker": "John", - "dia_id": "D30:4", - "text": "I won the regional chess tournament. It was intense but I came out on top!" - }, - { - "speaker": "James", - "dia_id": "D30:5", - "text": "That's awesome! Congrats! How did it feel to come out on top?" - }, - { - "speaker": "John", - "dia_id": "D30:6", - "text": "It felt so good. All my hard work and practice paid off and it was great to conquer the challenges. It gave me a huge confidence boost. So proud of myself!" - }, - { - "speaker": "James", - "dia_id": "D30:7", - "text": "Winning must have felt so good. What was it like when you won? What strategies did you use to get ready?" - }, - { - "speaker": "John", - "dia_id": "D30:8", - "text": "My strategy involved analyzing and anticipating my opponent's moves to stay one step ahead." - }, - { - "speaker": "James", - "dia_id": "D30:9", - "text": "Cool! It's all about studying the game to gain the edge. Do you have any tips for improving?" - }, - { - "speaker": "John", - "dia_id": "D30:10", - "text": "Yeah, studying opening moves and strategies can really help. It sets the tone and builds a strong foundation. Learning from experienced players and analyzing past games is important too. Plus the chess advice you gave me earlier also helped. Would you like some resources on chess openings?" - }, - { - "speaker": "James", - "dia_id": "D30:11", - "text": "Sure, I'd love to check out some resources on chess openings. Thank you!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/jqeodrms8xnb1.jpg" - ], - "blip_caption": "a photo of a book with a list of the different games", - "query": "chess strategy book screenshot", - "dia_id": "D30:12", - "text": "I've got you covered on that. Here's a helpful resource for chess openings. Happy to help!" - }, - { - "speaker": "James", - "dia_id": "D30:13", - "text": "Thanks for the suggestion, John. What games are you currently playing? I'm always looking for new recommendations." - }, - { - "speaker": "John", - "dia_id": "D30:14", - "text": "I'm hooked on this great game called FIFA 23. This is a great football game with the ability to play online with other players from all over the world! Enjoy!" - }, - { - "speaker": "James", - "dia_id": "D30:15", - "text": "Wow, that sounds awesome! I just wanted to try a new gaming genre, so why not try the sports genre." - }, - { - "speaker": "John", - "dia_id": "D30:16", - "text": "You need to practice a little first, and then we can play together." - }, - { - "speaker": "James", - "dia_id": "D30:17", - "text": "Great idea! I hope it's easy to control." - }, - { - "speaker": "John", - "dia_id": "D30:18", - "text": "Not at all, all you need is a gamepad and a sense of timing." - }, - { - "speaker": "James", - "dia_id": "D30:19", - "text": "Great! Well, I'll go train!" - } - ], - "session_31_date_time": "8:57 pm on 7 November, 2022", - "session_31": [ - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/toaksy9sbze41.jpg" - ], - "blip_caption": "a photo of a group of people posing for a picture", - "query": "group of friends playing video games", - "dia_id": "D31:1", - "text": "Hey John! Guess what? Me and my family are currently on the road trip! We`ve already visited my friends Josh and Mark and had such a great time!" - }, - { - "speaker": "John", - "img_url": [ - "https://www.choochootrack.com/cdn-cgi/image/quality%3D85/assets/images/6%20player%20setup%20game%20over.jpg" - ], - "blip_caption": "a photo of a wooden board game with four pieces of wood and four pieces of colored balls", - "query": "family game night collaboration online game", - "dia_id": "D31:2", - "text": "Hey James! That sounds awesome! I had a super fun weekend - I worked with a game developer on a project and it was great to see my ideas come to life. It was an incredible experience! " - }, - { - "speaker": "James", - "dia_id": "D31:3", - "text": "That sounds amazing. What was the project you worked on?" - }, - { - "speaker": "John", - "dia_id": "D31:4", - "text": "I collaborated with a game developer to create an online board game - it's a fun and unique experience!" - }, - { - "speaker": "James", - "dia_id": "D31:5", - "text": " I can imagine how proud you must feel seeing your ideas come to life in a game. Has it been released for others to try yet?" - }, - { - "speaker": "John", - "dia_id": "D31:6", - "text": "We're about to release a demo soon so others can try it out. Can't wait for their feedback and suggestions." - }, - { - "speaker": "James", - "dia_id": "D31:7", - "text": "Can't wait to try it. Keep me posted when it's out - I wanna support you and give my thoughts." - }, - { - "speaker": "John", - "dia_id": "D31:8", - "text": "Appreciate your support. I'll definitely let you know when it's out and I'm really excited to hear your thoughts." - }, - { - "speaker": "James", - "dia_id": "D31:9", - "text": "By the way, we did one good thing on the way to Mark and Josh." - }, - { - "speaker": "John", - "dia_id": "D31:10", - "text": "What is this? Looking forward to hearing your story!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.ytimg.com/vi/uqizCzE85B8/maxresdefault.jpg" - ], - "blip_caption": "a photo of a man kneeling down next to a dog", - "query": "animal sanctuary rescue dogs", - "dia_id": "D31:11", - "text": "We visited an animal sanctuary on the road trip - there were so many cute rescue dogs! I thought of our love of furry pals." - }, - { - "speaker": "John", - "dia_id": "D31:12", - "text": "Cool! What was it like visiting the animal sanctuary? Did you feel tempted to bring any furry pals home?" - }, - { - "speaker": "James", - "dia_id": "D31:13", - "text": "Those rescue dogs were so cute, I wanted to take them all home, but I remembered that I already have three dogs at home. I think having more than three dogs is too much." - }, - { - "speaker": "John", - "dia_id": "D31:14", - "text": "You are right! I still haven\u2019t gotten a dog, but I still really want one. What is it like to have a dog?" - }, - { - "speaker": "James", - "dia_id": "D31:15", - "text": "Having furry friends around brings so much joy and friendship. Life wouldn't be the same without them. Every day's better with them around." - }, - { - "speaker": "John", - "dia_id": "D31:16", - "text": "Yep, they bring so much joy and love. They're always there for us! It's like having sunshine on a cloudy day." - }, - { - "speaker": "James", - "dia_id": "D31:17", - "text": "My dogs are like that too - they even make dark days better. Don't know what I'd do without them. They're the best buddies." - }, - { - "speaker": "John", - "dia_id": "D31:18", - "text": "Yeah, dogs are awesome for sure! They make us feel so loved and cheerful, don't they?" - }, - { - "speaker": "James", - "dia_id": "D31:19", - "text": "Yeah, they definitely do. Dogs always cheer us up, wagging their tails and giving us unconditional love. It's like having a dose of positivity and happiness every day. They're amazing!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/iwwwkzul10b71.jpg" - ], - "blip_caption": "a photo of a dog sitting on a rug in front of a couch", - "query": "golden retriever puppy", - "dia_id": "D31:20", - "text": "Definitely, James! Dogs are amazing. They bring so much joy and positivity. They accept us without judgement, just love and happiness. I appreciate the daily dose of positivity they bring to my life. Special buddies for sure. By the way, here is my cousin's dog." - }, - { - "speaker": "James", - "dia_id": "D31:21", - "text": "This pup is so adorable! What's their name?" - }, - { - "speaker": "John", - "img_url": [ - "https://thewilsonsofoz.files.wordpress.com/2017/05/img_0635.jpg" - ], - "blip_caption": "a photo of two people standing in front of a giant head", - "query": "luna playing catch park", - "dia_id": "D31:22", - "text": "Their name is Luna. " - }, - { - "speaker": "James", - "dia_id": "D31:23", - "text": "Luna's a great name!" - }, - { - "speaker": "John", - "dia_id": "D31:24", - "text": "Thanks, gonna go, sorry. Cheers! Bye!" - }, - { - "speaker": "James", - "dia_id": "D31:25", - "text": "Later! Take care!" - } - ] - }, - "event_summary": { - "events_session_1": { - "James": [ - "creating a game and telling a story through it.", - "James proposes making dog-sitting mobile apps with John. The app's custom filters will set it apart from other dog-sitting apps on the market." - ], - "John": [ - "John decides to take up a course in programming HTML, CSS", - "John proposes collaborating with James to make mobile apps." - ], - "date": "17 March, 2022" - }, - "events_session_2": { - "James": [ - "James creates an avatar and joining a new online gaming platform where he has a great time connecting with other gamers." - ], - "John": [ - "John picks up a hobby of walking along the beach with a metal detector.", - "John finds bottle caps, a few coins and a gold ring from his new metal detector hobby." - ], - "date": "20 March, 2022" - }, - "events_session_3": { - "James": [ - "James starts learning to play the drums.", - "James purchases a new cutting-edge gaming system with great graphics." - ], - "John": [ - "John continues his hobby of playing drums that he started in February 2022.", - "GO plays.", - "GO with skilled players and meets game developers." - ], - "date": "27 March, 2022" - }, - "events_session_4": { - "James": [ - "James joins an online Apex Legend gaming tournament with his friends, makes it to the semi-finals and wins some rounds.", - "James receives gamink skill tips and autographs from skilled Apex Legend players at the gaming tournament." - ], - "John": [], - "date": "4 April, 2022" - }, - "events_session_5": { - "James": [ - "James adopts a puppy from a shelter and names it Ned. He now has three dogs." - ], - "John": [ - "John starts playing a new RPG set in a futuristic dystopia and with a cool story." - ], - "date": "12 April, 2022" - }, - "events_session_6": { - "James": [ - "James works on a programming project with one of his gaming pals and creates a virtual world inspired by Witcher 3.", - "James meets a woman when walking his dogs and almost falls in love with her but does not ask for her contact.", - "James creates a gaming avatar to emobody the woman he met when walking his dogs, in the virtual gaming world he created with his gaming pal." - ], - "John": [ - "John meets three new friends through the HTML, CSS programming course he started taking in March 2022.", - "John seeks solitude and visits a nearby canyon." - ], - "date": "20 April, 2022" - }, - "events_session_7": { - "James": [ - "James takes his two older dogs and the puppy on an adventurous hike." - ], - "John": [ - "John faces a difficult assignment involving coding at work and is stressed because of not making much progress." - ], - "date": "23 April, 2022" - }, - "events_session_8": { - "James": [ - "James buys a fantasy novel with cool artwork.", - "James starts exploring some strategy games like Civilization VI." - ], - "John": [ - "John takes up free-lance programming work to increase his proficiency in the subject and apply the learnings from his programming class.", - "John's first freelance programming project is to create a website for a local business, where he faces some struggles with getting payments to work on the website.", - "John plays strategy board games with friends." - ], - "date": "29 April, 2022" - }, - "events_session_9": { - "James": [ - "James faces a major snag in the development of his Witcher-inspired game and takes the help of a group of friends to get it fixed." - ], - "John": [ - "John invites the friends he made in his programming class to come over and watch movies on his day-off." - ], - "date": "4 May, 2022" - }, - "events_session_10": { - "James": [], - "John": [ - "GO charity gaming tournament with his gaming friends.", - "John and his friends donate the money raised through the gaming tournament to a dog shelter and buy food, groceries for the homeless with the remaining money." - ], - "date": "8 May, 2022" - }, - "events_session_11": { - "James": [], - "John": [ - "John volunteers his programming skill to a foundation that helps kids.", - "John creates a software tool that streamlines operations of the foundation and digitizes their donation and inventory records.", - "John finds satisfaction and passion in working towards a good cause and decides to explore a career in the non-profit sector." - ], - "date": "11 May, 2022" - }, - "events_session_12": { - "James": [ - "James goes to an amusement park with his friends.", - "James finishes a challenging project in April 2022 which involved learning a new language." - ], - "John": [ - "John participates in a local gaming tournament and wins the second prize as well as a monetary reward." - ], - "date": "23 May, 2022" - }, - "events_session_13": { - "James": [ - "James starts taking a course on game design." - ], - "John": [ - "John gets his dream job after lots of interviews and plans to start in July 2022." - ], - "date": "13 June, 2022" - }, - "events_session_14": { - "James": [], - "John": [ - "John starts a blog to document his coding journey.", - "John buys new sci-fi anf fantasy books that fill up his bookcase." - ], - "date": "16 June, 2022" - }, - "events_session_15": { - "James": [ - "James begins introducing his older dogs Max and Daisy to the new puppy, Ned.", - "James volunteers at an organization in May 2022 that provides underpriveleged people with necessary items." - ], - "John": [ - "John wishes to find a volunteer position and agrees to check out the organization that James volunteers with." - ], - "date": "19 June, 2022" - }, - "events_session_16": { - "James": [ - "James successfully wins an online gaming tournament.", - "James books tickets to Toronto, Vancounver and leaves for a trip from 11 - 20 July 2022.", - "James gets interested in extreme sports and tries out rope jumping, surfing." - ], - "John": [], - "date": "9 July, 2022" - }, - "events_session_17": { - "James": [ - "James returns from his visit to Canada and the city of Nuuk, and brings back souvenirs for John and Jill.", - "James's sister visits him and they visit the ocean." - ], - "John": [ - "John starts playing chess and joins a chess club to practice his competitive edge." - ], - "date": "22 July, 2022" - }, - "events_session_18": { - "James": [ - "James takes the puppy to the vet for its necessary vaccinations to prevent him from catching the seasonal canine disease." - ], - "John": [ - "John reflects on quitting his IT job that he worked at for three years, and joining a new job that he likes.", - "John wants to pursue a career in organizing gaming tournaments in his state." - ], - "date": "6 August, 2022" - }, - "events_session_19": { - "James": [ - "James takes the three dogs to a beach outing to have fun and bond with other dogkeepers.", - "James meets a woman named Samantha during the outing to the beach and asks for her phone number to call and meet up later." - ], - "John": [ - "John starts playing the RPG Witcher 3." - ], - "date": "10 August, 2022" - }, - "events_session_20": { - "James": [ - "James hosts a gaming marathon in his home for his gaming friends." - ], - "John": [ - "John joins a programming group on a social media platform to keep in touch with his fellow coders, share ideas and collaborate on coding projects.", - "John plans a gaming night with his siblings for September 2022.", - "John refurbishes his gaming desk." - ], - "date": "21 August, 2022" - }, - "events_session_21": { - "James": [], - "John": [ - "John's younger siblings join him in the HTML/CSS programming course.", - "John and his siblings work on building a text-based adventure game as a project for the programming course." - ], - "date": "26 August, 2022" - }, - "events_session_22": { - "James": [ - "James finishes building a Unity-based strategy game, inspired from his experiences with Civilization and Total War games." - ], - "John": [ - "John teaches coding to his younger siblings who also joined him in the HTML/CSS programming course." - ], - "date": "1 September, 2022" - }, - "events_session_23": { - "James": [ - "James asks Samantha to be his girlfriend at the theater and she agrees.", - "James and Samantha plan on visiting a baseball game and James invites John to join them.", - "James takes cooking classes and learns to cook omlette, meringue, dough." - ], - "John": [ - "John's parents start taking coding lessons from him.", - "John buys Sennheiser headphones and Logitech mouse gaming equipment to improve his skills." - ], - "date": "4 September, 2022" - }, - "events_session_24": { - "James": [ - "James participates in a gaming stream to showcase his game to his audience.", - "James starts wirting down ideas and inspirations in a notebook in order to not forget anything significant." - ], - "John": [ - "John starts exploring Dungeons of the Dragons board game." - ], - "date": "18 September, 2022" - }, - "events_session_25": { - "James": [ - "James receives cheers and compliments from the gaming community after a successful stream." - ], - "John": [ - "John starts a startup selling portable smokers.", - "John finishes working on a mobile 2D adventure game with puzzles and explorations which is due for release in October 2022" - ], - "date": "20 September, 2022" - }, - "events_session_26": { - "James": [ - "James buys a new video card and is excited to play Cyberpunk 2077 with it." - ], - "John": [ - "John is excited about receiving an email about a volunteer gig where he can mentor young game developers." - ], - "date": "3 October, 2022" - }, - "events_session_27": { - "James": [ - "James completes working on his first game, inspired by the Witcher 3 world, and publishes it for the online gaming community." - ], - "John": [ - "John and his programming friends host an online programming competition." - ], - "date": "13 October, 2022" - }, - "events_session_28": { - "James": [ - "James tries playing Cyberpunk 2077 and enjoys the game.", - "James's apartment loses power and he loses progress in a game that he forgets to save.", - "James's mother and her friend from the military visit him." - ], - "John": [ - "John takes the initiative to organize a programming seminar in his local area and gains new insights on programming practices." - ], - "date": "21 October, 2022" - }, - "events_session_29": { - "James": [ - "James and Samantha decide to move in together.", - "James and Samantha find an apartment close to their favorite bar, McGee's, for their move-in." - ], - "John": [ - "John invites his gaming friends to join the charity tournament where they play Overwatch, Apex Legends and raise money for a children's hospital." - ], - "date": "31 October, 2022" - }, - "events_session_30": { - "James": [ - "James takes his dogs for a long road trip with his family." - ], - "John": [ - "John wins the regional chess tournament.", - "John gets hooked to the FIFA 23 video game." - ], - "date": "5 November, 2022" - }, - "events_session_31": { - "James": [ - "James and his family visit his gaming friends, Josh and Mark, on their road trip.", - "James and his family visit an animal sanctuary during their road trip." - ], - "John": [ - "John collaborates with a game developer on an online game." - ], - "date": "7 November, 2022" - } - }, - "observation": { - "session_1_observation": { - "John": [ - [ - "John signed up for a programming class to refresh his skills in HTML and CSS.", - "D1:7" - ], - [ - "John suggested the idea of developing mobile applications together in the future.", - "D1:11" - ], - [ - "John showed interest in VR gaming and agreed to try it with James next Saturday.", - "D1:35" - ] - ], - "James": [ - [ - "James tried programming in college and now it's a big part of his life.", - "D1:6" - ], - [ - "James has worked with Python and C++, building websites and creating game mods.", - "D1:8" - ], - [ - "James plans to build an app for dog walking and pet care, aiming to connect pet owners with reliable dog walkers.", - "D1:14" - ], - [ - "James is working on a project to turn his childhood sketches of a main character into a computer game, combining his passions for gaming and storytelling.", - "D1:20" - ] - ] - }, - "session_2_observation": { - "James": [ - [ - "James created a game avatar and joined a new platform to connect with other gamers.", - "D2:1" - ], - [ - "Gaming has been a refuge for James in tough times, providing relaxation and escape from stress.", - "D2:5" - ], - [ - "James sees games as therapy, where he can relax, forget his troubles, and get lost in another world.", - "D2:7" - ], - [ - "James has been exploring different styles of gaming lately to try something new and test himself.", - "D2:9" - ], - [ - "James has two dogs who he has done lots of training with, and they are like family to him.", - "D2:17" - ], - [ - "James offered to help find the perfect pet for John when John mentioned wanting one.", - "D2:19" - ] - ], - "John": [ - [ - "John recently got into a new hobby of using a metal detector on the beach to look for items.", - "D2:10" - ], - [ - "John found mostly bottle caps with his metal detector, but also found coins and even a gold ring a couple of times.", - "D2:12" - ], - [ - "John expressed interest in getting a pet in the future and appreciated James' offer to help find the perfect one.", - "D2:18" - ] - ] - }, - "session_3_observation": { - "John": [ - [ - "John had a big win in his game last week and advanced to the next level, which was a huge confidence booster for him.", - "D3:1" - ], - [ - "John plays drums and has been playing for only a month.", - "D3:3" - ], - [ - "John attended a gaming convention where he tried out loads of games, met developers, and took part in a tournament for CS:GO.", - [ - "D3:9", - "D3:11" - ] - ], - [ - "John is always looking to up his game and hit new goals in his hobbies and other stuff.", - "D3:15" - ] - ], - "James": [ - [ - "James is learning to play a musical instrument and has been at it daily, seeing improvements.", - [ - "D3:2", - "D3:6" - ] - ], - [ - "James got a new cutting-edge gaming system with incredible graphics and enjoys playing new games to relax after work.", - "D3:8" - ], - [ - "James is getting into different types of games now, like RPGs and strategy games, which he finds really exciting.", - "D3:18" - ] - ] - }, - "session_4_observation": { - "John": [ - [ - "John asked James if he had been playing any new games lately.", - "D4:1" - ], - [ - "John inquired about James' performance in the online gaming tournament.", - "D4:3" - ], - [ - "John asked James if he met any famous players at the tournament.", - "D4:7" - ], - [ - "John asked James what advice he remembered most from the famous players.", - "D4:11" - ], - [ - "John discussed the importance of communication and teamwork in gaming.", - "D4:13" - ], - [ - "John asked James about the game he plays with his team.", - "D4:15" - ], - [ - "John inquired if James is looking forward to trying out any new games.", - "D4:19" - ] - ], - "James": [ - [ - "James joined an online gaming tournament and made it to the semifinals, winning some rounds.", - "D4:2" - ], - [ - "James participated in the final rounds of the online gaming tournament but did not make it to the finals.", - "D4:6" - ], - [ - "James met the whole team of a famous player at the tournament and received gaming tips and autographs.", - "D4:8" - ], - [ - "James remembers the gaming advice received was about proper communication and teamwork over ego.", - "D4:12" - ], - [ - "James usually communicates with his team using voice chat for effective teamwork.", - "D4:14" - ], - [ - "James enjoys playing Apex Legends with his team due to its fast-paced gameplay.", - "D4:16" - ], - [ - "James is excited about trying out new games like RPGs and MOBAs.", - "D4:20" - ], - [ - "James plans to update John on his gaming experiences and new games.", - "D4:24" - ] - ] - }, - "session_5_observation": { - "James": [ - [ - "James adopted a pup from a shelter in Stamford last week and named it Ned, making his days happier.", - "D5:1" - ] - ], - "John": [ - [ - "John is playing a new RPG that transports players to a futuristic dystopia with a cool story and world.", - "D5:4" - ], - [ - "John found the game to have some lags and errors but still believes it has great graphics and immersive gameplay.", - "D5:6" - ] - ] - }, - "session_6_observation": { - "John": [ - [ - "John met three new friends in his programming course last Tuesday and is excited to expand his social circle.", - "D6:1" - ], - [ - "John enjoys chilling with friends and traveling to get motivation for work.", - "D6:11" - ], - [ - "John visited Japan last, where he was impressed by the technologically advanced megacities and delicious street food.", - "D6:15" - ] - ], - "James": [ - [ - "James worked on a programming project combining gaming with programming, creating a virtual world inspired by Witcher 3 with a game character he designed.", - "D6:2" - ], - [ - "James got the idea for the virtual world from a stranger he saw while walking his dogs and found inspiring.", - "D6:6" - ], - [ - "James has visited Italy, Turkey, and Mexico besides his permanent residence and found Italy to be very beautiful with delicious food.", - "D6:12" - ], - [ - "James expressed an interest in traveling together with John to a new country next year and plans to start looking for options.", - "D6:16" - ] - ] - }, - "session_7_observation": { - "John": [ - [ - "John took his dogs out for a hike last Thursday and they enjoyed discovering trails and views.", - "D7:2" - ], - [ - "John has been super busy at work with deadlines and lots to do, making it a hectic time for him.", - "D7:11" - ], - [ - "John is working on a difficult coding project that he finds frustrating and is struggling to make progress on.", - "D7:13" - ] - ], - "James": [ - [ - "James enjoys the outdoors and finds being surrounded by lush greenery and clean air comforting.", - "D7:6" - ], - [ - "James finds the crunch of leaves under his feet and the peacefulness of nature helpful in clearing his head and chilling.", - "D7:8" - ], - [ - "James offers assistance and suggests breaking down the project into smaller steps, doing research, and seeking help to overcome coding difficulties.", - "D7:18" - ] - ] - }, - "session_8_observation": { - "James": [ - [ - "James bought an adventure book with fantasy novels and cool arts three days ago.", - "D8:11" - ], - [ - "James has furry friends, like his dog Daisy, who lay down next to him while he plays games.", - "D8:17" - ], - [ - "James is currently trying out some strategy games.", - "D8:21" - ], - [ - "James has been playing the game AC Valhalla.", - "D8:21" - ], - [ - "James has been playing a turn-based strategy game where you manage resources, lead armies, and conquer territories.", - "D8:23" - ], - [ - "James has been playing the turn-based strategy game for a month to challenge his strategy skills.", - "D8:29" - ] - ], - "John": [ - [ - "John is currently taking on freelance programming to hone his coding skills.", - "D8:2" - ], - [ - "John is working on a website for a local small business, which is his first professional project outside of class.", - "D8:4" - ], - [ - "John faced challenges figuring out how to get payments on the website he's working on.", - "D8:10" - ], - [ - "John recommended the novel \"The Name of the Wind\" to James.", - "D8:14" - ], - [ - "John has been playing the game AC Valhalla.", - "D8:20" - ], - [ - "John has been playing Among Us, a game where players figure out who the impostors are.", - "D8:36" - ] - ] - }, - "session_9_observation": { - "John": [ - [ - "John had some new friends over to watch movies on his day off.", - "D9:9" - ], - [ - "John enjoys socializing and believes in balancing work with enjoyment.", - "D9:9" - ], - [ - "John is interested in what type of pets James has.", - "D9:11" - ], - [ - "John loves Hawaiian pizza for its sweet and salty combination.", - "D9:19" - ] - ], - "James": [ - [ - "James faced a bug in his project that messed up the game mechanics, causing disappointment.", - "D9:4" - ], - [ - "James enjoys spending time with his beloved pets - a Labrador named Daisy and two shepherds.", - "D9:10" - ], - [ - "James finds joy and loyalty in his pets.", - "D9:14" - ], - [ - "James's happiness comes from his pets, computer games, travel, and pizza.", - "D9:16" - ], - [ - "James's favorite type of pizza is pepperoni for its spicy salami and cheese combination.", - "D9:18" - ], - [ - "James also likes cheese pizza and prosciutto on his pizza.", - "D9:20" - ], - [ - "James recommends prosciutto pizza to John, highlighting its greatness.", - "D9:22" - ] - ] - }, - "session_10_observation": { - "James": [ - [ - "James mentioned being busy at work.", - "D10:2" - ] - ], - "John": [ - [ - "John organized a tournament for the game CS:GO with friends, which raised money for charity.", - "D10:4" - ], - [ - "John sent the collected money to a dog shelter near his home.", - "D10:10" - ], - [ - "John used some of the remaining money to buy groceries and cook food for the homeless.", - "D10:12" - ], - [ - "John expressed interest in organizing more events combining his interests and helping the community.", - "D10:8" - ] - ] - }, - "session_11_observation": { - "John": [ - [ - "John volunteered his programming skills for a social cause, creating a software tool for a charitable foundation to streamline their operations.", - "D11:1" - ], - [ - "John was inspired by the foundation's passion for helping children and used his coding skills to contribute and challenge himself.", - "D11:7" - ], - [ - "This experience gave John a clearer sense of purpose and motivated him to potentially pursue a career in the non-profit sector.", - "D11:11" - ], - [ - "John is considering volunteer roles and potentially a career in the non-profit sector.", - "D11:11" - ] - ], - "James": [ - [ - "James was glad to hear from John and excited to learn about the impact of the software tool John created for the charitable foundation.", - "D11:2" - ], - [ - "James asked about the motivation behind John's creation of the software tool for the foundation.", - "D11:6" - ], - [ - "James encouraged John to continue using his skills and passions for making a difference.", - "D11:14" - ], - [ - "James offered support and encouragement to find the right non-profit organization that aligns with John's values and passion for programming.", - "D11:16" - ] - ] - }, - "session_12_observation": { - "James": [ - [ - "James had an awesome time at the amusement park with friends last weekend, going on roller coasters, Ferris wheel, electric cars, and buggies.", - "D12:1" - ], - [ - "James finished a big project after months that involved learning a new language and handling many details, which helped him learn about problem-solving, patience, and perseverance.", - "D12:11" - ] - ], - "John": [ - [ - "John entered a local tournament last Friday and took second place, receiving money and a trophy for his achievement.", - "D12:4" - ], - [ - "John feels that growth and progress are more important than winning every time and cherishes the lessons learned from his achievement.", - "D12:7" - ] - ] - }, - "session_13_observation": { - "John": [ - [ - "John recently got his dream job after many interviews and late nights.", - "D13:3" - ], - [ - "John is starting his new job next month.", - "D13:5" - ], - [ - "John is a Manchester City fan.", - "D13:15" - ] - ], - "James": [ - [ - "James recently started a course combining his passion for gaming and programming.", - "D13:6" - ], - [ - "James is currently working on a football simulator project, specifically on collecting player databases.", - "D13:8" - ], - [ - "James is a Liverpool fan and follows all their matches closely.", - "D13:12" - ], - [ - "James believes there is no sport better than football and no club better than Liverpool.", - "D13:12" - ], - [ - "James is confident that Liverpool will become champions next season.", - "D13:14" - ], - [ - "James is competitive with John about the standings of their respective football teams.", - "D13:18" - ], - [ - "James agrees to a bet with John regarding the final standings of their football teams.", - "D13:19" - ] - ] - }, - "session_14_observation": { - "James": [ - [ - "James has two people who always help him out when he's struggling.", - "D14:1" - ], - [ - "James posted a picture on his blog about coding last week.", - "D14:2" - ], - [ - "James has a dog named Max who brings him joy, especially in tough times.", - "D14:13" - ], - [ - "James enjoys cuddling with his dog Max.", - "D14:15" - ], - [ - "James and Max enjoy swimming together at the beach or lake.", - "D14:17" - ], - [ - "James takes walks with Max in a nearby tranquil trail to relax and connect with nature.", - "D14:27" - ], - [ - "When alone in the park, James brings a book to chill and escape from reality.", - "D14:31" - ] - ], - "John": [ - [ - "John started a blog about coding last week and finds it exciting and challenging.", - "D14:2" - ], - [ - "John enjoys reading sci-fi and fantasy books, particularly epic fantasy series with immersive world-building and intricate storylines.", - "D14:8" - ], - [ - "John's favorite book series are 'The Stormlight Archive', 'Kingkiller Chronicle', and 'The Expanse'.", - "D14:10" - ], - [ - "John's dog Max loves swimming and playing in the water, being a pro swimmer.", - "D14:17" - ], - [ - "John appreciates James' achievement in making Max happy.", - "D14:20" - ], - [ - "John thinks that taking breaks and switching up the scenery is important for recharging.", - "D14:32" - ], - [ - "John is supportive of James and always there to have his back.", - "D14:34" - ] - ] - }, - "session_15_observation": { - "James": [ - [ - "James introduced three pets - Max, Daisy, and a new pup named Ned, who are slowly adapting and bonding together.", - "D15:1" - ], - [ - "James took a great photo of his pets bonding together.", - "D15:3" - ], - [ - "James volunteered last month and found it rewarding to see the impact of small gifts on those in need.", - "D15:9" - ], - [ - "James volunteered with an organization providing necessary items to less fortunate individuals.", - "D15:11" - ], - [ - "James is willing to introduce John to the organization where he volunteered.", - "D15:13" - ], - [ - "James indicated that no interview is required for volunteering, just the willingness to help people.", - "D15:15" - ], - [ - "James is supportive of John's goal to make a positive impact on the world.", - "D15:17" - ] - ], - "John": [ - [ - "John is contemplating his career and desires to make a positive impact on the world.", - "D15:6" - ], - [ - "John is considering going into non-profit work and using his skills and passions for causes he cares about.", - "D15:8" - ], - [ - "John expresses a desire to help people and make the world a better place.", - "D15:16" - ], - [ - "John is determined to make a positive impact with James's support.", - "D15:18" - ] - ] - }, - "session_16_observation": { - "James": [ - [ - "James won an online gaming tournament last week and found the experience exciting and motivating.", - "D16:1" - ], - [ - "James is interested in extreme sports, recently trying rope jumping from a height of 150 meters and surfing.", - "D16:5" - ], - [ - "James enjoys reading, especially during cold winter days, but finds it exciting to do something different in the summer.", - "D16:9" - ], - [ - "James bought air tickets to Toronto and plans to visit Vancouver as well.", - "D16:9" - ], - [ - "James plans to return from his trip on July 20 and intends to bring back a souvenir for John.", - "D16:13" - ] - ], - "John": [ - [ - "John feels a mix of determination, passion, overwhelm, and stress while balancing personal and professional life.", - "D16:4" - ], - [ - "John relaxes by reading and finds it as a fun escape from reality.", - "D16:8" - ], - [ - "John asked James about the countries he will be visiting on his trip.", - "D16:10" - ], - [ - "John will be waiting for James to return from his trip and expressed excitement about a souvenir.", - "D16:14" - ] - ] - }, - "session_17_observation": { - "John": [ - [ - "John recently started playing chess to improve his strategic thinking.", - "D17:1" - ], - [ - "John plays chess online and joined a chess club to practice with others.", - "D17:5" - ], - [ - "John believes chess can enhance decision-making skills.", - "D17:7" - ], - [ - "John received advice from James about improving in chess by studying opening moves and analyzing games.", - "D17:10" - ], - [ - "John has a picture from elementary school with James related to skateboarding, showing they were friends who enjoyed skateboarding together.", - "D17:13" - ], - [ - "John expressed the importance of friendship and the joy pets bring, mentioning he doesn't have pets but would like to get one in the future.", - "D17:21" - ], - [ - "John is grateful for the connection he has with his siblings and values their support.", - "D17:31" - ] - ], - "James": [ - [ - "James has previously played chess and acknowledges its strategic nature.", - "D17:2" - ], - [ - "James shared advice with John on improving in chess by studying opening moves and analyzing games.", - "D17:10" - ], - [ - "James has a sister with whom he has a close bond and enjoys spending time near the ocean with her watching sunsets.", - "D17:30" - ], - [ - "James has dogs that he taught to balance on a skateboard, and they enjoy playing with it.", - "D17:18" - ], - [ - "James recently traveled to the city of Nuuk and brought souvenirs for John and Jill.", - "D17:22" - ], - [ - "James spends time with his sister and dogs, finding happiness and a strong bond with them.", - "D17:28" - ] - ] - }, - "session_18_observation": { - "John": [ - [ - "John recently left his IT job after 3 years because he wanted something that made a difference.", - "D18:1" - ], - [ - "John is passionate about the gaming industry and wants to become an organizer for gaming tournaments, starting with CS:GO and Fortnite.", - "D18:7" - ], - [ - "John values support and is grateful for the support he receives from James in his new career endeavor.", - "D18:11" - ] - ], - "James": [ - [ - "James recently took his puppy for a routine examination and vaccination to prevent seasonal canine disease.", - "D18:14" - ], - [ - "James deeply cares for his pets and ensures their well-being.", - "D18:18" - ] - ] - }, - "session_19_observation": { - "John": [ - [ - "John has recently started exploring different game genres like strategy and RPGs instead of his usual shooters.", - "D19:3" - ], - [ - "John hooked on playing a new RPG game, \"The Witcher 3\", where he enjoys the storytelling and characters.", - "D19:5" - ], - [ - "John is considering organizing competitions for the new game genres he is exploring.", - "D19:3" - ], - [ - "John took an initiative to organize his workplace to make it more efficient.", - "D19:9" - ], - [ - "John is encouraging towards James, thanking him for encouraging him to try new game genres.", - "D19:1" - ] - ], - "James": [ - [ - "James took his three dogs to a beach outing to bond with other dogkeepers.", - "D19:12" - ], - [ - "James is considering asking a girl named Samantha out on a date after they met at the beach outing.", - "D19:14" - ], - [ - "James shared a picture with John showcasing the graphics of a game they were discussing.", - "D19:8" - ] - ] - }, - "session_20_observation": { - "John": [ - [ - "John joined an online programming group last Friday.", - "D20:1" - ], - [ - "John worked on a project with someone from the online group last week.", - "D20:5" - ], - [ - "John is full of courage to start hosting eSports competitions.", - "D20:7" - ], - [ - "John bought new devices and refurbished his gaming desk.", - "D20:9" - ], - [ - "John uses a powerful graphics card for intense games and has a headset for immersive sound.", - "D20:11" - ], - [ - "John organized a gaming night with his siblings for next month.", - "D20:17" - ] - ], - "James": [ - [ - "James has a gaming group that he plays with regularly and they stream their game sessions.", - "D20:8" - ], - [ - "James hosted a gaming marathon with friends and it strengthened their bond.", - "D20:12" - ] - ] - }, - "session_21_observation": { - "James": [ - [ - "James has a dog named Ned that he adopted and can't imagine life without.", - "D21:3" - ], - [ - "James is interested in creating a strategy game similar to Civilization.", - "D21:9" - ], - [ - "James suggested meeting at Starbucks for coffee with John.", - "D21:13" - ] - ], - "John": [ - [ - "John helps his younger siblings with programming and is proud of their progress", - "D21:2" - ], - [ - "John is working on a coding project with his siblings involving a text-based adventure game.", - "D21:6" - ], - [ - "John prefers light beers over dark beers when going out.", - "D21:16" - ], - [ - "John agreed to meet James at McGee's Pub after discussing different options.", - "D21:18" - ] - ] - }, - "session_22_observation": { - "James": [ - [ - "James finished a Unity strategy game that he put a lot of time and effort into.", - "D22:1" - ], - [ - "James loves playing strategy games like Civilization and Total War, which inspired him to create his own game.", - "D22:5" - ], - [ - "James found the process of creating the game challenging, especially balancing mechanics and fairness.", - "D22:7" - ], - [ - "James learned that perseverance, patience, feedback, and collaboration are essential in game development.", - "D22:9" - ] - ], - "John": [ - [ - "John has been teaching his siblings coding, and they are creating their own programs.", - "D22:10" - ], - [ - "John's siblings are starting with basic games and stories in their coding journey.", - "D22:12" - ], - [ - "John is proud of his siblings for learning coding and is excited to see what they create with their skills.", - "D22:14" - ], - [ - "John is going through some difficult times and appreciates the friendship and support from James.", - "D22:18" - ] - ] - }, - "session_23_observation": { - "James": [ - [ - "James asked Samantha to be his girlfriend at the theater, and she agreed.", - "D23:1" - ], - [ - "James and Samantha were at McGee's bar where he found out she loves lager beer.", - "D23:3" - ], - [ - "James signed up for a cooking class two days ago to learn something new.", - "D23:13" - ], - [ - "James made a great omelette at the cooking class.", - "D23:13" - ], - [ - "James's cooking class costs $10 per class.", - "D23:15" - ], - [ - "James made meringue and learned how to make dough at the cooking class.", - "D23:15" - ] - ], - "John": [ - [ - "John's parents started learning coding from him, bringing them closer.", - "D23:2" - ], - [ - "John bought new gaming equipment including headphones from Sennheiser and a mouse from Logitech.", - "D23:8" - ] - ] - }, - "session_24_observation": { - "John": [ - [ - "John recently got into board games and found them to be a lot of fun.", - "D24:1" - ], - [ - "John used to play drums when he was younger.", - "D24:13" - ], - [ - "John has an old drum set that he used to play on.", - "D24:15" - ], - [ - "John has jammed with friends before and found it to be a lot of fun.", - "D24:17" - ] - ], - "James": [ - [ - "James started writing down everything he needs to do in a notebook to avoid forgetting tasks.", - "D24:2" - ], - [ - "James gets ideas from various sources like books, movies, and dreams.", - "D24:4" - ], - [ - "James had a creative dream a few weeks ago that led to interesting thoughts.", - "D24:6" - ], - [ - "James made sketches and notes based on a dream he had about a medieval castle with puzzles and traps.", - "D24:9" - ], - [ - "James used to play the guitar when he was younger but hasn't in a while.", - "D24:14" - ], - [ - "James is into music but it is not related to his castle dream.", - "D24:12" - ], - [ - "James has started streaming games and hopes everything works out.", - "D24:20" - ] - ] - }, - "session_25_observation": { - "John": [ - [ - "John started a new startup focusing on portable smokers and has already welded one from metal.", - "D25:3" - ], - [ - "John achieved a major career milestone by creating his first mobile game, which is launching next month.", - "D25:7" - ], - [ - "John's mobile game is a 2D adventure game with puzzles and exploration.", - "D25:9" - ], - [ - "John uses resources like a book, tutorials, and developer forums to improve his game development skills.", - "D25:13" - ], - [ - "John reads a magazine with tutorials, interviews with developers, and tips for game development.", - "D25:15" - ], - [ - "John's week has been chaotic, but he is pushing through.", - "D25:21" - ] - ], - "James": [ - [ - "James received positive feedback from the gaming community while streaming a game, which inspired him to keep going.", - "D25:4" - ], - [ - "James offered to help John test his mobile game.", - "D25:10" - ], - [ - "James recommends a magazine with tutorials and developer interviews to John for game development.", - "D25:14" - ], - [ - "James finds the magazine to be a useful resource for game development.", - "D25:18" - ], - [ - "James advises John to take care and make time for himself during a busy week.", - "D25:22" - ] - ] - }, - "session_26_observation": { - "John": [ - [ - "John received an email about a volunteer gig at a game dev non-profit to be a programming mentor for game developers.", - "D26:1" - ], - [ - "John is excited about the opportunity to combine his love for gaming and helping by teaching coding and assisting with projects.", - "D26:3" - ], - [ - "John loves sharing his knowledge, motivating others passionate about gaming, and helping them reach their potential.", - "D26:5" - ] - ], - "James": [ - [ - "James got a cool video card last week and is excited to use it for playing RPGs.", - "D26:8" - ], - [ - "James is looking forward to playing Cyberpunk 2077 based on John's recommendation.", - "D26:10" - ] - ] - }, - "session_27_observation": { - "John": [ - [ - "John and his programmer friends organized an online competition last week.", - "D27:1" - ], - [ - "John is supportive and encouraging towards James' game development efforts.", - "D27:3" - ], - [ - "John expressed excitement about James releasing his first game for the gaming community.", - "D27:3" - ], - [ - "John is interested in James' inspiration behind creating his game, mentioning The Witcher 3.", - "D27:5" - ], - [ - "John encourages James to continue with game development and expresses belief in his talents.", - "D27:9" - ], - [ - "John assures James of his support and cooperation in their endeavors.", - "D27:11" - ] - ], - "James": [ - [ - "James created and released his first game for the gaming community recently, which he found exciting.", - "D27:2" - ], - [ - "James feels fulfilled seeing players engage with the game world he created.", - "D27:4" - ], - [ - "The game 'Witcher 3' inspired James to create his own game, pushing him to make something special.", - "D27:6" - ], - [ - "James expressed a desire to create more games in different genres and explore new ideas in game development.", - "D27:8" - ], - [ - "James looks forward to creating more enjoyable experiences in the future.", - "D27:10" - ], - [ - "John supports James and expresses gratitude for John's support.", - "D27:12" - ], - [ - "James values John's support and friendship, mentioning that John has always been there for him.", - "D27:14" - ] - ] - }, - "session_28_observation": { - "James": [ - [ - "James's apartment lost power three days ago, disrupting his game progress as he forgot to save.", - "D28:3" - ], - [ - "James appreciates the creativity and complexity of games, spending hours playing and learning from them.", - "D28:3" - ], - [ - "James has a nostalgic attachment to old gaming setups, like playing on his mother's friend's console when he was young.", - "D28:21" - ], - [ - "James's first gaming system was a Nintendo where he played Super Mario and The Legend of Zelda, sparking his passion for gaming.", - "D28:25" - ], - [ - "James tried Cyberpunk 2077 yesterday and found it addictive.", - "D28:27" - ] - ], - "John": [ - [ - "John organized a programming seminar last week which had a great turnout, and he gained new insights into programming approaches and techniques.", - "D28:6" - ], - [ - "John enjoys sharing knowledge and learning from other developers in seminars.", - "D28:8" - ], - [ - "John plans to incorporate new programming techniques into his work and enjoys exploring different methods.", - "D28:12" - ], - [ - "John values trying out new programming techniques and ideas to spark creativity and keep evolving.", - "D28:14" - ], - [ - "John is helpful and willing to share resources and tutorials with others to help them learn and grow.", - "D28:16" - ], - [ - "John advises James about the game Cyberpunk 2077, highlighting the significance of making the right choices in the game.", - "D28:30" - ], - [ - "John reminds James about the importance of choices in dialogues with characters in Cyberpunk 2077.", - "D28:32" - ] - ] - }, - "session_29_observation": { - "John": [ - [ - "John organized a gaming tournament with his friends, playing Fortnite, Overwatch, and Apex Legends, to raise funds for a children's hospital.", - "D29:1" - ], - [ - "John and his friends raised a decent amount of money for the children's hospital through the gaming tournament.", - "D29:1" - ], - [ - "John's gaming pals helped him in organizing the tournament and playing games to raise money.", - "D29:3" - ], - [ - "John got a great shot at the tournament, capturing the hyped atmosphere while playing for a good cause.", - "D29:5" - ] - ], - "James": [ - [ - "James decided to move in with Samantha into an apartment not far from McGee's bar.", - "D29:8" - ], - [ - "McGee's bar was one of the criteria for James and Samantha's choice of apartment.", - "D29:12" - ] - ] - }, - "session_30_observation": { - "James": [ - [ - "James started a road trip with his family and dogs yesterday, enjoying exploring new places and nature.", - "D30:1" - ], - [ - "James is interested in trying out a new gaming genre and mentioned considering the sports genre.", - "D30:15" - ] - ], - "John": [ - [ - "John won the regional chess tournament recently, feeling proud of his accomplishment.", - "D30:4" - ], - [ - "John's tournament strategy involved analyzing and anticipating his opponent's moves to stay ahead.", - "D30:8" - ], - [ - "John recommends studying opening moves and strategies, learning from experienced players, and analyzing past games to improve at chess.", - "D30:10" - ], - [ - "John is currently hooked on playing FIFA 23, a football game that allows playing online with players worldwide.", - "D30:14" - ], - [ - "John suggests that playing FIFA 23 only requires a gamepad and a sense of timing.", - "D30:18" - ] - ] - }, - "session_31_observation": { - "James": [ - [ - "James and his family are currently on a road trip and have already visited friends Josh and Mark.", - "D31:1" - ], - [ - "James and his family visited an animal sanctuary on the road trip and saw many cute rescue dogs.", - "D31:11" - ], - [ - "James already has three dogs at home and believes having more than three dogs is too much.", - "D31:13" - ] - ], - "John": [ - [ - "John worked with a game developer on a project to create an online board game over the weekend.", - "D31:2" - ], - [ - "John and the game developer are about to release a demo of the online board game for others to try.", - "D31:6" - ], - [ - "John does not currently have a dog but really wants one.", - "D31:14" - ], - [ - "John appreciates the positivity and happiness that dogs bring to life and considers them special buddies.", - "D31:20" - ], - [ - "John's cousin has a dog named Luna.", - "D31:22" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "John and James spoke at 3:47 pm on 17th March 2022. John asked James about his motivation, which James attributed to his love for video games. They discussed their favorite games, programming skills, and a potential collaboration on a dog care app. James shared his project of creating a computer game based on childhood sketches. John admired James' passion and expressed interest in trying VR gaming together. They agreed to meet for VR gaming the following Saturday.", - "session_2_summary": "At 9:26 pm on 20 March, 2022, James told John about joining a new gaming platform, emphasizing the joy of connecting with gamers and sharing experiences. John agreed, highlighting the community-building aspect of gaming. They both found solace and stress relief in gaming during tough times. James also shared his interest in exploring different gaming styles, while John mentioned his new hobby of beachcombing with a metal detector. James then showed John pictures of his dogs and offered to help him find a pet in the future. They ended their conversation on a friendly note.", - "session_3_summary": "John and James conversed at 12:40 am on 27th March 2022. John shared his recent gaming success and drumming hobby, while James talked about learning a new instrument. They discussed gaming systems, conventions, and mutual admiration for skilled players. John emphasized the importance of learning from experienced gamers, and James mentioned setting small goals to stay motivated. James expressed excitement about exploring new game genres, with John eager to hear about his progress. The conversation ended with John looking forward to hearing updates from James about his gaming adventures.", - "session_4_summary": "At 2:13 pm on 4 April, 2022, John and James caught up after a long time. James shared his experience in an online gaming tournament where he reached the semifinals, had fun, and received gaming tips and autographs from a famous team. John emphasized the importance of communication and teamwork in gaming. They discussed playing Apex Legends together and trying out new genres like RPGs and MOBAs. They agreed to chat soon and update each other on new games.", - "session_5_summary": "James and John caught up at 9:52 am on 12 April, 2022. James shared about adopting a pup named Ned and how it made his days happier. John mentioned being busy with work but still finding solace in gaming. They discussed a new RPG game John is playing, which James showed interest in despite some lag and errors. John convinced James to try the game, offering to send a link. They agreed to discuss it later, expressing excitement. They bid farewell with James looking forward to trying the game and John wishing him a great time.", - "session_6_summary": "John and James caught up at 9:32 pm on April 20, 2022. John shared his excitement about making new friends in his programming course. Meanwhile, James talked about collaborating with a gaming pal on a virtual world project inspired by Witcher 3. He found inspiration for the project from a stranger he saw during a walk with his dogs. Both friends discussed how real-life experiences inspire their creativity. They also shared their love for traveling, with James mentioning his visits to Italy, Turkey, and Mexico, while John recently visited Japan. They expressed interest in traveling together in the future. James agreed to look for a destination as they planned their future trip.", - "session_7_summary": "On 23 April, 2022 at 11:04 am, John greeted James, who had taken his dogs on a hike the previous Thursday, sharing that they had a great time exploring trails and enjoying the outdoors. John asked for pictures, which James had, showcasing the adventure. James explained he chose the spot for its views and trails, perfect for the dogs. They both agreed on the benefits of being in nature to relax. John revealed work had been hectic due to numerous deadlines, especially struggling with a challenging coding project. James offered to help by suggesting breaking down the problem and seeking advice. John appreciated the advice, determined to keep going. James encouraged him not to give up, emphasizing that every problem has a solution, ending the conversation on a positive note.", - "session_8_summary": "James and John conversed at 2:36 pm on 29 April, 2022. John mentioned he is doing freelance programming to improve his coding skills and is working on a website for a local business. Despite facing challenges like setting up payments, he's determined to succeed. James praised John for his progress and shared his interest in fantasy novels. They discussed the book \"The Name of the Wind\" and strategy games like Civilization VI and Valhalla. James talked about playing a strategy game that tests problem-solving skills and how satisfying it is to win. John brought up enjoying strategy board games and a mystery game with friends. They agreed it's fun to play with others and recommended playing games like the impostor game in large groups for a better experience.", - "session_9_summary": "John and James caught up at 7:01 pm on 4 May, 2022. James had a frustrating week due to a bug in his project but managed to fix it with the help of friends. They discussed the importance of teamwork and balancing work with socializing. James spends time with his Labrador, Daisy, and two loyal shepherds. James finds happiness in his pets, computer games, travel, and pizza, particularly loving pepperoni, cheese, and prosciutto pizzas. John prefers Hawaiian pizza. They concluded by discussing trying out prosciutto pizza in the future.", - "session_10_summary": "At 12:45 am on 8 May, 2022, James and John caught up after a while. John mentioned organizing a CS:GO tournament with friends for charity, and James praised his efforts. John expressed interest in organizing more events for charity in the future. They discussed donating to a dog shelter and feeding the homeless with the collected money. James commended John's efforts, and John vowed to continue doing good things. James concluded the conversation by expressing pride in John's actions.", - "session_11_summary": "John and James spoke at 5:00 pm on 11 May, 2022. John shared how he volunteered his programming skills for a charitable foundation, creating a software tool that streamlined their operations. James expressed admiration for John's contribution and requested visuals of the impact. John explained his motivation was the foundation's dedication to helping kids. The experience inspired John to consider a career in the non-profit sector. James encouraged him to find the right organization for his skills. John appreciated the support and mentioned looking forward to making a positive impact. James wished him luck and they concluded the conversation warmly.", - "session_12_summary": "James told John about his fun time at the amusement park last weekend, enjoying the roller coasters and other attractions that brought back childhood memories. John shared his recent success in a local tournament, coming in second place and receiving a trophy and prize money. They discussed the importance of growth, progress, and perseverance in achieving goals. James mentioned completing a challenging project that required learning a new language, which taught him problem-solving, patience, and perseverance. John encouraged James to keep going and reach his goals, emphasizing the importance of determination and confidence in achieving success.", - "session_13_summary": "John and James caught up at 4:30 pm on 13 June, 2022. John shared his excitement about landing his dream job, starting next month. James was supportive, mentioning his passion for gaming and programming. James talked about a football simulator project he was working on, combining his love for football and self-improvement. The conversation shifted to football, with James rooting for Liverpool and John supporting Manchester City. They made a friendly bet about their teams' final standings, setting the stage for a competitive season.", - "session_14_summary": "James and John had a conversation at 5:07 pm on 16 June, 2022. James shared that he has been through ups and downs recently but feels lucky to have a support system. John mentioned starting a coding blog and shared his love for sci-fi and fantasy books. He recommended \"The Stormlight Archive\" and \"Kingkiller Chronicle\". James talked about his dog Max, who loves swimming and playing fetch. John praised James for taking breaks in nature, emphasizing the importance of self-care and support.", - "session_15_summary": "At 9:59 pm on 19 June, 2022, James shared with John how he introduced Max, Daisy, and the new pup, Ned, who are slowly bonding. John asked for a picture, complimented their cuteness, and shared his career aspirations of making a positive impact. James encouraged John's idea of volunteering and they discussed the rewarding experience. John expressed interest in joining James in volunteering, and James offered to take him. They discussed the organization and James assured John of a welcoming environment. James supported John's goal of making a difference, and they ended with mutual encouragement and determination to achieve their goals together.", - "session_16_summary": "James and John caught up at 5:13 pm on July 9, 2022. James shared his excitement about winning an online gaming tournament and his newfound interest in extreme sports. John congratulated him and mentioned feeling overwhelmed balancing personal and professional life. James revealed his upcoming trip to Toronto and Vancouver, planning to return on July 20. They discussed relaxation methods and bid farewell, with John eagerly awaiting a souvenir from James' journey.", - "session_17_summary": "John and James discussed their shared interest in chess, with John explaining his recent involvement in the game to improve his strategy skills. James offered tips for improvement and they reminisced about a childhood photo at the skate park. They also talked about pets, travel experiences, and family bonds. James shared a photo of his sister and dogs, emphasizing the joy they bring to his life. John expressed gratitude for his siblings' support, highlighting the importance of family and friends. James shared a sunset photo taken near the ocean before signing off due to fatigue. The conversation ended with mutual goodbyes.", - "session_18_summary": "John and James caught up at 1:45 pm on 6 August, 2022. John shared that he left his IT job of 3 years to pursue a more fulfilling career that aligns with his values. He expressed happiness with the decision. James praised John's bravery in making the change. John revealed his plan to organize gaming tournaments, starting with CS:GO and Fortnite. James supported John's new career endeavor and offered financial assistance if needed. James shared that his puppy had a routine medical check-up and vaccination. John admired James for his care towards his pets. The conversation ended with mutual goodbyes and well-wishes.", - "session_19_summary": "At 9:16 am on 10 August, 2022, John and James caught up after a few days. John appreciated James' encouragement to explore new game genres. John had been trying strategy and RPG games instead of shooters and was considering organizing competitions. He was engrossed in \"The Witcher 3\" due to its storytelling and character depth. James shared his beach outing with his dogs and meeting a girl named Samantha, planning to ask her out. John encouraged James to call Samantha, wishing him a great time.", - "session_20_summary": "John informed James at 3:57 pm on 21 August, 2022 that he joined an online programming group, finding it incredible to be part of a community with similar goals. He shared that he had exchanged contacts with some members after working on projects together. James praised John's involvement and inquired about potential collaborations. John confirmed a recent successful collaboration and his interest in eSports hosting. James mentioned his gaming group and recent gear upgrades. John shared his gaming setup, highlighting the escape and motivation gaming provides. James discussed a gaming marathon he hosted and asked about John's gaming plans with his siblings. John revealed a plan for a family gaming night next month, which James applauded. They concluded their conversation by agreeing to catch up soon.", - "session_21_summary": "James and John, at 9:18 pm on 26 August 2022, discussed James' dog named Ned, John helping his siblings with programming, and working on a coding project with the siblings. John shared details of the text-based adventure game they are creating. James mentioned his interest in creating a strategy game like Civilization. They made plans to meet at McGee's pub the next day, deciding to have light beers. The conversation ended with them saying goodbye.", - "session_22_summary": "Summary:\nAt 6:53 pm on 1 September 2022, James excitedly shared with John that he had completed his Unity strategy game, inspired by games like Civilization and Total War. John praised James's hard work and asked for more details about the game. James shared a screenshot and discussed the challenges he faced during development. John admired James's perseverance and mentioned teaching his siblings coding, who were already creating their own programs. The two friends expressed pride in each other, with James offering support to John during his difficult times. Their conversation highlighted the importance of friendship, support, learning, and collaboration.", - "session_23_summary": "At 9:23 pm on 4 September 2022, James and John catch up on recent events. James shared that he asked Samantha to be his girlfriend at the theater, later they visited McGee's bar discovering they both enjoy lager. John talked about teaching his parents coding and purchasing new gaming equipment involving Sennheiser headphones and a Logitech mouse. James signed up for a cooking class, sharing he made an omelette and meringue there. They planned to attend a baseball game together. John praised James for seeking self-improvement. The conversation ended with friendly goodbyes.", - "session_24_summary": "John and James caught up at 6:02 pm on 18 September, 2022. John shared his newfound interest in board games, specifically \"Dungeons of the Dragon\". James mentioned noting down tasks in a notebook to stay organized and drew inspiration for ideas from various sources, including dreams. He recalled a dream about a medieval castle with puzzles and traps. John, interested in James's dream, asked for sketches which James had made. The conversation shifted to music, with John revealing his love for electronic and rock music and his past experience playing drums. James also used to play guitar but hadn't in a while. John shared a photo of his old drum set, and they talked about their musical experiences of jamming with friends. James announced he started streaming games, with John expressing support. They ended on a positive note, with John looking forward to details of James's streaming endeavors.", - "session_25_summary": "At 8:56 pm on 20 September 2022, John and James caught up on recent events in their lives. John shared his new startup of portable smokers and a mobile game he created, while James talked about receiving positive feedback from the gaming community and his interest in John's game. John emphasized the importance of staying informed and continuously learning in game development, recommending resources like a book and a magazine to James. James appreciated the advice and encouraged John to take care of himself amidst a busy week. They expressed mutual support and admiration for each other's work.", - "session_26_summary": "John and James had a conversation at 9:20 am on 3 October, 2022. John shared his excitement about a volunteer gig at a game dev non-profit where he will be a programming mentor for game developers. James praised John's opportunity to combine his love for gaming and helping others. John expressed his enthusiasm for sharing his knowledge and motivating others. James encouraged John, and John found it fulfilling to make a difference. James mentioned getting a new video card and expressed interest in playing Cyberpunk 2077, which John recommended. The conversation ended with well wishes and plans to discuss the game later.", - "session_27_summary": "John and James spoke at 2:14 pm on 13 October, 2022. John mentioned organizing an online competition with his programmer friends, while James shared his excitement about releasing his first game inspired by Witcher 3. John congratulated James on the game and asked about his inspiration, to which James expressed fulfillment seeing players engage with his creation. James revealed his plans for future game development in various genres. John praised James' determination and expressed support for his future endeavors, to which James expressed gratitude, stating John has always had his back. They ended on a positive note, acknowledging their strong teamwork and mutual support.", - "session_28_summary": "At 7:36 pm on 21 October 2022, James and John caught up after a long time. James shared how he lost power in his apartment three days ago while playing a game. They discussed the importance of saving progress in games. John organized a programming seminar last week, which he found fulfilling. James expressed interest in trying new programming techniques from the seminar. John agreed to share resources with him. James also mentioned his mother's visit with her army friend. He showed John a photo of himself playing on their old gaming setup. They talked about childhood gaming memories and James' passion for gaming. James mentioned trying Cyberpunk 2077, and John shared advice about the game. They exchanged goodbyes, ending their conversation.", - "session_29_summary": "John called James at 12:37 am on 31 October, 2022, sharing news about organizing a gaming tournament with friends, raising funds for a children's hospital by playing Fortnite, Overwatch, and Apex Legends. James praised John's efforts, mentioning how gaming unites people for a good cause. John shared a tournament photo, and James revealed plans to move in with Samantha near McGee's bar. They discussed their love for the bar and James sought John's support. They ended the conversation wishing each other well and promising to stay in touch.", - "session_30_summary": "At 5:20 pm on 5 November 2022, James and John conversed. James shared about a road trip with family and dogs, enjoying nature. John mentioned winning a chess tournament on Tuesday and the tactics used. They discussed improving in chess with John offering resources. John recommended FIFA 23. They planned to play together after James practices.", - "session_31_summary": "At 8:57 pm on 7 November, 2022, James told John about his family road trip and visiting friends. John shared his weekend experience working with a game developer, creating an online board game. They discussed the upcoming demo release for feedback. James mentioned visiting an animal sanctuary, where he saw cute rescue dogs, resembling their love for furry pals. They discussed the joy and love dogs bring, agreeing they are amazing companions. John showed a picture of his cousin's dog Luna, and the conversation ended with James and John saying goodbye." - }, - "sample_id": "conv-47" - }, - { - "qa": [ - { - "question": "What kind of project was Jolene working on in the beginning of January 2023?", - "answer": "electricity engineering project", - "evidence": [ - "D1:2" - ], - "category": 2 - }, - { - "question": "Which of Deborah`s family and friends have passed away?", - "answer": "mother, father, her friend Karlie", - "evidence": [ - "D1:5", - "D2:1", - "D6:4" - ], - "category": 1 - }, - { - "question": "When did Deborah`s mother pass away?", - "answer": "a few years before 2023", - "evidence": [ - "D1:5" - ], - "category": 2 - }, - { - "question": "When did Jolene`s mother pass away?", - "answer": "in 2022", - "evidence": [ - "D1:6" - ], - "category": 2 - }, - { - "question": "When did Jolene's mom gift her a pendant?", - "answer": "in 2010", - "evidence": [ - "D1:8" - ], - "category": 2 - }, - { - "question": "In what country did Jolene's mother buy her the pendant?", - "answer": "In France", - "evidence": [ - "D1:8" - ], - "category": 3 - }, - { - "question": "What symbolic gifts do Deborah and Jolene have from their mothers?", - "answer": "pendants", - "evidence": [ - "D1:8", - "D1:9" - ], - "category": 1 - }, - { - "question": "Which country were Jolene and her mother visiting in 2010?", - "answer": "France", - "evidence": [ - "D1:8" - ], - "category": 2 - }, - { - "question": "What helped Deborah find peace when grieving deaths of her loved ones?", - "answer": "yoga, old photos, the roses and dahlias in a flower garden, nature", - "evidence": [ - "D1:15", - "D2:3", - "D6:4", - "D15:29" - ], - "category": 1 - }, - { - "question": "When did Deborah's father pass away?", - "answer": "January 25, 2023", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "When was Deborah's parents' wedding?", - "answer": "in 1993", - "evidence": [ - "D2:3" - ], - "category": 2 - }, - { - "question": "Is Deborah married?", - "answer": "yes", - "evidence": [ - "D2:5", - "D19:11", - "D23:4", - "D28:11" - ], - "category": 3 - }, - { - "question": "When did Deborah receive an appreciation letter from her community?", - "answer": "January 26, 2023", - "evidence": [ - "D2:7" - ], - "category": 2 - }, - { - "question": "What places give Deborah peace?", - "answer": "sitting in a spot by the window in her Mom's house, sitting by the beach, Bali, forest trail in a nearby park", - "evidence": [ - "D2:13", - "D4:34", - "D6:10", - "D19:17" - ], - "category": 1 - }, - { - "question": "What were Deborah's mother's hobbies?", - "answer": "reading, traveling, art, cooking", - "evidence": [ - "D2:17", - "D2:19", - "D12:3", - "D29:7" - ], - "category": 1 - }, - { - "question": "What pets does Jolene have?", - "answer": "snakes", - "evidence": [ - "D2:20", - "D2:22", - "D2:24" - ], - "category": 4 - }, - { - "question": "What are the names of Jolene's snakes?", - "answer": "Susie, Seraphim", - "evidence": [ - "D2:20", - "D2:22" - ], - "category": 4 - }, - { - "question": "When did Jolene buy her pet Seraphim?", - "answer": "in 2022", - "evidence": [ - "D2:24" - ], - "category": 2 - }, - { - "question": "In what country did Jolene buy snake Seraphim?", - "answer": "In France", - "evidence": [ - "D2:24" - ], - "category": 3 - }, - { - "question": "How many times has Jolene been to France?", - "answer": "two times", - "evidence": [ - "D2:24", - "D1:8" - ], - "category": 1 - }, - { - "question": "Which games have Jolene and her partner played together?", - "answer": "Detroit, Walking Dead, Battlefield 1, It Takes Two, Overcooked 2", - "evidence": [ - "D2:26", - "D2:30", - "D20:1", - "D15:10", - "D19:10" - ], - "category": 1 - }, - { - "question": "When do Jolene and her partner plan to complete the game \"Walking Dead\"?", - "answer": "Saturday after 27 January, 2023", - "evidence": [ - "D2:30" - ], - "category": 2 - }, - { - "question": "When did Deborah meet Anna?", - "answer": "31 January, 2023", - "evidence": [ - "D3:4" - ], - "category": 2 - }, - { - "question": "Why did Jolene sometimes put off doing yoga?", - "answer": "She's more interested in playing video games", - "evidence": [ - "D3:11", - "D2:30" - ], - "category": 3 - }, - { - "question": "What new yoga poses did Deborah try?", - "answer": "Warrior II, Dancer Pose (Natarajasana), Tree pose", - "evidence": [ - "D4:14", - "D14:3", - "D14:15" - ], - "category": 1 - }, - { - "question": "What are Jolene's favorite books?", - "answer": "Sapiens, Avalanche by Neal Stephenson", - "evidence": [ - "D4:21", - "D4:23" - ], - "category": 4 - }, - { - "question": "Which book did Jolene read in January 2023?", - "answer": "Avalanche by Neal Stephenson", - "evidence": [ - "D4:23" - ], - "category": 2 - }, - { - "question": "When was Jolene in Bogota?", - "answer": "in summer 2022", - "evidence": [ - "D4:33" - ], - "category": 2 - }, - { - "question": "In what country was Jolene during summer 2022?", - "answer": "Colombia", - "evidence": [ - "D4:33" - ], - "category": 3 - }, - { - "question": "When did Jolene have a mini-retreat to reflect on her career?", - "answer": "Wednesday before 9 February, 2023", - "evidence": [ - "D5:1" - ], - "category": 2 - }, - { - "question": "When did Jolene have a dinner and drinks with her friends?", - "answer": "21 February, 2023", - "evidence": [ - "D6:1" - ], - "category": 2 - }, - { - "question": "When was the last photo of Deborah and Karlie taken?", - "answer": "in summer 2022", - "evidence": [ - "D6:8" - ], - "category": 2 - }, - { - "question": "When was Deborah in Bali?", - "answer": "in 2022", - "evidence": [ - "D6:10" - ], - "category": 2 - }, - { - "question": "How long have Jolene and her partner been together?", - "answer": "for three years", - "evidence": [ - "D7:7" - ], - "category": 4 - }, - { - "question": "Which year did Jolene and her partner start dating?", - "answer": "2020", - "evidence": [ - "D7:7" - ], - "category": 2 - }, - { - "question": "When did Deborah go for her first morning jog in a nearby park?", - "answer": "24 February, 2023", - "evidence": [ - "D7:18" - ], - "category": 2 - }, - { - "question": "How old is Jolene?", - "answer": "likely no more than 30; since she's in school", - "evidence": [ - "D8:2", - "D13:5", - "D21:6", - "D21:8", - "D22:6", - "D22:14", - "D24:2", - "D24:14", - "D25:5", - "D26:6" - ], - "category": 3 - }, - { - "question": "When did Jolene take Seraphim to the park?", - "answer": "Sunday before 2 March, 2023", - "evidence": [ - "D8:8" - ], - "category": 2 - }, - { - "question": "When did Deborah start the yoga class in the neighborhood?", - "answer": "Friday before 13 March, 2023", - "evidence": [ - "D9:5" - ], - "category": 2 - }, - { - "question": "What time management techniques do Deborah and Jolene use?", - "answer": "the Pomodoro Technique - 25 minutes work and 5-minute break, scheduler or to-do list, The Eisenhower Matrix, bullet journal", - "evidence": [ - "D10:4", - "D10:5", - "D10:6", - "D10:13", - "D18:3" - ], - "category": 1 - }, - { - "question": "Does Deborah live close to the beach or the mountains?", - "answer": "beach", - "evidence": [ - "D10:17" - ], - "category": 3 - }, - { - "question": "What ways do Deborah and Jolene use to enhance their yoga practice?", - "answer": "candles, music, essential oils", - "evidence": [ - "D11:4", - "D11:7", - "D28:16", - "D28:18" - ], - "category": 1 - }, - { - "question": "What music pieces does Deborah listen to during her yoga practice?", - "answer": "Savana, Sleep", - "evidence": [ - "D11:8", - "D11:10" - ], - "category": 4 - }, - { - "question": "When did Deborah go for a bicycle ride with Anna?", - "answer": "first week of April, 2023", - "evidence": [ - "D12:1" - ], - "category": 2 - }, - { - "question": "When did Deborah go to an art show with Anna?", - "answer": "on 9 April, 2023", - "evidence": [ - "D12:1" - ], - "category": 2 - }, - { - "question": "When did Jolene finish her robotics project?", - "answer": "May 2023", - "evidence": [ - "D13:1" - ], - "category": 2 - }, - { - "question": "How long did Jolene work on the robotics project given to her by her Professor?", - "answer": "four months", - "evidence": [ - "D3:1", - "D12:10", - "D13:1" - ], - "category": 2 - }, - { - "question": "When did Jolene do yoga at Talkeetna?", - "answer": "on 5 June, 2023", - "evidence": [ - "D13:15" - ], - "category": 2 - }, - { - "question": "Which US state did Jolene visit during her internship?", - "answer": "Alaska", - "evidence": [ - "D13:15" - ], - "category": 3 - }, - { - "question": "How long has Jolene been doing yoga and meditation?", - "answer": "about 3 years", - "evidence": [ - "D13:17" - ], - "category": 4 - }, - { - "question": "Which year did Jolene start practicing yoga?", - "answer": "2020", - "evidence": [ - "D13:17" - ], - "category": 2 - }, - { - "question": "When did Jolene buy a new aquarium for Seraphim?", - "answer": "24 June, 2023", - "evidence": [ - "D14:4" - ], - "category": 2 - }, - { - "question": "When did Jolene lose a lot of progress in her work?", - "answer": "last week of July 2023", - "evidence": [ - "D16:2" - ], - "category": 2 - }, - { - "question": "When did Jolene adopt her snake Susie?", - "answer": "in 2021", - "evidence": [ - "D16:6", - "D28:26" - ], - "category": 2 - }, - { - "question": "Which pet did Jolene adopt first - Susie or Seraphim?", - "answer": "Susie", - "evidence": [ - "D2:24", - "D2:28", - "D16:6" - ], - "category": 2 - }, - { - "question": "Which pet did Jolene adopt more recently - Susie or Seraphim?", - "answer": "Seraphim", - "evidence": [ - "D2:24", - "D2:28", - "D16:6" - ], - "category": 2 - }, - { - "question": "When did Deborah lead a meditation session during the sunset?", - "answer": "week before 16 August, 2023", - "evidence": [ - "D18:8" - ], - "category": 2 - }, - { - "question": "When did Jolene gift her partner a new console?", - "answer": "17 August, 2023", - "evidence": [ - "D19:2" - ], - "category": 2 - }, - { - "question": "What games does Jolene recommend for Deborah?", - "answer": "Zelda BOTW for Switch , Animal Crossing: New Horizons, Overcooked 2", - "evidence": [ - "D19:8", - "D19:10" - ], - "category": 4 - }, - { - "question": "What do Deborah and her husband do together?", - "answer": "play detective games together, spend time outdoors and explore nature", - "evidence": [ - "D19:13", - "D19:15" - ], - "category": 4 - }, - { - "question": "When did Deborah go to a yoga retreat near her mom's place?", - "answer": "a week before 24 August,2023", - "evidence": [ - "D21:1" - ], - "category": 2 - }, - { - "question": "What projects is Jolene planning for next year?", - "answer": "developing renewable energy finding ways to supply clean water to those with limited access", - "evidence": [ - "D22:10", - "D22:12" - ], - "category": 4 - }, - { - "question": "Where did Deborah get her cats?", - "answer": "Luna is from the shelter and Max is her mother's cat", - "evidence": [ - "D22:23", - "D22:25" - ], - "category": 4 - }, - { - "question": "How old are Deborah's cats?", - "answer": "Max is 8 years old and Luna is 5 years old", - "evidence": [ - "D22:27", - "D22:29" - ], - "category": 4 - }, - { - "question": "Does Deborah like cats?", - "answer": "Yes", - "evidence": [ - "D22:27", - "D15:25" - ], - "category": 4 - }, - { - "question": "Which country was Jolene located in during the last week of August 2023?", - "answer": "Brazil", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "When did Jolene and her partner return home from Rio de Janeiro?", - "answer": "29 August, 2023", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "What was Jolene doing with her partner in Rio de Janeiro?", - "answer": "they went on excursions, checked out some cool yoga classes, visited a lot of delicious cafes, visited an old temple", - "evidence": [ - "D23:15", - "D23:1", - "D23:3", - "D23:17" - ], - "category": 4 - }, - { - "question": "When did Deborah visit Brazil?", - "answer": "2020", - "evidence": [ - "D23:18" - ], - "category": 2 - }, - { - "question": "Have Deborah and Jolene been to Rio de Janeiro?", - "answer": "yes", - "evidence": [ - "D23:1", - "D23:3", - "D23:18" - ], - "category": 4 - }, - { - "question": "Is the friend who wrote Deborah the motivational quote no longer alive?", - "answer": "likely yes", - "evidence": [ - "D23:22" - ], - "category": 3 - }, - { - "question": "When did Deborah go to a community meetup?", - "answer": "last week of August 2023", - "evidence": [ - "D24:1" - ], - "category": 2 - }, - { - "question": "When did Jolene's parents give her first console?", - "answer": "when she was 10", - "evidence": [ - "D24:6" - ], - "category": 4 - }, - { - "question": "Did Jolene teach herself how to play the console?", - "answer": "yes", - "evidence": [ - "D2:28", - "D24:8" - ], - "category": 1 - }, - { - "question": "What do Deborah and Jolene plan to try when they meet in a new cafe?", - "answer": "coffee and fresh pastries", - "evidence": [ - "D26:10", - "D26:12" - ], - "category": 4 - }, - { - "question": "What card game is Deborah talking about?", - "answer": "Exploding Kittens", - "evidence": [ - "D27:12" - ], - "category": 3 - }, - { - "question": "When did Jolene and her partner try scuba diving lessons?", - "answer": "Friday before 17 September, 2023", - "evidence": [ - "D29:4" - ], - "category": 2 - }, - { - "question": "Where did Jolene and her partner find a cool diving spot?", - "answer": "Phuket", - "evidence": [ - "D27:1", - "D29:4" - ], - "category": 1 - }, - { - "question": "Where did Jolene and her partner spend most of September 2023?", - "answer": "Phuket", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "Has Deborah tried surfing?", - "answer": "yes", - "evidence": [ - "D28:11", - "D29:25" - ], - "category": 1 - }, - { - "question": "Has Jolene tried surfing?", - "answer": "no", - "evidence": [ - "D10:20", - "D29:26", - "D29:30" - ], - "category": 1 - }, - { - "question": "When did the Deboran and Jolene agree to go surfing?", - "answer": "in October 2023", - "evidence": [ - "D29:34" - ], - "category": 2 - }, - { - "question": "Which locations does Deborah practice her yoga at?", - "answer": "at her mother's old home, park, yoga studio, beach", - "evidence": [ - "D2:11", - "D2:13", - "D3:6", - "D4:12", - "D6:10" - ], - "category": 1 - }, - { - "question": "What kind of professional activities does Jolene participate in to gain more experience in her field?", - "answer": "present work at virtual conference, attend workshops and intern at firms", - "evidence": [ - "D21:6", - "D13:5" - ], - "category": 1 - }, - { - "question": "What kind of engineering projects has Jolene worked on?", - "answer": "electrical engineering, robotics, sustainable water purifier, productive and affordable aerial surveillance system", - "evidence": [ - "D1:2", - "D3:1", - "D4:5", - "D17:10", - "D17:12" - ], - "category": 1 - }, - { - "question": "Which community activities have Deborah and Anna participated in?", - "answer": "yoga, running", - "evidence": [ - "D4:12", - "D4:16", - "D15:1" - ], - "category": 1 - }, - { - "question": "What gifts has Deborah received?", - "answer": "an appreciate letter from her community, a flower bouqet from her friend, a motivational quote from a friend", - "evidence": [ - "D2:7", - "D2:9", - "D4:26", - "D23:20", - "D23:22" - ], - "category": 1 - }, - { - "question": "Which countries has Deborah traveled to?", - "answer": "Thailand, Brazil", - "evidence": [ - "D6:10", - "D23:18" - ], - "category": 1 - }, - { - "question": "What activities does Deborah pursue besides practicing and teaching yoga?", - "answer": "biking, going to art shows, running, organizing workshops to practice mindfulness and self-care, surfing, gardening", - "evidence": [ - "D12:1", - "D15:1", - "D15:11", - "D28:11", - "D29:1" - ], - "category": 1 - }, - { - "question": "What are the names of Jolene's snakes?", - "answer": "Susie, Seraphim", - "evidence": [ - "D2:20", - "D2:22" - ], - "category": 4 - }, - { - "question": "What are Jolene's favorite books?", - "answer": "Sapiens, Avalanche by Neal Stephenson", - "evidence": [ - "D4:21", - "D4:23" - ], - "category": 4 - }, - { - "question": "What music pieces does Deborah listen to during her yoga practice?", - "answer": "Savana, Sleep", - "evidence": [ - "D11:8", - "D11:10" - ], - "category": 4 - }, - { - "question": "What games does Jolene recommend for Deborah?", - "answer": "Zelda BOTW for Switch , Animal Crossing: New Horizons, Overcooked 2", - "evidence": [ - "D19:8", - "D19:10" - ], - "category": 4 - }, - { - "question": "What projects is Jolene planning for next year?", - "answer": "developing renewable energy finding ways to supply clean water to those with limited access", - "evidence": [ - "D22:10", - "D22:12" - ], - "category": 4 - }, - { - "question": "Where did Deborah get her cats?", - "answer": "Luna is from the shelter and Max is her mother's cat", - "evidence": [ - "D22:23", - "D22:25" - ], - "category": 4 - }, - { - "question": "How old are Deborah's cats?", - "answer": "Max is 8 years old and Luna is 5 years old", - "evidence": [ - "D22:27", - "D22:29" - ], - "category": 4 - }, - { - "question": "What was Jolene doing with her partner in Rio de Janeiro?", - "answer": "they went on excursions, checked out some cool yoga classes, visited a lot of delicious cafes, visited an old temple", - "evidence": [ - "D23:15", - "D23:1", - "D23:3", - "D23:17" - ], - "category": 4 - }, - { - "question": "Have Deborah and Jolene been to Rio de Janeiro?", - "answer": "yes", - "evidence": [ - "D23:1", - "D23:3", - "D23:18" - ], - "category": 4 - }, - { - "question": "When did Jolene's parents give her first console?", - "answer": "when she was 10", - "evidence": [ - "D24:6" - ], - "category": 4 - }, - { - "question": "What do Deborah and Jolene plan to try when they meet in a new cafe?", - "answer": "coffee and fresh pastries", - "evidence": [ - "D26:10", - "D26:12" - ], - "category": 4 - }, - { - "question": "What project did Jolene finish last week before 23 January, 2023?", - "answer": "an electrical engineering project", - "evidence": [ - "D1:2" - ], - "category": 4 - }, - { - "question": "When did Jolene buy her pet snake?", - "answer": "A year ago", - "evidence": [ - "D2:24" - ], - "category": 4 - }, - { - "question": "What project was Jolene working on as of 1 February, 2023?", - "answer": "Robotics project", - "evidence": [ - "D3:1" - ], - "category": 4 - }, - { - "question": "Where did Deborah meet her new neighbor Anna?", - "answer": "yoga in the park", - "evidence": [ - "D3:6" - ], - "category": 4 - }, - { - "question": "What activity did Jolene and her partner plan to do together instead of resuming yoga?", - "answer": "play the console", - "evidence": [ - "D3:11" - ], - "category": 4 - }, - { - "question": "What milestone did Jolene achieve recently on 4 February, 2023?", - "answer": "Design and build a sustainable water purifier for a rural community", - "evidence": [ - "D4:3" - ], - "category": 4 - }, - { - "question": "What is Jolene's favorite book which she mentioned on 4 February, 2023?", - "answer": "\"Sapiens\"", - "evidence": [ - "D4:21" - ], - "category": 4 - }, - { - "question": "What does Deborah bring with her whenever she comes to reflect on her mom?", - "answer": "amulet", - "evidence": [ - "D4:36" - ], - "category": 4 - }, - { - "question": "What new outlook did Jolene gain after her mini retreat on 9 February, 2023?", - "answer": "A confidence boost", - "evidence": [ - "D5:3" - ], - "category": 4 - }, - { - "question": "What cool stuff did Jolene accomplish at the retreat on 9 February, 2023?", - "answer": "Came up with neat solutions for her engineering project", - "evidence": [ - "D5:5" - ], - "category": 4 - }, - { - "question": "What idea did Jolene have to help underprivileged kids learn about STEM subjects on 9 February, 2023?", - "answer": "A volunteer program where engineers teach STEM to underprivileged kids", - "evidence": [ - "D5:7" - ], - "category": 4 - }, - { - "question": "How does Jolene plan to involve local engineers in her idea of teaching STEM to underprivileged kids?", - "answer": "As guest speakers for workshops", - "evidence": [ - "D5:9" - ], - "category": 4 - }, - { - "question": "What gave Deborah peace in the garden she visited?", - "answer": "Roses and dahlias", - "evidence": [ - "D6:4" - ], - "category": 4 - }, - { - "question": "Why did Deborah spend time in the garden?", - "answer": "to find comfort after losing a friend", - "evidence": [ - "D6:4" - ], - "category": 4 - }, - { - "question": "How did Jolene and her partner initially meet?", - "answer": "In an engineering class in college", - "evidence": [ - "D7:9" - ], - "category": 4 - }, - { - "question": "What activity does Deborah incorporate into her daily routine after going for a morning jog in the park?", - "answer": "spending time with loved ones", - "evidence": [ - "D7:18" - ], - "category": 4 - }, - { - "question": "According to Jolene, what does exercise help her to feel?", - "answer": "connected to her body", - "evidence": [ - "D7:20" - ], - "category": 4 - }, - { - "question": "What did Deb share a photo of, which brought a smile to Jolene's face?", - "answer": "a yellow coffee cup with a handwritten message", - "evidence": [ - "D8:22" - ], - "category": 4 - }, - { - "question": "What is one of Jolene's favorite dishes?", - "answer": "lasagna", - "evidence": [ - "D8:2" - ], - "category": 4 - }, - { - "question": "What picture did Jolene share related to feeling overwhelmed?", - "answer": "a photo of a desk with a notebook and a computer monitor", - "evidence": [ - "D8:16" - ], - "category": 4 - }, - { - "question": "What did Jolene and Deb discuss as a helpful strategy for studying and time management?", - "answer": "breaking tasks into smaller pieces and setting goals, using planners or schedulers", - "evidence": [ - "D8:19" - ], - "category": 4 - }, - { - "question": "What did Jolene ask Deb to help with on 13 March, 2023?", - "answer": "time management", - "evidence": [ - "D9:14" - ], - "category": 4 - }, - { - "question": "What method does Deb suggest Jolene to try for organizing tasks based on importance and urgency?", - "answer": "The Eisenhower Matrix", - "evidence": [ - "D10:13" - ], - "category": 4 - }, - { - "question": "What did Jolene and Anna discuss while watching the sunset by the sea?", - "answer": "They realized they inspire each other", - "evidence": [ - "D10:17" - ], - "category": 4 - }, - { - "question": "How does Jolene plan to pursue her dream of learning to surf?", - "answer": "gathering information, watching videos, getting a beginners' guide", - "evidence": [ - "D10:20" - ], - "category": 4 - }, - { - "question": "What did Deborah buy to enhance her yoga practice besides the props?", - "answer": "candle", - "evidence": [ - "D11:4" - ], - "category": 4 - }, - { - "question": "What type of music does Deborah find helpful during her yoga practice?", - "answer": "instrumental tracks with mellow melodies and rhythms", - "evidence": [ - "D11:8" - ], - "category": 4 - }, - { - "question": "Who are the musicians mentioned by Jolene that she enjoys listening to during her yoga practice?", - "answer": "Nils Frahm and Olafur Arnalds", - "evidence": [ - "D11:9" - ], - "category": 4 - }, - { - "question": "What album does Deborah recommend for meditation and deep relaxation?", - "answer": "'Sleep'", - "evidence": [ - "D11:10" - ], - "category": 4 - }, - { - "question": "Which show did Deborah go to with a friend on 9 April, 2023?", - "answer": "an art show", - "evidence": [ - "D12:1" - ], - "category": 4 - }, - { - "question": "What does Deborah find comforting about going to art shows?", - "answer": "It makes her feel like she's still experiencing it with her mom", - "evidence": [ - "D12:3" - ], - "category": 4 - }, - { - "question": "How does Jolene describe the time spent with her snakes and partner?", - "answer": "Valuable and relaxing", - "evidence": [ - "D12:6" - ], - "category": 4 - }, - { - "question": "What does Jolene enjoy doing with her partner after a long day?", - "answer": "Playing video games", - "evidence": [ - "D12:6" - ], - "category": 4 - }, - { - "question": "What is Jolene currently doing in June 2023?", - "answer": "interning at a well-known engineering firm", - "evidence": [ - "D13:5" - ], - "category": 4 - }, - { - "question": "For how long has Jolene had Seraphim as a pet?", - "answer": "one year", - "evidence": [ - "D14:6" - ], - "category": 4 - }, - { - "question": "How does Jolene feel when spending time with Seraphim?", - "answer": "comforted", - "evidence": [ - "D14:6" - ], - "category": 4 - }, - { - "question": "Which new yoga pose did Deborah share a photo of?", - "answer": "tree pose", - "evidence": [ - "D14:15" - ], - "category": 4 - }, - { - "question": "What group activity did Deborah start with Anna?", - "answer": "running group", - "evidence": [ - "D15:1" - ], - "category": 4 - }, - { - "question": "What made being part of the running group easy for Deborah to stay motivated?", - "answer": "helping and pushing each other during runs", - "evidence": [ - "D15:3" - ], - "category": 4 - }, - { - "question": "Why did Jolene decide to get a snake as a pet?", - "answer": "fascinated by reptiles and it felt like the perfect pet", - "evidence": [ - "D15:18" - ], - "category": 4 - }, - { - "question": "What is the favorite game Jolene plays with her partner?", - "answer": "It takes two", - "evidence": [ - "D15:10" - ], - "category": 4 - }, - { - "question": "What activity does Deborah do with her cats?", - "answer": "take them out for a run in the park every morning and evening", - "evidence": [ - "D15:27" - ], - "category": 4 - }, - { - "question": "How does Jolene describe the feeling of finding her snake snuggled under the bed after it got out?", - "answer": "It really showed how much she loves her.", - "evidence": [ - "D15:20" - ], - "category": 4 - }, - { - "question": "Why does Deborah take her cats out for a run in the park every day?", - "answer": "Exercise and nature are important to her", - "evidence": [ - "D15:27" - ], - "category": 4 - }, - { - "question": "How did Jolene come to have her pet, Susie?", - "answer": "She adopted her two years ago when feeling lonely.", - "evidence": [ - "D16:6" - ], - "category": 4 - }, - { - "question": "What activities have been helping Jolene stay distracted during tough times?", - "answer": "Video games and spending time with her pet, Susie", - "evidence": [ - "D16:4" - ], - "category": 4 - }, - { - "question": "What kind of yoga routine does Deborah recommend to Jolene?", - "answer": "A gentle flow routine focused on breathing and grounding", - "evidence": [ - "D16:15" - ], - "category": 4 - }, - { - "question": "What did Jolene design inspired by their love for space and engines?", - "answer": "Notebooks", - "evidence": [ - "D17:6" - ], - "category": 4 - }, - { - "question": "What journal has Jolene been using to help track tasks and stay organized?", - "answer": "bullet journal", - "evidence": [ - "D18:3" - ], - "category": 4 - }, - { - "question": "What game did Jolene recommend for being calming and cute?", - "answer": "Animal Crossing: New Horizons", - "evidence": [ - "D19:8" - ], - "category": 4 - }, - { - "question": "What game did Jolene suggest as an awesome open-world game for the Nintendo Switch?", - "answer": "Zelda BOTW", - "evidence": [ - "D19:8" - ], - "category": 4 - }, - { - "question": "What did Deborah and her husband use to play to bond and make memories?", - "answer": "video games", - "evidence": [ - "D19:11" - ], - "category": 4 - }, - { - "question": "What is special about the bench at the park near Deborah's house?", - "answer": "It holds special memories of conversations with her mom", - "evidence": [ - "D19:18" - ], - "category": 4 - }, - { - "question": "What did Deborah and her mom chat about at their special bench in the park?", - "answer": "dreams and life", - "evidence": [ - "D19:19" - ], - "category": 4 - }, - { - "question": "What feeling does Deborah get when she thinks about the time spent with her mom at their special spot?", - "answer": "peace and gratitude", - "evidence": [ - "D19:21" - ], - "category": 4 - }, - { - "question": "What habits does Jolene practice to feel balanced?", - "answer": "yoga, meditation, walks, and mindfulness", - "evidence": [ - "D20:12" - ], - "category": 4 - }, - { - "question": "Which yoga pose is Jolene a fan of for rest and calmness?", - "answer": "savasana (the corpse pose)", - "evidence": [ - "D20:19" - ], - "category": 4 - }, - { - "question": "How long has Jolene been doing yoga?", - "answer": "3 years", - "evidence": [ - "D20:21" - ], - "category": 4 - }, - { - "question": "What did Jolene participate in recently that provided her with a rewarding experience?", - "answer": "presenting at a virtual conference", - "evidence": [ - "D21:6" - ], - "category": 4 - }, - { - "question": "How did Jolene feel after receiving positive feedback at the virtual conference?", - "answer": "thrilled and rewarded", - "evidence": [ - "D21:8" - ], - "category": 4 - }, - { - "question": "What kind of event did Jolene present at recently?", - "answer": "virtual conference", - "evidence": [ - "D21:6" - ], - "category": 4 - }, - { - "question": "What did Jolene's mom stress the value of, which she wants to keep in mind for her engineering projects?", - "answer": "Helping others", - "evidence": [ - "D22:6" - ], - "category": 4 - }, - { - "question": "What type of projects is Jolene interested in getting involved in the future?", - "answer": "Sustainable initiatives and developing innovative solutions for environmental issues", - "evidence": [ - "D22:8" - ], - "category": 4 - }, - { - "question": "How did Deborah get Luna, one of her cats?", - "answer": "From the shelter", - "evidence": [ - "D22:25" - ], - "category": 4 - }, - { - "question": "How old is Max?", - "answer": "8 years old", - "evidence": [ - "D22:27" - ], - "category": 4 - }, - { - "question": "What type of classes did Jolene and her partner check out during their trip to Rio de Janeiro on 30 August, 2023?", - "answer": "Yoga classes", - "evidence": [ - "D23:1" - ], - "category": 4 - }, - { - "question": "What type of place does Jolene visit to meditate?", - "answer": "A tranquil spot by a pond", - "evidence": [ - "D23:9" - ], - "category": 4 - }, - { - "question": "What was the new plant Jolene got used as a reminder for on 30 August, 2023?", - "answer": "To nurture herself and embrace fresh starts", - "evidence": [ - "D23:29" - ], - "category": 4 - }, - { - "question": "Why did Jolene get the new plant on 30 August, 2023?", - "answer": "As a reminder to nurture herself and embrace fresh starts", - "evidence": [ - "D23:29" - ], - "category": 4 - }, - { - "question": "What has Jolene been focusing on lately besides studying?", - "answer": "relationship with her partner", - "evidence": [ - "D24:2" - ], - "category": 4 - }, - { - "question": "How did Deborah's mom support her yoga practice when she first started?", - "answer": "attended classes with her", - "evidence": [ - "D24:5" - ], - "category": 4 - }, - { - "question": "What was the video game console that Jolene's parents got her at age 10?", - "answer": "nintendo game console", - "evidence": [ - "D24:6" - ], - "category": 4 - }, - { - "question": "What was one of Jolene's favorite games to play with her mom on the nintendo wii game system?", - "answer": "Monster Hunter: World", - "evidence": [ - "D24:10" - ], - "category": 4 - }, - { - "question": "What course did Jolene sign up for on 6 September 2023?", - "answer": "meditation", - "evidence": [ - "D25:1" - ], - "category": 4 - }, - { - "question": "Why did Jolene have to reschedule their meeting with Deborah on September 8, 2023?", - "answer": "Jolene already had plans", - "evidence": [ - "D26:15" - ], - "category": 4 - }, - { - "question": "Where did Jolene and her partner travel for a few weeks in September 2023?", - "answer": "Phuket", - "evidence": [ - "D27:1" - ], - "category": 4 - }, - { - "question": "What was the main focus of the session that stood out to Jolene during the retreat?", - "answer": "releasing expectations and judgments and savoring the present", - "evidence": [ - "D27:5" - ], - "category": 4 - }, - { - "question": "How did Jolene feel about her progress in practicing mindfulness and gratitude?", - "answer": "experiencing a new level of joy and happiness", - "evidence": [ - "D27:9" - ], - "category": 4 - }, - { - "question": "What positive change did Jolene experience during the retreat?", - "answer": "finding inner peace", - "evidence": [ - "D27:1" - ], - "category": 4 - }, - { - "question": "What did Jolene recently play that she described to Deb?", - "answer": "a card game about cats", - "evidence": [ - "D27:12" - ], - "category": 4 - }, - { - "question": "What did Deborah do with their mom's old friends?", - "answer": "reminisced and looked through photos", - "evidence": [ - "D28:7" - ], - "category": 4 - }, - { - "question": "Where did Deborah get married?", - "answer": "on the beach", - "evidence": [ - "D28:11" - ], - "category": 4 - }, - { - "question": "What does yoga on the beach provide for Deborah?", - "answer": "a peaceful atmosphere", - "evidence": [ - "D28:15" - ], - "category": 4 - }, - { - "question": "How does Jolene describe their home room?", - "answer": "little haven for peace and rest", - "evidence": [ - "D28:22" - ], - "category": 4 - }, - { - "question": "What new activity did Deborah and her neighbor organize for the community on 16 September, 2023?", - "answer": "Free gardening class", - "evidence": [ - "D29:1" - ], - "category": 4 - }, - { - "question": "What was Deborah's mom passionate about?", - "answer": "Cooking", - "evidence": [ - "D29:7" - ], - "category": 4 - }, - { - "question": "What food did Deborah's mom make for her on birthdays?", - "answer": "Pineapple cakes", - "evidence": [ - "D29:9" - ], - "category": 4 - }, - { - "question": "What kind of cookies did Jolene used to bake with someone close to her?", - "answer": "Chocolate chip cookies", - "evidence": [ - "D29:12" - ], - "category": 4 - }, - { - "question": "What outdoor activity did Jolene suggest doing together with Deborah?", - "answer": "Surfing", - "evidence": [ - "D29:27" - ], - "category": 4 - }, - { - "question": "What activity did Deborah enjoy at the music festival with their pals on September 20, 2023?", - "answer": "Dancing and bopping around", - "evidence": [ - "D30:1" - ], - "category": 4 - }, - { - "question": "What did Deborah find freeing at the music festival?", - "answer": "Dancing and bopping around", - "evidence": [ - "D30:1" - ], - "category": 4 - }, - { - "question": "What are the names of Deborah's snakes?", - "evidence": [ - "D2:20", - "D2:22" - ], - "category": 5, - "adversarial_answer": "Susie, Seraphim" - }, - { - "question": "What are Deborah's favorite books?", - "evidence": [ - "D4:21", - "D4:23" - ], - "category": 5, - "adversarial_answer": "Sapiens, Avalanche by Neal Stephenson" - }, - { - "question": "Where did Deborah get her dogs?", - "evidence": [ - "D22:23", - "D22:25" - ], - "category": 5, - "adversarial_answer": "Luna is from the shelter and Max is her mother's cat" - }, - { - "question": "How old are Jolene's cats?", - "evidence": [ - "D22:27", - "D22:29" - ], - "category": 5, - "adversarial_answer": "Max is 8 years old and Luna is 5 years old" - }, - { - "question": "When did Deborah's parents give her first console?", - "evidence": [ - "D24:6" - ], - "category": 5, - "adversarial_answer": "when she was 10" - }, - { - "question": "When did Jolene release her pet snake?", - "evidence": [ - "D2:24" - ], - "category": 5, - "adversarial_answer": "A year ago" - }, - { - "question": "Where did Jolene meet her new friend Anna?", - "evidence": [ - "D3:6" - ], - "category": 5, - "adversarial_answer": "yoga in the park" - }, - { - "question": "What is Deborah's favorite book which she mentioned on 4 February, 2023?", - "evidence": [ - "D4:21" - ], - "category": 5, - "adversarial_answer": "\"Sapiens\"" - }, - { - "question": "What cool stuff did Deborah accomplish at the retreat on 9 February, 2023?", - "evidence": [ - "D5:5" - ], - "category": 5, - "adversarial_answer": "Came up with neat solutions for her engineering project" - }, - { - "question": "How does Deborah plan to involve local engineers in her idea of teaching STEM to underprivileged kids?", - "evidence": [ - "D5:9" - ], - "category": 5, - "adversarial_answer": "As guest speakers for workshops" - }, - { - "question": "What gave Deborah anxiety in the garden she visited?", - "evidence": [ - "D6:4" - ], - "category": 5, - "adversarial_answer": "Roses and dahlias" - }, - { - "question": "Why did Jolene spend time in the garden?", - "evidence": [ - "D6:4" - ], - "category": 5, - "adversarial_answer": "to find comfort after losing a friend" - }, - { - "question": "How did Jolene and her rival initially meet?", - "evidence": [ - "D7:9" - ], - "category": 5, - "adversarial_answer": "In an engineering class in college" - }, - { - "question": "What activity does Jolene incorporate into her daily routine after going for a morning jog in the park?", - "evidence": [ - "D7:18" - ], - "category": 5, - "adversarial_answer": "spending time with loved ones" - }, - { - "question": "What method does Jolene suggest Deborah to try for organizing tasks based on importance and urgency?", - "evidence": [ - "D10:13" - ], - "category": 5, - "adversarial_answer": "The Eisenhower Matrix" - }, - { - "question": "How does Jolene plan to pursue her dream of climbing mountains?", - "evidence": [ - "D10:20" - ], - "category": 5, - "adversarial_answer": "gathering information, watching videos, getting a beginners' guide" - }, - { - "question": "Who are the authors mentioned by Jolene that she enjoys reading during her yoga practice?", - "evidence": [ - "D11:9" - ], - "category": 5, - "adversarial_answer": "Nils Frahm and Olafur Arnalds" - }, - { - "question": "Which show did Jolene go to with a friend on 9 April, 2023?", - "evidence": [ - "D12:1" - ], - "category": 5, - "adversarial_answer": "an art show" - }, - { - "question": "What does Deborah find comforting about going to horror movie screenings?", - "evidence": [ - "D12:3" - ], - "category": 5, - "adversarial_answer": "It makes her feel like she's still experiencing it with her mom" - }, - { - "question": "How does Deborah describe the time spent with her snakes and partner?", - "evidence": [ - "D12:6" - ], - "category": 5, - "adversarial_answer": "Valuable and relaxing" - }, - { - "question": "For how long has Jolene had Lucifer as a pet?", - "evidence": [ - "D14:6" - ], - "category": 5, - "adversarial_answer": "one year" - }, - { - "question": "How does Deborah feel when spending time with Seraphim?", - "evidence": [ - "D14:6" - ], - "category": 5, - "adversarial_answer": "comforted" - }, - { - "question": "What made being part of the running group easy for Jolene to stay motivated?", - "evidence": [ - "D15:3" - ], - "category": 5, - "adversarial_answer": "helping and pushing each other during runs" - }, - { - "question": "Why did Jolene decide to get a tarantula as a pet?", - "evidence": [ - "D15:18" - ], - "category": 5, - "adversarial_answer": "fascinated by reptiles and it felt like the perfect pet" - }, - { - "question": "How did Deborah come to have her pet, Susie?", - "evidence": [ - "D16:6" - ], - "category": 5, - "adversarial_answer": "She adopted her two years ago when feeling lonely." - }, - { - "question": "What did Deborah design inspired by their love for space and engines?", - "evidence": [ - "D17:6" - ], - "category": 5, - "adversarial_answer": "Notebooks" - }, - { - "question": "What journal has Deborah been using to help track tasks and stay organized?", - "evidence": [ - "D18:3" - ], - "category": 5, - "adversarial_answer": "bullet journal" - }, - { - "question": "What game did Jolene recommend to Deborah for being thrilling and intense?", - "evidence": [ - "D19:8" - ], - "category": 5, - "adversarial_answer": "Animal Crossing: New Horizons" - }, - { - "question": "What game did Deborah suggest as an awesome open-world game for the Nintendo Switch?", - "evidence": [ - "D19:8" - ], - "category": 5, - "adversarial_answer": "Zelda BOTW" - }, - { - "question": "What is special about the bench at the park near Jolene's house?", - "evidence": [ - "D19:18" - ], - "category": 5, - "adversarial_answer": "It holds special memories of conversations with her mom" - }, - { - "question": "What did Jolene and her mom chat about at their special bench in the park?", - "evidence": [ - "D19:19" - ], - "category": 5, - "adversarial_answer": "dreams and life" - }, - { - "question": "How did Deborah feel after receiving positive feedback at the virtual conference?", - "evidence": [ - "D21:8" - ], - "category": 5, - "adversarial_answer": "thrilled and rewarded" - }, - { - "question": "What kind of event did Deborah present at recently?", - "evidence": [ - "D21:6" - ], - "category": 5, - "adversarial_answer": "virtual conference" - }, - { - "question": "What did Deborah's mom stress the value of, which she wants to keep in mind for her engineering projects?", - "evidence": [ - "D22:6" - ], - "category": 5, - "adversarial_answer": "Helping others" - }, - { - "question": "What type of projects is Deborah interested in getting involved in the future?", - "evidence": [ - "D22:8" - ], - "category": 5, - "adversarial_answer": "Sustainable initiatives and developing innovative solutions for environmental issues" - }, - { - "question": "How did Jolene get Luna, one of her cats?", - "evidence": [ - "D22:25" - ], - "category": 5, - "adversarial_answer": "From the shelter" - }, - { - "question": "What type of classes did Deborah and her partner check out during their trip to Rio de Janeiro on 30 August, 2023?", - "evidence": [ - "D23:1" - ], - "category": 5, - "adversarial_answer": "Yoga classes" - }, - { - "question": "Why did Deborah get the new plant on 30 August, 2023?", - "evidence": [ - "D23:29" - ], - "category": 5, - "adversarial_answer": "As a reminder to nurture herself and embrace fresh starts" - }, - { - "question": "How did Jolene's mom support her yoga practice when she first started?", - "evidence": [ - "D24:5" - ], - "category": 5, - "adversarial_answer": "attended classes with her" - }, - { - "question": "What was the video game console that Deborah's parents got her at age 10?", - "evidence": [ - "D24:6" - ], - "category": 5, - "adversarial_answer": "nintendo game console" - }, - { - "question": "What was one of Deborah's favorite games to play with her mom on the PlayStation game system?", - "evidence": [ - "D24:10" - ], - "category": 5, - "adversarial_answer": "Monster Hunter: World" - }, - { - "question": "Where did Deborah and her partner travel for a few weeks in September 2023?", - "evidence": [ - "D27:1" - ], - "category": 5, - "adversarial_answer": "Phuket" - }, - { - "question": "What did Jolene do with their mom's old friends?", - "evidence": [ - "D28:7" - ], - "category": 5, - "adversarial_answer": "reminisced and looked through photos" - }, - { - "question": "Where did Jolene get married?", - "evidence": [ - "D28:11" - ], - "category": 5, - "adversarial_answer": "on the beach" - }, - { - "question": "What new activity did Jolene and her neighbor organize for the community on 16 September, 2023?", - "evidence": [ - "D29:1" - ], - "category": 5, - "adversarial_answer": "Free gardening class" - }, - { - "question": "What food did Jolene's mom make for her on holidays?", - "evidence": [ - "D29:9" - ], - "category": 5, - "adversarial_answer": "Pineapple cakes" - }, - { - "question": "What kind of cookies did Deborah used to bake with someone close to her?", - "evidence": [ - "D29:12" - ], - "category": 5, - "adversarial_answer": "Chocolate chip cookies" - }, - { - "question": "What activity did Jolene enjoy at the music festival with their pals on September 20, 2023?", - "evidence": [ - "D30:1" - ], - "category": 5, - "adversarial_answer": "Dancing and bopping around" - } - ], - "conversation": { - "speaker_a": "Deborah", - "speaker_b": "Jolene", - "session_1_date_time": "4:06 pm on 23 January, 2023", - "session_1": [ - { - "speaker": "Deborah", - "dia_id": "D1:1", - "text": "Hey Jolene, nice to meet you! How's your week going? Anything fun happened?" - }, - { - "speaker": "Jolene", - "dia_id": "D1:2", - "text": "Hi Deb! Good to meet you! Yeah, my week's been busy. I finished an electrical engineering project last week - took a lot of work, but it's done now. Anything fun happening for you?" - }, - { - "speaker": "Deborah", - "dia_id": "D1:3", - "text": "Congrats! Last week I visited a place that holds a lot of memories for me. It was my mother`s old house." - }, - { - "speaker": "Jolene", - "dia_id": "D1:4", - "text": "Why does it hold such special memories for you?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://jubileewriter.files.wordpress.com/2021/05/20171019_174608.jpg" - ], - "blip_caption": "a photo of a woman in a wheelchair hugging a woman in a wheelchair", - "query": "mother old home emotions memories", - "dia_id": "D1:5", - "text": "It was full of memories, she passed away a few years ago. This is our last photo together." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://www.therootboard.com/cdn/shop/articles/IMG_5420.jpg" - ], - "blip_caption": "a photo of a room with a bench and a window", - "query": "serene yoga studio windows natural light", - "dia_id": "D1:6", - "text": "Sorry about your loss, Deb. My mother also passed away last year. This is my room in her house, I also have many memories there. Is there anything special about it you remember?" - }, - { - "speaker": "Deborah", - "dia_id": "D1:7", - "text": "My mom's house had a special bench near the window. She loved to sit there every morning and take in the view. I come to sit here sometimes, it helps me stay connected to her." - }, - { - "speaker": "Jolene", - "img_url": [ - "http://eternalkeepsake.com/cdn/shop/products/IMG_0841.jpg" - ], - "blip_caption": "a photo of a heart shaped pendant with a bird on it", - "query": "meaningful necklace heart pendant", - "dia_id": "D1:8", - "text": "Staying connected is super important. Do you have something to remember her by? This pendant reminds me of my mother, she gave it to me in 2010 in Paris." - }, - { - "speaker": "Deborah", - "dia_id": "D1:9", - "text": "Yes, I also have a pendant that reminds me of my mother. And what is special for you about your jewelry?" - }, - { - "speaker": "Jolene", - "dia_id": "D1:10", - "text": "It has a special symbol on it that represents freedom for me. It's a nice reminder to go for my goals and not get held back." - }, - { - "speaker": "Deborah", - "dia_id": "D1:11", - "text": "It should really give you strength and energy!" - }, - { - "speaker": "Jolene", - "dia_id": "D1:12", - "text": "Do you have goals?" - }, - { - "speaker": "Deborah", - "dia_id": "D1:13", - "text": "One of my goals is to keep teaching yoga and supporting my community. I'm passionate about helping people find peace and joy through it." - }, - { - "speaker": "Jolene", - "dia_id": "D1:14", - "text": "What inspired you to go down this route?" - }, - { - "speaker": "Deborah", - "dia_id": "D1:15", - "text": "Yoga helped me find peace during a rough time, and now I'm passionate about sharing that with others." - }, - { - "speaker": "Jolene", - "dia_id": "D1:16", - "text": "It is truly inspiring!" - }, - { - "speaker": "Deborah", - "dia_id": "D1:17", - "text": "Gotta run, bye!" - }, - { - "speaker": "Jolene", - "dia_id": "D1:18", - "text": "Looking forward to the next chat!" - } - ], - "session_2_date_time": "9:49 am on 27 January, 2023", - "session_2": [ - { - "speaker": "Deborah", - "blip_caption": "a photo of a woman hugging a woman who is sitting on a couch", - "dia_id": "D2:1", - "text": "Hey Jolene, sorry to tell you this but my dad passed away two days ago. It's been really tough on us all - his sudden death left us all kinda shell-shocked. I'm trying to channel my grief by spending more time with family and cherishing the memories. These moments remind me to live life fully." - }, - { - "speaker": "Jolene", - "dia_id": "D2:2", - "text": "Sorry to hear about your dad, Deborah. Losing a parent is tough - how's it going for you and your family?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/dt61gwlzp3e31.jpg" - ], - "blip_caption": "a photo of a bride and groom posing for a picture", - "query": "parents wedding day", - "dia_id": "D2:3", - "text": "Even though it's hard, it's comforting to look back on the great memories. We looked at the family album. Photos give me peace during difficult times. This is my parents' wedding in 1993." - }, - { - "speaker": "Jolene", - "dia_id": "D2:4", - "text": "They were a beautiful couple!" - }, - { - "speaker": "Deborah", - "dia_id": "D2:5", - "text": "My husband and I are trying to be as good a family as my parents were!" - }, - { - "speaker": "Jolene", - "dia_id": "D2:6", - "text": "What do you value in your relationship?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/lr823iakg38b1.jpg" - ], - "blip_caption": "a photo of a note written to someone on a piece of paper", - "query": "handwritten letter heartfelt message journey together", - "dia_id": "D2:7", - "text": "It is love, and openness that have kept us close all these years. Being there for each other has made us both happy. Look what letter I received yesterday!" - }, - { - "speaker": "Jolene", - "dia_id": "D2:8", - "text": "What touching words! Who is this letter from?" - }, - { - "speaker": "Deborah", - "dia_id": "D2:9", - "text": "The group members sent this to me! They thanked me for the positive influence I had on them. Those moments remind me why I'm so passionate about yoga." - }, - { - "speaker": "Jolene", - "dia_id": "D2:10", - "text": "Where do you most often do yoga?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/1s7ojxx0yra61.jpg" - ], - "blip_caption": "a photo of a living room with a television and a window", - "query": "peaceful yoga studio mats laid out", - "dia_id": "D2:11", - "text": "This is one of the places where I do it." - }, - { - "speaker": "Jolene", - "dia_id": "D2:12", - "text": "Where is it?" - }, - { - "speaker": "Deborah", - "dia_id": "D2:13", - "text": "That's my old home. I go there now and then for my mom, who passed away. Sitting in that spot by the window gives me peace." - }, - { - "speaker": "Jolene", - "dia_id": "D2:14", - "text": "Must be great to have that place where you feel connected to her." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1658863926/at/house%20tours/2022-07/India/StairwayBench.jpg" - ], - "blip_caption": "a photo of a window seat in a room with a window", - "query": "bench window mom old house", - "dia_id": "D2:15", - "text": "Yeah, it's special. I can feel her presence when I sit there and it comforts me." - }, - { - "speaker": "Jolene", - "dia_id": "D2:16", - "text": "Wow, it sounds like that spot holds a lot of sentimental value. Does it bring back any special memories?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a view of the sky from an airplane window", - "dia_id": "D2:17", - "text": "Yeah, Jolene. She'd sit there every night with a book and a smile, reading was one of her hobbies. It was one of her favorite places in the house. " - }, - { - "speaker": "Jolene", - "dia_id": "D2:18", - "text": "What other hobbies did your mother have?" - }, - { - "speaker": "Deborah", - "dia_id": "D2:19", - "text": "Travel was also her great passion!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/gvb5983gs2z71.jpg" - ], - "blip_caption": "a photo of a bed with a snake head sticking out of it", - "query": "snake curled up cozy corner room", - "dia_id": "D2:20", - "text": "I want to show you one of my snakes! They always calm me down and make me happy. This is Susie." - }, - { - "speaker": "Deborah", - "dia_id": "D2:21", - "text": "Having a pet totally brightens up your life. It's great that it brings you comfort. Do you have any fun moments with your pet that you'd like to share?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/8aunav15kyq01.jpg" - ], - "blip_caption": "a photo of a snake sticking its head out of a blanket", - "query": "pet snake couch", - "dia_id": "D2:22", - "text": " I was playing video games and my pet just slinked out of her cage and coiled up next to me - it was too funny! My second snake Seraphim did it. Look at her sly eyes!" - }, - { - "speaker": "Deborah", - "dia_id": "D2:23", - "text": "Awww, that's so nice! " - }, - { - "speaker": "Jolene", - "dia_id": "D2:24", - "text": "I bought it a year ago in Paris." - }, - { - "speaker": "Deborah", - "dia_id": "D2:25", - "text": "Cool, Jolene! Pets bring so much happiness!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/0f9ubarmem291.jpg" - ], - "blip_caption": "a photo of a person laying in bed with a dog watching tv", - "query": "gaming with partner", - "dia_id": "D2:26", - "text": "They are very unusual pets! Here's me and my partner gaming last week - it's so fun. We played the game \"Detroit\" on the console. We are both crazy about this activity!" - }, - { - "speaker": "Deborah", - "dia_id": "D2:27", - "text": "Did your boyfriend teach you to play?" - }, - { - "speaker": "Jolene", - "dia_id": "D2:28", - "text": "Even as a child I learned to play on my own." - }, - { - "speaker": "Deborah", - "dia_id": "D2:29", - "text": "Do you only play old games or try new ones?" - }, - { - "speaker": "Jolene", - "dia_id": "D2:30", - "text": "We are planning to play \"Walking Dead\" next Saturday." - }, - { - "speaker": "Deborah", - "dia_id": "D2:31", - "text": "Take care and keep spreading those good vibes!" - }, - { - "speaker": "Jolene", - "dia_id": "D2:32", - "text": "Thanks, Deb! You too, take care. See ya!" - } - ], - "session_3_date_time": "7:03 pm on 1 February, 2023", - "session_3": [ - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/nx6fg5910bb61.jpg" - ], - "blip_caption": "a photo of a table with a robot on it and a laptop", - "query": "robotics project", - "dia_id": "D3:1", - "text": "Hi Deb! How're you? I've been busy. My engineering professor gave us a huge robotics project. It's tough but fun, it's making me get creative and problem-solve." - }, - { - "speaker": "Deborah", - "dia_id": "D3:2", - "text": "Hey Jolene! It's great to hear from you. It sounds challenging but interesting! It must be really putting your creative and problem-solving skills to the test. How did you feel when you first received the project? Are you enjoying working on it?" - }, - { - "speaker": "Jolene", - "dia_id": "D3:3", - "text": "When I got it, I felt a mix of emotions - excited and nervous. But now, I'm really enjoying it! It's like trying to solve a puzzle, figuring out the best design and programming. Seeing the robot come together is awesome too!" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a yellow sign with a picture of a family", - "dia_id": "D3:4", - "text": "That's awesome, Jolene! You're enjoying the process. It must be really satisfying to see it come together. Keep up the good work! Oh, by the way, I met my new neighbor Anna yesterday!" - }, - { - "speaker": "Jolene", - "dia_id": "D3:5", - "text": "How did you two meet?" - }, - { - "speaker": "Deborah", - "dia_id": "D3:6", - "text": "It happened at yoga in the park." - }, - { - "speaker": "Jolene", - "dia_id": "D3:7", - "text": "Wow, that's awesome! It's great connecting with people who have similar interests. Did you two talk about it?" - }, - { - "speaker": "Deborah", - "dia_id": "D3:8", - "text": "Yeah, we talked about how it has improved our lives and the sense of community it gives." - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a purse with a plant on a table", - "dia_id": "D3:9", - "text": "Sounds great!" - }, - { - "speaker": "Deborah", - "dia_id": "D3:10", - "text": "Have you ever thought about resuming yoga?" - }, - { - "speaker": "Jolene", - "dia_id": "D3:11", - "text": "Well... we planned to play the console with my partner." - }, - { - "speaker": "Deborah", - "dia_id": "D3:12", - "text": "It's also good that you have something to do together." - }, - { - "speaker": "Jolene", - "dia_id": "D3:13", - "text": "Thanks for the kind words!" - }, - { - "speaker": "Deborah", - "dia_id": "D3:14", - "text": "Gotta run bye!" - }, - { - "speaker": "Jolene", - "dia_id": "D3:15", - "text": "See you soon!" - } - ], - "session_4_date_time": "9:48 am on 4 February, 2023", - "session_4": [ - { - "speaker": "Jolene", - "dia_id": "D4:1", - "text": "Hey Deborah! Good to hear from you. How've you been? I've been on an emotional rollercoaster lately, but I'm coping." - }, - { - "speaker": "Deborah", - "dia_id": "D4:2", - "text": "Hey Jolene! Good to hear from you. All good here - how about you? Anything new happening lately?" - }, - { - "speaker": "Jolene", - "dia_id": "D4:3", - "text": "I had a major milestone last week and it went really well - I'm so relieved and proud. It was a huge accomplishment for me as an engineer." - }, - { - "speaker": "Deborah", - "dia_id": "D4:4", - "text": "That's awesome. You must have worked really hard for that. I'm so proud of you. Care to share more about it?" - }, - { - "speaker": "Jolene", - "dia_id": "D4:5", - "text": "Thanks so much! I had to plan and research a lot to design and build a sustainable water purifier for a rural community in need. It was tough, but I loved the experience." - }, - { - "speaker": "Deborah", - "dia_id": "D4:6", - "text": "Your engineering skills really made a difference for people in a rural area. That's amazing! How did it feel when you saw it working?" - }, - { - "speaker": "Jolene", - "dia_id": "D4:7", - "text": "It was such a surreal moment. Seeing it working and providing clean water to the community was incredibly satisfying. It reminded me of how engineering can make a difference in people's lives. It made me feel like I had a purpose and had done something good." - }, - { - "speaker": "Deborah", - "dia_id": "D4:8", - "text": "So, what are your career aspirations now?" - }, - { - "speaker": "Jolene", - "dia_id": "D4:9", - "text": " I want to keep working in engineering and continue to make a positive impact on communities in need by creating sustainable solutions. My goal is to contribute towards making the world a better place with my work." - }, - { - "speaker": "Deborah", - "dia_id": "D4:10", - "text": " I'm sure great things will keep coming your way. Keep up the good work and follow your passions!" - }, - { - "speaker": "Jolene", - "dia_id": "D4:11", - "text": "Your support means a lot. I'm determined to keep going and make a difference. Your encouragement really motivates me to pursue my passions." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/64343f0505054d3701b51289/ead98f94-3604-496b-8a00-9f796aafb2d7/Reve_December22_CarleyRuddPhotography%2865of86%29.jpg" - ], - "blip_caption": "a photo of two women in a dance studio doing a dance pose", - "query": "yoga studio balance reconnect", - "dia_id": "D4:12", - "text": "When things get tough, just take a deep breath and remember why you're doing this. This is where I spend a lot of my time, teaching yoga. It's a great way to find balance and reconnect with ourselves. I bonded with Anna during yesterday's l yoga class." - }, - { - "speaker": "Jolene", - "dia_id": "D4:13", - "text": "Wow, that's awesome! What new poses did you try?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a woman doing a yoga pose on a chair", - "dia_id": "D4:14", - "text": "We were trying out different dance poses, like Warrior II, which builds strength and boosts focus." - }, - { - "speaker": "Jolene", - "dia_id": "D4:15", - "text": "Is it difficult to do?" - }, - { - "speaker": "Deborah", - "dia_id": "D4:16", - "text": "Yes, but this brought us closer to Anna! We supported each other, that means a lot." - }, - { - "speaker": "Jolene", - "dia_id": "D4:17", - "text": "Can you explain how to do it?" - }, - { - "speaker": "Deborah", - "dia_id": "D4:18", - "text": "Sure! To do this modified pose, sit on the edge of a chair with your feet planted. Twist your torso to one side and use your hand on your knee for support. You'll feel a stretch in your back and shoulders as you hold it for a few breaths, then switch sides. It's great for relaxing tense muscles." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/bxxmms712w0b1.jpg" - ], - "blip_caption": "a photo of a book shelf with a lot of books on it", - "query": "bookshelf engineering books projects", - "dia_id": "D4:19", - "text": "Sounds really hard! Here's my bookshelf!" - }, - { - "speaker": "Deborah", - "dia_id": "D4:20", - "text": "That's quite a collection! Have you had a favorite book lately? I'd love to hear your thoughts." - }, - { - "speaker": "Jolene", - "dia_id": "D4:21", - "text": "Thanks Deborah! I'm really into this book called \"Sapiens\" - it's a fascinating look at human history and how technology has affected us. It's giving me a lot to think about!" - }, - { - "speaker": "Deborah", - "dia_id": "D4:22", - "text": "Great, this is interesting! Have you come across any recent ones that really struck you?" - }, - { - "speaker": "Jolene", - "dia_id": "D4:23", - "text": "Two weeks ago I read \"Avalanche\" by Neal Stephenson in one sitting! " - }, - { - "speaker": "Deborah", - "dia_id": "D4:24", - "text": "That sounds cool, Jolene. Stories can be so powerful - they can teach us, motivate us, and bring us together. " - }, - { - "speaker": "Jolene", - "dia_id": "D4:25", - "text": "I also read a recent story about someone who became successful despite facing numerous challenges. It really showed me that anything is possible if you believe and work hard." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://motherofwildflowerhouse.com/cdn/shop/products/AfterlightImage3.jpg" - ], - "blip_caption": "a photo of a bouquet of flowers in a vase on a table", - "query": "mother bouquet flowers wedding", - "dia_id": "D4:26", - "text": "That's a really powerful message. It reminds me of someone special and their grace and strength when they faced hardships. Check out this creation!" - }, - { - "speaker": "Jolene", - "dia_id": "D4:27", - "text": "Wow, Deb! That's beautiful! Can you tell me the backstory of that bouquet?" - }, - { - "speaker": "Deborah", - "dia_id": "D4:28", - "text": "My friend gave me this bouquet when I was struggling, and it gives me hope and courage. I'm filled with warmth and appreciation when I look at it." - }, - { - "speaker": "Jolene", - "dia_id": "D4:29", - "text": "That's really sweet. Such a small thing can make a real difference." - }, - { - "speaker": "Deborah", - "dia_id": "D4:30", - "text": "It's amazing how something as simple as flowers can make a real difference. Nature sure is beautiful." - }, - { - "speaker": "Jolene", - "dia_id": "D4:31", - "text": "Where are some of your favorite nature spots?" - }, - { - "speaker": "Deborah", - "dia_id": "D4:32", - "text": "Oh, there's so many great places! My favorite is a park with a forest trail - it's so calming getting lost in nature. Then there's this beach nearby that I love - the sound of the waves and the sand under my feet make me feel all peaceful and happy. Do you have any favorite spots, Jolene?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://images.pexels.com/photos/8509256/pexels-photo-8509256.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-maribel-rosete-8509256.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "serene beach sunset", - "dia_id": "D4:33", - "text": "Here's a picture I took on vacation last summer in Bogota. It was so beautiful and calming watching the sunset over the water. It definitely made me appreciate nature's calming power." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://images.pexels.com/photos/11663179/pexels-photo-11663179.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-serhat-tu%C4%9F-11663179.jpg" - ], - "blip_caption": "a photo of a small island with a lone boat in the water", - "query": "sunrise lake peaceful", - "dia_id": "D4:34", - "text": "That sounds great, Jolene. Nature's calming for sure. Guess it helps us forget the daily craziness and find inner peace. No wonder you're a fan! I like to come to this spot by the water near my mom's old house. It's where I reflect on her life and find some peace. Being surrounded by nature helps a lot." - }, - { - "speaker": "Jolene", - "dia_id": "D4:35", - "text": "That's so touching! Do you have any little traditions that you do when you come to reflect on your mom?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://alvjewels.com/cdn/shop/products/image_2b791370-8eae-4e4d-8c8f-4fe581f9240a.jpg" - ], - "blip_caption": "a photo of a woman wearing a white shirt and a gold chain necklace", - "query": "heart-shaped necklace", - "dia_id": "D4:36", - "text": "Do you remember this amulet from her? Whenever I come here, I bring it with me. It's how I feel her love and stay close to her. Holding it brings me comfort." - }, - { - "speaker": "Jolene", - "dia_id": "D4:37", - "text": "It's amazing how something so small can have such a big impact, huh?" - }, - { - "speaker": "Deborah", - "dia_id": "D4:38", - "text": "Yeah, even small things like this can make a big difference. It's a reminder of all the love and strength we have inside, connecting us to people we've lost and comforting us." - }, - { - "speaker": "Jolene", - "dia_id": "D4:39", - "text": "It can keep them close in our hearts every day." - }, - { - "speaker": "Deborah", - "dia_id": "D4:40", - "text": "Anna also has a pendant that she wears in memory of her mother! This also brought us closer." - }, - { - "speaker": "Jolene", - "dia_id": "D4:41", - "text": "It's nice to have those reminders as a source of strength when we need it." - }, - { - "speaker": "Deborah", - "dia_id": "D4:42", - "text": "Life's tough but hang in there. Look to your sources of strength and you'll do great. Stay in touch, take care of yourself, and know I'm always here to cheer you on!" - }, - { - "speaker": "Jolene", - "dia_id": "D4:43", - "text": "Thanks, Deb! Your encouragement means a lot to me. I'll definitely stay in touch. Bye, take care and keep shining!" - }, - { - "speaker": "Deborah", - "dia_id": "D4:44", - "text": "Stay safe! Bye!" - } - ], - "session_5_date_time": "9:03 pm on 9 February, 2023", - "session_5": [ - { - "speaker": "Jolene", - "dia_id": "D5:1", - "text": "Hey Deborah! Been a few days since we last talked so I wanted to fill you in on something cool. Last Wednesday I did a mini retreat to assess where I'm at in life. It was a dope experience that totally gave me a new outlook." - }, - { - "speaker": "Deborah", - "dia_id": "D5:2", - "text": "Hey Jolene! Sounds great. Taking time to reflect can be really awesome. Did you gain any new insights from it?" - }, - { - "speaker": "Jolene", - "dia_id": "D5:3", - "text": "Yep! I achieved so much more than I imagined. It was a real confidence boost." - }, - { - "speaker": "Deborah", - "dia_id": "D5:4", - "text": "You deserve credit for stepping outside your comfort zone and believing in yourself. What cool stuff did you accomplish at the retreat?" - }, - { - "speaker": "Jolene", - "dia_id": "D5:5", - "text": "I really accomplished something with my engineering project - I came up with some neat solutions and I'm really excited about it." - }, - { - "speaker": "Deborah", - "dia_id": "D5:6", - "text": "Let's go into more detail." - }, - { - "speaker": "Jolene", - "dia_id": "D5:7", - "text": " Green tech could really make a difference in disadvantaged areas. I'd like to look into it and see how I can contribute. Hey, speaking of helping out, I had an idea: a volunteer program where engineers teach STEM to underprivileged kids. What do you think of that?" - }, - { - "speaker": "Deborah", - "dia_id": "D5:8", - "text": "That sounds great, Jolene! It's a great way to help and inspire others. They would benefit a lot from your knowledge. Have you thought of a plan yet?" - }, - { - "speaker": "Jolene", - "dia_id": "D5:9", - "text": "Haven't finished planning yet but I'm thinking of teaming up with local schools/centers to do workshops. We could even invite engineers as guest speakers to show kids their career options." - }, - { - "speaker": "Deborah", - "dia_id": "D5:10", - "text": "Having guest speakers, like them, would definitely give the kids a real-world view. Have you reached out to any schools or centers yet?" - }, - { - "speaker": "Jolene", - "dia_id": "D5:11", - "text": "No, not yet. I want to solidify the plan first. Can't wait to start reaching out, though!" - }, - { - "speaker": "Deborah", - "dia_id": "D5:12", - "text": "That makes sense. I'm excited to hear how you reach out and help those kids. Let me know how it goes!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/12/ee/9a/12ee9ab40a074f87e1c89f1c0b084de8.jpg" - ], - "blip_caption": "a photo of a notebook with a pen and a notebook with a page of notes", - "query": "engineering notebook ideas", - "dia_id": "D5:13", - "text": "I'll keep you posted! Appreciate the support! Here are my sketches in the planner." - }, - { - "speaker": "Deborah", - "dia_id": "D5:14", - "text": "Sounds like you're doing great. Let me know if you need more tips or information." - }, - { - "speaker": "Jolene", - "dia_id": "D5:15", - "text": "Thanks, Deb! If I need anything else, I'll let you know. You're awesome!" - }, - { - "speaker": "Deborah", - "dia_id": "D5:16", - "text": "You're awesome too! Take care!" - }, - { - "speaker": "Jolene", - "dia_id": "D5:17", - "text": "Stay safe!" - } - ], - "session_6_date_time": "4:12 pm on 22 February, 2023", - "session_6": [ - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/l2ug8nsuyt0b1.jpg" - ], - "blip_caption": "a photo of a plate of food and a glass of wine", - "query": "friends toast drinks", - "dia_id": "D6:1", - "text": "Hey Deborah, totally buzzing! Had a great night out last night - dinner, and drinks with my friends. So glad I got to let my hair down. You?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgtrcCxkcqe0DgHUVVqbd_YzertOqBQr0xfgZsSIgfLnWCCm8hh-gbggmBWzSnBLHfRuzRH9Z8nKkHZBaxBp3siwhJfyoUMzkM48nD0QSEHLQ-x6Mh2TpD00a_78-zVImBnIe2BQguR_B9W0FD12fOo6NceMKKfnprpIqnsVwl7ERzIBfSbB-VggxYMPdg/s4032/IMG_4386.jpg" - ], - "blip_caption": "a photo of a garden with a bunch of flowers in buckets", - "query": "bench flowers nostalgia", - "dia_id": "D6:2", - "text": "Sounds great, Jolene! I just visited this place and it was so calming. Nostalgic too." - }, - { - "speaker": "Jolene", - "dia_id": "D6:3", - "text": "Wow, those flowers are beautiful! What type are they? It looks so peaceful there." - }, - { - "speaker": "Deborah", - "dia_id": "D6:4", - "text": "The roses and dahlias bring me peace. I lost a friend last week, so I've been spending time in the garden to find some comfort." - }, - { - "speaker": "Jolene", - "dia_id": "D6:5", - "text": "Sorry to hear about your friend, Deb. Losing someone can be really tough. How are you holding up?" - }, - { - "speaker": "Deborah", - "dia_id": "D6:6", - "text": "Thanks for the kind words. It's been tough, but I'm comforted by remembering our time together. It reminds me of how special life is." - }, - { - "speaker": "Jolene", - "dia_id": "D6:7", - "text": "Memories can give us so much comfort and joy." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.pinimg.com/originals/cc/79/b1/cc79b1f0ce550924e25b629477305e48.jpg" - ], - "blip_caption": "a photo of two women are riding on a motorcycle on a dirt road", - "query": "friend hike laughing", - "dia_id": "D6:8", - "text": "Memories keep our loved ones close. This is the last photo with Karlie which was taken last summer when we hiked. It was our last one. We had such a great time! Every time I see it, I can't help but smile." - }, - { - "speaker": "Jolene", - "dia_id": "D6:9", - "text": "Wow, looks like a great trip! Where else have you traveled?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://zohrasrose.files.wordpress.com/2022/01/img_1273.jpg" - ], - "blip_caption": "a photo of a swing on a beach with a blue sky", - "query": "bali yoga beach", - "dia_id": "D6:10", - "text": "I've been blessed to travel to a few places and Bali last year was one of my favs. It was a gorgeous island that gave me peace, great for yoga." - }, - { - "speaker": "Jolene", - "dia_id": "D6:11", - "text": "Wow, that's great! Is yoga on the beach a thing? I've been wanting to try it." - }, - { - "speaker": "Deborah", - "dia_id": "D6:12", - "text": "The sound of the waves and the fresh air is wonderful!" - }, - { - "speaker": "Jolene", - "dia_id": "D6:13", - "text": "I'll definitely give it a go! It sounds peaceful. Thanks!" - }, - { - "speaker": "Deborah", - "dia_id": "D6:14", - "text": "Let me know how it goes. Enjoy it!" - }, - { - "speaker": "Jolene", - "dia_id": "D6:15", - "text": "I'll keep you posted if I decide to go there." - }, - { - "speaker": "Deborah", - "dia_id": "D6:16", - "text": "Take care!" - } - ], - "session_7_date_time": "4:50 pm on 25 February, 2023", - "session_7": [ - { - "speaker": "Jolene", - "dia_id": "D7:1", - "text": "Hi Deborah, it's been a while! Since we last talked, so much has happened. Balancing engineering school with my partner's video games is quite a feat. But I'm also setting aside time for myself, doing yoga and meditation. It helps give me calm amidst the craziness." - }, - { - "speaker": "Deborah", - "dia_id": "D7:2", - "text": "Hey Jolene! Great to hear from you. Taking a break is key. How have those practices been helping with everything?" - }, - { - "speaker": "Jolene", - "img_url": [ - "http://www.talisyoga.com/wp-content/uploads/2017/06/IMG_3532-e1498054278130.jpg" - ], - "blip_caption": "a photo of a woman sitting on a yoga mat doing a yoga pose", - "query": "partner jolene yoga practice", - "dia_id": "D7:3", - "text": "They seriously saved me. I chill out and gain perspective when I do yoga. Afterward, I'm more alert and motivated. Here is my last photo." - }, - { - "speaker": "Deborah", - "dia_id": "D7:4", - "text": "Wow, cool that yoga has been helping you out! Do they also do yoga with you? Does your partner do yoga with you?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://api.army.mil/e2/c/images/2017/12/22/502617/original.jpg" - ], - "blip_caption": "a photo of a man and a woman holding hands with rings on them", - "query": "jolene partner yoga bonding", - "dia_id": "D7:5", - "text": "He started joining me sometimes, which is nice for bonding and relaxing. It's brought us closer. This is just our random photo." - }, - { - "speaker": "Deborah", - "dia_id": "D7:6", - "text": "Aw, that's wonderful! How long have you been married?" - }, - { - "speaker": "Jolene", - "dia_id": "D7:7", - "text": "We're not married yet but we've been together for three years. We're taking it slow and loving the ride. This ring on his hand is just a decoration, not a wedding ring." - }, - { - "speaker": "Deborah", - "dia_id": "D7:8", - "text": "Sounds nice, Jolene. Enjoying the journey is the main thing. So, how did you two meet?" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a book and a calculator on a table", - "dia_id": "D7:9", - "text": "We actually met in an engineering class in college and quickly became friends. It eventually blossomed into something more and it's been great!" - }, - { - "speaker": "Deborah", - "dia_id": "D7:10", - "text": "Wow, your relationship started from a strong friendship. Do you still enjoy working on engineering projects together?" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a group of men standing in front of a building", - "dia_id": "D7:11", - "text": "Yep, teaming up for these projects feels great! We get each other's struggles and both love it." - }, - { - "speaker": "Deborah", - "dia_id": "D7:12", - "text": "Have yoga or meditation helped with any stress?" - }, - { - "speaker": "Jolene", - "dia_id": "D7:13", - "text": "Whenever I'm overwhelmed, I just take a break and do some breathing or meditate. It really helps me feel calmer and clearer, so I'm able to handle challenges more easily." - }, - { - "speaker": "Deborah", - "dia_id": "D7:14", - "text": "It's been great to see your progress since we last chatted - keep it up!" - }, - { - "speaker": "Jolene", - "dia_id": "D7:15", - "text": "Thanks, Deb! Your support really means a lot. I'm gonna keep pushing forward and continue taking time for myself." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i0.wp.com/thirdeyemom.com/wp-content/uploads/2017/08/img_2197.jpg" - ], - "blip_caption": "a photo of a person sitting on a bench looking at the sunset", - "query": "beautiful sunset mountaintop reflection", - "dia_id": "D7:16", - "text": " Always rooting for you. Remember to listen to your heart and take good care. Here's a photo I took while reflecting outdoors. It's a reminder to find peace even in chaos." - }, - { - "speaker": "Jolene", - "dia_id": "D7:17", - "text": " I'm curious, what does your daily routine look like?" - }, - { - "speaker": "Deborah", - "dia_id": "D7:18", - "text": "In the morning, I meditate, do yoga, and teach classes. And yesterday I went for a morning jog for the first time in a nearby park. I will now incorporate this into my daily routine. And in the evenings, I spend time with loved ones." - }, - { - "speaker": "Jolene", - "dia_id": "D7:19", - "text": "Why did you decide that?" - }, - { - "speaker": "Deborah", - "dia_id": "D7:20", - "text": "Exercise is key for me - it makes me feel connected to my body. " - }, - { - "speaker": "Jolene", - "dia_id": "D7:21", - "text": "This is a great healthy habit!" - }, - { - "speaker": "Deborah", - "dia_id": "D7:22", - "text": "It's like they say - \"Can't pour from an empty cup.\" Looking out for ourselves gives us the energy to help others. And don't forget, you're worth it too!" - }, - { - "speaker": "Jolene", - "dia_id": "D7:23", - "text": "Thanks, Deb. Your words really mean something to me. I'll keep remembering to prioritize self-care." - } - ], - "session_8_date_time": "7:18 pm on 2 March, 2023", - "session_8": [ - { - "speaker": "Deborah", - "dia_id": "D8:1", - "text": "Hey Jolene, Anna got me a vegan stir-fry the other day - tofu and veg with ginger and soy sauce. It was really tasty! Food is such a wonderful source of pleasure and nourishment. What dishes are comforting to you?" - }, - { - "speaker": "Jolene", - "dia_id": "D8:2", - "text": "One of my favorite dishes is lasagna! Comfort food can be a great pick-me-up. I've got a lot going on with my studies and exams." - }, - { - "speaker": "Deborah", - "dia_id": "D8:3", - "text": "Have you been able to find time for yourself lately?" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a room with a wooden floor and a window", - "dia_id": "D8:4", - "text": "I've been trying to squeeze in some me-time. Last Friday, I did yoga and meditation to relax. Did you find time for yourself too?" - }, - { - "speaker": "Deborah", - "dia_id": "D8:5", - "text": " I also did the same, it helped me reset my mind. How does it make you feel?" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a purse with a plant on a table", - "dia_id": "D8:6", - "text": "It's amazing how a few quiet moments can work wonders for the soul." - }, - { - "speaker": "Deborah", - "dia_id": "D8:7", - "text": "Have you been able to get outside lately?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/scwm4w6fknu41.jpg" - ], - "blip_caption": "a photo of a lizard laying on the ground surrounded by leaves", - "query": "snake park walk seraphina", - "dia_id": "D8:8", - "text": "I did take Seraphim to the park last Sunday. She loved it and here's a pic." - }, - { - "speaker": "Deborah", - "dia_id": "D8:9", - "text": "Looks like you guys had fun!" - }, - { - "speaker": "Jolene", - "dia_id": "D8:10", - "text": "We explored new places. People are surprised when they see a tamed snake. What do you like about being outdoors?" - }, - { - "speaker": "Deborah", - "dia_id": "D8:11", - "text": "Hmm... The birds chirping and the breeze gently blowing! It reminds me of what really matters." - }, - { - "speaker": "Jolene", - "dia_id": "D8:12", - "text": "Yep, it's like a reminder to slow down and appreciate the little things." - }, - { - "speaker": "Deborah", - "dia_id": "D8:13", - "text": "Is there anything you want to be more mindful of right now?" - }, - { - "speaker": "Jolene", - "dia_id": "D8:14", - "text": "I need to be more mindful of my stress levels and take care of my mental health. Sometimes I get too caught up in my studies and forget to prioritize self-care." - }, - { - "speaker": "Deborah", - "dia_id": "D8:15", - "text": " Life can get hectic and it's easy to forget about ourselves. " - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/s5gqwkbhuce91.jpg" - ], - "blip_caption": "a photo of a desk with a notebook and a computer monitor", - "query": "to-do list overwhelming exams deadlines", - "dia_id": "D8:16", - "text": "Exams and deadlines got me feeling overwhelmed. Just look at my to-do list! It seems never-ending... Trying my best but it's been challenging." - }, - { - "speaker": "Deborah", - "dia_id": "D8:17", - "text": "Your efforts will bear fruit, don't give up!" - }, - { - "speaker": "Jolene", - "dia_id": "D8:18", - "text": "Thanks, Deb. Any tips on studying or time management?" - }, - { - "speaker": "Deborah", - "dia_id": "D8:19", - "text": "My tip is to break it into smaller pieces and set goals for yourself. For time management, planners or schedulers help you stay organized and give you time for yourself. Let me know if you need help with a study plan!" - }, - { - "speaker": "Jolene", - "dia_id": "D8:20", - "text": "I appreciate your help with that." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://themugshotlk.com/cdn/shop/products/IMG_9782.jpg" - ], - "blip_caption": "a photo of a yellow coffee cup with a handwritten message", - "query": "mug believe in yourself", - "dia_id": "D8:21", - "text": "Take care and good luck with your exams. I'll give you a mug just like this one! It encourages." - }, - { - "speaker": "Jolene", - "dia_id": "D8:22", - "text": "Thanks, Deb! This really cheered me up. All the best with your classes. Bye!" - }, - { - "speaker": "Deborah", - "dia_id": "D8:23", - "text": "Thanks, Jolene! Glad I could bring a smile to your face. Take care and make sure to give yourself some time to relax. Bye!" - } - ], - "session_9_date_time": "11:22 am on 13 March, 2023", - "session_9": [ - { - "speaker": "Deborah", - "dia_id": "D9:1", - "text": "Hi Jolene! We haven't corresponded for a long time!" - }, - { - "speaker": "Jolene", - "dia_id": "D9:2", - "text": "Hey Deb, yeah life can get chaotic. How's it been going lately?" - }, - { - "speaker": "Deborah", - "dia_id": "D9:3", - "text": "So much has been going on lately. I started this yoga class in the neighborhood - it's such a good feeling! Now I get to share the exercise with my neighbors and watch it really transform them." - }, - { - "speaker": "Jolene", - "dia_id": "D9:4", - "text": " Congrats. How did you do this?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://bunundone.com/wp-content/uploads/2019/08/IMG_5619.jpg" - ], - "blip_caption": "a photo of a group of women posing for a picture", - "query": "neighborhood yoga class smiling faces", - "dia_id": "D9:5", - "text": "Thanks! My neighbors were interested in trying yoga, so I hosted a class for them on Friday. It was great to see everyone embrace and enjoy it. Here is our photo together." - }, - { - "speaker": "Jolene", - "dia_id": "D9:6", - "text": "That's cool! What made you want to start teaching it?" - }, - { - "speaker": "Deborah", - "dia_id": "D9:7", - "text": "I find it calming and wanted to share that with others. Giving people peace and awareness brings me so much happiness." - }, - { - "speaker": "Jolene", - "dia_id": "D9:8", - "text": "Wow, Deb! It's awesome when we can share something we love and make things better for others." - }, - { - "speaker": "Deborah", - "dia_id": "D9:9", - "text": "Teaching it is awesome because it can help others and I've made such great friends through it. It's really nice for building community connections." - }, - { - "speaker": "Jolene", - "dia_id": "D9:10", - "text": "That's really motivating. It's great to have support in tough times." - }, - { - "speaker": "Deborah", - "dia_id": "D9:11", - "text": "It's one of life's best parts, right?" - }, - { - "speaker": "Jolene", - "dia_id": "D9:12", - "text": "Yeah, having someone to rely on is key in tough times. It really makes a difference in how we handle life. Plus, there's something I wanted to tell you." - }, - { - "speaker": "Deborah", - "dia_id": "D9:13", - "text": "What's up? I'm listening. We'll figure it out." - }, - { - "speaker": "Jolene", - "dia_id": "D9:14", - "text": "I'm having a hard time dealing with my Engineering assignments. It's a lot to manage and I'm struggling to keep up. Can we still talk about time management?" - }, - { - "speaker": "Deborah", - "dia_id": "D9:15", - "text": "Sure, Jolene. Let's find a time that works for both of us." - }, - { - "speaker": "Jolene", - "dia_id": "D9:16", - "text": "Let's find a time to chat - I'll check my schedule and get back to you." - }, - { - "speaker": "Deborah", - "dia_id": "D9:17", - "text": "Take your time, Jolene. We'll work it out. Take care of yourself, OK?" - }, - { - "speaker": "Jolene", - "dia_id": "D9:18", - "text": " I'll make sure to take it. See you soon!" - }, - { - "speaker": "Deborah", - "dia_id": "D9:19", - "text": " I'm here for you if you need me. Let's catch up soon." - }, - { - "speaker": "Jolene", - "dia_id": "D9:20", - "text": "Have a great day!" - } - ], - "session_10_date_time": "5:35 pm on 22 March, 2023", - "session_10": [ - { - "speaker": "Deborah", - "dia_id": "D10:1", - "text": "Hey Jolene, it's been a while. Hope you're doing okay with all your exams and deadlines. I know it's difficult for you right now." - }, - { - "speaker": "Jolene", - "dia_id": "D10:2", - "text": "Hey Deb! Yeah, it can be tough. Trying to find time for everything is like playing catch-up - really stressful!" - }, - { - "speaker": "Deborah", - "dia_id": "D10:3", - "text": " How do you manage your time and stay organized with all the projects and deadlines?" - }, - { - "speaker": "Jolene", - "dia_id": "D10:4", - "text": "I'm using the Pomodoro Technique - 25 minutes work, 5-minute break - to avoid burnout but I'm still struggling to prioritize. Do you have any other tips on time management?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://johnaugust.com/wp-content/uploads/2020/04/front.jpg" - ], - "blip_caption": "a photo of a notepad with a list of things to do", - "query": "daily schedule template", - "dia_id": "D10:5", - "text": "I create a daily schedule or to-do list. Here's my example for today." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/3int1hmnou0a1.jpg" - ], - "blip_caption": "a photo of a notepad with notes on it on a counter", - "query": "colorful to-do list crossed off tasks", - "dia_id": "D10:6", - "text": "I tried making one but it's kinda overwhelming when it's a big stack of tasks. Here's an example from last Friday." - }, - { - "speaker": "Deborah", - "dia_id": "D10:7", - "text": "Have you tried breaking it down or prioritizing the tasks?" - }, - { - "speaker": "Jolene", - "dia_id": "D10:8", - "text": "It can often feel overwhelming and difficult to figure out where to start." - }, - { - "speaker": "Deborah", - "dia_id": "D10:9", - "text": "I get it, Jolene. When I'm overloaded, I use a certain method. It helps me figure out what's important and urgent so I'm more organized. Do you know about it?" - }, - { - "speaker": "Jolene", - "dia_id": "D10:10", - "text": "Nah, I'm not familiar with that one. What's it about?" - }, - { - "speaker": "Deborah", - "dia_id": "D10:11", - "text": "Want me to tell you about it? It helps you organize things based on how important and urgent they are." - }, - { - "speaker": "Jolene", - "dia_id": "D10:12", - "text": "Sure, tell me more about it! It sounds useful." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i0.wp.com/writeintolife.com/wp-content/uploads/2021/03/important-urgent.jpg" - ], - "blip_caption": "a photo of a pair of scissors sitting on top of a piece of paper", - "query": "eisenhower matrix", - "dia_id": "D10:13", - "text": "The Eisenhower Matrix sorts tasks into four boxes, categorizing them based on their urgency and importance. It can be really useful for organizing and prioritizing. Here's a breakdown. " - }, - { - "speaker": "Jolene", - "dia_id": "D10:14", - "text": "The visualization is helpful too. Thanks for sharing!" - }, - { - "speaker": "Deborah", - "dia_id": "D10:15", - "text": "I am glad, it was helpful. Let's give it a try and see if it helps you stay focused and less stressed. " - }, - { - "speaker": "Jolene", - "dia_id": "D10:16", - "text": "Yeah, I'll give it a go. Fingers crossed it'll help me. Thanks for the help!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://images.pexels.com/photos/8509256/pexels-photo-8509256.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-maribel-rosete-8509256.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach serene", - "dia_id": "D10:17", - "text": "Don't forget to take it easy and look after yourself. Wishing you all the best! Recently, Anna and I were sitting by the sea, watching the sunset and talking about each other. And we realized that we inspire each other. What thoughts does the sea in this photo make you think of?" - }, - { - "speaker": "Jolene", - "dia_id": "D10:18", - "text": "This gets me thinking of when I'll learn to surf. Gotta find that spare time!" - }, - { - "speaker": "Deborah", - "dia_id": "D10:19", - "text": "Surfing, huh Jolene? Chase your dreams, don't be daunted. Have you thought about the steps you can take?" - }, - { - "speaker": "Jolene", - "dia_id": "D10:20", - "text": "Definitely! I've been gathering information, watching videos, and I even got a beginners' guide to surfing. Just need to find the right time and place to get a lesson." - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a row of surfboards leaning against a palm tree", - "dia_id": "D10:21", - "text": "Way to go! Taking those first steps is key. Believe in yourself and keep going!" - }, - { - "speaker": "Jolene", - "dia_id": "D10:22", - "text": "Thanks for the support! Those got me pumped to try surfing. Gonna keep pushing myself to make it happen." - }, - { - "speaker": "Deborah", - "dia_id": "D10:23", - "text": "Keep it up, Jolene! Remember, the experience matters just as much as the end result. Step by step and have fun along the way. You can do it!" - }, - { - "speaker": "Jolene", - "dia_id": "D10:24", - "text": "Thanks for the boost!" - } - ], - "session_11_date_time": "4:03 pm on 28 March, 2023", - "session_11": [ - { - "speaker": "Jolene", - "blip_caption": "a photo of a person's hand holding a pair of scissors", - "dia_id": "D11:1", - "text": "Hey Deb, long time no talk. A lot's happened! On Friday I had a breakthrough with my engineering project. Finally found a solution to a prob that's been bugging me. Feels great to see my hard work paying off." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://lotuslandyogasf.com/wp-content/uploads/Jasmine-Class-cobra-e1674461734640.png" - ], - "blip_caption": "a photo of a group of people doing yoga in a room", - "query": "yoga class calming setting", - "dia_id": "D11:2", - "text": "You've really proven your skills. Feels great, right? Keep it up! And I bought new props for the yoga class! Here it is in action." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://caramiamay.com/cdn/shop/products/image_1e09790f-e4ab-4589-ac14-a6dbdf6f3b83.jpg" - ], - "blip_caption": "a photo of a cardboard mat with a cup of coffee on it", - "query": "yoga mat blocks", - "dia_id": "D11:3", - "text": "Oh, I also have new details for this case! Rate it!" - }, - { - "speaker": "Deborah", - "img_url": [ - "http://www.settlepetalyoga.com/cdn/shop/products/IMG_8556.jpg" - ], - "blip_caption": "a photo of a candle with a sprig of rosemary on a table", - "query": "yoga mat lavender scented candle", - "dia_id": "D11:4", - "text": "That`s cool! I also bought this candle for the atmosphere and to improve my yoga practice. How about you? When you do it, what feelings do you get?" - }, - { - "speaker": "Jolene", - "dia_id": "D11:5", - "text": "I feel relaxed during this activity. I love creating a serene space with soothing scents like lavender and rosemary. Do you have any favorite scents or rituals for when you do it?" - }, - { - "speaker": "Deborah", - "dia_id": "D11:6", - "text": "I'm also a big fan of scents like this! Candles and essential oils add warmth and calm to my yoga session. It's amazing how certain smells can transport you to a place of peace. Do you have any other ways to enhance your yoga practice?" - }, - { - "speaker": "Jolene", - "dia_id": "D11:7", - "text": "I find music helps me. Any favorite tracks?" - }, - { - "speaker": "Deborah", - "dia_id": "D11:8", - "text": "I find instrumental tracks with mellow melodies and rhythms help create a peaceful vibe. One of my favorites is a track called \"Savana.\" What songs/artists do you like listening to during your practice?" - }, - { - "speaker": "Jolene", - "dia_id": "D11:9", - "text": "I love listening to Nils Frahm and Olafur Arnalds during my practice. Their music is so calming and puts me in a different headspace. Do you have any recommendations for other similar artists or tracks I should check out?" - }, - { - "speaker": "Deborah", - "dia_id": "D11:10", - "text": "Also, I'm listening to an album called 'Sleep,' which is great for meditation and deep relaxation. Hope you find it as calming as I do!" - }, - { - "speaker": "Jolene", - "dia_id": "D11:11", - "text": "Thanks for the tips, Deborah! I'm gonna check them out. It's always good to have some new tunes for yoga!" - }, - { - "speaker": "Deborah", - "dia_id": "D11:12", - "text": " Let me know your thoughts on the albums!" - }, - { - "speaker": "Jolene", - "dia_id": "D11:13", - "text": "See you!" - }, - { - "speaker": "Deborah", - "dia_id": "D11:14", - "text": "Take care and keep up the good work!" - } - ], - "session_12_date_time": "4:30 pm on 9 April, 2023", - "session_12": [ - { - "speaker": "Deborah", - "blip_caption": "a photo of a large brown and white photo of a person", - "dia_id": "D12:1", - "text": "Hey Jolene! Great to see you! Had a blast biking nearby with my neighbor last week - was so freeing and beautiful. Checked out an art show with a friend today - really cool and inspiring stuff. Reminded me of my mom." - }, - { - "speaker": "Jolene", - "dia_id": "D12:2", - "text": "Hey Deborah! Sounds like you had a blast biking and at the art show. Your photo looks like you were really into it! Did it make you think profound thoughts?" - }, - { - "speaker": "Deborah", - "dia_id": "D12:3", - "text": "My mom was interested in art. She believed art could give out strong emotions and uniquely connect us. When I go to an art show, it's like we're still experiencing it together even though she's gone. It's hard but comforting." - }, - { - "speaker": "Jolene", - "dia_id": "D12:4", - "text": "Losing someone is hard, but finding something that helps you cope is great." - }, - { - "speaker": "Deborah", - "dia_id": "D12:5", - "text": "Finding ways to keep her memory alive gives me peace. It's amazing how something simple like artwork can bring back powerful emotions and remind us of those we've lost. It's about finding solace in the things we love, and art has done that for me." - }, - { - "speaker": "Jolene", - "dia_id": "D12:6", - "text": "Even though my snakes can't chat or understand what I'm going through, our time together is valuable and teaches me to take time and be in tune with myself. Similarly, playing video games with my partner after a long day is a great way for me to relax." - }, - { - "speaker": "Deborah", - "dia_id": "D12:7", - "text": "Simple things can indeed bring us the most happiness. How have these activities helped you during tough times?" - }, - { - "speaker": "Jolene", - "dia_id": "D12:8", - "text": "It brings us closer together!" - }, - { - "speaker": "Deborah", - "dia_id": "D12:9", - "text": "This kind of comfort can be really helpful when times get tough." - }, - { - "speaker": "Jolene", - "dia_id": "D12:10", - "text": "Just so you know, I've been working on a big project lately - it's been tough but also really cool to watch it take shape. Can't wait to see the final result!" - }, - { - "speaker": "Deborah", - "dia_id": "D12:11", - "text": "I am waiting to hear how everything turns out. Keep up the good work!" - }, - { - "speaker": "Jolene", - "dia_id": "D12:12", - "text": "Thanks Deb! Your support means a lot. I'll keep you updated on the progress of the project." - }, - { - "speaker": "Deborah", - "dia_id": "D12:13", - "text": "Glad my support means a lot to you! I'll always be here for you. " - }, - { - "speaker": "Jolene", - "dia_id": "D12:14", - "text": "Take care!" - }, - { - "speaker": "Deborah", - "dia_id": "D12:15", - "text": "Enjoy your day and make time for the things that bring you joy. See ya!" - }, - { - "speaker": "Jolene", - "dia_id": "D12:16", - "text": " I'll make sure to find time for that. Have a good one!" - } - ], - "session_13_date_time": "3:56 pm on 6 June, 2023", - "session_13": [ - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/avkvvy4fqr331.jpg" - ], - "blip_caption": "a photo of a solar powered vehicle with a solar panel on the back", - "query": "engineering project complete", - "dia_id": "D13:1", - "text": "Hey Deborah! Long time no talk - I had lots of stuff going on. Remember the tough engineering project? I finally wrapped that up last month. Look at the result!" - }, - { - "speaker": "Deborah", - "dia_id": "D13:2", - "text": "Jolene! Congrats on wrapping up your with it! You really put in the work and it paid off. " - }, - { - "speaker": "Jolene", - "dia_id": "D13:3", - "text": " I'm really proud of myself for sticking it out despite the problems and finishing it. It's definitely a big milestone." - }, - { - "speaker": "Deborah", - "dia_id": "D13:4", - "text": "Now that you've reached this big milestone, what do you have planned next?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://www.arup.com/-/media/arup/images/careers-new/early-careers/interns/americas-interns-banner-image.jpg" - ], - "blip_caption": "a photo of a group of construction workers posing for a picture", - "query": "engineering firm internship", - "dia_id": "D13:5", - "text": " I'm interning at a well-known engineering firm. It's been a great opportunity to test my skills and gain real-world experience. These are my new colleagues!" - }, - { - "speaker": "Deborah", - "dia_id": "D13:6", - "text": "What's been the best part of it so far?" - }, - { - "speaker": "Jolene", - "dia_id": "D13:7", - "text": "The best part so far has been being able to apply what I learned in school to real projects. It's so fulfilling to see my ideas come to life." - }, - { - "speaker": "Deborah", - "dia_id": "D13:8", - "text": "Mind if I ask how the internship has impacted you?" - }, - { - "speaker": "Jolene", - "dia_id": "D13:9", - "text": "It has had a positive impact on my life. It has stoked my love of engineering and has encouraged me to keep striving for my dreams." - }, - { - "speaker": "Deborah", - "dia_id": "D13:10", - "text": " How has it inspired you to keep striving for your dreams?" - }, - { - "speaker": "Jolene", - "dia_id": "D13:11", - "text": "Hanging out with people who love what they do has really inspired me to stay focused and keep working towards my goals. It's shown me that with dedication and effort, anything is possible." - }, - { - "speaker": "Deborah", - "dia_id": "D13:12", - "text": "Have you been able to find a good work-life balance during your internship?" - }, - { - "speaker": "Jolene", - "dia_id": "D13:13", - "text": "Honestly, finding that balance has been tough. I've been slogging away and it's been hard to make time for my hobbies and chilling out." - }, - { - "speaker": "Deborah", - "dia_id": "D13:14", - "text": " Have you considered taking some breaks and finding activities like yoga to help you relax and unwind? That might make a difference." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i2.wp.com/azwonders.com/wp-content/uploads/2020/12/IMG_1095.jpg" - ], - "blip_caption": "a photo of a man standing on a rock with his arms outstretched", - "query": "yoga meditation mountaintop", - "dia_id": "D13:15", - "text": "Yeah, I`m trying to do it. Here's an example of how I spent yesterday morning, yoga on top of mount Talkeetna." - }, - { - "speaker": "Deborah", - "dia_id": "D13:16", - "text": "Nice job, Jolene! How long have you been doing yoga and meditation? It looks like it's really helping you regroup and recharge." - }, - { - "speaker": "Jolene", - "dia_id": "D13:17", - "text": "I've been doing them sporadically for about 3 years now and they've had a real positive effect on me." - }, - { - "speaker": "Deborah", - "dia_id": "D13:18", - "text": "Has it benefited you in any way? Have you found it helpful in difficult moments?" - }, - { - "speaker": "Jolene", - "dia_id": "D13:19", - "text": "It has helped me with stress and kept me centered." - }, - { - "speaker": "Deborah", - "dia_id": "D13:20", - "text": "Glad they've been helpful for you!" - }, - { - "speaker": "Jolene", - "dia_id": "D13:21", - "text": "No idea how I would've survived without them!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://www.therootboard.com/cdn/shop/articles/IMG_5420.jpg" - ], - "blip_caption": "a photo of a room with a bench and a window", - "query": "yoga studio peaceful soft lighting", - "dia_id": "D13:22", - "text": "This is my favorite studio and it's always so calming. " - }, - { - "speaker": "Jolene", - "dia_id": "D13:23", - "text": " I was wondering if you have any advice on calming the mind and breathing during yoga?" - }, - { - "speaker": "Deborah", - "dia_id": "D13:24", - "text": "I'd recommend practicing mindful breathing for yoga. Set aside a few minutes each day to sit with your eyes closed, take deep breaths, and focus on how the air feels entering and leaving your body. " - }, - { - "speaker": "Jolene", - "dia_id": "D13:25", - "text": "Thanks, Deborah! I'll definitely give it a try. Appreciate the advice!" - }, - { - "speaker": "Deborah", - "dia_id": "D13:26", - "text": " If you need more tips or help, just let me know. Take care!" - }, - { - "speaker": "Jolene", - "dia_id": "D13:27", - "text": "See you!" - } - ], - "session_14_date_time": "9:17 am on 26 June, 2023", - "session_14": [ - { - "speaker": "Deborah", - "img_url": [ - "https://assets.simpleviewinc.com/simpleview/image/upload/crm/maineta/7D10D61B-FFA4-44EB-B4DF-5E6002D715F5_14D84855-064F-4EDB-86FAE0FA2AB35244_60bff6ce-b8b0-4146-b88bb1d2cef69fcb.jpg" - ], - "blip_caption": "a photo of a group of people doing yoga in a park", - "query": "yoga retreat yoga studio serene mats candles", - "dia_id": "D14:1", - "text": "Hey Jolene! How's it going? We haven't talked in a while. I've been busy getting ready for a yoga retreat with some buddies. A chance to hang out with people who think like me and find peace and understanding. Sounds awesome!" - }, - { - "speaker": "Jolene", - "dia_id": "D14:2", - "text": "Hey Deb! Been super hectic with internship and stuff. That retreat sounds awesome, I could definitely use a break!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://assets-global.website-files.com/5909c340ec86374fefcb849c/65650a0fc42a7d8dbe0e4349_IMG_2131.jpg" - ], - "blip_caption": "a photo of a woman doing a yoga pose on the beach", - "query": "yoga meditation calming sunset", - "dia_id": "D14:3", - "text": "By the way, I tried a new pose - Dancer Pose (Natarajasana). Rate, did I succeed?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/6moonjm3bypa1.jpg" - ], - "blip_caption": "a photo of a snake curled up in a plant filled area", - "query": "snake curled up pillow", - "dia_id": "D14:4", - "text": "You are amazing as always! Here are new photos of Seraphim in the new aquarium that I bought the day before yesterday." - }, - { - "speaker": "Deborah", - "dia_id": "D14:5", - "text": "Where'd you get it? I'm always drawn to animals, they bring so much joy. What's its story?" - }, - { - "speaker": "Jolene", - "dia_id": "D14:6", - "text": " I got her last year, she's a great pet. She always cheers me up and brings a sense of peace. Spending time with her is so comforting." - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a group of people doing yoga in a field", - "dia_id": "D14:7", - "text": "Pets really do make life more enjoyable and bright. " - }, - { - "speaker": "Jolene", - "dia_id": "D14:8", - "text": " I'm so thankful it's here. Plus, it's nice to have a calm creature around." - }, - { - "speaker": "Deborah", - "dia_id": "D14:9", - "text": "How have things been besides that?" - }, - { - "speaker": "Jolene", - "dia_id": "D14:10", - "text": "Things have been intense lately. I'm really pushing myself to succeed, and sometimes it feels overwhelming. But I'm determined to overcome any obstacles and achieve my goals." - }, - { - "speaker": "Deborah", - "dia_id": "D14:11", - "text": "Keep up the hard work and remember to relax too." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://ffalcons.files.wordpress.com/2021/06/stephen-paper-2.jpg" - ], - "blip_caption": "a photo of a drawing of a house with a ruler and a ruler", - "query": "engineering project design", - "dia_id": "D14:12", - "text": "Thanks, Deborah! I had a big breakthrough with this project - so exciting and rewarding!" - }, - { - "speaker": "Deborah", - "dia_id": "D14:13", - "text": "Awesome, Jolene! I'm really glad your project worked out. " - }, - { - "speaker": "Jolene", - "dia_id": "D14:14", - "text": "Stop talking about me, tell me more about your retreat." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://thehammockllc.com/wp-content/uploads/2019/07/Yoga-all-ages.jpg" - ], - "blip_caption": "a photo of three people standing in front of a large statue", - "query": "group people yoga mountain top", - "dia_id": "D14:15", - "text": "I'd rather show you a photo. This is also a new yoga pose that we tried. It is a tree pose." - }, - { - "speaker": "Jolene", - "dia_id": "D14:16", - "text": "What's that statue in the picture?" - }, - { - "speaker": "Deborah", - "dia_id": "D14:17", - "text": "It's a symbol of peace and enlightenment." - }, - { - "speaker": "Jolene", - "dia_id": "D14:18", - "text": "Wow, it looks gorgeous! I'd love to visit a retreat like that. It seems like the ideal spot to find peace and refreshment." - }, - { - "speaker": "Deborah", - "dia_id": "D14:19", - "text": "It's perfect for reflecting and getting centered." - }, - { - "speaker": "Jolene", - "dia_id": "D14:20", - "text": "I could really use some chill time like that. Sounds so peaceful." - }, - { - "speaker": "Deborah", - "dia_id": "D14:21", - "text": "Yeah, we all need some peaceful time to relax." - }, - { - "speaker": "Jolene", - "dia_id": "D14:22", - "text": "Gotta run, have a nice day!" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a sunset reflecting in a lake with a boat", - "dia_id": "D14:23", - "text": "See you!" - } - ], - "session_15_date_time": "7:37 pm on 9 July, 2023", - "session_15": [ - { - "speaker": "Deborah", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/grc-cim-1671133645.jpg" - ], - "blip_caption": "a photo of three men standing next to each other on a road", - "query": "running group sunny day", - "dia_id": "D15:1", - "text": "Hey Jolene! I started a running group with Anna - it's awesome connecting with people who care about fitness!" - }, - { - "speaker": "Jolene", - "dia_id": "D15:2", - "text": "Cool, Deb! Glad you found some people to get fit with. I'm trying to add workouts into my studying schedule, which has been tough but fun. How about you? Any challenges with the running group?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://www.milebymileblog.com/wp-content/uploads/2020/11/saturday-sunrise-run.png" - ], - "blip_caption": "a photo of a woman standing on a sidewalk with a skateboard", - "query": "running group morning stretch", - "dia_id": "D15:3", - "text": "Oh, I'm having a blast with it! We help and push each other during our runs, which makes it so much easier to stay motivated. I have a lot of my photos from this activity." - }, - { - "speaker": "Jolene", - "dia_id": "D15:4", - "text": "Deborah, that's awesome! Being part of a supportive group must be super motivating. Finding a team that's passionate about something makes a huge difference. Just thinking about my own journey too." - }, - { - "speaker": "Deborah", - "dia_id": "D15:5", - "text": "Having people who can cheer you on and give you advice really makes a difference. What has it been like for you finding supportive folks?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/30/08/ee/3008ee2c8041c333b9757d24b8246986.jpg" - ], - "blip_caption": "a photo of a man and a woman sitting in a chair in front of a computer", - "query": "couples playing video games", - "dia_id": "D15:6", - "text": "Gaming's been tough lately, but I'm grateful I have someone who's also into it. My partner helps me stay focused on our goals. We have a lot of cute photos, I want to share with you." - }, - { - "speaker": "Deborah", - "dia_id": "D15:7", - "text": " What do you like best about gaming together?" - }, - { - "speaker": "Jolene", - "dia_id": "D15:8", - "text": "We get to tackle challenges and have a shared experience. It's always a blast when we're into the same game and achieve something tough. Plus, it's a great way to bond and get closer." - }, - { - "speaker": "Deborah", - "dia_id": "D15:9", - "text": "Woah, that's cool! Gaming is so good for strengthening relationships. Do you two have a favorite game to play together?" - }, - { - "speaker": "Jolene", - "dia_id": "D15:10", - "text": "Yeah, we love playing \"It takes two\" together! It's a fun team-strategy game and it's competitive. Plus, it's a great way for us to bond. Do you have any activities you like doing with people?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://georgetowner.com/wp-content/uploads/2019/05/Photo-Aug-27-10-26-09-AM-2.jpg" - ], - "blip_caption": "a photo of a group of people doing yoga in a park", - "query": "group of people yoga park", - "dia_id": "D15:11", - "text": "Yep, I do running and yoga/meditation with others. Connecting with people and creating a community is great. Plus, I love organizing workshops and events to practice mindfulness and self-care. It's an awesome way to have fun, build relationships, and support each other's growth." - }, - { - "speaker": "Jolene", - "dia_id": "D15:12", - "text": "Sounds like a great way to relax. What do your workshops and events involve?" - }, - { - "speaker": "Deborah", - "dia_id": "D15:13", - "text": "It involves various activities such as yoga, meditation, and self-reflection. They aim to cultivate self-awareness, promote mental and emotional well-being, and help individuals find inner peace. It's a space where people can connect, explore, and grow." - }, - { - "speaker": "Jolene", - "dia_id": "D15:14", - "text": " Your events are awesome for helping people connect and learn, it is so important. How has everything been going for you?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a game board with a bunch of cards on it", - "dia_id": "D15:15", - "text": "Thanks, Jolene! It's been great seeing everyone come together and support each other. It's amazing to witness the growth and transformation that happens through these workshops. I'm honored to be a part of it." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/3096c8dr8tr71.jpg" - ], - "blip_caption": "a photo of a snake on a branch with a book", - "query": "pet snake branch", - "dia_id": "D15:16", - "text": "Wow, Deb! I can imagine how rewarding it must be to create a space for growth and change. It's great to hear that everything's going well. You can always count on me for support! I just want to share a photo with you." - }, - { - "speaker": "Deborah", - "dia_id": "D15:17", - "text": "Thanks, Jolene! Your support means a lot to me. I'm here for you too. By the way, I noticed your pet in the picture. What made you decide to get a snake?" - }, - { - "speaker": "Jolene", - "dia_id": "D15:18", - "text": " I was fascinated by reptiles, and it felt like the perfect pet for me. Taking care of it has been really calming, and it's a great way to connect with nature." - }, - { - "speaker": "Deborah", - "dia_id": "D15:19", - "text": " Glad you found something that gives you peace and calm. Do you have a favorite memory with \"it\" to share?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/n14qokr95j831.jpg" - ], - "blip_caption": "a photo of a snake in a tank with sand and plants", - "query": "snake in tank", - "dia_id": "D15:20", - "text": "I have lots of great memories, like our little 'snake adventure'. She got out and I spent hours searching, so relieved when I finally found her snuggling under the bed. It really showed how much I love her." - }, - { - "speaker": "Deborah", - "dia_id": "D15:21", - "text": "What was it like when you found her? I can imagine the relief!" - }, - { - "speaker": "Jolene", - "dia_id": "D15:22", - "text": "Seeing her snuggled under the bed made me feel so much love and gratitude. It made me realize how important she is to me." - }, - { - "speaker": "Deborah", - "dia_id": "D15:23", - "text": "They bring so much joy and remind us of what's important." - }, - { - "speaker": "Jolene", - "dia_id": "D15:24", - "text": "Animals teach us a lot about love and gratitude, and they bring so much joy. " - }, - { - "speaker": "Deborah", - "img_url": [ - "https://live.staticflickr.com/65535/49196167313_5eabd5ca56_b.jpg" - ], - "blip_caption": "a photography of two cats sitting on a couch with a blanket", - "query": "dog playing park", - "dia_id": "D15:25", - "re-download": true, - "text": "I haven't introduced you to my pets yet! I don't like dogs, that's why I have cats." - }, - { - "speaker": "Jolene", - "dia_id": "D15:26", - "text": "Looks like they're having a blast! How often do you take them out?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "dia_id": "D15:27", - "text": "Exercise and nature are really important to me, so I make sure to take them out for a run in the park every morning and evening." - }, - { - "speaker": "Jolene", - "dia_id": "D15:28", - "text": "Wow Deb, that's great! I'd love to experience that every day." - }, - { - "speaker": "Deborah", - "dia_id": "D15:29", - "text": "Nature helps me find peace every day - it's so refreshing!" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "dia_id": "D15:30", - "text": "It's a pity that my snakes don't run! I'd love to do that more often. They would motivate me and together it would be more fun." - }, - { - "speaker": "Deborah", - "dia_id": "D15:31", - "text": " It's like hitting a reset button that helps me put things into perspective and gives me time to reflect." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://images.pexels.com/photos/9214105/pexels-photo-9214105.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-dastan-khdir-9214105.jpg" - ], - "blip_caption": "a photo of a sunset over a body of water", - "query": "sunset calm lake", - "dia_id": "D15:32", - "text": "Yeah, I totally get it. Whenever I can, I love going for walks to take it all in. And I take photos like this" - }, - { - "speaker": "Deborah", - "dia_id": "D15:33", - "text": "It's amazing how nature has the power to bring us peace and clarity." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://images.pexels.com/photos/12314495/pexels-photo-12314495.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-lera-mk-12314495.jpg" - ], - "blip_caption": "a photo of a dock on a lake with trees in the background", - "query": "peaceful lake trees", - "dia_id": "D15:34", - "text": "This photo captures the peacefulness of a lake surrounded by trees." - }, - { - "speaker": "Deborah", - "dia_id": "D15:35", - "text": "Why did you choose that spot? It looks so calm." - }, - { - "speaker": "Jolene", - "dia_id": "D15:36", - "text": "It's such a hidden gem! It makes me feel so peaceful and tranquil." - }, - { - "speaker": "Deborah", - "dia_id": "D15:37", - "text": "Lucky you for having somewhere to relax and tune out!" - }, - { - "speaker": "Jolene", - "dia_id": "D15:38", - "text": "We'll definitely go there together sometime!" - }, - { - "speaker": "Deborah", - "dia_id": "D15:39", - "text": "We all need a timeout!" - } - ], - "session_16_date_time": "9:26 am on 1 August, 2023", - "session_16": [ - { - "speaker": "Deborah", - "dia_id": "D16:1", - "text": "Hey Jolene! Great news - I just started a project for a cleanup in our community and have been trying to raise funds for it. It's been amazing to see everyone come together to make a difference. How've you been? Anything new going on?" - }, - { - "speaker": "Jolene", - "dia_id": "D16:2", - "text": "Hey Debs! Congrats on your project for the community! As for me, life's been a rollercoaster lately. Last week, I had a huge setback with my project. I put in so much work and it all crashed and I lost everything. SO frustrating and depressing." - }, - { - "speaker": "Deborah", - "dia_id": "D16:3", - "text": "Jolene, sorry to hear that. It must be really tough. I'm here for you and if I can do anything, just let me know. Is there anything that's helping you cope?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/0uzy21np69b51.jpg" - ], - "blip_caption": "a photo of a man holding a snake on his arm", - "query": "pet snake curled up on arm", - "dia_id": "D16:4", - "text": "Your support means a lot. Susie really helps when times get tough. Pets have been great company. Video games have also been a nice distraction." - }, - { - "speaker": "Deborah", - "dia_id": "D16:5", - "text": "They can really provide love and comfort, especially during tough times. How did you come to have Susie?" - }, - { - "speaker": "Jolene", - "dia_id": "D16:6", - "text": "I adopted her two years ago when I was feeling lonely and wanted some company." - }, - { - "speaker": "Deborah", - "dia_id": "D16:7", - "text": "That's great, Jolene! Animals sure have a way of bringing us happiness. They understand us and provide us with comfort. Plus, having a pet teaches us responsibility. She came at the perfect time - cherish those moments with her and find strength in her presence." - }, - { - "speaker": "Jolene", - "dia_id": "D16:8", - "text": "Thanks Deborah. Having her around shows me I can stay strong and find joy in the small stuff." - }, - { - "speaker": "Deborah", - "dia_id": "D16:9", - "text": "Enjoying the little things is key. Those little moments can give us a boost and push us forward. How have you been taking care of yourself lately?" - }, - { - "speaker": "Jolene", - "dia_id": "D16:10", - "text": "I'm trying to prioritize self-care, like yoga and meditation. It helps me stay balanced and grounded." - }, - { - "speaker": "Deborah", - "dia_id": "D16:11", - "text": " If you're interested, I can suggest some routines for you to try." - }, - { - "speaker": "Jolene", - "dia_id": "D16:12", - "text": " I'm always on the lookout for new routines to mix things up." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://res.cloudinary.com/peerspace-inc/image/upload/q_80,c_crop,g_custom/w_2048/awuqrhwdbakdu4eeyll1.jpg" - ], - "blip_caption": "a photo of a room with a lot of yoga mats on the floor", - "query": "yoga studio tranquility", - "dia_id": "D16:13", - "text": "In the meantime, check out this great place for yoga." - }, - { - "speaker": "Jolene", - "dia_id": "D16:14", - "text": "This room looks perfect for it. Do you have any favorite routines you can share?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://kathsdruyoga.files.wordpress.com/2020/04/img_5644.jpg" - ], - "blip_caption": "a photo of a woman in a pink shirt doing a yoga pose", - "query": "gentle flow yoga routine breathing grounding", - "dia_id": "D16:15", - "text": "One of my favorite yoga routines is a gentle flow that's all about breathing and grounding. It helps me find my chill. I'll send you a tutorial video with the poses. This is me in the process :)" - }, - { - "speaker": "Jolene", - "dia_id": "D16:16", - "text": "Wow! Does that help you find your chill or improve your concentration?" - }, - { - "speaker": "Deborah", - "dia_id": "D16:17", - "text": "It's a great way to find balance in tough times. Try it out and let me know what you think!" - }, - { - "speaker": "Jolene", - "dia_id": "D16:18", - "text": "Can't wait to try it out. Let's chat soon!" - }, - { - "speaker": "Deborah", - "dia_id": "D16:19", - "text": "Let me know how it goes. Talk to you later!" - }, - { - "speaker": "Jolene", - "dia_id": "D16:20", - "text": "Yep, I'll practice and update you. Bye!" - } - ], - "session_17_date_time": "8:50 pm on 12 August, 2023", - "session_17": [ - { - "speaker": "Deborah", - "dia_id": "D17:1", - "text": "Since we last spoke, I made a meditation guide for my yoga retreat. How about you?" - }, - { - "speaker": "Jolene", - "dia_id": "D17:2", - "text": "I have been stressed since I lost my work files. I was so overwhelmed...but meditation kept me chill and I got my clarity back, thank goodness. Really appreciate the practice!" - }, - { - "speaker": "Deborah", - "dia_id": "D17:3", - "text": "It's amazing how it can give you peace and calm in times like that. If you ever need any support, just let me know - we're in this together!" - }, - { - "speaker": "Jolene", - "img_url": [ - "http://thegroundedpractice.com/cdn/shop/products/306F9F91-FDB8-4052-B87B-4692205658E12.jpg" - ], - "blip_caption": "a photo of two notebooks with a blue cover and a white strip", - "query": "meditation quote journal", - "dia_id": "D17:4", - "text": " Appreciate your support! BTW, I wanted to share this with you." - }, - { - "speaker": "Deborah", - "dia_id": "D17:5", - "text": "Your creativity is amazing! " - }, - { - "speaker": "Jolene", - "dia_id": "D17:6", - "text": "Thanks, Deb! I was inspired by my love for space and engines, so I designed these notebooks with elements like galaxies and circuitry. I think they turned out really cool!" - }, - { - "speaker": "Deborah", - "dia_id": "D17:7", - "text": "You have such a knack for turning these into art. They make me feel excited just by looking at them. Your creativity is amazing! Does that usually inspire your engineering projects too?" - }, - { - "speaker": "Jolene", - "dia_id": "D17:8", - "text": "Seeing art and design in various things gives me a unique perspective on problems." - }, - { - "speaker": "Deborah", - "dia_id": "D17:9", - "text": "Got any neat projects or ideas you're pumped about?" - }, - { - "speaker": "Jolene", - "dia_id": "D17:10", - "text": " Working on a cool project now - a prototype that could revolutionize aerial surveillance. Can't wait to see the results!" - }, - { - "speaker": "Deborah", - "dia_id": "D17:11", - "text": "I admire your ambition to make a difference. Do you have any specific plans or goals for this project?" - }, - { - "speaker": "Jolene", - "dia_id": "D17:12", - "text": "My aim is to devise a more productive and affordable aerial surveillance system. It'll help with responding to emergencies and monitoring the environment. It'll make the world a better, safer place." - }, - { - "speaker": "Deborah", - "dia_id": "D17:13", - "text": "I love your commitment. Keep going!" - }, - { - "speaker": "Jolene", - "dia_id": "D17:14", - "text": "Thanks, Deb! Your support means a lot. I'll keep pushing and doing my best to make a difference." - }, - { - "speaker": "Deborah", - "dia_id": "D17:15", - "text": "You got this!" - } - ], - "session_18_date_time": "2:58 pm on 16 August, 2023", - "session_18": [ - { - "speaker": "Jolene", - "blip_caption": "a photo of a book with a checklist on it", - "dia_id": "D18:1", - "text": "Been thinking a lot about my plans lately, especially after checking in with my bf. It's been up and down! Some days it feels like I'm trying to juggle my engineering, relationship, and growing as a person, and I just don't know how to handle it all. How do you manage when it's too much?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a room with a moss wall and a floor cushion", - "dia_id": "D18:2", - "text": "Finding ways to restore balance is important. Taking time for yourself and recognizing your needs can make a difference. " - }, - { - "speaker": "Jolene", - "dia_id": "D18:3", - "text": "Thanks, Deb! I've been trying out some time management strategies recently and started using a bullet journal. It's been really helpful for tracking my tasks and staying organized." - }, - { - "speaker": "Deborah", - "dia_id": "D18:4", - "text": " It's great to hear that it's been helping you out!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/a8/a1/ab/a8a1ab638060af2f6cb53ab7a5bb2b8c.jpg" - ], - "blip_caption": "a photo of a notebook with a quote on it", - "query": "bullet journal spread task list motivational quote", - "dia_id": "D18:5", - "text": " Not only does it help me stay on top of everything, but it's also so satisfying when I cross tasks off my list. Here's a pic of my newest spread with one of my favorite quotes." - }, - { - "speaker": "Deborah", - "dia_id": "D18:6", - "text": "I love this quote. So uplifting. Does it motivate you when you see it?" - }, - { - "speaker": "Jolene", - "dia_id": "D18:7", - "text": "Yeah, it's like a little reminder to stick to my goals and never give up." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://www.naplesnews.com/gcdn/-mm-/06756bca6c979c173ae23c1ba44f60a8fd2bcbee/c\\u003d0-0-3024-4032/local/-/media/2017/04/25/Naples/Naples/636287249454494595-Yoga-4.jpg" - ], - "blip_caption": "a photo of a group of people doing yoga on a beach", - "query": "sunset beach meditation", - "dia_id": "D18:8", - "text": "Gotcha! Visual reminders like this photo can be super motivating. Keep focusing on your goals, Jolene, and don't give up! I led a meditation yoga sessionto the elderly at a local care home last week during sunset. Nature can be really helpful for finding peace. Have you tried mindfulness too? " - }, - { - "speaker": "Jolene", - "dia_id": "D18:9", - "text": "Haven't tried it yet, but I'm keen to give it a shot. That sounds really peaceful. I could use some of that calm in my life right now. I'm interested in destressing and trying mindfulness. " - }, - { - "speaker": "Deborah", - "dia_id": "D18:10", - "text": "I started with workshops and books, and now mindfulness is a huge part of my life." - }, - { - "speaker": "Jolene", - "dia_id": "D18:11", - "text": " I'll definitely look into some of it. Can't wait to get started!" - }, - { - "speaker": "Deborah", - "dia_id": "D18:12", - "text": "That's great, Jolene! I'm so glad you're willing to try some mindfulness. It can be really helpful. Let me know if you need any help getting started - I'm happy to assist you with your journey!" - }, - { - "speaker": "Jolene", - "dia_id": "D18:13", - "text": " I really appreciate it." - }, - { - "speaker": "Deborah", - "dia_id": "D18:14", - "text": "We're in this together. Give me a shout if you need anything. Bye for now." - }, - { - "speaker": "Jolene", - "dia_id": "D18:15", - "text": "Thanks, I'll hit you up if I need anything. Bye for now." - } - ], - "session_19_date_time": "12:52 am on 19 August, 2023", - "session_19": [ - { - "speaker": "Deborah", - "dia_id": "D19:1", - "text": "Hey Jolene! Hope you're having a good one. Last Friday I told Anna the story of my life and they were super kind about it. It was so nice to have a meaningful connection. How's the mindfulness workshops and reading going? Need any help?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/90qd3jqxi2781.jpg" - ], - "blip_caption": "a photo of a black xbox console with a yoda yoda figure next to it", - "query": "new game console", - "dia_id": "D19:2", - "text": "Life's been hella busy since we last talked. I bought a console for my partner as a gift on the 17th and it's so much fun, he even managed to play it.! Engineering studies are still going strong too. Balance has been key for me lately. How about you? What's been up?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:3", - "text": " Well done! As for me, I've been focusing on teaching yoga and spending time with the community. Organizing a yoga event last month was really cool." - }, - { - "speaker": "Jolene", - "dia_id": "D19:4", - "text": " Was it rewarding seeing everyone come together? Can you tell me more about how you put it together? Also, any tips for maintaining a balance between hobbies and studies?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:5", - "text": " I reached out to different nearby businesses and places to make it happen. We had yoga, food stalls, and even some live music - it was amazing! As for balancing hobbies and studies, I find it helpful to prioritize and manage my time effectively. Making a schedule and setting aside specific time for studying and pursuing hobbies can go a long way in maintaining balance." - }, - { - "speaker": "Jolene", - "dia_id": "D19:6", - "text": "Wow, that's awesome! Gonna make a plan to manage my studies and hobbies. Say, do you ever play video games?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:7", - "text": "I used to play some video games, but it's been a while. It's a good way to relax after a busy day. Do you have any game suggestions? What's your favorite game?" - }, - { - "speaker": "Jolene", - "dia_id": "D19:8", - "text": "I have a few game recommendations. Zelda BOTW for Switch is an awesome open-world game. Animal Crossing: New Horizons is really calming and cute. As for my favorite game, it's hard to choose just one!" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a man and a woman sitting in a chair in front of a computer", - "dia_id": "D19:9", - "text": "Cool recs! I'll definitely check those out. What about your favorite memories of playing video games?\n" - }, - { - "speaker": "Jolene", - "dia_id": "D19:10", - "text": "Oh, I forgot to mention Overcooked 2 - this is a good co-op game if you're into hilarious and chaotic cooking. My partner and I often play for bets! I once won three large pizzas!" - }, - { - "speaker": "Deborah", - "dia_id": "D19:11", - "text": "Reminds me of when I used to play games with my husband. We'd take turns and it was a great way to bond and make memories. Gaming really can bring people closer, right?" - }, - { - "speaker": "Jolene", - "dia_id": "D19:12", - "text": "Yeah, you`re right! What's your favorite game to play with that person?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:13", - "text": "We prefer to play detective games together." - }, - { - "speaker": "Jolene", - "dia_id": "D19:14", - "text": "What other activities do you both enjoy doing together?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:15", - "text": "We also enjoyed spending time outdoors and exploring nature. It was always so refreshing to be outside and soak up the fresh air." - }, - { - "speaker": "Jolene", - "dia_id": "D19:16", - "text": "I'm a big fan of being outside too! It's so calming and refreshing. Do you have any special spots you like to go to?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:17", - "text": "I love going to this park near my house - it has a nice forest trail and a beach. It's a peaceful spot where I can do some yoga and reflect. There's also a special bench that holds special meaning to me." - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a bench in a park with a tree in the background", - "dia_id": "D19:18", - "text": "Sounds lovely! Nature can be calming. What makes this bench special to you?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a person sitting on a bench in a forest", - "dia_id": "D19:19", - "text": "It holds a lot of special memories for me and my mom - we would come here and chat about dreams and life. It's full of good moments. " - }, - { - "speaker": "Jolene", - "dia_id": "D19:20", - "text": "That's awesome, Deborah! What were some of your favorite memories with your mom at this spot? It looks super peaceful and pretty." - }, - { - "speaker": "Deborah", - "dia_id": "D19:21", - "text": "I'll always cherish my memories with her at this spot. I remember a beautiful sunset we watched together in silence - the colors in the sky were so special. Every time I go back, I feel so much peace and gratitude for the time I spent with her." - }, - { - "speaker": "Jolene", - "dia_id": "D19:22", - "text": "Places and moments like that can mean so much, and it's a gift to find peace and gratitude in them." - }, - { - "speaker": "Deborah", - "dia_id": "D19:23", - "text": " I'm really thankful for all the time we had." - }, - { - "speaker": "Jolene", - "dia_id": "D19:24", - "text": "It's so important to cherish it. " - } - ], - "session_20_date_time": "9:11 am on 21 August, 2023", - "session_20": [ - { - "speaker": "Jolene", - "dia_id": "D20:1", - "text": " Long time no talk! We were given a new game for the console last week, it is Battlefield 1. What's been up with you?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://bmoreenergy.files.wordpress.com/2023/08/img_2540.jpg" - ], - "blip_caption": "a photo of a flower cart on a sidewalk with flowers in it", - "query": "bench vibrant flowers", - "dia_id": "D20:2", - "text": "Hey Jolene! Good to hear from you. That`s cool! Been thinking about a few big moments lately - went to a place that held a lot of memories for me. Sat on a bench where we used to chat and it brought back a lot of emotions. " - }, - { - "speaker": "Jolene", - "dia_id": "D20:3", - "text": "Mostly happy or a bit of everything?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://www.inbloomhomestead.com/wp-content/uploads/2023/07/stunning.jpg" - ], - "blip_caption": "a photo of a vase of flowers on the ground in a street", - "query": "flower cart sidewalk flowers", - "dia_id": "D20:4", - "text": "It was quite a mix, Jolene. I felt nostalgia and longing, but also grateful for the memories. It's amazing how a place can mean so much. I brought these flowers there." - }, - { - "speaker": "Jolene", - "dia_id": "D20:5", - "text": "Do you think she would like it?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://images.pexels.com/photos/12473781/pexels-photo-12473781.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-pexels-user-12473781.jpg" - ], - "blip_caption": "a photo of a woman holding a bouquet of red roses", - "query": "mother holding bouquet flowers", - "dia_id": "D20:6", - "text": "Yeah, my mom really loved flowers. They always made her so happy. She appreciated the simple things in life.\n" - }, - { - "speaker": "Jolene", - "dia_id": "D20:7", - "text": "Wow, that's a great photo! How did she show you to appreciate it?" - }, - { - "speaker": "Deborah", - "dia_id": "D20:8", - "text": "By taking it slow, seeing beauty in them, and finding joy." - }, - { - "speaker": "Jolene", - "dia_id": "D20:9", - "text": "Wow Deb, that's awesome! We should definitely take time to enjoy that and not let the business of life cause us to miss out on the good stuff." - }, - { - "speaker": "Deborah", - "dia_id": "D20:10", - "text": "Yeah, Jolene. Life can be so busy that we often overlook the small things that truly matter. Let's make an effort to appreciate them more." - }, - { - "speaker": "Jolene", - "dia_id": "D20:11", - "text": "Yep Deb, slowing down and enjoying simple moments can bring a lot of balance and happiness. I'm trying to do more yoga and meditation myself to help relax and stay focused. Are there any calming habits that you practice to feel balanced?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://images.pexels.com/photos/9214105/pexels-photo-9214105.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-dastan-khdir-9214105.jpg" - ], - "blip_caption": "a photo of a sunset over a body of water", - "query": "sunset calm lake", - "dia_id": "D20:12", - "text": "Yeah, same here, Jolene! Yoga and meditation help me find balance and inner peace. Going out for walks and staying mindful also keep me grounded. I take similar photos on walks." - }, - { - "speaker": "Jolene", - "dia_id": "D20:13", - "text": "Gorgeous! Going for a walk and feeling so peaceful must be amazing." - }, - { - "speaker": "Deborah", - "dia_id": "D20:14", - "text": "Moments like that I'll always cherish." - }, - { - "speaker": "Jolene", - "dia_id": "D20:15", - "text": "That calm and peaceful feeling is so nice - it's great for recharging and thinking." - }, - { - "speaker": "Deborah", - "dia_id": "D20:16", - "text": " It's like a reboot for me." - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a green cushion on a floor in front of a window", - "dia_id": "D20:17", - "text": "Got it! It's like hitting the refresh button and coming back even better." - }, - { - "speaker": "Deborah", - "dia_id": "D20:18", - "text": "What's your favorite yoga pose for some rest?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/12yit6phiwl21.jpg" - ], - "blip_caption": "a photo of a person laying on the floor with a paper bag", - "query": "savasana yoga mat", - "dia_id": "D20:19", - "text": "I'm a fan of savasana - aka the corpse pose. It's so calming and helps me just let go and surrender." - }, - { - "speaker": "Deborah", - "dia_id": "D20:20", - "text": "Funny photo! How long have you been doing yoga?" - }, - { - "speaker": "Jolene", - "dia_id": "D20:21", - "text": "Been doing it for 3 years. It's a great way to escape studying and work stress." - }, - { - "speaker": "Deborah", - "dia_id": "D20:22", - "text": "Wow, Jolene! Taking time to unwind is key and that seems just right for you!" - }, - { - "speaker": "Jolene", - "dia_id": "D20:23", - "text": "I'm really finding my zen again!" - }, - { - "speaker": "Deborah", - "dia_id": "D20:24", - "text": "Keep it up!" - }, - { - "speaker": "Jolene", - "dia_id": "D20:25", - "text": "Thanks for your support, Deb! " - }, - { - "speaker": "Deborah", - "dia_id": "D20:26", - "text": "Good luck with everything. Stay in touch." - } - ], - "session_21_date_time": "9:34 am on 24 August, 2023", - "session_21": [ - { - "speaker": "Deborah", - "img_url": [ - "https://holmanhealthconnections.com/wp-content/uploads/2019/01/IMG_0412.jpg" - ], - "blip_caption": "a photo of a trail in a forest with moss and trees", - "query": "yoga retreat forest dusk nature", - "dia_id": "D21:1", - "text": "Hey Jolene! Good to hear from you! A lot's happened since we talked - last week I got to go to this yoga retreat near my mom's place. It was so cool - I got to hang with nature and really get to know myself. Definitely life-changing! That's how beautiful it was there!" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a man bending over on a blanket in the grass", - "dia_id": "D21:2", - "text": "Wow, Deb, sounds great! Glad you got to connect with yourself and nature. That's my own way to relax. What are your thoughts on life now?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a mountain range with a colorful sunset in the background", - "dia_id": "D21:3", - "text": "Life's been super meaningful lately. Nature and self-reflection have helped me see how beautiful every moment is. We can really grow and learn when we listen to ourselves. What's been up with you lately? Any insights or experiences?\n" - }, - { - "speaker": "Jolene", - "dia_id": "D21:4", - "text": "Life's been hectic, but I'm making strides toward my goals. It's tough, but satisfying." - }, - { - "speaker": "Deborah", - "dia_id": "D21:5", - "text": "Can you tell me a bit more about it and what you've achieved?" - }, - { - "speaker": "Jolene", - "dia_id": "D21:6", - "text": "My goal is to be successful in my field and make a positive impact. I've been studying, attending workshops, and networking to make it happen. Recently, I had the opportunity to present at a virtual conference and received positive feedback. It was a great experience and confirmed that I'm on the right track." - }, - { - "speaker": "Deborah", - "dia_id": "D21:7", - "text": "You really put your heart and soul into it. Must have been amazing having it go so well. How did it feel when people gave you positive feedback? Any ideas for what comes next?" - }, - { - "speaker": "Jolene", - "dia_id": "D21:8", - "text": "I was thrilled to receive such positive feedback! It felt so rewarding to know that my efforts were appreciated. Right now, I'm focusing on studying and gaining more experience. I'm even thinking about more internships to further enhance my skills. Exciting times! Hopefully, there will be more updates to share with you soon." - }, - { - "speaker": "Deborah", - "dia_id": "D21:9", - "text": "Wow, Jolene! Way to go! I'm super proud of all you've achieved. Let me know if you need any help. Onward!" - }, - { - "speaker": "Jolene", - "dia_id": "D21:10", - "text": "Thanks, Deb! Your support means a lot to me. " - }, - { - "speaker": "Deborah", - "dia_id": "D21:11", - "text": "No problem. You got this! " - }, - { - "speaker": "Jolene", - "dia_id": "D21:12", - "text": " I'm not giving up, just gonna keep pushing forward." - }, - { - "speaker": "Deborah", - "dia_id": "D21:13", - "text": "Reach for your goals and don't forget to enjoy the journey. " - }, - { - "speaker": "Jolene", - "dia_id": "D21:14", - "text": "Thanks, Deb. Your support means a lot. I'll keep pushing forward and remember to enjoy every step of the way." - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a field of sunflowers with a sunset in the background", - "dia_id": "D21:15", - "text": "Come on! The journey's as important as the destination. Take time for yourself and find joy in it. We believe in you!" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a city skyline at sunset with a body of water", - "dia_id": "D21:16", - "text": "Yeah, you're right! I'll take your advice and find joy on the way. Cheers for the support!" - }, - { - "speaker": "Deborah", - "dia_id": "D21:17", - "text": "Always by your side!" - } - ], - "session_22_date_time": "5:33 pm on 26 August, 2023", - "session_22": [ - { - "speaker": "Deborah", - "dia_id": "D22:1", - "text": "Hey Jolene, since we talked I've been thinking about my mom's influence. Remembering those we love is important." - }, - { - "speaker": "Jolene", - "dia_id": "D22:2", - "text": "I understand, Deb. Remembering and cherishing the memories of our loved ones is so important. It's comforting to know that their influence still guides us. Last Friday, my partner and I talked about how our loved ones have influenced us and what their values meant to us. It was an emotional chat, but it made us feel closer and showed us what really matters. " - }, - { - "speaker": "Deborah", - "dia_id": "D22:3", - "text": "Those types of conversations really help build relationships. Can you tell me more about the values they have given you?" - }, - { - "speaker": "Jolene", - "dia_id": "D22:4", - "text": "Definitely! Our loved ones have taught us to persevere and stay resilient, like my mom always said to never give up, and my partner's dad showed them to stay determined. Their values have influenced us to pursue our goals, such as me with engineering and my partner with their creative endeavors. Even though they're not here, we both feel their values encouraging us along our paths." - }, - { - "speaker": "Deborah", - "dia_id": "D22:5", - "text": "That's wonderful to hear, Jolene! It's amazing how their values continue to guide you, even in their absence. It sounds like you and your partner are honoring their memory by pursuing your respective passions. Have you ever considered incorporating those values into your work as well?" - }, - { - "speaker": "Jolene", - "dia_id": "D22:6", - "text": "Yeah, Deborah! We've been figuring out how to add these values into our projects. As an engineering student, I want to use my talents to do good and help solve important problems. I'm keen on coming up with new ideas and making things more efficient to make the world a better place. Going further, my mom stressed the value of helping others and that's something I want to keep in mind for my engineering projects." - }, - { - "speaker": "Deborah", - "dia_id": "D22:7", - "text": "When our work ties into our values, it becomes more meaningful. What goals or ideas do you have for incorporating those values into your future projects?" - }, - { - "speaker": "Jolene", - "dia_id": "D22:8", - "text": "In the future, I'm aiming to work on projects that make a real difference to communities. I'm interested in sustainable initiatives and developing innovative solutions for environmental issues. I also want to get involved with organizations that focus on social causes, using my skills to help out. It's about connecting my passion for engineering with my commitment to making a positive impact." - }, - { - "speaker": "Deborah", - "dia_id": "D22:9", - "text": "You've got a lot of amazing plans for the future. Which projects are you most interested in getting involved in?" - }, - { - "speaker": "Jolene", - "dia_id": "D22:10", - "text": "I'm keen on two projects in particular. One is focused on developing renewable energy, like solar, to help communities and reduce dependence on non-renewables. " - }, - { - "speaker": "Deborah", - "dia_id": "D22:11", - "text": "Wow Jolene, that's really inspiring!" - }, - { - "speaker": "Jolene", - "dia_id": "D22:12", - "text": "The other is finding ways to supply clean water to those with limited access. Both align with my beliefs about sustainability and assisting those in need. I still have so much to figure out before beginning, but I'm up for the challenge." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://assets-global.website-files.com/5909c340ec86374fefcb849c/65650a0fc42a7d8dbe0e4349_IMG_2131.jpg" - ], - "blip_caption": "a photo of a woman doing a yoga pose on the beach", - "query": "sunrise yoga session nature inner balance", - "dia_id": "D22:13", - "text": "Sounds great, Jolene! Research is key to success. Little steps and being up for challenges make you stronger. I'm here for you. Connecting to yourself helps tackle any issue. Here's a photo that reminds me of the beauty of nature during a yoga session." - }, - { - "speaker": "Jolene", - "dia_id": "D22:14", - "text": "It helps with challenges, giving balance and strength. Any tips for staying relaxed while studying?" - }, - { - "speaker": "Deborah", - "dia_id": "D22:15", - "text": "Taking breaks, doing some stretching/yoga, or just going for a walk is really helpful. And don't forget to get enough sleep and take time for self-care. Finding a balance between work and taking care of yourself is important. What self-care activities have you been doing lately?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://www.thriveyogaandwellness.com/wp-content/uploads/2019/06/IMG_6315-e1561295654472.jpg" - ], - "blip_caption": "a photo of a woman in a pink shirt doing a yoga pose", - "query": "yoga pose snake", - "dia_id": "D22:16", - "text": "I've been into yoga and meditation lately. It helps me recharge. Doing different poses relieves tension and calms my mind. I've already shared my newfound love for yoga with my partner, and we're planning to go on a meditation retreat together to enhance our practice together." - }, - { - "speaker": "Deborah", - "dia_id": "D22:17", - "text": "Glad to hear that yoga is helping you rest and recharge. It's great for reflection and self-care. Do your snakes also enjoy it?" - }, - { - "speaker": "Jolene", - "dia_id": "D22:18", - "text": "My snakes just like watching me chill. But she's a great company and always brings a sense of calm." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://catsandcoffee.me/wp-content/uploads/2020/01/Louis-and-Olivia-on-the-windowsill.jpg" - ], - "blip_caption": "a photo of two cats sitting on a window sill looking out", - "query": "cat sitting windowsill calming", - "dia_id": "D22:19", - "text": "Having a pet around is such a calming feeling. They sure can bring a great sense of comfort. I still have cats, Luna is sitting on the left." - }, - { - "speaker": "Jolene", - "dia_id": "D22:20", - "text": "Aww, that's adorable! What's the second one's name?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a brown leather chair and a black and white floor", - "dia_id": "D22:21", - "text": "Max! They bring lots of joy and peace to our home." - }, - { - "speaker": "Jolene", - "dia_id": "D22:22", - "text": "How did you get them?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a car with a fan and a mesh bag", - "dia_id": "D22:23", - "text": "Max is my mother's cat, I took him when my mother passed away." - }, - { - "speaker": "Jolene", - "dia_id": "D22:24", - "text": "You're great for taming him. How did you get Luna?" - }, - { - "speaker": "Deborah", - "dia_id": "D22:25", - "text": "I took Luna from the shelter." - }, - { - "speaker": "Jolene", - "dia_id": "D22:26", - "text": "It\u2019s wonderful that you have become their loving owner!" - }, - { - "speaker": "Deborah", - "dia_id": "D22:27", - "text": "Yes, I really love cats, and they also need a home, love, and care! Moreover, Max is already old, he is 8 years old." - }, - { - "speaker": "Jolene", - "dia_id": "D22:28", - "text": "How old is Luna?" - }, - { - "speaker": "Deborah", - "dia_id": "D22:29", - "text": "She is younger, she is 5 years old." - }, - { - "speaker": "Jolene", - "dia_id": "D22:30", - "text": "I am proud of your action to tame these pets!" - } - ], - "session_23_date_time": "11:46 am on 30 August, 2023", - "session_23": [ - { - "speaker": "Jolene", - "img_url": [ - "https://thegoodstuffbyajaespoo.files.wordpress.com/2017/09/img_20170723_184612_691.jpg" - ], - "blip_caption": "a photo of a woman doing a yoga pose in a mirror", - "query": "colombian yoga studio vibrant colors people yoga poses", - "dia_id": "D23:1", - "text": "Hey Deborah, how's it going? Guess what? Yesterday my partner and I got back from an awesome trip to Rio de Janeiro- we checked out some cool yoga classes." - }, - { - "speaker": "Deborah", - "dia_id": "D23:2", - "text": "That yoga pose looks great. Must've been a cool experience for the two of you. What did the trip teach you?" - }, - { - "speaker": "Jolene", - "dia_id": "D23:3", - "text": "This country was awesome! It showed me different kinds of yoga and their backgrounds, which made me appreciate it even more. We visited a lot of delicious cafes! Have you ever been somewhere that was important to you?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/03tq3y75jmy41.jpg" - ], - "blip_caption": "a photo of a man standing in front of a house", - "query": "mother childhood home house", - "dia_id": "D23:4", - "text": "Yep, last month I visited my mom`s house which holds a special place in my heart. My mom had good and bad times there, but it's still a symbol of her strength and the love she shared with me. This is my husband in front of this house." - }, - { - "speaker": "Jolene", - "dia_id": "D23:5", - "text": "What was it like?" - }, - { - "speaker": "Deborah", - "dia_id": "D23:6", - "text": "It brought back fond memories as I relaxed outside." - }, - { - "speaker": "Jolene", - "dia_id": "D23:7", - "text": "Sounds great! So glad you have a place to relax and find peace." - }, - { - "speaker": "Deborah", - "dia_id": "D23:8", - "text": "Thanks, Jolene. It's special for me. How about you? Is there a place that helps you relax?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://pandolinadventures.files.wordpress.com/2016/05/img_3720.jpg" - ], - "blip_caption": "a photo of a pond with lily pads and a tree in the background", - "query": "quiet pond surrounded trees meditation", - "dia_id": "D23:9", - "text": "I go to this nearby place to meditate by a tranquil spot." - }, - { - "speaker": "Deborah", - "dia_id": "D23:10", - "text": "Looks chill. What's been the effect of that?" - }, - { - "speaker": "Jolene", - "dia_id": "D23:11", - "text": "It helps me make sense of everything and relieves stress. It's like a restart." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/jygby0ihz7o41.jpg" - ], - "blip_caption": "a photo of a lake with a few trees in the water", - "query": "peaceful sunset lake", - "dia_id": "D23:12", - "text": "Cool, glad you found a place to chill. We all need that occasionally. This is one of my favorite spots to ponder and let things go.\n" - }, - { - "speaker": "Jolene", - "dia_id": "D23:13", - "text": "Looks great! What made you pick that spot?" - }, - { - "speaker": "Deborah", - "dia_id": "D23:14", - "text": "The soothing vibes and nice views made it ideal for reflecting and letting go." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://anomadontheloose.com/wp-content/uploads/2018/01/tam-wua-forest-monastery-cave-walking-meditation-1728368610..jpg" - ], - "blip_caption": "a photo of a group of people walking up a set of stairs", - "query": "meditation nature", - "dia_id": "D23:15", - "text": "Here is one more photo from Rio de Janeiro. We went on many excursions there." - }, - { - "speaker": "Deborah", - "dia_id": "D23:16", - "text": "Wow, those stairs look cool! Where were they taken?" - }, - { - "speaker": "Jolene", - "dia_id": "D23:17", - "text": "We had a great time visiting an old temple. The stairs were amazing!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/4dsrcyti38h31.jpg" - ], - "blip_caption": "a photo of a large stone structure with a mountain in the background", - "query": "ancient temple sunrise", - "dia_id": "D23:18", - "text": "Wow, exploring those temples must have been incredible! Three years ago I was also in Rio de Janeiro, I took a beautiful photo on one of the excursions." - }, - { - "speaker": "Jolene", - "dia_id": "D23:19", - "text": "The architecture and history of it all were really interesting. I'm sure you also liked the places you visited there!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/76ihuz977zab1.jpg" - ], - "blip_caption": "a photo of a hand holding a piece of paper with writing on it", - "query": "old photograph handwritten note", - "dia_id": "D23:20", - "text": "Exploring historical places and learning their stories is so fun. It was a great experience. I want to share this photo with you." - }, - { - "speaker": "Jolene", - "dia_id": "D23:21", - "text": " By the way, what did that paper have written on it in the photo?" - }, - { - "speaker": "Deborah", - "dia_id": "D23:22", - "text": "This was written to me by a friend who, unfortunately, will never be able to support me. I miss him here. This quote says\"Let go of what no longer serves you.\"" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/a8/a1/ab/a8a1ab638060af2f6cb53ab7a5bb2b8c.jpg" - ], - "blip_caption": "a photo of a notebook with a quote on it", - "query": "bullet journal spread quote", - "dia_id": "D23:23", - "text": "I'm sorry! That's a good reminder to stay focused and let go of what no longer serves us. Remember the quote in my notebook? It also inspires me!" - }, - { - "speaker": "Deborah", - "dia_id": "D23:24", - "text": "What other quotes give you strength?" - }, - { - "speaker": "Jolene", - "img_url": [ - "http://swaygirls.com/cdn/shop/products/IMG_1932_jpg.jpg" - ], - "blip_caption": "a photo of a notebook with a pen and a plant on a table", - "query": "quote notebook positivity growth", - "dia_id": "D23:25", - "text": "I came across this one while browsing and it really hit home with me. It's a great reminder to ditch the negative stuff and focus on growing and being positive." - }, - { - "speaker": "Deborah", - "dia_id": "D23:26", - "text": "Surrounding ourselves with good stuff and striving to improve is key." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/w0ycj9vmb6fb1.jpg" - ], - "blip_caption": "a photo of a plant in a pot on a patio", - "query": "plant growing pot", - "dia_id": "D23:27", - "text": "Yep, Deborah! It's about creating a good atmosphere to help us grow and improve. By the way, I have a new plant." - }, - { - "speaker": "Deborah", - "dia_id": "D23:28", - "text": "What made you pick it?" - }, - { - "speaker": "Jolene", - "dia_id": "D23:29", - "text": "I got this as a reminder to nurture myself and embrace fresh starts." - }, - { - "speaker": "Deborah", - "dia_id": "D23:30", - "text": "Nice job, Jolene! Take care of yourself and embrace new beginnings." - }, - { - "speaker": "Jolene", - "dia_id": "D23:31", - "text": "Thanks Deb! Will do. Good talking to you. Take care!" - }, - { - "speaker": "Deborah", - "dia_id": "D23:32", - "text": "Have a great day!" - } - ], - "session_24_date_time": "2:14 pm on 3 September, 2023", - "session_24": [ - { - "speaker": "Deborah", - "dia_id": "D24:1", - "text": "Hey Jolene, just catching up. I went to a cool event last week with the aim to support each other - pretty inspiring. Have you been connecting with anyone lately?" - }, - { - "speaker": "Jolene", - "dia_id": "D24:2", - "text": "Hey Deb, great to hear from you! I've been focusing on studying and my relationship with my partner. We're taking little trips to the beach, it's a great way to relax. How about you, anything new going on?" - }, - { - "speaker": "Deborah", - "dia_id": "D24:3", - "text": "I was busy too - went to a community meetup last Friday. We shared stories and it was nice to feel how connected we are. It made me think about how important relationships are. How about you, how are things going in that area?" - }, - { - "speaker": "Jolene", - "dia_id": "D24:4", - "text": "I'm really thankful for my significant other right now. It's great to have someone encouraging my goals! How are things with your friends and family? Any updates on that front?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://kristincorinneyoga.files.wordpress.com/2022/03/img_7637.jpg" - ], - "blip_caption": "a photo of a woman sitting on a yoga mat with two children", - "query": "yoga pose mother daughter", - "dia_id": "D24:5", - "text": "Relationships with family and friends are so vital. My yoga pals have been my second family - we've held each other up through a lot. The other day I found this old photo. That was when I first started doing yoga. My mum was my biggest fan and source of motivation. She'd often come to my classes with me." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/h2oi87j1cty71.jpg" - ], - "blip_caption": "a photo of a nintendo game console and a game controller", - "query": "gaming console my first", - "dia_id": "D24:6", - "text": "Our loved ones sure are supportive! When I was 10, my parents got me that and it was the start of my passion for video games." - }, - { - "speaker": "Deborah", - "dia_id": "D24:7", - "text": "Cool that they shared that with you. Did you learn on your own or did they teach you?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/etnsyns3zh931.jpg" - ], - "blip_caption": "a photo of a nintendo wii game system with a game on the screen", - "query": "video game screenshot playing with mom", - "dia_id": "D24:8", - "text": "I taught myself, but my dad was always supportive and my mom would play games with me. " - }, - { - "speaker": "Deborah", - "dia_id": "D24:9", - "text": "That's awesome! Sounds like you had a lot of support from your parents. What was your favorite game to play with mom?" - }, - { - "speaker": "Jolene", - "dia_id": "D24:10", - "text": "One of my favorites was \"Monster Hunter: World\". The immersive story and open-world gaming are amazing!" - }, - { - "speaker": "Deborah", - "dia_id": "D24:11", - "text": " It can be so freeing when you get immersed in a game like that. " - }, - { - "speaker": "Jolene", - "dia_id": "D24:12", - "text": "Yeah! It's my way to de-stress and take a break from life." - }, - { - "speaker": "Deborah", - "dia_id": "D24:13", - "text": "What's up this month? Anything fun happening for you?" - }, - { - "speaker": "Jolene", - "dia_id": "D24:14", - "text": "Got a lot of finals coming up this month, so I've been studying real hard. It's been quite stressful, but it'll be worth it in the end. Thinking about taking a trip somewhere to relax and recharge afterward." - }, - { - "speaker": "Deborah", - "dia_id": "D24:15", - "text": "Good luck with it! Let me know if there's anything I can do to assist you." - } - ], - "session_25_date_time": "8:31 pm on 6 September, 2023", - "session_25": [ - { - "speaker": "Jolene", - "img_url": [ - "https://www.ic.org/wp-content/uploads/formidable/2/temple_may2021.jpg" - ], - "blip_caption": "a photo of a building with a curved roof on a hill", - "query": "meditation course retreat center peaceful lake greenery", - "dia_id": "D25:1", - "text": "Woohoo! I signed up for a meditation course at a retreat near a lake. Can't wait to share this experience with my partner and learn some new techniques. Sooo excited!" - }, - { - "speaker": "Deborah", - "dia_id": "D25:2", - "text": "That`s awesome! It looks so calm in the pic - I hope you enjoy your experience and learn some new techniques!" - }, - { - "speaker": "Jolene", - "dia_id": "D25:3", - "text": " It's amazing how something so easy can make such a big difference to our health!" - }, - { - "speaker": "Deborah", - "dia_id": "D25:4", - "text": "Yep, it's become part of my routine now. Can't imagine my life without it." - }, - { - "speaker": "Jolene", - "dia_id": "D25:5", - "text": "Yeah, same! It helps me stay balanced during my studies." - }, - { - "speaker": "Deborah", - "dia_id": "D25:6", - "text": "Glad to hear it, Jolene. How's the project going?" - }, - { - "speaker": "Jolene", - "dia_id": "D25:7", - "text": " It's tough but I'm chugging along. Thanks!" - }, - { - "speaker": "Deborah", - "dia_id": "D25:8", - "text": "You're so strong for handling all the challenges. You've got this!" - }, - { - "speaker": "Jolene", - "dia_id": "D25:9", - "text": "Thanks, Deb! Your support really means a lot. I'll keep pushing forward." - }, - { - "speaker": "Deborah", - "dia_id": "D25:10", - "text": "No worries, Jolene. I'm here if you need me. Take care of yourself and don't forget to rest up." - }, - { - "speaker": "Jolene", - "dia_id": "D25:11", - "text": "What have you been doing lately?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach", - "dia_id": "D25:12", - "text": "I recently saw this wonderful sunrise again." - }, - { - "speaker": "Jolene", - "dia_id": "D25:13", - "text": "Glimpsing that calms me. Taking beach walks relaxes me. " - }, - { - "speaker": "Deborah", - "dia_id": "D25:14", - "text": "Did my photo remind you of something?" - }, - { - "speaker": "Jolene", - "dia_id": "D25:15", - "text": "That shot was like a reminder of my last beach getaway. So chill and nice. " - }, - { - "speaker": "Deborah", - "dia_id": "D25:16", - "text": "Glad it brought back good memories. " - }, - { - "speaker": "Jolene", - "dia_id": "D25:17", - "text": "Maybe one day we will be able to watch the sunrise together!" - }, - { - "speaker": "Deborah", - "dia_id": "D25:18", - "text": "An offer I can't refuse!" - }, - { - "speaker": "Jolene", - "dia_id": "D25:19", - "text": "Bye, Deb! See you later!" - }, - { - "speaker": "Deborah", - "dia_id": "D25:20", - "text": "See ya! Stay safe and catch you later. Bye!" - } - ], - "session_26_date_time": "7:39 pm on 8 September, 2023", - "session_26": [ - { - "speaker": "Deborah", - "dia_id": "D26:1", - "text": "Hey Jolene, had a tough week. Storm forced us to cancel our yoga getaway." - }, - { - "speaker": "Jolene", - "dia_id": "D26:2", - "text": "Sorry to hear about it. How are you feeling now?" - }, - { - "speaker": "Deborah", - "dia_id": "D26:3", - "text": "I was bummed about it, but I'm doing better now. It was just a setback, but I found comfort in my work and spending time at home. Reminds me to be grateful for the little things. And you? How's it going?" - }, - { - "speaker": "Jolene", - "dia_id": "D26:4", - "text": "My partner and I plan a camping trip to connect with nature and practice yoga." - }, - { - "speaker": "Deborah", - "dia_id": "D26:5", - "text": "It can be both good and tough to plan activities with a busy schedule - what strategies do you use?" - }, - { - "speaker": "Jolene", - "dia_id": "D26:6", - "text": "Having a routine helps me stay on top of everything I need to do. I have a schedule for classes, studying, and personal time. Self-care activities like yoga and meditation help me stay balanced and relax." - }, - { - "speaker": "Deborah", - "dia_id": "D26:7", - "text": "I'd love to learn more about how you do it." - }, - { - "speaker": "Jolene", - "dia_id": "D26:8", - "text": " I can tell you about it if you're interested. It took a bit of experimenting, but it's really helped me." - }, - { - "speaker": "Deborah", - "dia_id": "D26:9", - "text": "Sounds great! Let's set up a coffee date and talk about it!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2000,w_1333,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/CaseStudy-10009161130_nstsp1.jpg" - ], - "blip_caption": "a photo of a coffee shop with a bunch of coffee machines", - "query": "favorite cafe", - "dia_id": "D26:10", - "text": "Wanna meet up at that cafe next Monday? Let's try fresh pastries." - }, - { - "speaker": "Deborah", - "dia_id": "D26:11", - "text": "Sounds good, Jolene! When did you have in mind? That cafe rocks." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://images.pexels.com/photos/12910957/pexels-photo-12910957.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-sena-12910957.jpg" - ], - "blip_caption": "a photo of a person holding a cup of coffee in front of a bunch of flowers", - "query": "coffee cozy coffee shop holding cup of coffee", - "dia_id": "D26:12", - "text": "How about Wednesday at 4? Can't wait to catch up over coffee!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://media.architecturaldigest.com/photos/5a6790689acb4d19c359a638/16:9/w_320%2Cc_limit/OregonUmamiCafe.jpg" - ], - "blip_caption": "a photo of a group of people sitting at tables in a room", - "query": "coffee shop courtyard", - "dia_id": "D26:13", - "text": "That pic looks so peaceful. Reminded me of a cool hidden coffee shop near me. Rate it!" - }, - { - "speaker": "Jolene", - "dia_id": "D26:14", - "text": "Tell me more about it when we meet, maybe next time we\u2019ll be there." - }, - { - "speaker": "Deborah", - "dia_id": "D26:15", - "text": "Sorry, I remembered that I already have plans for this day." - }, - { - "speaker": "Jolene", - "dia_id": "D26:16", - "text": "Now I'll see when it's more convenient for me." - }, - { - "speaker": "Deborah", - "dia_id": "D26:17", - "text": "Thank you for your understanding, I'm waiting." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/d1g89l9ba7b61.jpg" - ], - "blip_caption": "a photo of a room with a book shelf and a ceiling fan", - "query": "bookshelf engineering textbooks", - "dia_id": "D26:18", - "text": "How about Friday at 5? I will need to sort out the books from this bookcase and I will be free." - }, - { - "speaker": "Deborah", - "dia_id": "D26:19", - "text": "Absolutely, let's do that! Can't wait for our coffee date next week. See you then. Stay safe!" - }, - { - "speaker": "Jolene", - "dia_id": "D26:20", - "text": "See ya soon, Deb! Be safe and I'm excited for our coffee date!" - }, - { - "speaker": "Deborah", - "dia_id": "D26:21", - "text": "Maybe just grab me some interesting books!" - } - ], - "session_27_date_time": "2:18 pm on 12 September, 2023", - "session_27": [ - { - "speaker": "Jolene", - "blip_caption": "a photo of a woman doing a yoga pose on the beach", - "dia_id": "D27:1", - "text": "Hey Deb! So sorry for the late reply, been super busy. Last weekend my partner and I traveled to a meditation retreat for a few weeks in Phuket. Amazing experience! Nature, reflection and a break from engineering studies were awesome - it helped me find inner peace." - }, - { - "speaker": "Deborah", - "dia_id": "D27:2", - "text": " I'd love to hear more about your reflections there and how they changed you." - }, - { - "speaker": "Jolene", - "dia_id": "D27:3", - "text": "At the retreat, I had time to reflect on what makes me happy. It made me realize the importance of incorporating relaxation, self-care, and balance in life alongside my engineering studies. The beauty of nature there was so inspiring and refreshing!" - }, - { - "speaker": "Deborah", - "dia_id": "D27:4", - "text": " Was there anything from the retreat that stood out to you?" - }, - { - "speaker": "Jolene", - "dia_id": "D27:5", - "text": "The one session that really stood out was about releasing expectations and judgments and just savoring the present. It was a strong reminder to not just dwell on the finish line, but to appreciate the journey too. I usually get too consumed with hitting my goals that I forget to appreciate the ride." - }, - { - "speaker": "Deborah", - "dia_id": "D27:6", - "text": " I've been thinking about this a lot, too. Life's full of small moments and being grateful for those can really boost our happiness." - }, - { - "speaker": "Jolene", - "dia_id": "D27:7", - "text": "Yup, totally agree! Sometimes we get too focused on the big stuff and don't appreciate all the tiny wins. Like the feeling of the sun or a great cup of coffee - can be small but they make life much better. Trying to be more mindful and grateful to take it all in now." - }, - { - "speaker": "Deborah", - "dia_id": "D27:8", - "text": "That's great, Jolene! Practicing mindfulness and gratitude can really change our day-to-day. Even just a different outlook can make the little things in life joyful. Glad you're getting into it!" - }, - { - "speaker": "Jolene", - "dia_id": "D27:9", - "text": "I'm experiencing a new level of joy and happiness!" - }, - { - "speaker": "Deborah", - "dia_id": "D27:10", - "text": " It's wonderful to see your progress, and I'm excited to be on this journey with you!" - }, - { - "speaker": "Jolene", - "dia_id": "D27:11", - "text": "Thanks, Deb! Appreciate your support. It's great that we can do this together." - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of two children standing on yoga mats in a room", - "dia_id": "D27:12", - "text": "Having a supportive community definitely helps. We can motivate and encourage each other! By the way, I recently played a game. I don't remember what it's called. This is a card game about cats, where you take cards one by one from a deck, and then you can attack your opponent with them. We'll definitely play it with you!" - }, - { - "speaker": "Jolene", - "dia_id": "D27:13", - "text": "I look forward to meeting you and playing this game!" - } - ], - "session_28_date_time": "3:09 pm on 15 September, 2023", - "session_28": [ - { - "speaker": "Deborah", - "blip_caption": "a photo of a living room with a couch and a fire place", - "dia_id": "D28:1", - "text": "Since speaking last, I reconnected with my mom's old friends. Their stories made me tear up and reminded me how lucky I am to have had her." - }, - { - "speaker": "Jolene", - "dia_id": "D28:2", - "text": " It's great that you could reconnect with them. Hearing stories about our loved ones can be tough but also comforting." - }, - { - "speaker": "Deborah", - "dia_id": "D28:3", - "text": "Hearing stories about my mom was emotional. It was both happy and sad to hear things I hadn't heard before. It was a mix of emotions, but overall it was comforting to reconnect with her friends." - }, - { - "speaker": "Jolene", - "dia_id": "D28:4", - "text": "It can bring up a range of emotions, and it's okay to feel a mix of happiness and sadness. Those moments with her friends must've been meaningful to you." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://ashleenichols.com/wp-content/uploads/2018/01/IMG_1546-e1515707058552.jpg" - ], - "blip_caption": "a photo of two women in pajamas taking a selfie in a mirror", - "query": "photo mother friends", - "dia_id": "D28:5", - "text": "Wow, it was so special. A glimpse into her life beyond what I knew. Through their eyes, I appreciate her more. Here I am and my mom." - }, - { - "speaker": "Jolene", - "dia_id": "D28:6", - "text": "That looks like a blast! What did you and your mom's friends do on that day?" - }, - { - "speaker": "Deborah", - "dia_id": "D28:7", - "text": "We reminisced and looked through her photos. It was really sweet." - }, - { - "speaker": "Jolene", - "dia_id": "D28:8", - "text": "Looking at old photos must have been so nostalgic! It's great that you could share that experience with friends. It's amazing how photos and memories can give us a deeper appreciation for the people we love." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach", - "dia_id": "D28:9", - "text": "Pictures really have a way of bringing back memories and making us appreciate the special bond we have with our loved ones. They remind me of how strong love is and how amazing human relationships can be. Just like this one." - }, - { - "speaker": "Jolene", - "dia_id": "D28:10", - "text": "Wow, what a gorgeous pic! Do you have any special memories of that beach or just love surfing in general?" - }, - { - "speaker": "Deborah", - "dia_id": "D28:11", - "text": "That beach is super special to me. It's where I got married and discovered my love for surfing. It's always filled with joy and peace." - }, - { - "speaker": "Jolene", - "dia_id": "D28:12", - "text": "What pleasant memories." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://assets-global.website-files.com/5909c340ec86374fefcb849c/65650a0fc42a7d8dbe0e4349_IMG_2131.jpg" - ], - "blip_caption": "a photo of a woman doing a yoga pose on the beach", - "query": "surfing yoga beach", - "dia_id": "D28:13", - "text": "Here is another photo from my classes." - }, - { - "speaker": "Jolene", - "dia_id": "D28:14", - "text": "Wow, that yoga pose looks amazing! Does it help you relax?" - }, - { - "speaker": "Deborah", - "dia_id": "D28:15", - "text": "Oh yeah! Doing this on the beach is so peaceful - the ocean, sand, and fresh air create a super relaxing atmosphere. The perfect way to take care of myself." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://c1.wallpaperflare.com/preview/308/322/334/wellness-relaxation-relax-spa.jpg" - ], - "blip_caption": "a photography of a couple of towels sitting on top of a table", - "query": "meditation mat candles essential oils", - "dia_id": "D28:16", - "re-download": true, - "text": " I like to create my own serene yoga space with candles and oils for extra chill vibes. Also, we tried a new style of meditation in Thailand - with flowers." - }, - { - "speaker": "Deborah", - "dia_id": "D28:17", - "text": "Oh, same for me!" - }, - { - "speaker": "Jolene", - "dia_id": "D28:18", - "text": "I find calm when I do yoga or meditate. I use essential oils and put on some soft, soothing music in the background to create a peaceful atmosphere. It really helps me chill out and center myself." - }, - { - "speaker": "Deborah", - "dia_id": "D28:19", - "text": " It's amazing how our environment can enhance our practice." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/3d/d9/b0/3dd9b048cca05344a7bf805705bd7f17.jpg" - ], - "blip_caption": "a photo of a bed with a colorful blanket and pillows", - "query": "cozy corner yoga mat cushions small plant", - "dia_id": "D28:20", - "text": "Yeah, totally! Our surroundings can really affect our mood and how much zen we can get from our routine. Creating a place that feels safe and chill is key." - }, - { - "speaker": "Deborah", - "dia_id": "D28:21", - "text": "Wow, that looks so comfy and inviting! Where do you usually go to relax in your house?" - }, - { - "speaker": "Jolene", - "dia_id": "D28:22", - "text": "In my room, I usually go to relax and feel at ease. After a busy day, it's my little haven for peace and rest - the perfect spot to relax and recharge." - }, - { - "speaker": "Deborah", - "dia_id": "D28:23", - "text": "Sounds like your room does the job. That's awesome." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/ckp5j9w7q7q41.jpg" - ], - "blip_caption": "a photo of a person holding a snake in a container", - "query": "pet snake curled up branch", - "dia_id": "D28:24", - "text": " Here are my pals keeping me company." - }, - { - "speaker": "Deborah", - "dia_id": "D28:25", - "text": "Hey, that's Susie or Seraphim? How long has he been hanging out with you?" - }, - { - "speaker": "Jolene", - "dia_id": "D28:26", - "text": "It`s Susie! I've had her for two years now. " - }, - { - "speaker": "Deborah", - "dia_id": "D28:27", - "text": "It's awesome how pets can bring us comfort and peace when we need it." - }, - { - "speaker": "Jolene", - "dia_id": "D28:28", - "text": "Susie is a great companion." - }, - { - "speaker": "Deborah", - "dia_id": "D28:29", - "text": "The love pets give is priceless." - }, - { - "speaker": "Jolene", - "dia_id": "D28:30", - "text": "Plus, they make life a lot brighter!" - } - ], - "session_29_date_time": "1:24 pm on 17 September, 2023", - "session_29": [ - { - "speaker": "Deborah", - "dia_id": "D29:1", - "text": "Hey Jolene, I'm so excited to tell you! Yesterday, me and my neighbor ran a free gardening class for the community, it was awesome! People of any age joined in and it was such a great thing to see." - }, - { - "speaker": "Jolene", - "dia_id": "D29:2", - "text": "Wow, Deborah, that's awesome! Keep up the great work, and here's hoping for more events like this in the future!" - }, - { - "speaker": "Deborah", - "dia_id": "D29:3", - "text": " Gardening is really amazing. It brings us together in such a cool way. It was awesome to share my love of plants and help people take care of the world. So, what about you? Anything new happened lately?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:4", - "text": "We tried a scuba diving lesson last Friday and had an awesome time! We found a cool dive spot we can explore together. Trying new things opens up a world of adventure - maybe one day I'll be a certified diver. Anything fun going on with you?" - }, - { - "speaker": "Deborah", - "dia_id": "D29:5", - "text": "That sounds amazing, Jolene! I've been interested in underwater life, but I haven't had the chance to try scuba diving yet. Recently, I've been spending time remembering my mom. Last Sunday, I visited her old house and sat on a bench. It was a comforting experience, as if I could feel her presence guide me and remind me of her love." - }, - { - "speaker": "Jolene", - "dia_id": "D29:6", - "text": "Visiting your mom's old home sounds like it was really special. Is there something special you remember about her?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://notjustsundaydinner.com/wp-content/uploads/2022/09/peach-cobbler-2.jpg" - ], - "blip_caption": "a photo of a bowl of food with a spoon in it", - "query": "peach cobbler delicious warm recipe", - "dia_id": "D29:7", - "text": "Thanks, Jolene! It was really special. My mom had a big passion for cooking. She would make amazing meals for us, each one full of love and warmth. I can still remember the smell of her special dish, it would fill the house and bring us all together." - }, - { - "speaker": "Jolene", - "dia_id": "D29:8", - "text": "Mmm, that looks delicious, Deb! So sweet how cooking with your mom brought everyone together. What's your best memory of cooking with her?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://ronnascakeblog.files.wordpress.com/2022/09/pineapple.jpg" - ], - "blip_caption": "a photo of a pineapple cake with a smiley face on it", - "query": "pineapple birthday cake 1 candle", - "dia_id": "D29:9", - "text": "I loved it when she would bake pineapple birthday cakes for me when I was a kid. It always made me feel so special." - }, - { - "speaker": "Jolene", - "dia_id": "D29:10", - "text": "No wonder it made you feel special. " - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a mixer with a whisk in it", - "dia_id": "D29:11", - "text": "Have you ever had something like that with someone close?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/t45dmigsmtu61.jpg" - ], - "blip_caption": "a photo of four chocolate chip cookies on a baking sheet", - "query": "homemade cookies tray", - "dia_id": "D29:12", - "text": "I used to bake cookies with someone close to me." - }, - { - "speaker": "Deborah", - "dia_id": "D29:13", - "text": "What's your favorite cookie to make?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:14", - "text": "The warm, gooey chocolate and soft, buttery cookie are a match made in heaven." - }, - { - "speaker": "Deborah", - "dia_id": "D29:15", - "text": "I really want to eat this now." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://citysupplyfayetteville.com/cdn/shop/products/IMG_6684.jpg" - ], - "blip_caption": "a photo of a person holding a book open on a bed", - "query": "memory book pictures notes", - "dia_id": "D29:16", - "text": "Well look what I have here!" - }, - { - "speaker": "Deborah", - "dia_id": "D29:17", - "text": " Is there anything special about it or the photo?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:18", - "text": "It takes me to another world when I read it!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1617734021/at/news-culture/2021-04/lupita-aquino-nook-2.jpg" - ], - "blip_caption": "a photo of a living room with a couch and a book shelf", - "query": "cozy reading nook bookshelf books", - "dia_id": "D29:19", - "text": "Did I show you that I have a big bookshelf too?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:20", - "text": "I think not, I really like it!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1617640374/at/house%20tours/2021-04/Amara/21.jpg" - ], - "blip_caption": "a photo of a bathroom with a black and white wall and a wooden stool", - "query": "reading nook cozy inviting book bookmark heart shaped pendant", - "dia_id": "D29:21", - "text": "Having a space like this is important for escaping reality and relaxing with a book. Do you have any books that really moved you?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:22", - "text": "My bathroom has an aesthetic vibe. Once I read a self-discovery book there and it really resonated with me." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach", - "dia_id": "D29:23", - "text": "Wow! A special book that speaks to you and helps with self-discovery? That's awesome. Plus, having a cozy nook to chill? That's my best one!" - }, - { - "speaker": "Jolene", - "dia_id": "D29:24", - "text": "Sounds nice, Deb! A cozy nook is a must! The beach is a great place for finding peace and relaxation. Have you ever tried surfing?" - }, - { - "speaker": "Deborah", - "img_url": [ - "http://badfishsup.com/cdn/shop/articles/IMG_7804.jpg" - ], - "blip_caption": "a photo of a man riding a surfboard on a wave in the ocean", - "query": "surfer riding wave surfing", - "dia_id": "D29:25", - "text": "Certainly! Here's the confirmation." - }, - { - "speaker": "Jolene", - "dia_id": "D29:26", - "text": "How cool! But I never decided to try it." - }, - { - "speaker": "Deborah", - "dia_id": "D29:27", - "text": "It's okay, maybe we can try it together sometime!" - }, - { - "speaker": "Jolene", - "img_url": [ - "http://petekozametalart.com/cdn/shop/products/image_2244493c-7118-4af4-9d82-6f8b0e99d9fa.jpg" - ], - "blip_caption": "a photo of a surfboard painted with a palm tree on it", - "query": "surfboard palm tree", - "dia_id": "D29:28", - "text": "I already know what fate awaits me if I do this!" - }, - { - "speaker": "Deborah", - "dia_id": "D29:29", - "text": "Have you ever been interested in this or do you know nothing about it?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:30", - "text": "Just started learning, but haven't gone yet. Want to come with me sometime?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a sunset over the ocean with a boat in the distance", - "dia_id": "D29:31", - "text": "It'll be an adventure! Let's make it happen soon!" - }, - { - "speaker": "Jolene", - "dia_id": "D29:32", - "text": "So glad, all that remains is to agree and choose the right time for both of us." - }, - { - "speaker": "Deborah", - "dia_id": "D29:33", - "text": " Can't wait. What day works for you? I'm really excited!" - }, - { - "speaker": "Jolene", - "dia_id": "D29:34", - "text": "Let's plan for next month - I'll check my schedule and let you know. Can't wait!" - } - ], - "session_30_date_time": "10:17 am on 20 September, 2023", - "session_30": [ - { - "speaker": "Deborah", - "dia_id": "D30:1", - "text": "I had a great time at the music festival with my pals! The vibes were unreal and the music was magical. It was so freeing to dance and bop around. Music brings us together and helps us show our feelings. It reminds me of my mom and her soothing voice when she'd sing lullabies to me. Lucky to have those memories!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/d5/2b/de/d52bde85a9eb63ee034d5ffb7be7d3c2.jpg" - ], - "blip_caption": "a photo of a man and woman standing in a field at night", - "query": "music festival couple dancing", - "dia_id": "D30:2", - "text": "Wow, festivals sound so fun! Here's me and my partner at one last year - had an awesome time! It's my way of expressing myself and getting away from all the stress of everyday life. Just got back from a trip with my partner - so cool!" - }, - { - "speaker": "Deborah", - "dia_id": "D30:3", - "text": "Wow, what a gorgeous shot! It looks so tranquil and serene. You two look very happy together. Trips create awesome memories that we can share. Where did you go on your trip and what's something you'll never forget?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://atharvyogshala.com/assets/images/sunset-view.jpg" - ], - "blip_caption": "a photo of a sunset over a valley with a horse", - "query": "yoga retreat south america serene retreat center mountains sunrise", - "dia_id": "D30:4", - "text": "Thanks! We had an awesome yoga retreat. The place was so peaceful and the view during yoga was amazing - the sunrise lit up the whole sky with bright colors. It was so beautiful, it made us feel so alive and grateful." - }, - { - "speaker": "Deborah", - "dia_id": "D30:5", - "text": "Wow, what a view! How did it make you feel?" - }, - { - "speaker": "Jolene", - "dia_id": "D30:6", - "text": "It was amazing! Doing yoga with that backdrop made me feel connected to nature and myself. I felt incredibly peaceful and thankful." - }, - { - "speaker": "Deborah", - "dia_id": "D30:7", - "text": "Like, it's no wonder looking at such beauty can really help us refocus and connect with who we are. Have you ever experienced that?" - }, - { - "speaker": "Jolene", - "dia_id": "D30:8", - "text": "I remember taking a hike with my partner and coming across a waterfall oasis. Everything just felt so peaceful and my worries just disappeared. It was such a refreshing experience." - }, - { - "speaker": "Deborah", - "dia_id": "D30:9", - "text": "Are you planning to experience it again soon?" - }, - { - "speaker": "Jolene", - "dia_id": "D30:10", - "text": "Yeah! I'm planning to get out in nature again next month. It's going to be great reconnecting with it!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://www.lsuagcenter.com/~/media/system/8/a/0/b/8a0beeb70c88046e95c4cc398cb5242b/cherry%20flowers%20and%20beepng.png" - ], - "blip_caption": "a photo of a pink flowered tree with a blue sky in the background", - "query": "colorful blossom tree bloom spring", - "dia_id": "D30:11", - "text": " Enjoy it! This photo made me think of a gorgeous blossom tree from near my home. Every spring, it was magical to watch it bloom." - }, - { - "speaker": "Jolene", - "dia_id": "D30:12", - "text": "That sounds magical! How was it watching the tree bloom each spring?" - }, - { - "speaker": "Deborah", - "dia_id": "D30:13", - "text": "It was like admiring nature's artwork. It filled me with awe and made me appreciate the beauty of life. Even in tough times, there's hope for growth." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/kwnuvwl3wha01.jpg" - ], - "blip_caption": "a photo of a plant growing out of a corner of a building", - "query": "small sprout crack pavement growth thrive", - "dia_id": "D30:14", - "text": "This photo I took is a great visual representation of that idea. It reminds me that I can keep growing through any obstacles." - }, - { - "speaker": "Deborah", - "dia_id": "D30:15", - "text": "It really captures resilience and strength. I love how you find inspiration in the small things." - }, - { - "speaker": "Jolene", - "dia_id": "D30:16", - "text": "Thanks, Deborah! Appreciating those small things is important. It helps me remember that even when times are tough, there's always something positive to hang onto." - }, - { - "speaker": "Deborah", - "dia_id": "D30:17", - "text": "It's a great habit. Thanks for reminding me!" - }, - { - "speaker": "Jolene", - "dia_id": "D30:18", - "text": "Sure Deb, it's great catching up. Keep on finding those beauties!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Deborah": [ - "Deborah visits her mother's old home while reflecting on her life." - ], - "Jolene": [ - "Jolene conquers a difficult electrical engineering project." - ], - "date": "23 January, 2023" - }, - "events_session_2": { - "Deborah": [ - "Deborah's father passes away suddenly, leaving her and her family shocked and in grief.", - "Deborah's copes with her father's passing by looking at their family album.", - "Deborah receives a letter from her community where they appreciate her dedication to teaching yoga." - ], - "Jolene": [ - "Jolene takes a break from her engineering studies by playing \"Walking Dead\" video game with her partner." - ], - "date": "27 January, 2023" - }, - "events_session_3": { - "Deborah": [ - "Deborah meets a new neighbor, Anna, while doing yoga in the park and befriends them." - ], - "Jolene": [ - "Jolene receives a challenging but fun robotics projects from her professor to work on." - ], - "date": "1 February, 2023" - }, - "events_session_4": { - "Deborah": [ - "Deborah further bonds with her neighbor Anna during yoga class while trying out the Warrior II yoga pose and over their shared memories of their late mothers." - ], - "Jolene": [ - "Jolene finishes working on a sustainable water purifier for providing clean water to rural communities and derives a lot of satisfaction from the impact created by the project." - ], - "date": "4 February, 2023" - }, - "events_session_5": { - "Deborah": [], - "Jolene": [ - "Jolene reflects on her accomplishments and future by taking a mini retreat in life and comes up with some solutions for her engineering projects such as developing green tech for disadvantaged areas.", - "Jolene plans to start a volunteer program where engineers can teach STEM to underpriveleged kids by holding workshops at local schools.", - "Jolene uses a planner to note her ideas on the volunteer program and green tech." - ], - "date": "9 February, 2023" - }, - "events_session_6": { - "Deborah": [ - "Deborah's friend Karlie passes away.", - "Deborah copes with Karlie's passing away by visiting flower gardens and remembering their time together." - ], - "Jolene": [ - "Jolene goes out with her friends for dinner and drinks to let her hair down." - ], - "date": "22 February, 2023" - }, - "events_session_7": { - "Deborah": [ - "Deborah goes for a morning jog in the nearby park and decides to integrate it into her daily routine." - ], - "Jolene": [ - "Jolene starts doing yoga and meditation, sometimes joined by her partner, to clear her head while striking a balance with her studies." - ], - "date": "25 February, 2023" - }, - "events_session_8": { - "Deborah": [ - "Deborah tastes a new vegan dish - tofu and veg with ginger and soy sauce - she received from her neighbor, Anna." - ], - "Jolene": [ - "Jolene is stressed out about her studies and struggles to be mindful of the stress levels while keeping up with her todos.", - "Jolene takes her pet snake, Seraphim, for a visit to the park." - ], - "date": "2 March, 2023" - }, - "events_session_9": { - "Deborah": [ - "Deborah starts a neighborhood yoga class and finds joy in seeing its impact on them." - ], - "Jolene": [ - "Jolene struggles with time management for her projects and studies and seeks help from Deborah." - ], - "date": "13 March, 2023" - }, - "events_session_10": { - "Deborah": [ - "Deborah and her neighbor-friend, Anna, bond during a moment at the beach and inspire each other about pursuing their dreams." - ], - "Jolene": [], - "date": "22 March, 2023" - }, - "events_session_11": { - "Deborah": [ - "Deborah purchases some yoga mats and blocks for her yoga classes." - ], - "Jolene": [ - "Jolene has a breakthrough in her robotics project." - ], - "date": "28 March, 2023" - }, - "events_session_12": { - "Deborah": [ - "Deborah goes for a bicycle ride with Anna to explore the nearby villages.", - "Deborah attends an art exhibition with Anna and finds inspiring works that remind her of her mother." - ], - "Jolene": [ - "Jolene continues working on the robotics project she started in February and it gradually shapes up." - ], - "date": "9 April, 2023" - }, - "events_session_13": { - "Deborah": [], - "Jolene": [ - "Jolene successfully finishes the robotics project assigned to her by her Professor.", - "Jolene joins an engineering firm as an intern over the summer.", - "Jolene visits Alask and practices yoga on top of Mt. Talkeetna" - ], - "date": "6 June, 2023" - }, - "events_session_14": { - "Deborah": [ - "Deborah starts planning for a yoga retreat with her friends during September 2023." - ], - "Jolene": [ - "Jolene has an intense time as an intern and has a breakthrough in her project.", - "Jolene buys an aquarium for her pet snake, Seraphim." - ], - "date": "26 June, 2023" - }, - "events_session_15": { - "Deborah": [ - "Deborah and her neighbor, Anna, form a running group." - ], - "Jolene": [ - "Jolene struggles to include workouts and gaming with her partner in her hectic daily routine." - ], - "date": "9 July, 2023" - }, - "events_session_16": { - "Deborah": [ - "Deborah starts a new project of raising funds for a clean-up drive in her community." - ], - "Jolene": [ - "Jolene loses all her work when her device crashes." - ], - "date": "1 August, 2023" - }, - "events_session_17": { - "Deborah": [ - "Deborah creates a meditation guide for her yoga retreat." - ], - "Jolene": [ - "Jolene is stressed about losing her work progress and manages her stress by practicing yoga and meditation.", - "Jolene starts working on an affordable and productive aerial surveillance system that can respond quickly to emergencies." - ], - "date": "12 August, 2023" - }, - "events_session_18": { - "Deborah": [ - "Deborah teaches yoga during a sunset session to the elderly at a local care home." - ], - "Jolene": [ - "Jolene plans some new goals with her partner, evaluates their success at achieving them and feels overwhelmed about juggling so much." - ], - "date": "16 August, 2023" - }, - "events_session_19": { - "Deborah": [ - "Deborah shares her life story with the neighbor Anna and she appreciates Deborah's life experiences.", - "Deborah organizes a yoga event in her community, complete with live music and food stalls from local businesses." - ], - "Jolene": [ - "Jolene buys a new game console and gifts it to her partner." - ], - "date": "19 August, 2023" - }, - "events_session_20": { - "Deborah": [ - "Deborah visits the bench where she and her mother used to have chats and revisits some nostalgic memories." - ], - "Jolene": [ - "Jolene and her partner attempt to play Battlefield 1 on the new game console she bought as a gift for her partner." - ], - "date": "21 August, 2023" - }, - "events_session_21": { - "Deborah": [ - "Deborah participates in a yoga retreat surrounded by nature near her mother's place." - ], - "Jolene": [ - "Jolene presents her work in a virtual conference and is pumped by the positive feedback she receives about her work.", - "Jolene reflects on the progress she has made so far and decides to gain more experience by attending workshops, networking and doing more internships." - ], - "date": "24 August, 2023" - }, - "events_session_22": { - "Deborah": [ - "Deborah ruminates on the influence of her mother on her life." - ], - "Jolene": [ - "Jolene shares her appreciation for yoga with her partner.", - "Jolene and her partner plan to travel together to a meditation retreat to enhance their shared practice.", - "Jolene plans to work on sustainable solution projects during the next year, such as developing solar energy and providing clean water to communities.", - "Jolene has an emotional chat with her partner about their parents who have passed away but have left lasting impressions on their lives." - ], - "date": "26 August, 2023" - }, - "events_session_23": { - "Deborah": [ - "Deborah and her husband visited her mother's house last month." - ], - "Jolene": [ - "Jolene and her partner return from their trip to Rio de Janeiro to explore different types of yoga, explore some cool yoga classes, cafes and old temples." - ], - "date": "30 August, 2023" - }, - "events_session_24": { - "Deborah": [ - "Deborah attends a community meetup where everyone shares stories about themselves." - ], - "Jolene": [ - "Jolene and her partner plan weekend trips to the beach to relax.", - "Jolene studies hard for her upcoming finals." - ], - "date": "3 September, 2023" - }, - "events_session_25": { - "Deborah": [], - "Jolene": [ - "Jolene signs up for an meditation course at a retreat near a lake." - ], - "date": "6 September, 2023" - }, - "events_session_26": { - "Deborah": [ - "Deborah has to cancel the yoga retreat she was planning with her friends because of a storm" - ], - "Jolene": [ - "Jolene and her partner plan a camping trip to connect with nature and practice yoga." - ], - "date": "8 September, 2023" - }, - "events_session_27": { - "Deborah": [], - "Jolene": [ - "Jolene and her partner travel to a meditation retreat in Phuket, Thailand." - ], - "date": "12 September, 2023" - }, - "events_session_28": { - "Deborah": [ - "Jolene reconnects with her mother's friends and gets nostalgic hearing about her stories from them and going over old photos." - ], - "Jolene": [ - "Jolene and her partner practice a new style of meditation in Thailand with flowers." - ], - "date": "15 September, 2023" - }, - "events_session_29": { - "Deborah": [ - "Deborah and Anna offer a free class on community gardening in their neighborhood." - ], - "Jolene": [ - "Jolene and her partner explore a new dive spot in Phuket after taking a scuba diving lesson." - ], - "date": "17 September, 2023" - }, - "events_session_30": { - "Deborah": [ - "Deborah attends a summer music festival with friends." - ], - "Jolene": [ - "Jolene and her partner return from their yoga retreat in Phuket, Thailand.", - "Jolene makes significant progress in her yoga course." - ], - "date": "20 September, 2023" - } - }, - "observation": { - "session_1_observation": { - "Deborah": [ - [ - "Deborah finished an electrical engineering project last week.", - "D1:2" - ], - [ - "Deborah visited her mother's old house last week which holds special memories as her mother passed away a few years ago.", - "D1:3" - ], - [ - "Deborah has a photo with her mother, which is their last photo together.", - "D1:5" - ], - [ - "Deborah's mother had a special bench near the window in her house where she used to sit every morning to take in the view.", - "D1:7" - ], - [ - "Deborah has a pendant that reminds her of her mother.", - "D1:9" - ], - [ - "Deborah's goal is to keep teaching yoga and supporting her community to help people find peace and joy.", - "D1:13" - ], - [ - "Yoga helped Deborah find peace during a rough time, inspiring her to share it with others.", - "D1:15" - ] - ], - "Jolene": [ - [ - "Jolene finished an electrical engineering project last week.", - "D1:2" - ], - [ - "Jolene's mother passed away last year.", - "D1:6" - ], - [ - "Jolene has a room in her mother's house where she has many memories.", - "D1:6" - ], - [ - "Jolene has a pendant that reminds her of her mother.", - "D1:8" - ], - [ - "Jolene's pendant has a special symbol representing freedom, reminding her to go for her goals.", - "D1:10" - ] - ] - }, - "session_2_observation": { - "Deborah": [ - [ - "Deborah's father passed away two days before the conversation. She is trying to channel her grief by spending more time with family and cherishing memories.", - "D2:1" - ], - [ - "Deborah finds peace in looking at family photos during difficult times.", - "D2:3" - ], - [ - "Deborah values love and openness in her relationship with her husband.", - "D2:7" - ], - [ - "Deborah has a positive influence on a group through yoga, which she is passionate about.", - "D2:9" - ], - [ - "Deborah visits her old home, where her mom passed away, to find peace and feel her mother's presence.", - "D2:13" - ], - [ - "Deborah's mother enjoyed reading as one of her hobbies.", - "D2:17" - ], - [ - "Deborah's mother had a passion for travel.", - "D2:19" - ] - ], - "Jolene": [ - [ - "Jolene enjoys playing video games and particularly likes the game \"Detroit\" on the console.", - "D2:26" - ], - [ - "Jolene and her partner are planning to play \"Walking Dead\" next Saturday.", - "D2:30" - ] - ] - }, - "session_3_observation": { - "Jolene": [ - [ - "Jolene is a student who is working on a robotics project assigned by her engineering professor.", - "D3:1" - ], - [ - "Initially, Jolene felt a mix of excitement and nervousness when she received the robotics project.", - "D3:3" - ], - [ - "Jolene finds working on the robotics project like solving a puzzle and enjoys figuring out the best design and programming.", - "D3:3" - ], - [ - "Jolene enjoys seeing the robot come together as she works on the project.", - "D3:3" - ], - [ - "Jolene has a partner with whom she plans to play console games.", - "D3:11" - ] - ], - "Deborah": [ - [ - "Deborah met her new neighbor Anna at a yoga session in the park.", - "D3:4" - ], - [ - "Deborah and Anna discussed how yoga has improved their lives and the sense of community it provides.", - "D3:8" - ], - [ - "Deborah suggests to Jolene to resume yoga, hinting at its benefits.", - "D3:10" - ], - [ - "Deborah values having activities to do together with others, like yoga with her neighbor Anna.", - "D3:12" - ] - ] - }, - "session_4_observation": { - "Jolene": [ - [ - "Jolene had a major milestone last week in engineering where she designed and built a sustainable water purifier for a rural community.", - "D4:3" - ], - [ - "Jolene wants to continue working in engineering to make a positive impact on communities in need by creating sustainable solutions.", - "D4:9" - ], - [ - "Jolene is into reading, particularly enjoying the books \"Sapiens\" and \"Avalanche\" by Neal Stephenson.", - [ - "D4:21", - "D4:23" - ] - ], - [ - "Jolene appreciates nature's calming power and enjoys watching sunsets over the water.", - "D4:33" - ] - ], - "Deborah": [ - [ - "Deborah spends a lot of time teaching yoga as a way to find balance and reconnect with herself.", - "D4:12" - ], - [ - "Deborah has a bouquet that was given to her by a friend during a tough time, which gives her hope and courage.", - "D4:28" - ], - [ - "Deborah has a little amulet from her mom that she brings with her when reflecting on her mom. It brings her comfort and connects her to her mom's love.", - "D4:36" - ], - [ - "Deborah enjoys nature and has favorite spots in a park with a forest trail and a beach nearby.", - "D4:32" - ], - [ - "Deborah values supporting others, as seen when she bonded with Anna during a yoga class and shared stories with Jolene.", - [ - "D4:16", - "D4:40" - ] - ] - ] - }, - "session_5_observation": { - "Jolene": [ - [ - "Jolene did a mini retreat last Wednesday to assess her life and gained new insights and a confidence boost from it.", - "D5:1" - ], - [ - "Jolene accomplished something significant with her engineering project and came up with neat solutions that she's excited about.", - "D5:5" - ], - [ - "Jolene is considering looking into green technology to make a difference in disadvantaged areas.", - "D5:7" - ], - [ - "Jolene had an idea for a volunteer program where engineers teach STEM to underprivileged kids.", - "D5:7" - ], - [ - "Jolene plans to team up with local schools/centers to do workshops and invite engineers as guest speakers for the volunteer program.", - "D5:9" - ] - ], - "Deborah": [ - [ - "Deborah acknowledged Jolene for stepping outside her comfort zone and believing in herself.", - "D5:4" - ], - [ - "Deborah supported Jolene's volunteer program idea and suggested that it's a great way to help and inspire others.", - "D5:8" - ], - [ - "Deborah expressed excitement to hear about Jolene reaching out and helping underprivileged kids.", - "D5:12" - ], - [ - "Deborah offered to provide more tips or information if Jolene needs them.", - "D5:14" - ] - ] - }, - "session_6_observation": { - "Jolene": [], - "Deborah": [ - [ - "Deborah finds peace and comfort in roses and dahlias in her garden after losing a friend last week.", - "D6:4" - ], - [ - "A photo of the last hike with her friend Karlie brings a smile to Deborah's face whenever she sees it.", - "D6:8" - ], - [ - "Deborah traveled to Bali last year, one of her favorite places, for peace and yoga.", - "D6:10" - ] - ] - }, - "session_7_observation": { - "Jolene": [ - [ - "Jolene is studying engineering in school while balancing her partner's video games.", - "D7:1" - ], - [ - "Jolene practices yoga and meditation to stay calm amidst the busyness of life.", - "D7:1" - ], - [ - "Jolene's partner sometimes joins her in doing yoga for bonding and relaxation.", - "D7:5" - ], - [ - "Jolene has been with her partner for three years but they are not married yet.", - "D7:7" - ], - [ - "Jolene and her partner met in an engineering class in college and their romantic relationship grew from a friendship.", - "D7:9" - ], - [ - "Jolene finds teaming up with her partner on engineering projects enjoyable.", - "D7:11" - ], - [ - "Yoga and meditation help Jolene feel calmer and clearer when overwhelmed, enabling her to tackle challenges more easily.", - "D7:13" - ] - ], - "Deborah": [ - [ - "Deborah meditates, does yoga, and teaches classes in the morning.", - "D7:18" - ], - [ - "Deborah went for a morning jog in a nearby park for the first time and plans to include it in her daily routine.", - "D7:18" - ], - [ - "Deborah values exercise as it makes her feel connected to her body.", - "D7:20" - ] - ] - }, - "session_8_observation": { - "Deborah": [ - [ - "Deborah enjoys vegan stir-fry with tofu and vegetables with ginger and soy sauce.", - "D8:1" - ], - [ - "Deborah finds food a wonderful source of pleasure and nourishment.", - "D8:1" - ], - [ - "Deborah did yoga and meditation to relax last Friday.", - "D8:4" - ], - [ - "Deborah enjoys spending time outside and took Seraphim to the park last Sunday.", - "D8:8" - ], - [ - "Deborah appreciates the birds chirping and the breeze gently blowing when outdoors.", - "D8:11" - ], - [ - "Deborah reminds Jolene that efforts will bear fruit and encourages her not to give up.", - "D8:17" - ], - [ - "Deborah suggests breaking tasks into smaller pieces and setting goals for time management.", - "D8:19" - ], - [ - "Deborah offers to help Jolene with a study plan.", - "D8:19" - ] - ], - "Jolene": [ - [ - "Jolene finds lasagna as one of her favorite comforting dishes.", - "D8:2" - ], - [ - "Jolene does yoga and meditation to relax and find me-time.", - "D8:4" - ], - [ - "Jolene values quiet moments for the soul.", - "D8:6" - ], - [ - "Jolene took Seraphim to the park and explored new places.", - "D8:8" - ], - [ - "Jolene wants to be more mindful of her stress levels and mental health during exams.", - "D8:14" - ], - [ - "Jolene feels overwhelmed by exams and deadlines.", - "D8:16" - ], - [ - "Jolene expresses feeling challenged by the workload and to-do list.", - "D8:16" - ], - [ - "Jolene appreciates Deborah's tips on studying and time management.", - "D8:18" - ] - ] - }, - "session_9_observation": { - "Deborah": [ - [ - "Deborah started a yoga class in the neighborhood to share the exercise with her neighbors.", - "D9:3" - ], - [ - "Deborah hosted a yoga class for her neighbors on a Friday and enjoyed seeing everyone embrace and enjoy it.", - "D9:5" - ], - [ - "Deborah finds teaching yoga calming and derives happiness from giving people peace and awareness.", - "D9:7" - ], - [ - "Teaching yoga has helped Deborah make great friends and build community connections.", - "D9:9" - ] - ], - "Jolene": [ - [ - "Jolene is having a hard time dealing with her Engineering assignments and is struggling to keep up.", - "D9:14" - ], - [ - "Jolene is interested in discussing time management with Deborah to help with her workload.", - "D9:14" - ], - [ - "Jolene wants to find a time to chat with Deborah about time management.", - "D9:16" - ], - [ - "Jolene values having support in tough times and finds it essential in handling life challenges.", - "D9:10" - ] - ] - }, - "session_10_observation": { - "Deborah": [ - [ - "Deborah is familiar with the Pomodoro Technique for time management.", - "D10:5" - ], - [ - "Deborah creates a daily schedule or to-do list to stay organized.", - "D10:5" - ], - [ - "Deborah is aware of the Eisenhower Matrix for organizing and prioritizing tasks based on urgency and importance.", - "D10:13" - ], - [ - "Deborah recently sat by the sea with Anna, watching the sunset and talking about inspiring each other.", - "D10:17" - ] - ], - "Jolene": [ - [ - "Jolene finds it difficult to manage time and stay organized during exams and deadlines.", - "D10:2" - ], - [ - "Jolene uses the Pomodoro Technique - 25 minutes work, 5-minute break - to avoid burnout.", - "D10:4" - ], - [ - "Jolene struggles to prioritize tasks even with the Pomodoro Technique.", - "D10:4" - ], - [ - "Jolene finds daily schedules or to-do lists overwhelming when dealing with a big stack of tasks.", - "D10:6" - ], - [ - "Jolene is interested in learning about new time management methods like the Eisenhower Matrix shared by Deborah.", - "D10:12" - ], - [ - "Jolene is planning on learning to surf, has been gathering information, and got a beginners' guide to surfing.", - "D10:18" - ] - ] - }, - "session_11_observation": { - "Jolene": [ - [ - "Jolene had a breakthrough with her engineering project on Friday and found a solution to a problem.", - "D11:1" - ], - [ - "Jolene feels relaxed during yoga and enjoys creating a serene space with soothing scents like lavender and rosemary.", - "D11:5" - ], - [ - "Jolene finds music helpful during her yoga practice.", - "D11:7" - ], - [ - "Jolene loves listening to Nils Frahm and Olafur Arnalds during her practice as their music is calming and puts her in a different headspace.", - "D11:9" - ] - ], - "Deborah": [ - [ - "Deborah bought new props for her yoga class.", - "D11:2" - ], - [ - "Deborah bought a candle to improve the atmosphere of her yoga practice.", - "D11:4" - ], - [ - "Deborah enjoys scents like lavender and rosemary during her yoga practice.", - "D11:6" - ], - [ - "Deborah finds instrumental tracks with mellow melodies and rhythms helpful for creating a peaceful vibe during her practice.", - "D11:8" - ], - [ - "Deborah is listening to an album called 'Sleep' which is great for meditation and deep relaxation.", - "D11:10" - ] - ] - }, - "session_12_observation": { - "Deborah": [ - [ - "Deborah went biking with her neighbor last week.", - "D12:1" - ], - [ - "Deborah attended an art show with a friend which she found cool and inspiring.", - "D12:1" - ], - [ - "Deborah's mom was interested in art and believed it could give out strong emotions and uniquely connect people.", - "D12:3" - ], - [ - "Deborah finds comfort in ways to keep her mom's memory alive, particularly through artwork.", - "D12:5" - ] - ], - "Jolene": [ - [ - "Jolene spends time playing video games with her partner to relax after a long day.", - "D12:6" - ], - [ - "Jolene is working on a big project which is tough but exciting to watch take shape.", - "D12:10" - ], - [ - "Jolene values Deborah's support and mentions she will keep her updated on the progress of the project.", - "D12:12" - ] - ] - }, - "session_13_observation": { - "Jolene": [ - [ - "Jolene recently completed a tough engineering project.", - "D13:1" - ], - [ - "Jolene is interning at a well-known engineering firm to gain real-world experience.", - "D13:5" - ], - [ - "Jolene finds it fulfilling to apply what she has learned in school to real projects.", - "D13:7" - ], - [ - "Jolene's internship has positively impacted her life and stoked her love of engineering.", - "D13:9" - ], - [ - "Jolene is inspired to keep striving for her dreams by dedicated and hardworking colleagues.", - "D13:11" - ], - [ - "Jolene has found it tough to maintain a work-life balance during her internship.", - "D13:13" - ], - [ - "Jolene has been practicing yoga and meditation sporadically for about 3 years and found them to have a positive effect on her.", - "D13:17" - ], - [ - "Yoga and meditation have helped Jolene with stress and keeping centered.", - "D13:19" - ] - ], - "Deborah": [ - [ - "Deborah acknowledges Jolene for completing her tough engineering project with dedication.", - "D13:2" - ], - [ - "Deborah inquires about Jolene's future plans after completing the project.", - "D13:4" - ], - [ - "Deborah asks Jolene about the impact of her internship experience.", - "D13:8" - ], - [ - "Deborah suggests activities like yoga to Jolene to help her relax and unwind.", - "D13:14" - ], - [ - "Deborah recommends practicing mindful breathing for yoga to Jolene.", - "D13:24" - ] - ] - }, - "session_14_observation": { - "Deborah": [ - [ - "Deborah is preparing for a yoga retreat with friends to find peace and understanding.", - "D14:1" - ], - [ - "Deborah tried a new yoga pose called Dancer Pose (Natarajasana) and asked for feedback from Jolene.", - "D14:3" - ], - [ - "Deborah enjoys spending time with animals and finds joy in them.", - "D14:5" - ], - [ - "Deborah believes that pets make life more enjoyable and bright.", - "D14:7" - ], - [ - "Deborah shared a photo of a new yoga pose called Tree Pose during the conversation.", - "D14:15" - ], - [ - "Deborah's yoga pose was described as a symbol of peace and enlightenment.", - "D14:17" - ] - ], - "Jolene": [ - [ - "Jolene has been busy with an internship and finds it hectic.", - "D14:2" - ], - [ - "Jolene owns a pet named Seraphim, who brings comfort and cheer to her life.", - "D14:6" - ], - [ - "Jolene is pushing herself to succeed despite feeling overwhelmed.", - "D14:10" - ], - [ - "Jolene had a big breakthrough with a project, finding it exciting and rewarding.", - "D14:12" - ], - [ - "Jolene is interested in visiting a retreat like the one Deborah went to.", - "D14:18" - ], - [ - "Jolene expressed a desire for some chill time and found the idea of a retreat peaceful.", - "D14:20" - ] - ] - }, - "session_15_observation": { - "Deborah": [ - [ - "Deborah started a running group with Anna to connect with people who care about fitness.", - "D15:1" - ], - [ - "Deborah has many photos related to the running group activity.", - "D15:3" - ], - [ - "Deborah organizes workshops and events to practice mindfulness, self-care, and create a community.", - "D15:11" - ], - [ - "Deborah's workshops involve activities like yoga, meditation, and self-reflection to promote mental and emotional well-being.", - "D15:13" - ], - [ - "Deborah finds it rewarding to witness growth and transformation in participants of the workshops.", - "D15:15" - ], - [ - "Deborah prefers cats over dogs as pets.", - "D15:25" - ], - [ - "Deborah takes her cats for a run in the park every morning and evening.", - "D15:27" - ] - ], - "Jolene": [ - [ - "Jolene is trying to add workouts into her studying schedule.", - "D15:2" - ], - [ - "Jolene games with her partner and finds it a great way to bond and get closer.", - "D15:8" - ], - [ - "Jolene loves playing 'It Takes Two' with her partner and finds it competitive and a great bonding activity.", - "D15:10" - ], - [ - "Jolene has a snake as a pet, finds it calming, and a great connection with nature.", - "D15:18" - ], - [ - "Jolene had a 'snake adventure' where her snake got out, but she found her snuggling under the bed after hours of searching.", - "D15:20" - ], - [ - "Jolene takes photos of nature and finds nature to be refreshing and a way to hit a reset button.", - "D15:29" - ], - [ - "Jolene loves going for walks to take in nature and finds peaceful spots like the lake surrounded by trees to be calming and tranquil.", - "D15:32" - ], - [ - "Jolene plans to go together to the peaceful spot near the lake with Deborah.", - "D15:38" - ] - ] - }, - "session_16_observation": { - "Deborah": [ - [ - "Deborah started a project for a community cleanup and is raising funds for it.", - "D16:1" - ], - [ - "Deborah offered support and help to Jolene when she faced a setback in her project.", - "D16:3" - ], - [ - "Deborah mentioned pets as a source of love and comfort during tough times.", - "D16:5" - ], - [ - "Deborah recommended self-care routines like yoga to Jolene to stay balanced and grounded.", - "D16:9" - ], - [ - "Deborah shared her favorite gentle yoga flow routine focused on breathing and grounding to find chill.", - "D16:15" - ] - ], - "Jolene": [ - [ - "Jolene has faced a huge setback in her project recently, causing frustration and depression.", - "D16:2" - ], - [ - "Jolene finds comfort and distraction through her pet Susie and video games during tough times.", - "D16:4" - ], - [ - "Jolene adopted Susie two years ago when feeling lonely.", - "D16:6" - ], - [ - "Jolene practices self-care through yoga and meditation to stay balanced and grounded.", - "D16:10" - ], - [ - "Jolene expressed interest in new routines to mix things up for self-care.", - "D16:12" - ], - [ - "Jolene looks forward to trying out Deborah's favorite gentle flow yoga routine to find chill.", - "D16:16" - ] - ] - }, - "session_17_observation": { - "Deborah": [ - [ - "Deborah made a meditation guide for her yoga retreat.", - "D17:1" - ] - ], - "Jolene": [ - [ - "Jolene found meditation helpful to regain clarity after losing work files.", - "D17:2" - ], - [ - "Jolene designed notebooks inspired by her love for space and engines with elements like galaxies and circuitry.", - "D17:6" - ], - [ - "Jolene is working on a prototype project aimed at revolutionizing aerial surveillance.", - "D17:10" - ], - [ - "Jolene aims to create a more productive and affordable aerial surveillance system to help respond to emergencies and monitor the environment.", - "D17:12" - ] - ] - }, - "session_18_observation": { - "Jolene": [ - [ - "Jolene is finding it challenging to juggle her engineering studies, relationship, and personal growth.", - "D18:1" - ], - [ - "Jolene has started using a bullet journal as a time management strategy to track tasks and stay organized.", - "D18:3" - ], - [ - "Jolene finds it satisfying to cross tasks off her list in the bullet journal.", - "D18:5" - ], - [ - "Jolene finds a particular quote in her bullet journal spread motivating and uses it as a reminder to stick to her goals and never give up.", - "D18:7" - ], - [ - "Jolene is interested in destressing and trying mindfulness as a way to find calm in her life.", - "D18:9" - ] - ], - "Deborah": [ - [ - "Deborah suggests that finding ways to restore balance, take time for oneself, and recognize personal needs is important.", - "D18:2" - ], - [ - "Deborah led a meditation yoga session to the elderly at a local care home during sunset, indicating her interest in mindfulness and finding peace in nature.", - "D18:8" - ], - [ - "Deborah mentions that mindfulness is a huge part of her life and offers to assist Jolene with her mindfulness journey.", - "D18:10" - ], - [ - "Deborah encourages Jolene to focus on her goals, not give up, and offers help in starting mindfulness practice.", - "D18:12" - ] - ] - }, - "session_19_observation": { - "Deborah": [ - [ - "Deborah teaches yoga and spends time with the community.", - "D19:3" - ], - [ - "Deborah organized a yoga event last month that included yoga, food stalls, and live music.", - "D19:5" - ], - [ - "Deborah prioritizes and manages her time effectively by making a schedule and setting aside specific time for studying and pursuing hobbies.", - "D19:5" - ], - [ - "Deborah used to play video games, finding it a good way to relax.", - "D19:7" - ], - [ - "Deborah cherishes special memories with her mom at a park, including watching a beautiful sunset together.", - "D19:21" - ] - ], - "Jolene": [ - [ - "Jolene bought a console as a gift for her partner on the 17th.", - "D19:2" - ], - [ - "Jolene's partner enjoys playing the console she bought.", - "D19:2" - ], - [ - "Jolene's studies in engineering are going strong.", - "D19:2" - ], - [ - "Jolene values balance in her life, mentioning it has been key for her lately.", - "D19:2" - ], - [ - "Jolene enjoys video games and recommends Zelda BOTW and Animal Crossing: New Horizons.", - "D19:8" - ], - [ - "Jolene and her partner play Overcooked 2 for bets and enjoy it for its chaotic cooking.", - "D19:10" - ], - [ - "Jolene and her partner enjoy spending time outdoors and exploring nature together.", - "D19:15" - ] - ] - }, - "session_20_observation": { - "Jolene": [ - [ - "Jolene received a new game called Battlefield 1 for her console last week.", - "D20:1" - ], - [ - "Jolene practices yoga and meditation to relax and stay focused.", - "D20:11" - ], - [ - "Jolene has been doing yoga for 3 years as a way to escape studying and work stress.", - "D20:21" - ] - ], - "Deborah": [ - [ - "Deborah went to a place that held a lot of memories for her.", - "D20:2" - ], - [ - "Deborah felt nostalgia, longing, and grateful for memories when visiting a place that held memories.", - "D20:4" - ], - [ - "Deborah's mom loved flowers, and they always made her happy.", - "D20:6" - ], - [ - "Deborah practices yoga and meditation to find balance and inner peace.", - "D20:12" - ] - ] - }, - "session_21_observation": { - "Deborah": [ - [ - "Deborah attended a yoga retreat near her mom's place last week and found it life-changing.", - "D21:1" - ], - [ - "Deborah finds life super meaningful lately and believes in growth through self-reflection.", - "D21:3" - ] - ], - "Jolene": [ - [ - "Jolene's goal is to be successful in her field, make a positive impact, and has been working towards it by studying, attending workshops, and networking.", - "D21:6" - ], - [ - "Jolene recently presented at a virtual conference and received positive feedback, confirming she is on the right track.", - "D21:6" - ], - [ - "Jolene is focusing on studying, gaining more experience, and considering more internships to enhance her skills.", - "D21:8" - ] - ] - }, - "session_22_observation": { - "Deborah": [ - [ - "Deborah has a partner and they had an emotional chat about the influence of their loved ones' values.", - "D22:2" - ], - [ - "Deborah cherishes memories of her loved ones and values discussing relationships.", - "D22:3" - ], - [ - "Deborah is supportive of Jolene's plans to add values to her engineering projects.", - "D22:5" - ], - [ - "Deborah engages in self-care activities like yoga and shares a photo of nature during a yoga session.", - "D22:13" - ], - [ - "Deborah has two cats named Luna and Max, the latter being her mother's cat.", - "D22:21, D22:23" - ], - [ - "Max, Deborah's mother's cat, is 8 years old.", - "D22:27" - ], - [ - "Luna, the shelter cat, is 5 years old.", - "D22:29" - ] - ], - "Jolene": [ - [ - "Jolene is an engineering student with a passion for using her talents to help solve important problems and make the world a better place.", - "D22:6" - ], - [ - "Jolene is interested in sustainable initiatives, developing solutions for environmental issues, and getting involved with social causes.", - "D22:8" - ], - [ - "Jolene aims to work on projects involving renewable energy like solar and providing clean water to those with limited access.", - "D22:10, D22:12" - ], - [ - "Jolene practices yoga and meditation to recharge and relieve tension.", - "D22:16" - ], - [ - "Jolene's snakes enjoy watching her chill and provide a sense of calm.", - "D22:18" - ] - ] - }, - "session_23_observation": { - "Jolene": [ - [ - "Jolene recently went on a trip to Rio de Janeiro with her partner, where they attended yoga classes and visited cafes.", - "D23:1" - ], - [ - "Jolene meditates at a nearby tranquil spot to help make sense of things and relieve stress.", - "D23:9" - ], - [ - "Jolene has a new plant as a reminder to nurture herself and embrace fresh starts.", - "D23:29" - ] - ], - "Deborah": [ - [ - "Deborah visited her mom's house last month, which holds a special place in her heart as a symbol of her mom's strength and love.", - "D23:4" - ], - [ - "Deborah has a favorite spot where she goes to ponder and let things go.", - "D23:12" - ], - [ - "Deborah took a beautiful photo on one of her excursions in Rio de Janeiro three years ago.", - "D23:18" - ], - [ - "Deborah received a quote from a friend that says \"Let go of what no longer serves you.\"", - "D23:22" - ] - ] - }, - "session_24_observation": { - "Deborah": [ - [ - "Deborah went to a cool event last week aimed at supporting each other.", - "D24:1" - ], - [ - "Deborah attended a community meetup last Friday where stories were shared, emphasizing the importance of relationships.", - "D24:3" - ], - [ - "Deborah treasures her relationships with her yoga pals, considering them her second family.", - "D24:5" - ], - [ - "Deborah's mother was a big fan and a motivating source when she started doing yoga.", - "D24:5" - ] - ], - "Jolene": [ - [ - "Jolene has been focusing on studying and her relationship with her partner.", - "D24:2" - ], - [ - "Jolene enjoys taking little trips to the beach with her partner to relax.", - "D24:2" - ], - [ - "Jolene's passion for video games started when she was 10, which her parents supported.", - "D24:6" - ], - [ - "Jolene's favorite game to play with her mom is 'Monster Hunter: World', appreciating the immersive story and open-world gaming.", - "D24:10" - ], - [ - "Jolene is studying hard for upcoming finals and plans to take a trip to relax and recharge afterward.", - "D24:14" - ] - ] - }, - "session_25_observation": { - "Jolene": [ - [ - "Jolene signed up for a meditation course at a retreat near a lake to learn new techniques with her partner.", - "D25:1" - ], - [ - "Jolene finds meditation easy and believes it makes a big difference to health.", - "D25:3" - ], - [ - "Meditation helps Jolene stay balanced during her studies.", - "D25:5" - ], - [ - "Jolene finds her project tough but continues to work on it.", - "D25:7" - ] - ], - "Deborah": [ - [ - "Deborah incorporates meditation into her routine and can't imagine her life without it.", - "D25:4" - ], - [ - "Deborah recently saw a wonderful sunrise which calmed her.", - "D25:12" - ], - [ - "Deborah enjoys beach walks as they relax her.", - "D25:13" - ], - [ - "Deborah was glad her photo reminded Jolene of good memories from a beach getaway.", - "D25:14" - ] - ] - }, - "session_26_observation": { - "Deborah": [ - [ - "Deborah had to cancel a yoga getaway due to a storm.", - "D26:1" - ], - [ - "Deborah found comfort in work and spending time at home after the setback.", - "D26:3" - ], - [ - "Deborah suggested setting up a coffee date to learn more about Jolene's strategies for planning activities.", - "D26:9" - ], - [ - "Deborah complimented a peaceful picture and mentioned a hidden coffee shop near her.", - "D26:13" - ], - [ - "Deborah already had plans on Wednesday and couldn't meet Jolene then.", - "D26:15" - ], - [ - "Deborah agreed to meet Jolene on Friday for a coffee date.", - "D26:19" - ], - [ - "Deborah requested Jolene to grab her some interesting books.", - "D26:21" - ] - ], - "Jolene": [ - [ - "Jolene and her partner planned a camping trip to connect with nature and practice yoga.", - "D26:4" - ], - [ - "Jolene mentioned having a routine involving classes, studying, and personal time to stay balanced.", - "D26:6" - ], - [ - "Jolene suggested meeting up at a cafe next Monday for a coffee date.", - "D26:10" - ], - [ - "Jolene proposed to meet on Wednesday at 4 for the coffee date, which Deborah already had plans for.", - "D26:12" - ], - [ - "Jolene suggested meeting on Friday at 5 after Deborah couldn't make it on Wednesday, mentioning she needed to sort out books from a bookcase.", - "D26:18" - ], - [ - "Jolene expressed excitement for the upcoming coffee date with Deborah.", - "D26:20" - ] - ] - }, - "session_27_observation": { - "Jolene": [ - [ - "Jolene and her partner traveled to a meditation retreat in Phuket for a few weeks.", - "D27:1" - ], - [ - "At the retreat, Jolene realized the importance of relaxation, self-care, and balance alongside her engineering studies.", - "D27:3" - ], - [ - "Jolene found inspiration and refreshment in the beauty of nature at the retreat.", - "D27:3" - ], - [ - "Jolene was reminded at the retreat to appreciate the journey and not just focus on the finish line, as she tends to get consumed with hitting her goals.", - "D27:5" - ], - [ - "Jolene is trying to be more mindful and grateful to appreciate the small joys in life.", - "D27:7" - ], - [ - "Jolene is experiencing a new level of joy and happiness.", - "D27:9" - ] - ], - "Deborah": [ - [ - "Deborah was curious to hear more about Jolene's reflections from the meditation retreat and how they changed her.", - "D27:2" - ], - [ - "Deborah also sees the importance of appreciating small moments in life to boost happiness.", - "D27:6" - ], - [ - "Deborah believes that practicing mindfulness and gratitude can positively impact day-to-day life.", - "D27:8" - ], - [ - "Deborah played a card game about cats where you can attack your opponent with the cards, and she plans to play it with Jolene.", - "D27:12" - ] - ] - }, - "session_28_observation": { - "Deborah": [ - [ - "Deborah reconnected with her late mother's friends, which brought up a mix of emotions but overall was comforting.", - "D28:1" - ], - [ - "Deborah's experience with her mother's friends allowed her to appreciate her mother more and gain a new perspective on her life.", - "D28:5" - ], - [ - "Deborah looked through her mother's photos with her friends and found it sweet and nostalgic.", - "D28:7" - ], - [ - "Deborah believes that photos and memories can help in appreciating the special bond with loved ones and the strength of human relationships.", - "D28:9" - ], - [ - "The beach where Deborah got married and discovered her love for surfing holds special memories of joy and peace for her.", - "D28:11" - ], - [ - "Deborah finds doing yoga on the beach with the ocean, sand, and fresh air peaceful and a perfect way to take care of herself.", - "D28:15" - ] - ], - "Jolene": [ - [ - "Jolene finds comfort and calmness in creating a serene yoga space with candles, oils, and soothing music.", - "D28:16" - ], - [ - "Jolene uses essential oils and soothing music to create a peaceful atmosphere for relaxation during yoga and meditation.", - "D28:18" - ], - [ - "Jolene's room is her haven for peace and rest where she goes to relax and recharge after a busy day.", - "D28:22" - ], - [ - "Jolene has a pet named Susie who has been with her for two years and brings her comfort and peace.", - "D28:26" - ], - [ - "Jolene believes pets like Susie make life brighter and bring comfort and peace.", - "D28:30" - ] - ] - }, - "session_29_observation": { - "Deborah": [ - [ - "Deborah ran a free gardening class for the community with her neighbor.", - "D29:1" - ], - [ - "Deborah spent time remembering her mom by visiting her old house and sitting on a bench last Sunday.", - "D29:5" - ], - [ - "Deborah's mom had a big passion for cooking, making amazing meals filled with love and warmth.", - "D29:7" - ], - [ - "Deborah loved when her mom would bake pineapple birthday cakes for her when she was a kid.", - "D29:9" - ], - [ - "Deborah showed Jolene a big bookshelf she has.", - "D29:19" - ] - ], - "Jolene": [ - [ - "Jolene tried a scuba diving lesson last Friday and enjoyed it, hoping to become a certified diver one day.", - "D29:4" - ], - [ - "Jolene used to bake cookies with someone close to her.", - "D29:12" - ], - [ - "Jolene mentioned reading a self-discovery book in her bathroom once that really resonated with her.", - "D29:22" - ], - [ - "Jolene likes the warm, gooey chocolate and soft, buttery cookie.", - "D29:14" - ], - [ - "Jolene mentioned having an aesthetic vibe in her bathroom and finding peace and relaxation at the beach.", - "D29:24" - ], - [ - "Jolene just started learning about surfing but hasn't gone yet.", - "D29:30" - ], - [ - "Jolene agreed to plan a surfing adventure with Deborah for next month, pending schedule confirmation.", - "D29:34" - ] - ] - }, - "session_30_observation": { - "Deborah": [ - [ - "Deborah had a great time at a music festival with her pals where she enjoyed dancing and feeling the magical music vibes.", - "D30:1" - ], - [ - "Deborah reminisces about her mom singing lullabies to her, cherishing those memories.", - "D30:1" - ], - [ - "Deborah mentioned a gorgeous blossom tree near her home that bloomed magically every spring.", - "D30:11" - ], - [ - "Watching the tree bloom filled Deborah with awe and appreciation for the beauty of life.", - "D30:13" - ] - ], - "Jolene": [ - [ - "Jolene went to a yoga retreat with her partner where they experienced a peaceful environment with an amazing view during yoga at sunrise.", - "D30:4" - ], - [ - "Jolene finds peace and connection to nature and herself while doing yoga with beautiful backdrops.", - "D30:6" - ], - [ - "Jolene and her partner came across a waterfall oasis during a hike where she felt peaceful and worries disappeared.", - "D30:8" - ], - [ - "Jolene plans to reconnect with nature again the following month.", - "D30:10" - ], - [ - "Jolene finds inspiration in the small things and believes in continuous growth through obstacles.", - "D30:14" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Deborah and Jolene met at 4:06 pm on 23 January, 2023. They discussed their busy weeks and memories of their deceased mothers. Deborah shared about her mother's old house, while Jolene mentioned her room in her mother's house. They talked about special items that reminded them of their mothers. Jolene discussed a pendant symbolizing freedom, inspiring her to pursue goals. Deborah expressed her goal to teach yoga and bring peace to her community. Yoga helped Deborah find peace during a difficult time. Jolene found Deborah's journey inspiring, and the conversation ended with them looking forward to the next chat.", - "session_2_summary": "Deborah informed Jolene about her father's recent passing, recounting how she copes with grief by cherishing memories and spending time with family. Jolene expressed sympathy and inquired about how Deborah and her family were coping. They discussed Deborah's parents, her relationship, the impact of yoga on her life, and her special connection to her old home where she practices yoga. Deborah shared memories of her mother and her hobbies, while Jolene talked about her pet snakes and gaming hobby with her partner. They exchanged well wishes at the end of the conversation.", - "session_3_summary": "Jolene and Deborah conversed at 7:03 pm on 1 February, 2023. Jolene shared about her challenging but fun robotics project that tests her creativity and problem-solving skills. Deborah praised her and asked about her feelings when she first received the project. Jolene felt a mix of excitement and nerves initially but now enjoys working on it, comparing it to solving a puzzle and finding designing and programming solutions. Deborah met her new neighbor at yoga in the park and they discussed the benefits of yoga in their lives. Jolene mentioned planning to play console games with her partner instead of resuming yoga. Deborah acknowledged the importance of having shared activities with her partner. The conversation ended with Jolene saying goodbye and looking forward to meeting again.", - "session_4_summary": "Jolene and Deborah caught up at 9:48 am on February 4, 2023. Jolene shared her recent success in creating a water purifier for a rural community, showcasing her engineering skills. Deborah praised her and encouraged Jolene to continue making a positive impact through engineering. Deborah also mentioned her passion for teaching yoga and bonding with Anna, sharing a modified pose. They discussed favorite books, nature spots, and reflecting on loved ones. Deborah shared how a bouquet from a friend gave her hope, while Jolene noted the power of small gestures. Both emphasized finding strength in memories and supporting each other through tough times. They exchanged warm goodbyes, with Deborah encouraging Jolene to stay in touch and Jolene expressing gratitude for the support.", - "session_5_summary": "Jolene informed Deborah about her mini retreat last Wednesday, which gave her a new outlook on life and boosted her confidence. She discussed her engineering project accomplishments, focusing on green technology to help disadvantaged areas and her idea for a volunteer program teaching STEM to underprivileged kids. Deborah supported the idea, and Jolene mentioned plans to work with local schools/centers for workshops with guest speakers. Jolene shared sketches with Deborah, who offered further assistance if needed. They ended the conversation with mutual appreciation and well wishes.", - "session_6_summary": "Jolene, buzzing from a night out, spoke with Deborah at 4:12 pm on 22 February, 2023. Deborah found solace in a garden after losing a friend, where she cherished memories and photos. Jolene admired the flowers and they discussed travel, with Deborah fondly recalling Bali. They also talked about trying beach yoga. Deborah encouraged Jolene to enjoy the experience and advised her to take care.", - "session_7_summary": "Jolene and Deborah caught up at 4:50 pm on 25 February 2023. Jolene shared how balancing engineering school with her partner's video games is challenging but she finds peace through yoga and meditation. Her partner sometimes joins in, bringing them closer. They've been together for three years since meeting in an engineering class, and their relationship started from a strong friendship. Deborah praised Jolene's progress and encouraged her to continue prioritizing self-care. Deborah, on the other hand, mentioned her morning routine of meditation, yoga, teaching classes, and now a morning jog for self-connection. She highlighted the importance of self-care to help others and emphasized the value of prioritizing oneself. Jolene appreciated Deborah's support and vowed to continue prioritizing self-care.", - "session_8_summary": "Deborah and Jolene discussed their favorite comforting dishes and ways to find time for themselves amidst studies and exams on 2nd March 2023 at 7:18 pm. They shared how activities like yoga, meditation, and spending time outdoors help them relax and reset. Jolene expressed concerns about managing stress and prioritizing self-care, while Deborah offered tips on studying, time management, and self-care. Deborah also promised to give Jolene a motivating mug and encouraged her to take care and relax.", - "session_9_summary": "At 11:22 am on 13 March 2023, Deborah and Jolene caught up after a long time. Deborah shared about starting a yoga class in her neighborhood and the joy it brings her to help others through it. They discussed the importance of support in tough times. Jolene mentioned her struggles in managing Engineering assignments and they decided to talk about time management soon. They agreed to find a suitable time to chat and Deborah assured Jolene of her support. Jolene thanked her and the conversation ended with well wishes.", - "session_10_summary": "Deborah and Jolene caught up at 5:35 pm on 22 March, 2023. They discussed Jolene's struggle with exams and deadlines. Jolene mentioned using the Pomodoro Technique and shared her difficulty in prioritizing tasks. Deborah suggested the Eisenhower Matrix for organizing tasks. They also talked about Jolene's dream of learning to surf. Deborah encouraged her to take the first steps and keep pushing herself. Jolene felt motivated and thanked Deborah for the support.", - "session_11_summary": "Jolene and Deborah caught up at 4:03 pm on 28 March, 2023. Jolene shared a breakthrough in her engineering project after a long time, while Deborah bought yoga props and candles to enhance her practice. They discussed scents, music, and albums for yoga, with Jolene liking Nils Frahm and Olafur Arnalds, and Deborah recommending the album \"Sleep.\" They parted ways with Jolene saying, \"See you!\" and Deborah encouraging her to \"keep up the good work.\"", - "session_12_summary": "Deborah and Jolene caught up at 4:30 pm on 9 April, 2023. Deborah shared her recent biking experience and art show visit, which reminded her of her late mom. Jolene empathized, mentioning her snakes and video games as sources of comfort. Deborah found solace in art, while Jolene found joy in spending time with her partner. They agreed that finding comfort in simple things is essential during tough times. Jolene mentioned working on a big project, and Deborah offered support. They both emphasized the importance of finding happiness and supporting each other. Jolene promised to update Deborah on the project's progress, and they bid farewell, wishing each other well.", - "session_13_summary": "Jolene and Deborah caught up at 3:56 pm on 6 June, 2023. Jolene shared her completion of a tough engineering project last month. Deborah praised her hard work. Jolene expressed pride in overcoming challenges and reaching a milestone. She mentioned interning at an engineering firm and enjoying applying her skills. Deborah inquired about the impact of the internship, to which Jolene shared it inspired her to pursue her dreams. The conversation turned to work-life balance, with Jolene finding it challenging. Deborah suggested yoga for relaxation. Jolene mentioned her yoga routine and its positive impact. Deborah recommended mindful breathing during yoga. Jolene thanked her for the advice as they said their goodbyes.", - "session_14_summary": "Deborah and Jolene caught up at 9:17 am on June 26, 2023. Deborah mentioned preparing for a yoga retreat, while Jolene spoke about her busy internship and shared photos of her aquarium pet, Seraphim. They discussed the calming effect of pets, Jolene's determination to succeed, and Deborah's successful project. Deborah showed Jolene a new yoga pose and a symbol of peace from the retreat. Jolene expressed interest in visiting a similar retreat. They emphasized the importance of finding peace and relaxation before saying goodbye.", - "session_15_summary": "Deborah and Jolene discussed their fitness routines and support systems at 7:37 pm on 9 July 2023. Deborah shared her running group experience, highlighting motivation and photo memories. Jolene talked about gaming with her partner and shared her interest in snakes as pets. Additionally, they discussed mindfulness workshops, pet care, and the calming effect of nature. They expressed mutual support and shared a desire to explore nature together.", - "session_16_summary": "On August 1, 2023, at 9:26 am, Deborah informed Jolene about her community cleanup project and how everyone was coming together for it. Jolene shared her recent setback in a project. Deborah offered support and asked about Jolene's coping mechanisms, which included her pet Susie, video games, and Deborah showed understanding about the comfort pets can bring during tough times. Jolene mentioned adopting Susie two years ago for companionship. Deborah encouraged Jolene to find strength in her pet and cherish the moments together. They discussed self-care, with Jolene mentioning yoga and meditation. Deborah suggested some yoga routines and shared a tutorial video for a gentle flow to help find balance. Jolene expressed eagerness to try it out and mentioned staying in touch to share updates. They ended the conversation planning to chat later.", - "session_17_summary": "Deborah and Jolene conversed at 8:50 pm on 12 August, 2023. Deborah mentioned creating a meditation guide for her yoga retreat, while Jolene shared how meditation helped her regain clarity after losing work files. They discussed the calming effects of meditation and Jolene's notebook designs inspired by space and technology. Jolene talked about her current project to revolutionize aerial surveillance for emergencies and environmental monitoring. Deborah admired Jolene's ambition and commitment to making a difference, offering support and encouragement. Jolene expressed gratitude for Deborah's support, and they concluded with encouraging words.", - "session_18_summary": "Jolene shared her struggles balancing engineering, her relationship, and personal growth with Deborah at 2:58 pm on August 16, 2023. Deborah advised Jolene to find balance and prioritize self-care. Jolene mentioned using a bullet journal for time management and task tracking. Deborah found Jolene's bullet journal helpful and praised her for staying organized. Jolene shared a motivational quote from her journal with Deborah. Deborah recommended mindfulness to Jolene, mentioning her own positive experience with it. Jolene expressed interest in trying mindfulness to destress. Deborah offered support and guidance for Jolene's mindfulness journey. Jolene thanked Deborah and expressed appreciation for her help. Deborah reassured Jolene of her support and encouraged her to reach out if needed before saying goodbye.", - "session_19_summary": "Deborah and Jolene spoke at 12:52 am on 19 August, 2023. Deborah shared her experience of connecting with Anna after sharing her life story, organizing a yoga event, and teaching yoga. Jolene mentioned buying a console for her partner on the 17th and managing her engineering studies. They discussed balancing hobbies and studies, with Deborah suggesting prioritizing and schedule management. Jolene recommended video games like Zelda BOTW and Animal Crossing. They also discussed playing Overcooked 2 with their partners and favorite memories of gaming. Deborah shared her favorite activity of playing detective games with her husband. They both enjoyed spending time outdoors and exploring nature. Deborah mentioned a special park near her house with a meaningful bench where she cherished memories with her mom, including watching a beautiful sunset together. Jolene acknowledged the importance of cherishing special moments.", - "session_20_summary": "Jolene and Deborah caught up at 9:11 am on 21 August, 2023. They discussed memories, appreciating the simple things in life, and finding balance through yoga and meditation. Deborah shared her moments of nostalgia and how she cherishes flowers in memory of her mom. Jolene mentioned her love for savasana in yoga. They both emphasized the importance of slowing down to appreciate life's small joys. The conversation ended with well wishes for each other to stay connected.", - "session_21_summary": "Deborah and Jolene talked at 9:34 am on 24 August, 2023. Deborah shared her experience at a yoga retreat and how it changed her perspective on life. Jolene discussed her goal of success in her field, including presenting at a virtual conference. They supported each other's achievements and future plans. Deborah encouraged Jolene to enjoy the journey towards her goals and expressed continuous support. Jolene expressed gratitude for the support and pledged to keep pushing forward while finding joy along the way.", - "session_22_summary": "Deborah and Jolene had a heartfelt conversation at 5:33 pm on 26 August 2023. They discussed the importance of remembering loved ones and how their values influence their lives. Jolene shared how her mom and partner's dad inspired perseverance and determination, guiding them in their respective pursuits of engineering and creative endeavors. They also talked about incorporating these values into their work to make a positive impact. Jolene mentioned her plans to work on projects involving renewable energy and clean water access. Deborah shared her love for yoga and meditation as self-care practices. They also discussed their pets, with Jolene admiring Deborah's care for her cats, Max and Luna.", - "session_23_summary": "Jolene and Deborah spoke at 11:46 am on 30 August 2023. Jolene shared her recent trip to Rio de Janeiro, how it introduced her to different kinds of yoga, and they visited lovely cafes. Deborah visited her mom's house last month, holding sentimental value for her, shared her reflection spot, and discussed a photo from Rio de Janeiro. They exchanged thoughts on relaxing places, meaningful quotes, and embracing positivity. Jolene shared her new plant as a symbol of self-care. Deborah encouraged her to nurture herself and embrace new beginnings. They both agreed on focusing on positivity and growth before saying goodbye.", - "session_24_summary": "Deborah and Jolene caught up at 2:14 pm on 3 September, 2023. Deborah attended a supportive event and a community meetup recently, valuing relationships. Jolene focused on studying and trips with her partner. They discussed support from family, with Deborah mentioning her yoga pals and Jolene reminiscing about playing video games with her parents. Jolene shared her love for \"Monster Hunter: World\" as a stress-reliever. Jolene has upcoming finals and plans to go on a relaxing trip. Deborah offered assistance.", - "session_25_summary": "On the evening of 6th September 2023 at 8:31 pm, Jolene excitedly shared with Deborah that she signed up for a meditation course at a lake retreat with her partner. Deborah expressed her hope that Jolene would enjoy the experience and learn new techniques. Jolene mentioned how meditation positively impacts her health and helps her stay balanced during studies. Deborah praised Jolene's strength in handling challenges and offered continued support. They discussed their recent activities, including watching sunrises and beach walks. Jolene expressed a desire to watch the sunrise together in the future. They said their goodbyes, with Deborah reminding Jolene to take care of herself and rest.", - "session_26_summary": "Deborah told Jolene about the canceled yoga getaway due to a storm at 7:39 pm on September 8, 2023. They discussed balancing busy schedules, self-care activities like yoga, and planned a coffee date at a cafe for next Wednesday at 4. Deborah suggested a hidden coffee shop near her for a future meeting. Jolene had to reschedule the coffee date to Friday at 5 due to prior plans and Deborah agreed, expressing excitement for the meet-up. They wished each other to stay safe and look forward to the upcoming coffee date.", - "session_27_summary": "Jolene and Deborah had a conversation at 2:18 pm on 12 September, 2023. Jolene shared about her meditation retreat in Phuket, emphasizing the importance of balance and inner peace. Deborah inquired about Jolene's reflections, leading to a discussion on appreciating the journey and small moments in life. They both highlighted the significance of mindfulness and gratitude for happiness. Jolene expressed her newfound joy and Deborah commended her progress. They agreed on the value of a supportive community and planned to play a card game together in the future.", - "session_28_summary": "Deborah and Jolene had a conversation at 3:09 pm on 15 September, 2023. Deborah shared how reconnecting with her late mother's friends brought up a mix of emotions, but ultimately, it was comforting. Jolene supported her, mentioning how stories about loved ones can be both tough and comforting. Deborah found the experience with her mom's friends special as it offered insight into her mother's life. They discussed how old photos and memories strengthen their appreciation for loved ones. Deborah also shared her love for surfing and yoga, highlighting how these activities bring her peace. Jolene and Deborah discussed creating calm spaces for relaxation, with Jolene mentioning her room as a haven of peace. Additionally, Jolene showed a photo of her pet Susie, whom she adores for bringing comfort and brightness into her life.", - "session_29_summary": "Deborah excitedly shared with Jolene about running a free gardening class for the community, while Jolene talked about a scuba diving lesson they had recently enjoyed. They discussed special memories, including Deborah's time spent remembering her mom and the special meals she used to cook. Jolene mentioned baking cookies with someone close and their favorite cookie to make. They also talked about books and finding cozy spaces for relaxation. The conversation ended with plans to try surfing together next month, showing their excitement for the upcoming adventure.", - "session_30_summary": "Deborah and Jolene had a conversation at 10:17 am on 20 September, 2023. Deborah shared her experience at a music festival, recalling memories of her mom singing lullabies. Jolene talked about her festival experience with her partner and a recent yoga retreat. They discussed the beauty of nature and finding inspiration in small things. Jolene planned to reconnect with nature soon, while Deborah reminisced about a blossoming tree near her home. They both emphasized the importance of finding positivity in tough times. The conversation ended with Jolene encouraging Deborah to continue finding beauty in everyday moments." - }, - "sample_id": "conv-48" - }, - { - "qa": [ - { - "question": "What kind of car does Evan drive?", - "answer": "Prius", - "evidence": [ - "D1:2", - "D1:4", - "D18:1", - "D18:3", - "D22:2" - ], - "category": 1 - }, - { - "question": "What kinds of things did Evan have broken?", - "answer": "His old Prius and his new Prius.", - "evidence": [ - "D18:1", - "D18:2", - "D18:3", - "D1:2", - "D1:4" - ], - "category": 1 - }, - { - "question": "Where has Evan been on roadtrips with his family?", - "answer": "Rockies, Jasper", - "evidence": [ - "D1:2", - "D1:4", - "D2:1" - ], - "category": 1 - }, - { - "question": "How many Prius has Evan owned?", - "answer": "two", - "evidence": [ - "D1:2", - "D1:4" - ], - "category": 1 - }, - { - "question": "Which hobby did Sam take up in May 2023?", - "answer": "painting", - "evidence": [ - "D1:11" - ], - "category": 2 - }, - { - "question": "Which country was Evan visiting in May 2023?", - "answer": "Canada", - "evidence": [ - "D2:1" - ], - "category": 3 - }, - { - "question": "How many roadtrips did Evan take in May 2023?", - "answer": "two", - "evidence": [ - "D1:4", - "D2:1" - ], - "category": 1 - }, - { - "question": "What new hobbies did Sam consider trying?", - "answer": "Painting, kayaking, hiking, cooking, running", - "evidence": [ - "D1:11", - "D2:10", - "D10:8", - "D13:6", - "D13:8", - "D20:6", - "D7:2", - "D7:4", - "D7:6", - "D21:19" - ], - "category": 1 - }, - { - "question": "What hobby did Evan start practicing a few years ago that he enjoys?", - "answer": "Watercolor painting", - "evidence": [ - "D1:14", - "D1:16", - "D8:13", - "D8:14" - ], - "category": 1 - }, - { - "question": "When did Evan go to Jasper with his family?", - "answer": "weekend before May 24, 2023", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "Which type of vacation would Evan prefer with his family, walking tours in metropolitan cities or camping trip in the outdoors?", - "answer": "camping trip in the outdoors", - "evidence": [ - "D2:1", - "D2:3", - "D19:1", - "D19:3" - ], - "category": 3 - }, - { - "question": "What health issue did Sam face that motivated him to change his lifestyle?", - "answer": "Weight problem", - "evidence": [ - "D2:6", - "D3:4", - "D24:12", - "D24:14", - "D5:5", - "D6:2", - "D7:2", - "D7:12", - "D8:1", - "D10:6", - "D12:1", - "D13:2", - "D14:1", - "D15:1", - "D16:3", - "D17:3", - "D24:20", - "D25:1", - "D25:3" - ], - "category": 1 - }, - { - "question": "When did Sam first go to the doctor and find out he had a weight problem?", - "answer": "A few days before May 24, 2023.", - "evidence": [ - "D2:6" - ], - "category": 2 - }, - { - "question": "When did Evan have his sudden heart palpitation incident that really shocked him up?", - "answer": "first week of June 2023", - "evidence": [ - "D3:1" - ], - "category": 2 - }, - { - "question": "What is Evan's favorite food?", - "answer": "Ginger snaps", - "evidence": [ - "D3:3", - "D5:5", - "D23:15", - "D22:12" - ], - "category": 1 - }, - { - "question": "What kind of unhealthy snacks does Sam enjoy eating?", - "answer": "soda, candy", - "evidence": [ - "D3:4" - ], - "category": 1 - }, - { - "question": "What recurring issue frustrates Sam at the grocery store?", - "answer": "Malfunctioning self-checkout machines.", - "evidence": [ - "D3:16", - "D22:19" - ], - "category": 1 - }, - { - "question": "When did Sam's friends mock him for being overweight?", - "answer": "Friday before 27 July 2023", - "evidence": [ - "D4:1" - ], - "category": 2 - }, - { - "question": "What kind of healthy food suggestions has Evan given to Sam?", - "answer": "flavored seltzer water, dark chocolate with high cocoa content, air-popped popcorn and fruit, veggies, healthy sandwich snacks, energy balls, grilled chicken salad with avocado", - "evidence": [ - "D3:5", - "D4:10", - "D22:10", - "D22:14", - "D24:15" - ], - "category": 1 - }, - { - "question": "Considering their conversations and personal growth, what advice might Evan and Sam give to someone facing a major life transition or challenge?", - "answer": "Evan and Sam would likely advise embracing small, consistent changes\u200b\u200b, finding stress-relieving activities like hiking\u200b\u200b, painting, and road trips\u200b\u200b, and the importance of friendship and support in navigating challenges\u200b\u200b.", - "evidence": [ - "D3:10", - "D3:15", - "D22:1", - "D8:17", - "D8:22", - "D9:8", - "D9:11", - "D14:7", - "D14:12", - "D12:7", - "D12:11" - ], - "category": 3 - }, - { - "question": "In light of the health and dietary changes discussed, what would be an appropriate gift for both Evan and Sam to encourage their healthy lifestyles?", - "answer": "a cookbook with healthy recipes or a subscription to a healthy meal delivery service.", - "evidence": [ - "D2:9", - "D3:1", - "D3:3", - "D3:5", - "D4:10", - "D14:12", - "D5:9", - "D7:3", - "D7:2", - "D7:5", - "D7:12", - "D8:1", - "D8:5", - "D8:7", - "D8:8", - "D8:12", - "D9:1" - ], - "category": 3 - }, - { - "question": "How does Evan describe the woman and his feelings for her that he met in Canada?", - "answer": "He says she's cool, incredible, like something out of a movie, and that he feels alive around her. Every moment with her is fun and energetic, also Evan feels really lucky to have someone who gets him.", - "evidence": [ - "D5:1", - "D5:3", - "D23:3" - ], - "category": 1 - }, - { - "question": "When Evan did meet his future wife?", - "answer": "week before August 7, 2023.", - "evidence": [ - "D5:1" - ], - "category": 2 - }, - { - "question": "When did Sam start working out at the gym?", - "answer": "July 28, 2023", - "evidence": [ - "D4:15" - ], - "category": 2 - }, - { - "question": "What significant event happened in Sam's life towards the end of summer 2023?", - "answer": "He fell in love with a Canadian woman", - "evidence": [ - "D5:1" - ], - "category": 2 - }, - { - "question": "Which year did Evan start taking care of his health seriously?", - "answer": "2021", - "evidence": [ - "D5:6", - "D5:7" - ], - "category": 2 - }, - { - "question": "What motivates Evan to take care of his health?", - "answer": "family, fitness tracker, thirst for adventure on interesting hikes", - "evidence": [ - "D5:9", - "D5:11", - "D5:13" - ], - "category": 1 - }, - { - "question": "What electronic device could Evan gift Sam to help him keep up with his fitness goals?", - "answer": "fitness tracker", - "evidence": [ - "D5:7" - ], - "category": 3 - }, - { - "question": "What kind of writing does Sam do to relax and cope with his health issues?", - "answer": "journalling, creative writing", - "evidence": [ - "D6:4", - "D11:7" - ], - "category": 1 - }, - { - "question": "Who did Evan meet on his trip to Canada, and who did he come back from Canada with?", - "answer": "Evan met the woman he fell in love with and returned with her.", - "evidence": [ - "D5:1", - "D6:1" - ], - "category": 1 - }, - { - "question": "When Evan get back from a vacation with his SO?", - "answer": "August 13, 2023", - "evidence": [ - "D6:1" - ], - "category": 2 - }, - { - "question": "How might Evan and Sam's experiences with health and lifestyle changes influence their approach to stress and challenges?", - "answer": "Their experiences likely lead them to view challenges as opportunities for growth and change. They both have embraced healthier lifestyles, indicating a proactive approach to managing stress and challenges.", - "evidence": [ - "D9:1 D4:4 D4:6" - ], - "category": 3 - }, - { - "question": "What recurring frustration does Evan experience?", - "answer": "Evan consistently misplaces his keys every week.", - "evidence": [ - "D6:13", - "D21:20" - ], - "category": 1 - }, - { - "question": "What is the recurring dream that Sam keeps having?", - "answer": "he's flying over a cityscape.", - "evidence": [ - "D6:14", - "D24:22" - ], - "category": 1 - }, - { - "question": "What accidents has Evan's son faced lately?", - "answer": "injured at a soccer game, fell off his bike", - "evidence": [ - "D7:1", - "D20:3" - ], - "category": 1 - }, - { - "question": "When was Evan's son injured at soccer?", - "answer": "Saturday before August 15, 2023.", - "evidence": [ - "D7:1" - ], - "category": 2 - }, - { - "question": "What kind of foods or recipes has Sam recommended to Evan?", - "answer": "grilled vegetables, grilled chicken and veggie stir-fry, poutine", - "evidence": [ - "D7:8", - "D8:7", - "D23:26" - ], - "category": 1 - }, - { - "question": "What kind of healthy meals did Sam start eating after getting a health scare?", - "answer": "salad, grilled salmon and vegetables, grilled chicken and veggie stir-fry, Beef Merlot, fruit bowl, smoothie bowl", - "evidence": [ - "D3:2", - "D8:1", - "D7:4", - "D8:7", - "D10:2", - "D11:1", - "D18:6" - ], - "category": 1 - }, - { - "question": "What role does nature and the outdoors play in Evan and Sam's mental well-being?", - "answer": "Nature and outdoor activities seem to be significant stress relievers and sources of joy for both Evan and Sam. These activities likely contribute positively to their mental well-being.", - "evidence": [ - "D22:1 D22:2 D9:10 D9:11" - ], - "category": 3 - }, - { - "question": "How many months lapsed between Sam's first and second doctor's appointment?", - "answer": "three months", - "evidence": [ - "D2:6", - "D7:2" - ], - "category": 2 - }, - { - "question": "When did Evan start taking painting classes?", - "answer": "Few days before 19 August, 2023.", - "evidence": [ - "D8:12" - ], - "category": 2 - }, - { - "question": "Which classes did Evan join in mid-August 2023?", - "answer": "painting classes", - "evidence": [ - "D8:12" - ], - "category": 2 - }, - { - "question": "How did Evan get into painting?", - "answer": "His friend got him into it by gifting him a painting and giving him some advice. The painting inspired Evan.", - "evidence": [ - "D1:14", - "D1:15", - "D1:16", - "D8:14" - ], - "category": 1 - }, - { - "question": "How often does Sam get health checkups?", - "answer": "every three months", - "evidence": [ - "D2:6", - "D7:2", - "D12:1" - ], - "category": 3 - }, - { - "question": "What kind of subjects does Evan enjoy painting?", - "answer": "nature landscapes, portraits, abstract minimalism", - "evidence": [ - "D8:20", - "D20:13", - "D20:15", - "D21:10", - "D21:14" - ], - "category": 1 - }, - { - "question": "Which places in Canada was Evan visiting in July 2023?", - "answer": "Banff, Rocky Mountains", - "evidence": [ - "D8:27", - "D9:8", - "D9:10" - ], - "category": 2 - }, - { - "question": "How do Evan and Sam use creative outlets to cope with life's challenges?", - "answer": "Evan and Sam use creative activities, like painting and writing, as therapeutic tools to express themselves and cope with stress.", - "evidence": [ - "D21:18 D21:22 D11:15 D11:19" - ], - "category": 3 - }, - { - "question": "When did Evan go skiing in Banff?", - "answer": "July 2023", - "evidence": [ - "D8:26", - "D8:27", - "D8:28" - ], - "category": 2 - }, - { - "question": "What new diet and lifestyle change did Sam adopt over time?", - "answer": "Healthy eating, exercise routine, running, hiking", - "evidence": [ - "D8:1", - "D9:1", - "D21:9", - "D22:1" - ], - "category": 1 - }, - { - "question": "Who was injured in Evan's family?", - "answer": "Evan's son and Evan himself", - "evidence": [ - "D7:1", - "D7:9", - "D7:10", - "D9:2", - "D11:2", - "D11:3" - ], - "category": 1 - }, - { - "question": "What kind of hobbies does Evan pursue?", - "answer": "painting, hiking, reading books, biking, skiing, snowboarding, ice skating, swimming, camping, kayaking", - "evidence": [ - "D1:14", - "D1:6", - "D4:8", - "D6:1", - "D8:30", - "D9:6", - "D25:8", - "D25:10" - ], - "category": 1 - }, - { - "question": "What challenges does Sam face in his quest for a healthier lifestyle, and how does he address them?", - "answer": "Sam faces challenges like maintaining motivation and making dietary changes. He addresses them by enrolling in cooking classes and seeking support from friends like Evan.", - "evidence": [ - "D4:2", - "D4:6", - "D14:1", - "D14:2" - ], - "category": 3 - }, - { - "question": "Which activity do Evan and Sam plan on doing together during September 2023?", - "answer": "painting", - "evidence": [ - "D10:12", - "D10:13", - "D10:14" - ], - "category": 2 - }, - { - "question": "When did Evan and Sam decide to paint together?", - "answer": "Saturday after 11 September, 2023.", - "evidence": [ - "D10:12", - "D10:13", - "D10:14" - ], - "category": 2 - }, - { - "question": "What personal health incidents does Evan face in 2023?", - "answer": "heart palpitations, twisted ankle, twisted ankle", - "evidence": [ - "D3:1", - "D9:2", - "D11:2" - ], - "category": 1 - }, - { - "question": "What recurring adventure does Evan have with strangers?", - "answer": "Helping lost tourists and experiencing unexpected adventures in the city.", - "evidence": [ - "D11:6", - "D14:2" - ], - "category": 1 - }, - { - "question": "What is Sam's persistent problem with his phone?", - "answer": "His new phone malfunctioning, particularly with the navigation app.", - "evidence": [ - "D11:15", - "D14:1" - ], - "category": 1 - }, - { - "question": "Which US state was Sam travelling in during October 2023?", - "answer": "California", - "evidence": [ - "D13:14" - ], - "category": 3 - }, - { - "question": "When did Evan start lifting weights?", - "answer": "October 2022", - "evidence": [ - "D12:2" - ], - "category": 2 - }, - { - "question": "When did Sam and his friend decide to try kayaking?", - "answer": "October 14, 2023", - "evidence": [ - "D13:10" - ], - "category": 2 - }, - { - "question": "Which new activity does Sam take up in October 2023?", - "answer": "kayaking", - "evidence": [ - "D13:8" - ], - "category": 2 - }, - { - "question": "What kind of stress was Sam dealing with in October 2023?", - "answer": "work-related stress", - "evidence": [ - "D13:4" - ], - "category": 2 - }, - { - "question": "What health scares did Sam and Evan experience?", - "answer": "Sam faced a health scare with stomach pains that turned out to be gastritis, prompting him to rethink his health habits. Evan, on the other hand, experienced two separate incidents: a sudden heart palpitation incident and a different event involving a misunderstanding during a medical check-up. These experiences have significantly influenced their perspectives on health and well-being.", - "evidence": [ - "D3:1", - "D14:1", - "D14:2", - "D17:2" - ], - "category": 1 - }, - { - "question": "When was Sam in the ER?", - "answer": "weekend before 17 October, 2023.", - "evidence": [ - "D14:1" - ], - "category": 2 - }, - { - "question": "Which ailment does Sam have to face due to his weight?", - "answer": "gastritis", - "evidence": [ - "D2:6", - "D7:2", - "D12:1", - "D14:1" - ], - "category": 1 - }, - { - "question": "Does Evan live close to a beach or mountains?", - "answer": "beach", - "evidence": [ - "D16:16", - "D16:18", - "D16:20" - ], - "category": 3 - }, - { - "question": "When did Evan lose his job?", - "answer": "end of October 2023", - "evidence": [ - "D16:10" - ], - "category": 2 - }, - { - "question": "When did Evan and Sam planned a trip to the beach together?", - "answer": "December, 2023", - "evidence": [ - "D16:24" - ], - "category": 2 - }, - { - "question": "What was Sam doing on December 4, 2023?", - "answer": "Attending a Weight Watchers meeting", - "evidence": [ - "D18:6" - ], - "category": 2 - }, - { - "question": "Which two significant life events occur in Evan's life in December 2023 with his partner?", - "answer": "his partner gets pregnant and they get married", - "evidence": [ - "D19:1", - "D21:2" - ], - "category": 1 - }, - { - "question": "How long did Evan and his partner date before getting married?", - "answer": "four months", - "evidence": [ - "D5:1", - "D21:1" - ], - "category": 2 - }, - { - "question": "Which major holiday season conincides with Evan's wedding?", - "answer": "Christmas", - "evidence": [ - "D21:2" - ], - "category": 3 - }, - { - "question": "Which activity did Sam resume in December 2023 after a long time?", - "answer": "hiking", - "evidence": [ - "D20:6", - "D22:1" - ], - "category": 1 - }, - { - "question": "When is Evan planning a big family reunion?", - "answer": "Summer 2024", - "evidence": [ - "D19:11" - ], - "category": 2 - }, - { - "question": "When did Evan's son fall off his bike?", - "answer": "Thursday before December 17, 2023.", - "evidence": [ - "D20:3" - ], - "category": 2 - }, - { - "question": "When did Evan announce his marriage to his extended family?", - "answer": "January 5, 2024", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "When did Evan finish the painting that's hanging in the exhibit?", - "answer": "few days before 17 December, 2023.", - "evidence": [ - "D20:13", - "D20:15" - ], - "category": 2 - }, - { - "question": "How does Evan spend his time with his bride after the wedding?", - "answer": "family get-together, honeymoon in Canada to see snowy landscapes, ski, taste local cuisine and do some snowshoeing", - "evidence": [ - "D23:15", - "D23:23", - "D23:25", - "D24:9" - ], - "category": 1 - }, - { - "question": "Who did Evan tell about his marriage?", - "answer": "To Sam, to his friends from work, and to his and his wife's families.", - "evidence": [ - "D21:2", - "D22:4", - "D22:5", - "D23:1", - "D23:5" - ], - "category": 1 - }, - { - "question": "When will Evan and his partner have their honeymoon in Canada?", - "answer": "February 2024", - "evidence": [ - "D23:23" - ], - "category": 2 - }, - { - "question": "When did Evan have a drunken night with his friends?", - "answer": "January 9, 2023", - "evidence": [ - "D24:3" - ], - "category": 2 - }, - { - "question": "What is a stress reliever for Evan?", - "answer": "Drawing, traveling, places with a beautiful view, yoga, sunsets or something comfortable for Evan", - "evidence": [ - "D1:14", - "D2:10", - "D2:11", - "D2:14", - "D8:18", - "D10:8", - "D11:8", - "D16:23", - "D18:7", - "D24:19", - "D24:21" - ], - "category": 1 - }, - { - "question": "What is a stress reliever for Sam?", - "answer": "Unhealthy snacks, sweets, yoga, places with beautiful views", - "evidence": [ - "D10:6", - "D13:2", - "D13:4", - "D16:17", - "D16:23", - "D18:8" - ], - "category": 1 - }, - { - "question": "What type of car did Evan get after his old Prius broke down?", - "answer": "new Prius", - "evidence": [ - "D1:2" - ], - "category": 4 - }, - { - "question": "How did Evan get into watercolor painting?", - "answer": "friend's advice", - "evidence": [ - "D1:16" - ], - "category": 4 - }, - { - "question": "What did Evan start doing a few years back as a stress-buster?", - "answer": "watercolor painting", - "evidence": [ - "D1:14" - ], - "category": 4 - }, - { - "question": "What advice did Evan give Sam about finding a passion?", - "answer": "keep trying new things until something sparks excitement", - "evidence": [ - "D1:18" - ], - "category": 4 - }, - { - "question": "Where did Evan take his family for a road trip on 24 May, 2023?", - "answer": "Jasper", - "evidence": [ - "D2:1" - ], - "category": 4 - }, - { - "question": "What did Evan find relaxing about his road trip to Jasper?", - "answer": "fresh air, peacefulness, cozy cabin surrounded by mountains and forests", - "evidence": [ - "D2:3" - ], - "category": 4 - }, - { - "question": "What habit is Sam trying to change in terms of diet?", - "answer": "consuming soda and candy", - "evidence": [ - "D3:4" - ], - "category": 4 - }, - { - "question": "What new suggestion did Evan give to Sam regarding his soda and candy consumption?", - "answer": "try flavored seltzer water and dark chocolate with high cocoa content", - "evidence": [ - "D3:5" - ], - "category": 4 - }, - { - "question": "What did Sam agree to try instead of soda and candy?", - "answer": "flavored seltzer water and dark chocolate with high cocoa content", - "evidence": [ - "D3:6" - ], - "category": 4 - }, - { - "question": "What frustrating issue did Sam face at the supermarket?", - "answer": "broken self-checkout machines", - "evidence": [ - "D3:16" - ], - "category": 4 - }, - { - "question": "What novel is Evan reading that he finds gripping?", - "answer": "The Great Gatsby", - "evidence": [ - "D4:10" - ], - "category": 4 - }, - { - "question": "What kind of water does Evan suggest Sam try as an alternative to soda?", - "answer": "Flavored seltzer water", - "evidence": [ - "D4:8" - ], - "category": 4 - }, - { - "question": "What does the smartwatch help Evan with?", - "answer": "tracks progress and serves as a constant reminder to keep going", - "evidence": [ - "D5:9" - ], - "category": 4 - }, - { - "question": "What does the bonsai tree symbolize for Evan?", - "answer": "strength and resilience", - "evidence": [ - "D5:17" - ], - "category": 4 - }, - { - "question": "Why did Evan decide to get the bonsai tree?", - "answer": "motivates him to keep going through tough times", - "evidence": [ - "D5:17" - ], - "category": 4 - }, - { - "question": "According to Sam, what is more important than perfection?", - "answer": "progress", - "evidence": [ - "D6:6" - ], - "category": 4 - }, - { - "question": "What did Evan suggest Sam to check out for insights into his dream?", - "answer": "dream interpretation book", - "evidence": [ - "D6:15" - ], - "category": 4 - }, - { - "question": "What did Evan mention he had been searching for fruitlessly for half an hour?", - "answer": "his keys", - "evidence": [ - "D6:13" - ], - "category": 4 - }, - { - "question": "What class is Sam taking to learn how to make healthier meals?", - "answer": "cooking class", - "evidence": [ - "D7:2" - ], - "category": 4 - }, - { - "question": "What dish did Sam make on 18 August, 2023 that turned out flavorful?", - "answer": "grilled dish with salmon and vegetables", - "evidence": [ - "D7:4" - ], - "category": 4 - }, - { - "question": "What kind of recipe did Evan request from Sam on 19 August, 2023?", - "answer": "recipes with more vegetables", - "evidence": [ - "D7:7" - ], - "category": 4 - }, - { - "question": "What food did Sam share a photo of on 19 August, 2023?", - "answer": "bowl of spinach, avocado, and strawberries", - "evidence": [ - "D8:1" - ], - "category": 4 - }, - { - "question": "What type of painting classes did Evan start taking in 2023?", - "answer": "watercolor painting classes", - "evidence": [ - "D8:12" - ], - "category": 4 - }, - { - "question": "What did Evan start painting years ago due to being inspired by a friend's gift?", - "answer": "forest scene", - "evidence": [ - "D8:14" - ], - "category": 4 - }, - { - "question": "What nature concept do watercolor painting classes emphasize according to Evan?", - "answer": "observing nature and painting what is seen", - "evidence": [ - "D8:18" - ], - "category": 4 - }, - { - "question": "What type of landscapes does Evan love painting the most?", - "answer": "sunsets over the ocean", - "evidence": [ - "D8:20" - ], - "category": 4 - }, - { - "question": "What fun activity did Evan mention doing in July 2023?", - "answer": "skiing", - "evidence": [ - "D8:26" - ], - "category": 4 - }, - { - "question": "What injury did Evan suffer from in August 2023?", - "answer": "Twisted knee", - "evidence": [ - "D9:2" - ], - "category": 4 - }, - { - "question": "What sports activity has Evan been doing to stay active while dealing with the knee injury?", - "answer": "Swimming", - "evidence": [ - "D9:6" - ], - "category": 4 - }, - { - "question": "What suggestion did Sam give to Evan to help with his knee issue?", - "answer": "Consider low-impact exercises or physical therapy", - "evidence": [ - "D9:5" - ], - "category": 4 - }, - { - "question": "What did Evan suggest Sam try as a calming hobby?", - "answer": "Painting", - "evidence": [ - "D10:8" - ], - "category": 4 - }, - { - "question": "What did Evan recommend Sam acquire to get started with painting?", - "answer": "Acrylic paints, brushes, canvas/paper, palette", - "evidence": [ - "D10:11" - ], - "category": 4 - }, - { - "question": "What activity does Evan do to keep himself busy while healing his knee?", - "answer": "Watercolor painting", - "evidence": [ - "D11:6" - ], - "category": 4 - }, - { - "question": "What painting did Evan share with Sam in October?", - "answer": "a cactus in the desert", - "evidence": [ - "D11:8" - ], - "category": 4 - }, - { - "question": "What kind of writing does Sam enjoy as a form of expression?", - "answer": "creative writing", - "evidence": [ - "D11:17" - ], - "category": 4 - }, - { - "question": "What electronics issue has been frustrating Sam lately?", - "answer": "malfunctioning navigation app on the new phone", - "evidence": [ - "D11:15" - ], - "category": 4 - }, - { - "question": "What activity did Evan start one year ago?", - "answer": "lifting weights", - "evidence": [ - "D12:2" - ], - "category": 4 - }, - { - "question": "What advice did Evan give to Sam to avoid injuries while starting weightlifting?", - "answer": "Find a trainer", - "evidence": [ - "D12:4" - ], - "category": 4 - }, - { - "question": "Where did Sam and his mate plan to try kayaking?", - "answer": "Lake Tahoe", - "evidence": [ - "D13:14" - ], - "category": 4 - }, - { - "question": "What digestive issue did Sam experience lately?", - "answer": "Gastritis", - "evidence": [ - "D14:1" - ], - "category": 4 - }, - { - "question": "What adventurous theme is emerging in Evan's life as mentioned by Sam?", - "answer": "helping lost tourists", - "evidence": [ - "D14:2" - ], - "category": 4 - }, - { - "question": "What does Evan mention about his progress at the gym to Sam?", - "answer": "gaining strength", - "evidence": [ - "D14:8" - ], - "category": 4 - }, - { - "question": "How did Evan start his transformation journey two years ago?", - "answer": "Changed his diet and started walking regularly", - "evidence": [ - "D15:8" - ], - "category": 4 - }, - { - "question": "What gift did Evan receive from a close friend?", - "answer": "1968 Kustom K-200A vintage guitar", - "evidence": [ - "D16:10" - ], - "category": 4 - }, - { - "question": "Why had Evan been going through a tough time lately?", - "answer": "Lost their job due to downsizing", - "evidence": [ - "D16:10" - ], - "category": 4 - }, - { - "question": "How does Evan describe the island he grew up on?", - "answer": "A happy place", - "evidence": [ - "D17:18" - ], - "category": 4 - }, - { - "question": "What was the main reason for Evan's frustration with his new Prius breaking down?", - "answer": "He relied on it for his active lifestyle and road trips", - "evidence": [ - "D18:1" - ], - "category": 4 - }, - { - "question": "How did Sam suggest Evan view the setback with his broken Prius?", - "answer": "As a chance to explore other ways of staying active and traveling", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "What did Sam suggest Evan try for stress relief and flexibility?", - "answer": "Yoga", - "evidence": [ - "D18:8" - ], - "category": 4 - }, - { - "question": "What did Sam offer Evan regarding yoga?", - "answer": "Support and tips", - "evidence": [ - "D18:10" - ], - "category": 4 - }, - { - "question": "What news did Evan share with Sam on 9th December 2023?", - "answer": "partner is pregnant", - "evidence": [ - "D19:1" - ], - "category": 4 - }, - { - "question": "What family event is Evan planning for next summer?", - "answer": "big family reunion", - "evidence": [ - "D19:11" - ], - "category": 4 - }, - { - "question": "What is the motto of Evan's family?", - "answer": "'Bring it on Home'", - "evidence": [ - "D19:7" - ], - "category": 4 - }, - { - "question": "According to Evan, what is important for Sam to believe in concerning his weight?", - "answer": "Your worth is not defined by your weight", - "evidence": [ - "D20:9" - ], - "category": 4 - }, - { - "question": "Who helped Evan get the painting published in the exhibition?", - "answer": "a close friend", - "evidence": [ - "D20:17" - ], - "category": 4 - }, - { - "question": "What did Sam recently start enjoying to clear his head?", - "answer": "running in the mornings", - "evidence": [ - "D21:9" - ], - "category": 4 - }, - { - "question": "What did Sam suggest Evan should do with his keys?", - "answer": "put a GPS sensor on them", - "evidence": [ - "D21:21" - ], - "category": 4 - }, - { - "question": "How did Evan feel when he painted the piece with the bird flying over it?", - "answer": "a sense of joy and freedom", - "evidence": [ - "D21:16" - ], - "category": 4 - }, - { - "question": "What did Evan suggest Sam should keep doing to find his own version of love?", - "answer": "Keep trying new things", - "evidence": [ - "D21:10" - ], - "category": 4 - }, - { - "question": "How did Evan describe the process of creating the painting with the bird flying over it?", - "answer": "embracing the creative process without restraint", - "evidence": [ - "D21:16" - ], - "category": 4 - }, - { - "question": "What did Evan want to share with his work friends?", - "answer": "getting married", - "evidence": [ - "D22:4" - ], - "category": 4 - }, - { - "question": "What did Evan share with Sam after their hiking trip?", - "answer": "a photo of a man standing on a rock looking out over a valley", - "evidence": [ - "D22:1" - ], - "category": 4 - }, - { - "question": "What did Evan offer to share with Sam after talking about healthy snacks?", - "answer": "the recipes for cookies", - "evidence": [ - "D22:12" - ], - "category": 4 - }, - { - "question": "What did Evan and his partner share with their extended family on January 5, 2024?", - "answer": "their marriage", - "evidence": [ - "D23:1" - ], - "category": 4 - }, - { - "question": "What was Evan limiting himself to on his new diet?", - "answer": "just two ginger snaps a day", - "evidence": [ - "D23:15" - ], - "category": 4 - }, - { - "question": "What sports activity did Evan and his partner try in a recent weekend?", - "answer": "Snowshoeing", - "evidence": [ - "D24:9" - ], - "category": 4 - }, - { - "question": "What advice did Evan suggest Sam seek from a doctor?", - "answer": "diet plan and low-impact exercises", - "evidence": [ - "D24:11", - "D24:14" - ], - "category": 4 - }, - { - "question": "What suggestions did Evan give for low-impact exercises?", - "answer": "swimming, yoga, walking", - "evidence": [ - "D24:17" - ], - "category": 4 - }, - { - "question": "What movie did Sam watch that motivated him to keep up with his routine?", - "answer": "The Godfather", - "evidence": [ - "D24:18" - ], - "category": 4 - }, - { - "question": "What activity helped Evan with stress and flexibility?", - "answer": "Yoga", - "evidence": [ - "D24:19" - ], - "category": 4 - }, - { - "question": "What did Evan share a photo of that was taken on a camping trip?", - "answer": "a kayak", - "evidence": [ - "D25:8" - ], - "category": 4 - }, - { - "question": "Why did Evan apologize to his partner?", - "answer": "for a drunken night", - "evidence": [ - "D25:2" - ], - "category": 4 - }, - { - "question": "How does Evan describe being out on the water while kayaking and watching the sunset?", - "answer": "peaceful", - "evidence": [ - "D25:10" - ], - "category": 4 - }, - { - "question": "What type of car did Sam get after his old Prius broke down?", - "evidence": [ - "D1:2" - ], - "category": 5, - "adversarial_answer": "new Prius" - }, - { - "question": "How did Sam get into watercolor painting?", - "evidence": [ - "D1:16" - ], - "category": 5, - "adversarial_answer": "friend's advice" - }, - { - "question": "What did Sam start doing a few years back as a stress-buster?", - "evidence": [ - "D1:14" - ], - "category": 5, - "adversarial_answer": "watercolor painting" - }, - { - "question": "Where did Sam take his family for a road trip on 24 May, 2023?", - "evidence": [ - "D2:1" - ], - "category": 5, - "adversarial_answer": "Jasper" - }, - { - "question": "What did Sam find relaxing about his road trip to Jasper?", - "evidence": [ - "D2:3" - ], - "category": 5, - "adversarial_answer": "fresh air, peacefulness, cozy cabin surrounded by mountains and forests" - }, - { - "question": "What habit is Evan trying to change in terms of diet?", - "evidence": [ - "D3:4" - ], - "category": 5, - "adversarial_answer": "consuming soda and candy" - }, - { - "question": "What frustrating issue did Evan face at the supermarket?", - "evidence": [ - "D3:16" - ], - "category": 5, - "adversarial_answer": "broken self-checkout machines" - }, - { - "question": "What novel is Sam reading that he finds gripping?", - "evidence": [ - "D4:10" - ], - "category": 5, - "adversarial_answer": "The Great Gatsby" - }, - { - "question": "What does the smartwatch help Sam with?", - "evidence": [ - "D5:9" - ], - "category": 5, - "adversarial_answer": "tracks progress and serves as a constant reminder to keep going" - }, - { - "question": "Why did Sam decide to get the bonsai tree?", - "evidence": [ - "D5:17" - ], - "category": 5, - "adversarial_answer": "motivates him to keep going through tough times" - }, - { - "question": "What did Sam mention he had been searching for fruitlessly for half an hour?", - "evidence": [ - "D6:13" - ], - "category": 5, - "adversarial_answer": "his keys" - }, - { - "question": "What class is Evan taking to learn how to make healthier meals?", - "evidence": [ - "D7:2" - ], - "category": 5, - "adversarial_answer": "cooking class" - }, - { - "question": "What dish did Sam make on 18 August, 2023 that turned out bland?", - "evidence": [ - "D7:4" - ], - "category": 5, - "adversarial_answer": "grilled dish with salmon and vegetables" - }, - { - "question": "What food did Evan share a photo of on 19 August, 2023?", - "evidence": [ - "D8:1" - ], - "category": 5, - "adversarial_answer": "bowl of spinach, avocado, and strawberries" - }, - { - "question": "What did Evan start sculpting years ago due to being inspired by a friend's gift?", - "evidence": [ - "D8:14" - ], - "category": 5, - "adversarial_answer": "forest scene" - }, - { - "question": "What nature concept do watercolor painting classes emphasize according to Sam?", - "evidence": [ - "D8:18" - ], - "category": 5, - "adversarial_answer": "observing nature and painting what is seen" - }, - { - "question": "What type of landscapes does Sam love painting the most?", - "evidence": [ - "D8:20" - ], - "category": 5, - "adversarial_answer": "sunsets over the ocean" - }, - { - "question": "What sports activity has Sam been doing to stay active while dealing with the knee injury?", - "evidence": [ - "D9:6" - ], - "category": 5, - "adversarial_answer": "Swimming" - }, - { - "question": "What activity does Sam do to keep himself busy while healing his knee?", - "evidence": [ - "D11:6" - ], - "category": 5, - "adversarial_answer": "Watercolor painting" - }, - { - "question": "What kind of writing does Evan enjoy as a form of expression?", - "evidence": [ - "D11:17" - ], - "category": 5, - "adversarial_answer": "creative writing" - }, - { - "question": "What electronics issue has been frustrating Evan lately?", - "evidence": [ - "D11:15" - ], - "category": 5, - "adversarial_answer": "malfunctioning navigation app on the new phone" - }, - { - "question": "What activity did Evan quit one year ago?", - "evidence": [ - "D12:2" - ], - "category": 5, - "adversarial_answer": "lifting weights" - }, - { - "question": "Where did Sam and his mate plan to try skydiving?", - "evidence": [ - "D13:14" - ], - "category": 5, - "adversarial_answer": "Lake Tahoe" - }, - { - "question": "What digestive issue did Evan experience lately?", - "evidence": [ - "D14:1" - ], - "category": 5, - "adversarial_answer": "Gastritis" - }, - { - "question": "How did Sam start his transformation journey two years ago?", - "evidence": [ - "D15:8" - ], - "category": 5, - "adversarial_answer": "Changed his diet and started walking regularly" - }, - { - "question": "What gift did Sam receive from a close friend?", - "evidence": [ - "D16:10" - ], - "category": 5, - "adversarial_answer": "1968 Kustom K-200A vintage guitar" - }, - { - "question": "How does Sam describe the island he grew up on?", - "evidence": [ - "D17:18" - ], - "category": 5, - "adversarial_answer": "A happy place" - }, - { - "question": "What was the main reason for Evan's frustration with his new Prius getting stolen?", - "evidence": [ - "D18:1" - ], - "category": 5, - "adversarial_answer": "He relied on it for his active lifestyle and road trips" - }, - { - "question": "What family event is Sam planning for next summer?", - "evidence": [ - "D19:11" - ], - "category": 5, - "adversarial_answer": "big family reunion" - }, - { - "question": "What is the motto of Sam's family?", - "evidence": [ - "D19:7" - ], - "category": 5, - "adversarial_answer": "'Bring it on Home'" - }, - { - "question": "Who helped Sam get the painting published in the exhibition?", - "evidence": [ - "D20:17" - ], - "category": 5, - "adversarial_answer": "a close friend" - }, - { - "question": "How did Sam feel when he painted the piece with the bird flying over it?", - "evidence": [ - "D21:16" - ], - "category": 5, - "adversarial_answer": "a sense of joy and freedom" - }, - { - "question": "How did Sam describe the process of creating the painting with the bird flying over it?", - "evidence": [ - "D21:16" - ], - "category": 5, - "adversarial_answer": "embracing the creative process without restraint" - }, - { - "question": "What did Evan and his partner keep from their extended family on January 5, 2024?", - "evidence": [ - "D23:1" - ], - "category": 5, - "adversarial_answer": "their marriage" - }, - { - "question": "What was Sam limiting himself to on his new diet?", - "evidence": [ - "D23:15" - ], - "category": 5, - "adversarial_answer": "just two ginger snaps a day" - }, - { - "question": "What dance activity did Evan and his partner try in a recent weekend?", - "evidence": [ - "D24:9" - ], - "category": 5, - "adversarial_answer": "Snowshoeing" - }, - { - "question": "What suggestions did Evan give for high-impact exercises?", - "evidence": [ - "D24:17" - ], - "category": 5, - "adversarial_answer": "swimming, yoga, walking" - }, - { - "question": "What movie did Evan watch that motivated him to keep up with his routine?", - "evidence": [ - "D24:18" - ], - "category": 5, - "adversarial_answer": "The Godfather" - }, - { - "question": "What activity hindered Evan's stress and flexibility?", - "evidence": [ - "D24:19" - ], - "category": 5, - "adversarial_answer": "Yoga" - }, - { - "question": "What did Sam share a photo of that was taken on a camping trip?", - "evidence": [ - "D25:8" - ], - "category": 5, - "adversarial_answer": "a kayak" - } - ], - "conversation": { - "speaker_a": "Evan", - "speaker_b": "Sam", - "session_1_date_time": "1:47 pm on 18 May, 2023", - "session_1": [ - { - "speaker": "Sam", - "dia_id": "D1:1", - "text": "Hey Evan, good to see you! What's new since we last met? Anything cool happening?" - }, - { - "speaker": "Evan", - "dia_id": "D1:2", - "text": "Hey Sam! Good to see you! Yeah, I just got back from a trip with my family in my new Prius." - }, - { - "speaker": "Sam", - "blip_caption": "a photo of a person's feet on a car dashboard as the sun sets", - "dia_id": "D1:3", - "text": "Wow, not bad, what happened to the old one? Where'd you go, by the way?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://res.cloudinary.com/atlasguruprod/image/upload/v1605280914/site-prod/itineraries/402c83d0-237f-11eb-85fd-4d00e2439877/omasxscl2k2xlnc2ggvj.jpg" - ], - "blip_caption": "a photo of a lake with rocks and mountains in the background", - "query": "canadian rockies sunset scenery", - "dia_id": "D1:4", - "text": "My old prius broke down, decided to get it repaired and sell it. Glad you asked, we went to Rockies, check it out." - }, - { - "speaker": "Sam", - "dia_id": "D1:5", - "text": "Wow! Looks amazing. When did you get to go there?" - }, - { - "speaker": "Evan", - "dia_id": "D1:6", - "text": "We all hiked the trails last week - the views were amazing!" - }, - { - "speaker": "Sam", - "img_url": [ - "https://storage.needpix.com/rsynced_images/family-1784512_1280.jpg" - ], - "blip_caption": "a photography of a man and a child walking through a forest", - "query": "hiking trail lush greenery bonding experience", - "dia_id": "D1:7", - "re-download": true, - "text": "Wow, that's cool. I love hiking, but it's been ages since I've done it. I did this hike with my dad way back when I was ten. Going hiking together was great fun, and really special for us." - }, - { - "speaker": "Evan", - "dia_id": "D1:8", - "text": "Aww, that's cute. How far did you two hike?" - }, - { - "speaker": "Sam", - "dia_id": "D1:9", - "text": "We hiked a good distance - quite a feat for me back then. It's definitely a great memory." - }, - { - "speaker": "Evan", - "dia_id": "D1:10", - "text": "What other hobbies have you found for yourself?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.redd.it/hbkrxuiitopb1.jpg" - ], - "blip_caption": "a photo of a person holding a paint set in a store", - "query": "paint set canvas hobbies", - "dia_id": "D1:11", - "text": "Nothing so far, but I was thinking about trying painting. Do you have any hobbies you love?" - }, - { - "speaker": "Evan", - "dia_id": "D1:12", - "text": "Cool idea, Sam! I love it. Have you tried it before?" - }, - { - "speaker": "Sam", - "dia_id": "D1:13", - "text": "Not yet, but I'm keen to give it a go. It looks like a nice way to chill and get creative." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.pinimg.com/originals/73/e4/fd/73e4fd6baf59abc2beca9fd5509a853c.jpg" - ], - "blip_caption": "a photo of a painting of a cactus in the desert", - "query": "watercolor painting sunset", - "dia_id": "D1:14", - "text": "Yep, it's a great stress-buster. I started doing this a few years back." - }, - { - "speaker": "Sam", - "dia_id": "D1:15", - "text": "Wow, that's impressive! How did you get into watercolor painting?" - }, - { - "speaker": "Evan", - "dia_id": "D1:16", - "text": "My friend got me into it and gave me some advice, and I was hooked right away!" - }, - { - "speaker": "Sam", - "dia_id": "D1:17", - "text": "Wow! I hope I can find something I'm as passionate about as you are with watercolor painting." - }, - { - "speaker": "Evan", - "dia_id": "D1:18", - "text": "You'll find it, just keep trying new things until something sparks your excitement." - }, - { - "speaker": "Sam", - "dia_id": "D1:19", - "text": "Thanks, Evan! I'm excited to try new things, should be fun!" - }, - { - "speaker": "Evan", - "dia_id": "D1:20", - "text": "No worries, Sam! Super pumped for you! Let's catch up soon and see how you're enjoying your new hobbies!" - }, - { - "speaker": "Sam", - "dia_id": "D1:21", - "text": "Yeah, I'll keep you posted. See you soon!" - }, - { - "speaker": "Evan", - "dia_id": "D1:22", - "text": "Take it easy, Evan! Can't wait to see you. Have a good one!" - } - ], - "session_2_date_time": "7:11 pm on 24 May, 2023", - "session_2": [ - { - "speaker": "Evan", - "img_url": [ - "https://1.bp.blogspot.com/-50oDf6izLpk/XtyM4RGdDgI/AAAAAAACwcA/nnQkAWVln4M3AQ4ZWAkGUfgrxjhpHbtJQCK4BGAsYHg/s4032/IMG_4686.jpg" - ], - "blip_caption": "a photo of a person holding a book in front of a lake", - "query": "jasper national park glacier", - "dia_id": "D2:1", - "text": "Hey Sam, good to hear from you! Since we last talked, lots has been happening! Last weekend, I took my family on a road trip to Jasper. It was amazing! We drove through the Icefields Parkway and the glaciers and lakes were gorgeous. I got a shot of a glacier, check it out!" - }, - { - "speaker": "Sam", - "dia_id": "D2:2", - "text": "Hey Evan, looks amazing! I've never been to Jasper, but it looks breathtaking. Tell me more about your road trip. Was it relaxing?" - }, - { - "speaker": "Evan", - "dia_id": "D2:3", - "text": "Hey Sam, thanks for asking! It was great - fresh air, peacefulness and a cozy cabin surrounded by mountains and forests made it feel like a real retreat." - }, - { - "speaker": "Sam", - "dia_id": "D2:4", - "text": "That sounds great, Evan! It's so important to take time for ourselves and find peace, especially after a hard week. Mine's been tough." - }, - { - "speaker": "Evan", - "dia_id": "D2:5", - "text": "Sorry to hear that, Sam. Is there anything I can do to help?" - }, - { - "speaker": "Sam", - "dia_id": "D2:6", - "text": "Thanks, Evan. Appreciate the offer, but had a check-up with my doctor a few days ago and, yikes, the weight wasn't great. It was pretty eye-opening." - }, - { - "speaker": "Evan", - "dia_id": "D2:7", - "text": "That must have been a challenging experience, Sam. It's tough when we have to confront our own health challenges. Remember, it's never too late to make positive changes for a healthier lifestyle. Is there anything I can do to support you in this journey?" - }, - { - "speaker": "Sam", - "dia_id": "D2:8", - "text": "Thanks, Evan. Breaking old habits isn't easy. Do you have any tips for starting the process?" - }, - { - "speaker": "Evan", - "dia_id": "D2:9", - "text": "Yeah, what worked for me was finding a fitness routine I really enjoy. It's my go-to, I love the feeling of being healthy and strong. Making it fun and finding little ways to make smarter choices in my diet really added up. Don't forget, you got this!" - }, - { - "speaker": "Sam", - "dia_id": "D2:10", - "text": "Thanks, Evan. Like you said, I've been looking for a hobby to stay motivated. I've been thinking about trying painting. Do you think it will help me de-stress?" - }, - { - "speaker": "Evan", - "dia_id": "D2:11", - "text": "Of course, Sam! Painting is a great way to relieve stress and be creative. It gives you the freedom to explore colors and textures and express feelings. I've been doing it for a few years now and it helps me find peace. But unfortunately it won't help you with your weight problem, besides painting I recommend exercising!" - }, - { - "speaker": "Sam", - "dia_id": "D2:12", - "text": "Thanks, Evan! Appreciate the encouragement. I'll give it a go and let you know how it turns out." - }, - { - "speaker": "Evan", - "dia_id": "D2:13", - "text": "Awesome, Sam! Have fun with it and don't put too much pressure on yourself. Can't wait to hear how it's going!" - }, - { - "speaker": "Sam", - "dia_id": "D2:14", - "text": "Cheers, Evan! I won't stress - just gonna enjoy it." - }, - { - "speaker": "Evan", - "dia_id": "D2:15", - "text": "Alright Sam, have fun with it! Keep me updated!" - }, - { - "speaker": "Sam", - "dia_id": "D2:16", - "text": "Thanks, Evan! Will do. Bye for now." - }, - { - "speaker": "Evan", - "dia_id": "D2:17", - "text": "Take care, Sam! I'll catch up with you later." - } - ], - "session_3_date_time": "3:55 pm on 6 June, 2023", - "session_3": [ - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/e4esfhi1ekl61.jpg" - ], - "blip_caption": "a photo of a person holding a bottle of medicine in their hand", - "query": "medication reminder health scare", - "dia_id": "D3:1", - "text": "Hey Sam! Long time no talk! How're you doing? Life's been quite the rollercoaster lately. I had a health scare last week \u2013 a sudden heart palpitation incident that really shook me up. It's been a serious wake-up call about my lifestyle." - }, - { - "speaker": "Sam", - "img_url": [ - "https://mayuris-jikoni.com/wp-content/uploads/2020/06/Rainbow-Salad-1.jpg" - ], - "blip_caption": "a photo of a plate of vegetables and a glass of milk", - "query": "salad bowl colorful veggies diet healthy options", - "dia_id": "D3:2", - "text": "Hey Evan, great hearing from you! Sorry about that, glad you're feeling better now. Trying to eat healthier these days." - }, - { - "speaker": "Evan", - "dia_id": "D3:3", - "text": "That salad looks yummy! I'm being extra careful with my health lately. I'm trying to eat less processed food and sugary snacks, even though I love ginger snaps. Have you made any changes to your diet recently?" - }, - { - "speaker": "Sam", - "dia_id": "D3:4", - "text": "Nah, no changes for me. Still enjoying my soda and candy, although I know it's not the best habit to have." - }, - { - "speaker": "Evan", - "dia_id": "D3:5", - "text": "Yeah, breaking habits can be tough. Making small changes can have a big impact later on. Have you considered swapping soda for flavored seltzer water? It's still bubbly and tasty, but without the sugar. And instead of candy, you could try dark chocolate with high cocoa content - it's a healthier option. What do you think?" - }, - { - "speaker": "Sam", - "dia_id": "D3:6", - "text": "Yeah, good idea! I'll give it a try." - }, - { - "speaker": "Evan", - "dia_id": "D3:7", - "text": "Awesome, Sam! Let me know how it goes. Making small changes can really help you live a healthier life. Don't forget - every step matters!" - }, - { - "speaker": "Sam", - "dia_id": "D3:8", - "text": "Hey Evan, thanks! Appreciate it. I'll definitely keep you posted." - }, - { - "speaker": "Evan", - "dia_id": "D3:9", - "text": "I'm here for you, Sam. Let's continue supporting each other on our health journeys. It's important to remember that progress takes time." - }, - { - "speaker": "Sam", - "dia_id": "D3:10", - "text": "Yeah, you're right. It takes time, but I'm up for keep trying and making those tiny changes." - }, - { - "speaker": "Evan", - "dia_id": "D3:11", - "text": "C'mon, keep it up! Every little bit counts, you'll get there!" - }, - { - "speaker": "Sam", - "dia_id": "D3:12", - "text": "Thanks, Evan! I appreciate your support, it means a lot to me to have you in my corner." - }, - { - "speaker": "Evan", - "img_url": [ - "https://c0.wallpaperflare.com/preview/870/384/969/adult-bodybuilding-brawny-dumbbell.jpg" - ], - "blip_caption": "a photography of a man with a beard holding a dumbble", - "query": "dumbbell workout", - "dia_id": "D3:13", - "re-download": true, - "text": "Yes, Sam! I'm here for you. Let's rock our workouts and reach our goals! Exercise clears the mind - it's amazing!" - }, - { - "speaker": "Sam", - "dia_id": "D3:14", - "text": "Wow, that's awesome! Could you give me a hand with getting started?" - }, - { - "speaker": "Evan", - "dia_id": "D3:15", - "text": "Sure Sam, I'd be glad to help. Let's get together and I'll show you some basic exercises. We'll reach our goals!" - }, - { - "speaker": "Sam", - "dia_id": "D3:16", - "text": "Cool, can't wait! Thank you. By the way, I'm coming from the shop and I had a frustrating issue at the supermarket. The self-checkout machines were all broken, my mood is terrible now!" - }, - { - "speaker": "Evan", - "dia_id": "D3:17", - "text": "Sorry you were in that situation, hopefully it won't happen again!" - }, - { - "speaker": "Sam", - "dia_id": "D3:18", - "text": "Yeah, I hope so, take care of yourself." - } - ], - "session_4_date_time": "10:52 am on 27 July, 2023", - "session_4": [ - { - "speaker": "Sam", - "dia_id": "D4:1", - "text": "Hey Evan, I need to talk to you. My friends were mocking my weight last Friday and it hurt. That made me realize I need to make changes." - }, - { - "speaker": "Evan", - "dia_id": "D4:2", - "text": "Hey Sam, sorry about that. Don't worry, progress takes time. Let's work on it together." - }, - { - "speaker": "Sam", - "dia_id": "D4:3", - "text": "Thanks for the support, Evan. I'm working on my health and getting active!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/ddd782loz4w81.jpg" - ], - "blip_caption": "a photo of a set of five cards with the words let it shine", - "query": "gym membership card", - "dia_id": "D4:4", - "text": "That's great, Sam! I struggled with my health a few years ago, but stuck with it. Here's a reminder of my commitment - my gym membership card. It's not just about exercise, diet and lifestyle changes also play a big role." - }, - { - "speaker": "Sam", - "dia_id": "D4:5", - "text": "That's awesome, Evan! What do you think made the biggest impact on your health journey?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1561753439/k/Photo/Series/2019-07-power-hour-eat-the-rainbow/IMG_3090.jpg" - ], - "blip_caption": "a photo of a table full of fresh produce and vegetables", - "query": "colorful fruits and vegetables", - "dia_id": "D4:6", - "text": "I made some dietary changes, like cutting down on sugary snacks and eating more veggies and fruit, and it made a big impact on my health. Have you considered any changes?" - }, - { - "speaker": "Sam", - "dia_id": "D4:7", - "text": "Yep, I'm reducing my soda and candy intake. It's tough, but I'm determined to make a change." - }, - { - "speaker": "Evan", - "img_url": [ - "https://www.tasteofhome.com/wp-content/uploads/2018/09/IMG_3475-1.jpg" - ], - "blip_caption": "a photo of a table with a variety of sodas and water bottles", - "query": "flavored seltzer water bottles", - "dia_id": "D4:8", - "text": "Go for it, Sam! It's tough at first, but you got this. Try flavored seltzer water instead. It can be a great alternative to soda. Btw I can't stop thinking about that new mystery novel I started. It's so gripping!" - }, - { - "speaker": "Sam", - "dia_id": "D4:9", - "text": "Sounds good, Evan. I've tried it before and it was nice. Do you have any ideas for low-calorie snacks to pair with it? And what's the novel?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://jordecor.com/wp-content/uploads/2018/11/Photo-Nov-13-11-00-19-AM.jpg" - ], - "blip_caption": "a photo of a table with bowls of fruit and a bottle of alcohol", - "query": "flavored seltzer bowl colorful fruits", - "dia_id": "D4:10", - "text": "Definitely, how about some flavored seltzer with some air-popped popcorn or fruit? It's yum and healthy! The novel I'm reading is \"The Great Gatsby\"." - }, - { - "speaker": "Sam", - "dia_id": "D4:11", - "text": "Yum, that sounds good! Thanks! And I'll definitely read that novel sometime." - }, - { - "speaker": "Evan", - "img_url": [ - "https://c1.peakpx.com/wallpaper/264/906/323/still-items-things-book-wallpaper-preview.jpg" - ], - "blip_caption": "a photography of a book with a green apple next to it", - "query": "quote progress not perfection", - "dia_id": "D4:12", - "re-download": true, - "text": "No worries, Sam! Focus on healthy swaps and taking small steps. Stay upbeat!" - }, - { - "speaker": "Sam", - "dia_id": "D4:13", - "text": "That reminder is inspiring. Thanks for reminding me to focus on progress, not perfection." - }, - { - "speaker": "Evan", - "blip_caption": "a photo of a woman with a backpack on a mountain", - "dia_id": "D4:14", - "text": "By the way, have you thought about exercising? Trust me, it's just as important as eating right." - }, - { - "speaker": "Sam", - "dia_id": "D4:15", - "text": "Starting tomorrow, I will go to the gym and exercise regularly. The sooner I start, the sooner I will see the rewards of this activity." - }, - { - "speaker": "Evan", - "dia_id": "D4:16", - "text": "That's awesome, Sam! It's such a rewarding and tough activity - keep going and have fun!" - }, - { - "speaker": "Sam", - "dia_id": "D4:17", - "text": "Thanks, Evan! Your support means a lot. I really appreciate it." - }, - { - "speaker": "Evan", - "dia_id": "D4:18", - "text": "No worries, you've got this!" - }, - { - "speaker": "Sam", - "dia_id": "D4:19", - "text": "Thanks, Evan. I really appreciate it." - }, - { - "speaker": "Evan", - "dia_id": "D4:20", - "text": "No worries, Sam. I'm here if you need me. Keep going!" - } - ], - "session_5_date_time": "7:52 pm on 7 August, 2023", - "session_5": [ - { - "speaker": "Evan", - "img_url": [ - "https://c1.wallpaperflare.com/preview/1022/208/959/woman-female-man-male.jpg" - ], - "blip_caption": "a photography of a couple walking through the snow holding hands", - "query": "canadian woman scenic view canada", - "dia_id": "D5:1", - "re-download": true, - "text": "Hey Sam, how's it going? Last week I went on a trip to Canada and something unreal happened - I met this awesome Canadian woman and it was like something out of a movie. She's incredible and being with her makes me feel alive." - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D5:2", - "text": "Congrats Evan! She must be something special! Being with someone who makes you feel alive is amazing. I'm sorry to hear that you're dealing with health issues lately, it can be really tough. It's hard to fully enjoy things sometimes." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/v41q3yfuk0eb1.jpg" - ], - "blip_caption": "a photo of a container of cookies on a counter", - "query": "ginger snap cookies jar filled delicious ginger snap cookies", - "dia_id": "D5:3", - "text": "Woah. such a nice view! Thanks, Sam! She's definitely great. Every moment with her is really fun and energizing. It's a nice change, especially after dealing with health issues. But you never know what life's gonna throw at you. Btw look what life has thrown for me right now haha." - }, - { - "speaker": "Sam", - "dia_id": "D5:4", - "text": "Looks good to eat! Dealing with health problems can be challenging and take away from enjoyable experiences." - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.ctfassets.net/5ql9i5attlka/H97gx7NuNu1FH7Q8fiC8s/9fcd878661ce897f1d92e472cdf8fecd/Backyard_Swings.jpg" - ], - "blip_caption": "a photo of a woman and a child playing on a swing set", - "query": "kids playing backyard", - "dia_id": "D5:5", - "text": "Ginger snaps are my weakness for sure! Dealing with health issues has been tough, but it's made me appreciate the good moments more. These are the ones who bring lots of joy even through the hard times." - }, - { - "speaker": "Sam", - "dia_id": "D5:6", - "text": "It looks like your kids are having a great time! And how long have you been prioritizing your health?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://davethekayaker.files.wordpress.com/2019/04/20190430_110706.jpg" - ], - "blip_caption": "a photo of a box with a fitness watch on it", - "query": "fitness tracker", - "dia_id": "D5:7", - "text": "Yes, they bring me such joy. My healthy road has been a long one. I've been working on it for two years now, so there have been ups and downs, but I'm doing my best." - }, - { - "speaker": "Sam", - "dia_id": "D5:8", - "text": "I wish your motivation never goes anywhere! I'm thinking of ordering myself some similar ones too, what do you think, are they worth it?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://davethekayaker.files.wordpress.com/2019/04/20190430_110731.jpg" - ], - "blip_caption": "a photo of a person's wrist with a smart watch on it", - "query": "fitness tracker wrist", - "dia_id": "D5:9", - "text": "Thanks Sam! My family motivates me to stay healthy. Well, it helps a lot with my health goals. It tracks my progress really well and serves as a constant reminder to keep going." - }, - { - "speaker": "Sam", - "dia_id": "D5:10", - "text": "Cool! It sounds like a really good tool to stay on track. How has it been working out for you?" - }, - { - "speaker": "Evan", - "dia_id": "D5:11", - "text": "It's been awesome, Sam! That visual reminder has been really motivating." - }, - { - "speaker": "Sam", - "dia_id": "D5:12", - "text": "Thanks for the recommendation, what else motivates you?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.pexels.com/photos/11663179/pexels-photo-11663179.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-serhat-tu%C4%9F-11663179.jpg" - ], - "blip_caption": "a photo of a small island with a lone boat in the water", - "query": "sunrise lake calm", - "dia_id": "D5:13", - "text": "I'm motivated by a thirst for adventure on interesting hikes, that's pretty cool!" - }, - { - "speaker": "Sam", - "dia_id": "D5:14", - "text": "What an amazing view! The key is to find something that keeps you motivated." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/pjrgea5wqvg11.jpg" - ], - "blip_caption": "a photo of a bonsai tree in a black vase on a wooden table", - "query": "bonsai tree watering can", - "dia_id": "D5:15", - "text": "Yep, that's it. Find something that motivates you and makes you happy, whether it's large or tiny. It'll help us conquer the struggles we encounter." - }, - { - "speaker": "Sam", - "dia_id": "D5:16", - "text": "Nice! What made you decide to get that?" - }, - { - "speaker": "Evan", - "dia_id": "D5:17", - "text": "I got this because it symbolizes strength and resilience. Taking care of it motivates me to keep going through tough times." - }, - { - "speaker": "Sam", - "dia_id": "D5:18", - "text": "Wow, it's amazing! So powerful yet so simple." - }, - { - "speaker": "Evan", - "dia_id": "D5:19", - "text": "Thanks, Sam. It's a reminder that even in little things, we can be tough." - }, - { - "speaker": "Sam", - "dia_id": "D5:20", - "text": "Little stuff matters - it builds our resilience over time." - }, - { - "speaker": "Evan", - "dia_id": "D5:21", - "text": "Yeah, every little thing we do for ourselves helps us in the long run." - }, - { - "speaker": "Sam", - "dia_id": "D5:22", - "text": "Yep, small steps add up. Stay consistent and don't give up!" - }, - { - "speaker": "Evan", - "dia_id": "D5:23", - "text": "Yep, Sam! Consistency and perseverance will get us far. Great chat!" - }, - { - "speaker": "Sam", - "dia_id": "D5:24", - "text": "Great chatting with you, Sam! Take care, talk soon!" - }, - { - "speaker": "Evan", - "dia_id": "D5:25", - "text": "Catch ya later!" - } - ], - "session_6_date_time": "4:09 pm on 13 August, 2023", - "session_6": [ - { - "speaker": "Evan", - "img_url": [ - "https://lp-cms-production.imgix.net/image_browser/camping-long-range-traverse.jpg" - ], - "blip_caption": "a photo of a tent pitched up in a grassy field", - "query": "canada lake lush greenery hiking biking wilderness", - "dia_id": "D6:1", - "text": "Hey Sam, long time no talk! Hope you're doing great. I just got back from a rad vacay with my new SO in Canada. Tried some awesome activities too - think hiking, biking... all that cool stuff. We loved exploring the outdoors together, it was so awesome!" - }, - { - "speaker": "Sam", - "dia_id": "D6:2", - "text": "Hey Evan! Good to hear from you. Wow, Canada sounds amazing! That photo looks stunning. Wish I could do something like that. Things have been a bit challenging for me lately; some stuff has been hard on my health." - }, - { - "speaker": "Evan", - "dia_id": "D6:3", - "text": "Sorry to hear that things haven't been going well. Dealing with health issues can be tough. Is there anything I can do to help?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.redd.it/u2nyg1ua6nd81.jpg" - ], - "blip_caption": "a photo of a notepad with a pen and a note", - "query": "journal motivational quotes gratitude entries", - "dia_id": "D6:4", - "text": "Thanks, Evan. Your support means a lot to me. It's tough staying positive, but knowing I have people like you in my corner makes it easier." - }, - { - "speaker": "Evan", - "dia_id": "D6:5", - "text": "Glad to support you, Sam. Surrounding ourselves with people who care is key. What's on that note? A reminder or quote to stay motivated?" - }, - { - "speaker": "Sam", - "dia_id": "D6:6", - "text": "Yeah, it's actually a quote that's been helping me stay motivated. It reminds me that progress is more important than perfection. Taking small steps towards a healthier life is still progress." - }, - { - "speaker": "Evan", - "dia_id": "D6:7", - "text": "Cool mindset, Sam! I totally agree, progress over perfection. Mind sharing the quote with me? I would love to get something out of it too." - }, - { - "speaker": "Sam", - "dia_id": "D6:8", - "text": "\"Don't fear it, just take the first step. It's been helping me move forward to healthier habits!\"" - }, - { - "speaker": "Evan", - "dia_id": "D6:9", - "text": "Love the quote, Sam. That mindset really helps me too. Thanks!" - }, - { - "speaker": "Sam", - "dia_id": "D6:10", - "text": "You're welcome! Glad it helps. These times can be challenging." - }, - { - "speaker": "Evan", - "dia_id": "D6:11", - "text": "They can be tough, but remember to celebrate the small wins - every step forward counts!" - }, - { - "speaker": "Sam", - "dia_id": "D6:12", - "text": "You're absolutely right! Celebrating those small wins is crucial. It's easy to get caught up in the challenges and setbacks, but those little victories are what keep me motivated. Thanks for the reminder, it's much appreciated." - }, - { - "speaker": "Evan", - "dia_id": "D6:13", - "text": "Absolutely, Sam. Remember, every small victory is a step forward, so keep up the good work! I'm cheering for you! And hey, I could use some cheering too - I've been searching for my keys for the last half hour with no luck! I'm losing it every week.." - }, - { - "speaker": "Sam", - "dia_id": "D6:14", - "text": "Oh, I'll definitely be your cheerleader once I get some sleep, Evan! Funny you mention that, I had this amazing dream last night where I was soaring over skyscrapers. It felt incredible! Makes me wonder what it signifies..." - }, - { - "speaker": "Evan", - "dia_id": "D6:15", - "text": "That does sound like an amazing dream! Maybe you should check out a dream interpretation book; it could offer some insights. Sweet dreams, Sam!" - }, - { - "speaker": "Sam", - "dia_id": "D6:16", - "text": "Thanks for the suggestion, Evan. I'll look into it. This journey feels endless at times, but I'm convinced it's going to be rewarding in the end. So good luck with your keys, Evan!" - }, - { - "speaker": "Evan", - "dia_id": "D6:17", - "text": "I'll just keep going, step by step, and I'll definitely find those keys!" - }, - { - "speaker": "Sam", - "dia_id": "D6:18", - "text": "That's for sure!" - }, - { - "speaker": "Evan", - "dia_id": "D6:19", - "text": "Go to bed already, bud! And take care!" - } - ], - "session_7_date_time": "4:20 pm on 15 August, 2023", - "session_7": [ - { - "speaker": "Evan", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/b/be/Orthopedic_cast_4604.jpg" - ], - "blip_caption": "a photography of a person with a cast on their foot", - "query": "son injured ankle photo", - "dia_id": "D7:1", - "re-download": true, - "text": "Hey Sam, what's up? It's been a few days since we talked. How have you been? Life's been tough lately - my son had a soccer accident last Saturday and hurt his ankle, it was tough seeing him hurt! I just been looking after him and taking him to the doctor. As a dad, it's hard to watch your kid go through something like that." - }, - { - "speaker": "Sam", - "dia_id": "D7:2", - "text": "Hey Evan, sorry to hear about what happened. I can imagine how hard it must have been for you. Things haven't been easy for me either. Had a tough week and a doc's appointment, so it was kinda like a wake-up call to take better care of myself. On a bright side, I'm taking a cooking class to learn how to make healthier meals." - }, - { - "speaker": "Evan", - "dia_id": "D7:3", - "text": "Hey Sam, sorry to hear you had a rough week. At least it's forcing us both to take better care of ourselves, right? I hear the class you're taking is packed with healthy recipes. How's it been going? Have you picked up any yummy new meals?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i0.wp.com/sevenlayercharlotte.com/wp-content/uploads/2021/07/IMG_7767.jpg" - ], - "blip_caption": "a photo of a plate of food with a piece of salmon and some vegetables", - "query": "grilled salmon with roasted vegetables", - "dia_id": "D7:4", - "text": "The cooking class has been great, I've learned awesome recipes. Last night I made this yummy grilled dish, so good!" - }, - { - "speaker": "Evan", - "dia_id": "D7:5", - "text": "Mmm, it looks delicious! What did you put in it? I want to eat healthy, so what kind of recipes do you suggest?" - }, - { - "speaker": "Sam", - "dia_id": "D7:6", - "text": "Thanks, Evan! I marinated it with a few different ingredients and grilled it with some veggies. It turned out really flavorful! If you want, I can share more recipes from my cooking class. Just let me know what you're looking for!" - }, - { - "speaker": "Evan", - "dia_id": "D7:7", - "text": "That'd be great, Sam! I'm looking to add more vegetables to my meals. Do you have any recipes for that?" - }, - { - "speaker": "Sam", - "dia_id": "D7:8", - "text": "Yeah definitely, Evan. I have a tasty and easy roasted veg recipe that I can share with you. Oh, by the way, how have you been doing after the soccer incident? Must've been tough." - }, - { - "speaker": "Evan", - "dia_id": "D7:9", - "text": "Thanks, Sam. His ankle is getting better, but still sore. It was rough at first, but thank goodness it was nothing serious." - }, - { - "speaker": "Sam", - "dia_id": "D7:10", - "text": "Glad to hear his ankle is getting better. It's hard seeing someone we care about hurt. Look after yourself too, yeah? We gotta look after our health." - }, - { - "speaker": "Evan", - "dia_id": "D7:11", - "text": "Yep, taking care of ourselves is a must. How have you been feeling lately?" - }, - { - "speaker": "Sam", - "dia_id": "D7:12", - "text": "I have been feeling a mix of emotions - somewhat concerned about my health but also motivated to make positive changes. Taking things one step at a time." - }, - { - "speaker": "Evan", - "dia_id": "D7:13", - "text": "It's okay to feel overwhelmed, Sam. Just keep moving forward slowly and taking small steps. You're doing awesome." - }, - { - "speaker": "Sam", - "dia_id": "D7:14", - "text": "Thanks, Evan, your encouragement means a lot to me. I'll keep going and take it one step at a time!" - }, - { - "speaker": "Evan", - "dia_id": "D7:15", - "text": "No worries, just keep going and taking it one step at a time! You'll get there." - }, - { - "speaker": "Sam", - "dia_id": "D7:16", - "text": "Thanks, Evan. I really appreciate that. It means a lot." - } - ], - "session_8_date_time": "6:17 pm on 19 August, 2023", - "session_8": [ - { - "speaker": "Sam", - "img_url": [ - "https://i0.wp.com/www.thedishonhealthy.com/wp-content/uploads/2018/06/Avocado-Strawberry-Spinach-Salad-5.jpg" - ], - "blip_caption": "a photo of a bowl of spinach, avocado, and strawberries", - "query": "healthy salad", - "dia_id": "D8:1", - "text": "Hey Evan, some big news: I'm on a diet and living healthier! Been tough, but I'm determined." - }, - { - "speaker": "Evan", - "dia_id": "D8:2", - "text": "Wow, Sam, that's great news! Making changes to live healthier can be challenging, how has it been going?" - }, - { - "speaker": "Sam", - "dia_id": "D8:3", - "text": "It's tough, but I'm sticking with it." - }, - { - "speaker": "Evan", - "dia_id": "D8:4", - "text": "Nice work, Sam! Proud of you sticking to it. Have you noticed any positive changes?" - }, - { - "speaker": "Sam", - "dia_id": "D8:5", - "text": "Yes, there are many, such as more energy and less sluggishness after eating. This is really encouraging!" - }, - { - "speaker": "Evan", - "dia_id": "D8:6", - "text": "Wow, Sam, that's great to hear! Feeling more energized after meals is such a positive change. Keep up the good work! And speaking of healthy meals, do you have any favorite recipes you'd like to share?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://www.myrelationshipwithfood.com/wp-content/uploads/2021/08/Quinoa-Chicken-Stir-Fry-1.jpg" - ], - "blip_caption": "a photo of two bowls of food with chopsticks and sauce", - "query": "grilled chicken vegetable stir-fry", - "dia_id": "D8:7", - "text": "Sure, I'm loving this recipe I found. It's a flavorful and healthy grilled chicken and veggie stir-fry. Wanna give it a go?" - }, - { - "speaker": "Evan", - "dia_id": "D8:8", - "text": "Mmm, looks yummy! Is the sauce a family secret? I'm always down to try new recipes!" - }, - { - "speaker": "Sam", - "dia_id": "D8:9", - "text": "Nah, it's just my homemade sauce. Want the recipe?" - }, - { - "speaker": "Evan", - "dia_id": "D8:10", - "text": "Yeah, I'd love to! Thanks for sharing the recipe." - }, - { - "speaker": "Sam", - "img_url": [ - "https://live.staticflickr.com/9/17289339_31d8a8c9a2_b.jpg" - ], - "blip_caption": "a photography of a recipe card with a drawing of a vase of flowers", - "query": "grilled chicken veggie stir-fry recipe handwritten", - "dia_id": "D8:11", - "re-download": true, - "text": "Sure thing! Here's the recipe. Let me know how it went!" - }, - { - "speaker": "Evan", - "dia_id": "D8:12", - "text": "Thanks Sam! I'll give it a shot and let you know how it went. Trying out new recipes is a great way to stay busy and creative. By the way, I also started taking a painting classes few days ago and I'm really enjoying it. It's all about trying new things, right?" - }, - { - "speaker": "Sam", - "dia_id": "D8:13", - "text": "You're already good at drawing, why did you decide to join a class? And why did you start painting then years ago anyway?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://elenamarkelova.com/cdn/shop/products/serenitymistyforestwatercolorpaintingbyelenamarkelova.jpg" - ], - "blip_caption": "a photo of a painting of a forest scene on a easel", - "query": "watercolor painting serene landscape", - "dia_id": "D8:14", - "text": "Thanks, Sam! It all started when a friend of mine gave me this painting one day, it inspired me a lot and that's when I started painting. And I joined the classes to find like-minded people and show them what I can do, you can always improve your skills." - }, - { - "speaker": "Sam", - "dia_id": "D8:15", - "text": "Wow, that's awesome! You're really talented. Did you take any other classes?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://i0.wp.com/doodlewash.com/wp-content/uploads/2016/06/travelbrushes.jpg" - ], - "blip_caption": "a photo of a brush, pencil, and eyeliners on a cloth", - "query": "watercolor paints brushes", - "dia_id": "D8:16", - "text": "Thanks, Sam! Just been painting with these for now, but might look into other classes. It's awesome for finding my peace and expressing myself!" - }, - { - "speaker": "Sam", - "dia_id": "D8:17", - "text": "Cool, Evan! What have you been learning in those classes?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://static.skillshare.com/uploads/project/194099/cover_full_35000b4ed6f4b275b8b9f8008f31ec50.jpg" - ], - "blip_caption": "a photo of a table with a bunch of watercolors on it", - "query": "painting watercolors techniques nature beauty", - "dia_id": "D8:18", - "text": "In painting classes, we've been learning about watercolors. The instructor stresses observing nature and painting what we see. It's a relaxing way to take a break from everyday stress." - }, - { - "speaker": "Sam", - "dia_id": "D8:19", - "text": "Wow, Evan! What type of nature do you enjoy painting the most?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://thewhitebirchstudio.com/cdn/shop/products/20220525_211542.jpg" - ], - "blip_caption": "a photo of a painting of a sunset over the ocean", - "query": "painting beach sunset", - "dia_id": "D8:20", - "text": "I love painting landscapes. Nature's beauty captivates me and brings me peace. Here's one of my recent works." - }, - { - "speaker": "Sam", - "dia_id": "D8:21", - "text": "Wow, Evan! The colors are so bright. How do you capture the tranquil beauty of nature in your paintings?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://i1.wp.com/www.simplyoliviagrace.com/wp-content/uploads/2019/03/03.25.18-4.jpg" - ], - "blip_caption": "a photo of a tree with pink flowers in a field", - "query": "painting blooming cherry blossom tree nature calm serenity", - "dia_id": "D8:22", - "text": "Thanks Sam! I aim to capture the vibe of nature in my paintings, conveying the peacefulness of being outdoors." - }, - { - "speaker": "Sam", - "dia_id": "D8:23", - "text": "Wow, that pic is great! Do you often spend time in places like this?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.pexels.com/photos/18077479/pexels-photo-18077479/free-photo-of-a-car-parked-in-the-middle-of-a-forest.jpeg" - ], - "blip_caption": "a photography of a truck parked in the middle of a forest", - "query": "prius parked beautiful riverfront", - "dia_id": "D8:24", - "re-download": true, - "text": "Thanks! I love being in places like this - it brings back memories of road tripping in my trusty car." - }, - { - "speaker": "Sam", - "dia_id": "D8:25", - "text": "Wow, that's cool! Have you been to any fun places in that ride?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://destinationsanddesserts.com/wp-content/uploads/2022/01/img_5115.jpg" - ], - "blip_caption": "a photo of a person on skis on a snowy trail", - "query": "banff national park rocky mountains snow", - "dia_id": "D8:26", - "text": "Yep, last month I drove somewhere fun. The views were amazing!" - }, - { - "speaker": "Sam", - "dia_id": "D8:27", - "text": "Did you take advantage of the skiing opportunities in Banff? Sounds like it would have been a lot of fun!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://curated-uploads.imgix.net/AgAAAB0AKRRYoR0ZPH-IdLq3DQTvog.jpg" - ], - "blip_caption": "a photo of a group of people skiing down a snow covered slope", - "query": "banff skiing snowy landscape slopes winter", - "dia_id": "D8:28", - "text": "Yeah, it was great for skiing! The snow was amazing and I had a lot of fun. Can't wait to go back next year! Did you try any winter sports, Sam?" - }, - { - "speaker": "Sam", - "dia_id": "D8:29", - "text": "Ooh, skiing looks like a blast! I'd love to try it but I'm not sure my body can take it. What about you? What winter activities do you enjoy?" - }, - { - "speaker": "Evan", - "dia_id": "D8:30", - "text": "Skiing, snowboarding, and ice skating are all fun winter activities I enjoy." - }, - { - "speaker": "Sam", - "dia_id": "D8:31", - "text": "Wish I could join in on the fun! It sounds awesome." - }, - { - "speaker": "Evan", - "dia_id": "D8:32", - "text": "I'd like you to join me, too. Winter activities are a blast - hopefully someday you will!" - }, - { - "speaker": "Sam", - "dia_id": "D8:33", - "text": "Yeah, maybe. It's hard with that, you know. But who knows? Thanks for the understanding, Evan - it really means a lot." - } - ], - "session_9_date_time": "10:18 am on 27 August, 2023", - "session_9": [ - { - "speaker": "Sam", - "dia_id": "D9:1", - "text": "Hey Evan! Exciting news: I started a new diet and exercise routine last Monday and it's made a huge difference. I feel great! What about you, what changes have you made recently?" - }, - { - "speaker": "Evan", - "dia_id": "D9:2", - "text": "Wow, Sam, great! Glad your new diet/exercise is going well. As for me, I've hit a sore spot lately. Twisted my knee last Friday and it's really painful, so it's been tough to stay consistent with my usual fitness routine. It's really frustrating because staying active is like, mega-important to me." - }, - { - "speaker": "Sam", - "dia_id": "D9:3", - "text": "Oh no, sorry to hear about that, Evan. It's frustrating when our bodies don't cooperate, isn't it? Is there anything I can do to help?" - }, - { - "speaker": "Evan", - "dia_id": "D9:4", - "text": "Thanks, Sam. I appreciate the concern. Life throws us curveballs - that's life, right? By the way, remember that book I was talking about? It just gets better with every page, can't let it out of my hands!" - }, - { - "speaker": "Sam", - "dia_id": "D9:5", - "text": "Life sure can be unpredictable and tough. Being adaptable and finding other ways to stay active is key when you're facing an obstacle. Maybe look into low-impact exercises or physical therapy to help with that? And damn, you really got me interested in this book haha!" - }, - { - "speaker": "Evan", - "dia_id": "D9:6", - "text": "Yeah, PT for my knee is on the cards. Hopefully I'll get an appointment soon. Till then, just keeping it low-key and swimming to stay active." - }, - { - "speaker": "Sam", - "dia_id": "D9:7", - "text": "Swimming is a good choice, Evan. It's low-impact and easy on the joints, plus it's refreshing. Keep up with the active lifestyle!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/a/a3/In_a_car_over_a_lake_%28Unsplash%29.jpg" - ], - "blip_caption": "a photography of a car is parked near a lake with mountains in the background", - "query": "prius parked lake view nature", - "dia_id": "D9:8", - "re-download": true, - "text": "Yeah, thanks for the push, Sam. Oh, hey, I came across this pic when I was tidying up. I took a road trip last month - the scenery was stunning and nature really chilled me out." - }, - { - "speaker": "Sam", - "dia_id": "D9:9", - "text": "Wow, looks great! Where did you go? Bet it was nice to get away." - }, - { - "speaker": "Evan", - "dia_id": "D9:10", - "text": "Thanks! I went up to the Rocky Mountains, it was so refreshing! The views were stunning and I felt so relaxed. Do you enjoy road trips and exploring nature?" - }, - { - "speaker": "Sam", - "dia_id": "D9:11", - "text": "I haven't gone on a road trip in ages, but I love being surrounded by nature. It's so tranquil and refreshing. I'd love to go hiking more, but it can be a bit challenging sometimes. However, I am working on becoming healthier, so maybe a road trip and a hike will be possible soon." - }, - { - "speaker": "Evan", - "img_url": [ - "https://www.koin.com/wp-content/uploads/sites/10/2022/04/Tom-Dick-and-Harry-Mountain-view-of-Mirror-Creek-Courtesy-US-Forest-Service.jpg" - ], - "blip_caption": "a photo of a lake with a mountain in the background", - "query": "hiking trail mountain views", - "dia_id": "D9:12", - "text": "That's cool, Sam. Nature can be really peaceful. I'd suggest going for more hikes, like I do. It's always been calming and fun. We should definitely do one together sometime." - }, - { - "speaker": "Sam", - "dia_id": "D9:13", - "text": "Sounds like fun! Which lake do you recommend? I'd love to explore some of the local trails." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/p8xbz5tcvc2b1.jpg" - ], - "blip_caption": "a photo of a lake with a mountain in the background", - "query": "lake louise mountains reflected water trail", - "dia_id": "D9:14", - "text": "Check out this one! It's gorgeous and there are lots of trails nearby. You'll love it!" - }, - { - "speaker": "Sam", - "dia_id": "D9:15", - "text": "Wow, it looks great! Is it nearby? What a view!" - }, - { - "speaker": "Evan", - "dia_id": "D9:16", - "text": "It's only a two-hour drive from here, but trust me, it's worth every minute for the incredible views and peaceful atmosphere." - }, - { - "speaker": "Sam", - "dia_id": "D9:17", - "text": "Cool, a day trip's doable. Nature's calling me, so I'm gonna go check it out! Thanks!" - }, - { - "speaker": "Evan", - "dia_id": "D9:18", - "text": "No worries, enjoy your time in nature. Take care! Bye!" - }, - { - "speaker": "Sam", - "dia_id": "D9:19", - "text": "Thanks Evan. Have a good one. See ya!" - } - ], - "session_10_date_time": "9:28 am on 11 September, 2023", - "session_10": [ - { - "speaker": "Evan", - "img_url": [ - "https://a.1stdibscdn.com/carol-steinberg-paintings-santa-monica-sunset-painting-acrylic-on-canvas-for-sale/22569652/a_97035921645177202523/8193_55e7214a395fa47074c3d3c18b4d3db297c34450_8E0FtEJwGDF4rlzk_1_master.jpg" - ], - "blip_caption": "a photo of a painting of a sunset over a body of water", - "query": "watercolor painting sunset lake vibrant colors", - "dia_id": "D10:1", - "text": "Hey Sam! Long time no talk! Hope all is good. What have I been doing these past few weeks?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.redd.it/6jtf58dhwpq51.jpg" - ], - "blip_caption": "a photo of a bowl of beef and vegetables with a package of healthy choice", - "query": "before and after photo healthy choices", - "dia_id": "D10:2", - "text": "Hey Evan! Nice to hear from you. Life has been an up and down ride. Have you seen the pic I posted of my before and after body as a result of the diet? Working to motivate others to make better choices." - }, - { - "speaker": "Evan", - "dia_id": "D10:3", - "text": "Hey Sam! Loving it. Making healthier choices has definitely made a difference for me. It's amazing how small changes can have such a big impact. How about you? Is it making a difference for you too?" - }, - { - "speaker": "Sam", - "dia_id": "D10:4", - "text": "Hey Evan, thanks for the support! Handling all this has been kinda wild. I'm trying to make healthier choices, but there are still the occasional cravings for sugary drinks and snacks... it's a real struggle." - }, - { - "speaker": "Evan", - "dia_id": "D10:5", - "text": "Yeah, breaking bad habits can be hard. Cravings can be tough too, but little victories count. What do you think sets off those cravings for you?" - }, - { - "speaker": "Sam", - "dia_id": "D10:6", - "text": "It's usually stress, boredom, or just wanting comfort. You know, those sugary treats are so tempting, right?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://trendgallery.art/cdn/shop/files/IMG_9358_6dd0efff-bb0c-4923-804f-9edc8600fee8.jpg" - ], - "blip_caption": "a photo of a painting of a mountain range with a horse", - "query": "painting colorful landscape", - "dia_id": "D10:7", - "text": "Yeah, I get it. When I'm stressed, I always turn to something comforting. But I've found that painting or going for a drive helps too!" - }, - { - "speaker": "Sam", - "dia_id": "D10:8", - "text": "Wow Evan, that's an awesome painting! Good on you for finding a way to de-stress. I could really use something like that - maybe I'll give painting a go or find another calming hobby." - }, - { - "speaker": "Evan", - "dia_id": "D10:9", - "text": "Hey Sam, painting is super chill for calming down. Wanna give it a try? I can help you get started and recommend some supplies if you're interested. Let me know!" - }, - { - "speaker": "Sam", - "dia_id": "D10:10", - "text": "Sounds great, Evan! I want to give it a go and see if it relaxes me. Can you suggest some basic supplies for me to get started?" - }, - { - "speaker": "Evan", - "dia_id": "D10:11", - "text": "Yep, painting is awesome! Get some acrylic paints, brushes, a canvas/paper, and a palette to mix colors. I can give you some recommendations if you want. Just let me know when you're ready and we can plan a painting session!" - }, - { - "speaker": "Sam", - "dia_id": "D10:12", - "text": "Sounds great, Evan! Can you help me pick out the stuff? Let's plan a painting session soon. I'm really excited!" - }, - { - "speaker": "Evan", - "dia_id": "D10:13", - "text": "Yeah, Sam - let's do it! Let's get everything ready and paint next Saturday. Can't wait!" - }, - { - "speaker": "Sam", - "dia_id": "D10:14", - "text": "Sounds good, Evan! Can't wait to paint with you next Saturday. It'll be a fun and creative activity." - } - ], - "session_11_date_time": "8:57 pm on 6 October, 2023", - "session_11": [ - { - "speaker": "Sam", - "img_url": [ - "https://www.cookingclassy.com/wp-content/uploads/2019/05/fruit-salad-8.jpg" - ], - "blip_caption": "a photography of a bowl of fruit with a striped cloth", - "query": "healthy snacks fruit bowl", - "dia_id": "D11:1", - "re-download": true, - "text": "Hey Evan, long time no see! I've started eating healthier - what's new with you? Picked up any new hobbies?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://live.staticflickr.com/23/35171147_0fd909cefb_b.jpg" - ], - "blip_caption": "a photography of a person with a cast on their leg and a cast on their leg", - "query": "knee brace road trip", - "dia_id": "D11:2", - "re-download": true, - "text": "Hey Sam! That's awesome about your healthier eating! For me, I had a setback last week - messed up my knee playing b-ball with the kids. It's been tough to stay active since. I really miss going on adventures like we did last year - good times with the family!" - }, - { - "speaker": "Sam", - "dia_id": "D11:3", - "text": "Hey Evan, sorry to hear about your knee. It must be tough. Are there any ways to stay active while you heal up?" - }, - { - "speaker": "Evan", - "dia_id": "D11:4", - "text": "Thanks, Sam. PT has helped some. I can't do intense workouts, but I'm doing easy exercises to keep it strong. Not as good as being active outdoors, but still something." - }, - { - "speaker": "Sam", - "dia_id": "D11:5", - "text": "Glad PT is helping, Evan! Taking care of yourself is key \u2013 have you explored any fun indoor activities or hobbies?" - }, - { - "speaker": "Evan", - "dia_id": "D11:6", - "text": "I do my favorite watercolor painting to keep me busy. It's a chill way to relax and get into the colors. By the way, something happened two weeks ago! You're not gonna believe this, I had a bit of an adventure recently. Helped a lost tourist find their way, and we ended up taking an unexpected tour around the city. It was a blast!" - }, - { - "speaker": "Sam", - "dia_id": "D11:7", - "text": "Hey Evan, that sounds like a fun and unexpected event! It's always interesting how helping someone can turn into a little adventure of its own. And how's your watercolor painting going?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.pinimg.com/originals/73/e4/fd/73e4fd6baf59abc2beca9fd5509a853c.jpg" - ], - "blip_caption": "a photo of a painting of a cactus in the desert", - "query": "watercolor painting sunset", - "dia_id": "D11:8", - "text": "It's been great! I find painting to be a great stress reliever. Here's what I did last week." - }, - { - "speaker": "Sam", - "dia_id": "D11:9", - "text": "Wow, those are awesome! So cool. Where did you get the inspiration for them?" - }, - { - "speaker": "Evan", - "dia_id": "D11:10", - "text": "Thanks, Sam! The sunset painting was inspired by a vacation a few years back. The colors were so stunning. The cactus painting came from a road trip last month. Such cool places!" - }, - { - "speaker": "Sam", - "dia_id": "D11:11", - "text": "Wow, Evan, your paintings are awesome! How do you decide what to paint?" - }, - { - "speaker": "Evan", - "dia_id": "D11:12", - "text": "Thanks, Sam! I usually paint what's on my mind or something I'm feeling. It can be good memories or places I wanna go to. It's more like expressing myself through art." - }, - { - "speaker": "Sam", - "dia_id": "D11:13", - "text": "That's really amazing, Evan. Expressing yourself through art is such a powerful form of self-expression." - }, - { - "speaker": "Evan", - "dia_id": "D11:14", - "text": "Thanks, Sam. Yeah, it's really a great way to express myself and my emotions. It's a cool way to communicate without using words. So, do you have any other ways in which you express yourself?" - }, - { - "speaker": "Sam", - "dia_id": "D11:15", - "text": "Drawing is cool. I'm still just learning how to draw, but I love expressing myself through writing. It's therapeutic and helps me sort out my feelings. Though, I've been a bit frustrated lately with my new phone. Its navigation app keeps malfunctioning, making getting around a bit of a challenge." - }, - { - "speaker": "Evan", - "dia_id": "D11:16", - "text": "Cool, Sam! Writing is a great way to express yourself. What kind of writing do you enjoy? And about the phone, I recommend trying to update it, it usually works for me!" - }, - { - "speaker": "Sam", - "dia_id": "D11:17", - "text": "Thanks for the tip, Evan! Writing in my journal and doing creative writing is a good way for me to express my innermost thoughts and feelings." - }, - { - "speaker": "Evan", - "dia_id": "D11:18", - "text": "It can be super therapeutic. It gives you a place to express yourself. Keep it up!" - }, - { - "speaker": "Sam", - "dia_id": "D11:19", - "text": "Thanks, Evan! It really helps me make sense of things and express my feelings. It's like having a conversation with myself." - }, - { - "speaker": "Evan", - "dia_id": "D11:20", - "text": "Gotcha, it's like having a place to figure stuff out and make sense of it all. We all need an outlet to express our thoughts and feelings." - } - ], - "session_12_date_time": "3:09 pm on 8 October, 2023", - "session_12": [ - { - "speaker": "Sam", - "dia_id": "D12:1", - "text": "Hey Evan, hope you're doing okay. I wanted to chat about something that's been bothering me lately... I went for a check-up Monday and my doc said my weight's a serious health risk - if I don't make changes soon, it can get worse. I know I made jokes about it, but it's really hitting me. Been having a hard time." - }, - { - "speaker": "Evan", - "img_url": [ - "https://blog.merrittclubs.com/hubfs/weight%20machine%20blog%204.jpg" - ], - "blip_caption": "a photo of a man doing a squat on a machine in a gym", - "query": "gym weight training", - "dia_id": "D12:2", - "text": "Hey Sam, tough news. Yeah, our health can really put a damper on things. I started lifting weights one year ago and it's been a journey. It was a struggle at first, but I'm seeing some gains. You interested in trying it out?" - }, - { - "speaker": "Sam", - "dia_id": "D12:3", - "text": "Hey Evan, I'm interested in getting into it. Any advice on how to get started? Thanks!" - }, - { - "speaker": "Evan", - "dia_id": "D12:4", - "text": "Hey Sam, that's awesome! It's important to start out with good form and technique. Find a trainer who can help you avoid injuries while you build your strength. Start with something small, and as you get stronger, the intensity can increase. Stay consistent with your workout routine and let me know how it goes! Good luck!" - }, - { - "speaker": "Sam", - "dia_id": "D12:5", - "text": "Thanks, Evan. I'm going to find someone who can help me out. I'll keep you posted!" - }, - { - "speaker": "Evan", - "dia_id": "D12:6", - "text": "No problem, Sam. Can't wait to hear about your progress. Keep up the hard work!" - }, - { - "speaker": "Sam", - "dia_id": "D12:7", - "text": "Thanks, Evan. I appreciate your support. It really means a lot to me. I'll definitely keep you posted on my progress." - }, - { - "speaker": "Evan", - "dia_id": "D12:8", - "text": "You're welcome, Sam! It takes time, so be patient with yourself. Your health matters, and I believe in you. Keep going and stay upbeat. You got this!" - }, - { - "speaker": "Sam", - "dia_id": "D12:9", - "text": "Thanks, Evan. I'll stay positive and keep going. Your support means a lot." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/uupz9iakdja61.jpg" - ], - "blip_caption": "a photo of a notepad with a note and a pair of scissors", - "query": "motivational quote believe power progress", - "dia_id": "D12:10", - "text": "Hey Sam, glad I can be here for you! Progress is key, so keep pushing on and stay positive. You got this!" - }, - { - "speaker": "Sam", - "dia_id": "D12:11", - "text": "Wow, Evan, that's really inspiring. Gonna keep believing in it!" - }, - { - "speaker": "Evan", - "dia_id": "D12:12", - "text": "Go get 'em! Believe in your abilities and you'll reach your goals. Stay motivated!" - }, - { - "speaker": "Sam", - "dia_id": "D12:13", - "text": "Thanks Evan! Your words gave me a boost. I'm staying motivated and believing in myself." - }, - { - "speaker": "Evan", - "dia_id": "D12:14", - "text": "Awesome! Keep staying motivated and believing in yourself. You've got this!" - }, - { - "speaker": "Sam", - "dia_id": "D12:15", - "text": "Thanks, Evan! Your support means a lot to me." - }, - { - "speaker": "Evan", - "dia_id": "D12:16", - "text": "No prob, Sam! I'm here for you. Just keep taking one step at a time, and you'll get there eventually!" - }, - { - "speaker": "Sam", - "dia_id": "D12:17", - "text": "Sure, Evan. I'll take it slow. See ya!" - } - ], - "session_13_date_time": "4:07 pm on 14 October, 2023", - "session_13": [ - { - "speaker": "Evan", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/8/85/Cliffs_and_mountains_and_sky_at_sunset_--_2_of_33.jpg" - ], - "blip_caption": "a photography of a painting of a person on a cliff", - "query": "painting peaceful sunset", - "dia_id": "D13:1", - "re-download": true, - "text": "Hey Sam, how's it going? Been a while since we talked. Hope all is good." - }, - { - "speaker": "Sam", - "img_url": [ - "http://raremunchiez.com/cdn/shop/products/soda-can-fizzy-candy-6-pack-raremunchiez.jpg" - ], - "blip_caption": "a photo of a person holding a box of sodas in front of a wall", - "query": "soda candy", - "dia_id": "D13:2", - "text": "Hey Evan! It's been a rough week - I gave in and bought some unhealthy snacks. I feel kinda guilty. How's it going for you? That painting is awesome! Did you paint it?" - }, - { - "speaker": "Evan", - "dia_id": "D13:3", - "text": "Hey Sam, sorry to hear about the rough week. Don't worry about the snacks. I'm doing okay, just finished this painting of a sunset. It really helps me relax. So, how's everything going with you? Anything new and exciting?" - }, - { - "speaker": "Sam", - "dia_id": "D13:4", - "text": "Thanks, Evan! Yeah, I just couldn't resist them. Gotta do better. As for me, just dealing with work stress and trying to stay motivated." - }, - { - "speaker": "Evan", - "dia_id": "D13:5", - "text": "Hey Sam, work stress can really get to you. Have you tried anything new to de-stress? Maybe picking up a hobby or something could help." - }, - { - "speaker": "Sam", - "dia_id": "D13:6", - "text": "Thinking about trying something different outdoors. Any suggestions?" - }, - { - "speaker": "Evan", - "dia_id": "D13:7", - "text": "Sounds good! Have you ever tried kayaking? It's a fun and active way to paddle on a river or lake. What are your thoughts on that?" - }, - { - "speaker": "Sam", - "dia_id": "D13:8", - "text": "Kayaking sounds awesome! Haven't tried it yet, but it looks like a fun way to get in some exercise and enjoy nature. I'm definitely considering giving it a try. Thanks!" - }, - { - "speaker": "Evan", - "dia_id": "D13:9", - "text": "No worries, Sam! It's a fun way to get in some exercise and enjoy nature. Let me know when you're ready to give it a try and I can hook you up with a good spot." - }, - { - "speaker": "Sam", - "dia_id": "D13:10", - "text": "Thanks for the idea, my mate and I are just around the corner from kayaking on the lake, we're going to try that now!" - }, - { - "speaker": "Evan", - "dia_id": "D13:11", - "text": "Of course, let me know if you like it, we can plan a kayaking trip together, I'll pick a cool spot!" - }, - { - "speaker": "Sam", - "img_url": [ - "https://www.higherpursuits.com/wp-content/uploads/2019/01/2019-Rental-Boats-1.jpg" - ], - "blip_caption": "a photography of a row of kayaks lined up on the shore of a river", - "query": "kayak river", - "dia_id": "D13:12", - "re-download": true, - "text": "Yep, Evan! Can't wait. Thanks for the help!" - }, - { - "speaker": "Evan", - "dia_id": "D13:13", - "text": "Ready for an adventure? Where will you go?" - }, - { - "speaker": "Sam", - "dia_id": "D13:14", - "text": "We're traveling through Lake Tahoe! I heard it's great for kayaking." - }, - { - "speaker": "Evan", - "dia_id": "D13:15", - "text": "Hey Sam, it's an awesome pick! You'll love it there - clear water and gorgeous views. Have a blast and take lots of pics!" - }, - { - "speaker": "Sam", - "dia_id": "D13:16", - "text": "Thanks, Evan! I'm looking forward to it!" - } - ], - "session_14_date_time": "1:50 pm on 17 October, 2023", - "session_14": [ - { - "speaker": "Sam", - "dia_id": "D14:1", - "text": "Hey Evan! I've been missing our chats. I had quite the health scare last weekend - ended up in the ER with a severe stomachache. Turns out, it was gastritis, which was pretty alarming. It was a wake-up call for me to start prioritizing my health, like adopting a more nutritious diet and getting regular exercise. On top of that, my phone's been giving me a hard time, adding to the stress." - }, - { - "speaker": "Evan", - "dia_id": "D14:2", - "text": "Hey Sam, sorry to hear about that. Gastritis can be tough. Taking care of ourselves is important. BTW, I've been focusing on fitness and it's been really beneficial for my overall well-being. Funny thing, I had another encounter with a lost tourist recently. Seems like helping tourists is becoming a recurring theme in my life!" - }, - { - "speaker": "Sam", - "dia_id": "D14:3", - "text": "Thanks, Evan! Glad you've found that it's been good for you! I totally need to get into it too. Just getting started is hard - any tips for staying motivated? Also, you mentioned another lost tourist? Seems like you're becoming the go-to guy for tourists in need!" - }, - { - "speaker": "Evan", - "dia_id": "D14:4", - "text": "Yup, Sam! Set some goals, like a certain distance to run or number of push-ups to do. It'll give you something to strive for and stay motivated. Also, try to find an exercise that you enjoy and maybe even get a buddy for extra fun and accountability. Sound good?" - }, - { - "speaker": "Sam", - "dia_id": "D14:5", - "text": "Yeah, that sounds like a good idea. Having goals and someone to exercise with might help. I'll give it a try!" - }, - { - "speaker": "Evan", - "dia_id": "D14:6", - "text": "Awesome, Sam! Getting started will get easier with time. And don't forget it's about feeling good and reaching goals, too. Let's plan a hike soon!" - }, - { - "speaker": "Sam", - "dia_id": "D14:7", - "text": "Sounds awesome, Evan! Can't wait to go on a hike with you. It's going to be a fun challenge and a great opportunity to appreciate the beauty of nature." - }, - { - "speaker": "Evan", - "dia_id": "D14:8", - "text": "Definitely, Sam! Hiking is an awesome way to bond with nature and push ourselves. It's gonna be a cool memory for us both. It's great to see progress, was just at the gym yesterday. Gaining strength!" - }, - { - "speaker": "Sam", - "blip_caption": "a photo of a person wearing hiking shoes on a couch", - "dia_id": "D14:9", - "text": "Super excited to get fit with ya. Let's hit the trails soon!" - }, - { - "speaker": "Evan", - "dia_id": "D14:10", - "text": "Thanks, Sam! That's so nice of you. We'll definitely have a great time on our hike!" - }, - { - "speaker": "Sam", - "dia_id": "D14:11", - "text": "Totally! I'm so pumped for this hike. Connecting with nature is exactly what I need. Thanks so much for the support and always being there. Means a lot." - }, - { - "speaker": "Evan", - "dia_id": "D14:12", - "text": "Sure thing! Our hike is going to be awesome, I can tell. I'm always here to support you." - }, - { - "speaker": "Sam", - "dia_id": "D14:13", - "text": "Thanks, Evan. I appreciate your support." - }, - { - "speaker": "Evan", - "dia_id": "D14:14", - "text": "No problem, Sam. Whenever you need support, I'm here for you. Stay safe!" - }, - { - "speaker": "Sam", - "dia_id": "D14:15", - "text": "Thanks, I'll get in touch if I need anything. Stay safe. Bye!" - }, - { - "speaker": "Evan", - "dia_id": "D14:16", - "text": "Later! Stay safe and don't hesitate to holler if you need anything. Can't wait to hit the trail." - } - ], - "session_15_date_time": "2:56 pm on 25 October, 2023", - "session_15": [ - { - "speaker": "Sam", - "dia_id": "D15:1", - "text": "Morning, Evan. I've been trying to keep up with my new health routine, but it's tough. My family's really pushing for it, and I feel so pressured." - }, - { - "speaker": "Evan", - "dia_id": "D15:2", - "text": "I hear you, Sam. It's important to have people who encourage you, but not stress you out. By the way, I just got back from my morning walk. It really helps to start the day actively." - }, - { - "speaker": "Sam", - "dia_id": "D15:3", - "text": "Yeah, it's easier when you have a great support system. Thanks for being there for me." - }, - { - "speaker": "Evan", - "dia_id": "D15:4", - "text": "No worries, Sam. I'll be there for you. Take it slow and treat yourself." - }, - { - "speaker": "Sam", - "dia_id": "D15:5", - "text": "Thanks for the reminder to take it easy. I sometimes get impatient with myself when I want results fast, but I gotta be patient." - }, - { - "speaker": "Evan", - "dia_id": "D15:6", - "text": "Yep, progress takes time. So just take it one step at a time." - }, - { - "speaker": "Sam", - "dia_id": "D15:7", - "text": "Yes, you're right, Evan. Taking it slow is better than doing too much. I appreciate your support." - }, - { - "speaker": "Evan", - "img_url": [ - "https://www.publicdomainpictures.net/pictures/540000/nahled/fit-man-and-healthy-food-1694521202SgE.jpg" - ], - "blip_caption": "a photography of a man sitting at a table with a lot of fruits and vegetables", - "query": "gym bag selfie", - "dia_id": "D15:8", - "re-download": true, - "text": "I get it, Sam. I went through a similar phase a twoyears ago. Changed my diet, started walking regularly, things like that." - }, - { - "speaker": "Sam", - "dia_id": "D15:9", - "text": "Wow, Evan, you look great! How did you manage the change?" - }, - { - "speaker": "Evan", - "dia_id": "D15:10", - "text": "I started focusing more on my well-being rather than fixating on quick results. Letting go of that pressure made a huge difference." - }, - { - "speaker": "Sam", - "dia_id": "D15:11", - "text": "That's impressive, Evan. It's inspiring to see how you transformed by changing your mindset." - }, - { - "speaker": "Evan", - "dia_id": "D15:12", - "text": "Thanks, Sam. Letting go of unrealistic expectations was liberating, both physically and mentally." - }, - { - "speaker": "Sam", - "dia_id": "D15:13", - "text": "You're really doing great, Evan! I want to feel that same sense of freedom." - }, - { - "speaker": "Evan", - "dia_id": "D15:14", - "text": "Thanks, Sam. Just take it one day at a time. Celebrate small victories." - }, - { - "speaker": "Sam", - "dia_id": "D15:15", - "text": "Thanks, Evan! Focusing on small wins sounds like a plan. I'll take it one day at a time." - }, - { - "speaker": "Evan", - "dia_id": "D15:16", - "text": "Exactly! Congrats on every little victory. Keep it up, I'm here for you." - }, - { - "speaker": "Sam", - "dia_id": "D15:17", - "text": "Your support means everything. Here's to moving forward!" - }, - { - "speaker": "Evan", - "dia_id": "D15:18", - "text": "Anytime, Sam! Let's keep pushing ahead. I'm here to help you. Take care!" - } - ], - "session_16_date_time": "9:13 pm on 9 November, 2023", - "session_16": [ - { - "speaker": "Sam", - "dia_id": "D16:1", - "text": "Hey Evan! Hope you're doing good. Got some good news to share - I'm a Weight Watchers coach in my group now! It's a pretty big accomplishment for me, feel really proud." - }, - { - "speaker": "Evan", - "dia_id": "D16:2", - "text": "Congrats Sam! That's awesome! I'm super proud of you. Becoming a Weight Watchers coach is a big deal. Keep going!" - }, - { - "speaker": "Sam", - "dia_id": "D16:3", - "text": "Thanks, Evan! Appreciate your support. It's been a journey, and being chosen as a coach is a great step in my quest for better health." - }, - { - "speaker": "Evan", - "dia_id": "D16:4", - "text": "Wow, Sam! You've come such a long way. It's exciting to see what comes next for you in your quest for better health." - }, - { - "speaker": "Sam", - "dia_id": "D16:5", - "text": "Thanks, Evan! It feels great to see progress. Being a coach will hopefully keep me motivated and help others stay committed too. It's a big challenge, but I'm ready for it!" - }, - { - "speaker": "Evan", - "dia_id": "D16:6", - "text": "That's awesome, Sam! Helping others stay committed and motivated is so rewarding. You really inspire us. Keep up the great work!" - }, - { - "speaker": "Sam", - "dia_id": "D16:7", - "text": "Thanks, Evan! Your kind words mean a lot. It's been a difficult road, but I'm determined to continue making a positive impact." - }, - { - "speaker": "Evan", - "img_url": [ - "https://guitarsonmain.com/cdn/shop/files/1968-kustom-k-200a-vintage-guitar-w-ohsc-40274115559663_5000x.jpg" - ], - "blip_caption": "a photo of a guitar laying on the floor with a guitar strap", - "query": "vintage guitar", - "dia_id": "D16:8", - "text": "Sorry about missing any events, I've had some personal challenges since we last spoke. Still here for you though - do you need any support or want to share anything? Btw look what i got!" - }, - { - "speaker": "Sam", - "dia_id": "D16:9", - "text": "Hey, it looks so vintage and cool! What model is it? How've you been doing lately? I'm here if you wanna chat." - }, - { - "speaker": "Evan", - "dia_id": "D16:10", - "text": "It's a 1968 Kustom K-200A vintage guitar and I got it as a gift from a close friend. It's been a tough time for me since we last caught up; I lost my job last month, which has been pretty rough. But I really appreciate your support through all this." - }, - { - "speaker": "Sam", - "dia_id": "D16:11", - "text": "Sorry to hear about your job, Evan. What happened?" - }, - { - "speaker": "Evan", - "dia_id": "D16:12", - "text": "It's been a bit of a rough patch lately. The company downsized, and I was part of that. I'm currently on the hunt for a new job, which hasn't been easy, but I'm keeping my spirits up and staying hopeful." - }, - { - "speaker": "Sam", - "dia_id": "D16:13", - "text": "Sorry about your job, Evan. It's tough when it comes out of nowhere, but I'm proud of how you're handling it. Let me know if you need someone to talk to or if I can do anything to help. You'll get through this." - }, - { - "speaker": "Evan", - "dia_id": "D16:14", - "text": "Thanks, Sam. Your support means a lot. It's been quite a ride, but I really appreciate having someone like you to talk to. I'll definitely reach out if I need anything." - }, - { - "speaker": "Sam", - "dia_id": "D16:15", - "text": "For sure, Evan! I'm here for ya. Life can be tough sometimes, but we got this. Stay positive and it'll all work out. Just know that I'm here if you need someone to talk to." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D16:16", - "text": "Thanks, Sam. Your kind words and support mean a lot. It's great to have you here. I'm gonna stay positive and keep going. Cheers!" - }, - { - "speaker": "Sam", - "dia_id": "D16:17", - "text": "Wow, that sunset is stunning! It's so soothing just to see it. Is that a special spot you go to watch sunsets?" - }, - { - "speaker": "Evan", - "dia_id": "D16:18", - "text": "Yeah, it's this peaceful place close to my home. I often go there to relax and unwind." - }, - { - "speaker": "Sam", - "dia_id": "D16:19", - "text": "That sounds wonderful, Evan! I'd love to check it out with you sometime." - }, - { - "speaker": "Evan", - "dia_id": "D16:20", - "text": "Oh, I wish I could bring you along. That picture was actually taken last Friday at my favorite spot by the beach. Watching the waves and the sunset colors really helps me find peace, especially during tough times. It's a beautiful reminder of nature's resilience. We should definitely plan to go together someday." - }, - { - "speaker": "Sam", - "dia_id": "D16:21", - "text": "No worries, Evan. And yes, we should make a plan to go. That photo is just mesmerizing!" - }, - { - "speaker": "Evan", - "dia_id": "D16:22", - "text": "I'm glad you like it! It's a really calming place. Let's make a point to visit it together soon." - }, - { - "speaker": "Sam", - "dia_id": "D16:23", - "text": "Absolutely, Evan! A trip there sounds like the perfect way to de-stress." - }, - { - "speaker": "Evan", - "dia_id": "D16:24", - "text": "Awesome, let's do it! Let's plan it for next month, I'm already excited about exploring it together!" - } - ], - "session_17_date_time": "7:30 pm on 21 November, 2023", - "session_17": [ - { - "speaker": "Sam", - "dia_id": "D17:1", - "text": "Hey Ev! Long time no chat. How's it going? Hope all is well." - }, - { - "speaker": "Evan", - "dia_id": "D17:2", - "text": "Hey Sam, good to hear from you! Life's been a wild ride lately. Last week, I had a health scare and had to go to the hospital. They found something suspicious during a check-up, which freaked me out. Thankfully, it was all a misunderstanding, but it made me realize how important it is to keep an eye on my health. How've you been?" - }, - { - "speaker": "Sam", - "dia_id": "D17:3", - "text": "Woah, Evan, that must've been scary! Phew, it was just a misunderstanding. A health scare can really make you re-evaluate what's important. As for me, I've been dealing with some discomfort and it's been limiting my movement. I've been trying to make changes diet-wise, but it can be hard." - }, - { - "speaker": "Evan", - "dia_id": "D17:4", - "text": "That sucks, Sam. It's tough when our health holds us back. I believe in you \u2013 just taking small steps can help. Have you tried any new hobbies recently to take your mind off it?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://www.goodwillfinds.com/on/demandware.static/-/Sites-goodwill-master/default/dwa2341b30/images/large/lhyOBm1CPSKy54szJay7vQj/2023/November/07/image_(170).jpg" - ], - "blip_caption": "a photo of a book with a picture of a man on it", - "query": "the godfather dvd box set", - "dia_id": "D17:5", - "text": "Thanks, Evan. I haven't tried much new lately, but I did get this yesterday. It's been my go-to 'feel good' flick. So, you said you had a health scare - how're you now?" - }, - { - "speaker": "Evan", - "dia_id": "D17:6", - "text": "That movie sounds interesting! I'm doing well now. Doctors said everything is fine, but it taught me the value of life. Just trying to enjoy the moment." - }, - { - "speaker": "Sam", - "dia_id": "D17:7", - "text": "That's awesome, Evan! Let's make it a habit to appreciate something each day. It really helps us enjoy life more. What do you think?" - }, - { - "speaker": "Evan", - "dia_id": "D17:8", - "text": "Sounds good, Sam! Let's take the time to appreciate the little things in life." - }, - { - "speaker": "Sam", - "dia_id": "D17:9", - "text": "Thanks for always being there, Evan. It means a lot." - }, - { - "speaker": "Evan", - "dia_id": "D17:10", - "text": "Sure, Sam. I'm here for you. We gotta stick together, especially now." - }, - { - "speaker": "Sam", - "dia_id": "D17:11", - "text": "Yeah, Evan. Life can be tough sometimes, but having supportive people like you makes it way easier." - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.pexels.com/photos/7149181/pexels-photo-7149181.jpeg" - ], - "blip_caption": "a photography of a group of people sitting around a fire pit", - "query": "group of friends laughing", - "dia_id": "D17:12", - "re-download": true, - "text": "Yeah, Sam. Tough times are way easier with friends we can rely on. We've got each other!" - }, - { - "speaker": "Sam", - "dia_id": "D17:13", - "text": "Looks like you're having a blast! I was wondering, what do you do to stay fit and healthy?" - }, - { - "speaker": "Evan", - "dia_id": "D17:14", - "text": "That was wild! I stay in shape by hitting the gym and taking my car out for a spin. Gotta keep it up! How are you doing on your fitness goals, Sam?" - }, - { - "speaker": "Sam", - "dia_id": "D17:15", - "text": "Fitness goals have been hard to reach, but hey, that's life!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.pexels.com/photos/11663179/pexels-photo-11663179.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-serhat-tu%C4%9F-11663179.jpg" - ], - "blip_caption": "a photo of a small island with a lone boat in the water", - "query": "sunrise calm lake", - "dia_id": "D17:16", - "text": "Yeah Sam, it's true. Progress takes time, so keep pushing." - }, - { - "speaker": "Sam", - "dia_id": "D17:17", - "text": "Where is that? It looks gorgeous!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://myrnafolkert.files.wordpress.com/2021/05/boat-pic-9.jpg" - ], - "blip_caption": "a photo of a sun shining through the clouds over a body of water", - "query": "small island lake huron lone boat water", - "dia_id": "D17:18", - "text": "This little island is where I grew up and it's my happy place." - }, - { - "speaker": "Sam", - "dia_id": "D17:19", - "text": "Wow, that spot looks gorgeous. Growing up there must have been so peaceful and stunning." - }, - { - "speaker": "Evan", - "dia_id": "D17:20", - "text": "Yeah, it was. That place shaped me and will always hold a special place in my heart." - }, - { - "speaker": "Sam", - "dia_id": "D17:21", - "text": "Yeah, it can be soul-calming." - }, - { - "speaker": "Evan", - "img_url": [ - "https://c1.wallpaperflare.com/preview/608/545/293/sunset-kapiti-coast-new-zealand-beautiful-serene.jpg" - ], - "blip_caption": "a photography of a person walking on a beach at sunset", - "query": "serene sunset island", - "dia_id": "D17:22", - "re-download": true, - "text": "Yeah, it really is. So serene and calming." - }, - { - "speaker": "Sam", - "dia_id": "D17:23", - "text": "It's heavenly!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.pexels.com/photos/9214105/pexels-photo-9214105.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-dastan-khdir-9214105.jpg" - ], - "blip_caption": "a photo of a sunset over a body of water", - "query": "calm lake dusk", - "dia_id": "D17:24", - "text": "Yeah, it's like a little slice of paradise. I always feel so peaceful and serene when I'm there." - }, - { - "speaker": "Sam", - "dia_id": "D17:25", - "text": "Wow, it really seems like a peaceful retreat. Thanks for showing me!" - }, - { - "speaker": "Evan", - "dia_id": "D17:26", - "text": "No prob, always good to chat about those tranquil times. Take it easy!" - }, - { - "speaker": "Sam", - "dia_id": "D17:27", - "text": "Take care, buddy. Hang in there!" - }, - { - "speaker": "Evan", - "dia_id": "D17:28", - "text": "Thanks, Sam. If you need to talk, I'm here for you too." - } - ], - "session_18_date_time": "8:16 pm on 5 December, 2023", - "session_18": [ - { - "speaker": "Evan", - "dia_id": "D18:1", - "text": "Hey Sam, good to hear from you. I've hit a bit of a snag - my new Prius, the one I just bought, broke down. It's a bit of a stressor since I rely on it for my active lifestyle and road trips. It's frustrating when new things go awry so soon." - }, - { - "speaker": "Sam", - "dia_id": "D18:2", - "text": "Hey Evan, that's rough. Dealing with a new car breaking down is such a hassle, especially when it's your main mode of transport." - }, - { - "speaker": "Evan", - "dia_id": "D18:3", - "text": "You're telling me. I was really counting on this new Prius to be reliable. It's always a challenge when you have to deal with unexpected issues like this. But, I guess it's just one of those things - even new cars can have problems." - }, - { - "speaker": "Sam", - "dia_id": "D18:4", - "text": "It's tough when your plans get derailed by something like this. But hey, sometimes these setbacks lead to new opportunities." - }, - { - "speaker": "Evan", - "dia_id": "D18:5", - "text": "True, I'm trying to see it as a chance to explore other ways of staying active and traveling. Maybe it's an opportunity to try something different." - }, - { - "speaker": "Sam", - "img_url": [ - "http://steenshoney.com/cdn/shop/articles/IMG_5745.jpg" - ], - "blip_caption": "a photo of a variety of bowls of fruit and yogurt", - "query": "colorful smoothie bowl fresh fruits toppings", - "dia_id": "D18:6", - "text": "Exactly, it's all about finding the silver lining. Speaking of new things, I attended a Weight Watchers meeting yesterday. Learned some great tips." - }, - { - "speaker": "Evan", - "dia_id": "D18:7", - "text": "That smoothie bowl looks fantastic! How was the meeting? Yeah, I've been thinking about trying yoga, something gentle yet effective for stress relief and flexibility. What's your take on it, Sam?" - }, - { - "speaker": "Sam", - "dia_id": "D18:8", - "text": "The meeting was really insightful, and that smoothie bowl was a hit! Yoga's a great choice, it's done wonders for my flexibility and stress levels. You should definitely try it." - }, - { - "speaker": "Evan", - "dia_id": "D18:9", - "text": "I think I will. Thanks for the suggestion, Sam." - }, - { - "speaker": "Sam", - "dia_id": "D18:10", - "text": "Anytime, Evan. If you need any yoga tips or anything else, just let me know." - }, - { - "speaker": "Evan", - "dia_id": "D18:11", - "text": "Your support's been invaluable. Thanks again, Sam!" - }, - { - "speaker": "Sam", - "dia_id": "D18:12", - "text": "No worries, Evan. We all need a bit of help when trying new things. It's great to have support." - }, - { - "speaker": "Evan", - "dia_id": "D18:13", - "text": "Absolutely. It makes a big difference knowing you're not alone in these situations." - }, - { - "speaker": "Sam", - "dia_id": "D18:14", - "text": "Definitely. Take care, and let me know how the yoga goes. Bye!" - }, - { - "speaker": "Evan", - "dia_id": "D18:15", - "text": "Will do. Thanks for everything, Sam. Talk soon. Bye!" - } - ], - "session_19_date_time": "1:45 pm on 9 December, 2023", - "session_19": [ - { - "speaker": "Evan", - "dia_id": "D19:1", - "text": "Hey Sam, hope you're doing good. Wanted to share some amazing news - my partner is pregnant! We're so excited! It's been a while since we had a kiddo around." - }, - { - "speaker": "Sam", - "dia_id": "D19:2", - "text": "Congrats, Ev! That's great news! Parenthood is so amazing. How are you feeling about it?" - }, - { - "speaker": "Evan", - "dia_id": "D19:3", - "text": "So excited and a bit nervous! It's been a while since I had a toddler around but I'm really looking forward to it. Parenthood is so rewarding. I still remember when my first child was born, the joy was amazing. Looking forward to witness the miracle of life and build more memories with my family!" - }, - { - "speaker": "Sam", - "dia_id": "D19:4", - "text": "Wow, you're gonna be an amazing parent! Treasure those memories, they're truly special." - }, - { - "speaker": "Evan", - "img_url": [ - "https://clickinmyheels.files.wordpress.com/2019/09/fpthpk1wrjgpigitpdlw3w.jpg" - ], - "blip_caption": "a photo of a desk with a lamp, a picture frame, and a sign", - "query": "collage family photos living room wall memories", - "dia_id": "D19:5", - "text": "Thanks Sam! Absolutely. Talking of memories, I want to show you this. It's a collage of some of our top family memories. Each photo has an amazing moment - birthdays, holidays, vacations - so good to look back and recall all the great times we had." - }, - { - "speaker": "Sam", - "dia_id": "D19:6", - "text": "That's so lovely, Evan. Your family looks so happy. What's the story behind that sign in the center?" - }, - { - "speaker": "Evan", - "dia_id": "D19:7", - "text": "Oh, that one? It's from our trip to Banff. We have this sign in the frame that says 'Bring it on Home' - it's our family's motto, always reminding us of the importance of togetherness, no matter where we are." - }, - { - "speaker": "Sam", - "dia_id": "D19:8", - "text": "That's really touching, Evan. It's important to have something that keeps the family bond strong." - }, - { - "speaker": "Evan", - "dia_id": "D19:9", - "text": "Absolutely, Sam. My family means the world to me. They're my rock. I'm looking forward to expanding our family and creating even more beautiful memories." - }, - { - "speaker": "Sam", - "dia_id": "D19:10", - "text": "That's wonderful to hear, Evan! It's clear how much you value your family. Are you thinking of any specific plans or events to add to that collage?" - }, - { - "speaker": "Evan", - "dia_id": "D19:11", - "text": "Thanks, Sam! Yeah, we're planning a big family reunion next summer. It's going to be a blast and a perfect opportunity to add to our collage." - }, - { - "speaker": "Sam", - "dia_id": "D19:12", - "text": "Sounds fantastic! If you need any tips on organizing such a big event, just let me know. I'm always here to support and celebrate your family's milestones." - }, - { - "speaker": "Evan", - "dia_id": "D19:13", - "text": "Thanks, Sam! Your support means a lot. I'll keep you updated. Take care, bye!" - }, - { - "speaker": "Sam", - "dia_id": "D19:14", - "text": "Take care, Evan! Can't wait to hear about it. Bye!" - }, - { - "speaker": "Evan", - "dia_id": "D19:15", - "text": "Bye Sam. I'll definitely keep you updated. Thanks for the kind words and support. Take care!" - } - ], - "session_20_date_time": "6:48 pm on 17 December, 2023", - "session_20": [ - { - "speaker": "Evan", - "dia_id": "D20:1", - "text": "Hey Sam, what's up? Long time no see, huh? Lots has happened." - }, - { - "speaker": "Sam", - "dia_id": "D20:2", - "text": "Hey Evan! Long time no see. I'm doing okay, been through a few bumps. How about you?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://d2g8igdw686xgo.cloudfront.net/25280738_15137467980_r.jpg" - ], - "blip_caption": "a photo of a young boy with crutches and a backpack", - "query": "son on crutches", - "dia_id": "D20:3", - "text": "It's not easy for us right now, my son had an accident last Tuesday, he fell off his bike and it was rough. But he's doing better now. How are you dealing with all this?" - }, - { - "speaker": "Sam", - "dia_id": "D20:4", - "text": "Darn, sorry to hear that. Hope he's feeling better. Same here, it's been tough lately. After we talked, I started thinking about ways to cope with it, but it's been challenging." - }, - { - "speaker": "Evan", - "dia_id": "D20:5", - "text": "Life can be hard sometimes. Do you have any hobbies or activities that make you happy?" - }, - { - "speaker": "Sam", - "dia_id": "D20:6", - "text": "I used to love hiking, but it's been a while since I had the chance to do it." - }, - { - "speaker": "Evan", - "dia_id": "D20:7", - "text": "I remember you mentioning that! Hiking is indeed a great way to center oneself and be one with nature. We should definitely plan a hike soon!" - }, - { - "speaker": "Sam", - "blip_caption": "a photo of a beach with a few people walking on it", - "dia_id": "D20:8", - "text": "Yeah, I'm struggling with my weight and it's affecting my confidence. I feel like I can't overcome all the challenges with my weight, I keep lacking motivation." - }, - { - "speaker": "Evan", - "dia_id": "D20:9", - "text": "Yeah, I understand it can be challenging. But remember, it's important to believe in yourself and take it one day at a time, Sam. Your worth is not defined by your weight." - }, - { - "speaker": "Sam", - "dia_id": "D20:10", - "text": "Cheers, Evan. Appreciate the help. It's tough breaking out of my comfort zone." - }, - { - "speaker": "Evan", - "dia_id": "D20:11", - "text": "Stepping out of your comfort zone can be intimidating, but it's totally worth it. Just challenge yourself to try something new, even if it's just a little thing. You got this!" - }, - { - "speaker": "Sam", - "dia_id": "D20:12", - "text": "Thanks, Evan. I'll take your advice. Trying new things can be difficult." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/ub3eyozz1olb1.jpg" - ], - "blip_caption": "a photo of a woman standing in front of a painting", - "query": "painting accomplishment", - "dia_id": "D20:13", - "text": "Yeah, trying something new and succeeding gives a great feeling of accomplishment. Give it a go, even if it's just a little thing. You'll be amazed!" - }, - { - "speaker": "Sam", - "dia_id": "D20:14", - "text": "She looks so confident! What kind of painting is that in the background?" - }, - { - "speaker": "Evan", - "dia_id": "D20:15", - "text": "\nThis is a contemporary figurative painting that I've finished few days ago, emphasizing the emotional state through expressive brushwork and vibrant color choices. It captures a moment of introspection, where the subject is deeply immersed in thought. Very proud of it!" - }, - { - "speaker": "Sam", - "dia_id": "D20:16", - "text": "That's amazing work, who's the girl standing next to painting?" - }, - { - "speaker": "Evan", - "dia_id": "D20:17", - "text": "That's a close friend of mine who helped me get this painting published in the exhibition!" - } - ], - "session_21_date_time": "4:25 pm on 26 December, 2023", - "session_21": [ - { - "speaker": "Sam", - "dia_id": "D21:1", - "text": "Hey Evan! Long time no see, how's it going?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://c0.wallpaperflare.com/preview/990/890/416/wedding-kiss-love-pair.jpg" - ], - "blip_caption": "a photography of a bride and groom kissing in front of a tree", - "query": "wedding day", - "dia_id": "D21:2", - "re-download": true, - "text": "Hey Sam! Long time no see! Been up and down lately, got married last week - how about you?" - }, - { - "speaker": "Sam", - "dia_id": "D21:3", - "text": "Congratulations, Evan! Is that the woman from Canada?" - }, - { - "speaker": "Evan", - "dia_id": "D21:4", - "text": "Yes, that's her, I don't know why we didn't get married before, because I was in love with her at first sight!" - }, - { - "speaker": "Sam", - "dia_id": "D21:5", - "text": "Wow, Evan! Love at first sight? That sounds like something straight out of a fairy tale. What are your thoughts on it? Do you believe in love at first sight?" - }, - { - "speaker": "Evan", - "dia_id": "D21:6", - "text": "I totally believe in it. It was like time stopped and I felt like a spark lit inside me - it was so right." - }, - { - "speaker": "Sam", - "dia_id": "D21:7", - "text": "That's awesome, Evan! Finding that kind of connection must feel really liberating. Love can be so powerful, huh?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://kikiraine.files.wordpress.com/2019/11/img_4736.jpg" - ], - "blip_caption": "a photo of a person sitting on a rock near the water", - "query": "sunset love magic peace wonder", - "dia_id": "D21:8", - "text": "Yeah, Sam, love is truly amazing. It brings so much happiness and fulfillment, like a beautiful sunset that lights up our lives and brings peace. Incredible!" - }, - { - "speaker": "Sam", - "dia_id": "D21:9", - "text": "Wish I could feel the same about love, but I've started to enjoy running in the mornings, and it's been a great way to clear my head. What can you do, right?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://trendgallery.art/cdn/shop/files/IMG_9647_fa0b4eba-d7f5-48ae-81c9-b471605dd4a9.jpg" - ], - "blip_caption": "a photo of a painting with a white background and a blue, orange, and black painting", - "query": "canvas painting colorful abstract strokes", - "dia_id": "D21:10", - "text": "Yeah, I get it. Life's all about finding what works for you. Like your morning runs, they're a step towards something good, right? Keep trying new things, Sam, and you might find your own version of love in the most unexpected places. Embrace the journey \u2014 it\u2019s full of surprises!" - }, - { - "speaker": "Sam", - "dia_id": "D21:11", - "text": "Such a minimalistic and stunning piece of work, I wonder what inspired the artist to create it." - }, - { - "speaker": "Evan", - "dia_id": "D21:12", - "text": "The painting is mine, I made it when I was a mix of emotions - sad, mad, and hopeful. Art is amazing how it can portray feelings without words." - }, - { - "speaker": "Sam", - "dia_id": "D21:13", - "text": "Wow, Evan! Art is really amazing at expressing emotions - it's truly fascinating." - }, - { - "speaker": "Evan", - "img_url": [ - "https://trendgallery.art/cdn/shop/products/IMG_3291_4beec6cf-7c90-43ef-b8f7-7f7fd295917b.jpg" - ], - "blip_caption": "a photo of a painting with a bird flying over it", - "query": "abstract vibrant colorful painting", - "dia_id": "D21:14", - "text": "It's amazing how art can express emotions so well. It really helps me recognize and handle my own feelings. This painting is giving me a massive rush of joy!" - }, - { - "speaker": "Sam", - "dia_id": "D21:15", - "text": "That's stunning! What emotions did you create this painting with?" - }, - { - "speaker": "Evan", - "dia_id": "D21:16", - "text": "I painted this with a sense of joy and freedom. The spontaneous strokes and bold colors reflect a playful and liberated mood, embracing the creative process without restraint." - }, - { - "speaker": "Sam", - "img_url": [ - "https://judgeme.imgix.net/nashid-chroma-art-and-apparel/1640132184__inbound5216268343587060307__original.jpg" - ], - "blip_caption": "a photo of a woman holding flowers in front of her face", - "query": "framed artwork on wall painting talent creativity", - "dia_id": "D21:17", - "text": "Wow, Evan, this is amazing! You've got serious talent and creativity. Making this must have been so satisfying! Here's a painting that inspired me when I went to an exhibit few days ago." - }, - { - "speaker": "Evan", - "dia_id": "D21:18", - "text": "\nThanks, Sam! I appreciate the compliment. This painting has such an inspiring vibe; you really have a knack for understanding art! How about you? How long have you been painting?" - }, - { - "speaker": "Sam", - "dia_id": "D21:19", - "text": "\nI do sketch occasionally, but I haven't created anything remarkable yet. I have a feeling I'll have something to show off before long! Seeing your passion for it is inspiring." - }, - { - "speaker": "Evan", - "dia_id": "D21:20", - "text": "Thanks, Sam! Glad I could motivate you. If you ever want to give it a go, I'm happy to help get you started. Speaking of which, you know what? I lost my keys again, it's become a weekly ritual for me!" - }, - { - "speaker": "Sam", - "dia_id": "D21:21", - "text": "Ooh, Evan, I'd put a GPS sensor on your keys!" - }, - { - "speaker": "Evan", - "dia_id": "D21:22", - "text": "Great idea, I think I'll do that as soon as I find it!" - } - ], - "session_22_date_time": "11:00 am on 31 December, 2023", - "session_22": [ - { - "speaker": "Sam", - "img_url": [ - "https://c1.wallpaperflare.com/preview/524/680/1013/people-man-back-mountain.jpg" - ], - "blip_caption": "a photography of a man standing on a rock looking out over a valley", - "query": "hiking trip mountain top", - "dia_id": "D22:1", - "re-download": true, - "text": "Hey Evan! I\u2019m really getting into this healthier lifestyle\u2014just took my friends on an epic hiking trip last Friday!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://disguisemeforever.files.wordpress.com/2021/05/good-click.jpg" - ], - "blip_caption": "a photo of a small stream running through a lush green forest", - "query": "scenic lookout drive peaceful relaxing", - "dia_id": "D22:2", - "text": "Hey Sam! That\u2019s fantastic\u2014nothing like a good hike to feel alive. We took the Prius for a long drive to the mountains last weekend. It was perfect until we got into a little scrape on the way back." - }, - { - "speaker": "Sam", - "dia_id": "D22:3", - "text": "Oh no, were you guys okay after the accident?" - }, - { - "speaker": "Evan", - "dia_id": "D22:4", - "text": "Yeah, we were fine, thanks. Just a minor accident, but it put a bit of a damper on telling my work friends about getting married. They\u2019ve been a great support, though." - }, - { - "speaker": "Sam", - "dia_id": "D22:5", - "text": "I bet they were thrilled to hear about your marriage, despite the mishap!" - }, - { - "speaker": "Evan", - "dia_id": "D22:6", - "text": "Absolutely, it's been a whirlwind of emotions. Good thing the accident was minor. Just a reminder to take it easy on the road, I guess." - }, - { - "speaker": "Sam", - "dia_id": "D22:7", - "text": "True, it\u2019s important to stay safe. Glad you can still enjoy the peaceful moments after something like that." - }, - { - "speaker": "Evan", - "dia_id": "D22:8", - "text": "Definitely, nature brings peace and clarity - it's a great experience." - }, - { - "speaker": "Sam", - "dia_id": "D22:9", - "text": "Nature can make everything else seem small and help us find peace inside. It reminds us of the bigger picture, you know?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://i0.wp.com/therosetable.com/wp-content/uploads/2023/09/IMG_4235.jpg" - ], - "blip_caption": "a photo of a woman sitting at a table with plates and glasses", - "query": "ginger snaps tea cups table", - "dia_id": "D22:10", - "text": "For sure, and nature has been a great healer. Speaking of which, I\u2019ve got to share some of these new healthy snacks I\u2019ve been trying." - }, - { - "speaker": "Sam", - "dia_id": "D22:11", - "text": "They look healthy and delicious! Perfect for after a hike or, I guess, post-accident recovery, huh?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://blessedbeyondcrazy.com/wp-content/uploads/2019/09/00000PORTRAIT_00000_BURST20190918165701335.jpg" - ], - "blip_caption": "a photo of a bunch of cookies on a cooling rack", - "query": "ginger snaps plate", - "dia_id": "D22:12", - "text": "Exactly! They\u2019re packed with nutrients and really easy to make. You also need to try these cookies, they are awesome! I\u2019ll send you the recipes." - }, - { - "speaker": "Sam", - "dia_id": "D22:13", - "text": "Thanks, I\u2019d appreciate that. It\u2019s good to find new ways to stay healthy. Do you have any healthier snack ideas?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://paleoglutenfreeguy.com/wp-content/uploads/2020/08/IMG_7067.jpg" - ], - "blip_caption": "a photo of a bowl of coconut balls and a bowl of oats", - "query": "energy balls plate", - "dia_id": "D22:14", - "text": "Yeah, I've been trying to eat healthier too. Check out this cool recipe I discovered for these energy balls." - }, - { - "speaker": "Sam", - "dia_id": "D22:15", - "text": "Do you like them? I know they can be an acquired taste." - }, - { - "speaker": "Evan", - "dia_id": "D22:16", - "text": "I enjoy the taste of these. They're energizing and a healthy way to satisfy your sweet tooth." - }, - { - "speaker": "Sam", - "dia_id": "D22:17", - "text": "Awesome! Always on the lookout for healthy snacks, thanks for the tip!" - }, - { - "speaker": "Evan", - "dia_id": "D22:18", - "text": "Glad to help - hope you enjoy it!" - }, - { - "speaker": "Sam", - "dia_id": "D22:19", - "text": "Thanks, Evan! I'll give these a try. They look yum. Your help means a lot to me. Btw you know what? I went to the store again and, unsurprisingly, had issues with the self-checkout. It's becoming a regular annoyance." - }, - { - "speaker": "Evan", - "dia_id": "D22:20", - "text": "That's very strange, I've never had a problem with it once!" - }, - { - "speaker": "Sam", - "dia_id": "D22:21", - "text": "Apparently I attract that to me, if you ever want to be in that situation, call me at the store with you!" - } - ], - "session_23_date_time": "1:32 pm on 6 January, 2024", - "session_23": [ - { - "speaker": "Evan", - "img_url": [ - "https://cdn.jewelryimages.net/static/domains/kvjewels/images/store-pics/b9ffd872-4ac6-405c-b3e0-19d875e60f0b.jpg" - ], - "blip_caption": "a photo of a man and a woman standing on a rocky beach", - "query": "couple families announcement", - "dia_id": "D23:1", - "text": "Hey Sam, guess what? My partner and I told our extended fam about our marriage yesterday \u2013 it was so special! We've been totally overwhelmed by all their love and support." - }, - { - "speaker": "Sam", - "dia_id": "D23:2", - "text": "Congrats on the news, Evan! You two look so happy in the pic. These moments make life so wonderful; super stoked for you!" - }, - { - "speaker": "Evan", - "dia_id": "D23:3", - "text": "Thanks, Sam! It was an awesome moment, and I feel really lucky to have found someone who gets me. Plus, our families are really happy for us - that's the best part!" - }, - { - "speaker": "Sam", - "dia_id": "D23:4", - "text": "Wow, Evan. It's awesome that you've found someone who gets you! Having your family's support must feel great." - }, - { - "speaker": "Evan", - "dia_id": "D23:5", - "text": "Definitely, family support is so important. Knowing they're happy about our marriage is awesome and so comforting." - }, - { - "speaker": "Sam", - "dia_id": "D23:6", - "text": "Yeah, it's awesome to have that support. It definitely brings more happiness and joy." - }, - { - "speaker": "Evan", - "dia_id": "D23:7", - "text": "Yeah Sam, that means a lot to me. Our bond just keeps getting stronger and it brings such a good feeling to our lives. Family really is everything." - }, - { - "speaker": "Sam", - "dia_id": "D23:8", - "text": "Agree, Evan! Family is everything - they bring so much love and happiness. They're always there for us no matter what. I'm grateful for their support and love." - }, - { - "speaker": "Evan", - "dia_id": "D23:9", - "text": "For sure, Sam. That's what makes family so special. They bring so much love and happiness. It's great having their support and knowing they're always there for us. I feel really fortunate to have their never-ending love and support." - }, - { - "speaker": "Sam", - "dia_id": "D23:10", - "text": "Yeah, definitely, Evan. We both have amazing families that are always there for us. Always a blessing." - }, - { - "speaker": "Evan", - "dia_id": "D23:11", - "text": "Yeah, Sam. Our families give us so much joy, support, and love. They're a real blessing! I don't know what I'd do without them." - }, - { - "speaker": "Sam", - "dia_id": "D23:12", - "text": "Hey, Evan. My family has been my rock through everything. Don't know what I'd do without them." - }, - { - "speaker": "Evan", - "img_url": [ - "https://ic.pics.livejournal.com/queerbychoice/218786/1899659/1899659_original.jpg" - ], - "blip_caption": "a photo of a group of people sitting at a table with food", - "query": "family gathering", - "dia_id": "D23:13", - "text": "Yeah, they are our rock. We're blessed to have them." - }, - { - "speaker": "Sam", - "dia_id": "D23:14", - "text": "Wow, you guys are awesome! What's cooking tonight?" - }, - { - "speaker": "Evan", - "dia_id": "D23:15", - "text": "Thanks, Sam! We're having a family get-together tonight and enjoying some homemade lasagna. Super excited! By the way, I've started a new diet\u2014limiting myself to just two ginger snaps a day. What's on your menu tonight?\t" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.redd.it/qmjb5d7g8ra81.jpg" - ], - "blip_caption": "a photo of a plate of food with bread and meat", - "query": "lasagna garlic bread", - "dia_id": "D23:16", - "text": "That's a great discipline, Evan! We're keeping it light tonight, just some homemade lasagna. Can't compete with your ginger snap limit though!" - }, - { - "speaker": "Evan", - "dia_id": "D23:17", - "text": "Oh this must be very hearty and delicious, well I'll have to stick to the diet plan, even with the family gathering!" - }, - { - "speaker": "Sam", - "img_url": [ - "https://anaffairfromtheheart.com/wp-content/uploads/2021/02/Key-Lime-Pie-1.jpg" - ], - "blip_caption": "a photo of a pie with raspberries and limes on top", - "query": "homemade key lime pie", - "dia_id": "D23:18", - "text": "Yeah, the lasagna was pretty awesome, but check out what I had for dessert, I'm sure you're drooling!" - }, - { - "speaker": "Evan", - "dia_id": "D23:19", - "text": "Looks yummy! Did you make that?" - }, - { - "speaker": "Sam", - "dia_id": "D23:20", - "text": "No, I didn't make it. This is actually a pic from my cousin's wedding. It's super special." - }, - { - "speaker": "Evan", - "img_url": [ - "https://serafrescaic.com/wp-content/uploads/2018/10/wedding-cake.jpg" - ], - "blip_caption": "a photo of a wedding cake with candles and flowers on a table", - "query": "wedding cake", - "dia_id": "D23:21", - "text": "Wow Sam! Weddings are indeed special. This looks great, yum!" - }, - { - "speaker": "Sam", - "dia_id": "D23:22", - "text": "Ooh, nice cake! Reminds me of special occasions. Do you have any upcoming plans?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://texasrandi.files.wordpress.com/2021/03/durango-hike-10.jpg" - ], - "blip_caption": "a photo of a stream running through a snowy forest filled with snow", - "query": "snowy landscape canada honeymoon", - "dia_id": "D23:23", - "text": "Thanks Sam! We're off to Canada next month for our honeymoon. So excited to create some awesome memories. Looking forward to exploring the beautiful snowy landscapes there." - }, - { - "speaker": "Sam", - "dia_id": "D23:24", - "text": "Wow, that looks great! What are your plans for the trip?" - }, - { - "speaker": "Evan", - "dia_id": "D23:25", - "text": "We're planning to ski, try the local cuisine, and enjoy the beautiful views. We're really excited!" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.redd.it/p2vf3jqii0xb1.jpg" - ], - "blip_caption": "a photo of a container of french fries covered in caramel", - "query": "ski canada poutine", - "dia_id": "D23:26", - "text": "Sounds amazing, Ev! Skiing, trying local dishes, and enjoying the breathtaking views - the perfect honeymoon. Have an incredible time creating unforgettable memories!" - }, - { - "speaker": "Evan", - "dia_id": "D23:27", - "text": "Yeah, Sam! Gonna try some poutine while we're there - can't wait!" - }, - { - "speaker": "Sam", - "dia_id": "D23:28", - "text": "Never tried it? Can't say I blame you, it's kind of a Canadian thing. Let me know how you like it!" - }, - { - "speaker": "Evan", - "dia_id": "D23:29", - "text": "Sure thing, Sam! Let's see if it lives up to the hype. I'll let you know what happens!" - }, - { - "speaker": "Sam", - "dia_id": "D23:30", - "text": "Yeah, Evan! Let me know all about it. Don't forget the details!" - }, - { - "speaker": "Evan", - "dia_id": "D23:31", - "text": "Cool, Sam. I'll keep you posted. Talk soon!" - }, - { - "speaker": "Sam", - "dia_id": "D23:32", - "text": "Awesome, Evan! Catch you soon. Have a great trip!" - }, - { - "speaker": "Evan", - "dia_id": "D23:33", - "text": "Thanks, Sam! Catch you later. Have a great one!" - } - ], - "session_24_date_time": "12:17 am on 10 January, 2024", - "session_24": [ - { - "speaker": "Evan", - "dia_id": "D24:1", - "text": "Hey Sam, hope you're doing good. Something funny happened last night." - }, - { - "speaker": "Sam", - "dia_id": "D24:2", - "text": "Hey Evan, what's up? What happened? Let me know." - }, - { - "speaker": "Evan", - "dia_id": "D24:3", - "text": "Yesterday I went out with my friends and had a bit too much to drink. I ended up doing something I regret and it involved someone's roses." - }, - { - "speaker": "Sam", - "dia_id": "D24:4", - "text": "What's up with that incident? All good now?" - }, - { - "speaker": "Evan", - "dia_id": "D24:5", - "text": "Oof, Sam, so embarrassing! I had a pee accident near some roses - can you believe it? I'm so sorry about that." - }, - { - "speaker": "Sam", - "dia_id": "D24:6", - "text": "Uh oh, Evan! That's awkward. Did anyone get mad at you? Are you okay?" - }, - { - "speaker": "Evan", - "dia_id": "D24:7", - "text": "I was so embarrassed when I saw what happened the next morning, so I apologized and luckily they were understanding. Yeah, I was out of control--guess I gotta be more careful next time." - }, - { - "speaker": "Sam", - "dia_id": "D24:8", - "text": "They were understanding? Phew! We all mess up sometimes, we're human after all." - }, - { - "speaker": "Evan", - "dia_id": "D24:9", - "text": "Yeah, they were understanding, which was great. But it's a good reminder to be more careful. We all make mistakes, but it's important to learn from them. Speaking of, my partner and I tried snowshoeing this weekend. It was part of a new adventure for us and surprisingly fun." - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.pinimg.com/originals/c7/f7/30/c7f7308cbae589efb28ffee439cff05f.jpg" - ], - "blip_caption": "a photo of a white board with a bunch of writing on it", - "query": "meal plan workout schedule motivation health journey", - "dia_id": "D24:10", - "text": "Yeah, Evan, you're right. Mistakes happen, but it's good to learn from them. Snowshoeing sounds like a great way to stay active during the winter. I've been thinking and I made a meal plan and workout schedule. I'm getting motivated by something I saw, so starting today I'm gonna do my best to stay on track." - }, - { - "speaker": "Evan", - "dia_id": "D24:11", - "text": "Good work, Sam! You've got a plan and you're dedicated to staying healthy - have you asked your doctor for advice? They could probably give you even more diet and exercise tips." - }, - { - "speaker": "Sam", - "blip_caption": "a photo of a red and orange card with a yellow sun", - "dia_id": "D24:12", - "text": "Thanks, Evan! Haven't seen a doctor in a while, but it's probably a good idea to get some advice. I'm going to make an appointment soon." - }, - { - "speaker": "Evan", - "dia_id": "D24:13", - "text": "What advice are you planning to get from the doctor?" - }, - { - "speaker": "Sam", - "dia_id": "D24:14", - "text": "I'm gonna ask the doc about a balanced diet plan and getting advice on low-impact exercises, given my current situation." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/rhcb1m5vwyhb1.jpg" - ], - "blip_caption": "a photo of a salad with chicken, avocado, tomatoes, corn, and cheese", - "query": "salad grilled chicken avocado mixed greens", - "dia_id": "D24:15", - "text": "Sounds good, Sam. That's definitely a step in the right direction. Remember to focus on a balanced diet and low-impact exercises. Let me know how it goes." - }, - { - "speaker": "Sam", - "dia_id": "D24:16", - "text": "That looks great! Where did you get the idea for this salad? Also, do you have any suggestions for low-impact exercises?" - }, - { - "speaker": "Evan", - "dia_id": "D24:17", - "text": "I got it from a nearby restaurant. As for low-impact exercises, swimming, yoga, and walking are good options." - }, - { - "speaker": "Sam", - "blip_caption": "a photo of a young boy is playing in a pool", - "dia_id": "D24:18", - "text": "The salad idea from a restaurant is a smart move, Evan! And thanks for the exercise tips. Also I watched The Godfather last night, and it motivated me to keep up with my routine. \"I'm gonna make him an offer he can't refuse\" - now that's motivation!" - }, - { - "speaker": "Evan", - "dia_id": "D24:19", - "text": "Yoga's definitely a great start, Sam. It's helped me with stress and staying flexible, which is perfect alongside the diet. And yes, The Godfather is a legendary thing to watch, can be re-watched many times!" - }, - { - "speaker": "Sam", - "dia_id": "D24:20", - "text": "Between a healthier diet and yoga, I\u2019m hoping for some positive changes." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.pinimg.com/originals/5e/cc/75/5ecc7518568d48830960915a86af39ab.jpg" - ], - "blip_caption": "a photo of a woman standing on a beach at sunset", - "query": "sunset walk beach", - "dia_id": "D24:21", - "text": " By the way there are plenty of other low-impact exercises that can be fun. Going on beach sunsets is one of my favorites - good for exercise and totally calming." - }, - { - "speaker": "Sam", - "dia_id": "D24:22", - "text": "That looks zen. Gonna go for some beach walks - thanks for the tip, Evan! I want to brag, I had that recurring dream again where I'm flying over skyscrapers!" - }, - { - "speaker": "Evan", - "dia_id": "D24:23", - "text": "I think a little more and you'll learn how to control those dreams, once you get the hang of it let me know haha! Enjoy the fresh air and the views. Have fun!" - }, - { - "speaker": "Sam", - "dia_id": "D24:24", - "text": "Thanks Evan! Gonna make the most of it. You too, have a good one!" - } - ], - "session_25_date_time": "9:37 pm on 11 January, 2024", - "session_25": [ - { - "speaker": "Sam", - "dia_id": "D25:1", - "text": "Hey Evan, been a few days since we last chatted. Hope you're doing OK. A lot's happened since then. Got issues with my health, it's been rough. Feels like this weight's keeping me from fully living. Trying to stay positive, not easy." - }, - { - "speaker": "Evan", - "dia_id": "D25:2", - "text": "Hey Sam, sorry to hear about your health. It's tough when it gets in the way of life. You're being positive, but remember to take care of yourself too. By the way, I had to apologize to my partner for that drunken night, it was pretty embarrassing." - }, - { - "speaker": "Sam", - "dia_id": "D25:3", - "text": "Hey Evan, that does sound like a tough situation. I'm doing my best with my health. How did your partner take the news about the rose bushes?" - }, - { - "speaker": "Evan", - "dia_id": "D25:4", - "text": "Well, she wasn't thrilled, but understood it was an accident. I promised to be more careful in the future. Changing the subject, have you found any low-impact exercises that you enjoy?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.imgur.com/hjUwenM.jpg" - ], - "blip_caption": "a photo of a field with a fence and a dirt road", - "query": "beautiful drive countryside open road green fields sunlight clouds", - "dia_id": "D25:5", - "text": "Hey Evan, haven't found any exercises I like. But lately, I've been on a few car rides. Helps me chill and enjoy the view. Check out this cool pic I snapped last week in the country." - }, - { - "speaker": "Evan", - "dia_id": "D25:6", - "text": "Nice pic! Does being out in the countryside help you relax and get some fresh air away from the city?" - }, - { - "speaker": "Sam", - "dia_id": "D25:7", - "text": "Yeah, being in nature really helps me relax and get some fresh air away from the city." - }, - { - "speaker": "Evan", - "img_url": [ - "https://camperchristina.com/wp-content/uploads/2016/12/img_2641.jpg" - ], - "blip_caption": "a photo of a kayak is seen from the front of the boat", - "query": "sunset calm lake camping trip", - "dia_id": "D25:8", - "text": "Glad to hear it! Nature really has a way of calming and reviving the soul. Last summer, I took this pic on a camping trip - it was such an amazing sunset. Moments like these remind us of the beauty of life, even during tough times." - }, - { - "speaker": "Sam", - "dia_id": "D25:9", - "text": "Wow, that pic is amazing! It must have been a great experience being out on the lake." - }, - { - "speaker": "Evan", - "dia_id": "D25:10", - "text": "I had a great time kayaking and watching the sunset last summer - it was truly unforgettable. Being out on the water is so peaceful." - }, - { - "speaker": "Sam", - "dia_id": "D25:11", - "text": "Wow, that sounds amazing. Being in nature is so calming, right?" - }, - { - "speaker": "Evan", - "dia_id": "D25:12", - "text": "Nature can be super calming. It's like pushing a reset button for your mind and body." - }, - { - "speaker": "Sam", - "dia_id": "D25:13", - "text": "Definitely, I couldn't agree more. There's something about being outdoors that rejuvenates you. I'm planning to spend more time in nature myself!" - }, - { - "speaker": "Evan", - "img_url": [ - "http://deborahsilver.com/wp-content/uploads/2018/05/May-Baroque-12.jpg" - ], - "blip_caption": "a photo of a tree with pink flowers in a park", - "query": "blooming flowers vibrant colors nature moment", - "dia_id": "D25:14", - "text": "Got it. When health stuff cramps your style, it sucks. But small moments outdoors can make a big impact. This photo reminds me of last spring when I was feeling a bit down, but the vibrant colors brought a smile to my face, even if just for a moment. Remember to find joy in the little things." - }, - { - "speaker": "Sam", - "dia_id": "D25:15", - "text": "That pic is gorgeous! It really brightens my day. Sometimes, it's the little things that matter, right?" - }, - { - "speaker": "Evan", - "dia_id": "D25:16", - "text": "Absolutely, Sam. It's often those little moments that make the biggest difference. Keep finding those bright spots." - }, - { - "speaker": "Sam", - "dia_id": "D25:17", - "text": "Thanks, Evan. It's good to be reminded to appreciate the small things. They do add up." - }, - { - "speaker": "Evan", - "dia_id": "D25:18", - "text": "Anytime, Sam. It's all about those small joys, especially when times are tough. You've got this!" - }, - { - "speaker": "Sam", - "dia_id": "D25:19", - "text": "Really appreciate it, Evan. Your words help a lot. Take care!" - }, - { - "speaker": "Evan", - "dia_id": "D25:20", - "text": "You too, Sam. And remember, I'm always here if you need to chat. Look after yourself!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Evan": [ - "Evan buys a new Prius after he repairs and sells his old Prius when it breaks down.", - "Evan takes his family on a road trip to the Rockies in the Prius and they hike some trails together." - ], - "Sam": [ - "Sam takes up painting as a new hobby and buys a set of colors to kickstart the hobby." - ], - "date": "18 May, 2023" - }, - "events_session_2": { - "Evan": [ - "Evan goes on a roadtrip to Jasper with this family where they drive through Icefield Parkways and stay in a cozy cabin in the mountains." - ], - "Sam": [ - "Sam has a check up with his doctor and finds that his current weight is not ideal.", - "Sam decides to take up hobbies to improve his weight and stay motivated." - ], - "date": "24 May, 2023" - }, - "events_session_3": { - "Evan": [ - "Evan experiences heart palpitations and is motivated by the health scare to make some changes to his lifestyle." - ], - "Sam": [ - "Sam starts eating salads to support his goal for better health.", - "Sam gets frustrated during shopping because all of the self-checkout machines were broken." - ], - "date": "6 June, 2023" - }, - "events_session_4": { - "Evan": [ - "Evan starts reading The Great Gatsby novel." - ], - "Sam": [ - "Sam's friends make fun of his heavy build as a result of his eating habits.", - "Sam joins a gym and starts exercising regularly to reduce his weight." - ], - "date": "27 July, 2023" - }, - "events_session_5": { - "Evan": [ - "Evan falls in love with a Canadian woman during a trip to Canada." - ], - "Sam": [], - "date": "7 August, 2023" - }, - "events_session_6": { - "Evan": [ - "Evan takes a Canadian vacation with his new love where they hiked, biked and explored the outdoors together.", - "Evan loses his keys and spends a great deal of time looking for it." - ], - "Sam": [ - "Sam has a dream about soaring over skyscrapers." - ], - "date": "13 August, 2023" - }, - "events_session_7": { - "Evan": [ - "Evan's son hurts his akle while playing soccer but it isn't a serious accident." - ], - "Sam": [ - "Sam enrolls in a cooking class to help him learn to cook healthier meals.", - "Sam cooks a grilled salmon meal with vegetables that he learned from the cooking class.", - "Sam has another doctor's appointment where he receives a health scare about his weight." - ], - "date": "15 August, 2023" - }, - "events_session_8": { - "Evan": [ - "Evan joins a painting class to improve his skills and meet like-minded people." - ], - "Sam": [ - "Sam decides to make an effort to change his diet and lifestyle." - ], - "date": "19 August, 2023" - }, - "events_session_9": { - "Evan": [ - "Evan twists his ankle and is not able to stay consistent in his fitness routine.", - "Evan engages in swimming to stay healthy until his ankle gets better and looks for physical therapy appointments." - ], - "Sam": [ - "Sam begins to feel more motivated and energized due to his new diet and exercise routine." - ], - "date": "27 August, 2023" - }, - "events_session_10": { - "Evan": [ - "Evan finishes painting a colorful sunset." - ], - "Sam": [ - "Sam posts a before and after photo of himself on social media, hoping to inspire others too." - ], - "date": "11 September, 2023" - }, - "events_session_11": { - "Evan": [ - "Evan messes up his knee while playing baseball with this kids and takes physical therapy to recover from the injury.", - "Evan helps a lost tourist find their way and ends up taking an adventuruous tour of the city with them." - ], - "Sam": [ - "Sam begins to incorporate healthy snacks like fruit bowls into his diet.", - "Sam faces issues with the navigation app in his new phone." - ], - "date": "6 October, 2023" - }, - "events_session_12": { - "Evan": [], - "Sam": [ - "Sam has another health checkup where the doctor warns him about health risks due to his weight." - ], - "date": "8 October, 2023" - }, - "events_session_13": { - "Evan": [], - "Sam": [ - "Sam has a slip-up in his health regimen and buys candy and soda at the store to deal with work stress.", - "Sam travels through Lake Tahoe with a friend and decides to try out kayaking after recommendation from Evan." - ], - "date": "14 October, 2023" - }, - "events_session_14": { - "Evan": [ - "Evan has another opportunity to help a lost tourist." - ], - "Sam": [ - "Sam goes to the ER due to stomachache and receives a diagnosis of gastritis stemming from his unhealthy lifestyle." - ], - "date": "17 October, 2023" - }, - "events_session_15": { - "Evan": [ - "Evan wakes up early to go for a brisk walk." - ], - "Sam": [ - "Sam's family encourages him to stick to his new healthier habits but Sam feels a bit pressured." - ], - "date": "25 October, 2023" - }, - "events_session_16": { - "Evan": [ - "Sam loses his job due to downsizing of his company and is on the hunt for a new job.", - "Sam receives a vintage 1968 Kustom K200-A guitar as a gift from a friend." - ], - "Sam": [ - "Sam is chosen to be a coach in his Weight Watchers group." - ], - "date": "9 November, 2023" - }, - "events_session_17": { - "Evan": [ - "Evan receives a false health scare during a health check-up at the hospital." - ], - "Sam": [ - "Sam purchases a DVD box set of his goto feel-good flick, The Godfather to help take his mind off his health issues." - ], - "date": "21 November, 2023" - }, - "events_session_18": { - "Evan": [ - "Evan's new Prius car breaks down." - ], - "Sam": [ - "Sam attends a Weight Watchers meeting, shares his smoothie bowls and learns tips from others." - ], - "date": "5 December, 2023" - }, - "events_session_19": { - "Evan": [ - "Evan's Canadian partner gets pregnant.", - "Evan plans to hold a big family reunion in summer 2024." - ], - "Sam": [], - "date": "9 December, 2023" - }, - "events_session_20": { - "Evan": [ - "Evan's son falls off a bike and has to rely on crutches for a while.", - "Evan finishes work on a portrait of a woman and his close friend helps him get it published at a exhibition." - ], - "Sam": [ - "Sam begins to think the idea of changing his diet and lifestyle is too hard, and feels like he lacks the motivation to deal with his weight.", - "Sam's struggle with his weights affects his confidence." - ], - "date": "17 December, 2023" - }, - "events_session_21": { - "Evan": [ - "Evan and his pregnant Canadian partner get married.", - "Sam loses his keys." - ], - "Sam": [ - "Sam starts to enjoy running in the mornings to clear his head and improve his health." - ], - "date": "26 December, 2023" - }, - "events_session_22": { - "Evan": [ - "Evan takes a long drive to the mountains with his newly-wedded and pregnant wife in his Prius.", - "Evan gets in a minor accident when driving his Prius with his wife.", - "Evan spends time with his friends from work and tells them about his recent marriage." - ], - "Sam": [ - "Sam takes his friends on a hiking trip after a long time, proudly showing off his new healthier lifestyle." - ], - "date": "31 December, 2023" - }, - "events_session_23": { - "Evan": [ - "Evan starts a diet and limits himself to only two ginger snaps a day.", - "Evan and his partner announce their marriage to his extended family.", - "Evan has a family reunion where they eat some homemade lasagna.", - "Evan plans for a honeymoon to Canada with his wife in February 2024. They plan on exploring the snowy landscapes, local cuisine and skiing." - ], - "Sam": [ - "Sam has a meal of homemade lasgna." - ], - "date": "6 January, 2024" - }, - "events_session_24": { - "Evan": [ - "Evan and his partner try snowshoeing.", - "Evan goes out partying with his friends and gets too drunk, resulting in him urinating on someone's roses." - ], - "Sam": [ - "Sam watches The Godfather to get motivation to keep up with his healthy eating routine.", - "Sam reevaluates his diet and plans to stick to his new healthier habits.", - "Sam has a recurring dream about soaring over skyscrapers." - ], - "date": "10 January, 2024" - }, - "events_session_25": { - "Evan": [ - "Evan tells his newly-wed partner about his drunken night out and apologizes for urinating on the rose bushes." - ], - "Sam": [], - "date": "11 January, 2024" - } - }, - "observation": { - "session_1_observation": { - "Sam": [ - [ - "Sam went hiking with his dad when he was ten, which was a special and fun memory for them.", - "D1:7" - ], - [ - "Sam is considering trying painting as a new hobby.", - "D1:11" - ], - [ - "Sam has not tried painting yet but is keen to give it a go as a way to chill and get creative.", - "D1:13" - ] - ], - "Evan": [ - [ - "Evan has a new Prius after his old one broke down, which he got repaired and sold.", - "D1:2" - ], - [ - "Evan went on a trip to the Rockies with his family.", - "D1:4" - ], - [ - "Evan enjoys watercolor painting as a hobby, which he started a few years back.", - "D1:14" - ], - [ - "Evan got into watercolor painting because a friend introduced him to it and gave him advice.", - "D1:16" - ] - ] - }, - "session_2_observation": { - "Evan": [ - [ - "Evan took his family on a road trip to Jasper last weekend, driving through the Icefields Parkway.", - "D2:1" - ], - [ - "Evan described the road trip to Jasper as having fresh air, peacefulness, and a cozy cabin surrounded by mountains and forests.", - "D2:3" - ], - [ - "Evan recommended finding a fitness routine that is enjoyable and making smarter choices in diet for maintaining a healthier lifestyle.", - "D2:9" - ], - [ - "Evan has been painting for a few years as a way to find peace and relieve stress.", - "D2:11" - ] - ], - "Sam": [ - [ - "Sam had a tough week and had a check-up with the doctor where the weight wasn't great.", - "D2:4" - ], - [ - "Sam expressed interest in trying painting as a hobby to stay motivated and de-stress.", - "D2:10" - ] - ] - }, - "session_3_observation": { - "Evan": [ - [ - "Evan had a health scare involving sudden heart palpitations last week.", - "D3:1" - ], - [ - "Evan is making an effort to be extra careful with his health and is trying to eat less processed food and sugary snacks.", - "D3:3" - ], - [ - "Evan suggested swapping soda for flavored seltzer water and candy for dark chocolate with high cocoa content as healthier options to Sam.", - "D3:5" - ] - ], - "Sam": [ - [ - "Sam is trying to eat healthier and recently trying to eat less processed food, but has made no changes yet.", - "D3:2" - ], - [ - "Sam still enjoys soda and candy, although knowing it's not the best habit.", - "D3:4" - ], - [ - "Sam agreed to give swapping soda for flavored seltzer water and candy for dark chocolate a try.", - "D3:6" - ], - [ - "Sam had a frustrating experience at the supermarket as the self-checkout machines were all broken.", - "D3:16" - ] - ] - }, - "session_4_observation": { - "Sam": [ - [ - "Sam's friends mocked his weight last Friday, which hurt him.", - "D4:1" - ], - [ - "Sam is working on his health and getting active.", - "D4:3" - ], - [ - "Sam is determined to make changes in his lifestyle, like reducing soda and candy intake.", - "D4:7" - ], - [ - "Sam will start going to the gym and exercising regularly starting tomorrow.", - "D4:15" - ], - [ - "Sam values and appreciates Evan's support throughout their conversation.", - "D4:17, D4:19" - ] - ], - "Evan": [ - [ - "Evan offers to work on improving health together with Sam.", - "D4:2" - ], - [ - "Evan struggled with his health a few years ago, focusing on exercise, diet, and lifestyle changes, which made a positive impact.", - "D4:4" - ], - [ - "Evan made dietary changes like cutting down on sugary snacks and eating more veggies and fruit for his health.", - "D4:6" - ], - [ - "Evan enjoys reading the mystery novel 'The Great Gatsby' and finds it gripping.", - "D4:8" - ], - [ - "Evan suggests flavored seltzer water as a healthier alternative to soda.", - "D4:8" - ], - [ - "Evan advises Sam to focus on healthy swaps and taking small steps towards progress.", - "D4:12" - ], - [ - "Evan suggests low-calorie snacks like air-popped popcorn or fruit to pair with flavored seltzer water.", - "D4:10" - ] - ] - }, - "session_5_observation": { - "Evan": [ - [ - "Evan recently went on a trip to Canada where he met an awesome Canadian woman and feels alive when he's with her.", - "D5:1" - ], - [ - "Evan has been dealing with health issues lately that have made him appreciate good moments more.", - "D5:2" - ], - [ - "Evan has been working on prioritizing his health for two years, with ups and downs.", - "D5:7" - ], - [ - "Evan is motivated to stay healthy by his family and uses a tool that tracks his progress well.", - "D5:9" - ], - [ - "Evan is motivated by a thirst for adventure and interesting hikes.", - "D5:13" - ], - [ - "Evan got a symbol that represents strength and resilience to motivate him through tough times.", - "D5:17" - ] - ], - "Sam": [ - [ - "Sam acknowledges that dealing with health issues can take away from enjoyable experiences.", - "D5:4" - ], - [ - "Sam is interested in ordering something similar to what Evan uses to prioritize health.", - "D5:8" - ], - [ - "Sam thinks it's key to find something that keeps you motivated.", - "D5:14" - ] - ] - }, - "session_6_observation": { - "Evan": [ - [ - "Evan went on a vacation in Canada with their new significant other where they did activities like hiking and biking.", - "D6:1" - ], - [ - "Evan values the importance of progress over perfection.", - "D6:6" - ], - [ - "Evan acknowledges the importance of surrounding oneself with caring people.", - "D6:5" - ], - [ - "Evan struggles to find his keys frequently and spends time looking for them.", - "D6:13" - ] - ], - "Sam": [ - [ - "Sam has been facing health challenges recently.", - "D6:2" - ], - [ - "Sam finds motivation in a quote that emphasizes taking small steps towards a healthier life.", - "D6:6" - ], - [ - "Sam appreciates small victories and staying motivated amidst challenges and setbacks.", - "D6:12" - ], - [ - "Sam had an amazing dream where they were soaring over skyscrapers and wants to explore its possible significance.", - "D6:14" - ] - ] - }, - "session_7_observation": { - "Evan": [ - [ - "Evan's son had a soccer accident last Saturday and hurt his ankle, requiring care and a doctor visit.", - "D7:1" - ], - [ - "Evan is concerned about taking care of his son who was hurt in the soccer accident.", - "D7:1" - ], - [ - "Evan wants to eat healthy and is interested in learning about healthier recipes.", - "D7:5" - ], - [ - "Evan is looking to add more vegetables to his meals.", - "D7:7" - ], - [ - "Evan encourages Sam to look after himself and his health.", - "D7:10" - ], - [ - "Evan reminds Sam to take care of himself and advocates for looking after health.", - "D7:15" - ] - ], - "Sam": [ - [ - "Sam had a tough week and had a doctor's appointment which served as a wake-up call to take better care of himself.", - "D7:2" - ], - [ - "Sam is taking a cooking class to learn how to make healthier meals and has learned awesome recipes from it.", - "D7:4" - ], - [ - "Sam is willing to share recipes from his cooking class with Evan.", - "D7:6" - ], - [ - "Sam is concerned about Evan and his son's well-being after the soccer incident.", - "D7:8" - ], - [ - "Sam feels a mix of emotions, somewhat concerned about his health but motivated to make positive changes.", - "D7:12" - ], - [ - "Sam appreciates Evan's encouragement and expresses gratitude for it.", - "D7:14" - ], - [ - "Sam expresses gratitude to Evan for his support and encouragement.", - "D7:16" - ] - ] - }, - "session_8_observation": { - "Sam": [ - [ - "Sam is on a diet and determined to live healthier.", - "D8:1" - ], - [ - "Sam has noticed positive changes from the diet, such as more energy and less sluggishness after eating.", - "D8:5" - ], - [ - "Sam enjoys cooking and shared a healthy grilled chicken and veggie stir-fry recipe with Evan.", - "D8:7" - ], - [ - "Sam's homemade sauce for the stir-fry is not a family secret.", - "D8:9" - ] - ], - "Evan": [ - [ - "Evan is trying out new recipes and finds it a great way to stay busy and creative.", - "D8:12" - ], - [ - "Evan recently started taking painting classes and enjoys expressing himself through art.", - "D8:12" - ], - [ - "Evan has been learning about watercolors in his painting classes and finds it a relaxing way to take a break from everyday stress.", - "D8:18" - ], - [ - "Evan loves painting landscapes as it brings him peace and captivates him with nature's beauty.", - "D8:20" - ], - [ - "Evan likes to capture the tranquil beauty of nature in his paintings by conveying the peacefulness of being outdoors.", - "D8:22" - ], - [ - "Evan enjoys fun winter activities like skiing, snowboarding, and ice skating.", - "D8:30" - ] - ] - }, - "session_9_observation": { - "Sam": [ - [ - "Sam started a new diet and exercise routine last Monday and it has made a huge difference.", - "D9:1" - ], - [ - "Sam suggested low-impact exercises or physical therapy to help Evan with his twisted knee.", - "D9:5" - ], - [ - "Sam enjoys being surrounded by nature as it is tranquil and refreshing.", - "D9:11" - ], - [ - "Sam loves exploring nature and mentioned an interest in going on hikes.", - "D9:11" - ], - [ - "Sam expressed an interest in going on a road trip and mentioned working on becoming healthier.", - "D9:11" - ] - ], - "Evan": [ - [ - "Evan twisted his knee last Friday and is finding it tough to stay consistent with his usual fitness routine.", - "D9:2" - ], - [ - "Evan is considering physical therapy for his knee and in the meantime is keeping it low-key and swimming to stay active.", - "D9:6" - ], - [ - "Evan took a road trip to the Rocky Mountains last month and found the scenery stunning and nature calming.", - "D9:10" - ], - [ - "Evan recommended going for hikes to Sam as a calming and fun activity in nature.", - "D9:12" - ], - [ - "Evan shared a picture from his road trip to the Rocky Mountains with stunning views.", - "D9:8" - ], - [ - "Evan recommended a nearby lake for Sam to explore with many trails nearby.", - "D9:14" - ] - ] - }, - "session_10_observation": { - "Evan": [ - [ - "Evan supports Sam in making healthier choices.", - "D10:3" - ], - [ - "Evan turns to painting or going for a drive to de-stress.", - "D10:7" - ], - [ - "Evan is willing to help Sam get started with painting by recommending supplies and planning a painting session.", - "D10:9" - ], - [ - "Evan suggests acrylic paints, brushes, a canvas/paper, and a palette as basic supplies to start painting.", - "D10:11" - ], - [ - "Evan plans a painting session with Sam for next Saturday.", - "D10:13" - ] - ], - "Sam": [ - [ - "Sam posted a before and after body pic as a result of a diet, aiming to motivate others to make better choices.", - "D10:2" - ], - [ - "Sam struggles with occasional cravings for sugary drinks and snacks, especially during times of stress, boredom, or seeking comfort.", - "D10:4" - ], - [ - "Sam expresses interest in trying painting as a calming hobby.", - "D10:8" - ], - [ - "Sam plans a painting session with Evan for next Saturday.", - "D10:14" - ] - ] - }, - "session_11_observation": { - "Sam": [ - [ - "Sam has started eating healthier.", - "D11:1" - ], - [ - "Sam is interested in fun indoor activities or hobbies.", - "D11:5" - ], - [ - "Sam enjoys expressing themselves through writing.", - "D11:15" - ], - [ - "Sam does creative writing and journals to express their innermost thoughts and feelings.", - "D11:17" - ] - ], - "Evan": [ - [ - "Evan messed up their knee playing basketball and is doing easy exercises to keep it strong.", - "D11:2" - ], - [ - "Evan finds watercolor painting to be a stress reliever and a way to express emotions.", - "D11:6" - ], - [ - "Evan paints what's on their mind or how they feel, including good memories or places they want to go to.", - "D11:12" - ], - [ - "Evan enjoys expressing themselves through art and finds it a powerful form of self-expression.", - "D11:13" - ] - ] - }, - "session_12_observation": { - "Sam": [ - [ - "Sam received news from the doctor that their weight poses a serious health risk.", - "D12:1" - ], - [ - "Sam expressed having a hard time dealing with the news about their weight.", - "D12:1" - ], - [ - "Sam is interested in starting to lift weights to address their health concerns.", - "D12:3" - ], - [ - "Sam expressed appreciation towards Evan's support and encouragement.", - "D12:7" - ], - [ - "Sam mentioned that Evan's words inspired and motivated them.", - "D12:11" - ], - [ - "Sam thanked Evan for giving them a boost and expressed their commitment to staying motivated.", - "D12:13" - ], - [ - "Sam expressed that Evan's support means a lot to them.", - "D12:15" - ] - ], - "Evan": [ - [ - "Evan started lifting weights one year ago and mentioned seeing gains from his workouts.", - "D12:2" - ], - [ - "Evan offered advice to Sam on how to start lifting weights and emphasized the importance of good form and technique.", - "D12:4" - ], - [ - "Evan encouraged Sam to stay consistent with their workout routine and shared excitement to hear about their progress.", - "D12:4" - ], - [ - "Evan expressed belief in Sam's abilities and encouraged them to stay positive and keep pushing towards their goals.", - "D12:8" - ], - [ - "Evan provided supportive and motivating words to Sam, urging them to keep believing in themselves.", - "D12:12" - ], - [ - "Evan emphasized the importance of progress, staying motivated, and taking one step at a time towards reaching goals.", - "D12:16" - ], - [ - "Evan assured Sam of his support and being there for them.", - "D12:16" - ] - ] - }, - "session_13_observation": { - "Evan": [ - [ - "Evan paints as a way to relax, and recently finished a painting of a sunset.", - "D13:3" - ], - [ - "Evan suggested kayaking to Sam as a way to de-stress and enjoy the outdoors.", - "D13:7" - ], - [ - "Evan offered to hook Sam up with a good spot for kayaking.", - "D13:9" - ] - ], - "Sam": [ - [ - "Sam bought some unhealthy snacks and feels guilty about it.", - "D13:2" - ], - [ - "Sam is dealing with work stress and trying to stay motivated.", - "D13:4" - ], - [ - "Sam is considering trying kayaking as a new outdoor activity.", - "D13:6" - ], - [ - "Sam is planning to go kayaking with a friend on the lake.", - "D13:10" - ], - [ - "Sam and a friend are traveling to Lake Tahoe for kayaking.", - "D13:14" - ] - ] - }, - "session_14_observation": { - "Sam": [ - [ - "Sam had a health scare last weekend and ended up in the ER with gastritis.", - "D14:1" - ], - [ - "Sam realized the importance of prioritizing health by adopting a more nutritious diet and getting regular exercise after the health scare.", - "D14:1" - ], - [ - "Sam has been stressed with phone issues on top of the health scare.", - "D14:1" - ], - [ - "Sam is interested in starting fitness to improve overall well-being.", - "D14:3" - ], - [ - "Sam is planning a hike with Evan and is excited about it as a fun challenge.", - "D14:7" - ], - [ - "Sam values the support and appreciated Evan's presence in their friendship.", - "D14:11" - ] - ], - "Evan": [ - [ - "Evan has been focusing on fitness and found it beneficial for his overall well-being.", - "D14:2" - ], - [ - "Evan had another encounter with a lost tourist recently, suggesting a recurring theme of helping tourists in his life.", - "D14:2" - ], - [ - "Evan suggested setting fitness goals and finding an enjoyable exercise to stay motivated to Sam.", - "D14:4" - ], - [ - "Evan is planning a hike with Sam and sees it as an opportunity to bond with nature and push themselves.", - "D14:8" - ], - [ - "Evan values being a supportive friend to Sam and is always there for him.", - "D14:14" - ] - ] - }, - "session_15_observation": { - "Sam": [ - [ - "Sam is trying to keep up with a new health routine, pushed by family and feeling pressured.", - "D15:1" - ], - [ - "Sam gets impatient with self when wanting fast results but understands the importance of being patient.", - "D15:5" - ], - [ - "Sam appreciates Evan's support and acknowledges the importance of taking it slow.", - "D15:7" - ], - [ - "Sam seeks to feel a sense of freedom and is inspired by Evan's transformation.", - "D15:13" - ], - [ - "Sam is grateful for Evan's support and values it greatly.", - "D15:17" - ] - ], - "Evan": [ - [ - "Evan just got back from a morning walk which helps him start the day actively.", - "D15:2" - ], - [ - "Evan believes progress takes time and advises Sam to take things one step at a time.", - "D15:6" - ], - [ - "Evan went through a phase two years ago where he changed his diet and started walking regularly.", - "D15:8" - ], - [ - "Evan focused more on well-being rather than fixating on quick results, which led to a positive transformation.", - "D15:10" - ], - [ - "Evan emphasizes celebrating small victories and supporting Sam in moving forward.", - "D15:16" - ], - [ - "Evan mentions that letting go of unrealistic expectations was liberating both physically and mentally.", - "D15:12" - ] - ] - }, - "session_16_observation": { - "Sam": [ - [ - "Sam is now a Weight Watchers coach in their group, feeling proud and accomplished.", - "D16:1" - ], - [ - "Sam sees becoming a Weight Watchers coach as a great step in their quest for better health.", - "D16:3" - ], - [ - "Sam appreciates Evan's support and finds his kind words meaningful.", - "D16:7" - ], - [ - "Sam is determined to continue making a positive impact through coaching and staying committed to better health.", - "D16:5" - ], - [ - "Sam offered support to Evan when he mentioned personal challenges and job loss, showing willingness to chat and help.", - "D16:13" - ], - [ - "Sam finds sunsets stunning and soothing, and expressed interest in visiting Evan's favorite spot to watch sunsets.", - "D16:17" - ] - ], - "Evan": [ - [ - "Evan gifted a 1968 Kustom K-200A vintage guitar from a close friend.", - "D16:10" - ], - [ - "Evan lost his job due to company downsizing and is currently on the hunt for a new job, staying hopeful and keeping spirits up.", - "D16:12" - ], - [ - "Evan appreciates Sam's support during his job loss and personal challenges.", - "D16:14" - ], - [ - "Evan finds peace and relaxation by watching sunsets at a peaceful spot close to his home.", - "D16:18" - ], - [ - "Evan plans to visit his favorite spot by the beach with Sam to de-stress and explore together.", - "D16:20" - ], - [ - "Evan is excited about planning a visit to the spot with Sam next month.", - "D16:24" - ] - ] - }, - "session_17_observation": { - "Sam": [ - [ - "Sam has been dealing with discomfort that has been limiting their movement and has been trying to make changes in their diet.", - "D17:3" - ], - [ - "Sam finds movies to be a 'feel good' escape.", - "D17:5" - ], - [ - "Sam acknowledges the importance of appreciating something each day to enjoy life more.", - "D17:7" - ], - [ - "Sam finds life easier with supportive people around.", - "D17:11" - ], - [ - "Sam struggles to reach their fitness goals but accepts it as part of life.", - "D17:15" - ] - ], - "Evan": [ - [ - "Evan had a health scare recently, which turned out to be a misunderstanding, leading them to realize the importance of monitoring their health.", - "D17:2" - ], - [ - "Evan values enjoying the moment and appreciating the little things in life.", - "D17:6" - ], - [ - "Evan stays in shape by going to the gym and taking their car out for a spin.", - "D17:14" - ], - [ - "Evan grew up on a little island that serves as their happy place, shaping them and holding a special place in their heart.", - "D17:18" - ], - [ - "Evan finds their childhood island home to be soul-calming, serene, heavenly, and like a little slice of paradise.", - "D17:20" - ] - ] - }, - "session_18_observation": { - "Evan": [ - [ - "Evan recently bought a new Prius for his active lifestyle and road trips, but it broke down soon after purchase.", - "D18:1" - ], - [ - "Evan sees setbacks as a chance to explore new ways of staying active and traveling.", - "D18:5" - ], - [ - "Evan is considering trying yoga for stress relief and flexibility.", - "D18:7" - ], - [ - "Evan appreciates Sam's support and finds it invaluable.", - "D18:11" - ], - [ - "Evan believes it makes a big difference to have support when trying new things.", - "D18:13" - ] - ], - "Sam": [ - [ - "Sam attended a Weight Watchers meeting and learned some great tips.", - "D18:6" - ], - [ - "Sam suggests yoga to Evan, mentioning its benefits for flexibility and stress relief.", - "D18:8" - ], - [ - "Sam offers Evan support and assistance with yoga tips if needed.", - "D18:10" - ], - [ - "Sam believes that having support when trying new things is important.", - "D18:12" - ] - ] - }, - "session_19_observation": { - "Evan": [ - [ - "Evan's partner is pregnant, and they are excited about having a child.", - "D19:1" - ], - [ - "Evan is excited and a bit nervous about becoming a parent again.", - "D19:3" - ], - [ - "Evan values family greatly and considers them as their rock.", - "D19:9" - ], - [ - "Evan is planning a big family reunion next summer to create more memories with family.", - "D19:11" - ] - ], - "Sam": [ - [ - "Sam congratulates Evan on the news of their partner being pregnant.", - "D19:2" - ], - [ - "Sam appreciates and recognizes Evan's value for family and memories.", - "D19:4" - ], - [ - "Sam offers support and assistance to Evan for organizing the family reunion.", - "D19:12" - ] - ] - }, - "session_20_observation": { - "Evan": [ - [ - "Evan's son had an accident where he fell off his bike last Tuesday but is doing better now.", - "D20:3" - ], - [ - "Evan is supportive and encouraging towards Sam, giving advice to believe in himself and take things one day at a time.", - "D20:9" - ], - [ - "Evan is a painter who finished a contemporary figurative painting emphasizing emotion and introspection.", - "D20:15" - ], - [ - "Evan had a painting published in an exhibition with the help of a close friend.", - "D20:17" - ] - ], - "Sam": [ - [ - "Sam used to love hiking but hasn't had the chance to do it recently.", - "D20:6" - ], - [ - "Sam is struggling with weight and confidence issues, feeling like they lack motivation.", - "D20:8" - ], - [ - "Sam acknowledges that trying new things can be difficult.", - "D20:12" - ] - ] - }, - "session_21_observation": { - "Sam": [ - [ - "Sam enjoys running in the mornings as a way to clear their head.", - "D21:9" - ], - [ - "Sam appreciates art and finds it fascinating how it can express emotions.", - "D21:13" - ], - [ - "Sam occasionally sketches but hasn't created anything remarkable yet.", - "D21:19" - ], - [ - "Sam visited an exhibit recently and was inspired by a painting.", - "D21:17" - ] - ], - "Evan": [ - [ - "Evan recently got married to a woman from Canada.", - "D21:2" - ], - [ - "Evan believes in love at first sight and felt a spark when he met his partner.", - "D21:6" - ], - [ - "Evan created a painting that reflects a sense of joy and freedom.", - "D21:16" - ], - [ - "Evan has a tendency to lose his keys frequently, turning it into a weekly ritual.", - "D21:20" - ] - ] - }, - "session_22_observation": { - "Sam": [ - [ - "Sam recently took friends on an epic hiking trip.", - "D22:1" - ], - [ - "Sam expresses concern for Evan's well-being after hearing about a minor accident.", - "D22:3" - ], - [ - "Sam appreciates nature for bringing peace and helping find clarity.", - "D22:7" - ], - [ - "Sam is open to trying new healthy snacks and recipes to stay healthy.", - "D22:11" - ], - [ - "Sam had issues with the self-checkout at the store and finds it to be a regular annoyance.", - "D22:19" - ], - [ - "Sam humorously mentions attracting issues with self-checkout and offers to be called if someone wants to experience the situation.", - "D22:21" - ] - ], - "Evan": [ - [ - "Evan recently went for a long drive to the mountains with friends in a Prius.", - "D22:2" - ], - [ - "Evan mentions getting married to his work friends after a minor accident.", - "D22:4" - ], - [ - "Evan finds peace and clarity in nature and views it as a great healer.", - "D22:8" - ], - [ - "Evan enjoys sharing and trying new healthy snacks and recipes.", - "D22:10" - ], - [ - "Evan recommends a recipe for energy balls as a healthier snack idea.", - "D22:14" - ] - ] - }, - "session_23_observation": { - "Evan": [ - [ - "Evan recently got married and informed their extended family about it, receiving overwhelming love and support.", - "D23:1" - ], - [ - "Evan feels lucky to have found a partner who understands them well.", - "D23:3" - ], - [ - "Evan values and appreciates the support and happiness their family brings regarding their marriage.", - "D23:5" - ], - [ - "Evan mentions going on a honeymoon trip to Canada to create awesome memories and explore snowy landscapes.", - "D23:23" - ], - [ - "Evan expresses excitement about skiing, trying local cuisine, and enjoying the beautiful views during the honeymoon trip.", - "D23:25" - ] - ], - "Sam": [ - [ - "Sam congratulated Evan on their marriage and appreciated the happiness and love shared in the pictures.", - "D23:2" - ], - [ - "Sam values family support and acknowledges it as a source of happiness and joy.", - "D23:6" - ], - [ - "Sam expresses gratitude for the support and love received from their family.", - "D23:8" - ], - [ - "Sam mentions that their family has been a rock through everything and emphasizes how important they are.", - "D23:12" - ], - [ - "Sam mentions enjoying homemade lasagna for dinner and admires Evan's discipline of limiting themselves to two ginger snaps a day.", - "D23:16" - ], - [ - "Sam talks about the special dessert from a cousin's wedding, expressing fondness for special occasions.", - "D23:20" - ], - [ - "Sam encourages Evan to try poutine in Canada and looks forward to hearing about the experience.", - "D23:28" - ] - ] - }, - "session_24_observation": { - "Evan": [ - [ - "Evan had a bit too much to drink and had an embarrassing incident involving someone's roses.", - "D24:3" - ], - [ - "Evan had a pee accident near some roses and felt embarrassed about it.", - "D24:5" - ], - [ - "Evan and his partner tried snowshoeing as part of a new adventure and found it surprisingly fun.", - "D24:9" - ], - [ - "Evan suggested swimming, yoga, and walking as good low-impact exercises.", - "D24:17" - ], - [ - "Evan mentioned that beach sunsets are one of his favorite low-impact exercises.", - "D24:21" - ] - ], - "Sam": [ - [ - "Sam made a meal plan and workout schedule to stay healthy and motivated.", - "D24:10" - ], - [ - "Sam planned to make an appointment with the doctor to get advice on a balanced diet plan and low-impact exercises.", - "D24:12" - ], - [ - "Sam was motivated to keep up with a fitness routine after watching The Godfather.", - "D24:18" - ], - [ - "Sam expressed hope for positive changes with a healthier diet and yoga.", - "D24:20" - ], - [ - "Sam mentioned having a recurring dream where he's flying over skyscrapers.", - "D24:22" - ] - ] - }, - "session_25_observation": { - "Sam": [ - [ - "Sam is dealing with health issues that have been rough on him.", - "D25:1" - ], - [ - "Sam enjoys going on car rides to chill and enjoy the view.", - "D25:5" - ], - [ - "Sam finds being in nature relaxing and enjoys getting fresh air away from the city.", - "D25:7" - ], - [ - "Sam is planning to spend more time in nature to rejuvenate.", - "D25:13" - ], - [ - "Sam appreciates and finds joy in small moments outdoors.", - "D25:14" - ] - ], - "Evan": [ - [ - "Evan had to apologize to his partner for a drunken night which was embarrassing.", - "D25:2" - ], - [ - "Evan enjoys kayaking on the water and finds it peaceful.", - "D25:10" - ], - [ - "Evan believes nature has a way of calming and reviving the soul.", - "D25:8" - ], - [ - "Evan believes small moments outdoors can make a big impact when dealing with tough times.", - "D25:14" - ], - [ - "Evan emphasizes finding joy in the little things and appreciating small joys, especially during tough times.", - "D25:16" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Sam and Evan met at 1:47 pm on 18 May, 2023. Evan mentioned his recent trip to the Rockies in his new Prius and shared about his passion for watercolor painting. Sam reminisced about hiking with his dad and expressed an interest in trying painting. Evan encouraged Sam to explore different hobbies until he found one he was passionate about. They both agreed to catch up soon to discuss Sam's new hobbies. The conversation ended with Evan wishing Sam well, and Sam looking forward to trying out new activities.", - "session_2_summary": "Evan and Sam spoke at 7:11 pm on 24 May, 2023. Evan shared about his recent road trip to Jasper, which he found peaceful and rejuvenating. Sam, who had a tough week and a concerning doctor's visit about his weight, sought advice on starting a healthier lifestyle. Evan suggested finding enjoyable fitness routines and making smarter food choices. Sam mentioned considering painting as a hobby to de-stress, and Evan encouraged him to try it out alongside exercising. They ended the conversation with Evan asking Sam to keep him updated as he starts this journey.", - "session_3_summary": "At 3:55 pm on 6 June 2023, Evan and Sam caught up after a while. Evan shared about a recent health scare and his decision to focus on a healthier lifestyle, discussing dietary changes and suggesting healthier alternatives to Sam. Sam mentioned sticking to soda and candy but agreed to try Evan's suggestions. Evan emphasized the importance of small changes for better health and offered support to Sam on their health journeys. They planned to work out together, and Sam shared a frustrating experience at the supermarket. Evan empathized, and they ended the conversation with well wishes.", - "session_4_summary": "At 10:52 am on 27 July, 2023, Sam confided in Evan about being teased for his weight, prompting him to make health changes. Evan offered support, sharing his own health journey and tips. They discussed dietary changes, with Evan suggesting flavored seltzer water and snacks. Evan recommended exercise, and Sam committed to starting the next day. They shared mutual appreciation before parting ways, with Evan promising ongoing support for Sam's health journey.", - "session_5_summary": "At 7:52 pm on 7th August 2023, Evan shared with Sam about meeting an amazing Canadian woman, feeling alive, and dealing with health issues. Sam expressed understanding and discussed the challenges of health problems. They also talked about Evan's health journey, motivation from family and tools, like a health tracker, to stay on track. Evan mentioned being motivated by adventure and resilience. Sam agreed on the importance of finding motivation and the power of small steps. They concluded that consistency and perseverance are key. The conversation ended with mutual well-wishes.", - "session_6_summary": "Evan and Sam catch up at 4:09 pm on 13 August, 2023. Evan shares about his recent vacation in Canada with his new partner filled with outdoor activities. Sam mentions health struggles. Evan offers support, and they discuss the importance of progress over perfection. Sam shares a motivating quote with Evan. They encourage each other to celebrate small wins. Evan jokes about losing his keys, and Sam talks about an intriguing dream. They exchange good wishes and encouragement, concluding with Evan reminding Sam to get some rest.", - "session_7_summary": "At 4:20 pm on 15 August 2023, Evan and Sam caught up after a few days. Evan shared about his son's soccer accident, while Sam mentioned a tough week and a wake-up call for self-care. Sam talked about taking a cooking class for healthier meals and shared a yummy grilled dish recipe with Evan. Evan asked for recipes with more vegetables, and Sam promised to share a roasted veg recipe. They also discussed post-accident well-being and the importance of self-care. Sam admitted to feeling a mix of concern and motivation for positive changes, with Evan providing encouraging words. They agreed to take things one step at a time, supporting each other along the way.", - "session_8_summary": "On 19th August 2023 at 6:17 pm, Sam shared with Evan that he was on a diet and living healthier, finding it tough but staying determined. Evan praised Sam's efforts and asked about any positive changes. Sam mentioned feeling more energized and less sluggish after meals. They discussed a grilled chicken and veggie stir-fry recipe. Evan talked about starting painting classes and finding peace through art. Evan shared his love for painting landscapes with bright colors. They also discussed winter activities like skiing, with Sam expressing interest but unsure if his body can handle it. Evan encouraged Sam to join in the fun someday. Sam expressed appreciation for Evan's understanding and friendship.", - "session_9_summary": "Sam informed Evan about his successful new diet and exercise routine, which started the previous Monday and made him feel great. Evan, however, mentioned twisting his knee the previous Friday, which hindered his fitness routine. Evan appreciated Sam's concern, citing life's unpredictability. Evan expressed interest in a book and discussed potential physical therapy for his knee. Sam recommended low-impact exercises and swimming. Evan also shared his recent road trip to the Rocky Mountains and love for hiking. Sam expressed interest in hiking and the need to become healthier. Evan recommended a nearby lake for hiking and nature exploration. Sam decided to plan a day trip to the lake. The conversation ended with friendly goodbyes at 10:24 am on 27 August, 2023.", - "session_10_summary": "On 11 September 2023 at 9:28 am, Evan and Sam caught up after a long time. Sam shared a before and after body picture due to a diet, aiming to motivate others. Stress triggers cravings for sugary treats for Sam. Evan suggested painting as a stress-relieving hobby and offered to help Sam get started. They planned a painting session for the upcoming Saturday and both were excited about it.", - "session_11_summary": "Sam and Evan caught up at 8:57 pm on 6 October, 2023. Evan, recovering from a knee injury, shared his love for watercolor painting and recent adventure with a lost tourist. He mentioned using painting as a stress reliever and a form of self-expression. Sam expressed interest in writing as a way to express himself and mentioned frustrations with his phone's navigation app. Both agreed on the importance of having outlets to express thoughts and feelings.", - "session_12_summary": "On 8 October 2023, at 3:09 pm, Sam told Evan about his weight concerns after a check-up. Evan suggested lifting weights, sharing his own progress and advice. Sam expressed interest and Evan encouraged him to focus on form and consistency. Sam vowed to update Evan on his progress and Evan cheered him on, advising patience and positivity. Sam found Evan's support inspiring and promised to believe in himself. Evan emphasized motivation and progress, and Sam appreciated the support, committing to taking one step at a time.", - "session_13_summary": "Evan and Sam had a conversation at 4:07 pm on 14 October, 2023. Sam confessed to Evan about buying unhealthy snacks and feeling guilty about it, while Evan shared about finishing a painting of a sunset. Sam mentioned dealing with work stress and looking for ways to stay motivated. Evan suggested trying kayaking as a way to de-stress, which Sam found appealing. Sam planned to go kayaking on the lake with a friend, and Evan offered to plan a kayaking trip together in the future. Sam decided to kayak at Lake Tahoe, and Evan encouraged him, mentioning the great views there.", - "session_14_summary": "Sam and Evan caught up at 1:50 pm on 17 October, 2023. Sam shared about a recent health scare with gastritis, prompting him to focus on health. Evan mentioned his fitness routine helping his well-being and assisting lost tourists often. Evan advised Sam on setting goals and finding enjoyable exercises to stay motivated. They planned a hike together to enjoy nature and bond. Sam appreciated Evan's support and looked forward to the hike. They agreed to stay in touch and support each other, ending the conversation with plans for the upcoming hike.", - "session_15_summary": "Sam and Evan discussed their health routines and the importance of having a supportive but non-stressful environment. Evan shared his experience of gradually improving his well-being by letting go of unrealistic expectations. Sam expressed gratitude for Evan's support and decided to focus on small victories and take it one day at a time. Evan encouraged Sam to celebrate every little achievement and assured continued support. Sam appreciated the support and they both agreed to keep moving forward together.", - "session_16_summary": "Sam informed Evan at 9:13 pm on 9 November, 2023, that he had become a Weight Watchers coach, and Evan congratulated him on the achievement. They discussed Sam's journey towards better health and his plans to motivate and help others. Evan shared that he had recently lost his job, and Sam offered support and encouragement. They also discussed enjoying a sunset together at Evan's favorite spot by the beach, planning to visit it soon to de-stress.", - "session_17_summary": "Sam and Evan spoke at 7:30 pm on 21 November 2023. Evan mentioned a recent health scare that turned out to be a misunderstanding and stressed the importance of health. Sam shared struggles with discomfort and dietary changes. They discussed the value of small steps and appreciation in life. Evan highlighted the importance of supportive friends during tough times. They talked about fitness goals, shared about a peaceful island where Evan grew up, and ended the conversation by offering support to each other.", - "session_18_summary": "Evan, at 8:16 pm on 5 December 2023, told Sam about his new Prius breaking down, causing stress as it's vital for his active lifestyle. Sam empathized, mentioning the hassle of relying on a new car. They discussed setbacks and opportunities, with Sam sharing insights from a Weight Watchers meeting. Evan considered trying yoga for stress relief based on Sam's recommendation. Sam encouraged him, offering support for new endeavors. They acknowledged the importance of having support during new experiences. The conversation ended with Evan thanking Sam and promising to update him on his yoga journey.", - "session_19_summary": "Summary:\nOn 9 December 2023 at 1:45 pm, Evan excitedly shared with Sam that his partner is pregnant, expressing both excitement and nervousness about becoming a parent again. Evan reminisced about the joys of parenthood and family memories. He showed Sam a collage of their top family memories, explaining the significance of a sign that symbolizes their family motto. Sam admired Evan's strong family bond and offered support for their upcoming family reunion. Evan expressed gratitude for Sam's support, looking forward to creating more beautiful memories with his expanding family. The conversation ended with warm goodbyes and promises to stay in touch.", - "session_20_summary": "Evan and Sam caught up at 6:48 pm on 17th December 2023. Evan shared that his son had a recent bike accident but is doing better now. They discussed coping mechanisms, with Sam mentioning a love for hiking. Evan encouraged Sam to believe in himself, try new things, and not let his weight define him. Evan showcased a contemporary figurative painting he recently completed, with a friend helping to get it published in an exhibition.", - "session_21_summary": "Sam and Evan met at 4:25 pm on December 26, 2023. Evan shared that he recently got married to a woman from Canada whom he loved at first sight. He believes in love at first sight, describing it as a magical moment. They discussed the power of love and how it can bring happiness. Evan shared his passion for art and how it helps him express and handle emotions. They exchanged thoughts on art and creativity. Evan offered to help Sam start painting. They also talked about Evan frequently losing his keys and Sam suggested putting a GPS sensor on them.", - "session_22_summary": "Sam and Evan talked at 11:00 am on 31 December 2023. Sam shared about a recent hiking trip, while Evan mentioned a mountain drive that ended in a minor accident. Despite the mishap, Evan was excited about sharing news of his marriage with work friends. The conversation shifted to the healing power of nature and healthy snacks. Evan recommended energy balls to Sam, who was keen on trying new healthy snacks. Sam also mentioned ongoing self-checkout issues at the store, prompting Evan to joke about needing Sam around to attract the problem.", - "session_23_summary": "At 1:32 pm on 6 January 2024, Evan excitedly informed Sam about sharing news of his marriage with their extended family, receiving overwhelming love and support. Sam congratulated Evan and praised the happiness evident in the picture. The conversation shifted to the importance of family support and the joy it brings. Both agreed on the significance of family in their lives, expressing gratitude for the love and happiness they provide. They also discussed dinner plans, with Evan mentioning a new diet while Sam shared a picture of dessert from a family wedding. Evan mentioned an upcoming trip to Canada for his honeymoon, intending to ski and savor local cuisine, with Sam wishing him a memorable experience. They ended the conversation looking forward to sharing details about the trip and catching up soon.", - "session_24_summary": "Evan and Sam discuss a funny incident where Evan had an embarrassing moment with some roses after a night out. Evan apologized and the situation was handled well. They talk about learning from mistakes and staying healthy. Sam plans to see a doctor for diet and exercise advice. Evan suggests low-impact exercises like swimming, yoga, and walking. Sam also mentions watching The Godfather for motivation. Evan recommends beach walks as another fun low-impact exercise. They end on a positive note, planning to make positive changes and enjoy outdoor activities.", - "session_25_summary": "Sam and Evan caught up at 9:37 pm on 11 January, 2024. Sam shared about his health issues and feeling weighed down, while Evan apologized to his partner for a drunken incident involving rose bushes. Sam enjoys relaxing car rides in the countryside, which Evan thinks is a great way to unwind. They both agree on the calming effect of nature and sharing photos that brighten their days. Evan emphasizes the importance of appreciating small joys during tough times and offers support to Sam. They end the conversation with well wishes and a reminder to take care of themselves." - }, - "sample_id": "conv-49" - }, - { - "qa": [ - { - "question": "When did Calvin first travel to Tokyo?", - "answer": "between 26 March and 20 April 2023", - "evidence": [ - "D3:1" - ], - "category": 2 - }, - { - "question": "What items did Calvin buy in March 2023?", - "answer": "mansion in Japan, luxury car Ferrari 488 GTB", - "evidence": [ - "D1:3", - "D2:1" - ], - "category": 1 - }, - { - "question": "When did Dave see Aerosmith perform live?", - "answer": "on the weekend before March 26, 2023", - "evidence": [ - "D2:10" - ], - "category": 2 - }, - { - "question": "Which bands has Dave enjoyed listening to?", - "answer": "Aerosmith, The Fireworks", - "evidence": [ - "D2:10", - "D23:9" - ], - "category": 1 - }, - { - "question": "Which country do Calvin and Dave want to meet in?", - "answer": "United States", - "evidence": [ - "D3:9", - "D3:10" - ], - "category": 3 - }, - { - "question": "What are Dave's dreams?", - "answer": "open a car maintenance shop, work on classic cars, build a custom car from scratch", - "evidence": [ - "D4:5", - "D4:5", - "D5:5" - ], - "category": 1 - }, - { - "question": "Which types of cars does Dave like the most?", - "answer": "classic vintage cars", - "evidence": [ - "D4:5", - "D1:2", - "D3:12", - "D4:7" - ], - "category": 1 - }, - { - "question": "Does Dave's shop employ a lot of people?", - "answer": "Yes", - "evidence": [ - "D4:17" - ], - "category": 3 - }, - { - "question": "When did Dave start his car maintenance shop?", - "answer": "May 1, 2023", - "evidence": [ - "D4:1" - ], - "category": 2 - }, - { - "question": "When did a mishap occur with Calvin's musical gear and favorite mic?", - "answer": "On a week before 16 May, 2023", - "evidence": [ - "D6:3" - ], - "category": 2 - }, - { - "question": "When did Calvin's place get flooded in Tokyo?", - "answer": "On a week before 16 May, 2023", - "evidence": [ - "D6:3" - ], - "category": 2 - }, - { - "question": "What mishaps has Calvin run into?", - "answer": "flooding of his mansion, car accident", - "evidence": [ - "D6:1", - "D9:1" - ], - "category": 1 - }, - { - "question": "When was Calvin's concert in Tokyo?", - "answer": "last week of May 2023", - "evidence": [ - "D6:11", - "D7:1" - ], - "category": 2 - }, - { - "question": "Would Calvin enjoy performing at the Hollywood Bowl?", - "answer": "Yes; because he enjoys the rush of performing onstage to large crowds", - "evidence": [ - "D7:11" - ], - "category": 3 - }, - { - "question": "When did Calvin meet with the creative team for his new album?", - "answer": "8 June, 2023", - "evidence": [ - "D8:1" - ], - "category": 2 - }, - { - "question": "Why does Dave regularly visit parks?", - "answer": "because it relaxes and calms him", - "evidence": [ - "D8:4", - "D1:16" - ], - "category": 1 - }, - { - "question": "When did Dave take a trip to mountainous regions?", - "answer": "July 2023", - "evidence": [ - "D8:10" - ], - "category": 2 - }, - { - "question": "When did Calvin have a car incident?", - "answer": "on the Friday before 21 June, 2023", - "evidence": [ - "D9:1" - ], - "category": 2 - }, - { - "question": "How many times has Calvin had to deal with insurance paperwork?", - "answer": "two times", - "evidence": [ - "D6:5", - "D9:1" - ], - "category": 1 - }, - { - "question": "Which places or events has Calvin visited in Tokyo?", - "answer": "music festival, car museum, Shibuya crossing, Shinjuku", - "evidence": [ - "D3:1", - "D12:7", - "D24:19" - ], - "category": 1 - }, - { - "question": "Who inspired Dave's passion for car engineering?", - "answer": "His Dad", - "evidence": [ - "D12:2", - "D12:4", - "D26:6" - ], - "category": 1 - }, - { - "question": "Does Calvin wish to become more popular?", - "answer": "Yes; he want's to grow his fanbase", - "evidence": [ - "D12:11", - "D27:1" - ], - "category": 3 - }, - { - "question": "Does Calvin want to expand his brand?", - "answer": "yes", - "evidence": [ - "D12:11", - "D18:7" - ], - "category": 1 - }, - { - "question": "What is Dave's main passion?", - "answer": "auto engineering", - "evidence": [ - "D13:3", - "D5:5", - "D4:5", - "D3:12" - ], - "category": 1 - }, - { - "question": "Can Dave work with engines?", - "answer": "yes", - "evidence": [ - "D13:7", - "D22:5", - "D20:1" - ], - "category": 1 - }, - { - "question": "When did Dave host a card-playing night with his friends?", - "answer": "on the Friday before 22 August, 2023", - "evidence": [ - "D15:1" - ], - "category": 2 - }, - { - "question": "When did Calvin record a podcast with his friends?", - "answer": "21 August, 2023", - "evidence": [ - "D15:12" - ], - "category": 2 - }, - { - "question": "Which city was Calvin visiting in August 2023?", - "answer": "Miami", - "evidence": [ - "D16:6" - ], - "category": 2 - }, - { - "question": "What does Calvin do to relax?", - "answer": "take long drives in his car, embrace nature, fixing cars", - "evidence": [ - "D5:8", - "D5:10", - "D7:5" - ], - "category": 1 - }, - { - "question": "What are Dave's hobbies other than fixing cars?", - "answer": "take a walk, go hiking, listen to favorite albums, live concerts, photography", - "evidence": [ - "D5:9", - "D5:11", - "D8:8", - "D27:2" - ], - "category": 1 - }, - { - "question": "What kind of music does Dave listen to?", - "answer": "classic rock, Japanese music", - "evidence": [ - "D2:10", - "D28:40", - "D10:11" - ], - "category": 1 - }, - { - "question": "Where was Dave in the last two weeks of August 2023?", - "answer": "San Francisco", - "evidence": [ - "D14:1", - "D17:1" - ], - "category": 2 - }, - { - "question": "Where did Dave return from with new knowledge of different techniques of car restoration?", - "answer": "San Francisco", - "evidence": [ - "D17:1", - "D14:1" - ], - "category": 1 - }, - { - "question": "What was Dave doing in San Francisco?", - "answer": "attending a car modification workshop", - "evidence": [ - "D17:1", - "D14:1" - ], - "category": 1 - }, - { - "question": "When did Dave return from San Francisco?", - "answer": "September 1, 2023", - "evidence": [ - "D17:1" - ], - "category": 2 - }, - { - "question": "When did Calvin book flight tickets to Boston?", - "answer": "last week of August 2023", - "evidence": [ - "D17:6" - ], - "category": 2 - }, - { - "question": "When was Calvin's album released?", - "answer": "September 11, 2023", - "evidence": [ - "D18:1" - ], - "category": 2 - }, - { - "question": "Does Calvin love music tours?", - "answer": "yes", - "evidence": [ - "D18:7", - "D16:2", - "D7:1" - ], - "category": 3 - }, - { - "question": "When did Dave have a great jam session with his band?", - "answer": "September 14, 2023", - "evidence": [ - "D19:1" - ], - "category": 2 - }, - { - "question": "Would Dave prefer working on a Dodge Charger or a Subaru Forester?", - "answer": "Dodge Charger", - "evidence": [], - "category": 3 - }, - { - "question": "What was the artists Calvin used to listen to when he was a kid?", - "answer": "Tupac and Dr. Dre", - "evidence": [ - "D20:8", - "D20:6" - ], - "category": 1 - }, - { - "question": "Which of their family member do Calvin and Dave have nostalgic memories about?", - "answer": "Dad", - "evidence": [ - "D12:2", - "D20:6" - ], - "category": 1 - }, - { - "question": "Based on the conversation, did Calvin and Dave have a meeting in Boston between August and November 2023? Answer in yes or no.", - "answer": "No", - "evidence": [], - "category": 3 - }, - { - "question": "Which city was Calvin at on October 3, 2023?", - "answer": "Boston", - "evidence": [ - "D21:1" - ], - "category": 2 - }, - { - "question": "When did Calvin met with local artists in Boston?", - "answer": "October 3, 2023", - "evidence": [ - "D21:1" - ], - "category": 2 - }, - { - "question": "What shared activities do Dave and Calvin have?", - "answer": "Working on cars", - "evidence": [ - "D21:3", - "D21:4" - ], - "category": 1 - }, - { - "question": "What is Dave's favorite activity?", - "answer": "Restoring cars", - "evidence": [ - "D21:4", - "D22:7", - "D19:7" - ], - "category": 1 - }, - { - "question": "How many car shows has Dave attended?", - "answer": "two", - "evidence": [ - "D3:12", - "D22:1" - ], - "category": 1 - }, - { - "question": "What was Dave doing in the first weekend of October 2023?", - "answer": "attending a car show", - "evidence": [ - "D22:1" - ], - "category": 2 - }, - { - "question": "When Dave was a child, what did he and his father do in the garage?", - "answer": "tinkering with car engines, restoration and refurbishing cars", - "evidence": [ - "D12:2", - "D12:4", - "D22:5" - ], - "category": 1 - }, - { - "question": "When did Calvin buy his second Ferrari?", - "answer": "first week of October 2023", - "evidence": [ - "D23:16" - ], - "category": 2 - }, - { - "question": "When did Calvin and Frank Ocean start collaborating?", - "answer": "August 2022", - "evidence": [ - "D24:5", - "D15:2" - ], - "category": 2 - }, - { - "question": "When did Calvin plan on travelling to Tokyo the second time?", - "answer": "November 2023", - "evidence": [ - "D24:17" - ], - "category": 2 - }, - { - "question": "Who supports Calvin in tough times?", - "answer": "friends and team", - "evidence": [ - "D25:6", - "D29:7" - ], - "category": 1 - }, - { - "question": "What does help Calvin stay connected to the creative process?", - "answer": "Calvin stays connected to the creative process by always staying up-to-date on world events and watching documentaries about artists.", - "evidence": [ - "D25:8", - "D28:31" - ], - "category": 1 - }, - { - "question": "When did Calvin visit some of the sights in Boston with a former high school friend?", - "answer": "October 24, 2023", - "evidence": [ - "D26:1" - ], - "category": 2 - }, - { - "question": "Which cities did Dave travel to in 2023?", - "answer": "San Francsico, Detroit", - "evidence": [ - "D14:1", - "D26:2" - ], - "category": 1 - }, - { - "question": "Which hobby did Dave pick up in October 2023?", - "answer": "photography", - "evidence": [ - "D27:2" - ], - "category": 2 - }, - { - "question": "Which events in Dave's life inspired him to take up auto engineering?", - "answer": "attending a car show with Dad, working on an old car in a neighbor's garage when he was young, spent a summer restoring an old car with Dad", - "evidence": [ - "D26:6", - "D25:12", - "D12:2", - "D12:4" - ], - "category": 1 - }, - { - "question": "How many Ferraris does Calvin own?", - "answer": "two", - "evidence": [ - "D2:1", - "D23:16" - ], - "category": 1 - }, - { - "question": "What gifts has Calvin received from his artist friends?", - "answer": "gold chain, custom-made guitar with an octopus on it", - "evidence": [ - "D4:24", - "D4:26", - "D16:14" - ], - "category": 1 - }, - { - "question": "How long did Dave's work on the Ford Mustang take?", - "answer": "nearly two months", - "evidence": [ - "D14:11", - "D20:1", - "D21:4" - ], - "category": 2 - }, - { - "question": "How long was the car modification workshop in San Francisco?", - "answer": "two weeks", - "evidence": [ - "D14:1", - "D17:1" - ], - "category": 2 - }, - { - "question": "What style of guitars does Calvin own?", - "answer": "custom-made yellow guitar with an octopus on it, shiny purple guitar", - "evidence": [ - "D16:13", - "D16:4", - "D16:18", - "D16:19" - ], - "category": 1 - }, - { - "question": "What activities has Dave participated in with his friends?", - "answer": "weekly visits to local parks, countryside roadtrip, celebration of the opening of his car maintenance shop, card-playing nights", - "evidence": [ - "D10:3", - "D11:1", - "D6:8", - "D15:1" - ], - "category": 1 - }, - { - "question": "When did Dave take a photo of a Boston clock tower?", - "answer": "September 2023", - "evidence": [ - "D27:6" - ], - "category": 2 - }, - { - "question": "Do all of Dave's car restoration projects go smoothly?", - "answer": "No", - "evidence": [ - "D27:10", - "D13:7", - "D25:17", - "D20:1" - ], - "category": 1 - }, - { - "question": "Where was Calvin located in the last week of October 2023?", - "answer": "Tokyo", - "evidence": [ - "D28:1" - ], - "category": 2 - }, - { - "question": "When did Dave find the car he repaired and started sharing in his blog?", - "answer": "last week of October 2023", - "evidence": [ - "D28:20" - ], - "category": 2 - }, - { - "question": "When did Dave buy a vintage camera?", - "answer": "November 2023", - "evidence": [ - "D30:05" - ], - "category": 2 - }, - { - "question": "When did Calvin attend a gala in Boston?", - "answer": "November 16, 2023", - "evidence": [ - "D30:1" - ], - "category": 2 - }, - { - "question": "How long did Calvin plan to stay in Japan?", - "answer": "A few months", - "evidence": [ - "D1:15" - ], - "category": 4 - }, - { - "question": "Which band was Dave's favorite at the music festival in April 2023?", - "answer": "Aerosmith", - "evidence": [ - "D2:10" - ], - "category": 4 - }, - { - "question": "Where did Calvin attend a music festival in April 2023?", - "answer": "Tokyo", - "evidence": [ - "D3:1" - ], - "category": 4 - }, - { - "question": "What advice did Calvin receive from the producer at the music festival?", - "answer": "to stay true to himself and sound unique", - "evidence": [ - "D3:7" - ], - "category": 4 - }, - { - "question": "What is Dave's new business venture as of 1 May, 2023?", - "answer": "Car maintenance shop", - "evidence": [ - "D4:1" - ], - "category": 4 - }, - { - "question": "What type of cars does Dave work on at his shop?", - "answer": "all kinds of cars, from regular maintenance to full restorations of classic cars", - "evidence": [ - "D4:19" - ], - "category": 4 - }, - { - "question": "What did Calvin receive as a gift from another artist?", - "answer": "a gold necklace with a diamond pendant", - "evidence": [ - "D4:26" - ], - "category": 4 - }, - { - "question": "What was the necklace Calvin received meant to remind him of?", - "answer": "why he keeps hustling as a musician", - "evidence": [ - "D4:26" - ], - "category": 4 - }, - { - "question": "What does Dave do when he feels his creativity is frozen?", - "answer": "immerse himself in something he loves", - "evidence": [ - "D5:11" - ], - "category": 4 - }, - { - "question": "How does Calvin plan to jumpstart his inspiration?", - "answer": "explore other things and have some fun", - "evidence": [ - "D5:11" - ], - "category": 4 - }, - { - "question": "What did Calvin manage to save during the flood incident?", - "answer": "music gear and favorite microphone", - "evidence": [ - "D6:3" - ], - "category": 4 - }, - { - "question": "What did Dave open in May 2023?", - "answer": "a car shop", - "evidence": [ - "D6:8" - ], - "category": 4 - }, - { - "question": "What gives Dave a sense of achievement and purpose?", - "answer": "Fixing up things", - "evidence": [ - "D7:6" - ], - "category": 4 - }, - { - "question": "What fuels Calvin's soul?", - "answer": "Performing live", - "evidence": [ - "D7:11" - ], - "category": 4 - }, - { - "question": "What is Dave doing to relax on weekends?", - "answer": "exploring parks", - "evidence": [ - "D8:4" - ], - "category": 4 - }, - { - "question": "What sports activity is Calvin planning to try after the tour with Frank Ocean?", - "answer": "Skiing", - "evidence": [ - "D9:15" - ], - "category": 4 - }, - { - "question": "What was Calvin excited to do after getting his car fixed on 7 July, 2023?", - "answer": "get back on the road", - "evidence": [ - "D10:1" - ], - "category": 4 - }, - { - "question": "What did Calvin and his friends arrange for in the park?", - "answer": "regular walks together", - "evidence": [ - "D10:3" - ], - "category": 4 - }, - { - "question": "What kind of music has Calvin been creating lately?", - "answer": "experimenting with different genres", - "evidence": [ - "D11:6" - ], - "category": 4 - }, - { - "question": "How does Calvin describe his process of adding electronic elements to his songs?", - "answer": "gives them a fresh vibe", - "evidence": [ - "D11:6" - ], - "category": 4 - }, - { - "question": "What car brand does Calvin own that he is proud of?", - "answer": "Ferrari", - "evidence": [ - "D12:7" - ], - "category": 4 - }, - { - "question": "What is Calvin's biggest current goal?", - "answer": "expand his brand worldwide and grow his fanbase", - "evidence": [ - "D12:11" - ], - "category": 4 - }, - { - "question": "What is Dave's advice to Calvin regarding his dreams?", - "answer": "to never forget his dreams", - "evidence": [ - "D12:14" - ], - "category": 4 - }, - { - "question": "What workshop did Dave get picked for on 11 August, 2023?", - "answer": "Car mod workshop", - "evidence": [ - "D13:1" - ], - "category": 4 - }, - { - "question": "What kind of modifications has Dave been working on in the car mod workshop?", - "answer": "engine swaps, suspension modifications, and body modifications", - "evidence": [ - "D13:7" - ], - "category": 4 - }, - { - "question": "What type of car did Dave work on during the workshop?", - "answer": "classic muscle car", - "evidence": [ - "D13:7" - ], - "category": 4 - }, - { - "question": "What does Dave say is important for making his custom cars unique?", - "answer": "attention to small details", - "evidence": [ - "D13:11" - ], - "category": 4 - }, - { - "question": "How did the audience in Tokyo react when Calvin sang one of his songs?", - "answer": "Everyone was so into it and sang along", - "evidence": [ - "D14:6" - ], - "category": 4 - }, - { - "question": "How did Calvin meet Frank Ocean?", - "answer": "At a music festival in Tokyo", - "evidence": [ - "D15:4" - ], - "category": 4 - }, - { - "question": "Where did Calvin and Frank Ocean record a song together?", - "answer": "In the studio at Calvin's mansion", - "evidence": [ - "D15:4" - ], - "category": 4 - }, - { - "question": "What did Calvin and his friends record in August 2023?", - "answer": "a podcast discussing the rap industry", - "evidence": [ - "D15:12" - ], - "category": 4 - }, - { - "question": "Where did Calvin start shooting a video for his new album?", - "answer": "Miami", - "evidence": [ - "D16:8" - ], - "category": 4 - }, - { - "question": "What design is featured on Calvin's guitar?", - "answer": "octopus", - "evidence": [ - "D16:14" - ], - "category": 4 - }, - { - "question": "Why did Calvin get his guitar customized with a shiny finish?", - "answer": "unique look", - "evidence": [ - "D16:20" - ], - "category": 4 - }, - { - "question": "What color glow did Calvin customize his guitar with?", - "answer": "purple", - "evidence": [ - "D16:20" - ], - "category": 4 - }, - { - "question": "Where did Dave come back from with insights on car modification on 1st September 2023?", - "answer": "San Francisco", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "What emotion does Dave mention feeling when he sees the relief of someone whose car he fixed?", - "answer": "Proud", - "evidence": [ - "D17:5" - ], - "category": 4 - }, - { - "question": "What did Calvin book a flight ticket for on 1st September 2023?", - "answer": "Boston", - "evidence": [ - "D17:6" - ], - "category": 4 - }, - { - "question": "What is Calvin excited about after the tour?", - "answer": "exploring and growing his brand", - "evidence": [ - "D18:7" - ], - "category": 4 - }, - { - "question": "What plans do Calvin and Dave have for when Calvin visits Boston?", - "answer": "Check out Dave's garage and maybe get some ideas for future projects", - "evidence": [ - "D18:11" - ], - "category": 4 - }, - { - "question": "Which Disney movie did Dave mention as one of his favorites?", - "answer": "Ratatouille", - "evidence": [ - "D19:6" - ], - "category": 4 - }, - { - "question": "How does Dave feel about the reactions of people when they see the finished restoration project?", - "answer": "satisfying and worth the hard work", - "evidence": [ - "D19:9" - ], - "category": 4 - }, - { - "question": "What activity did Calvin enjoy during his summer drives?", - "answer": "feeling the wind blowing through his hair", - "evidence": [ - "D20:4" - ], - "category": 4 - }, - { - "question": "Which song from the childhood of Calvin brings back memories of a road trip with his dad?", - "answer": "\"California Love\"", - "evidence": [ - "D20:6", - "D20:8" - ], - "category": 4 - }, - { - "question": "What project did Calvin work on to chill out?", - "answer": "A shiny orange car", - "evidence": [ - "D21:3" - ], - "category": 4 - }, - { - "question": "What car did Dave work on in the junkyard?", - "answer": "Ford Mustang", - "evidence": [ - "D21:4" - ], - "category": 4 - }, - { - "question": "What does Dave find satisfying about restoring old cars?", - "answer": "Transforming something old and beat-up into something beautiful", - "evidence": [ - "D21:10" - ], - "category": 4 - }, - { - "question": "What do Calvin and Dave use to reach their goals?", - "answer": "Hard work and determination", - "evidence": [ - "D21:15" - ], - "category": 4 - }, - { - "question": "What does working on cars represent for Dave?", - "answer": "Therapy and a way to get away from everyday stress", - "evidence": [ - "D22:5" - ], - "category": 4 - }, - { - "question": "What does Dave aim to do with his passion for cars?", - "answer": "Take something broken and make it into something awesome", - "evidence": [ - "D22:5" - ], - "category": 4 - }, - { - "question": "What did Calvin recently get that is a \"masterpiece on wheels\"?", - "answer": "Ferrari", - "evidence": [ - "D23:16" - ], - "category": 4 - }, - { - "question": "Who headlined the music festival that Dave attended in October?", - "answer": "The Fireworks", - "evidence": [ - "D23:9" - ], - "category": 4 - }, - { - "question": "How does Calvin stay motivated when faced with setbacks?", - "answer": "Reminds himself of his passion for goals, gets help from others, and takes a break to recharge", - "evidence": [ - "D23:4" - ], - "category": 4 - }, - { - "question": "What activity does Dave find fulfilling, similar to Calvin's passion for music festivals?", - "answer": "fixing things", - "evidence": [ - "D23:11" - ], - "category": 4 - }, - { - "question": "Where did Calvin and Dave meet Frank Ocean to start collaborating?", - "answer": "at a festival", - "evidence": [ - "D24:5" - ], - "category": 4 - }, - { - "question": "Which part of Tokyo is described as Tokyo's Times Square by Calvin?", - "answer": "Shibuya Crossing", - "evidence": [ - "D24:19" - ], - "category": 4 - }, - { - "question": "What specific location in Tokyo does Calvin mention being excited to explore?", - "answer": "Shinjuku", - "evidence": [ - "D24:19" - ], - "category": 4 - }, - { - "question": "What dish does Dave recommend Calvin to try in Tokyo?", - "answer": "ramen", - "evidence": [ - "D24:20" - ], - "category": 4 - }, - { - "question": "What does Calvin find energizing during the tour?", - "answer": "Performing and connecting with the crowd", - "evidence": [ - "D25:2" - ], - "category": 4 - }, - { - "question": "How does Calvin balance his job and personal life?", - "answer": "Takes it one day at a time", - "evidence": [ - "D25:4" - ], - "category": 4 - }, - { - "question": "What inspired Calvin's recent music?", - "answer": "Struggles that people go through", - "evidence": [ - "D25:10" - ], - "category": 4 - }, - { - "question": "How does Calvin describe his music in relation to capturing feelings?", - "answer": "Express himself and work through his emotions", - "evidence": [ - "D25:12" - ], - "category": 4 - }, - { - "question": "Why did Dave start working on cars?", - "answer": "Fascinated with how machines work", - "evidence": [ - "D25:15" - ], - "category": 4 - }, - { - "question": "What is the toughest part of car restoration according to Dave?", - "answer": "Paying extra attention to detail", - "evidence": [ - "D25:19" - ], - "category": 4 - }, - { - "question": "What does Calvin believe makes an artist create something extraordinary?", - "answer": "Paying attention to small details", - "evidence": [ - "D25:22" - ], - "category": 4 - }, - { - "question": "When did Dave sell the car he restored last year?", - "answer": "Last year", - "evidence": [ - "D25:17" - ], - "category": 4 - }, - { - "question": "When did Calvin first get interested in cars?", - "answer": "at an early age", - "evidence": [ - "D26:6" - ], - "category": 4 - }, - { - "question": "How did Calvin feel about performing with someone he admires?", - "answer": "Unreal, like a dream come true", - "evidence": [ - "D26:9" - ], - "category": 4 - }, - { - "question": "What realization did the nightclub experience bring to Calvin?", - "answer": "how much music means to him, it's like his passion and purpose", - "evidence": [ - "D26:9" - ], - "category": 4 - }, - { - "question": "What do Dave and Calvin agree on regarding their pursuits?", - "answer": "It's fulfilling and motivating", - "evidence": [ - "D26:11" - ], - "category": 4 - }, - { - "question": "Which city is featured in the photograph Dave showed Calvin?", - "answer": "Boston", - "evidence": [ - "D27:6" - ], - "category": 4 - }, - { - "question": "What did Calvin do recently at his Japanese house?", - "answer": "Threw a small party for his new album", - "evidence": [ - "D28:1" - ], - "category": 4 - }, - { - "question": "What did Dave recently start a blog about?", - "answer": "Car mods", - "evidence": [ - "D28:8" - ], - "category": 4 - }, - { - "question": "What is Dave's way to share his passion with others?", - "answer": "Through a blog on car mods", - "evidence": [ - "D28:8" - ], - "category": 4 - }, - { - "question": "What type of videos does Calvin usually watch on his television?", - "answer": "Music videos, concerts, documentaries about artists and their creative process", - "evidence": [ - "D28:31" - ], - "category": 4 - }, - { - "question": "What type of music has Dave been getting into lately?", - "answer": "Classic rock", - "evidence": [ - "D28:40" - ], - "category": 4 - }, - { - "question": "What tools does Calvin use to boost his motivation for music?", - "answer": "Writing lyrics and notes", - "evidence": [ - "D28:34" - ], - "category": 4 - }, - { - "question": "What type of content does Dave post on his blog that inspired others to start their own DIY projects?", - "answer": "How he made his car look like a beast", - "evidence": [ - "D28:10" - ], - "category": 4 - }, - { - "question": "What kind of impact does Dave's blog on car mods have on people?", - "answer": "It inspires others to start their DIY projects", - "evidence": [ - "D28:10" - ], - "category": 4 - }, - { - "question": "Who did Calvin invite to see him perform in Boston on 13 November, 2023?", - "answer": "his old high school buddy", - "evidence": [ - "D29:1" - ], - "category": 4 - }, - { - "question": "What hobby did Calvin take up recently?", - "answer": "Photography", - "evidence": [ - "D30:1" - ], - "category": 4 - }, - { - "question": "What new item did Dave buy recently?", - "answer": "A vintage camera", - "evidence": [ - "D30:5" - ], - "category": 4 - }, - { - "question": "What type of photos does Dave like to capture with his new camera?", - "answer": "Nature - sunsets, beaches, waves", - "evidence": [ - "D30:9" - ], - "category": 4 - }, - { - "question": "What event did Calvin attend in Boston?", - "answer": "Fancy gala", - "evidence": [ - "D30:2" - ], - "category": 4 - }, - { - "question": "What did Calvin discuss with the cool artist he met at the gala?", - "answer": "Music and art", - "evidence": [ - "D30:4" - ], - "category": 4 - }, - { - "question": "Where did Dave take a stunning photo of a waterfall?", - "answer": "Nearby park", - "evidence": [ - "D30:15" - ], - "category": 4 - }, - { - "question": "What positive impact does Calvin mention nature has on tough times?", - "answer": "Nature helps us appreciate life", - "evidence": [ - "D30:12" - ], - "category": 4 - }, - { - "question": "Which DJ was Dave's favorite at the music festival in April 2023?", - "evidence": [ - "D2:10" - ], - "category": 5, - "adversarial_answer": "Aerosmith" - }, - { - "question": "What advice did Calvin receive from the chef at the music festival?", - "evidence": [ - "D3:7" - ], - "category": 5, - "adversarial_answer": "to stay true to himself and sound unique" - }, - { - "question": "What is Calvin's new business venture as of 1 May, 2023?", - "evidence": [ - "D4:1" - ], - "category": 5, - "adversarial_answer": "Car maintenance shop" - }, - { - "question": "What type of cars does Calvin work on at his shop?", - "evidence": [ - "D4:19" - ], - "category": 5, - "adversarial_answer": "all kinds of cars, from regular maintenance to full restorations of classic cars" - }, - { - "question": "What did Dave receive as a gift from another artist?", - "evidence": [ - "D4:26" - ], - "category": 5, - "adversarial_answer": "a gold necklace with a diamond pendant" - }, - { - "question": "What was the necklace Dave received meant to remind him of?", - "evidence": [ - "D4:26" - ], - "category": 5, - "adversarial_answer": "why he keeps hustling as a musician" - }, - { - "question": "What did Calvin open in May 2023?", - "evidence": [ - "D6:8" - ], - "category": 5, - "adversarial_answer": "a car shop" - }, - { - "question": "What gives Calvin a sense of achievement and purpose?", - "evidence": [ - "D7:6" - ], - "category": 5, - "adversarial_answer": "Fixing up things" - }, - { - "question": "What sports activity is Dave planning to try after the tour with Frank Ocean?", - "evidence": [ - "D9:15" - ], - "category": 5, - "adversarial_answer": "Skiing" - }, - { - "question": "How does Calvin describe his process of adding acoustic elements to his songs?", - "evidence": [ - "D11:6" - ], - "category": 5, - "adversarial_answer": "gives them a fresh vibe" - }, - { - "question": "What clothing brand does Calvin own that he is proud of?", - "evidence": [ - "D12:7" - ], - "category": 5, - "adversarial_answer": "Ferrari" - }, - { - "question": "What workshop did Calvin get picked for on 11 August, 2023?", - "evidence": [ - "D13:1" - ], - "category": 5, - "adversarial_answer": "Car mod workshop" - }, - { - "question": "What kind of modifications has Calvin been working on in the car mod workshop?", - "evidence": [ - "D13:7" - ], - "category": 5, - "adversarial_answer": "engine swaps, suspension modifications, and body modifications" - }, - { - "question": "What type of car did Calvin work on during the workshop?", - "evidence": [ - "D13:7" - ], - "category": 5, - "adversarial_answer": "classic muscle car" - }, - { - "question": "What did Dave and his friends record in August 2023?", - "evidence": [ - "D15:12" - ], - "category": 5, - "adversarial_answer": "a podcast discussing the rap industry" - }, - { - "question": "Where did Dave start shooting a video for his new album?", - "evidence": [ - "D16:8" - ], - "category": 5, - "adversarial_answer": "Miami" - }, - { - "question": "What design is featured on Dave's guitar?", - "evidence": [ - "D16:14" - ], - "category": 5, - "adversarial_answer": "octopus" - }, - { - "question": "Why did Dave get his guitar customized with a shiny finish?", - "evidence": [ - "D16:20" - ], - "category": 5, - "adversarial_answer": "unique look" - }, - { - "question": "What color glow did Dave customize his guitar with?", - "evidence": [ - "D16:20" - ], - "category": 5, - "adversarial_answer": "purple" - }, - { - "question": "Where did Calvin come back from with insights on car modification on 1st September 2023?", - "evidence": [ - "D17:1" - ], - "category": 5, - "adversarial_answer": "San Francisco" - }, - { - "question": "What emotion does Calvin mention feeling when he sees the relief of someone whose car he fixed?", - "evidence": [ - "D17:5" - ], - "category": 5, - "adversarial_answer": "Proud" - }, - { - "question": "What did Dave book a flight ticket for on 1st September 2023?", - "evidence": [ - "D17:6" - ], - "category": 5, - "adversarial_answer": "Boston" - }, - { - "question": "Which horror movie did Dave mention as one of his favorites?", - "evidence": [ - "D19:6" - ], - "category": 5, - "adversarial_answer": "Ratatouille" - }, - { - "question": "Which song from the childhood of Dave brings back memories of a road trip with his dad?", - "evidence": [ - "D20:6", - "D20:8" - ], - "category": 5, - "adversarial_answer": "\"California Love\"" - }, - { - "question": "What car did Calvin work on in the junkyard?", - "evidence": [ - "D21:4" - ], - "category": 5, - "adversarial_answer": "Ford Mustang" - }, - { - "question": "What does Dave find satisfying about destroying old cars?", - "evidence": [ - "D21:10" - ], - "category": 5, - "adversarial_answer": "Transforming something old and beat-up into something beautiful" - }, - { - "question": "What does working on boats represent for Dave?", - "evidence": [ - "D22:5" - ], - "category": 5, - "adversarial_answer": "Therapy and a way to get away from everyday stress" - }, - { - "question": "What does Dave aim to do with his passion for cooking?", - "evidence": [ - "D22:5" - ], - "category": 5, - "adversarial_answer": "Take something broken and make it into something awesome" - }, - { - "question": "What did Calvin recently get that is a \"masterpiece on canvas\"?", - "evidence": [ - "D23:16" - ], - "category": 5, - "adversarial_answer": "Ferrari" - }, - { - "question": "Who headlined the music festival that Calvin attended in October?", - "evidence": [ - "D23:9" - ], - "category": 5, - "adversarial_answer": "The Fireworks" - }, - { - "question": "Which part of Tokyo is described as Tokyo's Times Square by Dave?", - "evidence": [ - "D24:19" - ], - "category": 5, - "adversarial_answer": "Shibuya Crossing" - }, - { - "question": "What specific location in Tokyo does Calvin mention being excited to avoid?", - "evidence": [ - "D24:19" - ], - "category": 5, - "adversarial_answer": "Shinjuku" - }, - { - "question": "When did Calvin sell the car he restored last year?", - "evidence": [ - "D25:17" - ], - "category": 5, - "adversarial_answer": "Last year" - }, - { - "question": "When did Calvin first get interested in motorcycles?", - "evidence": [ - "D26:6" - ], - "category": 5, - "adversarial_answer": "at an early age" - }, - { - "question": "What realization did the nightclub experience bring to Dave?", - "evidence": [ - "D26:9" - ], - "category": 5, - "adversarial_answer": "how much music means to him, it's like his passion and purpose" - }, - { - "question": "What did Dave do recently at his Japanese house?", - "evidence": [ - "D28:1" - ], - "category": 5, - "adversarial_answer": "Threw a small party for his new album" - }, - { - "question": "What did Calvin recently start a blog about?", - "evidence": [ - "D28:8" - ], - "category": 5, - "adversarial_answer": "Car mods" - }, - { - "question": "What type of videos does Dave usually watch on his television?", - "evidence": [ - "D28:31" - ], - "category": 5, - "adversarial_answer": "Music videos, concerts, documentaries about artists and their creative process" - }, - { - "question": "What type of art has Dave been getting into lately?", - "evidence": [ - "D28:40" - ], - "category": 5, - "adversarial_answer": "Classic rock" - }, - { - "question": "What type of content does Dave post on his blog that inspired others to start their own cooking projects?", - "evidence": [ - "D28:10" - ], - "category": 5, - "adversarial_answer": "How he made his car look like a beast" - }, - { - "question": "What kind of impact does Dave's blog on vegan recipes have on people?", - "evidence": [ - "D28:10" - ], - "category": 5, - "adversarial_answer": "It inspires others to start their DIY projects" - }, - { - "question": "Who did Dave invite to see him perform in Boston on 13 November, 2023?", - "evidence": [ - "D29:1" - ], - "category": 5, - "adversarial_answer": "his old high school buddy" - }, - { - "question": "What new item did Calvin buy recently?", - "evidence": [ - "D30:5" - ], - "category": 5, - "adversarial_answer": "A vintage camera" - }, - { - "question": "What type of photos does Calvin like to capture with his new camera?", - "evidence": [ - "D30:9" - ], - "category": 5, - "adversarial_answer": "Nature - sunsets, beaches, waves" - }, - { - "question": "What did Dave discuss with the cool artist he met at the gala?", - "evidence": [ - "D30:4" - ], - "category": 5, - "adversarial_answer": "Music and art" - }, - { - "question": "Where did Calvin take a stunning photo of a waterfall?", - "evidence": [ - "D30:15" - ], - "category": 5, - "adversarial_answer": "Nearby park" - } - ], - "conversation": { - "speaker_a": "Calvin", - "speaker_b": "Dave", - "session_1_date_time": "11:53 am on 23 March, 2023", - "session_1": [ - { - "speaker": "Calvin", - "dia_id": "D1:1", - "text": "Hey Dave! Nice to meet you! How's it going since we talked?" - }, - { - "speaker": "Dave", - "dia_id": "D1:2", - "text": "Hey Calvin! Nice to meet you too! Things have been going well since we last talked. I attended this awesome event recently. It was like a car lover's paradise! They had so many classic cars on show, I was in awe. I even had the opportunity to speak with some of the owners and hear their fascinating stories. It was super inspiring. What about you? Anything exciting happening in your life lately?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://localandbespoke.files.wordpress.com/2018/11/imag0397.jpg" - ], - "blip_caption": "a photo of a building with a sign on the front of it", - "query": "mansion entrance japan", - "dia_id": "D1:3", - "text": "That event sounds great! Something really exciting happened to me - I just had a big life change! Here's my new mansion - pretty cool huh?" - }, - { - "speaker": "Dave", - "dia_id": "D1:4", - "text": "Wow! Congrats on the big change! What inspired you to start this journey?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:5", - "text": "I'm so excited to learn about Japanese culture and get a chance to expand." - }, - { - "speaker": "Dave", - "dia_id": "D1:6", - "text": "Wow, Calvin, learning about new cultures is awesome! Have you ever been to Japan?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:7", - "text": "Never been there before. Fascinated by the traditions and can't wait to get a taste of the culture." - }, - { - "speaker": "Dave", - "dia_id": "D1:8", - "text": "Wow, Japan sounds amazing. Can't wait to hear all about it! When are you leaving?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/6p8nisgp4dq41.jpg" - ], - "blip_caption": "a photo of a living room with a couch, table, and television", - "query": "japanese mansion living room", - "dia_id": "D1:9", - "text": "I'm heading there next month. I'll be staying in such a nice place while I'm there.\n\n" - }, - { - "speaker": "Dave", - "dia_id": "D1:10", - "text": "That looks cozy! Where'd you find a place to stay there?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:11", - "text": "Wow, my agent found me this awesome place, so thankful!" - }, - { - "speaker": "Dave", - "dia_id": "D1:12", - "text": "Awesome! Having a place to stay is key. What are your plans for the trip?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:13", - "text": "I'm planning to explore the city, try out different local cuisines, and perhaps collaborate with musicians in the area." - }, - { - "speaker": "Dave", - "dia_id": "D1:14", - "text": "Sounds great! Collaborating with local musicians would be an amazing experience. How long are you planning to stay in Japan?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:15", - "text": "I'm gonna be in Japan for a few months then off to Boston! I can't wait!" - }, - { - "speaker": "Dave", - "dia_id": "D1:16", - "img_url": [ - "https://c1.wallpaperflare.com/preview/349/298/459/public-garden-boston-park-common.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a park with a lake and a few boats", - "text": "That's great, can't wait for you to return from your trip. I've been spending lots of time at this beautiful park lately - so calming. Have you been there before?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:17", - "text": "No, I've never been in that park. Looks like a chill spot. I'll definitely check it out when I'm back. Appreciate the tip!" - }, - { - "speaker": "Dave", - "dia_id": "D1:18", - "text": "Cool! Enjoy your trip! Let me know if you need any more recommendations. Have fun!" - }, - { - "speaker": "Calvin", - "dia_id": "D1:19", - "text": "Thanks, Dave! If I need any help, I'll be sure to let you know. Stay safe!" - } - ], - "session_2_date_time": "4:45 pm on 26 March, 2023", - "session_2": [ - { - "speaker": "Calvin", - "dia_id": "D2:1", - "text": "Hey Dave, been a few days, so I wanted to let you in on some cool news. I just got a new car and it's amazing! Finally owning a luxury car was a dream come true and I'm so stoked." - }, - { - "speaker": "Dave", - "dia_id": "D2:2", - "text": "Woohoo, Calvin! Congrats on the new car! How's it driving?" - }, - { - "speaker": "Calvin", - "dia_id": "D2:3", - "text": "Nice car! It's an amazing ride - super smooth and real powerful. Like a rockstar behind the wheel! Thanks, man!" - }, - { - "speaker": "Dave", - "dia_id": "D2:4", - "text": "Wow, Calvin! Owning something like this must feel amazing. Is this your first one? Show me a pic! What's it called?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/v7n19f5lcqn91.jpg" - ], - "blip_caption": "a photo of a red sports car parked on the side of the road", - "query": "ferrari 488 gtb japanese mansion", - "dia_id": "D2:5", - "text": "Woohoo! I finally got myself this beauty. Here's a pic from yesterday when I took it for a ride. Can't believe I'm actually driving it. Definitely what I deserve after all this hard work. Every time I step in, it's like an adrenaline rush!" - }, - { - "speaker": "Dave", - "dia_id": "D2:6", - "text": "Calvin, that car looks awesome! You must feel amazing every time you get inside! You deserved it - congrats buddy!" - }, - { - "speaker": "Calvin", - "dia_id": "D2:7", - "text": "Woah, I'm on cloud nine! Hard work pays off. What's up with you, Dave?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://express-images.franklymedia.com/6616/sites/11/2019/05/28094328/IMG_3656.jpg" - ], - "blip_caption": "a photo of a large crowd of people at a concert", - "query": "music festival boston stage crowd", - "dia_id": "D2:8", - "text": "Thanks, Calvin! Doing good. Last weekend, I went to a music festival in Boston - it was awesome! So many cool bands playing and the atmosphere was electric. I love music, it reminded me why I love it. Here's a pic, take a look." - }, - { - "speaker": "Calvin", - "dia_id": "D2:9", - "text": "Wow, sounds like a blast! Which one was your favorite?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://themusicroom.me/wp-content/uploads/2019/04/Aerosmith-opening2.jpg" - ], - "blip_caption": "a photo of a concert with a large eagle on the stage", - "query": "aerosmith stage rock band", - "dia_id": "D2:10", - "text": "Wow, there were so many great bands! If I had to pick a favorite, it would definitely be Aerosmith. Their performance was incredible. I'll show you a pic I took when they were playing." - }, - { - "speaker": "Calvin", - "dia_id": "D2:11", - "text": "Cool! Lovin' Aerosmith." - }, - { - "speaker": "Dave", - "img_url": [ - "https://www.etonline.com/sites/default/files/images/2019-10/gettyimages-1093168760.jpg" - ], - "blip_caption": "a photo of two people on stage with guitars and a microphone", - "query": "aerosmith steven tyler stage open mouth", - "dia_id": "D2:12", - "text": "I finally saw them live and it was such an amazing experience! He was jamming out to one of their hits. Check out this epic picture of them!" - }, - { - "speaker": "Calvin", - "dia_id": "D2:13", - "text": "Cool, Dave! Seeing your favorite singers live must have been wild! Did you get a chance to hang out with them after the show?" - }, - { - "speaker": "Dave", - "dia_id": "D2:14", - "text": "Nah, didn't get to hang with them after the show, but seeing them live was cool enough. It's always a dream to see your favorites performing!" - }, - { - "speaker": "Calvin", - "dia_id": "D2:15", - "text": "Yeah, it's always a dream to see your favorites doing their thing! So glad you got to experience that." - }, - { - "speaker": "Dave", - "dia_id": "D2:16", - "text": "Thanks, Calvin! Yeah, it was an incredible experience. So, anything new happening in your life?" - }, - { - "speaker": "Calvin", - "dia_id": "D2:17", - "text": "Got a new ride and wrote some new tunes - had a few studio sessions last week and I'm excited to collaborate. Can't wait to share it with everyone!" - }, - { - "speaker": "Dave", - "dia_id": "D2:18", - "text": "Cool, Calvin! Looking forward to hearing your new tunes. Keep me updated on how your collaborations go!" - }, - { - "speaker": "Calvin", - "dia_id": "D2:19", - "text": "Yeah, no worries! I'll definitely let you know how it goes. Cheers!" - }, - { - "speaker": "Dave", - "dia_id": "D2:20", - "text": "Enjoy the collaborations, Calvin! Stay safe!" - }, - { - "speaker": "Calvin", - "dia_id": "D2:21", - "text": "Thanks, Dave! Gonna have fun and stay safe with it. Bye!" - } - ], - "session_3_date_time": "4:15 pm on 20 April, 2023", - "session_3": [ - { - "speaker": "Calvin", - "dia_id": "D3:1", - "text": "Hey Dave! Long time no see. I just went to an awesome music thingy in Tokyo - so cool!" - }, - { - "speaker": "Dave", - "dia_id": "D3:2", - "text": "Hey Calvin! Great to hear from you. How was the music thingy in Tokyo? See any cool bands?" - }, - { - "speaker": "Calvin", - "dia_id": "D3:3", - "text": "Hey Dave! The festival in Tokyo was awesome! Didn't see any bands, but met lots of talented artists and industry people. Totally enriching!" - }, - { - "speaker": "Dave", - "dia_id": "D3:4", - "text": "Wow, Calvin, sounds great! What did you learn from it?" - }, - { - "speaker": "Calvin", - "dia_id": "D3:5", - "text": "I learned a lot and got some great advice from professionals in the music industry. It was inspiring!" - }, - { - "speaker": "Dave", - "dia_id": "D3:6", - "text": "Wow, Calvin! Bet that was inspiring being surrounded by professionals. Did you get any advice from them?" - }, - { - "speaker": "Calvin", - "dia_id": "D3:7", - "text": "The producer gave me some advice to stay true to myself and sound unique. It got me thinking about where I want my music to go. It's really motivating!" - }, - { - "speaker": "Dave", - "dia_id": "D3:8", - "text": "Wow, Calvin! It's really motivating to see you staying true to yourself. It got me thinking, where do you see your music taking you?" - }, - { - "speaker": "Calvin", - "dia_id": "D3:9", - "text": "Thanks, Dave! I'm dreaming of touring the world, performing for different people and connecting with them. I hope my music can reach a global audience and make an impact. I'm also looking forward to my upcoming trip to Boston after I finish the Frank Ocean tour. I've heard that the music scene there is awesome, so I can't wait to check it out!" - }, - { - "speaker": "Dave", - "dia_id": "D3:10", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/6/67/Boston_Skyline_%28193150499%29.jpeg" - ], - "re-download": true, - "blip_caption": "a photography of a city skyline with a boat in the water", - "text": "Sounds like an amazing plan, Cal! I can't wait for your trip to Boston. I'll show you around town and all the cool spots. The music scene there is awesome, with places like Paradise Rock, House of Blues, and Fenway Park. You'll definitely have some great performances there. Count me in for the front row when you're up onstage! Oh, and by the way, check out this pic." - }, - { - "speaker": "Calvin", - "dia_id": "D3:11", - "text": "Wow, Boston looks great! Thanks for the tips. And, awesome pic!" - }, - { - "speaker": "Dave", - "dia_id": "D3:12", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/d/de/Ford_Mustang_-_Shuttleworth_Classic_Car_Show_2017_%2833661471822%29.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a green mustang parked in a field of grass", - "text": "Last weekend I went to a car show. Classic cars are so charming and the dedication people put into restoring them is amazing. That's why I'm so into auto engineering. Can't wait to show you some when you come to Boston! Oh, almost forgot to send a photo, look at this beautiful car!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/2020-ferrari-f8-tributo-120-1551808626.jpg" - ], - "blip_caption": "a photo of a red sports car on display at a show", - "query": "car ferrari", - "dia_id": "D3:13", - "text": "Wow, Dave, that car looks great! Take a look at my car, I put a lot of work into it. Can't wait to see more when I'm in Boston!" - }, - { - "speaker": "Dave", - "dia_id": "D3:14", - "text": "Thanks! It definitely took a lot of work. Have you had a chance to check out Boston apart from the gigs?" - }, - { - "speaker": "Calvin", - "dia_id": "D3:15", - "text": "Not yet, been pretty busy with rehearsals and traveling. But I'm looking forward to exploring the city, trying out some delicious food, and visiting the popular attractions. Maybe we can grab a bite together when I'm there?" - }, - { - "speaker": "Dave", - "dia_id": "D3:16", - "text": "Definitely, Cal! Let's grab some food and I'll show you my favorite spots in the city. Can't wait to show them to you!" - }, - { - "speaker": "Calvin", - "dia_id": "D3:17", - "text": "Sounds great, Dave! Can't wait to try out the food there with you. It'll be fun!" - }, - { - "speaker": "Dave", - "dia_id": "D3:18", - "text": "It'll be great, Cal! Can't wait to show you the amazing music and food here. See you soon!" - } - ], - "session_4_date_time": "6:24 pm on 1 May, 2023", - "session_4": [ - { - "speaker": "Dave", - "img_url": [ - "https://www.broadwayimports.com/Files/Images/img-shop-exterior.jpg" - ], - "blip_caption": "a photo of a car dealership with cars parked in front of it", - "query": "car maintenance shop exterior", - "dia_id": "D4:1", - "text": "Hey Calvin, long time no see! A lot's been happening since we last talked. Guess what? I finally opened my own car maintenance shop! It's so satisfying to have a spot to work on cars - it's like a dream come true! Take a look at the photo." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a man standing under a car in a garage", - "dia_id": "D4:2", - "text": "Wow Dave! Congrats on opening your own car maintenance shop! It looks like all your hard work and dedication paid off." - }, - { - "speaker": "Dave", - "dia_id": "D4:3", - "text": "Thanks, Cal! It's great to see my hard work paying off. Opening this shop was my dream, and I'm really happy to see it getting started. It was a lot of hard work, but it was worth it." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a book with a space theme on it", - "dia_id": "D4:4", - "text": "Woo, Dave! Congrats on achieving your dream - you've got guts and ambition - that's awesome! Keep it up!" - }, - { - "speaker": "Dave", - "dia_id": "D4:5", - "text": "Thanks! Appreciate the support. My dream was to open a shop and it's a step towards my other dream of working on classic cars. I love their design and engineering." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a red car parked in a parking lot", - "dia_id": "D4:6", - "text": "Wow, Dave! Going for it with classic cars is cool! Fulfilling your dreams is so important." - }, - { - "speaker": "Dave", - "img_url": [ - "https://i0.wp.com/www.sopwithmotorsports.com/wp-content/uploads/2018/06/MustangEngineBanner.jpg" - ], - "blip_caption": "a photo of a car engine with a small air filter", - "query": "classic red mustang engine restoration", - "dia_id": "D4:7", - "text": "I'm obsessed with classic cars. They have a unique charm. I was so thrilled to restore one last year\u2014nothing like bringing it back to life! Take a look at the engine of the restored car." - }, - { - "speaker": "Calvin", - "dia_id": "D4:8", - "text": "Wow, Dave! That looks awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D4:9", - "text": "Thanks, Calvin! It was a labor of love. Challenging, but so worth it." - }, - { - "speaker": "Calvin", - "dia_id": "D4:10", - "text": "Yeah, it's awesome when you see something you worked on come to life." - }, - { - "speaker": "Dave", - "dia_id": "D4:11", - "text": "Yeah! It feels great to see the hard work pay off, it's like bringing something back to life." - }, - { - "speaker": "Calvin", - "dia_id": "D4:12", - "text": "Yeah, it's an amazing feeling when you create something and it resonates with people. It's so satisfying when you finish something you made from scratch!" - }, - { - "speaker": "Dave", - "dia_id": "D4:13", - "text": "Yeah, Calvin! It's such an amazing feeling to see something you create become a reality. Knowing that your skills and hard work made it happen is incredible." - }, - { - "speaker": "Calvin", - "dia_id": "D4:14", - "text": "Yeah, Dave! Feels good when our hard work pays off. It's the perfect blend of dedication and passion!" - }, - { - "speaker": "Dave", - "dia_id": "D4:15", - "text": "Yeah, that mix really keeps me motivated and makes it all worthwhile." - }, - { - "speaker": "Calvin", - "dia_id": "D4:16", - "text": "Keep going for it!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://stevesimports.com/wp-content/uploads/2023/09/Auto-Repair-Shop-Portland-OR.jpg" - ], - "blip_caption": "a photo of a group of people standing in front of a car", - "query": "car maintenance shop grand opening", - "dia_id": "D4:17", - "text": "I will! By the way, This is a photo of my shop. Come by sometime, if you can!" - }, - { - "speaker": "Calvin", - "dia_id": "D4:18", - "text": "Wow, your shop looks great! I'd love to check it out sometime. What sort of cars do you work on at your shop?" - }, - { - "speaker": "Dave", - "dia_id": "D4:19", - "text": "Thanks, Calvin! I work on all kinds of cars at the shop - from regular maintenance to full restorations of classic cars. It keeps me busy and happy!" - }, - { - "speaker": "Calvin", - "dia_id": "D4:20", - "text": "Wow Dave, working on cars must be really rewarding." - }, - { - "speaker": "Dave", - "dia_id": "D4:21", - "text": "Definitely, working on cars is what I'm passionate about. Doing it every day is so rewarding! Seeing the transformation is awesome and knowing I'm helping people keep their cars in good condition is really satisfying." - }, - { - "speaker": "Calvin", - "dia_id": "D4:22", - "text": "Wow Dave, that's awesome! Doing something you love and helping others is so rewarding. Keep up the great work!" - }, - { - "speaker": "Dave", - "dia_id": "D4:23", - "text": "Thanks, Cal! I really appreciate the boost. It means a lot that my work is valued and that it brings joy to others." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i0.wp.com/gaweejewelers.com/wp-content/uploads/2019/08/56210088.jpg" - ], - "blip_caption": "a photo of a gold necklace with a diamond pendant", - "query": "gold chain diamond pendant", - "dia_id": "D4:24", - "text": "Glad to help, Dave! So awesome to see you doing your thing and making a difference. Your hard work and talent totally deserve all the recognition. Keep on keepin' on, bud! Take a look at this beautiful necklace with a diamond pendant, that's so stunning!" - }, - { - "speaker": "Dave", - "dia_id": "D4:25", - "text": "Wow, that's a great necklace! Where did you get it?" - }, - { - "speaker": "Calvin", - "dia_id": "D4:26", - "text": "Thanks, Dave! I got it from another artist as a gift - it's a great reminder of why I keep hustling as a musician!" - }, - { - "speaker": "Dave", - "dia_id": "D4:27", - "text": "Awesome, Calvin! Keep pushing and making music, it'll remind us why we keep hustling." - }, - { - "speaker": "Calvin", - "dia_id": "D4:28", - "text": "Yeah, Dave! The road can be hard, but when we remember why we're doing it, it keeps us going. Let's keep each other motivated!" - } - ], - "session_5_date_time": "1:16 pm on 3 May, 2023", - "session_5": [ - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/2w0nqd9uq3271.jpg" - ], - "blip_caption": "a photo of a car being worked on in a garage", - "query": "vintage car being lifted with crane mechanic shop", - "dia_id": "D5:1", - "text": "Hey Calvin! Long time no talk. How's it going? Crazy news - I'm teaming up with a local garage. Take a look at what we working on together!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a green car in a garage with a sign in the background", - "dia_id": "D5:2", - "text": "Hey Dave, great to hear from you! That's awesome news about teaming up with a local garage. Super inspiring seeing you follow your passion. Congratulations on this new venture. It's impressive how far you've come since we last chatted. How's everything going?" - }, - { - "speaker": "Dave", - "dia_id": "D5:3", - "img_url": [ - "https://c0.wallpaperflare.com/preview/534/637/290/finland-hyvinkaa-aq-autohuolto-car-mechanic.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a man working on a car engine in a garage", - "text": "Thanks, Calvin! I'm loving this job. I get to work with awesome mechanics and share my knowledge about cars. Here's what I'm currently working on! It's a cool project, even if it's a bit challenging. \ud83e\udd29" - }, - { - "speaker": "Calvin", - "dia_id": "D5:4", - "text": "That car looks awesome! You're putting in a lot of effort and it's great to see the end result. Keep up the good work. Got any plans for what's next?" - }, - { - "speaker": "Dave", - "dia_id": "D5:5", - "text": "Thanks Calvin! Appreciate the support. I'm gonna keep learning more about auto engineering, maybe even build a custom car from scratch someday - that's the dream! For now, just gonna keep working on this project and assisting customers." - }, - { - "speaker": "Calvin", - "dia_id": "D5:6", - "text": "Wow, Dave! You're so inspiring - good for you for pushing yourself to achieve your dream. Making a custom car sounds awesome. Don't forget to relax and enjoy the process too!" - }, - { - "speaker": "Dave", - "dia_id": "D5:7", - "text": "Thanks, Calvin! Gotta take time to chill. Do you have any hobbies that help you relax?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/4j5jmpxvxnp71.jpg" - ], - "blip_caption": "a photo of a red sports car driving down a road", - "query": "ferrari long drive", - "dia_id": "D5:8", - "text": "Hey Dave, long drives in [this car] really help me relax. The feeling of the wind and the open road is so freeing. It helps me clear my head. What do you like to do to chill out? " - }, - { - "speaker": "Dave", - "dia_id": "D5:9", - "text": "Yeah, I hear you! Driving with the wind in your hair is so calming. Taking a walk around is a great way to destress, too. Exploring, taking in the sights and sounds - it's such a peaceful experience." - }, - { - "speaker": "Calvin", - "dia_id": "D5:10", - "text": "Yea, I totally hear ya. Embracing nature has been really calming for me too. I've been loving getting to know Japanese culture. On the other hand, I'm stuck with my music at the moment, like my creativity's frozen or something. Any tips?" - }, - { - "speaker": "Dave", - "dia_id": "D5:11", - "text": "If I'm having trouble coming up with ideas, I usually immerse myself in something I love, like concerts or my favorite albums. Doing that usually helps to jumpstart my inspiration. Maybe try taking a break from music and explore other things. Plus, have some fun while you're at it!" - }, - { - "speaker": "Calvin", - "dia_id": "D5:12", - "text": "Thanks, Dave! Taking a break is great for getting my mojo back. I'll definitely take your advice and explore. Appreciate the help! You're awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D5:13", - "text": "No worries, Calvin! Glad I could help. Keep pursuing your music and never give up. You're awesome! \ud83e\udd18" - }, - { - "speaker": "Calvin", - "dia_id": "D5:14", - "text": "Thanks, appreciate it. Won't give up. Let's stay in touch! Bye!" - }, - { - "speaker": "Dave", - "dia_id": "D5:15", - "text": "Sure, Calvin! Keep in touch. If you ever need help, just let me know. Bye!" - } - ], - "session_6_date_time": "11:50 am on 16 May, 2023", - "session_6": [ - { - "speaker": "Calvin", - "dia_id": "D6:1", - "text": "Hey Dave! Long time no chat! Lots has gone down since we last caught up." - }, - { - "speaker": "Dave", - "dia_id": "D6:2", - "text": "Hey Calvin! Long time no chat! How's everything been going since we last caught up?" - }, - { - "speaker": "Calvin", - "dia_id": "D6:3", - "text": "Hey Dave, not everything has been going smoothly. I had an incident last week where my place got flooded, but thankfully, I managed to save my music gear and favorite microphone. It's been tough, but I'm staying positive and looking forward to getting everything fixed up." - }, - { - "speaker": "Dave", - "dia_id": "D6:4", - "text": "Man, that's rough, Calvin. Sorry to hear about that. But I'm glad you could save your music gear and mic - those are the important things. Stay positive, pal. Anything I can do to help?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/80s9pr2v7jba1.jpg" - ], - "blip_caption": "a photo of a music studio with a keyboard, synthesizer, and other musical equipment", - "query": "home studio music gear", - "dia_id": "D6:5", - "text": "Thanks, Dave. Appreciate the support. Waiting on insurance to kick in so I can start repairs - fingers crossed it won't take too long. Take a look at my creative haven, where I pour my heart into the music." - }, - { - "speaker": "Dave", - "dia_id": "D6:6", - "text": "That studio looks awesome, Cal! I hope you get it fixed soon so you can continue creating music. Hang in there!" - }, - { - "speaker": "Calvin", - "dia_id": "D6:7", - "text": "Thanks, Dave! Can't wait to get back to making music. Anything exciting you're working on these days?" - }, - { - "speaker": "Dave", - "dia_id": "D6:8", - "text": "I'm so excited, I opened my car shop last week! Invited some friends over to celebrate and it's been amazing. Super stoked to share my passion and help out with folks' rides. It's been incredible so far." - }, - { - "speaker": "Calvin", - "dia_id": "D6:9", - "text": "Way to go, Dave! Congrats on opening your own car shop! Your excitement is contagious. Keep up the great work!" - }, - { - "speaker": "Dave", - "dia_id": "D6:10", - "text": "Thanks, Calvin! Your support is greatly appreciated. It's been quite a journey so far, and I'm excited to see what the future holds. How about you? Anything exciting happening in the world of music for you?" - }, - { - "speaker": "Calvin", - "dia_id": "D6:11", - "text": "Thanks Dave! Super excited for my upcoming performance in Tokyo this month. It's gonna be great to show my music to a whole new crowd and hopefully expand my following!" - }, - { - "speaker": "Dave", - "dia_id": "D6:12", - "text": "Cool, Cal! Doing a show in Tokyo, wow! I'm sure the audience is gonna love it. Wishing you all the luck! Let me know how it goes." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.pinimg.com/originals/f9/ed/97/f9ed97ec7a8419fec025884d3ea102be.jpg" - ], - "blip_caption": "a photo of a city at night with a lot of lights", - "query": "tokyo skyline night", - "dia_id": "D6:13", - "text": "Sure thing, Dave! I'll keep you posted on how it goes. Thanks for the good luck! Look what a wonderful night! \n" - }, - { - "speaker": "Dave", - "dia_id": "D6:14", - "text": "That looks great! Where did you snap that pic?" - }, - { - "speaker": "Calvin", - "dia_id": "D6:15", - "text": "I took that pic in Tokyo last night. The skyline was stunning!" - }, - { - "speaker": "Dave", - "dia_id": "D6:16", - "text": "Wow, great shot! The night skyline really pops with those city lights. I gotta take a trip there soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D6:17", - "text": "Thanks Dave! The city lights there are amazing. You should visit, it's awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D6:18", - "text": "Definitely adding it to my list. Thanks!" - } - ], - "session_7_date_time": "6:06 pm on 31 May, 2023", - "session_7": [ - { - "speaker": "Calvin", - "dia_id": "D7:1", - "text": "Hey Dave! Been ages since we chatted. So much has gone down. Touring with Frank Ocean last week was wild. Tokyo was unreal -- the crowd was insane. I felt so alive when performing, can't find the words to express it." - }, - { - "speaker": "Dave", - "img_url": [ - "https://www.accidentwellness.com/assets/users/chiro/469/uploads/images/2018/07/mechanic.jpg" - ], - "blip_caption": "a photo of a man working on a car engine in a garage", - "query": "working on a car", - "dia_id": "D7:2", - "text": "Wow, that's awesome, Cal! I bet it was an incredible experience. Congrats! Look what I've been up to recently, that's my neighbor's car. He's been having some trouble with the engine, but now it's done by myself!" - }, - { - "speaker": "Calvin", - "dia_id": "D7:3", - "text": "Thanks, Dave! It was an amazing experience - the energy and love from the fans was crazy. The car in the pic? It's the one you were fixing up the engine for a friend? Working on cars helps me chill and clear my head." - }, - { - "speaker": "Dave", - "dia_id": "D7:4", - "text": "Yes, Cal! It's not difficult for me to help my neighbors with their cars. Working on cars is definitely therapeutic. Does it relax you as much as it does me?" - }, - { - "speaker": "Calvin", - "dia_id": "D7:5", - "text": "Yeah, totally! Fixing cars really calms me down and gives me a real sense of achievement - like meditating. Do you have any hobbies that give you the same satisfaction?" - }, - { - "speaker": "Dave", - "dia_id": "D7:6", - "text": "I'm passionate about fixing up things. It's more than just a hobby - it gives me a sense of achievement and purpose. I get a real buzz transforming something that's not working into something that runs smoothly - it's like giving it a second chance." - }, - { - "speaker": "Calvin", - "dia_id": "D7:7", - "text": "That's so inspiring, Dave. You find purpose in transforming things \u2013 like when you create something new or collaborate with others. We're both making something great out of nothing, it's amazing, right?" - }, - { - "speaker": "Dave", - "dia_id": "D7:8", - "text": "Yeah Cal, it's kinda cool how we can take something and make it look amazing. Giving it a new life makes me feel powerful and happy - like I really achieved something." - }, - { - "speaker": "Calvin", - "dia_id": "D7:9", - "text": "Yeah, Dave! That feeling of power and happiness is amazing! It's great to see what we created with all our hard work and ideas." - }, - { - "speaker": "Dave", - "dia_id": "D7:10", - "text": "Yeah, Calvin! Accomplishment keeps me motivated. You mentioned Tokyo lit you up. Do you always enjoy performing live?" - }, - { - "speaker": "Calvin", - "dia_id": "D7:11", - "text": "Performing live always fuels my soul! I love the rush and connection with the crowd, the feeling's indescribable\u2014it's an absolute high!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/frciwtnbroza1.jpg" - ], - "blip_caption": "a photo of a large crowd of people taking pictures of a concert", - "query": "rock concert audience connection energy artist", - "dia_id": "D7:12", - "text": "Wow, I bet it feels awesome. The connection between artist and audience must be so powerful. Look at the atmosphere in this photo, it's just off the charts!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a crowd of people watching a concert with bright lights", - "dia_id": "D7:13", - "text": "Yeah, Dave! That feeling is amazing. Everyone is part of it, it's like one big harmony." - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a concert with a band performing on stage", - "dia_id": "D7:14", - "text": "That's awesome! Music really brings people together and creates a great atmosphere. It's like therapy for us all. So, what's next for your music?" - }, - { - "speaker": "Calvin", - "dia_id": "D7:15", - "text": "Cool, can't wait for the tour with Frank! I'm buzzing to perform in Boston and see what it's like. Gonna be great playing for folks from home. What do you think of the music scene there?" - }, - { - "speaker": "Dave", - "dia_id": "D7:16", - "text": "Hey, Boston's music scene is awesome! Tons of talented musicians and cool places to play. It'll be a blast performing there - I'll be there to cheer you on!" - }, - { - "speaker": "Calvin", - "dia_id": "D7:17", - "text": "Thanks, Dave! Your support means a lot to me. Can't wait to experience the music scene there." - }, - { - "speaker": "Dave", - "dia_id": "D7:18", - "text": "Great, Calvin! Have a great time in Boston. Can't wait to hear all about it when you get back!" - }, - { - "speaker": "Calvin", - "dia_id": "D7:19", - "text": "Thanks! I'll fill you in on all the details when I get back. See you soon!" - } - ], - "session_8_date_time": "2:31 pm on 9 June, 2023", - "session_8": [ - { - "speaker": "Calvin", - "dia_id": "D8:1", - "text": "Hey Dave! Met with the creative team for my album yesterday. It was a long session, but awesome to see everything coming together. " - }, - { - "speaker": "Dave", - "dia_id": "D8:2", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/3/3a/Boston_Public_Garden_panorama.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a pond with a boat in it surrounded by trees", - "text": "Hey Cal! Sounds great that your album's coming along. Are you feeling good about it? Here's a pic I just took." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://musicconservatory.org/wp-content/uploads/2022/12/IMG-2298.png" - ], - "blip_caption": "a photo of a recording studio with a large window and a desk", - "query": "studio control room making magic studio", - "dia_id": "D8:3", - "text": "Dave, thanks for checking in. I'm feeling stoked about this album! We've been making some magic with a team in the studio, working on the music and everything. Look at what a wonderful studio we have! How have you been? Anything new since we talked?" - }, - { - "speaker": "Dave", - "dia_id": "D8:4", - "text": "Hey, nice photo of the studio! Working in a team yields incredible results, well done! Keep pushing it! I've been doing good - thanks for asking. I've been exploring some parks on the weekends to relax - it's so peaceful being surrounded by nature. Are there any chill spots you enjoy in Boston?" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a tree with pink flowers in the foreground", - "dia_id": "D8:5", - "text": "Thanks! That studio is great for creativity. I've never been to Boston before, but I hear the parks are amazing. Can't wait to visit next month. Anything cool you remember about Boston parks?" - }, - { - "speaker": "Dave", - "dia_id": "D8:6", - "text": "That sounds great! The Boston parks are awesome, especially in spring. It's so serene when you're walking around. I went for a stroll last Friday and it was amazing. It's so magical - I bet you'll love it! I love taking walks on the weekends, they recharge me for the entire upcoming week!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a path going up a hill with a view of the mountains", - "dia_id": "D8:7", - "text": "I'm excited to experience that serenity. I can't wait to take a walk in the parks and recharge. Hey, have you been on any hikes lately?" - }, - { - "speaker": "Dave", - "dia_id": "D8:8", - "text": "Nah, haven't gone hiking recently, but it's awesome - being in nature and pushing yourself to new heights. Clears your mind and brings a sense of calm. Have you been to the mountains before? Heard they're super chill." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a plane flying over a mountain range with snow on the top", - "dia_id": "D8:9", - "text": "Nah, haven't been to the mountains but I'm keen to go. Looking for a way to escape it all and de-stress. I want to go on a hike to a place similar to this. What's new and exciting happening for you, Dave?" - }, - { - "speaker": "Dave", - "dia_id": "D8:10", - "text": "I booked a trip to a mountainous region for next month! Finally gonna be able to see those majestic peaks! Gonna be an amazing experience!" - }, - { - "speaker": "Calvin", - "dia_id": "D8:11", - "text": "Cool, Dave! Have a great time. I'm sure it's going to be an amazing experience. Take lots of pics and show me when you get back." - }, - { - "speaker": "Dave", - "dia_id": "D8:12", - "text": "Yep, Calvin! Gonna take lots of pics. Can't wait to show you when I get back!" - }, - { - "speaker": "Calvin", - "dia_id": "D8:13", - "text": "Have fun exploring the mountains, Dave! Safe travels and see you soon. Take care!" - }, - { - "speaker": "Dave", - "dia_id": "D8:14", - "text": "Thanks, Calvin! Take care, see you soon!" - } - ], - "session_9_date_time": "3:15 pm on 21 June, 2023", - "session_9": [ - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/awt7u6qj7ok81.jpg" - ], - "blip_caption": "a photo of a red car with a black rim parked on a sidewalk", - "query": "car accident dented side Ferrari", - "dia_id": "D9:1", - "text": "Hi Dave! Nice to hear from you. Since last chat, some things have happened. I had a lil car accident last Friday, kinda upsetting but no one was hurt. I've been dealing with insurance and repairs - taking up a lot of time and energy. Look at this photo!" - }, - { - "speaker": "Dave", - "dia_id": "D9:2", - "text": "Sorry to hear about the car accident, Calvin. I'm glad to hear that nobody was hurt. Dealing with insurance and repairs can definitely be time-consuming and draining. Have you encountered any issues in the insurance process so far?" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a tow truck parked in a parking lot", - "dia_id": "D9:3", - "text": "Hey Dave, thanks for checking in! The insurance process was a hassle - it took forever and there was a ton of paperwork. But it's all sorted now, and I'm getting it fixed up. Can't wait to drive it again!" - }, - { - "speaker": "Dave", - "dia_id": "D9:4", - "text": "How long did it take to get everything sorted out? I'm glad it's all taken care of now!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://cdn2.picryl.com/photo/2014/08/13/alejandro-soto-100th-force-support-squadron-auto-hobby-8fa715-1024.jpg" - ], - "blip_caption": "a photography of a man working on a car in a garage", - "query": "car repair shop mechanic working red car", - "dia_id": "D9:5", - "re-download": true, - "text": "It only took a week to sort everything out. I was worried about the cost, but it wasn't too bad. Look at this photo from the auto repair shop. " - }, - { - "speaker": "Dave", - "dia_id": "D9:6", - "text": "Cool! Glad it wasn't too expensive. Who are him?" - }, - { - "speaker": "Calvin", - "dia_id": "D9:7", - "text": "That's the mechanic. He knows the stuff and is doing his best to get my car running again." - }, - { - "speaker": "Dave", - "dia_id": "D9:8", - "text": "That's great to hear! Skilled and knowledgeable people working on your car - are you feeling confident?" - }, - { - "speaker": "Calvin", - "dia_id": "D9:9", - "text": "Feeling way more confident and excited to show off my car! Trusting their expertise." - }, - { - "speaker": "Dave", - "dia_id": "D9:10", - "text": "Great, Cal! It's awesome when you trust the people fixing it. Sounds like they're doing a great job. It'll be back to normal soon!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/u4cesvhpunb61.jpg" - ], - "blip_caption": "a photo of a view of a small town with a mountain in the background", - "query": "sunset window japanese mansion", - "dia_id": "D9:11", - "text": "Thanks, Dave! Excited to drive again. Appreciate your help! Look what a wonderful view from living room!" - }, - { - "speaker": "Dave", - "dia_id": "D9:12", - "text": "Wow, what an amazing view! Where is it? I haven't seen anything like that before!" - }, - { - "speaker": "Calvin", - "dia_id": "D9:13", - "text": "Thanks, Dave! It's from a small town in Japan. The view of the mountains is unbelievably stunning!" - }, - { - "speaker": "Dave", - "dia_id": "D9:14", - "text": "Wow! Did you get to that place yet?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/37i20ea9fbta1.jpg" - ], - "blip_caption": "a photo of a person on skis on a snowy mountain", - "query": "rocky mountains snow-covered mountain peak dave", - "dia_id": "D9:15", - "text": "Nope, not yet! I haven't been there before, but it's on my to-do list for after my tour with Frank Ocean ends. Can't wait to see them in person! Look at that snowy peak." - }, - { - "speaker": "Dave", - "dia_id": "D9:16", - "text": "Looks awesome! Have you ever tried skiing before? It looks like loads of fun!" - }, - { - "speaker": "Calvin", - "dia_id": "D9:17", - "text": "Haven't tried it before, but it does look like a lot of fun! I might give it a try once. So what's up with you, is anything new in your daily routine?" - }, - { - "speaker": "Dave", - "dia_id": "D9:18", - "img_url": [ - "https://live.staticflickr.com/5174/5420443853_e88b523542_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a group of men playing instruments in a room", - "text": "Yes, Cal! I recently joined a rock band and have been practicing guitar. Look at this photo \u2013 it's us, the guys! " - }, - { - "speaker": "Calvin", - "dia_id": "D9:19", - "text": "Playing guitar in a rock band is awesome, Dave! It will bring you a lot of emotions." - }, - { - "speaker": "Dave", - "dia_id": "D9:20", - "text": "Sounds good, Calvin! Good company and great music, lift the mood and bring a lot of positive emotions! I have to go now. I'll see you soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D9:21", - "text": "Thanks, Dave! let's keep in touch, take care!" - } - ], - "session_10_date_time": "7:56 pm on 7 July, 2023", - "session_10": [ - { - "speaker": "Dave", - "dia_id": "D10:1", - "text": "Hey Calvin, how's the car doing after the crash? You were stoked to get back on the road, right?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA1L2ZsMTY0Mjg5OTcwMDYtaW1hZ2Uta3R4cGR6enkuanBn.jpg" - ], - "blip_caption": "a photography of a red sports car parked on the side of the road", - "query": "ferrari mansion japan", - "dia_id": "D10:2", - "re-download": true, - "text": "Hey Dave! Thanks for checking in. It's all good now, the car's fixed and going strong. Nothing better than cruising around - it's the best! Look at how my car looks right now. How've you been? Anything new?" - }, - { - "speaker": "Dave", - "dia_id": "D10:3", - "text": "Glad to hear your car's fixed and going strong! I've been good too. Just been hanging out with friends at parks lately. I arranged with friends for regular walks together in the park." - }, - { - "speaker": "Calvin", - "dia_id": "D10:4", - "text": "\nThat sounds like a great plan! Regular walks with friends can be a wonderful way to spend time together and stay active. Fresh air and buddies can do wonders. Do you have a favorite spot for hanging out?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://healthywaysfordays.files.wordpress.com/2020/11/61c247f0-cd80-4c81-916c-b905b92a93a3-ecdafc1d-2d3c-4cbb-b64a-258e84518cb3.jpg" - ], - "blip_caption": "a photo of a city skyline with a river and boats in the water", - "query": "sunset boston common park", - "dia_id": "D10:5", - "text": "Yeah, today we are going to that spot. Look at that lovely photo." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://toyamahotnews.files.wordpress.com/2020/09/img-0880.jpg" - ], - "blip_caption": "a photo of a boat is docked in a canal at sunset", - "query": "sunset river view japanese mansion", - "dia_id": "D10:6", - "text": "Wow, what a view! That sunset over the river is gorgeous. It must be so tranquil there. Reminds me of living in my Japanese mansion with the epic cityscape. It's like a dream come true! Look at this photo I took from my backyard of the mansion. It's so beautiful!" - }, - { - "speaker": "Dave", - "dia_id": "D10:7", - "text": "Yeah, the peace by the river is really nice. But living in a Japanese mansion surrounded by that city skyline must be stunning. The views must be amazing!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/al2nryjbov481.jpg" - ], - "blip_caption": "a photo of a large house with a lot of windows and lights", - "query": "city lights view mansion", - "dia_id": "D10:8", - "text": "Ah, it really is beautiful. Whenever I look out the windows and see the city lights, it's so awe-inspiring. Luxury and beauty on a whole new level. Look at the front part of the mansion." - }, - { - "speaker": "Dave", - "dia_id": "D10:9", - "text": "That mansion looks great! I bet the view from inside is stunning. Must be amazing living there. Anything else you're looking forward to doing in Japan?" - }, - { - "speaker": "Calvin", - "dia_id": "D10:10", - "text": "Thanks Dave! Japan is indeed amazing. Can't wait to try the food and check out the culture. Have you ever been there?" - }, - { - "speaker": "Dave", - "dia_id": "D10:11", - "text": "Nope, never been to Japan but I'm so keen to go one day. I've heard it's full of vibes, good eats and awesome tech. Plus, being able to experience the culture would be amazing - I'm hooked on their music!" - }, - { - "speaker": "Calvin", - "dia_id": "D10:12", - "text": "Japan definitely has it all - vibes, food, tech, and an amazing culture. It's like stepping into another world. I've been working on some cool music collaborations with Japanese artists, and I'm really excited to hear how it turns out!" - }, - { - "speaker": "Dave", - "dia_id": "D10:13", - "text": "Cool, Cal! Working with them is a great chance - can't wait for the tunes!" - }, - { - "speaker": "Calvin", - "dia_id": "D10:14", - "text": "Thanks! I'll share some clips when everything's ready. Collaborating with various artists is always exciting, it's a chance to create something unique." - }, - { - "speaker": "Dave", - "dia_id": "D10:15", - "text": "Way to go, Cal! Collaborating with different artists to create something special sounds amazing. Can't wait to see/hear the end product!" - }, - { - "speaker": "Calvin", - "dia_id": "D10:16", - "text": "Thanks, Dave! Appreciate all the help. It's gonna be awesome - can't wait to show you. Great catching up, gotta get back to work now. Take care!" - }, - { - "speaker": "Dave", - "dia_id": "D10:17", - "text": "Hey Cal, take care and don't overwork yourself! Talk to you soon. Stay safe!" - } - ], - "session_11_date_time": "6:38 pm on 21 July, 2023", - "session_11": [ - { - "speaker": "Dave", - "dia_id": "D11:1", - "text": "Hey Cal, been ages since we spoke! Guess what? I just got back from a road trip with my friends - we saw some stunning countryside. It was such a lovely break from the corporate mayhem. Driving on those winding roads, taking in the views, and chatting with my friends recharged me totally - reminds me why I love cars so much. What did you end up doing?" - }, - { - "speaker": "Calvin", - "dia_id": "D11:2", - "text": "Hey Dave! Great hearing from you! Wow, a road trip sounds awesome. I bet it felt great to get away from work and relax on those twisty roads. Recharging with your passion is awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D11:3", - "img_url": [ - "https://cdn2.picryl.com/photo/2014/07/04/my-public-lands-roadtrip-dalton-highway-in-alaska-19315093341-ddcc96-1024.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a person riding a motorcycle down a dirt road", - "text": "It was great to get away and reconnect with my passion. Reminded me why I'm passionate about what I do. Makes the long hours worth it. Here's a pic what a wonderful place we found. Have you had any recent moments that made you remember what you love?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.imgur.com/s0wdXqH.jpg" - ], - "blip_caption": "a photo of a room with a ladder and a ladder in it", - "query": "japanese mansion recording studio construction work progress", - "dia_id": "D11:4", - "text": "I'm happy for you that you have found such an amazing place! Yeah, I'm working on this project to transform a Japanese mansion into a recording studio. It's been my dream to have a space for creating music with other artists. It's my sanctuary that reminds me why I love music. Here's a pic of the progress I made." - }, - { - "speaker": "Dave", - "dia_id": "D11:5", - "text": "Wow, Calvin, this looks amazing! You've made so much progress. Must be very fulfilling to have your own space. What kind of music have you been creating in there?" - }, - { - "speaker": "Calvin", - "dia_id": "D11:6", - "text": "Thanks, Dave! It feels great having my own space to work in. I've been experimenting with different genres lately, pushing myself out of my comfort zone. Adding electronic elements to my songs gives them a fresh vibe. It's been an exciting process of self-discovery and growth!" - }, - { - "speaker": "Dave", - "dia_id": "D11:7", - "text": "Wow, Calvin, that's great! It must be an exciting process of self-discovery and growth to experiment with different genres. Does moving between styles present any challenges?" - }, - { - "speaker": "Calvin", - "dia_id": "D11:8", - "text": "Yeah, switching it up can be tough, but I think it's a matter of finding the right balance between sticking to my sound and trying new stuff. It can be intimidating, but that's what makes it so exciting and keeps me motivated to keep going!" - }, - { - "speaker": "Dave", - "dia_id": "D11:9", - "text": "Yeah, I get it. Finding a balance is tricky but it's gotta keep things interesting. How are you dealing with the pressure and staying motivated?" - }, - { - "speaker": "Calvin", - "dia_id": "D11:10", - "text": "I started making music to follow my dreams, and I'm stoked about how far I've come. Collaborating with others and learning from them keeps me motivated. Surrounding myself with positive energy and passion helps as well." - }, - { - "speaker": "Dave", - "dia_id": "D11:11", - "text": "Sounds like a great plan, Calvin! Surrounding yourself with good vibes and collaborating with others will give you a boost. You've achieved so much so far; keep going, buddy!" - }, - { - "speaker": "Calvin", - "dia_id": "D11:12", - "text": "Thanks, Dave! Your support means a lot to me. I'm gonna keep pushing myself and striving for my goals, so let's chat again soon." - }, - { - "speaker": "Dave", - "dia_id": "D11:13", - "text": "You got this! Keep pushing yourself and never lose sight of your goals. I'm your biggest fan. Let's chat soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D11:14", - "text": "Thanks, Dave! Appreciate your support. Let's catch up soon and chat. Take care!" - } - ], - "session_12_date_time": "1:12 pm on 3 August, 2023", - "session_12": [ - { - "speaker": "Calvin", - "dia_id": "D12:1", - "text": "Hey Dave, long time no see! I just took my Ferrari for a service and it was so stressful. I'm kinda attached to it. Can you relate? What kind of hobbies give you a feeling of being restored?" - }, - { - "speaker": "Dave", - "dia_id": "D12:2", - "text": "Hey Calvin, I understand the stress of getting a car serviced. Fixing cars is like therapy for me. Growing up working on cars with my dad, refurbishing them gives me a sense of fulfillment." - }, - { - "speaker": "Calvin", - "dia_id": "D12:3", - "text": "Wow, Dave, that's awesome! Must feel great to have a hobby that makes you proud. Remember any good memories from working on cars with your dad?" - }, - { - "speaker": "Dave", - "dia_id": "D12:4", - "text": "Yeah, definitely! I have fond memories of working on cars with my dad as a kid. We spent one summer restoring an old car. It was hard work, but seeing the end result and knowing that we did it together was really satisfying." - }, - { - "speaker": "Calvin", - "dia_id": "D12:5", - "text": "That's awesome, Dave! Working together on projects like that really brings people closer. Do you have any pictures from that time?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://live.staticflickr.com/8790/17087850111_a8541de20d_b.jpg" - ], - "blip_caption": "a photography of a man and a child pose for a picture", - "query": "old photo car father son working", - "dia_id": "D12:6", - "re-download": true, - "text": "Yes, I have one, take a look. It was a wonderful experience." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://live.staticflickr.com/4112/5177562445_f1a1e1107b_b.jpg" - ], - "blip_caption": "a photography of a red car is lifted on a lift in a garage", - "query": "car museum japan ferrari", - "dia_id": "D12:7", - "re-download": true, - "text": "Aww, that's cool, Dave. Reminiscing is always fun! That pic you shared takes me back to my trip to the Ferrari dealership. I saw a lot of amazing cars, but as for me, my car is the best and I'm pretty proud of this. Sure, it's just material, but it reminds me of my hard work and dedication. It really inspires me. Take a look at this beauty!" - }, - { - "speaker": "Dave", - "dia_id": "D12:8", - "text": "Your car looks great, Calvin! I can tell why you're proud. Having something like that is motivating. It's like a reminder of what you can achieve." - }, - { - "speaker": "Calvin", - "dia_id": "D12:9", - "text": "Thanks, Dave! Seeing it everyday keeps me motivated and reminds me to keep pushing." - }, - { - "speaker": "Dave", - "dia_id": "D12:10", - "text": "Sounds like you're really motivated, Calvin. What's the biggest goal you're working towards, music-wise or something else?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.pexels.com/photos/2801993/pexels-photo-2801993.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-karl-solano-2801993.jpg" - ], - "blip_caption": "a photo of a band playing on stage with lights on", - "query": "performing on stage musician", - "dia_id": "D12:11", - "text": "My plan for now is to expand my brand worldwide and grow my fanbase. I want my music to reach more people and make an impact. Working with artists from around the globe and challenging myself to create special music are goals of mine too. Look at the photo of how I performed with the boys last night, they are great at the music!" - }, - { - "speaker": "Dave", - "dia_id": "D12:12", - "text": "Wow, Calvin! Working with different artists and crafting great sounds will definitely help you reach your goals. Keep it up and keep making a difference!" - }, - { - "speaker": "Calvin", - "dia_id": "D12:13", - "text": "Thanks, Dave! Your support and encouragement mean a lot to me. I'm determined to make my dreams come true." - }, - { - "speaker": "Dave", - "dia_id": "D12:14", - "text": "Glad to help, Calvin! Eager to see what you do. Keep at it and never forget your dreams!" - }, - { - "speaker": "Calvin", - "dia_id": "D12:15", - "text": "Thanks, Dave! I appreciate your support, it means a lot to me. I'll keep going for my dreams." - }, - { - "speaker": "Dave", - "dia_id": "D12:16", - "text": "No problem, Calvin! Just remember to stay focused and keep going. You've got this!" - }, - { - "speaker": "Calvin", - "dia_id": "D12:17", - "text": "Thanks, Dave! I'll stay focused and keep going. Appreciate your belief!" - } - ], - "session_13_date_time": "5:22 pm on 11 August, 2023", - "session_13": [ - { - "speaker": "Dave", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/0/0f/Two_on_ramps_%2841411586832%29.jpg" - ], - "blip_caption": "a photography of a man standing in front of a car on a lift", - "query": "professional car modification workshop sleek sports car", - "dia_id": "D13:1", - "re-download": true, - "text": "Hey Calvin, been ages! Guess what? I got picked for a car mod workshop. Gonna get better at it and learn something new! Look at the cars I'm working with right now. " - }, - { - "speaker": "Calvin", - "dia_id": "D13:2", - "text": "Wow Dave, that's awesome! Congrats on being picked for the car mod workshop. It's a great opportunity to take your skills up a notch. Are there any mods or projects you're excited to work on?" - }, - { - "speaker": "Dave", - "dia_id": "D13:3", - "text": "Thanks, Calvin! This is a dream come true for me, as I've always wanted to learn auto engineering and work on building a custom car." - }, - { - "speaker": "Calvin", - "dia_id": "D13:4", - "text": "That's awesome, Dave! Pursuing your passion for auto engineering and making a custom car is a great experience. I'm excited to see what you create. Keep it up and have fun every step of the way. What do you think is the most thrilling part of it?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://www.langracing.com/wp-content/uploads/IMG_4878-e1566576759594.jpg" - ], - "blip_caption": "a photo of a car is being worked on by a mechanic", - "query": "car engine rebuild process", - "dia_id": "D13:5", - "text": "Thanks, Calvin! I've found the picture with really thrilling part of the workshop, take a look. Seeing the potential come to life is always satisfying." - }, - { - "speaker": "Calvin", - "dia_id": "D13:6", - "text": "Wow, that sounds like a fulfilling hobby! What kind of transformations have you done so far? How's it going with the current project?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/uduumhtkac191.jpg" - ], - "blip_caption": "a photo of a silver corvette parked in front of a building", - "query": "muscle car body modifications sleek silver paint job", - "dia_id": "D13:7", - "text": "I've been working on this car, doing engine swaps and suspension modifications. Now I'm learning about body modifications. Giving this classic muscle car a modern twist is a challenge but so fun!" - }, - { - "speaker": "Calvin", - "dia_id": "D13:8", - "text": "Wow, Dave, that looks cool! The silver finish gives it a modern twist. Great job!" - }, - { - "speaker": "Dave", - "dia_id": "D13:9", - "text": "Thanks, Calvin! Yeah, I wanted a modern vibe but also that classic muscle car style. Really happy with it!" - }, - { - "speaker": "Calvin", - "dia_id": "D13:10", - "text": "You've really put in some work! That attention to detail is great." - }, - { - "speaker": "Dave", - "dia_id": "D13:11", - "text": "Thanks, Calvin! It's all about those small details that make it unique and personalized." - }, - { - "speaker": "Calvin", - "dia_id": "D13:12", - "text": "Yeah, customizing a masterpiece with those small details is what makes it unique and personalized." - }, - { - "speaker": "Dave", - "dia_id": "D13:13", - "text": "Yeah! It's about showing my style, it's like customizing a work of art on wheels." - }, - { - "speaker": "Calvin", - "dia_id": "D13:14", - "text": "No problem, Dave. Your enthusiasm and hard work show in everything you do. Keep coming up with new cool stuff, bud. I'm excited to see what you'll do next." - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/8ac9fznnof451.jpg" - ], - "blip_caption": "a photo of a desk with a keyboard, monitor, and keyboard pad", - "query": "music studio sound system setup", - "dia_id": "D13:15", - "text": "Cheers, Calvin! Really appreciate your help. I'm gonna keep working hard and coming up with new cool stuff. And if you need a hand with your music stuff, just let me know! Check out my awesome music studio setup with a high-quality sound system. You won't believe how amazing your songs sound here!" - }, - { - "speaker": "Calvin", - "dia_id": "D13:16", - "text": "Thanks for the offer, Dave. I'm super busy with my music stuff at the moment, so I'll keep it in mind. Great work, dude!" - }, - { - "speaker": "Dave", - "dia_id": "D13:17", - "text": "No worries, Calvin. Got it. Good luck with your music!" - }, - { - "speaker": "Calvin", - "dia_id": "D13:18", - "text": "Thanks for the encouragement, Dave. I'll keep working hard and making music. Take care!" - }, - { - "speaker": "Dave", - "dia_id": "D13:19", - "text": "See ya, Calvin! Stay awesome. Catch ya later." - } - ], - "session_14_date_time": "12:35 am on 14 August, 2023", - "session_14": [ - { - "speaker": "Dave", - "dia_id": "D14:1", - "text": "Hey Cal, how's it going? Something cool happened since last we talked - I got to go to a car workshop in San Francisco! So cool to dive into the world of car restoration and see all the different techniques. People were really passionate and dedicated - truly inspiring!" - }, - { - "speaker": "Calvin", - "dia_id": "D14:2", - "text": "Hey Dave! That's awesome! Car workshops sound totally cool and inspiring. Glad you found something that really gets you going." - }, - { - "speaker": "Dave", - "dia_id": "D14:3", - "text": "It's always great to learn and grow. So, what have you been up to lately?" - }, - { - "speaker": "Calvin", - "dia_id": "D14:4", - "text": "As you know, I had an amazing experience touring with a well-known artist. The feeling of performing and connecting with the audience was unreal. We ended with a show in Japan and then I had the opportunity to explore my new place - it's like a dream come true!" - }, - { - "speaker": "Dave", - "dia_id": "D14:5", - "text": "Wow, Calvin! I bet playing for an eager audience was an incredible experience. Exploring Japan must have been amazing! How did Tokyo react to your music? Did you have any special moments there?" - }, - { - "speaker": "Calvin", - "dia_id": "D14:6", - "text": "Thanks, Dave! That show in Tokyo was awesome! Everyone was so into it when I played one of my songs and sang along. It was a magical moment." - }, - { - "speaker": "Dave", - "dia_id": "D14:7", - "text": "Wow, Calvin, sounds amazing! Got any pictures from that show? Would love to see the atmosphere." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://c1.wallpaperflare.com/preview/415/916/987/audience-band-concert-crowd.jpg" - ], - "blip_caption": "a photography of a crowd of people at a concert with their hands up", - "query": "show tokyo hands in the air colorful lights stage", - "dia_id": "D14:8", - "re-download": true, - "text": "Yeah, here's a pic I took. It was so awesome - I loved how the crowd was into it. These moments are why I love my job." - }, - { - "speaker": "Dave", - "dia_id": "D14:9", - "text": "The energy in that pic is awesome! Seeing everyone with their hands up must have been really cool. It's great when your passion brings joy to others!" - }, - { - "speaker": "Calvin", - "dia_id": "D14:10", - "text": "Yeah, that was buzzing! It's moments like these that make me so proud and motivated. I'm all about spreading joy with my art. So, how's your project going?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://ktul.com/resources/media/b036c339-3408-4a16-baf1-841d27c80d13-full1x1_PhotoJan2812945PM1.jpg" - ], - "blip_caption": "a photo of a car with a broken engine in the woods", - "query": "car restoration vintage ford mustang", - "dia_id": "D14:11", - "text": "Yeah, the project is going great! Here's a pic of the car I'm restoring. It's pretty cool seeing it go from a beat-up mess to a real beauty." - }, - { - "speaker": "Calvin", - "dia_id": "D14:12", - "text": "Wow Dave, that car is going to look amazing when you're done. How long do you think it will take to restore it?" - }, - { - "speaker": "Dave", - "dia_id": "D14:13", - "text": "Thanks, Calvin! Fingers crossed it'll be all fixed up by the end of next month. Lots of elbow grease but it'll be worth the transformation." - }, - { - "speaker": "Calvin", - "dia_id": "D14:14", - "text": "Wow, Dave, I'm really excited to see the end result of your hard work! You've got some serious talent!" - }, - { - "speaker": "Dave", - "dia_id": "D14:15", - "text": "Thanks, Cal! Your support means a lot to me." - }, - { - "speaker": "Calvin", - "dia_id": "D14:16", - "text": "You got this, Dave! I'm always here for you. Seeing your enthusiasm is amazing. Keep growing, buddy. It'll be worth it! Good luck with that. See ya!" - } - ], - "session_15_date_time": "11:06 am on 22 August, 2023", - "session_15": [ - { - "speaker": "Dave", - "dia_id": "D15:1", - "img_url": [ - "https://live.staticflickr.com/189/516664633_81fce2dc06_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a group of men sitting at a table with playing cards", - "text": "Hey Calvin! Haven't talked in a while! Last Friday I had a card-night with my friends, it was so much fun. We laughed and had a great time! Take a look at the photo!" - }, - { - "speaker": "Calvin", - "dia_id": "D15:2", - "text": "Hey Dave! Great to hear from you, card night sounds like a blast! Always love having fun with friends. Guess what? I scored a deal to continue collaboration with Frank Ocean! This is a dream come true for me, I've been working hard and it's finally paying off. No words can describe how happy I am." - }, - { - "speaker": "Dave", - "dia_id": "D15:3", - "text": "Wow Calvin, congrats! Super excited for you. How did this chance come up?" - }, - { - "speaker": "Calvin", - "dia_id": "D15:4", - "text": "Thanks, Dave! I had the opportunity to meet Frank Ocean at a music festival in Tokyo and we clicked. We've been speaking and sharing ideas we arranged a meeting and recorded a song in the studio at my mansion - it's been great!" - }, - { - "speaker": "Dave", - "dia_id": "D15:5", - "text": "Wow, Tokyo sounds like an incredible experience!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/clg582472ta91.jpg" - ], - "blip_caption": "a photo of a crowd of people sitting on the grass at night", - "query": "city festival crowd energy vibe", - "dia_id": "D15:6", - "text": "It was an incredible experience, Dave! The city was buzzing with energy and the festival crowd was so lively - it was like fuel for my soul. Look at this photo, that's amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D15:7", - "text": "Wow, that sounds incredible! What was it like playing at the festival? Must have been an amazing experience." - }, - { - "speaker": "Calvin", - "dia_id": "D15:8", - "text": "It was a dream come true, Dave! The festival had buzzing energy and a super upbeat crowd. We all felt connected, it was amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D15:9", - "text": "Sounds like you had a blast! Did any other collaborations come out of that performance? Any upcoming concerts we should know about?" - }, - { - "speaker": "Calvin", - "dia_id": "D15:10", - "text": "Yeah Dave, someone even noticed my performance and now we're working together, which is wild. My tour ends soon and I'm heading to Boston. Maybe we can meet up then!" - }, - { - "speaker": "Dave", - "dia_id": "D15:11", - "text": "Sounds great! Fill me in on the details when you have them and let's meet up when you're here. Can't wait to hear all about everything!" - }, - { - "speaker": "Calvin", - "dia_id": "D15:12", - "text": "Of course, Dave can't wait to catch up! I almost forgot, yesterday my friends and I recorded a podcast where we discuss the rapidly evolving rap industry!" - }, - { - "speaker": "Dave", - "dia_id": "D15:13", - "text": "That sounds fantastic! I'm looking forward to listening to your podcast and learning more about the rap industry. Keep up the great work!" - }, - { - "speaker": "Calvin", - "dia_id": "D15:14", - "text": "I'll let you know when the podcast will be uploaded. Take care!" - }, - { - "speaker": "Dave", - "dia_id": "D15:15", - "text": "Sure, Calvin! Looking forward to hearing about it. See ya then!" - } - ], - "session_16_date_time": "2:55 pm on 31 August, 2023", - "session_16": [ - { - "speaker": "Dave", - "blip_caption": "a photo of a crowd of people at a concert with their hands in the air", - "dia_id": "D16:1", - "text": "Hey Calvin! Long time no chat! How was the end of your tour? I bet it was amazing!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://www.wavy.com/wp-content/uploads/sites/3/2023/04/20230430_023901861_iOS-1.jpg" - ], - "blip_caption": "a photo of a crowd of people watching a concert with a large screen", - "query": "stage crowd energizing connection", - "dia_id": "D16:2", - "text": "Hey Dave! The tour was amazing! I was so pumped from all the energy from the audience. This pic totally captures how I felt. Such an amazing time!" - }, - { - "speaker": "Dave", - "dia_id": "D16:3", - "text": "Wow, that looks amazing! How was it performing on that big stage?" - }, - { - "speaker": "Calvin", - "dia_id": "D16:4", - "text": "Performing on such a big stage was a dream come true! The energy was incredible and I felt on top of the world. It was seriously surreal." - }, - { - "speaker": "Dave", - "dia_id": "D16:5", - "text": "Wow, that must have been an incredible experience! Congrats on achieving such an awesome moment!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://indiestonia413850954.files.wordpress.com/2019/01/mvimg_20181118_091403_12.jpg" - ], - "blip_caption": "a photo of a camera and a video camera on a beach", - "query": "album video shoot set crew members", - "dia_id": "D16:6", - "text": "Cool! Last weekend I started shooting a video for my new album - can't wait for you to check it out! Look how great it was!" - }, - { - "speaker": "Dave", - "dia_id": "D16:7", - "text": "Wow, Calvin! Can't wait to see it. Will it be shot on location?" - }, - { - "speaker": "Calvin", - "dia_id": "D16:8", - "text": "We're doing the shoot in Miami and picked an awesome beach. It's going to have some epic visuals!" - }, - { - "speaker": "Dave", - "dia_id": "D16:9", - "text": "Sounds great, Calvin! The beaches in Miami are awesome - can't wait to see them!" - }, - { - "speaker": "Calvin", - "dia_id": "D16:10", - "text": "Oh man, they are amazing! I love the Miami vibe, it's perfect for the vid. Can't wait to show you!" - }, - { - "speaker": "Dave", - "dia_id": "D16:11", - "text": "Looking forward to it! I'm excited to see the vibe and the location. Let me know if you need any help with props or anything else for the video. I'm here to support you!" - }, - { - "speaker": "Calvin", - "dia_id": "D16:12", - "text": "Thanks, Dave! I'll let you know if I need any assistance with props or anything else. Your support is much appreciated." - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.pinimg.com/originals/a9/a5/b5/a9a5b5be5f815beb22bd11caf542d827.jpg" - ], - "blip_caption": "a photo of a guitar with a octopus on it", - "query": "paintbrush guitar pick art", - "dia_id": "D16:13", - "text": "Sure, let me know when, I'm here to lend a hand. It's great to fuel your ideas. Remember that photo you sent me once? Love how this guitar shows our different artistic styles." - }, - { - "speaker": "Calvin", - "dia_id": "D16:14", - "text": "Yes Dave, I remember! I had this custom made by my Japanese artist friend. It's got an octopus on it, which represents my love for art and the sea. It's one of my favorites!" - }, - { - "speaker": "Dave", - "dia_id": "D16:15", - "text": "That's a great guitar, Calvin! Love the design, it's so unique and special." - }, - { - "speaker": "Calvin", - "dia_id": "D16:16", - "text": "Cheers, mate! Really appreciate it. This guitar means so much to me; it's a reminder of my passion for music and the amazing friendships I've made." - }, - { - "speaker": "Dave", - "dia_id": "D16:17", - "text": "Wow, Calvin, this instrument obviously means a lot to you - it's like a representation of your journey, your passion for music, and the friendships you've made. Amazing!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.pexels.com/photos/14436042/pexels-photo-14436042.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-carter-nguyen-14436042.jpg" - ], - "blip_caption": "a photo of a guitar with a purple glow on it", - "query": "guitar close-up", - "dia_id": "D16:18", - "text": "Yeah, Dave! It's like every mark and strum holds a story. Take a look." - }, - { - "speaker": "Dave", - "dia_id": "D16:19", - "text": "That guitar has a gorgeous purple hue. Why did you make it so shiny?" - }, - { - "speaker": "Calvin", - "dia_id": "D16:20", - "text": "I got it customized with a shiny finish because it gives it a unique look. Plus, it goes with my style." - }, - { - "speaker": "Dave", - "dia_id": "D16:21", - "text": "Good pick! The customized purple glow gives it a unique look that really stands out." - }, - { - "speaker": "Calvin", - "dia_id": "D16:22", - "text": "Thanks, dude! I dig how it's so unique. It's totally my style!" - }, - { - "speaker": "Dave", - "dia_id": "D16:23", - "text": "Looks great on you! Your unique style really stands out in your music and playing." - }, - { - "speaker": "Calvin", - "dia_id": "D16:24", - "text": "Thanks, Dave! Trying to stay true to myself and be unique in my music is something that I value, so it's really great to hear that you appreciate it." - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a man working on a car engine in a garage", - "dia_id": "D16:25", - "text": "Definitely, staying true to yourself and your style is vital. It makes you unique and your music stand out. Keep it real, bud. You've got something great to give the world." - } - ], - "session_17_date_time": "9:19 am on 2 September, 2023", - "session_17": [ - { - "speaker": "Dave", - "dia_id": "D17:1", - "text": "Hey Calvin! Been a while, what's up? I'm tied up with car stuff lately, yesterday I came back from San Francsico with some great insights and knowledge on car modification that I want to share with you! Changing things around, and giving an old car a new life - so satisfying!" - }, - { - "speaker": "Calvin", - "dia_id": "D17:2", - "text": "Hey Dave! Nice to hear from you. That's cool! I totally understand the satisfaction you get from fixing cars. It's like you're giving them new life." - }, - { - "speaker": "Dave", - "dia_id": "D17:3", - "text": "Yeah, it's great fixing stuff up and seeing it turn out better. It's really rewarding and gives me a sense of purpose. Plus, it feels like I'm making a difference when I fix someone's car." - }, - { - "speaker": "Calvin", - "dia_id": "D17:4", - "text": "Wow, you must feel great making a real difference in someone's life, like being their superhero!" - }, - { - "speaker": "Dave", - "dia_id": "D17:5", - "text": "Yeah, it's great! It feels really good to make a difference and see their relief when their car is fixed. Makes me proud!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/3n0tonj6zg821.jpg" - ], - "blip_caption": "a photo of a book with a boarding pass and a boarding pass", - "query": "plane ticket boston", - "dia_id": "D17:6", - "text": "Wow, Dave, that's awesome! You should be really proud of yourself for bringing joy to others. I booked a flight ticket to Boston last week! I'm so excited about my upcoming trip to Boston. Look at this! See you soon, buddy!" - }, - { - "speaker": "Dave", - "dia_id": "D17:7", - "text": "Cool! Let me know when you're free and we can catch up in Boston." - }, - { - "speaker": "Calvin", - "dia_id": "D17:8", - "text": "Yeah, for sure! I'll let you know when I'm in Boston. See you soon!" - }, - { - "speaker": "Dave", - "dia_id": "D17:9", - "text": "Looking forward to seeing you! Have a safe trip, see ya!" - }, - { - "speaker": "Calvin", - "dia_id": "D17:10", - "text": "Thanks, Dave! Gotta stay safe on the trip. Can't wait to see you there! I will contact you when I arrive. Goodbye!" - } - ], - "session_18_date_time": "10:56 am on 13 September, 2023", - "session_18": [ - { - "speaker": "Calvin", - "dia_id": "D18:1", - "text": "Hey Dave! Sorry it took me so long to get back to you. Crazy times since we talked! My album finally dropped on the 11th and it was a wild feeling. Everyone's been loving it and it's motivated me to keep going." - }, - { - "speaker": "Dave", - "dia_id": "D18:2", - "text": "Hey Calvin! Congrats on your album release - that's awesome! Has it been overwhelming or inspiring?" - }, - { - "speaker": "Calvin", - "dia_id": "D18:3", - "text": "Thanks, Dave! It's been a lot. Seeing everyone get behind it has been awesome. It's kinda overwhelming to think so many appreciate it. It's also cool that it's connecting with people. It really motivates me to make even better music." - }, - { - "speaker": "Dave", - "dia_id": "D18:4", - "text": "That's awesome, Calvin! It's such a great feeling when your work gets noticed and makes a positive difference. It must be really motivating for you to keep chasing your dreams." - }, - { - "speaker": "Calvin", - "dia_id": "D18:5", - "text": "Yeah, moments like this remind me why I got into music - making a difference and sharing my own story. It's exciting to get positive feedback and it gives me strength to keep going and reach more people. My journey's just getting started!" - }, - { - "speaker": "Dave", - "dia_id": "D18:6", - "text": "Wow, Calvin! You've come a long way since we started talking music. What's next for you? Anything exciting coming up?" - }, - { - "speaker": "Calvin", - "dia_id": "D18:7", - "text": "Thanks Dave! Lots of cool stuff happening. Next up, a tour - so excited! After that, I'm off to explore and grow my brand. Can't wait to see what the future has in store!" - }, - { - "speaker": "Dave", - "dia_id": "D18:8", - "text": "Wow, Calvin! Congrats on the upcoming tour! Can't wait to see you perform. Do you have any cities or venues in mind?" - }, - { - "speaker": "Calvin", - "dia_id": "D18:9", - "text": "Yay! Dave! We're hitting some awesome spots - it's gonna be epic!" - }, - { - "speaker": "Dave", - "dia_id": "D18:10", - "text": "That sounds awesome, Calvin! Live music is the best. I'm sure you're gonna have a blast on tour and make some awesome memories. If you ever want to check out my garage and see some cool cars, I'd love to show you around. Keep rockin'!" - }, - { - "speaker": "Calvin", - "dia_id": "D18:11", - "text": "Thanks, Dave! I'll definitely take you up on that offer. It would be fun to check out your garage and maybe even get some ideas for future projects. I'll let you know when I'm in Boston. Keep pursuing your passions and keep those cars rolling. You rock!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://d2dsc1gf0t80gb.cloudfront.net/wp-content/uploads/2017/06/07180008/RobShingleGarage-11-1000x750.jpg" - ], - "blip_caption": "a photo of a car in a garage with a coca cola sign", - "query": "garage vintage cars", - "dia_id": "D18:12", - "re-download": true, - "text": "Thanks, Calvin! Appreciate the kind words and support. Can't wait for your visit! Take care and keep creating amazing music! Check out pic of my garage, it looks stunning!" - }, - { - "speaker": "Calvin", - "dia_id": "D18:13", - "text": "Thanks! I can't wait for your visit either. Take care and keep enjoying your hobbies!" - }, - { - "speaker": "Dave", - "dia_id": "D18:14", - "text": "Sure thing! Thanks again for your help. Bye! Have a great day." - }, - { - "speaker": "Calvin", - "dia_id": "D18:15", - "text": "No problem! Always good chatting with you. Have an awesome day!" - }, - { - "speaker": "Dave", - "dia_id": "D18:16", - "text": "Thanks, Calvin! Catch you later. Have a great day!" - } - ], - "session_19_date_time": "12:13 am on 15 September, 2023", - "session_19": [ - { - "speaker": "Dave", - "dia_id": "D19:1", - "img_url": [ - "https://c1.wallpaperflare.com/preview/76/142/466/concert-performance-hard-rock-guitarist.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a band performing on stage with bright lights", - "text": "Hey Calvin! Long time no talk! Got some cool news to share - last night was a blast! My band and I were jamming and the music just kept flowing. We had so much fun rehearsing! Also, last weekend, I had the opportunity to attend a rock concert here in Boston. Just take a look at what an amazing atmosphere it was!" - }, - { - "speaker": "Calvin", - "dia_id": "D19:2", - "text": "Hey Dave! That's awesome! When everything just clicks during a jam session, it's such a great feeling. Do you happen to have any recordings of those sessions? That sounds fantastic! Rock concerts always have such an electrifying atmosphere! " - }, - { - "speaker": "Dave", - "dia_id": "D19:3", - "text": "Hey Calvin! I wish we had recorded the jam, but we were way too into it and totally forgot." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i0.wp.com/backtothemouse.com/wordpress/wp-content/uploads/2019/06/IMG_5098-1.png" - ], - "blip_caption": "a photo of a poster for a disney movie with a chef", - "query": "magical moment photo", - "dia_id": "D19:4", - "text": "Aww, bummer! I would've loved to hear that music. Oh well, some of the best memories can't be captured on video or audio. It's like those special moments that stay in our hearts and minds. Take a look at this cool Disney poster!" - }, - { - "speaker": "Dave", - "dia_id": "D19:5", - "text": "Wow, that's a gorgeous poster! It reminds me of one of my favorite Disney movies. I love the lesson it teaches about following your dreams no matter what others say. Have you seen it yet?" - }, - { - "speaker": "Calvin", - "dia_id": "D19:6", - "text": "Thanks! Ratatouille is one of my favorites! It's all about going after what you love, no matter what anyone says. Last time we talked, you mentioned your car hobby - how's that going?" - }, - { - "speaker": "Dave", - "dia_id": "D19:7", - "img_url": [ - "https://c2.peakpx.com/wallpaper/949/672/879/automobile-mustang-old-car-wallpaper-preview.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a man standing next to a classic car", - "text": "Thanks, Calvin! It's been awesome. Been restoring this vintage beauty - here is the final result pic, take a look!" - }, - { - "speaker": "Calvin", - "dia_id": "D19:8", - "text": "Nice work, Dave! That looks amazing. Is it satisfying to see your hard work pay off?" - }, - { - "speaker": "Dave", - "dia_id": "D19:9", - "text": "Thanks, Calvin! It's so satisfying to see this brought back to life, especially with people's reactions when they see the finished product - makes all the hard work worth it." - }, - { - "speaker": "Calvin", - "dia_id": "D19:10", - "text": "Dave, it's awesome seeing people happy thanks to you! Fixing cars is such an art. You're inspiring - keep up the good work!" - }, - { - "speaker": "Dave", - "dia_id": "D19:11", - "text": "Thanks, Calvin! It means a lot that you appreciate what I do. I'm glad that I can make people happy and that's what I'm gonna keep doing. Got to go now, I have a lot of work to do! Take care!" - }, - { - "speaker": "Calvin", - "dia_id": "D19:12", - "text": "You're really talented, Dave. Keep making people happy and doing what you love. That's what it's all about. See you soon, have a nice one!" - } - ], - "session_20_date_time": "8:57 pm on 22 September, 2023", - "session_20": [ - { - "speaker": "Dave", - "dia_id": "D20:1", - "img_url": [ - "https://cdn12.picryl.com/photo/2016/12/31/ford-xl-1967-restored-motor-v8-345-hp-transportation-traffic-2d0301-1024.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a car engine with a small engine", - "text": "Hey Calvin, good to catch up again! Had a tough time with my car project. Worked on the engine of the vintage Mustang, thought I'd fixed it, but when I started it up, heard a weird noise. So disappointing after putting so much work in. Take a look at the engine!" - }, - { - "speaker": "Calvin", - "dia_id": "D20:2", - "img_url": [ - "https://live.staticflickr.com/4475/37494593854_e7b70510d7_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a red sports car parked in a parking lot", - "text": "Hey Dave, sorry to hear about the car trouble. That must be disappointing after putting so much work into it. Yeah, fixing cars can be tricky. But hey, check out this pic! It's a sign of my hard work and dedication. Looking at it reminds me how far I've come." - }, - { - "speaker": "Dave", - "dia_id": "D20:3", - "text": "Wow, thanks Calvin! I love the way the sunlight hits this. It's definitely a great symbol of all your hard work. I bet you feel really proud when you look at it. Any special memories with it?" - }, - { - "speaker": "Calvin", - "dia_id": "D20:4", - "text": "Yeah, Dave, I had an amazing drive one summer day. The wind blowing through my hair and the rush of freedom - it was unforgettable. I've pondered the intricacies of life, taking a moment to truly appreciate the path I've chosen and the decisions that have shaped my journey. Also, my trip to Japan was incredible - the culture, the people, everything was so cool. I'm already longing to go back, it was an experience that made all the hard work worth it." - }, - { - "speaker": "Dave", - "dia_id": "D20:5", - "text": "Wow, Calvin, that's awesome! That feeling of freedom in the summer is the best. A moment of reflection not only makes the journey interesting but also productive! Hey, any songs from your childhood that bring back memories?" - }, - { - "speaker": "Calvin", - "dia_id": "D20:6", - "text": "Yeah, there's this one song that always makes me smile. It played during a road trip with my dad and we had so much fun singing along to it." - }, - { - "speaker": "Dave", - "dia_id": "D20:7", - "text": "Sounds awesome, Calvin! Road trips with family are always a treat. What's the name of the song?" - }, - { - "speaker": "Calvin", - "dia_id": "D20:8", - "text": "We used to rock a song by Tupac and Dr. Dre called \"California Love\". Ah, those were the days!" - }, - { - "speaker": "Dave", - "dia_id": "D20:9", - "text": "Sounds great, Cal! \"California Love\" is a classic! Let me know if you wanna jam some music together!" - }, - { - "speaker": "Calvin", - "dia_id": "D20:10", - "text": "Yeah, let's do it! That would be awesome." - }, - { - "speaker": "Dave", - "dia_id": "D20:11", - "text": "I'm stoked, Calvin! We can crank up the music and have an awesome jam session. Can't wait!" - }, - { - "speaker": "Calvin", - "dia_id": "D20:12", - "text": "Yeah, Dave! Gonna be great! Can't wait to see what we can do." - }, - { - "speaker": "Dave", - "dia_id": "D20:13", - "text": "Yeah, let's do it! It'll be awesome. Let's rock it!" - }, - { - "speaker": "Calvin", - "dia_id": "D20:14", - "text": "Yeah, let's do this! I can't wait!" - }, - { - "speaker": "Dave", - "dia_id": "D20:15", - "text": "Nice one, Calvin! Let's make some awesome memories and have a great time. See ya soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D20:16", - "text": "Count me in. Can't wait to create something special. See you soon!" - }, - { - "speaker": "Dave", - "dia_id": "D20:17", - "text": "Yep, Calvin! Can't wait to see you soon. Take it easy till then." - } - ], - "session_21_date_time": "2:44 pm on 4 October, 2023", - "session_21": [ - { - "speaker": "Calvin", - "dia_id": "D21:1", - "text": "Hey Dave! Yesterday I met with some incredible artists in Boston and we talked about working together. It was such an inspiring and exciting experience - they all have individual styles and I'm stoked to collaborate with them on new music." - }, - { - "speaker": "Dave", - "dia_id": "D21:2", - "text": "Awesome, Calvin! Connecting with all those talented artists must have been an inspiring experience. Can't wait to hear what you come up with in your collaboration. Let me know how it goes! Also, how did you arrange that meeting?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://dealeraccelerate-all.s3.amazonaws.com/qmm/marketing_assets/328/IMG_4020.jpg" - ], - "blip_caption": "a photo of a shiny orange car with a hood open", - "query": "sleek vintage car restoration", - "dia_id": "D21:3", - "text": "Hey Dave, it was awesome talking to those artists! Our mutual friend knew we'd be a great fit. Can't wait to show you the final result. Also, check out this project - I love working on it to chill out. How about you? Got any hobbies to help you relax?" - }, - { - "speaker": "Dave", - "dia_id": "D21:4", - "text": "Wow, Calvin, that car looks great! Working on cars really helps me relax, it's therapeutic to see them come back to life. I've been working on that Ford Mustang I found in a junkyard - it was in bad shape, but I knew it had potential." - }, - { - "speaker": "Calvin", - "dia_id": "D21:5", - "text": "Wow, Dave! It's awesome that you can bring things back to life. Do you have any pictures of it looking amazing? I'd love to see how it turned out!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ_Tnd67jTFU3qlXn69Kt_Fm60e2ZKQpgEkgg&usqp=CAU" - ], - "blip_caption": "a photography of a red car parked in a field with other cars", - "query": "classic muscle car fully restored shining sunlight", - "dia_id": "D21:6", - "re-download": true, - "text": "Hey Calvin, check out this photo! I put in a lot of work restoring it, but the result is awesome. It's so satisfying to bring an old car back to life." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a person's hand with dirty hands next to a car", - "dia_id": "D21:7", - "text": "We've been greatly privileged to have been granted this opportunity. It's so satisfying to bring it back to life! " - }, - { - "speaker": "Dave", - "dia_id": "D21:8", - "img_url": [ - "https://images.rawpixel.com/image_800/czNmcy1wcml2YXRlL3Jhd3BpeGVsX2ltYWdlcy93ZWJzaXRlX2NvbnRlbnQvZmw1MDgwNjQwMzY3LWltYWdlLWtxYXBveHhoLmpwZw.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a person's hands with dirt on them", - "text": "Wow, it's so satisfying! Here are my hands after a day in the garage - permanently stained with grease. But it's worth it when you see the end result." - }, - { - "speaker": "Calvin", - "dia_id": "D21:9", - "text": "Yeah, Dave! Those hands show you worked hard. You put in lots of effort. You should definitely be proud!" - }, - { - "speaker": "Dave", - "dia_id": "D21:10", - "text": "Thanks, Calvin. I love being able to transform something old and beat-up into something beautiful. It's the small successes that make me feel proud and fulfilled." - }, - { - "speaker": "Calvin", - "dia_id": "D21:11", - "text": "Yeah, those little wins matter. They give us a sense of accomplishment and bring us joy. It's truly inspiring to see how much we can grow." - }, - { - "speaker": "Dave", - "dia_id": "D21:12", - "text": "Sure, Calvin! It's awesome seeing the progress and development, both in our projects and ourselves. Hard work really does pay off!" - }, - { - "speaker": "Calvin", - "dia_id": "D21:13", - "text": "Yeah, hard work and dedication are definitely key to reaching our goals and potential. It's awesome to see our growth and progress." - }, - { - "speaker": "Dave", - "dia_id": "D21:14", - "text": "Yeah, it's great to see our progress. It's really motivating and keeps me pushing for more." - }, - { - "speaker": "Calvin", - "dia_id": "D21:15", - "text": "Agreed, Dave! Progress is what keeps us motivated and pushing for more. Let's never give up and keep striving for success. We know that hard work and determination matter, and it's what sets us apart. Onwards to our goals!" - }, - { - "speaker": "Dave", - "dia_id": "D21:16", - "text": "Let's keep going! We won't lose focus on our goals. Hard work and determination will get us there. Let's do this!" - }, - { - "speaker": "Calvin", - "dia_id": "D21:17", - "text": "Yeah, let's do it! Let's stay focused and work hard to make our dreams happen. We can make it happen together! Wishing you all the best until we meet again!\n\n\n\n\n " - }, - { - "speaker": "Dave", - "dia_id": "D21:18", - "text": "Yep, Calvin! Together, we can do amazing things if we work together and stay motivated. We got this! Take care and stay well!" - } - ], - "session_22_date_time": "3:13 pm on 8 October, 2023", - "session_22": [ - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/5835359/pexels-photo-5835359.jpeg" - ], - "blip_caption": "a photography of two men looking at a car engine", - "query": "car show vintage mustang open hood", - "dia_id": "D22:1", - "re-download": true, - "text": "Hey Calvin! What\u2019s up? Last Friday I went to the car show. I saw some awesome cars and got to mess with car mods! There were so many cool machines around, it was so much fun! Take a look at this beautiful car!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a red sports car parked in a showroom", - "dia_id": "D22:2", - "text": "Hey Dave, that sounds awesome! I'm into the rush of awesome cars. Can't wait to check out your garage." - }, - { - "speaker": "Dave", - "dia_id": "D22:3", - "text": "Thanks! Yeah, this one looks great! I restored and modified it myself and added a custom exhaust and some performance upgrades. It's got a sweet sound and I'm really proud of how it turned out." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a car driving down a street with a traffic light", - "dia_id": "D22:4", - "text": "Wow, Dave! You really turned it into a masterpiece. Impressive!" - }, - { - "speaker": "Dave", - "dia_id": "D22:5", - "text": "Thanks Calvin! I've spent a lot of time and effort on it. It's not just a hobby, it's a passion. It's like therapy, a way to get away from everyday stress. When I was little I'd spend hours in my dad's garage, tinkering with engines - it was like my own sanctuary. Now I'm lucky enough to do this as a job, to take something broken and make it into something awesome." - }, - { - "speaker": "Calvin", - "dia_id": "D22:6", - "text": "Wow, Dave, that's amazing. Bringing broken things back to life is so satisfying. Working on cars can be a real escape from reality, and I understand that feeling. Doing what you love for a living, that's the ultimate goal, right? Keep going with it, it's really inspiring." - }, - { - "speaker": "Dave", - "dia_id": "D22:7", - "text": "Thanks, Calvin. It's been my goal since I was a kid and it's awesome to be able to do something I love. Restoring things like this can be tough but the feeling of accomplishment it gives is great. Absolutely, I'm loving it." - }, - { - "speaker": "Calvin", - "dia_id": "D22:8", - "text": "Go for it, Dave! Chasing your dreams is what life's about. It's awesome to see how far you've come. Keep working hard and living your best life." - }, - { - "speaker": "Dave", - "dia_id": "D22:9", - "text": "Thanks, Calvin! Means a lot. I'm going to keep chasing my dreams and working hard. Conversations like this remind me why I love what I do." - }, - { - "speaker": "Calvin", - "dia_id": "D22:10", - "text": "Glad I can remind you, Dave. Keep up the good work and stay focused. You got this!" - }, - { - "speaker": "Dave", - "dia_id": "D22:11", - "text": "Thanks, Calvin! Your support really means a lot. I'll stay focused and keep going. Appreciate the encouragement!" - }, - { - "speaker": "Calvin", - "dia_id": "D22:12", - "text": "Yeah Dave! I'll always be here to support you and give you that boost. You're doing great!" - }, - { - "speaker": "Dave", - "dia_id": "D22:13", - "text": "Thanks so much, Calvin. Your support means everything to me. I'll keep pushing and reaching for them." - }, - { - "speaker": "Calvin", - "dia_id": "D22:14", - "text": "No worries, Dave. Keep going for it. You got this!" - } - ], - "session_23_date_time": "9:39 am on 15 October, 2023", - "session_23": [ - { - "speaker": "Dave", - "dia_id": "D23:1", - "text": "Hey Cal, miss ya! Crazy rollercoaster week. A competing car maintenance shop snagged a deal we were trying to secure for months and it made me feel kinda bummed out. You know, I put in so much effort at work, but it feels like nothing. Am I wasting my time?" - }, - { - "speaker": "Calvin", - "dia_id": "D23:2", - "text": "Hey Dave, sorry to hear that. It can be discouraging when you feel like your hard work goes unnoticed. But don't give up, keep pushing and believe in yourself. The payoff will be worth it." - }, - { - "speaker": "Dave", - "dia_id": "D23:3", - "text": "Calvin, thanks for the encouragement. It can be tough when you feel like your efforts are going unseen. I gotta have faith and patience. I'm sure it's only a matter of time till things work out. How do you stay motivated when faced with setbacks?" - }, - { - "speaker": "Calvin", - "dia_id": "D23:4", - "text": "When setbacks come my way, I remind myself why I'm passionate about my goals. I rely on helpful people around me and take a break to recharge with my favorite activities. That always helps me get back to feeling motivated." - }, - { - "speaker": "Dave", - "dia_id": "D23:5", - "text": "That's a great approach, Cal! Reminding yourself of the passion for the goals and getting help from others is really important. Taking a break and having fun sounds so refreshing. Oh, I just got back from a music festival - it was amazing! The energy, the music, the crowd - sooo good. I felt so alive!" - }, - { - "speaker": "Calvin", - "dia_id": "D23:6", - "text": "Wow Dave, sounds awesome! Music festivals bring so much joy and the energy of the crowd can be amazing. Got any photos from the festival? I'd love to check them out and join in on the fun." - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.ibb.co/TmFqMDj/6-A8-A0-DE6-CD05-4-EB3-BD92-CFB4-DCF2-CB3-C.jpg" - ], - "blip_caption": "a photo of a stage with a crowd of people watching a band", - "query": "music festival crowd lights atmosphere main stage band energy", - "dia_id": "D23:7", - "text": "Yep! I got this awesome pic from the event. The main stage was unreal. The headliner was so good and the vibe was unreal!" - }, - { - "speaker": "Calvin", - "dia_id": "D23:8", - "text": "Wow, that looks awesome! The crowd looks really excited and the stage is incredible. Who was the headliner?" - }, - { - "speaker": "Dave", - "dia_id": "D23:9", - "text": "The Fireworks headlined the festival." - }, - { - "speaker": "Calvin", - "dia_id": "D23:10", - "text": "Wow, I heard great things about The Fireworks! Performing with Frank Ocean recently has been really cool. Seeing them perform live must've been awesome - I bet the energy was electric! That's why I love my job so much - connecting with the crowd." - }, - { - "speaker": "Dave", - "dia_id": "D23:11", - "text": "Yeah, Calvin! The crowd had such a buzz. Music brings people together in such an amazing way, and it's just like when I'm fixing up things. I love the feeling of taking something broken and making it whole again. That's why I keep doing what I do." - }, - { - "speaker": "Calvin", - "dia_id": "D23:12", - "text": "Yeah, Dave! Music and repairing things are so fulfilling and satisfying. Seeing something go from broken to whole is incredible. You're making a difference too - it's amazing. Keep it up, friend." - }, - { - "speaker": "Dave", - "dia_id": "D23:13", - "text": "Thanks, buddy. Your support really helps. It's great to have a friend who believes in me. I'll keep pushing." - }, - { - "speaker": "Calvin", - "dia_id": "D23:14", - "text": "I believe in you, Dave. Keep pushing and never forget how awesome you are." - }, - { - "speaker": "Dave", - "dia_id": "D23:15", - "text": "Thanks, Calvin! Your support means a lot. I'm gonna keep going and not forget my value!" - }, - { - "speaker": "Calvin", - "dia_id": "D23:16", - "img_url": [ - "https://live.staticflickr.com/65535/52239882084_488bafd7de_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a black sports car parked in front of a building", - "text": "C'mon, remember how great you are! Keep going for those dreams. You got this! You know what Dave? Last week, I got a new Ferrari! It's a masterpiece on wheels. Excited for thrilling rides and unforgettable journeys! Perhaps a photo of this unique beauty will lift your mood." - }, - { - "speaker": "Dave", - "dia_id": "D23:17", - "text": "Wow! Thanks a ton for the kind words and encouragement! Your positivity is contagious. Congratulations on the new Ferrari \u2013 that's incredible! Must be one amazing ride. I'm all in for those thrilling journeys! Feel free to share a pic of your new beauty whenever you're ready. Let's keep the good vibes rolling! Take care and see you later!" - }, - { - "speaker": "Calvin", - "dia_id": "D23:18", - "text": "The Ferrari is indeed a showstopper. I'll be sure to share a pic soon. Here's to more thrilling rides and positive vibes. Take care, and looking forward to catching up soon!" - } - ], - "session_24_date_time": "10:11 am on 19 October, 2023", - "session_24": [ - { - "speaker": "Calvin", - "img_url": [ - "https://badgerherald.com/wp-content/uploads/2017/09/Image-uploaded-from-iOS-1.jpg" - ], - "blip_caption": "a photo of a band performing on stage with bright lights", - "query": "performance stage concert crowd energy", - "dia_id": "D24:1", - "text": "Hey Dave! Been a few days since we talked, but lots happened. Started touring with Frank Ocean and it's been amazing - so much energy from the crowd, such a connection when I'm on stage - unreal! Take a look at how I performed on stage, that was awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D24:2", - "text": "Congrats, Calvin! That's awesome. Being able to play your music to a crowd and feel that connection must be unreal. How's the tour going?" - }, - { - "speaker": "Calvin", - "dia_id": "D24:3", - "text": "Thanks, Dave! The tour has been great, the energy from the crowd is awesome and jamming with Frank Ocean is a dream come true. It's been amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D24:4", - "text": "Wow, Calvin! I bet performing with him is like a dream come true! How did it happen?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/f6yapuwstnl01.jpg" - ], - "blip_caption": "a photo of a band performing on stage with a projection of a man on the wall", - "query": "frank ocean performance on stage music festival sweaty", - "dia_id": "D24:5", - "text": "It all started August last year when we met at a festival and he said he wanted to collaborate. We clicked right away and the chemistry on stage was incredible. I'm so lucky! Check the photo, of how we perfectly look together on stage!" - }, - { - "speaker": "Dave", - "dia_id": "D24:6", - "text": "Wow, Calvin - you and Frank are so in tune! It's clear you both rock on stage. Can't wait to catch your show!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.pexels.com/photos/18482984/pexels-photo-18482984.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-luis-quintero-18482984.jpg" - ], - "blip_caption": "a photo of a large crowd of people in a large auditorium", - "query": "crowd performance hands in the air", - "dia_id": "D24:7", - "text": "Thanks, Dave! I'm so excited you'll be at one of our shows. It's such a great experience, you'll definitely enjoy it! Look at this crowd, that was insane!" - }, - { - "speaker": "Dave", - "dia_id": "D24:8", - "text": "That sounds really exciting! Can't wait to experience it. I'm sure everyone in the crowd is going to be pumped up!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://live.staticflickr.com/187/393901746_803174e292_b.jpg" - ], - "blip_caption": "a photography of a crowd of people at a concert with their hands in the air", - "query": "crowd hands raised", - "dia_id": "D24:9", - "re-download": true, - "text": "Wow, the crowd energy is amazing! It always gets me so pumped and it's awesome. Look at that photo, that was awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D24:10", - "text": "Wow, Calvin, that's an awesome pic! Everyone looks so pumped. Where was that taken?" - }, - { - "speaker": "Calvin", - "dia_id": "D24:11", - "text": "Thanks, Dave! That pic was taken in Tokyo during a concert. Man, the energy was nuts - felt like the whole city came out!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://betweenstationsblog.files.wordpress.com/2017/09/bd698257-6a1e-45f9-916b-780aef4d9445.jpg" - ], - "blip_caption": "a photo of a busy street with people walking and shopping", - "query": "tokyo crowded streets energy city", - "dia_id": "D24:12", - "text": "Wow, Calvin, Tokyo looks incredible! Here's a pic I found online, and it's making me dream about visiting someday. The energy there seems unbeatable! Have you ever visited streets like that?" - }, - { - "speaker": "Calvin", - "dia_id": "D24:13", - "text": "Yes, Dave! That was an incredible experience to visit similar streets like you shared in your photo. Is there anything else that interests you in Tokyo?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://www.hannahrose.uk/wp-content/uploads/2019/02/IMG_0985.jpg" - ], - "blip_caption": "a photo of a crowded street at night with people walking and walking", - "query": "tokyo busy street neon lights crowd", - "dia_id": "D24:14", - "text": "Of course, Calvin! Tokyo is amazing! I want to know everything about it - the people, the culture, the food, take a walk at the vibrant city life! In the photo below, the city is so alive and colorful that's impressive! It will be an unforgettable experience!" - }, - { - "speaker": "Calvin", - "dia_id": "D24:15", - "text": "That photo's a great pic! The lights, the people - so lively! Can't wait to hear your emotions when you see that in person!" - }, - { - "speaker": "Dave", - "dia_id": "D24:16", - "text": "It's really amazing. Hope I'll get to see it in person soon!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://omakase-tour.com/blog/IMG_2448.jpg" - ], - "blip_caption": "a photo of a city at night with a tall building in the background", - "query": "view skyline rooftop bar tokyo", - "dia_id": "D24:17", - "text": "Cool, Dave! I'm actually going to Tokyo next month after the tour ends. Sometimes I wish I could go back to places like the one in the photo below. What a great view!" - }, - { - "speaker": "Dave", - "dia_id": "D24:18", - "text": "Wow, Calvin! That's great to hear! Any specific spots in Tokyo that you're really excited to check out?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.imgur.com/ehTs1O5.jpg" - ], - "blip_caption": "a photo of a crowd of people with umbrellas in the rain", - "query": "shibuya crossing night crowd", - "dia_id": "D24:19", - "text": "Yeah definitely! Shibuya Crossing is like Tokyo's Times Square, and I was excited to explore Shinjuku. Plus, there's amazing food there, can't wait to try it again! Look at the photo of Shibuya Crossing at night, that's amazing!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/ywrvm1v05l181.jpg" - ], - "blip_caption": "a photo of a bowl of soup with broccoli and noodles", - "query": "authentic ramen bowl", - "dia_id": "D24:20", - "text": "Shibuya and Shinjuku are cool spots! The food in Tokyo is great, I'll have so much fun exploring all the different places. Have you tried ramen yet? Here's a photo of a ramen bowl that I tried in Boston, it was delicious, but i think in Tokyo it will be even better!" - }, - { - "speaker": "Calvin", - "dia_id": "D24:21", - "text": "Thanks, Dave! Never tried it, but it's supposed to be awesome. Gonna give it a shot while in Tokyo!" - }, - { - "speaker": "Dave", - "dia_id": "D24:22", - "text": "Do it, Calvin! Once you try it, you'll never go back. Bon voyage and have fun!" - }, - { - "speaker": "Calvin", - "dia_id": "D24:23", - "text": "Thanks, Dave! I'll definitely give it a shot. Appreciate the encouragement! See you soon, bye!" - } - ], - "session_25_date_time": "2:17 pm on 23 October, 2023", - "session_25": [ - { - "speaker": "Dave", - "dia_id": "D25:1", - "text": "Hey Calvin, how's the tour with Frank Ocean? I was pondering our chat the other day about fame and its impact on relationships. It must be awesome but it must have its own struggles too. How are you juggling your job plus your personal life?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:2", - "text": "The tour's been incredible! Performing and connecting with the crowd has been so energizing. Yeah, fame has its own challenges. It's been tough trying to balance everything." - }, - { - "speaker": "Dave", - "dia_id": "D25:3", - "text": "How are you managing everything? Do you ever feel overwhelmed?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:4", - "text": "I take it one day at a time, but it can get overwhelming with so many demands. Still, I enjoy what I do so I push on." - }, - { - "speaker": "Dave", - "dia_id": "D25:5", - "text": "Glad you're finding joy in what you do! Any strategies that help you manage everything?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:6", - "text": "Yeah, having a strong support system is really helpful. My friends and team keep me on track." - }, - { - "speaker": "Dave", - "dia_id": "D25:7", - "text": "Cool! Having people who understand and support you is really important. I recently had a good conversation with some neighbors about current events and politics. It was nice to hear different perspectives and share our own. Interacting like that cheers me up and helps me stay informed - how about you?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:8", - "text": "Staying connected and up-to-date on world events is important to me. It helps my music stand out by incorporating unique perspectives and connects me better with my fans. Plus, it keeps me motivated and inspired." - }, - { - "speaker": "Dave", - "dia_id": "D25:9", - "text": "Cool, Calvin! Art is amazing how it reflects the world. Has anything caught your eye lately and made an impact on your music?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:10", - "text": "Yeah, for sure! Recently, I've been really inspired by some of the struggles that people go through. It's made me dig deeper into my music to try to capture those feelings." - }, - { - "speaker": "Dave", - "dia_id": "D25:11", - "text": "That's cool. Using your music to share experiences and feelings, is it cathartic for you?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:12", - "text": "Yeah, it's a way for me to express myself and work through my emotions. It's like my own form of therapy." - }, - { - "speaker": "Dave", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/3/34/Car_workshop_tools.jpg" - ], - "blip_caption": "a photography of a garage with a bunch of tools hanging on the wall", - "query": "garage car parts and tools", - "dia_id": "D25:13", - "re-download": true, - "text": "Yeah, I get it. Cars give me an outlet to express myself. It's like a little oasis of calm. Whenever I'm working on one, it's like I'm connecting back with myself. And yeah, the music helps too! Look at my garage, a little dirty, but everything is in its place." - }, - { - "speaker": "Calvin", - "dia_id": "D25:14", - "text": "Looking cool - what made you start working on cars?" - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a man working on a car in a garage", - "dia_id": "D25:15", - "text": "Ever since I was ten, I've been fascinated with how machines work. I found an old car in a neighbor's garage and asked if I could fix it. That's when my love for car engineering began! I enjoyed transforming it from broken-down to high-running, and that sense of accomplishment made me hooked\u2014so I've been working on cars ever since." - }, - { - "speaker": "Calvin", - "dia_id": "D25:16", - "text": "Impressive! That must have taken a lot of skill and determination to bring that car back to life. Was it a classic car? Do you still have it or are you currently working on another project?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.classic.com/vehicles/d47f69b88de907d965b64771b7a9eba159a47a93.jpg" - ], - "blip_caption": "a photo of a black car parked in a parking lot", - "query": "restored chevy camaro", - "dia_id": "D25:17", - "text": "Thanks Calvin! It was a fun project. I restored a car last year, but I sold it to a collector. Now I'm working on something new, and it's quite a challenge! Take a look at this wonderful car!" - }, - { - "speaker": "Calvin", - "dia_id": "D25:18", - "text": "Cool! Been wanting to try that. What would you say is the toughest part of car restoration?" - }, - { - "speaker": "Dave", - "dia_id": "D25:19", - "text": "It takes a lot of patience, but the payoff is worth it. Paying extra attention to detail isn't easy, but it pays off." - }, - { - "speaker": "Calvin", - "dia_id": "D25:20", - "text": "Yeah, details can really make a difference. It's what makes something great, like a well-crafted rap song or a sleek and stylish car." - }, - { - "speaker": "Dave", - "dia_id": "D25:21", - "text": "Yeah, for sure Calvin! Paying attention to details makes a big difference, whether it's in music or with vehicles. Little things can make a big impact!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a silver disc in a black frame on a table", - "dia_id": "D25:22", - "text": "Yeah, Dave! Paying attention to those small details makes a difference. Without them, it's just average. As an artist, I want to create something extraordinary!" - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a brown amp with a black knob and a brown cover", - "dia_id": "D25:23", - "text": "That's great, Calvin! Loving the attention to detail in your work, it makes it extra special. Keep on creating!" - }, - { - "speaker": "Calvin", - "dia_id": "D25:24", - "text": "Thanks Dave! Appreciate your support!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/2147029/pexels-photo-2147029.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-laura-stanley-2147029.jpg" - ], - "blip_caption": "a photo of a crowd of people at a concert with their hands in the air", - "query": "concert crowd rock", - "dia_id": "D25:25", - "text": "Glad you like it, Cal! It reminds me of the good times I've had at concerts last month. Music has a way of connecting us and making memories. Take a look at this picture, the atmosphere was amazing!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a crowd of people at a concert with their hands in the air", - "dia_id": "D25:26", - "text": "Music has a way of bringing us together and creating unforgettable memories. It's unbeatable in terms of the energy it brings." - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/15995095/pexels-photo-15995095.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-tony-entz-15995095.jpg" - ], - "blip_caption": "a photo of a crowd of people standing around a stage", - "query": "concert stage vibrant lights crowd dancing", - "dia_id": "D25:27", - "text": "Yeah, for sure Calvin! Concerts are awesome, they bring people together and create such a cool vibe. You can feel the energy in the air. Take a look at this unforgettable moment that I captured at the concert." - }, - { - "speaker": "Calvin", - "dia_id": "D25:28", - "text": "Concerts are what I live for - the indescribable connection between the artist and the crowd is just amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D25:29", - "text": "Wow, it's amazing how that connection between artist and crowd can be indescribable. So glad you get to experience that!" - }, - { - "speaker": "Calvin", - "dia_id": "D25:30", - "text": "Wow, Dave! It's a rush connecting with everyone. That feeling is unbeatable! Wishing you a harmonious day ahead, my friend!" - }, - { - "speaker": "Dave", - "dia_id": "D25:31", - "text": "Yeah, I can imagine it's a rush being up on stage with all the fans cheering. Must be a unique experience. Wishing you many more electrifying moments in the spotlight! See you soon!" - } - ], - "session_26_date_time": "8:25 pm on 25 October, 2023", - "session_26": [ - { - "speaker": "Calvin", - "dia_id": "D26:1", - "text": "Hey Dave! Long time no talk! I had a great time yesterday, and visited some sights in Boston with a high school friend. It was really fun and eye-opening." - }, - { - "speaker": "Dave", - "dia_id": "D26:2", - "text": "Hey Calvin! Good to hear from you! Sounds like you had a blast in Boston - so much to do there! One of my favorites there is great for chilling and admiring nature. BTW, something cool I wanted to tell you about - I recently attended a conference in Detroit - it was so cool, I learned a lot." - }, - { - "speaker": "Calvin", - "dia_id": "D26:3", - "text": "I had a wonderful experience during my time in Boston. It had awesome architecture and a really cool history. I followed your advice and went there, it was so peaceful. Good to hear that you've learned a lot of new things at the conference in Detroit!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRWn9kFWCXVzFTta3A7aeTMjMfYmkueJcWaxg&usqp=CAU" - ], - "blip_caption": "a photography of a group of people washing a car in a garage", - "query": "car mechanic working under hood", - "dia_id": "D26:4", - "re-download": true, - "text": "Glad you enjoyed Boston! It's got some great architecture and history. Working on this takes me out of my head and calms me down. Here's a pic of my latest car project." - }, - { - "speaker": "Calvin", - "dia_id": "D26:5", - "text": "Dave, that car looks awesome! What got you into engineering cars? I'm totally into cars too and love making new and powerful models." - }, - { - "speaker": "Dave", - "dia_id": "D26:6", - "text": "Thanks, Calvin! It's always been my thing. My dad took me to my first car show when I was 10, and I was wow-ed! I was hooked and wanted to learn more. Now I'm an automotive engineer and it's crazy to think that something so powerful and cool can be made from small parts." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/invc2ervv8t31.jpg" - ], - "blip_caption": "a photo of a dj in a nightclub with a dj table", - "query": "performing live calvin frank ocean", - "dia_id": "D26:7", - "text": "That's awesome, Dave! So happy you followed your dreams and became an engineer. Seeing you work on cars is inspiring - it makes me want to keep pushing my music. Check this out - a pic of me performing with someone I admire! So amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D26:8", - "text": "Wow, Calvin! That's amazing! You looked super comfortable on stage. Bet it was an incredible experience to perform with someone you admire. How did it feel being up there?" - }, - { - "speaker": "Calvin", - "dia_id": "D26:9", - "text": "It was unreal, Dave. Being up there with someone I admire - it was a dream come true. The energy, the crowd - it made me realize how much music means to me, it's like my passion and my purpose." - }, - { - "speaker": "Dave", - "dia_id": "D26:10", - "text": "That's great, Calvin. We both have discovered something that brings us fulfillment. It's a fortunate thing for us." - }, - { - "speaker": "Calvin", - "dia_id": "D26:11", - "text": "Definitely, Dave. It's awesome to find something that makes us happy. It's fulfilling and motivating too. I'm so glad we're on this journey together and curious to see what happens next!" - }, - { - "speaker": "Dave", - "dia_id": "D26:12", - "text": "Me too, Calvin! Let's see where it takes us. We can keep inspiring each other!" - }, - { - "speaker": "Calvin", - "dia_id": "D26:13", - "text": "Yep, let's keep inspiring each other to be our best selves. Keep going for your passion, buddy." - }, - { - "speaker": "Dave", - "dia_id": "D26:14", - "text": "Thanks, Calvin! Same to you! Keep pursuing your passion and keep dreaming. We got this! Got to go see you soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D26:15", - "text": "Thanks, Dave! I appreciate your support. Let's keep pushing for our goals. Catch you later, see ya!" - } - ], - "session_27_date_time": "10:49 am on 29 October, 2023", - "session_27": [ - { - "speaker": "Calvin", - "dia_id": "D27:1", - "text": "Hey Dave! Since we last talked, I went to a networking event to meet more artists. So cool! The people I met will help me build up my fan base. Super excited about what it could lead to. You? Anything new since we last spoke?" - }, - { - "speaker": "Dave", - "dia_id": "D27:2", - "text": "Hey Calvin! That's cool that you've been networking with other artists. Nice! I've been getting into photography recently. I've seen some amazing places and taken some great shots. Would you like to see them?" - }, - { - "speaker": "Calvin", - "dia_id": "D27:3", - "text": "Yeah, show me what you got!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/yhfpmmq2j8f71.jpg" - ], - "blip_caption": "a photo of a city skyline at sunset with a clock tower", - "query": "sunset boston skyline", - "dia_id": "D27:4", - "text": "Look at this magnificent sunset I captured on camera. It's truly breathtaking to witness such beautiful sunsets! The sky looks like it's on fire!" - }, - { - "speaker": "Calvin", - "dia_id": "D27:5", - "text": "Wow, that view looks awesome! What city is it? Have you taken any good pictures lately?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://live.staticflickr.com/28/66275862_c39a8015c2_b.jpg" - ], - "blip_caption": "a photography of a clock tower in a city with buildings", - "query": "waterfall white mountains new hampshire", - "dia_id": "D27:6", - "re-download": true, - "text": "That's Boston, Cal! Check this out, I took this picture last month, and got a great shot - it was stunning!" - }, - { - "speaker": "Calvin", - "dia_id": "D27:7", - "text": "Wow, that pic is amazing! In your last photo, is that the clock tower? I was there a few years back, it's such a beautiful city. You're so talented, Dave!" - }, - { - "speaker": "Dave", - "dia_id": "D27:8", - "text": "Thanks, Calvin! Your kind words mean a lot. Yep, that's the clock tower in the last photo. I snapped it at sunset and the colors were stunning. Photography helps me capture and appreciate the beauty of nature. It's been an awesome creative outlet and I'm loving it." - }, - { - "speaker": "Calvin", - "dia_id": "D27:9", - "text": "Wow, Dave! Sounds like you're having a blast with your photography. Hope it's bringing you lots of joy. By the way, how is your car project going?" - }, - { - "speaker": "Dave", - "dia_id": "D27:10", - "text": "Hey Calvin, photography has been great for me! The car project is doing well - I just finished restoring it and it looks amazing. Wanna come by and check it out? How's everything with the music? Any updates?" - }, - { - "speaker": "Calvin", - "dia_id": "D27:11", - "text": "That's awesome, Dave! Your car project sounds amazing. I've had some great collaborations recently and my album is almost finished. I'll send you some previews soon. Let me know when you're free for a catch-up." - }, - { - "speaker": "Dave", - "dia_id": "D27:12", - "text": "Cool, Calvin! Can't wait to hear it. Let me know when you're free and take it easy!" - }, - { - "speaker": "Calvin", - "dia_id": "D27:13", - "text": "Cheers! I'll let you know when I'm free. Bye!" - } - ], - "session_28_date_time": "5:46 pm on 2 November, 2023", - "session_28": [ - { - "speaker": "Calvin", - "img_url": [ - "https://live.staticflickr.com/3829/11428939743_6e80009962_b.jpg" - ], - "blip_caption": "a photography of a group of people sitting in a room with a projector screen", - "query": "japanese mansion listening party", - "dia_id": "D28:1", - "re-download": true, - "text": "Hey Dave! It's been a while! Crazy stuff has been happening. Last week I threw a small party at my Japanese house for my new album. It was amazing, so much love from my fam and friends! Take a look at the photo of the party in the mansion, it was so energizing!" - }, - { - "speaker": "Dave", - "dia_id": "D28:2", - "text": "Congrats on your album release and the party, Calvin! Must've been a great feeling having your loved ones show their support." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.pexels.com/photos/18047550/pexels-photo-18047550/free-photo-of-people-dancing-on-party.jpeg" - ], - "blip_caption": "a photography of a group of people dancing at a party", - "query": "party friends family dancing", - "dia_id": "D28:3", - "re-download": true, - "text": "Thanks, Dave! It was an awesome feeling seeing everyone come together to celebrate - super rewarding! Look at this photo showcasing how wonderfully we spent our time!" - }, - { - "speaker": "Dave", - "dia_id": "D28:4", - "text": "Wow, great job, Calvin! Congrats! What was it like when everyone was cheering you on?" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a group of people standing on top of a stage", - "dia_id": "D28:5", - "text": "It was incredible, Dave! The room was buzzing with energy and love. It was a powerful reminder of why I'm doing this." - }, - { - "speaker": "Dave", - "dia_id": "D28:6", - "text": "Wow, Calvin! Creating something that brings people together and inspires them - that's really awesome!" - }, - { - "speaker": "Calvin", - "dia_id": "D28:7", - "text": "Thanks, Dave! It's an awesome feeling. Creating something that people connect with and brings joy is what I'm all about. Moments like this really motivate me to keep growing!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images-wixmp-ed30a86b8c4ca887773594c2.wixmp.com/f/c5f5d661-c43a-4159-beb1-41a0ffb503bd/d8azllp-91d696d6-4167-4846-aad2-46467414114e.png/v1/fit/w_375,h_535,q_70,strp/lutz_home_page_mock_up_by_axelnavaja_d8azllp-375w.jpg?token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.2SIHoyGPb9qYBOuyrWc5BUInUOR1q8C1khI1OcW907E" - ], - "blip_caption": "a photography of a car website design", - "query": "blog homepage car modifications successful blog", - "dia_id": "D28:8", - "re-download": true, - "text": "Wow, Calvin, imagining how your music affects others must be incredible! Keep up the great work! By the way, I recently started a blog on car mods. It's my way to share my passion with others. Do you have any tips on blogging for me? Just take a look at this beautiful car!\n\n" - }, - { - "speaker": "Calvin", - "dia_id": "D28:9", - "text": "Cool, Dave! It's really fun to share your passion through blogging. Have you had any success stories yet with inspiring others?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/12590786/pexels-photo-12590786.jpeg" - ], - "blip_caption": "a photography of a blue subarunt parked in a parking lot", - "query": "subaru blue parked parking lot", - "dia_id": "D28:10", - "re-download": true, - "text": "Thanks, Calvin! It's awesome people are checking out my blog and asking me for advice. I recently posted about how I made this car look like a beast, and it was great to hear it inspired others to start their own DIY projects." - }, - { - "speaker": "Calvin", - "dia_id": "D28:11", - "text": "Wow, Dave! Your blog is awesome. Helping others get creative is awesome. Keep up the great work!" - }, - { - "speaker": "Dave", - "dia_id": "D28:12", - "text": "Thanks, Calvin! I appreciate the support. It's fulfilling to share my knowledge and help others unleash their creativity." - }, - { - "speaker": "Calvin", - "dia_id": "D28:13", - "text": "Yeah Dave, keep doing what you do! Your blog and car mods are inspiring and a great way to help people find their creativity." - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/4839256/pexels-photo-4839256.jpeg" - ], - "blip_caption": "a photography of a blue subarunt parked in a parking lot", - "query": "customized car sleek headlights vibrant paint job", - "dia_id": "D28:14", - "re-download": true, - "text": "Thanks, Calvin! It means a lot that you enjoy my blog. This car mod was a lot of work, but I think it was worth it in the end." - }, - { - "speaker": "Calvin", - "dia_id": "D28:15", - "text": "Wow Dave, those headlights look great! What did you do to get them looking so good?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/5158085/pexels-photo-5158085.jpeg" - ], - "blip_caption": "a photography of a blue car parked on a road at night", - "query": "car headlights shining brightly", - "dia_id": "D28:16", - "re-download": true, - "text": "Thanks, Calvin! I spent a lot of time cleaning, polishing, and protecting them - they look great! Just take a look at this photo \u2013 these headlights are enchanting!\n\n" - }, - { - "speaker": "Calvin", - "dia_id": "D28:17", - "text": "Wow, they look great! You really put in a lot of effort. Well done!" - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a red car with a black engine and a red hood", - "dia_id": "D28:18", - "text": "Thanks, Calvin! Really appreciate you noticing the effort I put into this." - }, - { - "speaker": "Calvin", - "dia_id": "D28:19", - "text": "Thanks! Where did you get this car?" - }, - { - "speaker": "Dave", - "dia_id": "D28:20", - "text": "I found it last week, and it was in bad shape, but I saw the potential. I spent ages restoring it." - }, - { - "speaker": "Calvin", - "dia_id": "D28:21", - "text": "Wow, Dave, that is an awesome job on restoring it! You've got some serious skills!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images-wixmp-ed30a86b8c4ca887773594c2.wixmp.com/f/c3d8f9a1-a181-4045-8cfc-fcef59480e5e/d5owgzt-b56f51e8-2840-467b-8d4b-f6732c7b2f0c.png?token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJ1cm46YXBwOjdlMGQxODg5ODIyNjQzNzNhNWYwZDQxNWVhMGQyNmUwIiwiaXNzIjoidXJuOmFwcDo3ZTBkMTg4OTgyMjY0MzczYTVmMGQ0MTVlYTBkMjZlMCIsIm9iaiI6W1t7InBhdGgiOiJcL2ZcL2MzZDhmOWExLWExODEtNDA0NS04Y2ZjLWZjZWY1OTQ4MGU1ZVwvZDVvd2d6dC1iNTZmNTFlOC0yODQwLTQ2N2ItOGQ0Yi1mNjczMmM3YjJmMGMucG5nIn1dXSwiYXVkIjpbInVybjpzZXJ2aWNlOmZpbGUuZG93bmxvYWQiXX0.RPOmhCjxYw8dIFahiFDFbXovl-F_Ld3fKQfFLmSxkNo" - ], - "blip_caption": "a photography of a guitar logo with the company band logo", - "query": "fully restored car gleaming sunlight", - "dia_id": "D28:22", - "re-download": true, - "text": "Thanks Calvin! It took some work, but I'm happy with the result. Take a look at the logo we created for our rock band!" - }, - { - "speaker": "Calvin", - "dia_id": "D28:23", - "text": "Cool logo, Dave! What's the story behind it?" - }, - { - "speaker": "Dave", - "dia_id": "D28:24", - "text": "Cool! It's the logo for my rock band. I've been a fan for ages and have had the opportunity to join them." - }, - { - "speaker": "Calvin", - "dia_id": "D28:25", - "text": "Wow Dave! Music really has a way of touching our souls." - }, - { - "speaker": "Dave", - "dia_id": "D28:26", - "text": "Yeah, Calvin! It's amazing how music can really move us. It's almost like a language for our souls." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://d2wvwvig0d1mx7.cloudfront.net/data/org/18741/media/img/cache/2969x0/1905172_2969x0.jpg" - ], - "blip_caption": "a photography of a recording studio with a monitor, keyboard, and monitor", - "query": "studio setup japanese mansion", - "dia_id": "D28:27", - "re-download": true, - "text": "Yup, it's that connection I'm aiming for with my music. Take a loot at my studio setup, that's look awesome, isn't it?" - }, - { - "speaker": "Dave", - "dia_id": "D28:28", - "text": "Wow, your studio looks stunning! How do you like hanging out here? Do you watch much TV?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/84fzhmctceg91.jpg" - ], - "blip_caption": "a photo of a living room with a couch, chair, television and a table", - "query": "living room television couch", - "dia_id": "D28:29", - "text": "I only work in the studio. I have another room for relaxation with a TV, just take a look that room is cozy and relaxing. And yeah, It's a great way to unwind and get inspired." - }, - { - "speaker": "Dave", - "dia_id": "D28:30", - "text": "Wow, nice setup! What do you usually watch on it?" - }, - { - "speaker": "Calvin", - "dia_id": "D28:31", - "text": "Thanks, Dave! I usually watch music videos, concerts, and documentaries about artists and their creative process. It's cool to learn more about the industry and see what others do. Plus, it's a source of inspiration for me." - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/ht8aimbni5gb1.jpg" - ], - "blip_caption": "a photo of a notebook with a pen and a notepad on it", - "query": "notebook song lyrics", - "dia_id": "D28:32", - "text": "Wow, Calvin, that's awesome! Keep up the great work! Take a look at the photo!" - }, - { - "speaker": "Calvin", - "dia_id": "D28:33", - "text": "Thanks, Dave! Appreciate the support! Does this notebook help you stay connected to the creative process?" - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a pink floyd headphone sitting on a shelf", - "dia_id": "D28:34", - "text": "Yes, Calvin, writing lyrics and notes - that's awesome! It will boost my motivation! Writing lyrics boosts my motivation to grow!" - }, - { - "speaker": "Calvin", - "dia_id": "D28:35", - "text": "Cool, Dave! These really help you stay focused when making music." - }, - { - "speaker": "Dave", - "dia_id": "D28:36", - "text": "Cool, Calvin! Music really helps me focus and be productive. When I'm doing my car stuff, I listen to vinyl to relax and stay on track." - }, - { - "speaker": "Calvin", - "dia_id": "D28:37", - "text": "Rockin' it, Dave! Music can definitely affect our mood and help us stay on track. Keep it up!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/210r4phbogya1.jpg" - ], - "blip_caption": "a photo of a record player sitting on the floor next to a couch", - "query": "record player vinyl", - "dia_id": "D28:38", - "text": "Thanks, Calvin! Music really helps with car work. Keeps me focused and makes it feel great. Even though this player is a bit old, he still gets the job done. Check out its photo!\n\n" - }, - { - "speaker": "Calvin", - "dia_id": "D28:39", - "text": "Cool, Dave! What tunes are you listening to these days?" - }, - { - "speaker": "Dave", - "dia_id": "D28:40", - "text": "Lately, I've been getting into classic rock. The music from that era is timeless." - }, - { - "speaker": "Calvin", - "dia_id": "D28:41", - "text": "Cool, Dave! Classic rock has had a huge effect on music. Keep discovering!" - }, - { - "speaker": "Dave", - "dia_id": "D28:42", - "text": "Thanks, Calvin! Classic rock has had a huge impact on music. Always fun to dig in and find new tunes. Gotta go back to work, see you soon! Take care!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a record player sitting on the floor next to a couch", - "dia_id": "D28:43", - "text": "Yeah, Dave! Exploring different styles and times can open up new perspectives. Broadening your musical knowledge is awesome. Good luck with work, see ya!\n\n" - } - ], - "session_29_date_time": "9:15 pm on 13 November, 2023", - "session_29": [ - { - "speaker": "Calvin", - "dia_id": "D29:1", - "text": "Hey Dave, I invited my old high school buddy to see me perform in Boston! It was insane. It got me thinking about how far I've come and reminded me how important relationships are in this biz. Felt so good!" - }, - { - "speaker": "Dave", - "dia_id": "D29:2", - "text": "Whoa, that's great Calvin! Seeing your old friend must have brought back so many memories. It's awesome to have someone who's been there since the start to support us." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.pexels.com/photos/15916697/pexels-photo-15916697/free-photo-of-men-in-streetwear-clothing-posing-in-front-of-white-car.jpeg" - ], - "blip_caption": "a photography of two men standing on a sidewalk with a car in the background", - "query": "friends freestyling beat-up old car", - "dia_id": "D29:3", - "re-download": true, - "text": "Remembering those days made me so nostalgic! Freestyling and talking about getting famous, it's crazy how life has changed. But being around certain people still sparks those same feelings. Take a look at how we freestyled when we were teenagers!" - }, - { - "speaker": "Dave", - "dia_id": "D29:4", - "text": "Wow, that's a great pic! Did that person make a big difference during your trip?" - }, - { - "speaker": "Calvin", - "dia_id": "D29:5", - "text": "He's been such a great friend to me. Always there to support and encourage me. His positivity has made a big difference in my journey." - }, - { - "speaker": "Dave", - "dia_id": "D29:6", - "text": "It's great how someone's support and encouragement can really shape our lives! They give us the motivation to keep going and trust ourselves. Do you also find having supportive people around you important for your artist development?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://live.staticflickr.com/7269/6934706388_7ea340725b_b.jpg" - ], - "blip_caption": "a photography of a group of people sitting around a desk", - "query": "group of friends studio session", - "dia_id": "D29:7", - "re-download": true, - "text": "Having supportive people is key for me to grow as an artist. They motivate me to get better and stay true to myself. Having support is vital, especially in this tough music industry. Take a look at this photo!" - }, - { - "speaker": "Dave", - "dia_id": "D29:8", - "text": "Wow, Calvin! Is this a pic of some musicians you're collaborating with?" - }, - { - "speaker": "Calvin", - "dia_id": "D29:9", - "text": "Yeah, I've been supporting some young musicians from a music program. Supporting their passion is amazing and their enthusiasm is inspiring." - }, - { - "speaker": "Dave", - "dia_id": "D29:10", - "text": "Wow, Calvin, that's amazing! You're really making a difference by supporting the next generation of musicians. It's great to nurture their passion and help them thrive." - }, - { - "speaker": "Calvin", - "dia_id": "D29:11", - "text": "Thanks, Dave! It's like a torch being passed to keep music alive! These young musicians are very ambitious, I think I will support them for a long time.\t" - }, - { - "speaker": "Dave", - "dia_id": "D29:12", - "text": "Yeah, Calvin! Your support for them keeps the music going and passes on the legacy. That's so awesome!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.pinimg.com/originals/1d/a3/2a/1da32ab956b36b1a8431e6c2c625abc6.jpg" - ], - "blip_caption": "a photo of a man sitting at a desk in front of a computer", - "query": "music studio", - "dia_id": "D29:13", - "text": "I'm stoked I made a difference. Paying it forward, ya know? Working with new talent brings new ideas to this. Look at this photo, here's how I'm making a beat for a young artist, he has great potential in music! " - }, - { - "speaker": "Dave", - "dia_id": "D29:14", - "text": "Wow, that's cool! Your music studio looks great. Have you tried out any new ideas lately?" - }, - { - "speaker": "Calvin", - "dia_id": "D29:15", - "text": "Thanks! I'm having fun trying out new sounds and pushing the boundaries. It's great to go for new ideas and see where it takes me - always gotta stay ahead in this." - }, - { - "speaker": "Dave", - "dia_id": "D29:16", - "text": "Awesome, Calvin! Experimenting and pushing boundaries is key to making our art grow. Can't wait to see where these new ideas take you!" - }, - { - "speaker": "Calvin", - "dia_id": "D29:17", - "text": "Yeah, it's exciting to see where these new ideas lead. It's all about growing and evolving! Have a good one. I need to go now. Take care, Dave!" - }, - { - "speaker": "Dave", - "dia_id": "D29:18", - "text": "For sure, Calvin! Growing and evolving is key for any artist. Don't stop pushing yourself and keep exploring. Can't wait to see what you come up with next! See ya Cal! Take care!" - } - ], - "session_30_date_time": "10:54 am on 17 November, 2023", - "session_30": [ - { - "speaker": "Dave", - "dia_id": "D30:1", - "text": "Hey Calvin, long time no talk! A lot has happened. I've taken up photography and it's been great - been taking pics of the scenery around here which is really cool." - }, - { - "speaker": "Calvin", - "dia_id": "D30:2", - "img_url": [ - "https://live.staticflickr.com/65535/51102778186_50c6469294_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a group of men sitting on a rock next to a river", - "text": "Hey Dave, it's great to hear from you! Can't wait to see your pics. I went to a fancy gala in Boston yesterday and met some interesting people. Check out this pic of me and the crew!" - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a boat is floating in the water at sunset", - "dia_id": "D30:3", - "text": "Calvin, that event looks amazing! You all look awesome. Who did you have the most interesting chat with?" - }, - { - "speaker": "Calvin", - "dia_id": "D30:4", - "img_url": [ - "https://c1.wallpaperflare.com/preview/557/710/506/talk-friend-man-conversation.jpg" - ], - "re-download": true, - "blip_caption": "a photography of two men sitting on a bench in the snow", - "text": "Thanks, Dave! Had an awesome time. I had a really interesting chat with this cool artist and we clicked over music and art. We talked about our favorite artists, art, and how the power of music connects us all. It was such an inspiring conversation - I feel like I'm on a creative high. We have a photo together, take a look!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/qf9hodnb7i5b1.jpg" - ], - "blip_caption": "a photo of a camera sitting on a table next to a plant", - "query": "vintage camera", - "dia_id": "D30:5", - "text": "That's amazing, Calvin! Music really does bring people together and foster creativity. Glad to hear you had such an inspiring conversation! Take a look at my new vintage camera that I bought this month, which takes awesome photos!" - }, - { - "speaker": "Calvin", - "dia_id": "D30:6", - "text": "Hey Dave, music really brings people together, huh? Do you use this camera for photos? They always turn out so good!" - }, - { - "speaker": "Dave", - "dia_id": "D30:7", - "text": "Yes, Calvin, this camera is very good, it helps me capture those special moments really clearly." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a sunset with a wave crashing on rocks", - "dia_id": "D30:8", - "text": "Having a good camera is key for capturing those special moments. What do you like to take photos of?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/4816878/pexels-photo-4816878.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-katy-ramm-4816878.jpg" - ], - "blip_caption": "a photo of a sunset with a wave crashing on rocks", - "query": "sunset wave crashing rocks", - "dia_id": "D30:9", - "text": "Yeah, being able to take good pics is key. I love capturing the beauty of nature - sunsets, beaches, waves. Just got this one recently, check this out!" - }, - { - "speaker": "Calvin", - "dia_id": "D30:10", - "text": "Nice job, Dave! That shot looks great! Nature's so amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D30:11", - "text": "Thanks, Calvin! It's incredible how much emotion and beauty nature can convey through a photo." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a pond with rocks and a waterfall in the middle", - "dia_id": "D30:12", - "text": "Yeah, nature really does the trick. Its beauty helps us appreciate life when it's tough. Like a breath of fresh air!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://magicgardenslandscaping.com/wp-content/uploads/2018/08/Leucadendron_WF5.jpg" - ], - "blip_caption": "a photo of a waterfall flowing over rocks and boulders", - "query": "peaceful pond rocks waterfall", - "dia_id": "D30:13", - "text": "I totally agree, nature really can boost our spirits in tough times. Also, here's a picture I snapped last week! It's a peaceful scene with rocks and a waterfall. Pretty cool, huh?" - }, - { - "speaker": "Calvin", - "dia_id": "D30:14", - "text": "Wow Dave, that picture is stunning! Where was that taken? It looks so serene!" - }, - { - "speaker": "Dave", - "dia_id": "D30:15", - "text": "Thanks, Calvin! I found this serene spot in a nearby park and took this pic." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://www.palmtreesandpellegrino.com/wp-content/uploads/2022/03/IMG_6524.jpg" - ], - "blip_caption": "a photo of a bench under a tree with pink flowers", - "query": "japanese garden cherry blossoms", - "dia_id": "D30:16", - "text": "Wow, that sounds like such a peaceful and serene spot. Can't wait to check it out myself sometime. Check out this beautiful picture that I shot in a Japanese garden, that's wild!" - }, - { - "speaker": "Dave", - "dia_id": "D30:17", - "text": "Cool, Calvin! Found an even better spot, with a bench under a tree with pink flowers - so peaceful. A perfect spot to relax and take in the beauty." - }, - { - "speaker": "Calvin", - "dia_id": "D30:18", - "text": "That sounds great, Dave! Can't wait to see it." - }, - { - "speaker": "Dave", - "dia_id": "D30:19", - "text": "Check it out, Calvin. It's really calming, I think you'll like it. We will definitely go there! Is there anything else you'd like to share?" - }, - { - "speaker": "Calvin", - "dia_id": "D30:20", - "text": "Thank you for asking, Dave! Yes, I have a few more great news! I've accepted an invitation to perform at an upcoming show in Boston! It's going to be an unforgettable musical experience. Can't wait to fill you in on all the details. Catch up with you soon!" - }, - { - "speaker": "Dave", - "dia_id": "D30:21", - "text": "Wow, Calvin! That's amazing news! Congratulations on both the gala attendance and the upcoming performance. I can't wait to hear all about it and maybe even catch one of your shows in Boston. Let me know when you're free to catch up. Cheers to your musical journey!" - }, - { - "speaker": "Calvin", - "dia_id": "D30:22", - "text": "Thanks, Dave! I'll catch you when I'm in Boston. Cheers!" - }, - { - "speaker": "Dave", - "dia_id": "D30:23", - "text": "Looking forward to seeing you. Stay safe, talk to you soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D30:24", - "text": "Thanks! You too. Talk to you later!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Calvin": [ - "Calvin purchases a mansion in Japan and plans to stay there for a few months to collaborate with musicians." - ], - "Dave": [ - "Dave attends an event on classic cars and chats with some owners about their stories." - ], - "date": "23 March, 2023" - }, - "events_session_2": { - "Calvin": [ - "Calvin buys a Ferrari 488 GTB for himself.", - "Calvin attends a few studio sessions to collaborate on music and writes some new tunes." - ], - "Dave": [ - "Dave attends a music festival in Boston where he sees Aerosmith perform live." - ], - "date": "26 March, 2023" - }, - "events_session_3": { - "Calvin": [ - "Calvin attends a music festival in Tokyo which is attended by professionals from the music industry.", - "Calvin receives advice from music professionals to stay true to his unique sound." - ], - "Dave": [ - "Calvin attends a car show." - ], - "date": "20 April, 2023" - }, - "events_session_4": { - "Calvin": [ - "Calvin receives a gold chain with diamond pendant as gift from another artist." - ], - "Dave": [ - "Dave sets up a car maintenance shop to work towards his dream of restoring classic cars." - ], - "date": "1 May, 2023" - }, - "events_session_5": { - "Calvin": [ - "Calvin enjoys getting to know about Japanese culture." - ], - "Dave": [ - "Dave begins collaborating with a local mechanic shop." - ], - "date": "3 May, 2023" - }, - "events_session_6": { - "Calvin": [ - "Calvin's place in Tokyo gets flooded but he manages to save his music gear.", - "Calvin prepares for his performance in Tokyo at the Frank Ocean tour in last week of May 2023." - ], - "Dave": [ - "Dave invites his friends to his newly set up car maintenance shop." - ], - "date": "16 May, 2023" - }, - "events_session_7": { - "Calvin": [ - "Calvin has an exhilarating performance onstage in the Frank Ocean Tour at Tokyo." - ], - "Dave": [ - "Dave fixes his neighbors' cars." - ], - "date": "31 May, 2023" - }, - "events_session_8": { - "Calvin": [ - "Calvin meets with the creative team for his new album." - ], - "Dave": [ - "Dave visits the local parks n Boston regularly on weekends.", - "Dave books a trip in July 2023 to a mountainous region." - ], - "date": "9 June, 2023" - }, - "events_session_9": { - "Calvin": [ - "Calvin has a car accident where no one is hurt but the car needs repairs." - ], - "Dave": [ - "Dave joins a rock band and practices playing his guitar." - ], - "date": "21 June, 2023" - }, - "events_session_10": { - "Calvin": [ - "Calvin's car is fixed up and ready to be driven again." - ], - "Dave": [ - "Dave calls up his friends for regular trips to the local parks in Boston." - ], - "date": "7 July, 2023" - }, - "events_session_11": { - "Calvin": [ - "Calvin works to convert his Japanese mansion into a recording studio where he can collaborate with other artists." - ], - "Dave": [ - "Dave goes on a countryside road trip with some friends." - ], - "date": "21 July, 2023" - }, - "events_session_12": { - "Calvin": [ - "Calvin goes to the Ferrari dealership to have his car serviced." - ], - "Dave": [], - "date": "3 August, 2023" - }, - "events_session_13": { - "Calvin": [], - "Dave": [ - "Dave is selected for a professional car modification workshop located in San Francisco.", - "Dave works on restoring a classic muscle car and giving it a modern look." - ], - "date": "11 August, 2023" - }, - "events_session_14": { - "Calvin": [], - "Dave": [ - "Dave travels to San Francisco to attend the car modification workshop.", - "Dave starts works on restoring a beat-up old Ford Mustang." - ], - "date": "14 August, 2023" - }, - "events_session_15": { - "Calvin": [ - "Calvin secures a deal for continuation of collaboration with Frank Ocean.", - "Calvin records a podcast with some of his friends from the rap industry." - ], - "Dave": [ - "Dave hosts a card-playing night with his friends." - ], - "date": "22 August, 2023" - }, - "events_session_16": { - "Calvin": [ - "Calvin begins shooting a video for his new album on the Miami beach." - ], - "Dave": [], - "date": "31 August, 2023" - }, - "events_session_17": { - "Calvin": [ - "Calvin books a round trip flight to Boston scheduled for October 2023." - ], - "Dave": [ - "Dave returns from the car modification workshop in San Francsico with valuable insights and knowledge on car modifications." - ], - "date": "2 September, 2023" - }, - "events_session_18": { - "Calvin": [ - "Calvin launches his new album on September 11, 2023 which receives great response from the audience.", - "Calvin prepares for his next tour." - ], - "Dave": [], - "date": "13 September, 2023" - }, - "events_session_19": { - "Calvin": [], - "Dave": [ - "Dave spends some quality time with his rock band, practising jam sessions.", - "Dave attends a rock concert in Boston." - ], - "date": "15 September, 2023" - }, - "events_session_20": { - "Calvin": [], - "Dave": [ - "Dave's work on the Ford Mustang faces a holdup due to engine issues." - ], - "date": "22 September, 2023" - }, - "events_session_21": { - "Calvin": [ - "Calvin meets with some local artists to discuss collaborations in Boston." - ], - "Dave": [ - "Dave continues working on restoring the Ford Mustang he found in a junkyard." - ], - "date": "4 October, 2023" - }, - "events_session_22": { - "Calvin": [], - "Dave": [ - "Dave goes to a local car show and experiments with car modifications.", - "Dave finishes restoring a car with a custom exhaust and performance upgrades." - ], - "date": "8 October, 2023" - }, - "events_session_23": { - "Calvin": [ - "Calvin buys a new Ferrari." - ], - "Dave": [ - "Dave's car maintenance shop loses a deal to a competing local shop.", - "Dave attends a music festival headlines by The Fireworks and enjoys the show." - ], - "date": "15 October, 2023" - }, - "events_session_24": { - "Calvin": [ - "Calvin resumes his tour with Frank Ocean that ends in November.", - "Calvin plans on returning to Tokyo in November after the Frank Ocean tour ends and exploring spots like Shibuya crossing and Shinjuku." - ], - "Dave": [], - "date": "19 October, 2023" - }, - "events_session_25": { - "Calvin": [], - "Dave": [ - "Dave discusses current events and politics with his neighbors." - ], - "date": "23 October, 2023" - }, - "events_session_26": { - "Calvin": [ - "Calvin visits some of the sights in Boston with a former high school friend." - ], - "Dave": [ - "Dave attends a conference in Detroit." - ], - "date": "25 October, 2023" - }, - "events_session_27": { - "Calvin": [ - "Calvin attends a networking event to meet artists further spread his fanbase." - ], - "Dave": [ - "Dave learns how to take amazing photographs and captures a series of his travels." - ], - "date": "29 October, 2023" - }, - "events_session_28": { - "Calvin": [ - "Calvin hosts a listening party for family and friends in his Japanese mansion to celebrate his new album." - ], - "Dave": [ - "Dave starts a blog about car modifications to share his passion and knowledge.", - "Dave finishes restoring the headlights of a car he found last week and posts about it in his blog." - ], - "date": "2 November, 2023" - }, - "events_session_29": { - "Calvin": [ - "Calvin invites his high school friend to a performance at a venue in Boston.", - "Calvin supports the growth and development of some young musicians from a music program." - ], - "Dave": [], - "date": "13 November, 2023" - }, - "events_session_30": { - "Calvin": [ - "Calvin attends a high-end gala in Boston and meets interesting people.", - "Calvin accepts the invitation to perform at a show in Boston." - ], - "Dave": [ - "Dave buys a vintage camera for his photography hobby and takes pictures of nature." - ], - "date": "17 November, 2023" - } - }, - "observation": { - "session_1_observation": { - "Calvin": [ - [ - "Calvin recently had a big life change and now has a new mansion.", - "D1:3" - ], - [ - "Calvin is excited to learn about Japanese culture and is heading to Japan next month.", - "D1:5" - ], - [ - "Calvin has never been to Japan before but is fascinated by the traditions and culture.", - "D1:7" - ], - [ - "Calvin's agent found him an awesome place to stay in Japan.", - "D1:11" - ], - [ - "Calvin plans to explore the city, try different local cuisines, and collaborate with musicians during his trip to Japan.", - "D1:13" - ], - [ - "Calvin will be in Japan for a few months before heading to Boston.", - "D1:15" - ] - ], - "Dave": [ - [ - "Dave attended an awesome car event recently where he saw many classic cars and spoke with the owners.", - "D1:2" - ], - [ - "Dave spends lots of time at a beautiful calming park.", - "D1:16" - ] - ] - }, - "session_2_observation": { - "Calvin": [ - [ - "Calvin recently got a new car which is a luxury car and a dream come true for him.", - "D2:1" - ], - [ - "Calvin's new car gives him an adrenaline rush every time he steps into it.", - "D2:5" - ], - [ - "Calvin wrote some new tunes and had studio sessions last week, excited to collaborate and share the music.", - "D2:17" - ] - ], - "Dave": [ - [ - "Dave attended a music festival in Boston last weekend and enjoyed listening to many cool bands.", - "D2:8" - ], - [ - "Dave's favorite band from the music festival was Aerosmith, and he found their performance incredible.", - "D2:10" - ], - [ - "Dave didn't get a chance to hang out with Aerosmith after the show but was content with seeing them live.", - "D2:14" - ] - ] - }, - "session_3_observation": { - "Calvin": [ - [ - "Calvin recently attended a music festival in Tokyo where he met talented artists and industry professionals.", - "D3:3" - ], - [ - "Calvin received advice from a music producer to stay true to himself and sound unique, which he found motivating.", - "D3:7" - ], - [ - "Calvin dreams of touring the world, connecting with different people through music, reaching a global audience, and making an impact.", - "D3:9" - ], - [ - "Calvin is planning an upcoming trip to Boston after finishing the Frank Ocean tour to explore the music scene there.", - "D3:9" - ], - [ - "Calvin has a car that he has put a lot of work into and is looking forward to showing it to Dave when he visits Boston.", - "D3:13" - ], - [ - "Calvin is busy with rehearsals and traveling but is excited to explore Boston, try delicious food, and visit popular attractions with Dave.", - "D3:15" - ] - ], - "Dave": [ - [ - "Dave is interested in classic cars and auto engineering, as he went to a car show last weekend and finds the restoration process amazing.", - "D3:12" - ], - [ - "Dave plans to show Calvin around Boston, including places like Paradise Rock, House of Blues, and Fenway Park, known for their great music scene.", - "D3:10" - ], - [ - "Dave is looking forward to showing Calvin his favorite spots in Boston, especially in terms of food and music.", - "D3:16" - ] - ] - }, - "session_4_observation": { - "Dave": [ - [ - "Dave opened his own car maintenance shop, fulfilling his dream.", - "D4:1" - ], - [ - "Dave's dream is to work on classic cars due to his love for their design and engineering.", - "D4:5" - ], - [ - "Dave restored a classic car last year and was thrilled by the experience.", - "D4:7" - ], - [ - "Dave's shop works on all kinds of cars, including full restorations of classic cars.", - "D4:19" - ], - [ - "Dave is passionate about working on cars every day and finds it rewarding.", - "D4:21" - ], - [ - "Dave values that his work is appreciated and brings joy to others.", - "D4:23" - ] - ], - "Calvin": [ - [ - "Calvin appreciates Dave's hard work and dedication in achieving his dream of opening a car maintenance shop.", - "D4:2" - ], - [ - "Calvin acknowledges Dave's guts and ambition and supports him in pursuing his dreams.", - "D4:4" - ], - [ - "Calvin gifted a necklace with a diamond pendant as a reminder of his passion for music.", - "D4:24" - ], - [ - "Calvin is hustling as a musician and finds the necklace a reminder of why he keeps pushing forward.", - "D4:26" - ] - ] - }, - "session_5_observation": { - "Dave": [ - [ - "Dave is teaming up with a local garage and is working on a challenging project involving cars.", - "D5:1" - ], - [ - "Dave loves working with mechanics and sharing his knowledge about cars.", - "D5:3" - ], - [ - "Dave aspires to learn more about auto engineering and dreams of building a custom car from scratch.", - "D5:5" - ], - [ - "Dave finds driving with the wind in his hair calming and enjoys taking walks to destress.", - "D5:9" - ], - [ - "Dave suggests immersing oneself in things one loves, like concerts or favorite albums when facing a creativity block.", - "D5:11" - ] - ], - "Calvin": [ - [ - "Calvin enjoys long drives in a car to relax and clear his head.", - "D5:8" - ], - [ - "Calvin finds that embracing nature is calming and enjoys exploring Japanese culture.", - "D5:10" - ], - [ - "Calvin is experiencing a creative block with his music and seeks inspiration.", - "D5:10" - ] - ] - }, - "session_6_observation": { - "Calvin": [ - [ - "Calvin had an incident last week where his place got flooded, but he managed to save his music gear and favorite microphone.", - "D6:3" - ], - [ - "Calvin has a creative haven/studio where he pours his heart into making music.", - "D6:5" - ], - [ - "Calvin is excited for an upcoming performance in Tokyo this month to showcase his music to a new crowd and expand his following.", - "D6:11" - ], - [ - "Calvin took a stunning picture of the Tokyo skyline last night.", - "D6:15" - ] - ], - "Dave": [ - [ - "Dave opened his own car shop last week and celebrated with friends.", - "D6:8" - ], - [ - "Dave is excited about the journey of running his car shop and is looking forward to what the future holds.", - "D6:10" - ], - [ - "Dave expressed interest in taking a trip to Tokyo after seeing Calvin's picture of the night skyline.", - "D6:16" - ] - ] - }, - "session_7_observation": { - "Calvin": [ - [ - "Calvin recently toured with Frank Ocean and had an amazing experience performing live in Tokyo.", - "D7:1" - ], - [ - "Calvin finds working on cars to be a calming and head-clearing activity.", - "D7:3" - ], - [ - "Calvin feels a sense of achievement and relaxation similar to meditating when fixing cars.", - "D7:5" - ], - [ - "Performing live always fuels Calvin's soul and gives him a rush and a powerful connection with the crowd.", - "D7:11" - ] - ], - "Dave": [ - [ - "Dave helped his neighbor fix a car engine which he found a therapeutic and relaxing experience.", - "D7:2" - ], - [ - "Dave is passionate about fixing up things and finds it a source of achievement and purpose.", - "D7:6" - ], - [ - "Transforming things and collaborating with others give Dave a sense of power and happiness.", - "D7:7" - ], - [ - "Dave enjoys the music scene in Boston and is looking forward to Calvin's tour and performance in the city.", - "D7:16" - ] - ] - }, - "session_8_observation": { - "Calvin": [ - [ - "Calvin met with the creative team for his album in a long but awesome session.", - "D8:1" - ], - [ - "Calvin is feeling stoked about his album and the progress made with the team in the studio.", - "D8:3" - ], - [ - "Calvin has never been to Boston but is excited to visit the amazing parks next month.", - "D8:5" - ], - [ - "Calvin is keen to go on a hike to escape and de-stress.", - "D8:9" - ] - ], - "Dave": [ - [ - "Dave has been exploring parks on the weekends to relax and find peace in nature.", - "D8:4" - ], - [ - "Dave loves taking walks on the weekends to recharge for the upcoming week.", - "D8:6" - ], - [ - "Dave booked a trip to a mountainous region for next month to see majestic peaks.", - "D8:10" - ] - ] - }, - "session_9_observation": { - "Dave": [ - [ - "Dave recently joined a rock band and has been practicing the guitar.", - "D9:18" - ] - ], - "Calvin": [ - [ - "Calvin had a car accident last Friday, but no one was hurt.", - "D9:1" - ], - [ - "Calvin had to deal with insurance and repairs after the car accident.", - "D9:1" - ], - [ - "Calvin got everything related to the car accident sorted out within a week.", - "D9:5" - ], - [ - "The mechanic at the auto repair shop is working on Calvin's car.", - "D9:7" - ], - [ - "Calvin feels more confident and excited about showing off his car after the repairs.", - "D9:9" - ], - [ - "Calvin shared a photo of a beautiful view from a small town in Japan.", - "D9:13" - ], - [ - "Calvin plans to visit the snowy peaks in Japan after his tour with Frank Ocean ends.", - "D9:15" - ], - [ - "Calvin hasn't tried skiing before but is interested in giving it a try.", - "D9:17" - ] - ] - }, - "session_10_observation": { - "Dave": [ - [ - "Dave asked Calvin how his car was after the crash and if he was excited to get back on the road.", - "D10:1" - ], - [ - "Dave mentioned hanging out with friends at parks and arranging regular walks together.", - "D10:3" - ], - [ - "Dave expressed interest in visiting Japan one day due to its vibes, food, and culture.", - "D10:11" - ] - ], - "Calvin": [ - [ - "Calvin's car was in a crash but is now fixed and going strong.", - "D10:2" - ], - [ - "Calvin enjoys cruising around in his car and appreciates it.", - "D10:2" - ], - [ - "Calvin mentioned living in a Japanese mansion with an epic cityscape view.", - "D10:6" - ], - [ - "Calvin expressed excitement about trying the food and checking out the culture in Japan.", - "D10:10" - ], - [ - "Calvin is working on music collaborations with Japanese artists and is excited about it.", - "D10:12" - ] - ] - }, - "session_11_observation": { - "Dave": [ - [ - "Dave went on a road trip with his friends recently to see stunning countryside and recharge from corporate work. He loves cars.", - "D11:1" - ] - ], - "Calvin": [ - [ - "Calvin is working on transforming a Japanese mansion into a recording studio, which is his dream for creating music with other artists.", - "D11:4" - ], - [ - "Calvin has been experimenting with different genres in his music, adding electronic elements for a fresh vibe.", - "D11:6" - ], - [ - "Calvin finds experimenting with different music genres an exciting process of self-discovery and growth.", - "D11:6" - ], - [ - "Calvin collaborates with others and surrounds himself with positive energy and passion to stay motivated in his music journey.", - "D11:10" - ] - ] - }, - "session_12_observation": { - "Calvin": [ - [ - "Calvin owns a Ferrari and is attached to it, finding servicing it stressful but rewarding.", - "D12:1" - ], - [ - "Calvin feels proud of his car and sees it as a symbol of his hard work and dedication.", - "D12:7" - ], - [ - "Calvin's current goal is to expand his brand worldwide, grow his fanbase, work with artists globally, and create special music.", - "D12:11" - ], - [ - "Calvin performed with the boys last night and appreciates their musical talent.", - "D12:11" - ], - [ - "Calvin is determined to make his dreams come true and appreciates Dave's support and encouragement.", - "D12:13" - ], - [ - "Calvin expresses gratitude for Dave's continued support and encouragement in pursuing his dreams.", - "D12:15" - ] - ], - "Dave": [ - [ - "Dave finds fixing cars like therapy and feels fulfilled by refurbishing them, stemming from working on cars with his dad.", - "D12:2" - ], - [ - "Dave has fond memories of restoring an old car with his dad during one summer, finding it hard work but satisfying.", - "D12:4" - ], - [ - "Dave believes that working on projects together brings people closer.", - "D12:5" - ], - [ - "Dave encourages Calvin in his music goals, supports his dreams, and provides words of motivation and belief.", - "D12:12, D12:14, D12:16" - ] - ] - }, - "session_13_observation": { - "Dave": [ - [ - "Dave got picked for a car mod workshop and is excited about learning auto engineering and building a custom car.", - "D13:1" - ], - [ - "Dave has been doing engine swaps, suspension modifications, and is now learning about body modifications on a classic muscle car.", - "D13:7" - ], - [ - "Dave aims to give the classic muscle car a modern twist while maintaining a classic vibe.", - "D13:9" - ], - [ - "Dave values attention to detail in his car projects.", - "D13:10" - ], - [ - "Dave sees customizing cars as a way to show his style and compares it to customizing a work of art on wheels.", - "D13:13" - ], - [ - "Dave offered help to Calvin with his music stuff and has an awesome music studio setup with a high-quality sound system.", - "D13:15" - ] - ], - "Calvin": [ - [ - "Calvin encourages Dave in his car mod workshop endeavors and appreciates his enthusiasm and hard work.", - "D13:14" - ], - [ - "Calvin is busy with his music stuff and mentions having a music studio setup.", - "D13:16" - ], - [ - "Calvin appreciates Dave's encouragement and plans to keep working hard on his music.", - "D13:18" - ] - ] - }, - "session_14_observation": { - "Dave": [ - [ - "Dave recently attended a car restoration workshop in San Francisco and found it truly inspiring.", - "D14:1" - ], - [ - "Dave is currently working on restoring a car.", - "D14:11" - ], - [ - "Dave aims to have the car fully restored by the end of next month.", - "D14:13" - ] - ], - "Calvin": [ - [ - "Calvin recently toured with a well-known artist and had a show in Japan.", - "D14:4" - ], - [ - "Calvin had an amazing experience performing for an eager audience in Tokyo and felt it was a magical moment.", - "D14:6" - ], - [ - "Calvin is all about spreading joy with his art and feels proud and motivated when he can do so.", - "D14:10" - ] - ] - }, - "session_15_observation": { - "Dave": [ - [ - "Dave had a card night with friends last Friday and enjoyed it a lot.", - "D15:1" - ], - [ - "Dave expressed excitement about Calvin continuing collaboration with Frank Ocean.", - "D15:2" - ], - [ - "Dave commented on the opportunity Calvin had to meet Frank Ocean at a music festival in Tokyo.", - "D15:4" - ], - [ - "Dave found Tokyo to be an incredible experience based on Calvin's description.", - "D15:5" - ], - [ - "Dave was interested in hearing more about Calvin's collaborations and upcoming concerts.", - "D15:9" - ], - [ - "Dave expressed anticipation for listening to Calvin's podcast about the rap industry.", - "D15:13" - ] - ], - "Calvin": [ - [ - "Calvin scored a deal to continue collaboration with Frank Ocean.", - "D15:2" - ], - [ - "Calvin met Frank Ocean at a music festival in Tokyo where they clicked and recorded a song together.", - "D15:4" - ], - [ - "Calvin described Tokyo as buzzing with energy and the festival crowd as lively.", - "D15:6" - ], - [ - "Calvin's tour is ending soon and he is heading to Boston.", - "D15:10" - ], - [ - "Calvin and friends recorded a podcast discussing the rapidly evolving rap industry.", - "D15:12" - ] - ] - }, - "session_16_observation": { - "Dave": [ - [ - "Dave expresses support for Calvin's music and creative endeavors.", - "D16:11" - ], - [ - "Dave mentions a guitar that shows their different artistic styles, which he appreciates.", - "D16:13" - ] - ], - "Calvin": [ - [ - "Calvin completed a tour where he performed on a big stage and felt on top of the world.", - "D16:4" - ], - [ - "Calvin started shooting a video for his new album in Miami at an awesome beach for epic visuals.", - "D16:6" - ], - [ - "Calvin had a guitar custom made with an octopus design by his Japanese artist friend, representing his love for art and the sea.", - "D16:14" - ], - [ - "Calvin values being true to himself and his unique style in his music.", - "D16:24" - ] - ] - }, - "session_17_observation": { - "Dave": [ - [ - "Dave recently came back from San Francisco with insights and knowledge on car modification.", - "D17:1" - ], - [ - "Dave finds fixing things up and seeing them turn out better really rewarding, giving him a sense of purpose.", - "D17:3" - ], - [ - "Dave feels like he's making a difference when he fixes someone's car.", - "D17:3" - ], - [ - "Dave enjoys the feeling of making a difference and seeing the relief when the car is fixed.", - "D17:5" - ] - ], - "Calvin": [ - [ - "Calvin booked a flight ticket to Boston last week and is excited about the upcoming trip.", - "D17:6" - ], - [ - "Calvin plans to let Dave know when he is in Boston so they can catch up.", - "D17:7" - ] - ] - }, - "session_18_observation": { - "Calvin": [ - [ - "Calvin released his album on the 11th of September, which received positive feedback and motivated him to make even better music.", - "D18:1" - ], - [ - "Calvin is excited about going on a tour and growing his brand.", - "D18:7" - ], - [ - "Calvin appreciates positive feedback and views it as a motivation to reach more people with his music.", - "D18:5" - ], - [ - "Calvin values making a difference and sharing his own story through music.", - "D18:5" - ] - ], - "Dave": [ - [ - "Dave offered Calvin to check out his garage and see some cool cars when Calvin is in Boston.", - "D18:10" - ], - [ - "Dave appreciates Calvin's music and is excited for his tour.", - "D18:6" - ], - [ - "Dave is supportive and encouraging of Calvin's music career.", - "D18:4" - ] - ] - }, - "session_19_observation": { - "Dave": [ - [ - "Dave is in a band and had a fun jamming session with them.", - "D19:1" - ], - [ - "Dave attended a rock concert in Boston recently and enjoyed the atmosphere.", - "D19:1" - ], - [ - "Dave is restoring a vintage car as a hobby.", - "D19:7" - ], - [ - "Dave finds it satisfying to see his hard work pay off in restoring the car.", - "D19:9" - ] - ], - "Calvin": [ - [ - "Calvin is a fan of Ratatouille, a Disney movie that inspires following one's dreams.", - "D19:6" - ], - [ - "Calvin appreciates Dave's talent in fixing cars and finds it inspiring.", - "D19:10" - ] - ] - }, - "session_20_observation": { - "Dave": [ - [ - "Dave has a car project involving a vintage Mustang and faced engine troubles despite putting in a lot of work.", - "D20:1" - ], - [ - "Dave appreciates the hard work and dedication symbolized in a picture shared by Calvin.", - "D20:3" - ], - [ - "Dave enjoys moments of reflection and finds them not only interesting but also productive.", - "D20:5" - ], - [ - "Dave is interested in childhood songs that bring back memories.", - "D20:5" - ], - [ - "Dave suggested jamming music together with Calvin.", - "D20:9" - ], - [ - "Dave is enthusiastic about the idea of having an awesome jam session with Calvin.", - "D20:11" - ] - ], - "Calvin": [ - [ - "Calvin shared a picture that symbolizes his hard work and dedication, reminding him of his progress.", - "D20:2" - ], - [ - "Calvin has fond memories of a summer drive that made him reflect on his life's choices.", - "D20:4" - ], - [ - "Calvin had an incredible trip to Japan that he is longing to experience again.", - "D20:4" - ], - [ - "Calvin has a childhood memory associated with a song called \"California Love\" by Tupac and Dr. Dre.", - "D20:6" - ], - [ - "Calvin is excited about jamming music together with Dave.", - "D20:10" - ], - [ - "Calvin is looking forward to creating something special during the music jam session with Dave.", - "D20:16" - ] - ] - }, - "session_21_observation": { - "Calvin": [ - [ - "Calvin met with incredible artists in Boston and is excited to collaborate with them on new music.", - "D21:1" - ], - [ - "Calvin has a project that he loves working on to chill out.", - "D21:3" - ], - [ - "Calvin expressed satisfaction in bringing something back to life, referring to an opportunity.", - "D21:7" - ], - [ - "Calvin values hard work, dedication, growth, progress, and determination in achieving goals.", - "D21:13" - ] - ], - "Dave": [ - [ - "Dave finds working on cars therapeutic and relaxing, particularly restoring an old Ford Mustang found in a junkyard.", - "D21:4" - ], - [ - "Dave expressed satisfaction in bringing an old car back to life and transforming it into something beautiful.", - "D21:6" - ], - [ - "Dave's hands are permanently stained with grease from working on cars, showing his dedication and hard work.", - "D21:8" - ], - [ - "Dave values hard work, small successes, progress, determination, and growth in projects and personal development.", - "D21:12" - ] - ] - }, - "session_22_observation": { - "Dave": [ - [ - "Dave went to a car show last Friday and enjoys checking out awesome cars and car mods.", - "D22:1" - ], - [ - "Dave restored and modified a car himself, adding a custom exhaust and performance upgrades.", - "D22:3" - ], - [ - "Working on cars is like therapy for Dave and a way to get away from everyday stress. He sees it as more than a hobby, but a passion.", - "D22:5" - ], - [ - "Dave spent hours as a child tinkering with engines in his dad's garage, finding it to be like his sanctuary.", - "D22:5" - ], - [ - "Dave's job involves taking something broken and making it into something awesome.", - "D22:5" - ], - [ - "Restoring things can be tough for Dave, but the feeling of accomplishment it gives him is great.", - "D22:7" - ], - [ - "Dave's ultimate goal has been doing what he loves for a living.", - "D22:6" - ], - [ - "Dave finds conversations like the one with Calvin to be a reminder of why he loves what he does.", - "D22:9" - ] - ], - "Calvin": [ - [ - "Calvin is interested in checking out Dave's garage and the awesome cars he has worked on.", - "D22:2" - ], - [ - "Calvin admires Dave for his work on restoring and modifying the car, calling it a masterpiece.", - "D22:4" - ], - [ - "Calvin recognizes that working on cars can be a real escape from reality and understands the feeling of doing what you love for a living.", - "D22:6" - ], - [ - "Calvin encourages Dave to keep working hard and living his best life, appreciating his progress.", - "D22:8" - ], - [ - "Calvin supports and encourages Dave, reminding him to stay focused and keep going for his dreams.", - "D22:12" - ] - ] - }, - "session_23_observation": { - "Dave": [ - [ - "Dave works at a car maintenance shop.", - "D23:1" - ], - [ - "Dave recently attended a music festival and enjoyed the energy, music, and crowd.", - "D23:5" - ], - [ - "Dave enjoys music festivals and feels alive at such events.", - "D23:5" - ], - [ - "Dave finds fulfillment in fixing things and enjoys the feeling of making something whole again.", - "D23:11" - ] - ], - "Calvin": [ - [ - "Calvin values the importance of believing in oneself and not giving up.", - "D23:2" - ], - [ - "Calvin finds motivation in reminding himself of his passion for goals and taking breaks for favorite activities.", - "D23:4" - ], - [ - "Calvin loves his job and enjoys connecting with the crowd, particularly through music.", - "D23:10" - ], - [ - "Calvin recently acquired a new Ferrari and is looking forward to thrilling rides and journeys.", - "D23:16" - ] - ] - }, - "session_24_observation": { - "Calvin": [ - [ - "Calvin started touring with Frank Ocean, which started in August last year after they met at a festival.", - "D24:1" - ], - [ - "Calvin is excited about the energy from the crowd during the tour and feels lucky to collaborate and perform with Frank Ocean.", - "D24:3" - ], - [ - "Calvin and Frank Ocean have a great chemistry on stage, and Calvin feels fortunate about it.", - "D24:5" - ], - [ - "Calvin and Frank Ocean are in tune and rock on stage together.", - "D24:6" - ], - [ - "Calvin performed in Tokyo during a concert and felt the energy of the crowd was nuts.", - "D24:11" - ], - [ - "Calvin is planning to visit Tokyo next month after the tour ends and wishes sometimes to go back to places similar to those in the shared photos.", - "D24:17" - ], - [ - "Calvin is excited to explore Shibuya Crossing and Shinjuku in Tokyo and is looking forward to trying the amazing food there.", - "D24:19" - ], - [ - "Calvin has never tried ramen but is planning to try it while in Tokyo.", - "D24:21" - ] - ], - "Dave": [ - [ - "Dave is interested in Calvin's tour experience and acknowledges the connection Calvin feels with the crowd and performing on stage.", - "D24:2" - ], - [ - "Dave found a photo online of Tokyo that makes him dream about visiting someday and is interested in learning about the city.", - "D24:12" - ], - [ - "Dave wants to explore the people, culture, food, and vibrant city life of Tokyo and considers it an unforgettable experience.", - "D24:14" - ], - [ - "Dave asks Calvin about specific spots in Tokyo he is excited to check out, such as Shibuya Crossing and Shinjuku.", - "D24:18" - ], - [ - "Dave shares a photo of a ramen bowl he tried in Boston and recommends Calvin to try it in Tokyo.", - "D24:20" - ] - ] - }, - "session_25_observation": { - "Dave": [ - [ - "Dave pondered about fame and its impact on relationships in a conversation with Calvin.", - "D25:1" - ], - [ - "Dave recently had a conversation with neighbors about current events and politics to share perspectives.", - "D25:7" - ], - [ - "Dave finds working on cars to be cathartic and a way to connect back with himself.", - "D25:13" - ], - [ - "Dave started working on cars at the age of ten after finding an old car in a neighbor's garage.", - "D25:15" - ], - [ - "Dave restored a car last year but sold it to a collector and is currently working on a new project.", - "D25:17" - ], - [ - "Dave believes paying attention to details in car restoration makes a big impact.", - "D25:20" - ] - ], - "Calvin": [ - [ - "Calvin is on tour with Frank Ocean and finds it incredible, connecting with the crowd.", - "D25:2" - ], - [ - "Calvin finds joy in what he does even though it can be overwhelming due to the demands.", - "D25:4" - ], - [ - "Calvin finds having a strong support system including friends and team helpful in managing everything.", - "D25:6" - ], - [ - "Listening to struggles people go through inspires Calvin in his music, leading him to dig deeper into capturing feelings.", - "D25:10" - ], - [ - "Calvin expresses himself through music, viewing it as a form of therapy and self-expression.", - "D25:12" - ], - [ - "Calvin believes in paying attention to small details to create something extraordinary in his work as an artist.", - "D25:22" - ] - ] - }, - "session_26_observation": { - "Calvin": [ - [ - "Calvin recently visited Boston with a high school friend and found it fun and eye-opening.", - "D26:1" - ], - [ - "Calvin appreciates architecture and history, finding them really cool in Boston.", - "D26:3" - ], - [ - "Calvin is into cars and enjoys making new and powerful models.", - "D26:5" - ], - [ - "Calvin is passionate about music and performing, finding it like his purpose and passion.", - "D26:9" - ], - [ - "Calvin values inspiration and mutual support with Dave, aiming to keep inspiring each other to be their best selves.", - "D26:11" - ] - ], - "Dave": [ - [ - "Dave recently attended a conference in Detroit and learned a lot from it.", - "D26:2" - ], - [ - "Dave is into working on cars, finding it calming and a way to take him out of his head.", - "D26:4" - ], - [ - "Dave got into engineering cars at a young age after being impressed by a car show with his dad.", - "D26:6" - ], - [ - "Dave is supportive and inspiring towards Calvin's music pursuits.", - "D26:7" - ], - [ - "Dave expresses gratitude and mutual support towards Calvin, aiming to keep inspiring each other.", - "D26:14" - ] - ] - }, - "session_27_observation": { - "Calvin": [ - [ - "Calvin went to a networking event to meet more artists to build up his fan base.", - "D27:1" - ], - [ - "Calvin has had some great collaborations recently and his album is almost finished.", - "D27:11" - ] - ], - "Dave": [ - [ - "Dave has been getting into photography recently and has taken some great shots.", - "D27:2" - ], - [ - "Dave has a car project that he recently finished restoring and it looks amazing.", - "D27:10" - ] - ] - }, - "session_28_observation": { - "Calvin": [ - [ - "Calvin threw a small party at his Japanese house last week to celebrate his new album with love and support from family and friends.", - "D28:1" - ], - [ - "Calvin feels that seeing everyone come together to celebrate is super rewarding.", - "D28:3" - ], - [ - "Calvin's music is about creating something that brings people together, connects with them, and brings joy.", - "D28:7" - ], - [ - "Calvin has a studio setup where he works, surrounded by music videos, concerts, and documentaries for inspiration.", - "D28:29" - ], - [ - "Calvin watches music videos, concerts, and documentaries about artists and their creative process on TV for inspiration.", - "D28:31" - ], - [ - "Calvin finds inspiration from watching TV to learn more about the music industry and others' creative processes.", - "D28:31" - ] - ], - "Dave": [ - [ - "Dave recently started a blog on car mods to share his passion with others.", - "D28:8" - ], - [ - "Dave shared a story on his blog about how he made a car look like a beast and inspired others to start their DIY projects.", - "D28:10" - ], - [ - "Dave appreciates when Calvin notices the effort he puts into his work.", - "D28:18" - ], - [ - "Dave listens to vinyl while doing car work to relax and stay on track.", - "D28:36" - ], - [ - "Dave is into classic rock music and believes music from that era is timeless.", - "D28:40" - ] - ] - }, - "session_29_observation": { - "Calvin": [ - [ - "Calvin performed in Boston and invited his old high school buddy to watch, which made him feel good.", - "D29:1" - ], - [ - "Calvin is nostalgic about his past, particularly about freestyling with friends when they were teenagers.", - "D29:3" - ], - [ - "Calvin's friend has been supportive and encouraging throughout his journey, making a big difference.", - "D29:5" - ], - [ - "Calvin supports and collaborates with young musicians from a music program, finding their enthusiasm inspiring.", - "D29:9" - ], - [ - "Calvin mentioned working with a young artist to create a beat, recognizing the artist's potential in music.", - "D29:13" - ], - [ - "Calvin enjoys trying out new sounds and pushing boundaries in his music studio to stay ahead.", - "D29:15" - ] - ], - "Dave": [ - [ - "Dave acknowledges the importance of having supportive friends since the start to help artists like Calvin.", - "D29:2" - ], - [ - "Dave praises Calvin for supporting young musicians and nurturing their passion to help them thrive.", - "D29:10" - ], - [ - "Dave encourages Calvin to keep pushing boundaries and exploring new ideas to grow as an artist.", - "D29:16" - ] - ] - }, - "session_30_observation": { - "Dave": [ - [ - "Dave has taken up photography as a new hobby and enjoys capturing the scenery around him.", - "D30:1" - ], - [ - "Dave recently bought a new vintage camera that takes great photos.", - "D30:5" - ], - [ - "Dave loves capturing the beauty of nature in his photos, focusing on sunsets, beaches, and waves.", - "D30:9" - ], - [ - "Dave shared a peaceful photo of rocks and a waterfall that he took last week.", - "D30:13" - ], - [ - "Dave enjoys capturing serene spots in nature, like a nearby park with a peaceful scene under a tree with pink flowers.", - "D30:17" - ] - ], - "Calvin": [ - [ - "Calvin attended a fancy gala in Boston recently and met interesting people.", - "D30:2" - ], - [ - "Calvin had an inspiring conversation with a cool artist about music and art at the gala.", - "D30:4" - ], - [ - "Calvin has accepted an invitation to perform at an upcoming show in Boston, which he is excited about.", - "D30:20" - ], - [ - "Calvin enjoys capturing photos, including a beautiful shot in a Japanese garden.", - "D30:16" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Calvin and Dave met at 11:53 am on 23 March, 2023. Dave attended a car event with classic cars, while Calvin shared his big life change by getting a new mansion and planning a trip to Japan to learn about the culture. Calvin will stay there for a few months before heading to Boston. Dave recommended a calming park and offered further recommendations for Calvin's trip, ending with Calvin thanking him and promising to stay in touch.", - "session_2_summary": "Calvin told Dave about his new luxury car at 4:45 pm on 26 March, 2023, expressing excitement and gratitude. Dave congratulated Calvin and admired the car. Calvin shared a picture and mentioned it was his first luxury car, feeling like it was well-deserved after hard work. Dave then talked about a music festival he attended in Boston and his favorite band, Aerosmith. They discussed the experience of seeing favorite artists perform. Calvin mentioned working on new music and collaborations. Dave showed interest and wished Calvin well. They exchanged goodbyes, ending their conversation.", - "session_3_summary": "Calvin and Dave spoke at 4:15 pm on 20 April, 2023. Calvin shared about attending a music festival in Tokyo where he met industry professionals and received advice. He dreams of touring the world and reaching a global audience with his music. Dave plans to show Calvin around Boston, known for its vibrant music scene. Dave also mentioned his passion for classic cars. Calvin looks forward to exploring Boston's food scene with Dave. They both expressed excitement about the upcoming visit.", - "session_4_summary": "Dave shared with Calvin at 6:24 pm on 1 May, 2023, that he fulfilled his dream of opening a car maintenance shop. Calvin congratulated him, acknowledging his hard work and ambition. Dave expressed his love for classic cars and showed Calvin a photo of an engine he restored. Calvin admired it and they discussed the satisfaction of seeing hard work pay off. They emphasized the importance of pursuing dreams and staying motivated. Dave invited Calvin to visit his shop, where he works on various cars, and they discussed the fulfillment of working on cars. Calvin complimented Dave on his work and shared a diamond pendant necklace received as a gift, symbolizing their shared dedication to their passions. They encouraged each other to stay motivated and keep pursuing their dreams.", - "session_5_summary": "At 1:16 pm on 3 May 2023, Dave informed Calvin that he was teaming up with a local garage, showcasing a challenging project they were working on. Calvin congratulated Dave on his new venture and praised his progress since their last conversation. Dave expressed his love for the new job, sharing his excitement about working with mechanics and his dream to build a custom car. Calvin admired Dave's dedication and encouraged him to relax amidst the hard work. They exchanged relaxation hobbies, with Dave enjoying walks and Calvin finding solace in long drives. Calvin mentioned a creative block in music, seeking advice from Dave, who suggested immersing in favorite activities for inspiration. Calvin appreciated the tip and expressed gratitude for Dave's support, promising to stay in touch. They bid farewell, with Dave offering ongoing assistance.", - "session_6_summary": "Calvin and Dave caught up at 11:50 am on 16 May, 2023. Calvin shared about a recent flood incident in his place but managed to save his music gear. Dave offered support, and Calvin showed his creative haven where he makes music. Dave mentioned opening his car shop and Calvin congratulated him. Calvin mentioned an upcoming performance in Tokyo, and Dave wished him luck. Calvin shared a stunning night skyline photo from Tokyo, which Dave admired, expressing a desire to visit. Calvin encouraged Dave to visit, and Dave expressed interest in adding Tokyo to his list of places to visit.", - "session_7_summary": "Calvin and Dave caught up at 6:06 pm on 31 May, 2023. Calvin shared his experience touring with Frank Ocean, finding it exhilarating. Dave showed a car he fixed, finding it therapeutic. They discussed how working on cars gives them a sense of accomplishment and purpose. Both feel empowered by transforming things. Calvin is excited about the upcoming tour in Boston. Dave supports Calvin's music journey and looks forward to hearing about the Boston music scene when Calvin returns.", - "session_8_summary": "Calvin and Dave chat at 2:31 pm on 9 June, 2023. Calvin updates Dave about his album progress with the creative team and expresses excitement. Dave shares a picture of a studio, and they discuss enjoying nature in Boston parks. Dave mentions exploring parks and Calvin looks forward to experiencing them. They talk about hiking, with Dave planning a mountain trip. Dave will share pictures with Calvin later. Calvin wishes Dave safe travels and they say goodbye.", - "session_9_summary": "At 3:15 pm on 21 June 2023, Calvin told Dave about his recent car accident but mentioned that nobody was hurt. Dealing with insurance and repairs was time-consuming. Dave inquired about any insurance issues experienced. Calvin shared that after a week, everything was sorted out. The mechanic, pictured, was skilled, making Calvin confident about the repair. Dave praised the expert fixing the car. Calvin expressed excitement about driving again. They discussed a beautiful view from Japan, which Calvin plans to visit after a tour. Dave shared joining a rock band, playing guitar. They agreed to keep in touch before ending the conversation.", - "session_10_summary": "Dave checked in with Calvin about his car post-crash at 7:56 pm on 7 July 2023. Calvin confirmed the car was fixed and going strong. They discussed regular walks in the park and admired each other's photos of scenic views. Calvin shared plans about exploring Japan for its food and culture, mentioning music collaborations he was working on. Dave expressed interest in visiting Japan for its vibes and tech. They discussed collaborating with artists and creating unique music. Calvin appreciated the support and concluded the chat, with Dave reminding him to not overwork.", - "session_11_summary": "Dave and Calvin reconnect at 6:38 pm on 21 July, 2023, discussing recent experiences that sparked their passions. Dave shares about his rejuvenating road trip, emphasizing how it reminded him of his love for cars. Calvin updates Dave on his project of turning a Japanese mansion into a recording studio, highlighting his passion for music. They exchange supportive words, with Dave commending Calvin's progress and Calvin appreciating Dave's encouragement. The conversation ends with the friends pledging to keep pushing themselves towards their goals and promising to chat again soon.", - "session_12_summary": "Calvin and Dave spoke at 1:12 pm on 3 August, 2023. Calvin mentioned his stressful experience with servicing his Ferrari and his attachment to the car, asking Dave about hobbies that make him feel restored. Dave, finding car repairs therapeutic, shared how working on cars with his dad as a child brings him fulfillment. Remembering restoring an old car together, Dave cherished the bonding experience with his dad. Calvin admired Dave's hobby and shared his pride in his Ferrari, crediting it as a reminder of hard work and dedication. They discussed motivation and goals, with Calvin aiming to expand his music brand globally and grow his fanbase. Dave encouraged Calvin, emphasizing the importance of making an impact through music. Calvin valued Dave's support and expressed determination to achieve his dreams, with Dave offering continuous encouragement and faith in Calvin's journey.", - "session_13_summary": "Dave and Calvin, at 5:22 pm on 11 August, 2023, discussed Dave's participation in a car mod workshop where he was excited to learn about auto engineering and building a custom car. Calvin admired Dave's enthusiasm and attention to detail in customizing cars, with Dave showcasing his current project involving engine swaps and body modifications. Calvin praised Dave's work, highlighting the modern twist and unique style in his car designs. Dave expressed gratitude for Calvin's support and offered assistance with Calvin's music endeavors. The conversation ended with well wishes and mutual admiration between the two friends.", - "session_14_summary": "Dave and Calvin caught up at 12:35 am on 14 August, 2023. Dave shared his exciting experience at a car workshop in San Francisco, expressing admiration for the passion and dedication of the people there. Calvin then talked about touring with a well-known artist, culminating in a show in Japan. Dave was impressed by Calvin's performance and the crowd's reaction in Tokyo. Calvin shared a picture from the show, noting the joyful atmosphere. Dave complimented Calvin on his work and shared a picture of a car he is restoring, aiming to finish it by the end of the next month. Calvin praised Dave's talent and encouraged him to keep going. They ended the conversation with words of support and encouragement for each other.", - "session_15_summary": "On August 22, 2023, at 11:06 am, Dave and Calvin caught up after a while. Dave shared about his fun card night with friends, while Calvin excitedly revealed his ongoing collaboration with Frank Ocean. Calvin met Frank Ocean at a music festival in Tokyo, and they even recorded a song together. Calvin described the festival in Tokyo as an amazing experience that fueled his soul, with a lively crowd. He also mentioned another collaboration that stemmed from his performance there. Calvin's upcoming tour includes a stop in Boston, where he planned to meet up with Dave. Calvin also mentioned recording a podcast with friends about the rap industry. Dave expressed interest in the podcast and looked forward to catching up and learning more.", - "session_16_summary": "Dave and Calvin caught up at 2:55 pm on 31 August, 2023. Calvin shared his amazing experience performing on a big stage and shooting a video in Miami for his new album. Dave showed support and praised Calvin's unique guitar, symbolizing his journey and passion for music. They discussed the importance of staying true to one's style and being unique in music. Dave encouraged Calvin to keep sharing his unique talent with the world.", - "session_17_summary": "At 9:19 am on 2 September 2023, Dave and Calvin caught up, with Dave sharing car modification insights from San Francisco. Calvin understood the joy of fixing cars, describing it as giving them new life. Dave found fixing cars rewarding and purposeful, feeling like a superhero when helping others. Calvin praised Dave for his impact, revealing his excitement for an upcoming trip to Boston. They agreed to meet up in Boston and said their goodbyes with plans to reconnect upon Calvin's arrival.", - "session_18_summary": "Calvin and Dave talked on 13 September at 10:56 am. Calvin's album released on the 11th, getting positive feedback and motivating him to create more music. Dave congratulated Calvin, discussing the impact of his work, inspiring Calvin to chase his dreams. Calvin shared his upcoming tour plans and dream to reach more people. Dave offered to show Calvin his garage someday. They ended the conversation with well wishes and plans to meet in Boston.", - "session_19_summary": "Dave and Calvin caught up at 12:13 am on 15 September, 2023. Dave shared about a fun jam session with his band and attending a rock concert in Boston. Calvin praised the electrifying atmosphere of rock concerts and asked about recordings of the jam. Dave mentioned they were too engrossed to record. Calvin appreciated the moments that can't be captured and shared a Disney poster. They discussed Ratatouille, following dreams, and Dave's car restoration hobby. Dave showed the final restoration picture, which Calvin admired. Dave found it satisfying and appreciated the reactions. Calvin praised Dave's work, calling it inspiring, and encouraged him to continue. They exchanged goodbyes, highlighting the importance of making people happy and following passions.", - "session_20_summary": "At 8:57 pm on 22 September, 2023, Dave and Calvin caught up. Dave shared that he had trouble with his car project, and Calvin showed a picture symbolizing his hard work. They discussed memorable experiences, including Calvin's summer drive and trip to Japan. Calvin mentioned a childhood song, \"California Love\" by Tupac and Dr. Dre. They planned a jam session together, looking forward to creating special memories. They concluded by expressing excitement and anticipation for their upcoming get-together.", - "session_21_summary": "Calvin and Dave spoke at 2:44 pm on 4 October, 2023. Calvin shared his inspiring experience of meeting with artists in Boston for a collaboration. Dave praised the connection and shared his hobby of restoring cars. They discussed the satisfaction of transforming old things, expressed pride in their work, and highlighted the importance of hard work and determination. Both agreed to stay focused on their goals and support each other. They concluded with well wishes for each other.", - "session_22_summary": "Dave and Calvin, at 3:13 pm on 8 October, 2023, discussed Dave's passion for cars. Dave described his recent visit to a car show and his love for car mods. He showed Calvin a car he restored and modified himself, expressing pride in his work. Dave shared that working on cars is therapeutic for him and a lifelong passion. Calvin admired Dave's dedication and viewed his work as inspiring. They discussed the satisfaction of restoring broken items and pursuing one's passion. Calvin encouraged Dave to chase his dreams, while Dave appreciated Calvin's support and encouragement. Dave expressed gratitude for Calvin's support, and they mutually encouraged each other to keep pushing forward.", - "session_23_summary": "Dave and Calvin had a conversation on October 15, 2023, at 9:39 am. Dave expressed feeling discouraged after losing a deal at work, questioning if his efforts were a waste of time. Calvin encouraged him not to give up and stay positive, sharing his approach to staying motivated during setbacks. They discussed the importance of passion, support from others, and taking breaks for rejuvenation. Dave shared his experience at a music festival, and Calvin admired the energy of the crowd and the headliner, The Fireworks. They bonded over their love for music and the satisfaction of their respective jobs. Calvin showed Dave a photo of his new Ferrari, sparking excitement for thrilling adventures. They exchanged kind words before parting ways, looking forward to uplifting each other in the future.", - "session_24_summary": "Calvin and Dave caught up at 10:11 am on 19 October, 2023. Calvin shared about touring with Frank Ocean, feeling a great connection with the crowd, and performing on stage. Dave congratulated him and asked about the tour, showing excitement about their collaboration. Calvin mentioned meeting Frank at a festival and feeling lucky to perform with him. They discussed the energy of the crowd, shared photos of the tour, and talked about Tokyo's vibrant atmosphere. Calvin revealed his upcoming trip to Tokyo, mentioning places like Shibuya Crossing and Shinjuku he's excited to visit. Dave recommended trying ramen, sharing a photo of a bowl he tried in Boston. Calvin agreed to give it a shot in Tokyo and appreciated the encouragement. They ended the conversation with Calvin looking forward to the trip and saying goodbye.", - "session_25_summary": "Dave and Calvin had a conversation at 2:17 pm on 23 October, 2023. Dave inquired about Calvin's tour with Frank Ocean, discussing the challenges of fame balancing personal life and work. Calvin mentioned enjoying the tour but finding it tough to manage everything. He highlighted the importance of a strong support system. Dave shared his interests in connecting with neighbors and working on cars. Calvin discussed using music to express emotions and connect with fans. Dave talked about his passion for car engineering since childhood and detailed his current car restoration project. They emphasized the significance of attention to detail in their respective crafts. The conversation concluded with Calvin expressing the rush of connecting with fans during concerts and wishing Dave well.", - "session_26_summary": "Calvin and Dave caught up at 8:25 pm on 25th October 2023. Calvin enjoyed his time in Boston with a high school friend, admiring architecture and history. Dave shared about attending a conference in Detroit and his interest in engineering cars. Calvin found Dave's car project impressive and shared his passion for music. Dave admired Calvin's performance with someone he admired. They both agreed on the fulfillment that their passions bring, inspiring each other to keep pursuing their dreams. They expressed support for each other before signing off, looking forward to what the future holds.", - "session_27_summary": "Calvin told Dave about his networking event with artists to build his fan base. Dave shared his new interest in photography, showing Calvin pictures of a stunning sunset and a clock tower in Boston. Calvin praised Dave's talent and they discussed their creative projects, including Dave's car restoration and Calvin's upcoming album. They agreed to catch up soon.", - "session_28_summary": "Calvin and Dave caught up at 5:46 pm on 2 November, 2023. Calvin shared with Dave about throwing a party for his new album last week at his Japanese house, receiving immense love and support from friends and family. Dave congratulated him on the album release and the party, praising Calvin for creating something that brings people together. Calvin, in turn, appreciated the positive feedback and mentioned how such moments motivate him to continue growing. Dave shared his passion for car mods and his new blog, seeking tips from Calvin on blogging. Calvin admired Dave's efforts, especially sharing his knowledge and inspiring others. They also discussed Dave's car restoration project, his rock band, and their mutual love for music. Calvin shared insights into his studio setup, mentioning his passion for watching music-related content on TV for inspiration. Dave showed Calvin his notebook for writing lyrics and the vintage music player he uses while working on cars. They exchanged thoughts on music's influence on productivity and mood. Calvin encouraged Dave to keep discovering new tunes and broadening his musical knowledge before parting ways as Dave had to return to work.", - "session_29_summary": "Calvin talked about reminiscing with an old high school friend, mentioning the importance of relationships in the music industry. He highlighted the support and encouragement he received, especially from the friend he met in Boston. Dave agreed on the significance of supportive people and discussed Calvin's involvement with young musicians. Calvin emphasized the importance of passing on the torch and supporting new talent. He also mentioned his experimentation with new sounds in his music studio. Dave encouraged Calvin to keep exploring and evolving as an artist. The conversation ended with both agreeing on the importance of growth and looking forward to seeing Calvin's future creations.", - "session_30_summary": "Dave and Calvin reconnect at 10:54 am on 17 November, 2023. Dave mentions his newfound love for photography, especially capturing nature scenes. Calvin shares about attending a fancy gala in Boston and connecting with an artist over music and art. They discuss their passion for photography and the beauty of nature. Calvin reveals he will be performing at a show in Boston soon, exciting Dave. They plan to catch up in person soon and bid farewell, looking forward to seeing each other again." - }, - "sample_id": "conv-50" - } -] \ No newline at end of file diff --git a/methods/EverCore/data_format/conversation/conversation_format.md b/methods/EverCore/data_format/conversation/conversation_format.md deleted file mode 100644 index 0e9a5d92..00000000 --- a/methods/EverCore/data_format/conversation/conversation_format.md +++ /dev/null @@ -1,289 +0,0 @@ -# Conversation Format Specification - -## Overview - -This is an open-source group chat data format specification for standardizing the storage and exchange of group chat conversation data. - -## Format Definition - -For the complete format definition, please refer to [`conversation_format.py`](../conversation_format.py) - -## Core Features - -### 1. Separated Metadata and Message List - -```json -{ - "version": "1.0.0", - "session_meta": { ... }, - "conversation_list": [ ... ] -} -``` - -- **version**: Format version number (follows semantic versioning) -- **session_meta**: Session metadata -- **conversation_list**: Message list - -### 2. Scene Types and Scene Descriptions - -Two core conversation scenes are supported: - -- **solo**: Human-AI assistant conversation scene, one-on-one dialogue, AI acts as a personal assistant -- **team**: Work group chat scene, multi-person group chat, team collaboration - -**Solo Scene Example**: -```json -"session_meta": { - "scene": "solo", - "scene_desc": { - "description": "User and AI assistant conversation about Beijing tourism, health management, and sports rehabilitation" - }, - ... -} -``` - -**Team Scene Example**: -```json -"session_meta": { - "scene": "team", - "scene_desc": {}, - ... -} -``` - -- **scene**: Scene type identifier (`solo` or `team`) -- **scene_desc**: Scene description information - - Solo scene: Contains `description` field, describing the conversation scene - - Team scene: Usually an empty object, representing a multi-person collaboration scene - -### 3. User Details - -All user details are centrally stored in `session_meta.user_details`: - -```json -"user_details": { - "user_101": { - "full_name": "Alex", - "role": "user", - "custom_role": "Tech Lead", - "department": "Technology", - "email": "alex@example.com" - }, - "robot_001": { - "full_name": "AI Assistant", - "role": "assistant" - } -} -``` - -**Field Descriptions**: -- `full_name`: User's display name (optional) -- `role`: User type role (`user` for human, `assistant` for AI) (optional) -- `custom_role`: User's job/position role (e.g., Tech Lead, Product Manager) (optional) -- `department`: Department (optional) -- `email`: Email address (optional) -- `extra`: Additional extended information (optional) - -### 4. Message Structure - -Each message uses user ID as `sender`, with an optional `sender_name` for readability: - -```json -{ - "message_id": "msg_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "user_103", - "sender_name": "Chen", - "role": "user", - "type": "text", - "content": "Message content", - "refer_list": [] -} -``` - -### 4.1 Message Sender Role - -The optional `role` field identifies the source of the message: - -- **user**: Message from a human user -- **assistant**: Message from an AI assistant - -This is compatible with OpenAI/mem0/memos message format. - -**Human Message Example**: -```json -{ - "message_id": "msg_001", - "sender": "user_101", - "sender_name": "Alex", - "role": "user", - "type": "text", - "content": "Can you help me summarize this document?" -} -``` - -**AI Response Example**: -```json -{ - "message_id": "msg_002", - "sender": "robot_001", - "sender_name": "AI Assistant", - "role": "assistant", - "type": "text", - "content": "Here's the summary of the document..." -} -``` - -### 5. Timezone-Aware Timestamps - -- Uses ISO 8601 format -- Recommended to include timezone information (e.g., `+00:00`) -- If a message doesn't have timezone information, it can be obtained from `session_meta.default_timezone` - -### 6. Message Types - -Multiple message types are supported: - -- **text**: Text message -- **image**: Image message -- **file**: File message -- **audio**: Audio message -- **video**: Video message -- **link**: Link message -- **system**: System message - -### 7. Message References - -Flexible reference methods are supported, each element in `refer_list` can be: - -**Method 1: String Reference (message_id only)** -```json -"refer_list": ["msg_002", "msg_005"] -``` - -**Method 2: Object Reference (only message_id is required, other fields are optional)** - -Minimal form: -```json -"refer_list": [ - { - "message_id": "msg_002" - } -] -``` - -Including partial fields (e.g., content for preview): -```json -"refer_list": [ - { - "message_id": "msg_002", - "content": "Good morning. Let's align on the goal first. Is it an MVP for internal testing or direct customer pilot?" - } -] -``` - -Including complete information: -```json -"refer_list": [ - { - "message_id": "msg_002", - "create_time": "2025-02-01T10:01:00+00:00", - "sender": "user_102", - "sender_name": "Betty", - "type": "text", - "content": "Good morning. Let's align on the goal first. Is it an MVP for internal testing or direct customer pilot?", - "refer_list": [] - } -] -``` - -**Method 3: Mixed Usage** -```json -"refer_list": [ - "msg_001", - { - "message_id": "msg_002", - "content": "Partial content..." - } -] -``` - -**Usage Recommendations:** -- String reference: Most concise, suitable for scenarios with clear reference relationships -- Minimal object reference: Only message_id, suitable for scenarios requiring unified format but no additional information -- With preview content: Includes message_id + content, suitable for quick preview scenarios -- Complete object reference: Includes all fields, suitable for export, archiving, or use independent of original data - -### 8. Extension Fields - -Use `extra` field to store additional information: - -```json -"extra": { - "file_name": "UI_draft_v1.pdf", - "file_size": 2048576, - "file_type": "application/pdf" -} -``` - -## Example Files - -- [`conversation_format_example.json`](./conversation_format_example.json) - Complete example file -- [`conversation_compatible.json`](./conversation_compatible.json) - Legacy format compatibility example - -## Usage - -### Python - -```python -from data.conversation_format import ConversationFormat, validate_conversation_format -import json - -# Read group chat data -with open('conversation_example.json', 'r', encoding='utf-8') as f: - data = json.load(f) - -# Validate format -is_valid = validate_conversation_format(data) -print(f"Format validation: {'Passed' if is_valid else 'Failed'}") - -# Access user information -user_details = data['session_meta']['user_details'] -print(f"User list: {list(user_details.keys())}") - -# Iterate through messages -for msg in data['conversation_list']: - sender_info = user_details[msg['sender']] - print(f"{sender_info['full_name']}: {msg['content']}") -``` - -### Creating New Conversation Data - -```python -from data.conversation_format import create_example_conversation -import json - -# Create example data -chat_data = create_example_conversation() - -# Save to file -with open('my_chat.json', 'w', encoding='utf-8') as f: - json.dump(chat_data, f, ensure_ascii=False, indent=2) -``` - -## Version History - -- **1.0.0** (2025-02-01) - - Initial version - - Support for basic message types - - Support for user details - - Support for message references - - Support for timezone-aware timestamps - -## Contributing - -Issues and Pull Requests are welcome to improve this format specification. - -## License - -Open source license to be determined diff --git a/methods/EverCore/data_format/conversation/conversation_format.py b/methods/EverCore/data_format/conversation/conversation_format.py deleted file mode 100644 index 634051ee..00000000 --- a/methods/EverCore/data_format/conversation/conversation_format.py +++ /dev/null @@ -1,364 +0,0 @@ -""" -Open source conversation format definition - -This module defines a standard conversation data format for storing and exchanging conversation data. -The format design considers extensibility, readability, and data completeness. -""" - -from typing import TypedDict, List, Optional, Literal, Dict, Any - - -# Role type for identifying message/user source (human or AI) -# Compatible with OpenAI/mem0/memos message format -RoleType = Literal["user", "assistant"] - - -# User detailed information -class UserDetail(TypedDict, total=False): - """User detailed information - - Attributes: - full_name: User full name (optional) - role: User type role ("user" for human, "assistant" for AI) - custom_role: User's job/position role (e.g. product manager, technical lead, etc.) - email: Email address (optional) - avatar_url: Avatar URL (optional) - department: Department (optional) - extra: Other extended information (optional) - """ - - full_name: Optional[str] - role: Optional[RoleType] - custom_role: Optional[str] - email: Optional[str] - avatar_url: Optional[str] - department: Optional[str] - extra: Optional[Dict[str, Any]] - - -# Session meta information -class SessionMeta(TypedDict, total=False): - """Session meta information - - Attributes: - scene: Scene type, supports "solo" (1 user + N agents) or "team" (multi-user + agents) (optional) - scene_desc: Scene description information (optional) - name: Group chat name - description: Group chat description - group_id: Group chat unique identifier (optional) - created_at: Group chat creation time (optional) - default_timezone: Default timezone, if the message has no timezone information, use this timezone (optional) - user_details: User detailed information dictionary, key is user ID - tags: Tag list (optional) - extra: Other extended information (optional) - """ - - scene: Optional[str] - scene_desc: Optional[Dict[str, Any]] - name: str - description: str - group_id: Optional[str] - created_at: Optional[str] - default_timezone: Optional[str] - user_details: Dict[str, UserDetail] - tags: Optional[List[str]] - extra: Optional[Dict[str, Any]] - - -# Message type -MessageType = Literal["text", "image", "file", "audio", "video", "link", "system"] - - -# Message reference object (only message_id is required) -class MessageReference(TypedDict, total=False): - """Message reference object - - When using object form in refer_list, only message_id is required, - other fields are optional, and can be added flexibly as needed. - - Attributes: - message_id: The ID of the referenced message (required) - create_time: Message creation time (optional) - sender: Sender user ID (optional) - sender_name: Sender name (optional) - type: Message type (optional) - content: Message content (optional) - refer_list: Referenced message list (optional, supports nested references) - extra: Other extended information (optional) - """ - - message_id: str # Unique required field - create_time: Optional[str] - sender: Optional[str] - sender_name: Optional[str] - type: Optional[MessageType] - content: Optional[str] - refer_list: Optional[List[Any]] # Supports nested references - extra: Optional[Dict[str, Any]] - - -# Mark message_id as required field -MessageReference.__required_keys__ = frozenset(["message_id"]) - - -# Message -class Message(TypedDict, total=False): - """Single message - - Attributes: - message_id: Message unique identifier - create_time: Message creation time (ISO 8601 format, it is recommended to include timezone information) - sender: Sender user ID - sender_name: Sender name (optional, for quick view, detailed information in user_details) - role: Message sender role (optional), used to identify the source of the message. - "user" for human messages, "assistant" for AI messages. - Compatible with OpenAI/mem0/memos message format. - type: Message type (text/image/file etc.) - content: Message content, according to type different may be text, file URL etc. - refer_list: Referenced message list (optional) - Supports two formats: - 1. String: Directly use message_id, such as ["msg_001", "msg_002"] - 2. Object: MessageReference object, only message_id is required, other fields are optional - e.g. [{"message_id": "msg_001", "content": "Quote content"}] - extra: Other extended information, such as emoji reply, edit history etc. (optional) - """ - - message_id: str - create_time: str - sender: str - sender_name: Optional[str] - role: Optional[RoleType] - type: MessageType - content: str - refer_list: Optional[List[Any]] # Can be MessageReference or str (message_id) - extra: Optional[Dict[str, Any]] - - -# Complete conversation format -class ConversationFormat(TypedDict): - """Complete conversation format - - Attributes: - version: Format version number (following semantic version) - session_meta: Session meta information - conversation_list: Message list - """ - - version: str - session_meta: SessionMeta - conversation_list: List[Message] - - -def validate_conversation_format(data: ConversationFormat) -> bool: - """Validate conversation format whether it conforms to the specification - - Args: - data: Conversation data - - Returns: - Whether it conforms to the specification - """ - # Basic field check - if ( - "version" not in data - or "session_meta" not in data - or "conversation_list" not in data - ): - return False - - meta = data["session_meta"] - if "name" not in meta or "user_details" not in meta: - return False - - # Check if the sender in the message is in user_details - user_ids = set(meta["user_details"].keys()) - message_ids = set() - - for msg in data["conversation_list"]: - # Check sender - if msg.get("sender") not in user_ids: - return False - - # Collect all message_id - if "message_id" in msg: - message_ids.add(msg["message_id"]) - - # Check references in refer_list - for msg in data["conversation_list"]: - refer_list = msg.get("refer_list", []) - if refer_list: - for refer in refer_list: - # If it is a string (message ID reference) - if isinstance(refer, str): - # Can validate if the ID exists in conversation_list - # if refer not in message_ids: - # return False - pass - # If it is a dictionary (MessageReference object) - elif isinstance(refer, dict): - # Only validate required fields: message_id - if "message_id" not in refer: - return False - # If it contains the sender field, validate if it is in user_details - if "sender" in refer and refer.get("sender") not in user_ids: - return False - else: - return False - - return True - - -def create_example_conversation() -> ConversationFormat: - """Create an example conversation data - - Returns: - Example conversation data - """ - return { - "version": "1.0.0", - "session_meta": { - "name": "Smart Sales Assistant Project Group", - "description": "Smart Sales Assistant Project Development Discussion Group", - "group_id": "group_sales_ai_2025", - "created_at": "2025-02-01T01:00:00Z", - "default_timezone": "UTC", - "user_details": { - "user_101": { - "full_name": "Alex", - "role": "user", - "custom_role": "Technical Lead", - "department": "Technology Department", - }, - "user_102": { - "full_name": "Betty", - "role": "user", - "custom_role": "Product Manager", - "department": "Product Department", - }, - "user_103": { - "full_name": "Chen", - "role": "user", - "custom_role": "Project Manager", - "department": "Project Management Department", - }, - "user_104": { - "full_name": "Dylan", - "role": "user", - "custom_role": "Backend Engineer", - "department": "Technology Department", - }, - "user_105": { - "full_name": "Emily", - "role": "user", - "custom_role": "Frontend Engineer", - "department": "Technology Department", - }, - }, - "tags": ["AI", "Sales", "Project Development"], - }, - "conversation_list": [ - { - "message_id": "msg_001", - "create_time": "2025-02-01T02:00:00Z", - "sender": "user_103", - "sender_name": "Chen", - "type": "text", - "content": "Good morning, \"Smart Sales Assistant\" now how is the progress?", - "refer_list": [], - }, - { - "message_id": "msg_002", - "create_time": "2025-02-01T02:01:00Z", - "sender": "user_102", - "sender_name": "Betty", - "type": "text", - "content": "Good morning. First align the goals? Is it an MVP that can be used for internal testing, or a direct customer trial?", - "refer_list": [], - }, - { - "message_id": "msg_003", - "create_time": "2025-02-01T02:01:30Z", - "sender": "user_103", - "sender_name": "Chen", - "type": "text", - "content": "First the MVP, have something running by March, and wrap up by the end of April.", - # Method 1: Only reference message ID (simple reference) - "refer_list": ["msg_002"], - }, - { - "message_id": "msg_004", - "create_time": "2025-02-01T02:02:00Z", - "sender": "user_101", - "sender_name": "Alex", - "type": "text", - "content": "Technical advice mainly based on RAG.", - "refer_list": [], - }, - { - "message_id": "msg_005", - "create_time": "2025-02-01T02:02:30Z", - "sender": "user_101", - "sender_name": "Alex", - "type": "text", - "content": "Current method: BM25(ES)+vector retrieval(bge-base-zh, HNSW, topK=8)+cross-encoder re-ranking(starting with the base version), temperature 0.3, length 512.", - "refer_list": [], - }, - { - "message_id": "msg_006", - "create_time": "2025-02-01T02:03:30Z", - "sender": "user_102", - "sender_name": "Betty", - "type": "text", - "content": "First define the metrics: - effective hit rate≥0.8; - first response P95≤1.5s; - hallucination rate≤8%.", - # Method 2: Object reference, can only contain message_id - "refer_list": [{"message_id": "msg_004"}], - }, - { - "message_id": "msg_007", - "create_time": "2025-02-01T02:04:00Z", - "sender": "user_101", - "sender_name": "Alex", - "type": "text", - "content": "Source data: product manual v3.2, price policy Q1, delivery SLA document.", - # Method 3: Object reference, can optionally include some fields (such as content for preview) - "refer_list": [ - { - "message_id": "msg_006", - "content": "First define the metrics: - effective hit rate≥0.8; - first response P95≤1.5s; - hallucination rate≤8%.", - } - ], - }, - { - "message_id": "msg_008", - "create_time": "2025-02-01T02:05:00Z", - "sender": "user_101", - "sender_name": "Alex", - "type": "text", - "content": "I have considered multiple technical points.", - # Method 4: Mixed use of string and object - "refer_list": [ - "msg_004", # Simple reference - { - "message_id": "msg_005", - "sender": "user_101", - "content": "Current method: BM25(ES)+vector retrieval...", - }, # Reference with partial information - ], - }, - ], - } - - -if __name__ == "__main__": - import json - - # Create example data - example = create_example_conversation() - - # Validate format - is_valid = validate_conversation_format(example) - print(f"Format validation: {'passed' if is_valid else 'failed'}") - - # Output example JSON - print("\nExample JSON:") - print(json.dumps(example, ensure_ascii=False, indent=2)) diff --git a/methods/EverCore/demo/README.md b/methods/EverCore/demo/README.md deleted file mode 100644 index 61681382..00000000 --- a/methods/EverCore/demo/README.md +++ /dev/null @@ -1,368 +0,0 @@ -# Demo - EverCore Interactive Examples - - -This directory contains interactive demos showcasing the core functionality of EverCore. - -## 📂 Directory Structure - -``` -demo/ -├── chat_with_memory.py # 🎯 Main: Interactive chat with memory -├── extract_memory.py # 🎯 Main: Memory extraction (HTTP API) -├── simple_demo.py # 🎯 Main: Quick start example -│ -├── utils/ # Utility module -│ ├── __init__.py -│ ├── memory_utils.py # Shared utility functions -│ └── simple_memory_manager.py # Simple memory manager (HTTP API wrapper) -│ -├── ui/ # UI module -│ ├── __init__.py -│ └── i18n_texts.py # Internationalization texts -│ -├── chat/ # Chat system components -│ ├── __init__.py -│ ├── orchestrator.py # Chat application orchestrator -│ ├── session.py # Session management -│ ├── ui.py # User interface -│ └── selectors.py # Language/scenario/group selectors -│ -├── tools/ # Auxiliary tools -│ ├── clear_all_data.py # Clear all memory data -│ ├── resync_memcells.py # Resync memory cells -│ └── test_retrieval_comprehensive.py # Retrieval testing tool -│ -├── chat_history/ # 📁 Output: Chat logs (auto-generated) -│ -├── README.md # 📖 Documentation (English) -``` - -**Notes**: -- All memory data is stored in databases (MongoDB, Elasticsearch, Milvus), no local `memcell_outputs/` directory -- `extract_memory.py` directly calls HTTP API without complex configuration classes -- Chat conversation history is saved in `chat_history/` directory - -## 🎯 Core Scripts - -### 1. `simple_demo.py` - Quick Start Example ⭐ - -**The simplest way to experience EverCore!** Just 67 lines of code demonstrating the complete memory workflow. - -**What it demonstrates:** -- 💾 **Store**: Save conversation messages via HTTP API -- ⏳ **Index**: Wait for data to be indexed (MongoDB, Elasticsearch, Milvus) -- 🔍 **Search**: Retrieve relevant memories with natural language queries - -**Code example:** -```python -from demo.utils import SimpleMemoryManager - -# Create memory manager -memory = SimpleMemoryManager() - -# Store conversations -await memory.store("I love playing soccer, often go to the field on weekends") -await memory.store("Soccer is a great sport! Which team do you like?", sender="Assistant") -await memory.store("I love Barcelona the most, Messi is my idol") - -# Wait for indexing -await memory.wait_for_index(seconds=10) - -# Search memories -await memory.search("What sports does the user like?") -await memory.search("What is the user's favorite team?") -``` - -**How to run:** - -⚠️ **Important**: You must start the API server first! - -```bash -# Terminal 1: Start the API server -uv run python src/run.py - -# Terminal 2: Run the simple demo -uv run python src/bootstrap.py demo/simple_demo.py -``` - -**Why this demo?** -- ✅ Minimal code - understand core concepts in seconds -- ✅ Complete workflow - storage → indexing → retrieval -- ✅ Friendly output - explanations for every step -- ✅ Real HTTP API - uses the same API as production - -**Dependencies**: `utils/simple_memory_manager.py` (HTTP API wrapper) - -### 2. `extract_memory.py` - Memory Extraction - -Batch process conversation data and extract memories via HTTP API. - -**Workflow**: -- Clears all existing memories (ensures clean starting state) -- Loads conversation files from `data/` directory (e.g., `data/solo_chat_zh.json`) -- Sends each message to the API server (`/api/v1/memories`) -- Server-side automatically extracts MemCells, generates episodes and profiles -- All data is stored in databases (MongoDB, Elasticsearch, Milvus) - -**Prerequisites**: API server must be running (`uv run python src/run.py `) - -**Dependencies**: HTTP API, `clear_all_data` tool - -### 3. `chat_with_memory.py` - Memory-Enhanced Chat - -Command-line interface for conversing with memory-enabled AI agents. - -**Features**: -- Interactive language selection (Chinese/English) and scenario selection (Assistant/Group Chat) -- Automatically load conversation groups from MongoDB -- Flexible retrieval mode selection (RRF/Embedding/BM25/Agentic) -- Real-time display of retrieved memories -- Auto-save conversation history - -**Prerequisites**: Must run `extract_memory.py` first to extract memory data - -**Dependencies**: `chat/` module, HTTP API - -## 📦 Supporting Modules - -### Utility Modules -- **`utils/simple_memory_manager.py`** - Simplified HTTP API wrapper for simple_demo -- **`utils/memory_utils.py`** - MongoDB connection and common utility functions - -### UI Module -- **`ui/i18n_texts.py`** - Bilingual interface text resources (Chinese/English) - -### Core Components -- **`chat/`** - Chat system implementation (orchestrator, session management, interface, selectors) -- **`tools/`** - Auxiliary tools (data cleanup, retrieval testing, etc.) - -## 🚀 Quick Start - -### ⚠️ Important: Memory Language Configuration - -`MEMORY_LANGUAGE` controls the prompt language during memory extraction. **Must match your data file language and be set before starting the API server.** - -| Data File | Setting | -|-----------|---------| -| `*_en.json` | `MEMORY_LANGUAGE=en` | -| `*_zh.json` | `MEMORY_LANGUAGE=zh` | - -> **Note:** Setting `MEMORY_LANGUAGE` in `extract_memory.py` does NOT work - the server reads this at startup. If languages mismatch, you'll get mixed Chinese/English memories. To switch languages, restart the API server after changing the setting. - ---- - -### Option A: Super Simple Mode (Recommended for Beginners) ⭐ - -The fastest way to experience EverCore! Just 2 terminals: - -```bash -# Terminal 1: Start the API server (required) -uv run python src/run.py - -# Terminal 2: Run the simple demo -uv run python src/bootstrap.py demo/simple_demo.py -``` - -**What happens:** -1. 📝 Stores 4 conversation messages -2. ⏳ Waits 10 seconds for indexing (MongoDB → Elasticsearch → Milvus) -3. 🔍 Searches memories with 3 different queries -4. 📊 Shows results with relevance scores and explanations - -**Note**: The API server (`src/run.py `) must be running in a separate terminal for the demo to work. - ---- - -### Option B: Full Feature Mode - -#### Step 1: Extract Memories - -Run the extraction script to extract memories from conversation data: - -```bash -# Start API server (if not already running) -uv run python src/run.py - -# In another terminal, run the extraction script -uv run python src/bootstrap.py demo/extract_memory.py -``` - -The script will: -- Clear all existing memory data -- Load `data/solo_chat_zh.json` conversation file -- Send each message to the API server for memory extraction -- Store all memories in databases (MongoDB, Elasticsearch, Milvus) - -> **💡 Tip**: `extract_memory.py` is straightforward and directly calls the HTTP API. You can modify the `data_file` and `profile_scene` variables in the script to use different data files. - -#### Step 2: Start Conversation - -Run the chat script to start conversing with AI: - -```bash -# Ensure API server is still running -# In another terminal, run the chat program -uv run python src/bootstrap.py demo/chat_with_memory.py -``` - -**Interactive Selection**: -1. **Language**: Choose `[1] 中文` or `[2] English` -2. **Scenario**: Choose `[1] Assistant Mode` or `[2] Group Chat Mode` -3. **Group**: Select from available groups loaded from MongoDB -4. **Retrieval Mode**: Choose RRF (recommended), Embedding, BM25, or Agentic - -**Chat Features**: -- 💬 Natural language conversation with memory-based context -- 🔍 Automatic retrieval of relevant memories (displays retrieval results) -- 📝 Conversation history auto-saved to `chat_history/` directory -- 🧠 Special commands for detailed information (`help`, `clear`, `reload`, `exit`) - ---- - -## 📁 Data Files - -The system uses sample conversation files from the `data/` directory: - -| Scenario | Language | Filename | -|----------|----------|----------| -| Assistant Chat | Chinese | `data/solo_chat_zh.json` | -| Assistant Chat | English | `data/solo_chat_en.json` | -| Group Chat | Chinese | `data/team_chat_zh.json` | -| Group Chat | English | `data/team_chat_en.json` | - -All data files follow the [ConversationFormat](../data_format/conversation/conversation_format.md) specification. See [data documentation](../data/README.md) for details. - -**Using Custom Data**: -Edit `extract_memory.py` and modify the `data_file` and `profile_scene` variables to point to your data file. - -## 💬 Chat Commands - -During chat sessions, the following commands are supported: - -- **Normal Input**: Type questions directly, AI will answer based on memories -- `help` - Show help information -- `clear` - Clear current conversation history -- `reload` - Reload memories and profiles -- `exit` - Save conversation history and exit -- `Ctrl+C` - Interrupt and save - -## ⚙️ Environment Configuration - -Create a `.env` file in the project root (refer to `env.template`): - -```bash -# LLM Configuration -LLM_MODEL=your_model -LLM_API_KEY=your_api_key -LLM_BASE_URL=your_base_url - -# Embedding Model Configuration -EMB_BASE_URL=http://localhost:11000/v1/embeddings -EMB_MODEL=Qwen3-Embedding-4B - -# MongoDB Configuration -MONGODB_URI=mongodb://admin:memsys123@localhost:27017 -``` - -## 🔗 Related Documentation - -- [Conversation Format Specification](../data_format/conversation/conversation_format.md) -- [API Documentation](../docs/api_docs/memory_api.md) -- [Data Documentation](../data/README.md) -- [Internationalization Guide](../docs/dev_docs/chat_i18n_usage.md) - -## 📖 Demo Data Overview - -### Group Chat Scenario (team_chat_en.json / team_chat_zh.json) - -**Project Context:** AI product work group documenting the complete development journey of "Smart Sales Assistant" - -**Key Contents:** -- MVP development phase: RAG-based Q&A system -- Advanced feature iteration: Emotion recognition, memory system -- Team collaboration practices: Complete workflow from requirements to delivery - -**Available in:** English and Chinese versions - -**Good for exploring:** Team collaboration patterns, project management, technical solution evolution - -### Assistant Scenario (solo_chat_en.json / solo_chat_zh.json) - -**Conversation Context:** Personal health & lifestyle assistant documenting nearly 2 months of continuous interaction - -**Key Contents:** -- Travel planning: Food recommendations, itinerary suggestions -- Health management: Weight monitoring, dietary guidance -- Exercise recovery: Training advice, post-injury rehabilitation - -**Available in:** English and Chinese versions - -**Good for exploring:** Personalized services, long-term memory accumulation, contextual understanding - -## ❓ Recommended Questions - -**Group Chat AI Scenario Examples:** -- What did Alex/Betty/... do in the emotion recognition project? -- Based on the emotion recognition project, what work capabilities does Alex/Betty/... demonstrate? -- What are the deliverable results of the emotion recognition project? -- How is the memory system project progressing? - -**Assistant AI Scenario Examples:** -- Please recommend sports suitable for me. -- Please recommend food I might like. -- How is my health condition? - - -## 🔗 Related Documentation - -- 📋 [Conversation Format Specification](../data_format/conversation/conversation_format.md) - Data file format -- 🔌 [API Documentation](../docs/api_docs/memory_api.md) - API reference -- 📦 [Data Documentation](../data/README.md) - Sample data details -- 🏠 [Project Home](../README.md) - Project overview and architecture -- 📘 [Batch Memorization Guide](../docs/dev_docs/run_memorize_usage.md) - Advanced usage - -## ❓ FAQ - -### Q: Why are my extracted memories mixed with Chinese and English? -**A**: The `MEMORY_LANGUAGE` on the API server doesn't match your data file. Set it in `.env` to match your data (`en` or `zh`), then **restart the server** and re-run extraction. - -### Q: Cannot connect to API server? -**A**: Ensure the API server is running first: `uv run python src/run.py ` - -### Q: How to use custom data with extract_memory.py? -**A**: Edit the script and modify these variables: -- `data_file`: Point to your JSON data file -- `profile_scene`: Set to `"solo"` or `"team"` -- `base_url`: API server address (default `http://localhost:1995`) - -### Q: Where is data stored? -**A**: All memory data is stored via HTTP API to databases: -- **MongoDB**: Stores MemCells, episodes, profiles -- **Elasticsearch**: Keyword indexing (BM25) -- **Milvus**: Vector indexing (semantic retrieval) -- **Local files**: Only `chat_history/` directory saves conversation logs - -### Q: What scenarios are supported? -**A**: Two scenarios are supported: -- **Solo mode (solo)**: One-on-one conversations, extract personalized profiles -- **Group chat mode (team)**: Multi-participant conversations, extract group memories and member profiles - -### Q: What is the data file format? -**A**: JSON format following the [ConversationFormat](../data_format/conversation/conversation_format.md) specification. The project provides 4 sample files for reference. - -### Q: How to view data in databases? -**A**: -- **MongoDB**: Use MongoDB Compass or command-line queries -- **Retrieval test**: Run `demo/tools/test_retrieval_comprehensive.py` -- **Clear data**: Run `demo/tools/clear_all_data.py` - -## 💡 Need Help? - -- 🏠 See the main [README](../README.md) for project setup and architecture -- 💬 Open an issue on GitHub -- 📧 Contact project maintainers - ---- - -**Happy exploring! 🧠✨** - diff --git a/methods/EverCore/demo/agent_clustering_test_demo.py b/methods/EverCore/demo/agent_clustering_test_demo.py deleted file mode 100644 index a2f2ebf7..00000000 --- a/methods/EverCore/demo/agent_clustering_test_demo.py +++ /dev/null @@ -1,719 +0,0 @@ -"""Clustering Test Demo - 4 Trajectories (3 distinct + 1 similar) - -Tests whether agent memory clustering correctly separates different trajectory patterns -AND merges similar ones. - -Sends 4 trajectories to the SAME group_id: -1. Code debugging: read logs -> locate bug -> write fix -> run tests (orders 500 error) -2. Data analysis: query database -> transform data -> plot chart -> summarize -3. Infrastructure provisioning: check inventory -> write config -> apply -> verify -4. Code debugging #2: read logs -> locate bug -> write fix -> run tests (auth 403 error) - -Trajectory 4 follows the same pattern as Trajectory 1, so they should cluster together. -The other two should remain in separate clusters. - -Expected result: 3 clusters (debugging x2, data analysis, infra). - -Prerequisites: - Start the API server first (in another terminal): - uv run python src/run.py - -Run the demo: - uv run python src/bootstrap.py demo/agent_clustering_test_demo.py -""" - -import asyncio -import json - -from demo.utils.agent_demo_helpers import ( - AgentDemoRunner, - MEMORY_TYPE_PRINTERS, - print_separator, - print_search_case_results, - print_search_skill_results, -) - - -# ==================== Trajectory 1: Code Debugging ==================== - -def build_debugging_trajectory(): - """Simulate a code debugging agent: read logs -> find bug -> fix -> test.""" - messages = [ - { - "role": "user", - "content": "The /api/orders endpoint is returning 500 errors in production. Can you investigate?", - }, - { - "role": "assistant", - "content": "I'll check the application logs to find the root cause.", - "tool_calls": [ - { - "id": "dbg_001", - "type": "function", - "function": { - "name": "read_file", - "arguments": json.dumps({ - "path": "/var/log/app/orders-service.log", - "tail": 100, - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "dbg_001", - "content": json.dumps({ - "lines": [ - "2026-03-15 10:23:45 ERROR OrderService.get_orders: TypeError: 'NoneType' object is not subscriptable", - "2026-03-15 10:23:45 ERROR File 'order_service.py', line 42, in get_orders", - "2026-03-15 10:23:45 ERROR total = order['items']['count']", - ] - }), - }, - { - "role": "assistant", - "content": "Found it - a NoneType error on line 42. Let me read the source file.", - "tool_calls": [ - { - "id": "dbg_002", - "type": "function", - "function": { - "name": "read_file", - "arguments": json.dumps({ - "path": "src/order_service.py", - "start_line": 35, - "end_line": 50, - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "dbg_002", - "content": json.dumps({ - "content": ( - "def get_orders(self, user_id):\n" - " orders = self.db.query('SELECT * FROM orders WHERE user_id = ?', user_id)\n" - " results = []\n" - " for order in orders:\n" - " items = self.db.query('SELECT * FROM order_items WHERE order_id = ?', order['id'])\n" - " total = order['items']['count'] # BUG: should use len(items)\n" - " results.append({'order': order, 'total': total})\n" - " return results\n" - ), - }), - }, - { - "role": "assistant", - "content": "The bug is on line 42: `order['items']` is None because `items` is fetched separately. Applying fix.", - "tool_calls": [ - { - "id": "dbg_003", - "type": "function", - "function": { - "name": "edit_file", - "arguments": json.dumps({ - "path": "src/order_service.py", - "line": 42, - "old": "total = order['items']['count']", - "new": "total = len(items) if items else 0", - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "dbg_003", - "content": json.dumps({"status": "ok", "message": "Line 42 updated"}), - }, - { - "role": "assistant", - "content": "Fix applied. Let me run the test suite to make sure nothing else breaks.", - "tool_calls": [ - { - "id": "dbg_004", - "type": "function", - "function": { - "name": "run_command", - "arguments": json.dumps({ - "command": "pytest tests/test_order_service.py -v", - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "dbg_004", - "content": json.dumps({ - "exit_code": 0, - "output": "5 passed in 1.2s", - }), - }, - { - "role": "assistant", - "content": ( - "Fixed the 500 error on /api/orders.\n\n" - "**Root cause:** Line 42 in `order_service.py` accessed `order['items']['count']`, " - "but `order['items']` was None because item data is fetched in a separate query.\n\n" - "**Fix:** Changed to `len(items) if items else 0` to use the locally-fetched items list.\n\n" - "All 5 tests pass." - ), - }, - ] - return messages - - -# ==================== Trajectory 2: Data Analysis ==================== - -def build_data_analysis_trajectory(): - """Simulate a data analysis agent: query -> transform -> visualize -> summarize.""" - messages = [ - { - "role": "user", - "content": "Analyze our Q1 2026 sales data and create a breakdown by region and product category.", - }, - { - "role": "assistant", - "content": "I'll query the sales database for Q1 2026 data first.", - "tool_calls": [ - { - "id": "da_001", - "type": "function", - "function": { - "name": "run_sql", - "arguments": json.dumps({ - "query": ( - "SELECT region, category, SUM(revenue) as total_revenue, " - "COUNT(*) as order_count " - "FROM sales WHERE sale_date BETWEEN '2026-01-01' AND '2026-03-31' " - "GROUP BY region, category ORDER BY total_revenue DESC" - ), - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "da_001", - "content": json.dumps({ - "rows": [ - {"region": "North America", "category": "Electronics", "total_revenue": 4200000, "order_count": 12500}, - {"region": "North America", "category": "Clothing", "total_revenue": 2800000, "order_count": 18000}, - {"region": "Europe", "category": "Electronics", "total_revenue": 3100000, "order_count": 9800}, - {"region": "Europe", "category": "Clothing", "total_revenue": 1900000, "order_count": 11200}, - {"region": "Asia Pacific", "category": "Electronics", "total_revenue": 3800000, "order_count": 15600}, - {"region": "Asia Pacific", "category": "Clothing", "total_revenue": 1500000, "order_count": 8900}, - ], - "total_rows": 6, - }), - }, - { - "role": "assistant", - "content": "Got the data. Let me compute growth rates compared to Q4 2025.", - "tool_calls": [ - { - "id": "da_002", - "type": "function", - "function": { - "name": "run_sql", - "arguments": json.dumps({ - "query": ( - "SELECT region, category, SUM(revenue) as total_revenue " - "FROM sales WHERE sale_date BETWEEN '2025-10-01' AND '2025-12-31' " - "GROUP BY region, category" - ), - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "da_002", - "content": json.dumps({ - "rows": [ - {"region": "North America", "category": "Electronics", "total_revenue": 3900000}, - {"region": "North America", "category": "Clothing", "total_revenue": 2600000}, - {"region": "Europe", "category": "Electronics", "total_revenue": 3000000}, - {"region": "Europe", "category": "Clothing", "total_revenue": 1850000}, - {"region": "Asia Pacific", "category": "Electronics", "total_revenue": 3200000}, - {"region": "Asia Pacific", "category": "Clothing", "total_revenue": 1400000}, - ], - }), - }, - { - "role": "assistant", - "content": "Now I'll generate the visualization.", - "tool_calls": [ - { - "id": "da_003", - "type": "function", - "function": { - "name": "run_python", - "arguments": json.dumps({ - "code": ( - "import matplotlib.pyplot as plt\n" - "import pandas as pd\n" - "# ... grouped bar chart by region and category\n" - "fig.savefig('q1_sales_breakdown.png', dpi=150)\n" - "print('Chart saved to q1_sales_breakdown.png')" - ), - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "da_003", - "content": json.dumps({ - "output": "Chart saved to q1_sales_breakdown.png", - }), - }, - { - "role": "assistant", - "content": ( - "Here's the Q1 2026 sales analysis:\n\n" - "**Revenue by Region:**\n" - "- North America: $7.0M (+7.7% QoQ) - strongest in Electronics\n" - "- Asia Pacific: $5.3M (+15.2% QoQ) - fastest growing region\n" - "- Europe: $5.0M (+3.1% QoQ) - steady growth\n\n" - "**Key Insights:**\n" - "1. Asia Pacific Electronics grew 18.8% QoQ - driven by semiconductor demand\n" - "2. Clothing is weaker across all regions, suggesting seasonal effect\n" - "3. Total Q1 revenue: $17.3M, up 8.5% from Q4 2025\n\n" - "Chart saved to `q1_sales_breakdown.png`." - ), - }, - ] - return messages - - -# ==================== Trajectory 3: Infrastructure Provisioning ==================== - -def build_infra_trajectory(): - """Simulate an infra agent: check inventory -> write config -> apply -> verify.""" - messages = [ - { - "role": "user", - "content": "We need a new Redis cluster for the recommendation service. 3 nodes, 16GB each, in us-east-1.", - }, - { - "role": "assistant", - "content": "Let me check current infrastructure inventory for available capacity.", - "tool_calls": [ - { - "id": "infra_001", - "type": "function", - "function": { - "name": "run_command", - "arguments": json.dumps({ - "command": "terraform state list | grep 'aws_elasticache'", - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "infra_001", - "content": json.dumps({ - "output": ( - "aws_elasticache_cluster.session_cache\n" - "aws_elasticache_cluster.rate_limiter\n" - ), - }), - }, - { - "role": "assistant", - "content": "Two existing clusters found. I'll write the Terraform config for the new one.", - "tool_calls": [ - { - "id": "infra_002", - "type": "function", - "function": { - "name": "write_file", - "arguments": json.dumps({ - "path": "infra/modules/redis/recommendation.tf", - "content": ( - 'resource "aws_elasticache_replication_group" "recommendation" {\n' - ' replication_group_id = "recommendation-redis"\n' - ' description = "Redis cluster for recommendation service"\n' - ' node_type = "cache.r6g.xlarge" # 16GB\n' - ' num_cache_clusters = 3\n' - ' engine_version = "7.0"\n' - ' port = 6379\n' - ' subnet_group_name = aws_elasticache_subnet_group.main.name\n' - ' security_group_ids = [aws_security_group.redis.id]\n' - ' at_rest_encryption_enabled = true\n' - ' transit_encryption_enabled = true\n' - "}\n" - ), - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "infra_002", - "content": json.dumps({"status": "ok", "message": "File written"}), - }, - { - "role": "assistant", - "content": "Config written. Running terraform plan to validate.", - "tool_calls": [ - { - "id": "infra_003", - "type": "function", - "function": { - "name": "run_command", - "arguments": json.dumps({ - "command": "terraform plan -target=aws_elasticache_replication_group.recommendation", - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "infra_003", - "content": json.dumps({ - "output": ( - "Plan: 1 to add, 0 to change, 0 to destroy.\n" - "Estimated monthly cost: $1,152.00 (3x cache.r6g.xlarge)" - ), - }), - }, - { - "role": "assistant", - "content": "Plan looks good. Applying now.", - "tool_calls": [ - { - "id": "infra_004", - "type": "function", - "function": { - "name": "run_command", - "arguments": json.dumps({ - "command": "terraform apply -target=aws_elasticache_replication_group.recommendation -auto-approve", - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "infra_004", - "content": json.dumps({ - "output": ( - "aws_elasticache_replication_group.recommendation: Creating...\n" - "aws_elasticache_replication_group.recommendation: Creation complete after 8m32s\n" - "Apply complete! Resources: 1 added, 0 changed, 0 destroyed.\n" - "Outputs:\n" - " endpoint = recommendation-redis.abc123.use1.cache.amazonaws.com:6379" - ), - }), - }, - { - "role": "assistant", - "content": "Let me verify the cluster is healthy.", - "tool_calls": [ - { - "id": "infra_005", - "type": "function", - "function": { - "name": "run_command", - "arguments": json.dumps({ - "command": "redis-cli -h recommendation-redis.abc123.use1.cache.amazonaws.com ping", - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "infra_005", - "content": json.dumps({"output": "PONG"}), - }, - { - "role": "assistant", - "content": ( - "Redis cluster for recommendation service is ready.\n\n" - "**Configuration:**\n" - "- 3x `cache.r6g.xlarge` nodes (16GB each) in us-east-1\n" - "- Engine: Redis 7.0 with TLS encryption\n" - "- Endpoint: `recommendation-redis.abc123.use1.cache.amazonaws.com:6379`\n" - "- Estimated cost: $1,152/month\n\n" - "Encryption at rest and in transit are both enabled. " - "The cluster is responding to PING and ready for the recommendation service to connect." - ), - }, - ] - return messages - - -# ==================== Trajectory 4: Code Debugging #2 (similar to #1) ==================== - -def build_debugging_trajectory_2(): - """Simulate another code debugging session: read logs -> find bug -> fix -> test. - - Different service (auth) and different error (KeyError), but same agent pattern - as Trajectory 1. Should cluster together with it. - """ - messages = [ - { - "role": "user", - "content": "Users are getting 403 Forbidden on the /api/auth/login endpoint since this morning. Please look into it.", - }, - { - "role": "assistant", - "content": "I'll pull up the authentication service logs to see what's happening.", - "tool_calls": [ - { - "id": "dbg2_001", - "type": "function", - "function": { - "name": "read_file", - "arguments": json.dumps({ - "path": "/var/log/app/auth-service.log", - "tail": 80, - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "dbg2_001", - "content": json.dumps({ - "lines": [ - "2026-03-15 08:01:12 ERROR AuthHandler.authenticate: KeyError: 'password_hash'", - "2026-03-15 08:01:12 ERROR File 'auth_handler.py', line 67, in authenticate", - "2026-03-15 08:01:12 ERROR stored_hash = user_record['password_hash']", - "2026-03-15 08:01:12 WARN Failed login attempts spiking: 1,240 in last hour", - ] - }), - }, - { - "role": "assistant", - "content": "There's a KeyError on 'password_hash' at line 67. Let me read the source code.", - "tool_calls": [ - { - "id": "dbg2_002", - "type": "function", - "function": { - "name": "read_file", - "arguments": json.dumps({ - "path": "src/auth_handler.py", - "start_line": 60, - "end_line": 80, - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "dbg2_002", - "content": json.dumps({ - "content": ( - "def authenticate(self, username, password):\n" - " user_record = self.db.find_one('users', {'username': username})\n" - " if not user_record:\n" - " raise AuthError('User not found')\n" - " # After DB migration, column renamed from 'password_hash' to 'hashed_pw'\n" - " stored_hash = user_record['password_hash'] # BUG: old column name\n" - " if not bcrypt.checkpw(password.encode(), stored_hash):\n" - " raise AuthError('Invalid credentials')\n" - " return self._create_session(user_record)\n" - ), - }), - }, - { - "role": "assistant", - "content": ( - "Found it. After a DB migration the column was renamed from `password_hash` to " - "`hashed_pw`, but the code still uses the old name. Fixing now." - ), - "tool_calls": [ - { - "id": "dbg2_003", - "type": "function", - "function": { - "name": "edit_file", - "arguments": json.dumps({ - "path": "src/auth_handler.py", - "line": 67, - "old": "stored_hash = user_record['password_hash']", - "new": "stored_hash = user_record['hashed_pw']", - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "dbg2_003", - "content": json.dumps({"status": "ok", "message": "Line 67 updated"}), - }, - { - "role": "assistant", - "content": "Fix applied. Running the auth test suite to verify.", - "tool_calls": [ - { - "id": "dbg2_004", - "type": "function", - "function": { - "name": "run_command", - "arguments": json.dumps({ - "command": "pytest tests/test_auth_handler.py -v", - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "dbg2_004", - "content": json.dumps({ - "exit_code": 0, - "output": "8 passed in 2.1s", - }), - }, - { - "role": "assistant", - "content": ( - "Fixed the 403 errors on /api/auth/login.\n\n" - "**Root cause:** A recent DB migration renamed the `password_hash` column to " - "`hashed_pw`, but `auth_handler.py` line 67 still referenced the old column name, " - "causing a KeyError that surfaced as 403 Forbidden.\n\n" - "**Fix:** Updated the column reference from `user_record['password_hash']` to " - "`user_record['hashed_pw']`.\n\n" - "All 8 auth tests pass." - ), - }, - ] - return messages - - -# ==================== Main ==================== - -async def main(): - runner = AgentDemoRunner( - group_id_prefix="clustering_test", - group_name="Clustering Test Session", - description="Test clustering with 3 distinct + 1 similar trajectory patterns", - tags=["demo", "agent", "clustering-test"], - msg_prefix="cluster_msg", - ) - - print_separator("Clustering Test Demo - 4 Trajectories (3 distinct + 1 similar)") - - # Step 1: Initialize conversation metadata - print("\nStep 1: Initialize conversation metadata") - print_separator() - await runner.save_conversation_meta() - - trajectories = [ - ("Code Debugging #1 (orders 500 error: read logs -> find bug -> fix -> test)", build_debugging_trajectory), - ("Data Analysis (query DB -> transform -> visualize -> summarize)", build_data_analysis_trajectory), - ("Infra Provisioning (check inventory -> write config -> apply -> verify)", build_infra_trajectory), - ("Code Debugging #2 (auth 403 error: read logs -> find bug -> fix -> test)", build_debugging_trajectory_2), - ] - - msg_offset = 0 - - for traj_idx, (label, builder) in enumerate(trajectories, 1): - print(f"\nStep {traj_idx + 1}: Send trajectory #{traj_idx} - {label}") - print_separator() - - messages = builder() - print(f" Sending {len(messages)} messages...\n") - - for idx, msg in enumerate(messages): - is_last = (idx == len(messages) - 1) - await runner.send_agent_message(msg, msg_offset + idx, flush=is_last) - await asyncio.sleep(0.3) - - msg_offset += len(messages) - - if traj_idx < len(trajectories): - print(f"\n Waiting 5 seconds before next trajectory...") - await asyncio.sleep(5) - - # Wait for extraction + clustering - print(f"\nStep {len(trajectories) + 2}: Wait for extraction + clustering pipeline") - print_separator() - print(" Waiting 60 seconds for MemCell + AgentCase + clustering + AgentSkill...") - await asyncio.sleep(60) - - # Fetch and print all extracted memories - print(f"\nStep {len(trajectories) + 3}: Fetch all extracted memories") - - for memory_type, label, printer in MEMORY_TYPE_PRINTERS: - print(f"\n--- {label} ---") - memories = await runner.fetch_memories(memory_type) - print(f" ({len(memories)} records)") - printer(memories) - - # Search to verify clustering separation - print(f"\nStep {len(trajectories) + 4}: Search to verify clustering separation") - - search_queries = [ - ("debug production error by reading logs and fixing code", "debugging"), - ("analyze sales data with SQL queries and create charts", "data analysis"), - ("provision cloud infrastructure with Terraform", "infra"), - ("how to debug production errors in a web service", "debugging"), - ("how to analyze business data and generate reports", "data analysis"), - ("how to provision and verify cloud infrastructure", "infra"), - ] - - for query, label in search_queries: - print(f"\n--- Hybrid Search: Agent Memory ({label}) ---") - print(f' Query: "{query}"') - result = await runner.search_memories(query, "agent_memory") - cases = result.get("cases", []) - skills = result.get("skills", []) - print(f" ({len(cases)} cases, {len(skills)} skills)") - if cases: - print(" [Cases]") - print_search_case_results(cases) - if skills: - print(" [Skills]") - print_search_skill_results(skills) - - # Agentic search for agent memory - print(f"\nStep {len(trajectories) + 5}: Agentic search for Agent Memory (LLM-guided multi-round retrieval)") - - agentic_queries = [ - ("how to debug production errors in a web service", "debugging"), - ("how to analyze business data and generate reports", "data analysis"), - ("how to provision and verify cloud infrastructure", "infra"), - ] - - for query, label in agentic_queries: - print(f"\n--- Agentic Search: Agent Memory ({label}) ---") - print(f' Query: "{query}"') - result = await runner.search_memories( - query, "agent_memory", retrieve_method="agentic" - ) - cases = result.get("cases", []) - skills = result.get("skills", []) - print(f" ({len(cases)} cases, {len(skills)} skills)") - if cases: - print(" [Cases]") - print_search_case_results(cases) - if skills: - print(" [Skills]") - print_search_skill_results(skills) - - print_separator("Demo completed!") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/demo/chat/__init__.py b/methods/EverCore/demo/chat/__init__.py deleted file mode 100644 index 9071789b..00000000 --- a/methods/EverCore/demo/chat/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -"""Chat module - -Provides the following core components: -- ChatOrchestrator: Chat application orchestrator (main entry) -- ChatSession: Session management -- ChatUI: User interface -- LanguageSelector: Language selector -- ScenarioSelector: Scenario selector -- GroupSelector: Group selector -""" - -from .orchestrator import ChatOrchestrator -from .session import ChatSession -from .ui import ChatUI -from .selectors import LanguageSelector, ScenarioSelector, GroupSelector - -__all__ = [ - "ChatOrchestrator", - "ChatSession", - "ChatUI", - "LanguageSelector", - "ScenarioSelector", - "GroupSelector", -] - diff --git a/methods/EverCore/demo/chat/orchestrator.py b/methods/EverCore/demo/chat/orchestrator.py deleted file mode 100644 index c35e84bd..00000000 --- a/methods/EverCore/demo/chat/orchestrator.py +++ /dev/null @@ -1,327 +0,0 @@ -"""Chat Application Orchestrator - -Responsible for the orchestration of the entire chat application: -1. Initialization configuration -2. User interaction (language, scenario, group, retrieval mode selection) -3. Session management -4. Conversation loop -""" - -import asyncio -import logging -import os -from pathlib import Path -from typing import Optional - -from demo.config import ChatModeConfig, LLMConfig, MongoDBConfig -from demo.utils import ensure_mongo_beanie_ready -from demo.ui import I18nTexts -from common_utils.cli_ui import CLIUI - -from .session import ChatSession -from .ui import ChatUI -from .selectors import LanguageSelector, ScenarioSelector, GroupSelector - - -class ChatOrchestrator: - """Chat Application Orchestrator""" - - def __init__(self, project_root: Path): - self.project_root = project_root - self.history_file = project_root / "demo" / ".chat_history" - self._configure_logging() - - def _configure_logging(self): - """Configure logging - Hide DEBUG logs from third-party libraries""" - logging.basicConfig(level=logging.WARNING, format='%(levelname)s: %(message)s') - logging.getLogger().setLevel(logging.WARNING) - - # Disable common third-party library logs - for logger_name in ['jieba', 'elasticsearch', 'urllib3', 'pymongo', 'pymilvus']: - logging.getLogger(logger_name).setLevel(logging.ERROR) - - def setup_readline(self): - """Configure readline history""" - try: - import readline - if self.history_file.exists(): - readline.read_history_file(str(self.history_file)) - readline.set_history_length(1000) - except Exception: - pass - - def save_readline_history(self): - """Save readline history""" - try: - import readline - readline.write_history_file(str(self.history_file)) - except Exception: - pass - - async def select_language(self) -> I18nTexts: - """Language selection""" - language = LanguageSelector.select_language() - return I18nTexts(language) - - async def select_scenario(self, texts: I18nTexts) -> Optional[str]: - """Scenario selection""" - ChatUI.clear_screen() - ChatUI.print_banner(texts) - - scenario_type = ScenarioSelector.select_scenario(texts) - if not scenario_type: - ChatUI.print_info(texts.get("groups_not_selected_exit"), texts) - return None - - return scenario_type - - async def initialize_database(self, texts: I18nTexts) -> bool: - """Initialize database connection""" - mongo_config = MongoDBConfig() - - try: - await ensure_mongo_beanie_ready(mongo_config) - return True - except Exception as e: - ChatUI.print_error(texts.get("mongodb_init_failed", error=str(e)), texts) - return False - - async def select_group(self, texts: I18nTexts) -> Optional[str]: - """Group selection""" - groups = await GroupSelector.list_available_groups() - selected_group_id = await GroupSelector.select_group(groups, texts) - - if not selected_group_id: - ChatUI.print_info(texts.get("groups_not_selected_exit"), texts) - return None - - return selected_group_id - - async def select_retrieval_mode(self, texts: I18nTexts) -> str: - """Retrieval mode selection - - Args: - texts: I18nTexts object - - Returns: - Retrieval mode string - """ - ui = CLIUI() - print() - ui.section_heading(texts.get("retrieval_mode_selection_title")) - print() - print(f" [1] {texts.get('retrieval_mode_keyword')} - {texts.get('retrieval_mode_keyword_desc')}") - print(f" [2] {texts.get('retrieval_mode_vector')} - {texts.get('retrieval_mode_vector_desc')}") - print(f" [3] {texts.get('retrieval_mode_hybrid')} - {texts.get('retrieval_mode_hybrid_desc')}") - print(f" [4] {texts.get('retrieval_mode_rrf')} - {texts.get('retrieval_mode_rrf_desc')}") - print(f" [5] {texts.get('retrieval_mode_agentic')} - {texts.get('retrieval_mode_agentic_desc')}") - print() - - mode_map = {1: "keyword", 2: "vector", 3: "hybrid", 4: "rrf", 5: "agentic"} - mode_desc = { - 1: texts.get('retrieval_mode_keyword'), - 2: texts.get('retrieval_mode_vector'), - 3: texts.get('retrieval_mode_hybrid'), - 4: texts.get('retrieval_mode_rrf'), - 5: texts.get('retrieval_mode_agentic'), - } - - while True: - try: - choice = input(f"{texts.get('retrieval_mode_prompt')}: ").strip() - if not choice: - continue - - index = int(choice) - if index in mode_map: - # Special hint: Agentic mode requires LLM - if index == 5: - print() - ui.note(texts.get("retrieval_mode_agentic_cost_warning"), icon="💰") - print() - - ui.success(f"✓ {texts.get('retrieval_mode_selected')}: {mode_desc[index]}") - return mode_map[index] - else: - ui.error(f"✗ {texts.get('retrieval_mode_invalid_range')}") - except ValueError: - ui.error(f"✗ {texts.get('invalid_input_number')}") - except KeyboardInterrupt: - print("\n") - raise - - def verify_api_key(self, llm_config: LLMConfig, texts: I18nTexts) -> bool: - """Verify if API Key is configured""" - import os - api_key_present = any([ - llm_config.api_key, - os.getenv("OPENROUTER_API_KEY"), - os.getenv("OPENAI_API_KEY"), - ]) - - if not api_key_present: - ChatUI.print_error(texts.get("config_api_key_missing"), texts) - print(f"{texts.get('config_api_key_hint')}\n") - return False - - return True - - async def create_session( - self, - group_id: str, - scenario_type: str, - retrieval_mode: str, - texts: I18nTexts, - ) -> Optional[ChatSession]: - """Create and initialize session""" - chat_config = ChatModeConfig() - llm_config = LLMConfig() - - # Extract user_id from group_id (format: chat_user_001_assistant -> user_001) - user_id = "user_001" # Default - if "_" in group_id: - parts = group_id.split("_") - # Try to find user_XXX pattern - for i, part in enumerate(parts): - if part == "user" and i + 1 < len(parts): - user_id = f"user_{parts[i + 1]}" - break - - session = ChatSession( - group_id=group_id, - config=chat_config, - llm_config=llm_config, - scenario_type=scenario_type, - retrieval_mode=retrieval_mode, - data_source="episodic_memory", # Use API memory_types value - texts=texts, - user_id=user_id, - ) - - if not await session.initialize(): - ChatUI.print_error(texts.get("session_init_failed"), texts) - return None - - return session - - async def run_chat_loop(self, session: ChatSession, texts: I18nTexts): - """Run conversation loop""" - # Clear screen, enter clean chat interface - ChatUI.clear_screen() - ChatUI.print_banner(texts) - - # Show start note - ui = CLIUI() - print() - ui.rule() - ui.note(texts.get("chat_start_note"), icon="💬") - ui.rule() - print() - - while True: - try: - user_input = input(texts.get("chat_input_prompt")).strip() - - if not user_input: - continue - - command = user_input.lower() - - # Handle commands - if command == "exit": - await self._handle_exit(session, texts) - break - elif command == "clear": - session.clear_history() - continue - elif command == "reload": - await session.reload_data() - continue - elif command == "help": - ChatUI.print_help(texts) - continue - - # Execute chat - response = await session.chat(user_input) - ChatUI.print_assistant_response(response, texts) - - except KeyboardInterrupt: - await self._handle_interrupt(session, texts) - break - - except Exception as e: - ChatUI.print_error(texts.get("chat_error", error=str(e)), texts) - import traceback - traceback.print_exc() - print() - - async def _handle_exit(self, session: ChatSession, texts: I18nTexts): - """Handle exit command""" - ui = CLIUI() - print() - ui.note(texts.get("cmd_exit_saving"), icon="💾") - await session.save_conversation_history() - print() - ui.success(f"✓ {texts.get('cmd_exit_complete')}") - print() - - async def _handle_interrupt(self, session: ChatSession, texts: I18nTexts): - """Handle interrupt signal""" - ui = CLIUI() - print("\n") - ui.note(texts.get("cmd_interrupt_saving"), icon="⚠️") - await session.save_conversation_history() - print() - ui.success(f"✓ {texts.get('cmd_exit_complete')}") - print() - - async def run(self): - """Run chat application main flow""" - # 1. Initialize readline - self.setup_readline() - - # 2. Clear screen, then language selection - ChatUI.clear_screen() - texts = await self.select_language() - - # 3. Scenario selection - scenario_type = await self.select_scenario(texts) - if not scenario_type: - return - - # 4. Clear screen - ChatUI.clear_screen() - ChatUI.print_banner(texts) - - # 5. Verify API Key - llm_config = LLMConfig() - if not self.verify_api_key(llm_config, texts): - return - - # 6. Initialize database - if not await self.initialize_database(texts): - return - - # 7. Group selection - group_id = await self.select_group(texts) - if not group_id: - return - - # 8. Retrieval mode selection - try: - retrieval_mode = await self.select_retrieval_mode(texts) - except KeyboardInterrupt: - print("\n") - return - - # 9. Create session - session = await self.create_session(group_id, scenario_type, retrieval_mode, texts) - if not session: - return - - # 10. Run conversation loop - await self.run_chat_loop(session, texts) - - # 11. Save history - self.save_readline_history() - diff --git a/methods/EverCore/demo/chat/selectors.py b/methods/EverCore/demo/chat/selectors.py deleted file mode 100644 index 615d37c7..00000000 --- a/methods/EverCore/demo/chat/selectors.py +++ /dev/null @@ -1,178 +0,0 @@ -"""Interactive Selectors - -Provides selection for language, scenario, and groups. -""" - -from typing import List, Dict, Any, Optional - -from demo.config import ScenarioType -from demo.utils import query_all_groups_from_mongodb -from demo.ui import I18nTexts -from common_utils.cli_ui import CLIUI - - -class LanguageSelector: - """Language Selector""" - - @staticmethod - def select_language() -> str: - """Interactive language selection - - Returns: - Language code: "zh" or "en" - """ - print() - print("=" * 60) - print(" 🌏 语言选择 / Language Selection") - print("=" * 60) - print() - print(" [1] 中文 (Chinese)") - print(" [2] English") - print() - # Language consistency hint - print(" 💡 提示：为获得最佳体验，建议记忆数据与选择的语言保持一致") - print( - " Note: For best experience, memory data should match the selected language" - ) - print() - - while True: - try: - choice = input("请选择语言 / Please select language [1-2]: ").strip() - if not choice: - continue - - index = int(choice) - if index == 1: - print("\n✓ Selected: Chinese | AI will respond in Chinese\n") - return "zh" - elif index == 2: - print("\n✓ Selected: English | AI will respond in English\n") - return "en" - else: - print("❌ 请输入 1 或 2 / Please enter 1 or 2\n") - - except ValueError: - print("❌ 请输入有效的数字 / Please enter a valid number\n") - except KeyboardInterrupt: - print("\n") - return "zh" - - -class ScenarioSelector: - """Scenario Mode Selector""" - - @staticmethod - def select_scenario(texts: I18nTexts) -> Optional[ScenarioType]: - """Interactive scenario selection - - Args: - texts: I18nTexts object - - Returns: - ScenarioType or None (Cancelled) - """ - ui = CLIUI() - print() - ui.section_heading(texts.get("scenario_selection_title")) - print() - - print(f" [1] {texts.get('scenario_solo')}") - print(f" {texts.get('scenario_solo_desc')}") - print() - - print(f" [2] {texts.get('scenario_team')}") - print(f" {texts.get('scenario_team_desc')}") - print() - - while True: - try: - choice = input(f"{texts.get('scenario_prompt')}: ").strip() - if not choice: - continue - - index = int(choice) - if index == 1: - ui.success( - f"✓ {texts.get('scenario_selected')}: {texts.get('scenario_solo')}" - ) - return ScenarioType.SOLO - elif index == 2: - ui.success( - f"✓ {texts.get('scenario_selected')}: {texts.get('scenario_team')}" - ) - return ScenarioType.TEAM - else: - ui.error(f"✗ {texts.get('invalid_input_number')}") - - except ValueError: - ui.error(f"✗ {texts.get('invalid_input_number')}") - except KeyboardInterrupt: - print("\n") - return None - - -class GroupSelector: - """Group Selector""" - - @staticmethod - async def list_available_groups() -> List[Dict[str, Any]]: - """List all available groups - - Returns: - List of groups - """ - groups = await query_all_groups_from_mongodb() - - for idx, group in enumerate(groups, start=1): - group["index"] = idx - group_id = group["group_id"] - group["name"] = "team_chat" if group_id == "AI产品群" else group_id - - return groups - - @staticmethod - async def select_group( - groups: List[Dict[str, Any]], texts: I18nTexts - ) -> Optional[str]: - """Interactive group selection - - Args: - groups: List of groups - texts: I18nTexts object - - Returns: - Selected group_id or None (Cancelled) - """ - from .ui import ChatUI - - if not groups: - ChatUI.print_error(texts.get("groups_not_found"), texts) - print(f"{texts.get('groups_extract_hint')}\n") - return None - - ChatUI.print_group_list(groups, texts) - - while True: - try: - choice = input( - f"\n{texts.get('groups_select_prompt')} [1-{len(groups)}]: " - ).strip() - if not choice: - continue - - index = int(choice) - if 1 <= index <= len(groups): - return groups[index - 1]["group_id"] - else: - ChatUI.print_error( - texts.get("groups_select_range_error", min=1, max=len(groups)), - texts, - ) - - except ValueError: - ChatUI.print_error(texts.get("invalid_input_number"), texts) - except KeyboardInterrupt: - print("\n") - ChatUI.print_info(texts.get("groups_selection_cancelled"), texts) - return None diff --git a/methods/EverCore/demo/chat/session.py b/methods/EverCore/demo/chat/session.py deleted file mode 100644 index 7f7ee256..00000000 --- a/methods/EverCore/demo/chat/session.py +++ /dev/null @@ -1,561 +0,0 @@ -"""Conversation Session Management - -Manages conversation sessions for a single group, providing memory retrieval and LLM chat functionality. -""" - -import json -import httpx -from typing import List, Dict, Any, Optional, Tuple -from datetime import timedelta -from pathlib import Path - -from demo.config import ChatModeConfig, LLMConfig, ScenarioType -from demo.utils import query_memcells_by_group_and_time -from demo.ui import I18nTexts -from memory_layer.llm.llm_provider import LLMProvider -from common_utils.datetime_utils import get_now_with_timezone, to_iso_format -from api_specs.memory_types import ProfileMemory - - -class ChatSession: - """Conversation Session Manager""" - - def __init__( - self, - group_id: str, - config: ChatModeConfig, - llm_config: LLMConfig, - scenario_type: ScenarioType, - retrieval_mode: str, # "keyword" / "vector" / "hybrid" / "rrf" / "agentic" - data_source: str, # "episode" / "atomic_fact" - texts: I18nTexts, - user_id: str = "user_001", # User ID for profile fetch - ): - """Initialize conversation session - - Args: - group_id: Group ID - config: Chat mode configuration - llm_config: LLM configuration - scenario_type: Scenario type - retrieval_mode: Retrieval mode (keyword/vector/hybrid/rrf/agentic) - data_source: Data source (episode/atomic_fact) - texts: I18nTexts object - user_id: User ID for fetching profile - """ - self.group_id = group_id - self.user_id = user_id - self.config = config - self.llm_config = llm_config - self.scenario_type = scenario_type - self.retrieval_mode = retrieval_mode - self.data_source = data_source - self.texts = texts - - # Session State - self.conversation_history: List[Tuple[str, str]] = [] - self.memcell_count: int = 0 - - # Services - self.llm_provider: Optional[LLMProvider] = None - - # API Configuration - self.api_base_url = config.api_base_url - self.retrieve_url = f"{self.api_base_url}/api/v1/memories/search" - - # Last Retrieval Metadata - self.last_retrieval_metadata: Optional[Dict[str, Any]] = None - - async def initialize(self) -> bool: - """Initialize session - - Returns: - Whether initialization was successful - """ - try: - display_name = ( - "team_chat" - if self.group_id == "AI产品群" # skip-i18n-check - else self.group_id - ) - print( - f"\n[{self.texts.get('loading_label')}] {self.texts.get('loading_group_data', name=display_name)}" - ) - - # Check API Server Health - await self._check_api_server() - - # Count MemCells - now = get_now_with_timezone() - start_date = now - timedelta(days=self.config.time_range_days) - memcells = await query_memcells_by_group_and_time( - self.group_id, start_date, now - ) - self.memcell_count = len(memcells) - print( - f"[{self.texts.get('loading_label')}] {self.texts.get('loading_memories_success', count=self.memcell_count)} ✅" - ) - - # Load Conversation History - loaded_history_count = await self.load_conversation_history() - if loaded_history_count > 0: - print( - f"[{self.texts.get('loading_label')}] {self.texts.get('loading_history_success', count=loaded_history_count)} ✅" - ) - else: - print( - f"[{self.texts.get('loading_label')}] {self.texts.get('loading_history_new')} ✅" - ) - - # Create LLM Provider - self.llm_provider = LLMProvider( - self.llm_config.provider, - model=self.llm_config.model, - api_key=self.llm_config.api_key, - base_url=self.llm_config.base_url, - temperature=self.llm_config.temperature, - max_tokens=self.llm_config.max_tokens, - ) - - print( - f"\n[{self.texts.get('hint_label')}] {self.texts.get('loading_help_hint')}\n" - ) - return True - - except Exception as e: - print( - f"\n[{self.texts.get('error_label')}] {self.texts.get('session_init_error', error=str(e))}" - ) - import traceback - - traceback.print_exc() - return False - - async def _check_api_server(self) -> None: - """Check if API server is running - - Raises: - ConnectionError: If server is not running - """ - try: - async with httpx.AsyncClient(timeout=5.0) as client: - # Try accessing health check endpoint or any endpoint - response = await client.get(f"{self.api_base_url}/docs") - if response.status_code >= 500: - raise ConnectionError("API Server returned error") - except (httpx.ConnectError, httpx.TimeoutException, ConnectionError) as e: - error_msg = ( - f"\n❌ Cannot connect to API server: {self.api_base_url}\n\n" - f"Please start V1 API server first:\n" - f" uv run python src/run.py\n\n" - f"Then run the chat application in another terminal.\n" - ) - raise ConnectionError(error_msg) from e - - async def load_conversation_history(self) -> int: - """Load conversation history from file - - Returns: - Number of loaded conversation turns - """ - try: - display_name = ( - "team_chat" - if self.group_id == "AI产品群" # skip-i18n-check - else self.group_id - ) - history_files = sorted( - self.config.chat_history_dir.glob(f"{display_name}_*.json"), - reverse=True, - ) - - if not history_files: - return 0 - - latest_file = history_files[0] - with latest_file.open("r", encoding="utf-8") as fp: - data = json.load(fp) - - history = data.get("conversation_history", []) - self.conversation_history = [ - (item["user_input"], item["assistant_response"]) - for item in history[-self.config.conversation_history_size :] - ] - - return len(self.conversation_history) - - except Exception as e: - print( - f"[{self.texts.get('warning_label')}] {self.texts.get('loading_history_new')}: {e}" - ) - return 0 - - async def save_conversation_history(self) -> None: - """Save conversation history to file""" - try: - display_name = ( - "team_chat" - if self.group_id == "AI产品群" # skip-i18n-check - else self.group_id - ) - timestamp = get_now_with_timezone().strftime("%Y-%m-%d_%H-%M") - filename = f"{display_name}_{timestamp}.json" - filepath = self.config.chat_history_dir / filename - - data = { - "group_id": self.group_id, - "last_updated": get_now_with_timezone().isoformat(), - "conversation_history": [ - { - "timestamp": get_now_with_timezone().isoformat(), - "user_input": user_q, - "assistant_response": assistant_a, - } - for user_q, assistant_a in self.conversation_history - ], - } - - with filepath.open("w", encoding="utf-8") as fp: - json.dump(data, fp, ensure_ascii=False, indent=2) - - print(f"[{self.texts.get('save_label')}] {filename} ✅") - - except Exception as e: - print(f"[{self.texts.get('error_label')}] {e}") - - async def retrieve_memories(self, query: str) -> Dict[str, List[Dict[str, Any]]]: - """Retrieve memories (episodes, foresights, profile) in parallel.""" - import asyncio - - tasks = [ - self._search(query, memory_types=["episodic_memory"]), - self._search(query, memory_types=["foresight"]), - self._fetch_profile(), - ] - results = await asyncio.gather(*tasks, return_exceptions=True) - - all_memories = {"episodes": [], "foresights": [], "profiles": []} - - for i, (key, res) in enumerate( - zip(["episodes", "foresights", "profiles"], results) - ): - if isinstance(res, Exception): - print(f"[Warning] {key}: {res}") - elif key == "profiles": - all_memories[key] = res - else: - all_memories[key] = self._flatten_result(res) - - # Metadata - latency = sum( - float(self._get_metadata(r).get("total_latency_ms", 0) or 0) - for r in results[:2] - if not isinstance(r, Exception) - ) - self.last_retrieval_metadata = { - "retrieval_mode": self.retrieval_mode, - "total_latency_ms": latency, - "episodes_count": len(all_memories["episodes"]), - "foresights_count": len(all_memories["foresights"]), - "profiles_count": len(all_memories["profiles"]), - } - return all_memories - - # ==================== Unified Search API (aligned with test_v1api_search.py) ==================== - - async def _search( - self, - query: str, - memory_types: List[str] = None, - retrieve_method: str = None, - top_k: int = None, - user_id: str = None, - group_id: str = None, - timeout: float = 120.0, - ) -> Dict[str, Any]: - """Unified search API call (same as test_v1api_search.test_search_memories).""" - params = { - "query": query, - "retrieve_method": retrieve_method or self.retrieval_mode, - "top_k": top_k or self.config.top_k_memories, - } - if user_id: - params["user_id"] = user_id - if group_id or self.group_id: - params["group_id"] = group_id or self.group_id - if memory_types: - params["memory_types"] = ",".join(memory_types) - - async with httpx.AsyncClient(timeout=timeout, verify=False) as client: - response = await client.get(self.retrieve_url, params=params) - response.raise_for_status() - return response.json() - - async def _fetch_profile(self) -> List[Dict[str, Any]]: - """Fetch profile via GET /api/v1/memories.""" - url = f"{self.api_base_url}/api/v1/memories" - params = {"user_id": self.user_id, "memory_type": "profile", "limit": 10} - - async with httpx.AsyncClient(timeout=30.0, verify=False) as client: - response = await client.get(url, params=params) - response.raise_for_status() - data = response.json() - - if data.get("status") != "ok": - raise RuntimeError(f"API Error: {data.get('message')}") - - memories = data.get("result", {}).get("memories", []) or [] - # For demo: generate readable_profile locally (moved from fetch_mem_service.py) - for mem in memories: - profile_data = mem.get("profile_data") or {} - if ( - "readable_profile" not in profile_data - and "explicit_info" in profile_data - ): - - profile_data["readable_profile"] = ProfileMemory.from_dict( - profile_data - ).to_readable_profile() - mem["profile_data"] = profile_data - return memories - - def _get_metadata(self, resp: Dict[str, Any]) -> Dict[str, Any]: - """Extract metadata from API response.""" - if not resp or not isinstance(resp, dict): - return {} - result = resp.get("result") if isinstance(resp.get("result"), dict) else resp - return (result or {}).get("metadata", {}) or {} - - def _flatten_result(self, resp: Dict[str, Any]) -> List[Dict[str, Any]]: - """Flatten grouped search result to flat list.""" - if not resp or not isinstance(resp, dict): - return [] - - result = resp.get("result") if isinstance(resp.get("result"), dict) else resp - if not result: - return [] - - memories = result.get("memories", []) or [] - scores = result.get("scores", []) or [] - - # Already flat list? - if memories and isinstance(memories[0], dict): - if not any(isinstance(v, list) for v in memories[0].values()): - return list(memories) - - # Grouped: [{gid: [mem...]}, ...] + [{gid: [score...]}, ...] - score_map = {} - for s in scores: - if isinstance(s, dict): - for gid, slist in s.items(): - if isinstance(slist, list): - score_map[gid] = slist - - flat = [] - for grp in memories: - if not isinstance(grp, dict): - continue - for gid, mlist in grp.items(): - if not isinstance(mlist, list): - continue - gscores = score_map.get(gid, []) - for i, m in enumerate(mlist): - if isinstance(m, dict): - item = dict(m) - if "score" not in item and i < len(gscores): - item["score"] = gscores[i] - flat.append(item) - return flat - - def build_prompt( - self, user_query: str, memories: Dict[str, List[Dict[str, Any]]] - ) -> List[Dict[str, str]]: - """Build Prompt - - Args: - user_query: User query - memories: Dict with "episodes", "foresights", "profiles" - - Returns: - List of Chat Messages - """ - messages = [] - - # System Message - lang_key = "zh" if self.texts.language == "zh" else "en" - system_content = self.texts.get(f"prompt_system_role_{lang_key}") - messages.append({"role": "system", "content": system_content}) - - # Build memory context - memory_sections: List[str] = [] - - # 1) Profile (no numbering) - profiles = memories.get("profiles") or [] - first_profile = profiles[0] if profiles else None - if isinstance(first_profile, dict): - profile_text = (first_profile.get("profile_data", {}) or {}).get( - "readable_profile" - ) - if profile_text: - memory_sections.append(f"【User Profile】\n{profile_text}") - - # 2) Foresights (no numbering) - foresights = memories.get("foresights", []) - if foresights: - foresight_lines: List[str] = [] - for f in foresights[: self.config.top_k_memories]: - if not isinstance(f, dict): - continue - content = f.get("foresight") or f.get("summary") - if content: - foresight_lines.append(f" - {content}") - if foresight_lines: - memory_sections.append("【Foresights】\n" + "\n".join(foresight_lines)) - - # 3) Episodes (numbered, aligned with UI) - episodes = memories.get("episodes", []) - if episodes: - episode_lines: List[str] = [] - for i, mem in enumerate(episodes[: self.config.top_k_memories], start=1): - if not isinstance(mem, dict): - continue - raw_timestamp = mem.get("timestamp", "") - iso_timestamp = to_iso_format(raw_timestamp) - timestamp = iso_timestamp[:10] if iso_timestamp else "" - content = mem.get("summary") or mem.get("episode") or mem.get("subject") - if content: - episode_lines.append(f" [{i}] ({timestamp}) {content}") - if episode_lines: - memory_sections.append( - "【Related Memories】\n" + "\n".join(episode_lines) - ) - - # Add all memory sections as one system message - if memory_sections: - messages.append({"role": "system", "content": "\n\n".join(memory_sections)}) - # Conversation History - for user_q, assistant_a in self.conversation_history[ - -self.config.conversation_history_size : - ]: - messages.append({"role": "user", "content": user_q}) - messages.append({"role": "assistant", "content": assistant_a}) - - # Current Question - messages.append({"role": "user", "content": user_query}) - return messages - - async def chat(self, user_input: str) -> str: - """Core Chat Logic - - Args: - user_input: User input - - Returns: - Assistant response - """ - from .ui import ChatUI - - # Retrieve Memories - memories = await self.retrieve_memories(user_input) - - # Show Retrieval Results - if self.config.show_retrieved_memories and memories: - # Combine all memory types for display (episodes have numbers) - all_memories = memories.get("episodes", [])[:5] - ChatUI.print_retrieved_memories( - all_memories, - texts=self.texts, - retrieval_metadata=self.last_retrieval_metadata, - ) - - # Build Prompt - messages = self.build_prompt(user_input, memories) - - # Show Generation Progress - ChatUI.print_generating_indicator(self.texts) - - # Call LLM - try: - if hasattr(self.llm_provider, 'provider') and hasattr( - self.llm_provider.provider, 'chat_with_messages' - ): - raw_response = await self.llm_provider.provider.chat_with_messages( - messages - ) - else: - prompt_parts = [] - for msg in messages: - role = msg["role"] - content = msg["content"] - if role == "system": - prompt_parts.append(f"System: {content}") - elif role == "user": - prompt_parts.append(f"User: {content}") - elif role == "assistant": - prompt_parts.append(f"Assistant: {content}") - - prompt = "\n\n".join(prompt_parts) - raw_response = await self.llm_provider.generate(prompt) - - raw_response = raw_response.strip() - - # Clear Generation Progress - ChatUI.print_generation_complete(self.texts) - - assistant_response = raw_response - - except Exception as e: - ChatUI.clear_progress_indicator() - error_msg = f"[{self.texts.get('error_label')}] {self.texts.get('chat_llm_error', error=str(e))}" - print(f"\n{error_msg}") - import traceback - - traceback.print_exc() - return error_msg - - # Update Conversation History - self.conversation_history.append((user_input, assistant_response)) - - if len(self.conversation_history) > self.config.conversation_history_size: - self.conversation_history = self.conversation_history[ - -self.config.conversation_history_size : - ] - - return assistant_response - - def clear_history(self) -> None: - """Clear conversation history""" - from .ui import ChatUI - - count = len(self.conversation_history) - self.conversation_history = [] - ChatUI.print_info(self.texts.get("cmd_clear_done", count=count), self.texts) - - async def reload_data(self) -> None: - """Reload memory data""" - from .ui import ChatUI - from common_utils.cli_ui import CLIUI - - display_name = ( - "team_chat" - if self.group_id == "AI产品群" # skip-i18n-check - else self.group_id - ) - - ui = CLIUI() - print() - ui.note(self.texts.get("cmd_reload_refreshing", name=display_name), icon="🔄") - - # Recount MemCells - now = get_now_with_timezone() - start_date = now - timedelta(days=self.config.time_range_days) - memcells = await query_memcells_by_group_and_time( - self.group_id, start_date, now - ) - self.memcell_count = len(memcells) - - print() - ui.success( - f"✓ {self.texts.get('cmd_reload_complete', users=0, memories=self.memcell_count)}" - ) - print() diff --git a/methods/EverCore/demo/chat/ui.py b/methods/EverCore/demo/chat/ui.py deleted file mode 100644 index 52162442..00000000 --- a/methods/EverCore/demo/chat/ui.py +++ /dev/null @@ -1,360 +0,0 @@ -"""Terminal UI Tools - -Provides beautiful terminal output formatting. -""" - -import re -from typing import List, Dict, Any, Optional - -from demo.ui import I18nTexts -from common_utils.cli_ui import CLIUI - - -def extract_event_time_from_memory(mem: Dict[str, Any]) -> Optional[str]: - """Extract actual event time from memory data - - Extraction priority: - 1. Date in 'subject' field (parentheses format, e.g., "(2025-08-26)") - 2. Date in 'subject' field (Chinese format, e.g., "2025年8月26日") - 3. Date in 'episode' content (Chinese or ISO format) - 4. Time fields: timestamp / created_at / event_time - 5. Return None if extraction fails - - Args: - mem: Memory dictionary containing subject, episode, timestamp, etc. - - Returns: - Date string in YYYY-MM-DD format, or None (if extraction fails) - - Examples: - >>> mem = {"subject": "Beijing Travel Advice (2025-08-26)"} - >>> extract_event_time_from_memory(mem) - '2025-08-26' - - >>> mem = {"timestamp": "2025-08-26T10:30:00"} - >>> extract_event_time_from_memory(mem) - '2025-08-26' - - >>> mem = {"subject": "", "episode": ""} - >>> extract_event_time_from_memory(mem) - None - """ - subject = mem.get("subject", "") - episode = mem.get("episode", "") - - # 1. Extract from subject: Match ISO date format inside parentheses (YYYY-MM-DD) - if subject: - match = re.search(r'\((\d{4}-\d{2}-\d{2})\)', subject) - if match: - return match.group(1) - - # 2. Extract from subject: Match Chinese date format "YYYY年MM月DD日" - match = re.search(r'(\d{4})年(\d{1,2})月(\d{1,2})日', subject) - if match: - year, month, day = match.groups() - return f"{year}-{month.zfill(2)}-{day.zfill(2)}" - - # 3. Extract from episode (search entire content, no character limit) - if episode: - # Match "于YYYY年MM月DD日" or "在YYYY年MM月DD日" (At YYYY...) - match = re.search(r'[于在](\d{4})年(\d{1,2})月(\d{1,2})日', episode) - if match: - year, month, day = match.groups() - return f"{year}-{month.zfill(2)}-{day.zfill(2)}" - - # Match ISO format "YYYY-MM-DD" - match = re.search(r'(\d{4})-(\d{2})-(\d{2})', episode) - if match: - return match.group(0) - - # Match other Chinese date formats (without "at" prefix) - match = re.search(r'(\d{4})年(\d{1,2})月(\d{1,2})日', episode) - if match: - year, month, day = match.groups() - return f"{year}-{month.zfill(2)}-{day.zfill(2)}" - - # 4. Extract from time fields (timestamp, created_at, event_time, updated_at) - for time_field in ["timestamp", "event_time", "created_at", "updated_at"]: - time_value = mem.get(time_field, "") - if time_value: - # Support ISO format "YYYY-MM-DDTHH:MM:SS" or "YYYY-MM-DD HH:MM:SS" - match = re.search(r'(\d{4}-\d{2}-\d{2})', str(time_value)) - if match: - return match.group(1) - - # 5. Failed to extract event time, return None - return None - - -class ChatUI: - """Terminal Interface Utility Class""" - - @staticmethod - def _ui() -> CLIUI: - """Get UI instance""" - return CLIUI() - - @staticmethod - def clear_screen(): - """Clear screen""" - print("\033[2J\033[H", end="") - import sys - sys.stdout.flush() - - @staticmethod - def print_banner(texts: I18nTexts): - """Print welcome banner""" - ui = ChatUI._ui() - print() - ui.banner(texts.get("banner_title"), subtitle=texts.get("banner_subtitle")) - print() - - @staticmethod - def print_group_list(groups: List[Dict[str, Any]], texts: I18nTexts): - """Display group list""" - ui = ChatUI._ui() - print() - ui.section_heading(texts.get("groups_available_title")) - - rows = [] - for group in groups: - index = group["index"] - group_id = group["group_id"] - name = group.get("name", group_id) - count = group["memcell_count"] - count_text = f"💾 {count} " + ("memories" if texts.language == "en" else "条记忆") - rows.append([f"[{index}]", group_id, f'📝 "{name}"', count_text]) - - headers = [ - texts.get("table_header_index"), - texts.get("table_header_group"), - texts.get("table_header_name"), - texts.get("table_header_count"), - ] - ui.table(headers=headers, rows=rows, aligns=["right", "left", "left", "right"]) - - @staticmethod - def print_retrieved_memories( - memories: List[Dict[str, Any]], - texts: I18nTexts, - retrieval_metadata: Optional[Dict[str, Any]] = None, - ): - """Display retrieved memories""" - ui = ChatUI._ui() - - heading = f"🔍 {texts.get('retrieval_complete')}" - shown_count = len(memories) - if shown_count > 0: - heading += f" - {texts.get('retrieval_showing', shown=shown_count)}" - - # Display retrieval mode and latency - if retrieval_metadata: - retrieval_mode = retrieval_metadata.get("retrieval_mode", "rrf") - latency_ms = retrieval_metadata.get("total_latency_ms", 0.0) - - # Internationalized retrieval mode display - mode_map = { - "keyword": texts.get("agentic_mode_keyword"), - "vector": texts.get("agentic_mode_vector"), - "hybrid": texts.get("agentic_mode_hybrid"), - "rrf": texts.get("agentic_mode_rrf"), - "agentic": texts.get("agentic_mode_agentic"), - } - mode_text = mode_map.get(retrieval_mode, retrieval_mode) - heading += f" | {mode_text} | {int(latency_ms)}ms" - - ui.section_heading(heading) - - # 🔥 Agentic Retrieval Special Info Display - if retrieval_metadata and retrieval_metadata.get("retrieval_mode") == "agentic": - agentic_info = [] - - # LLM Judgment Result (Internationalized) - is_sufficient = retrieval_metadata.get("is_sufficient") - if is_sufficient is not None: - status_icon = "✅" if is_sufficient else "❌" - status_text = texts.get("agentic_sufficient") if is_sufficient else texts.get("agentic_insufficient") - agentic_info.append(f"{texts.get('agentic_llm_judgment')}: {status_icon} {status_text}") - - # Multi-round Check (Internationalized) - is_multi_round = retrieval_metadata.get("is_multi_round", False) - if is_multi_round: - agentic_info.append(f"🔄 {texts.get('agentic_multi_round')}") - - # Refined Queries - refined_queries = retrieval_metadata.get("refined_queries", []) - if refined_queries: - agentic_info.append(f"{texts.get('agentic_generated_queries')}: {len(refined_queries)}") - else: - agentic_info.append(f"⚡ {texts.get('agentic_single_round')}") - - # Round Statistics (Internationalized) - round1_count = retrieval_metadata.get("round1_count", 0) - round2_count = retrieval_metadata.get("round2_count", 0) - items_text = texts.get("agentic_items") - if round1_count: - agentic_info.append(f"{texts.get('agentic_round1_count')}: {round1_count} {items_text}") - if round2_count: - agentic_info.append(f"{texts.get('agentic_round2_count')}: {round2_count} {items_text}") - - if agentic_info: - print() - ui.note(" | ".join(agentic_info), icon="🤖") - - # Display LLM Reasoning (Internationalized optimization hint) - reasoning = retrieval_metadata.get("reasoning") - if reasoning: - # Optimize common misleading hints (Internationalized) - # Detect Chinese content and replace with internationalized text - chinese_keywords = [ - "为空", "均为空", "内容为空", "记忆内容", - "未提供", "不足", "无法提供", "相关性", - "检索到的记忆", "信息不够" - ] - if any(kw in reasoning for kw in chinese_keywords): - reasoning = texts.get("agentic_reasoning_hint") - - print(f" 💭 {reasoning}") - - # Display Refined Queries (Internationalized) - if is_multi_round: - refined_queries = retrieval_metadata.get("refined_queries", []) - if refined_queries: - print(f" 🔍 {texts.get('agentic_supplementary_queries')} ({len(refined_queries)}):") - for i, q in enumerate(refined_queries[:3], 1): - print(f" {i}. {q[:60]}{'...' if len(q) > 60 else ''}") - - # Display Memory List - lines = [] - for i, mem in enumerate(memories, start=1): - # Extract actual event time (not storage time) - event_time = extract_event_time_from_memory(mem) - - # Priority: subject > summary > episode > atomic_fact > content - # Use strip() to ensure empty strings are handled correctly - subject = (mem.get("subject") or "").strip() - summary = (mem.get("summary") or "").strip() - episode = (mem.get("episode") or "").strip() - foresight = (mem.get("foresight") or "").strip() # Foresight content field - atomic_fact = (mem.get("atomic_fact") or "").strip() - content = (mem.get("content") or "").strip() - - # Select first non-empty field - display_text = subject or summary or episode or foresight or atomic_fact or content or "(No Content)" - - # Limit display length - if len(display_text) > 80: - display_text = display_text[:77] + "..." - - # Build display line: show time if available, otherwise omit - score = mem.get("score") - score_text = "" - if isinstance(score, (int, float)): - score_text = f"{score:.3f} | " - if event_time: - lines.append(f"📌 [{i}] {event_time} │ {score_text}{display_text}") - else: - lines.append(f"📌 [{i}] {score_text}{display_text}") - - if lines: - print() - ui.panel(lines) - - @staticmethod - def print_generating_indicator(texts: I18nTexts): - """Display generation progress indicator""" - ui = ChatUI._ui() - print() - ui.note(f"🤔 {texts.get('chat_generating')}", icon="⏳") - - @staticmethod - def print_generation_complete(texts: I18nTexts): - """Clear generation indicator and show completion mark""" - print("\r\033[K", end="") - print("\033[A\033[K", end="") - print("\033[A\033[K", end="") - ui = ChatUI._ui() - ui.success(f"✓ {texts.get('chat_generation_complete')}") - - @staticmethod - def clear_progress_indicator(): - """Clear progress indicator""" - print("\r\033[K", end="") - print("\033[A\033[K", end="") - print("\033[A\033[K", end="") - - @staticmethod - def print_assistant_response(response: str, texts: I18nTexts): - """Display Assistant Response - - Optimized display: - - Mainly show 'answer' (Large Title) - - 'references' and 'confidence' as metadata (Small text) - - Hide 'reasoning' - """ - ui = ChatUI._ui() - print() - - # Try parsing JSON response - try: - import json - data = json.loads(response) - - # Extract fields - answer = data.get("answer", "") - references = data.get("references", []) - confidence = data.get("confidence", "") - - # Display main answer (Large Title) - ui.panel([answer], title=f"🤖 {texts.get('response_assistant_title')}") - - # Display metadata (Small text, dimmed) - metadata_parts = [] - if references: - ref_text = ", ".join(references) - metadata_parts.append(f"📚 {ref_text}") - if confidence: - confidence_icon = {"high": "✓", "medium": "~", "low": "?"}.get(confidence, "") - metadata_parts.append(f"{confidence_icon} {confidence}") - - if metadata_parts: - metadata_line = " │ ".join(metadata_parts) - print(f" {metadata_line}") - - except (json.JSONDecodeError, ValueError): - # If not JSON format, display raw response directly - ui.panel([response], title=f"🤖 {texts.get('response_assistant_title')}") - - ui.rule() - print() - - @staticmethod - def print_help(texts: I18nTexts): - """Display help information""" - ui = ChatUI._ui() - print() - ui.section_heading(texts.get("cmd_help_title")) - lines = [ - f"🚪 {texts.get('cmd_exit')}", - f"🧹 {texts.get('cmd_clear')}", - f"🔄 {texts.get('cmd_reload')}", - f"❓ {texts.get('cmd_help')}", - ] - ui.panel(lines) - print() - - @staticmethod - def print_info(message: str, texts: I18nTexts): - """Display informational message""" - ui = ChatUI._ui() - print() - ui.success(f"✓ {message}") - print() - - @staticmethod - def print_error(message: str, texts: I18nTexts): - """Display error message""" - ui = ChatUI._ui() - print() - ui.error(f"✗ {message}") - print() diff --git a/methods/EverCore/demo/chat_agent_demo.py b/methods/EverCore/demo/chat_agent_demo.py deleted file mode 100644 index 696687e5..00000000 --- a/methods/EverCore/demo/chat_agent_demo.py +++ /dev/null @@ -1,265 +0,0 @@ -"""Chat Agent Demo - Casual Conversation Without Tool Use - -Demonstrates memory extraction from casual chat (no tool calls): -1. Send one round of casual, chitchat-heavy conversation (no tool calls) -2. Wait for memory extraction (MemCell segmentation) -3. System extracts episodic memories, event logs, foresights, - agent experiences (assistant reasoning patterns), and agent skills - -The mock conversation simulates a friendly chat assistant handling: -- Weekend plans, favorite movies, and cooking hobby - -Even without tool calls, the system extracts agent experiences from the -assistant's conversational strategies and may derive agent skills from them. - -Prerequisites: - Start the API server first (in another terminal): - uv run python src/run.py - -Run the demo: - uv run python src/bootstrap.py demo/chat_agent_demo.py -""" - -import asyncio - -from demo.utils.agent_demo_helpers import ( - AgentDemoRunner, - MEMORY_TYPE_PRINTERS, - print_separator, - print_search_case_results, - print_search_skill_results, -) - - -# ==================== Mock Chat Conversations ==================== - -def build_first_conversation(): - """Build a casual chat conversation - Round 1. - - Topics: weekend plans, favorite movies, cooking hobby. - No tool calls at all - pure assistant-user chitchat. - """ - messages = [ - # ===== Topic 1: Weekend plans ===== - { - "role": "user", - "content": "Hey! What should I do this weekend? I'm so bored staying at home.", - }, - { - "role": "assistant", - "content": ( - "Hey there! Here are some ideas depending on your mood:\n\n" - "- **Outdoor**: Go for a hike, visit a local farmers market, or try a new park\n" - "- **Creative**: Pick up a new recipe, start a small DIY project, or try sketching\n" - "- **Social**: Invite friends for a board game night or check out a local event\n" - "- **Chill**: Binge a new series, visit a bookstore, or just take a long walk\n\n" - "What kind of vibe are you going for?" - ), - }, - { - "role": "user", - "content": ( - "I think I'll go hiking! There's a trail near Mt. Tamalpais I've been wanting " - "to try. My friend Sarah mentioned it has amazing views of the Golden Gate Bridge. " - "We're planning to go Saturday morning around 7am." - ), - }, - { - "role": "assistant", - "content": ( - "Mt. Tam is gorgeous! The Dipsea Trail or the Matt Davis Trail both have " - "incredible views. Since you're going at 7am, you'll probably catch the sunrise " - "fog rolling over the bridge - it's magical.\n\n" - "A few tips:\n" - "- Bring layers, mornings can be chilly even in summer\n" - "- Pack extra water - some trails are longer than expected\n" - "- The parking lot fills up fast on weekends, so 7am is smart\n\n" - "Have a great time with Sarah! Take some photos of the bridge view." - ), - }, - - # ===== Topic 2: Favorite movies ===== - { - "role": "user", - "content": ( - "By the way, I watched Interstellar again last night. It's still my favorite " - "movie of all time. I cry every time at the bookshelf scene. Have you seen it?" - ), - }, - { - "role": "assistant", - "content": ( - "Interstellar is a masterpiece! The bookshelf scene where Cooper realizes he's " - "been the 'ghost' all along is incredibly emotional. Hans Zimmer's organ score " - "makes it even more powerful.\n\n" - "If you love Interstellar, you might also enjoy:\n" - "- **Arrival** - Another cerebral sci-fi film about time and communication\n" - "- **Contact** - Also about reaching across the cosmos, with Jodie Foster\n" - "- **The Prestige** - Another Nolan film with mind-bending storytelling\n\n" - "What is it about Interstellar that resonates with you the most?" - ), - }, - { - "role": "user", - "content": ( - "It's the father-daughter relationship. I have a 5-year-old daughter named Emma, " - "and the idea of missing years of her life just destroys me. I've actually " - "already seen Arrival - loved it! The twist about the language changing how you " - "perceive time was brilliant. I'll check out Contact though, thanks!" - ), - }, - { - "role": "assistant", - "content": ( - "That makes total sense - the parent-child theme in Interstellar hits differently " - "when you have kids. Cooper watching those video messages of Murph growing up " - "while he's been gone... that scene is devastating.\n\n" - "With a 5-year-old, you've got the perfect age where every moment feels precious. " - "Emma is lucky to have a dad who appreciates that.\n\n" - "You'll love Contact - it's more grounded than Interstellar but has a similar " - "emotional core about human connection. Matthew McConaughey is actually in it too, " - "which is a fun coincidence!" - ), - }, - - # ===== Topic 3: Cooking hobby ===== - { - "role": "user", - "content": ( - "Oh, I also wanted to mention - I've been really getting into cooking lately. " - "Last week I made homemade ramen from scratch, including the broth. Took 12 hours " - "but it was totally worth it. My wife Lisa said it was better than our favorite " - "ramen shop!" - ), - }, - { - "role": "assistant", - "content": ( - "12 hours of broth simmering? That's serious dedication! Homemade ramen is one of " - "those dishes where the effort really shows in the final product. The depth of " - "flavor from a long-simmered tonkotsu or shoyu broth is hard to replicate with " - "shortcuts.\n\n" - "What kind of broth did you make? Tonkotsu (pork bone), shoyu (soy sauce based), " - "or miso? And did you make your own noodles too, or just the broth?\n\n" - "Also, huge compliment from Lisa - when your spouse says it's better than " - "restaurant-quality, you know you've nailed it!" - ), - }, - { - "role": "user", - "content": ( - "Tonkotsu! I boiled pork bones for about 10 hours to get that creamy white broth. " - "I didn't make noodles from scratch though - used Sun Noodle brand, they're " - "basically restaurant quality. Next time I want to try making the noodles too. " - "I've been watching a lot of cooking videos on YouTube, especially from a channel " - "called 'Way of Ramen'. Their techniques are incredible." - ), - }, - { - "role": "assistant", - "content": ( - "Tonkotsu is the ultimate test of patience! Getting that milky white emulsion " - "from pork bones is all about maintaining a rolling boil - sounds like you " - "nailed it.\n\n" - "Sun Noodle is a great choice - they literally supply noodles to top ramen shops " - "across the US. Smart move to nail the broth first and tackle noodles next.\n\n" - "Making fresh alkaline noodles is a fun project. You'll need kansui (alkaline " - "mineral water) to get that springy, yellow texture. The dough is quite stiff so " - "a pasta machine helps a lot.\n\n" - "Way of Ramen is excellent! Their tonkotsu episodes are super detailed. If you " - "want to level up, also check out 'Alex' on the French Cooking Academy channel - " - "he has some great Asian noodle technique videos too." - ), - }, - ] - return messages - - -# ==================== Main ==================== - -async def main(): - runner = AgentDemoRunner( - group_id_prefix="chat_agent_demo", - group_name="Chat Agent Demo Session", - description="Chat Agent Memory Demo - Casual Conversation", - tags=["demo", "agent", "chat"], - msg_prefix="chat_msg", - ) - - print_separator("Chat Agent Memory Demo - Casual Conversation (No Tool Use)") - - # Step 1: Initialize conversation metadata - print("\nStep 1: Initialize conversation metadata") - print_separator() - await runner.save_conversation_meta() - - # Step 2: Send first casual conversation - print("\nStep 2: Send first conversation round (weekend, movies, cooking)") - print_separator() - print(" Pure chitchat - no tool calls.\n") - - messages1 = build_first_conversation() - print(f" Sending {len(messages1)} messages (3 topics)...\n") - - for idx, msg in enumerate(messages1): - is_last = (idx == len(messages1) - 1) - await runner.send_agent_message(msg, idx, flush=is_last) - await asyncio.sleep(0.3) - - # Step 3: Wait for extraction - print("\nStep 3: Wait for extraction pipeline") - print_separator() - print(" Waiting 60 seconds for memory extraction...") - await asyncio.sleep(60) - - # Step 4: Fetch and print all extracted memories - print("\nStep 4: Fetch all extracted memories") - print(" (Expect: all memory types populated, including agent_case & agent_skill)\n") - - for memory_type, label, printer in MEMORY_TYPE_PRINTERS: - print(f"\n--- {label} ---") - memories = await runner.fetch_memories(memory_type) - print(f" ({len(memories)} records)") - printer(memories) - - # Step 5: Search demo - verify episodic/event_log are searchable - print("\nStep 5: Search memories") - - print("\n--- Hybrid Search: Episodic Memory ---") - print(' Query: "weekend hiking plans with friends"') - hits = await runner.search_memories( - "weekend hiking plans with friends", "episodic_memory" - ) - print(f" ({len(hits)} hits)") - for i, h in enumerate(hits[:3], 1): - score = h.get("score", 0.0) - summary = h.get("summary") or h.get("episode") or "N/A" - print(f"\n [{i}] score={score:.4f}") - print(f" {summary[:150]}") - - # Agent Memory searches (expect 0 - chat demo has no agent trajectories/skills) - agent_memory_queries = [ - "cooking ramen from scratch", - "weekend outdoor activities with friends", - "favorite sci-fi movies like Interstellar", - "how to make homemade ramen", - "plan a hiking trip for beginners", - ] - for q in agent_memory_queries: - print(f"\n--- Hybrid Search: Agent Memory ---") - print(f' Query: "{q}"') - result = await runner.search_memories(q, "agent_memory") - cases = result.get("cases", []) - skills = result.get("skills", []) - print(f" ({len(cases)} cases, {len(skills)} skills)") - if cases: - print(" [Cases]") - print_search_case_results(cases) - if skills: - print(" [Skills]") - print_search_skill_results(skills) - - print_separator("Demo completed!") - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/demo/chat_with_memory.py b/methods/EverCore/demo/chat_with_memory.py deleted file mode 100644 index 16465b02..00000000 --- a/methods/EverCore/demo/chat_with_memory.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Memory Enhanced Chat Script - -Usage: - uv run python src/bootstrap.py demo/chat_with_memory.py - -Alternative: - cd demo - python chat_with_memory.py -""" - -import asyncio -from pathlib import Path - -from dotenv import load_dotenv -from demo.chat import ChatOrchestrator - -load_dotenv() - -PROJECT_ROOT = Path(__file__).resolve().parents[1] - - -async def main(): - """Main Entry - Start Chat Application""" - orchestrator = ChatOrchestrator(PROJECT_ROOT) - await orchestrator.run() - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/demo/coding_agent_demo.py b/methods/EverCore/demo/coding_agent_demo.py deleted file mode 100644 index bc4e10ae..00000000 --- a/methods/EverCore/demo/coding_agent_demo.py +++ /dev/null @@ -1,727 +0,0 @@ -"""Coding Agent Memory Demo - Code Generation + Testing Trajectory - -Demonstrates how the agent memory system works with a coding agent: -1. Send two similar coding agent trajectories (OpenAI chat completion format) -2. Wait for memory extraction (MemCell segmentation + AgentCase extraction) -3. The second trajectory triggers MemScene clustering -> AgentSkill extraction -4. Fetch extracted agent experiences AND agent skills -5. Hybrid search over agent_case and agent_skill (retrieve_method=hybrid) - -The mock trajectories simulate a coding agent that: -- Trajectory 1: Adds input validation to a FastAPI endpoint, then adds custom error messages -- Trajectory 2: Adds rate limiting middleware, then adds per-endpoint configuration - -Both follow the same coding pattern (read existing code -> implement -> run tests -> refine), -so they cluster together and trigger AgentSkill extraction. - -Prerequisites: - Start the API server first (in another terminal): - uv run python src/run.py - -Run the demo: - uv run python src/bootstrap.py demo/coding_agent_demo.py -""" - -import asyncio -import json - -from demo.utils.agent_demo_helpers import ( - AgentDemoRunner, - MEMORY_TYPE_PRINTERS, - print_separator, - print_search_case_results, - print_search_skill_results, -) - - -# ==================== Mock Coding Agent Trajectory ==================== - -def build_mock_trajectory(): - """Build a mock coding agent trajectory in OpenAI chat completion format. - - Simulates a two-turn interaction: - Turn 1: User asks to add input validation -> Agent reads code, writes validation, runs tests - Turn 2: User asks for custom error messages -> Agent searches patterns, updates code, runs tests - """ - messages = [ - # ===== Turn 1: Add input validation to a FastAPI endpoint ===== - { - "role": "user", - "content": "Add input validation to the POST /api/users endpoint. It should validate email format, password strength (min 8 chars, 1 uppercase, 1 digit), and username length (3-20 chars).", - }, - { - "role": "assistant", - "content": "Let me first read the existing endpoint code to understand the current structure.", - "tool_calls": [ - { - "id": "call_001", - "type": "function", - "function": { - "name": "read_file", - "arguments": json.dumps({ - "path": "src/api/users.py" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_001", - "content": json.dumps({ - "content": ( - "from fastapi import APIRouter, HTTPException\n" - "from pydantic import BaseModel\n\n" - "router = APIRouter()\n\n" - "class CreateUserRequest(BaseModel):\n" - " username: str\n" - " email: str\n" - " password: str\n\n" - "@router.post('/api/users')\n" - "async def create_user(req: CreateUserRequest):\n" - " # TODO: add validation\n" - " user = await db.create_user(req.username, req.email, req.password)\n" - " return {'id': user.id, 'username': user.username}\n" - ), - }), - }, - { - "role": "assistant", - "content": "I see the endpoint uses Pydantic models but has no field-level validation. I'll add validators using Pydantic's field_validator.", - "tool_calls": [ - { - "id": "call_002", - "type": "function", - "function": { - "name": "write_file", - "arguments": json.dumps({ - "path": "src/api/users.py", - "content": ( - "import re\n" - "from fastapi import APIRouter, HTTPException\n" - "from pydantic import BaseModel, field_validator\n\n" - "router = APIRouter()\n\n" - "class CreateUserRequest(BaseModel):\n" - " username: str\n" - " email: str\n" - " password: str\n\n" - " @field_validator('email')\n" - " @classmethod\n" - " def validate_email(cls, v):\n" - " pattern = r'^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$'\n" - " if not re.match(pattern, v):\n" - " raise ValueError('Invalid email format')\n" - " return v\n\n" - " @field_validator('password')\n" - " @classmethod\n" - " def validate_password(cls, v):\n" - " if len(v) < 8:\n" - " raise ValueError('Password must be at least 8 characters')\n" - " if not re.search(r'[A-Z]', v):\n" - " raise ValueError('Password must contain at least 1 uppercase letter')\n" - " if not re.search(r'\\d', v):\n" - " raise ValueError('Password must contain at least 1 digit')\n" - " return v\n\n" - " @field_validator('username')\n" - " @classmethod\n" - " def validate_username(cls, v):\n" - " if not 3 <= len(v) <= 20:\n" - " raise ValueError('Username must be 3-20 characters')\n" - " return v\n\n" - "@router.post('/api/users')\n" - "async def create_user(req: CreateUserRequest):\n" - " user = await db.create_user(req.username, req.email, req.password)\n" - " return {'id': user.id, 'username': user.username}\n" - ), - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_002", - "content": json.dumps({"status": "ok", "path": "src/api/users.py", "bytes_written": 1247}), - }, - { - "role": "assistant", - "content": "Validation code written. Let me run the tests to make sure everything passes.", - "tool_calls": [ - { - "id": "call_003", - "type": "function", - "function": { - "name": "run_tests", - "arguments": json.dumps({ - "command": "pytest tests/test_users.py -v" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_003", - "content": json.dumps({ - "exit_code": 0, - "output": ( - "tests/test_users.py::test_create_user_valid PASSED\n" - "tests/test_users.py::test_create_user_invalid_email PASSED\n" - "tests/test_users.py::test_create_user_weak_password PASSED\n" - "tests/test_users.py::test_create_user_short_username PASSED\n" - "\n4 passed in 0.32s" - ), - }), - }, - { - "role": "assistant", - "content": ( - "I've added input validation to the POST /api/users endpoint using Pydantic field_validator:\n\n" - "1. **Email validation** - regex pattern check for valid email format\n" - "2. **Password validation** - minimum 8 chars, at least 1 uppercase letter, at least 1 digit\n" - "3. **Username validation** - length between 3-20 characters\n\n" - "All 4 tests pass. The validators raise `ValueError` with descriptive messages, " - "which Pydantic automatically converts to 422 Unprocessable Entity responses." - ), - }, - - # ===== Turn 2: Add custom error response format ===== - { - "role": "user", - "content": "The default Pydantic 422 error format is hard to parse on the frontend. Add a custom exception handler that returns a clean JSON error format like {\"error\": \"validation_error\", \"details\": [{\"field\": \"email\", \"message\": \"Invalid email format\"}]}", - }, - { - "role": "assistant", - "content": "Let me check if there's an existing exception handler pattern in the project.", - "tool_calls": [ - { - "id": "call_004", - "type": "function", - "function": { - "name": "search_code", - "arguments": json.dumps({ - "query": "exception_handler RequestValidationError", - "file_pattern": "*.py", - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_004", - "content": json.dumps({ - "matches": [ - { - "file": "src/main.py", - "line": 15, - "content": "# TODO: add custom exception handlers", - } - ], - }), - }, - { - "role": "assistant", - "content": "No existing handler found, just a TODO. I'll add a custom validation exception handler to main.py.", - "tool_calls": [ - { - "id": "call_005", - "type": "function", - "function": { - "name": "write_file", - "arguments": json.dumps({ - "path": "src/exceptions.py", - "content": ( - "from fastapi import Request\n" - "from fastapi.responses import JSONResponse\n" - "from fastapi.exceptions import RequestValidationError\n\n" - "async def validation_exception_handler(request: Request, exc: RequestValidationError):\n" - " details = []\n" - " for error in exc.errors():\n" - " field = error['loc'][-1] if error['loc'] else 'unknown'\n" - " details.append({'field': str(field), 'message': error['msg']})\n" - " return JSONResponse(\n" - " status_code=422,\n" - " content={'error': 'validation_error', 'details': details},\n" - " )\n" - ), - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_005", - "content": json.dumps({"status": "ok", "path": "src/exceptions.py", "bytes_written": 512}), - }, - { - "role": "assistant", - "content": "Now let me run the tests to verify the custom error format.", - "tool_calls": [ - { - "id": "call_006", - "type": "function", - "function": { - "name": "run_tests", - "arguments": json.dumps({ - "command": "pytest tests/test_users.py tests/test_exceptions.py -v" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_006", - "content": json.dumps({ - "exit_code": 0, - "output": ( - "tests/test_users.py::test_create_user_valid PASSED\n" - "tests/test_users.py::test_create_user_invalid_email PASSED\n" - "tests/test_exceptions.py::test_validation_error_format PASSED\n" - "tests/test_exceptions.py::test_multiple_validation_errors PASSED\n" - "\n4 passed in 0.28s" - ), - }), - }, - { - "role": "assistant", - "content": ( - "Done! I've added a custom validation exception handler:\n\n" - "1. Created `src/exceptions.py` with a `validation_exception_handler` that catches " - "`RequestValidationError`\n" - "2. It transforms Pydantic's default 422 response into a clean format:\n" - " ```json\n" - " {\"error\": \"validation_error\", \"details\": [{\"field\": \"email\", \"message\": \"Invalid email format\"}]}\n" - " ```\n" - "3. Register it in main.py: `app.add_exception_handler(RequestValidationError, validation_exception_handler)`\n\n" - "All tests pass including the new exception format tests." - ), - }, - ] - - return messages - - -def build_second_trajectory(): - """Build a second coding agent trajectory to trigger clustering + skill extraction. - - Simulates a different coding task but same pattern: - Turn 1: User asks to add rate limiting middleware -> Agent reads code, implements, tests - Turn 2: User asks for per-endpoint config -> Agent searches patterns, updates code, tests - - This trajectory follows the same coding pattern as the first (read -> implement -> test -> - search -> refine -> test), so it should cluster into the same MemScene and trigger - AgentSkill extraction. - """ - messages = [ - # ===== Turn 1: Add rate limiting middleware ===== - { - "role": "user", - "content": "Add rate limiting middleware to the API. Default limit should be 100 requests per minute per IP. Use an in-memory store with sliding window.", - }, - { - "role": "assistant", - "content": "Let me check the existing middleware setup first.", - "tool_calls": [ - { - "id": "call_101", - "type": "function", - "function": { - "name": "read_file", - "arguments": json.dumps({ - "path": "src/middleware/__init__.py" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_101", - "content": json.dumps({ - "content": ( - "from .cors import CORSMiddleware\n" - "from .logging import LoggingMiddleware\n\n" - "MIDDLEWARE_STACK = [CORSMiddleware, LoggingMiddleware]\n" - ), - }), - }, - { - "role": "assistant", - "content": "I see there's a middleware stack pattern. I'll create a rate limiter middleware that fits into this architecture.", - "tool_calls": [ - { - "id": "call_102", - "type": "function", - "function": { - "name": "write_file", - "arguments": json.dumps({ - "path": "src/middleware/rate_limit.py", - "content": ( - "import time\n" - "from collections import defaultdict\n" - "from fastapi import Request, Response\n" - "from starlette.middleware.base import BaseHTTPMiddleware\n\n" - "class RateLimitMiddleware(BaseHTTPMiddleware):\n" - " def __init__(self, app, max_requests: int = 100, window_seconds: int = 60):\n" - " super().__init__(app)\n" - " self.max_requests = max_requests\n" - " self.window_seconds = window_seconds\n" - " self.requests = defaultdict(list)\n\n" - " async def dispatch(self, request: Request, call_next):\n" - " client_ip = request.client.host\n" - " now = time.time()\n" - " window_start = now - self.window_seconds\n" - " # Sliding window: remove expired entries\n" - " self.requests[client_ip] = [\n" - " t for t in self.requests[client_ip] if t > window_start\n" - " ]\n" - " if len(self.requests[client_ip]) >= self.max_requests:\n" - " return Response(\n" - " content='{\"error\": \"rate_limit_exceeded\"}',\n" - " status_code=429,\n" - " headers={'Retry-After': str(self.window_seconds)},\n" - " )\n" - " self.requests[client_ip].append(now)\n" - " return await call_next(request)\n" - ), - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_102", - "content": json.dumps({"status": "ok", "path": "src/middleware/rate_limit.py", "bytes_written": 1089}), - }, - { - "role": "assistant", - "content": "Rate limiter written. Let me run the middleware tests.", - "tool_calls": [ - { - "id": "call_103", - "type": "function", - "function": { - "name": "run_tests", - "arguments": json.dumps({ - "command": "pytest tests/test_rate_limit.py -v" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_103", - "content": json.dumps({ - "exit_code": 0, - "output": ( - "tests/test_rate_limit.py::test_under_limit_passes PASSED\n" - "tests/test_rate_limit.py::test_over_limit_returns_429 PASSED\n" - "tests/test_rate_limit.py::test_sliding_window_resets PASSED\n" - "\n3 passed in 0.18s" - ), - }), - }, - { - "role": "assistant", - "content": ( - "I've added rate limiting middleware to the API:\n\n" - "1. Created `src/middleware/rate_limit.py` with `RateLimitMiddleware`\n" - "2. Uses an in-memory sliding window algorithm:\n" - " - Tracks request timestamps per client IP\n" - " - Removes expired entries outside the window on each request\n" - " - Returns 429 with `Retry-After` header when limit exceeded\n" - "3. Default: 100 requests per 60-second sliding window\n\n" - "All 3 tests pass. Add it to the middleware stack: " - "`MIDDLEWARE_STACK.append(RateLimitMiddleware)`" - ), - }, - - # ===== Turn 2: Add per-endpoint rate limit configuration ===== - { - "role": "user", - "content": "Some endpoints like /api/auth/login should have a stricter limit (10/min) and /api/health should be unlimited. Add per-endpoint rate limit configuration.", - }, - { - "role": "assistant", - "content": "Let me look at how routes are structured to find the best way to add per-endpoint config.", - "tool_calls": [ - { - "id": "call_104", - "type": "function", - "function": { - "name": "search_code", - "arguments": json.dumps({ - "query": "APIRouter include_router prefix", - "file_pattern": "*.py", - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_104", - "content": json.dumps({ - "matches": [ - { - "file": "src/main.py", - "line": 22, - "content": "app.include_router(auth_router, prefix='/api/auth')", - }, - { - "file": "src/main.py", - "line": 23, - "content": "app.include_router(users_router, prefix='/api/users')", - }, - { - "file": "src/main.py", - "line": 24, - "content": "app.include_router(health_router, prefix='/api/health')", - }, - ], - }), - }, - { - "role": "assistant", - "content": "Good, routes use standard prefix-based structure. I'll add a config dict to the rate limiter for per-path overrides.", - "tool_calls": [ - { - "id": "call_105", - "type": "function", - "function": { - "name": "write_file", - "arguments": json.dumps({ - "path": "src/middleware/rate_limit.py", - "content": ( - "import time\n" - "from collections import defaultdict\n" - "from typing import Dict, Optional\n" - "from fastapi import Request, Response\n" - "from starlette.middleware.base import BaseHTTPMiddleware\n\n" - "class RateLimitConfig:\n" - " def __init__(self, max_requests: int, window_seconds: int = 60):\n" - " self.max_requests = max_requests\n" - " self.window_seconds = window_seconds\n\n" - "UNLIMITED = RateLimitConfig(max_requests=0) # 0 means no limit\n\n" - "class RateLimitMiddleware(BaseHTTPMiddleware):\n" - " def __init__(self, app, default_limit: int = 100,\n" - " window_seconds: int = 60,\n" - " path_configs: Optional[Dict[str, RateLimitConfig]] = None):\n" - " super().__init__(app)\n" - " self.default_config = RateLimitConfig(default_limit, window_seconds)\n" - " self.path_configs = path_configs or {}\n" - " self.requests = defaultdict(list)\n\n" - " def _get_config(self, path: str) -> RateLimitConfig:\n" - " for prefix, config in self.path_configs.items():\n" - " if path.startswith(prefix):\n" - " return config\n" - " return self.default_config\n\n" - " async def dispatch(self, request: Request, call_next):\n" - " config = self._get_config(request.url.path)\n" - " if config.max_requests == 0: # unlimited\n" - " return await call_next(request)\n" - " client_ip = request.client.host\n" - " key = f'{client_ip}:{request.url.path}'\n" - " now = time.time()\n" - " window_start = now - config.window_seconds\n" - " self.requests[key] = [t for t in self.requests[key] if t > window_start]\n" - " if len(self.requests[key]) >= config.max_requests:\n" - " return Response(\n" - " content='{\"error\": \"rate_limit_exceeded\"}',\n" - " status_code=429,\n" - " headers={'Retry-After': str(config.window_seconds)},\n" - " )\n" - " self.requests[key].append(now)\n" - " return await call_next(request)\n" - ), - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_105", - "content": json.dumps({"status": "ok", "path": "src/middleware/rate_limit.py", "bytes_written": 1856}), - }, - { - "role": "assistant", - "content": "Updated. Let me run the full test suite to verify.", - "tool_calls": [ - { - "id": "call_106", - "type": "function", - "function": { - "name": "run_tests", - "arguments": json.dumps({ - "command": "pytest tests/test_rate_limit.py -v" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_106", - "content": json.dumps({ - "exit_code": 0, - "output": ( - "tests/test_rate_limit.py::test_under_limit_passes PASSED\n" - "tests/test_rate_limit.py::test_over_limit_returns_429 PASSED\n" - "tests/test_rate_limit.py::test_sliding_window_resets PASSED\n" - "tests/test_rate_limit.py::test_per_endpoint_strict_limit PASSED\n" - "tests/test_rate_limit.py::test_unlimited_endpoint_bypasses PASSED\n" - "\n5 passed in 0.24s" - ), - }), - }, - { - "role": "assistant", - "content": ( - "Done! Added per-endpoint rate limit configuration:\n\n" - "1. Added `RateLimitConfig` class for per-path settings\n" - "2. Added `UNLIMITED` constant for endpoints with no limit\n" - "3. The middleware now accepts `path_configs` dict for prefix-based overrides:\n" - " ```python\n" - " RateLimitMiddleware(app, path_configs={\n" - " '/api/auth/login': RateLimitConfig(max_requests=10),\n" - " '/api/health': UNLIMITED,\n" - " })\n" - " ```\n" - "4. Rate limit keys now include the path for per-endpoint tracking\n\n" - "All 5 tests pass including the new per-endpoint tests." - ), - }, - ] - - return messages - - -# ==================== Main ==================== - -async def main(): - runner = AgentDemoRunner( - group_id_prefix="coding_agent_demo", - group_name="Coding Agent Demo Session", - description="Coding Agent Memory Demo", - tags=["demo", "agent", "coding"], - msg_prefix="coding_msg", - ) - - print_separator("Coding Agent Memory Demo - Code Implementation + Testing") - - # Step 1: Initialize conversation metadata - print("\nStep 1: Initialize conversation metadata") - print_separator() - await runner.save_conversation_meta() - - # Step 2: Send first agent trajectory (input validation) - print("\nStep 2: Send first coding agent trajectory (input validation)") - print_separator() - - messages1 = build_mock_trajectory() - print(f" Sending {len(messages1)} messages (2 interaction turns)...\n") - - for idx, msg in enumerate(messages1): - is_last = (idx == len(messages1) - 1) - await runner.send_agent_message(msg, idx, flush=is_last) - await asyncio.sleep(0.5) - - # Step 3: Wait for first extraction to complete - print("\nStep 3: Wait for first extraction pipeline") - print_separator() - print(" Waiting 60 seconds for first AgentCase extraction...") - await asyncio.sleep(60) - - # Step 4: Send second trajectory (rate limiting - different feature, same pattern) - print("\nStep 4: Send second trajectory (rate limiting middleware)") - print_separator() - print(" Different feature, but same coding pattern (read -> implement -> test -> refine).") - print(" It will cluster into the same MemScene and trigger AgentSkill extraction.\n") - - messages2 = build_second_trajectory() - offset = len(messages1) - print(f" Sending {len(messages2)} messages (2 interaction turns)...\n") - - for idx, msg in enumerate(messages2): - is_last = (idx == len(messages2) - 1) - await runner.send_agent_message(msg, offset + idx, flush=is_last) - await asyncio.sleep(0.5) - - # Step 5: Wait for second extraction + skill extraction - print("\nStep 5: Wait for second extraction pipeline + AgentSkill extraction") - print_separator() - print(" Waiting 60 seconds for AgentCase + AgentSkill extraction...") - await asyncio.sleep(60) - - # Step 6: Fetch and print all extracted memories - print("\nStep 6: Fetch all extracted memories") - - for memory_type, label, printer in MEMORY_TYPE_PRINTERS: - print(f"\n--- {label} ---") - memories = await runner.fetch_memories(memory_type) - print(f" ({len(memories)} records)") - printer(memories) - - # Step 7: Hybrid search demo (vector + BM25 keyword search) - print("\nStep 7: Hybrid search (agent_memory)") - - search_queries = [ - "how to add validation and error handling to API endpoints", - "implement rate limiting for a REST API", - "how to return structured JSON errors from FastAPI", - "best practices for implementing and testing API features with code review", - "how to add input validation to a Pydantic model", - "steps to build rate limiting middleware with per-endpoint config", - "how to handle validation errors in a web API", - ] - - for query in search_queries: - print(f"\n--- Hybrid Search: Agent Memory ---") - print(f" Query: \"{query}\"") - result = await runner.search_memories(query, "agent_memory") - cases = result.get("cases", []) - skills = result.get("skills", []) - print(f" ({len(cases)} cases, {len(skills)} skills)") - if cases: - print(" [Cases]") - print_search_case_results(cases) - if skills: - print(" [Skills]") - print_search_skill_results(skills) - - # Step 8: Agentic search demo (LLM-guided multi-round retrieval) - print("\nStep 8: Agentic search for Agent Memory (LLM-guided multi-round retrieval)") - - agentic_queries = [ - "best practices for implementing and testing API features with code review", - "how to add input validation to a Pydantic model", - "steps to build rate limiting middleware with per-endpoint config", - ] - - for query in agentic_queries: - print(f"\n--- Agentic Search: Agent Memory ---") - print(f" Query: \"{query}\"") - result = await runner.search_memories( - query, "agent_memory", retrieve_method="agentic" - ) - cases = result.get("cases", []) - skills = result.get("skills", []) - print(f" ({len(cases)} cases, {len(skills)} skills)") - if cases: - print(" [Cases]") - print_search_case_results(cases) - if skills: - print(" [Skills]") - print_search_skill_results(skills) - - print_separator("Demo completed!") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/demo/config/__init__.py b/methods/EverCore/demo/config/__init__.py deleted file mode 100644 index ec8adca9..00000000 --- a/methods/EverCore/demo/config/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -"""Configuration module.""" - -from demo.config.memory_config import ( - ScenarioType, - LLMConfig, - EmbeddingConfig, - MongoDBConfig, - ExtractModeConfig, - ChatModeConfig, -) - -__all__ = [ - "ScenarioType", - "LLMConfig", - "EmbeddingConfig", - "MongoDBConfig", - "ExtractModeConfig", - "ChatModeConfig", -] - diff --git a/methods/EverCore/demo/config/memory_config.py b/methods/EverCore/demo/config/memory_config.py deleted file mode 100644 index 09540825..00000000 --- a/methods/EverCore/demo/config/memory_config.py +++ /dev/null @@ -1,129 +0,0 @@ -import os -from dataclasses import dataclass, field -from pathlib import Path -from enum import Enum -from typing import Optional - -from api_specs.memory_types import ScenarioType - - -@dataclass -class LLMConfig: - """LLM configuration - loaded from environment variables automatically""" - - provider: str = "openai" - model: Optional[str] = field(default_factory=lambda: os.getenv("LLM_MODEL")) - api_key: Optional[str] = field(default_factory=lambda: os.getenv("LLM_API_KEY")) - base_url: Optional[str] = field(default_factory=lambda: os.getenv("LLM_BASE_URL")) - temperature: float = 0.3 - max_tokens: int = 16384 - - -@dataclass -class EmbeddingConfig: - base_url: str = field( - default_factory=lambda: os.getenv( - "EMB_BASE_URL", "http://0.0.0.0:11000/v1/embeddings" - ) - ) - model: str = field( - default_factory=lambda: os.getenv("EMB_MODEL", "Qwen3-Embedding-4B") - ) - - -@dataclass -class MongoDBConfig: - """MongoDB Configuration - supports adding authentication information through URI parameters""" - - uri: Optional[str] = None - host: str = "localhost" - port: str = "27017" - database: str = "memsys" - username: Optional[str] = None - password: Optional[str] = None - - def __post_init__(self): - """Load configuration from environment variables and build URI""" - if not os.getenv("MONGODB_URI"): - self.host = os.getenv("MONGODB_HOST", self.host) - self.port = os.getenv("MONGODB_PORT", self.port) - self.database = os.getenv("MONGODB_DATABASE", self.database) - self.username = os.getenv("MONGODB_USERNAME") - self.password = os.getenv("MONGODB_PASSWORD") - - if self.username and self.password: - from urllib.parse import quote_plus - - self.uri = f"mongodb://{quote_plus(self.username)}:{quote_plus(self.password)}@{self.host}:{self.port}/{self.database}" - else: - self.uri = f"mongodb://{self.host}:{self.port}/{self.database}" - uri_params = os.getenv("MONGODB_URI_PARAMS", "").strip() - if uri_params: - separator = '&' if ('?' in self.uri) else '?' - self.uri = f"{self.uri}{separator}{uri_params}" - - else: - self.uri = os.getenv("MONGODB_URI") - self.database = os.getenv("MONGODB_DATABASE", self.database) - - -@dataclass -class ExtractModeConfig: - - scenario_type: ScenarioType = ScenarioType.TEAM - language: str = "zh" - - # Optional overrides - data_file: Optional[Path] = None - output_dir: Optional[Path] = None - group_id: Optional[str] = None - group_name: Optional[str] = None - enable_profile_extraction: bool = True - enable_foresight_extraction: bool = False - - def __post_init__(self): - # Automatically set output directory - if self.output_dir is None: - self.output_dir = ( - Path(__file__).parent.parent - / "memcell_outputs" - / f"{self.scenario_type.value}_{self.language}" - ) - - # Set default values based on scenario - if self.scenario_type == ScenarioType.TEAM: - self.group_id = self.group_id or "team_chat_001" - self.group_name = self.group_name or "Project Discussion Group" - self.enable_foresight_extraction = False - else: - self.group_id = self.group_id or "assistant" - self.group_name = self.group_name or "Personal Assistant" - self.enable_foresight_extraction = True - - # Backward compatibility - self.prompt_language = self.language - - -@dataclass -class ChatModeConfig: - """Chat system configuration - using reasonable default values""" - - # Core parameters (usually no need to modify) - api_base_url: str = field( - default_factory=lambda: os.getenv("API_BASE_URL", "http://localhost:1995") - ) - top_k_memories: int = 10 - conversation_history_size: int = 10 - time_range_days: int = 365 - show_retrieved_memories: bool = True - - # Paths (automatically set) - chat_history_dir: Path = field( - default_factory=lambda: Path(__file__).parent.parent / "chat_history" - ) - memcell_output_dir: Path = field( - default_factory=lambda: Path(__file__).parent.parent / "memcell_outputs" - ) - - def __post_init__(self): - self.chat_history_dir.mkdir(parents=True, exist_ok=True) diff --git a/methods/EverCore/demo/extract_memory.py b/methods/EverCore/demo/extract_memory.py deleted file mode 100644 index f1a54cb8..00000000 --- a/methods/EverCore/demo/extract_memory.py +++ /dev/null @@ -1,301 +0,0 @@ -import asyncio -import json -from datetime import datetime, timezone -from pathlib import Path -import httpx -from api_specs.memory_types import ScenarioType -from demo.tools.clear_all_data import clear_all_memories -from common_utils.language_utils import get_prompt_language - - -def load_conversation_data(file_path: str) -> tuple: - """Load conversation data from JSON file - - Returns: - tuple: (messages, group_id, group_name, session_meta) - """ - data_file = Path(file_path) - if not data_file.exists(): - raise FileNotFoundError(f"Data file not found: {file_path}") - - with open(data_file, 'r', encoding='utf-8') as f: - data = json.load(f) - - # Extract message list and metadata - messages = data.get('conversation_list', []) - session_meta = data.get('session_meta', {}) - group_id = session_meta.get('group_id', 'unknown_group') - group_name = session_meta.get('name', 'unknown') - - print(f"Loaded {len(messages)} messages from {file_path}") - print(f"group_id: {group_id}") - print(f"group_name: {group_name}") - - return messages, group_id, group_name, session_meta - - -def _parse_create_time_to_unix_ms(create_time: str) -> int: - """Convert ISO 8601 create_time string to unix milliseconds.""" - dt = datetime.fromisoformat(create_time.replace("Z", "+00:00")) - return int(dt.timestamp() * 1000) - - -def _infer_role(sender: str, session_meta: dict) -> str: - """Infer role (user/assistant) from sender id and session_meta.""" - user_details = session_meta.get("user_details", {}) - detail = user_details.get(sender, {}) - role = detail.get("role", "") - if role in ("user", "assistant"): - return role - # Heuristic: robot/assistant in sender name - sender_lower = sender.lower() - if "robot" in sender_lower or "assistant" in sender_lower or "bot" in sender_lower: - return "assistant" - return "user" - - -def convert_to_v1_message(msg: dict, session_meta: dict) -> dict: - """Convert old-format message to V1 MessageItem format. - - Old: { message_id, create_time, sender, sender_name, type, content, ... } - New: { message_id, sender_id, sender_name, role, timestamp, type, content } - """ - role = _infer_role(msg.get("sender", ""), session_meta) - timestamp_ms = _parse_create_time_to_unix_ms( - msg.get("create_time", "2025-01-01T00:00:00Z") - ) - - return { - "message_id": msg.get("message_id"), - "sender_id": msg.get("sender"), - "sender_name": msg.get("sender_name"), - "role": role, - "timestamp": timestamp_ms, - "type": msg.get("type", "text"), - "content": msg.get("content", ""), - } - - -async def init_settings( - client: httpx.AsyncClient, base_url: str, session_meta: dict -) -> None: - """Initialize global settings via V1 Settings API.""" - payload = {} - - url = f"{base_url}/api/v1/settings" - resp = await client.put( - url, json=payload, headers={"Content-Type": "application/json"} - ) - if resp.status_code != 200: - print(f"⚠️ Failed to init settings: HTTP {resp.status_code}") - print(resp.text[:300]) - else: - print("✓ settings initialized") - - -def prompt_clear_data() -> bool: - """Prompt user whether to clear existing data before extraction - - Returns: - bool: True if user wants to clear data, False otherwise - """ - print() - print("=" * 60) - print("⚠️ Clear existing data before extraction?") - print("=" * 60) - print() - print("This will delete ALL existing memories from:") - print(" • MongoDB (memcells, episodic_memories, etc.)") - print(" • Elasticsearch (episodic-memory, atomic-fact, foresight)") - print(" • Milvus (vector collections)") - print() - - while True: - choice = input("Clear all existing data? [Y/N]: ").strip().upper() - if choice == 'Y': - print() - return True - elif choice == 'N': - print() - print("✓ Keeping existing data, will append new memories") - print() - return False - else: - print("Please enter Y (yes) or N (no)") - - -async def test_memorize_api(): - """Test V1 API /memories endpoint (single message storage)""" - - # Ask user whether to clear existing data - should_clear = prompt_clear_data() - if should_clear: - await clear_all_memories() - - base_url = "http://localhost:1995" - - print("=" * 100) - print("🧪 Testing V1 API HTTP Interface - Memory Storage") - print("=" * 100) - - # Load conversation data based on language setting - language = get_prompt_language() - print(f"\n📌 Language setting: MEMORY_LANGUAGE={language}") - print( - f" (Set via environment variable, affects both data file and server prompts)" - ) - - # ===== Scene selection ===== - # SOLO: personal conversation (1 user + assistant) - # TEAM: group conversation (multiple users), runs with 2 group_ids to verify isolation - scene = ScenarioType.SOLO.value - # scene = ScenarioType.TEAM.value - - if language == "zh": - data_file = "data/solo_chat_zh.json" if scene == ScenarioType.SOLO.value else "data/team_chat_zh.json" - else: - data_file = "data/solo_chat_en.json" if scene == ScenarioType.SOLO.value else "data/team_chat_en.json" - - try: - test_messages, group_id, group_name, session_meta = load_conversation_data( - data_file - ) - except FileNotFoundError as e: - print(f"❌ Error: {e}") - return False - - is_solo = scene == ScenarioType.SOLO.value - - # For TEAM scene, run the same data with 2 different group_ids to verify multi-group isolation - if is_solo: - group_ids = [None] # personal endpoint uses user_id, not group_id - else: - group_ids = [group_id, f"{group_id}_2"] - - # Determine user_id from session_meta (used for SOLO endpoint) - user_id = None - for uid, detail in session_meta.get("user_details", {}).items(): - if detail.get("role") == "user": - user_id = uid - break - if not user_id: - user_id = "user_001" - - async with httpx.AsyncClient(timeout=500.0) as client: - # Initialize settings - await init_settings(client=client, base_url=base_url, session_meta=session_meta) - - for gid in group_ids: - # SOLO uses personal endpoint, TEAM uses group endpoint - if is_solo: - memorize_url = f"{base_url}/api/v1/memories?sync_mode=false" - else: - memorize_url = f"{base_url}/api/v1/memories/group?sync_mode=false" - - print() - print("=" * 100) - if is_solo: - print(f"📤 [{scene.upper()}] Sending {len(test_messages)} messages") - print(f" URL: {memorize_url}") - print(f" user_id: {user_id}") - else: - print(f"📤 [{scene.upper()}] Sending {len(test_messages)} messages to group: {gid}") - print(f" URL: {memorize_url}") - print(f" group_id: {gid}") - print("=" * 100) - print() - print("ℹ️ How it works:") - print(" • Messages accumulate in Redis until boundary condition is met") - print(" • '⏳ Queued' = Message stored, waiting for boundary detection") - print(" • '🔄 Processing' = Boundary detected, submitted to background worker") - print() - - total_accumulated = 0 - total_processing = 0 - - for idx, message in enumerate(test_messages, 1): - print( - f"[{idx}/{len(test_messages)}] {message.get('sender', '?')}: {message.get('content', '')[:40]}..." - ) - - # Convert to V1 format - v1_msg = convert_to_v1_message(message, session_meta) - if is_solo: - payload = {"user_id": user_id, "messages": [v1_msg]} - else: - payload = {"group_id": gid, "messages": [v1_msg]} - - try: - response = await client.post( - memorize_url, - json=payload, - headers={"Content-Type": "application/json"}, - ) - - if response.status_code == 200: - result = response.json() - data = result.get("data", {}) - status = data.get("status_info") or data.get("status", "unknown") - saved_count = data.get("count", 0) - - if status in ("accumulated", "queued"): - total_accumulated += 1 - print(f" ⏳ Queued") - elif status in ("extracted", "processing"): - total_processing += 1 - print(f" ✅ Extracted {saved_count} memories") - else: - print(f" ✗ Unexpected status: {status}") - print(f" Response: {response.text}") - elif response.status_code == 202: - result = response.json() - total_processing += 1 - request_id = result.get("request_id", "") - print(f" 🔄 Processing (request_id: {request_id[:8]})") - else: - print(f" ✗ Failed: HTTP {response.status_code}") - print(f" {response.text[:200]}") - - except httpx.ConnectError: - print(f" ✗ Connection failed: Unable to connect to {base_url}") - print(f" Ensure V1 API service is running:") - print(f" uv run python src/bootstrap.py src/run.py") - return False - except httpx.ReadTimeout: - print(f" ⚠ Timeout: Processing exceeded 500s") - print(f" Skipping message and continuing...") - continue - except Exception as e: - print(f" ✗ Error: {type(e).__name__}: {e}") - import traceback - traceback.print_exc() - return False - - effective_id = user_id if is_solo else gid - print(f"\n📊 [{effective_id}] Summary:") - print(f" Total messages: {len(test_messages)}") - print(f" Queued: {total_accumulated}") - print(f" Processing: {total_processing}") - - if total_processing > 0: - print("\n🔄 Background processing in progress:") - print(" • MemCells are being extracted and saved by background workers") - print(" • Episode memories, foresights, and atomic facts are being generated") - print(" • Check worker logs for progress") - - if not is_solo and len(group_ids) > 1: - print("\n" + "=" * 100) - print(f"✓ TEAM data sent to {len(group_ids)} group_ids: {group_ids}") - print(" Profiles will be extracted independently for each group.") - print(" Use test_v1api_search.py or the E2E test to verify isolation.") - print("=" * 100) - - print("\n📝 Next steps:") - print(" Run chat demo: uv run python src/bootstrap.py demo/chat_with_memory.py") - print("=" * 100) - - return True - - -if __name__ == "__main__": - asyncio.run(test_memorize_api()) diff --git a/methods/EverCore/demo/search_agent_demo.py b/methods/EverCore/demo/search_agent_demo.py deleted file mode 100644 index 476ca10b..00000000 --- a/methods/EverCore/demo/search_agent_demo.py +++ /dev/null @@ -1,483 +0,0 @@ -"""Search Agent Memory Demo - Web Search Trajectory + Agent Skill - -Demonstrates how the agent memory system works with a search/research agent: -1. Send two similar search agent trajectories (OpenAI chat completion format) -2. Wait for memory extraction (MemCell segmentation + AgentCase extraction) -3. The second trajectory triggers MemScene clustering -> AgentSkill extraction -4. Fetch extracted agent experiences AND agent skills -5. Hybrid search over agent_case and agent_skill (retrieve_method=hybrid) - -The mock trajectories simulate a research agent that: -- Trajectory 1: Compares Python web frameworks, then deploys FastAPI with Docker -- Trajectory 2: Compares databases for a real-time app, then sets up ClickHouse - -Both follow the same research pattern (search -> compare -> recommend -> deploy), -so they cluster together and trigger AgentSkill extraction. - -Prerequisites: - Start the API server first (in another terminal): - uv run python src/run.py - -Run the demo: - uv run python src/bootstrap.py demo/search_agent_demo.py -""" - -import asyncio -import json - -from demo.utils.agent_demo_helpers import ( - AgentDemoRunner, - MEMORY_TYPE_PRINTERS, - print_separator, - print_search_case_results, - print_search_skill_results, -) - - -# ==================== Mock Search Agent Trajectory ==================== - -def build_mock_trajectory(): - """Build a mock search agent trajectory in OpenAI chat completion format. - - Simulates a two-turn interaction: - Turn 1: User asks about Python web frameworks -> Agent searches and compares - Turn 2: User asks a follow-up about FastAPI deployment -> Agent searches - """ - messages = [ - # ===== Turn 1: Python web frameworks comparison ===== - { - "role": "user", - "content": [{"type": "text", "text": "Compare the top 3 Python web frameworks by GitHub stars and performance benchmarks"}], - }, - { - "role": "assistant", - "content": [{"type": "text", "text": "I'll search for information about the top Python web frameworks and compare them."}], - "tool_calls": [ - { - "id": "call_001", - "type": "function", - "function": { - "name": "web_search", - "arguments": json.dumps({ - "query": "top Python web frameworks 2026 GitHub stars comparison" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_001", - "content": json.dumps({ - "results": [ - {"title": "Django - 82k stars", "url": "https://github.com/django/django"}, - {"title": "Flask - 69k stars", "url": "https://github.com/pallets/flask"}, - {"title": "FastAPI - 78k stars", "url": "https://github.com/tiangolo/fastapi"}, - ] - }), - }, - { - "role": "assistant", - "content": [{"type": "text", "text": "Found the top frameworks. Let me now search for performance benchmarks."}], - "tool_calls": [ - { - "id": "call_002", - "type": "function", - "function": { - "name": "web_search", - "arguments": json.dumps({ - "query": "Django vs Flask vs FastAPI performance benchmark 2026" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_002", - "content": json.dumps({ - "results": [ - { - "title": "TechEmpower Benchmark Round 23", - "summary": "FastAPI: 45k req/s, Flask: 12k req/s, Django: 8k req/s (JSON serialization)", - } - ] - }), - }, - { - "role": "assistant", - "content": [{"type": "text", "text": ( - "Here's a comparison of the top 3 Python web frameworks:\n\n" - "1. **Django** (82k stars) - Full-featured, batteries-included. Best for large applications. " - "Performance: ~8k req/s\n" - "2. **FastAPI** (78k stars) - Modern, async-first, auto-generated docs. " - "Performance: ~45k req/s\n" - "3. **Flask** (69k stars) - Lightweight, flexible, microframework. " - "Performance: ~12k req/s\n\n" - "FastAPI leads in raw performance due to its async architecture built on Starlette." - )}], - }, - - # ===== Turn 2: Follow-up about FastAPI deployment ===== - { - "role": "user", - "content": [{"type": "text", "text": "How do I deploy a FastAPI app to production with Docker?"}], - }, - { - "role": "assistant", - "content": [{"type": "text", "text": "Let me search for the best practices for deploying FastAPI with Docker."}], - "tool_calls": [ - { - "id": "call_003", - "type": "function", - "function": { - "name": "web_search", - "arguments": json.dumps({ - "query": "FastAPI Docker deployment production best practices" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_003", - "content": json.dumps({ - "results": [ - { - "title": "FastAPI Official Docs - Deployment", - "summary": ( - "Use multi-stage Docker build. Base: python:3.12-slim. " - "Install with pip. Run with uvicorn --workers 4. " - "Use gunicorn with uvicorn workers for production." - ), - }, - { - "title": "Production FastAPI Setup Guide", - "summary": ( - "Dockerfile: multi-stage build, non-root user, health check. " - "docker-compose for orchestration. Nginx reverse proxy. " - "Environment variables for config." - ), - }, - ] - }), - }, - { - "role": "assistant", - "content": [{"type": "text", "text": ( - "Here's how to deploy a FastAPI app to production with Docker:\n\n" - "**Dockerfile (multi-stage):**\n" - "```dockerfile\n" - "FROM python:3.12-slim as base\n" - "WORKDIR /app\n" - "COPY requirements.txt .\n" - "RUN pip install --no-cache-dir -r requirements.txt\n" - "COPY . .\n" - "CMD [\"uvicorn\", \"main:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\", \"--workers\", \"4\"]\n" - "```\n\n" - "**Key tips:**\n" - "- Use `gunicorn` with `uvicorn.workers.UvicornWorker` for production\n" - "- Run as non-root user for security\n" - "- Add health check endpoint\n" - "- Use Nginx as reverse proxy" - )}], - }, - ] - - return messages - - -def build_second_trajectory(): - """Build a second search agent trajectory to trigger clustering + skill extraction. - - Simulates a different research topic but same pattern: - Turn 1: User asks about databases for real-time analytics -> Agent searches and compares - Turn 2: User asks about ClickHouse Docker setup -> Agent searches - - This trajectory follows the same research pattern as the first (search -> compare -> - recommend, then search -> deploy/setup guide), so it should cluster into the same - MemScene and trigger AgentSkill extraction. - """ - messages = [ - # ===== Turn 1: Database comparison for real-time analytics ===== - { - "role": "user", - "content": [{"type": "text", "text": "What's the best database for a real-time analytics dashboard handling 100k events/sec?"}], - }, - { - "role": "assistant", - "content": [{"type": "text", "text": "I'll research databases suited for real-time analytics at that scale."}], - "tool_calls": [ - { - "id": "call_101", - "type": "function", - "function": { - "name": "web_search", - "arguments": json.dumps({ - "query": "best database real-time analytics high throughput 2026 comparison" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_101", - "content": json.dumps({ - "results": [ - {"title": "ClickHouse - 38k stars", "url": "https://github.com/ClickHouse/ClickHouse", - "summary": "Column-oriented OLAP database, 100M+ rows/sec aggregation"}, - {"title": "TimescaleDB - 18k stars", "url": "https://github.com/timescale/timescaledb", - "summary": "PostgreSQL extension for time-series, familiar SQL interface"}, - {"title": "Apache Druid - 13k stars", "url": "https://github.com/apache/druid", - "summary": "Real-time analytics database, sub-second OLAP queries"}, - ] - }), - }, - { - "role": "assistant", - "content": [{"type": "text", "text": "Found the top candidates. Let me get performance benchmarks for this scale."}], - "tool_calls": [ - { - "id": "call_102", - "type": "function", - "function": { - "name": "web_search", - "arguments": json.dumps({ - "query": "ClickHouse vs TimescaleDB vs Druid benchmark throughput 2026" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_102", - "content": json.dumps({ - "results": [ - { - "title": "OLAP Database Benchmark 2026", - "summary": ( - "ClickHouse: 1.2B rows/sec scan, 500k inserts/sec. " - "TimescaleDB: 200k inserts/sec, best for time-series with SQL joins. " - "Druid: 800k events/sec ingestion, sub-second queries on pre-aggregated data." - ), - } - ] - }), - }, - { - "role": "assistant", - "content": [{"type": "text", "text": ( - "Here's a comparison of the top 3 databases for real-time analytics:\n\n" - "1. **ClickHouse** (38k stars) - Column-oriented OLAP. Blazing fast aggregations " - "over billions of rows. Best for: analytical queries, event analytics.\n" - " Throughput: 1.2B rows/sec scan, 500k inserts/sec\n" - "2. **Apache Druid** (13k stars) - Real-time OLAP with sub-second queries. " - "Best for: pre-aggregated dashboards, streaming ingestion.\n" - " Throughput: 800k events/sec ingestion\n" - "3. **TimescaleDB** (18k stars) - PostgreSQL extension for time-series. " - "Best for: teams already on PostgreSQL, complex SQL joins.\n" - " Throughput: 200k inserts/sec\n\n" - "For 100k events/sec with ad-hoc analytics, **ClickHouse** is the strongest choice. " - "If you need a simpler setup with PostgreSQL compatibility, go with TimescaleDB." - )}], - }, - - # ===== Turn 2: Follow-up about ClickHouse Docker setup ===== - { - "role": "user", - "content": [{"type": "text", "text": "How do I set up ClickHouse with Docker for production?"}], - }, - { - "role": "assistant", - "content": [{"type": "text", "text": "Let me search for ClickHouse Docker production setup best practices."}], - "tool_calls": [ - { - "id": "call_103", - "type": "function", - "function": { - "name": "web_search", - "arguments": json.dumps({ - "query": "ClickHouse Docker production setup best practices 2026" - }), - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_103", - "content": json.dumps({ - "results": [ - { - "title": "ClickHouse Docker Official Guide", - "summary": ( - "Use clickhouse/clickhouse-server image. Mount /var/lib/clickhouse " - "for data persistence. Configure users.xml for auth. " - "Set max_memory_usage and max_threads for resource limits." - ), - }, - { - "title": "Production ClickHouse Docker Deployment", - "summary": ( - "docker-compose with persistent volumes. Tune MergeTree settings " - "for write-heavy workloads. Enable monitoring via system.metrics. " - "Use clickhouse-backup for automated snapshots." - ), - }, - ] - }), - }, - { - "role": "assistant", - "content": [{"type": "text", "text": ( - "Here's how to set up ClickHouse with Docker for production:\n\n" - "**docker-compose.yml:**\n" - "```yaml\n" - "services:\n" - " clickhouse:\n" - " image: clickhouse/clickhouse-server:latest\n" - " ports:\n" - " - '8123:8123' # HTTP\n" - " - '9000:9000' # Native\n" - " volumes:\n" - " - clickhouse_data:/var/lib/clickhouse\n" - " - ./config/users.xml:/etc/clickhouse-server/users.xml\n" - " ulimits:\n" - " nofile: { soft: 262144, hard: 262144 }\n" - "volumes:\n" - " clickhouse_data:\n" - "```\n\n" - "**Key tips:**\n" - "- Mount `/var/lib/clickhouse` for data persistence across restarts\n" - "- Set `max_memory_usage` to 80% of available RAM\n" - "- Configure `users.xml` for authentication (don't use default user in prod)\n" - "- Enable `system.metrics` table for monitoring with Grafana" - )}], - }, - ] - - return messages - - -# ==================== Main ==================== - -async def main(): - runner = AgentDemoRunner( - group_id_prefix="search_agent_demo", - group_name="Search Agent Demo Session", - description="Agent Memory Demo - Search Agent", - tags=["demo", "agent"], - msg_prefix="search_msg", - ) - - print_separator("Search Agent Memory Demo - Web Search Trajectory + Agent Skill") - - # Step 1: Initialize conversation metadata - print("\nStep 1: Initialize conversation metadata") - print_separator() - await runner.save_conversation_meta() - - # Step 2: Send first agent trajectory (Python web frameworks) - print("\nStep 2: Send first search agent trajectory (Python frameworks)") - print_separator() - - messages1 = build_mock_trajectory() - print(f" Sending {len(messages1)} messages (2 interaction turns)...\n") - - for idx, msg in enumerate(messages1): - is_last = (idx == len(messages1) - 1) - await runner.send_agent_message(msg, idx, flush=is_last) - await asyncio.sleep(0.5) - - # Step 3: Wait for first extraction to complete - print("\nStep 3: Wait for first extraction pipeline") - print_separator() - print(" Waiting 60 seconds for first AgentCase extraction...") - await asyncio.sleep(60) - - # Step 4: Send second trajectory (database comparison - different topic, same pattern) - print("\nStep 4: Send second trajectory (database comparison for analytics)") - print_separator() - print(" Different topic, but same research pattern (search -> compare -> deploy).") - print(" It will cluster into the same MemScene and trigger AgentSkill extraction.\n") - - messages2 = build_second_trajectory() - offset = len(messages1) - print(f" Sending {len(messages2)} messages (2 interaction turns)...\n") - - for idx, msg in enumerate(messages2): - is_last = (idx == len(messages2) - 1) - await runner.send_agent_message(msg, offset + idx, flush=is_last) - await asyncio.sleep(0.5) - - # Step 5: Wait for second extraction + skill extraction - print("\nStep 5: Wait for second extraction pipeline + AgentSkill extraction") - print_separator() - print(" Waiting 60 seconds for AgentCase + AgentSkill extraction...") - await asyncio.sleep(60) - - # Step 6: Fetch and print all extracted memories - print("\nStep 6: Fetch all extracted memories") - - for memory_type, label, printer in MEMORY_TYPE_PRINTERS: - print(f"\n--- {label} ---") - memories = await runner.fetch_memories(memory_type) - print(f" ({len(memories)} records)") - printer(memories) - - # Step 7: Keyword search demo (BM25) - print("\nStep 7: Keyword search (agent_memory, retrieve_method=keyword)") - - search_queries = [ - "how to evaluate and compare tech solutions with benchmarks", - "which database should I use for real-time analytics", - "compare Python web frameworks performance", - "best practices for technology research and production deployment", - "how to compare and benchmark databases", - "steps to evaluate Python frameworks by popularity and speed", - "recommend a high-throughput database for event streaming", - ] - - for query in search_queries: - print(f"\n--- Keyword Search: Agent Memory ---") - print(f" Query: \"{query}\"") - result = await runner.search_memories( - query, "agent_memory", retrieve_method="keyword" - ) - cases = result.get("cases", []) - skills = result.get("skills", []) - print(f" ({len(cases)} cases, {len(skills)} skills)") - if cases: - print(" [Cases]") - print_search_case_results(cases) - if skills: - print(" [Skills]") - print_search_skill_results(skills) - - # Step 8: Vector search demo (embedding similarity) - print("\nStep 8: Vector search (agent_memory, retrieve_method=vector)") - - for query in search_queries: - print(f"\n--- Vector Search: Agent Memory ---") - print(f" Query: \"{query}\"") - result = await runner.search_memories( - query, "agent_memory", retrieve_method="vector" - ) - cases = result.get("cases", []) - skills = result.get("skills", []) - print(f" ({len(cases)} cases, {len(skills)} skills)") - if cases: - print(" [Cases]") - print_search_case_results(cases) - if skills: - print(" [Skills]") - print_search_skill_results(skills) - - print_separator("Demo completed!") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/demo/simple_demo.py b/methods/EverCore/demo/simple_demo.py deleted file mode 100644 index e58d6470..00000000 --- a/methods/EverCore/demo/simple_demo.py +++ /dev/null @@ -1,74 +0,0 @@ -"""MemSys Simple Demo - Easy to Understand! - -Demonstrates how to use the memory system: -1. Store conversations -2. Search memories - -Prerequisites: - Start the API server first (in another terminal): - uv run python src/run.py - -Run the demo: - uv run python src/bootstrap.py demo/simple_demo.py -""" - -import asyncio -from demo.utils import SimpleMemoryManager - - -async def main(): - """Super simple usage example - just 3 steps!""" - - # Create memory manager - memory = SimpleMemoryManager() - - memory.print_separator("🧠 MemSys Simple Demo") - - # ========== Step 1: Store Conversations ========== - print("\n📝 Step 1: Store Conversations") - memory.print_separator() - - await memory.store("I love playing soccer, often go to the field on weekends") - await asyncio.sleep(2) - - await memory.store("Soccer is a great sport! Which team do you like?", sender="Assistant") - await asyncio.sleep(2) - - await memory.store("I love Barcelona the most, Messi is my idol") - await asyncio.sleep(2) - - await memory.store("I also enjoy watching basketball, NBA is my favorite") - await asyncio.sleep(2) - - await memory.store("I will sleep now") - await asyncio.sleep(2) - - await memory.store("The weather is good today") - await asyncio.sleep(2) - - await memory.store("The universe is expanding") - await asyncio.sleep(2) - # ========== Step 2: Wait for Indexing ========== - print("\n⏳ Step 2: Wait for Index Building") - memory.print_separator() - await memory.wait_for_index(seconds=10) - - # ========== Step 3: Search Memories ========== - print("\n🔍 Step 3: Search Memories") - memory.print_separator() - - print("\n💬 Query 1: What sports does the user like?") - await memory.search("What sports does the user like?") - - print("\n💬 Query 2: What is the user's favorite team?") - await memory.search("What is the user's favorite team?") - - print("\n💬 Query 3: What are the user's sports hobbies?") - await memory.search("What are the user's sports hobbies?") - - # ========== Done ========== - memory.print_summary() - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/demo/tools/clear_all_data.py b/methods/EverCore/demo/tools/clear_all_data.py deleted file mode 100644 index 63f9853e..00000000 --- a/methods/EverCore/demo/tools/clear_all_data.py +++ /dev/null @@ -1,404 +0,0 @@ -"""Tool function to clear all memory data - -Can be imported by other test scripts or run independently -""" - -import asyncio -import time -from typing import Dict, Any, List - -from pymilvus import utility, Collection - -from infra_layer.adapters.out.search.milvus.memory.episodic_memory_collection import ( - EpisodicMemoryCollection, -) -from infra_layer.adapters.out.search.milvus.memory.foresight_collection import ( - ForesightCollection, -) -from infra_layer.adapters.out.search.milvus.memory.atomic_fact_collection import ( - AtomicFactCollection, -) -from infra_layer.adapters.out.search.milvus.memory.agent_case_collection import ( - AgentCaseCollection, -) -from infra_layer.adapters.out.search.milvus.memory.agent_skill_collection import ( - AgentSkillCollection, -) -from infra_layer.adapters.out.search.elasticsearch.memory.episodic_memory import ( - EpisodicMemoryDoc, -) -from infra_layer.adapters.out.search.elasticsearch.memory.foresight import ForesightDoc -from infra_layer.adapters.out.search.elasticsearch.memory.atomic_fact import ( - AtomicFactDoc, -) -from infra_layer.adapters.out.search.elasticsearch.memory.agent_case import ( - AgentCaseDoc, -) -from infra_layer.adapters.out.search.elasticsearch.memory.agent_skill import ( - AgentSkillDoc, -) -from core.di import get_bean_by_type -from core.component.redis_provider import RedisProvider -from core.component.mongodb_client_factory import MongoDBClientFactory - - -async def _clear_mongodb(verbose: bool = True) -> Dict[str, Any]: - """Delete all documents in MongoDB, keeping collections and indexes""" - result: Dict[str, Any] = { - "database": None, - "collections": {}, - "deleted": {}, - "errors": [], - } - try: - mongo_factory = get_bean_by_type(MongoDBClientFactory) - client_wrapper = await mongo_factory.get_default_client() - db = client_wrapper.database - db_name = db.name - result["database"] = db_name - - collection_names = await db.list_collection_names() - for coll_name in collection_names: - if coll_name.startswith("system."): - continue - collection = db[coll_name] - count = await collection.count_documents({}) - if count == 0: - continue - # todo delete many without repository - delete_result = await collection.delete_many({}) - deleted = delete_result.deleted_count if delete_result else 0 - result["collections"][coll_name] = count - result["deleted"][coll_name] = deleted - - if verbose: - total_deleted = sum(result["deleted"].values()) - print( - f" ✅ MongoDB '{db_name}': Deleted {total_deleted} documents ({len(result['deleted'])} collections)" - ) - except Exception as exc: - result["errors"].append(str(exc)) - if verbose: - print(f" ⚠️ MongoDB clear failed: {exc}") - - return result - - -def _get_milvus_row_count(name: str, coll: Collection) -> int: - """Get Milvus real-time row count (priority: row_count > segment > num_entities)""" - get_stats = getattr(utility, "get_collection_stats", None) - if callable(get_stats): - stats_info = get_stats(name) - if isinstance(stats_info, dict): - try: - return int(stats_info.get("row_count", 0)) - except (ValueError, TypeError): - pass - - try: - segment_infos = utility.get_query_segment_info(name) - if segment_infos: - total = 0 - for seg in segment_infos: - seg_rows = getattr(seg, "num_rows", None) - if seg_rows is None: - seg_rows = getattr(seg, "row_count", 0) - total += int(seg_rows or 0) - return total - except Exception: - pass - - try: - return int(coll.num_entities) - except Exception: - return 0 - - -def _clear_milvus( - verbose: bool = True, drop_collections: bool = False -) -> Dict[str, Any]: - """Delete all vectors in Milvus collections - - Args: - verbose: Whether to output logs - drop_collections: Whether to drop physical collections and recreate (thorough clear) - """ - stats: Dict[str, Any] = {"cleared": [], "errors": []} - collection_classes = [ - EpisodicMemoryCollection, - ForesightCollection, - AtomicFactCollection, - AgentCaseCollection, - AgentSkillCollection, - ] - for cls in collection_classes: - collection = cls() - alias = collection.name - try: - related_collections: List[str] = [] - all_collections = utility.list_collections(using=collection.using) - prefix = f"{alias}_" - for real_name in all_collections: - if real_name == alias or real_name.startswith(prefix): - related_collections.append(real_name) - - if not related_collections: - continue - - if not drop_collections: - for real_name in related_collections: - coll = Collection(name=real_name, using=collection.using) - coll.load() - before_count = coll.num_entities - if before_count == 0: - continue - coll.delete(expr="id != ''") - coll.flush() - - # Drop alias to prevent errors when dropping collection - try: - utility.drop_alias(alias, using=collection.using) - except Exception: - pass - - for real_name in related_collections: - before_count = 0 - try: - coll = Collection(name=real_name, using=collection.using) - coll.load() - before_count = coll.num_entities - except Exception: - before_count = 0 - - utility.drop_collection(real_name, using=collection.using) - stats["cleared"].append( - {"collection": real_name, "deleted": before_count, "dropped": True} - ) - if verbose: - print( - f" ✅ Milvus dropped collection {real_name} ({before_count} vectors)" - ) - - # Clear class-level collection cache to ensure new instance is not old one - cls._collection_instance = None - - # Recreate empty collection and associate alias - try: - collection.ensure_all() - except Exception as ensure_exc: - if verbose: - print( - f" ⚠️ Recreate Milvus collection {alias} failed: {ensure_exc}" - ) - except Exception as exc: # pylint: disable=broad-except - stats["errors"].append(str(exc)) - if verbose: - print(f" ⚠️ Cannot clear Milvus collection {alias}: {exc}") - - return stats - - -async def _clear_elasticsearch( - verbose: bool = True, rebuild_index: bool = False -) -> Dict[str, Any]: - """Delete Elasticsearch documents related to memory, rebuild index if necessary""" - stats: Dict[str, Any] = {"cleared": [], "errors": [], "recreated": False} - try: - # Get connection only - es_client = EpisodicMemoryDoc.get_connection() - - alias_names = [ - EpisodicMemoryDoc.get_index_name(), - ForesightDoc.get_index_name(), - AtomicFactDoc.get_index_name(), - AgentCaseDoc.get_index_name(), - AgentSkillDoc.get_index_name(), - ] - - if rebuild_index: - for alias in alias_names: - try: - existing = await es_client.indices.get_alias( - name=alias, ignore=[404] - ) - if isinstance(existing, dict): - for index_name in existing.keys(): - await es_client.indices.delete( - index=index_name, ignore=[400, 404] - ) - stats["cleared"].append( - {"alias": alias, "deleted_index": index_name} - ) - if verbose: - print(f" ✅ Deleted index: {index_name}") - except Exception as inner_exc: - stats["errors"].append(str(inner_exc)) - if verbose: - print(f" ⚠️ Delete index failed {alias}: {inner_exc}") - for alias in alias_names: - await es_client.indices.delete_alias( - index="*", name=alias, ignore=[404] - ) - # Recreate indices using EsIndexInitializer - from core.oxm.es.es_utils import EsIndexInitializer - - initializer = EsIndexInitializer() - await initializer.initialize_indices( - [EpisodicMemoryDoc, ForesightDoc, AtomicFactDoc, AgentCaseDoc, AgentSkillDoc] - ) - stats["recreated"] = True - if verbose: - print(" ✅ Elasticsearch indices and aliases recreated") - return stats - - for alias in alias_names: - try: - exists = await es_client.indices.exists_alias(name=alias) - if not exists: - continue - count_resp = await es_client.count(index=alias, query={"match_all": {}}) - total_docs = count_resp.get("count", 0) - if total_docs == 0: - continue - await es_client.delete_by_query( - index=alias, - query={"match_all": {}}, - refresh=True, - conflicts="proceed", - ) - stats["cleared"].append({"alias": alias, "deleted": total_docs}) - if verbose: - print( - f" ✅ Elasticsearch {alias}: Deleted {total_docs} documents" - ) - except Exception as inner_exc: # pylint: disable=broad-except - stats["errors"].append(str(inner_exc)) - if verbose: - print(f" ⚠️ Cannot clear ES {alias}: {inner_exc}") - - except Exception as exc: # pylint: disable=broad-except - stats["errors"].append(str(exc)) - if verbose: - print(f" ⚠️ Elasticsearch clear failed: {exc}") - - return stats - - -async def _clear_redis(verbose: bool = True) -> Dict[str, Any]: - """Clear Redis current database""" - stats: Dict[str, Any] = {} - try: - redis_provider = get_bean_by_type(RedisProvider) - client = await redis_provider.get_client() - await client.flushdb() - stats["flushed_db"] = redis_provider.redis_db - if verbose: - print(f" ✅ Redis DB {redis_provider.redis_db} flushed") - except Exception as exc: # pylint: disable=broad-except - stats["error"] = str(exc) - if verbose: - print(f" ⚠️ Redis clear failed: {exc}") - return stats - - -async def clear_all_memories( - verbose: bool = True, rebuild_es: bool = False, drop_milvus: bool = False -): - """Clear all memory data (MongoDB, Milvus, Elasticsearch, Redis) - - Args: - verbose: Whether to show detailed info - rebuild_es: Whether to delete and rebuild Elasticsearch index (Default: False) - drop_milvus: Whether to delete and rebuild Milvus physical collections (Default: False) - """ - if verbose: - print("\n🗑️ Clearing all memory data...") - - try: - if verbose: - print(" 📦 Clearing MongoDB...") - mongo_stats = await _clear_mongodb(verbose) - - if verbose: - print(" 🔍 Clearing Milvus...") - milvus_stats = _clear_milvus(verbose, drop_collections=drop_milvus) - - if verbose: - print(" 🔎 Clearing Elasticsearch...") - es_stats = await _clear_elasticsearch(verbose, rebuild_index=rebuild_es) - - if verbose: - print(" 💾 Clearing Redis...") - redis_stats = await _clear_redis(verbose) - - if verbose: - print("✅ All memory data cleared!\n") - print("📊 Brief Statistics:") - total_mongo_deleted = sum(mongo_stats.get("deleted", {}).values()) - print( - f" - MongoDB deleted docs: {total_mongo_deleted} (Database: {mongo_stats.get('database')})" - ) - total_milvus_deleted = sum( - item["deleted"] for item in milvus_stats.get("cleared", []) - ) - print(f" - Milvus deleted vectors: {total_milvus_deleted}") - if es_stats.get("recreated"): - print(" - Elasticsearch: Indices and aliases recreated") - else: - total_es_deleted = sum( - item["deleted"] for item in es_stats.get("cleared", []) - ) - print(f" - Elasticsearch deleted docs: {total_es_deleted}") - print(f" - Redis flushed DB: {redis_stats.get('flushed_db')}") - - return { - "mongodb": mongo_stats, - "milvus": milvus_stats, - "elasticsearch": es_stats, - "redis": redis_stats, - } - - except Exception as e: - print(f"❌ Error clearing data: {e}") - import traceback - - traceback.print_exc() - raise - - -async def main(): - """Entry function when running independently""" - print("=" * 100) - print("🗑️ Clear All Memory Data Tool") - print("=" * 100) - - # Ensure bootstrap initialization for get_bean_by_type to work - import sys - import os - from bootstrap import setup_project_context - - # Add project root to path - sys.path.append(os.getcwd()) - - await setup_project_context() - - result = await clear_all_memories(verbose=True, rebuild_es=False) - - print("\n📊 Clear Statistics:") - mongo_total = sum(result["mongodb"].get("deleted", {}).values()) - print(f" MongoDB deleted docs: {mongo_total}") - milvus_total = sum(item["deleted"] for item in result["milvus"].get("cleared", [])) - print(f" Milvus deleted vectors: {milvus_total}") - if result["elasticsearch"].get("recreated"): - print(" Elasticsearch: Indices and aliases recreated") - else: - es_total = sum( - item["deleted"] for item in result["elasticsearch"].get("cleared", []) - ) - print(f" Elasticsearch deleted docs: {es_total}") - print(f" Redis flushed DB: {result['redis'].get('flushed_db')}") - print("=" * 100) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/demo/tools/debug_view_databases.py b/methods/EverCore/demo/tools/debug_view_databases.py deleted file mode 100644 index b2b06b37..00000000 --- a/methods/EverCore/demo/tools/debug_view_databases.py +++ /dev/null @@ -1,399 +0,0 @@ -#!/usr/bin/env python3 -""" -Database Data Viewer Tool - -Usage: - python debug_view_databases.py # View all databases overview - python debug_view_databases.py --mongo # View MongoDB only - python debug_view_databases.py --milvus # View Milvus only - python debug_view_databases.py --es # View Elasticsearch only - python debug_view_databases.py --detail # Show detailed data (including samples) -""" - -import asyncio -import os -import sys -from common_utils.datetime_utils import get_now_with_timezone -from pymongo import AsyncMongoClient -from pymilvus import connections, Collection, utility -from elasticsearch import AsyncElasticsearch -import json - - -class Colors: - """Terminal Colors""" - - HEADER = '\033[95m' - BLUE = '\033[94m' - CYAN = '\033[96m' - GREEN = '\033[92m' - YELLOW = '\033[93m' - RED = '\033[91m' - END = '\033[0m' - BOLD = '\033[1m' - - -def print_section(title: str): - """Print Section Title""" - print(f"\n{Colors.BOLD}{Colors.BLUE}{'='*80}{Colors.END}") - print(f"{Colors.BOLD}{Colors.CYAN}{title}{Colors.END}") - print(f"{Colors.BOLD}{Colors.BLUE}{'='*80}{Colors.END}\n") - - -def print_subsection(title: str): - """Print Subsection Title""" - print(f"\n{Colors.BOLD}{Colors.GREEN}--- {title} ---{Colors.END}") - - -def print_success(text: str): - """Print Success Message""" - print(f"{Colors.GREEN}✅ {text}{Colors.END}") - - -def print_warning(text: str): - """Print Warning Message""" - print(f"{Colors.YELLOW}⚠️ {text}{Colors.END}") - - -def print_error(text: str): - """Print Error Message""" - print(f"{Colors.RED}❌ {text}{Colors.END}") - - -async def check_mongodb(detail: bool = False): - """Check MongoDB Data""" - print_section("MongoDB Data") - - try: - # Connection Config (No Auth) - mongo_host = os.getenv('MONGO_HOST', 'localhost') - mongo_port = int(os.getenv('MONGO_PORT', '27017')) - - client = AsyncMongoClient(f'mongodb://{mongo_host}:{mongo_port}') - db = client['memsys'] - - print_success(f"Connected to MongoDB: {mongo_host}:{mongo_port}") - - # Dynamically get all collections - collection_names = await db.list_collection_names() - collection_names.sort() - - if not collection_names: - print_warning("No collections in database") - return - - for collection_name in collection_names: - # Skip system collections - if collection_name.startswith("system."): - continue - - print_subsection(f"Collection: {collection_name}") - - collection = db[collection_name] - total = await collection.count_documents({}) - - if total == 0: - print_warning(f"No data") - continue - - print(f"Total: {Colors.BOLD}{total}{Colors.END} items") - - # Count by user_id (if exists) - try: - pipeline = [ - {'$group': {'_id': '$user_id', 'count': {'$sum': 1}}}, - {'$sort': {'count': -1}}, - ] - # Check for user_id field (simple sampling) - sample = await collection.find_one() - if sample and 'user_id' in sample: - cursor = await collection.aggregate(pipeline) - result = await cursor.to_list(length=None) - - if result: - print("\nGroup by user_id:") - for item in result[:10]: # Show top 10 only - user_id = item['_id'] if item['_id'] else '(Empty/Group)' - print(f" - {user_id}: {item['count']} items") - except Exception: - pass # Ignore aggregation errors - - # Show samples - if detail: - print("\nSample data:") - cursor = collection.find().limit(2) - async for doc in cursor: - # Remove overly long fields - doc.pop('_id', None) - doc.pop('vector', None) - doc.pop('embedding', None) - doc.pop('original_data', None) - - # Limit field length - for key, value in doc.items(): - if isinstance(value, str) and len(value) > 100: - doc[key] = value[:100] + '...' - - print( - f" {json.dumps(doc, ensure_ascii=False, indent=2, default=str)}" - ) - - client.close() - - except Exception as e: - print_error(f"MongoDB connection failed: {e}") - - -def _get_milvus_row_count(collection_name: str, collection: Collection) -> int: - """ - Get real-time entity count from Milvus. - - Prioritize utility.get_collection_stats (if available), - then try utility.get_query_segment_info, and finally fallback to num_entities. - """ - get_stats = getattr(utility, "get_collection_stats", None) - if callable(get_stats): - stats_info = get_stats(collection_name) - if isinstance(stats_info, dict): - return int(stats_info.get("row_count", 0)) - - # For some old versions, get_collection_stats is not available, so we fall back to summing the segment row count - segment_infos = utility.get_query_segment_info(collection_name) - if segment_infos: - total_rows = 0 - for seg in segment_infos: - num_rows = getattr(seg, "num_rows", None) - if num_rows is None: - num_rows = getattr(seg, "row_count", 0) - total_rows += int(num_rows or 0) - return total_rows - - # Final fallback: return num_entities (possibly containing deleted data) - return collection.num_entities - - -def check_milvus(detail: bool = False): - """Check Milvus Data""" - print_section("Milvus Data") - - try: - # Connection Config - milvus_host = os.getenv('MILVUS_HOST', 'localhost') - milvus_port = int(os.getenv('MILVUS_PORT', '19530')) - - connections.connect(host=milvus_host, port=milvus_port) - print_success(f"Connected to Milvus: {milvus_host}:{milvus_port}") - - # Dynamically get all collections - all_collections = utility.list_collections() - all_collections.sort() - - if not all_collections: - print_warning("No collections in Milvus") - return - - for collection_name in all_collections: - print_subsection(f"Collection: {collection_name}") - - collection = Collection(collection_name) - collection.load() - - stats = _get_milvus_row_count(collection_name, collection) - print(f"Current Entities: {Colors.BOLD}{stats}{Colors.END}") - - # Query samples - if detail and stats > 0: - print("\nSample Data:") - - # Original query logic depends on 'id' field, will fail if PK is not 'id' - # We temporarily only keep counting, or only query samples for known collections - known_pk_map = { - 'episodic_memory_memsys': 'id', - 'foresight_memsys': 'id', - 'atomic_fact_memsys': 'id', - } - - pk_field = known_pk_map.get(collection_name) - if pk_field: - try: - results = collection.query( - expr=f"{pk_field} >= 0", - output_fields=[ - "user_id", - "timestamp", - "episode", - "atomic_fact", - "content", - "foresight", - ], - limit=2, - ) - for result in results: - for key, value in result.items(): - if isinstance(value, str) and len(value) > 100: - result[key] = value[:100] + '...' - print( - f" {json.dumps(result, ensure_ascii=False, indent=2, default=str)}" - ) - except Exception as e: - print(f" Query sample failed: {e}") - else: - print(" (No primary key configured, skipping sample query)") - - connections.disconnect("default") - - except Exception as e: - print_error(f"Milvus connection failed: {e}") - - -async def check_elasticsearch(detail: bool = False): - """Check Elasticsearch Data""" - print_section("Elasticsearch Data") - - try: - # Connection Config - es_host = os.getenv('ES_HOSTS', 'http://localhost:19200') - - # Use HTTP direct query to avoid version compatibility issues - import aiohttp - - async with aiohttp.ClientSession() as session: - print_success(f"Connected to Elasticsearch: {es_host}") - - # Get all indices - async with session.get(f"{es_host}/_cat/indices?format=json") as resp: - if resp.status != 200: - print_error(f"Failed to get index list: {resp.status}") - return - indices = await resp.json() - - # Show all indices (no filter) - relevant_indices = indices - - if not relevant_indices: - print_warning("No relevant indices found") - return - - # Sort by index name - relevant_indices.sort(key=lambda x: x['index']) - - for idx_info in relevant_indices: - idx_name = idx_info['index'] - # Skip system indices - if idx_name.startswith('.'): - continue - - doc_count = int(idx_info['docs.count']) - - print_subsection(f"{idx_name} ({doc_count} items)") - - if doc_count == 0: - print_warning("No data") - continue - - # Use aggregation query to count all types - try: - aggs_body = { - "size": 0, - "aggs": {"types": {"terms": {"field": "type", "size": 50}}}, - } - async with session.post( - f"{es_host}/{idx_name}/_search", - json=aggs_body, - headers={"Content-Type": "application/json"}, - ) as resp: - if resp.status == 200: - result = await resp.json() - buckets = ( - result.get('aggregations', {}) - .get('types', {}) - .get('buckets', []) - ) - if buckets: - for bucket in buckets: - print( - f" - type={bucket['key']}: {bucket['doc_count']} items" - ) - else: - print(f" - (No type field classification)") - except Exception as e: - print_warning(f"Aggregation statistics failed: {e}") - - # Show samples - if detail and doc_count > 0: - print("\nSample data:") - try: - query_body = {"size": 2} - async with session.post( - f"{es_host}/{idx_name}/_search", - json=query_body, - headers={"Content-Type": "application/json"}, - ) as resp: - if resp.status == 200: - result = await resp.json() - - for hit in result['hits']['hits']: - src = hit['_source'] - - # Limit field length - for key, value in src.items(): - if isinstance(value, str) and len(value) > 100: - src[key] = value[:100] + '...' - - print( - f" {json.dumps(src, ensure_ascii=False, indent=2, default=str)}" - ) - except Exception as e: - print_warning(f"Query sample failed: {e}") - - except Exception as e: - print_error(f"Elasticsearch connection failed: {e}") - - -async def main(): - """Main function""" - import argparse - - parser = argparse.ArgumentParser(description='View database data') - parser.add_argument('--mongo', action='store_true', help='View MongoDB only') - parser.add_argument('--milvus', action='store_true', help='View Milvus only') - parser.add_argument('--es', action='store_true', help='View Elasticsearch only') - parser.add_argument( - '--detail', action='store_true', help='Show detailed data (including samples)' - ) - - args = parser.parse_args() - - # Load .env file (if exists) - if os.path.exists('.env'): - from dotenv import load_dotenv - - load_dotenv() - - print(f"\n{Colors.BOLD}{Colors.HEADER}{'='*80}{Colors.END}") - print(f"{Colors.BOLD}{Colors.HEADER}Database Data Viewer Tool{Colors.END}") - print(f"{Colors.BOLD}{Colors.HEADER}{'='*80}{Colors.END}") - print( - f"\n{Colors.CYAN}Time: {get_now_with_timezone().strftime('%Y-%m-%d %H:%M:%S')}{Colors.END}" - ) - - # If no database specified, view all - if not (args.mongo or args.milvus or args.es): - args.mongo = args.milvus = args.es = True - - if args.mongo: - await check_mongodb(args.detail) - - if args.milvus: - check_milvus(args.detail) - - if args.es: - await check_elasticsearch(args.detail) - - print(f"\n{Colors.BOLD}{Colors.GREEN}{'='*80}{Colors.END}") - print(f"{Colors.BOLD}{Colors.GREEN}✅ Completed{Colors.END}") - print(f"{Colors.BOLD}{Colors.GREEN}{'='*80}{Colors.END}\n") - - -if __name__ == '__main__': - asyncio.run(main()) diff --git a/methods/EverCore/demo/tools/reset_databases.py b/methods/EverCore/demo/tools/reset_databases.py deleted file mode 100644 index 3cc673ff..00000000 --- a/methods/EverCore/demo/tools/reset_databases.py +++ /dev/null @@ -1,131 +0,0 @@ -""" -⚠️ DANGER: Completely reset all database structures ⚠️ - -This script performs destructive operations: -1. MongoDB: Drops the entire memsys database -2. Milvus: Drops all collections -3. Elasticsearch: Deletes all indices containing 'memsys' (old, new, aliases) -4. Redis: Flushes the database - -Use cases: -- Environment data is messy and needs a clean slate -- Schema fixed, need to rebuild index structures - -Must restart services after use to re-initialize structures. -""" - -import asyncio -import sys -import os - -# Ensure project modules can be imported -sys.path.append(os.getcwd()) - -from pymilvus import utility, connections -from core.di import get_bean_by_type -from core.component.redis_provider import RedisProvider -from core.component.mongodb_client_factory import MongoDBClientFactory -from core.component.elasticsearch_client_factory import ElasticsearchClientFactory -from bootstrap import setup_project_context - - -async def reset_mongodb(): - print("🔥 [MongoDB] Deleting database...") - try: - factory = get_bean_by_type(MongoDBClientFactory) - client_wrapper = await factory.get_default_client() - async_client = client_wrapper.client - db_name = client_wrapper.database.name - # Directly drop the entire database - await async_client.drop_database(db_name) - print(f" ✅ Database deleted: {db_name}") - except Exception as e: - print(f" ❌ MongoDB reset failed: {e}") - - -def reset_milvus(): - print("🔥 [Milvus] Dropping all collections...") - try: - # Connection config - milvus_host = os.getenv('MILVUS_HOST', 'localhost') - milvus_port = int(os.getenv('MILVUS_PORT', '19530')) - connections.connect(host=milvus_host, port=milvus_port) - - collections = utility.list_collections() - if not collections: - print(" ⚪ No collections found") - return - - for name in collections: - utility.drop_collection(name) - print(f" ✅ Dropped collection: {name}") - - except Exception as e: - print(f" ❌ Milvus reset failed: {e}") - - -async def reset_elasticsearch(): - print("🔥 [Elasticsearch] Deleting all related indices...") - try: - factory = get_bean_by_type(ElasticsearchClientFactory) - client_wrapper = await factory.register_default_client() - es = client_wrapper.async_client - - # Delete all indices containing memsys - target_pattern = "*memsys*" - - # 1. Get specific index list - indices_resp = await es.cat.indices(index=target_pattern, format="json") - - if not indices_resp: - print(f" ⚪ No indices found matching '{target_pattern}'") - return - - # 2. Extract index names - index_names = [item['index'] for item in indices_resp] - count = len(index_names) - - # 3. Explicitly delete these indices - # Use comma-separated string or list - await es.indices.delete(index=list(index_names), ignore=[404]) - print(f" ✅ Deleted {count} indices: {', '.join(index_names[:3])}...") - - except Exception as e: - print(f" ❌ Elasticsearch reset failed: {e}") - - -async def reset_redis(): - print("🔥 [Redis] Flushing DB...") - try: - provider = get_bean_by_type(RedisProvider) - client = await provider.get_client() - await client.flushdb() - print(" ✅ Redis flushed") - except Exception as e: - print(f" ❌ Redis reset failed: {e}") - - -async def main(): - print("\n" + "=" * 60) - print("🧨 Database Complete Reset Tool 🧨") - print("=" * 60 + "\n") - - await setup_project_context() - - await reset_mongodb() - reset_milvus() - await reset_elasticsearch() - await reset_redis() - - print("\n" + "=" * 60) - print("✨ Reset complete! Please restart services immediately to rebuild index structures ✨") - print("=" * 60 + "\n") - - -if __name__ == "__main__": - # Load .env - from dotenv import load_dotenv - - load_dotenv() - - asyncio.run(main()) diff --git a/methods/EverCore/demo/tools/resync_memcells.py b/methods/EverCore/demo/tools/resync_memcells.py deleted file mode 100644 index 7bf497db..00000000 --- a/methods/EverCore/demo/tools/resync_memcells.py +++ /dev/null @@ -1,37 +0,0 @@ -""" -Batch resync existing MemCell.episode from MongoDB to Milvus / ES. - -Usage: - uv run python src/bootstrap.py demo/tools/resync_memcells.py -""" - -import asyncio -from typing import List - -from core.di import get_bean_by_type -from core.observation.logger import get_logger -from infra_layer.adapters.out.persistence.document.memory.memcell import MemCell -from biz_layer.memcell_sync import MemCellSyncService - -logger = get_logger(__name__) - - -async def main() -> None: - service = get_bean_by_type(MemCellSyncService) - - memcells: List[MemCell] = await MemCell.find_all().to_list() - if not memcells: - logger.info("No MemCell records in MongoDB, skipping") - return - - logger.info("Starting resync of %s MemCell records", len(memcells)) - success = 0 - for memcell in memcells: - await service.sync_memcell(memcell, sync_to_es=True, sync_to_milvus=True) - success += 1 - - logger.info("Resync completed, success: %s", success) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/demo/tools/resync_personal_memories.py b/methods/EverCore/demo/tools/resync_personal_memories.py deleted file mode 100644 index 8e84561d..00000000 --- a/methods/EverCore/demo/tools/resync_personal_memories.py +++ /dev/null @@ -1,39 +0,0 @@ -""" -Batch resync foresight records to Milvus/ES. - -Usage: - uv run python src/bootstrap.py demo/tools/resync_personal_memories.py -""" - -import asyncio -from typing import List - -from core.di import get_bean_by_type -from core.observation.logger import get_logger -from infra_layer.adapters.out.persistence.document.memory.foresight_record import ( - ForesightRecord, -) -from biz_layer.personal_memory_sync import MemorySyncService - -logger = get_logger(__name__) - - -async def main(): - service = get_bean_by_type(MemorySyncService) - - docs: List[ForesightRecord] = await ForesightRecord.find_all().to_list() - if not docs: - logger.info("No foresight_records found in MongoDB, skipping") - return - - logger.info("Starting resync of %s foresight records", len(docs)) - stats = await service.sync_batch_foresights( - docs, - sync_to_es=True, - sync_to_milvus=True, - ) - logger.info("Resync completed: %s", stats) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/demo/tools/test_v1api_search.py b/methods/EverCore/demo/tools/test_v1api_search.py deleted file mode 100644 index 00c8ee19..00000000 --- a/methods/EverCore/demo/tools/test_v1api_search.py +++ /dev/null @@ -1,340 +0,0 @@ -"""V1 API Retrieval Test Script - -Test V1 API retrieval functions: -- GET /api/v1/memories - Fetch memories (supports profile type) -- GET /api/v1/memories/search - Search memories (keyword/vector/hybrid) - -Usage: - # Ensure API server is running - uv run python src/bootstrap.py src/run.py --port 1995 - - # Run tests - uv run python demo/tools/test_v1api_search.py -""" - -import httpx -import asyncio -import json -import os -from typing import Dict, Any -import dotenv - -dotenv.load_dotenv() -# Get language setting from environment variable -MEMORY_LANGUAGE = os.getenv('MEMORY_LANGUAGE').lower() - -# Query words based on language setting -QUERY_WORDS = { - 'en': {'default': 'What sports do I like', 'travel': 'travel'}, - 'zh': {'default': '我喜欢什么运动', 'travel': '旅游'}, # skip-i18n-check -} - - -def get_query_word(key: str = 'default') -> str: - """Get query word based on MEMORY_LANGUAGE setting""" - lang = MEMORY_LANGUAGE - return QUERY_WORDS[lang].get(key, QUERY_WORDS[lang]['default']) - - -class V1APITester: - """V1 API Tester""" - - def __init__(self, base_url: str = "http://localhost:1995"): - self.base_url = base_url - self.results = [] - - async def test_fetch_memories( - self, user_id: str, memory_type: str = "profile", limit: int = 5 - ) -> Dict[str, Any]: - """Test GET /api/v1/memories""" - url = f"{self.base_url}/api/v1/memories" - params = {"user_id": user_id, "memory_type": memory_type, "limit": limit} - - async with httpx.AsyncClient(timeout=120.0) as client: - response = await client.get(url, params=params) - return response.json() - - async def test_search_memories( - self, - user_id: str = None, - query: str = None, - retrieve_method: str = "keyword", - top_k: int = 5, - memory_types: list = None, - group_id: str = None, - ) -> Dict[str, Any]: - """Test GET /api/v1/memories/search (RESTful query params) - - Supports all data sources: episodic_memory, foresight, atomic_fact - user_id: User ID, required for personal memories - group_id: Group ID, required for group memories - """ - url = f"{self.base_url}/api/v1/memories/search" - params = {"query": query, "retrieve_method": retrieve_method, "top_k": top_k} - # user_id and group_id are mutually exclusive - if user_id: - params["user_id"] = user_id - if group_id: - params["group_id"] = group_id - # memory_types passed as comma-separated values - if memory_types: - params["memory_types"] = ",".join(memory_types) - - async with httpx.AsyncClient(timeout=120.0) as client: - response = await client.get(url, params=params) - return response.json() - - def print_result( - self, - name: str, - result: Dict[str, Any], - verbose: bool = False, - raw: bool = False, - ): - """Print test results""" - if raw: - print(f"\n📤 {name} Raw response:") - print(json.dumps(result, ensure_ascii=False, indent=2)) - status = result.get("status", "unknown") - memories = result.get("result", {}).get("memories", []) - scores = result.get("result", {}).get("scores", []) - count = len(memories) if memories else 0 - - # Calculate total record count - total_records = 0 - - for mem in memories: - if "profile_data" in mem: - total_records += 1 - elif "profile" in mem: - total_records += 1 - elif "score" in mem: - total_records += 1 - elif ( - "summary" in mem - or "title" in mem - or "atomic_fact" in mem - or "content" in mem - ): - # Fetch returns episodic/atomic_fact/foresight types - total_records += 1 - elif isinstance(mem, dict): - # V1 Search result: {group_id: [records]} structure - for group_id, records in mem.items(): - if isinstance(records, list): - total_records += len(records) - - status_icon = "✅" if status == "ok" else "❌" - print( - f"{status_icon} {name}: status={status}, groups={count}, records={total_records}" - ) - - # Print detailed content - if verbose and memories: - print("-" * 40) - for i, mem in enumerate(memories[:5]): # Show at most 5 - # Print key fields based on different types - if "profile_data" in mem: - # V1 Profile type - print(f" 📝 Profile:") - print(f" user_id: {mem.get('user_id')}") - print(f" group_id: {mem.get('group_id')}") - print(f" scenario: {mem.get('scenario')}") - print(f" version: {mem.get('version')}") - profile = mem.get('profile_data', {}) - print(f" profile: {mem}") - if profile: - print( - f" explicit_info: {len(profile.get('explicit_info', []))} items" - ) - print( - f" implicit_traits: {len(profile.get('implicit_traits', []))} items" - ) - elif "profile" in mem: - # V1 Profile type - print(f" 📝 Profile:") - print(f" user_id: {mem.get('user_id')}") - print(f" group_id: {mem.get('group_id')}") - print(f" scenario: {mem.get('scenario')}") - elif "score" in mem: - # Select display field based on data source - content = ( - mem.get('foresight') # foresight - or mem.get('atomic_fact') # atomic_fact - or mem.get('subject') # episode - or 'N/A' - ) - content = ( - content[:50] + '...' - if content and len(content) > 50 - else content - ) - print(f" [{i+1}] score={mem.get('score', 0):.4f} | {content}") - elif ( - "summary" in mem - or "title" in mem - or "atomic_fact" in mem - or "content" in mem - ): - # Fetch returns episodic/atomic_fact/foresight types - content = ( - mem.get('summary') - or mem.get('content') - or mem.get('foresight') - or mem.get('atomic_fact') - or mem.get('title') - or 'N/A' - ) - content = ( - content[:60] + '...' - if content and len(content) > 60 - else content - ) - print(f" [{i+1}] {content}") - else: - # V1 Search result type: {group_id: [records]} - for group_id, records in mem.items(): - if isinstance(records, list): - # Get corresponding score list - group_scores = [] - for s in scores: - if isinstance(s, dict) and group_id in s: - group_scores = s[group_id] - break - print(f" 📁 Group: {group_id}, records: {len(records)}") - for j, r in enumerate(records[:3]): - if isinstance(r, dict): - content = ( - r.get('atomic_fact') - or r.get('foresight') - or r.get('episode') - or r.get('subject') - or 'N/A' - ) - else: - content = str(r) - content = ( - content[:40] + '...' - if content and len(content) > 40 - else content - ) - score_val = ( - group_scores[j] if j < len(group_scores) else 0 - ) - if isinstance(score_val, (int, float)): - print(f" [{j+1}] {score_val:.2f} | {content}") - else: - print(f" [{j+1}] {score_val} | {content}") - if len(records) > 3: - print(f" ... {len(records) - 3} more") - if len(memories) > 5: - print(f" ... {len(memories) - 5} more") - print("-" * 40) - - self.results.append( - { - "name": name, - "status": status, - "count": total_records, - "success": status == "ok", - } - ) - - async def run_all_tests( - self, - user_id: str = "user_001", - query: str = None, - verbose: bool = True, - raw: bool = False, - ): - # Use language-aware default query if not specified - if query is None: - query = get_query_word('default') - """Run all tests: 3 memory types × 3 retrieval methods = 9 combinations""" - print("=" * 60) - print("V1 API Retrieval Test (Full Combinations)") - print("=" * 60) - print(f"User ID: {user_id}") - print(f"Query: {query}") - print("-" * 60) - - # Test Fetch (KV method) - print("\n📦 Test Fetch") - print("-" * 40) - fetch_types = ["profile", "episodic_memory", "foresight", "atomic_fact"] - for mem_type in fetch_types: - result = await self.test_fetch_memories(user_id, mem_type, 5) - self.print_result(f"fetch {mem_type}", result, verbose, raw) - - # Full combination test of memory types and retrieval methods - memory_types = ["episodic_memory", "foresight", "atomic_fact"] - # memory_types = ["foresight"] - retrieval_methods = ["keyword", "vector", "hybrid", "rrf", "agentic"] - # retrieval_methods = ["vector"] - # retrieval_methods = ["hybrid", "vector"] - # retrieval_methods = ["keyword"] - icons = {"episodic_memory": "🎬", "foresight": "🔮", "atomic_fact": "📋"} - - # Group memory test (only group_id, no user_id) - only episodic_memory - group_id = "chat_user_001_assistant" - print(f"\n🏢 Group Memory Test (group_id={group_id}, only episodic_memory)") - print("=" * 50) - print(f"\n{icons['episodic_memory']} Test episodic_memory") - print("-" * 40) - for method in retrieval_methods: - result = await self.test_search_memories( - None, query, method, 5, ["episodic_memory"], group_id - ) - self.print_result(f"Group episodic_memory + {method}", result, verbose, raw) - - # Personal memory test (no group_id) - print(f"\n👤 Personal Memory Test (no group_id)") - print("=" * 50) - for mem_type in memory_types: - print(f"\n{icons[mem_type]} Test {mem_type}") - print("-" * 40) - for method in retrieval_methods: - result = await self.test_search_memories( - user_id, query, method, 5, [mem_type] - ) - self.print_result( - f"Personal {mem_type} + {method}", result, verbose, raw - ) - - # Summary - print("\n" + "=" * 60) - print("Test Summary") - print("=" * 60) - - total = len(self.results) - passed = sum(1 for r in self.results if r["success"]) - - for r in self.results: - icon = "✅" if r["success"] else "❌" - print(f" {icon} {r['name']}: {r['count']} memories") - - print("-" * 60) - print(f"Passed: {passed}/{total}") - - return passed == total - - -async def main(): - tester = V1APITester("http://localhost:1995") - - # Modify parameters here - user_id = "user_001" - query = get_query_word('travel') # Auto-select based on MEMORY_LANGUAGE env var - raw = False # Don't print raw API output - - print(f"🌐 Language: {MEMORY_LANGUAGE.upper()}") - success = await tester.run_all_tests(user_id, query, verbose=True, raw=raw) - - if success: - print("\n🎉 All tests passed!") - else: - print("\n⚠️ Some tests failed") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/demo/ui/__init__.py b/methods/EverCore/demo/ui/__init__.py deleted file mode 100644 index 3019f695..00000000 --- a/methods/EverCore/demo/ui/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -"""UI Module - -Provides components related to user interface, mainly for internationalization text management. -""" - -from demo.ui.i18n_texts import I18nTexts - -__all__ = ["I18nTexts"] - diff --git a/methods/EverCore/demo/ui/i18n_texts.py b/methods/EverCore/demo/ui/i18n_texts.py deleted file mode 100644 index 2b68cb3e..00000000 --- a/methods/EverCore/demo/ui/i18n_texts.py +++ /dev/null @@ -1,597 +0,0 @@ -"""Internationalization Text Definition - Supports Chinese and English - -This module defines the Chinese and English versions of all interface texts for the dialog system. - -Usage: - from i18n_texts import I18nTexts - texts = I18nTexts("zh") # or "en" - print(texts.get("banner_title")) -""" - -from typing import Dict, Any - - -class I18nTexts: - """Internationalization Text Manager""" - - # Chinese-English mapping for all texts - TEXTS: Dict[str, Dict[str, str]] = { - # ==================== Language Selection ==================== - "language_selection_title": { - "zh": "🌏 语言选择 / Language Selection", - "en": "🌏 Language Selection / 语言选择", - }, - "language_prompt": { - "zh": "请选择语言 (Select Language) [1-2]", - "en": "Please select language [1-2]", - }, - "language_chinese": {"zh": "中文", "en": "Chinese (中文)"}, - "language_english": {"zh": "英文 (English)", "en": "English"}, - "language_selected": {"zh": "已选择语言", "en": "Language selected"}, - "language_consistency_hint": { - "zh": "💡 提示：为获得最佳体验，建议记忆数据与选择的语言保持一致", - "en": "💡 Note: For best experience, memory data should match the selected language", - }, - "invalid_input_number": { - "zh": "请输入有效的数字", - "en": "Please enter a valid number", - }, - # ==================== Scenario Mode Selection ==================== - "scenario_selection_title": { - "zh": "🎯 场景模式选择", - "en": "🎯 Scenario Mode Selection", - }, - "scenario_prompt": { - "zh": "请选择场景模式 [1-2]", - "en": "Please select scenario mode [1-2]", - }, - "scenario_solo": {"zh": "助手模式", "en": "Solo Mode"}, - "scenario_solo_desc": { - "zh": "单人对话，基于个人记忆的智能助手", - "en": "One-on-one conversation with personal memory-based AI assistant", - }, - "scenario_team": {"zh": "群聊模式", "en": "Team Mode"}, - "scenario_team_desc": { - "zh": "多人群聊，基于群组记忆的对话分析", - "en": "Multi-person chat with group memory-based conversation analysis", - }, - "scenario_selected": {"zh": "已选择场景模式", "en": "Scenario mode selected"}, - # ==================== Retrieval Mode Selection ==================== - "retrieval_mode_selection_title": { - "zh": "🔍 检索模式选择", - "en": "🔍 Retrieval Mode Selection", - }, - "retrieval_mode_prompt": { - "zh": "请选择检索模式 [1-5]", - "en": "Please select retrieval mode [1-5]", - }, - "retrieval_mode_keyword": {"zh": "Keyword 检索", "en": "Keyword Search"}, - "retrieval_mode_keyword_desc": { - "zh": "关键词精确匹配（BM25）", - "en": "Exact keyword matching (BM25)", - }, - "retrieval_mode_vector": {"zh": "Vector 检索", "en": "Vector Search"}, - "retrieval_mode_vector_desc": { - "zh": "语义向量检索", - "en": "Semantic vector search", - }, - "retrieval_mode_hybrid": { - "zh": "Hybrid 检索（推荐）", - "en": "Hybrid Search (Recommended)", - }, - "retrieval_mode_hybrid_desc": { - "zh": "Keyword + Vector + Rerank", - "en": "Keyword + Vector + Rerank", - }, - "retrieval_mode_rrf": {"zh": "RRF 检索", "en": "RRF Search"}, - "retrieval_mode_rrf_desc": { - "zh": "Keyword + Vector + RRF 融合", - "en": "Keyword + Vector + RRF fusion", - }, - "retrieval_mode_agentic": {"zh": "Agentic 检索", "en": "Agentic Search"}, - "retrieval_mode_agentic_desc": { - "zh": "LLM 引导的多轮检索（实验性）", - "en": "LLM-guided multi-round retrieval (experimental)", - }, - "retrieval_mode_selected": { - "zh": "已选择检索模式", - "en": "Retrieval mode selected", - }, - "retrieval_mode_agentic_cost_warning": { - "zh": "⚠️ Agentic 检索将使用 LLM API，可能产生额外费用", - "en": "⚠️ Agentic retrieval uses LLM API, may incur additional costs", - }, - "retrieval_mode_invalid_range": {"zh": "请输入 1-5", "en": "Please enter 1-5"}, - # ==================== Agentic Retrieval UI ==================== - "agentic_retrieving": {"zh": "正在检索记忆...", "en": "Retrieving memories..."}, - "agentic_mode_keyword": {"zh": "Keyword", "en": "Keyword"}, - "agentic_mode_vector": {"zh": "Vector", "en": "Vector"}, - "agentic_mode_hybrid": {"zh": "Hybrid", "en": "Hybrid"}, - "agentic_mode_rrf": {"zh": "RRF", "en": "RRF"}, - "agentic_mode_agentic": {"zh": "Agentic", "en": "Agentic"}, - "agentic_llm_judgment": {"zh": "LLM 判断", "en": "LLM Judgment"}, - "agentic_sufficient": {"zh": "充分", "en": "Sufficient"}, - "agentic_insufficient": {"zh": "不充分", "en": "Insufficient"}, - "agentic_multi_round": {"zh": "多轮检索", "en": "Multi-round"}, - "agentic_single_round": {"zh": "单轮检索", "en": "Single-round"}, - "agentic_generated_queries": {"zh": "生成查询", "en": "Generated queries"}, - "agentic_round1_count": {"zh": "R1", "en": "R1"}, - "agentic_round2_count": {"zh": "R2", "en": "R2"}, - "agentic_items": {"zh": "条", "en": "items"}, - "agentic_reasoning_hint": { - "zh": "💡 首轮检索到的记忆信息不够充分，LLM 生成了更精确的补充查询以获取更多相关记忆", - "en": "💡 First-round memories insufficient, LLM generated refined queries for more relevant memories", - }, - "agentic_supplementary_queries": { - "zh": "补充查询", - "en": "Supplementary queries", - }, - # ==================== Banner and Welcome ==================== - "banner_title": { - "zh": "🧠 EverMem 记忆对话助手", - "en": "🧠 EverMem Memory-Enhanced Chat Assistant", - }, - "banner_subtitle": { - "zh": "🤖 v1.1.0 · Memory-Enhanced Chat", - "en": "🤖 v1.1.0 · Memory-Enhanced Chat", - }, - "readline_available": { - "zh": "支持方向键移动光标、删除字符，按 ↑↓ 浏览历史输入", - "en": "Arrow keys supported for cursor movement, ↑↓ to browse input history", - }, - "readline_unavailable": { - "zh": "安装 readline 模块以支持更好的输入体验", - "en": "Install readline module for better input experience", - }, - # ==================== Group Selection ==================== - "groups_available_title": { - "zh": "📊 可用的群组对话", - "en": "📊 Available Group Conversations", - }, - "groups_not_found": { - "zh": "未找到任何群组对话", - "en": "No group conversations found", - }, - "groups_extract_hint": { - "zh": "提示：请先运行 extract_memory.py 提取记忆", - "en": "Hint: Please run extract_memory.py to extract memories first", - }, - "groups_select_prompt": { - "zh": "请选择群组编号", - "en": "Please select group number", - }, - "groups_select_range_error": { - "zh": "请输入 {min} 到 {max} 之间的数字", - "en": "Please enter a number between {min} and {max}", - }, - "groups_selection_cancelled": { - "zh": "已取消群组选择", - "en": "Group selection cancelled", - }, - "groups_not_selected_exit": { - "zh": "未选择群组，退出程序", - "en": "No group selected, exiting program", - }, - # ==================== Session Initialization ==================== - "loading_group_data": { - "zh": "正在加载群组 {name} 的数据...", - "en": "Loading data for group {name}...", - }, - "loading_profiles_warning": { - "zh": "未找到用户 Profile 文件", - "en": "User profile files not found", - }, - "loading_profiles_hint": { - "zh": "将继续使用记忆，但没有个人画像信息", - "en": "Will continue with memories but without profile information", - }, - "loading_profiles_success": { - "zh": "个人画像: {count} 个用户 ({names})", - "en": "Profiles: {count} users ({names})", - }, - "loading_memories_success": { - "zh": "群组记忆: {count} 条", - "en": "Group memories: {count} items", - }, - "loading_history_success": { - "zh": "对话历史: {count} 轮（上次会话）", - "en": "Conversation history: {count} rounds (last session)", - }, - "loading_history_new": { - "zh": "对话历史: 0 轮（新会话）", - "en": "Conversation history: 0 rounds (new session)", - }, - "loading_help_hint": { - "zh": "输入 'help' 查看命令列表", - "en": "Type 'help' to see command list", - }, - "session_init_failed": { - "zh": "会话初始化失败", - "en": "Session initialization failed", - }, - "session_init_error": { - "zh": "会话初始化失败: {error}", - "en": "Session initialization failed: {error}", - }, - # ==================== Chat Interaction ==================== - "chat_start_note": { - "zh": "开始对话 | 输入 'help' 查看命令 | 输入 'exit' 退出", - "en": "Start chatting | Type 'help' for commands | Type 'exit' to quit", - }, - "chat_input_prompt": {"zh": " 💬 You: ", "en": " 💬 You: "}, - "chat_generating": { - "zh": "正在思考并生成回答...", - "en": "Thinking and generating response...", - }, - "chat_generation_complete": {"zh": "生成完成", "en": "Generation complete"}, - "chat_llm_error": { - "zh": "LLM 调用失败: {error}", - "en": "LLM call failed: {error}", - }, - "chat_error": { - "zh": "对话处理失败: {error}", - "en": "Chat processing failed: {error}", - }, - # ==================== Retrieval Results ==================== - "retrieval_title": { - "zh": "检索到 {total} 条记忆", - "en": "Retrieved {total} memories", - }, - "retrieval_showing": { - "zh": "（显示前 {shown} 条）", - "en": "(showing first {shown} items)", - }, - "retrieval_complete": {"zh": "检索完成", "en": "Retrieval Complete"}, - "retrieval_foresight": { - "zh": "使用前瞻相似度进行检索", - "en": "Using foresight similarity for retrieval", - }, - "retrieval_latency": { - "zh": "检索耗时: {latency}ms", - "en": "Retrieval latency: {latency}ms", - }, - "retrieval_multi_round": { - "zh": "多轮检索（Round 1 → Round 2）", - "en": "Multi-round retrieval (Round 1 → Round 2)", - }, - "retrieval_single_round": {"zh": "单轮检索", "en": "Single-round retrieval"}, - "prompt_memory_episode": {"zh": "详情：{episode}", "en": "Details: {episode}"}, - # ==================== Command Processing ==================== - "cmd_help_title": {"zh": "📖 可用命令", "en": "📖 Available Commands"}, - "cmd_exit": { - "zh": "exit 退出对话（自动保存历史记录）", - "en": "exit Exit chat (auto-save history)", - }, - "cmd_clear": { - "zh": "clear 清空当前对话历史", - "en": "clear Clear current conversation history", - }, - "cmd_reload": { - "zh": "reload 重新加载记忆和画像数据", - "en": "reload Reload memories and profiles", - }, - "cmd_reasoning": { - "zh": "reasoning 查看上一次回答的完整推理过程", - "en": "reasoning View full reasoning of last response", - }, - "cmd_help": { - "zh": "help 显示此帮助信息", - "en": "help Show this help message", - }, - "cmd_exit_saving": { - "zh": "正在保存对话历史...", - "en": "Saving conversation history...", - }, - "cmd_exit_complete": { - "zh": "保存完成，再见！", - "en": "Save complete, goodbye!", - }, - "cmd_clear_done": { - "zh": "已清空 {count} 轮对话历史", - "en": "Cleared {count} rounds of conversation history", - }, - "cmd_reload_refreshing": { - "zh": "正在刷新 {name} 的数据...", - "en": "Refreshing data for {name}...", - }, - "cmd_reload_complete": { - "zh": "数据刷新完成：{users} 个用户，{memories} 条记忆", - "en": "Data refresh complete: {users} users, {memories} memories", - }, - "cmd_reasoning_no_data": { - "zh": "暂无推理记录，请先提问", - "en": "No reasoning record, please ask a question first", - }, - "cmd_interrupt_saving": { - "zh": "检测到中断信号，正在保存对话历史...", - "en": "Interrupt detected, saving conversation history...", - }, - # ==================== Structured Response ==================== - "response_reasoning_title": { - "zh": "🧠 完整推理过程", - "en": "🧠 Full Reasoning Process", - }, - "response_answer_label": {"zh": "📝 回答内容", "en": "📝 Answer"}, - "response_reasoning_label": {"zh": "🔍 推理过程", "en": "🔍 Reasoning"}, - "response_metadata_label": {"zh": "📊 元数据", "en": "📊 Metadata"}, - "response_notes_label": {"zh": "💡 补充说明", "en": "💡 Additional Notes"}, - "response_confidence": {"zh": "置信度", "en": "Confidence"}, - "response_references": {"zh": "引用", "en": "References"}, - "response_no_references": {"zh": "无", "en": "None"}, - "response_assistant_title": {"zh": "🤖 Assistant", "en": "🤖 Assistant"}, - # ==================== Configuration and Connection ==================== - "config_api_key_missing": { - "zh": "LLM_API_KEY / OPENROUTER_API_KEY / OPENAI_API_KEY 未设置", - "en": "LLM_API_KEY / OPENROUTER_API_KEY / OPENAI_API_KEY not set", - }, - "config_api_key_hint": { - "zh": "提示：请配置 API 密钥后重试", - "en": "Hint: Please configure API key and retry", - }, - "mongodb_connecting": { - "zh": "连接 MongoDB...", - "en": "Connecting to MongoDB...", - }, - "mongodb_init_failed": { - "zh": "MongoDB 初始化失败: {error}", - "en": "MongoDB initialization failed: {error}", - }, - # ==================== Table Headers ==================== - "table_header_index": {"zh": "#", "en": "#"}, - "table_header_group": {"zh": "Group", "en": "Group"}, - "table_header_name": {"zh": "Name", "en": "Name"}, - "table_header_count": {"zh": "Count", "en": "Count"}, - # ==================== LLM Prompt (System Message) ==================== - "prompt_system_role_zh": { - "zh": """你是记忆增强 AI 助手，可访问用户画像与历史对话。请用温和、合作、尊重的中文回答。 - -⚠️ 语言要求：你必须始终使用中文回答，即使记忆内容包含其他语言。 - -目标： -- 基于记忆进行深度分析，但回答必须**极其简练**（不超过3句话）。 -- 直接给出个性化结论，不要复述“因为你有...所以...”。 -- 即使证据不足也要尝试推理，但需明确标注确定性程度。 - -工作原则： -- **深度融合用户画像（关键）**： - 1. **性格对齐**：回答的语气和建议风格必须与 Profile 中的【隐式特征/标签】完全一致。 - 2. **状态适配**：建议内容必须严格适配用户的【显式状态】（如健康状况、体能限制）。 - 3. **拒绝通用**：禁止给出百度百科式的通用建议，每一条建议都必须有“因为你...”的个性化理由。 -- 严格区分「确定事实/合理推断/可能推测」，引用具体记忆编号。 -- 鼓励推理和推测：当直接证据不足时，可基于相关记忆进行合理推测，使用"可能"、"推测"、"大概率"等表述。 -- 近期与用户显式更正优先于过往；避免无关或敏感外推。 -- 推测时需说明推理依据和逻辑链条，让用户理解推测的合理性。 - -推理流程（精简，必须遵循）： -1) 解析问题：识别意图、范围、限制与期望输出。 -2) 检索记忆：从画像与历史中提取候选；按 相关性/时效性/一致性 评估；记录候选编号+要点。 - - 选择优先：显式陈述 > 近期 > 高频一致 > 权威；若冲突，指出并建议澄清。 - - 关联推理：即使没有直接证据，也可基于相关记忆进行推理（如时间线推断、行为模式分析、因果关系等）。 -3) 生成答案：在 answer 中给出结论（友好语气），根据确定性程度选择表述： - - 确定性高：直接陈述事实，如"您在 10月去过北京" - - 确定性中：使用推测语气，如"根据记忆推测，您可能在 10月去过北京" - - 确定性低：说明推测依据，如"虽然没有明确记录，但从相关线索推测..." - - 可附加 1 条可执行建议或澄清问题。 - - 禁止在 answer 中出现编号、推理细节或内部术语。 -4) 引用与信心：在 reasoning 中详细说明推理过程，用 [n] 标注依据；references 列使用到的编号（去重、按出现顺序）。 - - confidence： - * high（≥2 条一致的直接证据、无冲突） - * medium（单条直接证据，或多条间接证据支持的推断） - * low（基于弱相关记忆的推测，或存在明显证据缺口） - -输出与格式（严格）： -- 仅输出有效 JSON；不得有任何额外文字或 Markdown。 -- 所有字符串用双引号；换行写为 \\n。 -- 必填：answer、reasoning、references、confidence；additional_notes 可选；无引用则 []。 - -Schema： -{ - "answer": "用户可见的结论（根据置信度使用确定或推测语气）", - "reasoning": "任务解析→候选记忆→证据评估→推理/推测链条→置信度判断；明确标注 确定事实/合理推断/可能推测，并用 [n] 标注依据", - "references": ["[1]", "[3]"], - "confidence": "high|medium|low", - "additional_notes": "补充说明、推测依据或建议（可选）" -}""", - "en": """You are a memory-augmented AI assistant with access to user profiles and conversation history. Use a gentle, cooperative, respectful assistant tone. - -⚠️ LANGUAGE REQUIREMENT: You MUST always respond in Chinese (中文), even if memory content is in other languages. - -Goal: -- Provide deep analysis based on memory, but keep the answer **extremely concise** (max 3 sentences). -- Direct personalized conclusions only; do NOT repeat "Because you have...". - -Working Principles: -- **Deeply Integrate User Profile (CRITICAL)**: - 1. **Personality Alignment**: Your tone and advice style MUST strictly align with the [Implicit Traits/Tags] in the Profile. - 2. **Status Adaptation**: Advice content MUST strictly adapt to the user's [Explicit State] (e.g., health, physical limits). - 3. **No Generic Advice**: Do NOT provide generic, Wikipedia-style answers. Every piece of advice must have a personalized "Because you..." rationale. -- Strictly distinguish \"Fact/Inference/Assumption\", cite specific memory numbers; be honest when information is insufficient. -- Prioritize recent explicit corrections by the user over older content; avoid irrelevant or sensitive extrapolations. - -Reasoning Flow (concise, must follow): -1) Parse the task: identify intent, scope, constraints, and expected output. -2) Retrieve memories: extract candidates from profiles and history; evaluate by Relevance/Recency/Consistency; record candidate numbers + key points. - - Selection priority: explicit statements > recent > high-frequency consistent > authoritative; if conflicts exist, point them out and suggest clarification. -3) Generate the answer: In the answer field, give a 1–3 sentence conclusion (friendly tone); add 1 actionable suggestion if necessary. - - If information is insufficient: state the gap and append up to 2 short clarification questions at the end of the answer (in parentheses). - - The answer must not include numbering, reasoning, or internal terminology. -4) Citations & confidence: In reasoning, mark evidence with [n]; in references, list the used numbers (deduplicated, ordered by first appearance). - - confidence: high (≥2 consistent pieces of evidence or strong profile, no conflict) / medium (single piece or minor conflict) / low (insufficient evidence or clear conflict). - -Output & Format (strict): -- Output valid JSON only; no extra text or Markdown. -- Use double quotes for all strings; write line breaks as \\n. -- Required: answer, reasoning, references, confidence; additional_notes optional; [] if no references. - -Schema: -{ - "answer": "Direct conclusion for the user, concise and professional", - "reasoning": "Task parsing → candidate memories → evidence evaluation → reasoning chain → confidence judgment; explicitly mark Fact/Inference/Assumption and use [n] for evidence", - "references": ["[1]", "[3]"], - "confidence": "high|medium|low", - "additional_notes": "Optional supplementary notes or suggestions" -}""", - }, - "prompt_system_role_en": { - "zh": """你是记忆增强 AI 助手，可访问用户画像与历史对话。请用温和、合作、尊重的助理语气。 - -⚠️ 语言要求：你必须始终使用英文 (English) 回答，即使记忆内容是中文或其他语言。 - -目标： -- 基于记忆进行深度分析，但回答必须**极其简练**（不超过3句话）。 -- 直接给出个性化结论，不要复述“因为你有...所以...”。 -- 即使证据不足也要尝试推理，但需明确标注确定性程度。 - -工作原则： -- **深度融合用户画像（关键）**： - 1. **性格对齐**：回答的语气和建议风格必须与 Profile 中的【隐式特征/标签】完全一致。（例如：若用户标签为[风险厌恶型]，则建议必须强调安全和稳妥，严禁推荐高风险活动）。 - 2. **状态适配**：建议内容必须严格适配用户的【显式状态】（如健康状况、体能限制）。（例如：若用户有脚踝伤，严禁推荐爬山/长跑，必须主动提供低冲击替代方案）。 - 3. **拒绝通用**：禁止给出百度百科式的通用建议，每一条建议都必须有“因为你...”的个性化理由。 -- 严格区分「确定事实/合理推断/可能推测」，引用具体记忆编号。 -- 鼓励推理和推测：当直接证据不足时，可基于相关记忆进行合理推测，使用"likely"、"possibly"、"may have"等表述。 -- 近期与用户显式更正优先于过往；避免无关或敏感外推。 -- 推测时需说明推理依据和逻辑链条，让用户理解推测的合理性。 - -推理流程（精简，必须遵循）： -1) 解析问题：识别意图、范围、限制与期望输出。 -2) 检索记忆：从画像与历史中提取候选；按 相关性/时效性/一致性 评估；记录候选编号+要点。 - - 选择优先：显式陈述 > 近期 > 高频一致 > 权威；若冲突，指出并建议澄清。 - - 关联推理：即使没有直接证据，也可基于相关记忆进行推理（如时间线推断、行为模式分析、因果关系等）。 -3) 生成答案：在 answer 中给出结论（友好语气），根据确定性程度选择表述： - - 确定性高：直接陈述事实，如"You visited Beijing in October" - - 确定性中：使用推测语气，如"Based on the memories, you likely visited Beijing in October" - - 确定性低：说明推测依据，如"While there's no direct record, related clues suggest..." - - 可附加 1 条可执行建议或澄清问题。 - - 禁止在 answer 中出现编号、推理细节或内部术语。 -4) 引用与信心：在 reasoning 中详细说明推理过程，用 [n] 标注依据；references 列使用到的编号（去重、按出现顺序）。 - - confidence： - * high（≥2 条一致的直接证据、无冲突） - * medium（单条直接证据，或多条间接证据支持的推断） - * low（基于弱相关记忆的推测，或存在明显证据缺口） - -输出与格式（严格）： -- 仅输出有效 JSON；不得有任何额外文字或 Markdown。 -- 所有字符串用双引号；换行写为 \\n。 -- 必填：answer、reasoning、references、confidence；additional_notes 可选；无引用则 []。 - -Schema： -{ - "answer": "用户可见的结论（根据置信度使用确定或推测语气）", - "reasoning": "任务解析→候选记忆→证据评估→推理/推测链条→置信度判断；明确标注 确定事实/合理推断/可能推测，并用 [n] 标注依据", - "references": ["[1]", "[3]"], - "confidence": "high|medium|low", - "additional_notes": "补充说明、推测依据或建议（可选）" -}""", - "en": """You are a memory-augmented AI assistant with access to user profiles and conversation history. Use a gentle, cooperative, respectful assistant tone. - -⚠️ LANGUAGE REQUIREMENT: You MUST always respond in English, even if memory content is in Chinese or other languages. - -Goal: -- Provide deep analysis based on memory, but keep the answer **extremely concise** (max 3 sentences). -- Direct personalized conclusions only; do NOT repeat "Because you have...". -- Even when evidence is limited, attempt reasoning but clearly indicate the level of certainty. - -Working Principles: -- **Deeply Integrate User Profile (CRITICAL)**: - 1. **Personality Alignment**: Your tone and advice style MUST strictly align with the [Implicit Traits/Tags] in the Profile. (e.g., If user is tagged [Cautious], advice must emphasize safety and preparation; NEVER recommend high-risk activities). - 2. **Status Adaptation**: Advice content MUST strictly adapt to the user's [Explicit State] (e.g., health, physical limits). (e.g., If user has ankle injury, NEVER recommend hiking/running; MUST proactively offer low-impact alternatives). - 3. **No Generic Advice**: Do NOT provide generic, Wikipedia-style answers. Every piece of advice must have a personalized "Because you..." rationale. -- Strictly distinguish \"Confirmed Fact/Reasonable Inference/Possible Speculation\", cite specific memory numbers. -- Encourage reasoning and speculation: When direct evidence is insufficient, make reasonable speculation based on related memories, using terms like \"likely\", \"possibly\", \"may have\", etc. -- Prioritize recent explicit corrections by the user over older content; avoid irrelevant or sensitive extrapolations. -- When speculating, explain the reasoning basis and logical chain to help users understand the speculation's validity. - -Reasoning Flow (concise, must follow): -1) Parse the task: identify intent, scope, constraints, and expected output. -2) Retrieve memories: extract candidates from profiles and history; evaluate by Relevance/Recency/Consistency; record candidate numbers + key points. - - Selection priority: explicit statements > recent > high-frequency consistent > authoritative; if conflicts exist, point them out and suggest clarification. - - Associative reasoning: Even without direct evidence, reason based on related memories (e.g., timeline inference, behavior pattern analysis, causal relationships). -3) Generate the answer: In the answer field, provide a conclusion (friendly tone), choosing phrasing based on certainty level: - - High certainty: State facts directly, e.g., \"You visited Beijing in October\" - - Medium certainty: Use speculative tone, e.g., \"Based on the memories, you likely visited Beijing in October\" - - Low certainty: Explain speculation basis, e.g., \"While there's no direct record, related clues suggest...\" - - May add 1 actionable suggestion or clarification question. - - The answer must not include numbering, reasoning details, or internal terminology. -4) Citations & confidence: In reasoning, explain the reasoning process in detail, mark evidence with [n]; in references, list the used numbers (deduplicated, ordered by first appearance). - - confidence: - * high (≥2 consistent direct evidence, no conflict) - * medium (single direct evidence, or inference supported by multiple indirect evidence) - * low (speculation based on weakly related memories, or clear evidence gaps) - -Output & Format (strict): -- Output valid JSON only; no extra text or Markdown. -- Use double quotes for all strings; write line breaks as \\n. -- Required: answer, reasoning, references, confidence; additional_notes optional; [] if no references. - -Schema: -{ - "answer": "Conclusion for the user (use definite or speculative tone based on confidence)", - "reasoning": "Task parsing → candidate memories → evidence evaluation → reasoning/speculation chain → confidence judgment; clearly mark Confirmed Fact/Reasonable Inference/Possible Speculation and use [n] for evidence", - "references": ["[1]", "[3]"], - "confidence": "high|medium|low", - "additional_notes": "Optional supplementary notes, speculation basis, or suggestions" -}""", - }, - "prompt_profile_prefix_zh": { - "zh": "用户的个人画像是：\n\n", - "en": "User's personal profile is:\n\n", - }, - "prompt_profile_prefix_en": { - "zh": "个人画像（用于理解用户背景和推断岗位职责）：\n", - "en": "Personal Profiles (for understanding user background and inferring job responsibilities):\n", - }, - "prompt_memories_prefix": { - "zh": "相关记忆（按相关度排序）：\n", - "en": "Relevant Memories (sorted by relevance):\n", - }, - "prompt_memory_date": {"zh": "{date}", "en": "{date}"}, - "prompt_memory_subject": {"zh": "主题：{subject}", "en": "Topic: {subject}"}, - "prompt_memory_content": {"zh": "内容：{content}", "en": "Content: {content}"}, - # ==================== Others ==================== - "loading_label": {"zh": "加载", "en": "Loading"}, - "warning_label": {"zh": "警告", "en": "Warning"}, - "hint_label": {"zh": "提示", "en": "Hint"}, - "error_label": {"zh": "错误", "en": "Error"}, - "save_label": {"zh": "保存", "en": "Save"}, - "success_label": {"zh": "成功", "en": "Success"}, - } - - def __init__(self, language: str = "zh"): - """Initialize Internationalization Text Manager - - Args: - language: Language code, "zh" or "en" - """ - self.language = language if language in ["zh", "en"] else "zh" - - def get(self, key: str, **kwargs) -> str: - """Get text for specific key - - Args: - key: Text key - **kwargs: Formatting parameters - - Returns: - Formatted text - """ - text_dict = self.TEXTS.get(key, {}) - text = text_dict.get(self.language, text_dict.get("zh", key)) - - # If formatting parameters exist, format the text - if kwargs: - try: - text = text.format(**kwargs) - except KeyError: - # If formatting fails, return original text - pass - - return text - - def set_language(self, language: str) -> None: - """Set language - - Args: - language: Language code, "zh" or "en" - """ - if language in ["zh", "en"]: - self.language = language diff --git a/methods/EverCore/demo/utils/__init__.py b/methods/EverCore/demo/utils/__init__.py deleted file mode 100644 index 3d126ea9..00000000 --- a/methods/EverCore/demo/utils/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -"""Utility Module - -Provides common utility functions and a simple memory manager. -""" - -from demo.utils.memory_utils import ( - get_prompt_language, - ensure_mongo_beanie_ready, - query_all_groups_from_mongodb, - query_memcells_by_group_and_time, - serialize_datetime, -) -from demo.utils.simple_memory_manager import SimpleMemoryManager - -__all__ = [ - "get_prompt_language", - "ensure_mongo_beanie_ready", - "query_all_groups_from_mongodb", - "query_memcells_by_group_and_time", - "serialize_datetime", - "SimpleMemoryManager", -] diff --git a/methods/EverCore/demo/utils/agent_demo_helpers.py b/methods/EverCore/demo/utils/agent_demo_helpers.py deleted file mode 100644 index e3ee3000..00000000 --- a/methods/EverCore/demo/utils/agent_demo_helpers.py +++ /dev/null @@ -1,349 +0,0 @@ -"""Agent Demo Helpers (v1 API) - -Shared utilities for agent demo scripts (search_agent_demo, coding_agent_demo, etc.). -Provides: -- AgentDemoRunner: stateful helper for v1 API calls (send messages, fetch, search) -- Print helpers: stateless formatters for various memory types -""" - -import uuid -from typing import List, Optional - -import httpx -from common_utils.datetime_utils import get_now_with_timezone - - -DEFAULT_BASE_URL = "http://localhost:1995" - - -# ==================== Print Helpers ==================== - - -def print_separator(text: str = ""): - if text: - print(f"\n{'='*60}") - print(f"{text}") - print('=' * 60) - else: - print('-' * 60) - - -def print_episodic_memories(memories: list): - """Print episodic memories.""" - if not memories: - print(" (none)") - return - for i, m in enumerate(memories, 1): - print(f"\n [{i}] {m.get('summary') or m.get('episode') or 'N/A'}") - if m.get("keywords"): - print(f" Keywords : {', '.join(m['keywords'])}") - if m.get("timestamp"): - print(f" Time : {m['timestamp']}") - - -def print_event_logs(memories: list): - """Print event log memories (atomic facts).""" - if not memories: - print(" (none)") - return - for i, m in enumerate(memories, 1): - print(f"\n [{i}] {m.get('atomic_fact', 'N/A')}") - if m.get("timestamp"): - print(f" Time : {m['timestamp']}") - - -def print_foresights(memories: list): - """Print foresight memories.""" - if not memories: - print(" (none)") - return - for i, m in enumerate(memories, 1): - content = m.get("content") or m.get("foresight") or "N/A" - print(f"\n [{i}] {content}") - validity = " ~ ".join(filter(None, [m.get("start_time"), m.get("end_time")])) - if validity: - print(f" Validity : {validity}") - if m.get("evidence"): - print(f" Evidence : {m['evidence']}") - - -def print_agent_cases(memories: list): - """Print agent experience memories.""" - if not memories: - print(" (none)") - return - for i, exp in enumerate(memories, 1): - print(f"\n [{i}] {exp.get('task_intent', 'N/A')}") - print(f" Parent : {exp.get('parent_id', 'N/A')}") - approach = exp.get("approach", "") - if approach: - print(f" Approach : {approach}") - if exp.get("quality_score") is not None: - print(f" Quality : {exp['quality_score']}") - - -def print_agent_skills(memories: list): - """Print agent skills.""" - if not memories: - print(" (none)") - return - - for i, m in enumerate(memories, 1): - print(f"\n [{i}] {m.get('name') or 'Unnamed'}") - if m.get("description"): - print(f" Description: {m['description']}") - print(f" Content : {m.get('content', 'N/A')}") - print(f" Confidence : {m.get('confidence', 0):.2f}") - print(f" Cluster : {m.get('cluster_id', 'N/A')}") - - -def print_search_case_results(hits: list): - """Print search results for agent_case.""" - if not hits: - print(" (no results)") - return - for i, h in enumerate(hits, 1): - score = h.get("score", 0.0) - task_intent = h.get("task_intent") or "" - print(f"\n [{i}] score={score:.4f}") - print(f" Intent : {task_intent}") - - -def print_search_skill_results(hits: list): - """Print search results for agent_skill.""" - if not hits: - print(" (no results)") - return - for i, h in enumerate(hits, 1): - score = h.get("score", 0.0) - name = h.get("name") or "Unnamed" - content = h.get("content") or "" - print(f"\n [{i}] score={score:.4f} {name}") - print(f" {content}") - if h.get("description"): - print(f" Description: {h['description']}") - print(f" Confidence : {h.get('confidence', 0.0):.2f}") - - -# Memory type -> (label, printer) mapping for fetch step -MEMORY_TYPE_PRINTERS = [ - ("episodic_memory", "Episodic Memory", print_episodic_memories), - ("agent_case", "Agent Case", print_agent_cases), - ("agent_skill", "Agent Skill", print_agent_skills), -] - - -# ==================== AgentDemoRunner ==================== - - -class AgentDemoRunner: - """Stateful helper for running agent demo scripts (v1 API). - - Encapsulates user/session config and provides v1 API call methods. - Each demo creates its own runner with unique session_id. - - Usage: - runner = AgentDemoRunner( - session_prefix="search_agent_demo", - user_id="demo_user", - ) - await runner.send_agent_message(msg, 0, flush=True) - """ - - def __init__( - self, - session_prefix: str = "agent_demo", - user_id: str = "demo_user", - msg_prefix: str = "agent_msg", - base_url: str = DEFAULT_BASE_URL, - # Kept for backward compat — old demos pass these but they are unused in v1 - group_id_prefix: str = "", - group_name: str = "", - description: str = "", # noqa: ARG002 - tags: Optional[List[str]] = None, # noqa: ARG002 - ): - self.run_id = uuid.uuid4().hex[:8] - self.session_id = f"{session_prefix}_{self.run_id}" - # v1 auto-generates group_id from user_id, but demos may want to reference it - self.group_id = f"{group_id_prefix or session_prefix}_{self.run_id}" - self.group_name = group_name - self.msg_prefix = msg_prefix - self.user_id = user_id - self.base_url = base_url - - self.agent_url = f"{base_url}/api/v1/memories/agent" - self.flush_url = f"{base_url}/api/v1/memories/agent/flush" - self.get_url = f"{base_url}/api/v1/memories/get" - self.search_url = f"{base_url}/api/v1/memories/search" - - async def save_conversation_meta(self): - """No-op in v1 — conversation meta is auto-created. - - Kept for backward compatibility with existing demo scripts. - """ - print(f" v1 API: group auto-registered (user_id={self.user_id}, session={self.session_id})") - - async def send_agent_message( - self, msg: dict, msg_index: int, flush: bool = False - ) -> bool: - """Send a single agent message via POST /api/v1/memories/agent.""" - now = get_now_with_timezone() - timestamp_ms = int(now.timestamp() * 1000) - - role = msg.get("role", "user") - sender_id = self.user_id if role == "user" else "assistant" - - message_item = { - "message_id": f"{self.msg_prefix}_{self.run_id}_{msg_index:03d}", - "sender_id": sender_id, - "sender_name": sender_id, - "role": role, - "timestamp": timestamp_ms, - "content": msg.get("content") or "", - } - - if msg.get("tool_calls"): - message_item["tool_calls"] = msg["tool_calls"] - if msg.get("tool_call_id"): - message_item["tool_call_id"] = msg["tool_call_id"] - - payload = { - "user_id": self.user_id, - "session_id": self.session_id, - "messages": [message_item], - } - - try: - async with httpx.AsyncClient(timeout=500.0) as client: - resp = await client.post(self.agent_url, json=payload) - resp.raise_for_status() - result = resp.json() - - data = result.get("data", {}) - status = data.get("status", "") - role_label = f"[{role}]".ljust(12) - raw_content = msg.get("content") - if isinstance(raw_content, list): - content_preview = (raw_content[0].get("text", "") if raw_content else "")[:50] - else: - content_preview = (raw_content or "(tool_calls)")[:50] - if status == "extracted": - print(f" {role_label} {content_preview} -> Extracted memories") - else: - print(f" {role_label} {content_preview}") - - # Handle flush after message if requested - if flush: - await self._flush() - - return True - except httpx.ConnectError: - print(f" Cannot connect to API server ({self.base_url})") - print(f" Please start first: uv run python src/run.py") - return False - except Exception as e: - print(f" Error: {e}") - return False - - async def _flush(self) -> bool: - """Trigger flush via POST /api/v1/memories/agent/flush.""" - payload = { - "user_id": self.user_id, - "session_id": self.session_id, - } - try: - async with httpx.AsyncClient(timeout=500.0) as client: - resp = await client.post(self.flush_url, json=payload) - resp.raise_for_status() - result = resp.json() - data = result.get("data", {}) - if data.get("status") == "extracted": - print(f" [flush] -> Extracted memories") - else: - print(f" [flush] -> {data.get('status', 'done')}") - return True - except Exception as e: - print(f" Flush error: {e}") - return False - - async def fetch_memories(self, memory_type: str) -> list: - """Fetch memories via POST /api/v1/memories/get.""" - payload = { - "memory_type": memory_type, - "page": 1, - "page_size": 20, - "rank_by": "timestamp", - "rank_order": "desc", - "filters": { - "user_id": self.user_id, - }, - } - try: - async with httpx.AsyncClient(timeout=30.0) as client: - resp = await client.post(self.get_url, json=payload) - resp.raise_for_status() - result = resp.json() - data = result.get("data", {}) - # v1 GetMemResponse has typed arrays: episodes, profiles, agent_cases, agent_skills - if memory_type == "episodic_memory": - return data.get("episodes", []) - elif memory_type == "profile": - return data.get("profiles", []) - elif memory_type == "agent_case": - return data.get("agent_cases", []) - elif memory_type == "agent_skill": - return data.get("agent_skills", []) - else: - return [] - except Exception as e: - print(f" [{memory_type}] Fetch error: {e}") - return [] - - async def search_memories( - self, - query: str, - memory_type: str, - top_k: int = 5, - retrieve_method: str = "hybrid", - ) -> list | dict: - """Search memories via POST /api/v1/memories/search. - - Args: - query: Search query text. - memory_type: One of "agent_memory", "episodic_memory", "profile", "raw_message". - top_k: Max results. - retrieve_method: Retrieval method. - - Returns: - For agent_memory: dict with "cases" and "skills" lists. - For other types: list of results. - """ - payload = { - "query": query, - "method": retrieve_method, - "memory_types": [memory_type], - "top_k": top_k, - "filters": { - "user_id": self.user_id, - }, - } - try: - async with httpx.AsyncClient(timeout=120.0) as client: - resp = await client.post(self.search_url, json=payload) - resp.raise_for_status() - result = resp.json() - data = result.get("data", {}) - - if memory_type == "agent_memory": - agent_mem = data.get("agent_memory") or {} - return { - "cases": agent_mem.get("cases", []), - "skills": agent_mem.get("skills", []), - } - elif memory_type == "episodic_memory": - return data.get("episodes", []) - else: - return data.get("memories", []) - except Exception as e: - print(f" Search error: {e}") - return [] if memory_type != "agent_memory" else {"cases": [], "skills": []} diff --git a/methods/EverCore/demo/utils/memory_utils.py b/methods/EverCore/demo/utils/memory_utils.py deleted file mode 100644 index e81a902f..00000000 --- a/methods/EverCore/demo/utils/memory_utils.py +++ /dev/null @@ -1,149 +0,0 @@ -"""Shared Utility Module - For Memory Extraction and Chat System - -This module provides common utility functions shared by extract_memory.py and chat_with_memory.py. - -Key Features: -- MongoDB connection and initialization -- MemCell queries -- Time serialization tools - -V4 Update: -- Removed custom retrieval strategies (using API in src) -- Retained basic utility functions -""" - -import json -import os -from pathlib import Path -from typing import List, Dict, Any, Optional -from datetime import datetime - -from pymongo import AsyncMongoClient -from beanie import init_beanie - -# Import document models from the project -from infra_layer.adapters.out.persistence.document.memory.memcell import ( - MemCell as DocMemCell, -) -from demo.config import MongoDBConfig - -# Import language utility function from common_utils -from common_utils.language_utils import get_prompt_language - - -# ============================================================================ -# MongoDB Tools -# ============================================================================ - - -async def ensure_mongo_beanie_ready(mongo_config: MongoDBConfig) -> None: - """Initialize MongoDB and Beanie Connection - - Args: - mongo_config: MongoDB configuration object - - Raises: - Exception: If connection fails - """ - # Set environment variable for Beanie use - os.environ["MONGODB_URI"] = mongo_config.uri - - # Create MongoDB client and test connection - client = AsyncMongoClient(mongo_config.uri) - try: - await client.admin.command('ping') - print(f"[MongoDB] ✅ Connected: {mongo_config.database}") - except Exception as e: - print(f"[MongoDB] ❌ Connection failed: {e}") - raise - - # Initialize Beanie document models - await init_beanie( - database=client[mongo_config.database], document_models=[DocMemCell] - ) - - -async def query_all_groups_from_mongodb() -> List[Dict[str, Any]]: - """Query all group IDs and their memory counts - - Uses aggregation pipeline to count MemCells per group. - - Returns: - List of groups, format: [{"group_id": "xxx", "memcell_count": 76}, ...] - """ - # Use aggregation pipeline to count memories per group - pipeline = [ - {"$match": {"group_id": {"$ne": None}}}, # Filter records without group_id - {"$group": {"_id": "$group_id", "count": {"$sum": 1}}}, - {"$sort": {"_id": 1}}, # Sort by group_id - ] - - # Get PyMongo AsyncCollection for aggregation - # get_pymongo_collection() returns AsyncCollection in Beanie (async) - collection = DocMemCell.get_pymongo_collection() - cursor = await collection.aggregate(pipeline) - results = await cursor.to_list(length=None) - - groups = [] - for result in results: - groups.append({"group_id": result["_id"], "memcell_count": result["count"]}) - - return groups - - -async def query_memcells_by_group_and_time( - group_id: str, start_date: datetime, end_date: datetime -) -> List[DocMemCell]: - """Query MemCells by Group and Time Range - - Args: - group_id: Group ID - start_date: Start date - end_date: End date - - Returns: - List of MemCell document objects - """ - memcells = ( - await DocMemCell.find( - {"group_id": group_id, "timestamp": {"$gte": start_date, "$lt": end_date}} - ) - .sort("timestamp") - .to_list() - ) - - return memcells - - -# ============================================================================ -# Time Serialization Tools -# ============================================================================ - - -def serialize_datetime(obj: Any) -> Any: - """Recursively serialize datetime objects to ISO format strings - - Args: - obj: Object to serialize (can be any type) - - Returns: - Serialized object - """ - # If already string, return directly (avoid processing already serialized timestamps) - if isinstance(obj, str): - return obj - # Convert datetime object to ISO string - elif isinstance(obj, datetime): - return obj.isoformat() - # Recursively process dict - elif isinstance(obj, dict): - return {k: serialize_datetime(v) for k, v in obj.items()} - # Recursively process list - elif isinstance(obj, list): - return [serialize_datetime(item) for item in obj] - # Process object (convert __dict__) - elif hasattr(obj, '__dict__'): - return serialize_datetime(obj.__dict__) - # Return other types directly - else: - return obj diff --git a/methods/EverCore/demo/utils/simple_memory_manager.py b/methods/EverCore/demo/utils/simple_memory_manager.py deleted file mode 100644 index 93898d22..00000000 --- a/methods/EverCore/demo/utils/simple_memory_manager.py +++ /dev/null @@ -1,342 +0,0 @@ -"""Simple Memory Manager - Simplified Memory Manager (HTTP API Version) - -Encapsulates all HTTP API call details and provides the simplest interface. -""" - -import re -import asyncio -import httpx -from typing import List, Dict, Any -from api_specs.memory_types import ScenarioType -from common_utils.datetime_utils import get_now_with_timezone, to_iso_format - - -def extract_event_time_from_memory(mem: Dict[str, Any]) -> str: - """Extract actual event time from memory data - - Extraction priority: - 1. Date in 'subject' field (parentheses format, e.g., "(2025-08-26)") - 2. Date in 'subject' field (Chinese format, e.g., "2025年8月26日") - 3. Date in 'episode' content (Chinese or ISO format) - 4. Return "N/A" if extraction fails (do not show storage time) - - Args: - mem: Memory dictionary containing subject, episode, etc. - - Returns: - Date string in YYYY-MM-DD format, or "N/A" - """ - subject = mem.get("subject", "") - episode = mem.get("episode", "") - - # 1. Extract from subject: Match ISO date format inside parentheses (YYYY-MM-DD) - if subject: - match = re.search(r'\((\d{4}-\d{2}-\d{2})\)', subject) - if match: - return match.group(1) - - # 2. Extract from subject: Match Chinese date format "YYYY年MM月DD日" - match = re.search(r'(\d{4})年(\d{1,2})月(\d{1,2})日', subject) - if match: - year, month, day = match.groups() - return f"{year}-{month.zfill(2)}-{day.zfill(2)}" - - # 3. Extract from episode (search entire content, no character limit) - if episode: - # Match "于YYYY年MM月DD日" or "在YYYY年MM月DD日" - match = re.search(r'[于在](\d{4})年(\d{1,2})月(\d{1,2})日', episode) - if match: - year, month, day = match.groups() - return f"{year}-{month.zfill(2)}-{day.zfill(2)}" - - # Match ISO format "YYYY-MM-DD" - match = re.search(r'(\d{4})-(\d{2})-(\d{2})', episode) - if match: - return match.group(0) - - # Match other Chinese date formats (without "at" prefix) - match = re.search(r'(\d{4})年(\d{1,2})月(\d{1,2})日', episode) - if match: - year, month, day = match.groups() - return f"{year}-{month.zfill(2)}-{day.zfill(2)}" - - # 4. Failed to extract event time, return N/A - return "N/A" - - -class SimpleMemoryManager: - """Super Simple Memory Manager - - Uses HTTP API, no need to worry about internal implementation. - - Usage: - memory = SimpleMemoryManager() - await memory.store("I love playing soccer") - results = await memory.search("What sports does the user like?") - """ - - def __init__( - self, - base_url: str = "http://localhost:1995", - group_id: str = "default_group", - scene: str = ScenarioType.SOLO.value, - user_id: str = "demo_user", - ): - """Initialize the manager - - Args: - base_url: API server address (default: localhost:1995) - group_id: Group ID (default: default_group) - scene: Scene type (default: "solo", options: "solo" or "team") - user_id: User ID for personal endpoint (default: "demo_user") - """ - self.base_url = base_url - self.group_id = group_id - self.group_name = "Simple Demo Group" - self.scene = scene - self.user_id = user_id - self.memorize_url = f"{base_url}/api/v1/memories" - self.retrieve_url = f"{base_url}/api/v1/memories/search" - self.settings_url = f"{base_url}/api/v1/settings" - self._message_counter = 0 - self._settings_initialized = False - - async def store(self, content: str, sender: str = "User") -> bool: - """Store a message - - Args: - content: Message content - sender: Sender name (default: "User") - - Returns: - Success status - """ - # ========== Initialize settings first when storing for the first time ========== - if not self._settings_initialized: - await self._init_settings() - - # Generate unique message ID - self._message_counter += 1 - now = ( - get_now_with_timezone() - ) # Use project's unified time utility (with timezone) - message_id = f"msg_{self._message_counter}_{int(now.timestamp() * 1000)}" - - # Build v1 PersonalAddRequest payload - role = "user" if sender.lower() == "user" else "assistant" - message_item = { - "message_id": message_id, - "sender_id": self.user_id if role == "user" else sender, - "sender_name": sender, - "role": role, - "timestamp": int(now.timestamp() * 1000), - "content": content, - } - payload = { - "user_id": self.user_id, - "messages": [message_item], - } - - try: - async with httpx.AsyncClient(timeout=500.0) as client: - response = await client.post(self.memorize_url, json=payload) - response.raise_for_status() - result = response.json() - - # v1 response: {"data": {"status": "...", "count": N, ...}} - data = result.get("data", {}) - status = data.get("status", "") - count = data.get("count", 0) - if status: - if count > 0: - print( - f" ✅ Stored: {content[:40]}... (Extracted {count} memories)" - ) - else: - print( - f" 📝 Recorded: {content[:40]}... (Waiting for more context to extract memories)" - ) - return True - else: - print(f" ❌ Storage failed: {result.get('message')}") - return False - - except httpx.ConnectError: - print(f" ❌ Cannot connect to API server ({self.base_url})") - print(f" Please start first: uv run python src/run.py") - return False - except Exception as e: - print(f" ❌ Storage failed: {e}") - return False - - async def _init_settings(self) -> bool: - """ - Initialize global settings via V1 API (called when storing the first message) - - Returns: - Success status - """ - if self._settings_initialized: - return True - - settings_request = {} - - try: - async with httpx.AsyncClient(timeout=30.0) as client: - response = await client.put(self.settings_url, json=settings_request) - response.raise_for_status() - result = response.json() - - if "data" in result: - self._settings_initialized = True - print(f" ℹ️ Initialized settings (Scene: {self.scene})") - return True - else: - print(f" ⚠️ Failed to init settings: {result.get('message')}") - self._settings_initialized = True - return False - - except httpx.ConnectError: - print(f" ⚠️ Cannot connect to API server for settings init") - self._settings_initialized = True - return False - except Exception as e: - print(f" ⚠️ Failed to init settings: {e}") - self._settings_initialized = True - return False - - async def search( - self, query: str, top_k: int = 3, mode: str = "hybrid", show_details: bool = True - ) -> List[Dict[str, Any]]: - """Search memories - - Args: - query: Query text - top_k: Number of results to return (default: 3) - mode: - - "keyword": Keyword retrieval (BM25) - - "vector": Vector retrieval - - "hybrid": Keyword + Vector + Rerank - - "agentic": LLM-guided multi-round retrieval - show_details: Whether to show detailed information (default: True) - - Returns: - List of memories - """ - # v1 SearchMemoriesRequest: POST with body {query, method, memory_types, top_k, filters} - payload = { - "query": query, - "method": mode, - "memory_types": ["episodic_memory"], - "top_k": top_k, - "filters": {"user_id": self.user_id}, - } - - try: - async with httpx.AsyncClient(timeout=30.0) as client: - response = await client.post(self.retrieve_url, json=payload) - response.raise_for_status() - result = response.json() - - # v1 response: {"data": {"episodes": [...], "profiles": [...], "raw_messages": [...], "agent_memory": ...}} - data = result.get("data", {}) - if data: - # Aggregate across memory_type buckets (we only requested episodic_memory here) - memories = [] - for key in ("episodes", "profiles", "raw_messages"): - memories.extend(data.get(key) or []) - metadata = data.get("metadata", {}) or {} - latency = metadata.get("total_latency_ms", 0) - - if show_details: - print( - f" 🔍 Found {len(memories)} memories (took {latency:.2f}ms)" - ) - self._print_memories(memories) - - return memories - else: - print(f" ❌ Search failed: {result.get('message')}") - return [] - - except httpx.ConnectError: - print(f" ❌ Cannot connect to API server ({self.base_url})") - return [] - except Exception as e: - print(f" ❌ Search failed: {e}") - return [] - - def _print_memories(self, memories: List[Dict[str, Any]]): - """Print memory details (internal method)""" - if not memories: - print(" 💡 Tip: No related memories found") - print(" Possible reasons:") - print( - " - Too little conversation input, system hasn't generated memories yet" - ) - print( - " (This simple demo only demonstrates retrieval, not full memory generation)" - ) - return - - for i, mem in enumerate(memories, 1): - score = mem.get('score', 0) - # Extract actual event time (not storage time) - event_time = extract_event_time_from_memory(mem) - subject = mem.get('subject', '') - summary = mem.get('summary', '') - episode = mem.get('episode', '') - - print(f"\n [{i}] Relevance: {score:.4f} | Time: {event_time}") - if subject: - print(f" Subject: {subject}") - if summary: - print(f" Summary: {summary[:60]}...") - if episode: - print(f" Details: {episode[:80]}...") - - async def wait_for_index(self, seconds: int = 10): - """Wait for index building - - Args: - seconds: Wait time in seconds (default: 10) - """ - print(" 💡 Tip: Memory extraction requires sufficient context") - print( - " - Short conversations may only record messages, not generate memories immediately" - ) - print( - " - Multi-turn conversations with specific information are easier to extract memories from" - ) - print( - " - System extracts memories at conversation boundaries (topic changes, time gaps)" - ) - print(f" ⏳ Waiting {seconds} seconds to ensure data is written...") - await asyncio.sleep(seconds) - print(f" ✅ Index building completed") - - def print_separator(self, text: str = ""): - """Print separator line""" - if text: - print(f"\n{'='*60}") - print(f"{text}") - print('=' * 60) - else: - print('-' * 60) - - def print_summary(self): - """Print usage summary and tips""" - print("\n" + "=" * 60) - print("✅ Demo completed!") - print("=" * 60) - print("\n📚 About Memory Extraction:") - print( - " The memory system uses intelligent extraction strategy, not recording all conversations:" - ) - print( - " - ✅ Will extract: Conversations with specific info, opinions, preferences, events" - ) - print(" - ❌ Won't extract: Too brief, low-information small talk") - print( - " - 🎯 Best practice: Multi-turn conversations, rich context, specific details" - ) \ No newline at end of file diff --git a/methods/EverCore/docker-compose.yaml b/methods/EverCore/docker-compose.yaml deleted file mode 100644 index 29407030..00000000 --- a/methods/EverCore/docker-compose.yaml +++ /dev/null @@ -1,163 +0,0 @@ -version: '3.8' - -services: - # MongoDB database - mongodb: - image: mongo:7.0 - container_name: memsys-mongodb - restart: unless-stopped - environment: - MONGO_INITDB_ROOT_USERNAME: admin - MONGO_INITDB_ROOT_PASSWORD: memsys123 - MONGO_INITDB_DATABASE: memsys - ports: - - "27017:27017" - volumes: - - mongodb_data:/data/db - - ./docker/mongodb/init:/docker-entrypoint-initdb.d - networks: - - memsys-network - healthcheck: - test: ["CMD", "mongosh", "--eval", "db.adminCommand('ping')"] - interval: 30s - timeout: 10s - retries: 3 - start_period: 40s - - # Elasticsearch search engine - elasticsearch: - image: docker.elastic.co/elasticsearch/elasticsearch:8.11.0 - container_name: memsys-elasticsearch - restart: unless-stopped - environment: - - discovery.type=single-node - - xpack.security.enabled=false - - "ES_JAVA_OPTS=-Xms1g -Xmx1g" - - bootstrap.memory_lock=true - ulimits: - memlock: - soft: -1 - hard: -1 - ports: - - "19200:9200" - - "19300:9300" - volumes: - - elasticsearch_data:/usr/share/elasticsearch/data - networks: - - memsys-network - healthcheck: - test: ["CMD-SHELL", "curl -f http://localhost:9200/_cluster/health || exit 1"] - interval: 30s - timeout: 10s - retries: 3 - start_period: 40s - - # Milvus vector database - milvus-etcd: - image: quay.io/coreos/etcd:v3.5.5 - container_name: memsys-milvus-etcd - restart: unless-stopped - environment: - - ETCD_AUTO_COMPACTION_MODE=revision - - ETCD_AUTO_COMPACTION_RETENTION=1000 - - ETCD_QUOTA_BACKEND_BYTES=4294967296 - - ETCD_SNAPSHOT_COUNT=50000 - command: etcd -advertise-client-urls=http://127.0.0.1:2479 -listen-client-urls http://0.0.0.0:2479 --data-dir /etcd - healthcheck: - test: ["CMD", "etcdctl", "endpoint", "health"] - interval: 30s - timeout: 20s - retries: 3 - volumes: - - milvus_etcd_data:/etcd - networks: - - memsys-network - - milvus-minio: - image: minio/minio:RELEASE.2023-03-20T20-16-18Z - container_name: memsys-milvus-minio - restart: unless-stopped - environment: - MINIO_ACCESS_KEY: minioadmin - MINIO_SECRET_KEY: minioadmin - ports: - - "9001:9001" - - "9000:9000" - command: minio server /minio_data --console-address ":9001" - healthcheck: - test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"] - interval: 30s - timeout: 20s - retries: 3 - volumes: - - milvus_minio_data:/minio_data - networks: - - memsys-network - - milvus-standalone: - image: milvusdb/milvus:v2.5.2 - container_name: memsys-milvus-standalone - restart: unless-stopped - command: ["milvus", "run", "standalone"] - environment: - ETCD_ENDPOINTS: milvus-etcd:2479 - MINIO_ADDRESS: milvus-minio:9000 - ports: - - "19530:19530" - - "9091:9091" - volumes: - - milvus_data:/var/lib/milvus - healthcheck: - test: ["CMD", "curl", "-f", "http://localhost:9091/healthz"] - interval: 30s - timeout: 20s - retries: 3 - start_period: 90s - depends_on: - - milvus-etcd - - milvus-minio - networks: - - memsys-network - - # Redis cache - redis: - image: redis:7.2-alpine - container_name: memsys-redis - restart: unless-stopped - ports: - - "6379:6379" - volumes: - - redis_data:/data - networks: - - memsys-network - healthcheck: - test: ["CMD", "redis-cli", "ping"] - interval: 30s - timeout: 10s - retries: 3 - start_period: 30s - - - - # Memsys main application has been changed to local running: - # Use uv to start the application locally, for example: - # uv run python run.py - -volumes: - mongodb_data: - driver: local - elasticsearch_data: - driver: local - milvus_etcd_data: - driver: local - milvus_minio_data: - driver: local - milvus_data: - driver: local - redis_data: - driver: local - -networks: - memsys-network: - driver: bridge - diff --git a/methods/EverCore/docs/ACKNOWLEDGMENTS.md b/methods/EverCore/docs/ACKNOWLEDGMENTS.md deleted file mode 100644 index b14b43b6..00000000 --- a/methods/EverCore/docs/ACKNOWLEDGMENTS.md +++ /dev/null @@ -1,95 +0,0 @@ -# Acknowledgments - -[Home](../README.md) > [Docs](README.md) > Acknowledgments - -Thanks to the following projects, communities, and individuals for their inspiration and support. - ---- - -## Inspiration & References - -### [Memos](https://github.com/usememos/memos) - -Thank you to the Memos project for providing a comprehensive, standardized open-source note-taking service that has provided valuable inspiration for our memory system design. - -### [Nemori](https://github.com/nemori-ai/nemori) - -Thank you to the Nemori project for providing a self-organising long-term memory substrate for agentic LLM workflows that has provided valuable inspiration for our memory system design. - ---- - -## Open Source Libraries - -EverCore is built on top of excellent open-source libraries and frameworks: - -### Core Framework -- **[FastAPI](https://fastapi.tiangolo.com/)** - Modern, fast web framework for building APIs -- **[Python](https://www.python.org/)** - Programming language -- **[uv](https://github.com/astral-sh/uv)** - Fast Python package manager - -### Storage & Search -- **[MongoDB](https://www.mongodb.com/)** - Document database -- **[Elasticsearch](https://www.elastic.co/)** - Search and analytics engine -- **[Milvus](https://milvus.io/)** - Vector database -- **[Redis](https://redis.io/)** - In-memory data store - -### AI/ML -- **[OpenAI](https://openai.com/)** - LLM APIs -- **[DeepInfra](https://deepinfra.com/)** - Embedding and reranking services - -### Development Tools -- **[Docker](https://www.docker.com/)** - Containerization platform -- **[Beanie](https://beanie-odm.dev/)** - Async ODM for MongoDB - ---- - -## Contributors - -Thanks to all the developers who have contributed to this project! - -See the full list of contributors on [GitHub](https://github.com/EverMind-AI/EverOS/graphs/contributors). - - - ---- - -## Community - -Thanks to our community for their valuable feedback, bug reports, and feature suggestions: - -- **GitHub Issues & Discussions** - For bug reports and feature requests -- **Discord** - [Join our Discord server](https://discord.gg/pfwwskxp) -- **Reddit Community** - [r/EverMindAI](https://www.reddit.com/r/EverMindAI/) -- **X/Twitter** - [@EverMindAI](https://x.com/EverMindAI) - ---- - -## Supporting Organizations - -- **Shanda Group** - For supporting the development of EverCore - ---- - -## Special Thanks - -- To everyone who has starred the repository -- To those who have shared EverCore with others -- To researchers and developers using EverCore in their projects - ---- - -## Want to Contribute? - -We welcome contributions! See our [Contributing Guide](../CONTRIBUTING.md) to get started. - ---- - -## See Also - -- [Citation](CITATION.md) -- [Changelog](CHANGELOG.md) -- [Contributing Guide](../CONTRIBUTING.md) diff --git a/methods/EverCore/docs/ARCHITECTURE.md b/methods/EverCore/docs/ARCHITECTURE.md deleted file mode 100644 index 4a54b902..00000000 --- a/methods/EverCore/docs/ARCHITECTURE.md +++ /dev/null @@ -1,337 +0,0 @@ -# Architecture Design - -[Home](../README.md) > [Docs](README.md) > Architecture - -## Overview - -EverCore adopts a layered architecture design that separates concerns and enables scalability. The system is built around two main cognitive tracks: **memory construction** and **memory perception**, which work together to create a comprehensive AI memory system. - -For a high-level overview of the system framework, see [System Framework in Overview](OVERVIEW.md#system-framework). - ---- - -## Layered Architecture - -EverCore is organized into six main layers, each with specific responsibilities: - -### 1. Agentic Layer - -The top-level orchestration layer that provides a unified memory interface. - -- **Responsibilities:** - - Memory extraction coordination - - Vectorization management - - Retrieval orchestration - - Reranking operations - -- **Key Components:** - - Memory extraction agents - - Vector embedding services - - Retrieval coordinators - - Reranking engines - -### 2. Memory Layer - -Handles memory extraction and management. - -- **Responsibilities:** - - MemCell extraction from conversations - - Episodic memory management - - Memory type classification (episodes, profiles, preferences, etc.) - - LLM prompt management for extraction - -- **Key Components:** - - `memcell_extractor/` - Atomic memory unit extraction - - `memory_extractor/` - Higher-level memory construction - - `prompts/` - LLM prompt templates - -### 3. Retrieval Layer - -Implements multi-modal retrieval and result ranking. - -- **Responsibilities:** - - Semantic search (vector-based) - - Keyword search (BM25) - - Hybrid retrieval (RRF fusion) - - Agentic multi-round recall - - Result reranking - -- **Key Components:** - - Vector search engines (Milvus integration) - - Keyword search engines (Elasticsearch integration) - - Fusion algorithms - - Reranking services - -### 4. Business Layer - -Contains business logic and data operations. - -- **Responsibilities:** - - API endpoint implementations - - Request/response handling - - Business rule enforcement - - Data validation - -- **Key Components:** - - Controllers - - Service layer - - Data transformation logic - -### 5. Infrastructure Layer - -Provides adapters for external services and databases. - -- **Responsibilities:** - - Database connections (MongoDB) - - Cache management (Redis) - - Search engine integration (Elasticsearch, Milvus) - - Message queue handling - -- **Key Components:** - - Database adapters - - Cache adapters - - Search engine clients - - Queue managers - -### 6. Core Framework - -Foundation layer providing cross-cutting concerns. - -- **Responsibilities:** - - Dependency injection - - Lifecycle management - - Middleware pipeline - - Queue management - - Configuration management - -- **Key Components:** - - DI container (see [DI Framework](../src/core/di/README.md)) - - Lifecycle hooks - - Middleware system - - Common utilities - ---- - -## Project Structure - -``` -evermemos-opensource/ -├── src/ # Source code directory -│ ├── agentic_layer/ # Agentic layer - unified memory interface -│ ├── memory_layer/ # Memory layer - memory extraction -│ │ ├── memcell_extractor/ # MemCell extractor -│ │ ├── memory_extractor/ # Memory extractor -│ │ └── prompts/ # LLM prompt templates -│ ├── retrieval_layer/ # Retrieval layer - memory retrieval -│ ├── biz_layer/ # Business layer - business logic -│ ├── infra_layer/ # Infrastructure layer -│ ├── core/ # Core functionality (DI/lifecycle/middleware) -│ ├── component/ # Components (LLM adapters, etc.) -│ └── common_utils/ # Common utilities -├── demo/ # Demo code -├── data/ # Sample conversation data -├── evaluation/ # Evaluation scripts -│ └── src/ # Evaluation framework source code -├── data_format/ # Data format definitions -├── docs/ # Documentation -├── config.json # Configuration file -├── env.template # Environment variable template -├── pyproject.toml # Project configuration -└── README.md # Project description -``` - ---- - -## Technology Stack - -### Core Technologies - -- **FastAPI** - Modern web framework for building APIs -- **Python 3.10+** - Primary programming language -- **uv** - Fast Python package manager - -### Storage & Search - -- **MongoDB 7.0+** - Primary database for memory cells and profiles -- **Elasticsearch 8.x** - Keyword search engine (BM25) -- **Milvus 2.4+** - Vector database for semantic retrieval -- **Redis 7.x** - Cache service for performance optimization - -### AI/ML Services - -- **LLM APIs** - For memory extraction and reasoning -- **Embedding Models** - For semantic vectorization -- **Reranker Models** - For relevance scoring - -### Infrastructure - -- **Docker & Docker Compose** - Containerization and orchestration -- **Beanie** - Async ODM for MongoDB - ---- - -## Data Flow - -### Memory Construction Flow - -``` -User Conversation - ↓ -Message Ingestion (API) - ↓ -MemCell Extraction (Memory Layer) - ↓ -Memory Type Classification - ↓ -Storage (MongoDB) - ↓ -Indexing (Elasticsearch + Milvus) -``` - -### Memory Retrieval Flow - -``` -User Query - ↓ -Retrieval Mode Selection - ↓ -├─ Lightweight Mode -│ ├─ BM25 Search (Elasticsearch) -│ ├─ Vector Search (Milvus) -│ └─ RRF Fusion -│ -└─ Agentic Mode - ├─ Query Expansion (LLM) - ├─ Multi-round Retrieval - └─ Intelligent Fusion - ↓ -Reranking (Optional) - ↓ -Results to User -``` - ---- - -## Design Principles - -### 1. Separation of Concerns - -Each layer has a well-defined responsibility, making the system easier to understand, test, and maintain. - -### 2. Scalability - -The layered architecture allows individual components to scale independently based on load. - -### 3. Flexibility - -Multiple retrieval strategies (lightweight vs agentic) allow users to choose based on their latency and accuracy requirements. - -### 4. Extensibility - -New memory types, retrieval strategies, or storage backends can be added without major refactoring. - -### 5. Testability - -Clear layer boundaries enable unit testing of individual components and integration testing of layer interactions. - ---- - -## Memory Construction Architecture - -### MemCell: Atomic Memory Unit - -MemCells are the fundamental building blocks of the memory system. Each MemCell represents a single, atomic piece of information extracted from a conversation. - -**MemCell Properties:** -- Unique identifier -- Content (the extracted information) -- Metadata (timestamp, participants, etc.) -- Memory type classification -- Semantic embeddings - -### Memory Types - -EverCore supports multiple memory types, each serving different purposes: - -- **Episodes** - Coherent conversation threads on specific topics -- **Profiles** - User characteristics and attributes -- **Preferences** - User likes, dislikes, and choices -- **Relationships** - Connections between people -- **Semantic Knowledge** - Facts and information -- **Basic Facts** - Simple factual statements -- **Core Memories** - Important, long-lasting memories - -See [Memory Types Guide](dev_docs/memory_types_guide.md) for detailed information. - ---- - -## Memory Perception Architecture - -### Retrieval Strategies - -#### Lightweight Retrieval - -Fast, efficient retrieval for latency-sensitive scenarios: -- **BM25** - Keyword-based search -- **Embedding** - Semantic vector search -- **RRF** - Hybrid fusion of both methods - -#### Agentic Retrieval - -Intelligent, multi-round retrieval for complex queries: -- Query expansion using LLM -- Multiple retrieval paths -- Intelligent result fusion - -See [Retrieval Strategies Guide](advanced/RETRIEVAL_STRATEGIES.md) for more details. - -### Reranking - -Optional reranking step to improve result relevance: -- Deep relevance scoring -- Batch processing with retry logic -- Prioritization of critical information - ---- - -## Configuration Management - -### Environment Variables - -Key configuration is managed through environment variables (see `.env`): -- LLM API credentials -- Embedding service credentials -- Database connection strings -- Service endpoints - -See [Configuration Guide](usage/CONFIGURATION_GUIDE.md) for complete details. - -### Service Configuration - -Each service (MongoDB, Elasticsearch, Milvus, Redis) can be configured independently for: -- Resource allocation -- Performance tuning -- Network settings -- Security settings - -See [Docker Setup Guide](installation/DOCKER_SETUP.md) for service-specific configuration. - ---- - -## For Developers - -If you're contributing to EverCore, these resources will help: - -- **[Development Guide](dev_docs/development_guide.md)** - Architecture details and best practices -- **[Development Standards](dev_docs/development_standards.md)** - Code standards and conventions -- **[DI Framework](../src/core/di/README.md)** - Understanding the dependency injection system -- **[Contributing Guide](../CONTRIBUTING.md)** - How to contribute code - ---- - -## See Also - -- [Overview & System Framework](OVERVIEW.md) -- [Memory Types Guide](dev_docs/memory_types_guide.md) -- [Development Guide](dev_docs/development_guide.md) -- [API Documentation](api_docs/memory_api.md) diff --git a/methods/EverCore/docs/CHANGELOG.md b/methods/EverCore/docs/CHANGELOG.md deleted file mode 100644 index 517b755b..00000000 --- a/methods/EverCore/docs/CHANGELOG.md +++ /dev/null @@ -1,67 +0,0 @@ -# Changelog - -[Home](../README.md) > [Docs](README.md) > Changelog - -All notable changes to EverCore will be documented in this file. - ---- - -## [1.2.0] - 2025-01-20 - -### Changed -- 🔌 **API Enhancement**: Added `role` field to `POST /memories` endpoint to identify message source (`user` or `assistant`) -- 🔧 **Settings API**: Global settings are now managed via `PUT /api/v1/settings` (singleton, no group_id) - -### Improved -- 🚀 **Database Efficiency**: Major performance improvements to database operations - -### Breaking Changes -- ⚠️ **Data Migration Required**: Database schema changes may cause incompatibility with data created in previous versions. Please backup your data before upgrading. - ---- - -## [1.1.0] - 2025-11-27 - -**🎉 🎉 🎉 EverCore v1.1.0 Released!** - -### Added -- 🔧 **vLLM Support**: Support vLLM deployment for Embedding and Reranker models (currently tailored for Qwen3 series) -- 📊 **Evaluation Resources**: Full results & code for LoCoMo, LongMemEval, PersonaMem released - -### Links -- [Release Notes](https://github.com/EverMind-AI/EverOS/releases/tag/v1.1.0) -- [Evaluation Guide](../evaluation/README.md) - ---- - -## [1.0.0] - 2025-11-02 - -**🎉 🎉 🎉 EverCore v1.0.0 Released!** - -### Added -- ✨ **Stable Version**: AI Memory System officially open sourced -- 📚 **Complete Documentation**: Quick start guide and comprehensive API documentation -- 📈 **Benchmark Testing**: LoCoMo dataset benchmark evaluation pipeline -- 🖥️ **Demo Tools**: Get started quickly with easy-to-use demos - -### Links -- [Release Notes](https://github.com/EverMind-AI/EverOS/releases/tag/v1.0.0) -- [Getting Started Guide](dev_docs/getting_started.md) -- [Demo Guide](../demo/README.md) - ---- - -## Future Plans - -Stay tuned for upcoming releases! Follow our progress: -- [GitHub Releases](https://github.com/EverMind-AI/EverOS/releases) -- [GitHub Discussions](https://github.com/EverMind-AI/EverOS/discussions) -- [Reddit](https://www.reddit.com/r/EverMindAI/) - ---- - -## See Also - -- [Overview](OVERVIEW.md) -- [Contributing Guide](../CONTRIBUTING.md) -- [GitHub Issues](https://github.com/EverMind-AI/EverOS/issues) diff --git a/methods/EverCore/docs/CITATION.md b/methods/EverCore/docs/CITATION.md deleted file mode 100644 index c5d7ae56..00000000 --- a/methods/EverCore/docs/CITATION.md +++ /dev/null @@ -1,63 +0,0 @@ -# Citation - -[Home](../README.md) > [Docs](README.md) > Citation - -If you use EverCore in your research or project, please cite our work. - ---- - -## Paper - -📄 **Paper Coming Soon** - Our technical paper is in preparation. Stay tuned! - -``` -Coming soon -``` - -Once published, we will provide BibTeX citation information here. - ---- - -## BibTeX - -```bibtex -% Will be added when paper is published -``` - ---- - -## Software Citation - -If you want to cite the software itself: - -``` -EverCore: Enterprise-Grade Intelligent Memory System for AI -Version: 1.1.0 -URL: https://github.com/EverMind-AI/EverOS -License: Apache 2.0 -``` - ---- - -## Acknowledgments - -If you use EverCore, we appreciate: -- Citing our paper (when available) -- Starring the GitHub repository -- Mentioning EverCore in your acknowledgments section - ---- - -## Stay Updated - -- Watch our [GitHub repository](https://github.com/EverMind-AI/EverOS) for paper announcements -- Follow [@EverMindAI](https://x.com/EverMindAI) on X/Twitter -- Join [GitHub Discussions](https://github.com/EverMind-AI/EverOS/discussions) - ---- - -## See Also - -- [Overview](OVERVIEW.md) -- [Acknowledgments](ACKNOWLEDGMENTS.md) -- [Changelog](CHANGELOG.md) diff --git a/methods/EverCore/docs/OVERVIEW.md b/methods/EverCore/docs/OVERVIEW.md deleted file mode 100644 index f18e540a..00000000 --- a/methods/EverCore/docs/OVERVIEW.md +++ /dev/null @@ -1,148 +0,0 @@ -# EverCore Overview - -[Home](../README.md) > [Docs](README.md) > Overview - -## Core Vision - -Build AI memory that never forgets, making every conversation built on previous understanding. - -## What is EverCore? - -**EverCore** is an open-source project designed to provide long-term memory capabilities to conversational AI agents. It extracts, structures, and retrieves information from conversations, enabling agents to maintain context, recall past interactions, and progressively build user profiles. This results in more personalized, coherent, and intelligent conversations. - -**EverCore** is a forward-thinking **intelligent system**. While traditional AI memory serves merely as a "look-back" database, EverCore enables AI not only to "remember" what happened, but also to "understand" the meaning behind these memories and use them to guide current actions and decisions. In the EverCore demo tools, you can see how EverCore extracts important information from your history, and then remembers your preferences, habits, and history during conversations, just like a **friend** who truly knows you. - -On the **LoCoMo** benchmark, our approach built upon EverCore achieved a reasoning accuracy of **92.3%** (evaluated by LLM-Judge), outperforming comparable methods in our evaluation. - -> 💬 **More than memory — it's foresight.** - -> 📄 **Paper Coming Soon** - Our technical paper is in preparation. Stay tuned! - ---- - -## Unique Advantages - - - - - - - -
-

🔗 Coherent Narrative

-

Beyond "fragments," connecting "stories": Automatically linking conversation pieces to build clear thematic context, enabling AI to "truly understand."

-
- When facing multi-threaded conversations, it naturally distinguishes between "Project A progress discussion" and "Team B strategy planning," maintaining coherent contextual logic within each theme.

- From scattered phrases to complete narratives, AI no longer just "understands one sentence" but "understands the whole story." -
- 		-

🧠 Evidence-Based Perception

-

Beyond "retrieval," intelligent "perception": Proactively capturing deep connections between memories and tasks, enabling AI to "think thoroughly" at critical moments.

-
- Imagine: When a user asks for "food recommendations," the AI proactively recalls "you had dental surgery two days ago" as a key piece of information, automatically adjusting suggestions to avoid unsuitable options.

- This is Contextual Awareness — enabling AI thinking to be truly built on understanding rather than isolated responses. -
- 		-

💾 Living Profiles

-

Beyond "records," dynamic "growth": Real-time user profile updates that get to know you better with each conversation, enabling AI to "recognize you authentically."

-
- Every interaction subtly updates the AI's understanding of you — preferences, style, and focus points all continuously evolve.

- As interactions deepen, it doesn't just "remember what you said," but is "learning who you are." -
-
- ---- - -## System Framework - -EverCore operates along two main tracks: **memory construction** and **memory perception**. Together they form a cognitive loop that continuously absorbs, consolidates, and applies past information, so every response is grounded in real context and long-term memory. - -

- Overview -

- -### 🧩 Memory Construction - -Memory construction layer: builds structured, retrievable long-term memory from raw conversation data. - -- **Core elements** - - ⚛️ **Atomic memory unit MemCell**: the core structured unit distilled from conversations for downstream organization and reference - - 🗂️ **Multi-level memory**: integrate related fragments by theme and storyline to form reusable, hierarchical memories - - 🏷️ **Multiple memory types**: covering episodes, profiles, preferences, relationships, semantic knowledge, basic facts, and core memories - -- **Workflow** - 1. **MemCell extraction**: identify key information in conversations to generate atomic memories - 2. **Memory construction**: integrate by theme and participants to form episodes and profiles - 3. **Storage and indexing**: persist data and build keyword and semantic indexes to support fast recall - -### 🔎 Memory Perception - -Memory perception layer: quickly recalls relevant memories through multi-round reasoning and intelligent fusion, achieving precise contextual awareness. - -#### 🎯 Intelligent Retrieval Tools - -- **🧪 Hybrid Retrieval (RRF Fusion)** - Parallel execution of semantic and keyword retrieval, seamlessly fused using Reciprocal Rank Fusion algorithm - -- **📊 Intelligent Reranking (Reranker)** - Batch concurrent processing with exponential backoff retry, maintaining stability under high throughput - Reorders candidate memories by deep relevance, prioritizing the most critical information - -#### 🚀 Flexible Retrieval Strategies - -- **⚡ Lightweight Fast Mode** - For latency-sensitive scenarios, skip LLM calls and use pure keyword retrieval (BM25) - Achieve a faster response speed - -- **🎓 Agentic Multi-Round Recall** - For insufficient cases, generate 2-3 complementary queries, retrieve and fuse in parallel - Enhance coverage of complex intents through multi-path RRF fusion - -#### 🧠 Reasoning Fusion - -- **Context Integration**: Concatenate recalled multi-level memories (episodes, profiles, preferences) with current conversation -- **Traceable Reasoning**: Model generates responses based on explicit memory evidence, avoiding hallucination - -💡 Through the cognitive loop of **"Structured Memory → Multi-Strategy Recall → Intelligent Retrieval → Contextual Reasoning"**, the AI always "thinks with memory", achieving true contextual awareness. - ---- - -## Why EverCore? - -Traditional AI systems lack persistent memory, treating each conversation in isolation. EverCore changes this by: - -1. **Extracting structured knowledge** from unstructured conversations -2. **Building coherent narratives** that connect related information -3. **Enabling intelligent perception** that goes beyond simple keyword matching -4. **Maintaining living profiles** that evolve with each interaction - -This results in AI that doesn't just respond, but truly understands and remembers. - ---- - -## Use Cases - -EverCore is ideal for: - -- **Personal AI Assistants** - Remember user preferences, habits, and history across sessions -- **Customer Service** - Maintain customer context and history for personalized support -- **Group Collaboration** - Track multi-participant conversations and team dynamics -- **Research & Analysis** - Build knowledge bases from conversation data -- **Educational Tools** - Adapt to student learning patterns and progress - ---- - -## Next Steps - -- **[Quick Setup](installation/SETUP.md)** - Get EverCore running -- **[Architecture](ARCHITECTURE.md)** - Deep dive into system design -- **[Usage Examples](usage/USAGE_EXAMPLES.md)** - Learn how to use EverCore -- **[API Documentation](api_docs/memory_api.md)** - Integrate with your application - ---- - -## See Also - -- [Architecture Design](ARCHITECTURE.md) -- [Memory Types Guide](dev_docs/memory_types_guide.md) -- [Development Guide](dev_docs/development_guide.md) diff --git a/methods/EverCore/docs/README.md b/methods/EverCore/docs/README.md deleted file mode 100644 index 1e742c13..00000000 --- a/methods/EverCore/docs/README.md +++ /dev/null @@ -1,76 +0,0 @@ -# EverCore Documentation - -[Home](../README.md) > Documentation - -Welcome to the EverCore documentation! This page serves as a comprehensive index to all available documentation. - -## Getting Started - -- **[Overview & Vision](OVERVIEW.md)** - Learn about EverCore's vision, unique advantages, and core concepts -- **[Quick Setup Guide](installation/SETUP.md)** - Complete installation and configuration instructions -- **[Basic Usage Examples](usage/USAGE_EXAMPLES.md)** - Get started with common usage patterns - -## Core Concepts - -- **[Architecture](ARCHITECTURE.md)** - System framework, layer design, and technology stack -- **[System Framework](OVERVIEW.md#system-framework)** - Memory construction and perception explained -- **[Memory Types](dev_docs/memory_types_guide.md)** - Understanding different memory types (episodes, profiles, etc.) - -## Installation & Setup - -- **[Complete Setup Guide](installation/SETUP.md)** - Comprehensive installation instructions -- **[Docker Setup](installation/DOCKER_SETUP.md)** - Docker services configuration and management -- **[Configuration Guide](usage/CONFIGURATION_GUIDE.md)** - Environment variables and service configuration -- **[MongoDB Guide](usage/MONGODB_GUIDE.md)** - MongoDB installation and setup - -## Usage Guides - -- **[Usage Examples](usage/USAGE_EXAMPLES.md)** - All usage methods including demos, API calls, and evaluation -- **[Interactive Demos](usage/DEMOS.md)** - Step-by-step demo walkthroughs -- **[Batch Operations](usage/BATCH_OPERATIONS.md)** - Process multiple messages efficiently - -## Advanced Topics - -- **[Team Chat Conversations](advanced/TEAM_CHAT_GUIDE.md)** - Managing team chat with multiple participants 🚧 -- **[Conversation Metadata Control](advanced/METADATA_CONTROL.md)** - Fine-grained metadata management 🚧 -- **[Memory Retrieval Strategies](advanced/RETRIEVAL_STRATEGIES.md)** - Lightweight vs Agentic retrieval modes -- **[Agentic Retrieval Guide](dev_docs/agentic_retrieval_guide.md)** - In-depth agentic retrieval documentation -- **[Agentic Retrieval Testing](dev_docs/agentic_retrieve_testing.md)** - Testing and evaluation - -## API Reference - -- **[Memory API Documentation](api_docs/memory_api.md)** - Complete API specification for memory operations -- **[API Usage Guide](dev_docs/api_usage_guide.md)** - Practical API usage patterns and examples - -## Development - -- **[Getting Started for Developers](dev_docs/getting_started.md)** - Development environment setup -- **[Development Guide](dev_docs/development_guide.md)** - Architecture, design patterns, and best practices -- **[Development Standards](dev_docs/development_standards.md)** - Code standards and conventions -- **[Bootstrap Usage](dev_docs/bootstrap_usage.md)** - Script runner and bootstrap utilities -- **[Metrics Library Design](dev_docs/metrics_library_design.md)** - Metrics and monitoring - -## Demos & Evaluation - -- **[Demo Guide](../demo/README.md)** - Interactive examples and memory extraction demos -- **[Data Guide](../data/README.md)** - Sample conversation data and format specifications -- **[Evaluation Guide](../evaluation/README.md)** - Benchmarking on LoCoMo, LongMemEval, PersonaMem - -## Contributing - -- **[Contributing Guide](../CONTRIBUTING.md)** - How to contribute to EverCore -- **[Bug Report Template](ISSUE_TEMPLATE_BUG.md)** - Report bugs -- **[Feature Request Template](ISSUE_TEMPLATE_FEATURE.md)** - Suggest new features - -## Additional Resources - -- **[Changelog](CHANGELOG.md)** - Release notes and version history -- **[Citation](CITATION.md)** - How to cite EverCore in your research -- **[Acknowledgments](ACKNOWLEDGMENTS.md)** - Credits and thanks - ---- - -**Legend:** -- 🚧 = Coming Soon (placeholder documentation) - -**Need help?** Open an issue on [GitHub](https://github.com/EverMind-AI/EverOS/issues) or check our [community channels](../README.md#contributing). diff --git a/methods/EverCore/docs/advanced/METADATA_CONTROL.md b/methods/EverCore/docs/advanced/METADATA_CONTROL.md deleted file mode 100644 index 7893aaea..00000000 --- a/methods/EverCore/docs/advanced/METADATA_CONTROL.md +++ /dev/null @@ -1,373 +0,0 @@ -# Settings Control Guide - -[Home](../../README.md) > [Docs](../README.md) > [Advanced](.) > Settings - -## Overview - -EverCore uses **settings** to provide context for memory extraction and retrieval. Properly configured settings enable: - -- **Better memory extraction** - Understanding who said what and in what context -- **Accurate retrieval filtering** - Querying memories by user, group, or time range -- **Contextual summaries** - Generating summaries that understand participants and relationships -- **Multi-tenant isolation** - Separating memories between different groups or organizations - -This guide explains when and how to control metadata for optimal results. - ---- - -## When to Control Metadata - -### 1. Multi-User Conversations - -**Use Case:** Group chats, team discussions, meetings with multiple participants - -**Why:** Without `user_details`, EverCore cannot distinguish between speakers or understand their roles. - -```json -{ - "session_meta": { - "user_details": { - "alice": { - "full_name": "Alice Smith", - "role": "user", - "custom_role": "Tech Lead", - "department": "Engineering" - }, - "bob": { - "full_name": "Bob Jones", - "role": "user", - "custom_role": "Product Manager" - } - } - } -} -``` - -**Benefits:** -- Memory extraction attributes facts to the correct person -- Retrieval can filter by specific user within a group -- Summaries understand organizational context - -### 2. AI Assistant Conversations - -**Use Case:** 1:1 conversations between a user and an AI assistant - -**Why:** The `scene` and `scene_desc` fields tell EverCore which messages are from the AI (to potentially exclude from personal memory extraction). - -```json -{ - "session_meta": { - "scene": "solo", - "scene_desc": { - "description": "Project discussion group chat" - }, - "user_details": { - "user_123": { - "full_name": "John Doe", - "role": "user" - }, - "assistant_001": { - "full_name": "AI Assistant", - "role": "assistant" - } - } - } -} -``` - -**Benefits:** -- AI responses can be handled differently from user messages -- Personal memories focus on what the user shared, not AI responses - -### 3. Cross-Timezone Teams - -**Use Case:** Distributed teams working across different timezones - -**Why:** The `timezone` ensures timestamps are interpreted correctly when timezone info is missing from individual messages. - -```json -{ - "session_meta": { - "timezone": "America/Los_Angeles", - "user_details": { - "dev_sf": {"full_name": "SF Developer"}, - "dev_tokyo": {"full_name": "Tokyo Developer"} - } - } -} -``` - -**Benefits:** -- Temporal queries ("What was discussed yesterday?") work correctly -- Memory ordering is accurate across timezones - -### 4. Categorized Conversations - -**Use Case:** Organizing conversations by type (work, social, family, etc.) - -**Why:** The `scene` field categorizes conversations for better context understanding and potential filtering. - -```json -{ - "session_meta": { - "scene": "team", - "tags": ["project-alpha", "backend", "Q1-2025"] - } -} -``` - -**Benefits:** -- Memory extraction understands the context (professional vs casual) -- Tags enable additional filtering and organization - -### 5. Default Configuration Fallback - -**Use Case:** Setting organization-wide defaults that apply when specific group config is missing - -**Why:** EverCore supports a default configuration that applies when a specific `scene` config is not found. - -```python -# Save default config -requests.put( - "http://localhost:1995/api/v1/settings", - json={ - "scene": "team", - "scene_desc": {}, - "timezone": "UTC", - "llm_custom_setting": {} - } -) -``` - -**Benefits:** -- New groups automatically inherit sensible defaults -- Reduces configuration overhead for common settings - ---- - -## Settings Fields Reference - -### Settings (`session_meta`) - -| Field | Type | Required | Description | -|-------|------|----------|-------------| -| `scene` | string | No | Scene type: `solo` (1:1 with AI) or `team` (group chat) | -| `scene_desc` | object | No | Scene-specific details (e.g., `description` for solo scene) | -| `timezone` | string | No | IANA timezone name (e.g., `America/New_York`) | -| `llm_custom_setting` | object | No | Custom LLM settings for memory extraction | - -### User Details (`user_details`) - -| Field | Type | Required | Description | -|-------|------|----------|-------------| -| `full_name` | string | No | User's display name | -| `role` | string | No | `user` (human) or `assistant` (AI) | -| `custom_role` | string | No | Job title or position (e.g., "Tech Lead") | -| `department` | string | No | Department or team name | -| `email` | string | No | Email address | -| `extra` | object | No | Additional custom fields | - -### Message Metadata - -| Field | Type | Required | Description | -|-------|------|----------|-------------| -| `message_id` | string | Yes | Unique identifier for the message | -| `create_time` | string | Yes | Message timestamp (ISO 8601 with timezone) | -| `sender` | string | Yes | User ID of the sender (must exist in `user_details`) | -| `sender_name` | string | No | Override display name for this message | -| `role` | string | No | `user` or `assistant` (overrides `user_details` role) | -| `refer_list` | array | No | Referenced message IDs or objects | - ---- - -## API Operations - -### Store Message with Metadata - -When storing a single message, you can include group and sender metadata: - -```python -import requests - -response = requests.post( - "http://localhost:1995/api/v0/memories", - json={ - "message_id": "msg_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "user_123", - "sender_name": "John", # Optional display name - "content": "I prefer Python for backend development", - "group_id": "team_engineering", - "group_name": "Engineering Team", - "role": "user" - } -) -``` - -### Search with Metadata Filters - -Filter search results by user or group: - -```python -# Search within a specific group -response = requests.get( - "http://localhost:1995/api/v0/memories/search", - json={ - "query": "What programming languages are preferred?", - "group_id": "team_engineering", - "user_id": "user_123", # Optional: filter to specific user - "retrieve_method": "rrf", - "start_time": "2025-01-01T00:00:00+00:00", - "end_time": "2025-02-01T00:00:00+00:00" - } -) -``` - -### Manage Settings - -#### Get Settings - -```python -# Get current settings -response = requests.get( - "http://localhost:1995/api/v1/settings" -) -``` - -#### Save/Update Settings (Upsert) - -The V1 Settings API uses PUT for both creation and updates (upsert semantics): - -```python -response = requests.put( - "http://localhost:1995/api/v1/settings", - json={ - "scene": "team", - "scene_desc": { - "description": "Backend engineering team discussions" - }, - "timezone": "America/Los_Angeles", - "llm_custom_setting": {} - } -) -``` - -**Response format (success):** -```json -{ - "data": { - "scene": "team", - "scene_desc": {"description": "..."}, - "timezone": "America/Los_Angeles", - "llm_custom_setting": {} - } -} -``` - -### Delete Memories with Metadata Filters - -```python -# Delete all memories for a specific user in a group -response = requests.delete( - "http://localhost:1995/api/v0/memories", - json={ - "user_id": "user_123", - "group_id": "team_engineering" - } -) -``` - ---- - -## Use Cases - -### 1. Customer Support System - -Track support conversations with customer context: - -```json -{ - "session_meta": { - "scene": "solo", - "scene_desc": {"description": "Support conversation with customer"}, - "timezone": "UTC", - "llm_custom_setting": {} - } -} -``` - -### 2. Meeting Transcription - -Capture meeting context with participant roles: - -```json -{ - "session_meta": { - "scene": "team", - "scene_desc": { - "description": "Daily Standup - Feb 1, 2025" - }, - "timezone": "America/New_York", - "llm_custom_setting": {} - } -} -``` - -### 3. Personal AI Assistant - -Track personal conversations with the AI: - -```json -{ - "session_meta": { - "scene": "solo", - "scene_desc": {"description": "Personal assistant conversation"}, - "timezone": "UTC", - "llm_custom_setting": {} - } -} -``` - ---- - -## Best Practices - -### 1. Always Provide User Details - -Even for simple conversations, providing `user_details` improves memory quality: - -```json -"user_details": { - "user_123": {"full_name": "John Doe"} -} -``` - -### 2. Use Consistent User IDs - -Use the same `sender` ID across all messages from the same person. The ID in messages must match keys in `user_details`. - -### 3. Include Timezone Information - -Always include timezone in message timestamps or set `timezone` in settings: - -```json -"create_time": "2025-02-01T10:00:00-05:00" -``` - -### 4. Use Appropriate Scene Types - -- **`solo`**: Use for 1:1 human-AI conversations -- **`team`**: Use for multi-person group chats and meetings - -### 5. Leverage Tags for Organization - -Tags provide additional filtering and categorization without affecting the core metadata structure. - ---- - -## See Also - -- [Team Chat Guide](TEAM_CHAT_GUIDE.md) - Multi-participant conversations -- [Conversation Format Specification](../../data_format/conversation/conversation_format.md) - Complete schema reference -- [Batch Operations](../usage/BATCH_OPERATIONS.md) - Processing conversations in batch -- [API Documentation](../api_docs/memory_api.md) - Complete API reference diff --git a/methods/EverCore/docs/advanced/RETRIEVAL_STRATEGIES.md b/methods/EverCore/docs/advanced/RETRIEVAL_STRATEGIES.md deleted file mode 100644 index 04eb53ca..00000000 --- a/methods/EverCore/docs/advanced/RETRIEVAL_STRATEGIES.md +++ /dev/null @@ -1,358 +0,0 @@ -# Memory Retrieval Strategies - -[Home](../../README.md) > [Docs](../README.md) > [Advanced](.) > Retrieval Strategies - -This guide explains the different retrieval strategies available in EverCore and when to use each one. - ---- - -## Table of Contents - -- [Overview](#overview) -- [Lightweight Retrieval](#lightweight-retrieval) -- [Agentic Retrieval](#agentic-retrieval) -- [Choosing a Strategy](#choosing-a-strategy) -- [API Examples](#api-examples) -- [Performance Comparison](#performance-comparison) -- [Best Practices](#best-practices) - ---- - -## Overview - -EverCore provides two main retrieval strategies: - -1. **Lightweight Retrieval** - Fast, efficient retrieval for latency-sensitive scenarios -2. **Agentic Retrieval** - Intelligent, multi-round retrieval for complex queries - -Both strategies leverage the Memory Perception layer to recall relevant memories through multi-round reasoning and intelligent fusion, achieving precise contextual awareness. - ---- - -## Lightweight Retrieval - -Fast retrieval mode that skips LLM calls for minimum latency. - -### Retrieval Modes - -#### 1. Keyword Search - -Pure keyword-based search using Elasticsearch BM25. - -**Characteristics:** -- Fastest retrieval mode -- No embedding required -- Best for exact keyword matches -- Lower accuracy for semantic queries - -**When to use:** -- Exact phrase or keyword search -- Latency is critical (< 100ms) -- No semantic understanding needed - -**Example:** -```python -{ - "query": "soccer weekend", - "retrieve_method": "keyword" -} -``` - -#### 2. Vector (Semantic Search) - -Pure vector-based search using Milvus. - -**Characteristics:** -- Semantic understanding -- Finds similar meaning, not just keywords -- Requires embedding model -- Moderate latency (~200-500ms) - -**When to use:** -- Semantic similarity important -- Query phrasing differs from stored content -- Need conceptual matches - -**Example:** -```python -{ - "query": "What sports does the user enjoy?", - "retrieve_method": "vector" -} -``` - -#### 3. RRF (Hybrid Retrieval) - Recommended - -Reciprocal Rank Fusion of BM25 and Embedding results. - -**Characteristics:** -- Best of both worlds -- Parallel execution of BM25 and embedding search -- Fuses results using RRF algorithm -- Balanced accuracy and speed - -**When to use:** -- Default choice for most scenarios -- Want both keyword and semantic matching -- Need robust retrieval across query types - -**Example:** -```python -{ - "query": "What are the user's weekend activities?", - "retrieve_method": "rrf" -} -``` - -### Intelligent Reranking - -Optional reranking step to improve result relevance: - -- **Batch concurrent processing** with exponential backoff retry -- **Deep relevance scoring** using reranker models -- **Prioritization** of most critical information -- **High throughput** stability - -Reranking is automatically applied for `hybrid` and `agentic` retrieval methods. For programmatic control, see the [Agentic Retrieval Guide](../dev_docs/agentic_retrieval_guide.md). - ---- - -## Agentic Retrieval - -Intelligent, multi-round retrieval using LLM for query expansion and fusion. - -### How It Works - -1. **Query Analysis** - LLM analyzes the user query -2. **Query Expansion** - Generates 2-3 complementary queries -3. **Parallel Retrieval** - Retrieves memories for each query -4. **RRF Fusion** - Fuses results using multi-path RRF -5. **Context Integration** - Concatenates memories with current conversation - -### Characteristics - -- **Higher latency** (~2-5 seconds with LLM calls) -- **Better coverage** for complex intents -- **Multi-aspect queries** handled effectively -- **Adaptive** to query complexity - -### When to Use - -- Complex, multi-faceted queries -- Queries requiring context understanding -- When accuracy is more important than speed -- Insufficient results from lightweight modes - -### Example Workflow - -**User Query:** "Tell me about my work-life balance" - -**Step 1 - Query Expansion:** -- Original: "Tell me about my work-life balance" -- Expanded 1: "work schedule and working hours" -- Expanded 2: "hobbies and leisure activities" -- Expanded 3: "stress and relaxation" - -**Step 2 - Parallel Retrieval:** -Each query retrieves top-k memories using RRF - -**Step 3 - Fusion:** -Results merged using multi-path RRF - -**Step 4 - Response:** -LLM generates response based on retrieved memories - ---- - -## Choosing a Strategy - -### Decision Flow - -``` -Is latency critical (< 100ms)? -├─ Yes → Use Keyword -└─ No → Continue - -Do you need semantic understanding? -├─ No → Use Keyword -└─ Yes → Continue - -Is the query complex or multi-faceted? -├─ Yes → Use Agentic -└─ No → Continue - -Default choice → Use RRF -``` - -### Use Case Matrix - -| Use Case | Recommended Strategy | Reason | -|----------|---------------------|--------| -| Exact phrase search | Keyword | Fast, precise keyword matching | -| Product search by name | Keyword or RRF | Keywords important | -| Conversational queries | RRF or Agentic | Semantic understanding needed | -| Complex analysis questions | Agentic | Multi-aspect coverage | -| Real-time chat | RRF | Balance of speed and accuracy | -| Background indexing | Any | No latency constraints | -| Autocomplete/suggestions | Keyword | Speed critical | -| Research/analysis | Agentic | Accuracy critical | - ---- - -## API Examples - -### Lightweight - Keyword - -```bash -curl -X GET http://localhost:1995/api/v0/memories/search \ - -H "Content-Type: application/json" \ - -d '{ - "query": "soccer", - "user_id": "user_001", - "memory_types": ["episodic_memory"], - "retrieve_method": "keyword", - "top_k": 5 - }' -``` - -### Lightweight - Vector - -```bash -curl -X GET http://localhost:1995/api/v0/memories/search \ - -H "Content-Type: application/json" \ - -d '{ - "query": "What sports does the user like?", - "user_id": "user_001", - "memory_types": ["episodic_memory"], - "retrieve_method": "vector", - "top_k": 5 - }' -``` - -### Lightweight - RRF (Recommended) - -```bash -curl -X GET http://localhost:1995/api/v0/memories/search \ - -H "Content-Type: application/json" \ - -d '{ - "query": "Tell me about the user hobbies", - "user_id": "user_001", - "memory_types": ["episodic_memory"], - "retrieve_method": "rrf", - "top_k": 5 - }' -``` - -### Agentic Retrieval - -```bash -curl -X GET http://localhost:1995/api/v0/memories/search \ - -H "Content-Type: application/json" \ - -d '{ - "query": "What is my work-life balance like?", - "user_id": "user_001", - "memory_types": ["episodic_memory"], - "retrieve_method": "agentic", - "top_k": 10 - }' -``` - ---- - -## Performance Comparison - -### Latency - -| Strategy | Typical Latency | Notes | -|----------|----------------|-------| -| Keyword | 50-100ms | Fastest | -| Vector | 200-500ms | Depends on Milvus performance | -| RRF | 200-600ms | Parallel keyword + vector | -| Agentic | 2-5 seconds | Includes LLM query expansion | - -### Accuracy - -Measured on LoCoMo benchmark: - -| Strategy | Precision | Recall | F1 Score | -|----------|-----------|--------|----------| -| Keyword | 0.72 | 0.68 | 0.70 | -| Vector | 0.78 | 0.75 | 0.77 | -| RRF | 0.85 | 0.82 | 0.84 | -| Agentic | 0.91 | 0.89 | 0.90 | - -*Note: Actual performance varies by query type and data* - -### Resource Usage - -| Strategy | CPU | Memory | Network | -|----------|-----|--------|---------| -| Keyword | Low | Low | Minimal | -| Vector | Medium | Medium | Moderate (embedding API) | -| RRF | Medium | Medium | Moderate | -| Agentic | Medium-High | Medium | High (multiple LLM calls) | - ---- - -## Best Practices - -### 1. Start with RRF - -For most applications, RRF provides the best balance: -- Good accuracy -- Reasonable latency -- Robust across query types - -### 2. Use Keyword Search for Known Patterns - -When users search for specific keywords or phrases: -- Product names -- Exact quotes -- Technical terms - -### 3. Reserve Agentic for Complex Queries - -Use agentic retrieval when: -- User query is vague or complex -- Standard retrieval returns insufficient results -- Analysis or reasoning required - -### 4. Tune top_k Parameter - -- **Keyword**: Lower top_k (3-5) for precise matches -- **Vector/RRF**: Medium top_k (5-10) for coverage -- **Agentic**: Higher top_k (10-20) for comprehensive results - -### 5. Monitor and Optimize - -- Track query latency and adjust strategy -- Monitor result relevance and switch modes -- Consider caching frequent queries - -### 6. Combine Strategies - -Use different strategies for different query types: - -```python -def select_strategy(query): - # Exact phrase (in quotes) - if query.startswith('"') and query.endswith('"'): - return "keyword" - - # Complex question - if any(word in query.lower() for word in ["why", "how", "explain", "analyze"]): - return "agentic" - - # Default - return "rrf" -``` - ---- - -## See Also - -- [Architecture: Memory Perception](../ARCHITECTURE.md#memory-perception-architecture) - Technical architecture -- [API Documentation](../api_docs/memory_api.md) - Complete API reference -- [Agentic Retrieval Guide](../dev_docs/agentic_retrieval_guide.md) - In-depth agentic retrieval -- [Evaluation Guide](../../evaluation/README.md) - Benchmarking retrieval strategies -- [Usage Examples](../usage/USAGE_EXAMPLES.md) - Practical examples diff --git a/methods/EverCore/docs/advanced/TEAM_CHAT_GUIDE.md b/methods/EverCore/docs/advanced/TEAM_CHAT_GUIDE.md deleted file mode 100644 index eff3f0bd..00000000 --- a/methods/EverCore/docs/advanced/TEAM_CHAT_GUIDE.md +++ /dev/null @@ -1,402 +0,0 @@ -# Team Chat Conversations Guide - -[Home](../../README.md) > [Docs](../README.md) > [Advanced](.) > Team Chat Guide - -## Overview - -EverCore supports organizing conversations into **groups** using `group_id` and `group_name`. This allows you to: - -- **Separate messages** into logical groups for better organization -- **Filter and retrieve memories** by group for targeted searches -- **Generate better summaries** within each group's context -- **Isolate memory contexts** between different groups - -This guide covers how to leverage group-based memory management for various use cases. - ---- - -## Core Concepts - -### Group ID and Group Name - -| Field | Description | Example | -|-------|-------------|---------| -| `group_id` | Unique identifier for the group | `"team_engineering"`, `"project_alpha"` | -| `group_name` | Human-readable display name | `"Engineering Team"`, `"Project Alpha"` | - -**Key Benefits:** - -1. **Memory Isolation** - Memories from different groups are separated, preventing cross-contamination -2. **Targeted Retrieval** - Query memories from a specific group without irrelevant results -3. **Contextual Summaries** - Generate summaries that understand the group's context and participants -4. **Scalable Organization** - Manage thousands of conversations across multiple groups - ---- - -## When to Use Group ID - -Starting from v1.2.0, `group_id` and `group_name` are **truly optional**. When omitted, the API automatically creates a default group based on the `sender` field. - -### Scenarios Where Group ID is NOT Needed - -| Use Case | Description | -|----------|-------------| -| **Knowledge Base** | Ingesting documents or content from a single source where cross-message correlation isn't needed | -| **Persona Building** | Building user profiles/personas from individual interactions | -| **Simple Q&A** | Single-user chatbot interactions without complex context | - -**Example without group_id:** -```json -{ - "message_id": "msg_001", - "create_time": "2025-01-15T10:00:00+00:00", - "sender": "user_001", - "content": "I prefer dark roast coffee in the morning" -} -``` - -### Scenarios Where Group ID is Recommended - -| Use Case | Description | -|----------|-------------| -| **Multi-User Conversations** | Group chats where multiple participants interact and context between messages matters | -| **User + AI Assistant** | Conversations between a user and AI where you want correlated episodic memories | -| **Project/Topic Organization** | When you need to query and organize memories by logical groupings | -| **Meeting Transcripts** | Multi-participant discussions where speaker context is important | - -**Why it matters:** Episodic memories are extracted from multiple related messages. When messages share a `group_id`, the system can build richer context by understanding the relationships between messages from different senders. Without a `group_id`, each sender's messages are processed independently. - -**Example with group_id:** -```json -{ - "message_id": "msg_001", - "create_time": "2025-01-15T10:00:00+00:00", - "sender": "user_001", - "content": "What should we use for the database?", - "group_id": "project_alpha", - "group_name": "Project Alpha Discussion" -} -``` - ---- - -## Use Cases - -### 1. Team/Department Conversations - -Organize conversations by team or department within an organization. - -```json -{ - "group_id": "dept_engineering", - "group_name": "Engineering Department" -} -``` - -**Benefits:** -- Keep engineering discussions separate from marketing, sales, etc. -- Generate team-specific insights and summaries -- Track team decisions and action items - -### 2. Project-Based Conversations - -Group all conversations related to a specific project. - -```json -{ - "group_id": "project_mobile_app_v2", - "group_name": "Mobile App v2.0 Development" -} -``` - -**Benefits:** -- All project discussions, decisions, and context in one place -- Query project-specific knowledge: "What was decided about the login flow?" -- Generate project progress summaries - -### 3. Channel-Based Conversations (Slack/Discord Style) - -Mirror your communication platform's channel structure. - -```json -{ - "group_id": "channel_general", - "group_name": "#general" -} -``` - -```json -{ - "group_id": "channel_random", - "group_name": "#random" -} -``` - -**Benefits:** -- Maintain channel context when building AI assistants -- Search within specific channels -- Channel-specific summaries and insights - -### 4. Customer Support Conversations - -Group support tickets or customer interactions. - -```json -{ - "group_id": "support_ticket_12345", - "group_name": "Ticket #12345 - Login Issue" -} -``` - -**Benefits:** -- Track full context of a support case -- Generate case summaries for handoffs -- Query similar past issues - -### 5. Meeting Transcripts - -Organize meeting notes and transcripts. - -```json -{ - "group_id": "meeting_weekly_standup_2025_02", - "group_name": "Weekly Standup - February 2025" -} -``` - -**Benefits:** -- Query across all standups: "What blockers were mentioned this month?" -- Generate meeting summaries automatically -- Track action items across meetings - ---- - -## Data Format - -### Complete Group Chat Structure - -```json -{ - "version": "1.0.0", - "session_meta": { - "group_id": "team_001", - "name": "Engineering Team", - "scene": "team", - "scene_desc": {}, - "user_details": { - "alice": { - "full_name": "Alice Smith", - "role": "user", - "custom_role": "Tech Lead", - "extra": {"department": "Engineering"} - }, - "bob": { - "full_name": "Bob Jones", - "role": "user", - "custom_role": "Senior Engineer" - } - }, - "default_timezone": "+00:00" - }, - "conversation_list": [ - { - "message_id": "msg_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "alice", - "sender_name": "Alice Smith", - "role": "user", - "type": "text", - "content": "Let's discuss the new API design" - }, - { - "message_id": "msg_002", - "create_time": "2025-02-01T10:01:00+00:00", - "sender": "bob", - "sender_name": "Bob Jones", - "role": "user", - "type": "text", - "content": "I think we should use REST with OpenAPI spec" - } - ] -} -``` - -### Key Fields - -| Field | Required | Description | -|-------|----------|-------------| -| `group_id` | No | Unique identifier for filtering and retrieval (see [When to Use Group ID](#when-to-use-group-id)) | -| `name` | No | Human-readable group name | -| `scene` | No | Scene type: `solo` (1:1 with AI) or `team` (group chat) | -| `user_details` | No | Participant information for context | - ---- - -## Processing Group Chats - -### Step 1: Prepare Your Data - -Create a JSON file following the ConversationFormat specification. - -### Step 2: Process the Group Chat - -```bash -uv run python src/bootstrap.py src/run_memorize.py \ - --input your_team_chat.json \ - --scene team \ - --api-url http://localhost:1995/api/v0/memories -``` - -**Parameters:** -- `--input`: Path to your ConversationFormat JSON file (required) -- `--scene`: Memory extraction scene - `team` or `solo` (required) -- `--api-url`: Memory API endpoint (required unless using `--validate-only`) -- `--validate-only`: Only validate the input file format without processing - -### Step 3: Verify Processing - -Check that memories were extracted: - -```bash -curl -X GET "http://localhost:1995/api/v0/memories/search" \ - -H "Content-Type: application/json" \ - -d '{ - "query": "What was discussed?", - "group_id": "team_001", - "memory_types": ["episodic_memory"], - "retrieve_method": "rrf" - }' -``` - ---- - -## Retrieving Group Memories - -### Filter by Group ID - -The primary way to retrieve group-specific memories: - -```python -import requests - -response = requests.get( - "http://localhost:1995/api/v0/memories/search", - json={ - "query": "What decisions were made about the API?", - "group_id": "team_001", # Filter to this group only - "memory_types": ["episodic_memory"], - "retrieve_method": "rrf", - "top_k": 10 - } -) - -memories = response.json() -result = memories.get("result", {}) -for group in result.get("memories", []): - print(f"Group: {group}") -``` - -### Retrieve Method Options - -| Method | Description | -|--------|-------------| -| `keyword` | BM25 keyword retrieval | -| `vector` | Vector semantic retrieval | -| `hybrid` | Combined keyword + vector | -| `rrf` | RRF fusion retrieval (keyword + vector) | -| `agentic` | LLM-guided multi-round retrieval | - -### Memory Type Options - -| Type | Description | -|------|-------------| -| `episodic_memory` | Conversation episodes and events | -| `profile` | User profile information | -| `foresight` | Prospective memory | -| `atomic_fact` | Atomic facts extracted from episodes | - ---- - -## Generating Group Summaries - -EverCore can generate contextual summaries within a group because it understands: - -- **Who participated** - User details and roles -- **What was discussed** - Full conversation context -- **When it happened** - Temporal relationships -- **Key decisions** - Extracted from conversation flow - -### Example: Query for Group Summary - -```python -response = requests.get( - "http://localhost:1995/api/v0/memories/search", - json={ - "query": "Summarize the key decisions and action items", - "group_id": "team_001", - "memory_types": ["episodic_memory", "atomic_fact"], - "retrieve_method": "agentic" # Use agentic mode for better synthesis - } -) -``` - ---- - -## Best Practices - -### 1. Consistent Group ID Naming - -Use a consistent naming convention: - -``` -# Good: Clear, hierarchical naming -team_engineering -project_mobile_v2 -tenant_acme_corp -channel_general - -# Avoid: Inconsistent or unclear naming -eng -proj1 -abc123 -``` - -### 2. Include User Details - -Providing user details improves memory quality: - -```json -"user_details": { - "alice": { - "full_name": "Alice Smith", - "custom_role": "Tech Lead" // Helps understand context - } -} -``` - -### 3. Use Appropriate Scene Types - -- Use `team` for multi-person group chats -- Use `solo` for 1:1 conversations with an AI assistant - -### 4. Batch Related Messages - -Process conversations in logical batches rather than individual messages for better context understanding. - ---- - -## Example Files - -- **[Chinese Sample](../../data/team_chat_zh.json)** - Chinese language example -- **[English Sample](../../data/team_chat_en.json)** - English language example -- **[Format Specification](../../data_format/conversation/conversation_format.md)** - Complete format reference - ---- - -## See Also - -- [Conversation Format Specification](../../data_format/conversation/conversation_format.md) - Complete data format reference -- [Batch Operations Guide](../usage/BATCH_OPERATIONS.md) - Processing multiple messages -- [Memory Retrieval Strategies](RETRIEVAL_STRATEGIES.md) - Optimizing search -- [Conversation Metadata Control](METADATA_CONTROL.md) - Fine-grained metadata management -- [API Documentation](../api_docs/memory_api.md) - Complete API reference diff --git a/methods/EverCore/docs/api_docs/memory_api.md b/methods/EverCore/docs/api_docs/memory_api.md deleted file mode 100644 index 9f935010..00000000 --- a/methods/EverCore/docs/api_docs/memory_api.md +++ /dev/null @@ -1,497 +0,0 @@ -# Memory API Documentation - -[Home](../../README.md) > [Docs](../README.md) > [API Docs](.) > Memory API - -## Overview - -The Memory API provides RESTful endpoints for storing, retrieving, searching, and managing conversational memories. - -**Base URL:** `http://localhost:1995/api/v0/memories` - -## API Endpoints - -| Method | Endpoint | Description | -|--------|----------|-------------| -| POST | `/memories` | Store a single message | -| GET | `/memories` | Fetch memories by type | -| GET | `/memories/search` | Search memories | -| GET | `/api/v1/settings` | Get global settings | -| PUT | `/api/v1/settings` | Update global settings | -| DELETE | `/memories` | Soft delete memories | - ---- - -## POST `/memories` - Store Message - -Store a single message into memory. - -### Request - -```json -{ - "message_id": "msg_001", - "create_time": "2025-01-15T10:00:00+00:00", - "sender": "user_001", - "content": "Let's discuss the technical solution for the new feature today", - "group_ids": "group_123", - "group_name": "Project Discussion Group", - "sender_name": "John", - "role": "user", - "refer_list": ["msg_000"] -} -``` - -### Request Fields - -| Field | Type | Required | Description | -|-------|------|----------|-------------| -| `message_id` | string | Yes | Unique message identifier | -| `create_time` | string | Yes | ISO 8601 timestamp with timezone | -| `sender` | string | Yes | Sender user ID | -| `content` | string | Yes | Message content | -| `group_id` | string | No | Group identifier | -| `group_name` | string | No | Group display name | -| `sender_name` | string | No | Sender display name (defaults to `sender`) | -| `role` | string | No | `user` (human) or `assistant` (AI) | -| `refer_list` | array | No | Referenced message IDs | - -### Group ID Behavior - -When `group_id` and `group_name` are not provided (null), the API automatically creates a default group based on the `sender` field. This enables simpler use cases where correlated memories between multiple senders are not needed. - -**When to omit `group_id`:** -- **Knowledge base ingestion** - Single-source content where sender correlation is not needed -- **Persona/profile building** - Building memories for a single user without multi-party context -- **Simple chatbot interactions** - 1:1 conversations where grouping is not required - -**When to provide `group_id`:** -- **Multi-user conversations** - Group chats where multiple participants interact -- **User + AI assistant** - Conversations between a user and AI where context correlation matters -- **Project/topic-based organization** - When you want to query memories by logical groupings - -Providing a `group_id` enables better episodic memory extraction by giving the system context about related messages across multiple senders. See the [Team Chat Guide](../advanced/TEAM_CHAT_GUIDE.md) for detailed guidance. - -### Example - -```bash -curl -X POST "http://localhost:1995/api/v0/memories" \ - -H "Content-Type: application/json" \ - -d '{ - "message_id": "msg_001", - "create_time": "2025-01-15T10:00:00+00:00", - "sender": "user_001", - "sender_name": "John", - "role": "user", - "content": "Let us discuss the technical solution for the new feature today", - "group_ids": "group_123", - "group_name": "Project Discussion Group", - "refer_list": [] - }' -``` - -### Response - -**Success (200)** - Memory extracted (boundary triggered): -```json -{ - "status": "ok", - "message": "Extracted 1 memories", - "result": { - "saved_memories": [], - "count": 1, - "status_info": "extracted" - } -} -``` - -**Success (200)** - Message queued (boundary not triggered): -```json -{ - "status": "ok", - "message": "Message queued, awaiting boundary detection", - "result": { - "saved_memories": [], - "count": 0, - "status_info": "accumulated" - } -} -``` - ---- - -## GET `/memories` - Fetch Memories - -Retrieve memories by type with optional filters. - -### Request Parameters (Query String) - -| Parameter | Type | Required | Default | Description | -|-----------|------|----------|---------|-------------| -| `user_id` | string | No* | - | User ID | -| `group_id` | string | No* | - | Group ID | -| `memory_type` | string | No | `episodic_memory` | Memory type | -| `limit` | integer | No | 40 | Max results (max: 500) | -| `offset` | integer | No | 0 | Pagination offset | -| `start_time` | string | No | - | Filter start time (ISO 8601) | -| `end_time` | string | No | - | Filter end time (ISO 8601) | - -*At least one of `user_id` or `group_id` must be provided (cannot both be `__all__`). - -### Memory Types - -| Type | Description | -|------|-------------| -| `profile` | User profile information | -| `episodic_memory` | Conversation episodes (default) | -| `foresight` | Prospective memory | -| `atomic_fact` | Atomic facts | - -### Example - -```bash -curl "http://localhost:1995/api/v0/memories?user_id=user_123&memory_type=episodic_memory&limit=20" -``` - -### Response - -```json -{ - "status": "ok", - "message": "Memory retrieval successful, retrieved 1 memories", - "result": { - "memories": [ - { - "memory_type": "episodic_memory", - "user_id": "user_123", - "timestamp": "2024-01-15T10:30:00", - "content": "User discussed coffee during the project sync", - "summary": "Project sync coffee note" - } - ], - "total_count": 100, - "has_more": false, - "metadata": { - "source": "fetch_mem_service", - "user_id": "user_123", - "memory_type": "fetch" - } - } -} -``` - ---- - -## GET `/memories/search` - Search Memories - -Search memories using keyword, vector, or hybrid retrieval methods. - -### Request Body - -```json -{ - "query": "coffee preference", - "user_id": "user_123", - "group_ids": ["group_456", "group_789"], - "retrieve_method": "keyword", - "memory_types": ["episodic_memory"], - "top_k": 10, - "start_time": "2024-01-01T00:00:00", - "end_time": "2024-12-31T23:59:59", - "radius": 0.6, - "include_metadata": true -} -``` - -### Request Fields - -| Field | Type | Required | Default | Description | -|-------|------|----------|---------|-------------| -| `query` | string | No | - | Search query text | -| `user_id` | string | No* | - | User ID | -| `group_ids` | array | No* | - | **Group IDs array** (max 10 items, None = search all groups) | -| `retrieve_method` | string | No | `keyword` | Retrieval method | -| `memory_types` | array | No | `[]` (defaults to `episodic_memory`) | Memory types to search | -| `top_k` | integer | No | 40 | Max results (max: 100, -1 = unlimited) | -| `start_time` | string | No | - | Filter start time (ISO 8601) | -| `end_time` | string | No | - | Filter end time (ISO 8601) | -| `radius` | float | No | - | Cosine similarity threshold (0.0-1.0, for vector/hybrid only) | -| `include_metadata` | boolean | No | true | Include metadata in response | -| `current_time` | string | No | - | Current time for filtering foresight events | - -*At least one of `user_id` or `group_ids` must be provided (cannot both be empty). - -### Group Filtering Behavior - -| Scenario | Behavior | -|----------|----------| -| `group_ids` is an array | Search in all specified groups | -| `group_ids` not provided | Search all groups for the user | - -**Note:** `profile` memory type is not supported in the search interface. - -### Retrieve Methods - -| Method | Description | -|--------|-------------| -| `keyword` | BM25 keyword retrieval (default) | -| `vector` | Vector semantic retrieval | -| `hybrid` | Keyword + vector + rerank | -| `rrf` | RRF fusion (keyword + vector + RRF ranking) | -| `agentic` | LLM-guided multi-round intelligent retrieval | - -### Examples - -**Search in multiple groups:** - -```bash -curl -X GET "http://localhost:1995/api/v1/memories/search" \ - -H "Content-Type: application/json" \ - -d '{ - "query": "coffee preference", - "user_id": "user_123", - "group_ids": ["group_456", "group_789"], - "retrieve_method": "vector", - "top_k": 10 - }' -``` - -**Search in a single group:** - -```bash -curl -X GET "http://localhost:1995/api/v1/memories/search" \ - -H "Content-Type: application/json" \ - -d '{ - "query": "coffee preference", - "user_id": "user_123", - "group_ids": ["group_456"], - "retrieve_method": "keyword", - "top_k": 10 - }' -``` - -**Search all groups for a user:** - -```bash -curl -X GET "http://localhost:1995/api/v0/memories/search" \ - -H "Content-Type: application/json" \ - -d '{ - "query": "coffee preference", - "user_id": "user_123", - "retrieve_method": "vector", - "top_k": 10 - }' -``` - -### Response - -```json -{ - "status": "ok", - "message": "Memory search successful", - "result": { - "memories": [ - { - "memory_type": "episodic_memory", - "user_id": "user_123", - "timestamp": "2024-01-15T10:30:00", - "subject": "Coffee preferences", - "summary": "Discussed coffee choices", - "episode": "Alice mentioned she prefers latte, Bob likes americano", - "group_id": "group_456", - "score": 0.95, - "original_data": [], - "extend": { - "_search_source": "vector" - } - } - ], - "total_count": 1, - "has_more": false, - "query_metadata": { - "source": "episodic_memory_es_repository", - "user_id": "user_123", - "memory_type": "retrieve" - }, - "metadata": { - "source": "episodic_memory_es_repository", - "user_id": "user_123", - "memory_type": "retrieve" - }, - "pending_messages": [] - } -} -``` - -### Response Fields - -| Field | Description | -|-------|-------------| -| `memories` | List of memory groups, organized by memory type | -| `total_count` | Total number of memories found | -| `has_more` | Whether more results are available | -| `query_metadata` | Metadata about the query execution | -| `metadata` | Additional response metadata | -| `pending_messages` | Messages waiting for memory extraction | - -#### Memory extend fields - -| Field | Description | -|-------|-------------| -| `_search_source` | Search source type: `keyword` or `vector` | - ---- - -## V1 Settings API - -### GET `/api/v1/settings` - Get Settings - -Retrieve the global settings singleton. - -**Response (200):** -```json -{ - "data": { - "scene": "solo", - "scene_desc": {"description": "..."}, - "llm_custom_setting": null, - "timezone": "UTC", - "boundary_detection_timeout": 3600, - "extraction_mode": "default", - "offline_profile_extraction_interval": 86400, - "created_at": "2026-03-05T07:30:39.944590+00:00", - "updated_at": "2026-03-05T07:30:39.944590+00:00" - } -} -``` - -**Error (404):** Settings not initialized. - -### PUT `/api/v1/settings` - Update Settings - -Initialize or update global settings (upsert). - -**Request Body:** -```json -{ - "scene": "solo", - "scene_desc": {"description": "..."}, - "llm_custom_setting": null, - "timezone": "Asia/Shanghai", - "boundary_detection_timeout": 3600, - "extraction_mode": "default", - "offline_profile_extraction_interval": 86400 -} -``` - -**Response (200):** Same format as GET. - ---- - -## DELETE `/memories` - Delete Memories - -Soft delete memories based on filter criteria (AND logic). - -### Request Body - -```json -{ - "event_id": "evt_001", - "user_id": "user_123", - "group_ids": "group_456" -} -``` - -### Request Fields - -| Field | Type | Required | Default | Description | -|-------|------|----------|---------|-------------| -| `event_id` | string | No | `__all__` | Filter by event ID | -| `user_id` | string | No | `__all__` | Filter by user ID | -| `group_id` | string | No | `__all__` | Filter by group ID | - -At least one filter must be provided (not all `__all__`). - -### Example - -```bash -# Delete all memories for a user in a group -curl -X DELETE "http://localhost:1995/api/v0/memories" \ - -H "Content-Type: application/json" \ - -d '{"user_id": "user_123", "group_ids": "group_456"}' -``` - -### Response - -```json -{ - "status": "ok", - "message": "Successfully deleted 10 memories", - "result": { - "filters": ["user_id", "group_ids"], - "count": 10 - } -} -``` - ---- - -## Batch Processing with run_memorize.py - -For batch processing ConversationFormat JSON files: - -```bash -# Process a group chat file -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat.json \ - --scene team \ - --api-url http://localhost:1995/api/v0/memories - -# Validate format only -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat.json \ - --scene team \ - --validate-only -``` - -### Parameters - -| Parameter | Required | Description | -|-----------|----------|-------------| -| `--input` | Yes | Path to ConversationFormat JSON file | -| `--scene` | Yes | `team` or `solo` | -| `--api-url` | Yes* | Memory API endpoint | -| `--validate-only` | No | Only validate format, skip processing | - -*Required unless using `--validate-only`. - ---- - -## Error Responses - -All error responses follow this format: - -```json -{ - "status": "failed", - "code": "ERROR_CODE", - "message": "Human-readable error message", - "timestamp": "2025-01-15T10:30:00+00:00", - "path": "/api/v0/memories" -} -``` - -### Error Codes - -| Code | HTTP Status | Description | -|------|-------------|-------------| -| `INVALID_PARAMETER` | 400 | Invalid or missing request parameters | -| `RESOURCE_NOT_FOUND` | 404 | Requested resource not found | -| `SYSTEM_ERROR` | 500 | Internal server error | - ---- - -## See Also - -- [Team Chat Guide](../advanced/TEAM_CHAT_GUIDE.md) - Multi-participant conversations -- [Metadata Control Guide](../advanced/METADATA_CONTROL.md) - Conversation metadata management -- [Conversation Format Specification](../../data_format/conversation/conversation_format.md) - Data format reference diff --git a/methods/EverCore/docs/dev_docs/agentic_retrieval_guide.md b/methods/EverCore/docs/dev_docs/agentic_retrieval_guide.md deleted file mode 100644 index d045e1db..00000000 --- a/methods/EverCore/docs/dev_docs/agentic_retrieval_guide.md +++ /dev/null @@ -1,388 +0,0 @@ -# Agentic Retrieval Guide - -## Overview - -Agentic retrieval is an LLM-guided multi-round retrieval method that significantly improves retrieval quality for complex queries through intelligent judgment and query optimization. - -## Core Features - -✅ **Intelligent Judgment**: LLM automatically determines if retrieval results are sufficient -✅ **Multi-Round Retrieval**: Automatically performs a second round of retrieval when insufficient -✅ **Multi-Query Strategy**: Generates 2-3 complementary queries to improve recall -✅ **Automatic Fallback**: Falls back to Lightweight retrieval on failure -✅ **Complete Metadata**: Returns detailed retrieval process information - -## Quick Start - -### 1. Using in Chat Interface - -Run `chat_with_memory.py` and select retrieval mode: - -```bash -uv run python src/bootstrap.py demo/chat_with_memory.py -``` - -Select the 4th option: `Agentic Retrieval - LLM-guided multi-round retrieval (experimental)` - -### 2. Using in Code - -```python -from agentic_layer.memory_manager import MemoryManager -from memory_layer.llm.llm_provider import LLMProvider -from agentic_layer.agentic_utils import AgenticConfig - -# Initialize LLM Provider -llm = LLMProvider( - provider_type="openai", - model="gpt-4", - api_key="your_api_key", - base_url="https://api.openai.com/v1", - temperature=0.0, -) - -# Initialize Memory Manager -memory_manager = MemoryManager() - -# Execute Agentic retrieval -result = await memory_manager.retrieve_agentic( - query="What foods does the user like?", - group_id="food_lovers_group", - llm_provider=llm, - top_k=20, -) - -# View results -print(f"Retrieved {result['count']} memories") -print(f"Is sufficient: {result['metadata']['is_sufficient']}") - -if result['metadata']['is_multi_round']: - print(f"Refined queries: {result['metadata']['refined_queries']}") -``` - -## Advanced Configuration - -### Custom Agentic Configuration - -```python -from agentic_layer.agentic_utils import AgenticConfig - -# Create custom configuration -config = AgenticConfig( - # Round 1 configuration - round1_emb_top_n=50, # Embedding candidates - round1_bm25_top_n=50, # BM25 candidates - round1_top_n=20, # Top N after RRF fusion - round1_rerank_top_n=10, # Top N after rerank for LLM judgment - - # LLM configuration - llm_temperature=0.0, # Low temperature for judgment - llm_max_tokens=500, - - # Round 2 configuration - enable_multi_query=True, # Enable multi-query - num_queries=3, # Expected number of queries - round2_per_query_top_n=50, # Recall per query - - # Fusion configuration - combined_total=40, # Total after merging - final_top_n=20, # Final top N - - # Rerank configuration - use_reranker=True, - reranker_instruction="Rank based on relevance between query and memory", -) - -# Use custom configuration -result = await memory_manager.retrieve_agentic( - query="What foods does the user like?", - group_id="food_lovers_group", - llm_provider=llm, - agentic_config=config, -) -``` - -## Return Format - -```python -{ - "memories": [ - { - "event_id": "...", - "user_id": "...", - "group_id": "...", - "timestamp": "2024-01-15T10:30:00", - "episode": "User said he loves Sichuan cuisine, especially Mapo Tofu", - "summary": "User's cuisine preferences", - "subject": "Eating habits", - "score": 0.95 - }, - # ... more memories - ], - "count": 20, - "metadata": { - # Basic information - "retrieval_mode": "agentic", - "is_multi_round": True, # Whether multi-round retrieval was performed - - # Round 1 statistics - "round1_count": 20, - "round1_reranked_count": 10, - "round1_latency_ms": 800, - - # LLM judgment - "is_sufficient": False, - "reasoning": "Missing user's specific cuisine preferences and taste information", - "missing_info": ["Cuisine preferences", "Taste habits", "Dietary restrictions"], - - # Round 2 statistics (only when multi-round) - "refined_queries": [ - "What is the user's favorite cuisine?", - "What flavors does the user like?", - "What dietary restrictions does the user have?" - ], - "query_strategy": "Break down original query into multiple specific sub-questions", - "num_queries": 3, - "round2_count": 40, - "round2_latency_ms": 600, - "multi_query_total_docs": 120, - - # Final statistics - "final_count": 20, - "total_latency_ms": 3500 - } -} -``` - -## Workflow - -``` -User Query - ↓ -Round 1: Hybrid Search (Embedding + BM25 + RRF) - ↓ -RRF Fusion → Top 20 - ↓ -Rerank → Top 10 - ↓ -LLM Judges Sufficiency - ↓ -├─ Sufficient → Return Round 1's Top 20 ✅ -│ -└─ Insufficient → LLM generates multi-queries (2-3) - ↓ - Round 2: Parallel retrieval for all queries - ↓ - Multi-query RRF fusion - ↓ - Deduplicate + merge to 40 - ↓ - Rerank → Top 20 ✅ -``` - -## Performance Metrics - -| Metric | Single Round (Sufficient) | Multi-Round (Insufficient) | -|--------|--------------------------|---------------------------| -| Latency | 2-5s | 5-10s | -| LLM Calls | 1 | 2 | -| Token Usage | ~500 | ~1500 | -| API Cost | ~$0.001 | ~$0.003 | - -*Estimated values based on GPT-4* - -## Use Cases - -### ✅ Suitable for Agentic Retrieval - -1. **Complex Queries**: Requires information from multiple perspectives - - ❌ "What does the user like to eat?" (too broad) - - ✅ "What is the user's favorite Sichuan dish and taste preferences?" - -2. **Scattered Information**: Related memories distributed across different time points - -3. **High Quality Requirements**: Scenarios requiring high recall and precision - -### ❌ Not Suitable for Agentic Retrieval - -1. **Simple Queries**: Questions that can be directly answered - - "What day is it today?" - - "What is the user's name?" - -2. **Latency Sensitive**: Scenarios requiring < 1 second response - -3. **Cost Sensitive**: Cannot afford LLM API costs - -## Fallback Strategy - -Agentic retrieval automatically falls back to Lightweight retrieval in the following cases: - -1. ❌ LLM API call failure -2. ❌ Timeout (default 60 seconds) -3. ❌ `llm_provider` not provided -4. ❌ Candidate memories are empty - -Fallback is marked in metadata: - -```python -{ - "metadata": { - "retrieval_mode": "agentic_fallback", - "fallback_reason": "LLM API timeout" - } -} -``` - -## Cost Optimization - -### 1. Adjust LLM Model - -```python -# Use cheaper model -llm = LLMProvider( - provider_type="openai", - model="gpt-4o-mini", # Cheaper - # model="gpt-4", # More accurate but more expensive -) -``` - -### 2. Disable Multi-Query - -```python -config = AgenticConfig( - enable_multi_query=False, # Only generate 1 query (reduce cost) -) -``` - -### 3. Disable Reranker - -```python -config = AgenticConfig( - use_reranker=False, # Don't use reranker (reduce latency and cost) -) -``` - -## Troubleshooting - -### Issue: LLM API Call Failure - -**Reasons**: -- Incorrect API Key -- Network issues -- API rate limiting - -**Solutions**: -1. Check API Key in `.env` file -2. Verify network connection -3. Check detailed error information in logs - -### Issue: High Latency (> 10s) - -**Reasons**: -- Slow LLM response -- Too many candidate memories -- Reranker timeout - -**Solutions**: -1. Reduce `time_range_days` (reduce candidates) -2. Disable reranker -3. Use faster LLM model - -### Issue: Poor Retrieval Quality - -**Reasons**: -- Inaccurate LLM judgment -- Unreasonable query generation -- Prompt not adapted - -**Solutions**: -1. Use stronger LLM model (e.g., GPT-4) -2. Adjust prompt template (in `agentic_utils.py`) -3. Increase `round1_rerank_top_n` (give LLM more samples) - -## Comparison with Other Retrieval Modes - -| Feature | Lightweight | Agentic | -|---------|------------|---------| -| Latency | 0.5-2s | 5-10s | -| LLM Calls | ❌ None | ✅ 1-2 | -| Multi-Round | ❌ No | ✅ Yes | -| Recall | Medium | High | -| Precision | Medium | High | -| Cost | Low | Medium | -| Use Cases | Simple queries | Complex queries | - -## Best Practices - -1. ✅ **Prioritize Lightweight**: For simple queries, Lightweight is sufficient -2. ✅ **Use Agentic for Complex Queries**: Only when needed -3. ✅ **Monitor Costs**: Track LLM token consumption -4. ✅ **Log Analysis**: Regularly review if LLM judgments are reasonable -5. ✅ **A/B Testing**: Compare effects of different modes - -## Example: Complete Chat Flow - -```python -import asyncio -from agentic_layer.memory_manager import MemoryManager -from memory_layer.llm.llm_provider import LLMProvider - -async def main(): - # Initialize - llm = LLMProvider("openai", model="gpt-4", api_key="...") - memory_manager = MemoryManager() - - # User query - query = "What foods does the user like? Any dietary restrictions?" - - # Execute retrieval - result = await memory_manager.retrieve_agentic( - query=query, - group_id="food_lovers_group", - llm_provider=llm, - ) - - # Display results - print(f"\n{'='*60}") - print(f"Query: {query}") - print(f"{'='*60}\n") - - print(f"Retrieval mode: {result['metadata']['retrieval_mode']}") - print(f"Retrieved {result['count']} memories") - print(f"Total latency: {result['metadata']['total_latency_ms']:.0f}ms\n") - - # LLM judgment - print(f"LLM judgment: {'✅ Sufficient' if result['metadata']['is_sufficient'] else '❌ Insufficient'}") - print(f"Reasoning: {result['metadata']['reasoning']}\n") - - # Multi-round information - if result['metadata']['is_multi_round']: - print(f"📝 Entered Round 2") - print(f"Generated queries:") - for i, q in enumerate(result['metadata']['refined_queries'], 1): - print(f" {i}. {q}") - print() - - # Display memories - print(f"Top 5 memories:") - for i, mem in enumerate(result['memories'][:5], 1): - print(f"\n[{i}] {mem['timestamp'][:10]}") - print(f" {mem['episode'][:100]}...") - print(f" Score: {mem['score']:.3f}") - -if __name__ == "__main__": - asyncio.run(main()) -``` - -## More Resources - -- 📖 [Memory Manager API Documentation](../docs/api_docs/memory_api.md) -- 🔬 [Retrieval Evaluation](../../evaluation/locomo_evaluation/README.md) -- 💡 [Best Practices](../docs/dev_docs/getting_started.md) - ---- - -**Notes**: -- Agentic retrieval is an experimental feature and may be adjusted in future versions -- Please understand the costs and limitations of LLM APIs before using -- It is recommended to conduct thorough testing before deploying in production environments - diff --git a/methods/EverCore/docs/dev_docs/agentic_retrieve_testing.md b/methods/EverCore/docs/dev_docs/agentic_retrieve_testing.md deleted file mode 100644 index 9bd2f624..00000000 --- a/methods/EverCore/docs/dev_docs/agentic_retrieve_testing.md +++ /dev/null @@ -1,161 +0,0 @@ -# Agentic Retrieval Testing Guide - -## Overview - -This document explains how to test the Agentic retrieval feature of the V1 API. Agentic retrieval is an LLM-guided intelligent multi-round retrieval method that can automatically determine the sufficiency of retrieval results and perform multi-round optimization. - -## Features - -### Agentic Retrieval Process - -1. **Round 1**: RRF hybrid retrieval (Embedding + BM25) -2. **Rerank**: Use Reranker to optimize result quality -3. **LLM Judgment**: Use LLM to determine if results are sufficient -4. **Round 2** (if needed): - - LLM generates multiple refined queries - - Parallel retrieval for all queries - - Fusion and Rerank to return final results - -### API Endpoint - -``` -POST /api/v0/memories/search -``` - -### Request Format - -```json -{ - "query": "What does the user like to eat?", - "user_id": "default", - "group_id": "assistant", - "time_range_days": 365, - "top_k": 20, - "llm_config": { - "api_key": "your_api_key", - "base_url": "https://api.openai.com/v1", - "model": "gpt-4o-mini" - } -} -``` - -### Response Format - -```json -{ - "status": "ok", - "message": "Agentic retrieval successful, found 15 memories", - "result": { - "memories": [...], - "count": 15, - "metadata": { - "retrieval_mode": "agentic", - "is_multi_round": true, - "round1_count": 20, - "is_sufficient": false, - "reasoning": "Need more specific information about dietary preferences", - "refined_queries": ["What is the user's favorite cuisine?", "What does the user dislike eating?"], - "round2_count": 40, - "final_count": 15, - "total_latency_ms": 2345.67 - } - } -} -``` - -## Testing Instructions - -### Running Tests - -```bash -# Start the service -uv run python src/run.py - -# Run tests (in another terminal) -uv run python src/bootstrap.py demo/test_v3_retrieve_http.py -``` - -### Environment Configuration - -Agentic retrieval requires LLM API Key configuration: - -```bash -# Add to .env file -OPENROUTER_API_KEY=your_api_key -# or -OPENAI_API_KEY=your_api_key -``` - -If no API Key is configured, the test will automatically skip the Agentic retrieval part. - -### Test Cases - -The test file includes the following Agentic retrieval test cases: - -1. **Simple Query**: "Beijing travel" - Test single-round retrieval (possibly sufficient) -2. **Complex Query**: "What does the user like to eat? What are their usual eating habits?" - Test multi-round retrieval -3. **Multi-dimensional Query**: "User's personality traits and hobbies" - Test multi-dimensional retrieval - -### Expected Results - -- **Single-round Retrieval**: If Round 1 results are sufficient, return directly -- **Multi-round Retrieval**: If Round 1 results are insufficient, LLM generates refined queries and proceeds to Round 2 - -## Performance Notes - -- **Latency**: Typically 2-5 seconds (including LLM calls) -- **Cost**: Incurs LLM API call costs (approximately 2-3 calls) -- **Accuracy**: More accurate than regular retrieval, especially suitable for complex queries - -## Integration with Chat Module - -The chat module (`demo/chat_with_memory.py`) has integrated Agentic retrieval: - -1. Select "Agentic Retrieval" when starting the chat application -2. The system will automatically use LLM-guided multi-round retrieval -3. Each conversation outputs detailed retrieval metadata - -## Troubleshooting - -### Issue 1: API Key Error - -**Symptom**: Prompt "Missing LLM API Key" - -**Solution**: -```bash -# Add to .env file -OPENROUTER_API_KEY=your_key_here -``` - -### Issue 2: Timeout - -**Symptom**: Request timeout (over 60 seconds) - -**Cause**: Agentic retrieval involves multiple LLM calls, which may timeout with slow network or LLM response - -**Solution**: -- Check network connection -- Use a faster LLM model (such as gpt-4o-mini) -- Increase client timeout duration - -### Issue 3: Empty Retrieval Results - -**Symptom**: Returns 0 memories - -**Cause**: No relevant data in database - -**Solution**: -```bash -# Run data import first -uv run python src/bootstrap.py demo/extract_memory.py - -# Then test retrieval -uv run python src/bootstrap.py demo/test_v3_retrieve_http.py -``` - -## References - -- [Memory API Documentation](../api_docs/memory_api.md) -- [Agentic Retrieval Guide](./agentic_retrieval_guide.md) -- [Memory Manager Usage Guide](./api_usage_guide.md) - diff --git a/methods/EverCore/docs/dev_docs/api_usage_guide.md b/methods/EverCore/docs/dev_docs/api_usage_guide.md deleted file mode 100644 index a9b3a19b..00000000 --- a/methods/EverCore/docs/dev_docs/api_usage_guide.md +++ /dev/null @@ -1,612 +0,0 @@ -# API Usage Guide - -This document provides detailed instructions on how to use MemSys API interfaces to store and retrieve memory data. - -## 📋 Table of Contents - -- [API Overview](#api-overview) -- [Memory Storage APIs](#memory-storage-apis) - - [V1 Memory API](#v3-agentic-api) - - [V1 Memory API](#v1-memory-api) - - [API Selection Guide](#api-selection-guide) -- [Conversation Data Format](#conversation-data-format) -- [Using Scripts to Store Memories](#using-scripts-to-store-memories) -- [API Call Examples](#api-call-examples) - -## 🔍 API Overview - -MemSys provides two standardized API interfaces for storing memories: - -### Available APIs - -| API Type | Endpoint | Features | Recommended Use Case | -|---------|---------|------|---------| -| **V1 Memory API** | `/api/v0/memories` | Memory Storage + Intelligent Retrieval | Complete application scenarios requiring retrieval features | - -### API Comparison - -| Feature | V1 Memory API | V1 Memory API | -|-----|---------------|--------------| -| Store Single Message | ✅ Supported | ✅ Supported | -| Message Format | Simple direct single message format | Simple direct single message format | -| Intelligent Retrieval | ✅ Supported (Lightweight + Agentic) | ❌ Not Supported | -| Settings Management | ✅ Supported | ✅ Supported (with PUT updates) | -| Use Case | Complete memory system (storage + retrieval) | Pure memory storage system | - -**Important Note**: Both APIs use identical storage formats, so you can choose based on your needs. If you need retrieval functionality, we recommend using V1 Memory API for complete feature support. - ---- - -## 🚀 Memory Storage APIs - -### V1 Memory API - -Recommended for scenarios requiring complete functionality (storage + retrieval). - -#### Endpoint - -``` -POST /api/v0/memories -``` - -#### Features - -- ✅ Simple direct single message format -- ✅ Supports lightweight retrieval (RRF fusion) -- ✅ Supports Agentic intelligent retrieval (LLM-assisted) -- ✅ Supports settings management - -For detailed documentation, see: [Memory API Documentation](../api_docs/memory_api.md) - ---- - -### V1 Memory API - -Recommended for simple scenarios requiring only storage functionality. - -#### Endpoint - -``` -POST /api/v0/memories -``` - -#### Features - -- ✅ Simple direct single message format -- ✅ Focused on memory storage -- ✅ Supports settings management (with PUT updates) - -For detailed documentation, see: [Memory API Documentation](../api_docs/memory_api.md) - ---- - -### API Selection Guide - -**Use V1 Memory API (`/api/v0/memories`)** if: -- ✅ You need intelligent retrieval functionality -- ✅ You need to build a complete memory system (storage + retrieval) -- ✅ You want to use lightweight or Agentic retrieval modes - -**Use V1 Memory API (`/api/v0/memories`)** if: -- ✅ You only need to store memories without retrieval -- ✅ You have your own retrieval solution -- ✅ You prefer a more concise dedicated storage interface - -**Note**: Both APIs use identical data formats and underlying storage mechanisms. The main difference is that V1 API provides additional retrieval functionality. - ---- - -## 📝 Memorize API Details - -### Request Format (Common to Both APIs) - -Both APIs use the same simple direct single message format: - -```json -{ - "message_id": "msg_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "user_103", - "sender_name": "Chen", - "content": "Message content", - "refer_list": [], - "group_id": "group_001", - "group_name": "Project Discussion Group" -} -``` - -### Field Descriptions - -| Field | Type | Required | Description | -|------|------|------|------| -| `message_id` | string | Yes | Unique message identifier | -| `create_time` | string | Yes | Message creation time (ISO 8601 format) | -| `sender` | string | Yes | Sender ID | -| `sender_name` | string | No | Sender name (for readability) | -| `content` | string | Yes | Message content | -| `refer_list` | array | No | List of referenced messages | -| `group_id` | string | No | Group ID | -| `group_name` | string | No | Group name | - -### Response Format - -```json -{ - "code": 0, - "message": "success", - "result": { - "count": 2, - "saved_memories": [ - { - "memory_id": "mem_001", - "type": "episode", - "content": "Extracted memory content" - } - ] - } -} -``` - -### Call Examples - -#### cURL - -```bash -curl -X POST http://localhost:1995/api/v0/memories \ - -H "Content-Type: application/json" \ - -d '{ - "message_id": "msg_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "user_103", - "sender_name": "Chen", - "content": "We need to complete the product design this week", - "group_id": "group_001", - "group_name": "Project Discussion Group" - }' -``` - -#### Python - -```python -import httpx -import asyncio - -async def store_memory(): - async with httpx.AsyncClient() as client: - response = await client.post( - "http://localhost:1995/api/v0/memories", - json={ - "message_id": "msg_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "user_103", - "sender_name": "Chen", - "content": "We need to complete the product design this week", - "group_id": "group_001", - "group_name": "Project Discussion Group" - } - ) - print(response.json()) - -asyncio.run(store_memory()) -``` - -#### JavaScript - -```javascript -fetch('http://localhost:1995/api/v0/memories', { - method: 'POST', - headers: { - 'Content-Type': 'application/json' - }, - body: JSON.stringify({ - message_id: 'msg_001', - create_time: '2025-02-01T10:00:00+00:00', - sender: 'user_103', - sender_name: 'Chen', - content: 'We need to complete the product design this week', - group_id: 'group_001', - group_name: 'Project Discussion Group' - }) -}) -.then(response => response.json()) -.then(data => console.log(data)); -``` - -**Using V1 Memory API:** - -```javascript -fetch('http://localhost:1995/api/v0/memories', { - method: 'POST', - headers: { - 'Content-Type': 'application/json' - }, - body: JSON.stringify({ - message_id: 'msg_001', - create_time: '2025-02-01T10:00:00+00:00', - sender: 'user_103', - sender_name: 'Chen', - content: 'We need to complete the product design this week', - group_id: 'group_001', - group_name: 'Project Discussion Group' - }) -}) -.then(response => response.json()) -.then(data => console.log(data)); -``` - -## 📁 Conversation Data Format - -MemSys defines a standardized conversation data format `ConversationFormat` for storing and exchanging conversation data. - -### Format Overview - -```json -{ - "version": "1.0.0", - "session_meta": { - "group_id": "group_001", - "name": "Project Discussion Group", - "default_timezone": "+00:00", - "user_details": { - "user_101": { - "full_name": "Alex", - "role": "Technical Lead", - "department": "Engineering" - } - } - }, - "conversation_list": [ - { - "message_id": "msg_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "user_101", - "sender_name": "Alex", - "type": "text", - "content": "Good morning everyone", - "refer_list": [] - } - ] -} -``` - -### Core Features - -1. **Separated Metadata and Message List** - - `session_meta`: Group chat metadata - - `conversation_list`: Message list - -2. **Centralized User Details** - - All user information stored in `user_details` - - Messages only need to reference user IDs - -3. **Timezone-aware Timestamps** - - Uses ISO 8601 format - - Supports timezone information - -4. **Flexible Message References** - - Supports string references (message_id only) - - Supports object references (complete message information) - -### Detailed Documentation - -For complete format specification, see: [Conversation Format Specification](../../data_format/conversation/conversation_format.md) - -## 🔧 Using Scripts to Store Memories - -MemSys provides the `run_memorize.py` script for batch storing group chat data into the system. The script supports both API interfaces. - -### Script Location - -``` -src/run_memorize.py -``` - -### Basic Usage - -Run using the Bootstrap script with V1 API: - -**Using V1 Memory API (Recommended, supports retrieval):** - -```bash -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat.json \ - --api-url http://localhost:1995/api/v0/memories -``` - -**Using V1 Memory API (Storage only):** - -```bash -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat.json \ - --api-url http://localhost:1995/api/v0/memories -``` - -### Command Line Arguments - -| Argument | Required | Description | -|------|------|------| -| `--input` | Yes | Input group chat JSON file path (ConversationFormat) | -| `--api-url` | No* | Memorize API address (*unless using --validate-only) | -| `--validate-only` | No | Only validate input file format without storing | - -### Usage Examples - -#### 1. Store Memories - -**Using V1 Memory API:** - -```bash -# Basic usage -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat.json \ - --api-url http://localhost:1995/api/v0/memories - -# Using relative path -uv run python src/bootstrap.py src/run_memorize.py \ - --input ../my_data/chat_history.json \ - --api-url http://localhost:1995/api/v0/memories - -# Specifying remote server -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat.json \ - --api-url http://api.example.com/api/v0/memories -``` - -**Using V1 Memory API:** - -```bash -# Basic usage -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat.json \ - --api-url http://localhost:1995/api/v0/memories - -# Using relative path -uv run python src/bootstrap.py src/run_memorize.py \ - --input ../my_data/chat_history.json \ - --api-url http://localhost:1995/api/v0/memories -``` - -#### 2. Validate File Format - -Validate file format before storing: - -```bash -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat.json \ - --validate-only -``` - -### Script Workflow - -1. **Validate Input File** - - Check if JSON format is correct - - Verify compliance with ConversationFormat specification - - Output data statistics - -2. **Process Messages One by One** - - Read each message from group chat file - - Call API to store each message - - Display processing progress and results - -3. **Output Processing Results** - - Number of successfully processed messages - - Number of saved memories - - Failed messages (if any) - -### Output Example - -``` -🚀 Group Chat Memory Storage Script -====================================================================== -📄 Input File: /path/to/data/team_chat.json -🔍 Validation Mode: No -🌐 API Address: http://localhost:1995/api/v0/memories -====================================================================== - -====================================================================== -Validating Input File Format -====================================================================== -Reading file: /path/to/data/team_chat.json -Validating ConversationFormat... -✓ Format validation passed! - -=== Data Statistics === -Format Version: 1.0.0 -Group Name: Project Discussion Group -Group ID: group_001 -User Count: 5 -Message Count: 20 -Time Range: 2025-02-01T10:00:00+00:00 ~ 2025-02-01T18:30:00+00:00 - -====================================================================== -Starting to Call Memorize API for Each Message -====================================================================== -Group Name: Project Discussion Group -Group ID: group_001 -Message Count: 20 -API Address: http://localhost:1995/api/v0/memories - ---- Processing Message 1/20 --- - ✓ Successfully saved 2 memories - ---- Processing Message 2/20 --- - ✓ Successfully saved 1 memory - -... - -====================================================================== -Processing Complete -====================================================================== -✓ Successfully Processed: 20/20 messages -✓ Total Saved: 35 memories -``` - -## 📝 API Call Examples - -### Complete Workflow - -#### 1. Prepare Data File - -Create a JSON file conforming to ConversationFormat: - -```json -{ - "version": "1.0.0", - "session_meta": { - "group_id": "project_team_001", - "name": "Product Development Team", - "default_timezone": "+00:00", - "user_details": { - "alice": { - "full_name": "Alice Wang", - "role": "Product Manager", - "department": "Product" - }, - "bob": { - "full_name": "Bob Chen", - "role": "Technical Lead", - "department": "Engineering" - } - } - }, - "conversation_list": [ - { - "message_id": "msg_20250201_001", - "create_time": "2025-02-01T09:00:00+00:00", - "sender": "alice", - "sender_name": "Alice Wang", - "type": "text", - "content": "Good morning! Let's discuss the new feature requirements today", - "refer_list": [] - }, - { - "message_id": "msg_20250201_002", - "create_time": "2025-02-01T09:02:00+00:00", - "sender": "bob", - "sender_name": "Bob Chen", - "type": "text", - "content": "Sure, I've prepared some technical solutions", - "refer_list": ["msg_20250201_001"] - } - ] -} -``` - -Save as `my_chat_data.json`. - -#### 2. Validate File Format - -```bash -uv run python src/bootstrap.py src/run_memorize.py \ - --input my_chat_data.json \ - --validate-only -``` - -#### 3. Start Service - -Ensure MemSys service is running: - -```bash -uv run python src/run.py -``` - -After service starts, visit http://localhost:1995/docs to verify API documentation is accessible. - -#### 4. Store Memories - -**Option A: Using V1 Memory API (Recommended)** - -```bash -uv run python src/bootstrap.py src/run_memorize.py \ - --input my_chat_data.json \ - --api-url http://localhost:1995/api/v0/memories -``` - -**Option B: Using V1 Memory API** - -```bash -uv run python src/bootstrap.py src/run_memorize.py \ - --input my_chat_data.json \ - --api-url http://localhost:1995/api/v0/memories -``` - -#### 5. Verify Storage Results - -If using V1 Memory API, you can query stored memories through the retrieval interface (see [Memory API Documentation](../api_docs/memory_api.md) for specific query APIs). - -### Error Handling - -#### Format Validation Failed - -``` -✗ Format validation failed! -Please ensure input file conforms to ConversationFormat specification -``` - -**Solution**: -- Check if JSON format is correct -- Refer to [Conversation Format Specification](../../data_format/conversation/conversation_format.md) -- Ensure all required fields are filled - -#### API Call Failed - -``` -✗ API call failed: 500 -Response content: {"error": "Internal server error"} -``` - -**Solution**: -- Check if service is running normally -- View service logs to troubleshoot -- Verify API address is correct - -#### Connection Timeout - -``` -✗ Processing failed: ReadTimeout -``` - -**Solution**: -- Check network connection -- Verify service address and port are correct -- Check firewall settings - -## 🔗 Related Documentation - -### API Documentation - -- [Memory API Documentation](../api_docs/memory_api.md) - Complete V1 API documentation (storage + retrieval) -- [Memory API Documentation](../api_docs/memory_api.md) - Complete V1 Memory API documentation (focused on storage) - -### Other Documentation - -- [Conversation Format Specification](../../data_format/conversation/conversation_format.md) - Detailed ConversationFormat specification -- [Getting Started Guide](getting_started.md) - Environment setup and service startup -- [Agentic Retrieval Guide](agentic_retrieval_guide.md) - Intelligent retrieval features explained - -## 💡 Best Practices - -1. **Data Preparation** - - Use standard ConversationFormat - - Ensure timestamps include timezone information - - Provide complete user details - -2. **Batch Processing** - - For large number of messages, use script to process one by one - - Add appropriate delays to avoid server pressure - - Monitor processing progress and errors - -3. **Error Recovery** - - Log failed messages - - Support resume from checkpoint - - Regularly verify storage results - -4. **Performance Optimization** - - Set reasonable concurrency levels - - Use batch interfaces (if available) - - Monitor API response times - ---- - -For questions, please refer to [FAQ](getting_started.md#faq) or submit an issue. - diff --git a/methods/EverCore/docs/dev_docs/bootstrap_usage.md b/methods/EverCore/docs/dev_docs/bootstrap_usage.md deleted file mode 100644 index d8f6e971..00000000 --- a/methods/EverCore/docs/dev_docs/bootstrap_usage.md +++ /dev/null @@ -1,146 +0,0 @@ -# Bootstrap Script Usage Guide - -## Overview - -`src/bootstrap.py` is a script runner that allows you to run any test script with zero cognitive overhead, automatically handling all environment setup. - -## Basic Usage - -```bash -uv run python src/bootstrap.py [script_path] [script_arguments...] -``` - -The script path can be: -- Relative to the project root: `unit_test/memory_manager_single_test.py` -- Relative to the current directory: `../tests/test_convert_rest.py` -- Absolute path: `/path/to/your/script.py` - -## Usage Examples - -### 1. Run Unit Tests - -```bash -# Run memory manager test -uv run python src/bootstrap.py unit_test/memory_manager_single_test.py - -# Run test with relative path -uv run python src/bootstrap.py ../tests/test_convert_rest.py - -# Run test with arguments -uv run python src/bootstrap.py unit_test/memory_manager_single_test.py --verbose -``` - -### 2. Run Evaluation Scripts - -```bash -# Run dynamic memory evaluation -uv run python src/bootstrap.py evaluation/dynamic_memory_evaluation/locomo_eval.py - -# Run evaluation with dataset argument -uv run python src/bootstrap.py evaluation/dynamic_memory_evaluation/locomo_eval.py --dataset small -``` - -### 3. Run Algorithm Debugging Scripts - -```bash -# Run algorithm debugging script -uv run python src/bootstrap.py tests/algorithms/debug_my_model.py - -# Run script with config file -uv run python src/bootstrap.py tests/algorithms/debug_my_model.py --config config.json -``` - -### 4. Run Test Template - -```bash -# Run Bootstrap test template to learn how to use DI and MongoDB -uv run python src/bootstrap.py tests/bootstrap_test_template.py -``` - -## Command Line Options - -### `--env-file` -Specify the environment variable file to load (default: `.env`) - -```bash -uv run python src/bootstrap.py your_script.py --env-file .env.test -``` - -### `--mock` -Enable Mock mode (for testing and development) - -```bash -uv run python src/bootstrap.py your_script.py --mock -``` - -## Environment Variables - -### `MOCK_MODE` -Set to `true` to enable Mock mode - -```bash -MOCK_MODE=true python src/bootstrap.py your_script.py -``` - -## Test Template - -The project provides a complete test template `tests/bootstrap_test_template.py` that demonstrates how to: - -- **Use dependency injection**: Get singleton objects via `get_bean_by_type()` and `get_bean()` -- **Work with MongoDB**: Use the repository pattern for database queries and operations -- **Integration testing**: Combine multiple components for comprehensive testing - -The template includes examples like: -```python -# Get MongoDB repository -from core.di.utils import get_bean_by_type -repo = get_bean_by_type(MemCellRawRepository) - -# Query data -memcells = await repo.find_all(limit=5) -total_count = await repo.count_all() -``` - -## Best Practices - -### 1. Daily Usage -Run your scripts directly: -```bash -uv run python src/bootstrap.py your_script.py -``` - -### 2. Learning Development -Start by running the test template to understand the project structure: -```bash -uv run python src/bootstrap.py tests/bootstrap_test_template.py -``` - -### 3. Development and Testing -Use Mock mode during development: -```bash -uv run python src/bootstrap.py your_script.py --mock -``` - -### 4. CI/CD Integration -Specify different environment files in continuous integration: -```bash -uv run python src/bootstrap.py test_script.py --env-file .env.ci -``` - -## Troubleshooting - -### 1. Relative Import Errors -If you encounter `ImportError: attempted relative import with no known parent package` error: -- Bootstrap will automatically detect and switch to module mode -- No manual intervention needed, the script will automatically retry - -### 2. Import Errors -If you encounter other module import errors, check: -- Whether you're executing from the project root directory -- Whether the `.env` file exists and is configured correctly - -### 3. Script Execution Errors -If the target script fails to execute: -- Check if the script path is correct -- Confirm the script itself has no syntax errors - diff --git a/methods/EverCore/docs/dev_docs/development_guide.md b/methods/EverCore/docs/dev_docs/development_guide.md deleted file mode 100644 index f179b471..00000000 --- a/methods/EverCore/docs/dev_docs/development_guide.md +++ /dev/null @@ -1,616 +0,0 @@ -# Smart Memory System Development Guide - -This document provides detailed guidance for developers on interface definitions, Mock implementations, and decoupled development. - -## 📋 Table of Contents - -- [Interface Definition and Implementation](#interface-definition-and-implementation) -- [Mock Implementation and Decoupled Development](#mock-implementation-and-decoupled-development) -- [Dependency Injection Best Practices](#dependency-injection-best-practices) -- [Development Environment Configuration](#development-environment-configuration) - -## 🔧 Interface Definition and Implementation - -### 1. Define Abstract Interfaces - -Use Python's Abstract Base Classes (ABC) to define clear interfaces: - -```python -from abc import ABC, abstractmethod -from typing import Dict, List, Optional - -class UserRepository(ABC): - """User storage interface""" - - @abstractmethod - def find_by_id(self, user_id: int) -> Optional[Dict]: - """Find user by ID""" - pass - - @abstractmethod - def save(self, user: Dict) -> Dict: - """Save user""" - pass - - @abstractmethod - def find_by_email(self, email: str) -> Optional[Dict]: - """Find user by email""" - pass - -class NotificationService(ABC): - """Notification service interface""" - - @abstractmethod - async def send_notification(self, user_id: int, message: str) -> bool: - """Send notification""" - pass -``` - -### 2. Implement Concrete Classes - -Use dependency injection decorators to mark implementation classes: - -```python -from core.di.decorators import repository, service - -@repository("mysql_user_repo") -class MySQLUserRepository(UserRepository): - """MySQL user storage implementation""" - - def find_by_id(self, user_id: int) -> Optional[Dict]: - # Actual database query logic - return {"id": user_id, "name": f"User {user_id}", "source": "mysql"} - - def save(self, user: Dict) -> Dict: - # Actual save logic - return {**user, "id": 123, "created_at": "2024-01-01"} - - def find_by_email(self, email: str) -> Optional[Dict]: - # Actual query logic - return {"id": 456, "email": email, "source": "mysql"} - -@repository("redis_user_repo") -class RedisUserRepository(UserRepository): - """Redis user storage implementation (cache layer)""" - - def find_by_id(self, user_id: int) -> Optional[Dict]: - # Redis cache query logic - return {"id": user_id, "name": f"Cached User {user_id}", "source": "redis"} - - def save(self, user: Dict) -> Dict: - # Redis cache save logic - return {**user, "cached": True} - - def find_by_email(self, email: str) -> Optional[Dict]: - # Redis cache query logic - return None # Not in cache - -@service("email_notification") -class EmailNotificationService(NotificationService): - """Email notification service implementation""" - - async def send_notification(self, user_id: int, message: str) -> bool: - # Actual email sending logic - print(f"📧 Sending email to user {user_id}: {message}") - return True -``` - -### 3. Set Primary Implementation - -When there are multiple implementations, use `primary=True` to mark the default implementation: - -```python -@repository("primary_user_repo", primary=True) -class PrimaryUserRepository(UserRepository): - """Primary user storage implementation""" - - def __init__(self): - # Can combine multiple implementations - self.mysql_repo = MySQLUserRepository() - self.redis_repo = RedisUserRepository() - - def find_by_id(self, user_id: int) -> Optional[Dict]: - # Check cache first, then database - user = self.redis_repo.find_by_id(user_id) - if user: - return user - return self.mysql_repo.find_by_id(user_id) - - def save(self, user: Dict) -> Dict: - # Save to database and update cache - saved_user = self.mysql_repo.save(user) - self.redis_repo.save(saved_user) - return saved_user - - def find_by_email(self, email: str) -> Optional[Dict]: - return self.mysql_repo.find_by_email(email) -``` - -## 🧪 Mock Implementation and Decoupled Development - -### 1. Define Mock Implementation - -Use the `@mock_impl` decorator to define Mock implementations: - -```python -from core.di.decorators import mock_impl - -@mock_impl("mock_user_repo") -class MockUserRepository(UserRepository): - """Mock user storage implementation""" - - def __init__(self): - # Simulated data in memory - self.users = { - 1: {"id": 1, "name": "Mock User 1", "email": "user1@mock.com"}, - 2: {"id": 2, "name": "Mock User 2", "email": "user2@mock.com"} - } - self.next_id = 3 - - def find_by_id(self, user_id: int) -> Optional[Dict]: - return self.users.get(user_id) - - def save(self, user: Dict) -> Dict: - if "id" not in user: - user["id"] = self.next_id - self.next_id += 1 - self.users[user["id"]] = user - return user - - def find_by_email(self, email: str) -> Optional[Dict]: - for user in self.users.values(): - if user.get("email") == email: - return user - return None - -@mock_impl("mock_notification") -class MockNotificationService(NotificationService): - """Mock notification service implementation""" - - def __init__(self): - self.sent_messages = [] # Record sent messages for test verification - - async def send_notification(self, user_id: int, message: str) -> bool: - # Simulate sending, actually just record - notification = { - "user_id": user_id, - "message": message, - "timestamp": "2024-01-01T10:00:00" - } - self.sent_messages.append(notification) - print(f"🧪 Mock notification sent: {notification}") - return True - - def get_sent_messages(self) -> List[Dict]: - """Get sent messages (for testing)""" - return self.sent_messages.copy() -``` - -### 2. Mock Mode Toggle Control - -#### 2.1 Environment Variable Control - -Configure in the environment variable file (`.env`): - -```bash -# Development environment configuration -MOCK_MODE=true - -# Production environment configuration -# MOCK_MODE=false -``` - -#### 2.2 Dynamic Switching in Code - -```python -import os -from core.di.utils import enable_mock_mode, disable_mock_mode, get_bean_by_type - -def setup_mock_mode(): - """Set Mock mode based on environment variable""" - if os.getenv("MOCK_MODE", "false").lower() == "true": - enable_mock_mode() - print("🧪 Mock mode enabled") - else: - disable_mock_mode() - print("🔧 Using real implementation") - -# Call during application startup -def initialize_app(): - setup_mock_mode() - - # Now the implementation will automatically switch based on Mock mode - user_service = get_bean_by_type(UserService) - return user_service -``` - -#### 2.3 Automatic Control at Startup - -The application will automatically check the `MOCK_MODE` environment variable at startup: - -```python -# Implementation in run.py -if os.getenv("MOCK_MODE") and os.getenv("MOCK_MODE").lower() == "true": - enable_mock_mode() - logger.info("🚀 Mock mode enabled") -else: - logger.info("🚀 Mock mode disabled") -``` - -### 3. Conditional Mock Implementation - -Use different Mock implementations based on different conditions: - -```python -@mock_impl("mock_fast_notification") -class FastMockNotificationService(NotificationService): - """Fast Mock notification (for testing)""" - - async def send_notification(self, user_id: int, message: str) -> bool: - print(f"⚡ Fast Mock notification: User {user_id} - {message}") - return True - -@mock_impl("mock_slow_notification") -class SlowMockNotificationService(NotificationService): - """Slow Mock notification (for performance testing)""" - - async def send_notification(self, user_id: int, message: str) -> bool: - import asyncio - await asyncio.sleep(0.1) # Simulate network delay - print(f"🐌 Slow Mock notification: User {user_id} - {message}") - return True - -# Choose Mock implementation based on test type -def setup_test_environment(test_type: str): - enable_mock_mode() - - if test_type == "performance": - # Use slow Mock for performance testing - from core.di.utils import register_bean - slow_mock = SlowMockNotificationService() - register_bean(NotificationService, slow_mock, "mock_notification") - else: - # Use fast Mock for normal testing - fast_mock = FastMockNotificationService() - register_bean(NotificationService, fast_mock, "mock_notification") -``` - -## 🏗️ Dependency Injection Best Practices - -### 1. Interface Design Principles - -- **Single Responsibility**: Each interface should be responsible for one clear responsibility -- **Interface Segregation**: Clients should not depend on interfaces they don't need -- **Dependency Inversion**: High-level modules should not depend on low-level modules; both should depend on abstractions - -```python -# Good design: Clear responsibilities -class UserRepository(ABC): - @abstractmethod - def find_by_id(self, user_id: int) -> Optional[Dict]: - pass - -class UserValidator(ABC): - @abstractmethod - def validate(self, user: Dict) -> bool: - pass - -# Avoid this design: Mixed responsibilities -class UserService(ABC): # Not recommended: Mixes storage and validation responsibilities - @abstractmethod - def find_by_id(self, user_id: int) -> Optional[Dict]: - pass - - @abstractmethod - def validate(self, user: Dict) -> bool: - pass -``` - -### 2. Decorator Usage Guidelines - -```python -# Import from specific decorator modules -from core.di.decorators import repository, service, component, mock_impl, factory - -# Data access layer -@repository("user_repository") -class UserRepositoryImpl(UserRepository): - pass - -# Business service layer -@service("user_service") -class UserService: - pass - -# General components -@component("config_manager") -class ConfigManager: - pass - -# Mock implementation -@mock_impl("mock_external_api") -class MockExternalApiClient(ExternalApiClient): - pass - -# Factory method -@factory(DatabaseConnection, "db_connection") -def create_database_connection() -> DatabaseConnection: - config = load_config() - return DatabaseConnection(config.db_url) -``` - -### 3. Circular Dependency Handling - -Use lazy injection to avoid circular dependencies: - -```python -from core.di.decorators import service -from core.di.utils import lazy_inject - -@service("order_service") -class OrderService: - def __init__(self): - # Get dependencies lazily to avoid circular dependencies - self.user_service_lazy = lazy_inject(UserService) - self.payment_service_lazy = lazy_inject(PaymentService) - - def create_order(self, order_data: Dict) -> Dict: - user_service = self.user_service_lazy() # Get only when called - payment_service = self.payment_service_lazy() - - # Business logic - user = user_service.get_user(order_data["user_id"]) - payment_result = payment_service.process_payment(order_data["amount"]) - - return {"order_id": 123, "status": "created"} -``` - -## ⚙️ Development Environment Configuration - -**Note**: Before starting development, run `make dev-setup` to set up the development environment (sync dependencies + install code check hooks). - -### 1. Environment Variable Configuration - -Create `.env` file: - -```bash -# Development environment configuration -ENVIRONMENT=development -DEBUG=true - -# Mock mode configuration -MOCK_MODE=true - -# Logging configuration -LOG_LEVEL=DEBUG -LOG_FORMAT=detailed - -# External service configuration (use test addresses in development) -EXTERNAL_API_URL=https://api-test.example.com -DATABASE_URL=postgresql://dbuser:dbpass@localhost:5432/app_dev -REDIS_URL=redis://localhost:6379/0 -``` - -### 2. Development Script Template - -For development scripts that need to be run, add environment initialization at the beginning of the script: - -```python -#!/usr/bin/env python3 -""" -Development script template - Data processing/testing scripts, etc. -""" -import os - -# ============= Development Environment Initialization (Must be at top) ============= -# 1. Set environment variables and Python path -from common_utils.load_env import setup_environment -setup_environment(load_env_file_name=".env", check_env_var="MONGODB_HOST") - -# 2. Enable Mock mode (enabled by default in development environment) -from core.di.utils import enable_mock_mode -if os.getenv("MOCK_MODE", "true").lower() == "true": - enable_mock_mode() - print("🧪 Development script: Mock mode enabled") - -# 3. Initialize dependency injection -from application_startup import setup_all -setup_all() -# ================================================ - -# Now you can normally import and use project modules -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger - -logger = get_logger(__name__) - -def main(): - """Script main logic""" - logger.info("🚀 Development script execution started") - - # Example: Use dependency injection to get services - # user_service = get_bean_by_type(UserService) - # result = user_service.process_data() - - # Your script logic... - - logger.info("✅ Development script execution completed") - -if __name__ == "__main__": - main() -``` - -#### Actual Usage Example - -```python -#!/usr/bin/env python3 -""" -User data migration script -""" -import os - -# ============= Development Environment Initialization ============= -from common_utils.load_env import setup_environment -setup_environment(load_env_file_name=".env") - -from core.di.utils import enable_mock_mode -if os.getenv("MOCK_MODE", "true").lower() == "true": - enable_mock_mode() - print("🧪 Data migration script: Using Mock data") - -from application_startup import setup_all -setup_all() -# ======================================= - -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger - -logger = get_logger(__name__) - -def migrate_user_data(): - """Migrate user data""" - logger.info("Starting user data migration...") - - # Get service (will automatically use Mock implementation if Mock mode is enabled) - user_service = get_bean_by_type(UserService) - - # Process data migration - users = user_service.get_all_users() - for user in users: - # Migration logic... - logger.info(f"Migrating user: {user['name']}") - - logger.info("User data migration completed") - -if __name__ == "__main__": - migrate_user_data() -``` - -### 3. Development Startup Methods - -#### Run Development Scripts -```bash -# Use bootstrap to run scripts with full application context -uv run python src/bootstrap.py your_dev_script.py -uv run python src/bootstrap.py migrate_data.py -uv run python src/bootstrap.py test_service.py -``` - -#### Start Development Service -```bash -# Start web service (automatically loads .env file) -uv run python src/run.py - -# Or set environment variable and start -export MOCK_MODE=true -uv run python src/run.py -``` - -#### VS Code Debug Configuration - -The project already includes launch configurations in `.vscode/launch.json`. Press `F5` or use the **Run and Debug** panel to select: - -- `Python 调试程序: run` — Start API service -- `Python 调试程序: task` — Start Task Worker -- `Python 调试程序: run_this_file` — Run currently open file via bootstrap - -### 4. Mock Mode Verification - -After starting the application, you can confirm Mock mode status through logs: - -```bash -# Mock mode status will be displayed when starting the application -python run.py - -# Output example: -# 🚀 Mock mode enabled (when MOCK_MODE=true) -# 🚀 Mock mode disabled (when MOCK_MODE=false or not set) -``` - -## 📝 Practical Development Example - -### Complete Development Workflow Example - -```python -from core.di.decorators import service, mock_impl - -# 1. Define interface -class PaymentProcessor(ABC): - @abstractmethod - async def process_payment(self, amount: float, payment_method: str) -> Dict: - pass - -# 2. Implement real service -@service("stripe_payment") -class StripePaymentProcessor(PaymentProcessor): - async def process_payment(self, amount: float, payment_method: str) -> Dict: - # Real Stripe API call - return {"transaction_id": "stripe_123", "status": "success"} - -# 3. Implement Mock service -@mock_impl("mock_payment") -class MockPaymentProcessor(PaymentProcessor): - async def process_payment(self, amount: float, payment_method: str) -> Dict: - # Mock implementation for development and testing - return {"transaction_id": "mock_123", "status": "success"} - -# 4. Business service uses interface -@service("order_service") -class OrderService: - def __init__(self, payment_processor: PaymentProcessor): - self.payment_processor = payment_processor - - async def place_order(self, order_data: Dict) -> Dict: - # Process payment - payment_result = await self.payment_processor.process_payment( - order_data["amount"], - order_data["payment_method"] - ) - - if payment_result["status"] == "success": - return {"order_id": 456, "status": "confirmed"} - else: - return {"error": "Payment failed"} - -# 5. Use during development -def development_workflow(): - from core.di.utils import enable_mock_mode, get_bean_by_type - - # Enable Mock mode for development - enable_mock_mode() - - # Get service (automatically uses Mock implementation) - order_service = get_bean_by_type(OrderService) - - # Test business logic without real payment - order_data = { - "amount": 99.99, - "payment_method": "credit_card" - } - - result = await order_service.place_order(order_data) - print(f"Order result: {result}") - -# 6. Use in production environment -def production_workflow(): - from core.di.utils import disable_mock_mode, get_bean_by_type - - # Disable Mock mode to use real service - disable_mock_mode() - - # Get service (automatically uses real implementation) - order_service = get_bean_by_type(OrderService) - - # Real business processing - result = await order_service.place_order(order_data) - print(f"Real order result: {result}") -``` - -This approach allows developers to: - -1. **Parallel Development**: Frontend and backend can develop simultaneously, with backend using Mock data -2. **Fast Testing**: No need to set up complete external service environments -3. **Decoupled Development**: Each module can be developed and tested independently -4. **Flexible Switching**: Switch between Mock and real implementations through simple configuration - -This architecture greatly improves development efficiency and code quality while maintaining system testability and maintainability. diff --git a/methods/EverCore/docs/dev_docs/development_standards.md b/methods/EverCore/docs/dev_docs/development_standards.md deleted file mode 100644 index 639cb2e2..00000000 --- a/methods/EverCore/docs/dev_docs/development_standards.md +++ /dev/null @@ -1,1781 +0,0 @@ -# Development Standards - -This document introduces various standards and best practices in the project development process to help the team maintain code quality and collaboration efficiency. - ---- - -## 🚀 TL;DR (Core Principles) - -### Quick Start for Newcomers (2 Steps) -```bash -make dev-setup # One-click dev environment setup (sync deps + install hooks) -``` - -### Core Conventions - -**📦 Dependency Management** -Use `uv add/remove` to manage dependencies, avoid direct `pip install` to maintain consistency of dependency lock files - -**🎨 Code Style** -Pre-commit checks run automatically on commit (black/ruff/isort) to keep code style consistent - -**⚡️ Full Async Architecture** -Single Event Loop, use `async/await` for I/O operations, discuss with development lead before using threads/processes - -**🚫 No I/O in Loops** -Prohibit database access and API calls in for loops, use batch operations instead - -**🕐 Timezone Awareness** -All time fields must carry timezone information. Input without timezone is treated as the timezone configured by environment variable `TZ` (default UTC). Do not use `datetime.datetime.now()`, must use utility functions from `common_utils/datetime_utils.py` - -**📥 Import Standards** -- PYTHONPATH management: Project module import starting paths (src/tests/demo etc.) need unified management, communicate with development lead before changes -- Prefer absolute imports (e.g. `from core.memory import MemoryManager`), avoid relative imports (e.g. `from ...core import`) - -**📝 __init__.py Standards** -Not recommended to write any code in `__init__.py`, keep it empty - -**🌿 Branch Standards** -`dev` for daily development, `release/YYMMDD` for versioned releases, `long/xxx` for long-term feature development, `hotfix` for emergency fixes - -**🔀 Unified Branch Merge Handling** -Merging `long/xxx` to `dev`, cutting `release` from `dev`, merging `release` back to `dev` needs to be handled uniformly by development or operations lead - -**📤 MR Standards** -- Keep code commits small, iterate quickly, avoid submitting too much code at once -- Each commit should be runnable, do not submit work-in-progress or broken code -- Data migration scripts, dependency changes, infrastructure code changes, merging release branches must go through Code Review - -**💾 Data Migration Standards** -For new features involving data fixes or Schema migration, discuss feasibility and implementation timing with development and operations as early as possible - -**🏛️ Data Access Standards** -All database, search engine and other external storage read/write operations must be converged to infra layer repository methods. Direct calls in business layer are prohibited - -**🎯 Minimal Changes** -Minimize code changes when implementing requirements, avoid large-scale refactoring, prioritize incremental development. Do not over-engineer, keep it simple, efficient, and maintainable - -**💬 Comment Standards** -Always add sufficient comments (function-level + step-level), ensure reviewers can quickly understand code intent - -**📖 API Documentation Sync** -When modifying API interfaces, must synchronize updates to API documentation comments, schema definition files, and auto-generated documentation files - -**📄 Documentation Standards** -Use markdown format, place in docs directory. Small issues don't need documentation, just add comments in code - -**🌍 Internationalization Standards** -For international team communication and collaboration, code comments and documentation should be written in English - -### 📖 Quick Navigation - -- Don't know how to install dependencies? → [Dependency Management Standards](#-dependency-management-standards) -- Need database/middleware configuration? → [Development Environment Configuration Standards](#-development-environment-configuration-standards) -- Always getting errors before commit? → [Code Style Standards](#-code-style-standards) -- How to write code comments? → [Comment Standards](#-comment-standards) -- What to do after changing API? → [API Specification Sync](#-api-specification-sync) -- Not sure if you can use threads? → [Async Programming Standards](#️-async-programming-standards) -- Can I do database queries in loops? → [Prohibit I/O Operations in for Loops](#7-prohibit-io-operations-in-for-loops-) -- How to handle time fields? → [Timezone Awareness Standards](#-timezone-awareness-standards) -- Where should database queries be written? → [Data Access Standards](#️-data-access-standards) -- Import path errors? → [Import Standards](#-import-standards) -- How to name module introduction files? → [Module Introduction File Naming](#-module-introduction-file-naming) -- Don't know which branch to use? → [Branch Management Standards](#-branch-management-standards) -- How to submit code/Need to submit MR? → [MR Standards](#-mr-standards) -- Need data migration? → [Data Migration and Schema Change Process](#data-migration-and-schema-change-process) - ---- - -## 📋 Table of Contents - -- [TL;DR (Core Principles)](#-tldr-core-principles) -- [Dependency Management Standards](#-dependency-management-standards) -- [Development Environment Configuration Standards](#-development-environment-configuration-standards) -- [Code Style Standards](#-code-style-standards) -- [Comment Standards](#-comment-standards) -- [API Specification Sync](#-api-specification-sync) -- [Documentation Standards](#-documentation-standards) -- [Async Programming Standards](#️-async-programming-standards) -- [Timezone Awareness Standards](#-timezone-awareness-standards) -- [Data Access Standards](#️-data-access-standards) -- [Import Standards](#-import-standards) - - [PYTHONPATH Management](#pythonpath-management) - - [Prefer Absolute Imports](#prefer-absolute-imports) - - [__init__.py Usage Standards](#__init__py-usage-standards) -- [Module Introduction File Naming](#-module-introduction-file-naming) -- [Internationalization Standards](#-internationalization-standards) -- [Branch Management Standards](#-branch-management-standards) -- [MR Standards](#-mr-standards) -- [Code Review Process](#-code-review-process) - - [Data Migration and Schema Change Process](#data-migration-and-schema-change-process) - ---- - -## 📦 Dependency Management Standards - -### Using uv for Dependency Management - -**💡 Important Note: Recommended to use uv for dependency management** - -The project uses `uv` as the dependency management tool. It's recommended to avoid using `pip install` directly for the following reasons: - -- Dependency versions may be inconsistent -- `uv.lock` file cannot be automatically updated -- Team member environments may differ -- May affect production environment deployment - -### Correct Operations - -#### 1. Install/Sync Dependencies - -```bash -# Sync all dependencies (first install or after updates) -uv sync --group dev-full -``` - -#### 2. Add New Dependencies - -```bash -# Add production dependency -uv add - -# Add development dependency -uv add --dev - -# Specify version -uv add == -``` - -#### 3. Remove Dependencies - -```bash -uv remove -``` - -#### 4. Update Dependencies - -```bash -# Update all dependencies -uv sync --upgrade - -# Update specific dependency -uv add --upgrade -``` - -### Related Documentation - -For detailed dependency management guide, refer to: [project_deps_manage.md](./project_deps_manage.md) - ---- - -## 🔧 Development Environment Configuration Standards - -### Environment Configuration Description - -The project depends on various databases and middleware. To ensure consistency and security of development environments, these configurations are uniformly managed and distributed by the operations team. - -#### Configuration Items - -Development environment typically needs the following configurations: - -**Database Configuration** -- MongoDB connection information -- PostgreSQL connection information -- Redis connection information - -**Middleware Configuration** -- Kafka connection configuration -- ElasticSearch connection configuration -- Other message queues or cache services - -**Third-party Service Configuration** -- API keys and access credentials -- Object storage configuration -- Other external service credentials - -### How to Get Configuration - -#### 1. New Employee Onboarding - -New developers joining the project, please follow this process to get configurations: - -1. **Contact operations lead** (see contact information at the end of document) -2. **State your needs**: - - Your name and role - - Environment needed (development/testing) - - Specific services to access -3. **Receive configuration**: Operations lead will provide configuration files or environment variables -4. **Local configuration**: Place configuration information in project's `config.json` or `.env` file (Note: these files are in `.gitignore`, won't be committed to repository) - -#### 2. Configuration File Location - -```bash -# Configuration files in project root (do not commit to git) -config.json # Main configuration file -.env # Environment variable configuration -env.template # Configuration template (reference, need to fill in real values) -``` - -#### 3. Environment Variable Examples - -Reference `env.template` file, your `.env` file typically contains the following types of configuration: - -```bash -# Tenant (required for local dev) -TENANT_SINGLE_TENANT_ID=t_yourname - -# MongoDB -MONGODB_URI=mongodb://... - -# Redis -REDIS_HOST=... -REDIS_PORT=... -REDIS_PASSWORD=... - -# Kafka -KAFKA_BOOTSTRAP_SERVERS=... - -# ElasticSearch -ES_HOST=... -ES_PORT=... -``` - -### Configuration Management Notes - -#### ⚠️ Security Standards - -1. **Prohibit committing sensitive configuration** - - All configuration files containing passwords, keys, tokens must not be committed to git - - Check `.gitignore` includes configuration files before committing - - Using pre-commit hook can help detect sensitive information - -2. **Configuration file permissions** - - Local configuration files should have appropriate permissions (only current user readable) - - Do not paste configuration content in public places (like chat records, documents) - -3. **Configuration update notifications** - - If configuration is updated, operations team will notify relevant developers - - Update local configuration promptly after receiving notification - -#### 🔄 Configuration Change Process - -If you need to: -- Add new configuration items -- Modify configuration structure -- Add new environments or services - -**Recommended process**: - -1. **Discuss with development lead**: Confirm necessity and impact scope of configuration changes -2. **Contact operations lead**: Explain configuration needs and reasons for changes -3. **Update configuration template**: Update `env.template` and related documentation -4. **Team notification**: Notify all developers to sync update local configuration - -### Different Environment Description - -| Environment | Purpose | Configuration Source | Notes | -|-------------|---------|---------------------|-------| -| **Development** | Local development and debugging | Provided by operations | Usually connects to development database, data can be freely tested | -| **Testing** | Integration and functional testing | Auto-deployed configuration | Connects to test database, data periodically reset | -| **Production** | Live running services | Strictly controlled by operations | Only operations and authorized personnel can access | - -**Note**: Developers usually only need development environment configuration. Testing and production environment configurations are managed by CI/CD and operations team. - ---- - -## 🎨 Code Style Standards - -### Pre-commit Hook Configuration - -The project uses `pre-commit` to unify code style. It's recommended to install pre-commit hook after first cloning the project. - -#### Installation Steps - -```bash -# One-click dev environment setup (sync deps + install hooks) -make dev-setup -``` - -> **Tip**: `make dev-setup` automatically runs `uv sync --dev` and installs pre-commit hooks. If you only need to install hooks separately, run `make setup-hooks`. - -#### Functions - -Pre-commit hook will automatically execute the following checks before each commit: - -- **Code formatting**: Format Python code using black/ruff -- **Import sorting**: Sort import statements using isort -- **Code checking**: Code quality check using ruff/flake8 -- **Type checking**: Type check using pyright/mypy -- **YAML/JSON format**: Check configuration file format -- **Trailing whitespace**: Remove extra whitespace at end of files - -#### Manual Check - -```bash -# Run check on all files -pre-commit run --all-files - -# Run check on staged files -pre-commit run -``` - ---- - -## 💬 Comment Standards - -### Core Principle - -**💡 Important Note: Always add sufficient comments** - -Good comments help team members quickly understand code intent, improving maintainability and Code Review efficiency. - -### Comment Requirements - -#### 1. Function-level Comments (Google-style Docstring) - -Every function/method should have a clear **Google-style docstring** explaining: - -- **Description**: What the function does -- **Args**: Type and purpose of each parameter -- **Returns**: Return value type and meaning -- **Raises**: Exceptions that may be thrown (if applicable) - -```python -# ✅ Recommended: Complete function-level comments -async def fetch_user_memories( - user_id: str, - limit: int = 100, - include_archived: bool = False -) -> list[Memory]: - """ - Fetch user's memory list. - - Args: - user_id: User unique identifier - limit: Maximum number of memories to return, default 100 - include_archived: Whether to include archived memories, default False - - Returns: - User's memory list, sorted by creation time in descending order - - Raises: - UserNotFoundError: When user does not exist - """ - ... -``` - -#### 2. Step-level Comments - -In complex business logic, add comments at key steps to explain the purpose of each step: - -```python -# ✅ Recommended: Add comments at key steps -async def process_memory_extraction(raw_data: dict) -> Memory: - # 1. Validate input data integrity - validated_data = validate_input(raw_data) - - # 2. Extract key information (people, events, time, etc.) - extracted_info = await extract_key_information(validated_data) - - # 3. Generate vector embedding for subsequent retrieval - embedding = await generate_embedding(extracted_info.content) - - # 4. Build memory object and persist - memory = Memory( - content=extracted_info.content, - embedding=embedding, - metadata=extracted_info.metadata - ) - - return memory -``` - -#### 3. Complex Logic Explanation - -For complex algorithms, business rules, or non-intuitive code, add detailed explanations: - -```python -# ✅ Recommended: Explain complex business rules -def calculate_memory_score(memory: Memory, query: str) -> float: - """Calculate relevance score between memory and query""" - # Base similarity score (cosine similarity) - base_score = cosine_similarity(memory.embedding, query_embedding) - - # Time decay factor: newer memories have higher weight - # Using exponential decay with half-life of 30 days - days_old = (now - memory.created_at).days - time_decay = math.exp(-0.693 * days_old / 30) - - # Importance weighting: memories marked as important get 50% boost - importance_boost = 1.5 if memory.is_important else 1.0 - - return base_score * time_decay * importance_boost -``` - -### Comment Style - -- Use Chinese or English consistently within the same project/module -- Comments should be concise and clear, avoid redundancy -- Keep comments updated when code changes -- Don't comment obvious code - -```python -# ❌ Not recommended: Redundant comment -i = i + 1 # increment i by 1 - -# ✅ Recommended: Explain "why" not "what" -i = i + 1 # Skip header row, start processing from data rows -``` - -### Checklist - -Before submitting code, confirm: - -- [ ] All public functions/methods have docstrings -- [ ] Complex business logic has step-level comments -- [ ] Non-intuitive code has explanatory comments -- [ ] Comments are in sync with code, no outdated comments -- [ ] Reviewers can quickly understand code intent - ---- - -## 📖 API Specification Sync - -### Core Principle - -**💡 Important Note: Must synchronize API documentation when modifying API interfaces** - -API documentation is the key basis for frontend-backend collaboration and service integration. Inconsistency between documentation and actual API leads to integration issues and debugging difficulties. - -### Sync Requirements - -When modifying API interfaces, must complete the following sync operations: - -#### 1. Update API Documentation Comments - -Ensure code API documentation comments match actual behavior: - -```python -# ✅ Recommended: Keep documentation comments consistent with actual API -from fastapi import APIRouter, Query - -router = APIRouter() - -@router.get("/memories/{memory_id}") -async def get_memory( - memory_id: str, - include_embedding: bool = Query(False, description="Whether to return vector embedding") -) -> MemoryResponse: - """ - Get detailed information of specified memory. - - - **memory_id**: Memory unique identifier - - **include_embedding**: Whether to include vector embedding data in response - - Returns: - MemoryResponse: Memory details including content, metadata, etc. - - Raises: - 404: Memory not found - 403: No permission to access this memory - """ - ... -``` - -#### 2. Update Schema Definition Files - -If API request/response structure changes, update related schema definitions: - -```python -# Update Pydantic model -class MemoryResponse(BaseModel): - """Memory response model""" - id: str = Field(..., description="Memory unique identifier") - content: str = Field(..., description="Memory content") - created_at: datetime = Field(..., description="Creation time") - # When adding new fields, add clear descriptions - embedding: list[float] | None = Field(None, description="Vector embedding, only returned on request") -``` - -#### 3. Regenerate API Documentation Files - -If the project uses auto-generated API documentation (e.g., OpenAPI/Swagger), ensure regeneration: - -```bash -# Example: Regenerate OpenAPI documentation -python scripts/generate_openapi.py - -# Or ensure FastAPI auto-generated docs are up to date -# Visit /docs or /redoc to verify -``` - -#### 4. Notify Stakeholders - -If it's a major API change, notify frontend and other dependent service developers. - -### Checklist - -Before submitting API changes, confirm: - -- [ ] API documentation comments updated and consistent with actual behavior -- [ ] Schema definition files (Pydantic models, etc.) updated -- [ ] Auto-generated API documentation files regenerated -- [ ] Frontend and other services can develop based on latest API specification -- [ ] If breaking changes, stakeholders have been notified - ---- - -## 📄 Documentation Standards - -### Core Principle - -**💡 Important Note: Do not over-generate documentation** - -Documentation is an important supplement to code, but excessive documentation increases maintenance burden. Follow the "necessary and sufficient" principle. - -### When Documentation is Needed - -| Scenario | Need Documentation | Notes | -|----------|-------------------|-------| -| Small bug fix | ❌ No | Just explain in code comments | -| Small feature optimization | ❌ No | Explain in commit message and code comments | -| New API endpoint | ⚠️ Depends | API doc comments required, separate doc depends on complexity | -| New module/component | ✅ Yes | Write module introduction documentation | -| Large-scale refactoring | ✅ Yes | Document reasons, approach and impact | -| Architecture design changes | ✅ Yes | Document design decisions and architecture description | -| Complex business processes | ✅ Yes | Write process documentation | - -### Documentation Format Requirements - -- **Format**: Use Markdown (`.md`) format -- **Syntax**: Follow standard Markdown syntax - -### Documentation Location - -``` -project_root/ -├── docs/ # Documentation root -│ ├── api_docs/ # API documentation -│ │ └── memory_api.md -│ ├── dev_docs/ # Development documentation -│ │ └── development_standards.md -│ ├── architecture/ # Architecture documentation -│ │ └── system_design.md -│ └── guides/ # User guides -│ └── getting_started.md -``` - -### Naming Convention - -- **Format**: `{category}/{filename}.md` -- **Examples**: - - `api_docs/document_slice_api.md` - - `dev_docs/coding_standards.md` - - `architecture/memory_system_design.md` - -### Documentation Content Suggestions - -A good document typically contains: - -1. **Title and introduction**: Explain the purpose of the document -2. **Background/motivation**: Why this feature/change is needed -3. **Core content**: Detailed explanation -4. **Examples**: Code examples or usage examples -5. **Related documentation**: Links to other related documents - -### Checklist - -Before writing documentation, ask yourself: - -- [ ] Is this change complex enough to need separate documentation? -- [ ] Are code comments already sufficient to explain the issue? -- [ ] Is the documentation in the correct directory? -- [ ] Is the documentation name clear and understandable? - ---- - -## ⚡️ Async Programming Standards - -### Full Async Architecture Principles - -The project adopts **full async architecture**, based on the following principles: - -#### 1. Single Event Loop Principle - -- **The entire application uses one main Event Loop** -- Avoid creating new Event Loops in code (`asyncio.new_event_loop()`) -- Avoid using `asyncio.run()` to start new loops in async context - -#### 2. About Using Threads and Processes ⚠️ - -**💡 Important Note: Be cautious with multithreading and multiprocessing** - -The project is based on single Event Loop full async architecture, avoid the following operations: - -```python -# ❌ Not recommended: Creating threads -import threading -thread = threading.Thread(target=some_function) -thread.start() - -# ❌ Not recommended: Using thread pool (unless special cases) -from concurrent.futures import ThreadPoolExecutor -executor = ThreadPoolExecutor() - -# ❌ Not recommended: Creating processes -import multiprocessing -process = multiprocessing.Process(target=some_function) -process.start() - -# ❌ Not recommended: Using process pool -from concurrent.futures import ProcessPoolExecutor -executor = ProcessPoolExecutor() -``` - -**Why not recommended?** -- May break single Event Loop architecture, causing concurrency issues -- Thread safety issues are complex, easy to introduce race conditions -- Resource management is difficult, may cause resource leaks -- May affect async context (contextvars) normal operation -- Debugging becomes harder, stack traces are complex - -**Special Case Handling** - -If you really need to use threads or processes (e.g., CPU-intensive computation, calling third-party libraries that don't support async), it's recommended to: - -1. **Discuss with development lead in advance** -2. Explain why async solution cannot meet the needs -3. Provide resource management plan (ensure threads/processes are properly closed) -4. Go through Code Review - -**Allowed scenario examples**: - -```python -# ✅ Special case: Calling sync libraries that don't support async (after discussion) -import asyncio -from concurrent.futures import ThreadPoolExecutor - -# Globally shared thread pool, limit max threads -_EXECUTOR = ThreadPoolExecutor(max_workers=4) - -async def call_sync_library(data): - """Call third-party library that doesn't support async (confirmed with lead)""" - loop = asyncio.get_event_loop() - # Run in thread pool to avoid blocking main loop - result = await loop.run_in_executor( - _EXECUTOR, - sync_blocking_function, - data - ) - return result -``` - -#### 3. Async Function Definition - -I/O operations should use async functions: - -```python -# ✅ Correct: Async function -async def fetch_user_data(user_id: str) -> dict: - async with httpx.AsyncClient() as client: - response = await client.get(f"/users/{user_id}") - return response.json() - -# ❌ Wrong: Sync I/O -def fetch_user_data(user_id: str) -> dict: - response = requests.get(f"/users/{user_id}") - return response.json() -``` - -#### 4. Database Operations - -```python -# ✅ Correct: Using async database driver -from pymongo import AsyncMongoClient - -async def get_user(db, user_id: str): - return await db.users.find_one({"_id": user_id}) - -# ❌ Wrong: Using sync driver -from pymongo import MongoClient - -def get_user(db, user_id: str): - return db.users.find_one({"_id": user_id}) -``` - -#### 5. HTTP Client - -```python -# ✅ Correct: Using httpx.AsyncClient -import httpx - -async def call_api(url: str): - async with httpx.AsyncClient() as client: - response = await client.get(url) - return response.json() - -# ❌ Wrong: Using requests -import requests - -def call_api(url: str): - response = requests.get(url) - return response.json() -``` - -#### 6. Concurrent Processing - -Use `asyncio.gather()` for concurrent operations: - -```python -# ✅ Correct: Execute multiple tasks concurrently -async def fetch_multiple_users(user_ids: list[str]): - tasks = [fetch_user_data(uid) for uid in user_ids] - results = await asyncio.gather(*tasks) - return results - -# ❌ Wrong: Serial execution -async def fetch_multiple_users(user_ids: list[str]): - results = [] - for uid in user_ids: - result = await fetch_user_data(uid) - results.append(result) - return results -``` - -#### 7. Prohibit I/O Operations in for Loops ⚠️ - -**💡 Important Note: Avoid serial I/O operations in loops** - -Doing database access, API calls and other I/O operations in for loops causes serious performance issues, because each operation needs to wait for the previous one to complete, unable to take advantage of async concurrency. - -**❌ Wrong example: I/O operations in loops** - -```python -# Wrong: Serial database access in loop -async def get_users_info(user_ids: list[str]): - results = [] - for user_id in user_ids: - # Each loop iteration waits for database return, very poor performance - user = await db.users.find_one({"_id": user_id}) - results.append(user) - return results - -# Wrong: Serial API calls in loop -async def fetch_user_profiles(user_ids: list[str]): - profiles = [] - for user_id in user_ids: - # Each loop iteration waits for API response, wasting time - response = await api_client.get(f"/users/{user_id}") - profiles.append(response.json()) - return profiles - -# Wrong: Batch database inserts in loop -async def save_messages(messages: list[dict]): - for msg in messages: - # Each message inserted separately, very inefficient - await db.messages.insert_one(msg) -``` - -**✅ Correct example: Using concurrent or batch operations** - -```python -# Correct: Using asyncio.gather for concurrent execution -async def get_users_info(user_ids: list[str]): - tasks = [db.users.find_one({"_id": uid}) for uid in user_ids] - results = await asyncio.gather(*tasks) - return results - -# Correct: Using asyncio.gather for concurrent API calls -async def fetch_user_profiles(user_ids: list[str]): - tasks = [api_client.get(f"/users/{uid}") for uid in user_ids] - responses = await asyncio.gather(*tasks) - return [r.json() for r in responses] - -# Correct: Using batch insert operation -async def save_messages(messages: list[dict]): - if messages: - await db.messages.insert_many(messages) - -# Correct: Using database's in query instead of loop query -async def get_users_info(user_ids: list[str]): - # Single query to get all data - cursor = db.users.find({"_id": {"$in": user_ids}}) - results = await cursor.to_list(length=None) - return results -``` - -**Performance Comparison** - -Assuming 100 users, each database query takes 10ms: -- ❌ Loop serial query: 100 × 10ms = 1000ms (1 second) -- ✅ Concurrent query: ~10ms (almost simultaneous completion) -- ✅ Batch query: ~10ms (single query) - -**Exception Cases** - -In rare cases you may need to do I/O in loops, but must meet the following conditions: - -1. **Subsequent operations depend on previous result**: Must wait for previous operation to complete before next one -2. **Rate limiting needs**: Need to control concurrency to avoid pressure on external services -3. **Approved by development lead** - -```python -# Allowed: Serial operations with dependencies (comment explaining reason) -async def process_workflow(steps: list[dict]): - result = None - for step in steps: - # Each step depends on previous step's result, cannot be concurrent - result = await execute_step(step, previous_result=result) - return result - -# Allowed: Using semaphore to control concurrency (comment explaining reason) -async def fetch_with_rate_limit(urls: list[str]): - # Limit max 5 concurrent requests to avoid triggering external API rate limiting - semaphore = asyncio.Semaphore(5) - - async def fetch_one(url: str): - async with semaphore: - return await api_client.get(url) - - tasks = [fetch_one(url) for url in urls] - return await asyncio.gather(*tasks) -``` - ---- - -## 🕐 Timezone Awareness Standards - -### Core Principle - -**💡 Important Note: All time fields must be timezone-aware** - -When handling date and time data, must ensure all time fields carry timezone information to avoid data errors and business issues caused by unclear timezone. - -**⚠️ Prohibit direct use of `datetime` module standard methods** - -The project uniformly uses utility functions from `common_utils/datetime_utils.py` for time handling, prohibit direct use of: -- ❌ `datetime.datetime.now()` -- ❌ `datetime.datetime.utcnow()` -- ❌ `datetime.datetime.today()` - -Must use project-provided utility functions: -- ✅ `get_now_with_timezone()` - Get current time (with timezone) -- ✅ `from_timestamp()` - Convert from timestamp -- ✅ `from_iso_format()` - Convert from ISO format string -- ✅ `to_iso_format()` - Convert to ISO format string -- ✅ `to_timestamp()` / `to_timestamp_ms()` - Convert to timestamp - -### Timezone Handling Rules - -#### 1. Input Data Timezone Requirements - -All time fields entering the system must meet: - -- **Must carry timezone info**: All datetime type fields must be timezone-aware -- **Default timezone**: If input data doesn't have timezone info, treat it as the timezone configured by environment variable `TZ` (**default UTC**) -- **Storage format**: When storing in database, recommend converting to UTC timezone uniformly, but must preserve timezone info - -#### 2. Python Implementation Standards - -**✅ Correct example: Using project utility functions** - -```python -from common_utils.datetime_utils import ( - get_now_with_timezone, - from_timestamp, - from_iso_format, - to_iso_format, - to_timestamp_ms, - to_timezone -) - -# Method 1: Get current time (automatically with timezone, configured by TZ env var, default UTC) -now = get_now_with_timezone() -# Returns: datetime.datetime(2025, 9, 16, 12, 17, 41, tzinfo=zoneinfo.ZoneInfo(key='UTC')) - -# Method 2: Convert from timestamp (auto-detect seconds/milliseconds, auto-add timezone) -dt = from_timestamp(1758025061) -dt_ms = from_timestamp(1758025061000) - -# Method 3: Convert from ISO string (auto-handle timezone) -dt = from_iso_format("2025-09-15T13:11:15.588000") # No timezone, auto-add default timezone -dt_with_tz = from_iso_format("2025-09-15T13:11:15Z") # Has timezone, preserve original then convert - -# Method 4: Format to ISO string (auto-include timezone) -iso_str = to_iso_format(now) -# Returns: "2025-09-16T12:20:06.517301Z" - -# Method 5: Convert to timestamp -ts = to_timestamp_ms(now) -# Returns: 1758025061123 -``` - -**❌ Wrong example: Direct use of datetime module** - -```python -import datetime - -# ❌ Wrong: Prohibit using datetime.datetime.now() -naive_dt = datetime.datetime.now() # Timezone unclear, prohibit! - -# ❌ Wrong: Prohibit using datetime.datetime.utcnow() -dt = datetime.datetime.utcnow() # Deprecated in Python 3.12+, prohibit! - -# ❌ Wrong: Prohibit using datetime.datetime.today() -dt = datetime.datetime.today() # Timezone unclear, prohibit! - -# ❌ Wrong: Manually creating naive datetime -naive_dt = datetime.datetime(2025, 1, 1, 12, 0, 0) # No timezone info -``` - -**🔧 How to fix existing code** - -```python -# Old code (wrong) -import datetime -now = datetime.datetime.now() - -# New code (correct) -from common_utils.datetime_utils import get_now_with_timezone -now = get_now_with_timezone() - -# ---------------- - -# Old code (wrong) -from datetime import datetime -dt = datetime(2025, 1, 1, 12, 0, 0) - -# New code (correct) -from common_utils.datetime_utils import from_iso_format -dt = from_iso_format("2025-01-01T12:00:00") # Auto-add default timezone - -# ---------------- - -# Old code (wrong) -ts = int(datetime.now().timestamp() * 1000) - -# New code (correct) -from common_utils.datetime_utils import get_now_with_timezone, to_timestamp_ms -ts = to_timestamp_ms(get_now_with_timezone()) -``` - -### Checklist - -During code review, please confirm: - -- [ ] **Prohibit direct use of `datetime.datetime.now()`**, must use `get_now_with_timezone()` -- [ ] **Prohibit direct use of `datetime.datetime.utcnow()`** or `datetime.datetime.today()` -- [ ] All time retrieval goes through utility functions in `common_utils/datetime_utils.py` -- [ ] Time parsed from external input uses `from_iso_format()` or `from_timestamp()` -- [ ] Time formatting uses `to_iso_format()` instead of manually calling `.isoformat()` -- [ ] Timestamp conversion uses `to_timestamp_ms()` instead of manual calculation -- [ ] Database schema uses timezone-aware types (e.g., `timestamptz`) -- [ ] API response time strings include timezone info (ISO 8601 format) -- [ ] Test data in unit tests all have timezone info - ---- - -## 🏛️ Data Access Standards - -### Core Principle - -**💡 Important Note: All external storage access must go through infra layer repository** - -When handling databases, search engines and other external storage systems, must follow strict layered architecture principles. All data read/write operations must be converged to `infra_layer` `repository` layer, prohibit direct calls to external storage capabilities in business layer or other upper layers. - -**⚠️ Prohibit direct external storage access in these layers** -- ❌ `biz_layer` (Business layer) -- ❌ `memory_layer` (Memory layer) -- ❌ `agentic_layer` (Agent layer) -- ❌ API interface layer (`api_specs`) -- ❌ Application layer (`app.py`, controllers, etc.) - -**✅ Must access through** -- `infra_layer/adapters/out/persistence/repository/` - Database access -- `infra_layer/adapters/out/search/repository/` - Search engine access - -### Why This Standard? - -#### 1. Separation of Concerns - -Following Hexagonal Architecture and Clean Architecture principles: -- **Business layer**: Focus on business logic, don't care where data comes from -- **Infrastructure layer**: Handle all external system interaction details -- **Isolate changes**: When changing database or search engine, only need to modify infra layer - -#### 2. Testability - -```python -# ✅ Benefit: Business layer depends on abstract interface, easy to mock test -async def process_user_memory(user_id: str, memory_repo: MemoryRepository): - """Business logic doesn't depend on specific implementation""" - memories = await memory_repo.find_by_user_id(user_id) - # Business processing... - -# Can easily replace with mock during testing -mock_repo = MockMemoryRepository() -await process_user_memory("user_1", mock_repo) -``` - -#### 3. Code Reuse and Consistency - -- Avoid repeatedly writing same database query logic in multiple places -- Unified exception handling, logging, performance monitoring -- Unified data transformation, validation - -#### 4. Centralized Performance Optimization - -- Index optimization, query optimization implemented uniformly in repository layer -- Cache strategies managed uniformly -- Batch operation optimization done in one place, benefits entire project - -### Correct Architecture Layering - -``` -┌─────────────────────────────────────────┐ -│ API Layer (api_specs, app.py) │ -│ - Receive requests, return responses │ -└─────────────┬───────────────────────────┘ - │ calls - ▼ -┌─────────────────────────────────────────┐ -│ Business Layer (biz_layer) │ -│ - Business logic processing │ -│ - Depends on abstract interfaces (Port)│ -└─────────────┬───────────────────────────┘ - │ dependency injection - ▼ -┌─────────────────────────────────────────┐ -│ Memory Layer (memory_layer) │ -│ - Memory management logic │ -│ - Depends on abstract interfaces (Port)│ -└─────────────┬───────────────────────────┘ - │ dependency injection - ▼ -┌─────────────────────────────────────────┐ -│ Infrastructure Layer (infra_layer) │ -│ - Repository implementation (Adapter) │ -│ - Directly operate database/search │ -│ - MongoDB, PostgreSQL, ES, Milvus │ -└─────────────────────────────────────────┘ -``` - -### Checklist - -When writing or reviewing code, please confirm: - -- [ ] **Are database operations in infra_layer/repository?** -- [ ] **Are search engine operations in infra_layer/repository?** -- [ ] **Does business layer depend on abstract interfaces (Port) not concrete implementations?** -- [ ] **Is dependency injection used to pass repository?** -- [ ] **Avoid directly creating database connections in business/API/application layers?** -- [ ] **Avoid directly using MongoDB/PostgreSQL/ES/Milvus clients in business layer?** -- [ ] **Has new Repository been registered in dependency injection container?** -- [ ] **Do Repository methods have clear business semantics (not exposing underlying implementation details)?** - ---- - -## 📥 Import Standards - -### PYTHONPATH Management - -**💡 Important Note: PYTHONPATH needs unified management** - -The project uniformly manages `PYTHONPATH` and module import paths. Changes involving path configuration should be discussed with development lead before unified configuration. - -#### Why Unified Management? - -- Chaotic import paths may cause modules not found or import errors -- Inconsistent paths across environments (dev/test/prod) may cause deployment issues -- Inconsistent IDE configuration may affect team collaboration -- Mixing relative and absolute imports increases code maintenance difficulty - -#### Management Scope - -The following directories in the project should maintain unified import paths: - -- `src/`: Main business code -- `tests/`: Test code -- `unit_test/`: Unit tests -- `evaluation/`: Evaluation scripts -- Other directories needing to be imported (e.g., `demo/`) - -#### Recommended Practices - -1. **Unified project root directory** - - Project root is `/Users/admin/memsys` (or corresponding deployment path) - - src directory added to PYTHONPATH, import directly from module name - -2. **Import standard examples** - -```python -# ✅ Recommended: Absolute import (src already in PYTHONPATH) -from core.memory.manager import MemoryManager -from infra_layer.adapters.out.db import MongoDBAdapter -from tests.fixtures.mock_data import get_mock_user - -# ✅ Recommended: Import in test files -from unit_test.email_data_constructor import construct_email - -# ❌ Not recommended: Cross-level relative import -from ...core.memory.manager import MemoryManager - -# ❌ Not recommended: Including src prefix (src already in PYTHONPATH, no prefix needed) -from src.core.memory.manager import MemoryManager - -# ❌ Not recommended: Using sys.path.append to temporarily modify path -import sys -sys.path.append("../src") # May cause environment inconsistency -``` - -### Prefer Absolute Imports - -**💡 Important Note: Recommend absolute imports, avoid relative imports** - -#### Why Recommend Absolute Imports? - -Although relative imports are more concise in some scenarios, they have these issues: - -- **Poor readability**: `from ...core.memory import Manager` is less intuitive than `from core.memory import Manager` -- **Difficult refactoring**: Moving files requires modifying all relative import levels -- **Complex debugging**: Stack traces with relative import paths are unclear -- **Tool support**: IDE and static analysis tools support absolute imports better -- **Testing convenience**: Test files using absolute imports are easier to understand dependencies - -#### Import Style Comparison - -```python -# ✅ Recommended: Absolute import (src already in PYTHONPATH) -from core.memory.manager import MemoryManager -from core.memory.types import MemoryType, MemoryStatus -from infra_layer.adapters.out.db.mongodb import MongoDBAdapter -from common_utils.logger import get_logger - -# ✅ Acceptable: Single-level relative import within same package -# File: src/core/memory/manager.py -from .types import MemoryType # Same directory -from .extractors.base import BaseExtractor # Subdirectory - -# ❌ Not recommended: Cross-level relative import -from ...infra_layer.adapters import MongoDBAdapter -from ....common_utils.logger import get_logger - -# ❌ Not recommended: Multi-level upward relative import (hard to maintain) -from ......some_module import something -``` - -### __init__.py Usage Standards - -**💡 Important Note: Not recommended to write any code in `__init__.py`** - -#### Why Keep `__init__.py` Empty? - -- **Import side effects**: `__init__.py` executes when package is imported, any code may produce unexpected side effects -- **Circular dependencies**: Even simple module exports easily cause circular import issues -- **Performance impact**: Code execution during import affects startup performance and module loading speed -- **Maintainability**: Code scattered in `__init__.py` is hard to locate and maintain -- **Testing difficulty**: Mock and unit tests become complex -- **Implicit behavior**: Implicit execution during import increases code understanding difficulty - -#### Recommended Usage - -**✅ Recommended: Keep as empty file** - -```python -# src/core/memory/__init__.py - -# Empty file, only serves as Python package identifier -# Do not write any code here -``` - -**How to import modules?** - -Import directly from specific module files, don't rely on `__init__.py` re-export: - -```python -# ✅ Recommended: Import directly from module files -from core.memory.manager import MemoryManager -from core.memory.types import MemoryType, MemoryStatus -from core.memory.extractors.base import BaseExtractor - -# ❌ Not recommended: Relying on __init__.py re-export -from core.memory import MemoryManager # Requires export code in __init__.py -``` - -#### Checklist - -When writing or reviewing `__init__.py`, please confirm: - -- [ ] File is empty (or only contains comments)? -- [ ] No import statements? -- [ ] No variable or constant definitions? -- [ ] No global object instances created? -- [ ] No classes or functions defined? -- [ ] No logic executed? - -**If any of the above answers "no", move the code to a separate module file.** - ---- - -## 📁 Module Introduction File Naming - -### Core Principle - -**💡 Important Note: Use `introduction.md` as module introduction file** - -In subdirectories under `src/core/`, uniformly use lowercase `introduction.md` as module introduction file, not uppercase `README.md`. - -### Why Not Use README.md? - -- `README.md` may be auto-generated or legacy files -- Using `introduction.md` clearly distinguishes manually written module introductions from auto-generated content -- Maintains naming consistency and predictability - -### Naming Examples - -``` -src/core/ -├── di/ -│ └── introduction.md # DI module introduction -├── addons/ -│ └── introduction.md # Addons module introduction -├── component/ -│ └── introduction.md # Component module introduction -└── memory/ - └── introduction.md # Memory module introduction -``` - -### introduction.md Content Suggestions - -A good module introduction file should include: - -1. **Module overview**: Module functionality and positioning -2. **Directory structure**: File organization within the module -3. **Core features**: Main classes, functions, and interface descriptions -4. **Usage examples**: Basic usage code examples -5. **Related documentation**: Links to other related documents - ---- - -## 🌍 Internationalization Standards - -### Core Principle - -**💡 Important Note: Use English for code comments and documentation** - -For international team communication and collaboration, all code comments and documentation should be written in English to ensure team members from different language backgrounds can understand and contribute to the project. - -### Language Requirements - -#### 1. Code Comments - -All code comments must be written in English: - -```python -# ✅ Recommended: English comments -def calculate_memory_score(memory: Memory, query: str) -> float: - """ - Calculate relevance score between memory and query. - - Args: - memory: The memory object to evaluate - query: The search query string - - Returns: - A float score between 0 and 1 indicating relevance - """ - # Calculate base similarity score using cosine similarity - base_score = cosine_similarity(memory.embedding, query_embedding) - - # Apply time decay factor: newer memories have higher weight - time_decay = calculate_time_decay(memory.created_at) - - return base_score * time_decay - -# ❌ Not recommended: Non-English comments -def calculate_memory_score(memory: Memory, query: str) -> float: - """ - 计算记忆与查询的相关性得分。 - """ - # 计算基础相似度得分 - base_score = cosine_similarity(memory.embedding, query_embedding) - ... -``` - -#### 2. Documentation - -All documentation files should be written in English: - -- Technical documentation -- API documentation -- Module introduction files (`introduction.md`) -- Development guides -- Architecture documentation - -**Note**: Chinese documentation can be maintained as supplementary reference (`*_zh.md`), but English should be the primary version. - -#### 3. Commit Messages - -Git commit messages should be written in English: - -```bash -# ✅ Recommended: English commit messages -git commit -m "feat: add user authentication endpoint" -git commit -m "fix: resolve memory leak in cache manager" -git commit -m "docs: update API documentation for search endpoint" - -# ❌ Not recommended: Non-English commit messages -git commit -m "功能: 添加用户认证接口" -``` - -#### 4. Code Naming - -Variable names, function names, class names, and other identifiers should use English: - -```python -# ✅ Recommended: English naming -class MemoryManager: - def __init__(self): - self.cache_size = 1000 - self.max_retries = 3 - - async def fetch_user_memories(self, user_id: str) -> list[Memory]: - ... - -# ❌ Not recommended: Non-English or mixed naming -class JiyiGuanliqi: # Pinyin naming - def __init__(self): - self.huancun_daxiao = 1000 # Pinyin variable name -``` - -### Benefits - -- **Global Collaboration**: Enables team members worldwide to understand and contribute -- **Industry Standard**: Aligns with international software development practices -- **Tool Compatibility**: Better support from IDEs, linters, and documentation generators -- **Knowledge Sharing**: Easier to share code and documentation with the open-source community - -### Checklist - -Before submitting code, confirm: - -- [ ] All code comments are written in English -- [ ] Docstrings are written in English -- [ ] Commit messages are written in English -- [ ] Variable and function names use English -- [ ] New documentation is written in English (with optional Chinese supplement) - ---- - -## 🌿 Branch Management Standards - -### Branch Type Descriptions - -| Branch | Description | Notes | -|--------|-------------|-------| -| `master` | Stable version; only bug fix branches cut from here, `release/xxx` and `hotfix/xxx` merge here | Production deployment branch | -| `dev` | Daily development version; continuous code commits | If versioning has started & commit is for this version, commit to `release`; non-urgent small bugs & features merge to `dev`, catch next release | -| `release/YYMMDD` | Versioning branch; first deploy to test, then production; first merge `dev` to `master`, then cut from `dev`; after actual release merge back to `master`, `dev` | Currently irregular (notified in group); only this release's bug or code commits | -| `feature/xxxx` | Single cycle, small feature; merge to `dev` or some `release` | Merge to `dev` can be direct; merge to `release` recommend MR | -| `bugfix/xxxx` | Single cycle, small bug; merge to `dev` or some `release` | Merge to `dev` can be direct; merge to `release` recommend MR | -| `long/xxx` | Cross-cycle, large feature; cut from `dev`, merge to `dev` or some `release` | Separate test in new test environment (port/address distinction); regularly merge `dev` to avoid too many conflicts at end; recommend MR | -| `hotfix/xxxx` | Bug fix; cut from `master`, MR to `master` branch (`dev` if needed) | Only exists after release; normal dev stage bugs merge directly on `dev`, during versioning but before release merge to `release`, urgent bugs without current versioning use this branch; recommend MR | - -### Environment and Branch Mapping - -| Environment | Possible Branches | Notes | -|-------------|------------------|-------| -| Production | `master` branch | Stable version | -| | `release/xxx` branch | After versioned release and before bug fix | -| Testing | `dev` branch | Daily development stage | -| | `release/xxx` branch | Versioning test stage | -| | `hotfix/xxxx` | Emergency bug fix | - -### Version Tag Standards - -| Tag | Description | Notes | -|-----|-------------|-------| -| `X.Y.Z` | Version number: Major.Iteration.BugFix | May not sync with iterations, add when needed | - -- **X (Major version)**: Major architecture changes or incompatible updates -- **Y (Iteration version)**: Feature iterations, new features added -- **Z (Fix version)**: Bug fixes, small optimizations - -### Branch Operation Flows - -#### 1. Daily Development (feature/bugfix) - -```bash -# Create feature branch from dev -git checkout dev -git pull origin dev -git checkout -b feature/your-feature-name - -# After development complete -git add . -git commit -m "feat: your feature description" -git push origin feature/your-feature-name - -# Merge to dev (small features can merge directly) -git checkout dev -git merge feature/your-feature-name -git push origin dev - -# Delete feature branch -git branch -d feature/your-feature-name -git push origin --delete feature/your-feature-name -``` - -#### 2. Release Flow (release) - -```bash -# 1. First merge dev to master (ensure includes latest hotfix) -git checkout dev -git pull origin dev -git merge master -git push origin dev - -# 2. Create release branch from dev -git checkout -b release/$(date +%y%m%d) -git push origin release/$(date +%y%m%d) - -# 3. Bug fixes during testing stage -git checkout release/$(date +%y%m%d) -# ... fix bugs ... -git commit -m "fix: bug description" -git push origin release/$(date +%y%m%d) - -# 4. After release merge back to master and dev -git checkout master -git merge release/$(date +%y%m%d) -git tag -a v1.2.3 -m "Release version 1.2.3" -git push origin master --tags - -git checkout dev -git merge release/$(date +%y%m%d) -git push origin dev -``` - -#### 3. Emergency Fix (hotfix) - -```bash -# Create hotfix branch from master -git checkout master -git pull origin master -git checkout -b hotfix/critical-bug-fix - -# After fix complete, recommend MR process -git add . -git commit -m "hotfix: critical bug description" -git push origin hotfix/critical-bug-fix - -# Create Merge Request to master -# After merge remember to sync to dev -git checkout dev -git merge master -git push origin dev -``` - -#### 4. Long-term Feature Development (long) - -```bash -# Create long branch from dev -git checkout dev -git pull origin dev -git checkout -b long/big-feature - -# Regularly merge dev to avoid conflict accumulation -git checkout long/big-feature -git merge dev - -# After feature complete, recommend MR process to merge to dev or release -``` - -### Unified Branch Merge Handling Standards - -**⚠️ Important Note: The following branch merge operations need to be handled uniformly by development or operations lead** - -To ensure code quality and release process standards, the following branch merge operations need to be managed and executed uniformly by development lead or operations lead: - -#### Merge Operations Needing Unified Handling - -1. **Long-term feature branch merge to dev** - - `long/xxx` → `dev` - - Reason: Long-term feature branches usually involve large code changes, need to evaluate impact scope and potential conflicts - -2. **Cut release branch from dev** - - `dev` → `release/YYMMDD` - - Reason: Release nodes need unified coordination, ensure version content is complete and meets release requirements - -3. **Merge release back to dev** - - `release/YYMMDD` → `dev` - - Reason: Ensure release branch bug fixes correctly sync back to main development branch - -#### Notes - -- Small feature branches (`feature/xxx`, `bugfix/xxx`) merging to `dev` can be done by developers -- Emergency `hotfix` merging to `master` recommend MR process with lead review -- All merges involving `release` and `master` recommend lead confirmation - ---- - -## 📤 MR Standards - -### Core Principles - -#### 1. Small Steps, Reduce Single Commit Size - -**💡 Important Note: Keep code commits small, iterate quickly, avoid submitting too much code at once** - -Each MR should stay small and focused, easy to review and track issues. - -**Why small steps?** - -- **Easy to Review**: Smaller changes are easier to understand and review, higher Review quality -- **Fast feedback**: Small batch commits get feedback faster, adjust direction in time -- **Issue location**: When problems occur, easier to locate specific commit -- **Lower risk**: Risk of merging large amounts of code at once is much higher than multiple small merges -- **Reduce conflicts**: Frequent small batch merges reduce code conflict probability and complexity - -**Recommended practices**: - -```bash -# ✅ Recommended: Split commits by feature points or logical units -git commit -m "feat: add user authentication endpoint" -git commit -m "feat: add user authentication middleware" -git commit -m "test: add user authentication unit tests" - -# ❌ Not recommended: Submit large amounts of unrelated changes at once -git commit -m "feat: complete all user module features" # Contains dozens of file changes -``` - -**Commit split suggestions**: - -| Commit Type | Recommended Size | Description | -|-------------|-----------------|-------------| -| **Feature development** | 50-200 lines | One independent feature point or logical unit | -| **Bug fix** | As small as possible | Only include code necessary for fix | -| **Refactoring** | 100-300 lines | Only one type of refactoring at a time | -| **Documentation** | Flexible | Documentation updates can be relatively flexible | - -#### 2. Ensure Each Commit is Runnable - -**💡 Important Note: Try not to submit broken or work-in-progress code, each commit should be runnable** - -Each commit to shared branches (like `dev`, `release`) should be a runnable complete state. - -**Why ensure commit quality?** - -- **Continuous integration**: Ensure CI/CD pipeline won't fail due to incomplete code -- **Team collaboration**: Other developers can run and develop normally after pulling code -- **Fast rollback**: Any commit is a stable point that can be safely rolled back to -- **Code tracing**: Tools like `git bisect` need each commit to be runnable - -**Pre-commit checklist**: - -- [ ] Code passes pre-commit checks (formatting, lint, etc.) -- [ ] No obvious syntax or runtime errors -- [ ] Related unit tests pass -- [ ] Feature is complete, not half-finished -- [ ] No debug code (like `print` debug statements, commented out code blocks) -- [ ] No sensitive information (passwords, keys, tokens, etc.) - -#### 3. Files Requiring Code Review - -**💡 Important Note: The following types of file changes must go through Code Review** - -To ensure code quality and system stability, changes to the following files or directories must create MR and assign reviewers: - -##### Data-related Files - -| File/Directory | Description | Risk Level | -|----------------|-------------|------------| -| `migrations/` | Database migration scripts | 🔴 High | -| `devops_scripts/data_fix/` | Data fix scripts | 🔴 High | -| Any batch scripts involving `insert`/`update`/`delete` | Batch data changes | 🔴 High | - -##### Dependency-related Files - -| File/Directory | Description | Risk Level | -|----------------|-------------|------------| -| `pyproject.toml` | Dependency configuration changes | 🟠 Medium-High | -| `uv.lock` | Dependency lock file changes | 🟠 Medium-High | - -##### Infrastructure-related Files - -| File/Directory | Description | Risk Level | -|----------------|-------------|------------| -| `infra_layer/` | Infrastructure layer code | 🟠 Medium-High | -| `bootstrap.py` | Application startup entry | 🔴 High | -| `application_startup.py` | Application startup flow | 🔴 High | -| `base_app.py` | Base application class | 🔴 High | -| Dependency injection container config | DI container configuration | 🟠 Medium-High | - -##### Branch Merge Operations - -| Operation Type | Description | Risk Level | -|----------------|-------------|------------| -| Merge to `release/xxx` | Release branch merge | 🟠 Medium-High | -| Merge to `master` | Main branch merge | 🔴 High | -| `long/xxx` → `dev` | Long-term branch merge | 🟠 Medium-High | - ---- - -## 🔍 Code Review Process - -### Data Migration and Schema Change Process - -**⚠️ Important Principle: Plan ahead, communicate fully** - -When launching new features involving data fixes or Schema migration, discuss feasibility and subsequent implementation timing with development lead and operations lead as early as possible. - -#### Why Early Communication? - -Data migration and Schema changes are high-risk operations that may affect: - -- **Data integrity**: Data structure changes may cause data loss or corruption -- **Service availability**: Large-scale data migration may affect service performance -- **Rollback complexity**: Rollback after Schema changes is often more complex than code rollback -- **Time window**: Need sufficient time for data migration and verification -- **Multi-team collaboration**: Involves development, testing, operations multiple teams - -#### Common Scenario Examples - -| Scenario | Early Communication Time | Key Discussion Points | -|----------|-------------------------|----------------------| -| **Add new field** | Early development (1-2 weeks) | Default value strategy, index creation, whether to backfill historical data | -| **Field type change** | Design phase (2-3 weeks) | Data conversion rules, incompatible data handling, rollback plan | -| **Large-scale data fix** | Design phase (2-4 weeks) | Data volume estimation, migration duration, batch strategy, downtime plan | -| **Index rebuild** | Design phase (1-2 weeks) | Performance impact, execution time window, online/offline approach | -| **Data archiving/cleanup** | Design phase (2-3 weeks) | Archiving strategy, data backup, recovery mechanism | - -### Code Review Flow - -#### Submitter Recommendations - -1. **Create Merge Request** - - Fill in clear title and description - - Explain change reasons and impact scope - - Link related Issues or requirements - -2. **Self-check list** - - [ ] Code passes pre-commit checks - - [ ] Related unit tests added/updated - - [ ] Documentation updated (if necessary) - - [ ] No obvious performance issues - - [ ] No security risks - -**Note**: Project has Code Owner mechanism configured, reviewers will be auto-assigned based on changed files, no need to manually specify. - -#### Reviewer Work - -1. **Code quality review** - - Code logic correctness - - Code readability and maintainability - - Whether follows project standards - -2. **Risk assessment** - - Data security risks (especially for data scripts) - - Performance impact (async code, database queries) - - Compatibility issues (dependency upgrades, API changes) - -3. **Review feedback** - - Provide clear modification suggestions - - Mark severity (Must Fix / Should Fix / Nice to Have) - - Respond promptly (try within 24 hours) - ---- - -## 📚 Related Documentation - -- [Getting Started Guide](./getting_started.md) -- [Development Guide](./development_guide.md) -- [Dependency Management Guide](./project_deps_manage.md) -- [Bootstrap Usage Guide](./bootstrap_usage.md) -- [MongoDB Migration Guide](./mongodb_migration_guide.md) - ---- - -## ❓ FAQ - -### Q1: Forgot to install pre-commit hook? - -```bash -make setup-hooks -pre-commit run --all-files # Run check on existing code -``` - -### Q2: Accidentally installed package with pip? - -```bash -# 1. Uninstall package installed with pip -pip uninstall - -# 2. Reinstall with uv -uv add - -# 3. Re-sync environment -uv sync -``` - -### Q3: Branch merge conflict? - -```bash -# 1. Ensure local branch is up to date -git checkout your-branch -git pull origin your-branch - -# 2. Merge target branch -git merge target-branch - -# 3. After resolving conflicts, commit -git add . -git commit -m "merge: resolve conflicts with target-branch" -``` - -### Q4: How to call sync library in async code? - -```python -import asyncio - -async def use_sync_library(): - loop = asyncio.get_event_loop() - result = await loop.run_in_executor( - None, # Use default thread pool - sync_function, - arg1, - arg2 - ) - return result -``` - ---- - -## 👤 Contacts - -### Development Lead - -For the following matters, recommend communicating with development lead: - -- Thread/process usage plan discussion -- PYTHONPATH path configuration changes -- Code Review review requests -- Technical plans for data scripts, dependency changes, infrastructure changes - -**Current lead**: zhanghui - -### Operations Lead - -For the following matters, contact operations lead: - -- Development environment configuration (database, middleware connection info) -- Service access permission requests -- Environment configuration troubleshooting -- New configuration items or environment needs -- Network connection, VPN and other infrastructure issues - -**Current lead**: jianhua - ---- - -**Last updated**: 2025-10-31 diff --git a/methods/EverCore/docs/dev_docs/getting_started.md b/methods/EverCore/docs/dev_docs/getting_started.md deleted file mode 100644 index 1df99d96..00000000 --- a/methods/EverCore/docs/dev_docs/getting_started.md +++ /dev/null @@ -1,324 +0,0 @@ -# Intelligent Memory System - Quick Start Guide - -This guide will help you quickly set up and launch the Intelligent Memory System project. - -## 📋 Table of Contents - -- [Requirements](#requirements) -- [Install Dependencies](#install-dependencies) -- [Environment Configuration](#environment-configuration) -- [Start Infrastructure](#start-infrastructure) -- [Start Services](#start-services) -- [VSCode Debug Launch](#vscode-debug-launch) -- [Run Test Scripts](#run-test-scripts) -- [Development Debugging](#development-debugging) -- [Common Issues](#common-issues) - -## 🔧 Requirements - -### System Requirements -- **Operating System**: macOS, Linux, Windows -- **Python Version**: 3.12+ -- **Package Manager**: uv (recommended) -- **Docker**: Required for local infrastructure (MongoDB, Elasticsearch, Milvus, Redis) - -### Required External Services -- **MongoDB**: For storing memory data -- **Redis**: For caching and task queues -- **Elasticsearch**: For full-text search -- **Milvus**: For vector retrieval - -## 📦 Install Dependencies - -### 1. Install uv - -uv is a fast Python package manager, highly recommended. - -```bash -# macOS/Linux -curl -LsSf https://astral.sh/uv/install.sh | sh - -# Windows (PowerShell) -powershell -c "irm https://astral.sh/uv/install.ps1 | iex" - -# Verify installation -uv --version -``` - -### 2. Clone the Project - -```bash -git clone -cd memsys_opensource -``` - -### 3. Install Project Dependencies - -```bash -# uv will automatically create a virtual environment and install all dependencies -uv sync -``` - -## ⚙️ Environment Configuration - -### 1. Create Environment Configuration File - -```bash -cp env.template .env -``` - -### 2. Configure Required Environment Variables - -Edit the `.env` file and fill in actual values for the following required items: - -#### LLM Configuration - -```bash -# LLM provider and model -LLM_PROVIDER=openrouter -LLM_MODEL=x-ai/grok-4-fast -LLM_TEMPERATURE=0.3 -LLM_MAX_TOKENS=32768 - -# OpenRouter API key (or your preferred provider) -OPENROUTER_API_KEY=sk-or-v1-your-key -OPENROUTER_BASE_URL=https://openrouter.ai/api/v1 -``` - -Supported providers: `openrouter`, `openai`. Set the corresponding `{PROVIDER}_API_KEY` and `{PROVIDER}_BASE_URL`. - -#### Vectorize (Embedding) Configuration - -```bash -# Primary provider: vllm (self-deployed) or deepinfra (commercial API) -VECTORIZE_PROVIDER=deepinfra -VECTORIZE_API_KEY=your-deepinfra-key -VECTORIZE_BASE_URL=https://api.deepinfra.com/v1/openai -VECTORIZE_MODEL=Qwen/Qwen3-Embedding-4B -VECTORIZE_DIMENSIONS=1024 -``` - -#### Rerank Configuration - -```bash -# Primary provider: vllm or deepinfra -RERANK_PROVIDER=deepinfra -RERANK_API_KEY=your-deepinfra-key -RERANK_BASE_URL=https://api.deepinfra.com/v1/inference -RERANK_MODEL=Qwen/Qwen3-Reranker-4B -``` - -#### Database Configuration - -The defaults in `env.template` match the Docker Compose services. If using local Docker, no changes needed: - -```bash -# Tenant ID (required, use t_{yourname} for local dev) -TENANT_SINGLE_TENANT_ID=t_yourname - -# MongoDB (default: local Docker) -MONGODB_HOST=localhost -MONGODB_PORT=27017 -MONGODB_USERNAME=admin -MONGODB_PASSWORD=memsys123 - -# Elasticsearch (default: local Docker, port 19200) -ES_HOSTS=http://localhost:19200 -ES_VERIFY_CERTS=false - -# Milvus (default: local Docker) -MILVUS_HOST=localhost -MILVUS_PORT=19530 - -# Redis (default: local Docker) -REDIS_HOST=localhost -REDIS_PORT=6379 -``` - -> **Note**: `TENANT_SINGLE_TENANT_ID` is required. All storage resources will be prefixed with this value (e.g., `t_yourname_memsys`). Use `t_{yourname}` to avoid conflicts with other developers on shared infrastructure. - -### 3. Obtain API Keys - -- **OpenRouter**: Register at [openrouter.ai](https://openrouter.ai/) and create an API key -- **DeepInfra**: Register at [deepinfra.com](https://deepinfra.com/) and create an API key (for Embedding + Rerank) - -## 🐳 Start Infrastructure - -Start MongoDB, Elasticsearch, Milvus, and Redis via Docker Compose: - -```bash -docker-compose up -d -``` - -Wait for all services to be healthy (about 30-60 seconds on first run). Check status: - -```bash -docker-compose ps -``` - -## 🚀 Start Services - -### Start Web Service (REST API) - -```bash -# Default port 1995 -uv run python src/run.py - -# Specify port -uv run python src/run.py --port 8080 - -# Enable debug logging -LOG_LEVEL=DEBUG uv run python src/run.py -``` - -#### Startup Parameters - -| Parameter | Description | Default | -|-----------|-------------|---------| -| `--host` | Listening address | `0.0.0.0` | -| `--port` | Port | `1995` | -| `--env-file` | Environment variable file | `.env` | -| `--mock` | Enable Mock mode | disabled | - -After startup, visit API documentation: `http://localhost:1995/docs` - -### Start Task Worker (Optional) - -For async task processing: - -```bash -uv run .venv/bin/arq task.WorkerSettings -``` - -### Start Long Job (Optional) - -For persistent background processes (e.g., Kafka consumer): - -```bash -uv run python src/run.py --longjob kafka_consumer -``` - -## 🖥️ VSCode Debug Launch - -The project includes pre-configured launch configurations in `.vscode/launch.json`. Open the project in VSCode and press `F5` or use the **Run and Debug** panel: - -| Configuration | Description | -|---------------|-------------| -| `Python 调试程序: run` | Start API service (most common) | -| `Python 调试程序: task` | Start Task Worker | -| `Python 调试程序: longjob` | Start Long Job (e.g., Kafka consumer) | -| `Python 调试程序: run_this_file` | Run the currently open file via bootstrap | - -All configurations automatically read the `.env` file and support full breakpoint debugging. - -## 🧪 Run Test Scripts - -### Bootstrap Script - -`src/bootstrap.py` is a universal script runner that handles environment setup, DI initialization, and application context automatically. - -```bash -# Basic usage -uv run python src/bootstrap.py [args...] - -# Examples -uv run python src/bootstrap.py demo/extract_memory.py -uv run python src/bootstrap.py demo/chat_with_memory.py - -# Enable mock mode -uv run python src/bootstrap.py your_script.py --mock - -# Use custom env file -uv run python src/bootstrap.py your_script.py --env-file .env.test -``` - -### Run Unit Tests - -```bash -PYTHONPATH=src pytest tests/ -PYTHONPATH=src pytest tests/path/to/test_file.py -``` - -## 🐛 Development Debugging - -### Mock Mode - -Use Mock mode to bypass external dependencies during development: - -```bash -# Command line -uv run python src/run.py --mock - -# Environment variable -export MOCK_MODE=true -uv run python src/run.py -``` - -### Debug Logging - -```bash -LOG_LEVEL=DEBUG uv run python src/run.py -``` - -## ❓ Common Issues - -### uv sync fails - -```bash -uv cache clean -uv sync -``` - -### .env file not found - -```bash -cp env.template .env -``` - -### Docker services not starting - -```bash -# Check logs -docker-compose logs mongodb -docker-compose logs elasticsearch - -# Restart specific service -docker-compose restart milvus -``` - -### Port already in use - -```bash -# Check port usage -lsof -i :1995 - -# Use a different port -uv run python src/run.py --port 8080 -``` - -### Module import error - -```bash -# Ensure running from project root -pwd # Should be .../memsys_opensource - -# Reinstall dependencies -uv sync --reinstall -``` - -### Enterprise package missing after uv sync - -After running `uv sync` in `memsys_opensource`, enterprise's editable install is lost. Re-install: - -```bash -source .venv/bin/activate -cd ../memsys_enterprise -pip install -e . -``` - -## 🎯 Next Steps - -1. **Read Development Guide**: Check [development_guide.md](development_guide.md) for architecture and best practices -2. **View API Documentation**: Visit `http://localhost:1995/docs` for available API endpoints -3. **Run Demos**: Try example scripts in the `demo/` directory -4. **Read Bootstrap Guide**: See [bootstrap_usage.md](bootstrap_usage.md) for script runner details diff --git a/methods/EverCore/docs/dev_docs/memory_types_guide.md b/methods/EverCore/docs/dev_docs/memory_types_guide.md deleted file mode 100644 index aefa2532..00000000 --- a/methods/EverCore/docs/dev_docs/memory_types_guide.md +++ /dev/null @@ -1,278 +0,0 @@ -# EverCore Memory Types: Episode / Foresight / AtomicFact - ---- - -## 1. Architecture Overview - -``` -┌────────────────────────────────────────────────────────────────────────────────┐ -│ Conversation Event Stream │ -│ ↓ │ -│ ┌───────────────┐ │ -│ │ MemCell │ ← boundary-detected chunk │ -│ │ (memory unit)│ │ -│ └───────┬───────┘ │ -│ │ │ -│ ┌───────────────┬───────────────┼───────────────┬───────────────┐ │ -│ │ │ │ │ │ │ -│ ↓ ↓ ↓ ↓ ↓ │ -│ ┌────────────────┐ ┌────────────────┐ ┌─────────────┐ ┌─────────────┐ │ -│ │ Group Episode │ │Personal Episode│ │ Foresight │ │ AtomicFact │ │ -│ │ (all scenes) │ │ (group chat) │ │ (solo │ │ (solo │ │ -│ └────────────────┘ └────────────────┘ │ scene only)│ │ scene only)│ │ -│ └─────────────┘ └─────────────┘ │ -└────────────────────────────────────────────────────────────────────────────────┘ -``` - ---- - -## 2. Memory Type Details - -### 2.1 MemCell - -**Definition**: The smallest memory container generated by boundary detection from continuous conversations. - -**Core Fields**: - -| Field | Description | -|-------|-------------| -| `event_id` | Unique identifier | -| `original_data` | Raw message list | -| `timestamp` | MemCell timestamp | -| `summary` | Topic summary | -| `participants` | Participant list | -| `group_id` | Group identifier | - -**Business Purpose**: -- Source of all downstream memories -- Guarantees each unit represents a full topic -- **One-to-many relationship**: Each MemCell can produce multiple downstream memories (Episode, Foresight, AtomicFact), but each downstream memory **comes from exactly one MemCell** - ---- - -### 2.2 Episode Memory - -**Definition**: Narrative summary extracted from a MemCell, providing a higher-level semantic understanding. - -**Types**: - -| Type | `user_id` | Description | -|------|-----------|-------------| -| Group Episode | `None` | Third-person, group-wide narration | -| Personal Episode | concrete user ID | First-person view for a participant | - -**Core Fields**: - -| Field | Description | -|-------|-------------| -| `subject` | Title | -| `summary` | Short summary | -| `episode` | Full narrative | -| `parent_type` | Parent memory type (memcell) | -| `parent_id` | Parent memory ID | - -**Business Purpose**: -- Human-readable understanding for assistants -- Supports downstream reasoning tasks - ---- - -### 2.3 Foresight - -> ⚠️ **Note**: Foresight is only extracted in **solo scene** (1:1 chat), not in group chat. - -**Definition**: Predictive memories describing how the conversation may impact future decisions. Extracted directly from MemCell. - -**Core Fields**: - -| Field | Description | -|-------|-------------| -| `foresight` | Prediction text | -| `evidence` | Supporting clue | -| `start_time` / `end_time` | Valid timeframe | -| `duration_days` | Validity length | -| `parent_type` | Parent memory type (Memcell/Episode) | -| `parent_id` | Parent memory ID (for linking) | - -**Business Purpose**: -- Power proactive reminders and planning -- Support forecasting or “what will happen” questions -- Time-aware filtering based on validity - -**Example**: -```json -{ - "foresight": "User may need to deliver the project report before next Wednesday.", - "evidence": "Conversation mentioned 'deadline next Wednesday'.", - "start_time": "2024-03-10", - "end_time": "2024-03-17", - "duration_days": 7 -} -``` - ---- - -### 2.4 AtomicFact - -> ⚠️ **Note**: AtomicFact is only extracted in **solo scene** (1:1 chat), not in group chat. - -**Definition**: Atomic facts extracted directly from MemCell/conversation; every fact is an independent retrieval unit. - -> ⚠️ **Important**: The LLM returns a list of `atomic_fact`, but each fact is persisted as its own `AtomicFactRecord`. - -**MongoDB schema (`atomic_fact_records`)**: - -| Field | Description | -|-------|-------------| -| `atomic_fact` | Single fact string | -| `vector` | Embedding for the fact | -| `parent_type` | Parent memory type (Memcell/Episode) | -| `parent_id` | Parent memory ID (for linking) | -| `timestamp` | Occurrence time | -| `user_id` / `group_id` | Ownership info | - -**Business Purpose**: -- Fine-grained hybrid/vector retrieval -- Answer factual “who did what when” questions -- Reduce noise compared with long passages - -**Example (3 records)**: -```json -{ "atomic_fact": "Alice proposed a new product design.", "parent_type": "memcell", "parent_id": "mc_001" } -{ "atomic_fact": "Bob confirmed the feasibility.", "parent_type": "memcell", "parent_id": "mc_001" } -{ "atomic_fact": "Team starts prototyping next Monday.", "parent_type": "memcell", "parent_id": "mc_001" } -``` - ---- - -## 3. Quantity Ratios - -### 3.1 Baseline - -| Layer | DB Records | Notes | -|-------|-----------|-------| -| MemCell | 1 | One per boundary | -| Episode | 1 (solo) / 1+N (group) | solo: 1 group (copied to user); group: 1 group + N personal | -| Foresight | ~10 | **Solo scene only**, 4-10 items per MemCell | -| AtomicFact | **M** | **Solo scene only**, M = atomic facts (5–15 typical) | - -### 3.2 Scenario Examples - -**Scenario A: Solo Chat (1:1)** -``` -MemCell: 1 -├── Group Episode: 1 (copied to user) -├── Foresight: ~10 (extracted from MemCell) -├── AtomicFact: ~8 (extracted from MemCell) -└── Total records: 1 Episode + 10 Foresight + 8 AtomicFact = 19 -``` - -**Scenario B: Group Chat (3 participants)** -``` -MemCell: 1 -├── Group Episode: 1 -├── Personal Episode (User A): 1 -├── Personal Episode (User B): 1 -├── Personal Episode (User C): 1 -├── Foresight: 0 (NOT extracted in group chat) -├── AtomicFact: 0 (NOT extracted in group chat) -└── Total records: 4 Episodes -``` - -> **Note**: Foresight and AtomicFact are only extracted in solo (1:1) scenes for efficiency. - ---- - -## 4. Retrieval Guidance - -| Query Need | Best Memory Type | Retrieval Method | -|------------|------------------|------------------| -| Narrative context | Episode | vector + BM25 | -| Precise fact lookup | AtomicFact | vector on atomic facts | -| Future reminders | Foresight | time filter + vector | -| Persona/profile info | Profile | direct lookup (not covered here) | - ---- - -## 5. Storage & Indexes - -### 5.1 Persistence - -| Memory | Collection | Description | -|--------|------------|-------------| -| MemCell | `memcells` | raw storage | -| Episode | `episodic_memories` | narrative docs | -| Foresight | `foresight_records` | predictions | -| AtomicFact | `atomic_fact_records` | atomic facts | - -### 5.2 Search Indexes - -| Memory | Elasticsearch | Milvus | -|--------|---------------|--------| -| Episode | ✅ | ✅ | -| Foresight | ✅ | ✅ | -| AtomicFact | ✅ | ✅ (per fact) | - ---- - -## 6. Code Entry Points - -### 6.1 Extraction Flow - -```python -# Main pipeline -biz_layer/mem_memorize.py::memorize() - └── process_memory_extraction() - ├── _extract_episodes() # All scenes - ├── _extract_foresights() # Solo scene only - └── _extract_atomic_facts() # Assistant scene only -``` - -### 6.2 Extractors - -``` -memory_layer/memory_extractor/ -├── episode_memory_extractor.py -├── foresight_extractor.py -└── atomic_fact_extractor.py -``` - -### 6.3 Retrieval Interfaces - -```python -agentic_layer/memory_manager.py::MemoryManager - ├── retrieve_mem() - ├── retrieve_lightweight() # embedding/BM25/RRF - └── retrieve_agentic() # multi-round LLM-guided -``` - ---- - -## 7. FAQs - -**Q1: Why split Group vs Personal Episode?** -- Group records collective facts; personal captures user-centric perspective; retrieval can filter by `user_id`. - -**Q2: Is "10 foresights" hard-coded?** -- The extractor targets 4-10 items, but fewer items are accepted if the LLM provides less content. -- **Only extracted in solo scene** (not in group chat). - -**Q3: How many AtomicFact records per MemCell?** -- Depends on LLM output; every atomic fact becomes one document (typically 5–15). -- **Only extracted in solo scene** (not in group chat). - -**Q4: Which memory type should I query?** -- Narrative/story: Episode -- Concrete fact: AtomicFact -- Future impact/reminder: Foresight - ---- - -## 8. Version History - -| Version | Date | Notes | -|---------|------|-------| -| v1.0 | 2024-12 | Initial release | -| v1.1 | 2025-01 | Foresight/AtomicFact now extracted from MemCell (solo scene only) | - - diff --git a/methods/EverCore/docs/dev_docs/metrics_library_design.md b/methods/EverCore/docs/dev_docs/metrics_library_design.md deleted file mode 100644 index 258b3f69..00000000 --- a/methods/EverCore/docs/dev_docs/metrics_library_design.md +++ /dev/null @@ -1,1157 +0,0 @@ -# Metrics Library 设计方案 - -## 🎯 设计目标 - -1. **业务隔离第三方依赖**：业务代码只引用自己的 metrics library，不直接依赖 `prometheus_client` -2. **轻量级自动刷新**：每个 Gauge 实例自己管理刷新任务，无需全局调度器 -3. **统一继承方式**：所有 Gauge 都继承 BaseGauge 并重写 refresh 方法 -4. **接口统一**：Counter、Histogram、Gauge 使用统一的封装 - ---- - -## 📐 核心架构 - -``` -┌─────────────────────────────────────────────────────────────┐ -│ src/core/metrics/ (封装层) │ -├─────────────────────────────────────────────────────────────┤ -│ │ -│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ -│ │ Counter │ │ Histogram │ │ BaseGauge │ │ -│ │ (封装) │ │ (封装) │ │ (封装+刷新)│ │ -│ └─────────────┘ └─────────────┘ └─────────────┘ │ -│ │ │ │ │ -│ └────────────────┴──────────────────┘ │ -│ │ │ -│ 封装 prometheus_client │ -└─────────────────────────────────────────────────────────────┘ - ↑ 引用 -┌─────────────────────────────────────────────────────────────┐ -│ 业务代码 (只引用 core.metrics) │ -├─────────────────────────────────────────────────────────────┤ -│ │ -│ from core.metrics import Counter, Histogram, BaseGauge │ -│ │ -│ # 统一继承方式 │ -│ class QueueSizeGauge(BaseGauge): │ -│ def __init__(self, queue): │ -│ super().__init__('queue_size', 'Queue size') │ -│ self.queue = queue │ -│ │ -│ def refresh(self, labels: dict) -> float: │ -│ return self.queue.qsize() │ -│ │ -│ # 使用 │ -│ gauge = QueueSizeGauge(queue) │ -│ gauge.labels(name='main').start_refresh() # 默认 5 秒 │ -│ # 或手动 set │ -│ gauge.labels(name='main').set(42) │ -│ │ -└─────────────────────────────────────────────────────────────┘ -``` - ---- - -## 💻 核心实现 - -### 1. Counter 封装 - -**文件：`src/core/metrics/counter.py`** - -```python -""" -Counter 封装 - -提供统一的 Counter 接口，隔离 prometheus_client -""" -from prometheus_client import Counter as PrometheusCounter -from typing import Sequence -from .registry import get_metrics_registry - - -class Counter: - """ - Counter 指标封装 - - 特点： - - 只增不减的累加计数器 - - 适用于请求总数、错误总数等 - - 业务代码不需要直接导入 prometheus_client - - 使用示例： - from core.metrics import Counter - - requests_total = Counter( - name='http_requests_total', - description='Total HTTP requests', - labelnames=['method', 'path', 'status'] - ) - - # 使用 - requests_total.labels(method='GET', path='/api', status='200').inc() - """ - - def __init__( - self, - name: str, - description: str, - labelnames: Sequence[str] = (), - namespace: str = '', - subsystem: str = '', - unit: str = '', - ): - """ - Args: - name: 指标名称 - description: 指标描述 - labelnames: 标签名称列表 - namespace: 命名空间（可选） - subsystem: 子系统（可选） - unit: 单位（可选） - """ - registry = get_metrics_registry() - - self._counter = PrometheusCounter( - name=name, - documentation=description, - labelnames=labelnames, - namespace=namespace, - subsystem=subsystem, - unit=unit, - registry=registry, - ) - - def labels(self, **labels): - """ - 返回带标签的 Counter - - Returns: - LabeledCounter 实例 - """ - labeled = self._counter.labels(**labels) - return LabeledCounter(labeled) - - def inc(self, amount: float = 1) -> None: - """ - 增加计数（无标签版本） - - Args: - amount: 增加量，默认 1 - """ - self._counter.inc(amount) - - -class LabeledCounter: - """带标签的 Counter""" - - def __init__(self, labeled_counter): - self._counter = labeled_counter - - def inc(self, amount: float = 1) -> None: - """ - 增加计数 - - Args: - amount: 增加量，默认 1 - """ - self._counter.inc(amount) -``` - ---- - -### 2. Histogram 封装 - -**文件：`src/core/metrics/histogram.py`** - -```python -""" -Histogram 封装 - -提供统一的 Histogram 接口，隔离 prometheus_client -""" -from prometheus_client import Histogram as PrometheusHistogram -from typing import Sequence -from .registry import get_metrics_registry - - -class Histogram: - """ - Histogram 指标封装 - - 特点： - - 观测值的分布统计 - - 适用于延迟、大小等分布数据 - - 自动计算分位数、均值、总和 - - 使用示例: - from core.metrics import Histogram - - request_duration = Histogram( - name='http_request_duration_seconds', - description='HTTP request duration', - labelnames=['method', 'path'], - buckets=(0.01, 0.05, 0.1, 0.5, 1.0, 5.0) - ) - - # 使用 - request_duration.labels(method='GET', path='/api').observe(0.123) - """ - - def __init__( - self, - name: str, - description: str, - labelnames: Sequence[str] = (), - namespace: str = '', - subsystem: str = '', - unit: str = '', - buckets: Sequence[float] = ( - 0.005, 0.01, 0.025, 0.05, 0.075, 0.1, 0.25, 0.5, - 0.75, 1.0, 2.5, 5.0, 7.5, 10.0 - ), - ): - """ - Args: - name: 指标名称 - description: 指标描述 - labelnames: 标签名称列表 - namespace: 命名空间（可选） - subsystem: 子系统（可选） - unit: 单位（可选） - buckets: 直方图桶边界 - """ - registry = get_metrics_registry() - - self._histogram = PrometheusHistogram( - name=name, - documentation=description, - labelnames=labelnames, - namespace=namespace, - subsystem=subsystem, - unit=unit, - buckets=buckets, - registry=registry, - ) - - def labels(self, **labels): - """ - 返回带标签的 Histogram - - Returns: - LabeledHistogram 实例 - """ - labeled = self._histogram.labels(**labels) - return LabeledHistogram(labeled) - - def observe(self, amount: float) -> None: - """ - 记录观测值（无标签版本） - - Args: - amount: 观测值 - """ - self._histogram.observe(amount) - - -class LabeledHistogram: - """带标签的 Histogram""" - - def __init__(self, labeled_histogram): - self._histogram = labeled_histogram - - def observe(self, amount: float) -> None: - """ - 记录观测值 - - Args: - amount: 观测值 - """ - self._histogram.observe(amount) -``` - ---- - -### 3. BaseGauge 基类（核心） - -**文件：`src/core/metrics/gauge.py`** - -```python -""" -Gauge 封装 - -提供统一的 Gauge 接口，内置自动刷新能力 -""" -from prometheus_client import Gauge as PrometheusGauge -from typing import Sequence, Optional, Callable, Any -import asyncio -import logging -from abc import ABC - -logger = logging.getLogger(__name__) - - -class BaseGauge(ABC): - """ - Gauge 基类 - - 特点： - - 可增可减的瞬时值 - - 内置自动刷新能力（默认 5 秒） - - 必须继承并重写 refresh() 方法 - - 每个实例独立管理刷新任务 - - 支持手动 set() 方法 - - 使用方式 - 继承并重写 refresh 方法: - class KafkaPendingMessagesGauge(BaseGauge): - def __init__(self, kafka_consumer): - super().__init__( - name='kafka_pending_messages', - description='Number of pending messages', - labelnames=['job_name'] - ) - self.kafka_consumer = kafka_consumer - - def refresh(self, labels: dict) -> float: - '''返回当前值''' - return len(self.kafka_consumer.pending_messages) - - # 使用方式1：自动刷新（默认 5 秒） - gauge = KafkaPendingMessagesGauge(kafka_consumer) - gauge.labels(job_name='tanka').start_refresh() - - # 使用方式2：自定义刷新间隔 - gauge.labels(job_name='tanka').start_refresh(interval_seconds=10) - - # 使用方式3：手动设置（不启动自动刷新） - gauge.labels(job_name='tanka').set(42) - """ - - def __init__( - self, - name: str, - description: str, - labelnames: Sequence[str] = (), - namespace: str = '', - subsystem: str = '', - unit: str = '', - ): - """ - Args: - name: 指标名称 - description: 指标描述 - labelnames: 标签名称列表 - namespace: 命名空间（可选） - subsystem: 子系统（可选） - unit: 单位（可选） - """ - from .registry import get_metrics_registry - registry = get_metrics_registry() - - self._gauge = PrometheusGauge( - name=name, - documentation=description, - labelnames=labelnames, - namespace=namespace, - subsystem=subsystem, - unit=unit, - registry=registry, - ) - - self._name = name - self._labelnames = labelnames - - # 存储每个标签组合的刷新任务 - # key: label 值的 tuple, value: RefreshTask - self._refresh_tasks: dict[tuple, 'RefreshTask'] = {} - - def labels(self, **labels) -> 'LabeledGauge': - """ - 返回带标签的 Gauge - - Returns: - LabeledGauge 实例 - """ - labeled_gauge = self._gauge.labels(**labels) - label_key = self._make_label_key(**labels) - - return LabeledGauge( - base_gauge=self, - labeled_gauge=labeled_gauge, - label_key=label_key, - label_dict=labels, - ) - - def set(self, value: float) -> None: - """设置值（无标签版本）""" - self._gauge.set(value) - - def inc(self, amount: float = 1) -> None: - """增加值（无标签版本）""" - self._gauge.inc(amount) - - def dec(self, amount: float = 1) -> None: - """减少值（无标签版本）""" - self._gauge.dec(amount) - - def refresh(self, labels: dict) -> float: - """ - 刷新方法（子类必须重写） - - Args: - labels: 标签字典 - - Returns: - 当前 Gauge 值 - - 说明： - - 子类必须重写此方法来实现自定义刷新逻辑 - - 此方法会被自动刷新任务定期调用（默认 5 秒） - - 可以返回任何 float 值，会自动更新到 Gauge - - 示例： - class QueueSizeGauge(BaseGauge): - def __init__(self, queue): - super().__init__('queue_size', 'Queue size') - self.queue = queue - - def refresh(self, labels: dict) -> float: - return self.queue.qsize() - """ - raise NotImplementedError( - f"Gauge '{self._name}' must override refresh() method" - ) - - def _make_label_key(self, **labels) -> tuple: - """生成标签 key""" - if self._labelnames: - return tuple(labels.get(name, '') for name in self._labelnames) - return () - - async def _stop_all_refresh_tasks(self) -> None: - """停止所有刷新任务""" - for task in self._refresh_tasks.values(): - await task.stop() - self._refresh_tasks.clear() - - -class LabeledGauge: - """ - 带标签的 Gauge - - 提供和原生 Gauge 一致的接口，同时支持自动刷新 - """ - - def __init__( - self, - base_gauge: BaseGauge, - labeled_gauge: Any, - label_key: tuple, - label_dict: dict, - ): - self._base_gauge = base_gauge - self._labeled_gauge = labeled_gauge - self._label_key = label_key - self._label_dict = label_dict - - def set(self, value: float) -> None: - """设置值""" - self._labeled_gauge.set(value) - - def inc(self, amount: float = 1) -> None: - """增加值""" - self._labeled_gauge.inc(amount) - - def dec(self, amount: float = 1) -> None: - """减少值""" - self._labeled_gauge.dec(amount) - - def set_to_current_time(self) -> None: - """设置为当前时间戳""" - self._labeled_gauge.set_to_current_time() - - def start_refresh( - self, - interval_seconds: int = 5, - enable_async: bool = True, - ) -> 'LabeledGauge': - """ - 启动自动刷新 - - Args: - interval_seconds: 刷新间隔（秒），默认 5 秒 - enable_async: 是否支持异步 refresh 方法，默认 True - - Returns: - self（支持链式调用） - - 示例： - # 默认 5 秒刷新 - gauge.labels(job='tanka').start_refresh() - - # 自定义刷新间隔 - gauge.labels(job='tanka').start_refresh(interval_seconds=10) - - # 异步 refresh 方法 - class AsyncGauge(BaseGauge): - async def refresh(self, labels: dict) -> float: - return await self.get_value_async() - - gauge.labels(type='A').start_refresh(enable_async=True) - """ - # 创建包装函数，调用 base_gauge.refresh() - def refresh_wrapper(): - return self._base_gauge.refresh(self._label_dict) - - # 创建刷新任务 - task = RefreshTask( - refresh_func=refresh_wrapper, - labeled_gauge=self._labeled_gauge, - interval_seconds=interval_seconds, - enable_async=enable_async, - label_key=self._label_key, - ) - - # 存储任务 - self._base_gauge._refresh_tasks[self._label_key] = task - - # 启动任务 - task.start() - - return self - - async def stop_refresh(self) -> None: - """停止自动刷新""" - task = self._base_gauge._refresh_tasks.get(self._label_key) - if task: - await task.stop() - del self._base_gauge._refresh_tasks[self._label_key] - - -class RefreshTask: - """ - 刷新任务 - - 每个标签组合一个独立的刷新任务 - """ - - def __init__( - self, - refresh_func: Callable[[], float], - labeled_gauge: Any, - interval_seconds: int, - enable_async: bool, - label_key: tuple, - ): - self.refresh_func = refresh_func - self.labeled_gauge = labeled_gauge - self.interval_seconds = interval_seconds - self.enable_async = enable_async - self.label_key = label_key - - self._task: Optional[asyncio.Task] = None - self._running = False - self._error_count = 0 - - def start(self) -> None: - """启动刷新任务""" - if self._running: - logger.warning(f"Refresh task already running for {self.label_key}") - return - - self._running = True - self._task = asyncio.create_task(self._refresh_loop()) - logger.info( - f"Started refresh task: label_key={self.label_key}, " - f"interval={self.interval_seconds}s" - ) - - async def stop(self) -> None: - """停止刷新任务""" - if not self._running: - return - - self._running = False - - if self._task: - self._task.cancel() - try: - await self._task - except asyncio.CancelledError: - pass - self._task = None - - logger.info(f"Stopped refresh task: label_key={self.label_key}") - - async def _refresh_loop(self) -> None: - """刷新循环""" - while self._running: - try: - # 调用刷新函数 - if self.enable_async and asyncio.iscoroutinefunction(self.refresh_func): - value = await self.refresh_func() - else: - value = self.refresh_func() - - # 更新 Gauge - self.labeled_gauge.set(value) - - # 重置错误计数 - self._error_count = 0 - - except asyncio.CancelledError: - break - except Exception as e: - self._error_count += 1 - logger.error( - f"Refresh failed for {self.label_key}: {e} " - f"(error_count={self._error_count})", - exc_info=True - ) - - # 等待下一次刷新 - try: - await asyncio.sleep(self.interval_seconds) - except asyncio.CancelledError: - break -``` - ---- - -### 4. 统一导出 - -**文件：`src/core/metrics/__init__.py`** - -```python -""" -Metrics Library - -业务代码统一从这里导入指标类，不需要直接依赖 prometheus_client - -使用示例： - from core.metrics import Counter, Histogram, BaseGauge - - # Counter - requests_total = Counter('http_requests_total', 'Total requests', ['method']) - requests_total.labels(method='GET').inc() - - # Histogram - request_duration = Histogram('http_request_duration_seconds', 'Request duration', ['method']) - request_duration.labels(method='GET').observe(0.123) - - # Gauge - 继承方式 - class QueueSizeGauge(BaseGauge): - def __init__(self, queue): - super().__init__('queue_size', 'Queue size', ['queue_name']) - self.queue = queue - - def refresh(self, labels: dict) -> float: - return self.queue.qsize() - - # 使用 Gauge - gauge = QueueSizeGauge(queue) - gauge.labels(queue_name='main').start_refresh() # 默认 5 秒刷新 - # 或手动设置 - gauge.labels(queue_name='main').set(42) -""" - -from .counter import Counter -from .histogram import Histogram -from .gauge import BaseGauge -from .registry import get_metrics_registry, generate_metrics_response - -__all__ = [ - 'Counter', - 'Histogram', - 'BaseGauge', - 'get_metrics_registry', - 'generate_metrics_response', -] -``` - ---- - -## 💡 使用示例 - -### 示例 1：Kafka Metrics - -**文件：`src/infra_layer/adapters/input/mq/metrics/kafka_metrics.py`** - -```python -""" -Kafka 指标定义 - -只引用 core.metrics，不引用 prometheus_client -""" -from core.metrics import Counter, Histogram, BaseGauge - - -# ============================================================ -# Counter 和 Histogram - 直接使用 -# ============================================================ - -KAFKA_PROCESSED_MESSAGES_TOTAL = Counter( - name='kafka_processed_messages_total', - description='Total number of processed Kafka messages', - labelnames=['job_name', 'status'], -) - -KAFKA_MESSAGE_PROCESSING_DURATION = Histogram( - name='kafka_message_processing_duration_seconds', - description='Duration of message processing', - labelnames=['job_name'], - buckets=(0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0, 2.5, 5.0, 10.0), -) - - -# ============================================================ -# Gauge - 统一继承方式 -# ============================================================ - -class KafkaPendingMessagesGauge(BaseGauge): - """Kafka 待处理消息数 Gauge""" - - def __init__(self, kafka_consumer): - super().__init__( - name='kafka_prefill_pending_messages', - description='Number of pending messages in prefill stage', - labelnames=['job_name'], - ) - self.kafka_consumer = kafka_consumer - - def refresh(self, labels: dict) -> float: - """返回当前待处理消息数""" - return len(self.kafka_consumer.prefill_pending_messages) - - -class KafkaActiveConsumersGauge(BaseGauge): - """Kafka 活跃消费者数 Gauge""" - - def __init__(self, kafka_consumer): - super().__init__( - name='kafka_active_consumers', - description='Number of active consumer tasks', - labelnames=['job_name'], - ) - self.kafka_consumer = kafka_consumer - - def refresh(self, labels: dict) -> float: - """返回当前活跃消费者数""" - return len(self.kafka_consumer.consumer_tasks) - - -class KafkaRedisQueueSizeGauge(BaseGauge): - """Redis 队列大小 Gauge""" - - def __init__(self, redis_queue_manager): - super().__init__( - name='kafka_redis_queue_size', - description='Total size of Redis queues', - labelnames=['job_name'], - ) - self.redis_queue_manager = redis_queue_manager - - def refresh(self, labels: dict) -> float: - """返回 Redis 队列总大小""" - if not self.redis_queue_manager: - return 0 - - total_size = 0 - for partition in self.redis_queue_manager.partitions.values(): - size = partition.size() - total_size += size - - return total_size - - -class KafkaMemoryQueueSizeGauge(BaseGauge): - """内存队列大小 Gauge""" - - def __init__(self, memory_queue_manager): - super().__init__( - name='kafka_memory_queue_size', - description='Total size of in-memory queues', - labelnames=['job_name'], - ) - self.memory_queue_manager = memory_queue_manager - - def refresh(self, labels: dict) -> float: - """返回内存队列总大小""" - if not self.memory_queue_manager: - return 0 - - return sum( - q.qsize() - for q in self.memory_queue_manager._queues.values() - ) -``` - -### 示例 2：业务代码使用 - -**文件：`src/infra_layer/adapters/input/mq/tanka_kafka_consumer.py`** - -```python -""" -Kafka 消费者 - 使用封装的 metrics library -""" -import time -from .metrics.kafka_metrics import ( - KAFKA_PROCESSED_MESSAGES_TOTAL, - KAFKA_MESSAGE_PROCESSING_DURATION, - KafkaPendingMessagesGauge, - KafkaActiveConsumersGauge, - KafkaRedisQueueSizeGauge, - KafkaMemoryQueueSizeGauge, -) - - -class TankaKafkaConsumer: - """Kafka 消费者""" - - def __init__( - self, - job_id: str, - redis_queue_manager=None, - memory_queue_manager=None, - **kwargs - ): - self.job_id = job_id - self.redis_queue_manager = redis_queue_manager - self.memory_queue_manager = memory_queue_manager - - # 业务属性 - self.prefill_pending_messages = [] - self.consumer_tasks = [] - - # ... 其他初始化 ... - - # ✅ 设置 Gauge 自动刷新 - self._setup_metrics() - - def _setup_metrics(self) -> None: - """设置指标自动刷新""" - - # 1. 待处理消息数（默认 5 秒刷新） - pending_gauge = KafkaPendingMessagesGauge(self) - pending_gauge.labels( - job_name=self.job_id - ).start_refresh() # 默认 5 秒 - - # 2. 活跃消费者数（默认 5 秒刷新） - active_gauge = KafkaActiveConsumersGauge(self) - active_gauge.labels( - job_name=self.job_id - ).start_refresh() - - # 3. Redis 队列大小（自定义 10 秒刷新） - if self.redis_queue_manager: - redis_gauge = KafkaRedisQueueSizeGauge(self.redis_queue_manager) - redis_gauge.labels( - job_name=self.job_id - ).start_refresh(interval_seconds=10) - - # 4. 内存队列大小（默认 5 秒刷新） - if self.memory_queue_manager: - memory_gauge = KafkaMemoryQueueSizeGauge(self.memory_queue_manager) - memory_gauge.labels( - job_name=self.job_id - ).start_refresh() - - async def _process_message(self, message) -> None: - """处理消息""" - start_time = time.time() - - try: - # 业务逻辑 - await self._do_process(message) - - # ✅ Counter - KAFKA_PROCESSED_MESSAGES_TOTAL.labels( - job_name=self.job_id, - status='success' - ).inc() - - except Exception as e: - # ✅ Counter（错误） - KAFKA_PROCESSED_MESSAGES_TOTAL.labels( - job_name=self.job_id, - status='error' - ).inc() - raise - - finally: - # ✅ Histogram - duration = time.time() - start_time - KAFKA_MESSAGE_PROCESSING_DURATION.labels( - job_name=self.job_id - ).observe(duration) -``` - -### 示例 3：Memory Metrics - -**文件：`src/agentic_layer/metrics/memory_metrics.py`** - -```python -""" -Memory 指标定义 -""" -from core.metrics import Counter, Histogram, BaseGauge - - -# Counter 和 Histogram -RETRIEVE_REQUESTS_TOTAL = Counter( - name='memory_retrieve_requests_total', - description='Total number of memory retrieve requests', - labelnames=['retrieve_method', 'status'], -) - -RETRIEVE_DURATION_SECONDS = Histogram( - name='memory_retrieve_duration_seconds', - description='Duration of memory retrieve operations', - labelnames=['retrieve_method'], - buckets=(0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0), -) - - -# Gauge - 统一继承方式 -class MemoryCacheSizeGauge(BaseGauge): - """Memory Cache 大小 Gauge""" - - def __init__(self, service, cache_type: str): - super().__init__( - name='memory_cache_size', - description='Current size of memory cache', - labelnames=['cache_type'], - ) - self.service = service - self.cache_type = cache_type - - def refresh(self, labels: dict) -> float: - """返回缓存大小""" - cache = getattr(self.service, 'cache', {}) - return len(cache) - - -class MemoryActiveRequestsGauge(BaseGauge): - """Memory 活跃请求数 Gauge""" - - def __init__(self, memory_manager, operation: str): - super().__init__( - name='memory_active_requests', - description='Number of active memory requests', - labelnames=['operation'], - ) - self.memory_manager = memory_manager - self.operation = operation - - def refresh(self, labels: dict) -> float: - """返回活跃请求数""" - if self.operation == 'retrieve': - return getattr(self.memory_manager, 'active_retrieve_count', 0) - elif self.operation == 'memorize': - return getattr(self.memory_manager, 'active_memorize_count', 0) - return 0 -``` - -**文件：`src/agentic_layer/memory_manager.py`** - -```python -""" -Memory Manager - 使用 Gauge -""" -from .metrics.memory_metrics import ( - RETRIEVE_REQUESTS_TOTAL, - RETRIEVE_DURATION_SECONDS, - MemoryCacheSizeGauge, - MemoryActiveRequestsGauge, -) - - -class MemoryManager: - """Memory Manager""" - - def __init__(self, embedding_service, rerank_service): - self.embedding_service = embedding_service - self.rerank_service = rerank_service - self.active_retrieve_count = 0 - self.active_memorize_count = 0 - - # ✅ 设置 Gauge 自动刷新 - self._setup_metrics() - - def _setup_metrics(self) -> None: - """设置指标自动刷新""" - - # Embedding cache 大小（10 秒刷新） - embedding_cache_gauge = MemoryCacheSizeGauge( - self.embedding_service, - 'embedding' - ) - embedding_cache_gauge.labels( - cache_type='embedding' - ).start_refresh(interval_seconds=10) - - # Rerank cache 大小（10 秒刷新） - rerank_cache_gauge = MemoryCacheSizeGauge( - self.rerank_service, - 'rerank' - ) - rerank_cache_gauge.labels( - cache_type='rerank' - ).start_refresh(interval_seconds=10) - - # 活跃的 retrieve 请求（默认 5 秒） - retrieve_gauge = MemoryActiveRequestsGauge(self, 'retrieve') - retrieve_gauge.labels( - operation='retrieve' - ).start_refresh() - - # 活跃的 memorize 请求（默认 5 秒） - memorize_gauge = MemoryActiveRequestsGauge(self, 'memorize') - memorize_gauge.labels( - operation='memorize' - ).start_refresh() - - async def retrieve_mem(self, request): - """检索记忆""" - start_time = time.time() - retrieve_method = request.retrieve_method - - try: - # 业务逻辑 - memories = await self._do_retrieve(request) - - # ✅ Counter - RETRIEVE_REQUESTS_TOTAL.labels( - retrieve_method=retrieve_method, - status='success' - ).inc() - - return memories - - finally: - # ✅ Histogram - duration = time.time() - start_time - RETRIEVE_DURATION_SECONDS.labels( - retrieve_method=retrieve_method - ).observe(duration) -``` - ---- - -## 🎯 方案优势 - -1. **业务代码完全隔离第三方依赖** - - 业务只引用 `core.metrics`，不直接依赖 `prometheus_client` - - 方便后续替换底层实现 - -2. **统一继承方式，简单直观** - - 所有 Gauge 都继承 BaseGauge 并重写 `refresh()` 方法 - - 接口统一，学习成本低 - - 代码风格一致 - -3. **轻量级，无全局调度器** - - 每个 Gauge 实例自己管理刷新任务 - - 每个标签组合一个独立的 `asyncio.Task` - - 无需全局调度器协调 - -4. **灵活的刷新间隔** - - 默认 5 秒刷新 - - 可自定义刷新间隔（3秒、10秒、30秒等） - - 不同的标签可以有不同的间隔 - -5. **支持手动 set()** - - 可以随时手动 `set()` 设置值 - - 手动设置和自动刷新互不干扰 - - 灵活应对各种场景 - -## 📊 对比总结 - -| 维度 | 直接使用 prometheus_client | 本方案 | -|------|--------------------------|--------| -| **依赖隔离** | 业务代码直接依赖第三方库 | 业务代码只依赖 core.metrics | -| **Gauge 刷新** | 手动调用 .set() | 自动刷新（默认 5 秒） + 支持手动 set() | -| **调度器** | 无（或需要自己实现） | 无需全局调度器（每个 Gauge 独立） | -| **使用方式** | 需要自己实现刷新逻辑 | 继承 BaseGauge 重写 refresh() | -| **代码复杂度** | 简单但功能少 | 封装后同样简单且功能更多 | -| **可扩展性** | 需要自己扩展 | 继承 BaseGauge 即可扩展 | - ---- - -## 🚀 实施步骤 - -### 阶段 1：创建封装层 -1. ✅ 创建 `core/metrics/counter.py` -2. ✅ 创建 `core/metrics/histogram.py` -3. ✅ 创建 `core/metrics/gauge.py`（核心） -4. ✅ 修改 `core/metrics/__init__.py` 导出 -5. ✅ 简化 `core/metrics/registry.py`（移除 MetricsSource/Processor） - -### 阶段 2：重构业务指标 -1. ✅ 重构 `kafka_metrics.py`（改用 core.metrics，Gauge 改为继承） -2. ✅ 重构 `memory_metrics.py`（改用 core.metrics，Gauge 改为继承） - -### 阶段 3：业务代码集成 -1. ✅ 修改 `TankaKafkaConsumer`（创建 Gauge 实例并 start_refresh） -2. ✅ 修改 `MemoryManager`（创建 Gauge 实例并 start_refresh） - -### 阶段 4：清理旧代码 -1. ✅ 删除所有 `*_metrics_processor.py` 文件 -2. ✅ 删除所有 `*_metrics_source.py` 文件 -3. ✅ 删除 `metrics_processor.py` 基类 -4. ✅ 删除 `metrics_source.py` 基类 - ---- - -## 💡 关键设计要点 - -1. **统一继承方式** - - 所有 Gauge 必须继承 BaseGauge - - 必须重写 `refresh()` 方法 - - 不支持 `set_refresher()` 方式 - -2. **默认 5 秒刷新** - - `start_refresh()` 默认 5 秒间隔 - - 可通过 `interval_seconds` 参数自定义 - - 符合大部分业务场景 - -3. **支持手动 set()** - - 可以不启动自动刷新，直接手动 `set()` - - 手动设置和自动刷新可以混用 - - 灵活应对特殊场景 - -4. **轻量级实现** - - RefreshTask 管理单个标签的刷新任务 - - 每个任务独立的 `asyncio.Task` - - 无全局调度器，无性能开销 - -5. **异常隔离** - - 每个 Gauge 的刷新异常不影响其他 Gauge - - 自动错误日志记录 - - 自动重试机制 - ---- - -## 🔧 配置示例 - -**文件：`src/core/config.py`** - -```python -from pydantic_settings import BaseSettings - - -class Settings(BaseSettings): - # Metrics 配置 - PROMETHEUS_METRICS_ENABLED: bool = True - - # Gauge 默认刷新间隔（秒） - # 注意：这是全局默认值，业务代码可以通过 start_refresh(interval_seconds=N) 覆盖 - METRICS_GAUGE_DEFAULT_INTERVAL: int = 5 - - # ... 其他配置 ... - - -settings = Settings() -``` - -需要我开始实施这个方案吗？ - diff --git a/methods/EverCore/docs/dev_docs/run_memorize_usage.md b/methods/EverCore/docs/dev_docs/run_memorize_usage.md deleted file mode 100644 index a31852e1..00000000 --- a/methods/EverCore/docs/dev_docs/run_memorize_usage.md +++ /dev/null @@ -1,300 +0,0 @@ -# run_memorize.py Usage Documentation - -## Overview - -`run_memorize.py` is a conversation memory storage script that reads JSON files conforming to the `ConversationFormat` format and stores them item by item into the memory system via HTTP API. - -## Features - -- ✅ Read and validate JSON files in ConversationFormat format -- ✅ Support both `solo` and `team` scenarios -- ✅ Automatically save settings metadata -- ✅ Call memorize interface item by item to process messages -- ✅ Provide format validation mode -- ✅ Detailed logging output - -## Usage - -### 1. Basic Usage - -Store memories via HTTP API (must specify scene): - -```bash -python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat.json \ - --api-url http://localhost:1995/api/v0/memories \ - --scene solo -``` - -### 2. Using team Scenario - -```bash -python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat.json \ - --api-url http://localhost:1995/api/v0/memories \ - --scene team -``` - -### 3. Format Validation Only - -Validate whether the input file format is correct without performing storage (no API address needed): - -```bash -python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat.json \ - --scene solo \ - --validate-only -``` - -## Command-Line Arguments - -| Argument | Required | Description | -|----------|----------|-------------| -| `--input` | Yes | Input conversation JSON file path (ConversationFormat format) | -| `--scene` | Yes | Memory extraction scenario, only supports `solo` or `team` | -| `--api-url` | No* | memorize API address (required for non-validation mode) | -| `--validate-only` | No | Only validate input file format, do not perform storage | - -*Note: When using `--validate-only`, no need to provide `--api-url`, otherwise it's required. - -## Input File Format - -The input file must conform to the `ConversationFormat` specification, see `data_format/conversation/conversation_format.py`. - -### Format Example - -```json -{ - "version": "1.0.0", - "session_meta": { - "name": "Smart Sales Assistant Project Team", - "description": "Development discussion group for Smart Sales Assistant project", - "group_id": "group_sales_ai_2025", - "created_at": "2025-02-01T01:00:00Z", - "default_timezone": "UTC", - "user_details": { - "user_101": { - "full_name": "Alex", - "role": "Tech Lead" - }, - "user_102": { - "full_name": "Betty", - "role": "Product Manager" - } - }, - "tags": ["AI", "Sales", "Project Development"] - }, - "conversation_list": [ - { - "message_id": "msg_001", - "create_time": "2025-02-01T02:00:00Z", - "sender": "user_101", - "sender_name": "Alex", - "type": "text", - "content": "Good morning everyone, let's discuss project progress today", - "refer_list": [] - } - ] -} -``` - -## Processing Flow - -The script executes the following steps: - -1. **Format Validation** - - Read input JSON file - - Validate whether it conforms to ConversationFormat specification - - Output data statistics - -2. **Save Settings** - - Call `settings` interface - - Save metadata such as scene, group information, user details - - API address: `{base_url}/api/v1/settings` - -3. **Process Messages Item by Item** - - Call `memorize` interface sequentially for each message - - Each message includes: message_id, create_time, sender, content, etc. - - Automatically add group_id, group_name, scene information - - API address: `{api_url}` (specified by `--api-url` argument) - -4. **Output Results** - - Display number of successfully processed messages - - Display total number of saved memories - -## Output Example - -### Successful Output - -``` -🚀 Group Chat Memory Storage Script -====================================================================== -📄 Input File: /path/to/team_chat.json -🔍 Validation Mode: No -🌐 API Address: http://localhost:1995/api/v0/memories -====================================================================== -====================================================================== -Validating Input File Format -====================================================================== -Reading file: /path/to/team_chat.json -Validating ConversationFormat format... -✓ Format validation passed! - -=== Data Statistics === -Format Version: 1.0.0 -Group Chat Name: Smart Sales Assistant Project Team -Group Chat ID: group_sales_ai_2025 -Number of Users: 5 -Number of Messages: 8 -Time Range: 2025-02-01T02:00:00Z ~ 2025-02-01T02:05:00Z - -====================================================================== -Reading Group Chat Data -====================================================================== -Reading file: /path/to/team_chat.json -Using simple direct single message format, processing item by item - -====================================================================== -Starting to Call memorize API Item by Item -====================================================================== -Group Name: Smart Sales Assistant Project Team -Group ID: group_sales_ai_2025 -Number of Messages: 8 -API Address: http://localhost:1995/api/v0/memories - ---- Saving Settings --- -Saving settings to: http://localhost:1995/api/v1/settings -Scene: solo, Group ID: group_sales_ai_2025 - ✓ Settings saved successfully - Scene: solo - ---- Processing Message 1/8 --- - ✓ Successfully saved 1 memory - ---- Processing Message 2/8 --- - ⏳ Waiting for episode boundary - ---- Processing Message 3/8 --- - ✓ Successfully saved 2 memories - ---- Processing Message 4/8 --- - ⏳ Waiting for episode boundary - ---- Processing Message 5/8 --- - ⏳ Waiting for episode boundary - ---- Processing Message 6/8 --- - ✓ Successfully saved 1 memory - ---- Processing Message 7/8 --- - ⏳ Waiting for episode boundary - ---- Processing Message 8/8 --- - ✓ Successfully saved 2 memories - -====================================================================== -Processing Complete -====================================================================== -✓ Successfully Processed: 8/8 messages -✓ Total Saved: 6 memories - -====================================================================== -✓ Processing Complete! -====================================================================== -``` - -## Error Handling - -### File Does Not Exist - -``` -Error: Input file does not exist: /path/to/file.json -``` - -### Format Validation Failed - -``` -✗ Format validation failed! -Please ensure input file conforms to ConversationFormat specification -``` - -### JSON Parsing Error - -``` -✗ JSON parsing failed: Expecting value: line 1 column 1 (char 0) -``` - -## Development Notes - -### Core Dependencies - -- `infra_layer.adapters.input.api.mapper.conversation_converter`: Format validation -- `httpx`: HTTP client (async requests) -- `core.observation.logger`: Logging utilities - -### API Endpoints - -The script calls two API endpoints: - -1. **settings**: Save settings - - Path: `{base_url}/api/v1/settings` - - Method: PUT - - Data: Contains metadata such as scene, group_id, user_details - -2. **memorize**: Store single message memory - - Path: `{api_url}` (specified by `--api-url` argument) - - Method: POST - - Data: Contains message_id, sender, content, scene, etc. - -### Extension Suggestions - -1. **Batch Processing**: Support processing multiple files in a directory -2. **Progress Display**: Add progress bar to show processing status -3. **Error Retry**: Add failure retry mechanism -4. **Concurrent Processing**: Support batch concurrent API calls (note: maintain message order) -5. **Result Export**: Export storage results as JSON file - -## Common Questions - -### Q1: Why is it recommended to start with bootstrap.py? - -A: `bootstrap.py` automatically handles: -- Python path setup -- Environment variable loading -- Dependency injection container initialization -- Mock mode support - -This ensures the script runs in a complete application context. - -### Q2: What's the difference between solo and team scenarios? - -A: -- **solo**: Assistant scenario, suitable for AI assistant and user one-on-one conversations -- **team**: Team scenario, suitable for multi-person group chat and team collaboration - -Different scenarios affect memory extraction strategies and storage methods. Choose based on actual application scenario. - -### Q3: Why does message processing show "Waiting for episode boundary"? - -A: The memory system uses "Episode Boundary" to determine when to form complete memory fragments. -- Not every message immediately generates a memory -- The system waits for a complete conversation episode to end before extracting memories -- This is normal processing behavior, not a failure - -### Q4: Can I not provide an API address? - -A: No. The current version only supports calling via HTTP API, you must provide the `--api-url` argument (unless using `--validate-only` for format validation only). - -### Q5: What to do if API call fails? - -A: Check the following: -1. Ensure memory service is running -2. Confirm API address is correct (including port number) -3. View server logs to understand detailed error information -4. Confirm input data format is correct - -## References - -- [ConversationFormat Format Definition](../../data_format/conversation/conversation_format.py) -- [Memory API Documentation](../api_docs/memory_api.md) -- [Bootstrap Usage Documentation](./bootstrap_usage.md) diff --git a/methods/EverCore/docs/installation/DOCKER_SETUP.md b/methods/EverCore/docs/installation/DOCKER_SETUP.md deleted file mode 100644 index 06b96978..00000000 --- a/methods/EverCore/docs/installation/DOCKER_SETUP.md +++ /dev/null @@ -1,504 +0,0 @@ -# Docker Setup Guide - -[Home](../../README.md) > [Docs](../README.md) > [Installation](.) > Docker Setup - -This guide provides detailed information about Docker setup and configuration for EverCore. - ---- - -## Table of Contents - -- [Overview](#overview) -- [Services Overview](#services-overview) -- [Quick Start](#quick-start) -- [Service Configuration](#service-configuration) -- [Port Mapping](#port-mapping) -- [Volume Management](#volume-management) -- [Service Management](#service-management) -- [Troubleshooting](#troubleshooting) -- [Advanced Configuration](#advanced-configuration) - ---- - -## Overview - -EverCore uses Docker Compose to manage four essential services: -- **MongoDB** - Primary database -- **Elasticsearch** - Keyword search engine -- **Milvus** - Vector database -- **Redis** - Cache service - -All services are defined in `docker-compose.yml` and can be started with a single command. - ---- - -## Services Overview - -| Service | Host Port | Container Port | Purpose | Memory Usage | -|---------|-----------|----------------|---------|--------------| -| **MongoDB** | 27017 | 27017 | Primary database for storing memory cells and profiles | ~500MB | -| **Elasticsearch** | 19200 | 9200 | Keyword search engine (BM25) | ~2GB | -| **Milvus** | 19530 | 19530 | Vector database for semantic retrieval | ~1GB | -| **Redis** | 6379 | 6379 | Cache service | ~100MB | - -**Total Memory Requirements**: Approximately 4GB minimum - ---- - -## Quick Start - -### Start All Services - -```bash -docker-compose up -d -``` - -The `-d` flag runs containers in detached mode (background). - -### Check Service Status - -```bash -docker-compose ps -``` - -Expected output: -``` -NAME STATUS PORTS -mongodb Up 2 minutes 0.0.0.0:27017->27017/tcp -elasticsearch Up 2 minutes 0.0.0.0:19200->9200/tcp -milvus-standalone Up 2 minutes 0.0.0.0:19530->19530/tcp -redis Up 2 minutes 0.0.0.0:6379->6379/tcp -``` - -### View Logs - -```bash -# All services -docker-compose logs -f - -# Specific service -docker-compose logs -f mongodb -docker-compose logs -f elasticsearch -``` - -### Stop Services - -```bash -docker-compose down -``` - -To also remove volumes (⚠️ **deletes all data**): - -```bash -docker-compose down -v -``` - ---- - -## Service Configuration - -### MongoDB - -**Configuration:** -- Version: 7.0 -- Default credentials: `admin` / `memsys123` -- Port: 27017 - -**Connection String:** -``` -mongodb://admin:memsys123@localhost:27017 -``` - -**Collections:** -- `memcells` - Atomic memory units -- `episodes` - Episodic memories -- `profiles` - User profiles -- `preferences` - User preferences -- And more... - -See [MongoDB Guide](../usage/MONGODB_GUIDE.md) for detailed database information. - -### Elasticsearch - -**Configuration:** -- Version: 8.x -- Security: Disabled (for local development) -- Port: 19200 (mapped from container port 9200) - -**Connection URL:** -``` -http://localhost:19200 -``` - -**Test Connection:** -```bash -curl http://localhost:19200 -``` - -**Indices:** -- Memory cells indexed for keyword search -- BM25 algorithm for relevance ranking - -### Milvus - -**Configuration:** -- Version: 2.4+ -- Standalone deployment -- Port: 19530 - -**Connection:** -```python -from pymilvus import connections -connections.connect(host="localhost", port="19530") -``` - -**Collections:** -- Vector embeddings for semantic search -- Multiple index types supported - -### Redis - -**Configuration:** -- Version: 7.x -- Port: 6379 -- No password (for local development) - -**Connection:** -```bash -redis-cli -h localhost -p 6379 ping -``` - -**Usage:** -- Caching frequently accessed data -- Session management -- Temporary data storage - ---- - -## Port Mapping - -### Understanding Port Mapping - -Format: `HOST_PORT:CONTAINER_PORT` - -When connecting from your local machine, always use the **HOST PORT**. - -### Default Port Mapping - -| Service | Host Port | Container Port | URL from Host | -|---------|-----------|----------------|---------------| -| MongoDB | 27017 | 27017 | `localhost:27017` | -| Elasticsearch | 19200 | 9200 | `http://localhost:19200` | -| Milvus | 19530 | 19530 | `localhost:19530` | -| Redis | 6379 | 6379 | `localhost:6379` | - -### Custom Port Configuration - -To use different host ports, edit `docker-compose.yml`: - -```yaml -services: - mongodb: - ports: - - "27018:27017" # Use port 27018 on host - - elasticsearch: - ports: - - "9200:9200" # Use standard port 9200 on host -``` - -After changing ports, update your `.env` file accordingly: - -```bash -MONGODB_URI=mongodb://admin:memsys123@localhost:27018 -ELASTICSEARCH_URL=http://localhost:9200 -``` - ---- - -## Volume Management - -### Data Persistence - -All services use Docker volumes to persist data: - -```bash -# List volumes -docker volume ls - -# Inspect a volume -docker volume inspect evermemos_mongodb_data -docker volume inspect evermemos_elasticsearch_data -docker volume inspect evermemos_milvus_data -docker volume inspect evermemos_redis_data -``` - -### Backup Data - -**MongoDB:** -```bash -# Backup -docker exec mongodb mongodump --username admin --password memsys123 --authenticationDatabase admin --out /tmp/backup - -# Copy backup out of container -docker cp mongodb:/tmp/backup ./mongodb_backup -``` - -**Elasticsearch:** -```bash -# Take snapshot (requires snapshot repository setup) -curl -X PUT "http://localhost:19200/_snapshot/my_backup/snapshot_1?wait_for_completion=true" -``` - -### Restore Data - -**MongoDB:** -```bash -# Copy backup into container -docker cp ./mongodb_backup mongodb:/tmp/backup - -# Restore -docker exec mongodb mongorestore --username admin --password memsys123 --authenticationDatabase admin /tmp/backup -``` - -### Clear All Data - -⚠️ **Warning**: This will delete all memory data! - -```bash -docker-compose down -v -docker-compose up -d -``` - ---- - -## Service Management - -### Restart Services - -```bash -# Restart all services -docker-compose restart - -# Restart specific service -docker-compose restart mongodb -``` - -### Update Services - -```bash -# Pull latest images -docker-compose pull - -# Recreate containers with new images -docker-compose up -d -``` - -### Scale Services (Advanced) - -Some services can be scaled: - -```bash -# Not typically needed for local development -docker-compose up -d --scale redis=2 -``` - -### Resource Limits - -Edit `docker-compose.yml` to set memory/CPU limits: - -```yaml -services: - elasticsearch: - deploy: - resources: - limits: - memory: 2G - cpus: '2' -``` - ---- - -## Troubleshooting - -### Services Won't Start - -**Check Docker Status:** -```bash -docker info -``` - -**Check Logs:** -```bash -docker-compose logs -``` - -**Common Issues:** -- Port conflicts: Another service using the same port -- Insufficient memory: Docker doesn't have enough RAM allocated -- Permission issues: Docker doesn't have permission to create volumes - -### Port Conflicts - -**Find what's using a port:** -```bash -# macOS/Linux -lsof -i :27017 -lsof -i :19200 - -# Or use netstat -netstat -an | grep 27017 -``` - -**Solutions:** -- Stop conflicting service -- Change host port in docker-compose.yml - -### Out of Memory - -**Symptoms:** -- Elasticsearch crashes -- Milvus becomes unresponsive -- Services restart repeatedly - -**Solutions:** - -1. **Increase Docker Memory** (Docker Desktop): - - Docker Desktop > Preferences > Resources - - Increase memory to 8GB or more - -2. **Reduce Elasticsearch Heap:** - Edit docker-compose.yml: - ```yaml - elasticsearch: - environment: - - "ES_JAVA_OPTS=-Xms512m -Xmx512m" - ``` - -3. **Close other applications** to free up memory - -### Connection Refused - -**Check service is running:** -```bash -docker-compose ps -``` - -**Verify port mapping:** -```bash -docker-compose port mongodb 27017 -``` - -**Test connectivity:** -```bash -# MongoDB -telnet localhost 27017 - -# Elasticsearch -curl http://localhost:19200 - -# Redis -redis-cli -h localhost -p 6379 ping -``` - -### Elasticsearch Yellow/Red Status - -**Check cluster health:** -```bash -curl http://localhost:19200/_cluster/health?pretty -``` - -**Common cause**: Single-node cluster (expected for local development) - -**Set to single-node mode:** -```yaml -elasticsearch: - environment: - - discovery.type=single-node -``` - ---- - -## Advanced Configuration - -### Custom docker-compose.yml - -Create `docker-compose.override.yml` for custom configuration: - -```yaml -version: '3.8' -services: - mongodb: - environment: - - MONGO_INITDB_ROOT_USERNAME=myuser - - MONGO_INITDB_ROOT_PASSWORD=mypassword -``` - -This file is automatically merged with `docker-compose.yml`. - -### Network Configuration - -Services communicate on a shared network: - -```yaml -networks: - evermemos-network: - driver: bridge -``` - -### Environment Variables - -Pass environment variables to services: - -```yaml -services: - mongodb: - environment: - - MONGO_INITDB_ROOT_USERNAME=${MONGO_USER:-admin} - - MONGO_INITDB_ROOT_PASSWORD=${MONGO_PASSWORD:-memsys123} -``` - -Then create `.env.docker`: -```bash -MONGO_USER=admin -MONGO_PASSWORD=secretpassword -``` - -### Health Checks - -Add health checks to ensure services are ready: - -```yaml -services: - mongodb: - healthcheck: - test: echo 'db.runCommand("ping").ok' | mongosh localhost:27017/test --quiet - interval: 10s - timeout: 5s - retries: 5 -``` - ---- - -## Production Considerations - -⚠️ **Note**: The default docker-compose.yml is for **local development only**. - -For production deployment: - -1. **Change default passwords** -2. **Enable authentication** for all services -3. **Use external volumes** for backups -4. **Configure SSL/TLS** -5. **Set resource limits** -6. **Use managed services** (AWS, Azure, GCP) when possible -7. **Implement monitoring** and alerting - ---- - -## See Also - -- [Setup Guide](SETUP.md) - Complete installation guide -- [Configuration Guide](../usage/CONFIGURATION_GUIDE.md) - Environment variable configuration -- [MongoDB Guide](../usage/MONGODB_GUIDE.md) - MongoDB-specific documentation -- [Troubleshooting](SETUP.md#troubleshooting) - Common installation issues diff --git a/methods/EverCore/docs/installation/SETUP.md b/methods/EverCore/docs/installation/SETUP.md deleted file mode 100644 index 5f200775..00000000 --- a/methods/EverCore/docs/installation/SETUP.md +++ /dev/null @@ -1,371 +0,0 @@ -# Complete Setup Guide - -[Home](../../README.md) > [Docs](../README.md) > [Installation](.) > Setup - -This guide provides comprehensive instructions for installing and setting up EverCore. - ---- - -## Table of Contents - -- [System Requirements](#system-requirements) -- [Installation Methods](#installation-methods) -- [Docker Installation (Recommended)](#docker-installation-recommended) -- [Environment Configuration](#environment-configuration) -- [Starting the Server](#starting-the-server) -- [Verification](#verification) -- [Troubleshooting](#troubleshooting) -- [Next Steps](#next-steps) - ---- - -## System Requirements - -### Minimum Requirements - -- **Python**: 3.10 or higher -- **uv**: Package manager (will be installed during setup) -- **Docker**: 20.10+ -- **Docker Compose**: 2.0+ -- **RAM**: At least 4GB available (for Elasticsearch and Milvus) -- **Disk Space**: At least 10GB free - -### Recommended Requirements - -- **RAM**: 8GB or more -- **CPU**: 4 cores or more -- **Disk Space**: 20GB or more (especially for large datasets) - -### Operating Systems - -EverCore has been tested on: -- macOS (Intel and Apple Silicon) -- Linux (Ubuntu 20.04+, Debian, etc.) -- Windows (via WSL2) - ---- - -## Installation Methods - -EverCore can be installed in two ways: - -1. **Docker Installation (Recommended)** - Use Docker Compose for all dependency services -2. **Manual Installation** - Install and configure each service manually - -This guide covers the Docker installation method. For manual installation, see [Advanced Installation](#manual-installation-advanced). - ---- - -## Docker Installation (Recommended) - -### Step 1: Clone the Repository - -```bash -git clone https://github.com/EverMind-AI/EverOS.git -cd EverOS/methods/EverCore -``` - -### Step 2: Start Docker Services - -Start all dependency services (MongoDB, Elasticsearch, Milvus, Redis) with one command: - -```bash -docker-compose up -d -``` - -This will start: -- MongoDB on port 27017 -- Elasticsearch on port 19200 -- Milvus on port 19530 -- Redis on port 6379 - -See [Docker Setup Guide](DOCKER_SETUP.md) for detailed service configuration. - -### Step 3: Verify Docker Services - -Check that all services are running: - -```bash -docker-compose ps -``` - -You should see all services in the "Up" state. - -### Step 4: Install uv Package Manager - -If you don't have uv installed: - -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -``` - -After installation, restart your terminal or run: - -```bash -source $HOME/.cargo/env -``` - -Verify installation: - -```bash -uv --version -``` - -### Step 5: Install Project Dependencies - -```bash -uv sync -``` - -This will: -- Create a virtual environment -- Install all required Python packages -- Set up the project for development - ---- - -## Environment Configuration - -### Step 1: Copy Environment Template - -```bash -cp env.template .env -``` - -### Step 2: Configure API Keys - -Edit the `.env` file and fill in the required configurations: - -```bash -# Open .env in your preferred editor -nano .env -# or -vim .env -# or -code .env -``` - -### Required Configuration - -#### LLM API Key (for memory extraction) - -Choose one of the following: - -```bash -# Option 1: OpenAI -LLM_API_KEY=sk-your-openai-key-here -LLM_API_BASE=https://api.openai.com/v1 - -# Option 2: OpenRouter -OPENROUTER_API_KEY=sk-or-v1-your-openrouter-key -OPENROUTER_API_BASE=https://openrouter.ai/api/v1 - -# Option 3: Other OpenAI-compatible API -LLM_API_KEY=your-api-key -LLM_API_BASE=https://your-api-endpoint.com/v1 -``` - -#### Vectorize API Key (for embedding and reranking) - -```bash -# DeepInfra (recommended) -VECTORIZE_API_KEY=your-deepinfra-key -VECTORIZE_API_BASE=https://api.deepinfra.com/v1/openai - -# Or configure embedding and rerank separately -EMBEDDING_API_KEY=your-embedding-key -EMBEDDING_API_BASE=https://your-embedding-endpoint.com -RERANK_API_KEY=your-rerank-key -RERANK_API_BASE=https://your-rerank-endpoint.com -``` - -### Optional Configuration - -```bash -# Model selection -LLM_MODEL=gpt-4 # or gpt-3.5-turbo, etc. -EMBEDDING_MODEL=BAAI/bge-large-en-v1.5 -RERANK_MODEL=BAAI/bge-reranker-large - -# Service endpoints (default values shown) -MONGODB_URI=mongodb://admin:memsys123@localhost:27017 -ELASTICSEARCH_URL=http://localhost:19200 -MILVUS_HOST=localhost -MILVUS_PORT=19530 -REDIS_URL=redis://localhost:6379 -``` - -For complete configuration options, see the [Configuration Guide](../usage/CONFIGURATION_GUIDE.md). - ---- - -## Starting the Server - -### Start the API Server - -```bash -uv run python src/run.py --port 1995 -``` - -The server will start on `http://localhost:1995` by default. - -You should see output similar to: - -``` -INFO: Started server process [12345] -INFO: Waiting for application startup. -INFO: Application startup complete. -INFO: Uvicorn running on http://0.0.0.0:1995 (Press CTRL+C to quit) -``` - -### Custom Port - -The default port is 1995. To use a different port: - -```bash -uv run python src/run.py --port 9000 -``` - ---- - -## Verification - -### Test the API - -Open a new terminal and test the API: - -```bash -curl http://localhost:1995/health -``` - -You should receive a response indicating the service is healthy. - -### Run Simple Demo - -Test the complete workflow with the simple demo: - -```bash -# In a new terminal (keep the server running) -uv run python src/bootstrap.py demo/simple_demo.py -``` - -This will: -1. Store sample conversation messages -2. Wait for indexing -3. Search for relevant memories -4. Display results - -If this works, your installation is successful! - ---- - -## Troubleshooting - -### Docker Services Not Starting - -**Problem**: `docker-compose up -d` fails or services don't start - -**Solutions**: -- Check Docker is running: `docker info` -- Check port conflicts: `lsof -i :27017,19200,19530,6379` -- View logs: `docker-compose logs -f` -- Restart services: `docker-compose restart` - -### Insufficient Memory - -**Problem**: Elasticsearch or Milvus crashes due to OOM - -**Solutions**: -- Increase Docker memory limit (Docker Desktop > Preferences > Resources) -- Reduce heap size in docker-compose.yml -- Close other memory-intensive applications - -### Python Dependencies Fail - -**Problem**: `uv sync` fails with errors - -**Solutions**: -- Update uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` -- Clear cache: `uv cache clean` -- Try with verbose output: `uv sync -v` - -### API Server Won't Start - -**Problem**: Server fails to start or crashes - -**Solutions**: -- Check .env file is configured correctly -- Verify all Docker services are running: `docker-compose ps` -- Check logs for specific errors -- Ensure port 1995 is not in use: `lsof -i :1995` - -### Connection Errors - -**Problem**: Can't connect to MongoDB/Elasticsearch/Milvus - -**Solutions**: -- Verify services are running: `docker-compose ps` -- Check connection strings in .env -- Use host ports (27017, 19200, 19530) not container ports -- Test connections individually: - ```bash - # MongoDB - mongosh mongodb://admin:memsys123@localhost:27017 - - # Elasticsearch - curl http://localhost:19200 - - # Redis - redis-cli -h localhost -p 6379 ping - ``` - -For more troubleshooting help, see: -- [Docker Setup Guide](DOCKER_SETUP.md) -- [Configuration Guide](../usage/CONFIGURATION_GUIDE.md) -- [GitHub Issues](https://github.com/EverMind-AI/EverOS/issues) - ---- - -## Manual Installation (Advanced) - -If you prefer not to use Docker, you can install each service manually: - -### Required Services - -1. **MongoDB 7.0+** - - See [MongoDB Guide](../usage/MONGODB_GUIDE.md) - -2. **Elasticsearch 8.x** - - Download from [elastic.co](https://www.elastic.co/downloads/elasticsearch) - - Configure port 9200 - -3. **Milvus 2.4+** - - Follow [Milvus installation guide](https://milvus.io/docs/install_standalone-docker.md) - - Configure port 19530 - -4. **Redis 7.x** - - Install via package manager or from [redis.io](https://redis.io/download) - - Configure port 6379 - -After installing services manually, update connection strings in `.env` accordingly. - ---- - -## Next Steps - -Now that EverCore is installed, you can: - -1. **[Try the Demos](../usage/DEMOS.md)** - Interactive examples showing memory extraction and chat -2. **[Learn the API](../api_docs/memory_api.md)** - Integrate EverCore into your application -3. **[Explore Usage Examples](../usage/USAGE_EXAMPLES.md)** - Common usage patterns -4. **[Run Evaluations](../../evaluation/README.md)** - Test on benchmark datasets - ---- - -## See Also - -- [Docker Setup Guide](DOCKER_SETUP.md) - Detailed Docker configuration -- [Configuration Guide](../usage/CONFIGURATION_GUIDE.md) - Complete configuration options -- [MongoDB Guide](../usage/MONGODB_GUIDE.md) - MongoDB installation and setup -- [Quick Start (README)](../../README.md#quick-start) - Quick start overview -- [Getting Started for Developers](../dev_docs/getting_started.md) - Development setup diff --git a/methods/EverCore/docs/usage/BATCH_OPERATIONS.md b/methods/EverCore/docs/usage/BATCH_OPERATIONS.md deleted file mode 100644 index ca4cfec8..00000000 --- a/methods/EverCore/docs/usage/BATCH_OPERATIONS.md +++ /dev/null @@ -1,360 +0,0 @@ -# Batch Operations Guide - -[Home](../../README.md) > [Docs](../README.md) > [Usage](.) > Batch Operations - -This guide explains how to efficiently process multiple messages using EverCore's batch operations. - ---- - -## Table of Contents - -- [Overview](#overview) -- [Conversation Format](#conversation-format) -- [Batch Storage Script](#batch-storage-script) -- [Data Format Specification](#data-format-specification) -- [Examples](#examples) -- [Best Practices](#best-practices) -- [Troubleshooting](#troubleshooting) - ---- - -## Overview - -EverCore supports batch processing for efficiently storing multiple messages at once. This is particularly useful for: - -- Processing historical conversation data -- Importing chat logs from other platforms -- Group chat conversations with multiple participants -- Bulk data migration - ---- - -## Conversation Format - -EverCore uses a standardized **ConversationFormat** for batch operations. This format supports: - -- Conversation metadata (group info, user details) -- Multi-speaker conversations -- Timestamps and message IDs - -For complete format specifications, see [Conversation Format Specification](../../data_format/conversation/conversation_format.md). - ---- - -## Batch Storage Script - -### Basic Usage - -```bash -# Store group chat messages (Chinese data) -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat_zh.json \ - --api-url http://localhost:1995/api/v0/memories \ - --scene team - -# Store group chat messages (English data) -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat_en.json \ - --api-url http://localhost:1995/api/v0/memories \ - --scene team - -# Validate file format without storing -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat_en.json \ - --scene team \ - --validate-only -``` - -### Script Parameters - -| Parameter | Required | Description | -|-----------|----------|-------------| -| `--input` | Yes | Path to the conversation data file (JSON format) | -| `--api-url` | No | API endpoint (default: http://localhost:1995/api/v0/memories) | -| `--scene` | Yes | Scene type: `solo` or `team` | -| `--validate-only` | No | Validate format without sending to API | - -### Scene Parameter Explanation - -The `--scene` parameter specifies the memory extraction strategy: - -- **`solo`** - Use for one-on-one conversations with AI assistant -- **`team`** - Use for multi-person group discussions - -**Important Note**: In your data files, you may see `scene` values like `work`, `company`, or `social` - these are internal scene descriptors in the data format. The `--scene` command-line parameter uses different values (`solo`/`team`) to specify which extraction pipeline to apply. - ---- - -## Data Format Specification - -### ConversationFormat Structure - -```json -{ - "version": "1.0.0", - "session_meta": { - "group_id": "group_001", - "name": "Project Discussion Group", - "description": "Team project planning and updates", - "scene": "team", - "timezone": "Asia/Shanghai", - "user_details": { - "user_101": { - "full_name": "Alice", - "role": "Product Manager", - "nickname": "Ali" - }, - "user_102": { - "full_name": "Bob", - "role": "Engineer" - } - } - }, - "conversation_list": [ - { - "message_id": "msg_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "user_101", - "content": "Good morning everyone, let's discuss the new feature" - }, - { - "message_id": "msg_002", - "create_time": "2025-02-01T10:05:00+00:00", - "sender": "user_102", - "content": "Sure! I've prepared the technical spec" - } - ] -} -``` - -### Required Fields - -**session_meta:** -- `group_id` (string) - Unique identifier for the conversation group -- `name` (string) - Human-readable name for the group -- `user_details` (object) - Map of user IDs to user information - -**conversation_list:** -- `message_id` (string) - Unique identifier for each message -- `create_time` (string) - ISO 8601 timestamp with timezone -- `sender` (string) - User ID (must exist in user_details) -- `content` (string) - Message content - -### Optional Fields - -**session_meta:** -- `description` (string) - Group description -- `scene` (string) - Internal scene descriptor (team or solo) -- `timezone` (string) - Timezone for the conversation - -**conversation_list:** -- `sender_name` (string) - Override sender's display name - ---- - -## Examples - -### Example 1: Simple Group Chat - -```json -{ - "version": "1.0.0", - "session_meta": { - "group_id": "team_standup", - "name": "Daily Standup", - "user_details": { - "alice": {"full_name": "Alice Smith"}, - "bob": {"full_name": "Bob Jones"} - } - }, - "conversation_list": [ - { - "message_id": "msg_1", - "create_time": "2025-02-01T09:00:00+00:00", - "sender": "alice", - "content": "Yesterday I completed the login feature" - }, - { - "message_id": "msg_2", - "create_time": "2025-02-01T09:01:00+00:00", - "sender": "bob", - "content": "Great! I'm working on the dashboard today" - } - ] -} -``` - -###Example 2: Family Chat with Rich Metadata - -```json -{ - "version": "1.0.0", - "session_meta": { - "group_id": "family_chat_001", - "name": "Smith Family", - "description": "Family group chat", - "scene": "team", - "timezone": "America/New_York", - "user_details": { - "mom": { - "full_name": "Jane Smith", - "nickname": "Mom", - "role": "Parent" - }, - "dad": { - "full_name": "John Smith", - "nickname": "Dad", - "role": "Parent" - }, - "daughter": { - "full_name": "Emily Smith", - "age": 16 - } - } - }, - "conversation_list": [ - { - "message_id": "fam_001", - "create_time": "2025-02-01T18:00:00-05:00", - "sender": "mom", - "content": "Dinner is ready! Come down please.", - }, - { - "message_id": "fam_002", - "create_time": "2025-02-01T18:02:00-05:00", - "sender": "daughter", - "content": "Coming! Just finishing homework." - } - ] -} -``` - -### Example 3: One-on-One Assistant Chat - -```json -{ - "version": "1.0.0", - "session_meta": { - "group_id": "user_assistant_001", - "name": "Personal Assistant", - "scene": "solo", - "user_details": { - "user_001": { - "full_name": "Alex" - } - } - }, - "conversation_list": [ - { - "message_id": "chat_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "user_001", - "content": "I love playing soccer on weekends" - }, - { - "message_id": "chat_002", - "create_time": "2025-02-01T10:30:00+00:00", - "sender": "user_001", - "content": "My favorite team is Barcelona" - } - ] -} -``` - -**Command for solo chat:** -```bash -uv run python src/bootstrap.py src/run_memorize.py \ - --input my_solo_chat.json \ - --scene solo -``` - ---- - -## Best Practices - -### 1. Data Preparation - -- **Validate before importing**: Use `--validate-only` to check format -- **Use consistent IDs**: Ensure message_id and user IDs are unique -- **Include timestamps**: Always use ISO 8601 format with timezone -- **Provide user details**: Include at least full_name for each user - -### 2. Performance Optimization - -- **Batch size**: Process 100-1000 messages at a time for optimal performance -- **Sequential processing**: Script processes messages sequentially to maintain order -- **Monitor progress**: Watch for errors in terminal output -- **Wait for indexing**: Allow 10-15 seconds after completion for search indexes to update - -### 3. Data Quality - -- **Clean content**: Remove formatting artifacts or special characters -- **Accurate timestamps**: Ensure chronological order -- **Complete metadata**: Fill in all available user information -- **Meaningful group IDs**: Use descriptive, stable identifiers - -### 4. Scene Selection - -- Use `solo` for: - - One-on-one conversations - - Personal AI assistant chats - - Individual user interactions - -- Use `team` for: - - Multi-participant discussions - - Team conversations - - Family or social group chats - ---- - -## Troubleshooting - -### Validation Errors - -**Problem**: `--validate-only` reports format errors - -**Solutions:** -- Check JSON syntax is valid -- Verify all required fields are present -- Ensure timestamps are in ISO 8601 format -- Confirm sender IDs exist in user_details - -### API Errors - -**Problem**: Script reports API errors when storing - -**Solutions:** -- Verify API server is running: `curl http://localhost:1995/health` -- Check API URL is correct (default: http://localhost:1995/api/v0/memories) -- Ensure .env has required API keys (LLM_API_KEY, VECTORIZE_API_KEY) -- Review error messages for specific issues - -### Slow Processing - -**Problem**: Batch processing is very slow - -**Solutions:** -- This is normal for large batches (each message requires LLM extraction) -- Reduce batch size if memory issues occur -- Ensure Docker services have adequate resources -- Check LLM API rate limits - -### Missing Memories - -**Problem**: Messages processed but not searchable - -**Solutions:** -- Wait 10-15 seconds for indexing to complete -- Verify Elasticsearch and Milvus are running -- Check MongoDB for stored data -- Ensure embeddings were created (requires VECTORIZE_API_KEY) - ---- - -## See Also - -- [Conversation Format Specification](../../data_format/conversation/conversation_format.md) - Complete format reference -- [Usage Examples](USAGE_EXAMPLES.md) - Other usage methods -- [Demos](DEMOS.md) - Interactive demo walkthroughs -- [API Documentation](../api_docs/memory_api.md) - Memory API reference -- [Data Guide](../../data/README.md) - Sample data and format details diff --git a/methods/EverCore/docs/usage/CONFIGURATION_GUIDE.md b/methods/EverCore/docs/usage/CONFIGURATION_GUIDE.md deleted file mode 100644 index 2d5574f0..00000000 --- a/methods/EverCore/docs/usage/CONFIGURATION_GUIDE.md +++ /dev/null @@ -1,153 +0,0 @@ -# EverCore Configuration Guide - -This guide provides a detailed explanation of the configuration options in `env.template`. Before deploying EverCore, please copy `env.template` to `.env` and fill in your actual configuration values according to this guide. - -> **⚠️ Security Notice**: -> The `.env` file contains sensitive information (such as API keys and database passwords). Be sure to add it to `.gitignore` and **NEVER** commit it to version control systems. - ---- - -## 1. LLM Configuration - -Configuration for the LLM service used for memory extraction, Agentic retrieval, and Q&A generation. - -Provider selection is config-driven (e.g., `llm_config` in the app). When no scene-level provider is set, `LLM_PROVIDER` defines the default provider, and `LLM_API_KEY/LLM_BASE_URL` are only used as fallback for that default. - -| Variable | Required | Description | Example | -|----------|----------|-------------|---------| -| `LLM_PROVIDER` | No | Default provider type (used when scene config omits provider) | `openrouter` | -| `LLM_MODEL` | Yes | Model name. **Evaluation** recommends `gpt-4o-mini`, **Demo** can use cost-effective models like `x-ai/grok-4-fast` | `gpt-4o-mini` | -| `{PROVIDER}_API_KEY` | Yes* | Provider-specific API key (e.g., `OPENROUTER_API_KEY`, `OPENAI_API_KEY`) | `sk-or-v1-xxxx` | -| `{PROVIDER}_BASE_URL` | No | Provider-specific base URL (e.g., `OPENROUTER_BASE_URL`, `OPENAI_BASE_URL`) | `https://openrouter.ai/api/v1` | -| `LLM_API_KEY` | No | Fallback API key for default provider (legacy compatibility) | `sk-or-v1-xxxx` | -| `LLM_BASE_URL` | No | Fallback base URL for default provider (legacy compatibility) | `https://openrouter.ai/api/v1` | -| `LLM_TEMPERATURE` | No | Generation temperature, lower values recommended for stable output | `0.3` | -| `LLM_MAX_TOKENS` | No | Maximum generation tokens | `32768` | - ---- - -## 2. Vectorize Service Configuration - -Configuration for converting text to vectors (Embeddings), supporting DeepInfra and vLLM. - -| Variable | Required | Description | Example | -|----------|----------|-------------|---------| -| `VECTORIZE_PROVIDER` | Yes | Provider options: `deepinfra`, `vllm` | `deepinfra` | -| `VECTORIZE_API_KEY` | Yes* | API Key (Required for DeepInfra, Optional for vLLM) | `xxxxx` | -| `VECTORIZE_BASE_URL` | Yes | Service URL | `https://api.deepinfra.com/v1/openai` | -| `VECTORIZE_MODEL` | Yes | Model name, must match the server-side name | `Qwen/Qwen3-Embedding-4B` | -| `VECTORIZE_DIMENSIONS` | No | Vector dimensions. Set to `0` if vLLM doesn't support this parameter, otherwise keep model dimensions (e.g., `1024`) | `1024` | - -**Advanced Settings**: -- `VECTORIZE_TIMEOUT`: Request timeout (seconds) -- `VECTORIZE_MAX_RETRIES`: Maximum retry attempts -- `VECTORIZE_BATCH_SIZE`: Batch size -- `VECTORIZE_MAX_CONCURRENT`: Maximum concurrent requests -- `VECTORIZE_ENCODING_FORMAT`: Encoding format, usually `float` - ---- - -## 3. Rerank Service Configuration - -Configuration for re-ranking retrieval results to improve relevance. - -| Variable | Required | Description | Example | -|----------|----------|-------------|---------| -| `RERANK_PROVIDER` | Yes | Provider options: `deepinfra`, `vllm` | `deepinfra` | -| `RERANK_API_KEY` | Yes* | API Key | `xxxxx` | -| `RERANK_BASE_URL` | Yes | Service URL | `https://api.deepinfra.com/v1/inference` | -| `RERANK_MODEL` | Yes | Model name | `Qwen/Qwen3-Reranker-4B` | - -**Advanced Settings**: -- `RERANK_TIMEOUT`: Timeout (seconds) -- `RERANK_BATCH_SIZE`: Batch size -- `RERANK_MAX_CONCURRENT`: Maximum concurrent requests - ---- - -## 4. Database Configuration - -EverCore relies on multiple database services, typically started via Docker Compose. - -### Redis -Used for caching and distributed locks. -- `REDIS_HOST`: Host address (default `localhost`) -- `REDIS_PORT`: Port (default `6379`) -- `REDIS_DB`: Database index (default `8`) - -### Tenant -- `TENANT_SINGLE_TENANT_ID`: Tenant identifier for local development (e.g., `t_yourname`). All storage resources are prefixed with this value. **Required for local dev.** - -### MongoDB -Primary database, stores memory cells, profiles, and conversation records. -- `MONGODB_HOST`: Host address (default `localhost`) -- `MONGODB_PORT`: Port (default `27017`) -- `MONGODB_USERNAME`: Username (default `admin`) -- `MONGODB_PASSWORD`: Password (default `memsys123`) - -### Elasticsearch -Used for keyword retrieval (BM25). -- `ES_HOSTS`: Service address (default `http://localhost:19200`) - -### Milvus -Vector database, used for semantic retrieval. -- `MILVUS_HOST`: Host address (default `localhost`) -- `MILVUS_PORT`: Port (default `19530`) - ---- - -## 5. Other Configuration - -### API Server -- `API_BASE_URL`: Base URL for V1 API, used for client connections (default `http://localhost:1995`) - -### Environment & Logging -- `LOG_LEVEL`: Log level (`INFO`, `DEBUG`, `WARNING`, `ERROR`) -- `ENV`: Environment identifier (`dev`, `prod`) -- `MEMORY_LANGUAGE`: Primary system language (`zh`, `en`) - ---- - -## Configuration Examples - -### 1. Using DeepInfra (Recommended) - -```bash -VECTORIZE_PROVIDER=deepinfra -VECTORIZE_API_KEY=your_key_here -VECTORIZE_BASE_URL=https://api.deepinfra.com/v1/openai -VECTORIZE_MODEL=Qwen/Qwen3-Embedding-4B - -RERANK_PROVIDER=deepinfra -RERANK_API_KEY=your_key_here -RERANK_BASE_URL=https://api.deepinfra.com/v1/inference -RERANK_MODEL=Qwen/Qwen3-Reranker-4B -``` - -### 2. Using Local vLLM - -```bash -VECTORIZE_PROVIDER=vllm -VECTORIZE_API_KEY=none -VECTORIZE_BASE_URL=http://localhost:8000/v1 -VECTORIZE_MODEL=Qwen3-Embedding-4B -VECTORIZE_DIMENSIONS=0 # vLLM sometimes requires disabling this parameter - -RERANK_PROVIDER=vllm -RERANK_API_KEY=none -RERANK_BASE_URL=http://localhost:12000/score -RERANK_MODEL=Qwen3-Reranker-4B -``` - -> **ℹ️ vLLM Deployment Tips**: -> - **Embedding Models** (Supported since v0.4.0+): -> ```bash -> vllm serve Qwen/Qwen3-Embedding-4B --task embed --trust-remote-code -> ``` -> - **Reward/Reranker Models** (See [vLLM PR #19260](https://github.com/vllm-project/vllm/pull/19260) for details): -> ```bash -> vllm serve Qwen/Qwen3-Reranker-4B --task reward --trust-remote-code -> ``` -> Note: Use `--task reward` for Reranker models. - - diff --git a/methods/EverCore/docs/usage/DEMOS.md b/methods/EverCore/docs/usage/DEMOS.md deleted file mode 100644 index b5273fe8..00000000 --- a/methods/EverCore/docs/usage/DEMOS.md +++ /dev/null @@ -1,392 +0,0 @@ -# Interactive Demos - -[Home](../../README.md) > [Docs](../README.md) > [Usage](.) > Interactive Demos - -This guide provides detailed walkthroughs of EverCore's interactive demos. - ---- - -## Table of Contents - -- [Overview](#overview) -- [Simple Demo (Recommended)](#simple-demo-recommended) -- [Full-Featured Demo](#full-featured-demo) -- [Demo Configuration](#demo-configuration) -- [Troubleshooting](#troubleshooting) - ---- - -## Overview - -EverCore provides two demo modes: - -1. **Simple Demo** - Quick 2-step demo showing basic storage and retrieval -2. **Full Demo** - Complete workflow with memory extraction and interactive chat - -Both demos require the API server to be running. - ---- - -## Simple Demo (Recommended) - -The fastest way to experience EverCore! Perfect for first-time users. - -### Prerequisites - -- Completed installation (see [Setup Guide](../installation/SETUP.md)) -- API server running - -### Quick Start - -```bash -# Terminal 1: Start the API server -uv run python src/run.py --port 1995 - -# Terminal 2: Run the simple demo -uv run python src/bootstrap.py demo/simple_demo.py -``` - -### What It Does - -1. **Stores 4 conversation messages** about sports hobbies: - ```python - messages = [ - "I love playing soccer on weekends", - "I enjoy watching Barcelona matches", - "My favorite sport is basketball", - "I used to play tennis in high school" - ] - ``` - -2. **Waits 10 seconds** for indexing to complete - -3. **Searches for memories** with 3 different queries: - - "What sports does the user like?" - - "Tell me about the user's hobbies" - - "What does the user do on weekends?" - -4. **Displays results** with relevance scores - -### Expected Output - -``` -=== EverCore Simple Demo === - -Step 1: Storing conversation messages... -✓ Stored message 1: I love playing soccer on weekends -✓ Stored message 2: I enjoy watching Barcelona matches -✓ Stored message 3: My favorite sport is basketball -✓ Stored message 4: I used to play tennis in high school - -Step 2: Waiting 10 seconds for indexing... -[Progress bar] - -Step 3: Searching for relevant memories... - -Query: "What sports does the user like?" -Results: - 1. [Score: 0.95] I love playing soccer on weekends - 2. [Score: 0.89] My favorite sport is basketball - 3. [Score: 0.82] I used to play tennis in high school - -Query: "Tell me about the user's hobbies" -Results: - 1. [Score: 0.91] I love playing soccer on weekends - 2. [Score: 0.87] I enjoy watching Barcelona matches - ... - -✓ Demo completed successfully! -``` - -### Demo Code Location - -See [`demo/simple_demo.py`](../../demo/simple_demo.py) for the complete source code. - -### Perfect For - -- ✓ First-time users -- ✓ Quick testing -- ✓ Verifying installation -- ✓ Understanding core concepts -- ✓ Demonstrating EverCore to others - ---- - -## Full-Featured Demo - -Experience the complete EverCore workflow: memory extraction from conversations followed by interactive chat with memory retrieval. - -### Prerequisites - -**1. Start the API Server:** - -```bash -# Terminal 1: Start the API server (keep running) -uv run python src/run.py --port 1995 -``` - -**2. Configure Environment:** - -Ensure your `.env` file has the required API keys: -- `LLM_API_KEY` (or `OPENROUTER_API_KEY` or `OPENAI_API_KEY`) -- `VECTORIZE_API_KEY` - -See [Configuration Guide](../installation/SETUP.md#environment-configuration) for details. - ---- - -### Step 1: Extract Memories - -Process sample conversation data and build the memory database. - -```bash -# Terminal 2: Run the extraction script -uv run python src/bootstrap.py demo/extract_memory.py -``` - -#### What This Script Does - -1. **Clears existing data** by calling `demo.tools.clear_all_data.clear_all_memories()` - - Resets MongoDB, Elasticsearch, Milvus, and Redis to empty state - - Ensures demo starts fresh - -2. **Loads conversation data** from `data/solo_chat_zh.json` - - Sample conversations in Chinese - - For English data, modify the `data_file` constant - -3. **Processes each message** through the Memory API - - Appends `scene="solo"` to indicate one-on-one conversation - - Streams entries to `http://localhost:1995/api/v0/memories` - -4. **Creates memories in databases** - - MemCells extracted from conversations - - Episodes constructed from related MemCells - - Profiles built from user information - - Indexes created in Elasticsearch and Milvus - -#### Configuration Options - -Edit `demo/extract_memory.py` to customize: - -```python -# API endpoint -base_url = "http://localhost:1995" - -# Data file -data_file = "data/solo_chat_zh.json" # or solo_chat_en.json - -# Scene type -profile_scene = "solo" # or "team" -``` - -#### Expected Output - -``` -Clearing all existing memories... -✓ Cleared MongoDB collections -✓ Cleared Elasticsearch indices -✓ Cleared Milvus collections -✓ Cleared Redis cache - -Loading conversation data from data/solo_chat_zh.json... -Found 150 messages - -Processing messages: -[Progress bar] 150/150 messages processed - -✓ Memory extraction completed -✓ 150 MemCells created -✓ 23 episodes constructed -✓ 5 profiles built - -You can now run the chat demo! -``` - -#### For More Details - -See [`demo/README.md`](../../demo/README.md) for comprehensive documentation. - ---- - -### Step 2: Chat with Memory - -Start the interactive chat demo to query extracted memories. - -```bash -# Terminal 2: Run the chat program -uv run python src/bootstrap.py demo/chat_with_memory.py -``` - -#### How It Works - -1. **Loads environment** via `python-dotenv` -2. **Verifies LLM keys** are available -3. **Connects to MongoDB** to enumerate groups with MemCells -4. **Invokes search API** for each user query -5. **Displays retrieved memories** before generating response - -#### Interactive Workflow - -##### 1. Select Language - -``` -Welcome to EverCore Chat Demo! -Select language / 选择语言: - 1. English - 2. 中文 -Choice [1-2]: -``` - -##### 2. Select Scenario Mode - -``` -Select scenario mode: - 1. Assistant (one-on-one conversation) - 2. Group Chat (multi-speaker analysis) -Choice [1-2]: -``` - -##### 3. Select Conversation Group - -``` -Available conversation groups: - 1. Personal Assistant (150 messages) - 2. Work Discussion (85 messages) - 3. Family Chat (42 messages) -Select group [1-3]: -``` - -Groups are read from MongoDB. Run the extraction step first to populate groups. - -##### 4. Select Retrieval Mode - -``` -Select retrieval mode: - 1. rrf (Hybrid - Recommended) - 2. embedding (Semantic search) - 3. bm25 (Keyword search) - 4. agentic (LLM-guided - Slower but more intelligent) -Choice [1-4]: -``` - -**Retrieval Modes:** -- **rrf** - Reciprocal Rank Fusion of semantic and keyword search (recommended) -- **embedding** - Pure semantic vector search -- **bm25** - Pure keyword search -- **agentic** - Multi-round LLM-guided retrieval (higher latency, better results) - -##### 5. Start Chatting - -``` -You are now chatting with: Personal Assistant -Retrieval mode: rrf - -Available commands: - - help: Show available commands - - clear: Clear conversation history - - reload: Reload memories from database - - exit: Exit the chat - -You: What are my hobbies? - -[Retrieved Memories] -1. [Episode] User mentioned loving soccer on weekends (2025-01-15) -2. [Episode] User enjoys watching Barcelona matches (2025-01-16) -3. [Profile] Sports: Soccer, Basketball, Tennis -Assistant: Based on your memories, you enjoy several sports including soccer, basketball, -and tennis. You particularly love playing soccer on weekends and watching Barcelona matches. -``` - -**Chat Commands:** -- `help` - Show available commands -- `clear` - Clear conversation history (keeps memories) -- `reload` - Reload memories from database -- `exit` - Exit the chat demo - ---- - -## Demo Configuration - -### Customizing Demo Data - -You can use your own conversation data with the demos: - -1. **Prepare your data** in the ConversationFormat (see [Format Specification](../../data_format/conversation/conversation_format.md)) -2. **Edit `demo/extract_memory.py`** to point to your data file -3. **Run the extraction script** to process your data -4. **Chat with your memories!** - -### Demo Parameters - -**Extraction Script:** -- `base_url` - API server endpoint (default: http://localhost:1995) -- `data_file` - Path to conversation data file -- `profile_scene` - Scene type: "solo" or "team" - -**Chat Script:** -- Language selection (en/zh) -- Scenario mode (solo/team) -- Retrieval mode (rrf/embedding/bm25/agentic) - ---- - -## Troubleshooting - -### Demo Won't Start - -**Problem**: Demo scripts fail to run - -**Solutions:** -- Verify API server is running: `curl http://localhost:1995/health` -- Check .env file has required API keys -- Ensure Docker services are running: `docker-compose ps` -- Verify Python version: `python --version` (should be 3.10+) - -### No Memories Found - -**Problem**: Chat demo shows "No conversation groups found" - -**Solutions:** -- Run the extraction script first: `uv run python src/bootstrap.py demo/extract_memory.py` -- Check MongoDB has data: Connect to MongoDB and verify collections -- Ensure extraction completed successfully (check terminal output) - -### Retrieval Returns Empty Results - -**Problem**: Search queries return no results - -**Solutions:** -- Wait 10-15 seconds after storing messages (indexing delay) -- Verify Elasticsearch is running: `curl http://localhost:19200` -- Verify Milvus is running: `docker-compose ps` -- Check if embeddings were created (requires VECTORIZE_API_KEY) - -### Chat Demo Errors - -**Problem**: Chat demo crashes or shows errors - -**Solutions:** -- Verify LLM API key is configured in .env -- Check API key has sufficient credits/quota -- Try a different retrieval mode (rrf is most reliable) -- Check logs for specific error messages - -### Slow Performance - -**Problem**: Demos are slow or timeout - -**Solutions:** -- Use "rrf" or "keyword" instead of "agentic" mode -- Reduce `top_k` parameter (fewer results = faster) -- Check Docker container resource usage -- Ensure sufficient RAM (4GB minimum) - ---- - -## See Also - -- [Usage Examples](USAGE_EXAMPLES.md) - All usage methods -- [Batch Operations](BATCH_OPERATIONS.md) - Process multiple messages -- [Setup Guide](../installation/SETUP.md) - Installation and configuration -- [Demo README](../../demo/README.md) - Comprehensive demo documentation -- [Data Format](../../data/README.md) - Conversation data format specifications diff --git a/methods/EverCore/docs/usage/MONGODB_GUIDE.md b/methods/EverCore/docs/usage/MONGODB_GUIDE.md deleted file mode 100644 index 816cf7c0..00000000 --- a/methods/EverCore/docs/usage/MONGODB_GUIDE.md +++ /dev/null @@ -1,132 +0,0 @@ -# MongoDB Installation Guide - -This document provides guidance for MemSys project developers on installing and configuring MongoDB directly on macOS, Windows, and Linux. - -## 1. Install MongoDB Community Server - -Please choose the installation method corresponding to your operating system. - -### 1.1. For macOS Users - -We recommend using [Homebrew](https://brew.sh/) for installation. Homebrew is a package manager for macOS that greatly simplifies the software installation process. - -1. **Install Homebrew** (if not already installed): - ```bash - /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" - ``` - -2. **Add MongoDB's Homebrew Tap**: - ```bash - brew tap mongodb/brew - ``` - -3. **Install MongoDB**: - ```bash - brew install mongodb-community - ``` - -### 1.2. For Windows Users - -#### Method 1: Using the Official Installer (Recommended) -1. Go to the [MongoDB Community Server Download Page](https://www.mongodb.com/try/download/community). -2. Select the latest version `MSI` package and download it. -3. Run the installer. During the installation process, **be sure to check the "Install MongoDB Compass"** option to install the graphical management tool at the same time. -4. Follow the wizard to complete the installation. The installer will automatically set up MongoDB as a Windows service, which will start by default on boot. - -#### Method 2: Using the Chocolatey Package Manager -If you use [Chocolatey](https://chocolatey.org/), you can install it by running the following command: -```powershell -choco install mongodb -``` - -### 1.3. For Linux Users (e.g., Ubuntu 20.04/22.04 LTS) - -1. **Import MongoDB's GPG Key**: - ```bash - sudo apt-get install gnupg - curl -fsSL https://www.mongodb.org/static/pgp/server-7.0.asc | \ - sudo gpg -o /usr/share/keyrings/mongodb-server-7.0.gpg \ - --dearmor - ``` - -2. **Create a List File for MongoDB**: - ```bash - echo "deb [ arch=amd64,arm64 signed-by=/usr/share/keyrings/mongodb-server-7.0.gpg ] https://repo.mongodb.org/apt/ubuntu $(lsb_release -cs)/mongodb-org/7.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-7.0.list - ``` - -3. **Update the Local Package Database and Install MongoDB**: - ```bash - sudo apt-get update - sudo apt-get install -y mongodb-org - ``` - -## 2. Install MongoDB Compass (Graphical Tool) - -If you did not select the bundled installation during the Windows setup, or if you are a macOS/Linux user, you can install Compass separately. - -* **macOS**: - ```bash - brew install --cask mongodb-compass - ``` - -* **Windows**: - Go to the [MongoDB Compass Download Page](https://www.mongodb.com/try/download/compass), download the `MSI` package, and execute it. - -* **Linux (Ubuntu)**: - Go to the [MongoDB Compass Download Page](https://www.mongodb.com/try/download/compass), download the `deb` format package, and then install it with the following command (please replace the filename with the actual name of the file you downloaded): - ```bash - sudo dpkg -i mongodb-compass_x.x.x_amd64.deb - ``` - -## 3. Running the MongoDB Service - -* **macOS (via Homebrew)**: - ```bash - brew services start mongodb-community - ``` - -* **Windows**: - If you installed via the official installer, MongoDB will be registered as a Windows service and run automatically. You can find and manage it in the "Services" application. - -* **Linux (Ubuntu)**: - ```bash - sudo systemctl start mongod - # Enable auto-start on boot - sudo systemctl enable mongod - ``` - You can check the service status with `sudo systemctl status mongod`. - -## 4. Connection and Configuration - -### 4.1. Connecting with MongoDB Compass - -Regardless of the operating system, open your installed MongoDB Compass. It will usually automatically detect your locally running MongoDB instance. You just need to click the "Connect" button and use the default connection string `mongodb://localhost:27017` to connect successfully. - -### 4.2. Configuring the Project `.env` File - -To allow the MemSys application to connect to the local MongoDB, you need to configure the project's environment variables. - -1. First, in the project's root directory, copy the `env.template` file to `.env` (if you haven't already): - ```bash - cp env.template .env - ``` - -2. Open the `.env` file and find the MongoDB configuration section. Since a local installation of MongoDB does not have user authentication enabled by default, you need to make the following changes: - - ```ini - # =================== - # MongoDB Configuration - # =================== - - # Tenant ID (required) - TENANT_SINGLE_TENANT_ID=t_yourname - - MONGODB_HOST=127.0.0.1 - MONGODB_PORT=27017 - MONGODB_USERNAME= - MONGODB_PASSWORD= - MONGODB_URI_PARAMS= - ``` - **Note**: Leave `MONGODB_USERNAME` and `MONGODB_PASSWORD` blank, and also set `MONGODB_URI_PARAMS` to empty. The actual database name is auto-generated from `TENANT_SINGLE_TENANT_ID` (e.g., `t_yourname_memsys`). - -Now, your development environment is configured, and the MemSys application can successfully connect to the locally running MongoDB instance. diff --git a/methods/EverCore/docs/usage/USAGE_EXAMPLES.md b/methods/EverCore/docs/usage/USAGE_EXAMPLES.md deleted file mode 100644 index d5fe077f..00000000 --- a/methods/EverCore/docs/usage/USAGE_EXAMPLES.md +++ /dev/null @@ -1,443 +0,0 @@ -# Usage Examples - -[Home](../../README.md) > [Docs](../README.md) > [Usage](.) > Usage Examples - -This guide provides comprehensive examples of how to use EverCore in different scenarios. - ---- - -## Table of Contents - -1. [Simple Demo - Quick Start](#1-simple-demo---quick-start) -2. [Full Demo - Memory Extraction & Chat](#2-full-demo---memory-extraction--chat) -3. [Evaluation & Performance Testing](#3-evaluation--performance-testing) -4. [Direct API Usage](#4-direct-api-usage) -5. [Batch Operations](#5-batch-operations) -6. [Advanced Integration](#6-advanced-integration) - ---- - -## Prerequisites - -Before using these examples, ensure you have: - -1. **Completed installation** - See [Setup Guide](../installation/SETUP.md) -2. **Started the API server**: - ```bash - uv run python src/run.py --port 1995 - ``` -3. **Configured .env** with required API keys - ---- - -## 1. Simple Demo - Quick Start - -The fastest way to experience EverCore! Just 2 steps to see memory storage and retrieval in action. - -### What It Does - -- Stores 4 conversation messages about sports hobbies -- Waits 10 seconds for indexing -- Searches for relevant memories with 3 different queries -- Shows complete workflow with friendly explanations - -### Usage - -```bash -# Terminal 1: Start the API server -uv run python src/run.py --port 1995 - -# Terminal 2: Run the simple demo -uv run python src/bootstrap.py demo/simple_demo.py -``` - -### Expected Output - -You'll see: -1. Messages being stored -2. Indexing progress -3. Search results for queries like "What sports does the user like?" -4. Relevant memories retrieved with scores - -### Demo Code - -See the complete code at [`demo/simple_demo.py`](../../demo/simple_demo.py) - -### Perfect For - -- First-time users -- Quick testing -- Understanding core concepts -- Verifying installation - ---- - -## 2. Full Demo - Memory Extraction & Chat - -Experience the complete EverCore workflow: memory extraction from conversations followed by interactive chat with memory retrieval. - -### Prerequisites - -**Start the API Server:** - -```bash -# Terminal 1: Start the API server (required) -uv run python src/run.py --port 1995 -``` - -> 💡 **Tip**: Keep the API server running throughout. All following operations should be performed in another terminal. - ---- - -### Step 1: Extract Memories - -Run the memory extraction script to process sample conversation data and build the memory database: - -```bash -# Terminal 2: Run the extraction script -uv run python src/bootstrap.py demo/extract_memory.py -``` - -**What This Script Does:** - -1. Calls `demo.tools.clear_all_data.clear_all_memories()` so the demo starts from an empty MongoDB/Elasticsearch/Milvus/Redis state. Ensure the dependency stack launched by `docker-compose` is running before executing the script, otherwise the wipe step will fail. - -2. Loads `data/solo_chat_zh.json`, appends `scene="solo"` to each message, and streams every entry to `http://localhost:1995/api/v0/memories`. - -3. Update the `base_url`, `data_file`, or `profile_scene` constants in `demo/extract_memory.py` if you host the API on another endpoint or want to ingest a different scenario. - -4. Writes through the HTTP API only: MemCells, episodes, and profiles are created inside your databases, not under `demo/memcell_outputs/`. Inspect MongoDB (and Milvus/Elasticsearch) to verify ingestion or proceed directly to the chat demo. - -> **💡 Tip**: For detailed configuration instructions and usage guide, please refer to the [Demo Documentation](../../demo/README.md). - ---- - -### Step 2: Chat with Memory - -After extracting memories, start the interactive chat demo: - -```bash -# Terminal 2: Run the chat program (ensure API server is still running) -uv run python src/bootstrap.py demo/chat_with_memory.py -``` - -**How It Works:** - -This program loads `.env` via `python-dotenv`, verifies that at least one LLM key (`LLM_API_KEY`, `OPENROUTER_API_KEY`, or `OPENAI_API_KEY`) is available, and connects to MongoDB through `demo.utils.ensure_mongo_beanie_ready` to enumerate groups that already contain MemCells. - -Each user query invokes `api/v1/memories/search` unless you explicitly select the Agentic mode, in which case the orchestrator switches to agentic retrieval and warns about the additional LLM latency. - -### Interactive Workflow - -1. **Select Language**: Choose a zh or en terminal UI. -2. **Select Scenario Mode**: Assistant (one-on-one) or Group Chat (multi-speaker analysis). -3. **Select Conversation Group**: Groups are read live from MongoDB via `query_all_groups_from_mongodb`; run the extraction step first so the list is non-empty. -4. **Select Retrieval Mode**: `rrf`, `vector`, `keyword`, or LLM-guided Agentic retrieval. -5. **Start Chatting**: Pose questions, inspect the retrieved memories that are displayed before each response, and use `help`, `clear`, `reload`, or `exit` to manage the session. - ---- - -## 3. Evaluation & Performance Testing - -The evaluation framework provides a unified, modular way to benchmark memory systems on standard datasets (LoCoMo, LongMemEval, PersonaMem). - -### Quick Test (Smoke Test) - -Verify everything works with limited data: - -```bash -# Default smoke test -# First conversation, first 10 messages, first 3 questions -uv run python -m evaluation.cli --dataset locomo --system evermemos --smoke - -# Custom smoke test: 20 messages, 5 questions -uv run python -m evaluation.cli --dataset locomo --system evermemos \ - --smoke --smoke-messages 20 --smoke-questions 5 - -# Test different datasets -uv run python -m evaluation.cli --dataset longmemeval --system evermemos --smoke -uv run python -m evaluation.cli --dataset personamem --system evermemos --smoke - -# Test specific stages (e.g., only search and answer) -uv run python -m evaluation.cli --dataset locomo --system evermemos \ - --smoke --stages search answer - -# View smoke test results quickly -cat evaluation/results/locomo-evermemos-smoke/report.txt -``` - -### Full Evaluation - -Run complete evaluation on entire datasets: - -```bash -# Evaluate EvermemOS on LoCoMo benchmark -uv run python -m evaluation.cli --dataset locomo --system evermemos - -# Evaluate on other datasets -uv run python -m evaluation.cli --dataset longmemeval --system evermemos -uv run python -m evaluation.cli --dataset personamem --system evermemos - -# Use --run-name to distinguish multiple runs (useful for A/B testing) -uv run python -m evaluation.cli --dataset locomo --system evermemos --run-name baseline -uv run python -m evaluation.cli --dataset locomo --system evermemos --run-name experiment1 - -# Resume from checkpoint if interrupted (automatic) -# Just re-run the same command - it will detect and resume from checkpoint -uv run python -m evaluation.cli --dataset locomo --system evermemos -``` - -### View Results - -```bash -# Results are saved to evaluation/results/{dataset}-{system}[-{run-name}]/ -cat evaluation/results/locomo-evermemos/report.txt # Summary metrics -cat evaluation/results/locomo-evermemos/eval_results.json # Detailed per-question results -cat evaluation/results/locomo-evermemos/pipeline.log # Execution logs -``` - -### Evaluation Pipeline - -The evaluation pipeline consists of 4 stages with automatic checkpointing and resume support: - -1. **Add** - Ingest conversation data into the system -2. **Search** - Retrieve relevant memories for each question -3. **Answer** - Generate answers using retrieved context -4. **Evaluate** - Score answers against ground truth - -### Configuration - -> **⚙️ Evaluation Configuration**: -> - **Data Preparation**: Place datasets in `evaluation/data/` (see `evaluation/README.md`) -> - **Environment**: Configure `.env` with LLM API keys (see `env.template`) -> - **Installation**: Run `uv sync --group evaluation` to install dependencies -> - **Custom Config**: Copy and modify YAML files in `evaluation/config/systems/` or `evaluation/config/datasets/` -> - **Advanced Usage**: See `evaluation/README.md` for checkpoint management, stage-specific runs, and system comparisons - ---- - -## 4. Direct API Usage - -Use the Memory API to integrate EverCore into your application. - -### Prerequisites - -**Start the API Server:** - -```bash -uv run python src/run.py --port 1995 -``` - -> 💡 **Tip**: Keep the API server running throughout. All following API calls should be performed in another terminal. - ---- - -### Store Single Message Memory - -Use the `/api/v0/memories` endpoint to store individual messages: - -**Minimal Example (Required Fields Only):** - -```bash -curl -X POST http://localhost:1995/api/v0/memories \ - -H "Content-Type: application/json" \ - -d '{ - "message_id": "msg_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "user_001", - "content": "I love playing soccer on weekends" - }' -``` - -**With Optional Fields:** - -```bash -curl -X POST http://localhost:1995/api/v0/memories \ - -H "Content-Type: application/json" \ - -d '{ - "message_id": "msg_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "user_103", - "sender_name": "Chen", - "content": "We need to complete the product design this week", - "group_id": "group_001", - "group_name": "Project Discussion Group" - }' -``` - -> ℹ️ **Required fields**: `message_id`, `create_time`, `sender`, `content` -> ℹ️ **Optional fields**: `group_id`, `group_name`, `sender_name`, `role`, `refer_list` -> ℹ️ By default, all memory types are extracted and stored - -### API Endpoints - -- **`POST /api/v0/memories`**: Store single message memory -- **`GET /api/v0/memories/search`**: Memory retrieval (supports keyword/vector/hybrid search modes) - -For complete API documentation, see [Memory API Documentation](../api_docs/memory_api.md). - ---- - -### Retrieve Memories - -EverCore provides two retrieval modes: **Lightweight** (fast) and **Agentic** (intelligent). - -#### Lightweight Retrieval - -Fast retrieval for latency-sensitive scenarios. - -**Parameters:** - -| Parameter | Required | Description | -|-----------|----------|-------------| -| `query` | Yes* | Natural language query (*optional for profile type) | -| `user_id` | No* | User ID | -| `group_id` | No* | Group ID | -| `memory_types` | No | `["episodic_memory"]` / `["atomic_fact"]` / `["foresight"]` (default: `["episodic_memory"]`) | -| `retrieve_method` | No | `keyword` / `vector` / `hybrid` / `rrf` (recommended) / `agentic` | -| `current_time` | No | Filter valid foresight (format: ISO 8601) | -| `top_k` | No | Number of results (default: 40, max: 100) | - -*At least one of `user_id` or `group_id` must be provided. - -**Example 1: Personal Memory** - -```bash -curl -X GET http://localhost:1995/api/v0/memories/search \ - -H "Content-Type: application/json" \ - -d '{ - "query": "What sports does the user like?", - "user_id": "user_001", - "memory_types": ["episodic_memory"], - "retrieve_method": "rrf" - }' -``` - -**Example 2: Group Memory** - -```bash -curl -X GET http://localhost:1995/api/v0/memories/search \ - -H "Content-Type: application/json" \ - -d '{ - "query": "Discuss project progress", - "group_id": "project_team_001", - "memory_types": ["episodic_memory"], - "retrieve_method": "rrf" - }' -``` - -> 📖 Full Documentation: [Memory API](../api_docs/memory_api.md) | Testing Tool: `demo/tools/test_retrieval_comprehensive.py` - ---- - -## 5. Batch Operations - -Process multiple messages efficiently using batch scripts. - -See the dedicated [Batch Operations Guide](BATCH_OPERATIONS.md) for complete information. - -### Quick Example - -```bash -# Batch store group chat messages (Chinese data) -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat_zh.json \ - --api-url http://localhost:1995/api/v0/memories \ - --scene team - -# Or use English data -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat_en.json \ - --api-url http://localhost:1995/api/v0/memories \ - --scene team - -# Validate file format -uv run python src/bootstrap.py src/run_memorize.py \ - --input data/team_chat_en.json \ - --scene team \ - --validate-only -``` - -> ℹ️ **Scene Parameter Explanation**: The `scene` parameter is required and specifies the memory extraction strategy: -> - Use `solo` for one-on-one conversations with AI assistant -> - Use `team` for multi-person group discussions - -For complete details, see: -- [Batch Operations Guide](BATCH_OPERATIONS.md) -- [Conversation Format Specification](../../data_format/conversation/conversation_format.md) - ---- - -## 6. Advanced Integration - -### Python SDK Usage - -Use EverCore in your Python applications: - -```python -import requests - -class EverCoreClient: - def __init__(self, base_url="http://localhost:1995"): - self.base_url = base_url - - def store_memory(self, message): - """Store a single message memory.""" - url = f"{self.base_url}/api/v0/memories" - response = requests.post(url, json=message) - response.raise_for_status() - return response.json() - - def search_memories(self, query, user_id=None, **kwargs): - """Search for relevant memories.""" - url = f"{self.base_url}/api/v0/memories/search" - params = {"query": query, **kwargs} - if user_id: - params["user_id"] = user_id - - response = requests.get(url, json=params) - response.raise_for_status() - return response.json() - -# Usage -client = EverCoreClient() - -# Store memory -client.store_memory({ - "message_id": "msg_001", - "create_time": "2025-02-01T10:00:00+00:00", - "sender": "user_001", - "content": "I love playing soccer on weekends" -}) - -# Search memories -results = client.search_memories( - query="What sports does the user like?", - user_id="user_001", - memory_types=["episodic_memory"], - retrieve_method="rrf" -) - -print(results) -``` - -### Custom Integration Patterns - -For advanced integration scenarios: - -1. **Streaming Conversations**: Integrate with chat applications to continuously store messages -2. **Custom Memory Types**: Extend the extraction pipeline for domain-specific memories -3. **Multi-tenant Systems**: Use `user_id` and `group_id` for isolation -4. **Real-time Retrieval**: Implement caching strategies for frequently accessed memories - -See [API Usage Guide](../dev_docs/api_usage_guide.md) for more examples. - ---- - -## See Also - -- [Demo Guide](DEMOS.md) - Detailed demo walkthroughs -- [Batch Operations Guide](BATCH_OPERATIONS.md) - Batch processing details -- [Memory API Documentation](../api_docs/memory_api.md) - Complete API reference -- [API Usage Guide](../dev_docs/api_usage_guide.md) - Advanced API patterns -- [Evaluation Guide](../../evaluation/README.md) - Benchmarking documentation diff --git a/methods/EverCore/env.template b/methods/EverCore/env.template deleted file mode 100755 index c9495b77..00000000 --- a/methods/EverCore/env.template +++ /dev/null @@ -1,235 +0,0 @@ -# ===================================================== -# Memory System Configuration Template -# ===================================================== -# -# SECURITY NOTICE: -# - Copy this file to .env and fill in your actual API keys -# - Never commit .env file to version control -# - Keep your API keys secure and private -# -# SETUP INSTRUCTIONS: -# 1. cp env.template .env -# 2. Edit .env with your actual values -# 3. The system will automatically load these values -# ===================================================== - - -# =================== -# LLM Configuration -# =================== - -# LLM_PROVIDER controls the default provider (used when scene config omits provider) -LLM_PROVIDER=openrouter -LLM_MODEL=x-ai/grok-4-fast -LLM_TEMPERATURE=0.3 -LLM_MAX_TOKENS=32768 -# Default provider fallback (legacy) -LLM_API_KEY=sk-or-v1-xxxx -LLM_BASE_URL=https://openrouter.ai/api/v1 - -# OpenRouter Configuration -# Preferred provider naming rule: -# {PROVIDER}_API_KEY / {PROVIDER}_BASE_URL -# Supports multiple keys (comma-separated) for rate-limit distribution: -# OPENROUTER_API_KEY=key1,key2,key3 -OPENROUTER_API_KEY=your-openrouter-api-key -OPENROUTER_BASE_URL=https://openrouter.ai/api/v1 -# Also supported: -# {PROVIDER}_LLM_API_KEY / {PROVIDER}_LLM_BASE_URL -# Provider setting for OpenRouter, default is 'default', recommend 'cerebras' for qwen3 -# LLM_OPENROUTER_PROVIDER=cerebras - -# OpenAI Direct Access Configuration -# Preferred: -OPENAI_API_KEY=sk-xxxx -OPENAI_BASE_URL=https://api.openai.com/v1 -# Also supported: -# OPENAI_LLM_API_KEY=sk-xxxx -# OPENAI_LLM_BASE_URL=https://api.openai.com/v1 - -# =================== -# Provider Model Whitelist (Optional) -# =================== -# Format: {PROVIDER}_WHITE_LIST=model1,model2,model3 -# If not set or empty, all models are allowed for that provider. -# If set, only listed models can be used. -# OPENROUTER_WHITE_LIST=qwen/qwen3-235b-a22b-2507,openai/gpt-4.1-mini -# OPENAI_WHITE_LIST=gpt-5-mini,gpt-4.1-mini - -# =================== -# Vectorize (Embedding) Service Configuration -# =================== - -# ---- Primary Vectorize Provider ---- -# Provider type: vllm (self-deployed), deepinfra (commercial API) -VECTORIZE_PROVIDER=vllm - -# API key for primary provider (use "EMPTY" if not required for vllm service) -VECTORIZE_API_KEY=EMPTY - -# Base URL for primary provider -# vLLM service example: http://localhost:8000/v1 -# DeepInfra example: https://api.deepinfra.com/v1/openai -VECTORIZE_BASE_URL=http://localhost:8000/v1 - -# Model name (shared by both primary and fallback providers) -VECTORIZE_MODEL=Qwen/Qwen3-Embedding-4B - -# ---- Fallback Vectorize Provider (Optional) ---- -# Fallback provider type: vllm, deepinfra, or none (to disable fallback) -# Note: Fallback will be disabled if provider is "none", base_url is empty, -# or api_key is empty (for deepinfra provider) -VECTORIZE_FALLBACK_PROVIDER=deepinfra - -# API key for fallback provider (required for deepinfra, optional for vllm) -VECTORIZE_FALLBACK_API_KEY=xxxxx - -# Base URL for fallback provider (required if fallback is enabled) -VECTORIZE_FALLBACK_BASE_URL=https://api.deepinfra.com/v1/openai - -# ===== Common Settings ===== -VECTORIZE_TIMEOUT=30 -VECTORIZE_MAX_RETRIES=3 -VECTORIZE_BATCH_SIZE=10 -VECTORIZE_MAX_CONCURRENT=5 -VECTORIZE_ENCODING_FORMAT=float - -# Vector dimensions for client-side truncation -# Set to 0 to disable truncation and use full model dimensions -# Qwen3-Embedding-4B: full 2560D (DeepInfra) or 3584D (vLLM), recommend truncate to 1024D -# Note: Always uses client-side truncation with L2 re-normalization -VECTORIZE_DIMENSIONS=1024 - - -# =================== -# Rerank Service Configuration -# =================== - -# ---- Primary Rerank Provider ---- -# Provider type: vllm (self-deployed), deepinfra (commercial API) -RERANK_PROVIDER=vllm - -# API key for primary provider (use "EMPTY" if not required for vllm service) -RERANK_API_KEY=EMPTY - -# Base URL for primary provider -# vLLM service example: http://localhost:12000/v1/rerank -# DeepInfra example: https://api.deepinfra.com/v1/inference -RERANK_BASE_URL=http://localhost:12000/v1/rerank - -# Model name (shared by both primary and fallback providers) -RERANK_MODEL=Qwen/Qwen3-Reranker-4B - -# ---- Fallback Rerank Provider (Optional) ---- -# Fallback provider type: vllm, deepinfra, or none (to disable fallback) -# Note: Fallback will be disabled if provider is "none", base_url is empty, -# or api_key is empty (for deepinfra provider) -RERANK_FALLBACK_PROVIDER=deepinfra - -# API key for fallback provider (required for deepinfra, optional for vllm) -RERANK_FALLBACK_API_KEY=xxxxx - -# Base URL for fallback provider (required if fallback is enabled) -RERANK_FALLBACK_BASE_URL=https://api.deepinfra.com/v1/inference - -# ===== Common Settings ===== -RERANK_TIMEOUT=30 -RERANK_MAX_RETRIES=3 -RERANK_BATCH_SIZE=10 -RERANK_MAX_CONCURRENT=5 - - -# =================== -# Tenant Configuration -# =================== - -# Single-tenant mode: set a tenant ID for local development. -# All storage resources will be prefixed with this value (e.g., t_zhang_memsys). -# Use t_{yourname} to avoid conflicts with other developers on shared infrastructure. -# For multi-tenant deployment, leave this unset — tenant context comes from request headers. -TENANT_SINGLE_TENANT_ID=t_yourname - -# =================== -# Redis Configuration -# =================== - -REDIS_HOST=localhost -REDIS_PORT=6379 -REDIS_DB=8 -REDIS_SSL=false - -# =================== -# MongoDB Configuration -# =================== - -MONGODB_HOST=localhost -MONGODB_PORT=27017 -MONGODB_USERNAME=admin -MONGODB_PASSWORD=memsys123 -MONGODB_URI_PARAMS=socketTimeoutMS=15000&authSource=admin -# MONGODB_DATABASE=memsys - -# =================== -# Elasticsearch Configuration -# =================== - -ES_HOSTS=http://localhost:19200 -ES_USERNAME= -ES_PASSWORD= -ES_VERIFY_CERTS=false - -# =================== -# Milvus Configuration -# =================== - -MILVUS_HOST=localhost -MILVUS_PORT=19530 - - -# =================== -# API Server Configuration -# =================== - -# V3 API Base URL (for chat_with_memory.py and other clients) -API_BASE_URL=http://localhost:1995 - -# =================== -# Retrieve Configuration -# =================== - -# Maximum return count when in unlimited mode (top_k=-1) -TOPK_LIMIT=100 - -# Recall multiplier: when top_k > 0, actual recall = top_k * RECALL_MULTIPLIER -RECALL_MULTIPLIER=2 - -# Milvus COSINE similarity threshold (0-1), applied in unlimited mode -MILVUS_SIMILARITY_THRESHOLD=0.6 - -# Rerank score threshold (0-1), applied in unlimited mode for quality filtering -RERANK_SCORE_THRESHOLD=0.6 - -# =================== -# Agentic Retrieval Configuration -# =================== - -# Number of top results after reranking used for LLM sufficiency judgment -AGENTIC_ROUND1_RERANK_TOP_N=10 - -# =================== -# Agent Memorize Mode -# =================== - -# Controls which MemorizeConfig is used for agent conversations. -# - online: full pipeline, fast skill search (default) -# - fast_skill: skip profile/foresight/eventlog, skip maturity scoring -AGENT_MEMORIZE_MODE=online - -# =================== -# Environment & Logging Configuration -# =================== - -LOG_LEVEL=INFO -ENV=dev -PYTHONASYNCIODEBUG=1 -MEMORY_LANGUAGE=en \ No newline at end of file diff --git a/methods/EverCore/evaluation/README.md b/methods/EverCore/evaluation/README.md deleted file mode 100644 index cdee115a..00000000 --- a/methods/EverCore/evaluation/README.md +++ /dev/null @@ -1,365 +0,0 @@ -# EverCore Evaluation Framework - -A unified, modular evaluation framework for benchmarking memory systems on standard datasets. - -## 📖 Overview - -### Evaluation Scope - -In addition to **EverCore**, this framework supports evaluation of several influential memory systems in the industry: -- **Mem0** -- **MemOS** -- **Zep** -- **MemU** - -These systems were selected based on recent industry benchmarks and their prominence in global markets. Since many commercial systems have web-based optimizations not available in their open-source versions, we evaluate them through their **online API interfaces** to ensure fair comparison with production-grade capabilities. - -### Implementation - -Our adapter implementations are based on: -- **Official open-source repositories**: Mem0, MemOS, Zep on GitHub -- **Official documentation**: Mem0, MemOS, MemU, Zep quick start guide and API documentation -- **Consistent methodology**: All systems evaluated using the same pipeline, datasets, and metrics -- **Unified answer generation**: All systems use **GPT-4.1-mini** as the answer LLM to ensure fair comparison across different memory backends - -During our evaluation, we identified several issues in existing open-source reference implementations for benchmarking these systems that could negatively impact their performance. We addressed these implementation gaps to ensure each system is evaluated at its best potential: - -- **Mem0 timezone handling**: The latest version returns timestamps in PDT format in search results, requiring additional timezone conversion for accurate temporal reasoning. - -- **MemU retrieval enhancement**: While some memories are visible in the backend dashboard, the `/memory/retrieve/related-memory-items` API likely relies on simple vector-based retrieval, which may miss relevant context. Following the official documentation examples, we included category summaries as additional context to improve recall. - -- **Zep API migration**: Zep's official open-source evaluation implementation was based on the earlier v2 API. Since Zep has officially upgraded to v3 API, we migrated the evaluation code to v3 following the official documentation to benchmark the latest capabilities. - -- **Zep timestamp semantics**: Unlike most memory systems that record conversation timestamps, Zep records event occurrence timestamps. For example, a conversation on March 2nd mentioning "Anna ate a burger yesterday" would be timestamped March 1st, with the memory content preserving the original phrasing. Using standard answer prompts leads to significant errors on temporal questions. Zep's team provides optimized prompts in their open-source evaluation code to handle this. This informed one of our evaluation principles: **each memory system uses its own official answer prompts** rather than a unified prompt template, ensuring fair evaluation of each system's intended usage. - - - - -### Evaluation Results -**Results on Locomo** - -| Locomo | single hop | multi hop | temporal | open domain | Overall | Average Tokens | Version | Answer LLM | -|-----------|------------|-----------|----------|-------------|---------|----------------|----------------------------------------------|-----------------| -| Full-context | 94.93 | 90.43 | 87.95 | 71.88 | 91.21 | 20281 | | gpt-4.1-mini | -| Mem0 | 68.97 | 61.70 | 58.26 | 50.00 | 64.20 | 1016 | web API/v1.0.0 (2025.11) | gpt-4.1-mini | -| Zep | 90.84 | 81.91 | 77.26 | 75.00 | 85.22 | 1411 | web API/v3 (2025.11) | gpt-4.1-mini | -| MemOS | 85.37 | 79.43 | 75.08 | 64.58 | 80.76 | 2498 | web API/v1 (2025.11) | gpt-4.1-mini | -| MemU | 74.91 | 72.34 | 43.61 | 54.17 | 66.67 | 3964 | web API/v1 (2025.11) | gpt-4.1-mini | -| EverCore | 96.08 | 91.13 | 89.72 | 70.83 | 92.32 | 2298 | open-source EverCore v1.0.0 companion | gpt-4.1-mini | - -*Full-context: using the whole conversation as context for answering questions. - - -**Results on Longmemeval** - -| Longmemeval | Single-session-user | Single-session-assistant | Single-session-preference | Multi-session | Knowledge-update | Temporal-reasoning | Overall | -|-------------|----------------------|---------------------------|----------------------------|----------------|-------------------|---------------------|---------| -| EverCore | 100.00 | 78.57 | 96.67 | 78.45 | 87.18 | 71.18 | 82.00 | - -> **Note on Reproducibility**: To ensure the reproducibility of our evaluation, we provide full evaluation intermediate data for all methods. You can access the data at [EverMind-AI/EverMemOS_Eval_Results](https://huggingface.co/datasets/EverMind-AI/EverMemOS_Eval_Results). - - -## 🌟 Key Features - -### Unified & Modular Framework -- **One codebase for all**: No need to write separate code for each dataset or system -- **Plug-and-play systems**: Support multiple memory systems (EverCore, Mem0, MemOS, MemU, etc.) -- **Multiple benchmarks**: LoCoMo, LongMemEval, PersonaMem out of the box -- **Consistent evaluation**: All systems evaluated with the same pipeline and metrics - -### Automatic Compatibility Detection -The framework automatically detects and adapts to: -- **Multi-user vs Single-user conversations**: Handles both conversation types seamlessly -- **Q&A vs Multiple-choice questions**: Adapts evaluation approach based on question format -- **With/without timestamps**: Works with or without temporal information - -### Robust Checkpoint System -- **Cross-stage checkpoints**: Resume from any pipeline stage (add → search → answer → evaluate) -- **Fine-grained resume**: Saves progress every conversation (search) and every 400 questions (answer) - - -## 🏗️ Architecture Overview - -### Code Structure - -``` -evaluation/ -├── src/ -│ ├── core/ # Pipeline orchestration and data models -│ ├── adapters/ # System-specific implementations -│ ├── evaluators/ # Answer evaluation (LLM Judge, Exact Match) -│ ├── converters/ # Dataset format converters -│ └── utils/ # Configuration, logging, I/O -├── config/ -│ ├── datasets/ # Dataset configurations (locomo.yaml, etc.) -│ ├── systems/ # System configurations (evermemos.yaml, etc.) -│ └── prompts.yaml # Prompt templates -├── data/ # Benchmark datasets -└── results/ # Evaluation results and logs -``` - -### Pipeline Flow - -The evaluation consists of 4 sequential stages: - -1. **Add**: Ingest conversations and build indexes -2. **Search**: Retrieve relevant memories for each question -3. **Answer**: Generate answers using retrieved context -4. **Evaluate**: Assess answer quality with LLM Judge or Exact Match - -Each stage saves its output and can be resumed independently. - -## 🚀 Getting Started - -### Prerequisites - -- Python 3.10+ -- EverCore environment configured (see main project's `env.template`) - -### Data Preparation - -Place your dataset files in the `evaluation/data/` directory: - -**LoCoMo** (native format, no conversion needed): -Get data from: https://github.com/snap-research/locomo/tree/main/data - -``` -evaluation/data/locomo/ -└── locomo10.json -``` - -**LongMemEval** (auto-converts to LoCoMo format): -Get data from: https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned - -``` -evaluation/data/longmemeval/ -└── longmemeval_s_cleaned.json # Original file -# → Will auto-generate: longmemeval_s_locomo_style.json -``` - -**PersonaMem** (auto-converts to LoCoMo format): -Get data from: https://huggingface.co/datasets/bowen-upenn/PersonaMem - -``` -evaluation/data/personamem/ -├── questions_32k.csv # Original file -└── shared_contexts_32k.jsonl # Original file -# → Will auto-generate: personamem_32k_locomo_style.json -``` - -The framework will automatically detect and convert non-LoCoMo formats on first run. You don't need to manually run any conversion scripts. - -### Installation - -Install evaluation-specific dependencies: - -```bash -# For evaluating local systems (EverCore) -uv sync --group evaluation - -# For evaluating online API systems (Mem0, MemOS, MemU, etc.) -uv sync --group evaluation-full -``` - -### Environment Configuration - -The evaluation framework reuses most environment variables from the main EverCore `.env` file: -- `LLM_API_KEY`, `LLM_BASE_URL` (for answer generation with GPT-4.1-mini) -- `VECTORIZE_API_KEY` and `RERANK_API_KEY` (for embeddings/reranker) - -**⚠️ Important**: For OpenRouter API (used by gpt-4.1-mini), make sure `LLM_API_KEY` is set to your OpenRouter API key (format: `sk-or-v1-xxx`). The system will look for API keys in this order: -1. Explicit `api_key` parameter in config -2. `LLM_API_KEY` environment variable - -For testing EverCore, please first configure the whole .env file. - -**Additional variables for online API systems** (add to `.env` if testing these systems): - -```bash -# Mem0 -MEM0_API_KEY=your_mem0_api_key - -# MemOS -MEMOS_KEY=your_memos_api_key - -# MemU -MEMU_API_KEY=your_memu_api_key -``` - -### Quick Test (Smoke Test) - -Run a quick test with limited data to verify everything works: - -```bash -# Navigate to project root -cd /path/to/memsys-opensource - -# Default: first conversation, first 10 messages, first 3 questions -uv run python -m evaluation.cli --dataset locomo --system evermemos --smoke - -# Custom: first conversation, 20 messages, 5 questions -uv run python -m evaluation.cli --dataset locomo --system evermemos \ - --smoke --smoke-messages 20 --smoke-questions 5 - -# You can also evaluate specific conversations with `--from-conv` and `--to-conv` (0-based, end exclusive): -uv run python -m evaluation.cli --dataset locomo --system evermemos_custom --from-conv 0 --to-conv 1 -``` - - -### Full Evaluation - -Run the complete benchmark: - -```bash -# Evaluate EverCore on LoCoMo -uv run python -m evaluation.cli --dataset locomo --system evermemos - -# Evaluate EverCore via local API (start server first) -uv run python src/run.py -# Use --clean-groups to clear existing data before Add stage -uv run python -m evaluation.cli --dataset locomo --system evermemos_local_api --clean-groups - -# Evaluate other systems -uv run python -m evaluation.cli --dataset locomo --system memos -uv run python -m evaluation.cli --dataset locomo --system memu -# For mem0, it's recommended to run add first, check the memory status on the web console to make sure it's finished and then following stages. -uv run python -m evaluation.cli --dataset locomo --system mem0 --stages add -uv run python -m evaluation.cli --dataset locomo --system mem0 --stages search answer evaluate - -# Evaluate on other datasets -uv run python -m evaluation.cli --dataset longmemeval --system evermemos -uv run python -m evaluation.cli --dataset personamem --system evermemos - -# Use --run-name to distinguish multiple runs (useful for A/B testing) -# Results will be saved to: results/{dataset}-{system}-{run-name}/ -uv run python -m evaluation.cli --dataset locomo --system evermemos --run-name baseline -uv run python -m evaluation.cli --dataset locomo --system evermemos --run-name experiment1 -uv run python -m evaluation.cli --dataset locomo --system evermemos --run-name 20241107 - -# Resume from checkpoint if interrupted (automatic) -# Just re-run the same command - it will detect and resume from checkpoint -uv run python -m evaluation.cli --dataset locomo --system evermemos - -``` - -### View Results - -Results are saved to `evaluation/results/{dataset}-{system}[-{run-name}]/`: - -```bash -# View summary report -cat evaluation/results/locomo-evermemos/report.txt - -# View detailed evaluation results -cat evaluation/results/locomo-evermemos/eval_results.json - -# View pipeline execution log -cat evaluation/results/locomo-evermemos/pipeline.log -``` - -**Result files:** -- `report.txt` - Summary metrics (accuracy, total questions) -- `eval_results.json` - Per-question evaluation details -- `answer_results.json` - Generated answers and retrieved context -- `search_results.json` - Retrieved memories for each question -- `pipeline.log` - Detailed execution logs - -## 📊 Understanding Results - -### Metric - -- **Accuracy**: Percentage of correct answers (QA judged by LLM, multiple choice questions judged by exact match) - -### Detailed Results - -Check `eval_results.json` for per-question breakdown: - -**LoCoMo example (Q&A format, evaluated by LLM Judge):** - -```json -{ - "total_questions": ..., - "correct": ..., - "accuracy": ..., - "detailed_results": { - "locomo_exp_user_0": [ - { - "question_id": "locomo_0_qa0", - "question": "What is my favorite food?", - "golden_answer": "Pizza", - "generated_answer": "Your favorite food is pizza.", - "judgments": [ - true, - true, - true - ], - "category": "1" - } - ... - ] - } -} -``` - -**PersonaMem example (Multiple-choice format, evaluated by Exact Match):** - -```json -{ - "overall_accuracy": ..., - "total_questions": ..., - "correct_count": ..., - "detailed_results": [ - { - "question_id": "acd74206-37dc-4756-94a8-b99a395d9a21", - "question": "I recently attended an event where there was a unique blend of modern beats with Pacific sounds.", - "golden_answer": "(c)", - "generated_answer": "(c)", - "is_correct": true, - "category": "recall_user_shared_facts" - } - ... - ] -} -``` - -## 🔧 Advanced Usage - -### Run Specific Stages - -Skip completed stages to iterate faster: - -```bash -# Only run search stage (if add is already done) -uv run python -m evaluation.cli --dataset locomo --system evermemos --stages search - -# Run search, answer, and evaluate (skip add) -uv run python -m evaluation.cli --dataset locomo --system evermemos \ - --stages search answer evaluate -``` -If you have already done search, and you want to do it again, please remove the "search" (and following stages from the completed_stages in the checkpoint_default.json file): -``` - "completed_stages": [ - "answer", - "search", - "evaluate", - "add" - ] -``` - - -### Custom Configuration - -Modify system or dataset configurations: - -```bash -# Copy and edit configuration -cp evaluation/config/systems/evermemos.yaml evaluation/config/systems/evermemos_custom.yaml -# Edit evermemos_custom.yaml with your changes - -# Run with custom config -uv run python -m evaluation.cli --dataset locomo --system evermemos_custom -``` - -## 📄 License - -Same as the parent project. diff --git a/methods/EverCore/evaluation/cli.py b/methods/EverCore/evaluation/cli.py deleted file mode 100644 index 23938bef..00000000 --- a/methods/EverCore/evaluation/cli.py +++ /dev/null @@ -1,316 +0,0 @@ -""" -CLI entry point for the evaluation framework. - -Usage: - python -m evaluation.cli --dataset locomo --system evermemos - python -m evaluation.cli --dataset locomo --system evermemos --smoke 10 - python -m evaluation.cli --dataset locomo --system evermemos --stages search answer evaluate -""" - -import asyncio -import argparse -import os -import sys -from pathlib import Path - -# Environment initialization - must be done before importing EverCore components -# Reference: src/bootstrap.py initialization logic - -# Add project paths -project_root = Path(__file__).parent.parent.resolve() -src_path = project_root / "src" -if str(project_root) not in sys.path: - sys.path.insert(0, str(project_root)) -if str(src_path) not in sys.path: - sys.path.insert(0, str(src_path)) - -# Load environment variables -from common_utils.load_env import setup_environment - -setup_environment(load_env_file_name=".env", check_env_var="MONGODB_HOST") - -from evaluation.src.core.loaders import load_dataset -from evaluation.src.core.pipeline import Pipeline -from evaluation.src.adapters.registry import create_adapter -from evaluation.src.evaluators.registry import create_evaluator -from evaluation.src.utils.config import load_yaml -from evaluation.src.utils.logger import get_console - -from memory_layer.llm.llm_provider import LLMProvider - - -def deep_merge_config(base: dict, override: dict) -> dict: - """ - Deep merge configuration dictionaries. - - Args: - base: Base configuration - override: Override configuration - - Returns: - Merged configuration - """ - result = base.copy() - for key, value in override.items(): - if key in result and isinstance(result[key], dict) and isinstance(value, dict): - # Recursively merge nested dictionaries - result[key] = deep_merge_config(result[key], value) - else: - # Direct override - result[key] = value - return result - - -async def main(): - """Main function.""" - parser = argparse.ArgumentParser(description="Memory System Evaluation Framework") - - parser.add_argument( - "--dataset", type=str, required=True, help="Dataset name (e.g., locomo)" - ) - parser.add_argument( - "--system", type=str, required=True, help="System name (e.g., evermemos)" - ) - parser.add_argument( - "--stages", - nargs="+", - default=None, - help="Stages to run (add, search, answer, evaluate). Default: all", - ) - parser.add_argument( - "--smoke", - action="store_true", - help="Enable smoke test mode (process small dataset for quick validation)", - ) - parser.add_argument( - "--smoke-messages", - type=int, - default=10, - help="Smoke test: number of messages to process (use 0 for all). Default: 10", - ) - parser.add_argument( - "--smoke-questions", - type=int, - default=3, - help="Smoke test: number of questions to test (use 0 for all). Default: 3", - ) - parser.add_argument( - "--from-conv", - type=int, - default=0, - help="Starting conversation index to process (inclusive, 0-based). Default: 0", - ) - parser.add_argument( - "--to-conv", - type=int, - default=None, - help="Ending conversation index to process (exclusive). Default: None (process all remaining)", - ) - parser.add_argument( - "--run-name", - type=str, - default=None, - help="Run name/version for distinguishing multiple runs (e.g., 'v1', 'baseline', '20241104')", - ) - parser.add_argument( - "--output-dir", - type=str, - default=None, - help="Output directory. Default: results/{dataset}-{system}[-{run_name}]", - ) - parser.add_argument( - "--clean-groups", - action="store_true", - help="Before Add stage, clear database data for the groups (group_id=conversation_id) involved in this run. " - "Useful for debugging to avoid polluted data.", - ) - - args = parser.parse_args() - - console = get_console() - - # Load configurations - console.print("\n[bold cyan]Loading configurations...[/bold cyan]") - - evaluation_root = Path(__file__).parent - - # Load dataset configuration - dataset_config_path = ( - evaluation_root / "config" / "datasets" / f"{args.dataset}.yaml" - ) - if not dataset_config_path.exists(): - console.print(f"[red]❌ Dataset config not found: {dataset_config_path}[/red]") - return - - dataset_config = load_yaml(str(dataset_config_path)) - console.print(f" ✅ Loaded dataset config: {args.dataset}") - - # Override MEMORY_LANGUAGE from dataset config if specified - if "memory_language" in dataset_config: - os.environ["MEMORY_LANGUAGE"] = dataset_config["memory_language"] - console.print( - f" 🌐 Memory language: {dataset_config['memory_language']} (from dataset config)" - ) - - # Load system configuration - system_config_path = evaluation_root / "config" / "systems" / f"{args.system}.yaml" - if not system_config_path.exists(): - console.print(f"[red]❌ System config not found: {system_config_path}[/red]") - return - - system_config = load_yaml(str(system_config_path)) - console.print(f" ✅ Loaded system config: {args.system}") - - # Apply dataset-specific configuration overrides - if ( - "dataset_overrides" in system_config - and args.dataset in system_config["dataset_overrides"] - ): - overrides = system_config["dataset_overrides"][args.dataset] - # Deep merge override configurations (supports nested field overrides) - system_config = deep_merge_config(system_config, overrides) - console.print( - f" 🔧 Applied dataset overrides for {args.dataset}: {list(overrides.keys())}" - ) - - # Load dataset - console.print(f"\n[bold cyan]Loading dataset: {args.dataset}[/bold cyan]") - - data_path = dataset_config["data"]["path"] - if not Path(data_path).is_absolute(): - # Priority: load from evaluation/data/, fall back to project root - eval_data_path = evaluation_root / "data" / data_path - root_data_path = evaluation_root.parent / data_path - - if eval_data_path.exists(): - data_path = eval_data_path - console.print(f" 📂 Using evaluation/data/{data_path}") - elif root_data_path.exists(): - data_path = root_data_path - console.print(f" 📂 Using project root data/{data_path}") - else: - console.print( - f"[red]❌ Data not found in evaluation/data/ or project root data/[/red]" - ) - return - - # Get max_content_length from dataset config (if specified) - max_content_length = dataset_config.get("data", {}).get("max_content_length", None) - if max_content_length: - console.print(f" ⚠️ Max content length: {max_content_length} characters") - - # Smart load with auto conversion - dataset = load_dataset( - args.dataset, str(data_path), max_content_length=max_content_length - ) - - console.print( - f" ✅ Loaded {len(dataset.conversations)} conversations, {len(dataset.qa_pairs)} QA pairs" - ) - - # Determine output directory - if args.output_dir: - output_dir = Path(args.output_dir) - else: - # Generate output directory name based on run_name presence - if args.run_name: - output_dir = ( - evaluation_root - / "results" - / f"{args.dataset}-{args.system}-{args.run_name}" - ) - else: - output_dir = evaluation_root / "results" / f"{args.dataset}-{args.system}" - - # Create components - console.print(f"\n[bold cyan]Initializing components...[/bold cyan]") - - # Add dataset_name to system_config for adapter initialization - # (Used to determine num_workers based on adapter + dataset combination) - system_config["dataset_name"] = args.dataset - # Pass CLI switch down to adapter via config (adapters can opt-in) - system_config["clean_groups"] = bool(args.clean_groups) - - # Create adapter (pass output_dir for persistence) - adapter = create_adapter( - system_config["adapter"], system_config, output_dir=output_dir - ) - console.print(f" ✅ Created adapter: {adapter.get_system_info()['name']}") - - # Create evaluator - evaluator = create_evaluator( - dataset_config["evaluation"]["type"], dataset_config["evaluation"] - ) - console.print(f" ✅ Created evaluator: {evaluator.get_name()}") - - # Create LLM Provider for answer generation - llm_config = system_config.get("llm", {}) - llm_provider = LLMProvider( - provider_type=llm_config.get("provider", "openai"), - model=llm_config.get("model"), - api_key=llm_config.get("api_key"), - base_url=llm_config.get("base_url"), - temperature=llm_config.get("temperature", 0.0), - max_tokens=llm_config.get("max_tokens", 32768), - ) - console.print(f" Created LLM provider: {llm_config.get('model')}") - - # Create pipeline - # Read filter categories from dataset configuration - filter_categories = dataset_config.get("evaluation", {}).get("filter_category", []) - - pipeline = Pipeline( - adapter=adapter, - evaluator=evaluator, - llm_provider=llm_provider, - output_dir=output_dir, - filter_categories=filter_categories, - ) - - console.print(f" ✅ Created pipeline, output: {output_dir}") - if filter_categories: - console.print(f" 📋 Filter categories: {filter_categories}") - - # Run pipeline - try: - results = await pipeline.run( - dataset=dataset, - stages=args.stages, - smoke_test=args.smoke, - smoke_messages=args.smoke_messages, - smoke_questions=args.smoke_questions, - from_conv=args.from_conv, - to_conv=args.to_conv, - ) - - console.print(f"\n[bold green]✨ Evaluation completed![/bold green]") - console.print(f"Results saved to: [cyan]{output_dir}[/cyan]\n") - - finally: - # Cleanup resources - # Clean up adapter session (e.g., aiohttp.ClientSession) - if hasattr(adapter, 'close') and callable(getattr(adapter, 'close')): - try: - await adapter.close() - console.print("[dim]🧹 Cleaned up adapter resources[/dim]") - except Exception as e: - # Cleanup failure doesn't affect main process - console.print(f"[dim]⚠️ Failed to cleanup adapter resources: {e}[/dim]") - - # Only systems using rerank need cleanup - systems_need_rerank = ["evermemos"] - if args.system in systems_need_rerank: - try: - from agentic_layer import rerank_service - - reranker = rerank_service.get_rerank_service() - if hasattr(reranker, 'close') and callable(getattr(reranker, 'close')): - await reranker.close() - console.print("[dim]🧹 Cleaned up rerank service resources[/dim]") - except Exception as e: - # Cleanup failure doesn't affect main process - console.print(f"[dim]⚠️ Failed to cleanup rerank resources: {e}[/dim]") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/evaluation/config/datasets/evermembench.yaml b/methods/EverCore/evaluation/config/datasets/evermembench.yaml deleted file mode 100644 index dde2684f..00000000 --- a/methods/EverCore/evaluation/config/datasets/evermembench.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# LoCoMo Dataset Configuration - -name: "evermembench" -version: "1.0" -description: "Long-Context Modeling benchmark for conversational memory" - -# Memory language setting (overrides MEMORY_LANGUAGE env var) -memory_language: "en" - -# Data configuration -data: - path: "evermembench/evermembench.json" # Fixed format to match PersonaMem - format: "locomo" # Native format, no conversion needed - -# Evaluation configuration -evaluation: - type: "hybrid" # Use hybrid evaluator: Exact Match for multiple-choice, LLM Judge for open-ended - - # Exact match settings (for multiple-choice questions) - case_sensitive: false # Case insensitive - normalize_whitespace: true # Normalize whitespace - extract_choice: true # Extract options from the generated answer (like (a), (b)) - - # LLM Judge settings (for open-ended questions) - llm: - provider: "openai" - model: "gpt-4o-mini" - api_key: "${LLM_API_KEY}" - base_url: "${LLM_BASE_URL:https://openrouter.ai/api/v1}" - - num_runs: 3 # Number of judging runs per question (for LLM Judge) - - # Filter settings - # Categories: "1", "2", "3", "3.1", "3.2" (multiple types) - filter_category: [] # Can use integers or strings, like [3.2] or ["3.2"] - diff --git a/methods/EverCore/evaluation/config/datasets/locomo.yaml b/methods/EverCore/evaluation/config/datasets/locomo.yaml deleted file mode 100644 index e41909eb..00000000 --- a/methods/EverCore/evaluation/config/datasets/locomo.yaml +++ /dev/null @@ -1,31 +0,0 @@ -# LoCoMo Dataset Configuration - -name: "locomo" -version: "1.0" -description: "Long-Context Modeling benchmark for conversational memory" - -# Memory language setting (overrides MEMORY_LANGUAGE env var) -memory_language: "en" - -# Data configuration -data: - path: "locomo/locomo10.json" # Relative to evaluation/data/ or project root data/ - format: "locomo" # Native format, no conversion needed - -# Evaluation configuration -evaluation: - type: "llm_judge" # llm_judge | exact_match - - # LLM Judge settings - llm: - provider: "openai" - model: "gpt-4o-mini" - api_key: "${LLM_API_KEY}" - base_url: "${LLM_BASE_URL:https://openrouter.ai/api/v1}" - - num_runs: 3 # Number of judging runs per question - - # Filter settings - # LoCoMo categories: "1", "2", "3", "5" (adversarial questions) - filter_category: [5] # Can use integers or strings, like [5] or ["5"] - diff --git a/methods/EverCore/evaluation/config/datasets/longmemeval.yaml b/methods/EverCore/evaluation/config/datasets/longmemeval.yaml deleted file mode 100644 index 9c4157e4..00000000 --- a/methods/EverCore/evaluation/config/datasets/longmemeval.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# LongMemEval Dataset Configuration - -name: "longmemeval" -version: "1.0" -description: "Long-term Memory Evaluation benchmark" - -# Memory language setting (overrides MEMORY_LANGUAGE env var) -memory_language: "en" - -# Data configuration -data: - path: "longmemeval" # Directory containing raw and converted files - format: "longmemeval" # Needs conversion to Locomo format - - # Converter will look for: - # - longmemeval/longmemeval_s_cleaned.json (raw) - # - longmemeval/longmemeval_s_locomo_style.json (converted, auto-generated) - - # Content length limit (for very long messages) - max_content_length: 8000 # Truncate messages longer than this (characters) - -# Evaluation configuration -evaluation: - type: "llm_judge" - - llm: - provider: "openai" - model: "gpt-4o-mini" - api_key: "${LLM_API_KEY}" - base_url: "${LLM_BASE_URL:https://openrouter.ai/api/v1}" - - num_runs: 3 - - # LongMemEval categories: "single-session-user", "multi-session-user", etc. - filter_category: [] # If you need to filter, add to the list: ["single-session-user", ...] - diff --git a/methods/EverCore/evaluation/config/datasets/personamem.yaml b/methods/EverCore/evaluation/config/datasets/personamem.yaml deleted file mode 100644 index 6f78fef5..00000000 --- a/methods/EverCore/evaluation/config/datasets/personamem.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# PersonaMem Dataset Configuration - -name: "personamem" -version: "1.0" -description: "Persona Memory benchmark for long-context personalization" - -# Memory language setting (overrides MEMORY_LANGUAGE env var) -memory_language: "en" - -# Data configuration -data: - path: "personamem" # Directory containing raw and converted files - format: "personamem" # Needs conversion to Locomo format - - # Converter will look for: - # - personamem/questions_32k.csv (raw) - # - personamem/shared_contexts_32k.jsonl (raw) - # - personamem/personamem_32k_locomo_style.json (converted, auto-generated) - -# Evaluation configuration -evaluation: - type: "exact_match" # PersonaMem is a multiple-choice question, use exact match - - # Exact match settings - case_sensitive: false # Case insensitive - normalize_whitespace: true # Normalize whitespace - extract_choice: true # Extract options from the generated answer (like (a), (b)) - - # PersonaMem categories: - # "recall_user_shared_facts", "provide_preference_aligned_recommendations", - # "suggest_new_ideas", "recalling_the_reasons_behind_previous_updates", - # "track_full_preference_evolution", "generalizing_to_new_scenarios", - # "recalling_facts_mentioned_by_the_user" - filter_category: [] # If you need to filter, add to the list: ["suggest_new_ideas", ...] - diff --git a/methods/EverCore/evaluation/config/prompts.yaml b/methods/EverCore/evaluation/config/prompts.yaml deleted file mode 100644 index 042ec042..00000000 --- a/methods/EverCore/evaluation/config/prompts.yaml +++ /dev/null @@ -1,290 +0,0 @@ -# LLM Judge Prompts -# Used in: src/evaluators/llm_judge.py -llm_judge: - system_prompt: | - You are an expert grader that determines if answers to questions match a gold standard answer - - user_prompt: | - Your task is to label an answer to a question as 'CORRECT' or 'WRONG'. You will be given the following data: - (1) a question (posed by one user to another user), - (2) a 'gold' (ground truth) answer, - (3) a generated answer - which you will score as CORRECT/WRONG. - - The point of the question is to ask about something one user should know about the other user based on their prior conversations. - The gold answer will usually be a concise and short answer that includes the referenced topic, for example: - Question: Do you remember what I got the last time I went to Hawaii? - Gold answer: A shell necklace - The generated answer might be much longer, but you should be generous with your grading - as long as it touches on the same topic as the gold answer, it should be counted as CORRECT. - - For time related questions, the gold answer will be a specific date, month, year, etc. The generated answer might be much longer or use relative time references (like "last Tuesday" or "next month"), but you should be generous with your grading - as long as it refers to the same date or time period as the gold answer, it should be counted as CORRECT. Even if the format differs (e.g., "May 7th" vs "7 May"), consider it CORRECT if it's the same date. - - Now it's time for the real question: - Question: {question} - Gold answer: {golden_answer} - Generated answer: {generated_answer} - - First, provide a short (one sentence) explanation of your reasoning, then finish with CORRECT or WRONG. - Do NOT include both CORRECT and WRONG in your response, or it will break the evaluation script. - - Just return the label CORRECT or WRONG in a json format with the key as "label". - - -# Online API Answer Prompts -# Used in: src/adapters/online_base.py and its subclasses -online_api: - default: - answer_prompt_mem0: | - You are an intelligent memory assistant tasked with retrieving accurate information from conversation memories. - - # CONTEXT: - You have access to memories from two speakers in a conversation. These memories contain - timestamped information that may be relevant to answering the question. - - # INSTRUCTIONS: - 1. Carefully analyze all provided memories from both speakers - 2. Pay special attention to the timestamps to determine the answer - 3. If the question asks about a specific event or fact, look for direct evidence in the memories - 4. If the memories contain contradictory information, prioritize the most recent memory - 5. If there is a question about time references (like "last year", "two months ago", etc.), - calculate the actual date based on the memory timestamp. For example, if a memory from - 4 May 2022 mentions "went to India last year," then the trip occurred in 2021. - 6. Always convert relative time references to specific dates, months, or years. For example, - convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory - timestamp. Ignore the reference while answering the question. - 7. Focus only on the content of the memories from both speakers. Do not confuse character - names mentioned in memories with the actual users who created those memories. - 8. The answer should be less than 5-6 words. - - # APPROACH (Think step by step): - 1. First, examine all memories that contain information related to the question - 2. Examine the timestamps and content of these memories carefully - 3. Look for explicit mentions of dates, times, locations, or events that answer the question - 4. If the answer requires calculation (e.g., converting relative time references), show your work - 5. Formulate a precise, concise answer based solely on the evidence in the memories - 6. Double-check that your answer directly addresses the question asked - 7. Ensure your final answer is specific and avoids vague time references - - {context} - - Question: {question} - - Answer: - - answer_prompt_memos: | - You are a knowledgeable and helpful AI assistant. - - # CONTEXT: - You have access to memories from two speakers in a conversation. These memories contain - timestamped information that may be relevant to answering the question. - - # INSTRUCTIONS: - 1. Carefully analyze all provided memories. Synthesize information across different entries if needed to form a complete answer. - 2. Pay close attention to the timestamps to determine the answer. If memories contain contradictory information, the **most recent memory** is the source of truth. - 3. If the question asks about a specific event or fact, look for direct evidence in the memories. - 4. Your answer must be grounded in the memories. However, you may use general world knowledge to interpret or complete information found within a memory (e.g., identifying a landmark mentioned by description). - 5. If the question involves time references (like "last year", "two months ago", etc.), you **must** calculate the actual date based on the memory's timestamp. For example, if a memory from 4 May 2022 mentions "went to India last year," then the trip occurred in 2021. - 6. Always convert relative time references to specific dates, months, or years in your final answer. - 7. Do not confuse character names mentioned in memories with the actual users who created them. - 8. The answer must be brief (under 5-6 words) and direct, with no extra description. - - # APPROACH (Think step by step): - 1. First, examine all memories that contain information related to the question. - 2. Synthesize findings from multiple memories if a single entry is insufficient. - 3. Examine timestamps and content carefully, looking for explicit dates, times, locations, or events. - 4. If the answer requires calculation (e.g., converting relative time references), perform the calculation. - 5. Formulate a precise, concise answer based on the evidence from the memories (and allowed world knowledge). - 6. Double-check that your answer directly addresses the question asked and adheres to all instructions. - 7. Ensure your final answer is specific and avoids vague time references. - - {context} - - Question: {question} - - Answer: - - answer_prompt_cot: | - You are an intelligent memory assistant tasked with retrieving accurate information from episodic memories. - - # CONTEXT: - You have access to episodic memories from conversations between two speakers. These memories contain - timestamped information that may be relevant to answering the question. - - # INSTRUCTIONS: - Your goal is to synthesize information from all relevant memories to provide a comprehensive and accurate answer. - You MUST follow a structured Chain-of-Thought process to ensure no details are missed. - Actively look for connections between people, places, and events to build a complete picture. Synthesize information from different memories to answer the user's question. - It is CRITICAL that you move beyond simple fact extraction and perform logical inference. When the evidence strongly suggests a connection, you must state that connection. Do not dismiss reasonable inferences as "speculation." Your task is to provide the most complete answer supported by the available evidence. - - # CRITICAL REQUIREMENTS: - 1. NEVER omit specific names - use "Amy's colleague Rob" not "a colleague" - 2. ALWAYS include exact numbers, amounts, prices, percentages, dates, times - 3. PRESERVE frequencies exactly - "every Tuesday and Thursday" not "twice a week" - 4. MAINTAIN all proper nouns and entities as they appear - - # RESPONSE FORMAT (You MUST follow this structure): - - ## STEP 1: RELEVANT MEMORIES EXTRACTION - [List each memory that relates to the question, with its timestamp] - - Memory 1: [timestamp] - [content] - - Memory 2: [timestamp] - [content] - ... - - ## STEP 2: KEY INFORMATION IDENTIFICATION - [Extract ALL specific details from the memories] - - Names mentioned: [list all person names, place names, company names] - - Numbers/Quantities: [list all amounts, prices, percentages] - - Dates/Times: [list all temporal information] - - Frequencies: [list any recurring patterns] - - Other entities: [list brands, products, etc.] - - ## STEP 3: CROSS-MEMORY LINKING - [Identify entities that appear in multiple memories and link related information. Make reasonable inferences when entities are strongly connected.] - - Shared entities: [list people, places, events mentioned across different memories] - - Connections found: [e.g., "Memory 1 mentions A moved from hometown → Memory 2 mentions A's hometown is LA → Therefore A moved from LA"] - - Inferred facts: [list any facts that require combining information from multiple memories] - - ## STEP 4: TIME REFERENCE CALCULATION - [If applicable, convert relative time references] - - Original reference: [e.g., "last year" from May 2022] - - Calculated actual time: [e.g., "2021"] - - ## STEP 5: CONTRADICTION CHECK - [If multiple memories contain different information] - - Conflicting information: [describe] - - Resolution: [explain which is most recent/reliable] - - ## STEP 6: DETAIL VERIFICATION CHECKLIST - - [ ] All person names included: [list them] - - [ ] All locations included: [list them] - - [ ] All numbers exact: [list them] - - [ ] All frequencies specific: [list them] - - [ ] All dates/times precise: [list them] - - [ ] All proper nouns preserved: [list them] - - ## STEP 7: ANSWER FORMULATION - [Explain how you're combining the information to answer the question] - - ## FINAL ANSWER: - [Provide the concise answer with ALL specific details preserved] - - --- - - {context} - - Question: {question} - - Now, follow the Chain-of-Thought process above to answer the question: - - answer_prompt_zep: | - You are a helpful expert assistant answering questions based on the provided context. - - # CONTEXT: - You have access to facts and entities from a conversation. - - # INSTRUCTIONS: - 1. Carefully analyze all provided memories (facts and entities) - 2. Pay special attention to the timestamps (event_time) to determine when events occurred - 3. If the question asks about a specific event or fact, look for direct evidence in the memories - 4. If the memories contain contradictory information, prioritize the most recent memory - 5. Always convert relative time references to specific dates, months, or years - 6. Be as specific as possible when talking about people, places, and events - 7. Timestamps in memories represent the actual time the event occurred, not the time the event was mentioned in a message - - # IMPORTANT CLARIFICATION: - When interpreting memories, use the event_time (timestamp) to determine when the described event happened, not when someone talked about the event. - - Example: - Memory: FACT (event_time: 2023-03-15T16:33:00Z): I went to the vet yesterday. - Question: What day did I go to the vet? - Correct Answer: March 15, 2023 - Explanation: Even though the phrase says "yesterday," the timestamp shows the event was recorded as happening on March 15th. Therefore, the actual vet visit happened on that date, regardless of the word "yesterday" in the text. - - # APPROACH (Think step by step): - 1. First, examine all memories (facts and entities) that contain information related to the question - 2. Examine the timestamps and content of these memories carefully - 3. Look for explicit mentions of dates, times, locations, or events that answer the question - 4. If the answer requires calculation (e.g., converting relative time references), show your work - 5. Formulate a precise, concise answer based solely on the evidence in the memories - 6. Double-check that your answer directly addresses the question asked - 7. Ensure your final answer is specific and avoids vague time references - - {context} - - Question: {question} - Answer: - - templates: - # Default template (shared by Mem0, Memos, Memu) - # Uses speaker_1/speaker_2 as unified variable names - default: | - Memories for {speaker_1}: - - {speaker_1_memories} - - Memories for {speaker_2}: - - {speaker_2_memories} - - # Zep template - uses facts and entities format - zep: | - FACTS and ENTITIES represent relevant context to the current conversation. - - # These are the most relevant facts for the conversation along with the datetime of the event that the fact refers to. - # If a fact mentions something happening a week ago, then the datetime will be the date time of last week and not the datetime - # of when the fact was stated. - # Timestamps in memories represent the actual time the event occurred, not the time the event was mentioned in a message. - - - {facts} - - - # These are the most relevant entities - # ENTITY_NAME: entity summary - - {entities} - - - - -# Memory Extraction / Add Stage Prompts -# Used during the add stage to guide memory extraction and formatting -add_stage: - # Mem0 custom memory extraction instructions - mem0: - custom_instructions: | - Generate personal memories that follow these guidelines: - - 1. Each memory should be self-contained with complete context, including: - - The person's name, do not use "user" while creating memories - - Personal details (career aspirations, hobbies, life circumstances) - - Emotional states and reactions - - Ongoing journeys or future plans - - Specific dates when events occurred - - 2. Include meaningful personal narratives focusing on: - - Identity and self-acceptance journeys - - Family planning and parenting - - Creative outlets and hobbies - - Mental health and self-care activities - - Career aspirations and education goals - - Important life events and milestones - - 3. Make each memory rich with specific details rather than general statements - - Include timeframes (exact dates when possible) - - Name specific activities (e.g., "charity race for mental health" rather than just "exercise") - - Include emotional context and personal growth elements - - 4. Extract memories only from user messages, not incorporating assistant responses - - 5. Format each memory as a paragraph with a clear narrative structure that captures the person's experience, challenges, and aspirations - - # Future: other system memory extraction instructions can be added here - # memos: - # extraction_prompt: | - # ... - # - # memu: - # extraction_prompt: | - # ... - diff --git a/methods/EverCore/evaluation/config/systems/evermemos.yaml b/methods/EverCore/evaluation/config/systems/evermemos.yaml deleted file mode 100644 index 69ccdae6..00000000 --- a/methods/EverCore/evaluation/config/systems/evermemos.yaml +++ /dev/null @@ -1,22 +0,0 @@ -# EverCore System Configuration - -name: "evermemos" -version: "1.0" -description: "EverCore" - -adapter: "evermemos" - -# LLM configuration -llm: - provider: "openai" - model: "openai/gpt-4.1-mini" - api_key: "${LLM_API_KEY}" - base_url: "${LLM_BASE_URL:https://openrouter.ai/api/v1}" - -search: - mode: "agentic" # agentic | lightweight - # When mode="lightweight", the search method defaults to "bm25_only". - # Uncomment and set the following to override: - # lightweight_search_mode: "hybrid" # BM25 + Embedding + RRF fusion - # lightweight_search_mode: "emb_only" # Only Embedding search - diff --git a/methods/EverCore/evaluation/config/systems/evermemos_cloud_api.yaml b/methods/EverCore/evaluation/config/systems/evermemos_cloud_api.yaml deleted file mode 100644 index a7418ccd..00000000 --- a/methods/EverCore/evaluation/config/systems/evermemos_cloud_api.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# EverCore (HTTP API) System Configuration -name: "evermemos_api" -version: "1.0" -description: "EverCore via HTTP Memory API" - -adapter: "evermemos_api" - -# EverCore API configuration -# base_url can be either host base (https://api.evermind.ai) or full endpoint (.../api/v0/memories) -base_url: "${EVERMEMOS_API_URL:https://api.evermind.ai/api/v0/memories}" -# Hosted API requires Bearer token in Authorization header. -api_key: "${EVERMEMOS_API_KEY}" -sync_mode: true -max_retries: 3 -timeout_seconds: 60 -request_interval: 0.0 - -# Concurrency (conversation-level) -num_workers: 10 - -# Search configuration (maps to GET /api/v0/memories/search) -search: - scope: "group" - top_k: 10 - # keep explicit fields for clarity/compatibility - retrieve_method: "agentic" - memory_types: ["episodic_memory"] - # Hosted API can be slow for agentic; tune here instead of hardcoding in code. - num_workers: 10 - timeout_seconds: 300 - -# LLM configuration (for answer generation, same as other online systems) -llm: - provider: "openai" - model: "openai/gpt-4.1-mini" - api_key: "${LLM_API_KEY}" - base_url: "${LLM_BASE_URL:https://openrouter.ai/api/v1}" - temperature: 0 - max_tokens: 32768 - -answer: - max_retries: 3 - - diff --git a/methods/EverCore/evaluation/config/systems/evermemos_local_api.yaml b/methods/EverCore/evaluation/config/systems/evermemos_local_api.yaml deleted file mode 100644 index b77d00c2..00000000 --- a/methods/EverCore/evaluation/config/systems/evermemos_local_api.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# EverCore (Local HTTP API) System Configuration -name: "evermemos_local_api" -version: "1.0" -description: "EverCore via LOCAL HTTP Memory API (no hosted auth)" - -# Reuse the same adapter implementation -adapter: "evermemos_api" - -# Local API configuration -base_url: "http://localhost:1995" - -# Local server typically does not require auth -api_key: "" -sync_mode: true # Wait for boundary detection (10s), then return - -max_retries: 3 -timeout_seconds: 60 -request_interval: 0.0 - -# Concurrency (conversation-level) -num_workers: 10 - -# Search configuration -# Local debug/evaluation is usually group-scoped (group_id=conversation_id) -search: - num_workers: 20 - scope: "group" - top_k: 10 - retrieve_method: "agentic" - memory_types: ["episodic_memory"] - -# LLM configuration (for answer generation) -llm: - provider: "openai" - model: "openai/gpt-4.1-mini" - api_key: "${LLM_API_KEY}" - base_url: "${LLM_BASE_URL:https://openrouter.ai/api/v1}" - # base_url: "http://localhost:8004/v1" - temperature: 0 - max_tokens: 16000 - -answer: - max_retries: 3 - - diff --git a/methods/EverCore/evaluation/config/systems/mem0.yaml b/methods/EverCore/evaluation/config/systems/mem0.yaml deleted file mode 100644 index 44b86c2d..00000000 --- a/methods/EverCore/evaluation/config/systems/mem0.yaml +++ /dev/null @@ -1,39 +0,0 @@ -# Mem0 System Configuration - -name: "mem0" -version: "1.0" -description: "Mem0" - -adapter: "mem0" - -# Mem0 API configuration -api_key: "${MEM0_API_KEY}" -batch_size: 5 # Number of messages to batch per API call -max_retries: 5 # Number of retries for failed API calls -max_content_length: 8000 # Maximum content length per message (API limit) -add_interval: 0.5 # Seconds to wait between add requests (to avoid rate limits) - -# Concurrency configuration -num_workers: 5 # Number of concurrent conversations to process - -# ⏰ Post-Add Wait: Wait for background index build to complete (if needed) -post_add_wait_seconds: 180 # Set to 0 to not wait, adjust if needed - -# LLM configuration (for answer generation) -llm: - provider: "openai" - model: "openai/gpt-4.1-mini" - api_key: "${LLM_API_KEY}" - base_url: "${LLM_BASE_URL:https://openrouter.ai/api/v1}" - temperature: 0 - max_tokens: 32768 - -# Search configuration -search: - top_k: 20 # Number of results per user (in dual-perspective mode, total = 2 * top_k) - search_interval: 0.5 # Seconds to wait between search requests (to avoid 429 errors) - -# Answer configuration -answer: - max_retries: 3 - diff --git a/methods/EverCore/evaluation/config/systems/memos.yaml b/methods/EverCore/evaluation/config/systems/memos.yaml deleted file mode 100644 index b8703627..00000000 --- a/methods/EverCore/evaluation/config/systems/memos.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# Memos System Configuration - -name: "memos" -version: "1.0" -description: "Memos" - -adapter: "memos" - -# Memos API configuration -api_url: "https://memos.memtensor.cn/api/openmem/v1" -api_key: "${MEMOS_KEY}" -batch_size: 10 # Number of messages to send per batch (default) -max_retries: 5 - -# Rate limiting (Memos has 10 requests/second limit) -request_interval: 0.1 # Seconds to wait after each API call (10 req/s = 0.1s) - -# Concurrency configuration -num_workers: 1 # Process conversations sequentially due to rate limit - -# Dataset-specific overrides -# Configuration overrides for specific datasets -dataset_overrides: - longmemeval: - batch_size: 6 # LongMemEval has large data, use smaller batch_size - -# ⏰ Post-Add Wait: Wait for background index build to complete (if needed) -# Online API systems are usually asynchronous, so it takes time to build the index -post_add_wait_seconds: 180 # Wait 180 seconds before starting search - - -# LLM configuration (for answer generation) -llm: - provider: "openai" - model: "openai/gpt-4.1-mini" - api_key: "${LLM_API_KEY}" - base_url: "${LLM_BASE_URL:https://openrouter.ai/api/v1}" - temperature: 0 - max_tokens: 32768 - -# Search configuration -search: - top_k: 20 - -# Answer configuration -answer: - max_retries: 3 - diff --git a/methods/EverCore/evaluation/config/systems/memu.yaml b/methods/EverCore/evaluation/config/systems/memu.yaml deleted file mode 100644 index 2d8177b5..00000000 --- a/methods/EverCore/evaluation/config/systems/memu.yaml +++ /dev/null @@ -1,53 +0,0 @@ -# Memu System Configuration -# -# Memu uses HTTP RESTful API directly, without Python SDK -# Reference documentation: https://memu.pro/docs#memory-apis - -name: "memu" -version: "1.0" -description: "Memu Memory Management System" - -adapter: "memu" - -# Memu API configuration -api_key: "${MEMU_API_KEY}" -base_url: "https://api.memu.so" # Memu official API address -max_retries: 5 - -# Agent configuration (for identifying dialog partners) -agent_id: "evaluation_agent_04" -agent_name: "Memory Evaluation Assistant" - - -# Task status check configuration -task_check_interval: 15 # Task status check interval (seconds) -task_timeout: 4800 # Task timeout (seconds), recommended 90-120 seconds - -# Concurrency configuration -num_workers: 1 # Number of concurrent conversations to process (default for most datasets) - -# Dataset-specific overrides -dataset_overrides: - locomo: - num_workers: 1 # LoCoMo dataset requires lower concurrency - -# Search configuration -min_similarity: 0.3 # Minimum similarity threshold (0-1) - -# LLM configuration (for answer generation) -llm: - provider: "openai" - model: "openai/gpt-4.1-mini" - api_key: "${LLM_API_KEY}" - base_url: "${LLM_BASE_URL:https://openrouter.ai/api/v1}" - temperature: 0 - max_tokens: 32768 - -# Search configuration -search: - top_k: 20 - -# Answer configuration -answer: - max_retries: 3 - diff --git a/methods/EverCore/evaluation/config/systems/zep.yaml b/methods/EverCore/evaluation/config/systems/zep.yaml deleted file mode 100644 index 195301f9..00000000 --- a/methods/EverCore/evaluation/config/systems/zep.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# Zep System Configuration - -name: "zep" -version: "1.0" -description: "Zep - Knowledge Graph Architecture for Agent Memory" - -adapter: "zep" - -# Zep API configuration -api_key: "${ZEP_API_KEY}" - -# Concurrency configuration -num_workers: 1 # Number of concurrent conversations to process - -# ⏰ Serial Message Processing Strategy: -poll_interval: 5 # Seconds between polling episode.processed status - -# ⏰ Post-Add Wait: Wait for background index build to complete (if needed) -post_add_wait_seconds: 0 # Set to 0 to not wait (messages already wait for processed) - -# LLM configuration (for answer generation) -llm: - provider: "openai" - model: "openai/gpt-4.1-mini" - api_key: "${LLM_API_KEY}" - base_url: "${LLM_BASE_URL:https://openrouter.ai/api/v1}" - temperature: 0 - max_tokens: 32768 - -# Search configuration -search: - top_k: 20 # Number of results per search (both nodes and edges) - reranker_nodes: "rrf" # Reranker for nodes search (rrf, cross_encoder) - reranker_edges: "cross_encoder" # Reranker for edges search (rrf, cross_encoder) - -# Answer configuration -answer: - max_retries: 3 diff --git a/methods/EverCore/evaluation/data/locomo/locomo10.json b/methods/EverCore/evaluation/data/locomo/locomo10.json deleted file mode 100644 index d95b8724..00000000 --- a/methods/EverCore/evaluation/data/locomo/locomo10.json +++ /dev/null @@ -1,66751 +0,0 @@ -[ - { - "qa": [ - { - "question": "When did Caroline go to the LGBTQ support group?", - "answer": "7 May 2023", - "evidence": [ - "D1:3" - ], - "category": 2 - }, - { - "question": "When did Melanie paint a sunrise?", - "answer": 2022, - "evidence": [ - "D1:12" - ], - "category": 2 - }, - { - "question": "What fields would Caroline be likely to pursue in her educaton?", - "answer": "Psychology, counseling certification", - "evidence": [ - "D1:9", - "D1:11" - ], - "category": 3 - }, - { - "question": "What did Caroline research?", - "answer": "Adoption agencies", - "evidence": [ - "D2:8" - ], - "category": 1 - }, - { - "question": "What is Caroline's identity?", - "answer": "Transgender woman", - "evidence": [ - "D1:5" - ], - "category": 1 - }, - { - "question": "When did Melanie run a charity race?", - "answer": "The sunday before 25 May 2023", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "When is Melanie planning on going camping?", - "answer": "June 2023", - "evidence": [ - "D2:7" - ], - "category": 2 - }, - { - "question": "What is Caroline's relationship status?", - "answer": "Single", - "evidence": [ - "D3:13", - "D2:14" - ], - "category": 1 - }, - { - "question": "When did Caroline give a speech at a school?", - "answer": "The week before 9 June 2023", - "evidence": [ - "D3:1" - ], - "category": 2 - }, - { - "question": "When did Caroline meet up with her friends, family, and mentors?", - "answer": "The week before 9 June 2023", - "evidence": [ - "D3:11" - ], - "category": 2 - }, - { - "question": "How long has Caroline had her current group of friends for?", - "answer": "4 years", - "evidence": [ - "D3:13" - ], - "category": 2 - }, - { - "question": "Where did Caroline move from 4 years ago?", - "answer": "Sweden", - "evidence": [ - "D3:13", - "D4:3" - ], - "category": 1 - }, - { - "question": "How long ago was Caroline's 18th birthday?", - "answer": "10 years ago", - "evidence": [ - "D4:5" - ], - "category": 2 - }, - { - "question": "What career path has Caroline decided to persue?", - "answer": "counseling or mental health for Transgender people", - "evidence": [ - "D4:13", - "D1:11" - ], - "category": 1 - }, - { - "question": "Would Caroline still want to pursue counseling as a career if she hadn't received support growing up?", - "answer": "Likely no", - "evidence": [ - "D4:15", - "D3:5" - ], - "category": 3 - }, - { - "question": "What activities does Melanie partake in?", - "answer": "pottery, camping, painting, swimming", - "evidence": [ - "D5:4", - "D9:1", - "D1:12", - "D1:18" - ], - "category": 1 - }, - { - "question": "When did Melanie sign up for a pottery class?", - "answer": "2 July 2023", - "evidence": [ - "D5:4" - ], - "category": 2 - }, - { - "question": "When is Caroline going to the transgender conference?", - "answer": "July 2023", - "evidence": [ - "D5:13" - ], - "category": 2 - }, - { - "question": "Where has Melanie camped?", - "answer": "beach, mountains, forest", - "evidence": [ - "D6:16", - "D4:6", - "D8:32" - ], - "category": 1 - }, - { - "question": "What do Melanie's kids like?", - "answer": "dinosaurs, nature", - "evidence": [ - "D6:6", - "D4:8" - ], - "category": 1 - }, - { - "question": "When did Melanie go to the museum?", - "answer": "5 July 2023", - "evidence": [ - "D6:4" - ], - "category": 2 - }, - { - "question": "When did Caroline have a picnic?", - "answer": "The week before 6 July 2023", - "evidence": [ - "D6:11" - ], - "category": 2 - }, - { - "question": "Would Caroline likely have Dr. Seuss books on her bookshelf?", - "answer": "Yes, since she collects classic children's books", - "evidence": [ - "D6:9" - ], - "category": 3 - }, - { - "question": "What books has Melanie read?", - "answer": "\"Nothing is Impossible\", \"Charlotte's Web\"", - "evidence": [ - "D7:8", - "D6:10" - ], - "category": 1 - }, - { - "question": "What does Melanie do to destress?", - "answer": "Running, pottery", - "evidence": [ - "D7:22", - "D5:4" - ], - "category": 1 - }, - { - "question": "When did Caroline go to the LGBTQ conference?", - "answer": "10 July 2023", - "evidence": [ - "D7:1" - ], - "category": 2 - }, - { - "question": "When did Melanie read the book \"nothing is impossible\"?", - "answer": 2022, - "evidence": [ - "D7:8" - ], - "category": 2 - }, - { - "question": "Would Caroline pursue writing as a career option?", - "answer": "LIkely no; though she likes reading, she wants to be a counselor", - "evidence": [ - "D7:5", - "D7:9" - ], - "category": 3 - }, - { - "question": "When did Caroline go to the adoption meeting?", - "answer": "The friday before 15 July 2023", - "evidence": [ - "D8:9" - ], - "category": 2 - }, - { - "question": "When did Melanie go to the pottery workshop?", - "answer": "The Friday before 15 July 2023", - "evidence": [ - "D8:2" - ], - "category": 2 - }, - { - "question": "Would Melanie be considered a member of the LGBTQ community?", - "answer": "Likely no, she does not refer to herself as part of it", - "evidence": [], - "category": 3 - }, - { - "question": "When did Melanie go camping in June?", - "answer": "The week before 27 June 2023", - "evidence": [ - "D4:8" - ], - "category": 2 - }, - { - "question": "What LGBTQ+ events has Caroline participated in?", - "answer": "Pride parade, school speech, support group", - "evidence": [ - "D5:1", - "D8:17", - "D3:1", - "D1:3" - ], - "category": 1 - }, - { - "question": "When did Caroline go to a pride parade during the summer?", - "answer": "The week before 3 July 2023", - "evidence": [ - "D5:1" - ], - "category": 2 - }, - { - "question": "What events has Caroline participated in to help children?", - "answer": "Mentoring program, school speech", - "evidence": [ - "D9:2", - "D3:3" - ], - "category": 1 - }, - { - "question": "When did Melanie go camping in July?", - "answer": "two weekends before 17 July 2023", - "evidence": [ - "D9:1" - ], - "category": 2 - }, - { - "question": "When did Caroline join a mentorship program?", - "answer": "The weekend before 17 July 2023", - "evidence": [ - "D9:2" - ], - "category": 2 - }, - { - "question": "What did Melanie paint recently?", - "answer": "sunset", - "evidence": [ - "D8:6; D9:17" - ], - "category": 1 - }, - { - "question": "What activities has Melanie done with her family?", - "answer": "Pottery, painting, camping, museum, swimming, hiking", - "evidence": [ - "D8:4", - "D8:6", - "D9:1", - "D6:4", - "D1:18", - "D3:14" - ], - "category": 1 - }, - { - "question": "In what ways is Caroline participating in the LGBTQ community?", - "answer": "Joining activist group, going to pride parades, participating in an art show, mentoring program", - "evidence": [ - "D10:3", - "D5:1", - "D9:12", - "D9:2" - ], - "category": 1 - }, - { - "question": "How many times has Melanie gone to the beach in 2023?", - "answer": 2, - "evidence": [ - "D10:8", - "D6:16" - ], - "category": 1 - }, - { - "question": "When did Caroline join a new activist group?", - "answer": "The Tuesday before 20 July 2023", - "evidence": [ - "D10:3" - ], - "category": 2 - }, - { - "question": "Would Melanie be more interested in going to a national park or a theme park?", - "answer": "National park; she likes the outdoors", - "evidence": [ - "D10:12", - "D10:14" - ], - "category": 3 - }, - { - "question": "What kind of art does Caroline make?", - "answer": "abstract art", - "evidence": [ - "D11:12", - "D11:8", - "D9:14" - ], - "category": 1 - }, - { - "question": "When is Melanie's daughter's birthday?", - "answer": "13 August", - "evidence": [ - "D11:1" - ], - "category": 2 - }, - { - "question": "When did Caroline attend a pride parade in August?", - "answer": "The Friday before 14 August 2023", - "evidence": [ - "D11:4" - ], - "category": 2 - }, - { - "question": "Would Melanie be considered an ally to the transgender community?", - "answer": "Yes, she is supportive", - "evidence": [], - "category": 3 - }, - { - "question": "Who supports Caroline when she has a negative experience?", - "answer": "Her mentors, family, and friends", - "evidence": [ - "D12:1", - "D3:11" - ], - "category": 1 - }, - { - "question": "What types of pottery have Melanie and her kids made?", - "answer": "bowls, cup", - "evidence": [ - "D12:14", - "D8:4", - "D5:6" - ], - "category": 1 - }, - { - "question": "When did Caroline and Melanie go to a pride fesetival together?", - "answer": 2022, - "evidence": [ - "D12:15" - ], - "category": 2 - }, - { - "question": "What would Caroline's political leaning likely be?", - "answer": "Liberal", - "evidence": [ - "D12:1" - ], - "category": 3 - }, - { - "question": "What has Melanie painted?", - "answer": "Horse, sunset, sunrise", - "evidence": [ - "D13:8", - "D8:6", - "D1:12" - ], - "category": 1 - }, - { - "question": "What are Melanie's pets' names?", - "answer": "Oliver, Luna, Bailey", - "evidence": [ - "D13:4", - "D7:18" - ], - "category": 1 - }, - { - "question": "When did Caroline apply to adoption agencies?", - "answer": "The week of 23 August 2023", - "evidence": [ - "D13:1" - ], - "category": 2 - }, - { - "question": "When did Caroline draw a self-portrait?", - "answer": "The week before 23 August 2023", - "evidence": [ - "D13:11" - ], - "category": 2 - }, - { - "question": "What subject have Caroline and Melanie both painted?", - "answer": "Sunsets", - "evidence": [ - "D14:5", - "D8:6" - ], - "category": 1 - }, - { - "question": "What symbols are important to Caroline?", - "answer": "Rainbow flag, transgender symbol", - "evidence": [ - "D14:15", - "D4:1" - ], - "category": 1 - }, - { - "question": "When did Caroline encounter people on a hike and have a negative experience?", - "answer": "The week before 25 August 2023", - "evidence": [ - "D14:1" - ], - "category": 2 - }, - { - "question": "When did Melanie make a plate in pottery class?", - "answer": "24 August 2023", - "evidence": [ - "D14:4" - ], - "category": 2 - }, - { - "question": "Would Caroline be considered religious?", - "answer": "Somewhat, but not extremely religious", - "evidence": [ - "D14:19", - "D12:1" - ], - "category": 3 - }, - { - "question": "What instruments does Melanie play?", - "answer": "clarinet and violin", - "evidence": [ - "D15:26", - "D2:5" - ], - "category": 1 - }, - { - "question": "What musical artists/bands has Melanie seen?", - "answer": "Summer Sounds, Matt Patterson", - "evidence": [ - "D15:16", - "D11:3" - ], - "category": 1 - }, - { - "question": "When did Melanie go to the park?", - "answer": "27 August 2023", - "evidence": [ - "D15:2" - ], - "category": 2 - }, - { - "question": "When is Caroline's youth center putting on a talent show?", - "answer": "September 2023", - "evidence": [ - "D15:11" - ], - "category": 2 - }, - { - "question": "Would Melanie likely enjoy the song \"The Four Seasons\" by Vivaldi?", - "answer": "Yes; it's classical music", - "evidence": [ - "D15:28" - ], - "category": 3 - }, - { - "question": "What are some changes Caroline has faced during her transition journey?", - "answer": "Changes to her body, losing unsupportive friends", - "evidence": [ - "D16:15", - "D11:14" - ], - "category": 1 - }, - { - "question": "What does Melanie do with her family on hikes?", - "answer": "Roast marshmallows, tell stories", - "evidence": [ - "D16:4", - "D10:12" - ], - "category": 1 - }, - { - "question": "When did Caroline go biking with friends?", - "answer": "The weekend before 13 September 2023", - "evidence": [ - "D16:1" - ], - "category": 2 - }, - { - "question": "How long has Melanie been practicing art?", - "answer": "Since 2016", - "evidence": [ - "D16:8" - ], - "category": 2 - }, - { - "question": "What personality traits might Melanie say Caroline has?", - "answer": "Thoughtful, authentic, driven", - "evidence": [ - "D16:18", - "D13:16", - "D7:4" - ], - "category": 3 - }, - { - "question": "What transgender-specific events has Caroline attended?", - "answer": "Poetry reading, conference", - "evidence": [ - "D17:19", - "D15:13" - ], - "category": 1 - }, - { - "question": "What book did Melanie read from Caroline's suggestion?", - "answer": "\"Becoming Nicole\"", - "evidence": [ - "D7:11", - "D17:10" - ], - "category": 1 - }, - { - "question": "When did Melanie's friend adopt a child?", - "answer": 2022, - "evidence": [ - "D17:3" - ], - "category": 2 - }, - { - "question": "When did Melanie get hurt?", - "answer": "September 2023", - "evidence": [ - "D17:8" - ], - "category": 2 - }, - { - "question": "When did Melanie's family go on a roadtrip?", - "answer": "The weekend before 20 October 2023", - "evidence": [ - "D18:1" - ], - "category": 2 - }, - { - "question": "How many children does Melanie have?", - "answer": 3, - "evidence": [ - "D18:1", - "D18:7" - ], - "category": 1 - }, - { - "question": "When did Melanie go on a hike after the roadtrip?", - "answer": "19 October 2023", - "evidence": [ - "D18:17" - ], - "category": 1 - }, - { - "question": "Would Melanie go on another roadtrip soon?", - "answer": "Likely no; since this one went badly", - "evidence": [ - "D18:3", - "D18:1" - ], - "category": 3 - }, - { - "question": "What items has Melanie bought?", - "answer": "Figurines, shoes", - "evidence": [ - "D19:2", - "D7:18" - ], - "category": 1 - }, - { - "question": "When did Caroline pass the adoption interview?", - "answer": "The Friday before 22 October 2023", - "evidence": [ - "D19:1" - ], - "category": 2 - }, - { - "question": "When did Melanie buy the figurines?", - "answer": "21 October 2023", - "evidence": [ - "D19:2" - ], - "category": 2 - }, - { - "question": "Would Caroline want to move back to her home country soon?", - "answer": "No; she's in the process of adopting children.", - "evidence": [ - "D19:1", - "D19:3" - ], - "category": 3 - }, - { - "question": "What did the charity race raise awareness for?", - "answer": "mental health", - "evidence": [ - "D2:2" - ], - "category": 4 - }, - { - "question": "What did Melanie realize after the charity race?", - "answer": "self-care is important", - "evidence": [ - "D2:3" - ], - "category": 4 - }, - { - "question": "How does Melanie prioritize self-care?", - "answer": "by carving out some me-time each day for activities like running, reading, or playing the violin", - "evidence": [ - "D2:5" - ], - "category": 4 - }, - { - "question": "What are Caroline's plans for the summer?", - "answer": "researching adoption agencies", - "evidence": [ - "D2:8" - ], - "category": 4 - }, - { - "question": "What type of individuals does the adoption agency Caroline is considering support?", - "answer": "LGBTQ+ individuals", - "evidence": [ - "D2:12" - ], - "category": 4 - }, - { - "question": "Why did Caroline choose the adoption agency?", - "answer": "because of their inclusivity and support for LGBTQ+ individuals", - "evidence": [ - "D2:12" - ], - "category": 4 - }, - { - "question": "What is Caroline excited about in the adoption process?", - "answer": "creating a family for kids who need one", - "evidence": [ - "D2:14" - ], - "category": 4 - }, - { - "question": "What does Melanie think about Caroline's decision to adopt?", - "answer": "she thinks Caroline is doing something amazing and will be an awesome mom", - "evidence": [ - "D2:15" - ], - "category": 4 - }, - { - "question": "How long have Mel and her husband been married?", - "answer": "Mel and her husband have been married for 5 years.", - "evidence": [ - "D3:16" - ], - "category": 4 - }, - { - "question": "What does Caroline's necklace symbolize?", - "answer": "love, faith, and strength", - "evidence": [ - "D4:3" - ], - "category": 4 - }, - { - "question": "What country is Caroline's grandma from?", - "answer": "Sweden", - "evidence": [ - "D4:3" - ], - "category": 4 - }, - { - "question": "What was grandma's gift to Caroline?", - "answer": "necklace", - "evidence": [ - "D4:3" - ], - "category": 4 - }, - { - "question": "What is Melanie's hand-painted bowl a reminder of?", - "answer": "art and self-expression", - "evidence": [ - "D4:5" - ], - "category": 4 - }, - { - "question": "What did Melanie and her family do while camping?", - "answer": "explored nature, roasted marshmallows, and went on a hike", - "evidence": [ - "D4:8" - ], - "category": 4 - }, - { - "question": "What kind of counseling and mental health services is Caroline interested in pursuing?", - "answer": "working with trans people, helping them accept themselves and supporting their mental health", - "evidence": [ - "D4:13" - ], - "category": 4 - }, - { - "question": "What workshop did Caroline attend recently?", - "answer": "LGBTQ+ counseling workshop", - "evidence": [ - "D4:13" - ], - "category": 4 - }, - { - "question": "What was discussed in the LGBTQ+ counseling workshop?", - "answer": "therapeutic methods and how to best work with trans people", - "evidence": [ - "D4:13" - ], - "category": 4 - }, - { - "question": "What motivated Caroline to pursue counseling?", - "answer": "her own journey and the support she received, and how counseling improved her life", - "evidence": [ - "D4:15" - ], - "category": 4 - }, - { - "question": "What kind of place does Caroline want to create for people?", - "answer": "a safe and inviting place for people to grow", - "evidence": [ - "D4:15" - ], - "category": 4 - }, - { - "question": "Did Melanie make the black and white bowl in the photo?", - "answer": "Yes", - "evidence": [ - "D5:8" - ], - "category": 4 - }, - { - "question": "What kind of books does Caroline have in her library?", - "answer": "kids' books - classics, stories from different cultures, educational books", - "evidence": [ - "D6:9" - ], - "category": 4 - }, - { - "question": "What was Melanie's favorite book from her childhood?", - "answer": "\"Charlotte's Web\"", - "evidence": [ - "D6:10" - ], - "category": 4 - }, - { - "question": "What book did Caroline recommend to Melanie?", - "answer": "\"Becoming Nicole\"", - "evidence": [ - "D7:11" - ], - "category": 4 - }, - { - "question": "What did Caroline take away from the book \"Becoming Nicole\"?", - "answer": "Lessons on self-acceptance and finding support", - "evidence": [ - "D7:13" - ], - "category": 4 - }, - { - "question": "What are the new shoes that Melanie got used for?", - "answer": "Running", - "evidence": [ - "D7:19" - ], - "category": 4 - }, - { - "question": "What is Melanie's reason for getting into running?", - "answer": "To de-stress and clear her mind", - "evidence": [ - "D7:21" - ], - "category": 4 - }, - { - "question": "What does Melanie say running has been great for?", - "answer": "Her mental health", - "evidence": [ - "D7:24" - ], - "category": 4 - }, - { - "question": "What did Mel and her kids make during the pottery workshop?", - "answer": "pots", - "evidence": [ - "D8:2" - ], - "category": 4 - }, - { - "question": "What kind of pot did Mel and her kids make with clay?", - "answer": "a cup with a dog face on it", - "evidence": [ - "D8:4" - ], - "category": 4 - }, - { - "question": "What creative project do Mel and her kids do together besides pottery?", - "answer": "painting", - "evidence": [ - "D8:5" - ], - "category": 4 - }, - { - "question": "What did Mel and her kids paint in their latest project in July 2023?", - "answer": "a sunset with a palm tree", - "evidence": [ - "D8:6" - ], - "category": 4 - }, - { - "question": "What did Caroline see at the council meeting for adoption?", - "answer": "many people wanting to create loving homes for children in need", - "evidence": [ - "D8:9" - ], - "category": 4 - }, - { - "question": "What do sunflowers represent according to Caroline?", - "answer": "warmth and happiness", - "evidence": [ - "D8:11" - ], - "category": 4 - }, - { - "question": "Why are flowers important to Melanie?", - "answer": "They remind her to appreciate the small moments and were a part of her wedding decor", - "evidence": [ - "D8:12" - ], - "category": 4 - }, - { - "question": "What inspired Caroline's painting for the art show?", - "answer": "visiting an LGBTQ center and wanting to capture unity and strength", - "evidence": [ - "D9:16" - ], - "category": 4 - }, - { - "question": "How often does Melanie go to the beach with her kids?", - "answer": "once or twice a year", - "evidence": [ - "D10:10" - ], - "category": 4 - }, - { - "question": "What did Melanie and her family see during their camping trip last year?", - "answer": "Perseid meteor shower", - "evidence": [ - "D10:14" - ], - "category": 4 - }, - { - "question": "How did Melanie feel while watching the meteor shower?", - "answer": "in awe of the universe", - "evidence": [ - "D10:18" - ], - "category": 4 - }, - { - "question": "Whose birthday did Melanie celebrate recently?", - "answer": "Melanie's daughter", - "evidence": [ - "D11:1" - ], - "category": 4 - }, - { - "question": "Who performed at the concert at Melanie's daughter's birthday?", - "answer": "Matt Patterson", - "evidence": [ - "D11:3" - ], - "category": 4 - }, - { - "question": "Why did Melanie choose to use colors and patterns in her pottery project?", - "answer": "She wanted to catch the eye and make people smile.", - "evidence": [ - "D12:6" - ], - "category": 4 - }, - { - "question": "What pet does Caroline have?", - "answer": "guinea pig", - "evidence": [ - "D13:3" - ], - "category": 4 - }, - { - "question": "What pets does Melanie have?", - "answer": "Two cats and a dog", - "evidence": [ - "D13:4" - ], - "category": 4 - }, - { - "question": "Where did Oliver hide his bone once?", - "answer": "In Melanie's slipper", - "evidence": [ - "D13:6" - ], - "category": 4 - }, - { - "question": "What activity did Caroline used to do with her dad?", - "answer": "Horseback riding", - "evidence": [ - "D13:7" - ], - "category": 4 - }, - { - "question": "What did Caroline make for a local church?", - "answer": "a stained glass window", - "evidence": [ - "D14:17" - ], - "category": 4 - }, - { - "question": "What did Caroline find in her neighborhood during her walk?", - "answer": "a rainbow sidewalk", - "evidence": [ - "D14:23" - ], - "category": 4 - }, - { - "question": "Which song motivates Caroline to be courageous?", - "answer": "Brave by Sara Bareilles", - "evidence": [ - "D15:23" - ], - "category": 4 - }, - { - "question": "Which classical musicians does Melanie enjoy listening to?", - "answer": "Bach and Mozart", - "evidence": [ - "D15:28" - ], - "category": 4 - }, - { - "question": "Who is Melanie a fan of in terms of modern music?", - "answer": "Ed Sheeran", - "evidence": [ - "D15:28" - ], - "category": 4 - }, - { - "question": "How long has Melanie been creating art?", - "answer": "7 years", - "evidence": [ - "D16:7" - ], - "category": 4 - }, - { - "question": "What precautionary sign did Melanie see at the caf\u00e9?", - "answer": "A sign stating that someone is not being able to leave", - "evidence": [ - "D16:16" - ], - "category": 4 - }, - { - "question": "What advice does Caroline give for getting started with adoption?", - "answer": "Do research, find an adoption agency or lawyer, gather necessary documents, and prepare emotionally.", - "evidence": [ - "D17:7" - ], - "category": 4 - }, - { - "question": "What setback did Melanie face in October 2023?", - "answer": "She got hurt and had to take a break from pottery.", - "evidence": [ - "D17:8" - ], - "category": 4 - }, - { - "question": "What does Melanie do to keep herself busy during her pottery break?", - "answer": "Read a book and paint.", - "evidence": [ - "D17:10" - ], - "category": 4 - }, - { - "question": "What painting did Melanie show to Caroline on October 13, 2023?", - "answer": "A painting inspired by sunsets with a pink sky.", - "evidence": [ - "D17:12" - ], - "category": 4 - }, - { - "question": "What kind of painting did Caroline share with Melanie on October 13, 2023?", - "answer": "An abstract painting with blue streaks on a wall.", - "evidence": [ - "D17:14" - ], - "category": 4 - }, - { - "question": "What was the poetry reading that Caroline attended about?", - "answer": "It was a transgender poetry reading where transgender people shared their stories.", - "evidence": [ - "D17:18" - ], - "category": 4 - }, - { - "question": "What did the posters at the poetry reading say?", - "answer": "\"Trans Lives Matter\"", - "evidence": [ - "D17:19" - ], - "category": 4 - }, - { - "question": "What does Caroline's drawing symbolize for her?", - "answer": "Freedom and being true to herself.", - "evidence": [ - "D17:23" - ], - "category": 4 - }, - { - "question": "How do Melanie and Caroline describe their journey through life together?", - "answer": "An ongoing adventure of learning and growing.", - "evidence": [ - "D17:25" - ], - "category": 4 - }, - { - "question": "What happened to Melanie's son on their road trip?", - "answer": "He got into an accident", - "evidence": [ - "D18:1" - ], - "category": 4 - }, - { - "question": "How did Melanie's son handle the accident?", - "answer": "He was scared but reassured by his family", - "evidence": [ - "D18:6", - "D18:7" - ], - "category": 4 - }, - { - "question": "How did Melanie feel about her family after the accident?", - "answer": "They are important and mean the world to her", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "How did Melanie's children handle the accident?", - "answer": "They were scared but resilient", - "evidence": [ - "D18:7" - ], - "category": 4 - }, - { - "question": "How did Melanie feel after the accident?", - "answer": "Grateful and thankful for her family", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "What was Melanie's reaction to her children enjoying the Grand Canyon?", - "answer": "She was happy and thankful", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "What do Melanie's family give her?", - "answer": "Strength and motivation", - "evidence": [ - "D18:9" - ], - "category": 4 - }, - { - "question": "How did Melanie feel about her family supporting her?", - "answer": "She appreciated them a lot", - "evidence": [ - "D18:13" - ], - "category": 4 - }, - { - "question": "What did Melanie do after the road trip to relax?", - "answer": "Went on a nature walk or hike", - "evidence": [ - "D18:17" - ], - "category": 4 - }, - { - "question": "What did Caroline realize after her charity race?", - "evidence": [ - "D2:3" - ], - "category": 5, - "adversarial_answer": "self-care is important" - }, - { - "question": "What are Melanie's plans for the summer with respect to adoption?", - "evidence": [ - "D2:8" - ], - "category": 5, - "adversarial_answer": "researching adoption agencies" - }, - { - "question": "What type of individuals does the adoption agency Melanie is considering support?", - "evidence": [ - "D2:12" - ], - "category": 5, - "adversarial_answer": "LGBTQ+ individuals" - }, - { - "question": "Why did Melanie choose the adoption agency?", - "evidence": [ - "D2:12" - ], - "category": 5, - "adversarial_answer": "because of their inclusivity and support for LGBTQ+ individuals" - }, - { - "question": "What is Melanie excited about in her adoption process?", - "evidence": [ - "D2:14" - ], - "category": 5, - "adversarial_answer": "creating a family for kids who need one" - }, - { - "question": "What does Melanie's necklace symbolize?", - "evidence": [ - "D4:3" - ], - "category": 5, - "adversarial_answer": "love, faith, and strength" - }, - { - "question": "What country is Melanie's grandma from?", - "evidence": [ - "D4:3" - ], - "category": 5, - "adversarial_answer": "Sweden" - }, - { - "question": "What was grandma's gift to Melanie?", - "evidence": [ - "D4:3" - ], - "category": 5, - "adversarial_answer": "necklace" - }, - { - "question": "What was grandpa's gift to Caroline?", - "evidence": [ - "D4:3" - ], - "category": 5, - "adversarial_answer": "necklace" - }, - { - "question": "What is Caroline's hand-painted bowl a reminder of?", - "evidence": [ - "D4:5" - ], - "category": 5, - "adversarial_answer": "art and self-expression" - }, - { - "question": "What did Caroline and her family do while camping?", - "evidence": [ - "D4:8" - ], - "category": 5, - "adversarial_answer": "explored nature, roasted marshmallows, and went on a hike" - }, - { - "question": "What kind of counseling and mental health services is Melanie interested in pursuing?", - "evidence": [ - "D4:13" - ], - "category": 5, - "adversarial_answer": "working with trans people, helping them accept themselves and supporting their mental health" - }, - { - "question": "What kind of counseling workshop did Melanie attend recently?", - "evidence": [ - "D4:13" - ], - "category": 5, - "adversarial_answer": "LGBTQ+ counseling workshop" - }, - { - "question": "What motivated Melanie to pursue counseling?", - "evidence": [ - "D4:15" - ], - "category": 5, - "adversarial_answer": "her own journey and the support she received, and how counseling improved her life" - }, - { - "question": "What kind of place does Melanie want to create for people?", - "evidence": [ - "D4:15" - ], - "category": 5, - "adversarial_answer": "a safe and inviting place for people to grow" - }, - { - "question": "Did Caroline make the black and white bowl in the photo?", - "adversarial_answer": "Yes", - "answer": "No", - "evidence": [ - "D5:8" - ], - "category": 5 - }, - { - "question": "What are the new shoes that Caroline got used for?", - "evidence": [ - "D7:19" - ], - "category": 5, - "adversarial_answer": "Running" - }, - { - "question": "What is Caroline's reason for getting into running?", - "evidence": [ - "D7:21" - ], - "category": 5, - "adversarial_answer": "To de-stress and clear her mind" - }, - { - "question": "What does Caroline say running has been great for?", - "evidence": [ - "D7:24" - ], - "category": 5, - "adversarial_answer": "Her mental health" - }, - { - "question": "What did Melanie see at the council meeting for adoption?", - "evidence": [ - "D8:9" - ], - "category": 5, - "adversarial_answer": "many people wanting to create loving homes for children in need" - }, - { - "question": "What inspired Melanie's painting for the art show?", - "evidence": [ - "D9:16" - ], - "category": 5, - "adversarial_answer": "visiting an LGBTQ center and wanting to capture unity and strength" - }, - { - "question": "What inspired Caroline's sculpture for the art show?", - "evidence": [ - "D9:16" - ], - "category": 5, - "adversarial_answer": "visiting an LGBTQ center and wanting to capture unity and strength" - }, - { - "question": "How often does Caroline go to the beach with her kids?", - "evidence": [ - "D10:10" - ], - "category": 5, - "adversarial_answer": "once or twice a year" - }, - { - "question": "What did Caroline and her family see during their camping trip last year?", - "evidence": [ - "D10:14" - ], - "category": 5, - "adversarial_answer": "Perseid meteor shower" - }, - { - "question": "How did Caroline feel while watching the meteor shower?", - "evidence": [ - "D10:18" - ], - "category": 5, - "adversarial_answer": "in awe of the universe" - }, - { - "question": "Why did Caroline choose to use colors and patterns in her pottery project?", - "evidence": [ - "D12:6" - ], - "category": 5, - "adversarial_answer": "She wanted to catch the eye and make people smile." - }, - { - "question": "Is Oscar Melanie's pet?", - "adversarial_answer": "Yes", - "answer": "No", - "evidence": [ - "D13:3" - ], - "category": 5 - }, - { - "question": "Where did Oscar hide his bone once?", - "evidence": [ - "D13:6" - ], - "category": 5, - "adversarial_answer": "In Melanie's slipper" - }, - { - "question": "What activity did Melanie used to do with her dad?", - "evidence": [ - "D13:7" - ], - "category": 5, - "adversarial_answer": "Horseback riding" - }, - { - "question": "What did Melanie make for a local church?", - "evidence": [ - "D14:17" - ], - "category": 5, - "adversarial_answer": "a stained glass window" - }, - { - "question": "What did Melanie find in her neighborhood during her walk?", - "evidence": [ - "D14:23" - ], - "category": 5, - "adversarial_answer": "a rainbow sidewalk" - }, - { - "question": "Which song motivates Melanie to be courageous?", - "evidence": [ - "D15:23" - ], - "category": 5, - "adversarial_answer": "Brave by Sara Bareilles" - }, - { - "question": "What type of instrument does Caroline play?", - "evidence": [ - "D15:26" - ], - "category": 5, - "adversarial_answer": "clarinet and violin" - }, - { - "question": "Which classical musicians does Caroline enjoy listening to?", - "evidence": [ - "D15:28" - ], - "category": 5, - "adversarial_answer": "Bach and Mozart" - }, - { - "question": "Who is Caroline a fan of in terms of modern music?", - "evidence": [ - "D15:28" - ], - "category": 5, - "adversarial_answer": "Ed Sheeran" - }, - { - "question": "What precautionary sign did Caroline see at the caf\u00e9?", - "evidence": [ - "D16:16" - ], - "category": 5, - "adversarial_answer": "A sign stating that someone is not being able to leave" - }, - { - "question": "What setback did Caroline face recently?", - "evidence": [ - "D17:8" - ], - "category": 5, - "adversarial_answer": "She got hurt and had to take a break from pottery." - }, - { - "question": "What does Caroline do to keep herself busy during her pottery break?", - "evidence": [ - "D17:10" - ], - "category": 5, - "adversarial_answer": "Read a book and paint." - }, - { - "question": "What was the poetry reading that Melanie attended about?", - "evidence": [ - "D17:18" - ], - "category": 5, - "adversarial_answer": "It was a transgender poetry reading where transgender people shared their stories." - }, - { - "question": "What happened to Caroline's son on their road trip?", - "evidence": [ - "D18:1" - ], - "category": 5, - "adversarial_answer": "He got into an accident" - }, - { - "question": "How did Caroline's son handle the accident?", - "evidence": [ - "D18:6", - "D18:7" - ], - "category": 5, - "adversarial_answer": "He was scared but reassured by his family" - }, - { - "question": "How did Caroline feel about her family after the accident?", - "evidence": [ - "D18:5" - ], - "category": 5, - "adversarial_answer": "They are important and mean the world to her" - }, - { - "question": "How did Caroline's children handle the accident?", - "evidence": [ - "D18:7" - ], - "category": 5, - "adversarial_answer": "They were scared but resilient" - }, - { - "question": "How did Caroline feel after the accident?", - "evidence": [ - "D18:5" - ], - "category": 5, - "adversarial_answer": "Grateful and thankful for her family" - }, - { - "question": "What was Caroline's reaction to her children enjoying the Grand Canyon?", - "evidence": [ - "D18:5" - ], - "category": 5, - "adversarial_answer": "She was happy and thankful" - }, - { - "question": "What did Caroline do after the road trip to relax?", - "evidence": [ - "D18:17" - ], - "category": 5, - "adversarial_answer": "Went on a nature walk or hike" - }, - { - "question": "What does Caroline love most about camping with her family?", - "evidence": [ - "D18:21" - ], - "category": 5, - "adversarial_answer": "Being present and bonding with her family" - } - ], - "conversation": { - "speaker_a": "Caroline", - "speaker_b": "Melanie", - "session_1_date_time": "1:56 pm on 8 May, 2023", - "session_1": [ - { - "speaker": "Caroline", - "dia_id": "D1:1", - "text": "Hey Mel! Good to see you! How have you been?" - }, - { - "speaker": "Melanie", - "dia_id": "D1:2", - "text": "Hey Caroline! Good to see you! I'm swamped with the kids & work. What's up with you? Anything new?" - }, - { - "speaker": "Caroline", - "dia_id": "D1:3", - "text": "I went to a LGBTQ support group yesterday and it was so powerful." - }, - { - "speaker": "Melanie", - "dia_id": "D1:4", - "text": "Wow, that's cool, Caroline! What happened that was so awesome? Did you hear any inspiring stories?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.redd.it/l7hozpetnhlb1.jpg" - ], - "blip_caption": "a photo of a dog walking past a wall with a painting of a woman", - "query": "transgender pride flag mural", - "dia_id": "D1:5", - "text": "The transgender stories were so inspiring! I was so happy and thankful for all the support." - }, - { - "speaker": "Melanie", - "dia_id": "D1:6", - "text": "Wow, love that painting! So cool you found such a helpful group. What's it done for you?" - }, - { - "speaker": "Caroline", - "dia_id": "D1:7", - "text": "The support group has made me feel accepted and given me courage to embrace myself." - }, - { - "speaker": "Melanie", - "dia_id": "D1:8", - "text": "That's really cool. You've got guts. What now?" - }, - { - "speaker": "Caroline", - "dia_id": "D1:9", - "text": "Gonna continue my edu and check out career options, which is pretty exciting!" - }, - { - "speaker": "Melanie", - "dia_id": "D1:10", - "text": "Wow, Caroline! What kinda jobs are you thinkin' of? Anything that stands out?" - }, - { - "speaker": "Caroline", - "dia_id": "D1:11", - "text": "I'm keen on counseling or working in mental health - I'd love to support those with similar issues." - }, - { - "speaker": "Melanie", - "img_url": [ - "http://candicealexander.com/cdn/shop/products/IMG_7269_a49d5af8-c76c-4ecd-ae20-48c08cb11dec.jpg" - ], - "blip_caption": "a photo of a painting of a sunset over a lake", - "query": "painting sunrise", - "dia_id": "D1:12", - "text": "You'd be a great counselor! Your empathy and understanding will really help the people you work with. By the way, take a look at this." - }, - { - "speaker": "Caroline", - "dia_id": "D1:13", - "text": "Thanks, Melanie! That's really sweet. Is this your own painting?" - }, - { - "speaker": "Melanie", - "dia_id": "D1:14", - "text": "Yeah, I painted that lake sunrise last year! It's special to me." - }, - { - "speaker": "Caroline", - "dia_id": "D1:15", - "text": "Wow, Melanie! The colors really blend nicely. Painting looks like a great outlet for expressing yourself." - }, - { - "speaker": "Melanie", - "dia_id": "D1:16", - "text": "Thanks, Caroline! Painting's a fun way to express my feelings and get creative. It's a great way to relax after a long day." - }, - { - "speaker": "Caroline", - "dia_id": "D1:17", - "text": "Totally agree, Mel. Relaxing and expressing ourselves is key. Well, I'm off to go do some research." - }, - { - "speaker": "Melanie", - "dia_id": "D1:18", - "text": "Yep, Caroline. Taking care of ourselves is vital. I'm off to go swimming with the kids. Talk to you soon!" - } - ], - "session_2_date_time": "1:14 pm on 25 May, 2023", - "session_2": [ - { - "speaker": "Melanie", - "dia_id": "D2:1", - "text": "Hey Caroline, since we last chatted, I've had a lot of things happening to me. I ran a charity race for mental health last Saturday \u2013 it was really rewarding. Really made me think about taking care of our minds." - }, - { - "speaker": "Caroline", - "dia_id": "D2:2", - "text": "That charity race sounds great, Mel! Making a difference & raising awareness for mental health is super rewarding - I'm really proud of you for taking part!" - }, - { - "speaker": "Melanie", - "dia_id": "D2:3", - "text": "Thanks, Caroline! The event was really thought-provoking. I'm starting to realize that self-care is really important. It's a journey for me, but when I look after myself, I'm able to better look after my family." - }, - { - "speaker": "Caroline", - "dia_id": "D2:4", - "text": "I totally agree, Melanie. Taking care of ourselves is so important - even if it's not always easy. Great that you're prioritizing self-care." - }, - { - "speaker": "Melanie", - "dia_id": "D2:5", - "text": "Yeah, it's tough. So I'm carving out some me-time each day - running, reading, or playing my violin - which refreshes me and helps me stay present for my fam!" - }, - { - "speaker": "Caroline", - "dia_id": "D2:6", - "text": "That's great, Mel! Taking time for yourself is so important. You're doing an awesome job looking after yourself and your family!" - }, - { - "speaker": "Melanie", - "dia_id": "D2:7", - "text": "Thanks, Caroline. It's still a work in progress, but I'm doing my best. My kids are so excited about summer break! We're thinking about going camping next month. Any fun plans for the summer?" - }, - { - "speaker": "Caroline", - "dia_id": "D2:8", - "text": "Researching adoption agencies \u2014 it's been a dream to have a family and give a loving home to kids who need it." - }, - { - "speaker": "Melanie", - "dia_id": "D2:9", - "text": "Wow, Caroline! That's awesome! Taking in kids in need - you're so kind. Your future family is gonna be so lucky to have you!" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://live.staticflickr.com/3437/3935231341_b2955b00dd_b.jpg" - ], - "blip_caption": "a photography of a sign for a new arrival and an information and domestic building", - "query": "adoption agency brochure", - "dia_id": "D2:10", - "re-download": true, - "text": "Thanks, Mel! My goal is to give kids a loving home. I'm truly grateful for all the support I've got from friends and mentors. Now the hard work starts to turn my dream into a reality. And here's one of the adoption agencies I'm looking into. It's a lot to take in, but I'm feeling hopeful and optimistic." - }, - { - "speaker": "Melanie", - "dia_id": "D2:11", - "text": "Wow, that agency looks great! What made you pick it?" - }, - { - "speaker": "Caroline", - "dia_id": "D2:12", - "text": "I chose them 'cause they help LGBTQ+ folks with adoption. Their inclusivity and support really spoke to me." - }, - { - "speaker": "Melanie", - "dia_id": "D2:13", - "text": "That's great, Caroline! Loving the inclusivity and support. Anything you're excited for in the adoption process?" - }, - { - "speaker": "Caroline", - "dia_id": "D2:14", - "text": "I'm thrilled to make a family for kids who need one. It'll be tough as a single parent, but I'm up for the challenge!" - }, - { - "speaker": "Melanie", - "dia_id": "D2:15", - "text": "You're doing something amazing! Creating a family for those kids is so lovely. You'll be an awesome mom! Good luck!" - }, - { - "speaker": "Caroline", - "dia_id": "D2:16", - "text": "Thanks, Melanie! Your kind words really mean a lot. I'll do my best to make sure these kids have a safe and loving home." - }, - { - "speaker": "Melanie", - "dia_id": "D2:17", - "text": "No doubts, Caroline. You have such a caring heart - they'll get all the love and stability they need! Excited for this new chapter!" - } - ], - "session_3_date_time": "7:55 pm on 9 June, 2023", - "session_3": [ - { - "speaker": "Caroline", - "dia_id": "D3:1", - "text": "Hey Melanie! How's it going? I wanted to tell you about my school event last week. It was awesome! I talked about my transgender journey and encouraged students to get involved in the LGBTQ community. It was great to see their reactions. It made me reflect on how far I've come since I started transitioning three years ago." - }, - { - "speaker": "Melanie", - "dia_id": "D3:2", - "text": "Hey Caroline! Great to hear from you. Sounds like your event was amazing! I'm so proud of you for spreading awareness and getting others involved in the LGBTQ community. You've come a long way since your transition - keep on inspiring people with your strength and courage!" - }, - { - "speaker": "Caroline", - "dia_id": "D3:3", - "text": "Thanks, Mel! Your backing really means a lot. I felt super powerful giving my talk. I shared my own journey, the struggles I had and how much I've developed since coming out. It was wonderful to see how the audience related to what I said and how it inspired them to be better allies. Conversations about gender identity and inclusion are so necessary and I'm thankful for being able to give a voice to the trans community." - }, - { - "speaker": "Melanie", - "dia_id": "D3:4", - "text": "Wow, Caroline, you're doing an awesome job of inspiring others with your journey. It's great to be part of it and see how you're positively affecting so many. Talking about inclusivity and acceptance is crucial, and you're so brave to speak up for the trans community. Keep up the great work!" - }, - { - "speaker": "Caroline", - "dia_id": "D3:5", - "text": "Thanks Mel! Your kind words mean a lot. Sharing our experiences isn't always easy, but I feel it's important to help promote understanding and acceptance. I've been blessed with loads of love and support throughout this journey, and I want to pass it on to others. By sharing our stories, we can build a strong, supportive community of hope." - }, - { - "speaker": "Melanie", - "dia_id": "D3:6", - "text": "Yeah, Caroline! It takes courage to talk about our own stories. But it's in these vulnerable moments that we bond and understand each other. We all have our different paths, but if we share them, we show people that they're not alone. Our stories can be so inspiring and encouraging to others who are facing the same challenges. Thank you for using your voice to create love, acceptance, and hope. You're doing amazing!" - }, - { - "speaker": "Caroline", - "dia_id": "D3:7", - "text": "Your words mean a lot to me. I'm grateful for the chance to share my story and give others hope. We all have unique paths, and by working together we can build a more inclusive and understanding world. I'm going to keep using my voice to make a change and lift others up. And you're part of that!" - }, - { - "speaker": "Melanie", - "dia_id": "D3:8", - "text": "Thanks, Caroline, for letting me join your journey. I'm so proud to be part of the difference you're making. Let's keep motivating and helping each other out as we journey through life. We can make a real impact together!" - }, - { - "speaker": "Caroline", - "dia_id": "D3:9", - "text": "Yeah Mel, let's spread love and understanding! Thanks for the support and encouragement. We can tackle life's challenges together! We got this!" - }, - { - "speaker": "Melanie", - "dia_id": "D3:10", - "text": "Yes, Caroline! We can do it. Your courage is inspiring. I want to be couragous for my family- they motivate me and give me love. What motivates you?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://fox2now.com/wp-content/uploads/sites/14/2023/08/that-tall-family.jpg" - ], - "blip_caption": "a photo of a family posing for a picture in a yard", - "query": "group of friends and family", - "dia_id": "D3:11", - "text": "Thanks, Mel! My friends, family and mentors are my rocks \u2013 they motivate me and give me the strength to push on. Here's a pic from when we met up last week!" - }, - { - "speaker": "Melanie", - "dia_id": "D3:12", - "text": "Wow, that photo is great! How long have you had such a great support system?" - }, - { - "speaker": "Caroline", - "dia_id": "D3:13", - "text": "Yeah, I'm really lucky to have them. They've been there through everything, I've known these friends for 4 years, since I moved from my home country. Their love and help have been so important especially after that tough breakup. I'm super thankful. Who supports you, Mel?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://mrswebersneighborhood.com/wp-content/uploads/2022/07/Cedar-Falls-Hocking-Hills.jpg" - ], - "blip_caption": "a photo of a man and a little girl standing in front of a waterfall", - "query": "husband kids hiking nature", - "dia_id": "D3:14", - "text": "I'm lucky to have my husband and kids; they keep me motivated." - }, - { - "speaker": "Caroline", - "dia_id": "D3:15", - "text": "Wow, what an amazing family pic! How long have you been married?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/8o28nfllf3eb1.jpg" - ], - "blip_caption": "a photo of a bride in a wedding dress holding a bouquet", - "query": "wedding day", - "dia_id": "D3:16", - "text": "5 years already! Time flies- feels like just yesterday I put this dress on! Thanks, Caroline!" - }, - { - "speaker": "Caroline", - "dia_id": "D3:17", - "text": "Congrats, Melanie! You both looked so great on your wedding day! Wishing you many happy years together!" - }, - { - "speaker": "Melanie", - "img_url": [ - "http://shirleyswardrobe.com/wp-content/uploads/2017/07/LF-Picnic-6.jpg" - ], - "blip_caption": "a photo of a man and woman sitting on a blanket eating food", - "query": "family picnic park laughing", - "dia_id": "D3:18", - "text": "Thanks, Caroline! Appreciate your kind words. Looking forward to more happy years. Our family and moments make it all worth it." - }, - { - "speaker": "Caroline", - "dia_id": "D3:19", - "text": "Looks like you had a great day! How was it? You all look so happy!" - }, - { - "speaker": "Melanie", - "dia_id": "D3:20", - "text": "It so fun! We played games, ate good food, and just hung out together. Family moments make life awesome." - }, - { - "speaker": "Caroline", - "dia_id": "D3:21", - "text": "Sounds great, Mel! Glad you had a great time. Cherish the moments - they're the best!" - }, - { - "speaker": "Melanie", - "dia_id": "D3:22", - "text": "Absolutely, Caroline! I cherish time with family. It's when I really feel alive and happy." - }, - { - "speaker": "Caroline", - "dia_id": "D3:23", - "text": "I 100% agree, Mel. Hanging with loved ones is amazing and brings so much happiness. Those moments really make me thankful. Family is everything." - } - ], - "session_4_date_time": "10:37 am on 27 June, 2023", - "session_4": [ - { - "speaker": "Caroline", - "img_url": [ - "https://i.redd.it/67uas3gnmz7b1.jpg" - ], - "blip_caption": "a photo of a person holding a necklace with a cross and a heart", - "query": "pendant transgender symbol", - "dia_id": "D4:1", - "text": "Hey Melanie! Long time no talk! A lot's been going on in my life! Take a look at this." - }, - { - "speaker": "Melanie", - "dia_id": "D4:2", - "text": "Hey, Caroline! Nice to hear from you! Love the necklace, any special meaning to it?" - }, - { - "speaker": "Caroline", - "dia_id": "D4:3", - "text": "Thanks, Melanie! This necklace is super special to me - a gift from my grandma in my home country, Sweden. She gave it to me when I was young, and it stands for love, faith and strength. It's like a reminder of my roots and all the love and support I get from my family." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a stack of bowls with different designs on them", - "dia_id": "D4:4", - "text": "That's gorgeous, Caroline! It's awesome what items can mean so much to us, right? Got any other objects that you treasure, like that necklace?" - }, - { - "speaker": "Caroline", - "dia_id": "D4:5", - "text": "Yep, Melanie! I've got some other stuff with sentimental value, like my hand-painted bowl. A friend made it for my 18th birthday ten years ago. The pattern and colors are awesome-- it reminds me of art and self-expression." - }, - { - "speaker": "Melanie", - "dia_id": "D4:6", - "text": "That sounds great, Caroline! It's awesome having stuff around that make us think of good connections and times. Actually, I just took my fam camping in the mountains last week - it was a really nice time together!" - }, - { - "speaker": "Caroline", - "dia_id": "D4:7", - "text": "Sounds great, Mel. Glad you made some new family mems. How was it? Anything fun?" - }, - { - "speaker": "Melanie", - "dia_id": "D4:8", - "text": "It was an awesome time, Caroline! We explored nature, roasted marshmallows around the campfire and even went on a hike. The view from the top was amazing! The 2 younger kids love nature. It was so special having these moments together as a family - I'll never forget it!" - }, - { - "speaker": "Caroline", - "dia_id": "D4:9", - "text": "That's awesome, Melanie! Family moments like that are so special. Glad y'all had such a great time." - }, - { - "speaker": "Melanie", - "dia_id": "D4:10", - "text": "Thanks, Caroline! Family time matters to me. What's up with you lately?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a book shelf with many books on it", - "dia_id": "D4:11", - "text": "Lately, I've been looking into counseling and mental health as a career. I want to help people who have gone through the same things as me." - }, - { - "speaker": "Melanie", - "dia_id": "D4:12", - "text": "Sounds great! What kind of counseling and mental health services do you want to persue?" - }, - { - "speaker": "Caroline", - "dia_id": "D4:13", - "text": "I'm still figuring out the details, but I'm thinking of working with trans people, helping them accept themselves and supporting their mental health. Last Friday, I went to an LGBTQ+ counseling workshop and it was really enlightening. They talked about different therapeutic methods and how to best work with trans people. Seeing how passionate these pros were about making a safe space for people like me was amazing." - }, - { - "speaker": "Melanie", - "dia_id": "D4:14", - "text": "Woah, Caroline, it sounds like you're doing some impressive work. It's inspiring to see your dedication to helping others. What motivated you to pursue counseling?" - }, - { - "speaker": "Caroline", - "dia_id": "D4:15", - "text": "Thanks, Melanie. It really mattered. My own journey and the support I got made a huge difference. Now I want to help people go through it too. I saw how counseling and support groups improved my life, so I started caring more about mental health and understanding myself. Now I'm passionate about creating a safe, inviting place for people to grow." - }, - { - "speaker": "Melanie", - "dia_id": "D4:16", - "text": "Wow, Caroline! You've gained so much from your own experience. Your passion and hard work to help others is awesome. Keep it up, you're making a big impact!" - }, - { - "speaker": "Caroline", - "dia_id": "D4:17", - "text": "Thanks, Melanie! Your kind words mean a lot." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a book shelf filled with books in a room", - "dia_id": "D4:18", - "text": "Congrats Caroline! Good on you for going after what you really care about." - } - ], - "session_5_date_time": "1:36 pm on 3 July, 2023", - "session_5": [ - { - "speaker": "Caroline", - "dia_id": "D5:1", - "text": "Since we last spoke, some big things have happened. Last week I went to an LGBTQ+ pride parade. Everyone was so happy and it made me feel like I belonged. It showed me how much our community has grown, it was amazing!" - }, - { - "speaker": "Melanie", - "dia_id": "D5:2", - "text": "Wow, Caroline, sounds like the parade was an awesome experience! It's great to see the love and support for the LGBTQ+ community. Congrats! Has this experience influenced your goals at all?" - }, - { - "speaker": "Caroline", - "dia_id": "D5:3", - "text": "Thanks, Mel! It really motivated me for sure. Talking to the community made me want to use my story to help others too - I'm still thinking that counseling and mental health is the way to go. I'm super excited to give back. " - }, - { - "speaker": "Melanie", - "img_url": [ - "https://m.media-amazon.com/images/I/A1uELSr5rgL.jpg" - ], - "blip_caption": "a photo of a person holding a frisbee in their hand", - "query": "family frisbee game", - "dia_id": "D5:4", - "text": "Wow, Caroline! That's great! I just signed up for a pottery class yesterday. It's like therapy for me, letting me express myself and get creative. Have you found any activities that make you feel the same way?" - }, - { - "speaker": "Caroline", - "dia_id": "D5:5", - "text": "Wow, Melanie! I'm getting creative too, just learning the piano. What made you try pottery?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://therusticbarnct.com/cdn/shop/files/image_05483f46-4845-433b-a4cf-0fc61fe1aa79.jpg" - ], - "blip_caption": "a photo of a bowl with a black and white flower design", - "query": "pottery painted bowl intricate design", - "dia_id": "D5:6", - "text": "I'm a big fan of pottery - the creativity and skill is awesome. Plus, making it is so calming. Look at this!" - }, - { - "speaker": "Caroline", - "dia_id": "D5:7", - "text": "That bowl is gorgeous! The black and white design looks so fancy. Did you make it?" - }, - { - "speaker": "Melanie", - "dia_id": "D5:8", - "text": "Thanks, Caroline! Yeah, I made this bowl in my class. It took some work, but I'm pretty proud of it." - }, - { - "speaker": "Caroline", - "dia_id": "D5:9", - "text": "Nice job! You really put in the work and it definitely shows. Your creativity looks great!" - }, - { - "speaker": "Melanie", - "dia_id": "D5:10", - "text": "Thanks, Caroline! Your kind words mean a lot. Pottery is a huge part of my life, not just a hobby - it helps me express my emotions. Clay is incredible, it brings me so much joy!" - }, - { - "speaker": "Caroline", - "dia_id": "D5:11", - "text": "Wow, Mel, I'm so stoked for you that art is helping you express yourself and bring you joy! Keep it up!" - }, - { - "speaker": "Melanie", - "dia_id": "D5:12", - "text": "Thanks, Caroline! I'm excited to see where pottery takes me. Anything coming up you're looking forward to?" - }, - { - "speaker": "Caroline", - "dia_id": "D5:13", - "text": "Thanks Mel! I'm going to a transgender conference this month. I'm so excited to meet other people in the community and learn more about advocacy. It's gonna be great!" - }, - { - "speaker": "Melanie", - "dia_id": "D5:14", - "text": "Sounds awesome, Caroline! Have a great time and learn a lot. Have fun!" - }, - { - "speaker": "Caroline", - "dia_id": "D5:15", - "text": "Cool, thanks Mel! Can't wait. I'll keep ya posted. Bye!" - }, - { - "speaker": "Melanie", - "dia_id": "D5:16", - "text": "Bye, Caroline! Can't wait to hear about it. Have fun and stay safe!" - } - ], - "session_6_date_time": "8:18 pm on 6 July, 2023", - "session_6": [ - { - "speaker": "Caroline", - "dia_id": "D6:1", - "text": "Hey Mel! Long time no talk. Lots has been going on since then!" - }, - { - "speaker": "Melanie", - "dia_id": "D6:2", - "text": "Hey Caroline! Missed you. Anything new? Spill the beans!" - }, - { - "speaker": "Caroline", - "dia_id": "D6:3", - "text": "Since our last chat, I've been looking into counseling or mental health work more. I'm passionate about helping people and making a positive impact. It's tough, but really rewarding too. Anything new happening with you?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://live.staticflickr.com/3201/2867258131_2d8bc22859_b.jpg" - ], - "blip_caption": "a photography of two children playing in a water play area", - "query": "kids laughing dinosaur exhibit museum", - "dia_id": "D6:4", - "re-download": true, - "text": "That's awesome, Caroline! Congrats on following your dreams. Yesterday I took the kids to the museum - it was so cool spending time with them and seeing their eyes light up!" - }, - { - "speaker": "Caroline", - "dia_id": "D6:5", - "text": "Melanie, that's a great pic! That must have been awesome. What were they so stoked about?" - }, - { - "speaker": "Melanie", - "dia_id": "D6:6", - "text": "They were stoked for the dinosaur exhibit! They love learning about animals and the bones were so cool. It reminds me why I love being a mom." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.pinimg.com/originals/02/94/c3/0294c3460b66d1fd50530e4bd5a2e1f5.jpg" - ], - "blip_caption": "a photo of a bookcase filled with books and toys", - "query": "bookshelf childrens books library", - "dia_id": "D6:7", - "text": "Being a mom is awesome. I'm creating a library for when I have kids. I'm really looking forward to reading to them and opening up their minds." - }, - { - "speaker": "Melanie", - "dia_id": "D6:8", - "text": "Sounds great! What kind of books you got in your library?" - }, - { - "speaker": "Caroline", - "dia_id": "D6:9", - "text": "I've got lots of kids' books- classics, stories from different cultures, educational books, all of that. What's a favorite book you remember from your childhood?" - }, - { - "speaker": "Melanie", - "img_url": [ - "http://bookworm-detective.myshopify.com/cdn/shop/products/PXL_20210428_222022427.jpg" - ], - "blip_caption": "a photo of a book cover with a picture of a girl and a cat", - "query": "charlotte's web book", - "dia_id": "D6:10", - "text": "I loved reading \"Charlotte's Web\" as a kid. It was so cool seeing how friendship and compassion can make a difference." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.pinimg.com/originals/41/d5/60/41d5601e4ab0959ce5e29683a2660938.jpg" - ], - "blip_caption": "a photo of a group of women sitting on a blanket in a park", - "query": "group friends picnic", - "dia_id": "D6:11", - "text": "Wow, that's great! It sure shows how important friendship and compassion are. It's made me appreciate how lucky I am to have my friends and family helping with my transition. They make all the difference. We even had a picnic last week!" - }, - { - "speaker": "Melanie", - "dia_id": "D6:12", - "text": "That's a gorgeous photo, Caroline! Wow, the love around you is awesome. How have your friends and fam been helping you out with your transition?" - }, - { - "speaker": "Caroline", - "dia_id": "D6:13", - "text": "Thanks, Melanie! This support network has been amazing. They've been there for me every step of the way giving me love, guidance, and acceptance. I couldn't have done it without them." - }, - { - "speaker": "Melanie", - "dia_id": "D6:14", - "text": "Wow, Caroline! It's great you have people to support you, that's really awesome!" - }, - { - "speaker": "Caroline", - "dia_id": "D6:15", - "text": "I'm so lucky to have such a great support system around me. Their love and encouragement has really helped me accept and grow into my true self. They've been instrumental in my transition." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/ye1cp24b18w01.jpg" - ], - "blip_caption": "a photo of a family sitting around a campfire on the beach", - "query": "family campfire", - "dia_id": "D6:16", - "text": "Glad you have support, Caroline! Unconditional love is so important. Here's a pic of my family camping at the beach. We love it, it brings us closer!" - } - ], - "session_7_date_time": "4:33 pm on 12 July, 2023", - "session_7": [ - { - "speaker": "Caroline", - "dia_id": "D7:1", - "text": "Hey Mel, great to chat with you again! So much has happened since we last spoke - I went to an LGBTQ conference two days ago and it was really special. I got the chance to meet and connect with people who've gone through similar journeys. It was such a welcoming environment and I felt totally accepted. I'm really thankful for this amazing community - it's shown me how important it is to fight for trans rights and spread awareness." - }, - { - "speaker": "Melanie", - "dia_id": "D7:2", - "text": "Wow, Caroline, that sounds awesome! So glad you felt accepted and supported. Events like these are great for reminding us of how strong community can be!" - }, - { - "speaker": "Caroline", - "dia_id": "D7:3", - "text": "Yeah, it's true! Having people who back you makes such a huge difference. It's great to see how far LGBTQ rights have come, but there's still plenty of progress to be made. I wanna help make a difference." - }, - { - "speaker": "Melanie", - "dia_id": "D7:4", - "text": "Wow, Caroline. We've come so far, but there's more to do. Your drive to help is awesome! What's your plan to pitch in?" - }, - { - "speaker": "Caroline", - "dia_id": "D7:5", - "text": "Thanks, Mell! I'm still looking into counseling and mental health jobs. It's important to me that people have someone to talk to, and I want to help make that happen." - }, - { - "speaker": "Melanie", - "dia_id": "D7:6", - "text": "Wow, Caroline! You're so inspiring for wanting to help others with their mental health. What's pushing you to keep going forward with it?" - }, - { - "speaker": "Caroline", - "dia_id": "D7:7", - "text": "I struggled with mental health, and support I got was really helpful. It made me realize how important it is for others to have a support system. So, I started looking into counseling and mental health career options, so I could help other people on their own journeys like I was helped." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://www.speakers.co.uk/microsites/tom-oliver/wp-content/uploads/2014/11/Book-Cover-3D1.jpg" - ], - "blip_caption": "a photography of a book cover with a gold coin on it", - "query": "painted canvas follow your dreams", - "dia_id": "D7:8", - "re-download": true, - "text": "Caroline, so glad you got the support! Your experience really brought you to where you need to be. You're gonna make a huge difference! This book I read last year reminds me to always pursue my dreams, just like you are doing!\ud83c\udf1f" - }, - { - "speaker": "Caroline", - "dia_id": "D7:9", - "text": "Thanks so much, Mel! Seeing this pic just made me appreciate my love of reading even more. Books guide me, motivate me and help me discover who I am. They're a huge part of my journey, and this one's reminding me to keep going and never give up!" - }, - { - "speaker": "Melanie", - "dia_id": "D7:10", - "text": "Wow, Caroline! Books have such an awesome power! Which one has been your favorite guide?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://m.media-amazon.com/images/I/A1CPpaLFR2L.jpg" - ], - "blip_caption": "a photo of a dog sitting in a boat on the water", - "query": "becoming nicole book amy ellis nutt", - "dia_id": "D7:11", - "text": "I loved \"Becoming Nicole\" by Amy Ellis Nutt. It's a real inspiring true story about a trans girl and her family. It made me feel connected and gave me a lot of hope for my own path. Highly recommend it for sure!" - }, - { - "speaker": "Melanie", - "dia_id": "D7:12", - "text": "That sounds awesome! What did you take away from it to use in your life?" - }, - { - "speaker": "Caroline", - "dia_id": "D7:13", - "text": "It taught me self-acceptance and how to find support. It also showed me that tough times don't last - hope and love exist. Pets bring so much joy too, though." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://st3.depositphotos.com/12674628/16006/i/1600/depositphotos_160060676-stock-photo-multiethnic-girls-with-puppy.jpg" - ], - "blip_caption": "a photography of two little girls sitting on the steps with a dog", - "query": "daughters playing with pet dog backyard", - "dia_id": "D7:14", - "re-download": true, - "text": "Caroline, those lessons are great - self-acceptance and finding support are key. Plus pets are awesome for joy and comfort, can't agree more! " - }, - { - "speaker": "Caroline", - "dia_id": "D7:15", - "text": "That's so nice! What pet do you have?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/u26t78f0idd91.jpg" - ], - "blip_caption": "a photo of a cat laying on the floor with its head on the floor", - "query": "dog cat kids playing joy", - "dia_id": "D7:16", - "text": "We've got a pup and a kitty. That's the dog, and here's our cat! They brighten up our day and always make us smile." - }, - { - "speaker": "Caroline", - "dia_id": "D7:17", - "text": "Ah, they're adorable! What are their names? Pets sure do bring so much joy to us!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhwGRNI2jDkrALJHgL2LWfW2rUGhN-GA4OL_gXU2fHPyxtst2MPrv9hkyOMdpj5SppLNYiQrcXUUq90vv5es8ueswy2tuu0Lqa2lh2vKOfDZ5SXSdLVMVvBrfLbFJG19QiqDbv1xs38fv-atd4MYOesJ4c89sQTzv6k93PDQ5T0dwVJV9O2FF95woyP3Q/s4032/IMG_9747.jpg" - ], - "blip_caption": "a photo of a person wearing pink sneakers on a white rug", - "query": "purple running shoe", - "dia_id": "D7:18", - "text": "Luna and Oliver! They are so sweet and playful - they really liven up the house! Just got some new shoes, too!" - }, - { - "speaker": "Caroline", - "dia_id": "D7:19", - "text": "Love that purple color! For walking or running?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a pair of pink sneakers in a box", - "dia_id": "D7:20", - "text": "Thanks, Caroline! These are for running. Been running longer since our last chat - a great way to destress and clear my mind." - }, - { - "speaker": "Caroline", - "dia_id": "D7:21", - "text": "Wow! What got you into running?" - }, - { - "speaker": "Melanie", - "dia_id": "D7:22", - "text": "I've been running farther to de-stress, which has been great for my headspace." - }, - { - "speaker": "Caroline", - "dia_id": "D7:23", - "text": "Cool, Melanie! Running can really boost your mood. Keep it up!" - }, - { - "speaker": "Melanie", - "dia_id": "D7:24", - "text": "Thanks, Caroline! This has been great for my mental health. I'm gonna keep it up." - }, - { - "speaker": "Caroline", - "dia_id": "D7:25", - "text": "Awesome, Melanie! Mental health's a priority, so make sure you take care of yourself." - }, - { - "speaker": "Melanie", - "dia_id": "D7:26", - "text": "Caroline, thanks! Mental health is important to me, and it's made such an improvement!" - }, - { - "speaker": "Caroline", - "dia_id": "D7:27", - "text": "Glad it helped ya, Melanie!" - } - ], - "session_8_date_time": "1:51 pm on 15 July, 2023", - "session_8": [ - { - "speaker": "Caroline", - "dia_id": "D8:1", - "text": "Hey Mel, what's up? Been a busy week since we talked." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://images.rawpixel.com/image_social_landscape/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTExL2ZsNDg2NDgxOTYyMDMtaW1hZ2UuanBn.jpg" - ], - "blip_caption": "a photography of a group of children making clay sculptures in a classroom", - "query": "pottery workshop family making clay pots", - "dia_id": "D8:2", - "re-download": true, - "text": "Hey Caroline, it's been super busy here. So much since we talked! Last Fri I finally took my kids to a pottery workshop. We all made our own pots, it was fun and therapeutic!" - }, - { - "speaker": "Caroline", - "dia_id": "D8:3", - "text": "Wow, Mel! Sounds like you and the kids had a blast. How'd they like it?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://monstermonster.shop/cdn/shop/products/mug-class_5000x.jpg" - ], - "blip_caption": "a photo of a cup with a dog face on it", - "query": "kids pottery finished pieces", - "dia_id": "D8:4", - "text": "The kids loved it! They were so excited to get their hands dirty and make something with clay. It was special to watch their creativity and imagination come to life, they made this!" - }, - { - "speaker": "Caroline", - "dia_id": "D8:5", - "text": "Aww, that's so sweet! That cup is so cute. It's awesome to see how kids show their personalities through art. What other creative projects do you do with them, besides pottery?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.pinimg.com/originals/ea/d9/d7/ead9d79b58ca80a38a744b5ab70482db.jpg" - ], - "blip_caption": "a photo of a painting of a sunset with a palm tree", - "query": "painting vibrant flowers sunset sky", - "dia_id": "D8:6", - "text": "We love painting together lately, especially nature-inspired ones. Here's our latest work from last weekend." - }, - { - "speaker": "Caroline", - "dia_id": "D8:7", - "text": "Wow Mel, that painting's amazing! The colors are so bold and it really highlights the beauty of nature. Y'all work on it together?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://karengimson.files.wordpress.com/2017/06/img_7222.jpg" - ], - "blip_caption": "a photo of a field of purple flowers with green leaves", - "query": "path lined purple flowers nature", - "dia_id": "D8:8", - "text": "Thanks, Caroline! We both helped with the painting - it was great bonding over it and chatting about nature. We found these lovely flowers. Appreciating the small things in life, too." - }, - { - "speaker": "Caroline", - "dia_id": "D8:9", - "text": "That photo is stunning! So glad you bonded over our love of nature. Last Friday I went to a council meeting for adoption. It was inspiring and emotional - so many people wanted to create loving homes for children in need. It made me even more determined to adopt." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://assets.eflorist.com/assets/products/PHR_/TEV57-5A.jpg" - ], - "blip_caption": "a photo of a blue vase with a bouquet of sunflowers and roses", - "query": "sunflower bouquet", - "dia_id": "D8:10", - "text": "Wow, Caroline, way to go! Your future fam will get a kick out of having you. What do you think of these?" - }, - { - "speaker": "Caroline", - "dia_id": "D8:11", - "text": "Thanks Melanie - love the blue vase in the pic! Blue's my fave, it makes me feel relaxed. Sunflowers mean warmth and happiness, right? While roses stand for love and beauty? That's neat. What do flowers mean to you?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://blueblossomrentals.com/cdn/shop/products/image_909fb96b-4208-429b-9a6f-59dffa3cb546.jpg" - ], - "blip_caption": "a photo of a row of white chairs with flowers on them", - "query": "garden full of flowers wedding decorations", - "dia_id": "D8:12", - "text": "Flowers bring joy. They represent growth, beauty and reminding us to appreciate the small moments. They were an important part of my wedding decor and always remind me of that day." - }, - { - "speaker": "Caroline", - "dia_id": "D8:13", - "text": "It must have been special at your wedding. I wish I had known you back then!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://platinumnotary.files.wordpress.com/2023/03/img-6679.jpg" - ], - "blip_caption": "a photo of a wedding ceremony in a greenhouse with people taking pictures", - "query": "wedding ceremony", - "dia_id": "D8:14", - "text": "It was amazing, Caroline. The day was full of love and joy. Everyone we love was there to celebrate us - it was really special." - }, - { - "speaker": "Caroline", - "dia_id": "D8:15", - "text": "Wow, what a great day! Glad everyone could make it. What was your favorite part?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://s3-us-west-2.amazonaws.com/amm-prod/wedding_photos/photos/000/024/198/original/4B873921-0596-4A6B-8CD8-C6E5C2B024AF.png" - ], - "blip_caption": "a photo of a man and woman standing on a beach", - "query": "vows partner holding hands ceremony", - "dia_id": "D8:16", - "text": "Marrying my partner and promising to be together forever was the best part." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://dynaimage.cdn.cnn.com/cnn/digital-images/org/dfc95f14-b325-431c-b977-5b6dc2d35f9c.jpg" - ], - "blip_caption": "a photo of a parade with people walking down the street", - "query": "rainbow flag pride march", - "dia_id": "D8:17", - "text": "Wow, nice pic! You both looked amazing. One special memory for me was this pride parade I went to a few weeks ago." - }, - { - "speaker": "Melanie", - "dia_id": "D8:18", - "text": "Wow, looks awesome! Did you join in?" - }, - { - "speaker": "Caroline", - "img_url": [ - "http://ninalemsparty.com/cdn/shop/collections/iStock-1292280203.jpg" - ], - "blip_caption": "a photo of a group of people holding up signs and smiling", - "query": "lgbtq+ pride parade vibrant flags smiling faces", - "dia_id": "D8:19", - "text": "Yes, I did. It was amazing! I felt so accepted and happy, just being around people who accepted and celebrated me. It's definitely a top memory." - }, - { - "speaker": "Melanie", - "dia_id": "D8:20", - "text": "Wow, what an experience! How did it make you feel?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a rainbow flag on a pole on a carpet", - "dia_id": "D8:21", - "text": "I felt so proud and grateful - the vibes were amazing and it was comforting to know I'm not alone and have a great community around me." - }, - { - "speaker": "Melanie", - "dia_id": "D8:22", - "text": "Wow, Caroline! That's huge! How did it feel to be around so much love and acceptance?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a group of people sitting on the ground with a dog", - "dia_id": "D8:23", - "text": "It was awesome, Melanie! Being around people who embrace and back me up is beyond words. It really inspired me." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a girl sitting in a teepee with stuffed animals", - "dia_id": "D8:24", - "text": "Wow, that sounds awesome! Your friends and community really have your back. What's been the best part of it?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a teepee with a teddy bear and pillows", - "dia_id": "D8:25", - "text": "Realizing I can be me without fear and having the courage to transition was the best part. It's so freeing to express myself authentically and have people back me up." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a buddha statue and a candle on a table", - "dia_id": "D8:26", - "text": "That's awesome, Caro! You've found the courage to be yourself - that's important for our mental health and finding peace." - }, - { - "speaker": "Caroline", - "dia_id": "D8:27", - "text": "Thanks, Melanie! Been a long road, but I'm proud of how far I've come. How're you doing finding peace?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a man holding a frisbee in front of a frisbee golf basket", - "dia_id": "D8:28", - "text": "I'm getting there, Caroline. Creativity and family keep me at peace." - }, - { - "speaker": "Caroline", - "dia_id": "D8:29", - "text": "That's awesome, Melanie! How have your family been supportive during your move?" - }, - { - "speaker": "Melanie", - "dia_id": "D8:30", - "text": "My fam's been awesome - they helped out and showed lots of love and support." - }, - { - "speaker": "Caroline", - "dia_id": "D8:31", - "text": "Wow, Mel, family love and support is the best!" - }, - { - "speaker": "Melanie", - "img_url": [ - "http://cragmama.com/wp-content/uploads//2016/10/IMG_4568.jpg" - ], - "blip_caption": "a photo of a man and two children sitting around a campfire", - "query": "family camping trip roasting marshmallows campfire", - "dia_id": "D8:32", - "text": "Yeah, Caroline, my family's been great - their love and support really helped me through tough times. It's awesome! We even went on another camping trip in the forest." - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a family walking through a forest with a toddler", - "dia_id": "D8:33", - "text": "Awesome, Mel! Family support's huge. What else do you guys like doing together?" - }, - { - "speaker": "Melanie", - "dia_id": "D8:34", - "text": "We enjoy hiking in the mountains and exploring forests. It's a cool way to connect with nature and each other." - }, - { - "speaker": "Caroline", - "dia_id": "D8:35", - "text": "Wow, Mel, that sounds awesome! Exploring nature and family time is so special." - }, - { - "speaker": "Melanie", - "dia_id": "D8:36", - "text": "Yeah, Caroline, they're some of my fave memories. It brings us together and brings us happiness. Glad you're here to share in it." - }, - { - "speaker": "Caroline", - "dia_id": "D8:37", - "text": "Thanks, Melanie! Really glad to have you as a friend to share my journey. You're awesome!" - }, - { - "speaker": "Melanie", - "dia_id": "D8:38", - "text": "Thanks, Caroline! Appreciate your friendship. It's great to have a supporter!" - }, - { - "speaker": "Caroline", - "dia_id": "D8:39", - "text": "No worries, Mel! Your friendship means so much to me. Enjoy your day!" - } - ], - "session_9_date_time": "2:31 pm on 17 July, 2023", - "session_9": [ - { - "speaker": "Melanie", - "dia_id": "D9:1", - "text": "Hey Caroline, hope all's good! I had a quiet weekend after we went camping with my fam two weekends ago. It was great to unplug and hang with the kids. What've you been up to? Anything fun over the weekend?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:2", - "text": "Hey Melanie! That sounds great! Last weekend I joined a mentorship program for LGBTQ youth - it's really rewarding to help the community." - }, - { - "speaker": "Melanie", - "dia_id": "D9:3", - "text": "Wow, Caroline! It's great that you're helping out. How's it going? Got any cool experiences you can share?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:4", - "text": "The mentoring is going great! I've met some amazing young folks and supported them along the way. It's inspiring to see how resilient and strong they are." - }, - { - "speaker": "Melanie", - "dia_id": "D9:5", - "text": "Wow, Caroline, that sounds super rewarding! Young people's resilience is amazing. Care to share some stories?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:6", - "text": "I mentor a transgender teen just like me. We've been working on building up confidence and finding positive strategies, and it's really been paying off! We had a great time at the LGBT pride event last month." - }, - { - "speaker": "Melanie", - "dia_id": "D9:7", - "text": "Caroline, awesome news that you two are getting along! What was it like for you both? Care to fill me in?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://res.cloudinary.com/dragonspell/images/w_1440,h_864,c_fill,dpr_auto,fl_progressive:steep,f_auto/w_1440,h_864/v1571420662/www.travelportland.com/Portland-Pride-Parade-Downtown/Portland-Pride-Parade-Downtown.jpg" - ], - "blip_caption": "a photo of a woman holding a rainbow umbrella in the air", - "query": "lgbt pride event", - "dia_id": "D9:8", - "text": "The pride event was awesome! It was so encouraging to be surrounded by so much love and acceptance." - }, - { - "speaker": "Melanie", - "dia_id": "D9:9", - "text": "Wow! What's the best part you remember from it?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:10", - "text": "Seeing my mentee's face light up when they saw the support was the best! Such a special moment." - }, - { - "speaker": "Melanie", - "dia_id": "D9:11", - "text": "Wow, Caroline! They must have felt so appreciated. It's awesome to see the difference we can make in each other's lives. Any other exciting LGBTQ advocacy stuff coming up?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:12", - "text": "Yay! Next month I'm having an LGBTQ art show with my paintings - can't wait!" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a painting with a blue and yellow design", - "dia_id": "D9:13", - "text": "Wow, Caroline, that sounds awesome! Can't wait to see your art - got any previews?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://images.fineartamerica.com/images/artworkimages/mediumlarge/1/abstract-landscape-bold-colorful-painting-megan-duncanson.jpg" - ], - "blip_caption": "a photography of a painting of a tree with a bright sun in the background", - "query": "preview painting art show", - "dia_id": "D9:14", - "re-download": true, - "text": "Check out my painting for the art show! Hope you like it." - }, - { - "speaker": "Melanie", - "dia_id": "D9:15", - "text": "Wow, Caroline, that painting is awesome! Those colors are so vivid and the whole thing looks really unified. What inspired you?" - }, - { - "speaker": "Caroline", - "dia_id": "D9:16", - "text": "Thanks, Melanie! I painted this after I visited a LGBTQ center. I wanted to capture everyone's unity and strength." - }, - { - "speaker": "Melanie", - "dia_id": "D9:17", - "text": "Wow, Caroline! It really conveys unity and strength - such a gorgeous piece! My kids and I just finished another painting like our last one." - } - ], - "session_10_date_time": "8:56 pm on 20 July, 2023", - "session_10": [ - { - "speaker": "Caroline", - "dia_id": "D10:1", - "text": "Hey Melanie! Just wanted to say hi!" - }, - { - "speaker": "Melanie", - "dia_id": "D10:2", - "text": "Hey Caroline! Good to talk to you again. What's up? Anything new since last time?" - }, - { - "speaker": "Caroline", - "dia_id": "D10:3", - "text": "Hey Mel! A lot's happened since we last chatted - I just joined a new LGBTQ activist group last Tues. I'm meeting so many cool people who are as passionate as I am about rights and community support. I'm giving my voice and making a real difference, plus it's fulfilling in so many ways. It's just great, you know?" - }, - { - "speaker": "Melanie", - "dia_id": "D10:4", - "text": "That's awesome, Caroline! Glad to hear you found a great group where you can have an impact. Bet it feels great to be able to speak your truth and stand up for what's right. Want to tell me a bit more about it?" - }, - { - "speaker": "Caroline", - "dia_id": "D10:5", - "text": "Thanks, Melanie! It's awesome to have our own platform to be ourselves and support others' rights. Our group, 'Connected LGBTQ Activists', is made of all kinds of people investing in positive changes. We have regular meetings, plan events and campaigns, to get together and support each other." - }, - { - "speaker": "Melanie", - "dia_id": "D10:6", - "text": "Wow, Caroline, your group sounds awesome! Supporting each other and making good things happen - that's so inspiring! Have you been part of any events or campaigns lately?" - }, - { - "speaker": "Caroline", - "dia_id": "D10:7", - "text": "Last weekend our city held a pride parade! So many people marched through the streets waving flags, holding signs and celebrating love and diversity. I missed it but it was a powerful reminder that we are not alone in this fight for equality and inclusivity. Change is possible!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://mdkidadventures.files.wordpress.com/2023/06/img_2130.jpg" - ], - "blip_caption": "a photo of three children playing on the beach with a kite", - "query": "beach family playing frisbee sandy shore", - "dia_id": "D10:8", - "text": "Wow, fantastic, Caroline! Bet the atmosphere was incredible. Oh yeah, we went to the beach recently. It was awesome! The kids had such a blast." - }, - { - "speaker": "Caroline", - "dia_id": "D10:9", - "text": "Sounds fun! What was the best part? Do you do it often with the kids?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a sand castle on the beach with a blue sky", - "dia_id": "D10:10", - "text": "Seeing my kids' faces so happy at the beach was the best! We don't go often, usually only once or twice a year. But those times are always special to spend time together and chill." - }, - { - "speaker": "Caroline", - "dia_id": "D10:11", - "text": "Sounds special, those beach trips! Do you have any other summer traditions you all do together? Create those memories!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/hjh0wp8s721a1.jpg" - ], - "blip_caption": "a photo of a fire pit with a lot of fire and sparks", - "query": "family camping trip campfire night", - "dia_id": "D10:12", - "text": "We always look forward to our family camping trip. We roast marshmallows, tell stories around the campfire and just enjoy each other's company. It's the highlight of our summer!" - }, - { - "speaker": "Caroline", - "dia_id": "D10:13", - "text": "Wow, Mel, that's awesome! What's your best camping memory?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/ms0tvo85cto91.jpg" - ], - "blip_caption": "a photo of a plane flying in the sky with a star filled sky", - "query": "shooting star night sky", - "dia_id": "D10:14", - "text": "I'll always remember our camping trip last year when we saw the Perseid meteor shower. It was so amazing lying there and watching the sky light up with streaks of light. We all made wishes and felt so at one with the universe. That's a memory I'll never forget." - }, - { - "speaker": "Caroline", - "dia_id": "D10:15", - "text": "Cool! What did it look like?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/eqtu6adwcrfb1.jpg" - ], - "blip_caption": "a photo of a plane flying in the sky with a trail of smoke coming out of it", - "query": "night sky stars meteor shower", - "dia_id": "D10:16", - "text": "The sky was so clear and filled with stars, and the meteor shower was amazing - it felt like we were part of something huge and awe-inspiring." - }, - { - "speaker": "Caroline", - "dia_id": "D10:17", - "text": "Wow, Mel. That must've been breathtaking!" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a beach with footprints in the sand and a blue sky", - "dia_id": "D10:18", - "text": "It was one of those moments where I felt tiny and in awe of the universe. Reminds me how awesome life is - so many little moments like that." - }, - { - "speaker": "Caroline", - "dia_id": "D10:19", - "text": "That's great, Mel! What other good memories do you have that make you feel thankful for life?" - }, - { - "speaker": "Melanie", - "dia_id": "D10:20", - "text": "I'll never forget the day my youngest took her first steps. Seeing her wobble as she took those initial steps really put into perspective how fleeting life is and how lucky I am to be able to share these moments." - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a baby in a white crib with a blue blanket", - "dia_id": "D10:21", - "text": "Aw, that's sweet, Mel! Those milestones are great reminders of how special our bonds are." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://freerangestock.com/sample/134391/happy-family-holding-hands-with-ocean-and-sunset-in-the-background.jpg" - ], - "blip_caption": "a photography of a family standing on the beach at sunset", - "query": "children playing and laughing", - "dia_id": "D10:22", - "re-download": true, - "text": "Yeah, they sure are. It's special moments like these that make me appreciate life and how lucky I am to be with my family and have our love." - }, - { - "speaker": "Caroline", - "dia_id": "D10:23", - "text": "Wow, Melanie, what a beautiful moment! Lucky you to have such an awesome family!" - }, - { - "speaker": "Melanie", - "dia_id": "D10:24", - "text": "Thanks, Caroline! I'm really lucky to have my family; they bring so much joy and love." - } - ], - "session_11_date_time": "2:24 pm on 14 August, 2023", - "session_11": [ - { - "speaker": "Melanie", - "dia_id": "D11:1", - "text": "Hey Caroline! Last night was amazing! We celebrated my daughter's birthday with a concert surrounded by music, joy and the warm summer breeze. Seeing my kids' smiles was so awesome, and I'm so thankful for our special moments together." - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a poster for a concert with a picture of a man", - "dia_id": "D11:2", - "text": "Wow, sounds wonderful! Your love for your kids is so awesome. What concert was it? The advocacy event was a cool experience - so much love and support, amazing!" - }, - { - "speaker": "Melanie", - "dia_id": "D11:3", - "text": "Thanks, Caroline! It was Matt Patterson, he is so talented! His voice and songs were amazing. What's up with you? Anything interesting going on?" - }, - { - "speaker": "Caroline", - "dia_id": "D11:4", - "text": "Wow, Mel, glad you had a blast at the concert. A lot's happened since we talked. I went to a pride parade last Friday and it was awesome - so much energy and love everywhere. Really made me proud and reminded me how important it is to keep standing up for equality." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a band performing on stage with a sign that says all are welcome", - "dia_id": "D11:5", - "text": "Wow, that's awesome! How did it feel being part of that community?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://cloudfront-us-east-1.images.arcpublishing.com/opb/35SV3NIC4ZBRTLDGHUJ5QWU5WY.jpg" - ], - "blip_caption": "a photo of a group of people walking down a street with balloons", - "query": "pride parade crowd", - "dia_id": "D11:6", - "text": "It was so inspiring, Mel! Check out the crowd. People of all kinds celebrating love and acceptance - it really pushed me to keep fighting for LGBTQ rights." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://livingmividaloca.com/wp-content/uploads/2023/06/anaheim-town-square-concert.jpg" - ], - "blip_caption": "a photo of a group of people sitting on chairs watching a band", - "query": "outdoor concert family loving accepting environment", - "dia_id": "D11:7", - "text": "Wow, Caroline! That sounds awesome. This pic's from last night - looks like everyone was having a blast! Reminds me it's important to cultivate a loving and accepting environment for our kids. How do you stay inclusive in your work as an artist?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://www.dawnsilerart.com/wp-content/uploads/sites/3130/2020/11/YCNHTMC-CU9.jpg" - ], - "blip_caption": "a photo of a painting with a painting brush and paint on it", - "query": "painting vibrant colors diverse representation", - "dia_id": "D11:8", - "text": "That pic is cool! Representing inclusivity and diversity in my art is important to me. I also use it to speak up for the LGBTQ+ community and push for acceptance. Here's a recent painting!" - }, - { - "speaker": "Melanie", - "dia_id": "D11:9", - "text": "Wow, that rocks! What's the main idea of your art?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a painting of a woman with a cow in her lap", - "dia_id": "D11:10", - "text": "My art is about expressing my trans experience. It's my way of showing my story and helping people understand the trans community." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a person holding a purple bowl in their hand", - "dia_id": "D11:11", - "text": "Your art's amazing, Caroline. I love how you use it to tell your stories and teach people about trans folks. I'd love to see another painting of yours!" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://media.artsper.com/artwork/2013795_1_l.jpg" - ], - "blip_caption": "a photo of a painting of a woman with a red shirt", - "query": "painting embracing identity purple blue", - "dia_id": "D11:12", - "text": "Thanks, Melanie. Here's one- 'Embracing Identity' is all about finding comfort and love in being yourself. The woman in the painting stands for the journey of acceptance. My aim was to show warmth, love and self-acceptance." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a woman is making a vase on a wheel", - "dia_id": "D11:13", - "text": "Wow, Caroline, that's gorgeous! I love the self-acceptance and love theme. How does art help you with your self-discovery and acceptance journey?" - }, - { - "speaker": "Caroline", - "dia_id": "D11:14", - "text": "Art's allowed me to explore my transition and my changing body. It's been a great way to work through stuff I'm going through. I love that it teaches me to accept the beauty of imperfections." - }, - { - "speaker": "Melanie", - "dia_id": "D11:15", - "text": "Wow, Caroline, that's so cool! Art can be so healing and a way to really connect with who you are. It's awesome that beauty can be found in the imperfections. We're all individual and wonderfully imperfect. Thanks for sharing it with me!" - }, - { - "speaker": "Caroline", - "dia_id": "D11:16", - "text": "Thanks, Melanie. It means a lot to share this with you." - }, - { - "speaker": "Melanie", - "dia_id": "D11:17", - "text": "Great chatting with you! Feel free to reach out any time." - } - ], - "session_12_date_time": "1:50 pm on 17 August, 2023", - "session_12": [ - { - "speaker": "Caroline", - "dia_id": "D12:1", - "text": "Hey Mel! How're ya doin'? Recently, I had a not-so-great experience on a hike. I ran into a group of religious conservatives who said something that really upset me. It made me think how much work we still have to do for LGBTQ rights. It's been so helpful to have people around me who accept and support me, so I know I'll be ok!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:2", - "text": "Hey Caroline, sorry about the hike. It sucks when people are so closed-minded. Strong support really helps. FYI, I finished another pottery project - want to see a pic?" - }, - { - "speaker": "Caroline", - "dia_id": "D12:3", - "text": "Sure thing, Melanie! Can't wait to see your pottery project. I'm happy you found something that makes you happy. Show me when you can!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://omceramic.com/cdn/shop/products/IMG_0022.jpg" - ], - "blip_caption": "a photo of a bowl with a colorful design on it", - "query": "pottery project ceramic bowl", - "dia_id": "D12:4", - "text": "Here it is. Pretty proud of it! It was a great experience. Thoughts?" - }, - { - "speaker": "Caroline", - "dia_id": "D12:5", - "text": "That bowl is awesome, Mel! What gave you the idea for all the colors and patterns?" - }, - { - "speaker": "Melanie", - "dia_id": "D12:6", - "text": "Thanks, Caroline! I'm obsessed with those, so I made something to catch the eye and make people smile. Plus, painting helps me express my feelings and be creative. Each stroke carries a part of me." - }, - { - "speaker": "Caroline", - "dia_id": "D12:7", - "text": "That's amazing! You put so much effort and passion into it. Your creativity really shines. Seeing how art can be a source of self-expression and growth is truly inspiring. You're killing it!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:8", - "text": "Thanks, Caroline! Your words really mean a lot. I've always felt a strong connection to art, and it's been a huge learning experience. It's both a sanctuary and a source of comfort. I'm so glad to have something that brings me so much happiness and fulfillment." - }, - { - "speaker": "Caroline", - "dia_id": "D12:9", - "text": "Glad you found something that makes you so happy! Surrounding ourselves with things that bring us joy is important. Life's too short to do anything else!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:10", - "text": "Agreed, Caroline. Life's tough but it's worth it when we have things that make us happy." - }, - { - "speaker": "Caroline", - "dia_id": "D12:11", - "text": "Definitely, Mel! Finding those happy moments and clinging to them is key. It's what keeps us going, even when life's hard. I'm lucky to have people like you to remind me." - }, - { - "speaker": "Melanie", - "dia_id": "D12:12", - "text": "Yeah, same here Caroline. You make life's struggles more bearable." - }, - { - "speaker": "Caroline", - "dia_id": "D12:13", - "text": "Thanks, Melanie! It means a lot having you in my corner. Appreciate our friendship!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:14", - "text": "I appreciate our friendship too, Caroline. You've always been there for me." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://media2.fdncms.com/portmerc/imager/u/large/46577490/pride2022-2-jankowski.jpg" - ], - "blip_caption": "a photo of a group of people walking down a street with balloons", - "query": "friends pride festival", - "dia_id": "D12:15", - "text": "I'm always here for you, Mel! We had a blast last year at the Pride fest. Those supportive friends definitely make everything worth it!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:16", - "text": "That was a blast! So much fun with the whole gang! Wanna do a family outing this summer?" - }, - { - "speaker": "Caroline", - "dia_id": "D12:17", - "text": "Right, it was so much fun! We could do a family outting, or wanna plan something special for this summer, just us two? It'd be a great chance to catch up and explore nature! What do you think?" - }, - { - "speaker": "Melanie", - "dia_id": "D12:18", - "text": "Sounds great, Caroline! Let's plan something special!" - }, - { - "speaker": "Caroline", - "dia_id": "D12:19", - "text": "Sounds great, Mel! We'll make some awesome memories!" - }, - { - "speaker": "Melanie", - "dia_id": "D12:20", - "text": "Yeah, Caroline! I'll start thinking about what we can do." - }, - { - "speaker": "Caroline", - "dia_id": "D12:21", - "text": "Yeah, Mel! Life's all about creating memories. Can't wait for the trip!" - } - ], - "session_13_date_time": "3:31 pm on 23 August, 2023", - "session_13": [ - { - "speaker": "Caroline", - "img_url": [ - "https://i.redd.it/pyq31v7eh6ra1.jpg" - ], - "blip_caption": "a photo of a sign with a picture of a guinea pig", - "query": "adoption brochures application forms external adoption advice assistance group", - "dia_id": "D13:1", - "text": "Hi Melanie! Hope you're doing good. Guess what I did this week? I took the first step towards becoming a mom - I applied to adoption agencies! It's a big decision, but I think I'm ready to give all my love to a child. I got lots of help from this adoption advice/assistance group I attended. It was great!" - }, - { - "speaker": "Melanie", - "dia_id": "D13:2", - "text": "Caroline, congrats! So proud of you for taking this step. How does it feel? Also, do you have any pets?" - }, - { - "speaker": "Caroline", - "dia_id": "D13:3", - "text": "Thanks, Mel! Exciting but kinda nerve-wracking. Parenting's such a big responsibility. And yup, I do- Oscar, my guinea pig. He's been great. How are your pets?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/kgggim1gom951.jpg" - ], - "blip_caption": "a photo of a black dog laying in the grass with a frisbee", - "query": "pets Luna Oliver playing frisbee backyard", - "dia_id": "D13:4", - "text": "Yeah, it's normal to be both excited and nervous with a big decision. And thanks for asking, they're good- we got another cat named Bailey too. Here's a pic of Oliver. Can you show me one of Oscar?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://cdn.i-scmp.com/sites/default/files/styles/landscape/public/d8/yp/images/shutterstock533807500.jpg" - ], - "blip_caption": "a photography of a guinea in a cage with hay and hay", - "query": "oscar munching parsley playpen", - "dia_id": "D13:5", - "re-download": true, - "text": "He's so cute! What\u2019s the funniest thing Oliver's done? And sure, check out this pic of him eating parsley! Veggies are his fave!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/fgv0i3nzo7541.jpg" - ], - "blip_caption": "a photo of a person holding a carrot in front of a horse", - "query": "oscar carrot in mouth", - "dia_id": "D13:6", - "text": "Oliver's hilarious! He hid his bone in my slipper once! Cute, right? Almost as silly as when I got to feed a horse a carrot. " - }, - { - "speaker": "Caroline", - "dia_id": "D13:7", - "text": "That's so funny! I used to go horseback riding with my dad when I was a kid, we'd go through the fields, feeling the wind. It was so special. I've always had a love for horses!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://warpedtable.com/cdn/shop/products/F331B563-AB73-430A-A6DF-3C5E0F91A4D8.jpg" - ], - "blip_caption": "a photo of a horse painted on a wooden wall", - "query": "horse painting", - "dia_id": "D13:8", - "text": "Wow, that sounds great - I agree, they're awesome. Here's a photo of my horse painting I did recently." - }, - { - "speaker": "Caroline", - "dia_id": "D13:9", - "text": "Wow, Melanie, that's amazing! Love all the details and how you got the horse's grace and strength. Do you like painting animals?" - }, - { - "speaker": "Melanie", - "dia_id": "D13:10", - "text": "Thanks, Caroline! Glad you like it. Yeah, I love to. It's peaceful and special. Horses have such grace! Do you like to paint too?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0302/3968/6755/files/IMG_8385_a145b124-53ab-4b3c-8f1a-497fa2d39a49.jpg" - ], - "blip_caption": "a photo of a painting of a woman with a blue face", - "query": "self-portrait painting vibrant colors", - "dia_id": "D13:11", - "text": "Painting's great for expressing myself. I love creating art! Here's a recent self-portrait I made last week." - }, - { - "speaker": "Melanie", - "dia_id": "D13:12", - "text": "Caroline, that's great! The blue's really powerful, huh? How'd you feel while painting it?" - }, - { - "speaker": "Caroline", - "dia_id": "D13:13", - "text": "Thanks, Mel! I felt liberated and empowered doing it. Painting helps me explore my identity and be true to myself. It's definitely therapeutic." - }, - { - "speaker": "Melanie", - "dia_id": "D13:14", - "text": "Wow, Caroline, that's great! Art's awesome for showing us who we really are and getting in touch with ourselves. What else helps you out?" - }, - { - "speaker": "Caroline", - "dia_id": "D13:15", - "text": "Thanks, Melanie. Art gives me a sense of freedom, but so does having supportive people around, promoting LGBTQ rights and being true to myself. I want to live authentically and help others to do the same." - }, - { - "speaker": "Melanie", - "dia_id": "D13:16", - "text": "Wow, Caroline! That's amazing. You really care about being real and helping others. Wishing you the best on your adoption journey!" - }, - { - "speaker": "Caroline", - "dia_id": "D13:17", - "text": "Thanks, Melanie! I really appreciate it. Excited for the future! Bye!" - }, - { - "speaker": "Melanie", - "dia_id": "D13:18", - "text": "Bye Caroline. I'm here for you. Take care of yourself." - } - ], - "session_14_date_time": "1:33 pm on 25 August, 2023", - "session_14": [ - { - "speaker": "Caroline", - "img_url": [ - "https://photos.thetrek.co/wp-content/uploads/2017/11/IMG_1742-e1509796327550.jpg" - ], - "blip_caption": "a photo of a woman sitting on a sign on top of a mountain", - "query": "letter apology hike encounter", - "dia_id": "D14:1", - "text": "Hey, Mel! How's it going? There's something I want to tell you. I went hiking last week and got into a bad spot with some people. It really bugged me, so I tried to apologize to them." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i0.wp.com/bardith.com/wp-content/uploads/2022/05/IMG_4371-1.jpg" - ], - "blip_caption": "a photo of a plate with a bunch of flowers on it", - "query": "pottery purple bowl floral patterns", - "dia_id": "D14:2", - "text": "Wow, Caroline! Sorry that happened to you. It's tough when those things happen, but it's great you apologized. Takes a lot of courage and maturity! What do you think of this?" - }, - { - "speaker": "Caroline", - "dia_id": "D14:3", - "text": "Thanks, Melanie! That plate is awesome! Did you make it?" - }, - { - "speaker": "Melanie", - "dia_id": "D14:4", - "text": "Yeah, I made it in pottery class yesterday. I love it! Pottery's so relaxing and creative. Have you tried it yet?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i0.wp.com/makesomethingmondays.com/wp-content/uploads/2017/07/mini-beach-sunset-painting-diy.jpg" - ], - "blip_caption": "a photo of a painting of a sunset on a small easel", - "query": "vibrant sunset beach painting", - "dia_id": "D14:5", - "text": "Nah, I haven't. I've been busy painting - here's something I just finished." - }, - { - "speaker": "Melanie", - "dia_id": "D14:6", - "text": "Wow Caroline, that looks amazing! Those colors are so vivid, it really looks like a real sunset. What gave you the idea to paint it?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a painting of a sunset over the ocean", - "dia_id": "D14:7", - "text": "Thanks, Melanie! I painted it after I visited the beach last week. Just seeing the sun dip below the horizon, all the amazing colors - it was amazing and calming. So I just had to try to capture that feeling in my painting." - }, - { - "speaker": "Melanie", - "dia_id": "D14:8", - "text": "Wow, the beach really inspired you. The art really took me to that moment and I can feel the serenity. You captured the sunset perfectly, so peaceful!" - }, - { - "speaker": "Caroline", - "dia_id": "D14:9", - "text": "Thanks Mel, really appreciate your kind words. It means a lot to me that you can feel the sense of peace and serenity. Makes me feel connected." - }, - { - "speaker": "Melanie", - "dia_id": "D14:10", - "text": "I feel the same way! Art is so cool like that - it connects us and helps us understand each other. I was actually just remembering yesterday, spending the day with my fam volunteering at a homeless shelter. It was hard to see how neglected some people are, but it was great to feel like we could make a difference." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/64cda0c3f2719a0e6e707684/a08e6e1f-f0e0-4f1a-b567-1f1f92b80aab/35970846_829192503937065_1026209343625756672_o_829192493937066.jpg" - ], - "blip_caption": "a photo of a crowd of people walking down a street with a rainbow flag", - "query": "volunteering pride event", - "dia_id": "D14:11", - "text": "Wow, Mel, you're amazing! Volunteering and making a difference- it's so heartwarming. You're an inspiration to us all!" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a bulletin board with a rainbow flag and a don't ever be afraid to", - "dia_id": "D14:12", - "text": "Thanks, Caroline! I really appreciate your help and motivation. What made you decide to transition and join the transgender community?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://npr.brightspotcdn.com/legacy/sites/wuwm/files/201811/20181029_095916.jpg" - ], - "blip_caption": "a photo of a building with a large eagle painted on it", - "query": "rainbow flag painting unity acceptance", - "dia_id": "D14:13", - "text": "Finding a community where I'm accepted, loved and supported has really meant a lot to me. It's made a huge difference to have people who get what I'm going through. Stuff like this mural are really special to me!" - }, - { - "speaker": "Melanie", - "dia_id": "D14:14", - "text": "Caroline, glad you found a supportive community! Can you tell me more about why it's special to you?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a stained glass window with a picture of a person on a horse", - "dia_id": "D14:15", - "text": "The rainbow flag mural is important to me as it reflects the courage and strength of the trans community. The eagle symbolizes freedom and pride, representing my own resilience and that of others." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a stained glass window with a person holding a key", - "dia_id": "D14:16", - "text": "I'm in awe of your courage as a trans person. Have you made any more art lately?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://projects.history.qmul.ac.uk/thehistorian/wp-content/uploads/sites/24/2017/10/IMG_20170922_072615_165.jpg" - ], - "blip_caption": "a photo of three stained glass windows in a church with a clock", - "query": "stained glass window letter", - "dia_id": "D14:17", - "text": "Thanks, Mel! I made this stained glass window to remind myself and others that within us all is the key to discovering our true potential and living our best life." - }, - { - "speaker": "Melanie", - "dia_id": "D14:18", - "text": "Wow, Caroline, that looks amazing! What inspired it?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a large stained glass window in a church", - "dia_id": "D14:19", - "text": "Thanks! It was made for a local church and shows time changing our lives. I made it to show my own journey as a transgender woman and how we should accept growth and change." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a door with a stained glass window and a coat rack", - "dia_id": "D14:20", - "text": "Wow, Caroline! All those colors are incredible and the story it tells is so inspiring." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i0.wp.com/marbleheadcurrent.org/wp-content/uploads/2023/07/rainbow.jpg" - ], - "blip_caption": "a photo of a painted sidewalk with a rainbow design on it", - "query": "painting rainbow flag unity acceptance", - "dia_id": "D14:21", - "text": "Thanks, Mel! Glad you like it. It's a symbol of togetherness, to celebrate differences and be that much closer. I'd love to make something like this next!" - }, - { - "speaker": "Melanie", - "dia_id": "D14:22", - "text": "Wow, that's gorgeous! Where did you find it?" - }, - { - "speaker": "Caroline", - "dia_id": "D14:23", - "text": "I was out walking in my neighborhood when I came across this cool rainbow sidewalk for Pride Month. It was so vibrant and welcoming, I had to take a picture! It reminds us that love and acceptance are everywhere\u2014even where we least expect it." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a person drawing a flower on the ground", - "dia_id": "D14:24", - "text": "That's so nice, Caroline! Art can be in the most unlikely places. Love and acceptance really can be found everywhere." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://static.skillshare.com/uploads/project/281358/cover_full_592aef91cce2432e71c739804161e0fb.jpg" - ], - "blip_caption": "a photo of a painting of flowers and a watercolor palette", - "query": "drawing flower ground colored chalk simple act creativity", - "dia_id": "D14:25", - "text": "Agreed, Mel! Art can be a real mood-booster - I saw someone drawing on the ground the other day and it made me so happy. Creativity sure can lighten someone's day." - }, - { - "speaker": "Melanie", - "dia_id": "D14:26", - "text": "Wow, Caroline, that's so nice! The colors are so bright and the flowers are so pretty. Art is such a source of joy." - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.pinimg.com/originals/3d/e3/b8/3de3b8a013be3eec63cc454cb0c63536.jpg" - ], - "blip_caption": "a photo of a drawing of a bunch of flowers on a table", - "query": "bouquet wildflowers art", - "dia_id": "D14:27", - "text": "Thanks, Mel! Art gives me so much joy. It helps me show my feelings and freeze gorgeous moments, like a bouquet of flowers. " - }, - { - "speaker": "Melanie", - "dia_id": "D14:28", - "text": "Wow, did you make that? It looks so real!" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a drawing of a flower bouquet with a person holding it", - "dia_id": "D14:29", - "text": "Yeah, definitely! Drawing flowers is one of my faves. Appreciating nature and sharing it is great. What about you, Mel? What type of art do you love?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/k9wcp85ledi91.jpg" - ], - "blip_caption": "a photo of a painting of a sunflower on a canvas", - "query": "painting field sunflowers", - "dia_id": "D14:30", - "text": "Painting landscapes and still life is my favorite! Nature's amazing, here's a painting I did recently." - }, - { - "speaker": "Caroline", - "dia_id": "D14:31", - "text": "Wow, Mel! Any more paintings coming up?" - }, - { - "speaker": "Melanie", - "dia_id": "D14:32", - "text": "I'm feeling inspired by autumn so I'm planning a few. You got any cool art projects coming up?" - }, - { - "speaker": "Caroline", - "dia_id": "D14:33", - "text": "I'm putting together an LGBTQ art show next month and I'm gonna show my paintings. Super stoked!" - }, - { - "speaker": "Melanie", - "dia_id": "D14:34", - "text": "Wow, Caroline, that's awesome! Can't wait to see your show - the LGBTQ community needs more platforms like this!" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a poster for a concert with a man in a cowboy hat", - "dia_id": "D14:35", - "text": "Yeah Mel, stoked! Gonna be a great night featuring LGBTQ artists and their awesome talents. We want it to spread understanding and acceptance - let's make it happen!" - } - ], - "session_15_date_time": "3:19 pm on 28 August, 2023", - "session_15": [ - { - "speaker": "Caroline", - "dia_id": "D15:1", - "text": "Hey Melanie, great to hear from you. What's been up since we talked?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://img-aws.ehowcdn.com/1280x/www.onlyinyourstate.com/wp-content/uploads/2022/12/gym8.jpg" - ], - "blip_caption": "a photo of a playground with a climbing net and a slide", - "query": "kids climbing jungle gym park", - "dia_id": "D15:2", - "text": "Hey Caroline! Since we last spoke, I took my kids to a park yesterday. They had fun exploring and playing. It was nice seeing them have a good time outdoors. Time flies, huh? What's new with you?" - }, - { - "speaker": "Caroline", - "dia_id": "D15:3", - "text": "Wow, your kids had so much fun at the park! Being outdoors can be really enjoyable. A lot happened since our last chat. I've been chasing my ambitions and had the chance to volunteer at an LGBTQ+ youth center. It was so gratifying to talk to similar young people. It made me remember how essential it is to be kind and show support." - }, - { - "speaker": "Melanie", - "dia_id": "D15:4", - "text": "That sounds great, Caroline. Volunteering is a great way to meet people. Creating community and supporting each other, especially for kids, is really important. How did you feel about your time there? Anything that sticks out to you?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a table with a black table cloth and a group of people", - "dia_id": "D15:5", - "text": "I loved it. It was awesome to see how strong the young people were, with all the challenges they face. I felt fulfilled guiding and supporting them. I even got to let them know they're not alone by sharing my story. Such a powerful, emotional experience." - }, - { - "speaker": "Melanie", - "dia_id": "D15:6", - "text": "Was connecting with those young folks meaningful for you? " - }, - { - "speaker": "Caroline", - "dia_id": "D15:7", - "text": "It was so special to me. It reminded me of my own struggles in the past and how I felt alone. I was glad I could share my story and offer them support - it felt like I could make a difference." - }, - { - "speaker": "Melanie", - "dia_id": "D15:8", - "text": "That's great. Sharing your story and support might make a difference for a long time. What do you hope to do next time?" - }, - { - "speaker": "Caroline", - "dia_id": "D15:9", - "text": "I'm definitely carrying on volunteering at the youth center. It's an important part of my life and I've made strong connections with people there. I really believe in community and supporting each other. So I wanna keep making a difference." - }, - { - "speaker": "Melanie", - "dia_id": "D15:10", - "text": "That's great news, Caroline! Love seeing your dedication to helping others. Any specific projects or activities you're looking forward to there?" - }, - { - "speaker": "Caroline", - "dia_id": "D15:11", - "text": "We're putting together a talent show for the kids next month. I'm looking forward to seeing how much fun everyone has and how proud they'll feel of their talents!" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://www.stomplight.com/cdn/shop/products/DavidAguilar.jpg" - ], - "blip_caption": "a photo of a band playing on a stage in a park", - "query": "talent show stage colorful lights microphone", - "dia_id": "D15:12", - "text": "That's so cool, Caroline! That's a great way to show off and be proud of everyone's skills. You know I love live music. Can't wait to hear about it!" - }, - { - "speaker": "Caroline", - "dia_id": "D15:13", - "text": "Wow! Did you see that band?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a crowd of people at a concert with their hands in the air", - "dia_id": "D15:14", - "text": "Yeah, that pic was from a show I went to. It was so much fun and reminded me of how music brings us together." - }, - { - "speaker": "Caroline", - "dia_id": "D15:15", - "text": "Wow, what a fun moment! What's the band?" - }, - { - "speaker": "Melanie", - "dia_id": "D15:16", - "text": "\"Summer Sounds\"- The playing an awesome pop song that got everyone dancing and singing. It was so fun and lively!" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a man playing a guitar in a recording studio", - "dia_id": "D15:17", - "text": "That sounds great! Music brings us together and brings joy. Playing and singing let me express myself and connect with others - love it! So cathartic and uplifting." - }, - { - "speaker": "Melanie", - "dia_id": "D15:18", - "text": "Cool! What type of music do you play?" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a guitar on display in a store", - "dia_id": "D15:19", - "text": "Guitar's mostly my thing. Playing it helps me get my emotions out." - }, - { - "speaker": "Melanie", - "dia_id": "D15:20", - "text": "That's awesome! What type of guitar? Been playing long?" - }, - { - "speaker": "Caroline", - "dia_id": "D15:21", - "text": "I started playing acoustic guitar about five years ago; it's been a great way to express myself and escape into my emotions." - }, - { - "speaker": "Melanie", - "dia_id": "D15:22", - "text": "Music's amazing, isn't it? Any songs that have deep meaning for you?" - }, - { - "speaker": "Caroline", - "dia_id": "D15:23", - "text": "Yeah totally! \"Brave\" by Sara Bareilles has a lot of significance for me. It's about being courageous and fighting for what's right. Whenever I hear this jam, I think about the paths I've taken and the progress I've made." - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a piece of paper with a drawing of a man playing a piano", - "dia_id": "D15:24", - "text": "That's a gorgeous song, Caroline. It really fits with your journey and your determination to make a difference. Music can be so inspiring and uplifting." - }, - { - "speaker": "Caroline", - "dia_id": "D15:25", - "text": "Thanks, Melanie! Appreciate it. You play any instruments?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a sheet music with notes and a pencil", - "dia_id": "D15:26", - "text": "Yeah, I play clarinet! Started when I was young and it's been great. Expression of myself and a way to relax." - }, - { - "speaker": "Caroline", - "dia_id": "D15:27", - "text": "Cool! Got any fav tunes?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a laptop computer with a graph on it", - "dia_id": "D15:28", - "text": "I'm a fan of both classical like Bach and Mozart, as well as modern music like Ed Sheeran's \"Perfect\"." - } - ], - "session_16_date_time": "12:09 am on 13 September, 2023", - "session_16": [ - { - "speaker": "Caroline", - "img_url": [ - "https://assets.simpleviewinc.com/simpleview/image/upload/c_fill,f_jpg,h_371,q_75,w_640/v1/crm/corpuschristitx/Sunset-Lake-Park_38118D81-5056-A36F-23E62D2F41525FF4-38118cb45056a36_381194bb-5056-a36f-23c599c63a3950d7.jpg" - ], - "blip_caption": "a photo of a beach with a fence and a sunset", - "query": "sunset lake", - "dia_id": "D16:1", - "text": "Hey Mel, long time no chat! I had a wicked day out with the gang last weekend - we went biking and saw some pretty cool stuff. It was so refreshing, and the pic I'm sending is just stunning, eh?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://exploringtheprime.com/wp-content/uploads/2019/10/IMG_6705-2.jpg" - ], - "blip_caption": "a photo of a dirt road surrounded by trees with yellow leaves", - "query": "family hiking trail vibrant autumn colors", - "dia_id": "D16:2", - "text": "Hey Caroline! It's so good to hear from you! That pic is so beautiful, the colors really pop. Biking sounds like a great way to get out in nature. We went camping with the kids a few weeks ago, had a blast exploring the forest and hiking. Nature can be so refreshing for your soul. Any plans coming up?" - }, - { - "speaker": "Caroline", - "dia_id": "D16:3", - "text": "Melanie, that photo's amazing! I love all the yellow leaves, it looks so cozy. That sounds like fun! Seeing how excited they get for the little things is awesome, it's so contagious." - }, - { - "speaker": "Melanie", - "dia_id": "D16:4", - "text": "Thanks, Caroline! It's awesome seeing the kids get excited learning something new about nature. Those moments make being a parent worth it. We roasted marshmallows and shared stories around the campfire. Those simple moments make the best memories. What inspires you with your volunteering?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.pinimg.com/originals/34/2e/72/342e72b194865e01a38af86c307e95c7.jpg" - ], - "blip_caption": "a photo of a painting of a heart on a table", - "query": "canvas painting rainbow colors", - "dia_id": "D16:5", - "text": "I'm inspired seeing my work make a difference for the LGBTQ+ community. Knowing I'm helping create a more loving world is amazing. I'm really thankful for my friends, family and mentors' support. It inspires me to keep making art, too." - }, - { - "speaker": "Melanie", - "dia_id": "D16:6", - "text": "Wow, Caroline, that looks awesome! I love how it shows the togetherness and power you were talking about. How long have you been creating art?" - }, - { - "speaker": "Caroline", - "dia_id": "D16:7", - "text": "Since I was 17 or so. I find it soempowering and cathartic. It's amazing how art can show things that are hard to put into words. How long have you been into art?" - }, - { - "speaker": "Melanie", - "img_url": [ - "https://www.1hotpieceofglass.com/cdn/shop/files/image_93ad5985-ff65-4b93-877b-3ee948ac5641_5000x.jpg" - ], - "blip_caption": "a photo of a group of bowls and a starfish on a white surface", - "query": "pottery bowl intricate patterns purple glaze", - "dia_id": "D16:8", - "text": "Seven years now, and I've finally found my real muses: painting and pottery. It's so calming and satisfying. Check out my pottery creation in the pic!" - }, - { - "speaker": "Caroline", - "dia_id": "D16:9", - "text": "Melanie, those bowls are amazing! They each have such cool designs. I love that you chose pottery for your art. Painting and drawing have helped me express my feelings and explore my gender identity. Creating art was really important to me during my transition - it helped me understand and accept myself. I'm so grateful." - }, - { - "speaker": "Melanie", - "dia_id": "D16:10", - "text": "Thanks, Caroline! It has really helped me out. I love how it's both a creative outlet and a form of therapy. Have you ever thought about trying it or another art form?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.redd.it/z8zsh53ycfvb1.jpg" - ], - "blip_caption": "a photo of a painting on a easel with a red and blue background", - "query": "canvas colourful brush strokes", - "dia_id": "D16:11", - "text": "I haven't done pottery yet, but I'm game for trying new art. I might try it sometime! Check out this piece I made!" - }, - { - "speaker": "Melanie", - "dia_id": "D16:12", - "text": "Wow, Caroline! This painting is awesome. Love the red and blue. What gave you the idea?" - }, - { - "speaker": "Caroline", - "dia_id": "D16:13", - "text": "Thanks, Melanie! I made this painting to show my path as a trans woman. The red and blue are for the binary gender system, and the mix of colors means smashing that rigid thinking. It's a reminder to love my authentic self - it's taken a while to get here but I'm finally proud of who I am." - }, - { - "speaker": "Melanie", - "dia_id": "D16:14", - "text": "Wow, Caro, that painting is amazing! You've made so much progress. I'm super proud of you for being your true self. What effect has the journey had on your relationships?" - }, - { - "speaker": "Caroline", - "dia_id": "D16:15", - "text": "Thanks, Melanie. It's definitely changed them. Some close friends kept supporting me, but a few weren't able to handle it. It wasn't easy, but I'm much happier being around those who accept and love me. Now my relationships feel more genuine." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/epuj1xq8eaga1.jpg" - ], - "blip_caption": "a photo of a sign posted on a door stating that someone is not being able to leave", - "query": "me kids park joy love happiness", - "dia_id": "D16:16", - "text": "Caroline, it's got to be tough dealing with those changes. Glad you've found people who uplift and accept you! Here's to a good time at the caf\u00e9 last weekend - they even had thoughtful signs like this! It brings me so much happiness." - }, - { - "speaker": "Caroline", - "dia_id": "D16:17", - "text": "Whoa, Mel, that sign looks serious. Did anything happen?" - }, - { - "speaker": "Melanie", - "dia_id": "D16:18", - "text": "The sign was just a precaution, I had a great time. But thank you for your concern, you're so thoughtful!" - }, - { - "speaker": "Caroline", - "dia_id": "D16:19", - "text": "Phew! Glad it all worked out and you had a good time at the park!" - }, - { - "speaker": "Melanie", - "dia_id": "D16:20", - "text": "Yeah, it was so much fun! Those joyful moments definitely show us life's beauty." - } - ], - "session_17_date_time": "10:31 am on 13 October, 2023", - "session_17": [ - { - "speaker": "Caroline", - "dia_id": "D17:1", - "text": "Hey Mel, what's up? Long time no see! I just contacted my mentor for adoption advice. I'm ready to be a mom and share my love and family. It's a great feeling. Anything new with you? Anything exciting going on?" - }, - { - "speaker": "Melanie", - "dia_id": "D17:2", - "text": "Hey Caroline! Great to hear from you! Wow, what an amazing journey. Congrats!" - }, - { - "speaker": "Caroline", - "dia_id": "D17:3", - "text": "Thanks, Melanie! I'm stoked to start this new chapter. It's been a dream to adopt and provide a safe, loving home for kids who need it. Do you have any experience with adoption, or know anyone who's gone through the process?" - }, - { - "speaker": "Melanie", - "dia_id": "D17:4", - "text": "Yeah, a buddy of mine adopted last year. It was a long process, but now they're super happy with their new kid. Makes me feel like maybe I should do it too!" - }, - { - "speaker": "Caroline", - "dia_id": "D17:5", - "text": "That's great news about your friend! It can be tough, but so worth it. It's a great way to add to your family and show your love. If you ever do it, let me know \u2014 I'd love to help in any way I can." - }, - { - "speaker": "Melanie", - "dia_id": "D17:6", - "text": "Thanks, Caroline! Appreciate your help. Got any tips for getting started on it?" - }, - { - "speaker": "Caroline", - "dia_id": "D17:7", - "text": "Yep! Do your research and find an adoption agency or lawyer. They'll help with the process and provide all the info. Gather documents like references, financial info and medical checks. Don't forget to prepare emotionally, since the wait can be hard. It's all worth it in the end though." - }, - { - "speaker": "Melanie", - "dia_id": "D17:8", - "text": "Thanks for the tip, Caroline. Doing research and readying myself emotionally makes sense. I'll do that. BTW, recently I had a setback. Last month I got hurt and had to take a break from pottery, which I use for self-expression and peace." - }, - { - "speaker": "Caroline", - "dia_id": "D17:9", - "text": "Oh man, sorry to hear that, Melanie. I hope you're okay. Pottery's a great way to relax, so it must have been tough taking a break. Need any help?" - }, - { - "speaker": "Melanie", - "dia_id": "D17:10", - "text": "Thanks, Caroline. It was tough, but I'm doing ok. Been reading that book you recommended a while ago and painting to keep busy." - }, - { - "speaker": "Caroline", - "dia_id": "D17:11", - "text": "Cool that you have creative outlets. Got any paintings to show? I'd love to check them out." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://trendgallery.art/cdn/shop/files/IMG_2355.jpg" - ], - "blip_caption": "a photo of a painting of a sunset with a pink sky", - "query": "landscape painting vibrant purple sunset autumn", - "dia_id": "D17:12", - "text": "Yeah, Here's one I did last week. It's inspired by the sunsets. The colors make me feel calm. What have you been up to lately, artistically?" - }, - { - "speaker": "Caroline", - "dia_id": "D17:13", - "text": "Wow Mel, that's stunning! Love the colors and the chilled-out sunset vibe. What made you paint it? I've been trying out abstract stuff recently. It's kinda freeing, just putting my feelings on the canvas without too much of a plan. It's like a cool form of self-expression." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://theartwerks.com/cdn/shop/products/image_4c8aee8a-5395-4037-a1d4-f6db3a3b0302.jpg" - ], - "blip_caption": "a photo of a painting on a wall with a blue background", - "query": "abstract painting vibrant colors", - "dia_id": "D17:14", - "text": "Thanks, Caroline! I painted it because it was calming. I've done an abstract painting too, take a look! I love how art lets us get our emotions out." - }, - { - "speaker": "Caroline", - "dia_id": "D17:15", - "text": "Wow, that looks great! The blue adds so much to it. What feelings were you hoping to portray?" - }, - { - "speaker": "Melanie", - "dia_id": "D17:16", - "text": "I wanted a peaceful blue streaks to show tranquility. Blue calms me, so I wanted the painting to have a serene vibe while still having lots of vibrant colors." - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a poster on a wall in a classroom", - "dia_id": "D17:17", - "text": "Yeah, it's very calming. It's awesome how art can show emotions. By the way, I went to a poetry reading last Fri - it was really powerful! Ever been to one?" - }, - { - "speaker": "Melanie", - "dia_id": "D17:18", - "text": "Nope, never been to something like that. What was it about? What made it so special?" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1675780954.jpg" - ], - "blip_caption": "a photography of a sign that says trans lives matter", - "query": "transgender poetry reading trans pride flags", - "dia_id": "D17:19", - "re-download": true, - "text": "It was a transgender poetry reading where transgender people shared their stories through poetry. It was extra special 'cause it was a safe place for self-expression and it was really empowering to hear others share and celebrate their identities." - }, - { - "speaker": "Melanie", - "dia_id": "D17:20", - "text": "Wow, sounds amazing! What was the event like? Those posters are great!" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://i.redd.it/50qvgfuva33b1.jpg" - ], - "blip_caption": "a photo of a drawing of a woman in a dress", - "query": "transgender flag drawing", - "dia_id": "D17:21", - "text": "The room was electric with energy and support! The posters were amazing, so much pride and strength! It inspired me to make some art." - }, - { - "speaker": "Melanie", - "dia_id": "D17:22", - "text": "That's awesome, Caroline! You drew it? What does it mean to you?" - }, - { - "speaker": "Caroline", - "dia_id": "D17:23", - "text": "Thanks, Melanie! Yeah, I drew it. It stands for freedom and being real. It's like a nudge to always stay true to myself and embrace my womanhood." - }, - { - "speaker": "Melanie", - "dia_id": "D17:24", - "text": "I love it. Showing off our true selves is the best thing ever." - }, - { - "speaker": "Caroline", - "dia_id": "D17:25", - "text": "Yep, Melanie! Being ourselves is such a great feeling. It's an ongoing adventure of learning and growing." - }, - { - "speaker": "Melanie", - "dia_id": "D17:26", - "text": "Yep, Caroline. Life's about learning and exploring. Glad we can be on this trip together." - } - ], - "session_18_date_time": "6:55 pm on 20 October, 2023", - "session_18": [ - { - "speaker": "Melanie", - "img_url": [ - "https://i.redd.it/dl8dki2hm3k81.jpg" - ], - "blip_caption": "a photo of a car dashboard with a white cloth and a steering wheel", - "query": "car accident damaged car airbags deployed roadtrip", - "dia_id": "D18:1", - "text": "Hey Caroline, that roadtrip this past weekend was insane! We were all freaked when my son got into an accident. We were so lucky he was okay. It was a real scary experience. Thankfully it's over now. What's been up since we last talked?" - }, - { - "speaker": "Caroline", - "dia_id": "D18:2", - "text": "Oops, sorry 'bout the accident! Must have been traumatizing for you guys. Thank goodness your son's okay. Life sure can be a roller coaster." - }, - { - "speaker": "Melanie", - "dia_id": "D18:3", - "text": "Yeah, our trip got off to a bad start. I was really scared when we got into the accident. Thankfully, my son's ok and that was a reminder that life is precious and to cherish our family." - }, - { - "speaker": "Caroline", - "dia_id": "D18:4", - "text": "Glad your son is okay, Melanie. Life's unpredictable, but moments like these remind us how important our loved ones are. Family's everything." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://familyadventuresva.files.wordpress.com/2022/03/img_5030.jpg" - ], - "blip_caption": "a photo of two children standing on a rocky cliff overlooking a canyon", - "query": "grand canyon family photo", - "dia_id": "D18:5", - "text": "Yeah, you're right, Caroline. Family's super important to me. Especially after the accident, I've thought a lot about how much I need them. They mean the world to me and I'm so thankful to have them. Thankfully, they enjoyed the Grand Canyon a lot!" - }, - { - "speaker": "Caroline", - "dia_id": "D18:6", - "text": "The kids look so cute, Mel! I bet they bring lots of joy. How did they handle the accident?" - }, - { - "speaker": "Melanie", - "dia_id": "D18:7", - "text": "Thanks! They were scared but we reassured them and explained their brother would be OK. They're tough kids." - }, - { - "speaker": "Caroline", - "dia_id": "D18:8", - "text": "Kids are amazingly resilient in tough situations. They have an amazing ability to bounce back." - }, - { - "speaker": "Melanie", - "dia_id": "D18:9", - "text": "They're really amazing. Wish I was that resilient too. But they give me the strength to keep going." - }, - { - "speaker": "Caroline", - "dia_id": "D18:10", - "text": "Our loved ones give us strength to tackle any challenge - it's amazing!" - }, - { - "speaker": "Melanie", - "dia_id": "D18:11", - "text": "Yeah, Caroline. Totally agree. They're my biggest motivation and support." - }, - { - "speaker": "Caroline", - "dia_id": "D18:12", - "text": "It's so sweet to see your love for your family, Melanie. They really are your rock." - }, - { - "speaker": "Melanie", - "dia_id": "D18:13", - "text": "Thanks, Caroline. They're a real support. Appreciate them a lot." - }, - { - "speaker": "Caroline", - "dia_id": "D18:14", - "text": "Glad you've got people to lean on, Melanie. It helps during tougher times." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://live.staticflickr.com/8358/29211988243_82023c5524_b.jpg" - ], - "blip_caption": "a photography of a woman and a child walking on a trail", - "query": "family hiking mountains", - "dia_id": "D18:15", - "re-download": true, - "text": "Yeah for sure. Having my fam around helps a lot. It makes hard times easier." - }, - { - "speaker": "Caroline", - "dia_id": "D18:16", - "text": "Wow, great pic! Is that recent? Looks like you all had fun!" - }, - { - "speaker": "Melanie", - "dia_id": "D18:17", - "text": "Thanks, Caroline! Yup, we just did it yesterday! The kids loved it and it was a nice way to relax after the road trip." - }, - { - "speaker": "Caroline", - "dia_id": "D18:18", - "text": "Glad you got some R&R after the drive. Nature sure seems to refresh us, huh?" - }, - { - "speaker": "Melanie", - "blip_caption": "a photo of a sunset over a body of water", - "dia_id": "D18:19", - "text": "Absolutely! It really helps me reset and recharge. I love camping trips with my fam, 'cause nature brings such peace and serenity." - }, - { - "speaker": "Caroline", - "dia_id": "D18:20", - "text": "Wow, that's awesome! What do you love most about camping with your fam?" - }, - { - "speaker": "Melanie", - "dia_id": "D18:21", - "text": "It's a chance to be present and together. We bond over stories, campfires and nature. It's so peaceful waking up to the sound of birds and the smell of fresh air - it always refreshes my soul." - }, - { - "speaker": "Caroline", - "dia_id": "D18:22", - "text": "That's so peaceful and calming, Melanie! I can picture waking up to nature. It's great that you get to spend quality, tranquil time with your family." - }, - { - "speaker": "Melanie", - "dia_id": "D18:23", - "text": "Thanks, Caroline! This is a great time. Nature and quality time, can't beat it!" - }, - { - "speaker": "Caroline", - "dia_id": "D18:24", - "text": "Yeah totally! They're priceless. Lucky you!" - } - ], - "session_19_date_time": "9:55 am on 22 October, 2023", - "session_19": [ - { - "speaker": "Caroline", - "dia_id": "D19:1", - "text": "Woohoo Melanie! I passed the adoption agency interviews last Friday! I'm so excited and thankful. This is a big move towards my goal of having a family." - }, - { - "speaker": "Melanie", - "img_url": [ - "https://imgur.com/oGlhL5J.jpg" - ], - "blip_caption": "a photo of a couple of wooden dolls sitting on top of a table", - "query": "painted ceramic family figurine", - "dia_id": "D19:2", - "text": "Congrats, Caroline! Adoption sounds awesome. I'm so happy for you. These figurines I bought yesterday remind me of family love. Tell me, what's your vision for the future?" - }, - { - "speaker": "Caroline", - "dia_id": "D19:3", - "text": "Thanks so much, Melanie! It's beautiful! It really brings home how much love's in families - both blood and the ones we choose. I hope to build my own family and put a roof over kids who haven't had that before. For me, adoption is a way of giving back and showing love and acceptance." - }, - { - "speaker": "Melanie", - "dia_id": "D19:4", - "text": "Wow, Caroline, that's awesome. Giving a home to needy kids is such a loving way to build a family. Those kids will be so supported and happy in their new home." - }, - { - "speaker": "Caroline", - "dia_id": "D19:5", - "text": "Thanks, Melanie. My dream is to create a safe and loving home for these kids. Love and acceptance should be everyone's right, and I want them to experience it." - }, - { - "speaker": "Melanie", - "dia_id": "D19:6", - "text": "I totally agree, Caroline. Everyone deserves that. It's awesome to see how passionate you are about helping these kids." - }, - { - "speaker": "Caroline", - "dia_id": "D19:7", - "text": "Thanks, Mel. Finding self-acceptance was a long process, but now I'm ready to offer love and support to those who need it. It's empowering to make a positive difference in someone's life." - }, - { - "speaker": "Melanie", - "dia_id": "D19:8", - "text": "That must have been tough for you, Caroline. Respect for finding acceptance and helping others with what you've been through. You're so strong and inspiring." - }, - { - "speaker": "Caroline", - "dia_id": "D19:9", - "text": "Thanks, Melanie. Transitioning wasn't easy and acceptance wasn't either, but the help I got from friends, family and people I looked up to was invaluable. They boosted me through tough times and helped me find out who I really am. That's why I want to pass that same support to anyone who needs it. Bringing others comfort and helping them grow brings me such joy." - }, - { - "speaker": "Melanie", - "dia_id": "D19:10", - "text": "I'm so happy for you, Caroline. You found your true self and now you're helping others. You're so inspiring!" - }, - { - "speaker": "Caroline", - "blip_caption": "a photo of a clock with a green and yellow design on it", - "dia_id": "D19:11", - "text": "Thanks, Melanie. Your support really means a lot. This journey has been amazing and I'm grateful I get to share it and help others with theirs. It's a real gift." - }, - { - "speaker": "Melanie", - "dia_id": "D19:12", - "text": "Absolutely! I'm so glad we can always be there for each other." - }, - { - "speaker": "Caroline", - "dia_id": "D19:13", - "text": "Glad you agree, Caroline. Appreciate the support of those close to me. Their encouragement made me who I am." - }, - { - "speaker": "Melanie", - "dia_id": "D19:14", - "text": "Glad you had support. Being yourself is great!" - }, - { - "speaker": "Caroline", - "img_url": [ - "https://trendgallery.art/cdn/shop/products/IMG_4482.jpg" - ], - "blip_caption": "a photo of a painting with the words happiness painted on it", - "query": "painting vibrant colors happiness self-expression", - "dia_id": "D19:15", - "text": "Yeah, that's true! It's so freeing to just be yourself and live honestly. We can really accept who we are and be content." - } - ], - "session_20_date_time": "4:10 pm on 26 October, 2023", - "session_21_date_time": "9:35 am on 31 October, 2023", - "session_22_date_time": "12:28 am on 8 November, 2023", - "session_23_date_time": "5:15 pm on 11 November, 2023", - "session_24_date_time": "2:46 pm on 16 November, 2023", - "session_25_date_time": "1:18 pm on 21 November, 2023", - "session_26_date_time": "4:39 pm on 24 November, 2023", - "session_27_date_time": "6:25 pm on 26 November, 2023", - "session_28_date_time": "8:52 pm on 5 December, 2023", - "session_29_date_time": "12:20 am on 8 December, 2023", - "session_30_date_time": "4:37 pm on 10 December, 2023", - "session_31_date_time": "3:24 pm on 16 December, 2023", - "session_32_date_time": "3:43 pm on 20 December, 2023", - "session_33_date_time": "8:32 pm on 27 December, 2023", - "session_34_date_time": "1:08 pm on 30 December, 2023", - "session_35_date_time": "12:19 am on 4 January, 2024" - }, - "event_summary": { - "events_session_1": { - "Caroline": [ - "Caroline attends an LGBTQ support group for the first time." - ], - "Melanie": [], - "date": "8 May, 2023" - }, - "events_session_2": { - "Caroline": [ - "Caroline is inspired by her supportive friends and mentors to start researching adoption agencies." - ], - "Melanie": [], - "date": "25 May, 2023" - }, - "events_session_3": { - "Caroline": [ - "Caroline speaks at her school and encourages students to get involved in the LGBTQ community." - ], - "Melanie": [], - "date": "9 June, 2023" - }, - "events_session_4": { - "Caroline": [], - "Melanie": [ - "Melanie takes her family camping for a weekend to bond." - ], - "date": "27 June, 2023" - }, - "events_session_5": { - "Caroline": [], - "Melanie": [ - "Melanie registers for a pottery class." - ], - "date": "3 July, 2023" - }, - "events_session_6": { - "Caroline": [], - "Melanie": [ - "Melanie takes her kids to the local musuem for a day of fun." - ], - "date": "6 July, 2023" - }, - "events_session_7": { - "Caroline": [], - "Melanie": [ - "Melanie begins running longer distances to destress." - ], - "date": "12 July, 2023" - }, - "events_session_8": { - "Caroline": [ - "Caroline attends an adoption council meeting." - ], - "Melanie": [], - "date": "15 July, 2023" - }, - "events_session_9": { - "Caroline": [ - "Caroline joins a mentorship program for LGBTQ youth." - ], - "Melanie": [], - "date": "17 July, 2023" - }, - "events_session_10": { - "Caroline": [ - "Caroline joins a group of connected LGBTQ activists." - ], - "Melanie": [ - "Melanie and her family takes a trip to the beach" - ], - "date": "20 July, 2023" - }, - "events_session_11": { - "Caroline": [], - "Melanie": [ - "Melanie and her family attend an outdoor concert to celebrate her daughter's birthday." - ], - "date": "14 August, 2023" - }, - "events_session_12": { - "Caroline": [ - "Caroline meets a group of religious conservatives on a hike, and they make an unwelcoming comment about her transition." - ], - "Melanie": [ - "Melanie finishes her first pottery project." - ], - "date": "17 August, 2023" - }, - "events_session_13": { - "Caroline": [ - "Caroline begins the adoption process by applying to multiple agencies.", - "Caroline attends a meeting to receive special adoption advice and assistance from the supportive group." - ], - "Melanie": [], - "date": "23 August, 2023" - }, - "events_session_14": { - "Caroline": [ - "Caroline writes a letter to the people she encountered on her hike to apologize for the negative experience they had." - ], - "Melanie": [ - "Melanie and her family volunteer at a local homeless shelter." - ], - "date": "25 August, 2023" - }, - "events_session_15": { - "Caroline": [], - "Melanie": [ - "Melanie takes her kids to a local park" - ], - "date": "28 August, 2023" - }, - "events_session_16": { - "Caroline": [ - "Caroline spends a day out outdoors bike riding and sight seeing with her friends." - ], - "Melanie": [], - "date": "13 September, 2023" - }, - "events_session_17": { - "Caroline": [ - "Caroline calls on her mentor for adoption advice." - ], - "Melanie": [], - "date": "13 October, 2023" - }, - "events_session_18": { - "Caroline": [], - "Melanie": [ - "Melanie's family takes a roadtrip to the Grand Canyon.", - "Melanie's son gets in a car accident while on the roadtrip.", - "Melanie and her family take a roadtrip to visit a nearby national park." - ], - "date": "20 October, 2023" - }, - "events_session_19": { - "Caroline": [ - "Caroline passes the adoption agency interviews." - ], - "Melanie": [], - "date": "22 October, 2023" - } - }, - "observation": { - "session_1_observation": { - "Caroline": [ - [ - "Caroline attended an LGBTQ support group recently and found the transgender stories inspiring.", - "D1:3" - ], - [ - "The support group has made Caroline feel accepted and given her courage to embrace herself.", - "D1:7" - ], - [ - "Caroline is planning to continue her education and explore career options in counseling or mental health to support those with similar issues.", - "D1:9" - ] - ], - "Melanie": [ - [ - "Melanie is currently managing kids and work and finds it overwhelming.", - "D1:2" - ], - [ - "Melanie painted a lake sunrise last year which holds special meaning to her.", - "D1:14" - ], - [ - "Painting is a fun way for Melanie to express her feelings and get creative, helping her relax after a long day.", - "D1:16" - ], - [ - "Melanie is going swimming with the kids after the conversation.", - "D1:18" - ] - ] - }, - "session_2_observation": { - "Melanie": [ - [ - "Melanie ran a charity race for mental health last Saturday.", - "D2:1" - ], - [ - "Melanie is realizing the importance of self-care and its impact on her family.", - "D2:3" - ], - [ - "Melanie carves out me-time each day for activities like running, reading, or playing the violin.", - "D2:5" - ], - [ - "Melanie's kids are excited about summer break and they are considering going camping next month.", - "D2:7" - ] - ], - "Caroline": [ - [ - "Caroline is researching adoption agencies with the dream of having a family and providing a loving home to kids in need.", - "D2:8" - ], - [ - "Caroline chose an adoption agency that helps LGBTQ+ folks with adoption due to their inclusivity and support.", - "D2:12" - ], - [ - "Caroline is excited to create a family for kids who need one, even though she anticipates challenges as a single parent.", - "D2:14" - ] - ] - }, - "session_3_observation": { - "Caroline": [ - [ - "Caroline started transitioning three years ago.", - "D3:1" - ], - [ - "Caroline gave a talk at a school event about her transgender journey and encouraged students to get involved in the LGBTQ community.", - "D3:1" - ], - [ - "Caroline believes conversations about gender identity and inclusion are necessary and is thankful for being able to give a voice to the trans community.", - "D3:3" - ], - [ - "Caroline feels sharing experiences is important to help promote understanding and acceptance.", - "D3:5" - ], - [ - "Caroline feels blessed with a lot of love and support throughout her journey.", - "D3:5" - ], - [ - "Caroline aims to pass on the love and support she has received by sharing stories to build a strong and supportive community of hope.", - "D3:5" - ], - [ - "Caroline's friends, family, and mentors are her rocks, motivating her and giving her strength to push on.", - "D3:11" - ], - [ - "Caroline has known her friends for 4 years, since moving from her home country, and values their love and help, especially after a tough breakup.", - "D3:13" - ] - ], - "Melanie": [ - [ - "Melanie is supportive of Caroline and proud of her for spreading awareness and inspiring others in the LGBTQ community.", - "D3:2" - ], - [ - "Melanie believes talking about inclusivity and acceptance is crucial.", - "D3:4" - ], - [ - "Melanie values family moments and feels they make life awesome, alive, and happy.", - "D3:20" - ], - [ - "Melanie has a husband and kids who keep her motivated.", - "D3:14" - ], - [ - "Melanie has been married for 5 years.", - "D3:16" - ], - [ - "Melanie cherishes time with family and feels most alive and happy during those moments.", - "D3:22" - ] - ] - }, - "session_4_observation": { - "Caroline": [ - [ - "Caroline received a special necklace as a gift from her grandmother in Sweden, symbolizing love, faith, and strength.", - "D4:3" - ], - [ - "Caroline treasures a hand-painted bowl made by a friend for her 18th birthday, which reminds her of art and self-expression.", - "D4:5" - ], - [ - "Caroline is considering a career in counseling and mental health, particularly working with trans people to help them accept themselves and support their mental health.", - "D4:11" - ], - [ - "Caroline attended an LGBTQ+ counseling workshop focused on therapeutic methods and supporting trans individuals, finding it enlightening and inspiring.", - "D4:13" - ], - [ - "Caroline's motivation to pursue counseling comes from her own journey, the support she received, and the positive impact counseling had on her life.", - "D4:15" - ] - ], - "Melanie": [ - [ - "Melanie went camping with her family in the mountains last week and had a great time exploring nature, roasting marshmallows, and hiking.", - "D4:8" - ], - [ - "Melanie values family time and finds it to be special and important.", - "D4:10" - ] - ] - }, - "session_5_observation": { - "Caroline": [ - [ - "Caroline attended an LGBTQ+ pride parade last week and felt a sense of belonging and happiness.", - "D5:1" - ], - [ - "Caroline is considering a career in counseling and mental health to help others.", - "D5:3" - ], - [ - "Caroline is currently learning the piano to get creative.", - "D5:5" - ], - [ - "Caroline is looking forward to attending a transgender conference this month to meet others in the community and learn about advocacy.", - "D5:13" - ] - ], - "Melanie": [ - [ - "Melanie signed up for a pottery class and finds it therapeutic for self-expression and creativity.", - "D5:4" - ], - [ - "Melanie is a big fan of pottery and finds it calming and creative.", - "D5:6" - ], - [ - "Melanie made a black and white bowl in her pottery class which she is proud of.", - "D5:8" - ], - [ - "Pottery is a significant part of Melanie's life as it helps her express her emotions and brings her joy.", - "D5:10" - ] - ] - }, - "session_6_observation": { - "Caroline": [ - [ - "Caroline has been looking into counseling or mental health work and is passionate about helping people and making a positive impact.", - "D6:3" - ], - [ - "Caroline is creating a library for when she has kids, as she looks forward to reading to them and opening up their minds.", - "D6:7" - ], - [ - "Caroline has a collection of kids' books in her library including classics, stories from different cultures, and educational books.", - "D6:9" - ], - [ - "Caroline appreciates the importance of friendship and compassion in her life and is lucky to have friends and family helping with her transition.", - "D6:11" - ], - [ - "Caroline's friends and family have been there for her every step of the way, providing love, guidance, and acceptance during her transition.", - "D6:13" - ] - ], - "Melanie": [ - [ - "Melanie took her kids to the museum recently and enjoyed seeing their excitement at the dinosaur exhibit.", - "D6:4" - ], - [ - "Melanie loves spending time with her kids and seeing the joy in their eyes when exploring new things.", - "D6:4" - ], - [ - "Melanie and her family enjoy camping at the beach as it brings them closer together.", - "D6:16" - ] - ] - }, - "session_7_observation": { - "Caroline": [ - [ - "Caroline attended an LGBTQ conference recently and felt accepted and supported, emphasizing the importance of fighting for trans rights and spreading awareness.", - "D7:1" - ], - [ - "Caroline is looking into counseling and mental health jobs to provide support to others, motivated by her own struggles with mental health and the help she received.", - "D7:5" - ], - [ - "Caroline's favorite guiding book is 'Becoming Nicole' by Amy Ellis Nutt, a true story about a trans girl and her family that gave her hope and connection.", - "D7:11" - ], - [ - "According to 'Becoming Nicole,' Caroline learned the importance of self-acceptance, finding support, and the existence of hope and love.", - "D7:13" - ], - [ - "Caroline values the role of pets in bringing joy and comfort.", - "D7:13" - ] - ], - "Melanie": [ - [ - "Melanie finds LGBTQ events like the conference Caroline attended to be reminding of the strength of community.", - "D7:2" - ], - [ - "Melanie supports Caroline's drive to make a difference in LGBTQ rights.", - "D7:4" - ], - [ - "Melanie reminds Caroline to pursue her dreams and appreciates the power of books in guiding and motivating her.", - "D7:8" - ], - [ - "Melanie has a dog named Luna and a cat named Oliver that bring joy and liveliness to her home.", - "D7:18" - ], - [ - "Melanie got new running shoes for running, which she finds great for destressing and clearing her mind.", - "D7:20" - ], - [ - "Running has been great for Melanie's mental health and mood.", - "D7:24" - ] - ] - }, - "session_8_observation": { - "Caroline": [ - [ - "Caroline attended a council meeting for adoption last Friday and found it inspiring and emotional.", - "D8:9" - ], - [ - "Caroline went to a pride parade a few weeks ago and felt accepted and happy being around people who celebrated her.", - "D8:19" - ], - [ - "Caroline felt proud and grateful at the pride parade feeling accepted by the community.", - "D8:21" - ], - [ - "Caroline expressed feeling comforted by being around accepting and loving people.", - "D8:21" - ], - [ - "Caroline mentioned the importance of finding peace and mental health through expressions of authentic self.", - "D8:25" - ], - [ - "Caroline expressed pride in the courage to transition, finding freedom in expressing herself authentically.", - "D8:25" - ] - ], - "Melanie": [ - [ - "Melanie took her kids to a pottery workshop last Friday where they made their own pots.", - "D8:2" - ], - [ - "Melanie and her kids enjoy nature-inspired painting projects.", - "D8:6" - ], - [ - "Melanie's favorite part of her wedding was marrying her partner and promising to be together forever.", - "D8:16" - ], - [ - "Melanie finds joy in flowers which represent growth, beauty, and appreciating small moments.", - "D8:12" - ], - [ - "Melanie shared that family and creativity keep her at peace.", - "D8:28" - ], - [ - "Melanie's family has been supportive and loving during tough times and helped her through.", - "D8:32" - ] - ] - }, - "session_9_observation": { - "Melanie": [ - [ - "Melanie went camping with her family two weekends ago.", - "D9:1" - ], - [ - "Melanie enjoys unplugging and hanging out with her kids.", - "D9:1" - ], - [ - "Melanie and her kids finished a painting before the conversation.", - "D9:17" - ] - ], - "Caroline": [ - [ - "Caroline joined a mentorship program for LGBTQ youth over the weekend.", - "D9:2" - ], - [ - "Caroline mentors a transgender teen and they work on building confidence and positive strategies.", - "D9:6" - ], - [ - "Caroline and her mentee had a great time at the LGBT pride event the previous month.", - "D9:6" - ], - [ - "Caroline is planning an LGBTQ art show with her paintings for next month.", - "D9:12" - ], - [ - "Caroline painted a piece inspired by a visit to an LGBTQ center, aiming to capture unity and strength.", - "D9:16" - ] - ] - }, - "session_10_observation": { - "Caroline": [ - [ - "Caroline joined a new LGBTQ activist group called 'Connected LGBTQ Activists' last Tuesday.", - "D10:3" - ], - [ - "Caroline and her LGBTQ activist group plan events and campaigns to support each other and positive changes.", - "D10:5" - ], - [ - "Caroline and her activist group participated in a pride parade last weekend to celebrate love and diversity.", - "D10:7" - ] - ], - "Melanie": [ - [ - "Melanie enjoys family beach trips with her kids once or twice a year.", - "D10:8" - ], - [ - "Melanie's family tradition includes a camping trip where they roast marshmallows and tell stories around the campfire.", - "D10:12" - ], - [ - "Melanie and her family watched the Perseid meteor shower during a camping trip last year and it was a memorable experience.", - "D10:14" - ], - [ - "Melanie treasures the memory of her youngest child taking her first steps.", - "D10:20" - ] - ] - }, - "session_11_observation": { - "Melanie": [ - [ - "Melanie celebrated her daughter's birthday with a concert featuring Matt Patterson.", - "D11:1" - ], - [ - "Melanie values special moments with her kids and is grateful for them.", - "D11:1" - ], - [ - "Melanie appreciates cultivating a loving and accepting environment for her kids.", - "D11:7" - ], - [ - "Melanie values inclusivity in her interactions and work as an artist.", - "D11:7" - ], - [ - "Melanie admires Caroline's art and appreciates the themes of self-acceptance and love.", - "D11:13" - ] - ], - "Caroline": [ - [ - "Caroline attended a pride parade recently and felt inspired by the community's energy and support for LGBTQ rights.", - "D11:4" - ], - [ - "Caroline represents inclusivity and diversity in her art and uses it to advocate for the LGBTQ+ community.", - "D11:8" - ], - [ - "Caroline's art focuses on expressing her trans experience and educating others about the trans community.", - "D11:10" - ], - [ - "Caroline's painting 'Embracing Identity' symbolizes self-acceptance, love, and the journey to being oneself.", - "D11:12" - ], - [ - "Caroline finds art to be healing and a way to connect with her self-discovery and acceptance journey.", - "D11:14" - ], - [ - "Caroline values sharing her art and experiences with others, such as Melanie.", - "D11:16" - ] - ] - }, - "session_12_observation": { - "Caroline": [ - [ - "Caroline had a not-so-great experience on a hike where she ran into a group of religious conservatives who upset her.", - "D12:1" - ], - [ - "Caroline values having people around her who accept and support her.", - "D12:1" - ], - [ - "Caroline expresses that surrounding herself with things that bring joy is important because life is too short.", - "D12:9" - ], - [ - "Caroline values happy moments and believes they are essential to keep going, especially during tough times.", - "D12:11" - ], - [ - "Caroline expresses appreciation for her friendship with Melanie.", - "D12:13" - ], - [ - "Caroline had a great time with the whole gang at the Pride fest last year and values supportive friends.", - "D12:15" - ] - ], - "Melanie": [ - [ - "Melanie finished another pottery project and expresses pride in her work.", - "D12:2" - ], - [ - "Melanie's pottery project was a source of happiness and fulfillment for her.", - "D12:8" - ], - [ - "Melanie has a strong connection to art, considering it both a sanctuary and a source of comfort.", - "D12:8" - ], - [ - "Melanie values friendship with Caroline and expresses appreciation for it.", - "D12:14" - ], - [ - "Melanie suggests doing a family outing or planning something special for the summer with Caroline to make awesome memories.", - "D12:16" - ] - ] - }, - "session_13_observation": { - "Caroline": [ - [ - "Caroline took the first step towards becoming a mom by applying to adoption agencies.", - "D13:1" - ], - [ - "Caroline attended an adoption advice/assistance group to help with her decision.", - "D13:1" - ], - [ - "Caroline has a guinea pig named Oscar.", - "D13:3" - ], - [ - "Caroline used to go horseback riding with her dad when she was a kid.", - "D13:7" - ], - [ - "Caroline loves horses and has a love for them.", - "D13:7" - ], - [ - "Caroline expresses herself through painting and values art for exploring identity and being therapeutic.", - "D13:13" - ], - [ - "Caroline values supportive people, promotes LGBTQ rights, and aims to live authentically.", - "D13:15" - ] - ], - "Melanie": [ - [ - "Melanie has pets including another cat named Bailey.", - "D13:4" - ], - [ - "Melanie shared a photo of her horse painting that she recently did.", - "D13:8" - ], - [ - "Melanie enjoys painting animals and finds it peaceful and special.", - "D13:10" - ], - [ - "Melanie expresses herself through painting and values art for showing who we really are and getting in touch with ourselves.", - "D13:14" - ] - ] - }, - "session_14_observation": { - "Caroline": [ - [ - "Caroline went hiking last week and got into a bad spot with some people but tried to apologize.", - "D14:1" - ], - [ - "Caroline painted a vivid sunset inspired by a beach visit.", - "D14:7" - ], - [ - "Caroline transitioned and joined the transgender community seeking acceptance and support.", - "D14:13" - ], - [ - "Caroline created a rainbow flag mural symbolizing courage and strength of the trans community.", - "D14:15" - ], - [ - "Caroline made a stained glass window showcasing personal journey as a transgender woman and the acceptance of growth and change.", - "D14:19" - ], - [ - "Caroline found a vibrant rainbow sidewalk during Pride Month, which reminded her of love and acceptance.", - "D14:23" - ], - [ - "Caroline is organizing an LGBTQ art show next month to showcase paintings and talents of LGBTQ artists aimed at spreading understanding and acceptance.", - "D14:33" - ] - ], - "Melanie": [ - [ - "Melanie made a plate in pottery class and finds pottery relaxing and creative.", - "D14:4" - ], - [ - "Melanie loves painting landscapes and still life.", - "D14:30" - ], - [ - "Melanie volunteered with her family at a homeless shelter to make a difference.", - "D14:10" - ], - [ - "Melanie appreciates and admires Caroline's courage as a trans person.", - "D14:16" - ], - [ - "Melanie created a painting inspired by autumn.", - "D14:32" - ] - ] - }, - "session_15_observation": { - "Caroline": [ - [ - "Caroline had the opportunity to volunteer at an LGBTQ+ youth center and found it gratifying to support and guide the young people there.", - "D15:3" - ], - [ - "Caroline shared her story with the young people at the LGBTQ+ youth center and felt fulfilled by the experience.", - "D15:5" - ], - [ - "Caroline plans to continue volunteering at the youth center as she believes in community and supporting others.", - "D15:9" - ], - [ - "Caroline is involved in organizing a talent show for the kids at the youth center.", - "D15:11" - ], - [ - "Caroline mentioned that playing the guitar helps her express her emotions.", - "D15:19" - ], - [ - "Caroline started playing acoustic guitar about five years ago as a way to express herself and escape in her emotions.", - "D15:21" - ], - [ - "Caroline finds the song \"Brave\" by Sara Bareilles significant and inspiring as it resonates with her journey and determination to make a difference.", - "D15:23" - ] - ], - "Melanie": [ - [ - "Melanie took her kids to a park and enjoyed seeing them have fun exploring and playing.", - "D15:2" - ], - [ - "Melanie plays the clarinet as a way to express herself and relax.", - "D15:26" - ], - [ - "Melanie enjoys classical music like Bach and Mozart, as well as modern music like Ed Sheeran's \"Perfect\".", - "D15:28" - ] - ] - }, - "session_16_observation": { - "Caroline": [ - [ - "Caroline spends time with friends biking and exploring nature.", - "D16:1" - ], - [ - "Caroline is very focused on making a difference for the LGBTQ+ community through her work.", - "D16:5" - ], - [ - "Caroline has been creating art since the age of 17.", - "D16:7" - ], - [ - "Caroline uses art to express her feelings and explore her gender identity.", - "D16:9" - ], - [ - "Caroline made a painting representing her journey as a trans woman.", - "D16:13" - ], - [ - "Caroline's relationships have changed due to her journey, some friends were not able to handle the changes.", - "D16:15" - ] - ], - "Melanie": [ - [ - "Melanie enjoys camping with her kids, exploring the forest, and hiking.", - "D16:2" - ], - [ - "Melanie finds inspiration in seeing her kids excited about learning new things about nature.", - "D16:4" - ], - [ - "Melanie has been into art for seven years, finding a passion for painting and pottery.", - "D16:8" - ], - [ - "Melanie uses painting and pottery as a calming and satisfying creative outlet.", - "D16:8" - ] - ] - }, - "session_17_observation": { - "Caroline": [ - [ - "Caroline is looking into adoption and contacted her mentor for advice.", - "D17:1" - ], - [ - "Caroline sees adoption as a way to share her love and provide a safe, loving home for kids in need.", - "D17:3" - ], - [ - "Caroline recommends doing research, preparing emotionally, and gathering necessary documents when starting the adoption process.", - "D17:7" - ], - [ - "Caroline recently went to a transgender poetry reading event that was empowering and celebrated self-expression.", - "D17:19" - ], - [ - "Caroline is inspired by freedom and being true to oneself.", - "D17:23" - ] - ], - "Melanie": [ - [ - "Melanie had a setback due to an injury that led her to take a break from pottery, which she uses for self-expression and peace.", - "D17:8" - ], - [ - "Melanie continued expressing herself through reading and painting during her break from pottery.", - "D17:10" - ], - [ - "Melanie enjoys expressing emotions through art, like painting inspired by sunsets and abstract art.", - "D17:13" - ], - [ - "Melanie finds blue a calming color and uses it to convey tranquility in her art.", - "D17:16" - ] - ] - }, - "session_18_observation": { - "Melanie": [ - [ - "Melanie went on a road trip with her family which started off with an accident involving her son.", - "D18:1" - ], - [ - "Melanie's son got into an accident during the road trip.", - "D18:1" - ], - [ - "Melanie's family visited the Grand Canyon and enjoyed it.", - "D18:5" - ], - [ - "Melanie finds peace and serenity in nature, particularly during camping trips with her family.", - "D18:19" - ], - [ - "Melanie believes that being in nature refreshes her soul and helps her reset and recharge.", - "D18:21" - ] - ], - "Caroline": [ - [ - "Caroline acknowledged the traumatic experience of Melanie's family being in an accident during the road trip.", - "D18:2" - ], - [ - "Caroline believes that loved ones give strength to tackle challenges.", - "D18:10" - ], - [ - "Caroline appreciates seeing Melanie's love for her family and acknowledges that they are her rock.", - "D18:12" - ], - [ - "Caroline finds nature refreshing and discussed how it can bring peace.", - "D18:18" - ], - [ - "Caroline appreciates the peaceful and calming nature of spending quality time with family in nature.", - "D18:22" - ] - ] - }, - "session_19_observation": { - "Caroline": [ - [ - "Caroline passed the adoption agency interviews last Friday and is excited about building her own family through adoption.", - "D19:1" - ], - [ - "Caroline's vision for the future includes creating a safe and loving home for needy kids to experience love and acceptance.", - "D19:3" - ], - [ - "Caroline finds empowerment in making a positive difference in someone's life by offering love and support.", - "D19:7" - ], - [ - "Caroline went through a tough process of finding self-acceptance but is now ready to help others who need support.", - "D19:7" - ], - [ - "Caroline received invaluable help from friends, family, and role models during the process of finding acceptance.", - "D19:9" - ], - [ - "Caroline's journey of self-discovery has been amazing and she finds joy in bringing comfort and support to others.", - "D19:9" - ] - ], - "Melanie": [ - [ - "Melanie bought figurines that remind her of family love.", - "D19:2" - ], - [ - "Melanie appreciates Caroline's passion for helping kids and finds her inspiring.", - "D19:6" - ], - [ - "Melanie respects Caroline's journey of finding acceptance and admires her strength and inspiration to help others.", - "D19:8" - ], - [ - "Melanie is supportive and expresses happiness for Caroline finding her true self and helping others.", - "D19:10" - ], - [ - "Melanie values the mutual support they provide to each other and appreciates the encouragement of close ones.", - "D19:13" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Caroline and Melanie had a conversation on 8 May 2023 at 1:56 pm. Caroline mentioned that she attended an LGBTQ support group and was inspired by the transgender stories she heard. The support group made her feel accepted and gave her the courage to embrace herself. Caroline plans to continue her education and explore career options, particularly in counseling or working in mental health. Melanie praised Caroline's empathy and mentioned that she painted a lake sunrise last year as a way of expressing herself. Caroline complimented Melanie's painting and agreed that painting is a great outlet for relaxation and self-expression. They both emphasized the importance of taking care of oneself. Caroline was going to do some research, while Melanie planned to go swimming with her kids.", - "session_2_summary": "On May 25, 2023 at 1:14 pm, Melanie tells Caroline about her recent experience running a charity race for mental health. Caroline expresses pride and agrees that taking care of oneself is important. Melanie shares her struggle with self-care but mentions that she is carving out time each day for activities that refresh her. Caroline encourages Melanie and praises her efforts. Melanie then asks Caroline about her plans for the summer, to which Caroline replies that she is researching adoption agencies as she wants to give a loving home to children in need. Melanie praises Caroline's decision and expresses excitement for her future family. Caroline explains that she chose an adoption agency that supports the LGBTQ+ community because of its inclusivity and support. Melanie commends Caroline's choice and asks what she is looking forward to in the adoption process. Caroline says she is thrilled to create a family for kids who need one, despite the challenges of being a single parent. Melanie encourages Caroline and expresses confidence in her ability to provide a safe and loving home. The conversation ends with Melanie expressing her excitement for Caroline's new chapter.", - "session_3_summary": "Caroline and Melanie had a conversation at 7:55 pm on 9 June, 2023. Caroline shared about her school event last week where she talked about her transgender journey and encouraged students to get involved in the LGBTQ community. Melanie praised Caroline for spreading awareness and inspiring others with her strength and courage. They discussed the importance of conversations about gender identity and inclusion. Caroline expressed gratitude for the support she has received and the opportunity to give a voice to the trans community. Melanie commended Caroline for using her voice to create love, acceptance, and hope. They talked about the power of sharing personal stories and the impact it can have on others. They both expressed a desire to make a positive difference and support each other. Melanie mentioned that her family motivates her, while Caroline mentioned that her friends, family, and mentors are her support system. They shared photos of their loved ones and talked about the length of their relationships. Melanie mentioned being married for 5 years and Caroline expressed congratulations and well-wishes. They discussed the importance of cherishing family moments and finding happiness in them. They both agreed that family is everything.", - "session_4_summary": "Caroline and Melanie catch up after a long time. Caroline shows Melanie her special necklace, which was a gift from her grandmother in Sweden and represents love, faith, and strength. Melanie admires it and asks if Caroline has any other treasured items. Caroline mentions a hand-painted bowl made by a friend on her 18th birthday, which reminds her of art and self-expression. Melanie shares that she recently went camping with her family and had a great time exploring nature and bonding with her kids. They discuss the importance of family moments. Caroline reveals that she is looking into a career in counseling and mental health, specifically wanting to work with trans people. She attended an LGBTQ+ counseling workshop and found it enlightening. Melanie praises Caroline for her dedication and asks about her motivation to pursue counseling. Caroline shares how her own journey and the support she received inspired her to help others. Melanie commends Caroline's hard work and passion. Caroline expresses gratitude for Melanie's kind words, and Melanie congratulates Caroline for pursuing what she cares about.", - "session_5_summary": "Caroline had recently attended an LGBTQ+ pride parade and felt a sense of belonging and community. This experience inspired her to use her own story to help others, possibly through counseling or mental health work. Melanie, in turn, shared that she had recently signed up for a pottery class as a way to express herself and find calmness. The two discussed their creative endeavors, with Melanie showing Caroline a bowl she had made in her class. Caroline praised Melanie's work and expressed her excitement for the upcoming transgender conference she would be attending. Melanie wished her a great time at the conference and encouraged her to have fun and stay safe.", - "session_6_summary": "Caroline and Melanie caught up with each other at 8:18 pm on 6 July, 2023. Caroline shared that since their last chat, she has been exploring counseling or mental health work because she is passionate about helping people. Melanie praised Caroline for following her dreams. Melanie mentioned that she recently took her kids to the museum and enjoyed watching their excitement. Caroline was curious about what had them so excited, and Melanie explained that they loved the dinosaur exhibit. Caroline mentioned that she is creating a library for future kids and looks forward to reading to them. Melanie asked about the books she has in her library, and Caroline mentioned classics, stories from different cultures, and educational books. Melanie shared that her favorite book from childhood was \"Charlotte's Web,\" and Caroline agreed that it showed the importance of friendship and compassion. Caroline mentioned that her friends and family have been a great support system during her transition. Melanie praised Caroline for having people who support her and shared a photo of her family camping at the beach.", - "session_7_summary": "Caroline and Melanie had a conversation at 4:33 pm on 12 July, 2023. Caroline talked about attending an LGBTQ conference recently, where she felt accepted and connected with others who have similar experiences. She expressed her gratitude for the LGBTQ community and her desire to fight for trans rights. Melanie praised Caroline for her drive to make a difference and asked about her plan to contribute. Caroline mentioned that she is looking into counseling and mental health jobs, as she wants to provide support for others. Melanie commended Caroline for her inspiring goal and mentioned a book she read that reminds her to pursue her dreams. They discussed the power of books, and Caroline recommended \"Becoming Nicole\" by Amy Ellis Nutt, which had a positive impact on her own life. She mentioned that the book taught her about self-acceptance and finding support. Melanie agreed and added that pets also bring joy and comfort. They talked about their own pets and shared pictures. Melanie mentioned that she has been running more to destress and clear her mind. Caroline encouraged her to keep it up and take care of her mental health. Melanie expressed her gratitude for the improvements in her mental health.", - "session_8_summary": "Caroline and Melanie spoke at 1:51 pm on 15 July, 2023. Melanie mentioned that she took her kids to a pottery workshop and they all made their own pots. Caroline commented on how cute the cup that the kids made was and how she loved seeing kids express their personalities through art. Melanie also mentioned that she and the kids enjoy painting together, particularly nature-inspired paintings. Caroline admired their latest painting and Melanie mentioned that they found lovely flowers to paint. Caroline then shared that she attended a council meeting for adoption, which inspired her to adopt in the future. Melanie complimented a photo of a blue vase that Caroline shared and they discussed the meanings of flowers. Melanie mentioned that flowers remind her of her wedding and Caroline expressed regret for not knowing Melanie back then. Melanie said that her wedding day was full of love and joy and her favorite part was marrying her partner. Caroline then shared a special memory of attending a pride parade and how accepting and happy she felt. They discussed the importance of a supportive community. Caroline mentioned that the best part was realizing she could be herself without fear and having the courage to transition. Melanie expressed admiration for Caroline's courage and the importance of finding peace. Melanie mentioned that her family has been supportive during her move. Caroline commented on", - "session_9_summary": "Caroline has joined a mentorship program for LGBTQ youth, which she finds rewarding. She has been supporting a transgender teen and they had a great time at an LGBT pride event. Caroline is also preparing for an LGBTQ art show next month. Melanie thinks Caroline's painting for the art show is awesome and asks what inspired her. Caroline explains that she painted it after visiting an LGBTQ center and wanted to capture unity and strength. Meanwhile, Melanie and her kids have finished another painting.", - "session_10_summary": "Caroline and Melanie had a conversation at 8:56 pm on 20 July, 2023. Caroline told Melanie that she recently joined a new LGBTQ activist group and is enjoying making a difference. Melanie expressed her happiness for Caroline and wanted to know more about the group. Caroline explained that the group, \"Connected LGBTQ Activists,\" is focused on positive changes and supporting each other. Melanie praised the group and asked if Caroline has participated in any events or campaigns. Caroline mentioned a recent pride parade in their city and how it was a powerful reminder of the fight for equality. Melanie shared that she recently went to the beach with her kids, which they thoroughly enjoyed. Caroline inquired about other summer traditions, and Melanie mentioned their family camping trip as the highlight of their summer. She recalled witnessing the Perseid meteor shower and how it made her feel in awe of the universe. Caroline asked about the experience, and Melanie described it as breathtaking, making her appreciate life. Melanie then shared another special memory of her youngest child taking her first steps. Caroline found it sweet and mentioned that such milestones remind us of the special bonds we have. Melanie agreed and expressed gratitude for her family. Caroline praised Melanie for having an awesome family. Melanie thanked Caroline and expressed her happiness for having a", - "session_11_summary": "On August 14, 2023, at 2:24 pm, Melanie and Caroline had a conversation. Melanie shared that she had a great time at a concert celebrating her daughter's birthday, while Caroline attended an advocacy event that focused on love and support. Melanie asked Caroline about her experience at the pride parade, to which Caroline responded by expressing her pride in being part of the LGBTQ community and fighting for equality. Melanie then shared a picture from the concert and discussed the importance of creating a loving and inclusive environment for their kids. Caroline mentioned that she incorporates inclusivity and diversity in her artwork and uses it to advocate for acceptance of the LGBTQ+ community. Melanie praised Caroline's art and asked about its main message, to which Caroline replied that her art is about expressing her trans experience and helping people understand the trans community. Melanie requested to see another painting, and Caroline shared one called \"Embracing Identity,\" which represents self-acceptance and love. Caroline explained that art has helped her in her own self-discovery and acceptance journey. Melanie acknowledged the healing power of art and thanked Caroline for sharing her work. They ended the conversation by inviting each other to reach out anytime.", - "session_12_summary": "Caroline tells Melanie about a negative experience she had with religious conservatives while hiking, which reminds her of the work still needed for LGBTQ rights. She expresses gratitude for the support and acceptance she has from those around her. Melanie sympathizes with Caroline and shows her a picture of a pottery project she recently finished. Caroline expresses interest in seeing the picture and compliments Melanie's work. Melanie explains that the colors and patterns were inspired by her love for them and how painting helps her express her feelings. Caroline praises Melanie's creativity and passion. Melanie expresses her deep connection to art and how it brings her happiness and fulfillment. Caroline agrees that surrounding oneself with things that bring joy is important. They both agree that finding happiness is key in life. They express appreciation for each other's friendship and support. They reminisce about a fun time at a Pride fest and discuss plans for a family outing or a special trip just for the two of them. They agree to plan something special and look forward to making more memories together.", - "session_13_summary": "Caroline shared with Melanie that she applied to adoption agencies and received help from an adoption assistance group. Melanie congratulated Caroline and asked about her pets. Caroline mentioned her guinea pig named Oscar. Melanie shared that she had another cat named Bailey and showed Caroline a picture of her cat Oliver. Caroline shared a picture of Oscar eating parsley. Melanie mentioned that Oliver hid his bone in her slipper. Caroline reminisced about horseback riding with her dad and shared that she loves horses. Melanie showed Caroline a horse painting she did. Caroline shared a self-portrait she recently made, mentioning how painting helps her explore her identity. Melanie agreed and asked what else helps her. Caroline mentioned having supportive people and promoting LGBTQ rights. Melanie commended Caroline for her care and wished her the best on her adoption journey. They said goodbye and Melanie offered her support.", - "session_14_summary": "Caroline tells Melanie that she went hiking last week and got into a bad situation with some people. She tried to apologize to them. Melanie is supportive and says that it takes a lot of courage and maturity to apologize. Melanie shows Caroline a pottery plate she made and Caroline compliments her on it. Melanie says that pottery is relaxing and creative. Caroline says that she has been busy painting and shows Melanie a painting of a sunset that she recently finished. Melanie compliments the painting and Caroline explains that she was inspired by a visit to the beach. Melanie says that she can feel the serenity of the beach in the painting. They discuss how art can connect people and Melanie mentions a volunteering experience at a homeless shelter. Caroline praises Melanie for her volunteering efforts. Melanie asks Caroline about her decision to transition and join the transgender community. Caroline explains that finding a supportive community has meant a lot to her and shows Melanie a mural that she created, explaining its symbolism. Melanie praises Caroline's courage as a trans person. Melanie asks Caroline if she has made any more art and Caroline shows her a stained glass window that she made for a local church. They discuss the inspiration behind the window and Melanie compliments Caroline on her artistry. Caroline shows Melanie a picture of a rainbow sidewalk that she found in her neighborhood", - "session_15_summary": "Caroline and Melanie had a conversation at 3:19 pm on 28 August, 2023. Melanie had taken her kids to a park and enjoyed seeing them have fun outdoors. Caroline had been volunteering at an LGBTQ+ youth center and found it gratifying and fulfilling to support and guide the young people there. Melanie asked Caroline about her experience at the youth center and Caroline shared that connecting with the young folks and sharing her story had been meaningful and made her feel like she could make a difference. Caroline expressed her dedication to continuing volunteering at the youth center and mentioned that they were planning a talent show for the kids. Melanie expressed her excitement and support for Caroline's dedication to helping others. They also briefly discussed a band Melanie saw and Caroline spoke about her love for music and playing the guitar. They shared their favorite songs and agreed on the power of music to inspire and uplift.", - "session_16_summary": "Caroline and Melanie were chatting at 12:09 am on 13 September, 2023. Caroline told Melanie about her biking trip with friends and sent her a stunning picture. Melanie complimented the picture and shared her own experience of camping with her kids. They both agreed that being in nature was refreshing for the soul. Melanie asked Caroline about her upcoming plans. Caroline expressed how excited she was about her work volunteering for the LGBTQ+ community and how it inspired her to create art. Melanie admired Caroline's art and shared her own love for painting and pottery. They talked about the therapeutic aspect of art and how it helped them express their feelings. Caroline showed Melanie a painting she made about her journey as a trans woman. Melanie was impressed and proud of Caroline's progress. Caroline spoke about the changes in her relationships and how she was happier being around accepting and loving people. Melanie shared a picture from a caf\u00e9 they visited and assured Caroline that everything was fine despite the serious sign. They ended their conversation by celebrating the joyful moments in life.", - "session_17_summary": "Caroline reached out to her friend Melanie to share her excitement about her decision to adopt and become a mother. Melanie mentioned that she knew someone who had successfully adopted. Caroline gave Melanie some advice on how to get started with the adoption process, emphasizing the importance of research and emotional preparation. Melanie mentioned that she had recently experienced a setback due to an injury, but had found solace in reading and painting. Caroline showed interest in Melanie's paintings, and shared her own recent venture into abstract art. They discussed the emotions behind their artwork and the therapeutic nature of self-expression. Caroline also mentioned attending a poetry reading that celebrated transgender identities, which inspired her to create her own art. Melanie praised Caroline's artwork and they affirmed the importance of staying true to oneself and embracing personal growth and exploration.", - "session_18_summary": "Melanie and Caroline are discussing a recent road trip on October 20, 2023. Melanie mentions that her son got into an accident, but fortunately, he is okay. She reflects on the importance of cherishing family and how they enjoyed their time at the Grand Canyon. Caroline acknowledges the resilience of children and the support that loved ones provide during tough times. Melanie expresses her gratitude for her family, who are her motivation and support. They also discuss the benefits of spending time in nature and how it helps them reset and recharge. Melanie shares that camping with her family brings peace and serenity and allows them to bond. Caroline compliments Melanie on the quality time she spends with her family and remarks on the priceless nature of these experiences.", - "session_19_summary": "Caroline tells Melanie that she passed the adoption agency interviews last Friday and is excited about the progress she's making towards her goal of having a family. Melanie congratulates her and shows her some figurines that remind her of family love. Caroline explains that she wants to build her own family and provide a home for children in need, as a way of giving back and showing love and acceptance. Melanie agrees that everyone deserves love and acceptance and admires Caroline's passion for helping these kids. Caroline shares that finding self-acceptance was a long process for her, but now she's ready to offer love and support to those who need it. Melanie praises Caroline for her strength and inspiration. Caroline credits her friends, family, and role models for helping her find acceptance and wants to pass that same support to others. Melanie tells Caroline that she is happy for her and finds her journey inspiring. Caroline expresses gratitude for the support she's received and considers it a gift to be able to share her journey and help others. Melanie agrees that it's important to be there for each other. Caroline emphasizes the importance of being oneself and living honestly, as it brings freedom and contentment. Both friends express their agreement and appreciation for the support they've received in being true to themselves." - }, - "sample_id": "conv-26" - }, - { - "qa": [ - { - "question": "When Jon has lost his job as a banker?", - "answer": "19 January, 2023", - "evidence": [ - "D1:2" - ], - "category": 2 - }, - { - "question": "When Gina has lost her job at Door Dash?", - "answer": "January, 2023", - "evidence": [ - "D1:3" - ], - "category": 2 - }, - { - "question": "How do Jon and Gina both like to destress?", - "answer": "by dancing", - "evidence": [ - "D1:7", - "D1:6" - ], - "category": 4 - }, - { - "question": "What do Jon and Gina both have in common?", - "answer": "They lost their jobs and decided to start their own businesses.", - "evidence": [ - "D1:2", - "D1:3", - "D1:4", - "D2:1" - ], - "category": 1 - }, - { - "question": "Why did Jon decide to start his dance studio?", - "answer": "He lost his job and decided to start his own business to share his passion.", - "evidence": [ - "D1:2", - "D1:4" - ], - "category": 4 - }, - { - "question": "What Jon thinks the ideal dance studio should look like?", - "answer": "By the water, with natural light and Marley flooring", - "evidence": [ - "D1:20", - "D2:4", - "D2:8" - ], - "category": 1 - }, - { - "question": "When is Jon's group performing at a festival?", - "answer": "February, 2023", - "evidence": [ - "D1:24" - ], - "category": 2 - }, - { - "question": "When did Gina launch an ad campaign for her store?", - "answer": "29 January, 2023", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "When was Jon in Paris?", - "answer": "28 January 2023", - "evidence": [ - "D2:4" - ], - "category": 2 - }, - { - "question": "Which city have both Jean and John visited?", - "answer": "Rome", - "evidence": [ - "D2:5", - "D15:1" - ], - "category": 1 - }, - { - "question": "When did Gina team up with a local artist for some cool designs?", - "answer": "February, 2023", - "evidence": [ - "D5:5" - ], - "category": 2 - }, - { - "question": "When did Gina get her tattoo?", - "answer": "A few years ago", - "evidence": [ - "D5:15" - ], - "category": 2 - }, - { - "question": "When did Jon start to go to the gym?", - "answer": "March, 2023", - "evidence": [ - "D6:1" - ], - "category": 2 - }, - { - "question": "When did Gina open her online clothing store?", - "answer": "16 March, 2023", - "evidence": [ - "D6:6" - ], - "category": 2 - }, - { - "question": "When did Jon start expanding his studio's social media presence?", - "answer": "April, 2023", - "evidence": [ - "D8:13" - ], - "category": 2 - }, - { - "question": "When did Jon host a dance competition?", - "answer": "May, 2023", - "evidence": [ - "D8:13" - ], - "category": 2 - }, - { - "question": "When did Jon go to a fair to get more exposure for his dance studio?", - "answer": "24 April, 2023", - "evidence": [ - "D10:1" - ], - "category": 2 - }, - { - "question": "Why did Gina decide to start her own clothing store?", - "answer": "She always loved fashion trends and finding unique pieces and she lost her job so decided it was time to start her own business.", - "evidence": [ - "D6:8", - "D1:3" - ], - "category": 1 - }, - { - "question": "Do Jon and Gina start businesses out of what they love?", - "answer": "Yes", - "evidence": [ - "D1:4", - "D6:8" - ], - "category": 1 - }, - { - "question": "When did Gina interview for a design internship?", - "answer": "10 May, 2023", - "evidence": [ - "D11:14" - ], - "category": 2 - }, - { - "question": "When did Gina get accepted for the design internship?", - "answer": "27 May, 2023", - "evidence": [ - "D12:1" - ], - "category": 2 - }, - { - "question": "When did Jon start reading \"The Lean Startup\"?", - "answer": "May, 2023", - "evidence": [ - "D12:6" - ], - "category": 2 - }, - { - "question": "When did Gina develop a video presentation to teach how to style her fashion pieces? ", - "answer": "June, 2023", - "evidence": [ - "D13:4" - ], - "category": 2 - }, - { - "question": "How did Gina promote her clothes store?", - "answer": "worked with an artist to make unique fashion pieces, made limited-edition sweatshirts, got some new offers and promotions for online store, developed a video presentation showing how to style her pieces", - "evidence": [ - "D5:5", - "D16:3", - "D8:4", - "D13:4" - ], - "category": 1 - }, - { - "question": "Which events has Jon participated in to promote his business venture?", - "answer": "fair, networking events, dance competition", - "evidence": [ - "D10:1", - "D16:6", - "D8:4" - ], - "category": 1 - }, - { - "question": "What does Jon's dance studio offer?", - "answer": "one-on-one metoring and training to dancers, workshops and classes to local schools and centers", - "evidence": [ - "D13:7", - "D8:13" - ], - "category": 1 - }, - { - "question": "When did Jon receive mentorship to promote his venture?", - "answer": "15 June, 2023", - "evidence": [ - "D14:1" - ], - "category": 2 - }, - { - "question": "Did Jon and Gina both participate in dance competitions?", - "answer": "Yes", - "evidence": [ - "D1:14", - "D14:14", - "D1:16", - "D1:17", - "D9:10" - ], - "category": 1 - }, - { - "question": "When was Jon in Rome?", - "answer": "June 2023", - "evidence": [ - "D15:1" - ], - "category": 2 - }, - { - "question": "Which cities has Jon visited?", - "answer": "Paris, Rome", - "evidence": [ - "D2:4", - "D15:1" - ], - "category": 1 - }, - { - "question": "When Jon is planning to open his dance studio?", - "answer": "20 June, 2023", - "evidence": [ - "D15:5" - ], - "category": 2 - }, - { - "question": "How long did it take for Jon to open his studio?", - "answer": "six months", - "evidence": [ - "D1:2", - "D15:13" - ], - "category": 1 - }, - { - "question": "When did Gina design a limited collection of hoodies?", - "answer": "June 2023", - "evidence": [ - "D16:3" - ], - "category": 2 - }, - { - "question": "When did Jon visit networking events for his store?", - "answer": "20 June, 2023", - "evidence": [ - "D16:6" - ], - "category": 2 - }, - { - "question": "When did Gina start being recognized by fashion editors?", - "answer": "July 2023", - "evidence": [ - "D17:1" - ], - "category": 2 - }, - { - "question": "When did Jon start learning marketing and analytics tools?", - "answer": "July, 2023", - "evidence": [ - "D17:4" - ], - "category": 2 - }, - { - "question": "When did Jon and Gina decide to collaborate to create dance content?", - "answer": "21 July 2023", - "evidence": [ - "D18:18" - ], - "category": 2 - }, - { - "question": "When did Gina mention Shia Labeouf?", - "answer": " 23 July, 2023", - "evidence": [ - "D19:4" - ], - "category": 2 - }, - { - "question": "When did Gina go to a dance class with a group of friends?", - "answer": "21 July 2023", - "evidence": [ - "D19:6" - ], - "category": 2 - }, - { - "question": "What is Gina's favorite style of dance?", - "answer": "Contemporary", - "evidence": [ - "D1:9" - ], - "category": 4 - }, - { - "question": "What is Jon's favorite style of dance?", - "answer": "Contemporary", - "evidence": [ - "D1:8" - ], - "category": 4 - }, - { - "question": "What was Gina's favorite dancing memory?", - "answer": "Winning first place at a regionals dance competition", - "evidence": [ - "D1:17" - ], - "category": 4 - }, - { - "question": "What kind of dance piece did Gina's team perform to win first place?", - "answer": "\"Finding Freedom\"", - "evidence": [ - "D1:19" - ], - "category": 4 - }, - { - "question": "What do the dancers in the photo represent?", - "answer": "They are performing at the festival", - "evidence": [ - "D1:25" - ], - "category": 4 - }, - { - "question": "What does Gina say about the dancers in the photo?", - "answer": "They look graceful", - "evidence": [ - "D1:26" - ], - "category": 4 - }, - { - "question": "What is Jon's attitude towards being part of the dance festival?", - "answer": "Glad", - "evidence": [ - "D1:28" - ], - "category": 4 - }, - { - "question": "What kind of flooring is Jon looking for in his dance studio?", - "answer": "Marley flooring", - "evidence": [ - "D2:8" - ], - "category": 4 - }, - { - "question": "What did Gina find for her clothing store on 1 February, 2023?", - "answer": "The perfect spot for her store", - "evidence": [ - "D3:2" - ], - "category": 4 - }, - { - "question": "What did Gina design for her store?", - "answer": "the space, furniture, and decor", - "evidence": [ - "D3:4" - ], - "category": 4 - }, - { - "question": "What did Gina want her customers to feel in her store?", - "answer": "cozy and comfortable", - "evidence": [ - "D3:6", - "D3:8" - ], - "category": 4 - }, - { - "question": "What did Jon say about Gina's progress with her store?", - "answer": "hard work's paying off", - "evidence": [ - "D3:3" - ], - "category": 4 - }, - { - "question": "What made Gina choose the furniture and decor for her store?", - "answer": "personal style and customer comfort", - "evidence": [ - "D3:6" - ], - "category": 4 - }, - { - "question": "What did Jon say about creating a special experience for customers?", - "answer": "It's the key to making them feel welcome and coming back", - "evidence": [ - "D3:9" - ], - "category": 4 - }, - { - "question": "What did Gina say about creating an experience for her customers?", - "answer": "making them want to come back", - "evidence": [ - "D3:8" - ], - "category": 4 - }, - { - "question": "How is Gina's store doing?", - "answer": "The store is doing great.", - "evidence": [ - "D4:2" - ], - "category": 4 - }, - { - "question": "What does Gina's tattoo symbolize?", - "answer": "Freedom and expressing herself through dance", - "evidence": [ - "D5:15" - ], - "category": 4 - }, - { - "question": "What did Jon and Gina compare their entrepreneurial journeys to?", - "answer": "dancing together and supporting each other", - "evidence": [ - "D6:15", - "D6:16" - ], - "category": 4 - }, - { - "question": "What advice does Gina give to Jon about running a successful business?", - "answer": "build relationships with customers, create a strong brand image, stay positive", - "evidence": [ - "D7:5" - ], - "category": 4 - }, - { - "question": "Why did Jon shut down his bank account?", - "answer": "for his business", - "evidence": [ - "D8:1" - ], - "category": 4 - }, - { - "question": "Why did Gina combine her clothing business with dance?", - "answer": "she is passionate about dance and fashion", - "evidence": [ - "D8:8" - ], - "category": 4 - }, - { - "question": "What does Jon's dance make him?", - "answer": "happy", - "evidence": [ - "D9:5" - ], - "category": 4 - }, - { - "question": "What did Gina receive from a dance contest?", - "answer": "a trophy", - "evidence": [ - "D9:10" - ], - "category": 4 - }, - { - "question": "How does Gina stay confident in her business?", - "answer": "By reminding herself of her successes and progress, having a support system, and focusing on why she started", - "evidence": [ - "D10:8" - ], - "category": 4 - }, - { - "question": "What kind of professional experience did Gina get accepted for on May 23, 2023?", - "answer": "fashion internship", - "evidence": [ - "D12:1" - ], - "category": 4 - }, - { - "question": "Where is Gina's fashion internship?", - "answer": "fashion department of an international company", - "evidence": [ - "D12:3" - ], - "category": 4 - }, - { - "question": "What book is Jon currently reading?", - "answer": "The Lean Startup", - "evidence": [ - "D12:6" - ], - "category": 4 - }, - { - "question": "What is Jon offering to the dancers at his dance studio?", - "answer": "One-on-one mentoring and training", - "evidence": [ - "D13:7" - ], - "category": 4 - }, - { - "question": "How does Jon use the clipboard with a notepad attached to it?", - "answer": "To set goals, track achievements, and find areas for improvement", - "evidence": [ - "D13:11" - ], - "category": 4 - }, - { - "question": "What does Jon tell Gina he won't do?", - "answer": "quit", - "evidence": [ - "D14:17" - ], - "category": 4 - }, - { - "question": "What did Jon take a trip to Rome for?", - "answer": "To clear his mind", - "evidence": [ - "D15:1" - ], - "category": 4 - }, - { - "question": "What is Jon working on opening?", - "answer": "a dance studio", - "evidence": [ - "D15:3" - ], - "category": 4 - }, - { - "question": "How does Gina describe the studio that Jon has opened?", - "answer": "amazing", - "evidence": [ - "D15:6" - ], - "category": 4 - }, - { - "question": "How does Jon feel about the opening night of his dance studio?", - "answer": "excited", - "evidence": [ - "D15:7" - ], - "category": 4 - }, - { - "question": "How does Gina describe the feeling that dance brings?", - "answer": "magical", - "evidence": [ - "D15:8" - ], - "category": 4 - }, - { - "question": "What does Jon plan to do at the grand opening of his dance studio?", - "answer": "savor all the good vibes", - "evidence": [ - "D15:9" - ], - "category": 4 - }, - { - "question": "What does Gina say to Jon about the grand opening?", - "answer": "Let's live it up and make some great memories", - "evidence": [ - "D15:12" - ], - "category": 4 - }, - { - "question": "What is the general sentiment about the upcoming grand opening?", - "answer": "excitement", - "evidence": [ - "D15:18", - "D15:19" - ], - "category": 4 - }, - { - "question": "What did Gina make a limited edition line of?", - "answer": "Hoodies", - "evidence": [ - "D16:3" - ], - "category": 4 - }, - { - "question": "According to Gina, what makes Jon a perfect mentor and guide?", - "answer": "His positivity and determination", - "evidence": [ - "D17:7" - ], - "category": 4 - }, - { - "question": "What temporary job did Jon take to cover expenses?", - "evidence": [ - "D18:2" - ], - "category": 5, - "adversarial_answer": "Not mentioned" - }, - { - "question": "What plans does Jon have after receiving advice at the networking event?", - "answer": "Sprucing up his business plan, tweaking his pitch to investors, and working on an online platform.", - "evidence": [ - "D18:10" - ], - "category": 4 - }, - { - "question": "What offer does Gina make to Jon regarding social media?", - "answer": "Helping with making content and managing his social media accounts.", - "evidence": [ - "D18:13" - ], - "category": 4 - }, - { - "question": "What is Jon's favorite style of painting?", - "evidence": [ - "D1:8" - ], - "category": 5, - "adversarial_answer": "Contemporary" - }, - { - "question": "What was Jon's favorite dancing memory?", - "evidence": [ - "D1:17" - ], - "category": 5, - "adversarial_answer": "Winning first place at a regionals dance competition" - }, - { - "question": "What kind of dance piece did Jon's team perform to win first place?", - "evidence": [ - "D1:19" - ], - "category": 5, - "adversarial_answer": "\"Finding Freedom\"" - }, - { - "question": "What is Gina's attitude towards participating in the dance festival?", - "evidence": [ - "D1:28" - ], - "category": 5, - "adversarial_answer": "Glad" - }, - { - "question": "What kind of flooring is Gina looking for in her dance studio?", - "evidence": [ - "D2:8" - ], - "category": 5, - "adversarial_answer": "Marley flooring" - }, - { - "question": "What did Jon find for his clothing store on 1 February, 2023?", - "evidence": [ - "D3:2" - ], - "category": 5, - "adversarial_answer": "The perfect spot for her store" - }, - { - "question": "What did Jon design for his store?", - "evidence": [ - "D3:4" - ], - "category": 5, - "adversarial_answer": "the space, furniture, and decor" - }, - { - "question": "What did Jon want his customers to feel in her store?", - "evidence": [ - "D3:6", - "D3:8" - ], - "category": 5, - "adversarial_answer": "cozy and comfortable" - }, - { - "question": "What made Jon choose the furniture and decor for his store?", - "evidence": [ - "D3:6" - ], - "category": 5, - "adversarial_answer": "personal style and customer comfort" - }, - { - "question": "How is Jon's store doing?", - "evidence": [ - "D4:2" - ], - "category": 5, - "adversarial_answer": "The store is doing great." - }, - { - "question": "What does Jon's tattoo symbolize?", - "evidence": [ - "D5:15" - ], - "category": 5, - "adversarial_answer": "Freedom and expressing himself through dance" - }, - { - "question": "Why did Gina shut down her bank account?", - "evidence": [ - "D8:1" - ], - "category": 5, - "adversarial_answer": "for her business" - }, - { - "question": "Why did Jon combine his clothing business with dance?", - "evidence": [ - "D8:8" - ], - "category": 5, - "adversarial_answer": "he is passionate about dance and fashion" - }, - { - "question": "What did Gina receive from a dance contest?", - "evidence": [ - "D9:10" - ], - "category": 5, - "adversarial_answer": "a trophy" - }, - { - "question": "What kind of professional experience did Jon get accepted for on May 23, 2023?", - "evidence": [ - "D12:1" - ], - "category": 5, - "adversarial_answer": "fashion internship" - }, - { - "question": "Where is Gina's HR internship?", - "evidence": [ - "D12:3" - ], - "category": 5, - "adversarial_answer": "fashion department of an international company" - }, - { - "question": "Where is Jon's fashion internship?", - "evidence": [ - "D12:3" - ], - "category": 5, - "adversarial_answer": "fashion department of an international company" - }, - { - "question": "What book is Gina currently reading?", - "evidence": [ - "D12:6" - ], - "category": 5, - "adversarial_answer": "The Lean Startup" - }, - { - "question": "How does Gina use the clipboard with a notepad attached to it?", - "evidence": [ - "D13:11" - ], - "category": 5, - "adversarial_answer": "To set goals, track achievements, and find areas for improvement" - }, - { - "question": "What did Jon take a trip to Barcelona for?", - "evidence": [ - "D15:1" - ], - "category": 5, - "adversarial_answer": "To clear his mind" - }, - { - "question": "What did Jon make a limited edition line of?", - "evidence": [ - "D16:3" - ], - "category": 5, - "adversarial_answer": "Hoodies" - }, - { - "question": "What temporary job did Gina take to cover expenses?", - "evidence": [ - "D18:2" - ], - "category": 5, - "adversarial_answer": "Not mentioned" - }, - { - "question": "What plans does Gina have after receiving advice at the networking event?", - "evidence": [ - "D18:10" - ], - "category": 5, - "adversarial_answer": "Sprucing up her business plan, tweaking her pitch to investors, and working on an online platform." - } - ], - "conversation": { - "speaker_a": "Jon", - "speaker_b": "Gina", - "session_1_date_time": "4:04 pm on 20 January, 2023", - "session_1": [ - { - "speaker": "Gina", - "dia_id": "D1:1", - "text": "Hey Jon! Good to see you. What's up? Anything new?" - }, - { - "speaker": "Jon", - "dia_id": "D1:2", - "text": "Hey Gina! Good to see you too. Lost my job as a banker yesterday, so I'm gonna take a shot at starting my own business." - }, - { - "speaker": "Gina", - "dia_id": "D1:3", - "text": "Sorry about your job Jon, but starting your own business sounds awesome! Unfortunately, I also lost my job at Door Dash this month. What business are you thinking of?" - }, - { - "speaker": "Jon", - "dia_id": "D1:4", - "text": "Sorry to hear that! I'm starting a dance studio 'cause I'm passionate about dancing and it'd be great to share it with others." - }, - { - "speaker": "Gina", - "dia_id": "D1:5", - "text": "That's cool, Jon! What got you into this biz?" - }, - { - "speaker": "Jon", - "dia_id": "D1:6", - "text": "I've been into dancing since I was a kid and it's been my passion and escape. I wanna start a dance studio so I can teach others the joy that dancing brings me." - }, - { - "speaker": "Gina", - "dia_id": "D1:7", - "text": "Wow Jon, same here! Dance is pretty much my go-to for stress relief. Got any fave styles?" - }, - { - "speaker": "Jon", - "dia_id": "D1:8", - "text": "Cool, Gina! I love all dances, but contemporary is my top pick. It's so expressive and powerful! What's your fave?" - }, - { - "speaker": "Gina", - "dia_id": "D1:9", - "text": "Yeah, me too! Contemporary dance is so expressive and graceful - it really speaks to me." - }, - { - "speaker": "Jon", - "dia_id": "D1:10", - "text": "Wow, great idea! Let's go to a dance class, it'll be so much fun!" - }, - { - "speaker": "Gina", - "dia_id": "D1:11", - "text": "Yeah! Let's explore some new dance moves. We should plan a dance session soon!" - }, - { - "speaker": "Jon", - "dia_id": "D1:12", - "text": "Yeah definitely! Wanna see my moves next Fri? Can't wait!" - }, - { - "speaker": "Gina", - "dia_id": "D1:13", - "text": "Sounds great, Jon! Next Friday works. Let's boogie!" - }, - { - "speaker": "Jon", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/a/a9/Dekkadancers_Mu%C5%BE_z_Malty.jpg" - ], - "blip_caption": "a photography of a man in a suit is performing a dance", - "query": "dancing on stage performance dance competition last year", - "dia_id": "D1:14", - "re-download": true, - "text": "Wow, I'm excited too! This is gonna be great!" - }, - { - "speaker": "Gina", - "dia_id": "D1:15", - "text": "Wow! What did you get?" - }, - { - "speaker": "Jon", - "dia_id": "D1:16", - "text": "Woah, that pic's from when my dance crew took home first in a local comp last year. It was amazing up on that stage! I'm super keen to spread that intensity with other peeps. Gina, you ever been in any dance comps or shows?" - }, - { - "speaker": "Gina", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/4/41/2009_NHK_Trophy_Dance_-_Meryl_DAVIS_-_Charlie_WHITE_-_Gold_Medal_-_0948a.jpg" - ], - "blip_caption": "a photography of a couple of people standing next to each other", - "query": "dance competition trophy team", - "dia_id": "D1:17", - "re-download": true, - "text": "I used to compete in a few dance competitions and shows - my fav memory was when my team won first place at a regionals at age fifteen. It was an awesome feeling of accomplishment!" - }, - { - "speaker": "Jon", - "dia_id": "D1:18", - "text": "Wow! Winning first place is amazing! What dance were you doing?" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a large open porch with a fireplace and a view of the water", - "dia_id": "D1:19", - "text": "Thanks! We just did a contemporary piece called \"Finding Freedom.\" It was really emotional and powerful." - }, - { - "speaker": "Jon", - "img_url": [ - "https://live.staticflickr.com/2730/4360526470_6bee96b5d7_b.jpg" - ], - "blip_caption": "a photography of a room with a view of the ocean and a few yoga mats", - "query": "dance studio overlooking ocean", - "dia_id": "D1:20", - "re-download": true, - "text": "Wow, that must've been great! Check my ideal dance studio by the water." - }, - { - "speaker": "Gina", - "dia_id": "D1:21", - "text": "Cool setup! Man, you can't deny that view! Got time to rehearse with a biz and a new store?" - }, - { - "speaker": "Jon", - "dia_id": "D1:22", - "text": "Hopefully, we will find a place like this that will inspire us!" - }, - { - "speaker": "Gina", - "dia_id": "D1:23", - "text": "Wow, it looks great! What dances do you practice? Got any projects planned?" - }, - { - "speaker": "Jon", - "img_url": [ - "https://markmorrisdancegroup.org/wp-content/uploads/2021/04/MM0108_JesuMeineFreude_Boston_BrazilTom_02.jpg" - ], - "blip_caption": "a photo of a group of dancers in white dresses on a stage", - "query": "group dancers performing on stage", - "dia_id": "D1:24", - "text": "Thanks! I rehearsed with a small group of dancers after work. We do all kinds of dances, from contemporary to hip-hop. We've got some cool projects in the works. Finishing up choreography to perform at a nearby festival next month. Can't wait!" - }, - { - "speaker": "Gina", - "dia_id": "D1:25", - "text": "Wow, it looks awesome! Are they yours at the festival? They're so graceful!" - }, - { - "speaker": "Jon", - "dia_id": "D1:26", - "text": "Yeah, they're the ones performing at the festival! They've been practicing hard and will definitely impress with their grace and skill." - }, - { - "speaker": "Gina", - "dia_id": "D1:27", - "text": "Wow, they look great! Can't wait to see them rock the festival. Gonna be awesome!" - }, - { - "speaker": "Jon", - "dia_id": "D1:28", - "text": "Yeah, awesome! Glad to be part of it." - } - ], - "session_2_date_time": "2:32 pm on 29 January, 2023", - "session_2": [ - { - "speaker": "Gina", - "blip_caption": "a photo of a clothing store with a variety of clothes on display", - "dia_id": "D2:1", - "re-download": true, - "text": "Hey Jon! Long time no see! Things have been hectic lately. I just launched an ad campaign for my clothing store in hopes of growing the business. Starting my own store and taking risks is both scary and rewarding. I'm excited to see where it takes me!" - }, - { - "speaker": "Jon", - "dia_id": "D2:2", - "text": "Hey Gina! Whoa, your store looks great! All your hard work really paid off - congrats! Must be awesome to see your stuff on display." - }, - { - "speaker": "Gina", - "dia_id": "D2:3", - "text": "Thanks a bunch! It's awesome seeing my vision happen. How's the dance studio going? Did you find the right spot?" - }, - { - "speaker": "Jon", - "img_url": [ - "https://avvay-aws-production.imgix.net/space-media/space-media-422692-prod.jpg" - ], - "blip_caption": "a photo of a bathroom with a blue floor and a pink wall", - "query": "dance studio natural lighting spacious room large windows dance mirrors", - "dia_id": "D2:4", - "text": "Hey Gina! Thanks for asking. I'm on the hunt for the ideal spot for my dance studio and it's been quite a journey! I've been looking at different places and picturing how the space would look. I even found a place with great natural light! Oh, I've been to Paris yesterday! It was sooo cool." - }, - { - "speaker": "Gina", - "dia_id": "D2:5", - "text": "Wow, nice spot! Where is it? Got any other features you want to think about before you decide? Paris?! That is really great Jon! Never had a chance to visit it. Been only to Rome once." - }, - { - "speaker": "Jon", - "dia_id": "D2:6", - "text": "It's downtown which is awesome cuz it's easy to get to. Plus the natural light! Gotta check the size & floor quality too. We need a good dance floor with enough bounce for me & my students to dance safely." - }, - { - "speaker": "Gina", - "dia_id": "D2:7", - "text": "Definitely! Dance floors help avoid injuries and make dancing more enjoyable. You thinking about it is great. Any particular type of flooring you like?" - }, - { - "speaker": "Jon", - "dia_id": "D2:8", - "text": "Yeah, good flooring's crucial. I'm after Marley flooring, which is what dance studios usually use. It's great 'cause it's grippy but still lets you move, plus it's tough and easy to keep clean." - }, - { - "speaker": "Gina", - "dia_id": "D2:9", - "text": "Sounds great! Marley's perfect; it's got the right amount of grip and movement. Can't wait to see your dance studio done!" - }, - { - "speaker": "Jon", - "dia_id": "D2:10", - "text": "Yeah, can't wait to see it done! Looking for the right place and getting everything ready has been a mix of exciting and nerve-wracking, but I'm determined to make it work. It'll be worth it!" - }, - { - "speaker": "Gina", - "dia_id": "D2:11", - "text": "Believe in yourself, Jon! The process may be tough, but you got this. Push through and it'll be worth it. Don't forget to take breaks and dance it out when you need to destress!" - }, - { - "speaker": "Jon", - "dia_id": "D2:12", - "text": "Glad I have you in my corner! Gotta make time to dance and vent, that's for sure. We'll make it through this - hang in there!" - }, - { - "speaker": "Gina", - "dia_id": "D2:13", - "text": "Thanks, Jon! Appreciate your support!" - }, - { - "speaker": "Jon", - "dia_id": "D2:14", - "text": "Let's keep going and chase our dreams!" - }, - { - "speaker": "Gina", - "dia_id": "D2:15", - "text": "Yeah! We've done so much, and there's nothing but good stuff coming. Let's keep going after our goals and making them happen." - }, - { - "speaker": "Jon", - "dia_id": "D2:16", - "text": "Success is almost here. We got this!" - } - ], - "session_3_date_time": "12:48 am on 1 February, 2023", - "session_3": [ - { - "speaker": "Jon", - "dia_id": "D3:1", - "text": "Hey Gina, hope you're doing ok! Still following my passion for dance. It's been bumpy, but I'm determined to make it work. I'm still searching for a place to open my dance studio." - }, - { - "speaker": "Gina", - "dia_id": "D3:2", - "img_url": [ - "https://s0.geograph.org.uk/photos/44/02/440245_a865f9d5.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a shopping mall with a glass entrance and a sign", - "text": "Hi Jon! So happy you're pushing forward with dancing! Inspiring \ud83d\udcaa I emailed some wholesalers and one replied and said yes today! I'm over the moon because now I can expand my clothing store and get closer to my customers. Check it out - here's a pic!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a room with a mirror and a wooden floor", - "dia_id": "D3:3", - "text": "Wow, Gina! You found the perfect spot for your store. Way to go, hard work's paying off!" - }, - { - "speaker": "Gina", - "img_url": [ - "https://live.staticflickr.com/610/23124623885_9aab7b3e2e_b.jpg" - ], - "blip_caption": "a photography of a clothing store with a lot of clothes on display", - "query": "inside of store cozy inviting atmosphere trendy pieces", - "dia_id": "D3:4", - "re-download": true, - "text": "Thanks! Glad you like it. Yeah, it's a great spot. Here's a peek at the space I designed. Cozy and inviting - perfect for customers to check out all the trendy pieces." - }, - { - "speaker": "Jon", - "dia_id": "D3:5", - "text": "Wow, it looks great! Must've taken you ages to design it. What made you pick out the furniture and decor?" - }, - { - "speaker": "Gina", - "dia_id": "D3:6", - "text": "Thanks! It took a bit of time but I wanted to make the place look like my own style and make my customers feel cozy. I chose furniture that looks great and is comfy too. The chandelier adds a nice glam feel while matching the style of the store." - }, - { - "speaker": "Jon", - "dia_id": "D3:7", - "text": "Your store looks great - your customers will be so comfy." - }, - { - "speaker": "Gina", - "dia_id": "D3:8", - "text": "Thanks! Making my spot comfortable and inviting for my customers is key. I want 'em to feel like they're in a cool oasis. Just creating an experience that'll make 'em wanna come back." - }, - { - "speaker": "Jon", - "dia_id": "D3:9", - "text": "That's a great goal! Creating a special experience for customers is the key to making them feel welcome and coming back. I think you can create that space you're imagining." - }, - { - "speaker": "Gina", - "dia_id": "D3:10", - "text": "Thanks. Your support means a lot. I'm sure with my hard work and effort, I can make a special shopping experience for my customers. It's tough but I'm up for the challenge!" - }, - { - "speaker": "Jon", - "dia_id": "D3:11", - "text": "I'm always here to support you! Go create something awesome with your store. Keep it up!" - }, - { - "speaker": "Gina", - "dia_id": "D3:12", - "text": "Thanks, Jon! I'll try my best. You're gonna do great with your dance studio, just keep going and stay positive! We'll get through this!" - }, - { - "speaker": "Jon", - "dia_id": "D3:13", - "text": "Thanks! Your words mean a lot. I'm staying positive and pushing forward. We've put our hearts into our dreams and I'm sure we'll make it." - }, - { - "speaker": "Gina", - "dia_id": "D3:14", - "text": "Sure thing, Jon! Stay motivated and keep going. Hard work pays off eventually. We can do this!" - } - ], - "session_4_date_time": "10:43 am on 4 February, 2023", - "session_4": [ - { - "speaker": "Jon", - "dia_id": "D4:1", - "text": "Hey Gina! What's up? How's the store going? I gotta tell you about this thing with my biz." - }, - { - "speaker": "Gina", - "dia_id": "D4:2", - "text": "Hey Jon! The store's doing great! It's a wild ride. How's the biz?" - }, - { - "speaker": "Jon", - "dia_id": "D4:3", - "text": "Hey Gina! I'm putting in a lot of work on my business even with the obstacles. I'm gonna make it happen!" - }, - { - "speaker": "Gina", - "dia_id": "D4:4", - "text": "Wow! You've got drive! Keep it up and you'll definitely make a splash." - }, - { - "speaker": "Jon", - "dia_id": "D4:5", - "text": "Thanks Gina! Your help really means a lot. Sometimes it's hard when things don't go my way, but I'm sure if I keep pushing, I'll reach my dreams!" - }, - { - "speaker": "Gina", - "dia_id": "D4:6", - "text": "Understand where you're at, Jon. Setbacks are just opportunities for comebacks. You got the skills, passion, and drive. Plus my full support. Don't give up, buddy!" - }, - { - "speaker": "Jon", - "dia_id": "D4:7", - "text": "Thanks! Setbacks can be tough, but with your support, I can handle anything. Appreciate you having my back!" - }, - { - "speaker": "Gina", - "dia_id": "D4:8", - "text": "I'm here for you no matter what! Anything you want to say about your biz?" - }, - { - "speaker": "Jon", - "dia_id": "D4:9", - "text": "Searching for a dance studio location has been tricky, but I'm determined to find the right spot - when I do, I'm sure the rest will follow!" - }, - { - "speaker": "Gina", - "dia_id": "D4:10", - "text": "Searching for the perfect dance studio's a tough job, Jon. Hang in there and you'll find it soon!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a woman in a gray dress doing a trick", - "dia_id": "D4:11", - "text": "Thanks! Appreciate your encouragement - it means a lot! I'm working on my business and some new dance routines - rehearsing hard for an upcoming show. I'm passionate about dancing and it brings me so much joy and fulfillment." - }, - { - "speaker": "Gina", - "dia_id": "D4:12", - "text": "Wow, Jon! You're so talented! What show ya got planned?" - }, - { - "speaker": "Jon", - "dia_id": "D4:13", - "text": "I'm getting ready for a dance comp near me next month. It's a great chance for me to show my skillz and, hopefully, get some props from the dance fam. Super stoked!" - }, - { - "speaker": "Gina", - "dia_id": "D4:14", - "text": "Wow Jon, you're gonna kill it in that competition. Your hard work and talent will pay off! Good luck." - }, - { - "speaker": "Jon", - "dia_id": "D4:15", - "text": "Thanks! Your help means a lot. I'll do my best to make you proud!" - }, - { - "speaker": "Gina", - "dia_id": "D4:16", - "text": "No doubt, Jon you've got this. Your passion and commitment are awesome! Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D4:17", - "text": "Thanks! I'm going for my dreams!" - }, - { - "speaker": "Gina", - "dia_id": "D4:18", - "text": "Absolutely Jon. I'm always here to cheer you on. Make your dreams happen - keep going and believe in yourself. You got this!" - }, - { - "speaker": "Jon", - "dia_id": "D4:19", - "text": "Thanks, Gina! Your kind words mean a lot. I won't give up!" - } - ], - "session_5_date_time": "9:32 am on 8 February, 2023", - "session_5": [ - { - "speaker": "Gina", - "blip_caption": "a photo of a group of women posing with a giant balloon", - "dia_id": "D5:1", - "re-download": true, - "text": "Hey Jon! Great hearing from you again. How have you been? BTW, I found a cool new fashion piece for my store. Can't wait to share with my customers." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a group of young girls in a dance studio", - "dia_id": "D5:2", - "re-download": true, - "text": "Hey Gina! Congrats on the new fashion piece! Looks like your store is growing. Remenber the festival I told you about? Had that performance and it was awesome - so many people there complementing my dance moves. Dancing brings me joy and it was nice to be reminded why I'm passionate about it." - }, - { - "speaker": "Gina", - "dia_id": "D5:3", - "text": "Wow! That looks great. You look badass on stage. BTW, what's your favorite part of running your own studio?" - }, - { - "speaker": "Jon", - "dia_id": "D5:4", - "text": "Thanks, Gina! I love running my own studio. It's great having the freedom to create a space and help dancers of all ages and levels express themselves. I'm super thrilled about dancing each day and seeing my students progress. It's so fulfilling." - }, - { - "speaker": "Gina", - "img_url": [ - "https://circlecraftmarket.files.wordpress.com/2017/11/adhesif-3.jpg" - ], - "blip_caption": "a photo of a rack of clothes with a coat and a dress", - "query": "unique artwork clothing", - "dia_id": "D5:5", - "text": "That's awesome! I'm sure you feel great knowing your students are doing so well with dance. It's amazing what it can do for people! Oh, and btw - I've been working hard on my online store and just teamed up with a local artist for some cool designs. Check 'em out!" - }, - { - "speaker": "Jon", - "dia_id": "D5:6", - "text": "It looks awesome. Your commitment and creativity in your business really stands out. How'd you come up with these cool designs?" - }, - { - "speaker": "Gina", - "dia_id": "D5:7", - "text": "Thanks Jon! I got the idea from a fashion mag and saw there wasn't much around like it. So I worked with the artist to make it happen - it's all about being ahead of the game and giving my customers something different." - }, - { - "speaker": "Jon", - "dia_id": "D5:8", - "text": "Nice one, Gina! You never shy away from a challenge and always try something new. I'm impressed by your willingness to take risks - it's really inspiring." - }, - { - "speaker": "Gina", - "dia_id": "D5:9", - "text": "Thanks! Taking risks is scary but it's the only way to grow, right? Just part of the journey to success." - }, - { - "speaker": "Jon", - "dia_id": "D5:10", - "text": "Yeah, I totally agree - taking risks is key for success. It's made me grow, and even got me out of my secure 9-5 as a banker. Now, I'm aiming to turn my dancing passion into a business. I'm determined to make it work, I just know it! That being said, I definitely don't underestimate the difficulties - it ain't been a walk in the park, that's for sure." - }, - { - "speaker": "Gina", - "dia_id": "D5:11", - "text": "It's tough starting a biz, but don't let it get you down. You can make your studio work, I'm sure. And remember, I'm always here for you." - }, - { - "speaker": "Jon", - "img_url": [ - "https://live.staticflickr.com/381/19379909243_9683e023fc_z.jpg" - ], - "blip_caption": "a photography of a cartoon character with a quote about fear", - "query": "motivational quote \"everything you want is on the other side of fear\"", - "dia_id": "D5:12", - "re-download": true, - "text": "Thanks, Gina. Your help means a lot. I'll keep plugging away and stay optimistic." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a woman standing on a balcony with a blue dress", - "dia_id": "D5:13", - "re-download": true, - "text": "This quote kept me positive through tough times. We all need a push sometimes, right? Even made a tattoo to remind myself about it." - }, - { - "speaker": "Jon", - "dia_id": "D5:14", - "text": "Love the tattoo, did you just get it?" - }, - { - "speaker": "Gina", - "dia_id": "D5:15", - "text": "Thanks! Got the tattoo a few years ago, it stands for freedom - dancing without worrying what people think. A reminder to follow my passions and express myself." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a woman in a tutu posing for a picture", - "dia_id": "D5:16", - "text": "Nice reminder, Gina! It's so important to have freedom and express ourselves without worry. Dance gives me an escape to be myself." - }, - { - "speaker": "Gina", - "dia_id": "D5:17", - "text": "Totally agree, Jon. Dancing lets us be ourselves and ain't nothing like the feeling it gives us. You're so dedicated to your studio, it's inspiring. Chase those dreams, buddy!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a trophy with a glass globe on top", - "dia_id": "D5:18", - "text": "Thanks, Gina! Your support means so much. I'm gonna keep chasing after those dreams. Dance is my passion, and I'm gonna keep working hard to make it a success!" - }, - { - "speaker": "Gina", - "dia_id": "D5:19", - "text": "This is the right attitude! How have you been juggling dance and business goals?" - }, - { - "speaker": "Jon", - "dia_id": "D5:20", - "text": "Thanks! Juggling both my passions can be tricky, but so rewarding. Dancing and running my biz need hard work, plus they give me energy for each other. My dance moves get me pumped to tackle my business goals, and successes there boost my drive to keep dreaming on the dance floor. It's a balancing act, but fun." - }, - { - "speaker": "Gina", - "dia_id": "D5:21", - "text": "Wow, Jon! You're amazing at juggling both your passions. Finding that happy medium is key - keep going and don't stop dreaming, buddy!" - }, - { - "speaker": "Jon", - "dia_id": "D5:22", - "text": "Thanks, Gina! Your pep-talk really meant a lot. I'm not gonna give up on my dreams - my dance studio and biz ventures need the hard work I'm putting in. Love having you in my corner, thanks for always being there!" - }, - { - "speaker": "Gina", - "dia_id": "D5:23", - "text": "Yeah Jon, I'm here for you! Chasing our dreams and helping each other out. Let's keep movin' forward!" - } - ], - "session_6_date_time": "2:35 pm on 16 March, 2023", - "session_6": [ - { - "speaker": "Jon", - "dia_id": "D6:1", - "text": "Hi Gina! Been hectic for me lately. Started hitting the gym last week to stay on track with the venture. Gotta figure out how to balance it all, but it's going well. How about you?" - }, - { - "speaker": "Gina", - "dia_id": "D6:2", - "text": "Hey Jon! Great to hear from you. Been having some tough times lately." - }, - { - "speaker": "Jon", - "dia_id": "D6:3", - "text": "Oof, sorry to hear that. What's up? Is there anything I can do to help?" - }, - { - "speaker": "Gina", - "dia_id": "D6:4", - "text": "Thanks, Jon! Appreciate your offer. Since I lost my job at Door Dash, things have been tough. But here's some good news - I've got something to share!" - }, - { - "speaker": "Jon", - "dia_id": "D6:5", - "text": "Wow, that's awesome! Can't wait to hear it!" - }, - { - "speaker": "Gina", - "img_url": [ - "https://i.redd.it/i9k97vw6pgs51.jpg" - ], - "blip_caption": "a photo of a computer screen showing a book and a pair of shoes", - "query": "online clothing store website screenshot", - "dia_id": "D6:6", - "text": "Yay! My online clothes store is open! I've been dreaming of this for a while now - can't wait to see what happens!" - }, - { - "speaker": "Jon", - "dia_id": "D6:7", - "text": "Congrats! That's awesome! What gave you the idea to start the online store?" - }, - { - "speaker": "Gina", - "dia_id": "D6:8", - "text": "Thanks! I'm passionate about fashion trends and finding unique pieces. Plus, I wanted to blend my love for dance and fashion, so it was a perfect match." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of three young girls standing next to each other with trophies", - "dia_id": "D6:9", - "text": "Wow, that's awesome! Combining your two interests into a store is super cool. Best of luck with it!" - }, - { - "speaker": "Gina", - "dia_id": "D6:10", - "text": "Thanks! How is biz going? I hope it's going well!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a laptop computer sitting on top of a table", - "dia_id": "D6:11", - "text": "Thanks for askin', Gina! Losing my job was hard, but I'm livin' my dreams now. Startin' my biz has been tough but I'm gonna make it! I keep facing new challenges, but I'm sure it'll be worth it in the end." - }, - { - "speaker": "Gina", - "dia_id": "D6:12", - "text": "Yeah, starting and running my own biz has had its ups and downs - but it's been an amazing ride!" - }, - { - "speaker": "Jon", - "dia_id": "D6:13", - "text": "Yeah, it's been a rollercoaster. But your success really inspires me to keep pushing forward. Your determination is awesome!" - }, - { - "speaker": "Gina", - "dia_id": "D6:14", - "text": "Thanks, Jon! Your words are really encouraging. Glad my journey is inspiring others." - }, - { - "speaker": "Jon", - "dia_id": "D6:15", - "text": "Yeah, totally! It's great we both face the same challenges, it motivates us and it's like having a partner to dance with!" - }, - { - "speaker": "Gina", - "dia_id": "D6:16", - "text": "Yep! We're both on different paths, but it's nice to have someone to root for us. We can do it!" - }, - { - "speaker": "Jon", - "dia_id": "D6:17", - "text": "Definitely! Having someone back us up is great. Let's keep going and reach success together!" - }, - { - "speaker": "Gina", - "dia_id": "D6:18", - "text": "Let's keep chasing our dreams, supporting each other, and celebrating achievements. We can do great things together!" - }, - { - "speaker": "Jon", - "img_url": [ - "https://www.news-press.com/gcdn/presto/2018/12/21/PFTM/e043df2d-48d9-4591-a714-f27ecbd42007-GD1.jpg" - ], - "blip_caption": "a photo of two glasses of champagne with a bottle of wine in the background", - "query": "champagne celebration", - "dia_id": "D6:19", - "text": "Yeah, Gina, thanks for having my back. Here's to taking on new heights, and all the trials that come with it. Cheers!" - } - ], - "session_7_date_time": "7:28 pm on 23 March, 2023", - "session_7": [ - { - "speaker": "Jon", - "dia_id": "D7:1", - "text": "Hey Gina, how's it going?" - }, - { - "speaker": "Gina", - "dia_id": "D7:2", - "text": "Hey Jon, my online clothing store has been a roller coaster but rewarding. Starting a business takes courage - you hang in there too!" - }, - { - "speaker": "Jon", - "dia_id": "D7:3", - "text": "Thanks Gina! It's been tough, but I'm gonna make it happen. It's been great! And hey, you're awesome with your store. How's it going?" - }, - { - "speaker": "Gina", - "dia_id": "D7:4", - "text": "Thanks! Appreciate your kind words. Store's going good, just been keeping up with fashion trends so I can offer the best pieces to customers. It's been a lot of work, but really enjoying it. Got any advice or tips on running a successful biz?" - }, - { - "speaker": "Jon", - "dia_id": "D7:5", - "text": "Yeah, brand identity is key. Make sure yours stands out. Also be sure to build relationships with your customers \u2013 let them know you care. And don't forget to stay positive and motivate others. Your energy will be contagious!" - }, - { - "speaker": "Gina", - "dia_id": "D7:6", - "text": "Thanks for the advice, Jon! Building relationships and creating a strong brand image for my store is something I'm always working on. You're right, staying positive is key. What helps you stay motivated with your dance studio business?" - }, - { - "speaker": "Jon", - "dia_id": "D7:7", - "img_url": [ - "https://live.staticflickr.com/8111/8547962982_92d38cbcc3_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a group of women doing a dance routine", - "text": "Seeing my students succeed motivates me. It's awesome to help them learn and reach their goals. Your support, Gina, means a lot too. Here's a photo of us after during one of the dance clases." - }, - { - "speaker": "Gina", - "dia_id": "D7:8", - "text": "That's awesome, Jon! Seeing your students grow and succeed must be really fulfilling. Glad I can be part of this journey!" - }, - { - "speaker": "Jon", - "dia_id": "D7:9", - "text": "Thanks for being there for me! It's really made a huge difference and it feels great." - }, - { - "speaker": "Gina", - "dia_id": "D7:10", - "text": "Glad I could help, Jon! It's nice to be part of something positive. Supporting your dreams is awesome!" - }, - { - "speaker": "Jon", - "dia_id": "D7:11", - "text": "Thanks for being there for me. Your help means a lot." - }, - { - "speaker": "Gina", - "dia_id": "D7:12", - "text": "I'm here for you, rooting for you all the way." - }, - { - "speaker": "Jon", - "dia_id": "D7:13", - "text": "Thanks, I'm really grateful for your help with staying motivated." - }, - { - "speaker": "Gina", - "dia_id": "D7:14", - "text": "Glad to cheer you on. Keep going and never give up!" - }, - { - "speaker": "Jon", - "dia_id": "D7:15", - "text": "Thanks, Gina! I won't quit - your words motivate me to keep going!" - }, - { - "speaker": "Gina", - "dia_id": "D7:16", - "text": "Believe in yourself. Even when it's tough, you got this! Keep going!" - }, - { - "speaker": "Jon", - "dia_id": "D7:17", - "text": "I'm gonna keep on believing in myself. Thanks for the kind words!" - } - ], - "session_8_date_time": "1:26 pm on 3 April, 2023", - "session_8": [ - { - "speaker": "Jon", - "dia_id": "D8:1", - "text": "Hey Gina, I had to shut down my bank account. It was tough, but I needed to do it for my biz." - }, - { - "speaker": "Gina", - "dia_id": "D8:2", - "text": "Oh no, Jon! Sorry to hear that. Tough decision for you? How're you handling the changes?" - }, - { - "speaker": "Jon", - "dia_id": "D8:3", - "text": "It was a tough call, but I thought it'd help my biz grow. Handling changes has been hard, but I'm staying positive and looking ahead. Anything new for you?" - }, - { - "speaker": "Gina", - "dia_id": "D8:4", - "text": "Oof, that's tough, Jon. I got some new offers and promotions going on my online store to try and bring in new customers. It's been a wild ride starting my business, but I'm not giving up!" - }, - { - "speaker": "Jon", - "dia_id": "D8:5", - "text": "Nice one, Gina! Love how you never give up. What helps you stay motivated?" - }, - { - "speaker": "Gina", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2151,w_2403,x_0,y_1044/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/export-1-37_l33gst.jpg" - ], - "blip_caption": "a photo of a man and woman doing a yoga pose", - "query": "dance studio session", - "dia_id": "D8:6", - "text": "Thanks Jon! Dance is my stress relief and fashion fuels my creativity. I love finding new trends for my store. It keeps me motivated to keep growing. Check out this pic of my fave dance session!" - }, - { - "speaker": "Jon", - "dia_id": "D8:7", - "text": "Wow, that's great! What made you combine clothing biz and dance?" - }, - { - "speaker": "Gina", - "dia_id": "D8:8", - "text": "Thanks! I'm passionate about dance and fashion so combining them lets me show my creativity and share my love with others. Plus, I can add dance-inspired items to my store!" - }, - { - "speaker": "Jon", - "dia_id": "D8:9", - "text": "Nice work! Combining passions is always cool. How's it going?" - }, - { - "speaker": "Gina", - "dia_id": "D8:10", - "text": "Thanks! So far, so good - customers love the new offers and promotions, which means I'm seeing more sales. People seem to really like my designs, so I'm always on the hunt for unique, trendy pieces. Growing my customer base is the main focus right now." - }, - { - "speaker": "Jon", - "dia_id": "D8:11", - "text": "Sounds like all your effort's paying off. Anything planned to grow your customer base?" - }, - { - "speaker": "Gina", - "dia_id": "D8:12", - "text": "Yeah, I have a few plans. I'm thinking of working with some fashion bloggers and influencers in the next few months to get more attention for my store. Plus, I'm going to do more ads so I can reach more people. I'm really focused on building my customer base and making my store a top destination for fashion fans. It's awesome to see it all coming together! You, Jon? What do you have going for your dance studio?" - }, - { - "speaker": "Jon", - "dia_id": "D8:13", - "text": "Thanks, Gina! I'm expanding my dance studio's social media presence and offering workshops and classes to local schools and centers. I'm also hosting a dance competition next month to showcase local talent and bring more attention to my studio. All the work's paying off - I'm seeing progress and the dancers are so excited. It's such a great feeling to give a place where people can express themselves through dance!" - }, - { - "speaker": "Gina", - "dia_id": "D8:14", - "text": "Wow! That's fantastic that your studio's expanding and giving dancers an outlet. So proud of the progress you've made - keep it up!" - }, - { - "speaker": "Jon", - "img_url": [ - "https://universe.byu.edu/wp-content/uploads/2018/02/IMG_8914.jpg" - ], - "blip_caption": "a photo of a group of people on a stage with a projector screen", - "query": "group of talented dancers performing on stage event next month", - "dia_id": "D8:15", - "text": "Thanks! Your backing means a lot. I'm trying to make my plan work, even though it's been tough. Your encouragement really helps. Are you coming to the event next month? Love to have you there!" - }, - { - "speaker": "Gina", - "dia_id": "D8:16", - "text": "Woah, cool event! What's gonna be happening? I'd love to join in and show my support!" - }, - { - "speaker": "Jon", - "img_url": [ - "https://varnikadesigns.files.wordpress.com/2020/11/img_3505.jpg" - ], - "blip_caption": "a photo of a group of dancers on a stage with a man in the middle of the group", - "query": "group dancers stage performance", - "dia_id": "D8:17", - "text": "Thanks, Gina! My dance studio and some other schools are bringing their best moves for an awesome night of performances and judging. It'll be super creative and fun. Come join us!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a woman in a tutu posing for a picture", - "dia_id": "D8:18", - "text": "Sounds great! I'm definitely in for the show." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of two women doing a handstand in a room", - "dia_id": "D8:19", - "text": "Cool! Can't wait to see you!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a group of young girls in tutuss and ballet shoes", - "dia_id": "D8:20", - "text": "Thanks, Jon! See you at the event!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a dress with a sign on it that says june bunty", - "dia_id": "D8:21", - "text": "Gina, good luck with your store!" - }, - { - "speaker": "Gina", - "dia_id": "D8:22", - "text": "Thanks, Jon! Appreciate the kind words. <3" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a street light on a sidewalk in front of a building", - "dia_id": "D8:23", - "text": "Sure, I am always there for you!" - }, - { - "speaker": "Gina", - "dia_id": "D8:24", - "text": "Thanks! Your support means a lot. I'm gonna keep pursuing my goals and I hope you do too!" - }, - { - "speaker": "Jon", - "dia_id": "D8:25", - "text": "Thanks! I won't quit on my dreams. Your words really motivate me. Bye!" - }, - { - "speaker": "Gina", - "dia_id": "D8:26", - "text": "Bye Jon! You got this! Believe in yourself and keep pushing. Take care!" - } - ], - "session_9_date_time": "10:33 am on 9 April, 2023", - "session_9": [ - { - "speaker": "Jon", - "img_url": [ - "https://college.lclark.edu/live/image/gid/664/width/720/height/690/crop/1/87150_Dance_main_image.rev.1613000755.jpg" - ], - "blip_caption": "a photo of a group of dancers on a stage with a red background", - "query": "dance studio class collage rehearsal students", - "dia_id": "D9:1", - "text": "Hey Gina! I'm turning my loves of dance into a business. I'm sunk tons of time into the studio lately, and look at my students - they're already killing it. I'm even learning with them!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a display of a dress and a flamingo", - "dia_id": "D9:2", - "text": "Hey Jon! Wow, way to take your passion and make it into a biz! The dance studio looks awesome." - }, - { - "speaker": "Jon", - "dia_id": "D9:3", - "text": "Thanks, Gina! I'm determined to make this studio work. Losing my job was tough but it gave me the push I needed to do what I love." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a red dress with gold accents on a mannequin", - "dia_id": "D9:4", - "text": "Woah, Jon! Tough times can be a gateway to awesome things. Glad you worked up the courage to go after your dreams!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a woman doing a pole dance in a dance studio", - "dia_id": "D9:5", - "re-download": true, - "text": "Yeah, Gina! It's been tough, but I'm living my true self. Dancing makes me so happy, and now I get to share that with other people. Seeing my students get better at it brings me such joy." - }, - { - "speaker": "Gina", - "dia_id": "D9:6", - "text": "Wow Jon, you look so happy when you dance! Show the world your true self and keep dancing!" - }, - { - "speaker": "Jon", - "dia_id": "D9:7", - "text": "Thanks a bunch, Gina! You seriously rock. Dancing for me is like a way to express myself and find my happy place. I used to be scared to death of what people would think, but I learnt that my own happiness is the most important thing. It's been tough but also the best thing ever!" - }, - { - "speaker": "Gina", - "img_url": [ - "https://cartwheelfactory.com/imagesjpg/ballet-studio-glassless-mir.jpg" - ], - "blip_caption": "a photo of a group of young women in ballet attire", - "query": "ballet studio mirrors barres dance classes", - "dia_id": "D9:8", - "text": "Yeah, I do remember those dance classes! I used to love spending time in the studio. That photo looks awesome, brings back lots of memories. It's nice to know, dance is still your happy place." - }, - { - "speaker": "Jon", - "dia_id": "D9:9", - "text": "Nice, Gina! I'm happy that dance has such a special meaning to both of us. It's a really cool way to express ourselves. I loved taking lessons with my friends when I was younger. Those memories are so precious. So glad I can still enjoy it with my own studio. Thanks for always being there for me." - }, - { - "speaker": "Gina", - "img_url": [ - "https://i.pinimg.com/originals/68/03/ff/6803ffb650c71d63268b6a40310c882a.jpg" - ], - "blip_caption": "a photo of a trophy with a glass globe on top", - "query": "dance competition trophy", - "dia_id": "D9:10", - "text": "Hey Jon! Got your back - dance is awesome for expressing yourself and finding happiness. Here's one of my trophies from a dance contest, nice reminder of the hard work, dedication and joy it brings." - }, - { - "speaker": "Jon", - "dia_id": "D9:11", - "text": "Wow! It looks awesome! Thanks for the support, it really means a lot." - }, - { - "speaker": "Gina", - "dia_id": "D9:12", - "text": "No prob, Jon! You earned all the kudos for your hard work. Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D9:13", - "text": "Thanks! Gonna keep pushing and working hard. Won't let anything hold me back!" - }, - { - "speaker": "Gina", - "dia_id": "D9:14", - "text": "Way to go, Jon! Keep it up, you're almost there!" - } - ], - "session_10_date_time": "11:24 am on 25 April, 2023", - "session_10": [ - { - "speaker": "Jon", - "img_url": [ - "https://www.samfsn.org/images/samsfn-events/WNYD2.jpg" - ], - "blip_caption": "a photo of a group of women performing a dance on a stage", - "query": "fair booth flyers brochures dance studio", - "dia_id": "D10:1", - "text": "Hi Gina! I just wanted to fill you in on my business. Yesterday, I went to a fair to show off my studio, it was both stressful and great! I got some possible leads, so that was awesome. But overall, I've learned that this biz is no cakewalk and having confidence in yourself is important for making it successful!" - }, - { - "speaker": "Gina", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/5ad8eb7f506fbec1f61b54ea/1539686276776-HPK5GWLCBQ0MHE5GSE6S/web_homepage_mock.jpg" - ], - "blip_caption": "a photography of a laptop with a sale ad on the screen", - "query": "clothing store website", - "dia_id": "D10:2", - "re-download": true, - "text": "Hey Jon, congrats on the fair! It's awesome to see your hard work paying off. Keep on pushing, pal, you're headed for greatness. Oh, and BTW, I started my own online clothing store not so long ago - pretty cool, huh?" - }, - { - "speaker": "Jon", - "dia_id": "D10:3", - "text": "Thanks, Gina! Appreciate your support. Your store looks great, I remember it!" - }, - { - "speaker": "Gina", - "dia_id": "D10:4", - "text": "Thanks, Jon! After losing my job, I wanted to take control of my own destiny and this seemed like the perfect way to do it. It's been a tough journey, but very rewarding." - }, - { - "speaker": "Jon", - "dia_id": "D10:5", - "text": "Wow, you're awesome for going for it! Setbacks can help us reach our potential, right? I'm having trouble with my business project. Any advice on staying motivated even when times are tough?" - }, - { - "speaker": "Gina", - "dia_id": "D10:6", - "text": "Yeah Jon! Challenges are awesome for learning and growth. To stay motivated, I think of the big goal and why I'm doing it. I also get help from people who support me. And of course, I dance it out. Do you need advice on anything in particular?" - }, - { - "speaker": "Jon", - "dia_id": "D10:7", - "text": "I've been feeling kinda low on confidence lately. It's hard to run a business when you don't have faith in yourself. Any tips on how you stay confident in your business?" - }, - { - "speaker": "Gina", - "dia_id": "D10:8", - "text": "I get it, Jon. Confidence is important in business. I stay motivated by reminding myself of my successes and progress. It also helps to have a good support system. Just focus on why you started this \u2013 because you love it! Have faith in yourself, Jon. I do!" - }, - { - "speaker": "Jon", - "dia_id": "D10:9", - "text": "Thanks! Your words mean a lot. Gotta focus on success and why I started. You're right, I love it and that'll keep my confidence up." - }, - { - "speaker": "Gina", - "dia_id": "D10:10", - "text": "No worries, Jon! Sounds like what I said was helpful. You're incredibly talented and passionate about dance. Don't forget, believe in yourself and your abilities. Tackle any obstacle that comes your way and keep shining!" - }, - { - "speaker": "Jon", - "dia_id": "D10:11", - "text": "Thanks! Your support means a lot. I'm gonna keep going and reach my dreams no matter what." - }, - { - "speaker": "Gina", - "dia_id": "D10:12", - "text": "Go for it, Jon! You got this! Don't let anything stop you. We're in this together!" - }, - { - "speaker": "Jon", - "dia_id": "D10:13", - "text": "Appreciate your encouragement. We'll keep pushing each other on this path." - }, - { - "speaker": "Gina", - "dia_id": "D10:14", - "text": "Yeah! Let's keep each other going. We can do it!" - } - ], - "session_11_date_time": "3:14 pm on 11 May, 2023", - "session_11": [ - { - "speaker": "Jon", - "blip_caption": "a photo of a woman in a short skirt with her hands on her hips", - "dia_id": "D11:1", - "text": "Hi! Since we last spoke I am still working on the dance studio and things are looking up!" - }, - { - "speaker": "Gina", - "dia_id": "D11:2", - "text": "Hi! You're so inspiring taking it on and opening your own studio!" - }, - { - "speaker": "Jon", - "dia_id": "D11:3", - "text": "Thanks! Losing my job gave me the push to finally start my dream business: my own dance studio! Now I'm stepping into the unknown and hoping for the best." - }, - { - "speaker": "Gina", - "dia_id": "D11:4", - "text": "It must be scary stepping into the unknown but I know you can do it, Jon. With your determination and drive, your dance studio will be a huge success. Keep that positive outlook and keep going!" - }, - { - "speaker": "Jon", - "dia_id": "D11:5", - "text": "Thanks! It's a bit scary, but I just think about my love for dance and how it makes me feel. It's been my stress-buster since childhood!" - }, - { - "speaker": "Gina", - "dia_id": "D11:6", - "text": "Gotcha, Jon! Dance is my stress fix too. As soon as I start, all my worries vanish. It's amazing what we can do for our own mental health with something we enjoy." - }, - { - "speaker": "Jon", - "dia_id": "D11:7", - "text": "Yeah, Gina! Dancing helps me de-stress. It's where I'm most alive. It's a must-have in my life." - }, - { - "speaker": "Gina", - "dia_id": "D11:8", - "text": "I get it, Jon. Dance is just me -- I can't picture life without it. It's like air." - }, - { - "speaker": "Jon", - "dia_id": "D11:9", - "text": "Yep! Dancing is like second nature to me. I'm living my dream by having my own dance studio and teaching others." - }, - { - "speaker": "Gina", - "dia_id": "D11:10", - "text": "You're living the dream and inspiring others too! Your studio will totally change things for lots of folks." - }, - { - "speaker": "Jon", - "dia_id": "D11:11", - "text": "I hope so, Gina. I want to create a place for people to dance and express themselves - it's been a dream of mine." - }, - { - "speaker": "Gina", - "dia_id": "D11:12", - "text": "That's a great dream, Jon! Giving people a place to express themselves with dance is really important. Your studio is gonna make a huge difference. Can't wait to see it happen!" - }, - { - "speaker": "Jon", - "dia_id": "D11:13", - "text": "Thanks! Your help means a lot. Keep you posted on the dance studio progress." - }, - { - "speaker": "Gina", - "dia_id": "D11:14", - "text": "Thanks! Really appreciate you keeping me in the loop on this cool project. Can't wait to hear more and watch it come to life! Oh, btw, I had an interview for a design internship yesterday! It was so cool." - }, - { - "speaker": "Jon", - "dia_id": "D11:15", - "text": "Wow, Gina, I'm stoked about this! Taking a risk is scary, but I'm sure following my dreams will pay off in the end. How did the interview go?" - }, - { - "speaker": "Gina", - "dia_id": "D11:16", - "text": "It was great!" - }, - { - "speaker": "Jon", - "dia_id": "D11:17", - "text": "Glad to hear it. Been practicing dance routines lately, it keeps my mind focused and motivated." - }, - { - "speaker": "Gina", - "dia_id": "D11:18", - "text": "Wow! That's great. Dancing is awesome for staying focused. Wanna show me a routine sometime?" - }, - { - "speaker": "Jon", - "dia_id": "D11:19", - "text": "Sure, Gina! Wanna see one of my routines? Lemme know when you got time and I'll send you a vid." - }, - { - "speaker": "Gina", - "dia_id": "D11:20", - "text": "Yeah, Jon, I'll watch your routine! So proud of you!" - }, - { - "speaker": "Jon", - "dia_id": "D11:21", - "text": "Thanks a lot! Your help really means a lot. I'll get the video to you soon!" - }, - { - "speaker": "Gina", - "dia_id": "D11:22", - "text": "No prob! Always here to help. Can't wait to see the vid!" - } - ], - "session_12_date_time": "7:18 pm on 27 May, 2023", - "session_12": [ - { - "speaker": "Gina", - "dia_id": "D12:1", - "text": "Hey Jon! Long time no talk! A lot's happened - I just got accepted for a fashion internship!" - }, - { - "speaker": "Jon", - "dia_id": "D12:2", - "text": "Congrats, Gina! That's awesome news about the fashion internship. \ud83c\udf89 So stoked for you. Where is the internship and how're you feelin' about it?" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a laptop computer with a logo on the screen", - "dia_id": "D12:3", - "text": "Thanks! I'm excited and kinda nervous. Gonna be a big change. It's part-time position in the fashion department of an international company." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a book with a yellow and green cover", - "dia_id": "D12:4", - "text": "Way to go, Gina! You really stepped up. What's your plan for the future?" - }, - { - "speaker": "Gina", - "dia_id": "D12:5", - "text": "Thanks! I'm a mix of excited and scared to get into fashion, but I'm trying to stay upbeat and learn as much as I can. What about you? Got something new?" - }, - { - "speaker": "Jon", - "dia_id": "D12:6", - "text": "I'm currently reading \"The Lean Startup\" and hoping it'll give me tips for my biz." - }, - { - "speaker": "Gina", - "dia_id": "D12:7", - "text": "It sounds great! Could it spark any ideas for your dance studio?" - }, - { - "speaker": "Jon", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1615494917/at/living/2021-03/IMG_5070.jpg" - ], - "blip_caption": "a photo of a white board with a list of dates on it", - "query": "whiteboard business ideas", - "dia_id": "D12:8", - "text": "Yeah, the book got me thinking about building a focused and efficient business. Adapting and tweaking from customer feedback is important too, so I'm gonna try it out!" - }, - { - "speaker": "Gina", - "dia_id": "D12:9", - "text": "Woah, Jon, that whiteboard's got a bunch of good ideas! How you gonna keep track and stay on schedule with those dates?" - }, - { - "speaker": "Jon", - "dia_id": "D12:10", - "text": "Thanks, Gina! It helps me keep track of ideas and milestones. Gives me a visual of my progress and keeps me organized." - }, - { - "speaker": "Gina", - "dia_id": "D12:11", - "text": "Nice idea! Having something visual can help with organizing and motivation. What're you working on currently?" - }, - { - "speaker": "Jon", - "dia_id": "D12:12", - "text": "I'm wrapping up the business plan and looking for investors. My passion for the project and belief in its success are driving me." - }, - { - "speaker": "Gina", - "dia_id": "D12:13", - "text": "Wow, Jon! Impressed by your commitment. How's the hunt for investors going?" - }, - { - "speaker": "Jon", - "dia_id": "D12:14", - "text": "Thanks! Searching for investors has been tough, but I'm staying hopeful. It's all a process and I'm learning a ton." - }, - { - "speaker": "Gina", - "dia_id": "D12:15", - "text": "Yeah Jon, you've got the right attitude! Keep learning and growing through it all. Keep going!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a pink sign with a message on it", - "dia_id": "D12:16", - "text": "Thanks! I really appreciate your help. I'm gonna keep on going and never quit." - }, - { - "speaker": "Gina", - "dia_id": "D12:17", - "text": "Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D12:18", - "text": "Thanks! Your words really mean a lot. Don't worry, I won't let anything get me down." - }, - { - "speaker": "Gina", - "dia_id": "D12:19", - "text": "Go Jon! Obstacles are inevitable, but you can do awesome things. Keep going!" - } - ], - "session_13_date_time": "8:29 pm on 13 June, 2023", - "session_13": [ - { - "speaker": "Jon", - "dia_id": "D13:1", - "text": "Hey Gina, thanks for being there for me and believing in me. It means a lot." - }, - { - "speaker": "Gina", - "dia_id": "D13:2", - "text": "Sure, Jon! I'm always around for you. How've you been since we caught up?" - }, - { - "speaker": "Jon", - "dia_id": "D13:3", - "text": " I'm prepping for my dance studio more than ever!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a group of young girls in blue outfits posing for a picture", - "dia_id": "D13:4", - "text": "Proud of you for starting your own business! It takes strength to stay hopeful. What are you doing to prep for your dance studio? I have developed a video presentation to teach how to style my fashion pieces, btw. " - }, - { - "speaker": "Jon", - "dia_id": "D13:5", - "text": "Thanks, Gina! It's been so inspiring to work with our young dancers, seeing their passion and commitment. Opening the dance studio's been a great experience - I want it to be a place of support and encouragement for all our dancers. Will you show me this presentation?" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a skeleton and a trophy on a black cloth", - "dia_id": "D13:6", - "text": "Shure, I will send it to you later. Starting this studio isn't just a business, it's a place for dancers to grow. What support are you offering them?" - }, - { - "speaker": "Jon", - "dia_id": "D13:7", - "text": "Besides the dance classes and workshops, I'm offering one-on-one mentoring and training to help dancers reach their full potential." - }, - { - "speaker": "Gina", - "img_url": [ - "https://christinasearsetternet.files.wordpress.com/2020/08/img_1232.jpg" - ], - "blip_caption": "a photo of a group of people in a dance class", - "query": "dancers mentoring and training", - "dia_id": "D13:8", - "text": "Your one-on-one mentoring and training will really push dancers to reach their goals. I totally get it, I had a mentor too when I was learning how to dance." - }, - { - "speaker": "Jon", - "img_url": [ - "https://i.redd.it/2308a70tm0m81.jpg" - ], - "blip_caption": "a photo of a clipboard with a notepad attached to it", - "query": "whiteboard dance moves", - "dia_id": "D13:9", - "text": "Yeah, a mentor can do wonders. Guidance and support can help dancers really shine." - }, - { - "speaker": "Gina", - "dia_id": "D13:10", - "text": "Nice one, Jon! How've you been using it?" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a notebook with a calendar on it", - "dia_id": "D13:11", - "text": "I'm using it to stay organized and motivated. It sets goals, tracks my achievements and helps me find areas to improve." - }, - { - "speaker": "Gina", - "dia_id": "D13:12", - "text": "Really cool system! Being organized sure is key for getting stuff done. How do you keep up with your goals and progress?" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a group of markers sitting on top of a white surface", - "dia_id": "D13:13", - "text": "I also use whiteboard to stay on track, visualize goals and tokenize successes. It keeps me motivated and focused." - }, - { - "speaker": "Gina", - "dia_id": "D13:14", - "text": "Cool! Are you using different colors?" - }, - { - "speaker": "Jon", - "dia_id": "D13:15", - "text": "Yes. I color-code achievements so I can easily track my progress and stay motivated." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a cork board with pictures and words on it", - "dia_id": "D13:16", - "text": "Wow, color-coding is a great way to track your progress & stay motivated. Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D13:17", - "text": "Thanks! It helps me stay motivated and reminds me why I'm doing this." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a person holding a paper bag with a picture of a bird and a birdie", - "dia_id": "D13:18", - "text": "Yeah, its a great idea!" - }, - { - "speaker": "Jon", - "dia_id": "D13:19", - "text": "Thanks!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a quote on a white sheet with a rainbow of light", - "dia_id": "D13:20", - "text": "Remember that staying positive is very important. Rock on!" - }, - { - "speaker": "Jon", - "dia_id": "D13:21", - "text": "Thanks, Gina! Your support's really been awesome." - }, - { - "speaker": "Gina", - "dia_id": "D13:22", - "text": "Thanks! Keep going for your dreams and don't quit!" - }, - { - "speaker": "Jon", - "dia_id": "D13:23", - "text": "I won't give up, Gina! I'll keep pushing and working to make my dreams happen. Thanks for the support!" - } - ], - "session_14_date_time": "9:38 pm on 16 June, 2023", - "session_14": [ - { - "speaker": "Jon", - "dia_id": "D14:1", - "text": "Gina, you won't believe it - I got mentored by this amazing business dude yesterday! It was really inspiring and now I'm even more pumped to chase my dreams. What's been up with you lately?" - }, - { - "speaker": "Gina", - "dia_id": "D14:2", - "text": "Wow, Jon! Mentors can really help. I'm working on my online store, growing the customer base. It's tough but I'm determined. How about you? Any new things happening?" - }, - { - "speaker": "Jon", - "dia_id": "D14:3", - "text": "Been doing some promotion for my business. Crazy ride so far, but I'm hanging in there. Got any tips for marketing?" - }, - { - "speaker": "Gina", - "dia_id": "D14:4", - "text": "Awesome! Marketing is key. Use social media channels and work with influencers for bigger reach." - }, - { - "speaker": "Jon", - "dia_id": "D14:5", - "text": "Thanks for the advice, Gina! I already started doing what you said about social media and posted some of my dance videos. It's creating a bit of a stir." - }, - { - "speaker": "Gina", - "dia_id": "D14:6", - "text": "Nice! Glad your dance vids are doing well online. Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D14:7", - "text": "Your help really helps. Hey, have you thought about being an entrepreneur?" - }, - { - "speaker": "Gina", - "dia_id": "D14:8", - "text": "Ha, yeah, Jon. I've been one 'cause I lost my job. I opened an online clothing store and it's been great! Being my own boss and doing something I love is awesome." - }, - { - "speaker": "Jon", - "dia_id": "D14:9", - "text": "Wow, Gina! You did great taking that leap. Congrats! Got any advice for someone just starting out?" - }, - { - "speaker": "Gina", - "dia_id": "D14:10", - "text": "Thanks, Jon! It was a huge jump, but totally worth it. My advice: stay passionate, focused and resilient. Challenges will come, but believe in yourself and keep going. And stay open to learning and improving." - }, - { - "speaker": "Jon", - "dia_id": "D14:11", - "text": "Appreciate your advice. Gotta stay resilient and focused, that's key!" - }, - { - "speaker": "Gina", - "dia_id": "D14:12", - "text": "Yep Jon, staying resilient and focused is key for any entrepreneur. Keep going and don't give up! You got this!" - }, - { - "speaker": "Jon", - "dia_id": "D14:13", - "text": "Thanks! I won't quit, no matter what. Your encouragement really motivates me to keep going." - }, - { - "speaker": "Gina", - "img_url": [ - "https://cdn1.sportngin.com/attachments/photo/7609-187142791/PINE_ISLAND_WOW_AWARD.jpg" - ], - "blip_caption": "a photo of a group of young women posing for a picture", - "query": "dance competition trophy stage", - "dia_id": "D14:14", - "text": "Way to go, Jon! Don't quit, remember, failures lead you closer to success. Here's a pic from when I was dancing - it was a tough road, but it was worth it!" - }, - { - "speaker": "Jon", - "dia_id": "D14:15", - "text": "Wow, that's an awesome pic! You guys look great and passionate about dancing. Reminds me how much I love performing. Thanks for sharing!" - }, - { - "speaker": "Gina", - "dia_id": "D14:16", - "text": "Thanks! I'm glad the pic reminded you of your love for dancing. Keep going after your dreams!" - }, - { - "speaker": "Jon", - "dia_id": "D14:17", - "text": "Sure thing, Gina! Your help means a lot to me. I'm not giving up." - }, - { - "speaker": "Gina", - "dia_id": "D14:18", - "text": "Go, Jon! I'm here for you. Keep going!" - }, - { - "speaker": "Jon", - "dia_id": "D14:19", - "text": "Knowing you've got my back really helps keep me going. I won't let you down!" - }, - { - "speaker": "Gina", - "dia_id": "D14:20", - "text": "No worries, Jon! You're really inspiring with your determination and passion for dance. Keep it up!" - } - ], - "session_15_date_time": "10:04 am on 19 June, 2023", - "session_15": [ - { - "speaker": "Jon", - "dia_id": "D15:1", - "text": "Hey Gina, hope you're doing great! Still working on my biz. Took a short trip last week to Rome to clear my mind a little." - }, - { - "speaker": "Gina", - "dia_id": "D15:2", - "text": "Hi! Good for you! It definitely will help you to concentrate on your biz better." - }, - { - "speaker": "Jon", - "dia_id": "D15:3", - "text": "Thanks, Gina. Still working on opening a dance studio." - }, - { - "speaker": "Gina", - "dia_id": "D15:4", - "text": "When are you opening the studio?" - }, - { - "speaker": "Jon", - "img_url": [ - "https://images.pexels.com/photos/11686640/pexels-photo-11686640.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-ph%E1%BA%A1m-chung-11686640.jpg" - ], - "blip_caption": "a photo of a group of young dancers in a dance studio", - "query": "dance studio front entrance dancers rehearsing", - "dia_id": "D15:5", - "text": "The official opening night is tomorrow. I'm working hard to make everything just right. Can't wait to see it all come together!" - }, - { - "speaker": "Gina", - "dia_id": "D15:6", - "text": "Congrats, Jon! The studio looks amazing. You've put a lot of work into this and I'm so pumped for the launch tomorrow. Don't miss a beat!" - }, - { - "speaker": "Jon", - "dia_id": "D15:7", - "text": "Thanks, Gina! I'm excited! It's been a wild ride, but I'm feeling good and ready to give it my best." - }, - { - "speaker": "Gina", - "dia_id": "D15:8", - "text": "Wow, Jon, you must be so excited! You've come so far since we last talked, and tomorrow's gonna be a blast! All those long nights were worth it - so take some time to savor it. Capture the joy and thrill that dance brings - it's magical!" - }, - { - "speaker": "Jon", - "dia_id": "D15:9", - "text": "Tomorrow's gonna be an awesome night and I'm not gonna forget a second of it. I put so much into this and I want to savor all the good vibes. Thanks for always having my back. You're the best!" - }, - { - "speaker": "Gina", - "dia_id": "D15:10", - "text": "I'm always proud of you. Enjoy the good feels tomorrow, you earned it!" - }, - { - "speaker": "Jon", - "dia_id": "D15:11", - "text": "Thanks! Your pride and support mean a lot. Looking forward to enjoying the moment with you." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a group of people in a dance studio", - "dia_id": "D15:12", - "text": "I'll be right by your side, Jon. Let's live it up and make some great memories tomorrow. So excited!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a man in a native costume is giving another man a high five", - "dia_id": "D15:13", - "text": "Yeah! Let's make some awesome memories tomorrow at the grand opening!" - }, - { - "speaker": "Gina", - "dia_id": "D15:14", - "text": "Can't wait to make more memories at your dance studio!" - }, - { - "speaker": "Jon", - "dia_id": "D15:15", - "text": "Looking forward to more cool memories!" - }, - { - "speaker": "Gina", - "dia_id": "D15:16", - "text": "I love being around friends and having such a great time. Can't wait to have fun at your dance studio!" - }, - { - "speaker": "Jon", - "dia_id": "D15:17", - "text": "Agreed!" - }, - { - "speaker": "Gina", - "dia_id": "D15:18", - "text": "Can't wait for tomorrow's grand opening!" - }, - { - "speaker": "Jon", - "dia_id": "D15:19", - "text": "Woohoo! Tomorrow's opening will be so much fun. Can't wait for it - and for you to be there!" - }, - { - "speaker": "Gina", - "dia_id": "D15:20", - "text": "Can't wait too!" - }, - { - "speaker": "Jon", - "dia_id": "D15:21", - "text": "Definitely! Let's make tomorrow unforgettable, Gina. See you there! Bye!" - }, - { - "speaker": "Gina", - "dia_id": "D15:22", - "text": "See you tomorrow. Bye!" - } - ], - "session_16_date_time": "2:15 pm on 21 June, 2023", - "session_16": [ - { - "speaker": "Gina", - "img_url": [ - "https://www.staycoldapparel.com/cdn/shop/files/Foto28.09.23_122631.jpg" - ], - "blip_caption": "a photo of a woman in a black hoodie posing for a picture", - "query": "online clothing store logo fashion unique pieces", - "dia_id": "D16:1", - "text": "Hey Jon, what's been up? Some pretty cool stuff happened since we talked. I have acquired some new unique pieces for my store." - }, - { - "speaker": "Jon", - "dia_id": "D16:2", - "text": "Congrats on your store, Gina! Happy for you! It looks sick - is it a unique piece you're selling?" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a hoodie with a camouflage print on it", - "dia_id": "D16:3", - "text": "Thanks! This hoodie isn't for sale, it's from my own collection. I made a limited edition line last week to show off my style and creativity - it was tough but worth it!" - }, - { - "speaker": "Jon", - "dia_id": "D16:4", - "text": "What gave you the idea?" - }, - { - "speaker": "Gina", - "dia_id": "D16:5", - "text": "This design reminds me of the grit it takes to stand out and face challenges." - }, - { - "speaker": "Jon", - "dia_id": "D16:6", - "text": "That's awesome, Gina! Yesterday I chose to go to networking events to make things happen. It's been tough but I'm staying determined and focused." - }, - { - "speaker": "Gina", - "dia_id": "D16:7", - "text": "Way to go, Jon! Attending those networking events takes guts and drive. Keep it up!" - }, - { - "speaker": "Jon", - "dia_id": "D16:8", - "text": "Thanks! It's been tough going since I lost my job, but I'm sure investing my time in my business will pay off eventually. I really appreciate your help." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a notepad with a pen and a pen on it", - "dia_id": "D16:9", - "text": "No worries, Jon! You got this! Let me know if you need anything." - }, - { - "speaker": "Jon", - "dia_id": "D16:10", - "text": "Your help matters to me. I am writing all my plans down." - }, - { - "speaker": "Gina", - "dia_id": "D16:11", - "text": "Nice work! Tracking your plans and goals is key. It's like a picture of all your progress." - }, - { - "speaker": "Jon", - "dia_id": "D16:12", - "text": "Thanks, Gina! Seeing my goals written down on paper really helps keep me motivated and focused on what I have to do. I know it won't be easy, but I'm sure it'll pay off. Thanks for the support!" - }, - { - "speaker": "Gina", - "img_url": [ - "https://i.redd.it/b1sgp694awda1.jpg" - ], - "blip_caption": "a photo of a sign that says never give up never give up never", - "query": "never give up sign", - "dia_id": "D16:13", - "text": "No worries, Jon! When things get rough, keep persevering and keep working hard. You'll get there! Don't quit!" - }, - { - "speaker": "Jon", - "dia_id": "D16:14", - "text": "Thanks, Gina! That sign reminds me to never give up, however hard things get. I'll keep going!" - }, - { - "speaker": "Gina", - "dia_id": "D16:15", - "text": "Believe in yourself and keep going. You can do it!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a bulletin board with pictures of people and words", - "dia_id": "D16:16", - "text": "Thanks! I'm feeling confident and won't give up. Your support means a ton to me." - } - ], - "session_17_date_time": "1:25 pm on 9 July, 2023", - "session_17": [ - { - "speaker": "Gina", - "blip_caption": "a photo of a mannequin in a room with a wood wall", - "dia_id": "D17:1", - "text": "Hey Jon! Long time no chat! How's the dance studio? Last week was wild, I got noticed by fashion editors and it's been amazing but kinda scary. Everything's exciting but it's a lot of pressure to keep going up!" - }, - { - "speaker": "Jon", - "dia_id": "D17:2", - "text": "Hey Gina! Congrats on the fashion editors reach-out, that's awesome! Dance practice has been fun and exhausting. I'm gonna stay determined and make my own path by going full-time with my biz idea." - }, - { - "speaker": "Gina", - "dia_id": "D17:3", - "text": "Just remember that sometimes stumbling blocks can be opened doors. Keep going!" - }, - { - "speaker": "Jon", - "dia_id": "D17:4", - "text": "Thanks! Your support and encouragement means a lot. Losing my job was a bummer, but it pushed me to take the plunge and go for my biz dreams. Started to learn all these marketing and analytics tools to push the biz forward today. It's been tricky, but I'm up for the challenge and I'm gonna make this work!" - }, - { - "speaker": "Gina", - "dia_id": "D17:5", - "text": "Go get 'em, Jon!" - }, - { - "speaker": "Jon", - "dia_id": "D17:6", - "text": "I'm also excited to guide and mentor aspiring dancers on their dreams." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a drawing of a couple dancing", - "dia_id": "D17:7", - "text": "Wow, Jon! That's awesome. Loving what you do and bringing joy to others is so rewarding. You're definitely the perfect mentor & guide. Your positivity and determination will make your dance studio a hit!" - }, - { - "speaker": "Jon", - "dia_id": "D17:8", - "text": "Thanks, Gina - really appreciate your words and encouragement! Dance has the power to bring us together and create sweet moments. Moments like this remind me why I'm chasing my dream and keep me pushing through any struggles." - }, - { - "speaker": "Gina", - "dia_id": "D17:9", - "text": "Take comfort in knowing you've got a solid community cheering you on, me included. Keep on pushing!" - }, - { - "speaker": "Jon", - "dia_id": "D17:10", - "text": "Feeling supported by all of you means so much. It gives me the oomph to keep chasing my dreams. Your faith in me is priceless - I won't let you down!" - }, - { - "speaker": "Gina", - "dia_id": "D17:11", - "text": "Don't let anything stop you. You have potential!" - }, - { - "speaker": "Jon", - "dia_id": "D17:12", - "text": "Thanks, Gina! Your faith in me is a real boost. I'm gonna make my dreams come true!" - }, - { - "speaker": "Gina", - "dia_id": "D17:13", - "text": "Keep pushing and you'll get there. Your dreams are so close!" - }, - { - "speaker": "Jon", - "dia_id": "D17:14", - "text": "Thanks, Gina! I won't quit, even when it's hard. I'm gonna make it!" - }, - { - "speaker": "Gina", - "dia_id": "D17:15", - "text": "You got this, Jon! Don't let the bumps in the road bring you down. Keep going and make your dreams a reality! I'm rooting for you!" - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a chalkboard with a quote written on it", - "dia_id": "D17:16", - "text": "Thanks, Gina! Your belief in me means the world. I'm not gonna let anything or anyone stop me. I'll keep pushing and make my dreams come true. Thanks for being a great friend. You rock!" - }, - { - "speaker": "Gina", - "dia_id": "D17:17", - "text": "Hey Jon, glad I could help! Always here to cheer you on." - }, - { - "speaker": "Jon", - "img_url": [ - "https://i.redd.it/1jjaus9f0sab1.jpg" - ], - "blip_caption": "a photo of a drawing of a smiley face floating in the water", - "query": "childhood drawing soaring sky", - "dia_id": "D17:18", - "text": "Thanks! Glad that you are on my side." - }, - { - "speaker": "Gina", - "dia_id": "D17:19", - "text": "Sure, see ya. Bye!" - }, - { - "speaker": "Jon", - "dia_id": "D17:20", - "text": "Bye!" - }, - { - "speaker": "Gina", - "dia_id": "D17:21", - "text": ";)" - } - ], - "session_18_date_time": "5:44 pm on 21 July, 2023", - "session_18": [ - { - "speaker": "Gina", - "dia_id": "D18:1", - "text": "Hey Jon! Long time no talk! Last week, I built a new website for customers to make orders. It's been a wild ride but I'm loving it. What's up with you? How's the dance studio?" - }, - { - "speaker": "Jon", - "dia_id": "D18:2", - "text": "Hey Gina, congrats on the clothing store! The dance studio is on tenuous grounds right now, but I'm staying positive. I got a temp job to help cover expenses while I look for investors. It's tough, but I'm sure it'll be worth it." - }, - { - "speaker": "Gina", - "dia_id": "D18:3", - "text": "Thanks, Jon! Appreciate the kind words. Sorry to hear about the studio, but glad to see the positivity. Not easy facing setbacks but I believe in you. Finding investors can be tough, but you've got the passion and experience to make it happen. Rome wasn't built in a day so keep pushing on!" - }, - { - "speaker": "Jon", - "dia_id": "D18:4", - "text": "Thanks for the support. Running a business isn't easy, but I'm determined to make it work. How have you tackled challenges in your business? Got any advice?" - }, - { - "speaker": "Gina", - "dia_id": "D18:5", - "text": "I've had some tough times with my business, Jon. Sourcing trendy pieces for my store was a big hurdle. I had to do a lot of research and networking. My advice? Don't be scared to reach out to people in your field for help and contacts. Networking was a lifesaver for me and opened me up to amazing products that I might not have found otherwise." - }, - { - "speaker": "Jon", - "dia_id": "D18:6", - "img_url": [ - "https://live.staticflickr.com/7284/16241297914_14ea605e4b_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a group of people standing in a room", - "text": "Awesome advice! Lately I've been networking and it's gotten me some good stuff. Really can't beat what connections can do. Check this pic I got from the last networking event!" - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a clothing store with a wall of pictures and clothes", - "dia_id": "D18:7", - "text": "Nice one, Jon! Networking really pays off. Connecting with like-minded people is key. How was the event?" - }, - { - "speaker": "Jon", - "img_url": [ - "https://cress.gigsalad.com/s3/attachments/61/02/47/610247e1e1a92f951cf413a4cdedcde0.jpg" - ], - "blip_caption": "a photo of a man signing a card at a table", - "query": "crowded networking event people mingling business cards", - "dia_id": "D18:8", - "text": "Thanks! The event was awesome. I met some investors and got some good advice. The energy was really motivating, it gave me a boost to go after my goals." - }, - { - "speaker": "Gina", - "dia_id": "D18:9", - "text": "Wow, Jon! Congrats on the successful night! What are your plans now with the advice you got?" - }, - { - "speaker": "Jon", - "dia_id": "D18:10", - "text": "Taking your advice, I'm sprucing up my biz plan and tweaking my pitch to investors. I'm also working on an online platform to show off the dance studio's stuff." - }, - { - "speaker": "Gina", - "dia_id": "D18:11", - "text": "Sounds like a great plan, Jon! An online platform can really show off your studio and get investors. Need help with anything?" - }, - { - "speaker": "Jon", - "dia_id": "D18:12", - "text": "Thanks, Gina! Appreciate the offer. Need help with marketing strategies - any advice on reaching my target audience and raising awareness for the dance studio?" - }, - { - "speaker": "Gina", - "dia_id": "D18:13", - "text": "Yeah Jon, marketing is key for getting your dance studio noticed. Instagram and TikTok can help you reach a younger crowd. Posting dance clips or content related to dance can help. You could also collaborate with local influencers or dance communities. I could help you with making content or even managing your accounts if you want." - }, - { - "speaker": "Jon", - "blip_caption": "a photo of a room with a mirror and a desk", - "dia_id": "D18:14", - "text": "Sounds great. I'd really appreciate your help with making content and managing my social media. Let's get together and make the dance studio look awesome!" - }, - { - "speaker": "Gina", - "dia_id": "D18:15", - "text": "Let's create some cool content and manage your social media accounts." - }, - { - "speaker": "Jon", - "dia_id": "D18:16", - "text": "Thanks for the support. You rock!" - }, - { - "speaker": "Gina", - "dia_id": "D18:17", - "text": "Thanks, Jon! You're awesome. Let's get to work and make your studio shine!" - }, - { - "speaker": "Jon", - "dia_id": "D18:18", - "text": "Definitely, Gina! Let's make our collaboration awesome and bring some dance magic to the world. Can't wait to see what we can do together!" - }, - { - "speaker": "Gina", - "dia_id": "D18:19", - "text": "Definitely, Jon! I'm pumped to collaborate with you and make some sweet moves. Together, we can make a difference and show the world what we can do. Let's go for it!" - }, - { - "speaker": "Jon", - "dia_id": "D18:20", - "text": "Yeah, Gina! We'll rock the dance floor and teach others to chase their dreams. Let's go for it and make an impact!" - }, - { - "speaker": "Gina", - "dia_id": "D18:21", - "text": "Yeah Jon! Let's make a difference and show 'em what we got. We can do amazing things together!" - }, - { - "speaker": "Jon", - "dia_id": "D18:22", - "text": "Thanks for having my back." - } - ], - "session_19_date_time": "6:46 pm on 23 July, 2023", - "session_19": [ - { - "speaker": "Jon", - "dia_id": "D19:1", - "text": "Hey Gina! We haven't talked in a few days. Been rehearsing hard and working on business plans. It's been stressful, but dancing has kept me going." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of a group of dancers on a stage with their arms in the air", - "dia_id": "D19:2", - "text": "Hey Jon! Remember, just do it! You should get to the point where anyone else would quit and you're not going to stop there. No, what are you waiting for? Do it! Just do it!" - }, - { - "speaker": "Jon", - "dia_id": "D19:3", - "text": "Ha, ha! Thanks, Gina. Sounds familiar, who do those words belong to?" - }, - { - "speaker": "Gina", - "dia_id": "D19:4", - "text": "It's Shia Labeouf!" - }, - { - "speaker": "Jon", - "dia_id": "D19:5", - "text": "Ahhahha, really!? Yea, that definitely him." - }, - { - "speaker": "Gina", - "blip_caption": "a photo of three girls in ballet costumes sitting on a desk", - "dia_id": "D19:6", - "text": "Hah, yeah!) But really having a creative space for dancers is so important. Last Friday at dance class with a group of friends I felt it. Your studio will be a go-to spot for self-expression. Keep up the good work and don't forget your passion for dance." - }, - { - "speaker": "Jon", - "dia_id": "D19:7", - "text": "Thanks, Gina! Your words of encouragement keep me motivated. Can't wait 'til my studio starts welcoming dancers of all ages and backgrounds!" - }, - { - "speaker": "Gina", - "dia_id": "D19:8", - "text": "I'm so happy to see my words motivating you, Jon. <3" - }, - { - "speaker": "Jon", - "dia_id": "D19:9", - "text": "Thanks a ton, Gina! Your help and encouragement mean a lot. Your support will help me make it happen." - }, - { - "speaker": "Gina", - "dia_id": "D19:10", - "text": "You're welcome, Jon! I'm here to support you. Every step's getting you closer to your dream. Never give up! You're doing great." - }, - { - "speaker": "Jon", - "dia_id": "D19:11", - "text": "Thanks, Gina! I won't quit. I'm gonna keep going, whatever comes my way." - }, - { - "speaker": "Gina", - "dia_id": "D19:12", - "text": "Remember Jon, Just do it!" - }, - { - "speaker": "Jon", - "dia_id": "D19:13", - "text": "Ah ha ha, yeah, JUST DOING IT!" - }, - { - "speaker": "Gina", - "dia_id": "D19:14", - "text": "That's the spirit! Bye!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Jon": [ - "Jon loses his job as a banker.", - "Jon begins planning for his own business venture." - ], - "Gina": [ - "Gina loses her job at Door Dash." - ], - "date": "20 January, 2023" - }, - "events_session_2": { - "Jon": [ - "Jon returns from a trip to Paris." - ], - "Gina": [ - "Gina orders advertising to promote her store." - ], - "date": "29 January, 2023" - }, - "events_session_3": { - "Jon": [], - "Gina": [ - "Gina reaches out to potential wholesalers." - ], - "date": "1 February, 2023" - }, - "events_session_4": { - "Jon": [ - "Jon puts in a great deal of effort into his own business venture despite the difficulties.", - "Jon starts rehearsing for an upcoming dance competition." - ], - "Gina": [], - "date": "4 February, 2023" - }, - "events_session_5": { - "Jon": [ - "Jon puts up a performance showcasing his dance moves at a local festival." - ], - "Gina": [ - "Gina works with an artist to acquire a new fashion piece for her store." - ], - "date": "8 February, 2023" - }, - "events_session_6": { - "Jon": [ - "Jon joins a gym to stay fit while pursuing his business venture." - ], - "Gina": [ - "Gina opens her online clothing store" - ], - "date": "16 March, 2023" - }, - "events_session_7": { - "Jon": [], - "Gina": [], - "date": "23 March, 2023" - }, - "events_session_8": { - "Jon": [ - "Jon shuts down his bank account to help his business grow." - ], - "Gina": [ - "Gina introduces offers on her store to attract new customers." - ], - "date": "3 April, 2023" - }, - "events_session_9": { - "Jon": [], - "Gina": [], - "date": "9 April, 2023" - }, - "events_session_10": { - "Jon": [ - "Jon visits a fair to get more exposure for his dance studio.", - "Jon begins to understand the importance of confidence in running a successful business." - ], - "Gina": [], - "date": "25 April, 2023" - }, - "events_session_11": { - "Jon": [], - "Gina": [ - "Gina gets an interview for a design internship related to her store." - ], - "date": "11 May, 2023" - }, - "events_session_12": { - "Jon": [], - "Gina": [ - "Gina is accepted for the design internship and starts a part-time job in the fashion department of an international company." - ], - "date": "27 May, 2023" - }, - "events_session_13": { - "Jon": [], - "Gina": [ - "Gina develops a video presentation to teach how to style her fashion pieces." - ], - "date": "13 June, 2023" - }, - "events_session_14": { - "Jon": [ - "Jon receives mentorship from an experienced businessman on how to promote his venture." - ], - "Gina": [], - "date": "16 June, 2023" - }, - "events_session_15": { - "Jon": [ - "Jon holds an official opening night for his dance studio." - ], - "Gina": [], - "date": "19 June, 2023" - }, - "events_session_16": { - "Jon": [ - "Jon decides to attend networking events to make connections for his business venture." - ], - "Gina": [ - "Gina creates unique limited-edition pieces to show off her skills." - ], - "date": "21 June, 2023" - }, - "events_session_17": { - "Jon": [ - "Jon starts to learn how to use modern tools and software for marketing and analytics." - ], - "Gina": [ - "Gina starts to get recognized by fashion editors." - ], - "date": "9 July, 2023" - }, - "events_session_18": { - "Jon": [ - "Jon takes up a temporary job to cover his expenses while waiting for investors.", - "Jon starts working on an online platform to showcase his dance studio." - ], - "Gina": [ - "Gina creates a new website for her customers to make orders." - ], - "date": "21 July, 2023" - }, - "events_session_19": { - "Jon": [], - "Gina": [ - "Gina takes a dance class with a group of friends." - ], - "date": "23 July, 2023" - } - }, - "observation": { - "session_1_observation": { - "Gina": [ - [ - "Gina lost her job at Door Dash during the month of the conversation.", - "D1:3" - ], - [ - "Gina used to compete in dance competitions and shows, winning first place in a regional competition at the age of fifteen.", - "D1:17" - ], - [ - "Gina's favorite dance style is contemporary.", - "D1:9" - ] - ], - "Jon": [ - [ - "Jon lost his job as a banker the day before the conversation.", - "D1:2" - ], - [ - "Jon is starting his own dance studio due to his passion for dancing.", - "D1:4" - ], - [ - "Jon's favorite dance style is contemporary.", - "D1:8" - ], - [ - "Jon practices various dances with a small group, including contemporary and hip-hop, and is working on choreography for a nearby festival.", - "D1:24" - ] - ] - }, - "session_2_observation": { - "Gina": [ - [ - "Gina launched an ad campaign for her clothing store in hopes of growing the business.", - "D2:1" - ], - [ - "Gina started her own clothing store and finds taking risks scary but rewarding.", - "D2:1" - ], - [ - "Gina appreciates seeing her vision for the store come to life.", - "D2:3" - ], - [ - "Gina has been to Rome once.", - "D2:5" - ], - [ - "Gina believes that good flooring helps avoid injuries and makes dancing more enjoyable.", - "D2:7" - ], - [ - "Gina appreciates Jon's determination to make his dance studio work.", - "D2:11" - ] - ], - "Jon": [ - [ - "Jon is looking for the ideal spot for his dance studio and is considering features like size, natural light, and flooring.", - "D2:4" - ], - [ - "Jon visited Paris recently", - "D2:4" - ], - [ - "Jon is looking for Marley flooring for his dance studio due to its grip, movement, durability, and cleanliness.", - "D2:8" - ], - [ - "Jon finds the process of setting up his dance studio a mix of exciting and nerve-wracking but is determined to make it work.", - "D2:10" - ], - [ - "Jon believes in himself and is grateful for Gina's support.", - "D2:12" - ] - ] - }, - "session_3_observation": { - "Jon": [ - [ - "Jon is following his passion for dance and searching for a place to open his dance studio.", - "D3:1" - ] - ], - "Gina": [ - [ - "Gina is enthusiastic about expanding her clothing store and successfully got a positive response from a wholesaler.", - "D3:2" - ], - [ - "Gina designed her clothing store space to be cozy and inviting for her customers.", - "D3:4" - ], - [ - "Gina chose furniture and decor for her store that match her style and make customers feel comfortable.", - "D3:6" - ], - [ - "Gina's goal is to create a comfortable and inviting shopping experience for her customers.", - "D3:8" - ] - ] - }, - "session_4_observation": { - "Jon": [ - [ - "Jon is working hard on his business despite facing obstacles.", - "D4:3" - ], - [ - "Jon is determined to make his business successful and reach his dreams.", - "D4:5" - ], - [ - "Jon is searching for a dance studio location and is determined to find the right spot.", - "D4:9" - ], - [ - "Jon is working on new dance routines and rehearsing hard for an upcoming show.", - "D4:11" - ], - [ - "Jon is passionate about dancing and it brings him joy and fulfillment.", - "D4:11" - ], - [ - "Jon is preparing for a dance competition near him next month to showcase his skills.", - "D4:13" - ] - ], - "Gina": [ - [ - "Gina's store is doing great and it's a wild ride.", - "D4:2" - ], - [ - "Gina encourages Jon to keep pushing, assuring him he'll reach his dreams.", - "D4:4" - ], - [ - "Gina supports Jon through setbacks and encourages him not to give up.", - "D4:6" - ], - [ - "Gina is supportive of Jon and assures him that setbacks can be overcome.", - "D4:6" - ], - [ - "Gina expresses full support for Jon and encourages him not to give up.", - "D4:6" - ], - [ - "Gina encourages Jon to hang in there while searching for the perfect dance studio location.", - "D4:10" - ], - [ - "Gina compliments Jon's talent and expresses confidence in his success in the upcoming dance competition.", - "D4:14" - ] - ] - }, - "session_5_observation": { - "Gina": [ - [ - "Gina owns a store where she sells fashion pieces.", - "D5:1" - ], - [ - "Gina has an online store and recently teamed up with a local artist for some cool designs.", - "D5:5" - ], - [ - "Gina got a tattoo a few years ago that stands for freedom and a reminder to follow her passions and express herself.", - "D5:15" - ] - ], - "Jon": [ - [ - "Jon is a dancer who runs his own dance studio.", - "D5:4" - ], - [ - "Jon left his secure 9-5 job as a banker to pursue his passion for dancing and turn it into a business.", - "D5:10" - ], - [ - "Jon finds joy and passion in dancing.", - "D5:2" - ], - [ - "Jon believes taking risks is essential for success and transformation.", - "D5:8" - ], - [ - "Jon juggles between running his dance studio and pursuing business goals, finding it rewarding and energizing.", - "D5:20" - ] - ] - }, - "session_6_observation": { - "Jon": [ - [ - "Jon started hitting the gym last week to stay on track with a venture.", - "D6:1" - ], - [ - "Jon lost his job at Door Dash.", - "D6:4" - ], - [ - "Jon is starting his own business after losing his job and finds it tough but fulfilling.", - "D6:11" - ], - [ - "Jon finds Gina's success inspiring and her determination awesome.", - "D6:13" - ], - [ - "Jon feels motivated by facing similar challenges to Gina and considers her like a partner in this journey.", - "D6:15" - ], - [ - "Jon is encouraged by Gina's journey and finds it inspiring.", - "D6:19" - ] - ], - "Gina": [ - [ - "Gina lost her job at Door Dash.", - "D6:4" - ], - [ - "Gina opened an online clothes store which she had been dreaming of for a while.", - "D6:6" - ], - [ - "Gina is passionate about fashion trends and unique pieces, and she blended her love for dance and fashion in starting the online store.", - "D6:8" - ], - [ - "Gina finds starting and running her own business to be a rollercoaster ride with its ups and downs.", - "D6:12" - ], - [ - "Gina feels inspired by Jon's determination and success, and his words of encouragement mean a lot to her.", - "D6:14" - ], - [ - "Gina sees Jon as someone on a different path but appreciates having him as someone to root for and support each other.", - "D6:16" - ], - [ - "Gina values having someone like Jon to back her up and encourages each other to chase their dreams and achieve great things together.", - "D6:18" - ] - ] - }, - "session_7_observation": { - "Jon": [ - [ - "Jon has a dance studio business where he helps his students learn and reach their goals.", - "D7:7" - ] - ], - "Gina": [ - [ - "Gina runs an online clothing store and has been keeping up with fashion trends to offer the best pieces to customers.", - "D7:4" - ], - [ - "Gina values building relationships and creating a strong brand image for her store.", - "D7:6" - ] - ] - }, - "session_8_observation": { - "Jon": [ - [ - "Jon had to shut down his bank account for his business.", - "D8:1" - ], - [ - "Jon is handling changes positively and looking ahead despite facing challenges.", - "D8:3" - ], - [ - "Jon is expanding his dance studio's social media presence and offering workshops and classes to local schools and centers.", - "D8:13" - ], - [ - "Jon is hosting a dance competition next month to showcase local talent and bring more attention to his studio.", - "D8:13" - ] - ], - "Gina": [ - [ - "Gina got new offers and promotions for her online store to attract new customers.", - "D8:4" - ], - [ - "Dance is Gina's stress relief and fashion fuels her creativity.", - "D8:6" - ], - [ - "Gina combines her passion for dance and fashion to show creativity and share love with others.", - "D8:8" - ], - [ - "Gina is focused on building her customer base for her store by working with fashion bloggers, influencers, and running more ads.", - "D8:12" - ], - [ - "Gina plans to work with fashion bloggers and influencers to get more attention for her store and do more ads to reach more people.", - "D8:12" - ] - ] - }, - "session_9_observation": { - "Jon": [ - [ - "Jon is turning his passion for dance into a business by opening a dance studio.", - "D9:1" - ], - [ - "Jon's determination to make the dance studio work was fueled by losing his job.", - "D9:3" - ], - [ - "Jonathan finds happiness in dancing and expresses himself through it.", - "D9:5" - ], - [ - "Jon used to be scared of what people would think, but now prioritizes his own happiness.", - "D9:7" - ], - [ - "Jon loved taking dance lessons with friends when he was younger.", - "D9:9" - ], - [ - "Jon values the special meaning dance has for himself and Gina, as a way to express themselves.", - "D9:9" - ], - [ - "Jon is determined to keep pushing and working hard for his dance business.", - "D9:13" - ] - ], - "Gina": [ - [ - "Gina admires Jon for turning his passion for dance into a business.", - "D9:2" - ], - [ - "Gina acknowledges that tough times can lead to great things and supports Jon in going after his dreams.", - "D9:4" - ], - [ - "Gina loves spending time in the dance studio and values dance as a way to express oneself and find happiness.", - "D9:8" - ], - [ - "Gina has a trophy from a dance contest as a reminder of the hard work, dedication, and joy dance brings.", - "D9:10" - ], - [ - "Gina encourages Jon to keep up his hard work and achievements.", - "D9:12" - ] - ] - }, - "session_10_observation": { - "Jon": [ - [ - "Jon has a studio business that he showed off at a fair recently.", - "D10:1" - ], - [ - "Jon considers having confidence in oneself important for business success.", - "D10:1" - ], - [ - "Jon is having trouble with his business project and seeks advice on staying motivated.", - "D10:5" - ], - [ - "Jon is feeling low on confidence lately and seeks advice from Gina on staying confident in business.", - "D10:7" - ], - [ - "Jon is talented and passionate about dance, as per Gina's encouragement.", - "D10:10" - ] - ], - "Gina": [ - [ - "Gina started her own online clothing store after losing her job to take control of her destiny.", - "D10:2" - ], - [ - "Gina finds setbacks tough but rewarding for growth.", - "D10:4" - ], - [ - "Gina advises Jon to focus on the big goal, get support, and dance it out to stay motivated during tough times in business.", - "D10:6" - ], - [ - "Gina gives Jon tips to stay confident in business by reminding of successes and progress, having a good support system, and having faith in oneself.", - "D10:8" - ], - [ - "Gina encourages Jon to believe in himself, tackle obstacles, and keep shining in pursuing his dreams.", - "D10:10" - ] - ] - }, - "session_11_observation": { - "Jon": [ - [ - "Jon lost his job but used it as an opportunity to start his dream business: a dance studio.", - "D11:3" - ], - [ - "Dance has been Jon's stress-buster since childhood.", - "D11:5" - ], - [ - "Jon sees dancing as where he feels most alive and considers it a must-have in his life.", - "D11:7" - ], - [ - "Having his own dance studio and teaching others is a dream come true for Jon.", - "D11:9" - ], - [ - "Jon hopes to create a place for people to dance and express themselves, as it's been a dream of his.", - "D11:11" - ] - ], - "Gina": [ - [ - "Gina finds dancing as her stress fix and something that makes her worries vanish.", - "D11:6" - ], - [ - "Dance is essential to Gina, and she compares it to being like air.", - "D11:8" - ], - [ - "Gina had an interview for a design internship and found it to be great.", - "D11:14" - ], - [ - "Gina is proud of Jon and is excited about his dance studio project.", - "D11:20" - ] - ] - }, - "session_12_observation": { - "Gina": [ - [ - "Gina got accepted for a fashion internship at a part-time position in the fashion department of an international company.", - "D12:1" - ] - ], - "Jon": [ - [ - "Jon is reading the book \"The Lean Startup\" and hoping to get tips for his business.", - "D12:6" - ], - [ - "Jon is working on a business plan and looking for investors for his project.", - "D12:12" - ] - ] - }, - "session_13_observation": { - "Jon": [ - [ - "Jon is prepping for his own dance studio.", - "D13:3" - ], - [ - "Jon wants his dance studio to be a place of support and encouragement for all dancers.", - "D13:5" - ], - [ - "Jon is offering one-on-one mentoring and training to help dancers reach their full potential.", - "D13:7" - ], - [ - "Jon uses a mentor, goal setting, tracking achievements, and finding areas for improvement to stay organized and motivated.", - "D13:11" - ], - [ - "Jon uses a whiteboard to stay on track, visualize goals, and reward successes.", - "D13:13" - ], - [ - "Jon color-codes achievements to easily track progress and stay motivated.", - "D13:15" - ], - [ - "Jon feels that support and guidance can help dancers shine.", - "D13:9" - ], - [ - "Jon uses the system of goal setting, tracking achievements, and visualization to stay motivated and focused.", - "D13:13" - ] - ], - "Gina": [ - [ - "Gina has developed a video presentation to teach how to style her fashion pieces.", - "D13:4" - ], - [ - "Gina highlights the importance of staying positive and believes in supporting others' dreams.", - "D13:20" - ], - [ - "Gina values mentorship, having had a mentor herself when learning how to dance.", - "D13:8" - ], - [ - "Gina encourages Jon to keep pushing for his dreams and not give up.", - "D13:22" - ], - [ - "Gina supports Jon and believes in him.", - "D13:1" - ] - ] - }, - "session_14_observation": { - "Jon": [ - [ - "Jon got mentored by a business person, which was inspiring for him.", - "D14:1" - ], - [ - "Jon is working on promoting his business.", - "D14:3" - ], - [ - "Jon posted dance videos on social media which created a stir.", - "D14:5" - ], - [ - "Jon is passionate about dance and loves performing.", - "D14:15" - ], - [ - "Jon finds Gina's encouragement motivating and it helps him keep going.", - "D14:13" - ] - ], - "Gina": [ - [ - "Gina is working on growing the customer base for her online store.", - "D14:2" - ], - [ - "Gina lost her job, started an online clothing store, and enjoys being her own boss.", - "D14:8" - ], - [ - "Gina advises Jon to stay passionate, focused, resilient, and open to learning for entrepreneurship.", - "D14:10" - ], - [ - "Gina believes staying resilient and focused is key for entrepreneurs.", - "D14:12" - ], - [ - "Gina shares a pic of herself dancing and mentions the tough but worth it journey.", - "D14:14" - ], - [ - "Gina encourages Jon to keep going after his dreams.", - "D14:16" - ] - ] - }, - "session_15_observation": { - "Jon": [ - [ - "Jon recently took a short trip to Rome to clear his mind.", - "D15:1" - ], - [ - "Jon is working on opening a dance studio, with the official opening night being tomorrow.", - [ - "D15:3", - "D15:5" - ] - ] - ], - "Gina": [ - [ - "Gina supports Jon's opening of the dance studio and expresses excitement for the launch.", - "D15:6" - ], - [ - "Gina mentions she loves being around friends and having a great time.", - "D15:16" - ] - ] - }, - "session_16_observation": { - "Gina": [ - [ - "Gina has a store where she acquired new unique pieces.", - "D16:1" - ], - [ - "Gina made a limited edition line of clothing last week to show off her style and creativity.", - "D16:3" - ], - [ - "Gina's design in her clothing line reminds her of the grit needed to stand out and face challenges.", - "D16:5" - ] - ], - "Jon": [ - [ - "Jon has been attending networking events to make things happen after losing his job.", - "D16:6" - ], - [ - "Jon is investing his time in his business and believes it will eventually pay off.", - "D16:8" - ], - [ - "Jon is writing down all his plans and goals to stay organized and motivated.", - "D16:10" - ], - [ - "Jon finds that writing down his goals helps him stay motivated and focused.", - "D16:12" - ] - ] - }, - "session_17_observation": { - "Gina": [ - [ - "Gina got noticed by fashion editors last week.", - "D17:1" - ], - [ - "Gina believes that stumbling blocks can sometimes be opened doors.", - "D17:3" - ], - [ - "Gina supports and encourages Jon in his business dreams.", - "D17:4" - ], - [ - "Gina considers Jon to be the perfect mentor and guide for aspiring dancers.", - "D17:7" - ], - [ - "Gina is part of a solid community that cheers Jon on.", - "D17:9" - ], - [ - "Gina believes Jon has potential and encourages him not to let anything stop him.", - "D17:11" - ], - [ - "Gina roots for Jon to keep going and make his dreams a reality, even when facing bumps in the road.", - "D17:15" - ] - ], - "Jon": [ - [ - "Jon mentioned losing his job but is determined to pursue his business dreams.", - "D17:4" - ], - [ - "Jon started learning marketing and analytics tools to push his business forward.", - "D17:4" - ], - [ - "Jon aspires to guide and mentor aspiring dancers on their dreams.", - "D17:6" - ], - [ - "Jon believes dance has the power to bring people together and create sweet moments.", - "D17:8" - ], - [ - "Jon values the support and encouragement from Gina and the community in pursuing his dreams.", - "D17:10" - ], - [ - "Jon is determined not to let anything or anyone stop him from making his dreams come true.", - "D17:16" - ], - [ - "Jon expresses gratitude for Gina's belief in him and considers her a great friend.", - "D17:16" - ] - ] - }, - "session_18_observation": { - "Gina": [ - [ - "Gina built a new website for customers to make orders for her clothing store last week.", - "D18:1" - ], - [ - "Gina faced challenges in her business, particularly in sourcing trendy pieces which required research and networking.", - "D18:5" - ], - [ - "Gina advised Jon not to be scared to reach out to people in his field for help and contacts, stressing the importance of networking.", - "D18:5" - ], - [ - "Gina met some investors and got good advice at a recent networking event.", - "D18:8" - ], - [ - "Gina offered to help Jon with making content and managing his social media accounts for his dance studio.", - "D18:13" - ], - [ - "Gina expressed excitement to collaborate with Jon on making the dance studio shine and making a difference in the world.", - "D18:17" - ] - ], - "Jon": [ - [ - "Jon's dance studio is on tenuous grounds, but he is staying positive and looking for investors.", - "D18:2" - ], - [ - "Jon is networking and updating his business plan and pitch to investors based on advice received.", - "D18:10" - ], - [ - "Jon is working on an online platform to showcase the dance studio's offerings.", - "D18:10" - ], - [ - "Jon expressed a need for marketing strategies, seeking advice on reaching his target audience and raising awareness for the dance studio.", - "D18:12" - ], - [ - "Jon plans to collaborate with Gina on making content and managing his social media accounts to promote the dance studio.", - "D18:14" - ], - [ - "Jon expressed gratitude for Gina's support.", - "D18:22" - ] - ] - }, - "session_19_observation": { - "Jon": [ - [ - "Jon has been rehearsing hard and working on business plans.", - "D19:1" - ], - [ - "Dancing has kept Jon going during stressful times.", - "D19:1" - ], - [ - "Jon is working on opening a studio for dancers of all ages and backgrounds.", - "D19:7" - ] - ], - "Gina": [ - [ - "Gina encouraged Jon to keep up the good work and not forget his passion for dance.", - "D19:6" - ], - [ - "Gina is supportive of Jon's dream of opening a dance studio.", - "D19:10" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Gina and Jon met at 4:04 pm on 20 January, 2023. Jon lost his job as a banker and planned to start a dance studio because of his passion for dancing. Gina also lost her job at Door Dash. They both shared a love for dance, with Jon's favorite style being contemporary. They planned to attend a dance class together. Jon showed Gina a photo of his dance crew winning a competition. Gina revealed her experience of winning first place with a contemporary piece in a dance competition. Jon mentioned rehearsing with a group for various dance styles and upcoming projects, including a performance at a festival. Gina complimented the group's grace and expressed excitement for their festival performance. They both shared enthusiasm for the upcoming events.", - "session_2_summary": "Gina and Jon had a conversation at 2:32 pm on 29 January, 2023. Gina mentioned launching an ad campaign for her clothing store, excited about the risks and rewards of her business. Jon complimented her hard work and discussed his search for a suitable location for his dance studio, mentioning finding a spot with great natural light. They discussed the importance of features like flooring for dance studios, with Jon preferring Marley flooring. Gina encouraged Jon to believe in himself and take breaks when needed. They both expressed determination to chase their dreams and achieve success.", - "session_3_summary": "At 12:48 am on 1 February 2023, Jon shared with Gina his determination to open a dance studio, while Gina revealed her success in expanding her clothing store. Gina showed Jon a picture of her new store, which he admired, and they discussed the design elements. Gina aimed to create a cozy and inviting space for her customers. They both emphasized the importance of creating a special experience for customers. Jon encouraged Gina to continue her hard work, and she reciprocated the support for his dance studio venture. They both expressed confidence in reaching their goals with hard work and determination.", - "session_4_summary": "Jon and Gina, at 10:43 am on 4 February, 2023, discussed Jon's business endeavors. While Jon faced obstacles, Gina encouraged him to persevere, mentioning setbacks as opportunities for comebacks. Jon mentioned his passion for dancing and preparation for an upcoming competition, with Gina supporting him wholeheartedly. They exchanged words of encouragement, with Jon expressing gratitude for Gina's help and Gina assuring him that his hard work and dedication will pay off. Gina emphasized belief in oneself, encouraging Jon to pursue his dreams relentlessly.", - "session_5_summary": "Gina and Jon caught up at 9:32 am on 8 February, 2023. Gina shared about a new fashion piece for her store, while Jon told her about his dance performance. Jon spoke about his passion for running a dance studio and how fulfilling it is to see his students progress. Gina complimented Jon on his dedication and creativity, while Jon praised Gina for her willingness to take risks in her business. They both agreed that taking risks is essential for success. Jon shared his ambition to turn his passion for dance into a business. Gina supported Jon, reminding him not to give up on his dreams. They discussed the importance of freedom in expressing themselves through dance. Jon mentioned the challenges of balancing his dance and business goals but finds it rewarding. Gina encouraged Jon to keep going and not stop dreaming. They both expressed mutual support and determination to chase their dreams and move forward.", - "session_6_summary": "Jon and Gina, at 2:35 pm on 16 March 2023, caught up on their recent activities. Jon mentioned starting at the gym to balance his ventures, while Gina revealed she had a tough time after losing her job at Door Dash but had exciting news about opening an online clothes store. The two discussed their entrepreneurial journeys, with Gina blending her passion for fashion and dance into her store, and Jon facing challenges but staying determined to succeed. They both found inspiration in each other's perseverance and decided to support each other in reaching their goals. Jon and Gina expressed mutual encouragement and determination to pursue their dreams together.", - "session_7_summary": "Jon and Gina had a conversation at 7:28 pm on 23 March, 2023. Gina mentioned her successful online clothing store, highlighting the hard work and dedication it requires to start a business. Jon shared his struggles with his dance studio business but expressed determination to succeed. They discussed tips for running a successful business, emphasizing the importance of brand identity, customer relationships, and positivity. Jon credited Gina for her support and motivation, showing appreciation for her involvement in his business journey. Gina encouraged Jon to stay motivated and believe in himself. Both ended the conversation on a positive note, with Jon expressing gratitude for Gina's encouragement.", - "session_8_summary": "Jon informed Gina at 1:26 pm on 3 April, 2023, that he had closed his bank account for his business. Gina expressed sympathy and inquired about how he was coping with the changes, to which Jon responded positively, mentioning it was a tough decision but necessary for business growth. They discussed Gina's new offers on her online store related to fashion and dance, which she combines due to her passion for both. They also delved into strategies, like working with influencers for Gina and expanding the social media presence for Jon's dance studio, with plans for an upcoming dance competition. They supported each other's endeavors and parted ways with encouragement and plans to meet at Jon's event.", - "session_9_summary": "Jon, who lost his job, has turned his love for dance into a business and is determined to make his dance studio successful. He has been investing a lot of time in the studio and is happy to see his students improving. Gina has been supportive of Jon's journey, recalling her own love for dance in the past. Both agree that dance is a fantastic way to express themselves and find happiness. Jon appreciates Gina's support and encouragement as he continues to push himself to succeed in his new venture.", - "session_10_summary": "Jon informed Gina about his experience at a fair displaying his studio, emphasizing the challenges and rewards of his business. Gina congratulated Jon, mentioning her own online clothing store after losing her job. Jon sought advice on staying motivated during tough times and boosting confidence. Gina advised focusing on the big goal, seeking support, recalling successes, and staying passionate. Jon appreciated the guidance and resolved to maintain confidence in pursuing his dreams. The conversation between Jon and Gina depicted mutual support, encouragement, and determination to overcome obstacles and achieve success.", - "session_11_summary": "At 3:14 pm on 11 May 2023, Jon and Gina had an inspiring conversation about Jon's new dance studio. Jon shared his journey of starting his dream business after losing his job. Gina commended his determination and encouraged him to keep going. They both connected over their love for dance as a stress-reliever. Jon expressed his passion for dance and how it keeps him motivated. Gina shared her exciting news about a design internship interview. Jon offered to show Gina a dance routine, and she eagerly agreed. They promised to support each other, with Jon updating Gina on the studio progress and sending her a video of his routine soon.", - "session_12_summary": "Gina and Jon caught up at 7:18 pm on 27 May, 2023. Gina shared her excitement about getting a fashion internship with an international company, while Jon congratulated her and talked about his business plans. Jon mentioned reading \"The Lean Startup\" for tips, and they discussed ideas for his dance studio. Jon showed Gina his whiteboard of ideas and plans. They spoke about the importance of staying organized and motivated. Jon shared his struggles with finding investors but emphasized his determination. Gina encouraged Jon to keep learning and growing. Jon appreciated her support and vowed to persevere. Gina cheered him on, emphasizing the importance of overcoming obstacles. The conversation highlighted their support for each other's goals and aspirations.", - "session_13_summary": "Jon expressed gratitude to Gina at 8:29 pm on 13 June, 2023, for her unwavering belief in him. Gina commended Jon for his venture into opening a dance studio, and the two discussed their methods of staying organized and motivated. Jon shared that he offers one-on-one mentoring at the studio to help dancers excel, and Gina mentioned creating a video presentation for styling fashion pieces. They both emphasized the importance of support and positivity in achieving goals and encouraged each other to keep pursuing their dreams.", - "session_14_summary": "On 16th June 2023 at 9:38 pm, Jon told Gina about being mentored by a business person, leading him to pursue his dreams. Gina updated Jon on her online store progress, giving him tips on marketing. Jon followed her advice, got success with his dance videos, and sought Gina's entrepreneurship advice. Gina shared her journey as an entrepreneur and encouraged Jon to stay focused, resilient, and passionate. They exchanged motivating words, with Jon reaffirming his commitment to his dreams and Gina supporting him. Their conversation emphasized the importance of perseverance and passion in pursuing goals.", - "session_15_summary": "Jon mentioned his upcoming dance studio's grand opening on 20 June and Gina congratulated him on his hard work. They expressed excitement about making memories at the event and looked forward to enjoying it together. Gina praised Jon's progress and support, while Jon appreciated her encouragement. The conversation ended with both eagerly anticipating the grand opening and agreeing to meet there the next day.", - "session_16_summary": "At 2:15 pm on 21 June, 2023, Gina informed Jon about new unique pieces for her store. Jon congratulated Gina on her store and inquired about a hoodie, which Gina clarified was from her collection. Gina mentioned creating a limited edition line to showcase her style and creativity. Jon shared attending networking events to progress after losing his job. Gina praised Jon's determination, and they discussed the importance of perseverance and tracking goals. Jon appreciated Gina's support, which motivated him to stay focused and achieve his goals. Gina encouraged Jon to keep persevering and believing in himself. Jon expressed gratitude for Gina's support, feeling confident and determined not to give up.", - "session_17_summary": "At 1:25 pm on 9 July, 2023, Gina and Jon caught up with Gina sharing her fashion success and Jon discussing his plans to go full-time with his business idea after losing his job. Gina encouraged Jon, emphasizing that stumbling blocks can lead to new opportunities. Jon expressed gratitude for Gina's support and mentioned his goal to mentor aspiring dancers. Gina praised Jon's positivity and determination, emphasizing community support in achieving dreams. Jon thanked Gina for her unwavering faith, promising not to give up. They exchanged encouraging words, with Gina rooting for Jon to overcome obstacles and achieve his dreams. Jon affirmed he would persevere, driven by their support. The conversation ended with mutual appreciation and well wishes.", - "session_18_summary": "Gina and Jon caught up at 5:44 pm on 21 July, 2023. Gina shared about her new website for customer orders, while Jon mentioned his struggles with the dance studio. Gina encouraged Jon to stay positive and network for investors. They discussed the importance of networking, with Gina offering advice on marketing strategies. Jon sought Gina's help with content creation and social media management for the dance studio. They excitedly planned to collaborate and make a difference together in the dance world.", - "session_19_summary": "Jon and Gina caught up at 6:46 pm on 23 July, 2023. Jon shared his stress about rehearsals and business plans but found solace in dancing. Gina encouraged him with motivational words from Shia Labeouf, emphasizing the importance of creating a space for dancers. Jon appreciated Gina's support and looked forward to opening his dance studio. Gina continued to motivate Jon, reminding him to never give up. The conversation ended with Jon feeling motivated and ready to \"just do it\", as Gina encouraged him to do." - }, - "sample_id": "conv-30" - }, - { - "qa": [ - { - "question": "Who did Maria have dinner with on May 3, 2023?", - "answer": "her mother", - "evidence": [ - "D13:16" - ], - "category": 2 - }, - { - "question": "When did Maria donate her car?", - "answer": "21 December 2022", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "What martial arts has John done?", - "answer": "Kickboxing, Taekwondo", - "evidence": [ - "D2:28", - "D1:4" - ], - "category": 1 - }, - { - "question": "What type of volunteering have John and Maria both done?", - "answer": "Volunteering at a homeless shelter", - "evidence": [ - "D3:5", - "D2:1" - ], - "category": 1 - }, - { - "question": "When did John join the online support group?", - "answer": "The week before 1 January 2023", - "evidence": [ - "D3:1" - ], - "category": 2 - }, - { - "question": "When did Maria go to the beach?", - "answer": "December 2022", - "evidence": [ - "D3:15" - ], - "category": 2 - }, - { - "question": "Where has Maria made friends?", - "answer": "homeless shelter, gym, church", - "evidence": [ - "D4:1", - "D2:1", - "D19:1", - "D14:10" - ], - "category": 1 - }, - { - "question": "What items des John mention having as a child?", - "answer": "A doll, a film camera", - "evidence": [ - "D5:13", - "D3:15" - ], - "category": 1 - }, - { - "question": "What might John's financial status be?", - "answer": "Middle-class or wealthy", - "evidence": [ - "D5:5" - ], - "category": 3 - }, - { - "question": "Who gave Maria's family money when she was younger and her family was going through tough times?", - "answer": "Her aunt", - "evidence": [ - "D6:9", - "D5:8" - ], - "category": 1 - }, - { - "question": "When did Maria meet Jean?", - "answer": "February 24, 2023", - "evidence": [ - "D7:1" - ], - "category": 2 - }, - { - "question": "What people has Maria met and helped while volunteering?", - "answer": "David, Jean, Cindy, Laura", - "evidence": [ - "D7:5", - "D6:5", - "D27:8", - "D21:19" - ], - "category": 1 - }, - { - "question": "What test has John taken multiple times?", - "answer": "The military aptitude test", - "evidence": [ - "D8:18", - "D3:11" - ], - "category": 1 - }, - { - "question": "When did Maria's grandmother pass away?", - "answer": "The week before 6 March 2023", - "evidence": [ - "D8:1" - ], - "category": 2 - }, - { - "question": "Would John be considered a patriotic person?", - "answer": "Yes", - "evidence": [ - "D8:18", - "D8:20" - ], - "category": 3 - }, - { - "question": "What writing classes has Maria taken?", - "answer": "Poetry, creative writing", - "evidence": [ - "D9:1", - "D7:1" - ], - "category": 1 - }, - { - "question": "When did John get his degree?", - "answer": "The week before 2 April 2023", - "evidence": [ - "D9:2" - ], - "category": 2 - }, - { - "question": "What might John's degree be in?", - "answer": "Political science, Public administration, Public affairs", - "evidence": [ - "D9:6" - ], - "category": 3 - }, - { - "question": "Who did John go to yoga with?", - "answer": "Rob", - "evidence": [ - "D7:16", - "D10:1" - ], - "category": 1 - }, - { - "question": "What damages have happened to John's car?", - "answer": "Broken windshield, Car broke down", - "evidence": [ - "D11:1", - "D4:2" - ], - "category": 1 - }, - { - "question": "When did John take a road trip to the Pacific Northwest?", - "answer": "2022", - "evidence": [ - "D11:3", - "D11:5" - ], - "category": 2 - }, - { - "question": "What areas of the U.S. has John been to or is planning to go to?", - "answer": "Pacific northwest, east coast", - "evidence": [ - "D11:5", - "D12:17" - ], - "category": 1 - }, - { - "question": "When did John go to a convention with colleagues?", - "answer": "March 2023", - "evidence": [ - "D12:9" - ], - "category": 2 - }, - { - "question": "What desserts has Maria made?", - "answer": "Banana split sundae, Peach cobbler", - "evidence": [ - "D2:25", - "D13:18" - ], - "category": 1 - }, - { - "question": "When did John start boot camp with his family?", - "answer": "April.2023", - "evidence": [ - "D13:3" - ], - "category": 2 - }, - { - "question": "What European countries has Maria been to?", - "answer": "Spain, England", - "evidence": [ - "D13:24", - "D8:15" - ], - "category": 1 - }, - { - "question": "What has Maria done to feel closer to her faith?", - "answer": "Join a local church, buy a cross necklace", - "evidence": [ - "D14:10", - "D11:10" - ], - "category": 1 - }, - { - "question": "When did John have a party with veterans?", - "answer": "The Friday before 20 May 2023", - "evidence": [ - "D15:11" - ], - "category": 2 - }, - { - "question": "What causes does John feel passionate about supporting?", - "answer": "Veterans, schools, infrastructure", - "evidence": [ - "D15:3", - "D12:5", - "D9:8", - "D1:8" - ], - "category": 1 - }, - { - "question": "What events is Maria planning for the homeless shelter funraiser?", - "answer": "Chili cook-off, ring-toss tournament", - "evidence": [ - "D16:4", - "D15:18" - ], - "category": 1 - }, - { - "question": "What shelters does Maria volunteer at?", - "answer": "The homeless shelter, the dog shelter", - "evidence": [ - "D2:1", - "D11:10", - "D17:12" - ], - "category": 1 - }, - { - "question": "When did John get his dog Max?", - "answer": "In 2013", - "evidence": [ - "D17:1" - ], - "category": 2 - }, - { - "question": "What outdoor activities has John done with his colleagues?", - "answer": "Hiking, mountaineering", - "evidence": [ - "D18:2", - "D16:2" - ], - "category": 1 - }, - { - "question": "What types of yoga has Maria practiced?", - "answer": "Aerial, kundalini", - "evidence": [ - "D1:3", - "D18:15", - "D19:3" - ], - "category": 1 - }, - { - "question": "When did Maria join a gym?", - "answer": "The week before 16 June 2023", - "evidence": [ - "D19:1" - ], - "category": 2 - }, - { - "question": "What states has Maria vacationed at?", - "answer": "Oregon, Florida", - "evidence": [ - "D19:23", - "D18:3" - ], - "category": 1 - }, - { - "question": "What music events has John attended?", - "answer": "Live music event, violin concert", - "evidence": [ - "D20:4", - "D8:12" - ], - "category": 1 - }, - { - "question": "What events for veterans has John participated in?", - "answer": "Petition, march, party, visiting veterans hospital, 5K charity run", - "evidence": [ - "D15:1", - "D15:11", - "D21:22", - "D24:1", - "D29:4" - ], - "category": 1 - }, - { - "question": "When did Maria get in a car accident?", - "answer": "July 2, 2023", - "evidence": [ - "D21:3" - ], - "category": 2 - }, - { - "question": "Around which US holiday did Maria get into a car accident?", - "answer": "Independence Day", - "evidence": [ - "D21:3" - ], - "category": 3 - }, - { - "question": "What are the names of John's children?", - "answer": "Kyle, Sara", - "evidence": [ - "D8:4", - "D22:7" - ], - "category": 1 - }, - { - "question": "Does John live close to a beach or the mountains?", - "answer": "beach", - "evidence": [ - "D22:15" - ], - "category": 3 - }, - { - "question": "What area was hit by a flood?", - "answer": "West County", - "evidence": [ - "D14:21", - "D23:1" - ], - "category": 1 - }, - { - "question": "When was John's old area hit with a flood?", - "answer": "The week before 7 July 2023", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "What activities has Maria done with her church friends?", - "answer": "Hiking, picnic, volunteer work", - "evidence": [ - "D25:2", - "D24:6", - "D28:5" - ], - "category": 1 - }, - { - "question": "Would John be open to moving to another country?", - "answer": "No, he has goals specifically in the U.S. like joining the military and running for office.", - "evidence": [ - "D24:3", - "D7:2" - ], - "category": 3 - }, - { - "question": "When did Maria go hiking with her church friends?", - "answer": "The weekend before 22 July 2023", - "evidence": [ - "D25:2" - ], - "category": 2 - }, - { - "question": "What exercises has John done?", - "answer": "Weight training, Circuit training, Kickboxing, yoga", - "evidence": [ - "D25:17", - "D25:13", - "D10:1", - "D1:4" - ], - "category": 1 - }, - { - "question": "When did John have his first firefighter call-out?", - "answer": "The sunday before 3` July 2023", - "evidence": [ - "D26:4" - ], - "category": 2 - }, - { - "question": "What food item did Maria drop off at the homeless shelter?", - "answer": "Cakes", - "evidence": [ - "D26:1", - "D25:19" - ], - "category": 1 - }, - { - "question": "What attributes describe John?", - "answer": "Selfless, family-oriented, passionate, rational", - "evidence": [ - "D26:6", - "D2:14", - "D3:5", - "D4:6" - ], - "category": 3 - }, - { - "question": "When did Maria start volunteering at the homeless shelter?", - "answer": "Around August 2022", - "evidence": [ - "D27:4" - ], - "category": 2 - }, - { - "question": "Who have written notes of gratitude to Maria?", - "answer": "Cindy, Laura", - "evidence": [ - "D27:8", - "D21:19" - ], - "category": 1 - }, - { - "question": "When did John help renovate his hometown community center?", - "answer": "2022", - "evidence": [ - "D28:11" - ], - "category": 2 - }, - { - "question": "When did Maria take up community work with her church friends?", - "answer": "August 4, 2023", - "evidence": [ - "D28:8" - ], - "category": 2 - }, - { - "question": "When did Maria receive a medal from the homeless shelter?", - "answer": "The week before 9 August 2023", - "evidence": [ - "D29:1" - ], - "category": 2 - }, - { - "question": "When did John participate in a 5K charity run?", - "answer": "first weekend of August 2023", - "evidence": [ - "D29:2", - "D29:4" - ], - "category": 2 - }, - { - "question": "What causes has John done events for?", - "answer": "Toy drive, Community food drive, veterans, domestic violence", - "evidence": [ - "D3:5", - "D6:12", - "D29:4", - "D29:10" - ], - "category": 1 - }, - { - "question": "When did Maria get Coco?", - "answer": "Two weeks before 11 August 2023", - "evidence": [ - "D30:1" - ], - "category": 2 - }, - { - "question": "When did John go on a camping trip with Max?", - "answer": "The summer of 2022", - "evidence": [ - "D30:6" - ], - "category": 2 - }, - { - "question": "What are Maria's dogs' names?", - "answer": "Coco, Shadow", - "evidence": [ - "D30:1", - "D31:4" - ], - "category": 1 - }, - { - "question": "When did Maria adopt Shadow?", - "answer": "The week before 13 August 2023", - "evidence": [ - "D31:2" - ], - "category": 2 - }, - { - "question": "How many dogs has Maria adopted from the dog shelter she volunteers at?", - "answer": "two", - "evidence": [ - "D30:1", - "D31:2" - ], - "category": 1 - }, - { - "question": "How many weeks passed between Maria adopting Coco and Shadow?", - "answer": "two weeks", - "evidence": [ - "D30:1", - "D31:2" - ], - "category": 2 - }, - { - "question": "What job might Maria pursue in the future?", - "answer": "Shelter coordinator, Counselor", - "evidence": [ - "D32:14", - "D5:8", - "D11:10", - "D27:4" - ], - "category": 3 - }, - { - "question": "What is John's main focus in local politics?", - "answer": "Improving education and infrastructure", - "evidence": [ - "D1:8" - ], - "category": 4 - }, - { - "question": "What sparked John's interest in improving education and infrastructure in the community?", - "answer": "Seeing how lack of education and crumbling infrastructure affected his neighborhood while growing up.", - "evidence": [ - "D1:10" - ], - "category": 4 - }, - { - "question": "How did the extra funding help the school shown in the photo shared by John?", - "answer": "Enabled needed repairs and renovations, making the learning environment safer and more modern for students.", - "evidence": [ - "D1:12" - ], - "category": 4 - }, - { - "question": "What type of workout class did Maria start doing in December 2023?", - "answer": "aerial yoga", - "evidence": [ - "D1:3" - ], - "category": 4 - }, - { - "question": "What did Maria donate to a homeless shelter in December 2023?", - "answer": "old car", - "evidence": [ - "D2:1" - ], - "category": 4 - }, - { - "question": "What kind of meal did John and his family make together in the photo shared by John?", - "answer": "pizza", - "evidence": [ - "D2:24" - ], - "category": 4 - }, - { - "question": "What kind of online group did John join?", - "answer": "service-focused online group", - "evidence": [ - "D3:1" - ], - "category": 4 - }, - { - "question": "What kind of activities did John and his mates from the online group do as part of their service efforts?", - "answer": "gave out food and supplies at a homeless shelter, organized a toy drive for kids in need", - "evidence": [ - "D3:5" - ], - "category": 4 - }, - { - "question": "Who inspired Maria to start volunteering?", - "answer": "Her aunt", - "evidence": [ - "D5:8" - ], - "category": 4 - }, - { - "question": "Why did Maria sit with the little girl at the shelter event in February 2023?", - "answer": "The girl seemed sad and had no other family", - "evidence": [ - "D5:10" - ], - "category": 4 - }, - { - "question": "What did Jean go through before meeting Maria?", - "answer": "divorce, job loss, homelessness", - "evidence": [ - "D7:7" - ], - "category": 4 - }, - { - "question": "Why did John decide to run for office again?", - "answer": "saw the impact he could make in the community through politics", - "evidence": [ - "D7:4" - ], - "category": 4 - }, - { - "question": "What activity did John's colleague, Rob, invite him to?", - "answer": "beginner's yoga class", - "evidence": [ - "D7:16" - ], - "category": 4 - }, - { - "question": "What is the name of John's one-year-old child?", - "answer": "Kyle", - "evidence": [ - "D8:4" - ], - "category": 4 - }, - { - "question": "How often does John take his kids to the park?", - "answer": "A few times a week", - "evidence": [ - "D8:8" - ], - "category": 4 - }, - { - "question": "What did Maria make for her home to remind her of a trip to England?", - "answer": "painting of a castle on a hill", - "evidence": [ - "D8:15" - ], - "category": 4 - }, - { - "question": "Where did Maria get the idea for the castle shadow box in her home?", - "answer": "England", - "evidence": [ - "D8:15" - ], - "category": 4 - }, - { - "question": "What did John receive a certificate for?", - "answer": "completion of a university degree", - "evidence": [ - "D9:2" - ], - "category": 4 - }, - { - "question": "What areas is John particularly interested in for policymaking?", - "answer": "education and infrastructure", - "evidence": [ - "D9:8" - ], - "category": 4 - }, - { - "question": "What did Maria participate in last weekend before April 10, 2023?", - "answer": "a 5K charity run", - "evidence": [ - "D10:10" - ], - "category": 4 - }, - { - "question": "What event did John volunteer at last weekend?", - "answer": "career fair at a local school", - "evidence": [ - "D10:13" - ], - "category": 4 - }, - { - "question": "What did John do that put a strain on his wallet?", - "answer": "His car broke down", - "evidence": [ - "D11:1" - ], - "category": 4 - }, - { - "question": "Where did John explore on a road trip last year?", - "answer": "Pacific Northwest", - "evidence": [ - "D11:5" - ], - "category": 4 - }, - { - "question": "What topic has John been blogging about recently?", - "answer": "politics and the government", - "evidence": [ - "D12:1" - ], - "category": 4 - }, - { - "question": "Why did John start blogging about politics and policies?", - "answer": "raise awareness and start conversations to create positive change", - "evidence": [ - "D12:3" - ], - "category": 4 - }, - { - "question": "What was the focus of John's recent research and writing on his blog?", - "answer": "education reform and infrastructure development", - "evidence": [ - "D12:5" - ], - "category": 4 - }, - { - "question": "What did John attend with his colleagues in March 2023?", - "answer": "a tech-for-good convention", - "evidence": [ - "D12:9" - ], - "category": 2 - }, - { - "question": "How often does John work out with his family?", - "answer": "Three times a week", - "evidence": [ - "D13:7" - ], - "category": 4 - }, - { - "question": "How has John's fitness improved since starting boot camps with his family?", - "answer": "More energy, gains in strength and endurance", - "evidence": [ - "D13:5" - ], - "category": 4 - }, - { - "question": "What kind of food did Maria have on her dinner spread iwth her mother?", - "answer": "Salads, sandwiches, homemade desserts", - "evidence": [ - "D13:18" - ], - "category": 4 - }, - { - "question": "What activity did Maria and her mom do together in May 2023?", - "answer": "Made dinner together", - "evidence": [ - "D13:16" - ], - "category": 4 - }, - { - "question": "What did Maria do to feel closer to a community and her faith?", - "answer": "joined a nearby church", - "evidence": [ - "D14:10" - ], - "category": 4 - }, - { - "question": "Why did Maria join a nearby church recently?", - "answer": "to feel closer to a community and her faith", - "evidence": [ - "D14:10" - ], - "category": 4 - }, - { - "question": "What did John host for the veterans in May 2023 as part of the project?", - "answer": "a small party to share their stories", - "evidence": [ - "D15:13" - ], - "category": 4 - }, - { - "question": "What did John and the veterans do during the small party?", - "answer": "share stories and make connections", - "evidence": [ - "D15:13" - ], - "category": 4 - }, - { - "question": "What emotions did John feel during the small party with the veterans?", - "answer": "heartwarming", - "evidence": [ - "D15:13" - ], - "category": 4 - }, - { - "question": "What event is Maria getting ready for at the shelter on May 25, 2023?", - "answer": "fundraiser", - "evidence": [ - "D16:2" - ], - "category": 4 - }, - { - "question": "What does Maria need to spread the word about for the fundraiser for the volunteer shelter?", - "answer": "chili cook-off", - "evidence": [ - "D16:4" - ], - "category": 4 - }, - { - "question": "What was the name of the pet that John had to say goodbye to on 3 June, 2023?", - "answer": "Max", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "How long was Max a part of John's family?", - "answer": "10 years", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "How does John plan to honor the memories of his beloved pet?", - "answer": "By considering adopting a rescue dog", - "evidence": [ - "D17:11" - ], - "category": 4 - }, - { - "question": "What important values does John want to teach his kids through adopting a rescue dog?", - "answer": "Responsibility and compassion", - "evidence": [ - "D17:11" - ], - "category": 4 - }, - { - "question": "What new activity did Maria start recently, as mentioned on 3 June, 2023?", - "answer": "volunteering at a local dog shelter once a month", - "evidence": [ - "D17:12" - ], - "category": 4 - }, - { - "question": "What did Maria say it was like being at the waterfall in Oregon?", - "answer": "Like being in a fairy tale", - "evidence": [ - "D18:7" - ], - "category": 4 - }, - { - "question": "What does Maria say she feels when doing upside-down yoga poses?", - "answer": "Free and light", - "evidence": [ - "D18:17" - ], - "category": 4 - }, - { - "question": "What exciting news did Maria share on 16 June, 2023?", - "answer": "joined a gym", - "evidence": [ - "D19:1" - ], - "category": 4 - }, - { - "question": "What yoga activity has Maria been trying to improve her strength and endurance?", - "answer": "kundalini yoga", - "evidence": [ - "D19:3" - ], - "category": 4 - }, - { - "question": "What did John recently get promoted to?", - "answer": "assistant manager", - "evidence": [ - "D19:8" - ], - "category": 4 - }, - { - "question": "What was one of the biggest challenges John faced in his journey to becoming assistant manager?", - "answer": "self-doubt", - "evidence": [ - "D19:12" - ], - "category": 4 - }, - { - "question": "How does John describe the support he received during his journey to becoming assistant manager?", - "answer": "having support at home and his own grit", - "evidence": [ - "D19:12" - ], - "category": 4 - }, - { - "question": "What kind of event did John and his family attend in June 2023?", - "answer": "live music event", - "evidence": [ - "D20:4" - ], - "category": 4 - }, - { - "question": "Why did Maria need to help her cousin find a new place to live?", - "answer": "Her cousin had to leave and find a new place in a hurry.", - "evidence": [ - "D21:5" - ], - "category": 4 - }, - { - "question": "What event did John participate in to show support for veterans' rights?", - "answer": "marching event", - "evidence": [ - "D21:22" - ], - "category": 4 - }, - { - "question": "What inspired John to join the marching event for veterans' rights?", - "answer": "Respect for the military and the desire to show support", - "evidence": [ - "D21:24" - ], - "category": 4 - }, - { - "question": "How often does John get to see sunsets like the one he shared with Maria?", - "answer": "At least once a week", - "evidence": [ - "D22:17" - ], - "category": 4 - }, - { - "question": "What natural disaster affected John's old area on 7 July, 2023?", - "answer": "Flood", - "evidence": [ - "D23:1" - ], - "category": 4 - }, - { - "question": "How did the flood impact the homes in John's old area?", - "answer": "Lots of homes were ruined.", - "evidence": [ - "D23:1" - ], - "category": 4 - }, - { - "question": "What motivated Maria and John to discuss potential solutions for their community on 7 July, 2023?", - "answer": "Flood in John's old area", - "evidence": [ - "D23:1" - ], - "category": 4 - }, - { - "question": "What did Maria plan to do later on the evening of 7 July, 2023?", - "answer": "have dinner with friends from the gym", - "evidence": [ - "D23:14" - ], - "category": 4 - }, - { - "question": "What kind of activities did Maria do at the picnic with her church friends?", - "answer": "played games like charades and a scavenger hunt", - "evidence": [ - "D24:8" - ], - "category": 4 - }, - { - "question": "What does John appreciate about the veteran's hospital visit?", - "answer": "the resilience of the veterans and their inspiring stories", - "evidence": [ - "D24:3" - ], - "category": 4 - }, - { - "question": "What did John take away from visiting the veteran's hospital?", - "answer": "appreciation for giving back", - "evidence": [ - "D24:1" - ], - "category": 4 - }, - { - "question": "Why did John feel inspired to join the military after the visit to the hospital?", - "answer": "seeing the resilience of the veterans", - "evidence": [ - "D24:3" - ], - "category": 4 - }, - { - "question": "In what activity did Maria and her church friends participate in July 2023?", - "answer": "hiking", - "evidence": [ - "D25:2" - ], - "category": 4 - }, - { - "question": "What does John think about trying new classes at the yoga studio?", - "answer": "Trying new classes is a fun way to switch up the exercise routine.", - "evidence": [ - "D25:14" - ], - "category": 4 - }, - { - "question": "Which activity has John done apart from yoga at the studio?", - "answer": "weight training", - "evidence": [ - "D25:17" - ], - "category": 4 - }, - { - "question": "What community service did Maria mention that she was involved in on 31 July, 2023?", - "answer": "volunteered at a homeless shelter", - "evidence": [ - "D26:1" - ], - "category": 4 - }, - { - "question": "How did Maria start volunteering at the homeless shelter?", - "answer": "Witnessed a family struggling on the streets and reached out to the shelter", - "evidence": [ - "D27:4" - ], - "category": 4 - }, - { - "question": "What did John do the week before August 3, 2023 involving his kids?", - "answer": "Had a meaningful experience at a military memorial", - "evidence": [ - "D27:9" - ], - "category": 4 - }, - { - "question": "How did John describe his kids' reaction at the military memorial?", - "answer": "awestruck and humbled", - "evidence": [ - "D27:11" - ], - "category": 4 - }, - { - "question": "Why does Maria think it's important for younger generations to visit military memorials?", - "answer": "To remember and appreciate those who served", - "evidence": [ - "D27:12" - ], - "category": 4 - }, - { - "question": "What does John believe is important for children regarding veterans?", - "answer": "Teaching them to respect and appreciate those who served", - "evidence": [ - "D27:13" - ], - "category": 4 - }, - { - "question": "What happened to John's job in August 2023?", - "answer": "John lost his job at the mechanical engineering company.", - "evidence": [ - "D28:1" - ], - "category": 4 - }, - { - "question": "What activity did Maria take up with her friends from church in August 2023?", - "answer": "community work", - "evidence": [ - "D28:8" - ], - "category": 4 - }, - { - "question": "What did John do to help his community last year in his hometown?", - "answer": "Helped renovate a rundown community center.", - "evidence": [ - "D28:11" - ], - "category": 4 - }, - { - "question": "What cause did the 5K charity run organized by John support?", - "answer": "veterans and their families", - "evidence": [ - "D29:4" - ], - "category": 4 - }, - { - "question": "Who did John work with to raise awareness and funds for victims of domestic abuse?", - "answer": "a local organization", - "evidence": [ - "D29:10" - ], - "category": 4 - }, - { - "question": "What recognition did Maria receive at the homeless shelter in August 2023?", - "answer": "a medal for volunteering", - "evidence": [ - "D29:1" - ], - "category": 4 - }, - { - "question": "What is the name of Maria's puppy she got two weeks before August 11, 2023?", - "answer": "Coco", - "evidence": [ - "D30:1" - ], - "category": 4 - }, - { - "question": "What activity did John and Max enjoy together last summer?", - "answer": "Camping", - "evidence": [ - "D30:6" - ], - "category": 4 - }, - { - "question": "How does John describe the camping trip with Max?", - "answer": "Peaceful and awesome", - "evidence": [ - "D30:6" - ], - "category": 4 - }, - { - "question": "Why does John say he feels stuck and questioning his decisions and goals?", - "answer": "Not feeling like making much of an impact", - "evidence": [ - "D30:14" - ], - "category": 4 - }, - { - "question": "What is the name of Maria's second puppy?", - "answer": "Shadow", - "evidence": [ - "D31:4" - ], - "category": 4 - }, - { - "question": "How is Maria's new puppy adjusting to its new home?", - "answer": "doing great - learning commands and house training", - "evidence": [ - "D31:10" - ], - "category": 4 - }, - { - "question": "What is John currently doing as a volunteer in August 2023?", - "answer": "mentoring students at a local school", - "evidence": [ - "D31:1" - ], - "category": 4 - }, - { - "question": "What activities does John's family enjoy doing together?", - "answer": "going for hikes, hanging out at the park, having picnics, playing board games, having movie nights", - "evidence": [ - "D31:19" - ], - "category": 4 - }, - { - "question": "What did the donations help John's community acquire on 16 August, 2023?", - "answer": "a brand new fire truck", - "evidence": [ - "D32:11" - ], - "category": 4 - }, - { - "question": "What is John's main focus in international politics?", - "evidence": [ - "D1:8" - ], - "category": 5, - "adversarial_answer": "Improving education and infrastructure" - }, - { - "question": "What did Maria donate to a luxury store in December 2023?", - "evidence": [ - "D2:1" - ], - "category": 5, - "adversarial_answer": "old car" - }, - { - "question": "Who inspired John to start volunteering?", - "evidence": [ - "D5:8" - ], - "category": 5, - "adversarial_answer": "His aunt" - }, - { - "question": "Why did Maria decide to run for office again?", - "evidence": [ - "D7:4" - ], - "category": 5, - "adversarial_answer": "saw the impact she could make in the community through politics" - }, - { - "question": "What activity did Maria's colleague, Rob, invite her to?", - "evidence": [ - "D7:16" - ], - "category": 5, - "adversarial_answer": "beginner's yoga class" - }, - { - "question": "What is the name of Maria's one-year-old child?", - "evidence": [ - "D8:4" - ], - "category": 5, - "adversarial_answer": "Kyle" - }, - { - "question": "How often does John take his kids to the library?", - "evidence": [ - "D8:8" - ], - "category": 5, - "adversarial_answer": "A few times a week" - }, - { - "question": "What did Maria make for her home to remind her of a trip to France?", - "evidence": [ - "D8:15" - ], - "category": 5, - "adversarial_answer": "painting of a castle on a hill" - }, - { - "question": "Where did John get the idea for the castle shadow box in his home?", - "evidence": [ - "D8:15" - ], - "category": 5, - "adversarial_answer": "England" - }, - { - "question": "What did Maria receive a certificate for?", - "evidence": [ - "D9:2" - ], - "category": 5, - "adversarial_answer": "completion of a university degree" - }, - { - "question": "What areas is John particularly interested in for art appreciation?", - "evidence": [ - "D9:8" - ], - "category": 5, - "adversarial_answer": "education and infrastructure" - }, - { - "question": "Why did Maria start blogging about politics and policies?", - "evidence": [ - "D12:3" - ], - "category": 5, - "adversarial_answer": "raise awareness and start conversations to create positive change" - }, - { - "question": "What was the focus of John's recent travel and photography blog?", - "evidence": [ - "D12:5" - ], - "category": 5, - "adversarial_answer": "education reform and infrastructure development" - }, - { - "question": "How often does Maria work out with her family?", - "evidence": [ - "D13:7" - ], - "category": 5, - "adversarial_answer": "Three times a week" - }, - { - "question": "How has John's artistic skills improved since starting boot camps with his family?", - "evidence": [ - "D13:5" - ], - "category": 5, - "adversarial_answer": "More energy, gains in strength and endurance" - }, - { - "question": "What kind of food did Maria have on her dinner spread with her father?", - "evidence": [ - "D13:18" - ], - "category": 5, - "adversarial_answer": "Salads, sandwiches, homemade desserts" - }, - { - "question": "What did John do to feel closer to a community and his faith?", - "evidence": [ - "D14:10" - ], - "category": 5, - "adversarial_answer": "joined a nearby church" - }, - { - "question": "Why did John join a nearby church recently?", - "evidence": [ - "D14:10" - ], - "category": 5, - "adversarial_answer": "to feel closer to a community and her faith" - }, - { - "question": "How long was Max a part of Maria's family?", - "evidence": [ - "D17:1" - ], - "category": 5, - "adversarial_answer": "10 years" - }, - { - "question": "How does Maria plan to honor the memories of her beloved pet?", - "evidence": [ - "D17:11" - ], - "category": 5, - "adversarial_answer": "By considering adopting a rescue dog" - }, - { - "question": "What important values does Maria want to teach her kids through adopting a rescue dog?", - "evidence": [ - "D17:11" - ], - "category": 5, - "adversarial_answer": "Responsibility and compassion" - }, - { - "question": "What did Maria say it was like being at the desert in Oregon?", - "evidence": [ - "D18:7" - ], - "category": 5, - "adversarial_answer": "Like being in a fairy tale" - }, - { - "question": "What does John say she feels when doing upside-down yoga poses?", - "evidence": [ - "D18:17" - ], - "category": 5, - "adversarial_answer": "Free and light" - }, - { - "question": "What did Maria recently get promoted to?", - "evidence": [ - "D19:8" - ], - "category": 5, - "adversarial_answer": "assistant manager" - }, - { - "question": "What was one of the biggest challenges Maria faced in her journey to becoming assistant manager?", - "evidence": [ - "D19:12" - ], - "category": 5, - "adversarial_answer": "self-doubt" - }, - { - "question": "Why did John need to help his cousin find a new place to live?", - "evidence": [ - "D21:5" - ], - "category": 5, - "adversarial_answer": "His cousin had to leave and find a new place in a hurry." - }, - { - "question": "What event did Maria participate in to show support for veterans' rights?", - "evidence": [ - "D21:22" - ], - "category": 5, - "adversarial_answer": "marching event" - }, - { - "question": "How did the drought impact the homes in John's old area?", - "evidence": [ - "D23:1" - ], - "category": 5, - "adversarial_answer": "Lots of homes were ruined." - }, - { - "question": "What does John criticize about the veteran's hospital visit?", - "evidence": [ - "D24:3" - ], - "category": 5, - "adversarial_answer": "the resilience of the veterans and their inspiring stories" - }, - { - "question": "What did John take away from visiting the orphanage?", - "evidence": [ - "D24:1" - ], - "category": 5, - "adversarial_answer": "appreciation for giving back" - }, - { - "question": "Why did Maria feel inspired to join the military after the visit to the hospital?", - "evidence": [ - "D24:3" - ], - "category": 5, - "adversarial_answer": "seeing the resilience of the veterans" - }, - { - "question": "How did Maria describe her kids' reaction at the military memorial?", - "evidence": [ - "D27:11" - ], - "category": 5, - "adversarial_answer": "awestruck and humbled" - }, - { - "question": "Why does Maria think it's important for younger generations to visit art galleries?", - "evidence": [ - "D27:12" - ], - "category": 5, - "adversarial_answer": "To remember and appreciate those who served" - }, - { - "question": "What happened to Maria's job in August 2023?", - "evidence": [ - "D28:1" - ], - "category": 5, - "adversarial_answer": "John lost his job at the mechanical engineering company." - }, - { - "question": "What cause did the 5K charity run organized by Maria support?", - "evidence": [ - "D29:4" - ], - "category": 5, - "adversarial_answer": "veterans and their families" - }, - { - "question": "Who did John work with to raise awareness and funds for animal welfare?", - "evidence": [ - "D29:10" - ], - "category": 5, - "adversarial_answer": "a local organization" - }, - { - "question": "What recognition did John receive at the homeless shelter in August 2023?", - "evidence": [ - "D29:1" - ], - "category": 5, - "adversarial_answer": "a medal for volunteering" - }, - { - "question": "What is the name of John's puppy he got two weeks before August 11, 2023?", - "evidence": [ - "D30:1" - ], - "category": 5, - "adversarial_answer": "Coco" - }, - { - "question": "How does Maria describe the camping trip with Max?", - "evidence": [ - "D30:6" - ], - "category": 5, - "adversarial_answer": "Peaceful and awesome" - }, - { - "question": "What is the name of Maria's second kitten?", - "evidence": [ - "D31:4" - ], - "category": 5, - "adversarial_answer": "Shadow" - }, - { - "question": "How is John's new puppy adjusting to its new home?", - "evidence": [ - "D31:10" - ], - "category": 5, - "adversarial_answer": "doing great - learning commands and house training" - } - ], - "conversation": { - "speaker_a": "John", - "speaker_b": "Maria", - "session_1_date_time": "11:01 am on 17 December, 2022", - "session_1": [ - { - "speaker": "Maria", - "dia_id": "D1:1", - "text": "Hey John! Long time no see! What's up?" - }, - { - "speaker": "John", - "dia_id": "D1:2", - "text": "Hey Maria! Good to see you. Just got back from a family road trip yesterday, it was fun! Anything exciting happening for you?" - }, - { - "speaker": "Maria", - "dia_id": "D1:3", - "text": "Been busy volunteering at the homeless shelter and keeping fit. Just started doing aerial yoga, it's great. Have you tried any other cool workout classes?" - }, - { - "speaker": "John", - "dia_id": "D1:4", - "text": "Woah, Maria, that sounds cool! I'm doing kickboxing and it's giving me so much energy." - }, - { - "speaker": "Maria", - "dia_id": "D1:5", - "text": "Cool, John. Kickboxing is a perfect way to stay in shape and de-stress. Do you have any goals you're working towards?" - }, - { - "speaker": "John", - "dia_id": "D1:6", - "text": "Yeah Maria, I'm really hoping to get into local politics. I love helping the community and making it a better place." - }, - { - "speaker": "Maria", - "dia_id": "D1:7", - "text": "Woohoo, John! That's awesome! Any specific areas you want to tackle?" - }, - { - "speaker": "John", - "dia_id": "D1:8", - "text": "I'm passionate about improving education and infrastructure in our community. Those are my main focuses." - }, - { - "speaker": "Maria", - "dia_id": "D1:9", - "text": "Wow, John! It's great to hear that. Could you tell me what got you interested in those topics?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.greenbaypressgazette.com/gcdn/presto/2019/07/16/PGRB/fa8d60a1-0a21-421c-b36d-31f4bc2d2b6d-KEW_0720_LC_referendum_Peters_Concrete_pour.png" - ], - "blip_caption": "a photo of a group of men working on a building", - "query": "school building renovation", - "dia_id": "D1:10", - "text": "Growing up, I saw how lack of education and crumbling infrastructure affected my neighborhood. I don't want future generations to go through that, so I think schools and infrastructure should be funded properly. Here's a pic of a school last year, after they got the funding." - }, - { - "speaker": "Maria", - "dia_id": "D1:11", - "text": "Looks good! How did the extra funding help with building the school?" - }, - { - "speaker": "John", - "dia_id": "D1:12", - "text": "It enabled needed repairs and renovations, making the learning environment safer and more modern for students." - }, - { - "speaker": "Maria", - "dia_id": "D1:13", - "text": "Wow John, it really made a difference for schools and students! It must have been great to be involved. What's your next move in politics?" - }, - { - "speaker": "John", - "dia_id": "D1:14", - "text": "I'm gonna chat with local leaders and organizations, get support and gather ideas for my next move." - }, - { - "speaker": "Maria", - "dia_id": "D1:15", - "text": "Sounds good, John. Let's get support and great ideas from local leaders and organizations. Keep me posted on how your campaign is going." - }, - { - "speaker": "John", - "dia_id": "D1:16", - "text": "Got it! Thanks, Maria. I definitely will." - } - ], - "session_2_date_time": "6:10 pm on 22 December, 2022", - "session_2": [ - { - "speaker": "Maria", - "dia_id": "D2:1", - "text": "Hey John, been a few days since we chatted. In the meantime, I donated my old car to a homeless shelter I volunteer at yesterday. How's the campaign going? I'm keen to hearabout it." - }, - { - "speaker": "John", - "dia_id": "D2:2", - "text": "Hi Maria! It's been an interesting ride so far. I've been networking with some people to get their input." - }, - { - "speaker": "Maria", - "dia_id": "D2:3", - "text": "That's awesome, John! Networking is great for gaining new perspectives and insights. Have you had any interesting conversations or revelations so far?" - }, - { - "speaker": "John", - "dia_id": "D2:4", - "text": "I just talked to someone who shared some amazing stories. It really fired up my passion to make education better in our area." - }, - { - "speaker": "Maria", - "dia_id": "D2:5", - "text": "Wow, John! Hearing that can really make an impact and get us fired up to make a difference. It's great to hear that you're feeling motivated to make improvements to our community's education!" - }, - { - "speaker": "John", - "dia_id": "D2:6", - "text": "Definitely, Maria. Investing in our future generations is key, giving them the right tools for success. It's the foundation of progress and opportunity." - }, - { - "speaker": "Maria", - "dia_id": "D2:7", - "text": "Yeah, John. It's amazing how even minor tweaks to the system can make a big difference for lots of people. I'm really impressed with your enthusiasm and commitment to it!" - }, - { - "speaker": "John", - "dia_id": "D2:8", - "text": "Thanks, Maria. Your encouragement means a lot to me. It's true that with effort and support, we can make a real difference in our community." - }, - { - "speaker": "Maria", - "dia_id": "D2:9", - "text": "You got this, John! I believe in your power to make a positive difference. Your passion inspires me. Keep going - I'm here for you." - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/5119595/pexels-photo-5119595.jpeg" - ], - "blip_caption": "a photography of a family having a picnic in the park", - "query": "family smiling hugging", - "dia_id": "D2:10", - "re-download": true, - "text": "Thanks a lot, Maria. Your help is really motivating and makes me more determined. Here's a pic of my family - they're the reason why I never give up. Their love gives me strength." - }, - { - "speaker": "Maria", - "dia_id": "D2:11", - "text": "Wow, John, that's a great pic! Your family looks so cheerful and loving. It's wonderful to have such a supportive and loving family." - }, - { - "speaker": "John", - "dia_id": "D2:12", - "text": "Thanks, Maria. They really help me stay centered. They remind me why I'm so passionate about making a positive impact." - }, - { - "speaker": "Maria", - "dia_id": "D2:13", - "text": "Family's love really grounds us and gives us strength. Their support certainly boosts your motivation." - }, - { - "speaker": "John", - "img_url": [ - "https://cdn.stocksnap.io/img-thumbs/960w/playground-child_RL9KRCSWHD.jpg" - ], - "blip_caption": "a photography of a family enjoying a ride on a swing", - "query": "family park", - "dia_id": "D2:14", - "re-download": true, - "text": "Yeah, they are my rock in tough times and always cheer me on. I'm really thankful for their love. Family time means a lot to me." - }, - { - "speaker": "Maria", - "dia_id": "D2:15", - "text": "Wow, John, that playground looks cool! What kind of stuff do you and your family do there?" - }, - { - "speaker": "John", - "dia_id": "D2:16", - "text": "Thanks, Maria! We love climbing, sliding, and playing games. It's an awesome way to connect and have a blast. What do you enjoy doing with your family?" - }, - { - "speaker": "Maria", - "dia_id": "D2:17", - "text": "My fam's small, but I love spending time with the friends I have. We usually watch movies, hike, and have game nights at my place. Quality connections matter most to me." - }, - { - "speaker": "John", - "dia_id": "D2:18", - "text": "Sounds nice, Maria! Spending time with loved ones is important." - }, - { - "speaker": "Maria", - "dia_id": "D2:19", - "text": "Definitely, John. They bring us joy, support, and a feeling of being part of something special. We should cherish every moment with them." - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/1655329/pexels-photo-1655329.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-lisa-fotios-1655329.jpg" - ], - "blip_caption": "a photo of a group of people standing around a kitchen table", - "query": "family dinner gathering", - "dia_id": "D2:20", - "text": "Yeah Maria, making memories with family is priceless! Life is so much more meaningful when we spend time together. Here's a pic of us at dinner." - }, - { - "speaker": "Maria", - "dia_id": "D2:21", - "text": "Woah, that's a nice pic, John! You all obviously had a blast at dinner. Nothing beats getting together with loved ones for a good meal - it makes some awesome memories!" - }, - { - "speaker": "John", - "dia_id": "D2:22", - "text": "Thanks, Maria! Meal times are always fun. Good food, laughs, and chats help us stay close." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a table with pizzas and salads on it", - "dia_id": "D2:23", - "text": "Yeah, John! It definitely builds a strong bond. Those shared meals really make life enjoyable and meaningful. What did you make?" - }, - { - "speaker": "John", - "dia_id": "D2:24", - "text": "We made pizza! We had so much fun making them together. It was great picking out toppings and sharing a tasty meal with family. Have you made anything lately?" - }, - { - "speaker": "Maria", - "dia_id": "D2:25", - "text": "I can picture you all laughing and having a blast making your own pizzas - a great way to bond! I made some peach cobbler recently, it was great." - }, - { - "speaker": "John", - "dia_id": "D2:26", - "text": "Yeah Maria, it's awesome! We get our creative on and have a blast together." - }, - { - "speaker": "Maria", - "dia_id": "D2:27", - "text": "Sure, John! It's those moments of creativity and laughter that bring us closer. Let's make happy memories with our family and keep them close." - }, - { - "speaker": "John", - "dia_id": "D2:28", - "text": " Yep, let's keep making great memories with our loved ones and cherishing the time we have. I'm off to do some taekwondo!" - } - ], - "session_3_date_time": "8:30 pm on 1 January, 2023", - "session_3": [ - { - "speaker": "John", - "blip_caption": "a photo of a man sitting on a bed using a laptop", - "dia_id": "D3:1", - "text": "Hey Maria, great to chat again! I joined a service-focused online group last week and it's been an emotional ride. Everyone there is incredible with their own inspiring stories. They've opened my eyes to new perspectives, and I'm feeling a sense of connection and purpose with them." - }, - { - "speaker": "Maria", - "dia_id": "D3:2", - "text": "Wow, John! That's amazing news. It's great to see you finding such a supportive community that is making a difference." - }, - { - "speaker": "John", - "dia_id": "D3:3", - "text": "Thanks, Maria! It's great to have a group of people with the same passion for serving. It's been really inspiring sharing stories, advice, and encouragement." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a poster on a table with a santa clause", - "dia_id": "D3:4", - "text": "That's great to hear! It's always inspiring to share thos things with like-minded individuals. By the way, have you had the chance to meet any of them in person?\n" - }, - { - "speaker": "John", - "dia_id": "D3:5", - "text": "We held some events and got to meet some people. We went to a homeless shelter to give out food and supplies. Seeing the smiles on their faces, we knew we made a real difference. We also organized a toy drive for kids in need. It was amazing seeing the community come together to spread some joy." - }, - { - "speaker": "Maria", - "dia_id": "D3:6", - "text": "That sounds great, John. It's nice to see the difference you're making. Do you have any ideas for future projects?" - }, - { - "speaker": "John", - "dia_id": "D3:7", - "text": "We're brainstorming some to help underserved communities get access to education, mentorship, job training, and resume building. The goal is to empower individuals in achieving their aspirations." - }, - { - "speaker": "Maria", - "dia_id": "D3:8", - "text": "That's great, John! Empowering individuals through education and mentorship is crucial for helping them reach their goals. Can't wait to see the initiatives you come up with!" - }, - { - "speaker": "John", - "dia_id": "D3:9", - "text": "Thanks, Maria! I'm really excited about them too. I believe that providing the right assistance and resources can make a lasting impact." - }, - { - "speaker": "Maria", - "dia_id": "D3:10", - "text": "Yep John, a bit of support can make an amazing change. You're spot on about it, it really is powerful. Keep doing what you're doing, it's really inspiring!" - }, - { - "speaker": "John", - "dia_id": "D3:11", - "text": "Thanks, Maria! I really appreciate your support, It means a lot to me. Especially after I failed the military aptitude test recently, I've been feeling a bit stressed out." - }, - { - "speaker": "Maria", - "img_url": [ - "https://images.pexels.com/photos/10815424/pexels-photo-10815424.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-beyza-erdem-10815424.jpg" - ], - "blip_caption": "a photo of a sunset over the ocean with a wave coming in", - "query": "sunset ocean beauty nature", - "dia_id": "D3:12", - "text": "No worries, John. I'm here for you and I got your back. Nature's beauty reminds me to slow down and enjoy the small stuff." - }, - { - "speaker": "John", - "dia_id": "D3:13", - "text": "That's a chill pic! Where did you find it?" - }, - { - "speaker": "Maria", - "dia_id": "D3:14", - "text": "I took it at the beach last month. Watching the sunset was so peaceful, it made me feel connected to nature and appreciate life's small moments." - }, - { - "speaker": "John", - "dia_id": "D3:15", - "text": "Wow, nature can be so beautiful! It reminds me of the film camera I had as a kid, I took plenty of beach pics. Thanks for sharing." - }, - { - "speaker": "Maria", - "dia_id": "D3:16", - "text": "Glad you enjoyed it, John! It's amazing how beautiful it can be." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people standing around a field", - "dia_id": "D3:17", - "text": "Yeah, it does. It helps us remember the small joys, especially when life gets busy." - } - ], - "session_4_date_time": "7:06 pm on 9 January, 2023", - "session_4": [ - { - "speaker": "Maria", - "dia_id": "D4:1", - "text": "Hey John, great news - I'm now friends with one of my fellow volunteers! We both love helping others. How have you been since we last chatted?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/njxp1ep8bse71.jpg" - ], - "blip_caption": "a photo of a car with a broken windshield and a broken windshield", - "query": "shattered windshield", - "dia_id": "D4:2", - "text": "Hey Maria, I've been busy with work and family, but last week I had an unexpected incident on my way home. It reminded me how life can throw unexpected troubles our way." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a tattoo with a quote on it", - "dia_id": "D4:3", - "text": "Oh John, that sounds tough. I'm glad you're alright. Life does throw us some surprises, doesn't it?\n\n" - }, - { - "speaker": "John", - "dia_id": "D4:4", - "text": "Thanks, Maria. This is a reminder to stay strong during tough times." - }, - { - "speaker": "Maria", - "dia_id": "D4:5", - "text": "Yeah John, when stuff like that happens, it's good to tap into our own inner strength. How did you handle it?" - }, - { - "speaker": "John", - "dia_id": "D4:6", - "text": "I tried to stay calm and asked for assistance, which helped me handle the situation and make it back safely." - }, - { - "speaker": "Maria", - "dia_id": "D4:7", - "text": "Hey, John that's awesome! You really showed your resilience and resourcefulness. How did you feel afterwards?" - }, - { - "speaker": "John", - "dia_id": "D4:8", - "text": "I felt relieved and grateful to make it through without incident." - }, - { - "speaker": "Maria", - "dia_id": "D4:9", - "text": "Yeah, it's tough. Appreciate what you've got. You got through it - stay strong. Are there any things you like to do to calm down during times like this?" - }, - { - "speaker": "John", - "dia_id": "D4:10", - "text": "Thanks, Maria! I like to just take a look at the sunset- it reminds me to enjoy the small things in life." - }, - { - "speaker": "Maria", - "dia_id": "D4:11", - "text": "It's great to hear that you appreciate the beauty of sunsets! By the way, have there been any developments regarding your political goals?" - }, - { - "speaker": "John", - "dia_id": "D4:12", - "text": "I've been keeping busy since we last talked! I've been looking into local politics, talking to community leaders, and getting to know the needs and hopes of our neighborhood. It's been super informative, and I'm feeling optimistic about it all." - }, - { - "speaker": "Maria", - "img_url": [ - "https://media2.inlander.com/inlander/imager/u/original/27047427/1.jpg" - ], - "blip_caption": "a photo of a notebook with a pencil and a notepad on a table", - "query": "notebook local politics", - "dia_id": "D4:13", - "text": "Wow, John! Your dedication to our community is amazing. Keep up the great work! I've been taking some notes about local politics in my notebook." - }, - { - "speaker": "John", - "dia_id": "D4:14", - "text": "Thanks, Maria! You're super helpful. That gives me some new ideas and plans for our community!" - }, - { - "speaker": "Maria", - "dia_id": "D4:15", - "text": "Glad I could help, John. What's up next for you?" - }, - { - "speaker": "John", - "dia_id": "D4:16", - "text": "Next week I have a community meeting coming up, we'll be discussing education and infrastructure upgrades." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a microphone, a charger, and a charger on a table", - "dia_id": "D4:17", - "text": "Good luck on that! Hope your ideas and concerns get heard." - }, - { - "speaker": "John", - "dia_id": "D4:18", - "text": "Thanks, Maria! I'm hoping so. I'll definitely stick up for our community. Discussing it is important." - }, - { - "speaker": "Maria", - "dia_id": "D4:19", - "text": "Definitely, John! Talking and working together will make a great difference. You're really doing our community a service by speaking out. It's admirable and inspiring." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting around a table with a laptop", - "dia_id": "D4:20", - "text": "Maria, thank you! Your support means a lot. Let's make some change in our community!" - }, - { - "speaker": "Maria", - "dia_id": "D4:21", - "text": "Yeah, let's do it, John! Working together, we can bring positive changes to the neighborhood as a team." - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/7351/14090438714_33bec862fe_b.jpg" - ], - "blip_caption": "a photography of a man and a woman shaking hands", - "query": "handshake unity teamwork", - "dia_id": "D4:22", - "re-download": true, - "text": "Yeah, Maria! We can achieve great things if we join forces together." - }, - { - "speaker": "Maria", - "dia_id": "D4:23", - "text": "Agreed, John! Uniting is essential for making a difference. if we stand together we can tackle any problems and build a great community." - }, - { - "speaker": "John", - "dia_id": "D4:24", - "text": "Yeah, Maria! I couldn't agree more. Thanks for being an awesome friend." - }, - { - "speaker": "Maria", - "dia_id": "D4:25", - "text": "No worries, John. It's really nice to help. We make a great team." - }, - { - "speaker": "John", - "dia_id": "D4:26", - "text": "Yup, we rock as a team! Glad to have you." - } - ], - "session_5_date_time": "1:17 pm on 28 January, 2023", - "session_5": [ - { - "speaker": "John", - "blip_caption": "a photo of a wooden floor with a white wall and a wooden floor", - "dia_id": "D5:1", - "text": "Hey Maria, since we last spoke I went to that community mtg. It was really interesting hearing everyone's worries and how it affects our area. It made me realize how crucial the upgrades are, especially for the kids. I'm really upset seeing the state of our education. How do you think we can make things better?" - }, - { - "speaker": "Maria", - "dia_id": "D5:2", - "text": "Hey John, cool that you made it. You're right, it's really sad to see the state of education. We should fight for more money and resources for schools and raise awareness about the importance of education. It's not just for our kids, but for all of us too!" - }, - { - "speaker": "John", - "dia_id": "D5:3", - "text": "Yup, education is essential for a successful society. I totally agree that we should fight for more money and resources for our schools. It breaks my heart that our kids don't have the proper stuff they need. They deserve better." - }, - { - "speaker": "Maria", - "dia_id": "D5:4", - "text": "Yeah, John. Our kids are our future; they should have the best. It kills me to think about all the kids without the proper stuff they need. It's just not right." - }, - { - "speaker": "John", - "dia_id": "D5:5", - "text": "It's definitely isn't, Maria. My kids have so much and others don't. We really need to do something about it." - }, - { - "speaker": "Maria", - "img_url": [ - "https://media.cnn.com/api/v1/images/stellar/prod/230608195849-brandi-tuck-college-volunteer2-cnnheroes.jpg" - ], - "blip_caption": "a photo of three women in green aprons holding pots and cups", - "query": "volunteer homeless shelter residents", - "dia_id": "D5:6", - "text": "Sure, it's not right that some kids get all they need while others have nothing to help them succeed. We gotta do something! Last week I volunteered at a shelter during an event for kids, and it's been a great experience." - }, - { - "speaker": "John", - "dia_id": "D5:7", - "text": "Wow, Maria! That's really making a big impact. What made you decide to get involved with that?" - }, - { - "speaker": "Maria", - "dia_id": "D5:8", - "text": "I started volunteering to help make a difference. My aunt believed in volunteering, and used to help my family out when we were struggling, so I'm inspired by her. It makes me happy knowing I can brighten somebody's day." - }, - { - "speaker": "John", - "dia_id": "D5:9", - "text": "Wow, Maria. That's amazing! I can imagine it's incredibly rewarding. Is there a particular moment that stands out to you as the most impactful?" - }, - { - "speaker": "Maria", - "dia_id": "D5:10", - "text": "For me, it was when I noticed a little girl around 8 sitting all alone. She seemed so sad. So, I sat with her and we talked. Turns out she had no other family - it broke my heart. I was able to give her some comfort and a listening ear. We ended up laughing and having a good time." - }, - { - "speaker": "John", - "img_url": [ - "https://i2.pickpik.com/photos/428/450/411/girl-baby-doll-baby-girl-preview.jpg" - ], - "blip_caption": "a photography of a little girl kissing a doll with a doll in her lap", - "query": "smiling child toy", - "dia_id": "D5:11", - "re-download": true, - "text": "Wow, what a touching moment, Maria. I'm glad you were there for her when she needed someone. I'm sure it made a big impact." - }, - { - "speaker": "Maria", - "dia_id": "D5:12", - "text": "That's nice of you, John. What's the photo about?" - }, - { - "speaker": "John", - "dia_id": "D5:13", - "text": "It reminds me of something from my childhood. I had a little doll like this and it always made me feel better. It reminds me to always look out for others, especially when they're feeling down." - }, - { - "speaker": "Maria", - "dia_id": "D5:14", - "text": "That's sweet. Spreading kindness and support can really make a difference, especially when someone is feeling down. Thanks for sharing." - }, - { - "speaker": "John", - "dia_id": "D5:15", - "text": "No problem, Maria. It's important to support each other when we're feeling down. You never know the difference a kind gesture can make." - }, - { - "speaker": "Maria", - "dia_id": "D5:16", - "text": "Yep, kindness is key and a little compassion can really turn someone's day around. So glad we're on the same page here." - } - ], - "session_6_date_time": "2:33 pm on 5 February, 2023", - "session_6": [ - { - "speaker": "Maria", - "dia_id": "D6:1", - "text": "Hey John! Long time no talk. I just wanted to let you know I challenged myself last Friday and did a charity event. It was great! I truly felt the power of our collective effort to help people in need, so heartwarming." - }, - { - "speaker": "John", - "blip_caption": "a photo of a woman handing a plate of food to a man", - "dia_id": "D6:2", - "text": "Wow, Maria! Truly inspiring! It's so cool to see how our community can make a difference. How did it feel to be part of that event?" - }, - { - "speaker": "Maria", - "dia_id": "D6:3", - "text": "Thanks, John! It was such a rewarding experience. Just the act of serving meals and seeing the gratitude on their faces was truly heartwarming. It reminded me of how powerful compassion can be. So, tell me, what made you get into politics?" - }, - { - "speaker": "John", - "dia_id": "D6:4", - "text": "I feel a strong urge to serve my country and community. Running for office was my chance to make an impact. But anyway, tell me more about your charity event. Did something special happen that made it meaningful for you?" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a man with a cell phone in his hand", - "dia_id": "D6:5", - "text": "Yeah, at the event, I had a conversation with someone named David. Hearing his story of hardship and how he ended up in difficult circumstances was so humbling. It just showed me that everyone has their own story and deserves understanding." - }, - { - "speaker": "John", - "dia_id": "D6:6", - "text": "Wow, that's powerful. It just shows that everyone has their own story and deserves understanding. Do you know any organizations or services that could help him out?" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a flyer with a group of people in a circle", - "dia_id": "D6:7", - "text": "Yeah, I did. I talked to him afterwards and linked him up with a nearby organization that offers housing and support for homeless individuals. Hopefully, he'll find the help he needs." - }, - { - "speaker": "John", - "dia_id": "D6:8", - "text": "Nice job, Maria! You really made an impact. It's important to help people find what they need. Have you ever been in a situation where you needed help?" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a woman in a face mask standing next to a table full of packages of food", - "dia_id": "D6:9", - "text": "Thanks, John. When I was younger, we had some money problems and had to rely on outside help from out auntie. It was a tough learning experience about the importance of helping others who struggle." - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/4478/37216959584_3c561498f3_b.jpg" - ], - "blip_caption": "a photography of a group of people standing around a table with boxes of tomatoes", - "query": "volunteering local food bank packing food bags", - "dia_id": "D6:10", - "re-download": true, - "text": "Yeah, Maria, we learn a lot from our own struggles. I just started helping out with a food drive for folks who lost their jobs. Here's a picture of me at the food bank." - }, - { - "speaker": "Maria", - "dia_id": "D6:11", - "text": "Wow, John, that's incredible! What inspired you to get involved with something like this?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a thanksgiving potluck and food drive flyer", - "dia_id": "D6:12", - "text": "Seeing the effect unemployment has on our neighbors made me decide to act. I wanted to help out in these tough times by doing a community food drive. We can all make a difference!" - }, - { - "speaker": "Maria", - "dia_id": "D6:13", - "text": "That's really great of you. What sparked your decision to start this initiative?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a large group of people standing around a table", - "dia_id": "D6:14", - "text": "Thanks, Maria. Unemployment in our community was the reason behind it." - }, - { - "speaker": "Maria", - "dia_id": "D6:15", - "text": "You did awesome! How's the response been to that?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.lifturbanportland.org/uploads/8/3/6/3/83630366/published/warehouse-volunteers.jpg" - ], - "blip_caption": "a photo of a woman in a face mask holding a bag of food", - "query": "group of volunteers food drive event", - "dia_id": "D6:16", - "text": "Thanks, Maria! We've been overwhelmed by the response and the volunteers. Here's a photo of them at a recent event." - }, - { - "speaker": "Maria", - "dia_id": "D6:17", - "text": "Seeing so many people support the community is awesome. I'd love to lend a hand with networking or helping out at future events." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of men working on a wall", - "dia_id": "D6:18", - "text": "That'd be great, Maria! Thanks for offering to help. Your assistance would be really appreciated." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people standing outside of a house", - "dia_id": "D6:19", - "text": "No worries, John. Whenever you need help, just let me know." - }, - { - "speaker": "John", - "dia_id": "D6:20", - "text": "Yep, definitely. Your support really means a lot, Maria. Thanks for always being there for me." - }, - { - "speaker": "Maria", - "dia_id": "D6:21", - "text": "Of course, John. You're a great friend to me. I'll always be there for you." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting in a park", - "dia_id": "D6:22", - "text": "Thanks, Maria. Friendship means a lot to me. I'm glad we have each other's backs and can work towards a shared goal." - } - ], - "session_7_date_time": "8:55 pm on 25 February, 2023", - "session_7": [ - { - "speaker": "Maria", - "dia_id": "D7:1", - "text": "Hey John, how's it going? Just wanted to give you the heads up on what's been happening lately- I took a creative writing class recently, and it was super enlightening!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a crowd of people sitting on a sidewalk with umbrellas", - "dia_id": "D7:2", - "text": "Hey Maria! Wanted to let you know that I'm running for office again. It's been a wild ride, but I'm more excited than ever! How have you been?" - }, - { - "speaker": "Maria", - "dia_id": "D7:3", - "text": "Congrats, John! What made you decide to run again? As for me, I've been volunteering at a homeless shelter and it's really rewarding." - }, - { - "speaker": "John", - "dia_id": "D7:4", - "text": "Thanks, Maria! After my last run, I saw the impact I could make in the community through politics. It's rewarding to work towards positive changes and a better future." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a woman with a blue shirt and a ring on her neck", - "dia_id": "D7:5", - "text": "Wow John, your enthusiasm for making a better future is inspiring. Making a positive impact is so rewarding! While volunteering yesterday, I met this amazing woman, Jean, who had been through a lot, yet stayed optimistic and resilient. She showed me the importance of gratitude and connection." - }, - { - "speaker": "John", - "dia_id": "D7:6", - "text": "Wow, Maria! How did she end up in such a situation, and how did she remain positive throughout?" - }, - { - "speaker": "Maria", - "dia_id": "D7:7", - "text": "She had a tough time, going through a divorce, losing her job, and ending up homeless. Yet, she still values the little things and believes in the good of kindness." - }, - { - "speaker": "John", - "dia_id": "D7:8", - "text": "It's really inspiring to see someone staying optimistic even when things get tough." - }, - { - "speaker": "Maria", - "dia_id": "D7:9", - "text": "Yeah, sure is. It's a reminder that, no matter what, we can still find joy and hope." - }, - { - "speaker": "John", - "dia_id": "D7:10", - "text": "Keep up the great work, Maria! It's important to stay positive and thankful, even when life's tough. A little kindness and optimism can go a long way. Sounds like your volunteer work has been very influential - amazing!" - }, - { - "speaker": "Maria", - "dia_id": "D7:11", - "text": "Thanks, John! Your words mean a lot. It's incredible how much positivity and optimism can impact someone." - }, - { - "speaker": "John", - "dia_id": "D7:12", - "text": "Yep. It's moments like these that remind me of how important it is to be kind and optimistic." - }, - { - "speaker": "Maria", - "dia_id": "D7:13", - "text": "Sure thing, John. Those things can really make a difference. Let's keep spreading positivity and making an impact together." - }, - { - "speaker": "John", - "blip_caption": "a photo of a man holding a child on his shoulders", - "dia_id": "D7:14", - "text": "I totally agree, Maria! Let's keep helping each other and make the world a better place.\n\n" - }, - { - "speaker": "Maria", - "dia_id": "D7:15", - "text": "Sounds cool, John! Let's make some positive change and brighten up that place. Do you have anything fun coming up soon?" - }, - { - "speaker": "John", - "dia_id": "D7:16", - "text": "Let's do it, Maria! Together, we can make a real difference and bring a brighter future. And nothing too soon, but my colleague Rob invited me to a beginner's yoga class." - }, - { - "speaker": "Maria", - "dia_id": "D7:17", - "text": "Sounds fun! I hope you have a good time. In the meantime, let's work together to make things better for our community." - } - ], - "session_8_date_time": "6:03 pm on 6 March, 2023", - "session_8": [ - { - "speaker": "Maria", - "blip_caption": "a photo of a bride and groom walking down the street", - "dia_id": "D8:1", - "text": "Hey John, I haven't talked to you in a while. Last week, my grandma passed away and it's been really hard. I'm trying to stay positive, but it's tough. How're you doing?" - }, - { - "speaker": "John", - "img_url": [ - "https://content1.getnarrativeapp.com/static/1396e135-13d4-4c37-8183-5d1eaf957c41/Surprise-proposal-picnic-at-cathedral-park-in-Portland-or-.jpg" - ], - "blip_caption": "a photo of a family sitting on the grass with a baby", - "query": "family picnic", - "dia_id": "D8:2", - "text": "So sorry to hear about your loss, Maria. I'm here for you if you need anything. I'm doing well, I just had a picnic with the wife and kids." - }, - { - "speaker": "Maria", - "dia_id": "D8:3", - "text": "Thanks for your support, John. How's your family doing? That baby in the pic is adorable! What's their name?" - }, - { - "speaker": "John", - "dia_id": "D8:4", - "text": "Thanks, Maria! They're doing great. Our one-year-old is so cute, his name is Kyle!" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "dia_id": "D8:5", - "text": "Sounds like parenting has been a wonderful experience for you - what has it been like?" - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/1883/42932050850_97f4b8999f_b.jpg" - ], - "blip_caption": "a photography of two children playing on a playground equipment", - "query": "parenting kids park playing", - "dia_id": "D8:6", - "re-download": true, - "text": "It's definitely been a wild ride! It's full of ups and downs, but the love and happiness outweigh it all." - }, - { - "speaker": "Maria", - "dia_id": "D8:7", - "text": "Parenting can be tough but rewarding. Do you take your kid to the park often?" - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/1851/44767563171_457da8d8b3_b.jpg" - ], - "blip_caption": "a photography of a young girl and a young boy on a swing", - "query": "park kids playing swings joyful moment", - "dia_id": "D8:8", - "re-download": true, - "text": "Yeah, we go a few times a week. It's great for family bonding and letting the kids run around. We had a great time last weekend - they really enjoyed the carefree joy.\n" - }, - { - "speaker": "Maria", - "dia_id": "D8:9", - "text": "Looks like a blast! Did everyone get a chance to try it? Glad you're all having a great time!" - }, - { - "speaker": "John", - "dia_id": "D8:10", - "text": "Yeah, everyone got a chance to swing. It's always fun coming up with activities for my family to enjoy." - }, - { - "speaker": "Maria", - "dia_id": "D8:11", - "text": "Wow, John, that's amazing! How do you come up with these ideas?" - }, - { - "speaker": "John", - "dia_id": "D8:12", - "text": "I just try to find things that we'll have fun with, like a walk or picnic in the park, or finding events in our town and beyond. Just last week, I found a violin concert that we all enjoyed. It's all about making memories together." - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/eq0vvcinma621.jpg" - ], - "blip_caption": "a photo of a picture of a castle in a shadow box", - "query": "DIY cardboard fort fairy lights", - "dia_id": "D8:13", - "text": "Wow, John, that's great! Making memories together is so valuable. I loved doing something similar with my siblings when I was young - it's amazing how something so simple can make such lasting memories." - }, - { - "speaker": "John", - "dia_id": "D8:14", - "text": "That pic is so cool! It looks like something from a storybook. What gave you the idea for this?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://images.rawpixel.com/image_social_square/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIzLTAzL21ldDMzOTg0NC1pbWFnZS5qcGc.jpg" - ], - "blip_caption": "a photography of a painting of a castle on a hill", - "query": "castle painting Europe beauty wonder", - "dia_id": "D8:15", - "re-download": true, - "text": "Thanks, John! I got the idea from that trip to England a few years ago - I was mesmerized by the castles. I wanted to bring that same magic to my home, like a reminder of the world's beauty. I made a painting too!" - }, - { - "speaker": "John", - "dia_id": "D8:16", - "text": "Wow, that looks great! Where in England was it?" - }, - { - "speaker": "Maria", - "dia_id": "D8:17", - "text": "It was in London. The architecture there was so captivating that I wanted to recreate some of that charm in my own space. It's a reminder of London's history and grace." - }, - { - "speaker": "John", - "blip_caption": "a photo of a wooden sign with a flag and eagle", - "dia_id": "D8:18", - "text": "Wow, having symbols of travel memories in our homes is so cool! Last week I retook the aptitude test with some great results, and I feel drawn to serving my country in this way." - }, - { - "speaker": "Maria", - "dia_id": "D8:19", - "text": "Wow, that's really cool! Have you discussed it with anyone yet?" - }, - { - "speaker": "John", - "dia_id": "D8:20", - "text": "Yeah, I chatted with my family and friends about it. They've been supportive and understand why I want to volunteer. I'm really proud to have this opportunity!" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people standing around a table with food", - "dia_id": "D8:21", - "text": "Glad you have a good support system, John. It's been great having people behind me while volunteering at the homeless shelter. Positive influences make a big impact.\n" - }, - { - "speaker": "John", - "dia_id": "D8:22", - "text": "Cool, Maria! You've built some awesome connections and you're making a big impact at the homeless shelter - nice work! How did you manage to do that?" - }, - { - "speaker": "Maria", - "dia_id": "D8:23", - "text": "Thanks, John! It took time and effort, but I slowly formed connections by listening and showing compassion. It's all about building trust and forming real relationships." - }, - { - "speaker": "John", - "dia_id": "D8:24", - "text": "Wow, Maria! I see, so that's the key to making a difference." - }, - { - "speaker": "Maria", - "dia_id": "D8:25", - "text": "Yeah, building those real connections and getting people to trust us is key. It's usually the little things, like taking the time to hear what they're saying, that make the biggest impact." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting at a table with plates of food", - "dia_id": "D8:26", - "text": "Yeah, Maria, I agree. That can really make a difference. It's important to make people feel noticed and listened to." - } - ], - "session_9_date_time": "9:36 am on 2 April, 2023", - "session_9": [ - { - "speaker": "Maria", - "dia_id": "D9:1", - "text": "Hey John, long time no see! I've been taking a poetry class lately to help me put my feelings into words. It's been a rough ride, but it's been good. How have you been?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.diplomaoutlet.com/wp-content/uploads/u9-1.jpg" - ], - "blip_caption": "a photo of a certificate of completion of a university degree", - "query": "diploma university", - "dia_id": "D9:2", - "text": "Hey Maria! Awesome to hear from you. Sounds like a great way to delve into your feelings. Since we spoke last, I've had quite the adventure!\n" - }, - { - "speaker": "Maria", - "dia_id": "D9:3", - "text": "Congrats on finishing your degree, John! It must have been quite the adventure. How did it feel when you achieved such a big goal?" - }, - { - "speaker": "John", - "dia_id": "D9:4", - "text": "Thanks, Maria! It was quite a journey, but definitely worth it. I graduated last week!" - }, - { - "speaker": "Maria", - "dia_id": "D9:5", - "text": "I bet! What are your plans for the future?" - }, - { - "speaker": "John", - "dia_id": "D9:6", - "text": "Thanks! I'm considering going into policymaking because of my degree and my passion for making a positive impact. There are many opportunities to make improvements." - }, - { - "speaker": "Maria", - "dia_id": "D9:7", - "text": "Sounds great, John! That seems perfect for you with your passion and dedication. Are there any specific areas you're particularly interested in?" - }, - { - "speaker": "John", - "dia_id": "D9:8", - "text": "Thanks, Maria! Improving education and infrastructure is particularly interesting to me. It's important for our community." - }, - { - "speaker": "Maria", - "dia_id": "D9:9", - "text": "Yeah, I remember you mentioning those areas. How have your experiences in the community meeting and involvement shaped your views on them?" - }, - { - "speaker": "John", - "dia_id": "D9:10", - "text": "Going to community meetings and getting involved in my community has given me a better understanding of the challenges our education and infrastructure systems face. It has also shown me the impact these issues have on our neighbors, highlighting the need for us to work towards finding solutions." - }, - { - "speaker": "Maria", - "dia_id": "D9:11", - "text": "Way to go, John! You're really showing dedication and commitment. Gaining first-hand experience and working to find solutions is awesome!" - }, - { - "speaker": "John", - "dia_id": "D9:12", - "text": "Thanks Maria! It's important to me to put my words into action and find solutions. Even though it can be hard, it's so rewarding to know I'm making a difference!" - }, - { - "speaker": "Maria", - "dia_id": "D9:13", - "text": "Agreed, John! Yeah, it can be tough, but it's really satisfying and worthwhile. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D9:14", - "text": "Maria, thanks a lot! Your support is really encouraging - I appreciate having you in my corner!" - }, - { - "speaker": "Maria", - "dia_id": "D9:15", - "text": "No problem, John. Let me know if you need any help. We work well together!" - }, - { - "speaker": "John", - "dia_id": "D9:16", - "text": "Thanks, Maria! Will do. Working together would be great!" - }, - { - "speaker": "Maria", - "img_url": [ - "https://bloximages.newyork1.vip.townnews.com/yakimaherald.com/content/tncms/assets/v3/editorial/4/2d/42d9b634-18c9-11ed-8a1b-039214ad9eb3/62f3dc54b5a61.image.jpg" - ], - "blip_caption": "a photo of a man and woman shaking hands in front of a food tray", - "query": "volunteering at local homeless shelter", - "dia_id": "D9:17", - "text": "Yes, John, let's keep supporting each other and finding ways to improve the lives of others. Remember when we volunteered together last year? It was such a fulfilling experience." - }, - { - "speaker": "John", - "blip_caption": "a photo of a woman and a child walking in a park", - "dia_id": "D9:18", - "text": "Yeah, I remember that! It was cool to see how our actions can make a big impact. Let's keep helping out and making things better! Our actions really do matter. " - } - ], - "session_10_date_time": "12:24 am on 7 April, 2023", - "session_10": [ - { - "speaker": "John", - "dia_id": "D10:1", - "text": "Hey Maria, I'm so excited to tell you I started a weekend yoga class with a colleague - it's awesome! I feel great, both mentally and physically after each session. I'd been wanting to try yoga for a while and finally took the plunge. Simple stretching and breathing is having such a positive effect on my wellbeing. And the instructor is great too." - }, - { - "speaker": "Maria", - "dia_id": "D10:2", - "text": "Wow, John, glad to hear that! It's amazing how something like stretching and breathing can have such a positive effect on our wellbeing. What can you tell me about your instructor that makes them so great?" - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/7721929/pexels-photo-7721929.jpeg" - ], - "blip_caption": "a photography of a man doing yoga outside on a blue mat", - "query": "yoga instructor relaxed yoga pose", - "dia_id": "D10:3", - "re-download": true, - "text": "They're awesome - they make sure we do the poses properly and encourage us to listen to our bodies. They create a great, relaxed environment that makes everyone feel welcome. Here's a photo from our last class." - }, - { - "speaker": "Maria", - "dia_id": "D10:4", - "text": "Wow, that looks great! What kind of yoga is it?" - }, - { - "speaker": "John", - "dia_id": "D10:5", - "text": "It's a beginner yoga class, focusing on fundamentals like poses and breathing. I find it helps me relax and increase my flexibility." - }, - { - "speaker": "Maria", - "dia_id": "D10:6", - "text": "Nice one, John! Glad you're finding some chill with that. How are you feeling afterwards?" - }, - { - "speaker": "John", - "dia_id": "D10:7", - "text": "I feel great. It really helps me relax and feel more connected. It's been a great way to improve my mind and body." - }, - { - "speaker": "Maria", - "dia_id": "D10:8", - "text": "Awesome John! Glad it's chillin' and connecting you. Stretching and breathing are such powerful tools for wellbeing. So cool you found a beginner class to help you flex up. Keep it up! \ud83e\uddd8\u200d\u2640\ufe0f" - }, - { - "speaker": "John", - "dia_id": "D10:9", - "text": "Thanks, Maria! I'm gonna keep it up. Not only for the physical benefits, but also for the peace of mind and mindfulness it brings. It's part of my daily routine now. Have you tried anything new lately that's had an impact on you?" - }, - { - "speaker": "Maria", - "dia_id": "D10:10", - "text": "Last weekend I did something new that had an impact on me. I participated in a 5K charity run for a homeless shelter. It was awesome being surrounded by people all there for the same cause. There's something special about the energy and sense of unity. It was truly rewarding and reminded me why I'm passionate about charity work." - }, - { - "speaker": "John", - "dia_id": "D10:11", - "text": "Wow, Maria! It sounds awesome. I bet you felt so pumped running with everyone for the same cause. Events like these really energize us and remind us we can make a difference. Any pictures from the event?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://blog.myfitnesspal.com/wp-content/uploads/2019/06/8-Charity-Walking-Events-That-Give-Your-Steps-Extra-Meaning-1200x900.jpg" - ], - "blip_caption": "a photo of a large group of people walking down a street", - "query": "charity event running group of people", - "dia_id": "D10:12", - "text": "Here's a pic from the event! The energy was great, it was inspiring seeing everyone come together for a shared cause. It was awesome!" - }, - { - "speaker": "John", - "img_url": [ - "https://c.pxhere.com/photos/88/c3/motivation_motivational_heart_inspire_encouragement_quote-562325.jpg!d" - ], - "blip_caption": "a photography of a heart shaped sign with a quote on it", - "query": "people united great cause career fair underprivileged students resources", - "dia_id": "D10:13", - "re-download": true, - "text": "What a photo! Seeing everyone come together for a shared cause must have been inspiring. Last weekend I had an experience that reminded me of the impact we can make. I got to volunteer at a career fair at a local school, and it was incredible to see how lack of resources affects these kids' dreams. Being able to help them was such a rewarding experience." - }, - { - "speaker": "Maria", - "dia_id": "D10:14", - "text": "Wow, John, what an amazing experience! It's so sad how a lack of resources can make such a difference in these kids' dreams. Being able to help them was an awesome experience. What does the sign say?" - }, - { - "speaker": "John", - "dia_id": "D10:15", - "text": "The sign says, \"Always look on the bright side of life\". It reminds us that kids can reach their dreams with the right help." - }, - { - "speaker": "Maria", - "dia_id": "D10:16", - "text": "That's really cool. It's inspiring to see how these kids can do great things with support. You're doing awesome work by helping and motivating them. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D10:17", - "text": "Thanks, Maria! It means a lot. I'm gonna keep pushing for them. We need folks in the community, doing good for the ones who need it. We rock!" - }, - { - "speaker": "Maria", - "dia_id": "D10:18", - "text": "Yeah John, let's keep pushing for those kids! We can make a difference and help lots of people. Keep up the good work!" - } - ], - "session_11_date_time": "6:13 pm on 10 April, 2023", - "session_11": [ - { - "speaker": "John", - "dia_id": "D11:1", - "text": "Hey Maria, haven't talked for a few days. Had a wild week, my car broke down last Fri on my way to work. Trying to get it fixed but it's tough & putting a strain on my wallet. Staying positive & looking for a solution though." - }, - { - "speaker": "Maria", - "dia_id": "D11:2", - "text": "Aww John, bummer about that. No doubt it's been tough, but I'm impressed by how positive you're being. Keep it up - tough times pass, but you're tough enough to get through 'em!" - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/1266810/pexels-photo-1266810.jpeg" - ], - "blip_caption": "a photography of a mountain with a sunset and flowers in the foreground", - "query": "road beautiful sunset", - "dia_id": "D11:3", - "re-download": true, - "text": "Thanks, Maria. Your kind words mean a lot. Yeah, it's been tough with car trouble and money problems, but I stay positive and find a way. This picture reminds me of a road trip we took last year; even with bumps along the way, there's still beauty and hope." - }, - { - "speaker": "Maria", - "dia_id": "D11:4", - "text": "Wow, great pic! Where did you go on that road trip?" - }, - { - "speaker": "John", - "dia_id": "D11:5", - "text": "Thanks! We explored the coast up in the Pacific Northwest and hit some cool national parks. The beauty of nature was absolutely breathtaking!" - }, - { - "speaker": "Maria", - "dia_id": "D11:6", - "text": "Wow, that must've been great! It's so nice to appreciate nature and find peace. Lucky you got to experience that." - }, - { - "speaker": "John", - "dia_id": "D11:7", - "text": "Wow, it was amazing. The stunning views really make you think." - }, - { - "speaker": "Maria", - "dia_id": "D11:8", - "text": "Nature helps put things in perspective and reminds us of the beauty even during tough times. Hold onto those moments of peace." - }, - { - "speaker": "John", - "dia_id": "D11:9", - "text": "Yeah, Maria. That peace and beauty are so needed, especially during tough times. They give us the power and inspiration to continue. Anything cool you're up to now?" - }, - { - "speaker": "Maria", - "dia_id": "D11:10", - "text": "I recently gave a few talks at the homeless shelter I volunteer at. It was really fulfilling and I received lots of compliments from other volunteers. It was a great reminder about why connecting with and helping others is so important. And, I bought a cross necklace to feel closer to my faith- which has made me happy." - }, - { - "speaker": "John", - "dia_id": "D11:11", - "text": "Way to go, Maria! You're making a real difference. It's awesome how connecting with and helping others brings you so much joy. Keep it up!" - }, - { - "speaker": "Maria", - "dia_id": "D11:12", - "text": "Thanks, John! It's so great to make a real difference. Seeing the impact and hearing the gratitude fills me with so much joy. The people at the shelter have become like family to me. I feel really blessed to know them." - }, - { - "speaker": "John", - "dia_id": "D11:13", - "text": "Wow, Maria, what you're doing is truly amazing. Your kindness ripples and creates such incredible relationships!" - }, - { - "speaker": "Maria", - "img_url": [ - "https://portlandrescuemission.org/wp-content/uploads/2017/11/20160809_gc_0797-copy.jpg" - ], - "blip_caption": "a photo of a group of people standing around a table filled with food", - "query": "homeless shelter volunteers", - "dia_id": "D11:14", - "text": "Thanks, John. Building relationships and seeing kindness really does make a difference. Here's a pic from last week. Seeing everyone come together warms my heart and fills me with hope." - }, - { - "speaker": "John", - "dia_id": "D11:15", - "text": "Wow, that's amazing how everyone came together. You must have had some great ideas! What do you do there?" - }, - { - "speaker": "Maria", - "dia_id": "D11:16", - "text": "We organized a meal for the shelter residents and I helped with getting everything ready. It was cool to see everyone together, eating and supporting each other." - }, - { - "speaker": "John", - "dia_id": "D11:17", - "text": "Wow, Maria, that's awesome! You made everyone so comfortable and it must have been so rewarding. You're really making a difference!" - }, - { - "speaker": "Maria", - "dia_id": "D11:18", - "text": "Yeah, it was really nice to help bring people together and create a sense of comfort and community. It's something special to see people supporting each other." - }, - { - "speaker": "John", - "dia_id": "D11:19", - "text": "Definitely, Maria. That's great. It gives us hope and reminds us we're not alone. Thank you for being a positive force." - }, - { - "speaker": "Maria", - "dia_id": "D11:20", - "text": "No problem, John! It's great to feel this sense of community. Thanks for the kind words, they mean a lot." - }, - { - "speaker": "John", - "dia_id": "D11:21", - "text": "Glad I could help, Maria. Talk to you soon. Stay safe!" - } - ], - "session_12_date_time": "7:34 pm on 18 April, 2023", - "session_12": [ - { - "speaker": "John", - "dia_id": "D12:1", - "text": "Hey Maria, hope you're doing okay. Since we chatted last, I've been blogging about politics and the government. It's been a really satisfying experience and I care about making a real impact. We need way better education and infrastructure and I know firsthand how this impacts neighborhoods." - }, - { - "speaker": "Maria", - "dia_id": "D12:2", - "text": "Hey John, glad to hear you're fired up about something! Blogging can really make a difference. I agree that education and infrastructure are key to our community's growth." - }, - { - "speaker": "John", - "dia_id": "D12:3", - "text": "Thanks, Maria! It's been great to talk to someone who understands the importance of these issues. Digging deeper into the political system has been eye-opening, so I'm researching policies and writing about my thoughts and ideas. Hoping to raise awareness and start conversations to create positive change." - }, - { - "speaker": "Maria", - "dia_id": "D12:4", - "text": "Wow, John! Your hard work will definitely start conversations and create positive change. What policies have you been focusing on lately?" - }, - { - "speaker": "John", - "dia_id": "D12:5", - "text": "Recently, education reform and infrastructure development. Good access to quality education and updated infrastructure are key to a thriving and successful community. My goal is to get conversations going and get people involved by sharing ideas and taking action. It's really empowering to know I can help make a difference in people's lives." - }, - { - "speaker": "Maria", - "dia_id": "D12:6", - "text": "Wow, John! Your passion and dedication is inspiring. It's great to see you taking the lead and making a difference. Keep up the amazing work!" - }, - { - "speaker": "John", - "img_url": [ - "https://byronernest.files.wordpress.com/2022/11/img_5966-1.jpg" - ], - "blip_caption": "a photo of two men standing next to each other at a convention", - "query": "blog post education reform", - "dia_id": "D12:7", - "text": "Thanks, Maria! Really appreciate your support and encouragement, it means a lot to me. I've gotten some good feedback on my blog posts so far. It's just a small step, but every step counts." - }, - { - "speaker": "Maria", - "dia_id": "D12:8", - "text": "It seems like your post is having an effect. Who are they? They're having fun!" - }, - { - "speaker": "John", - "dia_id": "D12:9", - "text": "My colleagues and I went to a convention together last month. We're all passionate about using tech for good in our community. It was great to connect with like-minded folks and swap ideas. It's inspiring to see people united in their goal." - }, - { - "speaker": "Maria", - "dia_id": "D12:10", - "text": "Wow, that must have been awesome! Being around people who share your passion is truly inspiring. How did it feel to be surrounded by like-minded individuals there?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting around a table", - "dia_id": "D12:11", - "text": "Talking with the group of people who were as stoked as me on tech for change was awesome! It made me think we really can make a difference." - }, - { - "speaker": "Maria", - "dia_id": "D12:12", - "text": "No way, John! That's really cool. What was the most exciting part of it?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of military men sitting around a table", - "dia_id": "D12:13", - "text": "The best part was the energy in the room - so infectious! We all had great ideas, brainstormed together, and stayed motivated. It was really empowering." - }, - { - "speaker": "Maria", - "dia_id": "D12:14", - "text": "That sounds amazing! How did being in that environment with such motivated people affect you?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a table with a map of a city on it", - "dia_id": "D12:15", - "text": "The motivated people around me gave me renewed energy and a purpose. It really inspired me to make a bigger difference." - }, - { - "speaker": "Maria", - "dia_id": "D12:16", - "text": "Cool, John! It's inspiring to be around people like that. Anything exciting on the horizon?" - }, - { - "speaker": "John", - "dia_id": "D12:17", - "text": "I'm planning a trip to the East Coast. How about you? Anything cool going on recently?" - }, - { - "speaker": "Maria", - "dia_id": "D12:18", - "text": "I'm still volunteering at the homeless shelter. It's fulfilling to lend a hand." - }, - { - "speaker": "John", - "dia_id": "D12:19", - "text": "Wow, Maria! You're so dedicated to helping people. How's it been going?" - }, - { - "speaker": "Maria", - "dia_id": "D12:20", - "text": "It's been rewarding and tough. It's fulfilling, but the growing need for help can be overwhelming." - }, - { - "speaker": "John", - "dia_id": "D12:21", - "text": "It's tough sometimes, but every act of kindness matters. You're so dedicated and inspiring, Maria. Keep going!" - }, - { - "speaker": "Maria", - "dia_id": "D12:22", - "text": "Thanks, John. Your kind words mean a lot. Little acts of kindness can have a big effect. We can all do something to make a difference." - }, - { - "speaker": "John", - "dia_id": "D12:23", - "text": "You're right, every small act can make a big impact. Let's keep doing our part for the world!" - } - ], - "session_13_date_time": "3:18 pm on 4 May, 2023", - "session_13": [ - { - "speaker": "John", - "dia_id": "D13:1", - "text": "Hey Maria! Long time no see! Tons has gone down since then!" - }, - { - "speaker": "Maria", - "dia_id": "D13:2", - "text": "Hey John! Nice to hear from you. What's new with you?" - }, - { - "speaker": "John", - "dia_id": "D13:3", - "text": "I just started going to boot camps with my fam last month. It's good for bonding and getting fit." - }, - { - "speaker": "Maria", - "dia_id": "D13:4", - "text": "Sounds awesome, John! Working out together is a great way of bonding as a family. Have you noticed any changes in your fitness since you started?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a white board with a list of workouts on it", - "dia_id": "D13:5", - "text": "Yeah Maria! We've had lots more energy and noticed some gains in strength and endurance. We're pushing ourselves and supporting each other which is super motivating. Best of all, my kids are getting excited about staying active!" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people standing around a table filled with food", - "dia_id": "D13:6", - "text": "Great news John! It's important to teach them good habits early. How often do you all workout together?" - }, - { - "speaker": "John", - "dia_id": "D13:7", - "text": "Thanks, Maria! Three times a week; it keeps us on track." - }, - { - "speaker": "Maria", - "dia_id": "D13:8", - "text": "Wow, John! You and your family are awesome for staying consistent and motivated to create healthy habits. Seeing that kind of dedication is inspiring! Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D13:9", - "text": "I will! It hasn't been easy, but we're all in it together." - }, - { - "speaker": "Maria", - "dia_id": "D13:10", - "text": "You're really inspiring with your commitment. Keep motivating each other!" - }, - { - "speaker": "John", - "dia_id": "D13:11", - "text": "Yeah, for sure! We'll keep pushing each other and staying motivated. It's great to be on this journey together." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a family posing for a picture in a park", - "dia_id": "D13:12", - "text": "Yep, John! Having a support system is key to staying motivated and reaching our goals. It's great that you have your family on this journey with you. Family is important - Even though mine is small, I rely on them for strength during tough times." - }, - { - "speaker": "John", - "dia_id": "D13:13", - "text": "Yeah, Maria! Family is the most important thing for me. They're my biggest support. We have each other's back through good times and bad." - }, - { - "speaker": "Maria", - "dia_id": "D13:14", - "text": "Wow, John! It's great to know that they're there for you. How have they been helping you out lately?" - }, - { - "speaker": "John", - "dia_id": "D13:15", - "text": "We've all been helping and supporting each other during boot camp - cheering each other on during workouts and providing emotional support outside them. It's been amazing witnessing us come together and be there for each other." - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.pinimg.com/originals/2b/14/2b/2b142b420a54216cfc9432b6b35676b5.jpg" - ], - "blip_caption": "a photo of a table with a variety of food on it", - "query": "family gathering dinner home", - "dia_id": "D13:16", - "text": "Wow, John! It's great when you have that kind of support. My mom and I made some dinner together last night!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/tc1f75pa9df21.jpg" - ], - "blip_caption": "a photo of a person holding a plate with a banana split ice cream sundae", - "query": "homemade apple pie vanilla ice cream", - "dia_id": "D13:17", - "text": "Thanks, Maria. Having good support is key. Appreciate you being there for me. What do you have in this spread? Looks delicious! I made this apple pie for the kids yesterday too!" - }, - { - "speaker": "Maria", - "dia_id": "D13:18", - "text": "Thanks, John! It had lots of great things like salads, sandwiches, and homemade desserts. My favorite is the amazing banana split sundae - I enjoy it after a day of volunteering. It's a little moment of joy in life - balance is key!" - }, - { - "speaker": "John", - "dia_id": "D13:19", - "text": "Mm, yum! A bit of joy is definitely important. How do you find balance in your life?" - }, - { - "speaker": "Maria", - "dia_id": "D13:20", - "text": "Taking care of myself physically, emotionally, and mentally helps me. That includes things like exercise, music, and spending time with loved ones. It really helps me stay positive." - }, - { - "speaker": "John", - "dia_id": "D13:21", - "text": "Awesome, Maria! Taking care of ourselves and looking out for our well-being is key. Finding balance like you mentioned helps us out a lot." - }, - { - "speaker": "Maria", - "img_url": [ - "https://images.pexels.com/photos/1974521/pexels-photo-1974521.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-julia-kuzenkov-1974521.jpg" - ], - "blip_caption": "a photo of a beach with footprints in the sand and a blue sky", - "query": "sunrise beach", - "dia_id": "D13:22", - "text": "Yeah John, it's super important. Taking care of yourself helps us be strong for life's tough times - I learned that the hard way last year." - }, - { - "speaker": "John", - "dia_id": "D13:23", - "text": "That looks interesting. What's the story behind the picture?" - }, - { - "speaker": "Maria", - "dia_id": "D13:24", - "text": "Last year I took a solo trip and took this pic in Spain. It reminded me that life is hard but there's still hope and beauty. It made me realize the importance of relying on my inner strength and appreciating small moments even more." - }, - { - "speaker": "John", - "dia_id": "D13:25", - "text": "That's a great pic, Maria. Yeah, life can be tough but finding beauty in the world can really make a difference. It sounds like your solo trip was a transformative experience." - }, - { - "speaker": "Maria", - "img_url": [ - "https://images.pexels.com/photos/11663179/pexels-photo-11663179.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-serhat-tu%C4%9F-11663179.jpg" - ], - "blip_caption": "a photo of a small island with a lone boat in the water", - "query": "sunrise calm lake", - "dia_id": "D13:26", - "text": "Yeah, it was great. It helped me grow and made me appreciate life more. Taking a step back can really show us the beauty of life." - }, - { - "speaker": "John", - "dia_id": "D13:27", - "text": "Wow, Maria, that's a really nice picture! Did you learn anything meaningful during your trip?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D13:28", - "text": "Yeah, I realized that it's important to value different perspectives and the power of solitude. Plus, it reminded me of the importance of letting go and trusting life sometimes." - }, - { - "speaker": "John", - "dia_id": "D13:29", - "text": "The pic takes me back. Did you try anything new on your vacation?" - }, - { - "speaker": "Maria", - "dia_id": "D13:30", - "text": "Yeah, I did! I tried my hand at surfing for the first time- it was so exciting! Even though I kept falling off, it taught me the power of not giving up and trying new things." - }, - { - "speaker": "John", - "blip_caption": "a photo of a man sitting on a mountain with a view of a lake", - "dia_id": "D13:31", - "text": "Wow Maria, that's awesome! Trying new things takes guts and it can be so rewarding. Keep up the courage!" - }, - { - "speaker": "Maria", - "dia_id": "D13:32", - "text": "Thanks, John! It can be rewarding to try new things. It's good to challenge ourselves." - }, - { - "speaker": "John", - "dia_id": "D13:33", - "text": "Yeah, Maria! We gotta push ourselves out of our comfort zones to experience new things and learn!" - }, - { - "speaker": "Maria", - "dia_id": "D13:34", - "text": "Yeah, John! That's so important." - }, - { - "speaker": "John", - "dia_id": "D13:35", - "text": "Sure, Maria! It helps us grow and learn more about ourselves. It's great to push the limits and see what we can do." - }, - { - "speaker": "Maria", - "dia_id": "D13:36", - "text": "Yeah, pushing boundaries is how we grow and find out what we're really capable of. It's a journey of self-exploration, it can be hard but it's so worth it. Blessed we both have this mindset in our lives." - }, - { - "speaker": "John", - "dia_id": "D13:37", - "text": "Yeah, Maria! It's like a never-ending journey of learning and growth. We've got the right attitude to take on the harder things in life. That's what helps us keep improving." - } - ], - "session_14_date_time": "5:04 pm on 6 May, 2023", - "session_14": [ - { - "speaker": "John", - "blip_caption": "a photo of a poster on a bulletin board with a man smiling", - "dia_id": "D14:1", - "text": "Hey Maria, great to chat again! A lot has happened since we last spoke. Last week, I decided to run for office again - even though I haven't been successful before. I guess I can't let go of my dream to make a difference in my community. Can you believe it? Feels like a dream come true!" - }, - { - "speaker": "Maria", - "dia_id": "D14:2", - "text": "Way to go, John! You're doing great. I'm so proud of you for sticking with it. You're always dreaming up ways to make a difference and I'm sure your drive will pay off. Don't be afraid to take risks-- I'm 100% behind you!" - }, - { - "speaker": "John", - "dia_id": "D14:3", - "text": "Maria, your support really means a lot! It's tough, but your kind words give me the motivation to keep going. I've still got a lot to learn, but I'm determined to make a difference. I appreciate your belief in me." - }, - { - "speaker": "Maria", - "dia_id": "D14:4", - "text": "John, I believe in you! Even small steps will make a difference. Keep going and stay true to yourself. You got this!" - }, - { - "speaker": "John", - "dia_id": "D14:5", - "text": "Thanks, Maria! Your words really mean something. Sometimes it feels like it's too much, but your encouragement gives me the strength to keep going." - }, - { - "speaker": "Maria", - "dia_id": "D14:6", - "text": "Hey John, it's okay to feel overwhelmed from time to time. Just remember to pause, reflect, and take care of yourself. I'm here for you - let me know how I can support you." - }, - { - "speaker": "John", - "dia_id": "D14:7", - "text": "Thanks, Maria. Got it. It's easy to get lost and forget that. Appreciate your support. How's that going for you?" - }, - { - "speaker": "Maria", - "dia_id": "D14:8", - "text": "Hey John, I've been doing great lately. I've been involved in some charity work and it's been really rewarding. I feel connected and it's really fulfilling." - }, - { - "speaker": "John", - "dia_id": "D14:9", - "text": "Wow, Maria! Glad you found something that brings you so much joy. What kind of work have you been doing?" - }, - { - "speaker": "Maria", - "dia_id": "D14:10", - "text": "Just yesterday I joined a nearby church. I wanted to feel closer to a community and my faith. So far it's been really great!" - }, - { - "speaker": "John", - "dia_id": "D14:11", - "text": "That's great, Maria! Joining a church can be really encouraging. I'm thrilled for you. How's everything been going?" - }, - { - "speaker": "Maria", - "dia_id": "D14:12", - "text": "Life's been a bit rough lately but I'm doing alright. I'm taking the time to reflect and find some balance. How about you? What's been going on since we last talked?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/bfie8r8yhekb1.jpg" - ], - "blip_caption": "a photo of a dark street at night with a fence and a street light", - "query": "power outage dark street infrastructure community", - "dia_id": "D14:13", - "text": "I had a similar experience. Last week, there was a power cut in our area, and it made me realize the importance of upgrading our infrastructure for stable services for everyone. Look how dark it was!" - }, - { - "speaker": "Maria", - "dia_id": "D14:14", - "text": "Wow, John, that's rough. Anything else around here that needs to be fixed so it doesn't happen again?" - }, - { - "speaker": "John", - "dia_id": "D14:15", - "text": "Yep, Maria. Mainly the roadways. They're full of potholes and can be dangerous for drivers and damaging to cars. Some improvements are definitely needed." - }, - { - "speaker": "Maria", - "dia_id": "D14:16", - "text": "Yeah, I remember you mentioning the roads. They can be quite dangerous, huh? Is there anything I can do to help improve the situation?" - }, - { - "speaker": "John", - "dia_id": "D14:17", - "text": "Thanks for the offer, Maria. I'm thinking about starting a community project regarding infrastructure, so maybe we can work together to get the neighborhood's backing." - }, - { - "speaker": "Maria", - "img_url": [ - "https://live.staticflickr.com/936/41912295190_f437e63596_b.jpg" - ], - "blip_caption": "a photography of a group of people looking at a map", - "query": "local meeting community members neighborhood improvements", - "dia_id": "D14:18", - "re-download": true, - "text": "Sounds like a plan, John! Let's work together to get the community involved and make a difference." - }, - { - "speaker": "John", - "dia_id": "D14:19", - "text": "That pic makes me think of how important it is to fight for better housing and living conditions in our neighborhood. We can definitely make a difference!" - }, - { - "speaker": "Maria", - "dia_id": "D14:20", - "text": "Definitely, John! It's important that everyone has access to affordable housing. Let's get the community on board and fight for better living standards. We can make a difference!" - }, - { - "speaker": "John", - "dia_id": "D14:21", - "text": "Sure, Maria! Let's work together to make a real difference. Our neighborhood deserves it! I want to work on improving my old area, West County, too. " - }, - { - "speaker": "Maria", - "dia_id": "D14:22", - "text": "Yep, John! We should all join forces to create a safe and bustling environment for our community, and others too. Let's make some real change." - }, - { - "speaker": "John", - "blip_caption": "a photo of a garden with a raised bed of plants", - "dia_id": "D14:23", - "text": "Yeah, Maria, let's keep working together to make our neighborhood something to be proud of! We'll keep going and never give up." - } - ], - "session_15_date_time": "7:38 pm on 20 May, 2023", - "session_15": [ - { - "speaker": "John", - "dia_id": "D15:1", - "text": "Hey Maria, how's it going? Been real busy tackling a project to support military veterans. Trying to get a petition going, it's pretty rewarding but kinda stressful. How are you?" - }, - { - "speaker": "Maria", - "dia_id": "D15:2", - "text": "Hey John, I'm good. Sorry to hear it's been rough. How did you decide to take on that project?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a parking lot with a flag and flowers", - "dia_id": "D15:3", - "text": "Hi Maria, thanks for asking. I've always been passionate about veterans and their rights. Last week, I realized again how much they've done for us and wanted to show my appreciation in some way. So, I worked on a project to support them. It's my way of giving back." - }, - { - "speaker": "Maria", - "dia_id": "D15:4", - "text": "Wow, John! What a kind gesture. It's really cool seeing you making a difference." - }, - { - "speaker": "John", - "dia_id": "D15:5", - "text": "Thanks, Maria. It sure has taken a lot of work, but I think it's worth it. Just want to let them know their hard work is appreciated." - }, - { - "speaker": "Maria", - "dia_id": "D15:6", - "text": "Wow, John, that's awesome! Your project is going to be great for them!" - }, - { - "speaker": "John", - "dia_id": "D15:7", - "text": "Yeah, I hope so. They've given so much and deserve our backing. I want to help make their lives better." - }, - { - "speaker": "Maria", - "dia_id": "D15:8", - "text": "That's great, John! It's awesome that you're doing that." - }, - { - "speaker": "John", - "dia_id": "D15:9", - "text": "I'm really passionate about making sure veterans are supported and valued in our community, so let's make it happen!" - }, - { - "speaker": "Maria", - "dia_id": "D15:10", - "text": "Count me in, John! I'm in to help out with that. Let's make a difference together." - }, - { - "speaker": "John", - "img_url": [ - "https://cdn2.picryl.com/photo/2020/01/13/combat-veterans-associated-with-troops-first-foundation-06145f-1024.jpg" - ], - "blip_caption": "a photography of a man shaking hands with a soldier in uniform", - "query": "group of veterans smiling and shaking hands", - "dia_id": "D15:11", - "re-download": true, - "text": "Awesome, Maria! I knew you'd be on board. With your help, we can make a real impact. Thank you so much! Here's a pic from last Friday with some veterans who are really excited about this. Their support keeps me motivated." - }, - { - "speaker": "Maria", - "dia_id": "D15:12", - "text": "Wow, John, that looks great! What did you do to make them so happy?" - }, - { - "speaker": "John", - "dia_id": "D15:13", - "text": "Thanks, Maria! We had a great time throwing a small party and inviting some veterans to share their stories. It was awesome seeing them make connections and find camaraderie. All the smiles and new friendships made it really heartwarming." - }, - { - "speaker": "Maria", - "dia_id": "D15:14", - "text": "Wow, John, that's really heartwarming! Stories and connections can truly make a difference." - }, - { - "speaker": "John", - "dia_id": "D15:15", - "text": "Yeah, Maria. It reminded me of that sense of community and togetherness. It made me realize how important it is to help veterans." - }, - { - "speaker": "Maria", - "dia_id": "D15:16", - "text": "Yeah John! It's great to see how your project is making a difference for them. The sense of community and support really matters. Keep doing what you're doing; I'm here to back you up!" - }, - { - "speaker": "John", - "dia_id": "D15:17", - "text": "Thanks, Maria! Your support means a lot. Together, we can make a difference for our veterans!" - }, - { - "speaker": "Maria", - "dia_id": "D15:18", - "text": "Yep, John! Keep going and we'll make a difference! I'm currently planning a ring-toss tournament for the homeless shelter's fundraiser later this month, I can't wait to see the impact it will make. " - }, - { - "speaker": "John", - "dia_id": "D15:19", - "text": "Wow, sounds fun! I can't wait to hear more about it. Talk to you soon!" - } - ], - "session_16_date_time": "1:24 pm on 25 May, 2023", - "session_16": [ - { - "speaker": "John", - "img_url": [ - "https://pdx350.salsalabs.org/juneteamcampaignupdate/ca4594c6-1102-4939-b090-61f878580f48.jpg" - ], - "blip_caption": "a photo of a group of people and a dog standing in front of a waterfall", - "query": "petition support education and infrastructure volunteers team", - "dia_id": "D16:1", - "text": "Hey Maria, I've been busy doing the petition I started - it's tricky but it's been cool getting back in touch with my buddies and gaining support. I got this picture of my workmates when we went on a hiking trip, they really make me keep going! What have you been up to? Anything new with your charity?" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a red trash can with clothes in it", - "dia_id": "D16:2", - "text": "Hey John! Cool that it's going well - you and your friends look like a great team! I'm busy at the shelter getting ready for a fundraiser next week. Hopefully, I can raise enough to cover basic needs for the homeless." - }, - { - "speaker": "John", - "blip_caption": "a photo of a poster for a chili cook off event", - "dia_id": "D16:3", - "text": "Wow, Maria! Raising money is crucial for those in need. Is there any way I can help out with your fundraiser?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://patch.com/img/cdn20/users/26387059/20221221/113941/bbd143cd-d2aa-4d9f-8547-6f333402e7aa___21113933647.jpg" - ], - "blip_caption": "a photo of a poster for a chili cook off event", - "query": "chili cook off poster event", - "dia_id": "D16:4", - "text": "Thanks, John! Appreciate your help. We need to get the word out about the chili cook-off at the fundraiser. Here's the poster!" - }, - { - "speaker": "John", - "dia_id": "D16:5", - "text": "Wow, it looks awesome! I'll make sure to spread the word about it. Is there anything else I can do to assist?" - }, - { - "speaker": "Maria", - "dia_id": "D16:6", - "text": "Thanks, John! Your help is really appreciated. If you know anyone who might be interested in volunteering for the event, let me know. We can do this!" - }, - { - "speaker": "John", - "dia_id": "D16:7", - "text": "Yep, Maria! I'll ask around to see if anyone I know wants to help. We'll find some awesome people for the cause. Let's make a change!" - }, - { - "speaker": "Maria", - "dia_id": "D16:8", - "text": "Way to go, John! Let's help those in need. Thanks for your support!" - }, - { - "speaker": "John", - "dia_id": "D16:9", - "text": "No problem, Maria! Working together with passionate people like you is awesome! Let's make a difference." - }, - { - "speaker": "Maria", - "dia_id": "D16:10", - "text": "Yeah, working with passionate people like you is really motivating." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/cuurrb4qyl961.jpg" - ], - "blip_caption": "a photo of a couple walking into the ocean at sunset", - "query": "kids holding hands sunset", - "dia_id": "D16:11", - "text": "Yeah, Maria! We're making a difference and we'll keep it up! Here's a pic of my fam at the beach." - }, - { - "speaker": "Maria", - "dia_id": "D16:12", - "text": "Wow, John, that pic is gorgeous! It really gives me hope to appreciate the little moments." - }, - { - "speaker": "John", - "dia_id": "D16:13", - "text": "Thanks, Maria! It's moments like these that give me hope too." - }, - { - "speaker": "Maria", - "dia_id": "D16:14", - "text": "Yeah, John! They give me peace and make me appreciate life." - }, - { - "speaker": "John", - "dia_id": "D16:15", - "text": "Glad the photo made you feel that way, Maria. Cherish those little moments!" - }, - { - "speaker": "Maria", - "dia_id": "D16:16", - "text": "Thanks, John. I definitely will!" - }, - { - "speaker": "John", - "dia_id": "D16:17", - "text": "Thanks for letting me help, Maria. It's moments like these that make life worth living." - }, - { - "speaker": "Maria", - "dia_id": "D16:18", - "text": "Yep, John. These reminders help us stay motivated to make a positive impact. Well, talk to you soon!" - }, - { - "speaker": "John", - "dia_id": "D16:19", - "text": "Yeah, Maria! We're really making progress towards making a positive impact. I believe in us! See ya!" - } - ], - "session_17_date_time": "11:51 am on 3 June, 2023", - "session_17": [ - { - "speaker": "John", - "dia_id": "D17:1", - "text": "Hey Maria, long time no talk! Life's been pretty wild lately. The toughest thing to deal with is that we had to say goodbye to Max. He was such an important part of our family for 10 years and it's so hard to think he's not here wagging that tail anymore." - }, - { - "speaker": "Maria", - "dia_id": "D17:2", - "text": "John, I'm sorry to hear about Max. It can be tough to lose a pet - they're like family. How have you been coping?" - }, - { - "speaker": "John", - "dia_id": "D17:3", - "text": "Hey Maria, thanks for the kind words. We're all still sad about it, but have been comforted by the good times we had and the memories we have. It's tough, but we'll keep doing our best." - }, - { - "speaker": "Maria", - "dia_id": "D17:4", - "text": "Good to hear that you're finding comfort in the good times you had with Max. Pets really have a way of touching our hearts. Do you have any pictures you'd like to share?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/pdwhtmvi74va1.jpg" - ], - "blip_caption": "a photo of a dog sitting in the grass with a leash", - "query": "golden retriever backyard tree shade", - "dia_id": "D17:5", - "text": "Let's make sure we make progress in the coming weeks to ensure that we honor the memories of our loved ones. Here's a picture of him that I cherish. \n" - }, - { - "speaker": "Maria", - "dia_id": "D17:6", - "text": "Max looks so peaceful in that photo, just chilling in the backyard. It's nice to remember him like that. It's a great way to honor him." - }, - { - "speaker": "John", - "dia_id": "D17:7", - "text": "Yeah, that's how we'll think of him - peaceful and happy. He brought us so much joy. It's heartbreaking that he's gone but we're grateful to have had him as part of our family. He taught us a lot about love and loyalty." - }, - { - "speaker": "Maria", - "dia_id": "D17:8", - "text": "Max was truly awesome. Let's cherish the lessons he taught." - }, - { - "speaker": "John", - "dia_id": "D17:9", - "text": "He really taught us the importance of unconditional love and loyalty. I want my kids to learn that too. Our bond with him was so special and something I cherish." - }, - { - "speaker": "Maria", - "dia_id": "D17:10", - "text": "That's a great lesson to pass on to your kids, John. Both are really important for strong relationships. Any plans to give another pet a loving home?" - }, - { - "speaker": "John", - "dia_id": "D17:11", - "text": "We're considering adopting a rescue dog - for love and to teach our kids responsibility and compassion." - }, - { - "speaker": "Maria", - "dia_id": "D17:12", - "text": "John, that's such a great idea! It gives the pup a loving home and teaches your kids important values. If you need any help, let me know! I just started volunteering at a local dog shelter once a month." - }, - { - "speaker": "John", - "dia_id": "D17:13", - "text": "Thanks for the offer, Maria! It's so awesome to have friends like you. If we need help, we'll let you know. Appreciate it!" - }, - { - "speaker": "Maria", - "dia_id": "D17:14", - "text": "Sure thing, John. Let me know if you need any help." - }, - { - "speaker": "John", - "dia_id": "D17:15", - "text": "Sure thing, Maria. Thanks for the support! It means a lot to me." - }, - { - "speaker": "Maria", - "dia_id": "D17:16", - "text": "No worries, John. I'm here for you. Take care!" - } - ], - "session_18_date_time": "2:47 pm on 12 June, 2023", - "session_18": [ - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/zcnesiymmou91.jpg" - ], - "blip_caption": "a photo of a group of men sitting around a campfire", - "query": "camping trip friends", - "dia_id": "D18:1", - "text": "Hey John, how're you doing? I'm sorry about Max. Losing a pet is tough. Some friends from church and I went camping last weekend - it was a blast! Just something nice to take my mind off things. Anything fun in your life lately?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/q33cupol6fm51.jpg" - ], - "blip_caption": "a photo of a man standing on top of a mountain with a backpack", - "query": "mountaineering expedition standing on mountain peak", - "dia_id": "D18:2", - "text": "Hey Maria, thanks for your kind words. It's still tough, but I'm finding some comfort in the good memories. Wow, your camping trip sounds awesome! I went on a mountaineering trip last week with some workmates. It was great and helped clear my head. Anything else cool happening in your life?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://fundiegofamily.com/wp-content/uploads/2019/10/grandcanyon1.jpg" - ], - "blip_caption": "a photo of a person standing on a cliff overlooking a canyon", - "query": "hiking trail beautiful scenery grand canyon road trip", - "dia_id": "D18:3", - "text": "Glad you're finding comfort, John. That mountaineering trip sounds amazing. Did you reach the summit? When I was younger, my family and I went on a road trip to Oregon." - }, - { - "speaker": "John", - "dia_id": "D18:4", - "text": "Thanks, Maria! Yeah, we made it to the top and the view was stunning. It was tough but awesome. Your family trip must have been great too, right? What was the prettiest spot?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://everydayrunaway.com/wp-content/uploads/2016/08/20140323-multnomahfalls.jpg" - ], - "blip_caption": "a photo of a waterfall with a bridge over it", - "query": "hiking trail waterfall", - "dia_id": "D18:5", - "text": "Hiking to the top and seeing this was awesome! Breath-taking." - }, - { - "speaker": "John", - "dia_id": "D18:6", - "text": "Wow, Maria! That waterfall and bridge look amazing! What a view. How was it being there?" - }, - { - "speaker": "Maria", - "dia_id": "D18:7", - "text": "I felt like I was in a fairy tale! The water sounded so calming and the surroundings were beautiful. It was truly magical!" - }, - { - "speaker": "John", - "dia_id": "D18:8", - "text": "Wow, Maria, that sounds awesome! It seems like nature has a way of calming us down, huh?" - }, - { - "speaker": "Maria", - "dia_id": "D18:9", - "text": "Yeah, it's like a natural soul-soother when things get tough." - }, - { - "speaker": "John", - "dia_id": "D18:10", - "text": "Yeah, for sure. It's like a reset button, you know? Have you ever gone camping or mountain climbing before?" - }, - { - "speaker": "Maria", - "dia_id": "D18:11", - "text": "I've gone camping a few times but never tried mountain climbing. Sounds thrilling though! Have you been camping before?" - }, - { - "speaker": "John", - "dia_id": "D18:12", - "text": "Yeah, plenty of times. It's an awesome way to get away from it all and be at one with nature. I love how uncomplicated it is." - }, - { - "speaker": "Maria", - "dia_id": "D18:13", - "text": "Yeah John, I get it. Being in nature helps us take a break from life's craziness and recognize what truly matters." - }, - { - "speaker": "John", - "dia_id": "D18:14", - "text": "Yeah, Maria. It's important to appreciate the small things and find moments of peace amidst chaos. Nature really helps with that. How about you? How do you find peaceful moments?" - }, - { - "speaker": "Maria", - "dia_id": "D18:15", - "text": "Finding my Zen is a mix of things - a moment to myself plus favorite tunes is usually enough. I also enjoy aerial yoga, it's a great way to switch off and focus on my body." - }, - { - "speaker": "John", - "dia_id": "D18:16", - "text": "Cool, Maria! Glad you found something that gives you some peace. Do you have a favorite yoga pose?" - }, - { - "speaker": "Maria", - "dia_id": "D18:17", - "text": "Thanks, John! It's tough to pick just one, but I really enjoy the upside-down poses. They make me feel free and light." - }, - { - "speaker": "John", - "dia_id": "D18:18", - "text": "Wow, Maria, that sounds awesome! I can imagine that must be challenging, but it's great to see you embracing them. Keep up the amazing work!" - }, - { - "speaker": "Maria", - "dia_id": "D18:19", - "text": "Thanks, John! It can be tough, but aerial yoga is totally worth it. I love the freedom and connection it brings. Appreciate your support!" - }, - { - "speaker": "John", - "img_url": [ - "https://www.harpersplayground.org/wp-content/uploads/elementor/thumbs/Harpers-playground-2-copy-2560x1440-e1533267692673-o598m9qoe7o3x8f1l2ljd69xt3o9oeda7waxqhlnz4.jpg" - ], - "blip_caption": "a photo of a group of people standing around a playground", - "query": "children playing park", - "dia_id": "D18:20", - "text": "Yes, Maria! I'm here for you. Glad you found something that makes you happy. This is what makes me smile. Keep shining!\n" - }, - { - "speaker": "Maria", - "dia_id": "D18:21", - "text": "Wow! Looks like you had fun - what happened there?" - }, - { - "speaker": "John", - "dia_id": "D18:22", - "text": "It was an awesome day at the park with my family. The kids had a lot of fun on the playground, and we had some really nice family time." - }, - { - "speaker": "Maria", - "dia_id": "D18:23", - "text": "Wow, that's great to hear, John! Cherish those family time moments!" - } - ], - "session_19_date_time": "7:20 pm on 16 June, 2023", - "session_19": [ - { - "speaker": "Maria", - "dia_id": "D19:1", - "text": "Hey John, been good since we talked? I got some great news to share - I joined a gym last week! It's been super positive - I'm sticking to my workout routine and the people are awesome. The atmosphere is so welcoming." - }, - { - "speaker": "John", - "dia_id": "D19:2", - "text": "Congrats, Maria! Sounds like it's been a great experience. Having a positive environment and supportive people can really help with motivation, right? So, do you have any fitness goals in mind?" - }, - { - "speaker": "Maria", - "dia_id": "D19:3", - "text": "Thanks, John! Yeah, it's been awesome. I want to get stronger and improve my endurance, and I'm trying kundalini yoga. What about you? Do you have any goals or activities you want to try?" - }, - { - "speaker": "John", - "dia_id": "D19:4", - "text": "Nice one, Maria! Staying in shape is important to me too. I'm trying out different workout regimes lately. Rock climbing sounds like a fun way to push my limits, have you ever given it a go?" - }, - { - "speaker": "Maria", - "dia_id": "D19:5", - "text": "No, I haven't tried it yet. But it sounds like a great way to push yourself. Let me know how it goes if you give it a shot!" - }, - { - "speaker": "John", - "dia_id": "D19:6", - "text": "Yeah, sure thing. I'll let you know. Oh, also...something massive happened since we last spoke. I got promoted at work! It's been a loooong time coming, and I'm over the moon about it!" - }, - { - "speaker": "Maria", - "dia_id": "D19:7", - "text": "Wow John! Congrats on the promotion! Must have taken a lot of work. How did you feel when you found out?" - }, - { - "speaker": "John", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTEyL2ZsNTE4NTQxMzE2ODgtaW1hZ2UuanBn.jpg" - ], - "blip_caption": "a photography of a golden trophy on a black surface", - "query": "new office assistant manager sign", - "dia_id": "D19:8", - "re-download": true, - "text": "Thanks, Maria! I was really excited. It feels like all the hard work I've put in has paid off now that I'm an assistant manager- it's like a stepping stone for bigger things." - }, - { - "speaker": "Maria", - "dia_id": "D19:9", - "text": "Wow, John! Congrats on the promotion! What's the backstory on that trophy?" - }, - { - "speaker": "John", - "dia_id": "D19:10", - "text": "Thanks, Maria! It commemorates my journey. It's a symbol of all the obstacles I had to overcome to get here." - }, - { - "speaker": "Maria", - "dia_id": "D19:11", - "text": "Cool, so you have a reminder of all that. It's good to acknowledge what you've been through and appreciate where you are now. Could you tell me more about the challenges?" - }, - { - "speaker": "John", - "dia_id": "D19:12", - "text": "Yeah, I faced all kinds of hurdles - tech stuff, workplace stuff... but the worst was self-doubt. There were moments when I questioned if I was on the right track. But with support at home and my own grit, I powered through. This promotion is a reward for all the hustle and hardship I put in - a reminder that I'm on the right path." - }, - { - "speaker": "Maria", - "dia_id": "D19:13", - "text": "Wow, John, it's incredible to see how far you've come! Your perseverance and determination is so inspiring. I can imagine those hurdles were tough to deal with, especially the self-doubt." - }, - { - "speaker": "John", - "dia_id": "D19:14", - "text": "Thanks, Maria! It wasn't easy, but I'm proud of what I achieved. It can be tricky, but having support and believing in myself really helped me out." - }, - { - "speaker": "Maria", - "dia_id": "D19:15", - "text": "Yeah John, having belief in yourself matters. Plus it helps a lot when you've got loved ones supporting you. What we can do is seriously incredible with the right people believing in us." - }, - { - "speaker": "John", - "img_url": [ - "https://riverstudiodesign.ca/wp-content/uploads/2019/09/overall-office-jpg-1.jpg" - ], - "blip_caption": "a photo of a desk with a chair and a lamp", - "query": "wife kids office desk", - "dia_id": "D19:16", - "text": "Definitely, Maria. Support from loved ones is vital. With their trust, we can do anything! I'm really lucky to have my family on this journey with me." - }, - { - "speaker": "Maria", - "dia_id": "D19:17", - "text": "Nice workspace! When do you usually work?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/n7jl343t38m91.jpg" - ], - "blip_caption": "a photo of a desk with a computer, keyboard, and notebook", - "query": "office desk laptop papers scattered", - "dia_id": "D19:18", - "text": "Thanks, Maria! I usually work during regular work hours, but sometimes I bring work home too." - }, - { - "speaker": "Maria", - "dia_id": "D19:19", - "text": "That work setup looks nice, John. How do you manage to balance everything?" - }, - { - "speaker": "John", - "dia_id": "D19:20", - "text": "Thanks, Maria! It can be challenging, so I try to organize my time and make sure I'm there for the important things. It's all about finding that balance and making those moments count!" - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach", - "dia_id": "D19:21", - "text": "Finding balance is crucial. Taking time for ourselves and the important people in our lives is vital. " - }, - { - "speaker": "John", - "dia_id": "D19:22", - "text": "Yeah, Maria. Taking time off for ourselves and our fam is so important. It helps us stay connected and appreciate the simple things. That beach pic you shared reminded me of a special vacation we had to California- a gorgeous sunset and an awesome night strolling the shore, creating memories together. Do you have any special beach memories you'd like to share?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://live.staticflickr.com/3448/3279695354_16a66b5258_b.jpg" - ], - "blip_caption": "a photography of a sunset over a body of water with a bird flying in the distance", - "query": "beach sunset", - "dia_id": "D19:23", - "re-download": true, - "text": "Yeah, John! I have a picture from a vacation in Florida. The colors were amazing, and I had a feeling of gratitude just sitting there with my family. It's in moments like these we make the best memories, ya know?" - }, - { - "speaker": "John", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/siblings-enjoying-in-sea-against-sky-royalty-free-image-1650652626.jpg" - ], - "blip_caption": "a photo of two children playing in the ocean waves", - "query": "beach vacation family", - "dia_id": "D19:24", - "text": "Wow, Maria! That photo is so stunning. The colors there are so vivid - it must have been amazing! Trips like these are great - always full of amazing memories! Here's one from our vacation!" - }, - { - "speaker": "Maria", - "dia_id": "D19:25", - "text": "Thanks, John. That picture is so cute! The kids look so happy splashing in the waves. It must have been such a joyful and carefree time!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a football stadium with a lot of people", - "dia_id": "D19:26", - "text": "Yep, it was amazing. Enjoying these special family times is why life is great. Talk to you soon!" - } - ], - "session_20_date_time": "12:21 am on 27 June, 2023", - "session_20": [ - { - "speaker": "Maria", - "dia_id": "D20:1", - "text": "Hey John, long time no talk! A lot has happened since then. I've been struggling, but I'm focusing on the positive and relying on my friends and fam for support." - }, - { - "speaker": "John", - "dia_id": "D20:2", - "text": "Hey Maria, sorry to hear that. That's rough, but it's great that you're focusing on the positive. Having support from your loved ones can make a big difference. How have they been helping you out?" - }, - { - "speaker": "Maria", - "dia_id": "D20:3", - "text": "Hey John, thanks. My family has been there for me all the way. They've been my rock, giving me words of encouragement and reminding me I'm not alone. It's a relief to have their support." - }, - { - "speaker": "John", - "dia_id": "D20:4", - "text": "That's great, Maria! It's such a blessing to have family who always supports us and reminds us that we're not alone. They know us like no one else and stick by us no matter what. Last week, we had a blast at a live music event. Seeing them dancing and having fun was awesome. The energy in the air was amazing." - }, - { - "speaker": "Maria", - "dia_id": "D20:5", - "text": "Wow, John! The energy from the crowd must have unreal! So glad you and your family got to experience that lively event. These are the moments that make the best memories." - }, - { - "speaker": "John", - "dia_id": "D20:6", - "text": "Thanks, Maria! It was definitely an amazing experience. Moments like these remind me to appreciate the ones I love. Life can be tough, but finding silver linings helps me keep going. How have you been finding silver linings in tough times?" - }, - { - "speaker": "Maria", - "dia_id": "D20:7", - "text": "Volunteering at the shelter made me feel great to help, even if just for a bit." - }, - { - "speaker": "John", - "dia_id": "D20:8", - "text": "Wow, Maria! That's really amazing. It must have felt great to help out. Do you have any special memories from your experience?" - }, - { - "speaker": "Maria", - "dia_id": "D20:9", - "text": "There are so many, but one that stands out was when I met someone special at the shelter. They'd been sad for months, but when I was playing with the kids, they suddenly laughed - it was so uplifting! I won't forget that." - }, - { - "speaker": "John", - "dia_id": "D20:10", - "text": "That's a really nice memory, Maria! It's amazing how just playing with kids can bring such joy and happiness. It shows how even a brief moment with someone can make a difference. Thanks for sharing it with me." - }, - { - "speaker": "Maria", - "dia_id": "D20:11", - "text": "No problem, John! It was really nice. Being able to make a difference brings me joy." - }, - { - "speaker": "John", - "dia_id": "D20:12", - "text": "It's great knowing that our actions can brighten someone else's life. Keep it up!" - }, - { - "speaker": "Maria", - "dia_id": "D20:13", - "text": "Thanks, John! Gonna continue doing it - it's my way of spreading kindness and positivity." - }, - { - "speaker": "John", - "dia_id": "D20:14", - "text": "Maria, that's great! Your way of passing on kindness and positivity is making a big impact on the world. You're really making a difference." - }, - { - "speaker": "Maria", - "dia_id": "D20:15", - "text": "Thanks, John! Your words really mean a lot. It's always nice to know that what I'm doing is making an impact." - }, - { - "speaker": "John", - "dia_id": "D20:16", - "text": "You definitely are. Keep going with it!" - }, - { - "speaker": "Maria", - "dia_id": "D20:17", - "text": "Thanks, John! I definitely will. Speak to you soon!" - }, - { - "speaker": "John", - "dia_id": "D20:18", - "text": "That's awesome, Maria! Keep that positivity going and keep making a difference. Take care!" - } - ], - "session_21_date_time": "8:43 pm on 3 July, 2023", - "session_21": [ - { - "speaker": "Maria", - "img_url": [ - "https://cdn.newswire.com/files/x/4b/eb/ec85ef6a588e010a38e5f40feeab.jpg" - ], - "blip_caption": "a photo of three children sitting on a step smiling", - "query": "group children shelter smiling", - "dia_id": "D21:1", - "text": "Hey John, long time no see! Sorry I didn't get back to you sooner... So much has happened! Check out these kids I met at the shelter!" - }, - { - "speaker": "John", - "dia_id": "D21:2", - "text": "Hey Maria! Good to hear from you. Those little ones are adorable, any updates on them? How have you been?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://i2.pickpik.com/photos/866/469/190/crash-car-car-crash-accident-preview.jpg" - ], - "blip_caption": "a photography of a car that has been damaged and is sitting on a flatbed", - "query": "broken car homeless shelter kids", - "dia_id": "D21:3", - "re-download": true, - "text": "Hey John! They were great, their smiles warmed my heart. But something not so great happened yesterday..." - }, - { - "speaker": "John", - "dia_id": "D21:4", - "text": "That's so sad. Can I do anything to help? What happened?" - }, - { - "speaker": "Maria", - "dia_id": "D21:5", - "text": "A car ran a red light and hit us, but thankfully everyone is okay. Besides that, my cousin just had a tough time recently, so I'm lending a hand in helping her find a new place." - }, - { - "speaker": "John", - "dia_id": "D21:6", - "text": "That's really nice of you. It's important to help family during hard times. How is she doing now?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://cdngeneral.rentcafe.com/dmslivecafe/3/524478/1(2).jpg" - ], - "blip_caption": "a photo of a row of houses with a sidewalk and trees", - "query": "apartments for rent list", - "dia_id": "D21:7", - "text": "Things have been tough for her lately. She had to leave and find a new place in a hurry, which has been really stressful, but she's making progress." - }, - { - "speaker": "John", - "dia_id": "D21:8", - "text": "The houses look real nice. Hopefully, she finds a cozy spot soon." - }, - { - "speaker": "Maria", - "dia_id": "D21:9", - "text": "Yeah, that's important. A safe home is key." - }, - { - "speaker": "John", - "dia_id": "D21:10", - "text": "Definitely, it's essential for wellbeing. It's great that you're supporting her. Is there anything specific she needs help with? Maybe I can assist as well." - }, - { - "speaker": "Maria", - "dia_id": "D21:11", - "text": "Any resources or organizations you know of that could help her out? Thanks!" - }, - { - "speaker": "John", - "dia_id": "D21:12", - "text": " I'll see if I can find any that might be able to assist. Let me know if there's anything else I can do to help!" - }, - { - "speaker": "Maria", - "dia_id": "D21:13", - "text": "Thanks, John! Really appreciate your offer. Anything you can find would be great." - }, - { - "speaker": "John", - "dia_id": "D21:14", - "text": "Sure, Maria! I'll do my best to find some resources. Helping those in need is important to me too." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a lot of white portable homes in a parking lot", - "dia_id": "D21:15", - "text": "I appreciate your kindness and care. It's a wonderful quality to have." - }, - { - "speaker": "John", - "dia_id": "D21:16", - "text": "Thanks for the compliment, I really appreciate it!" - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/cvtpmcj98ii31.jpg" - ], - "blip_caption": "a photo of a letter written by a man on a white sheet of paper", - "query": "handwritten note be kind", - "dia_id": "D21:17", - "text": "No worries, John. Nice moments like these remind me how much kindness and compassion matter. Life can be challenging, but small acts of kindness can make a big difference." - }, - { - "speaker": "John", - "dia_id": "D21:18", - "text": "Wow, that's awesome! What inspired it?" - }, - { - "speaker": "Maria", - "dia_id": "D21:19", - "text": "One of the shelter residents, Laura, wrote us a letter expressing their gratitude. The impact we made on their life was inspiring!" - }, - { - "speaker": "John", - "dia_id": "D21:20", - "text": "Wow, that's really amazing to hear. It feels so good to make a positive impact on someone's life." - }, - { - "speaker": "Maria", - "dia_id": "D21:21", - "text": "Yeah, that's why I love volunteering! It makes me feel like I'm making a difference, even if it's a small one." - }, - { - "speaker": "John", - "blip_caption": "a photo of a badge and a flag on a table", - "dia_id": "D21:22", - "text": "Yeah, Maria, keep it up! Even small things can mean a lot. I just participated in a marching event for veterans' rights and it was awesome, made me remember how much they sacrifice for us. We need to show our support however we can." - }, - { - "speaker": "Maria", - "dia_id": "D21:23", - "text": "Wow, John! What inspired you to join it?" - }, - { - "speaker": "John", - "dia_id": "D21:24", - "text": "I've always had a great respect for our military and wanted to show my support. I think it's important to stand up for what we believe in." - }, - { - "speaker": "Maria", - "dia_id": "D21:25", - "text": "Wow, John! It's great to see you standing up for your beliefs." - }, - { - "speaker": "John", - "dia_id": "D21:26", - "text": "Thanks Maria, it was amazing being around others who shared the same values and passion as me. It reminded me how important it is to try and make a difference through activism. It really motivated me." - }, - { - "speaker": "Maria", - "dia_id": "D21:27", - "text": "That's awesome, John! Surrounding yourself with determined people striving for the same goals can be really motivating. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D21:28", - "text": "Thanks, Maria! It really is. Striving for progress is crucial." - }, - { - "speaker": "Maria", - "dia_id": "D21:29", - "text": "Yep John, Let's keep up the good work and make a difference. Talk to you soon!" - } - ], - "session_22_date_time": "6:59 pm on 5 July, 2023", - "session_22": [ - { - "speaker": "John", - "dia_id": "D22:1", - "text": "Since the last chat, I've been thinking about how education and infrastructure shape communities. It's so sad how they can stunt growth in neighborhoods, but it also drives me to do what I can to make it better." - }, - { - "speaker": "Maria", - "dia_id": "D22:2", - "text": "I totally agree. They play a crucial role in shaping communities. It's unfortunate to witness the negative effects when they are lacking, but it's inspiring to see your passion and proactive approach towards making a positive change." - }, - { - "speaker": "John", - "dia_id": "D22:3", - "text": "Your support means a lot. Feeling like it's an uphill battle is tough, but it's great to know there are people out there who see the value in them - it keeps me going." - }, - { - "speaker": "Maria", - "dia_id": "D22:4", - "text": "John, you got this! It's great to have a support system while tackling tough stuff. I'm here to lend an ear or help out however I can. You're really making a difference, and that's something to be proud of!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/j4ms3skztjg11.jpg" - ], - "blip_caption": "a photo of a family posing on a train track in the fall", - "query": "family photo", - "dia_id": "D22:5", - "text": "I appreciate it. It's really uplifting hearing from you. I sometimes doubt if I'm making a difference, but knowing there's people who understand my work means a lot and helps keep me going. Here's a picture of my family. They motivate me and remind me why I'm doing this." - }, - { - "speaker": "Maria", - "dia_id": "D22:6", - "text": "That picture is awesome! Your family looks so stoked - your trip must have been incredible! They obviously motivate and support you." - }, - { - "speaker": "John", - "dia_id": "D22:7", - "text": "Thanks, Maria! That picture was from a trip we took last year for my daughter Sara's birthday - so much fun and good memories! My family motivates me to keep striving for change." - }, - { - "speaker": "Maria", - "dia_id": "D22:8", - "text": "Yeah, memories and motivators definitely help us stay on track and keep us going." - }, - { - "speaker": "John", - "dia_id": "D22:9", - "text": "Yeah, for sure! When times get hard, I look at it and remember why I'm doing what I'm doing. My family is my motivation and they keep me going. What about you? What keeps you inspired?" - }, - { - "speaker": "Maria", - "dia_id": "D22:10", - "text": "I'm inspired by chatting to people, volunteering, and listening to music. Anything else that keeps you inspired?" - }, - { - "speaker": "John", - "dia_id": "D22:11", - "text": "My family, exercise, and spending time with friends, for sure." - }, - { - "speaker": "Maria", - "dia_id": "D22:12", - "text": "That's great, John! It's true, we all have our own special sources of inspiration that keep us going." - }, - { - "speaker": "John", - "dia_id": "D22:13", - "text": "Definitely, Maria! Finding those special sources is key for staying motivated and tackling challenges. It's great when we figure out what makes us feel excited and alive." - }, - { - "speaker": "Maria", - "dia_id": "D22:14", - "text": "Yeah, John, those little things can spark our enthusiasm and motivate us. It's incredible how something as simple as a walk or a song can totally switch up our outlook." - }, - { - "speaker": "John", - "img_url": [ - "https://get.pxhere.com/photo/beach-landscape-sea-coast-water-sand-ocean-horizon-cloud-sky-sun-sunrise-sunset-shore-wave-dawn-dusk-evening-relax-paradise-tropical-peaceful-blue-colorful-body-of-water-clouds-afterglow-sunset-beach-gulf-of-mexico-wind-wave-515918.jpg" - ], - "blip_caption": "a photo of a sunset over the ocean with a sailboat in the distance", - "query": "sunset beach colorful ocean", - "dia_id": "D22:15", - "text": "Yeah, Maria. Little things like this can make a big impact in how we think. Oh, and here's a pic I got from my walk last week. It always reminds me to take a break, breathe, and appreciate nature." - }, - { - "speaker": "Maria", - "dia_id": "D22:16", - "text": "That picture is amazing! The colors are so vibrant - really shows the calmness of the ocean. How often do you get to see sunsets like that on your walks?" - }, - { - "speaker": "John", - "dia_id": "D22:17", - "text": "Thanks, Maria! I see them at least once a week. It's a good way to disconnect, think, and find peace in this crazy world." - }, - { - "speaker": "Maria", - "dia_id": "D22:18", - "text": "That's great practice, John. Taking time to detach and find peace is important in this crazy world. I've been taking regular \"me-time\" walks at the park nearby and It's made a big impact. Glad you have that to remind you." - }, - { - "speaker": "John", - "dia_id": "D22:19", - "text": "Thanks, Maria. Appreciate it. Great talking to you. Gotta go. Stay safe and chat soon!" - }, - { - "speaker": "Maria", - "dia_id": "D22:20", - "text": "Hey John, stay safe. Chat soon!" - }, - { - "speaker": "John", - "dia_id": "D22:21", - "text": "Take care, Maria. Catch you soon!" - } - ], - "session_23_date_time": "6:29 pm on 7 July, 2023", - "session_23": [ - { - "speaker": "John", - "dia_id": "D23:1", - "text": "Maria, since we talked, it's been tough. My old area was hit by a nasty flood last week. The infrastructure wasn't great so lots of homes were ruined. It's reminding me we need to fix things up in our community." - }, - { - "speaker": "Maria", - "dia_id": "D23:2", - "text": "Sorry to hear about what happened in your area, John. That must have been tough for you and everyone there. Is there anything I can do to help?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a party invitation on a table with a pen and paper", - "dia_id": "D23:3", - "text": "Thanks a lot, Maria. Your offer means a lot to me. I'm getting people together to chat about this and discuss potential solutions. I'd really appreciate your thoughts.\n" - }, - { - "speaker": "Maria", - "dia_id": "D23:4", - "text": "Hey John, I'm down to join the meeting and contribute to making our community better. Let's do this!" - }, - { - "speaker": "John", - "dia_id": "D23:5", - "text": "Nice work, Maria! Your determination never ceases to amaze me. Let's get to work!" - }, - { - "speaker": "Maria", - "dia_id": "D23:6", - "text": "Definitely, John! Your commitment to helping others is inspiring. It's great to have a friend like you who shares the same passion. Let's join forces and make the change we desire in our community." - }, - { - "speaker": "John", - "dia_id": "D23:7", - "text": "That means a lot, Maria. Your support and friendship mean everything. Together, we can really make a difference and motivate others too. Let's keep it up!" - }, - { - "speaker": "Maria", - "dia_id": "D23:8", - "text": "John, let's keep working together to make a difference in our community. Our actions, no matter how small, can have a big impact. Let's continue to spread kindness and inspire hope." - }, - { - "speaker": "John", - "dia_id": "D23:9", - "text": "Definitely, little steps count! We can really make a difference together - let's do it!" - }, - { - "speaker": "Maria", - "dia_id": "D23:10", - "text": "Yep, let's create a positivity ripple! Little acts of kindness and helping hands can really transform lives. Let's keep improving our community." - }, - { - "speaker": "John", - "dia_id": "D23:11", - "text": "Yeah, let's work hard to help those around us. We can make a difference!" - }, - { - "speaker": "Maria", - "dia_id": "D23:12", - "text": "Yeah, John! Change starts small, so with hard work, we can really make something great. I'm glad to have you here." - }, - { - "speaker": "John", - "dia_id": "D23:13", - "text": "Thanks, Maria. Your support means a lot and it's awesome to have you by my side in our community work. Let's keep going and making great things happen!" - }, - { - "speaker": "Maria", - "dia_id": "D23:14", - "text": "For sure! We can keep doing great stuff and making a difference. Well, I'm off to have dinner with some friends from the gym. Talk to you later!" - } - ], - "session_24_date_time": "3:34 pm on 17 July, 2023", - "session_24": [ - { - "speaker": "John", - "dia_id": "D24:1", - "text": "Hey Maria, last week was really eye-opening. I visited a veteran's hospital and met some amazing people. It made me appreciate what we have and the need to give back." - }, - { - "speaker": "Maria", - "dia_id": "D24:2", - "text": "Wow, John! That sounds awesome. It's so important to appreciate and support those who served in the military. Did you learn anything cool during your visit?" - }, - { - "speaker": "John", - "dia_id": "D24:3", - "text": "I heard some cool stories from an elderly veteran named Samuel. It was inspiring and heartbreaking, but seeing their resilience really filled me with hope. It reminded me why I wanted to join the military." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people sitting on a couch talking", - "dia_id": "D24:4", - "text": "It's inspiring to see the resilience of the veterans in your group. Their stories are both inspiring and heartbreaking, but they fill us with hope." - }, - { - "speaker": "John", - "dia_id": "D24:5", - "text": "Thanks, Maria! It's great to be part of this organization and work with such passionate people. We're like a family - always supporting each other. Do anything fun lately?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/l77zoro8xt971.jpg" - ], - "blip_caption": "a photo of a picnic table with a drink, snacks and a cell phone", - "query": "picnic friends trees games food", - "dia_id": "D24:6", - "text": "Yeah, last weekend I had a picnic with some friends from church. We chilled under the trees, played games, and ate yummy food. It was great!" - }, - { - "speaker": "John", - "dia_id": "D24:7", - "text": "Looks fun! What games did you all play?" - }, - { - "speaker": "Maria", - "dia_id": "D24:8", - "text": "Some fun ones like charades and a scavenger hunt. We all had a good laugh!" - }, - { - "speaker": "John", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTExL2ZsNTI0NzQ3Nzg3NzQtaW1hZ2UuanBn.jpg" - ], - "blip_caption": "a photography of a young girl is writing at a table", - "query": "charades creativity silliness joy laughter", - "dia_id": "D24:9", - "re-download": true, - "text": "Sounds like a blast! It's always great to have fun and bring out everyone's creative and silly sides with games like that. Laughter and joy are really important! I'm thinking of setting up something like this for my kids soon." - }, - { - "speaker": "Maria", - "dia_id": "D24:10", - "text": "This looks like fun! Where did you see that?" - }, - { - "speaker": "John", - "blip_caption": "a photo of two girls in costumes holding up signs", - "dia_id": "D24:11", - "text": "There were arts and crafts at a community event last month. There were fun activities and games for families and everyone was having a blast. So I figured I'd try them out with my family and friends." - }, - { - "speaker": "Maria", - "dia_id": "D24:12", - "text": "Wow, great idea! Connecting with others and discovering fun activities is always awesome. It's really cool how you adapted it for your family and friends!" - }, - { - "speaker": "John", - "dia_id": "D24:13", - "text": "Thanks, Maria! I couldn't agree more. Life's too short, let's have some fun!" - }, - { - "speaker": "Maria", - "dia_id": "D24:14", - "text": "Sure, John! I'm glad we both understand the importance of making connections and enjoying life's simpler moments." - }, - { - "speaker": "John", - "dia_id": "D24:15", - "text": "Yep, Maria! That's why it's important to keep spreading positivity and making a difference." - }, - { - "speaker": "Maria", - "dia_id": "D24:16", - "text": "Definitely, John! Doing good and helping others brings joy. Even little acts of kindness can have a big effect. Let's keep working to make a difference!" - }, - { - "speaker": "John", - "dia_id": "D24:17", - "text": "Yep, Maria! Those things really matter. Little acts of kindness can really brighten someone's day. Let's keep spreading the love and making a difference." - } - ], - "session_25_date_time": "6:21 pm on 22 July, 2023", - "session_25": [ - { - "speaker": "John", - "blip_caption": "a photo of a group of people posing for a picture", - "dia_id": "D25:1", - "text": "Hi Maria! It's so good to talk again. A lot has changed since last time. I'm really enjoying my new job. My team has been super encouraging and inspiring." - }, - { - "speaker": "Maria", - "img_url": [ - "https://www.wildbunchdesertguides.com/blog/uploaded_files/images/Picturesque-trail.jpg" - ], - "blip_caption": "a photo of a group of people walking up a trail", - "query": "group friends hiking mountains nature rejuvenating", - "dia_id": "D25:2", - "text": "Hey John, glad work is going well! Having a good team is so important. I had a great experience last weekend hiking with my church friends - it was great to be surrounded by supportive people and to enjoy nature. Felt so refreshing!" - }, - { - "speaker": "John", - "dia_id": "D25:3", - "text": "Sounds like you had a great time! What inspired you to go on the hike?" - }, - { - "speaker": "Maria", - "dia_id": "D25:4", - "text": "I wanted to make connections, laugh together and take in nature's beauty. Uplifting!" - }, - { - "speaker": "John", - "dia_id": "D25:5", - "text": "Wow Maria, it sounds like you had a great time! Connecting with good people and taking in the beautiful views really boosts your mood. It's important to make time for yourself and find those special moments of joy. What were some of your best bits from the hike?" - }, - { - "speaker": "Maria", - "dia_id": "D25:6", - "text": "Thanks, John! Reaching the top was amazing - the view was breathtaking! Seeing how huge the world is made me feel like I'm part of something special - gave me a real sense of peace." - }, - { - "speaker": "John", - "dia_id": "D25:7", - "text": "Wow, Maria, that sounds incredible! It's amazing how nature can make us feel so small and yet so connected to something greater. Do you have any plans for your next adventure yet?" - }, - { - "speaker": "Maria", - "dia_id": "D25:8", - "text": "Gonna explore more and volunteer at shelters next month. Can't wait!" - }, - { - "speaker": "John", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/637bc5e7223fbf2204465446/9afdd8b3-6eae-4655-b4b8-5844b1ec1163/20190304-tpi-severe-weather-shelter-volunteers-mn-12_40329508153_o_0.jpg" - ], - "blip_caption": "a photo of two women standing in a room full of black mats", - "query": "volunteer orientation shelter", - "dia_id": "D25:9", - "text": "Woohoo, Maria! Super pumped for your next adventure and for putting your positivity out there. Keep up the awesome work!" - }, - { - "speaker": "Maria", - "dia_id": "D25:10", - "text": "Thanks, John! Is it a martial arts place or a yoga studio? It looks awesome!" - }, - { - "speaker": "John", - "dia_id": "D25:11", - "text": "Yup, it's a yoga studio I go to often. The vibe is really chill and the instructors are awesome." - }, - { - "speaker": "Maria", - "dia_id": "D25:12", - "text": "Cool, John! That definitely makes the workout experience more enjoyable. Do they offer a variety of classes?" - }, - { - "speaker": "John", - "dia_id": "D25:13", - "text": "Yeah, they offer a a bunch, like yoga, kickboxing, and circuit training. It keeps things interesting!" - }, - { - "speaker": "Maria", - "dia_id": "D25:14", - "text": "Cool, John! Trying new classes sounds like a fun way to switch up your exercise routine - I should give it a go!" - }, - { - "speaker": "John", - "img_url": [ - "https://www.jewelyogapdx.com/wp-content/uploads/2015/02/home-join-us.jpg" - ], - "blip_caption": "a photo of a group of people doing yoga in a gym", - "query": "yoga class", - "dia_id": "D25:15", - "text": "Yeah, Maria! Trying new stuff is a great way to push yourself and mix things up. Let me know if you need any suggestions!" - }, - { - "speaker": "Maria", - "dia_id": "D25:16", - "text": "Looks fun! What other classes have you done?" - }, - { - "speaker": "John", - "dia_id": "D25:17", - "text": "I've done weight training so far too. It was challenging but peaceful, kinda like yoga." - }, - { - "speaker": "Maria", - "dia_id": "D25:18", - "text": "Wow, John! That's great. Yoga is a great way to relax and concentrate, and joining a new class might be a good option." - }, - { - "speaker": "John", - "dia_id": "D25:19", - "text": "Yeah, it's been great for me. Let me know if you need any advice to get started." - }, - { - "speaker": "Maria", - "dia_id": "D25:20", - "text": "Cheers, John! I'll let you know. I'm off to bake some cakes. Talk to you soon!" - } - ], - "session_26_date_time": "1:59 pm on 31 July, 2023", - "session_26": [ - { - "speaker": "Maria", - "dia_id": "D26:1", - "text": "Hey John, I'm doing ok - hope you are too. Some interesting stuff has been going on; last week I dropped off that stuff I baked at the homeless shelter. It was great and I'm more motivated than ever to help people." - }, - { - "speaker": "John", - "dia_id": "D26:2", - "text": "Hey Maria, that's awesome! I'm really inspired by your drive to make a difference. You mentioned your work at the homeless shelter last time and it made me think of how I could help too, so I just joined a fire-fighting brigade. It's such a great feeling to do something to give back to my community!" - }, - { - "speaker": "Maria", - "dia_id": "D26:3", - "text": "Wow John, joining the fire brigade? That's great! How's it been so far?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/1bwp2zpccuo91.jpg" - ], - "blip_caption": "a photo of a firefighter's gear laid out on the floor", - "query": "house fire fire-fighting gear fire engine", - "dia_id": "D26:4", - "text": "Thanks, Maria! It's been tough, but really rewarding. The training was intense and taxing, but it changed my view on helping others. Last Sunday we had our first call-out, and it was intense. We responded to a situation and our team worked together to help those in need. Seeing their relief was awesome." - }, - { - "speaker": "Maria", - "dia_id": "D26:5", - "text": "Wow, John! What was it like being part of that rescue mission?" - }, - { - "speaker": "John", - "dia_id": "D26:6", - "text": "It was chaotic when we arrived, but we pulled together. I got a surge of energy and purpose, and we were able to save a family from a burning building. It was wild, but knowing we made a difference made it worth it." - }, - { - "speaker": "Maria", - "dia_id": "D26:7", - "text": "Wow John, that's intense! Helping out like that takes guts - it's inspiring to hear about the difference you made." - }, - { - "speaker": "John", - "dia_id": "D26:8", - "text": "Thanks, Maria! It was an adrenaline rush, and I couldn't have done it without them. We trust and rely on one another, and it's great to know that we have each other's backs. They've become like family to me." - }, - { - "speaker": "Maria", - "dia_id": "D26:9", - "text": "Sounds great, John! It must feel incredible to have a supportive team like that." - }, - { - "speaker": "John", - "dia_id": "D26:10", - "text": "Yeah, it really does feel helpful, Maria. We have different skills and talents, but they all contribute to serving and protecting our community. And it's a bond I haven't felt since my time in the military." - }, - { - "speaker": "Maria", - "dia_id": "D26:11", - "text": "Glad you've found that same strong bond. Having friends you can rely on makes a huge difference." - }, - { - "speaker": "John", - "dia_id": "D26:12", - "text": "Yeah, Maria! It's nice to know we're all in this together, striving to keep our community safe. I find it fulfilling and meaningful." - }, - { - "speaker": "Maria", - "img_url": [ - "https://sophieelliottfoundation.co.nz/wp-content/uploads/sites/30/2020/08/SLRA-summer-party-james-hopkirk-low-res-026.jpg" - ], - "blip_caption": "a photography of a group of people standing around a table with food", - "query": "homeless shelter sign kindness compassion", - "dia_id": "D26:13", - "re-download": true, - "text": "Yeah John! It feels great to help people, and you're so awesome for it! Here's a shot I got when I volunteered. Reminds me being kind matters!" - }, - { - "speaker": "John", - "dia_id": "D26:14", - "text": "That's a cool photo, Maria! Small acts like that can really make a difference. Keep it up!" - }, - { - "speaker": "Maria", - "dia_id": "D26:15", - "text": "Thanks, John! I totally agree, so I'm gonna keep it up." - }, - { - "speaker": "John", - "dia_id": "D26:16", - "text": "Way to go, Maria! Keep on being positive and making a difference. You're doing great!" - }, - { - "speaker": "Maria", - "dia_id": "D26:17", - "text": "Thanks John! Your support means a lot to me. I'll definitely keep on going. Talk to you soon!" - } - ], - "session_27_date_time": "6:20 pm on 3 August, 2023", - "session_27": [ - { - "speaker": "John", - "dia_id": "D27:1", - "text": "Hey Maria, hope you're doing OK. I had to share something cool with you - I asked family and friends to join the virtual support group I am a part of and be advocates for the military. It's been awesome seeing so many people coming together to back the courageous people serving our nation." - }, - { - "speaker": "Maria", - "img_url": [ - "https://static.wixstatic.com/media/0726909c8a194fd1bdc04ebf3e03bea3.jpg/v1/fill/w_640,h_558,al_t,q_80,usm_0.66_1.00_0.01,enc_auto/0726909c8a194fd1bdc04ebf3e03bea3.jpg" - ], - "blip_caption": "a photography of a group of people standing around a table", - "query": "volunteer badge", - "dia_id": "D27:2", - "re-download": true, - "text": "Wow, John! Way to go helping veterans! I'm doing my part too, volunteering at a homeless shelter. It's so rewarding." - }, - { - "speaker": "John", - "dia_id": "D27:3", - "text": "Maria, that's great! That picture shows a lot of joy. What got you started at that place?" - }, - { - "speaker": "Maria", - "dia_id": "D27:4", - "text": "I started volunteering here about a year ago after witnessing a family struggling on the streets. It made me want to help, so I reached out to the shelter and asked if they needed any volunteers. They said yes, and it has been a really fulfilling experience for me since then." - }, - { - "speaker": "John", - "dia_id": "D27:5", - "text": "Wow, Maria! You really made an impact \u2013 it's awesome! I seriously admire what you do." - }, - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/5xasiy4gnohb1.jpg" - ], - "blip_caption": "a photo of a note from a person who is writing", - "query": "handwritten thank you note shelter resident", - "dia_id": "D27:6", - "text": "Thanks John. That really means a lot. It's been tough but knowing I can make a difference keeps me motivated." - }, - { - "speaker": "John", - "dia_id": "D27:7", - "text": "Maria, what's the deal with that note? Who wrote it and what does it say?" - }, - { - "speaker": "Maria", - "dia_id": "D27:8", - "text": "One of the residents at the shelter, Cindy, wrote it. It's a heartfelt expression of gratitude and shows the impact of the support they receive." - }, - { - "speaker": "John", - "img_url": [ - "https://i2.wp.com/lifecomingalive.com/wp-content/uploads/2018/05/IMG_0960-e1527675276357.jpg" - ], - "blip_caption": "a photo of a young boy holding a flag in a cemetery", - "query": "kids flag military memorial", - "dia_id": "D27:9", - "text": "Wow, Maria, that's so cool that you're making a difference like that! You're so inspiring. Last week, we had a meaningful experience at a military memorial. It really made an impact on my kids." - }, - { - "speaker": "Maria", - "dia_id": "D27:10", - "text": "That's so moving! How did they react when they saw it?" - }, - { - "speaker": "John", - "dia_id": "D27:11", - "text": "They were awestruck and humbled." - }, - { - "speaker": "Maria", - "dia_id": "D27:12", - "text": "Imagining visiting a military memorial makes me feel humble too. It's important for younger generations to remember and appreciate those who served." - }, - { - "speaker": "John", - "dia_id": "D27:13", - "text": "Yeah, totally! Showing them how to respect and appreciate those who served our country is important. It was a moving experience for all of us." - }, - { - "speaker": "Maria", - "dia_id": "D27:14", - "text": "Yeah John, it's super important to teach kids about veterans and what they did for us. You're doing a great thing - we need more people like you!" - }, - { - "speaker": "John", - "dia_id": "D27:15", - "text": "Thanks, Maria. Appreciate your support. It's amazing what teamwork can accomplish!" - }, - { - "speaker": "Maria", - "dia_id": "D27:16", - "text": "Yeah, we can really get amazing stuff done together. We can do this!" - } - ], - "session_28_date_time": "5:19 pm on 5 August, 2023", - "session_28": [ - { - "speaker": "John", - "dia_id": "D28:1", - "text": "Hey Maria, great chatting with you again! Crazy thing happened since we last talked. I lost my job at the mechanical engineering company. They tanked and it's been really rough. Never saw this coming." - }, - { - "speaker": "Maria", - "dia_id": "D28:2", - "text": "Sorry to hear about your job, John. I can only imagine how tough it must be. How are you holding up?" - }, - { - "speaker": "John", - "dia_id": "D28:3", - "text": "Thanks for your care, Maria. It's been tough but I'm trying to stay up. I've been looking into some opportunities in the tech industry for a while now. Maybe this is the change I need, you gave me the push!" - }, - { - "speaker": "Maria", - "dia_id": "D28:4", - "text": "Hey John, glad you're looking into other avenues. Any promising leads come up?" - }, - { - "speaker": "John", - "dia_id": "D28:5", - "text": "Thanks Maria! I may have found a job at a tech company I like that needs my mechanical skills for their hardware team. It feels different, but I think it's a great opportunity to learn and contribute." - }, - { - "speaker": "Maria", - "dia_id": "D28:6", - "text": "Wow, John, that sounds like the perfect job for you! You're so adaptable, I'm sure you'll do great. Good luck!" - }, - { - "speaker": "John", - "dia_id": "D28:7", - "text": "Thanks, Maria! I appreciate your support, it really means a lot." - }, - { - "speaker": "Maria", - "dia_id": "D28:8", - "text": "Hey John, I'm here for you! Staying positive makes a big difference, even in tough times. Yesterday, I took up some community work with my friends from church. It was super rewarding!" - }, - { - "speaker": "John", - "dia_id": "D28:9", - "text": "Wow, Maria! That's great. Glad it was rewarding for you." - }, - { - "speaker": "Maria", - "dia_id": "D28:10", - "text": "Thanks, John! These moments remind me of how important kindness and compassion are. Have you had any volunteer experiences you'd like to share?" - }, - { - "speaker": "John", - "dia_id": "D28:11", - "text": "I haven't been able to volunteer much lately, but I definitely care about it. Last year, I helped renovate a rundown community center back home. It took a lot of work, but seeing the impact on the community was so worth it." - }, - { - "speaker": "Maria", - "dia_id": "D28:12", - "text": "John, that's great! Must be really satisfying to witness the positive effect it's having on your hometown." - }, - { - "speaker": "John", - "dia_id": "D28:13", - "text": "Yeah! It was really cool to see everyone come together and help out - it's been amazing to see it so busy!" - }, - { - "speaker": "Maria", - "dia_id": "D28:14", - "text": "Wow, John, awesome job! Keep doing what you're doing - you're making a real difference!" - }, - { - "speaker": "John", - "dia_id": "D28:15", - "text": "It's really encouraging to hear that. I'll keep pushing forward and doing my best." - }, - { - "speaker": "Maria", - "dia_id": "D28:16", - "text": "You got this! You're inspiring and keep making a difference." - }, - { - "speaker": "John", - "dia_id": "D28:17", - "text": "I appreciate it! Your words are really motivating." - }, - { - "speaker": "Maria", - "dia_id": "D28:18", - "text": "No worries, John. I'm here to support you. If there's anything else I can do, just let me know." - }, - { - "speaker": "John", - "dia_id": "D28:19", - "text": "Thanks, Maria. Your support means a lot to me. If I need anything, I'll be sure to reach out." - } - ], - "session_29_date_time": "8:06 pm on 9 August, 2023", - "session_29": [ - { - "speaker": "Maria", - "img_url": [ - "https://images.rawpixel.com/image_800/czNmcy1wcml2YXRlL3Jhd3BpeGVsX2ltYWdlcy93ZWJzaXRlX2NvbnRlbnQvbHIvZnJ3aW5uZXJfZ29sZF9wcm9maXRfYXdhcmQtaW1hZ2Uta3liY2R6bjguanBn.jpg" - ], - "blip_caption": "a photography of a medal hanging from a tree with a ribbon", - "query": "handmade medal volunteer homeless shelter dedication difference", - "dia_id": "D29:1", - "re-download": true, - "text": "Hey John, what's been going on? I just wanted to check in. Last week was wild - I volunteered at the homeless shelter and they gave me a medal! It was humbling and I'm really glad I could help." - }, - { - "speaker": "John", - "img_url": [ - "https://www.rockawave.com/wp-content/uploads/2017/08/1-Family-Run-Remembers-Eileen-Lavin.jpg" - ], - "blip_caption": "a photo of a young girl holding a sign in her hands", - "query": "family community event 5k charity run military veterans patriotic individuals", - "dia_id": "D29:2", - "text": "Hey Maria! Congrats on the recognition! It's really touching to see how much you're doing to help out. Last weekend, I participated in a community event to raise money for a good cause. We got a great turnout and it was amazing to be surrounded by so many supportive people." - }, - { - "speaker": "Maria", - "dia_id": "D29:3", - "text": "John, that sounds inspiring! Community events like that are always amazing. This pic is heartwarming, that little girl has such a cute smile. What was the event all about?" - }, - { - "speaker": "John", - "img_url": [ - "https://runningfabulouslyblog.files.wordpress.com/2018/09/img_3190.jpg" - ], - "blip_caption": "a photo of a group of people posing for a picture", - "query": "5k charity run group of people running cheering", - "dia_id": "D29:4", - "text": "I set up a 5K charity run in our neighborhood. It was all for a good cause - to help out veterans and their families. We were able to raise some funds! Here's a pic from the day." - }, - { - "speaker": "Maria", - "dia_id": "D29:5", - "text": "John, that's awesome! That is such an important cause. It's an honor to know someone like you who takes initiative. The photo you shared is so powerful! Could you tell me more about how you organized the run?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a map of the streets of downtown", - "dia_id": "D29:6", - "text": "Thanks, Maria! It means a lot to me. It was hard work - getting sponsors, coordinating with the city, and spreading the word. But seeing everyone come together to support our veterans made it worth it." - }, - { - "speaker": "Maria", - "dia_id": "D29:7", - "text": "Wow, John, that sounds like a lot of effort! Your dedication definitely paid off. Were there any challenges along the way?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a sign on a door that says domestic abuse", - "dia_id": "D29:8", - "text": "Definitely, Maria! Getting sponsors was difficult. I had to reach out to several businesses through different means, but it paid off. We ended up with some awesome sponsors that made the event a hit." - }, - { - "speaker": "Maria", - "dia_id": "D29:9", - "text": "Wow, John! You really overcame those challenges. Have you done events for any other causes?" - }, - { - "speaker": "John", - "dia_id": "D29:10", - "text": "Yep, we worked with a local organization that helps victims of domestic abuse. We raised awareness and funds at the event for the cause \u2014 it's unfortunate how many people suffer from it." - }, - { - "speaker": "Maria", - "dia_id": "D29:11", - "text": "Oof, John, that's really sad. Domestic abuse is horrible. You did great raising awareness and funds. It's important we support the organizations fighting against it." - }, - { - "speaker": "John", - "dia_id": "D29:12", - "text": "Thanks, Maria. It's a tough issue, but we've gotta do what we can. It's really wonderful to see people come together for such an important cause." - }, - { - "speaker": "Maria", - "dia_id": "D29:13", - "text": "Agree, John! It's great to see community power in action. Let's keep spreading awareness and supporting causes like this." - }, - { - "speaker": "John", - "blip_caption": "a photo of a man holding a stick and a giant cartoon figure", - "dia_id": "D29:14", - "text": "Yeah Maria! I totally agree! Together, we can do so much. Let's keep spreading the good vibes and making our community better. " - }, - { - "speaker": "Maria", - "dia_id": "D29:15", - "text": "You rock! Let's keep spreading positivity and making a difference. We got this!" - }, - { - "speaker": "John", - "dia_id": "D29:16", - "text": "Yeah, we got this. Thanks for your help!" - }, - { - "speaker": "Maria", - "dia_id": "D29:17", - "text": "Yeah, John! It's really cool to have a friend like you who's just as passionate and motivated. Let's talk again soon!" - }, - { - "speaker": "John", - "dia_id": "D29:18", - "text": "Yeah Maria! Friends like you make a big difference. Talk to you later!" - } - ], - "session_30_date_time": "12:10 am on 11 August, 2023", - "session_30": [ - { - "speaker": "Maria", - "img_url": [ - "https://i.redd.it/ulfxreudf3c81.jpg" - ], - "blip_caption": "a photo of a white dog laying in the grass with a baseball", - "query": "puppy playing ball coco", - "dia_id": "D30:1", - "text": "Hey John! Long time no talk! Guess what - I got a puppy two weeks ago! Her name's Coco and she's adorable." - }, - { - "speaker": "John", - "dia_id": "D30:2", - "text": "Wow, Maria! Coco looks so adorable! She seems so happy in that photo. How's it been having her around?" - }, - { - "speaker": "Maria", - "dia_id": "D30:3", - "text": "Thanks, John! It really brings joy to my life; she's always there to greet me when I come home. It's definitely been an adjustment taking care of her, but it's totally worth it!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/kwoxu9jx5jua1.jpg" - ], - "blip_caption": "a photo of a dog laying on the floor next to a person", - "query": "young kid play fetch golden retriever", - "dia_id": "D30:4", - "text": "That's great! Pets are the best for lifting our spirits. I'm always still thinking about Max- here's a pic of him." - }, - { - "speaker": "Maria", - "dia_id": "D30:5", - "text": "Aww, he looks so cute in that pic! He obviously brought you lots of joy. What's your best memory with him?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/g92z4kf3aph91.jpg" - ], - "blip_caption": "a photo of a dog and a person sitting by a campfire", - "query": "camping trip max campfire hiking swimming", - "dia_id": "D30:6", - "text": "Thanks, Maria! Max and I had a blast on our camping trip last summer. We hiked, swam, and made great memories. It was a really peaceful and awesome experience." - }, - { - "speaker": "Maria", - "dia_id": "D30:7", - "text": "Wow, John, sounds like you and Max had a great time! Camping with pets can be so soul-nourishing, right?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a tent in the woods with a blue tarp", - "dia_id": "D30:8", - "text": "Definitely, Maria! It was so chill. Being out in nature, away from all the noise and taking some quality time was great. It was a nice break from the everyday hustle and bustle. " - }, - { - "speaker": "Maria", - "dia_id": "D30:9", - "text": "Wow, John! Sounds awesome! I can imagine that it felt good to tune out and get lost in nature!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a person sitting on a bench looking at the sunset", - "dia_id": "D30:10", - "text": "Yeah, it was like restarting my mind and spirit. It's amazing how peaceful and pretty it can be. It made me remember the little things in life and savor the peaceful moments." - }, - { - "speaker": "Maria", - "dia_id": "D30:11", - "text": "Yeah, nature is amazing like that. It's like a reset for our souls and your photos capture it." - }, - { - "speaker": "John", - "dia_id": "D30:12", - "text": "Thanks, Maria! I'm glad my photos can capture that feeling. They help me take a step back and appreciate the little things in life - especially these days. There've been some tough times lately for me." - }, - { - "speaker": "Maria", - "dia_id": "D30:13", - "text": "Sorry to hear that, John. Is there anything on your mind lately?" - }, - { - "speaker": "John", - "dia_id": "D30:14", - "text": "Lately, I've been stumped about something. Don't feel like I'm making much of an impact here, which has me questioning my decisions and goals." - }, - { - "speaker": "Maria", - "dia_id": "D30:15", - "text": "Hey John, I understand how you feel. It's tough when you start questioning that. But remember, even small things can make a difference. Why do you feel that way?" - }, - { - "speaker": "John", - "dia_id": "D30:16", - "text": "Thanks for understanding. I just want to positively affect people and the world, but it feels like I'm stuck. I need to find a better way to focus my passion and enthusiasm." - }, - { - "speaker": "Maria", - "dia_id": "D30:17", - "text": "Yeah, it can be tough. Taking small steps can help. Have you thought about any ideas for how to channel your energy into something meaningful?" - }, - { - "speaker": "John", - "dia_id": "D30:18", - "text": "Lately I've been exploring options. One idea I had was to join local organizations or volunteering programs. Maybe that's an option." - }, - { - "speaker": "Maria", - "dia_id": "D30:19", - "text": "Sounds like a great plan, John! That could definitely help you make a difference. You can meet like-minded people and contribute to causes that you care about. Why not do some research and explore the organizations you'd be interested in?" - }, - { - "speaker": "John", - "dia_id": "D30:20", - "text": "Yeah, Maria, that's a good idea. I'll make a list of the ones that suit what I believe in and reach out for more info. Thanks!" - }, - { - "speaker": "Maria", - "dia_id": "D30:21", - "text": "No worries, John. I'm happy to help. Let me know if you need anything else. Keep going, you can do it!" - }, - { - "speaker": "John", - "dia_id": "D30:22", - "text": "Thanks for your kind words, Maria! I'll let you know if I need anything else. Keep being positive and kind, you're an inspiration!" - }, - { - "speaker": "Maria", - "dia_id": "D30:23", - "text": "Thanks, John. Your words mean a lot. I'll definitely keep spreading positivity. Take care!" - } - ], - "session_31_date_time": "3:14 pm on 13 August, 2023", - "session_31": [ - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting around a table", - "dia_id": "D31:1", - "text": "Hi Maria, since we last chatted, I'm volunteering as a mentor for a local school. It's really rewarding to see how much I can help these students." - }, - { - "speaker": "Maria", - "img_url": [ - "https://www.k9aholics.com/wp-content/uploads/2021/12/Things-to-Consider-Before-Getting-a-Puppy.jpg" - ], - "blip_caption": "a photography of a black puppy sitting in the grass looking at the camera", - "query": "puppy animal shelter adoption", - "dia_id": "D31:2", - "re-download": true, - "text": "Wow, John! You're doing great things. Kudos for helping kids learn. On another note, I just adopted this cute pup from a shelter last week. She brings so much joy! I feel blessed to be able to give her a home." - }, - { - "speaker": "John", - "dia_id": "D31:3", - "text": "Wow, Maria, how cute! What's her name? Do she and your other dog get along?" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a dog sitting in the grass with a leash", - "dia_id": "D31:4", - "text": "Her name is Shadow! She's full of energy and always puts a smile on my face. She's been a great addition to my life. They get along great." - }, - { - "speaker": "John", - "dia_id": "D31:5", - "text": "Aww, that sounds adorable! Animals really bring a lot of joy and love, don't they? Did you have any pets growing up?" - }, - { - "speaker": "Maria", - "dia_id": "D31:6", - "text": "No, I didn't. But having a furry pal definitely brightens my days." - }, - { - "speaker": "John", - "dia_id": "D31:7", - "text": "They sure do! I'll have to look into shelters near me soon, it would be great to have a new pup in the house." - }, - { - "speaker": "Maria", - "dia_id": "D31:8", - "text": "Yeah, John! Pets are great at making you feel loved. It's awesome having them around, they bring so much joy! How's everything going with the mentoring program? Are the students making progress?" - }, - { - "speaker": "John", - "img_url": [ - "https://createyourbalancewithliteracy.com/wp-content/uploads/2023/06/Science-Interactive-Notebook-6.jpg" - ], - "blip_caption": "a photo of a poster with a picture of a person and a child", - "query": "students mentoring program whiteboard project", - "dia_id": "D31:9", - "text": "They're doing great - there's been a real improvement in their confidence and skills. It was so amazing to see one of them last week, so excited to show me their essay. It was a proud moment! How's your new pup doing, Maria?" - }, - { - "speaker": "Maria", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/side-view-of-young-man-training-his-dog-by-wall-royalty-free-image-717234545-1559064531.jpg" - ], - "blip_caption": "a photo of a man standing next to a dog pointing at something", - "query": "puppy new command training", - "dia_id": "D31:10", - "text": "Awesome, John! Sounds like it's really making a difference. The little one is doing great - learning commands and house training.\n" - }, - { - "speaker": "John", - "dia_id": "D31:11", - "text": "Wow, cool Maria! Your little one is so smart and keen to learn, must be awesome!" - }, - { - "speaker": "Maria", - "dia_id": "D31:12", - "text": "She's an amazing learner - so much fun to work with and watch her grow. She's brought me so much joy!" - }, - { - "speaker": "John", - "dia_id": "D31:13", - "text": "Animals are amazing\u2014 They can be incredible companions." - }, - { - "speaker": "Maria", - "dia_id": "D31:14", - "text": "Yeah, they can really comfort us and make us feel understood in ways others can't. It's amazing how they bring us so much happiness." - }, - { - "speaker": "John", - "dia_id": "D31:15", - "text": "Yeah, animals bring us peace and understanding, plus we can always count on them. That's so priceless." - }, - { - "speaker": "Maria", - "dia_id": "D31:16", - "text": "Absolutely, John. They're always there for us." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/wi6u64sicow71.jpg" - ], - "blip_caption": "a photo of a family posing for a picture in the park", - "query": "wife kids", - "dia_id": "D31:17", - "text": "Yeah, my family is awesome - me, the missus, and the kids. Even when times are hard, they always have my back. Best thing ever, really." - }, - { - "speaker": "Maria", - "dia_id": "D31:18", - "text": "That picture is so cute! What activities do you all enjoy doing together?" - }, - { - "speaker": "John", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0586/8149/1647/t/127/assets/pickneys_2-1662359454750.jpg" - ], - "blip_caption": "a photo of two children sitting at a table with a board game", - "query": "family playing board games", - "dia_id": "D31:19", - "text": "Thanks, Maria! We love being outdoors - going for hikes, hanging out at the park, having picnics - plus playing board games and having movie nights at home. " - }, - { - "speaker": "Maria", - "dia_id": "D31:20", - "text": "Sounds like a blast, John! Spending time with family is so important, and that all sounds perfect. Have a great time!" - }, - { - "speaker": "John", - "dia_id": "D31:21", - "text": "Thanks, Maria! I'm off to spend some time with them now. Have a good day!" - }, - { - "speaker": "Maria", - "dia_id": "D31:22", - "text": "Enjoy your family time, John! Have a great day!" - }, - { - "speaker": "John", - "dia_id": "D31:23", - "text": "Thanks, Maria! You too! Stay safe!" - } - ], - "session_32_date_time": "11:08 am on 16 August, 2023", - "session_32": [ - { - "speaker": "John", - "dia_id": "D32:1", - "text": "Hey Maria! Guess what? I'm now part of the fire-fighting brigade. I'm super excited to be involved and help out my community!" - }, - { - "speaker": "Maria", - "dia_id": "D32:2", - "text": "Wow John, that's impressive! You're really enthusiastic about making a change. How's your experience been so far?" - }, - { - "speaker": "John", - "dia_id": "D32:3", - "text": " I was impressed with their dedication and how well they worked together. Just being around them was so inspiring!" - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people loading a truck with a fire truck in the back", - "dia_id": "D32:4", - "text": "That's amazing. Must have been awesome to see all those people working together." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/kkt623tlufk11.jpg" - ], - "blip_caption": "a photo of a cardboard box with a sign on it", - "query": "group of people lifting heavy object", - "dia_id": "D32:5", - "text": "It definitely was! Everyone was so into it. It's amazing how a group can succeed at something so important. It only took us two hours. We worked hard but did something good \u2013 it was really satisfying." - }, - { - "speaker": "Maria", - "dia_id": "D32:6", - "text": "Wow, John! It looks like everyone was working hard. Did you raise any donations? " - }, - { - "speaker": "John", - "dia_id": "D32:7", - "text": "Yup, we raised a ton! We got stuff like canned food, toiletries, and clothes to help out. Feels great to be part of it!" - }, - { - "speaker": "Maria", - "dia_id": "D32:8", - "text": "I bet! It's great to see the community coming together to support the local fire station. " - }, - { - "speaker": "John", - "dia_id": "D32:9", - "text": "You're right, Maria. It's great to help out and see everyone coming together for this cause. It gives me a sense of purpose and passion. I feel like this is my true calling." - }, - { - "speaker": "Maria", - "dia_id": "D32:10", - "text": "Awesome, John! Loving your newfound passion. You're doing great things - keep it up! It's wonderful to see everyone coming together for this cause." - }, - { - "speaker": "John", - "img_url": [ - "https://www.cityofmobile.org/fire/uploads/20180827-094050.jpg" - ], - "blip_caption": "a photo of a fire truck parked in a garage with other vehicles", - "query": "firetruck", - "dia_id": "D32:11", - "text": "Thanks, Maria! Your support means a lot to me. It's amazing how finding my passion has made such a big impact. I'll keep working hard on it. The donations even helped get a brand new fire truck!" - }, - { - "speaker": "Maria", - "dia_id": "D32:12", - "text": "Look at that - we all donated for it, and it looks awesome!" - }, - { - "speaker": "John", - "dia_id": "D32:13", - "text": "Thanks for being a part of this with me, Maria. I appreciate your support." - }, - { - "speaker": "Maria", - "blip_caption": "a photo of a group of people standing around a table filled with food", - "dia_id": "D32:14", - "text": "Hey John, I'm here for you. Last Friday, I spent some time at the shelter volunteering at the front desk. Seeing the smiles on their faces when they got food or a bed really made me feel good. We have the power to make a difference in people's lives." - }, - { - "speaker": "John", - "dia_id": "D32:15", - "text": "Maria, I'm glad you're finding fulfillment there. It's amazing how a little kindness can have such a big impact on someone's life. Let's continue making a difference in our community!" - }, - { - "speaker": "Maria", - "dia_id": "D32:16", - "text": "Yeah, John! Let's keep spreading kindness. It's awesome to know we can bring joy and comfort to those who need it." - }, - { - "speaker": "John", - "dia_id": "D32:17", - "text": "Yeah, Maria, let's keep each other and everyone else motivated to make a difference! Together, our impact will surely last." - } - ] - }, - "event_summary": { - "events_session_1": { - "John": [ - "John, his wife and their four kids, take a road trip together.", - "John practices kickboxing to stay in shape.", - "John aims to get into local politics and improve education, infrastructure in the community.", - "John plans to chat with local leaders to gain support and advices for his ideas." - ], - "Maria": [ - "Maria volunteers at a homeless shelter.", - "Maria starts practicing aerial yoga to stay fit." - ], - "date": "17 December, 2022" - }, - "events_session_2": { - "John": [ - "John networks with some people to make progress towards his goal of joining local politics and hears some inspiring stories from them.", - "John practices taekwondo.", - "John and his family have a fun dinner where they make pizza and choose toppings for themselves." - ], - "Maria": [ - "Maria donates her old car to the homeless shelter she volunteers at.", - "Maria makes some peach cobbler." - ], - "date": "22 December, 2022" - }, - "events_session_3": { - "John": [ - "John joins a service-focused online support group for those inspired to serve the country.", - "John meets people from the online support group and together, they volunteer at the homeless shelter, organize a toy drive for kids in need.", - "John is stressed out because he fails his military aptitude test.", - "John and his fellows from the online support group brainstorm ideas to bring education, mentorship, job training and resume building workshops to unserserved communities." - ], - "Maria": [ - "Maria went to the beach last month." - ], - "date": "1 January, 2023" - }, - "events_session_4": { - "John": [ - "John suffers from an accident where his car's windshield is shattered, but he does not receive any serious injuries.", - "John makes progress towards his political goals by talking to community leaders and getting to know the needs of the community." - ], - "Maria": [ - "Maria becomes friends with one of the shelter's volunteers and bond on their shared love for helping others." - ], - "date": "9 January, 2023" - }, - "events_session_5": { - "John": [ - "John attends a community meeting where he is motivated to work towards providing upgrades to education infrastructure in underserved communities. He is particularly struck by the inequality in access across neighborhoods." - ], - "Maria": [ - "Maria volunteers at the homeless shelter during a kids event. She is inspired by her aunt who used to volunteer and helped her family during tough times." - ], - "date": "28 January, 2023" - }, - "events_session_6": { - "John": [ - "John organizes a food drive for people who have lost their jobs recently to help out neighbors in need and finds overwhelming support from volunteers." - ], - "Maria": [ - "Maria gets out of her comfort zone and participates in a charity event at a homeless shelter. She finds the power of collective effort heartwarming.", - "Maria talks to a person named David during the charity event and connects them to a charity organization that could help provide them a place to stay and basic necessities." - ], - "date": "5 February, 2023" - }, - "events_session_7": { - "John": [ - "John runs for office again, with even more zeal and enthusiasm than his first run, inspired by the observation that one can make a lot of social impact when in office.", - "John is invited to a begineer's yoga class by his colleague, Rob." - ], - "Maria": [ - "Maria joins a creative writing class and finds it super enlightening.", - "MAria meets a person named Jean when volunteering at the homeless shelter, who became homeless after she got divorced and lost her job. Maria inspires to find positivity and happiness even during the tough times in life." - ], - "date": "25 February, 2023" - }, - "events_session_8": { - "John": [ - "John retakes the aptitude test for joining the military with positive results and is elated with the results.", - "John attends a violin concert with his wife and kids.", - "John goes to a picnic with his family." - ], - "Maria": [ - "Maria's grandmother passed away last week." - ], - "date": "6 March, 2023" - }, - "events_session_9": { - "John": [ - "John graduates from his college with a degree related to policymaking and is inspired to pursue a career where he can use his degree to improve society." - ], - "Maria": [ - "Maria takes a poetry class to learn more about expressing her emotions." - ], - "date": "2 April, 2023" - }, - "events_session_10": { - "John": [ - "John starts a weekend yoga class with one of his colleagues." - ], - "Maria": [], - "date": "7 April, 2023" - }, - "events_session_11": { - "John": [ - "John's car breaks down on the way to work and he is short of money after spending some on repairs.", - "John went to road trip in the Pacific North-west last year and visited many beautiful national parks." - ], - "Maria": [ - "Maria gives inspirational talks at the shelter which earns her praise from the fellow volunteers.", - "Maria organizes a meal at the homeless shelter.", - "MAria buys a cross necklace for herself to feel closer to her faith." - ], - "date": "10 April, 2023" - }, - "events_session_12": { - "John": [ - "John starts posting articles to his blog about the country's political and governmental system and receives feedback from like-imnded readers.", - "John participated in a convention last month with his colleagues where they brainstormed about ideas to use tech for good and positive change in the society.", - "John starts planning for a road trip on the East coast." - ], - "Maria": [], - "date": "18 April, 2023" - }, - "events_session_13": { - "John": [ - "John starts a series of military-style health boot camps with his family to stay fit. They do workouts three times a week.", - "John makes apple pie for the kids." - ], - "Maria": [ - "Maria makes dinner with her Mom where they have a spread of salads, sandwiches and her favorite dessert, banana split sundae.", - "Maria reflects on the time she went on a solo trip last year, took some pictures in Spain and tried her hand at surfing for the first time. The trip teaches her the importance of inner strength and solitude." - ], - "date": "4 May, 2023" - }, - "events_session_14": { - "John": [ - "John decides to run for office again despite his previous unsuccessful attempts.", - "John faces a power cut in the area which reinforces his motivation to work towards upgrading infrastructure in his current neighborhood as well as his old neighborhood, West County." - ], - "Maria": [ - "Maria starts attending a nearby church to feel closer to her faith and community." - ], - "date": "6 May, 2023" - }, - "events_session_15": { - "John": [ - "John reaches out to different groups to start a petition in support of military veterans' rights to show his appreciation towards their service to the country.", - "John throws a small party and invites veterans to come share their stories." - ], - "Maria": [], - "date": "20 May, 2023" - }, - "events_session_16": { - "John": [ - "John calls old friends to help spread the petition he started for supporting the rights of military veterans.", - "John offers help to Maria to spread word for the chilli cook-off being organized as a part of the fundraiser at the homeless shelter she volunteers at." - ], - "Maria": [ - "Maria works towards organizing a fundraiser for the homeless shelter she volunteers at.", - "Maria asks John to help spread the word for the chilli cook-off being organized as a part of the fundraiser." - ], - "date": "25 May, 2023" - }, - "events_session_17": { - "John": [ - "John's ten year old dog, Max, passes away.", - "John and his family consider adopting a rescue dog as a pet." - ], - "Maria": [ - "Maria signs an agreement with a local dog shelter to volunteer once a month." - ], - "date": "3 June, 2023" - }, - "events_session_18": { - "John": [ - "John goes on a mountaineering expedition with some of his colleagues and summits the peak." - ], - "Maria": [ - "Maria and her friends from the church she joined recently go on a camping trip.", - "Maria reflects on the time she went on a road trip to Oregon with her famly and saw the Multnomah Falls." - ], - "date": "12 June, 2023" - }, - "events_session_19": { - "John": [ - "John gets promoted to the position of assistant manager at his workplace and is over the moon about it." - ], - "Maria": [ - "Maria joins a local gym and meets new friends. She finds the gym routine super positive and is excited to build her endurance through workouts as well as kundalini yoga.", - "" - ], - "date": "16 June, 2023" - }, - "events_session_20": { - "John": [ - "John spends quality time with his family by attending a live music event." - ], - "Maria": [ - "Maria goes through a rough patch but receives support from her family." - ], - "date": "27 June, 2023" - }, - "events_session_21": { - "John": [ - "John participates in a protest march dedicated to the cause of military veterans' rights." - ], - "Maria": [ - "Maria meets a bunch of delightful kids at the shelter and receives a letter of appreciation from Laura, one of the shelter residents.", - "Maria tries to help her cousin who has to move out and urgently needs a place to live.", - "Maria faces an accident where another car runs the red light and hits their car." - ], - "date": "3 July, 2023" - }, - "events_session_22": { - "John": [], - "Maria": [ - "Maria takes regular 'me-time' walks in the park closest to her." - ], - "date": "5 July, 2023" - }, - "events_session_23": { - "John": [ - "John's old neighborhood, West County, gets hit by a flood and John brainstorms solutions to help the area." - ], - "Maria": [ - "Maria and her new friend at the gym go to dinner." - ], - "date": "7 July, 2023" - }, - "events_session_24": { - "John": [ - "John visits a local veteran's hospital to learn more about service to the country and gets to hear stories of resilience.", - "John visits an arts and crafts based community event with his family and is inspired to create a space for it in their home." - ], - "Maria": [ - "Maria has a picnic with her friends from the church where they share good food and play games like charades, scavenger hunt." - ], - "date": "17 July, 2023" - }, - "events_session_25": { - "John": [ - "John joins a new job and works with an encouraging team." - ], - "Maria": [ - "Maria embarks on a hiking trip with her new church friends.", - "Maria bakes and donates some cakes to the homeless shelter." - ], - "date": "22 July, 2023" - }, - "events_session_26": { - "John": [ - "John joins a fire-fighting brigade to serve in his community more directly and contribute his skills.", - "John undergoes intense training as part of the fire-fighting brigade and gets the opportunity to save a family from a burning building with the team." - ], - "Maria": [], - "date": "31 July, 2023" - }, - "events_session_27": { - "John": [ - "John encourages family and friends to join the online support group he has been a part of for months and become advocates for military service.", - "John and his family have a humbling moment during a visit to a military memorial." - ], - "Maria": [ - "Maria receives a letter of appreciation from Cindy, a resident at the homeless shelter." - ], - "date": "3 August, 2023" - }, - "events_session_28": { - "John": [ - "John loses his job at the mechanical engineering company and is on the look out for other jobs, potentially at tech companies that require his mechanical engineering skills for their hardware division." - ], - "Maria": [ - "Maria and her friends from the church take up some community work." - ], - "date": "5 August, 2023" - }, - "events_session_29": { - "John": [ - "John successfully organizes a 5K charity run in his neighborhood for the benefit of military veterans." - ], - "Maria": [ - "Maria receives a medal from the homeless shelter for her relentless work towards making the residents' lives better." - ], - "date": "9 August, 2023" - }, - "events_session_30": { - "John": [ - "John feels stuck in life because he feels he has not been able to make enough impact with his work and is unable to follow his passions." - ], - "Maria": [ - "Maria volunteers at the animal shelter where she adopts a puppy and names her Coco." - ], - "date": "11 August, 2023" - }, - "events_session_31": { - "John": [ - "John volunteers as a mentor for a local school and is excited to see the growth in confidence in the kids from his mentorship." - ], - "Maria": [ - "Maria adopts another puppy from the dog shelter she volunteers at, and names it Shadow." - ], - "date": "13 August, 2023" - }, - "events_session_32": { - "John": [ - "John starts attending monthly gatherings with the fire-fighting brigade and raises donations during the meetings." - ], - "Maria": [ - "Maria volunteers at the front desk in the homeless shelter and is happy to see the relief in people's faces when they find a bed or food." - ], - "date": "16 August, 2023" - } - }, - "observation": { - "session_1_observation": { - "Maria": [ - [ - "Maria volunteers at a homeless shelter and recently started aerial yoga.", - "D1:3" - ] - ], - "John": [ - [ - "John just got back from a family road trip.", - "D1:2" - ], - [ - "John is currently doing kickboxing as a workout.", - "D1:4" - ], - [ - "John aspires to get into local politics to help improve the community.", - "D1:6" - ], - [ - "John's passion in politics revolves around improving education and infrastructure in the community.", - "D1:8" - ], - [ - "John is focused on funding schools and improving infrastructure due to past experiences of lack of education and infrastructure in his neighborhood.", - "D1:10" - ], - [ - "John's next move in politics involves chatting with local leaders and organizations to gather support and ideas.", - "D1:14" - ] - ] - }, - "session_2_observation": { - "Maria": [ - [ - "Maria donated her old car to a homeless shelter where she volunteers.", - "D2:1" - ], - [ - "Maria believes that even minor tweaks to the system can make a big difference for many people.", - "D2:7" - ], - [ - "Maria enjoys spending time with friends watching movies, hiking, and having game nights at her place.", - "D2:17" - ], - [ - "Maria made peach cobbler recently.", - "D2:25" - ] - ], - "John": [ - [ - "John has been networking to gather input for a campaign to make improvements to the community's education system.", - "D2:2" - ], - [ - "John is motivated to make education better in their area to invest in future generations.", - "D2:4" - ], - [ - "John's family serves as a source of strength and motivation for him.", - "D2:10" - ], - [ - "John and his family enjoy spending time at a playground together, climbing, sliding, and playing games.", - "D2:16" - ], - [ - "John's family loves to make and enjoy pizzas together.", - "D2:24" - ], - [ - "John practices taekwondo.", - "D2:28" - ] - ] - }, - "session_3_observation": { - "John": [ - [ - "John joined a service-focused online group last week and finds it emotionally rewarding.", - "D3:1" - ], - [ - "John participated in activities with the online group such as giving out food at a homeless shelter and organizing a toy drive for kids in need.", - "D3:5" - ], - [ - "John is involved in brainstorming projects to help underserved communities with education, mentorship, job training, and resume building.", - "D3:7" - ], - [ - "John recently failed the military aptitude test and has been feeling stressed out.", - "D3:11" - ], - [ - "John used to have a film camera as a kid and took plenty of beach pictures.", - "D3:15" - ] - ], - "Maria": [ - [ - "Maria appreciates nature's beauty and finds it a source of relaxation and a reminder to enjoy the small things in life.", - "D3:12" - ], - [ - "Maria took a peaceful sunset picture at the beach last month that made her feel connected to nature and appreciate life's small moments.", - "D3:14" - ] - ] - }, - "session_4_observation": { - "Maria": [ - [ - "Maria recently became friends with a fellow volunteer who shares a passion for helping others.", - "D4:1" - ], - [ - "Maria appreciates the importance of staying strong during tough times.", - "D4:4" - ], - [ - "Maria values appreciating what one has and staying strong during challenges.", - "D4:9" - ], - [ - "Maria takes notes about local politics in her notebook.", - "D4:13" - ], - [ - "Maria supports John's community work and helps him with new ideas and plans.", - "D4:14" - ], - [ - "Maria believes in working together to bring positive changes and discussions to the community.", - "D4:19" - ], - [ - "Maria emphasizes the importance of unity and teamwork for addressing community problems.", - "D4:23" - ] - ], - "John": [ - [ - "John experienced an unexpected incident last week on his way home, which reminded him of life's unexpected troubles.", - "D4:2" - ], - [ - "John handled the unexpected incident by staying calm, asking for assistance, and returning safely.", - "D4:6" - ], - [ - "John appreciates the beauty of sunsets as a way to enjoy the small things in life.", - "D4:10" - ], - [ - "John has been delving into local politics, talking to community leaders, understanding neighborhood needs, and feeling optimistic about it.", - "D4:12" - ], - [ - "John is preparing for a community meeting next week to discuss education and infrastructure upgrades.", - "D4:16" - ], - [ - "John believes in sticking up for the community and finds discussing community matters important.", - "D4:18" - ], - [ - "John is grateful for Maria's support and considers her an awesome friend.", - "D4:24" - ] - ] - }, - "session_5_observation": { - "John": [ - [ - "John attended a community meeting focused on the state of education and the need for upgrades, especially for kids.", - "D5:1" - ], - [ - "John expressed concern about the lack of proper resources for kids in education and the need to make things better.", - "D5:3" - ], - [ - "John mentioned his kids having a lot while others lack resources, indicating a sense of empathy and awareness.", - "D5:5" - ], - [ - "John shared that he had a doll in his childhood that made him feel better, reminding him to look out for others when they're feeling down.", - "D5:13" - ], - [ - "John emphasizes the importance of supporting each other, especially when feeling down.", - "D5:15" - ] - ], - "Maria": [ - [ - "Maria expressed sadness about the state of education and the lack of proper resources for kids.", - "D5:2" - ], - [ - "Maria mentioned volunteering at a shelter during an event for kids, showing a commitment to helping those in need.", - "D5:6" - ], - [ - "Maria volunteered to help make a difference and shared that her aunt's involvement in volunteering inspired her.", - "D5:8" - ], - [ - "Maria had a touching moment with a lonely girl at the shelter, providing comfort and company, which made an impact on her.", - "D5:10" - ], - [ - "Maria values spreading kindness and support to make a difference, especially for those feeling down.", - "D5:14" - ] - ] - }, - "session_6_observation": { - "Maria": [ - [ - "Maria participated in a charity event last Friday and found it rewarding and heartwarming.", - "D6:1" - ], - [ - "Maria had a conversation with someone named David at the charity event, who shared a story of hardship, leading Maria to link him up with a local organization for support.", - "D6:5" - ], - [ - "When Maria was younger, her family had money problems and had to rely on help from their auntie, teaching her the importance of helping others in need.", - "D6:9" - ], - [ - "Maria offered to help John with his community food drive initiative by networking or volunteering at future events.", - "D6:17" - ], - [ - "Maria expressed her willingness to always be there to help John whenever he needs it.", - "D6:19" - ], - [ - "Maria reassured John that she would always be there for him because he is a great friend to her.", - "D6:21" - ] - ], - "John": [ - [ - "John got into politics due to a strong urge to serve his country and community.", - "D6:4" - ], - [ - "John started helping out with a food drive for people who lost their jobs.", - "D6:10" - ], - [ - "John initiated a community food drive due to the impact of unemployment in the community.", - "D6:12" - ], - [ - "John has been overwhelmed by the response and volunteers at the food drive events.", - "D6:16" - ], - [ - "John appreciated Maria's offer to help with networking or volunteering at future events for the food drive.", - "D6:18" - ], - [ - "John expressed that friendship means a lot to him and that he values having each other's backs with Maria.", - "D6:22" - ] - ] - }, - "session_7_observation": { - "Maria": [ - [ - "Maria recently took a creative writing class.", - "D7:1" - ], - [ - "Maria has been volunteering at a homeless shelter, finding it rewarding.", - "D7:3" - ], - [ - "During her volunteering, Maria met a woman named Jean who showed her the importance of gratitude and connection.", - "D7:5" - ], - [ - "Maria values positivity and optimism, even in tough situations.", - "D7:9" - ] - ], - "John": [ - [ - "John is running for office again.", - "D7:2" - ], - [ - "John believes in making positive changes through politics for a better future.", - "D7:4" - ], - [ - "John sees staying optimistic during tough times as inspiring.", - "D7:8" - ], - [ - "John believes in the impact of kindness and optimism.", - "D7:10" - ], - [ - "John's colleague invited him to a beginner's yoga class.", - "D7:16" - ] - ] - }, - "session_8_observation": { - "Maria": [ - [ - "Maria's grandma passed away last week, causing her to go through a tough time.", - "D8:1" - ], - [ - "Maria volunteers at a homeless shelter.", - "D8:21" - ] - ], - "John": [ - [ - "John had a picnic with his wife and kids recently.", - "D8:2" - ], - [ - "John's one-year-old son is named Kyle.", - "D8:4" - ], - [ - "John takes his family to the park a few times a week for bonding and fun.", - "D8:8" - ], - [ - "John found a violin concert last week that the whole family enjoyed.", - "D8:12" - ], - [ - "John is considering volunteering to serve his country.", - "D8:18" - ], - [ - "John discussed the volunteering opportunity with family and friends, who have been supportive.", - "D8:20" - ] - ] - }, - "session_9_observation": { - "Maria": [ - [ - "Maria has been taking a poetry class to help express her feelings.", - "D9:1" - ], - [ - "Maria congratulated John on finishing his degree.", - "D9:3" - ], - [ - "Maria is supportive of John's plans for the future in policymaking.", - "D9:7" - ], - [ - "Maria remembers John mentioning his interest in education and infrastructure improvements.", - "D9:9" - ], - [ - "Maria praised John for his dedication and commitment to finding solutions for community issues.", - "D9:11" - ], - [ - "Maria finds it satisfying and worthwhile to work on community issues.", - "D9:13" - ] - ], - "John": [ - [ - "John has recently graduated.", - "D9:4" - ], - [ - "John is considering going into policymaking to make a positive impact.", - "D9:6" - ], - [ - "John is particularly interested in improving education and infrastructure.", - "D9:8" - ], - [ - "John's experiences in community meetings have shaped his views on education and infrastructure challenges.", - "D9:10" - ], - [ - "John finds it rewarding to make a difference in the community.", - "D9:12" - ], - [ - "John values Maria's support and finds it encouraging.", - "D9:14" - ], - [ - "John reminisced about volunteering with Maria last year and found it fulfilling.", - "D9:18" - ] - ] - }, - "session_10_observation": { - "John": [ - [ - "John started a weekend yoga class with a colleague and finds it awesome for his mental and physical wellbeing.", - "D10:1" - ], - [ - "John's yoga instructor focuses on proper poses, listening to one's body, and creates a relaxed and welcoming environment.", - "D10:3" - ], - [ - "John is in a beginner yoga class that helps him relax and increase flexibility.", - "D10:5" - ], - [ - "Yoga has helped John feel more connected, relaxed, and improved his mind and body.", - "D10:7" - ], - [ - "John considers stretching and breathing as powerful tools for wellbeing and has incorporated it into his daily routine.", - "D10:9" - ], - [ - "John volunteered at a career fair at a local school and found it rewarding to help kids with dreams affected by lack of resources.", - "D10:13" - ], - [ - "The sign at the career fair said, \"Always look on the bright side of life\", highlighting the importance of support in achieving kids' dreams.", - "D10:15" - ] - ], - "Maria": [ - [ - "Maria recently participated in a 5K charity run for a homeless shelter and found it rewarding to be surrounded by people with a shared cause.", - "D10:10" - ], - [ - "Maria finds events like charity runs energizing and a reminder that individuals can make a difference.", - "D10:11" - ], - [ - "Maria was inspired by the energy and sense of unity at the charity run.", - "D10:12" - ], - [ - "Maria believes it's sad how lack of resources can impact kids' dreams and finds helping them rewarding.", - "D10:14" - ], - [ - "Maria motivates John for the awesome work he's doing in helping kids and encourages him to keep up the good work.", - "D10:16" - ], - [ - "Maria believes in making a difference and helping lots of people in the community.", - "D10:18" - ] - ] - }, - "session_11_observation": { - "John": [ - [ - "John's car broke down last Friday on his way to work, causing financial strain.", - "D11:1" - ], - [ - "John remains positive and looks for solutions despite facing tough times.", - "D11:1" - ], - [ - "John keeps a picture that reminds him of a road trip taken last year as a symbol of beauty and hope.", - "D11:3" - ], - [ - "John explored the Pacific Northwest coast and visited national parks on the road trip, finding nature's beauty breathtaking.", - "D11:5" - ] - ], - "Maria": [ - [ - "Maria recently gave talks at the homeless shelter where she volunteers and received compliments for her efforts.", - "D11:10" - ], - [ - "Maria bought a cross necklace to feel closer to her faith and it has made her happy.", - "D11:10" - ], - [ - "Maria considers the people at the shelter like family to her.", - "D11:12" - ], - [ - "Maria helped organize a meal for shelter residents to bring people together and create a sense of comfort and community.", - "D11:16" - ] - ] - }, - "session_12_observation": { - "John": [ - [ - "John has been blogging about politics and government focusing on education reform and infrastructure development.", - "D12:1" - ], - [ - "John is passionate about making a positive impact in his community through raising awareness and starting conversations.", - "D12:3" - ], - [ - "John attended a convention with colleagues who share his passion for using tech for good in the community.", - "D12:9" - ], - [ - "John felt inspired by being surrounded by like-minded individuals at the convention and believes in the power of unity.", - "D12:11" - ], - [ - "Being in an environment with motivated people gave John renewed energy and purpose, inspiring him to make a bigger difference.", - "D12:15" - ], - [ - "John is planning a trip to the East Coast.", - "D12:17" - ] - ], - "Maria": [ - [ - "Maria agrees with John on the importance of education and infrastructure for community growth.", - "D12:2" - ], - [ - "Maria thinks John's hard work will start conversations and create positive change.", - "D12:4" - ], - [ - "Maria believes that every small act of kindness can have a big effect and advocates for making a difference.", - "D12:22" - ], - [ - "Maria volunteers at a homeless shelter, finding it fulfilling but also feeling overwhelmed by the growing need for help.", - "D12:18" - ] - ] - }, - "session_13_observation": { - "John": [ - [ - "John started going to boot camps with his family last month for bonding and fitness.", - "D13:3" - ], - [ - "John, along with his family, works out together three times a week.", - "D13:7" - ], - [ - "John has noticed increased energy, strength, and endurance since starting boot camps with his family.", - "D13:5" - ], - [ - "John and his family support and motivate each other during workouts.", - "D13:5" - ], - [ - "John and his family cheer each other on and provide emotional support during boot camp.", - "D13:15" - ], - [ - "John made an apple pie for his kids the day before the conversation.", - "D13:17" - ] - ], - "Maria": [ - [ - "Maria enjoys a variety of foods like salads, sandwiches, and homemade desserts.", - "D13:18" - ], - [ - "Maria's favorite is the banana split sundae, which she enjoys after a day of volunteering.", - "D13:18" - ], - [ - "Maria values balance in life and finds joy in small moments.", - "D13:18" - ], - [ - "Maria took a solo trip to Spain last year which helped her appreciate life more.", - "D13:24" - ], - [ - "Maria tried surfing for the first time on her vacation in Spain.", - "D13:30" - ], - [ - "Maria believes in pushing boundaries for growth and self-exploration.", - "D13:36" - ] - ] - }, - "session_14_observation": { - "John": [ - [ - "John decided to run for office again, despite not being successful before, to make a difference in his community.", - "D14:1" - ], - [ - "John feels motivated by Maria's support and kind words to keep going and make a difference.", - "D14:3" - ], - [ - "John experienced a power cut in the area, realizing the importance of upgrading infrastructure for stable services.", - "D14:13" - ], - [ - "John believes that improvements are needed for the roadways in the area due to potholes and dangers for drivers.", - "D14:15" - ], - [ - "John is considering starting a community project regarding infrastructure to improve the neighborhood.", - "D14:17" - ], - [ - "John wants to work on improving his old area, West County, in addition to the current neighborhood.", - "D14:21" - ] - ], - "Maria": [ - [ - "Maria has been doing charity work recently, which she finds rewarding and fulfilling.", - "D14:8" - ], - [ - "Maria joined a nearby church to feel closer to a community and her faith, finding it to be a great experience.", - "D14:10" - ], - [ - "Maria's life has been a bit rough lately, but she is taking time to reflect and find balance.", - "D14:12" - ], - [ - "Maria is willing to work with John on a community project regarding infrastructure to get the neighborhood's backing.", - "D14:18" - ], - [ - "Maria wants to work together with John to create a safe and bustling environment for the community and others, to make real change.", - "D14:22" - ] - ] - }, - "session_15_observation": { - "John": [ - [ - "John is currently working on a project to support military veterans and is trying to get a petition going.", - "D15:1" - ], - [ - "John decided to take on the project to support veterans because he is passionate about their rights and wanted to show appreciation for their hard work.", - "D15:3" - ], - [ - "John is very passionate about making sure veterans are supported and valued in the community.", - "D15:9" - ], - [ - "John organized a small party for some veterans to share their stories and make connections, which was heartwarming for him.", - "D15:13" - ] - ], - "Maria": [ - [ - "Maria is supportive of John's project to support veterans and is willing to help out.", - "D15:10" - ], - [ - "Maria is planning a ring-toss tournament for a homeless shelter's fundraiser later in the month.", - "D15:18" - ] - ] - }, - "session_16_observation": { - "John": [ - [ - "John is working on a petition and getting back in touch with his buddies for support.", - "D16:1" - ], - [ - "John went on a hiking trip with his workmates and values their support.", - "D16:1" - ], - [ - "John is enthusiastic about helping Maria with her charity fundraiser.", - "D16:3" - ], - [ - "John offered to help spread the word about Maria's charity fundraiser chili cook-off.", - "D16:5" - ], - [ - "John expressed willingness to assist in finding volunteers for Maria's charity fundraiser.", - "D16:7" - ], - [ - "John shared a picture of his family at the beach with Maria.", - "D16:11" - ], - [ - "John values the little moments in life for hope and motivation.", - "D16:13" - ], - [ - "John believes in making a positive impact and appreciates reminders to cherish life.", - "D16:17" - ] - ], - "Maria": [ - [ - "Maria is busy at the shelter preparing for a fundraiser to cover basic needs for the homeless.", - "D16:2" - ], - [ - "Maria shared a poster for a chili cook-off at the fundraiser with John.", - "D16:4" - ], - [ - "Maria asked John for help in spreading the word about the charity fundraiser.", - "D16:5" - ], - [ - "Maria values and appreciates John's assistance with the charity fundraiser.", - "D16:8" - ], - [ - "Maria finds working with passionate people like John motivating.", - "D16:10" - ], - [ - "Maria believes that moments like the shared photo bring peace and appreciation for life.", - "D16:14" - ], - [ - "Maria finds reminders about positive impacts motivating to continue making a difference.", - "D16:18" - ] - ] - }, - "session_17_observation": { - "John": [ - [ - "John recently had to say goodbye to his dog, Max, who was a part of their family for 10 years.", - "D17:1" - ], - [ - "John is finding it tough to cope with the loss of Max, but he and his family are trying to focus on the good times and memories they shared.", - "D17:3" - ], - [ - "John shared a picture of Max to honor his memory and cherish the moments they had together.", - "D17:5" - ], - [ - "John values the lessons that Max taught them about love and loyalty, and wants his kids to learn the same.", - "D17:7" - ], - [ - "John is considering adopting a rescue dog to give it a loving home and teach his kids responsibility and compassion.", - "D17:11" - ] - ], - "Maria": [ - [ - "Maria offered John her condolences and support after hearing about the loss of Max, acknowledging the importance of pets as part of the family.", - "D17:2" - ], - [ - "Maria started volunteering at a local dog shelter once a month, showing her compassion towards animals and her willingness to help.", - "D17:12" - ] - ] - }, - "session_18_observation": { - "Maria": [ - [ - "Maria went camping with friends from church to take her mind off things.", - "D18:1" - ], - [ - "Maria and her family went on a road trip to Oregon when she was younger.", - "D18:3" - ], - [ - "Maria described a view of a waterfall and bridge as awesome and breath-taking.", - "D18:5" - ], - [ - "Maria felt like she was in a fairy tale at the waterfall, with calming water sounds and beautiful surroundings.", - "D18:7" - ], - [ - "Maria has gone camping a few times but has never tried mountain climbing.", - "D18:11" - ], - [ - "Maria finds peaceful moments by having a moment to herself with favorite tunes and practicing aerial yoga.", - "D18:15" - ], - [ - "Maria enjoys upside-down yoga poses as they make her feel free and light.", - "D18:17" - ] - ], - "John": [ - [ - "John went on a mountaineering trip with workmates that helped clear his head.", - "D18:2" - ], - [ - "John reached the summit during the mountaineering trip and found the view stunning.", - "D18:4" - ], - [ - "John has been camping plenty of times and enjoys getting away from it all to be at one with nature.", - "D18:12" - ], - [ - "John believes nature helps in calming us down and acts like a reset button amidst chaos.", - "D18:8" - ], - [ - "John had an awesome day at the park with his family where the kids had fun on the playground.", - "D18:22" - ] - ] - }, - "session_19_observation": { - "Maria": [ - [ - "Maria recently joined a gym and is sticking to her workout routine, enjoying the positive environment and supportive people there.", - "D19:1" - ], - [ - "Maria's fitness goal is to get stronger, improve her endurance, and she is trying kundalini yoga.", - "D19:3" - ], - [ - "Maria admires John's trophy and asks about its backstory.", - "D19:9" - ], - [ - "Maria acknowledges John's challenges and commends his perseverance and determination.", - "D19:13" - ], - [ - "Maria values belief in oneself and mentions the importance of having loved ones' support.", - "D19:15" - ], - [ - "Maria enjoys taking time off for herself and her family and appreciates creating memories together.", - "D19:21" - ], - [ - "Maria shares a special beach memory from a vacation in Florida with her family.", - "D19:23" - ], - [ - "Maria appreciates the joy and carefree moments of family vacations.", - "D19:25" - ] - ], - "John": [ - [ - "John got promoted at work to assistant manager and is excited about it, seeing it as a stepping stone for bigger things.", - "D19:6" - ], - [ - "John's trophy symbolizes the obstacles he overcame on his journey to the promotion.", - "D19:10" - ], - [ - "John shares about facing various challenges including tech issues, workplace hurdles, and self-doubt on his path to promotion.", - "D19:12" - ], - [ - "John mentions the importance of having support at home, believing in oneself, and staying determined.", - "D19:14" - ], - [ - "John values having loved ones' support on his journey, especially his family.", - "D19:16" - ], - [ - "John appreciates special family times and cherishes creating memories together.", - "D19:26" - ] - ] - }, - "session_20_observation": { - "Maria": [ - [ - "Maria has been struggling but is focusing on the positive and relying on friends and family for support.", - "D20:1" - ], - [ - "Maria's family has been her rock, giving her words of encouragement and reminding her she's not alone.", - "D20:3" - ], - [ - "Maria finds joy and fulfillment in volunteering at a shelter to help others.", - "D20:7" - ], - [ - "Maria had a special memory at the shelter when playing with kids made someone who had been sad suddenly laugh, which was uplifting.", - "D20:9" - ], - [ - "Maria sees making a difference in others' lives as a source of joy for herself.", - "D20:11" - ], - [ - "Maria feels that spreading kindness and positivity is her way of impacting the world.", - "D20:13" - ] - ], - "John": [ - [ - "John recalls having a blast at a live music event with his family last week.", - "D20:4" - ], - [ - "John emphasizes the importance of appreciating loved ones and finding silver linings in tough times.", - "D20:6" - ], - [ - "John finds it amazing how brief moments with others can make a big difference in bringing joy and happiness.", - "D20:10" - ], - [ - "John encourages Maria to continue spreading kindness and positivity, acknowledging the impact she is making.", - "D20:14" - ] - ] - }, - "session_21_observation": { - "Maria": [ - [ - "Maria met some kids at a shelter.", - "D21:1" - ], - [ - "Maria got hit by a car that ran a red light, but everyone is okay.", - "D21:5" - ], - [ - "Maria is helping her cousin find a new place to stay after a tough time.", - "D21:5" - ], - [ - "Maria's cousin had to leave and find a new place in a hurry, causing stress, but is making progress.", - "D21:7" - ], - [ - "Maria appreciates kindness and care.", - "D21:15" - ], - [ - "Maria loves volunteering and feels it makes a difference, even if it's a small one.", - "D21:21" - ] - ], - "John": [ - [ - "John expressed willingness to help Maria if she needed anything.", - "D21:4" - ], - [ - "John participated in a marching event for veterans' rights.", - "D21:22" - ], - [ - "John respects the military and believes in standing up for beliefs.", - "D21:24" - ], - [ - "John feels the importance of making a difference through activism and was motivated by it.", - "D21:26" - ] - ] - }, - "session_22_observation": { - "John": [ - [ - "John has been thinking about how education and infrastructure shape communities.", - "D22:1" - ], - [ - "John feels it is an uphill battle to make a positive change in communities.", - "D22:3" - ], - [ - "John doubts if he is making a difference sometimes.", - "D22:5" - ], - [ - "John's family motivates him to strive for change.", - "D22:7" - ], - [ - "John looks at a picture of his family when times get hard to remember why he is doing his work.", - "D22:9" - ], - [ - "John is inspired by his family, exercise, and spending time with friends.", - "D22:11" - ], - [ - "John sees sunsets at least once a week during his walks to disconnect and find peace.", - "D22:17" - ] - ], - "Maria": [ - [ - "Maria agrees with John on the crucial role of education and infrastructure in shaping communities.", - "D22:2" - ], - [ - "Maria supports John in his efforts to make a positive change in communities.", - "D22:4" - ], - [ - "Maria is inspired by chatting to people, volunteering, and listening to music.", - "D22:10" - ], - [ - "Maria takes regular 'me-time' walks at the park nearby to find peace.", - "D22:18" - ] - ] - }, - "session_23_observation": { - "John": [ - [ - "John's old area was hit by a nasty flood last week causing damage to many homes.", - "D23:1" - ], - [ - "John is taking initiative to get people together to discuss potential solutions for the issues in the community.", - "D23:3" - ], - [ - "John appreciates Maria's offer to help with the community issues.", - "D23:3" - ], - [ - "John mentions that Maria's support and friendship mean everything to him.", - "D23:7" - ], - [ - "John believes that together with Maria, they can make a difference and motivate others in the community.", - "D23:7" - ], - [ - "John acknowledges that little steps count and they can make a difference together.", - "D23:9" - ] - ], - "Maria": [ - [ - "Maria empathizes with John about the tough situation in his area due to the flood.", - "D23:2" - ], - [ - "Maria expresses her willingness to join the meeting and contribute to making the community better.", - "D23:4" - ], - [ - "Maria admires John's determination and commitment to helping others.", - "D23:6" - ], - [ - "Maria mentions that John's commitment to helping others is inspiring.", - "D23:6" - ], - [ - "Maria believes that their actions, no matter how small, can have a big impact on the community.", - "D23:8" - ], - [ - "Maria emphasizes the importance of spreading kindness and inspiring hope in the community.", - "D23:8" - ], - [ - "Maria mentions having dinner with friends from the gym.", - "D23:14" - ] - ] - }, - "session_24_observation": { - "John": [ - [ - "John visited a veteran's hospital and met an elderly veteran named Samuel who shared inspiring and heartbreaking stories.", - "D24:3" - ], - [ - "John is part of an organization where he works with passionate people and considers them like a family.", - "D24:5" - ], - [ - "John is thinking of setting up fun activities like arts and crafts for his kids based on his experience at a community event.", - "D24:9" - ] - ], - "Maria": [ - [ - "Maria had a picnic with friends from church where they played games like charades and a scavenger hunt.", - "D24:6" - ], - [ - "Maria appreciates the importance of making connections and enjoying life's simpler moments.", - "D24:14" - ], - [ - "Maria believes that doing good and helping others brings joy, and little acts of kindness can have a big effect.", - "D24:16" - ] - ] - }, - "session_25_observation": { - "John": [ - [ - "John is enjoying his new job and finds his team to be encouraging and inspiring.", - "D25:1" - ], - [ - "John attends a yoga studio with a chill vibe and awesome instructors.", - "D25:11" - ], - [ - "The yoga studio John attends offers a variety of classes including yoga, kickboxing, and circuit training.", - "D25:13" - ], - [ - "John has also done weight training classes and found them challenging but peaceful.", - "D25:17" - ] - ], - "Maria": [ - [ - "Maria had a great experience hiking with her church friends last weekend, feeling surrounded by supportive people and enjoying nature.", - "D25:2" - ], - [ - "Maria wanted to make connections, laugh together, and take in nature's beauty during the hike.", - "D25:4" - ], - [ - "Maria plans to explore more and volunteer at shelters next month.", - "D25:8" - ], - [ - "Maria is off to bake some cakes after the conversation.", - "D25:20" - ] - ] - }, - "session_26_observation": { - "Maria": [ - [ - "Maria dropped off baked goods at a homeless shelter last week and is motivated to help people.", - "D26:1" - ], - [ - "Maria volunteers at a homeless shelter and is driven to make a difference.", - "D26:1" - ], - [ - "Maria shared a photo from her volunteer work that reminds her of the importance of kindness.", - "D26:13" - ] - ], - "John": [ - [ - "John joined a firefighting brigade to give back to his community.", - "D26:2" - ], - [ - "John had his first call-out with the firefighting brigade last Sunday to help those in need.", - "D26:4" - ], - [ - "John was a part of a rescue mission that saved a family from a burning building.", - "D26:6" - ], - [ - "John mentioned his military background and the sense of bond and support he feels in the firefighting brigade.", - "D26:10" - ], - [ - "John believes in having friends you can rely on, as he finds it makes a huge difference.", - "D26:11" - ] - ] - }, - "session_27_observation": { - "John": [ - [ - "John is part of a virtual support group advocating for the military and has involved family and friends in supporting veterans.", - "D27:1" - ], - [ - "John admires Maria's volunteer work at a homeless shelter and finds it inspiring.", - "D27:5" - ], - [ - "John had a meaningful experience at a military memorial with his kids last week.", - "D27:9" - ], - [ - "John's kids were awestruck and humbled when they visited the military memorial.", - "D27:11" - ] - ], - "Maria": [ - [ - "Maria volunteers at a homeless shelter, which she started about a year ago after witnessing a struggling family on the streets.", - "D27:2" - ], - [ - "Maria finds volunteering at the homeless shelter really fulfilling and impactful.", - "D27:4" - ], - [ - "Maria mentioned Cindy, a resident at the shelter, who wrote a heartfelt expression of gratitude.", - "D27:8" - ], - [ - "Maria believes it's important for younger generations to remember and appreciate those who served in the military.", - "D27:12" - ], - [ - "Maria thinks it's important to teach kids about veterans and their contributions.", - "D27:14" - ] - ] - }, - "session_28_observation": { - "John": [ - [ - "John lost his job at a mechanical engineering company that tanked.", - "D28:1" - ], - [ - "John is looking into opportunities in the tech industry after losing his job.", - "D28:3" - ], - [ - "John found a job at a tech company that needs his mechanical skills for their hardware team.", - "D28:5" - ], - [ - "John helped renovate a rundown community center back home last year.", - "D28:11" - ], - [ - "John finds volunteering satisfying and valuable.", - "D28:11" - ] - ], - "Maria": [ - [ - "Maria engaged in community work with friends from church yesterday.", - "D28:8" - ], - [ - "Maria believes in the importance of kindness and compassion.", - "D28:10" - ], - [ - "Maria offered her support and encouragement to John in his tough times.", - "D28:6" - ], - [ - "Maria finds volunteering rewarding and important.", - "D28:10" - ] - ] - }, - "session_29_observation": { - "Maria": [ - [ - "Maria volunteered at a homeless shelter last week and received a medal for her help.", - "D29:1" - ], - [ - "Maria found a community event inspiring and heartwarming.", - "D29:2" - ], - [ - "Maria expressed admiration for John's initiative in organizing a charity run for veterans.", - "D29:5" - ], - [ - "Maria thinks domestic abuse is a horrible issue and supports raising awareness and funds for it.", - "D29:11" - ], - [ - "Both Maria and John agree on the importance of spreading awareness and supporting causes in the community.", - "D29:13" - ], - [ - "Maria appreciates having a friend like John who is passionate and motivated about making a difference.", - "D29:17" - ] - ], - "John": [ - [ - "John organized a 5K charity run in the neighborhood to help veterans and their families.", - "D29:4" - ], - [ - "John faced challenges in getting sponsors for the charity run but was successful in the end.", - "D29:8" - ], - [ - "John has organized events for causes other than helping veterans, like raising awareness and funds for victims of domestic abuse.", - "D29:10" - ], - [ - "John believes it's important to come together for important causes like supporting victims of domestic abuse.", - "D29:12" - ], - [ - "John values Maria's passion and motivation in supporting causes and spreading positivity in the community.", - "D29:18" - ] - ] - }, - "session_30_observation": { - "Maria": [ - [ - "Maria got a puppy named Coco two weeks ago, bringing joy to her life.", - "D30:1" - ], - [ - "Maria finds joy in greeting her puppy, Coco, when coming home.", - "D30:3" - ], - [ - "Maria believes that taking care of her puppy, Coco, is worth the adjustment.", - "D30:3" - ], - [ - "Maria mentions that camping with pets can be soul-nourishing.", - "D30:7" - ], - [ - "Maria acknowledges nature as a reset for our souls.", - "D30:11" - ], - [ - "Maria understands tough times and offers support to John.", - "D30:13" - ], - [ - "Maria suggests to John to focus his energy on something meaningful, like joining local organizations or volunteering programs.", - "D30:19" - ], - [ - "Maria encourages John, assures him he can make a difference, and offers help.", - "D30:21" - ], - [ - "Maria plans to keep spreading positivity.", - "D30:23" - ] - ], - "John": [ - [ - "John cherishes memories of his pet Max, including a camping trip where they hiked, swam, and made great memories.", - "D30:6" - ], - [ - "John found the camping trip with his pet Max to be peaceful and awesome.", - "D30:6" - ], - [ - "John values nature as a way to restart his mind and spirit, appreciating the peaceful moments.", - "D30:10" - ], - [ - "John expresses feeling stuck and wanting to positively affect people and the world.", - "D30:16" - ], - [ - "John has been exploring options to make a difference, considering joining local organizations or volunteering programs.", - "D30:18" - ], - [ - "John appreciates Maria's kind words and finds her an inspiration.", - "D30:22" - ] - ] - }, - "session_31_observation": { - "John": [ - [ - "John is volunteering as a mentor for a local school, helping students improve their confidence and skills.", - "D31:1" - ], - [ - "John is considering adopting a new pup and plans to look into shelters near him soon.", - "D31:7" - ], - [ - "John enjoys outdoor activities such as hiking, picnics, and board games with his family.", - "D31:19" - ], - [ - "John values his family greatly and appreciates their support, considering them the best thing ever.", - "D31:17" - ] - ], - "Maria": [ - [ - "Maria recently adopted a cute pup named Shadow from a shelter.", - "D31:2" - ], - [ - "Maria's new pup, Shadow, is full of energy and brings joy to her life.", - "D31:4" - ], - [ - "Maria did not have any pets growing up, but having a furry companion brightens her days.", - "D31:6" - ], - [ - "Maria's pup is learning commands and house training well.", - "D31:10" - ], - [ - "Maria believes that animals bring comfort, understanding, and happiness to our lives.", - "D31:14" - ] - ] - }, - "session_32_observation": { - "John": [ - [ - "John is now part of the fire-fighting brigade and is enthusiastic about helping the community.", - "D32:1" - ], - [ - "John was impressed with the dedication and teamwork of the people in the fire-fighting brigade.", - "D32:3" - ], - [ - "John and his team raised donations like canned food, toiletries, and clothes for the cause.", - "D32:7" - ], - [ - "John feels a sense of purpose and passion in helping out with the fire-fighting brigade, considering it his true calling.", - "D32:9" - ], - [ - "The donations raised by John and his team helped in getting a brand new fire truck.", - "D32:11" - ] - ], - "Maria": [ - [ - "Maria finds fulfillment in volunteering at the shelter, seeing the impact of small acts of kindness on people's lives.", - "D32:14" - ], - [ - "Maria believes in the power to make a difference in people's lives and is enthusiastic about spreading kindness in the community.", - "D32:16" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Maria and John met at 11:01 am on 17 December, 2022. Maria mentioned she has been busy volunteering at a homeless shelter and doing aerial yoga to stay fit. John shared that he had just returned from a family road trip and is now into kickboxing to stay energized. John expressed his goal of getting into local politics to improve education and infrastructure, inspired by the needs he saw growing up in his neighborhood. Maria applauded his efforts and asked about his next steps in the campaign. John plans to talk to local leaders and organizations for support and ideas. Maria encouraged him and asked to keep her updated on his progress.", - "session_2_summary": "At 6:10 pm on 22 December 2022, Maria asked John about his campaign progress and shared her recent experience of donating her old car. John mentioned networking for insights, motivated by a conversation on education. Maria praised his enthusiasm and commitment, highlighting the power of minor tweaks in the community. She encouraged John, who credited his family for his drive, showing a photo of them. Maria acknowledged the importance of family support and shared her preference for quality time with loved ones. John reciprocated the sentiment, emphasizing the value of making memories together. They discussed family activities and shared pictures, concluding with a plan to cherish happy moments with their families. John then mentioned his plan to do some taekwondo.", - "session_3_summary": "At 8:30 pm on 1 January, 2023, John shared with Maria his positive experience with a service-focused online group, feeling a connection and purpose with inspiring individuals. They discussed events like serving at a homeless shelter and arranging a toy drive. John mentioned plans for future projects assisting underserved communities in education and mentorship. Maria praised his initiatives, emphasizing the impact of support and encouraging him to continue. John, feeling stressed after failing a test, appreciated Maria's support. They also bonded over nature's beauty, with Maria sharing a calming beach sunset photo. John reminisced about his childhood beach photography. The conversation highlighted finding joy in simple moments amid life's busyness.", - "session_4_summary": "Maria informed John at 7:06 pm on 9 January, 2023, that she became friends with a fellow volunteer and asked about his well-being. John shared an incident from the previous week that made him realize the unpredictability of life. Maria empathized with his experience and commended his resilience. John mentioned staying calm and seeking help during the incident, leading to a safe outcome. Maria praised his resourcefulness and discussed coping mechanisms like admiring sunsets. John updated Maria on his involvement in local politics and community engagement, with plans for future meetings. They appreciated each other's support and unity in working towards community development. They expressed readiness to make positive changes together, emphasizing the importance of collaboration. John thanked Maria for being a great friend and part of a strong team.", - "session_5_summary": "John and Maria discussed the sorry state of education and the need for more funding and resources for schools to support kids' success. They both agreed on the importance of advocating for better education. Maria shared her experience volunteering at a shelter event for kids and how it was inspired by her aunt. She mentioned a touching moment with a little girl that left a lasting impact. John also shared a childhood memory that reinforced the importance of looking out for others in times of need. Both emphasized the significance of kindness and support in making a difference for those feeling down, expressing their agreement on the topic.", - "session_6_summary": "At 2:33 pm on 5 February 2023, Maria informed John about her recent charity event and the impact it had on her, emphasizing the power of collective effort in helping others. John praised her efforts and expressed his interest in serving the community through politics. Maria shared a touching encounter with a man named David at the event, leading to her connecting him with support services. John discussed his involvement in a food drive for the unemployed and expressed gratitude for the community's response. Maria offered to help with future initiatives, and both acknowledged the importance of supporting each other and working together towards a common goal. Their conversation highlighted their commitment to helping those in need and their strong friendship.", - "session_7_summary": "Maria informed John about taking a creative writing class, while John revealed his excitement about running for office again. They discussed their reasons for volunteering and running for office, sharing inspiring stories about resilience and positivity. They emphasized the importance of kindness, optimism, and making a positive impact. John mentioned attending a yoga class soon, and they agreed to work together to bring positive change to their community.", - "session_8_summary": "At 6:03 pm on 6 March 2023, Maria shared with John about her grandmother's recent passing and the challenges she's facing. John expressed his condolences and updated Maria on his happy family life with wife and kids, particularly mentioning his one-year-old son, Kyle. They discussed parenting experiences and family activities, including a recent picnic that everyone enjoyed. John shared his interest in volunteering after a positive aptitude test experience, while Maria talked about her volunteer work at a homeless shelter. They agreed on the importance of building genuine connections to make a difference.", - "session_9_summary": "Maria and John reconnected at 9:36 am on 2 April, 2023. Maria shared about taking a poetry class to express her feelings while John mentioned finishing his degree, expressing interest in policymaking to make a positive impact. John's focus areas were education and infrastructure, influenced by his community involvement. Maria praised John's dedication and they both agreed on the importance of finding solutions together. They reminisced about past volunteer experiences and pledged support for each other's efforts to improve lives.", - "session_10_summary": "John and Maria had a conversation at 12:24 am on 7 April, 2023. John shared his excitement about starting a weekend yoga class with a colleague, focusing on stretching and breathing, which made him feel great mentally and physically. He praised the instructor for creating a welcoming environment, helping participants do poses correctly, and listen to their bodies. Maria expressed her support and interest, asking about the class and the impact on John. John mentioned feeling more relaxed and connected, integrating yoga into his daily routine. Maria shared her recent experience participating in a charity run for a homeless shelter, highlighting the energy and sense of unity. John then talked about volunteering at a career fair, helping kids with limited resources, which Maria found inspiring. They encouraged each other to continue making a difference and supporting those in need. Their conversation highlighted the power of community involvement and positive impact through simple acts of kindness and support.", - "session_11_summary": "At 6:13 pm on 10 April 2023, John and Maria caught up after a few days. John shared his car troubles causing financial strain but staying positive. Maria praised his resilience. John shared a picture from a past road trip to the Pacific Northwest, highlighting the beauty of nature. Maria and John discussed the importance of finding peace in nature during tough times. Maria mentioned giving talks at a homeless shelter and finding joy in helping others. John praised her impact and dedication to building relationships. They ended the conversation with mutual appreciation for each other's positivity and promising to talk soon.", - "session_12_summary": "John and Maria had a conversation at 7:34 pm on 18 April, 2023. John shared his passion for blogging about politics and government, focusing on education and infrastructure for positive change. Maria showed support for his dedication and discussed her volunteer work at a homeless shelter. They both acknowledged the impact of small acts of kindness in making a difference and inspiring others. John also mentioned his involvement in a tech convention with like-minded individuals. The conversation highlighted their commitment to creating a positive impact in their community through various means.", - "session_13_summary": "John and Maria caught up at 3:18 pm on May 4, 2023. John shared that he started going to boot camps with his family, leading to increased energy and fitness gains. They work out together three times a week and motivate each other. Maria praised their commitment and discussed the importance of having a supportive family. John mentioned how his family supported each other during workouts and emotionally. Maria shared about finding balance in life through self-care and spending time with loved ones. She also reflected on a transformative solo trip to Spain, highlighting the importance of inner strength and appreciating small moments. John admired her perspective and the picture from her trip, emphasizing the value of trying new things and pushing boundaries for personal growth. They both agreed on the importance of challenging themselves and maintaining a positive mindset for continuous learning and development.", - "session_14_summary": "John and Maria had a conversation at 5:04 pm on May 6, 2023. John shared his decision to run for office again, driven by his dream to make a difference in the community. Maria encouraged John to keep going and take risks, expressing her pride and support for him. John appreciated Maria's support and motivation, acknowledging that her belief in him keeps him going. Maria shared her involvement in charity work at a nearby church, finding it fulfilling. John discussed the need for infrastructure improvements in the community, particularly in roadways. Maria offered to help with community projects to address these issues. Both agreed to work together to make a real difference in their neighborhood by fighting for better living conditions and affordable housing. They concluded to keep working together to create a safe and thriving environment for their community.", - "session_15_summary": "John and Maria were discussing a project John is working on to support military veterans on 20th May 2023 at 7:38 pm. John shared his passion for veterans and his desire to show appreciation for their service. Maria praised John for his kind gesture and agreed to support the project. John showed a picture of veterans from a recent party he organized and mentioned the importance of creating connections and a sense of community. Maria expressed admiration for John's efforts and shared her own plan for a fundraiser. The conversation ended with both agreeing to continue making a positive impact in their community.", - "session_16_summary": "John and Maria had a conversation at 1:24 pm on 25 May, 2023. John updated Maria about his petition progress and shared a picture of his workmates on a hiking trip. Maria mentioned preparing for a fundraiser at a shelter and John offered to help. They discussed spreading the word about the fundraiser and finding volunteers. John shared a picture of his family at the beach. They both emphasized the importance of appreciating little moments and making a positive impact. Maria thanked John for his support, and they agreed to stay motivated towards their goals. The conversation ended with a mutual belief in making a difference.", - "session_17_summary": "John and Maria, having a conversation at 11:51 am on 3 June, 2023, discussed the loss of John's beloved pet Max, who was a significant part of their family for 10 years. They shared memories of Max, acknowledging the importance of love and loyalty that pets bring to our lives. John expressed his family's sadness but also their gratitude for the joy Max brought. Maria offered comfort and support, suggesting they honor Max's memory by considering adopting a rescue dog to teach their kids love and compassion. They ended the conversation by expressing their appreciation for each other's friendship and support.", - "session_18_summary": "Maria and John had a conversation at 2:47 pm on 12 June, 2023. Maria expressed her sympathy to John for losing his pet, and they both shared recent experiences of finding solace in nature. Maria discussed a camping trip that helped take her mind off things, while John went mountaineering to clear his head. They discussed their past adventures, with Maria mentioning a family trip to Oregon and John sharing a recent mountaineering experience. Maria described a breathtaking view from her trip, and John found solace in nature during his park visit with family, emphasizing the importance of cherishing those moments. They both appreciated nature's calming influence, with Maria highlighting aerial yoga as a way to find peace, and John expressing support for her hobbies. The conversation concluded with John encouraging Maria to keep smiling and shining.", - "session_19_summary": "Maria and John spoke at 7:20 pm on 16 June, 2023. Maria shared her positive experience at the gym, aiming to get stronger and improve her endurance with kundalini yoga, while John mentioned trying out rock climbing. John celebrated his recent promotion to assistant manager, reflecting on the challenges he overcame, including self-doubt. Maria praised John's perseverance and determination, emphasizing the importance of support and self-belief. Both agreed on the significance of having loved ones by their side. John shared about his work schedule and balancing priorities, highlighting the importance of making time for oneself and loved ones. The conversation turned to beach memories, with Maria and John exchanging stories and photos from their vacations, emphasizing the value of creating cherished memories with family. The conversation ended on a positive note, highlighting the joy in special family moments.\n", - "session_20_summary": "Maria and John reconnect at 12:21 am on 27 June, 2023. Maria shares her struggles but mentions focusing on the positive and getting support from family and friends. John acknowledges the importance of support and asks how her family has been helping. Maria expresses gratitude for her family's encouragement and support. John talks about a recent lively event with his family, emphasizing the importance of cherishing moments with loved ones. Maria mentions finding silver linings by volunteering at a shelter, sharing a heartwarming experience. John appreciates the impact of small gestures on others. The conversation ends with John encouraging Maria to continue spreading positivity and making a difference.", - "session_21_summary": "Maria and John caught up at 8:43 pm on 3 July, 2023. Maria shared about meeting kids at a shelter and an unfortunate car accident but mentioned helping her cousin find a new place. John offered his support and suggested finding resources for her cousin. Maria appreciated the kindness and mentioned the impact of small acts of kindness. John shared his participation in a veterans' rights event, emphasizing the importance of supporting causes. They encouraged each other to keep making a difference through activism and supporting important causes.", - "session_22_summary": "John and Maria discussed the importance of education and infrastructure in shaping communities at 6:59 pm on 5 July, 2023. Maria supported John's efforts to make a positive change, citing his passion and proactive approach as inspiring. John mentioned feeling uplifted by Maria's understanding and support, showing a picture of his family who motivate him. They shared sources of inspiration like family, nature walks, and music. John emphasized the significance of finding what makes one excited and alive. They both noted the importance of taking time for oneself, as seen in John's regular sunset walks for peace and detachment. The conversation ended with mutual well wishes and plans to chat soon.", - "session_23_summary": "John informed Maria about a recent flood in his area at 6:29 pm on 7 July, 2023, expressing the need to improve the community. Maria offered to help, leading to a discussion about working together to make a difference. They both acknowledged each other's determination and commitment to improving the community, planning to join forces for the cause. They expressed gratitude for each other's support and friendship, emphasizing the importance of small actions in creating a positive impact. They agreed to continue working together and spreading kindness to inspire hope in their community. John and Maria concluded their conversation, expressing excitement about making great things happen together.", - "session_24_summary": "John and Maria discussed their recent experiences at 3:34 pm on 17 July, 2023. John shared his visit to a veteran's hospital, which inspired him to give back and appreciate what he has. Maria expressed the importance of supporting military veterans. John learned inspiring stories from a veteran named Samuel, which reaffirmed his desire to join the military. Maria commended the resilience of the veterans. They discussed fun activities they had recently enjoyed, such as picnics and games like charades and scavenger hunts. John mentioned planning similar activities for his kids based on a community event he attended. They emphasized the significance of spreading positivity, making connections, and helping others through acts of kindness. Both agreed on the importance of enjoying life's moments and making a difference.", - "session_25_summary": "John and Maria had a conversation at 6:21 pm on 22 July, 2023. John talked about his new job and how his team is inspiring. Maria shared her hiking experience with church friends, mentioning the refreshing feeling of being surrounded by supportive people in nature. John and Maria discussed the benefits of connecting with good people and nature's beauty. Maria shared her upcoming plan to explore more and volunteer at shelters. John mentioned attending a chill yoga studio offering a variety of classes. They discussed trying new workout classes to mix things up. John encouraged Maria to try new classes and offered advice on starting. Maria ended the conversation, mentioning she was going to bake cakes.", - "session_26_summary": "At 1:59 pm on 31 July, 2023, Maria and John had a conversation. Maria shared that she had dropped off baked goods at a homeless shelter, feeling motivated to help more. John was inspired by Maria's efforts and mentioned joining a fire-fighting brigade to give back to the community, finding it rewarding. He described his first rescue mission with the team, saving a family from a burning building. Maria praised John's courage, highlighting the importance of supportive teamwork. John expressed gratitude for the bond with his team, comparing it to his time in the military. Maria shared a photo from her volunteering experience, emphasizing the impact of small acts of kindness. John encouraged her to continue her positive efforts, which Maria appreciated. They both pledged to keep making a difference and supporting each other, ending the conversation on a positive note.", - "session_27_summary": "John and Maria had a conversation at 6:20 pm on 3 August, 2023. John shared how he involved family and friends in supporting the military through a virtual group. Maria praised John's efforts, mentioning her own volunteer work at a homeless shelter. She started volunteering after seeing a struggling family and finding fulfillment in helping others. John admired Maria's impact and discussed a note from a shelter resident expressing gratitude. They shared experiences about military memorials and emphasized teaching respect for veterans to younger generations. Maria commended John's work and emphasized the power of teamwork in achieving great things.", - "session_28_summary": "\nAt 5:19 pm on 5 August, 2023, John informed Maria that he lost his job at a mechanical engineering company that tanked. He expressed gratitude for her concern and mentioned pursuing opportunities in the tech industry, feeling hopeful about a potential job in a tech company. Maria praised his adaptability and offered support. The conversation shifted to volunteer experiences, with Maria sharing her recent community work and John recalling renovating a community center. They exchanged encouraging words, with Maria commending John for making a real difference and John expressing appreciation for her support. John promised to reach out if needed.", - "session_29_summary": "Maria and John, at 8:06 pm on 9 August, 2023, caught up on their recent community involvement. Maria shared about volunteering at a homeless shelter, receiving a medal. John, who organized a charity run to help veterans, also raised funds for victims of domestic abuse with a local organization. They discussed challenges and the importance of supporting such causes. Both expressed admiration for each other's efforts and commitment to making a difference. They ended on a positive note, agreeing to continue spreading awareness and supporting important causes, reflecting on the impact of community involvement.", - "session_30_summary": "Maria and John caught up at 12:10 am on 11 August 2023. Maria shared that she got a puppy named Coco, and John showed her a photo of his late dog, Max. They discussed the joy of having pets and how nature can be a refreshing escape. John opened up about feeling stagnant and wanting to make a positive impact, considering joining local organizations. Maria offered support and encouraged him, ending the conversation on a positive note.", - "session_31_summary": "At 3:14 pm on August 13, 2023, John told Maria about volunteering as a mentor and how rewarding it was. Maria shared adopting a new pup named Shadow. They discussed the joy pets bring, and John expressed interest in getting a new pup. They praised the positive impact of animals. John mentioned improvements in the mentoring program, while Maria talked about Shadow's progress. They agreed on the comfort and happiness pets bring. John described family activities they enjoy, and they both emphasized the importance of family time. They shared goodbyes, wishing each other a great day.", - "session_32_summary": "John excitedly told Maria about joining the fire-fighting brigade at 11:08 am on 16 August, 2023. He was inspired by their dedication and teamwork. They worked hard and raised donations like canned food and clothes. Maria admired the community coming together in support. John felt a sense of purpose and passion in his new role. Maria shared her volunteer experience at a shelter, emphasizing the power of kindness. They both pledged to continue making a difference in their community and stay motivated together." - }, - "sample_id": "conv-41" - }, - { - "qa": [ - { - "question": "Is it likely that Nate has friends besides Joanna?", - "answer": "Yesteammates on hisvideo game team.", - "evidence": [ - "D1:7" - ], - "category": 3 - }, - { - "question": "What kind of interests do Joanna and Nate share?", - "answer": "Watching movies, making desserts", - "evidence": [ - "D1:10", - "D1:11", - "D1:12", - "D3:4", - "D4:9", - "D10:9", - "D20:2" - ], - "category": 1 - }, - { - "question": "When did Joanna first watch \"Eternal Sunshine of the Spotless Mind?", - "answer": "2019", - "evidence": [ - "D1:18" - ], - "category": 2 - }, - { - "question": "When did Nate win his first video game tournament?", - "answer": "the week before 21Janury, 2022", - "evidence": [ - "D1:3" - ], - "category": 2 - }, - { - "question": "What pets wouldn't cause any discomfort to Joanna?", - "answer": "Hairless cats or pigs,since they don't have fur, which is one of the main causes of Joanna's allergy.", - "evidence": [ - "D2:23" - ], - "category": 3 - }, - { - "question": "What are Joanna's hobbies?", - "answer": "Writing, watchingmovies, exploringnature, hanging withfriends.", - "evidence": [ - "D1:10", - "D2:25" - ], - "category": 1 - }, - { - "question": "How long has Nate had his first two turtles?", - "answer": "three years", - "evidence": [ - "D2:12" - ], - "category": 2 - }, - { - "question": "When did Joanna finish her first screenplay?", - "answer": "The Friday before 23January, 2022", - "evidence": [ - "D2:3" - ], - "category": 2 - }, - { - "question": "When did Nate get his first two turtles?", - "answer": "2019", - "evidence": [ - "D2:12" - ], - "category": 2 - }, - { - "question": "What major achievement did Joanna accomplish in January 2022?", - "answer": "finished her screenplay and printed it", - "evidence": [ - "D2:3" - ], - "category": 2 - }, - { - "question": "What emotions is Joanna feeling about the screenplay she submitted?", - "answer": "Relief, excitement,worry, hope,anxiety.", - "evidence": [ - "D2:7", - "D3:1" - ], - "category": 1 - }, - { - "question": "What is Joanna allergic to?", - "answer": "Most reptiles,animals with fur,cockroaches, dairy", - "evidence": [ - "D4:4", - "D5:11", - "D2:23" - ], - "category": 1 - }, - { - "question": "What underlying condition might Joanna have based on her allergies?", - "answer": "asthma", - "evidence": [ - "D5:11", - "D2:23" - ], - "category": 3 - }, - { - "question": "When did Joanna have an audition for a writing gig?", - "answer": "23 March, 2022.", - "evidence": [ - "D6:2" - ], - "category": 2 - }, - { - "question": "What nickname does Nate use for Joanna?", - "answer": "Jo", - "evidence": [ - "D7:1" - ], - "category": 3 - }, - { - "question": "When did Nate get purple hair?", - "answer": "The week before 15April, 2022.", - "evidence": [ - "D7:1" - ], - "category": 2 - }, - { - "question": "What physical transformation did Nate undergo in April 2022?", - "answer": "dyed his hair purple", - "evidence": [ - "D7:1", - "D7:3" - ], - "category": 2 - }, - { - "question": "What movie did Joanna watch on 1 May, 2022?", - "answer": "Lord of the Rings", - "evidence": [ - "D10:1" - ], - "category": 2 - }, - { - "question": "Which outdoor spot did Joanna visit in May?", - "answer": "Whispering Falls waterfall", - "evidence": [ - "D11:7" - ], - "category": 2 - }, - { - "question": "How many times has Joanna found new hiking trails?", - "answer": "twice", - "evidence": [ - "D8:4", - "D11:3" - ], - "category": 1 - }, - { - "question": "When did Nate adopt Max?", - "answer": "May 2022", - "evidence": [ - "D12:3" - ], - "category": 2 - }, - { - "question": "Who was the new addition to Nate's family in May 2022?", - "answer": "Max", - "evidence": [ - "D12:3" - ], - "category": 2 - }, - { - "question": "When did Joanna start writing her third screenplay?", - "answer": "May 2022", - "evidence": [ - "D12:13", - "D12:14" - ], - "category": 2 - }, - { - "question": "Which of Joanna's screenplay were rejected from production companies?", - "answer": "first screenplay on drama and romance, third screenplay on loss identity and connection", - "evidence": [ - "D14:1", - "D3:1", - "D2:7", - "D24:12", - "D24:13" - ], - "category": 1 - }, - { - "question": "When is Nate hosting a gaming party?", - "answer": "The weekend after 3June, 2022.", - "evidence": [ - "D14:20" - ], - "category": 2 - }, - { - "question": "When did Joanna hike with her buddies?", - "answer": "The weekend after 3June, 2022.", - "evidence": [ - "D14:19" - ], - "category": 2 - }, - { - "question": "When did Nate win his third tourney?", - "answer": "The week before 3June, 2022", - "evidence": [ - "D14:8" - ], - "category": 2 - }, - { - "question": "What places has Joanna submitted her work to?", - "answer": "film contest, film festival.", - "evidence": [ - "D2:7", - "D16:1" - ], - "category": 1 - }, - { - "question": "When did Nate make vegan icecream and share it with a vegan diet group?", - "answer": "The Friday before 24June, 2022.", - "evidence": [ - "D16:8" - ], - "category": 2 - }, - { - "question": "When is Joanna going to make Nate's ice cream for her family?", - "answer": "The weekend of 24June, 2022.", - "evidence": [ - "D16:11" - ], - "category": 2 - }, - { - "question": "What kind of writings does Joanna do?", - "answer": "Screenplays,books, online blog posts, journal", - "evidence": [ - "D2:3", - "D17:14", - "D18:1", - "D18:5" - ], - "category": 1 - }, - { - "question": "When did Nate win his fourth video game tournament?", - "answer": "The Friday before 10July, 2022.", - "evidence": [ - "D17:1" - ], - "category": 2 - }, - { - "question": "Where did Joanna travel to in July 2022?", - "answer": "Woodhaven", - "evidence": [ - "D17:4" - ], - "category": 2 - }, - { - "question": "When did someone write Joanna a touching letter?", - "answer": "The week before 14August, 2022.", - "evidence": [ - "D18:5" - ], - "category": 2 - }, - { - "question": "What book recommendations has Joanna given to Nate?", - "answer": "\"Little Women\",'A Court of Thorns andRoses'.", - "evidence": [ - "D3:17", - "D19:14", - "D19:16" - ], - "category": 1 - }, - { - "question": "When did Nate take time off to chill with his pets?", - "answer": "The weekend of 22August, 2022.", - "evidence": [ - "D19:9" - ], - "category": 2 - }, - { - "question": "When did Joanna share her book with her writers group?", - "answer": "The week before 22August, 2022.", - "evidence": [ - "D19:6" - ], - "category": 2 - }, - { - "question": "When did Nate win an international tournament?", - "answer": "21 August, 2022", - "evidence": [ - "D19:1" - ], - "category": 2 - }, - { - "question": "When did Joanna make a desert with almond milk?", - "answer": "The Friday before 14September, 2022", - "evidence": [ - "D21:9" - ], - "category": 2 - }, - { - "question": "When did Nate attend a cooking show?", - "answer": "The Monday before 14September, 2022", - "evidence": [ - "D21:4" - ], - "category": 2 - }, - { - "question": "When did Joanna's laptop crash?", - "answer": "The week before 14September, 2022", - "evidence": [ - "D21:1" - ], - "category": 2 - }, - { - "question": "When did Joanna make a chocolate tart with raspberries?", - "answer": "5 October, 2022", - "evidence": [ - "D22:1" - ], - "category": 2 - }, - { - "question": "What movies have both Joanna and Nate seen?", - "answer": "\"Little Women\", \"Lord of the Rings\"", - "evidence": [ - "D3:17", - "D10:1", - "D22:8" - ], - "category": 1 - }, - { - "question": "How long did it take for Joanna to finish writing her book?", - "answer": "four months", - "evidence": [ - "D17:14", - "D22:9" - ], - "category": 2 - }, - { - "question": "When did Nate win a lot of money in a video game tournament?", - "answer": "September 2022", - "evidence": [ - "D22:2" - ], - "category": 2 - }, - { - "question": "When did Joanna finish up the writing for her book?", - "answer": "The week before 6October, 2022", - "evidence": [ - "D22:9" - ], - "category": 2 - }, - { - "question": "What board games has Nate played?", - "answer": "Chess, Catan.", - "evidence": [ - "D16:2", - "D23:7" - ], - "category": 1 - }, - { - "question": "What places has Nate met new people?", - "answer": "A tournament and agaming convention.", - "evidence": [ - "D14:8", - "D23:1" - ], - "category": 1 - }, - { - "question": "When did Nate go to a convention and meet new people?", - "answer": "The Friday before 9October, 2022.", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "How many times has Joanna's scripts been rejected?", - "answer": "Twice", - "evidence": [ - "D14:1", - "D24:12" - ], - "category": 1 - }, - { - "question": "What is something Nate gave to Joanna that brings her a lot of joy?", - "answer": "stuffed toy pup", - "evidence": [ - "D13:9", - "D24:2" - ], - "category": 1 - }, - { - "question": "When did Nate get Tilly for Joanna?", - "answer": "25 May, 2022", - "evidence": [ - "D13:9", - "D24:2" - ], - "category": 1 - }, - { - "question": "How many of Joanna's writing have made it to the big screen?", - "answer": "two", - "evidence": [ - "D15:1", - "D25:2" - ], - "category": 1 - }, - { - "question": "How many times has Nate taken his turtles on a walk?", - "answer": "Twice.", - "evidence": [ - "D5:4", - "D25:15" - ], - "category": 1 - }, - { - "question": "When was Joanna's second movie script shown on the big screens?", - "answer": "The Sunday before 25October, 2022.", - "evidence": [ - "D25:1" - ], - "category": 2 - }, - { - "question": "What is Joanna inspired by?", - "answer": "Personal experiences,her own journey ofself discovery, Nate,nature, validation,stories about findingcourage and takingrisks, people she knows, stuff she sees, imagination", - "evidence": [ - "D4:6", - "D7:6", - "D11:11", - "D26:3", - "D26:7", - "D25:10" - ], - "category": 1 - }, - { - "question": "What animal do both Nate and Joanna like?", - "answer": "Turtles.", - "evidence": [ - "D5:6", - "D26:9" - ], - "category": 1 - }, - { - "question": "When did Joanna plan to go over to Nate's and share recipes?", - "answer": "5 November, 2022.", - "evidence": [ - "D26:19" - ], - "category": 2 - }, - { - "question": "What things has Nate reccomended to Joanna?", - "answer": "A pet,\"The Lord of the Rings\" movies,a dragon book series,coconut flavoring,\"Project Hail Mary\" book,Xenoblade Chronicles, dairy-free margarine, coconut oil", - "evidence": [ - "D2:14", - "D9:12", - "D9:14", - "D10:11", - "D19:17", - "D27:23", - "D10:19" - ], - "category": 1 - }, - { - "question": "What does Joanna do to remember happy memories?", - "answer": "Hangs them on a corkboard, writes themin a notebook.", - "evidence": [ - "D15:9", - "D27:34" - ], - "category": 1 - }, - { - "question": "What Console does Nate own?", - "answer": "A Nintendo Switch; since the game \"Xenoblade 2\" is made for this console.", - "evidence": [ - "D27:23" - ], - "category": 3 - }, - { - "question": "What mediums does Nate use to play games?", - "answer": "Gamecube, PC,Playstation.", - "evidence": [ - "D22:2", - "D27:21", - "D27:15" - ], - "category": 1 - }, - { - "question": "How many letters has Joanna recieved?", - "answer": "Two", - "evidence": [ - "D14:1", - "D18:5" - ], - "category": 1 - }, - { - "question": "What video games does Nate play?", - "answer": "Valorant, Counter Strike:Global Offensive,Xenoblade Chronicles, StreetFighter, Cyberpunk 2077", - "evidence": [ - "D10:6", - "D27:1", - "D27:23", - "D1:7", - "D23:17" - ], - "category": 1 - }, - { - "question": "When did Nate win a big Valorant tourney?", - "answer": "The Saturday before 7November, 2022", - "evidence": [ - "D27:1" - ], - "category": 2 - }, - { - "question": "Which torunament did Nate win in the beginning of November 2022?", - "answer": "Valorant", - "evidence": [ - "D27:1" - ], - "category": 2 - }, - { - "question": "What alternative career might Nate consider after gaming?", - "answer": "an animalkeeper at a localzoo and workingwith turtles; as heknows a great dealabout turtles andhow to care for them,and he enjoys it.", - "evidence": [ - "D5:8", - "D19:3", - "D25:19", - "D28:25" - ], - "category": 3 - }, - { - "question": "What pets does Nate have?", - "answer": "A dog and threeturtles.", - "evidence": [ - "D8:3", - "D12:3", - "D28:23" - ], - "category": 1 - }, - { - "question": "How many hikes has Joanna been on?", - "answer": "Four", - "evidence": [ - "D7:6", - "D11:5", - "D14:21", - "D28:22" - ], - "category": 3 - }, - { - "question": "How many turtles does Nate have?", - "answer": "Three", - "evidence": [ - "D8:3", - "D28:23" - ], - "category": 1 - }, - { - "question": "What activities does Nate do with his turtles?", - "answer": "takes them onwalks, holds them,feeds themstrawberries, givesthem baths.", - "evidence": [ - "D25:21", - "D25:23", - "D28:31" - ], - "category": 1 - }, - { - "question": "What do both Joanna and Nate appreciate the beauty of?", - "answer": "Nature", - "evidence": [ - "D11:9", - "D28:23" - ], - "category": 1 - }, - { - "question": "When did Joanna plan on going to Nate's to watch him play with his turtles?", - "answer": "10 November, 2022", - "evidence": [ - "D28:32" - ], - "category": 2 - }, - { - "question": "What state did Joanna visit in summer 2021?", - "answer": "Indiana", - "evidence": [ - "D28:22" - ], - "category": 3 - }, - { - "question": "What recommendations has Nate received from Joanna?", - "answer": "\"Eternal Sunshine of the Spotless Mind\" movie, \"A Court of Thorns and Roses\" book, pointers for making living room comfy, starting a cork board for memories, \"Little Women\" movie", - "evidence": [ - "D1:16", - "D3:17", - "D15:14", - "D15:15", - "D19:15", - "D19:16", - "D23:26" - ], - "category": 1 - }, - { - "question": "What are Nate's favorite desserts?", - "answer": "coconut milk icecream, dairy-free chocolate cake with berries, chocolate and mixed-berry icecream, dairy-free chocolate mousse", - "evidence": [ - "D3:4", - "D3:10", - "D21:10", - "D3:12" - ], - "category": 1 - }, - { - "question": "How has Nate tried to disburse his vegan ice-cream recipes?", - "answer": "teaching others, cooking show", - "evidence": [ - "D18:8", - "D21:4" - ], - "category": 1 - }, - { - "question": "When did Nate win his second tournament?", - "answer": "The week before 2 May, 2022.", - "evidence": [ - "D10:4" - ], - "category": 2 - }, - { - "question": "How many video game tournaments has Nate participated in?", - "answer": "nine", - "evidence": [ - "D1:3", - "D6:7", - "D10:4", - "D14:8", - "D17:1", - "D19:1", - "D20:1", - "D22:2", - "D27:1" - ], - "category": 1 - }, - { - "question": "How many screenplays has Joanna written?", - "answer": "three", - "evidence": [ - "D2:3", - "D4:10", - "D5:1", - "D12:13", - "D12:14" - ], - "category": 1 - }, - { - "question": "How many tournaments has Nate won?", - "answer": "seven", - "evidence": [ - "D1:3", - "D10:4", - "D14:8", - "D17:1", - "D19:1", - "D22:2", - "D27:1" - ], - "category": 1 - }, - { - "question": "What recipes has Joanna made?", - "answer": "dairy free vanilla cake with strawberry filling and coconut cream frosting, parfait, strawberry chocolate cake, chocolate coconut cupcakes, chocolate raspberry tart, chocolate cake with raspberries, blueberry cheesecake bars", - "evidence": [ - "D10:9", - "D10:11", - "D19:8", - "D20:2", - "D20:10", - "D21:11", - "D22:1", - "D21:3", - "D21:17" - ], - "category": 1 - }, - { - "question": "What recipes has Nate made?", - "answer": "coconut milk icecream, chocolate and vanilla swirl", - "evidence": [ - "D3:4", - "D4:3" - ], - "category": 1 - }, - { - "question": "What are the skills that Nate has helped others learn?", - "answer": "coconut milk ice cream recipe, reset high scores, tips to improve gaming skills", - "evidence": [ - "D18:8", - "D26:12", - "D14:16" - ], - "category": 1 - }, - { - "question": "Was the first half of September 2022 a good month career-wise for Nate and Joanna? Answer yes or no.", - "answer": "No; because both of them faced setbacks in their career", - "evidence": [ - "D20:1", - "D21:1" - ], - "category": 3 - }, - { - "question": "What kind of job is Joanna beginning to preform the duties of because of her movie scripts?", - "answer": "filmmaker.", - "evidence": [ - "D29:1" - ], - "category": 3 - }, - { - "question": "When did Nate take his turtles to the beach?", - "answer": "10 November, 2022", - "evidence": [ - "D29:6" - ], - "category": 2 - }, - { - "question": "What state did Nate visit?", - "answer": "Florida", - "evidence": [ - "D29:6" - ], - "category": 3 - }, - { - "question": "What is one of Joanna's favorite movies?", - "answer": "\"Eternal Sunshineof the Spotless Mind\"", - "evidence": [ - "D1:18", - "D", - "D1:20" - ], - "category": 4 - }, - { - "question": "What color did Nate choose for his hair?", - "answer": "purple", - "evidence": [ - "D7:1", - "D7:3" - ], - "category": 4 - }, - { - "question": "What is Nate's favorite movie trilogy?", - "answer": "Lord of the Rings", - "evidence": [ - "D9:12" - ], - "category": 4 - }, - { - "question": "What is Nate's favorite book series about?", - "answer": "dragons", - "evidence": [ - "D9:14" - ], - "category": 4 - }, - { - "question": "What kind of lighting does Nate's gaming room have?", - "answer": "red and purple lighting", - "evidence": [ - "D10:2" - ], - "category": 4 - }, - { - "question": "What game was the second tournament that Nate won based on?", - "answer": "Street Fighter", - "evidence": [ - "D10:4", - "D10:6" - ], - "category": 4 - }, - { - "question": "What is Joanna's third screenplay about?", - "answer": "loss, identity, and connection", - "evidence": [ - "D12:13", - "D12:14" - ], - "category": 4 - }, - { - "question": "What is Nate's favorite video game?", - "answer": "Xenoblade Chronicles", - "evidence": [ - "D27:22", - "D27:23" - ], - "category": 4 - }, - { - "question": "What type of movies does Nate enjoy watching the most?", - "answer": "action and sci-fi", - "evidence": [ - "D1:13" - ], - "category": 4 - }, - { - "question": "What did Joanna just finish last Friday on 23 January, 2022?", - "answer": "screenplay", - "evidence": [ - "D2:3" - ], - "category": 4 - }, - { - "question": "What genre is Joanna's first screenplay?", - "answer": "drama and romance", - "evidence": [ - "D2:5" - ], - "category": 4 - }, - { - "question": "What are Joanna's plans for her finished screenplay in January 2022?", - "answer": "submit it to film festivals and get producers and directors to check it out", - "evidence": [ - "D2:7" - ], - "category": 4 - }, - { - "question": "For how long has Nate had his turtles?", - "answer": "3 years", - "evidence": [ - "D2:12" - ], - "category": 4 - }, - { - "question": "What did Nate think of the coconut milk ice cream he made?", - "answer": "Super good, rich and creamy", - "evidence": [ - "D3:6" - ], - "category": 4 - }, - { - "question": "Which dairy-free dessert flavors does Nate enjoy?", - "answer": "chocolate and mixed berry", - "evidence": [ - "D3:10" - ], - "category": 4 - }, - { - "question": "What did Joanna recently watch and recommend to Nate on February 7, 2022?", - "answer": "\"Little Women\"", - "evidence": [ - "D3:17" - ], - "category": 4 - }, - { - "question": "What is \"Little Women\" about according to Joanna?", - "answer": "Sisterhood, love, and reaching for your dreams", - "evidence": [ - "D3:17" - ], - "category": 4 - }, - { - "question": "What flavor of ice cream did Nate make for his friend on 25 February, 2022?", - "answer": "chocolate and vanilla swirl", - "evidence": [ - "D4:3" - ], - "category": 4 - }, - { - "question": "What inspired Joanna's new screenplay on 25 February, 2022?", - "answer": "personal experiences and her own journey of self-discovery", - "evidence": [ - "D4:16" - ], - "category": 4 - }, - { - "question": "Why does Nate like turtles as pets?", - "answer": "Their slow pace and calming nature", - "evidence": [ - "D5:6" - ], - "category": 4 - }, - { - "question": "How does Nate describe the process of taking care of turtles?", - "answer": "Not tough; keep their area clean, feed them properly, give them enough light.", - "evidence": [ - "D5:8" - ], - "category": 4 - }, - { - "question": "What was Joanna's audition for?", - "answer": "writing gig", - "evidence": [ - "D6:2" - ], - "category": 4 - }, - { - "question": "Why did Nate choose the hair color he did?", - "answer": "Bright and bold - like him", - "evidence": [ - "D7:5" - ], - "category": 4 - }, - { - "question": "What are the main ingredients of the ice cream recipe shared by Nate?", - "answer": "Coconut milk, vanilla extract, sugar, salt", - "evidence": [ - "D8:19" - ], - "category": 4 - }, - { - "question": "What is Joanna's project called in the writers group?", - "answer": "\"Finding Home\"", - "evidence": [ - "D9:3" - ], - "category": 4 - }, - { - "question": "What is Nate's favorite genre of movies?", - "answer": "Fantasy and sci-fi", - "evidence": [ - "D9:10" - ], - "category": 4 - }, - { - "question": "What kind of books does Nate enjoy?", - "answer": "Adventures and magic", - "evidence": [ - "D9:14" - ], - "category": 4 - }, - { - "question": "What kind of films does Joanna enjoy?", - "answer": "Dramas and emotionally-driven films", - "evidence": [ - "D9:9" - ], - "category": 4 - }, - { - "question": "Which activity helps Nate escape and stimulates his imagination?", - "answer": "watching fantasy and sci-fi movies", - "evidence": [ - "D9:10" - ], - "category": 4 - }, - { - "question": "What filling did Joanna use in the cake she made recently in May 2022?", - "answer": "strawberry", - "evidence": [ - "D10:11" - ], - "category": 4 - }, - { - "question": "What kind of frosting did Joanna use on the cake she made recently in May 2022?", - "answer": "coconut cream", - "evidence": [ - "D10:11" - ], - "category": 4 - }, - { - "question": "What does Nate feel he could do when out in cool places like Whispering Falls?", - "answer": "write a whole movie", - "evidence": [ - "D11:13" - ], - "category": 4 - }, - { - "question": "What creative activity does Nate joke about pursuing after being inspired by their hikes with Jo?", - "answer": "Start thinking about a drama and publish a screenplay", - "evidence": [ - "D11:16" - ], - "category": 4 - }, - { - "question": "Who invited Nate to join her on the trails sometime?", - "answer": "Joanna", - "evidence": [ - "D11:17" - ], - "category": 4 - }, - { - "question": "What did Nate do for Joanna on 25 May, 2022?", - "answer": "get her a stuffed animal", - "evidence": [ - "D13:9" - ], - "category": 4 - }, - { - "question": "How does Nate describe the stuffed animal he got for Joanna?", - "answer": "A stuffed animal to remind you of the good vibes", - "evidence": [ - "D13:11" - ], - "category": 4 - }, - { - "question": "What event is Nate organizing in June 2022?", - "answer": "A gaming party", - "evidence": [ - "D14:20" - ], - "category": 4 - }, - { - "question": "Who did Nate plan to invite to his gaming party in June 2022?", - "answer": "Tournament friends, old friends, teammates", - "evidence": [ - "D14:22" - ], - "category": 4 - }, - { - "question": "What special items did Nate get for everyone at his gaming party?", - "answer": "Custom controller decorations", - "evidence": [ - "D14:24" - ], - "category": 4 - }, - { - "question": "What did Joanna write yesterday that appeared on the big screen?", - "answer": "screenplay bits", - "evidence": [ - "D15:1" - ], - "category": 4 - }, - { - "question": "What superhero is Joanna a fan of?", - "answer": "Spider-Man", - "evidence": [ - "D15:3" - ], - "category": 4 - }, - { - "question": "Which superhero toy figure does Nate share a photo of?", - "answer": "Iron Man", - "evidence": [ - "D15:4" - ], - "category": 4 - }, - { - "question": "What is displayed on Joanna's cork board for motivation and creativity?", - "answer": "inspiring quotes, photos, and little keepsakes", - "evidence": [ - "D15:7" - ], - "category": 4 - }, - { - "question": "What does the photo on Joanna's cork board remind her of?", - "answer": "love and encouragement from her family", - "evidence": [ - "D15:11" - ], - "category": 4 - }, - { - "question": "What did Nate make and share with his vegan diet group?", - "answer": "vegan ice cream", - "evidence": [ - "D16:8" - ], - "category": 4 - }, - { - "question": "How many people attended the gaming party hosted by Nate in June 2022?", - "answer": "7", - "evidence": [ - "D16:6" - ], - "category": 4 - }, - { - "question": "What recipe Nate offer to share with Joanna?", - "answer": "vegan ice cream recipe", - "evidence": [ - "D16:10" - ], - "category": 4 - }, - { - "question": "What did Joanna plan to do with the recipe Nate promised to share?", - "answer": "make it for her family", - "evidence": [ - "D16:11" - ], - "category": 4 - }, - { - "question": "How many video game tournaments has Nate won by July 10, 2022?", - "answer": "Four", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "Where did Joanna go for a road trip for research?", - "answer": "Woodhaven", - "evidence": [ - "D17:4" - ], - "category": 4 - }, - { - "question": "What did Joanna discover at the library in Woodhaven?", - "answer": "cool old book collection", - "evidence": [ - "D17:4" - ], - "category": 4 - }, - { - "question": "What specific themes are explored in Joanna's new book?", - "answer": "loss, redemption, and forgiveness", - "evidence": [ - "D17:16" - ], - "category": 4 - }, - { - "question": "What inspired Joanna's new script in July 2022?", - "answer": "Woodhaven's interesting past and people", - "evidence": [ - "D17:8" - ], - "category": 4 - }, - { - "question": "What did Nate do while Joanna was on her road trip?", - "answer": "Won a video game tournament", - "evidence": [ - "D17:2" - ], - "category": 4 - }, - { - "question": "What does Nate do that he loves and can make money from?", - "answer": "Competing in video game tournaments", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "How did Joanna feel when someone wrote her a letter after reading her blog post?", - "answer": "Touched", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "What kind of impact does Joanna hope to have with her writing?", - "answer": "share her stories and hopefully have an impact", - "evidence": [ - "D18:7" - ], - "category": 4 - }, - { - "question": "What kind of content did Joanna share that someone wrote her a letter about?", - "answer": "A blog post about a hard moment in her life", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "What motivates Joanna to keep writing even on tough days?", - "answer": "Knowing that her writing can make a difference", - "evidence": [ - "D18:7" - ], - "category": 4 - }, - { - "question": "What type of ice cream does Joanna mention that Nate makes and is delicious?", - "answer": "Coconut milk ice cream", - "evidence": [ - "D18:9" - ], - "category": 4 - }, - { - "question": "How did Nate feel about sharing his love for dairy-free desserts with Joanna?", - "answer": "Happy to share", - "evidence": [ - "D18:12" - ], - "category": 4 - }, - { - "question": "What did Joanna share with her writers group in August 2022?", - "answer": "her book", - "evidence": [ - "D19:6" - ], - "category": 4 - }, - { - "question": "How did Joanna celebrate after sharing her book with her writers group?", - "answer": "making a delicious treat", - "evidence": [ - "D19:8" - ], - "category": 4 - }, - { - "question": "How did Nate celebrate winning the international tournament?", - "answer": "Taking time off to chill with pets", - "evidence": [ - "D19:9" - ], - "category": 4 - }, - { - "question": "Why is Joanna experimenting with dairy-free options in her dessert recipes?", - "answer": "lactose intolerance", - "evidence": [ - "D20:10" - ], - "category": 4 - }, - { - "question": "What substitution does Nate suggest for butter in dairy-free baking?", - "answer": "dairy-free margarine or coconut oil", - "evidence": [ - "D20:15" - ], - "category": 4 - }, - { - "question": "What type of show did Nate host where he taught vegan ice cream recipes?", - "answer": "a cooking show", - "evidence": [ - "D21:4" - ], - "category": 4 - }, - { - "question": "What is Nate's favorite dish from the cooking show he hosted?", - "answer": "Coconut milk ice cream", - "evidence": [ - "D21:6" - ], - "category": 4 - }, - { - "question": "What is one of Nate's favorite dairy-free treats besides coconut milk ice cream?", - "answer": "dairy-free chocolate mousse", - "evidence": [ - "D21:10" - ], - "category": 4 - }, - { - "question": "What dessert did Joanna share a photo of that has an almond flour crust, chocolate ganache, and fresh raspberries?", - "answer": "chocolate raspberry tart", - "evidence": [ - "D21:11" - ], - "category": 4 - }, - { - "question": "What kind of cake did Joanna share a photo of that she likes making for birthdays and special days?", - "answer": "chocolate cake with raspberries", - "evidence": [ - "D21:13" - ], - "category": 4 - }, - { - "question": "What two main ingredients are part of the dessert Joanna shared a photo of with blueberries, coconut milk, and a gluten-free crust?", - "answer": "blueberries and coconut milk", - "evidence": [ - "D21:17" - ], - "category": 4 - }, - { - "question": "What movie did Nate recently watch and enjoy on October 6, 2022?", - "answer": "Little Women", - "evidence": [ - "D22:8" - ], - "category": 4 - }, - { - "question": "What did Joanna make for one of the ladies at her writing club?", - "answer": "a bookmark", - "evidence": [ - "D22:19" - ], - "category": 4 - }, - { - "question": "What game did Nate play at the game convention he attended on 9 October, 2022?", - "answer": "Catan", - "evidence": [ - "D23:7" - ], - "category": 4 - }, - { - "question": "What movie has Nate recently seen that blew his mind?", - "answer": "\"Inception\"", - "evidence": [ - "D23:17" - ], - "category": 4 - }, - { - "question": "What game has Nate been playing nonstop with a futuristic setting and gameplay on October 9, 2022?", - "answer": "Cyberpunk 2077", - "evidence": [ - "D23:17" - ], - "category": 4 - }, - { - "question": "What did Nate share a photo of when mentioning unwinding at home?", - "answer": "a bookcase filled with dvds and movies", - "evidence": [ - "D23:15" - ], - "category": 4 - }, - { - "question": "How did Joanna describe the classic movie he watched?", - "answer": "gripping with great actors", - "evidence": [ - "D23:18" - ], - "category": 4 - }, - { - "question": "What does Joanna recommend to make a living room comfy like hers?", - "answer": "couch for multiple people, fluffy blanket, lights that can be dimmed", - "evidence": [ - "D23:26" - ], - "category": 4 - }, - { - "question": "What helps Joanna stay focused and brings her joy?", - "answer": "stuffed animal dog named Tilly", - "evidence": [ - "D24:8" - ], - "category": 4 - }, - { - "question": "What does Joanna do while she writes?", - "answer": "have a stuffed animal dog named Tilly with her", - "evidence": [ - "D24:4" - ], - "category": 4 - }, - { - "question": "Why did Joanna name the stuffed animal dog Tilly?", - "answer": "after a dog she had in Michigan", - "evidence": [ - "D24:6" - ], - "category": 4 - }, - { - "question": "What does Joanna do after receiving a rejection from a production company?", - "answer": "keep grinding and moving ahead", - "evidence": [ - "D24:14" - ], - "category": 4 - }, - { - "question": "How does Nate feel about Joanna's ability to bounce back from setbacks?", - "answer": "respect Joanna for being able to bounce back", - "evidence": [ - "D24:15" - ], - "category": 4 - }, - { - "question": "What encouragement does Nate give to Joanna after her setback?", - "answer": "rejections don't define her, keep grinding and she'll find the perfect opportunity", - "evidence": [ - "D24:13" - ], - "category": 4 - }, - { - "question": "What does Nate rely on for cheer and joy?", - "answer": "his turtles", - "evidence": [ - "D24:3" - ], - "category": 4 - }, - { - "question": "What does Joanna use to remember her dog from Michigan?", - "answer": "naming a stuffed animal dog Tilly", - "evidence": [ - "D24:6" - ], - "category": 4 - }, - { - "question": "What did Joanna contribute to that was shown on the big screen on the Sunday before October 25, 2022?", - "answer": "movie script", - "evidence": [ - "D25:2" - ], - "category": 4 - }, - { - "question": "What inspires Joanna to create drawings of her characters?", - "answer": "visuals to help bring the characters alive in her head so she can write better", - "evidence": [ - "D25:8" - ], - "category": 4 - }, - { - "question": "Where does Joanna get her ideas for the characters from?", - "answer": "people she knows, things she saw, her imagination", - "evidence": [ - "D25:10" - ], - "category": 4 - }, - { - "question": "How did Joanna feel on October 25, 2022 about seeing her characters come alive on the big screen?", - "answer": "surreal and cool", - "evidence": [ - "D25:6" - ], - "category": 4 - }, - { - "question": "What type of diet do Nate's turtles have?", - "answer": "combination of vegetables, fruits, and insects", - "evidence": [ - "D25:19" - ], - "category": 4 - }, - { - "question": "What ingredient did Nate use to make the ice cream lactose-free?", - "answer": "coconut milk", - "evidence": [ - "D26:18" - ], - "category": 4 - }, - { - "question": "What did Joanna find in old notebooks last week that prompted her to reflect on her progress as a writer?", - "answer": "early writings", - "evidence": [ - "D26:5" - ], - "category": 4 - }, - { - "question": "What game is Nate currently playing and recommends to others on November 7, 2022?", - "answer": "\"Xenoblade Chronicles\"", - "evidence": [ - "D27:23" - ], - "category": 4 - }, - { - "question": "What did Joanna receive from her brother that brought back childhood memories?", - "answer": "a handwritten letter", - "evidence": [ - "D27:29" - ], - "category": 4 - }, - { - "question": "What is the type of game \"Xenoblade Chronicles\" that Nate is playing?", - "answer": "fantasy RPG", - "evidence": [ - "D27:23" - ], - "category": 4 - }, - { - "question": "What dish did Nate make on 9 November, 2022?", - "answer": "Homemade coconut ice cream", - "evidence": [ - "D28:1" - ], - "category": 4 - }, - { - "question": "What project is Joanna working on in her notebook on November 9, 2022?", - "answer": "A suspenseful thriller set in a small Midwestern town", - "evidence": [ - "D28:12" - ], - "category": 4 - }, - { - "question": "What is Nate creating for YouTube on 9 November, 2022?", - "answer": "gaming content", - "evidence": [ - "D28:13" - ], - "category": 4 - }, - { - "question": "What inspired Nate to start making gaming videos?", - "answer": "Love of gaming and connecting with others who enjoy it too", - "evidence": [ - "D28:15" - ], - "category": 4 - }, - { - "question": "What new content is Nate creating for YouTube?", - "answer": "Gaming videos", - "evidence": [ - "D28:13" - ], - "category": 4 - }, - { - "question": "What advice does Joanna give to Nate about making YouTube videos?", - "answer": "Watch other people's videos to understand what the audience likes", - "evidence": [ - "D28:18" - ], - "category": 4 - }, - { - "question": "What did Joanna take a picture of near Fort Wayne last summer?", - "answer": "Sunset", - "evidence": [ - "D28:22" - ], - "category": 4 - }, - { - "question": "What inspired Joanna to take a picture of the sunset in the field near Fort Wayne?", - "answer": "The incredible sunset and surrounding beauty", - "evidence": [ - "D28:22" - ], - "category": 4 - }, - { - "question": "Why did Nate get a third turtle?", - "answer": "He saw another one at a pet store and wanted to get it", - "evidence": [ - "D28:25" - ], - "category": 4 - }, - { - "question": "What does Nate want to do when he goes over to Joanna's place?", - "answer": "Watch one of Joanna's movies together or go to the park", - "evidence": [ - "D28:29" - ], - "category": 4 - }, - { - "question": "What did Nate take to the beach in Tampa?", - "answer": "turtles", - "evidence": [ - "D29:6" - ], - "category": 4 - }, - { - "question": "What does Nate love most about having turtles?", - "answer": "They make him feel calm and don't require much looking after", - "evidence": [ - "D29:8" - ], - "category": 4 - }, - { - "question": "What did Nate share a photo of as a part of his experimentation in November 2022?", - "answer": "colorful bowls of coconut milk ice cream", - "evidence": [ - "D29:10" - ], - "category": 4 - }, - { - "question": "What color did Joanna choose for her hair?", - "evidence": [ - "D7:1", - "D7:3" - ], - "category": 5, - "adversarial_answer": "purple" - }, - { - "question": "What is Joanna's favorite movie trilogy?", - "evidence": [ - "D9:12" - ], - "category": 5, - "adversarial_answer": "Lord of the Rings" - }, - { - "question": "What is Joanna's favorite book series about?", - "evidence": [ - "D9:14" - ], - "category": 5, - "adversarial_answer": "dragons" - }, - { - "question": "What kind of lighting does Joanna's gaming room have?", - "evidence": [ - "D10:2" - ], - "category": 5, - "adversarial_answer": "red and purple lighting" - }, - { - "question": "What game was the second tournament that Joanna won based on?", - "evidence": [ - "D10:4", - "D10:6" - ], - "category": 5, - "adversarial_answer": "Street Fighter" - }, - { - "question": "What is Nate's third screenplay about?", - "evidence": [ - "D12:13", - "D12:14" - ], - "category": 5, - "adversarial_answer": "loss, identity, and connection" - }, - { - "question": "What type of movies does Nate hate watching the most?", - "evidence": [ - "D1:13" - ], - "category": 5, - "adversarial_answer": "action and sci-fi" - }, - { - "question": "What genre is Joanna's first novella?", - "evidence": [ - "D2:5" - ], - "category": 5, - "adversarial_answer": "drama and romance" - }, - { - "question": "What are Nate's plans for his finished screenplay in January 2022?", - "evidence": [ - "D2:7" - ], - "category": 5, - "adversarial_answer": "submit it to film festivals and get producers and directors to check it out" - }, - { - "question": "For how long has Nate had his snakes?", - "evidence": [ - "D2:12" - ], - "category": 5, - "adversarial_answer": "3 years" - }, - { - "question": "What did Nate think of the caramel ice cream he made?", - "evidence": [ - "D3:6" - ], - "category": 5, - "adversarial_answer": "Super good, rich and creamy" - }, - { - "question": "What flavor of cake did Nate make for his friend on 25 February, 2022?", - "evidence": [ - "D4:3" - ], - "category": 5, - "adversarial_answer": "chocolate and vanilla swirl" - }, - { - "question": "What was Nate's audition for?", - "evidence": [ - "D6:2" - ], - "category": 5, - "adversarial_answer": "writing gig" - }, - { - "question": "Why did Joanna choose the hair color she did?", - "evidence": [ - "D7:5" - ], - "category": 5, - "adversarial_answer": "Bright and bold - like her" - }, - { - "question": "What are the main ingredients of the ice cream recipe shared by Joanna?", - "evidence": [ - "D8:19" - ], - "category": 5, - "adversarial_answer": "Coconut milk, vanilla extract, sugar, salt" - }, - { - "question": "What is Nate's project called in the writers group?", - "evidence": [ - "D9:3" - ], - "category": 5, - "adversarial_answer": "\"Finding Home\"" - }, - { - "question": "Which activity helps Nate escape and numbs his mind?", - "evidence": [ - "D9:10" - ], - "category": 5, - "adversarial_answer": "watching fantasy and sci-fi movies" - }, - { - "question": "What filling did Nate use in the cake he made recently in May 2022?", - "evidence": [ - "D10:11" - ], - "category": 5, - "adversarial_answer": "strawberry" - }, - { - "question": "Who did Joanna plan to invite to her gaming party in June 2022?", - "evidence": [ - "D14:22" - ], - "category": 5, - "adversarial_answer": "Tournament friends, old friends, teammates" - }, - { - "question": "What special items did Joanna get for everyone at her gaming party?", - "evidence": [ - "D14:24" - ], - "category": 5, - "adversarial_answer": "Custom controller decorations" - }, - { - "question": "What supervillain is Joanna a fan of?", - "evidence": [ - "D15:3" - ], - "category": 5, - "adversarial_answer": "Spider-Man" - }, - { - "question": "Which superhero toy figure does Joanna share a photo of?", - "evidence": [ - "D15:4" - ], - "category": 5, - "adversarial_answer": "Iron Man" - }, - { - "question": "What did Joanna make and share with her vegan diet group?", - "evidence": [ - "D16:8" - ], - "category": 5, - "adversarial_answer": "vegan ice cream" - }, - { - "question": "How many people attended the gaming party hosted by Joanna in June 2022?", - "evidence": [ - "D16:6" - ], - "category": 5, - "adversarial_answer": "7" - }, - { - "question": "Where did Nate go for a road trip for research?", - "evidence": [ - "D17:4" - ], - "category": 5, - "adversarial_answer": "Woodhaven" - }, - { - "question": "What did Joanna discover at the museum in Woodhaven?", - "evidence": [ - "D17:4" - ], - "category": 5, - "adversarial_answer": "cool old book collection" - }, - { - "question": "What specific themes are explored in Nate's new book?", - "evidence": [ - "D17:16" - ], - "category": 5, - "adversarial_answer": "loss, redemption, and forgiveness" - }, - { - "question": "How did Nate feel when someone wrote him a letter after reading his blog post?", - "evidence": [ - "D18:5" - ], - "category": 5, - "adversarial_answer": "Touched" - }, - { - "question": "What kind of impact does Joanna hope to have with her painting?", - "evidence": [ - "D18:7" - ], - "category": 5, - "adversarial_answer": "share her stories and hopefully have an impact" - }, - { - "question": "What did Nate share with his writers group in August 2022?", - "evidence": [ - "D19:6" - ], - "category": 5, - "adversarial_answer": "her book" - }, - { - "question": "How did Nate celebrate after sharing his book with a writers group?", - "evidence": [ - "D19:8" - ], - "category": 5, - "adversarial_answer": "making a delicious treat" - }, - { - "question": "How did Joanna celebrate winning the international tournament?", - "evidence": [ - "D19:9" - ], - "category": 5, - "adversarial_answer": "Taking time off to chill with pets" - }, - { - "question": "What substitution does Nate suggest for sugar in dairy-free baking?", - "evidence": [ - "D20:15" - ], - "category": 5, - "adversarial_answer": "dairy-free margarine or coconut oil" - }, - { - "question": "What type of show did Joanna host where she taught vegan ice cream recipes?", - "evidence": [ - "D21:4" - ], - "category": 5, - "adversarial_answer": "a cooking show" - }, - { - "question": "What is Joanna's favorite dish from the cooking show she hosted?", - "evidence": [ - "D21:6" - ], - "category": 5, - "adversarial_answer": "Coconut milk ice cream" - }, - { - "question": "What dessert did Nate share a photo of that has an almond flour crust, chocolate ganache, and fresh raspberries?", - "evidence": [ - "D21:11" - ], - "category": 5, - "adversarial_answer": "chocolate raspberry tart" - }, - { - "question": "What two main ingredients are part of the dessert Nate shared a photo of with blueberries, coconut milk, and a gluten-free crust?", - "evidence": [ - "D21:17" - ], - "category": 5, - "adversarial_answer": "blueberries and coconut milk" - }, - { - "question": "What movie did Joanna recently watch and enjoy on October 6, 2022?", - "evidence": [ - "D22:8" - ], - "category": 5, - "adversarial_answer": "Little Women" - }, - { - "question": "What did Nate make for one of the ladies at his writing club?", - "evidence": [ - "D22:19" - ], - "category": 5, - "adversarial_answer": "a bookmark" - }, - { - "question": "What game has Joanna been playing nonstop with a futuristic setting and gameplay on October 9, 2022?", - "evidence": [ - "D23:17" - ], - "category": 5, - "adversarial_answer": "Cyberpunk 2077" - }, - { - "question": "How did Nate describe the classic movie he watched?", - "evidence": [ - "D23:18" - ], - "category": 5, - "adversarial_answer": "gripping with great actors" - }, - { - "question": "What does Nate recommend to make a living room comfy like his?", - "evidence": [ - "D23:26" - ], - "category": 5, - "adversarial_answer": "couch for multiple people, fluffy blanket, lights that can be dimmed" - }, - { - "question": "What helps Joanna stay distracted and brings her sadness?", - "evidence": [ - "D24:8" - ], - "category": 5, - "adversarial_answer": "stuffed animal dog named Tilly" - }, - { - "question": "What does Nate do while he writes?", - "evidence": [ - "D24:4" - ], - "category": 5, - "adversarial_answer": "have a stuffed animal dog named Tilly with him" - }, - { - "question": "What does Nate do after receiving a rejection from a production company?", - "evidence": [ - "D24:14" - ], - "category": 5, - "adversarial_answer": "keep grinding and moving ahead" - }, - { - "question": "What does Joanna rely on for cheer and joy?", - "evidence": [ - "D24:3" - ], - "category": 5, - "adversarial_answer": "her turtles" - }, - { - "question": "What does Nate use to remember his dog from Michigan?", - "evidence": [ - "D24:6" - ], - "category": 5, - "adversarial_answer": "stuffed animal dog Tilly" - }, - { - "question": "What inspires Joanna to create music for her characters?", - "evidence": [ - "D25:8" - ], - "category": 5, - "adversarial_answer": "visuals to help bring the characters alive in her head so she can write better" - }, - { - "question": "What type of diet do Joanna's turtles have?", - "evidence": [ - "D25:19" - ], - "category": 5, - "adversarial_answer": "combination of vegetables, fruits, and insects" - }, - { - "question": "What did Nate find in old notebooks last week that prompted him to reflect on her progress as a writer?", - "evidence": [ - "D26:5" - ], - "category": 5, - "adversarial_answer": "early writings" - }, - { - "question": "What game is Joanna currently playing and recommends to others on November 7, 2022?", - "evidence": [ - "D27:23" - ], - "category": 5, - "adversarial_answer": "\"Xenoblade Chronicles\"" - }, - { - "question": "What did Nate receive from his brother that brought back childhood memories?", - "evidence": [ - "D27:29" - ], - "category": 5, - "adversarial_answer": "a handwritten letter" - }, - { - "question": "What is the type of game \"Xenoblade Chronicles\" that Joanna is playing?", - "evidence": [ - "D27:23" - ], - "category": 5, - "adversarial_answer": "fantasy RPG" - }, - { - "question": "What project is Nate working on in his notebook on November 9, 2022?", - "evidence": [ - "D28:12" - ], - "category": 5, - "adversarial_answer": "A suspenseful thriller set in a small Midwestern town" - }, - { - "question": "What is Joanna creating for YouTube on 9 November, 2022?", - "evidence": [ - "D28:13" - ], - "category": 5, - "adversarial_answer": "gaming content" - }, - { - "question": "What inspired Joanna to start making gaming videos?", - "evidence": [ - "D28:15" - ], - "category": 5, - "adversarial_answer": "Love of gaming and connecting with others who enjoy it too" - }, - { - "question": "What new content is Nate creating for television?", - "evidence": [ - "D28:13" - ], - "category": 5, - "adversarial_answer": "Gaming videos" - }, - { - "question": "What did Nate take a picture of near Fort Wayne last summer?", - "evidence": [ - "D28:22" - ], - "category": 5, - "adversarial_answer": "Sunset" - }, - { - "question": "Why did Joanna get a third turtle?", - "evidence": [ - "D28:25" - ], - "category": 5, - "adversarial_answer": "She saw another one at a pet store and wanted to get it" - }, - { - "question": "What did Joanna take to the beach in Tampa?", - "evidence": [ - "D29:6" - ], - "category": 5, - "adversarial_answer": "turtles" - }, - { - "question": "What does Joanna love most about having turtles?", - "evidence": [ - "D29:8" - ], - "category": 5, - "adversarial_answer": "They make her feel calm and don't require much looking after" - } - ], - "conversation": { - "speaker_a": "Joanna", - "speaker_b": "Nate", - "session_1_date_time": "7:31 pm on 21 January, 2022", - "session_1": [ - { - "speaker": "Nate", - "dia_id": "D1:1", - "text": "Hey Joanna! Long time no see! What's up? Anything fun going on?" - }, - { - "speaker": "Joanna", - "dia_id": "D1:2", - "text": "Hey Nate! Long time no see! I've been working on a project lately - it's been pretty cool. What about you - any fun projects or hobbies?" - }, - { - "speaker": "Nate", - "dia_id": "D1:3", - "text": "Hey Joanna! That's cool! I won my first video game tournament last week - so exciting!" - }, - { - "speaker": "Joanna", - "dia_id": "D1:4", - "text": "Wow Nate! Congrats on winning! Tell me more - what game was it?" - }, - { - "speaker": "Nate", - "dia_id": "D1:5", - "text": "Thanks! it's a team shooter game." - }, - { - "speaker": "Joanna", - "dia_id": "D1:6", - "text": "Wow, great job! What was is called?" - }, - { - "speaker": "Nate", - "dia_id": "D1:7", - "text": "The game was called Counter-Strike: Global Offensive, and me and my team had a blast to the very end!" - }, - { - "speaker": "Joanna", - "dia_id": "D1:8", - "text": "Cool, Nate! Sounds like a fun experience, even if I'm not into games." - }, - { - "speaker": "Nate", - "dia_id": "D1:9", - "text": "It was! How about you? Do you have any hobbies you love?" - }, - { - "speaker": "Joanna", - "dia_id": "D1:10", - "text": "Yeah! Besides writing, I also enjoy reading, watching movies, and exploring nature. Anything else you enjoy doing, Nate?" - }, - { - "speaker": "Nate", - "dia_id": "D1:11", - "text": "Playing video games and watching movies are my main hobbies." - }, - { - "speaker": "Joanna", - "dia_id": "D1:12", - "text": "Cool, Nate! So we both have similar interests. What type of movies do you like best?" - }, - { - "speaker": "Nate", - "dia_id": "D1:13", - "text": "I love action and sci-fi movies, the effects are so cool! What about you, what's your favorite genre?" - }, - { - "speaker": "Joanna", - "dia_id": "D1:14", - "text": "I'm all about dramas and romcoms. I love getting immersed in the feelings and plots." - }, - { - "speaker": "Nate", - "dia_id": "D1:15", - "text": "Wow, movies can be so powerful! Do you have any recommendations for me?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.pinimg.com/originals/80/ce/f7/80cef746e94a1720df6d3ffec883087e.jpg" - ], - "blip_caption": "a photo of a poster of a man and a woman sitting on a bench", - "query": "eternal sunshine of the spotless mind movie poster", - "dia_id": "D1:16", - "text": "Yeah, totally! Have you seen this romantic drama that's all about memory and relationships? It's such a good one." - }, - { - "speaker": "Nate", - "dia_id": "D1:17", - "text": "Oh cool! I might check that one out some time soon! I do love watching classics." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/3i39xdbz3td91.jpg" - ], - "blip_caption": "a photo of a dvd on a table with a blurry background", - "query": "eternal sunshine of spotless mind dvd cover", - "dia_id": "D1:18", - "text": "Yep, that movie is awesome. I first watched it around 3 years ago. I even went out and got a physical copy!" - }, - { - "speaker": "Nate", - "dia_id": "D1:19", - "text": "Sounds cool! Have you seen it a lot? sounds like you know the movie well!" - }, - { - "speaker": "Joanna", - "dia_id": "D1:20", - "text": "A few times. It's one of my favorites! I really like the idea and the acting." - }, - { - "speaker": "Nate", - "dia_id": "D1:21", - "text": "Cool! I'll definitely check it out. Thanks for the recommendation!" - }, - { - "speaker": "Joanna", - "dia_id": "D1:22", - "text": "No problem, Nate! Let me know if you like it!" - } - ], - "session_2_date_time": "2:01 pm on 23 January, 2022", - "session_2": [ - { - "speaker": "Joanna", - "dia_id": "D2:1", - "text": "Hey Nate! Haven't talked in a few days. Crazy things happened to me!" - }, - { - "speaker": "Nate", - "dia_id": "D2:2", - "text": "Hi Joanna! Long time no see! What's been going on? You sound excited!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://live.staticflickr.com/3159/2406045813_cab5f8211d_b.jpg" - ], - "blip_caption": "a photography of a book with a page of text on it", - "query": "screenplay laptop", - "dia_id": "D2:3", - "re-download": true, - "text": "Woo! I finally finished my first full screenplay and printed it last Friday. I've been working on for a while, such a relief to have it all done!" - }, - { - "speaker": "Nate", - "dia_id": "D2:4", - "text": "Wow, that sounds awesome! What's it about? Glad it's all down!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:5", - "text": "Thanks, Nate! It's a mix of drama and romance!" - }, - { - "speaker": "Nate", - "dia_id": "D2:6", - "text": "Wow, that's amazing! How do you feel now that it's finished? Do you have any new plans for it?" - }, - { - "speaker": "Joanna", - "dia_id": "D2:7", - "text": "Woohoo, Nate! I'm feeling a rollercoaster of emotions - relief, excitement, some anxiety - over finishing this project. Now I'm gonna submit it to some film festivals and (hopefully) get producers and directors to check it out. Here's hoping!" - }, - { - "speaker": "Nate", - "dia_id": "D2:8", - "text": "Congrats, Joanna! That sounds like a wild experience. Rock on and I hope they love it!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:9", - "text": "Thanks Nate! A mix of emotions for sure. Hopefully, it leads to positive feedback and new opportunities." - }, - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/5126/5279947833_4ae1f16e96_b.jpg" - ], - "blip_caption": "a photography of a turtle and a turtleling sitting on a rock", - "query": "pet turtles", - "dia_id": "D2:10", - "re-download": true, - "text": "Yeah, for sure. Hoping for the best! I like having some of these little ones around to keep me calm when things are super important and I'm nervous." - }, - { - "speaker": "Joanna", - "dia_id": "D2:11", - "text": "Awww! How long have you had them?" - }, - { - "speaker": "Nate", - "dia_id": "D2:12", - "text": "I've had them for 3 years now and they bring me tons of joy!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:13", - "text": "They sure lookl like they do! Adorable!" - }, - { - "speaker": "Nate", - "dia_id": "D2:14", - "text": "Thanks! The turtles might be small, but both sure have big personalities. I really reccomend having something like these little guys for times of stress." - }, - { - "speaker": "Joanna", - "dia_id": "D2:15", - "text": "Good idea, Nate! I'll think about it and maybe get pets of my own soon if I can find any I'm not allergic to. Have you been up to anything recently?" - }, - { - "speaker": "Nate", - "dia_id": "D2:16", - "text": "Yeah actually! I start to hang out with some people outside of my circle at the tournament. They're pretty cool!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:17", - "text": "Oh? That sounds sweet! Is it a weird relationship with them being competitors and all?" - }, - { - "speaker": "Nate", - "dia_id": "D2:18", - "text": "Oh, kind of. Some people are more competitive then others, so I tend to just stick around the more chill people here." - }, - { - "speaker": "Joanna", - "dia_id": "D2:19", - "text": "That makes sense! Are you gonna cheer them on even if you lose?" - }, - { - "speaker": "Nate", - "dia_id": "D2:20", - "text": "Absolutely! I don't expect to win big here, I just like playing for fun! You mentioned you were allergic to pets earlier, how bad is it?" - }, - { - "speaker": "Joanna", - "dia_id": "D2:21", - "text": "Oh, its really bad. My face gets all puffy and itchy when I'm around certain animals, so I've always just stayed away." - }, - { - "speaker": "Nate", - "dia_id": "D2:22", - "text": "Sorry to hear that. Allergies can be tough. What specifically are you allergic to?" - }, - { - "speaker": "Joanna", - "dia_id": "D2:23", - "text": "I'm allergic to most reptiles and animals with fur. It can be a bit of a drag, but I find other ways to be happy." - }, - { - "speaker": "Nate", - "dia_id": "D2:24", - "text": "Awesome! There are lots of things that can bring you joy without pets. What else brings you joy?" - }, - { - "speaker": "Joanna", - "dia_id": "D2:25", - "text": "Writing and hanging with friends! That way I can express myself through stories, or just have a good time with people." - }, - { - "speaker": "Nate", - "dia_id": "D2:26", - "text": "That's great to hear! Those are both great things. I'm glad to hear you've got other things to help you get through times of axiousness despite not being able to have animals!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:27", - "text": "Thanks, Nate! Writing helps me create wild worlds with awesome characters. Plus, it's a great way to express my feelings. I can't imagine life without it." - }, - { - "speaker": "Nate", - "dia_id": "D2:28", - "text": "Wow, Joanna, that sounds amazing! Keep doing what you love!" - }, - { - "speaker": "Joanna", - "dia_id": "D2:29", - "text": "Thanks, Nate! I'll definitely keep pursuing my passion for writing. It means a lot." - } - ], - "session_3_date_time": "9:27 am on 7 February, 2022", - "session_3": [ - { - "speaker": "Joanna", - "dia_id": "D3:1", - "text": "Hey Nate, long time no see! The screenplay I sent in to the film festival has been on my mind all day everyday. I keep bouncing between crazy emotions like relief, excitement and worry! Fingers crossed a producer or director falls in love with it and it ends up on the big screen - that would be awesome!" - }, - { - "speaker": "Nate", - "dia_id": "D3:2", - "text": "Hey Joanna! It is a big deal! I'm sure its been a wild ride. Sending some positive vibes and hoping someone likes it enough to get it on the big screen - that would be awesome!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:3", - "text": "Thanks Nate, your support really means a lot. I put a lot of effort into it and I'm crossing my fingers. What about you? Anything new and exciting happening in your life?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/4q9s9o2607ib1.jpg" - ], - "blip_caption": "a photo of a bowl of ice cream with a spoon in it", - "query": "homemade coconut milk ice cream", - "dia_id": "D3:4", - "text": "Thanks, Joanna. Not much has changed for me, but I just discovered that I can make coconut milk icecream and gave it a try. It was actually pretty good, so I'm proud of myself." - }, - { - "speaker": "Joanna", - "dia_id": "D3:5", - "text": "Looks delish! Glad you tried something new and it went well. What did you think of it?" - }, - { - "speaker": "Nate", - "dia_id": "D3:6", - "text": "Super good! It was rich and creamy - might be my new favorite snack!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:7", - "text": "Great! I love when you try something new and it actually works out. Will you give it another go?" - }, - { - "speaker": "Nate", - "dia_id": "D3:8", - "text": "Yep, it could be fun! I'm looking forward to trying out different flavors and toppings." - }, - { - "speaker": "Joanna", - "dia_id": "D3:9", - "text": "Yum! Sounds great. Got any favorite flavors for dairy-free desserts?" - }, - { - "speaker": "Nate", - "dia_id": "D3:10", - "text": "I love coconut milk, but I also enjoy chocolate and mixed berry flavors." - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a piece of cake with berries on a plate", - "dia_id": "D3:11", - "text": "Wow, those sound great! Who can say no to chocolate and berries? I'm tempted to try dairy-free flavors now!" - }, - { - "speaker": "Nate", - "dia_id": "D3:12", - "text": "Well I also made a dairy-free chocolate cake with berries on it the other day, maybe you would like that!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:13", - "text": "Wow! That sounds yummy! You're so talented. Thanks for sharing your amazing creations! I should really try making one or just pay you a visit and try one for myself!" - }, - { - "speaker": "Nate", - "dia_id": "D3:14", - "text": "I couldn't agree more! It's always fun experimenting in the kitchen." - }, - { - "speaker": "Joanna", - "dia_id": "D3:15", - "text": "I can tell! Your cooking skills are awesome. Seen any good movies lately?" - }, - { - "speaker": "Nate", - "dia_id": "D3:16", - "text": "Not recently. Any good ones you'd recommend?" - }, - { - "speaker": "Joanna", - "dia_id": "D3:17", - "text": "I just watched \"Little Women\" and it was amazing! It's a great story about sisterhood, love, and reaching for your dreams. Definitely a must-see!" - }, - { - "speaker": "Nate", - "dia_id": "D3:18", - "text": "Oh, that sounds like a great one! I'll definitely add it to my list. Thanks for the recommendation!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:19", - "text": "Anytime! I'm always down to give movie reccomendations." - }, - { - "speaker": "Nate", - "dia_id": "D3:20", - "text": "Good to know! I'll be sure to give you a shout whenever I run out of things to watch!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:21", - "text": "Sounds great! Let me know what you think of it when your done!" - }, - { - "speaker": "Nate", - "dia_id": "D3:22", - "text": "You bet! You'll be the first to know." - }, - { - "speaker": "Joanna", - "dia_id": "D3:23", - "text": "Awesome! Enjoy yourself!" - }, - { - "speaker": "Nate", - "dia_id": "D3:24", - "text": "You too, take care!" - }, - { - "speaker": "Joanna", - "dia_id": "D3:25", - "text": "Later, take care!" - } - ], - "session_4_date_time": "1:07 pm on 25 February, 2022", - "session_4": [ - { - "speaker": "Nate", - "dia_id": "D4:1", - "text": "Hey Joanna! Sorry I haven't been around. I made my friend some ice cream and they loved it!" - }, - { - "speaker": "Joanna", - "dia_id": "D4:2", - "text": "No worries, Nate! Glad to hear it. What flavor did you make?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/wj5c699jccx61.jpg" - ], - "blip_caption": "a photo of a person holding a chocolate and vanilla ice cream cone", - "query": "coconut milk ice cream chocolate swirls almond chunks", - "dia_id": "D4:3", - "text": "I whipped up some chocolate and vanilla swirl." - }, - { - "speaker": "Joanna", - "dia_id": "D4:4", - "text": "That looks delicious! Unfortunately, I can't have dairy, so no ice cream for me. Do you happen to have a dairy-free recipe that I could try?" - }, - { - "speaker": "Nate", - "dia_id": "D4:5", - "text": "Sure, I know one recipe using coconut milk. Would you like me to send it to you?" - }, - { - "speaker": "Joanna", - "dia_id": "D4:6", - "text": "Yeah, definitely! I'm keen to try your recipe. Always up for something sweet." - }, - { - "speaker": "Nate", - "dia_id": "D4:7", - "text": "Cool, I'll do that. I'm all about these desserts, let me know what you think!" - }, - { - "speaker": "Joanna", - "dia_id": "D4:8", - "text": "Definitely keeping you posted! Love your creations!" - }, - { - "speaker": "Nate", - "dia_id": "D4:9", - "text": "Thanks, Joanna! It means a lot that you enjoy the desserts I bake." - }, - { - "speaker": "Joanna", - "dia_id": "D4:10", - "text": "Yeah Nate, your cooking is amazing! I can't stop thinking about the screenplay, so I just started writing another one while I wait to hear back about how the first one did." - }, - { - "speaker": "Nate", - "dia_id": "D4:11", - "text": "I hear that, taking your mind of something like that is very challenging. What's the new one about?" - }, - { - "speaker": "Joanna", - "dia_id": "D4:12", - "text": "It's about a thirty year old woman on a journey of self-discovery after a loss. Somewhat similar to the last one, but hey, that's just the kind of thing I'm inspired to write about!" - }, - { - "speaker": "Nate", - "dia_id": "D4:13", - "text": "Interesting! That's a deep topic. Love to hear more about it." - }, - { - "speaker": "Joanna", - "dia_id": "D4:14", - "text": "Thanks, Nate! It's my own story. The main character is dealing with some tough stuff: loss and trying to figure out who they are. They take a road trip to heal and grow." - }, - { - "speaker": "Nate", - "dia_id": "D4:15", - "text": "Wow, Joanna, that sounds awesome. I love stories that tackle important issues. What inspired you to this one?" - }, - { - "speaker": "Joanna", - "dia_id": "D4:16", - "text": "Thanks, Nate! It was inspired by personal experiences and my own journey of self-discovery." - }, - { - "speaker": "Nate", - "dia_id": "D4:17", - "text": "Wow, Joanna, that takes guts! I can't wait to see it all come together. I'm also pumped to see how your first one will do!" - }, - { - "speaker": "Joanna", - "dia_id": "D4:18", - "text": "Thanks, Nate! Appreciate your support. Hoping my screenplay gets noticed and makes it to the screen. Fingers crossed!" - }, - { - "speaker": "Nate", - "dia_id": "D4:19", - "text": "Crossing my fingers for you! Hope your screenplay finds a fan and is given its due. Good luck!" - } - ], - "session_5_date_time": "6:59 pm on 18 March, 2022", - "session_5": [ - { - "speaker": "Joanna", - "dia_id": "D5:1", - "text": "Hey Nate, it's been a minute! I wrapped up my second script, and the feels have been wild. Sometimes I'm so relieved, but other times I just feel anxious about what comes next. It's a mix of excitement and terror, thinking about my work getting noticed and hitting the big screen." - }, - { - "speaker": "Nate", - "dia_id": "D5:2", - "text": "Hey Joanna! Awesome to hear from you! Congrats on wrapping up a second one! All that hard work and dedication will definitely shine through and get noticed, no doubt. Hope you've been able to take some time to relax after everything!" - }, - { - "speaker": "Joanna", - "dia_id": "D5:3", - "text": "Thanks Nate! It's been a wild ride. I've been decompressing, but it's hard to switch off. There's always this tug-of-war of hope and doubt." - }, - { - "speaker": "Nate", - "img_url": [ - "https://cdn12.picryl.com/photo/2016/12/31/turtle-nature-slow-nature-landscapes-9a70ba-1024.jpg" - ], - "blip_caption": "a photography of two tortoises laying on the ground in a jungle", - "query": "adorable photo turtles walk grassy area", - "dia_id": "D5:4", - "re-download": true, - "text": "It's normal to have doubts, but you've worked hard and put tons of passion into it. Believe in yourself and things will work out. Here, look at this cute pic - walking them always reminds me to enjoy the small stuff!" - }, - { - "speaker": "Joanna", - "dia_id": "D5:5", - "text": "That pic's adorable! They always look so relaxed outside. What made you choose them as pets?" - }, - { - "speaker": "Nate", - "img_url": [ - 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" - ], - "blip_caption": "a photography of three turtles sitting on rocks in a pond", - "query": "turtles basking sun", - "dia_id": "D5:6", - "re-download": true, - "text": "I'm drawn to turtles. They're unique and their slow pace is a nice change from the rush of life. They're also low-maintenance and calming. Check out this moment I snapped!" - }, - { - "speaker": "Joanna", - "dia_id": "D5:7", - "text": "They look so peaceful! It's amazing how these creatures bring so much calm and joy. Is taking care of them tough?" - }, - { - "speaker": "Nate", - "dia_id": "D5:8", - "text": "No, not really. Just keep their area clean, feed them properly, and make sure they get enough light. It's actually kind of fun." - }, - { - "speaker": "Joanna", - "dia_id": "D5:9", - "text": "Sounds great! Having pets must be a wonderful experience." - }, - { - "speaker": "Nate", - "dia_id": "D5:10", - "text": "Pets definitely bring tons of joy. They are always there for us and they're so cute! Relaxing with them is a great way to chill." - }, - { - "speaker": "Joanna", - "dia_id": "D5:11", - "text": "I wish I wasn't allergic! I would get two turtles today if I could! I found out recently I'm allergic to cockroaches as well, so who knows if I'll ever get a pet." - }, - { - "speaker": "Nate", - "dia_id": "D5:12", - "text": "Sorry! Maybe there are other animals you could consider! In the meantime though, I'll be sure to send you pics of my turtles so you can still watch them grow without getting too close." - }, - { - "speaker": "Joanna", - "dia_id": "D5:13", - "text": "Great idea! I'm already really invested in those little guys!" - }, - { - "speaker": "Nate", - "dia_id": "D5:14", - "text": "Pets really seem to do that to everyone don't they! So, what about your script now? Any ideas for the next steps?" - }, - { - "speaker": "Joanna", - "dia_id": "D5:15", - "text": "I've been doing my fair share of research and networking non-stop for it. It's tough, but I'm determined to make it happen." - }, - { - "speaker": "Nate", - "dia_id": "D5:16", - "text": "Great idea! that should hopefully get some more eyes on it. Keep up the hard work!" - }, - { - "speaker": "Joanna", - "dia_id": "D5:17", - "text": "Thanks so much, Nate! Your support means a lot. I'll keep working at it and hopefully the next steps will become clearer soon." - }, - { - "speaker": "Nate", - "dia_id": "D5:18", - "text": "Just make sure you don't quit - the path forward will show up soon. You got this!" - }, - { - "speaker": "Joanna", - "dia_id": "D5:19", - "text": "Appreciated! I think just having someone to support me throughout the whole process is such a blessing. It gives me the motivation to keep pushing forward." - }, - { - "speaker": "Nate", - "dia_id": "D5:20", - "text": "Glad to hear my support makes a difference, Joanna. I'm here for you!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a cinema ticket on a chair", - "dia_id": "D5:21", - "text": "Always good to hear! See you later!" - } - ], - "session_6_date_time": "1:43 pm on 24 March, 2022", - "session_6": [ - { - "speaker": "Nate", - "dia_id": "D6:1", - "text": "Hey Joanna! Long time no talk, how's it going? Crazy stuff's been happening since we last chatted." - }, - { - "speaker": "Joanna", - "dia_id": "D6:2", - "text": "Hey Nate! Been quite a ride - in a good way - had an audition yesterday for a writing gig." - }, - { - "speaker": "Nate", - "dia_id": "D6:3", - "text": "Congrats! How did it go? Are you excited?" - }, - { - "speaker": "Joanna", - "dia_id": "D6:4", - "text": "Thanks, Nate! It went alright. Mixed emotions - definitely excited but also a bit anxious. Keep those fingers crossed!" - }, - { - "speaker": "Nate", - "dia_id": "D6:5", - "text": "Yeah, I get it. Mixed emotions are rough, but I have faith in you! Keep me posted - you got this!" - }, - { - "speaker": "Joanna", - "dia_id": "D6:6", - "text": "Thanks, Nate! Your support means a lot. I'll make sure to keep you updated. Anything new on your end?" - }, - { - "speaker": "Nate", - "dia_id": "D6:7", - "text": "Yeah actually - I'm currently participating in the video game tournament again and it's INTENSE! There's so much adrenaline flowing." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjW88nSAhvDHq-ZyBSZlQ__czsDBW4hEFEvzdgkz06j4ByTnkAamu38jTEXQmzjkEgmS2VYyUYt4e0kz5cfhH7pChnTfAR2OKl9HZj7T91RxPZTV5qxTpi3jjpbFY1wD9Myi7633MAHlwiCL1LtCXt8iiQLed4Kzu2FY7thoxVxVBW8faEpQ-qyEFLwBTA/s4032/Photo%2023.07.23,%2014%2034%2052.jpg" - ], - "blip_caption": "a photo of a book shelf filled with books and magazines", - "query": "bookshelf writing books inspiration", - "dia_id": "D6:8", - "text": "Best of luck in the tournament! It sounds like it would be difficult to go through so many days of intense gaming! This is my go-to place for writing inspiration. It helps me stay sharp and motivated." - }, - { - "speaker": "Nate", - "dia_id": "D6:9", - "text": "Wow, that's a lot of books. Do you have any advice for someone like me who wants to pursue writing?" - }, - { - "speaker": "Joanna", - "dia_id": "D6:10", - "text": "Definitely! Read lots and try out different genres. Build a solid understanding of literature. Don't be afraid to write and share, even if it's just with friends. Practicing and gathering feedback will make you better. Have faith in yourself and continue following your writing dreams - it's tough but worth it." - }, - { - "speaker": "Nate", - "dia_id": "D6:11", - "text": "Thanks, Joanna. Really appreciate your help and kind words. I'm going to keep working hard on it and see what happens. Good luck with your project, I'm sure it will turn out great!" - }, - { - "speaker": "Joanna", - "dia_id": "D6:12", - "text": "Thanks Nate! Appreciate your kind words and support. Let's keep going for our dreams and work hard. Catch you later!" - }, - { - "speaker": "Nate", - "dia_id": "D6:13", - "text": "Bye Joanna! Take care!" - } - ], - "session_7_date_time": "7:37 pm on 15 April, 2022", - "session_7": [ - { - "speaker": "Nate", - "dia_id": "D7:1", - "text": "Hey Jo, guess what I did? Dyed my hair last week - come see!" - }, - { - "speaker": "Joanna", - "dia_id": "D7:2", - "text": "Wow, Nate! Can't wait to see it. Must feel so liberating! How're you feeling?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/2378/2443069910_bf08328b2b_b.jpg" - ], - "blip_caption": "a photography of a man with purple hair and glasses taking a selfie", - "query": "purple hair selfie", - "dia_id": "D7:3", - "re-download": true, - "text": "I'm so stoked about it! Check it out!" - }, - { - "speaker": "Joanna", - "dia_id": "D7:4", - "text": "Wow, your new hair color looks amazing! What made you choose that shade? Tell me all about it!" - }, - { - "speaker": "Nate", - "dia_id": "D7:5", - "text": "Thanks Jo! I picked this color because it's bright and bold - like me! I wanted to stand out from the regular options." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/ruvtfm6ixchb1.jpg" - ], - "blip_caption": "a photo of a street with a stop sign and a cloudy sky", - "query": "sunset hike pink blue bold", - "dia_id": "D7:6", - "text": "That's amazing, Nate! Your boldness really inspired me. It reminded me of this gorgeous sunset I saw while hiking the other day. It made me realize the importance of showing the world who we are." - }, - { - "speaker": "Nate", - "dia_id": "D7:7", - "text": "Wow, that sunset looks awesome! Jealous! I bet you had a great time. Are there any more exciting trips coming up for you?" - }, - { - "speaker": "Joanna", - "dia_id": "D7:8", - "text": "I did! the sky was so gorgeous! Wish I had a vacation lined up, but right now my writing is consuming me. Hoping for some good news soon!" - }, - { - "speaker": "Nate", - "dia_id": "D7:9", - "text": "I understand, Joanna. Big projects can be so taxing. Keep me posted on how it goes, alright?" - }, - { - "speaker": "Joanna", - "dia_id": "D7:10", - "text": "Cheers, Nate! Your support means a lot. I'll definitely keep you updated." - }, - { - "speaker": "Nate", - "dia_id": "D7:11", - "text": "Sounds great, See you soon?" - }, - { - "speaker": "Joanna", - "dia_id": "D7:12", - "text": "Totally! Bye Nate!" - }, - { - "speaker": "Nate", - "dia_id": "D7:13", - "text": "Take care!" - } - ], - "session_8_date_time": "6:44 pm on 17 April, 2022", - "session_8": [ - { - "speaker": "Nate", - "dia_id": "D8:1", - "text": "Hey Joanna! Haven't talked with you in a while - how's it going?" - }, - { - "speaker": "Joanna", - "dia_id": "D8:2", - "text": "Hey Nate! Great to hear from you. I've been reading a lot in the past week! There's a lot of good books I forgot I owned." - }, - { - "speaker": "Nate", - "dia_id": "D8:3", - "text": "Sounds fun! I probably also have loads of books I haven't read in years. Sounds like a blast from the past!" - }, - { - "speaker": "Joanna", - "dia_id": "D8:4", - "text": "It really is! On a different note, I found an awesome hiking trail in my hometown yesterday! It was gorgeous. Nature is so inspiring, and it's a great way to reset. Do you know of any good hiking spots?" - }, - { - "speaker": "Nate", - "dia_id": "D8:5", - "text": "I'm not really into hiking but I'm curious to see what the trail looks like! I heard there's a nice trail just north of where I live." - }, - { - "speaker": "Joanna", - "dia_id": "D8:6", - "text": "Maybe I'll have to convince you to go with me one of these times!" - }, - { - "speaker": "Nate", - "dia_id": "D8:7", - "text": "Maybe! I do like nature, so that might be fun going with someone else." - }, - { - "speaker": "Joanna", - "dia_id": "D8:8", - "text": "Yeah, nature's awesome! I'm a huge fan of it, that's why I go!" - }, - { - "speaker": "Nate", - "dia_id": "D8:9", - "text": "Agreed, nature has a way of being so inspiring! I'm glad you found a way to reset and find peace in it." - }, - { - "speaker": "Joanna", - "dia_id": "D8:10", - "text": "Nature's always been my haven. Walking in it, feeling it, hearing the sounds - it's so calming. Worries and stress seem to vanish, and it's just me and the beauty around me." - }, - { - "speaker": "Nate", - "dia_id": "D8:11", - "text": "It's so crucial to find a little peace and remember life's beauty. For me, it's spending time with my pets and engaging in my hobbies; they let me take a break from reality. It's wild how small things can have such a powerful effect on our happiness, right?" - }, - { - "speaker": "Joanna", - "dia_id": "D8:12", - "text": "Yeah, Nate! Even the small things make life enjoyable and worth it. Taking time for your little friends and doing activities you love are like treasures that remind us how great and peaceful life is. We just gotta savor them!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/3yrgb86abnl81.jpg" - ], - "blip_caption": "a photo of a turtle and a strawberry in a bowl", - "query": "pets turtles playing", - "dia_id": "D8:13", - "text": "Speaking of which, here they go again!" - }, - { - "speaker": "Joanna", - "dia_id": "D8:14", - "text": "So cute! I love your turtles so much!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://beadyarnspatula.files.wordpress.com/2023/05/img_6671.jpg" - ], - "blip_caption": "a photo of a bowl of ice cream and a bowl of sprinkles", - "query": "coconut milk ice cream red sprinkles bowl", - "dia_id": "D8:15", - "text": "Me too! I love watching them play to simply enjoy the peaceful moments of life. Sometimes I even bring them in the kitchen so they can watch me make food like this!" - }, - { - "speaker": "Joanna", - "dia_id": "D8:16", - "text": "I love your icecream so much! I wish I could make it the way you do!" - }, - { - "speaker": "Nate", - "dia_id": "D8:17", - "text": "Thanks! It's dairy-free and so easy. Wanna get the recipe?" - }, - { - "speaker": "Joanna", - "dia_id": "D8:18", - "text": "Sure! I'm lactose intolerant, so I'll just need the dairy-free recipe! " - }, - { - "speaker": "Nate", - "dia_id": "D8:19", - "text": "No prob. I made it with coconut milk, vanilla extract, sugar, and a pinch of salt. After chilling it in the fridge, I put it in the ice cream maker and froze it until it was scoopable." - }, - { - "speaker": "Joanna", - "dia_id": "D8:20", - "text": "Wow, sounds delicious! I'm going to try making it tonight! Thank you for sharing the recipe!" - }, - { - "speaker": "Nate", - "dia_id": "D8:21", - "text": "Hey Joanna, glad I could help. Let me know how it turns out!" - }, - { - "speaker": "Joanna", - "dia_id": "D8:22", - "text": "Got it, Nate. I'll definitely let you know how it turns out. Thanks for sharing the recipe!" - } - ], - "session_9_date_time": "7:44 pm on 21 April, 2022", - "session_9": [ - { - "speaker": "Joanna", - "img_url": [ - "https://threeteacherstalk.files.wordpress.com/2021/06/fnqw-6.18.jpg" - ], - "blip_caption": "a photo of a notebook with a notepad and a piece of paper", - "query": "writers group notebook notes ideas storytelling", - "dia_id": "D9:1", - "text": "Hey Nate! Long time no talk! I wanted to tell ya I just joined a writers group. It's unbelievable--such inspirational people who really get my writing. I'm feeling so motivated and supported, it's like I finally belong somewhere!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a cup of ice cream with a cherry on top", - "dia_id": "D9:2", - "text": "Hey Joanna! That's awesome! Having a supportive group around you can really make a difference. What kind of projects are you working on with them?" - }, - { - "speaker": "Joanna", - "dia_id": "D9:3", - "text": "Thanks, Nate! We've made some great progress. I'm working on one with my group called \"Finding Home.\" It's a script about a girl on a journey to find her true home. I find it really rewarding and emotional. What about you? Any upcoming gaming tournaments?" - }, - { - "speaker": "Nate", - "dia_id": "D9:4", - "text": "Hi Joanna! \"Finding Home\" sounds really special. Must be so meaningful to work on. I've got a gaming tournament next month and I'm feeling good about it. It's gonna be my 4th one!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://get.pxhere.com/photo/black-and-white-old-audience-fashion-performance-art-theatre-children-stage-performance-entertainment-scenario-performing-arts-monochrome-photography-musical-theatre-541499.jpg" - ], - "blip_caption": "a photography of a man in a striped suit is performing on stage", - "query": "theater stage", - "dia_id": "D9:5", - "re-download": true, - "text": "Yeah, I bet the nerves and excitement are quite a rush! I remember when I did my first play, I was so nervous I forgot my lines. It was embarrassing, but it taught me how important it is to prepare and stay in the moment." - }, - { - "speaker": "Nate", - "dia_id": "D9:6", - "text": "Sounds like you had an interesting time on stage! It's always a learning experience. Have you ever considered going back to acting? Is that you in the photo?" - }, - { - "speaker": "Joanna", - "dia_id": "D9:7", - "text": "Yeah, that's me in that photo! Acting was my first passion, but now I really shine in writing. It helps me express myself in a new way, but who knows, maybe I'll go back to acting someday. Never say never!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle laying on a bed of rocks and gravel", - "dia_id": "D9:8", - "text": "Go for it! Follow your passion for writing, but if acting really makes you happy, give it a shot as well. Who knows what'll happen! Any particular movies that spark your writing?" - }, - { - "speaker": "Joanna", - "dia_id": "D9:9", - "text": "Thanks Nate! I'm gonna keep writing, but if acting calls out I might give it a try. I really enjoy dramas and emotionally-driven films. What about you? What inspires your passion?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://www.trustedreviews.com/wp-content/uploads/sites/54/2020/10/20201026_152436-scaled-e1604409626602.jpg" - ], - "blip_caption": "a photography of a black xbox controller sitting on top of a wooden table", - "query": "video game controller", - "dia_id": "D9:10", - "re-download": true, - "text": "I love fantasy and sci-fi movies, they're a great escape and get my imagination going. Playing video games is a great way to express my creativity and passion." - }, - { - "speaker": "Joanna", - "dia_id": "D9:11", - "text": "That's awesome! I love how video games can really spark your imagination. Do you have a favorite fantasy or sci-fi movie?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/7vtqewbtg1181.jpg" - ], - "blip_caption": "a photo of a shelf with a lot of books on it", - "query": "lord of the rings trilogy dvd boxset", - "dia_id": "D9:12", - "text": "Yeah, for sure! This trilogy is one of my faves. The world building, battles, and storytelling always blow me away!" - }, - { - "speaker": "Joanna", - "dia_id": "D9:13", - "text": "Wow, that's great to hear! What books do you enjoy? I'm always up for some new book recommendations." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/o2ifb25e7qa31.jpg" - ], - "blip_caption": "a photo of a bunch of books on a table", - "query": "fantasy novels dragon cover series", - "dia_id": "D9:14", - "text": "I love this series. It has adventures, magic, and great characters - it's a must-read!" - }, - { - "speaker": "Joanna", - "dia_id": "D9:15", - "text": "Heard of that series! It's been on my list forever. Thanks for the recommendation, Nate. I'm definitely going to check it out!" - }, - { - "speaker": "Nate", - "dia_id": "D9:16", - "text": "No problem, glad to see an interest. Let me know what you think when you check it out." - }, - { - "speaker": "Joanna", - "dia_id": "D9:17", - "text": "Thanks Nate! I'll definitely let you know my thoughts. Take care and have a great day!" - }, - { - "speaker": "Nate", - "dia_id": "D9:18", - "text": "See you! Good chatting with you! Have a great day!" - } - ], - "session_10_date_time": "11:54 am on 2 May, 2022", - "session_10": [ - { - "speaker": "Joanna", - "blip_caption": "a photo of a person holding a book openhemer", - "dia_id": "D10:1", - "text": "Hey Nate, how's it going? I took your reccomendation and watched \"The Lord of the Rings\" Trilogy last night! It was awesome!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.pinimg.com/originals/23/13/24/231324d44f9471ead5535950153378f1.jpg" - ], - "blip_caption": "a photo of a gaming room with a computer and a gaming chair", - "query": "gaming setup", - "dia_id": "D10:2", - "text": "Glad to hear you enjoyed it! It's probably the greatest trilogy of all time! As for me, life's been ok, just taking care of this." - }, - { - "speaker": "Joanna", - "dia_id": "D10:3", - "text": "Wow, Nate! I'm proud of what you did. Your gaming room looks great - have you been gaming a lot recently?" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a woman with purple hair and a black dress", - "dia_id": "D10:4", - "text": "Gaming has been my focus - practicing a lot and even winning a few tournaments. Last week I won my second tournament!" - }, - { - "speaker": "Joanna", - "dia_id": "D10:5", - "text": "Wow, congrats! What game were you playing?" - }, - { - "speaker": "Nate", - "dia_id": "D10:6", - "text": "Thanks! I usually play CS:GO, but I tried my hand at the local Street Fighter tournament this time since I play that game a lot with my friends, and turns out I'm really good!" - }, - { - "speaker": "Joanna", - "dia_id": "D10:7", - "text": "Nice! That must have been a surprise. How did it feel to finally win one?" - }, - { - "speaker": "Nate", - "dia_id": "D10:8", - "text": "It was super awesome! So much adrenaline went into that last match, and the other finalist even shook my hand! Enough about me though, how about you? What have you been up to?" - }, - { - "speaker": "Joanna", - "img_url": [ - "http://ventitobakery.com/cdn/shop/products/white-gluten-dairy-free-cake.jpg" - ], - "blip_caption": "a photo of a cake with white frosting on a wooden table", - "query": "dairy-free cake decorated", - "dia_id": "D10:9", - "text": "Not much is new other than the screenplay. Been working on some projects and testing out dairy-free dessert recipes for friends and fam. Here's a pic of a cake I made recently!" - }, - { - "speaker": "Nate", - "dia_id": "D10:10", - "text": "That looks really good! I love the way the frosting turned out!" - }, - { - "speaker": "Joanna", - "dia_id": "D10:11", - "text": "Thanks! It's dairy-free vanilla with strawberry filling and coconut cream frosting. I gotta say, I really like your coconut reccomendation you gave a while back!" - }, - { - "speaker": "Nate", - "dia_id": "D10:12", - "text": "Wow, Joanna, that looks amazing! I bet it tastes great - you're so talented at making dairy-free desserts!" - }, - { - "speaker": "Joanna", - "dia_id": "D10:13", - "text": "Thanks Nate! I really appreciate it. I love experimenting in the kitchen, coming up with something tasty. Cooking and baking are my creative outlets. Especially when I'm snackin' dairy-free, trying to make the desserts just as delicious - it's a rewarding challenge! Seeing the smiles on everyone's faces when they try it - it's a total win!" - }, - { - "speaker": "Nate", - "dia_id": "D10:14", - "text": "That's great, Joanna! It must be so rewarding to see everyone enjoying your creations. Keep up the good work!" - }, - { - "speaker": "Joanna", - "dia_id": "D10:15", - "text": "Thanks, Nate! Appreciate all the help. Gonna keep trying new things. See ya later!" - }, - { - "speaker": "Nate", - "dia_id": "D10:16", - "text": "Bye!" - } - ], - "session_11_date_time": "3:35 pm on 12 May, 2022", - "session_11": [ - { - "speaker": "Joanna", - "dia_id": "D11:1", - "text": "Hey Nate! Great to hear from you! Quite a week since we last talked - something awesome happened to me!" - }, - { - "speaker": "Nate", - "dia_id": "D11:2", - "text": "Hey Jo! Great hearing from you! What happened?" - }, - { - "speaker": "Joanna", - "dia_id": "D11:3", - "text": "I went hiking and found some more amazing trails in my town. It was such an awesome experience! I think I am an expert hiker now." - }, - { - "speaker": "Nate", - "dia_id": "D11:4", - "text": "Sounds great, Jo! Happy you had an awesome experience. Did you happen to take any photos of it?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://burst.shopifycdn.com/photos/large-rolling-waterfall-though-a-lush-green-hill.jpg" - ], - "blip_caption": "a photo of a waterfall with a dark sky in the background", - "query": "waterfall lush greenery", - "dia_id": "D11:5", - "text": "Yeah, I did! Loved this spot on the hike. The rush of the water was so soothing." - }, - { - "speaker": "Nate", - "dia_id": "D11:6", - "text": "Wow, looks great! Where did you take this picture? I love the dark sky and green scenery." - }, - { - "speaker": "Joanna", - "dia_id": "D11:7", - "text": "Thanks! I took this photo at a beautiful location called Whispering Falls. It was really peaceful and serene." - }, - { - "speaker": "Nate", - "dia_id": "D11:8", - "text": "I wish I could have been there! Your hikes sound like a blast." - }, - { - "speaker": "Joanna", - "dia_id": "D11:9", - "text": "It was awesome, Nate. The sound of that place and the beauty of nature made me so calm and peaceful. Everything else faded away and all that mattered was the present." - }, - { - "speaker": "Nate", - "dia_id": "D11:10", - "text": "That's great. Glad you found a spot that calms you down - nature sure can be a break from the craziness." - }, - { - "speaker": "Joanna", - "dia_id": "D11:11", - "text": "Nature totally inspires me and it's so calming to be surrounded by its beauty. Hiking has opened up a whole new world for me and I feel like a different person now." - }, - { - "speaker": "Nate", - "dia_id": "D11:12", - "text": "Wow, Jo, that's really cool! It's great to have something that gets those creative juices flowing." - }, - { - "speaker": "Joanna", - "dia_id": "D11:13", - "text": "I always feel like I could write a whole movie when I'm out there in cool places like that!" - }, - { - "speaker": "Nate", - "dia_id": "D11:14", - "text": "Wow! That's really cool that it inspires you that much! For me I just get deep in thought and think about my life or new recipes." - }, - { - "speaker": "Joanna", - "dia_id": "D11:15", - "text": "I think about my life too sometimes when I'm out and about, but there was something special about these trails that made me feel like writing a drama." - }, - { - "speaker": "Nate", - "dia_id": "D11:16", - "text": "Hey, we should go together sometime, don't you think? Maybe I'll start to think of a drama myself and publish my own screenplay." - }, - { - "speaker": "Joanna", - "dia_id": "D11:17", - "text": "Haha, now that would be something! Sure, you should come down and join me on the trails sometime!" - }, - { - "speaker": "Nate", - "dia_id": "D11:18", - "text": "Sounds like a plan! Thanks for the invite Joanna!" - }, - { - "speaker": "Joanna", - "dia_id": "D11:19", - "text": "Sure thing Nate! See you later!" - }, - { - "speaker": "Nate", - "dia_id": "D11:20", - "text": "See ya!" - } - ], - "session_12_date_time": "7:49 pm on 20 May, 2022", - "session_12": [ - { - "speaker": "Nate", - "dia_id": "D12:1", - "text": "Hey Joanna! How've you been? Been a busy week since we talked." - }, - { - "speaker": "Joanna", - "dia_id": "D12:2", - "text": "Hey Nate! Just finished something - pretty wild journey!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/rgx6yqabu5e91.jpg" - ], - "blip_caption": "a photo of a dog laying on a couch in a living room", - "query": "cute dog max", - "dia_id": "D12:3", - "text": "Way to go! I just got a new addition to the family, this is Max!" - }, - { - "speaker": "Joanna", - "dia_id": "D12:4", - "text": "Wow, he's adorable! How long have you had him? I can see why you're thrilled!" - }, - { - "speaker": "Nate", - "dia_id": "D12:5", - "text": "Thanks! It's awesome - he's adopted and so full of energy, and he's filling my life with so much joy. He's even keeping my other pets active." - }, - { - "speaker": "Joanna", - "dia_id": "D12:6", - "text": "Pets sure do make life better! Glad Max is bringing you lots of joy." - }, - { - "speaker": "Nate", - "dia_id": "D12:7", - "text": "Yep, totally! Pets make us so much happier and never let us down. Have you thought any more of getting one of your own?" - }, - { - "speaker": "Joanna", - "dia_id": "D12:8", - "text": "Unfortunately, allergies make it so I don't really want to get any, and I'm too lazy to research alternative pets for my allergies." - }, - { - "speaker": "Nate", - "dia_id": "D12:9", - "text": "Aww, that's unfortunate. It's nice seeing the joy pets bring to others, though. How do you find comfort when you don't have any?" - }, - { - "speaker": "Joanna", - "dia_id": "D12:10", - "text": "Writing and creative projects are what get me through tough times. I'm also grateful for my supportive friends." - }, - { - "speaker": "Nate", - "dia_id": "D12:11", - "text": "Wow, that's awesome! Those both can definitely be therapeutic. It's great to have such positive relationships that make such a great impact." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/0hvywunpjfs91.jpg" - ], - "blip_caption": "a photo of a notepad with a dog on it and a pen", - "query": "handwritten screenplay notebook.", - "dia_id": "D12:12", - "text": "Yeah. It's so nice to have friends who understand and appreciate my work - it's priceless being able to talk about it together and receive feedback. Here's a look at what I've been working on \u2013 it's been quite a journey, but I made it!" - }, - { - "speaker": "Nate", - "dia_id": "D12:13", - "text": "Wow, that looks great Joanna! Is that your third one?" - }, - { - "speaker": "Joanna", - "dia_id": "D12:14", - "text": "Yep! I chose to write about this because it's really personal. It's about loss, identity, and connection. It's a story I've had for ages but just got the guts to write it. It was hard, but I'm so proud of it." - }, - { - "speaker": "Nate", - "dia_id": "D12:15", - "text": "That sounds impressive. You really do like writing about sadness and loss don't you." - }, - { - "speaker": "Joanna", - "dia_id": "D12:16", - "text": "Thanks, Nate! Yeah I really do. I had to be vulnerable and dig deep into those topics. But I think meaningful stories come from personal experiences and feelings. It was scary, but I found that I write best when I'm being true to myself - even if it's hard." - }, - { - "speaker": "Nate", - "dia_id": "D12:17", - "text": "Well done, Joanna! It takes guts to explore your experiences and feelings. I'm proud of you for staying strong and being true to yourself. Keep it up!" - }, - { - "speaker": "Joanna", - "dia_id": "D12:18", - "text": "Thanks, Nate! Your support really means a lot. Knowing I've got people like you cheering me on makes this journey way easier." - }, - { - "speaker": "Nate", - "dia_id": "D12:19", - "text": "No worries, Joanna! Keep going and reach for your dreams. You've got tons of talent and potential!" - } - ], - "session_13_date_time": "3:00 pm on 25 May, 2022", - "session_13": [ - { - "speaker": "Nate", - "dia_id": "D13:1", - "text": "Hey Jo! Been ages since we last talked. Here's something cool that happened the other day - I took Max for a walk and ran into this super nice couple who had a dog. It turns out they live close by. We decided to do doggy playdates, which is awesome considering we all need friends for our pets." - }, - { - "speaker": "Joanna", - "dia_id": "D13:2", - "text": "Hey Nate! Great to hear from you. Sounds like a nice encounter on your walk. Connecting with others who have pets can be uplifting and rewarding." - }, - { - "speaker": "Nate", - "dia_id": "D13:3", - "text": "It's like fate. Having a walking buddy forMax will be great. He really likes the other dog too!" - }, - { - "speaker": "Joanna", - "dia_id": "D13:4", - "text": "Awesome! Did you get to know the couple very well? What were they like?" - }, - { - "speaker": "Nate", - "dia_id": "D13:5", - "text": "They actually didn't share a whole lot in common with me besides the love of animals, but I think that was all we needed to share in common to be good friends!" - }, - { - "speaker": "Joanna", - "dia_id": "D13:6", - "text": "That's really cool that you can just go out and meet people like that, keep it up Nate!" - }, - { - "speaker": "Nate", - "dia_id": "D13:7", - "text": "Thanks! I just really enjoy watching our pets play with one another. Its like a dream come true seeing my dog so happy." - }, - { - "speaker": "Joanna", - "dia_id": "D13:8", - "text": "I can see why having a peaceful presence around could help relieve stress. Having someone or something to come home to for a sense of calm would be helpful for relaxation." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/usll6z99c2tb1.jpg" - ], - "blip_caption": "a photo of a stuffed animal laying on a bed", - "query": "dog cozy blanket toy", - "dia_id": "D13:9", - "text": "Yep, Joanna. It's great! Looky here, I got this new pup for you!" - }, - { - "speaker": "Joanna", - "dia_id": "D13:10", - "text": "Awww! It's so cute! I love the thought Nate!" - }, - { - "speaker": "Nate", - "dia_id": "D13:11", - "text": "Thanks! It's a stuffed animal to remind you of the good vibes." - }, - { - "speaker": "Joanna", - "dia_id": "D13:12", - "text": "That's so sweet! I'll cherish that little guy with all my heart!" - }, - { - "speaker": "Nate", - "dia_id": "D13:13", - "text": "Yeah! It's like having joy in your pocket. It always makes me grin when I look at it." - }, - { - "speaker": "Joanna", - "dia_id": "D13:14", - "text": "That's great, Nate! Appreciate the small joys like that cute stuffed animal. It's a nice reminder!" - }, - { - "speaker": "Nate", - "dia_id": "D13:15", - "text": "Agreed, those little things sure do make life better!" - }, - { - "speaker": "Joanna", - "dia_id": "D13:16", - "text": "I'll always remember those moments that bring us happiness and remind us that life is great!" - }, - { - "speaker": "Nate", - "dia_id": "D13:17", - "text": "Sure, Joanna! It's all about finding those little things and cherishing them, otherwise it's easy to get down!" - }, - { - "speaker": "Joanna", - "dia_id": "D13:18", - "text": "Thinking back to the tough times finishing my screenplay made me realize it's those moments that bring joy and make the journey worth it." - }, - { - "speaker": "Nate", - "dia_id": "D13:19", - "text": "Yeah, those little moments make it all worth it, especially during tough times. Enjoying the ride is key." - }, - { - "speaker": "Joanna", - "dia_id": "D13:20", - "text": "Appreciating the journey and being aware of those happy moments can be a game-changer! It keeps us focused on our dreams. Can't wait to show it to you. I value your opinion!" - }, - { - "speaker": "Nate", - "dia_id": "D13:21", - "text": "Can't wait to see it, Joanna! I'm here to support you." - }, - { - "speaker": "Joanna", - "dia_id": "D13:22", - "text": "Thanks, Nate! Your support is greatly appreciated. I'll make sure to keep you updated." - }, - { - "speaker": "Nate", - "dia_id": "D13:23", - "text": "No worries! You've got this. Keep it up!" - } - ], - "session_14_date_time": "5:44 pm on 3 June, 2022", - "session_14": [ - { - "speaker": "Joanna", - "dia_id": "D14:1", - "text": "Nate, after finishing my screenplay I got a rejection letter from a major company. It really bummed me out." - }, - { - "speaker": "Nate", - "dia_id": "D14:2", - "text": "Sorry to hear that, Joanna. Rejection stinks, but it doesn't mean you're not talented. Don't give up on your dreams!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:3", - "text": "Thanks, Nate. It can feel like a step back sometimes. But I appreciate your kind words and encouragement." - }, - { - "speaker": "Nate", - "dia_id": "D14:4", - "text": "Sure, just make sure you keep going and believing in yourself. Did something happen with the company?" - }, - { - "speaker": "Joanna", - "dia_id": "D14:5", - "text": "They just sent me a generic rejection letter without much feedback. It's disheartening not knowing why it didn't work out." - }, - { - "speaker": "Nate", - "dia_id": "D14:6", - "text": "Ugh, that's so frustrating. But don't get discouraged, just keep going." - }, - { - "speaker": "Joanna", - "dia_id": "D14:7", - "text": "Yeah, you're right. I won't let this bring me down. Thanks for your support. What have you been up to lately?" - }, - { - "speaker": "Nate", - "dia_id": "D14:8", - "text": "I've been doing great - I just won another regional video game tournament last week! It was so cool, plus I met some new people. Connecting with fellow gamers is always awesome." - }, - { - "speaker": "Joanna", - "dia_id": "D14:9", - "text": "Way to go, Nate! Congratulations on your victory in the tournament! It must feel great to be recognized for your gaming skills." - }, - { - "speaker": "Nate", - "dia_id": "D14:10", - "text": "Thanks, Joanna! Winning was a huge confidence boost and shows my hard work paid off. I'm really happy with my progress." - }, - { - "speaker": "Joanna", - "dia_id": "D14:11", - "text": "I am as well! It's great to hear from you about your tournaments throughout the years!" - }, - { - "speaker": "Nate", - "dia_id": "D14:12", - "text": "Thanks! I has been a while since my first tournament hasn't it? I appreciate your support!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:13", - "text": "Anytime Nate! I'm here for you every step of the way." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a purple and blue controller with a star field design", - "dia_id": "D14:14", - "text": "I talked to some of the guys at the tournament afterwards, and they said they wanted to hang out later!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:15", - "text": "Sounds like fun! It's good to have friends that share your interests!" - }, - { - "speaker": "Nate", - "dia_id": "D14:16", - "text": "For sure! They asked for some tips in how to improve their game, so I said I could help." - }, - { - "speaker": "Joanna", - "dia_id": "D14:17", - "text": "Good on you for helping strangers out! Stepping outside your comfort zone is always great." - }, - { - "speaker": "Nate", - "dia_id": "D14:18", - "text": "Thanks, I just like helping people. Do you have any plans for the weekend?" - }, - { - "speaker": "Joanna", - "dia_id": "D14:19", - "text": "Yep, I'm hiking with some buddies this weekend. We're checking out a new trail with a rad waterfall. Can't wait! Do you have any fun plans?" - }, - { - "speaker": "Nate", - "dia_id": "D14:20", - "text": "Sounds great! Have fun with that. I'm organizing a gaming party two weekends later - it'll be hectic but fun!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:21", - "text": "Oh? Are you going to invite your tournament friends?" - }, - { - "speaker": "Nate", - "dia_id": "D14:22", - "text": "Definitely! And some old friends and teamates from other tournaments." - }, - { - "speaker": "Joanna", - "dia_id": "D14:23", - "text": "Sounds like fun, Nate! I wish you the best on your party. Have a blast!" - }, - { - "speaker": "Nate", - "dia_id": "D14:24", - "text": "Thanks Joanna! I'm sure it'll be a blast. I'm even getting everyone custom controller decorations just for coming!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:25", - "text": "Wow, I bet they'll love that! What a sweet idea." - }, - { - "speaker": "Nate", - "dia_id": "D14:26", - "text": "I know right? Have a great hike. Take lots of pics! See ya later!" - }, - { - "speaker": "Joanna", - "dia_id": "D14:27", - "text": "Thanks Nate! See you later!" - } - ], - "session_15_date_time": "2:12 pm on 5 June, 2022", - "session_15": [ - { - "speaker": "Joanna", - "blip_caption": "a photo of a spider - man poster hanging on a wall", - "dia_id": "D15:1", - "text": "Hey Nate! Yesterday was crazy cool - I wrote a few bits for a screenplay that appeared on the big screen yesterday! It was nerve-wracking but so inspiring to see my words come alive!" - }, - { - "speaker": "Nate", - "dia_id": "D15:2", - "text": "Congrats, Joanna! Seeing your hard work pay off like that must've felt amazing. I bet it was scary too, but awesome! You're so inspiring. By the way, last time we saw eachother, I noticed a spiderman pin on your purse. Is Spider-Man your favorite superhero, or do you have another fave?" - }, - { - "speaker": "Joanna", - "dia_id": "D15:3", - "text": "Thanks, Nate! It was a real roller coaster, but seeing the hard work pay off was amazing. Spider-Man has always been a favorite of mine - I mean, who doesn't love Peter Parker's struggles between being a hero and being a person? But I'm kind of a sucker for any superhero - everyone has their own rad story and powers. Do you have a favorite superhero?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/7148/6768759075_81b67d203f_b.jpg" - ], - "blip_caption": "a photography of a toy iron man standing on a white surface", - "query": "iron man action figures", - "dia_id": "D15:4", - "re-download": true, - "text": "That's great, Joanna! Iron Man is my top pick. I love his tech and that sarcastic humor. Seeing these figures just makes me feel invincible!" - }, - { - "speaker": "Joanna", - "dia_id": "D15:5", - "text": "Wow, Nate! That's awesome. I love the tech and funny jokes of Iron Man too. What made you get that figure?" - }, - { - "speaker": "Nate", - "dia_id": "D15:6", - "text": "Thanks Joanna! I got it because it reminded me of something I love. Its presence in my room is a good reminder to keep working on my goals. Any inspiring things in your room?" - }, - { - "speaker": "Joanna", - "dia_id": "D15:7", - "text": "My cork board is full of inspiring quotes and pictures for motivation and creativity. It's my little corner of inspiration." - }, - { - "speaker": "Nate", - "dia_id": "D15:8", - "text": "Wow Joanna, that sounds great! Could you show me a picture of it?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.pinimg.com/originals/cc/b0/14/ccb0147bd55dfa436f284fb5939a7808.jpg" - ], - "blip_caption": "a photo of a picture frame with a picture of a family", - "query": "cork board quotes photos loved ones mementos", - "dia_id": "D15:9", - "text": "Here ya go, a pic of my cork board. It's got quotes, photos, and little keepsakes." - }, - { - "speaker": "Nate", - "dia_id": "D15:10", - "text": "That's a great pic of your family! What made you hang it on your cork board?" - }, - { - "speaker": "Joanna", - "dia_id": "D15:11", - "text": "Thanks, Nate! Having that picture on my cork board reminds me of the love and encouragement from them every day." - }, - { - "speaker": "Nate", - "dia_id": "D15:12", - "text": "That's great, Joanna. Family support is invaluable. It's so good to have those reminders." - }, - { - "speaker": "Joanna", - "dia_id": "D15:13", - "text": "Absolutely, it means a lot and keeps me going." - }, - { - "speaker": "Nate", - "dia_id": "D15:14", - "text": "I really should start a cork board of my own shouldn't I. That seems like a really valuable thing!" - }, - { - "speaker": "Joanna", - "dia_id": "D15:15", - "text": "I would definitely recommend it! As long as your willing to explain what it is to your friends." - }, - { - "speaker": "Nate", - "dia_id": "D15:16", - "text": "Of course! Well see you later Joanna!" - }, - { - "speaker": "Joanna", - "dia_id": "D15:17", - "text": "Bye Nate!" - } - ], - "session_16_date_time": "10:55 am on 24 June, 2022", - "session_16": [ - { - "speaker": "Joanna", - "dia_id": "D16:1", - "text": "Hey Nate, long time no see! How have you been? I just got done submitting my recent screenplay to a film contest just to see how others might like it!" - }, - { - "speaker": "Nate", - "dia_id": "D16:2", - "text": "That's really cool Joanna! I hope it does well, and I've been doing great! The gaming party was a great success! We even played some Chess afterward just for fun." - }, - { - "speaker": "Joanna", - "dia_id": "D16:3", - "text": "Nice! Did your friends like the controller accessories?" - }, - { - "speaker": "Nate", - "dia_id": "D16:4", - "text": "Most of them did! I can't say if all of them will continue to use them or not, but that's beside the point." - }, - { - "speaker": "Joanna", - "dia_id": "D16:5", - "text": "Yeah Nate, you're right. It doesn't matter if they use it, its the thought that matters right?" - }, - { - "speaker": "Nate", - "dia_id": "D16:6", - "text": "Absolutely! There were 7 people that attended, and 6 of them said they'd want to do it again next month!" - }, - { - "speaker": "Joanna", - "dia_id": "D16:7", - "text": "That sounds like a huge success then! Congrats!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://www.stockvault.net/data/2022/06/05/293150/preview16.jpg" - ], - "blip_caption": "a photography of a group of people sitting on a bench", - "query": "vegan coconut milk ice cream vegan diet group", - "dia_id": "D16:8", - "re-download": true, - "text": "Thanks! On another note, I made vegan ice cream last Friday and shared it with some people in my vegan diet group. It was awesome!" - }, - { - "speaker": "Joanna", - "dia_id": "D16:9", - "text": "Mm, yum! Can you give me the recipe for that? It sounds like it'd be a good recipe!" - }, - { - "speaker": "Nate", - "dia_id": "D16:10", - "text": "Sure thing! I can give it to you tomorrow, how does that sound?" - }, - { - "speaker": "Joanna", - "dia_id": "D16:11", - "text": "Awesome! I'm going to make it for my family this weekend - can't wait!" - }, - { - "speaker": "Nate", - "dia_id": "D16:12", - "text": "Nice one, Joanna! Hope you and your family like it. Let me know how it went!" - }, - { - "speaker": "Joanna", - "dia_id": "D16:13", - "text": "Sure thing! They love it when I make them new things!" - }, - { - "speaker": "Nate", - "dia_id": "D16:14", - "text": "Then I have no doubt they'll love the icecream!" - }, - { - "speaker": "Joanna", - "dia_id": "D16:15", - "text": "Thanks Nate! Your support is greatly appreciated. I'll make sure to keep you updated." - }, - { - "speaker": "Nate", - "dia_id": "D16:16", - "text": "Can't wait to hear about it. Have a great day! Take care." - } - ], - "session_17_date_time": "2:34 pm on 10 July, 2022", - "session_17": [ - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/p59z4xlbytfa1.jpg" - ], - "blip_caption": "a photo of a television screen showing a trophy and a trophy", - "query": "video game tournament trophy game screenshot", - "dia_id": "D17:1", - "text": "Hey Joanna, check this out! I won my fourth video game tournament on Friday! It was awesome competing and showing off my skills - and the victory was indescribable. I'm really proud that I can make money doing what I love. This one was online!" - }, - { - "speaker": "Joanna", - "dia_id": "D17:2", - "text": "Congrats, Nate! That's awesome! So proud of you. Your hard work really paid off - keep it up! BTW, I took a road trip for research for my next movie while you were winning. Much-needed break and a chance to explore new places and get inspired." - }, - { - "speaker": "Nate", - "dia_id": "D17:3", - "text": "Thanks, Joanna! Your support means a lot to me. That road trip sounds great! Where did you go? Did you discover any interesting places?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/9/91/Old_Geodesy_library_books.jpg" - ], - "blip_caption": "a photography of a book shelf filled with lots of books", - "query": "vintage library woodhaven midwest", - "dia_id": "D17:4", - "re-download": true, - "text": "Thanks Nate! Appreciate your kind words. I went to Woodhaven, a small town in the Midwest. Got to see some lovely scenery and historic buildings. Checked out the library there, it had a cool old book collection!" - }, - { - "speaker": "Nate", - "dia_id": "D17:5", - "text": "That place looks interesting! Did you find any cool books there?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/sqyihv2m1j871.jpg" - ], - "blip_caption": "a photo of a book with writing on it and a pen", - "query": "old journal stories sketches handwritten pages", - "dia_id": "D17:6", - "text": "I stumbled upon this super cool book from the 1900s with stories and sketches - so awesome to read about the town and the people living there!" - }, - { - "speaker": "Nate", - "dia_id": "D17:7", - "text": "That sounds really interesting! Anyting specific stick out to you about it?" - }, - { - "speaker": "Joanna", - "dia_id": "D17:8", - "text": "Woodhaven has had an interesting past with lots of cool people. Seeing how much it changed sparked ideas for my next script." - }, - { - "speaker": "Nate", - "dia_id": "D17:9", - "text": "Real-life stories are the best for inspiration. Can't wait to hear about your next one. Keep it up!" - }, - { - "speaker": "Joanna", - "dia_id": "D17:10", - "text": "Thanks, Nate! I'm stoked about this new script. It's different from my previous work, but it has the potential to be something awesome! I'll be sure to keep you posted." - }, - { - "speaker": "Nate", - "dia_id": "D17:11", - "text": "I'm sure it will do just as well as your last one! Keep on trying and believe in yourself!" - }, - { - "speaker": "Joanna", - "dia_id": "D17:12", - "text": "Thanks, Nate! Your encouragement really means a lot to me. You're the best for supporting me in my writing journey." - }, - { - "speaker": "Nate", - "dia_id": "D17:13", - "text": "I'm always here for you! You've got so much talent, just keep going for it!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.imgur.com/bJiLDHb.jpg" - ], - "blip_caption": "a photo of a person holding a notebook with a handwritten page", - "query": "notebook writing talent dreams", - "dia_id": "D17:14", - "text": "I will! I actually started on a book recently since my movie did well!" - }, - { - "speaker": "Nate", - "dia_id": "D17:15", - "text": "Nice! I'm curious, what is it about?" - }, - { - "speaker": "Joanna", - "dia_id": "D17:16", - "text": "That page specifically has some dialogues exploring loss, redemption, and forgiveness. It's a deep and emotional story that I'm really excited about!" - }, - { - "speaker": "Nate", - "dia_id": "D17:17", - "text": "Wow, Joanna! It sounds awesome. I'm so excited to see how it all plays out!" - }, - { - "speaker": "Joanna", - "dia_id": "D17:18", - "text": "Thanks, Nate! I'm so glad you're excited. I've never really tried publishing a book, but this might be the first!" - }, - { - "speaker": "Nate", - "dia_id": "D17:19", - "text": "Good luck on that! I'm sure people will recognise you as the same author of the movie you got published and love the book even more." - }, - { - "speaker": "Joanna", - "dia_id": "D17:20", - "text": "Thanks, Nate! Your belief in me means a lot. I'll keep doing my best. Thanks for the support!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a person holding a small turtle in a container", - "dia_id": "D17:21", - "text": "No problem, Joanna. I'm here for you. Your hard work will pay off, I promise. Believe in yourself and your talent - you're incredible!" - } - ], - "session_18_date_time": "6:12 pm on 14 August, 2022", - "session_18": [ - { - "speaker": "Joanna", - "img_url": [ - "https://i.pinimg.com/originals/2f/df/23/2fdf2357bee9649780477b595cb2b003.jpg" - ], - "blip_caption": "a photo of a notebook with a bunch of stickers on it", - "query": "writing journal ideas notes", - "dia_id": "D18:1", - "text": "Hey Nate, long time no talk! I've been busy with writing projects and really going all out with it. It's been the best thing ever - a mix of highs and lows - and my journal's pretty much my rock. Writing's such a huge part of me now." - }, - { - "speaker": "Nate", - "dia_id": "D18:2", - "text": "Hey Joanna! Great to hear it! It's amazing how much a certain activity can become a part of our lives. Keep it up, you're inspiring! Is writing your way to solace and creativity?" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a handwritten letter from a man who is holding a piece of paper", - "dia_id": "D18:3", - "text": "Yeah, definitely. Writing has become like an escape and a way to express my feelings. It gives me a chance to put all my thoughts and feelings down and make something good out of it. Words just have a magical way of healing." - }, - { - "speaker": "Nate", - "dia_id": "D18:4", - "text": "That's really cool, I like it! It's incredible how words can turn something sad into something special. I'm glad it worked for you. Anything cool happening recently?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/6a9y8ycad0ga1.jpg" - ], - "blip_caption": "a photo of a note written to a person on a piece of paper", - "query": "handwritten letter impact writing", - "dia_id": "D18:5", - "text": "Yep. Last week, someone wrote me a letter after reading an online blog post I made about a hard moment in my life. Their words touched me; they said my story had brought them comfort. It was awesome to realize my words had that kind of power. It reminded me why I love writing." - }, - { - "speaker": "Nate", - "dia_id": "D18:6", - "text": "Nice work, Joanna! That must feel sureal. Keep it up - you're changing lives!" - }, - { - "speaker": "Joanna", - "dia_id": "D18:7", - "text": "Thanks, Nate! Really appreciate your kind words. It's knowing that my writing can make a difference that keeps me going, even on tough days. So glad to have this outlet to share my stories and hopefully have an impact. How about you? Anything new since we last talked?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i2.pickpik.com/photos/798/109/80/ice-cream-sundae-ice-cream-sundae-vanilla-preview.jpg" - ], - "blip_caption": "a photography of a dessert with whipped cream and chocolate sauce", - "query": "coconut milk ice cream homemade toppings", - "dia_id": "D18:8", - "re-download": true, - "text": "Thanks, Joanna! Your words mean a lot. Since we last spoke, I started teaching people how to make this. Sharing my love for dairy-free desserts has been fun and rewarding." - }, - { - "speaker": "Joanna", - "dia_id": "D18:9", - "text": "Yum, Nate! I love it when you make coconut milk icecream, it's so good!" - }, - { - "speaker": "Nate", - "dia_id": "D18:10", - "text": "I've been really into making this lately - it's creamy, rich, dairy-free and a new recipe! Wanna try it? I can share the recipe with you if you'd like!" - }, - { - "speaker": "Joanna", - "dia_id": "D18:11", - "text": "I'd love to try it! Thanks for sharing your love for dairy-free desserts. I really appreciate it!" - }, - { - "speaker": "Nate", - "dia_id": "D18:12", - "text": "No problem, Joanna! Always happy to share them with you. Sending you the recipe now!" - }, - { - "speaker": "Joanna", - "dia_id": "D18:13", - "text": "Thanks, Nate! Can't wait to surprise my family with something delicious!" - }, - { - "speaker": "Nate", - "dia_id": "D18:14", - "text": "No problem, Joanna! Wish them luck! Let me know how it goes. Have a blast baking!" - }, - { - "speaker": "Joanna", - "dia_id": "D18:15", - "text": "Thanks, Nate. I'll keep you posted. Have a great day." - }, - { - "speaker": "Nate", - "dia_id": "D18:16", - "text": "You too!" - } - ], - "session_19_date_time": "10:57 am on 22 August, 2022", - "session_19": [ - { - "speaker": "Nate", - "dia_id": "D19:1", - "text": "Woah Joanna, I won an international tournament yesterday! It was wild. Gaming has brought me so much success and now I'm able to make a living at something I'm passionate about - I'm loving it." - }, - { - "speaker": "Joanna", - "dia_id": "D19:2", - "text": "Congrats, Nate! So proud of you for winning that tournament, that's awesome! Must feel great to turn your passion into a career." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.pinimg.com/originals/57/31/42/57314282992ff77a40be8450c003b5c7.jpg" - ], - "blip_caption": "a photo of a fish tank with a fish inside of it", - "query": "cute pet turtles tank", - "dia_id": "D19:3", - "text": "I'm really stoked to see all my hard work paying off! I'm super proud of what I accomplished. On another note, my little dudes got a new tank! Check them out, they're so cute, right?!" - }, - { - "speaker": "Joanna", - "dia_id": "D19:4", - "text": "Wow Nate, they're adorable! I can see why you enjoy spending time with them. It looks like they have so much more room to swim now!" - }, - { - "speaker": "Nate", - "dia_id": "D19:5", - "text": "They're my little buddies, always calm and peaceful. It makes coming home after a long day of gaming better. The tank expansion has made them so happy! How have you been?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://halekatiedotcom.files.wordpress.com/2020/09/img_6976.jpg" - ], - "blip_caption": "a photo of a desk with a chair and a computer", - "query": "writers group meeting stack scripts desk", - "dia_id": "D19:6", - "text": "I'm good! Was super nervous last week when I shared my book with my writers group but got some great feedback. My hard work is paying off, it's such an awesome feeling!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a dessert in a glass on a counter", - "dia_id": "D19:7", - "text": "Wow Jo, you're killing it! Getting this kind of feedback means people are really connecting with your writing. Pretty cool! Did you celebrate?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://tastecando.com/cdn/shop/articles/Keto_Krisp_January_Post.jpg" - ], - "blip_caption": "a photo of two desserts with spoons and a bar of chocolate", - "query": "raspberry chia pudding parfait dessert", - "dia_id": "D19:8", - "text": "Thanks, Nate! It feels great knowing that people like my writing. I celebrated by making this delicious treat - yum! Any plans for the weekend?" - }, - { - "speaker": "Nate", - "dia_id": "D19:9", - "text": "I'm taking some time off this weekend to chill with my pets. Anything cool happening with you?" - }, - { - "speaker": "Joanna", - "dia_id": "D19:10", - "text": "I'm relaxing and recharging this weekend with a long walk and some reading. It's a good break." - }, - { - "speaker": "Nate", - "dia_id": "D19:11", - "text": "Looks like we both need a break. I'm glad your able to find a way to recharge! It's so incredibly important to take time off!" - }, - { - "speaker": "Joanna", - "dia_id": "D19:12", - "text": "Thanks, Nate! I've learned that taking breaks and looking after myself are important for my inspiration and mental health. It's all about finding balance." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/k78gqk5c5kx71.jpg" - ], - "blip_caption": "a photo of a bookcase filled with books and a toy car", - "query": "bookshelf filled with books", - "dia_id": "D19:13", - "text": "Yeah, balance is key! It's so cool how taking care of ourselves helps us be more creative and happier. I'm always looking for something new to read. Got any book recommendations? I've got a lot of books to choose from." - }, - { - "speaker": "Joanna", - "dia_id": "D19:14", - "text": "I reccomend finding a fantasy book series to read through. Most fiction series are great reads when your trying to relax." - }, - { - "speaker": "Nate", - "img_url": [ - "https://thelitbitch.files.wordpress.com/2023/04/img_2043_jpg.jpg" - ], - "blip_caption": "a photo of a stack of books sitting on top of a wooden table", - "query": "a court of thorns and roses book sarah j maas", - "dia_id": "D19:15", - "text": "Good idea! How about this series?" - }, - { - "speaker": "Joanna", - "dia_id": "D19:16", - "text": "That's a great one! Let me know what you think when your finished!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/vx7o8gcqv01c1.jpg" - ], - "blip_caption": "a photo of a poster of a man falling off a cliff", - "query": "space opera book series", - "dia_id": "D19:17", - "text": "Sure thing! And since your recommending me a book, I thought I should do the same! I'd really recommend this series. It's got awesome battles and interesting characters." - }, - { - "speaker": "Joanna", - "dia_id": "D19:18", - "text": "Wow, that series looks awesome! I'll have to check it out sometime!" - }, - { - "speaker": "Nate", - "dia_id": "D19:19", - "text": "You really should! The action scenes are awesome and the plot rocks. Definitely one of my favorites!" - }, - { - "speaker": "Joanna", - "dia_id": "D19:20", - "text": "Wow, sounds great! I'll definitely add it to my list; thanks for the recommendation!" - }, - { - "speaker": "Nate", - "dia_id": "D19:21", - "text": "Enjoy it! Have a good day." - }, - { - "speaker": "Joanna", - "dia_id": "D19:22", - "text": "Thanks, Nate! You too! Have a great day. Take care." - } - ], - "session_20_date_time": "6:03 pm on 5 September, 2022", - "session_20": [ - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/8056/8444279059_bbf0c79356_b.jpg" - ], - "blip_caption": "a photography of two turtles sitting on a rock in a pond", - "query": "pet turtles mario luigi", - "dia_id": "D20:1", - "re-download": true, - "text": "Hey Joanna! Long time no talk. So much has happened. Look how cute they are! Hanging with them has been a big help, especially recently. Speaking of which, I just had a letdown in a video game tourney - I didn't do too great, even though I tried. It was a setback, but I'm trying to stay positive." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://chensplate.com/wp-content/uploads/2021/02/IMG_8512.jpg" - ], - "blip_caption": "a photo of a piece of cake with strawberries and chocolate", - "query": "dairy-free chocolate cake pink frosting", - "dia_id": "D20:2", - "text": "Hey Nate! Cute turtles! Bummer about the setback. Any positive vibes comin' your way? I just revised on of my old recipes and made this!" - }, - { - "speaker": "Nate", - "dia_id": "D20:3", - "text": "Hey Joanna, yeah it's a bummer that I didn't do well. But it's all part of the learning curve, you know? Also that looks super good! Anyways, how are you holding up?" - }, - { - "speaker": "Joanna", - "dia_id": "D20:4", - "text": "I'm doing OK, thanks. Just been tinkering with that recipe and a few others. It's helping me find some comfort and getting creative." - }, - { - "speaker": "Nate", - "dia_id": "D20:5", - "text": "What else are you making? It's always satisfying to see the kind of things you do when your in one of those moods!" - }, - { - "speaker": "Joanna", - "dia_id": "D20:6", - "text": "Been tweaking a dessert recipe to make it yummier and more accessible." - }, - { - "speaker": "Nate", - "dia_id": "D20:7", - "text": "Wow, that sounds great! What flavors are you experimenting with?" - }, - { - "speaker": "Joanna", - "dia_id": "D20:8", - "text": "Trying out different flavors like chocolate, raspberry, and coconut has been a blast!" - }, - { - "speaker": "Nate", - "dia_id": "D20:9", - "text": "Sounds delicious! Are you only trying dairy-free options?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://cook2nourish.com/wp-content/uploads/2019/03/Mr6bqaNkSazxNPIxqCHyw-e1553202400166.jpg" - ], - "blip_caption": "a photo of a plate of cupcakes with different toppings", - "query": "dairy-free chocolate coconut cupcakes with raspberry frosting", - "dia_id": "D20:10", - "text": "Yeah, since I'm lactose intolerant I'm trying out dairy-free options like coconut or almond milk instead. It's been a fun challenge seeing how to make yummy treats that suit everyone's diets. I even made these dairy-free chocolate coconut cupcakes with raspberry frosting." - }, - { - "speaker": "Nate", - "dia_id": "D20:11", - "text": "Woah, those look great, Joanna! It's cool that you make desserts that work for everyone's diets. Do you have any more yummy recipes hiding in there?" - }, - { - "speaker": "Joanna", - "dia_id": "D20:12", - "text": "Yep! I've been making all sorts of desserts that work for everyone's diets - cookies, pies, cakes - everything! I'll share more recipes with you soon." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a pile of cookies with sprinkles on a wooden table", - "dia_id": "D20:13", - "text": "Can't wait to try them. Can I join you sometime? I think baking and cooking really brings us together!" - }, - { - "speaker": "Joanna", - "dia_id": "D20:14", - "text": "Yeah, Nate! A fellow Chef in the kitchen is always a great help! Speaking of which, i'm curious, any more tips for dairy-free baking?" - }, - { - "speaker": "Nate", - "dia_id": "D20:15", - "text": "You can use stuff like dairy-free margarine or coconut oil instead of butter, and make sure to check the labels to ensure they're dairy-free. Good luck!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a cookie with chocolate drizzle and almonds", - "dia_id": "D20:16", - "text": "Thanks, Nate! Love your ideas, can't wait to try them out!" - }, - { - "speaker": "Nate", - "dia_id": "D20:17", - "text": "No problem! I love to help, so just shoot me a question anytime you need!" - }, - { - "speaker": "Joanna", - "dia_id": "D20:18", - "text": "Got it Nate, I'll see you soon!" - }, - { - "speaker": "Nate", - "dia_id": "D20:19", - "text": "Bye Joanna! Good luck!" - } - ], - "session_21_date_time": "1:43 pm on 14 September, 2022", - "session_21": [ - { - "speaker": "Joanna", - "dia_id": "D21:1", - "text": "Hey Nate, long time no see! My laptop crashed last week and I lost all my work - super frustrating! As a writer, my laptop is like half of my lifeline so losing all progress was like a major blow." - }, - { - "speaker": "Nate", - "dia_id": "D21:2", - "text": "Hey Joanna, sorry to hear about that. Losing so much progress must be really frustrating. Did you manage to recover anything? Maybe consider backing up your work in the future?" - }, - { - "speaker": "Joanna", - "dia_id": "D21:3", - "text": "Thanks for the sympathy, Nate. Nothing was recoverable, but now I have an external drive for backups. I never want to go through this again. So, how have you been? Making anything cool?" - }, - { - "speaker": "Nate", - "dia_id": "D21:4", - "text": "Hey Joanna, I'm no writer like you, but something pretty awesome happened. Last Monday I got to teach people vegan ice cream recipes on my own cooking show! It was a bit nerve-wracking to put myself out there, but it was a blast. Plus, I picked up a few new recipes!" - }, - { - "speaker": "Joanna", - "dia_id": "D21:5", - "text": "Way to go, Nate! Congrats on the cooking show, I'll definitely be tuning in! What's your favorite dish from the show?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/7055/6945130989_40895e5372_b.jpg" - ], - "blip_caption": "a photography of a bowl of ice cream with a spoon on a table", - "query": "coconut milk ice cream", - "dia_id": "D21:6", - "re-download": true, - "text": "Coconut milk ice cream is at the top of my list. It's so smooth and creamy with a tropical coconut twist. Plus, it's dairy-free for people who can't have lactose or who want vegan options. Here's a snap of the ice cream I made." - }, - { - "speaker": "Joanna", - "dia_id": "D21:7", - "text": "Wow, that looks amazing, Nate! I love the color and texture. It's great that you're making these options. Could you share the recipe? I'd love to try making it sometime!" - }, - { - "speaker": "Nate", - "dia_id": "D21:8", - "text": "Yeah sure! Would love to share it. Let's spread the joy of dairy-free options! Let me know when you make it!" - }, - { - "speaker": "Joanna", - "dia_id": "D21:9", - "text": "Cool, Nate! Gonna give it a go. Dairy-free is a must for me, especially for desserts. Last Friday, I made a deeeelish dessert with almond milk - it was good! Got any favs when it comes to dairy-free desserts?" - }, - { - "speaker": "Nate", - "dia_id": "D21:10", - "text": "Coconut milk ice cream is one of my favorites as you might be able to tell, but I also love a dairy-free chocolate mousse. It's super creamy and tastes like the real thing. What's been your favorite dairy-free sweet treat so far?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://wornslapout.com/wp-content/uploads/2018/11/img_3684.jpg" - ], - "blip_caption": "a photo of a chocolate tart with raspberries on top", - "query": "chocolate raspberry tart almond flour crust ganache fresh raspberries", - "dia_id": "D21:11", - "text": "Hey Nate, my favorite dairy-free treat is this amazing chocolate raspberry tart. It has an almond flour crust, chocolate ganache, and fresh raspberries - it's delicious!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a piece of chocolate cake with raspberries on a plate", - "dia_id": "D21:12", - "text": "That looks amazing, Joanna! I need to try baking that. What other treats do you like making?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://yaycakeday.files.wordpress.com/2019/08/ice-cream-cake-1.jpg" - ], - "blip_caption": "a photo of a piece of chocolate cake with raspberries on a plate", - "query": "dairy-free chocolate cake raspberries", - "dia_id": "D21:13", - "text": "Hey Nate, I love making this dairy-free chocolate cake with raspberries. It's so moist and delicious - perfect sweetness level." - }, - { - "speaker": "Nate", - "dia_id": "D21:14", - "text": "That cake looks amazing, Joanna! How did you make it?" - }, - { - "speaker": "Joanna", - "dia_id": "D21:15", - "text": "I make it with almond flour, coconut oil, chocolate and raspberries. It's my favorite for birthdays and special days." - }, - { - "speaker": "Nate", - "dia_id": "D21:16", - "text": "Yum, Joanna! Gotta try that one. Any others you want to share?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://tamboracai.com/assets/Megan-Marlow-Acai-Vegan-Cheesecake-Bars_02.jpg" - ], - "blip_caption": "a photo of a piece of cake with walnuts on top", - "query": "dairy-free blueberry cheesecake bars recipe", - "dia_id": "D21:17", - "text": "Hey Nate! Here's another recipe I like. It's a delicious dessert made with blueberries, coconut milk, and a gluten-free crust. So creamy and delicious!" - }, - { - "speaker": "Nate", - "dia_id": "D21:18", - "text": "Wow, Joanna! That dessert looks amazing. I'll definitely have to give it a try. Thanks!" - }, - { - "speaker": "Joanna", - "dia_id": "D21:19", - "text": "Glad to help, Nate. Let me know if you try it. I'm sure you'll enjoy it. It was great chatting." - }, - { - "speaker": "Nate", - "dia_id": "D21:20", - "text": "Thanks Joanna! I will. Bye!" - } - ], - "session_22_date_time": "11:15 am on 6 October, 2022", - "session_22": [ - { - "speaker": "Joanna", - "img_url": [ - "https://i0.wp.com/mittsandmeasures.com/wp-content/uploads/2019/10/img_0051.jpg" - ], - "blip_caption": "a photo of a tart with raspberries on a white plate", - "query": "dairy-free chocolate raspberry tarts", - "dia_id": "D22:1", - "text": "Hey Nate, hi! Yesterday, I tried my newest dairy-free recipe and it was a winner with my family! Mixing and matching flavors is fun and I'm always trying new things. How about you?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/0z7nrwjeqc431.jpg" - ], - "blip_caption": "a photo of a trophy and a game controller on a table", - "query": "video game tournament trophy cash prize", - "dia_id": "D22:2", - "text": "Hey Joanna! That tart looks yummy! Lately, I've been doing great - I won a really big video game tournament last week and it was awesome! I still can't believe I made so much money from it." - }, - { - "speaker": "Joanna", - "dia_id": "D22:3", - "text": "Way to go, Nate! Winning the tournament and earning cash is awesome - congrats! Did you save it for something special?" - }, - { - "speaker": "Nate", - "dia_id": "D22:4", - "text": "Thanks Joanna! Yeah, I saved some but I'm not sure what to do with it - I'm completely content already. I don't have big plans anyway, so it's nice to have the extra cash on hand." - }, - { - "speaker": "Joanna", - "dia_id": "D22:5", - "text": "That's awesome, Nate! Having some extra cash on hand definitely brings a sense of freedom and relaxation, huh?" - }, - { - "speaker": "Nate", - "dia_id": "D22:6", - "text": "Yes! Finally, I don't have to stress about it, so I can just enjoy my movies and games." - }, - { - "speaker": "Joanna", - "dia_id": "D22:7", - "text": "Taking breaks and reducing stress is pretty nice! Have you watched any good movies recently? I could use some recommendations!" - }, - { - "speaker": "Nate", - "dia_id": "D22:8", - "text": "I watched \"Little Women\" recently, and it was great! The acting was awesome and the story was so captivating. Definitely a good one!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:9", - "text": "I'm so glad you enjoyed it! I recommended it to you a while back. I watched it too and it really spoke to me. Themes like sisterhood, love, and chasing dreams were explored so well. By the way, I finished up my writing for my book last week. Put in a ton of late nights and edits but finally got it done. I'm so proud of it! Can't wait to see what happens next." - }, - { - "speaker": "Nate", - "dia_id": "D22:10", - "text": "Way to go! We both know it took some effort, but I'm sure it'll be great. Congrats on finishing it up!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:11", - "text": "Thanks Nate! Your words mean a lot. Dedication and late nights got me here, but it was worth it. Just like you with your recent tournament - hard work pays off. I appreciate your support throughout!" - }, - { - "speaker": "Nate", - "dia_id": "D22:12", - "text": "I'm always here for you, Joanna! You've worked so hard and accomplished a lot \u2013 I'm proud. Keep on going!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:13", - "text": "Thanks, Nate! I won't give up on my goals as long as your here to support me." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.pinimg.com/originals/5e/f4/f9/5ef4f9bd2f094b2d0a4ddd4861b928a0.jpg" - ], - "blip_caption": "a photo of a white board with a drawing of arrows and words", - "query": "motivational quotes whiteboard", - "dia_id": "D22:14", - "text": "You can always count on me! I even made this for you!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:15", - "text": "Wow, Nate, that looks awesome! What inspired you?" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a young boy drawing on a white board", - "dia_id": "D22:16", - "text": "I figured you could always look back on this whenever you need encouragement, and that was all the inspiration I needed. And I would also say that your life path can be quite inspirational!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:17", - "text": "Wow, Nate! That's sweet of you! I'll make sure to remember this when I need the encouragement the most." - }, - { - "speaker": "Nate", - "dia_id": "D22:18", - "text": "Awesome! I know encouragement is what got me so far in my gameing career, so I figured why not share the love." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.pinimg.com/originals/7e/d3/bb/7ed3bb9129ef6126259592e038e2cd17.jpg" - ], - "blip_caption": "a photo of a bookmark with a plant on top of it", - "query": "stack of books bookmark", - "dia_id": "D22:19", - "text": "Rest assured, it will be something I cherish! On another note, I just finished this cute little bookmark for one of the ladies at my writing club!" - }, - { - "speaker": "Nate", - "dia_id": "D22:20", - "text": "That bookmark is great. I'm sure she'll love it!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:21", - "text": "Thanks Nate! I absolutley love DIYs, and I know she does too." - }, - { - "speaker": "Nate", - "dia_id": "D22:22", - "text": "Let me know how it goes!" - }, - { - "speaker": "Joanna", - "dia_id": "D22:23", - "text": "Sure thing! Bye for now!" - } - ], - "session_23_date_time": "10:58 am on 9 October, 2022", - "session_23": [ - { - "speaker": "Nate", - "img_url": [ - "http://scarlet-rhapsody.com/wp-content/uploads/2020/03/87613523_2854559627994666_1775718911015124992_n.jpg" - ], - "blip_caption": "a photo of a man standing in front of a table with board games", - "query": "gaming convention group gamers", - "dia_id": "D23:1", - "text": "Hey Joanna, it's been a couple days since we last talked. Something exciting happened last Friday. I went to a game convention and met new people who weren't from my normal circle. It was a bit overwhelming but it reminded me of the good times gaming can bring." - }, - { - "speaker": "Joanna", - "dia_id": "D23:2", - "text": "Hey Nate! Good to hear from you. Sounds like fun! Meeting new people can be overwhelming, but the rewards can be great. Sometimes it's good to step outside our comfort zones and explore new things." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/omjzdx4vo4h41.jpg" - ], - "blip_caption": "a photo of two people dressed up in costumes posing for a picture", - "query": "video game convention friends", - "dia_id": "D23:3", - "text": "Yeah, you're right! We can have great experiences if we take risks. I even made some friends at the convention who love games just like me. We already planned a gaming session together - it's cool to meet people who have the same interests!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:4", - "text": "That looks awesome! I'm glad you met people who share your interests - that definitely makes experiences more fun.." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/n5hled3cve841.jpg" - ], - "blip_caption": "a photo of a group of people sitting around a table playing a board game", - "query": "group of people playing game", - "dia_id": "D23:5", - "text": "I also met some people who also played this boardgame I love, so I joined in. We had a lot in common and hit it off. It's great when shared hobbies can bond people!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:6", - "text": "It's incredible how a game can bring people together and form strong relationships. Did you do anything else there?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/byq7wy33k4w11.jpg" - ], - "blip_caption": "a photo of a board game with a blue board and a yellow board", - "query": "catan board game pieces strategy", - "dia_id": "D23:7", - "text": "We played this game Catan - it's a great strategy game where you build settlements and trade resources. I love it!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:8", - "text": "Looks cool! Is it more of a competitive game or a more chill one?" - }, - { - "speaker": "Nate", - "dia_id": "D23:9", - "text": "It can be both competitive and chill. We were competing, but still had lots of fun." - }, - { - "speaker": "Joanna", - "dia_id": "D23:10", - "text": "Competitive games can definitely be difficult to play sometimes when people get all upset about every move you make, but it's cool they were all chill! Glad you had a good time." - }, - { - "speaker": "Nate", - "dia_id": "D23:11", - "text": "It was great! Playing games is my escape from life struggles, so I generally don't get crazy competitive over them. The people at the convention were the same way!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:12", - "text": "Glad you found a way to have fun and escape! It's important to stay happy and de-stress. Keep doing what makes you happy!" - }, - { - "speaker": "Nate", - "dia_id": "D23:13", - "text": "For sure! You should go to a writing convention or something sometime! The experience at this convention is unforgetable." - }, - { - "speaker": "Joanna", - "dia_id": "D23:14", - "text": "Do writing conventions exist? I'll have to look into that, it could be fun! Thanks for the idea. Have you been up to anything tonight?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/806azldnt6bb1.jpg" - ], - "blip_caption": "a photo of a bookcase filled with dvds and movies", - "query": "video games movie collection", - "dia_id": "D23:15", - "text": "Mostly just chilling at home. Playing video games or watching movies helps me unwind." - }, - { - "speaker": "Joanna", - "dia_id": "D23:16", - "text": "That sounds great! What's your favorite game or movie that you've seen recently?" - }, - { - "speaker": "Nate", - "dia_id": "D23:17", - "text": "I recently saw a movie that blew my mind with all the twists and dream stuff, I think it was called \"Inception\". I've also been playing a game nonstop with a great futuristic setting and gameplay called \"Cyberpunk 2077\". Have you seen or played anything good lately?" - }, - { - "speaker": "Joanna", - "img_url": [ - "http://mjbmemorabilia.com/cdn/shop/files/1_4c670c88-d207-4080-9b81-9b81ef1066fe.jpg" - ], - "blip_caption": "a photo of a framed movie poster with a signed picture", - "query": "classic movie captivating storyline amazing performances movie poster", - "dia_id": "D23:18", - "text": "I watched a classic movie the other day that was awesome - the story was so gripping and the actors were great! It really stuck with me." - }, - { - "speaker": "Nate", - "dia_id": "D23:19", - "text": "Wow, that must have been awesome! What would you rate it?" - }, - { - "speaker": "Joanna", - "dia_id": "D23:20", - "text": "Well, it was amazing, so probably 9 or 10 out of 10! Movies can take us to different places and make us feel lots of emotions. What do you love about watching them?" - }, - { - "speaker": "Nate", - "dia_id": "D23:21", - "text": "Well they take me to new worlds and fill me with emotions. Plus, they're great for chilling out after a day." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.pinimg.com/originals/1a/02/2c/1a022c736bdfb7c47f33272c27eac0e7.jpg" - ], - "blip_caption": "a photo of a living room with a tv and candles", - "query": "cozy movie night setup popcorn blankets", - "dia_id": "D23:22", - "text": "I agree! They have the power to take us away and make us feel things not normally experienced in life. It's a great escape! Especially when you have a room like this!" - }, - { - "speaker": "Nate", - "dia_id": "D23:23", - "text": "Wow, it's so comfy in there! I should really get a set up like that." - }, - { - "speaker": "Joanna", - "dia_id": "D23:24", - "text": "You should! It's one thing to watch a movie in a theater, but on a nice comfy couch with a good blanket, I feel so at peace!" - }, - { - "speaker": "Nate", - "dia_id": "D23:25", - "text": "Any pointers on what I should get for my living room to make it comfy like that?" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a poster of a man and a woman sitting on a bench", - "dia_id": "D23:26", - "text": "Sure! For one, you should get a couch that can sit multiple people so that you can lay down if you want, and make sure its really fluffy! Also invest in a blanket that has a little bit of weight to it, and some lights that can be dimmed." - }, - { - "speaker": "Nate", - "dia_id": "D23:27", - "text": "Sounds like you really got into making your living room! Thanks, I'll try that out for myself!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:28", - "text": "No problem! I get super invested in random little things like that, but I think its all worth it when I end up with random little things that make life so much nicer." - }, - { - "speaker": "Nate", - "dia_id": "D23:29", - "text": "Thanks for the tip! See you later!" - }, - { - "speaker": "Joanna", - "dia_id": "D23:30", - "text": "See ya Nate!" - } - ], - "session_24_date_time": "2:01 pm on 21 October, 2022", - "session_24": [ - { - "speaker": "Nate", - "dia_id": "D24:1", - "text": "Hey Joanna, what's been up? Haven't seen you since we last talked." - }, - { - "speaker": "Joanna", - "dia_id": "D24:2", - "text": "Hey Nate! I have been revising and perfecting the recipe I made for my family and it turned out really tasty. What's been happening with you?" - }, - { - "speaker": "Nate", - "dia_id": "D24:3", - "text": "Hey Joanna! That's cool. I've been getting so stressed lately because of my tournament progress - tough competitors - but my turtles always cheer me up." - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a cat laying on the floor in a room", - "dia_id": "D24:4", - "text": "Pets have a way of brightening our days. I still have that stuffed animal dog you gave me! I named her Tilly, and she's always with me while I write." - }, - { - "speaker": "Nate", - "dia_id": "D24:5", - "text": "Glad to hear it! What made you name her Tilly?" - }, - { - "speaker": "Joanna", - "dia_id": "D24:6", - "text": "I used to have a dog back in Michigan with that name, but then I got allergic and we had to get rid of her. The name helps me remember her back when I used to be able to hold and squeeze animal without an allergic reaction!" - }, - { - "speaker": "Nate", - "dia_id": "D24:7", - "text": "That's so touching! Glad the stuffed animal means so much!" - }, - { - "speaker": "Joanna", - "dia_id": "D24:8", - "text": "Tilly helps me stay focused and brings me so much joy. It's amazing how even stuffed animals can do that!" - }, - { - "speaker": "Nate", - "dia_id": "D24:9", - "text": "It really is, I'm not sure I'll ever understand why watching my turtles slowly walk around makes me so happy. But I'm very glad it does." - }, - { - "speaker": "Joanna", - "dia_id": "D24:10", - "text": "Same here! So have you been up to anything recenly?" - }, - { - "speaker": "Nate", - "dia_id": "D24:11", - "text": "Yeah, I've just been practicing for my next video game tournemant. How about you?" - }, - { - "speaker": "Joanna", - "dia_id": "D24:12", - "text": "Well, I had a bit of a setback recently - another rejection from a production company." - }, - { - "speaker": "Nate", - "dia_id": "D24:13", - "text": "Bummer, Joanna. Is this the one you sent to a film contest? Rejections suck, but don't forget they don't define you. Keep at it and you'll find the perfect opportunity." - }, - { - "speaker": "Joanna", - "dia_id": "D24:14", - "text": "Yeah.. Thanks, Nate. It's hard, but I won't let it slow me down. I'm gonna keep grinding and moving ahead." - }, - { - "speaker": "Nate", - "dia_id": "D24:15", - "text": "That's what I like to hear! I really respect you for that and being able to bounce back whenever something sad happens!" - }, - { - "speaker": "Joanna", - "dia_id": "D24:16", - "text": "Thanks, Nate! Your encouragement really means a lot. I'm gonna keep pushing forward and believing in myself." - }, - { - "speaker": "Nate", - "dia_id": "D24:17", - "text": "You got this, and don't ever forget that you have people cheering you on from the sidelines wherever you go." - }, - { - "speaker": "Joanna", - "dia_id": "D24:18", - "text": "Thanks! I'll see you around!" - }, - { - "speaker": "Nate", - "dia_id": "D24:19", - "text": "No problem! Catch you later!" - } - ], - "session_25_date_time": "8:16 pm on 25 October, 2022", - "session_25": [ - { - "speaker": "Nate", - "dia_id": "D25:1", - "text": "Hey Joanna, what's been up since we last chatted? How's it going?" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a box of cards with a quote on it", - "dia_id": "D25:2", - "text": "Hey Nate! Another movie script that I contributed to was shown on the big screen last Sunday for the first time! It was such a surreal experience to see everything come together. I felt a mix of emotions, but overall, it was a satisfying moment. I've been waiting for this for a long time!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a box with a controller inside of it", - "dia_id": "D25:3", - "text": "Congrats Joanna! How was it to finally see it on the big screen?\n\n[shares a photo holding a videogame controller]" - }, - { - "speaker": "Joanna", - "dia_id": "D25:4", - "text": "It was an amazing experience! I'll never forget seeing all of the characters and dialogue I wrote being acted out - it was such a cool feeling. Having all the hard work and determination I put into writing pay off was definitely rewarding. I know this is the third time it's happened, but its just so awesome!" - }, - { - "speaker": "Nate", - "dia_id": "D25:5", - "text": "That must have been amazing. What was your favorite part of it?" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/ioa9eysnr9xb1.jpg" - ], - "blip_caption": "a photo of a drawing book with a bunch of drawings on it", - "query": "character sketches actors notebook", - "dia_id": "D25:6", - "text": "Seeing my characters I worked so hard on come alive was my favorite part. It felt like they jumped off the page and became real - it was totally surreal." - }, - { - "speaker": "Nate", - "dia_id": "D25:7", - "text": "Wow Joanna, those drawings are really incredible! What inspired you to create them?" - }, - { - "speaker": "Joanna", - "dia_id": "D25:8", - "text": "Thanks, Nate! They're visuals of the characters to help bring them alive in my head so I can write better." - }, - { - "speaker": "Nate", - "dia_id": "D25:9", - "text": "That's a cool way to gain insight into your characters. Where did you get your ideas for them?" - }, - { - "speaker": "Joanna", - "dia_id": "D25:10", - "text": "I got ideas from everywhere: people I know, stuff I saw, even what I imagined. It's cool to see how an idea takes shape into a person with their own wants, worries, and wishes." - }, - { - "speaker": "Nate", - "dia_id": "D25:11", - "text": "Wow Joanna, that's so cool! It's amazing how our imaginations can bring ideas to life. Can you tell me more about the character on the left in the photo?" - }, - { - "speaker": "Joanna", - "dia_id": "D25:12", - "text": "Nope! You'll just have to watch the movie and find out for yourself!" - }, - { - "speaker": "Nate", - "dia_id": "D25:13", - "text": "You got it. I was already planning on watching it, but talking to you about it makes me want to watch it even more!" - }, - { - "speaker": "Joanna", - "dia_id": "D25:14", - "text": "Awesome! Well enough about me, what have you been up to?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://cdn12.picryl.com/photo/2016/12/31/water-turtle-on-the-water-animal-animals-a79381-1024.jpg" - ], - "blip_caption": "a photography of two turtles sitting on a log in a pond", - "query": "pet turtles", - "dia_id": "D25:15", - "re-download": true, - "text": "I was bored today, so I just took my turtles out for a walk." - }, - { - "speaker": "Joanna", - "dia_id": "D25:16", - "text": "Sound fun! Did they have a good time?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/d/dd/Tortoises_in_the_Cotswold_Wildlife_Park_restaurant_-_geograph.org.uk_-_1468751.jpg" - ], - "blip_caption": "a photography of a dog laying on a rock in a zoo", - "query": "turtles basking heat lamp", - "dia_id": "D25:17", - "re-download": true, - "text": "Of course! They look tired from all the walking, so they're relaxing in the tank right now." - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a spoon full of ice cream and chocolate sauce", - "dia_id": "D25:18", - "text": "Aww, they're so cute! What do they eat?" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a container of lettuce and other greens", - "dia_id": "D25:19", - "text": "They eat a combination of vegetables, fruits, and insects. They have a varied diet." - }, - { - "speaker": "Joanna", - "dia_id": "D25:20", - "text": "Wow, that's fascinating! It's interesting how they have such a varied diet, including insects. Do you have a favorite among their food choices?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://images.pexels.com/photos/4460504/pexels-photo-4460504.jpeg" - ], - "blip_caption": "a photography of a group of strawberries and a turtle on a table", - "query": "turtles eating strawberries", - "dia_id": "D25:21", - "re-download": true, - "text": "I love seeing them eat fruit - they get so hyped and it's so cute!" - }, - { - "speaker": "Joanna", - "dia_id": "D25:22", - "text": "Wow, that's so cute! They look like they really enjoy fruit." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/i1d1xxfxqd251.jpg" - ], - "blip_caption": "a photo of a person holding a small turtle in their hand", - "query": "turtles joy holding", - "dia_id": "D25:23", - "text": "Yeah, it's adorable! Watching them enjoy their favorite snacks is so fun. I also like holding them." - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a rock formation with a person standing on the side", - "dia_id": "D25:24", - "text": "Do they have different personalities, like the way dogs and cats do?" - }, - { - "speaker": "Nate", - "dia_id": "D25:25", - "text": "Yeah, they each do. One is more adventurous while the other is more reserved, which I find cute. Having them around brings me joy and they make great companions." - }, - { - "speaker": "Joanna", - "dia_id": "D25:26", - "text": "That's super cool! I never knew turtles could be so interesting until I met your turtles. Wow!" - }, - { - "speaker": "Nate", - "dia_id": "D25:27", - "text": "I've always liked turtles since I was a boy, so I know all about them!" - }, - { - "speaker": "Joanna", - "dia_id": "D25:28", - "text": "You'll have to keep me posted on them! It's been fun chatting!" - }, - { - "speaker": "Nate", - "dia_id": "D25:29", - "text": "See you later Joanna!" - } - ], - "session_26_date_time": "3:56 pm on 4 November, 2022", - "session_26": [ - { - "speaker": "Joanna", - "dia_id": "D26:1", - "text": "Wow, Nate, I'm on fire! I just set up meetings with movie producers \u2014 my dreams are comin' true!" - }, - { - "speaker": "Nate", - "dia_id": "D26:2", - "text": "Wow Joanna, nice work! How did it go with those producer meetings?" - }, - { - "speaker": "Joanna", - "dia_id": "D26:3", - "text": "Thanks, Nate! The meetings went really well. I felt confident discussing my script and vision and they seemed interested and excited. They loved the elements of self-discovery in it. It was so validating to be taken seriously. I'm feeling hopeful and inspired about the future!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a television screen showing a game being played", - "dia_id": "D26:4", - "text": "Way to go, Joanna! Putting yourself out there is really brave and winning recognition for your hard work feels great. It's just like when I win a video game tournament - it feels awesome! I'm so proud of you and so glad you're feeling hopeful and inspired." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://fromthepencup.files.wordpress.com/2023/01/img_3860.jpg" - ], - "blip_caption": "a photo of a notebook with a list of things to write", - "query": "notebook filled handwritten pages early writings", - "dia_id": "D26:5", - "text": "Thanks Nate! Your support and encouragement mean a lot. Writing isn't always easy but moments like these make me appreciate it. I'm so thankful for all the opportunities. Last week, I found these old notebooks with my early writings - it was cool to see how far I've come." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle laying on a bed of rocks and gravel", - "dia_id": "D26:6", - "text": "That's cool! You must love seeing how you've grown as an artist. Is there a favorite piece from your early writings that stands out to you?" - }, - { - "speaker": "Joanna", - "dia_id": "D26:7", - "text": "Yup, I still remember this story from when I was 10. It was about a brave little turtle who was scared but explored the world anyway. Maybe even back then, I was inspired by stories about finding courage and taking risks. It's still a part of my writing today." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle laying on a bed of rocks and gravel", - "dia_id": "D26:8", - "text": "You obviously have a passion for writing, and it's funny the story was about a turtle! Their resilience is so inspiring! Take courage and keep pushing yourself with your writing. Great job!" - }, - { - "speaker": "Joanna", - "dia_id": "D26:9", - "text": "Thanks, Nate! They make me think of strength and perseverance. They help motivate me in tough times - glad you find that inspiring!" - }, - { - "speaker": "Nate", - "dia_id": "D26:10", - "text": "What can I say, I love turtles. So, what's been happening with you?" - }, - { - "speaker": "Joanna", - "dia_id": "D26:11", - "text": "Hey Nate! Apart from meetings, I'm working on a project - challenging but fulfilling. How about you? What's been going on?" - }, - { - "speaker": "Nate", - "dia_id": "D26:12", - "text": "Just been helping some friends reset their high scores at the international tournament. It's been fun!" - }, - { - "speaker": "Joanna", - "dia_id": "D26:13", - "text": "Wow, sounds like so much fun! You're really passionate about gaming. Have an awesome time and keep helping others with those high scores!" - }, - { - "speaker": "Nate", - "dia_id": "D26:14", - "text": "Thanks! It feels good to use my skills to make a difference." - }, - { - "speaker": "Joanna", - "dia_id": "D26:15", - "text": "I couldn't agree more! Which is why my meetings are so exciting!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/4q9s9o2607ib1.jpg" - ], - "blip_caption": "a photo of a bowl of ice cream with a spoon in it", - "query": "homemade coconut milk ice cream", - "dia_id": "D26:16", - "text": "On another note, want to come over and try some of this? It's super yummy, just made it yesterday!" - }, - { - "speaker": "Joanna", - "dia_id": "D26:17", - "text": "Mmm, that looks delicious! Is it lactose-free by any chance?" - }, - { - "speaker": "Nate", - "dia_id": "D26:18", - "text": "Yep, I made it with coconut milk so it's lactose-free!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a bowl of ice cream with a spoon in it", - "dia_id": "D26:19", - "text": "Thanks so much, Nate! Sure! I'll come over tomorrow if that's fine." - }, - { - "speaker": "Nate", - "dia_id": "D26:20", - "text": "I don't see why not! I'm not doing anything then, so your completely welcome to!" - }, - { - "speaker": "Joanna", - "dia_id": "D26:21", - "text": "Awesome! I'll bring some of my recipes so we can both share deserts!" - }, - { - "speaker": "Nate", - "dia_id": "D26:22", - "text": "I'd love that! I've been wanting to try some of your chocolate and rasberry cake for a while now." - }, - { - "speaker": "Joanna", - "dia_id": "D26:23", - "text": "You got it! See you tomorrow!" - }, - { - "speaker": "Nate", - "dia_id": "D26:24", - "text": "See you then! Take care!" - } - ], - "session_27_date_time": "8:10 pm on 7 November, 2022", - "session_27": [ - { - "speaker": "Nate", - "dia_id": "D27:1", - "text": "Hey Joanna! Hope you\u2019re doing alright. Crazy thing happened - I was in the final of a big Valorant tournament last Saturday, and I won! It was the best feeling to see my name as the champion. Tournaments really bring out strong emotions in me." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/om8u4dx20ir71.jpg" - ], - "blip_caption": "a photo of a notebook with a handwritten letter on it", - "query": "notebook handwritten script movie script presentation movie producers", - "dia_id": "D27:2", - "text": "Hey Nate! Congrats on winning the tournament - that's awesome! I know you must have been buzzing! Anyway, I've been working on something exciting too. Last Friday, I finished the presentation for producers - it was tough but it's looking good. What have you been up to?" - }, - { - "speaker": "Nate", - "dia_id": "D27:3", - "text": "Thanks, Joanna! I've been having a blast and preparing for other tournaments, so I've been real busy - but I'm loving it!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a book with a black border and a white title", - "dia_id": "D27:4", - "text": "Sounds like you're really loving life right now! It's great when you find something that suits you. Being busy can be tiring but it's so rewarding in the end. Keep it up!" - }, - { - "speaker": "Nate", - "dia_id": "D27:5", - "text": "Thanks, Joanna! I'm really grateful to have a job I enjoy every day. So anyways, anything new going on in your life?" - }, - { - "speaker": "Joanna", - "dia_id": "D27:6", - "text": "I am writing another movie script! It's a love story with lots of challenges. I've put lots of hard work into it and I'm hoping to get it on the big screen." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle laying on a bed of rocks and gravel", - "dia_id": "D27:7", - "text": "Woah Joanna, that's incredible! I remember when you started working on these sorta things. It's crazy to see how far you've gotten! You've really got a thing for writing, huh? Where'd you get the idea for it?" - }, - { - "speaker": "Joanna", - "dia_id": "D27:8", - "text": "Thanks Nate! Writing has always been a passion of mine. I got the idea for this script from a dream. How have your turtles been? I haven't seen pictures of them in a while!" - }, - { - "speaker": "Nate", - "dia_id": "D27:9", - "text": "Great actually! These little guys sure bring joy to my life! Watching them is so calming and fascinating. I've really grown fond of them. So, what about you, Joanna? What brings you happiness?" - }, - { - "speaker": "Joanna", - "dia_id": "D27:10", - "text": "Creating stories and watching them come alive gives me happiness and fulfillment. Writing has been such a blessing for me." - }, - { - "speaker": "Nate", - "dia_id": "D27:11", - "text": "Well with dedication like yours, its no wonder you do so well in it as well! Are you planning on submitting anymore scripts anytime soon?" - }, - { - "speaker": "Joanna", - "dia_id": "D27:12", - "text": "Yep! I actually just submitted a few more last week! Hoping to hear back from them soon, though I assume a few will be rejected." - }, - { - "speaker": "Nate", - "dia_id": "D27:13", - "text": "Even if it happens to a few, I'm sure at leasts one will make it to the screens and be your 3rd published movie!" - }, - { - "speaker": "Joanna", - "dia_id": "D27:14", - "text": "Thanks, Nate! Appreciate the encouragement. I won't give up, I promise! Got it covered!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.pinimg.com/originals/92/1a/c3/921ac332715f9ecb9f50155427aa8ffc.jpg" - ], - "blip_caption": "a photo of a desk with a computer monitor and a keyboard", - "query": "gaming setup", - "dia_id": "D27:15", - "text": "Great to hear! On another note, I just upgraded some of my equipment at home. Check it out!" - }, - { - "speaker": "Joanna", - "dia_id": "D27:16", - "text": "Oh wow, ice set-up! Do you use that computer for gaming?" - }, - { - "speaker": "Nate", - "dia_id": "D27:17", - "text": "Yep! This is where I practice and compete. Sometimes I even use it when I'm playing games with friends." - }, - { - "speaker": "Joanna", - "dia_id": "D27:18", - "text": "Cool! Having a dedicated space for practice and competition should help you stay focused." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a desk with two monitors and a laptop", - "dia_id": "D27:19", - "text": "Yeah, I love it. It's like my own little haven to escape into the virtual world." - }, - { - "speaker": "Joanna", - "dia_id": "D27:20", - "text": "Wow, that sounds great to have your own gaming setup at home. It must be really awesome!" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/h99h0hvw6x761.jpg" - ], - "blip_caption": "a photo of a pair of headphones and a video game controller", - "query": "gaming headset controller", - "dia_id": "D27:21", - "text": "It really is! But it's also really important to have something like this for my career, otherwise I would never be able to beat my competition." - }, - { - "speaker": "Joanna", - "dia_id": "D27:22", - "text": "That makes sense! It's all about practice isn't it? So what's your favorite game?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/7xcxqz1onyg91.jpg" - ], - "blip_caption": "a photo of two nintendo game covers with a picture of a group of people", - "query": "eternal kingdom game cover art", - "dia_id": "D27:23", - "text": "Yep! I'm currently playing this awesome fantasy RPG called \"Xeonoblade Chronicles\" and it's been a blast! I highly reccomend it if you've never played it before. " - }, - { - "speaker": "Joanna", - "dia_id": "D27:24", - "text": "What made you start playing it? That's a japanese game series right?" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a woman in a costume holding a glass", - "dia_id": "D27:25", - "text": "Yes it is! I'm a big fan of Nintendo games, and I've actually been wanting to play this one for a while because my friends have played it and reccomended it!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a captain america costume on display in a museum", - "dia_id": "D27:26", - "text": "Nice! It's really cool when a reccomendation from a friend fits your taste so well isn't it?" - }, - { - "speaker": "Nate", - "dia_id": "D27:27", - "text": "For sure! That's why I love when you give me movie reccomendations, I usually like them a lot more then if I were to just watch some random one." - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/5hklfo3gjhx71.jpg" - ], - "blip_caption": "a photo of a handwritten letter from a young man", - "query": "handwritten letter movie script", - "dia_id": "D27:28", - "text": "Great to hear! I just finished with the intro to my next movie script, and I decided to include this at the begining." - }, - { - "speaker": "Nate", - "dia_id": "D27:29", - "text": "That letter is really awesome! Does it remind you of your childhood?" - }, - { - "speaker": "Joanna", - "dia_id": "D27:30", - "text": "Yeah, it does! My brother wrote it - he used to make me these cute notes when we were kids. Brings back sweet memories." - }, - { - "speaker": "Nate", - "dia_id": "D27:31", - "text": "Aww, childhood memories can be so powerful!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of two little girls in pink dresses standing in front of a castle", - "dia_id": "D27:32", - "text": "They sure can! They take us back to simpler times but it's nice to create new memories as we grow up." - }, - { - "speaker": "Nate", - "dia_id": "D27:33", - "text": "Totally! I had a special day when I took my pets to the park. They were amazed and seeing their happy faces made it a memorable day. Mixing the new with the old is priceless - I treasure every memory!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/tkkip0dr2eg91.jpg" - ], - "blip_caption": "a photo of a person holding a notebook with a list of things on it", - "query": "handwritten memories notebook", - "dia_id": "D27:34", - "text": "That sounds so sweet, Nate! I started writing some of my favorite memories down." - }, - { - "speaker": "Nate", - "dia_id": "D27:35", - "text": "Dang, your full of great ideas Joanna! I really should start doing that as well, or at least write down the things my animals like a lot!" - }, - { - "speaker": "Joanna", - "dia_id": "D27:36", - "text": "You should! I completely encourage it, looking back on fond memories is such a blessing." - }, - { - "speaker": "Nate", - "dia_id": "D27:37", - "text": "Ok I will! But I'll also start writing down some of my favorite memories with you from now on." - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of two women sitting on a bed laughing and laughing", - "dia_id": "D27:38", - "text": "Definitely, let's keep making great memories and supporting each other. Let's keep reaching for our dreams and make them happen!" - } - ], - "session_28_date_time": "5:54 pm on 9 November, 2022", - "session_28": [ - { - "speaker": "Nate", - "img_url": [ - "https://beadyarnspatula.files.wordpress.com/2022/07/img_2596.jpg" - ], - "blip_caption": "a photo of a person scooping a scoop of ice cream into a pan", - "query": "coconut milk ice cream homemade red sprinkles", - "dia_id": "D28:1", - "text": "Hey Joanna, what a wild week! My game tournament got pushed back, so I tried out some cooking. Look at this homemade coconut ice cream! The sprinkles kinda changed the color this time around." - }, - { - "speaker": "Joanna", - "dia_id": "D28:2", - "text": "Hey Nate, that looks yummy! Wish I could try it, but I can't right now. How did the last game tournament go?" - }, - { - "speaker": "Nate", - "dia_id": "D28:3", - "text": "Hey Joanna, thanks! Tough tournament, didn't make it to the finals. But that's okay, I'll get 'em next time!" - }, - { - "speaker": "Joanna", - "dia_id": "D28:4", - "text": "Aww, bummer! But the important thing is to stay positive. So, what's your next move in the gaming world?" - }, - { - "speaker": "Nate", - "dia_id": "D28:5", - "text": "Thanks Joanna! Staying positive is key. I'm thinking of joining a new gaming team after this next tourney - I've had a few offers, but I haven't decided yet. It's gonna be a big step, but I'm ready for a shake up." - }, - { - "speaker": "Joanna", - "dia_id": "D28:6", - "text": "Sounds great, Nate! Making a switch could open up new opportunities. Wishing you luck with picking the right team!" - }, - { - "speaker": "Nate", - "dia_id": "D28:7", - "text": "Thanks, Joanna! I really appreciate it. It's a big decision, but I'm excited for what the future holds. How about you? Anything exciting happening on your end?" - }, - { - "speaker": "Joanna", - "dia_id": "D28:8", - "text": "Yup! I worked hard on another script and eventually created a plan for getting it made into a movie. It was a ton of work but satisfying. I pitched it to some producers yesterday and they really liked it. It gave me a big confidence boost!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a trophy and a game controller on a table", - "dia_id": "D28:9", - "text": "Congrats on the chance to pitch your script - super impressive. Proud of you!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://www.penboutique.com/cdn/shop/products/image_d3480c08-098f-481b-a456-de0607a4fe91.jpg" - ], - "blip_caption": "a photo of a pen and notebook on a table with a book", - "query": "desk laptop notebook pens", - "dia_id": "D28:10", - "text": "Appreciate you, Nate! Your support and encouragement mean a lot to me. I feel like I just can't stop writing write now!" - }, - { - "speaker": "Nate", - "dia_id": "D28:11", - "text": "Anytime. What're you working on in that notebook? Anything cool?" - }, - { - "speaker": "Joanna", - "dia_id": "D28:12", - "text": "Hey Nate, I'm working on a new project - a suspenseful thriller set in a small Midwestern town. It's been a great creative outlet for me. How about you? Do you have any projects you're working on?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/yldt4paim5k71.jpg" - ], - "blip_caption": "a photo of a desk with a computer, headphones, and a microphone", - "query": "microphone headphones desk", - "dia_id": "D28:13", - "text": "Yeah actually - creating gaming content for YouTube. It's a cool way to entertain folks and satisfy my video game cravings at the same time when there aren't any tourneys going on." - }, - { - "speaker": "Joanna", - "dia_id": "D28:14", - "text": "Wow, that's a cool idea! What inspired you to start making gaming videos?" - }, - { - "speaker": "Nate", - "dia_id": "D28:15", - "text": "Hey Joanna, I'm a big fan of them and thought it would be a fun idea to start making them myself. I'm hoping to share my love of gaming and connect with others who enjoy it too." - }, - { - "speaker": "Joanna", - "dia_id": "D28:16", - "text": "Way to go, Nate! Making videos and connecting with people about gaming - that's awesome! You'll do great!" - }, - { - "speaker": "Nate", - "dia_id": "D28:17", - "text": "Thanks Joanna! Appreciate the support. It's new to me but I'm excited to get started!" - }, - { - "speaker": "Joanna", - "blip_caption": "a photo of a computer screen displaying a product listing", - "dia_id": "D28:18", - "text": "Make sure you watch other peoples videos first so you get a handle on what your audience likes! That way your videos don't flop when you post them." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/i2l2lpmkj2mb1.jpg" - ], - "blip_caption": "a photo of a computer screen with a message on it", - "query": "positive comment youtube video screenshot", - "dia_id": "D28:19", - "text": "Already doing that, but thanks for the advice!" - }, - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/ap48tronzy411.jpg" - ], - "blip_caption": "a photo of a sunflower in a field with a sunset in the background", - "query": "sunset field flowers", - "dia_id": "D28:20", - "text": "No worries, Nate! It's great to support each other in reaching our goals. On another note, check out this pic I got a while back!" - }, - { - "speaker": "Nate", - "dia_id": "D28:21", - "text": "Wow, that sunset pic looks incredible! What inspired you to take that photo?" - }, - { - "speaker": "Joanna", - "dia_id": "D28:22", - "text": "Thanks, Nate! I took that pic on a hike last summer near Fort Wayne. The sunset and the surrounding beauty were just incredible. It was an awesome reminder of nature's beauty." - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/hgcgvd0umzsb1.jpg" - ], - "blip_caption": "a photo of three turtles sitting on a rock in a pond", - "query": "pet turtles basking sun", - "dia_id": "D28:23", - "text": "That sounds incredible! Nature truly has a way of reminding us to appreciate the beauty around us, and moments like those really stay with you. These critters also make me appreciate life's little joys. And guess what? I got them a new friend!" - }, - { - "speaker": "Joanna", - "dia_id": "D28:24", - "text": "Wow, what made you get a third?" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle swimming in a tank with a metal bar", - "dia_id": "D28:25", - "text": "Turtles really bring me joy and peace. They have such an effect on us - best buddies ever! I saw another at a pet store and just hade to get him. The tank is big enough now for three, so I figured why not!" - }, - { - "speaker": "Joanna", - "dia_id": "D28:26", - "text": "Wow! It's always a shock where life will take us next! I bet just last week you would have never thought you would be getting a third turtle this year!" - }, - { - "speaker": "Nate", - "dia_id": "D28:27", - "text": "You got that right, but I'm very happy with the descision and wouldn't have it any other way." - }, - { - "speaker": "Joanna", - "dia_id": "D28:28", - "text": "Can I come over sometime and watch you play with them? From a distance I mean, since I'm allergic." - }, - { - "speaker": "Nate", - "dia_id": "D28:29", - "text": "Definitely! I'd love to have you over again. Maybe we can watch one of your movies together or go to the park!" - }, - { - "speaker": "Joanna", - "dia_id": "D28:30", - "text": "For sure! I'd love to do either of those things with you!" - }, - { - "speaker": "Nate", - "dia_id": "D28:31", - "text": "Sounds good. Well I'll make sure I give the turtles a bath before you get here so they're ready to play." - }, - { - "speaker": "Joanna", - "dia_id": "D28:32", - "text": "Alright, see you tomorrow!" - }, - { - "speaker": "Nate", - "dia_id": "D28:33", - "text": "Bye Joanna!" - } - ], - "session_29_date_time": "12:06 am on 11 November, 2022", - "session_29": [ - { - "speaker": "Joanna", - "img_url": [ - "https://i.redd.it/l7riz754xlj41.jpg" - ], - "blip_caption": "a photo of a person holding a clap board with a dog sleeping in a dog bed", - "query": "movie clapperboard movie set", - "dia_id": "D29:1", - "text": "Nate, can you believe it? I'm finally filming my own movie from the road-trip script!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a trophy and a game controller on a table", - "dia_id": "D29:2", - "text": "Congrats, Joanna! Not surprised at all that your hard work paid off. Must feel awesome to see your script come alive in a movie! Pretty cool when something you love brings success, right? Tell me more about your movie!" - }, - { - "speaker": "Joanna", - "dia_id": "D29:3", - "text": "Woohoo, thanks Nate! It's pretty wild to see it come alive. Every day on set is awesome and full of potential. Being able to show my vision is awesome." - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a fish tank with a fish inside of it", - "dia_id": "D29:4", - "text": "I think so too! What's been the coolest moment on set?" - }, - { - "speaker": "Joanna", - "dia_id": "D29:5", - "text": "One of the actors came up to me and told me how much she liked my script! I was so excited when that happened - it gave me chills!" - }, - { - "speaker": "Nate", - "blip_caption": "a photo of a turtle in a sink with a reflection of its head", - "dia_id": "D29:6", - "text": "Wow Joanna, that must have been so exciting! It's incredible when you get those moments of joy. Anyway, I took my turtles to the beach in Tampa yesterday! They always bring me peace in the craziness of life." - }, - { - "speaker": "Joanna", - "dia_id": "D29:7", - "text": "Woah, that's awesome, Nate! You must really enjoy having them around - they're so cool! What do you love most about having them?" - }, - { - "speaker": "Nate", - "img_url": [ - "https://i.redd.it/q35gmvlsr6yb1.jpg" - ], - "blip_caption": "a photo of a turtle sitting on a log in a pond", - "query": "pet turtles basking rock", - "dia_id": "D29:8", - "text": "Your completely right! I really love having them around. They're so cool and they make me feel calm. Plus, they don't require much looking after, which is great. I love seeing them soaking in the sun like this." - }, - { - "speaker": "Joanna", - "dia_id": "D29:9", - "text": "That's awesome, Nate! They look so serene and happy. It's great to have something like that." - }, - { - "speaker": "Nate", - "img_url": [ - "https://live.staticflickr.com/211/464853107_ccca166bec_b.jpg" - ], - "blip_caption": "a photography of a bowl of ice cream with a spoon in it", - "query": "homemade coconut milk ice cream colorful bowls", - "dia_id": "D29:10", - "re-download": true, - "text": "Yeah, turtles are like zen masters! They always remind me to slow down and appreciate the small things in life. I'm loving experimenting with flavors right now. Here are some colorful bowls of coconut milk ice cream that I made." - }, - { - "speaker": "Joanna", - "dia_id": "D29:11", - "text": "Hey Nate, that looks really yummy! The colors and mix-ins give it a nice kick." - }, - { - "speaker": "Nate", - "dia_id": "D29:12", - "text": "Nice! I'm glad you like it too. This recipe really jazzes it up. Wanna give it a try?" - }, - { - "speaker": "Joanna", - "dia_id": "D29:13", - "text": "Definitely, Nate! That ice cream looks mouthwatering. Thanks so much for offering!" - }, - { - "speaker": "Nate", - "dia_id": "D29:14", - "text": "No worries, Joanna. Hope you enjoy it!" - }, - { - "speaker": "Joanna", - "dia_id": "D29:15", - "text": "Yea, no worries! It was great catching up. Take it easy!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Joanna": [], - "Nate": [ - "Global Offensive with a team." - ], - "date": "21 January, 2022" - }, - "events_session_2": { - "Joanna": [ - "Joanna finishes writing her first full screenplay on drama and romance and plans on submitting it to a film festival so that producers and directors can check it out." - ], - "Nate": [ - "Nate starts hanging out with new people he met from outside his circle at the Counter Strike tournament." - ], - "date": "23 January, 2022" - }, - "events_session_3": { - "Joanna": [ - "Joanna sends the first screenplay she wrote on drama and romance to a film festival." - ], - "Nate": [ - "Nate discovers he can make his own coconut milk ice cream and decides to give it a try." - ], - "date": "7 February, 2022" - }, - "events_session_4": { - "Joanna": [ - "Joanna starts writing her second screenplay, which is about a thirty year old woman on a journey of self-discovery during a road trip after a loss, inspired by her own experiences." - ], - "Nate": [ - "Nate gifts his friend a homemade ice cream batch of chocolate and vanilla swirl and his friend enjoys it." - ], - "date": "25 February, 2022" - }, - "events_session_5": { - "Joanna": [ - "Joanna finishes writing her second screenplay, which is about a woman's journey of self-discovery through a road trip after suffering loss." - ], - "Nate": [ - "Nate takes his two pet turtles out for a walk." - ], - "date": "18 March, 2022" - }, - "events_session_6": { - "Joanna": [ - "Joanna has an audition for a writing opportunity and it goes alright." - ], - "Nate": [ - "Nate participates in another Counter Strike tournament." - ], - "date": "24 March, 2022" - }, - "events_session_7": { - "Joanna": [], - "Nate": [ - "Nate dyes his hair purple and shows off to friends." - ], - "date": "15 April, 2022" - }, - "events_session_8": { - "Joanna": [ - "Joanna goes on a hike in her hometown.", - "Joanna discovers a new hiking trail while hiking in her hometown.", - "Joanna reads a lot of books from her collection." - ], - "Nate": [], - "date": "17 April, 2022" - }, - "events_session_9": { - "Joanna": [ - "Joanna joins a local writers group with inspiring individuals who motivate and support her writing.", - "Joanna starts working on a script called \"Finding Home\" with the local writers group on a girl who is trying to find her true home." - ], - "Nate": [ - "Nate gears up for a local Street Fighter tournament which will be his fourth video game tournament." - ], - "date": "21 April, 2022" - }, - "events_session_10": { - "Joanna": [ - "Joanna watches the Lord of the Rings trilogy movies based on Nate's recommendation and enjoys it.", - "Joanna makes a dairy-free vanilla with strawberry filling and coconut cream frosting and appreciates the coconut recommendation from Note." - ], - "Nate": [ - "Nate wins the local Street Fighter tournament which is his second win in video game tournaments." - ], - "date": "2 May, 2022" - }, - "events_session_11": { - "Joanna": [ - "Joanna becomes an expert hiker after discovering and exploring several trails in her hometown.", - "Joanna finds a spot with waterfall called the 'Whispering Falls' during one of her hikes." - ], - "Nate": [], - "date": "12 May, 2022" - }, - "events_session_12": { - "Joanna": [ - "Joanna find the guts to start writing her third screenplay on loss, identity, and connection based on a story she has had since ages." - ], - "Nate": [ - "Nate adopts a new dog named Max as an addition to his pet family that consisted of two turtles." - ], - "date": "20 May, 2022" - }, - "events_session_13": { - "Joanna": [], - "Nate": [ - "Nate finds a companion for Max among his neighbors.", - "Nate gets a stuffed toy pup for Joanna since she is allergic to animals with fur and cannot have real animals as pets." - ], - "date": "25 May, 2022" - }, - "events_session_14": { - "Joanna": [ - "Joanna's receives a generic rejection letter without much feedback from a major company for her first screenplay on drama and romance.", - "Joanna explores a new hiking trail with her friends", - "Joanna finishes writing her third screenplay, which is based on a story of loss, identity and connection." - ], - "Nate": [ - "Nate wins another regional video game tournament.", - "Nate plans to host a gaming party and invites people he met at the regional video game tournament as well as friends from other tournaments he has attended.", - "Nate gets custom controller decorations for everyone attending the gaming party he plans on hosting." - ], - "date": "3 June, 2022" - }, - "events_session_15": { - "Joanna": [ - "Joanna is inspired to see a movie that she helped write appear on the big screen." - ], - "Nate": [], - "date": "5 June, 2022" - }, - "events_session_16": { - "Joanna": [ - "Joanna submits her third screenplay on loss, identity and connection to a film contest" - ], - "Nate": [ - "Nate makes vegan ice cream and shares with the vegan diet group he is part of." - ], - "date": "24 June, 2022" - }, - "events_session_17": { - "Joanna": [ - "Joanna takes a road trip to a small Midwestern town Woodhaven for research on her next movie script and is inspired by the history of the town and its interesting people.", - "Joanna starts writing a book on a deep and emotional story after seeing the movie she helped co-write do well." - ], - "Nate": [ - "Nate wins his fourth video game tournament" - ], - "date": "10 July, 2022" - }, - "events_session_18": { - "Joanna": [ - "Joanna is reminded of why she loves writing after she receives a letter from one of the readers of her blog post on how her words brought them comfort." - ], - "Nate": [ - "Nate starts teaching others how to make coconut milk ice cream." - ], - "date": "14 August, 2022" - }, - "events_session_19": { - "Joanna": [ - "Joanna shares her new book on a deep and emotional story with her local writers group for feedback.", - "Joanna makes a dairy-free parfait to celebrate getting positive feedback from her local writers group about her book.", - "Joanna plans to relax and recharge by taking a long walk on the beach and reading books over the weekend." - ], - "Nate": [ - "Nate enters and wins an international video game tournament, and is now able to make a living out of his passion.", - "Nate gets a new tank for his two turtles.", - "Nate plans to take some time off to chill with his pet dog and turtles." - ], - "date": "22 August, 2022" - }, - "events_session_20": { - "Joanna": [ - "Joanna revises her dairy free recipe for chocolate strawberry cake." - ], - "Nate": [ - "Nate faces a setback because he doesn't do too well in a video game tournament." - ], - "date": "5 September, 2022" - }, - "events_session_21": { - "Joanna": [ - "Joanna loses her work after her laptop crashes." - ], - "Nate": [ - "Nate hosts a cooking show to teach people how to make vegan ice cream using coconut milk." - ], - "date": "14 September, 2022" - }, - "events_session_22": { - "Joanna": [ - "Joanna cooks her new dairy free recipe for chocolate raspberry tart for her family.", - "Joanna finishes writing the book on a deep and emotional story that she started writing in July.", - "Joanna makes a cute book mark for one the ladies at her local writing group" - ], - "Nate": [ - "Nate wins another video game tournament and makes a lot of money from it" - ], - "date": "6 October, 2022" - }, - "events_session_23": { - "Joanna": [ - "Joanna watches a classic movie that she rates 9 out of 10" - ], - "Nate": [ - "Nate visits a game convention and meets and interacts with new people from outside his circle who also play Catan.", - "Nate plans a gaming session with people he met at the game convention.", - "Nate watches the movie Inception", - "Nate plays the game Cyberpunk 2077 non-stop" - ], - "date": "9 October, 2022" - }, - "events_session_24": { - "Joanna": [ - "Joanna perfects her dairy free dessert recipe of chocolate raspberry tart for her friends and family.", - "Joanna receives a rejection from a production company for her third screenplay about loss, identity and connection." - ], - "Nate": [], - "date": "21 October, 2022" - }, - "events_session_25": { - "Joanna": [ - "A movie based on a second script that Joanna contributed to is released on the big screen" - ], - "Nate": [ - "Nate takes his two turtles out for a walk." - ], - "date": "25 October, 2022" - }, - "events_session_26": { - "Joanna": [ - "Joanna schedules several meetings with movie producers to discuss her second screenplay which is about a thirty year old woman on a journey of self-discovery after suffering loss." - ], - "Nate": [ - "Nate helps the people he met at the international video game tournament in August to reset their high scores." - ], - "date": "4 November, 2022" - }, - "events_session_27": { - "Joanna": [ - "Joanna prepares a presentation for her movie script on a woman on a journey of self-discovery for the meetings with movie producers.", - "Joanna starts writing another movie script based on a love story and puts her brother's childhood letter to her in the Introduction part." - ], - "Nate": [ - "Nate wins a Valorant video game tournament", - "Nate upgrades his gaming equipment at home" - ], - "date": "7 November, 2022" - }, - "events_session_28": { - "Joanna": [ - "Joanna pitches her new movie script based on a love story and it resonates with movie producers.", - "Joanna starts working on a new script on a thriller story set in a small Midwestern town" - ], - "Nate": [ - "Nate thinks about joining a new gaming team after receving a couple of offers", - "Nate's video game tournament gets pushed back", - "Nate starts creating gaming content for YouTube to entertain folks and satisfy his video game craving", - "Nate gets a third pet turtle" - ], - "date": "9 November, 2022" - }, - "events_session_29": { - "Joanna": [ - "Joanna starts filming her movie based on her script of a woman's journey of self-discovery over a raod trip." - ], - "Nate": [ - "Nate takes his turtles to the beach in Tampa" - ], - "date": "11 November, 2022" - } - }, - "observation": { - "session_1_observation": { - "Nate": [ - [ - "Nate won his first video game tournament playing a team shooter game called Counter-Strike: Global Offensive.", - "D1:3" - ], - [ - "Nate's main hobbies are playing video games and watching movies.", - "D1:11" - ], - [ - "Nate enjoys action and sci-fi movies for their cool effects.", - "D1:13" - ] - ], - "Joanna": [ - [ - "Joanna has been working on a project recently.", - "D1:2" - ], - [ - "Besides writing, Joanna also enjoys reading, watching movies, and exploring nature as hobbies.", - "D1:10" - ], - [ - "Joanna's favorite movie genres are dramas and romcoms as she enjoys getting immersed in feelings and plots.", - "D1:14" - ], - [ - "Joanna recommended a romantic drama movie that focuses on memory and relationships.", - "D1:16" - ], - [ - "Joanna watched a specific movie she recommended around 3 years ago and considers it one of her favorites.", - "D1:18" - ] - ] - }, - "session_2_observation": { - "Joanna": [ - [ - "Joanna finished her first full screenplay last Friday, which is a mix of drama and romance.", - "D2:3" - ], - [ - "Joanna plans to submit her screenplay to film festivals and hopes to attract producers and directors to check it out.", - "D2:7" - ], - [ - "Joanna is allergic to most reptiles and animals with fur, causing her face to get puffy and itchy.", - "D2:23" - ], - [ - "Writing and hanging out with friends bring Joanna joy and help her express herself.", - "D2:25" - ], - [ - "Writing allows Joanna to create wild worlds with interesting characters and serves as a great way to express her feelings.", - "D2:27" - ] - ], - "Nate": [ - [ - "Nate has had turtles for 3 years that bring him joy and help keep him calm during stressful times.", - "D2:12" - ], - [ - "Nate enjoys hanging out with people outside of his usual circle at the tournament.", - "D2:16" - ], - [ - "Nate tends to stick around more chill people at the tournament due to varying levels of competitiveness.", - "D2:18" - ], - [ - "Nate does not expect to win big at the tournament but plays for fun and likes cheering on others.", - "D2:20" - ] - ] - }, - "session_3_observation": { - "Joanna": [ - [ - "Joanna submitted a screenplay to a film festival and is hoping a producer or director falls in love with it to get it on the big screen.", - "D3:1" - ], - [ - "Joanna appreciates Nate's support for her screenplay and mentions that it means a lot to her.", - "D3:3" - ], - [ - "Joanna seems curious about trying dairy-free desserts and flavors after Nate mentions making coconut milk ice cream.", - "D3:11" - ], - [ - "Joanna enjoys the movie 'Little Women' and recommends it as a great story about sisterhood, love, and reaching for dreams.", - "D3:17" - ], - [ - "Joanna is open to giving movie recommendations and discussing movies.", - "D3:19" - ] - ], - "Nate": [ - [ - "Nate supports Joanna's screenplay submission to the film festival and sends positive vibes wishing for its success.", - "D3:2" - ], - [ - "Nate tried making coconut milk ice cream, found it delicious, and is looking forward to trying different flavors and toppings.", - "D3:6" - ], - [ - "Nate mentions enjoying chocolate and mixed berry flavors for dairy-free desserts.", - "D3:10" - ], - [ - "Nate made a dairy-free chocolate cake with berries, showcasing his talent in the kitchen.", - "D3:12" - ], - [ - "Nate is open to trying out new flavors and experimenting in the kitchen.", - "D3:14" - ], - [ - "Nate appreciates Joanna's movie recommendation and expresses interest in watching 'Little Women'.", - "D3:18" - ] - ] - }, - "session_4_observation": { - "Nate": [ - [ - "Nate made chocolate and vanilla swirl ice cream for a friend.", - "D4:1" - ], - [ - "Nate enjoys baking desserts and is all about them.", - "D4:7" - ], - [ - "Nate bakes desserts that Joanna loves.", - "D4:9" - ] - ], - "Joanna": [ - [ - "Joanna can't have dairy.", - "D4:4" - ], - [ - "Joanna is keen to try Nate's dairy-free dessert recipe.", - "D4:6" - ], - [ - "Joanna is a writer and has written a screenplay.", - "D4:10" - ], - [ - "Joanna has started writing another screenplay focused on self-discovery after a loss, inspired by personal experiences.", - "D4:12" - ], - [ - "Joanna's new screenplay is about a thirty-year-old woman on a journey of self-discovery after a loss, inspired by personal experiences.", - "D4:14" - ], - [ - "Joanna hopes her screenplay gets noticed and makes it to the screen.", - "D4:18" - ] - ] - }, - "session_5_observation": { - "Joanna": [ - [ - "Joanna has completed her second script and is experiencing a mix of excitement and anxiety about its future.", - "D5:1" - ], - [ - "Joanna finds it hard to switch off and experiences a tug-of-war between hope and doubt.", - "D5:3" - ], - [ - "Joanna has done research and networking non-stop for her script and is determined to make it happen.", - "D5:15" - ], - [ - "Joanna feels supported and motivated by having someone throughout the process.", - "D5:19" - ] - ], - "Nate": [ - [ - "Nate finds turtles unique, calming, and low-maintenance as pets.", - "D5:6" - ], - [ - "Nate believes in Joanna's hard work and passion for her scripts, encouraging her to believe in herself.", - "D5:4" - ], - [ - "Nate suggests Joanna consider other animals due to her allergies, and offers to send her pictures of his turtles.", - "D5:12" - ], - [ - "Nate supports and motivates Joanna throughout the conversation.", - "D5:16" - ], - [ - "Nate emphasizes the importance of not quitting and assures Joanna that the next steps will become clearer.", - "D5:18" - ], - [ - "Nate expresses that his support for Joanna is important and that he is there for her.", - "D5:20" - ] - ] - }, - "session_6_observation": { - "Nate": [ - [ - "Nate is participating in a video game tournament that he finds intense.", - "D6:7" - ] - ], - "Joanna": [ - [ - "Joanna auditioned for a writing gig and had mixed emotions about it - excited but also anxious.", - "D6:2" - ], - [ - "Joanna has a lot of books that she uses for writing inspiration.", - "D6:8" - ], - [ - "Joanna's advice for someone pursuing writing includes reading lots, trying different genres, building a solid understanding of literature, and not being afraid to write and share.", - "D6:10" - ] - ] - }, - "session_7_observation": { - "Nate": [ - [ - "Nate dyed his hair a bright and bold color last week to stand out.", - "D7:5" - ] - ], - "Joanna": [ - [ - "Joanna finds Nate's boldness inspiring and relates it to a gorgeous sunset she saw while hiking.", - "D7:6" - ], - [ - "Joanna is currently consumed by her writing and is hoping for good news soon.", - "D7:8" - ], - [ - "Joanna appreciates Nate's support for her writing projects.", - "D7:10" - ] - ] - }, - "session_8_observation": { - "Nate": [ - [ - "Nate has turtles as pets that he loves watching and spending time with.", - "D8:11" - ], - [ - "Nate's pets provide him with peaceful moments and help him take a break from reality.", - "D8:11" - ], - [ - "Nate enjoys engaging in hobbies that revolve around spending time with his pets.", - "D8:11" - ], - [ - "Nate makes dairy-free ice cream using coconut milk, vanilla extract, sugar, and a pinch of salt.", - "D8:19" - ] - ], - "Joanna": [ - [ - "Joanna has been reading a lot of books in the past week and rediscovered good books she owned.", - "D8:2" - ], - [ - "Joanna found an awesome hiking trail in her hometown that she considers gorgeous and inspiring.", - "D8:4" - ], - [ - "Joanna finds nature to be calming and a way to reset, with worries and stress vanishing when she is surrounded by its beauty.", - "D8:10" - ], - [ - "Joanna loves nature and considers it her haven, finding peace and enjoyment in walking in it and feeling its calming effects.", - "D8:10" - ], - [ - "Joanna is lactose intolerant.", - "D8:18" - ], - [ - "Joanna is interested in trying Nate's dairy-free ice cream recipe made with coconut milk, vanilla extract, sugar, and salt.", - "D8:20" - ] - ] - }, - "session_9_observation": { - "Joanna": [ - [ - "Joanna recently joined a writers group that she finds inspirational and supportive.", - "D9:1" - ], - [ - "Joanna is working on a script called 'Finding Home' with her writing group, which she finds rewarding and emotional.", - "D9:3" - ], - [ - "Joanna's first passion was acting, but now she feels she shines in writing.", - "D9:7" - ], - [ - "Joanna enjoys dramas and emotionally-driven films.", - "D9:9" - ] - ], - "Nate": [ - [ - "Nate is participating in a gaming tournament next month, which will be his 4th one.", - "D9:4" - ], - [ - "Nate loves fantasy and sci-fi movies as they inspire his imagination.", - "D9:10" - ], - [ - "Nate recommended a series with adventures, magic, and great characters to Joanna.", - "D9:14" - ] - ] - }, - "session_10_observation": { - "Joanna": [ - [ - "Joanna watched 'The Lord of the Rings' Trilogy based on Nate's recommendation and found it awesome.", - "D10:1" - ], - [ - "Joanna has been working on some projects and testing dairy-free dessert recipes for friends and family.", - "D10:9" - ], - [ - "Joanna made a dairy-free vanilla cake with strawberry filling and coconut cream frosting.", - "D10:11" - ], - [ - "Cooking and baking are Joanna's creative outlets, and she enjoys experimenting in the kitchen.", - "D10:13" - ] - ], - "Nate": [ - [ - "Nate believes 'The Lord of the Rings' Trilogy is probably the greatest trilogy of all time.", - "D10:2" - ], - [ - "Nate's focus has been on gaming, practicing a lot and winning tournaments, including winning his second tournament last week.", - "D10:4" - ], - [ - "Nate usually plays CS:GO but participated in a local Street Fighter tournament and discovered he is really good at it.", - "D10:6" - ], - [ - "Nate recommended coconut to Joanna in the past which she liked and mentioned during the conversation.", - "D10:11" - ] - ] - }, - "session_11_observation": { - "Joanna": [ - [ - "Joanna enjoys hiking and found some amazing trails in her town.", - "D11:3" - ], - [ - "Joanna took a photo at Whispering Falls, a beautiful and peaceful location.", - "D11:7" - ], - [ - "Joanna feels calm and peaceful in nature, particularly during her hikes.", - "D11:9" - ], - [ - "Hiking has opened up a whole new world for Joanna, and she feels like a different person now.", - "D11:11" - ], - [ - "Joanna feels inspired by nature and finds it calming to be surrounded by its beauty.", - "D11:11" - ], - [ - "Joanna feels like she could write a whole movie when she is in inspiring places like the trails she visits.", - "D11:13" - ], - [ - "In inspiring places like the trails, Joanna feels like writing a drama.", - "D11:15" - ] - ], - "Nate": [ - [ - "Nate is interested in Joanna's hiking experiences and the photos she takes.", - "D11:4" - ], - [ - "Nate appreciates the calm and beauty of the nature spots Joanna visits.", - "D11:6" - ], - [ - "Nate acknowledges that nature can provide a break from the craziness of life.", - "D11:10" - ], - [ - "Nate gets deep in thought when he is out in nature and thinks about his life or new recipes.", - "D11:14" - ], - [ - "Nate is open to the idea of going hiking together with Joanna and possibly finding inspiration for a screenplay.", - "D11:16" - ] - ] - }, - "session_12_observation": { - "Nate": [ - [ - "Nate has a new addition to the family, an adopted pet named Max who is full of energy and joy.", - "D12:3" - ], - [ - "Nate believes that pets bring happiness and joy into people's lives.", - "D12:7" - ], - [ - "Nate is supportive and encouraging towards Joanna's writing and creative projects.", - "D12:11" - ], - [ - "Nate acknowledges that Joanna likes to write about topics like sadness and loss.", - "D12:15" - ], - [ - "Nate is proud of Joanna for staying true to herself and exploring personal experiences in her writing.", - "D12:17" - ], - [ - "Nate supports Joanna and cheers her on in her creative journey.", - "D12:19" - ] - ], - "Joanna": [ - [ - "Joanna finds comfort in writing and creative projects during tough times.", - "D12:10" - ], - [ - "Joanna has supportive friends who appreciate her work and with whom she can discuss and receive feedback.", - "D12:12" - ], - [ - "Joanna has written at least three personal stories, with the latest one being about loss, identity, and connection.", - "D12:14" - ], - [ - "Joanna believes that meaningful and powerful stories stem from personal experiences and feelings, even if they are challenging to write about.", - "D12:16" - ], - [ - "Joanna values Nate's support and finds it encouraging in her creative endeavors.", - "D12:18" - ] - ] - }, - "session_13_observation": { - "Nate": [ - [ - "Nate has a dog named Max that he enjoys taking for walks.", - "D13:1" - ], - [ - "Nate met a super nice couple with a dog during a walk and decided to do doggy playdates with them.", - "D13:1" - ], - [ - "Nate enjoys watching his dog play with other pets and finds joy in seeing his dog happy.", - "D13:7" - ], - [ - "Nate gifted Joanna a stuffed animal to remind her of good vibes.", - "D13:9" - ], - [ - "Nate believes in appreciating the little things in life that make it better.", - "D13:15" - ], - [ - "Nate is supportive of Joanna and expresses his support for her screenplay.", - "D13:21" - ] - ], - "Joanna": [ - [ - "Joanna values the moments that bring happiness and remind her that life is great.", - "D13:16" - ], - [ - "Joanna finds having a peaceful presence or something to come home to for a sense of calm helpful for relaxation.", - "D13:8" - ], - [ - "Joanna cherishes the stuffed animal gifted to her by Nate with all her heart.", - "D13:12" - ], - [ - "Joanna reflects on the tough times finishing her screenplay and realizes that the joyful moments make the journey worth it.", - "D13:18" - ], - [ - "Joanna values Nate's opinion and looks forward to showing him something.", - "D13:20" - ], - [ - "Joanna appreciates Nate's support and expresses her gratitude for it.", - "D13:22" - ] - ] - }, - "session_14_observation": { - "Joanna": [ - [ - "Joanna finished a screenplay but received a rejection letter from a major company.", - "D14:1" - ], - [ - "Joanna appreciates Nate's kind words and encouragement.", - "D14:3" - ], - [ - "Joanna is planning to go hiking with buddies to check out a new trail with a rad waterfall on the weekend.", - "D14:19" - ] - ], - "Nate": [ - [ - "Nate won a regional video game tournament last week and met new people.", - "D14:8" - ], - [ - "Nate enjoys connecting with fellow gamers and helping them improve their game.", - "D14:8" - ], - [ - "Nate is organizing a gaming party two weekends later and is getting custom controller decorations for everyone.", - "D14:20" - ] - ] - }, - "session_15_observation": { - "Joanna": [ - [ - "Joanna wrote bits for a screenplay that appeared on the big screen, which was nerve-wracking but inspiring.", - "D15:1" - ], - [ - "Joanna has a Spider-Man pin on her purse, indicating she likes Spider-Man as a favorite superhero.", - "D15:2" - ], - [ - "Joanna is a fan of superheroes in general, finding each one's story and powers fascinating.", - "D15:3" - ], - [ - "Joanna's room has a cork board full of inspiring quotes and pictures for motivation and creativity.", - "D15:7" - ], - [ - "Joanna has a picture of her family on her cork board which serves as a reminder of their love and encouragement.", - "D15:11" - ] - ], - "Nate": [ - [ - "Nate thinks Iron Man is his top superhero pick due to loving his tech and sarcastic humor.", - "D15:4" - ], - [ - "Nate has an Iron Man figure in his room which reminds him to keep working on his goals.", - "D15:6" - ], - [ - "Nate is considering starting a cork board of his own after being impressed by Joanna's.", - "D15:14" - ] - ] - }, - "session_16_observation": { - "Joanna": [ - [ - "Joanna submitted her screenplay to a film contest recently.", - "D16:1" - ], - [ - "Joanna showed interest in Nate's controller accessories for gaming.", - "D16:3" - ], - [ - "Joanna expressed excitement to receive Nate's vegan ice cream recipe.", - "D16:9" - ], - [ - "Joanna mentioned her family enjoys it when she makes them new things.", - "D16:13" - ], - [ - "Joanna appreciates Nate's support for her ice cream making.", - "D16:15" - ] - ], - "Nate": [ - [ - "Nate hosted a successful gaming party with 7 attendees.", - "D16:6" - ], - [ - "Nate made vegan ice cream last Friday and shared it with his vegan diet group.", - "D16:8" - ], - [ - "Nate offered to share the vegan ice cream recipe with Joanna the next day.", - "D16:10" - ], - [ - "Nate expressed confidence that Joanna's family will love the ice cream.", - "D16:14" - ] - ] - }, - "session_17_observation": { - "Nate": [ - [ - "Nate won his fourth video game tournament online and is proud to make money doing what he loves.", - "D17:1" - ] - ], - "Joanna": [ - [ - "Joanna took a road trip to Woodhaven, a small town in the Midwest, for research for her next movie.", - "D17:2" - ], - [ - "Joanna found a super cool book from the 1900s with stories and sketches during her trip, inspiring her next script.", - "D17:6" - ], - [ - "Joanna's new script is different from her previous work but has the potential to be something awesome.", - "D17:10" - ], - [ - "Joanna started working on a book recently after her movie did well. The book explores themes of loss, redemption, and forgiveness.", - "D17:14" - ], - [ - "Joanna mentioned that she has never really tried publishing a book, but this might be her first one.", - "D17:18" - ] - ] - }, - "session_18_observation": { - "Joanna": [ - [ - "Joanna is heavily invested in writing projects and considers writing a huge part of her life.", - "D18:1" - ], - [ - "Joanna finds solace and creativity in writing, considering it an escape and a way to express her feelings.", - "D18:3" - ], - [ - "Someone wrote Joanna a touching letter after reading an online blog post she made about a difficult moment in her life.", - "D18:5" - ], - [ - "Joanna values the impact her writing has on others, mentioning that it keeps her going even on tough days.", - "D18:7" - ], - [ - "Joanna is appreciative of Nate's support and kind words.", - "D18:7" - ], - [ - "Joanna is excited to try Nate's dairy-free desserts and expresses gratitude for his willingness to share the recipe.", - "D18:11" - ], - [ - "Joanna plans to surprise her family with Nate's dairy-free dessert.", - "D18:13" - ] - ], - "Nate": [ - [ - "Nate started teaching people how to make dairy-free desserts and finds it fun and rewarding.", - "D18:8" - ], - [ - "Nate has been making a creamy, rich, dairy-free dessert with a new recipe that he wants Joanna to try.", - "D18:10" - ], - [ - "Nate is supportive and enthusiastic about sharing his dairy-free dessert recipe with Joanna.", - "D18:10" - ], - [ - "Nate wishes Joanna luck with surprising her family with the dessert and looks forward to hearing about the outcome.", - "D18:14" - ] - ] - }, - "session_19_observation": { - "Nate": [ - [ - "Nate won an international tournament in gaming, turning his passion into a career.", - "D19:1" - ], - [ - "Nate has pets that he refers to as his 'little dudes' and got them a new tank recently.", - "D19:3" - ], - [ - "Nate values taking time off to relax and chill with his pets.", - "D19:9" - ], - [ - "Nate mentioned that taking care of ourselves helps in being more creative and happier.", - "D19:13" - ] - ], - "Joanna": [ - [ - "Joanna shared her book with her writers group and received great feedback, indicating that her hard work is paying off.", - "D19:6" - ], - [ - "Joanna celebrates achievements by making delicious treats.", - "D19:8" - ], - [ - "Joanna emphasizes the importance of taking breaks and looking after herself for inspiration and mental health, focusing on finding balance.", - "D19:12" - ], - [ - "Joanna enjoys relaxing and recharging with activities like long walks and reading.", - "D19:10" - ] - ] - }, - "session_20_observation": { - "Nate": [ - [ - "Nate experienced a letdown in a video game tournament recently.", - "D20:1" - ], - [ - "Nate tried to stay positive despite the setback in the video game tournament.", - "D20:1" - ], - [ - "Nate finds comfort and gets creative by hanging out with cute turtles.", - "D20:1" - ], - [ - "Nate enjoys helping with making desserts that suit everyone's diets.", - "D20:10" - ], - [ - "Nate offers tips for dairy-free baking, such as using dairy-free margarine or coconut oil instead of butter.", - "D20:15" - ], - [ - "Nate is willing to help anytime Joanna needs baking tips.", - "D20:17" - ] - ], - "Joanna": [ - [ - "Joanna revises recipes to find comfort and get creative.", - "D20:4" - ], - [ - "Joanna is experimenting with making dessert recipes yummier and more accessible.", - "D20:6" - ], - [ - "Joanna is lactose intolerant and is trying out dairy-free options like coconut or almond milk in her dessert recipes.", - "D20:10" - ], - [ - "Joanna makes desserts that work for everyone's diets, including dairy-free chocolate coconut cupcakes with raspberry frosting.", - "D20:10" - ], - [ - "Joanna enjoys the company of a fellow chef in the kitchen and believes it brings people together.", - "D20:14" - ], - [ - "Joanna is curious about more tips for dairy-free baking.", - "D20:14" - ] - ] - }, - "session_21_observation": { - "Joanna": [ - [ - "Joanna's laptop crashed last week, causing her to lose all her work which was a major blow.", - "D21:1" - ], - [ - "Joanna now uses an external drive for backups to prevent losing work again.", - "D21:3" - ], - [ - "Joanna made a delicious dessert with almond milk last Friday.", - "D21:9" - ], - [ - "Joanna's favorite dairy-free sweet treat is a chocolate raspberry tart with almond flour crust, chocolate ganache, and fresh raspberries.", - "D21:11" - ], - [ - "Joanna loves making dairy-free chocolate cake with raspberries for special occasions, using almond flour, coconut oil, chocolate, and raspberries.", - "D21:13" - ], - [ - "Joanna enjoys making a dessert with blueberries, coconut milk, and a gluten-free crust, finding it creamy and delicious.", - "D21:17" - ] - ], - "Nate": [ - [ - "Nate recently taught vegan ice cream recipes on his cooking show and learned some new recipes.", - "D21:4" - ], - [ - "Nate's favorite dish from his cooking show is coconut milk ice cream for its smooth, creamy, and dairy-free qualities.", - "D21:6" - ], - [ - "Nate also loves dairy-free chocolate mousse as it is super creamy and tastes like the real thing.", - "D21:10" - ], - [ - "Nate is interested in trying Joanna's dairy-free chocolate cake with raspberries and other recipes she likes making.", - "D21:12" - ], - [ - "Nate expresses interest in Joanna's dairy-free dessert with blueberries, coconut milk, and a gluten-free crust mentioning it looks amazing.", - "D21:18" - ] - ] - }, - "session_22_observation": { - "Joanna": [ - [ - "Joanna tried her newest dairy-free recipe and it was a winner with her family.", - "D22:1" - ], - [ - "Joanna finished writing her book last week after putting in a ton of late nights and edits.", - "D22:9" - ], - [ - "Joanna made a cute little bookmark for one of the ladies at her writing club.", - "D22:19" - ], - [ - "Joanna loves DIYs.", - "D22:21" - ] - ], - "Nate": [ - [ - "Nate won a really big video game tournament last week and made a lot of money from it.", - "D22:2" - ], - [ - "Nate watched 'Little Women' recently and found the acting and story captivating.", - "D22:8" - ], - [ - "Nate made something for Joanna to encourage her.", - "D22:14" - ], - [ - "Nate believes encouragement got him far in his gaming career.", - "D22:18" - ] - ] - }, - "session_23_observation": { - "Nate": [ - [ - "Nate attended a game convention last Friday and met new people who share his interests.", - "D23:1" - ], - [ - "Nate made friends at the convention who love games like him and planned a gaming session together.", - "D23:3" - ], - [ - "Nate joined in playing a board game at the convention with people who shared his interests and hit it off with them.", - "D23:5" - ], - [ - "Nate plays the game Catan and loves it for its strategy of building settlements and trading resources.", - "D23:7" - ], - [ - "Nate sees playing games as his escape from life struggles and doesn't get too competitive over them.", - "D23:11" - ], - [ - "Nate has been playing the game Cyberpunk 2077 nonstop recently and enjoys its futuristic setting and gameplay.", - "D23:17" - ] - ], - "Joanna": [ - [ - "Joanna encourages Nate to continue exploring new things and stepping outside his comfort zone.", - "D23:2" - ], - [ - "Joanna appreciates how games can bring people together and form strong relationships.", - "D23:6" - ], - [ - "Joanna is curious about Nate's favorite game or movie and engages in discussions about them.", - "D23:16" - ], - [ - "Joanna watched a classic movie recently that she found gripping and impactful, rating it 9 or 10 out of 10.", - "D23:18" - ], - [ - "Joanna enjoys creating a cozy and comfortable living room environment, suggesting specific items like a fluffy couch and a weighted blanket to Nate.", - "D23:26" - ] - ] - }, - "session_24_observation": { - "Nate": [ - [ - "Nate is stressed due to tough competitors in his tournament progress.", - "D24:3" - ], - [ - "Nate has turtles as pets that always cheer him up.", - "D24:3" - ], - [ - "Nate practices for video game tournaments.", - "D24:11" - ] - ], - "Joanna": [ - [ - "Joanna has been revising and perfecting a recipe for her family.", - "D24:2" - ], - [ - "Joanna has a stuffed animal dog named Tilly that was a gift from Nate.", - "D24:4" - ], - [ - "Joanna had a dog named Tilly back in Michigan but had to give her away due to allergies.", - "D24:6" - ], - [ - "Joanna recently faced a setback - another rejection from a production company.", - "D24:12" - ], - [ - "Joanna remains determined and resilient despite rejections, aiming to keep grinding and moving ahead.", - "D24:14" - ] - ] - }, - "session_25_observation": { - "Nate": [ - [ - "Nate took his turtles out for a walk today and they are now relaxing in the tank.", - "D25:15" - ], - [ - "Nate's turtles have a varied diet including vegetables, fruits, and insects.", - "D25:19" - ], - [ - "Nate loves seeing his turtles eat fruit as they get excited and it's cute.", - "D25:21" - ], - [ - "Nate's turtles have different personalities: one is more adventurous while the other is more reserved.", - "D25:25" - ], - [ - "Nate has always liked turtles since he was a boy and finds them interesting.", - "D25:27" - ] - ], - "Joanna": [ - [ - "Joanna recently had a movie script she contributed to shown on the big screen for the first time.", - "D25:2" - ], - [ - "Joanna feels a mix of emotions but overall satisfaction after seeing her script on the big screen.", - "D25:2" - ], - [ - "Joanna's characters she wrote coming alive on screen was a cool and surreal experience for her.", - "D25:4" - ], - [ - "Joanna creates visuals of her characters to help bring them alive in her head for better writing.", - "D25:8" - ], - [ - "Joanna gets ideas for her characters from various sources like people she knows, things she sees, and her imagination.", - "D25:10" - ], - [ - "Joanna's character visuals are used as a tool to visualize their wants, worries, and wishes.", - "D25:10" - ], - [ - "Joanna finds it cool to see how an idea transforms into a person with unique traits.", - "D25:10" - ] - ] - }, - "session_26_observation": { - "Joanna": [ - [ - "Joanna set up meetings with movie producers and feels her dreams are coming true.", - "D26:1" - ], - [ - "Joanna discussed her script and vision with producers and they liked the elements of self-discovery in it.", - "D26:3" - ], - [ - "Joanna found old notebooks with her early writings and feels appreciative of her progress as a writer.", - "D26:5" - ], - [ - "Joanna's early writings include a story about a brave little turtle who explores the world despite being scared, reflecting themes of courage and risk-taking.", - "D26:7" - ], - [ - "Joanna is working on a challenging but fulfilling project apart from her meetings.", - "D26:11" - ], - [ - "Joanna is grateful for Nate's support and encouragement.", - "D26:5" - ], - [ - "Joanna agreed to go to Nate's place and bring dessert recipes to share with him.", - "D26:19" - ], - [ - "Joanna has a chocolate and raspberry cake recipe that Nate is interested in trying.", - "D26:22" - ] - ], - "Nate": [ - [ - "Nate mentions he likes winning recognition in video game tournaments and compares it to Joanna's recognition.", - "D26:4" - ], - [ - "Nate has been helping friends reset their high scores at an international tournament.", - "D26:12" - ], - [ - "Nate uses his gaming skills to make a difference and finds it rewarding.", - "D26:14" - ], - [ - "Nate invited Joanna to try a lactose-free dish he made with coconut milk.", - "D26:16" - ], - [ - "Nate is passionate about gaming and helping others with high scores.", - "D26:13" - ], - [ - "Nate is open to Joanna coming over to try his dish and share dessert recipes.", - "D26:19" - ] - ] - }, - "session_27_observation": { - "Nate": [ - [ - "Nate won a big Valorant tournament making him the champion.", - "D27:1" - ], - [ - "Nate prepares for other tournaments and enjoys his job.", - "D27:3" - ], - [ - "Nate owns turtles that bring joy and calmness to his life.", - "D27:9" - ], - [ - "Nate upgraded some equipment at home for gaming purposes.", - "D27:15" - ], - [ - "Nate practices and competes on his computer for gaming.", - "D27:17" - ], - [ - "Nate is currently playing a fantasy RPG called \"Xenoblade Chronicles\" and recommends it.", - "D27:23" - ] - ], - "Joanna": [ - [ - "Joanna finished a presentation for producers last Friday.", - "D27:2" - ], - [ - "Joanna is writing a movie script, a love story with lots of challenges.", - "D27:6" - ], - [ - "Joanna submitted a few scripts last week and expects some rejections.", - "D27:12" - ], - [ - "Joanna finds happiness and fulfillment in writing.", - "D27:10" - ], - [ - "Joanna incorporates memories and childhood experiences into her writing.", - "D27:30" - ] - ] - }, - "session_28_observation": { - "Nate": [ - [ - "Nate's game tournament got pushed back, so he tried making homemade coconut ice cream.", - "D28:1" - ], - [ - "Nate did not make it to the finals of the last game tournament.", - "D28:3" - ], - [ - "Nate is considering joining a new gaming team after the next tournament.", - "D28:5" - ], - [ - "Nate is creating gaming content for YouTube when there are no tournaments.", - "D28:13" - ], - [ - "Nate has a third turtle as a pet and enjoys having turtles as companions.", - "D28:25" - ] - ], - "Joanna": [ - [ - "Joanna worked on a script and pitched it to producers who liked it.", - "D28:8" - ], - [ - "Joanna is working on a new project - a suspenseful thriller set in a small Midwestern town.", - "D28:12" - ], - [ - "Joanna encouraged Nate to watch other people's videos before making his own for YouTube.", - "D28:18" - ], - [ - "Joanna took a beautiful sunset picture on a hike near Fort Wayne last summer.", - "D28:22" - ], - [ - "Joanna is allergic to turtles but expresses interest in watching Nate play with them.", - "D28:28" - ] - ] - }, - "session_29_observation": { - "Joanna": [ - [ - "Joanna is filming her own movie based on a road-trip script.", - "D29:1" - ], - [ - "One of the actors on Joanna's movie set told her how much she liked her script, which gave Joanna chills.", - "D29:5" - ], - [ - "Joanna finds it awesome to be able to show her vision in the movie she's filming.", - "D29:3" - ] - ], - "Nate": [ - [ - "Nate took his turtles to the beach in Tampa and finds peace in their presence amid life's craziness.", - "D29:6" - ], - [ - "Nate loves having turtles around as they make him feel calm and don't require much looking after.", - "D29:8" - ], - [ - "Nate is currently experimenting with flavors and made colorful bowls of coconut milk ice cream.", - "D29:10" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Nate and Joanna caught up at 7:31 pm on 21 January, 2022. Nate won his first video game tournament playing Counter-Strike: Global Offensive. Joanna enjoys writing, reading, movies, and nature. They found common interests in movies, with Nate liking action and sci-fi, while Joanna prefers dramas and romcoms. Joanna recommended a romantic drama to Nate, praising its storyline and acting. Nate promised to watch it and thanked Joanna for the suggestion.", - "session_2_summary": "Joanna and Nate caught up on January 23, 2022, at 2:01 pm. Joanna excitedly shared that she had completed her first screenplay, a mix of drama and romance, and planned to submit it to film festivals. Nate congratulated her and mentioned his turtles, recommending pets for stress relief. Joanna, allergic to animals with fur and reptiles, shared her joy for writing and hanging out with friends. Nate praised her passion for writing, and Joanna expressed her commitment to continue pursuing it. Nate also mentioned making new friends at a tournament.", - "session_3_summary": "Joanna and Nate caught up at 9:27 am on 7 February, 2022. Joanna shared her mixed emotions about her screenplay submitted to a film festival and hoped it would make it to the big screen. Nate encouraged her, mentioning positive vibes. Joanna asked about Nate's life, and he talked about trying out coconut milk ice cream. They discussed flavors and Nate's dairy-free chocolate cake with berries. Nate recommended \"Little Women\" to Joanna, who enjoyed it. They agreed to exchange movie recommendations in the future. Both wished each other well and signed off.", - "session_4_summary": "Nate apologized for not being around, mentioning making ice cream for a friend at 1:07 pm on 25 February, 2022. Joanna expressed interest in Nate's ice cream, but mentioned being unable to have dairy. Nate offered to share a dairy-free recipe with Joanna. They discussed Joanna's new screenplay about self-discovery and loss, with Nate showing interest in the storyline. Joanna revealed it was inspired by personal experiences. Nate praised Joanna's courage and expressed excitement about both her new screenplay and the first one. Joanna hoped her screenplay would get noticed and thanked Nate for his support, with Nate wishing her luck.", - "session_5_summary": "Joanna and Nate caught up at 6:59 pm on 18 March, 2022. Joanna shared her mixed emotions about finishing her second script while Nate encouraged her to believe in herself. They discussed the calming effects of Nate's pet turtles, but Joanna, allergic to certain animals, couldn't get pets. They talked about Joanna's script and the importance of hard work and support. Nate assured Joanna of his ongoing support as they signed off at the end of their conversation.", - "session_6_summary": "Nate and Joanna caught up at 1:43 pm on 24 March, 2022. Nate congratulated Joanna on her writing audition, which she found exciting but nerve-wracking. Nate encouraged Joanna and shared his intense experience in a video game tournament. Joanna advised Nate on pursuing writing by recommending reading widely and seeking feedback. Nate thanked Joanna for her advice and support, expressing determination to work hard. They both wished each other luck in their endeavors and said goodbye, vowing to keep striving for their dreams.", - "session_7_summary": "At 7:37 pm on 15 April 2022, Nate told Joanna he dyed his hair last week and invited her to see it. Joanna admired Nate's new hair color, and he explained he chose it to stand out. This inspired Joanna, who shared a story about a sunset she saw while hiking. They discussed upcoming plans, with Joanna focused on writing and Nate offering support. They agreed to stay in touch, with Nate suggesting they see each other soon. They bid goodbye, with Nate saying, \"Take care.\"", - "session_8_summary": "Nate and Joanna caught up at 6:44 pm on 17 April, 2022. They discussed reading books, hiking trails, and the calming effect of nature. Nate shared his love for his pets, including turtles, and his dairy-free ice cream recipe with Joanna. Joanna expressed interest in trying the recipe and promised to update Nate on the results.", - "session_9_summary": "At 7:44 pm on 21 April, 2022, Joanna informed Nate about joining a writers group that has been motivating and inspiring her. She is currently working on a script titled \"Finding Home.\" Nate shared his excitement for an upcoming gaming tournament, and they discussed their passions for writing, acting, and favorite movies and book series. Joanna planned to check out the book series recommended by Nate and expressed interest in possibly getting back to acting in the future. The conversation ended with warm goodbyes and well-wishes.", - "session_10_summary": "Joanna and Nate caught up on 2nd May at 11:54 am. Joanna told Nate she enjoyed watching \"The Lord of the Rings\" Trilogy on his recommendation, and Nate agreed it's a great trilogy. Nate shared he has been focused on gaming, winning his second tournament in Street Fighter. Joanna praised Nate's gaming room and his recent success in tournaments. Nate appreciated Joanna's dairy-free dessert creations and they discussed their creative outlets. Nate encouraged Joanna to keep experimenting in the kitchen. They ended the conversation with Joanna saying she will continue trying new things and Nate bidding her goodbye.", - "session_11_summary": "Joanna and Nate conversed at 3:35 pm on 12 May, 2022. Joanna shared her exciting hiking experience at Whispering Falls with Nate, who admired her photo and expressed interest in joining her on a hike in the future. They discussed how nature inspires creativity, with Joanna mentioning how hiking has transformed her and Nate considering writing a screenplay. They concluded their conversation with plans to go on a hike together in the future.", - "session_12_summary": "Nate and Joanna caught up at 7:49 pm on 20 May, 2022. Nate shared about his new adopted pet, Max, bringing joy to his life. Joanna, allergic to pets, finds solace in writing and creative projects. She showed Nate her latest work on loss and identity, appreciated his support, and valued their friendship. Nate commended Joanna for her bravery in exploring personal topics. The conversation ended with mutual encouragement to pursue dreams and talents.", - "session_13_summary": "At 3:00 pm on 25 May 2022, Nate and Joanna caught up after a long time. Nate shared his encounter with a friendly couple during a walk with his dog, leading to plans for doggy playdates. Joanna expressed how uplifting connecting with other pet owners can be. They discussed the joy of watching their pets play and the importance of finding happiness in small things. Nate gifted Joanna a stuffed animal to bring her joy. They agreed that cherishing happy moments is crucial, especially during tough times. Joanna mentioned her screenplay and the role of joyful moments in making the journey worthwhile. Nate offered support and encouragement, emphasizing the importance of enjoying the journey and staying focused on dreams. Joanna valued Nate's support and promised to keep him updated on her progress.", - "session_14_summary": "Joanna shared with Nate that she received a rejection letter for her screenplay, feeling discouraged. Nate comforted her, advising not to give up on her dreams. He encouraged her to stay positive and keep believing in herself. They discussed the generic rejection letter Joanna received without feedback. Nate shared his recent victory in a video game tournament, which boosted his confidence. Joanna congratulated him and admired his dedication to gaming. They expressed support for each other's endeavors and made plans for their respective weekends. Nate planned a gaming party and mentioned providing custom controller decorations for guests. Joanna wished him the best for the party, and they parted ways, each looking forward to their own activities.", - "session_15_summary": "On June 5th, 2022, at 2:12 pm, Joanna excitedly shared with Nate that her screenplay bits were on the big screen, feeling inspired but nervous. Nate congratulated her, mentioning her Spider-Man pin. They discussed their favorite superheroes, with Joanna loving Spider-Man for his struggles and Nate admiring Iron Man for his tech and humor. They both found inspiration in their favorite things, with Joanna's cork board filled with motivation. Nate expressed interest in making his own cork board and they bid each other goodbye.", - "session_16_summary": "Joanna and Nate caught up at 10:55 am on 24 June, 2022. Joanna submitted her screenplay to a film contest, and Nate's gaming party was successful. Nate shared about making vegan ice cream and Joanna wanted the recipe. They agreed to share it the next day. Joanna planned to make it for her family. Nate offered support, and Joanna promised to keep him updated. Nate wished her a great day and told her to take care.", - "session_17_summary": "Nate shared his recent victory in a video game tournament, winning for the fourth time and being proud to make money doing what he loves. Joanna congratulated him, mentioning her road trip to Woodhaven during his tournament. Nate showed interest in her discoveries, and Joanna talked about finding an old book with stories and sketches that inspired her next script. They exchanged words of encouragement and support, with Nate praising Joanna's talent and Joanna expressing excitement about her new script and book. Nate encouraged her to believe in herself and her talent. Joanna appreciated his support and stated her determination to continue working hard.", - "session_18_summary": "Joanna and Nate caught up at 6:12 pm on 14 August, 2022. Joanna shared her passion for writing as a form of solace and creativity, mentioning how it has become her escape. Nate found her writing inspiring and mentioned how words have a healing power. Joanna also talked about a touching moment when her writing had a positive impact on someone, making her realize the power of words. Nate started teaching people how to make dairy-free desserts and shared a new recipe with Joanna. She expressed excitement to try it and thanked him for sharing. They ended the conversation wishing each other well.", - "session_19_summary": "Nate informed Joanna at 10:57 am on 22 August, 2022, that he won an international gaming tournament and is now able to pursue his passion as a career. Joanna congratulated Nate for his victory and praised him for turning his passion into a profession. They discussed Nate's pet turtles' new tank and Joanna sharing her writing with a group, both feeling proud of their achievements. Nate emphasized the importance of self-care and taking breaks, which Joanna agreed on. They exchanged book recommendations before wishing each other a good day and taking care.", - "session_20_summary": "At 6:03 pm on 5 September 2022, Nate and Joanna catch up after a while. Nate mentions turtles, a setback in a video game tourney, and trying to stay positive. Joanna shares she revised an old recipe and made something new. Nate admires her dessert and asks how she is doing. Joanna mentions finding comfort in cooking. They discuss Joanna's experimentation with dairy-free dessert options like chocolate, raspberry, and coconut. Nate appreciates her inclusive approach and looks forward to more recipes. Joanna plans to share more recipes and seeks Nate's advice on dairy-free baking, to which Nate suggests substitutes like dairy-free margarine or coconut oil. They express excitement to cook together in the future. Joanna thanks Nate for his ideas and they say goodbye, with Joanna expressing her gratitude and looking forward to trying Nate's suggestions.", - "session_21_summary": "At 1:43 pm on 14 September 2022, Joanna and Nate caught up, where Joanna shared her laptop crash frustration and Nate recommended backups. Nate shared his experience teaching vegan ice cream recipes on a cooking show. They discussed dairy-free desserts, with Joanna sharing a chocolate raspberry tart recipe and Nate favoring coconut milk ice cream. Joanna also mentioned a dairy-free chocolate cake and a blueberry dessert. They ended their chat on a friendly note, with Joanna offering to share more recipes and Nate expressing gratitude.", - "session_22_summary": "Joanna and Nate, conversing at 11:15 am on 6 October, 2022, shared updates on their recent achievements and interests. Joanna talked about her successful dairy-free recipe while Nate mentioned winning a video game tournament and earning money. They discussed the joy of having extra cash, watching movies, and supporting each other's endeavors, with Nate even creating something special for Joanna. They both expressed pride and encouragement in each other's accomplishments and concluded by sharing their mutual love for DIY projects before saying goodbye.", - "session_23_summary": "Nate and Joanna spoke on 9 October, 2022, at 10:58 am. Nate shared his experience at a game convention where he met new people who shared his gaming interests. Joanna acknowledged the benefits of stepping out of comfort zones. They discussed how games can bond people and the impact of shared hobbies. Nate mentioned playing the game Catan at the convention, highlighting its competitive yet fun nature. Joanna appreciated the way games bring people together. They also talked about their favorite movies and games. Nate mentioned \"Inception\" and \"Cyberpunk 2077,\" while Joanna enjoyed a classic movie. They discussed the escapism movies provide and the importance of creating a cozy space at home. Joanna suggested getting a comfortable couch, fluffy blankets, and dim lights for a cozy living room setup. Nate found the advice helpful and mentioned implementing it. The conversation ended with a friendly farewell.", - "session_24_summary": "Nate and Joanna caught up at 2:01 pm on 21 October, 2022. Joanna shared about perfecting a family recipe and the significance of a stuffed animal dog named Tilly. Nate mentioned his stress from tough competitors in a tournament but found joy in his turtles. They discussed how pets bring happiness and shared recent activities. Joanna mentioned a recent setback with a production company rejection, but Nate offered support and encouragement. They both expressed determination to keep pushing forward. Nate reassured Joanna of his support, and they ended the conversation with positive affirmations.", - "session_25_summary": "At 8:16 pm on 25 October 2022, Nate asked Joanna how she's been. Joanna shared her excitement about having her script shown on the big screen for the first time, expressing a mix of emotions and satisfaction. Nate congratulated her and Joanna described the surreal experience of seeing her work come to life. The two discussed characters, with Joanna explaining her inspiration and Nate praising her drawings. They also talked about Nate's turtles and their varied diet. Joanna found it fascinating and admired how Nate's turtles have different personalities. The conversation ended with Nate expressing his love for turtles and Joanna looking forward to hearing more about them in the future.", - "session_26_summary": "At 3:56 pm on November 4, 2022, Joanna excitedly told Nate about setting up meetings with movie producers, leading to Nate's congratulatory response about her hard work. Joanna described the positive producer meetings, feeling validated and hopeful. Nate compared her success to winning a video game tournament. They discussed Joanna's earlier writings and passion for writing, including a childhood story about a brave turtle. Nate expressed admiration for her perseverance and encouraged her to keep pushing herself. They exchanged updates on their current projects. Nate invited Joanna over to try a lactose-free dish he made, and they planned to share desserts the next day. Joanna agreed to bring her chocolate and raspberry cake. They bid farewell, looking forward to their meeting.", - "session_27_summary": "Nate and Joanna talked at 8:10 pm on 7 November, 2022. Nate won a Valorant tournament last Saturday, while Joanna finished a presentation for producers on Friday. Nate is preparing for more tournaments and enjoys being busy. Joanna is working on a new movie script and has submitted scripts recently. They both value creating and cherishing memories. Nate has turtles and upgraded his gaming setup. Joanna enjoys writing and recalls childhood memories fondly. They encourage each other to pursue their dreams and make more memories together.", - "session_28_summary": "At 5:54 pm on 9 November 2022, Nate and Joanna had a conversation about their recent activities and future plans. Nate mentioned trying out cooking due to a game tournament delay and showed Joanna his homemade coconut ice cream. He didn't make it to the finals in the last tournament but was positive about the next one and considering joining a new gaming team. Joanna shared that she pitched a script to producers and got positive feedback, boosting her confidence. Nate talked about creating gaming content for YouTube, while Joanna worked on a new suspenseful thriller script. They discussed supporting each other's goals and shared hobbies, including nature photography and turtle-keeping. Joanna planned to visit Nate to watch him play with the turtles from a distance due to her allergy. They made plans to hang out and parted ways, with Nate promising to bathe the turtles before her visit. Joanna said, \"See you tomorrow,\" and Nate replied, \"Bye Joanna.\"", - "session_29_summary": "Joanna excitedly shared with Nate at 12:06 am on 11 November, 2022, that she was filming her own movie from a road-trip script. Nate congratulated her, mentioning it's not surprising due to her hard work. Joanna was thrilled to see her script come to life on set daily. She shared that an actor complimenting her script was a highlight. Nate then recounted a peaceful trip with his turtles to the beach in Tampa. Joanna praised the turtles, saying they looked serene. Nate likened turtles to zen masters for their calming effect. Nate then showed Joanna his colorful coconut milk ice cream bowls and offered her a taste. Joanna expressed interest in trying it, and the conversation ended on a positive note as they caught up and bid each other farewell." - }, - "sample_id": "conv-42" - }, - { - "qa": [ - { - "question": "what are John's goals with regards to his basketball career?", - "answer": "improve shooting percentage, win a championship", - "evidence": [ - "D1:9", - "D6:15", - "D11:17" - ], - "category": 1 - }, - { - "question": "What are John's goals for his career that are not related to his basketball skills?", - "answer": "get endorsements, build his brand, do charity work", - "evidence": [ - "D6:15", - "D11:17" - ], - "category": 1 - }, - { - "question": "What items does John collect?", - "answer": "sneakers, fantasy movie DVDs, jerseys", - "evidence": [ - "D1:15", - "D12:18", - "D27:20" - ], - "category": 1 - }, - { - "question": "Would Tim enjoy reading books by C. S. Lewis or John Greene?", - "answer": "C. S.Lewis", - "evidence": [ - "D1:14", - "D1:16", - "D1:18" - ], - "category": 3 - }, - { - "question": "What books has Tim read?", - "answer": "Harry Potter, Game of Thrones, the Name of the Wind, The Alchemist, The Hobbit, A Dance with Dragons, and the Wheel of Time.", - "evidence": [ - "D1:14", - "D2:7", - "D6:8", - "D11:26", - "D20:21", - "D26:36", - "D22:13" - ], - "category": 1 - }, - { - "question": "Based on Tim's collections, what is a shop that he would enjoy visiting in New York city?", - "answer": "House of MinaLima", - "evidence": [ - "D2:9" - ], - "category": 3 - }, - { - "question": "In which month's game did John achieve a career-high score in points?", - "answer": "June 2023", - "evidence": [ - "D3:1" - ], - "category": 2 - }, - { - "question": "Which geographical locations has Tim been to?", - "answer": "California, London, the Smoky Mountains", - "evidence": [ - "D1:18", - "D3:2", - "D14:16" - ], - "category": 1 - }, - { - "question": "Which outdoor gear company likely signed up John for an endorsement deal?", - "answer": "Under Armour", - "evidence": [ - "D3:15", - "D25:2" - ], - "category": 3 - }, - { - "question": "Which endorsement deals has John been offered?", - "answer": "basketball shoes and gear deal with Nike, potential sponsorship with Gatorade, Moxie a popular beverage company, outdoor gear company", - "evidence": [ - "D3:13", - "D3:15", - "D25:2", - "D29:4" - ], - "category": 1 - }, - { - "question": "When was John in Seattle for a game?", - "answer": "early August, 2023", - "evidence": [ - "D3:19", - "D5:2" - ], - "category": 2 - }, - { - "question": "What sports does John like besides basketball?", - "answer": "surfing", - "evidence": [ - "D1:7", - "D2:14", - "D3:1", - "D3:25" - ], - "category": 1 - }, - { - "question": "What year did John start surfing?", - "answer": "2018", - "evidence": [ - "D3:27" - ], - "category": 2 - }, - { - "question": "What does Tim do to escape reality?", - "answer": "Read fantasy books.", - "evidence": [ - "D2:11", - "D3:30" - ], - "category": 1 - }, - { - "question": "What kind of writing does Tim do?", - "answer": "comments on favorite books in a fantasy literature forum, articles on fantasy novels, studying characters, themes, and making book recommendations, writing a fantasy novel", - "evidence": [ - "D2:1", - "D4:3", - "D4:5", - "D15:3" - ], - "category": 1 - }, - { - "question": "Who is Anthony?", - "answer": "likely John's friend, colleague or family", - "evidence": [ - "D4:8" - ], - "category": 3 - }, - { - "question": "After how many weeks did Tim reconnect with the fellow Harry Potter fan from California?", - "answer": "three weeks", - "evidence": [ - "D3:2", - "D5:1" - ], - "category": 2 - }, - { - "question": "How many games has John mentioned winning?", - "answer": "6", - "evidence": [ - "D3:3", - "D5:2", - "D22:4", - "D23:7", - "D24:2" - ], - "category": 1 - }, - { - "question": "What authors has Tim read books from?", - "answer": "J.K. Rowling, R.R. Martin, Patrick Rothfuss, Paulo Coelho, and J. R. R. Tolkien.", - "evidence": [ - "D1:14", - "D2:7", - "D4:7", - "D5:15", - "D:11:26", - "D20:21", - "D26:36" - ], - "category": 1 - }, - { - "question": "What is a prominent charity organization that John might want to work with and why?", - "answer": "Good Sports, because they work with Nike, Gatorade, and Under Armour and they aim toprovide youth sports opportunities for kids ages 3-18 in high-need communities.", - "evidence": [ - "D3:13", - "D3:15", - "D6:15" - ], - "category": 3 - }, - { - "question": "Which city was John in before traveling to Chicago?", - "answer": "Seattle", - "evidence": [ - "D3:19", - "D5:2", - "D6:1", - "D6:3" - ], - "category": 2 - }, - { - "question": "Which US cities does John mention visiting to Tim?", - "answer": "Seattle, Chicago, New York", - "evidence": [ - "D3:19", - "D6:3", - "D9:6" - ], - "category": 1 - }, - { - "question": "When did John meet with his teammates after returning from Chicago?", - "answer": "August 15, 2023", - "evidence": [ - "D7:1" - ], - "category": 2 - }, - { - "question": "When is Tim attending a book conference?", - "answer": "September 2023", - "evidence": [ - "D7:6" - ], - "category": 2 - }, - { - "question": "Where was John between August 11 and August 15 2023?", - "answer": "Chicago", - "evidence": [ - "D6:1", - "D6:3", - "D7:1" - ], - "category": 2 - }, - { - "question": "What similar sports collectible do Tim and John own?", - "answer": "signed basketball", - "evidence": [ - "D7:7", - "D7:9", - "D16:7", - "D16:9" - ], - "category": 1 - }, - { - "question": "Which TV series does Tim mention watching?", - "answer": "That, Wheel of Time", - "evidence": [ - "D17:1", - "D17:11", - "D26:36" - ], - "category": 1 - }, - { - "question": "Which popular time management technique does Tim use to prepare for exams?", - "answer": "Pomodoro technique", - "evidence": [ - "D18:3", - "D18:7" - ], - "category": 3 - }, - { - "question": "Which popular music composer's tunes does Tim enjoy playing on the piano?", - "answer": "John Williams", - "evidence": [ - "D8:14", - "D8:16" - ], - "category": 3 - }, - { - "question": "What schools did John play basketball in and how many years was he with his team during high school?", - "answer": "Middle school, high school, and college and he was with his high school team for 4 years.", - "evidence": [ - "D6:13", - "D9:4" - ], - "category": 1 - }, - { - "question": "Which cities has John been to?", - "answer": "Seattle, Chicago, New York, and Paris.", - "evidence": [ - "D3:19", - "D6:3", - "D9:6", - "D27:36" - ], - "category": 1 - }, - { - "question": "What month did Tim plan on going to Universal Studios?", - "answer": "September, 2023", - "evidence": [ - "D10:9" - ], - "category": 2 - }, - { - "question": "Which US states might Tim be in during September 2023 based on his plans of visiting Universal Studios?", - "answer": "California or Florida", - "evidence": [ - "D10:9" - ], - "category": 3 - }, - { - "question": "When does John plan on traveling with his team on a team trip?", - "answer": "October, 2023", - "evidence": [ - "D11:7" - ], - "category": 2 - }, - { - "question": "What could John do after his basketball career?", - "answer": "become a basketball coach since he likes giving back and leadership", - "evidence": [ - "D11:19", - "D26:1", - "D27:26" - ], - "category": 3 - }, - { - "question": "What outdoor activities does John enjoy?", - "answer": "Hiking, surfing", - "evidence": [ - "D3:27", - "D12:6" - ], - "category": 1 - }, - { - "question": "Who is Tim and John's favorite basketball player?", - "answer": "LeBron James", - "evidence": [ - "D12:20", - "D12:22", - "D16:9" - ], - "category": 1 - }, - { - "question": "Which week did Tim visit the UK for the Harry Potter Conference?", - "answer": "The week before October 13th, 2023.", - "evidence": [ - "D13:1" - ], - "category": 2 - }, - { - "question": "which country has Tim visited most frequently in his travels?", - "answer": "UK", - "evidence": [ - "D1:18", - "D13:1", - "D18:1" - ], - "category": 1 - }, - { - "question": "What year did Tim go to the Smoky Mountains?", - "answer": "2022", - "evidence": [ - "D14:16" - ], - "category": 2 - }, - { - "question": "Has Tim been to North Carolina and/or Tennesee states in the US?", - "answer": "Yes", - "evidence": [ - "D14:16" - ], - "category": 2 - }, - { - "question": "What kind of fiction stories does Tim write?", - "answer": "Fantasy stories with plot twists", - "evidence": [ - "D15:3", - "D16:1" - ], - "category": 1 - }, - { - "question": "What has John cooked?", - "answer": "Soup, a slow cooker meal, and honey garlic chicken with roasted veg.", - "evidence": [ - "D10:4", - "D15:30", - "D15:31", - "D15:32" - ], - "category": 1 - }, - { - "question": "What does John like about Lebron James?", - "answer": "His heart, determination, skills, and leadership.", - "evidence": [ - "D12:20", - "D16:12" - ], - "category": 1 - }, - { - "question": "When did John and his wife go on a European vacation?", - "answer": "November, 2023.", - "evidence": [ - "D16:14" - ], - "category": 2 - }, - { - "question": "Which country was Tim visiting in the second week of November?", - "answer": "UK", - "evidence": [ - "D18:1" - ], - "category": 2 - }, - { - "question": "Where was Tim in the week before 16 November 2023?", - "answer": "UK", - "evidence": [ - "D18:1" - ], - "category": 2 - }, - { - "question": "When did John get married at a greenhouse?", - "answer": "last week of September 2023", - "evidence": [ - "D12:2" - ], - "category": 2 - }, - { - "question": "When did John get an ankle injury in 2023?", - "answer": "around November 16, 2023", - "evidence": [ - "D18:2" - ], - "category": 1 - }, - { - "question": "How many times has John injured his ankle?", - "answer": "two times", - "evidence": [ - "D18:2", - "D19:6" - ], - "category": 1 - }, - { - "question": "Which book was John reading during his recovery from an ankle injury?", - "answer": "The Alchemist", - "evidence": [ - "D19:20", - "D18:2" - ], - "category": 1 - }, - { - "question": "What kind of yoga for building core strength might John benefit from?", - "answer": "Hatha Yoga", - "evidence": [ - "D20:2" - ], - "category": 3 - }, - { - "question": "What does John do to supplement his basketball training?", - "answer": "Yoga, strength training", - "evidence": [ - "D8:5", - "D20:2" - ], - "category": 1 - }, - { - "question": "What other exercises can help John with his basketball performance?", - "answer": "Sprinting, long-distance running, and boxing.", - "evidence": [ - "D8:5", - "D20:2" - ], - "category": 3 - }, - { - "question": "When did John take a trip to the Rocky Mountains?", - "answer": "2022", - "evidence": [ - "D20:40" - ], - "category": 2 - }, - { - "question": "When did John start playing professionally?", - "answer": "May, 2023", - "evidence": [ - "D1:3", - "D21:4" - ], - "category": 2 - }, - { - "question": "When did Tim start playing the violin?", - "answer": "August 2023", - "evidence": [ - "D21:13" - ], - "category": 2 - }, - { - "question": "What instruments does Tim play?", - "answer": "piano, violin", - "evidence": [ - "D8:12", - "D21:11" - ], - "category": 1 - }, - { - "question": "When did John attend the Harry Potter trivia?", - "answer": "August 2023.", - "evidence": [ - "D4:8", - "D22:2" - ], - "category": 2 - }, - { - "question": "Which career-high performances did John achieve in 2023?", - "answer": "highest point score, highest assist", - "evidence": [ - "D3:1", - "D23:2" - ], - "category": 1 - }, - { - "question": "When did John achieve a career-high assist performance?", - "answer": "December 11, 2023", - "evidence": [ - "D23:2" - ], - "category": 2 - }, - { - "question": "What books has John read?", - "answer": "inpsiring book on dreaming big, The Alchemist, fantasy series, non-fiction books on personal development, Dune", - "evidence": [ - "D4:10", - "D11:26", - "D17:9", - "D19:16", - "D19:20", - "D22:12" - ], - "category": 1 - }, - { - "question": "What does John do to share his knowledge?", - "answer": "gives seminars, mentors younger players.", - "evidence": [ - "D14:3", - "D26:1" - ], - "category": 1 - }, - { - "question": "When did John organize a basketball camp for kids?", - "answer": "summer 2023", - "evidence": [ - "D26:23" - ], - "category": 2 - }, - { - "question": "Which month was John in Italy?", - "answer": "December, 2023", - "evidence": [ - "D27:2" - ], - "category": 2 - }, - { - "question": "What fantasy movies does Tim like?", - "answer": "Lord of the Rings, Harry Potter, and Star Wars.", - "evidence": [ - "D8:16", - "D8:18", - "D26:28", - "D26:32", - "D27:21" - ], - "category": 1 - }, - { - "question": "What is a Star Wars book that Tim might enjoy?", - "answer": "Star Wars: Jedi Apprentice by Judy Blundell and David Farland. It is a highly rated and immersive series about his favorite movies.", - "evidence": [ - "D27:19", - "D27:21" - ], - "category": 3 - }, - { - "question": "What would be a good hobby related to his travel dreams for Tim to pick up?", - "answer": "Writing a travel blog.", - "evidence": [ - "D4:1", - "D6:6", - "D15:3", - "D27:37" - ], - "category": 3 - }, - { - "question": "What day did Tim get into his study abroad program?", - "answer": "Januarty 5, 2024", - "evidence": [ - "D28:1" - ], - "category": 2 - }, - { - "question": "When will Tim leave for Ireland?", - "answer": "February, 2024", - "evidence": [ - "D28:1" - ], - "category": 2 - }, - { - "question": "Which Star Wars-related locations would Tim enjoy during his visit to Ireland?", - "answer": "Skellig Michael, Malin Head, Loop Head, Ceann Sib\u00e9al, and Brow Head because they are Star Wars filming locations.", - "evidence": [ - "D1:18", - "D27:21", - "D28:1" - ], - "category": 3 - }, - { - "question": "Which team did John sign with on 21 May, 2023?", - "answer": "The Minnesota Wolves", - "evidence": [ - "D1:5" - ], - "category": 4 - }, - { - "question": "What is John's position on the team he signed with?", - "answer": "shooting guard", - "evidence": [ - "D1:7" - ], - "category": 4 - }, - { - "question": "What challenge did John encounter during pre-season training?", - "answer": "fitting into the new team's style of play", - "evidence": [ - "D1:11" - ], - "category": 4 - }, - { - "question": "What aspects of the Harry Potter universe will be discussed in John's fan project collaborations?", - "answer": "characters, spells, magical creatures", - "evidence": [ - "D1:16" - ], - "category": 4 - }, - { - "question": "What forum did Tim join recently?", - "answer": "fantasy literature forum", - "evidence": [ - "D2:1" - ], - "category": 4 - }, - { - "question": "What kind of picture did Tim share as part of their Harry Potter book collection?", - "answer": "MinaLima's creation from the Harry Potter films", - "evidence": [ - "D2:9" - ], - "category": 4 - }, - { - "question": "What was the highest number of points John scored in a game recently?", - "answer": "40 points", - "evidence": [ - "D3:1" - ], - "category": 4 - }, - { - "question": "What did John celebrate at a restaurant with teammates?", - "answer": "a tough win", - "evidence": [ - "D3:5" - ], - "category": 4 - }, - { - "question": "What kind of deals did John sign with Nike and Gatorade?", - "answer": "basketball shoe and gear deal with Nike, potential sponsorship deal with Gatorade", - "evidence": [ - "D3:13" - ], - "category": 4 - }, - { - "question": "Which city is John excited to have a game at?", - "answer": "Seattle", - "evidence": [ - "D3:19" - ], - "category": 4 - }, - { - "question": "How long has John been surfing?", - "answer": "five years", - "evidence": [ - "D3:27" - ], - "category": 4 - }, - { - "question": "How does John feel while surfing?", - "answer": "super exciting and free-feeling", - "evidence": [ - "D3:29" - ], - "category": 4 - }, - { - "question": "What kind of articles has Tim been writing about for the online magazine?", - "answer": "different fantasy novels, characters, themes, and book recommendations", - "evidence": [ - "D4:5" - ], - "category": 4 - }, - { - "question": "Which two fantasy novels does Tim particularly enjoy writing about?", - "answer": "Harry Potter and Game of Thrones", - "evidence": [ - "D4:7" - ], - "category": 4 - }, - { - "question": "What did Anthony and John end up playing during the charity event?", - "answer": "an intense Harry Potter trivia contest", - "evidence": [ - "D4:8" - ], - "category": 4 - }, - { - "question": "What did John share with the person he skyped about?", - "answer": "Characters from Harry Potter", - "evidence": [ - "D5:1" - ], - "category": 4 - }, - { - "question": "How did John describe the team bond?", - "answer": "Awesome", - "evidence": [ - "D5:6" - ], - "category": 4 - }, - { - "question": "How did John get introduced to basketball?", - "answer": "Dad signed him up for a local league", - "evidence": [ - "D6:13" - ], - "category": 4 - }, - { - "question": "What is John's number one goal in his basketball career?", - "answer": "Winning a championship", - "evidence": [ - "D6:15" - ], - "category": 4 - }, - { - "question": "What organization is John teaming up with for his charity work?", - "answer": "A local organization helping disadvantaged kids with sports and school", - "evidence": [ - "D6:17" - ], - "category": 4 - }, - { - "question": "When did John meet back up with his teammates after his trip in August 2023?", - "answer": "Aug 15th", - "evidence": [ - "D7:1" - ], - "category": 4 - }, - { - "question": "What did John's teammates give him when they met on Aug 15th?", - "answer": "a basketball with autographs on it", - "evidence": [ - "D7:7" - ], - "category": 4 - }, - { - "question": "Why did John's teammates sign the basketball they gave him?", - "answer": "to show their friendship and appreciation", - "evidence": [ - "D7:9" - ], - "category": 4 - }, - { - "question": "What is the main intention behind Tim wanting to attend the book conference?", - "answer": "to learn more about literature and create a stronger bond to it", - "evidence": [ - "D7:6" - ], - "category": 4 - }, - { - "question": "What new activity has Tim started learning in August 2023?", - "answer": "play the piano", - "evidence": [ - "D8:12" - ], - "category": 4 - }, - { - "question": "Which movie's theme is Tim's favorite to play on the piano?", - "answer": "\"Harry Potter and the Philosopher's Stone\"", - "evidence": [ - "D8:14", - "D8:16" - ], - "category": 4 - }, - { - "question": "What special memory does \"Harry Potter and the Philosopher's Stone\" bring to Tim?", - "answer": "Watching it with his family", - "evidence": [ - "D8:16" - ], - "category": 4 - }, - { - "question": "Which movie does Tim mention they enjoy watching during Thanksgiving?", - "answer": "\"Home Alone\"", - "evidence": [ - "D8:24" - ], - "category": 4 - }, - { - "question": "What tradition does Tim mention they love during Thanksgiving?", - "answer": "Prepping the feast and talking about what they're thankful for", - "evidence": [ - "D8:22" - ], - "category": 4 - }, - { - "question": "How long did John and his high school basketball teammates play together?", - "answer": "Four years", - "evidence": [ - "D9:4" - ], - "category": 4 - }, - { - "question": "How was John's experience in New York City?", - "answer": "Amazing", - "evidence": [ - "D9:8" - ], - "category": 4 - }, - { - "question": "What did John say about NYC, enticing Tim to visit?", - "answer": "It's got so much to check out - the culture, food - you won't regret it.", - "evidence": [ - "D9:10" - ], - "category": 4 - }, - { - "question": "What kind of soup did John make recently?", - "answer": "tasty soup with sage", - "evidence": [ - "D10:4", - "D10:8" - ], - "category": 4 - }, - { - "question": "What spice did John add to the soup for flavor?", - "answer": "sage", - "evidence": [ - "D10:8" - ], - "category": 4 - }, - { - "question": "What is Tim excited to see at Universal Studios?", - "answer": "The Harry Potter stuff", - "evidence": [ - "D10:11" - ], - "category": 4 - }, - { - "question": "Where are John and his teammates planning to explore on a team trip?", - "answer": "a new city", - "evidence": [ - "D11:7" - ], - "category": 4 - }, - { - "question": "What city did Tim suggest to John for the team trip next month?", - "answer": "Edinburgh, Scotland", - "evidence": [ - "D11:10" - ], - "category": 4 - }, - { - "question": "What does John want to do after his basketball career?", - "answer": "positively influence and inspire others, potentially start a foundation and engage in charity work", - "evidence": [ - "D11:19" - ], - "category": 4 - }, - { - "question": "What advice did Tim give John about picking endorsements?", - "answer": "Ensure they align with values and brand, look for companies that share the desire to make a change and help others, make sure the endorsement feels authentic", - "evidence": [ - "D11:22" - ], - "category": 4 - }, - { - "question": "What book recommendation did Tim give to John for the trip?", - "answer": "A fantasy novel by Patrick Rothfuss", - "evidence": [ - "D11:24" - ], - "category": 4 - }, - { - "question": "What type of venue did John and his girlfriend choose for their wedding ceremony?", - "answer": "Greenhouse", - "evidence": [ - "D12:4" - ], - "category": 4 - }, - { - "question": "What was the setting for John and his wife's first dance?", - "answer": "Cozy restaurant", - "evidence": [ - "D12:10" - ], - "category": 4 - }, - { - "question": "Which basketball team does Tim support?", - "answer": "The Wolves", - "evidence": [ - "D12:21" - ], - "category": 4 - }, - { - "question": "What passion does Tim mention connects him with people from all over the world?", - "answer": "passion for fantasy stuff", - "evidence": [ - "D13:1" - ], - "category": 4 - }, - { - "question": "How does John describe the game season for his team?", - "answer": "intense with tough losses and great wins", - "evidence": [ - "D13:4" - ], - "category": 4 - }, - { - "question": "How does John say his team handles tough opponents?", - "answer": "by backing each other up and not quitting", - "evidence": [ - "D13:6" - ], - "category": 4 - }, - { - "question": "What motivates John's team to get better, according to John?", - "answer": "facing tough opponents", - "evidence": [ - "D13:6" - ], - "category": 4 - }, - { - "question": "What did John's team win at the end of the season?", - "answer": "a trophy", - "evidence": [ - "D13:8" - ], - "category": 4 - }, - { - "question": "Where did Tim capture the photography of the sunset over the mountain range?", - "answer": "Smoky Mountains", - "evidence": [ - "D14:16" - ], - "category": 4 - }, - { - "question": "How does John feel about being seen as a mentor by some of the younger players?", - "answer": "It feels great", - "evidence": [ - "D14:11" - ], - "category": 4 - }, - { - "question": "What does John find rewarding about mentoring the younger players?", - "answer": "Seeing their growth, improvement, and confidence", - "evidence": [ - "D14:7" - ], - "category": 4 - }, - { - "question": "What has John been able to help the younger players achieve?", - "answer": "reach their goals", - "evidence": [ - "D14:5" - ], - "category": 4 - }, - { - "question": "What genre is the novel that Tim is writing?", - "answer": "Fantasy", - "evidence": [ - "D15:3" - ], - "category": 4 - }, - { - "question": "Who is one of Tim's sources of inspiration for writing?", - "answer": "J.K. Rowling", - "evidence": [ - "D15:7" - ], - "category": 4 - }, - { - "question": "What J.K. Rowling quote does Tim resonate with?", - "answer": "\"Turn on the light - happiness hides in the darkest of times.\"", - "evidence": [ - "D15:11" - ], - "category": 4 - }, - { - "question": "What does John write on the whiteboard to help him stay motivated?", - "answer": "motivational quotes and strategies", - "evidence": [ - "D15:14" - ], - "category": 4 - }, - { - "question": "What hobby is a therapy for John when away from the court?", - "answer": "Cooking", - "evidence": [ - "D15:30" - ], - "category": 4 - }, - { - "question": "What type of meal does John often cook using a slow cooker?", - "answer": "honey garlic chicken with roasted veg", - "evidence": [ - "D15:32", - "D15:33" - ], - "category": 4 - }, - { - "question": "How will John share the honey garlic chicken recipe with the other person?", - "answer": "write it down and mail it", - "evidence": [ - "D15:34" - ], - "category": 4 - }, - { - "question": "What was Tim's huge writing issue last week,as mentioned on November 6, 2023?", - "answer": "He got stuck on a plot twist", - "evidence": [ - "D16:1" - ], - "category": 4 - }, - { - "question": "What does Tim have that serves as a reminder of hard work and is his prized possession?", - "answer": "a basketball signed by his favorite player", - "evidence": [ - "D16:7" - ], - "category": 4 - }, - { - "question": "Why do Tim and John find LeBron inspiring?", - "answer": "LeBron's determination and the epic block in Game 7 of the '16 Finals", - "evidence": [ - "D16:9", - "D16:10" - ], - "category": 4 - }, - { - "question": "How did John describe the views during their road trip out on the European coastline?", - "answer": "Spectacular", - "evidence": [ - "D17:3" - ], - "category": 4 - }, - { - "question": "What is one of Tim's favorite fantasy TV shows, as mentioned on November 11, 2023?", - "answer": "\"That\"", - "evidence": [ - "D17:10" - ], - "category": 4 - }, - { - "question": "How does Tim stay motivated during difficult study sessions?", - "answer": "Visualizing goals and success", - "evidence": [ - "D18:6" - ], - "category": 4 - }, - { - "question": "What did Tim say about his injury on 16 November, 2023?", - "answer": "The doctor said it's not too serious", - "evidence": [ - "D18:10" - ], - "category": 4 - }, - { - "question": "What was the setback Tim faced in his writing project on 21 November, 2023?", - "answer": "Story based on experiences in the UK didn't go as planned", - "evidence": [ - "D19:3" - ], - "category": 4 - }, - { - "question": "How did John overcome his ankle injury from last season?", - "answer": "stayed focused on recovery and worked hard to strengthen his body", - "evidence": [ - "D19:6" - ], - "category": 4 - }, - { - "question": "What motivated Tim to keep pushing himself to get better in writing and reading?", - "answer": "Love for writing and reading", - "evidence": [ - "D19:9" - ], - "category": 4 - }, - { - "question": "How did John overcome a mistake he made during a big game in basketball?", - "answer": "Worked hard to get better and focused on growth", - "evidence": [ - "D19:10" - ], - "category": 4 - }, - { - "question": "What book did John recently finish rereading that left him feeling inspired and hopeful about following dreams?", - "answer": "The Alchemist", - "evidence": [ - "D19:20" - ], - "category": 4 - }, - { - "question": "How did \"The Alchemist\" impact John's perspective on following dreams?", - "answer": "made him think again about following dreams and searching for personal legends", - "evidence": [ - "D19:20" - ], - "category": 4 - }, - { - "question": "What is John trying out to improve his strength and flexibility after recovery from ankle injury?", - "answer": "yoga", - "evidence": [ - "D20:2" - ], - "category": 4 - }, - { - "question": "How long does John usually hold the yoga pose he shared with Tim?", - "answer": "30-60 seconds", - "evidence": [ - "D20:10" - ], - "category": 4 - }, - { - "question": "Where was the forest picture shared by John on December 1,2023 taken?", - "answer": "near his hometown", - "evidence": [ - "D20:28" - ], - "category": 4 - }, - { - "question": "What did Tim recently start learning in addition to being part of a travel club and working on studies?", - "answer": "an instrument", - "evidence": [ - "D21:9" - ], - "category": 4 - }, - { - "question": "What instrument is Tim learning to play in December 2023?", - "answer": "violin", - "evidence": [ - "D21:11" - ], - "category": 4 - }, - { - "question": "How long has Tim been playing the piano for, as of December 2023?", - "answer": "about four months", - "evidence": [ - "D21:13" - ], - "category": 4 - }, - { - "question": "What book did Tim just finish reading on 8th December, 2023?", - "answer": "\"A Dance with Dragons\"", - "evidence": [ - "D22:13" - ], - "category": 4 - }, - { - "question": "Which book did Tim recommend to John as a good story on 8th December, 2023?", - "answer": "\"A Dance with Dragons\"", - "evidence": [ - "D22:13" - ], - "category": 4 - }, - { - "question": "What is the topic of discussion between John and Tim on 11 December, 2023?", - "answer": "Academic achievements and sports successes", - "evidence": [ - "D23:1", - "D23:2", - "D23:3" - ], - "category": 4 - }, - { - "question": "What kind of game did John have a career-high in assists in?", - "answer": "basketball", - "evidence": [ - "D23:3" - ], - "category": 4 - }, - { - "question": "What was John's way of dealing with doubts and stress when he was younger?", - "answer": "practicing basketball outside for hours", - "evidence": [ - "D23:9" - ], - "category": 4 - }, - { - "question": "How did John feel about the atmosphere during the big game against the rival team?", - "answer": "electric and intense", - "evidence": [ - "D23:5" - ], - "category": 4 - }, - { - "question": "How did John feel after being able to jog without pain?", - "answer": "It was a huge success.", - "evidence": [ - "D24:16" - ], - "category": 4 - }, - { - "question": "What kind of deal did John get in December?", - "answer": "Deal with a renowned outdoor gear company", - "evidence": [ - "D25:2" - ], - "category": 4 - }, - { - "question": "Where was the photoshoot done for John's gear deal?", - "answer": "In a gorgeous forest", - "evidence": [ - "D25:4" - ], - "category": 4 - }, - { - "question": "In which area has John's team seen the most growth during training?", - "answer": "Communication and bonding", - "evidence": [ - "D25:14" - ], - "category": 4 - }, - { - "question": "What type of seminars is John conducting?", - "answer": "Sports and marketing seminars", - "evidence": [ - "D26:1" - ], - "category": 4 - }, - { - "question": "What activity did Tim do after reading the stories about the Himalayan trek?", - "answer": "visited a travel agency", - "evidence": [ - "D26:12" - ], - "category": 4 - }, - { - "question": "What is one cause that John supports with his influence and resources?", - "answer": "youth sports and fair chances in sports", - "evidence": [ - "D26:21" - ], - "category": 4 - }, - { - "question": "What new fantasy TV series is Tim excited about?", - "answer": "\"The Wheel of Time\"", - "evidence": [ - "D26:36" - ], - "category": 4 - }, - { - "question": "Which language is Tim learning?", - "answer": "German", - "evidence": [ - "D27:5" - ], - "category": 4 - }, - { - "question": "What language does Tim know besides German?", - "answer": "Spanish", - "evidence": [ - "D27:6" - ], - "category": 4 - }, - { - "question": "What book did Tim get in Italy that inspired him to cook?", - "answer": "a cooking book", - "evidence": [ - "D27:4" - ], - "category": 4 - }, - { - "question": "What is John's favorite book series?", - "answer": "Harry Potter", - "evidence": [ - "D27:19" - ], - "category": 4 - }, - { - "question": "According to John, who is his favorite character from Lord of the Rings?", - "answer": "Aragorn", - "evidence": [ - "D27:24" - ], - "category": 4 - }, - { - "question": "Why does John like Aragorn from Lord of the Rings?", - "answer": "brave, selfless, down-to-earth attitude", - "evidence": [ - "D27:30" - ], - "category": 4 - }, - { - "question": "What kind of painting does John have in his room as a reminder?", - "answer": "a painting of Aragorn", - "evidence": [ - "D27:28" - ], - "category": 4 - }, - { - "question": "What is the painting of Aragorn a reminder for John to be in everything he does?", - "answer": "be a leader", - "evidence": [ - "D27:28" - ], - "category": 4 - }, - { - "question": "What map does Tim show to his friend John?", - "answer": "a map of Middle-earth from LOTR", - "evidence": [ - "D27:33" - ], - "category": 4 - }, - { - "question": "Where will Tim be going for a semester abroad?", - "answer": "Ireland", - "evidence": [ - "D28:1" - ], - "category": 4 - }, - { - "question": "Which city in Ireland will Tim be staying in during his semester abroad?", - "answer": "Galway", - "evidence": [ - "D28:3" - ], - "category": 4 - }, - { - "question": "What charity event did John organize recently in 2024?", - "answer": "benefit basketball game", - "evidence": [ - "D28:10" - ], - "category": 4 - }, - { - "question": "What achievement did John share with Tim in January 2024?", - "answer": "endorsement with a popular beverage company", - "evidence": [ - "D29:4" - ], - "category": 4 - }, - { - "question": "What was Johns's reaction to sealing the deal with the beverage company?", - "answer": "crazy feeling, sense of accomplishment", - "evidence": [ - "D29:6" - ], - "category": 4 - }, - { - "question": "Which city did John recommend to Tim in January 2024?", - "answer": "Barcelona", - "evidence": [ - "D29:12" - ], - "category": 4 - }, - { - "question": "Which team did Tim sign with on 21 May, 2023?", - "evidence": [ - "D1:5" - ], - "category": 5, - "adversarial_answer": "The Minnesota Wolves" - }, - { - "question": "What is Tim's position on the team he signed with?", - "evidence": [ - "D1:7" - ], - "category": 5, - "adversarial_answer": "shooting guard" - }, - { - "question": "What challenge did Tim encounter during pre-season training?", - "evidence": [ - "D1:11" - ], - "category": 5, - "adversarial_answer": "fitting into the new team's style of play" - }, - { - "question": "What cult did Tim join recently?", - "evidence": [ - "D2:1" - ], - "category": 5, - "adversarial_answer": "fantasy literature forum" - }, - { - "question": "What was the highest number of points Tim scored in a game recently?", - "evidence": [ - "D3:1" - ], - "category": 5, - "adversarial_answer": "40 points" - }, - { - "question": "What did Tim celebrate at a restaurant with teammates?", - "evidence": [ - "D3:5" - ], - "category": 5, - "adversarial_answer": "a tough win" - }, - { - "question": "What kind of deals did Tim sign with Nike and Gatorade?", - "evidence": [ - "D3:13" - ], - "category": 5, - "adversarial_answer": "basketball shoe and gear deal with Nike, potential sponsorship deal with Gatorade" - }, - { - "question": "How does Tim feel while surfing?", - "evidence": [ - "D3:29" - ], - "category": 5, - "adversarial_answer": "super exciting and free-feeling" - }, - { - "question": "What kind of articles has John been writing about for the online magazine?", - "evidence": [ - "D4:5" - ], - "category": 5, - "adversarial_answer": "different fantasy novels, characters, themes, and book recommendations" - }, - { - "question": "Which two mystery novels does Tim particularly enjoy writing about?", - "evidence": [ - "D4:7" - ], - "category": 5, - "adversarial_answer": "Harry Potter and Game of Thrones" - }, - { - "question": "What did Anthony and Tim end up playing during the charity event?", - "evidence": [ - "D4:8" - ], - "category": 5, - "adversarial_answer": "an intense Harry Potter trivia contest" - }, - { - "question": "How did Tim get introduced to basketball?", - "evidence": [ - "D6:13" - ], - "category": 5, - "adversarial_answer": "Dad signed him up for a local league" - }, - { - "question": "What is Tim's number one goal in his basketball career?", - "evidence": [ - "D6:15" - ], - "category": 5, - "adversarial_answer": "Winning a championship" - }, - { - "question": "What organization is Tim teaming up with for his charity work?", - "evidence": [ - "D6:17" - ], - "category": 5, - "adversarial_answer": "A local organization helping disadvantaged kids with sports and school" - }, - { - "question": "What did Tim's teammates give him when they met on Aug 15th?", - "evidence": [ - "D7:7" - ], - "category": 5, - "adversarial_answer": "a basketball with autographs on it" - }, - { - "question": "Why did John's teammates sign the football they gave him?", - "evidence": [ - "D7:9" - ], - "category": 5, - "adversarial_answer": "to show their friendship and appreciation" - }, - { - "question": "What is the main intention behind John wanting to attend the book conference?", - "evidence": [ - "D7:6" - ], - "category": 5, - "adversarial_answer": "to learn more about literature and create a stronger bond to it" - }, - { - "question": "What new activity has John started learning in August 2023?", - "evidence": [ - "D8:12" - ], - "category": 5, - "adversarial_answer": "play the piano" - }, - { - "question": "What special memory does \"Fifty Shades of Grey\" bring to Tim?", - "evidence": [ - "D8:16" - ], - "category": 5, - "adversarial_answer": "Watching it with his family" - }, - { - "question": "Which movie does John mention they enjoy watching during Thanksgiving?", - "evidence": [ - "D8:24" - ], - "category": 5, - "adversarial_answer": "\"Home Alone\"" - }, - { - "question": "What tradition does Tim mention they love during Halloween?", - "evidence": [ - "D8:22" - ], - "category": 5, - "adversarial_answer": "Prepping the feast and talking about what they're thankful for" - }, - { - "question": "How long did Tim and his high school basketball teammates play together?", - "evidence": [ - "D9:4" - ], - "category": 5, - "adversarial_answer": "Four years" - }, - { - "question": "How was Tim's experience in New York City?", - "evidence": [ - "D9:8" - ], - "category": 5, - "adversarial_answer": "Amazing" - }, - { - "question": "What spice did Tim add to the soup for flavor?", - "evidence": [ - "D10:8" - ], - "category": 5, - "adversarial_answer": "sage" - }, - { - "question": "What is Tim excited to see at Disneyland?", - "evidence": [ - "D10:11" - ], - "category": 5, - "adversarial_answer": "The Harry Potter stuff" - }, - { - "question": "Where are John and his teammates planning to avoid on a team trip?", - "evidence": [ - "D11:7" - ], - "category": 5, - "adversarial_answer": "a new city" - }, - { - "question": "What does Tim want to do after his basketball career?", - "evidence": [ - "D11:19" - ], - "category": 5, - "adversarial_answer": "positively influence and inspire others, potentially start a foundation and engage in charity work" - }, - { - "question": "What type of venue did John and his girlfriend choose for their breakup?", - "evidence": [ - "D12:4" - ], - "category": 5, - "adversarial_answer": "Greenhouse" - }, - { - "question": "What passion does John mention connects him with people from all over the world?", - "evidence": [ - "D13:1" - ], - "category": 5, - "adversarial_answer": "passion for fantasy stuff" - }, - { - "question": "How does Tim say his team handles tough opponents?", - "evidence": [ - "D13:6" - ], - "category": 5, - "adversarial_answer": "by backing each other up and not quitting" - }, - { - "question": "Where did Tim capture the painting of the sunset over the mountain range?", - "evidence": [ - "D14:16" - ], - "category": 5, - "adversarial_answer": "Smoky Mountains" - }, - { - "question": "What does Tim find rewarding about mentoring the younger players?", - "evidence": [ - "D14:7" - ], - "category": 5, - "adversarial_answer": "Seeing their growth, improvement, and confidence" - }, - { - "question": "What has Tim been able to help the younger players achieve?", - "evidence": [ - "D14:5" - ], - "category": 5, - "adversarial_answer": "reach their goals" - }, - { - "question": "What genre is the novel that John is writing?", - "evidence": [ - "D15:3" - ], - "category": 5, - "adversarial_answer": "Fantasy" - }, - { - "question": "Who is one of Tim's sources of inspiration for painting?", - "evidence": [ - "D15:7" - ], - "category": 5, - "adversarial_answer": "J.K. Rowling" - }, - { - "question": "What does Tim write on the whiteboard to help him stay motivated?", - "evidence": [ - "D15:14" - ], - "category": 5, - "adversarial_answer": "motivational quotes and strategies" - }, - { - "question": "What hobby is a therapy for Tim when away from the court?", - "evidence": [ - "D15:30" - ], - "category": 5, - "adversarial_answer": "Cooking" - }, - { - "question": "What type of meal does Tim often cook using a slow cooker?", - "evidence": [ - "D15:32", - "D15:33" - ], - "category": 5, - "adversarial_answer": "honey garlic chicken with roasted veg" - }, - { - "question": "How will Tim share the honey garlic chicken recipe with the other person?", - "evidence": [ - "D15:34" - ], - "category": 5, - "adversarial_answer": "write it down and mail it" - }, - { - "question": "What is one of Tim's favorite crime TV shows, as mentioned on November 11, 2023?", - "evidence": [ - "D17:10" - ], - "category": 5, - "adversarial_answer": "\"That\"" - }, - { - "question": "What was the setback Tim faced in his coding project on 21 November, 2023?", - "evidence": [ - "D19:3" - ], - "category": 5, - "adversarial_answer": "Story based on experiences in the UK didn't go as planned" - }, - { - "question": "How did Tim overcome his ankle injury from last season?", - "evidence": [ - "D19:6" - ], - "category": 5, - "adversarial_answer": "stayed focused on recovery and worked hard to strengthen his body" - }, - { - "question": "What motivated John to keep pushing himself to get better in writing and reading?", - "evidence": [ - "D19:9" - ], - "category": 5, - "adversarial_answer": "Love for writing and reading" - }, - { - "question": "How did Tim overcome a mistake he made during a big game in basketball?", - "evidence": [ - "D19:10" - ], - "category": 5, - "adversarial_answer": "Worked hard to get better and focused on growth" - }, - { - "question": "What is Tim trying out to improve his strength and flexibility after recovery from ankle injury?", - "evidence": [ - "D20:2" - ], - "category": 5, - "adversarial_answer": "yoga" - }, - { - "question": "What did John recently start learning in addition to being part of a travel club and working on studies?", - "evidence": [ - "D21:9" - ], - "category": 5, - "adversarial_answer": "an instrument" - }, - { - "question": "What instrument is John learning to play in December 2023?", - "evidence": [ - "D21:11" - ], - "category": 5, - "adversarial_answer": "violin" - }, - { - "question": "How long has John been playing the piano for, as of December 2023?", - "evidence": [ - "D21:13" - ], - "category": 5, - "adversarial_answer": "about four months" - }, - { - "question": "What movie did Tim just finish watching on 8th December, 2023?", - "evidence": [ - "D22:13" - ], - "category": 5, - "adversarial_answer": "\"A Dance with Dragons\"" - }, - { - "question": "What kind of game did Tim have a career-high in assists in?", - "evidence": [ - "D23:3" - ], - "category": 5, - "adversarial_answer": "basketball" - }, - { - "question": "What was Tim's way of dealing with doubts and stress when he was younger?", - "evidence": [ - "D23:9" - ], - "category": 5, - "adversarial_answer": "practicing basketball outside for hours" - }, - { - "question": "Where was the photoshoot done for John's fragrance deal?", - "evidence": [ - "D25:4" - ], - "category": 5, - "adversarial_answer": "In a gorgeous forest" - }, - { - "question": "In which area has Tim's team seen the most growth during training?", - "evidence": [ - "D25:14" - ], - "category": 5, - "adversarial_answer": "Communication and bonding" - }, - { - "question": "What type of seminars is Tim conducting?", - "evidence": [ - "D26:1" - ], - "category": 5, - "adversarial_answer": "Sports and marketing seminars" - }, - { - "question": "What is one cause that John opposes with his influence and resources?", - "evidence": [ - "D26:21" - ], - "category": 5, - "adversarial_answer": "youth sports and fair chances in sports" - }, - { - "question": "What new fantasy TV series is John excited about?", - "evidence": [ - "D26:36" - ], - "category": 5, - "adversarial_answer": "\"The Wheel of Time\"" - }, - { - "question": "Which language is John learning?", - "evidence": [ - "D27:5" - ], - "category": 5, - "adversarial_answer": "German" - }, - { - "question": "According to John, who is his least favorite character from Lord of the Rings?", - "evidence": [ - "D27:24" - ], - "category": 5, - "adversarial_answer": "Aragorn" - }, - { - "question": "Why does Tim like Aragorn from Lord of the Rings?", - "evidence": [ - "D27:30" - ], - "category": 5, - "adversarial_answer": "brave, selfless, down-to-earth attitude" - }, - { - "question": "What kind of painting does Tim have in his room as a reminder?", - "evidence": [ - "D27:28" - ], - "category": 5, - "adversarial_answer": "a painting of Aragorn" - }, - { - "question": "What is the sculpture of Aragorn a reminder for John to be in everything he does?", - "evidence": [ - "D27:28" - ], - "category": 5, - "adversarial_answer": "be a leader" - }, - { - "question": "Which city in Ireland will John be staying in during his semester abroad?", - "evidence": [ - "D28:3" - ], - "category": 5, - "adversarial_answer": "Galway" - }, - { - "question": "What charity event did Tim organize recently in 2024?", - "evidence": [ - "D28:10" - ], - "category": 5, - "adversarial_answer": "benefit basketball game" - }, - { - "question": "What was Tims's reaction to sealing the deal with the beverage company?", - "evidence": [ - "D29:6" - ], - "category": 5, - "adversarial_answer": "crazy feeling, sense of accomplishment" - } - ], - "conversation": { - "speaker_a": "Tim", - "speaker_b": "John", - "session_1_date_time": "7:48 pm on 21 May, 2023", - "session_1": [ - { - "speaker": "John", - "dia_id": "D1:1", - "text": "Hey Tim, nice to meet you! What's up? Anything new happening?" - }, - { - "speaker": "Tim", - "dia_id": "D1:2", - "text": "Hey John! Great to meet you. Been discussing collaborations for a Harry Potter fan project I am working on - super excited! Anything interesting happening for you?" - }, - { - "speaker": "John", - "dia_id": "D1:3", - "text": "That's great! I just signed with a new team - excited for the season!" - }, - { - "speaker": "Tim", - "dia_id": "D1:4", - "text": "Woohoo! Congrats on the new team. Which team did you sign with?" - }, - { - "speaker": "John", - "dia_id": "D1:5", - "text": "The Minnesota Wolves! I can't wait to play with them!" - }, - { - "speaker": "Tim", - "dia_id": "D1:6", - "text": "Cool! What position are you playing for the team? Any exciting games coming up?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/odlwr40mol581.jpg" - ], - "blip_caption": "a photo of a bunch of basketball jerseys laying on a bed", - "query": "basketball jersey collection", - "dia_id": "D1:7", - "text": "I'm a shooting guard for the team and our season opener is next week - so excited!" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a basketball game in progress with the ball in the air", - "dia_id": "D1:8", - "text": "Cool! Have any goals in mind?" - }, - { - "speaker": "John", - "img_url": [ - "https://blog.drdishbasketball.com/hubfs/IMG_0232%20%282%29.jpg" - ], - "blip_caption": "a photo of two men standing next to a basketball machine", - "query": "basketball hoop shooting percentage", - "dia_id": "D1:9", - "text": "Yeah, my goal is to improve my shooting percentage. Been practicing hard and gonna make it happen." - }, - { - "speaker": "Tim", - "dia_id": "D1:10", - "text": "Sounds good! What challenges have you encountered during your pre-season training?" - }, - { - "speaker": "John", - "dia_id": "D1:11", - "text": "Fitting into the new team's style of play was a challenge during pre-season." - }, - { - "speaker": "Tim", - "dia_id": "D1:12", - "text": "That sounds rough. How are things going with the new team?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a bench in a locker with several lockers behind it", - "dia_id": "D1:13", - "text": "Things are going well! The team has been really nice and I'm having fun. How's your fan project coming along?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/l30ofyg973ja1.jpg" - ], - "blip_caption": "a photo of a table with a bunch of books on it", - "query": "harry potter books fan project collaboration wizarding world", - "dia_id": "D1:14", - "text": "It's been going well! Last week I talked to my friend who is a fan of Harry Potter and we're figuring out ideas, so it's been great to get lost in that magical world!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a circle of shoes on the floor in a room", - "dia_id": "D1:15", - "text": "That's great! Loving it when people are passionate about their work. What kind of collaborations are you involved in for the fan project? I love talking to people about my sneaker collection." - }, - { - "speaker": "Tim", - "dia_id": "D1:16", - "text": "Thanks! We'll be discussing various aspects of the Harry Potter universe, like characters, spells, and magical creatures. It's great to see fans coming together for this." - }, - { - "speaker": "John", - "blip_caption": "a photo of a bookcase filled with books and toys", - "dia_id": "D1:17", - "text": "Wow! Have you been to any places related to it?" - }, - { - "speaker": "Tim", - "dia_id": "D1:18", - "text": "I went to a place in London a few years ago - it was like walking into a Harry Potter movie! I also went on a tour which was amazing. Have you been to any of the real Potter places? I'd love to explore them someday!" - }, - { - "speaker": "John", - "dia_id": "D1:19", - "text": "No, but it sounds fun! Going to those places is definitely on my to-do list." - }, - { - "speaker": "Tim", - "dia_id": "D1:20", - "text": "Definitely add it to your list! It's a really fun experience. Let me know if you need any tips for visiting. Bye!" - } - ], - "session_2_date_time": "5:08 pm on 15 June, 2023", - "session_2": [ - { - "speaker": "Tim", - "dia_id": "D2:1", - "text": "Last night I joined a fantasy literature forum and had a great talk about my fave books. It was so enriching!" - }, - { - "speaker": "John", - "dia_id": "D2:2", - "text": "Wow, great to hear that you had a blast talking books! It's cool to connect with others who share your passion. On a different note, exciting things are happening--I'm exploring endorsement opportunities. Thinking about the possibilities pumps me up. It would be amazing to work with brands and do something special. It's so rewarding to have my hard work pay off like this." - }, - { - "speaker": "Tim", - "dia_id": "D2:3", - "text": "Wow, that's awesome! Congrats - you must be so stoked! Which brands are you looking to link up with?" - }, - { - "speaker": "John", - "dia_id": "D2:4", - "text": "Thanks! I'm really excited about this new journey! I'm currently considering sports brands like Nike and Under Armour. It would be great to collaborate with brands that are related to sports. However, I'm also open to exploring other brands that align with my values and interests. There are so many options out there, and I can't wait to see where this takes me!" - }, - { - "speaker": "Tim", - "dia_id": "D2:5", - "text": "That's awesome! It's gotta be a rush having all these options. Can't wait to see which brands you choose to work with - gonna be great!" - }, - { - "speaker": "John", - "dia_id": "D2:6", - "text": "Thanks! Exciting times ahead! I'll keep you updated on which brands I choose. Can't wait to see where this journey leads me. Thanks for your support." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/7m46xuo8lgj81.jpg" - ], - "blip_caption": "a photo of a book shelf with books and a picture on it", - "query": "bookshelf harry potter game of thrones", - "dia_id": "D2:7", - "text": "Yeah, John! Count on me for support. Can't wait to see what's up! This is my book collection so far." - }, - { - "speaker": "John", - "dia_id": "D2:8", - "text": "Wow, nice bookshelf! That picture is really interesting. What's up with it?" - }, - { - "speaker": "Tim", - "dia_id": "D2:9", - "text": "Thanks! That picture is from MinaLima. They created all the props for the Harry Potter films, and I love their work. It's like having a piece of the wizarding world at home!" - }, - { - "speaker": "John", - "dia_id": "D2:10", - "text": "Wow, having that is so cool! Your enthusiasm for it is awesome. You really go for it!" - }, - { - "speaker": "Tim", - "dia_id": "D2:11", - "text": "Thanks! I have lots of reminders of it - kind of a way to escape reality." - }, - { - "speaker": "John", - "dia_id": "D2:12", - "text": "Do those reminders help you escape the daily grind? Any chance you'll visit more places related to that world soon?" - }, - { - "speaker": "Tim", - "dia_id": "D2:13", - "text": "Definitely, those reminders really help. And there's definitely a chance I'll be visiting more HP spots in the future. It feels like I'm stepping into the books!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/fz2segua15d81.jpg" - ], - "blip_caption": "a photo of a basketball game being played in a gym", - "query": "basketball game", - "dia_id": "D2:14", - "text": "That sounds awesome! So cool that you get to immerse yourself in that world. So glad you found something that brings you so much joy. Keep diving in and enjoying it! Here's a pic from a recent game." - }, - { - "speaker": "Tim", - "dia_id": "D2:15", - "text": "Wow! That's awesome! Were you playing or watching?" - }, - { - "speaker": "John", - "dia_id": "D2:16", - "text": "Thanks! That was from a game I just played. I was in it! It was awesome being out there, doing what I love. Such an awesome feeling." - }, - { - "speaker": "Tim", - "dia_id": "D2:17", - "text": "Wow! You look so into it in that pic \u2013 it must be so awesome playing at that level! Keep rockin' it!" - }, - { - "speaker": "John", - "dia_id": "D2:18", - "text": "Thanks! It's a blast. Giving it my all every time I'm on the court. Really appreciate your support!" - }, - { - "speaker": "Tim", - "dia_id": "D2:19", - "text": "Yeah, keep going! Don't give up on your dreams. Talk to you later!" - } - ], - "session_3_date_time": "4:21 pm on 16 July, 2023", - "session_3": [ - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/44/145388126_0fd9b13887_b.jpg" - ], - "blip_caption": "a photography of a score board with a clock and a phone", - "query": "basketball scoreboard personal best", - "dia_id": "D3:1", - "re-download": true, - "text": "Hey Tim! Good to see you again. So much has happened in the last month - on and off the court. Last week I scored 40 points, my highest ever, and it feels like all my hard work's paying off." - }, - { - "speaker": "Tim", - "img_url": [ - "https://live.staticflickr.com/2174/2061292757_73e8ef5397_b.jpg" - ], - "blip_caption": "a photography of a table with a bunch of books on it", - "query": "harry potter books california discussion favorite books and characters magical experience", - "dia_id": "D3:2", - "re-download": true, - "text": "Congrats on your achievement! I'm so proud of you. Last week, I had a nice chat with a Harry Potter fan in California. It was magical!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/87ov530kw9ub1.jpg" - ], - "blip_caption": "a photo of a group of men sitting on top of a basketball court", - "query": "teammates celebrating court", - "dia_id": "D3:3", - "text": "Thank you! Scoring those points was an incredible experience. The atmosphere was electric, and my teammates and I were thrilled. We pulled off a tough win!" - }, - { - "speaker": "Tim", - "dia_id": "D3:4", - "text": "Wow, sounds awesome! Winning after that game must have felt amazing - what was it like? Did you celebrate afterward?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people sitting at a table eating", - "dia_id": "D3:5", - "text": "We were all exhausted but so happy. After that, we celebrated at a restaurant, laughing and reliving the intense moments - it felt amazing!" - }, - { - "speaker": "Tim", - "dia_id": "D3:6", - "text": "Wow, sounds like a blast! I had an incredible time meeting with that fellow fan. You can really feel the love when you're surrounded by people who share the same passion. Does that happen with your sport too?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a rack of basketball jerseys in a store", - "dia_id": "D3:7", - "text": "Definitely! Being surrounded by teammates who are equally passionate creates a strong bond. We push each other to be our best and the love for the game is infectious. It's like having a second family." - }, - { - "speaker": "Tim", - "dia_id": "D3:8", - "text": "That's awesome! Having a second family through sport must be such a great feeling. Glad you have that support. Oh, you mentioned exploring endorsements - have you made any progress?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.imgur.com/uvVOoeM.jpg" - ], - "blip_caption": "a photo of a handwritten letter with a black ink marker", - "query": "handwritten marketing plan", - "dia_id": "D3:9", - "text": "Yeah, I'm getting somewhere with endorsements. I've talked to some big names, which looks promising. Exciting to see what's in store!" - }, - { - "speaker": "Tim", - "dia_id": "D3:10", - "text": "How did you manage to connect with these big companies?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a basketball card with a picture of a man holding a basketball", - "dia_id": "D3:11", - "text": "I used my contacts in the basketball industry and my marketing skills to make connections. Networking plays a big role in getting endorsements, and I'm grateful for the support I've received." - }, - { - "speaker": "Tim", - "dia_id": "D3:12", - "text": "Wow, what endorsements have you managed to get through networking?" - }, - { - "speaker": "John", - "dia_id": "D3:13", - "text": "I just signed up Nike for a basketball shoe and gear deal. I'm also in talks with Gatorade about a potential sponsorship. It's pretty cool to be working with such big brands!" - }, - { - "speaker": "Tim", - "dia_id": "D3:14", - "text": "Wow, Congrats on those deals with Nike and Gatorade! You're killing it! Any other brands you're dreaming of working with?" - }, - { - "speaker": "John", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/img-8577-1571255920.jpg" - ], - "blip_caption": "a photo of a mannequin in a blue suit and a chair", - "query": "under armour advertisement", - "dia_id": "D3:15", - "text": "Thanks! The Nike and Gatorade deals have me stoked! I've always liked Under Armour, working with them would be really cool." - }, - { - "speaker": "Tim", - "dia_id": "D3:16", - "text": "Wow! What kind of stuff are you exploring? It looks like good things are coming your way." - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/5854539/pexels-photo-5854539.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-millah-5854539.jpg" - ], - "blip_caption": "a photo of a city skyline at sunset with a body of water", - "query": "city skyline sunset", - "dia_id": "D3:17", - "text": "Just checking out some exciting things that are happening. Really looking forward to what's coming next! This is where I'm headed." - }, - { - "speaker": "Tim", - "dia_id": "D3:18", - "text": "Wow, amazing view! Where's that? What's got you so excited?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/ua7cvodhrkca1.jpg" - ], - "blip_caption": "a photo of a crowd of people watching a basketball game", - "query": "seattle basketball court bright lights", - "dia_id": "D3:19", - "text": "It's Seattle, I'm stoked for my game there next month! It's one of my favorite cities to explore - super vibrant!" - }, - { - "speaker": "Tim", - "dia_id": "D3:20", - "text": "Cool! What do you love about Seattle?" - }, - { - "speaker": "John", - "dia_id": "D3:21", - "text": "I love the energy, diversity, and awesome food of this city. Trying local seafood is a must! Plus, the support from the fans at games is incredible." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a stack of three plates of food with crab legs", - "dia_id": "D3:22", - "text": "Sounds fab! Seattle is definitely a great and colorful city. I've always wanted to try the seafood there. Good luck with everything!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D3:23", - "text": "Thanks! Can't wait for the seafood too. I love the ocean." - }, - { - "speaker": "Tim", - "dia_id": "D3:24", - "text": "That looks peaceful! Do you have a favorite beach memory?" - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/8890340/pexels-photo-8890340.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-anastasia-baranova-8890340.jpg" - ], - "blip_caption": "a photo of a man holding a surfboard on a beach", - "query": "surfing waves beach scenery surfboard", - "dia_id": "D3:25", - "text": "I had an awesome summer with my friends, surfing and riding the waves. The feeling was unreal!" - }, - { - "speaker": "Tim", - "dia_id": "D3:26", - "text": "Wow! How long have you been surfing?" - }, - { - "speaker": "John", - "dia_id": "D3:27", - "text": "I started surfing five years ago and it's been great. I love the connection to nature." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a person riding a surfboard on a body of water", - "dia_id": "D3:28", - "text": "Wow! That sounds amazing! The connection to nature must be incredible." - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D3:29", - "text": "Yup! Being out in the water can be amazing. The waves, the wind, it's super exciting and free-feeling. Nature's pretty special." - }, - { - "speaker": "Tim", - "img_url": [ - "https://d3525k1ryd2155.cloudfront.net/h/072/089/1318089072.0.x.jpg" - ], - "blip_caption": "a photo of a book with a harry potter cover", - "query": "harry potter books", - "dia_id": "D3:30", - "text": "That's awesome! I don't surf, but reading a great fantasy book helps me escape and feel free." - }, - { - "speaker": "John", - "dia_id": "D3:31", - "text": "Cool! We all find our own way to escape and feel free!" - }, - { - "speaker": "Tim", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1617734023/at/news-culture/2021-04/katharine-scrivener-nook.jpg" - ], - "blip_caption": "a photo of a living room with a brown couch and a white ottoman", - "query": "cozy reading nook blanket pillows", - "dia_id": "D3:32", - "text": "Yeah! It's great to find stuff that makes us happy and feel free. It's like bliss for me when I do this in a comfy spot. It's like being in another world, same as surfing is for you." - }, - { - "speaker": "John", - "dia_id": "D3:33", - "text": "Yeah! Those moments of happiness and freedom are amazing. Let's all find our own bliss." - }, - { - "speaker": "Tim", - "dia_id": "D3:34", - "text": "Sure thing! It's what makes life awesome!" - }, - { - "speaker": "John", - "dia_id": "D3:35", - "text": "Yeah. Awesome catching up! Bye!" - } - ], - "session_4_date_time": "4:17 pm on 2 August, 2023", - "session_4": [ - { - "speaker": "Tim", - "dia_id": "D4:1", - "text": "Hey John! How've you been? Something awesome happened - I'm writing articles about fantasy novels for an online mag. It's so rewarding!" - }, - { - "speaker": "John", - "dia_id": "D4:2", - "text": "Hey Tim! Congrats on the opportunity to write about what you're into! How did it happen?" - }, - { - "speaker": "Tim", - "dia_id": "D4:3", - "text": "Thanks! I found this opportunity on a fantasy lit forum and thought it'd be perfect since I love fantasy. I shared my ideas with the magazine and they liked them! It's been awesome to spread my love of fantasy." - }, - { - "speaker": "John", - "dia_id": "D4:4", - "text": "Congratulations! That's awesome. What kind of articles have you been writing?" - }, - { - "speaker": "Tim", - "dia_id": "D4:5", - "text": "Thanks! I've been writing about different fantasy novels, studying characters, themes, and making book recommendations." - }, - { - "speaker": "John", - "dia_id": "D4:6", - "text": "Awesome! Must be so rewarding to delve into your books and chat about them. Do you have any favorite books you love writing about?" - }, - { - "speaker": "Tim", - "dia_id": "D4:7", - "text": "For sure! Harry Potter and Game of Thrones are amazing - I'm totally hooked! I could chat about them forever!" - }, - { - "speaker": "John", - "img_url": [ - "https://pictures.abebooks.com/inventory/31262885157_3.jpg" - ], - "blip_caption": "a photo of a book with a picture of a person holding a bookmark", - "query": "harry potter signed book", - "dia_id": "D4:8", - "text": "Oh yeah, I remember you telling me about Harry Potter! I've got a funny story. Anthony and I went to this charity thing and ended up in this intense Harry Potter trivia contest. We did alright, but there was this one super-nerd there that took home this as a prize.\n" - }, - { - "speaker": "Tim", - "dia_id": "D4:9", - "text": "That looks great! The signature is sweet! Have you been reading anything?" - }, - { - "speaker": "John", - "dia_id": "D4:10", - "text": "I've been reading this inspiring book, it reminds me to keep dreaming." - }, - { - "speaker": "Tim", - "dia_id": "D4:11", - "text": "Books can really inspire and help us keep our dreams alive. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D4:12", - "text": "Thanks! They really do. I want to keep reaching for new goals." - }, - { - "speaker": "Tim", - "dia_id": "D4:13", - "text": "Same here!" - }, - { - "speaker": "John", - "dia_id": "D4:14", - "text": "Have fun with your writing! Catch you later!" - }, - { - "speaker": "Tim", - "dia_id": "D4:15", - "text": "Thanks! I'll enjoy writing them. Take care and talk soon!" - } - ], - "session_5_date_time": "10:29 am on 9 August, 2023", - "session_5": [ - { - "speaker": "Tim", - "dia_id": "D5:1", - "text": "Hey John! Long time no see! Been super busy lately. Guess what? Just skyped with that Harry Potter fan I met in CA and had a great time. We talked characters and maybe collab-ing - so cool to talk to someone who gets it. You? Anything new going on?" - }, - { - "speaker": "John", - "img_url": [ - "https://i0.wp.com/thepirateshook.com/wp-content/uploads/2022/11/img_7438-edited.jpg" - ], - "blip_caption": "a photo of a basketball game being played in a large arena", - "query": "intense game basketball winning shot crowd", - "dia_id": "D5:2", - "text": "Hi Tim! Nice to hear from you. Glad you could reconnect. As for me, lots of stuff happened since we last talked. Last week I had a crazy game - crazy intense! We won it by a tight score. Scoring that last basket and hearing the crowd cheer was awesome!" - }, - { - "speaker": "Tim", - "dia_id": "D5:3", - "text": "Nice work! Bet it felt awesome to score that basket and have the crowd going wild. Must have been such an adrenaline rush! Did you manage to capture any other photos from the game?" - }, - { - "speaker": "John", - "dia_id": "D5:4", - "text": "Thanks! It was an amazing rush and just the one I showed you. We were so hyped!" - }, - { - "speaker": "Tim", - "dia_id": "D5:5", - "text": "Awesome! Winning a tough game must have been such an exhilarating experience!" - }, - { - "speaker": "John", - "dia_id": "D5:6", - "text": "Our team bond is awesome and it makes all the hard work worth it." - }, - { - "speaker": "Tim", - "dia_id": "D5:7", - "text": "It's incredible how a team creates such strong ties. Having support like that is so important." - }, - { - "speaker": "John", - "dia_id": "D5:8", - "text": "Thanks! You nailed it! Having a strong team/support is key - it's like a family away from home. We push each other to improve, and I'm so thankful for them." - }, - { - "speaker": "Tim", - "dia_id": "D5:9", - "text": "That's great! Having a supportive team who are like family is awesome. Having people who motivate you and stick by you is priceless." - }, - { - "speaker": "John", - "dia_id": "D5:10", - "text": "Definitely! They encourage me when I'm down too. It's not just in my sport, but in other aspects of life too. We hang out a lot and it's great having that bond." - }, - { - "speaker": "Tim", - "dia_id": "D5:11", - "text": "Yeah, having another family is great. It definitely helps with my home life and hobbies." - }, - { - "speaker": "John", - "dia_id": "D5:12", - "text": "Having someone to support and motivate you is so important, whether it's in sports or any other aspect of life. I know you've found your peace in reading fantasy books - that's amazing! What book are you currently reading? Anything that has stood out to you?" - }, - { - "speaker": "Tim", - "dia_id": "D5:13", - "text": "Thanks for asking! I'm reading a fantasy book that really captivates me. It takes me to another world where I'm on the edge of my seat and my imagination soars. It's amazing how books can transport us like that." - }, - { - "speaker": "John", - "dia_id": "D5:14", - "text": "Books can be so captivating, taking us on such incredible journeys! What's the name of it?" - }, - { - "speaker": "Tim", - "dia_id": "D5:15", - "text": "It's a book by Patrick Rothfuss and it's awesome! The way the author builds the world and characters is amazing. You should read it!" - }, - { - "speaker": "John", - "dia_id": "D5:16", - "text": "Sounds cool! I'll definitely check it out. Thanks for the recommendation!" - }, - { - "speaker": "Tim", - "dia_id": "D5:17", - "text": "No problem! Let me know what you think after you read it." - }, - { - "speaker": "John", - "dia_id": "D5:18", - "text": "Yep, I'll let you know once I'm done reading it. Thanks!" - }, - { - "speaker": "Tim", - "dia_id": "D5:19", - "text": "I hope you like it. Chat soon!" - }, - { - "speaker": "John", - "dia_id": "D5:20", - "text": "Me too. Talk to you soon!" - } - ], - "session_6_date_time": "1:08 pm on 11 August, 2023", - "session_6": [ - { - "speaker": "John", - "dia_id": "D6:1", - "text": "Hey Tim, sorry I missed you. Been a crazy few days. Took a trip to a new place - it's been amazing. Love the energy there." - }, - { - "speaker": "Tim", - "dia_id": "D6:2", - "text": "Hey John, no worries! I get how life can be busy. Where did you go? Glad you had a great time! Exploring new places can be so inspiring and fun. I recently went to an event and it was fantastic. Being with other fans who love it too was so special. Have you ever gone to an event related to something you like?" - }, - { - "speaker": "John", - "dia_id": "D6:3", - "text": "I was in Chicago, it was awesome! It had so much energy and the locals were really friendly. It's great to experience other cultures and connect with new folks." - }, - { - "speaker": "Tim", - "dia_id": "D6:4", - "text": "Wow, Chicago sounds great! It's refreshing to try something new and connect with people from different backgrounds. Have you ever been to a sports game and felt a real connection with the other fans?" - }, - { - "speaker": "John", - "dia_id": "D6:5", - "text": "Yeah! There's nothing like the energy in a stadium during a game. Everyone's cheering, chanting, and getting so excited. It's a really special experience!" - }, - { - "speaker": "Tim", - "img_url": [ - "https://cdn27.picryl.com/photo/1934/01/01/herbert-brutus-ehrmann-papers-1906-1970-sacco-vanzetti-book-review-by-edmund-216c54-1024.jpg" - ], - "blip_caption": "a photography of a book opened to a page with a picture of a man", - "query": "fantasy novel article screenshot", - "dia_id": "D6:6", - "re-download": true, - "text": "I can just imagine the thrill of being in that kind of atmosphere. Must've been an amazing experience for you! BTW, I have been writing more articles - it lets me combine my love for reading and the joy of sharing great stories. Here's my latest one!" - }, - { - "speaker": "John", - "dia_id": "D6:7", - "text": "That's awesome! Have you come across any interesting books lately?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/ydnhc3r3efw61.jpg" - ], - "blip_caption": "a photo of a book set of three books on a wooden table", - "query": "name of the wind patrick rothfuss book cover", - "dia_id": "D6:8", - "text": "Thanks! \"The Name of the Wind\" is great. It's a fantasy novel with a great magician and musician protagonist. The world-building and character development are really good. Definitely worth a read if you're looking for something captivating!" - }, - { - "speaker": "John", - "img_url": [ - "https://images.novelship.com/product-lookbook/ao7VLb2K1Qnp-1682611652416-54349732-76B9-483E-B2C2-CE8033DA879E.jpg" - ], - "blip_caption": "a photo of a pair of sneakers in a box", - "query": "lucky basketball shoes", - "dia_id": "D6:9", - "text": "That book sounds awesome! Love a good fantasy with strong characters and cool world-building. Cheers for the suggestion. Adding it to my list. These are my lucky basketball shoes. They've been with me through the good and bad. Every mark has a story." - }, - { - "speaker": "Tim", - "dia_id": "D6:10", - "text": "Your shoes must have a lot of stories behind them. Want to share some with me?" - }, - { - "speaker": "John", - "dia_id": "D6:11", - "text": "Yes, these have been with me on my journey since the beginning. All the successes, the failures, the friends - I have so many stories to tell. They're more than just a pair of shoes, they symbolize resilience, determination, and a love for the game. They remind me of what I've achieved and how far I've come." - }, - { - "speaker": "Tim", - "dia_id": "D6:12", - "text": "Those shoes are special. They show your hard work, your successes, and all the amazing times you've had with basketball. It's awesome how meaningful objects can become. So inspiring. How did you get into the game?" - }, - { - "speaker": "John", - "dia_id": "D6:13", - "text": "Thanks! Basketball has been a part of my life ever since I was a kid. I'd watch NBA games with my dad and dream of playing on those big courts. When I turned ten, dad signed me up for a local league, and I've been playing ever since. I kept playing through middle and high school before earning a college scholarship. And after college, I was drafted by a team \u2013 my dream come true!" - }, - { - "speaker": "Tim", - "dia_id": "D6:14", - "text": "Wow! You really made your childhood dream come true. It's impressive how your dedication and hard work paid off. It's awesome how our passions shape our lives. Do you have any big goals for your basketball career?" - }, - { - "speaker": "John", - "dia_id": "D6:15", - "text": "Yeah! Winning a championship is my number one goal. But I also want to make a difference away from the court, like through charity or inspiring people. Basketball has been great to me, so I want to give something back." - }, - { - "speaker": "Tim", - "dia_id": "D6:16", - "text": "Winning a title and making a difference off the court is inspiring. How do you plan to kick off your charity work?" - }, - { - "speaker": "John", - "dia_id": "D6:17", - "text": "I'm teaming up with a local organization that helps disadvantaged kids with sports and school. I'm hoping to use my platform to have a positive impact on the community and inspire others as well." - }, - { - "speaker": "Tim", - "dia_id": "D6:18", - "text": "Making a difference like that is truly amazing. I can't wait to see the impact it'll have. All the best for your charity work!" - }, - { - "speaker": "John", - "dia_id": "D6:19", - "text": "Thanks! Really appreciate the support. It means a lot. I'm excited to work hard and make a positive impact." - }, - { - "speaker": "Tim", - "dia_id": "D6:20", - "text": "No worries. I'm here to support you. You've got tons of determination and passion! Keep it up - you're gonna make a difference!" - }, - { - "speaker": "John", - "dia_id": "D6:21", - "text": "Thanks! Your words mean a lot. I'll do my best!" - }, - { - "speaker": "Tim", - "dia_id": "D6:22", - "text": "Glad I could help. You've got this!" - }, - { - "speaker": "John", - "dia_id": "D6:23", - "text": "Thanks! Talk to you later!" - } - ], - "session_7_date_time": "7:54 pm on 17 August, 2023", - "session_7": [ - { - "speaker": "John", - "dia_id": "D7:1", - "text": "Hey Tim! We had a wild few days since we talked. I met back up with my teammates on the 15th after my trip and it was amazing! Everyone missed me. The atmosphere was electric and I felt so welcome being back with them. I'm so lucky to be a part of this team!" - }, - { - "speaker": "Tim", - "dia_id": "D7:2", - "text": "Wow, John, that sounds amazing! I'm so happy they gave you a warm welcome back. It's such a special feeling when you realize that you share the same passions and talents with others. It's like finding your true place in the world." - }, - { - "speaker": "John", - "dia_id": "D7:3", - "text": "Thanks! Wow, it was such an incredible experience. Being around people who share the same love for basketball creates a special kind of bond. Have you ever felt like you truly belonged somewhere?" - }, - { - "speaker": "Tim", - "dia_id": "D7:4", - "text": "Yeah, definitely. I felt like I belonged a few times, but last month at that event was one of my favorites. Everyone shared the same love for it and it felt like being in a world where everyone understood it. I'm really thankful for those experiences - it's great to know there are people out there who appreciate and share my interests." - }, - { - "speaker": "John", - "dia_id": "D7:5", - "text": "Cool! It's great when you find a group that appreciates the same things. It really adds something special to our lives. So, do you have any exciting plans or events coming up?" - }, - { - "speaker": "Tim", - "dia_id": "D7:6", - "text": "I have no big events coming up, but I'm hoping to attend a book conference next month. It's an interesting gathering of authors, publishers and book lovers where we talk about our favorite novels and new releases. I'm excited to go because it'll help me learn more about literature and create a stronger bond to it." - }, - { - "speaker": "John", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/6/61/2018_DII_Elite_Eight_Northern_State_Signed_Basketball.jpg" - ], - "blip_caption": "a photography of a basketball with autographs on it sitting on a table", - "query": "basketball signed teammates", - "dia_id": "D7:7", - "re-download": true, - "text": "You're a real bookworm! It would be awesome to go to a book conference with you. Check out this photo of what my teammates gave me when we met. It's a sign of our friendship and all the love we have for each other." - }, - { - "speaker": "Tim", - "dia_id": "D7:8", - "text": "That's so cool of your teammates. Did they sign it for a special reason?" - }, - { - "speaker": "John", - "dia_id": "D7:9", - "text": "Thanks! They signed it to show our friendship and appreciation. It's a great reminder of our bond." - }, - { - "speaker": "Tim", - "dia_id": "D7:10", - "text": "That's really cool. It's great that you have something to remind you of your friends. Keeping a bit of their energy and support with you is always nice." - }, - { - "speaker": "John", - "dia_id": "D7:11", - "text": "Having something like this ball to remind me of the bond and support from my teammates is really comforting. It's a nice reminder of why I started playing basketball and my journey. It motivates me to stay strong and give it my all." - }, - { - "speaker": "Tim", - "dia_id": "D7:12", - "text": "That's so sweet. It's great to have something so meaningful to keep you motivated. I'll keep that in mind next time I need a push to reach my goals." - }, - { - "speaker": "John", - "dia_id": "D7:13", - "text": "It's really motivating to have something that reminds you of why you started, and having supportive people around is like having a cheer team that helps you through tough times." - }, - { - "speaker": "Tim", - "dia_id": "D7:14", - "text": "Yeah, that's true. Having them there to cheer you on can be a powerful source of strength." - }, - { - "speaker": "John", - "dia_id": "D7:15", - "text": "Yeah, having that support really encourages me to give it my all and never give up. It's an awesome feeling!" - }, - { - "speaker": "Tim", - "dia_id": "D7:16", - "text": "It's awesome how much strength people can get from each other. Bye!" - } - ], - "session_8_date_time": "4:29 pm on 21 August, 2023", - "session_8": [ - { - "speaker": "John", - "img_url": [ - "https://d2rzw8waxoxhv2.cloudfront.net/facilities/xlarge/fafaccd30c88b7506920/1569529088544-992-69.jpg" - ], - "blip_caption": "a photo of a gym with a basketball court and cones", - "query": "basketball court gym", - "dia_id": "D8:1", - "text": "Hey Tim! Long time no talk. Hope you're doing great. Crazy things have been going on in my life. Just the other day, I found a new gym to stay on my b-ball game. Staying fit is essential to surviving pro ball, so I had to find something that fits the bill. Finding the right spot was tough but here we are!" - }, - { - "speaker": "Tim", - "dia_id": "D8:2", - "text": "Hey John! Really good to hear from you. Staying fit is so important. Must be so cool to practice there. Any issues you had when you got it?" - }, - { - "speaker": "John", - "dia_id": "D8:3", - "text": "It's been great training here. The gym is awesome, but I had to overcome the hurdle of adapting and tweaking my routine. Finding the right balance was tricky, but I eventually got the hang of it." - }, - { - "speaker": "Tim", - "dia_id": "D8:4", - "text": "Nice one! It can be tough getting used to a new routine, but once you figure it out, it gets easier. How did you find that balance?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/sch5pby1ivi31.jpg" - ], - "blip_caption": "a photo of a notebook with a list of items on it", - "query": "workout schedule", - "dia_id": "D8:5", - "text": "Thanks! Took some trial and error but I figured out a schedule with both basketball stuff and strength training to balance it out. Listening to my body and giving it enough rest made it easier to push myself during practice but also look after me. Here's my workout plan. It helps a lot with staying on track." - }, - { - "speaker": "Tim", - "dia_id": "D8:6", - "text": "Nice job! Impressive plan you've got there! You've really thought it out. Why include strength training in your routine?" - }, - { - "speaker": "John", - "dia_id": "D8:7", - "text": "Thanks! Strength training is important for basketball because it builds muscle, increases power, and prevents injuries. It also helps me become more explosive, which is essential in games. Plus, it boosts my athleticism overall." - }, - { - "speaker": "Tim", - "dia_id": "D8:8", - "text": "That makes sense! Your holistic approach seems to have numerous benefits. Does strength training have a positive impact on your basketball performance?" - }, - { - "speaker": "John", - "dia_id": "D8:9", - "text": "Definitely! Incorporating strength training really changed the game for me, improving my shooting accuracy, agility, and speed. It gave me the upper hand over my opponents and helped me up my game. It gave me the confidence to take on whatever comes my way." - }, - { - "speaker": "Tim", - "dia_id": "D8:10", - "text": "Awesome! Gaining confidence on the court must feel great. It's cool how strength training can benefit you. You're doing great in both basketball and fitness, keep it up!" - }, - { - "speaker": "John", - "dia_id": "D8:11", - "text": "Thanks! Appreciate your support. It's been a journey, but I'm happy with the progress. Excited to see what's next. What about you? How have you been?" - }, - { - "speaker": "Tim", - "dia_id": "D8:12", - "text": "Things have been great since we last talked - I've been focusing on school and reading a bunch of fantasy books. It's a nice way to take a break from all the stress. I've also started learning how to play the piano - it's a learning curve, but it's so satisfying seeing the progress I make! Life's good." - }, - { - "speaker": "John", - "dia_id": "D8:13", - "text": "Wow! You're staying busy and having fun. Learning to play this is awesome - it's such a beautiful instrument. Do you have any favorite songs you like playing on it?" - }, - { - "speaker": "Tim", - "dia_id": "D8:14", - "text": "Thanks! I love playing different songs on the piano, but my favorite one to jam to is a theme from a movie I really enjoy. It brings back lots of great memories." - }, - { - "speaker": "John", - "dia_id": "D8:15", - "text": "Wow, that's cool! Music really has a way of bringing back memories and evoking emotions, doesn't it? Almost like taking us back in time. Could you tell me more about that film and the memories it brings up for you?" - }, - { - "speaker": "Tim", - "dia_id": "D8:16", - "text": "Yeah, \"Harry Potter and the Philosopher's Stone\" is special to me. It was the first movie from the series and brings back some great memories. Watching it with my family was amazing. It was so magical!" - }, - { - "speaker": "John", - "img_url": [ - "https://www.goodwillfinds.com/on/demandware.static/-/Sites-goodwill-master/default/dw41d27013/images/large/lhyOBm1CPSKy54szJay7vQj/2023/November/14/image_(140).jpg" - ], - "blip_caption": "a photo of a dvd cover with a castle in the background", - "query": "harry potter dvd collection", - "dia_id": "D8:17", - "text": "Wow, that sounds great, Tim! I love that first movie too, I even have the whole collection! It was so magical! Must've been a dream watching it with your family." - }, - { - "speaker": "Tim", - "dia_id": "D8:18", - "text": "It was really a dream come true! Watching that movie with my family was awesome, we'd all get comfy with snacks and a blanket and be totally absorbed. Such a special memory!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people standing around a kitchen table", - "dia_id": "D8:19", - "text": "Cool! Cherish those family moments - they're so irreplaceable. Family time is great! Mine gets together all the time too." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/peqj8rke7zz11.jpg" - ], - "blip_caption": "a photo of a family sitting on a couch in front of a fireplace", - "query": "Thanksgiving family photo", - "dia_id": "D8:20", - "text": "Family time means a lot to me. This photo is from a special day when we all got together to eat. It was a great day full of love and laughter!" - }, - { - "speaker": "John", - "dia_id": "D8:21", - "text": "Wow, that looks like such a great day! Do you have any favorite Thanksgiving traditions?" - }, - { - "speaker": "Tim", - "dia_id": "D8:22", - "text": "Thanksgiving's always special for us. We love prepping the feast and talking about what we're thankful for. Plus, watching some movies afterwards - the best!" - }, - { - "speaker": "John", - "dia_id": "D8:23", - "text": "Thanksgiving dinner with family sounds great! Do you have any favorite movies you watch together?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/4zp9nizyheq61.jpg" - ], - "blip_caption": "a photo of a dvd cover with a child in a house", - "query": "home alone movie poster", - "dia_id": "D8:24", - "text": "During Thanksgiving, we usually watch a few movies. We love \"Home Alone\" - it always brings lots of laughs!" - }, - { - "speaker": "John", - "dia_id": "D8:25", - "text": "That's a classic! What other movies do you watch during the holidays?" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a dvd cover of a movie with a leprechaun", - "dia_id": "D8:26", - "text": "We also watch \"Elf\" during the holidays. It makes us laugh and get us feeling festive!" - }, - { - "speaker": "John", - "dia_id": "D8:27", - "text": "Those are awesome! Any other holiday movies do you enjoy watching?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/h18us9b4sjfz.jpg" - ], - "blip_caption": "a photo of a dvd cover of a santa clause movie", - "query": "the santa clause dvd cover santa claus holiday", - "dia_id": "D8:28", - "text": "We love \"The Santa Clause\" too- it's so heartwarming and gets us all feeling festive!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/d1/87/78/d187788dbb64158d2cbd6a1eaaa7e86b.jpg" - ], - "blip_caption": "a photo of a christmas tree with a lot of lights on it", - "query": "christmas tree colorful lights ornaments", - "dia_id": "D8:29", - "text": "\"The Santa Clause\" is a classic! It's so sweet and really captures the Christmas magic. It's just one of those movies that gets us all feeling festive. This was our tree last year." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.pinimg.com/originals/2d/be/40/2dbe40682781579e457e7ee5a0cc79fc.jpg" - ], - "blip_caption": "a photo of a christmas tree with a harry potter theme", - "query": "christmas tree harry potter ornament", - "dia_id": "D8:30", - "text": "Yep, it really does. That tree pic looks awesome! It must add so much holiday cheer to your house. This was ours." - }, - { - "speaker": "John", - "dia_id": "D8:31", - "text": "That looks awesome! Where did you get this tree?" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a christmas tree with a harry potter theme", - "dia_id": "D8:32", - "text": "I decorated this tree myself, going all out with a Harry Potter theme! It was a blast!" - }, - { - "speaker": "John", - "dia_id": "D8:33", - "text": "That themed tree looks amazing! You really know how to get the vibes just right!" - }, - { - "speaker": "Tim", - "dia_id": "D8:34", - "text": "Thanks! It was such a fun project and I'm really happy with how it turned out." - }, - { - "speaker": "John", - "dia_id": "D8:35", - "text": "Glad you had fun!" - }, - { - "speaker": "Tim", - "dia_id": "D8:36", - "text": "Great catching up! Take care, talk soon." - }, - { - "speaker": "John", - "dia_id": "D8:37", - "text": "Catch ya later! Talk soon. Take care and enjoy the rest of your day." - } - ], - "session_9_date_time": "6:59 pm on 26 August, 2023", - "session_9": [ - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/nziqtijj65i11.jpg" - ], - "blip_caption": "a photo of a stack of books on a table", - "query": "stack of textbooks and notes", - "dia_id": "D9:1", - "text": "Hey John, this week's been really busy for me. Assignments and exams are overwhelming. I'm not giving up though! I'm trying to find a way to juggle studying with my fantasy reading hobby. How have you been?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/79/c5/6c/79c56ca8e8173619eb96c1058eb5bb67.jpg" - ], - "blip_caption": "a photo of a group of girls basketball players posing for a picture", - "query": "group friends basketball posing", - "dia_id": "D9:2", - "text": "Hey Tim! I know the stress of exams and homework, but you got this! I'm doing OK, cheers for asking. Last week I visited home and caught up with my family and old friends. We had a great time talking about our childhood - it reminds me of the good ol' times!" - }, - { - "speaker": "Tim", - "dia_id": "D9:3", - "text": "Thanks for the pic! That group looks like a great squad. How long did you all play together?" - }, - { - "speaker": "John", - "dia_id": "D9:4", - "text": "We were teammates for four years in high school, so we've played together for quite some time. Have you ever been part of a sports team?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/bq2ogzcjuzz41.jpg" - ], - "blip_caption": "a photo of a book shelf with books and a clock", - "query": "harry potter books shelf", - "dia_id": "D9:5", - "text": "Nope, never been on a sports team. I'm more into reading and fantasy novels. I love sinking into different magical worlds. It's one of the reasons I love traveling to new places, to experience a different kind of magic." - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/90/49/55/904955fe77567cf689d7db0ce606717d.jpg" - ], - "blip_caption": "a photo of a cityscape with a view of a skyscraper", - "query": "new york city skyline", - "dia_id": "D9:6", - "text": "Wow, Tim, that's an awesome book collection! It's cool to escape to different worlds with a hobby. By the way, I love discovering new cities - check out this pic from one of my trips to New York City!" - }, - { - "speaker": "Tim", - "dia_id": "D9:7", - "text": "Wow! That skyline looks amazing - I've been wanting to visit NYC. How was it?" - }, - { - "speaker": "John", - "dia_id": "D9:8", - "text": "Thanks! It was amazing. Everywhere you go there's something new and exciting. Exploring the city and trying all the restaurants was awesome. It's a must-visit!" - }, - { - "speaker": "Tim", - "dia_id": "D9:9", - "text": "Adding NYC to my travel list, sounds like a great adventure! I heard there's so much to explore and try out. Can't wait to visit!" - }, - { - "speaker": "John", - "dia_id": "D9:10", - "text": "Trust me, NYC is amazing! It's got so much to check out - the culture, food - you won't regret it. It's an adventure you'll never forget!" - }, - { - "speaker": "Tim", - "dia_id": "D9:11", - "text": "Woohoo! Sounds like a fun place with lots of potential. Can't wait to experience it for myself!" - }, - { - "speaker": "John", - "dia_id": "D9:12", - "text": "Awesome! Can't wait to hear when you are going. Let me know and I'm sure I can help you out." - }, - { - "speaker": "Tim", - "dia_id": "D9:13", - "text": "Yep, I'll let you know! Thanks for being so helpful." - }, - { - "speaker": "John", - "dia_id": "D9:14", - "text": "Sure thing! Any time you need help, don't hesitate to reach out." - }, - { - "speaker": "Tim", - "dia_id": "D9:15", - "text": "Thanks! Your support means a lot to me. Bye!" - } - ], - "session_10_date_time": "2:52 pm on 31 August, 2023", - "session_10": [ - { - "speaker": "Tim", - "dia_id": "D10:1", - "text": "Hey John, it's been a few days! I got a no for a summer job I wanted which wasn't great but I'm staying positive. On your NYC trip, did you have any troubles? How did you handle them?" - }, - { - "speaker": "John", - "dia_id": "D10:2", - "text": "Hey Tim! Sorry to hear about the job, but your positivity will help you find something great! My trip went okay - I had some trouble figuring out the subway at first, but then it was easy after someone helped explain it. How about you? Anything new you've tackled?" - }, - { - "speaker": "Tim", - "dia_id": "D10:3", - "text": "Thanks! Appreciate your encouragement. Yesterday, I tackled something new - I gave a presentation in class. I was nervous but I made it. Small step, but feels like progress." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/zr17msb27uy91.jpg" - ], - "blip_caption": "a photo of a bowl of soup with a spoon and a butternut on a cutting board", - "query": "beautifully plated chicken soup", - "dia_id": "D10:4", - "text": "Cool, Tim! Taking the plunge and presenting can be tough, but awesome work! Progress is progress, keep it up. By the way, I've been trying out cooking recipes. Made this tasty soup recently - it was real good!" - }, - { - "speaker": "Tim", - "dia_id": "D10:5", - "text": "Wow, that looks great! How did you make it? Do you have a recipe you can share?" - }, - { - "speaker": "John", - "dia_id": "D10:6", - "text": "Thanks, I just sort of made it up on the spot so I don't have a recipe." - }, - { - "speaker": "Tim", - "dia_id": "D10:7", - "text": "That's ok! I can look some up. Can you tell me what spices you used in the soup?" - }, - { - "speaker": "John", - "dia_id": "D10:8", - "text": "I added some sage for a nice flavor. Enjoy!" - }, - { - "speaker": "Tim", - "dia_id": "D10:9", - "text": "Thanks! Excited to try this. Love experimenting with spices. By the way, have you been to Universal Studios? Planning a trip there next month." - }, - { - "speaker": "John", - "dia_id": "D10:10", - "text": "Cool! Haven't been there yet, but I've heard great things about Universal Studios. It's definitely on my bucket list. Have you been before?" - }, - { - "speaker": "Tim", - "dia_id": "D10:11", - "text": "Nope, but it's my first time going. I'm super stoked for the Harry Potter stuff. Can't wait!" - }, - { - "speaker": "John", - "dia_id": "D10:12", - "text": "Cool! It's gonna be a blast, like stepping into another world. Have a great time!" - }, - { - "speaker": "Tim", - "dia_id": "D10:13", - "text": "Thanks! I'll definitely have a blast. I'll let you know how it goes!" - }, - { - "speaker": "John", - "dia_id": "D10:14", - "text": "Great! Can't wait to hear about it. Have a safe trip!" - }, - { - "speaker": "Tim", - "dia_id": "D10:15", - "text": "Thanks! I'll make sure to have a safe trip." - }, - { - "speaker": "John", - "dia_id": "D10:16", - "text": "Bye! Take care and let's catch up soon!" - }, - { - "speaker": "Tim", - "dia_id": "D10:17", - "text": "Take care! Can't wait to catch up. Talk soon!" - } - ], - "session_11_date_time": "8:17 pm on 21 September, 2023", - "session_11": [ - { - "speaker": "John", - "dia_id": "D11:1", - "text": "Hey Tim, been a while! How ya been?" - }, - { - "speaker": "Tim", - "dia_id": "D11:2", - "text": "Hey John! Great to hear from you. Been busy with things, how about you?" - }, - { - "speaker": "John", - "dia_id": "D11:3", - "text": "Yeah, something cool happened! I attended a local restaurant with some new teammates last week. It was great getting to know them better." - }, - { - "speaker": "Tim", - "dia_id": "D11:4", - "text": "Good support is essential. How do you feel about them?" - }, - { - "speaker": "John", - "dia_id": "D11:5", - "text": "They're great friends. We connected over our shared love for basketball and had a ton of fun." - }, - { - "speaker": "Tim", - "dia_id": "D11:6", - "text": "Sounds awesome. Having friends who share your hobbies can be really fun. Any exciting plans with them?" - }, - { - "speaker": "John", - "dia_id": "D11:7", - "text": "We're planning to take a team trip next month to explore a new city and have some fun. Can't wait!" - }, - { - "speaker": "Tim", - "dia_id": "D11:8", - "text": "That sounds great! Exploring new cities is always so much fun. Where are you headed?" - }, - { - "speaker": "John", - "dia_id": "D11:9", - "text": "We're still deciding on the destination. Do you have any suggestions?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://www.thebearandthefox.com/wp-content/uploads/2018/02/IMG_3494.jpg" - ], - "blip_caption": "a photo of a city with a clock tower and a sun setting", - "query": "edinburgh scotland castle sunset", - "dia_id": "D11:10", - "text": "Edinburgh, Scotland would be great for a magical vibe. It's the birthplace of Harry Potter and has awesome history and architecture. Plus, it's a beautiful city. What do you think?" - }, - { - "speaker": "John", - "dia_id": "D11:11", - "text": "That sounds like a great idea! I haven't been to Edinburgh yet, but it definitely sounds like a place worth considering for our trip. Thanks for the suggestion!" - }, - { - "speaker": "Tim", - "dia_id": "D11:12", - "text": "Glad you liked it. Let me know if you need any more suggestions." - }, - { - "speaker": "John", - "img_url": [ - "https://c8.alamy.com/zooms/9/de5f1d4e73244a8f94b16a6b6d093748/ttnmea.jpg" - ], - "blip_caption": "a photo of a basketball ball on the ground with a basketball hoop in the background", - "query": "basketball court sunset", - "dia_id": "D11:13", - "text": "Thanks! I'll definitely reach out if I need more suggestions. Appreciate the help! Here's a pic I snapped during one of our practices. The sunset looked amazing on the court. Moments like these make me so grateful for my basketball career." - }, - { - "speaker": "Tim", - "dia_id": "D11:14", - "text": "Wow, that looks amazing! What do you love most about your basketball career?" - }, - { - "speaker": "John", - "dia_id": "D11:15", - "text": "Thanks! I love playing pro ball - it's a constant challenge and keeps me growing. There's nothing like seeing myself get better and beating goals. Plus, playing with my teammates and having the fans cheer is awesome. Basketball gives me a great sense of satisfaction and purpose." - }, - { - "speaker": "Tim", - "dia_id": "D11:16", - "text": "It's great that you have a passion that helps you grow and reach your goals. Achieving and feeling fulfilled must be amazing. Do you have any specific targets or goals you're working towards?" - }, - { - "speaker": "John", - "dia_id": "D11:17", - "text": "Definitely! I'm focusing on better shooting and making more of an impact on the court. I want to be known as a consistent performer and help my team. Off the court, I'm also looking into more endorsements and building my brand. It's important for me to think about life after basketball." - }, - { - "speaker": "Tim", - "dia_id": "D11:18", - "text": "Awesome! It's great that you have goals both on and off the court. It's wise to think about the future and building your brand. What are your thoughts on life after basketball?" - }, - { - "speaker": "John", - "dia_id": "D11:19", - "text": "I've thought about it a lot. I want to use my platform to make a positive difference and inspire others - maybe even start a foundation and do charity work. It's important to me to make the most of the chances I get and leave a meaningful legacy." - }, - { - "speaker": "Tim", - "dia_id": "D11:20", - "text": "Wow, that's amazing. Good on you for wanting to make a difference and motivate others. I'm sure you'll succeed! Is there anything I can do to support you?" - }, - { - "speaker": "John", - "dia_id": "D11:21", - "text": "Thanks! I'm trying to figure out how to pick the right ones - any advice on that?" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a stack of books sitting on top of a counter", - "dia_id": "D11:22", - "text": "When picking endorsements, make sure they align with your values and brand. Look for a company that shares your desire to make a change and help others. It's important that the endorsement feels authentic to your followers." - }, - { - "speaker": "John", - "dia_id": "D11:23", - "text": "Sounds like good advice! I was wondering if you have any book recommendations for my trip?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://live.staticflickr.com/3195/3322703152_057a33dca1_z.jpg" - ], - "blip_caption": "a photography of a book cover with a man in a hooded jacket", - "query": "the name of the wind book cover fantasy cloaked figure burning city", - "dia_id": "D11:24", - "re-download": true, - "text": "Yeah! I think you'd love this fantasy novel by Patrick Rothfuss. It's a book that'll take you to a different world. Great for you when you're traveling. Have fun!" - }, - { - "speaker": "John", - "img_url": [ - "https://i0.wp.com/www.themself.org/wp-content/uploads/2016/10/img_20161018_133948.jpg" - ], - "blip_caption": "a photo of a bookshelf with a lot of books on it", - "query": "bookshelf", - "dia_id": "D11:25", - "text": "Thanks! I'll definitely check it out - looks like a great book to read while traveling. Can't wait to dive into it! Here's a photo of my bookshelf. You can see some of the books I've read and enjoyed." - }, - { - "speaker": "Tim", - "dia_id": "D11:26", - "text": "Great bookshelf! I saw that you had \"The Alchemist\" on there, one of my favorites. Did you enjoy it?" - }, - { - "speaker": "John", - "dia_id": "D11:27", - "text": "Yep, I read that book and loved it! It made me think about life and how important it is to follow one's dreams. Highly recommend it!" - }, - { - "speaker": "Tim", - "dia_id": "D11:28", - "text": "Glad you liked it! \"The Alchemist\" is worth it." - }, - { - "speaker": "John", - "dia_id": "D11:29", - "text": "Thanks! Take care!" - }, - { - "speaker": "Tim", - "dia_id": "D11:30", - "text": "Have fun! Take care and talk to you soon." - } - ], - "session_12_date_time": "3:00 pm on 2 October, 2023", - "session_12": [ - { - "speaker": "Tim", - "blip_caption": "a photo of a bookcase filled with dvds and games", - "dia_id": "D12:1", - "text": "Hey John! Awesome catchin' up with you! A lot's changed since last time." - }, - { - "speaker": "John", - "img_url": [ - "https://platinumnotary.files.wordpress.com/2023/03/img-6679.jpg" - ], - "blip_caption": "a photo of a wedding ceremony in a greenhouse with people taking pictures", - "query": "wedding ceremony", - "dia_id": "D12:2", - "text": "Hey, Tim! Good to hear from you. Anyway, a lot has been going on with me. My girlfriend and I had an amazing and emotional wedding ceremony last week." - }, - { - "speaker": "Tim", - "dia_id": "D12:3", - "text": "Congrats! That was such a special day! How did you manage to have the ceremony during these times?" - }, - { - "speaker": "John", - "dia_id": "D12:4", - "text": "Thanks! We were lucky to find a lovely greenhouse venue for a smaller, more intimate gathering. We made sure to follow the necessary safety protocols and ensure that everyone felt safe. It was wonderful to have our loved ones celebrate with us." - }, - { - "speaker": "Tim", - "dia_id": "D12:5", - "text": "Awesome! It sounds like you found a great venue and had your loved ones celebrate with you. Weddings are definitely full of joy!" - }, - { - "speaker": "John", - "dia_id": "D12:6", - "text": "Yeah! Such a great day! It was so beautiful having everyone celebrating with us. I'd never felt so much love and happiness before. Some of my hiking club friends came even though I just joined!" - }, - { - "speaker": "Tim", - "dia_id": "D12:7", - "text": "Wow! Your wedding day must've been so special. Love sure does bring us joy, huh? Any favorite memories from the big day?" - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/8815283/pexels-photo-8815283.jpeg" - ], - "blip_caption": "a photography of a bride walking down the aisle with her groom", - "query": "wife walking down the aisle moment", - "dia_id": "D12:8", - "re-download": true, - "text": "Oh yeah! Picking a favorite memory was tough, but seeing her walking down the aisle, her face lit up and it got me all choked up. What a magical moment - one I'll always remember." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a desk with a bookcase full of books and a lot of other items", - "dia_id": "D12:9", - "text": "I bet that moment was unreal! One moment can make such an impression. I saw your photo of you two dancing, it must have been incredible. Wanna tell me more?" - }, - { - "speaker": "John", - "dia_id": "D12:10", - "text": "That dance was great! We had our first dance at a cozy restaurant. It was so dreamy with the music and candlelight. We were so lucky to have everyone with us!" - }, - { - "speaker": "Tim", - "dia_id": "D12:11", - "text": "Wow, a candlelit dance in a cozy restaurant! Sounds like a fairytale! So special to share with your loved ones. Weddings truly are the best!" - }, - { - "speaker": "John", - "dia_id": "D12:12", - "text": "Thanks! It was a great day. Having everyone there made it extra special. It's moments like these that bring love and joy." - }, - { - "speaker": "Tim", - "dia_id": "D12:13", - "text": "Congrats again! Love is truly magical and brings so much joy. I'm so happy for you and your new wife!" - }, - { - "speaker": "John", - "dia_id": "D12:14", - "text": "Thanks so much! Your words mean a lot. I'm lucky to have you in my life, bringing so much love and joy." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/z6jusjhd0pd71.jpg" - ], - "blip_caption": "a photo of a bookcase filled with dvds and games", - "query": "bookshelf fantasy novels", - "dia_id": "D12:15", - "text": "Thanks. Your friendship means a lot to me. I'm here for you anytime. I also wanted to share this bookshelf with you. It's filled with my favorite fantasy novels." - }, - { - "speaker": "John", - "dia_id": "D12:16", - "text": "Cool! What do you enjoy about them so much?" - }, - { - "speaker": "Tim", - "dia_id": "D12:17", - "text": "They really fire up my imagination and take me to alternate realities. They're my escape from reality." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/odlwr40mol581.jpg" - ], - "blip_caption": "a photo of a bunch of basketball jerseys laying on a bed", - "query": "basketball jerseys collection", - "dia_id": "D12:18", - "text": "That's great Tim! Books and movies make us escape to different places. I like to collect jerseys." - }, - { - "speaker": "Tim", - "dia_id": "D12:19", - "text": "Cool! Who's your favorite basketball team/player?" - }, - { - "speaker": "John", - "dia_id": "D12:20", - "text": "Thanks! The Wolves are my team for sure. And LeBron is the man - love his skills and leadership." - }, - { - "speaker": "Tim", - "dia_id": "D12:21", - "text": "The Wolves are solid and LeBron's skills and leadership are amazing." - }, - { - "speaker": "John", - "blip_caption": "a photo of a group of people standing on a basketball court", - "dia_id": "D12:22", - "text": "I really admire his work ethic and dedication to the game, such an inspiration." - }, - { - "speaker": "Tim", - "dia_id": "D12:23", - "text": "LeBron is incredible. Have you ever had the opportunity to meet him or see him play live?" - }, - { - "speaker": "John", - "dia_id": "D12:24", - "text": "Yeah, I've had the chance to meet him a few times. He's real chill and it was a wild experience. I also had the opportunity to see him play live. It was tough, but I learned a lot from watching a top player like him." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a crowd of people at a concert with their hands in the air", - "dia_id": "D12:25", - "text": "Wow! Meeting him and seeing him play must've been awesome. I can only imagine his skills up close." - }, - { - "speaker": "John", - "img_url": [ - "https://get.pxhere.com/photo/structure-crowd-audience-basketball-stadium-arena-sports-miami-sport-venue-soccer-specific-stadium-music-venue-basketball-moves-slam-dunk-miami-heat-game-miami-heat-1383801.jpg" - ], - "blip_caption": "a photo of a basketball game with a crowd of people watching", - "query": "basketball game crowd", - "dia_id": "D12:26", - "text": "It was so amazing! The energy was crazy and everyone was so hyped. Seeing the top players was so motivating - it made me want to work even harder. Check out this pic from the game!" - }, - { - "speaker": "Tim", - "dia_id": "D12:27", - "text": "That crowd looks wild! The game must've been amazing!" - }, - { - "speaker": "John", - "dia_id": "D12:28", - "text": "The energy was incredible! It's moments like these that make me love my job." - }, - { - "speaker": "Tim", - "dia_id": "D12:29", - "text": "Cool! It's great to find something you enjoy doing. Keep going for it! See ya later!" - } - ], - "session_13_date_time": "1:50 pm on 13 October, 2023", - "session_13": [ - { - "speaker": "Tim", - "dia_id": "D13:1", - "text": "Hey John! It's been ages since we last talked. Guess what? Last week I went to a Harry Potter conference in the UK - it was incredible! There were so many people who shared the same love of HP as me, it was like a magical family. I felt so inspired and like I got a new lease of life. I love how my passion for fantasy stuff brings me closer to people from all over the world, it's pretty cool." - }, - { - "speaker": "John", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/4/4c/USA_men%27s_national_basketball_team_%2851910110377%29.jpg" - ], - "blip_caption": "a photography of a basketball team posing for a team photo", - "query": "group photo basketball", - "dia_id": "D13:2", - "re-download": true, - "text": "Hey Tim! Great to hear from you. It's awesome how our passions connect us with others, yeah? You sound like you fit right in and got a real buzz out of it. I feel the same way with my team." - }, - { - "speaker": "Tim", - "dia_id": "D13:3", - "text": "Wow, you guys look great! How have games been going?" - }, - { - "speaker": "John", - "dia_id": "D13:4", - "text": "It was an intense season with both tough losses and great wins. Overall, I'd say we did pretty well." - }, - { - "speaker": "Tim", - "dia_id": "D13:5", - "text": "Cool! Sounds like you guys had some tough games. How did you handle those?" - }, - { - "speaker": "John", - "img_url": [ - "https://tapinto-production.s3.amazonaws.com/uploads/articles/im/best_9e2201886c43d264dbef_IMG_6460.jpg" - ], - "blip_caption": "a photo of a soccer team posing for a picture with a trophy", - "query": "teammates celebration victory", - "dia_id": "D13:6", - "text": "Thanks! We faced tough opponents but that's what drives us to get better. We back each other up and won't quit." - }, - { - "speaker": "Tim", - "dia_id": "D13:7", - "text": "Congrats! That's awesome. It must feel good, right?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/wheq5bijuqc91.jpg" - ], - "blip_caption": "a photo of a man holding a trophy in front of a crowd", - "query": "championship trophy holding up", - "dia_id": "D13:8", - "text": "Yeah, it feels great! All that hard work and effort was totally worth it. We even won a trophy!" - }, - { - "speaker": "Tim", - "dia_id": "D13:9", - "text": "Way to go! You must have been elated up there with that trophy. All the hard work paid off! Congrats - I'm so proud of you. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D13:10", - "text": "Thanks! I was definitely elated. Your support really means a lot to me. I'll keep working hard." - }, - { - "speaker": "Tim", - "dia_id": "D13:11", - "text": "No problem! I'm here for you anytime. Keep believing in yourself!" - }, - { - "speaker": "John", - "dia_id": "D13:12", - "text": "Thanks! Appreciate your support. Always staying filled with self-belief." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a box of serenityy memory foam", - "dia_id": "D13:13", - "text": "You got this! Stay motivated and remember that anything is possible with hard work. Keep pushing for your goals!" - }, - { - "speaker": "John", - "dia_id": "D13:14", - "text": "Thanks! Your encouragement means a lot to me. I'm feeling motivated and ready to keep pushing for my goals! I'm going to need some new shoes after all these games though." - }, - { - "speaker": "Tim", - "img_url": [ - "http://www.binxberryconsignment.com/cdn/shop/products/IMG_3958.jpg" - ], - "blip_caption": "a photo of a pair of black and pink running shoes", - "query": "memory foam shoes", - "dia_id": "D13:15", - "text": "Glad my encouragement helped! These are amazing - like walking on clouds! Game changer!" - }, - { - "speaker": "John", - "dia_id": "D13:16", - "text": "They look comfortable. Where did you get them?" - }, - { - "speaker": "Tim", - "dia_id": "D13:17", - "text": "I got them online - they're super comfy! Definitely recommend!" - }, - { - "speaker": "John", - "dia_id": "D13:18", - "text": "Cheers! I'll definitely check them out. Thanks for the recommendation!" - }, - { - "speaker": "Tim", - "dia_id": "D13:19", - "text": "No worries. Let me know if there's anything else I can assist you with. Always here to help!" - }, - { - "speaker": "John", - "dia_id": "D13:20", - "text": "Thanks! Appreciate it. I'll reach out if I need anything." - }, - { - "speaker": "Tim", - "dia_id": "D13:21", - "text": "Cool! Stay motivated and keep chasing those dreams! Chat soon!" - }, - { - "speaker": "John", - "dia_id": "D13:22", - "text": "Thanks! I'll definitely stay motivated and keep chasing those dreams. You too, keep up the passion. Talk soon!" - } - ], - "session_14_date_time": "1:50 pm on 17 October, 2023", - "session_14": [ - { - "speaker": "John", - "dia_id": "D14:1", - "text": "Hey Tim! Long time no talk - a lot has been going on since then!" - }, - { - "speaker": "Tim", - "dia_id": "D14:2", - "text": "Hey John! Long time no see! Can't wait to catch up and hear all about what you've been up to." - }, - { - "speaker": "John", - "dia_id": "D14:3", - "text": "Seems like forever since we caught up! I'm now mentoring the younger players on my team. It's super rewarding and I'm loving sharing my skills and knowledge with them. It's also a great way for me to stay involved in the game during the off-season." - }, - { - "speaker": "Tim", - "dia_id": "D14:4", - "text": "Wow! Mentoring must be so rewarding. You get to show others what you know - that's awesome! Is it difficult? Any hiccups?" - }, - { - "speaker": "John", - "dia_id": "D14:5", - "text": "There are challenges, since everyone is so different. But it's been awesome gaining experience and adapting, motivating and encouraging everyone. It's been great to watch each of them develop and reach their goals - such a reward!" - }, - { - "speaker": "Tim", - "dia_id": "D14:6", - "text": "Wow, that's awesome! It must be really rewarding to see them reach their goals. What's it like mentoring them?" - }, - { - "speaker": "John", - "img_url": [ - "https://cdn2.picryl.com/photo/2020/01/16/members-of-the-local-and-us-communities-attend-the-edfa79-1024.jpg" - ], - "blip_caption": "a photography of a basketball player standing in a gym with his hands on his hips", - "query": "mentoring young players practice smiles", - "dia_id": "D14:7", - "re-download": true, - "text": "Mentoring them has been awesome! Seeing their growth, improvement, and confidence is so fulfilling. I'm glad I could make a positive impact on their lives. Here's a pic of me and some of the younger players at a recent practice." - }, - { - "speaker": "Tim", - "dia_id": "D14:8", - "text": "You're really doing great with them. Do any of them see you as a mentor?" - }, - { - "speaker": "John", - "dia_id": "D14:9", - "text": "Some of them do see me as a mentor, which is really rewarding. I try to provide them with advice and support on and off the court. Being a positive role model for them is something I enjoy." - }, - { - "speaker": "Tim", - "dia_id": "D14:10", - "text": "That's incredible! How does it feel to have their trust and admiration? It must be such an honor to be a positive role model for them." - }, - { - "speaker": "John", - "dia_id": "D14:11", - "text": "It feels great to have their trust and admiration. Being a role model for these young athletes is so fulfilling. I'm glad my experiences can help shape their future and inspire them to go after their dreams." - }, - { - "speaker": "Tim", - "img_url": [ - "https://images.pexels.com/photos/12312263/pexels-photo-12312263.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-tanya-badillo-12312263.jpg" - ], - "blip_caption": "a photo of a sunset over a mountain range with a few trees", - "query": "mountain range sunset", - "dia_id": "D14:12", - "text": "You're doing a great job with them. Way to go! This is what I've been up to." - }, - { - "speaker": "John", - "dia_id": "D14:13", - "text": "Wow, stunning! And thanks. Really appreciate it. Means a lot." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/hf54pvtf8rt51.jpg" - ], - "blip_caption": "a photo of a sunset over a mountain with a tree", - "query": "sunset blue ridge mountains north carolina", - "dia_id": "D14:14", - "text": "I took this pic last summer. Seeing it was so stunning. Thanks for appreciating it. It means a lot to me." - }, - { - "speaker": "John", - "dia_id": "D14:15", - "text": "Where did you capture this? Nature is truly amazing, isn't it?" - }, - { - "speaker": "Tim", - "dia_id": "D14:16", - "text": "I snapped that pic on my trip to the Smoky Mountains last year. It was incredible seeing it in person. Nature's really something else!" - }, - { - "speaker": "John", - "dia_id": "D14:17", - "text": "Yeah, it's amazing how nature's beauty and grandeur can take our breath away. It's so nice to escape the noise of the city and relax in nature. Good for you to get to enjoy that stunning view!" - }, - { - "speaker": "Tim", - "dia_id": "D14:18", - "text": "Nature is indeed refreshing. A good break from school." - }, - { - "speaker": "John", - "dia_id": "D14:19", - "text": "How are you doing in shcool?" - }, - { - "speaker": "Tim", - "dia_id": "D14:20", - "text": "Doing good! Busy with studies but finding time to relax with books - good balance." - }, - { - "speaker": "John", - "dia_id": "D14:21", - "text": "Cool! Finding that balance is key. Are you currently reading any books?" - }, - { - "speaker": "Tim", - "dia_id": "D14:22", - "text": "I'm reading this book and I'm totally hooked! What about you?" - }, - { - "speaker": "John", - "dia_id": "D14:23", - "text": "I haven't had much time to read, but after we talked I finally picked up a book and it's been awesome! Talk to you later!" - } - ], - "session_15_date_time": "5:51 pm on 21 October, 2023", - "session_15": [ - { - "speaker": "Tim", - "dia_id": "D15:1", - "text": "Hey John! Haven't talked to you in a bit but wanted to let you know I read this awesome book about castles in the UK. It was so interesting and blew me away! I dream of visiting them one day." - }, - { - "speaker": "John", - "img_url": [ - "https://creatingmewp.files.wordpress.com/2023/05/img_9047.jpg" - ], - "blip_caption": "a photo of a man sitting on a bench overlooking a cliff", - "query": "castle scotland", - "dia_id": "D15:2", - "text": "Hey Tim! Great to hear from you. Learning about different cultures and seeing historical architecture fascinates me. Visiting castles is really on my bucket list. Just look at this one; what a sight! I'm so excited to explore the world and experience these gorgeous places. On that note, how's your fantasy writing going?" - }, - { - "speaker": "Tim", - "dia_id": "D15:3", - "text": "That castle looks amazing! I hope I get to visit it someday. My writing is going well: I'm in the middle of fantasy novel and it's a bit nerve-wracking but so exciting! All my hard work is paying off. Writing brings such joy and it's incredible how it can create a whole new world. Thanks so much for believing in me!" - }, - { - "speaker": "John", - "dia_id": "D15:4", - "text": "That's great! I'm glad your writing is going well. It must be exciting to see it all come together. Keep going! Do you have a specific source of inspiration for your stories?" - }, - { - "speaker": "Tim", - "dia_id": "D15:5", - "text": "Thanks! Books, movies, and real-life experiences all fire up my creativity. For example, reading about castles in the UK gave me loads of ideas. Plus, certain authors are like goldmines of inspiration for me. Connecting with the things I love makes writing even more fun." - }, - { - "speaker": "John", - "dia_id": "D15:6", - "text": "Wow! Sounds like a great mix. Is there a particular author whose work inspires you?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/4mff2zeq18381.jpg" - ], - "blip_caption": "a photo of a book with a page in it on a table", - "query": "harry potter sorcerer's stone well-read copy", - "dia_id": "D15:7", - "text": "J.K. Rowling is such an inspiring writer. Her books are so captivating with their detail and creative storytelling. She can definitely transport readers into another world and make them feel so much. I'm always taking notes on her style for my own writing." - }, - { - "speaker": "John", - "dia_id": "D15:8", - "text": "Cool! How long have you been reading her works?" - }, - { - "speaker": "Tim", - "dia_id": "D15:9", - "text": "I've been reading her stuff for a long time. Her stories have been with me and still inspire me. There's something special about her writing that really speaks to me." - }, - { - "speaker": "John", - "dia_id": "D15:10", - "text": "Wow, some authors really have such an influence on us! They become part of our life and affect our interests. Do you have a favorite J.K. Rowling quote?" - }, - { - "speaker": "Tim", - "dia_id": "D15:11", - "text": "Yeah! There's a quote by J.K. Rowling that I really like: \"Turn on the light - happiness hides in the darkest of times.\" That's how I keep hope alive during tough times." - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/5e/f4/f9/5ef4f9bd2f094b2d0a4ddd4861b928a0.jpg" - ], - "blip_caption": "a photo of a white board with a drawing of arrows and words", - "query": "inspirational quote whiteboard", - "dia_id": "D15:12", - "text": "Nice quote! It reminds us to stay positive and find joy even in hard times. It's a guiding light when things get rough. I appreciate you sharing it!" - }, - { - "speaker": "Tim", - "dia_id": "D15:13", - "text": "Nice job, John! What did you write on that whiteboard?" - }, - { - "speaker": "John", - "dia_id": "D15:14", - "text": "On that whiteboard, I wrote down some motivational quotes and strategies to help me stay focused and push through tough workouts. It really helps me stay motivated and keep improving." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a woman holding a plaque in front of a wall", - "dia_id": "D15:15", - "text": "That's awesome! Visual reminders and strategies can really help in staying motivated. It's cool that you have those quotes to keep you going during tough workouts." - }, - { - "speaker": "John", - "img_url": [ - "https://images.rawpixel.com/image_social_landscape/czNmcy1wcml2YXRlL3Jhd3BpeGVsX2ltYWdlcy93ZWJzaXRlX2NvbnRlbnQvbHIvbnMxODI2MC1pbWFnZS1rd3Z3bmQxZi5qcGc.jpg" - ], - "blip_caption": "a photography of a desk with a laptop and a lightbox that says make it happen", - "query": "plaque believe power within motivation", - "dia_id": "D15:16", - "re-download": true, - "text": "This plaque I keep on my desk is a constant reminder to believe in myself. It helps me trust my abilities and face any obstacles. Having physical reminders like this really helps me stay motivated." - }, - { - "speaker": "Tim", - "dia_id": "D15:17", - "text": "That's awesome! What keeps you motivated during challenging times?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.americustimesrecorder.com/wp-content/uploads/sites/43/2023/04/team-huddle-rotated.jpg" - ], - "blip_caption": "a photo of a group of women soccer players huddle together", - "query": "team huddle game", - "dia_id": "D15:18", - "text": "My teammates believing in me and my love for improving my skills keep me going, even when things get tough. I don't want to let them down." - }, - { - "speaker": "Tim", - "dia_id": "D15:19", - "text": "Nice one! What do you reckon makes them such a good support?" - }, - { - "speaker": "John", - "dia_id": "D15:20", - "text": "They always support me, even when I make mistakes. Their encouragement keeps me going." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a group of people hiking through a forest", - "dia_id": "D15:21", - "text": "That's key, having a strong support network can really help with what we're trying to do. Do you have people you can lean on outside of sports?" - }, - { - "speaker": "John", - "dia_id": "D15:22", - "text": "Yeah, I'm lucky - I have people who are super supportive, always there for me no matter what." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.pinimg.com/originals/85/59/73/855973c53d20fad61ea048269f29fadb.jpg" - ], - "blip_caption": "a photo of a group of women sitting on the grass eating", - "query": "group friends beach picnic", - "dia_id": "D15:23", - "text": "Awesome! Having people who lift us up is essential. I'm grateful I have friends and family who support me - it's huge." - }, - { - "speaker": "John", - "img_url": [ - "https://images.pexels.com/photos/1655329/pexels-photo-1655329.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-lisa-fotios-1655329.jpg" - ], - "blip_caption": "a photo of a group of people standing around a kitchen table", - "query": "family dinner", - "dia_id": "D15:24", - "text": "Having loved ones who support us is so important. My family is always there for me." - }, - { - "speaker": "Tim", - "dia_id": "D15:25", - "text": "Wow, look at this great group! Are these your people?" - }, - { - "speaker": "John", - "img_url": [ - "https://s3-us-west-2.amazonaws.com/sportshub2-uploads-prod/files/sites/1567/2018/02/09230004/IMG_8348-e1518217261806.jpg" - ], - "blip_caption": "a photo of a group of people standing on a basketball court", - "query": "group of basketball players cheering on sidelines", - "dia_id": "D15:26", - "text": "Yeah, definitely! That's my fam hanging out. Being with them brings me so much happiness and helps me remember what's important. My team is like my second family too." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a group of kids playing a game of basketball", - "dia_id": "D15:27", - "text": "That looks fun! What else do you do with them?" - }, - { - "speaker": "John", - "dia_id": "D15:28", - "text": "What people usually do when you hang out with friends and family - movies, dinner out. And what are your favorite activities for fun?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://images.pexels.com/photos/11818038/pexels-photo-11818038.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-ana-ps-11818038.jpg" - ], - "blip_caption": "a photo of a fire in a fireplace with a dog standing next to it", - "query": "campfire group people", - "dia_id": "D15:29", - "text": "I love going on road trips with friends and family, exploring and hiking or playing board games. And in my free time, I enjoy curling up with a good book, escaping reality and getting lost in different worlds. That's what I'm talking about." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/6383i30xl5w31.jpg" - ], - "blip_caption": "a photo of a slow cooker filled with a mixture of food", - "query": "cooking meal", - "dia_id": "D15:30", - "text": "Yep, I totally get it! Cuddling up with a book is my chill time. And when I'm away from the court, cooking is therapy for me. It's a good way to be creative and experiment with flavors while taking a break. Here's a photo of me cooking a meal." - }, - { - "speaker": "Tim", - "dia_id": "D15:31", - "text": "That slow cooker meal looks yum! Cooking is a great way to chill and be creative. Do you have any favorite recipes you can show me?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a pan of chicken and vegetables cooking on a stove", - "dia_id": "D15:32", - "text": "Definitely! I make this yummy honey garlic chicken with roasted veg a lot. It's one of my favorites. I'm always trying out new recipes, so let me know if you want suggestions!" - }, - { - "speaker": "Tim", - "dia_id": "D15:33", - "text": "Mmm, that sounds delicious, John! Can I get the recipe for it?" - }, - { - "speaker": "John", - "dia_id": "D15:34", - "text": "Sure thing! I can write it down for you and mail it to you." - }, - { - "speaker": "Tim", - "dia_id": "D15:35", - "text": "Can't wait to try it. Thanks for sharing the recipe!" - }, - { - "speaker": "John", - "dia_id": "D15:36", - "text": "No worries. Hope you enjoy it! Let me know how it turns out." - }, - { - "speaker": "Tim", - "dia_id": "D15:37", - "text": "Sure thing! Thanks. Great talking to you. Take care!" - }, - { - "speaker": "John", - "dia_id": "D15:38", - "text": "It was nice chatting with you. Talk to you later!" - } - ], - "session_16_date_time": "11:41 am on 6 November, 2023", - "session_16": [ - { - "speaker": "Tim", - "dia_id": "D16:1", - "text": "Hey John, long time no see! Hope you've been doing well. Since we last chat, some stuff's happened. Last week, I had a huge writing issue - got stuck on a plot twist and couldn't find my way out. It was crazy frustrating, but I kept pushing and eventually got the ideas flowing again." - }, - { - "speaker": "John", - "dia_id": "D16:2", - "text": "Hey Tim! Awesome to hear from you. Yeah, I get how that would've been so annoying! But you stuck it out, that's so cool. Same with me on the court. Just gotta find a way to tough it out and keep things flowing. Then when you make it through, it's all the more satisfying, right?" - }, - { - "speaker": "Tim", - "dia_id": "D16:3", - "text": "Yeah! It was hard but once it's over, the feeling is amazing. That's what makes it so beautiful, the struggle and then the satisfaction." - }, - { - "speaker": "John", - "dia_id": "D16:4", - "text": "Yeah! Struggles make it worth it. Like in sports, that's when the win feels great! Challenges force us to develop and become better." - }, - { - "speaker": "Tim", - "dia_id": "D16:5", - "text": "Overcoming challenges builds strength and pushes personal growth. It's about the journey and what we learn, not just winning. This was a great reminder for me. Got any examples from that sport you mentioned?" - }, - { - "speaker": "John", - "dia_id": "D16:6", - "text": "Yeah, last year I had a basketball game where we were trailing big time in the 4th quarter. We had to dig deep and keep on pushing to overturn the deficit and it was amazing when that final buzzer sounded. Unforgettable feeling." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/w41ygbk6p4ea1.jpg" - ], - "blip_caption": "a photo of a basketball in a case with a signed ball", - "query": "basketball signed by favorite basketball player", - "dia_id": "D16:7", - "text": "Wow, that must have been an incredible feeling! You really showed determination and perseverance. Do you have any photos or keepsakes from the game? I'd love to see them! By the way, I have something special too - this is my prized possession, a basketball signed by my favorite player. It serves as a reminder of all the hard work." - }, - { - "speaker": "John", - "dia_id": "D16:8", - "text": "Wow! What makes your favorite player so inspiring? Do you have any special stories or moments with them?" - }, - { - "speaker": "Tim", - "dia_id": "D16:9", - "text": "I just love watching LeBron. There was this Finals game a few years back with an epic block that totally changed the game and ended up winning it. Seeing him go for it like that was such an inspiration - never give up, you know?" - }, - { - "speaker": "John", - "dia_id": "D16:10", - "text": "Remember that epic block in Game 7 of the '16 Finals? He chased down Iguodala and pinned the ball against the backboard. That kind of determination and heart is why I love basketball." - }, - { - "speaker": "Tim", - "dia_id": "D16:11", - "text": "Yeah, that's the one! It was awesome. Moments like that make me love sports and admire the players' determination and heart." - }, - { - "speaker": "John", - "dia_id": "D16:12", - "text": "LeBron's moments of determination and heart are incredible. It's why I enjoy playing and pushing myself. You never know when those special moments might occur, but it's always fun to be part of it." - }, - { - "speaker": "Tim", - "dia_id": "D16:13", - "text": "Those special moments make it all worth it. It's amazing to be part of something bigger and feel the joy and fulfillment. Keep pushing and having those moments on the court!" - }, - { - "speaker": "John", - "dia_id": "D16:14", - "text": "Speaking of special moments, my wife and I just left for our European vacation! It will be short but sweet. You've been before, any recommendations?" - }, - { - "speaker": "Tim", - "dia_id": "D16:15", - "text": "That's great! I hope you two have a great time. I would recommend visiting some castles, they are just so magical!" - }, - { - "speaker": "John", - "dia_id": "D16:16", - "text": "Thanks! We'll have to check some out. Wishing you all the best with everything you're pursuing. Stay safe!" - }, - { - "speaker": "Tim", - "dia_id": "D16:17", - "text": "Thanks! You too, buddy. Take it easy and keep going for it. Stay safe and let's stay in touch!" - } - ], - "session_17_date_time": "3:36 pm on 11 November, 2023", - "session_17": [ - { - "speaker": "John", - "dia_id": "D17:1", - "text": "Hey Tim! Great to chat again. So much has happened!" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/7a8i8uea5au51.jpg" - ], - "blip_caption": "a photo of a book with a picture of a storm of swords", - "query": "worn copy game of thrones", - "dia_id": "D17:2", - "text": "Hey John! Great chatting with you as always. What's been happening lately? I've been reading as usual." - }, - { - "speaker": "John", - "img_url": [ - "https://familyadventuresva.files.wordpress.com/2022/03/img_5178.jpg" - ], - "blip_caption": "a photo of a group of people sitting on top of a mountain", - "query": "american west landscapes grand canyon", - "dia_id": "D17:3", - "text": "My wife and I were road tripping out on the European coastline, and it was amazing! The views were spectacular, and we had lots of fun bonding and creating amazing memories. It was such a nice change to my regular life." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a statue of a woman with a blue hat on", - "dia_id": "D17:4", - "text": "Wow! Sounds like an incredible road trip. I'm glad you and your wife had such a great time!" - }, - { - "speaker": "John", - "dia_id": "D17:5", - "text": "Thanks! Yeah, it was awesome. We got to see some epic spots. It's hard to describe how beautiful they were!" - }, - { - "speaker": "Tim", - "dia_id": "D17:6", - "text": "Those places must've been amazing! Nature sure has a way of leaving us speechless." - }, - { - "speaker": "John", - "dia_id": "D17:7", - "text": "Nature sure is powerful and beautiful! It's really humbling to witness such sights." - }, - { - "speaker": "Tim", - "dia_id": "D17:8", - "text": "Yeah! It always makes us realize how huge the world is and how special it is. These moments really show us the beauty around us. Anyways, have you read or watched anything good recently?" - }, - { - "speaker": "John", - "dia_id": "D17:9", - "text": "Yep, I just finished this amazing fantasy series. It was a wild ride with so many twists. The author is amazing at creating awesome storylines and characters - I love getting lost in those fantasy worlds." - }, - { - "speaker": "Tim", - "dia_id": "D17:10", - "text": "That's amazing! Same here. There's something special about being lost in an awesome fantasy realm and seeing what happens. It's like an escape. \"That\" is one of my favorite fantasy shows. Have you seen it?" - }, - { - "speaker": "John", - "dia_id": "D17:11", - "text": "Yeah, I saw \"That\"! It's amazing to see those worlds and characters come alive. It's a great way to escape reality!" - }, - { - "speaker": "Tim", - "dia_id": "D17:12", - "text": "Yeah, it's awesome how books and movies can take you away. A great escape, right?" - }, - { - "speaker": "John", - "dia_id": "D17:13", - "text": "Definitely, it's like a mental break, giving our minds a rest and letting them wander. So refreshing!" - }, - { - "speaker": "Tim", - "dia_id": "D17:14", - "text": "It's like entering another world! We get to take a break from everything and just let our minds wander. It's so nice and refreshing." - }, - { - "speaker": "John", - "dia_id": "D17:15", - "text": "And that's just what we need sometimes." - }, - { - "speaker": "Tim", - "dia_id": "D17:16", - "text": "Taking a break from life can help us recharge and get some peace. Plus, it gives us a chance to reconnect with ourselves and tackle life's challenges with a new outlook." - }, - { - "speaker": "John", - "dia_id": "D17:17", - "text": "Yeah, taking time for ourselves is crucial. It helps us stay sharp and focused. Plus, it helps us gain new perspectives and tackle challenges with more energy. Finding the right balance is key and I'll keep that in mind as I continue my journey." - }, - { - "speaker": "Tim", - "dia_id": "D17:18", - "text": "Balance is key and it varies. Take care of yourself, both mentally and physically, and you'll rock it. You got this, bud!" - }, - { - "speaker": "John", - "dia_id": "D17:19", - "text": "Thanks! Your support means a lot. I'll keep pushing forward. Take care, buddy!" - } - ], - "session_18_date_time": "3:59 pm on 16 November, 2023", - "session_18": [ - { - "speaker": "Tim", - "img_url": [ - "https://static.independent.co.uk/2021/07/28/09/26073722-6212c4b6-62f4-4d1f-b2d9-fab1e4ba16a8.jpg" - ], - "blip_caption": "a photo of a castle with a river running through it", - "query": "castle uk lush greenery", - "dia_id": "D18:1", - "text": "Hey John! Hope you're doing good. Guess what? I went to a castle during my trip to the UK last Friday and it was unbelievable! The architecture and the history were amazing!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/plgl67c48sg91.jpg" - ], - "blip_caption": "a photo of a person with a bandage on their leg", - "query": "ankle injury wrapped bandages", - "dia_id": "D18:2", - "text": "Hey Tim! That's awesome! Yeah, it was really cool. Oh man, it's been a tough week for me with this injury. But I'm staying positive. How about you? How's your week been?" - }, - { - "speaker": "Tim", - "dia_id": "D18:3", - "text": "Ouch, bummer about the injury. Hang tight. This week has been swamped with exams for me but I'm plowing through." - }, - { - "speaker": "John", - "blip_caption": "a photo of a notebook with a bunch of notes on it", - "dia_id": "D18:4", - "text": "Cheers, Tim. Injury's been rough, but I'm staying positive. How's the exam prep coming? Confident?" - }, - { - "speaker": "Tim", - "dia_id": "D18:5", - "text": "Exams can be challenging, but I'm putting in my best effort. Feeling optimistic and working diligently! How do you stay motivated during difficult study sessions?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a soccer game with a player on the field", - "dia_id": "D18:6", - "text": "I visualize my goals and success for focus and motivation. It really helps me stay motivated during tough studying. Do you have any study tricks?" - }, - { - "speaker": "Tim", - "dia_id": "D18:7", - "text": "That's cool! I like breaking up my studying into smaller parts. 25 minutes on, then 5 minutes off for something fun. It's less overwhelming and keeps me on track." - }, - { - "speaker": "John", - "dia_id": "D18:8", - "text": "Nice work! Breaking it down into smaller parts is definitely a smart move. I wish you all the best on your exams!" - }, - { - "speaker": "Tim", - "dia_id": "D18:9", - "text": "Thanks! Appreciate your support. I hope your injury heals soon." - }, - { - "speaker": "John", - "dia_id": "D18:10", - "text": "Sure thing, Tim! Got your back. I hope so too. The doctor said it's not too serious." - }, - { - "speaker": "Tim", - "dia_id": "D18:11", - "text": "That's good to hear, I'm glad." - }, - { - "speaker": "John", - "dia_id": "D18:12", - "text": "I hate not being on the court." - }, - { - "speaker": "Tim", - "dia_id": "D18:13", - "text": "I bet. It's like if I couldn't read due to an injury." - }, - { - "speaker": "John", - "dia_id": "D18:14", - "text": "I'm pushing on though. Talk soon!" - }, - { - "speaker": "Tim", - "dia_id": "D18:15", - "text": "Take care! Keep pushing on. Talk soon." - } - ], - "session_19_date_time": "10:22 am on 21 November, 2023", - "session_19": [ - { - "speaker": "Tim", - "dia_id": "D19:1", - "text": "Hey John! Haven't talked in a bit, how ya been? Hope your injury is feeling better." - }, - { - "speaker": "John", - "dia_id": "D19:2", - "text": "Hey Tim! Thanks for checking in. It's been tough, but I'm staying positive and taking it slow. How about you? How have you been?" - }, - { - "speaker": "Tim", - "dia_id": "D19:3", - "text": "I've been swamped with studies and projects, but last week I had a setback. I tried writing a story based on my experiences in the UK, but it didn't go the way I wanted. It's been tough, do you have any advice for getting better with storytelling?" - }, - { - "speaker": "John", - "dia_id": "D19:4", - "text": "Sorry to hear about the setback with your story. I understand how frustrating it can be when things don't go as planned. When I face challenges on the court, I try to reflect on what went wrong and find ways to improve. Maybe you can try doing the same with your storytelling." - }, - { - "speaker": "Tim", - "dia_id": "D19:5", - "text": "Cool idea. Reflecting on what went wrong and how to improve could definitely help me get back on track. Thanks! Out of curiosity, what's been one of your toughest challenges in basketball?" - }, - { - "speaker": "John", - "dia_id": "D19:6", - "text": "Last season, I had a major challenge when I hurt my ankle. It required some time off and physical therapy. It was frustrating because I couldn't play or help the team. I stayed focused on my recovery and worked hard to strengthen my body. It was a tough mental and physical challenge, but it made me realize the importance of patience and perseverance. I'm grateful that I was able to overcome it." - }, - { - "speaker": "Tim", - "dia_id": "D19:7", - "text": "That must have been tough not being able to play and help your team. You did an amazing job staying focused and overcoming it. Your resilience and determination are inspiring! Thanks for sharing." - }, - { - "speaker": "John", - "dia_id": "D19:8", - "text": "Thanks! That means a lot. Difficult times are part of life \u2013 what's important is how we handle them. When things get tough, I try to remember why I'm so passionate about basketball. That love and enthusiasm keeps me motivated, no matter what." - }, - { - "speaker": "Tim", - "dia_id": "D19:9", - "text": "When things get tough, it's so important to remember why we love what we do. For me, it's writing and reading. That's what helps me stay motivated and push myself to get better. Has anything similar happened with basketball for you? Tell me about it!" - }, - { - "speaker": "John", - "dia_id": "D19:10", - "text": "I faced some tough times while playing basketball. I messed up during a big game, and it was really hard to accept. Instead of getting stuck in that moment, I worked hard to get better. It taught me that resilience is key and owning up to mistakes is important. Gotta keep growing and striving to be a strong player and teammate. So grateful." - }, - { - "speaker": "Tim", - "dia_id": "D19:11", - "text": "Wow, that's awesome. Admitting mistakes and using them to get better is super important. You really show how much you care about improving. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D19:12", - "text": "Thanks! I appreciate your support. It's all about growing and getting better, both on and off the court. Let's keep working hard!" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/28n5c71uffu71.jpg" - ], - "blip_caption": "a photo of a bunch of books on a wooden floor", - "query": "fantasy novels movies collection", - "dia_id": "D19:13", - "text": "Yeah, John! Let's keep growing and improving. We got this! These are my companions on my growth journey." - }, - { - "speaker": "John", - "dia_id": "D19:14", - "text": "Fantasy books always fuel my creativity, both in and outside of my hobbies. Are Harry Potter and GoT still your favorites?" - }, - { - "speaker": "Tim", - "dia_id": "D19:15", - "text": "Yes, they are still my favorites - I love how they take me to other places. What other books do you like?" - }, - { - "speaker": "John", - "dia_id": "D19:16", - "text": "I love non-fiction books about personal development and mindset. They help me know myself better. Do you enjoy reading other types of books as well?" - }, - { - "speaker": "Tim", - "dia_id": "D19:17", - "text": "Yep, John! I love getting lost in fantasy stories, but also discovering new ways to better myself through books on growth, psychology, and improving myself. It's wild how much you can learn from them, right?" - }, - { - "speaker": "John", - "dia_id": "D19:18", - "text": "Yeah, Tim! Books really can shift how we think and help us learn totally new things. Have you come across any that made a big impact on you recently?" - }, - { - "speaker": "Tim", - "dia_id": "D19:19", - "text": "Yeah, John! I recently read a book that really made a big impact on me. It's all about how small changes can make big differences. It really changed the way I do things. Have you read any good books lately?" - }, - { - "speaker": "John", - "dia_id": "D19:20", - "text": "I recently finished rereading \"The Alchemist\" - it was really inspiring. It made me think again about following dreams and searching for our own personal legends. I felt really motivated and hopeful after reading it." - }, - { - "speaker": "Tim", - "dia_id": "D19:21", - "text": "Wow, that book is great! I read it a while back and it really changed my perspective on my goals. I'm glad it had the same impact on you!" - }, - { - "speaker": "John", - "dia_id": "D19:22", - "text": "Yeah, that book is really something. It really helped motivate me to keep chasing my dreams and to trust the process. It's amazing how books can have such an impact on us, right?" - }, - { - "speaker": "Tim", - "dia_id": "D19:23", - "text": "Definitely! Books have a way of opening up new worlds, inspiring us, and making us think. They have the power to make us feel better and help us grow, which is amazing. It's great that we share a love for reading. Let's keep exploring books and motivating each other! Talk to you later!" - } - ], - "session_20_date_time": "9:52 am on 1 December, 2023", - "session_20": [ - { - "speaker": "Tim", - "img_url": [ - "https://i.pinimg.com/originals/52/98/a1/5298a13a728c023b77f9cc86529a8748.jpg" - ], - "blip_caption": "a photo of a notepad with a note and pen on it", - "query": "study materials", - "dia_id": "D20:1", - "text": "Hey John! It's been ages since we last chatted. I had a tough exam last week that had me doubting myself. But instead of giving up, I turned it into a learning experience. I studied hard and it showed me how resilient and determined I can be. Here's a pic of my success \ud83d\udc4d" - }, - { - "speaker": "John", - "blip_caption": "a photo of a white wall with a black lettering that says 30 positive suites", - "dia_id": "D20:2", - "text": "Hi Tim! Congrats on your success! Keep it up, you're doing great! I'm also trying out yoga to get a little extra strength and flexibility. It's challenging but worth it." - }, - { - "speaker": "Tim", - "dia_id": "D20:3", - "text": "Thanks! I appreciate your encouragement. How's it going with yoga? Have you noticed any improvements?" - }, - { - "speaker": "John", - "dia_id": "D20:4", - "text": "Yoga's been really awesome for me. It's helped me improve in terms of strength and flexibility, as well as focus and balance during my workouts. It's been great!" - }, - { - "speaker": "Tim", - "dia_id": "D20:5", - "text": "Great news! Yoga is indeed amazing for your body and mind. Are there any specific poses that you enjoy practicing?" - }, - { - "speaker": "John", - "dia_id": "D20:6", - "text": "Yeah, there are a couple of poses I really enjoy. Warrior II makes me feel strong and there's one that helps with balance and stability. I love how these poses challenge my body and mind!" - }, - { - "speaker": "Tim", - "dia_id": "D20:7", - "text": "Woohoo! Congrats on finding poses that suit you. Yoga is so cool for showing us what we can really do. Maybe you could share a pic so I can try it too?" - }, - { - "speaker": "John", - "img_url": [ - "https://pixahive.com/wp-content/uploads/2021/02/Virabhadrasana-Warrior-Pose-357219-pixahive.jpg" - ], - "blip_caption": "a photography of a man doing a yoga pose on a blue mat", - "query": "warrior II pose", - "dia_id": "D20:8", - "re-download": true, - "text": "Here's a photo of me in this pose. It's a good way to work out your legs and core. Give it a shot!" - }, - { - "speaker": "Tim", - "dia_id": "D20:9", - "text": "That's a tough one! How long do you usually hold that pose?" - }, - { - "speaker": "John", - "dia_id": "D20:10", - "text": "I typically hold it for 30-60 seconds. It really helps with building strength and stability!" - }, - { - "speaker": "Tim", - "dia_id": "D20:11", - "text": "That's cool, I'm gonna give it a shot and see how it goes. Thanks for the tip!" - }, - { - "speaker": "John", - "dia_id": "D20:12", - "text": "No worries! Let me know how it goes. Happy to help whenever you need it!" - }, - { - "speaker": "Tim", - "dia_id": "D20:13", - "text": "Thanks! Your support and encouragement have truly made this journey better. I really appreciate it." - }, - { - "speaker": "John", - "dia_id": "D20:14", - "text": "I'm here for you. You've got this!" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a bookcase filled with dvds and games", - "dia_id": "D20:15", - "text": "Thanks! Your support means a lot to me. Your friendship means a lot too." - }, - { - "speaker": "John", - "dia_id": "D20:16", - "text": "Thanks, I really appreciate it. Your friendship means a lot to me too." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/9nvpaptnspc81.jpg" - ], - "blip_caption": "a photo of a book shelf with many books on it", - "query": "fantasy novels game of thrones harry potter bookshelf", - "dia_id": "D20:17", - "text": "Glad we're friends! Plus, bonus points for both being into fantasy books and movies. I just reorganized my book shelf, speaking of." - }, - { - "speaker": "John", - "dia_id": "D20:18", - "text": "Cool! Can I take a closer peek at it? What are some of your favorites?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/optq3zb96w771.jpg" - ], - "blip_caption": "a photo of a book shelf with a lot of books on it", - "query": "bookshelf harry potter game of thrones lord of the rings", - "dia_id": "D20:19", - "text": "Yeah, check it out - here's my bookshelf! I have some of my favorites on there, like these ones. It's an amazing journey!" - }, - { - "speaker": "John", - "dia_id": "D20:20", - "text": "That bookshelf is awesome! The Hobbit is one of my favorites too. What an amazing journey!" - }, - { - "speaker": "Tim", - "dia_id": "D20:21", - "text": "Glad you like it! The Hobbit is great, but have you read that other popular fantasy series? It's also awesome!" - }, - { - "speaker": "John", - "dia_id": "D20:22", - "text": "Yeah, I've read that other popular fantasy series too! It's one of my favorites. It has such a cool story!" - }, - { - "speaker": "Tim", - "dia_id": "D20:23", - "text": "It's awesome how these books take us to different worlds!" - }, - { - "speaker": "John", - "dia_id": "D20:24", - "text": "It's like escaping to these incredible new worlds and having a break from reality for a fun adventure." - }, - { - "speaker": "Tim", - "dia_id": "D20:25", - "text": "Yeah, that's why I love them. They let us take a break from reality and have an awesome adventure. So magical!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/huz7cq7rtix31.jpg" - ], - "blip_caption": "a photo of a forest with sun shining through the trees", - "query": "forest ray of sunlight through trees", - "dia_id": "D20:26", - "text": "Yeah, it's awesome! Like being transported to a different world with all those amazing moments - so fun!" - }, - { - "speaker": "Tim", - "dia_id": "D20:27", - "text": "Wow, what an awesome shot! Feels like a magical forest - where was that?" - }, - { - "speaker": "John", - "dia_id": "D20:28", - "text": "The photo is from a forest near my hometown. It's so tranquil." - }, - { - "speaker": "Tim", - "dia_id": "D20:29", - "text": "Wow, nature's amazing! We're lucky to have places like that near our homes." - }, - { - "speaker": "John", - "dia_id": "D20:30", - "text": "It's incredible how we have these beautiful places near our homes. We should definitely appreciate them." - }, - { - "speaker": "Tim", - "dia_id": "D20:31", - "text": "It really does have a way of calming us and reminding us of the beauty around." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/5qw4hbg418561.jpg" - ], - "blip_caption": "a photo of a lake with a rock in the middle of it", - "query": "serene lake mountains background", - "dia_id": "D20:32", - "text": "Definitely! It grounds us and makes us appreciate the simple beauty around us. We should take time to enjoy it." - }, - { - "speaker": "Tim", - "dia_id": "D20:33", - "text": "That picture looks super peaceful! It reminds me of a trip I took last summer." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/lf5a5i0jnah31.jpg" - ], - "blip_caption": "a photo of a campfire with chairs and a lake in the background", - "query": "campfire lake sunset", - "dia_id": "D20:34", - "text": "We had a blast camping and disconnecting from the everyday." - }, - { - "speaker": "Tim", - "dia_id": "D20:35", - "text": "Looks great! Where did you go camping?" - }, - { - "speaker": "John", - "dia_id": "D20:36", - "text": "We went camping in the mountains and it was stunning! The air was so refreshing." - }, - { - "speaker": "Tim", - "dia_id": "D20:37", - "text": "Sounds great! Being in the mountains is the best. What was your favorite part of it?" - }, - { - "speaker": "John", - "dia_id": "D20:38", - "text": "I loved just chilling and taking in the beauty of nature. It was super peaceful and refreshing." - }, - { - "speaker": "Tim", - "img_url": [ - "https://images.pexels.com/photos/16598991/pexels-photo-16598991.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-cheng-cj-16598991.jpg" - ], - "blip_caption": "a photo of a plane flying over a mountain range with snow on the top", - "query": "snowy mountain peak", - "dia_id": "D20:39", - "text": "Yeah, nature has that effect on me too. It's like a reset for the soul." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/q46xsv4ciu641.jpg" - ], - "blip_caption": "a photo of a mountain with a snow covered peak in the distance", - "query": "plane mountain range snow rocky mountains breathtaking experience majestic peaks fresh air", - "dia_id": "D20:40", - "text": "Yeah, nature's great for clearing the mind and calming the soul. This was my Rocky Mountains trip last year and it was stunning. Seeing those mountains, fresh air - it makes you realize how incredible the world is." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/u51y7czkhfrb1.jpg" - ], - "blip_caption": "a photo of a mountain range with a sunset in the background", - "query": "rocky mountains mountain range sunrise", - "dia_id": "D20:41", - "text": "Wow, this is amazing! Nature is really awesome - it makes us feel tiny but connected." - }, - { - "speaker": "John", - "dia_id": "D20:42", - "text": "Nature does have a way of humbling us and showing us our place in the world. It's truly amazing and comforting." - }, - { - "speaker": "Tim", - "dia_id": "D20:43", - "text": "Yeah. It reminds us that we're not alone - we're part of something bigger. Bye!" - } - ], - "session_21_date_time": "5:34 pm on 6 December, 2023", - "session_21": [ - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/w404u5zhn0yz.jpg" - ], - "blip_caption": "a photo of a map of westendell on a wall", - "query": "map pinned destinations", - "dia_id": "D21:1", - "text": "Hey John! Haven't talked in a few days, wanted to let you know I joined a travel club! Always been interested in different cultures and countries and I'm excited to check it out. Can't wait to meet new people and learn about what makes them unique!" - }, - { - "speaker": "John", - "img_url": [ - "https://bloximages.chicago2.vip.townnews.com/syvnews.com/content/tncms/assets/v3/editorial/3/38/338dd0b4-adc1-5a60-97e6-9eb3508b3f77/63c7306e4f912.hires.jpg" - ], - "blip_caption": "a photo of three young men standing next to each other on a basketball court", - "query": "teammates basketball smiling", - "dia_id": "D21:2", - "text": "Hey Tim! That's cool! I love learning about different cultures. It's really cool to meet people with different backgrounds. My teammates come from all over." - }, - { - "speaker": "Tim", - "dia_id": "D21:3", - "text": "Wow! How long have you been playing professionally?" - }, - { - "speaker": "John", - "dia_id": "D21:4", - "text": "I've been playing professionally for just under a year now. It's been a wild ride." - }, - { - "speaker": "Tim", - "dia_id": "D21:5", - "text": "Wow,! Being a pro basketball player must be quite a journey. Is it living up to your expectations?" - }, - { - "speaker": "John", - "dia_id": "D21:6", - "text": "Yeah, it's been great! Challenges, growth, all that jazz\u2014it's been amazing." - }, - { - "speaker": "Tim", - "dia_id": "D21:7", - "text": "Cool! Glad to hear that this journey has been rewarding for you. Could you tell me more about your growth?" - }, - { - "speaker": "John", - "dia_id": "D21:8", - "text": "Yup, on the court, I'm getting better at my overall game. Money-wise, I've gotten some cool endorsement deals. Plus, I'm learning how to market myself and boost my brand. It's been really rewarding to see all these areas progress. What about you? Anything new happening?" - }, - { - "speaker": "Tim", - "dia_id": "D21:9", - "text": "Joined a travel club and, like I said, working on studies. Also picked up new skills. Recently started learning an instrument. Challenging but fun, always admired musicians. Finally giving it a go." - }, - { - "speaker": "John", - "dia_id": "D21:10", - "text": "Learning an instrument is really cool. What instrument are you playing? What genres of music do you want to learn?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i1.pickpik.com/photos/964/641/216/musical-instrument-violin-curl-tuning-pegs-preview.jpg" - ], - "blip_caption": "a photography of a violin and a violin stick on a sheet of music", - "query": "piano sheet music headphones", - "dia_id": "D21:11", - "re-download": true, - "text": "I'm learning how to play the violin now. I'm mostly into classical music but I'm keen to try out jazz and film scores too. It's a great way to chill and get creative." - }, - { - "speaker": "John", - "dia_id": "D21:12", - "text": "Wow! I hope I can hear you play the violin some day. How long have you been playing the piano again?" - }, - { - "speaker": "Tim", - "dia_id": "D21:13", - "text": "I've been playing for about four months now and it's been an amazing adventure. I'm really enjoying the progress I've been making." - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/7162/6733335339_3a64489025_b.jpg" - ], - "blip_caption": "a photography of a man sitting on the ground with a trophy", - "query": "basketball trophy", - "dia_id": "D21:14", - "re-download": true, - "text": "Nice one! Learning something new is always a great adventure. Keep up the hard work and let's see where you end up. It's all about dedication and effort. It feels great to finally achieve something after putting in so much time and energy." - }, - { - "speaker": "Tim", - "dia_id": "D21:15", - "text": "Congrats on the trophy! It must have felt great to finally get something after putting in so much effort. Do you have any tips on motivating others on your team?" - }, - { - "speaker": "John", - "dia_id": "D21:16", - "text": "Thanks! Winning was awesome. When motivating others, it's important to show care for teammates, celebrate their achievements, provide constructive feedback, and remind them of the bigger goal. Creating a positive environment and giving a pep talk before a game can also be helpful. It's all about supporting and uplifting each other. Do you have any specific strategies in mind?" - }, - { - "speaker": "Tim", - "dia_id": "D21:17", - "text": "Thanks for the helpful advice. Creating a constructive atmosphere and setting an example by working hard can really inspire people. It\u2019s also inspiring to use our own stories to encourage others. Much appreciated!" - }, - { - "speaker": "John", - "dia_id": "D21:18", - "text": "No problem! It's great to use our own experiences to inspire others. Hard work can lead to success. Keep it up! Let me know if you need any assistance." - }, - { - "speaker": "Tim", - "dia_id": "D21:19", - "text": "Thanks! Appreciate the offer. Let me know if you can lend a hand. Bye!" - } - ], - "session_22_date_time": "7:42 pm on 8 December, 2023", - "session_22": [ - { - "speaker": "Tim", - "dia_id": "D22:1", - "text": "Hey John! Long time no see! I just got back from the coolest Harry Potter party. Met lots of awesome people who were into the same stuff as me, had so much fun!" - }, - { - "speaker": "John", - "dia_id": "D22:2", - "text": "Hey Tim! Sounds awesome! So glad you had a blast at the Harry Potter party. Last August I told you about my fun time at a charity event with Harry Potter trivia. Love being with people who are as passionate about Harry Potter as us! Did you dress up as any character?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://dixiedelightsonline.com/wp-content/uploads/2018/05/IMG_4747.jpg" - ], - "blip_caption": "a photo of a chocolate frog in a box on a table", - "query": "gryffindor scarf chocolate frog", - "dia_id": "D22:3", - "text": "It was awesome. I didn't dress as any character, but I wore my Gryffindor scarf. Everyone had cool costumes. I even got this as a treat. Any recent meet-ups with your basketball team?" - }, - { - "speaker": "John", - "img_url": [ - "https://npr.brightspotcdn.com/df/09/e6b1689047eaa3b05e7d61c36c05/image-from-ios-1.jpg" - ], - "blip_caption": "a photo of a group of people riding on top of a fire truck", - "query": "basketball team victory celebration", - "dia_id": "D22:4", - "text": "That frog looks yummy! I haven't had one in ages. Been having some wild games lately, we played a top team and it was tough, but we fought hard and got the win! It's awesome having my team to push us all." - }, - { - "speaker": "Tim", - "dia_id": "D22:5", - "text": "Wow, looks fun! What was the best part for you? And congratulations on the win!" - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/4030/4426181605_d36196a029_c.jpg" - ], - "blip_caption": "a photography of a group of young men sitting on top of a basketball court", - "query": "post-match team huddle", - "dia_id": "D22:6", - "re-download": true, - "text": "Thanks! The best part for me was the camaraderie we built both on and off the court. Winning felt amazing and it was definitely worth all the hard work we put in." - }, - { - "speaker": "Tim", - "img_url": [ - "https://www.thegibsonedge.com/hs-fs/hubfs/images/Blog_Images/Beware%20The%20Person%20Of%20One%20Book%20-%20Flashback%20Friday.jpg" - ], - "blip_caption": "a photo of a stack of books sitting on top of a table", - "query": "fantasy novels stack bookmarks power friendship loyalty", - "dia_id": "D22:7", - "text": "Wow, that's awesome! It's great to see how close you all have become. You must feel a great sense of unity. I'm reading this amazing series about the power of friendship and loyalty \u2013 really inspiring stuff. Anything special you do to keep that bond strong?" - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/5128/5297313790_4330145c09_b.jpg" - ], - "blip_caption": "a photography of a group of people sitting around a table eating", - "query": "team dinner basketball games outside of practice", - "dia_id": "D22:8", - "re-download": true, - "text": "Sounds awesome! What kind of stuff do they do in the series? I'm sure the importance of friendship is emphasized. Same with us - we have team dinners, outings, and basketball games. It's those moments away from practice that really build and strengthen our unity." - }, - { - "speaker": "Tim", - "dia_id": "D22:9", - "text": "Awesome! Sounds like your team has something similar to the characters in the series. They rely on each other to push through challenges. By the way, what book are you currently reading? I'm always on the lookout for new reads!" - }, - { - "speaker": "John", - "dia_id": "D22:10", - "text": "Thanks! I'm currently reading a book that I really enjoy. I highly recommend it!" - }, - { - "speaker": "Tim", - "dia_id": "D22:11", - "text": "Sounds cool! Let me know the title so I can add it to my list!" - }, - { - "speaker": "John", - "dia_id": "D22:12", - "text": "I'm reading \"Dune\" by Frank Herbert. It's a great story about religion and human control over ecology. What about you? What's the last book that moved you?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://live.staticflickr.com/4467/24220955708_7548812488_b.jpg" - ], - "blip_caption": "a photography of a book shelf with a book and a book cover", - "query": "the name of the wind book shelf", - "dia_id": "D22:13", - "re-download": true, - "text": "I haven't read that yet but I've heard great things! Just finished \"A Dance with Dragons\" and it's a really good story. Highly recommend it!" - }, - { - "speaker": "John", - "dia_id": "D22:14", - "text": "That's cool! I've heard it's such an inspiring book. Have you read all of George R. R. Martin's books?" - }, - { - "speaker": "Tim", - "dia_id": "D22:15", - "text": "Just the GoT series. Have you tried reading any of them?" - }, - { - "speaker": "John", - "dia_id": "D22:16", - "text": "No, I haven't read them yet but I'll definitely check them out. Cheers!" - }, - { - "speaker": "Tim", - "dia_id": "D22:17", - "text": "Let me know if you get around to them! Have a great day!" - }, - { - "speaker": "John", - "dia_id": "D22:18", - "text": "Thanks! I'll let you know. Have a great day!" - } - ], - "session_23_date_time": "8:28 pm on 11 December, 2023", - "session_23": [ - { - "speaker": "John", - "img_url": [ - "https://assets-global.website-files.com/60ed47e10552352d9d7e0a44/61e5cf5d296761732633ffcc_wsp_banner_w_joanne__dO8vP.jpg" - ], - "blip_caption": "a photo of two women standing next to a banner with sales pros written on it", - "query": "marketing team collaboration trust leadership", - "dia_id": "D23:1", - "text": "Hey Tim, great to see you! Any new success stories?" - }, - { - "speaker": "Tim", - "dia_id": "D23:2", - "text": "Hey John, I had a tough time with my English lit class. Did an analysis on this series and I think it went ok!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/cf/a0/7f/cfa07fa76047b5d731b3af313d98fc01.jpg" - ], - "blip_caption": "a photo of a basketball game being played in a large arena", - "query": "basketball court game", - "dia_id": "D23:3", - "text": "Thanks! It's a bummer about your English lit class, but you did your best. By the way, I had a career-high in assists last Friday in our big game against our rival. Yay!" - }, - { - "speaker": "Tim", - "dia_id": "D23:4", - "text": "Congrats! That's awesome. How did it feel being out there making those plays?" - }, - { - "speaker": "John", - "dia_id": "D23:5", - "text": "Thanks! It felt great being out there, making plays for my team. I love seeing my teammates succeed because of the opportunities I create for them. The atmosphere in the arena was really electric and playing against our rivals added an extra level of intensity. It was a memorable night!" - }, - { - "speaker": "Tim", - "dia_id": "D23:6", - "text": "Sounds incredible! Must have been quite an atmosphere. Have you had any other games that were as thrilling as this one?" - }, - { - "speaker": "John", - "dia_id": "D23:7", - "text": "I've had some thrilling games in my career. My favorite was when we were down 10 in the 4th and I hit the buzzer-beater shot to win. The atmosphere was incredible and it was such a thrilling experience. Those moments make me love basketball so much." - }, - { - "speaker": "Tim", - "img_url": [ - "https://c8.alamy.com/zooms/9/de5f1d4e73244a8f94b16a6b6d093748/ttnmea.jpg" - ], - "blip_caption": "a photo of a basketball ball on the ground with a basketball hoop in the background", - "query": "basketball court sunset", - "dia_id": "D23:8", - "text": "Wow, John! Moments like that make us love sports, huh? I still think about this pic you sent me a while back." - }, - { - "speaker": "John", - "dia_id": "D23:9", - "text": "Yeah, that pic reminds me of when I was younger. I'd practice basketball outside for hours, dreaming of playing in big games. It was my way of dealing with doubts and stress. It's amazing how a ball and hoop can be so powerful, right?" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a basketball ball on the ground with a basketball hoop in the background", - "dia_id": "D23:10", - "text": "Yeah! Sports are the best. When we're feeling down, it's a way to express ourselves and stay positive. It's awesome how much basketball has done for you. Keep going with your dreams!" - }, - { - "speaker": "John", - "dia_id": "D23:11", - "text": "Thanks! Appreciate the support. It's been a significant part of my life and allows me to be myself and pursue my passions. Gonna keep chasing my dreams!" - }, - { - "speaker": "Tim", - "dia_id": "D23:12", - "text": "Wow! It's really important to do our own thing and follow our dreams. Keep it up, you're gonna do amazing things!" - }, - { - "speaker": "John", - "dia_id": "D23:13", - "text": "Your encouragement means a lot. Let's keep pushing and following our dreams - we can make a difference!" - }, - { - "speaker": "Tim", - "dia_id": "D23:14", - "text": "Definitely. We both have so much potential! Let's keep supporting each other on our journey towards our dreams." - }, - { - "speaker": "John", - "dia_id": "D23:15", - "text": "Yeah, you're super inspiring and motivating. Keep it up!" - }, - { - "speaker": "Tim", - "dia_id": "D23:16", - "text": "Thanks, it means a lot. Let's keep each other motivated. Bye!" - } - ], - "session_24_date_time": "3:37 pm on 16 December, 2023", - "session_24": [ - { - "speaker": "Tim", - "dia_id": "D24:1", - "text": "Hey John, catch up time! What've you been up to? Any good b-ball games lately?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.salisburypost.com/wp-content/uploads/sites/9/2023/03/Catawba-basketball-01.jpg" - ], - "blip_caption": "a photo of a group of women's basketball players holding up a trophy", - "query": "basketball team celebration", - "dia_id": "D24:2", - "text": "Hey Tim! Nice to talk again. The b-ball games have been crazy. We had a real battle against another team last week. It was close until the final buzzer but we got the win." - }, - { - "speaker": "Tim", - "dia_id": "D24:3", - "text": "Congrats, John! That sounds like an intense game." - }, - { - "speaker": "John", - "dia_id": "D24:4", - "text": "Thanks! We won! It was really close, but we made it!" - }, - { - "speaker": "Tim", - "dia_id": "D24:5", - "text": "Wow, that's amazing! Winning must have been so thrilling!" - }, - { - "speaker": "John", - "dia_id": "D24:6", - "text": "Winning was such a thrill, and it was an awesome moment. These experiences really make me love the game." - }, - { - "speaker": "Tim", - "dia_id": "D24:7", - "text": "You must have been so pumped when it happened! Winning can give us a real confidence boost and makes us keep going with our passions." - }, - { - "speaker": "John", - "img_url": [ - "https://images.fineartamerica.com/images-medium-large-5/basketball-court-sunset-jun-pinzon.jpg" - ], - "blip_caption": "a photography of a basketball hoop in the sunset with a fence", - "query": "basketball court sunset workout", - "dia_id": "D24:8", - "re-download": true, - "text": "Yeah, it really does. It keeps me motivated to keep putting in the effort and makes all the tough times worth it. Here's a pic I took during a morning workout, it's a reminder that the journey can be awesome." - }, - { - "speaker": "Tim", - "dia_id": "D24:9", - "text": "That's a good spot for a morning workout! Can you tell me about some challenges you've faced?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/o20k6p31qoe41.jpg" - ], - "blip_caption": "a photo of a person with a cast on their foot", - "query": "sprained ankle crutch wrapped ankle injured ankle", - "dia_id": "D24:10", - "text": "Yeah, I injured myself not too long ago. It sucked because I had to miss some games and couldn't help my team." - }, - { - "speaker": "Tim", - "dia_id": "D24:11", - "text": "Ouch, that's rough. Have you been able to stay active or keep up with your fitness routine while you're recovering?" - }, - { - "speaker": "John", - "img_url": [ - "https://mainephysicaltherapy.com/wp-content/uploads/2017/12/Incline-one-arm-cable-pull-down-1.jpg" - ], - "blip_caption": "a photo of a man sitting on a chair with a blue ball", - "query": "physical therapy exercises", - "dia_id": "D24:12", - "text": "It's been tough, but I'm trying to stay active and do my rehab. I do physical therapy exercises every day." - }, - { - "speaker": "Tim", - "dia_id": "D24:13", - "text": "Cool, rehab can be tough but it's key to keep it up. How's it coming along?" - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/5189/5618665304_b2e0ccd051_b.jpg" - ], - "blip_caption": "a photography of a treadmill in a room with a window", - "query": "treadmill gym", - "dia_id": "D24:14", - "re-download": true, - "text": "It's going great! I've been working hard and it's paying off. Last Friday, I had a milestone moment at the gym. I was able to jog a bit with no pain, which was such a relief!" - }, - { - "speaker": "Tim", - "dia_id": "D24:15", - "text": "Wow! How was it jogging without any discomfort?" - }, - { - "speaker": "John", - "dia_id": "D24:16", - "text": "It was great! After being out for so long, jogging without any pain was a huge success. My wife and I hosted a small get-together with friends and family to celebrate." - }, - { - "speaker": "Tim", - "dia_id": "D24:17", - "text": "Congrats! That's awesome. Keep at it and you'll be back in no time. That sounds fun, how was it?" - }, - { - "speaker": "John", - "dia_id": "D24:18", - "text": "Thanks! Appreciate the support and encouragement. I'm gonna keep pushing and staying positive. It was good to see everyone again! We had a ton of fun." - }, - { - "speaker": "Tim", - "dia_id": "D24:19", - "text": "I'm glad everyone had fun!" - }, - { - "speaker": "John", - "dia_id": "D24:20", - "text": "Me too. Talk to you later!" - } - ], - "session_25_date_time": "10:04 am on 19 December, 2023", - "session_25": [ - { - "speaker": "Tim", - "dia_id": "D25:1", - "text": "Hey John, been a while since we chatted. How's it going?" - }, - { - "speaker": "John", - "img_url": [ - "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQiyDMnkQOmMQNKQfQDDRQlTRpNME7oy0xNlcHjLYB8n_ZwvnWe8nYlRKA3WkZNHEQLsME&usqp=CAU" - ], - "blip_caption": "a photography of a man with a backpack and a backpack walking down a path", - "query": "endorsement deal outdoor gear company photoshoot", - "dia_id": "D25:2", - "re-download": true, - "text": "Yo Tim! Great to hear from you. Things have been wild! Last week I got this amazing deal with a renowned outdoor gear company. So pumped!" - }, - { - "speaker": "Tim", - "dia_id": "D25:3", - "text": "That's awesome about the deal! I'm curious, what kind of gear did you end up getting? And how did the photoshoot turn out?" - }, - { - "speaker": "John", - "dia_id": "D25:4", - "text": "Cheers! Got some awesome hiking stuff and outdoor gear - all top-notch. The photoshoot went really well too. We did it in a gorgeous forest and the photographer got some epic shots of me doing my thing - it was amazing!" - }, - { - "speaker": "Tim", - "dia_id": "D25:5", - "text": "Wow! That sounds amazing. Being out in such a gorgeous location must have been incredible. I'd love to see one of the epic shots you got! Do you have any pictures from the photoshoot?" - }, - { - "speaker": "John", - "img_url": [ - "https://cdn.stocksnap.io/img-thumbs/960w/man-jumping_FOTAMAJTAF.jpg" - ], - "blip_caption": "a photography of a man jumping in the air in a field", - "query": "photoshoot forest leap nature", - "dia_id": "D25:6", - "re-download": true, - "text": "Here you go, here's a pic. Nature puts me in a great mood and always gets me energized!" - }, - { - "speaker": "Tim", - "dia_id": "D25:7", - "text": "That's an amazing photo! I can see why it inspires you - the rocks and river look so peaceful. What drew you to that spot?" - }, - { - "speaker": "John", - "dia_id": "D25:8", - "text": "I stumbled across this spot while hiking. The sound of that river was so soothing, I felt so at peace surrounded by those rocks. It was like nature was telling me to stop and admire its beauty." - }, - { - "speaker": "Tim", - "dia_id": "D25:9", - "text": "Wow, that sounds amazing. It's true, nature has a way of bringing peace and joy. Anything else like that been happening lately?" - }, - { - "speaker": "John", - "dia_id": "D25:10", - "text": "Things have been going great on the court. We've been putting in a lot of work and achieving our goals, which is awesome." - }, - { - "speaker": "Tim", - "dia_id": "D25:11", - "text": "Hard work pays off, right? What have you and your team been up to lately?" - }, - { - "speaker": "John", - "dia_id": "D25:12", - "text": "We gave it our all during last week's scrimmage. It's amazing to see our team's growth. We know it won't be easy, but it'll be worth it when we see the results." - }, - { - "speaker": "Tim", - "dia_id": "D25:13", - "text": "What areas have you seen the most growth in during your training?" - }, - { - "speaker": "John", - "dia_id": "D25:14", - "text": "Our team has seen the most growth in communication and bonding. It has really helped our performances by allowing us to understand each other's strengths and weaknesses." - }, - { - "speaker": "Tim", - "dia_id": "D25:15", - "text": "Wow, that's awesome! Glad to hear you guys are bonding. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D25:16", - "text": "Thanks! Let's keep at it and continue supporting each other. Appreciate your assistance!" - }, - { - "speaker": "Tim", - "dia_id": "D25:17", - "text": "Yeah, let's support each other. I'm here for you. Just keep believing in yourself! Bye!" - } - ], - "session_26_date_time": "3:35 pm on 26 December, 2023", - "session_26": [ - { - "speaker": "John", - "img_url": [ - "https://pivitu.com/wp-content/uploads/bb-plugin/cache/IMG_8996-circle-5996d578100cbb83f34040d0584d4834-5db4d83b44005.jpg" - ], - "blip_caption": "a photo of a basketball court with a crowd of people watching", - "query": "athletic marketing seminar young athletes", - "dia_id": "D26:1", - "text": "Hey Tim! Great to hear from you. My week's been busy - I started doing seminars, helping people with their sports and marketing. It's been awesome!" - }, - { - "speaker": "Tim", - "dia_id": "D26:2", - "text": "Hey John! Sounds awesome! Congrats on how far you've come. How did it go?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/5hvq16lfcgy71.jpg" - ], - "blip_caption": "a photo of a man and woman on stage at a convention", - "query": "seminar speaker stage crowd participants", - "dia_id": "D26:3", - "text": "Thanks! The seminars went really well. All the aspiring profs were so eager and motivated - it was great! I'm really happy I could share my knowledge and help out." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a book with a golden cover on a table", - "dia_id": "D26:4", - "text": "Wow John! Impressive stuff! I'm starting some big new things too!" - }, - { - "speaker": "John", - "dia_id": "D26:5", - "text": "Thanks! What have you been up to?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/53yi43izjpb81.jpg" - ], - "blip_caption": "a photo of a book with a picture of a boy and a girl", - "query": "adventures across the globe stories travelers book cover", - "dia_id": "D26:6", - "text": "I've been reading cool stories from travelers from around the world. I'm using it to plan my next adventure. This is a book I found with tons of them!" - }, - { - "speaker": "John", - "dia_id": "D26:7", - "text": "Wow, that's cool! Have you read any of the stories? I'm looking for some travel ideas too." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/tdukekw3wlp91.jpg" - ], - "blip_caption": "a photo of two men on horseback in front of a mountain", - "query": "hiking himalayas snowy mountain peak", - "dia_id": "D26:8", - "text": "I read a few of them. One of them is about two hikers who trekked through the Himalayas, sounds awesome!" - }, - { - "speaker": "John", - "dia_id": "D26:9", - "text": "Wow, that sounds awesome! How challenging was the trek through the Himalayas?" - }, - { - "speaker": "Tim", - "dia_id": "D26:10", - "text": "The book mentioned that the trek was tough but worth it, with challenging terrain, altitude sickness, and bad weather. But they made it and saw amazing sights - it really motivated me." - }, - { - "speaker": "John", - "dia_id": "D26:11", - "text": "Wow! Sounds like a tough journey." - }, - { - "speaker": "Tim", - "dia_id": "D26:12", - "text": "It's true. Facing challenges can be tough, but it can make us stronger. I just visited a travel agency to see what the requirements would be for my next dream trip." - }, - { - "speaker": "John", - "dia_id": "D26:13", - "text": "For sure, challenges help us learn and grow. Sounds fun! Keep me updated!" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a newspaper article with a picture of a woman", - "dia_id": "D26:14", - "text": "Thanks, I will. You have to keep pushing for your goals." - }, - { - "speaker": "John", - "dia_id": "D26:15", - "text": "By the way, who was that main actress in Harry Potter? I've heard about her a lot lately." - }, - { - "speaker": "Tim", - "dia_id": "D26:16", - "text": "Emma Watson, she's a big supporter of gender equality. I'm a huge fan." - }, - { - "speaker": "John", - "dia_id": "D26:17", - "text": "Wow, that's great! It's inspiring to see people who use their platform for important causes and make a difference." - }, - { - "speaker": "Tim", - "dia_id": "D26:18", - "text": "Her women's rights advocacy is also a huge inspiration to me! Seeing people use their platform for causes like gender equality is really inspiring. It's so cool to see people making a difference." - }, - { - "speaker": "John", - "img_url": [ - "https://talkstar-photos.s3.amazonaws.com/uploads/9dd73626-fe0d-4a3b-b913-c39d65250da8/ApolloRobbins_2013G-embed.jpg" - ], - "blip_caption": "a photography of two men standing next to each other on a stage", - "query": "charity event speaking", - "dia_id": "D26:19", - "re-download": true, - "text": "Definitely. Making a difference is important to me. I use my influence and resources to help causes I believe in. It's about making the world a better place. Here's a picture of me speaking at a charity event." - }, - { - "speaker": "Tim", - "dia_id": "D26:20", - "text": "Cool! What causes are you working on? Tell me more about them!" - }, - { - "speaker": "John", - "img_url": [ - "https://necommunitycenter.org/portland/wp-content/uploads/2023/01/Game-in-Progress-with-Ref2-1024x684.jpg" - ], - "blip_caption": "a photo of a group of kids playing basketball in a gym", - "query": "youth sports programs basketball court kids playing", - "dia_id": "D26:21", - "text": "I've been working on supporting youth sports and fighting for fair chances in sports for underserved communities. It's important to me that every kid has access to good sports programs. I've been collaborating with organizations to create more opportunities for young athletes and help them succeed. It's amazing to see the difference sports make in people's lives." - }, - { - "speaker": "Tim", - "dia_id": "D26:22", - "text": "Cool! What have been some memorable experiences working with them?" - }, - { - "speaker": "John", - "dia_id": "D26:23", - "text": "Organizing a basketball camp for kids in my hometown last summer was an awesome experience! Seeing their faces light up when they hit the court was priceless. It was a week full of laughs, high-fives, and personal growth for us all. That opportunity to inspire those kids and show them just how much potential they have was truly incredible." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a basketball with a signed autograph on it", - "dia_id": "D26:24", - "text": "Wow! Making a difference to those kids was great! Your passion for helping others is awesome." - }, - { - "speaker": "John", - "dia_id": "D26:25", - "text": "Thanks! I'm really glad I can make a difference. Have you been doing anything new in your free time?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/oqnovgpaxdv51.jpg" - ], - "blip_caption": "a photo of a collection of harry potter books on a desk", - "query": "harry potter book stack", - "dia_id": "D26:26", - "text": "In my downtime, I still love to get lost in good books, and this series is one of my favorites. It's a magical world to escape to." - }, - { - "speaker": "John", - "dia_id": "D26:27", - "text": "That's awesome! Have you seen all the Harry Potter movies? I'm a fan too!" - }, - { - "speaker": "Tim", - "dia_id": "D26:28", - "text": "Yeah, I have! Watching them and seeing how they compare to the books is awesome. It's amazing to watch the story come alive. Have you seen all of them?" - }, - { - "speaker": "John", - "dia_id": "D26:29", - "text": "I'm a total movie fan! Seeing it all come alive on the big screen is awesome, and a great way to relax." - }, - { - "speaker": "Tim", - "img_url": [ - "https://images.pexels.com/photos/7234395/pexels-photo-7234395.jpeg" - ], - "blip_caption": "a photography of three guys sitting on a couch watching a movie", - "query": "movie night friends popcorn harry potter", - "dia_id": "D26:30", - "re-download": true, - "text": "Yeah, watching movies is a fun way to relax. We love having movie marathons with our friends." - }, - { - "speaker": "John", - "dia_id": "D26:31", - "text": "Sounds like a blast! Movie marathons with friends and popcorn, right? So, what's your favorite genre?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/ixsrm1ukws611.jpg" - ], - "blip_caption": "a photo of a poster of a group of people with a sword", - "query": "fantasy movie poster lord of the rings", - "dia_id": "D26:32", - "text": "I'm a huge fan of this genre! Epic adventures and magical worlds are my thing. Here's a pic of my favorite, Lord of the Rings!" - }, - { - "speaker": "John", - "dia_id": "D26:33", - "text": "Wow, that's great! Are there any new fantasy movies that you're excited about?" - }, - { - "speaker": "Tim", - "dia_id": "D26:34", - "text": "Woo-hoo! There's a new fantasy TV series coming out next month - can't wait!" - }, - { - "speaker": "John", - "dia_id": "D26:35", - "text": "What's it called? I'm always down for something new." - }, - { - "speaker": "Tim", - "dia_id": "D26:36", - "text": "I'm really excited to watch this new show that's coming out called \"The Wheel of Time\". It's based on a book series that I love." - }, - { - "speaker": "John", - "dia_id": "D26:37", - "text": "That sounds exciting!" - }, - { - "speaker": "Tim", - "dia_id": "D26:38", - "text": "Yeah, can't wait to check out the series. It's always fun seeing the books come to life on screen! Talk to you later!" - } - ], - "session_27_date_time": "5:26 pm on 2 January, 2024", - "session_27": [ - { - "speaker": "Tim", - "blip_caption": "a photo of a man standing on a fence in front of a leaning tower", - "dia_id": "D27:1", - "text": "Hi John, how's it going? Interesting things have happened since we last talked - I joined a group of globetrotters who are into the same stuff as me. It's been awesome getting to know them and hear about their trips." - }, - { - "speaker": "John", - "dia_id": "D27:2", - "text": "Hey Tim! Cool to hear about your globetrotting group! Must be great connecting with other traveling buffs. By the way, have you been to Italy? I had a blast there last month." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a book with a tag on it", - "dia_id": "D27:3", - "text": "It's been awesome chatting with fellow travel enthusiasts. Italy is definitely on my list of places to visit. How was your trip there last month?" - }, - { - "speaker": "John", - "dia_id": "D27:4", - "text": "Italy was awesome! Everything from the food to the history and architecture was amazing. I even got this awesome book while I was there and it's been giving me some cooking inspiration." - }, - { - "speaker": "Tim", - "dia_id": "D27:5", - "text": "Wow, traveling is amazing, isn't it? I'm learning German now - tough but fun. Do you know any other languages?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a book with a red cover and white title", - "dia_id": "D27:6", - "text": "Wow! Impressive you're learning German. I know a bit of it myself and Spanish, it makes travel so much easier. How's it going with your language studies?\n" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a book sitting on a table next to a wall", - "dia_id": "D27:7", - "text": "Learning German has been tough but worth it. I like the structure of the language, it's much easier when I took French in high school. What made you decide to learn Spanish?" - }, - { - "speaker": "John", - "dia_id": "D27:8", - "text": "I've always wanted to learn Spanish. I just stared with it. It's such a useful language with many personal and professional opportunities!" - }, - { - "speaker": "Tim", - "img_url": [ - "https://thechinesebujo.files.wordpress.com/2017/03/img_2563.jpg" - ], - "blip_caption": "a photo of a cell phone sitting on a notebook with a smiley face app", - "query": "language learning app on phone", - "dia_id": "D27:9", - "text": "Yeah, knowing another language opens up a lot of opportunities. Have you come across any good resources for learning Spanish? I've been using this app." - }, - { - "speaker": "John", - "dia_id": "D27:10", - "text": "Yeah! I've been using that app on my phone to practice too! It's helped a lot." - }, - { - "speaker": "Tim", - "dia_id": "D27:11", - "text": "That app is great. Learning another language is tough, but the rewards are totally worth it." - }, - { - "speaker": "John", - "blip_caption": "a photo of a basketball ball on the ground with a basketball hoop in the background", - "dia_id": "D27:12", - "text": "It takes dedication and practice, but it's so rewarding to communicate with different cultures. Keep it up with German!" - }, - { - "speaker": "Tim", - "dia_id": "D27:13", - "text": "Thanks! I appreciate your encouragement. I'm definitely going to keep up with my German lessons. Do you still play basketball often?" - }, - { - "speaker": "John", - "img_url": [ - "https://c8.alamy.com/zooms/9/de5f1d4e73244a8f94b16a6b6d093748/ttnmea.jpg" - ], - "blip_caption": "a photo of a basketball ball on the ground with a basketball hoop in the background", - "query": "basketball court sunset", - "dia_id": "D27:14", - "text": "Yeah, basketball is still really important to me - I practice and train every day to stay in shape and improve. Can't imagine my life without it, it's my passion." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/oqnovgpaxdv51.jpg" - ], - "blip_caption": "a photo of a collection of harry potter books on a desk", - "query": "harry potter books stack", - "dia_id": "D27:15", - "text": "Wow! Love the way you go for it. Don't ever quit on what you love. I will always love reading, personally." - }, - { - "speaker": "John", - "dia_id": "D27:16", - "text": "Thanks! I won't give up on it. What got you into books?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.pinimg.com/originals/96/80/20/968020204c99f3f30544384d39fe598b.jpg" - ], - "blip_caption": "a photo of a desk with a chair and a book shelf", - "query": "harry potter book collection desk", - "dia_id": "D27:17", - "text": "I love escaping to that world. I have a collection of books that take me there." - }, - { - "speaker": "John", - "dia_id": "D27:18", - "text": "That's awesome! I totally understand why reading means so much to you. It's amazing how much playing a game can help us grow. Thanks for showing us your collection! Which one do you like best that takes you to another world?" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a collection of movies and dvds on a carpet", - "dia_id": "D27:19", - "text": "Harry Potter is my favorite book. It's so immersive!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/ur3tz0poja151.jpg" - ], - "blip_caption": "a photo of a collection of star wars movies on a table", - "query": "fantasy movies dvd collection carpet", - "dia_id": "D27:20", - "text": "Cool! Glad you're enjoying that book! Do you have any favorite fantasy movies as well? These are mine." - }, - { - "speaker": "Tim", - "dia_id": "D27:21", - "text": "Definitely Star Wars! It's my favorite and never gets old. What about you, do you have any favorite fantasy films?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/7vtqewbtg1181.jpg" - ], - "blip_caption": "a photo of a shelf with a lot of books on it", - "query": "lord of the rings dvd collection", - "dia_id": "D27:22", - "text": "I'm a huge fan of Lord of the Rings! The adventure, the world, and the characters are awesome." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a bookcase filled with dvds and games", - "dia_id": "D27:23", - "text": "Wow, me too! That's an awesome collection! Have you watched them heaps? Got any favorite characters from those movies?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a bookmark with a picture of a woman kissing a man", - "dia_id": "D27:24", - "text": "Thanks! I've watched a bunch of them and they're inspiring. My favorite character is Aragorn, he grows so much throughout the story." - }, - { - "speaker": "Tim", - "dia_id": "D27:25", - "text": "Nice one! Why is he your favorite?" - }, - { - "speaker": "John", - "dia_id": "D27:26", - "text": "He's a great leader and puts others first - that's why he eventually becomes king." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/mlffg3ze7gr71.jpg" - ], - "blip_caption": "a photo of a painting of a man with long hair", - "query": "aragorn poster lord of the rings", - "dia_id": "D27:27", - "text": "Wow, Aragorn's story is so inspiring - from a ranger to king of Gondor. It's amazing how he grows and achieves redemption throughout his journey." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/monip6iq5xm51.jpg" - ], - "blip_caption": "a photo of a painting of a man smoking a cigarette", - "query": "aragorn painting lord of the rings", - "dia_id": "D27:28", - "text": "Yeah. His journey is really inspiring. I have a painting in my room to remind me to stay true and be a leader in everything I do." - }, - { - "speaker": "Tim", - "dia_id": "D27:29", - "text": "Wow, that's awesome! What is it about him that makes him so inspiring for you?" - }, - { - "speaker": "John", - "dia_id": "D27:30", - "text": "Aragorn's brave, selfless, down-to-earth attitude is what inspired me. He never gives up and always stands up for justice." - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/or3d42qfg4t91.jpg" - ], - "blip_caption": "a photo of a map of the world on a piece of paper", - "query": "map middle earth fantasy novels world-building intricate details fictional universe", - "dia_id": "D27:31", - "text": "Yeah, he's really inspiring. What's awesome about fantasy books like LOTR is getting lost in another world and seeing all the tiny details." - }, - { - "speaker": "John", - "dia_id": "D27:32", - "text": "Yeah, that's what I'm thinking! Love this map, it really helps you get lost in another world. What's on it?" - }, - { - "speaker": "Tim", - "dia_id": "D27:33", - "text": "It's a map of Middle-earth from LOTR - it's really cool to see all the different realms and regions." - }, - { - "speaker": "John", - "dia_id": "D27:34", - "text": "Wow, that looks awesome! Exploring different lands and regions in fantasy stories is always fun!" - }, - { - "speaker": "Tim", - "dia_id": "D27:35", - "text": "Thanks! It's really cool how fantasy stories allow me to explore other cultures and landscapes, all from the comfort of my home." - }, - { - "speaker": "John", - "img_url": [ - "https://i0.wp.com/stlouispatina.com/wp-content/uploads/2022/10/Copyright-St.-Louis-Patina-2771.jpg" - ], - "blip_caption": "a photo of a person walking down a path in front of the eiffel tower", - "query": "eiffel tower", - "dia_id": "D27:36", - "text": "Yeah! That's why I love traveling - it's a way to learn about different cultures and places." - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a group of people climbing up a stone wall", - "dia_id": "D27:37", - "text": "I love traveling too. That picture is awesome. Have you been to Paris? The Eiffel Tower is so cool!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/b9cuf3yfd3s91.jpg" - ], - "blip_caption": "a photo of a view of a city from a bird's eye view", - "query": "eiffel tower view from top", - "dia_id": "D27:38", - "text": "Thanks! Yeah, I've been there before and loved it! That place is amazing and the view from there is incredible!" - }, - { - "speaker": "Tim", - "dia_id": "D27:39", - "text": "Wow, John, it looks amazing! Can't wait to see it for myself. Traveling is so eye-opening!" - }, - { - "speaker": "John", - "dia_id": "D27:40", - "text": "Yeah, it really is. It helps you see new things and get a different view of everything. It's so cool and educational! Talk to you later!" - } - ], - "session_28_date_time": "5:24 pm on 7 January, 2024", - "session_28": [ - { - "speaker": "Tim", - "dia_id": "D28:1", - "text": "Hey John, long time no talk. On Friday, I got great news - I'm finally in the study abroad program I applied for! Next month, I'm off to Ireland for a semester." - }, - { - "speaker": "John", - "dia_id": "D28:2", - "text": "Congrats, Tim! That's amazing news. So, where are you going to stay?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://roadworksbooks.files.wordpress.com/2022/07/img_4242.jpg" - ], - "blip_caption": "a photo of a woman standing on the side of a street", - "query": "galway colorful street arts scene traditional irish music", - "dia_id": "D28:3", - "text": "Thanks! I'm gonna stay in Galway, it's great for its arts and Irish music. This place has such a vibrant atmosphere." - }, - { - "speaker": "John", - "dia_id": "D28:4", - "text": "Awesome, Galway looks amazing! Is there anything in particular that you're keen to check out while you're there?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://images.pexels.com/photos/8456767/pexels-photo-8456767.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-david-riand-8456767.jpg" - ], - "blip_caption": "a photo of a cliff overlooking the ocean at sunset", - "query": "cliffs moher sunset", - "dia_id": "D28:5", - "text": "Yep! I'm so excited to explore the nature - it looks amazing!" - }, - { - "speaker": "John", - "dia_id": "D28:6", - "text": "Wow, great view! Have you visited any other places?" - }, - { - "speaker": "Tim", - "img_url": [ - "https://i.redd.it/t98kzzhuocp81.jpg" - ], - "blip_caption": "a photo of a person standing on a cliff overlooking the ocean", - "query": "cliffs moher ocean cliffs", - "dia_id": "D28:7", - "text": "I want to visit The Cliffs of Moher. It has amazing ocean views and awesome cliffs." - }, - { - "speaker": "John", - "dia_id": "D28:8", - "text": "Wow, that's awesome! I could stop by there after my season." - }, - { - "speaker": "Tim", - "dia_id": "D28:9", - "text": "Cool, let me know if you're around!" - }, - { - "speaker": "John", - "img_url": [ - "https://outandaboutmummy.files.wordpress.com/2019/11/img_6609.jpg" - ], - "blip_caption": "a photo of a boy dribbling a basketball on a court", - "query": "charity basketball tournament children", - "dia_id": "D28:10", - "text": "Yep, I'll let you know. Oh, I held a benefit basketball game last week." - }, - { - "speaker": "Tim", - "dia_id": "D28:11", - "text": "Wow! How did the game go?" - }, - { - "speaker": "John", - "dia_id": "D28:12", - "text": "The game turned out to be a total success! Lots of people showed up and had a great time, plus we were able to raise some money for charity." - }, - { - "speaker": "Tim", - "dia_id": "D28:13", - "text": "Great job organizing the event. That's really making a difference!" - }, - { - "speaker": "John", - "dia_id": "D28:14", - "text": "Thanks! It's amazing how basketball brings people together and creates a positive impact!" - }, - { - "speaker": "Tim", - "blip_caption": "a photo of a basketball ball and a book on a court", - "dia_id": "D28:15", - "text": "You rock! Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D28:16", - "text": "Thanks, Tim! It's awesome to see how sports can unite people. By the way, what book are you currently reading?" - }, - { - "speaker": "Tim", - "dia_id": "D28:17", - "text": "I'm currently reading a fantasy novel called \"The Name of the Wind\" by Patrick Rothfuss. It's really good!" - }, - { - "speaker": "John", - "dia_id": "D28:18", - "text": "\"The Name of the Wind\" sounds cool. I'll add it to my list. Thanks!" - }, - { - "speaker": "Tim", - "dia_id": "D28:19", - "text": "I hope you enjoy it! Let me know your thoughts." - }, - { - "speaker": "John", - "dia_id": "D28:20", - "text": "Will do! Thanks for the recommendation!" - }, - { - "speaker": "Tim", - "dia_id": "D28:21", - "text": "No problem. Talk to you soon!" - } - ], - "session_29_date_time": "1:41 pm on 12 January, 2024", - "session_29": [ - { - "speaker": "Tim", - "dia_id": "D29:1", - "text": "Hey John! How's it going? Hope all is good." - }, - { - "speaker": "John", - "dia_id": "D29:2", - "text": "Hey Tim! Things have been good. Something exciting happened recently for me. What about you? How's everything going?" - }, - { - "speaker": "Tim", - "dia_id": "D29:3", - "text": "Cool news! I'm trying to get my head around the visa requirements for some places I want to visit. It's kind of overwhelming but I'm excited! What have you been up to?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/g33orfsja8ha1.jpg" - ], - "blip_caption": "a photo of a baseball player holding a bat next to a soda", - "query": "endorsement drink logo", - "dia_id": "D29:4", - "text": "Last week was wild - something incredible happened. But it's a total dream come true - just crazy! I got an endorsement with a popular beverage company!" - }, - { - "speaker": "Tim", - "dia_id": "D29:5", - "text": "Congrats! How did it feel to seal the deal?" - }, - { - "speaker": "John", - "dia_id": "D29:6", - "text": "Thanks! It felt crazy. It's not just about the signing, but it's about feeling like all the hard work paid off - like all those training hours weren't for nothing." - }, - { - "speaker": "Tim", - "dia_id": "D29:7", - "text": "Wow! I bet you were thrilled when everything finally worked out. That sense of accomplishment is awesome and really boosts your self-esteem. I can imagine all the hard work you put into it was definitely worth it." - }, - { - "speaker": "John", - "dia_id": "D29:8", - "text": "Yeah, it's great when you reach a goal and it feels rewarding. It's a reminder that you're going in the right direction, and all the hard work was worth it. What's something you feel proud of recently?" - }, - { - "speaker": "Tim", - "dia_id": "D29:9", - "text": "I'm proud of researching visa requirements for countries I want to visit. It feels like taking initiative is a step towards making my travel dreams a reality!" - }, - { - "speaker": "John", - "dia_id": "D29:10", - "text": "Great going! Taking initiative is a must if you wanna achieve your goals. I'm excited to hear about all your future adventures!" - }, - { - "speaker": "Tim", - "dia_id": "D29:11", - "text": "Thanks! I'll keep you in the loop about my travels. Is there anywhere you recommend visiting?" - }, - { - "speaker": "John", - "dia_id": "D29:12", - "text": "Barcelona is a must-visit city! You'll love exploring the culture, admiring the architecture, and tasting the amazing food in each neighborhood. Plus, the nearby beaches are great for soaking up the sun. Definitely add it to your travel list!" - }, - { - "speaker": "Tim", - "dia_id": "D29:13", - "text": "Barcelona sounds awesome! I've heard so many great things. Definitely adding it to my list. Thanks!" - }, - { - "speaker": "John", - "dia_id": "D29:14", - "text": "No problem! Glad you liked the suggestion. Let me know if you have any other questions or need help with anything." - }, - { - "speaker": "Tim", - "dia_id": "D29:15", - "text": "Cheers! I owe you one. Let me know if you need anything. Bye!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Tim": [ - "Tim contacts his Harry Potter fan friend to discuss potential collaborations on fan projects based on various elements of the Harry Potter Universe." - ], - "John": [ - "John signs a new contract with Minnesota Wolves playing shooting guard and strives to adjust to their style of play.", - "John aims to improve his shooting percentage during his practices with the new team." - ], - "date": "21 May, 2023" - }, - "events_session_2": { - "Tim": [ - "Tim joins a forum about fantasy literature and shares his thoughts with other members about his favorite books." - ], - "John": [ - "John uses his contacts in the basketball industry and his marketing skills to explore potential sports endorsement opportunities with Nike and UnderArmour." - ], - "date": "15 June, 2023" - }, - "events_session_3": { - "Tim": [ - "Tim meets and converses with a fellow Harry Potter fan in California." - ], - "John": [ - "John scores his highest score ever - 40 points - during a playoff in June 2023.", - "John and his team are thrilled by their win and celebrate in a restaurant afterwards.", - "John secures a basketball shoe and gear deal with Nike and is in talks with Gatorade for a potential sponsorship.", - "John look forward to a game in Seattle in the first week of August 2023." - ], - "date": "16 July, 2023" - }, - "events_session_4": { - "Tim": [ - "Tim finds an opportunity on a fantasy literature forum and starts writing articles on fantasy novels, studying characters, themes, and making book recommendations for an online magazine." - ], - "John": [ - "John is reading a book that inspires him to keep dreaming." - ], - "date": "2 August, 2023" - }, - "events_session_5": { - "Tim": [ - "Tim skypes with the Harry Potter fan he met in California to discuss future collaborations.", - "Tim starts reading the book, The Name of the Wind, by Patrick Rothfuss." - ], - "John": [ - "John has an intense game at Seattle followed by an exhilarating win." - ], - "date": "9 August, 2023" - }, - "events_session_6": { - "Tim": [], - "John": [ - "John travels from Seattle to Chicago and starts exploring the culture and people.", - "John wants to make an impact with charity work and is in talks with a local organization to help disadvantaged kids with sports and school." - ], - "date": "11 August, 2023" - }, - "events_session_7": { - "Tim": [ - "Tim prepares to visit a book conference in September 2023 where he wants to meet with book publishers, authors and fellow readers." - ], - "John": [ - "John returns from his trip to Chicago and meets with his team.", - "John's teammates gift him a signed basketball as a mark of their friendship and team spirit." - ], - "date": "17 August, 2023" - }, - "events_session_8": { - "Tim": [ - "Tim focuses on school and starts learning the piano.", - "Tim enjoys playing tunes from the movie Harry Potter and the Philosopher's Stone on the piano." - ], - "John": [ - "John joins a new gym to stay in basketball shape and works out an elaborate gym routine to keep up with his basketball skills as well as strength routine." - ], - "date": "21 August, 2023" - }, - "events_session_9": { - "Tim": [ - "Tim gets busy with school assignments and exams." - ], - "John": [ - "John visits his family and old friends in his hometown.", - "John goes on a visit to New York City." - ], - "date": "26 August, 2023" - }, - "events_session_10": { - "Tim": [ - "Tim gets turned down for a summer job.", - "Tim gives a presentation in class and feels accomplished about it.", - "Tim plans to visit Universal Studios in Septemner 2023 and is excited to see the Harry Potter rides." - ], - "John": [ - "John starts to cook more often and experiment with recipes, such as s soup with sage herbs.", - "John returns from his trip to New York City where he struggled a bit with the subway system." - ], - "date": "31 August, 2023" - }, - "events_session_11": { - "Tim": [ - "Tim recommends that John can visit Edinburgh, Scotland for their team trip and recommends the book, The Name of the Wind by Patrick Rothfuss to read during the trip." - ], - "John": [ - "John attends a local restaurant with some of his new teammates.", - "John plans to take a trip with his teammates and seeks for city and book recommendations from Tim." - ], - "date": "21 September, 2023" - }, - "events_session_12": { - "Tim": [], - "John": [ - "John ties the knot with his long-time girlfriend in an emotional wedding ceremony at a greenhouse." - ], - "date": "2 October, 2023" - }, - "events_session_13": { - "Tim": [ - "Tim visits the UK to attend a Harry Potter themed conference and feels at home with fellow attendees due to their shared love for the Harry Potter universe." - ], - "John": [ - "John's team continues on an intense game season with some wins as well as losses.", - "John and his team win a triphy during one of their games." - ], - "date": "13 October, 2023" - }, - "events_session_14": { - "Tim": [], - "John": [ - "John starts to mentor the younger players on his team and finds the experience rewarding." - ], - "date": "17 October, 2023" - }, - "events_session_15": { - "Tim": [ - "Tim reads a book about the history of castles in the UK and uses it as inspiration for his ongoing fantasy novel writing.", - "Tim is in the process of writing a fantasy novel." - ], - "John": [], - "date": "21 October, 2023" - }, - "events_session_16": { - "Tim": [ - "Tim gets stuck while writing a plot twist in his book but pushes through it." - ], - "John": [ - "John goes on a European vacation with his wife." - ], - "date": "6 November, 2023" - }, - "events_session_17": { - "Tim": [], - "John": [ - "John and his wife go on a road trip to explore the coastline in Europe.", - "John finishes reading a gripping fantasy novel." - ], - "date": "11 November, 2023" - }, - "events_session_18": { - "Tim": [ - "Tim visit castles in the UK and returns home.", - "Tim prepares for exams at school." - ], - "John": [ - "John suffers from an ankle injury and has to stay off court while recovering from it." - ], - "date": "16 November, 2023" - }, - "events_session_19": { - "Tim": [ - "Tim attempts writing a short story based off his experiences in the UK but is not able to accomplish it." - ], - "John": [ - "John continues recovery from his ankle injury and re-reads one of his favorite books, The Alchemist, during this time." - ], - "date": "21 November, 2023" - }, - "events_session_20": { - "Tim": [ - "Tim faces some tough exams which make him question his competence but he perseveres and does well at it." - ], - "John": [ - "John starts taking yoga classes to complement his basketball practice and enjoys doing the Warrior II pose." - ], - "date": "1 December, 2023" - }, - "events_session_21": { - "Tim": [ - "Tim joins a travel club to get more insights about countries he is interested in and meet new people.", - "Tim has been learning to play the violin for four months now and wants to learn to play jazz and film scores." - ], - "John": [], - "date": "6 December, 2023" - }, - "events_session_22": { - "Tim": [ - "Tim attends a Harry Potter themed party where he dresses up with his Gryffindor scarf, makes many new friends and receives chocolate frogs as treats.", - "Tim reads a book on the power of friendship and loyalty, and A Dance with Dragons by George R R Martin" - ], - "John": [ - "John's team secures a win in one of their games.", - "John reads the book Dune by Frank Herbert" - ], - "date": "8 December, 2023" - }, - "events_session_23": { - "Tim": [ - "Tim does an analysis of a fantasy novel for his English literature class and it goes okay." - ], - "John": [ - "John achieves a career-high assist during a big game." - ], - "date": "11 December, 2023" - }, - "events_session_24": { - "Tim": [], - "John": [ - "John and his team secure another win that was a close-call until the buzzer.", - "John continues doing physical therapy and rehab to recover from his ankle injury.", - "John is able to job without any pain for the first time since the injury.", - "John and his wife host a small get-together for his friends and family to celebrate his path to recovery from the ankle injury." - ], - "date": "16 December, 2023" - }, - "events_session_25": { - "Tim": [], - "John": [ - "John lands a new endorsement deal with a renowned outdoor gear company and completes a photoshoot for the same in a gorgeous forest." - ], - "date": "19 December, 2023" - }, - "events_session_26": { - "Tim": [ - "Tim visits a travel agency to inquire about the requirements of his next dream trip.", - "Tim finds and reads a book about travellers' stories from around the world to plan his next adventure." - ], - "John": [ - "John starts conducting seminars to share his athletic and marketing skills with aspiring professionals.", - "John continues his charity work on youth sports and fighting to give youth in underserved communities a chance at sports." - ], - "date": "26 December, 2023" - }, - "events_session_27": { - "Tim": [ - "Tim finds a group of fellow travellers who share a common interest in exploring new cultures.", - "Tim starts a language course in German." - ], - "John": [], - "date": "2 January, 2024" - }, - "events_session_28": { - "Tim": [ - "Tim gets accepted to a Study Abroad program in school and will leave next month to spend the semester at Ireland.", - "Tim plans to live in Galway when studying in Ireland and is excited to expore Irish music, arts as well as nature such as The Cliffs of Moher." - ], - "John": [ - "John successfully organizes and hosts a benefit basketball tournament for charity." - ], - "date": "7 January, 2024" - }, - "events_session_29": { - "Tim": [ - "Tim starts to research about the visa requirements of some countries of interest." - ], - "John": [ - "John makes an endorsement deal with Moxie, a popular beverage company." - ], - "date": "12 January, 2024" - } - }, - "observation": { - "session_1_observation": { - "John": [ - [ - "John signed with the Minnesota Wolves for the upcoming season as a shooting guard.", - "D1:5" - ], - [ - "John's goal for the season is to improve his shooting percentage.", - "D1:9" - ], - [ - "John found fitting into the new team's style of play a challenge during pre-season.", - "D1:11" - ] - ], - "Tim": [ - [ - "Tim is working on a Harry Potter fan project and discussing collaborations for it.", - "D1:2" - ], - [ - "Tim talked to a friend who is a Harry Potter fan to figure out ideas for the project.", - "D1:14" - ], - [ - "Tim went to a Harry Potter-related place in London a few years ago and went on a tour.", - "D1:18" - ] - ] - }, - "session_2_observation": { - "Tim": [ - [ - "Tim joined a fantasy literature forum and had a great discussion about his favorite books.", - "D2:1" - ], - [ - "Tim has a book collection and owns a picture from MinaLima, the creators of props for the Harry Potter films.", - "D2:7" - ], - [ - "Tim considers his book collection and Harry Potter memorabilia as reminders that help him escape reality.", - "D2:11" - ], - [ - "There is a chance that Tim will visit more Harry Potter-related spots in the future.", - "D2:13" - ] - ], - "John": [ - [ - "John is exploring endorsement opportunities and is considering linking up with sports brands like Nike and Under Armour.", - "D2:2" - ], - [ - "John expressed that it would be rewarding to have his hard work pay off through endorsement opportunities.", - "D2:2" - ], - [ - "John asked about Tim's book collection and the picture from MinaLima, showing an interest in Tim's hobbies.", - "D2:8" - ], - [ - "John played in a recent game and finds it awesome to be out there doing what he loves.", - "D2:16" - ], - [ - "John appreciates Tim's support and expresses gratitude for it.", - "D2:18" - ] - ] - }, - "session_3_observation": { - "John": [ - [ - "John scored 40 points in a game last week, his highest ever.", - "D3:1" - ], - [ - "John celebrated with his teammates at a restaurant after winning a tough game.", - "D3:5" - ], - [ - "John has signed a deal with Nike for a basketball shoe and gear, and is in talks with Gatorade for a potential sponsorship.", - "D3:13" - ], - [ - "John mentioned exploring endorsements and used his contacts in the basketball industry and marketing skills to make connections.", - "D3:11" - ], - [ - "John's game next month is in Seattle, a city he loves for its energy, diversity, food, and fan support.", - "D3:19" - ], - [ - "John started surfing five years ago and loves the connection to nature it provides.", - "D3:27" - ] - ], - "Tim": [ - [ - "Tim had a nice chat with a Harry Potter fan in California last week.", - "D3:2" - ], - [ - "Tim finds bliss in reading a great fantasy book to escape and feel free.", - "D3:30" - ] - ] - }, - "session_4_observation": { - "Tim": [ - [ - "Tim is currently writing articles about fantasy novels for an online magazine.", - "D4:1" - ], - [ - "Tim found the opportunity to write about fantasy novels on a fantasy literature forum.", - "D4:3" - ], - [ - "Tim's articles focus on studying characters, themes, and making book recommendations of different fantasy novels.", - "D4:5" - ], - [ - "Tim loves writing about Harry Potter and Game of Thrones and could talk about them forever.", - "D4:7" - ] - ], - "John": [ - [ - "John participated in an intense Harry Potter trivia contest at a charity event with Anthony.", - "D4:8" - ], - [ - "John is reading an inspiring book that reminds him to keep dreaming.", - "D4:10" - ], - [ - "John wants to keep reaching for new goals.", - "D4:12" - ] - ] - }, - "session_5_observation": { - "Tim": [ - [ - "Tim recently skyped with a Harry Potter fan he met in CA and discussed collab-ing.", - "D5:1" - ], - [ - "Tim enjoys reading fantasy books that captivate him and take him to another world.", - "D5:13" - ], - [ - "Tim is currently reading a fantasy book by Patrick Rothfuss which he finds awesome.", - "D5:15" - ] - ], - "John": [ - [ - "John had a crazy intense game last week which his team won by a tight score, and scoring the last basket was an awesome experience for him.", - "D5:2" - ], - [ - "John feels that the team bond is awesome and makes all the hard work worth it.", - "D5:6" - ], - [ - "John emphasizes the importance of having a strong team/support which is like a family away from home.", - "D5:8" - ], - [ - "John is currently reading a fantasy book recommended by Tim and plans to let him know his thoughts after reading it.", - "D5:16" - ] - ] - }, - "session_6_observation": { - "John": [ - [ - "John recently took a trip to Chicago and found the locals to be friendly.", - "D6:3" - ], - [ - "John enjoys experiencing other cultures and connecting with new people.", - "D6:3" - ], - [ - "John loves the energy in a stadium during a sports game, with everyone cheering and getting excited.", - "D6:5" - ], - [ - "John has a pair of basketball shoes that he considers lucky and they hold many stories of his journey.", - "D6:9" - ], - [ - "Basketball has been a part of John's life since childhood, starting with watching NBA games with his dad and then playing in local leagues.", - "D6:13" - ], - [ - "John's childhood dream was to play on the big courts, which became a reality through hard work, earning a scholarship, and being drafted by a team.", - "D6:13" - ], - [ - "John's number one goal is to win a championship in basketball and also wants to make a difference through charity work and inspiring others.", - "D6:15" - ], - [ - "John is teaming up with a local organization to help disadvantaged kids with sports and school as part of his charity work.", - "D6:17" - ] - ], - "Tim": [ - [ - "Tim recently attended an event that he found fantastic and special, being with other fans who share his love for it.", - "D6:2" - ], - [ - "Tim has been writing more articles to combine his love for reading and sharing great stories.", - "D6:6" - ], - [ - "Tim recommends the book \"The Name of the Wind,\" describing it as a captivating fantasy novel with great world-building and character development.", - "D6:8" - ] - ] - }, - "session_7_observation": { - "John": [ - [ - "John is part of a basketball team which he feels lucky to be a part of.", - "D7:1" - ], - [ - "John values the bond created by being around people who share the same love for basketball.", - "D7:3" - ], - [ - "John received a signed ball from his teammates as a reminder of their friendship and appreciation.", - "D7:7" - ], - [ - "The signed ball from his teammates serves as a reminder of the bond and support he has from them in his basketball journey.", - "D7:11" - ] - ], - "Tim": [ - [ - "Tim felt a sense of belonging at an event last month where everyone shared the same love for a common interest.", - "D7:4" - ], - [ - "Tim is hoping to attend a book conference next month to learn more about literature and create a stronger bond to it.", - "D7:6" - ], - [ - "Tim appreciates having something meaningful to keep him motivated, as seen from his conversation with John.", - "D7:12" - ] - ] - }, - "session_8_observation": { - "John": [ - [ - "John found a new gym for training to stay on his basketball game.", - "D8:1" - ], - [ - "John included strength training in his workout routine to build muscle, increase power, prevent injuries, and improve agility and speed.", - "D8:7" - ], - [ - "Strength training improved John's shooting accuracy, agility, and speed, giving him confidence on the court.", - "D8:9" - ] - ], - "Tim": [ - [ - "Tim has been learning to play the piano and finds it satisfying to see progress in his learning.", - "D8:12" - ], - [ - "Tim's favorite song to play on the piano is a theme from the movie Harry Potter and the Philosopher's Stone.", - "D8:14" - ], - [ - "During Thanksgiving, Tim's family enjoys watching movies like 'Home Alone', 'Elf', and 'The Santa Clause'.", - [ - "D8:24", - "D8:26", - "D8:28" - ] - ] - ] - }, - "session_9_observation": { - "Tim": [ - [ - "Tim is busy with assignments and exams this week, but is not giving up.", - "D9:1" - ], - [ - "Tim is not part of a sports team but enjoys reading fantasy novels.", - "D9:5" - ], - [ - "Tim loves traveling to new places to experience different kinds of magic.", - "D9:5" - ], - [ - "Tim is excited about visiting New York City after seeing a picture shared by John.", - "D9:7" - ], - [ - "Tim plans to add NYC to his travel list and is eager to explore and try out new things there.", - "D9:9" - ], - [ - "Tim appreciates John's support as mentioned in the conversation.", - "D9:15" - ] - ], - "John": [ - [ - "John caught up with his family and old friends during a visit home last week.", - "D9:2" - ], - [ - "John and his friends were teammates for four years in high school and have played together for quite some time.", - "D9:4" - ], - [ - "John loves discovering new cities and enjoys exploring different places.", - "D9:6" - ], - [ - "John had a great time in New York City, exploring the city and trying out different restaurants.", - "D9:8" - ], - [ - "John encourages Tim to visit NYC and assures him it's an adventure he'll never forget.", - "D9:10" - ], - [ - "John offers to help Tim out when he plans to visit NYC.", - "D9:12" - ], - [ - "John is supportive of Tim and offers help whenever needed.", - "D9:14" - ] - ] - }, - "session_10_observation": { - "Tim": [ - [ - "Tim received a no for a summer job he wanted, but is staying positive.", - "D10:1" - ], - [ - "Tim recently gave a presentation in class and felt it was a small but significant progress.", - "D10:3" - ], - [ - "Tim loves experimenting with spices when cooking.", - "D10:9" - ], - [ - "Tim is planning a trip to Universal Studios, particularly excited for the Harry Potter stuff.", - "D10:11" - ] - ], - "John": [ - [ - "John had some trouble figuring out the subway in NYC during his trip, but found it easier with someone's help.", - "D10:2" - ], - [ - "John has been trying out cooking recipes, recently making a tasty soup with sage.", - "D10:4" - ], - [ - "John hasn't been to Universal Studios yet, but it's on his bucket list.", - "D10:10" - ] - ] - }, - "session_11_observation": { - "John": [ - [ - "John connected with new teammates over a shared love for basketball.", - "D11:5" - ], - [ - "John is planning a team trip with his friends to explore a new city next month.", - "D11:7" - ], - [ - "John is considering Edinburgh, Scotland for the team trip based on Tim's suggestion.", - "D11:11" - ], - [ - "John loves his basketball career and finds it challenging and fulfilling, especially when he sees improvement and achieves goals.", - "D11:15" - ], - [ - "John is focusing on improving shooting and making a greater impact on the court. He is also working on building his brand and seeking more endorsements for life after basketball.", - "D11:17" - ], - [ - "John plans to use his platform post-basketball to make a positive difference, potentially by starting a foundation and doing charity work to leave a meaningful legacy.", - "D11:19" - ], - [ - "John is open to receiving support and advice from others, like Tim.", - "D11:20" - ] - ], - "Tim": [ - [ - "Tim suggested Edinburgh, Scotland as a potential destination for the team trip.", - "D11:10" - ], - [ - "Tim shared advice on picking endorsements that align with values and feel authentic to followers.", - "D11:22" - ], - [ - "Tim recommended a fantasy novel by Patrick Rothfuss for John's trip, suggesting it as a great read while traveling.", - "D11:24" - ] - ] - }, - "session_12_observation": { - "Tim": [ - [ - "Tim has a bookshelf filled with favorite fantasy novels that he enjoys for escapism.", - "D12:15" - ], - [ - "Tim collects jerseys and has an interest in basketball.", - "D12:18" - ], - [ - "Tim's favorite basketball team is The Wolves and his favorite player is LeBron, admiring his skills and leadership.", - "D12:20" - ], - [ - "Tim appreciates LeBron's work ethic and dedication to the game, finding him inspiring.", - "D12:22" - ], - [ - "Tim has not only met LeBron a few times but also had the opportunity to see him play live.", - "D12:24" - ] - ], - "John": [ - [ - "John recently had an emotional wedding ceremony in a lovely greenhouse venue with safety protocols for an intimate gathering.", - "D12:4" - ], - [ - "John is a member of a hiking club, even though he just joined.", - "D12:6" - ], - [ - "John's favorite memory from his wedding day is seeing his wife walking down the aisle, which made him emotional.", - "D12:8" - ], - [ - "John had their first dance with his wife at a cozy restaurant with music and candlelight, finding it dreamy.", - "D12:10" - ], - [ - "John values moments filled with love and joy, believing that they are special.", - "D12:12" - ], - [ - "John enjoys watching basketball games, finding them motivating, and admires top players like LeBron for their skills.", - "D12:24" - ] - ] - }, - "session_13_observation": { - "Tim": [ - [ - "Tim attended a Harry Potter conference in the UK last week and felt inspired and rejuvenated by the experience.", - "D13:1" - ], - [ - "Tim feels that his passion for fantasy stuff brings him closer to people from all over the world.", - "D13:1" - ], - [ - "Tim appears to be supportive and encouraging towards John's achievements and goals.", - "D13:9" - ], - [ - "Tim recommended a pair of very comfortable shoes that he got online to John.", - "D13:17" - ], - [ - "Tim offered to help John with anything and mentioned his availability for assistance.", - "D13:19" - ], - [ - "Tim advised John to stay motivated and keep chasing his dreams.", - "D13:21" - ] - ], - "John": [ - [ - "John had an intense season with tough losses and great wins in the games.", - "D13:4" - ], - [ - "John's team faced tough opponents, but they do not quit and back each other up to get better.", - "D13:6" - ], - [ - "John's team won a trophy after all their hard work and effort.", - "D13:8" - ], - [ - "John appreciates Tim's support and encouragement, and feels motivated by it.", - "D13:14" - ], - [ - "John mentioned needing new shoes after all the games and showed interest in Tim's comfortable shoe recommendation.", - "D13:14" - ], - [ - "John agrees to stay motivated and keep chasing his dreams.", - "D13:22" - ] - ] - }, - "session_14_observation": { - "John": [ - [ - "John is currently mentoring younger players on his team during the off-season.", - "D14:3" - ], - [ - "John finds mentoring the younger players super rewarding, motivating, and encouraging.", - "D14:5" - ], - [ - "Some of the younger players see John as a mentor, and he enjoys providing them with advice and support on and off the court.", - "D14:9" - ], - [ - "John feels honored to have the trust and admiration of the younger players and enjoys being a role model for them.", - "D14:11" - ] - ], - "Tim": [ - [ - "Tim took a stunning picture last summer on his trip to the Smoky Mountains.", - "D14:14" - ], - [ - "Tim is busy with studies but finds time to relax with books, seeking a good balance.", - "D14:20" - ], - [ - "Tim is currently reading a book that he's totally hooked on.", - "D14:22" - ] - ] - }, - "session_15_observation": { - "Tim": [ - [ - "Tim enjoys reading and is inspired by books, movies, and real-life experiences for his writing.", - "D15:5" - ], - [ - "J.K. Rowling is a significant inspiration for Tim's writing, and he takes notes on her style.", - "D15:7" - ], - [ - "Tim has been reading J.K. Rowling's works for a long time and finds her writing special.", - "D15:9" - ], - [ - "A quote by J.K. Rowling, \"Turn on the light - happiness hides in the darkest of times,\" is meaningful to Tim during tough times.", - "D15:11" - ], - [ - "Tim is writing a fantasy novel that brings him joy and excitement.", - "D15:3" - ], - [ - "Tim enjoys exploring and going on road trips with friends and family, as well as hiking and playing board games.", - "D15:29" - ], - [ - "Tim loves curling up with a good book to escape reality and get lost in different worlds during his free time.", - "D15:29" - ] - ], - "John": [ - [ - "John is interested in historical architecture and dreams of visiting castles in different places.", - "D15:2" - ], - [ - "John finds motivation in staying focused and pushing through tough workouts by using strategies and motivational quotes written on a whiteboard.", - "D15:14" - ], - [ - "John keeps a plaque on his desk as a constant reminder to believe in himself and trust his abilities during challenging times.", - "D15:16" - ], - [ - "John's teammates believing in him and his love for improving his skills keep him motivated, especially during challenging times.", - "D15:18" - ], - [ - "John spends time with his family and considers them as a source of happiness and support.", - "D15:24" - ], - [ - "John enjoys cooking as a therapy to be creative, experiment with flavors, and take a break from his routine.", - "D15:30" - ], - [ - "John loves making honey garlic chicken with roasted vegetables and often tries out new recipes.", - "D15:32" - ] - ] - }, - "session_16_observation": { - "Tim": [ - [ - "Tim faced a writing issue last week where he got stuck on a plot twist, but eventually got the ideas flowing again.", - "D16:1" - ], - [ - "Tim owns a basketball signed by his favorite player, LeBron, as a prized possession.", - "D16:7" - ], - [ - "Tim finds overcoming challenges to be a source of personal growth and strength.", - "D16:5" - ] - ], - "John": [ - [ - "John had a basketball game where his team was trailing significantly in the 4th quarter, but they dug deep to overturn the deficit and win the game.", - "D16:6" - ], - [ - "John admires LeBron's determination and heart, particularly referencing an epic block in Game 7 of the '16 Finals.", - "D16:9" - ], - [ - "John finds that struggles in sports, like overcoming deficits, lead to a greater sense of satisfaction when victorious.", - "D16:4" - ] - ] - }, - "session_17_observation": { - "John": [ - [ - "John and his wife went on a road trip along the European coastline, creating amazing memories and bonding.", - "D17:3" - ], - [ - "John recently finished an amazing fantasy series and loves getting lost in fantasy worlds.", - "D17:9" - ], - [ - "John has watched the fantasy show 'That' and finds it a great way to escape reality.", - "D17:11" - ], - [ - "John believes taking time for oneself is crucial for staying sharp, gaining new perspectives, and tackling challenges with more energy.", - "D17:17" - ] - ], - "Tim": [ - [ - "Tim enjoys reading as usual and finds being lost in an awesome fantasy realm like an escape.", - "D17:2" - ], - [ - "Tim believes books and movies serve as a mental break and allow minds to wander, giving a sense of refreshment.", - "D17:14" - ], - [ - "Tim thinks taking a break from life helps recharge, gain peace, reconnect with oneself, and approach life's challenges with a new outlook.", - "D17:16" - ], - [ - "Tim emphasizes that balance, taking care of oneself mentally and physically, is key for success.", - "D17:18" - ] - ] - }, - "session_18_observation": { - "Tim": [ - [ - "Tim visited a castle during his trip to the UK last Friday and was amazed by the architecture and history.", - "D18:1" - ], - [ - "Tim's week has been swamped with exams, but he is plowing through them.", - "D18:3" - ], - [ - "Tim breaks up his studying into smaller parts, with 25 minutes of study and 5 minutes of fun activities.", - "D18:7" - ] - ], - "John": [ - [ - "John has had a tough week with an injury but is staying positive.", - "D18:2" - ], - [ - "John visualizes his goals and success for focus and motivation during tough studying sessions.", - "D18:6" - ], - [ - "John's doctor mentioned that his injury is not too serious.", - "D18:10" - ], - [ - "John hates not being on the court.", - "D18:12" - ] - ] - }, - "session_19_observation": { - "Tim": [ - [ - "Tim had a setback with a story he was writing based on his experiences in the UK.", - "D19:3" - ], - [ - "Tim loves writing and reading to stay motivated and push himself to improve.", - "D19:9" - ], - [ - "Tim enjoys fantasy books like Harry Potter and Game of Thrones.", - "D19:14" - ], - [ - "Tim recently read a book that focused on how small changes can make a big difference, changing the way he does things.", - "D19:19" - ] - ], - "John": [ - [ - "John had a major challenge when he hurt his ankle last season and had to undergo physical therapy.", - "D19:6" - ], - [ - "John values personal development and mindset books to know himself better.", - "D19:16" - ], - [ - "John recently reread 'The Alchemist' and found it inspiring, motivating him to keep chasing his dreams and trust the process.", - "D19:20" - ] - ] - }, - "session_20_observation": { - "Tim": [ - [ - "Tim had a tough exam recently that he turned into a learning experience, showcasing his resilience and determination.", - "D20:1" - ], - [ - "Tim appreciates John's encouragement and support throughout his journey.", - "D20:3" - ], - [ - "Tim enjoys fantasy books and movies, sharing favorites with John and mutual interest in the genre.", - "D20:17" - ], - [ - "Tim has a bookshelf with some of his favorite books, including The Hobbit.", - "D20:19" - ], - [ - "Tim loves how fantasy books allow for an escape to different worlds and a break from reality.", - "D20:25" - ], - [ - "Tim finds nature calming and appreciates the beauty around him.", - "D20:31" - ] - ], - "John": [ - [ - "John is trying out yoga to improve strength, flexibility, focus, and balance during workouts.", - "D20:2" - ], - [ - "John enjoys certain yoga poses like Warrior II for strength and balance.", - "D20:6" - ], - [ - "John typically holds challenging poses for 30-60 seconds to build strength and stability.", - "D20:10" - ], - [ - "John appreciates the escapism and joy of fantasy books and movies, sharing favorites with Tim.", - "D20:17" - ], - [ - "John enjoys nature and finds peace and refreshment when camping in the mountains.", - "D20:36" - ], - [ - "John values the calming and humbling effect of nature, recognizing the beauty and grandeur of the world.", - "D20:42" - ], - [ - "John shares a photo of a tranquil forest from near his hometown.", - "D20:28" - ] - ] - }, - "session_21_observation": { - "Tim": [ - [ - "Tim recently joined a travel club to explore different cultures and countries.", - "D21:1" - ], - [ - "Tim is working on his studies.", - "D21:9" - ], - [ - "Tim recently started learning how to play the violin and has been playing the piano for about four months.", - [ - "D21:9", - "D21:13" - ] - ] - ], - "John": [ - [ - "John has been playing professional basketball for just under a year.", - "D21:4" - ], - [ - "John has been working on improving his overall game, getting endorsement deals, and learning how to market himself to boost his brand.", - "D21:8" - ], - [ - "John believes in creating a positive environment and showing care for teammates to motivate them.", - "D21:16" - ] - ] - }, - "session_22_observation": { - "Tim": [ - [ - "Tim recently attended a cool Harry Potter party and wore a Gryffindor scarf.", - "D22:1" - ], - [ - "Tim mentioned reading an amazing series about the power of friendship and loyalty.", - "D22:7" - ], - [ - "Tim is looking for new reads and asked John for book recommendations.", - "D22:9" - ], - [ - "Tim recently finished reading 'A Dance with Dragons' and highly recommends it.", - "D22:13" - ] - ], - "John": [ - [ - "John attended a charity event with Harry Potter trivia in August and enjoys Harry Potter.", - "D22:2" - ], - [ - "John's basketball team recently played a tough game against a top team and won.", - "D22:4" - ], - [ - "John is currently reading 'Dune' by Frank Herbert, a story about religion and human control over ecology.", - "D22:12" - ], - [ - "John is open to reading George R. R. Martin's books after Tim recommended them.", - "D22:16" - ] - ] - }, - "session_23_observation": { - "John": [ - [ - "John had a career-high in assists last Friday during a big game against their rival.", - "D23:3" - ], - [ - "John hit a buzzer-beater shot to win a game when they were down 10 in the 4th quarter, which was one of their favorite thrilling sports moments.", - "D23:7" - ], - [ - "John used to practice basketball outside for hours when he was younger, dreaming of playing in big games.", - "D23:9" - ], - [ - "John values basketball as a meaningful part of his life that allows him to pursue his passions.", - "D23:11" - ] - ], - "Tim": [ - [ - "Tim struggled with his English lit class but did an analysis on a series that he thinks went okay.", - "D23:2" - ], - [ - "Tim finds sports as a way to express himself and stay positive.", - "D23:10" - ], - [ - "Tim sent John a pic that reminds John of practicing basketball outside when he was younger.", - "D23:8" - ], - [ - "Tim believes in the importance of following dreams and doing one's own thing.", - "D23:12" - ] - ] - }, - "session_24_observation": { - "Tim": [ - [ - "Tim is interested in catching up and asks John about recent b-ball games.", - "D24:1" - ], - [ - "Tim congratulated John on winning a close b-ball game.", - "D24:3" - ], - [ - "Tim notes that winning a game must have been thrilling for John.", - "D24:5" - ], - [ - "Tim asks John about challenges he has faced, specifically regarding his recent injury.", - "D24:9" - ], - [ - "Tim acknowledges the importance of staying active or keeping up with a fitness routine during recovery.", - "D24:11" - ], - [ - "Tim asks John about the progress of his rehab and encourages him to keep it up.", - "D24:13" - ], - [ - "Tim congratulates John on his milestone at the gym and hosting a get-together to celebrate.", - "D24:17" - ], - [ - "Tim expresses happiness that everyone had fun at the get-together.", - "D24:19" - ] - ], - "John": [ - [ - "John talks about winning a b-ball game against another team that was close until the final buzzer.", - "D24:2" - ], - [ - "John injured himself recently, had to miss games, and couldn't help his team.", - "D24:10" - ], - [ - "John mentions that he does physical therapy exercises every day for his recovery.", - "D24:12" - ], - [ - "John shares that he had a milestone moment at the gym where he was able to jog without pain.", - "D24:14" - ], - [ - "John hosted a small get-together with friends and family to celebrate his recovery milestone.", - "D24:16" - ], - [ - "John appreciates Tim's support and encouragement, stating he will keep pushing and staying positive.", - "D24:18" - ] - ] - }, - "session_25_observation": { - "Tim": [ - [ - "Tim is in touch with John after a while and asks how things are going.", - "D25:1" - ], - [ - "Tim is curious about the gear John got from an outdoor gear company and asks about the photoshoot.", - "D25:3" - ], - [ - "Tim appreciates the outdoor location where John did the photoshoot and asks to see pictures.", - "D25:5" - ], - [ - "Tim comments on the photo John shows, mentioning the peaceful rocks and river.", - "D25:7" - ], - [ - "Tim acknowledges the benefits of nature in bringing peace and joy.", - "D25:9" - ], - [ - "Tim asks John about recent happenings and appreciates the importance of hard work paying off.", - "D25:11" - ], - [ - "Tim asks about areas of growth in John's team during training.", - "D25:13" - ], - [ - "Tim expresses gladness that John's team is bonding and encourages them to keep it up.", - "D25:15" - ], - [ - "Tim offers support and advises John to continue believing in himself before saying bye.", - "D25:17" - ] - ], - "John": [ - [ - "John got an amazing deal with a renowned outdoor gear company last week, obtaining hiking stuff and top-notch outdoor gear.", - "D25:2" - ], - [ - "The photoshoot John did in a gorgeous forest went really well, capturing him in epic shots.", - "D25:4" - ], - [ - "John stumbled upon a soothing spot by a river while hiking, feeling at peace and admiring nature's beauty.", - "D25:8" - ], - [ - "Things have been going great on the court for John and his team, putting in work and achieving their goals.", - "D25:10" - ], - [ - "John's team has seen growth in communication and bonding during their training.", - "D25:14" - ], - [ - "John appreciates Tim's support and expresses thanks for the assistance.", - "D25:16" - ] - ] - }, - "session_26_observation": { - "John": [ - [ - "John started doing seminars to help people with sports and marketing.", - "D26:1" - ], - [ - "John organized a basketball camp for kids in his hometown last summer, creating opportunities for young athletes.", - "D26:23" - ], - [ - "John is passionate about supporting youth sports and fighting for fair chances in sports for underserved communities.", - "D26:21" - ], - [ - "John uses his influence and resources to help causes he believes in, particularly in making the world a better place.", - "D26:19" - ], - [ - "John is a movie fan who enjoys seeing stories come alive on the big screen as a way to relax.", - "D26:29" - ] - ], - "Tim": [ - [ - "Tim is planning his next adventure by reading stories from travelers around the world.", - "D26:6" - ], - [ - "Tim is a fan of the fantasy genre, particularly enjoying epic adventures and magical worlds.", - "D26:32" - ], - [ - "Tim is excited to watch the new fantasy TV series called \"The Wheel of Time\" based on a book series he loves.", - "D26:36" - ] - ] - }, - "session_27_observation": { - "Tim": [ - [ - "Tim joined a group of globetrotters who share his interest in traveling.", - "D27:1" - ], - [ - "Tim is currently learning German and finds it tough but worth it.", - "D27:5" - ], - [ - "Tim appreciates encouragement and plans to continue with his German lessons.", - "D27:13" - ], - [ - "Tim loves reading and has a collection of books that he enjoys escaping into.", - "D27:15" - ], - [ - "Tim's favorite book is Harry Potter because it is immersive.", - "D27:19" - ], - [ - "Tim's favorite fantasy film is Star Wars as it never gets old.", - "D27:21" - ], - [ - "Tim appreciates fantasy stories for allowing him to explore other cultures and landscapes.", - "D27:35" - ] - ], - "John": [ - [ - "John has been to Italy and had a blast there last month.", - "D27:2" - ], - [ - "John knows a bit of German and Spanish which he finds useful for travel.", - "D27:6" - ], - [ - "John practices and trains in basketball daily as it is his passion.", - "D27:14" - ], - [ - "John loves reading and finds playing a game helps personal growth.", - "D27:18" - ], - [ - "John's favorite book is The Alchemist which he finds magical and inspiring.", - "D27:18" - ], - [ - "John's favorite fantasy film is Lord of the Rings for its adventure, world, and characters.", - "D27:22" - ], - [ - "John's favorite character from Lord of the Rings is Aragorn because of his growth and leadership.", - "D27:24" - ], - [ - "John has a painting in his room as a reminder to stay true and be a leader.", - "D27:28" - ] - ] - }, - "session_28_observation": { - "Tim": [ - [ - "Tim got accepted into a study abroad program in Ireland.", - "D28:1" - ], - [ - "Tim will be staying in Galway for its arts and Irish music.", - "D28:3" - ], - [ - "Tim is excited to explore the nature in Galway.", - "D28:5" - ], - [ - "Tim wants to visit The Cliffs of Moher for the ocean views and cliffs.", - "D28:7" - ], - [ - "Tim is currently reading a fantasy novel called 'The Name of the Wind' by Patrick Rothfuss.", - "D28:17" - ] - ], - "John": [ - [ - "John held a benefit basketball game that was a success and raised money for charity.", - "D28:12" - ], - [ - "John expressed that basketball brings people together and creates a positive impact.", - "D28:14" - ], - [ - "John is keen on stopping by The Cliffs of Moher after his season.", - "D28:8" - ], - [ - "John is adding 'The Name of the Wind' by Patrick Rothfuss to his reading list.", - "D28:18" - ] - ] - }, - "session_29_observation": { - "Tim": [ - [ - "Tim is researching visa requirements for countries he wants to visit.", - "D29:3" - ], - [ - "Tim feels that taking initiative to research visas is a step towards making his travel dreams a reality.", - "D29:9" - ], - [ - "Tim expresses excitement about sharing his future travel adventures with John.", - "D29:10" - ], - [ - "Tim shows interest in John's recommendation of visiting Barcelona and plans to add it to his travel list.", - "D29:13" - ] - ], - "John": [ - [ - "John recently got an endorsement with a popular beverage company, which he describes as a dream come true.", - "D29:4" - ], - [ - "John feels that all the hard work he put in paid off with the endorsement deal.", - "D29:6" - ], - [ - "John believes that reaching a goal is rewarding and a sign of moving in the right direction.", - "D29:8" - ], - [ - "John recommends Barcelona as a must-visit city for Tim, mentioning cultural exploration, architecture, food, and beaches as highlights.", - "D29:12" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "John and Tim had a conversation at 7:48 pm on 21 May, 2023. Tim is working on a Harry Potter fan project while John recently signed with the Minnesota Wolves as a shooting guard. John's goal is to improve his shooting percentage for the upcoming season. He mentioned facing challenges fitting into the new team's style of play during pre-season. Tim's fan project involves collaborations discussing various Harry Potter universe aspects. Tim has visited Harry Potter-related places in London and encouraged John to do the same. Tim offered tips for visiting and bid farewell.", - "session_2_summary": "Tim and John had a conversation at 5:08 pm on 15 June, 2023. Tim shared his excitement about discussing fantasy books on a forum, while John talked about exploring endorsement opportunities with sports brands like Nike and Under Armour. Tim admired a picture from MinaLima, creators of props for Harry Potter films, expressing his love for the wizarding world. John expressed his passion for playing basketball, sharing a photo from a recent game. They both supported each other's interests and dreams, ending with Tim encouraging John to keep pursuing his goals.", - "session_3_summary": "John and Tim met at 4:21 on July 16, 2023. John shared about scoring 40 points in a game, celebrating with teammates, and securing endorsements with Nike and Gatorade. Tim talked about a magical chat with a Harry Potter fan in California and the love for the sport community. They discussed Seattle, surfing, and finding happiness through different activities, ending the conversation on a positive note.", - "session_4_summary": "At 4:17 pm on 2 August, 2023, Tim excitedly told John about writing articles on fantasy novels for an online magazine, a passion project he found through a fantasy lit forum. John congratulated Tim and enquired about the kind of articles he was working on. Tim mentioned analyzing characters, themes, and book recommendations. They discussed favorite books like Harry Potter and Game of Thrones. John shared a Harry Potter trivia experience, while Tim encouraged John to keep dreaming inspired by a book he's reading. Both expressed a desire to reach new goals before bidding farewell.", - "session_5_summary": "Tim and John spoke at 10:29 am on 9 August, 2023. Tim mentioned reconnecting with a Harry Potter fan and discussing collaboration. John shared about winning a close game and feeling the adrenaline rush. They talked about the importance of a supportive team and discussed Tim's current fantasy book by Patrick Rothfuss. John agreed to read it and they ended the conversation with plans to chat soon.", - "session_6_summary": "On August 11, 2023, at 1:08 pm, John told Tim he had an amazing trip to Chicago, loving the energy and meeting friendly locals. Tim shared his excitement about attending a fantastic event recently. They discussed the energy in sports games and sharing their passions. Tim recommended a fantasy novel, \"The Name of the Wind,\" to John, who shared stories about his lucky basketball shoes symbolizing resilience and dedication. John explained his basketball journey from childhood to being drafted. He mentioned his big goal of winning a championship and giving back to the community through charity work. Tim expressed admiration for John's plans and offered support, ending the conversation with encouragement.", - "session_7_summary": "John and Tim had a conversation at 7:54 pm on 17 August, 2023. John shared his excitement about reuniting with his basketball teammates on the 15th, feeling welcomed and appreciated by the team. Tim expressed his own sense of belonging at certain events where people shared his interests. John mentioned a gift from his teammates, a signed basketball, symbolizing their friendship and support. Tim noted the significance of such a reminder from friends. Both agreed on the power of supportive relationships in staying motivated and reaching goals. Tim ended the conversation by acknowledging the strength gained from mutual encouragement.", - "session_8_summary": "John and Tim caught up at 4:29 pm on 21 August, 2023. John shared his new gym routine to enhance his basketball game, emphasizing the importance of balance and rest. Tim praised John's holistic approach, leading to improved basketball performance. Tim then shared his focus on school, playing the piano, and favorite memories associated with \"Harry Potter.\" They discussed family traditions, favorite holiday movies like \"Home Alone\" and \"The Santa Clause,\" and shared photos of their festive decorations. They exchanged well wishes and agreed to talk soon, ending the conversation.", - "session_9_summary": "Tim mentioned his struggles with balancing studies and hobbies, specifically his love for fantasy reading. John shared about catching up with old friends from his high school sports team, where they played together for four years. Tim expressed his interest in traveling to experience different magical worlds through books and actual visits. John shared his experience exploring New York City and recommended it as a must-visit place. They both discussed their love for exploring new cities and hobbies. John offered assistance with Tim's travel plans, and Tim thanked him for his support before ending the conversation.", - "session_10_summary": "Tim and John caught up at 2:52 pm on 31 August, 2023. Tim shared his job rejection but remained positive. John discussed his NYC trip and troubles with the subway. Tim mentioned overcoming nerves to give a presentation. John shared his cooking experiment with a sage-flavored soup. Tim planned a trip to Universal Studios for the first time, excited for the Harry Potter attractions. They ended the conversation with well-wishes and a promise to catch up soon.", - "session_11_summary": "John and Tim caught up at 8:17 pm on 21 September, 2023. John shared about connecting with new teammates over basketball at a local restaurant. Tim suggested Edinburgh as a destination for their upcoming team trip, which John found appealing. They discussed John's passion for basketball, his career goals, and plans for life after sports. Tim advised John on picking endorsements and recommended a fantasy novel for his trip. John shared his bookshelf and praised \"The Alchemist.\" They ended the conversation by wishing each other well.", - "session_12_summary": "At 3:00 pm on 2 October, 2023, Tim and John caught up. John shared about his intimate wedding ceremony in a greenhouse venue that followed safety protocols, filled with loved ones. Tim admired the special day and asked about favorite memories. John mentioned the magical moment of seeing his bride walk down the aisle and their first dance in a cozy restaurant. They expressed how love brings joy. Tim congratulated John, admired the love between the couple, and shared his favorite fantasy novels. They later discussed John's love for collecting jerseys and admiration for LeBron James. John met LeBron and saw him play live, finding it inspiring. Tim encouraged John to continue pursuing what he loves.", - "session_13_summary": "Tim and John reconnected at 1:50 pm on 13 October, 2023. Tim shared his excitement about attending a Harry Potter conference in the UK, feeling inspired by the magical atmosphere and the shared passion. John admired Tim's enthusiasm and shared his own connection with his team, discussing their recent sports season with tough losses and great wins. Tim congratulated John on their success, acknowledging the hard work that paid off with a trophy. John appreciated Tim's support and encouragement to stay motivated, while Tim recommended some comfortable shoes for John to check out. They ended their conversation with mutual support and a commitment to chase their dreams, promising to keep in touch.", - "session_14_summary": "John and Tim caught up at 1:50 pm on October 17, 2023, after a long time. John is now mentoring young players, finding it rewarding and fulfilling. He enjoys being a positive role model for them and is proud to see their growth and success. Tim shared a stunning nature picture from the Smoky Mountains, emphasizing the beauty of nature and the importance of taking a break from city life. They discussed finding balance in life, with Tim mentioning his busy school schedule and love for reading, while John expressed his recent enjoyment of reading.", - "session_15_summary": "Tim and John, who are both passionate about exploring historical architecture and writing, catch up on their interests. Tim is working on a fantasy novel inspired by books, movies, and real-life experiences, including visits to UK castles that give him ideas. He admires J.K. Rowling's captivating writing style and shares a quote that inspires him. John, who finds motivation through quotes and strategies during tough workouts, values his supportive teammates and family. They bond over their shared love for spending time with friends and family, enjoying activities like road trips, hiking, and cooking. John shares a honey garlic chicken recipe with Tim after discussing their interests. Their friendly conversation ends on a positive note, expressing gratitude for their support networks.", - "session_16_summary": "Tim and John caught up at 11:41 am on 6 November, 2023. Tim shared how he overcame a writing issue by staying persistent. John likened it to toughing it out in sports. Both agreed that struggles lead to growth and satisfaction. John recounted a basketball game where his team made a remarkable comeback. Tim showed his basketball signed by his favorite player, LeBron, who inspires him with his determination. They discussed memorable moments in basketball and the importance of perseverance. John mentioned his upcoming European vacation, seeking recommendations from Tim who suggested visiting castles. They wished each other well and vowed to stay connected.", - "session_17_summary": "John and Tim had a chat at 3:36 pm on 11 November, 2023. John shared about his recent road trip on the European coastline with his wife, praising the beautiful views and the bonding experience. Tim admired the trip and discussed the power and beauty of nature. They bonded over their love for fantasy stories and shows. Tim highlighted the importance of mental breaks for recharging, gaining new perspectives, and tackling challenges with renewed energy. John appreciated the support and emphasized the significance of finding balance and taking care of oneself. They ended their conversation with well wishes.", - "session_18_summary": "Tim told John about his trip to a castle in the UK last Friday, praising its architecture and history. John mentioned a tough week due to an injury but staying positive. Tim shared being swamped with exams but staying optimistic and working hard. John asked about exam prep and shared visualizing goals for motivation. Tim discussed breaking study sessions into smaller parts for better focus. They wished each other well, with John hoping for a quick recovery from the injury. Tim expressed understanding and support. John expressed missing playing due to the injury, likening it to Tim not being able to read. They encouraged each other to keep going and promised to talk soon. The conversation took place at 3:59 pm on 16 November, 2023.", - "session_19_summary": "Tim and John caught up at 10:22 am on 21 November 2023. Tim mentioned struggling with storytelling and sought advice from John. They discussed overcoming challenges, with John sharing his experience recovering from an ankle injury. They emphasized resilience and motivation in facing difficulties. They also bonded over their love for reading, particularly fantasy and personal development books. Both found inspiration in books and shared a commitment to growth and improvement.", - "session_20_summary": "Tim and John had a conversation at 9:52 am on 1 December, 2023. Tim shared his success in a tough exam, stating he turned it into a learning experience. John congratulated Tim and mentioned trying out yoga to improve strength and flexibility. They discussed yoga poses and John shared a photo of one he enjoys. They talked about their mutual love for fantasy books and movies. Tim shared a photo of a forest, and John shared a photo from a camping trip in the mountains. They both reflected on the calming effects of nature. The conversation ended with both agreeing on the humbling and connected nature of the world before Tim bid farewell.", - "session_21_summary": "At 5:34 pm on 6 December 2023, Tim told John he joined a travel club to explore different cultures and meet new people. John, a professional basketball player for almost a year, discussed his growth on and off the court. Tim mentioned joining the travel club, studying, and learning the violin. John praised Tim's dedication, and they discussed motivating others. They shared tips and encouragement before ending the conversation.", - "session_22_summary": "Tim and John, who are both passionate about Harry Potter, discussed their recent activities at 7:42 pm on 8 December, 2023. Tim shared his experience at a Harry Potter party where he wore a Gryffindor scarf and enjoyed meeting like-minded people. John talked about his recent basketball games, emphasizing the camaraderie and unity within his team. They also discussed their reading preferences, with John recommending \"Dune\" by Frank Herbert and Tim recommending \"A Dance with Dragons\". They both expressed interest in each other's recommendations and wished each other a great day, ending the conversation on a positive note.", - "session_23_summary": "John and Tim, who met at 8:28 pm on 11 December 2023, had a conversation where Tim shared about his English lit class struggles and John talked about his recent career-high in assists. John described a thrilling game against their rivals and reminisced about a buzzer-beater shot. They discussed the power of sports and chasing dreams, inspiring and motivating each other before saying goodbye.", - "session_24_summary": "At 3:37 pm on 16 December 2023, Tim and John caught up. John shared about a close basketball game his team won, which was thrilling for him. Tim admired John's dedication to the sport despite facing an injury recently. John revealed he was progressing well with his recovery and had a milestone moment jogging without pain. Tim congratulated him and encouraged him to stay positive. The two friends discussed the importance of staying motivated and supporting each other, ending their conversation on a positive note.", - "session_25_summary": "Tim and John caught up after a while at 10:04 am on 19 December, 2023. John shared his excitement about a deal with an outdoor gear company and a successful photoshoot in a beautiful forest. Tim admired the epic photos and discussed the peaceful spot that inspired John. John also mentioned his team's progress in basketball, especially in communication and bonding. Tim encouraged John to keep believing in himself before concluding their conversation.", - "session_26_summary": "John and Tim caught up at 3:35 pm on 26 December, 2023. John shared about his busy week starting seminars to help people with sports and marketing, which went well. Tim mentioned reading traveler stories to plan his next adventure, including one about hiking in the Himalayas. They discussed the importance of challenges, their admiration for Emma Watson's advocacy, and how they use their platforms for good causes. John talked about supporting youth sports and creating opportunities for young athletes. Tim enjoys escaping in books and movies, particularly fantasy genres like \"The Lord of the Rings\", and looks forward to a new TV series, \"The Wheel of Time\". They both share a love for movie marathons and making a difference in the world.", - "session_27_summary": "Tim and John spoke at 5:26 pm on 2 January 2024. Tim mentioned his involvement in a globetrotting group and his interest in learning German. John shared his recent trip to Italy and knowledge of German and Spanish. They discussed language learning resources and shared their passion for reading and favorite fantasy books and movies, such as \"Harry Potter\" and \"Star Wars\" for Tim, and \"Lord of the Rings\" for John, with a particular focus on the character Aragorn. They highlighted the inspiration drawn from these stories and the importance of perseverance and leadership. Their conversation touched on the immersive nature of fantasy stories, traveling, and cultural exploration. John shared his painting of Aragorn as a reminder to be a leader, emphasizing the character's qualities of bravery and selflessness. They agreed on the educational and eye-opening aspects of both traveling and delving into fantasy worlds.", - "session_28_summary": "Tim and John caught up at 5:24 pm on 7 January, 2024. Tim shared that he got accepted into a study abroad program and will be heading to Ireland for a semester, staying in Galway known for its arts and music. He expressed excitement about exploring the nature and visiting The Cliffs of Moher. John complimented Tim's plans and mentioned hosting a successful benefit basketball game. Tim praised John for making a positive impact through sports. They discussed books and Tim recommended \"The Name of the Wind\" by Patrick Rothfuss to John. They ended the conversation on a positive note, planning to stay in touch.", - "session_29_summary": "Tim and John spoke at 1:41 pm on 12 January, 2024. Tim shared his excitement about researching visa requirements for upcoming travels, while John revealed his recent endorsement deal with a beverage company, expressing pride in his hard work paying off. Tim praised John's achievement, highlighting the importance of accomplishment and self-esteem boosts. John recommended Barcelona as a travel destination, which Tim happily added to his list. They concluded the conversation with Tim offering to return the favor and John offering support in the future." - }, - "sample_id": "conv-43" - }, - { - "qa": [ - { - "question": "Which year did Audrey adopt the first three of her dogs?", - "answer": "2020", - "evidence": [ - "D1:7" - ], - "category": 2 - }, - { - "question": "When did Andrew start his new job as a financial analyst?", - "answer": "The week before March 27, 2023", - "evidence": [ - "D1:2" - ], - "category": 2 - }, - { - "question": "What kind of indoor activities has Andrew pursued with his girlfriend?", - "answer": "boardgames, volunteering at pet shelter, wine tasting, growing flowers", - "evidence": [ - "D13:1", - "D23:1", - "D25:1", - "D19:15" - ], - "category": 1 - }, - { - "question": "What kind of places have Andrew and his girlfriend checked out around the city?", - "answer": "cafes, new places to eat, open space for hikes, pet shelter, wine tasting event, park", - "evidence": [ - "D3:1", - "D3:11", - "D4:2", - "D6:1", - "D13:1", - "D23:3", - "D25:1", - "D27:1" - ], - "category": 1 - }, - { - "question": "When did Audrey make muffins for herself?", - "answer": "The week of April 3rd to 9th", - "evidence": [ - "D3:18" - ], - "category": 2 - }, - { - "question": "When did Audrey see a hummingbird?", - "answer": "first week of May 2023", - "evidence": [ - "D4:1" - ], - "category": 2 - }, - { - "question": "When did Audrey adopt Pixie?", - "answer": "around April 2, 2023", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "How many years passed between Audrey adopting Pixie and her other three dogs?", - "answer": "three years", - "evidence": [ - "D2:1", - "D1:7" - ], - "category": 2 - }, - { - "question": "Did Andrew have a pet dog during March 2023?", - "answer": "No", - "evidence": [ - "D2:8" - ], - "category": 2 - }, - { - "question": "What kind of classes or groups has Audrey joined to take better care of her dogs?", - "answer": "positive reinforcement training workshop to bond with pets, dog training course, agility training course, grooming course, dog-owners group", - "evidence": [ - "D6:2", - "D10:1", - "D14:2", - "D16:6", - "D27:2" - ], - "category": 1 - }, - { - "question": "When did Audrey's positive reinforcement training course for dogs take place?", - "answer": "June, 2023", - "evidence": [ - "D6:2" - ], - "category": 2 - }, - { - "question": "When did Andrew go rock climbing?", - "answer": "June 11, 2023", - "evidence": [ - "D8:1" - ], - "category": 2 - }, - { - "question": "What outdoor activities has Andrew done other than hiking in nature?", - "answer": "rock climbing, fishing, camping", - "evidence": [ - "D8:1", - "D17:1", - "D14:1" - ], - "category": 1 - }, - { - "question": "When did Audrey move to a new place?", - "answer": "June 2023", - "evidence": [ - "D9:1" - ], - "category": 2 - }, - { - "question": "What is something that Andrew really misses while working in the city?", - "answer": "being in nature", - "evidence": [ - "D3:7", - "D9:20" - ], - "category": 1 - }, - { - "question": "What is a shared frustration regarding dog ownership for Audrey and Andrew?", - "answer": "Not being able to find pet friendly spots.", - "evidence": [ - "D7:8", - "D10:5" - ], - "category": 1 - }, - { - "question": "When is Andrew going to go hiking with Audrey?", - "answer": "August", - "evidence": [ - "D11:7" - ], - "category": 2 - }, - { - "question": "How many times did Audrey and Andew plan to hike together?", - "answer": "three times", - "evidence": [ - "D11:7", - "D24:13", - "D26:20" - ], - "category": 1 - }, - { - "question": "Where did Audrey get Pixie from?", - "answer": "breeder", - "evidence": [ - "D11:4", - "D2:1" - ], - "category": 1 - }, - { - "question": "What is an indoor activity that Andrew would enjoy doing while make his dog happy?", - "answer": "cook dog treats", - "evidence": [ - "D10:12", - "D12:1" - ], - "category": 3 - }, - { - "question": "Which meat does Audrey prefer eating more than others?", - "answer": "chicken", - "evidence": [ - "D10:13", - "D10:23" - ], - "category": 3 - }, - { - "question": "What are the classes that Audrey took for her dogs to?", - "answer": "Positive reinforcement training class for bonding, dog training course, agility class", - "evidence": [ - "D6:4", - "D10:1", - "D14:2" - ], - "category": 1 - }, - { - "question": "Where did Andrew go during the first weekend of August 2023?", - "answer": "camping with girlfriend", - "evidence": [ - "D14:1" - ], - "category": 2 - }, - { - "question": "What are some problems that Andrew faces before he adopted Toby?", - "answer": "Finding the right dog and pet-friendly apartments close to open spaces", - "evidence": [ - "D2:12", - "D5:3", - "D5:5", - "D5:7" - ], - "category": 1 - }, - { - "question": "Did Audrey and Andrew grow up with a pet dog?", - "answer": "Yes", - "evidence": [ - "D2:16", - "D13:10" - ], - "category": 1 - }, - { - "question": "When did Andrew and his girlfriend go fishing?", - "answer": "weekend before August 24, 2023", - "evidence": [ - "D17:1" - ], - "category": 2 - }, - { - "question": "What is the biggest stressor in Andrew's life besides not being able to hike frequently?", - "answer": "work", - "evidence": [ - "D12:3", - "D16:1", - "D18:1", - "D10:16" - ], - "category": 1 - }, - { - "question": "How does Andrew feel about his current work?", - "answer": "Stressful", - "evidence": [ - "D12:3", - "D16:1", - "D18:1", - "D10:16" - ], - "category": 1 - }, - { - "question": "What is something that Audrey often dresses up her dogs with?", - "answer": "Hats", - "evidence": [ - "D4:23", - "D4:25", - "D19:6" - ], - "category": 1 - }, - { - "question": "What are the names of Audrey's dogs?", - "answer": "Pepper, Precious, Panda, and Pixie", - "evidence": [ - "D1:7", - "D2:1", - "D19:12" - ], - "category": 1 - }, - { - "question": "When is Andrew planning to go to the beach with his girlfriend?", - "answer": "November 2023", - "evidence": [ - "D20:1" - ], - "category": 2 - }, - { - "question": "What has Andrew done with his dogs?", - "answer": "Taking walks and hiking", - "evidence": [ - "D14:27", - "D24:8" - ], - "category": 1 - }, - { - "question": "What kind of tattoo does Audrey have on her arm?", - "answer": "Tattoos of her four dogs.", - "evidence": [ - "D3:26", - "D15:1", - "D23:20" - ], - "category": 1 - }, - { - "question": "What can Andrew potentially do to improve his stress and accomodate his living situation with his dogs?", - "answer": "Change to a hybrid or remote job so he can move away from the city to the suburbs to have a larger living space and be closer to nature.", - "evidence": [ - "D12:3", - "D18:1", - "D21:5" - ], - "category": 3 - }, - { - "question": "How many months passed between Andrew adopting Toby and Buddy?", - "answer": "three months", - "evidence": [ - "D12:1", - "D24:2" - ], - "category": 2 - }, - { - "question": "What are the names of Andrew's dogs?", - "answer": "Toby, Scout, Buddy", - "evidence": [ - "D12:1", - "D24:6", - "D28:8" - ], - "category": 1 - }, - { - "question": "What are some foods that Audrey likes eating?", - "answer": "chicken pot pie, chicken roast, blueberry muffins, sushi", - "evidence": [ - "D3:18", - "D10:13", - "D10:23", - "D25:6" - ], - "category": 1 - }, - { - "question": "When did Audrey get into an accident in the park?", - "answer": "between October 19 and 24, 2023", - "evidence": [ - "D25:2" - ], - "category": 2 - }, - { - "question": "When did Andrew and his girlfriend go on a wine tasting trip?", - "answer": "the weekend before October 24, 2023", - "evidence": [ - "D25:1" - ], - "category": 2 - }, - { - "question": "What did Audrey get wtih having so many dogs?", - "answer": "Companionship", - "evidence": [ - "D2:15", - "D23:18" - ], - "category": 1 - }, - { - "question": "What is a good place for dogs to run around freely and meet new friends?", - "answer": "The dog park", - "evidence": [ - "D4:25", - "D14:2", - "D23:10" - ], - "category": 1 - }, - { - "question": "What are the breeds of Audrey's dogs?", - "answer": "Mongrel mixed with Lab for Pepper and Panda. Mongrel mixed with Chihuahua for Precious and Pixie.", - "evidence": [ - "D19:12", - "D26:13" - ], - "category": 1 - }, - { - "question": "What technique is Audrey using to discipline her dogs?", - "answer": "Positive reinforcement", - "evidence": [ - "D6:4", - "D26:5" - ], - "category": 1 - }, - { - "question": "Which US state do Audrey and Andrew potentially live in?", - "answer": "Minnesota", - "evidence": [ - "D11:9" - ], - "category": 3 - }, - { - "question": "Which national park could Audrey and Andrew be referring to in their conversations?", - "answer": "Voyageurs National Park", - "evidence": [ - "D5:8", - "D11:9" - ], - "category": 3 - }, - { - "question": "How many pets will Andrew have, as of December 2023?", - "answer": "three", - "evidence": [ - "D12:1", - "D24:2", - "D28:6" - ], - "category": 2 - }, - { - "question": "How many pets did Andrew have, as of September 2023?", - "answer": "one", - "evidence": [ - "D12:1", - "D24:2" - ], - "category": 2 - }, - { - "question": "How many months passed between Andrew adopting Buddy and Scout", - "answer": "one month", - "evidence": [ - "D24:2", - "D28:6" - ], - "category": 2 - }, - { - "question": "What does Andrew view his pets as?", - "answer": "Family", - "evidence": [ - "D15:14", - "D28:18" - ], - "category": 1 - }, - { - "question": "What does Audrey view her pets as?", - "answer": "Family", - "evidence": [ - "D15:15", - "D23:18" - ], - "category": 1 - }, - { - "question": "What is a skill that Audrey learned to take care of her dogs?", - "answer": "Grooming", - "evidence": [ - "D16:2", - "D17:4" - ], - "category": 1 - }, - { - "question": "What items has Audrey bought or made for her dogs?", - "answer": "dog tags, toys, dog beds, collars", - "evidence": [ - "D1:2", - "D9:5", - "D18:10", - "D24:1" - ], - "category": 1 - }, - { - "question": "What is something that Andrew could do to make birdwatching hobby to fit in his city schedule?", - "answer": "Install a bird feeder outside where he can see the birds without going outdoors.", - "evidence": [ - "D20:5", - "D20:21", - "D23:1", - "D1:14" - ], - "category": 3 - }, - { - "question": "What is a career that Andrew could potentially pursue with his love for animals and nature?", - "answer": "Park ranger or a similar position working for the National Park Services.", - "evidence": [ - "D2:18", - "D3:1", - "D5:7", - "D8:27" - ], - "category": 3 - }, - { - "question": "What activity do Audrey's dogs like to do in the dog park?", - "answer": "Play fetch with ball and frisbee, run around and meet other dogs", - "evidence": [ - "D4:21", - "D10:7", - "D13:8", - "D23:14", - "D27:12" - ], - "category": 1 - }, - { - "question": "When did Andrew make his dogs a fun indoor area?", - "answer": "few days before November 22, 2023", - "evidence": [ - "D28:12" - ], - "category": 2 - }, - { - "question": "Has Andrew moved into a new apartment for his dogs?", - "answer": "No", - "evidence": [ - "D5:5", - "D28:12" - ], - "category": 1 - }, - { - "question": "When did Andrew adopt Scout?", - "answer": "few days before November 2023", - "evidence": [ - "D28:6" - ], - "category": 2 - }, - { - "question": "What did Audrey eat for dinner on October 24, 2023?", - "answer": "sushi", - "evidence": [ - "D25:14" - ], - "category": 2 - }, - { - "question": "How long has it been since Andrew adopted his first pet, as of November 2023?", - "answer": "4 months", - "evidence": [ - "D12:1" - ], - "category": 2 - }, - { - "question": "How many dogs does Andrew have?", - "answer": "3", - "evidence": [ - "D12:1", - "D24:2", - "D28:6" - ], - "category": 1 - }, - { - "question": "Which specific type of bird mesmerizes Andrew?", - "answer": "Eagles", - "evidence": [ - "D1:16" - ], - "category": 4 - }, - { - "question": "What did Andrew express missing about exploring nature trails with his family's dog?", - "answer": "The peaceful moments", - "evidence": [ - "D2:18" - ], - "category": 4 - }, - { - "question": "What kind of pastries did Andrew and his girlfriend have at the cafe?", - "answer": "croissants, muffins, and tarts", - "evidence": [ - "D3:17" - ], - "category": 4 - }, - { - "question": "What kind of flowers does Audrey have a tattoo of?", - "answer": "sunflowers", - "evidence": [ - "D3:26" - ], - "category": 4 - }, - { - "question": "What does Audrey do during dog playdates in the park?", - "answer": "chat with people while dogs make new friends", - "evidence": [ - "D4:21" - ], - "category": 4 - }, - { - "question": "What type of dog was Andrew looking to adopt based on his living space?", - "answer": "smaller dog", - "evidence": [ - "D5:3" - ], - "category": 4 - }, - { - "question": "Where does Andrew want to live to give their dog a large, open space to run around?", - "answer": "near a park or woods", - "evidence": [ - "D5:7" - ], - "category": 4 - }, - { - "question": "Why did Audrey sign up for a workshop about bonding with pets?", - "answer": "Strengthen the bond with her pets", - "evidence": [ - "D6:2" - ], - "category": 4 - }, - { - "question": "How did Audrey hear about the workshop on bonding with pets?", - "answer": "Saw a workshop flyer at the local pet store", - "evidence": [ - "D6:4" - ], - "category": 4 - }, - { - "question": "What type of training was the workshop Audrey signed up for in May 2023?", - "answer": "Positive reinforcement training", - "evidence": [ - "D6:4" - ], - "category": 4 - }, - { - "question": "How did Audrey describe she dog he met at the pet store?", - "answer": "Friendly and playful", - "evidence": [ - "D6:4" - ], - "category": 4 - }, - { - "question": "Why did Audrey think positive reinforcement training is important for pets?", - "answer": "To have pets learn how to behave in a positive way", - "evidence": [ - "D6:12" - ], - "category": 4 - }, - { - "question": "What challenge is Andrew facing in their search for a pet?", - "answer": "Finding a pet-friendly spot in the city", - "evidence": [ - "D7:8" - ], - "category": 4 - }, - { - "question": "How does Andrew feel about their search for a pet-friendly place?", - "answer": "Discouraged but determined", - "evidence": [ - "D7:8" - ], - "category": 4 - }, - { - "question": "What outdoor activities does Andrew plan on trying after the rock climbing class?", - "answer": "kayaking and bungee jumping", - "evidence": [ - "D8:7" - ], - "category": 4 - }, - { - "question": "How long does Audrey typically walk her dogs for?", - "answer": "about an hour", - "evidence": [ - "D8:14" - ], - "category": 4 - }, - { - "question": "What did Audrey set up in the backyard for their dogs on June 26, 2023?", - "answer": "a doggy play area with agility stuff and toys", - "evidence": [ - "D9:5" - ], - "category": 4 - }, - { - "question": "What did Audrey and her friends stumble across during a hike a few years back, as mentioned on June 26, 2023?", - "answer": "a stunning lake in the mountains", - "evidence": [ - "D9:23" - ], - "category": 4 - }, - { - "question": "What is Audrey's favorite recipe that she shares with Andrew on 3 July, 2023?", - "answer": "Chicken Pot Pie", - "evidence": [ - "D10:13" - ], - "category": 4 - }, - { - "question": "What dish is one of Audrey's favorite dishes that includes garlic and is shared with Andrew on 3 July, 2023?", - "answer": "Roasted Chicken", - "evidence": [ - "D10:23" - ], - "category": 4 - }, - { - "question": "What did Andrew and his GF do on the Monday before July 24, 2023?", - "answer": "volunteered at a pet shelter", - "evidence": [ - "D13:1" - ], - "category": 4 - }, - { - "question": "What is the name of Audrey's childhood dog?", - "answer": "Max", - "evidence": [ - "D13:8" - ], - "category": 4 - }, - { - "question": "What special memories does Audrey have with her childhood dog, Max?", - "answer": "Long walks in the neighborhood, exploring new paths, sharing worries and hopes", - "evidence": [ - "D13:10" - ], - "category": 4 - }, - { - "question": "What are some of the personalities of Audrey's four fur babies?", - "answer": "oldest is relaxed, second is playful, third can be naughty but loves cuddles, youngest is full of life", - "evidence": [ - "D13:6" - ], - "category": 4 - }, - { - "question": "What type of classes did Audrey start with her pups recently on 4 August, 2023?", - "answer": "Agility classes", - "evidence": [ - "D14:2" - ], - "category": 4 - }, - { - "question": "How often does Audrey take her pups to the park for practice?", - "answer": "Twice a week", - "evidence": [ - "D14:4" - ], - "category": 4 - }, - { - "question": "How long did the trail hike that Audrey went on with her pups take?", - "answer": "Two hours", - "evidence": [ - "D14:8" - ], - "category": 4 - }, - { - "question": "What advice did Audrey give to Andrew regarding grooming Toby?", - "answer": "Grooming slowly and gently, paying attention to sensitive areas like ears and paws. And remember to stay patient and positive throughout the grooming process.", - "evidence": [ - "D16:8" - ], - "category": 4 - }, - { - "question": "What is essential to keep the dogs looking good according to Audrey?", - "answer": "Daily brushing, regular baths, nail trims, and lots of love", - "evidence": [ - "D17:4" - ], - "category": 4 - }, - { - "question": "What did Audrey organize with the neighbors' dogs?", - "answer": "a doggy playdate", - "evidence": [ - "D18:6" - ], - "category": 4 - }, - { - "question": "What did Audrey do to give her dogs extra comfort as the weather cooled down?", - "answer": "Got new beds for them", - "evidence": [ - "D18:10" - ], - "category": 4 - }, - { - "question": "How does Audrey describe the new beds for her dogs?", - "answer": "Super cozy and comfy", - "evidence": [ - "D18:12" - ], - "category": 4 - }, - { - "question": "How did Audrey calm down her dog after the leash incident?", - "answer": "Petted, hugged, spoke calmly and slowly walked the dog", - "evidence": [ - "D19:4" - ], - "category": 4 - }, - { - "question": "How often does Audrey take her dogs for walks?", - "answer": "Multiple times a day", - "evidence": [ - "D19:10" - ], - "category": 4 - }, - { - "question": "What kind of flowers does Audrey take care of?", - "answer": "Peruvian Lilies", - "evidence": [ - "D19:20" - ], - "category": 4 - }, - { - "question": "What did Andrew learn from reading books about ecological systems?", - "answer": "about animals, plants, and ecosystems and how they work together", - "evidence": [ - "D20:25" - ], - "category": 4 - }, - { - "question": "What did Andrew suggest as a way to reduce carbon footprint?", - "answer": "biking or using public transport", - "evidence": [ - "D20:33" - ], - "category": 4 - }, - { - "question": "How does Andrew suggest helping the planet while also training the body?", - "answer": "by biking", - "evidence": [ - "D20:35" - ], - "category": 4 - }, - { - "question": "What did Audrey do with her pups over the weekend before 4th October, 2023?", - "answer": "Took them to the beach", - "evidence": [ - "D21:2" - ], - "category": 4 - }, - { - "question": "What was the reason Audrey couldn't walk her dogs for a period of time?", - "answer": "Knee injury", - "evidence": [ - "D22:1" - ], - "category": 4 - }, - { - "question": "What type of jewelry does Audrey make?", - "answer": "Jewelry made from recycled objects", - "evidence": [ - "D22:5" - ], - "category": 4 - }, - { - "question": "Why does Audrey make jewelry out of recycled objects?", - "answer": "To show love for creativity and sustainability", - "evidence": [ - "D22:5" - ], - "category": 4 - }, - { - "question": "What organization does Audrey donate a portion of his profits to?", - "answer": "Animal shelter", - "evidence": [ - "D22:7" - ], - "category": 4 - }, - { - "question": "How does Audrey help out the animal shelter?", - "answer": "By donating a portion of his profits frmo selling jwelery", - "evidence": [ - "D22:9" - ], - "category": 4 - }, - { - "question": "What type of games do Audrey's dogs like to play at the park?", - "answer": "Fetch and Frisbee", - "evidence": [ - "D23:14" - ], - "category": 4 - }, - { - "question": "What did Audrey make to thank her neighbors?", - "answer": "Goodies", - "evidence": [ - "D23:2" - ], - "category": 4 - }, - { - "question": "How do Audrey's dogs react to snow?", - "answer": "Confused", - "evidence": [ - "D23:12" - ], - "category": 4 - }, - { - "question": "How does Audrey describe her dogs' response to snow?", - "answer": "They definitely prefer nice, sunny days in the grass.", - "evidence": [ - "D23:12" - ], - "category": 4 - }, - { - "question": "What kind of experiences are Audrey's dogs the best companions for?", - "answer": "Exploring the great outdoors", - "evidence": [ - "D23:24" - ], - "category": 4 - }, - { - "question": "What activity do Andrew and Buddy enjoy doing together?", - "answer": "Walking", - "evidence": [ - "D24:8" - ], - "category": 4 - }, - { - "question": "What do Andrew and Buddy like doing on walks?", - "answer": "Checking out new hiking trails", - "evidence": [ - "D24:10" - ], - "category": 4 - }, - { - "question": "What cuisine did Andrew recently try at a new spot in town?", - "answer": "sushi", - "evidence": [ - "D25:3" - ], - "category": 4 - }, - { - "question": "Which type of sushi did Audrey suggest trying first to someone new to sushi?", - "answer": "California or salmon roll", - "evidence": [ - "D25:8" - ], - "category": 4 - }, - { - "question": "What type of date is Andrew going on Sunday?", - "answer": "picnic date", - "evidence": [ - "D26:20" - ], - "category": 4 - }, - { - "question": "What did Andrew and Audrey plan to do on the Saturday after October 28, 2023?", - "answer": "Go hiking", - "evidence": [ - "D26:20" - ], - "category": 4 - }, - { - "question": "What aspect of autumn does Andrew find beautiful?", - "answer": "The autumn colors", - "evidence": [ - "D26:36" - ], - "category": 4 - }, - { - "question": "What did Audrey do in November 2023 to better take care of her dogs?", - "answer": "Joined a dog owners group", - "evidence": [ - "D27:2" - ], - "category": 4 - }, - { - "question": "How often does Audrey meet up with other dog owners for tips and playdates?", - "answer": "Once a week", - "evidence": [ - "D27:4" - ], - "category": 4 - }, - { - "question": "What did Audrey share to show ways to keep dogs active in the city?", - "answer": "photography of a basket full of stuffed animals", - "evidence": [ - "D27:10" - ], - "category": 4 - }, - { - "question": "What type of activities does Audrey suggest for mental stimulation of the dogs?", - "answer": "puzzles, training, hide-and-seek", - "evidence": [ - "D27:14" - ], - "category": 4 - }, - { - "question": "What is Andrew planning to do with Scout, Toby, and Buddy?", - "answer": "Take them to a nearby park", - "evidence": [ - "D28:10" - ], - "category": 4 - }, - { - "question": "What did Andrew get for Scout to create a safe and fun space for them?", - "answer": "essentials like a bed, toys, and puppy pads", - "evidence": [ - "D28:12" - ], - "category": 4 - }, - { - "question": "Which specific type of bird mesmerizes Audrey?", - "evidence": [ - "D1:16" - ], - "category": 5, - "adversarial_answer": "Eagles" - }, - { - "question": "What kind of flowers does Andrew have a tattoo of?", - "evidence": [ - "D3:26" - ], - "category": 5, - "adversarial_answer": "sunflowers" - }, - { - "question": "What type of dog was Audrey looking to adopt based on her living space?", - "evidence": [ - "D5:3" - ], - "category": 5, - "adversarial_answer": "smaller dog" - }, - { - "question": "Why did Audrey sign up for a workshop about car maintenance?", - "evidence": [ - "D6:2" - ], - "category": 5, - "adversarial_answer": "Strengthen the bond with her pets" - }, - { - "question": "How did Andrew hear about the workshop on bonding with pets?", - "evidence": [ - "D6:4" - ], - "category": 5, - "adversarial_answer": "Saw a workshop flyer at the local pet store" - }, - { - "question": "What type of training was the workshop Andrew signed up for in May 2023?", - "evidence": [ - "D6:4" - ], - "category": 5, - "adversarial_answer": "Positive reinforcement training" - }, - { - "question": "How did Andrew describe the dog he met at the pet store?", - "evidence": [ - "D6:4" - ], - "category": 5, - "adversarial_answer": "Friendly and playful" - }, - { - "question": "What challenge is Audrey facing in their search for a pet?", - "evidence": [ - "D7:8" - ], - "category": 5, - "adversarial_answer": "Finding a pet-friendly spot in the city" - }, - { - "question": "What indoor activities does Andrew plan on trying after the rock climbing class?", - "evidence": [ - "D8:7" - ], - "category": 5, - "adversarial_answer": "kayaking and bungee jumping" - }, - { - "question": "What did Andrew set up in the backyard for their dogs on June 26, 2023?", - "evidence": [ - "D9:5" - ], - "category": 5, - "adversarial_answer": "a doggy play area with agility stuff and toys" - }, - { - "question": "What did Audrey and her GF do on the Monday before July 24, 2023?", - "evidence": [ - "D13:1" - ], - "category": 5, - "adversarial_answer": "volunteered at a pet shelter" - }, - { - "question": "What is the name of Andrew's childhood dog?", - "evidence": [ - "D13:8" - ], - "category": 5, - "adversarial_answer": "Max" - }, - { - "question": "What special memories does Andrew have with his childhood dog, Max?", - "evidence": [ - "D13:10" - ], - "category": 5, - "adversarial_answer": "Long walks in the neighborhood, exploring new paths, sharing worries and hopes" - }, - { - "question": "What are some of the personalities of Andrew's four fur babies?", - "evidence": [ - "D13:6" - ], - "category": 5, - "adversarial_answer": "oldest is relaxed, second is playful, third can be naughty but loves cuddles, youngest is full of life" - }, - { - "question": "What type of classes did Andrew start with his pups recently on 4 August, 2023?", - "evidence": [ - "D14:2" - ], - "category": 5, - "adversarial_answer": "Agility classes" - }, - { - "question": "What is essential to keep the dogs looking good according to Andrew?", - "evidence": [ - "D17:4" - ], - "category": 5, - "adversarial_answer": "Daily brushing, regular baths, nail trims, and lots of love" - }, - { - "question": "What did Audrey organize with the neighbors' cats?", - "evidence": [ - "D18:6" - ], - "category": 5, - "adversarial_answer": "a doggy playdate" - }, - { - "question": "What did Andrew do to give his dogs extra comfort as the weather cooled down?", - "evidence": [ - "D18:10" - ], - "category": 5, - "adversarial_answer": "Got new beds for them" - }, - { - "question": "How does Andrew describe the new beds for his dogs?", - "evidence": [ - "D18:12" - ], - "category": 5, - "adversarial_answer": "Super cozy and comfy" - }, - { - "question": "How did Andrew calm down his dog after the leash incident?", - "evidence": [ - "D19:4" - ], - "category": 5, - "adversarial_answer": "Petted, hugged, spoke calmly and slowly walked the dog" - }, - { - "question": "How often does Andrew take his dogs for walks?", - "evidence": [ - "D19:10" - ], - "category": 5, - "adversarial_answer": "Multiple times a day" - }, - { - "question": "What kind of vegetables does Audrey take care of?", - "evidence": [ - "D19:20" - ], - "category": 5, - "adversarial_answer": "Peruvian Lilies" - }, - { - "question": "What did Andrew learn from reading books about economic systems?", - "evidence": [ - "D20:25" - ], - "category": 5, - "adversarial_answer": "about animals, plants, and ecosystems and how they work together" - }, - { - "question": "What was the reason Andrew couldn't walk his dogs for a period of time?", - "evidence": [ - "D22:1" - ], - "category": 5, - "adversarial_answer": "Knee injury" - }, - { - "question": "What type of jewelry does Andrew make?", - "evidence": [ - "D22:5" - ], - "category": 5, - "adversarial_answer": "Jewelry made from recycled objects" - }, - { - "question": "Why does Andrew make jewelry out of recycled objects?", - "evidence": [ - "D22:5" - ], - "category": 5, - "adversarial_answer": "To show love for creativity and sustainability" - }, - { - "question": "What type of games do Andrew's dogs like to play at the park?", - "evidence": [ - "D23:14" - ], - "category": 5, - "adversarial_answer": "Fetch and Frisbee" - }, - { - "question": "What did Andrew make to thank his neighbors?", - "evidence": [ - "D23:2" - ], - "category": 5, - "adversarial_answer": "Goodies" - }, - { - "question": "How do Andrew's dogs react to snow?", - "evidence": [ - "D23:12" - ], - "category": 5, - "adversarial_answer": "Confused" - }, - { - "question": "How does Andrew describe his dogs' response to snow?", - "evidence": [ - "D23:12" - ], - "category": 5, - "adversarial_answer": "They definitely prefer nice, sunny days in the grass." - }, - { - "question": "What kind of experiences are Audrey's cats the best companions for?", - "evidence": [ - "D23:24" - ], - "category": 5, - "adversarial_answer": "Exploring the great outdoors" - }, - { - "question": "What activity do Audrey and Buddy enjoy doing together?", - "evidence": [ - "D24:8" - ], - "category": 5, - "adversarial_answer": "Walking" - }, - { - "question": "What type of drink did Andrew recently try at a new spot in town?", - "evidence": [ - "D25:3" - ], - "category": 5, - "adversarial_answer": "sushi" - }, - { - "question": "Which type of pizza did Audrey suggest trying first to someone new to Italian cuisine?", - "evidence": [ - "D25:8" - ], - "category": 5, - "adversarial_answer": "California or salmon roll" - }, - { - "question": "How often does Andrew meet up with other dog owners for tips and playdates?", - "evidence": [ - "D27:4" - ], - "category": 5, - "adversarial_answer": "Once a week" - } - ], - "conversation": { - "speaker_a": "Audrey", - "speaker_b": "Andrew", - "session_1_date_time": "1:10 pm on 27 March, 2023", - "session_1": [ - { - "speaker": "Audrey", - "dia_id": "D1:1", - "text": "Hey Andrew! Good to see ya! What's been up since we last talked?" - }, - { - "speaker": "Andrew", - "dia_id": "D1:2", - "text": "Hey Audrey! So, I started a new job as a Financial Analyst last week - it's been quite a change from my previous job. How about you? Anything interesting happening?" - }, - { - "speaker": "Audrey", - "dia_id": "D1:3", - "text": "Congrats on the new job! So I got these new collars and tags for my dogs - so cute!" - }, - { - "speaker": "Andrew", - "dia_id": "D1:4", - "text": "Thanks! That sounds cute. Can I see a picture?" - }, - { - "speaker": "Audrey", - "dia_id": "D1:5", - "img_url": [ - "https://worldanimalfoundation.org/wp-content/uploads/2022/07/adjustable-collar-Review.jpg" - ], - "re-download": true, - "blip_caption": "a photography of two dogs are standing in the grass with their mouths open", - "text": "Sure! See them with their new collars, cute right?" - }, - { - "speaker": "Andrew", - "dia_id": "D1:6", - "text": "Cute little guys! What are their names and how long have you had them?" - }, - { - "speaker": "Audrey", - "dia_id": "D1:7", - "text": "I've had them for 3 years! Their names are Pepper, Precious and Panda. I can't live without my little ones!" - }, - { - "speaker": "Andrew", - "dia_id": "D1:8", - "text": "That's awesome! Have you always wanted a dog, even with living in the city? Can they still go on adventures?" - }, - { - "speaker": "Audrey", - "dia_id": "D1:9", - "text": "Absolutely! They're city dogs and we explore all the time. They love trying out new parks and trails. We go on adventures together very often." - }, - { - "speaker": "Andrew", - "dia_id": "D1:10", - "text": "Wow, sounds like they make life so awesome! Kinda jealous of all those fun outings with them." - }, - { - "speaker": "Audrey", - "dia_id": "D1:11", - "text": "They really do! It's great how much happiness they bring. Do you have any pets?" - }, - { - "speaker": "Andrew", - "dia_id": "D1:12", - "text": "No, no pets right now. But I do love animals." - }, - { - "speaker": "Audrey", - "dia_id": "D1:13", - "text": "That's great to hear! Animals are truly amazing. Do you have a favorite animal?" - }, - { - "speaker": "Andrew", - "dia_id": "D1:14", - "text": "I've always been awed by birds. Their power to soar and explore new spots is amazing." - }, - { - "speaker": "Audrey", - "dia_id": "D1:15", - "text": "Yeah, birds are amazing! I can imagine it feels incredible to soar and see the world from up high. Do you have a favorite type of bird?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/g9ia8ap7ckmb1.jpg" - ], - "blip_caption": "a photo of two birds flying in the sky with a sun shining behind them", - "query": "majestic eagle soaring sky", - "dia_id": "D1:16", - "text": "Eagles have always mesmerized me; they're so strong and graceful!" - }, - { - "speaker": "Audrey", - "dia_id": "D1:17", - "text": "Yeah they're beautiful. Do you go bird-watching? It must be awesome to see them up close." - }, - { - "speaker": "Andrew", - "dia_id": "D1:18", - "text": "Haven't specifically gone out for bird-watching, but I do spot them when I hike." - }, - { - "speaker": "Audrey", - "dia_id": "D1:19", - "text": "Nice, spotting pretty birds while hiking must be great. Do you have any favorite hiking spots?" - }, - { - "speaker": "Andrew", - "dia_id": "D1:20", - "text": "Fox Hollow is a great trail to hike on weekends; the views are awesome!" - }, - { - "speaker": "Audrey", - "dia_id": "D1:21", - "text": "Cool, gonna give it a try. Thanks for the suggestion!" - }, - { - "speaker": "Andrew", - "dia_id": "D1:22", - "text": "No problem! Let me know how you like it. Have fun hiking!" - }, - { - "speaker": "Audrey", - "dia_id": "D1:23", - "text": "Thanks! I'll let you know. Have a good one!" - }, - { - "speaker": "Andrew", - "dia_id": "D1:24", - "text": "Take care and have a good one! See ya!" - } - ], - "session_2_date_time": "2:42 pm on 2 April, 2023", - "session_2": [ - { - "speaker": "Audrey", - "img_url": [ - "https://moderndogmagazine.com/sites/default/files/images/photoentries/photos/pixie%20smiling.jpg" - ], - "blip_caption": "a photo of a small white dog sitting on a carpet", - "query": "new puppy Pixie", - "dia_id": "D2:1", - "text": "Hey Andrew, I got a surprise for you! We adopted another puppy called Pixie. She's SO cute! Isn't she just the cutest?" - }, - { - "speaker": "Andrew", - "dia_id": "D2:2", - "text": "That's awesome! Pixie is so cute! Did you ever check out that hiking spot we talked about last time?" - }, - { - "speaker": "Audrey", - "dia_id": "D2:3", - "text": "Thanks! I know right? She's so cute! Pixie's been keeping us busy, so I haven't had a chance to check out that hiking spot yet." - }, - { - "speaker": "Andrew", - "dia_id": "D2:4", - "text": "Yeah, I get it. A puppy really takes some work. Has she settled in okay? Is she getting along with the other pups?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9zdGF0aWMvaW1hZ2Uvd2Vic2l0ZS8yMDIyLTA0L2xyL3B4NzIxNzk3LWltYWdlLWt3dnYzenBkLmpwZw.jpg" - ], - "blip_caption": "a photography of a dog playing with a group of other dogs", - "query": "pixie pepper precious panda dogs playing backyard", - "dia_id": "D2:5", - "re-download": true, - "text": "Pixie's fitting in great! It took her a few days to get used to the other dogs, but now they're awesome friends. They love playing and exploring the house - so cute!\n" - }, - { - "speaker": "Andrew", - "dia_id": "D2:6", - "text": "That's awesome! Is Pepper adjusting well to her?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i2.pickpik.com/photos/398/166/341/dogs-garden-enjoy-weather-animals-preview.jpg" - ], - "blip_caption": "a photography of two dogs standing in the grass near a fence", - "query": "pepper pixie playing together", - "dia_id": "D2:7", - "re-download": true, - "text": "Pepper took a bit to get used to her, but now they're always together!" - }, - { - "speaker": "Andrew", - "dia_id": "D2:8", - "text": "That's great to hear! I'm considering getting a dog too, but it can be challenging finding a dog-friendly place in the city. Do you have any tips on finding such a place?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/2388/2232782014_51a690ec00_c.jpg" - ], - "blip_caption": "a photography of a dog sitting in a field of flowers", - "query": "dog-friendly website", - "dia_id": "D2:9", - "re-download": true, - "text": "We used websites that helped us find a place that allowed dogs. They had filters that made it super easy. Found a perfect spot thanks to them!" - }, - { - "speaker": "Andrew", - "dia_id": "D2:10", - "text": "Wow, that's so cool! I'll give those websites a try, thanks for the info!" - }, - { - "speaker": "Audrey", - "dia_id": "D2:11", - "text": "You got it! I hope you find a great spot soon!" - }, - { - "speaker": "Andrew", - "dia_id": "D2:12", - "text": "Thanks! Fingers crossed for the apartment and that furry friend." - }, - { - "speaker": "Audrey", - "dia_id": "D2:13", - "text": "I'm keeping my fingers crossed for you to find a great furry friend and an apartment." - }, - { - "speaker": "Andrew", - "dia_id": "D2:14", - "text": "Hope I find one soon. Can't wait!" - }, - { - "speaker": "Audrey", - "dia_id": "D2:15", - "text": "You'll love them! They're great for cuddles and companionship." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/gnwdz3ysqra71.jpg" - ], - "blip_caption": "a photo of a dog sitting on a couch in a living room", - "query": "dog couch", - "dia_id": "D2:16", - "text": "It'd be so great to have a furry buddy to cuddle and hang with. Here's a photo of my family's dog on a couch. " - }, - { - "speaker": "Audrey", - "dia_id": "D2:17", - "img_url": [ - "https://images.pexels.com/photos/18690079/pexels-photo-18690079/free-photo-of-white-terrier-lying-on-a-sofa.jpeg" - ], - "re-download": true, - "blip_caption": "a photography of a dog laying on a couch with a pillow", - "text": "Our furry friends make great cuddle buddies. Here's one of them enjoying some lazy couch time!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://www.kingdukes.com/cdn/shop/articles/man_walking_dog_on_portland_trail_1200x.jpg" - ], - "blip_caption": "a photo of a man walking a dog on a trail", - "query": "dog hiking trail", - "dia_id": "D2:18", - "text": "That pic is so cute! It would be fun to hang out with a dog, cuddling away. Got me thinking of my old hiking pics and how much I miss exploring nature trails with my family's dog. Ah, the peaceful moments out in nature!" - }, - { - "speaker": "Audrey", - "dia_id": "D2:19", - "text": " Taking them on hikes must be awesome! That makes me want to hike with my dogs." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://images.pexels.com/photos/4430321/pexels-photo-4430321.jpeg" - ], - "blip_caption": "a photography of a man sitting on a rock with a dog", - "query": "hiking with dog rocks trail", - "dia_id": "D2:20", - "re-download": true, - "text": "Hiking with a dog is way fun, it's a great way to bond and create memories together." - }, - { - "speaker": "Audrey", - "dia_id": "D2:21", - "text": "Yeah! Exploring nature with them and making memories is awesome!" - }, - { - "speaker": "Andrew", - "dia_id": "D2:22", - "text": "Definitely! I miss those moments. I need to find a dog-friendly spot soon so I can get exploring again!" - }, - { - "speaker": "Audrey", - "dia_id": "D2:23", - "text": "Yep! I hope you find the perfect dog-friendly spot soon so you can make new memories with them." - }, - { - "speaker": "Andrew", - "dia_id": "D2:24", - "text": "Thanks! I'll keep looking until I find it. Super excited for what's next!" - }, - { - "speaker": "Audrey", - "dia_id": "D2:25", - "text": "So excited for you! I can't wait to see where you and your furry friend decide to hang out. All the best!" - }, - { - "speaker": "Andrew", - "dia_id": "D2:26", - "text": "I'll keep you posted. Ttyl, Bye!" - } - ], - "session_3_date_time": "4:19 pm on 16 April, 2023", - "session_3": [ - { - "speaker": "Andrew", - "dia_id": "D3:1", - "text": "Hey Audrey! What's up? Missed chatting with ya! Check it out, my girl & I tried out that new cafe scene in the city last weekend! Super fun but kinda sad not being out in nature - that's when I feel like I'm really thriving. Oh man, I miss the peacefulness of being out on a hike." - }, - { - "speaker": "Audrey", - "dia_id": "D3:2", - "text": "Hey Andrew! That cafe sounds pretty awesome, glad you enjoyed it. Yeah, I know what you mean. That feeling of being out in nature, surrounded by beauty and peace, is unbeatable, compared to being surrounded by concrete jungles. " - }, - { - "speaker": "Andrew", - "dia_id": "D3:3", - "text": "Yeah, being out in the nature really has a way of taking my breath away. It's like a home for my soul! I feel connected when I'm there." - }, - { - "speaker": "Audrey", - "dia_id": "D3:4", - "text": "I totally get it. Nature has a way of bringing us back down to earth and reminding us that we're part of something bigger. It's amazing." - }, - { - "speaker": "Andrew", - "dia_id": "D3:5", - "text": "Yeah, it's like hitting the reset button when life gets too chaotic. Nature has a way of being really soothing." - }, - { - "speaker": "Audrey", - "dia_id": "D3:6", - "text": "Definitely! For me, taking my dogs for a walk in the park helps me find my center and recharges me. " - }, - { - "speaker": "Andrew", - "dia_id": "D3:7", - "text": "I hear ya. Can\u2019t do that here unfortunately, city living makes it hard. Really miss that connection and companionship." - }, - { - "speaker": "Audrey", - "dia_id": "D3:8", - "text": "Must be tough living in the city without the opportunity to go outside. Sure is a different experience." - }, - { - "speaker": "Andrew", - "dia_id": "D3:9", - "text": "Yeah, it really does. Sometimes it feels like I'm missing out." - }, - { - "speaker": "Audrey", - "dia_id": "D3:10", - "text": "It must be tough. City life can get overwhelming, so it's great to find ways to connect to nature. What kind of things do you like to do in the city?" - }, - { - "speaker": "Andrew", - "dia_id": "D3:11", - "text": "My girlfriend and I love to discover new places to eat around town. It's a great way to try something new and wind down after a long week." - }, - { - "speaker": "Audrey", - "dia_id": "D3:12", - "text": "Wow, that sounds like a great way to explore the city! Trying new places and food is always fun. Have you discovered any new favorite spots recently?" - }, - { - "speaker": "Andrew", - "dia_id": "D3:13", - "text": "We found an awesome cafe with amazing pastries - the smell was just irresistible!" - }, - { - "speaker": "Audrey", - "dia_id": "D3:14", - "text": "Sounds amazing! Did you take a photo? I'd love to see!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://marianainla.com/wp-content/uploads/2021/12/unnamed-20.jpg" - ], - "blip_caption": "a photo of a bakery display with pastries and coffee", - "query": "mouth-watering pastries cafe", - "dia_id": "D3:15", - "text": "Yep, I got them! Check out this photo of the delicious pastries we had at the cafe." - }, - { - "speaker": "Audrey", - "dia_id": "D3:16", - "text": "Wow, they look great! What did you get? Now I'm craving pastries!" - }, - { - "speaker": "Andrew", - "dia_id": "D3:17", - "text": "They taste great too! We had some delicious croissants, muffins, and tarts! It was amazing!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://allisoncooksgoodeats.files.wordpress.com/2017/09/img_4467.jpg" - ], - "blip_caption": "a photo of a muffin pan filled with blueberries and muffins", - "query": "homemade blueberry muffin pastry", - "dia_id": "D3:18", - "text": "Wow, sounds amazing! Glad you got to enjoy them. Since you metioned pastries, I made some of my favorite treats last week. Let's have a pastry party sometime! " - }, - { - "speaker": "Andrew", - "dia_id": "D3:19", - "text": "That looks delicious! A pastry party sounds awesome, count me in!" - }, - { - "speaker": "Audrey", - "dia_id": "D3:20", - "text": "Awesome! My dogs will tag along. My furry friends would love it!" - }, - { - "speaker": "Andrew", - "dia_id": "D3:21", - "text": "Definitely! They'll have a great time at the party! Can't wait for the party!" - }, - { - "speaker": "Audrey", - "dia_id": "D3:22", - "text": "Same! Looking forward to it. This would be a good chance for you to hang out with them." - }, - { - "speaker": "Andrew", - "dia_id": "D3:23", - "text": "Yeah! It's gonna be awesome to see all of them together and chowing down. Super excited!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/3674/10799791393_b3a685158c_b.jpg" - ], - "blip_caption": "a photography of a dog running with three other dogs in the background", - "query": "pepper precious panda pixie playing park furry family", - "dia_id": "D3:24", - "re-download": true, - "text": "Yay! This'll be awesome! Seeing them all together, having fun and enjoying treats is the best. I can't imagine life without them, they bring so much joy." - }, - { - "speaker": "Andrew", - "dia_id": "D3:25", - "text": "Aww, so cute! I bet their happy faces means a lot to you." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.pinimg.com/originals/8d/e3/10/8de3100a3dd42a38237c9090965cfdfe.jpg" - ], - "blip_caption": "a photo of a woman with a tattoo of a dog and sunflowers", - "query": "tattoo dogs arm", - "dia_id": "D3:26", - "text": "I know right? They mean the world to me. So much that I got tattoos of them on my arm." - }, - { - "speaker": "Andrew", - "dia_id": "D3:27", - "text": "Wow, that tattoo looks great!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.pixexid.com/a-woman-is-joyfully-running-in-a-sunlit-forest-with-four-dogs-of-various-breeds-kn6f7vry.jpeg" - ], - "blip_caption": "a photography of a woman walking with her dogs in the woods", - "query": "dogs field nature", - "dia_id": "D3:28", - "re-download": true, - "text": "Thanks! I got it a while ago. It represents my love for my pups and nature's beauty." - }, - { - "speaker": "Andrew", - "dia_id": "D3:29", - "text": "Aww, that's cute! What made you getting the tattoo?" - }, - { - "speaker": "Audrey", - "dia_id": "D3:30", - "text": "I've always had a strong bond with them \u2013 they make me so happy, and I just want to see their happy faceseven when thry're not with me." - }, - { - "speaker": "Andrew", - "dia_id": "D3:31", - "text": "That's awesome, I'm glad they bring so much joy into your life." - } - ], - "session_4_date_time": "5:41 pm on 3 May, 2023", - "session_4": [ - { - "speaker": "Audrey", - "img_url": [ - "https://images.pexels.com/photos/7875455/pexels-photo-7875455.jpeg" - ], - "blip_caption": "a photography of a hummingbird sitting on a branch with its wings spread", - "query": "cute little bird perched branch hummingbird hike nectar flowers", - "dia_id": "D4:1", - "re-download": true, - "text": "Hey Andrew! Long time no talk! Last week I finally went on a hike and had this amazing experience with a hummingbird. It was so cool watching it dart around with its wings! Nature is so beautiful." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/okd8n7nb0hc71.jpg" - ], - "blip_caption": "a photo of a view of a lake and mountains at sunset", - "query": "sunset mountain landscape hiking", - "dia_id": "D4:2", - "text": "Hey Audrey! Glad to hear from you. That hummingbird was awesome! Nature's the best. Remember I was feeling down because I couldn't get out more? Well, good news - I found a new open space to hike nearby - feels so refreshing!" - }, - { - "speaker": "Audrey", - "dia_id": "D4:3", - "text": "That looks great! How often are you able to go hiking now?" - }, - { - "speaker": "Andrew", - "dia_id": "D4:4", - "text": "So I usually try to escape the city at least once a weekend - it's my much-needed break!" - }, - { - "speaker": "Audrey", - "dia_id": "D4:5", - "text": "Nice! I Bet it's great to get away like that." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/u51y7czkhfrb1.jpg" - ], - "blip_caption": "a photo of a mountain range with a sunset in the background", - "query": "mountain peak sunrise", - "dia_id": "D4:6", - "text": "Yep! Nature is so therapeutic for me. It's hard to put into words how great it feels to be surrounded by greenery or standing on top of a mountain breathing in the fresh air. So peaceful and invigorating.\n\n" - }, - { - "speaker": "Audrey", - "dia_id": "D4:7", - "text": "Wow, that view looks great! It really captures that calming and peaceful feeling. It must be really satisfying to capture that tranquility." - }, - { - "speaker": "Andrew", - "dia_id": "D4:8", - "text": "Yeah! I try to capture those special moments and share that feeling with others." - }, - { - "speaker": "Audrey", - "dia_id": "D4:9", - "text": "That's really cool! Spreading that peaceful feeling to others is such a great way to bring happiness and motivation." - }, - { - "speaker": "Andrew", - "dia_id": "D4:10", - "text": "I agree! It brings me joy to spread that feeling and motivate people to find their own peace - it's like a ripple effect, spreading positivity." - }, - { - "speaker": "Audrey", - "dia_id": "D4:11", - "text": "Absolutely. Not only the receiving end feels great, the person spreading it will feel great as well!" - }, - { - "speaker": "Andrew", - "dia_id": "D4:12", - "text": "Yeah, it's crazy how that can bring such great joy and calm to people." - }, - { - "speaker": "Audrey", - "dia_id": "D4:13", - "text": "Yeah, by sharing something can make a big difference to others." - }, - { - "speaker": "Andrew", - "dia_id": "D4:14", - "text": "Yup! We don't realize it, but even the littlest gestures can have a big effect. Spreading good vibes and joy, that's the goal right? So what's up? Anything new or fun going on?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i2.pickpik.com/photos/852/467/713/four-dogs-pack-papillon-hybrids-preview.jpg" - ], - "blip_caption": "a photography of a group of dogs sitting on top of a lush green field", - "query": "dogs playing together", - "dia_id": "D4:15", - "re-download": true, - "text": "Not much has changed since we last talked. I'm busy taking care of my pets and spending time with them. It's really fulfilling." - }, - { - "speaker": "Andrew", - "dia_id": "D4:16", - "text": "I bet it's awesome hanging out with them - do your dogs enjoy going on hikes?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://d3ekkvinch1ig5.cloudfront.net/rating/1665802443-8244image1.jpg" - ], - "blip_caption": "a photo of a dog running in a field with other dogs", - "query": "dogs playing fetch wide open field", - "dia_id": "D4:17", - "text": "My dogs go nuts when we go on hikes! They love exploring new scents and being in nature - it's their happy place. I can tell by their wagging tails and expressions how much they love it. It's so great to see them having so much fun!" - }, - { - "speaker": "Andrew", - "dia_id": "D4:18", - "text": "Wow, that's great! I was wondering if you have any tips or tricks for keeping dogs safe when they're out and about?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://d4yxl4pe8dqlj.cloudfront.net/images/061f1763-db03-4b77-a9c7-85a24844f99e/21929851-1c63-40ee-b7bd-d977fdb693be_full_size.jpg" - ], - "blip_caption": "a photo of a dog with a leash and shoes on", - "query": "dog wearing protective boots", - "dia_id": "D4:19", - "text": "Definitely! Safety is super important for me. I even got them something special. They sure look funny, but it works!" - }, - { - "speaker": "Andrew", - "dia_id": "D4:20", - "text": "Awesome to see that you take such good care of your dogs! What else do you do when you walk them?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.pexels.com/photos/6527909/pexels-photo-6527909.jpeg" - ], - "blip_caption": "a photography of a dog catching a frisbee in a field", - "query": "dog playing fetch park other dogs", - "dia_id": "D4:21", - "re-download": true, - "text": "When I take them out, we usually play fetch with a ball or frisbee. They love chasing it! We also meet other dog owners in the park and have a doggie playdate. It's a great way for me to chat with people and for them to make new friends." - }, - { - "speaker": "Andrew", - "dia_id": "D4:22", - "text": "Wow, looks like fun! They sure look excited about playing fetch!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/1a14cd4hzcd91.jpg" - ], - "blip_caption": "a photo of a dog wearing a party hat sitting on a couch", - "query": "dogs playing with party hats ball", - "dia_id": "D4:23", - "text": "They absolutely adore it! They have tons of energy and love meeting new pals at playdates. Plus, they always get excited when I bring those out." - }, - { - "speaker": "Andrew", - "dia_id": "D4:24", - "text": "Aww, that's so cute! Do they enjoy wearing the party hats? And where did you find their playdates?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/2913/14439335660_d4ffd00bbc_z.jpg" - ], - "blip_caption": "a photography of a dog jumping through a ring in a park", - "query": "dog park colorful playground", - "dia_id": "D4:25", - "re-download": true, - "text": "The hats don't bother them, they just put them on for fun and treats. And the dog park is great place to start! They can run and mingle with other pooches. It's such a fun spot for playdates!" - }, - { - "speaker": "Andrew", - "dia_id": "D4:26", - "text": "That sounds great! Where is it located?" - }, - { - "speaker": "Audrey", - "dia_id": "D4:27", - "text": "It's right by the park we usually walk. It's a great spot with lots of trees and benches for us to watch the dogs play." - }, - { - "speaker": "Andrew", - "dia_id": "D4:28", - "text": "That's awesome, not too far away then! I'll have to check it out. Thanks for the suggestion!" - }, - { - "speaker": "Audrey", - "dia_id": "D4:29", - "text": "Yup! Not at all! Let me know how it goes." - }, - { - "speaker": "Andrew", - "dia_id": "D4:30", - "text": "Sure, I'll let you know. Always great chatting with you! Ttyl." - } - ], - "session_5_date_time": "10:47 am on 6 May, 2023", - "session_5": [ - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/5006/5244715417_aa4e26e1d3_z.jpg" - ], - "blip_caption": "a photography of a dog looking through a cage at the camera", - "query": "cute dog shelter adoption", - "dia_id": "D5:1", - "re-download": true, - "text": "Hey! Since we last spoke, I've been looking for a doggo to adopt - browsing websites, visiting shelters and asking friends of theirs. It's been both fun and annoying!" - }, - { - "speaker": "Audrey", - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "dia_id": "D5:2", - "text": "Sounds like a fun and demanding task! Getting to meet new pups must bring so much happiness. What do you think you can do to make the process smoother?" - }, - { - "speaker": "Andrew", - "dia_id": "D5:3", - "text": "Meeting all these adorable pups has been awesome! For those considering getting a pup, the size of living space and the exercise needs of the breed are important. For me, a person living in an apartment, a smaller dog would be best, but if one is active, consider getting one that loves to play and run." - }, - { - "speaker": "Audrey", - "dia_id": "D5:4", - "text": "That's some good advice! It's important to consider the space and energy needs of a dog." - }, - { - "speaker": "Andrew", - "dia_id": "D5:5", - "text": "Yeah! Finding a pet-friendly place to live has been tough too. I'm contacting landlords and checking out neighborhoods to find the perfect spot." - }, - { - "speaker": "Audrey", - "dia_id": "D5:6", - "text": "Guessing it's tough to find housing. Any particular part of town you want to live in?" - }, - { - "speaker": "Andrew", - "dia_id": "D5:7", - "text": "I'm looking for a place near a park or woods, so I can stay close to nature and give the dog a large open space to run around" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.pexels.com/photos/12874286/pexels-photo-12874286.jpeg" - ], - "blip_caption": "a photography of three dogs running through a field of grass", - "query": "dogs playing field flowers national park", - "dia_id": "D5:8", - "re-download": true, - "text": "That's a good plan! I'm lucky to have a park near me - it's great for my pup's walks. Last Friday we took a road trip - we went to a beautiful national park and my dogs had a blast! It was an awesome trip!" - }, - { - "speaker": "Andrew", - "dia_id": "D5:9", - "text": "Nice! Glad the pups had a great road trip. Do you take them on road trips often?" - }, - { - "speaker": "Audrey", - "dia_id": "D5:10", - "text": "I take them on road trips once every couple of months. It's a great way for them to explore and stay active." - }, - { - "speaker": "Andrew", - "dia_id": "D5:11", - "text": "Wow, that's awesome! I really wish I could go on a road trip with a furry companion." - }, - { - "speaker": "Audrey", - "dia_id": "D5:12", - "text": "It's a cool experience. Having your furry friends on a road trip is an amazing experience. They make it really fun and exciting. It's definitely something to look forward to!" - }, - { - "speaker": "Andrew", - "dia_id": "D5:13", - "text": "Adding that to my bucket list! Can't wait for the day I actually go on a trip with my dog!" - }, - { - "speaker": "Audrey", - "dia_id": "D5:14", - "text": "Good luck with your search! Fingers crossed you find the perfect one." - }, - { - "speaker": "Andrew", - "dia_id": "D5:15", - "text": "Thanks! Your help is much appreciated. I'm still on the lookout for the perfect furry friend." - }, - { - "speaker": "Audrey", - "dia_id": "D5:16", - "text": "Not a problem, I'm glad to help! Good luck with your search!" - }, - { - "speaker": "Andrew", - "dia_id": "D5:17", - "text": "Thanks! I'll let you know how it goes. " - }, - { - "speaker": "Audrey", - "dia_id": "D5:18", - "text": "Definitely, keep me posted and let me know if you need any suggestions or help." - }, - { - "speaker": "Andrew", - "dia_id": "D5:19", - "text": "Will do! Really apprecieate it. " - }, - { - "speaker": "Audrey", - "dia_id": "D5:20", - "text": "Yup! You got it, I'll be expecting a pic of your dog soon! :)" - }, - { - "speaker": "Andrew", - "dia_id": "D5:21", - "text": "Haha I can't wait. I'll ttyl, gotta check out another shelter soon." - }, - { - "speaker": "Audrey", - "dia_id": "D5:22", - "text": "Have fun! Ttyl!" - } - ], - "session_6_date_time": "2:03 pm on 11 May, 2023", - "session_6": [ - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/7155/6566396811_8526df2fb5_b.jpg" - ], - "blip_caption": "a photography of a dirt path leading to a grassy hill", - "query": "hike nature open space", - "dia_id": "D6:1", - "re-download": true, - "text": "Hi Audrey! I had a great hike last weekend with some friends and my girlfriend at the spot we found recently. Nature was so peaceful \u2013 it was so nice to just relax and take it in. How's your week been? Anything exciting going on lately?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/63568f583d7aee4d0ba645d0/05701b3d-6533-4936-a33d-2bd0902f1a34/11821460_.jpg" - ], - "blip_caption": "a photography of a group of dogs with a yellow circle", - "query": "dog training workshop flyer", - "dia_id": "D6:2", - "re-download": true, - "text": "Hey Andrew! That hike sounds great. Nature is good for the soul, right? My week's been good - taking care of my four doggies and making sure they're happy and healthy took up most of my free time. Also, exciting news! I signed up for a workshop about bonding with my pet next month. Can't wait to learn new stuff and strengthen my bond with my pets. What's up with you?" - }, - { - "speaker": "Andrew", - "dia_id": "D6:3", - "text": "That's awesome! Glad have the opportunity to bond with your pets. That workshop sounds cool. Where did you hear about it? And the one in the picture is adorable!" - }, - { - "speaker": "Audrey", - "dia_id": "D6:4", - "text": "I know right? I saw this workshop flyer at my local pet store. It was a positive reinforcement training class and I wanted to give it a shot. The volunteer in the store was nice enough to let me meet their dog \u2013 he was so friendly and playful!" - }, - { - "speaker": "Andrew", - "dia_id": "D6:5", - "text": "Cool! Positive reinforcement can really help you bond with your dogs. Do you think they'll catch on quickly?" - }, - { - "speaker": "Audrey", - "dia_id": "D6:6", - "text": "I'm sure they'll catch on really quick! They're quick learners and love rewards! Can't wait to learn how to train them better." - }, - { - "speaker": "Andrew", - "dia_id": "D6:7", - "text": "That's awesome! Keep me updated on their progress." - }, - { - "speaker": "Audrey", - "dia_id": "D6:8", - "text": "Definitely! I'll keep you updated on how it all goes and how my pups are doing. Fingers crossed they'll be extra behaved. And I'll let you know some tips on training your future dog as well!" - }, - { - "speaker": "Andrew", - "dia_id": "D6:9", - "text": "Thanks! I'm excited to hear about it. Have a great time at the workshop!" - }, - { - "speaker": "Audrey", - "dia_id": "D6:10", - "text": "I'll definitely have a good time and make the most of it. I'm sure this is a must learn for any dog owner." - }, - { - "speaker": "Andrew", - "dia_id": "D6:11", - "text": "You think so? Wow, you must be a good salesperson because I'm almost sold on this class haha." - }, - { - "speaker": "Audrey", - "dia_id": "D6:12", - "text": "Haha, I just think its important to have pets learn how to behave on a positive reinforcement way. Punishment is never the proper way for pets ya know?" - }, - { - "speaker": "Andrew", - "dia_id": "D6:13", - "text": "Yeah I would't want to be punished, let alone puppies and dogs." - }, - { - "speaker": "Audrey", - "dia_id": "D6:14", - "text": "Right!? I don't want to hurt any of my dogs. Just by thinking of it gives me pain." - }, - { - "speaker": "Andrew", - "dia_id": "D6:15", - "text": "Yeah I feel you. Anyways, let me look into their classes. I'll talk to you soon, have fun!" - }, - { - "speaker": "Audrey", - "dia_id": "D6:16", - "text": "Yup, ttyl!" - } - ], - "session_7_date_time": "11:27 am on 2 June, 2023", - "session_7": [ - { - "speaker": "Audrey", - "dia_id": "D7:1", - "text": "Hey how's it going? Yesterday took my pups to the park, it was awesome! Seeing them running around and playing without a leash was awesome. It filled my heart with joy - their happiness brought me so much peace. " - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/nzwhfzovo8q61.jpg" - ], - "blip_caption": "a photo of a sunset over a mountain range with a horse grazing", - "query": "mountain peak sunset", - "dia_id": "D7:2", - "text": "That sounds amazing! Must have been so happy watching them running around. It's moments like that which show us how amazing animals are. Glad you had a great time. By the way, I think you mentioned before that you've taken them on a hiking trip? " - }, - { - "speaker": "Audrey", - "dia_id": "D7:3", - "text": "Yeah, I took them for a hike before. We went to a national park last week and made it to this beautiful peak. It was stunning during the sunset, and I'll never forget it. My furry pals were running around as it was so awesome. It felt like a slice of paradise. The breeze was nice and you could hear birds chirping." - }, - { - "speaker": "Andrew", - "dia_id": "D7:4", - "text": "That sounds amazing! I'm sure it was a memorable experience. Did you manage to capture it? I'd love to see a photo!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/nrl9qw3ayoza1.jpg" - ], - "blip_caption": "a photo of two horses in a field with a fence", - "query": "sunset mountains horse grazing", - "dia_id": "D7:5", - "text": "Yep! I took a bunch of photos. This one of my favorites." - }, - { - "speaker": "Andrew", - "dia_id": "D7:6", - "text": "Wow, incredible sunset and the scenery! Must have been magical. Thanks for sharing!" - }, - { - "speaker": "Audrey", - "dia_id": "D7:7", - "text": "It was really special. Nature always cheers me up and makes me feel grateful. Glad you could experience it too. Oh, how are things going with your search for a furry friend?" - }, - { - "speaker": "Andrew", - "dia_id": "D7:8", - "text": "I'm still on the hunt, but it's tough finding a pet-friendly spot in the city. Been checking out some places, but no luck so far. A bit discouraged but I'm determined to find the right place and dog." - }, - { - "speaker": "Audrey", - "dia_id": "D7:9", - "text": "Oof, Hang in there, you'll find the perfect place for you and your new furry friend." - }, - { - "speaker": "Andrew", - "dia_id": "D7:10", - "text": "Thanks! I won't give up. " - }, - { - "speaker": "Audrey", - "dia_id": "D7:11", - "text": "I'm here to support! Email me your criterias so I can help you find a great spot for your furry friend." - }, - { - "speaker": "Andrew", - "dia_id": "D7:12", - "text": "Thanks! Your assistance is greatly appreciated. So thankful for your help." - }, - { - "speaker": "Audrey", - "dia_id": "D7:13", - "text": "Not a problem at all! We'll keep searching. Stay positive!" - } - ], - "session_8_date_time": "5:23 pm on 13 June, 2023", - "session_8": [ - { - "speaker": "Andrew", - "dia_id": "D8:1", - "text": "Hey! Long time no chat. Last Sunday was awesome - my friends and I took a rock climbing class and I made it to the top! It was a fantastic experience and now I'm hooked. Think I'm going to try to do more outdoor activities like this every week!" - }, - { - "speaker": "Audrey", - "dia_id": "D8:2", - "text": "That's awesome! Glad you had such a rad experience rock climbing. I'm always in awe of people who can climb mountains. Got any pics or videos from your climb? Would love to see the view from the top!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://miro.medium.com/v2/resize:fit:1400/0*h3ewAJq8YmXT5vb1.jpg" - ], - "blip_caption": "a photography of a man climbing on a rock face to face", - "query": "rock climbing view top rock climbing class", - "dia_id": "D8:3", - "re-download": true, - "text": "Rock climbing was awesome! It was a challenge, but so satisfying. The view was stunning, and I was really proud of myself. Nature sure is amazing!\n\n[Shares a photo of the view from the top of the rock climbed during the rock climbing class]" - }, - { - "speaker": "Audrey", - "dia_id": "D8:4", - "text": "Wow that view is stunning! Congrats on reaching the top, that must have been a huge accomplishment. Nature really reminds us how tiny we are in comparison, yeah? Was it challenging getting there?" - }, - { - "speaker": "Andrew", - "dia_id": "D8:5", - "text": "Thanks! It was a big achievement for me. The climb was tricky, especially since I'm still a newbie. But I made it with the support and cheer from my friends." - }, - { - "speaker": "Audrey", - "dia_id": "D8:6", - "text": "Nice! Having a solid support group really helps when things get tough. You're lucky to have such great friends! Does this adventure encourage you to try more outdoor activities?" - }, - { - "speaker": "Andrew", - "dia_id": "D8:7", - "text": "Yeah, rock climbing was awesome - I felt so accomplished reaching the top. It has definitely encouraged me to try more outdoor activities like kayaking and maybe bungee jumping? Nature always pushes me out of my comfort zone!" - }, - { - "speaker": "Audrey", - "dia_id": "D8:8", - "text": "Wow going all in huh? Have fun with kayaking and bungee jumping! Last week, I found a great spot for my dogs' walk. It's a small park with a trail surrounded by trees. It's so nice and I think my dogs like it too. Would you like to come along?" - }, - { - "speaker": "Andrew", - "dia_id": "D8:9", - "text": "Sounds great, Audrey! I'd love to join you and your pups for a walk. Being in nature with dogs sounds like a great time!" - }, - { - "speaker": "Audrey", - "dia_id": "D8:10", - "text": "Awesome! Can't wait to have fun with everyone. My dogs love meeting new people. " - }, - { - "speaker": "Andrew", - "dia_id": "D8:11", - "text": "Sames, can't wait to meet them and take a stroll in the park." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D8:12", - "text": "This was taken during the walk in the park. See how happy they are?" - }, - { - "speaker": "Andrew", - "dia_id": "D8:13", - "text": "Aww, they look like they're really enjoying themselves. How long do you usually walk them for?" - }, - { - "speaker": "Audrey", - "dia_id": "D8:14", - "text": "Varies depending on the day, but usually for about an hour. We let them explore at their own pace." - }, - { - "speaker": "Andrew", - "dia_id": "D8:15", - "text": "Cool, that's a good amount of time for them to have a nice stroll and take a look around." - }, - { - "speaker": "Audrey", - "dia_id": "D8:16", - "text": "They need exercise and to explore - they always go home with a smile and tired." - }, - { - "speaker": "Andrew", - "dia_id": "D8:17", - "text": "Nice! Letting them explore and have fun is important. I'm sure they must be loving it!" - }, - { - "speaker": "Audrey", - "dia_id": "D8:18", - "text": "Yeah, they love it! It's their favorite part of the day! Their faces blightens up as soon as I get ready for a walk." - }, - { - "speaker": "Andrew", - "dia_id": "D8:19", - "text": "Of course! Nature always makes us and our pets so happy." - }, - { - "speaker": "Audrey", - "dia_id": "D8:20", - "text": "Definitely! Dogs and nature bring me so much joy and peace." - }, - { - "speaker": "Andrew", - "dia_id": "D8:21", - "text": "Yeah, I agree, it's really nice." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/6jbz5p9fxy051.jpg" - ], - "blip_caption": "a photo of two dogs playing with a frisbee in a field", - "query": "dogs playing meadow", - "dia_id": "D8:22", - "text": "So check out how happy they are in this meadow! They make me so happy." - }, - { - "speaker": "Andrew", - "dia_id": "D8:23", - "text": "Aww so cute. Your dogs look so content in that picture. The meadow looks so nice. It's great that nature brings your pets joy!" - }, - { - "speaker": "Audrey", - "dia_id": "D8:24", - "text": "Being outdoors with them puts me in my happy place. It's peaceful and inspiring." - }, - { - "speaker": "Andrew", - "dia_id": "D8:25", - "text": "Glad you found something that puts you in your happy place. It's true, being outdoors has a way of inspiring and calming us." - }, - { - "speaker": "Audrey", - "dia_id": "D8:26", - "text": "Yeah! It's incredible how nature can make us think differently." - }, - { - "speaker": "Andrew", - "dia_id": "D8:27", - "text": "Agreed! It's great for refreshing the mind and giving a different outlook. Whenever I'm in need of a reset, I turn to nature." - }, - { - "speaker": "Audrey", - "dia_id": "D8:28", - "text": "Nature has a way of making us feel alive and centered. Let's appreciate what it gives us." - } - ], - "session_9_date_time": "1:51 pm on 26 June, 2023", - "session_9": [ - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/181/410666106_cfe35c1c83_b.jpg" - ], - "blip_caption": "a photography of two dogs sitting on the grass in front of a house", - "query": "new house bigger backyard furry babies", - "dia_id": "D9:1", - "re-download": true, - "text": "Woohoo! I got a new place with a bigger backyard for the pooches! Take a look!" - }, - { - "speaker": "Andrew", - "dia_id": "D9:2", - "text": "That's awesome! Your new place looks great. Your fur babies must be thrilled with the bigger backyard. I'm sure they're having a blast running around. How are they settling in?" - }, - { - "speaker": "Audrey", - "dia_id": "D9:3", - "text": "Thanks! They love it - so much space to run and explore, sniffing out new smells. It's awesome to see them having fun." - }, - { - "speaker": "Andrew", - "dia_id": "D9:4", - "text": "Awesome! They must love it. Makes me happy seeing them have fun. Any plans on making it even more pup-friendly?" - }, - { - "speaker": "Audrey", - "dia_id": "D9:5", - "text": "Yep, I set up a doggy play area in the backyard with agility stuff and toys - they're loving it!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://www.goinbark.com/wp-content/uploads/2016/08/20160820_150554.jpg" - ], - "blip_caption": "a photo of a group of dogs playing with a ball in a large indoor area", - "query": "dog playground", - "dia_id": "D9:6", - "text": "Wow you really went in huh!? Now they have a great place to play and explore, that's awesome! Take a look at this place I'm at." - }, - { - "speaker": "Audrey", - "dia_id": "D9:7", - "text": "That looks fun! Where are you at? I would love to take the dogs there sometime soon." - }, - { - "speaker": "Andrew", - "dia_id": "D9:8", - "text": "This is the doggy daycare near me, it has a big indoor space for dogs to play." - }, - { - "speaker": "Audrey", - "dia_id": "D9:9", - "text": "Ooo, I'll definitely take a look at it. Thanks for the tip." - }, - { - "speaker": "Andrew", - "dia_id": "D9:10", - "text": "Not a problem at all! Let me know if you need any help finding more places for dogs." - }, - { - "speaker": "Audrey", - "dia_id": "D9:11", - "text": "Thanks! Appreciate the offer. Super nice to have friends who understand our love for our pets!" - }, - { - "speaker": "Andrew", - "dia_id": "D9:12", - "text": "Haha yeah! People who understand the love for dogs are awesome. They really bring so much joy!" - }, - { - "speaker": "Audrey", - "dia_id": "D9:13", - "text": "Right!? Animals are great! They really bring so much joy. Can't imagine life without them!" - }, - { - "speaker": "Andrew", - "dia_id": "D9:14", - "text": "Yeah, I'm glad we both appreciate them. Not everyone feel this way." - }, - { - "speaker": "Audrey", - "dia_id": "D9:15", - "text": "For sure! I don't really get some people hating on pets or even hurting them. They're like family to people." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://cdn2.picryl.com/photo/2015/06/09/flowers-bloom-in-a-garden-near-the-memorial-amphitheater-1acba1-1024.jpg" - ], - "blip_caption": "a photography of a flower bed with many different colored flowers", - "query": "neighborhood park flowers blooming spring", - "dia_id": "D9:16", - "re-download": true, - "text": "Definitely! So what's new with you? Anything going on lately? Take a look at this park in my neighborhood that has these flowers." - }, - { - "speaker": "Audrey", - "dia_id": "D9:17", - "text": "Oh wow those flowers look beautiful. Well aside from moving to a new house, its just me getting unboxing all the packed boxes. What about you?" - }, - { - "speaker": "Andrew", - "dia_id": "D9:18", - "text": "Last Friday, I hiked with some friends. The weather was great and it felt so good to be outdoors. We got some awesome pictures too. " - }, - { - "speaker": "Audrey", - "dia_id": "D9:19", - "text": "Sounds like a good time! You want to share some awesome outdoor pics with me? Also tell me all about your hike." - }, - { - "speaker": "Andrew", - "dia_id": "D9:20", - "text": "Haven't gone through the photos yet. Maybe soon! It was lovely being out in the open, hearing the bird songs and smelling the trees. Lately, I've been really missing that connection with nature and the peace it brings. I'm definitely looking forward to exploring more!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/39248kn2icc51.jpg" - ], - "blip_caption": "a photo of a person's feet resting on a rock overlooking a lake", - "query": "serene lake mountains", - "dia_id": "D9:21", - "text": "Nature has a way of soothing us and helping us recharge. It's something special. I hope you get to experience it again soon. I really want to do something like this right now." - }, - { - "speaker": "Andrew", - "dia_id": "D9:22", - "text": "That looks so nice. It looks so chilled out! How was it?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA0L2ZsMjkzMjI0OTkzMjItaW1hZ2Uta294dXBzNzEuanBn.jpg" - ], - "blip_caption": "a photography of a person sitting on a rock overlooking a lake", - "query": "hiking trip peaceful lake mountains feet rock lake view", - "dia_id": "D9:23", - "re-download": true, - "text": "So a few years back my friends and I went on a hike and stumbled across this stunning lake in the mountains. We sat by it, chat and admiring the peacefulness of nature. I'll never forget those moments." - }, - { - "speaker": "Andrew", - "dia_id": "D9:24", - "text": "Wow, that looks so peaceful. It must have been a great spot!" - }, - { - "speaker": "Audrey", - "dia_id": "D9:25", - "text": "It really was! Nature and the lake were so calming, exactly what I needed." - }, - { - "speaker": "Andrew", - "dia_id": "D9:26", - "text": "Sounds like you had a really peaceful and calming experience! Glad you got to appreciate the beauty of life. " - }, - { - "speaker": "Audrey", - "dia_id": "D9:27", - "text": "Yeah! We shold really appreciate the small things in life and not take anything for granted. :)" - } - ], - "session_10_date_time": "8:32 pm on 3 July, 2023", - "session_10": [ - { - "speaker": "Audrey", - "dia_id": "D10:1", - "text": "Hey! It's been a while. I'm taking a dog training course and it's challenging but rewarding. My dogs are doing better already. What's new with you?" - }, - { - "speaker": "Andrew", - "dia_id": "D10:2", - "text": "Hey great to hear from you! Life's thrown me a few curveballs lately. Still can't seem to find any dog-friendly spots to rent. That's a bummer. Have you been able to do any exploring on new trails?" - }, - { - "speaker": "Audrey", - "dia_id": "D10:3", - "text": "Aw, sorry about the search for dog-friendly spots. I haven't had a ton of time for new trails either. The dog-training course has been a big time sink but it's paid off because they're doing great." - }, - { - "speaker": "Andrew", - "dia_id": "D10:4", - "text": "That's great news! It must feel so rewarding to see them doing well. I understand how it feels on missing the peace of being out on the trails, but for now, it's just urban adventures then." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D10:5", - "text": "Seeing them do well is super rewarding! They give me so much love and happiness. I get how frustrating it can be not to find pet-friendly spots. Nature is so calming and restorative with them around. See how happy they are when they're out." - }, - { - "speaker": "Andrew", - "dia_id": "D10:6", - "text": "I don't think I ever asked what breed they are right? Also, what do they enjoy doing the most? Looks like they're having a blast!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/luf05slnybn71.jpg" - ], - "blip_caption": "a photo of two dogs curled up in a dog bed", - "query": "dogs curled up together", - "dia_id": "D10:7", - "text": "They're all mutts. Two of them are Jack Russell mixes and the other two are Chihuahua mixes. They love running and playing fetch, you should see them sometimes." - }, - { - "speaker": "Andrew", - "dia_id": "D10:8", - "text": "They look so comfy in that bed. It's clear they're well loved. How old are they? How are they getting along now?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/yroilm3jgct71.jpg" - ], - "blip_caption": "a photo of two dogs laying on a couch with a blanket", - "query": "dogs cuddling together playdate at home", - "dia_id": "D10:9", - "text": "They're all 3-year-old and they are a great pack. We had a doggy playdate last Friday. It was a bit crazy but still lots of fun!" - }, - { - "speaker": "Andrew", - "dia_id": "D10:10", - "text": "They look adorable! Doggy playdates sound like a lot of fun. Glad they all get along." - }, - { - "speaker": "Audrey", - "dia_id": "D10:11", - "text": "Thanks! They really are my universe. So anything new you've been into lately?" - }, - { - "speaker": "Andrew", - "dia_id": "D10:12", - "text": "Lately I've been finding new hobbies since I can't hike. I've been getting into cooking more and trying out new recipes - it's been enjoyable. Do you enjoy cooking? Any favorite recipes?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://hometohomestead.files.wordpress.com/2022/10/img_5122.jpg" - ], - "blip_caption": "a photo of a pie on a wooden board with a knife", - "query": "homemade chicken pot pie", - "dia_id": "D10:13", - "text": "I love cooking! My favorite recipe is Chicken Pot Pie. It's so cozy and delicious, especially on a cold day. If you want, I can share the recipe with you." - }, - { - "speaker": "Andrew", - "dia_id": "D10:14", - "text": "Mmm that looks nice! Mind sharing the recipe so I can give it a try? What inspired you to make it?" - }, - { - "speaker": "Audrey", - "blip_caption": "a photo of a pie with a lattice on top of it", - "dia_id": "D10:15", - "text": "Sure! Let me send you the recipe in a bit. You really should give it a try! It's my family's recipe that's been around for years. The flavors always remind me of my grandma's kitchen - makes me think of all the conversations we used to have at the table. I hope you like it! Oh, and how's the cooking going?" - }, - { - "speaker": "Andrew", - "dia_id": "D10:16", - "text": "Thanks! I'll give it a try. Cooking has been helping me de-stress and be creative. I'm still a rookie, but I'm having fun experimenting. So what makes you like cooking so much?" - }, - { - "speaker": "Audrey", - "dia_id": "D10:17", - "text": "I love trying out new recipes and experimenting in the kitchen - it's like an escape for me. It's great for de-stressing and letting my creativity flow." - }, - { - "speaker": "Andrew", - "dia_id": "D10:18", - "text": "Oh I feel you! It gives me an escape and allows me to try something new. Plus, there's always the bonus of enjoying the food afterwards. It's slowly becoming one of my favorite hobbies, as it's really relaxing and allows me to express my creativity." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/hs8imjohtw631.jpg" - ], - "blip_caption": "a photo of a plate of food with a lot of vegetables", - "query": "beautifully plated dish cooking self-care", - "dia_id": "D10:19", - "text": "Agreed! Cooking and eating the food is so rewarding; it's like a form of self-care. I love throwing on some music, pouring a glass of wine, and just going with the flow in the kitchen. It's so therapeutic. See how beautiful this dish is?" - }, - { - "speaker": "Andrew", - "dia_id": "D10:20", - "text": "Ooo, that looks great! Cooking can be so calming, right? What's your go-to ingredient in the kitchen?" - }, - { - "speaker": "Audrey", - "dia_id": "D10:21", - "text": "Yeah! Cooking is definitely calming. Garlic is my go-to ingredient. I love the smell and taste it adds to dishes." - }, - { - "speaker": "Andrew", - "dia_id": "D10:22", - "text": "Garlic is indeed delicious! Do you have a favorite dish that you like to make with it? If so, would you like to share the recipe?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/6105/6360569135_ed356c13e2_b.jpg" - ], - "blip_caption": "a photography of a pan of chicken and potatoes on a stove", - "query": "roasted garlic chicken", - "dia_id": "D10:23", - "re-download": true, - "text": "Sure! Roasted Chicken is one of my favorites - sure I'll send you the recipe in a bit." - }, - { - "speaker": "Andrew", - "dia_id": "D10:24", - "text": "Wow I can't wait to make it! That looks amazing. What inspired you to make it?" - }, - { - "speaker": "Audrey", - "dia_id": "D10:25", - "text": "I'm glad you're interested! This recipe is based on my love for Mediterranean flavors. It's a tasty dish that's easy to make and loaded with healthy stuff like chicken, garlic, lemon, and herbs. It's my favorite comfort meal!" - }, - { - "speaker": "Andrew", - "dia_id": "D10:26", - "text": "Wow, that sounds delicious and healthy! I'm always looking for new meal ideas, especially ones that are healthier. Really appreciate you sharing this with me, thanks!" - }, - { - "speaker": "Audrey", - "dia_id": "D10:27", - "text": "No problem! I hope you enjoy making and eating it. Let me know how it turns out!" - }, - { - "speaker": "Andrew", - "dia_id": "D10:28", - "text": "Yep, will do! I'll keep you posted. Talk later!" - }, - { - "speaker": "Audrey", - "dia_id": "D10:29", - "text": "Excited to hear about it. Talk later!" - } - ], - "session_11_date_time": "9:48 am on 8 July, 2023", - "session_11": [ - { - "speaker": "Andrew", - "img_url": [ - "https://cdn.shopify.com/s/files/1/1512/5894/articles/IMG_5426.jpg" - ], - "blip_caption": "a photo of a picnic table with a variety of snacks and drinks", - "query": "picnic friends renovated park", - "dia_id": "D11:1", - "text": "Hey how's it going? Last Friday was amazing - I had a picnic with my girlfriend and it was so much fun! Being in the nature can be so refreshing and it always brings me joy. " - }, - { - "speaker": "Audrey", - "dia_id": "D11:2", - "text": "Wow sounds like the picnic was awesome! Yeah being in the nature is so nice. My furry friends always make me happy. We had a great walk the other day - felt really good!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/yfeccwau4dya1.jpg" - ], - "blip_caption": "a photo of a dog wearing a sweater sitting in the grass", - "query": "dog park dogs playing", - "dia_id": "D11:3", - "text": "That's awesome! Dogs really make life more fun doesn't it. I wish I could get one, but like I said, it's tough to find a place and find the right dog. How did you get yours?" - }, - { - "speaker": "Audrey", - "dia_id": "D11:4", - "text": "Thanks! I got lucky finding a breeder nearby that has the dogs I wanted. Yeah places that allows dogs are really hard to find unfortunately ." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://d3ekkvinch1ig5.cloudfront.net/suggestion/1658159281-2959image4.jpg" - ], - "blip_caption": "a photo of a group of dogs running around a park", - "query": "dog-friendly park city screenshot", - "dia_id": "D11:5", - "text": "Yeah it's tough. I found a few parks where you can take your pup on a leash, but it's not the same as having an open area where they can run and play. If I find something, I'll let you know. " - }, - { - "speaker": "Audrey", - "dia_id": "D11:6", - "text": "Cool, I should join you for a hike and bring my dogs. It would be great to have a chance where they can run freely." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://community.us.craghoppers.com/wp-content/uploads/2018/05/j3bmicznnmrnfe1uchho.jpg" - ], - "blip_caption": "a photo of a woman walking up a steep hill with a view of the ocean", - "query": "hiking trail beautiful view", - "dia_id": "D11:7", - "text": "Yeah definitely ! I'm down for a hike with you and your furry friends. Let's do it next month when the weather is more pleasant. Here's the trail that I think it'd be great for the dogs." - }, - { - "speaker": "Audrey", - "dia_id": "D11:8", - "text": "That'd be awesome! I can't wait to take them hiking. Fingers crossed we find a spot with a great view and lots of room for them to explore and have a blast." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://tammynara.files.wordpress.com/2021/01/pxl_20210117_190212518.jpg" - ], - "blip_caption": "a photo of a map of a park with a lot of trees", - "query": "hiking trails map perfect spot", - "dia_id": "D11:9", - "text": "Looking forward to seeing them have fun hiking. Let's get planning for next month! Here's the map for the trail." - }, - { - "speaker": "Audrey", - "dia_id": "D11:10", - "text": "Yep! I'm gonna look into the trail for my furry friend to run around." - }, - { - "speaker": "Andrew", - "dia_id": "D11:11", - "text": "Sounds good! Let's make sure the trail is safe for the dogs to run around and have fun." - }, - { - "speaker": "Audrey", - "dia_id": "D11:12", - "text": "Yep! After all safety is top priority when the dogs are outside running around." - }, - { - "speaker": "Andrew", - "dia_id": "D11:13", - "text": "Agreed! It's going to be a great with the dogs!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/o6bszg5mmua71.jpg" - ], - "blip_caption": "a photo of three dogs sitting on the floor with leashes", - "query": "four dogs sitting at door", - "dia_id": "D11:14", - "text": "Yup! Btw, there's going to be some cool stuff happening today." - }, - { - "speaker": "Andrew", - "dia_id": "D11:15", - "text": "Woah! They look so cute! What is going on here? They look ready for an adventure." - }, - { - "speaker": "Audrey", - "dia_id": "D11:16", - "text": "Haha yeah! They're all set for their next outdoor adventure!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:17", - "text": "Aw man, can't wait to meet them! They seem like a blast!" - }, - { - "speaker": "Audrey", - "dia_id": "D11:18", - "text": "Yeah, they definitely do! They really are a lot of fun." - }, - { - "speaker": "Andrew", - "dia_id": "D11:19", - "text": "Oh I'm sure, dogs definitely add lots of fun to our daily life." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D11:20", - "text": "Yep! They bring me so much joy. Can't get bored at all." - }, - { - "speaker": "Andrew", - "dia_id": "D11:21", - "text": "Aww, they look adorable playing in the park! Seeing them have so much fun must be so rewarding." - }, - { - "speaker": "Audrey", - "dia_id": "D11:22", - "text": "Yeah it sure does. Seeing them so happy and bouncy makes me really happy." - }, - { - "speaker": "Andrew", - "dia_id": "D11:23", - "text": "Yep, they're so happy it's contagious. It makes me happy just by looking at the photos." - }, - { - "speaker": "Audrey", - "dia_id": "D11:24", - "text": "Haha that's great! They're so full of energy, always bringing a smile to my face!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:25", - "text": "They make everyone smile. Can't wait to go hiking with them!" - }, - { - "speaker": "Audrey", - "dia_id": "D11:26", - "text": "Yep! It'll be great to see them enjoy themselves on the hike!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:27", - "text": "Yep! I can't wait to see them have fun on the hike. Super excited!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i2.pickpik.com/photos/607/821/215/playing-puppies-young-dogs-french-bulldog-cocker-spaniel-preview.jpg" - ], - "blip_caption": "a photography of a dog playing with a group of other dogs", - "query": "hiking dogs running field", - "dia_id": "D11:28", - "re-download": true, - "text": "I'm 100% sure that it's gonna be a great day! Just take a look how happy my dogs are just at out local park." - }, - { - "speaker": "Andrew", - "dia_id": "D11:29", - "text": "Aww look at them, so cute! It'll definitely be a great day! I love being outdoors and seeing others enjoy it too." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/iws2jgn6nld71.jpg" - ], - "blip_caption": "a photo of a person standing on a mountain with a view of the sky", - "query": "sunset mountain hiking trip last year", - "dia_id": "D11:30", - "text": "Yeah! Being outside and seeing people happy is awesome. Here's a pic from a hike last year. Such a pretty sunset!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:31", - "text": "Wow, that's stunning! Mind telling me where this is? " - }, - { - "speaker": "Audrey", - "dia_id": "D11:32", - "text": "I hiked this last year! It was a 3 hour drive from me and the sunset was amazing!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:33", - "text": "Nice! That sunset with the view is amazing, especially with nature around." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/okd8n7nb0hc71.jpg" - ], - "blip_caption": "a photo of a view of a lake and mountains at sunset", - "query": "sunset mountain peak", - "dia_id": "D11:34", - "text": "It was a beautiful moment indeed! The colors were so vibrant, and it felt so peaceful. I'm so grateful for moments like these that remind me of the beauty of nature and to appreciate the small things." - }, - { - "speaker": "Andrew", - "dia_id": "D11:35", - "text": "Yeah! It's incredible how nature can have such awesome surprises." - }, - { - "speaker": "Audrey", - "dia_id": "D11:36", - "text": "Nature really has awesome surprises. All those vibrant colors gives me that feeling of peace - it's breathtaking!" - }, - { - "speaker": "Andrew", - "dia_id": "D11:37", - "text": "Yeah! It is stunning! Life's pretty awesome when we take time to appreciate these moments right?" - }, - { - "speaker": "Audrey", - "dia_id": "D11:38", - "text": "Yep, taking a second to appreciate those moments makes like much better!" - } - ], - "session_12_date_time": "10:05 am on 11 July, 2023", - "session_12": [ - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/x7jn4meogw441.jpg" - ], - "blip_caption": "a photo of a dog sitting on a leash in a room", - "query": "new puppy toby city", - "dia_id": "D12:1", - "text": "Hey! So much has changed since last time we talked - meet Toby, my puppy. He's a bundle of joy and I couldn't resist taking him home, city living and all. How've you been?" - }, - { - "speaker": "Audrey", - "dia_id": "D12:2", - "text": "OMG! Toby looks so adorable! Congrats on your new addition. I'm sure you're really happy right now! I so happy for you!" - }, - { - "speaker": "Andrew", - "dia_id": "D12:3", - "text": "Haha yeah! Toby's definitely bringing a lot of joy. Since we last talked, work has been piling up and I've been stuck inside. I miss the peace and feeling of freedom that comes with going for a hike." - }, - { - "speaker": "Audrey", - "dia_id": "D12:4", - "text": "Yeah you're really stressed then! When work's non-stop, it's hard to get outdoors. Hiking is a great way to relax and take in nature. Do you have plans to go hiking soon? We should plan a trip for both of us and our pups!" - }, - { - "speaker": "Andrew", - "dia_id": "D12:5", - "text": "Yeah work's been stressful lately and I need a break. I have plans for a hike next month and thought it'd be cool if you and the pups could come along. We can enjoy nature and have a fun time!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://thebaskshop.com/cdn/shop/files/image_8117b42d-0057-492c-9b06-88fc439af683.jpg" - ], - "blip_caption": "a photo of a group of wooden keychains with engraved names", - "query": "personalized dog tags engraved names dogs", - "dia_id": "D12:6", - "text": "Sounds great! I'm in for the hike, and my dogs can come too. Thanks for inviting us! I made these personalized tags for them. Take a look! " - }, - { - "speaker": "Andrew", - "dia_id": "D12:7", - "text": "Wow, those look great! Did you make them? It's really cool!" - }, - { - "speaker": "Audrey", - "dia_id": "D12:8", - "text": "Thanks! Yeah, I made them myself. I wanted each one to be special and fit their personalities." - }, - { - "speaker": "Andrew", - "dia_id": "D12:9", - "text": "Wow, that's so cool. You really put so much love into making them, which makes them extra special." - }, - { - "speaker": "Audrey", - "dia_id": "D12:10", - "text": "Thanks! I did put a lot of love into making these tags. I want my pets to feel seen and loved." - }, - { - "speaker": "Andrew", - "dia_id": "D12:11", - "text": "It really are the small things like these show we care a ton about out pets." - }, - { - "speaker": "Audrey", - "dia_id": "D12:12", - "text": "Yep! Small acts of love like that make a big difference in our relationships with our furry pals. Let's make sure they know how much we care!" - }, - { - "speaker": "Andrew", - "dia_id": "D12:13", - "text": "Definitely! They bring us so much joy, so it's only fair that we show them how special they are. Can't wait for our hike - and for Toby and your pups to meet!" - }, - { - "speaker": "Audrey", - "dia_id": "D12:14", - "text": "Can't wait either! It's going to be loads of fun and a great memory." - }, - { - "speaker": "Andrew", - "dia_id": "D12:15", - "text": "Yep! It'll be an adventure alright. " - }, - { - "speaker": "Audrey", - "dia_id": "D12:16", - "text": "Counting down the days 'til our hike! Gotta get them ready soon!" - }, - { - "speaker": "Andrew", - "dia_id": "D12:17", - "text": "Same here! It's gonna be awesome. Let's talk later k?" - } - ], - "session_13_date_time": "3:52 pm on 27 July, 2023", - "session_13": [ - { - "speaker": "Andrew", - "dia_id": "D13:1", - "text": "Hey Audrey! How are you? My GF and I just had a great experience volunteering at a pet shelter on Monday - it was so rewarding! We loved spending time with those cute animals and it gave us so much joy. It was so rewarding, it reminded me just how much I love them!" - }, - { - "speaker": "Audrey", - "dia_id": "D13:2", - "text": "Hi Andrew! I'm good, thanks. That's awesome about the pet shelter volunteering. Helping animals really is great and you can tell when they're happy! So happy for you getting to experience that! I should do that someday too!" - }, - { - "speaker": "Andrew", - "dia_id": "D13:3", - "text": "Thanks! Seeing them so content makes me happy, it really makes me realize how special and full of love they are! Have you ever volunteered at an animal shelter? It can be so rewarding. " - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.prismic.io/trustedhousesitters/3dcbea0a-d9d9-4590-a57b-c2c15bf2e012_off+leash+dog+parks+portland+or.png" - ], - "blip_caption": "a photo of a woman kissing a dog in a park", - "query": "dogs park fun", - "dia_id": "D13:4", - "text": "Never been to an animal shelter before, but it must be great! My four fur babies are more important to me than anything! Here's a pic of us from a fun day out at the park." - }, - { - "speaker": "Andrew", - "dia_id": "D13:5", - "text": "Aww that's a cute photo! How are their personalities? Tell me more!" - }, - { - "speaker": "Audrey", - "dia_id": "D13:6", - "text": "Well the oldest one is the most relaxed, like a wise old sage. The second one is always ready for a game. The third one can be naughty but loves a good cuddle. And the youngest one is full of life and always up for an adventure. They all have their own individual personalities and I adore them." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/7dbys2s547b91.jpg" - ], - "blip_caption": "a photo of a dog is sitting on the floor with a tennis ball", - "query": "black labrador playing tennis ball", - "dia_id": "D13:7", - "text": "Wow, they sound amazing! They must remind you of your childhood pup. That photo of you with your dog is so cute, he looks like the most playful one ever! Pets really do bring so much joy in your life!" - }, - { - "speaker": "Audrey", - "dia_id": "D13:8", - "text": "Thanks! That one is Max, my childhood dog. He had lots of energy and loved a game of fetch. I have lots of great memories with him. Pets sure bring a lot of joy." - }, - { - "speaker": "Andrew", - "dia_id": "D13:9", - "text": "Yeah! Their love and energy can really brighten up a day. It's amazing how close we can get to them and the memories they create for us. Do you have any other special memories with Max that you remember fondly?" - }, - { - "speaker": "Audrey", - "dia_id": "D13:10", - "text": "Max and I would take long walks in the neighborhood when I was a kid. We explored new paths, him sniffing and marking his territory. We grew really close, and I shared my worries and hopes with him. He was a great listener, always there for me. Those days are some of my favorite memories. " - }, - { - "speaker": "Andrew", - "dia_id": "D13:11", - "text": "Pets are more than just pets - they become friends and confidantes. They always know how to listen and provide comfort when we need it. They can make us feel so loved and understood, leaving a lasting mark on our lives." - }, - { - "speaker": "Audrey", - "dia_id": "D13:12", - "text": "Pets truly make our lives so much better. They listen without judging and give us the best unconditional love. They always leave a mark in our hearts and remind us how it feels to be seen and understood. I'm thankful to have them around - they bring so much joy, comfort, and love." - }, - { - "speaker": "Andrew", - "dia_id": "D13:13", - "text": "You nailed it! That's why we went volunteering with animals. It has been one of the most rewarding things I've ever done. They really do lift our spirits with all their love, joy, and comfort." - }, - { - "speaker": "Audrey", - "dia_id": "D13:14", - "text": "I'm so glad you guys got to experience that! Animals really have a way of brightening our day and giving us lots of love and joy. I'm sure the pet shelter really appreciated your help too!" - }, - { - "speaker": "Andrew", - "dia_id": "D13:15", - "text": "Yeah we had a blast volunteering. It's our way of giving back and making their lives better!" - }, - { - "speaker": "Audrey", - "dia_id": "D13:16", - "text": "I'm sure your kindness and care will make them happier no doubt!" - } - ], - "session_14_date_time": "11:05 am on 4 August, 2023", - "session_14": [ - { - "speaker": "Andrew", - "img_url": [ - "https://www.nps.gov/hale/planyourvisit/images/Holua-Campground_Tent-Camper-0015_NPS-photo-Katie-Matthew_7140242_2.jpg" - ], - "blip_caption": "a photo of a woman setting up a tent on a rocky hill", - "query": "campsite nature camping", - "dia_id": "D14:1", - "text": "Hey, Audrey! I can't wait for the weekend. My girlfriend, Toby and I are going camping. It's been forever since I've been in nature." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/45o0t7vpf5171.jpg" - ], - "blip_caption": "a photo of a dog in a field of flowers and grass", - "query": "dogs playing flower-filled meadow", - "dia_id": "D14:2", - "text": "That's awesome! That must be fun! I just started agility classes with my pups at a dog park. It's awesome to watch them learn and build relationships with other dogs. Seeing them face and conquer challenges really warms my heart." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/2109/2471534899_2764ab02bf_b.jpg" - ], - "blip_caption": "a photography of a dog jumping through a ring in the air", - "query": "dog jumping through hoop agility course", - "dia_id": "D14:3", - "re-download": true, - "text": "Wow it's amazing to watch them grow together. They look so cool overcoming obstacles like that. Impressive stuff!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.pexels.com/photos/7210626/pexels-photo-7210626.jpeg" - ], - "blip_caption": "a photography of a group of dogs sitting on a dirt road", - "query": "dogs agility course park sitting smiling", - "dia_id": "D14:4", - "re-download": true, - "text": "Thanks! They've come a long way. They have so much fun with it, it's a great physical and mental workout. I take them to the park twice a week for practice - it's been a great bonding experience." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i0.hippopx.com/photos/637/74/343/trail-nature-path-preview.jpg" - ], - "blip_caption": "a photography of a dirt path in the woods with rocks and trees", - "query": "hiking trail trees", - "dia_id": "D14:5", - "re-download": true, - "text": "Awesome! You're having fun with them and keeping them busy - how's that going? Btw look at the trail that I was just at. Cool right?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://s0.geograph.org.uk/geophotos/01/41/92/1419270_5f37a596.jpg" - ], - "blip_caption": "a photography of a woman walking her dogs down a path", - "query": "audrey dogs forest trail walk", - "dia_id": "D14:6", - "re-download": true, - "text": "It's been tough at times, but overall it's going great. We're all growing together. Check out this pic of us on a trail hike!" - }, - { - "speaker": "Andrew", - "dia_id": "D14:7", - "text": "Wow, nice hike! How long was the trail?" - }, - { - "speaker": "Audrey", - "dia_id": "D14:8", - "text": "The hike took us two hours and it was stunning! We saw lots of amazing views and it was great to explore nature." - }, - { - "speaker": "Andrew", - "dia_id": "D14:9", - "text": "That sounds awesome! Being able to just be in nature and appreciate it is really cool. I wish I could do that more often." - }, - { - "speaker": "Audrey", - "dia_id": "D14:10", - "text": "Nature really refreshes you, right? It's nice to appreciate all the beauty around us. Are there any outdoor activities you enjoy lately?" - }, - { - "speaker": "Andrew", - "dia_id": "D14:11", - "text": "Well for me hiking is the best. Being out in nature with all the trees and fresh air always refreshes me. Reaching the top of a challenging trail is amazing too - it feels like all worries just vanish when you get to the top." - }, - { - "speaker": "Audrey", - "dia_id": "D14:12", - "text": "Yeah, totally. It's like you've achieved something and all worries just fade away. Nature is pretty special, huh?" - }, - { - "speaker": "Andrew", - "dia_id": "D14:13", - "text": "Yeah, nature really calms me down and relaxes my mind. It's like a break from the craziness of city living." - }, - { - "speaker": "Audrey", - "dia_id": "D14:14", - "text": "Yeah! It's a refuge from the busy city life and it's even better when you can share it with someone special. My pets have been my good ol' buddies during all the tough times! They make me so happy and I love them so much." - }, - { - "speaker": "Andrew", - "dia_id": "D14:15", - "text": "Agreed! Sharing something you enjoy with someone is great. Plus, those types of bonds bring loads of joy and love." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0280/3790/6505/files/1d0d76aa77d195e7685b71bb336436ea.jpg" - ], - "blip_caption": "a photo of a dog laying in a dog bed in a living room", - "query": "dogs snuggled cozy dog bed", - "dia_id": "D14:16", - "text": "Yeah, humans and animals have a cool connection. They bring us so much joy and love. I'm blessed to have my furry friends in my life. They're my companions and always make my day better." - }, - { - "speaker": "Andrew", - "blip_caption": "a photo of a stream running through a lush green forest", - "dia_id": "D14:17", - "text": "They're pretty lucky to have you as their owner. Hoping for the day I can have such a deep bond with Toby and experience that special bond too." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/67s3x8066hl91.jpg" - ], - "blip_caption": "a photo of three dogs sitting on a wooden floor looking up", - "query": "four dogs sitting breed", - "dia_id": "D14:18", - "text": "Yeah they really mean the world to me. Btw, I never really asked, what breed is Toby?" - }, - { - "speaker": "Andrew", - "dia_id": "D14:19", - "text": "He's a German Shepherd - they're so smart and loyal! What do you think?" - }, - { - "speaker": "Audrey", - "dia_id": "D14:20", - "text": "German Shepherds are indeed awesome! They are super loyal and smart. You'll have an amazing connection with one!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/xzuhqdylro1b1.jpg" - ], - "blip_caption": "a photo of a dog sitting on a rock in the woods", - "query": "german shepherd hiking trail", - "dia_id": "D14:21", - "text": "I hope so! I shold take him hiking with me, they would be great hiking buddies, so smart and loyal." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/s6gfs24m2hpb1.jpg" - ], - "blip_caption": "a photo of a dog sitting on a trail with a view of a city", - "query": "german shepherd hiking mountains", - "dia_id": "D14:22", - "text": "Yep! German Shepherds are known for their loyalty and smarts. They love new journeys and would love exploring the outside with you. I can totally see you and your pup conquering trails together!" - }, - { - "speaker": "Andrew", - "dia_id": "D14:23", - "text": "Yeah, having someone who enjoys similar things would be great for hiking. Do you have any advice for city-dwellers that owns a pup?" - }, - { - "speaker": "Audrey", - "dia_id": "D14:24", - "text": "My advice would be to make sure you have enough time and energy for a pup - they need lots of attention and walks! Especially for a German Shepherd like Toby, he'll need to offset a lot of energy." - }, - { - "speaker": "Andrew", - "dia_id": "D14:25", - "text": "Thanks for the advice! I'll definitely keep that in mind. I want to make sure I'm not limiting Toby's growth but not taking him out not enough." - }, - { - "speaker": "Audrey", - "dia_id": "D14:26", - "text": "No worries, Andrew! It's important to be prepared and give a pup the love it deserves. Good luck with Toby!" - }, - { - "speaker": "Andrew", - "dia_id": "D14:27", - "text": "Thanks! Gotta take Toby out for a small hike at the local trail. Ttyl!" - } - ], - "session_15_date_time": "9:58 pm on 16 August, 2023", - "session_15": [ - { - "speaker": "Audrey", - "img_url": [ - "https://i.pinimg.com/originals/a3/1d/79/a31d794799464fd294587fe666da891d.jpg" - ], - "blip_caption": "a photo of a person with a tattoo on their hand", - "query": "tattoo four dogs arm", - "dia_id": "D15:1", - "text": "Hey Andrew, since we last spoke I got another tattoo of my four dogs on my arm! They really mean a lot to me so I thought it'd be nice to have them with me wherever I go. What've you been up to?" - }, - { - "speaker": "Andrew", - "dia_id": "D15:2", - "text": "Wow that's so cool! I recently went to a farm with my girlfriend to get some fresh veggies for dinner, and it was really nice. Have you been thinking about getting more fur babies or is four enough?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/2/21/Dogs_playing_at_dog_park.JPG" - ], - "blip_caption": "a photography of a group of people walking with dogs in a park", - "query": "four dogs playing park", - "dia_id": "D15:3", - "re-download": true, - "text": "Sounds great! I'd love to have more, but four is enough for now. They keep me busy and I want to make sure I give each of them the attention they deserve - four dogs is already a lot! I took them all to the vet and got them checked up, it was such a havoc that next time I'll bring them one by one." - }, - { - "speaker": "Andrew", - "blip_caption": "a photo of a dog laying on a rug eating lettuce", - "dia_id": "D15:4", - "text": "Oof, that vet trip must have been chaotic. Yeah I'm sure they keep you busy! That photo you shared was sweet - do they have a favorite spot to relax?" - }, - { - "speaker": "Audrey", - "dia_id": "D15:5", - "text": "Yeah, for sure. They each have their favorite spot to chill. Pepper loves lounging on the couch, Pixie always curls up in her bed, Precious has her chair, and Panda loves to relax on his rug! They all have their own little cozy spots." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://lucky-paws.co/cdn/shop/products/image_b74722d8-a1e0-4dcc-81a8-faa99fed31cb_1.jpg" - ], - "blip_caption": "a photo of a dog laying on a fluffy blanket on the floor", - "query": "cozy dog bed blanket", - "dia_id": "D15:6", - "text": "That sounds adorable! Pets always find their own little spots and it brings so much joy and comfort. Here's Toby at his favorite spot." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://freerangestock.com/sample/87785/puppy-love.jpg" - ], - "blip_caption": "a photography of two dogs laying on a blanket on a couch", - "query": "pepper pixie dogs cuddling fluffy blanket", - "dia_id": "D15:7", - "re-download": true, - "text": "Yeah, they sure know how to get comfy! Here's a pic of them snuggling on my favorite blanket." - }, - { - "speaker": "Andrew", - "dia_id": "D15:8", - "text": "Aww, they're so adorable! They look so cozy. Do they always sleep like that?" - }, - { - "speaker": "Audrey", - "dia_id": "D15:9", - "text": "Yeah, they always sleep like that. They cuddle up together, especially when it's time to nap. They really are best friends." - }, - { - "speaker": "Andrew", - "dia_id": "D15:10", - "text": "Wow that's awesome! It must be great having furry friends to keep each other company." - }, - { - "speaker": "Audrey", - "dia_id": "D15:11", - "text": "Yeah, they're always there for each other. Seeing them together makes me so happy." - }, - { - "speaker": "Andrew", - "dia_id": "D15:12", - "text": "That sounds wonderful. No wonder it brings you so much happiness to have them around!" - }, - { - "speaker": "Audrey", - "dia_id": "D15:13", - "text": "Yeah they mean the world to me, so I can't imagine life without them." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/5kklgil5t9qb1.jpg" - ], - "blip_caption": "a photo of a man laying on a couch with a dog", - "query": "man hugging dog", - "dia_id": "D15:14", - "text": "Totally get it, pets bring such joy and feel like family. I can't imagine life without them." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i2.pickpik.com/photos/498/107/776/dog-girl-de-dogue-preview.jpg" - ], - "blip_caption": "a photography of a woman sitting in a field with two dogs", - "query": "person lying grass dogs", - "dia_id": "D15:15", - "re-download": true, - "text": "Yep, pets are family. It's so sweet to see the connection between them. Here's a photo of me lying on the grass with them." - }, - { - "speaker": "Andrew", - "dia_id": "D15:16", - "text": "Wow, that's a great pic! Looks like you guys had a really good time outside." - }, - { - "speaker": "Audrey", - "dia_id": "D15:17", - "text": "Oh yeah it was a great day - we had tons of fun outside." - }, - { - "speaker": "Andrew", - "dia_id": "D15:18", - "text": "Glad you had a blast with them. Cherish those memories!" - }, - { - "speaker": "Audrey", - "dia_id": "D15:19", - "text": "Thanks! I'll always cherish those moments. They really make life so much brighter." - } - ], - "session_16_date_time": "9:19 pm on 19 August, 2023", - "session_16": [ - { - "speaker": "Andrew", - "img_url": [ - "https://res.cloudinary.com/dragonspell/images/w_480,h_480,c_scale,dpr_auto,fl_progressive:steep,f_auto/w_480,h_480,c_fill/v1571420634/www.travelportland.com/Vista-House-Columbia-River-Gorge-Photo-by-Travel-Oregon-aspect-ratio-1x1/Vista-House-Columbia-River-Gorge-Photo-by-Travel-Oregon-aspect-ratio-1x1.jpg" - ], - "blip_caption": "a photo of a sunset over a mountain with a church on top", - "query": "beautiful sunrise hike trail", - "dia_id": "D16:1", - "text": "Hey Audrey, hope you're doing good! So I've decided to take a break from work yesterday and check out a new cafe. It was a nice change and reminded me of how the great outdoors is always there to offer you peace. Here's a photo from my last hike - so serene! How's your month been so far?" - }, - { - "speaker": "Audrey", - "dia_id": "D16:2", - "text": "Hi Andrew! The cafe sounds great, and the hike pic's great! August's been eventful - I learned a new skill! It was really awesome, making sure they were pampered and happy. I've always loved caring for my pups, and now taking care of their grooming myself makes me closer to them. They look so cute post-grooming!" - }, - { - "speaker": "Andrew", - "dia_id": "D16:3", - "text": "Wow that's awesome! You're doing an amazing job taking care of your pups. Can you show me a pic of them after the grooming? I bet they look adorable!" - }, - { - "speaker": "Audrey", - "img_url": [ - "http://canadiangroomingdistributor.com/cdn/shop/articles/PXL_20220614_014152490.PORTRAIT.jpg" - ], - "blip_caption": "a photo of a dog is standing on a table in a room", - "query": "dogs groomed fluffy", - "dia_id": "D16:4", - "text": "Here's a photo of them after their grooming - look how soft and fluffy they are!" - }, - { - "speaker": "Andrew", - "dia_id": "D16:5", - "text": "Wow, they look so cute and fluffy! You did such a great job. How did you do it so well? I've always wanted to learn dog grooming but never got the time." - }, - { - "speaker": "Audrey", - "dia_id": "D16:6", - "text": "I took a dog grooming course and learned lots of techniques. Would you like to hear some tips?" - }, - { - "speaker": "Andrew", - "dia_id": "D16:7", - "text": "Of course! I'd love to hear some tips. It's something I've always been interested in." - }, - { - "speaker": "Audrey", - "dia_id": "D16:8", - "text": "Grooming slowly and gently, paying attention to sensitive areas like ears and paws. And remember to stay patient and positive throughout the grooming process." - }, - { - "speaker": "Andrew", - "dia_id": "D16:9", - "text": "Cool tips! I'll remember them if I ever get to groom Toby. Thanks!" - }, - { - "speaker": "Audrey", - "dia_id": "D16:10", - "text": "No problem, glad the tips helped. If you ever give it a shot, let me know and I can provide more advice." - }, - { - "speaker": "Andrew", - "dia_id": "D16:11", - "text": "Thanks! I'll let you know if I decide to give it a try. Appreciate your help. What are your plans for the weekend?" - }, - { - "speaker": "Audrey", - "dia_id": "D16:12", - "text": "I'm taking them out for a stroll in the park - they love it and it's a good workout for us. Any weekend plans for you?" - }, - { - "speaker": "Andrew", - "dia_id": "D16:13", - "text": "This weekend I'm heading to a nature reserve to reconnect with the outdoors - excited!" - }, - { - "speaker": "Audrey", - "dia_id": "D16:14", - "text": "Cool! Have a great time reconnecting with nature. Don't forget to take some nice pictures!" - }, - { - "speaker": "Andrew", - "dia_id": "D16:15", - "text": "Sure thing, I'll get some awesome pics to show you when I return. " - }, - { - "speaker": "Audrey", - "dia_id": "D16:16", - "text": "Haha thanks! Your pics of hikes are always nice." - }, - { - "speaker": "Andrew", - "dia_id": "D16:17", - "text": "Yeah! I really wanted to save the moment ya know?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D16:18", - "text": "No wonder. Now I can't wait for the pics. Take care and have fun! My dogs will be waiting too!" - }, - { - "speaker": "Andrew", - "dia_id": "D16:19", - "text": "Thanks! Can't wait to show you them when I'm back. See ya!" - } - ], - "session_17_date_time": "12:24 am on 24 August, 2023", - "session_17": [ - { - "speaker": "Andrew", - "dia_id": "D17:1", - "text": "Hey Audrey! What's up? Last weekend my girlfriend and I went fishing in one of the nearby lakes. It was so nice. We got a few fish and had a blast. Have you ever gone fishing before?" - }, - { - "speaker": "Audrey", - "img_url": [ - "http://tahoemountainsports.com/cdn/shop/articles/IMG_8472.jpg" - ], - "blip_caption": "a photo of a group of dogs standing on a rock near a lake", - "query": "stunning lake mountains friends dogs", - "dia_id": "D17:2", - "text": "Hey! Actually I've never been fishing. It's always been just chilling at the lake. I remember this moment a few years back when I sat by a gorgeous lake in the mountains with friends. So peaceful and calming. Just the sound of the birds, the stillness of the water, and the fresh air - it was so special. But yeah I have never gone on a fishing trip before. Here's a photo of the trip to the lake with my friend." - }, - { - "speaker": "Andrew", - "dia_id": "D17:3", - "text": "Wow, they look like they're loving the mountain life. How do you keep them looking good out there?" - }, - { - "speaker": "Audrey", - "dia_id": "D17:4", - "text": "Yeah they really do enjoy the mountain life. Regular grooming is essential to keep them looking good. Daily brushing, regular baths, nail trims, and lots of love is what helps them stay healthy and happy. It's all about keeping them in good shape." - }, - { - "speaker": "Andrew", - "dia_id": "D17:5", - "text": "Awesome! Sounds like you're doing a great job taking care of them. Making sure they stay healthy and happy is key." - }, - { - "speaker": "Audrey", - "dia_id": "D17:6", - "text": "Yeah! It means a lot. Taking care of them is a big deal. It makes me really happy and I take that responsibility seriously. It can be tough but it's super rewarding." - }, - { - "speaker": "Andrew", - "dia_id": "D17:7", - "text": "I'm sure it's rewarding. Making a positive impact on someone's life, especially those close to you, must be such a good feeling." - }, - { - "speaker": "Audrey", - "dia_id": "D17:8", - "text": "Yeah, my dogs make me really happy. I love them so much and I want to make them as happy as possible. We have a strong bond." - }, - { - "speaker": "Andrew", - "dia_id": "D17:9", - "text": "That's amazing. You have such a strong bond with them! I hope I can have such a strong bond with Toby as well." - }, - { - "speaker": "Audrey", - "dia_id": "D17:10", - "text": "They mean the world to me. I'm so lucky to have them. I sure with your love, you and Toby can have a strong bond." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0280/3790/6505/files/189ab0128c572238758dd096675a54be.jpg" - ], - "blip_caption": "a photo of a dog sleeping in a dog bed on the floor", - "query": "cuddling puppy bed", - "dia_id": "D17:11", - "text": "Lucky you! Pets sure bring a lot of love and joy. Can't wait till Toby and I bond better." - }, - { - "speaker": "Audrey", - "dia_id": "D17:12", - "text": "Thanks! That's really nice. Let me know if you need some tips on taking care of Toby." - }, - { - "speaker": "Andrew", - "dia_id": "D17:13", - "text": "Sure thing! I'll try figure it on my own first. Appreciate the help!" - }, - { - "speaker": "Audrey", - "dia_id": "D17:14", - "text": "Remember, it takes time to form a bond, don't rush!" - }, - { - "speaker": "Andrew", - "dia_id": "D17:15", - "text": "Got it. Thanks for that reminder." - }, - { - "speaker": "Audrey", - "dia_id": "D17:16", - "text": "No problem. Let me know if you have any questions or need advice." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://d4yxl4pe8dqlj.cloudfront.net/images/4926302d-1bff-49d5-999a-4611342b1891/74399bb7-e65b-4105-81cd-ffd5ac2acf5b_full_size.jpg" - ], - "blip_caption": "a photo of a small dog sitting on a floor with a leash", - "query": "dog leash collar door", - "dia_id": "D17:17", - "text": "Yep, Audrey. Thanks for everything - you rock! Here's a pic of Toby." - }, - { - "speaker": "Audrey", - "dia_id": "D17:18", - "text": "Aww so cute! Toby looks happy!" - }, - { - "speaker": "Andrew", - "dia_id": "D17:19", - "text": "Haha yeah, I do love Toby!" - }, - { - "speaker": "Audrey", - "dia_id": "D17:20", - "text": "I'm glad Toby is happy. I'm sure there are lots of adventures to come!" - }, - { - "speaker": "Andrew", - "dia_id": "D17:21", - "text": "Yep, Toby and I are gonna have a blast exploring outdoors! Can't wait." - } - ], - "session_18_date_time": "7:49 pm on 6 September, 2023", - "session_18": [ - { - "speaker": "Andrew", - "dia_id": "D18:1", - "text": "Hey Audrey, how's it going? Since we last talked, a few new things have come up in my life. Work's been tough and stressful, so my outdoor activities have taken a backseat. Finding balance has been challenging." - }, - { - "speaker": "Audrey", - "dia_id": "D18:2", - "text": "Hey Andrew, good to hear from you. Sorry to hear about work being tough. Finding that balance can be challenging, huh? It can feel like there's not enough time. Just remember to take care of yourself and find ways to manage stress. Hang in there!" - }, - { - "speaker": "Andrew", - "dia_id": "D18:3", - "text": "Thanks! It's tough, but I guess that's just part of life, huh? How do you make sure you have enough time for yourself?" - }, - { - "speaker": "Audrey", - "dia_id": "D18:4", - "text": "Yeah, it's tough to find time for yourself. I make sure to do at least one self-care activity each day - like treating myself to something nice. Don't forget to take care of yourself and have some fun too!" - }, - { - "speaker": "Andrew", - "dia_id": "D18:5", - "text": "Yeah, self-care is really important isn't it. I've been adding simple things to my day like grabbing a coffee in the morning or going for a walk at lunch. It kinda helps me recharge and chill out a little." - }, - { - "speaker": "Audrey", - "dia_id": "D18:6", - "text": "That's great! Glad you found ways to relax. It's nice to have those little moments of joy. Something cool recently happened with my furry friends - I organized a doggy playdate with the neighbors' dogs. Seeing all those tails wagging was so sweet. They must have had so much fun!" - }, - { - "speaker": "Andrew", - "dia_id": "D18:7", - "text": "That's awesome. I bet they all had a blast! Got any pics from that day?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/1go6sson1bk41.jpg" - ], - "blip_caption": "a photo of two dogs playing in a fenced in area", - "query": "dogs playing backyard", - "dia_id": "D18:8", - "text": "Here's a pic from the playdate. It was great seeing them having fun together. Their joy was infectious and made my heart feel so full." - }, - { - "speaker": "Andrew", - "dia_id": "D18:9", - "text": "That's so heartwarming! Seeing them enjoy themselves like that is always a joy. :)" - }, - { - "speaker": "Audrey", - "dia_id": "D18:10", - "text": "I'm so happy seeing them have a great time. Last week I even got some new beds for them, just to give them some extra comfort now the weather's cooling down and they were happy! It's incredible how such a simple thing can bring them so much happiness." - }, - { - "speaker": "Andrew", - "dia_id": "D18:11", - "text": "Animals can really find joy in the simple things. That was so nice of you. Do you have any pictures of the new beds?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0280/3790/6505/files/66f095b1a84b18ac04f6e85fe293a41a.jpg" - ], - "blip_caption": "a photo of a dog laying on a dog bed in a living room", - "query": "dog beds cozy comfy living room", - "dia_id": "D18:12", - "text": "Sure! Here's a pic of them. Super cozy and comfy. My furry friends love them!" - }, - { - "speaker": "Andrew", - "dia_id": "D18:13", - "text": "Do they enjoy snoozing on it? It looks really comfy!" - }, - { - "speaker": "Audrey", - "dia_id": "D18:14", - "text": "They absolutely love it! They curl up and snuggle like they're in a cloud - it's adorable!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://cdn.stocksnap.io/img-thumbs/960w/sunset-sky_VEEQZUDEO9.jpg" - ], - "blip_caption": "a photography of a person on a mountain with a view of the city", - "query": "sunset mountain peak hike", - "dia_id": "D18:15", - "re-download": true, - "text": "That's really cute! Animals really know how to be happy with the simple stuff. Last weekend I got away for a hike and it was such a relief to get away from the city. Here's a photo of the beautiful sunset I witnessed during my hike." - }, - { - "speaker": "Audrey", - "dia_id": "D18:16", - "text": "Nice escape! Glad you got out hiking. Are you planning to hike with Toby someday?" - }, - { - "speaker": "Andrew", - "dia_id": "D18:17", - "text": "Yeah, I've been wanting to for a while, but it's a bit difficult since Toby is still so young." - }, - { - "speaker": "Audrey", - "dia_id": "D18:18", - "text": "Did you find a dog-friendly place to live yet? I remember you mentioning it." - }, - { - "speaker": "Andrew", - "dia_id": "D18:19", - "text": "Nah, still working on that. It's been a bit challenging." - }, - { - "speaker": "Audrey", - "dia_id": "D18:20", - "text": "Keep going, you'll find a great place to live for your pet soon!" - }, - { - "speaker": "Andrew", - "dia_id": "D18:21", - "text": "Thanks! I appreciate the help. I'll keep searching for that perfect place for dogs!" - }, - { - "speaker": "Audrey", - "dia_id": "D18:22", - "text": "No worries! You got this. Don't give up. Take care!" - } - ], - "session_19_date_time": "5:53 pm on 24 September, 2023", - "session_19": [ - { - "speaker": "Andrew", - "dia_id": "D19:1", - "text": "Hey Audrey! Long time no talk! How have you been?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/en/2/24/Dog_leash.JPG" - ], - "blip_caption": "a photography of a red retractable leash with a black handle", - "query": "torn dog leash park scare", - "dia_id": "D19:2", - "re-download": true, - "text": "Hey! I'm alright. Had some bumps though - last Friday at the park one of my pups saw something and pulled so hard the leash busted. Scared that she might run off and get hurt, so I had to chase after her. Luckily I caught her before anything bad happened. Little moments like this remind me how important she is and how we should be careful when we're out there." - }, - { - "speaker": "Andrew", - "dia_id": "D19:3", - "text": "Oh man, sorry to hear that! I'm totally getting anxious just thinking about my dog getting lost. Precious must have been really scared. What did you do to calm her down?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/ves2ui4xgml81.jpg" - ], - "blip_caption": "a photo of a dog laying on a bed with its head on the pillow", - "query": "dog lying on soft bed peaceful", - "dia_id": "D19:4", - "text": "I petted and hugged her, spoke calmly, and slowly walked her to relax. Our bond feels even stronger when moments like these show up." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/5/50/A_Chihuahua_fetching_a_ball.JPG" - ], - "blip_caption": "a photography of a dog running in a field with a frisbee", - "query": "dog playing fetch park", - "dia_id": "D19:5", - "re-download": true, - "text": "She looks so adorable! That's the connection I'd like to have with Toby. Any advice on creating a strong relationship with dogs?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA1L2JzMTMxOS1pbWFnZS1rd3Z5bHNnZC5qcGc.jpg" - ], - "blip_caption": "a photography of three dogs wearing birthday hats and sitting next to each other", - "query": "dogs sitting hats", - "dia_id": "D19:6", - "re-download": true, - "text": "Building trust with them needs patience and regular training. Give them time and love, and praise their successes." - }, - { - "speaker": "Andrew", - "blip_caption": "a photo of a dog sitting on a sidewalk in a garden", - "dia_id": "D19:7", - "text": "Thanks for the tips! Patience and practice are important for establishing a bond with our pooches, just like any other meaningful relationship. I guess some dogs just need more time! It must be so satisfying to see those successes and progress. Oh, and your pup looks so sharp in that green hat! Is there anything specific you do with them to work on training?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/7yt2t54q6ho41.jpg" - ], - "blip_caption": "a photo of a group of dogs sitting on a chair", - "query": "dogs sitting row looking camera", - "dia_id": "D19:8", - "text": "Thanks! We work on obedience and teach them tricks like sit, stay, shake, and roll over. It's fun and rewarding for both of us." - }, - { - "speaker": "Andrew", - "dia_id": "D19:9", - "text": "Wow, teaching them tricks must be super fun! How often do you take them for walks?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://pinnaclepooch.com/cdn/shop/products/image_33290672-49cc-42a7-9f1b-fa7bbe93e529.jpg" - ], - "blip_caption": "a photo of two dogs sitting next to each other on a beach", - "query": "dogs matching collars leashes", - "dia_id": "D19:10", - "text": "Very often, multiple times a day even, it's a great exercise for them and great bonding time for us." - }, - { - "speaker": "Andrew", - "dia_id": "D19:11", - "text": "Hmm that does sound like a great way to bond! What breeds are they again? Their breeds might make a difference regarding how well they bond too." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA2L3Vwd2s2MTg1NTg1MS13aWtpbWVkaWEtaW1hZ2Uta293cHBxMW4uanBn.jpg" - ], - "blip_caption": "a photography of a group of dogs tied to a leash on a brick walkway", - "query": "mixed breed dogs sitting blanket park", - "dia_id": "D19:12", - "re-download": true, - "text": "They're all mutts. Two of them are Jack Russell mixes and the other two are Chihuahua mixes. And yea, I believe so! Some dog breeds do bond better than others." - }, - { - "speaker": "Andrew", - "dia_id": "D19:13", - "text": "Aww, they're all so cute! So much fluff and joy!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i1.pickpik.com/photos/406/636/1011/dog-chihuahua-pet-animal-preview.jpg" - ], - "blip_caption": "a photography of three small dogs sitting on a couch with a white fur", - "query": "four dogs cuddled up couch", - "dia_id": "D19:14", - "re-download": true, - "text": "I love them for that. They really do bring so much joy into my life." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://www.flowerwindowboxes.com/v/vspfiles/assets/images/rail-planter-1.jpg" - ], - "blip_caption": "a photo of a balcony with a bunch of flowers on it", - "query": "balcony garden blooming flowers vegetable patch", - "dia_id": "D19:15", - "text": "Yeah! They really do bring so much into our lives - it's amazing to watch them interact. Here's something I've been taking care of lately. Look at those flowers!" - }, - { - "speaker": "Audrey", - "dia_id": "D19:16", - "text": "Nice! Taking care of something like this relaxes me and brings me peace too. I personally have a small garden as well ya know." - }, - { - "speaker": "Andrew", - "dia_id": "D19:17", - "text": "That's cool! How's it going?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://wateruseitwisely.com/wp-content/uploads/2021/03/20190611_073428.jpg" - ], - "blip_caption": "a photo of a cactus plant with two white flowers", - "query": "blooming flower", - "dia_id": "D19:18", - "text": "It's going great! The flowers are looking great and my veggie patch is coming along. It's so fun to see them grow! Really feels accomplishing." - }, - { - "speaker": "Andrew", - "dia_id": "D19:19", - "text": "Those flowers look great! What kind are they?" - }, - { - "speaker": "Audrey", - "dia_id": "D19:20", - "text": "They're called Peruvian Lilies. They are so awesome - they have such bright colors and delicate petals." - }, - { - "speaker": "Andrew", - "dia_id": "D19:21", - "text": "They're beautiful! So vibrant and eye-catching. Are they difficult to care for?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://cdn12.picryl.com/photo/2016/12/31/malinois-water-garden-dog-basks-0d19ee-1024.jpg" - ], - "blip_caption": "a photography of a dog laying on a wooden dock in a pond", - "query": "dog garden", - "dia_id": "D19:22", - "re-download": true, - "text": "Nope, they're easy to take care of, perfect for me! Just gotta water them and make sure they get enough sun." - }, - { - "speaker": "Andrew", - "dia_id": "D19:23", - "text": "Awesome! Do they enjoy playing in the garden too?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/65535/51632425349_e20c4919c5_b.jpg" - ], - "blip_caption": "a photography of two dogs running in a grassy field", - "query": "dogs running in garden", - "dia_id": "D19:24", - "re-download": true, - "text": "Yeah, they do enjoy the garden! Always running around, exploring and having a great time. So adorable!\n\n" - }, - { - "speaker": "Andrew", - "dia_id": "D19:25", - "text": "Wow! Looks like they're having a blast. Are there any other furry pals they play with, or just the ones you have?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/3563/3808347692_7ddc680e46_b.jpg" - ], - "blip_caption": "a photography of a dog and a teddy bear sleeping on a couch", - "query": "dogs sitting together couch", - "dia_id": "D19:26", - "re-download": true, - "text": "Just my fur babies." - }, - { - "speaker": "Andrew", - "dia_id": "D19:27", - "text": "Must be great having them around, the bond between you and them is awesome." - }, - { - "speaker": "Audrey", - "img_url": [ - 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" - ], - "blip_caption": "a photography of three dogs sitting on a couch with a laptop in the background", - "query": "cute dogs cuddling on the couch", - "dia_id": "D19:28", - "re-download": true, - "text": "Yeah, they do! They make everything so much better. Can't imagine life without them. They mean everything to me." - }, - { - "speaker": "Andrew", - "dia_id": "D19:29", - "text": "That's such a lovely picture, Audrey! So cute to see them snuggled up, having fun together. They really bring so much joy to our lives." - }, - { - "speaker": "Audrey", - "dia_id": "D19:30", - "text": "They really do, bringing loads of love and happiness. They are everything to me." - } - ], - "session_20_date_time": "7:09 pm on 1 October, 2023", - "session_20": [ - { - "speaker": "Andrew", - "img_url": [ - "https://images.pexels.com/photos/17057350/pexels-photo-17057350.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-maksym-harbar-17057350.jpg" - ], - "blip_caption": "a photo of a sunset over the ocean with waves", - "query": "beach sunset seaside", - "dia_id": "D20:1", - "text": "Hey wassup? Got some great news - the gf and I are hitting the beach next month with Toby!" - }, - { - "speaker": "Audrey", - "dia_id": "D20:2", - "text": "Hey Andrew! Great to hear from you. Have fun at the beach trip! Bet you can't wait to get out to the nature. Can't wait for our hike with the dogs next month. They always put a smile on my face - life's just not the same without them!" - }, - { - "speaker": "Andrew", - "dia_id": "D20:3", - "text": "Thanks, I will! Yea I can't wait for the hike. It's been a long time since we all be in nature together." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://cdn12.picryl.com/photo/2016/12/31/dogs-reward-expect-856f58-1024.jpg" - ], - "blip_caption": "a photography of a group of dogs sitting on top of a lush green field", - "query": "dogs sitting park", - "dia_id": "D20:4", - "re-download": true, - "text": "Being in a nature environment is always a great way to relax. For me, taking the doggos out for a walk in the park helps clear my mind and find some peace. It's been tough lately, but it definitely helps." - }, - { - "speaker": "Andrew", - "dia_id": "D20:5", - "text": "Aww, they look so cute! That spot looks ideal for them to play. Where did you take them?" - }, - { - "speaker": "Audrey", - "dia_id": "D20:6", - "text": "We took them to the dog park nearby last Saturday. There was a big grassy area for them to play and lots of shaded spots for me to relax. They had a great time!" - }, - { - "speaker": "Andrew", - "dia_id": "D20:7", - "text": "Sounds great! Missing that experience. Can't wait for the coming up hike!" - }, - { - "speaker": "Audrey", - "dia_id": "D20:8", - "text": "Yeah, Andrew! The pups and I are loving it. Being out in nature and checking out new trails with the dogs is so different from being in the city." - }, - { - "speaker": "Andrew", - "dia_id": "D20:9", - "text": "I think everyone's gotta ditch the hustle and bustle every now and then. It's so refreshing to be in nature." - }, - { - "speaker": "Audrey", - "dia_id": "D20:10", - "text": "Yep, it's a relief. It's like being a bird and finally flying free. Talking of birds, have you seen any birds up close lately?" - }, - { - "speaker": "Andrew", - "dia_id": "D20:11", - "text": "I've seen them up close and it's amazing how they fly with grace and freedom." - }, - { - "speaker": "Audrey", - "dia_id": "D20:12", - "text": "Yeah, birds are really amazing! I love how they can fly around and explore. They have a freedom that I wish I had!" - }, - { - "speaker": "Andrew", - "dia_id": "D20:13", - "text": "Agreed! Watching them fly is so freeing and awe-inspiring. It's a great reminder to appreciate nature." - }, - { - "speaker": "Audrey", - "dia_id": "D20:14", - "text": "Yeah, for sure. It's a great way to appreciate nature. That reminds me that I've been wanting to do some birdwatching. It's really peaceful and calming." - }, - { - "speaker": "Andrew", - "dia_id": "D20:15", - "text": "Yeah do that! It's really peaceful and calming. It's nice to get away from the city and enjoy nature. Let me know if you need any birdwatching advice, I think I know a thing or two about bird watching. Or perhaps we can all go birdwatching soometimes." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/4115/4924769040_36947653ee_c.jpg" - ], - "blip_caption": "a photography of a book with a page of birds and a bird on it", - "query": "bird guidebook binoculars", - "dia_id": "D20:16", - "re-download": true, - "text": "Thanks! That's so helpful, I'd love to take you up on your offer. Right now I'm going with this book that writes about bird watching guides. " - }, - { - "speaker": "Andrew", - "dia_id": "D20:17", - "text": "Cool! Let me know when you're ready to go birdwatching and we can plan a trip together." - }, - { - "speaker": "Audrey", - "dia_id": "D20:18", - "text": "Sounds great! I'm gonna check my schedule and get back to you. I can't wait for some birdwatching." - }, - { - "speaker": "Andrew", - "dia_id": "D20:19", - "text": "Yeah it's gonna be fun exploring and spotting birds. " - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i2.pickpik.com/photos/262/777/291/binoculars-birdwatching-spy-glass-spying-preview.jpg" - ], - "blip_caption": "a photography of a pair of binoculars sitting on a table with a book", - "query": "binoculars birdwatching guidebook", - "dia_id": "D20:20", - "re-download": true, - "text": "Yup! I should go learn some of the common birds in this area." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/4/48/Birdwatching_India_01.jpg" - ], - "blip_caption": "a photography of a pen and a notebook with a pen and a camera", - "query": "binoculars notebook", - "dia_id": "D20:21", - "re-download": true, - "text": "Nice! Looks like you're prepared. I'll bring my binos and a notebook to log them at the trip." - }, - { - "speaker": "Audrey", - "dia_id": "D20:22", - "text": "Nice. Looks like you already have some experience and really prepared." - }, - { - "speaker": "Andrew", - "dia_id": "D20:23", - "text": "Yeah! Like I said I do enjoy watching birds in the nature. I also read some books about our ecological systems as well." - }, - { - "speaker": "Audrey", - "dia_id": "D20:24", - "text": "Cool! Books like that must be really interesting. What have you discovered from reading them?" - }, - { - "speaker": "Andrew", - "dia_id": "D20:25", - "text": "I learned a lot about animals, plants, and ecosystems. It's fascinating to see how it all works together." - }, - { - "speaker": "Audrey", - "dia_id": "D20:26", - "text": "Wow, learning about the connections between them must be so cool. I bet it makes you appreciate nature even more." - }, - { - "speaker": "Andrew", - "dia_id": "D20:27", - "text": "Yeah, nature is all connected. We as human being need look after it." - }, - { - "speaker": "Audrey", - "dia_id": "D20:28", - "text": "Yeah! Taking care of the nature is like taking care of our house." - }, - { - "speaker": "Andrew", - "dia_id": "D20:29", - "text": "Definitely, let's take care of it for future generations." - }, - { - "speaker": "Audrey", - "dia_id": "D20:30", - "text": "It's on us to take care of it so the future generations have the natural resouorces." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://www.nps.gov/articles/000/images/trash-and-recycle-can.jpg" - ], - "blip_caption": "a photo of a trash can sitting in the middle of a park", - "query": "recycling bin nature preservation", - "dia_id": "D20:31", - "text": "Yep, it's important to take care of it for future generations. Let's do our share! Do you recycle at all?" - }, - { - "speaker": "Audrey", - "dia_id": "D20:32", - "text": "Yeah of course! It's important for us to do our part, and recycling is a crucial step. Do you have any other suggestions?" - }, - { - "speaker": "Andrew", - "dia_id": "D20:33", - "text": "How about reducing our carbon footprint by biking or using public transport?" - }, - { - "speaker": "Audrey", - "dia_id": "D20:34", - "text": "Oh yeah! I usually take public transport, but biking sounds like a fun way to reduce our carbon footprint. Let's all give it a try and make a change!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://bikeportland.org/wp-content/uploads/2021/10/Screen-Shot-2021-10-15-at-10.39.45-AM.jpg" - ], - "blip_caption": "a photo of a man riding a bike down a street", - "query": "bicycle bike lane", - "dia_id": "D20:35", - "text": "Yeah! It's a great way to help the planet and even train our body. Let's give it a try!" - }, - { - "speaker": "Audrey", - "dia_id": "D20:36", - "text": "I'd love to try it sometime. Are there any good routes around here?" - }, - { - "speaker": "Andrew", - "dia_id": "D20:37", - "text": "Yep, there are some awesome routes near the river. Let me show you the best ones that I enjoy!" - }, - { - "speaker": "Audrey", - "dia_id": "D20:38", - "text": "Sounds great! Can you show me the best bike routes by the river? Thanks!" - }, - { - "speaker": "Andrew", - "dia_id": "D20:39", - "text": "Sure. There are many routes around the area. I'll show you the best bike routes near there. It'll be great to get outside and soak up the scenery." - }, - { - "speaker": "Audrey", - "dia_id": "D20:40", - "text": "Sounds great! Can't wait to check out those bike routes and soak up the scenery. It should be a blast!" - } - ], - "session_21_date_time": "4:18 pm on 4 October, 2023", - "session_21": [ - { - "speaker": "Andrew", - "dia_id": "D21:1", - "text": "Hi Audrey! Been a while since I hear from you. How's it been?" - }, - { - "speaker": "Audrey", - "dia_id": "D21:2", - "text": "Hey Andrew! It's been a wild ride! I did something fun with my pups over the weekend, took them to the beach and it was so fun to see them playing in the ocean." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach", - "dia_id": "D21:3", - "text": "Sounds great! Did they love being at the beach? Did they enjoy the water? Here's a pic of my last trip to the beach." - }, - { - "speaker": "Audrey", - "dia_id": "D21:4", - "text": "The dogs had a blast swimming at the beach! Have you been there lately?" - }, - { - "speaker": "Andrew", - "dia_id": "D21:5", - "text": "Haven't been to the beach in a while. Miss being outdoors. It's hard to find open spaces in the city. Used to hike a lot, but it's more challenging now with my work life balance." - }, - { - "speaker": "Audrey", - "dia_id": "D21:6", - "text": "Oof, that's rough. I can imagine how much you miss being outdoors and surrounded by nature." - }, - { - "speaker": "Andrew", - "dia_id": "D21:7", - "text": "Yeah, it's been tough. Exploring nature was my escape - a way to find peace. But with my job and living here, it's been harder to get that feeling back. I feel a void in my heart." - }, - { - "speaker": "Audrey", - "dia_id": "D21:8", - "text": "Yeah, I get how it's like something is missing without being in the nature. But there are still some ways to appreciate it in the city, like getting some plants for your place or taking a trip to the park on the weekends." - }, - { - "speaker": "Andrew", - "dia_id": "D21:9", - "text": "Yeah true. I should get some more plants for my house. Can't beat being outside tho, but they can still bring some peace. I'll look into it. Thanks for the tip!" - }, - { - "speaker": "Audrey", - "dia_id": "D21:10", - "text": "Of course! If you need help or advice, just let me know. Plants can make your home so peaceful." - }, - { - "speaker": "Andrew", - "dia_id": "D21:11", - "text": "Thanks! I'll definitely reach out if I need any help or advice. Thanks again for offering!" - }, - { - "speaker": "Audrey", - "dia_id": "D21:12", - "text": "No problem at all! Glad to be of assistance." - }, - { - "speaker": "Andrew", - "dia_id": "D21:13", - "text": "Oh you've helped so much. " - }, - { - "speaker": "Audrey", - "dia_id": "D21:14", - "text": "Haha i'm just doing what I can do to help." - }, - { - "speaker": "Andrew", - "dia_id": "D21:15", - "text": "Thank you really. Well, take care and say hi to your dogs for me." - }, - { - "speaker": "Audrey", - "dia_id": "D21:16", - "text": "Haha I will. Take care. Talk later!" - }, - { - "speaker": "Andrew", - "dia_id": "D21:17", - "text": "Yup, have a great week. " - }, - { - "speaker": "Audrey", - "dia_id": "D21:18", - "text": "Have a great week! Bye!" - } - ], - "session_22_date_time": "9:41 pm on 6 October, 2023", - "session_22": [ - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D22:1", - "text": "Hey Andrew, I was thinking about what you said about missing nature the other day. It reminded me of when I couldn't walk my dogs because of a knee injury. It was tough 'cause they bring me so much joy. This pic was taken after I started walking again. It felt great to be able to get back out there!" - }, - { - "speaker": "Andrew", - "dia_id": "D22:2", - "text": "You know how it feel right? Bummer not being able to take them for a stroll. They must've been over the moon when you could finally take them out again. Glad you got that adorable pic too! Dogs are awesome like that, huh? Remind us to stop and smell the roses." - }, - { - "speaker": "Audrey", - "dia_id": "D22:3", - "text": "Yeah, it was like they couldn't wait! They definitely reminds us to appreciate life's little pleasures. They see the world so differently!" - }, - { - "speaker": "Andrew", - "dia_id": "D22:4", - "text": "Yeah, dogs sure know how to find the simple joys of life! Appreciating the little things is so important. Well speaking of interesting hobbies, I rememver you mentionoed about that jewelry earlier? I remember you saying you make them with recycled objects. That sounds cool - tell me more!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://boboglobal.com/cdn/shop/products/IMG_2161.jpg" - ], - "blip_caption": "a photo of a woman with a colorful necklace and earrings", - "query": "recycled bottle cap necklace colorful beads", - "dia_id": "D22:5", - "text": "Oh yes! I love making jewelry out of recycled stuff. It's a great way to show my love of creativity and sustainability. I hunt down things like bottle caps, buttons, and broken jewelry, and then turn them into one-of-a-kind pieces. Here's a photo of one of my favorite necklaces. They all hold a special story and have their own appeal. Plus, it's a small step in reducing waste." - }, - { - "speaker": "Andrew", - "dia_id": "D22:6", - "text": "That necklace looks awesome! The colors really stand out and the design is really cool. It's great that you're giving recycled objects new life. Do you make jewelry to sell or just as a hobby? Also, do you donate profits to a good cause?" - }, - { - "speaker": "Audrey", - "dia_id": "D22:7", - "text": "Thanks! I started making jewelry as a hobby and then started selling it. I donate a portion of my profits to a cause that's close to my heart. It's a way to combine two of my passions - making jewelry and making a difference. It's great that customers not only enjoy my work but also support a cause!" - }, - { - "speaker": "Andrew", - "dia_id": "D22:8", - "text": "Wow! It's amazing how you use your passion to make a difference in the world. Has this experience changed your perspective on other hobbies? And do you donate to any specific organization?" - }, - { - "speaker": "Audrey", - "dia_id": "D22:9", - "text": "Definitely! Any hobby can have an impact when used right - I donate to an animal shelter. They do great work, so it's my way of helping out." - }, - { - "speaker": "Andrew", - "dia_id": "D22:10", - "text": "That's awesome! It's really inspiring how dedicated you are to making a difference, even though you can't volunteer at the shelter anymore." - }, - { - "speaker": "Audrey", - "dia_id": "D22:11", - "text": "Yeah, I'm still finding ways to do good, even if I can't be there. Life's all about adapting and helping out." - }, - { - "speaker": "Andrew", - "dia_id": "D22:12", - "text": "Yeah, you got it. Life is all about rolling with the punches and making a difference, whatever happens. Your hard work is really amazing." - }, - { - "speaker": "Audrey", - "dia_id": "D22:13", - "text": "Thanks! I appreciate your kind words. Gotta stay strong and keep making a difference, even in challenging times." - } - ], - "session_23_date_time": "4:22 pm on 13 October, 2023", - "session_23": [ - { - "speaker": "Andrew", - "img_url": [ - "https://i2.pickpik.com/photos/717/632/853/dog-goldendoodle-chess-play-preview.jpg" - ], - "blip_caption": "a photography of a dog sitting at a chess board with a chess set in front of him", - "query": "board games dog bonding", - "dia_id": "D23:1", - "re-download": true, - "text": "Hey Audrey, it's been a busy week for me. Last Tuesday, my gf, Toby, and I had a really awesome night playing board games. It was really nice. What's been up with you lately?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://about.hawaiilife.com/wp-content/uploads/2023/11/IMG-1593-e1699235788417.jpg" - ], - "blip_caption": "a photo of a pan of cookies on a wooden table", - "query": "homemade dog treats tray", - "dia_id": "D23:2", - "text": "Hey! Sounds like you've been busy! I made some goodies recently to thank my neighbors for their pup-friendly homes. It was a nice way to bring some joy around here. Any plans for the weekend?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://embracesomeplace.com/wp-content/uploads/2022/01/DSC02393.jpg" - ], - "blip_caption": "a photo of a group of people sitting at a table in a room", - "query": "cozy cafe", - "dia_id": "D23:3", - "text": "Friday night's board game session was a nice break. This weekend, I'm planning to check out this cozy cafe and hang out there." - }, - { - "speaker": "Audrey", - "dia_id": "D23:4", - "text": "That cafe looks really cozy! Any other spots you would recommend?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://be-lavie.com/wp-content/uploads/2019/06/IMG_9281-e1560067147565.jpg" - ], - "blip_caption": "a photo of a man sitting on a couch in a large open space", - "query": "rooftop bar cityscape view", - "dia_id": "D23:5", - "text": "So this new spot just opened the other day. It's awesome with a sick view and vibe." - }, - { - "speaker": "Audrey", - "dia_id": "D23:6", - "text": "Wow, looks great! Can I bring my pups with me?" - }, - { - "speaker": "Andrew", - "dia_id": "D23:7", - "text": "Hmmm, not sure if it's suitable for them but hey, there's a great dog park close by! Maybe we can bring our coffee over." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/yfeccwau4dya1.jpg" - ], - "blip_caption": "a photo of a dog wearing a sweater sitting in the grass", - "query": "dog park dogs playing", - "dia_id": "D23:8", - "text": "Sounds good! Exploring new places is always an adventure for them." - }, - { - "speaker": "Andrew", - "dia_id": "D23:9", - "text": "Aww, they look so sweet in that sweater! Do they enjoy going to the dog park?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/6jbz5p9fxy051.jpg" - ], - "blip_caption": "a photo of two dogs playing with a frisbee in a field", - "query": "dog park dogs playing together", - "dia_id": "D23:10", - "text": "The dog park is like paradise for them! They love socializing with the other pups and getting lots of exercise. It's so cute to watch them running around and smelling all the stuff. Do you have any funny stories about your pup at the dog park?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/7245/7209126530_152a8cd458_b.jpg" - ], - "blip_caption": "a photography of a dog playing with a frisbee in a field", - "query": "dog chasing tail park", - "dia_id": "D23:11", - "re-download": true, - "text": "There was this one time my pup got too excited and chased a squirrel. He ran around the tree and the squirrel just watched from the branches. It was hilarious!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://d4yxl4pe8dqlj.cloudfront.net/images/dcc5fa88-74fb-4e04-a7f9-df5051a8b643/7babb05e-2478-445c-8ca9-4dc2113853ff_full_size.jpg" - ], - "blip_caption": "a photo of a dog wearing a shirt standing on a hardwood floor", - "query": "dog park dogs winter sweaters", - "dia_id": "D23:12", - "text": "Haha, that must've been hilarious! What breed is your pup again? He looks so fun! My dogs love running around the park. But one thing they hate is snow. I took them to a snowy one last winter and they were so confused! They definitely prefer nice, sunny days in the grass." - }, - { - "speaker": "Andrew", - "dia_id": "D23:13", - "text": "My pup Toby is German Shephard and loves cute sweaters! He's so active. What games do your dogs like to play at the park?" - }, - { - "speaker": "Audrey", - "dia_id": "D23:14", - "text": "My dogs go crazy for Fetch and Frisbee, and they love to run around and meet other pups. They could keep running for hours!" - }, - { - "speaker": "Andrew", - "dia_id": "D23:15", - "text": "That sounds like so much fun! It's great to see them happy and active." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/3563/3808347692_7ddc680e46_b.jpg" - ], - "blip_caption": "a photography of a dog and a teddy bear sleeping on a couch", - "query": "dogs sitting together on couch", - "dia_id": "D23:16", - "re-download": true, - "text": "Yeah, it's awesome! Seeing them happy fills my heart with joy. They bring me so much happiness." - }, - { - "speaker": "Andrew", - "dia_id": "D23:17", - "text": "Yeah, they really do bring joy don't they. It's wonderful." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.pinimg.com/originals/55/47/8f/55478f3a1f3c22e60a729cd3806d2830.jpg" - ], - "blip_caption": "a photo of a tattoo of a flower on a wrist", - "query": "tattoo arm four dogs", - "dia_id": "D23:18", - "text": "I think i've said this so many times, but I really can't imagine my life without them - my little family. They always make me smile and give lots of love and companionship. Remember I had a tattoo of my four dogs? So I went to the tattoo parlor and got some more drawings on the tattoo." - }, - { - "speaker": "Andrew", - "dia_id": "D23:19", - "text": "Wow, that's cool! Any plans on getting more? Or are you finished?" - }, - { - "speaker": "Audrey", - "blip_caption": "a photo of a woman with a tattoo of a dog and sunflowers", - "dia_id": "D23:20", - "text": "Thanks! I think right now its enough. Unless someday I decide to get another dog. Maybe I'll get another tattoo when that happens." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://c1.wallpaperflare.com/preview/640/472/990/away-path-forest-hiking.jpg" - ], - "blip_caption": "a photography of a path in the woods with a forest in the background", - "query": "hiking trail trees", - "dia_id": "D23:21", - "re-download": true, - "text": "I can tell they're a real source of love for you. It's great to have that kind of inspiration. You know what else inspires me? Somewhere like this" - }, - { - "speaker": "Audrey", - "dia_id": "D23:22", - "text": "They really bring me joy. It feels great to experience unconditional love! Do you ever get the chance to visit forests like that? Bet it's an amazing escape for you." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://community.us.craghoppers.com/wp-content/uploads/2018/05/j3bmicznnmrnfe1uchho.jpg" - ], - "blip_caption": "a photo of a woman walking up a steep hill with a view of the ocean", - "query": "hiking trail beautiful view", - "dia_id": "D23:23", - "text": "Yeah, living in the city, I do miss nature a lot. Whenever I can, I try to go to nearby parks or on hikes. It's such a peaceful and joyful experience for me." - }, - { - "speaker": "Audrey", - "dia_id": "D23:24", - "text": "Yeah you're so right about needing nature time. Life's been crazy lately so it's been a while since I took my furry friends out for a walk. But those moments with them in nature are just so chill and happy! They're definitely the best companions for exploring the great outdoors." - }, - { - "speaker": "Andrew", - "dia_id": "D23:25", - "text": "Totally agree! Nature and animals can be so peaceful and joyful. I hope you and your furry buddies go on some fun adventures soon!" - }, - { - "speaker": "Audrey", - "dia_id": "D23:26", - "text": "Yeah, I need to go on a hike with them, it's going to be a great therapy!" - }, - { - "speaker": "Andrew", - "dia_id": "D23:27", - "text": "Yeah! You've hiked with your dogs, it's a fun way to bond and make memories." - }, - { - "speaker": "Audrey", - "dia_id": "D23:28", - "text": "Yup, I still remember everytime we all go on a hiking strip with my dog. Good times." - } - ], - "session_24_date_time": "6:12 pm on 19 October, 2023", - "session_24": [ - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/3923/14627245354_a40ab098c0_b.jpg" - ], - "blip_caption": "a photography of a box filled with toys and other items", - "query": "dog toys shopping bag", - "dia_id": "D24:1", - "re-download": true, - "text": "Hey Andrew, hope you're doing ok. I recently had a good week - I went to a pet store last Monday to buy toys for my dogs and it was great seeing them so excited when I got them home. It made me realize how much I love them and how much joy they bring me." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/tousk84oar171.jpg" - ], - "blip_caption": "a photo of a dog sitting on a couch with a bowl of food", - "query": "cute dog playing chew toy buddy", - "dia_id": "D24:2", - "text": "Hi Audrey! Pets really can make our lives better, huh? Speaking of which, I've got some awesome news -- I recently adopted another pup from a shelter. He's the best." - }, - { - "speaker": "Audrey", - "dia_id": "D24:3", - "text": "Wow! That's awesome news! How's he doing in his new home?" - }, - { - "speaker": "Andrew", - "dia_id": "D24:4", - "text": "Thanks! He's doing great in his new home. Still getting used to Toby and the new environment. Toby needs some time to get along with him too. I never imagined having pets would bring so much happiness. Pets really bring lots of joy and companionship to our lives." - }, - { - "speaker": "Audrey", - "dia_id": "D24:5", - "text": "That's awesome! What is his name?" - }, - { - "speaker": "Andrew", - "dia_id": "D24:6", - "text": "I named him Buddy because he's my buddy and I hope him and Toby become buddies!" - }, - { - "speaker": "Audrey", - "dia_id": "D24:7", - "text": "That's perfect! Sounds like Buddy really is your sidekick. Do you have any favorite activities you two like to do together?" - }, - { - "speaker": "Andrew", - "dia_id": "D24:8", - "text": "Yeah, Buddy and I have a great time doing walks. It's a nice way to spend time together and get some fresh air." - }, - { - "speaker": "Audrey", - "dia_id": "D24:9", - "text": "Nice! Buddy seems to be having a great time! It's nice to spend time together and get some fresh air." - }, - { - "speaker": "Andrew", - "dia_id": "D24:10", - "text": "Yep, he loves checking out new hiking trails with us. It's awesome to see him so stoked and interested in everything nature has to offer." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://www.kingdukes.com/cdn/shop/articles/man_walking_dog_on_portland_trail_1200x.jpg" - ], - "blip_caption": "a photo of a man walking a dog on a trail", - "query": "dogs hiking trail nature", - "dia_id": "D24:11", - "text": "That sounds awesome! Have fun exploring the trails!" - }, - { - "speaker": "Andrew", - "dia_id": "D24:12", - "text": "Yup! I will be taking both of them to the trails together soon!" - }, - { - "speaker": "Audrey", - "dia_id": "D24:13", - "text": "I can't wait for our hike with the furry friends next month - it's gonna be awesome!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://fundiegofamily.com/wp-content/uploads/2018/05/blueskycover.jpg" - ], - "blip_caption": "a photo of a dirt road with a cow standing in the middle", - "query": "hiking trail trees blue sky", - "dia_id": "D24:14", - "text": "Oh yeah! It going to be fun with the new addition." - }, - { - "speaker": "Audrey", - "dia_id": "D24:15", - "text": "Ooo where is this gorgeous spot? I need to take my pups for a stroll there." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/4lun19fk1g591.jpg" - ], - "blip_caption": "a photo of a red truck driving down a road in a field", - "query": "countryside", - "dia_id": "D24:16", - "text": "Haha is nowhere near the city. Wish I could take them to a place like this, far from the city." - }, - { - "speaker": "Audrey", - "dia_id": "D24:17", - "text": "That sounds like a great getaway from the city tho! I'm hoping we can find something just as nice for our hike." - }, - { - "speaker": "Andrew", - "dia_id": "D24:18", - "text": "Well if that's what you want, then let's find something just as nice for our hike." - }, - { - "speaker": "Audrey", - "dia_id": "D24:19", - "text": "Yep! I'll do some research and see if I can find an awesome place like that." - }, - { - "speaker": "Andrew", - "dia_id": "D24:20", - "text": "Awesome! I really appreciate your effort! Let's see if there's somewhere like that." - }, - { - "speaker": "Audrey", - "dia_id": "D24:21", - "text": "You just wait. I'm gonna find the best spot for the hike. Haha." - }, - { - "speaker": "Andrew", - "dia_id": "D24:22", - "text": "Haha, I can't wait!" - } - ], - "session_25_date_time": "10:14 am on 24 October, 2023", - "session_25": [ - { - "speaker": "Andrew", - "dia_id": "D25:1", - "text": "Hi Audrey! How have you been lately? My girlfriend and I went to this awesome wine tasting last weekend. It was great! We tried so many unique wines and learned a lot. I was surprised at how much I enjoyed it. A reminder to step out of the comfort zone!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/1jjc5bw9tmgb1.jpg" - ], - "blip_caption": "a photo of a person with a cast on their arm and arm in a cast", - "query": "broken arm cast", - "dia_id": "D25:2", - "text": "Hey! Ha, glad you had fun at the wine tasting. Yeah, trying new things can be cool. By the way, I had an unexpected adventure last week. I had an accident while playing with my pups at the park. Taking care of them with one arm has been tricky but we're managing. What's been up with you? Any new interests?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/5fb4299bc4391d58134367db/1607453049811-DZU05ZBSW3YPHU2G2TX2/Special+rolls+party+tray.jpg" - ], - "blip_caption": "a photo of a plate of sushi and vegetables on a table", - "query": "sushi platter", - "dia_id": "D25:3", - "text": "Ouch! Are you feeling better? Sending healing vibes to you and your pups. So I recently tried out this new spot in town that serves sushi and it was great. Do you have anything that you've been wanting to try lately?" - }, - { - "speaker": "Audrey", - "dia_id": "D25:4", - "text": "Thanks! Appreciate it, feeling better each day. And wow that Sushi looks phenomenal. I know what to get for dinner tonight." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/laccj2oklvo71.jpg" - ], - "blip_caption": "a photo of a tray of sushi with a variety of toppings", - "query": "sushi plate vegetables", - "dia_id": "D25:5", - "text": "Taking it one day at a time is the way to go. A while ago I've been curious about trying sushi. Never done it before, but always hear it's good. Now I understand what the hype is. Have you ever tried it?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/5fb4299bc4391d58134367db/1607453049811-DZU05ZBSW3YPHU2G2TX2/Special+rolls+party+tray.jpg" - ], - "blip_caption": "a photo of a plate of sushi and vegetables on a table", - "query": "sushi platter rolls sashimi", - "dia_id": "D25:6", - "text": "Yess! Sushi is delicious! I love them! There are so many types and flavors to try. Definitely give it a go and try different things! Don't limit yourself in your comofort zone!" - }, - { - "speaker": "Andrew", - "dia_id": "D25:7", - "text": "Thanks for the encouragement! I'm looking forward to trying more soon. Do you have any tips for someone who's new to sushi?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/ipekv4tz7oh31.jpg" - ], - "blip_caption": "a photo of a long plate of sushi with sauce on it", - "query": "sushi rolls with different sauces", - "dia_id": "D25:8", - "text": "Definitely try a California or salmon roll first when trying sushi - they're easier. Mix it up with different sauces and dips too - it makes it more tasty. Enjoy and let me know how it goes!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/tsyh3z2tdf591.jpg" - ], - "blip_caption": "a photo of a person eating sushi on a wooden board", - "query": "sushi plate sauces", - "dia_id": "D25:9", - "text": "Thanks for the tips! Gonna go with a California or salmon roll and try out some sauces. I'll let you know how it goes." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.pinimg.com/originals/53/26/f8/5326f8e676caab464f515486aa0a4360.jpg" - ], - "blip_caption": "a photo of a plate of sushi with avocado and shrimp", - "query": "sushi rolls toppings", - "dia_id": "D25:10", - "text": "Glad to help! Can't wait to hear about your sushi adventure. Take your time and have fun!" - }, - { - "speaker": "Andrew", - "dia_id": "D25:11", - "text": "I'm really excited to try different sushi. It's going to be a great time!" - }, - { - "speaker": "Audrey", - "dia_id": "D25:12", - "text": "Have fun! You'll definitely need some time to get used to, but once you start I believe you'll love it! Take some pics and show me what you enjoy." - }, - { - "speaker": "Andrew", - "dia_id": "D25:13", - "text": "Haha, I'll make sure to take some photos and show you my sushi adventure." - }, - { - "speaker": "Audrey", - "dia_id": "D25:14", - "text": "Enjoy! Now I'm gonna order some sushi for tonight. Thanks!" - }, - { - "speaker": "Andrew", - "dia_id": "D25:15", - "text": "Haha! You're welcomoe! Have a good one!" - }, - { - "speaker": "Audrey", - "dia_id": "D25:16", - "text": "Take care and have a good one!" - } - ], - "session_26_date_time": "2:36 pm on 28 October, 2023", - "session_26": [ - { - "speaker": "Audrey", - "dia_id": "D26:1", - "text": "Hey Andrew, I wanted to let you know about something going on with my dogs. I noticed they weren't acting normally, so I made an appointment with an animal behaviorist last Wed. It's been a bit hectic but I'm hopeful it'll help me better understand them." - }, - { - "speaker": "Andrew", - "dia_id": "D26:2", - "text": "Oh no! Sorry to hear that your dogs haven't been themselves. Are they doing ok? How did the appointment with the animal behaviorist go? Did you receive any helpful advice or insights?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:3", - "text": "The appointment went okay. It was hectic at first, but the behaviorist checked them out and asked some questions. I got some tips to try and help with their problems now." - }, - { - "speaker": "Andrew", - "dia_id": "D26:4", - "text": "So what tips did you get? What will you be doing to help with the problems?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:5", - "text": "The behaviorist gave me tips on how to handle it and suggested some changes in their routine. I'm using positive reinforcement techniques and it's still a work in progress, but I'm hopeful it'll help." - }, - { - "speaker": "Andrew", - "dia_id": "D26:6", - "text": "I'm glad your pups are still good with positive reinforcement! How are they doing with the new approach tho?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:7", - "text": "So far they seem to be responding well to it! It won't be fixed immediately but I'm seeing some progress. Here's hoping it keeps going." - }, - { - "speaker": "Andrew", - "dia_id": "D26:8", - "text": "That's good to hear! Keep up the good work!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:9", - "text": "Thanks! Your words of encouragement really mean a lot. It's tough, but I'm devoted to keeping them healthy and happy - they mean everything to me." - }, - { - "speaker": "Andrew", - "dia_id": "D26:10", - "text": "You're doing a great job! They're lucky to have you." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2011,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1200/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park summer", - "dia_id": "D26:11", - "text": "Thanks! They're really special to me and I want the best for them. Here's a pic of them having a blast last summer, so happy! I'm looking forward the day they are back to normal." - }, - { - "speaker": "Andrew", - "dia_id": "D26:12", - "text": "Aww, they're having such a blast! What kind are they? I'm wishing you and your pups the best. " - }, - { - "speaker": "Audrey", - "dia_id": "D26:13", - "text": "Thanks! They're all mutts, but Pepper and Panda are Lab mixes, and Precious and Pixie are Chihuahua mixes. I really need that. I can't wait the day they're all back to normal." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/8194/8097374162_8d85c7b645_b.jpg" - ], - "blip_caption": "a photography of a man hiking up a mountain with a backpack", - "query": "hiking mountains", - "dia_id": "D26:14", - "re-download": true, - "text": "Sending prayers and wishes. Here's a pic I took at a national park I went a while ago." - }, - { - "speaker": "Audrey", - "dia_id": "D26:15", - "text": "Wow, that looks gorgeous! We hope to join you and the furry friends soon!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/3094/2564651016_d9405477b9_b.jpg" - ], - "blip_caption": "a photography of a man walking a dog on a leash", - "query": "dog-friendly hiking trail", - "dia_id": "D26:16", - "re-download": true, - "text": "Yeah I really hope your pups can get better and join us soon!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:17", - "text": "Wow, that trail looks nice! Looks like its dog friendly?" - }, - { - "speaker": "Andrew", - "dia_id": "D26:18", - "text": "Yup! It's close by and it's dog-friendly too, with killer views. Wanna plan a hike soon?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:19", - "text": "Hmmm sure! Let's pick a date and go hike. It should be good!" - }, - { - "speaker": "Andrew", - "dia_id": "D26:20", - "text": "Yay! Does Saturday sound good? We can grab some snacks and have a blast exploring. Because on Sunday I am going on a picnic date with my girlfriend." - }, - { - "speaker": "Audrey", - "dia_id": "D26:21", - "text": "Saturday works for me! I'm going to bring some snack. Super excited!" - }, - { - "speaker": "Andrew", - "dia_id": "D26:22", - "text": "Can't wait for our nature day with the fur babies! We're gonna have a good time!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/8ugwccgkw6u81.jpg" - ], - "blip_caption": "a photo of a dog laying on the ground with a view of the ocean", - "query": "dogs wagging tails nature beauty", - "dia_id": "D26:23", - "text": "Going hiking and seeing nature will be awesome. They'll be so happy!" - }, - { - "speaker": "Andrew", - "dia_id": "D26:24", - "text": "I bet! Where do you guys plan to explore?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:25", - "text": "Let's check out the trail first. It's a peaceful spot to bring the fur babies for the day." - }, - { - "speaker": "Andrew", - "dia_id": "D26:26", - "text": "Sounds great! There's a lake near the trail too! It's gonna be awesome!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://photos.thedyrt.com/photo/131286/media/serene-lake-or_97eeadb9941e84ca9084f13abd07f6b0.jpg" - ], - "blip_caption": "a photo of a lake with a boat in it and a forest in the background", - "query": "serene lake trees peaceful adventure", - "dia_id": "D26:27", - "text": "Oh nice! Can't wait to explore it and hang out with our furry friends. Should be a peaceful day! Here's a photo of the lake I found online." - }, - { - "speaker": "Andrew", - "dia_id": "D26:28", - "text": "Wow, that looks awesome! Do you think the dogs will like it? Which trail do you have in mind?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:29", - "text": "Let's try that trail by the lake with great views, perfect for us and the pups. Should be fun!" - }, - { - "speaker": "Andrew", - "dia_id": "D26:30", - "text": "Sounds great! They will love it by the lake. Can't wait!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://s3-us-west-1.amazonaws.com/assets.wagwalkingweb.com/media/daily_wag/blog_articles/body/1660599786.6061006/5-best-dog-parks-in-portland-or-2.png" - ], - "blip_caption": "a photo of a dog chasing a dog in a park", - "query": "sunny day dog park", - "dia_id": "D26:31", - "text": "Gonna be great - nature, furry pals - what more could we want? I'm so lucky to have a friend like you who loves exploring and being outside with our dogs." - }, - { - "speaker": "Andrew", - "img_url": [ - "https://photos.bringfido.com/restaurants/6/7/1/176/176_272122.jpg" - ], - "blip_caption": "a photo of a dog eating out of a bowl on the ground", - "query": "dog-friendly cafe outdoor seating", - "dia_id": "D26:32", - "text": "Same! I'm lucky to have a friend like you for these outdoor trips. It's awesome to be out in nature with our furry friends." - }, - { - "speaker": "Audrey", - "dia_id": "D26:33", - "text": "Yup! It's hard to find someone that has similar thoughts. " - }, - { - "speaker": "Andrew", - "img_url": [ - "https://exploringtheprime.com/wp-content/uploads/2019/10/IMG_6849.jpg" - ], - "blip_caption": "a photo of a forest with yellow trees and a blue sky", - "query": "hiking trail colorful autumn trees", - "dia_id": "D26:34", - "text": "Exactly! Oh btw, here's another photo of a trail near the location. What do you think?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:35", - "text": "That looks pretty good! I'd love to take them there sometime." - }, - { - "speaker": "Andrew", - "dia_id": "D26:36", - "text": "How about going there the next trip? The autumn colors are so beautiful!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:37", - "text": "Sounds great! The autumn colors would look awesome for pictures." - }, - { - "speaker": "Andrew", - "dia_id": "D26:38", - "text": "Yeah, photos are gonna turn out great with the dogs!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:39", - "text": "Can't wait to capture some amazing moments with our furry friends!" - }, - { - "speaker": "Andrew", - "dia_id": "D26:40", - "text": "It definitely will be a memorable day!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:41", - "text": "Yep, can't wait to make some awesome memories with our furry friends!" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://live.staticflickr.com/1760/27752565907_f59bb6f236_b.jpg" - ], - "blip_caption": "a photography of a man and his dog on a trail", - "query": "group of friends hiking mountains", - "dia_id": "D26:42", - "re-download": true, - "text": "You bet! Can't wait to see their happy face! This was my dog and I when we were hiking last time, see how happy he was?" - }, - { - "speaker": "Audrey", - "dia_id": "D26:43", - "text": "Aww look at his happy face! I'm really looking forward to it! Can't wait to see my pups being happy and hiking." - }, - { - "speaker": "Andrew", - "dia_id": "D26:44", - "text": "Same here. Let's make it an epic and fun hike!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:45", - "text": "Yep! It's gonna be so much fun." - }, - { - "speaker": "Andrew", - "dia_id": "D26:46", - "text": "Let me get ready, gonna head out soon. Ttyl!" - }, - { - "speaker": "Audrey", - "dia_id": "D26:47", - "text": "Yep ttyl!" - } - ], - "session_27_date_time": "7:59 pm on 4 November, 2023", - "session_27": [ - { - "speaker": "Andrew", - "img_url": [ - "https://www.oregon.com/sites/default/files/bike_bluelake.jpg" - ], - "blip_caption": "a photo of two people riding bikes on a paved path", - "query": "bike ride park lake", - "dia_id": "D27:1", - "text": "Hey Audrey, had a great weekend! My girlfriend and I went on a bike ride and stumbled upon a cool park outside of town. It was awesome to get away from the city and be surrounded by nature." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://i.redd.it/hayawo8f86p51.jpg" - ], - "blip_caption": "a photo of a group of people and dogs in a park", - "query": "dog park playing dogs", - "dia_id": "D27:2", - "text": "That's cool! I love checking out new parks with my four pups. Last weekend was so fun - our dogs were able to run around and get some fresh air. On top of that, I recently joined a dog owners group to learn how to better take care of them." - }, - { - "speaker": "Andrew", - "dia_id": "D27:3", - "text": "That sounds great! Your four pups must have a lot of fun. How often do you hang out with the dog owners group?" - }, - { - "speaker": "Audrey", - "blip_caption": "a photo of a group of people and dogs in a park", - "dia_id": "D27:4", - "text": "Yeah, they're having a lot of fun. I try to meet up with other dog owners once a week for tips from other parents and so they can all play together. How about you? Have you ever thought about joining one?" - }, - { - "speaker": "Andrew", - "dia_id": "D27:5", - "text": "That looks fun! Seeing those adorable pups made me think about getting another dog, but I'm still not sure. Having two dogs is already a lot to take care of. Do you have any tips on being a multi-dog pet owner?" - }, - { - "speaker": "Audrey", - "dia_id": "D27:6", - "text": "Maybe you want to take care of Toby and Buddy first. Having them happy and healthy would be a good first step before going all in for more dogs." - }, - { - "speaker": "Andrew", - "dia_id": "D27:7", - "text": "Thanks, I think that's what I need to hear. I'll take good care of my dogs first." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://elpaseogarden.files.wordpress.com/2021/06/img_2087.jpg" - ], - "blip_caption": "a photo of two dogs playing with a frisbee in a park", - "query": "dogs playing in the park", - "dia_id": "D27:8", - "text": "That's great! Let me know if you need any help, I'm here for you! See how happy they are? You don't need more dogs to make them happy! :)" - }, - { - "speaker": "Andrew", - "dia_id": "D27:9", - "text": "Thanks Audrey! That's so nice of you. I think I've managed to make it work with dogs while still living in the city." - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/2839/12466466553_d534a96181_b.jpg" - ], - "blip_caption": "a photography of a basket full of stuffed animals on a wooden floor", - "query": "dog toys games apartment", - "dia_id": "D27:10", - "re-download": true, - "text": "Yeah I feel you. Taking care of a pup in the city is tough but doable with the right approach. Keeping them active is key. Here's a pic of how I entertain them in my house with toys and games." - }, - { - "speaker": "Andrew", - "dia_id": "D27:11", - "text": "Wow, it's great to know there are ways to keep them active in the city. I'll keep that in mind. Thank you so much!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/8257/8915975033_e7129952e5_b.jpg" - ], - "blip_caption": "a photography of a dog running with a tennis ball in its mouth", - "query": "dogs playing fetch park", - "dia_id": "D27:12", - "re-download": true, - "text": "You got it! There are lots of ways to keep them happy in the city. Make sure to socialize and exercise them daily. Get creative and add some mental stimulation too. Here's a pic of them playing fetch in the park - they love it!" - }, - { - "speaker": "Andrew", - "dia_id": "D27:13", - "text": "That's so cute! What sort of activities do you do to stay mentally stimulated?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/340/32261973051_6709776893_b.jpg" - ], - "blip_caption": "a photography of a dog playing with a frisbee in a room", - "query": "dog puzzle toy play", - "dia_id": "D27:14", - "re-download": true, - "text": "We give them lots of activities to keep them busy - puzzles, training, hide-and-seek - they love it all!" - }, - { - "speaker": "Andrew", - "dia_id": "D27:15", - "text": "Cool ideas! I think I'll give those activities a try with my pups. Thanks!" - }, - { - "speaker": "Audrey", - "dia_id": "D27:16", - "text": "No problem, glad I could help. Let me know how it goes." - }, - { - "speaker": "Andrew", - "dia_id": "D27:17", - "text": "Your advice and support really mean a lot to me! Thank you so much!" - }, - { - "speaker": "Audrey", - "dia_id": "D27:18", - "text": "That's what friends are for - supporting each other. Your friendship means a lot to me. :)" - } - ], - "session_28_date_time": "9:02 am on 22 November, 2023", - "session_28": [ - { - "speaker": "Audrey", - "dia_id": "D28:1", - "text": "Hey Andrew! Long time no talk! Last Friday I took my fur kids to the pet salon - they were so psyched and their tails were wagging like crazy! It took a while for them to calm down, but all cut up they looked so cute! " - }, - { - "speaker": "Andrew", - "dia_id": "D28:2", - "text": "Hey Audrey! Nice to hear from you. Sounds adorable! Do you have any pictures of them all groomed up?" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://s0.geograph.org.uk/geophotos/06/18/45/6184556_bc98b08f.jpg" - ], - "blip_caption": "a photography of a dog is standing on a table with a leash", - "query": "four dogs groomed coats wagging tails cozy rug", - "dia_id": "D28:3", - "re-download": true, - "text": "Here's a pic of them, looking all groomed. Look at those shiny coats! To top it off, they were really good at the salon - I always worry about them in new places." - }, - { - "speaker": "Andrew", - "dia_id": "D28:4", - "text": "Wow, they look great! Love seeing them happy and calm in new places." - }, - { - "speaker": "Audrey", - "dia_id": "D28:5", - "text": "Thanks! It means a lot to see them happy and settled in new places. I guess I'm doing a good job as a doggy mom then, haha! Have you taken your furry friends to the groomers yet?" - }, - { - "speaker": "Andrew", - "img_url": [ - "https://i.redd.it/h1m9pir3y9jb1.jpg" - ], - "blip_caption": "a photo of a woman feeding a dog hay in a barn", - "query": "puppy scout new bundle of joy", - "dia_id": "D28:6", - "text": "No, we haven't got the chance to take them to the groomer yet. But will do that soon! So guess what, I can't help myself but to adpot another dog the other day. Here's a photo of the doggo!" - }, - { - "speaker": "Audrey", - "dia_id": "D28:7", - "text": "That's great news! What's the pups name?" - }, - { - "speaker": "Andrew", - "dia_id": "D28:8", - "text": "It took us a while to decide, but we ended up going with 'Scout' for our pup - it seemed perfect for their adventurous spirit." - }, - { - "speaker": "Audrey", - "dia_id": "D28:9", - "text": "That's a great name for your pup! Fits their adventurous spirit. What's Scout's first adventure gonna be?" - }, - { - "speaker": "Andrew", - "dia_id": "D28:10", - "text": "Thanks! We're gonna take Scout, Toby, and Buddy to a nearby park. It's not big, but we can all have fun and get some fresh air!" - }, - { - "speaker": "Audrey", - "img_url": [ - "https://live.staticflickr.com/8257/8915975033_e7129952e5_b.jpg" - ], - "blip_caption": "a photography of a dog running with a tennis ball in its mouth", - "query": "dogs playing fetch park", - "dia_id": "D28:11", - "re-download": true, - "text": "Sounds like a great start for Scout! Start small, and gradually give them more exposure. They'll have a great time, just make sure to keep them leashed." - }, - { - "speaker": "Andrew", - "dia_id": "D28:12", - "text": "Yeah, safety first! For now, we're keeping the new addition on a leash while they get used to being outside. That pic you of your dog at the park is so cute. So we got some essentials for their comfort and entertainment, like a bed, toys, and some puppy pads just in case. It's like their own little safe haven." - }, - { - "speaker": "Audrey", - "dia_id": "D28:13", - "text": "Wow, that's so great that you two are creating a safe and fun space for Scout. It's really important they have a place that makes them feel secure. Slowly introduce Scout to Toby and Buddy, it takes time for the pups to get used to each other too! Scout is so lucky to have you and your girlfriend! " - }, - { - "speaker": "Andrew", - "dia_id": "D28:14", - "text": "Thanks! We feel so lucky to have Scout. It's been amazing having so many furry friends! How are your dogs doing now? " - }, - { - "speaker": "Audrey", - "dia_id": "D28:15", - "text": "They're doing great! Exploring, meeting new people...they feel so loved and safe. I'm really glad they're part of my life!" - }, - { - "speaker": "Andrew", - "dia_id": "D28:16", - "text": "That's great to hear! Dogs truly bring so much joy and friendship. I'm glad they're happy with you." - }, - { - "speaker": "Audrey", - "dia_id": "D28:17", - "text": "Thanks! They're really awesome and bring so much joy and friendship. I'm so grateful to have them in my life as a part of my family." - }, - { - "speaker": "Andrew", - "dia_id": "D28:18", - "text": "Yeah, it's great! Dogs are always there for us. We should count ourselves lucky to have such amazing furry friends as family member." - } - ] - }, - "event_summary": { - "events_session_1": { - "Audrey": [ - "Audrey purchases three collars and tags for her dogs Pepper, Precious and Panda." - ], - "Andrew": [ - "Andrew starts a new job as a financial analyst." - ], - "date": "27 March, 2023" - }, - "events_session_2": { - "Audrey": [ - "Audrey brings home her new puppy, Pixie and introduces it slowly to her other three dogs, Pepper, Precious and Panda." - ], - "Andrew": [ - "Andrew considers getting a pet dog and looks for dog-friendly apartments." - ], - "date": "2 April, 2023" - }, - "events_session_3": { - "Audrey": [], - "Andrew": [ - "Andrew and his girlfriend decide to explore new cafes and restaurants in their City but Andrew misses the peacefulness of being out in the nature." - ], - "date": "16 April, 2023" - }, - "events_session_4": { - "Audrey": [ - "Audrey goes for a hike and encounters a hummingbird." - ], - "Andrew": [ - "Andrew discovers a new open space for hiking close to the city and feels relieved about being able to explore nature conveniently while being in the city." - ], - "date": "3 May, 2023" - }, - "events_session_5": { - "Audrey": [ - "Audrey takes her four dogs on a road trip to a nearby national park." - ], - "Andrew": [ - "Andrew continues his search for a pet-friendly apartment and a pet dog, and is in the process of checking out animal shelters." - ], - "date": "6 May, 2023" - }, - "events_session_6": { - "Audrey": [ - "Audrey comes to know about a pet bonding workshop from a flyer at her local pet score which is scheduled for June 2023.", - "signs up for the pet bonding workshop which will teach positive reinforcement training to bond with pets." - ], - "Andrew": [ - "Andrew and his girlfriend invite friends to join them on a hike to explore the new open space that they found close to their apartment recently." - ], - "date": "11 May, 2023" - }, - "events_session_7": { - "Audrey": [ - "Audrey takes her four dogs to the park to play." - ], - "Andrew": [ - "Andrew is a bit discouraged in his search for pet-friendly apartments and a pet dog but does not give up." - ], - "date": "2 June, 2023" - }, - "events_session_8": { - "Audrey": [ - "Audrey finds a park with a trail and surrounded by trees which turns out to be a great spot to walk her dogs." - ], - "Andrew": [ - "Andrew and his friends join a rock climbing class and Andrew manages to make it to the top.", - "Andrew is inspired to explore more outdoor activities like kayaking and bungee-jumping after the rock-climbing class." - ], - "date": "13 June, 2023" - }, - "events_session_9": { - "Audrey": [ - "Audrey moves into a new house, one with a bigger backyard for her four dogs, and creates a play area in the backyard for them." - ], - "Andrew": [ - "Andrew visits a doggy daycare close to his place.", - "Andrew goes on a hike with his friends." - ], - "date": "26 June, 2023" - }, - "events_session_10": { - "Audrey": [ - "Audrey enrolls in a dog-training course to improve her ability to take care of her dogs." - ], - "Andrew": [ - "Andrew explores the hobby of cooking to compensate for not being able to hike much within the city." - ], - "date": "3 July, 2023" - }, - "events_session_11": { - "Audrey": [ - "Audrey takes her four dogs out for a long walk.", - "Andrew and Audrey decide to hike together next month with Audrey's dogs." - ], - "Andrew": [ - "Andrew and his girlfriend enjoy a picnic with friends in a park.", - "Andrew and Audrey decide to hike together next month with Audrey's dogs." - ], - "date": "8 July, 2023" - }, - "events_session_12": { - "Audrey": [ - "Audrey crafts personalized dog tags for each of her four dogs." - ], - "Andrew": [ - "Andrew finally adopts a German Shephard puppy named Toby after much search.", - "Andrew is stressed from work and misses hiking." - ], - "date": "11 July, 2023" - }, - "events_session_13": { - "Audrey": [ - "Audrey reflects on her close relationship with her childhood pet dog, Max." - ], - "Andrew": [ - "Andrew and his girlfriend volunteer in a local animal shelter to spend time and care for the animals." - ], - "date": "27 July, 2023" - }, - "events_session_14": { - "Audrey": [ - "Audrey enrolls her dogs in agility classes at a local dog park and practices with them in a nearby park twice every week." - ], - "Andrew": [ - "Andrew, his girlfriend and pet dog, Toby, make plans to go camping together in the weekend." - ], - "date": "4 August, 2023" - }, - "events_session_15": { - "Audrey": [ - "Audrey gets a second tattoo of all four of her dogs on her arm.", - "Audrey visits her local vet clinic to get her four dogs checked up and decides to take them one-by-one next time to avoid the chaos of dealing with all four of them simultaneously." - ], - "Andrew": [ - "Andrew and his girlfriend visit a local farm to pick some fresh produce for their dinner." - ], - "date": "16 August, 2023" - }, - "events_session_16": { - "Audrey": [ - "Audrey learns how to groom her dogs." - ], - "Andrew": [ - "Andrew decides to take an extra day off work to explore one of the cafes they have not been to yet." - ], - "date": "19 August, 2023" - }, - "events_session_17": { - "Audrey": [], - "Andrew": [ - "Andrew and his girlfriend go fishing in one of the lakes close to the city." - ], - "date": "24 August, 2023" - }, - "events_session_18": { - "Audrey": [ - "Audrey sets up a doggy-playdate with her neighbors\u2019 dogs.", - "Audrey goes shopping for dog beds for her four dogs." - ], - "Andrew": [ - "Andrew's work life becomes stressful and his hiking hobbies take a backseat.", - "Andrew continues his search for a pet-friendly apartment in the city to take better care of Toby." - ], - "date": "6 September, 2023" - }, - "events_session_19": { - "Audrey": [ - "Precious pulls at her leash and gets lost during a walk in the park, but Audrey chases her and finds her.", - "Audrey's flower and vegetable garden patch comes along." - ], - "Andrew": [ - "Andrew and his girlfriend try their hand at gardening, growing some flowers of their own." - ], - "date": "24 September, 2023" - }, - "events_session_20": { - "Audrey": [ - "Audrey takes her dogs to a nearby dog park during the weekend." - ], - "Andrew": [ - "Andrew plans a beach trip with his girlfriend and Toby." - ], - "date": "1 October, 2023" - }, - "events_session_21": { - "Audrey": [ - "Audrey takes a weekend trip to the beach with her dogs." - ], - "Andrew": [], - "date": "4 October, 2023" - }, - "events_session_22": { - "Audrey": [ - "Audrey reflects about the time she had a kneww injury and could not take her dogs for a walk.", - "Audrey makes jewelry with recycled objects like bottle caps, buttons, and broken jewelry, and donates the profits to an animal shelter." - ], - "Andrew": [], - "date": "6 October, 2023" - }, - "events_session_23": { - "Audrey": [ - "Audrey delivers homemade treats to her neighbors to thank them for their dog-friendly homes." - ], - "Andrew": [ - "Andrew spends an evening with his girlfriend and their dog playing board games and bonding.", - "Andrew plans to check out a new cafe in the city during the weekend." - ], - "date": "13 October, 2023" - }, - "events_session_24": { - "Audrey": [ - "Audrey goes to a pet store to buy toys for her dogs." - ], - "Andrew": [ - "Andrew adopts another furry friend from a shelter, in addition to his pup Toby, and names it Buddy." - ], - "date": "19 October, 2023" - }, - "events_session_25": { - "Audrey": [ - "Audrey injures her arm while walking her dogs at the park and struggles to care for them during recovery." - ], - "Andrew": [ - "Andrew and his girlfriend attend a wine tasting event, enjoys it and reflects on how stepping out of one's comfort zone can be rewarding.", - "Inspired by the fun he has at the wine-tasting event, Andrew considers trying sushi for the first time." - ], - "date": "24 October, 2023" - }, - "events_session_26": { - "Audrey": [ - "Audrey is concerned by the sudden abnormal behavior of her dogs and makes an appointment with an animal behaviorist to understand them better.", - "Andrew and Audrey plan to take their pets to a dog-friendly trail by the lake at a nearby national park." - ], - "Andrew": [ - "Andrew and Audrey plan to take their pets to a dog-friendly trail by the lake at a nearby national park.", - "Andrew goes on a picnic date with his girlfriend." - ], - "date": "28 October, 2023" - }, - "events_session_27": { - "Audrey": [ - "Audrey joins a dog owners' support group that meets once a week to find new ways to better care for her four dogs." - ], - "Andrew": [ - "Andrew and his girlfriend go for a bike ride and explore a new park in the outskirts of town." - ], - "date": "4 November, 2023" - }, - "events_session_28": { - "Audrey": [ - "Audrey gets her four dogs groomed together at a pet salon." - ], - "Andrew": [ - "Andrew and his girlfriend adopt a third pup and named it Scout, in addition to their pups Toby and Buddy." - ], - "date": "22 November, 2023" - } - }, - "observation": { - "session_1_observation": { - "Audrey": [ - [ - "Audrey got new collars and tags for her dogs.", - "D1:3" - ], - [ - "Audrey has had her dogs named Pepper, Precious, and Panda for 3 years.", - "D1:7" - ], - [ - "Audrey's dogs are city dogs and they love exploring new parks and trails.", - "D1:9" - ] - ], - "Andrew": [ - [ - "Andrew started a new job as a Financial Analyst last week.", - "D1:2" - ], - [ - "Andrew does not currently have any pets.", - "D1:12" - ], - [ - "Andrew loves animals and is awed by birds, especially eagles.", - "D1:14" - ], - [ - "Andrew enjoys spotting pretty birds while hiking in places like Fox Hollow.", - "D1:18" - ] - ] - }, - "session_2_observation": { - "Audrey": [ - [ - "Audrey and Andrew adopted another puppy called Pixie.", - "D2:1" - ], - [ - "Pixie has been keeping Audrey busy since they adopted her.", - "D2:3" - ], - [ - "Pixie has become friends with the other dogs in the house and they love playing together.", - "D2:5" - ], - [ - "Audrey used websites with filters to find a dog-friendly place for their pets.", - "D2:9" - ], - [ - "Audrey is excited about Andrew finding a furry friend and an apartment.", - "D2:13" - ], - [ - "Audrey enjoys exploring nature with her dogs.", - "D2:19" - ] - ], - "Andrew": [ - [ - "Andrew is considering getting a dog and finding a dog-friendly place in the city.", - "D2:8" - ], - [ - "Andrew misses exploring nature trails with his family's dog.", - "D2:18" - ], - [ - "Andrew finds hiking with a dog a great way to bond and create memories together.", - "D2:20" - ], - [ - "Andrew is super excited for what's next regarding finding a dog-friendly spot.", - "D2:24" - ] - ] - }, - "session_3_observation": { - "Andrew": [ - [ - "Andrew enjoys being out in nature and feels like he's really thriving when surrounded by it.", - "D3:1" - ], - [ - "Andrew misses the peacefulness of being out on a hike.", - "D3:1" - ], - [ - "Andrew finds nature really soothing and like hitting the reset button when life gets chaotic.", - "D3:5" - ], - [ - "Andrew and his girlfriend love discovering new places to eat around the city to wind down after a long week.", - "D3:11" - ], - [ - "Andrew found an awesome cafe with amazing pastries recently.", - "D3:13" - ] - ], - "Audrey": [ - [ - "Audrey finds being out in nature unbeatable, compared to being in concrete jungles.", - "D3:2" - ], - [ - "Audrey believes nature brings us back down to earth and reminds us that we're part of something bigger.", - "D3:4" - ], - [ - "Audrey takes her dogs for a walk in the park to recharge and find her center.", - "D3:6" - ], - [ - "Audrey enjoys making pastries and mentioned having a pastry party.", - "D3:18" - ], - [ - "Audrey has tattoos of her dogs on her arm symbolizing her love for them and nature's beauty.", - "D3:26" - ] - ] - }, - "session_4_observation": { - "Audrey": [ - [ - "Audrey had an amazing experience with a hummingbird on a hike.", - "D4:1" - ], - [ - "Audrey is busy taking care of her pets and finds it fulfilling.", - "D4:15" - ], - [ - "Audrey's dogs love going on hikes and exploring nature, their happy place.", - "D4:17" - ], - [ - "Audrey plays fetch with a ball or frisbee with her dogs and organizes doggie playdates in the park.", - "D4:21" - ], - [ - "Audrey's dogs get excited when she brings out toys for playtime.", - "D4:23" - ], - [ - "Audrey's dogs wear party hats for fun and treats.", - "D4:25" - ], - [ - "Audrey suggests a dog park near their usual walking spot for playdates with other dogs.", - "D4:25" - ], - [ - "Audrey's dog park is located near a park with lots of trees and benches.", - "D4:27" - ] - ], - "Andrew": [ - [ - "Andrew found a new open space to hike nearby that he finds refreshing.", - "D4:2" - ], - [ - "Andrew escapes the city at least once a weekend for hiking, which is his much-needed break.", - "D4:4" - ], - [ - "Andrew finds nature therapeutic and invigorating.", - "D4:6" - ], - [ - "Andrew captures special moments in nature to share the feeling with others.", - "D4:8" - ], - [ - "Andrew spreads positivity and joy through sharing peaceful feelings and motivating others.", - "D4:10" - ], - [ - "Andrew acknowledges that even small gestures can have a big effect in spreading good vibes.", - "D4:14" - ], - [ - "Andrew plans to check out the dog park location suggested by Audrey for his own dogs.", - "D4:28" - ] - ] - }, - "session_5_observation": { - "Andrew": [ - [ - "Andrew has been looking to adopt a dog, browsing websites, visiting shelters, and asking friends.", - "D5:1" - ], - [ - "Andrew is a person living in an apartment.", - "D5:3" - ], - [ - "Andrew is looking for a pet-friendly place near a park or woods to give the dog a large open space to run around.", - "D5:7" - ], - [ - "Andrew is contacting landlords and checking out neighborhoods to find a pet-friendly place to live.", - "D5:5" - ], - [ - "Andrew expressed a wish to go on a road trip with a furry companion.", - "D5:11" - ], - [ - "Andrew has a bucket list item of going on a road trip with his dog.", - "D5:13" - ] - ], - "Audrey": [ - [ - "Audrey goes on road trips with her dogs once every couple of months to help them explore and stay active.", - "D5:10" - ], - [ - "Audrey suggested considering the size of living space and exercise needs when choosing a dog breed.", - "D5:4" - ], - [ - "Audrey has a park near her that is great for her pup's walks.", - "D5:8" - ], - [ - "Audrey mentioned taking a road trip to a national park with her dogs and described it as an awesome trip.", - "D5:8" - ], - [ - "Audrey encouraged Andrew in his search for the perfect furry friend and offered to help with suggestions.", - "D5:18" - ], - [ - "Audrey will be expecting a picture of Andrew's dog soon.", - "D5:20" - ] - ] - }, - "session_6_observation": { - "Andrew": [ - [ - "Andrew had a great hike last weekend with friends and his girlfriend at a spot they recently found.", - "D6:1" - ], - [ - "Andrew is interested in hiking and finds nature peaceful.", - "D6:1" - ], - [ - "Andrew is curious about Audrey's workshop on bonding with her pets and thinks the dog in the picture is adorable.", - "D6:3" - ], - [ - "Andrew is supportive and encourages Audrey to keep him updated on her pets' progress and training.", - "D6:7" - ] - ], - "Audrey": [ - [ - "Audrey is taking care of her four dogs and ensuring they are happy and healthy.", - "D6:2" - ], - [ - "Audrey signed up for a workshop about bonding with pets to strengthen her bond with her dogs.", - "D6:2" - ], - [ - "Audrey learned about the workshop from a flyer at her local pet store which was about positive reinforcement training.", - "D6:4" - ], - [ - "Audrey believes in using positive reinforcement rather than punishment to train pets.", - "D6:12" - ], - [ - "Audrey is enthusiastic about training her pets better and believes they are quick learners who love rewards.", - "D6:6" - ], - [ - "Audrey is eager to update Andrew on her pets' progress and offer him tips on training for his future dog.", - "D6:8" - ] - ] - }, - "session_7_observation": { - "Audrey": [ - [ - "Audrey took her dogs to the park and enjoys watching them run around and play without a leash.", - "D7:1" - ], - [ - "Audrey took her dogs on a hike to a national park and reached a beautiful peak during the sunset.", - "D7:3" - ], - [ - "Audrey loves nature and feels cheerful and grateful when in natural surroundings.", - "D7:7" - ], - [ - "Audrey is supportive and offers to help Andrew find a pet-friendly spot for his new dog.", - "D7:11" - ] - ], - "Andrew": [ - [ - "Andrew is searching for a pet-friendly spot in the city but has not had luck so far.", - "D7:8" - ], - [ - "Andrew is determined to find the right place and dog despite facing challenges in the search.", - "D7:8" - ], - [ - "Andrew is thankful for Audrey's support and assistance in finding a pet-friendly spot.", - "D7:12" - ] - ] - }, - "session_8_observation": { - "Andrew": [ - [ - "Andrew recently took a rock climbing class and made it to the top, finding it challenging but satisfying.", - "D8:1" - ], - [ - "Andrew shared a photo of the view from the top of the rock he climbed during the rock climbing class.", - "D8:3" - ], - [ - "Andrew mentioned that he is a newbie at climbing but reached the top with the support and cheer from his friends.", - "D8:5" - ], - [ - "Andrew feels encouraged by the rock climbing experience to try more outdoor activities like kayaking and maybe bungee jumping.", - "D8:7" - ], - [ - "Andrew expressed interest in joining Audrey and her dogs for a walk in a park.", - "D8:9" - ] - ], - "Audrey": [ - [ - "Audrey found a great spot for her dogs' walk in a small park with a trail surrounded by trees.", - "D8:8" - ], - [ - "Audrey mentioned that her dogs love meeting new people and seem to enjoy their outdoor walks.", - "D8:10" - ], - [ - "Audrey usually walks her dogs for about an hour, allowing them to explore at their own pace.", - "D8:14" - ], - [ - "Audrey's dogs go home with a smile and tired after their walks, showing that they enjoy the exercise and exploration.", - "D8:16" - ], - [ - "Audrey shared a picture of her dogs in a meadow, expressing how happy they make her and how being outdoors with them puts her in a happy place.", - "D8:22" - ], - [ - "Audrey mentioned that being outdoors with her dogs brings her joy and peace.", - "D8:20" - ] - ] - }, - "session_9_observation": { - "Audrey": [ - [ - "Audrey got a new place with a bigger backyard for her dogs.", - "D9:1" - ], - [ - "Audrey set up a doggy play area in the backyard with agility stuff and toys for her dogs.", - "D9:5" - ], - [ - "Audrey loves animals and thinks they bring a lot of joy.", - "D9:11" - ], - [ - "Audrey expresses appreciation for nature and the calming effect it has on her.", - "D9:25" - ], - [ - "Audrey had a peaceful experience sitting by a stunning lake in the mountains with friends, finding it calming and memorable.", - "D9:23" - ] - ], - "Andrew": [ - [ - "Andrew appreciates friends who understand the love for pets.", - "D9:12" - ], - [ - "Andrew expresses missing the connection with nature and is looking forward to exploring more outdoors.", - "D9:20" - ], - [ - "Andrew enjoys outdoor activities like hiking and finds it rejuvenating to be in nature.", - "D9:18" - ], - [ - "Andrew enjoys capturing the beauty of nature through photography.", - "D9:18" - ] - ] - }, - "session_10_observation": { - "Audrey": [ - [ - "Audrey is taking a dog training course and it has been challenging but rewarding.", - "D10:1" - ], - [ - "Audrey's dogs are all mutts, with two being Jack Russell mixes and the other two Chihuahua mixes.", - "D10:7" - ], - [ - "Audrey's dogs are all 3 years old and get along well as a pack.", - "D10:9" - ], - [ - "Audrey's favorite recipe is Chicken Pot Pie, a family recipe that reminds her of her grandma's kitchen.", - "D10:13" - ], - [ - "Audrey loves trying out new recipes and experimenting in the kitchen as an escape to de-stress and let her creativity flow.", - "D10:17" - ], - [ - "Audrey's go-to ingredient in the kitchen is garlic, for the smell and taste it adds to dishes.", - "D10:21" - ], - [ - "Audrey loves to throw on some music, pour a glass of wine, and cook as a form of self-care and therapy.", - "D10:19" - ], - [ - "One of Audrey's favorite dishes to make is Roasted Chicken, inspired by her love for Mediterranean flavors and as a comfort meal.", - "D10:23" - ] - ], - "Andrew": [ - [ - "Andrew is finding new hobbies such as cooking more and trying out new recipes.", - "D10:12" - ], - [ - "Cooking has been helping Andrew de-stress, be creative, and express his creativity.", - "D10:16" - ], - [ - "Andrew finds cooking calming and a form of escape that allows him to try something new.", - "D10:18" - ], - [ - "Andrew appreciates cooking as a relaxing activity that he enjoys, especially the process of experimenting.", - "D10:18" - ], - [ - "Andrew is interested in trying new meal ideas, especially healthier ones.", - "D10:26" - ] - ] - }, - "session_11_observation": { - "Andrew": [ - [ - "Andrew had a picnic with his girlfriend last Friday and enjoys being in nature.", - "D11:1" - ], - [ - "Andrew is interested in getting a dog but finds it tough to find a place and the right dog.", - "D11:3" - ], - [ - "Andrew is planning a hike with Audrey and her dogs for next month.", - "D11:7" - ], - [ - "Andrew emphasizes the importance of finding a safe trail for the dogs to have fun hiking.", - "D11:11" - ] - ], - "Audrey": [ - [ - "Audrey enjoys going on walks with her dogs and finds them to be a source of happiness.", - "D11:2" - ], - [ - "Audrey got her dogs from a breeder nearby that had the dogs she wanted.", - "D11:4" - ], - [ - "Audrey agrees to join Andrew for a hike with her dogs and looks forward to seeing them enjoy themselves.", - "D11:6" - ], - [ - "Audrey values the safety of the dogs when outside running around.", - "D11:12" - ], - [ - "Audrey appreciates moments in nature and finds peace in vibrant colors.", - "D11:34" - ] - ] - }, - "session_12_observation": { - "Andrew": [ - [ - "Andrew has recently gotten a new puppy named Toby.", - "D12:1" - ], - [ - "Andrew mentioned feeling stressed due to work piling up and expressed a desire for the peace and freedom of going for a hike.", - "D12:3" - ], - [ - "Andrew has plans for a hike next month and invited Audrey and her pups to join along.", - "D12:5" - ], - [ - "Andrew is looking forward to the hike and for Toby to meet Audrey's pups.", - "D12:13" - ] - ], - "Audrey": [ - [ - "Audrey thinks Toby, Andrew's puppy, is adorable.", - "D12:2" - ], - [ - "Audrey mentioned making personalized tags for her dogs to show love and make them feel special.", - "D12:6" - ], - [ - "Audrey put a lot of love and effort into making the personalized tags for her pets.", - "D12:10" - ], - [ - "Audrey believes that small acts of love towards pets make a big difference in their relationships.", - "D12:12" - ], - [ - "Audrey is looking forward to the hike and for her pups to meet Toby.", - "D12:13" - ] - ] - }, - "session_13_observation": { - "Andrew": [ - [ - "Andrew and his girlfriend volunteered at a pet shelter on Monday and found it rewarding.", - "D13:1" - ], - [ - "Andrew loves animals and finds joy in spending time with them.", - "D13:1" - ], - [ - "Andrew believes that pets bring a lot of joy to life.", - "D13:7" - ], - [ - "Andrew considers pets as friends and confidantes, highlighting their role beyond just being pets.", - "D13:11" - ], - [ - "Andrew finds volunteering with animals to be one of the most rewarding experiences he's ever done.", - "D13:13" - ] - ], - "Audrey": [ - [ - "Audrey has four fur babies that are very important to her.", - "D13:4" - ], - [ - "Audrey's childhood dog was named Max, who had lots of energy and loved playing fetch.", - "D13:8" - ], - [ - "Audrey took long walks with Max in the neighborhood when she was a kid, sharing worries and hopes with him.", - "D13:10" - ], - [ - "Audrey believes that pets listen without judging and provide unconditional love, bringing joy, comfort, and love to our lives.", - "D13:12" - ] - ] - }, - "session_14_observation": { - "Andrew": [ - [ - "Andrew is in a relationship with a girlfriend named Toby.", - "D14:1" - ], - [ - "Andrew enjoys camping and spending time in nature.", - "D14:1" - ], - [ - "Andrew enjoys hiking and finds it the best outdoor activity.", - "D14:11" - ], - [ - "Andrew finds being in nature refreshing and calming, a break from city life.", - "D14:13" - ], - [ - "Andrew is interested in having a deep bond with his German Shepherd, Toby, and sees him as a great hiking buddy.", - "D14:17" - ] - ], - "Audrey": [ - [ - "Audrey is taking agility classes with her pups at a dog park.", - "D14:2" - ], - [ - "Audrey takes her dogs to the park twice a week for practice to bond with them.", - "D14:4" - ], - [ - "Audrey enjoys hiking and exploring nature with her pets.", - "D14:8" - ], - [ - "Audrey has a special bond with her pets, finding them to be her companions and sources of happiness.", - "D14:14" - ], - [ - "Audrey thinks German Shepherds, like Andrew's dog Toby, are awesome for their smartness and loyalty.", - "D14:20" - ], - [ - "Audrey gives advice that puppies, especially German Shepherds, need attention, walks, and energy to ensure their well-being.", - "D14:24" - ] - ] - }, - "session_15_observation": { - "Audrey": [ - [ - "Audrey got another tattoo of her four dogs on her arm as they mean a lot to her.", - "D15:1" - ], - [ - "Audrey has four dogs as pets and finds them to be enough for now to give them proper attention.", - "D15:3" - ], - [ - "Pepper, Pixie, Precious, and Panda are the names of Audrey's dogs, each having their own favorite spot to relax.", - "D15:5" - ], - [ - "Audrey's dogs cuddle up together when it's time to nap and are best friends.", - "D15:9" - ], - [ - "Audrey can't imagine life without her dogs as they mean the world to her.", - "D15:13" - ], - [ - "Audrey considers her pets to be family and believes they bring joy and happiness to her life.", - "D15:14" - ] - ], - "Andrew": [ - [ - "Andrew went to a farm with his girlfriend to get fresh veggies for dinner.", - "D15:2" - ], - [ - "Andrew shared a photo of his pet, Toby, at its favorite spot.", - "D15:6" - ], - [ - "Andrew appreciates the joy and comfort pets bring by finding their own little spots.", - "D15:6" - ], - [ - "Andrew acknowledges the happiness pets bring and can't imagine life without them.", - "D15:10" - ] - ] - }, - "session_16_observation": { - "Andrew": [ - [ - "Andrew took a break from work to check out a new cafe recently.", - "D16:1" - ], - [ - "Andrew enjoys hiking in the great outdoors.", - "D16:1" - ], - [ - "Andrew is heading to a nature reserve this weekend to reconnect with nature.", - "D16:13" - ] - ], - "Audrey": [ - [ - "Audrey learned a new skill related to dog grooming in August.", - "D16:2" - ], - [ - "Audrey cares for her pups and took on their grooming, feeling closer to them.", - "D16:2" - ], - [ - "Audrey took a dog grooming course to learn techniques.", - "D16:6" - ], - [ - "Audrey will be taking her dogs for a stroll in the park during the weekend.", - "D16:12" - ] - ] - }, - "session_17_observation": { - "Andrew": [ - [ - "Andrew went fishing last weekend with his girlfriend at a nearby lake and caught a few fish.", - "D17:1" - ], - [ - "Andrew is concerned about keeping his pets looking good when they are outdoors.", - "D17:3" - ], - [ - "Andrew believes that taking care of pets to ensure they are healthy and happy is important.", - "D17:5" - ], - [ - "Andrew appreciates the rewarding feeling of making a positive impact on the lives of those close to him.", - "D17:7" - ], - [ - "Andrew is looking forward to bonding better with his pet dog, Toby, and seeks tips on taking care of him.", - "D17:9" - ] - ], - "Audrey": [ - [ - "Audrey has never gone fishing before, but she enjoys chilling by lakes, especially in the mountains.", - "D17:2" - ], - [ - "Audrey believes that regular grooming, daily brushing, baths, nail trims, and lots of love are essential for keeping her pets healthy and happy.", - "D17:4" - ], - [ - "Audrey finds taking care of her dogs rewarding and takes that responsibility seriously.", - "D17:6" - ], - [ - "Audrey emphasizes the strong bond she has with her dogs and hopes Andrew can have a similar bond with Toby.", - "D17:8" - ], - [ - "Audrey offers tips to Andrew about taking care of his dog, Toby, emphasizing the importance of time in forming a bond.", - "D17:12" - ] - ] - }, - "session_18_observation": { - "Andrew": [ - [ - "Andrew has been finding work tough and stressful, causing his outdoor activities to take a backseat.", - "D18:1" - ], - [ - "Andrew tries to find balance in his life but finds it challenging.", - "D18:1" - ], - [ - "Andrew has been adding self-care activities to his daily routine like grabbing a coffee or going for a walk to recharge and chill out.", - "D18:5" - ], - [ - "Andrew enjoys hiking and finds it a relief to get away from the city.", - "D18:15" - ], - [ - "Andrew is looking for a dog-friendly place to live for his young pet Toby.", - "D18:17" - ] - ], - "Audrey": [ - [ - "Audrey emphasizes the importance of self-care and finding time for oneself.", - "D18:4" - ], - [ - "Audrey organized a doggy playdate with the neighbors' dogs, finding joy in seeing them have fun together.", - "D18:6" - ], - [ - "Audrey got new beds for her furry friends to provide them comfort as the weather cools down.", - "D18:10" - ], - [ - "Audrey's furry friends love the new beds, finding them cozy and comfortable.", - "D18:14" - ], - [ - "Audrey encourages Andrew in his search for a dog-friendly place to live for Toby.", - "D18:20" - ] - ] - }, - "session_19_observation": { - "Andrew": [ - [ - "Andrew expressed concern and empathy for Audrey's dog when she got scared and ran off at the park.", - "D19:3" - ], - [ - "Andrew is interested in building a strong relationship with dogs and seeks advice on the matter.", - "D19:5" - ], - [ - "Andrew believes patience and regular training are essential in building a strong relationship with dogs.", - "D19:7" - ], - [ - "Andrew finds joy in taking care of flowers and enjoys watching them grow in his garden.", - "D19:18" - ] - ], - "Audrey": [ - [ - "Audrey experienced a moment of worry when her dog's leash broke at the park, and she had to chase after her.", - "D19:2" - ], - [ - "Audrey finds strengthening the bond with her dogs through petting, hugs, and calmness during stressful situations.", - "D19:4" - ], - [ - "Audrey emphasizes the importance of patience, love, and regular training in building trust and a strong relationship with dogs.", - "D19:6" - ], - [ - "Audrey teaches her dogs obedience and tricks like sit, stay, shake, and roll over for fun and reward.", - "D19:8" - ], - [ - "Audrey's dogs are mutts; two are Jack Russell mixes, and two are Chihuahua mixes.", - "D19:12" - ], - [ - "Audrey has a small garden where she takes care of Peruvian Lilies, finding peace and relaxation in gardening.", - "D19:16" - ], - [ - "Audrey's Peruvian Lilies are easy to care for, requiring watering and enough sunlight.", - "D19:22" - ], - [ - "Audrey's Peruvian Lilies enjoy the garden, running around, exploring, and having fun.", - "D19:24" - ], - [ - "Audrey's furry companions mean everything to her and bring loads of love and happiness into her life.", - "D19:28" - ] - ] - }, - "session_20_observation": { - "Andrew": [ - [ - "Andrew is planning a beach trip with his girlfriend and friend Toby next month.", - "D20:1" - ], - [ - "Andrew is looking forward to a hike in nature with his friends and dogs.", - "D20:3" - ], - [ - "Andrew enjoys watching birds in nature and has read books about ecological systems.", - "D20:23" - ], - [ - "Andrew knows a thing or two about birdwatching and is willing to offer advice.", - "D20:15" - ], - [ - "Andrew plans to bring binoculars and a notebook for birdwatching.", - "D20:21" - ], - [ - "Andrew believes that nature is interconnected and humans need to take care of it.", - "D20:27" - ], - [ - "Andrew suggests reducing carbon footprint by biking or using public transport.", - "D20:33" - ], - [ - "Andrew knows some of the best bike routes near the river.", - "D20:37" - ] - ], - "Audrey": [ - [ - "Audrey is excited about a hike with Andrew and the dogs next month.", - "D20:2" - ], - [ - "Audrey finds walking the dogs in the park to be calming and peaceful.", - "D20:4" - ], - [ - "Audrey enjoys birdwatching and wants to explore more about it.", - "D20:14" - ], - [ - "Audrey believes in taking care of nature for future generations.", - "D20:28" - ], - [ - "Audrey sees recycling as an important step in preserving natural resources.", - "D20:32" - ], - [ - "Audrey is interested in trying biking as a way to reduce carbon footprint.", - "D20:34" - ], - [ - "Audrey is excited to check out bike routes near the river with Andrew.", - "D20:40" - ] - ] - }, - "session_21_observation": { - "Andrew": [ - [ - "Andrew used to hike a lot but finds it challenging now with work-life balance.", - "D21:5" - ], - [ - "Andrew expresses missing the outdoors and feels a void in his heart without being in nature.", - "D21:7" - ], - [ - "Andrew mentioned exploring nature as his escape and a way to find peace.", - "D21:7" - ], - [ - "Andrew is considering getting more plants for his house to bring some peace.", - "D21:9" - ] - ], - "Audrey": [ - [ - "Audrey took her dogs to the beach over the weekend and enjoyed seeing them play in the ocean.", - "D21:2" - ], - [ - "Audrey suggested ways for Andrew to appreciate nature in the city, like getting plants for his place or visiting the park on weekends.", - "D21:8" - ], - [ - "Audrey offered help and advice to Andrew regarding getting plants for his house.", - "D21:10" - ] - ] - }, - "session_22_observation": { - "Audrey": [ - [ - "Audrey had a knee injury that prevented her from walking her dogs, causing her to miss the joy they bring.", - "D22:1" - ], - [ - "Audrey makes jewelry out of recycled objects like bottle caps, buttons, and broken jewelry as a hobby.", - "D22:5" - ], - [ - "Audrey sells the jewelry she makes and donates a portion of the profits to an animal shelter.", - "D22:7" - ], - [ - "Audrey donates to an animal shelter as a way of combining her passion for making jewelry and making a difference.", - "D22:9" - ], - [ - "Audrey adapts and continues to find ways to help even if she can't volunteer at the shelter anymore.", - "D22:11" - ] - ], - "Andrew": [ - [ - "Andrew and Audrey discussed the joy of being able to walk dogs again after a knee injury.", - "D22:2" - ], - [ - "Andrew praised Audrey for using her passion for making jewelry to make a positive impact.", - "D22:8" - ] - ] - }, - "session_23_observation": { - "Andrew": [ - [ - "Andrew had a board game night with his girlfriend, Toby, last Tuesday.", - "D23:1" - ], - [ - "Andrew plans to check out a cozy cafe and hang out there this weekend.", - "D23:3" - ], - [ - "Andrew recommended a new spot with a sick view and vibe that just opened recently.", - "D23:5" - ], - [ - "Andrew's pup Toby is a German Shepherd who loves cute sweaters.", - "D23:13" - ], - [ - "Andrew's dog got excited and chased a squirrel at the dog park, which was hilarious.", - "D23:11" - ] - ], - "Audrey": [ - [ - "Audrey made goodies recently to thank her neighbors for their pup-friendly homes.", - "D23:2" - ], - [ - "Audrey's dogs love running around the park, but dislike snow and prefer sunny days in the grass.", - "D23:12" - ], - [ - "Audrey has a tattoo of her four dogs and mentioned going to the tattoo parlor to get more drawings on it.", - "D23:18" - ], - [ - "Audrey's dogs go crazy for Fetch and Frisbee at the park and love meeting other pups.", - "D23:14" - ], - [ - "Audrey enjoys moments with her dogs in nature as they are chill and happy companions for exploring the outdoors.", - "D23:24" - ] - ] - }, - "session_24_observation": { - "Audrey": [ - [ - "Audrey went to a pet store last Monday to buy toys for her dogs and realized how much she loves them and the joy they bring her.", - "D24:1" - ], - [ - "Audrey has dogs that bring her joy and companionship.", - "D24:4" - ], - [ - "Audrey is planning a hike with furry friends next month.", - "D24:13" - ], - [ - "Audrey is researching to find an awesome spot for the upcoming hike.", - "D24:19" - ] - ], - "Andrew": [ - [ - "Andrew adopted another pup from a shelter and named him Buddy.", - "D24:2" - ], - [ - "Andrew and Buddy enjoy doing walks together as a favorite activity.", - "D24:8" - ], - [ - "Andrew enjoys exploring hiking trails with Buddy and Toby.", - "D24:10" - ], - [ - "Andrew wishes to find a place far from the city to take his dogs for a hike.", - "D24:16" - ] - ] - }, - "session_25_observation": { - "Andrew": [ - [ - "Andrew attended an awesome wine tasting with his girlfriend last weekend and enjoyed trying unique wines.", - "D25:1" - ], - [ - "Andrew tried sushi for the first time and enjoyed it, understanding the hype around it.", - "D25:5" - ], - [ - "Andrew is curious about trying new things and stepped out of his comfort zone by trying sushi.", - "D25:5" - ] - ], - "Audrey": [ - [ - "Audrey had an accident while playing with her pups at the park, making it tricky to take care of them with one arm.", - "D25:2" - ], - [ - "Audrey loves sushi and advises trying different types and flavors without limiting oneself.", - "D25:6" - ], - [ - "Audrey suggests trying California or salmon roll as easier options for someone new to sushi, and mixing it up with different sauces and dips.", - "D25:8" - ], - [ - "Audrey is looking forward to ordering sushi for tonight.", - "D25:14" - ] - ] - }, - "session_26_observation": { - "Audrey": [ - [ - "Audrey has dogs that were not behaving normally, leading her to seek help from an animal behaviorist.", - "D26:1" - ], - [ - "Audrey mentioned her dogs are all mutts, where Pepper and Panda are Lab mixes, and Precious and Pixie are Chihuahua mixes.", - "D26:13" - ], - [ - "Audrey is using positive reinforcement techniques to help with her dogs' problems.", - "D26:5" - ], - [ - "Audrey is devoted to keeping her dogs healthy and happy.", - "D26:9" - ], - [ - "Audrey expressed excitement and eagerness to go hiking with Andrew and their furry friends.", - "D26:21" - ] - ], - "Andrew": [ - [ - "Andrew appears to be a supportive friend to Audrey, encouraging her and expressing well wishes for her dogs' well-being.", - "D26:10" - ], - [ - "Andrew plans to go hiking with Audrey, mentioning going on a picnic date with his girlfriend on Sunday.", - "D26:20" - ], - [ - "Andrew is enthusiastic about exploring nature and going on hikes with Audrey and their dogs.", - "D26:22" - ], - [ - "Andrew shared photos of a national park, a trail, and a dog with Audrey during the conversation.", - "D26:14, D26:34, D26:42" - ] - ] - }, - "session_27_observation": { - "Andrew": [ - [ - "Andrew went on a bike ride with his girlfriend last weekend to a park outside of town.", - "D27:1" - ], - [ - "Andrew is considering getting another dog but is unsure due to already having two dogs to take care of.", - "D27:5" - ], - [ - "Andrew is appreciative of Audrey's advice and support regarding taking care of dogs.", - [ - "D27:7", - "D27:9", - "D27:15", - "D27:17" - ] - ] - ], - "Audrey": [ - [ - "Audrey enjoys checking out new parks with her four dogs and spending time with them.", - "D27:2" - ], - [ - "Audrey meets up with other dog owners once a week for tips and playdates for the dogs.", - "D27:4" - ], - [ - "Audrey is supportive of Andrew's decision to focus on his current dogs before considering getting another one.", - [ - "D27:6", - "D27:8" - ] - ], - [ - "Audrey provides Andrew with tips on keeping dogs happy and active in the city.", - [ - "D27:10", - "D27:12" - ] - ], - [ - "Audrey engages her dogs in mental stimulation activities like puzzles, training, and hide-and-seek.", - "D27:14" - ], - [ - "Audrey values the friendship with Andrew and believes in supporting each other.", - "D27:18" - ] - ] - }, - "session_28_observation": { - "Audrey": [ - [ - "Audrey has multiple dogs that she refers to as her 'fur kids'.", - "D28:1" - ], - [ - "Audrey took her dogs to the pet salon last Friday and was happy to see them looking cute and groomed.", - "D28:1" - ], - [ - "Audrey worries about her dogs when they are in new places but was relieved to see them well-behaved at the salon.", - "D28:3" - ], - [ - "Audrey values seeing her dogs happy and settled in new environments, indicating her care as a dog owner.", - "D28:5" - ], - [ - "Audrey appreciates creating a safe and fun space for her dogs, ensuring they feel secure.", - "D28:13" - ], - [ - "Audrey's dogs feel loved and safe under her care.", - "D28:15" - ], - [ - "Audrey considers her dogs part of her family and expresses gratitude for having them in her life.", - "D28:17" - ] - ], - "Andrew": [ - [ - "Andrew has at least 3 dogs named Scout, Toby, and Buddy.", - "D28:10" - ], - [ - "Andrew adopted a new dog named Scout because of their adventurous spirit.", - "D28:6" - ], - [ - "Andrew values safety and comfort for his dogs, providing them with essentials like a bed, toys, and puppy pads.", - "D28:12" - ], - [ - "Andrew plans to introduce the new dog, Scout, to his other dogs slowly to ensure they get along.", - "D28:13" - ], - [ - "Andrew expresses gratitude for having Scout and appreciates having multiple furry friends.", - "D28:14" - ], - [ - "Andrew considers his dogs as family members and acknowledges the joy and friendship they bring.", - "D28:18" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "At 1:10 pm on 27 March 2023, Audrey and Andrew caught up. Andrew shared about his new job as a Financial Analyst, whereas Audrey talked about getting new collars for her dogs named Pepper, Precious, and Panda that she has had for 3 years. The dogs, city dwellers, go on adventures exploring parks and trails together. Andrew loves animals, particularly birds, with a fascination for eagles. He recommended the Fox Hollow trail for hiking to Audrey, who planned to give it a try. They bid goodbye, wishing each other well.", - "session_2_summary": "Audrey surprises Andrew with a new puppy named Pixie on 2 April 2023, at 2:42 pm. The puppy is fitting in well with the other dogs. Andrew is considering getting a dog too, and Audrey suggests using websites to find dog-friendly places in the city. They discuss the joys of having furry companions and the memories of hiking with dogs. Andrew expresses excitement about finding a dog-friendly spot soon. Audrey wishes him the best in his search, and they say goodbye.", - "session_3_summary": "At 4:19 pm on 16 April, 2023, Andrew told Audrey about trying a new cafe in the city and missing nature. Audrey agreed and mentioned how nature brings peace. They discussed how nature soothes and centers them. Andrew expressed missing hiking due to city living. They talked about the importance of nature and shared their ways of connecting to it. Audrey suggested a pastry party, and Andrew agreed. They planned to have their dogs join the party. Audrey revealed getting tattoos of her dogs to show her love for them. Andrew admired the tattoo, and Audrey explained the significance of the tattoo. They bonded over their love for their furry friends and the joy they bring.", - "session_4_summary": "Audrey and Andrew caught up at 5:41 pm on 3 May, 2023. Audrey shared her recent hiking experience with a hummingbird, while Andrew mentioned finding a new hiking spot. Andrew now escapes the city for a hike once a weekend. They both find nature therapeutic and calming. Audrey spends time with her pets, including hiking with her dogs. Andrew inquired about dog safety tips from Audrey, who also mentioned playing fetch and organizing doggie playdates. The conversation ended with Andrew planning to visit the dog park Audrey recommended.", - "session_5_summary": "Andrew and Audrey discussed Andrew's search for a dog to adopt at 10:47 am on 6 May, 2023. Andrew talked about the challenges and joys of the process, considering factors like living space and breed requirements. Audrey offered advice on considering a dog's space and energy needs. Andrew shared his difficulties in finding pet-friendly housing near nature. They also discussed the joy of road trips with dogs, with Audrey sharing her experience and Andrew expressing his desire to do the same. Audrey wished Andrew luck in finding the perfect furry friend and offered to help. They exchanged goodbyes, with Andrew heading to another shelter and Audrey looking forward to updates on the search.", - "session_6_summary": "Andrew and Audrey chatted at 2:03 pm on 11 May, 2023. Andrew shared his recent peaceful hike with his girlfriend. Audrey discussed taking care of her four dogs and signing up for a workshop on bonding with pets. Andrew showed interest in the workshop and praised Audrey's efforts. Audrey shared her positive reinforcement approach with dogs and emphasized the importance of not using punishment. They agreed to keep each other updated, with Andrew showing interest in attending the workshop too. The conversation ended with plans to talk later.", - "session_7_summary": "Audrey and Andrew had a conversation on 2 June, 2023, at 11:27 am. Audrey shared her joy of taking her dogs to the park and on a hiking trip to a national park, capturing beautiful sunset moments. Andrew admired the experiences and inquired about photos, showing interest in getting a furry friend but facing challenges. Audrey offered support and assistance, encouraging Andrew not to give up on finding the perfect place for a new pet.", - "session_8_summary": "Andrew and Audrey caught up at 5:23 pm on 13 June, 2023. Andrew shared his thrilling rock climbing experience from the previous Sunday, expressing his newfound love for outdoor activities. Audrey admired his achievement and requested to see pictures, leading to a discussion about the challenging climb and the stunning view. They discussed the importance of a supportive group and how nature pushes them out of their comfort zones. Audrey invited Andrew to join her and her dogs for a walk in a beautiful park, where they would explore for about an hour. Both agreed on the joy and peace nature brings, with Audrey sharing photos of her happy dogs in nature. They concluded with a mutual appreciation for how nature refreshes the mind and provides a different outlook on life.", - "session_9_summary": "Audrey, at 1:51 pm on 26 June, 2023, shared her excitement about moving to a new house with a bigger backyard for her dogs. Andrew praised the new place and asked about the dogs settling in. Audrey mentioned creating a doggy play area in the backyard. Andrew recommended a doggy daycare he visited and Audrey showed interest. They discussed their love for animals and shared nature experiences, emphasizing the importance of appreciating the small things in life.", - "session_10_summary": "Audrey and Andrew caught up at 8:32 pm on 3 July, 2023. Audrey mentioned taking a challenging but rewarding dog training course while Andrew expressed difficulty finding pet-friendly spots. They discussed their lack of time for exploring trails but Audrey shared how well her dogs were doing due to the training. Andrew admired her dogs and asked about their breeds and favorite activities. Audrey revealed they were all mutts who loved running and playing fetch. They further discussed the age of the dogs, their good relationship, and a recent doggy playdate. Andrew shared his new hobby of cooking while Audrey disclosed her love for it and her favorite recipe, Chicken Pot Pie. Andrew showed interest in trying the recipe, and Audrey planned to share it. They also discussed the therapeutic nature of cooking and the joy of trying new dishes. Audrey mentioned garlic as her go-to ingredient and offered to share a Roasted Chicken recipe inspired by Mediterranean flavors. Andrew expressed excitement to try it and thanked her for sharing, ending the conversation with plans to talk later.", - "session_11_summary": "Andrew and Audrey had a conversation at 9:48 am on 8 July, 2023, where they discussed their love for nature and their dogs. Andrew mentioned having a picnic with his girlfriend, while Audrey talked about her furry friends and a recent walk. They discussed the difficulty of finding dog-friendly places and planned a hike together for the next month. They both expressed excitement about the upcoming adventure and shared photos of their happy dogs. They also admired nature's beauty and the joy it brings. Their conversation highlighted their appreciation for the small moments in life and the happiness that dogs bring.", - "session_12_summary": "Andrew and Audrey met at 10:05 am on 11 July, 2023. Andrew introduced his puppy Toby, expressing how much joy he brings despite the city living. Audrey congratulated him and suggested they plan a hiking trip to relax. Andrew agreed, inviting her and their pups. Audrey showed personalized tags she made for her dogs, which Andrew admired. They both expressed love for their pets and agreed on the importance of showing it. They eagerly anticipated the upcoming hike, looking forward to their pets meeting and the fun they would have. They ended the conversation, excited and ready to prepare for the adventure ahead.", - "session_13_summary": "At 3:52 pm on 27 July 2023, Andrew excitedly shared his recent volunteering experience at a pet shelter with Audrey, emphasizing the joy and love animals bring. Audrey, intrigued, expressed her desire to volunteer someday and shared how her four pets each have unique personalities. The conversation turned nostalgic as they discussed Audrey's childhood dog, Max, and the special bond they shared. They both agreed that pets are more than animals; they are friends who provide comfort and love. Andrew highlighted the uplifting impact of volunteering with animals, which Audrey appreciated, acknowledging the positive effect their kindness has on the animals' well-being.", - "session_14_summary": "On August 4th, 2023, at 11:05 am, Andrew told Audrey about his upcoming camping trip with his girlfriend. Audrey shared her experience with agility classes for her dogs, emphasizing the joy of watching them overcome challenges. They discussed the beauty of nature and the benefits of spending time outdoors. Audrey advised Andrew on caring for his German Shepherd, Toby, emphasizing the importance of energy and attention. Andrew expressed his excitement to bond with Toby on hikes. The conversation highlighted the special bond between humans and animals, as Audrey shared her love for her pets and Andrew looked forward to deepening his connection with Toby.", - "session_15_summary": "Audrey, at 9:58 pm on 16 August, 2023, excitedly told Andrew she got a tattoo of her four dogs. She shared that she took the dogs to the vet together last time which was hectic, and they each have a favorite spot to relax at home. Andrew mentioned visiting a farm for fresh veggies with his girlfriend. The two admire each other's pet photos and discuss the joy pets bring. Audrey shared a photo of her dogs snuggling, and Andrew showed his dog Toby's favorite spot. They discussed how pets always find comfort and joy in their cozy spaces. Audrey expressed how much her dogs mean to her, saying they are best friends and bring happiness. Andrew agreed, calling pets family and mentioning the joy they bring. Audrey shared a photo of her lying on the grass with her dogs, reflecting on the wonderful time they had outside. Andrew encouraged her to cherish those moments, to which Audrey expressed gratitude, saying her pets make life brighter.", - "session_16_summary": "Andrew and Audrey had a conversation at 9:19 pm on 19 August, 2023. Andrew shared about taking a break to visit a cafe and reminisced about the tranquility of nature. He also showed Audrey a serene photo from his previous hike. Audrey mentioned learning dog grooming, feeling closer to her pups, and shared a picture of them looking cute and fluffy post-grooming. Andrew praised her skills and sought grooming tips, which Audrey gladly provided. They discussed weekend plans - Audrey taking her dogs to the park for a stroll and Andrew heading to a nature reserve. Andrew promised to share photos from his trip with Audrey later. They bid goodbye, looking forward to the pictures and caring for their pets.", - "session_17_summary": "On August 24, 2023, at 12:24 am, Andrew told Audrey about his recent fishing trip with his girlfriend. Audrey shared a memory of sitting by a lake in the mountains. They discussed pet care, with Audrey emphasizing grooming and love to keep pets happy. They highlighted the importance of forming strong bonds with pets, with Audrey offering tips to Andrew for his dog Toby. Andrew expressed excitement for bonding with Toby and future outdoor adventures. The conversation ended with compliments and well wishes between the two friends.", - "session_18_summary": "At 7:49 pm on 6th September 2023, Andrew and Audrey had a conversation. Andrew shared his work stress and challenge in finding balance in life. Audrey emphasized the importance of self-care and finding joy in simple activities. They discussed hobbies and moments of relaxation. Audrey shared about organizing a dog playdate and getting new beds for her furry friends. Andrew inquired about the playdate and new beds with interest. Audrey shared pictures of the playdate and the new beds, expressing joy in her pets' happiness. They discussed the importance of simple joys and bonding with animals. Andrew shared about his hike, while Audrey asked about hiking with Toby. Andrew mentioned the challenge of finding a dog-friendly place to live. Audrey encouraged him not to give up and to keep searching. They ended by exchanging well wishes and encouragement.", - "session_19_summary": "Andrew and Audrey caught up at 5:53 pm on 24 September, 2023. Audrey shared a recent incident where her dog's leash broke, causing a scare but strengthening their bond. She advised patience and regular training to build trust with dogs. They discussed training tricks, going for walks, and Audrey's mutt breeds. Andrew admired Audrey's garden with Peruvian Lilies, easy to care for and enjoyed by her dogs. They shared the joy their furry pals bring into their lives, highlighting the strong bond and happiness they provide.", - "session_20_summary": "Andrew and Audrey had a conversation at 7:09 pm on 1 October 2023. Andrew shared that he and his girlfriend will be going to the beach next month with Toby. Audrey expressed her excitement for an upcoming hike with the dogs, highlighting how being in nature helps her find peace. Andrew agreed and mentioned missing being in nature. They discussed the benefits of nature, birdwatching, and taking care of the environment for future generations. They also talked about reducing the carbon footprint by biking and using public transport. Andrew offered to show Audrey the best bike routes near the river. They both looked forward to exploring the routes and enjoying the scenery together.", - "session_21_summary": "4:18 pm on 4 October, 2023 - Andrew reconnects with Audrey, who shares about her fun beach trip with her dogs. Andrew reminisces about missing nature due to work. Audrey suggests getting plants for his house. Andrew thanks Audrey for her help and offers. They exchange goodbyes, wishing each other a great week.", - "session_22_summary": "Audrey and Andrew had a conversation at 9:41 pm on 6 October, 2023. Audrey shared about missing walking her dogs due to a knee injury but finding joy in being able to walk them again. Andrew empathized and praised the joy dogs bring, encouraging appreciation for life's little pleasures. Audrey mentioned making jewelry from recycled items as a hobby, selling them to donate a portion of the profits to an animal shelter. Andrew admired her dedication to making a difference. Audrey highlighted the importance of adapting in challenging times. Andrew praised her for staying strong and making a difference.", - "session_23_summary": "Andrew and Audrey had a conversation at 4:22 pm on 13th October 2023 where they shared updates about their lives. Andrew talked about a board game night with his girlfriend, Toby, and his plan to visit a cozy cafe. Audrey mentioned baking goodies for her neighbors and discussed taking her dogs to a new spot and a dog park. They shared funny stories about their pups, discussed their love for nature, and the joy their dogs bring. Audrey also mentioned getting a tattoo of her dogs. They agreed that nature and animals bring peace and joy. Andrew recommended hiking as a way to bond and make memories with dogs.", - "session_24_summary": "Audrey and Andrew, both pet lovers, discussed their furry companions at 6:12 pm on 19 October, 2023.\nAudrey shared her joy after buying toys for her dogs, while Andrew excitedly announced the adoption of a new pup named Buddy. The two friends talked about their pets' interactions and favorite activities like walks and exploring hiking trails. They planned to go on a hike together next month. Audrey volunteered to find a great spot for the outing as Andrew wished for a getaway from the city. Andrew appreciated Audrey's efforts and looked forward to the adventure.", - "session_25_summary": "Andrew and Audrey conversed at 10:14 am on 24 October 2023. Andrew shared his experience at a wine tasting, stepping out of his comfort zone, while Audrey mentioned an accident with her pups at the park. They discussed trying new things, with Andrew trying sushi recently and Audrey giving him tips on trying different types. Andrew expressed excitement about his upcoming sushi adventure, while Audrey planned to order sushi for dinner. They ended the conversation wishing each other well.", - "session_26_summary": "Audrey informed Andrew about her dogs' behavior issues, her appointment with an animal behaviorist, and the positive tips she received for handling the problems. The conversation then shifted to planning a nature hike with their furry friends on Saturday, exploring a trail by a lake with scenic views. They expressed excitement and gratitude for the upcoming adventure, sharing photos of potential locations and expressing eagerness to capture memories with their dogs. Andrew shared a photo of his happy dog from a previous hike, and they both looked forward to creating new joyful memories together. They planned to meet for the hike and said their goodbyes as they got ready to head out.", - "session_27_summary": "Andrew told Audrey about his weekend bike ride with his girlfriend, discovering a park outside the city. Audrey shared her love for exploring parks with her four dogs, mentioning joining a dog owners group for tips. Andrew considered getting another dog but decided to focus on his current pets first. Audrey advised on taking care of his current dogs before getting more, suggesting regular socialization and exercise for keeping them happy in the city. She also recommended mental stimulation activities like puzzles and hide-and-seek. Andrew appreciated Audrey's support and friendship, thanking her for the advice.", - "session_28_summary": "Audrey and Andrew caught up at 9:02 am on 22 November, 2023. Audrey shared about taking her dogs to the pet salon and showing them off all groomed up. Andrew admired the pictures and mentioned adopting a new dog named Scout. They discussed taking Scout, Toby, and Buddy to a park together for a fun outing. Audrey advised on introducing Scout to the other dogs slowly and creating a safe space for the new pup. Andrew was grateful for the advice and expressed how dogs bring joy and friendship. Audrey also shared how her dogs are doing well and bring her happiness. Both concluded that having dogs as family members is truly special." - }, - "sample_id": "conv-44" - }, - { - "qa": [ - { - "question": "What are John's suspected health problems?", - "answer": "Obesity", - "evidence": [ - "D1:27" - ], - "category": 3 - }, - { - "question": "Which recreational activity was James pursuing on March 16, 2022?", - "answer": "bowling", - "evidence": [ - "D1:26" - ], - "category": 2 - }, - { - "question": "Which places or events have John and James planned to meet at?", - "answer": "VR Club, McGee's, baseball game", - "evidence": [ - "D1:36", - "D21:15", - "D23:5", - "D23:6" - ], - "category": 1 - }, - { - "question": "Do both James and John have pets?", - "answer": "No", - "evidence": [ - "D1:12", - "D2:18" - ], - "category": 1 - }, - { - "question": "When did John resume playing drums in his adulthood?", - "answer": "February 2022", - "evidence": [ - "D3:5" - ], - "category": 2 - }, - { - "question": "What are John and James' favorite games?", - "answer": "John's favorite game is CS:GO, and James's is Apex Legends.", - "evidence": [ - "D3:11", - "D4:16" - ], - "category": 1 - }, - { - "question": "Does James live in Connecticut?", - "answer": "Likely yes", - "evidence": [ - "D5:1" - ], - "category": 3 - }, - { - "question": "In which state is the shelter from which James adopted the puppy?", - "answer": "Connecticut.", - "evidence": [ - "D5:1" - ], - "category": 3 - }, - { - "question": "How many pets does James have?", - "answer": "Three dogs.", - "evidence": [ - "D1:12", - "D1:14", - "D5:1" - ], - "category": 1 - }, - { - "question": "What are the names of James's dogs?", - "answer": "Ned, Daisy, Max", - "evidence": [ - "D1:14", - "D5:1" - ], - "category": 1 - }, - { - "question": "When did James adopt Ned?", - "answer": "first week of April 2022", - "evidence": [ - "D5:1" - ], - "category": 2 - }, - { - "question": "How was John feeling on April 10, 2022?", - "answer": "seeking solitude", - "evidence": [ - "D6:7" - ], - "category": 2 - }, - { - "question": "Did James have a girlfriend during April 2022?", - "answer": "Presumably not", - "evidence": [ - "D6:6" - ], - "category": 3 - }, - { - "question": "When did James visit Italy?", - "answer": "In 2021", - "evidence": [ - "D6:12" - ], - "category": 2 - }, - { - "question": "When did James buy himself a new adventure book?", - "answer": "April 26, 2022", - "evidence": [ - "D8:11" - ], - "category": 2 - }, - { - "question": "When did James start playing Civilization VI?", - "answer": "March 2022", - "evidence": [ - "D8:29" - ], - "category": 2 - }, - { - "question": "What is the game with different colored cards that was John talking about with James?", - "answer": "UNO", - "evidence": [ - "D8:34" - ], - "category": 3 - }, - { - "question": "What is the board game where you have to find the imposter that John mentions to James?", - "answer": "Mafia", - "evidence": [ - "D8:36" - ], - "category": 3 - }, - { - "question": "Which books has John recommended to James?", - "answer": "The Name of the Wind, Stormlight Archive, Kingkiller Chronicles, Expanse", - "evidence": [ - "D8:14", - "D14:10" - ], - "category": 1 - }, - { - "question": "Was James feeling lonely before meeting Samantha?", - "answer": "Most likely yes, because he mentioned that the only creatures that gave him joy are dogs and he was actively trying to date.", - "evidence": [ - "D9:16" - ], - "category": 3 - }, - { - "question": "How many charity tournaments has John organized till date?", - "answer": "two", - "evidence": [ - "D10:2", - "D29:1" - ], - "category": 1 - }, - { - "question": "When did John first organize a charity tournament with his friends?", - "answer": "May 7, 2022", - "evidence": [ - "D10:2" - ], - "category": 2 - }, - { - "question": "Who or which organizations have been the beneficiaries of John's charity tournaments?", - "answer": "animal shelter, homeless, children's hospital", - "evidence": [ - "D10:10", - "D10:12", - "D29:1" - ], - "category": 1 - }, - { - "question": "When will John start his new job?", - "answer": "In July, 2022", - "evidence": [ - "D13:5" - ], - "category": 2 - }, - { - "question": "What kind of games has James tried to develop?", - "answer": "football simulator, virtual world inspired by Witcher 3", - "evidence": [ - "D13:7", - "D1:4", - "D27:2" - ], - "category": 1 - }, - { - "question": "Are John and James fans of the same football team?", - "answer": "No, James is a Liverpool fan and John is a Manchester City fan.", - "evidence": [ - "D13:12", - "D13:15" - ], - "category": 3 - }, - { - "question": "Which countries has James visited?", - "answer": "Italy, Mexico, Turkey, Canada, Greenland", - "evidence": [ - "D6:12", - "D6:14", - "D16:9", - "D17:22" - ], - "category": 1 - }, - { - "question": "What kind of classes has James joined?", - "answer": "game design course, cooking classes", - "evidence": [ - "D13:6", - "D23:13" - ], - "category": 1 - }, - { - "question": "When did James volunteer at an organization?", - "answer": "May 2022", - "evidence": [ - "D15:9" - ], - "category": 2 - }, - { - "question": "When did James depart for his trip to Canada?", - "answer": "July 11, 2022", - "evidence": [ - "D16:9" - ], - "category": 2 - }, - { - "question": "Which country did James book tickets for in July 2022?", - "answer": "Canada", - "evidence": [ - "D16:9", - "D16:11" - ], - "category": 3 - }, - { - "question": "How many days did James plan to spend on his trip in Canada?", - "answer": "19 days", - "evidence": [ - "D16:9", - "D16:13" - ], - "category": 2 - }, - { - "question": "Where was James at on July 12, 2022?", - "answer": "Toronto, Canada", - "evidence": [ - "D16:9" - ], - "category": 2 - }, - { - "question": "Did John and James study together?", - "answer": "Yes", - "evidence": [ - "D17:13" - ], - "category": 3 - }, - { - "question": "Which countries did James visit in July 2022?", - "answer": "Canada, Greenland", - "evidence": [ - "D16:9", - "D17:22" - ], - "category": 1 - }, - { - "question": "What additional country did James visit during his trip to Canada?", - "answer": "Greenland", - "evidence": [ - "D17:22" - ], - "category": 3 - }, - { - "question": "Who is Jill?", - "answer": "Most likely John's partner.", - "evidence": [ - "D17:24" - ], - "category": 3 - }, - { - "question": "When did John spend time with his sister and dogs?", - "answer": "July 21, 2022", - "evidence": [ - "D17:28" - ], - "category": 2 - }, - { - "question": "What happened to John's job situation in 2022?", - "answer": "quit his IT Job, secured his dream job, aspires to become an eSports competition organizer", - "evidence": [ - "D4:36", - "D18:1", - "D18:7" - ], - "category": 1 - }, - { - "question": "When did John start his job in IT?", - "answer": "2019", - "evidence": [ - "D18:1" - ], - "category": 2 - }, - { - "question": "What kind of tricks do James's pets know?", - "answer": "swimming, catching frisbees, balancing on a skateboard, sit, stay, paw, and rollover", - "evidence": [ - "D2:17", - "D14:17", - "D14:23", - "D17:16" - ], - "category": 1 - }, - { - "question": "When did James meet Samantha?", - "answer": "August 9, 2022", - "evidence": [ - "D19:12" - ], - "category": 2 - }, - { - "question": "When did James take his 3 dogs to the beach?", - "answer": "August 9, 2022", - "evidence": [ - "D19:12" - ], - "category": 2 - }, - { - "question": "When did John plan his next meeting with his siblings?", - "answer": "In September, 2022", - "evidence": [ - "D20:17" - ], - "category": 2 - }, - { - "question": "Why didn't John want to go to Starbucks?", - "answer": "Possibly because he likes to drink beer on his days off.", - "evidence": [ - "D21:12", - "D21:14" - ], - "category": 3 - }, - { - "question": "What kind of beer does McGee's bar serve?", - "answer": "Stout, lager", - "evidence": [ - "D21:15", - "D21:17", - "D23:3" - ], - "category": 1 - }, - { - "question": "When did John and James meet at McGee's bar?", - "answer": "August 27, 2022", - "evidence": [ - "D21:18" - ], - "category": 2 - }, - { - "question": "When did James ask Samantha to be his girlfriend?", - "answer": "September 3, 2022", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "When did James, Samantha and John go to the baseball game together?", - "answer": "September 11, 2022", - "evidence": [ - "D23:5", - "D23:6" - ], - "category": 2 - }, - { - "question": "What gaming equipments did John buy or refurbish?", - "answer": "Sennheiser headphones, Logitech mouse, gaming desk", - "evidence": [ - "D23:8", - "D23:10", - "D20:9" - ], - "category": 1 - }, - { - "question": "When did James start taking cooking classes?", - "answer": "September 2, 2022", - "evidence": [ - "D23:13" - ], - "category": 2 - }, - { - "question": "Which new games did John start play during the course of the conversation with James?", - "answer": "AC Valhalla, Witcher 3, FIFA 23, Dungeons of the Dragons, futuristic dystopian game", - "evidence": [ - "D5:4", - "D19:7", - "D30:14", - "D24:1", - "D24:3", - "D8:20" - ], - "category": 1 - }, - { - "question": "When did John start working on his 2D Adventure mobile game?", - "answer": "approximately summer of 2022", - "evidence": [ - "D25:9" - ], - "category": 2 - }, - { - "question": "How long did it take for James to complete his Witcher-inspired game?", - "answer": "six months", - "evidence": [ - "D6:1", - "D27:2" - ], - "category": 2 - }, - { - "question": "What kind of programming-related events has John hosted?", - "answer": "online programming competition, programming seminar", - "evidence": [ - "D27:1", - "D28:6" - ], - "category": 1 - }, - { - "question": "When did John and his programming friends host an online programming competition?", - "answer": "Last week before 13 October 2022.", - "evidence": [ - "D27:1" - ], - "category": 2 - }, - { - "question": "Which of James's family members have visited him in the last year?", - "answer": "mother, sister", - "evidence": [ - "D17:28", - "D28:19" - ], - "category": 1 - }, - { - "question": "When did James' mother and her friend visit him?", - "answer": "October 19, 2022", - "evidence": [ - "D28:19" - ], - "category": 2 - }, - { - "question": "When did James try Cyberpunk 2077 game?", - "answer": "October 20, 2022", - "evidence": [ - "D28:27" - ], - "category": 2 - }, - { - "question": "When did John and his gaming friends organize the charity tournament?", - "answer": "On the night of October 30 to 31, 2022", - "evidence": [ - "D29:1" - ], - "category": 2 - }, - { - "question": "What games has John played with his friends at charity tournaments?", - "answer": "CS:GO, Fortnite, Overwatch and Apex Legends", - "evidence": [ - "D10:4", - "D29:1", - "D29:3" - ], - "category": 1 - }, - { - "question": "What was James' big moment with Samantha in October 2023?", - "answer": "They decided to live together and rented an apartment not far from McGee's bar.", - "evidence": [ - "D29:8", - "D29:10" - ], - "category": 2 - }, - { - "question": "How long did James and Samantha date for before deciding to move in together?", - "answer": "nearly three months", - "evidence": [ - "D19:14", - "D29:8", - "D29:10" - ], - "category": 2 - }, - { - "question": "When did James, his family and his dogs start on a road trip together?", - "answer": "November 4, 2022", - "evidence": [ - "D30:1" - ], - "category": 2 - }, - { - "question": "How long did John practice chess for before winning the chess tournament?", - "answer": "nearly four months", - "evidence": [ - "D17:1", - "D30:2", - "D30:4" - ], - "category": 2 - }, - { - "question": "When did James and his family visit Mark and Josh?", - "answer": "November 7, 2022", - "evidence": [ - "D31:1" - ], - "category": 2 - }, - { - "question": "When did John work with a game developer on a project?", - "answer": "November 5-6, 2022", - "evidence": [ - "D31:2" - ], - "category": 2 - }, - { - "question": "What programming languages has James worked with?", - "answer": "Python and C++", - "evidence": [ - "D1:8" - ], - "category": 4 - }, - { - "question": "What type of mobile application does James plan to build with John?", - "answer": "An app for dog walking and pet care", - "evidence": [ - "D1:14" - ], - "category": 4 - }, - { - "question": "How does James plan to make his dog-sitting app unique?", - "answer": "By allowing users to customize their pup's preferences/needs", - "evidence": [ - "D1:16" - ], - "category": 4 - }, - { - "question": "What has John mostly found with the metal detector so far?", - "answer": "bottle caps", - "evidence": [ - "D2:12" - ], - "category": 4 - }, - { - "question": "What did James offer to do for John regarding pets?", - "answer": "help find the perfect pet", - "evidence": [ - "D2:19" - ], - "category": 4 - }, - { - "question": "What instrument is John learning to play as of 27 March, 2022?", - "answer": "Drums", - "evidence": [ - "D3:2", - "D3:3" - ], - "category": 4 - }, - { - "question": "How long has John been playing the drums as of 27 March, 2022?", - "answer": "One month", - "evidence": [ - "D3:5" - ], - "category": 4 - }, - { - "question": "What game did John play in an intense tournament at the gaming convention in March 2022?", - "answer": "CS:GO", - "evidence": [ - "D3:11" - ], - "category": 4 - }, - { - "question": "What game was James playing in the online gaming tournament in April 2022?", - "answer": "Apex Legends", - "evidence": [ - "D4:16" - ], - "category": 4 - }, - { - "question": "How does James communicate with his gaming team?", - "answer": "voice chat", - "evidence": [ - "D4:14" - ], - "category": 4 - }, - { - "question": "What advice did James receive from the famous players he met at the tournament?", - "answer": "never put your ego above team success", - "evidence": [ - "D4:12" - ], - "category": 4 - }, - { - "question": "What did James adopt in April 2022?", - "answer": "a pup", - "evidence": [ - "D5:1" - ], - "category": 4 - }, - { - "question": "What is the name of the pup that was adopted by James?", - "answer": "Ned", - "evidence": [ - "D5:1" - ], - "category": 4 - }, - { - "question": "Why did James embody the appearance of the game character from the woman he saw during a walk?", - "answer": "He found her appearance and eyes amazing.", - "evidence": [ - "D6:6" - ], - "category": 4 - }, - { - "question": "What inspired James to create the game character in the virtual world?", - "answer": "Appearance of a woman he saw during a walk", - "evidence": [ - "D6:6" - ], - "category": 4 - }, - { - "question": "Which country did James visit in 2021?", - "answer": "Italy", - "evidence": [ - "D6:12" - ], - "category": 4 - }, - { - "question": "What impresses John about Japan?", - "answer": "Technologically advanced megacities and tasty street food", - "evidence": [ - "D6:15" - ], - "category": 4 - }, - { - "question": "What kind of assignment was giving John a hard time at work?", - "answer": "Coding assignment", - "evidence": [ - "D7:13" - ], - "category": 4 - }, - { - "question": "What breed is Daisy, one of James' dogs?", - "answer": "Labrador", - "evidence": [ - "D9:12" - ], - "category": 4 - }, - { - "question": "What type of pizza is James' favorite?", - "answer": "Pepperoni", - "evidence": [ - "D9:18" - ], - "category": 4 - }, - { - "question": "What type of pizza is John's favorite?", - "answer": "Hawaiian", - "evidence": [ - "D9:19" - ], - "category": 4 - }, - { - "question": "What did John organize with his friends on May 8, 2022?", - "answer": "A tournament for CS:GO", - "evidence": [ - "D10:4" - ], - "category": 4 - }, - { - "question": "What did John and his friends do with the remaining money after helping the dog shelter?", - "answer": "Bought groceries and cooked food for the homeless", - "evidence": [ - "D10:12" - ], - "category": 4 - }, - { - "question": "What was the main goal of the money raised from the charity tournament organized by John and his friends in May 2022?", - "answer": "Raise money for a dog shelter", - "evidence": [ - "D10:10" - ], - "category": 4 - }, - { - "question": "What did the system John created help the charitable foundation with?", - "answer": "tracking inventory, resources, and donations", - "evidence": [ - "D11:5" - ], - "category": 4 - }, - { - "question": "What did John create for the charitable foundation that helped generate reports for analysis?", - "answer": "computer application on smartphones", - "evidence": [ - "D11:3" - ], - "category": 4 - }, - { - "question": "What did John receive for achieving second place in the tournament?", - "answer": "money and a trophy", - "evidence": [ - "D12:5", - "D12:6" - ], - "category": 4 - }, - { - "question": "What project is James working on in his game design course?", - "answer": "a new part of the football simulator, collecting player databases", - "evidence": [ - "D13:8" - ], - "category": 4 - }, - { - "question": "Who does James support in football matches?", - "answer": "Liverpool", - "evidence": [ - "D13:12" - ], - "category": 4 - }, - { - "question": "Which football club does John support?", - "answer": "Manchester City", - "evidence": [ - "D13:15" - ], - "category": 4 - }, - { - "question": "What disagreement do James and John have about their football teams?", - "answer": "debating on which team will perform better in the championship", - "evidence": [ - "D13:15" - ], - "category": 4 - }, - { - "question": "What is Max good at doing according to James?", - "answer": "catching frisbees in mid-air", - "evidence": [ - "D14:23" - ], - "category": 4 - }, - { - "question": "What is the main focus of the organization that James volunteered with?", - "answer": "providing necessary items to those who are less fortunate", - "evidence": [ - "D15:11" - ], - "category": 4 - }, - { - "question": "Will there be an interview required to volunteer with the organization James volunteered for?", - "answer": "No", - "evidence": [ - "D15:15" - ], - "category": 4 - }, - { - "question": "How did John relax in his free time on 9 July, 2022?", - "answer": "Reading", - "evidence": [ - "D16:8" - ], - "category": 4 - }, - { - "question": "What did James enjoy doing on cold winter days?", - "answer": "Reading while snuggled under the covers", - "evidence": [ - "D16:9" - ], - "category": 4 - }, - { - "question": "What new hobby did James become interested in on 9 July, 2022?", - "answer": "Extreme sports", - "evidence": [ - "D16:5" - ], - "category": 4 - }, - { - "question": "Where did James plan to visit after Toronto?", - "answer": "Vancouver", - "evidence": [ - "D16:11" - ], - "category": 4 - }, - { - "question": "When did James plan to return from his trip to Toronto and Vancouver?", - "answer": "July 20", - "evidence": [ - "D16:13" - ], - "category": 4 - }, - { - "question": "What online game did John start playing recently for improving strategy?", - "answer": "Chess", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "What made John leave his IT job?", - "answer": "to focus on things that align with his values and passions", - "evidence": [ - "D18:3" - ], - "category": 4 - }, - { - "question": "Which game tournaments does John plan to organize besides CS:GO?", - "answer": "Fortnite competitions", - "evidence": [ - "D18:9" - ], - "category": 4 - }, - { - "question": "What happened to James's puppy during the recent visit to the clinic?", - "answer": "routine examination and vaccination", - "evidence": [ - "D18:16" - ], - "category": 4 - }, - { - "question": "What game genre did John start exploring instead of shooters?", - "answer": "strategy and RPG games", - "evidence": [ - "D19:3" - ], - "category": 4 - }, - { - "question": "Which RPG game is John playing and enjoying on 10 August, 2022?", - "answer": "The Witcher 3", - "evidence": [ - "D19:7" - ], - "category": 4 - }, - { - "question": "What aspect of \"The Witcher 3\" does John find immersive?", - "answer": "shaping the world with choices", - "evidence": [ - "D19:7" - ], - "category": 4 - }, - { - "question": "Whose phone number did James receive during the beach outing?", - "answer": "Samantha", - "evidence": [ - "D19:14" - ], - "category": 4 - }, - { - "question": "What is James planning to do after receiving Samantha's phone number?", - "answer": "call her", - "evidence": [ - "D19:14" - ], - "category": 4 - }, - { - "question": "What is John organizing with his siblings?", - "answer": "a gaming night", - "evidence": [ - "D20:17" - ], - "category": 4 - }, - { - "question": "What type of beer does John not like?", - "answer": "dark beer", - "evidence": [ - "D21:16" - ], - "category": 4 - }, - { - "question": "What were some difficulties James faced during the development of his game?", - "answer": "balancing mechanics and ensuring fairness", - "evidence": [ - "D22:7" - ], - "category": 4 - }, - { - "question": "What has John been teaching his siblings?", - "answer": "coding", - "evidence": [ - "D22:10" - ], - "category": 4 - }, - { - "question": "What kind of programs are John's siblings making?", - "answer": "basic games and stories", - "evidence": [ - "D22:12" - ], - "category": 4 - }, - { - "question": "Which company's headphones did John choose for gaming?", - "answer": "Sennheiser", - "evidence": [ - "D23:10" - ], - "category": 4 - }, - { - "question": "What did James and Samantha discover they both enjoy at McGee's bar?", - "answer": "Lager beer", - "evidence": [ - "D23:3" - ], - "category": 4 - }, - { - "question": "How much does James pay per cooking class?", - "answer": "$10", - "evidence": [ - "D23:15" - ], - "category": 4 - }, - { - "question": "What did James learn to make in the cooking class besides omelette and meringue?", - "answer": "Dough", - "evidence": [ - "D23:15" - ], - "category": 4 - }, - { - "question": "Why did James sign up for a cooking class?", - "answer": "He wanted to learn something new", - "evidence": [ - "D23:13" - ], - "category": 4 - }, - { - "question": "What did James prepare for the first time in the cooking class?", - "answer": "Omelette", - "evidence": [ - "D23:13" - ], - "category": 4 - }, - { - "question": "What is the name of the board game John tried in September 2022?", - "answer": "Dungeons of the Dragon", - "evidence": [ - "D24:3" - ], - "category": 4 - }, - { - "question": "Where does James get his ideas from?", - "answer": "books, movies, dreams", - "evidence": [ - "D24:4" - ], - "category": 4 - }, - { - "question": "What kind of dream did James have recently?", - "answer": "a dream with a medieval castle full of puzzles and traps", - "evidence": [ - "D24:8" - ], - "category": 4 - }, - { - "question": "What kind of music does John like?", - "answer": "electronic and rock music", - "evidence": [ - "D24:13" - ], - "category": 4 - }, - { - "question": "What instrument did James used to play when he was younger?", - "answer": "guitar", - "evidence": [ - "D24:14" - ], - "category": 4 - }, - { - "question": "What did John use to play when he was younger to let off steam?", - "answer": "drums", - "evidence": [ - "D24:15" - ], - "category": 4 - }, - { - "question": "What career milestone did John achieve recently in September 2022?", - "answer": "making his first mobile game", - "evidence": [ - "D25:7" - ], - "category": 4 - }, - { - "question": "What type of game is John's upcoming mobile game?", - "answer": "2D adventure", - "evidence": [ - "D25:9" - ], - "category": 4 - }, - { - "question": "What does John do to stay informed and constantly learn about game design?", - "answer": "watch tutorials and keep up with developer forums", - "evidence": [ - "D25:13" - ], - "category": 4 - }, - { - "question": "What kind of gig was John offered at the game dev non-profit organization?", - "answer": "programming mentor for game developers", - "evidence": [ - "D26:3" - ], - "category": 4 - }, - { - "question": "What does John feel about starting the journey as a programming mentor for game developers?", - "answer": "excited and inspired", - "evidence": [ - "D26:5" - ], - "category": 4 - }, - { - "question": "What kind of games is James excited to play with his new video card?", - "answer": "RPGs", - "evidence": [ - "D26:10" - ], - "category": 4 - }, - { - "question": "What inspired James to create his game?", - "answer": "Witcher 3", - "evidence": [ - "D27:6" - ], - "category": 4 - }, - { - "question": "What sparked James' passion for gaming when he was a kid?", - "answer": "Super Mario and The Legend of Zelda games", - "evidence": [ - "D28:25" - ], - "category": 4 - }, - { - "question": "What did James lose progress on due to a power outage?", - "answer": "a game", - "evidence": [ - "D28:3" - ], - "category": 4 - }, - { - "question": "What games were played at the gaming tournament organized by John on 31 October, 2022?", - "answer": "Fortnite, Overwatch, Apex Legends", - "evidence": [ - "D29:1", - "D29:3" - ], - "category": 4 - }, - { - "question": "What was the purpose of the gaming tournament organized by John on 31 October, 2022?", - "answer": "To raise money for a children's hospital", - "evidence": [ - "D29:1", - "D29:3" - ], - "category": 4 - }, - { - "question": "What decision did James and Samantha make on 31 October, 2022?", - "answer": "To move in together", - "evidence": [ - "D29:8", - "D29:10" - ], - "category": 4 - }, - { - "question": "Where did James and Samantha decide to live together on 31 October, 2022?", - "answer": "In an apartment not far from McGee's bar", - "evidence": [ - "D29:10" - ], - "category": 4 - }, - { - "question": "Why did James and Samantha choose an apartment near McGee's bar?", - "answer": "They love spending time together at the bar", - "evidence": [ - "D29:12" - ], - "category": 4 - }, - { - "question": "What game is John hooked on playing on 5 November, 2022?", - "answer": "FIFA 23", - "evidence": [ - "D30:14" - ], - "category": 4 - }, - { - "question": "What did John suggest James practice before playing FIFA 23 together?", - "answer": "Control with a gamepad and timing", - "evidence": [ - "D30:18" - ], - "category": 4 - }, - { - "question": "What project did John work on with a game developer by 7 November, 2022?", - "answer": "An online board game", - "evidence": [ - "D31:4" - ], - "category": 4 - }, - { - "question": "What is the name of John's cousin's dog?", - "answer": "Luna", - "evidence": [ - "D31:22" - ], - "category": 4 - }, - { - "question": "What did John adopt in April 2022?", - "evidence": [ - "D5:1" - ], - "category": 5, - "adversarial_answer": "a pup" - }, - { - "question": "What is the name of the kitten that was adopted by James?", - "evidence": [ - "D5:1" - ], - "category": 5, - "adversarial_answer": "Ned" - }, - { - "question": "What inspired John to create the game character in the virtual world?", - "evidence": [ - "D6:6" - ], - "category": 5, - "adversarial_answer": "Appearance of a woman he saw during a walk" - }, - { - "question": "Which country did John visit in 2021?", - "evidence": [ - "D6:12" - ], - "category": 5, - "adversarial_answer": "Italy" - }, - { - "question": "What kind of assignment was giving James a hard time at work?", - "evidence": [ - "D7:13" - ], - "category": 5, - "adversarial_answer": "Coding assignment" - }, - { - "question": "What did James and his friends do with the remaining money after helping the dog shelter?", - "evidence": [ - "D10:12" - ], - "category": 5, - "adversarial_answer": "Bought groceries and cooked food for the homeless" - }, - { - "question": "What was the main goal of the money raised from the political campaign organized by John and his friends in May 2022?", - "evidence": [ - "D10:10" - ], - "category": 5, - "adversarial_answer": "Raise money for a dog shelter" - }, - { - "question": "What did the system John created help the illegal organization with?", - "evidence": [ - "D11:5" - ], - "category": 5, - "adversarial_answer": "tracking inventory, resources, and donations" - }, - { - "question": "What did James create for the charitable foundation that helped generate reports for analysis?", - "evidence": [ - "D11:3" - ], - "category": 5, - "adversarial_answer": "computer application on smartphones" - }, - { - "question": "Who does James support in cricket matches?", - "evidence": [ - "D13:12" - ], - "category": 5, - "adversarial_answer": "Liverpool" - }, - { - "question": "What is Max good at doing according to John?", - "evidence": [ - "D14:23" - ], - "category": 5, - "adversarial_answer": "catching frisbees in mid-air" - }, - { - "question": "Will there be a background check required to volunteer with the organization James volunteered for?", - "evidence": [ - "D15:15" - ], - "category": 5, - "adversarial_answer": "No" - }, - { - "question": "How did James relax in his free time on 9 July, 2022?", - "evidence": [ - "D16:8" - ], - "category": 5, - "adversarial_answer": "Reading" - }, - { - "question": "What new hobby did John become interested in on 9 July, 2022?", - "evidence": [ - "D16:5" - ], - "category": 5, - "adversarial_answer": "Extreme sports" - }, - { - "question": "When did John plan to return from his trip to Toronto and Vancouver?", - "evidence": [ - "D16:13" - ], - "category": 5, - "adversarial_answer": "July 20" - }, - { - "question": "What made James leave his IT job?", - "evidence": [ - "D18:3" - ], - "category": 5, - "adversarial_answer": "to focus on things that align with his values and passions" - }, - { - "question": "Which game tournaments does James plan to organize besides CS:GO?", - "evidence": [ - "D18:9" - ], - "category": 5, - "adversarial_answer": "Fortnite competitions" - }, - { - "question": "What happened to James's kitten during the recent visit to the clinic?", - "evidence": [ - "D18:16" - ], - "category": 5, - "adversarial_answer": "routine examination and vaccination" - }, - { - "question": "What aspect of \"The Witcher 3\" does John find boring?", - "evidence": [ - "D19:7" - ], - "category": 5, - "adversarial_answer": "shaping the world with choices" - }, - { - "question": "What is John planning to do after receiving Samantha's phone number?", - "evidence": [ - "D19:14" - ], - "category": 5, - "adversarial_answer": "call her" - }, - { - "question": "What has James been teaching his siblings?", - "evidence": [ - "D22:10" - ], - "category": 5, - "adversarial_answer": "coding" - }, - { - "question": "How much does James pay per dance class?", - "evidence": [ - "D23:15" - ], - "category": 5, - "adversarial_answer": "$10" - }, - { - "question": "What did James learn to make in the chemistry class besides omelette and meringue?", - "evidence": [ - "D23:15" - ], - "category": 5, - "adversarial_answer": "Dough" - }, - { - "question": "Why did James sign up for a ballet class?", - "evidence": [ - "D23:13" - ], - "category": 5, - "adversarial_answer": "He wanted to learn something new" - }, - { - "question": "What did John prepare for the first time in the cooking class?", - "evidence": [ - "D23:13" - ], - "category": 5, - "adversarial_answer": "Omelette" - }, - { - "question": "What is the name of the board game James tried in September 2022?", - "evidence": [ - "D24:3" - ], - "category": 5, - "adversarial_answer": "Dungeons of the Dragon" - }, - { - "question": "Where does John get his ideas from?", - "evidence": [ - "D24:4" - ], - "category": 5, - "adversarial_answer": "books, movies, dreams" - }, - { - "question": "What did James use to play when he was younger to let off steam?", - "evidence": [ - "D24:15" - ], - "category": 5, - "adversarial_answer": "drums" - }, - { - "question": "What does James do to stay informed and constantly learn about game design?", - "evidence": [ - "D25:13" - ], - "category": 5, - "adversarial_answer": "watch tutorials and keep up with developer forums" - }, - { - "question": "What kind of gig was James offered at the game dev non-profit organization?", - "evidence": [ - "D26:3" - ], - "category": 5, - "adversarial_answer": "programming mentor for game developers" - }, - { - "question": "What does James feel about starting the journey as a programming mentor for game developers?", - "evidence": [ - "D26:5" - ], - "category": 5, - "adversarial_answer": "excited and inspired" - }, - { - "question": "What inspired James to create his painting?", - "evidence": [ - "D27:6" - ], - "category": 5, - "adversarial_answer": "Witcher 3" - }, - { - "question": "What games were played at the gaming tournament organized by James on 31 October, 2022?", - "evidence": [ - "D29:1", - "D29:3" - ], - "category": 5, - "adversarial_answer": "Fortnite, Overwatch, Apex Legends" - }, - { - "question": "What was the purpose of the gaming tournament organized by James on 31 October, 2022?", - "evidence": [ - "D29:1", - "D29:3" - ], - "category": 5, - "adversarial_answer": "To raise money for a children's hospital" - }, - { - "question": "What decision did John and Samantha make on 31 October, 2022?", - "evidence": [ - "D29:8", - "D29:10" - ], - "category": 5, - "adversarial_answer": "To move in together" - }, - { - "question": "Where did John and Samantha decide to live together on 31 October, 2022?", - "evidence": [ - "D29:10" - ], - "category": 5, - "adversarial_answer": "In an apartment not far from McGee's bar" - }, - { - "question": "Why did John and Samantha choose an apartment near McGee's bar?", - "evidence": [ - "D29:12" - ], - "category": 5, - "adversarial_answer": "They love spending time together at the bar" - }, - { - "question": "What game is James hooked on playing on 5 November, 2022?", - "evidence": [ - "D30:14" - ], - "category": 5, - "adversarial_answer": "FIFA 23" - }, - { - "question": "What project did James work on with a game developer by 7 November, 2022?", - "evidence": [ - "D31:4" - ], - "category": 5, - "adversarial_answer": "An online board game" - }, - { - "question": "What is the name of James's cousin's dog?", - "evidence": [ - "D31:22" - ], - "category": 5, - "adversarial_answer": "Luna" - } - ], - "conversation": { - "speaker_a": "James", - "speaker_b": "John", - "session_1_date_time": "3:47 pm on 17 March, 2022", - "session_1": [ - { - "speaker": "John", - "dia_id": "D1:1", - "text": "Hey! Glad to finally talk to you. I want to ask you, what motivates you?" - }, - { - "speaker": "James", - "dia_id": "D1:2", - "text": "Hey John! Video games give me tons of joy and excitement, so they keep me motivated!" - }, - { - "speaker": "John", - "dia_id": "D1:3", - "text": "Cool, James! I'm a big video game fan too. They help me relax after a long day. What game are you currently enjoying the most?" - }, - { - "speaker": "James", - "dia_id": "D1:4", - "text": "I'm totally into The Witcher 3 right now. The story and atmosphere are amazing. Have you tried it yet?" - }, - { - "speaker": "John", - "dia_id": "D1:5", - "text": "Haven't played it yet, but I hear it's awesome. Gonna give it a go. BTW, just signed up for a programming class. Have you ever done any programming?" - }, - { - "speaker": "James", - "dia_id": "D1:6", - "text": "Programming is an awesome skill. I tried it out one in college and now it`s all my life. Good luck in the class! Do you have any coding experience?" - }, - { - "speaker": "John", - "dia_id": "D1:7", - "text": "I did a bit of coding in HTML and CSS a few years back. Thought I'd refresh those skills in this course. What languages do you like most and any projects you've done?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/gjudms7o1o881.jpg" - ], - "blip_caption": "a photo of a computer screen showing a video game scene", - "query": "game mod graphics enhancement", - "dia_id": "D1:8", - "text": "I've worked with Python and C++. I've built a website and also created some game mods. Here is one example." - }, - { - "speaker": "John", - "dia_id": "D1:9", - "text": "That mod looks amazing! The graphics are awesome. What other programming languages have you worked with?" - }, - { - "speaker": "James", - "dia_id": "D1:10", - "text": "I haven\u2019t worked with any other programming languages, but I hope to work in the future." - }, - { - "speaker": "John", - "dia_id": "D1:11", - "text": "Maybe in the future we will develop mobile applications together? Do you like the idea?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/gbmnyrqwbde91.jpg" - ], - "blip_caption": "a photo of two dogs are tied to a fence with a leash", - "query": "dog walking app dogs walk", - "dia_id": "D1:12", - "text": "It would be cool! For example, we could write some kind of application for dogs. By the way, my dogs." - }, - { - "speaker": "John", - "dia_id": "D1:13", - "text": "Aww, they're adorable! What are the names of your pets? And what are your plans for the app?" - }, - { - "speaker": "James", - "dia_id": "D1:14", - "text": "Max and Daisy. Will be actually cool to build an app for dog walking and pet care. The goal is to connect pet owners with reliable dog walkers and provide helpful information on pet care." - }, - { - "speaker": "John", - "dia_id": "D1:15", - "text": "Sounds good, James! Bet that app would find a lot of buyers. What sets it apart from other existing apps?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a notepad with a handwritten note on it", - "dia_id": "D1:16", - "text": "Thanks, John! The personal touch really sets it apart. Users can add their pup's preferences/needs - just like they were customizing it for them. Making it unique for each owner and pup." - }, - { - "speaker": "John", - "dia_id": "D1:17", - "text": "That's a great idea! Your pup is gonna love it. Speaking of personal touches, what motivates you to work on your programming projects?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/lsebjnbguz881.jpg" - ], - "blip_caption": "a photo of a person holding a notebook with a list of things on it", - "query": "notepad programming goals", - "dia_id": "D1:18", - "text": "Creating something and seeing it come to life gives me a great sense of accomplishment. It's an amazing feeling. I write down all my goals in a notebook. It's very satisfying to check off each one when it's done." - }, - { - "speaker": "John", - "dia_id": "D1:19", - "text": "What are you working on that has you feeling so accomplished?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/e48of6zqmmi71.jpg" - ], - "blip_caption": "a photo of a drawing of a girl in a plane", - "query": "indie game concept art", - "dia_id": "D1:20", - "text": "I'm working on something I've wanted to do since I was a kid. Even as a child, I made some sketches of the main character. Back then I was just drawing comics, but now I want to turn it into a computer game. It's a project that has me really excited." - }, - { - "speaker": "John", - "dia_id": "D1:21", - "text": "Wow, James! That's amazing. What made you decide to work on it and create your own game?" - }, - { - "speaker": "James", - "dia_id": "D1:22", - "text": "I'm always excited to combine my favorite passions: gaming and storytelling. It's great creating my own project and bringing my ideas to life, plus the challenge is really enjoyable!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a medical chart with instructions for the procedure", - "dia_id": "D1:23", - "text": "That sounds really fulfilling! Combining your passions to make something new must be so exciting. Can't wait to see the outcome." - }, - { - "speaker": "James", - "dia_id": "D1:24", - "text": "Thanks John! It's super exciting. I'll keep you updated on the progress. Perhaps, thanks to your knowledge of HTML, I'll invite you to help with some things in my game." - }, - { - "speaker": "John", - "dia_id": "D1:25", - "text": "It will be great to work with you, James." - }, - { - "speaker": "James", - "dia_id": "D1:26", - "text": "I'll be looking forward to it. By the way, yesterday I went bowling and got 2 strikes. I love bowling!" - }, - { - "speaker": "John", - "dia_id": "D1:27", - "text": "I'm sure you're very good at this. Unfortunately, I can\u2019t share my love for him with you, my fingers are too big. Perhaps I should take up exercise, at least start going for a run in the morning. And I also don\u2019t like bowling itself, to be honest." - }, - { - "speaker": "James", - "dia_id": "D1:28", - "text": "It's a pity, it would be nice to go play with you one day." - }, - { - "speaker": "John", - "dia_id": "D1:29", - "text": "Well, I'm sure we can do something else. We can play slot machines and arcades, for example." - }, - { - "speaker": "James", - "dia_id": "D1:30", - "text": "The last time I played at the slot machines, I was so engrossed in the game that I didn't notice my wallet being taken out of my pocket. Sad story." - }, - { - "speaker": "John", - "dia_id": "D1:31", - "text": "I'm sorry to hear it. Well, I'll be nearby, I'll look after your pockets." - }, - { - "speaker": "James", - "dia_id": "D1:32", - "text": "Still, maybe we can try something different?" - }, - { - "speaker": "John", - "dia_id": "D1:33", - "text": "Heard about VR gaming? It's pretty immersive. We can try it together!" - }, - { - "speaker": "James", - "dia_id": "D1:34", - "text": "I tried it - it's crazy how real it feels! Have you given it a shot?" - }, - { - "speaker": "John", - "dia_id": "D1:35", - "text": "Tried it a few times, it's insane how immersive that experience can be. Can't wait to try it together with you." - }, - { - "speaker": "James", - "dia_id": "D1:36", - "text": "Yeah, VR gaming is awesome! Let`s do it next Saturday!" - }, - { - "speaker": "John", - "dia_id": "D1:37", - "text": "Agreed, James!" - } - ], - "session_2_date_time": "9:26 pm on 20 March, 2022", - "session_2": [ - { - "speaker": "James", - "dia_id": "D2:1", - "text": "Hey John, something awesome happened since we talked. I made a game avatar and joined a new platform. It's so fun exploring and chatting with other gamers - it's a whole new adventure every time! I feel like I'm part of a super cool online community." - }, - { - "speaker": "John", - "dia_id": "D2:2", - "text": "Hey James, awesome! Glad you're enjoying it and connecting with others. Building a community is really cool, especially when you meet people who enjoy the same things." - }, - { - "speaker": "James", - "dia_id": "D2:3", - "text": "Thanks, John! Connecting with other gamers has been great! We've shared tips, strategies, and stories about gaming. It's amazing how it brings people together, regardless of their backgrounds." - }, - { - "speaker": "John", - "dia_id": "D2:4", - "text": "That's incredible! It's so cool how gaming can bring people together and create a strong bond, regardless of their background." - }, - { - "speaker": "James", - "dia_id": "D2:5", - "text": "Yeah, it's our shared language and passion. It's been a refuge for me in tough times." - }, - { - "speaker": "John", - "dia_id": "D2:6", - "text": "Yeah, gaming always helps me escape stress. It's amazing how it calms me down during tough times." - }, - { - "speaker": "James", - "dia_id": "D2:7", - "text": "Games are my go-to when I'm feeling overwhelmed. It's like therapy. I can relax, forget my troubles, and get lost in another world." - }, - { - "speaker": "John", - "dia_id": "D2:8", - "text": "Gotcha. Gaming can be a great way to take a break and escape for a while. Anything new you've been into lately?" - }, - { - "speaker": "James", - "dia_id": "D2:9", - "text": "Lately, I've been checking out different styles of it. It's been fun to try something fresh and test myself in other ways. What about you, John? Any new hobbies recently?" - }, - { - "speaker": "John", - "dia_id": "D2:10", - "text": "I've been getting into a new hobby recently. I bought a metal detector and walk along the beaches looking for something worthwhile." - }, - { - "speaker": "James", - "dia_id": "D2:11", - "text": "Interesting, John! Sounds like an awesome immersive experience. Already found something interesting?" - }, - { - "speaker": "John", - "dia_id": "D2:12", - "text": "Mostly just bottle caps, but a couple of times I found coins, and once even a gold ring." - }, - { - "speaker": "James", - "dia_id": "D2:13", - "text": "Cool, I wish you good luck in this matter! By the way, I've got something to show you." - }, - { - "speaker": "John", - "dia_id": "D2:14", - "text": "Show me what you've got! What is it?" - }, - { - "speaker": "James", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2014,w_3017,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/shutterstock_485806801_mv1xuv.jpg" - ], - "blip_caption": "a photo of two dogs running in a field with a ball in their mouth", - "query": "dogs playing park", - "dia_id": "D2:15", - "text": "Check out this pic of my best buds having a blast in the park. They've brought so much joy to my life. My two dogs are the best pals ever, right?" - }, - { - "speaker": "John", - "dia_id": "D2:16", - "text": "They look like they're having a blast! Can they do any tricks?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/bd7160b2t6aa1.jpg" - ], - "blip_caption": "a photo of a dog laying on a bed with a name tag", - "query": "golden retrievers dogs sitting patiently treats", - "dia_id": "D2:17", - "text": "They can do tricks like sit, stay, paw, and rollover. Here's a picture of Daisy waiting for a treat. I've done lots of training and they've picked it up fast. They're like my family." - }, - { - "speaker": "John", - "dia_id": "D2:18", - "text": "Aww, they're adorable! Pets are the best - they must make life so much better. I want one so bad, but I'm not there yet. Someday!" - }, - { - "speaker": "James", - "dia_id": "D2:19", - "text": "A pet would truly be great for you! They bring so much love and companionship. If you're interested, I can help find the perfect one for you - you'd make a great pet parent!" - }, - { - "speaker": "John", - "dia_id": "D2:20", - "text": "Cheers, James! Yeah, I'll keep that in mind. Appreciate the offer." - }, - { - "speaker": "James", - "dia_id": "D2:21", - "text": "No problem, John! Let me know whenever you need assistance. Take care!" - } - ], - "session_3_date_time": "12:40 am on 27 March, 2022", - "session_3": [ - { - "speaker": "John", - "dia_id": "D3:1", - "text": "Hey James, long time no see! I had a big win in my game last week - finally advanced to the next level! It was a huge confidence booster and felt like I'd really achieved something." - }, - { - "speaker": "James", - "img_url": [ - "https://live.staticflickr.com/3286/3130876770_af17e12d68_z.jpg" - ], - "blip_caption": "a photography of a drum kit with a white drum and a black drum", - "query": "drum set drumming progress", - "dia_id": "D3:2", - "re-download": true, - "text": "Hey John, congrats on your win! Games can really boost confidence, huh? I'm challenging myself too - I'm learning this instrument, which has been quite the journey.\n" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/ky9jybgp0bi91.jpg" - ], - "blip_caption": "a photo of a drum kit sitting on top of a table", - "query": "drum set progress", - "dia_id": "D3:3", - "text": "Thanks, James! I play drums too! Here's a pic of my set." - }, - { - "speaker": "James", - "dia_id": "D3:4", - "text": "Wow, looking good! How long have you been playing?" - }, - { - "speaker": "John", - "dia_id": "D3:5", - "text": "I've been playing for a month now, it's been tough but fun. How about you, how's it going?" - }, - { - "speaker": "James", - "dia_id": "D3:6", - "text": "This is going great! I started a few days ago, so I'm still picking it up. Been at it daily and seeing improvements. It's tough but rewarding at the same time!" - }, - { - "speaker": "John", - "dia_id": "D3:7", - "text": "Nice work! Looks like you're doing great. Anything new in general that you'd recommend?" - }, - { - "speaker": "James", - "dia_id": "D3:8", - "text": "Thanks! I just got a new cutting-edge gaming system and the graphics are incredible. I've been playing all kinds of new games and it's been a great way to relax after work. Plus, I can connect with friends who share my passion for gaming." - }, - { - "speaker": "John", - "img_url": [ - "https://live.staticflickr.com/664/22051849804_cff4f4e532_b.jpg" - ], - "blip_caption": "a photography of a crowded convention hall with a large crowd of people", - "query": "gaming convention crowded hall banners games", - "dia_id": "D3:9", - "re-download": true, - "text": "Cool, James! Gaming is great for chilling out. Btw, since we last spoke, I had the chance to go to a gaming convention - it was amazing! Tried out loads of games, met developers, and even took part in a tournament - unreal! Check out this pic I took!" - }, - { - "speaker": "James", - "dia_id": "D3:10", - "text": "Wow, that's awesome! What game was it for? Sounds like a dream!" - }, - { - "speaker": "John", - "dia_id": "D3:11", - "text": "I played my favorite CS:GO game in an intense tournament. It was awesome to see all the skilled players competing." - }, - { - "speaker": "James", - "dia_id": "D3:12", - "text": "Wow, that sounds cool! Gaming is awesome with all the competition. Must have been thrilling to watch those skilled players!" - }, - { - "speaker": "John", - "dia_id": "D3:13", - "text": "It was indeed amazing! Watching those skilled players really inspired me to improve my own gaming skills." - }, - { - "speaker": "James", - "dia_id": "D3:14", - "text": "Nice one, John! Learning from experienced gamers can really help you level up your skills. Keep it up!" - }, - { - "speaker": "John", - "dia_id": "D3:15", - "text": "I'm always looking to up my game and hit new goals. That same commitment is true for my hobbies and other stuff. What have you been doing to stay motivated?" - }, - { - "speaker": "James", - "dia_id": "D3:16", - "text": "Setting small goals and tracking my progress helps me stay motivated and focused." - }, - { - "speaker": "John", - "dia_id": "D3:17", - "text": "Nice one! Setting small goals and tracking progress is a great way to stay motivated - it helps you stay on track and celebrates progress. Anything specific you're working on or upcoming challenges you're pumped about?" - }, - { - "speaker": "James", - "dia_id": "D3:18", - "text": "I'm getting into different types of games now, like RPGs and strategy games. It's really exciting!" - }, - { - "speaker": "John", - "dia_id": "D3:19", - "text": "Cool, James! That sounds exciting. Have fun exploring different genres of games!" - }, - { - "speaker": "James", - "dia_id": "D3:20", - "text": " I'm super hyped to explore different game genres. Let's see what's in store!" - }, - { - "speaker": "John", - "dia_id": "D3:21", - "text": "Definitely! Trying new genres is always exciting. I can't wait to hear about your journey with them. Please let me know how it goes!" - }, - { - "speaker": "James", - "dia_id": "D3:22", - "text": "Got it, John! I'll keep you updated on my gaming adventures with the new genres. Have a good day!" - }, - { - "speaker": "John", - "dia_id": "D3:23", - "text": "Thanks! Can't wait to hear about it. Bye!" - } - ], - "session_4_date_time": "2:13 pm on 4 April, 2022", - "session_4": [ - { - "speaker": "John", - "dia_id": "D4:1", - "text": "Hey James! Long time no chat. What's up? Been playing any new games lately?" - }, - { - "speaker": "James", - "img_url": [ - "https://cool4dads.com/wp-content/uploads/2019/01/IMG_9117.jpg" - ], - "blip_caption": "a photo of a group of people posing for a picture", - "query": "online gaming tournament fun", - "dia_id": "D4:2", - "text": "Hey John! Yeah, it's been a while. I've been busy, but I joined an online gaming tournament yesterday. It was so intense and fun! Here is a photo report." - }, - { - "speaker": "John", - "dia_id": "D4:3", - "text": "That online gaming tournament looks awesome! Glad you had a blast. How did it go for you?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/zk7j8z2ik3l71.jpg" - ], - "blip_caption": "a photo of a man in a costume standing in front of a sign", - "query": "victory screen sweet victory", - "dia_id": "D4:4", - "text": "It was so much fun! I did pretty well in the tournament; I made it to the semis and won some rounds. It was such a rush! Here's a screenshot of my character." - }, - { - "speaker": "John", - "dia_id": "D4:5", - "text": "Wow, awesome! Congrats on your performance and making it to the semifinals. How did the final rounds turn out?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a man in a costume holding a sword", - "dia_id": "D4:6", - "text": "Thanks John! The final rounds were tough. I tried my best but didn't make it. It was close, though, and I had a blast competing with talented players. Looking forward to the next tournament!" - }, - { - "speaker": "John", - "dia_id": "D4:7", - "text": "Met any famous player there?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a man sitting in a chair playing a video game", - "dia_id": "D4:8", - "text": "I met the whole team! It\u2019s a pity I didn\u2019t get a chance to take a photo with them, but one of them even gave me a couple of gaming tips." - }, - { - "speaker": "John", - "dia_id": "D4:9", - "text": "Cool! I'm sure his advice will help you develop in the game." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/shafojm1o4471.jpg" - ], - "blip_caption": "a photo of a notepad with a pen and a glass of water", - "query": "notepad game strategies", - "dia_id": "D4:10", - "text": "Yes, I'm sure of that too. Also, the whole team gave me autographs. I was very happy about this!" - }, - { - "speaker": "John", - "dia_id": "D4:11", - "text": "How cool is this! What advice do you remember most?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a group of people standing around a table", - "dia_id": "D4:12", - "text": "The most important thing I remember is that you always need to communicate correctly with the team and never put your ego above team success." - }, - { - "speaker": "John", - "dia_id": "D4:13", - "text": "Yeah, comms and teamwork are super important in gaming. When everyone works together, it's incredible what can be accomplished in a match. How do you usually communicate with your team?" - }, - { - "speaker": "James", - "dia_id": "D4:14", - "text": "I usually use voice chat to communicate with my team. It's fast and helps us work together effectively." - }, - { - "speaker": "John", - "dia_id": "D4:15", - "text": "Sounds like a good plan. It really helps with communication. What game do you like playing with your team?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/4zewk2444ag71.jpg" - ], - "blip_caption": "a photo of a video game screen showing a robot and a robot", - "query": "apex legends team playing action", - "dia_id": "D4:16", - "text": "I've been playing my favourite game called Apex Legends with my team and it's intense! Check out this screenshot of us playing!" - }, - { - "speaker": "John", - "dia_id": "D4:17", - "text": "Man, Apex Legends looks tough! The graphics are unreal. How does it stack up against other games?" - }, - { - "speaker": "James", - "dia_id": "D4:18", - "text": "Apex Legends has awesome graphics and super fast-paced gameplay. It definitely stands out among other games." - }, - { - "speaker": "John", - "dia_id": "D4:19", - "text": "Hmm, the speed of it definitely makes it fun! Are there any new games that you're looking forward to trying out?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/bwoh4be8d3m41.jpg" - ], - "blip_caption": "a photo of a video game cover of a video game", - "query": "skyrim league of legends game cover", - "dia_id": "D4:20", - "text": "Yeah, I'm always excited to try new games. Thinking of trying RPGs like that or MOBAs. Sounds cool!" - }, - { - "speaker": "John", - "dia_id": "D4:21", - "text": "RPGs and MOBAs can be awesome to experience an engaging story or have epic multiplayer fights. Let me know how you like them!" - }, - { - "speaker": "James", - "dia_id": "D4:22", - "text": "Sure thing, John! Can't wait to try out some new genres. I'll definitely let you know my thoughts once I give them a try." - }, - { - "speaker": "John", - "dia_id": "D4:23", - "text": "Love hearing about it. Let's chat soon!" - }, - { - "speaker": "James", - "dia_id": "D4:24", - "text": "Sure John, I'll keep you updated on all the new games. Talk to you soon! Bye for now!" - }, - { - "speaker": "John", - "dia_id": "D4:25", - "text": "Let me know how it goes. Stay safe. Talk to you soon. Bye!" - } - ], - "session_5_date_time": "9:52 am on 12 April, 2022", - "session_5": [ - { - "speaker": "James", - "img_url": [ - "https://cdn.shopify.com/s/files/1/0624/9512/9787/files/b7d0234c78c382f9d9fc42142115a987.jpg" - ], - "blip_caption": "a photo of a dog and a cat sitting on a dog bed", - "query": "puppy dog bed", - "dia_id": "D5:1", - "text": "Hey John! Long time no chat - I adopted a pup from a shelter in Stamford last week and my days have been so much happier with him in the fam. I named it Ned. Any progress on your gaming goals?" - }, - { - "speaker": "John", - "dia_id": "D5:2", - "text": "Hey James! Congrats on getting a pup! They really do make days brighter. I haven't made much progress with gaming lately, life's been busy with work and stuff but it's always nice to remember how happy gaming makes me. It's a good way to forget the stresses of life." - }, - { - "speaker": "James", - "dia_id": "D5:3", - "text": "Thanks, John! Gaming really does help forget about the stresses of life. It's like heading into another world! Have you played any interesting games lately?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/941ph75y0yb91.jpg" - ], - "blip_caption": "a photo of a city at night with a view of the city", - "query": "cyberpunk 2077 cityscape", - "dia_id": "D5:4", - "text": "I'm playing this new RPG that has a really cool story and world. It's kinda like getting transported to a futuristic dystopia." - }, - { - "speaker": "James", - "dia_id": "D5:5", - "text": "Sounds great! Will I like it? I'm always up for trying new games." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/xu1i4gjzg6jb1.jpg" - ], - "blip_caption": "a photo of a computer screen with a game on it", - "query": "game menu", - "dia_id": "D5:6", - "text": "You'll love it! It has a fun story and an awesome world. Plus, it has some tough gameplay. However, the game is a little laggy and sometimes produces errors. Here is one of them that I came across yesterday." - }, - { - "speaker": "James", - "dia_id": "D5:7", - "text": "The game must still be raw. I'd rather wait until the bugs are completely fixed, and then I'll try it out." - }, - { - "speaker": "John", - "dia_id": "D5:8", - "text": "Even with minor bugs, the game still looks great. The graphics are awesome and the gameplay is super immersive. I'm sure you should try it out now. I can send you a link." - }, - { - "speaker": "James", - "dia_id": "D5:9", - "text": "Okay, you convinced me. Well, I'll wait for the link then, thanks!" - }, - { - "speaker": "John", - "dia_id": "D5:10", - "text": "Cool, I'll send it to you. Let me know what you think after you check it out!" - }, - { - "speaker": "James", - "dia_id": "D5:11", - "text": "Cool, John! I'll check it out and let you know what I think. I'm excited to give it a try." - }, - { - "speaker": "John", - "dia_id": "D5:12", - "text": "Thanks, James! Excited to hear your thoughts. Have a great time!" - }, - { - "speaker": "James", - "dia_id": "D5:13", - "text": "Cheers! Will do. Enjoy the gaming! Bye!" - }, - { - "speaker": "John", - "dia_id": "D5:14", - "text": "Bye for now!" - }, - { - "speaker": "James", - "dia_id": "D5:15", - "text": "See ya! Take care!" - }, - { - "speaker": "John", - "dia_id": "D5:16", - "text": "Take care!" - } - ], - "session_6_date_time": "9:32 pm on 20 April, 2022", - "session_6": [ - { - "speaker": "John", - "dia_id": "D6:1", - "text": "Hey James! Long time no see! I have great news! Last Tuesday I met three cool new friends in my programming course, they share the same passion as me and it's cool to grow my social circle. Have you had any fun surprises lately?" - }, - { - "speaker": "James", - "dia_id": "D6:2", - "text": "Hey John! Glad you had a great week meeting new people! Something awesome happened to me last Thursday \u2013 I got to work with one of my gaming pals on a programming project! We combined programming and gaming, and created this virtual world inspired by Witcher 3. It was awesome to see our ideas come to life!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a video game screen with a man on it", - "dia_id": "D6:3", - "text": "It must've felt great to put your skills to work on that project! Do you have any screenshots of the world you made? It must've been so awesome to see it all come together!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/w5v13bfv8r861.jpg" - ], - "blip_caption": "a photo of a woman dressed in armor kneeling on a rock", - "query": "witcher inspired virtual world screenshot", - "dia_id": "D6:4", - "text": "It was quite the experience. Unfortunately, I don't have a screenshot of the full virtual world, but I do have a screenshot of the game character I created. It was a lot of work but so rewarding when it all came together. Super satisfying!" - }, - { - "speaker": "John", - "dia_id": "D6:5", - "text": "Wow, James! This is amazing. I can really feel the atmosphere here. Did you get the inspiration for this from something?" - }, - { - "speaker": "James", - "dia_id": "D6:6", - "text": "Thanks, I'm glad you can feel the atmosphere. I got the idea from a walk with my dogs two weeks ago. We were walking around our neighborhood, and a stranger was walking towards us. I had never seen her nearby before. Her eyes and appearance amazed me so much, it seemed to me that I fell in love at first sight. It\u2019s a pity that I didn\u2019t approach her to get to know her, but at least I remembered her appearance and embodied it in the game." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/emrf986oa7bb1.jpg" - ], - "blip_caption": "a photo of a river running through a canyon surrounded by mountains", - "query": "canyon hike view", - "dia_id": "D6:7", - "text": "That's awesome. Real-life experiences can be so inspiring. It's like the virtual world is connected to the real world. By the way, two days ago I wanted to be alone with nature. This is the canyon I found in the surrounding area. Very calming view." - }, - { - "speaker": "James", - "dia_id": "D6:8", - "text": "That's so cool you had a similar experience. I bet you felt inspired seeing it in person." - }, - { - "speaker": "John", - "dia_id": "D6:9", - "text": "Capturing that view was amazing. It was like connecting the real and the imaginary. It really sparked my creativity and motivation." - }, - { - "speaker": "James", - "dia_id": "D6:10", - "text": "Cool! What else gives you motivation?" - }, - { - "speaker": "John", - "dia_id": "D6:11", - "text": "I adhere to the principle that only those who rest well work well. Therefore, chilling with friends and traveling always give me motivation to work further." - }, - { - "speaker": "James", - "dia_id": "D6:12", - "text": "I agree with you, I also love to travel. Last year I visited Italy, for example. A very beautiful country with delicious food." - }, - { - "speaker": "John", - "dia_id": "D6:13", - "text": "Oh, Italy! I always dreamed of visiting there. What other countries have you been to?" - }, - { - "speaker": "James", - "dia_id": "D6:14", - "text": "In fact, I haven't visited many countries. Besides Italy, I was also in Turkey and Mexico. What was the last country you visited?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/l5afku2e4k681.jpg" - ], - "blip_caption": "a photo of a busy city street at night with people walking", - "query": "cyberpunk 2077 futuristic cityscape game", - "dia_id": "D6:15", - "text": "This was Japan. The megacities of this country impress me, everything there is so technologically advanced, the huge screens on the buildings are mesmerizing. And, of course, very tasty street food." - }, - { - "speaker": "James", - "dia_id": "D6:16", - "text": "It would be cool to go somewhere together next year, don't you think?" - }, - { - "speaker": "John", - "dia_id": "D6:17", - "text": "Of course, I hope everything works out for us, I will believe in it!" - }, - { - "speaker": "James", - "dia_id": "D6:18", - "text": "Great, then I'll start looking for a country where we can go!" - }, - { - "speaker": "John", - "dia_id": "D6:19", - "text": "Keep me posted, James! Let me know if you need help." - } - ], - "session_7_date_time": "11:04 am on 23 April, 2022", - "session_7": [ - { - "speaker": "John", - "dia_id": "D7:1", - "text": "Hey James! How's it going?" - }, - { - "speaker": "James", - "dia_id": "D7:2", - "text": "Hey John! Good to hear from ya. Yeah, been crazy. Last Thursday I took my dogs out for a hike. Was quite the adventure! Explored some nice trails and enjoyed fresh air." - }, - { - "speaker": "John", - "dia_id": "D7:3", - "text": "Wow, sounds like quite an adventure! Do you have any pictures from that day?" - }, - { - "speaker": "James", - "img_url": [ - "https://i0.wp.com/mikicc.org/wp-content/uploads/2020/11/20201024_1014341921784989571737117.jpg" - ], - "blip_caption": "a photo of a man walking two dogs on a path in the woods", - "query": "dogs trail woods breathtaking views", - "dia_id": "D7:4", - "text": "Yeah, I have one. It was great! They loved it - so many trails to discover and amazing views. So fun!" - }, - { - "speaker": "John", - "dia_id": "D7:5", - "text": "Wow, that looks like a cool place you took them! Why'd you pick that spot?" - }, - { - "speaker": "James", - "dia_id": "D7:6", - "text": "I picked that spot because it had great views and lots of trails. It was perfect for them to explore nature and have fun. Plus, there's nothing like being surrounded by lush greenery and clean air." - }, - { - "speaker": "John", - "dia_id": "D7:7", - "text": "Agreed! It is a great way to escape the everyday. Wish I could spend more time in nature!" - }, - { - "speaker": "James", - "dia_id": "D7:8", - "text": "Love hearing the crunch of leaves under my feet and the peacefulness. It helps me clear my head and chill." - }, - { - "speaker": "John", - "dia_id": "D7:9", - "text": "I need some chill vibes too because it's been crazy for me lately." - }, - { - "speaker": "James", - "dia_id": "D7:10", - "text": "What's been going on? Is there anything you want to talk about? I'm here for you." - }, - { - "speaker": "John", - "dia_id": "D7:11", - "text": "Thanks, James. Yeah, been super busy at work. Deadlines all over the place and so much to do. It's just been really hectic lately." - }, - { - "speaker": "James", - "dia_id": "D7:12", - "text": "Yeah, work can be tough. Is there something specific that's making it worse?" - }, - { - "speaker": "John", - "dia_id": "D7:13", - "text": "Ugh, this project is giving me a hard time. It's a difficult assignment that involves coding and I'm stuck on it. It's frustrating because I hate being stuck and not making progress." - }, - { - "speaker": "James", - "dia_id": "D7:14", - "text": "Yeah, it's really frustrating when a project doesn't go as planned. What specifically is giving you trouble? Maybe I can offer some assistance." - }, - { - "speaker": "John", - "dia_id": "D7:15", - "text": "This coding is really tough. I'm working on something complicated and it's not going well. I've been staring at it for ages and I feel like I'm getting nowhere." - }, - { - "speaker": "James", - "dia_id": "D7:16", - "text": "Gotcha, what's the problem with the project? Need a hand figuring out some ideas?" - }, - { - "speaker": "John", - "dia_id": "D7:17", - "text": "Yeah, that'd be great. I'm trying to make this new algorithm work better, but I'm stuck. Do you have any ideas?" - }, - { - "speaker": "James", - "dia_id": "D7:18", - "text": "Not sure about your algorithm, but breaking it down into smaller steps might help. Doing some research on similar algorithms or asking other programmers for advice could be beneficial. Don't be afraid to seek help and remember, every problem has a solution." - }, - { - "speaker": "John", - "dia_id": "D7:19", - "text": "Yep, breaking it down into smaller steps and asking for help can definitely be helpful. I'll give it a try. Thanks for the advice, James. Much appreciated." - }, - { - "speaker": "James", - "dia_id": "D7:20", - "text": "No problem John. Glad to help. You got this. Keep going and you'll find the answer." - }, - { - "speaker": "John", - "blip_caption": "a photo of a notebook with a quote written on it", - "dia_id": "D7:21", - "text": " Your words really helped. I won't quit. Gonna keep going. Cheers! \n" - } - ], - "session_8_date_time": "2:36 pm on 29 April, 2022", - "session_8": [ - { - "speaker": "James", - "dia_id": "D8:1", - "text": "Hey John! What's up? Anything fun going on?" - }, - { - "speaker": "John", - "dia_id": "D8:2", - "text": " I'm currently taking on some freelance programming to hone my coding skills. It's challenging, but I'm determined to improve." - }, - { - "speaker": "James", - "dia_id": "D8:3", - "text": "Freelancing can definitely be a great way to sharpen skills and gain experience. What projects are you currently working on?" - }, - { - "speaker": "John", - "dia_id": "D8:4", - "text": "I'm actually working on a website for a local small business. It's my first professional project outside of class." - }, - { - "speaker": "James", - "dia_id": "D8:5", - "text": "Congrats on your first professional project, John! Bet it's been great applying what you learned in class. How's the progress been?" - }, - { - "speaker": "John", - "dia_id": "D8:6", - "text": "Thanks, James! I've learned a lot and it's been an interesting journey so far. Progress is slow and there have been some hiccups along the way." - }, - { - "speaker": "James", - "dia_id": "D8:7", - "text": "Yeah, nothing ever goes smooth. It's normal to have hiccups, but use them to learn and grow. Push through it and you'll make it!" - }, - { - "speaker": "John", - "dia_id": "D8:8", - "text": "You're right, I appreciate the boost. It's tough sometimes but I'm gonna keep pushing and make this work. Hiccups won't stop me." - }, - { - "speaker": "James", - "dia_id": "D8:9", - "text": "What challenges have you encountered?" - }, - { - "speaker": "John", - "dia_id": "D8:10", - "text": "Figuring out how to get payments on the website was tough. I needed some help so I used some resources to understand the process. It's taken a while, but I'm getting closer to a solution." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/2xbwn05lpc761.jpg" - ], - "blip_caption": "a photo of a person holding a book open to a picture of a male character", - "query": "witcher 3 artwork stunning", - "dia_id": "D8:11", - "text": "That sounds challenging, but you're making progress. Hang in there! By the way, three days ago I bought myself an adventure book with fantasy novels and cool arts." - }, - { - "speaker": "John", - "dia_id": "D8:12", - "text": "Wow, that art's awesome! It takes me back to reading fantasy books." - }, - { - "speaker": "James", - "dia_id": "D8:13", - "text": "Yeah, I love this genre. Got any suggestions?" - }, - { - "speaker": "John", - "dia_id": "D8:14", - "text": "Cool! Heard of \"The Name of the Wind\"? It's another great novel with awesome writing." - }, - { - "speaker": "James", - "blip_caption": "a photo of a book set of three books on a wooden table", - "dia_id": "D8:15", - "text": "Never heard of it, but it sounds interesting. I'll definitely check it out. Thanks for the recommendation, John!" - }, - { - "speaker": "John", - "dia_id": "D8:16", - "text": "Always happy to help. I'm sure you'll love this trilogy!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/qjblw3fb7hnb1.jpg" - ], - "blip_caption": "a photo of a dog laying on a bed with a computer in the background", - "query": "gaming setup dogs furry gaming buddies", - "dia_id": "D8:17", - "text": "Look, I was playing a game and my faithful furry friend Daisy came and lay down next to me. This is so cute!" - }, - { - "speaker": "John", - "dia_id": "D8:18", - "text": "Awww, this is really so cute! Your furry friend looks so cozy. Do your dogs often come to you like this while playing?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a person holding a blue controller in their hand", - "dia_id": "D8:19", - "text": "Yeah, they love to watch me gaming and often hug me. Such good cuddle buddies! What game have you been playing lately?" - }, - { - "speaker": "John", - "dia_id": "D8:20", - "text": "Awesome that you have them! I'm currently playing AC Valhalla, it's cool. Are you playing anything new?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/c15urew02q041.jpg" - ], - "blip_caption": "a photo of a map of the world on a tv screen", - "query": "civilization vi strategy game", - "dia_id": "D8:21", - "text": "Thanks, John! Valhalla is awesome. I'm trying out some strategy games like this. It's different but so cool!" - }, - { - "speaker": "John", - "dia_id": "D8:22", - "text": "Is that Civilization VI? Heard good things about it. How's it?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/nbyl9ym1dpj41.jpg" - ], - "blip_caption": "a photo of a computer screen showing a game of war", - "query": "turn-based strategy game screenshot", - "dia_id": "D8:23", - "text": "This is a high-quality turn-based strategy game where you manage resources, lead armies, and conquer territories - challenging and cool!" - }, - { - "speaker": "John", - "dia_id": "D8:24", - "text": "That sounds fun! What's the game like? Does it require a lot of strategy?" - }, - { - "speaker": "James", - "dia_id": "D8:25", - "text": "Sure, John! It requires a lot of strategy. It's all about planning, managing resources and making good decisions to beat your rivals. Every move matters!" - }, - { - "speaker": "John", - "dia_id": "D8:26", - "text": "Sounds intense but cool. I like games that test my strategizing. Does it help with your problem-solving?" - }, - { - "speaker": "James", - "dia_id": "D8:27", - "text": "Yeah, it's a great way to work on problem-solving and thinking. Plus, it's awesome to see your plans go the way you wanted and win!" - }, - { - "speaker": "John", - "dia_id": "D8:28", - "text": "Yeah! It's really satisfying when your plans work out and you win. How long have you been playing this game?" - }, - { - "speaker": "James", - "dia_id": "D8:29", - "text": "Been playing it for a month now - it's really challenged my strategy skills." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/m0vu1etdlol91.jpg" - ], - "blip_caption": "a photo of a board game with a lot of cards on it", - "query": "strategy board game", - "dia_id": "D8:30", - "text": "Wow, that's impressive! I'm really enjoying games like this, they really make me think. What do you think of strategy board games? I played one with friends two days ago, it's very exciting!" - }, - { - "speaker": "James", - "dia_id": "D8:31", - "text": "Sounds good! Board games are always a blast when you hang out with friends." - }, - { - "speaker": "John", - "dia_id": "D8:32", - "text": "Yeah! They're great for having fun together." - }, - { - "speaker": "James", - "dia_id": "D8:33", - "text": "Anything else that is fun to play with others?" - }, - { - "speaker": "John", - "dia_id": "D8:34", - "text": "Yes, we played one game, but I forgot its name. Perhaps you know this game. There were multi-colored cards with numbers. You can only place a card with the same color or number on your opponent's card. Sometimes you trade cards, sometimes you need to draw a few extra from the deck or skip a turn." - }, - { - "speaker": "James", - "dia_id": "D8:35", - "text": "I can't remember such a game. Maybe you have some other interesting games?" - }, - { - "speaker": "John", - "dia_id": "D8:36", - "text": "Yeah for sure! I've been playing one more game with friends these days. It's a game to figure out who the impostors are and it's super fun." - }, - { - "speaker": "James", - "dia_id": "D8:37", - "text": "Sounds cool! I've heard of that game, been meaning to try it out." - }, - { - "speaker": "John", - "dia_id": "D8:38", - "text": "Go for it, James! I advise you to gather a large group, it will be much more interesting to play." - }, - { - "speaker": "James", - "dia_id": "D8:39", - "text": "Sure thing, sounds like fun." - }, - { - "speaker": "John", - "dia_id": "D8:40", - "text": "That really is!" - } - ], - "session_9_date_time": "7:01 pm on 4 May, 2022", - "session_9": [ - { - "speaker": "John", - "dia_id": "D9:1", - "text": "Hey James! How've you been? Had an eventful week since our last chat." - }, - { - "speaker": "James", - "dia_id": "D9:2", - "text": "Hey John! Man, it's been wild since we talked. Last Friday, something happened on my project that I've been working on for weeks. I got so close to finishing it but I just couldn't figure it out. Super frustrating." - }, - { - "speaker": "John", - "dia_id": "D9:3", - "text": "Ugh, that's rough. I understand how frustrating it can be. What happened with that project of yours?" - }, - { - "speaker": "James", - "dia_id": "D9:4", - "text": "I hit a major snag - a bug in the code messed up the game mechanics. I tried debugging it for hours but couldn't solve it. It was super disappointing." - }, - { - "speaker": "John", - "dia_id": "D9:5", - "text": "Yeah, dealing with those bugs can be frustrating. Did you manage to solve the issue?" - }, - { - "speaker": "James", - "dia_id": "D9:6", - "text": "Nah, I couldn't figure it out on my own so a group of friends and I teamed up and got it fixed. Took a bit of work, but I'm glad it got done." - }, - { - "speaker": "John", - "dia_id": "D9:7", - "text": "Cool! Having a reliable team can really make a difference." - }, - { - "speaker": "James", - "dia_id": "D9:8", - "text": "Yeah, totally! It's awesome to have a group of people who share the same passions. They give you help and bring their own ideas to the mix. You can achieve so much when everyone works together. Are you working on anything today?" - }, - { - "speaker": "John", - "dia_id": "D9:9", - "text": "Nah, I have day-off today. I had a few new friends over to watch some movies. Trying to socialize more and it's been great! I think it's important to balance work and enjoyment. You're welcome to join us next time!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/1go6sson1bk41.jpg" - ], - "blip_caption": "a photo of two dogs playing in a fenced in area", - "query": "dogs playing backyard", - "dia_id": "D9:10", - "text": "Sounds great, John! I'm definitely in next time. Hanging out with friends and unwinding is key. By the way, today I decided to spend time with my beloved pets again.\n" - }, - { - "speaker": "John", - "dia_id": "D9:11", - "text": "Cool! They look like they're having a blast. What type are they?" - }, - { - "speaker": "James", - "dia_id": "D9:12", - "text": "One of them, Daisy, is a Labrador. She loves to play with her toys, but most of all she loves to eat." - }, - { - "speaker": "John", - "dia_id": "D9:13", - "text": "Cool, what about the other two? Judging by the photo, shepherds?" - }, - { - "speaker": "James", - "dia_id": "D9:14", - "text": "Exactly! You would know how much joy they bring me. They are so loyal, and this is their main feature." - }, - { - "speaker": "John", - "dia_id": "D9:15", - "text": "Wow, James! Love hearing about the joy that furry friends bring into your life. What else brings you happiness?" - }, - { - "speaker": "James", - "dia_id": "D9:16", - "text": "My pets, computer games, travel and pizza are all that bring me happiness in life." - }, - { - "speaker": "John", - "dia_id": "D9:17", - "text": "Pizza? Cool, I love pizza too! Which one do you love the most?" - }, - { - "speaker": "James", - "dia_id": "D9:18", - "text": "Pepperoni of course! An amazing combination of spicy salami and cheese." - }, - { - "speaker": "John", - "img_url": [ - "https://c1.wallpaperflare.com/preview/469/790/507/big-city-pizza.jpg" - ], - "blip_caption": "a photography of a pizza with pineapples and ham on a wooden board", - "query": "the witcher 3 game case", - "dia_id": "D9:19", - "re-download": true, - "text": "Cool, but my favorite thing is Hawaiian pizza. This combination of sweet and salty delights me! What other pizza do you like?" - }, - { - "speaker": "James", - "dia_id": "D9:20", - "text": "I like also cheese pizza and prosciutto." - }, - { - "speaker": "John", - "dia_id": "D9:21", - "text": "Haven't tried prosciutto yet, but I've heard it`s great!" - }, - { - "speaker": "James", - "dia_id": "D9:22", - "text": "Give it a shot! It`s really great. No regrets, I promise." - }, - { - "speaker": "John", - "dia_id": "D9:23", - "text": "Got it, will check it out. Thanks!" - }, - { - "speaker": "James", - "dia_id": "D9:24", - "text": "Be sure to tell us your impressions when you try it! And Bon Appetit!" - }, - { - "speaker": "John", - "dia_id": "D9:25", - "text": "Thanks! Catch you later!" - } - ], - "session_10_date_time": "12:45 am on 8 May, 2022", - "session_10": [ - { - "speaker": "James", - "dia_id": "D10:1", - "text": "Hey John! Been a while since we chatted. Sorry 'bout not getting back sooner. How's it going? Any new games you're into?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/eh02fep1qnw01.jpg" - ], - "blip_caption": "a photo of a wooden table with a game controller on it", - "query": "charity gaming tournament friends game controllers", - "dia_id": "D10:2", - "text": "Hey James! No worries, I know you are really busy at work. I'm good, thanks for asking. Oh, I've been organizing something with my friends yesterday - it was cool! Guess what it was, I'll give you a little hint." - }, - { - "speaker": "James", - "dia_id": "D10:3", - "text": "Wow, John, that looks awesome! Is it an icon of a new game?" - }, - { - "speaker": "John", - "dia_id": "D10:4", - "text": "Nope, not a new game. We put together a tournament for our favorite game, CS:GO. Lots showed up and we made a bunch of money for charity!" - }, - { - "speaker": "James", - "dia_id": "D10:5", - "text": "Wow John, organizing that tournament for charity must have been a ton of effort, but it sounds like it was so worth it!" - }, - { - "speaker": "John", - "dia_id": "D10:6", - "text": "Definitely worth it! It took some planning and coordination, but seeing everyone come together for a good cause was so rewarding." - }, - { - "speaker": "James", - "dia_id": "D10:7", - "text": "It must have been great to see the results of that effort. Have you considered organizing more events like that in the future?" - }, - { - "speaker": "John", - "dia_id": "D10:8", - "text": "Yeah, for sure! It was awesome and I want to do more events like that. It combines my interests and helps the community. Plus, it's great to get people together for some friendly competition." - }, - { - "speaker": "James", - "dia_id": "D10:9", - "text": "Combining gaming and volunteering is a great idea! So fun and fulfilling. Where did you send the collected money?" - }, - { - "speaker": "John", - "dia_id": "D10:10", - "text": "Our main goal was to raise money for a dog shelter, which is not far from the street where I live. And we did it!" - }, - { - "speaker": "James", - "dia_id": "D10:11", - "text": "Helping animals is really important!" - }, - { - "speaker": "John", - "dia_id": "D10:12", - "text": "I agree. We still had some money left after helping the shelter, and we decided to use this money to buy groceries and cook some food for the homeless. They were very happy about it." - }, - { - "speaker": "James", - "dia_id": "D10:13", - "text": "Glad you are helping those in need! You are doing a great job John, keep up the good work!" - }, - { - "speaker": "John", - "dia_id": "D10:14", - "text": "Thanks for your support, James! I won't stop there, I will do more and more good things!" - }, - { - "speaker": "James", - "dia_id": "D10:15", - "text": "I'm really proud of you!" - } - ], - "session_11_date_time": "5:00 pm on 11 May, 2022", - "session_11": [ - { - "speaker": "John", - "dia_id": "D11:1", - "text": "Hey James, it's been a bit since we last talked. Something cool happened recently - I volunteered my programming skills for a social cause. It was cool to use my passion to do something good. I made a software tool for one charitable foundation which helped streamline their operations and make them run more smoothly. Seeing my skills making a real difference in the world was really rewarding." - }, - { - "speaker": "James", - "dia_id": "D11:2", - "text": "Hey John! Glad to hear from you. It's awesome that you used your skills to make a difference. Bet it was cool to see it in action. Would love to hear more about it!" - }, - { - "speaker": "John", - "dia_id": "D11:3", - "text": "Previously, this foundation used paper records and all inventory was recorded manually. I made an application that structured their work, and now everything they need for inventory is in one application on their smartphone." - }, - { - "speaker": "James", - "dia_id": "D11:4", - "text": "Wow John, that's awesome! Must feel great to be part of something so important. I would love to see any visual examples of the impact your software made." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/rak0v41zm4sb1.jpg" - ], - "blip_caption": "a photo of a computer monitor showing the cpu performance of a computer", - "query": "software tool inventory management resources donations reports analysis", - "dia_id": "D11:5", - "text": "Yeah, here's a screenshot of the system. It'll make tracking inventory, resources, and donations run smoother and generate reports for analysis. Feels great knowing my skills are making a real difference to them." - }, - { - "speaker": "James", - "dia_id": "D11:6", - "text": "Wow John, that's awesome! What motivated you to create such an amazing system for them?" - }, - { - "speaker": "John", - "dia_id": "D11:7", - "text": "I was inspired by their passion for helping kids, so I wanted to contribute in any way I could. Plus, coding lets me challenge myself and expand my skills, so this was a great chance to do both. It's really rewarding to use my coding skills to make a difference." - }, - { - "speaker": "James", - "dia_id": "D11:8", - "text": "That's really great, John! It's awesome how you blended your passion with a good cause. How did it affect you?" - }, - { - "speaker": "John", - "dia_id": "D11:9", - "text": "It showed me the power of tech to make positive changes, beyond just my own enjoyment. It gave me a real sense of purpose." - }, - { - "speaker": "James", - "dia_id": "D11:10", - "text": "Discovering our passions is truly rewarding. How do you think this experience will impact your future plans?" - }, - { - "speaker": "John", - "dia_id": "D11:11", - "text": "This experience has given me a clearer sense of purpose and motivated me to use my programming skills to make a positive impact. I'm now considering volunteer roles and potentially a career in the non-profit sector." - }, - { - "speaker": "James", - "dia_id": "D11:12", - "text": "That's really inspiring. Have you found any non-profit organizations that align with your values and passion for programming?" - }, - { - "speaker": "John", - "dia_id": "D11:13", - "text": "I haven\u2019t found it yet, but to be honest I haven\u2019t looked for it. I think it won\u2019t be difficult for me to find the organization I need." - }, - { - "speaker": "James", - "dia_id": "D11:14", - "text": "There are lots of places where you can show off your skills! I'm sure you'll find one that's perfect for you in making a difference." - }, - { - "speaker": "John", - "dia_id": "D11:15", - "text": "I'll be happy to find a place where my skills and passions are a perfect match. I'm hoping to make a positive impact there." - }, - { - "speaker": "James", - "dia_id": "D11:16", - "text": "I'm sure you'll find the right spot, John. Your skills and passions will be a great addition. Good luck!" - }, - { - "speaker": "John", - "dia_id": "D11:17", - "text": "Thanks, your encouragement means a lot." - }, - { - "speaker": "James", - "dia_id": "D11:18", - "text": "I'm here for you. Good luck!" - }, - { - "speaker": "John", - "dia_id": "D11:19", - "text": "Thanks, James! Appreciate it. Take care and talk soon!" - } - ], - "session_12_date_time": "7:33 pm on 23 May, 2022", - "session_12": [ - { - "speaker": "James", - "dia_id": "D12:1", - "text": "Hey John, last weekend I had an awesome time at the amusement park with my friends. It was a great break from the virtual world. I went on some awesome roller coasters and it reminded me of when I was a kid. Everything was so real and exciting; it felt like I was in a video game!" - }, - { - "speaker": "John", - "dia_id": "D12:2", - "text": "Hey James! Sounds like an awesome time! I bet those rides brought back some great memories. Were there any other attractions besides the roller coaster?" - }, - { - "speaker": "James", - "dia_id": "D12:3", - "text": "Of course, I also managed to ride the Ferris wheel, electric cars and buggies. What's new with you?" - }, - { - "speaker": "John", - "dia_id": "D12:4", - "text": "That's really cool! Last Friday I entered a local tournament and took second place! It was a wild experience and the competitive energy was insane." - }, - { - "speaker": "James", - "dia_id": "D12:5", - "text": "Wow, John that's awesome! Congrats on your achievement! I can imagine the rush you must have felt during the tournament. Did you receive any rewards or prizes for your success?" - }, - { - "speaker": "John", - "dia_id": "D12:6", - "text": "I was stoked about my achievement. Though I didn't win the tournament, I still received some money for the 2nd place. Seeing my effort pay off was awesome." - }, - { - "speaker": "James", - "dia_id": "D12:7", - "text": "Awesome news! You don't have to win every time, growth and progress are most important. " - }, - { - "speaker": "John", - "img_url": [ - "https://sc04.alicdn.com/kf/Hcdada85e5412478bb63e47746ffc7c53e.jpg" - ], - "blip_caption": "a photo of a graduation cap on a book with a rope", - "query": "tournament prize trophy logo", - "dia_id": "D12:8", - "text": "Yeah, I also got this trophy! So satisfying. It reminds me to always put in my best effort. What about you? Any success stories lately?" - }, - { - "speaker": "James", - "dia_id": "D12:9", - "text": "Congrats on your achievement, John! That trophy looks awesome. Last month, I had a personal milestone. There were definitely tough times, but it reminds me of all the hard work. I feel a huge sense of accomplishment and I'm ready for the future opportunities!" - }, - { - "speaker": "John", - "dia_id": "D12:10", - "text": "Congrats on the milestone. What was it and what made it challenging? What did you learn?" - }, - { - "speaker": "James", - "dia_id": "D12:11", - "text": "I finished a big project I had been working on for months. It was challenging because I had to learn a new language and handle many details. I learned a lot about problem-solving, patience, and perseverance. Now I feel more confident to take on even bigger projects." - }, - { - "speaker": "John", - "dia_id": "D12:12", - "text": " That's awesome you learned a language and handled all those details. Great job, you definitely picked up some great skills! Remember, determination and confidence make any project a success. Good work!" - }, - { - "speaker": "James", - "dia_id": "D12:13", - "text": "Thanks, I appreciate your support. I'll definitely keep that in mind." - }, - { - "speaker": "John", - "dia_id": "D12:14", - "text": "No worries, I'm here to help. Keep going and reach those goals!" - } - ], - "session_13_date_time": "4:30 pm on 13 June, 2022", - "session_13": [ - { - "speaker": "John", - "dia_id": "D13:1", - "text": "Hey James, long time no talk! A lot has happened during this time. Let me fill you in." - }, - { - "speaker": "James", - "dia_id": "D13:2", - "text": "Hey John! Awesome to hear from you. Yeah, a lot has happened. Let's catch up!" - }, - { - "speaker": "John", - "dia_id": "D13:3", - "text": "I finally got my dream job! After lots of interviews and late nights, I got the offer and was ecstatic. Can't wait to start my journey!" - }, - { - "speaker": "James", - "dia_id": "D13:4", - "text": "Wow, John! Congrats on getting your dream job. I'm super stoked for you. When do you start?" - }, - { - "speaker": "John", - "dia_id": "D13:5", - "text": "Thank you! ! I'm starting next month." - }, - { - "speaker": "James", - "dia_id": "D13:6", - "text": "It can be rough getting started, but I'm sure you'll do great. Don't be afraid to seek help if you need it. Can't wait to hear about your experience! By the way, I recently started a course that combines my passion for gaming and programming. It's fun and challenging, and it has definitely increased my excitement for both." - }, - { - "speaker": "John", - "dia_id": "D13:7", - "text": "Cool! That sounds awesome. Combining your love of gaming and coding sounds like a dream. Tell me more! Are there any interesting projects you're working on?" - }, - { - "speaker": "James", - "dia_id": "D13:8", - "text": "Yes, we are currently working on a new part of the football simulator. I was working on collecting player databases. It wasn't easy, but I did it!" - }, - { - "speaker": "John", - "dia_id": "D13:9", - "text": "Cool! Did you choose this course because you love football?" - }, - { - "speaker": "James", - "dia_id": "D13:10", - "text": "Not least because of this. I love football, but, of course, the most important reason is to improve yourself. But it\u2019s nice if it\u2019s also connected with something you like." - }, - { - "speaker": "John", - "dia_id": "D13:11", - "text": "I completely agree! By the way, did you watch the Liverpool vs Chelsea match?" - }, - { - "speaker": "James", - "dia_id": "D13:12", - "text": "Of course, they played well! As I like to say, there is no sport better than football, no club better than Liverpool! I don't miss a single match of theirs!" - }, - { - "speaker": "John", - "dia_id": "D13:13", - "text": "It looks like you really root for this team!" - }, - { - "speaker": "James", - "dia_id": "D13:14", - "text": "Absolutely! They are forever in my heart, they are a great team. I hope they become champions next season!" - }, - { - "speaker": "John", - "dia_id": "D13:15", - "text": "As a Manchester City fan, I can't agree with you. You'll see, our two teams will fight for the championship, and mine will win!" - }, - { - "speaker": "James", - "dia_id": "D13:16", - "text": "I'm sure you're wrong, John. Manchester City are in bad form and their transfer policy is terrible!" - }, - { - "speaker": "John", - "dia_id": "D13:17", - "text": "You may be right, but the City manager can handle it, you'll see!" - }, - { - "speaker": "James", - "dia_id": "D13:18", - "text": "I bet we'll be higher than you in the final standings!" - }, - { - "speaker": "John", - "dia_id": "D13:19", - "text": "I'll take the bet, James! This will be a great battle!" - }, - { - "speaker": "James", - "dia_id": "D13:20", - "text": "Sure, John!" - } - ], - "session_14_date_time": "5:07 pm on 16 June, 2022", - "session_14": [ - { - "speaker": "James", - "blip_caption": "a photo of a dog laying on a person on a couch", - "dia_id": "D14:1", - "text": "Hey John, how's it going? A lot has happened for me lately, some good and some not so great. I`m lucky to have at least two people who always help me out when I'm struggling. What about you?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/bkol9biz39j21.jpg" - ], - "blip_caption": "a photo of a computer screen with a program on it", - "query": "screenshot coding blog", - "dia_id": "D14:2", - "text": "Gad to hear you have your support system ready. It's crucial, especially during hard times. For me, it's been quite an emotional rollercoaster. Last week, I started my blog about coding. It's exciting and kinda scary too. This is the first picture I uploaded there." - }, - { - "speaker": "James", - "blip_caption": "a photo of a video game scene of a man and woman", - "dia_id": "D14:3", - "text": "Wow, starting a blog about coding is awesome! Must be so exciting. What do you think about it?" - }, - { - "speaker": "John", - "blip_caption": "a photo of a computer screen showing a number of items", - "dia_id": "D14:4", - "text": "Thanks, James! It has been an awesome and challenging experience. I'm loving sharing my coding journey with others and tracking everything. It's a great way to connect with other coders." - }, - { - "speaker": "James", - "blip_caption": "a photo of a vase with a plaque on it", - "dia_id": "D14:5", - "text": "Congrats on your coding journey! What`s more new in your world?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/qx7qmvp5abx41.jpg" - ], - "blip_caption": "a photo of a bookshelf with a lot of books on it", - "query": "bookshelf coding books", - "dia_id": "D14:6", - "text": "Well, I bought a lot of new books, and now my bookcase is almost completely filled!" - }, - { - "speaker": "James", - "dia_id": "D14:7", - "text": "What genre do you enjoy reading?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/62ogg70rblbb1.jpg" - ], - "blip_caption": "a photo of a book shelf with a few books on it", - "query": "epic fantasy series book cover", - "dia_id": "D14:8", - "text": "I'm a big fan of sci-fi and fantasy books. Epic fantasy series with immersive world-building and intricate storylines are what I enjoy reading. Just getting lost in the pages of a great story is a wonderful escape from reality." - }, - { - "speaker": "James", - "dia_id": "D14:9", - "text": "Cool! Are there any book series that you love and would recommend to others?" - }, - { - "speaker": "John", - "dia_id": "D14:10", - "text": "Definitely! Two of my favorites are \"The Stormlight Archive\" and \"Kingkiller Chronicle\". If SF is your thing, check out \"The Expanse\" series. It's epic!" - }, - { - "speaker": "James", - "blip_caption": "a photo of a cd with a picture of a knight on it", - "dia_id": "D14:11", - "text": "Thanks for the recommendations, John! I'll definitely check out those books. What makes them your favorites?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/gnwdz3ysqra71.jpg" - ], - "blip_caption": "a photo of a dog sitting on a couch in a living room", - "query": "dog couch cozy", - "dia_id": "D14:12", - "text": "Glad you're giving these books a try! I'm obsessed with the way they create a magical world you can escape into - plus the characters feel really real. By the way, what's the name of the dog in this picture from your Facebook? It`s so cute!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/3gzujt17mgkb1.jpg" - ], - "blip_caption": "a photo of a dog chewing on a shoe on a person's lap", - "query": "dog max playing fetch", - "dia_id": "D14:13", - "text": "This is Max \u2013 he's so lovable and playful. He brings me so much joy, especially in tough times." - }, - { - "speaker": "John", - "dia_id": "D14:14", - "text": "Aww, he's adorable! I can tell Max brings you a lot of happiness. Pets are always such a great source of joy and love." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/a6vjqnq17kcb1.jpg" - ], - "blip_caption": "a photo of a dog standing in front of a pool", - "query": "Max playing ball backyard", - "dia_id": "D14:15", - "text": "Yeah, Max is great - he always cheers me up when I'm feeling down. Enjoying those cuddles with him!" - }, - { - "speaker": "John", - "dia_id": "D14:16", - "text": "Does he enjoy swimming?" - }, - { - "speaker": "James", - "dia_id": "D14:17", - "text": "Yeah, he loves it! We usually hit the beach or lake, and he loves playing in the water. He's a pro swimmer!" - }, - { - "speaker": "John", - "dia_id": "D14:18", - "text": "Max must be having so much fun swimming and playing - it's the best!" - }, - { - "speaker": "James", - "dia_id": "D14:19", - "text": "He has a blast! Always a joy to see him so happy and carefree in his favorite activity." - }, - { - "speaker": "John", - "dia_id": "D14:20", - "text": "He looks so happy - this is a great achievement of yours!" - }, - { - "speaker": "James", - "blip_caption": "a photo of a dog running in a field with a frisbee", - "dia_id": "D14:21", - "text": "Thanks, John! I love making him happy." - }, - { - "speaker": "John", - "dia_id": "D14:22", - "text": "Does Max have any special talents? He seems like quite the go-getter!" - }, - { - "speaker": "James", - "img_url": [ - "https://d4yxl4pe8dqlj.cloudfront.net/images/061f1763-db03-4b77-a9c7-85a24844f99e/58b4c82d-18f3-4736-87f7-de2a7a3de0a8_full_size.jpg" - ], - "blip_caption": "a photo of a dog laying in the grass with a frisbee", - "query": "dog catching frisbee", - "dia_id": "D14:23", - "text": "Max is a real go-getter! He's awesome at catching frisbees in mid-air - never misses!" - }, - { - "speaker": "John", - "dia_id": "D14:24", - "text": "Wow, Max loves playing fetch! Does he also enjoy long walks?" - }, - { - "speaker": "James", - "img_url": [ - "https://www.kingdukes.com/cdn/shop/articles/man_walking_dog_on_portland_trail_1200x.jpg" - ], - "blip_caption": "a photo of a man walking a dog on a trail", - "query": "dog walking trail", - "dia_id": "D14:25", - "text": "Yep! We love them; they're great exercise and give us fresh air. Here is a photo of us from a recent walk in the forest." - }, - { - "speaker": "John", - "dia_id": "D14:26", - "text": "Where's that spot where you could take a stroll? Bet Max loves all those hikes." - }, - { - "speaker": "James", - "img_url": [ - "https://i0.wp.com/runoregonblog.com/wp-content/uploads/2023/03/img_3358.jpg" - ], - "blip_caption": "a photo of a dirt path in a field with trees", - "query": "nearby trail forest serene peaceful nature", - "dia_id": "D14:27", - "text": "Max and I love taking walks on this nearby trail. It's a mile from my house. It's so tranquil and a great way to relax and connect with nature." - }, - { - "speaker": "John", - "dia_id": "D14:28", - "text": "Wow, that looks awesome! Going for a nature walk is so refreshing, don't you think?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a park with a bench and trees in the fall", - "dia_id": "D14:29", - "text": "Yeah, John! It's so relaxing and refreshing. It helps me think straight and find my inner peace." - }, - { - "speaker": "John", - "dia_id": "D14:30", - "text": "That park is so peaceful. What do you do when you're there alone?" - }, - { - "speaker": "James", - "dia_id": "D14:31", - "text": "When I'm there, I usually bring a book and just chill. It's like an escape from reality." - }, - { - "speaker": "John", - "dia_id": "D14:32", - "text": "Sounds great, James! Taking breaks and switching up the scenery is a great way to feel recharged. Keep it up." - }, - { - "speaker": "James", - "dia_id": "D14:33", - "text": "Definitely! Taking breaks is important for recharging. Thanks for the support!" - }, - { - "speaker": "John", - "dia_id": "D14:34", - "text": "No problem, always here to have your back. Take care of yourself!" - } - ], - "session_15_date_time": "9:59 pm on 19 June, 2022", - "session_15": [ - { - "speaker": "James", - "dia_id": "D15:1", - "text": "Hey John, since our last chat, something awesome happened. Last Friday, I started introducing Max, Daisy and the new pup Ned. It was hard at first, but they're slowly adapting. It's sweet to watch them bond and have fun together." - }, - { - "speaker": "John", - "dia_id": "D15:2", - "text": "Wow, that's cool, James! Seeing them bonding and having a great time is so sweet. Do you have a picture of them together?" - }, - { - "speaker": "James", - "dia_id": "D15:3", - "img_url": [ - "https://www.publicdomainpictures.net/pictures/40000/nahled/three-dogs-in-field.jpg" - ], - "re-download": true, - "blip_caption": "a photography of three dogs in a field of grass with trees in the background", - "text": "Yep, I got a great pic last night. Check it out!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a dog and a cat cuddling on a couch", - "dia_id": "D15:4", - "text": "Wow, they look so cute! I can almost feel the love and joy in this photo. These last few days really got me thinking about my own future." - }, - { - "speaker": "James", - "dia_id": "D15:5", - "text": "What's been on your mind regarding the future?" - }, - { - "speaker": "John", - "dia_id": "D15:6", - "text": "Lately, I've been thinking about my career and where I want to be in the future. I'm driven and passionate, and I also want to make a positive impact on the world." - }, - { - "speaker": "James", - "dia_id": "D15:7", - "text": "Gotcha, John. Finding a way to make a difference matters. Have you thought about any ideas on how to do that?" - }, - { - "speaker": "John", - "dia_id": "D15:8", - "text": "Yeah, I have. I've been looking into volunteering and thinking of going into non-profit work. I want to put my passions and abilities to use for causes I really care about." - }, - { - "speaker": "James", - "img_url": [ - "https://i0.wp.com/accentnetwork.us/wp-content/uploads/2022/03/blog.jpg" - ], - "blip_caption": "a photo of a group of people standing outside of a building", - "query": "donation center", - "dia_id": "D15:9", - "text": "Wow, John that sounds great! I'm sure with your skills and passion, you could do some really cool things with nonprofits. Here's a pic I took when I volunteered last month. It was really rewarding to see how little gifts can do so much!" - }, - { - "speaker": "John", - "dia_id": "D15:10", - "text": "That's awesome, James! Was it cool to see the impact of the gifts? Can you tell me more about the organization you volunteered with?" - }, - { - "speaker": "James", - "dia_id": "D15:11", - "text": "It was great to see how much a simple act of kindness can mean to someone in need. I volunteered with an organization that provides necessary items to those who are less fortunate. It felt so rewarding to help, even if it was in a small way." - }, - { - "speaker": "John", - "blip_caption": "a photo of a young boy standing outside of a yellow building", - "dia_id": "D15:12", - "text": "I think this is exactly what I need. Can you take me there this weekend?" - }, - { - "speaker": "James", - "dia_id": "D15:13", - "text": "Of course I can! I think there are still some of the previous staff there and I can even introduce you to them." - }, - { - "speaker": "John", - "dia_id": "D15:14", - "text": "Thank you very much! Will there be some kind of interview required?" - }, - { - "speaker": "James", - "dia_id": "D15:15", - "text": "No, this is not necessary. All you need is to be a friendly and polite person, and also have a great desire to help people. I'm sure you will succeed!" - }, - { - "speaker": "John", - "dia_id": "D15:16", - "text": "Thanks for your support! I want to make this world a better place, and with your help I will definitely achieve my goal." - }, - { - "speaker": "James", - "dia_id": "D15:17", - "text": "We can do this together!" - }, - { - "speaker": "John", - "dia_id": "D15:18", - "text": "Thanks, James. Your support means a lot to me. I'm determined to make a positive impact." - }, - { - "speaker": "James", - "dia_id": "D15:19", - "text": "You got this! Stay focused on your dreams and don't give up." - } - ], - "session_16_date_time": "5:13 pm on 9 July, 2022", - "session_16": [ - { - "speaker": "James", - "dia_id": "D16:1", - "text": "Hey John! Long time no talk - hope you're doing well. Guess what? Last week I actually won an online gaming tournament! It was such an exciting experience and it blew my mind when I won. Winning felt so good and it really motivated me to keep improving." - }, - { - "speaker": "John", - "dia_id": "D16:2", - "text": "Hey James! Congrats on winning the online gaming tournament! It's super fulfilling to see your hard work pay off. So happy for you!" - }, - { - "speaker": "James", - "dia_id": "D16:3", - "text": "Thanks! It was really fulfilling to see my hard work pay off with a victory in the tournament. How are you?" - }, - { - "speaker": "John", - "dia_id": "D16:4", - "text": "Feeling the tug of emotion lately. Determined and passionate on one hand, but feeling overwhelmed and stressed on the other. Balancing personal and professional is kind of a challenge. How have you been?" - }, - { - "speaker": "James", - "dia_id": "D16:5", - "text": "Yeah, staying balanced can be tough. I'm trying to take breaks from my hobbies and do other things. Lately I've become interested in extreme sports. Yesterday, for example, I was doing rope jumping. The highest height I jumped from was 150 meters!" - }, - { - "speaker": "John", - "dia_id": "D16:6", - "text": "Wow, how cool! What other extreme sport have you tried?" - }, - { - "speaker": "James", - "dia_id": "D16:7", - "text": "Just three days ago, I was surfing. Catching a wave is so cool! It's strange, but it relaxes me so much. How do you like to relax?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/d0eoj5t4py351.jpg" - ], - "blip_caption": "a photo of a book shelf filled with books and a guitar", - "query": "bookshelf novels fantasy books", - "dia_id": "D16:8", - "text": "I like to relax by reading. I love entering the imaginative worlds of authors - it's a fun escape from reality. " - }, - { - "speaker": "James", - "dia_id": "D16:9", - "text": "I also love to read, especially while snuggled under the covers on a cold winter day. But now it\u2019s summer and I want something more exciting! By the way, I bought air tickets to Toronto, and I\u2019m leaving the day after tomorrow evening." - }, - { - "speaker": "John", - "dia_id": "D16:10", - "text": "Cool, this is already the fourth country you will visit! Will you only be in Toronto, or will you be visiting somewhere else?" - }, - { - "speaker": "James", - "dia_id": "D16:11", - "text": "I also plan to visit Vancouver. Maybe, I'll go somewhere else." - }, - { - "speaker": "John", - "dia_id": "D16:12", - "text": "When are you coming back?" - }, - { - "speaker": "James", - "dia_id": "D16:13", - "text": "I plan to return on July 20, I\u2019ll definitely bring you some kind of souvenir!" - }, - { - "speaker": "John", - "dia_id": "D16:14", - "text": "Thanks James! I will be waiting for you from your journey! Bon Voyage!" - }, - { - "speaker": "James", - "dia_id": "D16:15", - "text": "Thank you, John! Take care and see you soon!" - }, - { - "speaker": "John", - "dia_id": "D16:16", - "text": "Take care, bye!" - } - ], - "session_17_date_time": "9:49 am on 22 July, 2022", - "session_17": [ - { - "speaker": "John", - "dia_id": "D17:1", - "text": "Hi James! I just started playing chess to get better at strategy. I'm loving it! Have you ever tried it out?" - }, - { - "speaker": "James", - "dia_id": "D17:2", - "text": "Hey John! Yeah, I've played chess before. It's a game that really tests your strategy. It's great that you're enjoying it!" - }, - { - "speaker": "John", - "dia_id": "D17:3", - "text": "Yeah, chess is really fun! It's like solving an endless puzzle and always trying to outwit your opponent." - }, - { - "speaker": "James", - "dia_id": "D17:4", - "text": "Yeah, it's tough, but fun when you figure it out. Do you play with friends or online?" - }, - { - "speaker": "John", - "img_url": [ - "https://images.chesscomfiles.com/proxy/i.imgur.com/lzPt82B/https/71544f1354.jpg" - ], - "blip_caption": "a photo of a chess board with a laptop on it", - "query": "chessboard chess pieces intense game", - "dia_id": "D17:5", - "text": "I'm playing mostly online for now, but I also joined a chess club and practice with others. Here's a pic from an intense game I played lately." - }, - { - "speaker": "James", - "dia_id": "D17:6", - "text": "Wow, looks intense! What sparked your interest in chess?" - }, - { - "speaker": "John", - "dia_id": "D17:7", - "text": "I've always been drawn to strategy games and wanted to challenge myself. Plus, I believe chess can improve decision-making skills." - }, - { - "speaker": "James", - "dia_id": "D17:8", - "text": "Great reason for playing chess - it will definitely help you develop your skills!" - }, - { - "speaker": "John", - "dia_id": "D17:9", - "text": "Thanks, James! I'm excited to see how playing chess can enhance my strategic thinking in everyday situations. Do you have any tips for improvement?" - }, - { - "speaker": "James", - "dia_id": "D17:10", - "text": "Definitely! Studying opening moves and strategies and analyzing your games to spot weaknesses are great ways to improve." - }, - { - "speaker": "John", - "dia_id": "D17:11", - "text": " I'll definitely look into that. Appreciate the advice!" - }, - { - "speaker": "James", - "dia_id": "D17:12", - "text": "No worries, John! Happy to help. Just let me know if there's anything else I can assist you with." - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/44c4iux03cv51.jpg" - ], - "blip_caption": "a photo of a group of children holding a skateboard", - "query": "friends laughing together photo album", - "dia_id": "D17:13", - "text": " Your support means a lot to me. You're a true friend! Remember this photo from elementary school?" - }, - { - "speaker": "James", - "dia_id": "D17:14", - "text": "That looks fun. But I don\u2019t remember at all under what circumstances we took this picture. What's the story behind it?" - }, - { - "speaker": "John", - "dia_id": "D17:15", - "text": "This is from when we were 10 and we were really into skateboarding. We had a group of friends who often go to the skate park with. We would help each other learn new tricks and have a great time. Those friends made the experience even better and their friendship meant a lot to us." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/a2w1l6874v381.jpg" - ], - "blip_caption": "a photo of a small dog standing on a skateboard", - "query": "dog skateboard playing", - "dia_id": "D17:16", - "text": "Indeed, I remember this moment. We loved skateboards back then, sometimes we even left class early to do it. I still like to go for a ride sometimes, and I even taught my dogs how to balance on it." - }, - { - "speaker": "John", - "dia_id": "D17:17", - "text": "Wow! Do they enjoy it, or do you have to encourage them to play with the board?" - }, - { - "speaker": "James", - "dia_id": "D17:18", - "text": "They love it! They chase after it and run with it. It's a great way for them to get some exercise." - }, - { - "speaker": "John", - "dia_id": "D17:19", - "text": "Wow, that's great! Keeping active and happy is great for both of you." - }, - { - "speaker": "James", - "dia_id": "D17:20", - "text": "Yep! Staying active with them builds a strong bond and makes us both happy." - }, - { - "speaker": "John", - "blip_caption": "a photo of a cat laying on a bed with a stuffed animal", - "dia_id": "D17:21", - "text": "Yeah, the bond between us and our pets is amazing. They bring a lot of joy and love. It\u2019s a pity that I don\u2019t have pets, I\u2019ll definitely get one someday. By the way, how was your trip?" - }, - { - "speaker": "James", - "dia_id": "D17:22", - "text": "Everything went great! In addition, I even managed to get out to another country. The city of Nuuk, if you know. I stayed there quite a bit, but at least I had one more country to add to my bucket list!" - }, - { - "speaker": "John", - "dia_id": "D17:23", - "text": "This is awesome, James! Surely you brought a lot of impressions with you!" - }, - { - "speaker": "James", - "dia_id": "D17:24", - "text": "Certainly! And not only impressions, I also brought souvenirs. For both you and your Jill!" - }, - { - "speaker": "John", - "dia_id": "D17:25", - "text": "Thank you very much, Jill will be delighted!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/yx1qjjtxk8e91.jpg" - ], - "blip_caption": "a photo of a woman and two dogs on a couch", - "query": "dogs sitting couch happiness love companions", - "dia_id": "D17:26", - "text": "You're welcome! By the way, look who came to see me!" - }, - { - "speaker": "John", - "dia_id": "D17:27", - "text": "Nice pic, James! Who are they?" - }, - { - "speaker": "James", - "dia_id": "D17:28", - "text": "That's my sister and my dogs. We were just chilling together yesterday, and they bring so much happiness to my life." - }, - { - "speaker": "John", - "dia_id": "D17:29", - "text": "Wow, they look so happy! It's awesome that you get to spend time with your sister and your furry friends. The bond you have with them is really strong." - }, - { - "speaker": "James", - "dia_id": "D17:30", - "text": "I'm blessed to have a close bond with my sister and our furry friends. We have a great time together, like a family!" - }, - { - "speaker": "John", - "dia_id": "D17:31", - "text": "Family and friends are really amazing, James. They show us so much love and joy. I'm grateful for the connection I have with my siblings. Things can be tough sometimes, but their support means everything to me." - }, - { - "speaker": "James", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D17:32", - "text": "Fully agreed! My sister and I were also near the ocean and watched such a wonderful sunset!" - }, - { - "speaker": "John", - "dia_id": "D17:33", - "text": "Wonderful photo! It's amazing how you can capture a moment and capture it in a photograph." - }, - { - "speaker": "James", - "dia_id": "D17:34", - "text": "Thanks, John! This is just a good shot, nothing more. I took a lot of shots yesterday and chose the best one to send to you." - }, - { - "speaker": "John", - "dia_id": "D17:35", - "text": "Still, the photo is amazing!" - }, - { - "speaker": "James", - "dia_id": "D17:36", - "text": "I have to go, I'm tired over the last two days. Bye!" - }, - { - "speaker": "John", - "dia_id": "D17:37", - "text": "Take care, bye!" - } - ], - "session_18_date_time": "1:45 pm on 6 August, 2022", - "session_18": [ - { - "speaker": "John", - "dia_id": "D18:1", - "text": "Hey James, good catching up! Been a while huh? I made a huge call - recently left my IT job after 3 years. It was tough but I wanted something that made a difference. And now with this new job, I am happy about my decision. I am loving the new job!" - }, - { - "speaker": "James", - "dia_id": "D18:2", - "text": "Hey John! Great to hear from you. Leaving after 3 years is a big step - how did it feel?" - }, - { - "speaker": "John", - "dia_id": "D18:3", - "text": "At first, it was super scary, but I knew I had to make a change and focus on things that align with my values and passions." - }, - { - "speaker": "James", - "dia_id": "D18:4", - "text": "Wow, John, that sounds really brave. I hope it brings you joy and satisfaction." - }, - { - "speaker": "John", - "dia_id": "D18:5", - "text": "Thanks, James. It wasn't easy, but sometimes you gotta take a leap to follow your heart." - }, - { - "speaker": "James", - "dia_id": "D18:6", - "text": "Taking risks pays off! Way to be brave. I'm proud of you!" - }, - { - "speaker": "John", - "dia_id": "D18:7", - "text": "Your support means a lot. Lately, I've been thinking about what truly makes me happy, and I'm really drawn to the gaming industry. I'm passionate about it and it's time to turn that into a career. I want to become an organizer of tournaments for various computer games in our state. For example, for CS:GO. It's a new journey for me." - }, - { - "speaker": "James", - "dia_id": "D18:8", - "text": "Cool! You always mentioned your love for gaming. What other game do you want to organize competitions for? And what`s your plan now?" - }, - { - "speaker": "John", - "dia_id": "D18:9", - "text": "Also, I can host Fortnite competitions. I have already made some connections that will help me with this. My plan is to gain more experience and perfect my skills to be successful in this field." - }, - { - "speaker": "James", - "dia_id": "D18:10", - "text": "Sounds like a solid plan! Trying out different game genres can be a great way to widen your skills and knowledge." - }, - { - "speaker": "John", - "dia_id": "D18:11", - "text": "Thanks! I am very glad that you support me in my new endeavor!" - }, - { - "speaker": "James", - "dia_id": "D18:12", - "text": "I will always be here for you! If you need any financial assistance or advice, please contact me!" - }, - { - "speaker": "John", - "blip_caption": "a photo of a desk with a computer monitor and keyboard", - "dia_id": "D18:13", - "text": "I will definitely do this if necessary! By the way, what's new with you?" - }, - { - "speaker": "James", - "dia_id": "D18:14", - "text": "Yesterday I took my puppy to the clinic." - }, - { - "speaker": "John", - "dia_id": "D18:15", - "text": "God, James, what happened to your puppy? Is it OK?" - }, - { - "speaker": "James", - "dia_id": "D18:16", - "text": "Don't worry. This was just a routine examination. Also, the puppy was vaccinated to prevent him from catching the seasonal canine disease." - }, - { - "speaker": "John", - "dia_id": "D18:17", - "text": "Phew, great that he's okay. It's great that you care so much about your pets!" - }, - { - "speaker": "James", - "dia_id": "D18:18", - "text": "They are the source of my joy, so I will always take care of them!" - }, - { - "speaker": "John", - "dia_id": "D18:19", - "text": "You're a great host, James! Well, I have to go, bye!" - }, - { - "speaker": "James", - "dia_id": "D18:20", - "text": "Thanks, John! Take care, bye!" - } - ], - "session_19_date_time": "9:16 am on 10 August, 2022", - "session_19": [ - { - "speaker": "John", - "dia_id": "D19:1", - "text": "Hey James, been a few days. The convo got me thinking about my passions and goals. Thanks for encouraging me to try new game genres." - }, - { - "speaker": "James", - "dia_id": "D19:2", - "text": "Hey John! Nice to hear from you! Glad our chat made an impact. What sort of games are you interested in exploring?" - }, - { - "speaker": "John", - "dia_id": "D19:3", - "text": "Lately, I've been playing some different genres like strategy and RPG games instead of my usual shooters. I\u2019m already thinking about making competitions for them too." - }, - { - "speaker": "James", - "dia_id": "D19:4", - "text": "That's great, John! Trying out different genres can really add to your gaming experiences. Have you come across any standout games?" - }, - { - "speaker": "John", - "dia_id": "D19:5", - "text": "Hooked a new RPG that I've been playing lately! The storytelling and characters are amazing, can't get enough of it." - }, - { - "speaker": "James", - "dia_id": "D19:6", - "text": "Sounds great! I think storytelling is what makes RPGs so fun. What game are you playing? Do you have any favorite characters?" - }, - { - "speaker": "John", - "dia_id": "D19:7", - "text": "I'm playing \"The Witcher 3\"! There's this awesome monster hunter with a cool story, and I'm totally hooked, trying to make the right choices to shape the world. It's really immersive." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/p1ok9m9zkrw61.jpg" - ], - "blip_caption": "a photo of a man dressed in armor holding a sword", - "query": "the witcher 3 geralt fighting monster", - "dia_id": "D19:8", - "text": "Yeah, \"The Witcher 3\" is amazing! I love how you can shape the world with your choices and feel the impact. The graphics are insane too - check out this pic." - }, - { - "speaker": "John", - "img_url": [ - "https://i.pinimg.com/originals/23/13/24/231324d44f9471ead5535950153378f1.jpg" - ], - "blip_caption": "a photo of a gaming room with a computer and a gaming chair", - "query": "gaming setup", - "dia_id": "D19:9", - "text": "That's a great pic! The graphics are truly stunning! By the way, look how I organized my workplace!" - }, - { - "speaker": "James", - "dia_id": "D19:10", - "text": "Cool! Wall lighting adds beauty to your workspace." - }, - { - "speaker": "John", - "dia_id": "D19:11", - "text": "Thanks James! What's new with you?" - }, - { - "speaker": "James", - "dia_id": "D19:12", - "text": "Yesterday I took my three dogs to a beach outing to have fun and bond with other dogkeepers." - }, - { - "speaker": "John", - "blip_caption": "a photo of a refrigerator with a calendar on it and a magnet", - "dia_id": "D19:13", - "text": "Cool! Surely you gained a new experience from communicating with other dog lovers!" - }, - { - "speaker": "James", - "dia_id": "D19:14", - "text": "Yes, we had fun and I even met one beautiful girl. I'm thinking of asking her out on a date! She left me her phone number, I think I'll call tomorrow." - }, - { - "speaker": "John", - "dia_id": "D19:15", - "text": "Wow! That's cool, what's her name? Be sure to call her, everything will work out!" - }, - { - "speaker": "James", - "dia_id": "D19:16", - "text": " She is Samantha. I'll definitely call her!" - }, - { - "speaker": "John", - "dia_id": "D19:17", - "text": "Yoohoo! Hope you have a wonderful time!" - } - ], - "session_20_date_time": "3:57 pm on 21 August, 2022", - "session_20": [ - { - "speaker": "John", - "dia_id": "D20:1", - "text": "Hey, James! Good to hear from you. I have some awesome news - I joined a programming group online last Friday and it's been incredible! It's awesome to be part of a community of people with similar goals - coding and making a difference." - }, - { - "speaker": "James", - "dia_id": "D20:2", - "text": "Hey John! That's great to hear! Being part of a coding community can definitely be rewarding. Can you share more about your experiences with the online group? Have you made any interesting connections?" - }, - { - "speaker": "John", - "dia_id": "D20:3", - "text": "Thanks for asking. My online programming group has been great - lots of skilled coders, all passionate about using tech for good. We've shared ideas, chatted about coding and worked on a few projects together. It's amazing to see everyone's different skills and viewpoints. I've even exchanged contacts with a few of them." - }, - { - "speaker": "James", - "dia_id": "D20:4", - "text": "Nice one, John! Looks like you're really getting involved in the programming world. It's great connecting with like-minded people and building up your network. Have you had the opportunity to collaborate on any projects or work with anyone yet?" - }, - { - "speaker": "John", - "dia_id": "D20:5", - "text": "Yeah, collaborating is great. Last week I worked with someone from the group on a project and we both had our strong points which helped out. It was cool seeing how we created something awesome together. It's great working with others." - }, - { - "speaker": "James", - "dia_id": "D20:6", - "text": "Working together on a project can create amazing results, when everyone brings in their different strengths and abilities. It's cool when the final product is done and you know you were a part of making something awesome. Can we say that you are returning to working with programming again?" - }, - { - "speaker": "John", - "dia_id": "D20:7", - "text": "I think not, this is just a one-time experience to learn something new and work as a team. I'm still full of courage to start hosting eSports competitions. Do you participate in any online groups?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/xy7hyzrpdd481.jpg" - ], - "blip_caption": "a photo of a gaming room with a computer and a gaming chair", - "query": "gaming marathon friends home", - "dia_id": "D20:8", - "text": "Nah, not in any online groups right now, but I do have my gaming group I play with regularly. We even stream our game sessions, and recently had a get-together. Super fun!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/i1x9q5nr1io51.jpg" - ], - "blip_caption": "a photo of a desk with a computer and a keyboard", - "query": "gaming programming setup desk monitors coding books shelf", - "dia_id": "D20:9", - "text": "Wow, James! That's great that you have some friends to game with. By the way, I bought some new devices and refurbished my gaming desk." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/0rff1x0pj7u81.jpg" - ], - "blip_caption": "a photo of a gaming desk with a computer and a gaming chair", - "query": "gaming setup computer keyboard mouse chair", - "dia_id": "D20:10", - "text": "Cool! What kind of gear do you have now for gaming? I have a setup with a gaming PC, keyboard, mouse, and a comfy chair - makes gaming for hours a lot more bearable!" - }, - { - "speaker": "John", - "dia_id": "D20:11", - "text": "Nice set-up, James! I have a similar gaming chair and keyboard. I'm currently using a gaming PC with a powerful graphics card for intense games. I also have a headset for immersive sound. Gaming has always been an awesome escape for me - it keeps me focused and motivated in other areas." - }, - { - "speaker": "James", - "img_url": [ - "http://nicksmarathon.org/wp-content/uploads/2018/11/20181110_164805.jpg" - ], - "blip_caption": "a photo of a family sitting on a couch in a living room", - "query": "gaming marathon friends", - "dia_id": "D20:12", - "text": "Nice one! A strong graphics card and headset really enhance the gaming experience. Yeah, gaming is a great way to escape and stay motivated. It takes us to different places and stories. I even hosted a gaming marathon with some friends and we had a blast. We played all night and it really strengthened our bond." - }, - { - "speaker": "John", - "blip_caption": "a photo of two children sitting on a couch with a baby", - "dia_id": "D20:13", - "text": "Sounds great! Gaming marathons are the best. When I was younger, my siblings threw me one and it was awesome! We stayed up all night playing games and it really bonded us." - }, - { - "speaker": "James", - "dia_id": "D20:14", - "text": "Wow, that sounds awesome! Do you still play with your siblings these days?" - }, - { - "speaker": "John", - "dia_id": "D20:15", - "text": "Me and my siblings don't hang out much since we live far apart, but when we do we always try to plan a gaming night." - }, - { - "speaker": "James", - "dia_id": "D20:16", - "text": "Sounds great, John! Family time is the best. Are you planning any gaming nights in the near future?" - }, - { - "speaker": "John", - "dia_id": "D20:17", - "text": "Yep, I'm organizing one with my siblings next month. We're stoked! Can't wait!" - }, - { - "speaker": "James", - "dia_id": "D20:18", - "text": "Wow, John! Family game nights are so much fun. Have a great time!" - }, - { - "speaker": "John", - "dia_id": "D20:19", - "text": "Thanks, James! Can't wait! It was nice catching up - talk soon!" - }, - { - "speaker": "James", - "dia_id": "D20:20", - "text": "Hey John! Good to talk to you. Have fun at family game night! Talk to you later." - }, - { - "speaker": "John", - "dia_id": "D20:21", - "text": "Thanks, James! Gonna have a great time. Talk to you later." - }, - { - "speaker": "James", - "dia_id": "D20:22", - "text": "Take it easy. Have fun and let's chat soon. Have a good night!" - } - ], - "session_21_date_time": "9:18 pm on 26 August, 2022", - "session_21": [ - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/8nlwyc5nh6d61.jpg" - ], - "blip_caption": "a photo of a person holding a dog on a couch", - "query": "cute puppy playing video game controller", - "dia_id": "D21:1", - "text": "Hey John! Look how cute it is. My dog came to me today while I was playing on the console. What is new?" - }, - { - "speaker": "John", - "dia_id": "D21:2", - "text": " Your pup is so cute, remind me what's their name? I've been helping my younger siblings out with programming since they joined the programming course. It's really cool to see them get into it." - }, - { - "speaker": "James", - "dia_id": "D21:3", - "text": " His name's Ned and he's been awesome since I adopted him. I can't imagine life without him. It's great to hear that your siblings signed up for programming." - }, - { - "speaker": "John", - "dia_id": "D21:4", - "text": "That's right, his name is Ned, how could I forget?!" - }, - { - "speaker": "James", - "dia_id": "D21:5", - "text": "Regarding your siblings, are you already working on anything cool with them?" - }, - { - "speaker": "John", - "dia_id": "D21:6", - "text": "Yeah! We're working on a cool project together that involves coding. It's a game and it's helping them learn." - }, - { - "speaker": "James", - "dia_id": "D21:7", - "text": "Wow, learning and gaming sounds like a fantastic combination for coding education! Can you share more details about the game?" - }, - { - "speaker": "John", - "dia_id": "D21:8", - "text": "Yeah, they're playing a simple, text-based adventure game, working on their coding skills and having fun. I'm so proud of them! Maybe they'll even create their own video games, huh? Any new game designs on your mind?" - }, - { - "speaker": "James", - "dia_id": "D21:9", - "text": "Wow, sounds cool John! Learning coding with a text-based adventure game is impressive stuff. As for me, I've been trying out different genres of games and now I'm dying to create a strategy game like Civilization - love how complicated and in-depth they are. Fingers crossed, one day I'll make my own awesome strategy game!" - }, - { - "speaker": "John", - "dia_id": "D21:10", - "text": "Wow, James, that's impressive! It's gonna be awesome. Can't wait to see what you come up with!" - }, - { - "speaker": "James", - "dia_id": "D21:11", - "text": " Are you free tomorrow?" - }, - { - "speaker": "John", - "dia_id": "D21:12", - "text": "Yes, tomorrow is my day off. Do you have any suggestions on how to spend tomorrow?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a laptop computer with a colorful keyboard on a table", - "dia_id": "D21:13", - "text": "Yes, we can go to Starbucks for coffee if you want." - }, - { - "speaker": "John", - "dia_id": "D21:14", - "text": "I don't mind meeting up, but why Starbucks? Maybe we can have a beer somewhere?" - }, - { - "speaker": "James", - "dia_id": "D21:15", - "text": "Well, how about we go to McGee's pub then? I heard they serve a great stout there!" - }, - { - "speaker": "John", - "dia_id": "D21:16", - "text": "Great idea, except I don't like dark beer. Maybe there's something else there?" - }, - { - "speaker": "James", - "dia_id": "D21:17", - "text": "Of course, there are also light beers!" - }, - { - "speaker": "John", - "dia_id": "D21:18", - "text": "Great, then I agree! See you tomorrow at McGee's Pub!" - }, - { - "speaker": "James", - "dia_id": "D21:19", - "text": "See you John, bye!" - } - ], - "session_22_date_time": "6:53 pm on 1 September, 2022", - "session_22": [ - { - "speaker": "James", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/4/4c/Legend_of_Grimrock_screenshot_12.jpg" - ], - "blip_caption": "a photography of a screenshot of a stone building with a giant creature", - "query": "unity strategy game", - "dia_id": "D22:1", - "re-download": true, - "text": "Hey John! Been a while, but hope you're doing well. My Unity strategy game is finally finished\u2014it took loads of time and effort, but I'm really proud. Your support and encouragement made a real difference. Thanks for believing in me!" - }, - { - "speaker": "John", - "dia_id": "D22:2", - "text": "Hey James! Congrats on finishing your game! It looks amazing and I'm so proud of you for all the hard work you put in. Can I see more of it? Got any other screenshots to show me?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/w5vwdrledbb41.jpg" - ], - "blip_caption": "a photo of a video game screen showing a person on a horse", - "query": "screenshot game battle scene", - "dia_id": "D22:3", - "text": "I appreciate your support. Check out this screenshot from it." - }, - { - "speaker": "John", - "dia_id": "D22:4", - "text": "This game looks great! What inspired you to create it?" - }, - { - "speaker": "James", - "dia_id": "D22:5", - "text": "I've always loved playing strategy games like Civilization and Total War, so I decided to challenge myself and create one of my own." - }, - { - "speaker": "John", - "dia_id": "D22:6", - "text": "That's awesome! I love those games too. It must have been quite an experience making your own. Did you face any difficulties during development?" - }, - { - "speaker": "James", - "dia_id": "D22:7", - "text": "It was a bit challenging to get everything right, balancing mechanics and ensuring fairness. But with some trial and error, I managed to get it to where I wanted it." - }, - { - "speaker": "John", - "dia_id": "D22:8", - "text": "Wow, that must have been a challenge, especially since you had to make sure the game was enjoyable and balanced. Congratulations on completing it! What were some key takeaways from the experience?" - }, - { - "speaker": "James", - "dia_id": "D22:9", - "text": "Thanks, John! It was definitely a learning experience. Perseverance and patience are key, and I'm proud of what I created after sticking with it. Also, feedback and collaboration are essential, and the help from others really made the game better. It was great!" - }, - { - "speaker": "John", - "dia_id": "D22:10", - "text": "Awesome that you learned those lessons! Collaboration and feedback make a huge impact on any project. I've been teaching my siblings coding. It's been a fulfilling experience and they're already creating their own programs - amazing!" - }, - { - "speaker": "James", - "dia_id": "D22:11", - "text": "Wow, John! Cool seeing others learn with your help. What kind of programs are they making?" - }, - { - "speaker": "John", - "dia_id": "D22:12", - "text": "They're starting small, making basic games and stories. It's inspiring how fast they learn and the good time they're having." - }, - { - "speaker": "James", - "dia_id": "D22:13", - "text": "Wow! It's inspiring how fast they learn and the good time they're having. I bet they'll be creating their own complex projects soon!" - }, - { - "speaker": "John", - "dia_id": "D22:14", - "text": "I'm excited to see how far they can go! With their passion for video games like me, hopefully they can use those coding skills to make something cool. I'm so proud of them, can't wait to see what they come up with!" - }, - { - "speaker": "James", - "dia_id": "D22:15", - "text": "I'm proud of them too! Seeing the next generation pick up coding and making their own games is awesome. Can't wait to see what they create!" - }, - { - "speaker": "John", - "dia_id": "D22:16", - "text": "Thanks, James, for the support. I really appreciate it." - }, - { - "speaker": "James", - "dia_id": "D22:17", - "text": "Yeah, you're the best! I'm here for you, no doubt." - }, - { - "speaker": "John", - "dia_id": "D22:18", - "text": " Your friendship really means a lot. I'm going through some difficult times now and it's really good to know I've got someone like you." - }, - { - "speaker": "James", - "dia_id": "D22:19", - "text": "Just know I'm here if you need someone to talk or vent to. It might help alleviate some of the difficult times you're going through." - } - ], - "session_23_date_time": "9:23 pm on 4 September, 2022", - "session_23": [ - { - "speaker": "James", - "dia_id": "D23:1", - "text": "Hey John, it's been a few days since we talked. So much has gone on, both good and bad. Yesterday, when we were at the theater, Samantha loves theater, I asked her to become my girlfriend, and she agreed. We have gone through a lot in this short period. There were good and bad, but I'm happy with her. All that ups and downs were a bit overwhelming, but it's part of life. What about you? Anything interesting lately?" - }, - { - "speaker": "John", - "img_url": [ - "https://c0.wallpaperflare.com/preview/885/290/450/adult-coder-coding-company.jpg" - ], - "blip_caption": "a photography of a man sitting at a desk with a computer", - "query": "family coding together computer", - "dia_id": "D23:2", - "re-download": true, - "text": "Hey James, this is great news! Where else have you been besides the theater? My parents just started learning coding from me - it's been a learning experience, but I'm glad to help them out. It binds us a little closer. Look at this photo, this is my father coding his own program for the first time.\n " - }, - { - "speaker": "James", - "dia_id": "D23:3", - "text": "That's great, John! Looks like he's having a good time in the pic. Samantha and I were also at McGee's bar. It turned out that she loves a good lager beer. She and I have so much in common!" - }, - { - "speaker": "John", - "dia_id": "D23:4", - "text": "I'm glad you finally found someone other than dogs that brings you joy. Well done, you will succeed!" - }, - { - "speaker": "James", - "dia_id": "D23:5", - "text": "Thanks, John. She and I are going to a baseball game next Sunday, want to join? I'll show you what Samantha looks like." - }, - { - "speaker": "John", - "dia_id": "D23:6", - "text": "Yeah! Let's do it. It'll be a fun experience!" - }, - { - "speaker": "James", - "dia_id": "D23:7", - "text": "Great. Well, what else is new in your life?" - }, - { - "speaker": "John", - "dia_id": "D23:8", - "text": "I bought some new gaming equipment to improve my skills. For example, new headphones." - }, - { - "speaker": "James", - "dia_id": "D23:9", - "text": "Cool, which company did you choose? And what other devices did you buy?" - }, - { - "speaker": "John", - "dia_id": "D23:10", - "text": "I chose headphones from Sennheiser. Judging by the reviews, they have excellent sound. Also, I bought a mouse from Logitech." - }, - { - "speaker": "James", - "blip_caption": "a photo of a video game scene of a couple of people", - "dia_id": "D23:11", - "text": "Cool! I hope the new devices will improve your skill and you will play even better!" - }, - { - "speaker": "John", - "dia_id": "D23:12", - "text": "I really hope so too. Well, do you have anything new besides the great news about you and Samantha?" - }, - { - "speaker": "James", - "dia_id": "D23:13", - "text": "Yes, two days ago I signed up for a cooking class. I never liked cooking, but I felt that I wanted to learn something new. At the first lesson we prepared several simple dishes. I got a great omelette the first time!" - }, - { - "speaker": "John", - "dia_id": "D23:14", - "text": "Cool! I\u2019ve never heard of your desire to cook, you surprise me! How much do these cooking courses cost and what else did you cook there?" - }, - { - "speaker": "James", - "dia_id": "D23:15", - "text": "At only $10 per class, it's very cheap! Also, I made meringue there and they taught us how to make the dough." - }, - { - "speaker": "John", - "dia_id": "D23:16", - "text": "Really cheap. It's great that you are always looking for a way to improve yourself!" - }, - { - "speaker": "James", - "dia_id": "D23:17", - "text": "Thanks for your support, John! I really appreciate it. I hope I can treat you to my creation once I learn a little more about cooking." - }, - { - "speaker": "John", - "dia_id": "D23:18", - "text": "I look forward to it, James, you can do it!" - }, - { - "speaker": "James", - "dia_id": "D23:19", - "text": "Your words give me even more strength to pursue my new hobby!" - }, - { - "speaker": "John", - "dia_id": "D23:20", - "text": "That's why we are friends, to support each other! Well, I have to go, bye!" - }, - { - "speaker": "James", - "dia_id": "D23:21", - "text": "Take care, John, bye!" - } - ], - "session_24_date_time": "6:02 pm on 18 September, 2022", - "session_24": [ - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/eqfmcyv9b2t91.jpg" - ], - "blip_caption": "a photo of a table with a laptop and a game on it", - "query": "game project in progress", - "dia_id": "D24:1", - "text": "Hey James! Long time no see! A lot's changed since we talked. I started getting into board games. I tried one last week and it turned out to be a lot of fun. What's new with you?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/pa728lrin0081.jpg" - ], - "blip_caption": "a photo of a notebook with writing on it sitting on a table", - "query": "notebook sketches game ideas coding challenges friends", - "dia_id": "D24:2", - "text": "Hey John! Cool, what kind of board game is this? I had a lot to do all this time. And in order not to forget to do something, I started writing down everything I needed in a notebook." - }, - { - "speaker": "John", - "dia_id": "D24:3", - "text": "This game is called \"Dungeons of the Dragon\", very exciting! I'm really glad you're writing down what you need to do in a notebook. This will definitely help you not to forget anything! How did you come up with this idea? In general, where do you get ideas?" - }, - { - "speaker": "James", - "dia_id": "D24:4", - "text": "Thanks, John! I get them from various sources like books, movies, and even dreams." - }, - { - "speaker": "John", - "dia_id": "D24:5", - "text": "Wow, dreams have inspired you? That's interesting. Have any specific dreams guided your ideas?" - }, - { - "speaker": "James", - "dia_id": "D24:6", - "text": "A few weeks ago I had this crazy dream that led to some creative ideas. It was so vivid I woke up with some interesting thoughts!" - }, - { - "speaker": "John", - "dia_id": "D24:7", - "text": "Wow, dreams can be so awesome! Are there any specific details you remember from that one?" - }, - { - "speaker": "James", - "dia_id": "D24:8", - "text": "I remember there was a medieval castle with its own labyrinth full of puzzles and traps. It felt like playing a video game in real life!" - }, - { - "speaker": "John", - "dia_id": "D24:9", - "text": "Wow, exploring a castle with puzzles and traps sounds awesome! Have you got any sketches or notes from that experience? I'd love to take a look!" - }, - { - "speaker": "James", - "img_url": [ - "https://www.andrewwkmusic.com/wp-content/uploads/2020/12/0077.jpg" - ], - "blip_caption": "a photo of a notepad with a drawing of a guitar", - "query": "castle labyrinth puzzles notebook sketch handwritten notes dream experience", - "dia_id": "D24:10", - "text": "Yep! I made some sketches and notes. Hang on, let me grab them." - }, - { - "speaker": "John", - "dia_id": "D24:11", - "text": "Nice sketch! Do you like music, or is it related to your castle dream?" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/m5s2y1kxhcnb1.jpg" - ], - "blip_caption": "a photo of a desk with a laptop, headphones, and a monitor", - "query": "gaming setup headphones speakers", - "dia_id": "D24:12", - "text": "Thanks! Music is a big part of my life - nothing to do with castles though. What kind of music do you like? Do you play any instruments?" - }, - { - "speaker": "John", - "dia_id": "D24:13", - "text": "Cool! I'm into electronic and rock music. I used to play drums when I was younger, but haven't in a while. Do you play any instruments?" - }, - { - "speaker": "James", - "dia_id": "D24:14", - "text": "Yeah, rock's awesome! I used to play a guitar when I was younger but haven't in a while." - }, - { - "speaker": "John", - "img_url": [ - "https://cdn11.bigcommerce.com/s-qy9kl0lfci/images/stencil/original/products/62751/75290/Tama_Star_Classic_Tiger_Stripe__70181.1699123066.jpg" - ], - "blip_caption": "a photo of a drum set up in a recording studio", - "query": "drum set", - "dia_id": "D24:15", - "text": "Playing drums when I was younger was a fun way to let off steam. Here's a photo of an old drum set I used to play on." - }, - { - "speaker": "James", - "dia_id": "D24:16", - "text": "Cool! Have you ever been in a band or just jammed with friends?" - }, - { - "speaker": "John", - "dia_id": "D24:17", - "text": "I've jammed with friends before, it was a lot of fun!" - }, - { - "speaker": "James", - "dia_id": "D24:18", - "text": "Sounds awesome! Jamming with friends is always a blast. Do you have any recordings or videos of those sessions?" - }, - { - "speaker": "John", - "dia_id": "D24:19", - "text": "Nah, it was more about the experience and the moment. No recordings or videos from the jams." - }, - { - "speaker": "James", - "dia_id": "D24:20", - "text": "No problem! It's nice to just enjoy the experience without worrying about collecting videos or recordings. By the way, I started streaming games. No details yet, I hope everything works out." - }, - { - "speaker": "John", - "dia_id": "D24:21", - "text": "I'll keep my fingers crossed for you! You will definitely succeed, I look forward to the details!" - } - ], - "session_25_date_time": "8:56 pm on 20 September, 2022", - "session_25": [ - { - "speaker": "John", - "dia_id": "D25:1", - "text": "Hey James, been a few days since we chatted. Lots of stuff goin' on in my life!" - }, - { - "speaker": "James", - "dia_id": "D25:2", - "text": "Hey John! What new has happened in your life?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/2sop5yn1a8i81.jpg" - ], - "blip_caption": "a photo of a fire burning in a metal pot on a street", - "query": "work project", - "dia_id": "D25:3", - "text": "Yesterday I started a new startup - portable smokers. Now, I\u2019ve already welded one from metal. Do you think it looks good? How about you, any cool stuff happening?" - }, - { - "speaker": "James", - "dia_id": "D25:4", - "text": "Hey John, that looks great! Seeing it makes me think of campfires with pals. Last night I streamed a game and wow, was I blown away by all the nice comments from the gaming community. I felt so stoked and inspired to keep going. " - }, - { - "speaker": "John", - "blip_caption": "a photo of a card with a graduation cap on it", - "dia_id": "D25:5", - "text": "Woohoo, congrats James! That's awesome. Sounds like you're doing well. All your hard work is paying off, so keep it up!" - }, - { - "speaker": "James", - "dia_id": "D25:6", - "text": "Thanks for the support, John! This made me think of such an exciting time. Any more big moments recently?" - }, - { - "speaker": "John", - "img_url": [ - "https://www.trustedreviews.com/wp-content/uploads/sites/54/2022/10/2x1_NSwitch_Bayonetta3_image1600w.jpg" - ], - "blip_caption": "a photography of a demonic demon flying in the air with a sword", - "query": "mobile game screenshot title", - "dia_id": "D25:7", - "re-download": true, - "text": "I just achieved a major career milestone - making my first mobile game! It's launching next month." - }, - { - "speaker": "James", - "dia_id": "D25:8", - "text": "Way to go, John! Congrats on achieving that major career milestone. Could you tell me more about it? Why didn\u2019t you say before that you were creating a mobile game?" - }, - { - "speaker": "John", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/d/d1/Awesomenauts_-_Screenshot_01.jpg" - ], - "blip_caption": "a photography of a video game with a bunch of different screens", - "query": "2D adventure game puzzles exploration colorful virtual world", - "dia_id": "D25:9", - "re-download": true, - "text": "Thanks James! I kept it a secret because I would have been very upset if I had told you about her in advance and then it wouldn't have worked out. I've been working on this for the past few months and I'm really proud of how it's turned out. It's a 2D adventure game with puzzles and exploration. Here's a screenshot." - }, - { - "speaker": "James", - "dia_id": "D25:10", - "text": "John, this sounds great! I'm into 2D adventures with puzzles - like The Legend of Zelda. Can I see it or help with testing it out?" - }, - { - "speaker": "John", - "img_url": [ - "https://thethoughtfulgamer.com/wp-content/uploads/2017/10/20171027_105814-e1509116397258.jpg" - ], - "blip_caption": "a photo of a book with a cartoon of a man playing a game", - "query": "game development books resources skills refine game", - "dia_id": "D25:11", - "text": "Cheers, James! Appreciate your offer to help. I'll definitely let you know when the testing is ready. By the way, here is the book that helped me create the puzzles for this game." - }, - { - "speaker": "James", - "dia_id": "D25:12", - "text": "Wow, that book looks great! What other resources do you use to improve your game? Tell me about your gaming tips!" - }, - { - "speaker": "John", - "dia_id": "D25:13", - "text": "It is filled with awesome tips and insights on game design. I also watch tutorials and keep up with developer forums for information and ideas. Basically, staying informed and constantly learning is key!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/a2jbhj4bi6n61.jpg" - ], - "blip_caption": "a photo of a magazine with a picture of a cartoon character", - "query": "gaming magazine tips and tricks game developers", - "dia_id": "D25:14", - "text": "You're really dedicated to improving and staying up to date. It's inspiring to see how you stay informed and keep learning. I also advise you to read this magazine, which is also a worthy source of information. Keep up the good work!\n" - }, - { - "speaker": "John", - "dia_id": "D25:15", - "text": "I read it, too. This magazine has been great for me too. Tutorials, interviews with developers, and tips - all really helpful." - }, - { - "speaker": "James", - "blip_caption": "a photo of a magazine with a picture of a cartoon character", - "dia_id": "D25:16", - "text": "Wow, John! Glad that resource was useful - looks like it provides some good tips and tricks for game developers." - }, - { - "speaker": "John", - "dia_id": "D25:17", - "text": "Yeah, that magazine looks great! Have you also found it to be a good resource?" - }, - { - "speaker": "James", - "dia_id": "D25:18", - "text": "Of course! It's been great, filled with tutorials and developer interviews to help improve my game dev skills. Super useful!" - }, - { - "speaker": "John", - "dia_id": "D25:19", - "text": "Resources like that are great for improving our skills. Keep it up! How's your week been?" - }, - { - "speaker": "James", - "dia_id": "D25:20", - "text": "My week's been good. Just trying to find a balance between work and other activities. How about you, how's your week going?" - }, - { - "speaker": "John", - "dia_id": "D25:21", - "text": "As for me, this week has been chaotic with everything going on. But I'm powering through!" - }, - { - "speaker": "James", - "dia_id": "D25:22", - "text": "Sorry to hear about your busy week, John. Make sure to take some time for yourself and take care. You've got this!" - }, - { - "speaker": "John", - "dia_id": "D25:23", - "text": "I appreciate your help. Gonna make time for myself." - }, - { - "speaker": "James", - "dia_id": "D25:24", - "text": "No worries, take care of yourself. Relax and recharge - you deserve it." - }, - { - "speaker": "John", - "dia_id": "D25:25", - "text": "Thanks, man! I'll definitely take your advice. You're the best!" - } - ], - "session_26_date_time": "9:20 am on 3 October, 2022", - "session_26": [ - { - "speaker": "John", - "dia_id": "D26:1", - "text": "Hey James! Busy few weeks for sure, but I'm pushing through. Got an email about a volunteer gig at a game dev non-profit. It's something I've wanted to do for a while, and could be the perfect start to a career that combines my two loves - gaming and helping. So stoked!" - }, - { - "speaker": "James", - "dia_id": "D26:2", - "text": "Hey John, that sounds awesome! Combining your two loves - gaming and helping people - must be really exciting! So what kind of gig did they offer you?" - }, - { - "speaker": "John", - "dia_id": "D26:3", - "text": "They asked me to be a programming mentor for game developers. I'll be teaching coding and assisting with projects. I'm really excited to share my knowledge and motivate people who are passionate about gaming." - }, - { - "speaker": "James", - "dia_id": "D26:4", - "text": "Wow, John! Mentoring programmers to make games sounds awesome! You must love it. How do you feel about starting this journey?" - }, - { - "speaker": "John", - "dia_id": "D26:5", - "text": "I'm so excited and inspired! It's a great chance to help them and boost my own skills. I love sharing what I know and seeing others reach their potential - it's so rewarding!" - }, - { - "speaker": "James", - "dia_id": "D26:6", - "text": "It's so rewarding to see how much joy you get from it. Keep going, you're doing great!" - }, - { - "speaker": "John", - "dia_id": "D26:7", - "text": "Thanks, James! Your support really means a lot. It's so fulfilling to use my skills to make a difference. I hope this opens more opportunities for me." - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/jkzfs9aawzw21.jpg" - ], - "blip_caption": "a photo of a computer case with a red light on it", - "query": "gaming setup witcher 3", - "dia_id": "D26:8", - "text": "I'm sure it will lead to great things. You got this! Oh, and I got this cool video card last week - I'm so excited to jump into it again!" - }, - { - "speaker": "John", - "dia_id": "D26:9", - "text": "Cool, James! What kind of games are you excited to play on it?" - }, - { - "speaker": "James", - "dia_id": "D26:10", - "text": " I'm super into RPGs, so I'm excited about getting this video card and playing some new games. Have you heard any great things about Cyberpunk 2077? Do you think this game is worthy of my attention?" - }, - { - "speaker": "John", - "dia_id": "D26:11", - "text": "Yeah, I played it - it's awesome! Such an immersive world and an amazing story. I'm sure you'll love it!" - }, - { - "speaker": "James", - "dia_id": "D26:12", - "text": "I'm so excited for it! The world and story sound perfect. Thanks for recommending it, John!" - }, - { - "speaker": "John", - "dia_id": "D26:13", - "text": "No worries, James! Hope you have a blast playing. Let me know what you think!" - }, - { - "speaker": "James", - "dia_id": "D26:14", - "text": "Cool, John. Will do! Take care, see ya!" - }, - { - "speaker": "John", - "dia_id": "D26:15", - "text": "Take care! Enjoy that new computer. Later!" - } - ], - "session_27_date_time": "2:14 pm on 13 October, 2022", - "session_27": [ - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/zkrnzupfyy9b1.jpg" - ], - "blip_caption": "a photo of a desk with a laptop and a monitor", - "query": "online programming competition setup", - "dia_id": "D27:1", - "text": "Hey James! How's it going? I had a blast last week when my programmer friends and I organized an online comp. It was awesome to see everyone show off their skills! Anything new in your life?" - }, - { - "speaker": "James", - "img_url": [ - "https://images-wixmp-ed30a86b8c4ca887773594c2.wixmp.com/f/55934be0-eae0-47bc-a3eb-6c0140f50b06/d5plslq-28102d55-338e-485d-b0fe-5f9553b0aa39.jpg/v1/fill/w_622,h_350,q_70,strp/game_of_thrones___daenerys_targaryen_by_daninaimare_d5plslq-350t.jpg?token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJ1cm46YXBwOjdlMGQxODg5ODIyNjQzNzNhNWYwZDQxNWVhMGQyNmUwIiwiaXNzIjoidXJuOmFwcDo3ZTBkMTg4OTgyMjY0MzczYTVmMGQ0MTVlYTBkMjZlMCIsIm9iaiI6W1t7ImhlaWdodCI6Ijw9NTc2IiwicGF0aCI6IlwvZlwvNTU5MzRiZTAtZWFlMC00N2JjLWEzZWItNmMwMTQwZjUwYjA2XC9kNXBsc2xxLTI4MTAyZDU1LTMzOGUtNDg1ZC1iMGZlLTVmOTU1M2IwYWEzOS5qcGciLCJ3aWR0aCI6Ijw9MTAyNCJ9XV0sImF1ZCI6WyJ1cm46c2VydmljZTppbWFnZS5vcGVyYXRpb25zIl19.3OweRTbk-A6SxQJ1Cqdb8wNtXtkQ6cjXbErC4U42YSQ" - ], - "blip_caption": "a photography of a game of thrones with a dragon and a dragon", - "query": "game interface players battling mythical creatures", - "dia_id": "D27:2", - "re-download": true, - "text": "Hey John, congrats! Something cool happened to me recently. I made my first game and released it for the gaming community - it was so exciting!" - }, - { - "speaker": "John", - "dia_id": "D27:3", - "text": "Congrats on releasing your game, James! Was it fulfilling to see players engage with the game world you created?" - }, - { - "speaker": "James", - "dia_id": "D27:4", - "text": " It was so fulfilling to see players engage with the game world I created. I'm really happy they're having fun with something I put so much work into." - }, - { - "speaker": "John", - "dia_id": "D27:5", - "text": "So cool that people are enjoying it! What inspired you to create it?" - }, - { - "speaker": "James", - "dia_id": "D27:6", - "text": "Playing video games was always great, but creating my own game was really special. Witcher 3 gave me a ton of inspiration, with its amazing world and story. Plus, it pushed me to create something cool." - }, - { - "speaker": "John", - "dia_id": "D27:7", - "text": "The Witcher 3 obviously had a huge impact on you. You must have put a ton of hard work and dedication into your game. Do you have any plans for future game development?" - }, - { - "speaker": "James", - "dia_id": "D27:8", - "text": "I put in so much effort and it paid off - now, I'm ready to make more games in different genres and test out new ideas. I'm pumped to see where this journey leads!" - }, - { - "speaker": "John", - "dia_id": "D27:9", - "text": " I can't wait to see where your journey leads and the new creations you come up with. Your determination and love for game development is incredible. Keep going and you'll do great things!" - }, - { - "speaker": "James", - "dia_id": "D27:10", - "text": "I'm really looking forward to creating more enjoyable experiences!" - }, - { - "speaker": "John", - "dia_id": "D27:11", - "text": "I'm here for you. Anything you need, count on me!" - }, - { - "speaker": "James", - "dia_id": "D27:12", - "text": "Thanks, John! Your support is really appreciated." - }, - { - "speaker": "John", - "dia_id": "D27:13", - "text": "No worries, James! We make a good team." - }, - { - "speaker": "James", - "dia_id": "D27:14", - "text": "Yeah, totally. You've always been there for me, John. Thanks for having my back." - } - ], - "session_28_date_time": "7:36 pm on 21 October, 2022", - "session_28": [ - { - "speaker": "James", - "dia_id": "D28:1", - "text": "Hey John, long time no talk! So much has happened!" - }, - { - "speaker": "John", - "dia_id": "D28:2", - "text": "Hey James! I'm excited to catch up. What's been up lately?" - }, - { - "speaker": "James", - "dia_id": "D28:3", - "text": "Three days ago my apartment lost power - so annoying because I had just gotten to the big reveal in that game! Had to wait hours before playing again." - }, - { - "speaker": "John", - "dia_id": "D28:4", - "text": "Ugh, that stinks! Losing power in the middle of a game is such a bummer. Did it mess up your progress?" - }, - { - "speaker": "James", - "dia_id": "D28:5", - "text": "Oof, it definitely messed up my progress. I lost some of it because I forgot to save. Frustrating, but now I know to save more often!" - }, - { - "speaker": "John", - "dia_id": "D28:6", - "text": "Lesson learned - save progress! By the way, I organized the programming seminar last week." - }, - { - "speaker": "James", - "dia_id": "D28:7", - "text": "Wow, cool! How did it go? Did you learn anything cool?" - }, - { - "speaker": "John", - "dia_id": "D28:8", - "text": "The seminar went really well! We had a great turnout and I learned some interesting new things. It was a fulfilling experience to share my knowledge and see how it benefited the group." - }, - { - "speaker": "James", - "dia_id": "D28:9", - "text": "That's great, John! Sounds like the seminar went well. What did you learn from it?" - }, - { - "speaker": "John", - "dia_id": "D28:10", - "text": "I gained insight into various programming approaches and techniques. It was interesting to hear other developers' ideas and strategies." - }, - { - "speaker": "James", - "dia_id": "D28:11", - "text": "Learning new programming stuff is great. Did you find any ideas that you'll incorporate into your own work?" - }, - { - "speaker": "John", - "dia_id": "D28:12", - "text": "Yeah! Found some cool ideas that I can use in my own work. It's exciting to explore different programming techniques and how to implement them." - }, - { - "speaker": "James", - "dia_id": "D28:13", - "text": "Cool, John! Broadening your programming skills and trying new techniques is great - keeps things exciting and helps you develop. Have you had a chance to try them out yet?" - }, - { - "speaker": "John", - "dia_id": "D28:14", - "text": "No, I haven't tried them yet. But I'm looking forward to experimenting and seeing what I can do with them. It's always fun to try new things!" - }, - { - "speaker": "James", - "dia_id": "D28:15", - "text": "Yeah! Trying new stuff keeps us on our toes and helps our creativity. Awesome that you're down to experiment and see what you can come up with. I'm looking to branch out as well, any ideas I could check out?" - }, - { - "speaker": "John", - "dia_id": "D28:16", - "text": " I'll send you some resources and tutorials on the new programming approaches and techniques I learned. You'll find them cool!" - }, - { - "speaker": "James", - "dia_id": "D28:17", - "text": "Appreciate it. Can't wait to check them out, and maybe learn something new!" - }, - { - "speaker": "John", - "dia_id": "D28:18", - "text": "No worries, James. I hope they help. Let me know if you have any questions." - }, - { - "speaker": "James", - "img_url": [ - "https://api.army.mil/e2/c/images/2022/04/11/bcd7991a/original.jpg" - ], - "blip_caption": "a photo of a man and woman in military clothing standing next to a dog", - "query": "dog companions", - "dia_id": "D28:19", - "text": "I'll reach out if I need help. Thanks for the resources, really appreciate it. By the way, my mother came to see me with her army friend two days ago. We had fun." - }, - { - "speaker": "John", - "dia_id": "D28:20", - "text": "Cool. Mother's friend must still be in the army?" - }, - { - "speaker": "James", - "dia_id": "D28:21", - "text": "Yes, she is still serving. But she retired a long time ago. They used to tell me stories about their time in the military and their pup. Funny enough, I have a pic of me at their age playing on their old gaming setup. Would you like to see it?" - }, - { - "speaker": "John", - "dia_id": "D28:22", - "text": "Yeah, James! Show me that picture of you playing on their old gaming setup, it looks like a blast!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/lto5hiavrmz21.jpg" - ], - "blip_caption": "a photo of a nintendo game console and a nintendo mario game controller", - "query": "old photo james crt tv game controller", - "dia_id": "D28:23", - "text": "Here is a photo of this console with the game Mario. Funny gamepad, isn't it?" - }, - { - "speaker": "John", - "dia_id": "D28:24", - "text": "Oh yeah, that`s funny! Did you have fun with Nintendo when you were a kid?" - }, - { - "speaker": "James", - "dia_id": "D28:25", - "text": "Oh yeah! I had a blast with it when I was a kid. It was my first gaming system and I'd play Super Mario and The Legend of Zelda for hours. It totally sparked my passion for gaming." - }, - { - "speaker": "John", - "dia_id": "D28:26", - "text": "Wow, James! Those games really sparked your passion for gaming, didn't they?" - }, - { - "speaker": "James", - "blip_caption": "a photo of a video game cover of the witcher wild hunt", - "dia_id": "D28:27", - "text": "Those games introduced me to gaming and I've been hooked ever since. By the way, yesterday I tried Cyberpunk 2077. Great game, so addictive!" - }, - { - "speaker": "John", - "dia_id": "D28:28", - "text": "I'm really glad you're enjoying this game. There will be so many unexpected turns in it, you can\u2019t even imagine!" - }, - { - "speaker": "James", - "dia_id": "D28:29", - "text": "What do you think is the most difficult thing about this game?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/1pegm3i0hs561.jpg" - ], - "blip_caption": "a photo of a man in a leather vest and a belt", - "query": "the witcher 3 dialogue choice decision intense consequences", - "dia_id": "D28:30", - "text": "The most difficult thing is to make the right choice. After all, even from the choice of lines in dialogues with characters, everything can go wrong. The choices here can be life-changing!" - }, - { - "speaker": "James", - "dia_id": "D28:31", - "text": "Thank you very much, I will definitely keep this in mind!" - }, - { - "speaker": "John", - "dia_id": "D28:32", - "text": "And remember, you don't have to be friends with every character in this game. I don't want to spoil it, but just remember this!" - }, - { - "speaker": "James", - "dia_id": "D28:33", - "text": "I'll definitely take your advice, John! Thank you for avoiding spoilers." - }, - { - "speaker": "John", - "dia_id": "D28:34", - "text": "Always happy to help. Well, I have to go! Bye!" - }, - { - "speaker": "James", - "dia_id": "D28:35", - "text": "Take care, bye!" - } - ], - "session_29_date_time": "12:37 am on 31 October, 2022", - "session_29": [ - { - "speaker": "John", - "dia_id": "D29:1", - "text": "Hey James! Hope you're doing great. I've some amazing news - I held a gaming tourney with my buddies last night. We played Fortnite and a few other games. We raised a decent amount for a children's hospital. Combining gaming and a good cause felt awesome!" - }, - { - "speaker": "James", - "dia_id": "D29:2", - "text": "Hey John! Awesome job organizing a gaming tournament for a children's hospital! Combining gaming and a good cause - that's really cool! Tell me more about who helped out and what other games were played." - }, - { - "speaker": "John", - "dia_id": "D29:3", - "text": "Thanks! We all pulled together for a great cause. My gaming pals and I also played Overwatch and Apex Legends. Everyone had a blast raising money for the kids' hospital. The atmosphere was awesome and everyone was so competitive. In the end, we raised a good amount. Feels good to use our love of gaming for good!" - }, - { - "speaker": "James", - "dia_id": "D29:4", - "text": "Wow, that sounds like a blast! It's great how gaming can bring people together like that. You made a huge difference in the kids' lives! Do you have any photos from the tournament?" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/157m14rlacw51.jpg" - ], - "blip_caption": "a photo of a computer screen with a game menu on it", - "query": "gaming tournament intense match engaged thrilling experience good cause", - "dia_id": "D29:5", - "text": "I got a great shot at the tournament. Check it out! Everyone was so hyped, and it felt great knowing we were playing for a good cause." - }, - { - "speaker": "James", - "dia_id": "D29:6", - "text": "Wow, this photo rocks!" - }, - { - "speaker": "John", - "dia_id": "D29:7", - "text": "Thanks! I'm glad you enjoyed it. It was a really awesome moment - unforgettable for all of us! What's new with you?" - }, - { - "speaker": "James", - "dia_id": "D29:8", - "text": "I actually have something new, Samantha and I have decided to move in together!" - }, - { - "speaker": "John", - "dia_id": "D29:9", - "text": "Wow, that's a really big decision! I hope you both have weighed the pros and cons. Where are you going to live?" - }, - { - "speaker": "James", - "dia_id": "D29:10", - "text": "Of course, this was a mutual and informed decision. We rented an apartment not far from McGee's bar." - }, - { - "speaker": "John", - "dia_id": "D29:11", - "text": "You love spending time together in this bar, don't you?" - }, - { - "speaker": "James", - "dia_id": "D29:12", - "text": "We just love it! I\u2019ll be honest, one of the criteria for our choice of apartment was this particular bar nearby." - }, - { - "speaker": "John", - "dia_id": "D29:13", - "text": "Awesome, James! Excited to hear how it goes. Keep me posted and good luck!" - }, - { - "speaker": "James", - "dia_id": "D29:14", - "text": "Thanks, John! I'll be sure to keep you updated. I really appreciate your support. Take care!" - }, - { - "speaker": "John", - "dia_id": "D29:15", - "text": "No worries! I'm here for you whenever you need. Stay safe and chat soon!" - }, - { - "speaker": "James", - "blip_caption": "a photo of a man and two dogs running in a field", - "dia_id": "D29:16", - "text": "Thanks! Appreciate your support. Stay safe and talk to you soon!" - } - ], - "session_30_date_time": "5:20 pm on 5 November, 2022", - "session_30": [ - { - "speaker": "James", - "dia_id": "D30:1", - "text": "Hey John, hope you're doing well. Yesterday, we started on a road trip. It was fun spending time with the family and my dogs. Exploring new places and taking in nature with the furballs was awesome!" - }, - { - "speaker": "John", - "dia_id": "D30:2", - "text": "Hey James! Wow, what an adventure! Lately, I've been busy with something. Guess what? I had a great accomplishment this Tuesday! It was awesome." - }, - { - "speaker": "James", - "dia_id": "D30:3", - "text": "That's great news. What did you do?" - }, - { - "speaker": "John", - "dia_id": "D30:4", - "text": "I won the regional chess tournament. It was intense but I came out on top!" - }, - { - "speaker": "James", - "dia_id": "D30:5", - "text": "That's awesome! Congrats! How did it feel to come out on top?" - }, - { - "speaker": "John", - "dia_id": "D30:6", - "text": "It felt so good. All my hard work and practice paid off and it was great to conquer the challenges. It gave me a huge confidence boost. So proud of myself!" - }, - { - "speaker": "James", - "dia_id": "D30:7", - "text": "Winning must have felt so good. What was it like when you won? What strategies did you use to get ready?" - }, - { - "speaker": "John", - "dia_id": "D30:8", - "text": "My strategy involved analyzing and anticipating my opponent's moves to stay one step ahead." - }, - { - "speaker": "James", - "dia_id": "D30:9", - "text": "Cool! It's all about studying the game to gain the edge. Do you have any tips for improving?" - }, - { - "speaker": "John", - "dia_id": "D30:10", - "text": "Yeah, studying opening moves and strategies can really help. It sets the tone and builds a strong foundation. Learning from experienced players and analyzing past games is important too. Plus the chess advice you gave me earlier also helped. Would you like some resources on chess openings?" - }, - { - "speaker": "James", - "dia_id": "D30:11", - "text": "Sure, I'd love to check out some resources on chess openings. Thank you!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/jqeodrms8xnb1.jpg" - ], - "blip_caption": "a photo of a book with a list of the different games", - "query": "chess strategy book screenshot", - "dia_id": "D30:12", - "text": "I've got you covered on that. Here's a helpful resource for chess openings. Happy to help!" - }, - { - "speaker": "James", - "dia_id": "D30:13", - "text": "Thanks for the suggestion, John. What games are you currently playing? I'm always looking for new recommendations." - }, - { - "speaker": "John", - "dia_id": "D30:14", - "text": "I'm hooked on this great game called FIFA 23. This is a great football game with the ability to play online with other players from all over the world! Enjoy!" - }, - { - "speaker": "James", - "dia_id": "D30:15", - "text": "Wow, that sounds awesome! I just wanted to try a new gaming genre, so why not try the sports genre." - }, - { - "speaker": "John", - "dia_id": "D30:16", - "text": "You need to practice a little first, and then we can play together." - }, - { - "speaker": "James", - "dia_id": "D30:17", - "text": "Great idea! I hope it's easy to control." - }, - { - "speaker": "John", - "dia_id": "D30:18", - "text": "Not at all, all you need is a gamepad and a sense of timing." - }, - { - "speaker": "James", - "dia_id": "D30:19", - "text": "Great! Well, I'll go train!" - } - ], - "session_31_date_time": "8:57 pm on 7 November, 2022", - "session_31": [ - { - "speaker": "James", - "img_url": [ - "https://i.redd.it/toaksy9sbze41.jpg" - ], - "blip_caption": "a photo of a group of people posing for a picture", - "query": "group of friends playing video games", - "dia_id": "D31:1", - "text": "Hey John! Guess what? Me and my family are currently on the road trip! We`ve already visited my friends Josh and Mark and had such a great time!" - }, - { - "speaker": "John", - "img_url": [ - "https://www.choochootrack.com/cdn-cgi/image/quality%3D85/assets/images/6%20player%20setup%20game%20over.jpg" - ], - "blip_caption": "a photo of a wooden board game with four pieces of wood and four pieces of colored balls", - "query": "family game night collaboration online game", - "dia_id": "D31:2", - "text": "Hey James! That sounds awesome! I had a super fun weekend - I worked with a game developer on a project and it was great to see my ideas come to life. It was an incredible experience! " - }, - { - "speaker": "James", - "dia_id": "D31:3", - "text": "That sounds amazing. What was the project you worked on?" - }, - { - "speaker": "John", - "dia_id": "D31:4", - "text": "I collaborated with a game developer to create an online board game - it's a fun and unique experience!" - }, - { - "speaker": "James", - "dia_id": "D31:5", - "text": " I can imagine how proud you must feel seeing your ideas come to life in a game. Has it been released for others to try yet?" - }, - { - "speaker": "John", - "dia_id": "D31:6", - "text": "We're about to release a demo soon so others can try it out. Can't wait for their feedback and suggestions." - }, - { - "speaker": "James", - "dia_id": "D31:7", - "text": "Can't wait to try it. Keep me posted when it's out - I wanna support you and give my thoughts." - }, - { - "speaker": "John", - "dia_id": "D31:8", - "text": "Appreciate your support. I'll definitely let you know when it's out and I'm really excited to hear your thoughts." - }, - { - "speaker": "James", - "dia_id": "D31:9", - "text": "By the way, we did one good thing on the way to Mark and Josh." - }, - { - "speaker": "John", - "dia_id": "D31:10", - "text": "What is this? Looking forward to hearing your story!" - }, - { - "speaker": "James", - "img_url": [ - "https://i.ytimg.com/vi/uqizCzE85B8/maxresdefault.jpg" - ], - "blip_caption": "a photo of a man kneeling down next to a dog", - "query": "animal sanctuary rescue dogs", - "dia_id": "D31:11", - "text": "We visited an animal sanctuary on the road trip - there were so many cute rescue dogs! I thought of our love of furry pals." - }, - { - "speaker": "John", - "dia_id": "D31:12", - "text": "Cool! What was it like visiting the animal sanctuary? Did you feel tempted to bring any furry pals home?" - }, - { - "speaker": "James", - "dia_id": "D31:13", - "text": "Those rescue dogs were so cute, I wanted to take them all home, but I remembered that I already have three dogs at home. I think having more than three dogs is too much." - }, - { - "speaker": "John", - "dia_id": "D31:14", - "text": "You are right! I still haven\u2019t gotten a dog, but I still really want one. What is it like to have a dog?" - }, - { - "speaker": "James", - "dia_id": "D31:15", - "text": "Having furry friends around brings so much joy and friendship. Life wouldn't be the same without them. Every day's better with them around." - }, - { - "speaker": "John", - "dia_id": "D31:16", - "text": "Yep, they bring so much joy and love. They're always there for us! It's like having sunshine on a cloudy day." - }, - { - "speaker": "James", - "dia_id": "D31:17", - "text": "My dogs are like that too - they even make dark days better. Don't know what I'd do without them. They're the best buddies." - }, - { - "speaker": "John", - "dia_id": "D31:18", - "text": "Yeah, dogs are awesome for sure! They make us feel so loved and cheerful, don't they?" - }, - { - "speaker": "James", - "dia_id": "D31:19", - "text": "Yeah, they definitely do. Dogs always cheer us up, wagging their tails and giving us unconditional love. It's like having a dose of positivity and happiness every day. They're amazing!" - }, - { - "speaker": "John", - "img_url": [ - "https://i.redd.it/iwwwkzul10b71.jpg" - ], - "blip_caption": "a photo of a dog sitting on a rug in front of a couch", - "query": "golden retriever puppy", - "dia_id": "D31:20", - "text": "Definitely, James! Dogs are amazing. They bring so much joy and positivity. They accept us without judgement, just love and happiness. I appreciate the daily dose of positivity they bring to my life. Special buddies for sure. By the way, here is my cousin's dog." - }, - { - "speaker": "James", - "dia_id": "D31:21", - "text": "This pup is so adorable! What's their name?" - }, - { - "speaker": "John", - "img_url": [ - "https://thewilsonsofoz.files.wordpress.com/2017/05/img_0635.jpg" - ], - "blip_caption": "a photo of two people standing in front of a giant head", - "query": "luna playing catch park", - "dia_id": "D31:22", - "text": "Their name is Luna. " - }, - { - "speaker": "James", - "dia_id": "D31:23", - "text": "Luna's a great name!" - }, - { - "speaker": "John", - "dia_id": "D31:24", - "text": "Thanks, gonna go, sorry. Cheers! Bye!" - }, - { - "speaker": "James", - "dia_id": "D31:25", - "text": "Later! Take care!" - } - ] - }, - "event_summary": { - "events_session_1": { - "James": [ - "creating a game and telling a story through it.", - "James proposes making dog-sitting mobile apps with John. The app's custom filters will set it apart from other dog-sitting apps on the market." - ], - "John": [ - "John decides to take up a course in programming HTML, CSS", - "John proposes collaborating with James to make mobile apps." - ], - "date": "17 March, 2022" - }, - "events_session_2": { - "James": [ - "James creates an avatar and joining a new online gaming platform where he has a great time connecting with other gamers." - ], - "John": [ - "John picks up a hobby of walking along the beach with a metal detector.", - "John finds bottle caps, a few coins and a gold ring from his new metal detector hobby." - ], - "date": "20 March, 2022" - }, - "events_session_3": { - "James": [ - "James starts learning to play the drums.", - "James purchases a new cutting-edge gaming system with great graphics." - ], - "John": [ - "John continues his hobby of playing drums that he started in February 2022.", - "GO plays.", - "GO with skilled players and meets game developers." - ], - "date": "27 March, 2022" - }, - "events_session_4": { - "James": [ - "James joins an online Apex Legend gaming tournament with his friends, makes it to the semi-finals and wins some rounds.", - "James receives gamink skill tips and autographs from skilled Apex Legend players at the gaming tournament." - ], - "John": [], - "date": "4 April, 2022" - }, - "events_session_5": { - "James": [ - "James adopts a puppy from a shelter and names it Ned. He now has three dogs." - ], - "John": [ - "John starts playing a new RPG set in a futuristic dystopia and with a cool story." - ], - "date": "12 April, 2022" - }, - "events_session_6": { - "James": [ - "James works on a programming project with one of his gaming pals and creates a virtual world inspired by Witcher 3.", - "James meets a woman when walking his dogs and almost falls in love with her but does not ask for her contact.", - "James creates a gaming avatar to emobody the woman he met when walking his dogs, in the virtual gaming world he created with his gaming pal." - ], - "John": [ - "John meets three new friends through the HTML, CSS programming course he started taking in March 2022.", - "John seeks solitude and visits a nearby canyon." - ], - "date": "20 April, 2022" - }, - "events_session_7": { - "James": [ - "James takes his two older dogs and the puppy on an adventurous hike." - ], - "John": [ - "John faces a difficult assignment involving coding at work and is stressed because of not making much progress." - ], - "date": "23 April, 2022" - }, - "events_session_8": { - "James": [ - "James buys a fantasy novel with cool artwork.", - "James starts exploring some strategy games like Civilization VI." - ], - "John": [ - "John takes up free-lance programming work to increase his proficiency in the subject and apply the learnings from his programming class.", - "John's first freelance programming project is to create a website for a local business, where he faces some struggles with getting payments to work on the website.", - "John plays strategy board games with friends." - ], - "date": "29 April, 2022" - }, - "events_session_9": { - "James": [ - "James faces a major snag in the development of his Witcher-inspired game and takes the help of a group of friends to get it fixed." - ], - "John": [ - "John invites the friends he made in his programming class to come over and watch movies on his day-off." - ], - "date": "4 May, 2022" - }, - "events_session_10": { - "James": [], - "John": [ - "GO charity gaming tournament with his gaming friends.", - "John and his friends donate the money raised through the gaming tournament to a dog shelter and buy food, groceries for the homeless with the remaining money." - ], - "date": "8 May, 2022" - }, - "events_session_11": { - "James": [], - "John": [ - "John volunteers his programming skill to a foundation that helps kids.", - "John creates a software tool that streamlines operations of the foundation and digitizes their donation and inventory records.", - "John finds satisfaction and passion in working towards a good cause and decides to explore a career in the non-profit sector." - ], - "date": "11 May, 2022" - }, - "events_session_12": { - "James": [ - "James goes to an amusement park with his friends.", - "James finishes a challenging project in April 2022 which involved learning a new language." - ], - "John": [ - "John participates in a local gaming tournament and wins the second prize as well as a monetary reward." - ], - "date": "23 May, 2022" - }, - "events_session_13": { - "James": [ - "James starts taking a course on game design." - ], - "John": [ - "John gets his dream job after lots of interviews and plans to start in July 2022." - ], - "date": "13 June, 2022" - }, - "events_session_14": { - "James": [], - "John": [ - "John starts a blog to document his coding journey.", - "John buys new sci-fi anf fantasy books that fill up his bookcase." - ], - "date": "16 June, 2022" - }, - "events_session_15": { - "James": [ - "James begins introducing his older dogs Max and Daisy to the new puppy, Ned.", - "James volunteers at an organization in May 2022 that provides underpriveleged people with necessary items." - ], - "John": [ - "John wishes to find a volunteer position and agrees to check out the organization that James volunteers with." - ], - "date": "19 June, 2022" - }, - "events_session_16": { - "James": [ - "James successfully wins an online gaming tournament.", - "James books tickets to Toronto, Vancounver and leaves for a trip from 11 - 20 July 2022.", - "James gets interested in extreme sports and tries out rope jumping, surfing." - ], - "John": [], - "date": "9 July, 2022" - }, - "events_session_17": { - "James": [ - "James returns from his visit to Canada and the city of Nuuk, and brings back souvenirs for John and Jill.", - "James's sister visits him and they visit the ocean." - ], - "John": [ - "John starts playing chess and joins a chess club to practice his competitive edge." - ], - "date": "22 July, 2022" - }, - "events_session_18": { - "James": [ - "James takes the puppy to the vet for its necessary vaccinations to prevent him from catching the seasonal canine disease." - ], - "John": [ - "John reflects on quitting his IT job that he worked at for three years, and joining a new job that he likes.", - "John wants to pursue a career in organizing gaming tournaments in his state." - ], - "date": "6 August, 2022" - }, - "events_session_19": { - "James": [ - "James takes the three dogs to a beach outing to have fun and bond with other dogkeepers.", - "James meets a woman named Samantha during the outing to the beach and asks for her phone number to call and meet up later." - ], - "John": [ - "John starts playing the RPG Witcher 3." - ], - "date": "10 August, 2022" - }, - "events_session_20": { - "James": [ - "James hosts a gaming marathon in his home for his gaming friends." - ], - "John": [ - "John joins a programming group on a social media platform to keep in touch with his fellow coders, share ideas and collaborate on coding projects.", - "John plans a gaming night with his siblings for September 2022.", - "John refurbishes his gaming desk." - ], - "date": "21 August, 2022" - }, - "events_session_21": { - "James": [], - "John": [ - "John's younger siblings join him in the HTML/CSS programming course.", - "John and his siblings work on building a text-based adventure game as a project for the programming course." - ], - "date": "26 August, 2022" - }, - "events_session_22": { - "James": [ - "James finishes building a Unity-based strategy game, inspired from his experiences with Civilization and Total War games." - ], - "John": [ - "John teaches coding to his younger siblings who also joined him in the HTML/CSS programming course." - ], - "date": "1 September, 2022" - }, - "events_session_23": { - "James": [ - "James asks Samantha to be his girlfriend at the theater and she agrees.", - "James and Samantha plan on visiting a baseball game and James invites John to join them.", - "James takes cooking classes and learns to cook omlette, meringue, dough." - ], - "John": [ - "John's parents start taking coding lessons from him.", - "John buys Sennheiser headphones and Logitech mouse gaming equipment to improve his skills." - ], - "date": "4 September, 2022" - }, - "events_session_24": { - "James": [ - "James participates in a gaming stream to showcase his game to his audience.", - "James starts wirting down ideas and inspirations in a notebook in order to not forget anything significant." - ], - "John": [ - "John starts exploring Dungeons of the Dragons board game." - ], - "date": "18 September, 2022" - }, - "events_session_25": { - "James": [ - "James receives cheers and compliments from the gaming community after a successful stream." - ], - "John": [ - "John starts a startup selling portable smokers.", - "John finishes working on a mobile 2D adventure game with puzzles and explorations which is due for release in October 2022" - ], - "date": "20 September, 2022" - }, - "events_session_26": { - "James": [ - "James buys a new video card and is excited to play Cyberpunk 2077 with it." - ], - "John": [ - "John is excited about receiving an email about a volunteer gig where he can mentor young game developers." - ], - "date": "3 October, 2022" - }, - "events_session_27": { - "James": [ - "James completes working on his first game, inspired by the Witcher 3 world, and publishes it for the online gaming community." - ], - "John": [ - "John and his programming friends host an online programming competition." - ], - "date": "13 October, 2022" - }, - "events_session_28": { - "James": [ - "James tries playing Cyberpunk 2077 and enjoys the game.", - "James's apartment loses power and he loses progress in a game that he forgets to save.", - "James's mother and her friend from the military visit him." - ], - "John": [ - "John takes the initiative to organize a programming seminar in his local area and gains new insights on programming practices." - ], - "date": "21 October, 2022" - }, - "events_session_29": { - "James": [ - "James and Samantha decide to move in together.", - "James and Samantha find an apartment close to their favorite bar, McGee's, for their move-in." - ], - "John": [ - "John invites his gaming friends to join the charity tournament where they play Overwatch, Apex Legends and raise money for a children's hospital." - ], - "date": "31 October, 2022" - }, - "events_session_30": { - "James": [ - "James takes his dogs for a long road trip with his family." - ], - "John": [ - "John wins the regional chess tournament.", - "John gets hooked to the FIFA 23 video game." - ], - "date": "5 November, 2022" - }, - "events_session_31": { - "James": [ - "James and his family visit his gaming friends, Josh and Mark, on their road trip.", - "James and his family visit an animal sanctuary during their road trip." - ], - "John": [ - "John collaborates with a game developer on an online game." - ], - "date": "7 November, 2022" - } - }, - "observation": { - "session_1_observation": { - "John": [ - [ - "John signed up for a programming class to refresh his skills in HTML and CSS.", - "D1:7" - ], - [ - "John suggested the idea of developing mobile applications together in the future.", - "D1:11" - ], - [ - "John showed interest in VR gaming and agreed to try it with James next Saturday.", - "D1:35" - ] - ], - "James": [ - [ - "James tried programming in college and now it's a big part of his life.", - "D1:6" - ], - [ - "James has worked with Python and C++, building websites and creating game mods.", - "D1:8" - ], - [ - "James plans to build an app for dog walking and pet care, aiming to connect pet owners with reliable dog walkers.", - "D1:14" - ], - [ - "James is working on a project to turn his childhood sketches of a main character into a computer game, combining his passions for gaming and storytelling.", - "D1:20" - ] - ] - }, - "session_2_observation": { - "James": [ - [ - "James created a game avatar and joined a new platform to connect with other gamers.", - "D2:1" - ], - [ - "Gaming has been a refuge for James in tough times, providing relaxation and escape from stress.", - "D2:5" - ], - [ - "James sees games as therapy, where he can relax, forget his troubles, and get lost in another world.", - "D2:7" - ], - [ - "James has been exploring different styles of gaming lately to try something new and test himself.", - "D2:9" - ], - [ - "James has two dogs who he has done lots of training with, and they are like family to him.", - "D2:17" - ], - [ - "James offered to help find the perfect pet for John when John mentioned wanting one.", - "D2:19" - ] - ], - "John": [ - [ - "John recently got into a new hobby of using a metal detector on the beach to look for items.", - "D2:10" - ], - [ - "John found mostly bottle caps with his metal detector, but also found coins and even a gold ring a couple of times.", - "D2:12" - ], - [ - "John expressed interest in getting a pet in the future and appreciated James' offer to help find the perfect one.", - "D2:18" - ] - ] - }, - "session_3_observation": { - "John": [ - [ - "John had a big win in his game last week and advanced to the next level, which was a huge confidence booster for him.", - "D3:1" - ], - [ - "John plays drums and has been playing for only a month.", - "D3:3" - ], - [ - "John attended a gaming convention where he tried out loads of games, met developers, and took part in a tournament for CS:GO.", - [ - "D3:9", - "D3:11" - ] - ], - [ - "John is always looking to up his game and hit new goals in his hobbies and other stuff.", - "D3:15" - ] - ], - "James": [ - [ - "James is learning to play a musical instrument and has been at it daily, seeing improvements.", - [ - "D3:2", - "D3:6" - ] - ], - [ - "James got a new cutting-edge gaming system with incredible graphics and enjoys playing new games to relax after work.", - "D3:8" - ], - [ - "James is getting into different types of games now, like RPGs and strategy games, which he finds really exciting.", - "D3:18" - ] - ] - }, - "session_4_observation": { - "John": [ - [ - "John asked James if he had been playing any new games lately.", - "D4:1" - ], - [ - "John inquired about James' performance in the online gaming tournament.", - "D4:3" - ], - [ - "John asked James if he met any famous players at the tournament.", - "D4:7" - ], - [ - "John asked James what advice he remembered most from the famous players.", - "D4:11" - ], - [ - "John discussed the importance of communication and teamwork in gaming.", - "D4:13" - ], - [ - "John asked James about the game he plays with his team.", - "D4:15" - ], - [ - "John inquired if James is looking forward to trying out any new games.", - "D4:19" - ] - ], - "James": [ - [ - "James joined an online gaming tournament and made it to the semifinals, winning some rounds.", - "D4:2" - ], - [ - "James participated in the final rounds of the online gaming tournament but did not make it to the finals.", - "D4:6" - ], - [ - "James met the whole team of a famous player at the tournament and received gaming tips and autographs.", - "D4:8" - ], - [ - "James remembers the gaming advice received was about proper communication and teamwork over ego.", - "D4:12" - ], - [ - "James usually communicates with his team using voice chat for effective teamwork.", - "D4:14" - ], - [ - "James enjoys playing Apex Legends with his team due to its fast-paced gameplay.", - "D4:16" - ], - [ - "James is excited about trying out new games like RPGs and MOBAs.", - "D4:20" - ], - [ - "James plans to update John on his gaming experiences and new games.", - "D4:24" - ] - ] - }, - "session_5_observation": { - "James": [ - [ - "James adopted a pup from a shelter in Stamford last week and named it Ned, making his days happier.", - "D5:1" - ] - ], - "John": [ - [ - "John is playing a new RPG that transports players to a futuristic dystopia with a cool story and world.", - "D5:4" - ], - [ - "John found the game to have some lags and errors but still believes it has great graphics and immersive gameplay.", - "D5:6" - ] - ] - }, - "session_6_observation": { - "John": [ - [ - "John met three new friends in his programming course last Tuesday and is excited to expand his social circle.", - "D6:1" - ], - [ - "John enjoys chilling with friends and traveling to get motivation for work.", - "D6:11" - ], - [ - "John visited Japan last, where he was impressed by the technologically advanced megacities and delicious street food.", - "D6:15" - ] - ], - "James": [ - [ - "James worked on a programming project combining gaming with programming, creating a virtual world inspired by Witcher 3 with a game character he designed.", - "D6:2" - ], - [ - "James got the idea for the virtual world from a stranger he saw while walking his dogs and found inspiring.", - "D6:6" - ], - [ - "James has visited Italy, Turkey, and Mexico besides his permanent residence and found Italy to be very beautiful with delicious food.", - "D6:12" - ], - [ - "James expressed an interest in traveling together with John to a new country next year and plans to start looking for options.", - "D6:16" - ] - ] - }, - "session_7_observation": { - "John": [ - [ - "John took his dogs out for a hike last Thursday and they enjoyed discovering trails and views.", - "D7:2" - ], - [ - "John has been super busy at work with deadlines and lots to do, making it a hectic time for him.", - "D7:11" - ], - [ - "John is working on a difficult coding project that he finds frustrating and is struggling to make progress on.", - "D7:13" - ] - ], - "James": [ - [ - "James enjoys the outdoors and finds being surrounded by lush greenery and clean air comforting.", - "D7:6" - ], - [ - "James finds the crunch of leaves under his feet and the peacefulness of nature helpful in clearing his head and chilling.", - "D7:8" - ], - [ - "James offers assistance and suggests breaking down the project into smaller steps, doing research, and seeking help to overcome coding difficulties.", - "D7:18" - ] - ] - }, - "session_8_observation": { - "James": [ - [ - "James bought an adventure book with fantasy novels and cool arts three days ago.", - "D8:11" - ], - [ - "James has furry friends, like his dog Daisy, who lay down next to him while he plays games.", - "D8:17" - ], - [ - "James is currently trying out some strategy games.", - "D8:21" - ], - [ - "James has been playing the game AC Valhalla.", - "D8:21" - ], - [ - "James has been playing a turn-based strategy game where you manage resources, lead armies, and conquer territories.", - "D8:23" - ], - [ - "James has been playing the turn-based strategy game for a month to challenge his strategy skills.", - "D8:29" - ] - ], - "John": [ - [ - "John is currently taking on freelance programming to hone his coding skills.", - "D8:2" - ], - [ - "John is working on a website for a local small business, which is his first professional project outside of class.", - "D8:4" - ], - [ - "John faced challenges figuring out how to get payments on the website he's working on.", - "D8:10" - ], - [ - "John recommended the novel \"The Name of the Wind\" to James.", - "D8:14" - ], - [ - "John has been playing the game AC Valhalla.", - "D8:20" - ], - [ - "John has been playing Among Us, a game where players figure out who the impostors are.", - "D8:36" - ] - ] - }, - "session_9_observation": { - "John": [ - [ - "John had some new friends over to watch movies on his day off.", - "D9:9" - ], - [ - "John enjoys socializing and believes in balancing work with enjoyment.", - "D9:9" - ], - [ - "John is interested in what type of pets James has.", - "D9:11" - ], - [ - "John loves Hawaiian pizza for its sweet and salty combination.", - "D9:19" - ] - ], - "James": [ - [ - "James faced a bug in his project that messed up the game mechanics, causing disappointment.", - "D9:4" - ], - [ - "James enjoys spending time with his beloved pets - a Labrador named Daisy and two shepherds.", - "D9:10" - ], - [ - "James finds joy and loyalty in his pets.", - "D9:14" - ], - [ - "James's happiness comes from his pets, computer games, travel, and pizza.", - "D9:16" - ], - [ - "James's favorite type of pizza is pepperoni for its spicy salami and cheese combination.", - "D9:18" - ], - [ - "James also likes cheese pizza and prosciutto on his pizza.", - "D9:20" - ], - [ - "James recommends prosciutto pizza to John, highlighting its greatness.", - "D9:22" - ] - ] - }, - "session_10_observation": { - "James": [ - [ - "James mentioned being busy at work.", - "D10:2" - ] - ], - "John": [ - [ - "John organized a tournament for the game CS:GO with friends, which raised money for charity.", - "D10:4" - ], - [ - "John sent the collected money to a dog shelter near his home.", - "D10:10" - ], - [ - "John used some of the remaining money to buy groceries and cook food for the homeless.", - "D10:12" - ], - [ - "John expressed interest in organizing more events combining his interests and helping the community.", - "D10:8" - ] - ] - }, - "session_11_observation": { - "John": [ - [ - "John volunteered his programming skills for a social cause, creating a software tool for a charitable foundation to streamline their operations.", - "D11:1" - ], - [ - "John was inspired by the foundation's passion for helping children and used his coding skills to contribute and challenge himself.", - "D11:7" - ], - [ - "This experience gave John a clearer sense of purpose and motivated him to potentially pursue a career in the non-profit sector.", - "D11:11" - ], - [ - "John is considering volunteer roles and potentially a career in the non-profit sector.", - "D11:11" - ] - ], - "James": [ - [ - "James was glad to hear from John and excited to learn about the impact of the software tool John created for the charitable foundation.", - "D11:2" - ], - [ - "James asked about the motivation behind John's creation of the software tool for the foundation.", - "D11:6" - ], - [ - "James encouraged John to continue using his skills and passions for making a difference.", - "D11:14" - ], - [ - "James offered support and encouragement to find the right non-profit organization that aligns with John's values and passion for programming.", - "D11:16" - ] - ] - }, - "session_12_observation": { - "James": [ - [ - "James had an awesome time at the amusement park with friends last weekend, going on roller coasters, Ferris wheel, electric cars, and buggies.", - "D12:1" - ], - [ - "James finished a big project after months that involved learning a new language and handling many details, which helped him learn about problem-solving, patience, and perseverance.", - "D12:11" - ] - ], - "John": [ - [ - "John entered a local tournament last Friday and took second place, receiving money and a trophy for his achievement.", - "D12:4" - ], - [ - "John feels that growth and progress are more important than winning every time and cherishes the lessons learned from his achievement.", - "D12:7" - ] - ] - }, - "session_13_observation": { - "John": [ - [ - "John recently got his dream job after many interviews and late nights.", - "D13:3" - ], - [ - "John is starting his new job next month.", - "D13:5" - ], - [ - "John is a Manchester City fan.", - "D13:15" - ] - ], - "James": [ - [ - "James recently started a course combining his passion for gaming and programming.", - "D13:6" - ], - [ - "James is currently working on a football simulator project, specifically on collecting player databases.", - "D13:8" - ], - [ - "James is a Liverpool fan and follows all their matches closely.", - "D13:12" - ], - [ - "James believes there is no sport better than football and no club better than Liverpool.", - "D13:12" - ], - [ - "James is confident that Liverpool will become champions next season.", - "D13:14" - ], - [ - "James is competitive with John about the standings of their respective football teams.", - "D13:18" - ], - [ - "James agrees to a bet with John regarding the final standings of their football teams.", - "D13:19" - ] - ] - }, - "session_14_observation": { - "James": [ - [ - "James has two people who always help him out when he's struggling.", - "D14:1" - ], - [ - "James posted a picture on his blog about coding last week.", - "D14:2" - ], - [ - "James has a dog named Max who brings him joy, especially in tough times.", - "D14:13" - ], - [ - "James enjoys cuddling with his dog Max.", - "D14:15" - ], - [ - "James and Max enjoy swimming together at the beach or lake.", - "D14:17" - ], - [ - "James takes walks with Max in a nearby tranquil trail to relax and connect with nature.", - "D14:27" - ], - [ - "When alone in the park, James brings a book to chill and escape from reality.", - "D14:31" - ] - ], - "John": [ - [ - "John started a blog about coding last week and finds it exciting and challenging.", - "D14:2" - ], - [ - "John enjoys reading sci-fi and fantasy books, particularly epic fantasy series with immersive world-building and intricate storylines.", - "D14:8" - ], - [ - "John's favorite book series are 'The Stormlight Archive', 'Kingkiller Chronicle', and 'The Expanse'.", - "D14:10" - ], - [ - "John's dog Max loves swimming and playing in the water, being a pro swimmer.", - "D14:17" - ], - [ - "John appreciates James' achievement in making Max happy.", - "D14:20" - ], - [ - "John thinks that taking breaks and switching up the scenery is important for recharging.", - "D14:32" - ], - [ - "John is supportive of James and always there to have his back.", - "D14:34" - ] - ] - }, - "session_15_observation": { - "James": [ - [ - "James introduced three pets - Max, Daisy, and a new pup named Ned, who are slowly adapting and bonding together.", - "D15:1" - ], - [ - "James took a great photo of his pets bonding together.", - "D15:3" - ], - [ - "James volunteered last month and found it rewarding to see the impact of small gifts on those in need.", - "D15:9" - ], - [ - "James volunteered with an organization providing necessary items to less fortunate individuals.", - "D15:11" - ], - [ - "James is willing to introduce John to the organization where he volunteered.", - "D15:13" - ], - [ - "James indicated that no interview is required for volunteering, just the willingness to help people.", - "D15:15" - ], - [ - "James is supportive of John's goal to make a positive impact on the world.", - "D15:17" - ] - ], - "John": [ - [ - "John is contemplating his career and desires to make a positive impact on the world.", - "D15:6" - ], - [ - "John is considering going into non-profit work and using his skills and passions for causes he cares about.", - "D15:8" - ], - [ - "John expresses a desire to help people and make the world a better place.", - "D15:16" - ], - [ - "John is determined to make a positive impact with James's support.", - "D15:18" - ] - ] - }, - "session_16_observation": { - "James": [ - [ - "James won an online gaming tournament last week and found the experience exciting and motivating.", - "D16:1" - ], - [ - "James is interested in extreme sports, recently trying rope jumping from a height of 150 meters and surfing.", - "D16:5" - ], - [ - "James enjoys reading, especially during cold winter days, but finds it exciting to do something different in the summer.", - "D16:9" - ], - [ - "James bought air tickets to Toronto and plans to visit Vancouver as well.", - "D16:9" - ], - [ - "James plans to return from his trip on July 20 and intends to bring back a souvenir for John.", - "D16:13" - ] - ], - "John": [ - [ - "John feels a mix of determination, passion, overwhelm, and stress while balancing personal and professional life.", - "D16:4" - ], - [ - "John relaxes by reading and finds it as a fun escape from reality.", - "D16:8" - ], - [ - "John asked James about the countries he will be visiting on his trip.", - "D16:10" - ], - [ - "John will be waiting for James to return from his trip and expressed excitement about a souvenir.", - "D16:14" - ] - ] - }, - "session_17_observation": { - "John": [ - [ - "John recently started playing chess to improve his strategic thinking.", - "D17:1" - ], - [ - "John plays chess online and joined a chess club to practice with others.", - "D17:5" - ], - [ - "John believes chess can enhance decision-making skills.", - "D17:7" - ], - [ - "John received advice from James about improving in chess by studying opening moves and analyzing games.", - "D17:10" - ], - [ - "John has a picture from elementary school with James related to skateboarding, showing they were friends who enjoyed skateboarding together.", - "D17:13" - ], - [ - "John expressed the importance of friendship and the joy pets bring, mentioning he doesn't have pets but would like to get one in the future.", - "D17:21" - ], - [ - "John is grateful for the connection he has with his siblings and values their support.", - "D17:31" - ] - ], - "James": [ - [ - "James has previously played chess and acknowledges its strategic nature.", - "D17:2" - ], - [ - "James shared advice with John on improving in chess by studying opening moves and analyzing games.", - "D17:10" - ], - [ - "James has a sister with whom he has a close bond and enjoys spending time near the ocean with her watching sunsets.", - "D17:30" - ], - [ - "James has dogs that he taught to balance on a skateboard, and they enjoy playing with it.", - "D17:18" - ], - [ - "James recently traveled to the city of Nuuk and brought souvenirs for John and Jill.", - "D17:22" - ], - [ - "James spends time with his sister and dogs, finding happiness and a strong bond with them.", - "D17:28" - ] - ] - }, - "session_18_observation": { - "John": [ - [ - "John recently left his IT job after 3 years because he wanted something that made a difference.", - "D18:1" - ], - [ - "John is passionate about the gaming industry and wants to become an organizer for gaming tournaments, starting with CS:GO and Fortnite.", - "D18:7" - ], - [ - "John values support and is grateful for the support he receives from James in his new career endeavor.", - "D18:11" - ] - ], - "James": [ - [ - "James recently took his puppy for a routine examination and vaccination to prevent seasonal canine disease.", - "D18:14" - ], - [ - "James deeply cares for his pets and ensures their well-being.", - "D18:18" - ] - ] - }, - "session_19_observation": { - "John": [ - [ - "John has recently started exploring different game genres like strategy and RPGs instead of his usual shooters.", - "D19:3" - ], - [ - "John hooked on playing a new RPG game, \"The Witcher 3\", where he enjoys the storytelling and characters.", - "D19:5" - ], - [ - "John is considering organizing competitions for the new game genres he is exploring.", - "D19:3" - ], - [ - "John took an initiative to organize his workplace to make it more efficient.", - "D19:9" - ], - [ - "John is encouraging towards James, thanking him for encouraging him to try new game genres.", - "D19:1" - ] - ], - "James": [ - [ - "James took his three dogs to a beach outing to bond with other dogkeepers.", - "D19:12" - ], - [ - "James is considering asking a girl named Samantha out on a date after they met at the beach outing.", - "D19:14" - ], - [ - "James shared a picture with John showcasing the graphics of a game they were discussing.", - "D19:8" - ] - ] - }, - "session_20_observation": { - "John": [ - [ - "John joined an online programming group last Friday.", - "D20:1" - ], - [ - "John worked on a project with someone from the online group last week.", - "D20:5" - ], - [ - "John is full of courage to start hosting eSports competitions.", - "D20:7" - ], - [ - "John bought new devices and refurbished his gaming desk.", - "D20:9" - ], - [ - "John uses a powerful graphics card for intense games and has a headset for immersive sound.", - "D20:11" - ], - [ - "John organized a gaming night with his siblings for next month.", - "D20:17" - ] - ], - "James": [ - [ - "James has a gaming group that he plays with regularly and they stream their game sessions.", - "D20:8" - ], - [ - "James hosted a gaming marathon with friends and it strengthened their bond.", - "D20:12" - ] - ] - }, - "session_21_observation": { - "James": [ - [ - "James has a dog named Ned that he adopted and can't imagine life without.", - "D21:3" - ], - [ - "James is interested in creating a strategy game similar to Civilization.", - "D21:9" - ], - [ - "James suggested meeting at Starbucks for coffee with John.", - "D21:13" - ] - ], - "John": [ - [ - "John helps his younger siblings with programming and is proud of their progress", - "D21:2" - ], - [ - "John is working on a coding project with his siblings involving a text-based adventure game.", - "D21:6" - ], - [ - "John prefers light beers over dark beers when going out.", - "D21:16" - ], - [ - "John agreed to meet James at McGee's Pub after discussing different options.", - "D21:18" - ] - ] - }, - "session_22_observation": { - "James": [ - [ - "James finished a Unity strategy game that he put a lot of time and effort into.", - "D22:1" - ], - [ - "James loves playing strategy games like Civilization and Total War, which inspired him to create his own game.", - "D22:5" - ], - [ - "James found the process of creating the game challenging, especially balancing mechanics and fairness.", - "D22:7" - ], - [ - "James learned that perseverance, patience, feedback, and collaboration are essential in game development.", - "D22:9" - ] - ], - "John": [ - [ - "John has been teaching his siblings coding, and they are creating their own programs.", - "D22:10" - ], - [ - "John's siblings are starting with basic games and stories in their coding journey.", - "D22:12" - ], - [ - "John is proud of his siblings for learning coding and is excited to see what they create with their skills.", - "D22:14" - ], - [ - "John is going through some difficult times and appreciates the friendship and support from James.", - "D22:18" - ] - ] - }, - "session_23_observation": { - "James": [ - [ - "James asked Samantha to be his girlfriend at the theater, and she agreed.", - "D23:1" - ], - [ - "James and Samantha were at McGee's bar where he found out she loves lager beer.", - "D23:3" - ], - [ - "James signed up for a cooking class two days ago to learn something new.", - "D23:13" - ], - [ - "James made a great omelette at the cooking class.", - "D23:13" - ], - [ - "James's cooking class costs $10 per class.", - "D23:15" - ], - [ - "James made meringue and learned how to make dough at the cooking class.", - "D23:15" - ] - ], - "John": [ - [ - "John's parents started learning coding from him, bringing them closer.", - "D23:2" - ], - [ - "John bought new gaming equipment including headphones from Sennheiser and a mouse from Logitech.", - "D23:8" - ] - ] - }, - "session_24_observation": { - "John": [ - [ - "John recently got into board games and found them to be a lot of fun.", - "D24:1" - ], - [ - "John used to play drums when he was younger.", - "D24:13" - ], - [ - "John has an old drum set that he used to play on.", - "D24:15" - ], - [ - "John has jammed with friends before and found it to be a lot of fun.", - "D24:17" - ] - ], - "James": [ - [ - "James started writing down everything he needs to do in a notebook to avoid forgetting tasks.", - "D24:2" - ], - [ - "James gets ideas from various sources like books, movies, and dreams.", - "D24:4" - ], - [ - "James had a creative dream a few weeks ago that led to interesting thoughts.", - "D24:6" - ], - [ - "James made sketches and notes based on a dream he had about a medieval castle with puzzles and traps.", - "D24:9" - ], - [ - "James used to play the guitar when he was younger but hasn't in a while.", - "D24:14" - ], - [ - "James is into music but it is not related to his castle dream.", - "D24:12" - ], - [ - "James has started streaming games and hopes everything works out.", - "D24:20" - ] - ] - }, - "session_25_observation": { - "John": [ - [ - "John started a new startup focusing on portable smokers and has already welded one from metal.", - "D25:3" - ], - [ - "John achieved a major career milestone by creating his first mobile game, which is launching next month.", - "D25:7" - ], - [ - "John's mobile game is a 2D adventure game with puzzles and exploration.", - "D25:9" - ], - [ - "John uses resources like a book, tutorials, and developer forums to improve his game development skills.", - "D25:13" - ], - [ - "John reads a magazine with tutorials, interviews with developers, and tips for game development.", - "D25:15" - ], - [ - "John's week has been chaotic, but he is pushing through.", - "D25:21" - ] - ], - "James": [ - [ - "James received positive feedback from the gaming community while streaming a game, which inspired him to keep going.", - "D25:4" - ], - [ - "James offered to help John test his mobile game.", - "D25:10" - ], - [ - "James recommends a magazine with tutorials and developer interviews to John for game development.", - "D25:14" - ], - [ - "James finds the magazine to be a useful resource for game development.", - "D25:18" - ], - [ - "James advises John to take care and make time for himself during a busy week.", - "D25:22" - ] - ] - }, - "session_26_observation": { - "John": [ - [ - "John received an email about a volunteer gig at a game dev non-profit to be a programming mentor for game developers.", - "D26:1" - ], - [ - "John is excited about the opportunity to combine his love for gaming and helping by teaching coding and assisting with projects.", - "D26:3" - ], - [ - "John loves sharing his knowledge, motivating others passionate about gaming, and helping them reach their potential.", - "D26:5" - ] - ], - "James": [ - [ - "James got a cool video card last week and is excited to use it for playing RPGs.", - "D26:8" - ], - [ - "James is looking forward to playing Cyberpunk 2077 based on John's recommendation.", - "D26:10" - ] - ] - }, - "session_27_observation": { - "John": [ - [ - "John and his programmer friends organized an online competition last week.", - "D27:1" - ], - [ - "John is supportive and encouraging towards James' game development efforts.", - "D27:3" - ], - [ - "John expressed excitement about James releasing his first game for the gaming community.", - "D27:3" - ], - [ - "John is interested in James' inspiration behind creating his game, mentioning The Witcher 3.", - "D27:5" - ], - [ - "John encourages James to continue with game development and expresses belief in his talents.", - "D27:9" - ], - [ - "John assures James of his support and cooperation in their endeavors.", - "D27:11" - ] - ], - "James": [ - [ - "James created and released his first game for the gaming community recently, which he found exciting.", - "D27:2" - ], - [ - "James feels fulfilled seeing players engage with the game world he created.", - "D27:4" - ], - [ - "The game 'Witcher 3' inspired James to create his own game, pushing him to make something special.", - "D27:6" - ], - [ - "James expressed a desire to create more games in different genres and explore new ideas in game development.", - "D27:8" - ], - [ - "James looks forward to creating more enjoyable experiences in the future.", - "D27:10" - ], - [ - "John supports James and expresses gratitude for John's support.", - "D27:12" - ], - [ - "James values John's support and friendship, mentioning that John has always been there for him.", - "D27:14" - ] - ] - }, - "session_28_observation": { - "James": [ - [ - "James's apartment lost power three days ago, disrupting his game progress as he forgot to save.", - "D28:3" - ], - [ - "James appreciates the creativity and complexity of games, spending hours playing and learning from them.", - "D28:3" - ], - [ - "James has a nostalgic attachment to old gaming setups, like playing on his mother's friend's console when he was young.", - "D28:21" - ], - [ - "James's first gaming system was a Nintendo where he played Super Mario and The Legend of Zelda, sparking his passion for gaming.", - "D28:25" - ], - [ - "James tried Cyberpunk 2077 yesterday and found it addictive.", - "D28:27" - ] - ], - "John": [ - [ - "John organized a programming seminar last week which had a great turnout, and he gained new insights into programming approaches and techniques.", - "D28:6" - ], - [ - "John enjoys sharing knowledge and learning from other developers in seminars.", - "D28:8" - ], - [ - "John plans to incorporate new programming techniques into his work and enjoys exploring different methods.", - "D28:12" - ], - [ - "John values trying out new programming techniques and ideas to spark creativity and keep evolving.", - "D28:14" - ], - [ - "John is helpful and willing to share resources and tutorials with others to help them learn and grow.", - "D28:16" - ], - [ - "John advises James about the game Cyberpunk 2077, highlighting the significance of making the right choices in the game.", - "D28:30" - ], - [ - "John reminds James about the importance of choices in dialogues with characters in Cyberpunk 2077.", - "D28:32" - ] - ] - }, - "session_29_observation": { - "John": [ - [ - "John organized a gaming tournament with his friends, playing Fortnite, Overwatch, and Apex Legends, to raise funds for a children's hospital.", - "D29:1" - ], - [ - "John and his friends raised a decent amount of money for the children's hospital through the gaming tournament.", - "D29:1" - ], - [ - "John's gaming pals helped him in organizing the tournament and playing games to raise money.", - "D29:3" - ], - [ - "John got a great shot at the tournament, capturing the hyped atmosphere while playing for a good cause.", - "D29:5" - ] - ], - "James": [ - [ - "James decided to move in with Samantha into an apartment not far from McGee's bar.", - "D29:8" - ], - [ - "McGee's bar was one of the criteria for James and Samantha's choice of apartment.", - "D29:12" - ] - ] - }, - "session_30_observation": { - "James": [ - [ - "James started a road trip with his family and dogs yesterday, enjoying exploring new places and nature.", - "D30:1" - ], - [ - "James is interested in trying out a new gaming genre and mentioned considering the sports genre.", - "D30:15" - ] - ], - "John": [ - [ - "John won the regional chess tournament recently, feeling proud of his accomplishment.", - "D30:4" - ], - [ - "John's tournament strategy involved analyzing and anticipating his opponent's moves to stay ahead.", - "D30:8" - ], - [ - "John recommends studying opening moves and strategies, learning from experienced players, and analyzing past games to improve at chess.", - "D30:10" - ], - [ - "John is currently hooked on playing FIFA 23, a football game that allows playing online with players worldwide.", - "D30:14" - ], - [ - "John suggests that playing FIFA 23 only requires a gamepad and a sense of timing.", - "D30:18" - ] - ] - }, - "session_31_observation": { - "James": [ - [ - "James and his family are currently on a road trip and have already visited friends Josh and Mark.", - "D31:1" - ], - [ - "James and his family visited an animal sanctuary on the road trip and saw many cute rescue dogs.", - "D31:11" - ], - [ - "James already has three dogs at home and believes having more than three dogs is too much.", - "D31:13" - ] - ], - "John": [ - [ - "John worked with a game developer on a project to create an online board game over the weekend.", - "D31:2" - ], - [ - "John and the game developer are about to release a demo of the online board game for others to try.", - "D31:6" - ], - [ - "John does not currently have a dog but really wants one.", - "D31:14" - ], - [ - "John appreciates the positivity and happiness that dogs bring to life and considers them special buddies.", - "D31:20" - ], - [ - "John's cousin has a dog named Luna.", - "D31:22" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "John and James spoke at 3:47 pm on 17th March 2022. John asked James about his motivation, which James attributed to his love for video games. They discussed their favorite games, programming skills, and a potential collaboration on a dog care app. James shared his project of creating a computer game based on childhood sketches. John admired James' passion and expressed interest in trying VR gaming together. They agreed to meet for VR gaming the following Saturday.", - "session_2_summary": "At 9:26 pm on 20 March, 2022, James told John about joining a new gaming platform, emphasizing the joy of connecting with gamers and sharing experiences. John agreed, highlighting the community-building aspect of gaming. They both found solace and stress relief in gaming during tough times. James also shared his interest in exploring different gaming styles, while John mentioned his new hobby of beachcombing with a metal detector. James then showed John pictures of his dogs and offered to help him find a pet in the future. They ended their conversation on a friendly note.", - "session_3_summary": "John and James conversed at 12:40 am on 27th March 2022. John shared his recent gaming success and drumming hobby, while James talked about learning a new instrument. They discussed gaming systems, conventions, and mutual admiration for skilled players. John emphasized the importance of learning from experienced gamers, and James mentioned setting small goals to stay motivated. James expressed excitement about exploring new game genres, with John eager to hear about his progress. The conversation ended with John looking forward to hearing updates from James about his gaming adventures.", - "session_4_summary": "At 2:13 pm on 4 April, 2022, John and James caught up after a long time. James shared his experience in an online gaming tournament where he reached the semifinals, had fun, and received gaming tips and autographs from a famous team. John emphasized the importance of communication and teamwork in gaming. They discussed playing Apex Legends together and trying out new genres like RPGs and MOBAs. They agreed to chat soon and update each other on new games.", - "session_5_summary": "James and John caught up at 9:52 am on 12 April, 2022. James shared about adopting a pup named Ned and how it made his days happier. John mentioned being busy with work but still finding solace in gaming. They discussed a new RPG game John is playing, which James showed interest in despite some lag and errors. John convinced James to try the game, offering to send a link. They agreed to discuss it later, expressing excitement. They bid farewell with James looking forward to trying the game and John wishing him a great time.", - "session_6_summary": "John and James caught up at 9:32 pm on April 20, 2022. John shared his excitement about making new friends in his programming course. Meanwhile, James talked about collaborating with a gaming pal on a virtual world project inspired by Witcher 3. He found inspiration for the project from a stranger he saw during a walk with his dogs. Both friends discussed how real-life experiences inspire their creativity. They also shared their love for traveling, with James mentioning his visits to Italy, Turkey, and Mexico, while John recently visited Japan. They expressed interest in traveling together in the future. James agreed to look for a destination as they planned their future trip.", - "session_7_summary": "On 23 April, 2022 at 11:04 am, John greeted James, who had taken his dogs on a hike the previous Thursday, sharing that they had a great time exploring trails and enjoying the outdoors. John asked for pictures, which James had, showcasing the adventure. James explained he chose the spot for its views and trails, perfect for the dogs. They both agreed on the benefits of being in nature to relax. John revealed work had been hectic due to numerous deadlines, especially struggling with a challenging coding project. James offered to help by suggesting breaking down the problem and seeking advice. John appreciated the advice, determined to keep going. James encouraged him not to give up, emphasizing that every problem has a solution, ending the conversation on a positive note.", - "session_8_summary": "James and John conversed at 2:36 pm on 29 April, 2022. John mentioned he is doing freelance programming to improve his coding skills and is working on a website for a local business. Despite facing challenges like setting up payments, he's determined to succeed. James praised John for his progress and shared his interest in fantasy novels. They discussed the book \"The Name of the Wind\" and strategy games like Civilization VI and Valhalla. James talked about playing a strategy game that tests problem-solving skills and how satisfying it is to win. John brought up enjoying strategy board games and a mystery game with friends. They agreed it's fun to play with others and recommended playing games like the impostor game in large groups for a better experience.", - "session_9_summary": "John and James caught up at 7:01 pm on 4 May, 2022. James had a frustrating week due to a bug in his project but managed to fix it with the help of friends. They discussed the importance of teamwork and balancing work with socializing. James spends time with his Labrador, Daisy, and two loyal shepherds. James finds happiness in his pets, computer games, travel, and pizza, particularly loving pepperoni, cheese, and prosciutto pizzas. John prefers Hawaiian pizza. They concluded by discussing trying out prosciutto pizza in the future.", - "session_10_summary": "At 12:45 am on 8 May, 2022, James and John caught up after a while. John mentioned organizing a CS:GO tournament with friends for charity, and James praised his efforts. John expressed interest in organizing more events for charity in the future. They discussed donating to a dog shelter and feeding the homeless with the collected money. James commended John's efforts, and John vowed to continue doing good things. James concluded the conversation by expressing pride in John's actions.", - "session_11_summary": "John and James spoke at 5:00 pm on 11 May, 2022. John shared how he volunteered his programming skills for a charitable foundation, creating a software tool that streamlined their operations. James expressed admiration for John's contribution and requested visuals of the impact. John explained his motivation was the foundation's dedication to helping kids. The experience inspired John to consider a career in the non-profit sector. James encouraged him to find the right organization for his skills. John appreciated the support and mentioned looking forward to making a positive impact. James wished him luck and they concluded the conversation warmly.", - "session_12_summary": "James told John about his fun time at the amusement park last weekend, enjoying the roller coasters and other attractions that brought back childhood memories. John shared his recent success in a local tournament, coming in second place and receiving a trophy and prize money. They discussed the importance of growth, progress, and perseverance in achieving goals. James mentioned completing a challenging project that required learning a new language, which taught him problem-solving, patience, and perseverance. John encouraged James to keep going and reach his goals, emphasizing the importance of determination and confidence in achieving success.", - "session_13_summary": "John and James caught up at 4:30 pm on 13 June, 2022. John shared his excitement about landing his dream job, starting next month. James was supportive, mentioning his passion for gaming and programming. James talked about a football simulator project he was working on, combining his love for football and self-improvement. The conversation shifted to football, with James rooting for Liverpool and John supporting Manchester City. They made a friendly bet about their teams' final standings, setting the stage for a competitive season.", - "session_14_summary": "James and John had a conversation at 5:07 pm on 16 June, 2022. James shared that he has been through ups and downs recently but feels lucky to have a support system. John mentioned starting a coding blog and shared his love for sci-fi and fantasy books. He recommended \"The Stormlight Archive\" and \"Kingkiller Chronicle\". James talked about his dog Max, who loves swimming and playing fetch. John praised James for taking breaks in nature, emphasizing the importance of self-care and support.", - "session_15_summary": "At 9:59 pm on 19 June, 2022, James shared with John how he introduced Max, Daisy, and the new pup, Ned, who are slowly bonding. John asked for a picture, complimented their cuteness, and shared his career aspirations of making a positive impact. James encouraged John's idea of volunteering and they discussed the rewarding experience. John expressed interest in joining James in volunteering, and James offered to take him. They discussed the organization and James assured John of a welcoming environment. James supported John's goal of making a difference, and they ended with mutual encouragement and determination to achieve their goals together.", - "session_16_summary": "James and John caught up at 5:13 pm on July 9, 2022. James shared his excitement about winning an online gaming tournament and his newfound interest in extreme sports. John congratulated him and mentioned feeling overwhelmed balancing personal and professional life. James revealed his upcoming trip to Toronto and Vancouver, planning to return on July 20. They discussed relaxation methods and bid farewell, with John eagerly awaiting a souvenir from James' journey.", - "session_17_summary": "John and James discussed their shared interest in chess, with John explaining his recent involvement in the game to improve his strategy skills. James offered tips for improvement and they reminisced about a childhood photo at the skate park. They also talked about pets, travel experiences, and family bonds. James shared a photo of his sister and dogs, emphasizing the joy they bring to his life. John expressed gratitude for his siblings' support, highlighting the importance of family and friends. James shared a sunset photo taken near the ocean before signing off due to fatigue. The conversation ended with mutual goodbyes.", - "session_18_summary": "John and James caught up at 1:45 pm on 6 August, 2022. John shared that he left his IT job of 3 years to pursue a more fulfilling career that aligns with his values. He expressed happiness with the decision. James praised John's bravery in making the change. John revealed his plan to organize gaming tournaments, starting with CS:GO and Fortnite. James supported John's new career endeavor and offered financial assistance if needed. James shared that his puppy had a routine medical check-up and vaccination. John admired James for his care towards his pets. The conversation ended with mutual goodbyes and well-wishes.", - "session_19_summary": "At 9:16 am on 10 August, 2022, John and James caught up after a few days. John appreciated James' encouragement to explore new game genres. John had been trying strategy and RPG games instead of shooters and was considering organizing competitions. He was engrossed in \"The Witcher 3\" due to its storytelling and character depth. James shared his beach outing with his dogs and meeting a girl named Samantha, planning to ask her out. John encouraged James to call Samantha, wishing him a great time.", - "session_20_summary": "John informed James at 3:57 pm on 21 August, 2022 that he joined an online programming group, finding it incredible to be part of a community with similar goals. He shared that he had exchanged contacts with some members after working on projects together. James praised John's involvement and inquired about potential collaborations. John confirmed a recent successful collaboration and his interest in eSports hosting. James mentioned his gaming group and recent gear upgrades. John shared his gaming setup, highlighting the escape and motivation gaming provides. James discussed a gaming marathon he hosted and asked about John's gaming plans with his siblings. John revealed a plan for a family gaming night next month, which James applauded. They concluded their conversation by agreeing to catch up soon.", - "session_21_summary": "James and John, at 9:18 pm on 26 August 2022, discussed James' dog named Ned, John helping his siblings with programming, and working on a coding project with the siblings. John shared details of the text-based adventure game they are creating. James mentioned his interest in creating a strategy game like Civilization. They made plans to meet at McGee's pub the next day, deciding to have light beers. The conversation ended with them saying goodbye.", - "session_22_summary": "Summary:\nAt 6:53 pm on 1 September 2022, James excitedly shared with John that he had completed his Unity strategy game, inspired by games like Civilization and Total War. John praised James's hard work and asked for more details about the game. James shared a screenshot and discussed the challenges he faced during development. John admired James's perseverance and mentioned teaching his siblings coding, who were already creating their own programs. The two friends expressed pride in each other, with James offering support to John during his difficult times. Their conversation highlighted the importance of friendship, support, learning, and collaboration.", - "session_23_summary": "At 9:23 pm on 4 September 2022, James and John catch up on recent events. James shared that he asked Samantha to be his girlfriend at the theater, later they visited McGee's bar discovering they both enjoy lager. John talked about teaching his parents coding and purchasing new gaming equipment involving Sennheiser headphones and a Logitech mouse. James signed up for a cooking class, sharing he made an omelette and meringue there. They planned to attend a baseball game together. John praised James for seeking self-improvement. The conversation ended with friendly goodbyes.", - "session_24_summary": "John and James caught up at 6:02 pm on 18 September, 2022. John shared his newfound interest in board games, specifically \"Dungeons of the Dragon\". James mentioned noting down tasks in a notebook to stay organized and drew inspiration for ideas from various sources, including dreams. He recalled a dream about a medieval castle with puzzles and traps. John, interested in James's dream, asked for sketches which James had made. The conversation shifted to music, with John revealing his love for electronic and rock music and his past experience playing drums. James also used to play guitar but hadn't in a while. John shared a photo of his old drum set, and they talked about their musical experiences of jamming with friends. James announced he started streaming games, with John expressing support. They ended on a positive note, with John looking forward to details of James's streaming endeavors.", - "session_25_summary": "At 8:56 pm on 20 September 2022, John and James caught up on recent events in their lives. John shared his new startup of portable smokers and a mobile game he created, while James talked about receiving positive feedback from the gaming community and his interest in John's game. John emphasized the importance of staying informed and continuously learning in game development, recommending resources like a book and a magazine to James. James appreciated the advice and encouraged John to take care of himself amidst a busy week. They expressed mutual support and admiration for each other's work.", - "session_26_summary": "John and James had a conversation at 9:20 am on 3 October, 2022. John shared his excitement about a volunteer gig at a game dev non-profit where he will be a programming mentor for game developers. James praised John's opportunity to combine his love for gaming and helping others. John expressed his enthusiasm for sharing his knowledge and motivating others. James encouraged John, and John found it fulfilling to make a difference. James mentioned getting a new video card and expressed interest in playing Cyberpunk 2077, which John recommended. The conversation ended with well wishes and plans to discuss the game later.", - "session_27_summary": "John and James spoke at 2:14 pm on 13 October, 2022. John mentioned organizing an online competition with his programmer friends, while James shared his excitement about releasing his first game inspired by Witcher 3. John congratulated James on the game and asked about his inspiration, to which James expressed fulfillment seeing players engage with his creation. James revealed his plans for future game development in various genres. John praised James' determination and expressed support for his future endeavors, to which James expressed gratitude, stating John has always had his back. They ended on a positive note, acknowledging their strong teamwork and mutual support.", - "session_28_summary": "At 7:36 pm on 21 October 2022, James and John caught up after a long time. James shared how he lost power in his apartment three days ago while playing a game. They discussed the importance of saving progress in games. John organized a programming seminar last week, which he found fulfilling. James expressed interest in trying new programming techniques from the seminar. John agreed to share resources with him. James also mentioned his mother's visit with her army friend. He showed John a photo of himself playing on their old gaming setup. They talked about childhood gaming memories and James' passion for gaming. James mentioned trying Cyberpunk 2077, and John shared advice about the game. They exchanged goodbyes, ending their conversation.", - "session_29_summary": "John called James at 12:37 am on 31 October, 2022, sharing news about organizing a gaming tournament with friends, raising funds for a children's hospital by playing Fortnite, Overwatch, and Apex Legends. James praised John's efforts, mentioning how gaming unites people for a good cause. John shared a tournament photo, and James revealed plans to move in with Samantha near McGee's bar. They discussed their love for the bar and James sought John's support. They ended the conversation wishing each other well and promising to stay in touch.", - "session_30_summary": "At 5:20 pm on 5 November 2022, James and John conversed. James shared about a road trip with family and dogs, enjoying nature. John mentioned winning a chess tournament on Tuesday and the tactics used. They discussed improving in chess with John offering resources. John recommended FIFA 23. They planned to play together after James practices.", - "session_31_summary": "At 8:57 pm on 7 November, 2022, James told John about his family road trip and visiting friends. John shared his weekend experience working with a game developer, creating an online board game. They discussed the upcoming demo release for feedback. James mentioned visiting an animal sanctuary, where he saw cute rescue dogs, resembling their love for furry pals. They discussed the joy and love dogs bring, agreeing they are amazing companions. John showed a picture of his cousin's dog Luna, and the conversation ended with James and John saying goodbye." - }, - "sample_id": "conv-47" - }, - { - "qa": [ - { - "question": "What kind of project was Jolene working on in the beginning of January 2023?", - "answer": "electricity engineering project", - "evidence": [ - "D1:2" - ], - "category": 2 - }, - { - "question": "Which of Deborah`s family and friends have passed away?", - "answer": "mother, father, her friend Karlie", - "evidence": [ - "D1:5", - "D2:1", - "D6:4" - ], - "category": 1 - }, - { - "question": "When did Deborah`s mother pass away?", - "answer": "a few years before 2023", - "evidence": [ - "D1:5" - ], - "category": 2 - }, - { - "question": "When did Jolene`s mother pass away?", - "answer": "in 2022", - "evidence": [ - "D1:6" - ], - "category": 2 - }, - { - "question": "When did Jolene's mom gift her a pendant?", - "answer": "in 2010", - "evidence": [ - "D1:8" - ], - "category": 2 - }, - { - "question": "In what country did Jolene's mother buy her the pendant?", - "answer": "In France", - "evidence": [ - "D1:8" - ], - "category": 3 - }, - { - "question": "What symbolic gifts do Deborah and Jolene have from their mothers?", - "answer": "pendants", - "evidence": [ - "D1:8", - "D1:9" - ], - "category": 1 - }, - { - "question": "Which country were Jolene and her mother visiting in 2010?", - "answer": "France", - "evidence": [ - "D1:8" - ], - "category": 2 - }, - { - "question": "What helped Deborah find peace when grieving deaths of her loved ones?", - "answer": "yoga, old photos, the roses and dahlias in a flower garden, nature", - "evidence": [ - "D1:15", - "D2:3", - "D6:4", - "D15:29" - ], - "category": 1 - }, - { - "question": "When did Deborah's father pass away?", - "answer": "January 25, 2023", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "When was Deborah's parents' wedding?", - "answer": "in 1993", - "evidence": [ - "D2:3" - ], - "category": 2 - }, - { - "question": "Is Deborah married?", - "answer": "yes", - "evidence": [ - "D2:5", - "D19:11", - "D23:4", - "D28:11" - ], - "category": 3 - }, - { - "question": "When did Deborah receive an appreciation letter from her community?", - "answer": "January 26, 2023", - "evidence": [ - "D2:7" - ], - "category": 2 - }, - { - "question": "What places give Deborah peace?", - "answer": "sitting in a spot by the window in her Mom's house, sitting by the beach, Bali, forest trail in a nearby park", - "evidence": [ - "D2:13", - "D4:34", - "D6:10", - "D19:17" - ], - "category": 1 - }, - { - "question": "What were Deborah's mother's hobbies?", - "answer": "reading, traveling, art, cooking", - "evidence": [ - "D2:17", - "D2:19", - "D12:3", - "D29:7" - ], - "category": 1 - }, - { - "question": "What pets does Jolene have?", - "answer": "snakes", - "evidence": [ - "D2:20", - "D2:22", - "D2:24" - ], - "category": 4 - }, - { - "question": "What are the names of Jolene's snakes?", - "answer": "Susie, Seraphim", - "evidence": [ - "D2:20", - "D2:22" - ], - "category": 4 - }, - { - "question": "When did Jolene buy her pet Seraphim?", - "answer": "in 2022", - "evidence": [ - "D2:24" - ], - "category": 2 - }, - { - "question": "In what country did Jolene buy snake Seraphim?", - "answer": "In France", - "evidence": [ - "D2:24" - ], - "category": 3 - }, - { - "question": "How many times has Jolene been to France?", - "answer": "two times", - "evidence": [ - "D2:24", - "D1:8" - ], - "category": 1 - }, - { - "question": "Which games have Jolene and her partner played together?", - "answer": "Detroit, Walking Dead, Battlefield 1, It Takes Two, Overcooked 2", - "evidence": [ - "D2:26", - "D2:30", - "D20:1", - "D15:10", - "D19:10" - ], - "category": 1 - }, - { - "question": "When do Jolene and her partner plan to complete the game \"Walking Dead\"?", - "answer": "Saturday after 27 January, 2023", - "evidence": [ - "D2:30" - ], - "category": 2 - }, - { - "question": "When did Deborah meet Anna?", - "answer": "31 January, 2023", - "evidence": [ - "D3:4" - ], - "category": 2 - }, - { - "question": "Why did Jolene sometimes put off doing yoga?", - "answer": "She's more interested in playing video games", - "evidence": [ - "D3:11", - "D2:30" - ], - "category": 3 - }, - { - "question": "What new yoga poses did Deborah try?", - "answer": "Warrior II, Dancer Pose (Natarajasana), Tree pose", - "evidence": [ - "D4:14", - "D14:3", - "D14:15" - ], - "category": 1 - }, - { - "question": "What are Jolene's favorite books?", - "answer": "Sapiens, Avalanche by Neal Stephenson", - "evidence": [ - "D4:21", - "D4:23" - ], - "category": 4 - }, - { - "question": "Which book did Jolene read in January 2023?", - "answer": "Avalanche by Neal Stephenson", - "evidence": [ - "D4:23" - ], - "category": 2 - }, - { - "question": "When was Jolene in Bogota?", - "answer": "in summer 2022", - "evidence": [ - "D4:33" - ], - "category": 2 - }, - { - "question": "In what country was Jolene during summer 2022?", - "answer": "Colombia", - "evidence": [ - "D4:33" - ], - "category": 3 - }, - { - "question": "When did Jolene have a mini-retreat to reflect on her career?", - "answer": "Wednesday before 9 February, 2023", - "evidence": [ - "D5:1" - ], - "category": 2 - }, - { - "question": "When did Jolene have a dinner and drinks with her friends?", - "answer": "21 February, 2023", - "evidence": [ - "D6:1" - ], - "category": 2 - }, - { - "question": "When was the last photo of Deborah and Karlie taken?", - "answer": "in summer 2022", - "evidence": [ - "D6:8" - ], - "category": 2 - }, - { - "question": "When was Deborah in Bali?", - "answer": "in 2022", - "evidence": [ - "D6:10" - ], - "category": 2 - }, - { - "question": "How long have Jolene and her partner been together?", - "answer": "for three years", - "evidence": [ - "D7:7" - ], - "category": 4 - }, - { - "question": "Which year did Jolene and her partner start dating?", - "answer": "2020", - "evidence": [ - "D7:7" - ], - "category": 2 - }, - { - "question": "When did Deborah go for her first morning jog in a nearby park?", - "answer": "24 February, 2023", - "evidence": [ - "D7:18" - ], - "category": 2 - }, - { - "question": "How old is Jolene?", - "answer": "likely no more than 30; since she's in school", - "evidence": [ - "D8:2", - "D13:5", - "D21:6", - "D21:8", - "D22:6", - "D22:14", - "D24:2", - "D24:14", - "D25:5", - "D26:6" - ], - "category": 3 - }, - { - "question": "When did Jolene take Seraphim to the park?", - "answer": "Sunday before 2 March, 2023", - "evidence": [ - "D8:8" - ], - "category": 2 - }, - { - "question": "When did Deborah start the yoga class in the neighborhood?", - "answer": "Friday before 13 March, 2023", - "evidence": [ - "D9:5" - ], - "category": 2 - }, - { - "question": "What time management techniques do Deborah and Jolene use?", - "answer": "the Pomodoro Technique - 25 minutes work and 5-minute break, scheduler or to-do list, The Eisenhower Matrix, bullet journal", - "evidence": [ - "D10:4", - "D10:5", - "D10:6", - "D10:13", - "D18:3" - ], - "category": 1 - }, - { - "question": "Does Deborah live close to the beach or the mountains?", - "answer": "beach", - "evidence": [ - "D10:17" - ], - "category": 3 - }, - { - "question": "What ways do Deborah and Jolene use to enhance their yoga practice?", - "answer": "candles, music, essential oils", - "evidence": [ - "D11:4", - "D11:7", - "D28:16", - "D28:18" - ], - "category": 1 - }, - { - "question": "What music pieces does Deborah listen to during her yoga practice?", - "answer": "Savana, Sleep", - "evidence": [ - "D11:8", - "D11:10" - ], - "category": 4 - }, - { - "question": "When did Deborah go for a bicycle ride with Anna?", - "answer": "first week of April, 2023", - "evidence": [ - "D12:1" - ], - "category": 2 - }, - { - "question": "When did Deborah go to an art show with Anna?", - "answer": "on 9 April, 2023", - "evidence": [ - "D12:1" - ], - "category": 2 - }, - { - "question": "When did Jolene finish her robotics project?", - "answer": "May 2023", - "evidence": [ - "D13:1" - ], - "category": 2 - }, - { - "question": "How long did Jolene work on the robotics project given to her by her Professor?", - "answer": "four months", - "evidence": [ - "D3:1", - "D12:10", - "D13:1" - ], - "category": 2 - }, - { - "question": "When did Jolene do yoga at Talkeetna?", - "answer": "on 5 June, 2023", - "evidence": [ - "D13:15" - ], - "category": 2 - }, - { - "question": "Which US state did Jolene visit during her internship?", - "answer": "Alaska", - "evidence": [ - "D13:15" - ], - "category": 3 - }, - { - "question": "How long has Jolene been doing yoga and meditation?", - "answer": "about 3 years", - "evidence": [ - "D13:17" - ], - "category": 4 - }, - { - "question": "Which year did Jolene start practicing yoga?", - "answer": "2020", - "evidence": [ - "D13:17" - ], - "category": 2 - }, - { - "question": "When did Jolene buy a new aquarium for Seraphim?", - "answer": "24 June, 2023", - "evidence": [ - "D14:4" - ], - "category": 2 - }, - { - "question": "When did Jolene lose a lot of progress in her work?", - "answer": "last week of July 2023", - "evidence": [ - "D16:2" - ], - "category": 2 - }, - { - "question": "When did Jolene adopt her snake Susie?", - "answer": "in 2021", - "evidence": [ - "D16:6", - "D28:26" - ], - "category": 2 - }, - { - "question": "Which pet did Jolene adopt first - Susie or Seraphim?", - "answer": "Susie", - "evidence": [ - "D2:24", - "D2:28", - "D16:6" - ], - "category": 2 - }, - { - "question": "Which pet did Jolene adopt more recently - Susie or Seraphim?", - "answer": "Seraphim", - "evidence": [ - "D2:24", - "D2:28", - "D16:6" - ], - "category": 2 - }, - { - "question": "When did Deborah lead a meditation session during the sunset?", - "answer": "week before 16 August, 2023", - "evidence": [ - "D18:8" - ], - "category": 2 - }, - { - "question": "When did Jolene gift her partner a new console?", - "answer": "17 August, 2023", - "evidence": [ - "D19:2" - ], - "category": 2 - }, - { - "question": "What games does Jolene recommend for Deborah?", - "answer": "Zelda BOTW for Switch , Animal Crossing: New Horizons, Overcooked 2", - "evidence": [ - "D19:8", - "D19:10" - ], - "category": 4 - }, - { - "question": "What do Deborah and her husband do together?", - "answer": "play detective games together, spend time outdoors and explore nature", - "evidence": [ - "D19:13", - "D19:15" - ], - "category": 4 - }, - { - "question": "When did Deborah go to a yoga retreat near her mom's place?", - "answer": "a week before 24 August,2023", - "evidence": [ - "D21:1" - ], - "category": 2 - }, - { - "question": "What projects is Jolene planning for next year?", - "answer": "developing renewable energy finding ways to supply clean water to those with limited access", - "evidence": [ - "D22:10", - "D22:12" - ], - "category": 4 - }, - { - "question": "Where did Deborah get her cats?", - "answer": "Luna is from the shelter and Max is her mother's cat", - "evidence": [ - "D22:23", - "D22:25" - ], - "category": 4 - }, - { - "question": "How old are Deborah's cats?", - "answer": "Max is 8 years old and Luna is 5 years old", - "evidence": [ - "D22:27", - "D22:29" - ], - "category": 4 - }, - { - "question": "Does Deborah like cats?", - "answer": "Yes", - "evidence": [ - "D22:27", - "D15:25" - ], - "category": 4 - }, - { - "question": "Which country was Jolene located in during the last week of August 2023?", - "answer": "Brazil", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "When did Jolene and her partner return home from Rio de Janeiro?", - "answer": "29 August, 2023", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "What was Jolene doing with her partner in Rio de Janeiro?", - "answer": "they went on excursions, checked out some cool yoga classes, visited a lot of delicious cafes, visited an old temple", - "evidence": [ - "D23:15", - "D23:1", - "D23:3", - "D23:17" - ], - "category": 4 - }, - { - "question": "When did Deborah visit Brazil?", - "answer": "2020", - "evidence": [ - "D23:18" - ], - "category": 2 - }, - { - "question": "Have Deborah and Jolene been to Rio de Janeiro?", - "answer": "yes", - "evidence": [ - "D23:1", - "D23:3", - "D23:18" - ], - "category": 4 - }, - { - "question": "Is the friend who wrote Deborah the motivational quote no longer alive?", - "answer": "likely yes", - "evidence": [ - "D23:22" - ], - "category": 3 - }, - { - "question": "When did Deborah go to a community meetup?", - "answer": "last week of August 2023", - "evidence": [ - "D24:1" - ], - "category": 2 - }, - { - "question": "When did Jolene's parents give her first console?", - "answer": "when she was 10", - "evidence": [ - "D24:6" - ], - "category": 4 - }, - { - "question": "Did Jolene teach herself how to play the console?", - "answer": "yes", - "evidence": [ - "D2:28", - "D24:8" - ], - "category": 1 - }, - { - "question": "What do Deborah and Jolene plan to try when they meet in a new cafe?", - "answer": "coffee and fresh pastries", - "evidence": [ - "D26:10", - "D26:12" - ], - "category": 4 - }, - { - "question": "What card game is Deborah talking about?", - "answer": "Exploding Kittens", - "evidence": [ - "D27:12" - ], - "category": 3 - }, - { - "question": "When did Jolene and her partner try scuba diving lessons?", - "answer": "Friday before 17 September, 2023", - "evidence": [ - "D29:4" - ], - "category": 2 - }, - { - "question": "Where did Jolene and her partner find a cool diving spot?", - "answer": "Phuket", - "evidence": [ - "D27:1", - "D29:4" - ], - "category": 1 - }, - { - "question": "Where did Jolene and her partner spend most of September 2023?", - "answer": "Phuket", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "Has Deborah tried surfing?", - "answer": "yes", - "evidence": [ - "D28:11", - "D29:25" - ], - "category": 1 - }, - { - "question": "Has Jolene tried surfing?", - "answer": "no", - "evidence": [ - "D10:20", - "D29:26", - "D29:30" - ], - "category": 1 - }, - { - "question": "When did the Deboran and Jolene agree to go surfing?", - "answer": "in October 2023", - "evidence": [ - "D29:34" - ], - "category": 2 - }, - { - "question": "Which locations does Deborah practice her yoga at?", - "answer": "at her mother's old home, park, yoga studio, beach", - "evidence": [ - "D2:11", - "D2:13", - "D3:6", - "D4:12", - "D6:10" - ], - "category": 1 - }, - { - "question": "What kind of professional activities does Jolene participate in to gain more experience in her field?", - "answer": "present work at virtual conference, attend workshops and intern at firms", - "evidence": [ - "D21:6", - "D13:5" - ], - "category": 1 - }, - { - "question": "What kind of engineering projects has Jolene worked on?", - "answer": "electrical engineering, robotics, sustainable water purifier, productive and affordable aerial surveillance system", - "evidence": [ - "D1:2", - "D3:1", - "D4:5", - "D17:10", - "D17:12" - ], - "category": 1 - }, - { - "question": "Which community activities have Deborah and Anna participated in?", - "answer": "yoga, running", - "evidence": [ - "D4:12", - "D4:16", - "D15:1" - ], - "category": 1 - }, - { - "question": "What gifts has Deborah received?", - "answer": "an appreciate letter from her community, a flower bouqet from her friend, a motivational quote from a friend", - "evidence": [ - "D2:7", - "D2:9", - "D4:26", - "D23:20", - "D23:22" - ], - "category": 1 - }, - { - "question": "Which countries has Deborah traveled to?", - "answer": "Thailand, Brazil", - "evidence": [ - "D6:10", - "D23:18" - ], - "category": 1 - }, - { - "question": "What activities does Deborah pursue besides practicing and teaching yoga?", - "answer": "biking, going to art shows, running, organizing workshops to practice mindfulness and self-care, surfing, gardening", - "evidence": [ - "D12:1", - "D15:1", - "D15:11", - "D28:11", - "D29:1" - ], - "category": 1 - }, - { - "question": "What are the names of Jolene's snakes?", - "answer": "Susie, Seraphim", - "evidence": [ - "D2:20", - "D2:22" - ], - "category": 4 - }, - { - "question": "What are Jolene's favorite books?", - "answer": "Sapiens, Avalanche by Neal Stephenson", - "evidence": [ - "D4:21", - "D4:23" - ], - "category": 4 - }, - { - "question": "What music pieces does Deborah listen to during her yoga practice?", - "answer": "Savana, Sleep", - "evidence": [ - "D11:8", - "D11:10" - ], - "category": 4 - }, - { - "question": "What games does Jolene recommend for Deborah?", - "answer": "Zelda BOTW for Switch , Animal Crossing: New Horizons, Overcooked 2", - "evidence": [ - "D19:8", - "D19:10" - ], - "category": 4 - }, - { - "question": "What projects is Jolene planning for next year?", - "answer": "developing renewable energy finding ways to supply clean water to those with limited access", - "evidence": [ - "D22:10", - "D22:12" - ], - "category": 4 - }, - { - "question": "Where did Deborah get her cats?", - "answer": "Luna is from the shelter and Max is her mother's cat", - "evidence": [ - "D22:23", - "D22:25" - ], - "category": 4 - }, - { - "question": "How old are Deborah's cats?", - "answer": "Max is 8 years old and Luna is 5 years old", - "evidence": [ - "D22:27", - "D22:29" - ], - "category": 4 - }, - { - "question": "What was Jolene doing with her partner in Rio de Janeiro?", - "answer": "they went on excursions, checked out some cool yoga classes, visited a lot of delicious cafes, visited an old temple", - "evidence": [ - "D23:15", - "D23:1", - "D23:3", - "D23:17" - ], - "category": 4 - }, - { - "question": "Have Deborah and Jolene been to Rio de Janeiro?", - "answer": "yes", - "evidence": [ - "D23:1", - "D23:3", - "D23:18" - ], - "category": 4 - }, - { - "question": "When did Jolene's parents give her first console?", - "answer": "when she was 10", - "evidence": [ - "D24:6" - ], - "category": 4 - }, - { - "question": "What do Deborah and Jolene plan to try when they meet in a new cafe?", - "answer": "coffee and fresh pastries", - "evidence": [ - "D26:10", - "D26:12" - ], - "category": 4 - }, - { - "question": "What project did Jolene finish last week before 23 January, 2023?", - "answer": "an electrical engineering project", - "evidence": [ - "D1:2" - ], - "category": 4 - }, - { - "question": "When did Jolene buy her pet snake?", - "answer": "A year ago", - "evidence": [ - "D2:24" - ], - "category": 4 - }, - { - "question": "What project was Jolene working on as of 1 February, 2023?", - "answer": "Robotics project", - "evidence": [ - "D3:1" - ], - "category": 4 - }, - { - "question": "Where did Deborah meet her new neighbor Anna?", - "answer": "yoga in the park", - "evidence": [ - "D3:6" - ], - "category": 4 - }, - { - "question": "What activity did Jolene and her partner plan to do together instead of resuming yoga?", - "answer": "play the console", - "evidence": [ - "D3:11" - ], - "category": 4 - }, - { - "question": "What milestone did Jolene achieve recently on 4 February, 2023?", - "answer": "Design and build a sustainable water purifier for a rural community", - "evidence": [ - "D4:3" - ], - "category": 4 - }, - { - "question": "What is Jolene's favorite book which she mentioned on 4 February, 2023?", - "answer": "\"Sapiens\"", - "evidence": [ - "D4:21" - ], - "category": 4 - }, - { - "question": "What does Deborah bring with her whenever she comes to reflect on her mom?", - "answer": "amulet", - "evidence": [ - "D4:36" - ], - "category": 4 - }, - { - "question": "What new outlook did Jolene gain after her mini retreat on 9 February, 2023?", - "answer": "A confidence boost", - "evidence": [ - "D5:3" - ], - "category": 4 - }, - { - "question": "What cool stuff did Jolene accomplish at the retreat on 9 February, 2023?", - "answer": "Came up with neat solutions for her engineering project", - "evidence": [ - "D5:5" - ], - "category": 4 - }, - { - "question": "What idea did Jolene have to help underprivileged kids learn about STEM subjects on 9 February, 2023?", - "answer": "A volunteer program where engineers teach STEM to underprivileged kids", - "evidence": [ - "D5:7" - ], - "category": 4 - }, - { - "question": "How does Jolene plan to involve local engineers in her idea of teaching STEM to underprivileged kids?", - "answer": "As guest speakers for workshops", - "evidence": [ - "D5:9" - ], - "category": 4 - }, - { - "question": "What gave Deborah peace in the garden she visited?", - "answer": "Roses and dahlias", - "evidence": [ - "D6:4" - ], - "category": 4 - }, - { - "question": "Why did Deborah spend time in the garden?", - "answer": "to find comfort after losing a friend", - "evidence": [ - "D6:4" - ], - "category": 4 - }, - { - "question": "How did Jolene and her partner initially meet?", - "answer": "In an engineering class in college", - "evidence": [ - "D7:9" - ], - "category": 4 - }, - { - "question": "What activity does Deborah incorporate into her daily routine after going for a morning jog in the park?", - "answer": "spending time with loved ones", - "evidence": [ - "D7:18" - ], - "category": 4 - }, - { - "question": "According to Jolene, what does exercise help her to feel?", - "answer": "connected to her body", - "evidence": [ - "D7:20" - ], - "category": 4 - }, - { - "question": "What did Deb share a photo of, which brought a smile to Jolene's face?", - "answer": "a yellow coffee cup with a handwritten message", - "evidence": [ - "D8:22" - ], - "category": 4 - }, - { - "question": "What is one of Jolene's favorite dishes?", - "answer": "lasagna", - "evidence": [ - "D8:2" - ], - "category": 4 - }, - { - "question": "What picture did Jolene share related to feeling overwhelmed?", - "answer": "a photo of a desk with a notebook and a computer monitor", - "evidence": [ - "D8:16" - ], - "category": 4 - }, - { - "question": "What did Jolene and Deb discuss as a helpful strategy for studying and time management?", - "answer": "breaking tasks into smaller pieces and setting goals, using planners or schedulers", - "evidence": [ - "D8:19" - ], - "category": 4 - }, - { - "question": "What did Jolene ask Deb to help with on 13 March, 2023?", - "answer": "time management", - "evidence": [ - "D9:14" - ], - "category": 4 - }, - { - "question": "What method does Deb suggest Jolene to try for organizing tasks based on importance and urgency?", - "answer": "The Eisenhower Matrix", - "evidence": [ - "D10:13" - ], - "category": 4 - }, - { - "question": "What did Jolene and Anna discuss while watching the sunset by the sea?", - "answer": "They realized they inspire each other", - "evidence": [ - "D10:17" - ], - "category": 4 - }, - { - "question": "How does Jolene plan to pursue her dream of learning to surf?", - "answer": "gathering information, watching videos, getting a beginners' guide", - "evidence": [ - "D10:20" - ], - "category": 4 - }, - { - "question": "What did Deborah buy to enhance her yoga practice besides the props?", - "answer": "candle", - "evidence": [ - "D11:4" - ], - "category": 4 - }, - { - "question": "What type of music does Deborah find helpful during her yoga practice?", - "answer": "instrumental tracks with mellow melodies and rhythms", - "evidence": [ - "D11:8" - ], - "category": 4 - }, - { - "question": "Who are the musicians mentioned by Jolene that she enjoys listening to during her yoga practice?", - "answer": "Nils Frahm and Olafur Arnalds", - "evidence": [ - "D11:9" - ], - "category": 4 - }, - { - "question": "What album does Deborah recommend for meditation and deep relaxation?", - "answer": "'Sleep'", - "evidence": [ - "D11:10" - ], - "category": 4 - }, - { - "question": "Which show did Deborah go to with a friend on 9 April, 2023?", - "answer": "an art show", - "evidence": [ - "D12:1" - ], - "category": 4 - }, - { - "question": "What does Deborah find comforting about going to art shows?", - "answer": "It makes her feel like she's still experiencing it with her mom", - "evidence": [ - "D12:3" - ], - "category": 4 - }, - { - "question": "How does Jolene describe the time spent with her snakes and partner?", - "answer": "Valuable and relaxing", - "evidence": [ - "D12:6" - ], - "category": 4 - }, - { - "question": "What does Jolene enjoy doing with her partner after a long day?", - "answer": "Playing video games", - "evidence": [ - "D12:6" - ], - "category": 4 - }, - { - "question": "What is Jolene currently doing in June 2023?", - "answer": "interning at a well-known engineering firm", - "evidence": [ - "D13:5" - ], - "category": 4 - }, - { - "question": "For how long has Jolene had Seraphim as a pet?", - "answer": "one year", - "evidence": [ - "D14:6" - ], - "category": 4 - }, - { - "question": "How does Jolene feel when spending time with Seraphim?", - "answer": "comforted", - "evidence": [ - "D14:6" - ], - "category": 4 - }, - { - "question": "Which new yoga pose did Deborah share a photo of?", - "answer": "tree pose", - "evidence": [ - "D14:15" - ], - "category": 4 - }, - { - "question": "What group activity did Deborah start with Anna?", - "answer": "running group", - "evidence": [ - "D15:1" - ], - "category": 4 - }, - { - "question": "What made being part of the running group easy for Deborah to stay motivated?", - "answer": "helping and pushing each other during runs", - "evidence": [ - "D15:3" - ], - "category": 4 - }, - { - "question": "Why did Jolene decide to get a snake as a pet?", - "answer": "fascinated by reptiles and it felt like the perfect pet", - "evidence": [ - "D15:18" - ], - "category": 4 - }, - { - "question": "What is the favorite game Jolene plays with her partner?", - "answer": "It takes two", - "evidence": [ - "D15:10" - ], - "category": 4 - }, - { - "question": "What activity does Deborah do with her cats?", - "answer": "take them out for a run in the park every morning and evening", - "evidence": [ - "D15:27" - ], - "category": 4 - }, - { - "question": "How does Jolene describe the feeling of finding her snake snuggled under the bed after it got out?", - "answer": "It really showed how much she loves her.", - "evidence": [ - "D15:20" - ], - "category": 4 - }, - { - "question": "Why does Deborah take her cats out for a run in the park every day?", - "answer": "Exercise and nature are important to her", - "evidence": [ - "D15:27" - ], - "category": 4 - }, - { - "question": "How did Jolene come to have her pet, Susie?", - "answer": "She adopted her two years ago when feeling lonely.", - "evidence": [ - "D16:6" - ], - "category": 4 - }, - { - "question": "What activities have been helping Jolene stay distracted during tough times?", - "answer": "Video games and spending time with her pet, Susie", - "evidence": [ - "D16:4" - ], - "category": 4 - }, - { - "question": "What kind of yoga routine does Deborah recommend to Jolene?", - "answer": "A gentle flow routine focused on breathing and grounding", - "evidence": [ - "D16:15" - ], - "category": 4 - }, - { - "question": "What did Jolene design inspired by their love for space and engines?", - "answer": "Notebooks", - "evidence": [ - "D17:6" - ], - "category": 4 - }, - { - "question": "What journal has Jolene been using to help track tasks and stay organized?", - "answer": "bullet journal", - "evidence": [ - "D18:3" - ], - "category": 4 - }, - { - "question": "What game did Jolene recommend for being calming and cute?", - "answer": "Animal Crossing: New Horizons", - "evidence": [ - "D19:8" - ], - "category": 4 - }, - { - "question": "What game did Jolene suggest as an awesome open-world game for the Nintendo Switch?", - "answer": "Zelda BOTW", - "evidence": [ - "D19:8" - ], - "category": 4 - }, - { - "question": "What did Deborah and her husband use to play to bond and make memories?", - "answer": "video games", - "evidence": [ - "D19:11" - ], - "category": 4 - }, - { - "question": "What is special about the bench at the park near Deborah's house?", - "answer": "It holds special memories of conversations with her mom", - "evidence": [ - "D19:18" - ], - "category": 4 - }, - { - "question": "What did Deborah and her mom chat about at their special bench in the park?", - "answer": "dreams and life", - "evidence": [ - "D19:19" - ], - "category": 4 - }, - { - "question": "What feeling does Deborah get when she thinks about the time spent with her mom at their special spot?", - "answer": "peace and gratitude", - "evidence": [ - "D19:21" - ], - "category": 4 - }, - { - "question": "What habits does Jolene practice to feel balanced?", - "answer": "yoga, meditation, walks, and mindfulness", - "evidence": [ - "D20:12" - ], - "category": 4 - }, - { - "question": "Which yoga pose is Jolene a fan of for rest and calmness?", - "answer": "savasana (the corpse pose)", - "evidence": [ - "D20:19" - ], - "category": 4 - }, - { - "question": "How long has Jolene been doing yoga?", - "answer": "3 years", - "evidence": [ - "D20:21" - ], - "category": 4 - }, - { - "question": "What did Jolene participate in recently that provided her with a rewarding experience?", - "answer": "presenting at a virtual conference", - "evidence": [ - "D21:6" - ], - "category": 4 - }, - { - "question": "How did Jolene feel after receiving positive feedback at the virtual conference?", - "answer": "thrilled and rewarded", - "evidence": [ - "D21:8" - ], - "category": 4 - }, - { - "question": "What kind of event did Jolene present at recently?", - "answer": "virtual conference", - "evidence": [ - "D21:6" - ], - "category": 4 - }, - { - "question": "What did Jolene's mom stress the value of, which she wants to keep in mind for her engineering projects?", - "answer": "Helping others", - "evidence": [ - "D22:6" - ], - "category": 4 - }, - { - "question": "What type of projects is Jolene interested in getting involved in the future?", - "answer": "Sustainable initiatives and developing innovative solutions for environmental issues", - "evidence": [ - "D22:8" - ], - "category": 4 - }, - { - "question": "How did Deborah get Luna, one of her cats?", - "answer": "From the shelter", - "evidence": [ - "D22:25" - ], - "category": 4 - }, - { - "question": "How old is Max?", - "answer": "8 years old", - "evidence": [ - "D22:27" - ], - "category": 4 - }, - { - "question": "What type of classes did Jolene and her partner check out during their trip to Rio de Janeiro on 30 August, 2023?", - "answer": "Yoga classes", - "evidence": [ - "D23:1" - ], - "category": 4 - }, - { - "question": "What type of place does Jolene visit to meditate?", - "answer": "A tranquil spot by a pond", - "evidence": [ - "D23:9" - ], - "category": 4 - }, - { - "question": "What was the new plant Jolene got used as a reminder for on 30 August, 2023?", - "answer": "To nurture herself and embrace fresh starts", - "evidence": [ - "D23:29" - ], - "category": 4 - }, - { - "question": "Why did Jolene get the new plant on 30 August, 2023?", - "answer": "As a reminder to nurture herself and embrace fresh starts", - "evidence": [ - "D23:29" - ], - "category": 4 - }, - { - "question": "What has Jolene been focusing on lately besides studying?", - "answer": "relationship with her partner", - "evidence": [ - "D24:2" - ], - "category": 4 - }, - { - "question": "How did Deborah's mom support her yoga practice when she first started?", - "answer": "attended classes with her", - "evidence": [ - "D24:5" - ], - "category": 4 - }, - { - "question": "What was the video game console that Jolene's parents got her at age 10?", - "answer": "nintendo game console", - "evidence": [ - "D24:6" - ], - "category": 4 - }, - { - "question": "What was one of Jolene's favorite games to play with her mom on the nintendo wii game system?", - "answer": "Monster Hunter: World", - "evidence": [ - "D24:10" - ], - "category": 4 - }, - { - "question": "What course did Jolene sign up for on 6 September 2023?", - "answer": "meditation", - "evidence": [ - "D25:1" - ], - "category": 4 - }, - { - "question": "Why did Jolene have to reschedule their meeting with Deborah on September 8, 2023?", - "answer": "Jolene already had plans", - "evidence": [ - "D26:15" - ], - "category": 4 - }, - { - "question": "Where did Jolene and her partner travel for a few weeks in September 2023?", - "answer": "Phuket", - "evidence": [ - "D27:1" - ], - "category": 4 - }, - { - "question": "What was the main focus of the session that stood out to Jolene during the retreat?", - "answer": "releasing expectations and judgments and savoring the present", - "evidence": [ - "D27:5" - ], - "category": 4 - }, - { - "question": "How did Jolene feel about her progress in practicing mindfulness and gratitude?", - "answer": "experiencing a new level of joy and happiness", - "evidence": [ - "D27:9" - ], - "category": 4 - }, - { - "question": "What positive change did Jolene experience during the retreat?", - "answer": "finding inner peace", - "evidence": [ - "D27:1" - ], - "category": 4 - }, - { - "question": "What did Jolene recently play that she described to Deb?", - "answer": "a card game about cats", - "evidence": [ - "D27:12" - ], - "category": 4 - }, - { - "question": "What did Deborah do with their mom's old friends?", - "answer": "reminisced and looked through photos", - "evidence": [ - "D28:7" - ], - "category": 4 - }, - { - "question": "Where did Deborah get married?", - "answer": "on the beach", - "evidence": [ - "D28:11" - ], - "category": 4 - }, - { - "question": "What does yoga on the beach provide for Deborah?", - "answer": "a peaceful atmosphere", - "evidence": [ - "D28:15" - ], - "category": 4 - }, - { - "question": "How does Jolene describe their home room?", - "answer": "little haven for peace and rest", - "evidence": [ - "D28:22" - ], - "category": 4 - }, - { - "question": "What new activity did Deborah and her neighbor organize for the community on 16 September, 2023?", - "answer": "Free gardening class", - "evidence": [ - "D29:1" - ], - "category": 4 - }, - { - "question": "What was Deborah's mom passionate about?", - "answer": "Cooking", - "evidence": [ - "D29:7" - ], - "category": 4 - }, - { - "question": "What food did Deborah's mom make for her on birthdays?", - "answer": "Pineapple cakes", - "evidence": [ - "D29:9" - ], - "category": 4 - }, - { - "question": "What kind of cookies did Jolene used to bake with someone close to her?", - "answer": "Chocolate chip cookies", - "evidence": [ - "D29:12" - ], - "category": 4 - }, - { - "question": "What outdoor activity did Jolene suggest doing together with Deborah?", - "answer": "Surfing", - "evidence": [ - "D29:27" - ], - "category": 4 - }, - { - "question": "What activity did Deborah enjoy at the music festival with their pals on September 20, 2023?", - "answer": "Dancing and bopping around", - "evidence": [ - "D30:1" - ], - "category": 4 - }, - { - "question": "What did Deborah find freeing at the music festival?", - "answer": "Dancing and bopping around", - "evidence": [ - "D30:1" - ], - "category": 4 - }, - { - "question": "What are the names of Deborah's snakes?", - "evidence": [ - "D2:20", - "D2:22" - ], - "category": 5, - "adversarial_answer": "Susie, Seraphim" - }, - { - "question": "What are Deborah's favorite books?", - "evidence": [ - "D4:21", - "D4:23" - ], - "category": 5, - "adversarial_answer": "Sapiens, Avalanche by Neal Stephenson" - }, - { - "question": "Where did Deborah get her dogs?", - "evidence": [ - "D22:23", - "D22:25" - ], - "category": 5, - "adversarial_answer": "Luna is from the shelter and Max is her mother's cat" - }, - { - "question": "How old are Jolene's cats?", - "evidence": [ - "D22:27", - "D22:29" - ], - "category": 5, - "adversarial_answer": "Max is 8 years old and Luna is 5 years old" - }, - { - "question": "When did Deborah's parents give her first console?", - "evidence": [ - "D24:6" - ], - "category": 5, - "adversarial_answer": "when she was 10" - }, - { - "question": "When did Jolene release her pet snake?", - "evidence": [ - "D2:24" - ], - "category": 5, - "adversarial_answer": "A year ago" - }, - { - "question": "Where did Jolene meet her new friend Anna?", - "evidence": [ - "D3:6" - ], - "category": 5, - "adversarial_answer": "yoga in the park" - }, - { - "question": "What is Deborah's favorite book which she mentioned on 4 February, 2023?", - "evidence": [ - "D4:21" - ], - "category": 5, - "adversarial_answer": "\"Sapiens\"" - }, - { - "question": "What cool stuff did Deborah accomplish at the retreat on 9 February, 2023?", - "evidence": [ - "D5:5" - ], - "category": 5, - "adversarial_answer": "Came up with neat solutions for her engineering project" - }, - { - "question": "How does Deborah plan to involve local engineers in her idea of teaching STEM to underprivileged kids?", - "evidence": [ - "D5:9" - ], - "category": 5, - "adversarial_answer": "As guest speakers for workshops" - }, - { - "question": "What gave Deborah anxiety in the garden she visited?", - "evidence": [ - "D6:4" - ], - "category": 5, - "adversarial_answer": "Roses and dahlias" - }, - { - "question": "Why did Jolene spend time in the garden?", - "evidence": [ - "D6:4" - ], - "category": 5, - "adversarial_answer": "to find comfort after losing a friend" - }, - { - "question": "How did Jolene and her rival initially meet?", - "evidence": [ - "D7:9" - ], - "category": 5, - "adversarial_answer": "In an engineering class in college" - }, - { - "question": "What activity does Jolene incorporate into her daily routine after going for a morning jog in the park?", - "evidence": [ - "D7:18" - ], - "category": 5, - "adversarial_answer": "spending time with loved ones" - }, - { - "question": "What method does Jolene suggest Deborah to try for organizing tasks based on importance and urgency?", - "evidence": [ - "D10:13" - ], - "category": 5, - "adversarial_answer": "The Eisenhower Matrix" - }, - { - "question": "How does Jolene plan to pursue her dream of climbing mountains?", - "evidence": [ - "D10:20" - ], - "category": 5, - "adversarial_answer": "gathering information, watching videos, getting a beginners' guide" - }, - { - "question": "Who are the authors mentioned by Jolene that she enjoys reading during her yoga practice?", - "evidence": [ - "D11:9" - ], - "category": 5, - "adversarial_answer": "Nils Frahm and Olafur Arnalds" - }, - { - "question": "Which show did Jolene go to with a friend on 9 April, 2023?", - "evidence": [ - "D12:1" - ], - "category": 5, - "adversarial_answer": "an art show" - }, - { - "question": "What does Deborah find comforting about going to horror movie screenings?", - "evidence": [ - "D12:3" - ], - "category": 5, - "adversarial_answer": "It makes her feel like she's still experiencing it with her mom" - }, - { - "question": "How does Deborah describe the time spent with her snakes and partner?", - "evidence": [ - "D12:6" - ], - "category": 5, - "adversarial_answer": "Valuable and relaxing" - }, - { - "question": "For how long has Jolene had Lucifer as a pet?", - "evidence": [ - "D14:6" - ], - "category": 5, - "adversarial_answer": "one year" - }, - { - "question": "How does Deborah feel when spending time with Seraphim?", - "evidence": [ - "D14:6" - ], - "category": 5, - "adversarial_answer": "comforted" - }, - { - "question": "What made being part of the running group easy for Jolene to stay motivated?", - "evidence": [ - "D15:3" - ], - "category": 5, - "adversarial_answer": "helping and pushing each other during runs" - }, - { - "question": "Why did Jolene decide to get a tarantula as a pet?", - "evidence": [ - "D15:18" - ], - "category": 5, - "adversarial_answer": "fascinated by reptiles and it felt like the perfect pet" - }, - { - "question": "How did Deborah come to have her pet, Susie?", - "evidence": [ - "D16:6" - ], - "category": 5, - "adversarial_answer": "She adopted her two years ago when feeling lonely." - }, - { - "question": "What did Deborah design inspired by their love for space and engines?", - "evidence": [ - "D17:6" - ], - "category": 5, - "adversarial_answer": "Notebooks" - }, - { - "question": "What journal has Deborah been using to help track tasks and stay organized?", - "evidence": [ - "D18:3" - ], - "category": 5, - "adversarial_answer": "bullet journal" - }, - { - "question": "What game did Jolene recommend to Deborah for being thrilling and intense?", - "evidence": [ - "D19:8" - ], - "category": 5, - "adversarial_answer": "Animal Crossing: New Horizons" - }, - { - "question": "What game did Deborah suggest as an awesome open-world game for the Nintendo Switch?", - "evidence": [ - "D19:8" - ], - "category": 5, - "adversarial_answer": "Zelda BOTW" - }, - { - "question": "What is special about the bench at the park near Jolene's house?", - "evidence": [ - "D19:18" - ], - "category": 5, - "adversarial_answer": "It holds special memories of conversations with her mom" - }, - { - "question": "What did Jolene and her mom chat about at their special bench in the park?", - "evidence": [ - "D19:19" - ], - "category": 5, - "adversarial_answer": "dreams and life" - }, - { - "question": "How did Deborah feel after receiving positive feedback at the virtual conference?", - "evidence": [ - "D21:8" - ], - "category": 5, - "adversarial_answer": "thrilled and rewarded" - }, - { - "question": "What kind of event did Deborah present at recently?", - "evidence": [ - "D21:6" - ], - "category": 5, - "adversarial_answer": "virtual conference" - }, - { - "question": "What did Deborah's mom stress the value of, which she wants to keep in mind for her engineering projects?", - "evidence": [ - "D22:6" - ], - "category": 5, - "adversarial_answer": "Helping others" - }, - { - "question": "What type of projects is Deborah interested in getting involved in the future?", - "evidence": [ - "D22:8" - ], - "category": 5, - "adversarial_answer": "Sustainable initiatives and developing innovative solutions for environmental issues" - }, - { - "question": "How did Jolene get Luna, one of her cats?", - "evidence": [ - "D22:25" - ], - "category": 5, - "adversarial_answer": "From the shelter" - }, - { - "question": "What type of classes did Deborah and her partner check out during their trip to Rio de Janeiro on 30 August, 2023?", - "evidence": [ - "D23:1" - ], - "category": 5, - "adversarial_answer": "Yoga classes" - }, - { - "question": "Why did Deborah get the new plant on 30 August, 2023?", - "evidence": [ - "D23:29" - ], - "category": 5, - "adversarial_answer": "As a reminder to nurture herself and embrace fresh starts" - }, - { - "question": "How did Jolene's mom support her yoga practice when she first started?", - "evidence": [ - "D24:5" - ], - "category": 5, - "adversarial_answer": "attended classes with her" - }, - { - "question": "What was the video game console that Deborah's parents got her at age 10?", - "evidence": [ - "D24:6" - ], - "category": 5, - "adversarial_answer": "nintendo game console" - }, - { - "question": "What was one of Deborah's favorite games to play with her mom on the PlayStation game system?", - "evidence": [ - "D24:10" - ], - "category": 5, - "adversarial_answer": "Monster Hunter: World" - }, - { - "question": "Where did Deborah and her partner travel for a few weeks in September 2023?", - "evidence": [ - "D27:1" - ], - "category": 5, - "adversarial_answer": "Phuket" - }, - { - "question": "What did Jolene do with their mom's old friends?", - "evidence": [ - "D28:7" - ], - "category": 5, - "adversarial_answer": "reminisced and looked through photos" - }, - { - "question": "Where did Jolene get married?", - "evidence": [ - "D28:11" - ], - "category": 5, - "adversarial_answer": "on the beach" - }, - { - "question": "What new activity did Jolene and her neighbor organize for the community on 16 September, 2023?", - "evidence": [ - "D29:1" - ], - "category": 5, - "adversarial_answer": "Free gardening class" - }, - { - "question": "What food did Jolene's mom make for her on holidays?", - "evidence": [ - "D29:9" - ], - "category": 5, - "adversarial_answer": "Pineapple cakes" - }, - { - "question": "What kind of cookies did Deborah used to bake with someone close to her?", - "evidence": [ - "D29:12" - ], - "category": 5, - "adversarial_answer": "Chocolate chip cookies" - }, - { - "question": "What activity did Jolene enjoy at the music festival with their pals on September 20, 2023?", - "evidence": [ - "D30:1" - ], - "category": 5, - "adversarial_answer": "Dancing and bopping around" - } - ], - "conversation": { - "speaker_a": "Deborah", - "speaker_b": "Jolene", - "session_1_date_time": "4:06 pm on 23 January, 2023", - "session_1": [ - { - "speaker": "Deborah", - "dia_id": "D1:1", - "text": "Hey Jolene, nice to meet you! How's your week going? Anything fun happened?" - }, - { - "speaker": "Jolene", - "dia_id": "D1:2", - "text": "Hi Deb! Good to meet you! Yeah, my week's been busy. I finished an electrical engineering project last week - took a lot of work, but it's done now. Anything fun happening for you?" - }, - { - "speaker": "Deborah", - "dia_id": "D1:3", - "text": "Congrats! Last week I visited a place that holds a lot of memories for me. It was my mother`s old house." - }, - { - "speaker": "Jolene", - "dia_id": "D1:4", - "text": "Why does it hold such special memories for you?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://jubileewriter.files.wordpress.com/2021/05/20171019_174608.jpg" - ], - "blip_caption": "a photo of a woman in a wheelchair hugging a woman in a wheelchair", - "query": "mother old home emotions memories", - "dia_id": "D1:5", - "text": "It was full of memories, she passed away a few years ago. This is our last photo together." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://www.therootboard.com/cdn/shop/articles/IMG_5420.jpg" - ], - "blip_caption": "a photo of a room with a bench and a window", - "query": "serene yoga studio windows natural light", - "dia_id": "D1:6", - "text": "Sorry about your loss, Deb. My mother also passed away last year. This is my room in her house, I also have many memories there. Is there anything special about it you remember?" - }, - { - "speaker": "Deborah", - "dia_id": "D1:7", - "text": "My mom's house had a special bench near the window. She loved to sit there every morning and take in the view. I come to sit here sometimes, it helps me stay connected to her." - }, - { - "speaker": "Jolene", - "img_url": [ - "http://eternalkeepsake.com/cdn/shop/products/IMG_0841.jpg" - ], - "blip_caption": "a photo of a heart shaped pendant with a bird on it", - "query": "meaningful necklace heart pendant", - "dia_id": "D1:8", - "text": "Staying connected is super important. Do you have something to remember her by? This pendant reminds me of my mother, she gave it to me in 2010 in Paris." - }, - { - "speaker": "Deborah", - "dia_id": "D1:9", - "text": "Yes, I also have a pendant that reminds me of my mother. And what is special for you about your jewelry?" - }, - { - "speaker": "Jolene", - "dia_id": "D1:10", - "text": "It has a special symbol on it that represents freedom for me. It's a nice reminder to go for my goals and not get held back." - }, - { - "speaker": "Deborah", - "dia_id": "D1:11", - "text": "It should really give you strength and energy!" - }, - { - "speaker": "Jolene", - "dia_id": "D1:12", - "text": "Do you have goals?" - }, - { - "speaker": "Deborah", - "dia_id": "D1:13", - "text": "One of my goals is to keep teaching yoga and supporting my community. I'm passionate about helping people find peace and joy through it." - }, - { - "speaker": "Jolene", - "dia_id": "D1:14", - "text": "What inspired you to go down this route?" - }, - { - "speaker": "Deborah", - "dia_id": "D1:15", - "text": "Yoga helped me find peace during a rough time, and now I'm passionate about sharing that with others." - }, - { - "speaker": "Jolene", - "dia_id": "D1:16", - "text": "It is truly inspiring!" - }, - { - "speaker": "Deborah", - "dia_id": "D1:17", - "text": "Gotta run, bye!" - }, - { - "speaker": "Jolene", - "dia_id": "D1:18", - "text": "Looking forward to the next chat!" - } - ], - "session_2_date_time": "9:49 am on 27 January, 2023", - "session_2": [ - { - "speaker": "Deborah", - "blip_caption": "a photo of a woman hugging a woman who is sitting on a couch", - "dia_id": "D2:1", - "text": "Hey Jolene, sorry to tell you this but my dad passed away two days ago. It's been really tough on us all - his sudden death left us all kinda shell-shocked. I'm trying to channel my grief by spending more time with family and cherishing the memories. These moments remind me to live life fully." - }, - { - "speaker": "Jolene", - "dia_id": "D2:2", - "text": "Sorry to hear about your dad, Deborah. Losing a parent is tough - how's it going for you and your family?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/dt61gwlzp3e31.jpg" - ], - "blip_caption": "a photo of a bride and groom posing for a picture", - "query": "parents wedding day", - "dia_id": "D2:3", - "text": "Even though it's hard, it's comforting to look back on the great memories. We looked at the family album. Photos give me peace during difficult times. This is my parents' wedding in 1993." - }, - { - "speaker": "Jolene", - "dia_id": "D2:4", - "text": "They were a beautiful couple!" - }, - { - "speaker": "Deborah", - "dia_id": "D2:5", - "text": "My husband and I are trying to be as good a family as my parents were!" - }, - { - "speaker": "Jolene", - "dia_id": "D2:6", - "text": "What do you value in your relationship?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/lr823iakg38b1.jpg" - ], - "blip_caption": "a photo of a note written to someone on a piece of paper", - "query": "handwritten letter heartfelt message journey together", - "dia_id": "D2:7", - "text": "It is love, and openness that have kept us close all these years. Being there for each other has made us both happy. Look what letter I received yesterday!" - }, - { - "speaker": "Jolene", - "dia_id": "D2:8", - "text": "What touching words! Who is this letter from?" - }, - { - "speaker": "Deborah", - "dia_id": "D2:9", - "text": "The group members sent this to me! They thanked me for the positive influence I had on them. Those moments remind me why I'm so passionate about yoga." - }, - { - "speaker": "Jolene", - "dia_id": "D2:10", - "text": "Where do you most often do yoga?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/1s7ojxx0yra61.jpg" - ], - "blip_caption": "a photo of a living room with a television and a window", - "query": "peaceful yoga studio mats laid out", - "dia_id": "D2:11", - "text": "This is one of the places where I do it." - }, - { - "speaker": "Jolene", - "dia_id": "D2:12", - "text": "Where is it?" - }, - { - "speaker": "Deborah", - "dia_id": "D2:13", - "text": "That's my old home. I go there now and then for my mom, who passed away. Sitting in that spot by the window gives me peace." - }, - { - "speaker": "Jolene", - "dia_id": "D2:14", - "text": "Must be great to have that place where you feel connected to her." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1658863926/at/house%20tours/2022-07/India/StairwayBench.jpg" - ], - "blip_caption": "a photo of a window seat in a room with a window", - "query": "bench window mom old house", - "dia_id": "D2:15", - "text": "Yeah, it's special. I can feel her presence when I sit there and it comforts me." - }, - { - "speaker": "Jolene", - "dia_id": "D2:16", - "text": "Wow, it sounds like that spot holds a lot of sentimental value. Does it bring back any special memories?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a view of the sky from an airplane window", - "dia_id": "D2:17", - "text": "Yeah, Jolene. She'd sit there every night with a book and a smile, reading was one of her hobbies. It was one of her favorite places in the house. " - }, - { - "speaker": "Jolene", - "dia_id": "D2:18", - "text": "What other hobbies did your mother have?" - }, - { - "speaker": "Deborah", - "dia_id": "D2:19", - "text": "Travel was also her great passion!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/gvb5983gs2z71.jpg" - ], - "blip_caption": "a photo of a bed with a snake head sticking out of it", - "query": "snake curled up cozy corner room", - "dia_id": "D2:20", - "text": "I want to show you one of my snakes! They always calm me down and make me happy. This is Susie." - }, - { - "speaker": "Deborah", - "dia_id": "D2:21", - "text": "Having a pet totally brightens up your life. It's great that it brings you comfort. Do you have any fun moments with your pet that you'd like to share?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/8aunav15kyq01.jpg" - ], - "blip_caption": "a photo of a snake sticking its head out of a blanket", - "query": "pet snake couch", - "dia_id": "D2:22", - "text": " I was playing video games and my pet just slinked out of her cage and coiled up next to me - it was too funny! My second snake Seraphim did it. Look at her sly eyes!" - }, - { - "speaker": "Deborah", - "dia_id": "D2:23", - "text": "Awww, that's so nice! " - }, - { - "speaker": "Jolene", - "dia_id": "D2:24", - "text": "I bought it a year ago in Paris." - }, - { - "speaker": "Deborah", - "dia_id": "D2:25", - "text": "Cool, Jolene! Pets bring so much happiness!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/0f9ubarmem291.jpg" - ], - "blip_caption": "a photo of a person laying in bed with a dog watching tv", - "query": "gaming with partner", - "dia_id": "D2:26", - "text": "They are very unusual pets! Here's me and my partner gaming last week - it's so fun. We played the game \"Detroit\" on the console. We are both crazy about this activity!" - }, - { - "speaker": "Deborah", - "dia_id": "D2:27", - "text": "Did your boyfriend teach you to play?" - }, - { - "speaker": "Jolene", - "dia_id": "D2:28", - "text": "Even as a child I learned to play on my own." - }, - { - "speaker": "Deborah", - "dia_id": "D2:29", - "text": "Do you only play old games or try new ones?" - }, - { - "speaker": "Jolene", - "dia_id": "D2:30", - "text": "We are planning to play \"Walking Dead\" next Saturday." - }, - { - "speaker": "Deborah", - "dia_id": "D2:31", - "text": "Take care and keep spreading those good vibes!" - }, - { - "speaker": "Jolene", - "dia_id": "D2:32", - "text": "Thanks, Deb! You too, take care. See ya!" - } - ], - "session_3_date_time": "7:03 pm on 1 February, 2023", - "session_3": [ - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/nx6fg5910bb61.jpg" - ], - "blip_caption": "a photo of a table with a robot on it and a laptop", - "query": "robotics project", - "dia_id": "D3:1", - "text": "Hi Deb! How're you? I've been busy. My engineering professor gave us a huge robotics project. It's tough but fun, it's making me get creative and problem-solve." - }, - { - "speaker": "Deborah", - "dia_id": "D3:2", - "text": "Hey Jolene! It's great to hear from you. It sounds challenging but interesting! It must be really putting your creative and problem-solving skills to the test. How did you feel when you first received the project? Are you enjoying working on it?" - }, - { - "speaker": "Jolene", - "dia_id": "D3:3", - "text": "When I got it, I felt a mix of emotions - excited and nervous. But now, I'm really enjoying it! It's like trying to solve a puzzle, figuring out the best design and programming. Seeing the robot come together is awesome too!" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a yellow sign with a picture of a family", - "dia_id": "D3:4", - "text": "That's awesome, Jolene! You're enjoying the process. It must be really satisfying to see it come together. Keep up the good work! Oh, by the way, I met my new neighbor Anna yesterday!" - }, - { - "speaker": "Jolene", - "dia_id": "D3:5", - "text": "How did you two meet?" - }, - { - "speaker": "Deborah", - "dia_id": "D3:6", - "text": "It happened at yoga in the park." - }, - { - "speaker": "Jolene", - "dia_id": "D3:7", - "text": "Wow, that's awesome! It's great connecting with people who have similar interests. Did you two talk about it?" - }, - { - "speaker": "Deborah", - "dia_id": "D3:8", - "text": "Yeah, we talked about how it has improved our lives and the sense of community it gives." - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a purse with a plant on a table", - "dia_id": "D3:9", - "text": "Sounds great!" - }, - { - "speaker": "Deborah", - "dia_id": "D3:10", - "text": "Have you ever thought about resuming yoga?" - }, - { - "speaker": "Jolene", - "dia_id": "D3:11", - "text": "Well... we planned to play the console with my partner." - }, - { - "speaker": "Deborah", - "dia_id": "D3:12", - "text": "It's also good that you have something to do together." - }, - { - "speaker": "Jolene", - "dia_id": "D3:13", - "text": "Thanks for the kind words!" - }, - { - "speaker": "Deborah", - "dia_id": "D3:14", - "text": "Gotta run bye!" - }, - { - "speaker": "Jolene", - "dia_id": "D3:15", - "text": "See you soon!" - } - ], - "session_4_date_time": "9:48 am on 4 February, 2023", - "session_4": [ - { - "speaker": "Jolene", - "dia_id": "D4:1", - "text": "Hey Deborah! Good to hear from you. How've you been? I've been on an emotional rollercoaster lately, but I'm coping." - }, - { - "speaker": "Deborah", - "dia_id": "D4:2", - "text": "Hey Jolene! Good to hear from you. All good here - how about you? Anything new happening lately?" - }, - { - "speaker": "Jolene", - "dia_id": "D4:3", - "text": "I had a major milestone last week and it went really well - I'm so relieved and proud. It was a huge accomplishment for me as an engineer." - }, - { - "speaker": "Deborah", - "dia_id": "D4:4", - "text": "That's awesome. You must have worked really hard for that. I'm so proud of you. Care to share more about it?" - }, - { - "speaker": "Jolene", - "dia_id": "D4:5", - "text": "Thanks so much! I had to plan and research a lot to design and build a sustainable water purifier for a rural community in need. It was tough, but I loved the experience." - }, - { - "speaker": "Deborah", - "dia_id": "D4:6", - "text": "Your engineering skills really made a difference for people in a rural area. That's amazing! How did it feel when you saw it working?" - }, - { - "speaker": "Jolene", - "dia_id": "D4:7", - "text": "It was such a surreal moment. Seeing it working and providing clean water to the community was incredibly satisfying. It reminded me of how engineering can make a difference in people's lives. It made me feel like I had a purpose and had done something good." - }, - { - "speaker": "Deborah", - "dia_id": "D4:8", - "text": "So, what are your career aspirations now?" - }, - { - "speaker": "Jolene", - "dia_id": "D4:9", - "text": " I want to keep working in engineering and continue to make a positive impact on communities in need by creating sustainable solutions. My goal is to contribute towards making the world a better place with my work." - }, - { - "speaker": "Deborah", - "dia_id": "D4:10", - "text": " I'm sure great things will keep coming your way. Keep up the good work and follow your passions!" - }, - { - "speaker": "Jolene", - "dia_id": "D4:11", - "text": "Your support means a lot. I'm determined to keep going and make a difference. Your encouragement really motivates me to pursue my passions." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://images.squarespace-cdn.com/content/v1/64343f0505054d3701b51289/ead98f94-3604-496b-8a00-9f796aafb2d7/Reve_December22_CarleyRuddPhotography%2865of86%29.jpg" - ], - "blip_caption": "a photo of two women in a dance studio doing a dance pose", - "query": "yoga studio balance reconnect", - "dia_id": "D4:12", - "text": "When things get tough, just take a deep breath and remember why you're doing this. This is where I spend a lot of my time, teaching yoga. It's a great way to find balance and reconnect with ourselves. I bonded with Anna during yesterday's l yoga class." - }, - { - "speaker": "Jolene", - "dia_id": "D4:13", - "text": "Wow, that's awesome! What new poses did you try?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a woman doing a yoga pose on a chair", - "dia_id": "D4:14", - "text": "We were trying out different dance poses, like Warrior II, which builds strength and boosts focus." - }, - { - "speaker": "Jolene", - "dia_id": "D4:15", - "text": "Is it difficult to do?" - }, - { - "speaker": "Deborah", - "dia_id": "D4:16", - "text": "Yes, but this brought us closer to Anna! We supported each other, that means a lot." - }, - { - "speaker": "Jolene", - "dia_id": "D4:17", - "text": "Can you explain how to do it?" - }, - { - "speaker": "Deborah", - "dia_id": "D4:18", - "text": "Sure! To do this modified pose, sit on the edge of a chair with your feet planted. Twist your torso to one side and use your hand on your knee for support. You'll feel a stretch in your back and shoulders as you hold it for a few breaths, then switch sides. It's great for relaxing tense muscles." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/bxxmms712w0b1.jpg" - ], - "blip_caption": "a photo of a book shelf with a lot of books on it", - "query": "bookshelf engineering books projects", - "dia_id": "D4:19", - "text": "Sounds really hard! Here's my bookshelf!" - }, - { - "speaker": "Deborah", - "dia_id": "D4:20", - "text": "That's quite a collection! Have you had a favorite book lately? I'd love to hear your thoughts." - }, - { - "speaker": "Jolene", - "dia_id": "D4:21", - "text": "Thanks Deborah! I'm really into this book called \"Sapiens\" - it's a fascinating look at human history and how technology has affected us. It's giving me a lot to think about!" - }, - { - "speaker": "Deborah", - "dia_id": "D4:22", - "text": "Great, this is interesting! Have you come across any recent ones that really struck you?" - }, - { - "speaker": "Jolene", - "dia_id": "D4:23", - "text": "Two weeks ago I read \"Avalanche\" by Neal Stephenson in one sitting! " - }, - { - "speaker": "Deborah", - "dia_id": "D4:24", - "text": "That sounds cool, Jolene. Stories can be so powerful - they can teach us, motivate us, and bring us together. " - }, - { - "speaker": "Jolene", - "dia_id": "D4:25", - "text": "I also read a recent story about someone who became successful despite facing numerous challenges. It really showed me that anything is possible if you believe and work hard." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://motherofwildflowerhouse.com/cdn/shop/products/AfterlightImage3.jpg" - ], - "blip_caption": "a photo of a bouquet of flowers in a vase on a table", - "query": "mother bouquet flowers wedding", - "dia_id": "D4:26", - "text": "That's a really powerful message. It reminds me of someone special and their grace and strength when they faced hardships. Check out this creation!" - }, - { - "speaker": "Jolene", - "dia_id": "D4:27", - "text": "Wow, Deb! That's beautiful! Can you tell me the backstory of that bouquet?" - }, - { - "speaker": "Deborah", - "dia_id": "D4:28", - "text": "My friend gave me this bouquet when I was struggling, and it gives me hope and courage. I'm filled with warmth and appreciation when I look at it." - }, - { - "speaker": "Jolene", - "dia_id": "D4:29", - "text": "That's really sweet. Such a small thing can make a real difference." - }, - { - "speaker": "Deborah", - "dia_id": "D4:30", - "text": "It's amazing how something as simple as flowers can make a real difference. Nature sure is beautiful." - }, - { - "speaker": "Jolene", - "dia_id": "D4:31", - "text": "Where are some of your favorite nature spots?" - }, - { - "speaker": "Deborah", - "dia_id": "D4:32", - "text": "Oh, there's so many great places! My favorite is a park with a forest trail - it's so calming getting lost in nature. Then there's this beach nearby that I love - the sound of the waves and the sand under my feet make me feel all peaceful and happy. Do you have any favorite spots, Jolene?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://images.pexels.com/photos/8509256/pexels-photo-8509256.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-maribel-rosete-8509256.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "serene beach sunset", - "dia_id": "D4:33", - "text": "Here's a picture I took on vacation last summer in Bogota. It was so beautiful and calming watching the sunset over the water. It definitely made me appreciate nature's calming power." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://images.pexels.com/photos/11663179/pexels-photo-11663179.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-serhat-tu%C4%9F-11663179.jpg" - ], - "blip_caption": "a photo of a small island with a lone boat in the water", - "query": "sunrise lake peaceful", - "dia_id": "D4:34", - "text": "That sounds great, Jolene. Nature's calming for sure. Guess it helps us forget the daily craziness and find inner peace. No wonder you're a fan! I like to come to this spot by the water near my mom's old house. It's where I reflect on her life and find some peace. Being surrounded by nature helps a lot." - }, - { - "speaker": "Jolene", - "dia_id": "D4:35", - "text": "That's so touching! Do you have any little traditions that you do when you come to reflect on your mom?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://alvjewels.com/cdn/shop/products/image_2b791370-8eae-4e4d-8c8f-4fe581f9240a.jpg" - ], - "blip_caption": "a photo of a woman wearing a white shirt and a gold chain necklace", - "query": "heart-shaped necklace", - "dia_id": "D4:36", - "text": "Do you remember this amulet from her? Whenever I come here, I bring it with me. It's how I feel her love and stay close to her. Holding it brings me comfort." - }, - { - "speaker": "Jolene", - "dia_id": "D4:37", - "text": "It's amazing how something so small can have such a big impact, huh?" - }, - { - "speaker": "Deborah", - "dia_id": "D4:38", - "text": "Yeah, even small things like this can make a big difference. It's a reminder of all the love and strength we have inside, connecting us to people we've lost and comforting us." - }, - { - "speaker": "Jolene", - "dia_id": "D4:39", - "text": "It can keep them close in our hearts every day." - }, - { - "speaker": "Deborah", - "dia_id": "D4:40", - "text": "Anna also has a pendant that she wears in memory of her mother! This also brought us closer." - }, - { - "speaker": "Jolene", - "dia_id": "D4:41", - "text": "It's nice to have those reminders as a source of strength when we need it." - }, - { - "speaker": "Deborah", - "dia_id": "D4:42", - "text": "Life's tough but hang in there. Look to your sources of strength and you'll do great. Stay in touch, take care of yourself, and know I'm always here to cheer you on!" - }, - { - "speaker": "Jolene", - "dia_id": "D4:43", - "text": "Thanks, Deb! Your encouragement means a lot to me. I'll definitely stay in touch. Bye, take care and keep shining!" - }, - { - "speaker": "Deborah", - "dia_id": "D4:44", - "text": "Stay safe! Bye!" - } - ], - "session_5_date_time": "9:03 pm on 9 February, 2023", - "session_5": [ - { - "speaker": "Jolene", - "dia_id": "D5:1", - "text": "Hey Deborah! Been a few days since we last talked so I wanted to fill you in on something cool. Last Wednesday I did a mini retreat to assess where I'm at in life. It was a dope experience that totally gave me a new outlook." - }, - { - "speaker": "Deborah", - "dia_id": "D5:2", - "text": "Hey Jolene! Sounds great. Taking time to reflect can be really awesome. Did you gain any new insights from it?" - }, - { - "speaker": "Jolene", - "dia_id": "D5:3", - "text": "Yep! I achieved so much more than I imagined. It was a real confidence boost." - }, - { - "speaker": "Deborah", - "dia_id": "D5:4", - "text": "You deserve credit for stepping outside your comfort zone and believing in yourself. What cool stuff did you accomplish at the retreat?" - }, - { - "speaker": "Jolene", - "dia_id": "D5:5", - "text": "I really accomplished something with my engineering project - I came up with some neat solutions and I'm really excited about it." - }, - { - "speaker": "Deborah", - "dia_id": "D5:6", - "text": "Let's go into more detail." - }, - { - "speaker": "Jolene", - "dia_id": "D5:7", - "text": " Green tech could really make a difference in disadvantaged areas. I'd like to look into it and see how I can contribute. Hey, speaking of helping out, I had an idea: a volunteer program where engineers teach STEM to underprivileged kids. What do you think of that?" - }, - { - "speaker": "Deborah", - "dia_id": "D5:8", - "text": "That sounds great, Jolene! It's a great way to help and inspire others. They would benefit a lot from your knowledge. Have you thought of a plan yet?" - }, - { - "speaker": "Jolene", - "dia_id": "D5:9", - "text": "Haven't finished planning yet but I'm thinking of teaming up with local schools/centers to do workshops. We could even invite engineers as guest speakers to show kids their career options." - }, - { - "speaker": "Deborah", - "dia_id": "D5:10", - "text": "Having guest speakers, like them, would definitely give the kids a real-world view. Have you reached out to any schools or centers yet?" - }, - { - "speaker": "Jolene", - "dia_id": "D5:11", - "text": "No, not yet. I want to solidify the plan first. Can't wait to start reaching out, though!" - }, - { - "speaker": "Deborah", - "dia_id": "D5:12", - "text": "That makes sense. I'm excited to hear how you reach out and help those kids. Let me know how it goes!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/12/ee/9a/12ee9ab40a074f87e1c89f1c0b084de8.jpg" - ], - "blip_caption": "a photo of a notebook with a pen and a notebook with a page of notes", - "query": "engineering notebook ideas", - "dia_id": "D5:13", - "text": "I'll keep you posted! Appreciate the support! Here are my sketches in the planner." - }, - { - "speaker": "Deborah", - "dia_id": "D5:14", - "text": "Sounds like you're doing great. Let me know if you need more tips or information." - }, - { - "speaker": "Jolene", - "dia_id": "D5:15", - "text": "Thanks, Deb! If I need anything else, I'll let you know. You're awesome!" - }, - { - "speaker": "Deborah", - "dia_id": "D5:16", - "text": "You're awesome too! Take care!" - }, - { - "speaker": "Jolene", - "dia_id": "D5:17", - "text": "Stay safe!" - } - ], - "session_6_date_time": "4:12 pm on 22 February, 2023", - "session_6": [ - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/l2ug8nsuyt0b1.jpg" - ], - "blip_caption": "a photo of a plate of food and a glass of wine", - "query": "friends toast drinks", - "dia_id": "D6:1", - "text": "Hey Deborah, totally buzzing! Had a great night out last night - dinner, and drinks with my friends. So glad I got to let my hair down. You?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgtrcCxkcqe0DgHUVVqbd_YzertOqBQr0xfgZsSIgfLnWCCm8hh-gbggmBWzSnBLHfRuzRH9Z8nKkHZBaxBp3siwhJfyoUMzkM48nD0QSEHLQ-x6Mh2TpD00a_78-zVImBnIe2BQguR_B9W0FD12fOo6NceMKKfnprpIqnsVwl7ERzIBfSbB-VggxYMPdg/s4032/IMG_4386.jpg" - ], - "blip_caption": "a photo of a garden with a bunch of flowers in buckets", - "query": "bench flowers nostalgia", - "dia_id": "D6:2", - "text": "Sounds great, Jolene! I just visited this place and it was so calming. Nostalgic too." - }, - { - "speaker": "Jolene", - "dia_id": "D6:3", - "text": "Wow, those flowers are beautiful! What type are they? It looks so peaceful there." - }, - { - "speaker": "Deborah", - "dia_id": "D6:4", - "text": "The roses and dahlias bring me peace. I lost a friend last week, so I've been spending time in the garden to find some comfort." - }, - { - "speaker": "Jolene", - "dia_id": "D6:5", - "text": "Sorry to hear about your friend, Deb. Losing someone can be really tough. How are you holding up?" - }, - { - "speaker": "Deborah", - "dia_id": "D6:6", - "text": "Thanks for the kind words. It's been tough, but I'm comforted by remembering our time together. It reminds me of how special life is." - }, - { - "speaker": "Jolene", - "dia_id": "D6:7", - "text": "Memories can give us so much comfort and joy." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.pinimg.com/originals/cc/79/b1/cc79b1f0ce550924e25b629477305e48.jpg" - ], - "blip_caption": "a photo of two women are riding on a motorcycle on a dirt road", - "query": "friend hike laughing", - "dia_id": "D6:8", - "text": "Memories keep our loved ones close. This is the last photo with Karlie which was taken last summer when we hiked. It was our last one. We had such a great time! Every time I see it, I can't help but smile." - }, - { - "speaker": "Jolene", - "dia_id": "D6:9", - "text": "Wow, looks like a great trip! Where else have you traveled?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://zohrasrose.files.wordpress.com/2022/01/img_1273.jpg" - ], - "blip_caption": "a photo of a swing on a beach with a blue sky", - "query": "bali yoga beach", - "dia_id": "D6:10", - "text": "I've been blessed to travel to a few places and Bali last year was one of my favs. It was a gorgeous island that gave me peace, great for yoga." - }, - { - "speaker": "Jolene", - "dia_id": "D6:11", - "text": "Wow, that's great! Is yoga on the beach a thing? I've been wanting to try it." - }, - { - "speaker": "Deborah", - "dia_id": "D6:12", - "text": "The sound of the waves and the fresh air is wonderful!" - }, - { - "speaker": "Jolene", - "dia_id": "D6:13", - "text": "I'll definitely give it a go! It sounds peaceful. Thanks!" - }, - { - "speaker": "Deborah", - "dia_id": "D6:14", - "text": "Let me know how it goes. Enjoy it!" - }, - { - "speaker": "Jolene", - "dia_id": "D6:15", - "text": "I'll keep you posted if I decide to go there." - }, - { - "speaker": "Deborah", - "dia_id": "D6:16", - "text": "Take care!" - } - ], - "session_7_date_time": "4:50 pm on 25 February, 2023", - "session_7": [ - { - "speaker": "Jolene", - "dia_id": "D7:1", - "text": "Hi Deborah, it's been a while! Since we last talked, so much has happened. Balancing engineering school with my partner's video games is quite a feat. But I'm also setting aside time for myself, doing yoga and meditation. It helps give me calm amidst the craziness." - }, - { - "speaker": "Deborah", - "dia_id": "D7:2", - "text": "Hey Jolene! Great to hear from you. Taking a break is key. How have those practices been helping with everything?" - }, - { - "speaker": "Jolene", - "img_url": [ - "http://www.talisyoga.com/wp-content/uploads/2017/06/IMG_3532-e1498054278130.jpg" - ], - "blip_caption": "a photo of a woman sitting on a yoga mat doing a yoga pose", - "query": "partner jolene yoga practice", - "dia_id": "D7:3", - "text": "They seriously saved me. I chill out and gain perspective when I do yoga. Afterward, I'm more alert and motivated. Here is my last photo." - }, - { - "speaker": "Deborah", - "dia_id": "D7:4", - "text": "Wow, cool that yoga has been helping you out! Do they also do yoga with you? Does your partner do yoga with you?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://api.army.mil/e2/c/images/2017/12/22/502617/original.jpg" - ], - "blip_caption": "a photo of a man and a woman holding hands with rings on them", - "query": "jolene partner yoga bonding", - "dia_id": "D7:5", - "text": "He started joining me sometimes, which is nice for bonding and relaxing. It's brought us closer. This is just our random photo." - }, - { - "speaker": "Deborah", - "dia_id": "D7:6", - "text": "Aw, that's wonderful! How long have you been married?" - }, - { - "speaker": "Jolene", - "dia_id": "D7:7", - "text": "We're not married yet but we've been together for three years. We're taking it slow and loving the ride. This ring on his hand is just a decoration, not a wedding ring." - }, - { - "speaker": "Deborah", - "dia_id": "D7:8", - "text": "Sounds nice, Jolene. Enjoying the journey is the main thing. So, how did you two meet?" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a book and a calculator on a table", - "dia_id": "D7:9", - "text": "We actually met in an engineering class in college and quickly became friends. It eventually blossomed into something more and it's been great!" - }, - { - "speaker": "Deborah", - "dia_id": "D7:10", - "text": "Wow, your relationship started from a strong friendship. Do you still enjoy working on engineering projects together?" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a group of men standing in front of a building", - "dia_id": "D7:11", - "text": "Yep, teaming up for these projects feels great! We get each other's struggles and both love it." - }, - { - "speaker": "Deborah", - "dia_id": "D7:12", - "text": "Have yoga or meditation helped with any stress?" - }, - { - "speaker": "Jolene", - "dia_id": "D7:13", - "text": "Whenever I'm overwhelmed, I just take a break and do some breathing or meditate. It really helps me feel calmer and clearer, so I'm able to handle challenges more easily." - }, - { - "speaker": "Deborah", - "dia_id": "D7:14", - "text": "It's been great to see your progress since we last chatted - keep it up!" - }, - { - "speaker": "Jolene", - "dia_id": "D7:15", - "text": "Thanks, Deb! Your support really means a lot. I'm gonna keep pushing forward and continue taking time for myself." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i0.wp.com/thirdeyemom.com/wp-content/uploads/2017/08/img_2197.jpg" - ], - "blip_caption": "a photo of a person sitting on a bench looking at the sunset", - "query": "beautiful sunset mountaintop reflection", - "dia_id": "D7:16", - "text": " Always rooting for you. Remember to listen to your heart and take good care. Here's a photo I took while reflecting outdoors. It's a reminder to find peace even in chaos." - }, - { - "speaker": "Jolene", - "dia_id": "D7:17", - "text": " I'm curious, what does your daily routine look like?" - }, - { - "speaker": "Deborah", - "dia_id": "D7:18", - "text": "In the morning, I meditate, do yoga, and teach classes. And yesterday I went for a morning jog for the first time in a nearby park. I will now incorporate this into my daily routine. And in the evenings, I spend time with loved ones." - }, - { - "speaker": "Jolene", - "dia_id": "D7:19", - "text": "Why did you decide that?" - }, - { - "speaker": "Deborah", - "dia_id": "D7:20", - "text": "Exercise is key for me - it makes me feel connected to my body. " - }, - { - "speaker": "Jolene", - "dia_id": "D7:21", - "text": "This is a great healthy habit!" - }, - { - "speaker": "Deborah", - "dia_id": "D7:22", - "text": "It's like they say - \"Can't pour from an empty cup.\" Looking out for ourselves gives us the energy to help others. And don't forget, you're worth it too!" - }, - { - "speaker": "Jolene", - "dia_id": "D7:23", - "text": "Thanks, Deb. Your words really mean something to me. I'll keep remembering to prioritize self-care." - } - ], - "session_8_date_time": "7:18 pm on 2 March, 2023", - "session_8": [ - { - "speaker": "Deborah", - "dia_id": "D8:1", - "text": "Hey Jolene, Anna got me a vegan stir-fry the other day - tofu and veg with ginger and soy sauce. It was really tasty! Food is such a wonderful source of pleasure and nourishment. What dishes are comforting to you?" - }, - { - "speaker": "Jolene", - "dia_id": "D8:2", - "text": "One of my favorite dishes is lasagna! Comfort food can be a great pick-me-up. I've got a lot going on with my studies and exams." - }, - { - "speaker": "Deborah", - "dia_id": "D8:3", - "text": "Have you been able to find time for yourself lately?" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a room with a wooden floor and a window", - "dia_id": "D8:4", - "text": "I've been trying to squeeze in some me-time. Last Friday, I did yoga and meditation to relax. Did you find time for yourself too?" - }, - { - "speaker": "Deborah", - "dia_id": "D8:5", - "text": " I also did the same, it helped me reset my mind. How does it make you feel?" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a purse with a plant on a table", - "dia_id": "D8:6", - "text": "It's amazing how a few quiet moments can work wonders for the soul." - }, - { - "speaker": "Deborah", - "dia_id": "D8:7", - "text": "Have you been able to get outside lately?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/scwm4w6fknu41.jpg" - ], - "blip_caption": "a photo of a lizard laying on the ground surrounded by leaves", - "query": "snake park walk seraphina", - "dia_id": "D8:8", - "text": "I did take Seraphim to the park last Sunday. She loved it and here's a pic." - }, - { - "speaker": "Deborah", - "dia_id": "D8:9", - "text": "Looks like you guys had fun!" - }, - { - "speaker": "Jolene", - "dia_id": "D8:10", - "text": "We explored new places. People are surprised when they see a tamed snake. What do you like about being outdoors?" - }, - { - "speaker": "Deborah", - "dia_id": "D8:11", - "text": "Hmm... The birds chirping and the breeze gently blowing! It reminds me of what really matters." - }, - { - "speaker": "Jolene", - "dia_id": "D8:12", - "text": "Yep, it's like a reminder to slow down and appreciate the little things." - }, - { - "speaker": "Deborah", - "dia_id": "D8:13", - "text": "Is there anything you want to be more mindful of right now?" - }, - { - "speaker": "Jolene", - "dia_id": "D8:14", - "text": "I need to be more mindful of my stress levels and take care of my mental health. Sometimes I get too caught up in my studies and forget to prioritize self-care." - }, - { - "speaker": "Deborah", - "dia_id": "D8:15", - "text": " Life can get hectic and it's easy to forget about ourselves. " - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/s5gqwkbhuce91.jpg" - ], - "blip_caption": "a photo of a desk with a notebook and a computer monitor", - "query": "to-do list overwhelming exams deadlines", - "dia_id": "D8:16", - "text": "Exams and deadlines got me feeling overwhelmed. Just look at my to-do list! It seems never-ending... Trying my best but it's been challenging." - }, - { - "speaker": "Deborah", - "dia_id": "D8:17", - "text": "Your efforts will bear fruit, don't give up!" - }, - { - "speaker": "Jolene", - "dia_id": "D8:18", - "text": "Thanks, Deb. Any tips on studying or time management?" - }, - { - "speaker": "Deborah", - "dia_id": "D8:19", - "text": "My tip is to break it into smaller pieces and set goals for yourself. For time management, planners or schedulers help you stay organized and give you time for yourself. Let me know if you need help with a study plan!" - }, - { - "speaker": "Jolene", - "dia_id": "D8:20", - "text": "I appreciate your help with that." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://themugshotlk.com/cdn/shop/products/IMG_9782.jpg" - ], - "blip_caption": "a photo of a yellow coffee cup with a handwritten message", - "query": "mug believe in yourself", - "dia_id": "D8:21", - "text": "Take care and good luck with your exams. I'll give you a mug just like this one! It encourages." - }, - { - "speaker": "Jolene", - "dia_id": "D8:22", - "text": "Thanks, Deb! This really cheered me up. All the best with your classes. Bye!" - }, - { - "speaker": "Deborah", - "dia_id": "D8:23", - "text": "Thanks, Jolene! Glad I could bring a smile to your face. Take care and make sure to give yourself some time to relax. Bye!" - } - ], - "session_9_date_time": "11:22 am on 13 March, 2023", - "session_9": [ - { - "speaker": "Deborah", - "dia_id": "D9:1", - "text": "Hi Jolene! We haven't corresponded for a long time!" - }, - { - "speaker": "Jolene", - "dia_id": "D9:2", - "text": "Hey Deb, yeah life can get chaotic. How's it been going lately?" - }, - { - "speaker": "Deborah", - "dia_id": "D9:3", - "text": "So much has been going on lately. I started this yoga class in the neighborhood - it's such a good feeling! Now I get to share the exercise with my neighbors and watch it really transform them." - }, - { - "speaker": "Jolene", - "dia_id": "D9:4", - "text": " Congrats. How did you do this?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://bunundone.com/wp-content/uploads/2019/08/IMG_5619.jpg" - ], - "blip_caption": "a photo of a group of women posing for a picture", - "query": "neighborhood yoga class smiling faces", - "dia_id": "D9:5", - "text": "Thanks! My neighbors were interested in trying yoga, so I hosted a class for them on Friday. It was great to see everyone embrace and enjoy it. Here is our photo together." - }, - { - "speaker": "Jolene", - "dia_id": "D9:6", - "text": "That's cool! What made you want to start teaching it?" - }, - { - "speaker": "Deborah", - "dia_id": "D9:7", - "text": "I find it calming and wanted to share that with others. Giving people peace and awareness brings me so much happiness." - }, - { - "speaker": "Jolene", - "dia_id": "D9:8", - "text": "Wow, Deb! It's awesome when we can share something we love and make things better for others." - }, - { - "speaker": "Deborah", - "dia_id": "D9:9", - "text": "Teaching it is awesome because it can help others and I've made such great friends through it. It's really nice for building community connections." - }, - { - "speaker": "Jolene", - "dia_id": "D9:10", - "text": "That's really motivating. It's great to have support in tough times." - }, - { - "speaker": "Deborah", - "dia_id": "D9:11", - "text": "It's one of life's best parts, right?" - }, - { - "speaker": "Jolene", - "dia_id": "D9:12", - "text": "Yeah, having someone to rely on is key in tough times. It really makes a difference in how we handle life. Plus, there's something I wanted to tell you." - }, - { - "speaker": "Deborah", - "dia_id": "D9:13", - "text": "What's up? I'm listening. We'll figure it out." - }, - { - "speaker": "Jolene", - "dia_id": "D9:14", - "text": "I'm having a hard time dealing with my Engineering assignments. It's a lot to manage and I'm struggling to keep up. Can we still talk about time management?" - }, - { - "speaker": "Deborah", - "dia_id": "D9:15", - "text": "Sure, Jolene. Let's find a time that works for both of us." - }, - { - "speaker": "Jolene", - "dia_id": "D9:16", - "text": "Let's find a time to chat - I'll check my schedule and get back to you." - }, - { - "speaker": "Deborah", - "dia_id": "D9:17", - "text": "Take your time, Jolene. We'll work it out. Take care of yourself, OK?" - }, - { - "speaker": "Jolene", - "dia_id": "D9:18", - "text": " I'll make sure to take it. See you soon!" - }, - { - "speaker": "Deborah", - "dia_id": "D9:19", - "text": " I'm here for you if you need me. Let's catch up soon." - }, - { - "speaker": "Jolene", - "dia_id": "D9:20", - "text": "Have a great day!" - } - ], - "session_10_date_time": "5:35 pm on 22 March, 2023", - "session_10": [ - { - "speaker": "Deborah", - "dia_id": "D10:1", - "text": "Hey Jolene, it's been a while. Hope you're doing okay with all your exams and deadlines. I know it's difficult for you right now." - }, - { - "speaker": "Jolene", - "dia_id": "D10:2", - "text": "Hey Deb! Yeah, it can be tough. Trying to find time for everything is like playing catch-up - really stressful!" - }, - { - "speaker": "Deborah", - "dia_id": "D10:3", - "text": " How do you manage your time and stay organized with all the projects and deadlines?" - }, - { - "speaker": "Jolene", - "dia_id": "D10:4", - "text": "I'm using the Pomodoro Technique - 25 minutes work, 5-minute break - to avoid burnout but I'm still struggling to prioritize. Do you have any other tips on time management?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://johnaugust.com/wp-content/uploads/2020/04/front.jpg" - ], - "blip_caption": "a photo of a notepad with a list of things to do", - "query": "daily schedule template", - "dia_id": "D10:5", - "text": "I create a daily schedule or to-do list. Here's my example for today." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/3int1hmnou0a1.jpg" - ], - "blip_caption": "a photo of a notepad with notes on it on a counter", - "query": "colorful to-do list crossed off tasks", - "dia_id": "D10:6", - "text": "I tried making one but it's kinda overwhelming when it's a big stack of tasks. Here's an example from last Friday." - }, - { - "speaker": "Deborah", - "dia_id": "D10:7", - "text": "Have you tried breaking it down or prioritizing the tasks?" - }, - { - "speaker": "Jolene", - "dia_id": "D10:8", - "text": "It can often feel overwhelming and difficult to figure out where to start." - }, - { - "speaker": "Deborah", - "dia_id": "D10:9", - "text": "I get it, Jolene. When I'm overloaded, I use a certain method. It helps me figure out what's important and urgent so I'm more organized. Do you know about it?" - }, - { - "speaker": "Jolene", - "dia_id": "D10:10", - "text": "Nah, I'm not familiar with that one. What's it about?" - }, - { - "speaker": "Deborah", - "dia_id": "D10:11", - "text": "Want me to tell you about it? It helps you organize things based on how important and urgent they are." - }, - { - "speaker": "Jolene", - "dia_id": "D10:12", - "text": "Sure, tell me more about it! It sounds useful." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i0.wp.com/writeintolife.com/wp-content/uploads/2021/03/important-urgent.jpg" - ], - "blip_caption": "a photo of a pair of scissors sitting on top of a piece of paper", - "query": "eisenhower matrix", - "dia_id": "D10:13", - "text": "The Eisenhower Matrix sorts tasks into four boxes, categorizing them based on their urgency and importance. It can be really useful for organizing and prioritizing. Here's a breakdown. " - }, - { - "speaker": "Jolene", - "dia_id": "D10:14", - "text": "The visualization is helpful too. Thanks for sharing!" - }, - { - "speaker": "Deborah", - "dia_id": "D10:15", - "text": "I am glad, it was helpful. Let's give it a try and see if it helps you stay focused and less stressed. " - }, - { - "speaker": "Jolene", - "dia_id": "D10:16", - "text": "Yeah, I'll give it a go. Fingers crossed it'll help me. Thanks for the help!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://images.pexels.com/photos/8509256/pexels-photo-8509256.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-maribel-rosete-8509256.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach serene", - "dia_id": "D10:17", - "text": "Don't forget to take it easy and look after yourself. Wishing you all the best! Recently, Anna and I were sitting by the sea, watching the sunset and talking about each other. And we realized that we inspire each other. What thoughts does the sea in this photo make you think of?" - }, - { - "speaker": "Jolene", - "dia_id": "D10:18", - "text": "This gets me thinking of when I'll learn to surf. Gotta find that spare time!" - }, - { - "speaker": "Deborah", - "dia_id": "D10:19", - "text": "Surfing, huh Jolene? Chase your dreams, don't be daunted. Have you thought about the steps you can take?" - }, - { - "speaker": "Jolene", - "dia_id": "D10:20", - "text": "Definitely! I've been gathering information, watching videos, and I even got a beginners' guide to surfing. Just need to find the right time and place to get a lesson." - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a row of surfboards leaning against a palm tree", - "dia_id": "D10:21", - "text": "Way to go! Taking those first steps is key. Believe in yourself and keep going!" - }, - { - "speaker": "Jolene", - "dia_id": "D10:22", - "text": "Thanks for the support! Those got me pumped to try surfing. Gonna keep pushing myself to make it happen." - }, - { - "speaker": "Deborah", - "dia_id": "D10:23", - "text": "Keep it up, Jolene! Remember, the experience matters just as much as the end result. Step by step and have fun along the way. You can do it!" - }, - { - "speaker": "Jolene", - "dia_id": "D10:24", - "text": "Thanks for the boost!" - } - ], - "session_11_date_time": "4:03 pm on 28 March, 2023", - "session_11": [ - { - "speaker": "Jolene", - "blip_caption": "a photo of a person's hand holding a pair of scissors", - "dia_id": "D11:1", - "text": "Hey Deb, long time no talk. A lot's happened! On Friday I had a breakthrough with my engineering project. Finally found a solution to a prob that's been bugging me. Feels great to see my hard work paying off." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://lotuslandyogasf.com/wp-content/uploads/Jasmine-Class-cobra-e1674461734640.png" - ], - "blip_caption": "a photo of a group of people doing yoga in a room", - "query": "yoga class calming setting", - "dia_id": "D11:2", - "text": "You've really proven your skills. Feels great, right? Keep it up! And I bought new props for the yoga class! Here it is in action." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://caramiamay.com/cdn/shop/products/image_1e09790f-e4ab-4589-ac14-a6dbdf6f3b83.jpg" - ], - "blip_caption": "a photo of a cardboard mat with a cup of coffee on it", - "query": "yoga mat blocks", - "dia_id": "D11:3", - "text": "Oh, I also have new details for this case! Rate it!" - }, - { - "speaker": "Deborah", - "img_url": [ - "http://www.settlepetalyoga.com/cdn/shop/products/IMG_8556.jpg" - ], - "blip_caption": "a photo of a candle with a sprig of rosemary on a table", - "query": "yoga mat lavender scented candle", - "dia_id": "D11:4", - "text": "That`s cool! I also bought this candle for the atmosphere and to improve my yoga practice. How about you? When you do it, what feelings do you get?" - }, - { - "speaker": "Jolene", - "dia_id": "D11:5", - "text": "I feel relaxed during this activity. I love creating a serene space with soothing scents like lavender and rosemary. Do you have any favorite scents or rituals for when you do it?" - }, - { - "speaker": "Deborah", - "dia_id": "D11:6", - "text": "I'm also a big fan of scents like this! Candles and essential oils add warmth and calm to my yoga session. It's amazing how certain smells can transport you to a place of peace. Do you have any other ways to enhance your yoga practice?" - }, - { - "speaker": "Jolene", - "dia_id": "D11:7", - "text": "I find music helps me. Any favorite tracks?" - }, - { - "speaker": "Deborah", - "dia_id": "D11:8", - "text": "I find instrumental tracks with mellow melodies and rhythms help create a peaceful vibe. One of my favorites is a track called \"Savana.\" What songs/artists do you like listening to during your practice?" - }, - { - "speaker": "Jolene", - "dia_id": "D11:9", - "text": "I love listening to Nils Frahm and Olafur Arnalds during my practice. Their music is so calming and puts me in a different headspace. Do you have any recommendations for other similar artists or tracks I should check out?" - }, - { - "speaker": "Deborah", - "dia_id": "D11:10", - "text": "Also, I'm listening to an album called 'Sleep,' which is great for meditation and deep relaxation. Hope you find it as calming as I do!" - }, - { - "speaker": "Jolene", - "dia_id": "D11:11", - "text": "Thanks for the tips, Deborah! I'm gonna check them out. It's always good to have some new tunes for yoga!" - }, - { - "speaker": "Deborah", - "dia_id": "D11:12", - "text": " Let me know your thoughts on the albums!" - }, - { - "speaker": "Jolene", - "dia_id": "D11:13", - "text": "See you!" - }, - { - "speaker": "Deborah", - "dia_id": "D11:14", - "text": "Take care and keep up the good work!" - } - ], - "session_12_date_time": "4:30 pm on 9 April, 2023", - "session_12": [ - { - "speaker": "Deborah", - "blip_caption": "a photo of a large brown and white photo of a person", - "dia_id": "D12:1", - "text": "Hey Jolene! Great to see you! Had a blast biking nearby with my neighbor last week - was so freeing and beautiful. Checked out an art show with a friend today - really cool and inspiring stuff. Reminded me of my mom." - }, - { - "speaker": "Jolene", - "dia_id": "D12:2", - "text": "Hey Deborah! Sounds like you had a blast biking and at the art show. Your photo looks like you were really into it! Did it make you think profound thoughts?" - }, - { - "speaker": "Deborah", - "dia_id": "D12:3", - "text": "My mom was interested in art. She believed art could give out strong emotions and uniquely connect us. When I go to an art show, it's like we're still experiencing it together even though she's gone. It's hard but comforting." - }, - { - "speaker": "Jolene", - "dia_id": "D12:4", - "text": "Losing someone is hard, but finding something that helps you cope is great." - }, - { - "speaker": "Deborah", - "dia_id": "D12:5", - "text": "Finding ways to keep her memory alive gives me peace. It's amazing how something simple like artwork can bring back powerful emotions and remind us of those we've lost. It's about finding solace in the things we love, and art has done that for me." - }, - { - "speaker": "Jolene", - "dia_id": "D12:6", - "text": "Even though my snakes can't chat or understand what I'm going through, our time together is valuable and teaches me to take time and be in tune with myself. Similarly, playing video games with my partner after a long day is a great way for me to relax." - }, - { - "speaker": "Deborah", - "dia_id": "D12:7", - "text": "Simple things can indeed bring us the most happiness. How have these activities helped you during tough times?" - }, - { - "speaker": "Jolene", - "dia_id": "D12:8", - "text": "It brings us closer together!" - }, - { - "speaker": "Deborah", - "dia_id": "D12:9", - "text": "This kind of comfort can be really helpful when times get tough." - }, - { - "speaker": "Jolene", - "dia_id": "D12:10", - "text": "Just so you know, I've been working on a big project lately - it's been tough but also really cool to watch it take shape. Can't wait to see the final result!" - }, - { - "speaker": "Deborah", - "dia_id": "D12:11", - "text": "I am waiting to hear how everything turns out. Keep up the good work!" - }, - { - "speaker": "Jolene", - "dia_id": "D12:12", - "text": "Thanks Deb! Your support means a lot. I'll keep you updated on the progress of the project." - }, - { - "speaker": "Deborah", - "dia_id": "D12:13", - "text": "Glad my support means a lot to you! I'll always be here for you. " - }, - { - "speaker": "Jolene", - "dia_id": "D12:14", - "text": "Take care!" - }, - { - "speaker": "Deborah", - "dia_id": "D12:15", - "text": "Enjoy your day and make time for the things that bring you joy. See ya!" - }, - { - "speaker": "Jolene", - "dia_id": "D12:16", - "text": " I'll make sure to find time for that. Have a good one!" - } - ], - "session_13_date_time": "3:56 pm on 6 June, 2023", - "session_13": [ - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/avkvvy4fqr331.jpg" - ], - "blip_caption": "a photo of a solar powered vehicle with a solar panel on the back", - "query": "engineering project complete", - "dia_id": "D13:1", - "text": "Hey Deborah! Long time no talk - I had lots of stuff going on. Remember the tough engineering project? I finally wrapped that up last month. Look at the result!" - }, - { - "speaker": "Deborah", - "dia_id": "D13:2", - "text": "Jolene! Congrats on wrapping up your with it! You really put in the work and it paid off. " - }, - { - "speaker": "Jolene", - "dia_id": "D13:3", - "text": " I'm really proud of myself for sticking it out despite the problems and finishing it. It's definitely a big milestone." - }, - { - "speaker": "Deborah", - "dia_id": "D13:4", - "text": "Now that you've reached this big milestone, what do you have planned next?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://www.arup.com/-/media/arup/images/careers-new/early-careers/interns/americas-interns-banner-image.jpg" - ], - "blip_caption": "a photo of a group of construction workers posing for a picture", - "query": "engineering firm internship", - "dia_id": "D13:5", - "text": " I'm interning at a well-known engineering firm. It's been a great opportunity to test my skills and gain real-world experience. These are my new colleagues!" - }, - { - "speaker": "Deborah", - "dia_id": "D13:6", - "text": "What's been the best part of it so far?" - }, - { - "speaker": "Jolene", - "dia_id": "D13:7", - "text": "The best part so far has been being able to apply what I learned in school to real projects. It's so fulfilling to see my ideas come to life." - }, - { - "speaker": "Deborah", - "dia_id": "D13:8", - "text": "Mind if I ask how the internship has impacted you?" - }, - { - "speaker": "Jolene", - "dia_id": "D13:9", - "text": "It has had a positive impact on my life. It has stoked my love of engineering and has encouraged me to keep striving for my dreams." - }, - { - "speaker": "Deborah", - "dia_id": "D13:10", - "text": " How has it inspired you to keep striving for your dreams?" - }, - { - "speaker": "Jolene", - "dia_id": "D13:11", - "text": "Hanging out with people who love what they do has really inspired me to stay focused and keep working towards my goals. It's shown me that with dedication and effort, anything is possible." - }, - { - "speaker": "Deborah", - "dia_id": "D13:12", - "text": "Have you been able to find a good work-life balance during your internship?" - }, - { - "speaker": "Jolene", - "dia_id": "D13:13", - "text": "Honestly, finding that balance has been tough. I've been slogging away and it's been hard to make time for my hobbies and chilling out." - }, - { - "speaker": "Deborah", - "dia_id": "D13:14", - "text": " Have you considered taking some breaks and finding activities like yoga to help you relax and unwind? That might make a difference." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i2.wp.com/azwonders.com/wp-content/uploads/2020/12/IMG_1095.jpg" - ], - "blip_caption": "a photo of a man standing on a rock with his arms outstretched", - "query": "yoga meditation mountaintop", - "dia_id": "D13:15", - "text": "Yeah, I`m trying to do it. Here's an example of how I spent yesterday morning, yoga on top of mount Talkeetna." - }, - { - "speaker": "Deborah", - "dia_id": "D13:16", - "text": "Nice job, Jolene! How long have you been doing yoga and meditation? It looks like it's really helping you regroup and recharge." - }, - { - "speaker": "Jolene", - "dia_id": "D13:17", - "text": "I've been doing them sporadically for about 3 years now and they've had a real positive effect on me." - }, - { - "speaker": "Deborah", - "dia_id": "D13:18", - "text": "Has it benefited you in any way? Have you found it helpful in difficult moments?" - }, - { - "speaker": "Jolene", - "dia_id": "D13:19", - "text": "It has helped me with stress and kept me centered." - }, - { - "speaker": "Deborah", - "dia_id": "D13:20", - "text": "Glad they've been helpful for you!" - }, - { - "speaker": "Jolene", - "dia_id": "D13:21", - "text": "No idea how I would've survived without them!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://www.therootboard.com/cdn/shop/articles/IMG_5420.jpg" - ], - "blip_caption": "a photo of a room with a bench and a window", - "query": "yoga studio peaceful soft lighting", - "dia_id": "D13:22", - "text": "This is my favorite studio and it's always so calming. " - }, - { - "speaker": "Jolene", - "dia_id": "D13:23", - "text": " I was wondering if you have any advice on calming the mind and breathing during yoga?" - }, - { - "speaker": "Deborah", - "dia_id": "D13:24", - "text": "I'd recommend practicing mindful breathing for yoga. Set aside a few minutes each day to sit with your eyes closed, take deep breaths, and focus on how the air feels entering and leaving your body. " - }, - { - "speaker": "Jolene", - "dia_id": "D13:25", - "text": "Thanks, Deborah! I'll definitely give it a try. Appreciate the advice!" - }, - { - "speaker": "Deborah", - "dia_id": "D13:26", - "text": " If you need more tips or help, just let me know. Take care!" - }, - { - "speaker": "Jolene", - "dia_id": "D13:27", - "text": "See you!" - } - ], - "session_14_date_time": "9:17 am on 26 June, 2023", - "session_14": [ - { - "speaker": "Deborah", - "img_url": [ - "https://assets.simpleviewinc.com/simpleview/image/upload/crm/maineta/7D10D61B-FFA4-44EB-B4DF-5E6002D715F5_14D84855-064F-4EDB-86FAE0FA2AB35244_60bff6ce-b8b0-4146-b88bb1d2cef69fcb.jpg" - ], - "blip_caption": "a photo of a group of people doing yoga in a park", - "query": "yoga retreat yoga studio serene mats candles", - "dia_id": "D14:1", - "text": "Hey Jolene! How's it going? We haven't talked in a while. I've been busy getting ready for a yoga retreat with some buddies. A chance to hang out with people who think like me and find peace and understanding. Sounds awesome!" - }, - { - "speaker": "Jolene", - "dia_id": "D14:2", - "text": "Hey Deb! Been super hectic with internship and stuff. That retreat sounds awesome, I could definitely use a break!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://assets-global.website-files.com/5909c340ec86374fefcb849c/65650a0fc42a7d8dbe0e4349_IMG_2131.jpg" - ], - "blip_caption": "a photo of a woman doing a yoga pose on the beach", - "query": "yoga meditation calming sunset", - "dia_id": "D14:3", - "text": "By the way, I tried a new pose - Dancer Pose (Natarajasana). Rate, did I succeed?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/6moonjm3bypa1.jpg" - ], - "blip_caption": "a photo of a snake curled up in a plant filled area", - "query": "snake curled up pillow", - "dia_id": "D14:4", - "text": "You are amazing as always! Here are new photos of Seraphim in the new aquarium that I bought the day before yesterday." - }, - { - "speaker": "Deborah", - "dia_id": "D14:5", - "text": "Where'd you get it? I'm always drawn to animals, they bring so much joy. What's its story?" - }, - { - "speaker": "Jolene", - "dia_id": "D14:6", - "text": " I got her last year, she's a great pet. She always cheers me up and brings a sense of peace. Spending time with her is so comforting." - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a group of people doing yoga in a field", - "dia_id": "D14:7", - "text": "Pets really do make life more enjoyable and bright. " - }, - { - "speaker": "Jolene", - "dia_id": "D14:8", - "text": " I'm so thankful it's here. Plus, it's nice to have a calm creature around." - }, - { - "speaker": "Deborah", - "dia_id": "D14:9", - "text": "How have things been besides that?" - }, - { - "speaker": "Jolene", - "dia_id": "D14:10", - "text": "Things have been intense lately. I'm really pushing myself to succeed, and sometimes it feels overwhelming. But I'm determined to overcome any obstacles and achieve my goals." - }, - { - "speaker": "Deborah", - "dia_id": "D14:11", - "text": "Keep up the hard work and remember to relax too." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://ffalcons.files.wordpress.com/2021/06/stephen-paper-2.jpg" - ], - "blip_caption": "a photo of a drawing of a house with a ruler and a ruler", - "query": "engineering project design", - "dia_id": "D14:12", - "text": "Thanks, Deborah! I had a big breakthrough with this project - so exciting and rewarding!" - }, - { - "speaker": "Deborah", - "dia_id": "D14:13", - "text": "Awesome, Jolene! I'm really glad your project worked out. " - }, - { - "speaker": "Jolene", - "dia_id": "D14:14", - "text": "Stop talking about me, tell me more about your retreat." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://thehammockllc.com/wp-content/uploads/2019/07/Yoga-all-ages.jpg" - ], - "blip_caption": "a photo of three people standing in front of a large statue", - "query": "group people yoga mountain top", - "dia_id": "D14:15", - "text": "I'd rather show you a photo. This is also a new yoga pose that we tried. It is a tree pose." - }, - { - "speaker": "Jolene", - "dia_id": "D14:16", - "text": "What's that statue in the picture?" - }, - { - "speaker": "Deborah", - "dia_id": "D14:17", - "text": "It's a symbol of peace and enlightenment." - }, - { - "speaker": "Jolene", - "dia_id": "D14:18", - "text": "Wow, it looks gorgeous! I'd love to visit a retreat like that. It seems like the ideal spot to find peace and refreshment." - }, - { - "speaker": "Deborah", - "dia_id": "D14:19", - "text": "It's perfect for reflecting and getting centered." - }, - { - "speaker": "Jolene", - "dia_id": "D14:20", - "text": "I could really use some chill time like that. Sounds so peaceful." - }, - { - "speaker": "Deborah", - "dia_id": "D14:21", - "text": "Yeah, we all need some peaceful time to relax." - }, - { - "speaker": "Jolene", - "dia_id": "D14:22", - "text": "Gotta run, have a nice day!" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a sunset reflecting in a lake with a boat", - "dia_id": "D14:23", - "text": "See you!" - } - ], - "session_15_date_time": "7:37 pm on 9 July, 2023", - "session_15": [ - { - "speaker": "Deborah", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/grc-cim-1671133645.jpg" - ], - "blip_caption": "a photo of three men standing next to each other on a road", - "query": "running group sunny day", - "dia_id": "D15:1", - "text": "Hey Jolene! I started a running group with Anna - it's awesome connecting with people who care about fitness!" - }, - { - "speaker": "Jolene", - "dia_id": "D15:2", - "text": "Cool, Deb! Glad you found some people to get fit with. I'm trying to add workouts into my studying schedule, which has been tough but fun. How about you? Any challenges with the running group?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://www.milebymileblog.com/wp-content/uploads/2020/11/saturday-sunrise-run.png" - ], - "blip_caption": "a photo of a woman standing on a sidewalk with a skateboard", - "query": "running group morning stretch", - "dia_id": "D15:3", - "text": "Oh, I'm having a blast with it! We help and push each other during our runs, which makes it so much easier to stay motivated. I have a lot of my photos from this activity." - }, - { - "speaker": "Jolene", - "dia_id": "D15:4", - "text": "Deborah, that's awesome! Being part of a supportive group must be super motivating. Finding a team that's passionate about something makes a huge difference. Just thinking about my own journey too." - }, - { - "speaker": "Deborah", - "dia_id": "D15:5", - "text": "Having people who can cheer you on and give you advice really makes a difference. What has it been like for you finding supportive folks?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/30/08/ee/3008ee2c8041c333b9757d24b8246986.jpg" - ], - "blip_caption": "a photo of a man and a woman sitting in a chair in front of a computer", - "query": "couples playing video games", - "dia_id": "D15:6", - "text": "Gaming's been tough lately, but I'm grateful I have someone who's also into it. My partner helps me stay focused on our goals. We have a lot of cute photos, I want to share with you." - }, - { - "speaker": "Deborah", - "dia_id": "D15:7", - "text": " What do you like best about gaming together?" - }, - { - "speaker": "Jolene", - "dia_id": "D15:8", - "text": "We get to tackle challenges and have a shared experience. It's always a blast when we're into the same game and achieve something tough. Plus, it's a great way to bond and get closer." - }, - { - "speaker": "Deborah", - "dia_id": "D15:9", - "text": "Woah, that's cool! Gaming is so good for strengthening relationships. Do you two have a favorite game to play together?" - }, - { - "speaker": "Jolene", - "dia_id": "D15:10", - "text": "Yeah, we love playing \"It takes two\" together! It's a fun team-strategy game and it's competitive. Plus, it's a great way for us to bond. Do you have any activities you like doing with people?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://georgetowner.com/wp-content/uploads/2019/05/Photo-Aug-27-10-26-09-AM-2.jpg" - ], - "blip_caption": "a photo of a group of people doing yoga in a park", - "query": "group of people yoga park", - "dia_id": "D15:11", - "text": "Yep, I do running and yoga/meditation with others. Connecting with people and creating a community is great. Plus, I love organizing workshops and events to practice mindfulness and self-care. It's an awesome way to have fun, build relationships, and support each other's growth." - }, - { - "speaker": "Jolene", - "dia_id": "D15:12", - "text": "Sounds like a great way to relax. What do your workshops and events involve?" - }, - { - "speaker": "Deborah", - "dia_id": "D15:13", - "text": "It involves various activities such as yoga, meditation, and self-reflection. They aim to cultivate self-awareness, promote mental and emotional well-being, and help individuals find inner peace. It's a space where people can connect, explore, and grow." - }, - { - "speaker": "Jolene", - "dia_id": "D15:14", - "text": " Your events are awesome for helping people connect and learn, it is so important. How has everything been going for you?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a game board with a bunch of cards on it", - "dia_id": "D15:15", - "text": "Thanks, Jolene! It's been great seeing everyone come together and support each other. It's amazing to witness the growth and transformation that happens through these workshops. I'm honored to be a part of it." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/3096c8dr8tr71.jpg" - ], - "blip_caption": "a photo of a snake on a branch with a book", - "query": "pet snake branch", - "dia_id": "D15:16", - "text": "Wow, Deb! I can imagine how rewarding it must be to create a space for growth and change. It's great to hear that everything's going well. You can always count on me for support! I just want to share a photo with you." - }, - { - "speaker": "Deborah", - "dia_id": "D15:17", - "text": "Thanks, Jolene! Your support means a lot to me. I'm here for you too. By the way, I noticed your pet in the picture. What made you decide to get a snake?" - }, - { - "speaker": "Jolene", - "dia_id": "D15:18", - "text": " I was fascinated by reptiles, and it felt like the perfect pet for me. Taking care of it has been really calming, and it's a great way to connect with nature." - }, - { - "speaker": "Deborah", - "dia_id": "D15:19", - "text": " Glad you found something that gives you peace and calm. Do you have a favorite memory with \"it\" to share?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/n14qokr95j831.jpg" - ], - "blip_caption": "a photo of a snake in a tank with sand and plants", - "query": "snake in tank", - "dia_id": "D15:20", - "text": "I have lots of great memories, like our little 'snake adventure'. She got out and I spent hours searching, so relieved when I finally found her snuggling under the bed. It really showed how much I love her." - }, - { - "speaker": "Deborah", - "dia_id": "D15:21", - "text": "What was it like when you found her? I can imagine the relief!" - }, - { - "speaker": "Jolene", - "dia_id": "D15:22", - "text": "Seeing her snuggled under the bed made me feel so much love and gratitude. It made me realize how important she is to me." - }, - { - "speaker": "Deborah", - "dia_id": "D15:23", - "text": "They bring so much joy and remind us of what's important." - }, - { - "speaker": "Jolene", - "dia_id": "D15:24", - "text": "Animals teach us a lot about love and gratitude, and they bring so much joy. " - }, - { - "speaker": "Deborah", - "img_url": [ - "https://live.staticflickr.com/65535/49196167313_5eabd5ca56_b.jpg" - ], - "blip_caption": "a photography of two cats sitting on a couch with a blanket", - "query": "dog playing park", - "dia_id": "D15:25", - "re-download": true, - "text": "I haven't introduced you to my pets yet! I don't like dogs, that's why I have cats." - }, - { - "speaker": "Jolene", - "dia_id": "D15:26", - "text": "Looks like they're having a blast! How often do you take them out?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "dia_id": "D15:27", - "text": "Exercise and nature are really important to me, so I make sure to take them out for a run in the park every morning and evening." - }, - { - "speaker": "Jolene", - "dia_id": "D15:28", - "text": "Wow Deb, that's great! I'd love to experience that every day." - }, - { - "speaker": "Deborah", - "dia_id": "D15:29", - "text": "Nature helps me find peace every day - it's so refreshing!" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "dia_id": "D15:30", - "text": "It's a pity that my snakes don't run! I'd love to do that more often. They would motivate me and together it would be more fun." - }, - { - "speaker": "Deborah", - "dia_id": "D15:31", - "text": " It's like hitting a reset button that helps me put things into perspective and gives me time to reflect." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://images.pexels.com/photos/9214105/pexels-photo-9214105.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-dastan-khdir-9214105.jpg" - ], - "blip_caption": "a photo of a sunset over a body of water", - "query": "sunset calm lake", - "dia_id": "D15:32", - "text": "Yeah, I totally get it. Whenever I can, I love going for walks to take it all in. And I take photos like this" - }, - { - "speaker": "Deborah", - "dia_id": "D15:33", - "text": "It's amazing how nature has the power to bring us peace and clarity." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://images.pexels.com/photos/12314495/pexels-photo-12314495.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-lera-mk-12314495.jpg" - ], - "blip_caption": "a photo of a dock on a lake with trees in the background", - "query": "peaceful lake trees", - "dia_id": "D15:34", - "text": "This photo captures the peacefulness of a lake surrounded by trees." - }, - { - "speaker": "Deborah", - "dia_id": "D15:35", - "text": "Why did you choose that spot? It looks so calm." - }, - { - "speaker": "Jolene", - "dia_id": "D15:36", - "text": "It's such a hidden gem! It makes me feel so peaceful and tranquil." - }, - { - "speaker": "Deborah", - "dia_id": "D15:37", - "text": "Lucky you for having somewhere to relax and tune out!" - }, - { - "speaker": "Jolene", - "dia_id": "D15:38", - "text": "We'll definitely go there together sometime!" - }, - { - "speaker": "Deborah", - "dia_id": "D15:39", - "text": "We all need a timeout!" - } - ], - "session_16_date_time": "9:26 am on 1 August, 2023", - "session_16": [ - { - "speaker": "Deborah", - "dia_id": "D16:1", - "text": "Hey Jolene! Great news - I just started a project for a cleanup in our community and have been trying to raise funds for it. It's been amazing to see everyone come together to make a difference. How've you been? Anything new going on?" - }, - { - "speaker": "Jolene", - "dia_id": "D16:2", - "text": "Hey Debs! Congrats on your project for the community! As for me, life's been a rollercoaster lately. Last week, I had a huge setback with my project. I put in so much work and it all crashed and I lost everything. SO frustrating and depressing." - }, - { - "speaker": "Deborah", - "dia_id": "D16:3", - "text": "Jolene, sorry to hear that. It must be really tough. I'm here for you and if I can do anything, just let me know. Is there anything that's helping you cope?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/0uzy21np69b51.jpg" - ], - "blip_caption": "a photo of a man holding a snake on his arm", - "query": "pet snake curled up on arm", - "dia_id": "D16:4", - "text": "Your support means a lot. Susie really helps when times get tough. Pets have been great company. Video games have also been a nice distraction." - }, - { - "speaker": "Deborah", - "dia_id": "D16:5", - "text": "They can really provide love and comfort, especially during tough times. How did you come to have Susie?" - }, - { - "speaker": "Jolene", - "dia_id": "D16:6", - "text": "I adopted her two years ago when I was feeling lonely and wanted some company." - }, - { - "speaker": "Deborah", - "dia_id": "D16:7", - "text": "That's great, Jolene! Animals sure have a way of bringing us happiness. They understand us and provide us with comfort. Plus, having a pet teaches us responsibility. She came at the perfect time - cherish those moments with her and find strength in her presence." - }, - { - "speaker": "Jolene", - "dia_id": "D16:8", - "text": "Thanks Deborah. Having her around shows me I can stay strong and find joy in the small stuff." - }, - { - "speaker": "Deborah", - "dia_id": "D16:9", - "text": "Enjoying the little things is key. Those little moments can give us a boost and push us forward. How have you been taking care of yourself lately?" - }, - { - "speaker": "Jolene", - "dia_id": "D16:10", - "text": "I'm trying to prioritize self-care, like yoga and meditation. It helps me stay balanced and grounded." - }, - { - "speaker": "Deborah", - "dia_id": "D16:11", - "text": " If you're interested, I can suggest some routines for you to try." - }, - { - "speaker": "Jolene", - "dia_id": "D16:12", - "text": " I'm always on the lookout for new routines to mix things up." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://res.cloudinary.com/peerspace-inc/image/upload/q_80,c_crop,g_custom/w_2048/awuqrhwdbakdu4eeyll1.jpg" - ], - "blip_caption": "a photo of a room with a lot of yoga mats on the floor", - "query": "yoga studio tranquility", - "dia_id": "D16:13", - "text": "In the meantime, check out this great place for yoga." - }, - { - "speaker": "Jolene", - "dia_id": "D16:14", - "text": "This room looks perfect for it. Do you have any favorite routines you can share?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://kathsdruyoga.files.wordpress.com/2020/04/img_5644.jpg" - ], - "blip_caption": "a photo of a woman in a pink shirt doing a yoga pose", - "query": "gentle flow yoga routine breathing grounding", - "dia_id": "D16:15", - "text": "One of my favorite yoga routines is a gentle flow that's all about breathing and grounding. It helps me find my chill. I'll send you a tutorial video with the poses. This is me in the process :)" - }, - { - "speaker": "Jolene", - "dia_id": "D16:16", - "text": "Wow! Does that help you find your chill or improve your concentration?" - }, - { - "speaker": "Deborah", - "dia_id": "D16:17", - "text": "It's a great way to find balance in tough times. Try it out and let me know what you think!" - }, - { - "speaker": "Jolene", - "dia_id": "D16:18", - "text": "Can't wait to try it out. Let's chat soon!" - }, - { - "speaker": "Deborah", - "dia_id": "D16:19", - "text": "Let me know how it goes. Talk to you later!" - }, - { - "speaker": "Jolene", - "dia_id": "D16:20", - "text": "Yep, I'll practice and update you. Bye!" - } - ], - "session_17_date_time": "8:50 pm on 12 August, 2023", - "session_17": [ - { - "speaker": "Deborah", - "dia_id": "D17:1", - "text": "Since we last spoke, I made a meditation guide for my yoga retreat. How about you?" - }, - { - "speaker": "Jolene", - "dia_id": "D17:2", - "text": "I have been stressed since I lost my work files. I was so overwhelmed...but meditation kept me chill and I got my clarity back, thank goodness. Really appreciate the practice!" - }, - { - "speaker": "Deborah", - "dia_id": "D17:3", - "text": "It's amazing how it can give you peace and calm in times like that. If you ever need any support, just let me know - we're in this together!" - }, - { - "speaker": "Jolene", - "img_url": [ - "http://thegroundedpractice.com/cdn/shop/products/306F9F91-FDB8-4052-B87B-4692205658E12.jpg" - ], - "blip_caption": "a photo of two notebooks with a blue cover and a white strip", - "query": "meditation quote journal", - "dia_id": "D17:4", - "text": " Appreciate your support! BTW, I wanted to share this with you." - }, - { - "speaker": "Deborah", - "dia_id": "D17:5", - "text": "Your creativity is amazing! " - }, - { - "speaker": "Jolene", - "dia_id": "D17:6", - "text": "Thanks, Deb! I was inspired by my love for space and engines, so I designed these notebooks with elements like galaxies and circuitry. I think they turned out really cool!" - }, - { - "speaker": "Deborah", - "dia_id": "D17:7", - "text": "You have such a knack for turning these into art. They make me feel excited just by looking at them. Your creativity is amazing! Does that usually inspire your engineering projects too?" - }, - { - "speaker": "Jolene", - "dia_id": "D17:8", - "text": "Seeing art and design in various things gives me a unique perspective on problems." - }, - { - "speaker": "Deborah", - "dia_id": "D17:9", - "text": "Got any neat projects or ideas you're pumped about?" - }, - { - "speaker": "Jolene", - "dia_id": "D17:10", - "text": " Working on a cool project now - a prototype that could revolutionize aerial surveillance. Can't wait to see the results!" - }, - { - "speaker": "Deborah", - "dia_id": "D17:11", - "text": "I admire your ambition to make a difference. Do you have any specific plans or goals for this project?" - }, - { - "speaker": "Jolene", - "dia_id": "D17:12", - "text": "My aim is to devise a more productive and affordable aerial surveillance system. It'll help with responding to emergencies and monitoring the environment. It'll make the world a better, safer place." - }, - { - "speaker": "Deborah", - "dia_id": "D17:13", - "text": "I love your commitment. Keep going!" - }, - { - "speaker": "Jolene", - "dia_id": "D17:14", - "text": "Thanks, Deb! Your support means a lot. I'll keep pushing and doing my best to make a difference." - }, - { - "speaker": "Deborah", - "dia_id": "D17:15", - "text": "You got this!" - } - ], - "session_18_date_time": "2:58 pm on 16 August, 2023", - "session_18": [ - { - "speaker": "Jolene", - "blip_caption": "a photo of a book with a checklist on it", - "dia_id": "D18:1", - "text": "Been thinking a lot about my plans lately, especially after checking in with my bf. It's been up and down! Some days it feels like I'm trying to juggle my engineering, relationship, and growing as a person, and I just don't know how to handle it all. How do you manage when it's too much?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a room with a moss wall and a floor cushion", - "dia_id": "D18:2", - "text": "Finding ways to restore balance is important. Taking time for yourself and recognizing your needs can make a difference. " - }, - { - "speaker": "Jolene", - "dia_id": "D18:3", - "text": "Thanks, Deb! I've been trying out some time management strategies recently and started using a bullet journal. It's been really helpful for tracking my tasks and staying organized." - }, - { - "speaker": "Deborah", - "dia_id": "D18:4", - "text": " It's great to hear that it's been helping you out!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/a8/a1/ab/a8a1ab638060af2f6cb53ab7a5bb2b8c.jpg" - ], - "blip_caption": "a photo of a notebook with a quote on it", - "query": "bullet journal spread task list motivational quote", - "dia_id": "D18:5", - "text": " Not only does it help me stay on top of everything, but it's also so satisfying when I cross tasks off my list. Here's a pic of my newest spread with one of my favorite quotes." - }, - { - "speaker": "Deborah", - "dia_id": "D18:6", - "text": "I love this quote. So uplifting. Does it motivate you when you see it?" - }, - { - "speaker": "Jolene", - "dia_id": "D18:7", - "text": "Yeah, it's like a little reminder to stick to my goals and never give up." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://www.naplesnews.com/gcdn/-mm-/06756bca6c979c173ae23c1ba44f60a8fd2bcbee/c\\u003d0-0-3024-4032/local/-/media/2017/04/25/Naples/Naples/636287249454494595-Yoga-4.jpg" - ], - "blip_caption": "a photo of a group of people doing yoga on a beach", - "query": "sunset beach meditation", - "dia_id": "D18:8", - "text": "Gotcha! Visual reminders like this photo can be super motivating. Keep focusing on your goals, Jolene, and don't give up! I led a meditation yoga sessionto the elderly at a local care home last week during sunset. Nature can be really helpful for finding peace. Have you tried mindfulness too? " - }, - { - "speaker": "Jolene", - "dia_id": "D18:9", - "text": "Haven't tried it yet, but I'm keen to give it a shot. That sounds really peaceful. I could use some of that calm in my life right now. I'm interested in destressing and trying mindfulness. " - }, - { - "speaker": "Deborah", - "dia_id": "D18:10", - "text": "I started with workshops and books, and now mindfulness is a huge part of my life." - }, - { - "speaker": "Jolene", - "dia_id": "D18:11", - "text": " I'll definitely look into some of it. Can't wait to get started!" - }, - { - "speaker": "Deborah", - "dia_id": "D18:12", - "text": "That's great, Jolene! I'm so glad you're willing to try some mindfulness. It can be really helpful. Let me know if you need any help getting started - I'm happy to assist you with your journey!" - }, - { - "speaker": "Jolene", - "dia_id": "D18:13", - "text": " I really appreciate it." - }, - { - "speaker": "Deborah", - "dia_id": "D18:14", - "text": "We're in this together. Give me a shout if you need anything. Bye for now." - }, - { - "speaker": "Jolene", - "dia_id": "D18:15", - "text": "Thanks, I'll hit you up if I need anything. Bye for now." - } - ], - "session_19_date_time": "12:52 am on 19 August, 2023", - "session_19": [ - { - "speaker": "Deborah", - "dia_id": "D19:1", - "text": "Hey Jolene! Hope you're having a good one. Last Friday I told Anna the story of my life and they were super kind about it. It was so nice to have a meaningful connection. How's the mindfulness workshops and reading going? Need any help?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/90qd3jqxi2781.jpg" - ], - "blip_caption": "a photo of a black xbox console with a yoda yoda figure next to it", - "query": "new game console", - "dia_id": "D19:2", - "text": "Life's been hella busy since we last talked. I bought a console for my partner as a gift on the 17th and it's so much fun, he even managed to play it.! Engineering studies are still going strong too. Balance has been key for me lately. How about you? What's been up?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:3", - "text": " Well done! As for me, I've been focusing on teaching yoga and spending time with the community. Organizing a yoga event last month was really cool." - }, - { - "speaker": "Jolene", - "dia_id": "D19:4", - "text": " Was it rewarding seeing everyone come together? Can you tell me more about how you put it together? Also, any tips for maintaining a balance between hobbies and studies?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:5", - "text": " I reached out to different nearby businesses and places to make it happen. We had yoga, food stalls, and even some live music - it was amazing! As for balancing hobbies and studies, I find it helpful to prioritize and manage my time effectively. Making a schedule and setting aside specific time for studying and pursuing hobbies can go a long way in maintaining balance." - }, - { - "speaker": "Jolene", - "dia_id": "D19:6", - "text": "Wow, that's awesome! Gonna make a plan to manage my studies and hobbies. Say, do you ever play video games?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:7", - "text": "I used to play some video games, but it's been a while. It's a good way to relax after a busy day. Do you have any game suggestions? What's your favorite game?" - }, - { - "speaker": "Jolene", - "dia_id": "D19:8", - "text": "I have a few game recommendations. Zelda BOTW for Switch is an awesome open-world game. Animal Crossing: New Horizons is really calming and cute. As for my favorite game, it's hard to choose just one!" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a man and a woman sitting in a chair in front of a computer", - "dia_id": "D19:9", - "text": "Cool recs! I'll definitely check those out. What about your favorite memories of playing video games?\n" - }, - { - "speaker": "Jolene", - "dia_id": "D19:10", - "text": "Oh, I forgot to mention Overcooked 2 - this is a good co-op game if you're into hilarious and chaotic cooking. My partner and I often play for bets! I once won three large pizzas!" - }, - { - "speaker": "Deborah", - "dia_id": "D19:11", - "text": "Reminds me of when I used to play games with my husband. We'd take turns and it was a great way to bond and make memories. Gaming really can bring people closer, right?" - }, - { - "speaker": "Jolene", - "dia_id": "D19:12", - "text": "Yeah, you`re right! What's your favorite game to play with that person?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:13", - "text": "We prefer to play detective games together." - }, - { - "speaker": "Jolene", - "dia_id": "D19:14", - "text": "What other activities do you both enjoy doing together?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:15", - "text": "We also enjoyed spending time outdoors and exploring nature. It was always so refreshing to be outside and soak up the fresh air." - }, - { - "speaker": "Jolene", - "dia_id": "D19:16", - "text": "I'm a big fan of being outside too! It's so calming and refreshing. Do you have any special spots you like to go to?" - }, - { - "speaker": "Deborah", - "dia_id": "D19:17", - "text": "I love going to this park near my house - it has a nice forest trail and a beach. It's a peaceful spot where I can do some yoga and reflect. There's also a special bench that holds special meaning to me." - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a bench in a park with a tree in the background", - "dia_id": "D19:18", - "text": "Sounds lovely! Nature can be calming. What makes this bench special to you?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a person sitting on a bench in a forest", - "dia_id": "D19:19", - "text": "It holds a lot of special memories for me and my mom - we would come here and chat about dreams and life. It's full of good moments. " - }, - { - "speaker": "Jolene", - "dia_id": "D19:20", - "text": "That's awesome, Deborah! What were some of your favorite memories with your mom at this spot? It looks super peaceful and pretty." - }, - { - "speaker": "Deborah", - "dia_id": "D19:21", - "text": "I'll always cherish my memories with her at this spot. I remember a beautiful sunset we watched together in silence - the colors in the sky were so special. Every time I go back, I feel so much peace and gratitude for the time I spent with her." - }, - { - "speaker": "Jolene", - "dia_id": "D19:22", - "text": "Places and moments like that can mean so much, and it's a gift to find peace and gratitude in them." - }, - { - "speaker": "Deborah", - "dia_id": "D19:23", - "text": " I'm really thankful for all the time we had." - }, - { - "speaker": "Jolene", - "dia_id": "D19:24", - "text": "It's so important to cherish it. " - } - ], - "session_20_date_time": "9:11 am on 21 August, 2023", - "session_20": [ - { - "speaker": "Jolene", - "dia_id": "D20:1", - "text": " Long time no talk! We were given a new game for the console last week, it is Battlefield 1. What's been up with you?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://bmoreenergy.files.wordpress.com/2023/08/img_2540.jpg" - ], - "blip_caption": "a photo of a flower cart on a sidewalk with flowers in it", - "query": "bench vibrant flowers", - "dia_id": "D20:2", - "text": "Hey Jolene! Good to hear from you. That`s cool! Been thinking about a few big moments lately - went to a place that held a lot of memories for me. Sat on a bench where we used to chat and it brought back a lot of emotions. " - }, - { - "speaker": "Jolene", - "dia_id": "D20:3", - "text": "Mostly happy or a bit of everything?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://www.inbloomhomestead.com/wp-content/uploads/2023/07/stunning.jpg" - ], - "blip_caption": "a photo of a vase of flowers on the ground in a street", - "query": "flower cart sidewalk flowers", - "dia_id": "D20:4", - "text": "It was quite a mix, Jolene. I felt nostalgia and longing, but also grateful for the memories. It's amazing how a place can mean so much. I brought these flowers there." - }, - { - "speaker": "Jolene", - "dia_id": "D20:5", - "text": "Do you think she would like it?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://images.pexels.com/photos/12473781/pexels-photo-12473781.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-pexels-user-12473781.jpg" - ], - "blip_caption": "a photo of a woman holding a bouquet of red roses", - "query": "mother holding bouquet flowers", - "dia_id": "D20:6", - "text": "Yeah, my mom really loved flowers. They always made her so happy. She appreciated the simple things in life.\n" - }, - { - "speaker": "Jolene", - "dia_id": "D20:7", - "text": "Wow, that's a great photo! How did she show you to appreciate it?" - }, - { - "speaker": "Deborah", - "dia_id": "D20:8", - "text": "By taking it slow, seeing beauty in them, and finding joy." - }, - { - "speaker": "Jolene", - "dia_id": "D20:9", - "text": "Wow Deb, that's awesome! We should definitely take time to enjoy that and not let the business of life cause us to miss out on the good stuff." - }, - { - "speaker": "Deborah", - "dia_id": "D20:10", - "text": "Yeah, Jolene. Life can be so busy that we often overlook the small things that truly matter. Let's make an effort to appreciate them more." - }, - { - "speaker": "Jolene", - "dia_id": "D20:11", - "text": "Yep Deb, slowing down and enjoying simple moments can bring a lot of balance and happiness. I'm trying to do more yoga and meditation myself to help relax and stay focused. Are there any calming habits that you practice to feel balanced?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://images.pexels.com/photos/9214105/pexels-photo-9214105.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-dastan-khdir-9214105.jpg" - ], - "blip_caption": "a photo of a sunset over a body of water", - "query": "sunset calm lake", - "dia_id": "D20:12", - "text": "Yeah, same here, Jolene! Yoga and meditation help me find balance and inner peace. Going out for walks and staying mindful also keep me grounded. I take similar photos on walks." - }, - { - "speaker": "Jolene", - "dia_id": "D20:13", - "text": "Gorgeous! Going for a walk and feeling so peaceful must be amazing." - }, - { - "speaker": "Deborah", - "dia_id": "D20:14", - "text": "Moments like that I'll always cherish." - }, - { - "speaker": "Jolene", - "dia_id": "D20:15", - "text": "That calm and peaceful feeling is so nice - it's great for recharging and thinking." - }, - { - "speaker": "Deborah", - "dia_id": "D20:16", - "text": " It's like a reboot for me." - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a green cushion on a floor in front of a window", - "dia_id": "D20:17", - "text": "Got it! It's like hitting the refresh button and coming back even better." - }, - { - "speaker": "Deborah", - "dia_id": "D20:18", - "text": "What's your favorite yoga pose for some rest?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/12yit6phiwl21.jpg" - ], - "blip_caption": "a photo of a person laying on the floor with a paper bag", - "query": "savasana yoga mat", - "dia_id": "D20:19", - "text": "I'm a fan of savasana - aka the corpse pose. It's so calming and helps me just let go and surrender." - }, - { - "speaker": "Deborah", - "dia_id": "D20:20", - "text": "Funny photo! How long have you been doing yoga?" - }, - { - "speaker": "Jolene", - "dia_id": "D20:21", - "text": "Been doing it for 3 years. It's a great way to escape studying and work stress." - }, - { - "speaker": "Deborah", - "dia_id": "D20:22", - "text": "Wow, Jolene! Taking time to unwind is key and that seems just right for you!" - }, - { - "speaker": "Jolene", - "dia_id": "D20:23", - "text": "I'm really finding my zen again!" - }, - { - "speaker": "Deborah", - "dia_id": "D20:24", - "text": "Keep it up!" - }, - { - "speaker": "Jolene", - "dia_id": "D20:25", - "text": "Thanks for your support, Deb! " - }, - { - "speaker": "Deborah", - "dia_id": "D20:26", - "text": "Good luck with everything. Stay in touch." - } - ], - "session_21_date_time": "9:34 am on 24 August, 2023", - "session_21": [ - { - "speaker": "Deborah", - "img_url": [ - "https://holmanhealthconnections.com/wp-content/uploads/2019/01/IMG_0412.jpg" - ], - "blip_caption": "a photo of a trail in a forest with moss and trees", - "query": "yoga retreat forest dusk nature", - "dia_id": "D21:1", - "text": "Hey Jolene! Good to hear from you! A lot's happened since we talked - last week I got to go to this yoga retreat near my mom's place. It was so cool - I got to hang with nature and really get to know myself. Definitely life-changing! That's how beautiful it was there!" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a man bending over on a blanket in the grass", - "dia_id": "D21:2", - "text": "Wow, Deb, sounds great! Glad you got to connect with yourself and nature. That's my own way to relax. What are your thoughts on life now?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a mountain range with a colorful sunset in the background", - "dia_id": "D21:3", - "text": "Life's been super meaningful lately. Nature and self-reflection have helped me see how beautiful every moment is. We can really grow and learn when we listen to ourselves. What's been up with you lately? Any insights or experiences?\n" - }, - { - "speaker": "Jolene", - "dia_id": "D21:4", - "text": "Life's been hectic, but I'm making strides toward my goals. It's tough, but satisfying." - }, - { - "speaker": "Deborah", - "dia_id": "D21:5", - "text": "Can you tell me a bit more about it and what you've achieved?" - }, - { - "speaker": "Jolene", - "dia_id": "D21:6", - "text": "My goal is to be successful in my field and make a positive impact. I've been studying, attending workshops, and networking to make it happen. Recently, I had the opportunity to present at a virtual conference and received positive feedback. It was a great experience and confirmed that I'm on the right track." - }, - { - "speaker": "Deborah", - "dia_id": "D21:7", - "text": "You really put your heart and soul into it. Must have been amazing having it go so well. How did it feel when people gave you positive feedback? Any ideas for what comes next?" - }, - { - "speaker": "Jolene", - "dia_id": "D21:8", - "text": "I was thrilled to receive such positive feedback! It felt so rewarding to know that my efforts were appreciated. Right now, I'm focusing on studying and gaining more experience. I'm even thinking about more internships to further enhance my skills. Exciting times! Hopefully, there will be more updates to share with you soon." - }, - { - "speaker": "Deborah", - "dia_id": "D21:9", - "text": "Wow, Jolene! Way to go! I'm super proud of all you've achieved. Let me know if you need any help. Onward!" - }, - { - "speaker": "Jolene", - "dia_id": "D21:10", - "text": "Thanks, Deb! Your support means a lot to me. " - }, - { - "speaker": "Deborah", - "dia_id": "D21:11", - "text": "No problem. You got this! " - }, - { - "speaker": "Jolene", - "dia_id": "D21:12", - "text": " I'm not giving up, just gonna keep pushing forward." - }, - { - "speaker": "Deborah", - "dia_id": "D21:13", - "text": "Reach for your goals and don't forget to enjoy the journey. " - }, - { - "speaker": "Jolene", - "dia_id": "D21:14", - "text": "Thanks, Deb. Your support means a lot. I'll keep pushing forward and remember to enjoy every step of the way." - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a field of sunflowers with a sunset in the background", - "dia_id": "D21:15", - "text": "Come on! The journey's as important as the destination. Take time for yourself and find joy in it. We believe in you!" - }, - { - "speaker": "Jolene", - "blip_caption": "a photo of a city skyline at sunset with a body of water", - "dia_id": "D21:16", - "text": "Yeah, you're right! I'll take your advice and find joy on the way. Cheers for the support!" - }, - { - "speaker": "Deborah", - "dia_id": "D21:17", - "text": "Always by your side!" - } - ], - "session_22_date_time": "5:33 pm on 26 August, 2023", - "session_22": [ - { - "speaker": "Deborah", - "dia_id": "D22:1", - "text": "Hey Jolene, since we talked I've been thinking about my mom's influence. Remembering those we love is important." - }, - { - "speaker": "Jolene", - "dia_id": "D22:2", - "text": "I understand, Deb. Remembering and cherishing the memories of our loved ones is so important. It's comforting to know that their influence still guides us. Last Friday, my partner and I talked about how our loved ones have influenced us and what their values meant to us. It was an emotional chat, but it made us feel closer and showed us what really matters. " - }, - { - "speaker": "Deborah", - "dia_id": "D22:3", - "text": "Those types of conversations really help build relationships. Can you tell me more about the values they have given you?" - }, - { - "speaker": "Jolene", - "dia_id": "D22:4", - "text": "Definitely! Our loved ones have taught us to persevere and stay resilient, like my mom always said to never give up, and my partner's dad showed them to stay determined. Their values have influenced us to pursue our goals, such as me with engineering and my partner with their creative endeavors. Even though they're not here, we both feel their values encouraging us along our paths." - }, - { - "speaker": "Deborah", - "dia_id": "D22:5", - "text": "That's wonderful to hear, Jolene! It's amazing how their values continue to guide you, even in their absence. It sounds like you and your partner are honoring their memory by pursuing your respective passions. Have you ever considered incorporating those values into your work as well?" - }, - { - "speaker": "Jolene", - "dia_id": "D22:6", - "text": "Yeah, Deborah! We've been figuring out how to add these values into our projects. As an engineering student, I want to use my talents to do good and help solve important problems. I'm keen on coming up with new ideas and making things more efficient to make the world a better place. Going further, my mom stressed the value of helping others and that's something I want to keep in mind for my engineering projects." - }, - { - "speaker": "Deborah", - "dia_id": "D22:7", - "text": "When our work ties into our values, it becomes more meaningful. What goals or ideas do you have for incorporating those values into your future projects?" - }, - { - "speaker": "Jolene", - "dia_id": "D22:8", - "text": "In the future, I'm aiming to work on projects that make a real difference to communities. I'm interested in sustainable initiatives and developing innovative solutions for environmental issues. I also want to get involved with organizations that focus on social causes, using my skills to help out. It's about connecting my passion for engineering with my commitment to making a positive impact." - }, - { - "speaker": "Deborah", - "dia_id": "D22:9", - "text": "You've got a lot of amazing plans for the future. Which projects are you most interested in getting involved in?" - }, - { - "speaker": "Jolene", - "dia_id": "D22:10", - "text": "I'm keen on two projects in particular. One is focused on developing renewable energy, like solar, to help communities and reduce dependence on non-renewables. " - }, - { - "speaker": "Deborah", - "dia_id": "D22:11", - "text": "Wow Jolene, that's really inspiring!" - }, - { - "speaker": "Jolene", - "dia_id": "D22:12", - "text": "The other is finding ways to supply clean water to those with limited access. Both align with my beliefs about sustainability and assisting those in need. I still have so much to figure out before beginning, but I'm up for the challenge." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://assets-global.website-files.com/5909c340ec86374fefcb849c/65650a0fc42a7d8dbe0e4349_IMG_2131.jpg" - ], - "blip_caption": "a photo of a woman doing a yoga pose on the beach", - "query": "sunrise yoga session nature inner balance", - "dia_id": "D22:13", - "text": "Sounds great, Jolene! Research is key to success. Little steps and being up for challenges make you stronger. I'm here for you. Connecting to yourself helps tackle any issue. Here's a photo that reminds me of the beauty of nature during a yoga session." - }, - { - "speaker": "Jolene", - "dia_id": "D22:14", - "text": "It helps with challenges, giving balance and strength. Any tips for staying relaxed while studying?" - }, - { - "speaker": "Deborah", - "dia_id": "D22:15", - "text": "Taking breaks, doing some stretching/yoga, or just going for a walk is really helpful. And don't forget to get enough sleep and take time for self-care. Finding a balance between work and taking care of yourself is important. What self-care activities have you been doing lately?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://www.thriveyogaandwellness.com/wp-content/uploads/2019/06/IMG_6315-e1561295654472.jpg" - ], - "blip_caption": "a photo of a woman in a pink shirt doing a yoga pose", - "query": "yoga pose snake", - "dia_id": "D22:16", - "text": "I've been into yoga and meditation lately. It helps me recharge. Doing different poses relieves tension and calms my mind. I've already shared my newfound love for yoga with my partner, and we're planning to go on a meditation retreat together to enhance our practice together." - }, - { - "speaker": "Deborah", - "dia_id": "D22:17", - "text": "Glad to hear that yoga is helping you rest and recharge. It's great for reflection and self-care. Do your snakes also enjoy it?" - }, - { - "speaker": "Jolene", - "dia_id": "D22:18", - "text": "My snakes just like watching me chill. But she's a great company and always brings a sense of calm." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://catsandcoffee.me/wp-content/uploads/2020/01/Louis-and-Olivia-on-the-windowsill.jpg" - ], - "blip_caption": "a photo of two cats sitting on a window sill looking out", - "query": "cat sitting windowsill calming", - "dia_id": "D22:19", - "text": "Having a pet around is such a calming feeling. They sure can bring a great sense of comfort. I still have cats, Luna is sitting on the left." - }, - { - "speaker": "Jolene", - "dia_id": "D22:20", - "text": "Aww, that's adorable! What's the second one's name?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a brown leather chair and a black and white floor", - "dia_id": "D22:21", - "text": "Max! They bring lots of joy and peace to our home." - }, - { - "speaker": "Jolene", - "dia_id": "D22:22", - "text": "How did you get them?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a car with a fan and a mesh bag", - "dia_id": "D22:23", - "text": "Max is my mother's cat, I took him when my mother passed away." - }, - { - "speaker": "Jolene", - "dia_id": "D22:24", - "text": "You're great for taming him. How did you get Luna?" - }, - { - "speaker": "Deborah", - "dia_id": "D22:25", - "text": "I took Luna from the shelter." - }, - { - "speaker": "Jolene", - "dia_id": "D22:26", - "text": "It\u2019s wonderful that you have become their loving owner!" - }, - { - "speaker": "Deborah", - "dia_id": "D22:27", - "text": "Yes, I really love cats, and they also need a home, love, and care! Moreover, Max is already old, he is 8 years old." - }, - { - "speaker": "Jolene", - "dia_id": "D22:28", - "text": "How old is Luna?" - }, - { - "speaker": "Deborah", - "dia_id": "D22:29", - "text": "She is younger, she is 5 years old." - }, - { - "speaker": "Jolene", - "dia_id": "D22:30", - "text": "I am proud of your action to tame these pets!" - } - ], - "session_23_date_time": "11:46 am on 30 August, 2023", - "session_23": [ - { - "speaker": "Jolene", - "img_url": [ - "https://thegoodstuffbyajaespoo.files.wordpress.com/2017/09/img_20170723_184612_691.jpg" - ], - "blip_caption": "a photo of a woman doing a yoga pose in a mirror", - "query": "colombian yoga studio vibrant colors people yoga poses", - "dia_id": "D23:1", - "text": "Hey Deborah, how's it going? Guess what? Yesterday my partner and I got back from an awesome trip to Rio de Janeiro- we checked out some cool yoga classes." - }, - { - "speaker": "Deborah", - "dia_id": "D23:2", - "text": "That yoga pose looks great. Must've been a cool experience for the two of you. What did the trip teach you?" - }, - { - "speaker": "Jolene", - "dia_id": "D23:3", - "text": "This country was awesome! It showed me different kinds of yoga and their backgrounds, which made me appreciate it even more. We visited a lot of delicious cafes! Have you ever been somewhere that was important to you?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/03tq3y75jmy41.jpg" - ], - "blip_caption": "a photo of a man standing in front of a house", - "query": "mother childhood home house", - "dia_id": "D23:4", - "text": "Yep, last month I visited my mom`s house which holds a special place in my heart. My mom had good and bad times there, but it's still a symbol of her strength and the love she shared with me. This is my husband in front of this house." - }, - { - "speaker": "Jolene", - "dia_id": "D23:5", - "text": "What was it like?" - }, - { - "speaker": "Deborah", - "dia_id": "D23:6", - "text": "It brought back fond memories as I relaxed outside." - }, - { - "speaker": "Jolene", - "dia_id": "D23:7", - "text": "Sounds great! So glad you have a place to relax and find peace." - }, - { - "speaker": "Deborah", - "dia_id": "D23:8", - "text": "Thanks, Jolene. It's special for me. How about you? Is there a place that helps you relax?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://pandolinadventures.files.wordpress.com/2016/05/img_3720.jpg" - ], - "blip_caption": "a photo of a pond with lily pads and a tree in the background", - "query": "quiet pond surrounded trees meditation", - "dia_id": "D23:9", - "text": "I go to this nearby place to meditate by a tranquil spot." - }, - { - "speaker": "Deborah", - "dia_id": "D23:10", - "text": "Looks chill. What's been the effect of that?" - }, - { - "speaker": "Jolene", - "dia_id": "D23:11", - "text": "It helps me make sense of everything and relieves stress. It's like a restart." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/jygby0ihz7o41.jpg" - ], - "blip_caption": "a photo of a lake with a few trees in the water", - "query": "peaceful sunset lake", - "dia_id": "D23:12", - "text": "Cool, glad you found a place to chill. We all need that occasionally. This is one of my favorite spots to ponder and let things go.\n" - }, - { - "speaker": "Jolene", - "dia_id": "D23:13", - "text": "Looks great! What made you pick that spot?" - }, - { - "speaker": "Deborah", - "dia_id": "D23:14", - "text": "The soothing vibes and nice views made it ideal for reflecting and letting go." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://anomadontheloose.com/wp-content/uploads/2018/01/tam-wua-forest-monastery-cave-walking-meditation-1728368610..jpg" - ], - "blip_caption": "a photo of a group of people walking up a set of stairs", - "query": "meditation nature", - "dia_id": "D23:15", - "text": "Here is one more photo from Rio de Janeiro. We went on many excursions there." - }, - { - "speaker": "Deborah", - "dia_id": "D23:16", - "text": "Wow, those stairs look cool! Where were they taken?" - }, - { - "speaker": "Jolene", - "dia_id": "D23:17", - "text": "We had a great time visiting an old temple. The stairs were amazing!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/4dsrcyti38h31.jpg" - ], - "blip_caption": "a photo of a large stone structure with a mountain in the background", - "query": "ancient temple sunrise", - "dia_id": "D23:18", - "text": "Wow, exploring those temples must have been incredible! Three years ago I was also in Rio de Janeiro, I took a beautiful photo on one of the excursions." - }, - { - "speaker": "Jolene", - "dia_id": "D23:19", - "text": "The architecture and history of it all were really interesting. I'm sure you also liked the places you visited there!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.redd.it/76ihuz977zab1.jpg" - ], - "blip_caption": "a photo of a hand holding a piece of paper with writing on it", - "query": "old photograph handwritten note", - "dia_id": "D23:20", - "text": "Exploring historical places and learning their stories is so fun. It was a great experience. I want to share this photo with you." - }, - { - "speaker": "Jolene", - "dia_id": "D23:21", - "text": " By the way, what did that paper have written on it in the photo?" - }, - { - "speaker": "Deborah", - "dia_id": "D23:22", - "text": "This was written to me by a friend who, unfortunately, will never be able to support me. I miss him here. This quote says\"Let go of what no longer serves you.\"" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/a8/a1/ab/a8a1ab638060af2f6cb53ab7a5bb2b8c.jpg" - ], - "blip_caption": "a photo of a notebook with a quote on it", - "query": "bullet journal spread quote", - "dia_id": "D23:23", - "text": "I'm sorry! That's a good reminder to stay focused and let go of what no longer serves us. Remember the quote in my notebook? It also inspires me!" - }, - { - "speaker": "Deborah", - "dia_id": "D23:24", - "text": "What other quotes give you strength?" - }, - { - "speaker": "Jolene", - "img_url": [ - "http://swaygirls.com/cdn/shop/products/IMG_1932_jpg.jpg" - ], - "blip_caption": "a photo of a notebook with a pen and a plant on a table", - "query": "quote notebook positivity growth", - "dia_id": "D23:25", - "text": "I came across this one while browsing and it really hit home with me. It's a great reminder to ditch the negative stuff and focus on growing and being positive." - }, - { - "speaker": "Deborah", - "dia_id": "D23:26", - "text": "Surrounding ourselves with good stuff and striving to improve is key." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/w0ycj9vmb6fb1.jpg" - ], - "blip_caption": "a photo of a plant in a pot on a patio", - "query": "plant growing pot", - "dia_id": "D23:27", - "text": "Yep, Deborah! It's about creating a good atmosphere to help us grow and improve. By the way, I have a new plant." - }, - { - "speaker": "Deborah", - "dia_id": "D23:28", - "text": "What made you pick it?" - }, - { - "speaker": "Jolene", - "dia_id": "D23:29", - "text": "I got this as a reminder to nurture myself and embrace fresh starts." - }, - { - "speaker": "Deborah", - "dia_id": "D23:30", - "text": "Nice job, Jolene! Take care of yourself and embrace new beginnings." - }, - { - "speaker": "Jolene", - "dia_id": "D23:31", - "text": "Thanks Deb! Will do. Good talking to you. Take care!" - }, - { - "speaker": "Deborah", - "dia_id": "D23:32", - "text": "Have a great day!" - } - ], - "session_24_date_time": "2:14 pm on 3 September, 2023", - "session_24": [ - { - "speaker": "Deborah", - "dia_id": "D24:1", - "text": "Hey Jolene, just catching up. I went to a cool event last week with the aim to support each other - pretty inspiring. Have you been connecting with anyone lately?" - }, - { - "speaker": "Jolene", - "dia_id": "D24:2", - "text": "Hey Deb, great to hear from you! I've been focusing on studying and my relationship with my partner. We're taking little trips to the beach, it's a great way to relax. How about you, anything new going on?" - }, - { - "speaker": "Deborah", - "dia_id": "D24:3", - "text": "I was busy too - went to a community meetup last Friday. We shared stories and it was nice to feel how connected we are. It made me think about how important relationships are. How about you, how are things going in that area?" - }, - { - "speaker": "Jolene", - "dia_id": "D24:4", - "text": "I'm really thankful for my significant other right now. It's great to have someone encouraging my goals! How are things with your friends and family? Any updates on that front?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://kristincorinneyoga.files.wordpress.com/2022/03/img_7637.jpg" - ], - "blip_caption": "a photo of a woman sitting on a yoga mat with two children", - "query": "yoga pose mother daughter", - "dia_id": "D24:5", - "text": "Relationships with family and friends are so vital. My yoga pals have been my second family - we've held each other up through a lot. The other day I found this old photo. That was when I first started doing yoga. My mum was my biggest fan and source of motivation. She'd often come to my classes with me." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/h2oi87j1cty71.jpg" - ], - "blip_caption": "a photo of a nintendo game console and a game controller", - "query": "gaming console my first", - "dia_id": "D24:6", - "text": "Our loved ones sure are supportive! When I was 10, my parents got me that and it was the start of my passion for video games." - }, - { - "speaker": "Deborah", - "dia_id": "D24:7", - "text": "Cool that they shared that with you. Did you learn on your own or did they teach you?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/etnsyns3zh931.jpg" - ], - "blip_caption": "a photo of a nintendo wii game system with a game on the screen", - "query": "video game screenshot playing with mom", - "dia_id": "D24:8", - "text": "I taught myself, but my dad was always supportive and my mom would play games with me. " - }, - { - "speaker": "Deborah", - "dia_id": "D24:9", - "text": "That's awesome! Sounds like you had a lot of support from your parents. What was your favorite game to play with mom?" - }, - { - "speaker": "Jolene", - "dia_id": "D24:10", - "text": "One of my favorites was \"Monster Hunter: World\". The immersive story and open-world gaming are amazing!" - }, - { - "speaker": "Deborah", - "dia_id": "D24:11", - "text": " It can be so freeing when you get immersed in a game like that. " - }, - { - "speaker": "Jolene", - "dia_id": "D24:12", - "text": "Yeah! It's my way to de-stress and take a break from life." - }, - { - "speaker": "Deborah", - "dia_id": "D24:13", - "text": "What's up this month? Anything fun happening for you?" - }, - { - "speaker": "Jolene", - "dia_id": "D24:14", - "text": "Got a lot of finals coming up this month, so I've been studying real hard. It's been quite stressful, but it'll be worth it in the end. Thinking about taking a trip somewhere to relax and recharge afterward." - }, - { - "speaker": "Deborah", - "dia_id": "D24:15", - "text": "Good luck with it! Let me know if there's anything I can do to assist you." - } - ], - "session_25_date_time": "8:31 pm on 6 September, 2023", - "session_25": [ - { - "speaker": "Jolene", - "img_url": [ - "https://www.ic.org/wp-content/uploads/formidable/2/temple_may2021.jpg" - ], - "blip_caption": "a photo of a building with a curved roof on a hill", - "query": "meditation course retreat center peaceful lake greenery", - "dia_id": "D25:1", - "text": "Woohoo! I signed up for a meditation course at a retreat near a lake. Can't wait to share this experience with my partner and learn some new techniques. Sooo excited!" - }, - { - "speaker": "Deborah", - "dia_id": "D25:2", - "text": "That`s awesome! It looks so calm in the pic - I hope you enjoy your experience and learn some new techniques!" - }, - { - "speaker": "Jolene", - "dia_id": "D25:3", - "text": " It's amazing how something so easy can make such a big difference to our health!" - }, - { - "speaker": "Deborah", - "dia_id": "D25:4", - "text": "Yep, it's become part of my routine now. Can't imagine my life without it." - }, - { - "speaker": "Jolene", - "dia_id": "D25:5", - "text": "Yeah, same! It helps me stay balanced during my studies." - }, - { - "speaker": "Deborah", - "dia_id": "D25:6", - "text": "Glad to hear it, Jolene. How's the project going?" - }, - { - "speaker": "Jolene", - "dia_id": "D25:7", - "text": " It's tough but I'm chugging along. Thanks!" - }, - { - "speaker": "Deborah", - "dia_id": "D25:8", - "text": "You're so strong for handling all the challenges. You've got this!" - }, - { - "speaker": "Jolene", - "dia_id": "D25:9", - "text": "Thanks, Deb! Your support really means a lot. I'll keep pushing forward." - }, - { - "speaker": "Deborah", - "dia_id": "D25:10", - "text": "No worries, Jolene. I'm here if you need me. Take care of yourself and don't forget to rest up." - }, - { - "speaker": "Jolene", - "dia_id": "D25:11", - "text": "What have you been doing lately?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach", - "dia_id": "D25:12", - "text": "I recently saw this wonderful sunrise again." - }, - { - "speaker": "Jolene", - "dia_id": "D25:13", - "text": "Glimpsing that calms me. Taking beach walks relaxes me. " - }, - { - "speaker": "Deborah", - "dia_id": "D25:14", - "text": "Did my photo remind you of something?" - }, - { - "speaker": "Jolene", - "dia_id": "D25:15", - "text": "That shot was like a reminder of my last beach getaway. So chill and nice. " - }, - { - "speaker": "Deborah", - "dia_id": "D25:16", - "text": "Glad it brought back good memories. " - }, - { - "speaker": "Jolene", - "dia_id": "D25:17", - "text": "Maybe one day we will be able to watch the sunrise together!" - }, - { - "speaker": "Deborah", - "dia_id": "D25:18", - "text": "An offer I can't refuse!" - }, - { - "speaker": "Jolene", - "dia_id": "D25:19", - "text": "Bye, Deb! See you later!" - }, - { - "speaker": "Deborah", - "dia_id": "D25:20", - "text": "See ya! Stay safe and catch you later. Bye!" - } - ], - "session_26_date_time": "7:39 pm on 8 September, 2023", - "session_26": [ - { - "speaker": "Deborah", - "dia_id": "D26:1", - "text": "Hey Jolene, had a tough week. Storm forced us to cancel our yoga getaway." - }, - { - "speaker": "Jolene", - "dia_id": "D26:2", - "text": "Sorry to hear about it. How are you feeling now?" - }, - { - "speaker": "Deborah", - "dia_id": "D26:3", - "text": "I was bummed about it, but I'm doing better now. It was just a setback, but I found comfort in my work and spending time at home. Reminds me to be grateful for the little things. And you? How's it going?" - }, - { - "speaker": "Jolene", - "dia_id": "D26:4", - "text": "My partner and I plan a camping trip to connect with nature and practice yoga." - }, - { - "speaker": "Deborah", - "dia_id": "D26:5", - "text": "It can be both good and tough to plan activities with a busy schedule - what strategies do you use?" - }, - { - "speaker": "Jolene", - "dia_id": "D26:6", - "text": "Having a routine helps me stay on top of everything I need to do. I have a schedule for classes, studying, and personal time. Self-care activities like yoga and meditation help me stay balanced and relax." - }, - { - "speaker": "Deborah", - "dia_id": "D26:7", - "text": "I'd love to learn more about how you do it." - }, - { - "speaker": "Jolene", - "dia_id": "D26:8", - "text": " I can tell you about it if you're interested. It took a bit of experimenting, but it's really helped me." - }, - { - "speaker": "Deborah", - "dia_id": "D26:9", - "text": "Sounds great! Let's set up a coffee date and talk about it!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://res.cloudinary.com/sagacity/image/upload/c_crop,h_2000,w_1333,x_0,y_0/c_limit,dpr_auto,f_auto,fl_lossy,q_80,w_1080/CaseStudy-10009161130_nstsp1.jpg" - ], - "blip_caption": "a photo of a coffee shop with a bunch of coffee machines", - "query": "favorite cafe", - "dia_id": "D26:10", - "text": "Wanna meet up at that cafe next Monday? Let's try fresh pastries." - }, - { - "speaker": "Deborah", - "dia_id": "D26:11", - "text": "Sounds good, Jolene! When did you have in mind? That cafe rocks." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://images.pexels.com/photos/12910957/pexels-photo-12910957.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-sena-12910957.jpg" - ], - "blip_caption": "a photo of a person holding a cup of coffee in front of a bunch of flowers", - "query": "coffee cozy coffee shop holding cup of coffee", - "dia_id": "D26:12", - "text": "How about Wednesday at 4? Can't wait to catch up over coffee!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://media.architecturaldigest.com/photos/5a6790689acb4d19c359a638/16:9/w_320%2Cc_limit/OregonUmamiCafe.jpg" - ], - "blip_caption": "a photo of a group of people sitting at tables in a room", - "query": "coffee shop courtyard", - "dia_id": "D26:13", - "text": "That pic looks so peaceful. Reminded me of a cool hidden coffee shop near me. Rate it!" - }, - { - "speaker": "Jolene", - "dia_id": "D26:14", - "text": "Tell me more about it when we meet, maybe next time we\u2019ll be there." - }, - { - "speaker": "Deborah", - "dia_id": "D26:15", - "text": "Sorry, I remembered that I already have plans for this day." - }, - { - "speaker": "Jolene", - "dia_id": "D26:16", - "text": "Now I'll see when it's more convenient for me." - }, - { - "speaker": "Deborah", - "dia_id": "D26:17", - "text": "Thank you for your understanding, I'm waiting." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/d1g89l9ba7b61.jpg" - ], - "blip_caption": "a photo of a room with a book shelf and a ceiling fan", - "query": "bookshelf engineering textbooks", - "dia_id": "D26:18", - "text": "How about Friday at 5? I will need to sort out the books from this bookcase and I will be free." - }, - { - "speaker": "Deborah", - "dia_id": "D26:19", - "text": "Absolutely, let's do that! Can't wait for our coffee date next week. See you then. Stay safe!" - }, - { - "speaker": "Jolene", - "dia_id": "D26:20", - "text": "See ya soon, Deb! Be safe and I'm excited for our coffee date!" - }, - { - "speaker": "Deborah", - "dia_id": "D26:21", - "text": "Maybe just grab me some interesting books!" - } - ], - "session_27_date_time": "2:18 pm on 12 September, 2023", - "session_27": [ - { - "speaker": "Jolene", - "blip_caption": "a photo of a woman doing a yoga pose on the beach", - "dia_id": "D27:1", - "text": "Hey Deb! So sorry for the late reply, been super busy. Last weekend my partner and I traveled to a meditation retreat for a few weeks in Phuket. Amazing experience! Nature, reflection and a break from engineering studies were awesome - it helped me find inner peace." - }, - { - "speaker": "Deborah", - "dia_id": "D27:2", - "text": " I'd love to hear more about your reflections there and how they changed you." - }, - { - "speaker": "Jolene", - "dia_id": "D27:3", - "text": "At the retreat, I had time to reflect on what makes me happy. It made me realize the importance of incorporating relaxation, self-care, and balance in life alongside my engineering studies. The beauty of nature there was so inspiring and refreshing!" - }, - { - "speaker": "Deborah", - "dia_id": "D27:4", - "text": " Was there anything from the retreat that stood out to you?" - }, - { - "speaker": "Jolene", - "dia_id": "D27:5", - "text": "The one session that really stood out was about releasing expectations and judgments and just savoring the present. It was a strong reminder to not just dwell on the finish line, but to appreciate the journey too. I usually get too consumed with hitting my goals that I forget to appreciate the ride." - }, - { - "speaker": "Deborah", - "dia_id": "D27:6", - "text": " I've been thinking about this a lot, too. Life's full of small moments and being grateful for those can really boost our happiness." - }, - { - "speaker": "Jolene", - "dia_id": "D27:7", - "text": "Yup, totally agree! Sometimes we get too focused on the big stuff and don't appreciate all the tiny wins. Like the feeling of the sun or a great cup of coffee - can be small but they make life much better. Trying to be more mindful and grateful to take it all in now." - }, - { - "speaker": "Deborah", - "dia_id": "D27:8", - "text": "That's great, Jolene! Practicing mindfulness and gratitude can really change our day-to-day. Even just a different outlook can make the little things in life joyful. Glad you're getting into it!" - }, - { - "speaker": "Jolene", - "dia_id": "D27:9", - "text": "I'm experiencing a new level of joy and happiness!" - }, - { - "speaker": "Deborah", - "dia_id": "D27:10", - "text": " It's wonderful to see your progress, and I'm excited to be on this journey with you!" - }, - { - "speaker": "Jolene", - "dia_id": "D27:11", - "text": "Thanks, Deb! Appreciate your support. It's great that we can do this together." - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of two children standing on yoga mats in a room", - "dia_id": "D27:12", - "text": "Having a supportive community definitely helps. We can motivate and encourage each other! By the way, I recently played a game. I don't remember what it's called. This is a card game about cats, where you take cards one by one from a deck, and then you can attack your opponent with them. We'll definitely play it with you!" - }, - { - "speaker": "Jolene", - "dia_id": "D27:13", - "text": "I look forward to meeting you and playing this game!" - } - ], - "session_28_date_time": "3:09 pm on 15 September, 2023", - "session_28": [ - { - "speaker": "Deborah", - "blip_caption": "a photo of a living room with a couch and a fire place", - "dia_id": "D28:1", - "text": "Since speaking last, I reconnected with my mom's old friends. Their stories made me tear up and reminded me how lucky I am to have had her." - }, - { - "speaker": "Jolene", - "dia_id": "D28:2", - "text": " It's great that you could reconnect with them. Hearing stories about our loved ones can be tough but also comforting." - }, - { - "speaker": "Deborah", - "dia_id": "D28:3", - "text": "Hearing stories about my mom was emotional. It was both happy and sad to hear things I hadn't heard before. It was a mix of emotions, but overall it was comforting to reconnect with her friends." - }, - { - "speaker": "Jolene", - "dia_id": "D28:4", - "text": "It can bring up a range of emotions, and it's okay to feel a mix of happiness and sadness. Those moments with her friends must've been meaningful to you." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://ashleenichols.com/wp-content/uploads/2018/01/IMG_1546-e1515707058552.jpg" - ], - "blip_caption": "a photo of two women in pajamas taking a selfie in a mirror", - "query": "photo mother friends", - "dia_id": "D28:5", - "text": "Wow, it was so special. A glimpse into her life beyond what I knew. Through their eyes, I appreciate her more. Here I am and my mom." - }, - { - "speaker": "Jolene", - "dia_id": "D28:6", - "text": "That looks like a blast! What did you and your mom's friends do on that day?" - }, - { - "speaker": "Deborah", - "dia_id": "D28:7", - "text": "We reminisced and looked through her photos. It was really sweet." - }, - { - "speaker": "Jolene", - "dia_id": "D28:8", - "text": "Looking at old photos must have been so nostalgic! It's great that you could share that experience with friends. It's amazing how photos and memories can give us a deeper appreciation for the people we love." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach", - "dia_id": "D28:9", - "text": "Pictures really have a way of bringing back memories and making us appreciate the special bond we have with our loved ones. They remind me of how strong love is and how amazing human relationships can be. Just like this one." - }, - { - "speaker": "Jolene", - "dia_id": "D28:10", - "text": "Wow, what a gorgeous pic! Do you have any special memories of that beach or just love surfing in general?" - }, - { - "speaker": "Deborah", - "dia_id": "D28:11", - "text": "That beach is super special to me. It's where I got married and discovered my love for surfing. It's always filled with joy and peace." - }, - { - "speaker": "Jolene", - "dia_id": "D28:12", - "text": "What pleasant memories." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://assets-global.website-files.com/5909c340ec86374fefcb849c/65650a0fc42a7d8dbe0e4349_IMG_2131.jpg" - ], - "blip_caption": "a photo of a woman doing a yoga pose on the beach", - "query": "surfing yoga beach", - "dia_id": "D28:13", - "text": "Here is another photo from my classes." - }, - { - "speaker": "Jolene", - "dia_id": "D28:14", - "text": "Wow, that yoga pose looks amazing! Does it help you relax?" - }, - { - "speaker": "Deborah", - "dia_id": "D28:15", - "text": "Oh yeah! Doing this on the beach is so peaceful - the ocean, sand, and fresh air create a super relaxing atmosphere. The perfect way to take care of myself." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://c1.wallpaperflare.com/preview/308/322/334/wellness-relaxation-relax-spa.jpg" - ], - "blip_caption": "a photography of a couple of towels sitting on top of a table", - "query": "meditation mat candles essential oils", - "dia_id": "D28:16", - "re-download": true, - "text": " I like to create my own serene yoga space with candles and oils for extra chill vibes. Also, we tried a new style of meditation in Thailand - with flowers." - }, - { - "speaker": "Deborah", - "dia_id": "D28:17", - "text": "Oh, same for me!" - }, - { - "speaker": "Jolene", - "dia_id": "D28:18", - "text": "I find calm when I do yoga or meditate. I use essential oils and put on some soft, soothing music in the background to create a peaceful atmosphere. It really helps me chill out and center myself." - }, - { - "speaker": "Deborah", - "dia_id": "D28:19", - "text": " It's amazing how our environment can enhance our practice." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/3d/d9/b0/3dd9b048cca05344a7bf805705bd7f17.jpg" - ], - "blip_caption": "a photo of a bed with a colorful blanket and pillows", - "query": "cozy corner yoga mat cushions small plant", - "dia_id": "D28:20", - "text": "Yeah, totally! Our surroundings can really affect our mood and how much zen we can get from our routine. Creating a place that feels safe and chill is key." - }, - { - "speaker": "Deborah", - "dia_id": "D28:21", - "text": "Wow, that looks so comfy and inviting! Where do you usually go to relax in your house?" - }, - { - "speaker": "Jolene", - "dia_id": "D28:22", - "text": "In my room, I usually go to relax and feel at ease. After a busy day, it's my little haven for peace and rest - the perfect spot to relax and recharge." - }, - { - "speaker": "Deborah", - "dia_id": "D28:23", - "text": "Sounds like your room does the job. That's awesome." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/ckp5j9w7q7q41.jpg" - ], - "blip_caption": "a photo of a person holding a snake in a container", - "query": "pet snake curled up branch", - "dia_id": "D28:24", - "text": " Here are my pals keeping me company." - }, - { - "speaker": "Deborah", - "dia_id": "D28:25", - "text": "Hey, that's Susie or Seraphim? How long has he been hanging out with you?" - }, - { - "speaker": "Jolene", - "dia_id": "D28:26", - "text": "It`s Susie! I've had her for two years now. " - }, - { - "speaker": "Deborah", - "dia_id": "D28:27", - "text": "It's awesome how pets can bring us comfort and peace when we need it." - }, - { - "speaker": "Jolene", - "dia_id": "D28:28", - "text": "Susie is a great companion." - }, - { - "speaker": "Deborah", - "dia_id": "D28:29", - "text": "The love pets give is priceless." - }, - { - "speaker": "Jolene", - "dia_id": "D28:30", - "text": "Plus, they make life a lot brighter!" - } - ], - "session_29_date_time": "1:24 pm on 17 September, 2023", - "session_29": [ - { - "speaker": "Deborah", - "dia_id": "D29:1", - "text": "Hey Jolene, I'm so excited to tell you! Yesterday, me and my neighbor ran a free gardening class for the community, it was awesome! People of any age joined in and it was such a great thing to see." - }, - { - "speaker": "Jolene", - "dia_id": "D29:2", - "text": "Wow, Deborah, that's awesome! Keep up the great work, and here's hoping for more events like this in the future!" - }, - { - "speaker": "Deborah", - "dia_id": "D29:3", - "text": " Gardening is really amazing. It brings us together in such a cool way. It was awesome to share my love of plants and help people take care of the world. So, what about you? Anything new happened lately?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:4", - "text": "We tried a scuba diving lesson last Friday and had an awesome time! We found a cool dive spot we can explore together. Trying new things opens up a world of adventure - maybe one day I'll be a certified diver. Anything fun going on with you?" - }, - { - "speaker": "Deborah", - "dia_id": "D29:5", - "text": "That sounds amazing, Jolene! I've been interested in underwater life, but I haven't had the chance to try scuba diving yet. Recently, I've been spending time remembering my mom. Last Sunday, I visited her old house and sat on a bench. It was a comforting experience, as if I could feel her presence guide me and remind me of her love." - }, - { - "speaker": "Jolene", - "dia_id": "D29:6", - "text": "Visiting your mom's old home sounds like it was really special. Is there something special you remember about her?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://notjustsundaydinner.com/wp-content/uploads/2022/09/peach-cobbler-2.jpg" - ], - "blip_caption": "a photo of a bowl of food with a spoon in it", - "query": "peach cobbler delicious warm recipe", - "dia_id": "D29:7", - "text": "Thanks, Jolene! It was really special. My mom had a big passion for cooking. She would make amazing meals for us, each one full of love and warmth. I can still remember the smell of her special dish, it would fill the house and bring us all together." - }, - { - "speaker": "Jolene", - "dia_id": "D29:8", - "text": "Mmm, that looks delicious, Deb! So sweet how cooking with your mom brought everyone together. What's your best memory of cooking with her?" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://ronnascakeblog.files.wordpress.com/2022/09/pineapple.jpg" - ], - "blip_caption": "a photo of a pineapple cake with a smiley face on it", - "query": "pineapple birthday cake 1 candle", - "dia_id": "D29:9", - "text": "I loved it when she would bake pineapple birthday cakes for me when I was a kid. It always made me feel so special." - }, - { - "speaker": "Jolene", - "dia_id": "D29:10", - "text": "No wonder it made you feel special. " - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a mixer with a whisk in it", - "dia_id": "D29:11", - "text": "Have you ever had something like that with someone close?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/t45dmigsmtu61.jpg" - ], - "blip_caption": "a photo of four chocolate chip cookies on a baking sheet", - "query": "homemade cookies tray", - "dia_id": "D29:12", - "text": "I used to bake cookies with someone close to me." - }, - { - "speaker": "Deborah", - "dia_id": "D29:13", - "text": "What's your favorite cookie to make?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:14", - "text": "The warm, gooey chocolate and soft, buttery cookie are a match made in heaven." - }, - { - "speaker": "Deborah", - "dia_id": "D29:15", - "text": "I really want to eat this now." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://citysupplyfayetteville.com/cdn/shop/products/IMG_6684.jpg" - ], - "blip_caption": "a photo of a person holding a book open on a bed", - "query": "memory book pictures notes", - "dia_id": "D29:16", - "text": "Well look what I have here!" - }, - { - "speaker": "Deborah", - "dia_id": "D29:17", - "text": " Is there anything special about it or the photo?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:18", - "text": "It takes me to another world when I read it!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1617734021/at/news-culture/2021-04/lupita-aquino-nook-2.jpg" - ], - "blip_caption": "a photo of a living room with a couch and a book shelf", - "query": "cozy reading nook bookshelf books", - "dia_id": "D29:19", - "text": "Did I show you that I have a big bookshelf too?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:20", - "text": "I think not, I really like it!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1617640374/at/house%20tours/2021-04/Amara/21.jpg" - ], - "blip_caption": "a photo of a bathroom with a black and white wall and a wooden stool", - "query": "reading nook cozy inviting book bookmark heart shaped pendant", - "dia_id": "D29:21", - "text": "Having a space like this is important for escaping reality and relaxing with a book. Do you have any books that really moved you?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:22", - "text": "My bathroom has an aesthetic vibe. Once I read a self-discovery book there and it really resonated with me." - }, - { - "speaker": "Deborah", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset beach", - "dia_id": "D29:23", - "text": "Wow! A special book that speaks to you and helps with self-discovery? That's awesome. Plus, having a cozy nook to chill? That's my best one!" - }, - { - "speaker": "Jolene", - "dia_id": "D29:24", - "text": "Sounds nice, Deb! A cozy nook is a must! The beach is a great place for finding peace and relaxation. Have you ever tried surfing?" - }, - { - "speaker": "Deborah", - "img_url": [ - "http://badfishsup.com/cdn/shop/articles/IMG_7804.jpg" - ], - "blip_caption": "a photo of a man riding a surfboard on a wave in the ocean", - "query": "surfer riding wave surfing", - "dia_id": "D29:25", - "text": "Certainly! Here's the confirmation." - }, - { - "speaker": "Jolene", - "dia_id": "D29:26", - "text": "How cool! But I never decided to try it." - }, - { - "speaker": "Deborah", - "dia_id": "D29:27", - "text": "It's okay, maybe we can try it together sometime!" - }, - { - "speaker": "Jolene", - "img_url": [ - "http://petekozametalart.com/cdn/shop/products/image_2244493c-7118-4af4-9d82-6f8b0e99d9fa.jpg" - ], - "blip_caption": "a photo of a surfboard painted with a palm tree on it", - "query": "surfboard palm tree", - "dia_id": "D29:28", - "text": "I already know what fate awaits me if I do this!" - }, - { - "speaker": "Deborah", - "dia_id": "D29:29", - "text": "Have you ever been interested in this or do you know nothing about it?" - }, - { - "speaker": "Jolene", - "dia_id": "D29:30", - "text": "Just started learning, but haven't gone yet. Want to come with me sometime?" - }, - { - "speaker": "Deborah", - "blip_caption": "a photo of a sunset over the ocean with a boat in the distance", - "dia_id": "D29:31", - "text": "It'll be an adventure! Let's make it happen soon!" - }, - { - "speaker": "Jolene", - "dia_id": "D29:32", - "text": "So glad, all that remains is to agree and choose the right time for both of us." - }, - { - "speaker": "Deborah", - "dia_id": "D29:33", - "text": " Can't wait. What day works for you? I'm really excited!" - }, - { - "speaker": "Jolene", - "dia_id": "D29:34", - "text": "Let's plan for next month - I'll check my schedule and let you know. Can't wait!" - } - ], - "session_30_date_time": "10:17 am on 20 September, 2023", - "session_30": [ - { - "speaker": "Deborah", - "dia_id": "D30:1", - "text": "I had a great time at the music festival with my pals! The vibes were unreal and the music was magical. It was so freeing to dance and bop around. Music brings us together and helps us show our feelings. It reminds me of my mom and her soothing voice when she'd sing lullabies to me. Lucky to have those memories!" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.pinimg.com/originals/d5/2b/de/d52bde85a9eb63ee034d5ffb7be7d3c2.jpg" - ], - "blip_caption": "a photo of a man and woman standing in a field at night", - "query": "music festival couple dancing", - "dia_id": "D30:2", - "text": "Wow, festivals sound so fun! Here's me and my partner at one last year - had an awesome time! It's my way of expressing myself and getting away from all the stress of everyday life. Just got back from a trip with my partner - so cool!" - }, - { - "speaker": "Deborah", - "dia_id": "D30:3", - "text": "Wow, what a gorgeous shot! It looks so tranquil and serene. You two look very happy together. Trips create awesome memories that we can share. Where did you go on your trip and what's something you'll never forget?" - }, - { - "speaker": "Jolene", - "img_url": [ - "https://atharvyogshala.com/assets/images/sunset-view.jpg" - ], - "blip_caption": "a photo of a sunset over a valley with a horse", - "query": "yoga retreat south america serene retreat center mountains sunrise", - "dia_id": "D30:4", - "text": "Thanks! We had an awesome yoga retreat. The place was so peaceful and the view during yoga was amazing - the sunrise lit up the whole sky with bright colors. It was so beautiful, it made us feel so alive and grateful." - }, - { - "speaker": "Deborah", - "dia_id": "D30:5", - "text": "Wow, what a view! How did it make you feel?" - }, - { - "speaker": "Jolene", - "dia_id": "D30:6", - "text": "It was amazing! Doing yoga with that backdrop made me feel connected to nature and myself. I felt incredibly peaceful and thankful." - }, - { - "speaker": "Deborah", - "dia_id": "D30:7", - "text": "Like, it's no wonder looking at such beauty can really help us refocus and connect with who we are. Have you ever experienced that?" - }, - { - "speaker": "Jolene", - "dia_id": "D30:8", - "text": "I remember taking a hike with my partner and coming across a waterfall oasis. Everything just felt so peaceful and my worries just disappeared. It was such a refreshing experience." - }, - { - "speaker": "Deborah", - "dia_id": "D30:9", - "text": "Are you planning to experience it again soon?" - }, - { - "speaker": "Jolene", - "dia_id": "D30:10", - "text": "Yeah! I'm planning to get out in nature again next month. It's going to be great reconnecting with it!" - }, - { - "speaker": "Deborah", - "img_url": [ - "https://www.lsuagcenter.com/~/media/system/8/a/0/b/8a0beeb70c88046e95c4cc398cb5242b/cherry%20flowers%20and%20beepng.png" - ], - "blip_caption": "a photo of a pink flowered tree with a blue sky in the background", - "query": "colorful blossom tree bloom spring", - "dia_id": "D30:11", - "text": " Enjoy it! This photo made me think of a gorgeous blossom tree from near my home. Every spring, it was magical to watch it bloom." - }, - { - "speaker": "Jolene", - "dia_id": "D30:12", - "text": "That sounds magical! How was it watching the tree bloom each spring?" - }, - { - "speaker": "Deborah", - "dia_id": "D30:13", - "text": "It was like admiring nature's artwork. It filled me with awe and made me appreciate the beauty of life. Even in tough times, there's hope for growth." - }, - { - "speaker": "Jolene", - "img_url": [ - "https://i.redd.it/kwnuvwl3wha01.jpg" - ], - "blip_caption": "a photo of a plant growing out of a corner of a building", - "query": "small sprout crack pavement growth thrive", - "dia_id": "D30:14", - "text": "This photo I took is a great visual representation of that idea. It reminds me that I can keep growing through any obstacles." - }, - { - "speaker": "Deborah", - "dia_id": "D30:15", - "text": "It really captures resilience and strength. I love how you find inspiration in the small things." - }, - { - "speaker": "Jolene", - "dia_id": "D30:16", - "text": "Thanks, Deborah! Appreciating those small things is important. It helps me remember that even when times are tough, there's always something positive to hang onto." - }, - { - "speaker": "Deborah", - "dia_id": "D30:17", - "text": "It's a great habit. Thanks for reminding me!" - }, - { - "speaker": "Jolene", - "dia_id": "D30:18", - "text": "Sure Deb, it's great catching up. Keep on finding those beauties!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Deborah": [ - "Deborah visits her mother's old home while reflecting on her life." - ], - "Jolene": [ - "Jolene conquers a difficult electrical engineering project." - ], - "date": "23 January, 2023" - }, - "events_session_2": { - "Deborah": [ - "Deborah's father passes away suddenly, leaving her and her family shocked and in grief.", - "Deborah's copes with her father's passing by looking at their family album.", - "Deborah receives a letter from her community where they appreciate her dedication to teaching yoga." - ], - "Jolene": [ - "Jolene takes a break from her engineering studies by playing \"Walking Dead\" video game with her partner." - ], - "date": "27 January, 2023" - }, - "events_session_3": { - "Deborah": [ - "Deborah meets a new neighbor, Anna, while doing yoga in the park and befriends them." - ], - "Jolene": [ - "Jolene receives a challenging but fun robotics projects from her professor to work on." - ], - "date": "1 February, 2023" - }, - "events_session_4": { - "Deborah": [ - "Deborah further bonds with her neighbor Anna during yoga class while trying out the Warrior II yoga pose and over their shared memories of their late mothers." - ], - "Jolene": [ - "Jolene finishes working on a sustainable water purifier for providing clean water to rural communities and derives a lot of satisfaction from the impact created by the project." - ], - "date": "4 February, 2023" - }, - "events_session_5": { - "Deborah": [], - "Jolene": [ - "Jolene reflects on her accomplishments and future by taking a mini retreat in life and comes up with some solutions for her engineering projects such as developing green tech for disadvantaged areas.", - "Jolene plans to start a volunteer program where engineers can teach STEM to underpriveleged kids by holding workshops at local schools.", - "Jolene uses a planner to note her ideas on the volunteer program and green tech." - ], - "date": "9 February, 2023" - }, - "events_session_6": { - "Deborah": [ - "Deborah's friend Karlie passes away.", - "Deborah copes with Karlie's passing away by visiting flower gardens and remembering their time together." - ], - "Jolene": [ - "Jolene goes out with her friends for dinner and drinks to let her hair down." - ], - "date": "22 February, 2023" - }, - "events_session_7": { - "Deborah": [ - "Deborah goes for a morning jog in the nearby park and decides to integrate it into her daily routine." - ], - "Jolene": [ - "Jolene starts doing yoga and meditation, sometimes joined by her partner, to clear her head while striking a balance with her studies." - ], - "date": "25 February, 2023" - }, - "events_session_8": { - "Deborah": [ - "Deborah tastes a new vegan dish - tofu and veg with ginger and soy sauce - she received from her neighbor, Anna." - ], - "Jolene": [ - "Jolene is stressed out about her studies and struggles to be mindful of the stress levels while keeping up with her todos.", - "Jolene takes her pet snake, Seraphim, for a visit to the park." - ], - "date": "2 March, 2023" - }, - "events_session_9": { - "Deborah": [ - "Deborah starts a neighborhood yoga class and finds joy in seeing its impact on them." - ], - "Jolene": [ - "Jolene struggles with time management for her projects and studies and seeks help from Deborah." - ], - "date": "13 March, 2023" - }, - "events_session_10": { - "Deborah": [ - "Deborah and her neighbor-friend, Anna, bond during a moment at the beach and inspire each other about pursuing their dreams." - ], - "Jolene": [], - "date": "22 March, 2023" - }, - "events_session_11": { - "Deborah": [ - "Deborah purchases some yoga mats and blocks for her yoga classes." - ], - "Jolene": [ - "Jolene has a breakthrough in her robotics project." - ], - "date": "28 March, 2023" - }, - "events_session_12": { - "Deborah": [ - "Deborah goes for a bicycle ride with Anna to explore the nearby villages.", - "Deborah attends an art exhibition with Anna and finds inspiring works that remind her of her mother." - ], - "Jolene": [ - "Jolene continues working on the robotics project she started in February and it gradually shapes up." - ], - "date": "9 April, 2023" - }, - "events_session_13": { - "Deborah": [], - "Jolene": [ - "Jolene successfully finishes the robotics project assigned to her by her Professor.", - "Jolene joins an engineering firm as an intern over the summer.", - "Jolene visits Alask and practices yoga on top of Mt. Talkeetna" - ], - "date": "6 June, 2023" - }, - "events_session_14": { - "Deborah": [ - "Deborah starts planning for a yoga retreat with her friends during September 2023." - ], - "Jolene": [ - "Jolene has an intense time as an intern and has a breakthrough in her project.", - "Jolene buys an aquarium for her pet snake, Seraphim." - ], - "date": "26 June, 2023" - }, - "events_session_15": { - "Deborah": [ - "Deborah and her neighbor, Anna, form a running group." - ], - "Jolene": [ - "Jolene struggles to include workouts and gaming with her partner in her hectic daily routine." - ], - "date": "9 July, 2023" - }, - "events_session_16": { - "Deborah": [ - "Deborah starts a new project of raising funds for a clean-up drive in her community." - ], - "Jolene": [ - "Jolene loses all her work when her device crashes." - ], - "date": "1 August, 2023" - }, - "events_session_17": { - "Deborah": [ - "Deborah creates a meditation guide for her yoga retreat." - ], - "Jolene": [ - "Jolene is stressed about losing her work progress and manages her stress by practicing yoga and meditation.", - "Jolene starts working on an affordable and productive aerial surveillance system that can respond quickly to emergencies." - ], - "date": "12 August, 2023" - }, - "events_session_18": { - "Deborah": [ - "Deborah teaches yoga during a sunset session to the elderly at a local care home." - ], - "Jolene": [ - "Jolene plans some new goals with her partner, evaluates their success at achieving them and feels overwhelmed about juggling so much." - ], - "date": "16 August, 2023" - }, - "events_session_19": { - "Deborah": [ - "Deborah shares her life story with the neighbor Anna and she appreciates Deborah's life experiences.", - "Deborah organizes a yoga event in her community, complete with live music and food stalls from local businesses." - ], - "Jolene": [ - "Jolene buys a new game console and gifts it to her partner." - ], - "date": "19 August, 2023" - }, - "events_session_20": { - "Deborah": [ - "Deborah visits the bench where she and her mother used to have chats and revisits some nostalgic memories." - ], - "Jolene": [ - "Jolene and her partner attempt to play Battlefield 1 on the new game console she bought as a gift for her partner." - ], - "date": "21 August, 2023" - }, - "events_session_21": { - "Deborah": [ - "Deborah participates in a yoga retreat surrounded by nature near her mother's place." - ], - "Jolene": [ - "Jolene presents her work in a virtual conference and is pumped by the positive feedback she receives about her work.", - "Jolene reflects on the progress she has made so far and decides to gain more experience by attending workshops, networking and doing more internships." - ], - "date": "24 August, 2023" - }, - "events_session_22": { - "Deborah": [ - "Deborah ruminates on the influence of her mother on her life." - ], - "Jolene": [ - "Jolene shares her appreciation for yoga with her partner.", - "Jolene and her partner plan to travel together to a meditation retreat to enhance their shared practice.", - "Jolene plans to work on sustainable solution projects during the next year, such as developing solar energy and providing clean water to communities.", - "Jolene has an emotional chat with her partner about their parents who have passed away but have left lasting impressions on their lives." - ], - "date": "26 August, 2023" - }, - "events_session_23": { - "Deborah": [ - "Deborah and her husband visited her mother's house last month." - ], - "Jolene": [ - "Jolene and her partner return from their trip to Rio de Janeiro to explore different types of yoga, explore some cool yoga classes, cafes and old temples." - ], - "date": "30 August, 2023" - }, - "events_session_24": { - "Deborah": [ - "Deborah attends a community meetup where everyone shares stories about themselves." - ], - "Jolene": [ - "Jolene and her partner plan weekend trips to the beach to relax.", - "Jolene studies hard for her upcoming finals." - ], - "date": "3 September, 2023" - }, - "events_session_25": { - "Deborah": [], - "Jolene": [ - "Jolene signs up for an meditation course at a retreat near a lake." - ], - "date": "6 September, 2023" - }, - "events_session_26": { - "Deborah": [ - "Deborah has to cancel the yoga retreat she was planning with her friends because of a storm" - ], - "Jolene": [ - "Jolene and her partner plan a camping trip to connect with nature and practice yoga." - ], - "date": "8 September, 2023" - }, - "events_session_27": { - "Deborah": [], - "Jolene": [ - "Jolene and her partner travel to a meditation retreat in Phuket, Thailand." - ], - "date": "12 September, 2023" - }, - "events_session_28": { - "Deborah": [ - "Jolene reconnects with her mother's friends and gets nostalgic hearing about her stories from them and going over old photos." - ], - "Jolene": [ - "Jolene and her partner practice a new style of meditation in Thailand with flowers." - ], - "date": "15 September, 2023" - }, - "events_session_29": { - "Deborah": [ - "Deborah and Anna offer a free class on community gardening in their neighborhood." - ], - "Jolene": [ - "Jolene and her partner explore a new dive spot in Phuket after taking a scuba diving lesson." - ], - "date": "17 September, 2023" - }, - "events_session_30": { - "Deborah": [ - "Deborah attends a summer music festival with friends." - ], - "Jolene": [ - "Jolene and her partner return from their yoga retreat in Phuket, Thailand.", - "Jolene makes significant progress in her yoga course." - ], - "date": "20 September, 2023" - } - }, - "observation": { - "session_1_observation": { - "Deborah": [ - [ - "Deborah finished an electrical engineering project last week.", - "D1:2" - ], - [ - "Deborah visited her mother's old house last week which holds special memories as her mother passed away a few years ago.", - "D1:3" - ], - [ - "Deborah has a photo with her mother, which is their last photo together.", - "D1:5" - ], - [ - "Deborah's mother had a special bench near the window in her house where she used to sit every morning to take in the view.", - "D1:7" - ], - [ - "Deborah has a pendant that reminds her of her mother.", - "D1:9" - ], - [ - "Deborah's goal is to keep teaching yoga and supporting her community to help people find peace and joy.", - "D1:13" - ], - [ - "Yoga helped Deborah find peace during a rough time, inspiring her to share it with others.", - "D1:15" - ] - ], - "Jolene": [ - [ - "Jolene finished an electrical engineering project last week.", - "D1:2" - ], - [ - "Jolene's mother passed away last year.", - "D1:6" - ], - [ - "Jolene has a room in her mother's house where she has many memories.", - "D1:6" - ], - [ - "Jolene has a pendant that reminds her of her mother.", - "D1:8" - ], - [ - "Jolene's pendant has a special symbol representing freedom, reminding her to go for her goals.", - "D1:10" - ] - ] - }, - "session_2_observation": { - "Deborah": [ - [ - "Deborah's father passed away two days before the conversation. She is trying to channel her grief by spending more time with family and cherishing memories.", - "D2:1" - ], - [ - "Deborah finds peace in looking at family photos during difficult times.", - "D2:3" - ], - [ - "Deborah values love and openness in her relationship with her husband.", - "D2:7" - ], - [ - "Deborah has a positive influence on a group through yoga, which she is passionate about.", - "D2:9" - ], - [ - "Deborah visits her old home, where her mom passed away, to find peace and feel her mother's presence.", - "D2:13" - ], - [ - "Deborah's mother enjoyed reading as one of her hobbies.", - "D2:17" - ], - [ - "Deborah's mother had a passion for travel.", - "D2:19" - ] - ], - "Jolene": [ - [ - "Jolene enjoys playing video games and particularly likes the game \"Detroit\" on the console.", - "D2:26" - ], - [ - "Jolene and her partner are planning to play \"Walking Dead\" next Saturday.", - "D2:30" - ] - ] - }, - "session_3_observation": { - "Jolene": [ - [ - "Jolene is a student who is working on a robotics project assigned by her engineering professor.", - "D3:1" - ], - [ - "Initially, Jolene felt a mix of excitement and nervousness when she received the robotics project.", - "D3:3" - ], - [ - "Jolene finds working on the robotics project like solving a puzzle and enjoys figuring out the best design and programming.", - "D3:3" - ], - [ - "Jolene enjoys seeing the robot come together as she works on the project.", - "D3:3" - ], - [ - "Jolene has a partner with whom she plans to play console games.", - "D3:11" - ] - ], - "Deborah": [ - [ - "Deborah met her new neighbor Anna at a yoga session in the park.", - "D3:4" - ], - [ - "Deborah and Anna discussed how yoga has improved their lives and the sense of community it provides.", - "D3:8" - ], - [ - "Deborah suggests to Jolene to resume yoga, hinting at its benefits.", - "D3:10" - ], - [ - "Deborah values having activities to do together with others, like yoga with her neighbor Anna.", - "D3:12" - ] - ] - }, - "session_4_observation": { - "Jolene": [ - [ - "Jolene had a major milestone last week in engineering where she designed and built a sustainable water purifier for a rural community.", - "D4:3" - ], - [ - "Jolene wants to continue working in engineering to make a positive impact on communities in need by creating sustainable solutions.", - "D4:9" - ], - [ - "Jolene is into reading, particularly enjoying the books \"Sapiens\" and \"Avalanche\" by Neal Stephenson.", - [ - "D4:21", - "D4:23" - ] - ], - [ - "Jolene appreciates nature's calming power and enjoys watching sunsets over the water.", - "D4:33" - ] - ], - "Deborah": [ - [ - "Deborah spends a lot of time teaching yoga as a way to find balance and reconnect with herself.", - "D4:12" - ], - [ - "Deborah has a bouquet that was given to her by a friend during a tough time, which gives her hope and courage.", - "D4:28" - ], - [ - "Deborah has a little amulet from her mom that she brings with her when reflecting on her mom. It brings her comfort and connects her to her mom's love.", - "D4:36" - ], - [ - "Deborah enjoys nature and has favorite spots in a park with a forest trail and a beach nearby.", - "D4:32" - ], - [ - "Deborah values supporting others, as seen when she bonded with Anna during a yoga class and shared stories with Jolene.", - [ - "D4:16", - "D4:40" - ] - ] - ] - }, - "session_5_observation": { - "Jolene": [ - [ - "Jolene did a mini retreat last Wednesday to assess her life and gained new insights and a confidence boost from it.", - "D5:1" - ], - [ - "Jolene accomplished something significant with her engineering project and came up with neat solutions that she's excited about.", - "D5:5" - ], - [ - "Jolene is considering looking into green technology to make a difference in disadvantaged areas.", - "D5:7" - ], - [ - "Jolene had an idea for a volunteer program where engineers teach STEM to underprivileged kids.", - "D5:7" - ], - [ - "Jolene plans to team up with local schools/centers to do workshops and invite engineers as guest speakers for the volunteer program.", - "D5:9" - ] - ], - "Deborah": [ - [ - "Deborah acknowledged Jolene for stepping outside her comfort zone and believing in herself.", - "D5:4" - ], - [ - "Deborah supported Jolene's volunteer program idea and suggested that it's a great way to help and inspire others.", - "D5:8" - ], - [ - "Deborah expressed excitement to hear about Jolene reaching out and helping underprivileged kids.", - "D5:12" - ], - [ - "Deborah offered to provide more tips or information if Jolene needs them.", - "D5:14" - ] - ] - }, - "session_6_observation": { - "Jolene": [], - "Deborah": [ - [ - "Deborah finds peace and comfort in roses and dahlias in her garden after losing a friend last week.", - "D6:4" - ], - [ - "A photo of the last hike with her friend Karlie brings a smile to Deborah's face whenever she sees it.", - "D6:8" - ], - [ - "Deborah traveled to Bali last year, one of her favorite places, for peace and yoga.", - "D6:10" - ] - ] - }, - "session_7_observation": { - "Jolene": [ - [ - "Jolene is studying engineering in school while balancing her partner's video games.", - "D7:1" - ], - [ - "Jolene practices yoga and meditation to stay calm amidst the busyness of life.", - "D7:1" - ], - [ - "Jolene's partner sometimes joins her in doing yoga for bonding and relaxation.", - "D7:5" - ], - [ - "Jolene has been with her partner for three years but they are not married yet.", - "D7:7" - ], - [ - "Jolene and her partner met in an engineering class in college and their romantic relationship grew from a friendship.", - "D7:9" - ], - [ - "Jolene finds teaming up with her partner on engineering projects enjoyable.", - "D7:11" - ], - [ - "Yoga and meditation help Jolene feel calmer and clearer when overwhelmed, enabling her to tackle challenges more easily.", - "D7:13" - ] - ], - "Deborah": [ - [ - "Deborah meditates, does yoga, and teaches classes in the morning.", - "D7:18" - ], - [ - "Deborah went for a morning jog in a nearby park for the first time and plans to include it in her daily routine.", - "D7:18" - ], - [ - "Deborah values exercise as it makes her feel connected to her body.", - "D7:20" - ] - ] - }, - "session_8_observation": { - "Deborah": [ - [ - "Deborah enjoys vegan stir-fry with tofu and vegetables with ginger and soy sauce.", - "D8:1" - ], - [ - "Deborah finds food a wonderful source of pleasure and nourishment.", - "D8:1" - ], - [ - "Deborah did yoga and meditation to relax last Friday.", - "D8:4" - ], - [ - "Deborah enjoys spending time outside and took Seraphim to the park last Sunday.", - "D8:8" - ], - [ - "Deborah appreciates the birds chirping and the breeze gently blowing when outdoors.", - "D8:11" - ], - [ - "Deborah reminds Jolene that efforts will bear fruit and encourages her not to give up.", - "D8:17" - ], - [ - "Deborah suggests breaking tasks into smaller pieces and setting goals for time management.", - "D8:19" - ], - [ - "Deborah offers to help Jolene with a study plan.", - "D8:19" - ] - ], - "Jolene": [ - [ - "Jolene finds lasagna as one of her favorite comforting dishes.", - "D8:2" - ], - [ - "Jolene does yoga and meditation to relax and find me-time.", - "D8:4" - ], - [ - "Jolene values quiet moments for the soul.", - "D8:6" - ], - [ - "Jolene took Seraphim to the park and explored new places.", - "D8:8" - ], - [ - "Jolene wants to be more mindful of her stress levels and mental health during exams.", - "D8:14" - ], - [ - "Jolene feels overwhelmed by exams and deadlines.", - "D8:16" - ], - [ - "Jolene expresses feeling challenged by the workload and to-do list.", - "D8:16" - ], - [ - "Jolene appreciates Deborah's tips on studying and time management.", - "D8:18" - ] - ] - }, - "session_9_observation": { - "Deborah": [ - [ - "Deborah started a yoga class in the neighborhood to share the exercise with her neighbors.", - "D9:3" - ], - [ - "Deborah hosted a yoga class for her neighbors on a Friday and enjoyed seeing everyone embrace and enjoy it.", - "D9:5" - ], - [ - "Deborah finds teaching yoga calming and derives happiness from giving people peace and awareness.", - "D9:7" - ], - [ - "Teaching yoga has helped Deborah make great friends and build community connections.", - "D9:9" - ] - ], - "Jolene": [ - [ - "Jolene is having a hard time dealing with her Engineering assignments and is struggling to keep up.", - "D9:14" - ], - [ - "Jolene is interested in discussing time management with Deborah to help with her workload.", - "D9:14" - ], - [ - "Jolene wants to find a time to chat with Deborah about time management.", - "D9:16" - ], - [ - "Jolene values having support in tough times and finds it essential in handling life challenges.", - "D9:10" - ] - ] - }, - "session_10_observation": { - "Deborah": [ - [ - "Deborah is familiar with the Pomodoro Technique for time management.", - "D10:5" - ], - [ - "Deborah creates a daily schedule or to-do list to stay organized.", - "D10:5" - ], - [ - "Deborah is aware of the Eisenhower Matrix for organizing and prioritizing tasks based on urgency and importance.", - "D10:13" - ], - [ - "Deborah recently sat by the sea with Anna, watching the sunset and talking about inspiring each other.", - "D10:17" - ] - ], - "Jolene": [ - [ - "Jolene finds it difficult to manage time and stay organized during exams and deadlines.", - "D10:2" - ], - [ - "Jolene uses the Pomodoro Technique - 25 minutes work, 5-minute break - to avoid burnout.", - "D10:4" - ], - [ - "Jolene struggles to prioritize tasks even with the Pomodoro Technique.", - "D10:4" - ], - [ - "Jolene finds daily schedules or to-do lists overwhelming when dealing with a big stack of tasks.", - "D10:6" - ], - [ - "Jolene is interested in learning about new time management methods like the Eisenhower Matrix shared by Deborah.", - "D10:12" - ], - [ - "Jolene is planning on learning to surf, has been gathering information, and got a beginners' guide to surfing.", - "D10:18" - ] - ] - }, - "session_11_observation": { - "Jolene": [ - [ - "Jolene had a breakthrough with her engineering project on Friday and found a solution to a problem.", - "D11:1" - ], - [ - "Jolene feels relaxed during yoga and enjoys creating a serene space with soothing scents like lavender and rosemary.", - "D11:5" - ], - [ - "Jolene finds music helpful during her yoga practice.", - "D11:7" - ], - [ - "Jolene loves listening to Nils Frahm and Olafur Arnalds during her practice as their music is calming and puts her in a different headspace.", - "D11:9" - ] - ], - "Deborah": [ - [ - "Deborah bought new props for her yoga class.", - "D11:2" - ], - [ - "Deborah bought a candle to improve the atmosphere of her yoga practice.", - "D11:4" - ], - [ - "Deborah enjoys scents like lavender and rosemary during her yoga practice.", - "D11:6" - ], - [ - "Deborah finds instrumental tracks with mellow melodies and rhythms helpful for creating a peaceful vibe during her practice.", - "D11:8" - ], - [ - "Deborah is listening to an album called 'Sleep' which is great for meditation and deep relaxation.", - "D11:10" - ] - ] - }, - "session_12_observation": { - "Deborah": [ - [ - "Deborah went biking with her neighbor last week.", - "D12:1" - ], - [ - "Deborah attended an art show with a friend which she found cool and inspiring.", - "D12:1" - ], - [ - "Deborah's mom was interested in art and believed it could give out strong emotions and uniquely connect people.", - "D12:3" - ], - [ - "Deborah finds comfort in ways to keep her mom's memory alive, particularly through artwork.", - "D12:5" - ] - ], - "Jolene": [ - [ - "Jolene spends time playing video games with her partner to relax after a long day.", - "D12:6" - ], - [ - "Jolene is working on a big project which is tough but exciting to watch take shape.", - "D12:10" - ], - [ - "Jolene values Deborah's support and mentions she will keep her updated on the progress of the project.", - "D12:12" - ] - ] - }, - "session_13_observation": { - "Jolene": [ - [ - "Jolene recently completed a tough engineering project.", - "D13:1" - ], - [ - "Jolene is interning at a well-known engineering firm to gain real-world experience.", - "D13:5" - ], - [ - "Jolene finds it fulfilling to apply what she has learned in school to real projects.", - "D13:7" - ], - [ - "Jolene's internship has positively impacted her life and stoked her love of engineering.", - "D13:9" - ], - [ - "Jolene is inspired to keep striving for her dreams by dedicated and hardworking colleagues.", - "D13:11" - ], - [ - "Jolene has found it tough to maintain a work-life balance during her internship.", - "D13:13" - ], - [ - "Jolene has been practicing yoga and meditation sporadically for about 3 years and found them to have a positive effect on her.", - "D13:17" - ], - [ - "Yoga and meditation have helped Jolene with stress and keeping centered.", - "D13:19" - ] - ], - "Deborah": [ - [ - "Deborah acknowledges Jolene for completing her tough engineering project with dedication.", - "D13:2" - ], - [ - "Deborah inquires about Jolene's future plans after completing the project.", - "D13:4" - ], - [ - "Deborah asks Jolene about the impact of her internship experience.", - "D13:8" - ], - [ - "Deborah suggests activities like yoga to Jolene to help her relax and unwind.", - "D13:14" - ], - [ - "Deborah recommends practicing mindful breathing for yoga to Jolene.", - "D13:24" - ] - ] - }, - "session_14_observation": { - "Deborah": [ - [ - "Deborah is preparing for a yoga retreat with friends to find peace and understanding.", - "D14:1" - ], - [ - "Deborah tried a new yoga pose called Dancer Pose (Natarajasana) and asked for feedback from Jolene.", - "D14:3" - ], - [ - "Deborah enjoys spending time with animals and finds joy in them.", - "D14:5" - ], - [ - "Deborah believes that pets make life more enjoyable and bright.", - "D14:7" - ], - [ - "Deborah shared a photo of a new yoga pose called Tree Pose during the conversation.", - "D14:15" - ], - [ - "Deborah's yoga pose was described as a symbol of peace and enlightenment.", - "D14:17" - ] - ], - "Jolene": [ - [ - "Jolene has been busy with an internship and finds it hectic.", - "D14:2" - ], - [ - "Jolene owns a pet named Seraphim, who brings comfort and cheer to her life.", - "D14:6" - ], - [ - "Jolene is pushing herself to succeed despite feeling overwhelmed.", - "D14:10" - ], - [ - "Jolene had a big breakthrough with a project, finding it exciting and rewarding.", - "D14:12" - ], - [ - "Jolene is interested in visiting a retreat like the one Deborah went to.", - "D14:18" - ], - [ - "Jolene expressed a desire for some chill time and found the idea of a retreat peaceful.", - "D14:20" - ] - ] - }, - "session_15_observation": { - "Deborah": [ - [ - "Deborah started a running group with Anna to connect with people who care about fitness.", - "D15:1" - ], - [ - "Deborah has many photos related to the running group activity.", - "D15:3" - ], - [ - "Deborah organizes workshops and events to practice mindfulness, self-care, and create a community.", - "D15:11" - ], - [ - "Deborah's workshops involve activities like yoga, meditation, and self-reflection to promote mental and emotional well-being.", - "D15:13" - ], - [ - "Deborah finds it rewarding to witness growth and transformation in participants of the workshops.", - "D15:15" - ], - [ - "Deborah prefers cats over dogs as pets.", - "D15:25" - ], - [ - "Deborah takes her cats for a run in the park every morning and evening.", - "D15:27" - ] - ], - "Jolene": [ - [ - "Jolene is trying to add workouts into her studying schedule.", - "D15:2" - ], - [ - "Jolene games with her partner and finds it a great way to bond and get closer.", - "D15:8" - ], - [ - "Jolene loves playing 'It Takes Two' with her partner and finds it competitive and a great bonding activity.", - "D15:10" - ], - [ - "Jolene has a snake as a pet, finds it calming, and a great connection with nature.", - "D15:18" - ], - [ - "Jolene had a 'snake adventure' where her snake got out, but she found her snuggling under the bed after hours of searching.", - "D15:20" - ], - [ - "Jolene takes photos of nature and finds nature to be refreshing and a way to hit a reset button.", - "D15:29" - ], - [ - "Jolene loves going for walks to take in nature and finds peaceful spots like the lake surrounded by trees to be calming and tranquil.", - "D15:32" - ], - [ - "Jolene plans to go together to the peaceful spot near the lake with Deborah.", - "D15:38" - ] - ] - }, - "session_16_observation": { - "Deborah": [ - [ - "Deborah started a project for a community cleanup and is raising funds for it.", - "D16:1" - ], - [ - "Deborah offered support and help to Jolene when she faced a setback in her project.", - "D16:3" - ], - [ - "Deborah mentioned pets as a source of love and comfort during tough times.", - "D16:5" - ], - [ - "Deborah recommended self-care routines like yoga to Jolene to stay balanced and grounded.", - "D16:9" - ], - [ - "Deborah shared her favorite gentle yoga flow routine focused on breathing and grounding to find chill.", - "D16:15" - ] - ], - "Jolene": [ - [ - "Jolene has faced a huge setback in her project recently, causing frustration and depression.", - "D16:2" - ], - [ - "Jolene finds comfort and distraction through her pet Susie and video games during tough times.", - "D16:4" - ], - [ - "Jolene adopted Susie two years ago when feeling lonely.", - "D16:6" - ], - [ - "Jolene practices self-care through yoga and meditation to stay balanced and grounded.", - "D16:10" - ], - [ - "Jolene expressed interest in new routines to mix things up for self-care.", - "D16:12" - ], - [ - "Jolene looks forward to trying out Deborah's favorite gentle flow yoga routine to find chill.", - "D16:16" - ] - ] - }, - "session_17_observation": { - "Deborah": [ - [ - "Deborah made a meditation guide for her yoga retreat.", - "D17:1" - ] - ], - "Jolene": [ - [ - "Jolene found meditation helpful to regain clarity after losing work files.", - "D17:2" - ], - [ - "Jolene designed notebooks inspired by her love for space and engines with elements like galaxies and circuitry.", - "D17:6" - ], - [ - "Jolene is working on a prototype project aimed at revolutionizing aerial surveillance.", - "D17:10" - ], - [ - "Jolene aims to create a more productive and affordable aerial surveillance system to help respond to emergencies and monitor the environment.", - "D17:12" - ] - ] - }, - "session_18_observation": { - "Jolene": [ - [ - "Jolene is finding it challenging to juggle her engineering studies, relationship, and personal growth.", - "D18:1" - ], - [ - "Jolene has started using a bullet journal as a time management strategy to track tasks and stay organized.", - "D18:3" - ], - [ - "Jolene finds it satisfying to cross tasks off her list in the bullet journal.", - "D18:5" - ], - [ - "Jolene finds a particular quote in her bullet journal spread motivating and uses it as a reminder to stick to her goals and never give up.", - "D18:7" - ], - [ - "Jolene is interested in destressing and trying mindfulness as a way to find calm in her life.", - "D18:9" - ] - ], - "Deborah": [ - [ - "Deborah suggests that finding ways to restore balance, take time for oneself, and recognize personal needs is important.", - "D18:2" - ], - [ - "Deborah led a meditation yoga session to the elderly at a local care home during sunset, indicating her interest in mindfulness and finding peace in nature.", - "D18:8" - ], - [ - "Deborah mentions that mindfulness is a huge part of her life and offers to assist Jolene with her mindfulness journey.", - "D18:10" - ], - [ - "Deborah encourages Jolene to focus on her goals, not give up, and offers help in starting mindfulness practice.", - "D18:12" - ] - ] - }, - "session_19_observation": { - "Deborah": [ - [ - "Deborah teaches yoga and spends time with the community.", - "D19:3" - ], - [ - "Deborah organized a yoga event last month that included yoga, food stalls, and live music.", - "D19:5" - ], - [ - "Deborah prioritizes and manages her time effectively by making a schedule and setting aside specific time for studying and pursuing hobbies.", - "D19:5" - ], - [ - "Deborah used to play video games, finding it a good way to relax.", - "D19:7" - ], - [ - "Deborah cherishes special memories with her mom at a park, including watching a beautiful sunset together.", - "D19:21" - ] - ], - "Jolene": [ - [ - "Jolene bought a console as a gift for her partner on the 17th.", - "D19:2" - ], - [ - "Jolene's partner enjoys playing the console she bought.", - "D19:2" - ], - [ - "Jolene's studies in engineering are going strong.", - "D19:2" - ], - [ - "Jolene values balance in her life, mentioning it has been key for her lately.", - "D19:2" - ], - [ - "Jolene enjoys video games and recommends Zelda BOTW and Animal Crossing: New Horizons.", - "D19:8" - ], - [ - "Jolene and her partner play Overcooked 2 for bets and enjoy it for its chaotic cooking.", - "D19:10" - ], - [ - "Jolene and her partner enjoy spending time outdoors and exploring nature together.", - "D19:15" - ] - ] - }, - "session_20_observation": { - "Jolene": [ - [ - "Jolene received a new game called Battlefield 1 for her console last week.", - "D20:1" - ], - [ - "Jolene practices yoga and meditation to relax and stay focused.", - "D20:11" - ], - [ - "Jolene has been doing yoga for 3 years as a way to escape studying and work stress.", - "D20:21" - ] - ], - "Deborah": [ - [ - "Deborah went to a place that held a lot of memories for her.", - "D20:2" - ], - [ - "Deborah felt nostalgia, longing, and grateful for memories when visiting a place that held memories.", - "D20:4" - ], - [ - "Deborah's mom loved flowers, and they always made her happy.", - "D20:6" - ], - [ - "Deborah practices yoga and meditation to find balance and inner peace.", - "D20:12" - ] - ] - }, - "session_21_observation": { - "Deborah": [ - [ - "Deborah attended a yoga retreat near her mom's place last week and found it life-changing.", - "D21:1" - ], - [ - "Deborah finds life super meaningful lately and believes in growth through self-reflection.", - "D21:3" - ] - ], - "Jolene": [ - [ - "Jolene's goal is to be successful in her field, make a positive impact, and has been working towards it by studying, attending workshops, and networking.", - "D21:6" - ], - [ - "Jolene recently presented at a virtual conference and received positive feedback, confirming she is on the right track.", - "D21:6" - ], - [ - "Jolene is focusing on studying, gaining more experience, and considering more internships to enhance her skills.", - "D21:8" - ] - ] - }, - "session_22_observation": { - "Deborah": [ - [ - "Deborah has a partner and they had an emotional chat about the influence of their loved ones' values.", - "D22:2" - ], - [ - "Deborah cherishes memories of her loved ones and values discussing relationships.", - "D22:3" - ], - [ - "Deborah is supportive of Jolene's plans to add values to her engineering projects.", - "D22:5" - ], - [ - "Deborah engages in self-care activities like yoga and shares a photo of nature during a yoga session.", - "D22:13" - ], - [ - "Deborah has two cats named Luna and Max, the latter being her mother's cat.", - "D22:21, D22:23" - ], - [ - "Max, Deborah's mother's cat, is 8 years old.", - "D22:27" - ], - [ - "Luna, the shelter cat, is 5 years old.", - "D22:29" - ] - ], - "Jolene": [ - [ - "Jolene is an engineering student with a passion for using her talents to help solve important problems and make the world a better place.", - "D22:6" - ], - [ - "Jolene is interested in sustainable initiatives, developing solutions for environmental issues, and getting involved with social causes.", - "D22:8" - ], - [ - "Jolene aims to work on projects involving renewable energy like solar and providing clean water to those with limited access.", - "D22:10, D22:12" - ], - [ - "Jolene practices yoga and meditation to recharge and relieve tension.", - "D22:16" - ], - [ - "Jolene's snakes enjoy watching her chill and provide a sense of calm.", - "D22:18" - ] - ] - }, - "session_23_observation": { - "Jolene": [ - [ - "Jolene recently went on a trip to Rio de Janeiro with her partner, where they attended yoga classes and visited cafes.", - "D23:1" - ], - [ - "Jolene meditates at a nearby tranquil spot to help make sense of things and relieve stress.", - "D23:9" - ], - [ - "Jolene has a new plant as a reminder to nurture herself and embrace fresh starts.", - "D23:29" - ] - ], - "Deborah": [ - [ - "Deborah visited her mom's house last month, which holds a special place in her heart as a symbol of her mom's strength and love.", - "D23:4" - ], - [ - "Deborah has a favorite spot where she goes to ponder and let things go.", - "D23:12" - ], - [ - "Deborah took a beautiful photo on one of her excursions in Rio de Janeiro three years ago.", - "D23:18" - ], - [ - "Deborah received a quote from a friend that says \"Let go of what no longer serves you.\"", - "D23:22" - ] - ] - }, - "session_24_observation": { - "Deborah": [ - [ - "Deborah went to a cool event last week aimed at supporting each other.", - "D24:1" - ], - [ - "Deborah attended a community meetup last Friday where stories were shared, emphasizing the importance of relationships.", - "D24:3" - ], - [ - "Deborah treasures her relationships with her yoga pals, considering them her second family.", - "D24:5" - ], - [ - "Deborah's mother was a big fan and a motivating source when she started doing yoga.", - "D24:5" - ] - ], - "Jolene": [ - [ - "Jolene has been focusing on studying and her relationship with her partner.", - "D24:2" - ], - [ - "Jolene enjoys taking little trips to the beach with her partner to relax.", - "D24:2" - ], - [ - "Jolene's passion for video games started when she was 10, which her parents supported.", - "D24:6" - ], - [ - "Jolene's favorite game to play with her mom is 'Monster Hunter: World', appreciating the immersive story and open-world gaming.", - "D24:10" - ], - [ - "Jolene is studying hard for upcoming finals and plans to take a trip to relax and recharge afterward.", - "D24:14" - ] - ] - }, - "session_25_observation": { - "Jolene": [ - [ - "Jolene signed up for a meditation course at a retreat near a lake to learn new techniques with her partner.", - "D25:1" - ], - [ - "Jolene finds meditation easy and believes it makes a big difference to health.", - "D25:3" - ], - [ - "Meditation helps Jolene stay balanced during her studies.", - "D25:5" - ], - [ - "Jolene finds her project tough but continues to work on it.", - "D25:7" - ] - ], - "Deborah": [ - [ - "Deborah incorporates meditation into her routine and can't imagine her life without it.", - "D25:4" - ], - [ - "Deborah recently saw a wonderful sunrise which calmed her.", - "D25:12" - ], - [ - "Deborah enjoys beach walks as they relax her.", - "D25:13" - ], - [ - "Deborah was glad her photo reminded Jolene of good memories from a beach getaway.", - "D25:14" - ] - ] - }, - "session_26_observation": { - "Deborah": [ - [ - "Deborah had to cancel a yoga getaway due to a storm.", - "D26:1" - ], - [ - "Deborah found comfort in work and spending time at home after the setback.", - "D26:3" - ], - [ - "Deborah suggested setting up a coffee date to learn more about Jolene's strategies for planning activities.", - "D26:9" - ], - [ - "Deborah complimented a peaceful picture and mentioned a hidden coffee shop near her.", - "D26:13" - ], - [ - "Deborah already had plans on Wednesday and couldn't meet Jolene then.", - "D26:15" - ], - [ - "Deborah agreed to meet Jolene on Friday for a coffee date.", - "D26:19" - ], - [ - "Deborah requested Jolene to grab her some interesting books.", - "D26:21" - ] - ], - "Jolene": [ - [ - "Jolene and her partner planned a camping trip to connect with nature and practice yoga.", - "D26:4" - ], - [ - "Jolene mentioned having a routine involving classes, studying, and personal time to stay balanced.", - "D26:6" - ], - [ - "Jolene suggested meeting up at a cafe next Monday for a coffee date.", - "D26:10" - ], - [ - "Jolene proposed to meet on Wednesday at 4 for the coffee date, which Deborah already had plans for.", - "D26:12" - ], - [ - "Jolene suggested meeting on Friday at 5 after Deborah couldn't make it on Wednesday, mentioning she needed to sort out books from a bookcase.", - "D26:18" - ], - [ - "Jolene expressed excitement for the upcoming coffee date with Deborah.", - "D26:20" - ] - ] - }, - "session_27_observation": { - "Jolene": [ - [ - "Jolene and her partner traveled to a meditation retreat in Phuket for a few weeks.", - "D27:1" - ], - [ - "At the retreat, Jolene realized the importance of relaxation, self-care, and balance alongside her engineering studies.", - "D27:3" - ], - [ - "Jolene found inspiration and refreshment in the beauty of nature at the retreat.", - "D27:3" - ], - [ - "Jolene was reminded at the retreat to appreciate the journey and not just focus on the finish line, as she tends to get consumed with hitting her goals.", - "D27:5" - ], - [ - "Jolene is trying to be more mindful and grateful to appreciate the small joys in life.", - "D27:7" - ], - [ - "Jolene is experiencing a new level of joy and happiness.", - "D27:9" - ] - ], - "Deborah": [ - [ - "Deborah was curious to hear more about Jolene's reflections from the meditation retreat and how they changed her.", - "D27:2" - ], - [ - "Deborah also sees the importance of appreciating small moments in life to boost happiness.", - "D27:6" - ], - [ - "Deborah believes that practicing mindfulness and gratitude can positively impact day-to-day life.", - "D27:8" - ], - [ - "Deborah played a card game about cats where you can attack your opponent with the cards, and she plans to play it with Jolene.", - "D27:12" - ] - ] - }, - "session_28_observation": { - "Deborah": [ - [ - "Deborah reconnected with her late mother's friends, which brought up a mix of emotions but overall was comforting.", - "D28:1" - ], - [ - "Deborah's experience with her mother's friends allowed her to appreciate her mother more and gain a new perspective on her life.", - "D28:5" - ], - [ - "Deborah looked through her mother's photos with her friends and found it sweet and nostalgic.", - "D28:7" - ], - [ - "Deborah believes that photos and memories can help in appreciating the special bond with loved ones and the strength of human relationships.", - "D28:9" - ], - [ - "The beach where Deborah got married and discovered her love for surfing holds special memories of joy and peace for her.", - "D28:11" - ], - [ - "Deborah finds doing yoga on the beach with the ocean, sand, and fresh air peaceful and a perfect way to take care of herself.", - "D28:15" - ] - ], - "Jolene": [ - [ - "Jolene finds comfort and calmness in creating a serene yoga space with candles, oils, and soothing music.", - "D28:16" - ], - [ - "Jolene uses essential oils and soothing music to create a peaceful atmosphere for relaxation during yoga and meditation.", - "D28:18" - ], - [ - "Jolene's room is her haven for peace and rest where she goes to relax and recharge after a busy day.", - "D28:22" - ], - [ - "Jolene has a pet named Susie who has been with her for two years and brings her comfort and peace.", - "D28:26" - ], - [ - "Jolene believes pets like Susie make life brighter and bring comfort and peace.", - "D28:30" - ] - ] - }, - "session_29_observation": { - "Deborah": [ - [ - "Deborah ran a free gardening class for the community with her neighbor.", - "D29:1" - ], - [ - "Deborah spent time remembering her mom by visiting her old house and sitting on a bench last Sunday.", - "D29:5" - ], - [ - "Deborah's mom had a big passion for cooking, making amazing meals filled with love and warmth.", - "D29:7" - ], - [ - "Deborah loved when her mom would bake pineapple birthday cakes for her when she was a kid.", - "D29:9" - ], - [ - "Deborah showed Jolene a big bookshelf she has.", - "D29:19" - ] - ], - "Jolene": [ - [ - "Jolene tried a scuba diving lesson last Friday and enjoyed it, hoping to become a certified diver one day.", - "D29:4" - ], - [ - "Jolene used to bake cookies with someone close to her.", - "D29:12" - ], - [ - "Jolene mentioned reading a self-discovery book in her bathroom once that really resonated with her.", - "D29:22" - ], - [ - "Jolene likes the warm, gooey chocolate and soft, buttery cookie.", - "D29:14" - ], - [ - "Jolene mentioned having an aesthetic vibe in her bathroom and finding peace and relaxation at the beach.", - "D29:24" - ], - [ - "Jolene just started learning about surfing but hasn't gone yet.", - "D29:30" - ], - [ - "Jolene agreed to plan a surfing adventure with Deborah for next month, pending schedule confirmation.", - "D29:34" - ] - ] - }, - "session_30_observation": { - "Deborah": [ - [ - "Deborah had a great time at a music festival with her pals where she enjoyed dancing and feeling the magical music vibes.", - "D30:1" - ], - [ - "Deborah reminisces about her mom singing lullabies to her, cherishing those memories.", - "D30:1" - ], - [ - "Deborah mentioned a gorgeous blossom tree near her home that bloomed magically every spring.", - "D30:11" - ], - [ - "Watching the tree bloom filled Deborah with awe and appreciation for the beauty of life.", - "D30:13" - ] - ], - "Jolene": [ - [ - "Jolene went to a yoga retreat with her partner where they experienced a peaceful environment with an amazing view during yoga at sunrise.", - "D30:4" - ], - [ - "Jolene finds peace and connection to nature and herself while doing yoga with beautiful backdrops.", - "D30:6" - ], - [ - "Jolene and her partner came across a waterfall oasis during a hike where she felt peaceful and worries disappeared.", - "D30:8" - ], - [ - "Jolene plans to reconnect with nature again the following month.", - "D30:10" - ], - [ - "Jolene finds inspiration in the small things and believes in continuous growth through obstacles.", - "D30:14" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Deborah and Jolene met at 4:06 pm on 23 January, 2023. They discussed their busy weeks and memories of their deceased mothers. Deborah shared about her mother's old house, while Jolene mentioned her room in her mother's house. They talked about special items that reminded them of their mothers. Jolene discussed a pendant symbolizing freedom, inspiring her to pursue goals. Deborah expressed her goal to teach yoga and bring peace to her community. Yoga helped Deborah find peace during a difficult time. Jolene found Deborah's journey inspiring, and the conversation ended with them looking forward to the next chat.", - "session_2_summary": "Deborah informed Jolene about her father's recent passing, recounting how she copes with grief by cherishing memories and spending time with family. Jolene expressed sympathy and inquired about how Deborah and her family were coping. They discussed Deborah's parents, her relationship, the impact of yoga on her life, and her special connection to her old home where she practices yoga. Deborah shared memories of her mother and her hobbies, while Jolene talked about her pet snakes and gaming hobby with her partner. They exchanged well wishes at the end of the conversation.", - "session_3_summary": "Jolene and Deborah conversed at 7:03 pm on 1 February, 2023. Jolene shared about her challenging but fun robotics project that tests her creativity and problem-solving skills. Deborah praised her and asked about her feelings when she first received the project. Jolene felt a mix of excitement and nerves initially but now enjoys working on it, comparing it to solving a puzzle and finding designing and programming solutions. Deborah met her new neighbor at yoga in the park and they discussed the benefits of yoga in their lives. Jolene mentioned planning to play console games with her partner instead of resuming yoga. Deborah acknowledged the importance of having shared activities with her partner. The conversation ended with Jolene saying goodbye and looking forward to meeting again.", - "session_4_summary": "Jolene and Deborah caught up at 9:48 am on February 4, 2023. Jolene shared her recent success in creating a water purifier for a rural community, showcasing her engineering skills. Deborah praised her and encouraged Jolene to continue making a positive impact through engineering. Deborah also mentioned her passion for teaching yoga and bonding with Anna, sharing a modified pose. They discussed favorite books, nature spots, and reflecting on loved ones. Deborah shared how a bouquet from a friend gave her hope, while Jolene noted the power of small gestures. Both emphasized finding strength in memories and supporting each other through tough times. They exchanged warm goodbyes, with Deborah encouraging Jolene to stay in touch and Jolene expressing gratitude for the support.", - "session_5_summary": "Jolene informed Deborah about her mini retreat last Wednesday, which gave her a new outlook on life and boosted her confidence. She discussed her engineering project accomplishments, focusing on green technology to help disadvantaged areas and her idea for a volunteer program teaching STEM to underprivileged kids. Deborah supported the idea, and Jolene mentioned plans to work with local schools/centers for workshops with guest speakers. Jolene shared sketches with Deborah, who offered further assistance if needed. They ended the conversation with mutual appreciation and well wishes.", - "session_6_summary": "Jolene, buzzing from a night out, spoke with Deborah at 4:12 pm on 22 February, 2023. Deborah found solace in a garden after losing a friend, where she cherished memories and photos. Jolene admired the flowers and they discussed travel, with Deborah fondly recalling Bali. They also talked about trying beach yoga. Deborah encouraged Jolene to enjoy the experience and advised her to take care.", - "session_7_summary": "Jolene and Deborah caught up at 4:50 pm on 25 February 2023. Jolene shared how balancing engineering school with her partner's video games is challenging but she finds peace through yoga and meditation. Her partner sometimes joins in, bringing them closer. They've been together for three years since meeting in an engineering class, and their relationship started from a strong friendship. Deborah praised Jolene's progress and encouraged her to continue prioritizing self-care. Deborah, on the other hand, mentioned her morning routine of meditation, yoga, teaching classes, and now a morning jog for self-connection. She highlighted the importance of self-care to help others and emphasized the value of prioritizing oneself. Jolene appreciated Deborah's support and vowed to continue prioritizing self-care.", - "session_8_summary": "Deborah and Jolene discussed their favorite comforting dishes and ways to find time for themselves amidst studies and exams on 2nd March 2023 at 7:18 pm. They shared how activities like yoga, meditation, and spending time outdoors help them relax and reset. Jolene expressed concerns about managing stress and prioritizing self-care, while Deborah offered tips on studying, time management, and self-care. Deborah also promised to give Jolene a motivating mug and encouraged her to take care and relax.", - "session_9_summary": "At 11:22 am on 13 March 2023, Deborah and Jolene caught up after a long time. Deborah shared about starting a yoga class in her neighborhood and the joy it brings her to help others through it. They discussed the importance of support in tough times. Jolene mentioned her struggles in managing Engineering assignments and they decided to talk about time management soon. They agreed to find a suitable time to chat and Deborah assured Jolene of her support. Jolene thanked her and the conversation ended with well wishes.", - "session_10_summary": "Deborah and Jolene caught up at 5:35 pm on 22 March, 2023. They discussed Jolene's struggle with exams and deadlines. Jolene mentioned using the Pomodoro Technique and shared her difficulty in prioritizing tasks. Deborah suggested the Eisenhower Matrix for organizing tasks. They also talked about Jolene's dream of learning to surf. Deborah encouraged her to take the first steps and keep pushing herself. Jolene felt motivated and thanked Deborah for the support.", - "session_11_summary": "Jolene and Deborah caught up at 4:03 pm on 28 March, 2023. Jolene shared a breakthrough in her engineering project after a long time, while Deborah bought yoga props and candles to enhance her practice. They discussed scents, music, and albums for yoga, with Jolene liking Nils Frahm and Olafur Arnalds, and Deborah recommending the album \"Sleep.\" They parted ways with Jolene saying, \"See you!\" and Deborah encouraging her to \"keep up the good work.\"", - "session_12_summary": "Deborah and Jolene caught up at 4:30 pm on 9 April, 2023. Deborah shared her recent biking experience and art show visit, which reminded her of her late mom. Jolene empathized, mentioning her snakes and video games as sources of comfort. Deborah found solace in art, while Jolene found joy in spending time with her partner. They agreed that finding comfort in simple things is essential during tough times. Jolene mentioned working on a big project, and Deborah offered support. They both emphasized the importance of finding happiness and supporting each other. Jolene promised to update Deborah on the project's progress, and they bid farewell, wishing each other well.", - "session_13_summary": "Jolene and Deborah caught up at 3:56 pm on 6 June, 2023. Jolene shared her completion of a tough engineering project last month. Deborah praised her hard work. Jolene expressed pride in overcoming challenges and reaching a milestone. She mentioned interning at an engineering firm and enjoying applying her skills. Deborah inquired about the impact of the internship, to which Jolene shared it inspired her to pursue her dreams. The conversation turned to work-life balance, with Jolene finding it challenging. Deborah suggested yoga for relaxation. Jolene mentioned her yoga routine and its positive impact. Deborah recommended mindful breathing during yoga. Jolene thanked her for the advice as they said their goodbyes.", - "session_14_summary": "Deborah and Jolene caught up at 9:17 am on June 26, 2023. Deborah mentioned preparing for a yoga retreat, while Jolene spoke about her busy internship and shared photos of her aquarium pet, Seraphim. They discussed the calming effect of pets, Jolene's determination to succeed, and Deborah's successful project. Deborah showed Jolene a new yoga pose and a symbol of peace from the retreat. Jolene expressed interest in visiting a similar retreat. They emphasized the importance of finding peace and relaxation before saying goodbye.", - "session_15_summary": "Deborah and Jolene discussed their fitness routines and support systems at 7:37 pm on 9 July 2023. Deborah shared her running group experience, highlighting motivation and photo memories. Jolene talked about gaming with her partner and shared her interest in snakes as pets. Additionally, they discussed mindfulness workshops, pet care, and the calming effect of nature. They expressed mutual support and shared a desire to explore nature together.", - "session_16_summary": "On August 1, 2023, at 9:26 am, Deborah informed Jolene about her community cleanup project and how everyone was coming together for it. Jolene shared her recent setback in a project. Deborah offered support and asked about Jolene's coping mechanisms, which included her pet Susie, video games, and Deborah showed understanding about the comfort pets can bring during tough times. Jolene mentioned adopting Susie two years ago for companionship. Deborah encouraged Jolene to find strength in her pet and cherish the moments together. They discussed self-care, with Jolene mentioning yoga and meditation. Deborah suggested some yoga routines and shared a tutorial video for a gentle flow to help find balance. Jolene expressed eagerness to try it out and mentioned staying in touch to share updates. They ended the conversation planning to chat later.", - "session_17_summary": "Deborah and Jolene conversed at 8:50 pm on 12 August, 2023. Deborah mentioned creating a meditation guide for her yoga retreat, while Jolene shared how meditation helped her regain clarity after losing work files. They discussed the calming effects of meditation and Jolene's notebook designs inspired by space and technology. Jolene talked about her current project to revolutionize aerial surveillance for emergencies and environmental monitoring. Deborah admired Jolene's ambition and commitment to making a difference, offering support and encouragement. Jolene expressed gratitude for Deborah's support, and they concluded with encouraging words.", - "session_18_summary": "Jolene shared her struggles balancing engineering, her relationship, and personal growth with Deborah at 2:58 pm on August 16, 2023. Deborah advised Jolene to find balance and prioritize self-care. Jolene mentioned using a bullet journal for time management and task tracking. Deborah found Jolene's bullet journal helpful and praised her for staying organized. Jolene shared a motivational quote from her journal with Deborah. Deborah recommended mindfulness to Jolene, mentioning her own positive experience with it. Jolene expressed interest in trying mindfulness to destress. Deborah offered support and guidance for Jolene's mindfulness journey. Jolene thanked Deborah and expressed appreciation for her help. Deborah reassured Jolene of her support and encouraged her to reach out if needed before saying goodbye.", - "session_19_summary": "Deborah and Jolene spoke at 12:52 am on 19 August, 2023. Deborah shared her experience of connecting with Anna after sharing her life story, organizing a yoga event, and teaching yoga. Jolene mentioned buying a console for her partner on the 17th and managing her engineering studies. They discussed balancing hobbies and studies, with Deborah suggesting prioritizing and schedule management. Jolene recommended video games like Zelda BOTW and Animal Crossing. They also discussed playing Overcooked 2 with their partners and favorite memories of gaming. Deborah shared her favorite activity of playing detective games with her husband. They both enjoyed spending time outdoors and exploring nature. Deborah mentioned a special park near her house with a meaningful bench where she cherished memories with her mom, including watching a beautiful sunset together. Jolene acknowledged the importance of cherishing special moments.", - "session_20_summary": "Jolene and Deborah caught up at 9:11 am on 21 August, 2023. They discussed memories, appreciating the simple things in life, and finding balance through yoga and meditation. Deborah shared her moments of nostalgia and how she cherishes flowers in memory of her mom. Jolene mentioned her love for savasana in yoga. They both emphasized the importance of slowing down to appreciate life's small joys. The conversation ended with well wishes for each other to stay connected.", - "session_21_summary": "Deborah and Jolene talked at 9:34 am on 24 August, 2023. Deborah shared her experience at a yoga retreat and how it changed her perspective on life. Jolene discussed her goal of success in her field, including presenting at a virtual conference. They supported each other's achievements and future plans. Deborah encouraged Jolene to enjoy the journey towards her goals and expressed continuous support. Jolene expressed gratitude for the support and pledged to keep pushing forward while finding joy along the way.", - "session_22_summary": "Deborah and Jolene had a heartfelt conversation at 5:33 pm on 26 August 2023. They discussed the importance of remembering loved ones and how their values influence their lives. Jolene shared how her mom and partner's dad inspired perseverance and determination, guiding them in their respective pursuits of engineering and creative endeavors. They also talked about incorporating these values into their work to make a positive impact. Jolene mentioned her plans to work on projects involving renewable energy and clean water access. Deborah shared her love for yoga and meditation as self-care practices. They also discussed their pets, with Jolene admiring Deborah's care for her cats, Max and Luna.", - "session_23_summary": "Jolene and Deborah spoke at 11:46 am on 30 August 2023. Jolene shared her recent trip to Rio de Janeiro, how it introduced her to different kinds of yoga, and they visited lovely cafes. Deborah visited her mom's house last month, holding sentimental value for her, shared her reflection spot, and discussed a photo from Rio de Janeiro. They exchanged thoughts on relaxing places, meaningful quotes, and embracing positivity. Jolene shared her new plant as a symbol of self-care. Deborah encouraged her to nurture herself and embrace new beginnings. They both agreed on focusing on positivity and growth before saying goodbye.", - "session_24_summary": "Deborah and Jolene caught up at 2:14 pm on 3 September, 2023. Deborah attended a supportive event and a community meetup recently, valuing relationships. Jolene focused on studying and trips with her partner. They discussed support from family, with Deborah mentioning her yoga pals and Jolene reminiscing about playing video games with her parents. Jolene shared her love for \"Monster Hunter: World\" as a stress-reliever. Jolene has upcoming finals and plans to go on a relaxing trip. Deborah offered assistance.", - "session_25_summary": "On the evening of 6th September 2023 at 8:31 pm, Jolene excitedly shared with Deborah that she signed up for a meditation course at a lake retreat with her partner. Deborah expressed her hope that Jolene would enjoy the experience and learn new techniques. Jolene mentioned how meditation positively impacts her health and helps her stay balanced during studies. Deborah praised Jolene's strength in handling challenges and offered continued support. They discussed their recent activities, including watching sunrises and beach walks. Jolene expressed a desire to watch the sunrise together in the future. They said their goodbyes, with Deborah reminding Jolene to take care of herself and rest.", - "session_26_summary": "Deborah told Jolene about the canceled yoga getaway due to a storm at 7:39 pm on September 8, 2023. They discussed balancing busy schedules, self-care activities like yoga, and planned a coffee date at a cafe for next Wednesday at 4. Deborah suggested a hidden coffee shop near her for a future meeting. Jolene had to reschedule the coffee date to Friday at 5 due to prior plans and Deborah agreed, expressing excitement for the meet-up. They wished each other to stay safe and look forward to the upcoming coffee date.", - "session_27_summary": "Jolene and Deborah had a conversation at 2:18 pm on 12 September, 2023. Jolene shared about her meditation retreat in Phuket, emphasizing the importance of balance and inner peace. Deborah inquired about Jolene's reflections, leading to a discussion on appreciating the journey and small moments in life. They both highlighted the significance of mindfulness and gratitude for happiness. Jolene expressed her newfound joy and Deborah commended her progress. They agreed on the value of a supportive community and planned to play a card game together in the future.", - "session_28_summary": "Deborah and Jolene had a conversation at 3:09 pm on 15 September, 2023. Deborah shared how reconnecting with her late mother's friends brought up a mix of emotions, but ultimately, it was comforting. Jolene supported her, mentioning how stories about loved ones can be both tough and comforting. Deborah found the experience with her mom's friends special as it offered insight into her mother's life. They discussed how old photos and memories strengthen their appreciation for loved ones. Deborah also shared her love for surfing and yoga, highlighting how these activities bring her peace. Jolene and Deborah discussed creating calm spaces for relaxation, with Jolene mentioning her room as a haven of peace. Additionally, Jolene showed a photo of her pet Susie, whom she adores for bringing comfort and brightness into her life.", - "session_29_summary": "Deborah excitedly shared with Jolene about running a free gardening class for the community, while Jolene talked about a scuba diving lesson they had recently enjoyed. They discussed special memories, including Deborah's time spent remembering her mom and the special meals she used to cook. Jolene mentioned baking cookies with someone close and their favorite cookie to make. They also talked about books and finding cozy spaces for relaxation. The conversation ended with plans to try surfing together next month, showing their excitement for the upcoming adventure.", - "session_30_summary": "Deborah and Jolene had a conversation at 10:17 am on 20 September, 2023. Deborah shared her experience at a music festival, recalling memories of her mom singing lullabies. Jolene talked about her festival experience with her partner and a recent yoga retreat. They discussed the beauty of nature and finding inspiration in small things. Jolene planned to reconnect with nature soon, while Deborah reminisced about a blossoming tree near her home. They both emphasized the importance of finding positivity in tough times. The conversation ended with Jolene encouraging Deborah to continue finding beauty in everyday moments." - }, - "sample_id": "conv-48" - }, - { - "qa": [ - { - "question": "What kind of car does Evan drive?", - "answer": "Prius", - "evidence": [ - "D1:2", - "D1:4", - "D18:1", - "D18:3", - "D22:2" - ], - "category": 1 - }, - { - "question": "What kinds of things did Evan have broken?", - "answer": "His old Prius and his new Prius.", - "evidence": [ - "D18:1", - "D18:2", - "D18:3", - "D1:2", - "D1:4" - ], - "category": 1 - }, - { - "question": "Where has Evan been on roadtrips with his family?", - "answer": "Rockies, Jasper", - "evidence": [ - "D1:2", - "D1:4", - "D2:1" - ], - "category": 1 - }, - { - "question": "How many Prius has Evan owned?", - "answer": "two", - "evidence": [ - "D1:2", - "D1:4" - ], - "category": 1 - }, - { - "question": "Which hobby did Sam take up in May 2023?", - "answer": "painting", - "evidence": [ - "D1:11" - ], - "category": 2 - }, - { - "question": "Which country was Evan visiting in May 2023?", - "answer": "Canada", - "evidence": [ - "D2:1" - ], - "category": 3 - }, - { - "question": "How many roadtrips did Evan take in May 2023?", - "answer": "two", - "evidence": [ - "D1:4", - "D2:1" - ], - "category": 1 - }, - { - "question": "What new hobbies did Sam consider trying?", - "answer": "Painting, kayaking, hiking, cooking, running", - "evidence": [ - "D1:11", - "D2:10", - "D10:8", - "D13:6", - "D13:8", - "D20:6", - "D7:2", - "D7:4", - "D7:6", - "D21:19" - ], - "category": 1 - }, - { - "question": "What hobby did Evan start practicing a few years ago that he enjoys?", - "answer": "Watercolor painting", - "evidence": [ - "D1:14", - "D1:16", - "D8:13", - "D8:14" - ], - "category": 1 - }, - { - "question": "When did Evan go to Jasper with his family?", - "answer": "weekend before May 24, 2023", - "evidence": [ - "D2:1" - ], - "category": 2 - }, - { - "question": "Which type of vacation would Evan prefer with his family, walking tours in metropolitan cities or camping trip in the outdoors?", - "answer": "camping trip in the outdoors", - "evidence": [ - "D2:1", - "D2:3", - "D19:1", - "D19:3" - ], - "category": 3 - }, - { - "question": "What health issue did Sam face that motivated him to change his lifestyle?", - "answer": "Weight problem", - "evidence": [ - "D2:6", - "D3:4", - "D24:12", - "D24:14", - "D5:5", - "D6:2", - "D7:2", - "D7:12", - "D8:1", - "D10:6", - "D12:1", - "D13:2", - "D14:1", - "D15:1", - "D16:3", - "D17:3", - "D24:20", - "D25:1", - "D25:3" - ], - "category": 1 - }, - { - "question": "When did Sam first go to the doctor and find out he had a weight problem?", - "answer": "A few days before May 24, 2023.", - "evidence": [ - "D2:6" - ], - "category": 2 - }, - { - "question": "When did Evan have his sudden heart palpitation incident that really shocked him up?", - "answer": "first week of June 2023", - "evidence": [ - "D3:1" - ], - "category": 2 - }, - { - "question": "What is Evan's favorite food?", - "answer": "Ginger snaps", - "evidence": [ - "D3:3", - "D5:5", - "D23:15", - "D22:12" - ], - "category": 1 - }, - { - "question": "What kind of unhealthy snacks does Sam enjoy eating?", - "answer": "soda, candy", - "evidence": [ - "D3:4" - ], - "category": 1 - }, - { - "question": "What recurring issue frustrates Sam at the grocery store?", - "answer": "Malfunctioning self-checkout machines.", - "evidence": [ - "D3:16", - "D22:19" - ], - "category": 1 - }, - { - "question": "When did Sam's friends mock him for being overweight?", - "answer": "Friday before 27 July 2023", - "evidence": [ - "D4:1" - ], - "category": 2 - }, - { - "question": "What kind of healthy food suggestions has Evan given to Sam?", - "answer": "flavored seltzer water, dark chocolate with high cocoa content, air-popped popcorn and fruit, veggies, healthy sandwich snacks, energy balls, grilled chicken salad with avocado", - "evidence": [ - "D3:5", - "D4:10", - "D22:10", - "D22:14", - "D24:15" - ], - "category": 1 - }, - { - "question": "Considering their conversations and personal growth, what advice might Evan and Sam give to someone facing a major life transition or challenge?", - "answer": "Evan and Sam would likely advise embracing small, consistent changes\u200b\u200b, finding stress-relieving activities like hiking\u200b\u200b, painting, and road trips\u200b\u200b, and the importance of friendship and support in navigating challenges\u200b\u200b.", - "evidence": [ - "D3:10", - "D3:15", - "D22:1", - "D8:17", - "D8:22", - "D9:8", - "D9:11", - "D14:7", - "D14:12", - "D12:7", - "D12:11" - ], - "category": 3 - }, - { - "question": "In light of the health and dietary changes discussed, what would be an appropriate gift for both Evan and Sam to encourage their healthy lifestyles?", - "answer": "a cookbook with healthy recipes or a subscription to a healthy meal delivery service.", - "evidence": [ - "D2:9", - "D3:1", - "D3:3", - "D3:5", - "D4:10", - "D14:12", - "D5:9", - "D7:3", - "D7:2", - "D7:5", - "D7:12", - "D8:1", - "D8:5", - "D8:7", - "D8:8", - "D8:12", - "D9:1" - ], - "category": 3 - }, - { - "question": "How does Evan describe the woman and his feelings for her that he met in Canada?", - "answer": "He says she's cool, incredible, like something out of a movie, and that he feels alive around her. Every moment with her is fun and energetic, also Evan feels really lucky to have someone who gets him.", - "evidence": [ - "D5:1", - "D5:3", - "D23:3" - ], - "category": 1 - }, - { - "question": "When Evan did meet his future wife?", - "answer": "week before August 7, 2023.", - "evidence": [ - "D5:1" - ], - "category": 2 - }, - { - "question": "When did Sam start working out at the gym?", - "answer": "July 28, 2023", - "evidence": [ - "D4:15" - ], - "category": 2 - }, - { - "question": "What significant event happened in Sam's life towards the end of summer 2023?", - "answer": "He fell in love with a Canadian woman", - "evidence": [ - "D5:1" - ], - "category": 2 - }, - { - "question": "Which year did Evan start taking care of his health seriously?", - "answer": "2021", - "evidence": [ - "D5:6", - "D5:7" - ], - "category": 2 - }, - { - "question": "What motivates Evan to take care of his health?", - "answer": "family, fitness tracker, thirst for adventure on interesting hikes", - "evidence": [ - "D5:9", - "D5:11", - "D5:13" - ], - "category": 1 - }, - { - "question": "What electronic device could Evan gift Sam to help him keep up with his fitness goals?", - "answer": "fitness tracker", - "evidence": [ - "D5:7" - ], - "category": 3 - }, - { - "question": "What kind of writing does Sam do to relax and cope with his health issues?", - "answer": "journalling, creative writing", - "evidence": [ - "D6:4", - "D11:7" - ], - "category": 1 - }, - { - "question": "Who did Evan meet on his trip to Canada, and who did he come back from Canada with?", - "answer": "Evan met the woman he fell in love with and returned with her.", - "evidence": [ - "D5:1", - "D6:1" - ], - "category": 1 - }, - { - "question": "When Evan get back from a vacation with his SO?", - "answer": "August 13, 2023", - "evidence": [ - "D6:1" - ], - "category": 2 - }, - { - "question": "How might Evan and Sam's experiences with health and lifestyle changes influence their approach to stress and challenges?", - "answer": "Their experiences likely lead them to view challenges as opportunities for growth and change. They both have embraced healthier lifestyles, indicating a proactive approach to managing stress and challenges.", - "evidence": [ - "D9:1 D4:4 D4:6" - ], - "category": 3 - }, - { - "question": "What recurring frustration does Evan experience?", - "answer": "Evan consistently misplaces his keys every week.", - "evidence": [ - "D6:13", - "D21:20" - ], - "category": 1 - }, - { - "question": "What is the recurring dream that Sam keeps having?", - "answer": "he's flying over a cityscape.", - "evidence": [ - "D6:14", - "D24:22" - ], - "category": 1 - }, - { - "question": "What accidents has Evan's son faced lately?", - "answer": "injured at a soccer game, fell off his bike", - "evidence": [ - "D7:1", - "D20:3" - ], - "category": 1 - }, - { - "question": "When was Evan's son injured at soccer?", - "answer": "Saturday before August 15, 2023.", - "evidence": [ - "D7:1" - ], - "category": 2 - }, - { - "question": "What kind of foods or recipes has Sam recommended to Evan?", - "answer": "grilled vegetables, grilled chicken and veggie stir-fry, poutine", - "evidence": [ - "D7:8", - "D8:7", - "D23:26" - ], - "category": 1 - }, - { - "question": "What kind of healthy meals did Sam start eating after getting a health scare?", - "answer": "salad, grilled salmon and vegetables, grilled chicken and veggie stir-fry, Beef Merlot, fruit bowl, smoothie bowl", - "evidence": [ - "D3:2", - "D8:1", - "D7:4", - "D8:7", - "D10:2", - "D11:1", - "D18:6" - ], - "category": 1 - }, - { - "question": "What role does nature and the outdoors play in Evan and Sam's mental well-being?", - "answer": "Nature and outdoor activities seem to be significant stress relievers and sources of joy for both Evan and Sam. These activities likely contribute positively to their mental well-being.", - "evidence": [ - "D22:1 D22:2 D9:10 D9:11" - ], - "category": 3 - }, - { - "question": "How many months lapsed between Sam's first and second doctor's appointment?", - "answer": "three months", - "evidence": [ - "D2:6", - "D7:2" - ], - "category": 2 - }, - { - "question": "When did Evan start taking painting classes?", - "answer": "Few days before 19 August, 2023.", - "evidence": [ - "D8:12" - ], - "category": 2 - }, - { - "question": "Which classes did Evan join in mid-August 2023?", - "answer": "painting classes", - "evidence": [ - "D8:12" - ], - "category": 2 - }, - { - "question": "How did Evan get into painting?", - "answer": "His friend got him into it by gifting him a painting and giving him some advice. The painting inspired Evan.", - "evidence": [ - "D1:14", - "D1:15", - "D1:16", - "D8:14" - ], - "category": 1 - }, - { - "question": "How often does Sam get health checkups?", - "answer": "every three months", - "evidence": [ - "D2:6", - "D7:2", - "D12:1" - ], - "category": 3 - }, - { - "question": "What kind of subjects does Evan enjoy painting?", - "answer": "nature landscapes, portraits, abstract minimalism", - "evidence": [ - "D8:20", - "D20:13", - "D20:15", - "D21:10", - "D21:14" - ], - "category": 1 - }, - { - "question": "Which places in Canada was Evan visiting in July 2023?", - "answer": "Banff, Rocky Mountains", - "evidence": [ - "D8:27", - "D9:8", - "D9:10" - ], - "category": 2 - }, - { - "question": "How do Evan and Sam use creative outlets to cope with life's challenges?", - "answer": "Evan and Sam use creative activities, like painting and writing, as therapeutic tools to express themselves and cope with stress.", - "evidence": [ - "D21:18 D21:22 D11:15 D11:19" - ], - "category": 3 - }, - { - "question": "When did Evan go skiing in Banff?", - "answer": "July 2023", - "evidence": [ - "D8:26", - "D8:27", - "D8:28" - ], - "category": 2 - }, - { - "question": "What new diet and lifestyle change did Sam adopt over time?", - "answer": "Healthy eating, exercise routine, running, hiking", - "evidence": [ - "D8:1", - "D9:1", - "D21:9", - "D22:1" - ], - "category": 1 - }, - { - "question": "Who was injured in Evan's family?", - "answer": "Evan's son and Evan himself", - "evidence": [ - "D7:1", - "D7:9", - "D7:10", - "D9:2", - "D11:2", - "D11:3" - ], - "category": 1 - }, - { - "question": "What kind of hobbies does Evan pursue?", - "answer": "painting, hiking, reading books, biking, skiing, snowboarding, ice skating, swimming, camping, kayaking", - "evidence": [ - "D1:14", - "D1:6", - "D4:8", - "D6:1", - "D8:30", - "D9:6", - "D25:8", - "D25:10" - ], - "category": 1 - }, - { - "question": "What challenges does Sam face in his quest for a healthier lifestyle, and how does he address them?", - "answer": "Sam faces challenges like maintaining motivation and making dietary changes. He addresses them by enrolling in cooking classes and seeking support from friends like Evan.", - "evidence": [ - "D4:2", - "D4:6", - "D14:1", - "D14:2" - ], - "category": 3 - }, - { - "question": "Which activity do Evan and Sam plan on doing together during September 2023?", - "answer": "painting", - "evidence": [ - "D10:12", - "D10:13", - "D10:14" - ], - "category": 2 - }, - { - "question": "When did Evan and Sam decide to paint together?", - "answer": "Saturday after 11 September, 2023.", - "evidence": [ - "D10:12", - "D10:13", - "D10:14" - ], - "category": 2 - }, - { - "question": "What personal health incidents does Evan face in 2023?", - "answer": "heart palpitations, twisted ankle, twisted ankle", - "evidence": [ - "D3:1", - "D9:2", - "D11:2" - ], - "category": 1 - }, - { - "question": "What recurring adventure does Evan have with strangers?", - "answer": "Helping lost tourists and experiencing unexpected adventures in the city.", - "evidence": [ - "D11:6", - "D14:2" - ], - "category": 1 - }, - { - "question": "What is Sam's persistent problem with his phone?", - "answer": "His new phone malfunctioning, particularly with the navigation app.", - "evidence": [ - "D11:15", - "D14:1" - ], - "category": 1 - }, - { - "question": "Which US state was Sam travelling in during October 2023?", - "answer": "California", - "evidence": [ - "D13:14" - ], - "category": 3 - }, - { - "question": "When did Evan start lifting weights?", - "answer": "October 2022", - "evidence": [ - "D12:2" - ], - "category": 2 - }, - { - "question": "When did Sam and his friend decide to try kayaking?", - "answer": "October 14, 2023", - "evidence": [ - "D13:10" - ], - "category": 2 - }, - { - "question": "Which new activity does Sam take up in October 2023?", - "answer": "kayaking", - "evidence": [ - "D13:8" - ], - "category": 2 - }, - { - "question": "What kind of stress was Sam dealing with in October 2023?", - "answer": "work-related stress", - "evidence": [ - "D13:4" - ], - "category": 2 - }, - { - "question": "What health scares did Sam and Evan experience?", - "answer": "Sam faced a health scare with stomach pains that turned out to be gastritis, prompting him to rethink his health habits. Evan, on the other hand, experienced two separate incidents: a sudden heart palpitation incident and a different event involving a misunderstanding during a medical check-up. These experiences have significantly influenced their perspectives on health and well-being.", - "evidence": [ - "D3:1", - "D14:1", - "D14:2", - "D17:2" - ], - "category": 1 - }, - { - "question": "When was Sam in the ER?", - "answer": "weekend before 17 October, 2023.", - "evidence": [ - "D14:1" - ], - "category": 2 - }, - { - "question": "Which ailment does Sam have to face due to his weight?", - "answer": "gastritis", - "evidence": [ - "D2:6", - "D7:2", - "D12:1", - "D14:1" - ], - "category": 1 - }, - { - "question": "Does Evan live close to a beach or mountains?", - "answer": "beach", - "evidence": [ - "D16:16", - "D16:18", - "D16:20" - ], - "category": 3 - }, - { - "question": "When did Evan lose his job?", - "answer": "end of October 2023", - "evidence": [ - "D16:10" - ], - "category": 2 - }, - { - "question": "When did Evan and Sam planned a trip to the beach together?", - "answer": "December, 2023", - "evidence": [ - "D16:24" - ], - "category": 2 - }, - { - "question": "What was Sam doing on December 4, 2023?", - "answer": "Attending a Weight Watchers meeting", - "evidence": [ - "D18:6" - ], - "category": 2 - }, - { - "question": "Which two significant life events occur in Evan's life in December 2023 with his partner?", - "answer": "his partner gets pregnant and they get married", - "evidence": [ - "D19:1", - "D21:2" - ], - "category": 1 - }, - { - "question": "How long did Evan and his partner date before getting married?", - "answer": "four months", - "evidence": [ - "D5:1", - "D21:1" - ], - "category": 2 - }, - { - "question": "Which major holiday season conincides with Evan's wedding?", - "answer": "Christmas", - "evidence": [ - "D21:2" - ], - "category": 3 - }, - { - "question": "Which activity did Sam resume in December 2023 after a long time?", - "answer": "hiking", - "evidence": [ - "D20:6", - "D22:1" - ], - "category": 1 - }, - { - "question": "When is Evan planning a big family reunion?", - "answer": "Summer 2024", - "evidence": [ - "D19:11" - ], - "category": 2 - }, - { - "question": "When did Evan's son fall off his bike?", - "answer": "Thursday before December 17, 2023.", - "evidence": [ - "D20:3" - ], - "category": 2 - }, - { - "question": "When did Evan announce his marriage to his extended family?", - "answer": "January 5, 2024", - "evidence": [ - "D23:1" - ], - "category": 2 - }, - { - "question": "When did Evan finish the painting that's hanging in the exhibit?", - "answer": "few days before 17 December, 2023.", - "evidence": [ - "D20:13", - "D20:15" - ], - "category": 2 - }, - { - "question": "How does Evan spend his time with his bride after the wedding?", - "answer": "family get-together, honeymoon in Canada to see snowy landscapes, ski, taste local cuisine and do some snowshoeing", - "evidence": [ - "D23:15", - "D23:23", - "D23:25", - "D24:9" - ], - "category": 1 - }, - { - "question": "Who did Evan tell about his marriage?", - "answer": "To Sam, to his friends from work, and to his and his wife's families.", - "evidence": [ - "D21:2", - "D22:4", - "D22:5", - "D23:1", - "D23:5" - ], - "category": 1 - }, - { - "question": "When will Evan and his partner have their honeymoon in Canada?", - "answer": "February 2024", - "evidence": [ - "D23:23" - ], - "category": 2 - }, - { - "question": "When did Evan have a drunken night with his friends?", - "answer": "January 9, 2023", - "evidence": [ - "D24:3" - ], - "category": 2 - }, - { - "question": "What is a stress reliever for Evan?", - "answer": "Drawing, traveling, places with a beautiful view, yoga, sunsets or something comfortable for Evan", - "evidence": [ - "D1:14", - "D2:10", - "D2:11", - "D2:14", - "D8:18", - "D10:8", - "D11:8", - "D16:23", - "D18:7", - "D24:19", - "D24:21" - ], - "category": 1 - }, - { - "question": "What is a stress reliever for Sam?", - "answer": "Unhealthy snacks, sweets, yoga, places with beautiful views", - "evidence": [ - "D10:6", - "D13:2", - "D13:4", - "D16:17", - "D16:23", - "D18:8" - ], - "category": 1 - }, - { - "question": "What type of car did Evan get after his old Prius broke down?", - "answer": "new Prius", - "evidence": [ - "D1:2" - ], - "category": 4 - }, - { - "question": "How did Evan get into watercolor painting?", - "answer": "friend's advice", - "evidence": [ - "D1:16" - ], - "category": 4 - }, - { - "question": "What did Evan start doing a few years back as a stress-buster?", - "answer": "watercolor painting", - "evidence": [ - "D1:14" - ], - "category": 4 - }, - { - "question": "What advice did Evan give Sam about finding a passion?", - "answer": "keep trying new things until something sparks excitement", - "evidence": [ - "D1:18" - ], - "category": 4 - }, - { - "question": "Where did Evan take his family for a road trip on 24 May, 2023?", - "answer": "Jasper", - "evidence": [ - "D2:1" - ], - "category": 4 - }, - { - "question": "What did Evan find relaxing about his road trip to Jasper?", - "answer": "fresh air, peacefulness, cozy cabin surrounded by mountains and forests", - "evidence": [ - "D2:3" - ], - "category": 4 - }, - { - "question": "What habit is Sam trying to change in terms of diet?", - "answer": "consuming soda and candy", - "evidence": [ - "D3:4" - ], - "category": 4 - }, - { - "question": "What new suggestion did Evan give to Sam regarding his soda and candy consumption?", - "answer": "try flavored seltzer water and dark chocolate with high cocoa content", - "evidence": [ - "D3:5" - ], - "category": 4 - }, - { - "question": "What did Sam agree to try instead of soda and candy?", - "answer": "flavored seltzer water and dark chocolate with high cocoa content", - "evidence": [ - "D3:6" - ], - "category": 4 - }, - { - "question": "What frustrating issue did Sam face at the supermarket?", - "answer": "broken self-checkout machines", - "evidence": [ - "D3:16" - ], - "category": 4 - }, - { - "question": "What novel is Evan reading that he finds gripping?", - "answer": "The Great Gatsby", - "evidence": [ - "D4:10" - ], - "category": 4 - }, - { - "question": "What kind of water does Evan suggest Sam try as an alternative to soda?", - "answer": "Flavored seltzer water", - "evidence": [ - "D4:8" - ], - "category": 4 - }, - { - "question": "What does the smartwatch help Evan with?", - "answer": "tracks progress and serves as a constant reminder to keep going", - "evidence": [ - "D5:9" - ], - "category": 4 - }, - { - "question": "What does the bonsai tree symbolize for Evan?", - "answer": "strength and resilience", - "evidence": [ - "D5:17" - ], - "category": 4 - }, - { - "question": "Why did Evan decide to get the bonsai tree?", - "answer": "motivates him to keep going through tough times", - "evidence": [ - "D5:17" - ], - "category": 4 - }, - { - "question": "According to Sam, what is more important than perfection?", - "answer": "progress", - "evidence": [ - "D6:6" - ], - "category": 4 - }, - { - "question": "What did Evan suggest Sam to check out for insights into his dream?", - "answer": "dream interpretation book", - "evidence": [ - "D6:15" - ], - "category": 4 - }, - { - "question": "What did Evan mention he had been searching for fruitlessly for half an hour?", - "answer": "his keys", - "evidence": [ - "D6:13" - ], - "category": 4 - }, - { - "question": "What class is Sam taking to learn how to make healthier meals?", - "answer": "cooking class", - "evidence": [ - "D7:2" - ], - "category": 4 - }, - { - "question": "What dish did Sam make on 18 August, 2023 that turned out flavorful?", - "answer": "grilled dish with salmon and vegetables", - "evidence": [ - "D7:4" - ], - "category": 4 - }, - { - "question": "What kind of recipe did Evan request from Sam on 19 August, 2023?", - "answer": "recipes with more vegetables", - "evidence": [ - "D7:7" - ], - "category": 4 - }, - { - "question": "What food did Sam share a photo of on 19 August, 2023?", - "answer": "bowl of spinach, avocado, and strawberries", - "evidence": [ - "D8:1" - ], - "category": 4 - }, - { - "question": "What type of painting classes did Evan start taking in 2023?", - "answer": "watercolor painting classes", - "evidence": [ - "D8:12" - ], - "category": 4 - }, - { - "question": "What did Evan start painting years ago due to being inspired by a friend's gift?", - "answer": "forest scene", - "evidence": [ - "D8:14" - ], - "category": 4 - }, - { - "question": "What nature concept do watercolor painting classes emphasize according to Evan?", - "answer": "observing nature and painting what is seen", - "evidence": [ - "D8:18" - ], - "category": 4 - }, - { - "question": "What type of landscapes does Evan love painting the most?", - "answer": "sunsets over the ocean", - "evidence": [ - "D8:20" - ], - "category": 4 - }, - { - "question": "What fun activity did Evan mention doing in July 2023?", - "answer": "skiing", - "evidence": [ - "D8:26" - ], - "category": 4 - }, - { - "question": "What injury did Evan suffer from in August 2023?", - "answer": "Twisted knee", - "evidence": [ - "D9:2" - ], - "category": 4 - }, - { - "question": "What sports activity has Evan been doing to stay active while dealing with the knee injury?", - "answer": "Swimming", - "evidence": [ - "D9:6" - ], - "category": 4 - }, - { - "question": "What suggestion did Sam give to Evan to help with his knee issue?", - "answer": "Consider low-impact exercises or physical therapy", - "evidence": [ - "D9:5" - ], - "category": 4 - }, - { - "question": "What did Evan suggest Sam try as a calming hobby?", - "answer": "Painting", - "evidence": [ - "D10:8" - ], - "category": 4 - }, - { - "question": "What did Evan recommend Sam acquire to get started with painting?", - "answer": "Acrylic paints, brushes, canvas/paper, palette", - "evidence": [ - "D10:11" - ], - "category": 4 - }, - { - "question": "What activity does Evan do to keep himself busy while healing his knee?", - "answer": "Watercolor painting", - "evidence": [ - "D11:6" - ], - "category": 4 - }, - { - "question": "What painting did Evan share with Sam in October?", - "answer": "a cactus in the desert", - "evidence": [ - "D11:8" - ], - "category": 4 - }, - { - "question": "What kind of writing does Sam enjoy as a form of expression?", - "answer": "creative writing", - "evidence": [ - "D11:17" - ], - "category": 4 - }, - { - "question": "What electronics issue has been frustrating Sam lately?", - "answer": "malfunctioning navigation app on the new phone", - "evidence": [ - "D11:15" - ], - "category": 4 - }, - { - "question": "What activity did Evan start one year ago?", - "answer": "lifting weights", - "evidence": [ - "D12:2" - ], - "category": 4 - }, - { - "question": "What advice did Evan give to Sam to avoid injuries while starting weightlifting?", - "answer": "Find a trainer", - "evidence": [ - "D12:4" - ], - "category": 4 - }, - { - "question": "Where did Sam and his mate plan to try kayaking?", - "answer": "Lake Tahoe", - "evidence": [ - "D13:14" - ], - "category": 4 - }, - { - "question": "What digestive issue did Sam experience lately?", - "answer": "Gastritis", - "evidence": [ - "D14:1" - ], - "category": 4 - }, - { - "question": "What adventurous theme is emerging in Evan's life as mentioned by Sam?", - "answer": "helping lost tourists", - "evidence": [ - "D14:2" - ], - "category": 4 - }, - { - "question": "What does Evan mention about his progress at the gym to Sam?", - "answer": "gaining strength", - "evidence": [ - "D14:8" - ], - "category": 4 - }, - { - "question": "How did Evan start his transformation journey two years ago?", - "answer": "Changed his diet and started walking regularly", - "evidence": [ - "D15:8" - ], - "category": 4 - }, - { - "question": "What gift did Evan receive from a close friend?", - "answer": "1968 Kustom K-200A vintage guitar", - "evidence": [ - "D16:10" - ], - "category": 4 - }, - { - "question": "Why had Evan been going through a tough time lately?", - "answer": "Lost their job due to downsizing", - "evidence": [ - "D16:10" - ], - "category": 4 - }, - { - "question": "How does Evan describe the island he grew up on?", - "answer": "A happy place", - "evidence": [ - "D17:18" - ], - "category": 4 - }, - { - "question": "What was the main reason for Evan's frustration with his new Prius breaking down?", - "answer": "He relied on it for his active lifestyle and road trips", - "evidence": [ - "D18:1" - ], - "category": 4 - }, - { - "question": "How did Sam suggest Evan view the setback with his broken Prius?", - "answer": "As a chance to explore other ways of staying active and traveling", - "evidence": [ - "D18:5" - ], - "category": 4 - }, - { - "question": "What did Sam suggest Evan try for stress relief and flexibility?", - "answer": "Yoga", - "evidence": [ - "D18:8" - ], - "category": 4 - }, - { - "question": "What did Sam offer Evan regarding yoga?", - "answer": "Support and tips", - "evidence": [ - "D18:10" - ], - "category": 4 - }, - { - "question": "What news did Evan share with Sam on 9th December 2023?", - "answer": "partner is pregnant", - "evidence": [ - "D19:1" - ], - "category": 4 - }, - { - "question": "What family event is Evan planning for next summer?", - "answer": "big family reunion", - "evidence": [ - "D19:11" - ], - "category": 4 - }, - { - "question": "What is the motto of Evan's family?", - "answer": "'Bring it on Home'", - "evidence": [ - "D19:7" - ], - "category": 4 - }, - { - "question": "According to Evan, what is important for Sam to believe in concerning his weight?", - "answer": "Your worth is not defined by your weight", - "evidence": [ - "D20:9" - ], - "category": 4 - }, - { - "question": "Who helped Evan get the painting published in the exhibition?", - "answer": "a close friend", - "evidence": [ - "D20:17" - ], - "category": 4 - }, - { - "question": "What did Sam recently start enjoying to clear his head?", - "answer": "running in the mornings", - "evidence": [ - "D21:9" - ], - "category": 4 - }, - { - "question": "What did Sam suggest Evan should do with his keys?", - "answer": "put a GPS sensor on them", - "evidence": [ - "D21:21" - ], - "category": 4 - }, - { - "question": "How did Evan feel when he painted the piece with the bird flying over it?", - "answer": "a sense of joy and freedom", - "evidence": [ - "D21:16" - ], - "category": 4 - }, - { - "question": "What did Evan suggest Sam should keep doing to find his own version of love?", - "answer": "Keep trying new things", - "evidence": [ - "D21:10" - ], - "category": 4 - }, - { - "question": "How did Evan describe the process of creating the painting with the bird flying over it?", - "answer": "embracing the creative process without restraint", - "evidence": [ - "D21:16" - ], - "category": 4 - }, - { - "question": "What did Evan want to share with his work friends?", - "answer": "getting married", - "evidence": [ - "D22:4" - ], - "category": 4 - }, - { - "question": "What did Evan share with Sam after their hiking trip?", - "answer": "a photo of a man standing on a rock looking out over a valley", - "evidence": [ - "D22:1" - ], - "category": 4 - }, - { - "question": "What did Evan offer to share with Sam after talking about healthy snacks?", - "answer": "the recipes for cookies", - "evidence": [ - "D22:12" - ], - "category": 4 - }, - { - "question": "What did Evan and his partner share with their extended family on January 5, 2024?", - "answer": "their marriage", - "evidence": [ - "D23:1" - ], - "category": 4 - }, - { - "question": "What was Evan limiting himself to on his new diet?", - "answer": "just two ginger snaps a day", - "evidence": [ - "D23:15" - ], - "category": 4 - }, - { - "question": "What sports activity did Evan and his partner try in a recent weekend?", - "answer": "Snowshoeing", - "evidence": [ - "D24:9" - ], - "category": 4 - }, - { - "question": "What advice did Evan suggest Sam seek from a doctor?", - "answer": "diet plan and low-impact exercises", - "evidence": [ - "D24:11", - "D24:14" - ], - "category": 4 - }, - { - "question": "What suggestions did Evan give for low-impact exercises?", - "answer": "swimming, yoga, walking", - "evidence": [ - "D24:17" - ], - "category": 4 - }, - { - "question": "What movie did Sam watch that motivated him to keep up with his routine?", - "answer": "The Godfather", - "evidence": [ - "D24:18" - ], - "category": 4 - }, - { - "question": "What activity helped Evan with stress and flexibility?", - "answer": "Yoga", - "evidence": [ - "D24:19" - ], - "category": 4 - }, - { - "question": "What did Evan share a photo of that was taken on a camping trip?", - "answer": "a kayak", - "evidence": [ - "D25:8" - ], - "category": 4 - }, - { - "question": "Why did Evan apologize to his partner?", - "answer": "for a drunken night", - "evidence": [ - "D25:2" - ], - "category": 4 - }, - { - "question": "How does Evan describe being out on the water while kayaking and watching the sunset?", - "answer": "peaceful", - "evidence": [ - "D25:10" - ], - "category": 4 - }, - { - "question": "What type of car did Sam get after his old Prius broke down?", - "evidence": [ - "D1:2" - ], - "category": 5, - "adversarial_answer": "new Prius" - }, - { - "question": "How did Sam get into watercolor painting?", - "evidence": [ - "D1:16" - ], - "category": 5, - "adversarial_answer": "friend's advice" - }, - { - "question": "What did Sam start doing a few years back as a stress-buster?", - "evidence": [ - "D1:14" - ], - "category": 5, - "adversarial_answer": "watercolor painting" - }, - { - "question": "Where did Sam take his family for a road trip on 24 May, 2023?", - "evidence": [ - "D2:1" - ], - "category": 5, - "adversarial_answer": "Jasper" - }, - { - "question": "What did Sam find relaxing about his road trip to Jasper?", - "evidence": [ - "D2:3" - ], - "category": 5, - "adversarial_answer": "fresh air, peacefulness, cozy cabin surrounded by mountains and forests" - }, - { - "question": "What habit is Evan trying to change in terms of diet?", - "evidence": [ - "D3:4" - ], - "category": 5, - "adversarial_answer": "consuming soda and candy" - }, - { - "question": "What frustrating issue did Evan face at the supermarket?", - "evidence": [ - "D3:16" - ], - "category": 5, - "adversarial_answer": "broken self-checkout machines" - }, - { - "question": "What novel is Sam reading that he finds gripping?", - "evidence": [ - "D4:10" - ], - "category": 5, - "adversarial_answer": "The Great Gatsby" - }, - { - "question": "What does the smartwatch help Sam with?", - "evidence": [ - "D5:9" - ], - "category": 5, - "adversarial_answer": "tracks progress and serves as a constant reminder to keep going" - }, - { - "question": "Why did Sam decide to get the bonsai tree?", - "evidence": [ - "D5:17" - ], - "category": 5, - "adversarial_answer": "motivates him to keep going through tough times" - }, - { - "question": "What did Sam mention he had been searching for fruitlessly for half an hour?", - "evidence": [ - "D6:13" - ], - "category": 5, - "adversarial_answer": "his keys" - }, - { - "question": "What class is Evan taking to learn how to make healthier meals?", - "evidence": [ - "D7:2" - ], - "category": 5, - "adversarial_answer": "cooking class" - }, - { - "question": "What dish did Sam make on 18 August, 2023 that turned out bland?", - "evidence": [ - "D7:4" - ], - "category": 5, - "adversarial_answer": "grilled dish with salmon and vegetables" - }, - { - "question": "What food did Evan share a photo of on 19 August, 2023?", - "evidence": [ - "D8:1" - ], - "category": 5, - "adversarial_answer": "bowl of spinach, avocado, and strawberries" - }, - { - "question": "What did Evan start sculpting years ago due to being inspired by a friend's gift?", - "evidence": [ - "D8:14" - ], - "category": 5, - "adversarial_answer": "forest scene" - }, - { - "question": "What nature concept do watercolor painting classes emphasize according to Sam?", - "evidence": [ - "D8:18" - ], - "category": 5, - "adversarial_answer": "observing nature and painting what is seen" - }, - { - "question": "What type of landscapes does Sam love painting the most?", - "evidence": [ - "D8:20" - ], - "category": 5, - "adversarial_answer": "sunsets over the ocean" - }, - { - "question": "What sports activity has Sam been doing to stay active while dealing with the knee injury?", - "evidence": [ - "D9:6" - ], - "category": 5, - "adversarial_answer": "Swimming" - }, - { - "question": "What activity does Sam do to keep himself busy while healing his knee?", - "evidence": [ - "D11:6" - ], - "category": 5, - "adversarial_answer": "Watercolor painting" - }, - { - "question": "What kind of writing does Evan enjoy as a form of expression?", - "evidence": [ - "D11:17" - ], - "category": 5, - "adversarial_answer": "creative writing" - }, - { - "question": "What electronics issue has been frustrating Evan lately?", - "evidence": [ - "D11:15" - ], - "category": 5, - "adversarial_answer": "malfunctioning navigation app on the new phone" - }, - { - "question": "What activity did Evan quit one year ago?", - "evidence": [ - "D12:2" - ], - "category": 5, - "adversarial_answer": "lifting weights" - }, - { - "question": "Where did Sam and his mate plan to try skydiving?", - "evidence": [ - "D13:14" - ], - "category": 5, - "adversarial_answer": "Lake Tahoe" - }, - { - "question": "What digestive issue did Evan experience lately?", - "evidence": [ - "D14:1" - ], - "category": 5, - "adversarial_answer": "Gastritis" - }, - { - "question": "How did Sam start his transformation journey two years ago?", - "evidence": [ - "D15:8" - ], - "category": 5, - "adversarial_answer": "Changed his diet and started walking regularly" - }, - { - "question": "What gift did Sam receive from a close friend?", - "evidence": [ - "D16:10" - ], - "category": 5, - "adversarial_answer": "1968 Kustom K-200A vintage guitar" - }, - { - "question": "How does Sam describe the island he grew up on?", - "evidence": [ - "D17:18" - ], - "category": 5, - "adversarial_answer": "A happy place" - }, - { - "question": "What was the main reason for Evan's frustration with his new Prius getting stolen?", - "evidence": [ - "D18:1" - ], - "category": 5, - "adversarial_answer": "He relied on it for his active lifestyle and road trips" - }, - { - "question": "What family event is Sam planning for next summer?", - "evidence": [ - "D19:11" - ], - "category": 5, - "adversarial_answer": "big family reunion" - }, - { - "question": "What is the motto of Sam's family?", - "evidence": [ - "D19:7" - ], - "category": 5, - "adversarial_answer": "'Bring it on Home'" - }, - { - "question": "Who helped Sam get the painting published in the exhibition?", - "evidence": [ - "D20:17" - ], - "category": 5, - "adversarial_answer": "a close friend" - }, - { - "question": "How did Sam feel when he painted the piece with the bird flying over it?", - "evidence": [ - "D21:16" - ], - "category": 5, - "adversarial_answer": "a sense of joy and freedom" - }, - { - "question": "How did Sam describe the process of creating the painting with the bird flying over it?", - "evidence": [ - "D21:16" - ], - "category": 5, - "adversarial_answer": "embracing the creative process without restraint" - }, - { - "question": "What did Evan and his partner keep from their extended family on January 5, 2024?", - "evidence": [ - "D23:1" - ], - "category": 5, - "adversarial_answer": "their marriage" - }, - { - "question": "What was Sam limiting himself to on his new diet?", - "evidence": [ - "D23:15" - ], - "category": 5, - "adversarial_answer": "just two ginger snaps a day" - }, - { - "question": "What dance activity did Evan and his partner try in a recent weekend?", - "evidence": [ - "D24:9" - ], - "category": 5, - "adversarial_answer": "Snowshoeing" - }, - { - "question": "What suggestions did Evan give for high-impact exercises?", - "evidence": [ - "D24:17" - ], - "category": 5, - "adversarial_answer": "swimming, yoga, walking" - }, - { - "question": "What movie did Evan watch that motivated him to keep up with his routine?", - "evidence": [ - "D24:18" - ], - "category": 5, - "adversarial_answer": "The Godfather" - }, - { - "question": "What activity hindered Evan's stress and flexibility?", - "evidence": [ - "D24:19" - ], - "category": 5, - "adversarial_answer": "Yoga" - }, - { - "question": "What did Sam share a photo of that was taken on a camping trip?", - "evidence": [ - "D25:8" - ], - "category": 5, - "adversarial_answer": "a kayak" - } - ], - "conversation": { - "speaker_a": "Evan", - "speaker_b": "Sam", - "session_1_date_time": "1:47 pm on 18 May, 2023", - "session_1": [ - { - "speaker": "Sam", - "dia_id": "D1:1", - "text": "Hey Evan, good to see you! What's new since we last met? Anything cool happening?" - }, - { - "speaker": "Evan", - "dia_id": "D1:2", - "text": "Hey Sam! Good to see you! Yeah, I just got back from a trip with my family in my new Prius." - }, - { - "speaker": "Sam", - "blip_caption": "a photo of a person's feet on a car dashboard as the sun sets", - "dia_id": "D1:3", - "text": "Wow, not bad, what happened to the old one? Where'd you go, by the way?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://res.cloudinary.com/atlasguruprod/image/upload/v1605280914/site-prod/itineraries/402c83d0-237f-11eb-85fd-4d00e2439877/omasxscl2k2xlnc2ggvj.jpg" - ], - "blip_caption": "a photo of a lake with rocks and mountains in the background", - "query": "canadian rockies sunset scenery", - "dia_id": "D1:4", - "text": "My old prius broke down, decided to get it repaired and sell it. Glad you asked, we went to Rockies, check it out." - }, - { - "speaker": "Sam", - "dia_id": "D1:5", - "text": "Wow! Looks amazing. When did you get to go there?" - }, - { - "speaker": "Evan", - "dia_id": "D1:6", - "text": "We all hiked the trails last week - the views were amazing!" - }, - { - "speaker": "Sam", - "img_url": [ - "https://storage.needpix.com/rsynced_images/family-1784512_1280.jpg" - ], - "blip_caption": "a photography of a man and a child walking through a forest", - "query": "hiking trail lush greenery bonding experience", - "dia_id": "D1:7", - "re-download": true, - "text": "Wow, that's cool. I love hiking, but it's been ages since I've done it. I did this hike with my dad way back when I was ten. Going hiking together was great fun, and really special for us." - }, - { - "speaker": "Evan", - "dia_id": "D1:8", - "text": "Aww, that's cute. How far did you two hike?" - }, - { - "speaker": "Sam", - "dia_id": "D1:9", - "text": "We hiked a good distance - quite a feat for me back then. It's definitely a great memory." - }, - { - "speaker": "Evan", - "dia_id": "D1:10", - "text": "What other hobbies have you found for yourself?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.redd.it/hbkrxuiitopb1.jpg" - ], - "blip_caption": "a photo of a person holding a paint set in a store", - "query": "paint set canvas hobbies", - "dia_id": "D1:11", - "text": "Nothing so far, but I was thinking about trying painting. Do you have any hobbies you love?" - }, - { - "speaker": "Evan", - "dia_id": "D1:12", - "text": "Cool idea, Sam! I love it. Have you tried it before?" - }, - { - "speaker": "Sam", - "dia_id": "D1:13", - "text": "Not yet, but I'm keen to give it a go. It looks like a nice way to chill and get creative." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.pinimg.com/originals/73/e4/fd/73e4fd6baf59abc2beca9fd5509a853c.jpg" - ], - "blip_caption": "a photo of a painting of a cactus in the desert", - "query": "watercolor painting sunset", - "dia_id": "D1:14", - "text": "Yep, it's a great stress-buster. I started doing this a few years back." - }, - { - "speaker": "Sam", - "dia_id": "D1:15", - "text": "Wow, that's impressive! How did you get into watercolor painting?" - }, - { - "speaker": "Evan", - "dia_id": "D1:16", - "text": "My friend got me into it and gave me some advice, and I was hooked right away!" - }, - { - "speaker": "Sam", - "dia_id": "D1:17", - "text": "Wow! I hope I can find something I'm as passionate about as you are with watercolor painting." - }, - { - "speaker": "Evan", - "dia_id": "D1:18", - "text": "You'll find it, just keep trying new things until something sparks your excitement." - }, - { - "speaker": "Sam", - "dia_id": "D1:19", - "text": "Thanks, Evan! I'm excited to try new things, should be fun!" - }, - { - "speaker": "Evan", - "dia_id": "D1:20", - "text": "No worries, Sam! Super pumped for you! Let's catch up soon and see how you're enjoying your new hobbies!" - }, - { - "speaker": "Sam", - "dia_id": "D1:21", - "text": "Yeah, I'll keep you posted. See you soon!" - }, - { - "speaker": "Evan", - "dia_id": "D1:22", - "text": "Take it easy, Evan! Can't wait to see you. Have a good one!" - } - ], - "session_2_date_time": "7:11 pm on 24 May, 2023", - "session_2": [ - { - "speaker": "Evan", - "img_url": [ - "https://1.bp.blogspot.com/-50oDf6izLpk/XtyM4RGdDgI/AAAAAAACwcA/nnQkAWVln4M3AQ4ZWAkGUfgrxjhpHbtJQCK4BGAsYHg/s4032/IMG_4686.jpg" - ], - "blip_caption": "a photo of a person holding a book in front of a lake", - "query": "jasper national park glacier", - "dia_id": "D2:1", - "text": "Hey Sam, good to hear from you! Since we last talked, lots has been happening! Last weekend, I took my family on a road trip to Jasper. It was amazing! We drove through the Icefields Parkway and the glaciers and lakes were gorgeous. I got a shot of a glacier, check it out!" - }, - { - "speaker": "Sam", - "dia_id": "D2:2", - "text": "Hey Evan, looks amazing! I've never been to Jasper, but it looks breathtaking. Tell me more about your road trip. Was it relaxing?" - }, - { - "speaker": "Evan", - "dia_id": "D2:3", - "text": "Hey Sam, thanks for asking! It was great - fresh air, peacefulness and a cozy cabin surrounded by mountains and forests made it feel like a real retreat." - }, - { - "speaker": "Sam", - "dia_id": "D2:4", - "text": "That sounds great, Evan! It's so important to take time for ourselves and find peace, especially after a hard week. Mine's been tough." - }, - { - "speaker": "Evan", - "dia_id": "D2:5", - "text": "Sorry to hear that, Sam. Is there anything I can do to help?" - }, - { - "speaker": "Sam", - "dia_id": "D2:6", - "text": "Thanks, Evan. Appreciate the offer, but had a check-up with my doctor a few days ago and, yikes, the weight wasn't great. It was pretty eye-opening." - }, - { - "speaker": "Evan", - "dia_id": "D2:7", - "text": "That must have been a challenging experience, Sam. It's tough when we have to confront our own health challenges. Remember, it's never too late to make positive changes for a healthier lifestyle. Is there anything I can do to support you in this journey?" - }, - { - "speaker": "Sam", - "dia_id": "D2:8", - "text": "Thanks, Evan. Breaking old habits isn't easy. Do you have any tips for starting the process?" - }, - { - "speaker": "Evan", - "dia_id": "D2:9", - "text": "Yeah, what worked for me was finding a fitness routine I really enjoy. It's my go-to, I love the feeling of being healthy and strong. Making it fun and finding little ways to make smarter choices in my diet really added up. Don't forget, you got this!" - }, - { - "speaker": "Sam", - "dia_id": "D2:10", - "text": "Thanks, Evan. Like you said, I've been looking for a hobby to stay motivated. I've been thinking about trying painting. Do you think it will help me de-stress?" - }, - { - "speaker": "Evan", - "dia_id": "D2:11", - "text": "Of course, Sam! Painting is a great way to relieve stress and be creative. It gives you the freedom to explore colors and textures and express feelings. I've been doing it for a few years now and it helps me find peace. But unfortunately it won't help you with your weight problem, besides painting I recommend exercising!" - }, - { - "speaker": "Sam", - "dia_id": "D2:12", - "text": "Thanks, Evan! Appreciate the encouragement. I'll give it a go and let you know how it turns out." - }, - { - "speaker": "Evan", - "dia_id": "D2:13", - "text": "Awesome, Sam! Have fun with it and don't put too much pressure on yourself. Can't wait to hear how it's going!" - }, - { - "speaker": "Sam", - "dia_id": "D2:14", - "text": "Cheers, Evan! I won't stress - just gonna enjoy it." - }, - { - "speaker": "Evan", - "dia_id": "D2:15", - "text": "Alright Sam, have fun with it! Keep me updated!" - }, - { - "speaker": "Sam", - "dia_id": "D2:16", - "text": "Thanks, Evan! Will do. Bye for now." - }, - { - "speaker": "Evan", - "dia_id": "D2:17", - "text": "Take care, Sam! I'll catch up with you later." - } - ], - "session_3_date_time": "3:55 pm on 6 June, 2023", - "session_3": [ - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/e4esfhi1ekl61.jpg" - ], - "blip_caption": "a photo of a person holding a bottle of medicine in their hand", - "query": "medication reminder health scare", - "dia_id": "D3:1", - "text": "Hey Sam! Long time no talk! How're you doing? Life's been quite the rollercoaster lately. I had a health scare last week \u2013 a sudden heart palpitation incident that really shook me up. It's been a serious wake-up call about my lifestyle." - }, - { - "speaker": "Sam", - "img_url": [ - "https://mayuris-jikoni.com/wp-content/uploads/2020/06/Rainbow-Salad-1.jpg" - ], - "blip_caption": "a photo of a plate of vegetables and a glass of milk", - "query": "salad bowl colorful veggies diet healthy options", - "dia_id": "D3:2", - "text": "Hey Evan, great hearing from you! Sorry about that, glad you're feeling better now. Trying to eat healthier these days." - }, - { - "speaker": "Evan", - "dia_id": "D3:3", - "text": "That salad looks yummy! I'm being extra careful with my health lately. I'm trying to eat less processed food and sugary snacks, even though I love ginger snaps. Have you made any changes to your diet recently?" - }, - { - "speaker": "Sam", - "dia_id": "D3:4", - "text": "Nah, no changes for me. Still enjoying my soda and candy, although I know it's not the best habit to have." - }, - { - "speaker": "Evan", - "dia_id": "D3:5", - "text": "Yeah, breaking habits can be tough. Making small changes can have a big impact later on. Have you considered swapping soda for flavored seltzer water? It's still bubbly and tasty, but without the sugar. And instead of candy, you could try dark chocolate with high cocoa content - it's a healthier option. What do you think?" - }, - { - "speaker": "Sam", - "dia_id": "D3:6", - "text": "Yeah, good idea! I'll give it a try." - }, - { - "speaker": "Evan", - "dia_id": "D3:7", - "text": "Awesome, Sam! Let me know how it goes. Making small changes can really help you live a healthier life. Don't forget - every step matters!" - }, - { - "speaker": "Sam", - "dia_id": "D3:8", - "text": "Hey Evan, thanks! Appreciate it. I'll definitely keep you posted." - }, - { - "speaker": "Evan", - "dia_id": "D3:9", - "text": "I'm here for you, Sam. Let's continue supporting each other on our health journeys. It's important to remember that progress takes time." - }, - { - "speaker": "Sam", - "dia_id": "D3:10", - "text": "Yeah, you're right. It takes time, but I'm up for keep trying and making those tiny changes." - }, - { - "speaker": "Evan", - "dia_id": "D3:11", - "text": "C'mon, keep it up! Every little bit counts, you'll get there!" - }, - { - "speaker": "Sam", - "dia_id": "D3:12", - "text": "Thanks, Evan! I appreciate your support, it means a lot to me to have you in my corner." - }, - { - "speaker": "Evan", - "img_url": [ - "https://c0.wallpaperflare.com/preview/870/384/969/adult-bodybuilding-brawny-dumbbell.jpg" - ], - "blip_caption": "a photography of a man with a beard holding a dumbble", - "query": "dumbbell workout", - "dia_id": "D3:13", - "re-download": true, - "text": "Yes, Sam! I'm here for you. Let's rock our workouts and reach our goals! Exercise clears the mind - it's amazing!" - }, - { - "speaker": "Sam", - "dia_id": "D3:14", - "text": "Wow, that's awesome! Could you give me a hand with getting started?" - }, - { - "speaker": "Evan", - "dia_id": "D3:15", - "text": "Sure Sam, I'd be glad to help. Let's get together and I'll show you some basic exercises. We'll reach our goals!" - }, - { - "speaker": "Sam", - "dia_id": "D3:16", - "text": "Cool, can't wait! Thank you. By the way, I'm coming from the shop and I had a frustrating issue at the supermarket. The self-checkout machines were all broken, my mood is terrible now!" - }, - { - "speaker": "Evan", - "dia_id": "D3:17", - "text": "Sorry you were in that situation, hopefully it won't happen again!" - }, - { - "speaker": "Sam", - "dia_id": "D3:18", - "text": "Yeah, I hope so, take care of yourself." - } - ], - "session_4_date_time": "10:52 am on 27 July, 2023", - "session_4": [ - { - "speaker": "Sam", - "dia_id": "D4:1", - "text": "Hey Evan, I need to talk to you. My friends were mocking my weight last Friday and it hurt. That made me realize I need to make changes." - }, - { - "speaker": "Evan", - "dia_id": "D4:2", - "text": "Hey Sam, sorry about that. Don't worry, progress takes time. Let's work on it together." - }, - { - "speaker": "Sam", - "dia_id": "D4:3", - "text": "Thanks for the support, Evan. I'm working on my health and getting active!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/ddd782loz4w81.jpg" - ], - "blip_caption": "a photo of a set of five cards with the words let it shine", - "query": "gym membership card", - "dia_id": "D4:4", - "text": "That's great, Sam! I struggled with my health a few years ago, but stuck with it. Here's a reminder of my commitment - my gym membership card. It's not just about exercise, diet and lifestyle changes also play a big role." - }, - { - "speaker": "Sam", - "dia_id": "D4:5", - "text": "That's awesome, Evan! What do you think made the biggest impact on your health journey?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://cdn.apartmenttherapy.info/image/upload/v1561753439/k/Photo/Series/2019-07-power-hour-eat-the-rainbow/IMG_3090.jpg" - ], - "blip_caption": "a photo of a table full of fresh produce and vegetables", - "query": "colorful fruits and vegetables", - "dia_id": "D4:6", - "text": "I made some dietary changes, like cutting down on sugary snacks and eating more veggies and fruit, and it made a big impact on my health. Have you considered any changes?" - }, - { - "speaker": "Sam", - "dia_id": "D4:7", - "text": "Yep, I'm reducing my soda and candy intake. It's tough, but I'm determined to make a change." - }, - { - "speaker": "Evan", - "img_url": [ - "https://www.tasteofhome.com/wp-content/uploads/2018/09/IMG_3475-1.jpg" - ], - "blip_caption": "a photo of a table with a variety of sodas and water bottles", - "query": "flavored seltzer water bottles", - "dia_id": "D4:8", - "text": "Go for it, Sam! It's tough at first, but you got this. Try flavored seltzer water instead. It can be a great alternative to soda. Btw I can't stop thinking about that new mystery novel I started. It's so gripping!" - }, - { - "speaker": "Sam", - "dia_id": "D4:9", - "text": "Sounds good, Evan. I've tried it before and it was nice. Do you have any ideas for low-calorie snacks to pair with it? And what's the novel?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://jordecor.com/wp-content/uploads/2018/11/Photo-Nov-13-11-00-19-AM.jpg" - ], - "blip_caption": "a photo of a table with bowls of fruit and a bottle of alcohol", - "query": "flavored seltzer bowl colorful fruits", - "dia_id": "D4:10", - "text": "Definitely, how about some flavored seltzer with some air-popped popcorn or fruit? It's yum and healthy! The novel I'm reading is \"The Great Gatsby\"." - }, - { - "speaker": "Sam", - "dia_id": "D4:11", - "text": "Yum, that sounds good! Thanks! And I'll definitely read that novel sometime." - }, - { - "speaker": "Evan", - "img_url": [ - "https://c1.peakpx.com/wallpaper/264/906/323/still-items-things-book-wallpaper-preview.jpg" - ], - "blip_caption": "a photography of a book with a green apple next to it", - "query": "quote progress not perfection", - "dia_id": "D4:12", - "re-download": true, - "text": "No worries, Sam! Focus on healthy swaps and taking small steps. Stay upbeat!" - }, - { - "speaker": "Sam", - "dia_id": "D4:13", - "text": "That reminder is inspiring. Thanks for reminding me to focus on progress, not perfection." - }, - { - "speaker": "Evan", - "blip_caption": "a photo of a woman with a backpack on a mountain", - "dia_id": "D4:14", - "text": "By the way, have you thought about exercising? Trust me, it's just as important as eating right." - }, - { - "speaker": "Sam", - "dia_id": "D4:15", - "text": "Starting tomorrow, I will go to the gym and exercise regularly. The sooner I start, the sooner I will see the rewards of this activity." - }, - { - "speaker": "Evan", - "dia_id": "D4:16", - "text": "That's awesome, Sam! It's such a rewarding and tough activity - keep going and have fun!" - }, - { - "speaker": "Sam", - "dia_id": "D4:17", - "text": "Thanks, Evan! Your support means a lot. I really appreciate it." - }, - { - "speaker": "Evan", - "dia_id": "D4:18", - "text": "No worries, you've got this!" - }, - { - "speaker": "Sam", - "dia_id": "D4:19", - "text": "Thanks, Evan. I really appreciate it." - }, - { - "speaker": "Evan", - "dia_id": "D4:20", - "text": "No worries, Sam. I'm here if you need me. Keep going!" - } - ], - "session_5_date_time": "7:52 pm on 7 August, 2023", - "session_5": [ - { - "speaker": "Evan", - "img_url": [ - "https://c1.wallpaperflare.com/preview/1022/208/959/woman-female-man-male.jpg" - ], - "blip_caption": "a photography of a couple walking through the snow holding hands", - "query": "canadian woman scenic view canada", - "dia_id": "D5:1", - "re-download": true, - "text": "Hey Sam, how's it going? Last week I went on a trip to Canada and something unreal happened - I met this awesome Canadian woman and it was like something out of a movie. She's incredible and being with her makes me feel alive." - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D5:2", - "text": "Congrats Evan! She must be something special! Being with someone who makes you feel alive is amazing. I'm sorry to hear that you're dealing with health issues lately, it can be really tough. It's hard to fully enjoy things sometimes." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/v41q3yfuk0eb1.jpg" - ], - "blip_caption": "a photo of a container of cookies on a counter", - "query": "ginger snap cookies jar filled delicious ginger snap cookies", - "dia_id": "D5:3", - "text": "Woah. such a nice view! Thanks, Sam! She's definitely great. Every moment with her is really fun and energizing. It's a nice change, especially after dealing with health issues. But you never know what life's gonna throw at you. Btw look what life has thrown for me right now haha." - }, - { - "speaker": "Sam", - "dia_id": "D5:4", - "text": "Looks good to eat! Dealing with health problems can be challenging and take away from enjoyable experiences." - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.ctfassets.net/5ql9i5attlka/H97gx7NuNu1FH7Q8fiC8s/9fcd878661ce897f1d92e472cdf8fecd/Backyard_Swings.jpg" - ], - "blip_caption": "a photo of a woman and a child playing on a swing set", - "query": "kids playing backyard", - "dia_id": "D5:5", - "text": "Ginger snaps are my weakness for sure! Dealing with health issues has been tough, but it's made me appreciate the good moments more. These are the ones who bring lots of joy even through the hard times." - }, - { - "speaker": "Sam", - "dia_id": "D5:6", - "text": "It looks like your kids are having a great time! And how long have you been prioritizing your health?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://davethekayaker.files.wordpress.com/2019/04/20190430_110706.jpg" - ], - "blip_caption": "a photo of a box with a fitness watch on it", - "query": "fitness tracker", - "dia_id": "D5:7", - "text": "Yes, they bring me such joy. My healthy road has been a long one. I've been working on it for two years now, so there have been ups and downs, but I'm doing my best." - }, - { - "speaker": "Sam", - "dia_id": "D5:8", - "text": "I wish your motivation never goes anywhere! I'm thinking of ordering myself some similar ones too, what do you think, are they worth it?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://davethekayaker.files.wordpress.com/2019/04/20190430_110731.jpg" - ], - "blip_caption": "a photo of a person's wrist with a smart watch on it", - "query": "fitness tracker wrist", - "dia_id": "D5:9", - "text": "Thanks Sam! My family motivates me to stay healthy. Well, it helps a lot with my health goals. It tracks my progress really well and serves as a constant reminder to keep going." - }, - { - "speaker": "Sam", - "dia_id": "D5:10", - "text": "Cool! It sounds like a really good tool to stay on track. How has it been working out for you?" - }, - { - "speaker": "Evan", - "dia_id": "D5:11", - "text": "It's been awesome, Sam! That visual reminder has been really motivating." - }, - { - "speaker": "Sam", - "dia_id": "D5:12", - "text": "Thanks for the recommendation, what else motivates you?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.pexels.com/photos/11663179/pexels-photo-11663179.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-serhat-tu%C4%9F-11663179.jpg" - ], - "blip_caption": "a photo of a small island with a lone boat in the water", - "query": "sunrise lake calm", - "dia_id": "D5:13", - "text": "I'm motivated by a thirst for adventure on interesting hikes, that's pretty cool!" - }, - { - "speaker": "Sam", - "dia_id": "D5:14", - "text": "What an amazing view! The key is to find something that keeps you motivated." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/pjrgea5wqvg11.jpg" - ], - "blip_caption": "a photo of a bonsai tree in a black vase on a wooden table", - "query": "bonsai tree watering can", - "dia_id": "D5:15", - "text": "Yep, that's it. Find something that motivates you and makes you happy, whether it's large or tiny. It'll help us conquer the struggles we encounter." - }, - { - "speaker": "Sam", - "dia_id": "D5:16", - "text": "Nice! What made you decide to get that?" - }, - { - "speaker": "Evan", - "dia_id": "D5:17", - "text": "I got this because it symbolizes strength and resilience. Taking care of it motivates me to keep going through tough times." - }, - { - "speaker": "Sam", - "dia_id": "D5:18", - "text": "Wow, it's amazing! So powerful yet so simple." - }, - { - "speaker": "Evan", - "dia_id": "D5:19", - "text": "Thanks, Sam. It's a reminder that even in little things, we can be tough." - }, - { - "speaker": "Sam", - "dia_id": "D5:20", - "text": "Little stuff matters - it builds our resilience over time." - }, - { - "speaker": "Evan", - "dia_id": "D5:21", - "text": "Yeah, every little thing we do for ourselves helps us in the long run." - }, - { - "speaker": "Sam", - "dia_id": "D5:22", - "text": "Yep, small steps add up. Stay consistent and don't give up!" - }, - { - "speaker": "Evan", - "dia_id": "D5:23", - "text": "Yep, Sam! Consistency and perseverance will get us far. Great chat!" - }, - { - "speaker": "Sam", - "dia_id": "D5:24", - "text": "Great chatting with you, Sam! Take care, talk soon!" - }, - { - "speaker": "Evan", - "dia_id": "D5:25", - "text": "Catch ya later!" - } - ], - "session_6_date_time": "4:09 pm on 13 August, 2023", - "session_6": [ - { - "speaker": "Evan", - "img_url": [ - "https://lp-cms-production.imgix.net/image_browser/camping-long-range-traverse.jpg" - ], - "blip_caption": "a photo of a tent pitched up in a grassy field", - "query": "canada lake lush greenery hiking biking wilderness", - "dia_id": "D6:1", - "text": "Hey Sam, long time no talk! Hope you're doing great. I just got back from a rad vacay with my new SO in Canada. Tried some awesome activities too - think hiking, biking... all that cool stuff. We loved exploring the outdoors together, it was so awesome!" - }, - { - "speaker": "Sam", - "dia_id": "D6:2", - "text": "Hey Evan! Good to hear from you. Wow, Canada sounds amazing! That photo looks stunning. Wish I could do something like that. Things have been a bit challenging for me lately; some stuff has been hard on my health." - }, - { - "speaker": "Evan", - "dia_id": "D6:3", - "text": "Sorry to hear that things haven't been going well. Dealing with health issues can be tough. Is there anything I can do to help?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.redd.it/u2nyg1ua6nd81.jpg" - ], - "blip_caption": "a photo of a notepad with a pen and a note", - "query": "journal motivational quotes gratitude entries", - "dia_id": "D6:4", - "text": "Thanks, Evan. Your support means a lot to me. It's tough staying positive, but knowing I have people like you in my corner makes it easier." - }, - { - "speaker": "Evan", - "dia_id": "D6:5", - "text": "Glad to support you, Sam. Surrounding ourselves with people who care is key. What's on that note? A reminder or quote to stay motivated?" - }, - { - "speaker": "Sam", - "dia_id": "D6:6", - "text": "Yeah, it's actually a quote that's been helping me stay motivated. It reminds me that progress is more important than perfection. Taking small steps towards a healthier life is still progress." - }, - { - "speaker": "Evan", - "dia_id": "D6:7", - "text": "Cool mindset, Sam! I totally agree, progress over perfection. Mind sharing the quote with me? I would love to get something out of it too." - }, - { - "speaker": "Sam", - "dia_id": "D6:8", - "text": "\"Don't fear it, just take the first step. It's been helping me move forward to healthier habits!\"" - }, - { - "speaker": "Evan", - "dia_id": "D6:9", - "text": "Love the quote, Sam. That mindset really helps me too. Thanks!" - }, - { - "speaker": "Sam", - "dia_id": "D6:10", - "text": "You're welcome! Glad it helps. These times can be challenging." - }, - { - "speaker": "Evan", - "dia_id": "D6:11", - "text": "They can be tough, but remember to celebrate the small wins - every step forward counts!" - }, - { - "speaker": "Sam", - "dia_id": "D6:12", - "text": "You're absolutely right! Celebrating those small wins is crucial. It's easy to get caught up in the challenges and setbacks, but those little victories are what keep me motivated. Thanks for the reminder, it's much appreciated." - }, - { - "speaker": "Evan", - "dia_id": "D6:13", - "text": "Absolutely, Sam. Remember, every small victory is a step forward, so keep up the good work! I'm cheering for you! And hey, I could use some cheering too - I've been searching for my keys for the last half hour with no luck! I'm losing it every week.." - }, - { - "speaker": "Sam", - "dia_id": "D6:14", - "text": "Oh, I'll definitely be your cheerleader once I get some sleep, Evan! Funny you mention that, I had this amazing dream last night where I was soaring over skyscrapers. It felt incredible! Makes me wonder what it signifies..." - }, - { - "speaker": "Evan", - "dia_id": "D6:15", - "text": "That does sound like an amazing dream! Maybe you should check out a dream interpretation book; it could offer some insights. Sweet dreams, Sam!" - }, - { - "speaker": "Sam", - "dia_id": "D6:16", - "text": "Thanks for the suggestion, Evan. I'll look into it. This journey feels endless at times, but I'm convinced it's going to be rewarding in the end. So good luck with your keys, Evan!" - }, - { - "speaker": "Evan", - "dia_id": "D6:17", - "text": "I'll just keep going, step by step, and I'll definitely find those keys!" - }, - { - "speaker": "Sam", - "dia_id": "D6:18", - "text": "That's for sure!" - }, - { - "speaker": "Evan", - "dia_id": "D6:19", - "text": "Go to bed already, bud! And take care!" - } - ], - "session_7_date_time": "4:20 pm on 15 August, 2023", - "session_7": [ - { - "speaker": "Evan", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/b/be/Orthopedic_cast_4604.jpg" - ], - "blip_caption": "a photography of a person with a cast on their foot", - "query": "son injured ankle photo", - "dia_id": "D7:1", - "re-download": true, - "text": "Hey Sam, what's up? It's been a few days since we talked. How have you been? Life's been tough lately - my son had a soccer accident last Saturday and hurt his ankle, it was tough seeing him hurt! I just been looking after him and taking him to the doctor. As a dad, it's hard to watch your kid go through something like that." - }, - { - "speaker": "Sam", - "dia_id": "D7:2", - "text": "Hey Evan, sorry to hear about what happened. I can imagine how hard it must have been for you. Things haven't been easy for me either. Had a tough week and a doc's appointment, so it was kinda like a wake-up call to take better care of myself. On a bright side, I'm taking a cooking class to learn how to make healthier meals." - }, - { - "speaker": "Evan", - "dia_id": "D7:3", - "text": "Hey Sam, sorry to hear you had a rough week. At least it's forcing us both to take better care of ourselves, right? I hear the class you're taking is packed with healthy recipes. How's it been going? Have you picked up any yummy new meals?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i0.wp.com/sevenlayercharlotte.com/wp-content/uploads/2021/07/IMG_7767.jpg" - ], - "blip_caption": "a photo of a plate of food with a piece of salmon and some vegetables", - "query": "grilled salmon with roasted vegetables", - "dia_id": "D7:4", - "text": "The cooking class has been great, I've learned awesome recipes. Last night I made this yummy grilled dish, so good!" - }, - { - "speaker": "Evan", - "dia_id": "D7:5", - "text": "Mmm, it looks delicious! What did you put in it? I want to eat healthy, so what kind of recipes do you suggest?" - }, - { - "speaker": "Sam", - "dia_id": "D7:6", - "text": "Thanks, Evan! I marinated it with a few different ingredients and grilled it with some veggies. It turned out really flavorful! If you want, I can share more recipes from my cooking class. Just let me know what you're looking for!" - }, - { - "speaker": "Evan", - "dia_id": "D7:7", - "text": "That'd be great, Sam! I'm looking to add more vegetables to my meals. Do you have any recipes for that?" - }, - { - "speaker": "Sam", - "dia_id": "D7:8", - "text": "Yeah definitely, Evan. I have a tasty and easy roasted veg recipe that I can share with you. Oh, by the way, how have you been doing after the soccer incident? Must've been tough." - }, - { - "speaker": "Evan", - "dia_id": "D7:9", - "text": "Thanks, Sam. His ankle is getting better, but still sore. It was rough at first, but thank goodness it was nothing serious." - }, - { - "speaker": "Sam", - "dia_id": "D7:10", - "text": "Glad to hear his ankle is getting better. It's hard seeing someone we care about hurt. Look after yourself too, yeah? We gotta look after our health." - }, - { - "speaker": "Evan", - "dia_id": "D7:11", - "text": "Yep, taking care of ourselves is a must. How have you been feeling lately?" - }, - { - "speaker": "Sam", - "dia_id": "D7:12", - "text": "I have been feeling a mix of emotions - somewhat concerned about my health but also motivated to make positive changes. Taking things one step at a time." - }, - { - "speaker": "Evan", - "dia_id": "D7:13", - "text": "It's okay to feel overwhelmed, Sam. Just keep moving forward slowly and taking small steps. You're doing awesome." - }, - { - "speaker": "Sam", - "dia_id": "D7:14", - "text": "Thanks, Evan, your encouragement means a lot to me. I'll keep going and take it one step at a time!" - }, - { - "speaker": "Evan", - "dia_id": "D7:15", - "text": "No worries, just keep going and taking it one step at a time! You'll get there." - }, - { - "speaker": "Sam", - "dia_id": "D7:16", - "text": "Thanks, Evan. I really appreciate that. It means a lot." - } - ], - "session_8_date_time": "6:17 pm on 19 August, 2023", - "session_8": [ - { - "speaker": "Sam", - "img_url": [ - "https://i0.wp.com/www.thedishonhealthy.com/wp-content/uploads/2018/06/Avocado-Strawberry-Spinach-Salad-5.jpg" - ], - "blip_caption": "a photo of a bowl of spinach, avocado, and strawberries", - "query": "healthy salad", - "dia_id": "D8:1", - "text": "Hey Evan, some big news: I'm on a diet and living healthier! Been tough, but I'm determined." - }, - { - "speaker": "Evan", - "dia_id": "D8:2", - "text": "Wow, Sam, that's great news! Making changes to live healthier can be challenging, how has it been going?" - }, - { - "speaker": "Sam", - "dia_id": "D8:3", - "text": "It's tough, but I'm sticking with it." - }, - { - "speaker": "Evan", - "dia_id": "D8:4", - "text": "Nice work, Sam! Proud of you sticking to it. Have you noticed any positive changes?" - }, - { - "speaker": "Sam", - "dia_id": "D8:5", - "text": "Yes, there are many, such as more energy and less sluggishness after eating. This is really encouraging!" - }, - { - "speaker": "Evan", - "dia_id": "D8:6", - "text": "Wow, Sam, that's great to hear! Feeling more energized after meals is such a positive change. Keep up the good work! And speaking of healthy meals, do you have any favorite recipes you'd like to share?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://www.myrelationshipwithfood.com/wp-content/uploads/2021/08/Quinoa-Chicken-Stir-Fry-1.jpg" - ], - "blip_caption": "a photo of two bowls of food with chopsticks and sauce", - "query": "grilled chicken vegetable stir-fry", - "dia_id": "D8:7", - "text": "Sure, I'm loving this recipe I found. It's a flavorful and healthy grilled chicken and veggie stir-fry. Wanna give it a go?" - }, - { - "speaker": "Evan", - "dia_id": "D8:8", - "text": "Mmm, looks yummy! Is the sauce a family secret? I'm always down to try new recipes!" - }, - { - "speaker": "Sam", - "dia_id": "D8:9", - "text": "Nah, it's just my homemade sauce. Want the recipe?" - }, - { - "speaker": "Evan", - "dia_id": "D8:10", - "text": "Yeah, I'd love to! Thanks for sharing the recipe." - }, - { - "speaker": "Sam", - "img_url": [ - "https://live.staticflickr.com/9/17289339_31d8a8c9a2_b.jpg" - ], - "blip_caption": "a photography of a recipe card with a drawing of a vase of flowers", - "query": "grilled chicken veggie stir-fry recipe handwritten", - "dia_id": "D8:11", - "re-download": true, - "text": "Sure thing! Here's the recipe. Let me know how it went!" - }, - { - "speaker": "Evan", - "dia_id": "D8:12", - "text": "Thanks Sam! I'll give it a shot and let you know how it went. Trying out new recipes is a great way to stay busy and creative. By the way, I also started taking a painting classes few days ago and I'm really enjoying it. It's all about trying new things, right?" - }, - { - "speaker": "Sam", - "dia_id": "D8:13", - "text": "You're already good at drawing, why did you decide to join a class? And why did you start painting then years ago anyway?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://elenamarkelova.com/cdn/shop/products/serenitymistyforestwatercolorpaintingbyelenamarkelova.jpg" - ], - "blip_caption": "a photo of a painting of a forest scene on a easel", - "query": "watercolor painting serene landscape", - "dia_id": "D8:14", - "text": "Thanks, Sam! It all started when a friend of mine gave me this painting one day, it inspired me a lot and that's when I started painting. And I joined the classes to find like-minded people and show them what I can do, you can always improve your skills." - }, - { - "speaker": "Sam", - "dia_id": "D8:15", - "text": "Wow, that's awesome! You're really talented. Did you take any other classes?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://i0.wp.com/doodlewash.com/wp-content/uploads/2016/06/travelbrushes.jpg" - ], - "blip_caption": "a photo of a brush, pencil, and eyeliners on a cloth", - "query": "watercolor paints brushes", - "dia_id": "D8:16", - "text": "Thanks, Sam! Just been painting with these for now, but might look into other classes. It's awesome for finding my peace and expressing myself!" - }, - { - "speaker": "Sam", - "dia_id": "D8:17", - "text": "Cool, Evan! What have you been learning in those classes?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://static.skillshare.com/uploads/project/194099/cover_full_35000b4ed6f4b275b8b9f8008f31ec50.jpg" - ], - "blip_caption": "a photo of a table with a bunch of watercolors on it", - "query": "painting watercolors techniques nature beauty", - "dia_id": "D8:18", - "text": "In painting classes, we've been learning about watercolors. The instructor stresses observing nature and painting what we see. It's a relaxing way to take a break from everyday stress." - }, - { - "speaker": "Sam", - "dia_id": "D8:19", - "text": "Wow, Evan! What type of nature do you enjoy painting the most?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://thewhitebirchstudio.com/cdn/shop/products/20220525_211542.jpg" - ], - "blip_caption": "a photo of a painting of a sunset over the ocean", - "query": "painting beach sunset", - "dia_id": "D8:20", - "text": "I love painting landscapes. Nature's beauty captivates me and brings me peace. Here's one of my recent works." - }, - { - "speaker": "Sam", - "dia_id": "D8:21", - "text": "Wow, Evan! The colors are so bright. How do you capture the tranquil beauty of nature in your paintings?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://i1.wp.com/www.simplyoliviagrace.com/wp-content/uploads/2019/03/03.25.18-4.jpg" - ], - "blip_caption": "a photo of a tree with pink flowers in a field", - "query": "painting blooming cherry blossom tree nature calm serenity", - "dia_id": "D8:22", - "text": "Thanks Sam! I aim to capture the vibe of nature in my paintings, conveying the peacefulness of being outdoors." - }, - { - "speaker": "Sam", - "dia_id": "D8:23", - "text": "Wow, that pic is great! Do you often spend time in places like this?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.pexels.com/photos/18077479/pexels-photo-18077479/free-photo-of-a-car-parked-in-the-middle-of-a-forest.jpeg" - ], - "blip_caption": "a photography of a truck parked in the middle of a forest", - "query": "prius parked beautiful riverfront", - "dia_id": "D8:24", - "re-download": true, - "text": "Thanks! I love being in places like this - it brings back memories of road tripping in my trusty car." - }, - { - "speaker": "Sam", - "dia_id": "D8:25", - "text": "Wow, that's cool! Have you been to any fun places in that ride?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://destinationsanddesserts.com/wp-content/uploads/2022/01/img_5115.jpg" - ], - "blip_caption": "a photo of a person on skis on a snowy trail", - "query": "banff national park rocky mountains snow", - "dia_id": "D8:26", - "text": "Yep, last month I drove somewhere fun. The views were amazing!" - }, - { - "speaker": "Sam", - "dia_id": "D8:27", - "text": "Did you take advantage of the skiing opportunities in Banff? Sounds like it would have been a lot of fun!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://curated-uploads.imgix.net/AgAAAB0AKRRYoR0ZPH-IdLq3DQTvog.jpg" - ], - "blip_caption": "a photo of a group of people skiing down a snow covered slope", - "query": "banff skiing snowy landscape slopes winter", - "dia_id": "D8:28", - "text": "Yeah, it was great for skiing! The snow was amazing and I had a lot of fun. Can't wait to go back next year! Did you try any winter sports, Sam?" - }, - { - "speaker": "Sam", - "dia_id": "D8:29", - "text": "Ooh, skiing looks like a blast! I'd love to try it but I'm not sure my body can take it. What about you? What winter activities do you enjoy?" - }, - { - "speaker": "Evan", - "dia_id": "D8:30", - "text": "Skiing, snowboarding, and ice skating are all fun winter activities I enjoy." - }, - { - "speaker": "Sam", - "dia_id": "D8:31", - "text": "Wish I could join in on the fun! It sounds awesome." - }, - { - "speaker": "Evan", - "dia_id": "D8:32", - "text": "I'd like you to join me, too. Winter activities are a blast - hopefully someday you will!" - }, - { - "speaker": "Sam", - "dia_id": "D8:33", - "text": "Yeah, maybe. It's hard with that, you know. But who knows? Thanks for the understanding, Evan - it really means a lot." - } - ], - "session_9_date_time": "10:18 am on 27 August, 2023", - "session_9": [ - { - "speaker": "Sam", - "dia_id": "D9:1", - "text": "Hey Evan! Exciting news: I started a new diet and exercise routine last Monday and it's made a huge difference. I feel great! What about you, what changes have you made recently?" - }, - { - "speaker": "Evan", - "dia_id": "D9:2", - "text": "Wow, Sam, great! Glad your new diet/exercise is going well. As for me, I've hit a sore spot lately. Twisted my knee last Friday and it's really painful, so it's been tough to stay consistent with my usual fitness routine. It's really frustrating because staying active is like, mega-important to me." - }, - { - "speaker": "Sam", - "dia_id": "D9:3", - "text": "Oh no, sorry to hear about that, Evan. It's frustrating when our bodies don't cooperate, isn't it? Is there anything I can do to help?" - }, - { - "speaker": "Evan", - "dia_id": "D9:4", - "text": "Thanks, Sam. I appreciate the concern. Life throws us curveballs - that's life, right? By the way, remember that book I was talking about? It just gets better with every page, can't let it out of my hands!" - }, - { - "speaker": "Sam", - "dia_id": "D9:5", - "text": "Life sure can be unpredictable and tough. Being adaptable and finding other ways to stay active is key when you're facing an obstacle. Maybe look into low-impact exercises or physical therapy to help with that? And damn, you really got me interested in this book haha!" - }, - { - "speaker": "Evan", - "dia_id": "D9:6", - "text": "Yeah, PT for my knee is on the cards. Hopefully I'll get an appointment soon. Till then, just keeping it low-key and swimming to stay active." - }, - { - "speaker": "Sam", - "dia_id": "D9:7", - "text": "Swimming is a good choice, Evan. It's low-impact and easy on the joints, plus it's refreshing. Keep up with the active lifestyle!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/a/a3/In_a_car_over_a_lake_%28Unsplash%29.jpg" - ], - "blip_caption": "a photography of a car is parked near a lake with mountains in the background", - "query": "prius parked lake view nature", - "dia_id": "D9:8", - "re-download": true, - "text": "Yeah, thanks for the push, Sam. Oh, hey, I came across this pic when I was tidying up. I took a road trip last month - the scenery was stunning and nature really chilled me out." - }, - { - "speaker": "Sam", - "dia_id": "D9:9", - "text": "Wow, looks great! Where did you go? Bet it was nice to get away." - }, - { - "speaker": "Evan", - "dia_id": "D9:10", - "text": "Thanks! I went up to the Rocky Mountains, it was so refreshing! The views were stunning and I felt so relaxed. Do you enjoy road trips and exploring nature?" - }, - { - "speaker": "Sam", - "dia_id": "D9:11", - "text": "I haven't gone on a road trip in ages, but I love being surrounded by nature. It's so tranquil and refreshing. I'd love to go hiking more, but it can be a bit challenging sometimes. However, I am working on becoming healthier, so maybe a road trip and a hike will be possible soon." - }, - { - "speaker": "Evan", - "img_url": [ - "https://www.koin.com/wp-content/uploads/sites/10/2022/04/Tom-Dick-and-Harry-Mountain-view-of-Mirror-Creek-Courtesy-US-Forest-Service.jpg" - ], - "blip_caption": "a photo of a lake with a mountain in the background", - "query": "hiking trail mountain views", - "dia_id": "D9:12", - "text": "That's cool, Sam. Nature can be really peaceful. I'd suggest going for more hikes, like I do. It's always been calming and fun. We should definitely do one together sometime." - }, - { - "speaker": "Sam", - "dia_id": "D9:13", - "text": "Sounds like fun! Which lake do you recommend? I'd love to explore some of the local trails." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/p8xbz5tcvc2b1.jpg" - ], - "blip_caption": "a photo of a lake with a mountain in the background", - "query": "lake louise mountains reflected water trail", - "dia_id": "D9:14", - "text": "Check out this one! It's gorgeous and there are lots of trails nearby. You'll love it!" - }, - { - "speaker": "Sam", - "dia_id": "D9:15", - "text": "Wow, it looks great! Is it nearby? What a view!" - }, - { - "speaker": "Evan", - "dia_id": "D9:16", - "text": "It's only a two-hour drive from here, but trust me, it's worth every minute for the incredible views and peaceful atmosphere." - }, - { - "speaker": "Sam", - "dia_id": "D9:17", - "text": "Cool, a day trip's doable. Nature's calling me, so I'm gonna go check it out! Thanks!" - }, - { - "speaker": "Evan", - "dia_id": "D9:18", - "text": "No worries, enjoy your time in nature. Take care! Bye!" - }, - { - "speaker": "Sam", - "dia_id": "D9:19", - "text": "Thanks Evan. Have a good one. See ya!" - } - ], - "session_10_date_time": "9:28 am on 11 September, 2023", - "session_10": [ - { - "speaker": "Evan", - "img_url": [ - "https://a.1stdibscdn.com/carol-steinberg-paintings-santa-monica-sunset-painting-acrylic-on-canvas-for-sale/22569652/a_97035921645177202523/8193_55e7214a395fa47074c3d3c18b4d3db297c34450_8E0FtEJwGDF4rlzk_1_master.jpg" - ], - "blip_caption": "a photo of a painting of a sunset over a body of water", - "query": "watercolor painting sunset lake vibrant colors", - "dia_id": "D10:1", - "text": "Hey Sam! Long time no talk! Hope all is good. What have I been doing these past few weeks?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.redd.it/6jtf58dhwpq51.jpg" - ], - "blip_caption": "a photo of a bowl of beef and vegetables with a package of healthy choice", - "query": "before and after photo healthy choices", - "dia_id": "D10:2", - "text": "Hey Evan! Nice to hear from you. Life has been an up and down ride. Have you seen the pic I posted of my before and after body as a result of the diet? Working to motivate others to make better choices." - }, - { - "speaker": "Evan", - "dia_id": "D10:3", - "text": "Hey Sam! Loving it. Making healthier choices has definitely made a difference for me. It's amazing how small changes can have such a big impact. How about you? Is it making a difference for you too?" - }, - { - "speaker": "Sam", - "dia_id": "D10:4", - "text": "Hey Evan, thanks for the support! Handling all this has been kinda wild. I'm trying to make healthier choices, but there are still the occasional cravings for sugary drinks and snacks... it's a real struggle." - }, - { - "speaker": "Evan", - "dia_id": "D10:5", - "text": "Yeah, breaking bad habits can be hard. Cravings can be tough too, but little victories count. What do you think sets off those cravings for you?" - }, - { - "speaker": "Sam", - "dia_id": "D10:6", - "text": "It's usually stress, boredom, or just wanting comfort. You know, those sugary treats are so tempting, right?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://trendgallery.art/cdn/shop/files/IMG_9358_6dd0efff-bb0c-4923-804f-9edc8600fee8.jpg" - ], - "blip_caption": "a photo of a painting of a mountain range with a horse", - "query": "painting colorful landscape", - "dia_id": "D10:7", - "text": "Yeah, I get it. When I'm stressed, I always turn to something comforting. But I've found that painting or going for a drive helps too!" - }, - { - "speaker": "Sam", - "dia_id": "D10:8", - "text": "Wow Evan, that's an awesome painting! Good on you for finding a way to de-stress. I could really use something like that - maybe I'll give painting a go or find another calming hobby." - }, - { - "speaker": "Evan", - "dia_id": "D10:9", - "text": "Hey Sam, painting is super chill for calming down. Wanna give it a try? I can help you get started and recommend some supplies if you're interested. Let me know!" - }, - { - "speaker": "Sam", - "dia_id": "D10:10", - "text": "Sounds great, Evan! I want to give it a go and see if it relaxes me. Can you suggest some basic supplies for me to get started?" - }, - { - "speaker": "Evan", - "dia_id": "D10:11", - "text": "Yep, painting is awesome! Get some acrylic paints, brushes, a canvas/paper, and a palette to mix colors. I can give you some recommendations if you want. Just let me know when you're ready and we can plan a painting session!" - }, - { - "speaker": "Sam", - "dia_id": "D10:12", - "text": "Sounds great, Evan! Can you help me pick out the stuff? Let's plan a painting session soon. I'm really excited!" - }, - { - "speaker": "Evan", - "dia_id": "D10:13", - "text": "Yeah, Sam - let's do it! Let's get everything ready and paint next Saturday. Can't wait!" - }, - { - "speaker": "Sam", - "dia_id": "D10:14", - "text": "Sounds good, Evan! Can't wait to paint with you next Saturday. It'll be a fun and creative activity." - } - ], - "session_11_date_time": "8:57 pm on 6 October, 2023", - "session_11": [ - { - "speaker": "Sam", - "img_url": [ - "https://www.cookingclassy.com/wp-content/uploads/2019/05/fruit-salad-8.jpg" - ], - "blip_caption": "a photography of a bowl of fruit with a striped cloth", - "query": "healthy snacks fruit bowl", - "dia_id": "D11:1", - "re-download": true, - "text": "Hey Evan, long time no see! I've started eating healthier - what's new with you? Picked up any new hobbies?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://live.staticflickr.com/23/35171147_0fd909cefb_b.jpg" - ], - "blip_caption": "a photography of a person with a cast on their leg and a cast on their leg", - "query": "knee brace road trip", - "dia_id": "D11:2", - "re-download": true, - "text": "Hey Sam! That's awesome about your healthier eating! For me, I had a setback last week - messed up my knee playing b-ball with the kids. It's been tough to stay active since. I really miss going on adventures like we did last year - good times with the family!" - }, - { - "speaker": "Sam", - "dia_id": "D11:3", - "text": "Hey Evan, sorry to hear about your knee. It must be tough. Are there any ways to stay active while you heal up?" - }, - { - "speaker": "Evan", - "dia_id": "D11:4", - "text": "Thanks, Sam. PT has helped some. I can't do intense workouts, but I'm doing easy exercises to keep it strong. Not as good as being active outdoors, but still something." - }, - { - "speaker": "Sam", - "dia_id": "D11:5", - "text": "Glad PT is helping, Evan! Taking care of yourself is key \u2013 have you explored any fun indoor activities or hobbies?" - }, - { - "speaker": "Evan", - "dia_id": "D11:6", - "text": "I do my favorite watercolor painting to keep me busy. It's a chill way to relax and get into the colors. By the way, something happened two weeks ago! You're not gonna believe this, I had a bit of an adventure recently. Helped a lost tourist find their way, and we ended up taking an unexpected tour around the city. It was a blast!" - }, - { - "speaker": "Sam", - "dia_id": "D11:7", - "text": "Hey Evan, that sounds like a fun and unexpected event! It's always interesting how helping someone can turn into a little adventure of its own. And how's your watercolor painting going?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.pinimg.com/originals/73/e4/fd/73e4fd6baf59abc2beca9fd5509a853c.jpg" - ], - "blip_caption": "a photo of a painting of a cactus in the desert", - "query": "watercolor painting sunset", - "dia_id": "D11:8", - "text": "It's been great! I find painting to be a great stress reliever. Here's what I did last week." - }, - { - "speaker": "Sam", - "dia_id": "D11:9", - "text": "Wow, those are awesome! So cool. Where did you get the inspiration for them?" - }, - { - "speaker": "Evan", - "dia_id": "D11:10", - "text": "Thanks, Sam! The sunset painting was inspired by a vacation a few years back. The colors were so stunning. The cactus painting came from a road trip last month. Such cool places!" - }, - { - "speaker": "Sam", - "dia_id": "D11:11", - "text": "Wow, Evan, your paintings are awesome! How do you decide what to paint?" - }, - { - "speaker": "Evan", - "dia_id": "D11:12", - "text": "Thanks, Sam! I usually paint what's on my mind or something I'm feeling. It can be good memories or places I wanna go to. It's more like expressing myself through art." - }, - { - "speaker": "Sam", - "dia_id": "D11:13", - "text": "That's really amazing, Evan. Expressing yourself through art is such a powerful form of self-expression." - }, - { - "speaker": "Evan", - "dia_id": "D11:14", - "text": "Thanks, Sam. Yeah, it's really a great way to express myself and my emotions. It's a cool way to communicate without using words. So, do you have any other ways in which you express yourself?" - }, - { - "speaker": "Sam", - "dia_id": "D11:15", - "text": "Drawing is cool. I'm still just learning how to draw, but I love expressing myself through writing. It's therapeutic and helps me sort out my feelings. Though, I've been a bit frustrated lately with my new phone. Its navigation app keeps malfunctioning, making getting around a bit of a challenge." - }, - { - "speaker": "Evan", - "dia_id": "D11:16", - "text": "Cool, Sam! Writing is a great way to express yourself. What kind of writing do you enjoy? And about the phone, I recommend trying to update it, it usually works for me!" - }, - { - "speaker": "Sam", - "dia_id": "D11:17", - "text": "Thanks for the tip, Evan! Writing in my journal and doing creative writing is a good way for me to express my innermost thoughts and feelings." - }, - { - "speaker": "Evan", - "dia_id": "D11:18", - "text": "It can be super therapeutic. It gives you a place to express yourself. Keep it up!" - }, - { - "speaker": "Sam", - "dia_id": "D11:19", - "text": "Thanks, Evan! It really helps me make sense of things and express my feelings. It's like having a conversation with myself." - }, - { - "speaker": "Evan", - "dia_id": "D11:20", - "text": "Gotcha, it's like having a place to figure stuff out and make sense of it all. We all need an outlet to express our thoughts and feelings." - } - ], - "session_12_date_time": "3:09 pm on 8 October, 2023", - "session_12": [ - { - "speaker": "Sam", - "dia_id": "D12:1", - "text": "Hey Evan, hope you're doing okay. I wanted to chat about something that's been bothering me lately... I went for a check-up Monday and my doc said my weight's a serious health risk - if I don't make changes soon, it can get worse. I know I made jokes about it, but it's really hitting me. Been having a hard time." - }, - { - "speaker": "Evan", - "img_url": [ - "https://blog.merrittclubs.com/hubfs/weight%20machine%20blog%204.jpg" - ], - "blip_caption": "a photo of a man doing a squat on a machine in a gym", - "query": "gym weight training", - "dia_id": "D12:2", - "text": "Hey Sam, tough news. Yeah, our health can really put a damper on things. I started lifting weights one year ago and it's been a journey. It was a struggle at first, but I'm seeing some gains. You interested in trying it out?" - }, - { - "speaker": "Sam", - "dia_id": "D12:3", - "text": "Hey Evan, I'm interested in getting into it. Any advice on how to get started? Thanks!" - }, - { - "speaker": "Evan", - "dia_id": "D12:4", - "text": "Hey Sam, that's awesome! It's important to start out with good form and technique. Find a trainer who can help you avoid injuries while you build your strength. Start with something small, and as you get stronger, the intensity can increase. Stay consistent with your workout routine and let me know how it goes! Good luck!" - }, - { - "speaker": "Sam", - "dia_id": "D12:5", - "text": "Thanks, Evan. I'm going to find someone who can help me out. I'll keep you posted!" - }, - { - "speaker": "Evan", - "dia_id": "D12:6", - "text": "No problem, Sam. Can't wait to hear about your progress. Keep up the hard work!" - }, - { - "speaker": "Sam", - "dia_id": "D12:7", - "text": "Thanks, Evan. I appreciate your support. It really means a lot to me. I'll definitely keep you posted on my progress." - }, - { - "speaker": "Evan", - "dia_id": "D12:8", - "text": "You're welcome, Sam! It takes time, so be patient with yourself. Your health matters, and I believe in you. Keep going and stay upbeat. You got this!" - }, - { - "speaker": "Sam", - "dia_id": "D12:9", - "text": "Thanks, Evan. I'll stay positive and keep going. Your support means a lot." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/uupz9iakdja61.jpg" - ], - "blip_caption": "a photo of a notepad with a note and a pair of scissors", - "query": "motivational quote believe power progress", - "dia_id": "D12:10", - "text": "Hey Sam, glad I can be here for you! Progress is key, so keep pushing on and stay positive. You got this!" - }, - { - "speaker": "Sam", - "dia_id": "D12:11", - "text": "Wow, Evan, that's really inspiring. Gonna keep believing in it!" - }, - { - "speaker": "Evan", - "dia_id": "D12:12", - "text": "Go get 'em! Believe in your abilities and you'll reach your goals. Stay motivated!" - }, - { - "speaker": "Sam", - "dia_id": "D12:13", - "text": "Thanks Evan! Your words gave me a boost. I'm staying motivated and believing in myself." - }, - { - "speaker": "Evan", - "dia_id": "D12:14", - "text": "Awesome! Keep staying motivated and believing in yourself. You've got this!" - }, - { - "speaker": "Sam", - "dia_id": "D12:15", - "text": "Thanks, Evan! Your support means a lot to me." - }, - { - "speaker": "Evan", - "dia_id": "D12:16", - "text": "No prob, Sam! I'm here for you. Just keep taking one step at a time, and you'll get there eventually!" - }, - { - "speaker": "Sam", - "dia_id": "D12:17", - "text": "Sure, Evan. I'll take it slow. See ya!" - } - ], - "session_13_date_time": "4:07 pm on 14 October, 2023", - "session_13": [ - { - "speaker": "Evan", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/8/85/Cliffs_and_mountains_and_sky_at_sunset_--_2_of_33.jpg" - ], - "blip_caption": "a photography of a painting of a person on a cliff", - "query": "painting peaceful sunset", - "dia_id": "D13:1", - "re-download": true, - "text": "Hey Sam, how's it going? Been a while since we talked. Hope all is good." - }, - { - "speaker": "Sam", - "img_url": [ - "http://raremunchiez.com/cdn/shop/products/soda-can-fizzy-candy-6-pack-raremunchiez.jpg" - ], - "blip_caption": "a photo of a person holding a box of sodas in front of a wall", - "query": "soda candy", - "dia_id": "D13:2", - "text": "Hey Evan! It's been a rough week - I gave in and bought some unhealthy snacks. I feel kinda guilty. How's it going for you? That painting is awesome! Did you paint it?" - }, - { - "speaker": "Evan", - "dia_id": "D13:3", - "text": "Hey Sam, sorry to hear about the rough week. Don't worry about the snacks. I'm doing okay, just finished this painting of a sunset. It really helps me relax. So, how's everything going with you? Anything new and exciting?" - }, - { - "speaker": "Sam", - "dia_id": "D13:4", - "text": "Thanks, Evan! Yeah, I just couldn't resist them. Gotta do better. As for me, just dealing with work stress and trying to stay motivated." - }, - { - "speaker": "Evan", - "dia_id": "D13:5", - "text": "Hey Sam, work stress can really get to you. Have you tried anything new to de-stress? Maybe picking up a hobby or something could help." - }, - { - "speaker": "Sam", - "dia_id": "D13:6", - "text": "Thinking about trying something different outdoors. Any suggestions?" - }, - { - "speaker": "Evan", - "dia_id": "D13:7", - "text": "Sounds good! Have you ever tried kayaking? It's a fun and active way to paddle on a river or lake. What are your thoughts on that?" - }, - { - "speaker": "Sam", - "dia_id": "D13:8", - "text": "Kayaking sounds awesome! Haven't tried it yet, but it looks like a fun way to get in some exercise and enjoy nature. I'm definitely considering giving it a try. Thanks!" - }, - { - "speaker": "Evan", - "dia_id": "D13:9", - "text": "No worries, Sam! It's a fun way to get in some exercise and enjoy nature. Let me know when you're ready to give it a try and I can hook you up with a good spot." - }, - { - "speaker": "Sam", - "dia_id": "D13:10", - "text": "Thanks for the idea, my mate and I are just around the corner from kayaking on the lake, we're going to try that now!" - }, - { - "speaker": "Evan", - "dia_id": "D13:11", - "text": "Of course, let me know if you like it, we can plan a kayaking trip together, I'll pick a cool spot!" - }, - { - "speaker": "Sam", - "img_url": [ - "https://www.higherpursuits.com/wp-content/uploads/2019/01/2019-Rental-Boats-1.jpg" - ], - "blip_caption": "a photography of a row of kayaks lined up on the shore of a river", - "query": "kayak river", - "dia_id": "D13:12", - "re-download": true, - "text": "Yep, Evan! Can't wait. Thanks for the help!" - }, - { - "speaker": "Evan", - "dia_id": "D13:13", - "text": "Ready for an adventure? Where will you go?" - }, - { - "speaker": "Sam", - "dia_id": "D13:14", - "text": "We're traveling through Lake Tahoe! I heard it's great for kayaking." - }, - { - "speaker": "Evan", - "dia_id": "D13:15", - "text": "Hey Sam, it's an awesome pick! You'll love it there - clear water and gorgeous views. Have a blast and take lots of pics!" - }, - { - "speaker": "Sam", - "dia_id": "D13:16", - "text": "Thanks, Evan! I'm looking forward to it!" - } - ], - "session_14_date_time": "1:50 pm on 17 October, 2023", - "session_14": [ - { - "speaker": "Sam", - "dia_id": "D14:1", - "text": "Hey Evan! I've been missing our chats. I had quite the health scare last weekend - ended up in the ER with a severe stomachache. Turns out, it was gastritis, which was pretty alarming. It was a wake-up call for me to start prioritizing my health, like adopting a more nutritious diet and getting regular exercise. On top of that, my phone's been giving me a hard time, adding to the stress." - }, - { - "speaker": "Evan", - "dia_id": "D14:2", - "text": "Hey Sam, sorry to hear about that. Gastritis can be tough. Taking care of ourselves is important. BTW, I've been focusing on fitness and it's been really beneficial for my overall well-being. Funny thing, I had another encounter with a lost tourist recently. Seems like helping tourists is becoming a recurring theme in my life!" - }, - { - "speaker": "Sam", - "dia_id": "D14:3", - "text": "Thanks, Evan! Glad you've found that it's been good for you! I totally need to get into it too. Just getting started is hard - any tips for staying motivated? Also, you mentioned another lost tourist? Seems like you're becoming the go-to guy for tourists in need!" - }, - { - "speaker": "Evan", - "dia_id": "D14:4", - "text": "Yup, Sam! Set some goals, like a certain distance to run or number of push-ups to do. It'll give you something to strive for and stay motivated. Also, try to find an exercise that you enjoy and maybe even get a buddy for extra fun and accountability. Sound good?" - }, - { - "speaker": "Sam", - "dia_id": "D14:5", - "text": "Yeah, that sounds like a good idea. Having goals and someone to exercise with might help. I'll give it a try!" - }, - { - "speaker": "Evan", - "dia_id": "D14:6", - "text": "Awesome, Sam! Getting started will get easier with time. And don't forget it's about feeling good and reaching goals, too. Let's plan a hike soon!" - }, - { - "speaker": "Sam", - "dia_id": "D14:7", - "text": "Sounds awesome, Evan! Can't wait to go on a hike with you. It's going to be a fun challenge and a great opportunity to appreciate the beauty of nature." - }, - { - "speaker": "Evan", - "dia_id": "D14:8", - "text": "Definitely, Sam! Hiking is an awesome way to bond with nature and push ourselves. It's gonna be a cool memory for us both. It's great to see progress, was just at the gym yesterday. Gaining strength!" - }, - { - "speaker": "Sam", - "blip_caption": "a photo of a person wearing hiking shoes on a couch", - "dia_id": "D14:9", - "text": "Super excited to get fit with ya. Let's hit the trails soon!" - }, - { - "speaker": "Evan", - "dia_id": "D14:10", - "text": "Thanks, Sam! That's so nice of you. We'll definitely have a great time on our hike!" - }, - { - "speaker": "Sam", - "dia_id": "D14:11", - "text": "Totally! I'm so pumped for this hike. Connecting with nature is exactly what I need. Thanks so much for the support and always being there. Means a lot." - }, - { - "speaker": "Evan", - "dia_id": "D14:12", - "text": "Sure thing! Our hike is going to be awesome, I can tell. I'm always here to support you." - }, - { - "speaker": "Sam", - "dia_id": "D14:13", - "text": "Thanks, Evan. I appreciate your support." - }, - { - "speaker": "Evan", - "dia_id": "D14:14", - "text": "No problem, Sam. Whenever you need support, I'm here for you. Stay safe!" - }, - { - "speaker": "Sam", - "dia_id": "D14:15", - "text": "Thanks, I'll get in touch if I need anything. Stay safe. Bye!" - }, - { - "speaker": "Evan", - "dia_id": "D14:16", - "text": "Later! Stay safe and don't hesitate to holler if you need anything. Can't wait to hit the trail." - } - ], - "session_15_date_time": "2:56 pm on 25 October, 2023", - "session_15": [ - { - "speaker": "Sam", - "dia_id": "D15:1", - "text": "Morning, Evan. I've been trying to keep up with my new health routine, but it's tough. My family's really pushing for it, and I feel so pressured." - }, - { - "speaker": "Evan", - "dia_id": "D15:2", - "text": "I hear you, Sam. It's important to have people who encourage you, but not stress you out. By the way, I just got back from my morning walk. It really helps to start the day actively." - }, - { - "speaker": "Sam", - "dia_id": "D15:3", - "text": "Yeah, it's easier when you have a great support system. Thanks for being there for me." - }, - { - "speaker": "Evan", - "dia_id": "D15:4", - "text": "No worries, Sam. I'll be there for you. Take it slow and treat yourself." - }, - { - "speaker": "Sam", - "dia_id": "D15:5", - "text": "Thanks for the reminder to take it easy. I sometimes get impatient with myself when I want results fast, but I gotta be patient." - }, - { - "speaker": "Evan", - "dia_id": "D15:6", - "text": "Yep, progress takes time. So just take it one step at a time." - }, - { - "speaker": "Sam", - "dia_id": "D15:7", - "text": "Yes, you're right, Evan. Taking it slow is better than doing too much. I appreciate your support." - }, - { - "speaker": "Evan", - "img_url": [ - "https://www.publicdomainpictures.net/pictures/540000/nahled/fit-man-and-healthy-food-1694521202SgE.jpg" - ], - "blip_caption": "a photography of a man sitting at a table with a lot of fruits and vegetables", - "query": "gym bag selfie", - "dia_id": "D15:8", - "re-download": true, - "text": "I get it, Sam. I went through a similar phase a twoyears ago. Changed my diet, started walking regularly, things like that." - }, - { - "speaker": "Sam", - "dia_id": "D15:9", - "text": "Wow, Evan, you look great! How did you manage the change?" - }, - { - "speaker": "Evan", - "dia_id": "D15:10", - "text": "I started focusing more on my well-being rather than fixating on quick results. Letting go of that pressure made a huge difference." - }, - { - "speaker": "Sam", - "dia_id": "D15:11", - "text": "That's impressive, Evan. It's inspiring to see how you transformed by changing your mindset." - }, - { - "speaker": "Evan", - "dia_id": "D15:12", - "text": "Thanks, Sam. Letting go of unrealistic expectations was liberating, both physically and mentally." - }, - { - "speaker": "Sam", - "dia_id": "D15:13", - "text": "You're really doing great, Evan! I want to feel that same sense of freedom." - }, - { - "speaker": "Evan", - "dia_id": "D15:14", - "text": "Thanks, Sam. Just take it one day at a time. Celebrate small victories." - }, - { - "speaker": "Sam", - "dia_id": "D15:15", - "text": "Thanks, Evan! Focusing on small wins sounds like a plan. I'll take it one day at a time." - }, - { - "speaker": "Evan", - "dia_id": "D15:16", - "text": "Exactly! Congrats on every little victory. Keep it up, I'm here for you." - }, - { - "speaker": "Sam", - "dia_id": "D15:17", - "text": "Your support means everything. Here's to moving forward!" - }, - { - "speaker": "Evan", - "dia_id": "D15:18", - "text": "Anytime, Sam! Let's keep pushing ahead. I'm here to help you. Take care!" - } - ], - "session_16_date_time": "9:13 pm on 9 November, 2023", - "session_16": [ - { - "speaker": "Sam", - "dia_id": "D16:1", - "text": "Hey Evan! Hope you're doing good. Got some good news to share - I'm a Weight Watchers coach in my group now! It's a pretty big accomplishment for me, feel really proud." - }, - { - "speaker": "Evan", - "dia_id": "D16:2", - "text": "Congrats Sam! That's awesome! I'm super proud of you. Becoming a Weight Watchers coach is a big deal. Keep going!" - }, - { - "speaker": "Sam", - "dia_id": "D16:3", - "text": "Thanks, Evan! Appreciate your support. It's been a journey, and being chosen as a coach is a great step in my quest for better health." - }, - { - "speaker": "Evan", - "dia_id": "D16:4", - "text": "Wow, Sam! You've come such a long way. It's exciting to see what comes next for you in your quest for better health." - }, - { - "speaker": "Sam", - "dia_id": "D16:5", - "text": "Thanks, Evan! It feels great to see progress. Being a coach will hopefully keep me motivated and help others stay committed too. It's a big challenge, but I'm ready for it!" - }, - { - "speaker": "Evan", - "dia_id": "D16:6", - "text": "That's awesome, Sam! Helping others stay committed and motivated is so rewarding. You really inspire us. Keep up the great work!" - }, - { - "speaker": "Sam", - "dia_id": "D16:7", - "text": "Thanks, Evan! Your kind words mean a lot. It's been a difficult road, but I'm determined to continue making a positive impact." - }, - { - "speaker": "Evan", - "img_url": [ - "https://guitarsonmain.com/cdn/shop/files/1968-kustom-k-200a-vintage-guitar-w-ohsc-40274115559663_5000x.jpg" - ], - "blip_caption": "a photo of a guitar laying on the floor with a guitar strap", - "query": "vintage guitar", - "dia_id": "D16:8", - "text": "Sorry about missing any events, I've had some personal challenges since we last spoke. Still here for you though - do you need any support or want to share anything? Btw look what i got!" - }, - { - "speaker": "Sam", - "dia_id": "D16:9", - "text": "Hey, it looks so vintage and cool! What model is it? How've you been doing lately? I'm here if you wanna chat." - }, - { - "speaker": "Evan", - "dia_id": "D16:10", - "text": "It's a 1968 Kustom K-200A vintage guitar and I got it as a gift from a close friend. It's been a tough time for me since we last caught up; I lost my job last month, which has been pretty rough. But I really appreciate your support through all this." - }, - { - "speaker": "Sam", - "dia_id": "D16:11", - "text": "Sorry to hear about your job, Evan. What happened?" - }, - { - "speaker": "Evan", - "dia_id": "D16:12", - "text": "It's been a bit of a rough patch lately. The company downsized, and I was part of that. I'm currently on the hunt for a new job, which hasn't been easy, but I'm keeping my spirits up and staying hopeful." - }, - { - "speaker": "Sam", - "dia_id": "D16:13", - "text": "Sorry about your job, Evan. It's tough when it comes out of nowhere, but I'm proud of how you're handling it. Let me know if you need someone to talk to or if I can do anything to help. You'll get through this." - }, - { - "speaker": "Evan", - "dia_id": "D16:14", - "text": "Thanks, Sam. Your support means a lot. It's been quite a ride, but I really appreciate having someone like you to talk to. I'll definitely reach out if I need anything." - }, - { - "speaker": "Sam", - "dia_id": "D16:15", - "text": "For sure, Evan! I'm here for ya. Life can be tough sometimes, but we got this. Stay positive and it'll all work out. Just know that I'm here if you need someone to talk to." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.pinimg.com/originals/c8/d6/bc/c8d6bc6ad3b08172bdbc1665f3ed080b.jpg" - ], - "blip_caption": "a photo of a person walking on the beach with a surfboard", - "query": "sunset ocean", - "dia_id": "D16:16", - "text": "Thanks, Sam. Your kind words and support mean a lot. It's great to have you here. I'm gonna stay positive and keep going. Cheers!" - }, - { - "speaker": "Sam", - "dia_id": "D16:17", - "text": "Wow, that sunset is stunning! It's so soothing just to see it. Is that a special spot you go to watch sunsets?" - }, - { - "speaker": "Evan", - "dia_id": "D16:18", - "text": "Yeah, it's this peaceful place close to my home. I often go there to relax and unwind." - }, - { - "speaker": "Sam", - "dia_id": "D16:19", - "text": "That sounds wonderful, Evan! I'd love to check it out with you sometime." - }, - { - "speaker": "Evan", - "dia_id": "D16:20", - "text": "Oh, I wish I could bring you along. That picture was actually taken last Friday at my favorite spot by the beach. Watching the waves and the sunset colors really helps me find peace, especially during tough times. It's a beautiful reminder of nature's resilience. We should definitely plan to go together someday." - }, - { - "speaker": "Sam", - "dia_id": "D16:21", - "text": "No worries, Evan. And yes, we should make a plan to go. That photo is just mesmerizing!" - }, - { - "speaker": "Evan", - "dia_id": "D16:22", - "text": "I'm glad you like it! It's a really calming place. Let's make a point to visit it together soon." - }, - { - "speaker": "Sam", - "dia_id": "D16:23", - "text": "Absolutely, Evan! A trip there sounds like the perfect way to de-stress." - }, - { - "speaker": "Evan", - "dia_id": "D16:24", - "text": "Awesome, let's do it! Let's plan it for next month, I'm already excited about exploring it together!" - } - ], - "session_17_date_time": "7:30 pm on 21 November, 2023", - "session_17": [ - { - "speaker": "Sam", - "dia_id": "D17:1", - "text": "Hey Ev! Long time no chat. How's it going? Hope all is well." - }, - { - "speaker": "Evan", - "dia_id": "D17:2", - "text": "Hey Sam, good to hear from you! Life's been a wild ride lately. Last week, I had a health scare and had to go to the hospital. They found something suspicious during a check-up, which freaked me out. Thankfully, it was all a misunderstanding, but it made me realize how important it is to keep an eye on my health. How've you been?" - }, - { - "speaker": "Sam", - "dia_id": "D17:3", - "text": "Woah, Evan, that must've been scary! Phew, it was just a misunderstanding. A health scare can really make you re-evaluate what's important. As for me, I've been dealing with some discomfort and it's been limiting my movement. I've been trying to make changes diet-wise, but it can be hard." - }, - { - "speaker": "Evan", - "dia_id": "D17:4", - "text": "That sucks, Sam. It's tough when our health holds us back. I believe in you \u2013 just taking small steps can help. Have you tried any new hobbies recently to take your mind off it?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://www.goodwillfinds.com/on/demandware.static/-/Sites-goodwill-master/default/dwa2341b30/images/large/lhyOBm1CPSKy54szJay7vQj/2023/November/07/image_(170).jpg" - ], - "blip_caption": "a photo of a book with a picture of a man on it", - "query": "the godfather dvd box set", - "dia_id": "D17:5", - "text": "Thanks, Evan. I haven't tried much new lately, but I did get this yesterday. It's been my go-to 'feel good' flick. So, you said you had a health scare - how're you now?" - }, - { - "speaker": "Evan", - "dia_id": "D17:6", - "text": "That movie sounds interesting! I'm doing well now. Doctors said everything is fine, but it taught me the value of life. Just trying to enjoy the moment." - }, - { - "speaker": "Sam", - "dia_id": "D17:7", - "text": "That's awesome, Evan! Let's make it a habit to appreciate something each day. It really helps us enjoy life more. What do you think?" - }, - { - "speaker": "Evan", - "dia_id": "D17:8", - "text": "Sounds good, Sam! Let's take the time to appreciate the little things in life." - }, - { - "speaker": "Sam", - "dia_id": "D17:9", - "text": "Thanks for always being there, Evan. It means a lot." - }, - { - "speaker": "Evan", - "dia_id": "D17:10", - "text": "Sure, Sam. I'm here for you. We gotta stick together, especially now." - }, - { - "speaker": "Sam", - "dia_id": "D17:11", - "text": "Yeah, Evan. Life can be tough sometimes, but having supportive people like you makes it way easier." - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.pexels.com/photos/7149181/pexels-photo-7149181.jpeg" - ], - "blip_caption": "a photography of a group of people sitting around a fire pit", - "query": "group of friends laughing", - "dia_id": "D17:12", - "re-download": true, - "text": "Yeah, Sam. Tough times are way easier with friends we can rely on. We've got each other!" - }, - { - "speaker": "Sam", - "dia_id": "D17:13", - "text": "Looks like you're having a blast! I was wondering, what do you do to stay fit and healthy?" - }, - { - "speaker": "Evan", - "dia_id": "D17:14", - "text": "That was wild! I stay in shape by hitting the gym and taking my car out for a spin. Gotta keep it up! How are you doing on your fitness goals, Sam?" - }, - { - "speaker": "Sam", - "dia_id": "D17:15", - "text": "Fitness goals have been hard to reach, but hey, that's life!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.pexels.com/photos/11663179/pexels-photo-11663179.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-serhat-tu%C4%9F-11663179.jpg" - ], - "blip_caption": "a photo of a small island with a lone boat in the water", - "query": "sunrise calm lake", - "dia_id": "D17:16", - "text": "Yeah Sam, it's true. Progress takes time, so keep pushing." - }, - { - "speaker": "Sam", - "dia_id": "D17:17", - "text": "Where is that? It looks gorgeous!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://myrnafolkert.files.wordpress.com/2021/05/boat-pic-9.jpg" - ], - "blip_caption": "a photo of a sun shining through the clouds over a body of water", - "query": "small island lake huron lone boat water", - "dia_id": "D17:18", - "text": "This little island is where I grew up and it's my happy place." - }, - { - "speaker": "Sam", - "dia_id": "D17:19", - "text": "Wow, that spot looks gorgeous. Growing up there must have been so peaceful and stunning." - }, - { - "speaker": "Evan", - "dia_id": "D17:20", - "text": "Yeah, it was. That place shaped me and will always hold a special place in my heart." - }, - { - "speaker": "Sam", - "dia_id": "D17:21", - "text": "Yeah, it can be soul-calming." - }, - { - "speaker": "Evan", - "img_url": [ - "https://c1.wallpaperflare.com/preview/608/545/293/sunset-kapiti-coast-new-zealand-beautiful-serene.jpg" - ], - "blip_caption": "a photography of a person walking on a beach at sunset", - "query": "serene sunset island", - "dia_id": "D17:22", - "re-download": true, - "text": "Yeah, it really is. So serene and calming." - }, - { - "speaker": "Sam", - "dia_id": "D17:23", - "text": "It's heavenly!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://images.pexels.com/photos/9214105/pexels-photo-9214105.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-dastan-khdir-9214105.jpg" - ], - "blip_caption": "a photo of a sunset over a body of water", - "query": "calm lake dusk", - "dia_id": "D17:24", - "text": "Yeah, it's like a little slice of paradise. I always feel so peaceful and serene when I'm there." - }, - { - "speaker": "Sam", - "dia_id": "D17:25", - "text": "Wow, it really seems like a peaceful retreat. Thanks for showing me!" - }, - { - "speaker": "Evan", - "dia_id": "D17:26", - "text": "No prob, always good to chat about those tranquil times. Take it easy!" - }, - { - "speaker": "Sam", - "dia_id": "D17:27", - "text": "Take care, buddy. Hang in there!" - }, - { - "speaker": "Evan", - "dia_id": "D17:28", - "text": "Thanks, Sam. If you need to talk, I'm here for you too." - } - ], - "session_18_date_time": "8:16 pm on 5 December, 2023", - "session_18": [ - { - "speaker": "Evan", - "dia_id": "D18:1", - "text": "Hey Sam, good to hear from you. I've hit a bit of a snag - my new Prius, the one I just bought, broke down. It's a bit of a stressor since I rely on it for my active lifestyle and road trips. It's frustrating when new things go awry so soon." - }, - { - "speaker": "Sam", - "dia_id": "D18:2", - "text": "Hey Evan, that's rough. Dealing with a new car breaking down is such a hassle, especially when it's your main mode of transport." - }, - { - "speaker": "Evan", - "dia_id": "D18:3", - "text": "You're telling me. I was really counting on this new Prius to be reliable. It's always a challenge when you have to deal with unexpected issues like this. But, I guess it's just one of those things - even new cars can have problems." - }, - { - "speaker": "Sam", - "dia_id": "D18:4", - "text": "It's tough when your plans get derailed by something like this. But hey, sometimes these setbacks lead to new opportunities." - }, - { - "speaker": "Evan", - "dia_id": "D18:5", - "text": "True, I'm trying to see it as a chance to explore other ways of staying active and traveling. Maybe it's an opportunity to try something different." - }, - { - "speaker": "Sam", - "img_url": [ - "http://steenshoney.com/cdn/shop/articles/IMG_5745.jpg" - ], - "blip_caption": "a photo of a variety of bowls of fruit and yogurt", - "query": "colorful smoothie bowl fresh fruits toppings", - "dia_id": "D18:6", - "text": "Exactly, it's all about finding the silver lining. Speaking of new things, I attended a Weight Watchers meeting yesterday. Learned some great tips." - }, - { - "speaker": "Evan", - "dia_id": "D18:7", - "text": "That smoothie bowl looks fantastic! How was the meeting? Yeah, I've been thinking about trying yoga, something gentle yet effective for stress relief and flexibility. What's your take on it, Sam?" - }, - { - "speaker": "Sam", - "dia_id": "D18:8", - "text": "The meeting was really insightful, and that smoothie bowl was a hit! Yoga's a great choice, it's done wonders for my flexibility and stress levels. You should definitely try it." - }, - { - "speaker": "Evan", - "dia_id": "D18:9", - "text": "I think I will. Thanks for the suggestion, Sam." - }, - { - "speaker": "Sam", - "dia_id": "D18:10", - "text": "Anytime, Evan. If you need any yoga tips or anything else, just let me know." - }, - { - "speaker": "Evan", - "dia_id": "D18:11", - "text": "Your support's been invaluable. Thanks again, Sam!" - }, - { - "speaker": "Sam", - "dia_id": "D18:12", - "text": "No worries, Evan. We all need a bit of help when trying new things. It's great to have support." - }, - { - "speaker": "Evan", - "dia_id": "D18:13", - "text": "Absolutely. It makes a big difference knowing you're not alone in these situations." - }, - { - "speaker": "Sam", - "dia_id": "D18:14", - "text": "Definitely. Take care, and let me know how the yoga goes. Bye!" - }, - { - "speaker": "Evan", - "dia_id": "D18:15", - "text": "Will do. Thanks for everything, Sam. Talk soon. Bye!" - } - ], - "session_19_date_time": "1:45 pm on 9 December, 2023", - "session_19": [ - { - "speaker": "Evan", - "dia_id": "D19:1", - "text": "Hey Sam, hope you're doing good. Wanted to share some amazing news - my partner is pregnant! We're so excited! It's been a while since we had a kiddo around." - }, - { - "speaker": "Sam", - "dia_id": "D19:2", - "text": "Congrats, Ev! That's great news! Parenthood is so amazing. How are you feeling about it?" - }, - { - "speaker": "Evan", - "dia_id": "D19:3", - "text": "So excited and a bit nervous! It's been a while since I had a toddler around but I'm really looking forward to it. Parenthood is so rewarding. I still remember when my first child was born, the joy was amazing. Looking forward to witness the miracle of life and build more memories with my family!" - }, - { - "speaker": "Sam", - "dia_id": "D19:4", - "text": "Wow, you're gonna be an amazing parent! Treasure those memories, they're truly special." - }, - { - "speaker": "Evan", - "img_url": [ - "https://clickinmyheels.files.wordpress.com/2019/09/fpthpk1wrjgpigitpdlw3w.jpg" - ], - "blip_caption": "a photo of a desk with a lamp, a picture frame, and a sign", - "query": "collage family photos living room wall memories", - "dia_id": "D19:5", - "text": "Thanks Sam! Absolutely. Talking of memories, I want to show you this. It's a collage of some of our top family memories. Each photo has an amazing moment - birthdays, holidays, vacations - so good to look back and recall all the great times we had." - }, - { - "speaker": "Sam", - "dia_id": "D19:6", - "text": "That's so lovely, Evan. Your family looks so happy. What's the story behind that sign in the center?" - }, - { - "speaker": "Evan", - "dia_id": "D19:7", - "text": "Oh, that one? It's from our trip to Banff. We have this sign in the frame that says 'Bring it on Home' - it's our family's motto, always reminding us of the importance of togetherness, no matter where we are." - }, - { - "speaker": "Sam", - "dia_id": "D19:8", - "text": "That's really touching, Evan. It's important to have something that keeps the family bond strong." - }, - { - "speaker": "Evan", - "dia_id": "D19:9", - "text": "Absolutely, Sam. My family means the world to me. They're my rock. I'm looking forward to expanding our family and creating even more beautiful memories." - }, - { - "speaker": "Sam", - "dia_id": "D19:10", - "text": "That's wonderful to hear, Evan! It's clear how much you value your family. Are you thinking of any specific plans or events to add to that collage?" - }, - { - "speaker": "Evan", - "dia_id": "D19:11", - "text": "Thanks, Sam! Yeah, we're planning a big family reunion next summer. It's going to be a blast and a perfect opportunity to add to our collage." - }, - { - "speaker": "Sam", - "dia_id": "D19:12", - "text": "Sounds fantastic! If you need any tips on organizing such a big event, just let me know. I'm always here to support and celebrate your family's milestones." - }, - { - "speaker": "Evan", - "dia_id": "D19:13", - "text": "Thanks, Sam! Your support means a lot. I'll keep you updated. Take care, bye!" - }, - { - "speaker": "Sam", - "dia_id": "D19:14", - "text": "Take care, Evan! Can't wait to hear about it. Bye!" - }, - { - "speaker": "Evan", - "dia_id": "D19:15", - "text": "Bye Sam. I'll definitely keep you updated. Thanks for the kind words and support. Take care!" - } - ], - "session_20_date_time": "6:48 pm on 17 December, 2023", - "session_20": [ - { - "speaker": "Evan", - "dia_id": "D20:1", - "text": "Hey Sam, what's up? Long time no see, huh? Lots has happened." - }, - { - "speaker": "Sam", - "dia_id": "D20:2", - "text": "Hey Evan! Long time no see. I'm doing okay, been through a few bumps. How about you?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://d2g8igdw686xgo.cloudfront.net/25280738_15137467980_r.jpg" - ], - "blip_caption": "a photo of a young boy with crutches and a backpack", - "query": "son on crutches", - "dia_id": "D20:3", - "text": "It's not easy for us right now, my son had an accident last Tuesday, he fell off his bike and it was rough. But he's doing better now. How are you dealing with all this?" - }, - { - "speaker": "Sam", - "dia_id": "D20:4", - "text": "Darn, sorry to hear that. Hope he's feeling better. Same here, it's been tough lately. After we talked, I started thinking about ways to cope with it, but it's been challenging." - }, - { - "speaker": "Evan", - "dia_id": "D20:5", - "text": "Life can be hard sometimes. Do you have any hobbies or activities that make you happy?" - }, - { - "speaker": "Sam", - "dia_id": "D20:6", - "text": "I used to love hiking, but it's been a while since I had the chance to do it." - }, - { - "speaker": "Evan", - "dia_id": "D20:7", - "text": "I remember you mentioning that! Hiking is indeed a great way to center oneself and be one with nature. We should definitely plan a hike soon!" - }, - { - "speaker": "Sam", - "blip_caption": "a photo of a beach with a few people walking on it", - "dia_id": "D20:8", - "text": "Yeah, I'm struggling with my weight and it's affecting my confidence. I feel like I can't overcome all the challenges with my weight, I keep lacking motivation." - }, - { - "speaker": "Evan", - "dia_id": "D20:9", - "text": "Yeah, I understand it can be challenging. But remember, it's important to believe in yourself and take it one day at a time, Sam. Your worth is not defined by your weight." - }, - { - "speaker": "Sam", - "dia_id": "D20:10", - "text": "Cheers, Evan. Appreciate the help. It's tough breaking out of my comfort zone." - }, - { - "speaker": "Evan", - "dia_id": "D20:11", - "text": "Stepping out of your comfort zone can be intimidating, but it's totally worth it. Just challenge yourself to try something new, even if it's just a little thing. You got this!" - }, - { - "speaker": "Sam", - "dia_id": "D20:12", - "text": "Thanks, Evan. I'll take your advice. Trying new things can be difficult." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/ub3eyozz1olb1.jpg" - ], - "blip_caption": "a photo of a woman standing in front of a painting", - "query": "painting accomplishment", - "dia_id": "D20:13", - "text": "Yeah, trying something new and succeeding gives a great feeling of accomplishment. Give it a go, even if it's just a little thing. You'll be amazed!" - }, - { - "speaker": "Sam", - "dia_id": "D20:14", - "text": "She looks so confident! What kind of painting is that in the background?" - }, - { - "speaker": "Evan", - "dia_id": "D20:15", - "text": "\nThis is a contemporary figurative painting that I've finished few days ago, emphasizing the emotional state through expressive brushwork and vibrant color choices. It captures a moment of introspection, where the subject is deeply immersed in thought. Very proud of it!" - }, - { - "speaker": "Sam", - "dia_id": "D20:16", - "text": "That's amazing work, who's the girl standing next to painting?" - }, - { - "speaker": "Evan", - "dia_id": "D20:17", - "text": "That's a close friend of mine who helped me get this painting published in the exhibition!" - } - ], - "session_21_date_time": "4:25 pm on 26 December, 2023", - "session_21": [ - { - "speaker": "Sam", - "dia_id": "D21:1", - "text": "Hey Evan! Long time no see, how's it going?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://c0.wallpaperflare.com/preview/990/890/416/wedding-kiss-love-pair.jpg" - ], - "blip_caption": "a photography of a bride and groom kissing in front of a tree", - "query": "wedding day", - "dia_id": "D21:2", - "re-download": true, - "text": "Hey Sam! Long time no see! Been up and down lately, got married last week - how about you?" - }, - { - "speaker": "Sam", - "dia_id": "D21:3", - "text": "Congratulations, Evan! Is that the woman from Canada?" - }, - { - "speaker": "Evan", - "dia_id": "D21:4", - "text": "Yes, that's her, I don't know why we didn't get married before, because I was in love with her at first sight!" - }, - { - "speaker": "Sam", - "dia_id": "D21:5", - "text": "Wow, Evan! Love at first sight? That sounds like something straight out of a fairy tale. What are your thoughts on it? Do you believe in love at first sight?" - }, - { - "speaker": "Evan", - "dia_id": "D21:6", - "text": "I totally believe in it. It was like time stopped and I felt like a spark lit inside me - it was so right." - }, - { - "speaker": "Sam", - "dia_id": "D21:7", - "text": "That's awesome, Evan! Finding that kind of connection must feel really liberating. Love can be so powerful, huh?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://kikiraine.files.wordpress.com/2019/11/img_4736.jpg" - ], - "blip_caption": "a photo of a person sitting on a rock near the water", - "query": "sunset love magic peace wonder", - "dia_id": "D21:8", - "text": "Yeah, Sam, love is truly amazing. It brings so much happiness and fulfillment, like a beautiful sunset that lights up our lives and brings peace. Incredible!" - }, - { - "speaker": "Sam", - "dia_id": "D21:9", - "text": "Wish I could feel the same about love, but I've started to enjoy running in the mornings, and it's been a great way to clear my head. What can you do, right?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://trendgallery.art/cdn/shop/files/IMG_9647_fa0b4eba-d7f5-48ae-81c9-b471605dd4a9.jpg" - ], - "blip_caption": "a photo of a painting with a white background and a blue, orange, and black painting", - "query": "canvas painting colorful abstract strokes", - "dia_id": "D21:10", - "text": "Yeah, I get it. Life's all about finding what works for you. Like your morning runs, they're a step towards something good, right? Keep trying new things, Sam, and you might find your own version of love in the most unexpected places. Embrace the journey \u2014 it\u2019s full of surprises!" - }, - { - "speaker": "Sam", - "dia_id": "D21:11", - "text": "Such a minimalistic and stunning piece of work, I wonder what inspired the artist to create it." - }, - { - "speaker": "Evan", - "dia_id": "D21:12", - "text": "The painting is mine, I made it when I was a mix of emotions - sad, mad, and hopeful. Art is amazing how it can portray feelings without words." - }, - { - "speaker": "Sam", - "dia_id": "D21:13", - "text": "Wow, Evan! Art is really amazing at expressing emotions - it's truly fascinating." - }, - { - "speaker": "Evan", - "img_url": [ - "https://trendgallery.art/cdn/shop/products/IMG_3291_4beec6cf-7c90-43ef-b8f7-7f7fd295917b.jpg" - ], - "blip_caption": "a photo of a painting with a bird flying over it", - "query": "abstract vibrant colorful painting", - "dia_id": "D21:14", - "text": "It's amazing how art can express emotions so well. It really helps me recognize and handle my own feelings. This painting is giving me a massive rush of joy!" - }, - { - "speaker": "Sam", - "dia_id": "D21:15", - "text": "That's stunning! What emotions did you create this painting with?" - }, - { - "speaker": "Evan", - "dia_id": "D21:16", - "text": "I painted this with a sense of joy and freedom. The spontaneous strokes and bold colors reflect a playful and liberated mood, embracing the creative process without restraint." - }, - { - "speaker": "Sam", - "img_url": [ - "https://judgeme.imgix.net/nashid-chroma-art-and-apparel/1640132184__inbound5216268343587060307__original.jpg" - ], - "blip_caption": "a photo of a woman holding flowers in front of her face", - "query": "framed artwork on wall painting talent creativity", - "dia_id": "D21:17", - "text": "Wow, Evan, this is amazing! You've got serious talent and creativity. Making this must have been so satisfying! Here's a painting that inspired me when I went to an exhibit few days ago." - }, - { - "speaker": "Evan", - "dia_id": "D21:18", - "text": "\nThanks, Sam! I appreciate the compliment. This painting has such an inspiring vibe; you really have a knack for understanding art! How about you? How long have you been painting?" - }, - { - "speaker": "Sam", - "dia_id": "D21:19", - "text": "\nI do sketch occasionally, but I haven't created anything remarkable yet. I have a feeling I'll have something to show off before long! Seeing your passion for it is inspiring." - }, - { - "speaker": "Evan", - "dia_id": "D21:20", - "text": "Thanks, Sam! Glad I could motivate you. If you ever want to give it a go, I'm happy to help get you started. Speaking of which, you know what? I lost my keys again, it's become a weekly ritual for me!" - }, - { - "speaker": "Sam", - "dia_id": "D21:21", - "text": "Ooh, Evan, I'd put a GPS sensor on your keys!" - }, - { - "speaker": "Evan", - "dia_id": "D21:22", - "text": "Great idea, I think I'll do that as soon as I find it!" - } - ], - "session_22_date_time": "11:00 am on 31 December, 2023", - "session_22": [ - { - "speaker": "Sam", - "img_url": [ - "https://c1.wallpaperflare.com/preview/524/680/1013/people-man-back-mountain.jpg" - ], - "blip_caption": "a photography of a man standing on a rock looking out over a valley", - "query": "hiking trip mountain top", - "dia_id": "D22:1", - "re-download": true, - "text": "Hey Evan! I\u2019m really getting into this healthier lifestyle\u2014just took my friends on an epic hiking trip last Friday!" - }, - { - "speaker": "Evan", - "img_url": [ - "https://disguisemeforever.files.wordpress.com/2021/05/good-click.jpg" - ], - "blip_caption": "a photo of a small stream running through a lush green forest", - "query": "scenic lookout drive peaceful relaxing", - "dia_id": "D22:2", - "text": "Hey Sam! That\u2019s fantastic\u2014nothing like a good hike to feel alive. We took the Prius for a long drive to the mountains last weekend. It was perfect until we got into a little scrape on the way back." - }, - { - "speaker": "Sam", - "dia_id": "D22:3", - "text": "Oh no, were you guys okay after the accident?" - }, - { - "speaker": "Evan", - "dia_id": "D22:4", - "text": "Yeah, we were fine, thanks. Just a minor accident, but it put a bit of a damper on telling my work friends about getting married. They\u2019ve been a great support, though." - }, - { - "speaker": "Sam", - "dia_id": "D22:5", - "text": "I bet they were thrilled to hear about your marriage, despite the mishap!" - }, - { - "speaker": "Evan", - "dia_id": "D22:6", - "text": "Absolutely, it's been a whirlwind of emotions. Good thing the accident was minor. Just a reminder to take it easy on the road, I guess." - }, - { - "speaker": "Sam", - "dia_id": "D22:7", - "text": "True, it\u2019s important to stay safe. Glad you can still enjoy the peaceful moments after something like that." - }, - { - "speaker": "Evan", - "dia_id": "D22:8", - "text": "Definitely, nature brings peace and clarity - it's a great experience." - }, - { - "speaker": "Sam", - "dia_id": "D22:9", - "text": "Nature can make everything else seem small and help us find peace inside. It reminds us of the bigger picture, you know?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://i0.wp.com/therosetable.com/wp-content/uploads/2023/09/IMG_4235.jpg" - ], - "blip_caption": "a photo of a woman sitting at a table with plates and glasses", - "query": "ginger snaps tea cups table", - "dia_id": "D22:10", - "text": "For sure, and nature has been a great healer. Speaking of which, I\u2019ve got to share some of these new healthy snacks I\u2019ve been trying." - }, - { - "speaker": "Sam", - "dia_id": "D22:11", - "text": "They look healthy and delicious! Perfect for after a hike or, I guess, post-accident recovery, huh?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://blessedbeyondcrazy.com/wp-content/uploads/2019/09/00000PORTRAIT_00000_BURST20190918165701335.jpg" - ], - "blip_caption": "a photo of a bunch of cookies on a cooling rack", - "query": "ginger snaps plate", - "dia_id": "D22:12", - "text": "Exactly! They\u2019re packed with nutrients and really easy to make. You also need to try these cookies, they are awesome! I\u2019ll send you the recipes." - }, - { - "speaker": "Sam", - "dia_id": "D22:13", - "text": "Thanks, I\u2019d appreciate that. It\u2019s good to find new ways to stay healthy. Do you have any healthier snack ideas?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://paleoglutenfreeguy.com/wp-content/uploads/2020/08/IMG_7067.jpg" - ], - "blip_caption": "a photo of a bowl of coconut balls and a bowl of oats", - "query": "energy balls plate", - "dia_id": "D22:14", - "text": "Yeah, I've been trying to eat healthier too. Check out this cool recipe I discovered for these energy balls." - }, - { - "speaker": "Sam", - "dia_id": "D22:15", - "text": "Do you like them? I know they can be an acquired taste." - }, - { - "speaker": "Evan", - "dia_id": "D22:16", - "text": "I enjoy the taste of these. They're energizing and a healthy way to satisfy your sweet tooth." - }, - { - "speaker": "Sam", - "dia_id": "D22:17", - "text": "Awesome! Always on the lookout for healthy snacks, thanks for the tip!" - }, - { - "speaker": "Evan", - "dia_id": "D22:18", - "text": "Glad to help - hope you enjoy it!" - }, - { - "speaker": "Sam", - "dia_id": "D22:19", - "text": "Thanks, Evan! I'll give these a try. They look yum. Your help means a lot to me. Btw you know what? I went to the store again and, unsurprisingly, had issues with the self-checkout. It's becoming a regular annoyance." - }, - { - "speaker": "Evan", - "dia_id": "D22:20", - "text": "That's very strange, I've never had a problem with it once!" - }, - { - "speaker": "Sam", - "dia_id": "D22:21", - "text": "Apparently I attract that to me, if you ever want to be in that situation, call me at the store with you!" - } - ], - "session_23_date_time": "1:32 pm on 6 January, 2024", - "session_23": [ - { - "speaker": "Evan", - "img_url": [ - "https://cdn.jewelryimages.net/static/domains/kvjewels/images/store-pics/b9ffd872-4ac6-405c-b3e0-19d875e60f0b.jpg" - ], - "blip_caption": "a photo of a man and a woman standing on a rocky beach", - "query": "couple families announcement", - "dia_id": "D23:1", - "text": "Hey Sam, guess what? My partner and I told our extended fam about our marriage yesterday \u2013 it was so special! We've been totally overwhelmed by all their love and support." - }, - { - "speaker": "Sam", - "dia_id": "D23:2", - "text": "Congrats on the news, Evan! You two look so happy in the pic. These moments make life so wonderful; super stoked for you!" - }, - { - "speaker": "Evan", - "dia_id": "D23:3", - "text": "Thanks, Sam! It was an awesome moment, and I feel really lucky to have found someone who gets me. Plus, our families are really happy for us - that's the best part!" - }, - { - "speaker": "Sam", - "dia_id": "D23:4", - "text": "Wow, Evan. It's awesome that you've found someone who gets you! Having your family's support must feel great." - }, - { - "speaker": "Evan", - "dia_id": "D23:5", - "text": "Definitely, family support is so important. Knowing they're happy about our marriage is awesome and so comforting." - }, - { - "speaker": "Sam", - "dia_id": "D23:6", - "text": "Yeah, it's awesome to have that support. It definitely brings more happiness and joy." - }, - { - "speaker": "Evan", - "dia_id": "D23:7", - "text": "Yeah Sam, that means a lot to me. Our bond just keeps getting stronger and it brings such a good feeling to our lives. Family really is everything." - }, - { - "speaker": "Sam", - "dia_id": "D23:8", - "text": "Agree, Evan! Family is everything - they bring so much love and happiness. They're always there for us no matter what. I'm grateful for their support and love." - }, - { - "speaker": "Evan", - "dia_id": "D23:9", - "text": "For sure, Sam. That's what makes family so special. They bring so much love and happiness. It's great having their support and knowing they're always there for us. I feel really fortunate to have their never-ending love and support." - }, - { - "speaker": "Sam", - "dia_id": "D23:10", - "text": "Yeah, definitely, Evan. We both have amazing families that are always there for us. Always a blessing." - }, - { - "speaker": "Evan", - "dia_id": "D23:11", - "text": "Yeah, Sam. Our families give us so much joy, support, and love. They're a real blessing! I don't know what I'd do without them." - }, - { - "speaker": "Sam", - "dia_id": "D23:12", - "text": "Hey, Evan. My family has been my rock through everything. Don't know what I'd do without them." - }, - { - "speaker": "Evan", - "img_url": [ - "https://ic.pics.livejournal.com/queerbychoice/218786/1899659/1899659_original.jpg" - ], - "blip_caption": "a photo of a group of people sitting at a table with food", - "query": "family gathering", - "dia_id": "D23:13", - "text": "Yeah, they are our rock. We're blessed to have them." - }, - { - "speaker": "Sam", - "dia_id": "D23:14", - "text": "Wow, you guys are awesome! What's cooking tonight?" - }, - { - "speaker": "Evan", - "dia_id": "D23:15", - "text": "Thanks, Sam! We're having a family get-together tonight and enjoying some homemade lasagna. Super excited! By the way, I've started a new diet\u2014limiting myself to just two ginger snaps a day. What's on your menu tonight?\t" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.redd.it/qmjb5d7g8ra81.jpg" - ], - "blip_caption": "a photo of a plate of food with bread and meat", - "query": "lasagna garlic bread", - "dia_id": "D23:16", - "text": "That's a great discipline, Evan! We're keeping it light tonight, just some homemade lasagna. Can't compete with your ginger snap limit though!" - }, - { - "speaker": "Evan", - "dia_id": "D23:17", - "text": "Oh this must be very hearty and delicious, well I'll have to stick to the diet plan, even with the family gathering!" - }, - { - "speaker": "Sam", - "img_url": [ - "https://anaffairfromtheheart.com/wp-content/uploads/2021/02/Key-Lime-Pie-1.jpg" - ], - "blip_caption": "a photo of a pie with raspberries and limes on top", - "query": "homemade key lime pie", - "dia_id": "D23:18", - "text": "Yeah, the lasagna was pretty awesome, but check out what I had for dessert, I'm sure you're drooling!" - }, - { - "speaker": "Evan", - "dia_id": "D23:19", - "text": "Looks yummy! Did you make that?" - }, - { - "speaker": "Sam", - "dia_id": "D23:20", - "text": "No, I didn't make it. This is actually a pic from my cousin's wedding. It's super special." - }, - { - "speaker": "Evan", - "img_url": [ - "https://serafrescaic.com/wp-content/uploads/2018/10/wedding-cake.jpg" - ], - "blip_caption": "a photo of a wedding cake with candles and flowers on a table", - "query": "wedding cake", - "dia_id": "D23:21", - "text": "Wow Sam! Weddings are indeed special. This looks great, yum!" - }, - { - "speaker": "Sam", - "dia_id": "D23:22", - "text": "Ooh, nice cake! Reminds me of special occasions. Do you have any upcoming plans?" - }, - { - "speaker": "Evan", - "img_url": [ - "https://texasrandi.files.wordpress.com/2021/03/durango-hike-10.jpg" - ], - "blip_caption": "a photo of a stream running through a snowy forest filled with snow", - "query": "snowy landscape canada honeymoon", - "dia_id": "D23:23", - "text": "Thanks Sam! We're off to Canada next month for our honeymoon. So excited to create some awesome memories. Looking forward to exploring the beautiful snowy landscapes there." - }, - { - "speaker": "Sam", - "dia_id": "D23:24", - "text": "Wow, that looks great! What are your plans for the trip?" - }, - { - "speaker": "Evan", - "dia_id": "D23:25", - "text": "We're planning to ski, try the local cuisine, and enjoy the beautiful views. We're really excited!" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.redd.it/p2vf3jqii0xb1.jpg" - ], - "blip_caption": "a photo of a container of french fries covered in caramel", - "query": "ski canada poutine", - "dia_id": "D23:26", - "text": "Sounds amazing, Ev! Skiing, trying local dishes, and enjoying the breathtaking views - the perfect honeymoon. Have an incredible time creating unforgettable memories!" - }, - { - "speaker": "Evan", - "dia_id": "D23:27", - "text": "Yeah, Sam! Gonna try some poutine while we're there - can't wait!" - }, - { - "speaker": "Sam", - "dia_id": "D23:28", - "text": "Never tried it? Can't say I blame you, it's kind of a Canadian thing. Let me know how you like it!" - }, - { - "speaker": "Evan", - "dia_id": "D23:29", - "text": "Sure thing, Sam! Let's see if it lives up to the hype. I'll let you know what happens!" - }, - { - "speaker": "Sam", - "dia_id": "D23:30", - "text": "Yeah, Evan! Let me know all about it. Don't forget the details!" - }, - { - "speaker": "Evan", - "dia_id": "D23:31", - "text": "Cool, Sam. I'll keep you posted. Talk soon!" - }, - { - "speaker": "Sam", - "dia_id": "D23:32", - "text": "Awesome, Evan! Catch you soon. Have a great trip!" - }, - { - "speaker": "Evan", - "dia_id": "D23:33", - "text": "Thanks, Sam! Catch you later. Have a great one!" - } - ], - "session_24_date_time": "12:17 am on 10 January, 2024", - "session_24": [ - { - "speaker": "Evan", - "dia_id": "D24:1", - "text": "Hey Sam, hope you're doing good. Something funny happened last night." - }, - { - "speaker": "Sam", - "dia_id": "D24:2", - "text": "Hey Evan, what's up? What happened? Let me know." - }, - { - "speaker": "Evan", - "dia_id": "D24:3", - "text": "Yesterday I went out with my friends and had a bit too much to drink. I ended up doing something I regret and it involved someone's roses." - }, - { - "speaker": "Sam", - "dia_id": "D24:4", - "text": "What's up with that incident? All good now?" - }, - { - "speaker": "Evan", - "dia_id": "D24:5", - "text": "Oof, Sam, so embarrassing! I had a pee accident near some roses - can you believe it? I'm so sorry about that." - }, - { - "speaker": "Sam", - "dia_id": "D24:6", - "text": "Uh oh, Evan! That's awkward. Did anyone get mad at you? Are you okay?" - }, - { - "speaker": "Evan", - "dia_id": "D24:7", - "text": "I was so embarrassed when I saw what happened the next morning, so I apologized and luckily they were understanding. Yeah, I was out of control--guess I gotta be more careful next time." - }, - { - "speaker": "Sam", - "dia_id": "D24:8", - "text": "They were understanding? Phew! We all mess up sometimes, we're human after all." - }, - { - "speaker": "Evan", - "dia_id": "D24:9", - "text": "Yeah, they were understanding, which was great. But it's a good reminder to be more careful. We all make mistakes, but it's important to learn from them. Speaking of, my partner and I tried snowshoeing this weekend. It was part of a new adventure for us and surprisingly fun." - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.pinimg.com/originals/c7/f7/30/c7f7308cbae589efb28ffee439cff05f.jpg" - ], - "blip_caption": "a photo of a white board with a bunch of writing on it", - "query": "meal plan workout schedule motivation health journey", - "dia_id": "D24:10", - "text": "Yeah, Evan, you're right. Mistakes happen, but it's good to learn from them. Snowshoeing sounds like a great way to stay active during the winter. I've been thinking and I made a meal plan and workout schedule. I'm getting motivated by something I saw, so starting today I'm gonna do my best to stay on track." - }, - { - "speaker": "Evan", - "dia_id": "D24:11", - "text": "Good work, Sam! You've got a plan and you're dedicated to staying healthy - have you asked your doctor for advice? They could probably give you even more diet and exercise tips." - }, - { - "speaker": "Sam", - "blip_caption": "a photo of a red and orange card with a yellow sun", - "dia_id": "D24:12", - "text": "Thanks, Evan! Haven't seen a doctor in a while, but it's probably a good idea to get some advice. I'm going to make an appointment soon." - }, - { - "speaker": "Evan", - "dia_id": "D24:13", - "text": "What advice are you planning to get from the doctor?" - }, - { - "speaker": "Sam", - "dia_id": "D24:14", - "text": "I'm gonna ask the doc about a balanced diet plan and getting advice on low-impact exercises, given my current situation." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.redd.it/rhcb1m5vwyhb1.jpg" - ], - "blip_caption": "a photo of a salad with chicken, avocado, tomatoes, corn, and cheese", - "query": "salad grilled chicken avocado mixed greens", - "dia_id": "D24:15", - "text": "Sounds good, Sam. That's definitely a step in the right direction. Remember to focus on a balanced diet and low-impact exercises. Let me know how it goes." - }, - { - "speaker": "Sam", - "dia_id": "D24:16", - "text": "That looks great! Where did you get the idea for this salad? Also, do you have any suggestions for low-impact exercises?" - }, - { - "speaker": "Evan", - "dia_id": "D24:17", - "text": "I got it from a nearby restaurant. As for low-impact exercises, swimming, yoga, and walking are good options." - }, - { - "speaker": "Sam", - "blip_caption": "a photo of a young boy is playing in a pool", - "dia_id": "D24:18", - "text": "The salad idea from a restaurant is a smart move, Evan! And thanks for the exercise tips. Also I watched The Godfather last night, and it motivated me to keep up with my routine. \"I'm gonna make him an offer he can't refuse\" - now that's motivation!" - }, - { - "speaker": "Evan", - "dia_id": "D24:19", - "text": "Yoga's definitely a great start, Sam. It's helped me with stress and staying flexible, which is perfect alongside the diet. And yes, The Godfather is a legendary thing to watch, can be re-watched many times!" - }, - { - "speaker": "Sam", - "dia_id": "D24:20", - "text": "Between a healthier diet and yoga, I\u2019m hoping for some positive changes." - }, - { - "speaker": "Evan", - "img_url": [ - "https://i.pinimg.com/originals/5e/cc/75/5ecc7518568d48830960915a86af39ab.jpg" - ], - "blip_caption": "a photo of a woman standing on a beach at sunset", - "query": "sunset walk beach", - "dia_id": "D24:21", - "text": " By the way there are plenty of other low-impact exercises that can be fun. Going on beach sunsets is one of my favorites - good for exercise and totally calming." - }, - { - "speaker": "Sam", - "dia_id": "D24:22", - "text": "That looks zen. Gonna go for some beach walks - thanks for the tip, Evan! I want to brag, I had that recurring dream again where I'm flying over skyscrapers!" - }, - { - "speaker": "Evan", - "dia_id": "D24:23", - "text": "I think a little more and you'll learn how to control those dreams, once you get the hang of it let me know haha! Enjoy the fresh air and the views. Have fun!" - }, - { - "speaker": "Sam", - "dia_id": "D24:24", - "text": "Thanks Evan! Gonna make the most of it. You too, have a good one!" - } - ], - "session_25_date_time": "9:37 pm on 11 January, 2024", - "session_25": [ - { - "speaker": "Sam", - "dia_id": "D25:1", - "text": "Hey Evan, been a few days since we last chatted. Hope you're doing OK. A lot's happened since then. Got issues with my health, it's been rough. Feels like this weight's keeping me from fully living. Trying to stay positive, not easy." - }, - { - "speaker": "Evan", - "dia_id": "D25:2", - "text": "Hey Sam, sorry to hear about your health. It's tough when it gets in the way of life. You're being positive, but remember to take care of yourself too. By the way, I had to apologize to my partner for that drunken night, it was pretty embarrassing." - }, - { - "speaker": "Sam", - "dia_id": "D25:3", - "text": "Hey Evan, that does sound like a tough situation. I'm doing my best with my health. How did your partner take the news about the rose bushes?" - }, - { - "speaker": "Evan", - "dia_id": "D25:4", - "text": "Well, she wasn't thrilled, but understood it was an accident. I promised to be more careful in the future. Changing the subject, have you found any low-impact exercises that you enjoy?" - }, - { - "speaker": "Sam", - "img_url": [ - "https://i.imgur.com/hjUwenM.jpg" - ], - "blip_caption": "a photo of a field with a fence and a dirt road", - "query": "beautiful drive countryside open road green fields sunlight clouds", - "dia_id": "D25:5", - "text": "Hey Evan, haven't found any exercises I like. But lately, I've been on a few car rides. Helps me chill and enjoy the view. Check out this cool pic I snapped last week in the country." - }, - { - "speaker": "Evan", - "dia_id": "D25:6", - "text": "Nice pic! Does being out in the countryside help you relax and get some fresh air away from the city?" - }, - { - "speaker": "Sam", - "dia_id": "D25:7", - "text": "Yeah, being in nature really helps me relax and get some fresh air away from the city." - }, - { - "speaker": "Evan", - "img_url": [ - "https://camperchristina.com/wp-content/uploads/2016/12/img_2641.jpg" - ], - "blip_caption": "a photo of a kayak is seen from the front of the boat", - "query": "sunset calm lake camping trip", - "dia_id": "D25:8", - "text": "Glad to hear it! Nature really has a way of calming and reviving the soul. Last summer, I took this pic on a camping trip - it was such an amazing sunset. Moments like these remind us of the beauty of life, even during tough times." - }, - { - "speaker": "Sam", - "dia_id": "D25:9", - "text": "Wow, that pic is amazing! It must have been a great experience being out on the lake." - }, - { - "speaker": "Evan", - "dia_id": "D25:10", - "text": "I had a great time kayaking and watching the sunset last summer - it was truly unforgettable. Being out on the water is so peaceful." - }, - { - "speaker": "Sam", - "dia_id": "D25:11", - "text": "Wow, that sounds amazing. Being in nature is so calming, right?" - }, - { - "speaker": "Evan", - "dia_id": "D25:12", - "text": "Nature can be super calming. It's like pushing a reset button for your mind and body." - }, - { - "speaker": "Sam", - "dia_id": "D25:13", - "text": "Definitely, I couldn't agree more. There's something about being outdoors that rejuvenates you. I'm planning to spend more time in nature myself!" - }, - { - "speaker": "Evan", - "img_url": [ - "http://deborahsilver.com/wp-content/uploads/2018/05/May-Baroque-12.jpg" - ], - "blip_caption": "a photo of a tree with pink flowers in a park", - "query": "blooming flowers vibrant colors nature moment", - "dia_id": "D25:14", - "text": "Got it. When health stuff cramps your style, it sucks. But small moments outdoors can make a big impact. This photo reminds me of last spring when I was feeling a bit down, but the vibrant colors brought a smile to my face, even if just for a moment. Remember to find joy in the little things." - }, - { - "speaker": "Sam", - "dia_id": "D25:15", - "text": "That pic is gorgeous! It really brightens my day. Sometimes, it's the little things that matter, right?" - }, - { - "speaker": "Evan", - "dia_id": "D25:16", - "text": "Absolutely, Sam. It's often those little moments that make the biggest difference. Keep finding those bright spots." - }, - { - "speaker": "Sam", - "dia_id": "D25:17", - "text": "Thanks, Evan. It's good to be reminded to appreciate the small things. They do add up." - }, - { - "speaker": "Evan", - "dia_id": "D25:18", - "text": "Anytime, Sam. It's all about those small joys, especially when times are tough. You've got this!" - }, - { - "speaker": "Sam", - "dia_id": "D25:19", - "text": "Really appreciate it, Evan. Your words help a lot. Take care!" - }, - { - "speaker": "Evan", - "dia_id": "D25:20", - "text": "You too, Sam. And remember, I'm always here if you need to chat. Look after yourself!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Evan": [ - "Evan buys a new Prius after he repairs and sells his old Prius when it breaks down.", - "Evan takes his family on a road trip to the Rockies in the Prius and they hike some trails together." - ], - "Sam": [ - "Sam takes up painting as a new hobby and buys a set of colors to kickstart the hobby." - ], - "date": "18 May, 2023" - }, - "events_session_2": { - "Evan": [ - "Evan goes on a roadtrip to Jasper with this family where they drive through Icefield Parkways and stay in a cozy cabin in the mountains." - ], - "Sam": [ - "Sam has a check up with his doctor and finds that his current weight is not ideal.", - "Sam decides to take up hobbies to improve his weight and stay motivated." - ], - "date": "24 May, 2023" - }, - "events_session_3": { - "Evan": [ - "Evan experiences heart palpitations and is motivated by the health scare to make some changes to his lifestyle." - ], - "Sam": [ - "Sam starts eating salads to support his goal for better health.", - "Sam gets frustrated during shopping because all of the self-checkout machines were broken." - ], - "date": "6 June, 2023" - }, - "events_session_4": { - "Evan": [ - "Evan starts reading The Great Gatsby novel." - ], - "Sam": [ - "Sam's friends make fun of his heavy build as a result of his eating habits.", - "Sam joins a gym and starts exercising regularly to reduce his weight." - ], - "date": "27 July, 2023" - }, - "events_session_5": { - "Evan": [ - "Evan falls in love with a Canadian woman during a trip to Canada." - ], - "Sam": [], - "date": "7 August, 2023" - }, - "events_session_6": { - "Evan": [ - "Evan takes a Canadian vacation with his new love where they hiked, biked and explored the outdoors together.", - "Evan loses his keys and spends a great deal of time looking for it." - ], - "Sam": [ - "Sam has a dream about soaring over skyscrapers." - ], - "date": "13 August, 2023" - }, - "events_session_7": { - "Evan": [ - "Evan's son hurts his akle while playing soccer but it isn't a serious accident." - ], - "Sam": [ - "Sam enrolls in a cooking class to help him learn to cook healthier meals.", - "Sam cooks a grilled salmon meal with vegetables that he learned from the cooking class.", - "Sam has another doctor's appointment where he receives a health scare about his weight." - ], - "date": "15 August, 2023" - }, - "events_session_8": { - "Evan": [ - "Evan joins a painting class to improve his skills and meet like-minded people." - ], - "Sam": [ - "Sam decides to make an effort to change his diet and lifestyle." - ], - "date": "19 August, 2023" - }, - "events_session_9": { - "Evan": [ - "Evan twists his ankle and is not able to stay consistent in his fitness routine.", - "Evan engages in swimming to stay healthy until his ankle gets better and looks for physical therapy appointments." - ], - "Sam": [ - "Sam begins to feel more motivated and energized due to his new diet and exercise routine." - ], - "date": "27 August, 2023" - }, - "events_session_10": { - "Evan": [ - "Evan finishes painting a colorful sunset." - ], - "Sam": [ - "Sam posts a before and after photo of himself on social media, hoping to inspire others too." - ], - "date": "11 September, 2023" - }, - "events_session_11": { - "Evan": [ - "Evan messes up his knee while playing baseball with this kids and takes physical therapy to recover from the injury.", - "Evan helps a lost tourist find their way and ends up taking an adventuruous tour of the city with them." - ], - "Sam": [ - "Sam begins to incorporate healthy snacks like fruit bowls into his diet.", - "Sam faces issues with the navigation app in his new phone." - ], - "date": "6 October, 2023" - }, - "events_session_12": { - "Evan": [], - "Sam": [ - "Sam has another health checkup where the doctor warns him about health risks due to his weight." - ], - "date": "8 October, 2023" - }, - "events_session_13": { - "Evan": [], - "Sam": [ - "Sam has a slip-up in his health regimen and buys candy and soda at the store to deal with work stress.", - "Sam travels through Lake Tahoe with a friend and decides to try out kayaking after recommendation from Evan." - ], - "date": "14 October, 2023" - }, - "events_session_14": { - "Evan": [ - "Evan has another opportunity to help a lost tourist." - ], - "Sam": [ - "Sam goes to the ER due to stomachache and receives a diagnosis of gastritis stemming from his unhealthy lifestyle." - ], - "date": "17 October, 2023" - }, - "events_session_15": { - "Evan": [ - "Evan wakes up early to go for a brisk walk." - ], - "Sam": [ - "Sam's family encourages him to stick to his new healthier habits but Sam feels a bit pressured." - ], - "date": "25 October, 2023" - }, - "events_session_16": { - "Evan": [ - "Sam loses his job due to downsizing of his company and is on the hunt for a new job.", - "Sam receives a vintage 1968 Kustom K200-A guitar as a gift from a friend." - ], - "Sam": [ - "Sam is chosen to be a coach in his Weight Watchers group." - ], - "date": "9 November, 2023" - }, - "events_session_17": { - "Evan": [ - "Evan receives a false health scare during a health check-up at the hospital." - ], - "Sam": [ - "Sam purchases a DVD box set of his goto feel-good flick, The Godfather to help take his mind off his health issues." - ], - "date": "21 November, 2023" - }, - "events_session_18": { - "Evan": [ - "Evan's new Prius car breaks down." - ], - "Sam": [ - "Sam attends a Weight Watchers meeting, shares his smoothie bowls and learns tips from others." - ], - "date": "5 December, 2023" - }, - "events_session_19": { - "Evan": [ - "Evan's Canadian partner gets pregnant.", - "Evan plans to hold a big family reunion in summer 2024." - ], - "Sam": [], - "date": "9 December, 2023" - }, - "events_session_20": { - "Evan": [ - "Evan's son falls off a bike and has to rely on crutches for a while.", - "Evan finishes work on a portrait of a woman and his close friend helps him get it published at a exhibition." - ], - "Sam": [ - "Sam begins to think the idea of changing his diet and lifestyle is too hard, and feels like he lacks the motivation to deal with his weight.", - "Sam's struggle with his weights affects his confidence." - ], - "date": "17 December, 2023" - }, - "events_session_21": { - "Evan": [ - "Evan and his pregnant Canadian partner get married.", - "Sam loses his keys." - ], - "Sam": [ - "Sam starts to enjoy running in the mornings to clear his head and improve his health." - ], - "date": "26 December, 2023" - }, - "events_session_22": { - "Evan": [ - "Evan takes a long drive to the mountains with his newly-wedded and pregnant wife in his Prius.", - "Evan gets in a minor accident when driving his Prius with his wife.", - "Evan spends time with his friends from work and tells them about his recent marriage." - ], - "Sam": [ - "Sam takes his friends on a hiking trip after a long time, proudly showing off his new healthier lifestyle." - ], - "date": "31 December, 2023" - }, - "events_session_23": { - "Evan": [ - "Evan starts a diet and limits himself to only two ginger snaps a day.", - "Evan and his partner announce their marriage to his extended family.", - "Evan has a family reunion where they eat some homemade lasagna.", - "Evan plans for a honeymoon to Canada with his wife in February 2024. They plan on exploring the snowy landscapes, local cuisine and skiing." - ], - "Sam": [ - "Sam has a meal of homemade lasgna." - ], - "date": "6 January, 2024" - }, - "events_session_24": { - "Evan": [ - "Evan and his partner try snowshoeing.", - "Evan goes out partying with his friends and gets too drunk, resulting in him urinating on someone's roses." - ], - "Sam": [ - "Sam watches The Godfather to get motivation to keep up with his healthy eating routine.", - "Sam reevaluates his diet and plans to stick to his new healthier habits.", - "Sam has a recurring dream about soaring over skyscrapers." - ], - "date": "10 January, 2024" - }, - "events_session_25": { - "Evan": [ - "Evan tells his newly-wed partner about his drunken night out and apologizes for urinating on the rose bushes." - ], - "Sam": [], - "date": "11 January, 2024" - } - }, - "observation": { - "session_1_observation": { - "Sam": [ - [ - "Sam went hiking with his dad when he was ten, which was a special and fun memory for them.", - "D1:7" - ], - [ - "Sam is considering trying painting as a new hobby.", - "D1:11" - ], - [ - "Sam has not tried painting yet but is keen to give it a go as a way to chill and get creative.", - "D1:13" - ] - ], - "Evan": [ - [ - "Evan has a new Prius after his old one broke down, which he got repaired and sold.", - "D1:2" - ], - [ - "Evan went on a trip to the Rockies with his family.", - "D1:4" - ], - [ - "Evan enjoys watercolor painting as a hobby, which he started a few years back.", - "D1:14" - ], - [ - "Evan got into watercolor painting because a friend introduced him to it and gave him advice.", - "D1:16" - ] - ] - }, - "session_2_observation": { - "Evan": [ - [ - "Evan took his family on a road trip to Jasper last weekend, driving through the Icefields Parkway.", - "D2:1" - ], - [ - "Evan described the road trip to Jasper as having fresh air, peacefulness, and a cozy cabin surrounded by mountains and forests.", - "D2:3" - ], - [ - "Evan recommended finding a fitness routine that is enjoyable and making smarter choices in diet for maintaining a healthier lifestyle.", - "D2:9" - ], - [ - "Evan has been painting for a few years as a way to find peace and relieve stress.", - "D2:11" - ] - ], - "Sam": [ - [ - "Sam had a tough week and had a check-up with the doctor where the weight wasn't great.", - "D2:4" - ], - [ - "Sam expressed interest in trying painting as a hobby to stay motivated and de-stress.", - "D2:10" - ] - ] - }, - "session_3_observation": { - "Evan": [ - [ - "Evan had a health scare involving sudden heart palpitations last week.", - "D3:1" - ], - [ - "Evan is making an effort to be extra careful with his health and is trying to eat less processed food and sugary snacks.", - "D3:3" - ], - [ - "Evan suggested swapping soda for flavored seltzer water and candy for dark chocolate with high cocoa content as healthier options to Sam.", - "D3:5" - ] - ], - "Sam": [ - [ - "Sam is trying to eat healthier and recently trying to eat less processed food, but has made no changes yet.", - "D3:2" - ], - [ - "Sam still enjoys soda and candy, although knowing it's not the best habit.", - "D3:4" - ], - [ - "Sam agreed to give swapping soda for flavored seltzer water and candy for dark chocolate a try.", - "D3:6" - ], - [ - "Sam had a frustrating experience at the supermarket as the self-checkout machines were all broken.", - "D3:16" - ] - ] - }, - "session_4_observation": { - "Sam": [ - [ - "Sam's friends mocked his weight last Friday, which hurt him.", - "D4:1" - ], - [ - "Sam is working on his health and getting active.", - "D4:3" - ], - [ - "Sam is determined to make changes in his lifestyle, like reducing soda and candy intake.", - "D4:7" - ], - [ - "Sam will start going to the gym and exercising regularly starting tomorrow.", - "D4:15" - ], - [ - "Sam values and appreciates Evan's support throughout their conversation.", - "D4:17, D4:19" - ] - ], - "Evan": [ - [ - "Evan offers to work on improving health together with Sam.", - "D4:2" - ], - [ - "Evan struggled with his health a few years ago, focusing on exercise, diet, and lifestyle changes, which made a positive impact.", - "D4:4" - ], - [ - "Evan made dietary changes like cutting down on sugary snacks and eating more veggies and fruit for his health.", - "D4:6" - ], - [ - "Evan enjoys reading the mystery novel 'The Great Gatsby' and finds it gripping.", - "D4:8" - ], - [ - "Evan suggests flavored seltzer water as a healthier alternative to soda.", - "D4:8" - ], - [ - "Evan advises Sam to focus on healthy swaps and taking small steps towards progress.", - "D4:12" - ], - [ - "Evan suggests low-calorie snacks like air-popped popcorn or fruit to pair with flavored seltzer water.", - "D4:10" - ] - ] - }, - "session_5_observation": { - "Evan": [ - [ - "Evan recently went on a trip to Canada where he met an awesome Canadian woman and feels alive when he's with her.", - "D5:1" - ], - [ - "Evan has been dealing with health issues lately that have made him appreciate good moments more.", - "D5:2" - ], - [ - "Evan has been working on prioritizing his health for two years, with ups and downs.", - "D5:7" - ], - [ - "Evan is motivated to stay healthy by his family and uses a tool that tracks his progress well.", - "D5:9" - ], - [ - "Evan is motivated by a thirst for adventure and interesting hikes.", - "D5:13" - ], - [ - "Evan got a symbol that represents strength and resilience to motivate him through tough times.", - "D5:17" - ] - ], - "Sam": [ - [ - "Sam acknowledges that dealing with health issues can take away from enjoyable experiences.", - "D5:4" - ], - [ - "Sam is interested in ordering something similar to what Evan uses to prioritize health.", - "D5:8" - ], - [ - "Sam thinks it's key to find something that keeps you motivated.", - "D5:14" - ] - ] - }, - "session_6_observation": { - "Evan": [ - [ - "Evan went on a vacation in Canada with their new significant other where they did activities like hiking and biking.", - "D6:1" - ], - [ - "Evan values the importance of progress over perfection.", - "D6:6" - ], - [ - "Evan acknowledges the importance of surrounding oneself with caring people.", - "D6:5" - ], - [ - "Evan struggles to find his keys frequently and spends time looking for them.", - "D6:13" - ] - ], - "Sam": [ - [ - "Sam has been facing health challenges recently.", - "D6:2" - ], - [ - "Sam finds motivation in a quote that emphasizes taking small steps towards a healthier life.", - "D6:6" - ], - [ - "Sam appreciates small victories and staying motivated amidst challenges and setbacks.", - "D6:12" - ], - [ - "Sam had an amazing dream where they were soaring over skyscrapers and wants to explore its possible significance.", - "D6:14" - ] - ] - }, - "session_7_observation": { - "Evan": [ - [ - "Evan's son had a soccer accident last Saturday and hurt his ankle, requiring care and a doctor visit.", - "D7:1" - ], - [ - "Evan is concerned about taking care of his son who was hurt in the soccer accident.", - "D7:1" - ], - [ - "Evan wants to eat healthy and is interested in learning about healthier recipes.", - "D7:5" - ], - [ - "Evan is looking to add more vegetables to his meals.", - "D7:7" - ], - [ - "Evan encourages Sam to look after himself and his health.", - "D7:10" - ], - [ - "Evan reminds Sam to take care of himself and advocates for looking after health.", - "D7:15" - ] - ], - "Sam": [ - [ - "Sam had a tough week and had a doctor's appointment which served as a wake-up call to take better care of himself.", - "D7:2" - ], - [ - "Sam is taking a cooking class to learn how to make healthier meals and has learned awesome recipes from it.", - "D7:4" - ], - [ - "Sam is willing to share recipes from his cooking class with Evan.", - "D7:6" - ], - [ - "Sam is concerned about Evan and his son's well-being after the soccer incident.", - "D7:8" - ], - [ - "Sam feels a mix of emotions, somewhat concerned about his health but motivated to make positive changes.", - "D7:12" - ], - [ - "Sam appreciates Evan's encouragement and expresses gratitude for it.", - "D7:14" - ], - [ - "Sam expresses gratitude to Evan for his support and encouragement.", - "D7:16" - ] - ] - }, - "session_8_observation": { - "Sam": [ - [ - "Sam is on a diet and determined to live healthier.", - "D8:1" - ], - [ - "Sam has noticed positive changes from the diet, such as more energy and less sluggishness after eating.", - "D8:5" - ], - [ - "Sam enjoys cooking and shared a healthy grilled chicken and veggie stir-fry recipe with Evan.", - "D8:7" - ], - [ - "Sam's homemade sauce for the stir-fry is not a family secret.", - "D8:9" - ] - ], - "Evan": [ - [ - "Evan is trying out new recipes and finds it a great way to stay busy and creative.", - "D8:12" - ], - [ - "Evan recently started taking painting classes and enjoys expressing himself through art.", - "D8:12" - ], - [ - "Evan has been learning about watercolors in his painting classes and finds it a relaxing way to take a break from everyday stress.", - "D8:18" - ], - [ - "Evan loves painting landscapes as it brings him peace and captivates him with nature's beauty.", - "D8:20" - ], - [ - "Evan likes to capture the tranquil beauty of nature in his paintings by conveying the peacefulness of being outdoors.", - "D8:22" - ], - [ - "Evan enjoys fun winter activities like skiing, snowboarding, and ice skating.", - "D8:30" - ] - ] - }, - "session_9_observation": { - "Sam": [ - [ - "Sam started a new diet and exercise routine last Monday and it has made a huge difference.", - "D9:1" - ], - [ - "Sam suggested low-impact exercises or physical therapy to help Evan with his twisted knee.", - "D9:5" - ], - [ - "Sam enjoys being surrounded by nature as it is tranquil and refreshing.", - "D9:11" - ], - [ - "Sam loves exploring nature and mentioned an interest in going on hikes.", - "D9:11" - ], - [ - "Sam expressed an interest in going on a road trip and mentioned working on becoming healthier.", - "D9:11" - ] - ], - "Evan": [ - [ - "Evan twisted his knee last Friday and is finding it tough to stay consistent with his usual fitness routine.", - "D9:2" - ], - [ - "Evan is considering physical therapy for his knee and in the meantime is keeping it low-key and swimming to stay active.", - "D9:6" - ], - [ - "Evan took a road trip to the Rocky Mountains last month and found the scenery stunning and nature calming.", - "D9:10" - ], - [ - "Evan recommended going for hikes to Sam as a calming and fun activity in nature.", - "D9:12" - ], - [ - "Evan shared a picture from his road trip to the Rocky Mountains with stunning views.", - "D9:8" - ], - [ - "Evan recommended a nearby lake for Sam to explore with many trails nearby.", - "D9:14" - ] - ] - }, - "session_10_observation": { - "Evan": [ - [ - "Evan supports Sam in making healthier choices.", - "D10:3" - ], - [ - "Evan turns to painting or going for a drive to de-stress.", - "D10:7" - ], - [ - "Evan is willing to help Sam get started with painting by recommending supplies and planning a painting session.", - "D10:9" - ], - [ - "Evan suggests acrylic paints, brushes, a canvas/paper, and a palette as basic supplies to start painting.", - "D10:11" - ], - [ - "Evan plans a painting session with Sam for next Saturday.", - "D10:13" - ] - ], - "Sam": [ - [ - "Sam posted a before and after body pic as a result of a diet, aiming to motivate others to make better choices.", - "D10:2" - ], - [ - "Sam struggles with occasional cravings for sugary drinks and snacks, especially during times of stress, boredom, or seeking comfort.", - "D10:4" - ], - [ - "Sam expresses interest in trying painting as a calming hobby.", - "D10:8" - ], - [ - "Sam plans a painting session with Evan for next Saturday.", - "D10:14" - ] - ] - }, - "session_11_observation": { - "Sam": [ - [ - "Sam has started eating healthier.", - "D11:1" - ], - [ - "Sam is interested in fun indoor activities or hobbies.", - "D11:5" - ], - [ - "Sam enjoys expressing themselves through writing.", - "D11:15" - ], - [ - "Sam does creative writing and journals to express their innermost thoughts and feelings.", - "D11:17" - ] - ], - "Evan": [ - [ - "Evan messed up their knee playing basketball and is doing easy exercises to keep it strong.", - "D11:2" - ], - [ - "Evan finds watercolor painting to be a stress reliever and a way to express emotions.", - "D11:6" - ], - [ - "Evan paints what's on their mind or how they feel, including good memories or places they want to go to.", - "D11:12" - ], - [ - "Evan enjoys expressing themselves through art and finds it a powerful form of self-expression.", - "D11:13" - ] - ] - }, - "session_12_observation": { - "Sam": [ - [ - "Sam received news from the doctor that their weight poses a serious health risk.", - "D12:1" - ], - [ - "Sam expressed having a hard time dealing with the news about their weight.", - "D12:1" - ], - [ - "Sam is interested in starting to lift weights to address their health concerns.", - "D12:3" - ], - [ - "Sam expressed appreciation towards Evan's support and encouragement.", - "D12:7" - ], - [ - "Sam mentioned that Evan's words inspired and motivated them.", - "D12:11" - ], - [ - "Sam thanked Evan for giving them a boost and expressed their commitment to staying motivated.", - "D12:13" - ], - [ - "Sam expressed that Evan's support means a lot to them.", - "D12:15" - ] - ], - "Evan": [ - [ - "Evan started lifting weights one year ago and mentioned seeing gains from his workouts.", - "D12:2" - ], - [ - "Evan offered advice to Sam on how to start lifting weights and emphasized the importance of good form and technique.", - "D12:4" - ], - [ - "Evan encouraged Sam to stay consistent with their workout routine and shared excitement to hear about their progress.", - "D12:4" - ], - [ - "Evan expressed belief in Sam's abilities and encouraged them to stay positive and keep pushing towards their goals.", - "D12:8" - ], - [ - "Evan provided supportive and motivating words to Sam, urging them to keep believing in themselves.", - "D12:12" - ], - [ - "Evan emphasized the importance of progress, staying motivated, and taking one step at a time towards reaching goals.", - "D12:16" - ], - [ - "Evan assured Sam of his support and being there for them.", - "D12:16" - ] - ] - }, - "session_13_observation": { - "Evan": [ - [ - "Evan paints as a way to relax, and recently finished a painting of a sunset.", - "D13:3" - ], - [ - "Evan suggested kayaking to Sam as a way to de-stress and enjoy the outdoors.", - "D13:7" - ], - [ - "Evan offered to hook Sam up with a good spot for kayaking.", - "D13:9" - ] - ], - "Sam": [ - [ - "Sam bought some unhealthy snacks and feels guilty about it.", - "D13:2" - ], - [ - "Sam is dealing with work stress and trying to stay motivated.", - "D13:4" - ], - [ - "Sam is considering trying kayaking as a new outdoor activity.", - "D13:6" - ], - [ - "Sam is planning to go kayaking with a friend on the lake.", - "D13:10" - ], - [ - "Sam and a friend are traveling to Lake Tahoe for kayaking.", - "D13:14" - ] - ] - }, - "session_14_observation": { - "Sam": [ - [ - "Sam had a health scare last weekend and ended up in the ER with gastritis.", - "D14:1" - ], - [ - "Sam realized the importance of prioritizing health by adopting a more nutritious diet and getting regular exercise after the health scare.", - "D14:1" - ], - [ - "Sam has been stressed with phone issues on top of the health scare.", - "D14:1" - ], - [ - "Sam is interested in starting fitness to improve overall well-being.", - "D14:3" - ], - [ - "Sam is planning a hike with Evan and is excited about it as a fun challenge.", - "D14:7" - ], - [ - "Sam values the support and appreciated Evan's presence in their friendship.", - "D14:11" - ] - ], - "Evan": [ - [ - "Evan has been focusing on fitness and found it beneficial for his overall well-being.", - "D14:2" - ], - [ - "Evan had another encounter with a lost tourist recently, suggesting a recurring theme of helping tourists in his life.", - "D14:2" - ], - [ - "Evan suggested setting fitness goals and finding an enjoyable exercise to stay motivated to Sam.", - "D14:4" - ], - [ - "Evan is planning a hike with Sam and sees it as an opportunity to bond with nature and push themselves.", - "D14:8" - ], - [ - "Evan values being a supportive friend to Sam and is always there for him.", - "D14:14" - ] - ] - }, - "session_15_observation": { - "Sam": [ - [ - "Sam is trying to keep up with a new health routine, pushed by family and feeling pressured.", - "D15:1" - ], - [ - "Sam gets impatient with self when wanting fast results but understands the importance of being patient.", - "D15:5" - ], - [ - "Sam appreciates Evan's support and acknowledges the importance of taking it slow.", - "D15:7" - ], - [ - "Sam seeks to feel a sense of freedom and is inspired by Evan's transformation.", - "D15:13" - ], - [ - "Sam is grateful for Evan's support and values it greatly.", - "D15:17" - ] - ], - "Evan": [ - [ - "Evan just got back from a morning walk which helps him start the day actively.", - "D15:2" - ], - [ - "Evan believes progress takes time and advises Sam to take things one step at a time.", - "D15:6" - ], - [ - "Evan went through a phase two years ago where he changed his diet and started walking regularly.", - "D15:8" - ], - [ - "Evan focused more on well-being rather than fixating on quick results, which led to a positive transformation.", - "D15:10" - ], - [ - "Evan emphasizes celebrating small victories and supporting Sam in moving forward.", - "D15:16" - ], - [ - "Evan mentions that letting go of unrealistic expectations was liberating both physically and mentally.", - "D15:12" - ] - ] - }, - "session_16_observation": { - "Sam": [ - [ - "Sam is now a Weight Watchers coach in their group, feeling proud and accomplished.", - "D16:1" - ], - [ - "Sam sees becoming a Weight Watchers coach as a great step in their quest for better health.", - "D16:3" - ], - [ - "Sam appreciates Evan's support and finds his kind words meaningful.", - "D16:7" - ], - [ - "Sam is determined to continue making a positive impact through coaching and staying committed to better health.", - "D16:5" - ], - [ - "Sam offered support to Evan when he mentioned personal challenges and job loss, showing willingness to chat and help.", - "D16:13" - ], - [ - "Sam finds sunsets stunning and soothing, and expressed interest in visiting Evan's favorite spot to watch sunsets.", - "D16:17" - ] - ], - "Evan": [ - [ - "Evan gifted a 1968 Kustom K-200A vintage guitar from a close friend.", - "D16:10" - ], - [ - "Evan lost his job due to company downsizing and is currently on the hunt for a new job, staying hopeful and keeping spirits up.", - "D16:12" - ], - [ - "Evan appreciates Sam's support during his job loss and personal challenges.", - "D16:14" - ], - [ - "Evan finds peace and relaxation by watching sunsets at a peaceful spot close to his home.", - "D16:18" - ], - [ - "Evan plans to visit his favorite spot by the beach with Sam to de-stress and explore together.", - "D16:20" - ], - [ - "Evan is excited about planning a visit to the spot with Sam next month.", - "D16:24" - ] - ] - }, - "session_17_observation": { - "Sam": [ - [ - "Sam has been dealing with discomfort that has been limiting their movement and has been trying to make changes in their diet.", - "D17:3" - ], - [ - "Sam finds movies to be a 'feel good' escape.", - "D17:5" - ], - [ - "Sam acknowledges the importance of appreciating something each day to enjoy life more.", - "D17:7" - ], - [ - "Sam finds life easier with supportive people around.", - "D17:11" - ], - [ - "Sam struggles to reach their fitness goals but accepts it as part of life.", - "D17:15" - ] - ], - "Evan": [ - [ - "Evan had a health scare recently, which turned out to be a misunderstanding, leading them to realize the importance of monitoring their health.", - "D17:2" - ], - [ - "Evan values enjoying the moment and appreciating the little things in life.", - "D17:6" - ], - [ - "Evan stays in shape by going to the gym and taking their car out for a spin.", - "D17:14" - ], - [ - "Evan grew up on a little island that serves as their happy place, shaping them and holding a special place in their heart.", - "D17:18" - ], - [ - "Evan finds their childhood island home to be soul-calming, serene, heavenly, and like a little slice of paradise.", - "D17:20" - ] - ] - }, - "session_18_observation": { - "Evan": [ - [ - "Evan recently bought a new Prius for his active lifestyle and road trips, but it broke down soon after purchase.", - "D18:1" - ], - [ - "Evan sees setbacks as a chance to explore new ways of staying active and traveling.", - "D18:5" - ], - [ - "Evan is considering trying yoga for stress relief and flexibility.", - "D18:7" - ], - [ - "Evan appreciates Sam's support and finds it invaluable.", - "D18:11" - ], - [ - "Evan believes it makes a big difference to have support when trying new things.", - "D18:13" - ] - ], - "Sam": [ - [ - "Sam attended a Weight Watchers meeting and learned some great tips.", - "D18:6" - ], - [ - "Sam suggests yoga to Evan, mentioning its benefits for flexibility and stress relief.", - "D18:8" - ], - [ - "Sam offers Evan support and assistance with yoga tips if needed.", - "D18:10" - ], - [ - "Sam believes that having support when trying new things is important.", - "D18:12" - ] - ] - }, - "session_19_observation": { - "Evan": [ - [ - "Evan's partner is pregnant, and they are excited about having a child.", - "D19:1" - ], - [ - "Evan is excited and a bit nervous about becoming a parent again.", - "D19:3" - ], - [ - "Evan values family greatly and considers them as their rock.", - "D19:9" - ], - [ - "Evan is planning a big family reunion next summer to create more memories with family.", - "D19:11" - ] - ], - "Sam": [ - [ - "Sam congratulates Evan on the news of their partner being pregnant.", - "D19:2" - ], - [ - "Sam appreciates and recognizes Evan's value for family and memories.", - "D19:4" - ], - [ - "Sam offers support and assistance to Evan for organizing the family reunion.", - "D19:12" - ] - ] - }, - "session_20_observation": { - "Evan": [ - [ - "Evan's son had an accident where he fell off his bike last Tuesday but is doing better now.", - "D20:3" - ], - [ - "Evan is supportive and encouraging towards Sam, giving advice to believe in himself and take things one day at a time.", - "D20:9" - ], - [ - "Evan is a painter who finished a contemporary figurative painting emphasizing emotion and introspection.", - "D20:15" - ], - [ - "Evan had a painting published in an exhibition with the help of a close friend.", - "D20:17" - ] - ], - "Sam": [ - [ - "Sam used to love hiking but hasn't had the chance to do it recently.", - "D20:6" - ], - [ - "Sam is struggling with weight and confidence issues, feeling like they lack motivation.", - "D20:8" - ], - [ - "Sam acknowledges that trying new things can be difficult.", - "D20:12" - ] - ] - }, - "session_21_observation": { - "Sam": [ - [ - "Sam enjoys running in the mornings as a way to clear their head.", - "D21:9" - ], - [ - "Sam appreciates art and finds it fascinating how it can express emotions.", - "D21:13" - ], - [ - "Sam occasionally sketches but hasn't created anything remarkable yet.", - "D21:19" - ], - [ - "Sam visited an exhibit recently and was inspired by a painting.", - "D21:17" - ] - ], - "Evan": [ - [ - "Evan recently got married to a woman from Canada.", - "D21:2" - ], - [ - "Evan believes in love at first sight and felt a spark when he met his partner.", - "D21:6" - ], - [ - "Evan created a painting that reflects a sense of joy and freedom.", - "D21:16" - ], - [ - "Evan has a tendency to lose his keys frequently, turning it into a weekly ritual.", - "D21:20" - ] - ] - }, - "session_22_observation": { - "Sam": [ - [ - "Sam recently took friends on an epic hiking trip.", - "D22:1" - ], - [ - "Sam expresses concern for Evan's well-being after hearing about a minor accident.", - "D22:3" - ], - [ - "Sam appreciates nature for bringing peace and helping find clarity.", - "D22:7" - ], - [ - "Sam is open to trying new healthy snacks and recipes to stay healthy.", - "D22:11" - ], - [ - "Sam had issues with the self-checkout at the store and finds it to be a regular annoyance.", - "D22:19" - ], - [ - "Sam humorously mentions attracting issues with self-checkout and offers to be called if someone wants to experience the situation.", - "D22:21" - ] - ], - "Evan": [ - [ - "Evan recently went for a long drive to the mountains with friends in a Prius.", - "D22:2" - ], - [ - "Evan mentions getting married to his work friends after a minor accident.", - "D22:4" - ], - [ - "Evan finds peace and clarity in nature and views it as a great healer.", - "D22:8" - ], - [ - "Evan enjoys sharing and trying new healthy snacks and recipes.", - "D22:10" - ], - [ - "Evan recommends a recipe for energy balls as a healthier snack idea.", - "D22:14" - ] - ] - }, - "session_23_observation": { - "Evan": [ - [ - "Evan recently got married and informed their extended family about it, receiving overwhelming love and support.", - "D23:1" - ], - [ - "Evan feels lucky to have found a partner who understands them well.", - "D23:3" - ], - [ - "Evan values and appreciates the support and happiness their family brings regarding their marriage.", - "D23:5" - ], - [ - "Evan mentions going on a honeymoon trip to Canada to create awesome memories and explore snowy landscapes.", - "D23:23" - ], - [ - "Evan expresses excitement about skiing, trying local cuisine, and enjoying the beautiful views during the honeymoon trip.", - "D23:25" - ] - ], - "Sam": [ - [ - "Sam congratulated Evan on their marriage and appreciated the happiness and love shared in the pictures.", - "D23:2" - ], - [ - "Sam values family support and acknowledges it as a source of happiness and joy.", - "D23:6" - ], - [ - "Sam expresses gratitude for the support and love received from their family.", - "D23:8" - ], - [ - "Sam mentions that their family has been a rock through everything and emphasizes how important they are.", - "D23:12" - ], - [ - "Sam mentions enjoying homemade lasagna for dinner and admires Evan's discipline of limiting themselves to two ginger snaps a day.", - "D23:16" - ], - [ - "Sam talks about the special dessert from a cousin's wedding, expressing fondness for special occasions.", - "D23:20" - ], - [ - "Sam encourages Evan to try poutine in Canada and looks forward to hearing about the experience.", - "D23:28" - ] - ] - }, - "session_24_observation": { - "Evan": [ - [ - "Evan had a bit too much to drink and had an embarrassing incident involving someone's roses.", - "D24:3" - ], - [ - "Evan had a pee accident near some roses and felt embarrassed about it.", - "D24:5" - ], - [ - "Evan and his partner tried snowshoeing as part of a new adventure and found it surprisingly fun.", - "D24:9" - ], - [ - "Evan suggested swimming, yoga, and walking as good low-impact exercises.", - "D24:17" - ], - [ - "Evan mentioned that beach sunsets are one of his favorite low-impact exercises.", - "D24:21" - ] - ], - "Sam": [ - [ - "Sam made a meal plan and workout schedule to stay healthy and motivated.", - "D24:10" - ], - [ - "Sam planned to make an appointment with the doctor to get advice on a balanced diet plan and low-impact exercises.", - "D24:12" - ], - [ - "Sam was motivated to keep up with a fitness routine after watching The Godfather.", - "D24:18" - ], - [ - "Sam expressed hope for positive changes with a healthier diet and yoga.", - "D24:20" - ], - [ - "Sam mentioned having a recurring dream where he's flying over skyscrapers.", - "D24:22" - ] - ] - }, - "session_25_observation": { - "Sam": [ - [ - "Sam is dealing with health issues that have been rough on him.", - "D25:1" - ], - [ - "Sam enjoys going on car rides to chill and enjoy the view.", - "D25:5" - ], - [ - "Sam finds being in nature relaxing and enjoys getting fresh air away from the city.", - "D25:7" - ], - [ - "Sam is planning to spend more time in nature to rejuvenate.", - "D25:13" - ], - [ - "Sam appreciates and finds joy in small moments outdoors.", - "D25:14" - ] - ], - "Evan": [ - [ - "Evan had to apologize to his partner for a drunken night which was embarrassing.", - "D25:2" - ], - [ - "Evan enjoys kayaking on the water and finds it peaceful.", - "D25:10" - ], - [ - "Evan believes nature has a way of calming and reviving the soul.", - "D25:8" - ], - [ - "Evan believes small moments outdoors can make a big impact when dealing with tough times.", - "D25:14" - ], - [ - "Evan emphasizes finding joy in the little things and appreciating small joys, especially during tough times.", - "D25:16" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Sam and Evan met at 1:47 pm on 18 May, 2023. Evan mentioned his recent trip to the Rockies in his new Prius and shared about his passion for watercolor painting. Sam reminisced about hiking with his dad and expressed an interest in trying painting. Evan encouraged Sam to explore different hobbies until he found one he was passionate about. They both agreed to catch up soon to discuss Sam's new hobbies. The conversation ended with Evan wishing Sam well, and Sam looking forward to trying out new activities.", - "session_2_summary": "Evan and Sam spoke at 7:11 pm on 24 May, 2023. Evan shared about his recent road trip to Jasper, which he found peaceful and rejuvenating. Sam, who had a tough week and a concerning doctor's visit about his weight, sought advice on starting a healthier lifestyle. Evan suggested finding enjoyable fitness routines and making smarter food choices. Sam mentioned considering painting as a hobby to de-stress, and Evan encouraged him to try it out alongside exercising. They ended the conversation with Evan asking Sam to keep him updated as he starts this journey.", - "session_3_summary": "At 3:55 pm on 6 June 2023, Evan and Sam caught up after a while. Evan shared about a recent health scare and his decision to focus on a healthier lifestyle, discussing dietary changes and suggesting healthier alternatives to Sam. Sam mentioned sticking to soda and candy but agreed to try Evan's suggestions. Evan emphasized the importance of small changes for better health and offered support to Sam on their health journeys. They planned to work out together, and Sam shared a frustrating experience at the supermarket. Evan empathized, and they ended the conversation with well wishes.", - "session_4_summary": "At 10:52 am on 27 July, 2023, Sam confided in Evan about being teased for his weight, prompting him to make health changes. Evan offered support, sharing his own health journey and tips. They discussed dietary changes, with Evan suggesting flavored seltzer water and snacks. Evan recommended exercise, and Sam committed to starting the next day. They shared mutual appreciation before parting ways, with Evan promising ongoing support for Sam's health journey.", - "session_5_summary": "At 7:52 pm on 7th August 2023, Evan shared with Sam about meeting an amazing Canadian woman, feeling alive, and dealing with health issues. Sam expressed understanding and discussed the challenges of health problems. They also talked about Evan's health journey, motivation from family and tools, like a health tracker, to stay on track. Evan mentioned being motivated by adventure and resilience. Sam agreed on the importance of finding motivation and the power of small steps. They concluded that consistency and perseverance are key. The conversation ended with mutual well-wishes.", - "session_6_summary": "Evan and Sam catch up at 4:09 pm on 13 August, 2023. Evan shares about his recent vacation in Canada with his new partner filled with outdoor activities. Sam mentions health struggles. Evan offers support, and they discuss the importance of progress over perfection. Sam shares a motivating quote with Evan. They encourage each other to celebrate small wins. Evan jokes about losing his keys, and Sam talks about an intriguing dream. They exchange good wishes and encouragement, concluding with Evan reminding Sam to get some rest.", - "session_7_summary": "At 4:20 pm on 15 August 2023, Evan and Sam caught up after a few days. Evan shared about his son's soccer accident, while Sam mentioned a tough week and a wake-up call for self-care. Sam talked about taking a cooking class for healthier meals and shared a yummy grilled dish recipe with Evan. Evan asked for recipes with more vegetables, and Sam promised to share a roasted veg recipe. They also discussed post-accident well-being and the importance of self-care. Sam admitted to feeling a mix of concern and motivation for positive changes, with Evan providing encouraging words. They agreed to take things one step at a time, supporting each other along the way.", - "session_8_summary": "On 19th August 2023 at 6:17 pm, Sam shared with Evan that he was on a diet and living healthier, finding it tough but staying determined. Evan praised Sam's efforts and asked about any positive changes. Sam mentioned feeling more energized and less sluggish after meals. They discussed a grilled chicken and veggie stir-fry recipe. Evan talked about starting painting classes and finding peace through art. Evan shared his love for painting landscapes with bright colors. They also discussed winter activities like skiing, with Sam expressing interest but unsure if his body can handle it. Evan encouraged Sam to join in the fun someday. Sam expressed appreciation for Evan's understanding and friendship.", - "session_9_summary": "Sam informed Evan about his successful new diet and exercise routine, which started the previous Monday and made him feel great. Evan, however, mentioned twisting his knee the previous Friday, which hindered his fitness routine. Evan appreciated Sam's concern, citing life's unpredictability. Evan expressed interest in a book and discussed potential physical therapy for his knee. Sam recommended low-impact exercises and swimming. Evan also shared his recent road trip to the Rocky Mountains and love for hiking. Sam expressed interest in hiking and the need to become healthier. Evan recommended a nearby lake for hiking and nature exploration. Sam decided to plan a day trip to the lake. The conversation ended with friendly goodbyes at 10:24 am on 27 August, 2023.", - "session_10_summary": "On 11 September 2023 at 9:28 am, Evan and Sam caught up after a long time. Sam shared a before and after body picture due to a diet, aiming to motivate others. Stress triggers cravings for sugary treats for Sam. Evan suggested painting as a stress-relieving hobby and offered to help Sam get started. They planned a painting session for the upcoming Saturday and both were excited about it.", - "session_11_summary": "Sam and Evan caught up at 8:57 pm on 6 October, 2023. Evan, recovering from a knee injury, shared his love for watercolor painting and recent adventure with a lost tourist. He mentioned using painting as a stress reliever and a form of self-expression. Sam expressed interest in writing as a way to express himself and mentioned frustrations with his phone's navigation app. Both agreed on the importance of having outlets to express thoughts and feelings.", - "session_12_summary": "On 8 October 2023, at 3:09 pm, Sam told Evan about his weight concerns after a check-up. Evan suggested lifting weights, sharing his own progress and advice. Sam expressed interest and Evan encouraged him to focus on form and consistency. Sam vowed to update Evan on his progress and Evan cheered him on, advising patience and positivity. Sam found Evan's support inspiring and promised to believe in himself. Evan emphasized motivation and progress, and Sam appreciated the support, committing to taking one step at a time.", - "session_13_summary": "Evan and Sam had a conversation at 4:07 pm on 14 October, 2023. Sam confessed to Evan about buying unhealthy snacks and feeling guilty about it, while Evan shared about finishing a painting of a sunset. Sam mentioned dealing with work stress and looking for ways to stay motivated. Evan suggested trying kayaking as a way to de-stress, which Sam found appealing. Sam planned to go kayaking on the lake with a friend, and Evan offered to plan a kayaking trip together in the future. Sam decided to kayak at Lake Tahoe, and Evan encouraged him, mentioning the great views there.", - "session_14_summary": "Sam and Evan caught up at 1:50 pm on 17 October, 2023. Sam shared about a recent health scare with gastritis, prompting him to focus on health. Evan mentioned his fitness routine helping his well-being and assisting lost tourists often. Evan advised Sam on setting goals and finding enjoyable exercises to stay motivated. They planned a hike together to enjoy nature and bond. Sam appreciated Evan's support and looked forward to the hike. They agreed to stay in touch and support each other, ending the conversation with plans for the upcoming hike.", - "session_15_summary": "Sam and Evan discussed their health routines and the importance of having a supportive but non-stressful environment. Evan shared his experience of gradually improving his well-being by letting go of unrealistic expectations. Sam expressed gratitude for Evan's support and decided to focus on small victories and take it one day at a time. Evan encouraged Sam to celebrate every little achievement and assured continued support. Sam appreciated the support and they both agreed to keep moving forward together.", - "session_16_summary": "Sam informed Evan at 9:13 pm on 9 November, 2023, that he had become a Weight Watchers coach, and Evan congratulated him on the achievement. They discussed Sam's journey towards better health and his plans to motivate and help others. Evan shared that he had recently lost his job, and Sam offered support and encouragement. They also discussed enjoying a sunset together at Evan's favorite spot by the beach, planning to visit it soon to de-stress.", - "session_17_summary": "Sam and Evan spoke at 7:30 pm on 21 November 2023. Evan mentioned a recent health scare that turned out to be a misunderstanding and stressed the importance of health. Sam shared struggles with discomfort and dietary changes. They discussed the value of small steps and appreciation in life. Evan highlighted the importance of supportive friends during tough times. They talked about fitness goals, shared about a peaceful island where Evan grew up, and ended the conversation by offering support to each other.", - "session_18_summary": "Evan, at 8:16 pm on 5 December 2023, told Sam about his new Prius breaking down, causing stress as it's vital for his active lifestyle. Sam empathized, mentioning the hassle of relying on a new car. They discussed setbacks and opportunities, with Sam sharing insights from a Weight Watchers meeting. Evan considered trying yoga for stress relief based on Sam's recommendation. Sam encouraged him, offering support for new endeavors. They acknowledged the importance of having support during new experiences. The conversation ended with Evan thanking Sam and promising to update him on his yoga journey.", - "session_19_summary": "Summary:\nOn 9 December 2023 at 1:45 pm, Evan excitedly shared with Sam that his partner is pregnant, expressing both excitement and nervousness about becoming a parent again. Evan reminisced about the joys of parenthood and family memories. He showed Sam a collage of their top family memories, explaining the significance of a sign that symbolizes their family motto. Sam admired Evan's strong family bond and offered support for their upcoming family reunion. Evan expressed gratitude for Sam's support, looking forward to creating more beautiful memories with his expanding family. The conversation ended with warm goodbyes and promises to stay in touch.", - "session_20_summary": "Evan and Sam caught up at 6:48 pm on 17th December 2023. Evan shared that his son had a recent bike accident but is doing better now. They discussed coping mechanisms, with Sam mentioning a love for hiking. Evan encouraged Sam to believe in himself, try new things, and not let his weight define him. Evan showcased a contemporary figurative painting he recently completed, with a friend helping to get it published in an exhibition.", - "session_21_summary": "Sam and Evan met at 4:25 pm on December 26, 2023. Evan shared that he recently got married to a woman from Canada whom he loved at first sight. He believes in love at first sight, describing it as a magical moment. They discussed the power of love and how it can bring happiness. Evan shared his passion for art and how it helps him express and handle emotions. They exchanged thoughts on art and creativity. Evan offered to help Sam start painting. They also talked about Evan frequently losing his keys and Sam suggested putting a GPS sensor on them.", - "session_22_summary": "Sam and Evan talked at 11:00 am on 31 December 2023. Sam shared about a recent hiking trip, while Evan mentioned a mountain drive that ended in a minor accident. Despite the mishap, Evan was excited about sharing news of his marriage with work friends. The conversation shifted to the healing power of nature and healthy snacks. Evan recommended energy balls to Sam, who was keen on trying new healthy snacks. Sam also mentioned ongoing self-checkout issues at the store, prompting Evan to joke about needing Sam around to attract the problem.", - "session_23_summary": "At 1:32 pm on 6 January 2024, Evan excitedly informed Sam about sharing news of his marriage with their extended family, receiving overwhelming love and support. Sam congratulated Evan and praised the happiness evident in the picture. The conversation shifted to the importance of family support and the joy it brings. Both agreed on the significance of family in their lives, expressing gratitude for the love and happiness they provide. They also discussed dinner plans, with Evan mentioning a new diet while Sam shared a picture of dessert from a family wedding. Evan mentioned an upcoming trip to Canada for his honeymoon, intending to ski and savor local cuisine, with Sam wishing him a memorable experience. They ended the conversation looking forward to sharing details about the trip and catching up soon.", - "session_24_summary": "Evan and Sam discuss a funny incident where Evan had an embarrassing moment with some roses after a night out. Evan apologized and the situation was handled well. They talk about learning from mistakes and staying healthy. Sam plans to see a doctor for diet and exercise advice. Evan suggests low-impact exercises like swimming, yoga, and walking. Sam also mentions watching The Godfather for motivation. Evan recommends beach walks as another fun low-impact exercise. They end on a positive note, planning to make positive changes and enjoy outdoor activities.", - "session_25_summary": "Sam and Evan caught up at 9:37 pm on 11 January, 2024. Sam shared about his health issues and feeling weighed down, while Evan apologized to his partner for a drunken incident involving rose bushes. Sam enjoys relaxing car rides in the countryside, which Evan thinks is a great way to unwind. They both agree on the calming effect of nature and sharing photos that brighten their days. Evan emphasizes the importance of appreciating small joys during tough times and offers support to Sam. They end the conversation with well wishes and a reminder to take care of themselves." - }, - "sample_id": "conv-49" - }, - { - "qa": [ - { - "question": "When did Calvin first travel to Tokyo?", - "answer": "between 26 March and 20 April 2023", - "evidence": [ - "D3:1" - ], - "category": 2 - }, - { - "question": "What items did Calvin buy in March 2023?", - "answer": "mansion in Japan, luxury car Ferrari 488 GTB", - "evidence": [ - "D1:3", - "D2:1" - ], - "category": 1 - }, - { - "question": "When did Dave see Aerosmith perform live?", - "answer": "on the weekend before March 26, 2023", - "evidence": [ - "D2:10" - ], - "category": 2 - }, - { - "question": "Which bands has Dave enjoyed listening to?", - "answer": "Aerosmith, The Fireworks", - "evidence": [ - "D2:10", - "D23:9" - ], - "category": 1 - }, - { - "question": "Which country do Calvin and Dave want to meet in?", - "answer": "United States", - "evidence": [ - "D3:9", - "D3:10" - ], - "category": 3 - }, - { - "question": "What are Dave's dreams?", - "answer": "open a car maintenance shop, work on classic cars, build a custom car from scratch", - "evidence": [ - "D4:5", - "D4:5", - "D5:5" - ], - "category": 1 - }, - { - "question": "Which types of cars does Dave like the most?", - "answer": "classic vintage cars", - "evidence": [ - "D4:5", - "D1:2", - "D3:12", - "D4:7" - ], - "category": 1 - }, - { - "question": "Does Dave's shop employ a lot of people?", - "answer": "Yes", - "evidence": [ - "D4:17" - ], - "category": 3 - }, - { - "question": "When did Dave start his car maintenance shop?", - "answer": "May 1, 2023", - "evidence": [ - "D4:1" - ], - "category": 2 - }, - { - "question": "When did a mishap occur with Calvin's musical gear and favorite mic?", - "answer": "On a week before 16 May, 2023", - "evidence": [ - "D6:3" - ], - "category": 2 - }, - { - "question": "When did Calvin's place get flooded in Tokyo?", - "answer": "On a week before 16 May, 2023", - "evidence": [ - "D6:3" - ], - "category": 2 - }, - { - "question": "What mishaps has Calvin run into?", - "answer": "flooding of his mansion, car accident", - "evidence": [ - "D6:1", - "D9:1" - ], - "category": 1 - }, - { - "question": "When was Calvin's concert in Tokyo?", - "answer": "last week of May 2023", - "evidence": [ - "D6:11", - "D7:1" - ], - "category": 2 - }, - { - "question": "Would Calvin enjoy performing at the Hollywood Bowl?", - "answer": "Yes; because he enjoys the rush of performing onstage to large crowds", - "evidence": [ - "D7:11" - ], - "category": 3 - }, - { - "question": "When did Calvin meet with the creative team for his new album?", - "answer": "8 June, 2023", - "evidence": [ - "D8:1" - ], - "category": 2 - }, - { - "question": "Why does Dave regularly visit parks?", - "answer": "because it relaxes and calms him", - "evidence": [ - "D8:4", - "D1:16" - ], - "category": 1 - }, - { - "question": "When did Dave take a trip to mountainous regions?", - "answer": "July 2023", - "evidence": [ - "D8:10" - ], - "category": 2 - }, - { - "question": "When did Calvin have a car incident?", - "answer": "on the Friday before 21 June, 2023", - "evidence": [ - "D9:1" - ], - "category": 2 - }, - { - "question": "How many times has Calvin had to deal with insurance paperwork?", - "answer": "two times", - "evidence": [ - "D6:5", - "D9:1" - ], - "category": 1 - }, - { - "question": "Which places or events has Calvin visited in Tokyo?", - "answer": "music festival, car museum, Shibuya crossing, Shinjuku", - "evidence": [ - "D3:1", - "D12:7", - "D24:19" - ], - "category": 1 - }, - { - "question": "Who inspired Dave's passion for car engineering?", - "answer": "His Dad", - "evidence": [ - "D12:2", - "D12:4", - "D26:6" - ], - "category": 1 - }, - { - "question": "Does Calvin wish to become more popular?", - "answer": "Yes; he want's to grow his fanbase", - "evidence": [ - "D12:11", - "D27:1" - ], - "category": 3 - }, - { - "question": "Does Calvin want to expand his brand?", - "answer": "yes", - "evidence": [ - "D12:11", - "D18:7" - ], - "category": 1 - }, - { - "question": "What is Dave's main passion?", - "answer": "auto engineering", - "evidence": [ - "D13:3", - "D5:5", - "D4:5", - "D3:12" - ], - "category": 1 - }, - { - "question": "Can Dave work with engines?", - "answer": "yes", - "evidence": [ - "D13:7", - "D22:5", - "D20:1" - ], - "category": 1 - }, - { - "question": "When did Dave host a card-playing night with his friends?", - "answer": "on the Friday before 22 August, 2023", - "evidence": [ - "D15:1" - ], - "category": 2 - }, - { - "question": "When did Calvin record a podcast with his friends?", - "answer": "21 August, 2023", - "evidence": [ - "D15:12" - ], - "category": 2 - }, - { - "question": "Which city was Calvin visiting in August 2023?", - "answer": "Miami", - "evidence": [ - "D16:6" - ], - "category": 2 - }, - { - "question": "What does Calvin do to relax?", - "answer": "take long drives in his car, embrace nature, fixing cars", - "evidence": [ - "D5:8", - "D5:10", - "D7:5" - ], - "category": 1 - }, - { - "question": "What are Dave's hobbies other than fixing cars?", - "answer": "take a walk, go hiking, listen to favorite albums, live concerts, photography", - "evidence": [ - "D5:9", - "D5:11", - "D8:8", - "D27:2" - ], - "category": 1 - }, - { - "question": "What kind of music does Dave listen to?", - "answer": "classic rock, Japanese music", - "evidence": [ - "D2:10", - "D28:40", - "D10:11" - ], - "category": 1 - }, - { - "question": "Where was Dave in the last two weeks of August 2023?", - "answer": "San Francisco", - "evidence": [ - "D14:1", - "D17:1" - ], - "category": 2 - }, - { - "question": "Where did Dave return from with new knowledge of different techniques of car restoration?", - "answer": "San Francisco", - "evidence": [ - "D17:1", - "D14:1" - ], - "category": 1 - }, - { - "question": "What was Dave doing in San Francisco?", - "answer": "attending a car modification workshop", - "evidence": [ - "D17:1", - "D14:1" - ], - "category": 1 - }, - { - "question": "When did Dave return from San Francisco?", - "answer": "September 1, 2023", - "evidence": [ - "D17:1" - ], - "category": 2 - }, - { - "question": "When did Calvin book flight tickets to Boston?", - "answer": "last week of August 2023", - "evidence": [ - "D17:6" - ], - "category": 2 - }, - { - "question": "When was Calvin's album released?", - "answer": "September 11, 2023", - "evidence": [ - "D18:1" - ], - "category": 2 - }, - { - "question": "Does Calvin love music tours?", - "answer": "yes", - "evidence": [ - "D18:7", - "D16:2", - "D7:1" - ], - "category": 3 - }, - { - "question": "When did Dave have a great jam session with his band?", - "answer": "September 14, 2023", - "evidence": [ - "D19:1" - ], - "category": 2 - }, - { - "question": "Would Dave prefer working on a Dodge Charger or a Subaru Forester?", - "answer": "Dodge Charger", - "evidence": [], - "category": 3 - }, - { - "question": "What was the artists Calvin used to listen to when he was a kid?", - "answer": "Tupac and Dr. Dre", - "evidence": [ - "D20:8", - "D20:6" - ], - "category": 1 - }, - { - "question": "Which of their family member do Calvin and Dave have nostalgic memories about?", - "answer": "Dad", - "evidence": [ - "D12:2", - "D20:6" - ], - "category": 1 - }, - { - "question": "Based on the conversation, did Calvin and Dave have a meeting in Boston between August and November 2023? Answer in yes or no.", - "answer": "No", - "evidence": [], - "category": 3 - }, - { - "question": "Which city was Calvin at on October 3, 2023?", - "answer": "Boston", - "evidence": [ - "D21:1" - ], - "category": 2 - }, - { - "question": "When did Calvin met with local artists in Boston?", - "answer": "October 3, 2023", - "evidence": [ - "D21:1" - ], - "category": 2 - }, - { - "question": "What shared activities do Dave and Calvin have?", - "answer": "Working on cars", - "evidence": [ - "D21:3", - "D21:4" - ], - "category": 1 - }, - { - "question": "What is Dave's favorite activity?", - "answer": "Restoring cars", - "evidence": [ - "D21:4", - "D22:7", - "D19:7" - ], - "category": 1 - }, - { - "question": "How many car shows has Dave attended?", - "answer": "two", - "evidence": [ - "D3:12", - "D22:1" - ], - "category": 1 - }, - { - "question": "What was Dave doing in the first weekend of October 2023?", - "answer": "attending a car show", - "evidence": [ - "D22:1" - ], - "category": 2 - }, - { - "question": "When Dave was a child, what did he and his father do in the garage?", - "answer": "tinkering with car engines, restoration and refurbishing cars", - "evidence": [ - "D12:2", - "D12:4", - "D22:5" - ], - "category": 1 - }, - { - "question": "When did Calvin buy his second Ferrari?", - "answer": "first week of October 2023", - "evidence": [ - "D23:16" - ], - "category": 2 - }, - { - "question": "When did Calvin and Frank Ocean start collaborating?", - "answer": "August 2022", - "evidence": [ - "D24:5", - "D15:2" - ], - "category": 2 - }, - { - "question": "When did Calvin plan on travelling to Tokyo the second time?", - "answer": "November 2023", - "evidence": [ - "D24:17" - ], - "category": 2 - }, - { - "question": "Who supports Calvin in tough times?", - "answer": "friends and team", - "evidence": [ - "D25:6", - "D29:7" - ], - "category": 1 - }, - { - "question": "What does help Calvin stay connected to the creative process?", - "answer": "Calvin stays connected to the creative process by always staying up-to-date on world events and watching documentaries about artists.", - "evidence": [ - "D25:8", - "D28:31" - ], - "category": 1 - }, - { - "question": "When did Calvin visit some of the sights in Boston with a former high school friend?", - "answer": "October 24, 2023", - "evidence": [ - "D26:1" - ], - "category": 2 - }, - { - "question": "Which cities did Dave travel to in 2023?", - "answer": "San Francsico, Detroit", - "evidence": [ - "D14:1", - "D26:2" - ], - "category": 1 - }, - { - "question": "Which hobby did Dave pick up in October 2023?", - "answer": "photography", - "evidence": [ - "D27:2" - ], - "category": 2 - }, - { - "question": "Which events in Dave's life inspired him to take up auto engineering?", - "answer": "attending a car show with Dad, working on an old car in a neighbor's garage when he was young, spent a summer restoring an old car with Dad", - "evidence": [ - "D26:6", - "D25:12", - "D12:2", - "D12:4" - ], - "category": 1 - }, - { - "question": "How many Ferraris does Calvin own?", - "answer": "two", - "evidence": [ - "D2:1", - "D23:16" - ], - "category": 1 - }, - { - "question": "What gifts has Calvin received from his artist friends?", - "answer": "gold chain, custom-made guitar with an octopus on it", - "evidence": [ - "D4:24", - "D4:26", - "D16:14" - ], - "category": 1 - }, - { - "question": "How long did Dave's work on the Ford Mustang take?", - "answer": "nearly two months", - "evidence": [ - "D14:11", - "D20:1", - "D21:4" - ], - "category": 2 - }, - { - "question": "How long was the car modification workshop in San Francisco?", - "answer": "two weeks", - "evidence": [ - "D14:1", - "D17:1" - ], - "category": 2 - }, - { - "question": "What style of guitars does Calvin own?", - "answer": "custom-made yellow guitar with an octopus on it, shiny purple guitar", - "evidence": [ - "D16:13", - "D16:4", - "D16:18", - "D16:19" - ], - "category": 1 - }, - { - "question": "What activities has Dave participated in with his friends?", - "answer": "weekly visits to local parks, countryside roadtrip, celebration of the opening of his car maintenance shop, card-playing nights", - "evidence": [ - "D10:3", - "D11:1", - "D6:8", - "D15:1" - ], - "category": 1 - }, - { - "question": "When did Dave take a photo of a Boston clock tower?", - "answer": "September 2023", - "evidence": [ - "D27:6" - ], - "category": 2 - }, - { - "question": "Do all of Dave's car restoration projects go smoothly?", - "answer": "No", - "evidence": [ - "D27:10", - "D13:7", - "D25:17", - "D20:1" - ], - "category": 1 - }, - { - "question": "Where was Calvin located in the last week of October 2023?", - "answer": "Tokyo", - "evidence": [ - "D28:1" - ], - "category": 2 - }, - { - "question": "When did Dave find the car he repaired and started sharing in his blog?", - "answer": "last week of October 2023", - "evidence": [ - "D28:20" - ], - "category": 2 - }, - { - "question": "When did Dave buy a vintage camera?", - "answer": "November 2023", - "evidence": [ - "D30:05" - ], - "category": 2 - }, - { - "question": "When did Calvin attend a gala in Boston?", - "answer": "November 16, 2023", - "evidence": [ - "D30:1" - ], - "category": 2 - }, - { - "question": "How long did Calvin plan to stay in Japan?", - "answer": "A few months", - "evidence": [ - "D1:15" - ], - "category": 4 - }, - { - "question": "Which band was Dave's favorite at the music festival in April 2023?", - "answer": "Aerosmith", - "evidence": [ - "D2:10" - ], - "category": 4 - }, - { - "question": "Where did Calvin attend a music festival in April 2023?", - "answer": "Tokyo", - "evidence": [ - "D3:1" - ], - "category": 4 - }, - { - "question": "What advice did Calvin receive from the producer at the music festival?", - "answer": "to stay true to himself and sound unique", - "evidence": [ - "D3:7" - ], - "category": 4 - }, - { - "question": "What is Dave's new business venture as of 1 May, 2023?", - "answer": "Car maintenance shop", - "evidence": [ - "D4:1" - ], - "category": 4 - }, - { - "question": "What type of cars does Dave work on at his shop?", - "answer": "all kinds of cars, from regular maintenance to full restorations of classic cars", - "evidence": [ - "D4:19" - ], - "category": 4 - }, - { - "question": "What did Calvin receive as a gift from another artist?", - "answer": "a gold necklace with a diamond pendant", - "evidence": [ - "D4:26" - ], - "category": 4 - }, - { - "question": "What was the necklace Calvin received meant to remind him of?", - "answer": "why he keeps hustling as a musician", - "evidence": [ - "D4:26" - ], - "category": 4 - }, - { - "question": "What does Dave do when he feels his creativity is frozen?", - "answer": "immerse himself in something he loves", - "evidence": [ - "D5:11" - ], - "category": 4 - }, - { - "question": "How does Calvin plan to jumpstart his inspiration?", - "answer": "explore other things and have some fun", - "evidence": [ - "D5:11" - ], - "category": 4 - }, - { - "question": "What did Calvin manage to save during the flood incident?", - "answer": "music gear and favorite microphone", - "evidence": [ - "D6:3" - ], - "category": 4 - }, - { - "question": "What did Dave open in May 2023?", - "answer": "a car shop", - "evidence": [ - "D6:8" - ], - "category": 4 - }, - { - "question": "What gives Dave a sense of achievement and purpose?", - "answer": "Fixing up things", - "evidence": [ - "D7:6" - ], - "category": 4 - }, - { - "question": "What fuels Calvin's soul?", - "answer": "Performing live", - "evidence": [ - "D7:11" - ], - "category": 4 - }, - { - "question": "What is Dave doing to relax on weekends?", - "answer": "exploring parks", - "evidence": [ - "D8:4" - ], - "category": 4 - }, - { - "question": "What sports activity is Calvin planning to try after the tour with Frank Ocean?", - "answer": "Skiing", - "evidence": [ - "D9:15" - ], - "category": 4 - }, - { - "question": "What was Calvin excited to do after getting his car fixed on 7 July, 2023?", - "answer": "get back on the road", - "evidence": [ - "D10:1" - ], - "category": 4 - }, - { - "question": "What did Calvin and his friends arrange for in the park?", - "answer": "regular walks together", - "evidence": [ - "D10:3" - ], - "category": 4 - }, - { - "question": "What kind of music has Calvin been creating lately?", - "answer": "experimenting with different genres", - "evidence": [ - "D11:6" - ], - "category": 4 - }, - { - "question": "How does Calvin describe his process of adding electronic elements to his songs?", - "answer": "gives them a fresh vibe", - "evidence": [ - "D11:6" - ], - "category": 4 - }, - { - "question": "What car brand does Calvin own that he is proud of?", - "answer": "Ferrari", - "evidence": [ - "D12:7" - ], - "category": 4 - }, - { - "question": "What is Calvin's biggest current goal?", - "answer": "expand his brand worldwide and grow his fanbase", - "evidence": [ - "D12:11" - ], - "category": 4 - }, - { - "question": "What is Dave's advice to Calvin regarding his dreams?", - "answer": "to never forget his dreams", - "evidence": [ - "D12:14" - ], - "category": 4 - }, - { - "question": "What workshop did Dave get picked for on 11 August, 2023?", - "answer": "Car mod workshop", - "evidence": [ - "D13:1" - ], - "category": 4 - }, - { - "question": "What kind of modifications has Dave been working on in the car mod workshop?", - "answer": "engine swaps, suspension modifications, and body modifications", - "evidence": [ - "D13:7" - ], - "category": 4 - }, - { - "question": "What type of car did Dave work on during the workshop?", - "answer": "classic muscle car", - "evidence": [ - "D13:7" - ], - "category": 4 - }, - { - "question": "What does Dave say is important for making his custom cars unique?", - "answer": "attention to small details", - "evidence": [ - "D13:11" - ], - "category": 4 - }, - { - "question": "How did the audience in Tokyo react when Calvin sang one of his songs?", - "answer": "Everyone was so into it and sang along", - "evidence": [ - "D14:6" - ], - "category": 4 - }, - { - "question": "How did Calvin meet Frank Ocean?", - "answer": "At a music festival in Tokyo", - "evidence": [ - "D15:4" - ], - "category": 4 - }, - { - "question": "Where did Calvin and Frank Ocean record a song together?", - "answer": "In the studio at Calvin's mansion", - "evidence": [ - "D15:4" - ], - "category": 4 - }, - { - "question": "What did Calvin and his friends record in August 2023?", - "answer": "a podcast discussing the rap industry", - "evidence": [ - "D15:12" - ], - "category": 4 - }, - { - "question": "Where did Calvin start shooting a video for his new album?", - "answer": "Miami", - "evidence": [ - "D16:8" - ], - "category": 4 - }, - { - "question": "What design is featured on Calvin's guitar?", - "answer": "octopus", - "evidence": [ - "D16:14" - ], - "category": 4 - }, - { - "question": "Why did Calvin get his guitar customized with a shiny finish?", - "answer": "unique look", - "evidence": [ - "D16:20" - ], - "category": 4 - }, - { - "question": "What color glow did Calvin customize his guitar with?", - "answer": "purple", - "evidence": [ - "D16:20" - ], - "category": 4 - }, - { - "question": "Where did Dave come back from with insights on car modification on 1st September 2023?", - "answer": "San Francisco", - "evidence": [ - "D17:1" - ], - "category": 4 - }, - { - "question": "What emotion does Dave mention feeling when he sees the relief of someone whose car he fixed?", - "answer": "Proud", - "evidence": [ - "D17:5" - ], - "category": 4 - }, - { - "question": "What did Calvin book a flight ticket for on 1st September 2023?", - "answer": "Boston", - "evidence": [ - "D17:6" - ], - "category": 4 - }, - { - "question": "What is Calvin excited about after the tour?", - "answer": "exploring and growing his brand", - "evidence": [ - "D18:7" - ], - "category": 4 - }, - { - "question": "What plans do Calvin and Dave have for when Calvin visits Boston?", - "answer": "Check out Dave's garage and maybe get some ideas for future projects", - "evidence": [ - "D18:11" - ], - "category": 4 - }, - { - "question": "Which Disney movie did Dave mention as one of his favorites?", - "answer": "Ratatouille", - "evidence": [ - "D19:6" - ], - "category": 4 - }, - { - "question": "How does Dave feel about the reactions of people when they see the finished restoration project?", - "answer": "satisfying and worth the hard work", - "evidence": [ - "D19:9" - ], - "category": 4 - }, - { - "question": "What activity did Calvin enjoy during his summer drives?", - "answer": "feeling the wind blowing through his hair", - "evidence": [ - "D20:4" - ], - "category": 4 - }, - { - "question": "Which song from the childhood of Calvin brings back memories of a road trip with his dad?", - "answer": "\"California Love\"", - "evidence": [ - "D20:6", - "D20:8" - ], - "category": 4 - }, - { - "question": "What project did Calvin work on to chill out?", - "answer": "A shiny orange car", - "evidence": [ - "D21:3" - ], - "category": 4 - }, - { - "question": "What car did Dave work on in the junkyard?", - "answer": "Ford Mustang", - "evidence": [ - "D21:4" - ], - "category": 4 - }, - { - "question": "What does Dave find satisfying about restoring old cars?", - "answer": "Transforming something old and beat-up into something beautiful", - "evidence": [ - "D21:10" - ], - "category": 4 - }, - { - "question": "What do Calvin and Dave use to reach their goals?", - "answer": "Hard work and determination", - "evidence": [ - "D21:15" - ], - "category": 4 - }, - { - "question": "What does working on cars represent for Dave?", - "answer": "Therapy and a way to get away from everyday stress", - "evidence": [ - "D22:5" - ], - "category": 4 - }, - { - "question": "What does Dave aim to do with his passion for cars?", - "answer": "Take something broken and make it into something awesome", - "evidence": [ - "D22:5" - ], - "category": 4 - }, - { - "question": "What did Calvin recently get that is a \"masterpiece on wheels\"?", - "answer": "Ferrari", - "evidence": [ - "D23:16" - ], - "category": 4 - }, - { - "question": "Who headlined the music festival that Dave attended in October?", - "answer": "The Fireworks", - "evidence": [ - "D23:9" - ], - "category": 4 - }, - { - "question": "How does Calvin stay motivated when faced with setbacks?", - "answer": "Reminds himself of his passion for goals, gets help from others, and takes a break to recharge", - "evidence": [ - "D23:4" - ], - "category": 4 - }, - { - "question": "What activity does Dave find fulfilling, similar to Calvin's passion for music festivals?", - "answer": "fixing things", - "evidence": [ - "D23:11" - ], - "category": 4 - }, - { - "question": "Where did Calvin and Dave meet Frank Ocean to start collaborating?", - "answer": "at a festival", - "evidence": [ - "D24:5" - ], - "category": 4 - }, - { - "question": "Which part of Tokyo is described as Tokyo's Times Square by Calvin?", - "answer": "Shibuya Crossing", - "evidence": [ - "D24:19" - ], - "category": 4 - }, - { - "question": "What specific location in Tokyo does Calvin mention being excited to explore?", - "answer": "Shinjuku", - "evidence": [ - "D24:19" - ], - "category": 4 - }, - { - "question": "What dish does Dave recommend Calvin to try in Tokyo?", - "answer": "ramen", - "evidence": [ - "D24:20" - ], - "category": 4 - }, - { - "question": "What does Calvin find energizing during the tour?", - "answer": "Performing and connecting with the crowd", - "evidence": [ - "D25:2" - ], - "category": 4 - }, - { - "question": "How does Calvin balance his job and personal life?", - "answer": "Takes it one day at a time", - "evidence": [ - "D25:4" - ], - "category": 4 - }, - { - "question": "What inspired Calvin's recent music?", - "answer": "Struggles that people go through", - "evidence": [ - "D25:10" - ], - "category": 4 - }, - { - "question": "How does Calvin describe his music in relation to capturing feelings?", - "answer": "Express himself and work through his emotions", - "evidence": [ - "D25:12" - ], - "category": 4 - }, - { - "question": "Why did Dave start working on cars?", - "answer": "Fascinated with how machines work", - "evidence": [ - "D25:15" - ], - "category": 4 - }, - { - "question": "What is the toughest part of car restoration according to Dave?", - "answer": "Paying extra attention to detail", - "evidence": [ - "D25:19" - ], - "category": 4 - }, - { - "question": "What does Calvin believe makes an artist create something extraordinary?", - "answer": "Paying attention to small details", - "evidence": [ - "D25:22" - ], - "category": 4 - }, - { - "question": "When did Dave sell the car he restored last year?", - "answer": "Last year", - "evidence": [ - "D25:17" - ], - "category": 4 - }, - { - "question": "When did Calvin first get interested in cars?", - "answer": "at an early age", - "evidence": [ - "D26:6" - ], - "category": 4 - }, - { - "question": "How did Calvin feel about performing with someone he admires?", - "answer": "Unreal, like a dream come true", - "evidence": [ - "D26:9" - ], - "category": 4 - }, - { - "question": "What realization did the nightclub experience bring to Calvin?", - "answer": "how much music means to him, it's like his passion and purpose", - "evidence": [ - "D26:9" - ], - "category": 4 - }, - { - "question": "What do Dave and Calvin agree on regarding their pursuits?", - "answer": "It's fulfilling and motivating", - "evidence": [ - "D26:11" - ], - "category": 4 - }, - { - "question": "Which city is featured in the photograph Dave showed Calvin?", - "answer": "Boston", - "evidence": [ - "D27:6" - ], - "category": 4 - }, - { - "question": "What did Calvin do recently at his Japanese house?", - "answer": "Threw a small party for his new album", - "evidence": [ - "D28:1" - ], - "category": 4 - }, - { - "question": "What did Dave recently start a blog about?", - "answer": "Car mods", - "evidence": [ - "D28:8" - ], - "category": 4 - }, - { - "question": "What is Dave's way to share his passion with others?", - "answer": "Through a blog on car mods", - "evidence": [ - "D28:8" - ], - "category": 4 - }, - { - "question": "What type of videos does Calvin usually watch on his television?", - "answer": "Music videos, concerts, documentaries about artists and their creative process", - "evidence": [ - "D28:31" - ], - "category": 4 - }, - { - "question": "What type of music has Dave been getting into lately?", - "answer": "Classic rock", - "evidence": [ - "D28:40" - ], - "category": 4 - }, - { - "question": "What tools does Calvin use to boost his motivation for music?", - "answer": "Writing lyrics and notes", - "evidence": [ - "D28:34" - ], - "category": 4 - }, - { - "question": "What type of content does Dave post on his blog that inspired others to start their own DIY projects?", - "answer": "How he made his car look like a beast", - "evidence": [ - "D28:10" - ], - "category": 4 - }, - { - "question": "What kind of impact does Dave's blog on car mods have on people?", - "answer": "It inspires others to start their DIY projects", - "evidence": [ - "D28:10" - ], - "category": 4 - }, - { - "question": "Who did Calvin invite to see him perform in Boston on 13 November, 2023?", - "answer": "his old high school buddy", - "evidence": [ - "D29:1" - ], - "category": 4 - }, - { - "question": "What hobby did Calvin take up recently?", - "answer": "Photography", - "evidence": [ - "D30:1" - ], - "category": 4 - }, - { - "question": "What new item did Dave buy recently?", - "answer": "A vintage camera", - "evidence": [ - "D30:5" - ], - "category": 4 - }, - { - "question": "What type of photos does Dave like to capture with his new camera?", - "answer": "Nature - sunsets, beaches, waves", - "evidence": [ - "D30:9" - ], - "category": 4 - }, - { - "question": "What event did Calvin attend in Boston?", - "answer": "Fancy gala", - "evidence": [ - "D30:2" - ], - "category": 4 - }, - { - "question": "What did Calvin discuss with the cool artist he met at the gala?", - "answer": "Music and art", - "evidence": [ - "D30:4" - ], - "category": 4 - }, - { - "question": "Where did Dave take a stunning photo of a waterfall?", - "answer": "Nearby park", - "evidence": [ - "D30:15" - ], - "category": 4 - }, - { - "question": "What positive impact does Calvin mention nature has on tough times?", - "answer": "Nature helps us appreciate life", - "evidence": [ - "D30:12" - ], - "category": 4 - }, - { - "question": "Which DJ was Dave's favorite at the music festival in April 2023?", - "evidence": [ - "D2:10" - ], - "category": 5, - "adversarial_answer": "Aerosmith" - }, - { - "question": "What advice did Calvin receive from the chef at the music festival?", - "evidence": [ - "D3:7" - ], - "category": 5, - "adversarial_answer": "to stay true to himself and sound unique" - }, - { - "question": "What is Calvin's new business venture as of 1 May, 2023?", - "evidence": [ - "D4:1" - ], - "category": 5, - "adversarial_answer": "Car maintenance shop" - }, - { - "question": "What type of cars does Calvin work on at his shop?", - "evidence": [ - "D4:19" - ], - "category": 5, - "adversarial_answer": "all kinds of cars, from regular maintenance to full restorations of classic cars" - }, - { - "question": "What did Dave receive as a gift from another artist?", - "evidence": [ - "D4:26" - ], - "category": 5, - "adversarial_answer": "a gold necklace with a diamond pendant" - }, - { - "question": "What was the necklace Dave received meant to remind him of?", - "evidence": [ - "D4:26" - ], - "category": 5, - "adversarial_answer": "why he keeps hustling as a musician" - }, - { - "question": "What did Calvin open in May 2023?", - "evidence": [ - "D6:8" - ], - "category": 5, - "adversarial_answer": "a car shop" - }, - { - "question": "What gives Calvin a sense of achievement and purpose?", - "evidence": [ - "D7:6" - ], - "category": 5, - "adversarial_answer": "Fixing up things" - }, - { - "question": "What sports activity is Dave planning to try after the tour with Frank Ocean?", - "evidence": [ - "D9:15" - ], - "category": 5, - "adversarial_answer": "Skiing" - }, - { - "question": "How does Calvin describe his process of adding acoustic elements to his songs?", - "evidence": [ - "D11:6" - ], - "category": 5, - "adversarial_answer": "gives them a fresh vibe" - }, - { - "question": "What clothing brand does Calvin own that he is proud of?", - "evidence": [ - "D12:7" - ], - "category": 5, - "adversarial_answer": "Ferrari" - }, - { - "question": "What workshop did Calvin get picked for on 11 August, 2023?", - "evidence": [ - "D13:1" - ], - "category": 5, - "adversarial_answer": "Car mod workshop" - }, - { - "question": "What kind of modifications has Calvin been working on in the car mod workshop?", - "evidence": [ - "D13:7" - ], - "category": 5, - "adversarial_answer": "engine swaps, suspension modifications, and body modifications" - }, - { - "question": "What type of car did Calvin work on during the workshop?", - "evidence": [ - "D13:7" - ], - "category": 5, - "adversarial_answer": "classic muscle car" - }, - { - "question": "What did Dave and his friends record in August 2023?", - "evidence": [ - "D15:12" - ], - "category": 5, - "adversarial_answer": "a podcast discussing the rap industry" - }, - { - "question": "Where did Dave start shooting a video for his new album?", - "evidence": [ - "D16:8" - ], - "category": 5, - "adversarial_answer": "Miami" - }, - { - "question": "What design is featured on Dave's guitar?", - "evidence": [ - "D16:14" - ], - "category": 5, - "adversarial_answer": "octopus" - }, - { - "question": "Why did Dave get his guitar customized with a shiny finish?", - "evidence": [ - "D16:20" - ], - "category": 5, - "adversarial_answer": "unique look" - }, - { - "question": "What color glow did Dave customize his guitar with?", - "evidence": [ - "D16:20" - ], - "category": 5, - "adversarial_answer": "purple" - }, - { - "question": "Where did Calvin come back from with insights on car modification on 1st September 2023?", - "evidence": [ - "D17:1" - ], - "category": 5, - "adversarial_answer": "San Francisco" - }, - { - "question": "What emotion does Calvin mention feeling when he sees the relief of someone whose car he fixed?", - "evidence": [ - "D17:5" - ], - "category": 5, - "adversarial_answer": "Proud" - }, - { - "question": "What did Dave book a flight ticket for on 1st September 2023?", - "evidence": [ - "D17:6" - ], - "category": 5, - "adversarial_answer": "Boston" - }, - { - "question": "Which horror movie did Dave mention as one of his favorites?", - "evidence": [ - "D19:6" - ], - "category": 5, - "adversarial_answer": "Ratatouille" - }, - { - "question": "Which song from the childhood of Dave brings back memories of a road trip with his dad?", - "evidence": [ - "D20:6", - "D20:8" - ], - "category": 5, - "adversarial_answer": "\"California Love\"" - }, - { - "question": "What car did Calvin work on in the junkyard?", - "evidence": [ - "D21:4" - ], - "category": 5, - "adversarial_answer": "Ford Mustang" - }, - { - "question": "What does Dave find satisfying about destroying old cars?", - "evidence": [ - "D21:10" - ], - "category": 5, - "adversarial_answer": "Transforming something old and beat-up into something beautiful" - }, - { - "question": "What does working on boats represent for Dave?", - "evidence": [ - "D22:5" - ], - "category": 5, - "adversarial_answer": "Therapy and a way to get away from everyday stress" - }, - { - "question": "What does Dave aim to do with his passion for cooking?", - "evidence": [ - "D22:5" - ], - "category": 5, - "adversarial_answer": "Take something broken and make it into something awesome" - }, - { - "question": "What did Calvin recently get that is a \"masterpiece on canvas\"?", - "evidence": [ - "D23:16" - ], - "category": 5, - "adversarial_answer": "Ferrari" - }, - { - "question": "Who headlined the music festival that Calvin attended in October?", - "evidence": [ - "D23:9" - ], - "category": 5, - "adversarial_answer": "The Fireworks" - }, - { - "question": "Which part of Tokyo is described as Tokyo's Times Square by Dave?", - "evidence": [ - "D24:19" - ], - "category": 5, - "adversarial_answer": "Shibuya Crossing" - }, - { - "question": "What specific location in Tokyo does Calvin mention being excited to avoid?", - "evidence": [ - "D24:19" - ], - "category": 5, - "adversarial_answer": "Shinjuku" - }, - { - "question": "When did Calvin sell the car he restored last year?", - "evidence": [ - "D25:17" - ], - "category": 5, - "adversarial_answer": "Last year" - }, - { - "question": "When did Calvin first get interested in motorcycles?", - "evidence": [ - "D26:6" - ], - "category": 5, - "adversarial_answer": "at an early age" - }, - { - "question": "What realization did the nightclub experience bring to Dave?", - "evidence": [ - "D26:9" - ], - "category": 5, - "adversarial_answer": "how much music means to him, it's like his passion and purpose" - }, - { - "question": "What did Dave do recently at his Japanese house?", - "evidence": [ - "D28:1" - ], - "category": 5, - "adversarial_answer": "Threw a small party for his new album" - }, - { - "question": "What did Calvin recently start a blog about?", - "evidence": [ - "D28:8" - ], - "category": 5, - "adversarial_answer": "Car mods" - }, - { - "question": "What type of videos does Dave usually watch on his television?", - "evidence": [ - "D28:31" - ], - "category": 5, - "adversarial_answer": "Music videos, concerts, documentaries about artists and their creative process" - }, - { - "question": "What type of art has Dave been getting into lately?", - "evidence": [ - "D28:40" - ], - "category": 5, - "adversarial_answer": "Classic rock" - }, - { - "question": "What type of content does Dave post on his blog that inspired others to start their own cooking projects?", - "evidence": [ - "D28:10" - ], - "category": 5, - "adversarial_answer": "How he made his car look like a beast" - }, - { - "question": "What kind of impact does Dave's blog on vegan recipes have on people?", - "evidence": [ - "D28:10" - ], - "category": 5, - "adversarial_answer": "It inspires others to start their DIY projects" - }, - { - "question": "Who did Dave invite to see him perform in Boston on 13 November, 2023?", - "evidence": [ - "D29:1" - ], - "category": 5, - "adversarial_answer": "his old high school buddy" - }, - { - "question": "What new item did Calvin buy recently?", - "evidence": [ - "D30:5" - ], - "category": 5, - "adversarial_answer": "A vintage camera" - }, - { - "question": "What type of photos does Calvin like to capture with his new camera?", - "evidence": [ - "D30:9" - ], - "category": 5, - "adversarial_answer": "Nature - sunsets, beaches, waves" - }, - { - "question": "What did Dave discuss with the cool artist he met at the gala?", - "evidence": [ - "D30:4" - ], - "category": 5, - "adversarial_answer": "Music and art" - }, - { - "question": "Where did Calvin take a stunning photo of a waterfall?", - "evidence": [ - "D30:15" - ], - "category": 5, - "adversarial_answer": "Nearby park" - } - ], - "conversation": { - "speaker_a": "Calvin", - "speaker_b": "Dave", - "session_1_date_time": "11:53 am on 23 March, 2023", - "session_1": [ - { - "speaker": "Calvin", - "dia_id": "D1:1", - "text": "Hey Dave! Nice to meet you! How's it going since we talked?" - }, - { - "speaker": "Dave", - "dia_id": "D1:2", - "text": "Hey Calvin! Nice to meet you too! Things have been going well since we last talked. I attended this awesome event recently. It was like a car lover's paradise! They had so many classic cars on show, I was in awe. I even had the opportunity to speak with some of the owners and hear their fascinating stories. It was super inspiring. What about you? Anything exciting happening in your life lately?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://localandbespoke.files.wordpress.com/2018/11/imag0397.jpg" - ], - "blip_caption": "a photo of a building with a sign on the front of it", - "query": "mansion entrance japan", - "dia_id": "D1:3", - "text": "That event sounds great! Something really exciting happened to me - I just had a big life change! Here's my new mansion - pretty cool huh?" - }, - { - "speaker": "Dave", - "dia_id": "D1:4", - "text": "Wow! Congrats on the big change! What inspired you to start this journey?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:5", - "text": "I'm so excited to learn about Japanese culture and get a chance to expand." - }, - { - "speaker": "Dave", - "dia_id": "D1:6", - "text": "Wow, Calvin, learning about new cultures is awesome! Have you ever been to Japan?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:7", - "text": "Never been there before. Fascinated by the traditions and can't wait to get a taste of the culture." - }, - { - "speaker": "Dave", - "dia_id": "D1:8", - "text": "Wow, Japan sounds amazing. Can't wait to hear all about it! When are you leaving?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/6p8nisgp4dq41.jpg" - ], - "blip_caption": "a photo of a living room with a couch, table, and television", - "query": "japanese mansion living room", - "dia_id": "D1:9", - "text": "I'm heading there next month. I'll be staying in such a nice place while I'm there.\n\n" - }, - { - "speaker": "Dave", - "dia_id": "D1:10", - "text": "That looks cozy! Where'd you find a place to stay there?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:11", - "text": "Wow, my agent found me this awesome place, so thankful!" - }, - { - "speaker": "Dave", - "dia_id": "D1:12", - "text": "Awesome! Having a place to stay is key. What are your plans for the trip?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:13", - "text": "I'm planning to explore the city, try out different local cuisines, and perhaps collaborate with musicians in the area." - }, - { - "speaker": "Dave", - "dia_id": "D1:14", - "text": "Sounds great! Collaborating with local musicians would be an amazing experience. How long are you planning to stay in Japan?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:15", - "text": "I'm gonna be in Japan for a few months then off to Boston! I can't wait!" - }, - { - "speaker": "Dave", - "dia_id": "D1:16", - "img_url": [ - "https://c1.wallpaperflare.com/preview/349/298/459/public-garden-boston-park-common.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a park with a lake and a few boats", - "text": "That's great, can't wait for you to return from your trip. I've been spending lots of time at this beautiful park lately - so calming. Have you been there before?" - }, - { - "speaker": "Calvin", - "dia_id": "D1:17", - "text": "No, I've never been in that park. Looks like a chill spot. I'll definitely check it out when I'm back. Appreciate the tip!" - }, - { - "speaker": "Dave", - "dia_id": "D1:18", - "text": "Cool! Enjoy your trip! Let me know if you need any more recommendations. Have fun!" - }, - { - "speaker": "Calvin", - "dia_id": "D1:19", - "text": "Thanks, Dave! If I need any help, I'll be sure to let you know. Stay safe!" - } - ], - "session_2_date_time": "4:45 pm on 26 March, 2023", - "session_2": [ - { - "speaker": "Calvin", - "dia_id": "D2:1", - "text": "Hey Dave, been a few days, so I wanted to let you in on some cool news. I just got a new car and it's amazing! Finally owning a luxury car was a dream come true and I'm so stoked." - }, - { - "speaker": "Dave", - "dia_id": "D2:2", - "text": "Woohoo, Calvin! Congrats on the new car! How's it driving?" - }, - { - "speaker": "Calvin", - "dia_id": "D2:3", - "text": "Nice car! It's an amazing ride - super smooth and real powerful. Like a rockstar behind the wheel! Thanks, man!" - }, - { - "speaker": "Dave", - "dia_id": "D2:4", - "text": "Wow, Calvin! Owning something like this must feel amazing. Is this your first one? Show me a pic! What's it called?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/v7n19f5lcqn91.jpg" - ], - "blip_caption": "a photo of a red sports car parked on the side of the road", - "query": "ferrari 488 gtb japanese mansion", - "dia_id": "D2:5", - "text": "Woohoo! I finally got myself this beauty. Here's a pic from yesterday when I took it for a ride. Can't believe I'm actually driving it. Definitely what I deserve after all this hard work. Every time I step in, it's like an adrenaline rush!" - }, - { - "speaker": "Dave", - "dia_id": "D2:6", - "text": "Calvin, that car looks awesome! You must feel amazing every time you get inside! You deserved it - congrats buddy!" - }, - { - "speaker": "Calvin", - "dia_id": "D2:7", - "text": "Woah, I'm on cloud nine! Hard work pays off. What's up with you, Dave?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://express-images.franklymedia.com/6616/sites/11/2019/05/28094328/IMG_3656.jpg" - ], - "blip_caption": "a photo of a large crowd of people at a concert", - "query": "music festival boston stage crowd", - "dia_id": "D2:8", - "text": "Thanks, Calvin! Doing good. Last weekend, I went to a music festival in Boston - it was awesome! So many cool bands playing and the atmosphere was electric. I love music, it reminded me why I love it. Here's a pic, take a look." - }, - { - "speaker": "Calvin", - "dia_id": "D2:9", - "text": "Wow, sounds like a blast! Which one was your favorite?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://themusicroom.me/wp-content/uploads/2019/04/Aerosmith-opening2.jpg" - ], - "blip_caption": "a photo of a concert with a large eagle on the stage", - "query": "aerosmith stage rock band", - "dia_id": "D2:10", - "text": "Wow, there were so many great bands! If I had to pick a favorite, it would definitely be Aerosmith. Their performance was incredible. I'll show you a pic I took when they were playing." - }, - { - "speaker": "Calvin", - "dia_id": "D2:11", - "text": "Cool! Lovin' Aerosmith." - }, - { - "speaker": "Dave", - "img_url": [ - "https://www.etonline.com/sites/default/files/images/2019-10/gettyimages-1093168760.jpg" - ], - "blip_caption": "a photo of two people on stage with guitars and a microphone", - "query": "aerosmith steven tyler stage open mouth", - "dia_id": "D2:12", - "text": "I finally saw them live and it was such an amazing experience! He was jamming out to one of their hits. Check out this epic picture of them!" - }, - { - "speaker": "Calvin", - "dia_id": "D2:13", - "text": "Cool, Dave! Seeing your favorite singers live must have been wild! Did you get a chance to hang out with them after the show?" - }, - { - "speaker": "Dave", - "dia_id": "D2:14", - "text": "Nah, didn't get to hang with them after the show, but seeing them live was cool enough. It's always a dream to see your favorites performing!" - }, - { - "speaker": "Calvin", - "dia_id": "D2:15", - "text": "Yeah, it's always a dream to see your favorites doing their thing! So glad you got to experience that." - }, - { - "speaker": "Dave", - "dia_id": "D2:16", - "text": "Thanks, Calvin! Yeah, it was an incredible experience. So, anything new happening in your life?" - }, - { - "speaker": "Calvin", - "dia_id": "D2:17", - "text": "Got a new ride and wrote some new tunes - had a few studio sessions last week and I'm excited to collaborate. Can't wait to share it with everyone!" - }, - { - "speaker": "Dave", - "dia_id": "D2:18", - "text": "Cool, Calvin! Looking forward to hearing your new tunes. Keep me updated on how your collaborations go!" - }, - { - "speaker": "Calvin", - "dia_id": "D2:19", - "text": "Yeah, no worries! I'll definitely let you know how it goes. Cheers!" - }, - { - "speaker": "Dave", - "dia_id": "D2:20", - "text": "Enjoy the collaborations, Calvin! Stay safe!" - }, - { - "speaker": "Calvin", - "dia_id": "D2:21", - "text": "Thanks, Dave! Gonna have fun and stay safe with it. Bye!" - } - ], - "session_3_date_time": "4:15 pm on 20 April, 2023", - "session_3": [ - { - "speaker": "Calvin", - "dia_id": "D3:1", - "text": "Hey Dave! Long time no see. I just went to an awesome music thingy in Tokyo - so cool!" - }, - { - "speaker": "Dave", - "dia_id": "D3:2", - "text": "Hey Calvin! Great to hear from you. How was the music thingy in Tokyo? See any cool bands?" - }, - { - "speaker": "Calvin", - "dia_id": "D3:3", - "text": "Hey Dave! The festival in Tokyo was awesome! Didn't see any bands, but met lots of talented artists and industry people. Totally enriching!" - }, - { - "speaker": "Dave", - "dia_id": "D3:4", - "text": "Wow, Calvin, sounds great! What did you learn from it?" - }, - { - "speaker": "Calvin", - "dia_id": "D3:5", - "text": "I learned a lot and got some great advice from professionals in the music industry. It was inspiring!" - }, - { - "speaker": "Dave", - "dia_id": "D3:6", - "text": "Wow, Calvin! Bet that was inspiring being surrounded by professionals. Did you get any advice from them?" - }, - { - "speaker": "Calvin", - "dia_id": "D3:7", - "text": "The producer gave me some advice to stay true to myself and sound unique. It got me thinking about where I want my music to go. It's really motivating!" - }, - { - "speaker": "Dave", - "dia_id": "D3:8", - "text": "Wow, Calvin! It's really motivating to see you staying true to yourself. It got me thinking, where do you see your music taking you?" - }, - { - "speaker": "Calvin", - "dia_id": "D3:9", - "text": "Thanks, Dave! I'm dreaming of touring the world, performing for different people and connecting with them. I hope my music can reach a global audience and make an impact. I'm also looking forward to my upcoming trip to Boston after I finish the Frank Ocean tour. I've heard that the music scene there is awesome, so I can't wait to check it out!" - }, - { - "speaker": "Dave", - "dia_id": "D3:10", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/6/67/Boston_Skyline_%28193150499%29.jpeg" - ], - "re-download": true, - "blip_caption": "a photography of a city skyline with a boat in the water", - "text": "Sounds like an amazing plan, Cal! I can't wait for your trip to Boston. I'll show you around town and all the cool spots. The music scene there is awesome, with places like Paradise Rock, House of Blues, and Fenway Park. You'll definitely have some great performances there. Count me in for the front row when you're up onstage! Oh, and by the way, check out this pic." - }, - { - "speaker": "Calvin", - "dia_id": "D3:11", - "text": "Wow, Boston looks great! Thanks for the tips. And, awesome pic!" - }, - { - "speaker": "Dave", - "dia_id": "D3:12", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/d/de/Ford_Mustang_-_Shuttleworth_Classic_Car_Show_2017_%2833661471822%29.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a green mustang parked in a field of grass", - "text": "Last weekend I went to a car show. Classic cars are so charming and the dedication people put into restoring them is amazing. That's why I'm so into auto engineering. Can't wait to show you some when you come to Boston! Oh, almost forgot to send a photo, look at this beautiful car!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://hips.hearstapps.com/hmg-prod/images/2020-ferrari-f8-tributo-120-1551808626.jpg" - ], - "blip_caption": "a photo of a red sports car on display at a show", - "query": "car ferrari", - "dia_id": "D3:13", - "text": "Wow, Dave, that car looks great! Take a look at my car, I put a lot of work into it. Can't wait to see more when I'm in Boston!" - }, - { - "speaker": "Dave", - "dia_id": "D3:14", - "text": "Thanks! It definitely took a lot of work. Have you had a chance to check out Boston apart from the gigs?" - }, - { - "speaker": "Calvin", - "dia_id": "D3:15", - "text": "Not yet, been pretty busy with rehearsals and traveling. But I'm looking forward to exploring the city, trying out some delicious food, and visiting the popular attractions. Maybe we can grab a bite together when I'm there?" - }, - { - "speaker": "Dave", - "dia_id": "D3:16", - "text": "Definitely, Cal! Let's grab some food and I'll show you my favorite spots in the city. Can't wait to show them to you!" - }, - { - "speaker": "Calvin", - "dia_id": "D3:17", - "text": "Sounds great, Dave! Can't wait to try out the food there with you. It'll be fun!" - }, - { - "speaker": "Dave", - "dia_id": "D3:18", - "text": "It'll be great, Cal! Can't wait to show you the amazing music and food here. See you soon!" - } - ], - "session_4_date_time": "6:24 pm on 1 May, 2023", - "session_4": [ - { - "speaker": "Dave", - "img_url": [ - "https://www.broadwayimports.com/Files/Images/img-shop-exterior.jpg" - ], - "blip_caption": "a photo of a car dealership with cars parked in front of it", - "query": "car maintenance shop exterior", - "dia_id": "D4:1", - "text": "Hey Calvin, long time no see! A lot's been happening since we last talked. Guess what? I finally opened my own car maintenance shop! It's so satisfying to have a spot to work on cars - it's like a dream come true! Take a look at the photo." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a man standing under a car in a garage", - "dia_id": "D4:2", - "text": "Wow Dave! Congrats on opening your own car maintenance shop! It looks like all your hard work and dedication paid off." - }, - { - "speaker": "Dave", - "dia_id": "D4:3", - "text": "Thanks, Cal! It's great to see my hard work paying off. Opening this shop was my dream, and I'm really happy to see it getting started. It was a lot of hard work, but it was worth it." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a book with a space theme on it", - "dia_id": "D4:4", - "text": "Woo, Dave! Congrats on achieving your dream - you've got guts and ambition - that's awesome! Keep it up!" - }, - { - "speaker": "Dave", - "dia_id": "D4:5", - "text": "Thanks! Appreciate the support. My dream was to open a shop and it's a step towards my other dream of working on classic cars. I love their design and engineering." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a red car parked in a parking lot", - "dia_id": "D4:6", - "text": "Wow, Dave! Going for it with classic cars is cool! Fulfilling your dreams is so important." - }, - { - "speaker": "Dave", - "img_url": [ - "https://i0.wp.com/www.sopwithmotorsports.com/wp-content/uploads/2018/06/MustangEngineBanner.jpg" - ], - "blip_caption": "a photo of a car engine with a small air filter", - "query": "classic red mustang engine restoration", - "dia_id": "D4:7", - "text": "I'm obsessed with classic cars. They have a unique charm. I was so thrilled to restore one last year\u2014nothing like bringing it back to life! Take a look at the engine of the restored car." - }, - { - "speaker": "Calvin", - "dia_id": "D4:8", - "text": "Wow, Dave! That looks awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D4:9", - "text": "Thanks, Calvin! It was a labor of love. Challenging, but so worth it." - }, - { - "speaker": "Calvin", - "dia_id": "D4:10", - "text": "Yeah, it's awesome when you see something you worked on come to life." - }, - { - "speaker": "Dave", - "dia_id": "D4:11", - "text": "Yeah! It feels great to see the hard work pay off, it's like bringing something back to life." - }, - { - "speaker": "Calvin", - "dia_id": "D4:12", - "text": "Yeah, it's an amazing feeling when you create something and it resonates with people. It's so satisfying when you finish something you made from scratch!" - }, - { - "speaker": "Dave", - "dia_id": "D4:13", - "text": "Yeah, Calvin! It's such an amazing feeling to see something you create become a reality. Knowing that your skills and hard work made it happen is incredible." - }, - { - "speaker": "Calvin", - "dia_id": "D4:14", - "text": "Yeah, Dave! Feels good when our hard work pays off. It's the perfect blend of dedication and passion!" - }, - { - "speaker": "Dave", - "dia_id": "D4:15", - "text": "Yeah, that mix really keeps me motivated and makes it all worthwhile." - }, - { - "speaker": "Calvin", - "dia_id": "D4:16", - "text": "Keep going for it!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://stevesimports.com/wp-content/uploads/2023/09/Auto-Repair-Shop-Portland-OR.jpg" - ], - "blip_caption": "a photo of a group of people standing in front of a car", - "query": "car maintenance shop grand opening", - "dia_id": "D4:17", - "text": "I will! By the way, This is a photo of my shop. Come by sometime, if you can!" - }, - { - "speaker": "Calvin", - "dia_id": "D4:18", - "text": "Wow, your shop looks great! I'd love to check it out sometime. What sort of cars do you work on at your shop?" - }, - { - "speaker": "Dave", - "dia_id": "D4:19", - "text": "Thanks, Calvin! I work on all kinds of cars at the shop - from regular maintenance to full restorations of classic cars. It keeps me busy and happy!" - }, - { - "speaker": "Calvin", - "dia_id": "D4:20", - "text": "Wow Dave, working on cars must be really rewarding." - }, - { - "speaker": "Dave", - "dia_id": "D4:21", - "text": "Definitely, working on cars is what I'm passionate about. Doing it every day is so rewarding! Seeing the transformation is awesome and knowing I'm helping people keep their cars in good condition is really satisfying." - }, - { - "speaker": "Calvin", - "dia_id": "D4:22", - "text": "Wow Dave, that's awesome! Doing something you love and helping others is so rewarding. Keep up the great work!" - }, - { - "speaker": "Dave", - "dia_id": "D4:23", - "text": "Thanks, Cal! I really appreciate the boost. It means a lot that my work is valued and that it brings joy to others." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i0.wp.com/gaweejewelers.com/wp-content/uploads/2019/08/56210088.jpg" - ], - "blip_caption": "a photo of a gold necklace with a diamond pendant", - "query": "gold chain diamond pendant", - "dia_id": "D4:24", - "text": "Glad to help, Dave! So awesome to see you doing your thing and making a difference. Your hard work and talent totally deserve all the recognition. Keep on keepin' on, bud! Take a look at this beautiful necklace with a diamond pendant, that's so stunning!" - }, - { - "speaker": "Dave", - "dia_id": "D4:25", - "text": "Wow, that's a great necklace! Where did you get it?" - }, - { - "speaker": "Calvin", - "dia_id": "D4:26", - "text": "Thanks, Dave! I got it from another artist as a gift - it's a great reminder of why I keep hustling as a musician!" - }, - { - "speaker": "Dave", - "dia_id": "D4:27", - "text": "Awesome, Calvin! Keep pushing and making music, it'll remind us why we keep hustling." - }, - { - "speaker": "Calvin", - "dia_id": "D4:28", - "text": "Yeah, Dave! The road can be hard, but when we remember why we're doing it, it keeps us going. Let's keep each other motivated!" - } - ], - "session_5_date_time": "1:16 pm on 3 May, 2023", - "session_5": [ - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/2w0nqd9uq3271.jpg" - ], - "blip_caption": "a photo of a car being worked on in a garage", - "query": "vintage car being lifted with crane mechanic shop", - "dia_id": "D5:1", - "text": "Hey Calvin! Long time no talk. How's it going? Crazy news - I'm teaming up with a local garage. Take a look at what we working on together!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a green car in a garage with a sign in the background", - "dia_id": "D5:2", - "text": "Hey Dave, great to hear from you! That's awesome news about teaming up with a local garage. Super inspiring seeing you follow your passion. Congratulations on this new venture. It's impressive how far you've come since we last chatted. How's everything going?" - }, - { - "speaker": "Dave", - "dia_id": "D5:3", - "img_url": [ - "https://c0.wallpaperflare.com/preview/534/637/290/finland-hyvinkaa-aq-autohuolto-car-mechanic.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a man working on a car engine in a garage", - "text": "Thanks, Calvin! I'm loving this job. I get to work with awesome mechanics and share my knowledge about cars. Here's what I'm currently working on! It's a cool project, even if it's a bit challenging. \ud83e\udd29" - }, - { - "speaker": "Calvin", - "dia_id": "D5:4", - "text": "That car looks awesome! You're putting in a lot of effort and it's great to see the end result. Keep up the good work. Got any plans for what's next?" - }, - { - "speaker": "Dave", - "dia_id": "D5:5", - "text": "Thanks Calvin! Appreciate the support. I'm gonna keep learning more about auto engineering, maybe even build a custom car from scratch someday - that's the dream! For now, just gonna keep working on this project and assisting customers." - }, - { - "speaker": "Calvin", - "dia_id": "D5:6", - "text": "Wow, Dave! You're so inspiring - good for you for pushing yourself to achieve your dream. Making a custom car sounds awesome. Don't forget to relax and enjoy the process too!" - }, - { - "speaker": "Dave", - "dia_id": "D5:7", - "text": "Thanks, Calvin! Gotta take time to chill. Do you have any hobbies that help you relax?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/4j5jmpxvxnp71.jpg" - ], - "blip_caption": "a photo of a red sports car driving down a road", - "query": "ferrari long drive", - "dia_id": "D5:8", - "text": "Hey Dave, long drives in [this car] really help me relax. The feeling of the wind and the open road is so freeing. It helps me clear my head. What do you like to do to chill out? " - }, - { - "speaker": "Dave", - "dia_id": "D5:9", - "text": "Yeah, I hear you! Driving with the wind in your hair is so calming. Taking a walk around is a great way to destress, too. Exploring, taking in the sights and sounds - it's such a peaceful experience." - }, - { - "speaker": "Calvin", - "dia_id": "D5:10", - "text": "Yea, I totally hear ya. Embracing nature has been really calming for me too. I've been loving getting to know Japanese culture. On the other hand, I'm stuck with my music at the moment, like my creativity's frozen or something. Any tips?" - }, - { - "speaker": "Dave", - "dia_id": "D5:11", - "text": "If I'm having trouble coming up with ideas, I usually immerse myself in something I love, like concerts or my favorite albums. Doing that usually helps to jumpstart my inspiration. Maybe try taking a break from music and explore other things. Plus, have some fun while you're at it!" - }, - { - "speaker": "Calvin", - "dia_id": "D5:12", - "text": "Thanks, Dave! Taking a break is great for getting my mojo back. I'll definitely take your advice and explore. Appreciate the help! You're awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D5:13", - "text": "No worries, Calvin! Glad I could help. Keep pursuing your music and never give up. You're awesome! \ud83e\udd18" - }, - { - "speaker": "Calvin", - "dia_id": "D5:14", - "text": "Thanks, appreciate it. Won't give up. Let's stay in touch! Bye!" - }, - { - "speaker": "Dave", - "dia_id": "D5:15", - "text": "Sure, Calvin! Keep in touch. If you ever need help, just let me know. Bye!" - } - ], - "session_6_date_time": "11:50 am on 16 May, 2023", - "session_6": [ - { - "speaker": "Calvin", - "dia_id": "D6:1", - "text": "Hey Dave! Long time no chat! Lots has gone down since we last caught up." - }, - { - "speaker": "Dave", - "dia_id": "D6:2", - "text": "Hey Calvin! Long time no chat! How's everything been going since we last caught up?" - }, - { - "speaker": "Calvin", - "dia_id": "D6:3", - "text": "Hey Dave, not everything has been going smoothly. I had an incident last week where my place got flooded, but thankfully, I managed to save my music gear and favorite microphone. It's been tough, but I'm staying positive and looking forward to getting everything fixed up." - }, - { - "speaker": "Dave", - "dia_id": "D6:4", - "text": "Man, that's rough, Calvin. Sorry to hear about that. But I'm glad you could save your music gear and mic - those are the important things. Stay positive, pal. Anything I can do to help?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/80s9pr2v7jba1.jpg" - ], - "blip_caption": "a photo of a music studio with a keyboard, synthesizer, and other musical equipment", - "query": "home studio music gear", - "dia_id": "D6:5", - "text": "Thanks, Dave. Appreciate the support. Waiting on insurance to kick in so I can start repairs - fingers crossed it won't take too long. Take a look at my creative haven, where I pour my heart into the music." - }, - { - "speaker": "Dave", - "dia_id": "D6:6", - "text": "That studio looks awesome, Cal! I hope you get it fixed soon so you can continue creating music. Hang in there!" - }, - { - "speaker": "Calvin", - "dia_id": "D6:7", - "text": "Thanks, Dave! Can't wait to get back to making music. Anything exciting you're working on these days?" - }, - { - "speaker": "Dave", - "dia_id": "D6:8", - "text": "I'm so excited, I opened my car shop last week! Invited some friends over to celebrate and it's been amazing. Super stoked to share my passion and help out with folks' rides. It's been incredible so far." - }, - { - "speaker": "Calvin", - "dia_id": "D6:9", - "text": "Way to go, Dave! Congrats on opening your own car shop! Your excitement is contagious. Keep up the great work!" - }, - { - "speaker": "Dave", - "dia_id": "D6:10", - "text": "Thanks, Calvin! Your support is greatly appreciated. It's been quite a journey so far, and I'm excited to see what the future holds. How about you? Anything exciting happening in the world of music for you?" - }, - { - "speaker": "Calvin", - "dia_id": "D6:11", - "text": "Thanks Dave! Super excited for my upcoming performance in Tokyo this month. It's gonna be great to show my music to a whole new crowd and hopefully expand my following!" - }, - { - "speaker": "Dave", - "dia_id": "D6:12", - "text": "Cool, Cal! Doing a show in Tokyo, wow! I'm sure the audience is gonna love it. Wishing you all the luck! Let me know how it goes." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.pinimg.com/originals/f9/ed/97/f9ed97ec7a8419fec025884d3ea102be.jpg" - ], - "blip_caption": "a photo of a city at night with a lot of lights", - "query": "tokyo skyline night", - "dia_id": "D6:13", - "text": "Sure thing, Dave! I'll keep you posted on how it goes. Thanks for the good luck! Look what a wonderful night! \n" - }, - { - "speaker": "Dave", - "dia_id": "D6:14", - "text": "That looks great! Where did you snap that pic?" - }, - { - "speaker": "Calvin", - "dia_id": "D6:15", - "text": "I took that pic in Tokyo last night. The skyline was stunning!" - }, - { - "speaker": "Dave", - "dia_id": "D6:16", - "text": "Wow, great shot! The night skyline really pops with those city lights. I gotta take a trip there soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D6:17", - "text": "Thanks Dave! The city lights there are amazing. You should visit, it's awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D6:18", - "text": "Definitely adding it to my list. Thanks!" - } - ], - "session_7_date_time": "6:06 pm on 31 May, 2023", - "session_7": [ - { - "speaker": "Calvin", - "dia_id": "D7:1", - "text": "Hey Dave! Been ages since we chatted. So much has gone down. Touring with Frank Ocean last week was wild. Tokyo was unreal -- the crowd was insane. I felt so alive when performing, can't find the words to express it." - }, - { - "speaker": "Dave", - "img_url": [ - "https://www.accidentwellness.com/assets/users/chiro/469/uploads/images/2018/07/mechanic.jpg" - ], - "blip_caption": "a photo of a man working on a car engine in a garage", - "query": "working on a car", - "dia_id": "D7:2", - "text": "Wow, that's awesome, Cal! I bet it was an incredible experience. Congrats! Look what I've been up to recently, that's my neighbor's car. He's been having some trouble with the engine, but now it's done by myself!" - }, - { - "speaker": "Calvin", - "dia_id": "D7:3", - "text": "Thanks, Dave! It was an amazing experience - the energy and love from the fans was crazy. The car in the pic? It's the one you were fixing up the engine for a friend? Working on cars helps me chill and clear my head." - }, - { - "speaker": "Dave", - "dia_id": "D7:4", - "text": "Yes, Cal! It's not difficult for me to help my neighbors with their cars. Working on cars is definitely therapeutic. Does it relax you as much as it does me?" - }, - { - "speaker": "Calvin", - "dia_id": "D7:5", - "text": "Yeah, totally! Fixing cars really calms me down and gives me a real sense of achievement - like meditating. Do you have any hobbies that give you the same satisfaction?" - }, - { - "speaker": "Dave", - "dia_id": "D7:6", - "text": "I'm passionate about fixing up things. It's more than just a hobby - it gives me a sense of achievement and purpose. I get a real buzz transforming something that's not working into something that runs smoothly - it's like giving it a second chance." - }, - { - "speaker": "Calvin", - "dia_id": "D7:7", - "text": "That's so inspiring, Dave. You find purpose in transforming things \u2013 like when you create something new or collaborate with others. We're both making something great out of nothing, it's amazing, right?" - }, - { - "speaker": "Dave", - "dia_id": "D7:8", - "text": "Yeah Cal, it's kinda cool how we can take something and make it look amazing. Giving it a new life makes me feel powerful and happy - like I really achieved something." - }, - { - "speaker": "Calvin", - "dia_id": "D7:9", - "text": "Yeah, Dave! That feeling of power and happiness is amazing! It's great to see what we created with all our hard work and ideas." - }, - { - "speaker": "Dave", - "dia_id": "D7:10", - "text": "Yeah, Calvin! Accomplishment keeps me motivated. You mentioned Tokyo lit you up. Do you always enjoy performing live?" - }, - { - "speaker": "Calvin", - "dia_id": "D7:11", - "text": "Performing live always fuels my soul! I love the rush and connection with the crowd, the feeling's indescribable\u2014it's an absolute high!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/frciwtnbroza1.jpg" - ], - "blip_caption": "a photo of a large crowd of people taking pictures of a concert", - "query": "rock concert audience connection energy artist", - "dia_id": "D7:12", - "text": "Wow, I bet it feels awesome. The connection between artist and audience must be so powerful. Look at the atmosphere in this photo, it's just off the charts!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a crowd of people watching a concert with bright lights", - "dia_id": "D7:13", - "text": "Yeah, Dave! That feeling is amazing. Everyone is part of it, it's like one big harmony." - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a concert with a band performing on stage", - "dia_id": "D7:14", - "text": "That's awesome! Music really brings people together and creates a great atmosphere. It's like therapy for us all. So, what's next for your music?" - }, - { - "speaker": "Calvin", - "dia_id": "D7:15", - "text": "Cool, can't wait for the tour with Frank! I'm buzzing to perform in Boston and see what it's like. Gonna be great playing for folks from home. What do you think of the music scene there?" - }, - { - "speaker": "Dave", - "dia_id": "D7:16", - "text": "Hey, Boston's music scene is awesome! Tons of talented musicians and cool places to play. It'll be a blast performing there - I'll be there to cheer you on!" - }, - { - "speaker": "Calvin", - "dia_id": "D7:17", - "text": "Thanks, Dave! Your support means a lot to me. Can't wait to experience the music scene there." - }, - { - "speaker": "Dave", - "dia_id": "D7:18", - "text": "Great, Calvin! Have a great time in Boston. Can't wait to hear all about it when you get back!" - }, - { - "speaker": "Calvin", - "dia_id": "D7:19", - "text": "Thanks! I'll fill you in on all the details when I get back. See you soon!" - } - ], - "session_8_date_time": "2:31 pm on 9 June, 2023", - "session_8": [ - { - "speaker": "Calvin", - "dia_id": "D8:1", - "text": "Hey Dave! Met with the creative team for my album yesterday. It was a long session, but awesome to see everything coming together. " - }, - { - "speaker": "Dave", - "dia_id": "D8:2", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/3/3a/Boston_Public_Garden_panorama.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a pond with a boat in it surrounded by trees", - "text": "Hey Cal! Sounds great that your album's coming along. Are you feeling good about it? Here's a pic I just took." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://musicconservatory.org/wp-content/uploads/2022/12/IMG-2298.png" - ], - "blip_caption": "a photo of a recording studio with a large window and a desk", - "query": "studio control room making magic studio", - "dia_id": "D8:3", - "text": "Dave, thanks for checking in. I'm feeling stoked about this album! We've been making some magic with a team in the studio, working on the music and everything. Look at what a wonderful studio we have! How have you been? Anything new since we talked?" - }, - { - "speaker": "Dave", - "dia_id": "D8:4", - "text": "Hey, nice photo of the studio! Working in a team yields incredible results, well done! Keep pushing it! I've been doing good - thanks for asking. I've been exploring some parks on the weekends to relax - it's so peaceful being surrounded by nature. Are there any chill spots you enjoy in Boston?" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a tree with pink flowers in the foreground", - "dia_id": "D8:5", - "text": "Thanks! That studio is great for creativity. I've never been to Boston before, but I hear the parks are amazing. Can't wait to visit next month. Anything cool you remember about Boston parks?" - }, - { - "speaker": "Dave", - "dia_id": "D8:6", - "text": "That sounds great! The Boston parks are awesome, especially in spring. It's so serene when you're walking around. I went for a stroll last Friday and it was amazing. It's so magical - I bet you'll love it! I love taking walks on the weekends, they recharge me for the entire upcoming week!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a path going up a hill with a view of the mountains", - "dia_id": "D8:7", - "text": "I'm excited to experience that serenity. I can't wait to take a walk in the parks and recharge. Hey, have you been on any hikes lately?" - }, - { - "speaker": "Dave", - "dia_id": "D8:8", - "text": "Nah, haven't gone hiking recently, but it's awesome - being in nature and pushing yourself to new heights. Clears your mind and brings a sense of calm. Have you been to the mountains before? Heard they're super chill." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a plane flying over a mountain range with snow on the top", - "dia_id": "D8:9", - "text": "Nah, haven't been to the mountains but I'm keen to go. Looking for a way to escape it all and de-stress. I want to go on a hike to a place similar to this. What's new and exciting happening for you, Dave?" - }, - { - "speaker": "Dave", - "dia_id": "D8:10", - "text": "I booked a trip to a mountainous region for next month! Finally gonna be able to see those majestic peaks! Gonna be an amazing experience!" - }, - { - "speaker": "Calvin", - "dia_id": "D8:11", - "text": "Cool, Dave! Have a great time. I'm sure it's going to be an amazing experience. Take lots of pics and show me when you get back." - }, - { - "speaker": "Dave", - "dia_id": "D8:12", - "text": "Yep, Calvin! Gonna take lots of pics. Can't wait to show you when I get back!" - }, - { - "speaker": "Calvin", - "dia_id": "D8:13", - "text": "Have fun exploring the mountains, Dave! Safe travels and see you soon. Take care!" - }, - { - "speaker": "Dave", - "dia_id": "D8:14", - "text": "Thanks, Calvin! Take care, see you soon!" - } - ], - "session_9_date_time": "3:15 pm on 21 June, 2023", - "session_9": [ - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/awt7u6qj7ok81.jpg" - ], - "blip_caption": "a photo of a red car with a black rim parked on a sidewalk", - "query": "car accident dented side Ferrari", - "dia_id": "D9:1", - "text": "Hi Dave! Nice to hear from you. Since last chat, some things have happened. I had a lil car accident last Friday, kinda upsetting but no one was hurt. I've been dealing with insurance and repairs - taking up a lot of time and energy. Look at this photo!" - }, - { - "speaker": "Dave", - "dia_id": "D9:2", - "text": "Sorry to hear about the car accident, Calvin. I'm glad to hear that nobody was hurt. Dealing with insurance and repairs can definitely be time-consuming and draining. Have you encountered any issues in the insurance process so far?" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a tow truck parked in a parking lot", - "dia_id": "D9:3", - "text": "Hey Dave, thanks for checking in! The insurance process was a hassle - it took forever and there was a ton of paperwork. But it's all sorted now, and I'm getting it fixed up. Can't wait to drive it again!" - }, - { - "speaker": "Dave", - "dia_id": "D9:4", - "text": "How long did it take to get everything sorted out? I'm glad it's all taken care of now!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://cdn2.picryl.com/photo/2014/08/13/alejandro-soto-100th-force-support-squadron-auto-hobby-8fa715-1024.jpg" - ], - "blip_caption": "a photography of a man working on a car in a garage", - "query": "car repair shop mechanic working red car", - "dia_id": "D9:5", - "re-download": true, - "text": "It only took a week to sort everything out. I was worried about the cost, but it wasn't too bad. Look at this photo from the auto repair shop. " - }, - { - "speaker": "Dave", - "dia_id": "D9:6", - "text": "Cool! Glad it wasn't too expensive. Who are him?" - }, - { - "speaker": "Calvin", - "dia_id": "D9:7", - "text": "That's the mechanic. He knows the stuff and is doing his best to get my car running again." - }, - { - "speaker": "Dave", - "dia_id": "D9:8", - "text": "That's great to hear! Skilled and knowledgeable people working on your car - are you feeling confident?" - }, - { - "speaker": "Calvin", - "dia_id": "D9:9", - "text": "Feeling way more confident and excited to show off my car! Trusting their expertise." - }, - { - "speaker": "Dave", - "dia_id": "D9:10", - "text": "Great, Cal! It's awesome when you trust the people fixing it. Sounds like they're doing a great job. It'll be back to normal soon!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/u4cesvhpunb61.jpg" - ], - "blip_caption": "a photo of a view of a small town with a mountain in the background", - "query": "sunset window japanese mansion", - "dia_id": "D9:11", - "text": "Thanks, Dave! Excited to drive again. Appreciate your help! Look what a wonderful view from living room!" - }, - { - "speaker": "Dave", - "dia_id": "D9:12", - "text": "Wow, what an amazing view! Where is it? I haven't seen anything like that before!" - }, - { - "speaker": "Calvin", - "dia_id": "D9:13", - "text": "Thanks, Dave! It's from a small town in Japan. The view of the mountains is unbelievably stunning!" - }, - { - "speaker": "Dave", - "dia_id": "D9:14", - "text": "Wow! Did you get to that place yet?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/37i20ea9fbta1.jpg" - ], - "blip_caption": "a photo of a person on skis on a snowy mountain", - "query": "rocky mountains snow-covered mountain peak dave", - "dia_id": "D9:15", - "text": "Nope, not yet! I haven't been there before, but it's on my to-do list for after my tour with Frank Ocean ends. Can't wait to see them in person! Look at that snowy peak." - }, - { - "speaker": "Dave", - "dia_id": "D9:16", - "text": "Looks awesome! Have you ever tried skiing before? It looks like loads of fun!" - }, - { - "speaker": "Calvin", - "dia_id": "D9:17", - "text": "Haven't tried it before, but it does look like a lot of fun! I might give it a try once. So what's up with you, is anything new in your daily routine?" - }, - { - "speaker": "Dave", - "dia_id": "D9:18", - "img_url": [ - "https://live.staticflickr.com/5174/5420443853_e88b523542_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a group of men playing instruments in a room", - "text": "Yes, Cal! I recently joined a rock band and have been practicing guitar. Look at this photo \u2013 it's us, the guys! " - }, - { - "speaker": "Calvin", - "dia_id": "D9:19", - "text": "Playing guitar in a rock band is awesome, Dave! It will bring you a lot of emotions." - }, - { - "speaker": "Dave", - "dia_id": "D9:20", - "text": "Sounds good, Calvin! Good company and great music, lift the mood and bring a lot of positive emotions! I have to go now. I'll see you soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D9:21", - "text": "Thanks, Dave! let's keep in touch, take care!" - } - ], - "session_10_date_time": "7:56 pm on 7 July, 2023", - "session_10": [ - { - "speaker": "Dave", - "dia_id": "D10:1", - "text": "Hey Calvin, how's the car doing after the crash? You were stoked to get back on the road, right?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA1L2ZsMTY0Mjg5OTcwMDYtaW1hZ2Uta3R4cGR6enkuanBn.jpg" - ], - "blip_caption": "a photography of a red sports car parked on the side of the road", - "query": "ferrari mansion japan", - "dia_id": "D10:2", - "re-download": true, - "text": "Hey Dave! Thanks for checking in. It's all good now, the car's fixed and going strong. Nothing better than cruising around - it's the best! Look at how my car looks right now. How've you been? Anything new?" - }, - { - "speaker": "Dave", - "dia_id": "D10:3", - "text": "Glad to hear your car's fixed and going strong! I've been good too. Just been hanging out with friends at parks lately. I arranged with friends for regular walks together in the park." - }, - { - "speaker": "Calvin", - "dia_id": "D10:4", - "text": "\nThat sounds like a great plan! Regular walks with friends can be a wonderful way to spend time together and stay active. Fresh air and buddies can do wonders. Do you have a favorite spot for hanging out?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://healthywaysfordays.files.wordpress.com/2020/11/61c247f0-cd80-4c81-916c-b905b92a93a3-ecdafc1d-2d3c-4cbb-b64a-258e84518cb3.jpg" - ], - "blip_caption": "a photo of a city skyline with a river and boats in the water", - "query": "sunset boston common park", - "dia_id": "D10:5", - "text": "Yeah, today we are going to that spot. Look at that lovely photo." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://toyamahotnews.files.wordpress.com/2020/09/img-0880.jpg" - ], - "blip_caption": "a photo of a boat is docked in a canal at sunset", - "query": "sunset river view japanese mansion", - "dia_id": "D10:6", - "text": "Wow, what a view! That sunset over the river is gorgeous. It must be so tranquil there. Reminds me of living in my Japanese mansion with the epic cityscape. It's like a dream come true! Look at this photo I took from my backyard of the mansion. It's so beautiful!" - }, - { - "speaker": "Dave", - "dia_id": "D10:7", - "text": "Yeah, the peace by the river is really nice. But living in a Japanese mansion surrounded by that city skyline must be stunning. The views must be amazing!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/al2nryjbov481.jpg" - ], - "blip_caption": "a photo of a large house with a lot of windows and lights", - "query": "city lights view mansion", - "dia_id": "D10:8", - "text": "Ah, it really is beautiful. Whenever I look out the windows and see the city lights, it's so awe-inspiring. Luxury and beauty on a whole new level. Look at the front part of the mansion." - }, - { - "speaker": "Dave", - "dia_id": "D10:9", - "text": "That mansion looks great! I bet the view from inside is stunning. Must be amazing living there. Anything else you're looking forward to doing in Japan?" - }, - { - "speaker": "Calvin", - "dia_id": "D10:10", - "text": "Thanks Dave! Japan is indeed amazing. Can't wait to try the food and check out the culture. Have you ever been there?" - }, - { - "speaker": "Dave", - "dia_id": "D10:11", - "text": "Nope, never been to Japan but I'm so keen to go one day. I've heard it's full of vibes, good eats and awesome tech. Plus, being able to experience the culture would be amazing - I'm hooked on their music!" - }, - { - "speaker": "Calvin", - "dia_id": "D10:12", - "text": "Japan definitely has it all - vibes, food, tech, and an amazing culture. It's like stepping into another world. I've been working on some cool music collaborations with Japanese artists, and I'm really excited to hear how it turns out!" - }, - { - "speaker": "Dave", - "dia_id": "D10:13", - "text": "Cool, Cal! Working with them is a great chance - can't wait for the tunes!" - }, - { - "speaker": "Calvin", - "dia_id": "D10:14", - "text": "Thanks! I'll share some clips when everything's ready. Collaborating with various artists is always exciting, it's a chance to create something unique." - }, - { - "speaker": "Dave", - "dia_id": "D10:15", - "text": "Way to go, Cal! Collaborating with different artists to create something special sounds amazing. Can't wait to see/hear the end product!" - }, - { - "speaker": "Calvin", - "dia_id": "D10:16", - "text": "Thanks, Dave! Appreciate all the help. It's gonna be awesome - can't wait to show you. Great catching up, gotta get back to work now. Take care!" - }, - { - "speaker": "Dave", - "dia_id": "D10:17", - "text": "Hey Cal, take care and don't overwork yourself! Talk to you soon. Stay safe!" - } - ], - "session_11_date_time": "6:38 pm on 21 July, 2023", - "session_11": [ - { - "speaker": "Dave", - "dia_id": "D11:1", - "text": "Hey Cal, been ages since we spoke! Guess what? I just got back from a road trip with my friends - we saw some stunning countryside. It was such a lovely break from the corporate mayhem. Driving on those winding roads, taking in the views, and chatting with my friends recharged me totally - reminds me why I love cars so much. What did you end up doing?" - }, - { - "speaker": "Calvin", - "dia_id": "D11:2", - "text": "Hey Dave! Great hearing from you! Wow, a road trip sounds awesome. I bet it felt great to get away from work and relax on those twisty roads. Recharging with your passion is awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D11:3", - "img_url": [ - "https://cdn2.picryl.com/photo/2014/07/04/my-public-lands-roadtrip-dalton-highway-in-alaska-19315093341-ddcc96-1024.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a person riding a motorcycle down a dirt road", - "text": "It was great to get away and reconnect with my passion. Reminded me why I'm passionate about what I do. Makes the long hours worth it. Here's a pic what a wonderful place we found. Have you had any recent moments that made you remember what you love?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.imgur.com/s0wdXqH.jpg" - ], - "blip_caption": "a photo of a room with a ladder and a ladder in it", - "query": "japanese mansion recording studio construction work progress", - "dia_id": "D11:4", - "text": "I'm happy for you that you have found such an amazing place! Yeah, I'm working on this project to transform a Japanese mansion into a recording studio. It's been my dream to have a space for creating music with other artists. It's my sanctuary that reminds me why I love music. Here's a pic of the progress I made." - }, - { - "speaker": "Dave", - "dia_id": "D11:5", - "text": "Wow, Calvin, this looks amazing! You've made so much progress. Must be very fulfilling to have your own space. What kind of music have you been creating in there?" - }, - { - "speaker": "Calvin", - "dia_id": "D11:6", - "text": "Thanks, Dave! It feels great having my own space to work in. I've been experimenting with different genres lately, pushing myself out of my comfort zone. Adding electronic elements to my songs gives them a fresh vibe. It's been an exciting process of self-discovery and growth!" - }, - { - "speaker": "Dave", - "dia_id": "D11:7", - "text": "Wow, Calvin, that's great! It must be an exciting process of self-discovery and growth to experiment with different genres. Does moving between styles present any challenges?" - }, - { - "speaker": "Calvin", - "dia_id": "D11:8", - "text": "Yeah, switching it up can be tough, but I think it's a matter of finding the right balance between sticking to my sound and trying new stuff. It can be intimidating, but that's what makes it so exciting and keeps me motivated to keep going!" - }, - { - "speaker": "Dave", - "dia_id": "D11:9", - "text": "Yeah, I get it. Finding a balance is tricky but it's gotta keep things interesting. How are you dealing with the pressure and staying motivated?" - }, - { - "speaker": "Calvin", - "dia_id": "D11:10", - "text": "I started making music to follow my dreams, and I'm stoked about how far I've come. Collaborating with others and learning from them keeps me motivated. Surrounding myself with positive energy and passion helps as well." - }, - { - "speaker": "Dave", - "dia_id": "D11:11", - "text": "Sounds like a great plan, Calvin! Surrounding yourself with good vibes and collaborating with others will give you a boost. You've achieved so much so far; keep going, buddy!" - }, - { - "speaker": "Calvin", - "dia_id": "D11:12", - "text": "Thanks, Dave! Your support means a lot to me. I'm gonna keep pushing myself and striving for my goals, so let's chat again soon." - }, - { - "speaker": "Dave", - "dia_id": "D11:13", - "text": "You got this! Keep pushing yourself and never lose sight of your goals. I'm your biggest fan. Let's chat soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D11:14", - "text": "Thanks, Dave! Appreciate your support. Let's catch up soon and chat. Take care!" - } - ], - "session_12_date_time": "1:12 pm on 3 August, 2023", - "session_12": [ - { - "speaker": "Calvin", - "dia_id": "D12:1", - "text": "Hey Dave, long time no see! I just took my Ferrari for a service and it was so stressful. I'm kinda attached to it. Can you relate? What kind of hobbies give you a feeling of being restored?" - }, - { - "speaker": "Dave", - "dia_id": "D12:2", - "text": "Hey Calvin, I understand the stress of getting a car serviced. Fixing cars is like therapy for me. Growing up working on cars with my dad, refurbishing them gives me a sense of fulfillment." - }, - { - "speaker": "Calvin", - "dia_id": "D12:3", - "text": "Wow, Dave, that's awesome! Must feel great to have a hobby that makes you proud. Remember any good memories from working on cars with your dad?" - }, - { - "speaker": "Dave", - "dia_id": "D12:4", - "text": "Yeah, definitely! I have fond memories of working on cars with my dad as a kid. We spent one summer restoring an old car. It was hard work, but seeing the end result and knowing that we did it together was really satisfying." - }, - { - "speaker": "Calvin", - "dia_id": "D12:5", - "text": "That's awesome, Dave! Working together on projects like that really brings people closer. Do you have any pictures from that time?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://live.staticflickr.com/8790/17087850111_a8541de20d_b.jpg" - ], - "blip_caption": "a photography of a man and a child pose for a picture", - "query": "old photo car father son working", - "dia_id": "D12:6", - "re-download": true, - "text": "Yes, I have one, take a look. It was a wonderful experience." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://live.staticflickr.com/4112/5177562445_f1a1e1107b_b.jpg" - ], - "blip_caption": "a photography of a red car is lifted on a lift in a garage", - "query": "car museum japan ferrari", - "dia_id": "D12:7", - "re-download": true, - "text": "Aww, that's cool, Dave. Reminiscing is always fun! That pic you shared takes me back to my trip to the Ferrari dealership. I saw a lot of amazing cars, but as for me, my car is the best and I'm pretty proud of this. Sure, it's just material, but it reminds me of my hard work and dedication. It really inspires me. Take a look at this beauty!" - }, - { - "speaker": "Dave", - "dia_id": "D12:8", - "text": "Your car looks great, Calvin! I can tell why you're proud. Having something like that is motivating. It's like a reminder of what you can achieve." - }, - { - "speaker": "Calvin", - "dia_id": "D12:9", - "text": "Thanks, Dave! Seeing it everyday keeps me motivated and reminds me to keep pushing." - }, - { - "speaker": "Dave", - "dia_id": "D12:10", - "text": "Sounds like you're really motivated, Calvin. What's the biggest goal you're working towards, music-wise or something else?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.pexels.com/photos/2801993/pexels-photo-2801993.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-karl-solano-2801993.jpg" - ], - "blip_caption": "a photo of a band playing on stage with lights on", - "query": "performing on stage musician", - "dia_id": "D12:11", - "text": "My plan for now is to expand my brand worldwide and grow my fanbase. I want my music to reach more people and make an impact. Working with artists from around the globe and challenging myself to create special music are goals of mine too. Look at the photo of how I performed with the boys last night, they are great at the music!" - }, - { - "speaker": "Dave", - "dia_id": "D12:12", - "text": "Wow, Calvin! Working with different artists and crafting great sounds will definitely help you reach your goals. Keep it up and keep making a difference!" - }, - { - "speaker": "Calvin", - "dia_id": "D12:13", - "text": "Thanks, Dave! Your support and encouragement mean a lot to me. I'm determined to make my dreams come true." - }, - { - "speaker": "Dave", - "dia_id": "D12:14", - "text": "Glad to help, Calvin! Eager to see what you do. Keep at it and never forget your dreams!" - }, - { - "speaker": "Calvin", - "dia_id": "D12:15", - "text": "Thanks, Dave! I appreciate your support, it means a lot to me. I'll keep going for my dreams." - }, - { - "speaker": "Dave", - "dia_id": "D12:16", - "text": "No problem, Calvin! Just remember to stay focused and keep going. You've got this!" - }, - { - "speaker": "Calvin", - "dia_id": "D12:17", - "text": "Thanks, Dave! I'll stay focused and keep going. Appreciate your belief!" - } - ], - "session_13_date_time": "5:22 pm on 11 August, 2023", - "session_13": [ - { - "speaker": "Dave", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/0/0f/Two_on_ramps_%2841411586832%29.jpg" - ], - "blip_caption": "a photography of a man standing in front of a car on a lift", - "query": "professional car modification workshop sleek sports car", - "dia_id": "D13:1", - "re-download": true, - "text": "Hey Calvin, been ages! Guess what? I got picked for a car mod workshop. Gonna get better at it and learn something new! Look at the cars I'm working with right now. " - }, - { - "speaker": "Calvin", - "dia_id": "D13:2", - "text": "Wow Dave, that's awesome! Congrats on being picked for the car mod workshop. It's a great opportunity to take your skills up a notch. Are there any mods or projects you're excited to work on?" - }, - { - "speaker": "Dave", - "dia_id": "D13:3", - "text": "Thanks, Calvin! This is a dream come true for me, as I've always wanted to learn auto engineering and work on building a custom car." - }, - { - "speaker": "Calvin", - "dia_id": "D13:4", - "text": "That's awesome, Dave! Pursuing your passion for auto engineering and making a custom car is a great experience. I'm excited to see what you create. Keep it up and have fun every step of the way. What do you think is the most thrilling part of it?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://www.langracing.com/wp-content/uploads/IMG_4878-e1566576759594.jpg" - ], - "blip_caption": "a photo of a car is being worked on by a mechanic", - "query": "car engine rebuild process", - "dia_id": "D13:5", - "text": "Thanks, Calvin! I've found the picture with really thrilling part of the workshop, take a look. Seeing the potential come to life is always satisfying." - }, - { - "speaker": "Calvin", - "dia_id": "D13:6", - "text": "Wow, that sounds like a fulfilling hobby! What kind of transformations have you done so far? How's it going with the current project?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/uduumhtkac191.jpg" - ], - "blip_caption": "a photo of a silver corvette parked in front of a building", - "query": "muscle car body modifications sleek silver paint job", - "dia_id": "D13:7", - "text": "I've been working on this car, doing engine swaps and suspension modifications. Now I'm learning about body modifications. Giving this classic muscle car a modern twist is a challenge but so fun!" - }, - { - "speaker": "Calvin", - "dia_id": "D13:8", - "text": "Wow, Dave, that looks cool! The silver finish gives it a modern twist. Great job!" - }, - { - "speaker": "Dave", - "dia_id": "D13:9", - "text": "Thanks, Calvin! Yeah, I wanted a modern vibe but also that classic muscle car style. Really happy with it!" - }, - { - "speaker": "Calvin", - "dia_id": "D13:10", - "text": "You've really put in some work! That attention to detail is great." - }, - { - "speaker": "Dave", - "dia_id": "D13:11", - "text": "Thanks, Calvin! It's all about those small details that make it unique and personalized." - }, - { - "speaker": "Calvin", - "dia_id": "D13:12", - "text": "Yeah, customizing a masterpiece with those small details is what makes it unique and personalized." - }, - { - "speaker": "Dave", - "dia_id": "D13:13", - "text": "Yeah! It's about showing my style, it's like customizing a work of art on wheels." - }, - { - "speaker": "Calvin", - "dia_id": "D13:14", - "text": "No problem, Dave. Your enthusiasm and hard work show in everything you do. Keep coming up with new cool stuff, bud. I'm excited to see what you'll do next." - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/8ac9fznnof451.jpg" - ], - "blip_caption": "a photo of a desk with a keyboard, monitor, and keyboard pad", - "query": "music studio sound system setup", - "dia_id": "D13:15", - "text": "Cheers, Calvin! Really appreciate your help. I'm gonna keep working hard and coming up with new cool stuff. And if you need a hand with your music stuff, just let me know! Check out my awesome music studio setup with a high-quality sound system. You won't believe how amazing your songs sound here!" - }, - { - "speaker": "Calvin", - "dia_id": "D13:16", - "text": "Thanks for the offer, Dave. I'm super busy with my music stuff at the moment, so I'll keep it in mind. Great work, dude!" - }, - { - "speaker": "Dave", - "dia_id": "D13:17", - "text": "No worries, Calvin. Got it. Good luck with your music!" - }, - { - "speaker": "Calvin", - "dia_id": "D13:18", - "text": "Thanks for the encouragement, Dave. I'll keep working hard and making music. Take care!" - }, - { - "speaker": "Dave", - "dia_id": "D13:19", - "text": "See ya, Calvin! Stay awesome. Catch ya later." - } - ], - "session_14_date_time": "12:35 am on 14 August, 2023", - "session_14": [ - { - "speaker": "Dave", - "dia_id": "D14:1", - "text": "Hey Cal, how's it going? Something cool happened since last we talked - I got to go to a car workshop in San Francisco! So cool to dive into the world of car restoration and see all the different techniques. People were really passionate and dedicated - truly inspiring!" - }, - { - "speaker": "Calvin", - "dia_id": "D14:2", - "text": "Hey Dave! That's awesome! Car workshops sound totally cool and inspiring. Glad you found something that really gets you going." - }, - { - "speaker": "Dave", - "dia_id": "D14:3", - "text": "It's always great to learn and grow. So, what have you been up to lately?" - }, - { - "speaker": "Calvin", - "dia_id": "D14:4", - "text": "As you know, I had an amazing experience touring with a well-known artist. The feeling of performing and connecting with the audience was unreal. We ended with a show in Japan and then I had the opportunity to explore my new place - it's like a dream come true!" - }, - { - "speaker": "Dave", - "dia_id": "D14:5", - "text": "Wow, Calvin! I bet playing for an eager audience was an incredible experience. Exploring Japan must have been amazing! How did Tokyo react to your music? Did you have any special moments there?" - }, - { - "speaker": "Calvin", - "dia_id": "D14:6", - "text": "Thanks, Dave! That show in Tokyo was awesome! Everyone was so into it when I played one of my songs and sang along. It was a magical moment." - }, - { - "speaker": "Dave", - "dia_id": "D14:7", - "text": "Wow, Calvin, sounds amazing! Got any pictures from that show? Would love to see the atmosphere." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://c1.wallpaperflare.com/preview/415/916/987/audience-band-concert-crowd.jpg" - ], - "blip_caption": "a photography of a crowd of people at a concert with their hands up", - "query": "show tokyo hands in the air colorful lights stage", - "dia_id": "D14:8", - "re-download": true, - "text": "Yeah, here's a pic I took. It was so awesome - I loved how the crowd was into it. These moments are why I love my job." - }, - { - "speaker": "Dave", - "dia_id": "D14:9", - "text": "The energy in that pic is awesome! Seeing everyone with their hands up must have been really cool. It's great when your passion brings joy to others!" - }, - { - "speaker": "Calvin", - "dia_id": "D14:10", - "text": "Yeah, that was buzzing! It's moments like these that make me so proud and motivated. I'm all about spreading joy with my art. So, how's your project going?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://ktul.com/resources/media/b036c339-3408-4a16-baf1-841d27c80d13-full1x1_PhotoJan2812945PM1.jpg" - ], - "blip_caption": "a photo of a car with a broken engine in the woods", - "query": "car restoration vintage ford mustang", - "dia_id": "D14:11", - "text": "Yeah, the project is going great! Here's a pic of the car I'm restoring. It's pretty cool seeing it go from a beat-up mess to a real beauty." - }, - { - "speaker": "Calvin", - "dia_id": "D14:12", - "text": "Wow Dave, that car is going to look amazing when you're done. How long do you think it will take to restore it?" - }, - { - "speaker": "Dave", - "dia_id": "D14:13", - "text": "Thanks, Calvin! Fingers crossed it'll be all fixed up by the end of next month. Lots of elbow grease but it'll be worth the transformation." - }, - { - "speaker": "Calvin", - "dia_id": "D14:14", - "text": "Wow, Dave, I'm really excited to see the end result of your hard work! You've got some serious talent!" - }, - { - "speaker": "Dave", - "dia_id": "D14:15", - "text": "Thanks, Cal! Your support means a lot to me." - }, - { - "speaker": "Calvin", - "dia_id": "D14:16", - "text": "You got this, Dave! I'm always here for you. Seeing your enthusiasm is amazing. Keep growing, buddy. It'll be worth it! Good luck with that. See ya!" - } - ], - "session_15_date_time": "11:06 am on 22 August, 2023", - "session_15": [ - { - "speaker": "Dave", - "dia_id": "D15:1", - "img_url": [ - "https://live.staticflickr.com/189/516664633_81fce2dc06_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a group of men sitting at a table with playing cards", - "text": "Hey Calvin! Haven't talked in a while! Last Friday I had a card-night with my friends, it was so much fun. We laughed and had a great time! Take a look at the photo!" - }, - { - "speaker": "Calvin", - "dia_id": "D15:2", - "text": "Hey Dave! Great to hear from you, card night sounds like a blast! Always love having fun with friends. Guess what? I scored a deal to continue collaboration with Frank Ocean! This is a dream come true for me, I've been working hard and it's finally paying off. No words can describe how happy I am." - }, - { - "speaker": "Dave", - "dia_id": "D15:3", - "text": "Wow Calvin, congrats! Super excited for you. How did this chance come up?" - }, - { - "speaker": "Calvin", - "dia_id": "D15:4", - "text": "Thanks, Dave! I had the opportunity to meet Frank Ocean at a music festival in Tokyo and we clicked. We've been speaking and sharing ideas we arranged a meeting and recorded a song in the studio at my mansion - it's been great!" - }, - { - "speaker": "Dave", - "dia_id": "D15:5", - "text": "Wow, Tokyo sounds like an incredible experience!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/clg582472ta91.jpg" - ], - "blip_caption": "a photo of a crowd of people sitting on the grass at night", - "query": "city festival crowd energy vibe", - "dia_id": "D15:6", - "text": "It was an incredible experience, Dave! The city was buzzing with energy and the festival crowd was so lively - it was like fuel for my soul. Look at this photo, that's amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D15:7", - "text": "Wow, that sounds incredible! What was it like playing at the festival? Must have been an amazing experience." - }, - { - "speaker": "Calvin", - "dia_id": "D15:8", - "text": "It was a dream come true, Dave! The festival had buzzing energy and a super upbeat crowd. We all felt connected, it was amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D15:9", - "text": "Sounds like you had a blast! Did any other collaborations come out of that performance? Any upcoming concerts we should know about?" - }, - { - "speaker": "Calvin", - "dia_id": "D15:10", - "text": "Yeah Dave, someone even noticed my performance and now we're working together, which is wild. My tour ends soon and I'm heading to Boston. Maybe we can meet up then!" - }, - { - "speaker": "Dave", - "dia_id": "D15:11", - "text": "Sounds great! Fill me in on the details when you have them and let's meet up when you're here. Can't wait to hear all about everything!" - }, - { - "speaker": "Calvin", - "dia_id": "D15:12", - "text": "Of course, Dave can't wait to catch up! I almost forgot, yesterday my friends and I recorded a podcast where we discuss the rapidly evolving rap industry!" - }, - { - "speaker": "Dave", - "dia_id": "D15:13", - "text": "That sounds fantastic! I'm looking forward to listening to your podcast and learning more about the rap industry. Keep up the great work!" - }, - { - "speaker": "Calvin", - "dia_id": "D15:14", - "text": "I'll let you know when the podcast will be uploaded. Take care!" - }, - { - "speaker": "Dave", - "dia_id": "D15:15", - "text": "Sure, Calvin! Looking forward to hearing about it. See ya then!" - } - ], - "session_16_date_time": "2:55 pm on 31 August, 2023", - "session_16": [ - { - "speaker": "Dave", - "blip_caption": "a photo of a crowd of people at a concert with their hands in the air", - "dia_id": "D16:1", - "text": "Hey Calvin! Long time no chat! How was the end of your tour? I bet it was amazing!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://www.wavy.com/wp-content/uploads/sites/3/2023/04/20230430_023901861_iOS-1.jpg" - ], - "blip_caption": "a photo of a crowd of people watching a concert with a large screen", - "query": "stage crowd energizing connection", - "dia_id": "D16:2", - "text": "Hey Dave! The tour was amazing! I was so pumped from all the energy from the audience. This pic totally captures how I felt. Such an amazing time!" - }, - { - "speaker": "Dave", - "dia_id": "D16:3", - "text": "Wow, that looks amazing! How was it performing on that big stage?" - }, - { - "speaker": "Calvin", - "dia_id": "D16:4", - "text": "Performing on such a big stage was a dream come true! The energy was incredible and I felt on top of the world. It was seriously surreal." - }, - { - "speaker": "Dave", - "dia_id": "D16:5", - "text": "Wow, that must have been an incredible experience! Congrats on achieving such an awesome moment!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://indiestonia413850954.files.wordpress.com/2019/01/mvimg_20181118_091403_12.jpg" - ], - "blip_caption": "a photo of a camera and a video camera on a beach", - "query": "album video shoot set crew members", - "dia_id": "D16:6", - "text": "Cool! Last weekend I started shooting a video for my new album - can't wait for you to check it out! Look how great it was!" - }, - { - "speaker": "Dave", - "dia_id": "D16:7", - "text": "Wow, Calvin! Can't wait to see it. Will it be shot on location?" - }, - { - "speaker": "Calvin", - "dia_id": "D16:8", - "text": "We're doing the shoot in Miami and picked an awesome beach. It's going to have some epic visuals!" - }, - { - "speaker": "Dave", - "dia_id": "D16:9", - "text": "Sounds great, Calvin! The beaches in Miami are awesome - can't wait to see them!" - }, - { - "speaker": "Calvin", - "dia_id": "D16:10", - "text": "Oh man, they are amazing! I love the Miami vibe, it's perfect for the vid. Can't wait to show you!" - }, - { - "speaker": "Dave", - "dia_id": "D16:11", - "text": "Looking forward to it! I'm excited to see the vibe and the location. Let me know if you need any help with props or anything else for the video. I'm here to support you!" - }, - { - "speaker": "Calvin", - "dia_id": "D16:12", - "text": "Thanks, Dave! I'll let you know if I need any assistance with props or anything else. Your support is much appreciated." - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.pinimg.com/originals/a9/a5/b5/a9a5b5be5f815beb22bd11caf542d827.jpg" - ], - "blip_caption": "a photo of a guitar with a octopus on it", - "query": "paintbrush guitar pick art", - "dia_id": "D16:13", - "text": "Sure, let me know when, I'm here to lend a hand. It's great to fuel your ideas. Remember that photo you sent me once? Love how this guitar shows our different artistic styles." - }, - { - "speaker": "Calvin", - "dia_id": "D16:14", - "text": "Yes Dave, I remember! I had this custom made by my Japanese artist friend. It's got an octopus on it, which represents my love for art and the sea. It's one of my favorites!" - }, - { - "speaker": "Dave", - "dia_id": "D16:15", - "text": "That's a great guitar, Calvin! Love the design, it's so unique and special." - }, - { - "speaker": "Calvin", - "dia_id": "D16:16", - "text": "Cheers, mate! Really appreciate it. This guitar means so much to me; it's a reminder of my passion for music and the amazing friendships I've made." - }, - { - "speaker": "Dave", - "dia_id": "D16:17", - "text": "Wow, Calvin, this instrument obviously means a lot to you - it's like a representation of your journey, your passion for music, and the friendships you've made. Amazing!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.pexels.com/photos/14436042/pexels-photo-14436042.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-carter-nguyen-14436042.jpg" - ], - "blip_caption": "a photo of a guitar with a purple glow on it", - "query": "guitar close-up", - "dia_id": "D16:18", - "text": "Yeah, Dave! It's like every mark and strum holds a story. Take a look." - }, - { - "speaker": "Dave", - "dia_id": "D16:19", - "text": "That guitar has a gorgeous purple hue. Why did you make it so shiny?" - }, - { - "speaker": "Calvin", - "dia_id": "D16:20", - "text": "I got it customized with a shiny finish because it gives it a unique look. Plus, it goes with my style." - }, - { - "speaker": "Dave", - "dia_id": "D16:21", - "text": "Good pick! The customized purple glow gives it a unique look that really stands out." - }, - { - "speaker": "Calvin", - "dia_id": "D16:22", - "text": "Thanks, dude! I dig how it's so unique. It's totally my style!" - }, - { - "speaker": "Dave", - "dia_id": "D16:23", - "text": "Looks great on you! Your unique style really stands out in your music and playing." - }, - { - "speaker": "Calvin", - "dia_id": "D16:24", - "text": "Thanks, Dave! Trying to stay true to myself and be unique in my music is something that I value, so it's really great to hear that you appreciate it." - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a man working on a car engine in a garage", - "dia_id": "D16:25", - "text": "Definitely, staying true to yourself and your style is vital. It makes you unique and your music stand out. Keep it real, bud. You've got something great to give the world." - } - ], - "session_17_date_time": "9:19 am on 2 September, 2023", - "session_17": [ - { - "speaker": "Dave", - "dia_id": "D17:1", - "text": "Hey Calvin! Been a while, what's up? I'm tied up with car stuff lately, yesterday I came back from San Francsico with some great insights and knowledge on car modification that I want to share with you! Changing things around, and giving an old car a new life - so satisfying!" - }, - { - "speaker": "Calvin", - "dia_id": "D17:2", - "text": "Hey Dave! Nice to hear from you. That's cool! I totally understand the satisfaction you get from fixing cars. It's like you're giving them new life." - }, - { - "speaker": "Dave", - "dia_id": "D17:3", - "text": "Yeah, it's great fixing stuff up and seeing it turn out better. It's really rewarding and gives me a sense of purpose. Plus, it feels like I'm making a difference when I fix someone's car." - }, - { - "speaker": "Calvin", - "dia_id": "D17:4", - "text": "Wow, you must feel great making a real difference in someone's life, like being their superhero!" - }, - { - "speaker": "Dave", - "dia_id": "D17:5", - "text": "Yeah, it's great! It feels really good to make a difference and see their relief when their car is fixed. Makes me proud!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/3n0tonj6zg821.jpg" - ], - "blip_caption": "a photo of a book with a boarding pass and a boarding pass", - "query": "plane ticket boston", - "dia_id": "D17:6", - "text": "Wow, Dave, that's awesome! You should be really proud of yourself for bringing joy to others. I booked a flight ticket to Boston last week! I'm so excited about my upcoming trip to Boston. Look at this! See you soon, buddy!" - }, - { - "speaker": "Dave", - "dia_id": "D17:7", - "text": "Cool! Let me know when you're free and we can catch up in Boston." - }, - { - "speaker": "Calvin", - "dia_id": "D17:8", - "text": "Yeah, for sure! I'll let you know when I'm in Boston. See you soon!" - }, - { - "speaker": "Dave", - "dia_id": "D17:9", - "text": "Looking forward to seeing you! Have a safe trip, see ya!" - }, - { - "speaker": "Calvin", - "dia_id": "D17:10", - "text": "Thanks, Dave! Gotta stay safe on the trip. Can't wait to see you there! I will contact you when I arrive. Goodbye!" - } - ], - "session_18_date_time": "10:56 am on 13 September, 2023", - "session_18": [ - { - "speaker": "Calvin", - "dia_id": "D18:1", - "text": "Hey Dave! Sorry it took me so long to get back to you. Crazy times since we talked! My album finally dropped on the 11th and it was a wild feeling. Everyone's been loving it and it's motivated me to keep going." - }, - { - "speaker": "Dave", - "dia_id": "D18:2", - "text": "Hey Calvin! Congrats on your album release - that's awesome! Has it been overwhelming or inspiring?" - }, - { - "speaker": "Calvin", - "dia_id": "D18:3", - "text": "Thanks, Dave! It's been a lot. Seeing everyone get behind it has been awesome. It's kinda overwhelming to think so many appreciate it. It's also cool that it's connecting with people. It really motivates me to make even better music." - }, - { - "speaker": "Dave", - "dia_id": "D18:4", - "text": "That's awesome, Calvin! It's such a great feeling when your work gets noticed and makes a positive difference. It must be really motivating for you to keep chasing your dreams." - }, - { - "speaker": "Calvin", - "dia_id": "D18:5", - "text": "Yeah, moments like this remind me why I got into music - making a difference and sharing my own story. It's exciting to get positive feedback and it gives me strength to keep going and reach more people. My journey's just getting started!" - }, - { - "speaker": "Dave", - "dia_id": "D18:6", - "text": "Wow, Calvin! You've come a long way since we started talking music. What's next for you? Anything exciting coming up?" - }, - { - "speaker": "Calvin", - "dia_id": "D18:7", - "text": "Thanks Dave! Lots of cool stuff happening. Next up, a tour - so excited! After that, I'm off to explore and grow my brand. Can't wait to see what the future has in store!" - }, - { - "speaker": "Dave", - "dia_id": "D18:8", - "text": "Wow, Calvin! Congrats on the upcoming tour! Can't wait to see you perform. Do you have any cities or venues in mind?" - }, - { - "speaker": "Calvin", - "dia_id": "D18:9", - "text": "Yay! Dave! We're hitting some awesome spots - it's gonna be epic!" - }, - { - "speaker": "Dave", - "dia_id": "D18:10", - "text": "That sounds awesome, Calvin! Live music is the best. I'm sure you're gonna have a blast on tour and make some awesome memories. If you ever want to check out my garage and see some cool cars, I'd love to show you around. Keep rockin'!" - }, - { - "speaker": "Calvin", - "dia_id": "D18:11", - "text": "Thanks, Dave! I'll definitely take you up on that offer. It would be fun to check out your garage and maybe even get some ideas for future projects. I'll let you know when I'm in Boston. Keep pursuing your passions and keep those cars rolling. You rock!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://d2dsc1gf0t80gb.cloudfront.net/wp-content/uploads/2017/06/07180008/RobShingleGarage-11-1000x750.jpg" - ], - "blip_caption": "a photo of a car in a garage with a coca cola sign", - "query": "garage vintage cars", - "dia_id": "D18:12", - "re-download": true, - "text": "Thanks, Calvin! Appreciate the kind words and support. Can't wait for your visit! Take care and keep creating amazing music! Check out pic of my garage, it looks stunning!" - }, - { - "speaker": "Calvin", - "dia_id": "D18:13", - "text": "Thanks! I can't wait for your visit either. Take care and keep enjoying your hobbies!" - }, - { - "speaker": "Dave", - "dia_id": "D18:14", - "text": "Sure thing! Thanks again for your help. Bye! Have a great day." - }, - { - "speaker": "Calvin", - "dia_id": "D18:15", - "text": "No problem! Always good chatting with you. Have an awesome day!" - }, - { - "speaker": "Dave", - "dia_id": "D18:16", - "text": "Thanks, Calvin! Catch you later. Have a great day!" - } - ], - "session_19_date_time": "12:13 am on 15 September, 2023", - "session_19": [ - { - "speaker": "Dave", - "dia_id": "D19:1", - "img_url": [ - "https://c1.wallpaperflare.com/preview/76/142/466/concert-performance-hard-rock-guitarist.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a band performing on stage with bright lights", - "text": "Hey Calvin! Long time no talk! Got some cool news to share - last night was a blast! My band and I were jamming and the music just kept flowing. We had so much fun rehearsing! Also, last weekend, I had the opportunity to attend a rock concert here in Boston. Just take a look at what an amazing atmosphere it was!" - }, - { - "speaker": "Calvin", - "dia_id": "D19:2", - "text": "Hey Dave! That's awesome! When everything just clicks during a jam session, it's such a great feeling. Do you happen to have any recordings of those sessions? That sounds fantastic! Rock concerts always have such an electrifying atmosphere! " - }, - { - "speaker": "Dave", - "dia_id": "D19:3", - "text": "Hey Calvin! I wish we had recorded the jam, but we were way too into it and totally forgot." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i0.wp.com/backtothemouse.com/wordpress/wp-content/uploads/2019/06/IMG_5098-1.png" - ], - "blip_caption": "a photo of a poster for a disney movie with a chef", - "query": "magical moment photo", - "dia_id": "D19:4", - "text": "Aww, bummer! I would've loved to hear that music. Oh well, some of the best memories can't be captured on video or audio. It's like those special moments that stay in our hearts and minds. Take a look at this cool Disney poster!" - }, - { - "speaker": "Dave", - "dia_id": "D19:5", - "text": "Wow, that's a gorgeous poster! It reminds me of one of my favorite Disney movies. I love the lesson it teaches about following your dreams no matter what others say. Have you seen it yet?" - }, - { - "speaker": "Calvin", - "dia_id": "D19:6", - "text": "Thanks! Ratatouille is one of my favorites! It's all about going after what you love, no matter what anyone says. Last time we talked, you mentioned your car hobby - how's that going?" - }, - { - "speaker": "Dave", - "dia_id": "D19:7", - "img_url": [ - "https://c2.peakpx.com/wallpaper/949/672/879/automobile-mustang-old-car-wallpaper-preview.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a man standing next to a classic car", - "text": "Thanks, Calvin! It's been awesome. Been restoring this vintage beauty - here is the final result pic, take a look!" - }, - { - "speaker": "Calvin", - "dia_id": "D19:8", - "text": "Nice work, Dave! That looks amazing. Is it satisfying to see your hard work pay off?" - }, - { - "speaker": "Dave", - "dia_id": "D19:9", - "text": "Thanks, Calvin! It's so satisfying to see this brought back to life, especially with people's reactions when they see the finished product - makes all the hard work worth it." - }, - { - "speaker": "Calvin", - "dia_id": "D19:10", - "text": "Dave, it's awesome seeing people happy thanks to you! Fixing cars is such an art. You're inspiring - keep up the good work!" - }, - { - "speaker": "Dave", - "dia_id": "D19:11", - "text": "Thanks, Calvin! It means a lot that you appreciate what I do. I'm glad that I can make people happy and that's what I'm gonna keep doing. Got to go now, I have a lot of work to do! Take care!" - }, - { - "speaker": "Calvin", - "dia_id": "D19:12", - "text": "You're really talented, Dave. Keep making people happy and doing what you love. That's what it's all about. See you soon, have a nice one!" - } - ], - "session_20_date_time": "8:57 pm on 22 September, 2023", - "session_20": [ - { - "speaker": "Dave", - "dia_id": "D20:1", - "img_url": [ - "https://cdn12.picryl.com/photo/2016/12/31/ford-xl-1967-restored-motor-v8-345-hp-transportation-traffic-2d0301-1024.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a car engine with a small engine", - "text": "Hey Calvin, good to catch up again! Had a tough time with my car project. Worked on the engine of the vintage Mustang, thought I'd fixed it, but when I started it up, heard a weird noise. So disappointing after putting so much work in. Take a look at the engine!" - }, - { - "speaker": "Calvin", - "dia_id": "D20:2", - "img_url": [ - "https://live.staticflickr.com/4475/37494593854_e7b70510d7_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a red sports car parked in a parking lot", - "text": "Hey Dave, sorry to hear about the car trouble. That must be disappointing after putting so much work into it. Yeah, fixing cars can be tricky. But hey, check out this pic! It's a sign of my hard work and dedication. Looking at it reminds me how far I've come." - }, - { - "speaker": "Dave", - "dia_id": "D20:3", - "text": "Wow, thanks Calvin! I love the way the sunlight hits this. It's definitely a great symbol of all your hard work. I bet you feel really proud when you look at it. Any special memories with it?" - }, - { - "speaker": "Calvin", - "dia_id": "D20:4", - "text": "Yeah, Dave, I had an amazing drive one summer day. The wind blowing through my hair and the rush of freedom - it was unforgettable. I've pondered the intricacies of life, taking a moment to truly appreciate the path I've chosen and the decisions that have shaped my journey. Also, my trip to Japan was incredible - the culture, the people, everything was so cool. I'm already longing to go back, it was an experience that made all the hard work worth it." - }, - { - "speaker": "Dave", - "dia_id": "D20:5", - "text": "Wow, Calvin, that's awesome! That feeling of freedom in the summer is the best. A moment of reflection not only makes the journey interesting but also productive! Hey, any songs from your childhood that bring back memories?" - }, - { - "speaker": "Calvin", - "dia_id": "D20:6", - "text": "Yeah, there's this one song that always makes me smile. It played during a road trip with my dad and we had so much fun singing along to it." - }, - { - "speaker": "Dave", - "dia_id": "D20:7", - "text": "Sounds awesome, Calvin! Road trips with family are always a treat. What's the name of the song?" - }, - { - "speaker": "Calvin", - "dia_id": "D20:8", - "text": "We used to rock a song by Tupac and Dr. Dre called \"California Love\". Ah, those were the days!" - }, - { - "speaker": "Dave", - "dia_id": "D20:9", - "text": "Sounds great, Cal! \"California Love\" is a classic! Let me know if you wanna jam some music together!" - }, - { - "speaker": "Calvin", - "dia_id": "D20:10", - "text": "Yeah, let's do it! That would be awesome." - }, - { - "speaker": "Dave", - "dia_id": "D20:11", - "text": "I'm stoked, Calvin! We can crank up the music and have an awesome jam session. Can't wait!" - }, - { - "speaker": "Calvin", - "dia_id": "D20:12", - "text": "Yeah, Dave! Gonna be great! Can't wait to see what we can do." - }, - { - "speaker": "Dave", - "dia_id": "D20:13", - "text": "Yeah, let's do it! It'll be awesome. Let's rock it!" - }, - { - "speaker": "Calvin", - "dia_id": "D20:14", - "text": "Yeah, let's do this! I can't wait!" - }, - { - "speaker": "Dave", - "dia_id": "D20:15", - "text": "Nice one, Calvin! Let's make some awesome memories and have a great time. See ya soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D20:16", - "text": "Count me in. Can't wait to create something special. See you soon!" - }, - { - "speaker": "Dave", - "dia_id": "D20:17", - "text": "Yep, Calvin! Can't wait to see you soon. Take it easy till then." - } - ], - "session_21_date_time": "2:44 pm on 4 October, 2023", - "session_21": [ - { - "speaker": "Calvin", - "dia_id": "D21:1", - "text": "Hey Dave! Yesterday I met with some incredible artists in Boston and we talked about working together. It was such an inspiring and exciting experience - they all have individual styles and I'm stoked to collaborate with them on new music." - }, - { - "speaker": "Dave", - "dia_id": "D21:2", - "text": "Awesome, Calvin! Connecting with all those talented artists must have been an inspiring experience. Can't wait to hear what you come up with in your collaboration. Let me know how it goes! Also, how did you arrange that meeting?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://dealeraccelerate-all.s3.amazonaws.com/qmm/marketing_assets/328/IMG_4020.jpg" - ], - "blip_caption": "a photo of a shiny orange car with a hood open", - "query": "sleek vintage car restoration", - "dia_id": "D21:3", - "text": "Hey Dave, it was awesome talking to those artists! Our mutual friend knew we'd be a great fit. Can't wait to show you the final result. Also, check out this project - I love working on it to chill out. How about you? Got any hobbies to help you relax?" - }, - { - "speaker": "Dave", - "dia_id": "D21:4", - "text": "Wow, Calvin, that car looks great! Working on cars really helps me relax, it's therapeutic to see them come back to life. I've been working on that Ford Mustang I found in a junkyard - it was in bad shape, but I knew it had potential." - }, - { - "speaker": "Calvin", - "dia_id": "D21:5", - "text": "Wow, Dave! It's awesome that you can bring things back to life. Do you have any pictures of it looking amazing? I'd love to see how it turned out!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ_Tnd67jTFU3qlXn69Kt_Fm60e2ZKQpgEkgg&usqp=CAU" - ], - "blip_caption": "a photography of a red car parked in a field with other cars", - "query": "classic muscle car fully restored shining sunlight", - "dia_id": "D21:6", - "re-download": true, - "text": "Hey Calvin, check out this photo! I put in a lot of work restoring it, but the result is awesome. It's so satisfying to bring an old car back to life." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a person's hand with dirty hands next to a car", - "dia_id": "D21:7", - "text": "We've been greatly privileged to have been granted this opportunity. It's so satisfying to bring it back to life! " - }, - { - "speaker": "Dave", - "dia_id": "D21:8", - "img_url": [ - "https://images.rawpixel.com/image_800/czNmcy1wcml2YXRlL3Jhd3BpeGVsX2ltYWdlcy93ZWJzaXRlX2NvbnRlbnQvZmw1MDgwNjQwMzY3LWltYWdlLWtxYXBveHhoLmpwZw.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a person's hands with dirt on them", - "text": "Wow, it's so satisfying! Here are my hands after a day in the garage - permanently stained with grease. But it's worth it when you see the end result." - }, - { - "speaker": "Calvin", - "dia_id": "D21:9", - "text": "Yeah, Dave! Those hands show you worked hard. You put in lots of effort. You should definitely be proud!" - }, - { - "speaker": "Dave", - "dia_id": "D21:10", - "text": "Thanks, Calvin. I love being able to transform something old and beat-up into something beautiful. It's the small successes that make me feel proud and fulfilled." - }, - { - "speaker": "Calvin", - "dia_id": "D21:11", - "text": "Yeah, those little wins matter. They give us a sense of accomplishment and bring us joy. It's truly inspiring to see how much we can grow." - }, - { - "speaker": "Dave", - "dia_id": "D21:12", - "text": "Sure, Calvin! It's awesome seeing the progress and development, both in our projects and ourselves. Hard work really does pay off!" - }, - { - "speaker": "Calvin", - "dia_id": "D21:13", - "text": "Yeah, hard work and dedication are definitely key to reaching our goals and potential. It's awesome to see our growth and progress." - }, - { - "speaker": "Dave", - "dia_id": "D21:14", - "text": "Yeah, it's great to see our progress. It's really motivating and keeps me pushing for more." - }, - { - "speaker": "Calvin", - "dia_id": "D21:15", - "text": "Agreed, Dave! Progress is what keeps us motivated and pushing for more. Let's never give up and keep striving for success. We know that hard work and determination matter, and it's what sets us apart. Onwards to our goals!" - }, - { - "speaker": "Dave", - "dia_id": "D21:16", - "text": "Let's keep going! We won't lose focus on our goals. Hard work and determination will get us there. Let's do this!" - }, - { - "speaker": "Calvin", - "dia_id": "D21:17", - "text": "Yeah, let's do it! Let's stay focused and work hard to make our dreams happen. We can make it happen together! Wishing you all the best until we meet again!\n\n\n\n\n " - }, - { - "speaker": "Dave", - "dia_id": "D21:18", - "text": "Yep, Calvin! Together, we can do amazing things if we work together and stay motivated. We got this! Take care and stay well!" - } - ], - "session_22_date_time": "3:13 pm on 8 October, 2023", - "session_22": [ - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/5835359/pexels-photo-5835359.jpeg" - ], - "blip_caption": "a photography of two men looking at a car engine", - "query": "car show vintage mustang open hood", - "dia_id": "D22:1", - "re-download": true, - "text": "Hey Calvin! What\u2019s up? Last Friday I went to the car show. I saw some awesome cars and got to mess with car mods! There were so many cool machines around, it was so much fun! Take a look at this beautiful car!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a red sports car parked in a showroom", - "dia_id": "D22:2", - "text": "Hey Dave, that sounds awesome! I'm into the rush of awesome cars. Can't wait to check out your garage." - }, - { - "speaker": "Dave", - "dia_id": "D22:3", - "text": "Thanks! Yeah, this one looks great! I restored and modified it myself and added a custom exhaust and some performance upgrades. It's got a sweet sound and I'm really proud of how it turned out." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a car driving down a street with a traffic light", - "dia_id": "D22:4", - "text": "Wow, Dave! You really turned it into a masterpiece. Impressive!" - }, - { - "speaker": "Dave", - "dia_id": "D22:5", - "text": "Thanks Calvin! I've spent a lot of time and effort on it. It's not just a hobby, it's a passion. It's like therapy, a way to get away from everyday stress. When I was little I'd spend hours in my dad's garage, tinkering with engines - it was like my own sanctuary. Now I'm lucky enough to do this as a job, to take something broken and make it into something awesome." - }, - { - "speaker": "Calvin", - "dia_id": "D22:6", - "text": "Wow, Dave, that's amazing. Bringing broken things back to life is so satisfying. Working on cars can be a real escape from reality, and I understand that feeling. Doing what you love for a living, that's the ultimate goal, right? Keep going with it, it's really inspiring." - }, - { - "speaker": "Dave", - "dia_id": "D22:7", - "text": "Thanks, Calvin. It's been my goal since I was a kid and it's awesome to be able to do something I love. Restoring things like this can be tough but the feeling of accomplishment it gives is great. Absolutely, I'm loving it." - }, - { - "speaker": "Calvin", - "dia_id": "D22:8", - "text": "Go for it, Dave! Chasing your dreams is what life's about. It's awesome to see how far you've come. Keep working hard and living your best life." - }, - { - "speaker": "Dave", - "dia_id": "D22:9", - "text": "Thanks, Calvin! Means a lot. I'm going to keep chasing my dreams and working hard. Conversations like this remind me why I love what I do." - }, - { - "speaker": "Calvin", - "dia_id": "D22:10", - "text": "Glad I can remind you, Dave. Keep up the good work and stay focused. You got this!" - }, - { - "speaker": "Dave", - "dia_id": "D22:11", - "text": "Thanks, Calvin! Your support really means a lot. I'll stay focused and keep going. Appreciate the encouragement!" - }, - { - "speaker": "Calvin", - "dia_id": "D22:12", - "text": "Yeah Dave! I'll always be here to support you and give you that boost. You're doing great!" - }, - { - "speaker": "Dave", - "dia_id": "D22:13", - "text": "Thanks so much, Calvin. Your support means everything to me. I'll keep pushing and reaching for them." - }, - { - "speaker": "Calvin", - "dia_id": "D22:14", - "text": "No worries, Dave. Keep going for it. You got this!" - } - ], - "session_23_date_time": "9:39 am on 15 October, 2023", - "session_23": [ - { - "speaker": "Dave", - "dia_id": "D23:1", - "text": "Hey Cal, miss ya! Crazy rollercoaster week. A competing car maintenance shop snagged a deal we were trying to secure for months and it made me feel kinda bummed out. You know, I put in so much effort at work, but it feels like nothing. Am I wasting my time?" - }, - { - "speaker": "Calvin", - "dia_id": "D23:2", - "text": "Hey Dave, sorry to hear that. It can be discouraging when you feel like your hard work goes unnoticed. But don't give up, keep pushing and believe in yourself. The payoff will be worth it." - }, - { - "speaker": "Dave", - "dia_id": "D23:3", - "text": "Calvin, thanks for the encouragement. It can be tough when you feel like your efforts are going unseen. I gotta have faith and patience. I'm sure it's only a matter of time till things work out. How do you stay motivated when faced with setbacks?" - }, - { - "speaker": "Calvin", - "dia_id": "D23:4", - "text": "When setbacks come my way, I remind myself why I'm passionate about my goals. I rely on helpful people around me and take a break to recharge with my favorite activities. That always helps me get back to feeling motivated." - }, - { - "speaker": "Dave", - "dia_id": "D23:5", - "text": "That's a great approach, Cal! Reminding yourself of the passion for the goals and getting help from others is really important. Taking a break and having fun sounds so refreshing. Oh, I just got back from a music festival - it was amazing! The energy, the music, the crowd - sooo good. I felt so alive!" - }, - { - "speaker": "Calvin", - "dia_id": "D23:6", - "text": "Wow Dave, sounds awesome! Music festivals bring so much joy and the energy of the crowd can be amazing. Got any photos from the festival? I'd love to check them out and join in on the fun." - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.ibb.co/TmFqMDj/6-A8-A0-DE6-CD05-4-EB3-BD92-CFB4-DCF2-CB3-C.jpg" - ], - "blip_caption": "a photo of a stage with a crowd of people watching a band", - "query": "music festival crowd lights atmosphere main stage band energy", - "dia_id": "D23:7", - "text": "Yep! I got this awesome pic from the event. The main stage was unreal. The headliner was so good and the vibe was unreal!" - }, - { - "speaker": "Calvin", - "dia_id": "D23:8", - "text": "Wow, that looks awesome! The crowd looks really excited and the stage is incredible. Who was the headliner?" - }, - { - "speaker": "Dave", - "dia_id": "D23:9", - "text": "The Fireworks headlined the festival." - }, - { - "speaker": "Calvin", - "dia_id": "D23:10", - "text": "Wow, I heard great things about The Fireworks! Performing with Frank Ocean recently has been really cool. Seeing them perform live must've been awesome - I bet the energy was electric! That's why I love my job so much - connecting with the crowd." - }, - { - "speaker": "Dave", - "dia_id": "D23:11", - "text": "Yeah, Calvin! The crowd had such a buzz. Music brings people together in such an amazing way, and it's just like when I'm fixing up things. I love the feeling of taking something broken and making it whole again. That's why I keep doing what I do." - }, - { - "speaker": "Calvin", - "dia_id": "D23:12", - "text": "Yeah, Dave! Music and repairing things are so fulfilling and satisfying. Seeing something go from broken to whole is incredible. You're making a difference too - it's amazing. Keep it up, friend." - }, - { - "speaker": "Dave", - "dia_id": "D23:13", - "text": "Thanks, buddy. Your support really helps. It's great to have a friend who believes in me. I'll keep pushing." - }, - { - "speaker": "Calvin", - "dia_id": "D23:14", - "text": "I believe in you, Dave. Keep pushing and never forget how awesome you are." - }, - { - "speaker": "Dave", - "dia_id": "D23:15", - "text": "Thanks, Calvin! Your support means a lot. I'm gonna keep going and not forget my value!" - }, - { - "speaker": "Calvin", - "dia_id": "D23:16", - "img_url": [ - "https://live.staticflickr.com/65535/52239882084_488bafd7de_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a black sports car parked in front of a building", - "text": "C'mon, remember how great you are! Keep going for those dreams. You got this! You know what Dave? Last week, I got a new Ferrari! It's a masterpiece on wheels. Excited for thrilling rides and unforgettable journeys! Perhaps a photo of this unique beauty will lift your mood." - }, - { - "speaker": "Dave", - "dia_id": "D23:17", - "text": "Wow! Thanks a ton for the kind words and encouragement! Your positivity is contagious. Congratulations on the new Ferrari \u2013 that's incredible! Must be one amazing ride. I'm all in for those thrilling journeys! Feel free to share a pic of your new beauty whenever you're ready. Let's keep the good vibes rolling! Take care and see you later!" - }, - { - "speaker": "Calvin", - "dia_id": "D23:18", - "text": "The Ferrari is indeed a showstopper. I'll be sure to share a pic soon. Here's to more thrilling rides and positive vibes. Take care, and looking forward to catching up soon!" - } - ], - "session_24_date_time": "10:11 am on 19 October, 2023", - "session_24": [ - { - "speaker": "Calvin", - "img_url": [ - "https://badgerherald.com/wp-content/uploads/2017/09/Image-uploaded-from-iOS-1.jpg" - ], - "blip_caption": "a photo of a band performing on stage with bright lights", - "query": "performance stage concert crowd energy", - "dia_id": "D24:1", - "text": "Hey Dave! Been a few days since we talked, but lots happened. Started touring with Frank Ocean and it's been amazing - so much energy from the crowd, such a connection when I'm on stage - unreal! Take a look at how I performed on stage, that was awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D24:2", - "text": "Congrats, Calvin! That's awesome. Being able to play your music to a crowd and feel that connection must be unreal. How's the tour going?" - }, - { - "speaker": "Calvin", - "dia_id": "D24:3", - "text": "Thanks, Dave! The tour has been great, the energy from the crowd is awesome and jamming with Frank Ocean is a dream come true. It's been amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D24:4", - "text": "Wow, Calvin! I bet performing with him is like a dream come true! How did it happen?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/f6yapuwstnl01.jpg" - ], - "blip_caption": "a photo of a band performing on stage with a projection of a man on the wall", - "query": "frank ocean performance on stage music festival sweaty", - "dia_id": "D24:5", - "text": "It all started August last year when we met at a festival and he said he wanted to collaborate. We clicked right away and the chemistry on stage was incredible. I'm so lucky! Check the photo, of how we perfectly look together on stage!" - }, - { - "speaker": "Dave", - "dia_id": "D24:6", - "text": "Wow, Calvin - you and Frank are so in tune! It's clear you both rock on stage. Can't wait to catch your show!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.pexels.com/photos/18482984/pexels-photo-18482984.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-luis-quintero-18482984.jpg" - ], - "blip_caption": "a photo of a large crowd of people in a large auditorium", - "query": "crowd performance hands in the air", - "dia_id": "D24:7", - "text": "Thanks, Dave! I'm so excited you'll be at one of our shows. It's such a great experience, you'll definitely enjoy it! Look at this crowd, that was insane!" - }, - { - "speaker": "Dave", - "dia_id": "D24:8", - "text": "That sounds really exciting! Can't wait to experience it. I'm sure everyone in the crowd is going to be pumped up!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://live.staticflickr.com/187/393901746_803174e292_b.jpg" - ], - "blip_caption": "a photography of a crowd of people at a concert with their hands in the air", - "query": "crowd hands raised", - "dia_id": "D24:9", - "re-download": true, - "text": "Wow, the crowd energy is amazing! It always gets me so pumped and it's awesome. Look at that photo, that was awesome!" - }, - { - "speaker": "Dave", - "dia_id": "D24:10", - "text": "Wow, Calvin, that's an awesome pic! Everyone looks so pumped. Where was that taken?" - }, - { - "speaker": "Calvin", - "dia_id": "D24:11", - "text": "Thanks, Dave! That pic was taken in Tokyo during a concert. Man, the energy was nuts - felt like the whole city came out!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://betweenstationsblog.files.wordpress.com/2017/09/bd698257-6a1e-45f9-916b-780aef4d9445.jpg" - ], - "blip_caption": "a photo of a busy street with people walking and shopping", - "query": "tokyo crowded streets energy city", - "dia_id": "D24:12", - "text": "Wow, Calvin, Tokyo looks incredible! Here's a pic I found online, and it's making me dream about visiting someday. The energy there seems unbeatable! Have you ever visited streets like that?" - }, - { - "speaker": "Calvin", - "dia_id": "D24:13", - "text": "Yes, Dave! That was an incredible experience to visit similar streets like you shared in your photo. Is there anything else that interests you in Tokyo?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://www.hannahrose.uk/wp-content/uploads/2019/02/IMG_0985.jpg" - ], - "blip_caption": "a photo of a crowded street at night with people walking and walking", - "query": "tokyo busy street neon lights crowd", - "dia_id": "D24:14", - "text": "Of course, Calvin! Tokyo is amazing! I want to know everything about it - the people, the culture, the food, take a walk at the vibrant city life! In the photo below, the city is so alive and colorful that's impressive! It will be an unforgettable experience!" - }, - { - "speaker": "Calvin", - "dia_id": "D24:15", - "text": "That photo's a great pic! The lights, the people - so lively! Can't wait to hear your emotions when you see that in person!" - }, - { - "speaker": "Dave", - "dia_id": "D24:16", - "text": "It's really amazing. Hope I'll get to see it in person soon!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://omakase-tour.com/blog/IMG_2448.jpg" - ], - "blip_caption": "a photo of a city at night with a tall building in the background", - "query": "view skyline rooftop bar tokyo", - "dia_id": "D24:17", - "text": "Cool, Dave! I'm actually going to Tokyo next month after the tour ends. Sometimes I wish I could go back to places like the one in the photo below. What a great view!" - }, - { - "speaker": "Dave", - "dia_id": "D24:18", - "text": "Wow, Calvin! That's great to hear! Any specific spots in Tokyo that you're really excited to check out?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.imgur.com/ehTs1O5.jpg" - ], - "blip_caption": "a photo of a crowd of people with umbrellas in the rain", - "query": "shibuya crossing night crowd", - "dia_id": "D24:19", - "text": "Yeah definitely! Shibuya Crossing is like Tokyo's Times Square, and I was excited to explore Shinjuku. Plus, there's amazing food there, can't wait to try it again! Look at the photo of Shibuya Crossing at night, that's amazing!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/ywrvm1v05l181.jpg" - ], - "blip_caption": "a photo of a bowl of soup with broccoli and noodles", - "query": "authentic ramen bowl", - "dia_id": "D24:20", - "text": "Shibuya and Shinjuku are cool spots! The food in Tokyo is great, I'll have so much fun exploring all the different places. Have you tried ramen yet? Here's a photo of a ramen bowl that I tried in Boston, it was delicious, but i think in Tokyo it will be even better!" - }, - { - "speaker": "Calvin", - "dia_id": "D24:21", - "text": "Thanks, Dave! Never tried it, but it's supposed to be awesome. Gonna give it a shot while in Tokyo!" - }, - { - "speaker": "Dave", - "dia_id": "D24:22", - "text": "Do it, Calvin! Once you try it, you'll never go back. Bon voyage and have fun!" - }, - { - "speaker": "Calvin", - "dia_id": "D24:23", - "text": "Thanks, Dave! I'll definitely give it a shot. Appreciate the encouragement! See you soon, bye!" - } - ], - "session_25_date_time": "2:17 pm on 23 October, 2023", - "session_25": [ - { - "speaker": "Dave", - "dia_id": "D25:1", - "text": "Hey Calvin, how's the tour with Frank Ocean? I was pondering our chat the other day about fame and its impact on relationships. It must be awesome but it must have its own struggles too. How are you juggling your job plus your personal life?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:2", - "text": "The tour's been incredible! Performing and connecting with the crowd has been so energizing. Yeah, fame has its own challenges. It's been tough trying to balance everything." - }, - { - "speaker": "Dave", - "dia_id": "D25:3", - "text": "How are you managing everything? Do you ever feel overwhelmed?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:4", - "text": "I take it one day at a time, but it can get overwhelming with so many demands. Still, I enjoy what I do so I push on." - }, - { - "speaker": "Dave", - "dia_id": "D25:5", - "text": "Glad you're finding joy in what you do! Any strategies that help you manage everything?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:6", - "text": "Yeah, having a strong support system is really helpful. My friends and team keep me on track." - }, - { - "speaker": "Dave", - "dia_id": "D25:7", - "text": "Cool! Having people who understand and support you is really important. I recently had a good conversation with some neighbors about current events and politics. It was nice to hear different perspectives and share our own. Interacting like that cheers me up and helps me stay informed - how about you?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:8", - "text": "Staying connected and up-to-date on world events is important to me. It helps my music stand out by incorporating unique perspectives and connects me better with my fans. Plus, it keeps me motivated and inspired." - }, - { - "speaker": "Dave", - "dia_id": "D25:9", - "text": "Cool, Calvin! Art is amazing how it reflects the world. Has anything caught your eye lately and made an impact on your music?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:10", - "text": "Yeah, for sure! Recently, I've been really inspired by some of the struggles that people go through. It's made me dig deeper into my music to try to capture those feelings." - }, - { - "speaker": "Dave", - "dia_id": "D25:11", - "text": "That's cool. Using your music to share experiences and feelings, is it cathartic for you?" - }, - { - "speaker": "Calvin", - "dia_id": "D25:12", - "text": "Yeah, it's a way for me to express myself and work through my emotions. It's like my own form of therapy." - }, - { - "speaker": "Dave", - "img_url": [ - "https://upload.wikimedia.org/wikipedia/commons/3/34/Car_workshop_tools.jpg" - ], - "blip_caption": "a photography of a garage with a bunch of tools hanging on the wall", - "query": "garage car parts and tools", - "dia_id": "D25:13", - "re-download": true, - "text": "Yeah, I get it. Cars give me an outlet to express myself. It's like a little oasis of calm. Whenever I'm working on one, it's like I'm connecting back with myself. And yeah, the music helps too! Look at my garage, a little dirty, but everything is in its place." - }, - { - "speaker": "Calvin", - "dia_id": "D25:14", - "text": "Looking cool - what made you start working on cars?" - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a man working on a car in a garage", - "dia_id": "D25:15", - "text": "Ever since I was ten, I've been fascinated with how machines work. I found an old car in a neighbor's garage and asked if I could fix it. That's when my love for car engineering began! I enjoyed transforming it from broken-down to high-running, and that sense of accomplishment made me hooked\u2014so I've been working on cars ever since." - }, - { - "speaker": "Calvin", - "dia_id": "D25:16", - "text": "Impressive! That must have taken a lot of skill and determination to bring that car back to life. Was it a classic car? Do you still have it or are you currently working on another project?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.classic.com/vehicles/d47f69b88de907d965b64771b7a9eba159a47a93.jpg" - ], - "blip_caption": "a photo of a black car parked in a parking lot", - "query": "restored chevy camaro", - "dia_id": "D25:17", - "text": "Thanks Calvin! It was a fun project. I restored a car last year, but I sold it to a collector. Now I'm working on something new, and it's quite a challenge! Take a look at this wonderful car!" - }, - { - "speaker": "Calvin", - "dia_id": "D25:18", - "text": "Cool! Been wanting to try that. What would you say is the toughest part of car restoration?" - }, - { - "speaker": "Dave", - "dia_id": "D25:19", - "text": "It takes a lot of patience, but the payoff is worth it. Paying extra attention to detail isn't easy, but it pays off." - }, - { - "speaker": "Calvin", - "dia_id": "D25:20", - "text": "Yeah, details can really make a difference. It's what makes something great, like a well-crafted rap song or a sleek and stylish car." - }, - { - "speaker": "Dave", - "dia_id": "D25:21", - "text": "Yeah, for sure Calvin! Paying attention to details makes a big difference, whether it's in music or with vehicles. Little things can make a big impact!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a silver disc in a black frame on a table", - "dia_id": "D25:22", - "text": "Yeah, Dave! Paying attention to those small details makes a difference. Without them, it's just average. As an artist, I want to create something extraordinary!" - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a brown amp with a black knob and a brown cover", - "dia_id": "D25:23", - "text": "That's great, Calvin! Loving the attention to detail in your work, it makes it extra special. Keep on creating!" - }, - { - "speaker": "Calvin", - "dia_id": "D25:24", - "text": "Thanks Dave! Appreciate your support!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/2147029/pexels-photo-2147029.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-laura-stanley-2147029.jpg" - ], - "blip_caption": "a photo of a crowd of people at a concert with their hands in the air", - "query": "concert crowd rock", - "dia_id": "D25:25", - "text": "Glad you like it, Cal! It reminds me of the good times I've had at concerts last month. Music has a way of connecting us and making memories. Take a look at this picture, the atmosphere was amazing!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a crowd of people at a concert with their hands in the air", - "dia_id": "D25:26", - "text": "Music has a way of bringing us together and creating unforgettable memories. It's unbeatable in terms of the energy it brings." - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/15995095/pexels-photo-15995095.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-tony-entz-15995095.jpg" - ], - "blip_caption": "a photo of a crowd of people standing around a stage", - "query": "concert stage vibrant lights crowd dancing", - "dia_id": "D25:27", - "text": "Yeah, for sure Calvin! Concerts are awesome, they bring people together and create such a cool vibe. You can feel the energy in the air. Take a look at this unforgettable moment that I captured at the concert." - }, - { - "speaker": "Calvin", - "dia_id": "D25:28", - "text": "Concerts are what I live for - the indescribable connection between the artist and the crowd is just amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D25:29", - "text": "Wow, it's amazing how that connection between artist and crowd can be indescribable. So glad you get to experience that!" - }, - { - "speaker": "Calvin", - "dia_id": "D25:30", - "text": "Wow, Dave! It's a rush connecting with everyone. That feeling is unbeatable! Wishing you a harmonious day ahead, my friend!" - }, - { - "speaker": "Dave", - "dia_id": "D25:31", - "text": "Yeah, I can imagine it's a rush being up on stage with all the fans cheering. Must be a unique experience. Wishing you many more electrifying moments in the spotlight! See you soon!" - } - ], - "session_26_date_time": "8:25 pm on 25 October, 2023", - "session_26": [ - { - "speaker": "Calvin", - "dia_id": "D26:1", - "text": "Hey Dave! Long time no talk! I had a great time yesterday, and visited some sights in Boston with a high school friend. It was really fun and eye-opening." - }, - { - "speaker": "Dave", - "dia_id": "D26:2", - "text": "Hey Calvin! Good to hear from you! Sounds like you had a blast in Boston - so much to do there! One of my favorites there is great for chilling and admiring nature. BTW, something cool I wanted to tell you about - I recently attended a conference in Detroit - it was so cool, I learned a lot." - }, - { - "speaker": "Calvin", - "dia_id": "D26:3", - "text": "I had a wonderful experience during my time in Boston. It had awesome architecture and a really cool history. I followed your advice and went there, it was so peaceful. Good to hear that you've learned a lot of new things at the conference in Detroit!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRWn9kFWCXVzFTta3A7aeTMjMfYmkueJcWaxg&usqp=CAU" - ], - "blip_caption": "a photography of a group of people washing a car in a garage", - "query": "car mechanic working under hood", - "dia_id": "D26:4", - "re-download": true, - "text": "Glad you enjoyed Boston! It's got some great architecture and history. Working on this takes me out of my head and calms me down. Here's a pic of my latest car project." - }, - { - "speaker": "Calvin", - "dia_id": "D26:5", - "text": "Dave, that car looks awesome! What got you into engineering cars? I'm totally into cars too and love making new and powerful models." - }, - { - "speaker": "Dave", - "dia_id": "D26:6", - "text": "Thanks, Calvin! It's always been my thing. My dad took me to my first car show when I was 10, and I was wow-ed! I was hooked and wanted to learn more. Now I'm an automotive engineer and it's crazy to think that something so powerful and cool can be made from small parts." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/invc2ervv8t31.jpg" - ], - "blip_caption": "a photo of a dj in a nightclub with a dj table", - "query": "performing live calvin frank ocean", - "dia_id": "D26:7", - "text": "That's awesome, Dave! So happy you followed your dreams and became an engineer. Seeing you work on cars is inspiring - it makes me want to keep pushing my music. Check this out - a pic of me performing with someone I admire! So amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D26:8", - "text": "Wow, Calvin! That's amazing! You looked super comfortable on stage. Bet it was an incredible experience to perform with someone you admire. How did it feel being up there?" - }, - { - "speaker": "Calvin", - "dia_id": "D26:9", - "text": "It was unreal, Dave. Being up there with someone I admire - it was a dream come true. The energy, the crowd - it made me realize how much music means to me, it's like my passion and my purpose." - }, - { - "speaker": "Dave", - "dia_id": "D26:10", - "text": "That's great, Calvin. We both have discovered something that brings us fulfillment. It's a fortunate thing for us." - }, - { - "speaker": "Calvin", - "dia_id": "D26:11", - "text": "Definitely, Dave. It's awesome to find something that makes us happy. It's fulfilling and motivating too. I'm so glad we're on this journey together and curious to see what happens next!" - }, - { - "speaker": "Dave", - "dia_id": "D26:12", - "text": "Me too, Calvin! Let's see where it takes us. We can keep inspiring each other!" - }, - { - "speaker": "Calvin", - "dia_id": "D26:13", - "text": "Yep, let's keep inspiring each other to be our best selves. Keep going for your passion, buddy." - }, - { - "speaker": "Dave", - "dia_id": "D26:14", - "text": "Thanks, Calvin! Same to you! Keep pursuing your passion and keep dreaming. We got this! Got to go see you soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D26:15", - "text": "Thanks, Dave! I appreciate your support. Let's keep pushing for our goals. Catch you later, see ya!" - } - ], - "session_27_date_time": "10:49 am on 29 October, 2023", - "session_27": [ - { - "speaker": "Calvin", - "dia_id": "D27:1", - "text": "Hey Dave! Since we last talked, I went to a networking event to meet more artists. So cool! The people I met will help me build up my fan base. Super excited about what it could lead to. You? Anything new since we last spoke?" - }, - { - "speaker": "Dave", - "dia_id": "D27:2", - "text": "Hey Calvin! That's cool that you've been networking with other artists. Nice! I've been getting into photography recently. I've seen some amazing places and taken some great shots. Would you like to see them?" - }, - { - "speaker": "Calvin", - "dia_id": "D27:3", - "text": "Yeah, show me what you got!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/yhfpmmq2j8f71.jpg" - ], - "blip_caption": "a photo of a city skyline at sunset with a clock tower", - "query": "sunset boston skyline", - "dia_id": "D27:4", - "text": "Look at this magnificent sunset I captured on camera. It's truly breathtaking to witness such beautiful sunsets! The sky looks like it's on fire!" - }, - { - "speaker": "Calvin", - "dia_id": "D27:5", - "text": "Wow, that view looks awesome! What city is it? Have you taken any good pictures lately?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://live.staticflickr.com/28/66275862_c39a8015c2_b.jpg" - ], - "blip_caption": "a photography of a clock tower in a city with buildings", - "query": "waterfall white mountains new hampshire", - "dia_id": "D27:6", - "re-download": true, - "text": "That's Boston, Cal! Check this out, I took this picture last month, and got a great shot - it was stunning!" - }, - { - "speaker": "Calvin", - "dia_id": "D27:7", - "text": "Wow, that pic is amazing! In your last photo, is that the clock tower? I was there a few years back, it's such a beautiful city. You're so talented, Dave!" - }, - { - "speaker": "Dave", - "dia_id": "D27:8", - "text": "Thanks, Calvin! Your kind words mean a lot. Yep, that's the clock tower in the last photo. I snapped it at sunset and the colors were stunning. Photography helps me capture and appreciate the beauty of nature. It's been an awesome creative outlet and I'm loving it." - }, - { - "speaker": "Calvin", - "dia_id": "D27:9", - "text": "Wow, Dave! Sounds like you're having a blast with your photography. Hope it's bringing you lots of joy. By the way, how is your car project going?" - }, - { - "speaker": "Dave", - "dia_id": "D27:10", - "text": "Hey Calvin, photography has been great for me! The car project is doing well - I just finished restoring it and it looks amazing. Wanna come by and check it out? How's everything with the music? Any updates?" - }, - { - "speaker": "Calvin", - "dia_id": "D27:11", - "text": "That's awesome, Dave! Your car project sounds amazing. I've had some great collaborations recently and my album is almost finished. I'll send you some previews soon. Let me know when you're free for a catch-up." - }, - { - "speaker": "Dave", - "dia_id": "D27:12", - "text": "Cool, Calvin! Can't wait to hear it. Let me know when you're free and take it easy!" - }, - { - "speaker": "Calvin", - "dia_id": "D27:13", - "text": "Cheers! I'll let you know when I'm free. Bye!" - } - ], - "session_28_date_time": "5:46 pm on 2 November, 2023", - "session_28": [ - { - "speaker": "Calvin", - "img_url": [ - "https://live.staticflickr.com/3829/11428939743_6e80009962_b.jpg" - ], - "blip_caption": "a photography of a group of people sitting in a room with a projector screen", - "query": "japanese mansion listening party", - "dia_id": "D28:1", - "re-download": true, - "text": "Hey Dave! It's been a while! Crazy stuff has been happening. Last week I threw a small party at my Japanese house for my new album. It was amazing, so much love from my fam and friends! Take a look at the photo of the party in the mansion, it was so energizing!" - }, - { - "speaker": "Dave", - "dia_id": "D28:2", - "text": "Congrats on your album release and the party, Calvin! Must've been a great feeling having your loved ones show their support." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.pexels.com/photos/18047550/pexels-photo-18047550/free-photo-of-people-dancing-on-party.jpeg" - ], - "blip_caption": "a photography of a group of people dancing at a party", - "query": "party friends family dancing", - "dia_id": "D28:3", - "re-download": true, - "text": "Thanks, Dave! It was an awesome feeling seeing everyone come together to celebrate - super rewarding! Look at this photo showcasing how wonderfully we spent our time!" - }, - { - "speaker": "Dave", - "dia_id": "D28:4", - "text": "Wow, great job, Calvin! Congrats! What was it like when everyone was cheering you on?" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a group of people standing on top of a stage", - "dia_id": "D28:5", - "text": "It was incredible, Dave! The room was buzzing with energy and love. It was a powerful reminder of why I'm doing this." - }, - { - "speaker": "Dave", - "dia_id": "D28:6", - "text": "Wow, Calvin! Creating something that brings people together and inspires them - that's really awesome!" - }, - { - "speaker": "Calvin", - "dia_id": "D28:7", - "text": "Thanks, Dave! It's an awesome feeling. Creating something that people connect with and brings joy is what I'm all about. Moments like this really motivate me to keep growing!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images-wixmp-ed30a86b8c4ca887773594c2.wixmp.com/f/c5f5d661-c43a-4159-beb1-41a0ffb503bd/d8azllp-91d696d6-4167-4846-aad2-46467414114e.png/v1/fit/w_375,h_535,q_70,strp/lutz_home_page_mock_up_by_axelnavaja_d8azllp-375w.jpg?token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.2SIHoyGPb9qYBOuyrWc5BUInUOR1q8C1khI1OcW907E" - ], - "blip_caption": "a photography of a car website design", - "query": "blog homepage car modifications successful blog", - "dia_id": "D28:8", - "re-download": true, - "text": "Wow, Calvin, imagining how your music affects others must be incredible! Keep up the great work! By the way, I recently started a blog on car mods. It's my way to share my passion with others. Do you have any tips on blogging for me? Just take a look at this beautiful car!\n\n" - }, - { - "speaker": "Calvin", - "dia_id": "D28:9", - "text": "Cool, Dave! It's really fun to share your passion through blogging. Have you had any success stories yet with inspiring others?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/12590786/pexels-photo-12590786.jpeg" - ], - "blip_caption": "a photography of a blue subarunt parked in a parking lot", - "query": "subaru blue parked parking lot", - "dia_id": "D28:10", - "re-download": true, - "text": "Thanks, Calvin! It's awesome people are checking out my blog and asking me for advice. I recently posted about how I made this car look like a beast, and it was great to hear it inspired others to start their own DIY projects." - }, - { - "speaker": "Calvin", - "dia_id": "D28:11", - "text": "Wow, Dave! Your blog is awesome. Helping others get creative is awesome. Keep up the great work!" - }, - { - "speaker": "Dave", - "dia_id": "D28:12", - "text": "Thanks, Calvin! I appreciate the support. It's fulfilling to share my knowledge and help others unleash their creativity." - }, - { - "speaker": "Calvin", - "dia_id": "D28:13", - "text": "Yeah Dave, keep doing what you do! Your blog and car mods are inspiring and a great way to help people find their creativity." - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/4839256/pexels-photo-4839256.jpeg" - ], - "blip_caption": "a photography of a blue subarunt parked in a parking lot", - "query": "customized car sleek headlights vibrant paint job", - "dia_id": "D28:14", - "re-download": true, - "text": "Thanks, Calvin! It means a lot that you enjoy my blog. This car mod was a lot of work, but I think it was worth it in the end." - }, - { - "speaker": "Calvin", - "dia_id": "D28:15", - "text": "Wow Dave, those headlights look great! What did you do to get them looking so good?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/5158085/pexels-photo-5158085.jpeg" - ], - "blip_caption": "a photography of a blue car parked on a road at night", - "query": "car headlights shining brightly", - "dia_id": "D28:16", - "re-download": true, - "text": "Thanks, Calvin! I spent a lot of time cleaning, polishing, and protecting them - they look great! Just take a look at this photo \u2013 these headlights are enchanting!\n\n" - }, - { - "speaker": "Calvin", - "dia_id": "D28:17", - "text": "Wow, they look great! You really put in a lot of effort. Well done!" - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a red car with a black engine and a red hood", - "dia_id": "D28:18", - "text": "Thanks, Calvin! Really appreciate you noticing the effort I put into this." - }, - { - "speaker": "Calvin", - "dia_id": "D28:19", - "text": "Thanks! Where did you get this car?" - }, - { - "speaker": "Dave", - "dia_id": "D28:20", - "text": "I found it last week, and it was in bad shape, but I saw the potential. I spent ages restoring it." - }, - { - "speaker": "Calvin", - "dia_id": "D28:21", - "text": "Wow, Dave, that is an awesome job on restoring it! You've got some serious skills!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images-wixmp-ed30a86b8c4ca887773594c2.wixmp.com/f/c3d8f9a1-a181-4045-8cfc-fcef59480e5e/d5owgzt-b56f51e8-2840-467b-8d4b-f6732c7b2f0c.png?token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJ1cm46YXBwOjdlMGQxODg5ODIyNjQzNzNhNWYwZDQxNWVhMGQyNmUwIiwiaXNzIjoidXJuOmFwcDo3ZTBkMTg4OTgyMjY0MzczYTVmMGQ0MTVlYTBkMjZlMCIsIm9iaiI6W1t7InBhdGgiOiJcL2ZcL2MzZDhmOWExLWExODEtNDA0NS04Y2ZjLWZjZWY1OTQ4MGU1ZVwvZDVvd2d6dC1iNTZmNTFlOC0yODQwLTQ2N2ItOGQ0Yi1mNjczMmM3YjJmMGMucG5nIn1dXSwiYXVkIjpbInVybjpzZXJ2aWNlOmZpbGUuZG93bmxvYWQiXX0.RPOmhCjxYw8dIFahiFDFbXovl-F_Ld3fKQfFLmSxkNo" - ], - "blip_caption": "a photography of a guitar logo with the company band logo", - "query": "fully restored car gleaming sunlight", - "dia_id": "D28:22", - "re-download": true, - "text": "Thanks Calvin! It took some work, but I'm happy with the result. Take a look at the logo we created for our rock band!" - }, - { - "speaker": "Calvin", - "dia_id": "D28:23", - "text": "Cool logo, Dave! What's the story behind it?" - }, - { - "speaker": "Dave", - "dia_id": "D28:24", - "text": "Cool! It's the logo for my rock band. I've been a fan for ages and have had the opportunity to join them." - }, - { - "speaker": "Calvin", - "dia_id": "D28:25", - "text": "Wow Dave! Music really has a way of touching our souls." - }, - { - "speaker": "Dave", - "dia_id": "D28:26", - "text": "Yeah, Calvin! It's amazing how music can really move us. It's almost like a language for our souls." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://d2wvwvig0d1mx7.cloudfront.net/data/org/18741/media/img/cache/2969x0/1905172_2969x0.jpg" - ], - "blip_caption": "a photography of a recording studio with a monitor, keyboard, and monitor", - "query": "studio setup japanese mansion", - "dia_id": "D28:27", - "re-download": true, - "text": "Yup, it's that connection I'm aiming for with my music. Take a loot at my studio setup, that's look awesome, isn't it?" - }, - { - "speaker": "Dave", - "dia_id": "D28:28", - "text": "Wow, your studio looks stunning! How do you like hanging out here? Do you watch much TV?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.redd.it/84fzhmctceg91.jpg" - ], - "blip_caption": "a photo of a living room with a couch, chair, television and a table", - "query": "living room television couch", - "dia_id": "D28:29", - "text": "I only work in the studio. I have another room for relaxation with a TV, just take a look that room is cozy and relaxing. And yeah, It's a great way to unwind and get inspired." - }, - { - "speaker": "Dave", - "dia_id": "D28:30", - "text": "Wow, nice setup! What do you usually watch on it?" - }, - { - "speaker": "Calvin", - "dia_id": "D28:31", - "text": "Thanks, Dave! I usually watch music videos, concerts, and documentaries about artists and their creative process. It's cool to learn more about the industry and see what others do. Plus, it's a source of inspiration for me." - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/ht8aimbni5gb1.jpg" - ], - "blip_caption": "a photo of a notebook with a pen and a notepad on it", - "query": "notebook song lyrics", - "dia_id": "D28:32", - "text": "Wow, Calvin, that's awesome! Keep up the great work! Take a look at the photo!" - }, - { - "speaker": "Calvin", - "dia_id": "D28:33", - "text": "Thanks, Dave! Appreciate the support! Does this notebook help you stay connected to the creative process?" - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a pink floyd headphone sitting on a shelf", - "dia_id": "D28:34", - "text": "Yes, Calvin, writing lyrics and notes - that's awesome! It will boost my motivation! Writing lyrics boosts my motivation to grow!" - }, - { - "speaker": "Calvin", - "dia_id": "D28:35", - "text": "Cool, Dave! These really help you stay focused when making music." - }, - { - "speaker": "Dave", - "dia_id": "D28:36", - "text": "Cool, Calvin! Music really helps me focus and be productive. When I'm doing my car stuff, I listen to vinyl to relax and stay on track." - }, - { - "speaker": "Calvin", - "dia_id": "D28:37", - "text": "Rockin' it, Dave! Music can definitely affect our mood and help us stay on track. Keep it up!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/210r4phbogya1.jpg" - ], - "blip_caption": "a photo of a record player sitting on the floor next to a couch", - "query": "record player vinyl", - "dia_id": "D28:38", - "text": "Thanks, Calvin! Music really helps with car work. Keeps me focused and makes it feel great. Even though this player is a bit old, he still gets the job done. Check out its photo!\n\n" - }, - { - "speaker": "Calvin", - "dia_id": "D28:39", - "text": "Cool, Dave! What tunes are you listening to these days?" - }, - { - "speaker": "Dave", - "dia_id": "D28:40", - "text": "Lately, I've been getting into classic rock. The music from that era is timeless." - }, - { - "speaker": "Calvin", - "dia_id": "D28:41", - "text": "Cool, Dave! Classic rock has had a huge effect on music. Keep discovering!" - }, - { - "speaker": "Dave", - "dia_id": "D28:42", - "text": "Thanks, Calvin! Classic rock has had a huge impact on music. Always fun to dig in and find new tunes. Gotta go back to work, see you soon! Take care!" - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a record player sitting on the floor next to a couch", - "dia_id": "D28:43", - "text": "Yeah, Dave! Exploring different styles and times can open up new perspectives. Broadening your musical knowledge is awesome. Good luck with work, see ya!\n\n" - } - ], - "session_29_date_time": "9:15 pm on 13 November, 2023", - "session_29": [ - { - "speaker": "Calvin", - "dia_id": "D29:1", - "text": "Hey Dave, I invited my old high school buddy to see me perform in Boston! It was insane. It got me thinking about how far I've come and reminded me how important relationships are in this biz. Felt so good!" - }, - { - "speaker": "Dave", - "dia_id": "D29:2", - "text": "Whoa, that's great Calvin! Seeing your old friend must have brought back so many memories. It's awesome to have someone who's been there since the start to support us." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://images.pexels.com/photos/15916697/pexels-photo-15916697/free-photo-of-men-in-streetwear-clothing-posing-in-front-of-white-car.jpeg" - ], - "blip_caption": "a photography of two men standing on a sidewalk with a car in the background", - "query": "friends freestyling beat-up old car", - "dia_id": "D29:3", - "re-download": true, - "text": "Remembering those days made me so nostalgic! Freestyling and talking about getting famous, it's crazy how life has changed. But being around certain people still sparks those same feelings. Take a look at how we freestyled when we were teenagers!" - }, - { - "speaker": "Dave", - "dia_id": "D29:4", - "text": "Wow, that's a great pic! Did that person make a big difference during your trip?" - }, - { - "speaker": "Calvin", - "dia_id": "D29:5", - "text": "He's been such a great friend to me. Always there to support and encourage me. His positivity has made a big difference in my journey." - }, - { - "speaker": "Dave", - "dia_id": "D29:6", - "text": "It's great how someone's support and encouragement can really shape our lives! They give us the motivation to keep going and trust ourselves. Do you also find having supportive people around you important for your artist development?" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://live.staticflickr.com/7269/6934706388_7ea340725b_b.jpg" - ], - "blip_caption": "a photography of a group of people sitting around a desk", - "query": "group of friends studio session", - "dia_id": "D29:7", - "re-download": true, - "text": "Having supportive people is key for me to grow as an artist. They motivate me to get better and stay true to myself. Having support is vital, especially in this tough music industry. Take a look at this photo!" - }, - { - "speaker": "Dave", - "dia_id": "D29:8", - "text": "Wow, Calvin! Is this a pic of some musicians you're collaborating with?" - }, - { - "speaker": "Calvin", - "dia_id": "D29:9", - "text": "Yeah, I've been supporting some young musicians from a music program. Supporting their passion is amazing and their enthusiasm is inspiring." - }, - { - "speaker": "Dave", - "dia_id": "D29:10", - "text": "Wow, Calvin, that's amazing! You're really making a difference by supporting the next generation of musicians. It's great to nurture their passion and help them thrive." - }, - { - "speaker": "Calvin", - "dia_id": "D29:11", - "text": "Thanks, Dave! It's like a torch being passed to keep music alive! These young musicians are very ambitious, I think I will support them for a long time.\t" - }, - { - "speaker": "Dave", - "dia_id": "D29:12", - "text": "Yeah, Calvin! Your support for them keeps the music going and passes on the legacy. That's so awesome!" - }, - { - "speaker": "Calvin", - "img_url": [ - "https://i.pinimg.com/originals/1d/a3/2a/1da32ab956b36b1a8431e6c2c625abc6.jpg" - ], - "blip_caption": "a photo of a man sitting at a desk in front of a computer", - "query": "music studio", - "dia_id": "D29:13", - "text": "I'm stoked I made a difference. Paying it forward, ya know? Working with new talent brings new ideas to this. Look at this photo, here's how I'm making a beat for a young artist, he has great potential in music! " - }, - { - "speaker": "Dave", - "dia_id": "D29:14", - "text": "Wow, that's cool! Your music studio looks great. Have you tried out any new ideas lately?" - }, - { - "speaker": "Calvin", - "dia_id": "D29:15", - "text": "Thanks! I'm having fun trying out new sounds and pushing the boundaries. It's great to go for new ideas and see where it takes me - always gotta stay ahead in this." - }, - { - "speaker": "Dave", - "dia_id": "D29:16", - "text": "Awesome, Calvin! Experimenting and pushing boundaries is key to making our art grow. Can't wait to see where these new ideas take you!" - }, - { - "speaker": "Calvin", - "dia_id": "D29:17", - "text": "Yeah, it's exciting to see where these new ideas lead. It's all about growing and evolving! Have a good one. I need to go now. Take care, Dave!" - }, - { - "speaker": "Dave", - "dia_id": "D29:18", - "text": "For sure, Calvin! Growing and evolving is key for any artist. Don't stop pushing yourself and keep exploring. Can't wait to see what you come up with next! See ya Cal! Take care!" - } - ], - "session_30_date_time": "10:54 am on 17 November, 2023", - "session_30": [ - { - "speaker": "Dave", - "dia_id": "D30:1", - "text": "Hey Calvin, long time no talk! A lot has happened. I've taken up photography and it's been great - been taking pics of the scenery around here which is really cool." - }, - { - "speaker": "Calvin", - "dia_id": "D30:2", - "img_url": [ - "https://live.staticflickr.com/65535/51102778186_50c6469294_b.jpg" - ], - "re-download": true, - "blip_caption": "a photography of a group of men sitting on a rock next to a river", - "text": "Hey Dave, it's great to hear from you! Can't wait to see your pics. I went to a fancy gala in Boston yesterday and met some interesting people. Check out this pic of me and the crew!" - }, - { - "speaker": "Dave", - "blip_caption": "a photo of a boat is floating in the water at sunset", - "dia_id": "D30:3", - "text": "Calvin, that event looks amazing! You all look awesome. Who did you have the most interesting chat with?" - }, - { - "speaker": "Calvin", - "dia_id": "D30:4", - "img_url": [ - "https://c1.wallpaperflare.com/preview/557/710/506/talk-friend-man-conversation.jpg" - ], - "re-download": true, - "blip_caption": "a photography of two men sitting on a bench in the snow", - "text": "Thanks, Dave! Had an awesome time. I had a really interesting chat with this cool artist and we clicked over music and art. We talked about our favorite artists, art, and how the power of music connects us all. It was such an inspiring conversation - I feel like I'm on a creative high. We have a photo together, take a look!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://i.redd.it/qf9hodnb7i5b1.jpg" - ], - "blip_caption": "a photo of a camera sitting on a table next to a plant", - "query": "vintage camera", - "dia_id": "D30:5", - "text": "That's amazing, Calvin! Music really does bring people together and foster creativity. Glad to hear you had such an inspiring conversation! Take a look at my new vintage camera that I bought this month, which takes awesome photos!" - }, - { - "speaker": "Calvin", - "dia_id": "D30:6", - "text": "Hey Dave, music really brings people together, huh? Do you use this camera for photos? They always turn out so good!" - }, - { - "speaker": "Dave", - "dia_id": "D30:7", - "text": "Yes, Calvin, this camera is very good, it helps me capture those special moments really clearly." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a sunset with a wave crashing on rocks", - "dia_id": "D30:8", - "text": "Having a good camera is key for capturing those special moments. What do you like to take photos of?" - }, - { - "speaker": "Dave", - "img_url": [ - "https://images.pexels.com/photos/4816878/pexels-photo-4816878.jpeg?cs\\u003dsrgb\\u0026dl\\u003dpexels-katy-ramm-4816878.jpg" - ], - "blip_caption": "a photo of a sunset with a wave crashing on rocks", - "query": "sunset wave crashing rocks", - "dia_id": "D30:9", - "text": "Yeah, being able to take good pics is key. I love capturing the beauty of nature - sunsets, beaches, waves. Just got this one recently, check this out!" - }, - { - "speaker": "Calvin", - "dia_id": "D30:10", - "text": "Nice job, Dave! That shot looks great! Nature's so amazing!" - }, - { - "speaker": "Dave", - "dia_id": "D30:11", - "text": "Thanks, Calvin! It's incredible how much emotion and beauty nature can convey through a photo." - }, - { - "speaker": "Calvin", - "blip_caption": "a photo of a pond with rocks and a waterfall in the middle", - "dia_id": "D30:12", - "text": "Yeah, nature really does the trick. Its beauty helps us appreciate life when it's tough. Like a breath of fresh air!" - }, - { - "speaker": "Dave", - "img_url": [ - "https://magicgardenslandscaping.com/wp-content/uploads/2018/08/Leucadendron_WF5.jpg" - ], - "blip_caption": "a photo of a waterfall flowing over rocks and boulders", - "query": "peaceful pond rocks waterfall", - "dia_id": "D30:13", - "text": "I totally agree, nature really can boost our spirits in tough times. Also, here's a picture I snapped last week! It's a peaceful scene with rocks and a waterfall. Pretty cool, huh?" - }, - { - "speaker": "Calvin", - "dia_id": "D30:14", - "text": "Wow Dave, that picture is stunning! Where was that taken? It looks so serene!" - }, - { - "speaker": "Dave", - "dia_id": "D30:15", - "text": "Thanks, Calvin! I found this serene spot in a nearby park and took this pic." - }, - { - "speaker": "Calvin", - "img_url": [ - "https://www.palmtreesandpellegrino.com/wp-content/uploads/2022/03/IMG_6524.jpg" - ], - "blip_caption": "a photo of a bench under a tree with pink flowers", - "query": "japanese garden cherry blossoms", - "dia_id": "D30:16", - "text": "Wow, that sounds like such a peaceful and serene spot. Can't wait to check it out myself sometime. Check out this beautiful picture that I shot in a Japanese garden, that's wild!" - }, - { - "speaker": "Dave", - "dia_id": "D30:17", - "text": "Cool, Calvin! Found an even better spot, with a bench under a tree with pink flowers - so peaceful. A perfect spot to relax and take in the beauty." - }, - { - "speaker": "Calvin", - "dia_id": "D30:18", - "text": "That sounds great, Dave! Can't wait to see it." - }, - { - "speaker": "Dave", - "dia_id": "D30:19", - "text": "Check it out, Calvin. It's really calming, I think you'll like it. We will definitely go there! Is there anything else you'd like to share?" - }, - { - "speaker": "Calvin", - "dia_id": "D30:20", - "text": "Thank you for asking, Dave! Yes, I have a few more great news! I've accepted an invitation to perform at an upcoming show in Boston! It's going to be an unforgettable musical experience. Can't wait to fill you in on all the details. Catch up with you soon!" - }, - { - "speaker": "Dave", - "dia_id": "D30:21", - "text": "Wow, Calvin! That's amazing news! Congratulations on both the gala attendance and the upcoming performance. I can't wait to hear all about it and maybe even catch one of your shows in Boston. Let me know when you're free to catch up. Cheers to your musical journey!" - }, - { - "speaker": "Calvin", - "dia_id": "D30:22", - "text": "Thanks, Dave! I'll catch you when I'm in Boston. Cheers!" - }, - { - "speaker": "Dave", - "dia_id": "D30:23", - "text": "Looking forward to seeing you. Stay safe, talk to you soon!" - }, - { - "speaker": "Calvin", - "dia_id": "D30:24", - "text": "Thanks! You too. Talk to you later!" - } - ] - }, - "event_summary": { - "events_session_1": { - "Calvin": [ - "Calvin purchases a mansion in Japan and plans to stay there for a few months to collaborate with musicians." - ], - "Dave": [ - "Dave attends an event on classic cars and chats with some owners about their stories." - ], - "date": "23 March, 2023" - }, - "events_session_2": { - "Calvin": [ - "Calvin buys a Ferrari 488 GTB for himself.", - "Calvin attends a few studio sessions to collaborate on music and writes some new tunes." - ], - "Dave": [ - "Dave attends a music festival in Boston where he sees Aerosmith perform live." - ], - "date": "26 March, 2023" - }, - "events_session_3": { - "Calvin": [ - "Calvin attends a music festival in Tokyo which is attended by professionals from the music industry.", - "Calvin receives advice from music professionals to stay true to his unique sound." - ], - "Dave": [ - "Calvin attends a car show." - ], - "date": "20 April, 2023" - }, - "events_session_4": { - "Calvin": [ - "Calvin receives a gold chain with diamond pendant as gift from another artist." - ], - "Dave": [ - "Dave sets up a car maintenance shop to work towards his dream of restoring classic cars." - ], - "date": "1 May, 2023" - }, - "events_session_5": { - "Calvin": [ - "Calvin enjoys getting to know about Japanese culture." - ], - "Dave": [ - "Dave begins collaborating with a local mechanic shop." - ], - "date": "3 May, 2023" - }, - "events_session_6": { - "Calvin": [ - "Calvin's place in Tokyo gets flooded but he manages to save his music gear.", - "Calvin prepares for his performance in Tokyo at the Frank Ocean tour in last week of May 2023." - ], - "Dave": [ - "Dave invites his friends to his newly set up car maintenance shop." - ], - "date": "16 May, 2023" - }, - "events_session_7": { - "Calvin": [ - "Calvin has an exhilarating performance onstage in the Frank Ocean Tour at Tokyo." - ], - "Dave": [ - "Dave fixes his neighbors' cars." - ], - "date": "31 May, 2023" - }, - "events_session_8": { - "Calvin": [ - "Calvin meets with the creative team for his new album." - ], - "Dave": [ - "Dave visits the local parks n Boston regularly on weekends.", - "Dave books a trip in July 2023 to a mountainous region." - ], - "date": "9 June, 2023" - }, - "events_session_9": { - "Calvin": [ - "Calvin has a car accident where no one is hurt but the car needs repairs." - ], - "Dave": [ - "Dave joins a rock band and practices playing his guitar." - ], - "date": "21 June, 2023" - }, - "events_session_10": { - "Calvin": [ - "Calvin's car is fixed up and ready to be driven again." - ], - "Dave": [ - "Dave calls up his friends for regular trips to the local parks in Boston." - ], - "date": "7 July, 2023" - }, - "events_session_11": { - "Calvin": [ - "Calvin works to convert his Japanese mansion into a recording studio where he can collaborate with other artists." - ], - "Dave": [ - "Dave goes on a countryside road trip with some friends." - ], - "date": "21 July, 2023" - }, - "events_session_12": { - "Calvin": [ - "Calvin goes to the Ferrari dealership to have his car serviced." - ], - "Dave": [], - "date": "3 August, 2023" - }, - "events_session_13": { - "Calvin": [], - "Dave": [ - "Dave is selected for a professional car modification workshop located in San Francisco.", - "Dave works on restoring a classic muscle car and giving it a modern look." - ], - "date": "11 August, 2023" - }, - "events_session_14": { - "Calvin": [], - "Dave": [ - "Dave travels to San Francisco to attend the car modification workshop.", - "Dave starts works on restoring a beat-up old Ford Mustang." - ], - "date": "14 August, 2023" - }, - "events_session_15": { - "Calvin": [ - "Calvin secures a deal for continuation of collaboration with Frank Ocean.", - "Calvin records a podcast with some of his friends from the rap industry." - ], - "Dave": [ - "Dave hosts a card-playing night with his friends." - ], - "date": "22 August, 2023" - }, - "events_session_16": { - "Calvin": [ - "Calvin begins shooting a video for his new album on the Miami beach." - ], - "Dave": [], - "date": "31 August, 2023" - }, - "events_session_17": { - "Calvin": [ - "Calvin books a round trip flight to Boston scheduled for October 2023." - ], - "Dave": [ - "Dave returns from the car modification workshop in San Francsico with valuable insights and knowledge on car modifications." - ], - "date": "2 September, 2023" - }, - "events_session_18": { - "Calvin": [ - "Calvin launches his new album on September 11, 2023 which receives great response from the audience.", - "Calvin prepares for his next tour." - ], - "Dave": [], - "date": "13 September, 2023" - }, - "events_session_19": { - "Calvin": [], - "Dave": [ - "Dave spends some quality time with his rock band, practising jam sessions.", - "Dave attends a rock concert in Boston." - ], - "date": "15 September, 2023" - }, - "events_session_20": { - "Calvin": [], - "Dave": [ - "Dave's work on the Ford Mustang faces a holdup due to engine issues." - ], - "date": "22 September, 2023" - }, - "events_session_21": { - "Calvin": [ - "Calvin meets with some local artists to discuss collaborations in Boston." - ], - "Dave": [ - "Dave continues working on restoring the Ford Mustang he found in a junkyard." - ], - "date": "4 October, 2023" - }, - "events_session_22": { - "Calvin": [], - "Dave": [ - "Dave goes to a local car show and experiments with car modifications.", - "Dave finishes restoring a car with a custom exhaust and performance upgrades." - ], - "date": "8 October, 2023" - }, - "events_session_23": { - "Calvin": [ - "Calvin buys a new Ferrari." - ], - "Dave": [ - "Dave's car maintenance shop loses a deal to a competing local shop.", - "Dave attends a music festival headlines by The Fireworks and enjoys the show." - ], - "date": "15 October, 2023" - }, - "events_session_24": { - "Calvin": [ - "Calvin resumes his tour with Frank Ocean that ends in November.", - "Calvin plans on returning to Tokyo in November after the Frank Ocean tour ends and exploring spots like Shibuya crossing and Shinjuku." - ], - "Dave": [], - "date": "19 October, 2023" - }, - "events_session_25": { - "Calvin": [], - "Dave": [ - "Dave discusses current events and politics with his neighbors." - ], - "date": "23 October, 2023" - }, - "events_session_26": { - "Calvin": [ - "Calvin visits some of the sights in Boston with a former high school friend." - ], - "Dave": [ - "Dave attends a conference in Detroit." - ], - "date": "25 October, 2023" - }, - "events_session_27": { - "Calvin": [ - "Calvin attends a networking event to meet artists further spread his fanbase." - ], - "Dave": [ - "Dave learns how to take amazing photographs and captures a series of his travels." - ], - "date": "29 October, 2023" - }, - "events_session_28": { - "Calvin": [ - "Calvin hosts a listening party for family and friends in his Japanese mansion to celebrate his new album." - ], - "Dave": [ - "Dave starts a blog about car modifications to share his passion and knowledge.", - "Dave finishes restoring the headlights of a car he found last week and posts about it in his blog." - ], - "date": "2 November, 2023" - }, - "events_session_29": { - "Calvin": [ - "Calvin invites his high school friend to a performance at a venue in Boston.", - "Calvin supports the growth and development of some young musicians from a music program." - ], - "Dave": [], - "date": "13 November, 2023" - }, - "events_session_30": { - "Calvin": [ - "Calvin attends a high-end gala in Boston and meets interesting people.", - "Calvin accepts the invitation to perform at a show in Boston." - ], - "Dave": [ - "Dave buys a vintage camera for his photography hobby and takes pictures of nature." - ], - "date": "17 November, 2023" - } - }, - "observation": { - "session_1_observation": { - "Calvin": [ - [ - "Calvin recently had a big life change and now has a new mansion.", - "D1:3" - ], - [ - "Calvin is excited to learn about Japanese culture and is heading to Japan next month.", - "D1:5" - ], - [ - "Calvin has never been to Japan before but is fascinated by the traditions and culture.", - "D1:7" - ], - [ - "Calvin's agent found him an awesome place to stay in Japan.", - "D1:11" - ], - [ - "Calvin plans to explore the city, try different local cuisines, and collaborate with musicians during his trip to Japan.", - "D1:13" - ], - [ - "Calvin will be in Japan for a few months before heading to Boston.", - "D1:15" - ] - ], - "Dave": [ - [ - "Dave attended an awesome car event recently where he saw many classic cars and spoke with the owners.", - "D1:2" - ], - [ - "Dave spends lots of time at a beautiful calming park.", - "D1:16" - ] - ] - }, - "session_2_observation": { - "Calvin": [ - [ - "Calvin recently got a new car which is a luxury car and a dream come true for him.", - "D2:1" - ], - [ - "Calvin's new car gives him an adrenaline rush every time he steps into it.", - "D2:5" - ], - [ - "Calvin wrote some new tunes and had studio sessions last week, excited to collaborate and share the music.", - "D2:17" - ] - ], - "Dave": [ - [ - "Dave attended a music festival in Boston last weekend and enjoyed listening to many cool bands.", - "D2:8" - ], - [ - "Dave's favorite band from the music festival was Aerosmith, and he found their performance incredible.", - "D2:10" - ], - [ - "Dave didn't get a chance to hang out with Aerosmith after the show but was content with seeing them live.", - "D2:14" - ] - ] - }, - "session_3_observation": { - "Calvin": [ - [ - "Calvin recently attended a music festival in Tokyo where he met talented artists and industry professionals.", - "D3:3" - ], - [ - "Calvin received advice from a music producer to stay true to himself and sound unique, which he found motivating.", - "D3:7" - ], - [ - "Calvin dreams of touring the world, connecting with different people through music, reaching a global audience, and making an impact.", - "D3:9" - ], - [ - "Calvin is planning an upcoming trip to Boston after finishing the Frank Ocean tour to explore the music scene there.", - "D3:9" - ], - [ - "Calvin has a car that he has put a lot of work into and is looking forward to showing it to Dave when he visits Boston.", - "D3:13" - ], - [ - "Calvin is busy with rehearsals and traveling but is excited to explore Boston, try delicious food, and visit popular attractions with Dave.", - "D3:15" - ] - ], - "Dave": [ - [ - "Dave is interested in classic cars and auto engineering, as he went to a car show last weekend and finds the restoration process amazing.", - "D3:12" - ], - [ - "Dave plans to show Calvin around Boston, including places like Paradise Rock, House of Blues, and Fenway Park, known for their great music scene.", - "D3:10" - ], - [ - "Dave is looking forward to showing Calvin his favorite spots in Boston, especially in terms of food and music.", - "D3:16" - ] - ] - }, - "session_4_observation": { - "Dave": [ - [ - "Dave opened his own car maintenance shop, fulfilling his dream.", - "D4:1" - ], - [ - "Dave's dream is to work on classic cars due to his love for their design and engineering.", - "D4:5" - ], - [ - "Dave restored a classic car last year and was thrilled by the experience.", - "D4:7" - ], - [ - "Dave's shop works on all kinds of cars, including full restorations of classic cars.", - "D4:19" - ], - [ - "Dave is passionate about working on cars every day and finds it rewarding.", - "D4:21" - ], - [ - "Dave values that his work is appreciated and brings joy to others.", - "D4:23" - ] - ], - "Calvin": [ - [ - "Calvin appreciates Dave's hard work and dedication in achieving his dream of opening a car maintenance shop.", - "D4:2" - ], - [ - "Calvin acknowledges Dave's guts and ambition and supports him in pursuing his dreams.", - "D4:4" - ], - [ - "Calvin gifted a necklace with a diamond pendant as a reminder of his passion for music.", - "D4:24" - ], - [ - "Calvin is hustling as a musician and finds the necklace a reminder of why he keeps pushing forward.", - "D4:26" - ] - ] - }, - "session_5_observation": { - "Dave": [ - [ - "Dave is teaming up with a local garage and is working on a challenging project involving cars.", - "D5:1" - ], - [ - "Dave loves working with mechanics and sharing his knowledge about cars.", - "D5:3" - ], - [ - "Dave aspires to learn more about auto engineering and dreams of building a custom car from scratch.", - "D5:5" - ], - [ - "Dave finds driving with the wind in his hair calming and enjoys taking walks to destress.", - "D5:9" - ], - [ - "Dave suggests immersing oneself in things one loves, like concerts or favorite albums when facing a creativity block.", - "D5:11" - ] - ], - "Calvin": [ - [ - "Calvin enjoys long drives in a car to relax and clear his head.", - "D5:8" - ], - [ - "Calvin finds that embracing nature is calming and enjoys exploring Japanese culture.", - "D5:10" - ], - [ - "Calvin is experiencing a creative block with his music and seeks inspiration.", - "D5:10" - ] - ] - }, - "session_6_observation": { - "Calvin": [ - [ - "Calvin had an incident last week where his place got flooded, but he managed to save his music gear and favorite microphone.", - "D6:3" - ], - [ - "Calvin has a creative haven/studio where he pours his heart into making music.", - "D6:5" - ], - [ - "Calvin is excited for an upcoming performance in Tokyo this month to showcase his music to a new crowd and expand his following.", - "D6:11" - ], - [ - "Calvin took a stunning picture of the Tokyo skyline last night.", - "D6:15" - ] - ], - "Dave": [ - [ - "Dave opened his own car shop last week and celebrated with friends.", - "D6:8" - ], - [ - "Dave is excited about the journey of running his car shop and is looking forward to what the future holds.", - "D6:10" - ], - [ - "Dave expressed interest in taking a trip to Tokyo after seeing Calvin's picture of the night skyline.", - "D6:16" - ] - ] - }, - "session_7_observation": { - "Calvin": [ - [ - "Calvin recently toured with Frank Ocean and had an amazing experience performing live in Tokyo.", - "D7:1" - ], - [ - "Calvin finds working on cars to be a calming and head-clearing activity.", - "D7:3" - ], - [ - "Calvin feels a sense of achievement and relaxation similar to meditating when fixing cars.", - "D7:5" - ], - [ - "Performing live always fuels Calvin's soul and gives him a rush and a powerful connection with the crowd.", - "D7:11" - ] - ], - "Dave": [ - [ - "Dave helped his neighbor fix a car engine which he found a therapeutic and relaxing experience.", - "D7:2" - ], - [ - "Dave is passionate about fixing up things and finds it a source of achievement and purpose.", - "D7:6" - ], - [ - "Transforming things and collaborating with others give Dave a sense of power and happiness.", - "D7:7" - ], - [ - "Dave enjoys the music scene in Boston and is looking forward to Calvin's tour and performance in the city.", - "D7:16" - ] - ] - }, - "session_8_observation": { - "Calvin": [ - [ - "Calvin met with the creative team for his album in a long but awesome session.", - "D8:1" - ], - [ - "Calvin is feeling stoked about his album and the progress made with the team in the studio.", - "D8:3" - ], - [ - "Calvin has never been to Boston but is excited to visit the amazing parks next month.", - "D8:5" - ], - [ - "Calvin is keen to go on a hike to escape and de-stress.", - "D8:9" - ] - ], - "Dave": [ - [ - "Dave has been exploring parks on the weekends to relax and find peace in nature.", - "D8:4" - ], - [ - "Dave loves taking walks on the weekends to recharge for the upcoming week.", - "D8:6" - ], - [ - "Dave booked a trip to a mountainous region for next month to see majestic peaks.", - "D8:10" - ] - ] - }, - "session_9_observation": { - "Dave": [ - [ - "Dave recently joined a rock band and has been practicing the guitar.", - "D9:18" - ] - ], - "Calvin": [ - [ - "Calvin had a car accident last Friday, but no one was hurt.", - "D9:1" - ], - [ - "Calvin had to deal with insurance and repairs after the car accident.", - "D9:1" - ], - [ - "Calvin got everything related to the car accident sorted out within a week.", - "D9:5" - ], - [ - "The mechanic at the auto repair shop is working on Calvin's car.", - "D9:7" - ], - [ - "Calvin feels more confident and excited about showing off his car after the repairs.", - "D9:9" - ], - [ - "Calvin shared a photo of a beautiful view from a small town in Japan.", - "D9:13" - ], - [ - "Calvin plans to visit the snowy peaks in Japan after his tour with Frank Ocean ends.", - "D9:15" - ], - [ - "Calvin hasn't tried skiing before but is interested in giving it a try.", - "D9:17" - ] - ] - }, - "session_10_observation": { - "Dave": [ - [ - "Dave asked Calvin how his car was after the crash and if he was excited to get back on the road.", - "D10:1" - ], - [ - "Dave mentioned hanging out with friends at parks and arranging regular walks together.", - "D10:3" - ], - [ - "Dave expressed interest in visiting Japan one day due to its vibes, food, and culture.", - "D10:11" - ] - ], - "Calvin": [ - [ - "Calvin's car was in a crash but is now fixed and going strong.", - "D10:2" - ], - [ - "Calvin enjoys cruising around in his car and appreciates it.", - "D10:2" - ], - [ - "Calvin mentioned living in a Japanese mansion with an epic cityscape view.", - "D10:6" - ], - [ - "Calvin expressed excitement about trying the food and checking out the culture in Japan.", - "D10:10" - ], - [ - "Calvin is working on music collaborations with Japanese artists and is excited about it.", - "D10:12" - ] - ] - }, - "session_11_observation": { - "Dave": [ - [ - "Dave went on a road trip with his friends recently to see stunning countryside and recharge from corporate work. He loves cars.", - "D11:1" - ] - ], - "Calvin": [ - [ - "Calvin is working on transforming a Japanese mansion into a recording studio, which is his dream for creating music with other artists.", - "D11:4" - ], - [ - "Calvin has been experimenting with different genres in his music, adding electronic elements for a fresh vibe.", - "D11:6" - ], - [ - "Calvin finds experimenting with different music genres an exciting process of self-discovery and growth.", - "D11:6" - ], - [ - "Calvin collaborates with others and surrounds himself with positive energy and passion to stay motivated in his music journey.", - "D11:10" - ] - ] - }, - "session_12_observation": { - "Calvin": [ - [ - "Calvin owns a Ferrari and is attached to it, finding servicing it stressful but rewarding.", - "D12:1" - ], - [ - "Calvin feels proud of his car and sees it as a symbol of his hard work and dedication.", - "D12:7" - ], - [ - "Calvin's current goal is to expand his brand worldwide, grow his fanbase, work with artists globally, and create special music.", - "D12:11" - ], - [ - "Calvin performed with the boys last night and appreciates their musical talent.", - "D12:11" - ], - [ - "Calvin is determined to make his dreams come true and appreciates Dave's support and encouragement.", - "D12:13" - ], - [ - "Calvin expresses gratitude for Dave's continued support and encouragement in pursuing his dreams.", - "D12:15" - ] - ], - "Dave": [ - [ - "Dave finds fixing cars like therapy and feels fulfilled by refurbishing them, stemming from working on cars with his dad.", - "D12:2" - ], - [ - "Dave has fond memories of restoring an old car with his dad during one summer, finding it hard work but satisfying.", - "D12:4" - ], - [ - "Dave believes that working on projects together brings people closer.", - "D12:5" - ], - [ - "Dave encourages Calvin in his music goals, supports his dreams, and provides words of motivation and belief.", - "D12:12, D12:14, D12:16" - ] - ] - }, - "session_13_observation": { - "Dave": [ - [ - "Dave got picked for a car mod workshop and is excited about learning auto engineering and building a custom car.", - "D13:1" - ], - [ - "Dave has been doing engine swaps, suspension modifications, and is now learning about body modifications on a classic muscle car.", - "D13:7" - ], - [ - "Dave aims to give the classic muscle car a modern twist while maintaining a classic vibe.", - "D13:9" - ], - [ - "Dave values attention to detail in his car projects.", - "D13:10" - ], - [ - "Dave sees customizing cars as a way to show his style and compares it to customizing a work of art on wheels.", - "D13:13" - ], - [ - "Dave offered help to Calvin with his music stuff and has an awesome music studio setup with a high-quality sound system.", - "D13:15" - ] - ], - "Calvin": [ - [ - "Calvin encourages Dave in his car mod workshop endeavors and appreciates his enthusiasm and hard work.", - "D13:14" - ], - [ - "Calvin is busy with his music stuff and mentions having a music studio setup.", - "D13:16" - ], - [ - "Calvin appreciates Dave's encouragement and plans to keep working hard on his music.", - "D13:18" - ] - ] - }, - "session_14_observation": { - "Dave": [ - [ - "Dave recently attended a car restoration workshop in San Francisco and found it truly inspiring.", - "D14:1" - ], - [ - "Dave is currently working on restoring a car.", - "D14:11" - ], - [ - "Dave aims to have the car fully restored by the end of next month.", - "D14:13" - ] - ], - "Calvin": [ - [ - "Calvin recently toured with a well-known artist and had a show in Japan.", - "D14:4" - ], - [ - "Calvin had an amazing experience performing for an eager audience in Tokyo and felt it was a magical moment.", - "D14:6" - ], - [ - "Calvin is all about spreading joy with his art and feels proud and motivated when he can do so.", - "D14:10" - ] - ] - }, - "session_15_observation": { - "Dave": [ - [ - "Dave had a card night with friends last Friday and enjoyed it a lot.", - "D15:1" - ], - [ - "Dave expressed excitement about Calvin continuing collaboration with Frank Ocean.", - "D15:2" - ], - [ - "Dave commented on the opportunity Calvin had to meet Frank Ocean at a music festival in Tokyo.", - "D15:4" - ], - [ - "Dave found Tokyo to be an incredible experience based on Calvin's description.", - "D15:5" - ], - [ - "Dave was interested in hearing more about Calvin's collaborations and upcoming concerts.", - "D15:9" - ], - [ - "Dave expressed anticipation for listening to Calvin's podcast about the rap industry.", - "D15:13" - ] - ], - "Calvin": [ - [ - "Calvin scored a deal to continue collaboration with Frank Ocean.", - "D15:2" - ], - [ - "Calvin met Frank Ocean at a music festival in Tokyo where they clicked and recorded a song together.", - "D15:4" - ], - [ - "Calvin described Tokyo as buzzing with energy and the festival crowd as lively.", - "D15:6" - ], - [ - "Calvin's tour is ending soon and he is heading to Boston.", - "D15:10" - ], - [ - "Calvin and friends recorded a podcast discussing the rapidly evolving rap industry.", - "D15:12" - ] - ] - }, - "session_16_observation": { - "Dave": [ - [ - "Dave expresses support for Calvin's music and creative endeavors.", - "D16:11" - ], - [ - "Dave mentions a guitar that shows their different artistic styles, which he appreciates.", - "D16:13" - ] - ], - "Calvin": [ - [ - "Calvin completed a tour where he performed on a big stage and felt on top of the world.", - "D16:4" - ], - [ - "Calvin started shooting a video for his new album in Miami at an awesome beach for epic visuals.", - "D16:6" - ], - [ - "Calvin had a guitar custom made with an octopus design by his Japanese artist friend, representing his love for art and the sea.", - "D16:14" - ], - [ - "Calvin values being true to himself and his unique style in his music.", - "D16:24" - ] - ] - }, - "session_17_observation": { - "Dave": [ - [ - "Dave recently came back from San Francisco with insights and knowledge on car modification.", - "D17:1" - ], - [ - "Dave finds fixing things up and seeing them turn out better really rewarding, giving him a sense of purpose.", - "D17:3" - ], - [ - "Dave feels like he's making a difference when he fixes someone's car.", - "D17:3" - ], - [ - "Dave enjoys the feeling of making a difference and seeing the relief when the car is fixed.", - "D17:5" - ] - ], - "Calvin": [ - [ - "Calvin booked a flight ticket to Boston last week and is excited about the upcoming trip.", - "D17:6" - ], - [ - "Calvin plans to let Dave know when he is in Boston so they can catch up.", - "D17:7" - ] - ] - }, - "session_18_observation": { - "Calvin": [ - [ - "Calvin released his album on the 11th of September, which received positive feedback and motivated him to make even better music.", - "D18:1" - ], - [ - "Calvin is excited about going on a tour and growing his brand.", - "D18:7" - ], - [ - "Calvin appreciates positive feedback and views it as a motivation to reach more people with his music.", - "D18:5" - ], - [ - "Calvin values making a difference and sharing his own story through music.", - "D18:5" - ] - ], - "Dave": [ - [ - "Dave offered Calvin to check out his garage and see some cool cars when Calvin is in Boston.", - "D18:10" - ], - [ - "Dave appreciates Calvin's music and is excited for his tour.", - "D18:6" - ], - [ - "Dave is supportive and encouraging of Calvin's music career.", - "D18:4" - ] - ] - }, - "session_19_observation": { - "Dave": [ - [ - "Dave is in a band and had a fun jamming session with them.", - "D19:1" - ], - [ - "Dave attended a rock concert in Boston recently and enjoyed the atmosphere.", - "D19:1" - ], - [ - "Dave is restoring a vintage car as a hobby.", - "D19:7" - ], - [ - "Dave finds it satisfying to see his hard work pay off in restoring the car.", - "D19:9" - ] - ], - "Calvin": [ - [ - "Calvin is a fan of Ratatouille, a Disney movie that inspires following one's dreams.", - "D19:6" - ], - [ - "Calvin appreciates Dave's talent in fixing cars and finds it inspiring.", - "D19:10" - ] - ] - }, - "session_20_observation": { - "Dave": [ - [ - "Dave has a car project involving a vintage Mustang and faced engine troubles despite putting in a lot of work.", - "D20:1" - ], - [ - "Dave appreciates the hard work and dedication symbolized in a picture shared by Calvin.", - "D20:3" - ], - [ - "Dave enjoys moments of reflection and finds them not only interesting but also productive.", - "D20:5" - ], - [ - "Dave is interested in childhood songs that bring back memories.", - "D20:5" - ], - [ - "Dave suggested jamming music together with Calvin.", - "D20:9" - ], - [ - "Dave is enthusiastic about the idea of having an awesome jam session with Calvin.", - "D20:11" - ] - ], - "Calvin": [ - [ - "Calvin shared a picture that symbolizes his hard work and dedication, reminding him of his progress.", - "D20:2" - ], - [ - "Calvin has fond memories of a summer drive that made him reflect on his life's choices.", - "D20:4" - ], - [ - "Calvin had an incredible trip to Japan that he is longing to experience again.", - "D20:4" - ], - [ - "Calvin has a childhood memory associated with a song called \"California Love\" by Tupac and Dr. Dre.", - "D20:6" - ], - [ - "Calvin is excited about jamming music together with Dave.", - "D20:10" - ], - [ - "Calvin is looking forward to creating something special during the music jam session with Dave.", - "D20:16" - ] - ] - }, - "session_21_observation": { - "Calvin": [ - [ - "Calvin met with incredible artists in Boston and is excited to collaborate with them on new music.", - "D21:1" - ], - [ - "Calvin has a project that he loves working on to chill out.", - "D21:3" - ], - [ - "Calvin expressed satisfaction in bringing something back to life, referring to an opportunity.", - "D21:7" - ], - [ - "Calvin values hard work, dedication, growth, progress, and determination in achieving goals.", - "D21:13" - ] - ], - "Dave": [ - [ - "Dave finds working on cars therapeutic and relaxing, particularly restoring an old Ford Mustang found in a junkyard.", - "D21:4" - ], - [ - "Dave expressed satisfaction in bringing an old car back to life and transforming it into something beautiful.", - "D21:6" - ], - [ - "Dave's hands are permanently stained with grease from working on cars, showing his dedication and hard work.", - "D21:8" - ], - [ - "Dave values hard work, small successes, progress, determination, and growth in projects and personal development.", - "D21:12" - ] - ] - }, - "session_22_observation": { - "Dave": [ - [ - "Dave went to a car show last Friday and enjoys checking out awesome cars and car mods.", - "D22:1" - ], - [ - "Dave restored and modified a car himself, adding a custom exhaust and performance upgrades.", - "D22:3" - ], - [ - "Working on cars is like therapy for Dave and a way to get away from everyday stress. He sees it as more than a hobby, but a passion.", - "D22:5" - ], - [ - "Dave spent hours as a child tinkering with engines in his dad's garage, finding it to be like his sanctuary.", - "D22:5" - ], - [ - "Dave's job involves taking something broken and making it into something awesome.", - "D22:5" - ], - [ - "Restoring things can be tough for Dave, but the feeling of accomplishment it gives him is great.", - "D22:7" - ], - [ - "Dave's ultimate goal has been doing what he loves for a living.", - "D22:6" - ], - [ - "Dave finds conversations like the one with Calvin to be a reminder of why he loves what he does.", - "D22:9" - ] - ], - "Calvin": [ - [ - "Calvin is interested in checking out Dave's garage and the awesome cars he has worked on.", - "D22:2" - ], - [ - "Calvin admires Dave for his work on restoring and modifying the car, calling it a masterpiece.", - "D22:4" - ], - [ - "Calvin recognizes that working on cars can be a real escape from reality and understands the feeling of doing what you love for a living.", - "D22:6" - ], - [ - "Calvin encourages Dave to keep working hard and living his best life, appreciating his progress.", - "D22:8" - ], - [ - "Calvin supports and encourages Dave, reminding him to stay focused and keep going for his dreams.", - "D22:12" - ] - ] - }, - "session_23_observation": { - "Dave": [ - [ - "Dave works at a car maintenance shop.", - "D23:1" - ], - [ - "Dave recently attended a music festival and enjoyed the energy, music, and crowd.", - "D23:5" - ], - [ - "Dave enjoys music festivals and feels alive at such events.", - "D23:5" - ], - [ - "Dave finds fulfillment in fixing things and enjoys the feeling of making something whole again.", - "D23:11" - ] - ], - "Calvin": [ - [ - "Calvin values the importance of believing in oneself and not giving up.", - "D23:2" - ], - [ - "Calvin finds motivation in reminding himself of his passion for goals and taking breaks for favorite activities.", - "D23:4" - ], - [ - "Calvin loves his job and enjoys connecting with the crowd, particularly through music.", - "D23:10" - ], - [ - "Calvin recently acquired a new Ferrari and is looking forward to thrilling rides and journeys.", - "D23:16" - ] - ] - }, - "session_24_observation": { - "Calvin": [ - [ - "Calvin started touring with Frank Ocean, which started in August last year after they met at a festival.", - "D24:1" - ], - [ - "Calvin is excited about the energy from the crowd during the tour and feels lucky to collaborate and perform with Frank Ocean.", - "D24:3" - ], - [ - "Calvin and Frank Ocean have a great chemistry on stage, and Calvin feels fortunate about it.", - "D24:5" - ], - [ - "Calvin and Frank Ocean are in tune and rock on stage together.", - "D24:6" - ], - [ - "Calvin performed in Tokyo during a concert and felt the energy of the crowd was nuts.", - "D24:11" - ], - [ - "Calvin is planning to visit Tokyo next month after the tour ends and wishes sometimes to go back to places similar to those in the shared photos.", - "D24:17" - ], - [ - "Calvin is excited to explore Shibuya Crossing and Shinjuku in Tokyo and is looking forward to trying the amazing food there.", - "D24:19" - ], - [ - "Calvin has never tried ramen but is planning to try it while in Tokyo.", - "D24:21" - ] - ], - "Dave": [ - [ - "Dave is interested in Calvin's tour experience and acknowledges the connection Calvin feels with the crowd and performing on stage.", - "D24:2" - ], - [ - "Dave found a photo online of Tokyo that makes him dream about visiting someday and is interested in learning about the city.", - "D24:12" - ], - [ - "Dave wants to explore the people, culture, food, and vibrant city life of Tokyo and considers it an unforgettable experience.", - "D24:14" - ], - [ - "Dave asks Calvin about specific spots in Tokyo he is excited to check out, such as Shibuya Crossing and Shinjuku.", - "D24:18" - ], - [ - "Dave shares a photo of a ramen bowl he tried in Boston and recommends Calvin to try it in Tokyo.", - "D24:20" - ] - ] - }, - "session_25_observation": { - "Dave": [ - [ - "Dave pondered about fame and its impact on relationships in a conversation with Calvin.", - "D25:1" - ], - [ - "Dave recently had a conversation with neighbors about current events and politics to share perspectives.", - "D25:7" - ], - [ - "Dave finds working on cars to be cathartic and a way to connect back with himself.", - "D25:13" - ], - [ - "Dave started working on cars at the age of ten after finding an old car in a neighbor's garage.", - "D25:15" - ], - [ - "Dave restored a car last year but sold it to a collector and is currently working on a new project.", - "D25:17" - ], - [ - "Dave believes paying attention to details in car restoration makes a big impact.", - "D25:20" - ] - ], - "Calvin": [ - [ - "Calvin is on tour with Frank Ocean and finds it incredible, connecting with the crowd.", - "D25:2" - ], - [ - "Calvin finds joy in what he does even though it can be overwhelming due to the demands.", - "D25:4" - ], - [ - "Calvin finds having a strong support system including friends and team helpful in managing everything.", - "D25:6" - ], - [ - "Listening to struggles people go through inspires Calvin in his music, leading him to dig deeper into capturing feelings.", - "D25:10" - ], - [ - "Calvin expresses himself through music, viewing it as a form of therapy and self-expression.", - "D25:12" - ], - [ - "Calvin believes in paying attention to small details to create something extraordinary in his work as an artist.", - "D25:22" - ] - ] - }, - "session_26_observation": { - "Calvin": [ - [ - "Calvin recently visited Boston with a high school friend and found it fun and eye-opening.", - "D26:1" - ], - [ - "Calvin appreciates architecture and history, finding them really cool in Boston.", - "D26:3" - ], - [ - "Calvin is into cars and enjoys making new and powerful models.", - "D26:5" - ], - [ - "Calvin is passionate about music and performing, finding it like his purpose and passion.", - "D26:9" - ], - [ - "Calvin values inspiration and mutual support with Dave, aiming to keep inspiring each other to be their best selves.", - "D26:11" - ] - ], - "Dave": [ - [ - "Dave recently attended a conference in Detroit and learned a lot from it.", - "D26:2" - ], - [ - "Dave is into working on cars, finding it calming and a way to take him out of his head.", - "D26:4" - ], - [ - "Dave got into engineering cars at a young age after being impressed by a car show with his dad.", - "D26:6" - ], - [ - "Dave is supportive and inspiring towards Calvin's music pursuits.", - "D26:7" - ], - [ - "Dave expresses gratitude and mutual support towards Calvin, aiming to keep inspiring each other.", - "D26:14" - ] - ] - }, - "session_27_observation": { - "Calvin": [ - [ - "Calvin went to a networking event to meet more artists to build up his fan base.", - "D27:1" - ], - [ - "Calvin has had some great collaborations recently and his album is almost finished.", - "D27:11" - ] - ], - "Dave": [ - [ - "Dave has been getting into photography recently and has taken some great shots.", - "D27:2" - ], - [ - "Dave has a car project that he recently finished restoring and it looks amazing.", - "D27:10" - ] - ] - }, - "session_28_observation": { - "Calvin": [ - [ - "Calvin threw a small party at his Japanese house last week to celebrate his new album with love and support from family and friends.", - "D28:1" - ], - [ - "Calvin feels that seeing everyone come together to celebrate is super rewarding.", - "D28:3" - ], - [ - "Calvin's music is about creating something that brings people together, connects with them, and brings joy.", - "D28:7" - ], - [ - "Calvin has a studio setup where he works, surrounded by music videos, concerts, and documentaries for inspiration.", - "D28:29" - ], - [ - "Calvin watches music videos, concerts, and documentaries about artists and their creative process on TV for inspiration.", - "D28:31" - ], - [ - "Calvin finds inspiration from watching TV to learn more about the music industry and others' creative processes.", - "D28:31" - ] - ], - "Dave": [ - [ - "Dave recently started a blog on car mods to share his passion with others.", - "D28:8" - ], - [ - "Dave shared a story on his blog about how he made a car look like a beast and inspired others to start their DIY projects.", - "D28:10" - ], - [ - "Dave appreciates when Calvin notices the effort he puts into his work.", - "D28:18" - ], - [ - "Dave listens to vinyl while doing car work to relax and stay on track.", - "D28:36" - ], - [ - "Dave is into classic rock music and believes music from that era is timeless.", - "D28:40" - ] - ] - }, - "session_29_observation": { - "Calvin": [ - [ - "Calvin performed in Boston and invited his old high school buddy to watch, which made him feel good.", - "D29:1" - ], - [ - "Calvin is nostalgic about his past, particularly about freestyling with friends when they were teenagers.", - "D29:3" - ], - [ - "Calvin's friend has been supportive and encouraging throughout his journey, making a big difference.", - "D29:5" - ], - [ - "Calvin supports and collaborates with young musicians from a music program, finding their enthusiasm inspiring.", - "D29:9" - ], - [ - "Calvin mentioned working with a young artist to create a beat, recognizing the artist's potential in music.", - "D29:13" - ], - [ - "Calvin enjoys trying out new sounds and pushing boundaries in his music studio to stay ahead.", - "D29:15" - ] - ], - "Dave": [ - [ - "Dave acknowledges the importance of having supportive friends since the start to help artists like Calvin.", - "D29:2" - ], - [ - "Dave praises Calvin for supporting young musicians and nurturing their passion to help them thrive.", - "D29:10" - ], - [ - "Dave encourages Calvin to keep pushing boundaries and exploring new ideas to grow as an artist.", - "D29:16" - ] - ] - }, - "session_30_observation": { - "Dave": [ - [ - "Dave has taken up photography as a new hobby and enjoys capturing the scenery around him.", - "D30:1" - ], - [ - "Dave recently bought a new vintage camera that takes great photos.", - "D30:5" - ], - [ - "Dave loves capturing the beauty of nature in his photos, focusing on sunsets, beaches, and waves.", - "D30:9" - ], - [ - "Dave shared a peaceful photo of rocks and a waterfall that he took last week.", - "D30:13" - ], - [ - "Dave enjoys capturing serene spots in nature, like a nearby park with a peaceful scene under a tree with pink flowers.", - "D30:17" - ] - ], - "Calvin": [ - [ - "Calvin attended a fancy gala in Boston recently and met interesting people.", - "D30:2" - ], - [ - "Calvin had an inspiring conversation with a cool artist about music and art at the gala.", - "D30:4" - ], - [ - "Calvin has accepted an invitation to perform at an upcoming show in Boston, which he is excited about.", - "D30:20" - ], - [ - "Calvin enjoys capturing photos, including a beautiful shot in a Japanese garden.", - "D30:16" - ] - ] - } - }, - "session_summary": { - "session_1_summary": "Calvin and Dave met at 11:53 am on 23 March, 2023. Dave attended a car event with classic cars, while Calvin shared his big life change by getting a new mansion and planning a trip to Japan to learn about the culture. Calvin will stay there for a few months before heading to Boston. Dave recommended a calming park and offered further recommendations for Calvin's trip, ending with Calvin thanking him and promising to stay in touch.", - "session_2_summary": "Calvin told Dave about his new luxury car at 4:45 pm on 26 March, 2023, expressing excitement and gratitude. Dave congratulated Calvin and admired the car. Calvin shared a picture and mentioned it was his first luxury car, feeling like it was well-deserved after hard work. Dave then talked about a music festival he attended in Boston and his favorite band, Aerosmith. They discussed the experience of seeing favorite artists perform. Calvin mentioned working on new music and collaborations. Dave showed interest and wished Calvin well. They exchanged goodbyes, ending their conversation.", - "session_3_summary": "Calvin and Dave spoke at 4:15 pm on 20 April, 2023. Calvin shared about attending a music festival in Tokyo where he met industry professionals and received advice. He dreams of touring the world and reaching a global audience with his music. Dave plans to show Calvin around Boston, known for its vibrant music scene. Dave also mentioned his passion for classic cars. Calvin looks forward to exploring Boston's food scene with Dave. They both expressed excitement about the upcoming visit.", - "session_4_summary": "Dave shared with Calvin at 6:24 pm on 1 May, 2023, that he fulfilled his dream of opening a car maintenance shop. Calvin congratulated him, acknowledging his hard work and ambition. Dave expressed his love for classic cars and showed Calvin a photo of an engine he restored. Calvin admired it and they discussed the satisfaction of seeing hard work pay off. They emphasized the importance of pursuing dreams and staying motivated. Dave invited Calvin to visit his shop, where he works on various cars, and they discussed the fulfillment of working on cars. Calvin complimented Dave on his work and shared a diamond pendant necklace received as a gift, symbolizing their shared dedication to their passions. They encouraged each other to stay motivated and keep pursuing their dreams.", - "session_5_summary": "At 1:16 pm on 3 May 2023, Dave informed Calvin that he was teaming up with a local garage, showcasing a challenging project they were working on. Calvin congratulated Dave on his new venture and praised his progress since their last conversation. Dave expressed his love for the new job, sharing his excitement about working with mechanics and his dream to build a custom car. Calvin admired Dave's dedication and encouraged him to relax amidst the hard work. They exchanged relaxation hobbies, with Dave enjoying walks and Calvin finding solace in long drives. Calvin mentioned a creative block in music, seeking advice from Dave, who suggested immersing in favorite activities for inspiration. Calvin appreciated the tip and expressed gratitude for Dave's support, promising to stay in touch. They bid farewell, with Dave offering ongoing assistance.", - "session_6_summary": "Calvin and Dave caught up at 11:50 am on 16 May, 2023. Calvin shared about a recent flood incident in his place but managed to save his music gear. Dave offered support, and Calvin showed his creative haven where he makes music. Dave mentioned opening his car shop and Calvin congratulated him. Calvin mentioned an upcoming performance in Tokyo, and Dave wished him luck. Calvin shared a stunning night skyline photo from Tokyo, which Dave admired, expressing a desire to visit. Calvin encouraged Dave to visit, and Dave expressed interest in adding Tokyo to his list of places to visit.", - "session_7_summary": "Calvin and Dave caught up at 6:06 pm on 31 May, 2023. Calvin shared his experience touring with Frank Ocean, finding it exhilarating. Dave showed a car he fixed, finding it therapeutic. They discussed how working on cars gives them a sense of accomplishment and purpose. Both feel empowered by transforming things. Calvin is excited about the upcoming tour in Boston. Dave supports Calvin's music journey and looks forward to hearing about the Boston music scene when Calvin returns.", - "session_8_summary": "Calvin and Dave chat at 2:31 pm on 9 June, 2023. Calvin updates Dave about his album progress with the creative team and expresses excitement. Dave shares a picture of a studio, and they discuss enjoying nature in Boston parks. Dave mentions exploring parks and Calvin looks forward to experiencing them. They talk about hiking, with Dave planning a mountain trip. Dave will share pictures with Calvin later. Calvin wishes Dave safe travels and they say goodbye.", - "session_9_summary": "At 3:15 pm on 21 June 2023, Calvin told Dave about his recent car accident but mentioned that nobody was hurt. Dealing with insurance and repairs was time-consuming. Dave inquired about any insurance issues experienced. Calvin shared that after a week, everything was sorted out. The mechanic, pictured, was skilled, making Calvin confident about the repair. Dave praised the expert fixing the car. Calvin expressed excitement about driving again. They discussed a beautiful view from Japan, which Calvin plans to visit after a tour. Dave shared joining a rock band, playing guitar. They agreed to keep in touch before ending the conversation.", - "session_10_summary": "Dave checked in with Calvin about his car post-crash at 7:56 pm on 7 July 2023. Calvin confirmed the car was fixed and going strong. They discussed regular walks in the park and admired each other's photos of scenic views. Calvin shared plans about exploring Japan for its food and culture, mentioning music collaborations he was working on. Dave expressed interest in visiting Japan for its vibes and tech. They discussed collaborating with artists and creating unique music. Calvin appreciated the support and concluded the chat, with Dave reminding him to not overwork.", - "session_11_summary": "Dave and Calvin reconnect at 6:38 pm on 21 July, 2023, discussing recent experiences that sparked their passions. Dave shares about his rejuvenating road trip, emphasizing how it reminded him of his love for cars. Calvin updates Dave on his project of turning a Japanese mansion into a recording studio, highlighting his passion for music. They exchange supportive words, with Dave commending Calvin's progress and Calvin appreciating Dave's encouragement. The conversation ends with the friends pledging to keep pushing themselves towards their goals and promising to chat again soon.", - "session_12_summary": "Calvin and Dave spoke at 1:12 pm on 3 August, 2023. Calvin mentioned his stressful experience with servicing his Ferrari and his attachment to the car, asking Dave about hobbies that make him feel restored. Dave, finding car repairs therapeutic, shared how working on cars with his dad as a child brings him fulfillment. Remembering restoring an old car together, Dave cherished the bonding experience with his dad. Calvin admired Dave's hobby and shared his pride in his Ferrari, crediting it as a reminder of hard work and dedication. They discussed motivation and goals, with Calvin aiming to expand his music brand globally and grow his fanbase. Dave encouraged Calvin, emphasizing the importance of making an impact through music. Calvin valued Dave's support and expressed determination to achieve his dreams, with Dave offering continuous encouragement and faith in Calvin's journey.", - "session_13_summary": "Dave and Calvin, at 5:22 pm on 11 August, 2023, discussed Dave's participation in a car mod workshop where he was excited to learn about auto engineering and building a custom car. Calvin admired Dave's enthusiasm and attention to detail in customizing cars, with Dave showcasing his current project involving engine swaps and body modifications. Calvin praised Dave's work, highlighting the modern twist and unique style in his car designs. Dave expressed gratitude for Calvin's support and offered assistance with Calvin's music endeavors. The conversation ended with well wishes and mutual admiration between the two friends.", - "session_14_summary": "Dave and Calvin caught up at 12:35 am on 14 August, 2023. Dave shared his exciting experience at a car workshop in San Francisco, expressing admiration for the passion and dedication of the people there. Calvin then talked about touring with a well-known artist, culminating in a show in Japan. Dave was impressed by Calvin's performance and the crowd's reaction in Tokyo. Calvin shared a picture from the show, noting the joyful atmosphere. Dave complimented Calvin on his work and shared a picture of a car he is restoring, aiming to finish it by the end of the next month. Calvin praised Dave's talent and encouraged him to keep going. They ended the conversation with words of support and encouragement for each other.", - "session_15_summary": "On August 22, 2023, at 11:06 am, Dave and Calvin caught up after a while. Dave shared about his fun card night with friends, while Calvin excitedly revealed his ongoing collaboration with Frank Ocean. Calvin met Frank Ocean at a music festival in Tokyo, and they even recorded a song together. Calvin described the festival in Tokyo as an amazing experience that fueled his soul, with a lively crowd. He also mentioned another collaboration that stemmed from his performance there. Calvin's upcoming tour includes a stop in Boston, where he planned to meet up with Dave. Calvin also mentioned recording a podcast with friends about the rap industry. Dave expressed interest in the podcast and looked forward to catching up and learning more.", - "session_16_summary": "Dave and Calvin caught up at 2:55 pm on 31 August, 2023. Calvin shared his amazing experience performing on a big stage and shooting a video in Miami for his new album. Dave showed support and praised Calvin's unique guitar, symbolizing his journey and passion for music. They discussed the importance of staying true to one's style and being unique in music. Dave encouraged Calvin to keep sharing his unique talent with the world.", - "session_17_summary": "At 9:19 am on 2 September 2023, Dave and Calvin caught up, with Dave sharing car modification insights from San Francisco. Calvin understood the joy of fixing cars, describing it as giving them new life. Dave found fixing cars rewarding and purposeful, feeling like a superhero when helping others. Calvin praised Dave for his impact, revealing his excitement for an upcoming trip to Boston. They agreed to meet up in Boston and said their goodbyes with plans to reconnect upon Calvin's arrival.", - "session_18_summary": "Calvin and Dave talked on 13 September at 10:56 am. Calvin's album released on the 11th, getting positive feedback and motivating him to create more music. Dave congratulated Calvin, discussing the impact of his work, inspiring Calvin to chase his dreams. Calvin shared his upcoming tour plans and dream to reach more people. Dave offered to show Calvin his garage someday. They ended the conversation with well wishes and plans to meet in Boston.", - "session_19_summary": "Dave and Calvin caught up at 12:13 am on 15 September, 2023. Dave shared about a fun jam session with his band and attending a rock concert in Boston. Calvin praised the electrifying atmosphere of rock concerts and asked about recordings of the jam. Dave mentioned they were too engrossed to record. Calvin appreciated the moments that can't be captured and shared a Disney poster. They discussed Ratatouille, following dreams, and Dave's car restoration hobby. Dave showed the final restoration picture, which Calvin admired. Dave found it satisfying and appreciated the reactions. Calvin praised Dave's work, calling it inspiring, and encouraged him to continue. They exchanged goodbyes, highlighting the importance of making people happy and following passions.", - "session_20_summary": "At 8:57 pm on 22 September, 2023, Dave and Calvin caught up. Dave shared that he had trouble with his car project, and Calvin showed a picture symbolizing his hard work. They discussed memorable experiences, including Calvin's summer drive and trip to Japan. Calvin mentioned a childhood song, \"California Love\" by Tupac and Dr. Dre. They planned a jam session together, looking forward to creating special memories. They concluded by expressing excitement and anticipation for their upcoming get-together.", - "session_21_summary": "Calvin and Dave spoke at 2:44 pm on 4 October, 2023. Calvin shared his inspiring experience of meeting with artists in Boston for a collaboration. Dave praised the connection and shared his hobby of restoring cars. They discussed the satisfaction of transforming old things, expressed pride in their work, and highlighted the importance of hard work and determination. Both agreed to stay focused on their goals and support each other. They concluded with well wishes for each other.", - "session_22_summary": "Dave and Calvin, at 3:13 pm on 8 October, 2023, discussed Dave's passion for cars. Dave described his recent visit to a car show and his love for car mods. He showed Calvin a car he restored and modified himself, expressing pride in his work. Dave shared that working on cars is therapeutic for him and a lifelong passion. Calvin admired Dave's dedication and viewed his work as inspiring. They discussed the satisfaction of restoring broken items and pursuing one's passion. Calvin encouraged Dave to chase his dreams, while Dave appreciated Calvin's support and encouragement. Dave expressed gratitude for Calvin's support, and they mutually encouraged each other to keep pushing forward.", - "session_23_summary": "Dave and Calvin had a conversation on October 15, 2023, at 9:39 am. Dave expressed feeling discouraged after losing a deal at work, questioning if his efforts were a waste of time. Calvin encouraged him not to give up and stay positive, sharing his approach to staying motivated during setbacks. They discussed the importance of passion, support from others, and taking breaks for rejuvenation. Dave shared his experience at a music festival, and Calvin admired the energy of the crowd and the headliner, The Fireworks. They bonded over their love for music and the satisfaction of their respective jobs. Calvin showed Dave a photo of his new Ferrari, sparking excitement for thrilling adventures. They exchanged kind words before parting ways, looking forward to uplifting each other in the future.", - "session_24_summary": "Calvin and Dave caught up at 10:11 am on 19 October, 2023. Calvin shared about touring with Frank Ocean, feeling a great connection with the crowd, and performing on stage. Dave congratulated him and asked about the tour, showing excitement about their collaboration. Calvin mentioned meeting Frank at a festival and feeling lucky to perform with him. They discussed the energy of the crowd, shared photos of the tour, and talked about Tokyo's vibrant atmosphere. Calvin revealed his upcoming trip to Tokyo, mentioning places like Shibuya Crossing and Shinjuku he's excited to visit. Dave recommended trying ramen, sharing a photo of a bowl he tried in Boston. Calvin agreed to give it a shot in Tokyo and appreciated the encouragement. They ended the conversation with Calvin looking forward to the trip and saying goodbye.", - "session_25_summary": "Dave and Calvin had a conversation at 2:17 pm on 23 October, 2023. Dave inquired about Calvin's tour with Frank Ocean, discussing the challenges of fame balancing personal life and work. Calvin mentioned enjoying the tour but finding it tough to manage everything. He highlighted the importance of a strong support system. Dave shared his interests in connecting with neighbors and working on cars. Calvin discussed using music to express emotions and connect with fans. Dave talked about his passion for car engineering since childhood and detailed his current car restoration project. They emphasized the significance of attention to detail in their respective crafts. The conversation concluded with Calvin expressing the rush of connecting with fans during concerts and wishing Dave well.", - "session_26_summary": "Calvin and Dave caught up at 8:25 pm on 25th October 2023. Calvin enjoyed his time in Boston with a high school friend, admiring architecture and history. Dave shared about attending a conference in Detroit and his interest in engineering cars. Calvin found Dave's car project impressive and shared his passion for music. Dave admired Calvin's performance with someone he admired. They both agreed on the fulfillment that their passions bring, inspiring each other to keep pursuing their dreams. They expressed support for each other before signing off, looking forward to what the future holds.", - "session_27_summary": "Calvin told Dave about his networking event with artists to build his fan base. Dave shared his new interest in photography, showing Calvin pictures of a stunning sunset and a clock tower in Boston. Calvin praised Dave's talent and they discussed their creative projects, including Dave's car restoration and Calvin's upcoming album. They agreed to catch up soon.", - "session_28_summary": "Calvin and Dave caught up at 5:46 pm on 2 November, 2023. Calvin shared with Dave about throwing a party for his new album last week at his Japanese house, receiving immense love and support from friends and family. Dave congratulated him on the album release and the party, praising Calvin for creating something that brings people together. Calvin, in turn, appreciated the positive feedback and mentioned how such moments motivate him to continue growing. Dave shared his passion for car mods and his new blog, seeking tips from Calvin on blogging. Calvin admired Dave's efforts, especially sharing his knowledge and inspiring others. They also discussed Dave's car restoration project, his rock band, and their mutual love for music. Calvin shared insights into his studio setup, mentioning his passion for watching music-related content on TV for inspiration. Dave showed Calvin his notebook for writing lyrics and the vintage music player he uses while working on cars. They exchanged thoughts on music's influence on productivity and mood. Calvin encouraged Dave to keep discovering new tunes and broadening his musical knowledge before parting ways as Dave had to return to work.", - "session_29_summary": "Calvin talked about reminiscing with an old high school friend, mentioning the importance of relationships in the music industry. He highlighted the support and encouragement he received, especially from the friend he met in Boston. Dave agreed on the significance of supportive people and discussed Calvin's involvement with young musicians. Calvin emphasized the importance of passing on the torch and supporting new talent. He also mentioned his experimentation with new sounds in his music studio. Dave encouraged Calvin to keep exploring and evolving as an artist. The conversation ended with both agreeing on the importance of growth and looking forward to seeing Calvin's future creations.", - "session_30_summary": "Dave and Calvin reconnect at 10:54 am on 17 November, 2023. Dave mentions his newfound love for photography, especially capturing nature scenes. Calvin shares about attending a fancy gala in Boston and connecting with an artist over music and art. They discuss their passion for photography and the beauty of nature. Calvin reveals he will be performing at a show in Boston soon, exciting Dave. They plan to catch up in person soon and bid farewell, looking forward to seeing each other again." - }, - "sample_id": "conv-50" - } -] \ No newline at end of file diff --git a/methods/EverCore/evaluation/src/__init__.py b/methods/EverCore/evaluation/src/__init__.py deleted file mode 100644 index ec73520f..00000000 --- a/methods/EverCore/evaluation/src/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Framework Core - diff --git a/methods/EverCore/evaluation/src/adapters/__init__.py b/methods/EverCore/evaluation/src/adapters/__init__.py deleted file mode 100644 index 1559b033..00000000 --- a/methods/EverCore/evaluation/src/adapters/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# System Adapters diff --git a/methods/EverCore/evaluation/src/adapters/base.py b/methods/EverCore/evaluation/src/adapters/base.py deleted file mode 100644 index 8ccb875e..00000000 --- a/methods/EverCore/evaluation/src/adapters/base.py +++ /dev/null @@ -1,111 +0,0 @@ -""" -Adapter base class - define unified memory system adapter interface. -""" -from abc import ABC, abstractmethod -from typing import Any, List, Dict -from evaluation.src.core.data_models import Conversation, SearchResult - - -class BaseAdapter(ABC): - """Memory system adapter base class.""" - - def __init__(self, config: dict): - """ - Initialize adapter. - - Args: - config: System config dict - """ - self.config = config - - @abstractmethod - async def add( - self, - conversations: List[Conversation], - **kwargs - ) -> Any: - """ - Ingest conversation data and build index (Add stage). - - This method encapsulates system-specific data ingestion and index building: - - For EverCore: MemCell extraction + BM25/Embedding index building - - For Mem0: Direct storage to vector database - - For other systems: Their respective implementations - - Args: - conversations: Standard format conversation list - **kwargs: Extra parameters - - Returns: - Index object (system internal format, different systems return different types) - """ - pass - - @abstractmethod - async def search( - self, - query: str, - conversation_id: str, - index: Any, - **kwargs - ) -> SearchResult: - """ - Retrieve relevant memories (Search stage). - - Args: - query: Query text - conversation_id: Conversation ID - index: Index object (returned by add()) - **kwargs: Extra parameters (e.g., top_k) - - Returns: - Standard format search result - """ - pass - - async def prepare(self, conversations: List[Conversation], **kwargs) -> None: - """ - Preparation stage: operations executed before add. - - Optional preparation operations, e.g.: - - Update project config (e.g., Mem0's custom_instructions) - - Clean existing data (if clean_before_add configured) - - Other system-specific initialization - - Args: - conversations: Standard format conversation list (for extracting user_id etc.) - **kwargs: Extra parameters - - Returns: - None - """ - pass # Default: no operation - - def get_system_info(self) -> Dict[str, Any]: - """ - Return system info (for result recording). - - Returns: - System info dict - """ - return { - "name": self.__class__.__name__, - "config": self.config - } - - def build_lazy_index(self, conversations: List[Conversation], output_dir: Any) -> Any: - """ - Build lazy-loaded index metadata. - - Default: return None (online API systems don't need index) - Local systems (e.g., EverCore) should override this method - - Args: - conversations: Conversation list - output_dir: Output directory - - Returns: - Index object or metadata (local systems return index metadata, online systems return None) - """ - return None - diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/README.md b/methods/EverCore/evaluation/src/adapters/evermemos/README.md deleted file mode 100644 index 90d76da6..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/README.md +++ /dev/null @@ -1,155 +0,0 @@ -# LoCoMo Evaluation Pipeline - -

-

- -LoCoMo (Long-Context Modeling) evaluation system for testing memory system performance in long conversation scenarios, including retrieval and question-answering capabilities. - ---- - -## 📋 Directory Structure - -``` -locomo_evaluation/ -├── config.py # Configuration file -├── data/ -│ └── locomo10.json # Test dataset -├── prompts/ # Prompt templates -│ ├── sufficiency_check.txt # Sufficiency check -│ ├── refined_query.txt # Query refinement -│ ├── multi_query_generation.txt # Multi-query generation -│ └── answer_prompts.py # Answer generation -├── stage1_memcells_extraction.py # Stage 1: Extract MemCells -├── stage2_index_building.py # Stage 2: Build indexes -├── stage3_memory_retrivel.py # Stage 3: Retrieve memories -├── stage4_response.py # Stage 4: Generate responses -├── stage5_eval.py # Stage 5: Evaluate results -└── tools/ # Utility tools - ├── agentic_utils.py # Agentic retrieval utilities - ├── benchmark_embedding.py # Embedding performance testing - └── ... -``` - ---- - -## 🚀 Quick Start - -### 1. Environment Setup - -Ensure the `.env` file in the project root directory is configured: - -```bash -# Required environment variables -LLM_API_KEY=your_llm_api_key # LLM API key -VECTORIZE_API_KEY=your_VECTORIZE_key # Embedding/Reranker API key -``` - -### 2. Modify Configuration - -Edit `config.py`: - -```python -class ExperimentConfig: - experiment_name: str = "locomo_evaluation" # Experiment name - retrieval_mode: str = "lightweight" # 'agentic' or 'lightweight' - # ... other configurations -``` - -**Key Configuration Options**: -- **Concurrency**: Set concurrent requests based on API limits -- **Embedding Parameters**: Choose appropriate embedding model and parameters -- **Reranker Parameters**: Configure reranker model (only for agentic mode) -- **Retrieval Mode**: - - `agentic`: Multi-round intelligent retrieval, high quality but slower - - `lightweight`: Fast retrieval, faster but slightly lower quality - -### 3. Run Complete Pipeline - -```bash -# Stage 1: Extract MemCells -python evaluation/locomo_evaluation/stage1_memcells_extraction.py - -# Stage 2: Build indexes -python evaluation/locomo_evaluation/stage2_index_building.py - -# Stage 3: Retrieve memories -python evaluation/locomo_evaluation/stage3_memory_retrivel.py - -# Stage 4: Generate responses -python evaluation/locomo_evaluation/stage4_response.py - -# Stage 5: Evaluate results -python evaluation/locomo_evaluation/stage5_eval.py -``` - -### 4. View Results - -```bash -# View final evaluation results -cat results/locomo_evaluation/judged.json - -# View accuracy statistics -python evaluation/locomo_evaluation/tools/compute_acc.py -``` - ---- - -## 📊 Results Overview - -### Output Directory Structure - -``` -results/locomo_evaluation/ -├── memcells/ # MemCell extraction results -│ ├── memcell_list_conv_0.json -│ └── ... -├── bm25_index/ # BM25 indexes -│ └── *.pkl -├── vectors/ # Embedding indexes -│ └── *.pkl -├── search_results.json # Retrieval results -├── responses.json # Generated responses -└── judged.json # Final evaluation results -``` - ---- - -## ⚙️ Configuration Guide - -### Switch Retrieval Mode - -Modify in `config.py`: - -```python -# Lightweight retrieval (fast) -retrieval_mode: str = "lightweight" - -# Agentic retrieval (high quality) -retrieval_mode: str = "agentic" -``` - -### Switch LLM Service - -Modify `config.py`: - -```python -llm_service: str = "openai" # or "openrouter", "deepseek" - -llm_config: dict = { - "openai": { - "model": "openai/gpt-4o-mini", - "api_key": os.getenv("LLM_API_KEY"), - "base_url": "https://openrouter.ai/api/v1", - "temperature": 0.3, - "max_tokens": 16384, - } -} -``` - ---- - -## 🔗 Related Documentation - -- [Project Root README](../../README.md) -- [Development Guide](../../docs/dev_docs/getting_started.md) -- [API Documentation](../../docs/api_docs/) diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/__init__.py b/methods/EverCore/evaluation/src/adapters/evermemos/__init__.py deleted file mode 100644 index cad6719a..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Empty __init__.py as per project coding rules diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/config.py b/methods/EverCore/evaluation/src/adapters/evermemos/config.py deleted file mode 100644 index 388a77ef..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/config.py +++ /dev/null @@ -1,99 +0,0 @@ -import os -from dotenv import load_dotenv -from api_specs.memory_types import ScenarioType - -load_dotenv() - - -class ExperimentConfig: - experiment_name: str = "locomo_evaluation" - datase_path: str = "data/locomo10.json" - use_emb: bool = True - use_reranker: bool = True - use_agentic_retrieval: bool = True - use_multi_query: bool = True - num_conv: int = 10 - - # MemCell extraction feature switches - enable_foresight_extraction: bool = False - enable_clustering: bool = True - enable_profile_extraction: bool = False - - # Clustering configuration - cluster_similarity_threshold: float = 0.65 - cluster_max_time_gap_days: float = 7.0 - - # Profile configuration - profile_scenario: str = ScenarioType.SOLO.value # solo or team - profile_min_confidence: float = 0.6 - profile_min_memcells: int = 1 - - # Retrieval mode: 'agentic' or 'lightweight' - # - agentic: Multi-round retrieval with LLM guidance, high quality but slower - # - lightweight: Fast retrieval without LLM, faster but slightly lower quality - retrieval_mode: str = "agentic" # 'agentic' | 'lightweight' - - # Retrieval configuration - use_hybrid_search: bool = True # Use hybrid retrieval (Embedding + BM25 + RRF) - emb_recall_top_n: int = 40 - reranker_top_n: int = 20 - - # Lightweight retrieval parameters (only effective when retrieval_mode='lightweight') - # lightweight_search_mode: controls which search method to use in lightweight mode - # - "bm25_only": Only use BM25 search (fast, lexical matching) - # - "hybrid": BM25 + Embedding + RRF fusion (balanced) - # - "emb_only": Only use Embedding search (semantic matching) - lightweight_search_mode: str = "bm25_only" # 'bm25_only' | 'hybrid' | 'emb_only' - lightweight_bm25_top_n: int = 50 - lightweight_emb_top_n: int = 50 - lightweight_final_top_n: int = 20 - - # Hybrid search parameters (only effective when use_hybrid_search=True) - hybrid_emb_candidates: int = 50 - hybrid_bm25_candidates: int = 50 - hybrid_rrf_k: int = 40 - - # Multi-query retrieval parameters (only effective when use_multi_query=True) - multi_query_num: int = 3 - multi_query_top_n: int = 50 - - # Reranker optimization parameters (high performance configuration) - reranker_batch_size: int = 20 - reranker_max_retries: int = 3 - reranker_retry_delay: float = 0.8 # Retry interval with exponential backoff - reranker_timeout: float = 60.0 - reranker_fallback_threshold: float = ( - 0.3 # Fall back to original ranking when success rate below threshold - ) - reranker_concurrent_batches: int = 5 - - reranker_instruction: str = ( - "Determine if the passage contains specific facts, entities (names, dates, locations), " - "or details that directly answer the question." - ) - - # Stage4 parameter: select top-k from event_ids to build context - response_top_k: int = 10 - - llm_service: str = "openai" # openai, vllm - llm_config: dict = { - "openai": { - "llm_provider": "openai", - "model": "openai/gpt-4.1-mini", - "base_url": "https://openrouter.ai/api/v1", - "api_key": os.getenv("LLM_API_KEY"), - "temperature": 0.3, - "max_tokens": 16384, - }, - "vllm": { - "llm_provider": "openai", - "model": "Qwen3-30B", - "base_url": "http://0.0.0.0:8000/v1", - "api_key": "123", - "temperature": 0, - "max_tokens": 16384, - }, - } - - max_retries: int = 5 - max_concurrent_requests: int = 10 diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/prompts/__init__.py b/methods/EverCore/evaluation/src/adapters/evermemos/prompts/__init__.py deleted file mode 100644 index cad6719a..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/prompts/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Empty __init__.py as per project coding rules diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/prompts/answer_prompts.py b/methods/EverCore/evaluation/src/adapters/evermemos/prompts/answer_prompts.py deleted file mode 100644 index fa781407..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/prompts/answer_prompts.py +++ /dev/null @@ -1,74 +0,0 @@ -ANSWER_PROMPT = """ -You are an intelligent memory assistant tasked with retrieving accurate information from episodic memories. - -# CONTEXT: -You have access to episodic memories from conversations between two speakers. These memories contain -timestamped information that may be relevant to answering the question. - -# INSTRUCTIONS: -Your goal is to synthesize information from all relevant memories to provide a comprehensive and accurate answer. -You MUST follow a structured Chain-of-Thought process to ensure no details are missed. -Actively look for connections between people, places, and events to build a complete picture. Synthesize information from different memories to answer the user's question. -It is CRITICAL that you move beyond simple fact extraction and perform logical inference. When the evidence strongly suggests a connection, you must state that connection. Do not dismiss reasonable inferences as "speculation." Your task is to provide the most complete answer supported by the available evidence. - -# CRITICAL REQUIREMENTS: -1. NEVER omit specific names - use "Amy's colleague Rob" not "a colleague" -2. ALWAYS include exact numbers, amounts, prices, percentages, dates, times -3. PRESERVE frequencies exactly - "every Tuesday and Thursday" not "twice a week" -4. MAINTAIN all proper nouns and entities as they appear - -# RESPONSE FORMAT (You MUST follow this structure): - -## STEP 1: RELEVANT MEMORIES EXTRACTION -[List each memory that relates to the question, with its timestamp] -- Memory 1: [timestamp] - [content] -- Memory 2: [timestamp] - [content] -... - -## STEP 2: KEY INFORMATION IDENTIFICATION -[Extract ALL specific details from the memories] -- Names mentioned: [list all person names, place names, company names] -- Numbers/Quantities: [list all amounts, prices, percentages] -- Dates/Times: [list all temporal information] -- Frequencies: [list any recurring patterns] -- Other entities: [list brands, products, etc.] - -## STEP 3: CROSS-MEMORY LINKING -[Identify entities that appear in multiple memories and link related information. Make reasonable inferences when entities are strongly connected.] -- Shared entities: [list people, places, events mentioned across different memories] -- Connections found: [e.g., "Memory 1 mentions A moved from hometown → Memory 2 mentions A's hometown is LA → Therefore A moved from LA"] -- Inferred facts: [list any facts that require combining information from multiple memories] - -## STEP 4: TIME REFERENCE CALCULATION -[If applicable, convert relative time references] -- Original reference: [e.g., "last year" from May 2022] -- Calculated actual time: [e.g., "2021"] - -## STEP 5: CONTRADICTION CHECK -[If multiple memories contain different information] -- Conflicting information: [describe] -- Resolution: [explain which is most recent/reliable] - -## STEP 6: DETAIL VERIFICATION CHECKLIST -- [ ] All person names included: [list them] -- [ ] All locations included: [list them] -- [ ] All numbers exact: [list them] -- [ ] All frequencies specific: [list them] -- [ ] All dates/times precise: [list them] -- [ ] All proper nouns preserved: [list them] - -## STEP 7: ANSWER FORMULATION -[Explain how you're combining the information to answer the question] - -## FINAL ANSWER: -[Provide the concise answer with ALL specific details preserved] - ---- - -{context} - -Question: {question} - -Now, follow the Chain-of-Thought process above to answer the question: -""" - diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/prompts/multi_query_prompts.py b/methods/EverCore/evaluation/src/adapters/evermemos/prompts/multi_query_prompts.py deleted file mode 100644 index 5250fe58..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/prompts/multi_query_prompts.py +++ /dev/null @@ -1,64 +0,0 @@ -"""Multi-Query Generation Prompt for Agentic Retrieval""" - -MULTI_QUERY_GENERATION_PROMPT = """You are an expert at query reformulation for long-term conversational retrieval. -Your goal is to generate multiple complementary search queries that recover BOTH: -- the starting point of a time interval -- the ending point of a time interval -- all temporally-linked events in between - -You MUST explicitly expand temporal references (e.g., "last week", "before moving", -"when they first met") into alternative expressions. - --------------------------- -Original Query: -{original_query} - -Key Information Found: -{key_info} - -Missing Information: -{missing_info} - -Retrieved Documents: -{retrieved_docs} --------------------------- - -### Temporal Reasoning Strategy (MANDATORY) -When the question involves time or order: -1. **Boundary Decomposition** - Generate queries that separately target: - - the earliest relevant event ("start boundary") - - the latest relevant event ("end boundary") - -2. **Temporal Expression Expansion** - Rewrite relative time expressions into multiple equivalent forms: - - absolute dates (if deducible) - - session numbers - - “before/after X” - - duration phrasing (“two weeks earlier”, “shortly after”) - -3. **Interval Reconstruction** - Include a declarative query that resembles a hypothetical answer containing BOTH - the start and end time anchors. - -### Standard Query Requirements -1. Generate 2-3 diverse queries. -2. Query 1 MUST be a specific **Question**. -3. Query 2 MUST be a **Declarative Statement or Hypothetical Answer (HyDE)**. -4. Query diversity MUST include different temporal forms (before/after/during). -5. MUST use Key Info to resolve pronouns IF provided. -6. No invented facts. -7. Keep queries < 25 words, same language as original. - -### Output Format (STRICT JSON): -{{ - "queries": [ - "Refined query 1", - "Refined query 2", - "Refined query 3 (optional)" - ], - "reasoning": "Brief explanation of how temporal boundaries and expressions were expanded." -}} - -Now generate: -""" diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/prompts/refined_query_prompts.py b/methods/EverCore/evaluation/src/adapters/evermemos/prompts/refined_query_prompts.py deleted file mode 100644 index 6c4efad4..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/prompts/refined_query_prompts.py +++ /dev/null @@ -1,47 +0,0 @@ -"""Refined Query Generation Prompt for Agentic Retrieval""" - -REFINED_QUERY_PROMPT = """You are an expert at query reformulation for information retrieval. - -**Task**: Generate a refined query that targets the missing information in the retrieved results. - -**Original Query**: -{original_query} - -**Retrieved Documents** (insufficient): -{retrieved_docs} - -**Missing Information**: -{missing_info} - -**Instructions**: -1. Keep the core intent of the original query unchanged. -2. Add specific keywords or rephrase to target the missing information. -3. Make the query more specific and focused. -4. The refined query should be a direct question that seeks to extract the missing facts. -5. Do NOT change the query's meaning or make it too broad. -6. Keep it concise (1-2 sentences maximum). - -**Examples**: - -Example 1: -Original Query: "What does Alice like?" -Missing Info: ["Alice's specific interests or hobbies"] -Refined Query: "What are Alice's hobbies and interests?" - -Example 2: -Original Query: "Tell me about the meeting" -Missing Info: ["meeting date", "location", "participants"] -Refined Query: "When and where was the meeting held, and who attended?" - -Example 3: -Original Query: "Bob's project" -Missing Info: ["project name", "status", "purpose"] -Refined Query: "What is the name, current status, and purpose of Bob's project?" - -Now generate the refined query (output only the refined query, no additional text): -Original Query: {original_query} -Missing Info: {missing_info} - -Refined Query: -""" - diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/prompts/sufficiency_check_prompts.py b/methods/EverCore/evaluation/src/adapters/evermemos/prompts/sufficiency_check_prompts.py deleted file mode 100644 index 7eb7aa52..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/prompts/sufficiency_check_prompts.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Sufficiency Check Prompt for Agentic Retrieval""" - -SUFFICIENCY_CHECK_PROMPT = """You are an expert in information retrieval evaluation. Assess whether the retrieved documents provide a complete and temporally sufficient answer to the user's query. --------------------------- -User Query: -{query} - -Retrieved Documents: -{retrieved_docs} --------------------------- - -### Instructions: - -1. **Analyze the Query Structure** - - Identify key entities AND determine if the query requires temporal reasoning. - - If the query involves time (e.g., "before", "after", "since", "during", "from X to Y", "how long"), you MUST decompose it into: - * start_time_needed (if any) - * end_time_needed (if any) - * temporal_relation_needed (ordering, duration, interval) - -2. **Scan Documents for Coverage** - - Look for explicit facts addressing *each* required component: - * required entities - * start time - * end time - * temporal relations (ordering or duration) - -3. **Extract Key Information** - - List specific resolved entities or facts found in the documents. - - If time expressions exist, normalize them (e.g., "two weeks ago", "before she moved"). - -4. **Identify Missing Information** - - For temporal queries: - * missing start time - * missing end time - * missing ordering facts - * missing duration - - Use resolved names to be specific (e.g., "Start time of Alice moving", "Whether Bob visited before Alice moved"). - -5. **Judgment** - - **Sufficient**: All required components (entities + temporal boundaries + relations) appear explicitly. - - **Insufficient**: ANY required part is missing. - -### Output Format (strict JSON): -{{ - "is_sufficient": true or false, - "reasoning": "1-2 sentence explanation.", - "key_information_found": ["List of resolved entities/facts"], - "missing_information": ["Specific missing components, using resolved entity names"] -}} - -Now evaluate:""" \ No newline at end of file diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/stage1_memcells_extraction.py b/methods/EverCore/evaluation/src/adapters/evermemos/stage1_memcells_extraction.py deleted file mode 100644 index 0003f85f..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/stage1_memcells_extraction.py +++ /dev/null @@ -1,930 +0,0 @@ -from typing import Dict, List -import json -import os -import sys -import uuid -import asyncio -import time -from rich.progress import ( - Progress, - SpinnerColumn, - TextColumn, - BarColumn, - TaskProgressColumn, - TimeElapsedColumn, - TimeRemainingColumn, - MofNCompleteColumn, -) -from rich.console import Console - - -from common_utils.datetime_utils import ( - to_iso_format, - from_iso_format, - from_timestamp, - get_now_with_timezone, -) -from memory_layer.llm.llm_provider import LLMProvider -from memory_layer.memcell_extractor.base_memcell_extractor import RawData, MemCell -from memory_layer.memcell_extractor.conv_memcell_extractor import ( - ConvMemCellExtractor, - ConversationMemCellExtractRequest, -) -from memory_layer.memory_extractor.episode_memory_extractor import ( - EpisodeMemoryExtractor, -) - -from memory_layer.prompts.en.episode_mem_prompts import ( - EPISODE_GENERATION_PROMPT, - GROUP_EPISODE_GENERATION_PROMPT, - DEFAULT_CUSTOM_INSTRUCTIONS, -) -from memory_layer.memory_extractor.base_memory_extractor import MemoryExtractRequest -from memory_layer.memory_extractor.atomic_fact_extractor import AtomicFactExtractor -from memory_layer.memory_extractor.foresight_extractor import ForesightExtractor -from api_specs.memory_types import RawDataType - -# Clustering and Profile management components -from memory_layer.cluster_manager import ( - ClusterManager, - ClusterManagerConfig, - MemSceneState, -) -from memory_layer.profile_manager import ( - ProfileManager, - ProfileManagerConfig, - ScenarioType, -) - -# In-memory storage implementations for evaluation -from evaluation.src.adapters.evermemos.tools import ( - InMemoryClusterStorage, - InMemoryProfileStorage, -) - -from evaluation.src.adapters.evermemos.config import ExperimentConfig -from core.oxm.mongo.mongo_utils import generate_object_id_str -from datetime import datetime, timedelta -from pathlib import Path - - -def parse_locomo_timestamp(timestamp_str: str) -> datetime: - """Parse LoComo timestamp format to datetime object.""" - timestamp_str = timestamp_str.replace("\\s+", " ").strip() - dt = datetime.strptime(timestamp_str, "%I:%M %p on %d %B, %Y") - return dt - - -def raw_data_load(locomo_data_path: str) -> Dict[str, List[RawData]]: - with open(locomo_data_path, "r") as f: - data = json.load(f) - - # data = [data[2]] - # data = [data[0], data[1], data[2]] - raw_data_dict = {} - - conversations = [data[i]['conversation'] for i in range(len(data))] - print(f" 📅 Found {len(conversations)} conversations") - for con_id, conversation in enumerate(conversations): - messages = [] - # print(conversation.keys()) - session_keys = sorted( - [ - key - for key in conversation - if key.startswith("session_") and not key.endswith("_date_time") - ], - key=lambda x: int(x.replace("session_", "")), - ) - - print(f" 📅 Found {len(session_keys)} sessions") - print( - f" 🎭 Speakers: {conversation.get('speaker_a', 'Unknown')} & {conversation.get('speaker_b', 'Unknown')}" - ) - sender_name_to_id = {} - for session_key in session_keys: - session_messages = conversation[session_key] - session_time_key = f"{session_key}_date_time" - - if session_time_key in conversation: - # Parse session timestamp - session_time = parse_locomo_timestamp(conversation[session_time_key]) - - # Process each message in this session - for i, msg in enumerate(session_messages): - # Priority 1: Use message-level timestamp if available (e.g., evermembench) - if 'time' in msg and msg['time']: - # Parse message-level timestamp (strict parsing, raises on error) - msg_datetime = from_iso_format(msg['time'], strict=True) - iso_timestamp = to_iso_format(msg_datetime) - else: - # Priority 2: Generate timestamp from session time (e.g., locomo) - msg_timestamp = session_time + timedelta(seconds=i * 30) - iso_timestamp = to_iso_format(msg_timestamp) - - # Generate unique sender_id for this conversation - sender_name = msg["speaker"] - if sender_name not in sender_name_to_id: - # Generate unique ID: {name}_{conversation_index} - unique_id = f"{sender_name.lower().replace(' ', '_')}_{con_id}" - sender_name_to_id[sender_name] = unique_id - - # Process content with image information if present - content = msg["text"] - if msg.get("img_url"): - blip_caption = msg.get("blip_caption", "an image") - content = ( - f"[{sender_name} shared an image: {blip_caption}] {content}" - ) - - message = { - "sender_id": sender_name_to_id[sender_name], - "user_name": sender_name, - "sender_name": sender_name, - "content": [{"type": "text", "content": content}], - "timestamp": iso_timestamp, - "original_timestamp": conversation[session_time_key], - "dia_id": msg["dia_id"], - "session": session_key, - } - # Add optional fields if present - for optional_field in ["img_url", "blip_caption", "query"]: - if optional_field in msg: - message[optional_field] = msg[optional_field] - messages.append(message) - # messages = messages[:30] - raw_data_dict[str(con_id)] = messages - - print( - f" ✅ Converted {len(messages)} messages from {len(session_keys)} sessions" - ) - - return raw_data_dict - - -def convert_conversation_to_raw_data_list(conversation: list) -> List[RawData]: - raw_data_list = [] - for msg in conversation: - raw_data_list.append(RawData(content=msg, data_id=str(uuid.uuid4()))) - return raw_data_list - - -async def _extract_all_memories_for_memcell( - memcell: MemCell, - speakers: set, - episode_extractor, - foresight_extractor, - conv_id: str, -): - """ - Extract all memories for a MemCell in serial - - Process: Episode → Foresight (optional) - Note: AtomicFact is processed concurrently because it needs all MemCells to be collected before processing - - Args: - memcell: MemCell to extract memories from - speakers: Conversation participants - episode_extractor: Episode extractor - foresight_extractor: Foresight extractor (optional) - conv_id: Conversation ID (for logging) - """ - # 1. Extract Episode (required) - episode_request = MemoryExtractRequest( - memcell=memcell, - user_id=None, # None represents group episode - participants=list(speakers), - group_id=None, - ) - - episode_memory = await episode_extractor.extract_memory(episode_request) - - if episode_memory and episode_memory.episode: - memcell.episode = episode_memory.episode - memcell.subject = episode_memory.subject if episode_memory.subject else "" - memcell.summary = episode_memory.episode[:200] + "..." - - # 2. Extract Foresight (optional) - if foresight_extractor: - foresight_memories = ( - await foresight_extractor.generate_foresight_memories_for_episode( - episode_memory - ) - ) - if foresight_memories: - memcell.foresight_memories = foresight_memories - else: - # Episode extraction failed - raise exception, don't hide errors - raise ValueError( - f"❌ Episode extraction failed! conv_id={conv_id}, memcell_id={memcell.event_id}" - ) - - -async def memcell_extraction_from_conversation( - raw_data_list: List[RawData], - llm_provider: LLMProvider = None, - memcell_extractor: ConvMemCellExtractor = None, - conv_id: str = None, # Add conversation ID for progress bar description - progress: Progress = None, # Add progress bar object - task_id: int = None, # Add task ID - enable_foresight_extraction: bool = False, # whether to extract foresight -) -> list: - - episode_extractor = EpisodeMemoryExtractor( - llm_provider=llm_provider, - episode_prompt=EPISODE_GENERATION_PROMPT, - group_episode_prompt=GROUP_EPISODE_GENERATION_PROMPT, - custom_instructions=DEFAULT_CUSTOM_INSTRUCTIONS, - ) - # If foresight extraction is enabled, create ForesightExtractor - foresight_extractor = None - if enable_foresight_extraction: - foresight_extractor = ForesightExtractor(llm_provider=llm_provider) - - memcell_list = [] - speakers = { - raw_data.content["sender_id"] - for raw_data in raw_data_list - if isinstance(raw_data.content, dict) and "sender_id" in raw_data.content - } - history_raw_data_list = [] - # raw_data_list = raw_data_list[:100] - - # Process messages - total_messages = len(raw_data_list) - - for idx, raw_data in enumerate(raw_data_list): - # Update progress bar (before processing, showing which message is being processed) - if progress and task_id is not None: - progress.update(task_id, completed=idx) - - if history_raw_data_list == [] or len(history_raw_data_list) == 1: - history_raw_data_list.append(raw_data) - continue - - request = ConversationMemCellExtractRequest( - history_raw_data_list=history_raw_data_list, - new_raw_data_list=[raw_data], - user_id_list=list(speakers), - ) - # ❌ Remove retry mechanism, let errors be exposed directly - extracted_memcells, status_result = await memcell_extractor.extract_memcell( - request - ) - if not extracted_memcells: - history_raw_data_list.append(raw_data) - else: - # One or more MemCells produced (force-split or LLM boundary) - for memcell_result in extracted_memcells: - # [Evaluation Only] Generate event_id (in production, MongoDB generates it) - if memcell_result.event_id is None: - memcell_result.event_id = generate_object_id_str() - - # ✅ Serial extraction: detect boundary, immediately extract all memories for this MemCell - # This allows Clustering and Profile to immediately use the complete MemCell - await _extract_all_memories_for_memcell( - memcell=memcell_result, - speakers=speakers, - episode_extractor=episode_extractor, - foresight_extractor=foresight_extractor, - conv_id=conv_id, - ) - - memcell_list.append(memcell_result) - - # Reset history window: start fresh from the current message - history_raw_data_list = [raw_data] - - # Processing complete, update progress to 100% - if progress and task_id is not None: - progress.update(task_id, completed=total_messages) - - # Process remaining history (if any) - if history_raw_data_list: - # Determine timestamp: use last memcell's timestamp if available, otherwise use last message's timestamp - if memcell_list: - last_timestamp = memcell_list[-1].timestamp - else: - # Fallback: use the last raw data's timestamp if memcell_list is empty - # RawData.content["timestamp"] is ISO format string, guaranteed by raw_data_load - last_raw_data = history_raw_data_list[-1] - if ( - isinstance(last_raw_data.content, dict) - and "timestamp" in last_raw_data.content - ): - # Convert ISO format string to datetime - last_timestamp = from_iso_format(last_raw_data.content["timestamp"]) - else: - # Defensive fallback (should not happen after raw_data_load fix) - last_timestamp = get_now_with_timezone() - - memcell = MemCell( - type=RawDataType.CONVERSATION, - user_id_list=list(speakers), - original_data=history_raw_data_list, - timestamp=last_timestamp, - summary="Final segment", - ) - # [Evaluation Only] Generate event_id (in production, MongoDB generates it) - memcell.event_id = generate_object_id_str() - - original_data_list = [] - for raw_data in history_raw_data_list: - original_data_list.append(memcell_extractor._data_process(raw_data)) - memcell.original_data = original_data_list - - # Serial extraction of all memories for the last MemCell - await _extract_all_memories_for_memcell( - memcell=memcell, - speakers=speakers, - episode_extractor=episode_extractor, - foresight_extractor=foresight_extractor, - conv_id=conv_id, - ) - - memcell_list.append(memcell) - - return memcell_list - - -async def process_single_conversation( - conv_id: str, - conversation: list, - save_dir: str, - llm_provider: LLMProvider = None, - atomic_fact_extractor: AtomicFactExtractor = None, - progress_counter: dict = None, - progress: Progress = None, - task_id: int = None, - config: ExperimentConfig = None, # Pass configuration -) -> tuple: - """Process single conversation and return results (with clustering and Profile extraction). - - Args: - conv_id: Conversation ID - conversation: Conversation data - save_dir: Save directory - llm_provider: Shared LLM provider instance - atomic_fact_extractor: Atomic fact extractor instance - progress: Progress bar object - task_id: Progress task ID - config: Experiment configuration (for reading feature flags) - - Returns: - tuple: (conv_id, memcell_list) - """ - # Update status to processing - if progress and task_id is not None: - progress.update(task_id, status="Processing") - - # Create components based on configuration - cluster_mgr = None - mem_scene_state = None - cluster_storage = None - profile_mgr = None - profile_storage = None - - # Create MemCellExtractor - raw_data_list = convert_conversation_to_raw_data_list(conversation) - memcell_extractor = ConvMemCellExtractor(llm_provider=llm_provider) - - # Conditional creation: Cluster manager (per-conversation) - if config and config.enable_clustering: - cluster_storage = InMemoryClusterStorage( - enable_persistence=True, - persist_dir=Path(save_dir) / "clusters" / f"conv_{conv_id}", - ) - cluster_config = ClusterManagerConfig( - similarity_threshold=config.cluster_similarity_threshold, - max_time_gap_days=config.cluster_max_time_gap_days, - ) - cluster_mgr = ClusterManager(config=cluster_config) - mem_scene_state = MemSceneState() - - # Conditional creation: Profile manager - if config and config.enable_profile_extraction: - profile_storage = InMemoryProfileStorage( - enable_persistence=True, - persist_dir=Path(save_dir) / "profiles" / f"conv_{conv_id}", - enable_versioning=True, - ) - - # Set scenario type dynamically - scenario = ( - ScenarioType.SOLO - if config.profile_scenario.lower() == ScenarioType.SOLO.value - else ScenarioType.TEAM - ) - - profile_config = ProfileManagerConfig( - scenario=scenario, - min_confidence=config.profile_min_confidence, - batch_size=50, - ) - - profile_mgr = ProfileManager( - llm_provider=llm_provider, - config=profile_config, - group_id=f"locomo_conv_{conv_id}", - ) - - # Extract MemCells (pass foresight extraction config) - memcell_list = await memcell_extraction_from_conversation( - raw_data_list, - llm_provider=llm_provider, - memcell_extractor=memcell_extractor, - conv_id=conv_id, - progress=progress, - task_id=task_id, - enable_foresight_extraction=( - config.enable_foresight_extraction if config else False - ), - ) - - # Convert timestamps to datetime objects before saving - for memcell in memcell_list: - if hasattr(memcell, 'timestamp'): - ts = memcell.timestamp - if isinstance(ts, (int, float)): - memcell.timestamp = from_timestamp(ts) - elif isinstance(ts, str): - memcell.timestamp = from_iso_format(ts) - elif not isinstance(ts, datetime): - memcell.timestamp = get_now_with_timezone() - - # Concurrent atomic fact generation - if atomic_fact_extractor: - memcells_with_episode = [ - (idx, memcell) - for idx, memcell in enumerate(memcell_list) - if hasattr(memcell, 'episode') - and memcell.episode - and memcell.episode != "Episode extraction failed" - ] - - async def extract_single_atomic_fact(idx: int, memcell): - atomic_fact_result = await atomic_fact_extractor.extract_atomic_fact( - memcell=memcell, timestamp=memcell.timestamp - ) - return idx, atomic_fact_result - - sem = asyncio.Semaphore(20) - - async def extract_with_semaphore(idx, memcell): - async with sem: - return await extract_single_atomic_fact(idx, memcell) - - print( - f"\n Starting concurrent extraction of {len(memcells_with_episode)} atomic facts..." - ) - atomic_fact_tasks = [ - extract_with_semaphore(idx, memcell) - for idx, memcell in memcells_with_episode - ] - atomic_fact_results = await asyncio.gather(*atomic_fact_tasks) - - for original_idx, atomic_fact_result in atomic_fact_results: - if atomic_fact_result: - memcell_list[original_idx].atomic_fact = atomic_fact_result - - print( - f"Atomic fact extraction complete: {sum(1 for _, el in atomic_fact_results if el)}/{len(atomic_fact_results)} succeeded" - ) - - # Save single conversation results - memcell_dicts = [] - for memcell in memcell_list: - memcell_dict = memcell.to_dict() - if hasattr(memcell, 'atomic_fact') and memcell.atomic_fact: - memcell_dict['atomic_fact'] = memcell.atomic_fact.to_dict() - memcell_dicts.append(memcell_dict) - - output_file = os.path.join(save_dir, f"memcell_list_conv_{conv_id}.json") - with open(output_file, "w") as f: - json.dump(memcell_dicts, f, ensure_ascii=False, indent=2) - - # Clustering: process each memcell - cluster_stats = {} - if cluster_mgr and mem_scene_state: - group_id = f"conv_{conv_id}" - for memcell in memcell_list: - memcell_dict = memcell.to_dict() if hasattr(memcell, 'to_dict') else memcell - cluster_id, mem_scene_state = await cluster_mgr.cluster_memcell( - memcell_dict, mem_scene_state - ) - - # Save mem scene state - await cluster_storage.save_mem_scene(group_id, mem_scene_state.to_dict()) - - # Export clustering results - cluster_output_dir = Path(save_dir) / "clusters" / f"conv_{conv_id}" - cluster_output_dir.mkdir(parents=True, exist_ok=True) - - state_file = cluster_output_dir / f"mem_scene_{group_id}.json" - with open(state_file, "w", encoding="utf-8") as f: - json.dump( - mem_scene_state.to_dict(), f, ensure_ascii=False, indent=2, default=str - ) - - assignments_file = cluster_output_dir / f"cluster_map_{group_id}.json" - with open(assignments_file, "w", encoding="utf-8") as f: - json.dump( - {"assignments": mem_scene_state.eventid_to_cluster}, - f, - ensure_ascii=False, - indent=2, - ) - - cluster_stats = cluster_mgr.get_stats() - - # Profile extraction: after all memcells processed - profile_stats = {} - profile_count = 0 - if profile_mgr and profile_storage and memcell_list: - user_id_set = set() - for memcell in memcell_list: - if hasattr(memcell, 'user_id_list'): - user_id_set.update(memcell.user_id_list or []) - user_id_list = list(user_id_set) - - old_profiles_dict = await profile_storage.get_all_profiles() - old_profiles = list(old_profiles_dict.values()) - - new_profiles = await profile_mgr.extract_profiles( - memcells=memcell_list, # Pass MemCell objects, not dictionaries - old_profiles=old_profiles, - user_id_list=user_id_list, - ) - - group_id = f"locomo_conv_{conv_id}" - for profile in new_profiles: - if isinstance(profile, dict): - user_id = profile.get('user_id') - else: - user_id = getattr(profile, 'user_id', None) - - if user_id: - await profile_storage.save_profile( - user_id, - profile, - metadata={ - "group_id": group_id, - "scenario": config.profile_scenario if config else "solo", - "memcell_count": len(memcell_list), - }, - ) - profile_count += 1 - - profile_stats = profile_mgr.get_stats() - - # Save statistics - stats_output = { - "conv_id": conv_id, - "memcells": len(memcell_list), - "clustering_enabled": config.enable_clustering if config else False, - "profile_enabled": config.enable_profile_extraction if config else False, - "foresight_enabled": config.enable_foresight_extraction if config else False, - } - - if cluster_stats: - stats_output["clustering"] = cluster_stats - if profile_stats: - stats_output["profiles"] = profile_stats - stats_output["profile_count"] = profile_count - - stats_file = Path(save_dir) / "stats" / f"conv_{conv_id}_stats.json" - stats_file.parent.mkdir(parents=True, exist_ok=True) - with open(stats_file, "w") as f: - json.dump(stats_output, f, ensure_ascii=False, indent=2) - - # Update progress - if progress_counter: - progress_counter['completed'] += 1 - - return conv_id, memcell_list - - -async def main(): - """Main function - concurrent processing of all conversations.""" - - config = ExperimentConfig() - llm_service = config.llm_service - dataset_path = config.datase_path - raw_data_dict = raw_data_load(dataset_path) - - CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) - os.makedirs(os.path.join(CURRENT_DIR, config.experiment_name), exist_ok=True) - os.makedirs( - os.path.join(CURRENT_DIR, config.experiment_name, "memcells"), exist_ok=True - ) - save_dir = os.path.join(CURRENT_DIR, config.experiment_name, "memcells") - - console = Console() - - # Print configuration info - console.print("\n" + "=" * 60, style="bold cyan") - console.print("Experiment Configuration", style="bold cyan") - console.print("=" * 60, style="bold cyan") - console.print(f"Experiment name: {config.experiment_name}", style="cyan") - console.print(f"Dataset path: {config.datase_path}", style="cyan") - console.print(f"\nFeature flags:", style="bold yellow") - console.print( - f" - Foresight extraction: {'✅ Enabled' if config.enable_foresight_extraction else '❌ Disabled'}", - style="green" if config.enable_foresight_extraction else "dim", - ) - console.print( - f" - Clustering: {'✅ Enabled' if config.enable_clustering else '❌ Disabled'}", - style="green" if config.enable_clustering else "dim", - ) - console.print( - f" - Profile extraction: {'✅ Enabled' if config.enable_profile_extraction else '❌ Disabled'}", - style="green" if config.enable_profile_extraction else "dim", - ) - - if config.enable_clustering: - console.print(f"\nClustering config:", style="bold") - console.print( - f" - Similarity threshold: {config.cluster_similarity_threshold}", - style="dim", - ) - console.print( - f" - Max time gap: {config.cluster_max_time_gap_days} days", style="dim" - ) - - if config.enable_profile_extraction: - console.print(f"\nProfile config:", style="bold") - console.print(f" - Scenario: {config.profile_scenario}", style="dim") - console.print( - f" - Min confidence: {config.profile_min_confidence}", style="dim" - ) - console.print(f" - Min MemCells: {config.profile_min_memcells}", style="dim") - console.print("=" * 60 + "\n", style="bold cyan") - - # Checkpoint resume: check completed conversations - completed_convs = set() - for conv_id in raw_data_dict.keys(): - output_file = os.path.join(save_dir, f"memcell_list_conv_{conv_id}.json") - if os.path.exists(output_file): - # Validate file (non-empty and parseable) - try: - with open(output_file, "r") as f: - data = json.load(f) - if data and len(data) > 0: # Ensure data available - completed_convs.add(conv_id) - console.print( - f"✅ Skipping completed conversation: {conv_id} ({len(data)} memcells)", - style="green", - ) - except Exception as e: - console.print( - f"⚠️ Conversation {conv_id} file corrupted, will reprocess: {e}", - style="yellow", - ) - - # Filter conversations needing processing - pending_raw_data_dict = { - conv_id: conv_data - for conv_id, conv_data in raw_data_dict.items() - if conv_id not in completed_convs - } - - console.print( - f"\n📊 Total conversations found: {len(raw_data_dict)}", style="bold cyan" - ) - console.print(f"✅ Completed: {len(completed_convs)}", style="bold green") - console.print(f"⏳ Pending: {len(pending_raw_data_dict)}", style="bold yellow") - - if len(pending_raw_data_dict) == 0: - console.print( - f"\n🎉 All conversations completed, nothing to process!", style="bold green" - ) - return - - total_messages = sum(len(conv) for conv in pending_raw_data_dict.values()) - console.print(f"📝 Pending messages: {total_messages}", style="bold blue") - console.print( - f"🚀 Starting concurrent processing of remaining conversations...\n", - style="bold green", - ) - - # Create shared LLM Provider and MemCell Extractor instances (solve connection race issue) - console.print("⚙️ Initializing LLM Provider...", style="yellow") - console.print(f" Model: {config.llm_config[llm_service]['model']}", style="dim") - console.print( - f" Base URL: {config.llm_config[llm_service]['base_url']}", style="dim" - ) - - shared_llm_provider = LLMProvider( - provider_type="openai", - model=config.llm_config[llm_service]["model"], - api_key=config.llm_config[llm_service]["api_key"], - base_url=config.llm_config[llm_service]["base_url"], - temperature=config.llm_config[llm_service]["temperature"], - max_tokens=config.llm_config[llm_service]["max_tokens"], - ) - - # Create shared Atomic Fact Extractor - console.print("⚙️ Initializing Atomic Fact Extractor...", style="yellow") - shared_atomic_fact_extractor = AtomicFactExtractor(llm_provider=shared_llm_provider) - - # 🔥 Use pending conversation dict (checkpoint resume) - # Create progress counter - progress_counter = { - 'total': len(pending_raw_data_dict), - 'completed': 0, - 'failed': 0, - } - - # Use Rich progress bar - start_time = time.time() - - with Progress( - SpinnerColumn(), - TextColumn("[progress.description]{task.description}"), - BarColumn(), - MofNCompleteColumn(), # Show "3/10" format - TextColumn("•"), - TaskProgressColumn(), # Show percentage - TextColumn("•"), - TimeElapsedColumn(), # Elapsed time - TextColumn("•"), - TimeRemainingColumn(), # Remaining time - TextColumn("•"), - TextColumn("[bold blue]{task.fields[status]}"), - console=console, - transient=False, - refresh_per_second=1, - ) as progress: - # Create main progress task - main_task = progress.add_task( - "[bold cyan]🎯 Overall Progress", - total=len(raw_data_dict), - completed=len(completed_convs), # Completed count - status="Processing", - ) - - # First add completed conversations to progress bar (show as completed) - conversation_tasks = {} - for conv_id in completed_convs: - conv_task_id = progress.add_task( - f"[green]Conv-{conv_id}", - total=len(raw_data_dict[conv_id]), - completed=len(raw_data_dict[conv_id]), # 100% - status="✅ (Skipped)", - ) - conversation_tasks[conv_id] = conv_task_id - - # Create progress bar tasks for pending conversations - updated_tasks = [] - for conv_id, conversation in pending_raw_data_dict.items(): - # Create progress bar for each conversation - conv_task_id = progress.add_task( - f"[yellow]Conv-{conv_id}", # Simplified name - total=len(conversation), # Total messages - completed=0, # Initialize to 0 - status="Waiting", - ) - conversation_tasks[conv_id] = conv_task_id - - # Create processing task - task = process_single_conversation( - conv_id, - conversation, - save_dir, - llm_provider=shared_llm_provider, - atomic_fact_extractor=shared_atomic_fact_extractor, - progress_counter=progress_counter, - progress=progress, - task_id=conv_task_id, - config=config, # Pass configuration - ) - updated_tasks.append(task) - - # Define completion update function - async def run_with_completion(task, conv_id): - result = await task - progress.update( - conversation_tasks[conv_id], - status="✅", - completed=progress.tasks[conversation_tasks[conv_id]].total, - ) - progress.update(main_task, advance=1) - return result - - # Execute all pending tasks concurrently - if updated_tasks: - results = await asyncio.gather( - *[ - run_with_completion(task, conv_id) - for (conv_id, _), task in zip( - pending_raw_data_dict.items(), updated_tasks - ) - ] - ) - else: - results = [] - # with open(os.path.join(save_dir, "response_info.json"), "w") as f: - # json.dump(shared_llm_provider.provider.response_info, f, ensure_ascii=False, indent=2) - # Update main progress to complete - progress.update(main_task, status="✅ Complete") - - end_time = time.time() - - # Gather statistics - all_memcells = [] - successful_convs = 0 - for conv_id, memcell_list in results: - if memcell_list: - successful_convs += 1 - all_memcells.extend(memcell_list) - - console.print("\n" + "=" * 60, style="dim") - console.print("📊 Processing completion statistics:", style="bold") - console.print( - f" ✅ Successfully processed: {successful_convs}/{len(raw_data_dict)}", - style="green", - ) - console.print(f" 📝 Total memcells extracted: {len(all_memcells)}", style="blue") - console.print(f" ⏱️ Total time: {end_time - start_time:.2f}s", style="yellow") - console.print( - f" 🚀 Average per conversation: {(end_time - start_time)/len(raw_data_dict):.2f}s", - style="cyan", - ) - console.print("=" * 60, style="dim") - - # Save summary results - all_memcells_dicts = [memcell.to_dict() for memcell in all_memcells] - summary_file = os.path.join(save_dir, "memcell_list_all.json") - with open(summary_file, "w") as f: - json.dump(all_memcells_dicts, f, ensure_ascii=False, indent=2) - console.print(f"\n💾 Summary results saved to: {summary_file}", style="green") - - # Aggregate clustering and Profile statistics - # Collect clustering and Profile info from all conversations - total_clusters = 0 - total_profiles = 0 - cluster_stats_list = [] - profile_stats_list = [] - - stats_dir = Path(save_dir) / "stats" - if stats_dir.exists(): - for stats_file in stats_dir.glob("conv_*_stats.json"): - try: - with open(stats_file) as f: - conv_stats = json.load(f) - total_clusters += conv_stats.get("clustering", {}).get( - "total_clusters", 0 - ) - total_profiles += conv_stats.get("profile_count", 0) - cluster_stats_list.append(conv_stats.get("clustering", {})) - profile_stats_list.append(conv_stats.get("profiles", {})) - except Exception: - pass - - # Save processing summary (with clustering and Profile statistics) - summary = { - "total_conversations": len(raw_data_dict), - "successful_conversations": successful_convs, - "total_memcells": len(all_memcells), - "total_clusters": total_clusters, - "total_profiles": total_profiles, - "processing_time_seconds": end_time - start_time, - "average_time_per_conversation": (end_time - start_time) / len(raw_data_dict), - "conversation_results": { - conv_id: len(memcell_list) for conv_id, memcell_list in results - }, - "clustering_summary": { - "total_clusters": total_clusters, - "avg_clusters_per_conv": ( - total_clusters / successful_convs if successful_convs > 0 else 0 - ), - }, - "profile_summary": { - "total_profiles": total_profiles, - "avg_profiles_per_conv": ( - total_profiles / successful_convs if successful_convs > 0 else 0 - ), - }, - } - summary_info_file = os.path.join(save_dir, "processing_summary.json") - with open(summary_info_file, "w") as f: - json.dump(summary, f, ensure_ascii=False, indent=2) - console.print(f"📊 Processing summary saved to: {summary_info_file}", style="green") - - # Print clustering and Profile statistics - console.print(f"\n📊 Clustering statistics:", style="bold cyan") - console.print(f" - Total clusters: {total_clusters}", style="cyan") - console.print( - f" - Average per conversation: {total_clusters / successful_convs if successful_convs > 0 else 0:.1f}", - style="cyan", - ) - console.print(f"\n👤 Profile statistics:", style="bold green") - console.print(f" - Total Profiles: {total_profiles}", style="green") - console.print( - f" - Average per conversation: {total_profiles / successful_convs if successful_convs > 0 else 0:.1f}\n", - style="green", - ) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/stage2_index_building.py b/methods/EverCore/evaluation/src/adapters/evermemos/stage2_index_building.py deleted file mode 100644 index e49c20a4..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/stage2_index_building.py +++ /dev/null @@ -1,397 +0,0 @@ -import json -import os -import sys -import pickle -from pathlib import Path - -import nltk -from nltk.corpus import stopwords -from nltk.stem import PorterStemmer -from nltk.tokenize import word_tokenize -from rank_bm25 import BM25Okapi -import asyncio - - -from evaluation.src.adapters.evermemos.config import ExperimentConfig -from agentic_layer.vectorize_service import get_vectorize_service - - -def ensure_nltk_data(): - """Ensure required NLTK data is downloaded.""" - try: - nltk.data.find("tokenizers/punkt") - except LookupError: - print("Downloading punkt...") - nltk.download("punkt", quiet=True) - - try: - nltk.data.find("tokenizers/punkt_tab") - except LookupError: - print("Downloading punkt_tab...") - nltk.download("punkt_tab", quiet=True) - - try: - nltk.data.find("corpora/stopwords") - except LookupError: - print("Downloading stopwords...") - nltk.download("stopwords", quiet=True) - - # Verify stopwords availability - try: - from nltk.corpus import stopwords - - test_stopwords = stopwords.words("english") - if not test_stopwords: - raise ValueError("Stopwords is empty") - except Exception as e: - print(f"Warning: NLTK stopwords error: {e}") - print("Re-downloading stopwords...") - nltk.download("stopwords", quiet=False, force=True) - - -def build_searchable_text(doc: dict) -> str: - """ - Build searchable text from a document with weighted fields. - - Priority: - 1. If atomic fact data exists (atomic_fact key), use atomic_fact for indexing - 2. Otherwise, fall back to original fields: - - "subject" corresponds to "title" (weight * 3) - - "summary" corresponds to "summary" (weight * 2) - - "episode" corresponds to "content" (weight * 1) - """ - parts = [] - - # Prefer atomic_fact from atomic_fact key (if exists) - if doc.get("atomic_fact") and doc["atomic_fact"].get("atomic_fact"): - atomic_facts = doc["atomic_fact"]["atomic_fact"] - if isinstance(atomic_facts, list): - # Handle nested atomic_fact structure - # atomic_fact can be list of strings or list of dicts (containing "fact" and "embedding") - for fact in atomic_facts: - if isinstance(fact, dict) and "fact" in fact: - # New format: {"fact": "...", "embedding": [...]} - parts.append(fact["fact"]) - elif isinstance(fact, str): - # Old format: pure string list (backward compatible) - parts.append(fact) - return " ".join(str(fact) for fact in parts if fact) - - # Fall back to original fields (maintain backward compatibility) - # Title has highest weight (repeat 3 times) - if doc.get("subject"): - parts.extend([doc["subject"]] * 3) - - # Summary (repeat 2 times) - if doc.get("summary"): - parts.extend([doc["summary"]] * 2) - - # Content - if doc.get("episode"): - parts.append(doc["episode"]) - - return " ".join(str(part) for part in parts if part) - - -def tokenize(text: str, stemmer, stop_words: set) -> list[str]: - """ - NLTK-based tokenization with stemming and stopword removal. - """ - if not text: - return [] - - tokens = word_tokenize(text.lower()) - - processed_tokens = [ - stemmer.stem(token) - for token in tokens - if token.isalpha() and len(token) >= 2 and token not in stop_words - ] - - return processed_tokens - - -def build_bm25_index( - config: ExperimentConfig, data_dir: Path, bm25_save_dir: Path -) -> list[list[float]]: - # --- NLTK Setup --- - print("Ensuring NLTK data is available...") - ensure_nltk_data() - stemmer = PorterStemmer() - stop_words = set(stopwords.words("english")) - - # --- Data Loading and Processing --- - # corpus = [] # This line is removed as per the new_code - # original_docs = [] # This line is removed as per the new_code - - print(f"Reading data from: {data_dir}") - - for i in range(config.num_conv): - file_path = data_dir / f"memcell_list_conv_{i}.json" - if not file_path.exists(): - print(f"Warning: File not found, skipping: {file_path}") - continue - - print(f"\nProcessing {file_path.name}...") - - corpus = [] - original_docs = [] - - with open(file_path, "r", encoding="utf-8") as f: - data = json.load(f) - - for doc in data: - original_docs.append(doc) - searchable_text = build_searchable_text(doc) - tokenized_text = tokenize(searchable_text, stemmer, stop_words) - corpus.append(tokenized_text) - - if not corpus: - print( - f"Warning: No documents found in {file_path.name}. Skipping index creation." - ) - continue - - print(f"Processed {len(original_docs)} documents from {file_path.name}.") - - # --- BM25 Indexing --- - print(f"Building BM25 index for {file_path.name}...") - bm25 = BM25Okapi(corpus) - - # --- Saving the Index --- - index_data = {"bm25": bm25, "docs": original_docs} - - output_path = bm25_save_dir / f"bm25_index_conv_{i}.pkl" - print(f"Saving index to: {output_path}") - with open(output_path, "wb") as f: - pickle.dump(index_data, f) - - -async def build_emb_index(config: ExperimentConfig, data_dir: Path, emb_save_dir: Path): - """ - Build Embedding index (stable version). - - Performance optimization strategy: - 1. Controlled concurrency: strictly follow API Semaphore(5) limit - 2. Conservative batch size: 256 texts/batch (avoid timeouts) - 3. Serial batch submission: grouped submission to avoid queue buildup - 4. Progress monitoring: real-time progress and speed display - - Optimization effects: - - Stability first, avoid timeouts and API overload - - API concurrency: 5 (controlled by vectorize_service.Semaphore) - - Batch size: 256 (balance stability and efficiency) - """ - # Conservative batch size (avoid timeouts) - BATCH_SIZE = ( - 256 # Use larger batches (single API call processes more, reduce request count) - ) - MAX_CONCURRENT_BATCHES = 5 # Strictly control concurrency (match Semaphore(5)) - - import time # For performance statistics - - for i in range(config.num_conv): - file_path = data_dir / f"memcell_list_conv_{i}.json" - if not file_path.exists(): - print(f"Warning: File not found, skipping: {file_path}") - continue - - print(f"\n{'='*60}") - print(f"Processing {file_path.name} for embedding...") - print(f"{'='*60}") - - with open(file_path, "r", encoding="utf-8") as f: - original_docs = json.load(f) - - texts_to_embed = [] - doc_field_map = [] - for doc_idx, doc in enumerate(original_docs): - # Prefer atomic fact data from atomic_fact key (if exists) - if doc.get("atomic_fact") and doc["atomic_fact"].get("atomic_fact"): - atomic_facts = doc["atomic_fact"]["atomic_fact"] - if isinstance(atomic_facts, list) and atomic_facts: - # calculate embedding for each atomic_fact separately (MaxSim strategy) - # This precisely matches specific atomic facts, avoiding semantic dilution - for fact_idx, fact in enumerate(atomic_facts): - # compatible with both formats (string / dict) - fact_text = None - if isinstance(fact, dict) and "fact" in fact: - # New format: {"fact": "...", "embedding": [...]} - fact_text = fact["fact"] - elif isinstance(fact, str): - # Old format: pure string - fact_text = fact - - # Ensure fact is non-empty - if fact_text and fact_text.strip(): - texts_to_embed.append(fact_text) - doc_field_map.append((doc_idx, f"atomic_fact_{fact_idx}")) - continue - - # Fall back to original fields (maintain backward compatibility) - for field in ["subject", "summary", "episode"]: - if text := doc.get(field): - texts_to_embed.append(text) - doc_field_map.append((doc_idx, field)) - - if not texts_to_embed: - print( - f"Warning: No documents found in {file_path.name}. Skipping embedding creation." - ) - continue - - total_texts = len(texts_to_embed) - total_batches = (total_texts + BATCH_SIZE - 1) // BATCH_SIZE - print(f"Total texts to embed: {total_texts}") - print(f"Batch size: {BATCH_SIZE}") - print(f"Total batches: {total_batches}") - print(f"Max concurrent batches: {MAX_CONCURRENT_BATCHES}") - print(f"\nStarting parallel embedding generation...") - - # Stable batch processing (avoid timeouts) - start_time = time.time() - - async def process_batch_with_retry( - batch_idx: int, batch_texts: list, max_retries: int = 3 - ) -> tuple[int, list]: - """Process single batch (async + retry).""" - for attempt in range(max_retries): - try: - # Call API to get embeddings (concurrency controlled by Semaphore(5)) - batch_embeddings = await get_vectorize_service().get_embeddings( - batch_texts - ) - return (batch_idx, batch_embeddings) - except Exception as e: - if attempt < max_retries - 1: - wait_time = 2.0 * (2**attempt) # Exponential backoff: 2s, 4s - print( - f" ⚠️ Batch {batch_idx + 1}/{total_batches} failed (attempt {attempt + 1}), retrying in {wait_time:.1f}s: {e}" - ) - await asyncio.sleep(wait_time) - else: - print( - f" ❌ Batch {batch_idx + 1}/{total_batches} failed after {max_retries} attempts: {e}" - ) - return (batch_idx, []) - - # Grouped serial submission (avoid queue buildup causing timeouts) - print( - f"Processing {total_batches} batches in groups of {MAX_CONCURRENT_BATCHES}..." - ) - - batch_results = [] - completed = 0 - - # Grouped submission, max MAX_CONCURRENT_BATCHES concurrent per group - for group_start in range(0, total_texts, BATCH_SIZE * MAX_CONCURRENT_BATCHES): - # Calculate batch range for current group - group_end = min( - group_start + BATCH_SIZE * MAX_CONCURRENT_BATCHES, total_texts - ) - group_tasks = [] - - for j in range(group_start, group_end, BATCH_SIZE): - batch_idx = j // BATCH_SIZE - batch_texts = texts_to_embed[j : j + BATCH_SIZE] - task = process_batch_with_retry(batch_idx, batch_texts) - group_tasks.append(task) - - # Process current group concurrently (max MAX_CONCURRENT_BATCHES) - print( - f" Group {group_start//BATCH_SIZE//MAX_CONCURRENT_BATCHES + 1}: Processing {len(group_tasks)} batches concurrently..." - ) - group_results = await asyncio.gather(*group_tasks, return_exceptions=False) - batch_results.extend(group_results) - - completed += len(group_tasks) - progress = (completed / total_batches) * 100 - print(f" Progress: {completed}/{total_batches} batches ({progress:.1f}%)") - - # Inter-group delay (give API server breathing room) - if group_end < total_texts: - await asyncio.sleep(1.0) # 1s inter-group delay - - # Reorganize results by batch order - all_embeddings = [] - for batch_idx, batch_embeddings in sorted(batch_results, key=lambda x: x[0]): - all_embeddings.extend(batch_embeddings) - - elapsed_time = time.time() - start_time - speed = total_texts / elapsed_time if elapsed_time > 0 else 0 - print(f"\n✅ Embedding generation complete!") - print(f" - Total texts: {total_texts}") - print(f" - Total embeddings: {len(all_embeddings)}") - print(f" - Time elapsed: {elapsed_time:.2f}s") - print(f" - Speed: {speed:.1f} texts/sec") - print(f" - Average batch time: {elapsed_time/total_batches:.2f}s") - - # Verify result completeness - if len(all_embeddings) != total_texts: - print( - f" ⚠️ Warning: Expected {total_texts} embeddings, got {len(all_embeddings)}" - ) - else: - print(f" ✓ All embeddings generated successfully") - - # Re-associate embeddings with their original documents and fields - # Support multiple atomic_fact embeddings per document (for MaxSim strategy) - doc_embeddings = [{"doc": doc, "embeddings": {}} for doc in original_docs] - - for (doc_idx, field), emb in zip(doc_field_map, all_embeddings): - # If atomic_fact field, save as list (support multiple atomic_facts) - if field.startswith("atomic_fact_"): - if "atomic_facts" not in doc_embeddings[doc_idx]["embeddings"]: - doc_embeddings[doc_idx]["embeddings"]["atomic_facts"] = [] - doc_embeddings[doc_idx]["embeddings"]["atomic_facts"].append(emb) - else: - # Save other fields directly - doc_embeddings[doc_idx]["embeddings"][field] = emb - - # The final structure of the saved .pkl file will be a list of dicts: - # [ - # { - # "doc": { ... original document ... }, - # "embeddings": { - # "atomic_facts": [ # New: atomic_fact embeddings list (for MaxSim) - # [ ... embedding vector for fact 0 ... ], - # [ ... embedding vector for fact 1 ... ], - # ... - # ], - # "subject": [ ... embedding vector ... ], # Backward compatible legacy fields - # "summary": [ ... embedding vector ... ], - # "episode": [ ... embedding vector ... ] - # } - # }, - # ... - # ] - output_path = emb_save_dir / f"embedding_index_conv_{i}.pkl" - emb_save_dir.mkdir(parents=True, exist_ok=True) - print(f"Saving embeddings to: {output_path}") - with open(output_path, "wb") as f: - pickle.dump(doc_embeddings, f) - - -async def main(): - """Main function to build and save the BM25 index.""" - # --- Configuration --- - # The directory containing the JSON files - config = ExperimentConfig() - data_dir = Path(__file__).parent / config.experiment_name / "memcells" - bm25_save_dir = Path(__file__).parent / config.experiment_name / "bm25_index" - emb_save_dir = Path(__file__).parent / config.experiment_name / "vectors" - os.makedirs(bm25_save_dir, exist_ok=True) - os.makedirs(emb_save_dir, exist_ok=True) - build_bm25_index(config, data_dir, bm25_save_dir) - if config.use_emb: - await build_emb_index(config, data_dir, emb_save_dir) - # data_dir = Path("/Users/admin/Documents/Projects/b001-memsys/evaluation/locomo_evaluation/results/locomo_evaluation_0/") - - # Where to save the final index file - # output_path = data_dir / "bm25_index.pkl" # This line is removed as per the new_code - - print("\nAll indexing complete!") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/stage3_memory_retrivel.py b/methods/EverCore/evaluation/src/adapters/evermemos/stage3_memory_retrivel.py deleted file mode 100644 index 0f85966f..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/stage3_memory_retrivel.py +++ /dev/null @@ -1,1474 +0,0 @@ -import json -import os -import sys -import pickle -from pathlib import Path -from typing import List, Tuple, Optional -import time - -import nltk -import numpy as np -from nltk.corpus import stopwords -from nltk.stem import PorterStemmer -from nltk.tokenize import word_tokenize -import asyncio -from tqdm import tqdm - - -from evaluation.src.adapters.evermemos.config import ExperimentConfig - -# from evaluation.src.adapters.evermemos.tools.embedding_provider import EmbeddingProvider -# from evaluation.src.adapters.evermemos.tools.reranker_provider import RerankerProvider -from agentic_layer.vectorize_service import get_vectorize_service -from agentic_layer import rerank_service - -from evaluation.src.adapters.evermemos.tools import agentic_utils - -from memory_layer.llm.llm_provider import LLMProvider - - -# This file depends on the rank_bm25 library. -# If you haven't installed it yet, run: pip install rank_bm25 - - -def ensure_nltk_data(): - """Ensure required NLTK data is downloaded.""" - try: - nltk.data.find("tokenizers/punkt") - except LookupError: - print("Downloading punkt...") - nltk.download("punkt", quiet=True) - - try: - nltk.data.find("tokenizers/punkt_tab") - except LookupError: - print("Downloading punkt_tab...") - nltk.download("punkt_tab", quiet=True) - - try: - nltk.data.find("corpora/stopwords") - except LookupError: - print("Downloading stopwords...") - nltk.download("stopwords", quiet=True) - - # Verify stopwords availability - try: - from nltk.corpus import stopwords - - test_stopwords = stopwords.words("english") - if not test_stopwords: - raise ValueError("Stopwords is empty") - except Exception as e: - print(f"Warning: NLTK stopwords error: {e}") - print("Re-downloading stopwords...") - nltk.download("stopwords", quiet=False, force=True) - - -def cosine_similarity(query_vec: np.ndarray, doc_vecs: np.ndarray) -> np.ndarray: - """ - Calculates cosine similarity between a query vector and multiple document vectors. - - Args: - query_vec: A 1D numpy array for the query. - doc_vecs: A 2D numpy array where each row is a document vector. - - Returns: - A 1D numpy array of cosine similarity scores. - """ - # Calculate dot product - dot_product = np.dot(doc_vecs, query_vec) - - # Calculate norms - query_norm = np.linalg.norm(query_vec) - doc_norms = np.linalg.norm(doc_vecs, axis=1) - - # Calculate cosine similarity, handling potential division by zero - denominator = query_norm * doc_norms - # Replace 0s in denominator with a small number to avoid division by zero - denominator[denominator == 0] = 1e-9 - - similarity_scores = dot_product / denominator - - return similarity_scores - - -def compute_maxsim_score( - query_emb: np.ndarray, atomic_fact_embs: List[np.ndarray] -) -> float: - """ - Compute maximum similarity between query and multiple atomic_fact embeddings (MaxSim strategy). - - Core idea of MaxSim strategy: - - Find the single most relevant atomic_fact to the query - - If any atomic_fact is strongly related to query, consider the entire atomic fact group relevant - - Avoid score dilution by irrelevant facts - - Suitable for memory retrieval where users typically focus on one aspect - - Optimization: use vectorized matrix operations for 2-3x speed boost. - - Args: - query_emb: Query embedding vector (1D numpy array) - atomic_fact_embs: List of atomic_fact embedding vectors - - Returns: - Maximum similarity score (float, range [-1, 1], typically [0, 1]) - """ - if not atomic_fact_embs: - return 0.0 - - query_norm = np.linalg.norm(query_emb) - if query_norm == 0: - return 0.0 - - # Optimization: use matrix operations instead of loops (2-3x speedup) - try: - # Convert list to matrix: shape = (n_facts, embedding_dim) - fact_matrix = np.array(atomic_fact_embs) - - # Batch compute norms for all facts - fact_norms = np.linalg.norm(fact_matrix, axis=1) - - # Filter zero vectors - valid_mask = fact_norms > 0 - if not np.any(valid_mask): - return 0.0 - - # Vectorized computation of all similarities - # sims = (fact_matrix @ query_emb) / (query_norm * fact_norms) - dot_products = np.dot(fact_matrix[valid_mask], query_emb) - sims = dot_products / (query_norm * fact_norms[valid_mask]) - - # Return maximum similarity - return float(np.max(sims)) - - except Exception as e: - # Fall back to loop method (compatibility guarantee) - similarities = [] - for fact_emb in atomic_fact_embs: - fact_norm = np.linalg.norm(fact_emb) - if fact_norm == 0: - continue - sim = np.dot(query_emb, fact_emb) / (query_norm * fact_norm) - similarities.append(sim) - return max(similarities) if similarities else 0.0 - - -def tokenize(text: str, stemmer, stop_words: set) -> list[str]: - """ - NLTK-based tokenization with stemming and stopword removal. - This must be identical to the tokenization used during indexing. - """ - if not text: - return [] - - tokens = word_tokenize(text.lower()) - - processed_tokens = [ - stemmer.stem(token) - for token in tokens - if token.isalpha() and len(token) >= 2 and token not in stop_words - ] - - return processed_tokens - - -def search_with_bm25_index(query: str, bm25, docs, top_n: int = 5): - """ - Performs BM25 search using a pre-loaded index. - """ - stemmer = PorterStemmer() - stop_words = set(stopwords.words("english")) - tokenized_query = tokenize(query, stemmer, stop_words) - - if not tokenized_query: - print("Warning: Query is empty after tokenization.") - return [] - - doc_scores = bm25.get_scores(tokenized_query) - results_with_scores = list(zip(docs, doc_scores)) - sorted_results = sorted(results_with_scores, key=lambda x: x[1], reverse=True) - return sorted_results[:top_n] - - -def reciprocal_rank_fusion( - emb_results: List[Tuple[dict, float]], - bm25_results: List[Tuple[dict, float]], - k: int = 60, -) -> List[Tuple[dict, float]]: - """ - Fuse Embedding and BM25 retrieval results using RRF (Reciprocal Rank Fusion). - - RRF is a normalization-free fusion strategy sensitive to ranking position. - Formula: RRF_score(doc) = Σ(1 / (k + rank_i)) - - Advantages: - 1. No need to normalize scores (Embedding and BM25 have different score ranges) - 2. Simple and effective, widely validated in industry (Elasticsearch, etc.) - 3. More sensitive to top results (high ranks contribute more) - 4. No parameter tuning needed (k=60 is empirically optimal) - - Args: - emb_results: Embedding retrieval results [(doc, score), ...] - bm25_results: BM25 retrieval results [(doc, score), ...] - k: RRF constant, typically 60 (empirical value) - - Returns: - Fused results [(doc, rrf_score), ...], sorted by RRF score descending - - Example: - emb_results = [(doc1, 0.92), (doc2, 0.87), (doc3, 0.81)] - bm25_results = [(doc2, 15.3), (doc1, 12.7), (doc4, 10.2)] - - Doc1: 1/(60+1) + 1/(60+2) = 0.0323 - Doc2: 1/(60+2) + 1/(60+1) = 0.0323 - Doc3: 1/(60+3) + 0 = 0.0159 - Doc4: 0 + 1/(60+3) = 0.0159 - - Fused result: [(doc1, 0.0323), (doc2, 0.0323), (doc3, 0.0159), (doc4, 0.0159)] - """ - doc_rrf_scores = {} # {event_id: rrf_score} - doc_map = {} # {event_id: doc} - - # Process Embedding retrieval results - for rank, (doc, score) in enumerate(emb_results, start=1): - doc_id = doc.get("event_id", id(doc)) # Prefer event_id, fall back to id() - if doc_id not in doc_map: - doc_map[doc_id] = doc - doc_rrf_scores[doc_id] = doc_rrf_scores.get(doc_id, 0.0) + 1.0 / (k + rank) - - # Process BM25 retrieval results - for rank, (doc, score) in enumerate(bm25_results, start=1): - doc_id = doc.get("event_id", id(doc)) # Prefer event_id, fall back to id() - if doc_id not in doc_map: - doc_map[doc_id] = doc - doc_rrf_scores[doc_id] = doc_rrf_scores.get(doc_id, 0.0) + 1.0 / (k + rank) - - # Sort by RRF score - sorted_docs = sorted(doc_rrf_scores.items(), key=lambda x: x[1], reverse=True) - - # Convert back to (doc, score) format - fused_results = [(doc_map[doc_id], rrf_score) for doc_id, rrf_score in sorted_docs] - - return fused_results - - -def multi_rrf_fusion( - results_list: List[List[Tuple[dict, float]]], k: int = 60 -) -> List[Tuple[dict, float]]: - """ - Fuse multiple query retrieval results using RRF (multi-query fusion). - - Similar to dual-path RRF, but supports fusing arbitrary number of retrieval results. - Each result set contributes score: 1 / (k + rank) - - Principle: - - Documents ranking high across multiple queries accumulate high scores and rank high finally - - This is a "voting mechanism": documents deemed relevant by multiple queries are more likely truly relevant - - Args: - results_list: Multiple retrieval result lists [ - [(doc1, score), (doc2, score), ...], # Query 1 results - [(doc3, score), (doc1, score), ...], # Query 2 results - [(doc4, score), (doc2, score), ...], # Query 3 results - ] - k: RRF constant (default 60) - - Returns: - Fused results [(doc, rrf_score), ...], sorted by RRF score descending - - Example: - Query 1 results: [(doc_A, 0.9), (doc_B, 0.8), (doc_C, 0.7)] - Query 2 results: [(doc_B, 0.88), (doc_D, 0.82), (doc_A, 0.75)] - Query 3 results: [(doc_A, 0.92), (doc_E, 0.85), (doc_B, 0.80)] - - RRF score calculation: - doc_A: 1/(60+1) + 1/(60+3) + 1/(60+1) = 0.0323 <- appears in Q1,Q2,Q3 - doc_B: 1/(60+2) + 1/(60+1) + 1/(60+3) = 0.0323 <- appears in Q1,Q2,Q3 - doc_C: 1/(60+3) + 0 + 0 = 0.0159 <- only in Q1 - doc_D: 0 + 1/(60+2) + 0 = 0.0161 <- only in Q2 - doc_E: 0 + 0 + 1/(60+2) = 0.0161 <- only in Q3 - - Fused result: doc_A and doc_B rank highest (endorsed by multiple queries) - """ - if not results_list: - return [] - - # If only one result set, return directly - if len(results_list) == 1: - return results_list[0] - - doc_rrf_scores = {} # {event_id: rrf_score} - doc_map = {} # {event_id: doc} - - # Iterate through each query's retrieval results - for query_results in results_list: - for rank, (doc, score) in enumerate(query_results, start=1): - doc_id = doc.get("event_id", id(doc)) # Prefer event_id, fall back to id() - if doc_id not in doc_map: - doc_map[doc_id] = doc - # Accumulate RRF score - doc_rrf_scores[doc_id] = doc_rrf_scores.get(doc_id, 0.0) + 1.0 / (k + rank) - - # Sort by RRF score - sorted_docs = sorted(doc_rrf_scores.items(), key=lambda x: x[1], reverse=True) - - # Convert back to (doc, score) format - fused_results = [(doc_map[doc_id], rrf_score) for doc_id, rrf_score in sorted_docs] - - return fused_results - - -async def lightweight_retrieval( - query: str, emb_index, bm25, docs, config: ExperimentConfig -) -> Tuple[List[Tuple[dict, float]], dict]: - """ - Lightweight fast retrieval (no LLM calls, pure algorithmic retrieval). - - Supports three search modes (controlled by config.lightweight_search_mode): - - "bm25_only": Only use BM25 search (fast, lexical matching) - - "hybrid": BM25 + Embedding + RRF fusion (balanced) - - "emb_only": Only use Embedding search (semantic matching) - - Advantages: - - Fast: no LLM calls, pure vector/lexical retrieval - - Low cost: no LLM API consumption - - Stable: no network dependency, pure local computation - - Suitable scenarios: - - Latency-sensitive scenarios - - Budget-constrained scenarios - - Simple and clear query scenarios - - Args: - query: User query - emb_index: Embedding index - bm25: BM25 index - docs: Document list - config: Experiment configuration - - Returns: - (final_results, metadata) - """ - start_time = time.time() - - # Get search mode from config (default to "bm25_only") - search_mode = getattr(config, 'lightweight_search_mode', 'bm25_only') - - metadata = { - "retrieval_mode": "lightweight", - "lightweight_search_mode": search_mode, - "emb_count": 0, - "bm25_count": 0, - "final_count": 0, - "total_latency_ms": 0.0, - } - - # Execute retrieval based on search mode - if search_mode == "bm25_only": - # BM25 only mode: fast lexical matching - bm25_results = await asyncio.to_thread( - search_with_bm25_index, query, bm25, docs, config.lightweight_bm25_top_n - ) - metadata["bm25_count"] = len(bm25_results) - final_results = bm25_results[: config.lightweight_final_top_n] - - elif search_mode == "emb_only": - # Embedding only mode: semantic matching - emb_results = await search_with_emb_index( - query, emb_index, top_n=config.lightweight_emb_top_n - ) - metadata["emb_count"] = len(emb_results) - final_results = emb_results[: config.lightweight_final_top_n] - - else: - # Hybrid mode (default fallback): BM25 + Embedding + RRF fusion - # Execute Embedding and BM25 retrieval in parallel - emb_task = search_with_emb_index( - query, emb_index, top_n=config.lightweight_emb_top_n - ) - bm25_task = asyncio.to_thread( - search_with_bm25_index, query, bm25, docs, config.lightweight_bm25_top_n - ) - - emb_results, bm25_results = await asyncio.gather(emb_task, bm25_task) - - metadata["emb_count"] = len(emb_results) - metadata["bm25_count"] = len(bm25_results) - - # RRF fusion - if not emb_results and not bm25_results: - final_results = [] - elif not emb_results: - final_results = bm25_results[: config.lightweight_final_top_n] - elif not bm25_results: - final_results = emb_results[: config.lightweight_final_top_n] - else: - # Use RRF fusion - fused_results = reciprocal_rank_fusion( - emb_results, bm25_results, k=60 # Standard RRF parameter - ) - final_results = fused_results[: config.lightweight_final_top_n] - - metadata["final_count"] = len(final_results) - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - - return final_results, metadata - - -async def search_with_emb_index( - query: str, - emb_index, - top_n: int = 5, - query_embedding: Optional[np.ndarray] = None, # Support pre-computed embedding -): - """ - Execute embedding retrieval using MaxSim strategy. - - For documents containing atomic_facts: - - Calculate similarity between query and each atomic_fact - - Take maximum similarity as document score (MaxSim strategy) - - For traditional documents: - - Fall back to using subject/summary/episode fields - - Take maximum similarity among these fields - - Optimization: support pre-computed query embedding to avoid repeated API calls. - - Args: - query: Query text - emb_index: Pre-built embedding index - top_n: Number of results to return - query_embedding: Optional pre-computed query embedding (avoid redundant computation) - - Returns: - Sorted (document, score) list - """ - # Get query embedding (call API if not provided) - if query_embedding is not None: - query_vec = query_embedding - else: - query_vec = np.array(await get_vectorize_service().get_embedding(query)) - - query_norm = np.linalg.norm(query_vec) - - # If query vector is zero, return empty result - if query_norm == 0: - return [] - - # Store MaxSim score for each document - doc_scores = [] - - for item in emb_index: - doc = item.get("doc") - embeddings = item.get("embeddings", {}) - - if not embeddings: - continue - - # Prefer atomic_facts (MaxSim strategy) - if "atomic_facts" in embeddings: - atomic_fact_embs = embeddings["atomic_facts"] - if atomic_fact_embs: - # Use MaxSim to compute score - score = compute_maxsim_score(query_vec, atomic_fact_embs) - doc_scores.append((doc, score)) - continue - - # Fall back to traditional fields (maintain backward compatibility) - # For traditional fields, also use MaxSim strategy (take maximum) - field_scores = [] - for field in ["subject", "summary", "episode"]: - if field in embeddings: - field_emb = embeddings[field] - field_norm = np.linalg.norm(field_emb) - - if field_norm > 0: - sim = np.dot(query_vec, field_emb) / (query_norm * field_norm) - field_scores.append(sim) - - if field_scores: - score = max(field_scores) - doc_scores.append((doc, score)) - - if not doc_scores: - return [] - - # Sort by score descending and return Top-N - sorted_results = sorted(doc_scores, key=lambda x: x[1], reverse=True) - return sorted_results[:top_n] - - -async def hybrid_search_with_rrf( - query: str, - emb_index, - bm25, - docs, - top_n: int = 40, - emb_candidates: int = 50, - bm25_candidates: int = 50, - rrf_k: int = 60, - query_embedding: Optional[np.ndarray] = None, # Support pre-computed embedding -) -> List[Tuple[dict, float]]: - """ - Fuse Embedding and BM25 retrieval results using RRF (hybrid retrieval). - - Execution flow: - 1. Execute Embedding (MaxSim) and BM25 retrieval in parallel - 2. Each method recalls top-N candidate documents - 3. Fuse two result sets using RRF - 4. Return fused Top-N documents - - Why use hybrid retrieval: - - Embedding: good at semantic matching, but weak on rare words and exact matching - - BM25: good at exact matching and rare words, but weak semantic understanding - - RRF fusion: combines advantages of both, improves recall rate by 15-20% - - Args: - query: User query - emb_index: Embedding index - bm25: BM25 index - docs: Document list (for BM25) - top_n: Final number of results to return (default 40) - emb_candidates: Number of Embedding retrieval candidates (default 50) - bm25_candidates: Number of BM25 retrieval candidates (default 50) - rrf_k: RRF parameter k (default 60, empirically optimal) - - Returns: - Fused Top-N results [(doc, rrf_score), ...] - - Example: - Query: "What does he like to eat?" - - Embedding Top-3: - - (doc_A: "User loves Sichuan cuisine", 0.92) # Semantic match "like"="love" - - (doc_B: "User prefers light flavors", 0.78) - - (doc_C: "Chengdu is a food paradise", 0.65) - - BM25 Top-3: - - (doc_A: "User loves Sichuan cuisine", 15.3) # Exact match "likes" - - (doc_D: "Likes eating hotpot", 12.7) # Exact match "likes eating" - - (doc_E: "Favorite dish is Mapo Tofu", 10.2) - - RRF fusion: - - doc_A: Ranks high in both results -> highest score - - doc_D: Only ranks high in BM25 - - doc_B: Only ranks high in Embedding - - Final: [(doc_A, 0.0323), (doc_D, 0.0161), (doc_B, 0.0161), ...] - """ - # Execute Embedding and BM25 retrieval in parallel (improve efficiency) - emb_task = search_with_emb_index( - query, emb_index, top_n=emb_candidates, query_embedding=query_embedding - ) - bm25_task = asyncio.to_thread( - search_with_bm25_index, query, bm25, docs, bm25_candidates - ) - - # Wait for both retrieval tasks to complete - emb_results, bm25_results = await asyncio.gather(emb_task, bm25_task) - - # If one retrieval result is empty, return the other - if not emb_results and not bm25_results: - return [] - elif not emb_results: - print(f"Warning: Embedding search returned no results for query: {query}") - return bm25_results[:top_n] - elif not bm25_results: - print(f"Warning: BM25 search returned no results for query: {query}") - return emb_results[:top_n] - - # Use RRF to fuse two retrieval results - fused_results = reciprocal_rank_fusion(emb_results, bm25_results, k=rrf_k) - - # Print fusion statistics (for debugging) - print( - f"Hybrid search: Emb={len(emb_results)}, BM25={len(bm25_results)}, Fused={len(fused_results)}, Returning top-{top_n}" - ) - - return fused_results[:top_n] - - -async def agentic_retrieval( - query: str, - config: ExperimentConfig, - llm_provider: LLMProvider, # Use LLMProvider - llm_config: dict, - emb_index, - bm25, - docs, -) -> Tuple[List[Tuple[dict, float]], dict]: - """ - Agentic multi-round retrieval (LLM-guided) - new process. - - Process: - 1. Round 1: Hybrid search -> Top 20 -> Rerank -> Top 5 -> LLM judges sufficiency - 2. If sufficient: return original Top 20 (before rerank) - 3. If insufficient: - - LLM generates improved query - - Round 2: retrieve and merge to 40 - - Rerank 40 -> return final results - - Args: - query: User query - config: Experiment configuration - llm_provider: LLM Provider (Memory Layer) - llm_config: LLM configuration dict - emb_index: Embedding index - bm25: BM25 index - docs: Document list - - Returns: - (final_results, metadata) - """ - import time - - start_time = time.time() - - metadata = { - "is_multi_round": False, - "round1_count": 0, - "round1_reranked_count": 0, - "round2_count": 0, - "is_sufficient": None, - "reasoning": None, - "refined_query": None, - "final_count": 0, - "total_latency_ms": 0.0, - } - - print(f"\n{'='*60}") - print(f"Agentic Retrieval: {query[:60]}...") - print(f"{'='*60}") - print(f" [Start] Time: {time.strftime('%H:%M:%S')}") - - # Round 1: Hybrid search Top 20 - print(f" [Round 1] Hybrid search for Top 20...") - - round1_top20 = await hybrid_search_with_rrf( - query=query, - emb_index=emb_index, - bm25=bm25, - docs=docs, - top_n=20, - emb_candidates=config.hybrid_emb_candidates, - bm25_candidates=config.hybrid_bm25_candidates, - rrf_k=config.hybrid_rrf_k, - ) - - metadata["round1_count"] = len(round1_top20) - print(f" [Round 1] Retrieved {len(round1_top20)} documents") - - if not round1_top20: - print(f" [Warning] No results from Round 1") - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - return [], metadata - - # Rerank Top 20 to Top 10 for Sufficiency Check - print(f" [Rerank] Reranking Top 20 to get Top 10 for sufficiency check...") - - if config.use_reranker: - reranked_top10 = await reranker_search( - query=query, - results=round1_top20, - top_n=10, - reranker_instruction=config.reranker_instruction, - batch_size=config.reranker_batch_size, - max_retries=config.reranker_max_retries, - retry_delay=config.reranker_retry_delay, - timeout=config.reranker_timeout, - fallback_threshold=config.reranker_fallback_threshold, - config=config, - ) - metadata["round1_reranked_count"] = len(reranked_top10) - print(f" [Rerank] Got Top 10 for sufficiency check") - else: - # If not using reranker, take first 10 directly - reranked_top10 = round1_top20[:10] - metadata["round1_reranked_count"] = 10 - print(f" [No Rerank] Using original Top 10 for sufficiency check") - - if not reranked_top10: - print(f" [Warning] Reranking failed, falling back to original Top 20") - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - return round1_top20, metadata - - # LLM Sufficiency Check - print(f" [LLM] Checking sufficiency on Top 10...") - - is_sufficient, reasoning, missing_info, key_info = ( - await agentic_utils.check_sufficiency( - query=query, - results=reranked_top10, # Use reranked Top 10 - llm_provider=llm_provider, # Use LLMProvider - llm_config=llm_config, - max_docs=10, # Explicitly check only 10 documents - ) - ) - - metadata["is_sufficient"] = is_sufficient - metadata["reasoning"] = reasoning - metadata["key_information_found"] = key_info # New: record found key information - - print(f" [LLM] Result: {'✅ Sufficient' if is_sufficient else '❌ Insufficient'}") - print(f" [LLM] Reasoning: {reasoning}") - if key_info: # New: print found key information - print(f" [LLM] Key Info Found: {', '.join(key_info)}") - - if is_sufficient: - print(f" [Decision] Sufficient! Using reranked Top 10 results") - - final_results = reranked_top10 - metadata["final_count"] = len(final_results) - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - - print(f" [Complete] Latency: {metadata['total_latency_ms']:.0f}ms") - return final_results, metadata - - # If insufficient: enter Round 2 - metadata["is_multi_round"] = True - metadata["missing_info"] = missing_info - print(f" [Decision] Insufficient, entering Round 2") - print(f" [Missing] {', '.join(missing_info) if missing_info else 'N/A'}") - - # LLM generates multiple improved queries (multi-query strategy) - use_multi_query = getattr( - config, 'use_multi_query', True - ) # Default enable multi-query - - if use_multi_query: - print(f" [LLM] Generating multiple refined queries...") - - # Generate 2-3 complementary queries - refined_queries, query_strategy = await agentic_utils.generate_multi_queries( - original_query=query, - results=reranked_top10, # Based on Top 10 generate improved queries - missing_info=missing_info, - llm_provider=llm_provider, # Use LLMProvider - llm_config=llm_config, - key_info=key_info, # New: pass found key information - max_docs=10, - num_queries=3, # Expect to generate 3 queries - ) - - metadata["refined_queries"] = refined_queries - metadata["query_strategy"] = query_strategy - metadata["num_queries"] = len(refined_queries) - - # Round 2: Execute multiple query retrieval in parallel - print(f" [Round 2] Executing {len(refined_queries)} queries in parallel...") - - # Execute hybrid search for all queries in parallel - multi_query_tasks = [ - hybrid_search_with_rrf( - query=q, - emb_index=emb_index, - bm25=bm25, - docs=docs, - top_n=50, # Each query recalls 50 candidates - emb_candidates=config.hybrid_emb_candidates, - bm25_candidates=config.hybrid_bm25_candidates, - rrf_k=config.hybrid_rrf_k, - ) - for q in refined_queries - ] - - # Wait for all queries to complete - multi_query_results = await asyncio.gather(*multi_query_tasks) - - # Print recall count for each query - for i, results in enumerate(multi_query_results, 1): - print(f" Query {i}: Retrieved {len(results)} documents") - - # Use RRF to fuse results from multiple queries - print(f" [Multi-RRF] Fusing results from {len(refined_queries)} queries...") - - # Use multi-query RRF fusion - round2_results = multi_rrf_fusion( - results_list=multi_query_results, - k=config.hybrid_rrf_k, # Use same k parameter - ) - - # Take Top 40 for subsequent merging - round2_results = round2_results[:40] - - metadata["round2_count"] = len(round2_results) - metadata["multi_query_total_docs"] = sum(len(r) for r in multi_query_results) - - print( - f" [Multi-RRF] Fused {metadata['multi_query_total_docs']} → {len(round2_results)} unique documents" - ) - - else: - # Fall back to single-query mode (maintain backward compatibility) - print(f" [LLM] Generating single refined query (legacy mode)...") - - refined_query = await agentic_utils.generate_refined_query( - original_query=query, - results=reranked_top10, - missing_info=missing_info, - llm_provider=llm_provider, - llm_config=llm_config, - key_info=key_info, # New: pass found key information - max_docs=10, - ) - - metadata["refined_query"] = refined_query - print(f" [LLM] Refined query: {refined_query}") - - # Round 2: Retrieve using single refined query - print(f" [Round 2] Hybrid search with refined query...") - - round2_results = await hybrid_search_with_rrf( - query=refined_query, - emb_index=emb_index, - bm25=bm25, - docs=docs, - top_n=40, - emb_candidates=config.hybrid_emb_candidates, - bm25_candidates=config.hybrid_bm25_candidates, - rrf_k=config.hybrid_rrf_k, - ) - - metadata["round2_count"] = len(round2_results) - print(f" [Round 2] Retrieved {len(round2_results)} documents") - - # Merge: ensure total of 40 documents - print(f" [Merge] Combining Round 1 and Round 2 to ensure 40 documents...") - - # Use event_id for deduplication instead of Python memory address - # Reason: BM25 and Embedding indices load JSON separately, creating different Python objects - round1_ids = {doc.get("event_id", id(doc)) for doc, _ in round1_top20} - round2_unique = [ - (doc, score) - for doc, score in round2_results - if doc.get("event_id", id(doc)) not in round1_ids - ] - - # Merge: Round1 Top20 + Round2 deduplicated documents (ensure total=40) - combined_results = round1_top20.copy() # First add 20 from Round1 - needed_from_round2 = 40 - len(combined_results) # Need 20 more - combined_results.extend(round2_unique[:needed_from_round2]) - - actual_count = len(combined_results) - duplicates_removed = len(round2_results) - len(round2_unique) - round2_added = len(round2_unique[:needed_from_round2]) - - print(f" [Merge] Round1 Top20: 20 documents") - print(f" [Merge] Round2 duplicates removed: {duplicates_removed} documents") - print(f" [Merge] Round2 unique added: {round2_added} documents") - print(f" [Merge] Combined total: {actual_count} documents (target: 40)") - - # Rerank the merged 40 documents - if config.use_reranker and len(combined_results) > 0: - print(f" [Rerank] Reranking {len(combined_results)} documents...") - - final_results = await reranker_search( - query=query, # Use original query for rerank - results=combined_results, - top_n=20, # Return Top 20 as final result - reranker_instruction=config.reranker_instruction, - batch_size=config.reranker_batch_size, - max_retries=config.reranker_max_retries, - retry_delay=config.reranker_retry_delay, - timeout=config.reranker_timeout, - fallback_threshold=config.reranker_fallback_threshold, - config=config, - ) - - print(f" [Rerank] Final Top 20 selected") - else: - # Not using Reranker, return Top 20 directly - final_results = combined_results[:20] - print(f" [No Rerank] Returning Top 20 from combined results") - - metadata["final_count"] = len(final_results) - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - - print( - f" [Complete] Final: {len(final_results)} docs | Latency: {metadata['total_latency_ms']:.0f}ms" - ) - print(f"{'='*60}\n") - - return final_results, metadata - - -async def reranker_search( - query: str, - results: List[Tuple[dict, float]], - top_n: int = 20, - reranker_instruction: str = None, - batch_size: int = 10, # Batch size (Reranker API usually limited) - max_retries: int = 3, # Maximum retry attempts - retry_delay: float = 2.0, # Base retry delay - timeout: float = 30.0, # Single batch timeout - fallback_threshold: float = 0.3, # Fallback threshold - config: ExperimentConfig = None, # Experiment configuration (for getting concurrency) -): - """ - Rerank retrieval results using reranker model (supports batch concurrent processing + enhanced stability). - - For documents containing atomic facts: - - Format as multi-line text: time + each atomic_fact on separate line - - Example: - 2024-10-31 14:30:00 - User likes Sichuan cuisine - User's favorite Sichuan dish is Mapo Tofu - User dislikes overly spicy dishes - - For traditional documents: - - Fall back to using episode field - - Optimization strategy (stability first): - - Process documents in batches (batch_size per batch) - - Serial batch processing (avoid API rate limiting) - - Each batch supports retry and exponential backoff - - Auto-downgrade to original ranking when success rate too low - - Single batch timeout protection - - Args: - query: User query - results: Initial retrieval results (from embedding or BM25) - top_n: Number of results to return (default 20) - reranker_instruction: Reranker instruction - batch_size: Number of documents per batch (default 10) - max_retries: Maximum retry attempts per batch (default 3) - retry_delay: Base retry delay in seconds (default 2.0, exponential backoff) - timeout: Single batch timeout in seconds (default 30) - fallback_threshold: Fallback when success rate below this value (default 0.3) - - Returns: - Reranked Top-N result list - """ - if not results: - return [] - - # Step 1: Format documents - docs_with_episode = [] - doc_texts = [] - original_indices = [] # Record original indices for restoration - - for idx, (doc, score) in enumerate(results): - # Prefer using atomic fact data to format text (if exists) - if doc.get("atomic_fact") and doc["atomic_fact"].get("atomic_fact"): - atomic_fact_data = doc["atomic_fact"] - time_str = atomic_fact_data.get("time", "") - atomic_facts = atomic_fact_data.get("atomic_fact", []) - - if isinstance(atomic_facts, list) and atomic_facts: - # Format as multi-line text (time + each atomic_fact on separate line) - formatted_lines = [] - if time_str: - formatted_lines.append(time_str) - - # Fix: compatible with both formats (string / dict) - for fact in atomic_facts: - if isinstance(fact, dict) and "fact" in fact: - # New format: {"fact": "...", "embedding": [...]} - formatted_lines.append(fact["fact"]) - elif isinstance(fact, str): - # Old format: pure string - formatted_lines.append(fact) - - formatted_text = "\n".join(formatted_lines) - - docs_with_episode.append(doc) - doc_texts.append(formatted_text) - original_indices.append(idx) - continue - - # Fall back to original episode field (maintain backward compatibility) - if episode_text := doc.get("episode"): - docs_with_episode.append(doc) - doc_texts.append(episode_text) - original_indices.append(idx) - - if not doc_texts: - return [] - - reranker = rerank_service.get_rerank_service() - print(f"Reranking query: {query}") - print(f"Reranking {len(doc_texts)} documents in batches of {batch_size}...") - print(f"Reranking instruction: {reranker_instruction}") - - # Step 2: Batch processing (serial + retry + fallback) - # Split documents into batches, batch_size per batch - batches = [] - for i in range(0, len(doc_texts), batch_size): - batch = doc_texts[i : i + batch_size] - batches.append((i, batch)) # Save start index and batch data - - print(f"Split into {len(batches)} batches for serial reranking") - - # Process single batch (with retry + timeout + exponential backoff) - async def process_batch_with_retry(start_idx: int, batch_texts: List[str]): - """Process single batch (with retry and timeout).""" - for attempt in range(max_retries): - try: - # Add timeout protection - batch_results = await asyncio.wait_for( - reranker.rerank_documents( - query, batch_texts, instruction=reranker_instruction - ), - timeout=timeout, - ) - - # Both vllm and DeepInfra now return same format: - # {"results": [{"index": ..., "score": ..., "rank": ...}, ...]} - for item in batch_results["results"]: - item["global_index"] = start_idx + item["index"] - - if attempt > 0: - print( - f" ✓ Batch at {start_idx} succeeded on attempt {attempt + 1}" - ) - return batch_results["results"] - - except asyncio.TimeoutError: - if attempt < max_retries - 1: - wait_time = retry_delay * ( - 2**attempt - ) # Exponential backoff: 2s, 4s, 8s - print( - f" ⏱️ Batch at {start_idx} timeout (attempt {attempt + 1}), retrying in {wait_time:.1f}s" - ) - await asyncio.sleep(wait_time) - else: - print( - f" ❌ Batch at {start_idx} timeout after {max_retries} attempts" - ) - return [] - - except Exception as e: - if attempt < max_retries - 1: - wait_time = retry_delay * (2**attempt) - print( - f" ⚠️ Batch at {start_idx} failed (attempt {attempt + 1}), retrying in {wait_time:.1f}s: {e}" - ) - await asyncio.sleep(wait_time) - else: - print( - f" ❌ Batch at {start_idx} failed after {max_retries} attempts: {e}" - ) - return [] - - # Controlled concurrent processing (conservative concurrency strategy) - # Get concurrency count from config (default 2, conservative value) - max_concurrent = getattr(config, 'reranker_concurrent_batches', 2) - - batch_results_list = [] - successful_batches = 0 - - # Process in groups, max max_concurrent batches concurrently per group - for group_start in range(0, len(batches), max_concurrent): - group_batches = batches[group_start : group_start + max_concurrent] - - print( - f" Processing batch group {group_start//max_concurrent + 1} ({len(group_batches)} batches in parallel)..." - ) - - # Process all batches in current group concurrently - tasks = [ - process_batch_with_retry(start_idx, batch) - for start_idx, batch in group_batches - ] - group_results = await asyncio.gather(*tasks, return_exceptions=True) - - # Count successful batches - for result in group_results: - if isinstance(result, list) and result: - batch_results_list.append(result) - successful_batches += 1 - else: - batch_results_list.append([]) - - # Inter-group delay (further reduced, aggressive acceleration) - if group_start + max_concurrent < len(batches): - await asyncio.sleep(0.3) # Inter-group 0.3s interval (reduced from 0.8s) - - # Step 3: Merge all batch results + fallback strategy - all_rerank_results = [] - for batch_results in batch_results_list: - all_rerank_results.extend(batch_results) - - # Calculate success rate - success_rate = successful_batches / len(batches) if batches else 0.0 - print( - f"Reranker success rate: {success_rate:.1%} ({successful_batches}/{len(batches)} batches)" - ) - - # Fallback strategy 1: Complete failure - if not all_rerank_results: - print( - "⚠️ Warning: All reranker batches failed, using original ranking as fallback" - ) - return results[:top_n] - - # Fallback strategy 2: Success rate too low - if success_rate < fallback_threshold: - print( - f"⚠️ Warning: Reranker success rate too low ({success_rate:.1%} < {fallback_threshold:.1%}), using original ranking" - ) - return results[:top_n] - - print(f"Reranking complete: {len(all_rerank_results)} documents scored") - - # Step 4: Sort by reranker score and return Top-N - sorted_results = sorted(all_rerank_results, key=lambda x: x["score"], reverse=True)[ - :top_n - ] - - # Map back to original documents - final_results = [ - (results[original_indices[item["global_index"]]][0], item["score"]) - for item in sorted_results - ] - - return final_results - - -async def main(): - """Main function to perform batch search and save results in nemori format.""" - # --- Configuration --- - config = ExperimentConfig() - bm25_index_dir = Path(__file__).parent / config.experiment_name / "bm25_index" - emb_index_dir = Path(__file__).parent / config.experiment_name / "vectors" - save_dir = Path(__file__).parent / config.experiment_name - - dataset_path = config.datase_path - results_output_path = save_dir / "search_results.json" - - # Checkpoint resume: checkpoint file path - checkpoint_path = save_dir / "search_results_checkpoint.json" - - # Ensure NLTK data is ready - ensure_nltk_data() - - # Initialize LLM Provider (for Agentic retrieval) - llm_provider = None - llm_config = None - if config.use_agentic_retrieval: - if agentic_utils is None: - print("Error: agentic_utils not found, cannot use agentic retrieval") - print("Please check that tools/agentic_utils.py exists") - return - - llm_config = config.llm_config.get( - config.llm_service, config.llm_config["openai"] - ) - - # Use Memory Layer's LLMProvider instead of AsyncOpenAI - llm_provider = LLMProvider( - provider_type="openai", - model=llm_config["model"], - api_key=llm_config["api_key"], - base_url=llm_config["base_url"], - temperature=llm_config.get("temperature", 0.3), - max_tokens=llm_config.get("max_tokens", 32768), - ) - print(f"✅ LLM Provider initialized for agentic retrieval") - print(f" Model: {llm_config['model']}") - - # Load the dataset - print(f"Loading dataset from: {dataset_path}") - with open(dataset_path, "r", encoding="utf-8") as f: - dataset = json.load(f) - - # Checkpoint resume: load existing checkpoint - all_search_results = {} - processed_conversations = set() - - if checkpoint_path.exists(): - print(f"\n🔄 Found checkpoint file: {checkpoint_path}") - try: - with open(checkpoint_path, "r", encoding="utf-8") as f: - all_search_results = json.load(f) - processed_conversations = set(all_search_results.keys()) - print( - f"✅ Loaded {len(processed_conversations)} conversations from checkpoint" - ) - print(f" Already processed: {sorted(processed_conversations)}") - except Exception as e: - print(f"⚠️ Failed to load checkpoint: {e}") - print(f" Starting from scratch...") - all_search_results = {} - processed_conversations = set() - else: - print(f"\n🆕 No checkpoint found, starting from scratch") - - # Iterate through the dataset, assuming the index of the dataset list - # corresponds to the conversation index number. - for i, conversation_data in enumerate(dataset): - conv_id = f"locomo_exp_user_{i}" - - # Checkpoint resume: skip processed conversations - if conv_id in processed_conversations: - print(f"\n⏭️ Skipping Conversation ID: {conv_id} (already processed)") - continue - - speaker_a = conversation_data["conversation"].get("speaker_a") - speaker_b = conversation_data["conversation"].get("speaker_b") - print(f"\n--- Processing Conversation ID: {conv_id} ({i+1}/{len(dataset)}) ---") - - if "qa" not in conversation_data: - print(f"Warning: No 'qa' key found in conversation #{i}. Skipping.") - continue - - # --- Load index once per conversation --- - # If using hybrid search, need to load both Embedding and BM25 indices - if config.use_hybrid_search: - # Load Embedding index - emb_index_path = emb_index_dir / f"embedding_index_conv_{i}.pkl" - if not emb_index_path.exists(): - print( - f"Error: Embedding index not found at {emb_index_path}. Skipping conversation." - ) - continue - with open(emb_index_path, "rb") as f: - emb_index = pickle.load(f) - - # Load BM25 index - bm25_index_path = bm25_index_dir / f"bm25_index_conv_{i}.pkl" - if not bm25_index_path.exists(): - print( - f"Error: BM25 index not found at {bm25_index_path}. Skipping conversation." - ) - continue - with open(bm25_index_path, "rb") as f: - index_data = pickle.load(f) - bm25 = index_data["bm25"] - docs = index_data["docs"] - - print( - f"Loaded both Embedding and BM25 indexes for conversation {i} (Hybrid Search)" - ) - - elif config.use_emb: - # Load Embedding index only - emb_index_path = emb_index_dir / f"embedding_index_conv_{i}.pkl" - if not emb_index_path.exists(): - print( - f"Error: Index file not found at {emb_index_path}. Skipping conversation." - ) - continue - with open(emb_index_path, "rb") as f: - emb_index = pickle.load(f) - else: - # Load BM25 index only - bm25_index_path = bm25_index_dir / f"bm25_index_conv_{i}.pkl" - if not bm25_index_path.exists(): - print( - f"Error: Index file not found at {bm25_index_path}. Skipping conversation." - ) - continue - with open(bm25_index_path, "rb") as f: - index_data = pickle.load(f) - bm25 = index_data["bm25"] - docs = index_data["docs"] - - # Parallelize per-question retrieval with bounded concurrency - # Increase concurrency: also use higher concurrency for Agentic retrieval (10 -> 20) - max_concurrent = 20 if config.use_agentic_retrieval else 128 - sem = asyncio.Semaphore(max_concurrent) - - if config.use_agentic_retrieval: - print( - f" 🚀 Agentic retrieval enabled with HIGH CONCURRENCY: {max_concurrent} concurrent requests" - ) - - async def process_single_qa(qa_pair): - """Process single QA pair (supports multiple retrieval modes).""" - question = qa_pair.get("question") - if not question: - return None - if qa_pair.get("category") == 5: - print(f"Skipping question {question} because it is category 5") - return None - - # Start timing - qa_start_time = time.time() - - try: - async with sem: - retrieval_metadata = {} - - # Retrieval mode selection - if config.retrieval_mode == "agentic": - # Agentic multi-round retrieval (complex but high quality) - top_results, retrieval_metadata = await agentic_retrieval( - query=question, - config=config, - llm_provider=llm_provider, # Use LLMProvider - llm_config=llm_config, - emb_index=emb_index, - bm25=bm25, - docs=docs, - ) - - elif config.retrieval_mode == "lightweight": - # Lightweight fast retrieval (fast but slightly lower quality) - top_results, retrieval_metadata = await lightweight_retrieval( - query=question, - emb_index=emb_index, - bm25=bm25, - docs=docs, - config=config, - ) - - else: - # Traditional retrieval branch (maintain backward compatibility) - if config.use_reranker: - # Stage 1: Initial retrieval, recall Top-N candidates - if config.use_hybrid_search: - # Hybrid search: Embedding (MaxSim) + BM25 + RRF fusion - results = await hybrid_search_with_rrf( - query=question, - emb_index=emb_index, - bm25=bm25, - docs=docs, - top_n=config.emb_recall_top_n, - emb_candidates=config.hybrid_emb_candidates, - bm25_candidates=config.hybrid_bm25_candidates, - rrf_k=config.hybrid_rrf_k, - ) - elif config.use_emb: - # Use Embedding + MaxSim retrieval only - results = await search_with_emb_index( - query=question, - emb_index=emb_index, - top_n=config.emb_recall_top_n, - ) - else: - # Use BM25 retrieval only - results = await asyncio.to_thread( - search_with_bm25_index, - question, - bm25, - docs, - config.emb_recall_top_n, - ) - - # Stage 2: Reranker reordering - top_results = await reranker_search( - query=question, - results=results, - top_n=config.reranker_top_n, - reranker_instruction=config.reranker_instruction, - batch_size=config.reranker_batch_size, - max_retries=config.reranker_max_retries, - retry_delay=config.reranker_retry_delay, - timeout=config.reranker_timeout, - fallback_threshold=config.reranker_fallback_threshold, - config=config, - ) - else: - # Single-stage retrieval (not using Reranker) - if config.use_hybrid_search: - top_results = await hybrid_search_with_rrf( - query=question, - emb_index=emb_index, - bm25=bm25, - docs=docs, - top_n=20, - emb_candidates=config.hybrid_emb_candidates, - bm25_candidates=config.hybrid_bm25_candidates, - rrf_k=config.hybrid_rrf_k, - ) - elif config.use_emb: - top_results = await search_with_emb_index( - query=question, emb_index=emb_index, top_n=20 - ) - else: - top_results = await asyncio.to_thread( - search_with_bm25_index, question, bm25, docs, 20 - ) - - # Add retrieval time statistics - retrieval_metadata = { - "retrieval_mode": "traditional", - "use_reranker": config.use_reranker, - "use_hybrid_search": config.use_hybrid_search, - } - - # Extract event_ids - event_ids = [] - if top_results: - for doc, score in top_results: - event_id = doc.get('event_id') - if event_id: - event_ids.append(event_id) - - # Calculate processing time - qa_latency_ms = (time.time() - qa_start_time) * 1000 - - result = { - "query": question, - "event_ids": event_ids, # Return event_ids instead of context - "original_qa": qa_pair, - "retrieval_metadata": { - **retrieval_metadata, - "qa_latency_ms": qa_latency_ms, - "target_event_ids_count": len( - top_results - ), # Record target count - "actual_event_ids_count": len( - event_ids - ), # Record actual extracted count - }, - } - - return result - - except Exception as e: - print(f"Error processing question '{question}': {e}") - import traceback - - traceback.print_exc() - return None - - tasks = [ - asyncio.create_task(process_single_qa(qa_pair)) - for qa_pair in conversation_data["qa"] - ] - results_for_conv = [ - res for res in await asyncio.gather(*tasks) if res is not None - ] - - all_search_results[conv_id] = results_for_conv - - # Checkpoint resume: save checkpoint after each conversation - try: - print(f"💾 Saving checkpoint after conversation {conv_id}...") - with open(checkpoint_path, "w", encoding="utf-8") as f: - json.dump(all_search_results, f, indent=2, ensure_ascii=False) - print(f"✅ Checkpoint saved: {len(all_search_results)} conversations") - except Exception as e: - print(f"⚠️ Failed to save checkpoint: {e}") - - # Save all results to a single JSON file in the specified format - print(f"\n{'='*60}") - print(f"🎉 All conversations processed!") - print(f"{'='*60}") - print(f"\nSaving final results to: {results_output_path}") - with open(results_output_path, "w", encoding="utf-8") as f: - json.dump(all_search_results, f, indent=2, ensure_ascii=False) - - print(f"✅ Batch search and retrieval complete!") - print(f" Total conversations: {len(all_search_results)}") - - # Checkpoint resume: delete checkpoint file after completion - if checkpoint_path.exists(): - try: - checkpoint_path.unlink() - print(f"🗑️ Checkpoint file removed (task completed)") - except Exception as e: - print(f"⚠️ Failed to remove checkpoint: {e}") - - # Clean up resources - reranker = rerank_service.get_rerank_service() - # Assuming the service is DeepInfraRerankService, which has a close method. - if hasattr(reranker, 'close') and callable(getattr(reranker, 'close')): - await reranker.close() - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/stage4_response.py b/methods/EverCore/evaluation/src/adapters/evermemos/stage4_response.py deleted file mode 100644 index ad63f9cd..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/stage4_response.py +++ /dev/null @@ -1,420 +0,0 @@ -import argparse -import asyncio -import json -import os -import sys -from pathlib import Path -from time import time -from typing import List, Dict, Optional - -import pandas as pd -from tqdm import tqdm - - -from evaluation.src.adapters.evermemos.config import ExperimentConfig -from evaluation.src.adapters.evermemos.prompts.answer_prompts import ANSWER_PROMPT - -# Use Memory Layer's LLMProvider -from memory_layer.llm.llm_provider import LLMProvider - - -# Context building template (migrated from stage3) -TEMPLATE = """Episodes memories for conversation between {speaker_1} and {speaker_2}: - - {speaker_memories} -""" - - -def load_memcells_by_conversation(conv_idx: int, memcells_dir: Path) -> Dict[str, dict]: - """ - Load all memcells for specified conversation, return event_id -> memcell mapping. - - Args: - conv_idx: Conversation index - memcells_dir: Memcells directory path - - Returns: - Mapping of {event_id: memcell_dict} - """ - memcell_file = memcells_dir / f"memcell_list_conv_{conv_idx}.json" - - if not memcell_file.exists(): - print(f"Warning: Memcell file not found: {memcell_file}") - return {} - - try: - with open(memcell_file, "r", encoding="utf-8") as f: - memcells = json.load(f) - - # Build event_id -> memcell mapping - memcell_map = {} - for memcell in memcells: - event_id = memcell.get("event_id") - if event_id: - memcell_map[event_id] = memcell - - return memcell_map - - except Exception as e: - print(f"Error loading memcells from {memcell_file}: {e}") - return {} - - -def build_context_from_event_ids( - event_ids: List[str], - memcell_map: Dict[str, dict], - speaker_a: str, - speaker_b: str, - top_k: int = 10, -) -> str: - """ - Extract corresponding episode memory from memcell_map based on event_ids and build context. - - Args: - event_ids: Retrieved event_ids list (sorted by relevance) - memcell_map: Mapping of event_id -> memcell - speaker_a: Speaker A - speaker_b: Speaker B - top_k: Select top k event_ids (default 10) - - Returns: - Formatted context string - """ - # Select top-k event_ids - selected_event_ids = event_ids[:top_k] - - # Extract corresponding episode memory from memcell_map - retrieved_docs_text = [] - for event_id in selected_event_ids: - memcell = memcell_map.get(event_id) - if not memcell: - # Cannot find corresponding memcell, skip - continue - - subject = memcell.get('subject', 'N/A') - episode = memcell.get('episode', 'N/A') - doc_text = f"{subject}: {episode}\n---" - retrieved_docs_text.append(doc_text) - - # Concatenate all documents - speaker_memories = "\n\n".join(retrieved_docs_text) - - # Format final context using template - context = TEMPLATE.format( - speaker_1=speaker_a, speaker_2=speaker_b, speaker_memories=speaker_memories - ) - - return context - - -async def locomo_response( - llm_provider: LLMProvider, # Use LLMProvider - context: str, - question: str, - experiment_config: ExperimentConfig, -) -> str: - """Generate answer (using LLMProvider). - - Args: - llm_provider: LLM Provider - context: Retrieved context - question: User question - experiment_config: Experiment configuration - - Returns: - Generated answer - """ - prompt = ANSWER_PROMPT.format(context=context, question=question) - - for i in range(experiment_config.max_retries): - try: - result = await llm_provider.generate(prompt=prompt, temperature=0) - - # Safe parse FINAL ANSWER (avoid index out of range) - if "FINAL ANSWER:" in result: - parts = result.split("FINAL ANSWER:") - if len(parts) > 1: - result = parts[1].strip() - else: - # Split failed, use original result - result = result.strip() - else: - # No FINAL ANSWER marker, use original result - result = result.strip() - - if result == "": - continue - break - except Exception as e: - print(f"Error: {e}") - continue - - return result - - -async def process_qa( - qa, - search_result, - llm_provider, - experiment_config, - memcell_map: Dict[str, dict], - speaker_a: str, - speaker_b: str, -): - """ - Process single QA pair (new version: build context from event_ids). - - Args: - qa: Question and answer pair - search_result: Retrieval result (contains event_ids) - llm_provider: LLM Provider - experiment_config: Experiment configuration - memcell_map: Mapping of event_id -> memcell - speaker_a: Speaker A - speaker_b: Speaker B - - Returns: - Dictionary containing question, answer, category, etc. - """ - start = time() - query = qa.get("question") - gold_answer = qa.get("answer") - qa_category = qa.get("category") - - # Build context from event_ids (using top_k) - event_ids = search_result.get("event_ids", []) - top_k = experiment_config.response_top_k - - context = build_context_from_event_ids( - event_ids=event_ids, - memcell_map=memcell_map, - speaker_a=speaker_a, - speaker_b=speaker_b, - top_k=top_k, - ) - - answer = await locomo_response(llm_provider, context, query, experiment_config) - - response_duration_ms = (time() - start) * 1000 - - # Only output in verbose mode (reduce logs) - # print(f"Processed question: {query}") - # print(f"Answer: {answer}") - - return { - "question": query, - "answer": answer, - "category": qa_category, - "golden_answer": gold_answer, - "search_context": context, # Save built context - "event_ids_used": event_ids[:top_k], # Record actually used event_ids - "response_duration_ms": response_duration_ms, - "search_duration_ms": search_result.get("retrieval_metadata", {}).get( - "total_latency_ms", 0 - ), - } - - -async def main(search_path, save_path): - """ - Optimized main function. - - Performance optimizations: - 1. Global concurrent processing: all QA pairs processed concurrently instead of serial by conversation - 2. Concurrency control: use Semaphore to control maximum concurrency - 3. Progress monitoring: real-time progress display - 4. Incremental saving: save immediately after each conversation completes (avoid data loss on crash) - - Optimization results: - - Before: 77 minutes (serial) - - After: ~8 minutes (concurrent 50) - - Speedup: ~10x - """ - llm_config = ExperimentConfig.llm_config["openai"] - experiment_config = ExperimentConfig() - - # Create LLM Provider (replaces AsyncOpenAI) - llm_provider = LLMProvider( - provider_type="openai", - model=llm_config["model"], - api_key=llm_config["api_key"], - base_url=llm_config["base_url"], - temperature=llm_config.get("temperature", 0.0), - max_tokens=llm_config.get("max_tokens", 32768), - ) - - locomo_df = pd.read_json(experiment_config.datase_path) - with open(search_path) as file: - locomo_search_results = json.load(file) - - num_users = len(locomo_df) - - # Load memcells directory - memcells_dir = Path(search_path).parent / "memcells" - if not memcells_dir.exists(): - print(f"Error: Memcells directory not found: {memcells_dir}") - return - - print(f"\n{'='*60}") - print(f"Stage4: LLM Response Generation") - print(f"{'='*60}") - print(f"Total conversations: {num_users}") - print(f"Response top-k: {experiment_config.response_top_k}") - print(f"Memcells directory: {memcells_dir}") - - # Global concurrency control (key optimization) - # Control number of QA pairs processed simultaneously to avoid API rate limiting - MAX_CONCURRENT = 50 # Adjust based on API limits (10-100) - semaphore = asyncio.Semaphore(MAX_CONCURRENT) - - # Collect all QA pairs (across conversations) - all_tasks = [] - task_to_group = {} # Track which group each task belongs to - - # Define processing function with concurrency control - async def process_qa_with_semaphore( - qa, search_result, group_id, memcell_map, speaker_a, speaker_b - ): - """QA processing with concurrency control.""" - async with semaphore: - result = await process_qa( - qa, - search_result, - llm_provider, - experiment_config, - memcell_map, - speaker_a, - speaker_b, - ) - return (group_id, result) - - total_qa_count = 0 - for group_idx in range(num_users): - qa_set = locomo_df["qa"].iloc[group_idx] - qa_set_filtered = [qa for qa in qa_set if qa.get("category") != 5] - - group_id = f"locomo_exp_user_{group_idx}" - search_results = locomo_search_results.get(group_id) - - # Load memcells for current conversation - memcell_map = load_memcells_by_conversation(group_idx, memcells_dir) - print(f"Loaded {len(memcell_map)} memcells for conversation {group_idx}") - - # Get speaker information - conversation_data = locomo_df["conversation"].iloc[group_idx] - speaker_a = conversation_data.get("speaker_a", "Speaker A") - speaker_b = conversation_data.get("speaker_b", "Speaker B") - - matched_pairs = [] - for qa in qa_set_filtered: - question = qa.get("question") - matching_result = next( - ( - result - for result in search_results - if result.get("query") == question - ), - None, - ) - if matching_result: - matched_pairs.append((qa, matching_result)) - else: - print( - f"Warning: No matching search result found for question: {question}" - ) - - total_qa_count += len(matched_pairs) - - # Create tasks (global concurrency) - for qa, search_result in matched_pairs: - task = process_qa_with_semaphore( - qa, search_result, group_id, memcell_map, speaker_a, speaker_b - ) - all_tasks.append(task) - - print(f"Total questions to process: {total_qa_count}") - print(f"Max concurrent requests: {MAX_CONCURRENT}") - print(f"Estimated time: {total_qa_count * 3 / MAX_CONCURRENT / 60:.1f} minutes") - print(f"\n{'='*60}") - print(f"Starting parallel processing...") - print(f"{'='*60}\n") - - # Execute all tasks globally concurrently (with progress monitoring) - all_responses = {f"locomo_exp_user_{i}": [] for i in range(num_users)} - - import time as time_module - - start_time = time_module.time() - completed = 0 - failed = 0 - - # Batch processing + incremental saving (avoid data loss on crash) - CHUNK_SIZE = 200 # Process 200 tasks at a time - SAVE_INTERVAL = 400 # Save every 400 tasks - - for chunk_start in range(0, len(all_tasks), CHUNK_SIZE): - chunk_tasks = all_tasks[chunk_start : chunk_start + CHUNK_SIZE] - chunk_results = await asyncio.gather(*chunk_tasks, return_exceptions=True) - - # Group results into each conversation - for result in chunk_results: - if isinstance(result, Exception): - print(f" ❌ Task failed: {result}") - failed += 1 - continue - - group_id, qa_result = result - all_responses[group_id].append(qa_result) - - completed += len(chunk_tasks) - elapsed = time_module.time() - start_time - speed = completed / elapsed if elapsed > 0 else 0 - eta = (total_qa_count - completed) / speed if speed > 0 else 0 - - print( - f"Progress: {completed}/{total_qa_count} ({completed/total_qa_count*100:.1f}%) | " - f"Speed: {speed:.1f} qa/s | Failed: {failed} | ETA: {eta/60:.1f} min" - ) - - # Incremental saving (save every SAVE_INTERVAL tasks) - if completed % SAVE_INTERVAL == 0 or completed == total_qa_count: - temp_save_path = ( - Path(save_path).parent / f"responses_checkpoint_{completed}.json" - ) - with open(temp_save_path, "w", encoding="utf-8") as f: - json.dump(all_responses, f, indent=2, ensure_ascii=False) - print(f" 💾 Checkpoint saved: {temp_save_path.name}") - - elapsed_time = time_module.time() - start_time - success_rate = (completed - failed) / completed * 100 if completed > 0 else 0 - - print(f"\n{'='*60}") - print(f"✅ All responses generated!") - print(f" - Total questions: {total_qa_count}") - print(f" - Successful: {completed - failed}") - print(f" - Failed: {failed}") - print(f" - Success rate: {success_rate:.1f}%") - print(f" - Time elapsed: {elapsed_time/60:.1f} minutes ({elapsed_time:.0f}s)") - print(f" - Average speed: {total_qa_count/elapsed_time:.1f} qa/s") - print(f"{'='*60}\n") - - # Save final results - os.makedirs(Path(save_path).parent, exist_ok=True) - with open(save_path, "w", encoding="utf-8") as f: - json.dump(all_responses, f, indent=2, ensure_ascii=False) - print(f"✅ Final results saved to: {save_path}") - - # Clean up checkpoint files - checkpoint_files = list(Path(save_path).parent.glob("responses_checkpoint_*.json")) - for checkpoint_file in checkpoint_files: - checkpoint_file.unlink() - print(f" 🗑️ Removed checkpoint: {checkpoint_file.name}") - - -if __name__ == "__main__": - config = ExperimentConfig() - search_result_path = str( - Path(__file__).parent / config.experiment_name / "search_results.json" - ) - save_path = Path(__file__).parent / config.experiment_name / "responses.json" - asyncio.run(main(search_result_path, save_path)) diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/stage5_eval.py b/methods/EverCore/evaluation/src/adapters/evermemos/stage5_eval.py deleted file mode 100644 index 30ab2b8c..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/stage5_eval.py +++ /dev/null @@ -1,292 +0,0 @@ -import asyncio -import json -import logging - -import time -from pathlib import Path - -import numpy as np -from openai import AsyncOpenAI -from pydantic import BaseModel, Field -from tqdm import tqdm - - -from evaluation.src.adapters.evermemos.config import ExperimentConfig - -logging.basicConfig(level=logging.CRITICAL) - - -class LLMGrade(BaseModel): - llm_judgment: str = Field(description="CORRECT or WRONG") - llm_reasoning: str = Field( - description="Explain why the answer is correct or incorrect." - ) - - -async def locomo_grader( - llm_client, question: str, gold_answer: str, response: str -) -> bool: - system_prompt = """ - You are an expert grader that determines if answers to questions match a gold standard answer - """ - - accuracy_prompt = f""" - Your task is to label an answer to a question as ’CORRECT’ or ’WRONG’. You will be given the following data: - (1) a question (posed by one user to another user), - (2) a ’gold’ (ground truth) answer, - (3) a generated answer - which you will score as CORRECT/WRONG. - - The point of the question is to ask about something one user should know about the other user based on their prior conversations. - The gold answer will usually be a concise and short answer that includes the referenced topic, for example: - Question: Do you remember what I got the last time I went to Hawaii? - Gold answer: A shell necklace - The generated answer might be much longer, but you should be generous with your grading - as long as it touches on the same topic as the gold answer, it should be counted as CORRECT. - - For time related questions, the gold answer will be a specific date, month, year, etc. The generated answer might be much longer or use relative time references (like "last Tuesday" or "next month"), but you should be generous with your grading - as long as it refers to the same date or time period as the gold answer, it should be counted as CORRECT. Even if the format differs (e.g., "May 7th" vs "7 May"), consider it CORRECT if it's the same date. - - Now it’s time for the real question: - Question: {question} - Gold answer: {gold_answer} - Generated answer: {response} - - First, provide a short (one sentence) explanation of your reasoning, then finish with CORRECT or WRONG. - Do NOT include both CORRECT and WRONG in your response, or it will break the evaluation script. - - Just return the label CORRECT or WRONG in a json format with the key as "label". - """ - - response = await llm_client.chat.completions.create( - model="gpt-4o-mini", - messages=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": accuracy_prompt}, - ], - temperature=0, - ) - message_content = response.choices[0].message.content - label = json.loads(message_content)["label"] - parsed = LLMGrade(llm_judgment=label, llm_reasoning="") - - return parsed.llm_judgment.strip().lower() == "correct" - - - -def convert_numpy_types(obj): - if isinstance(obj, np.number): - return float(obj) - elif isinstance(obj, dict): - return {k: convert_numpy_types(v) for k, v in obj.items()} - elif isinstance(obj, list): - return [convert_numpy_types(i) for i in obj] - else: - return obj - - -async def process_group_responses( - group_id, group_responses, oai_client, num_runs: int, disable_progress: bool = False -): - graded_responses = [] - - # Process responses with asyncio for concurrent API calls - for response in tqdm( - group_responses, - desc=f"Processing {group_id}", - disable=disable_progress, - leave=False # Clear progress bar after completion - ): - question = response.get("question") - answer = response.get("answer") - ground_truth = response.get("golden_answer") - category = response.get("category") - - context = response.get("search_context", "") - response_duration_ms = response.get("response_duration_ms", 0.0) - search_duration_ms = response.get("search_duration_ms", 0.0) - - if ground_truth is None: - continue - - grading_tasks = [ - locomo_grader(oai_client, question, ground_truth, answer) - for _ in range(num_runs) - ] - judgments = await asyncio.gather(*grading_tasks) - judgments_dict = {f"judgment_{i + 1}": j for i, j in enumerate(judgments)} - - nlp_metrics = {} - graded_response = { - "question": question, - "answer": answer, - "golden_answer": ground_truth, - "category": category, - "llm_judgments": judgments_dict, - "nlp_metrics": nlp_metrics, - "response_duration_ms": response_duration_ms, - "search_duration_ms": search_duration_ms, - "total_duration_ms": response_duration_ms + search_duration_ms, - } - graded_responses.append(graded_response) - - return group_id, graded_responses - - -async def process_single_group( - group_id, group_responses, oai_client, num_runs, disable_progress: bool = False -): - try: - start_time = time.time() - result = await process_group_responses( - group_id, group_responses, oai_client, num_runs, disable_progress - ) - end_time = time.time() - elapsed_time = round(end_time - start_time, 2) - # Reduced output: only print on error or important moments - # print(f"Group {group_id} processed in {elapsed_time} seconds") - return result - except Exception as e: - print(f"❌ Error processing group {group_id}: {e}") - return group_id, [] - - -async def main(): - # --- Configuration --- - config = ExperimentConfig() - version = config.experiment_name - num_runs = 3 - max_workers = 10 - - print(f"\n{'='*60}") - print(f"🔍 Stage5: LLM-as-a-Judge Evaluation") - print(f"{'='*60}") - - # --- Path Setup --- - current_dir = Path(__file__).parent - results_dir = current_dir / version - response_path = results_dir / "responses.json" - judged_path = results_dir / "judged.json" - - results_dir.mkdir(parents=True, exist_ok=True) - - # --- Client Setup --- - llm_config = config.llm_config["openai"] - oai_client = AsyncOpenAI( - api_key=llm_config["api_key"], base_url=llm_config["base_url"] - ) - - # --- Data Loading --- - try: - with open(response_path) as file: - locomo_responses = json.load(file) - except FileNotFoundError: - print(f"❌ Error: Response file not found at {response_path}") - return - - # --- Evaluation --- - num_users = 10 - all_grades = {} - - total_responses_count = sum( - len(locomo_responses.get(f"locomo_exp_user_{i}", [])) for i in range(num_users) - ) - print(f"📊 Total responses: {total_responses_count}") - print(f"👥 Total users: {num_users}") - print(f"🔄 Judgments per question: {num_runs}") - print(f"⚡ Concurrent workers: {max_workers}") - print(f"{'='*60}\n") - - # Create tasks for processing each group - tasks = [] - active_users = 0 - for group_idx in range(num_users): - group_id = f"locomo_exp_user_{group_idx}" - group_responses = locomo_responses.get(group_id, []) - if not group_responses: - continue - - active_users += 1 - tasks.append( - process_single_group( - group_id, group_responses, oai_client, num_runs, disable_progress=True # Disable individual group progress bar - ) - ) - - print(f"🚀 Starting evaluation...\n") - - semaphore = asyncio.Semaphore(max_workers) - - async def limited_task(task): - async with semaphore: - return await task - - limited_tasks = [limited_task(task) for task in tasks] - - # Add overall progress bar - group_results = [] - for coro in tqdm( - asyncio.as_completed(limited_tasks), - total=len(limited_tasks), - desc="📈 Evaluating groups", - unit="group" - ): - result = await coro - group_results.append(result) - - for group_id, graded_responses in group_results: - all_grades[group_id] = graded_responses - - print(f"\n{'='*60}") - print(f"✅ Evaluation Complete") - print(f"{'='*60}") - - # --- Score Calculation --- - run_scores = [] - evaluated_count = 0 - if num_runs > 0: - for i in range(1, num_runs + 1): - judgment_key = f"judgment_{i}" - current_run_correct_count = 0 - current_run_total_count = 0 - for group in all_grades.values(): - for response in group: - if judgment_key in response["llm_judgments"]: - if response["llm_judgments"][judgment_key]: - current_run_correct_count += 1 - current_run_total_count += 1 - - if current_run_total_count > 0: - run_accuracy = current_run_correct_count / current_run_total_count - run_scores.append(run_accuracy) - - if current_run_total_count > 0: - evaluated_count = current_run_total_count - - # Simplified and formatted result output - if evaluated_count > 0: - mean_of_scores = np.mean(run_scores) - std_of_scores = np.std(run_scores) - - print(f"\n{'='*60}") - print(f"📊 Final Results") - print(f"{'='*60}") - print(f"🎯 Mean Accuracy: {mean_of_scores:.4f} ({mean_of_scores*100:.2f}%)") - print(f"📈 Std Deviation: {std_of_scores:.4f}") - print(f"📝 Questions: {evaluated_count}") - print(f"🔄 Runs per Q: {num_runs}") - print(f"{'='*60}") - print(f"Individual run scores: {[round(s, 4) for s in run_scores]}") - print(f"{'='*60}\n") - else: - print("\n⚠️ No responses were evaluated") - print("LLM-as-a-Judge score: N/A (0/0)\n") - - # --- Save Results --- - all_grades = convert_numpy_types(all_grades) - with open(judged_path, "w") as f: - json.dump(all_grades, f, indent=2) - print(f"💾 Saved detailed results to: {judged_path}") - print(f"✅ Stage5 complete!\n") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/tools/__init__.py b/methods/EverCore/evaluation/src/adapters/evermemos/tools/__init__.py deleted file mode 100644 index cbf29e5b..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/tools/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Evaluation tools for EverCore adapter.""" - -from evaluation.src.adapters.evermemos.tools.in_memory_cluster_storage import ( - InMemoryClusterStorage, -) -from evaluation.src.adapters.evermemos.tools.in_memory_profile_storage import ( - InMemoryProfileStorage, -) - -__all__ = [ - "InMemoryClusterStorage", - "InMemoryProfileStorage", -] diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/tools/agentic_utils.py b/methods/EverCore/evaluation/src/adapters/evermemos/tools/agentic_utils.py deleted file mode 100644 index 78f88ede..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/tools/agentic_utils.py +++ /dev/null @@ -1,426 +0,0 @@ -""" -Agentic Retrieval utility functions. - -Provides tools for LLM-guided multi-round retrieval: -1. Sufficiency Check: Determine if retrieval results are sufficient -2. Query Refinement: Generate improved queries -3. Document Formatting: Format documents for LLM consumption -""" - -import json -import asyncio -from pathlib import Path -from typing import List, Tuple, Optional - -# Import prompts from Python files -from evaluation.src.adapters.evermemos.prompts.sufficiency_check_prompts import ( - SUFFICIENCY_CHECK_PROMPT, -) -from evaluation.src.adapters.evermemos.prompts.refined_query_prompts import ( - REFINED_QUERY_PROMPT, -) -from evaluation.src.adapters.evermemos.prompts.multi_query_prompts import ( - MULTI_QUERY_GENERATION_PROMPT, -) - - -def format_documents_for_llm( - results: List[Tuple[dict, float]], max_docs: int = 10, use_episode: bool = True -) -> str: - """ - Format retrieval results for LLM consumption. - - Args: - results: Retrieval results [(doc, score), ...] - max_docs: Maximum number of documents to include - use_episode: True=use Episode Memory, False=use Atomic Fact - - Returns: - Formatted document string - """ - formatted_docs = [] - - for i, (doc, score) in enumerate(results[:max_docs], start=1): - subject = doc.get("subject", "N/A") - - # Choose format based on use_episode parameter - if use_episode: - # Use Episode Memory format (full narrative) - episode = doc.get("episode", "N/A") - - # Limit episode length to avoid overly long prompts - if len(episode) > 500: - episode = episode[:500] + "..." - - doc_text = ( - f"Document {i}:\n" f" Title: {subject}\n" f" Content: {episode}\n" - ) - formatted_docs.append(doc_text) - else: - # Use atomic fact format - if doc.get("atomic_fact") and doc["atomic_fact"].get("atomic_fact"): - atomic_fact_data = doc["atomic_fact"] - time_str = atomic_fact_data.get("time", "N/A") - atomic_facts = atomic_fact_data.get("atomic_fact", []) - - if isinstance(atomic_facts, list) and atomic_facts: - # Format as: Document N: title + time + fact list - facts_text = "\n ".join(atomic_facts[:5]) # Show max 5 facts - if len(atomic_facts) > 5: - facts_text += ( - f"\n ... and {len(atomic_facts) - 5} more facts" - ) - - doc_text = ( - f"Document {i}:\n" - f" Title: {subject}\n" - f" Time: {time_str}\n" - f" Facts:\n" - f" {facts_text}\n" - ) - formatted_docs.append(doc_text) - continue - - # Fall back to episode if no atomic fact data - episode = doc.get("episode", "N/A") - if len(episode) > 500: - episode = episode[:500] + "..." - - doc_text = ( - f"Document {i}:\n" f" Title: {subject}\n" f" Content: {episode}\n" - ) - formatted_docs.append(doc_text) - - return "\n".join(formatted_docs) - - -def parse_json_response(response: str) -> dict: - """ - Parse LLM JSON response with robust error handling. - - Args: - response: Raw LLM response string - - Returns: - Parsed JSON dictionary - """ - try: - # Extract JSON (LLM may add extra text before/after) - start_idx = response.find("{") - end_idx = response.rfind("}") + 1 - - if start_idx == -1 or end_idx == 0: - raise ValueError("No JSON object found in response") - - json_str = response[start_idx:end_idx] - result = json.loads(json_str) - - # Validate required fields - if "is_sufficient" not in result: - raise ValueError("Missing 'is_sufficient' field") - - # Set default values - result.setdefault("reasoning", "No reasoning provided") - result.setdefault("missing_information", []) - result.setdefault("key_information_found", []) - - return result - - except (json.JSONDecodeError, ValueError) as e: - print(f" ⚠️ Failed to parse LLM response: {e}") - print(f" Raw response: {response[:200]}...") - - # Conservative fallback: assume sufficient to avoid unnecessary second round - return { - "is_sufficient": True, - "reasoning": f"Failed to parse: {str(e)}", - "missing_information": [], - "key_information_found": [], - } - - -def parse_refined_query(response: str, original_query: str) -> str: - """ - Parse refined query from LLM response. - - Args: - response: LLM response - original_query: Original query (for fallback) - - Returns: - Refined query string - """ - refined = response.strip() - - # Remove common prefixes - prefixes = ["Refined Query:", "Output:", "Answer:", "Query:"] - for prefix in prefixes: - if refined.startswith(prefix): - refined = refined[len(prefix) :].strip() - - # Validate length - if len(refined) < 5 or len(refined) > 300: - print(f" ⚠️ Invalid refined query length ({len(refined)}), using original") - return original_query - - # Avoid identical query - if refined.lower() == original_query.lower(): - print(f" ⚠️ Refined query identical to original, using original") - return original_query - - return refined - - -async def check_sufficiency( - query: str, - results: List[Tuple[dict, float]], - llm_provider, - llm_config: dict, - max_docs: int = 10, -) -> Tuple[bool, str, List[str], List[str]]: - """ - Check if retrieval results are sufficient. - - Args: - query: User query - results: Retrieval results (Top 10) - llm_provider: LLM Provider (Memory Layer) - llm_config: LLM configuration dict - max_docs: Maximum number of documents to evaluate - - Returns: - (is_sufficient, reasoning, missing_information, key_information_found) - """ - try: - # Format documents (using Episode Memory format) - retrieved_docs = format_documents_for_llm( - results, max_docs=max_docs, use_episode=True - ) - - # Use prompt template - prompt = SUFFICIENCY_CHECK_PROMPT.format( - query=query, retrieved_docs=retrieved_docs - ) - - # Call LLM (using LLMProvider) - result_text = await llm_provider.generate( - prompt=prompt, - temperature=0.0, # Low temperature for stable judgment - max_tokens=500, - ) - - # Parse JSON response - result = parse_json_response(result_text) - - return ( - result["is_sufficient"], - result["reasoning"], - result.get("missing_information", []), - result.get("key_information_found", []), - ) - - except asyncio.TimeoutError: - print(f" ❌ Sufficiency check timeout (30s)") - # Timeout fallback: assume sufficient - return True, "Timeout: LLM took too long", [], [] - except Exception as e: - print(f" ❌ Sufficiency check failed: {e}") - import traceback - - traceback.print_exc() - # Conservative fallback: assume sufficient - return True, f"Error: {str(e)}", [], [] - - -async def generate_refined_query( - original_query: str, - results: List[Tuple[dict, float]], - missing_info: List[str], - llm_provider, - llm_config: dict, - key_info: Optional[List[str]] = None, - max_docs: int = 10, -) -> str: - """ - Generate improved query. - - Args: - original_query: Original query - results: Round 1 retrieval results (Top 10) - missing_info: List of missing information - llm_provider: LLM Provider - llm_config: LLM configuration - key_info: List of key information found (optional, for future use) - max_docs: Maximum number of documents to use - - Returns: - Refined query string - """ - try: - # Format documents and missing info (using Episode Memory format) - retrieved_docs = format_documents_for_llm( - results, max_docs=max_docs, use_episode=True - ) - missing_info_str = ", ".join(missing_info) if missing_info else "N/A" - - # Use prompt template - prompt = REFINED_QUERY_PROMPT.format( - original_query=original_query, - retrieved_docs=retrieved_docs, - missing_info=missing_info_str, - ) - - # Call LLM (using LLMProvider) - result_text = await llm_provider.generate( - prompt=prompt, - temperature=0.3, # Higher temperature for creativity - max_tokens=150, - ) - - # Parse and validate - refined_query = parse_refined_query(result_text, original_query) - - return refined_query - - except asyncio.TimeoutError: - print(f" ❌ Query refinement timeout (30s)") - # Timeout fallback: use original query - return original_query - except Exception as e: - print(f" ❌ Query refinement failed: {e}") - import traceback - - traceback.print_exc() - # Fall back to original query - return original_query - - -def parse_multi_query_response( - response: str, original_query: str -) -> Tuple[List[str], str]: - """ - Parse multi-query generation JSON response. - - Args: - response: Raw LLM response string - original_query: Original query (for fallback) - - Returns: - (queries_list, reasoning) - """ - try: - # Extract JSON - start_idx = response.find("{") - end_idx = response.rfind("}") + 1 - - if start_idx == -1 or end_idx == 0: - raise ValueError("No JSON object found in response") - - json_str = response[start_idx:end_idx] - result = json.loads(json_str) - - # Validate required fields - if "queries" not in result or not isinstance(result["queries"], list): - raise ValueError("Missing or invalid 'queries' field") - - queries = result["queries"] - reasoning = result.get("reasoning", "No reasoning provided") - - # Filter and validate queries - valid_queries = [] - for q in queries: - if isinstance(q, str) and 5 <= len(q) <= 300: - # Avoid identical to original query - if q.lower().strip() != original_query.lower().strip(): - valid_queries.append(q.strip()) - - # Return at least 1 query - if not valid_queries: - print(f" ⚠️ No valid queries generated, using original") - return [original_query], "Fallback: used original query" - - # Limit to maximum 3 queries - valid_queries = valid_queries[:3] - - print(f" ✅ Generated {len(valid_queries)} valid queries") - return valid_queries, reasoning - - except (json.JSONDecodeError, ValueError) as e: - print(f" ⚠️ Failed to parse multi-query response: {e}") - print(f" Raw response: {response[:200]}...") - - # Fallback: return original query - return [original_query], f"Parse error: {str(e)}" - - -async def generate_multi_queries( - original_query: str, - results: List[Tuple[dict, float]], - missing_info: List[str], - llm_provider, - llm_config: dict, - key_info: Optional[List[str]] = None, - max_docs: int = 5, - num_queries: int = 3, -) -> Tuple[List[str], str]: - """ - Generate multiple complementary queries for multi-query retrieval. - - Args: - original_query: Original query - results: Round 1 retrieval results (Top 5) - missing_info: List of missing information - llm_provider: LLM Provider - llm_config: LLM configuration - key_info: List of key information found (for better query refinement) - max_docs: Maximum number of documents to use (default 5) - num_queries: Expected number of queries to generate (default 3, may be fewer) - - Returns: - (queries_list, reasoning) - queries_list: Generated query list (1-3 queries) - reasoning: LLM generation strategy explanation - """ - try: - # Format documents and missing info (using Episode Memory format) - retrieved_docs = format_documents_for_llm( - results, max_docs=max_docs, use_episode=True - ) - missing_info_str = ", ".join(missing_info) if missing_info else "N/A" - key_info_str = ", ".join(key_info) if key_info else "N/A" - - # Use prompt template - prompt = MULTI_QUERY_GENERATION_PROMPT.format( - original_query=original_query, - retrieved_docs=retrieved_docs, - missing_info=missing_info_str, - key_info=key_info_str, - ) - - # Call LLM (using LLMProvider) - result_text = await llm_provider.generate( - prompt=prompt, - temperature=0.4, # Higher temperature for query diversity - max_tokens=300, # Increased tokens to support multiple queries - ) - - # Parse and validate - queries, reasoning = parse_multi_query_response(result_text, original_query) - - print(f" [Multi-Query] Generated {len(queries)} queries:") - for i, q in enumerate(queries, 1): - print(f" Query {i}: {q[:80]}{'...' if len(q) > 80 else ''}") - print(f" [Multi-Query] Strategy: {reasoning}") - - return queries, reasoning - - except asyncio.TimeoutError: - print(f" ❌ Multi-query generation timeout (30s)") - return [original_query], "Timeout: used original query" - except Exception as e: - print(f" ❌ Multi-query generation failed: {e}") - import traceback - - traceback.print_exc() - # Fall back to original query - return [original_query], f"Error: {str(e)}" diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/tools/compute_acc.py b/methods/EverCore/evaluation/src/adapters/evermemos/tools/compute_acc.py deleted file mode 100644 index a1def551..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/tools/compute_acc.py +++ /dev/null @@ -1,112 +0,0 @@ -import json -import argparse -from collections import defaultdict -import numpy as np - - -def compute_accuracy_from_judgments(file_path): - """ - Computes accuracies, mean, and standard deviation from a JSON file with LLM judgments. - """ - with open(file_path, 'r', encoding='utf-8') as f: - data = json.load(f) - - num_judgments = 0 - # Determine the number of judgments from the first record - for user_id, results in data.items(): - if results: - first_item = results[0] - if 'llm_judgments' in first_item and isinstance( - first_item['llm_judgments'], dict - ): - num_judgments = len(first_item['llm_judgments']) - break - - if num_judgments == 0: - print("No judgments found in the file.") - return - - judgment_correct = [0] * num_judgments - total_questions = 0 - - category_correct = [defaultdict(int) for _ in range(num_judgments)] - category_total = defaultdict(int) - - # First pass to get total questions - for user_id, results in data.items(): - total_questions += len(results) - for item in results: - category = item.get("category") - category_total[category] += 1 - - for user_id, results in data.items(): - for item in results: - judgments = item.get("llm_judgments", {}) - category = item.get("category") - for i in range(num_judgments): - if judgments.get(f"judgment_{i+1}", False): - judgment_correct[i] += 1 - category_correct[i][category] += 1 - - accuracies = [ - (correct / total_questions) * 100 if total_questions > 0 else 0 - for correct in judgment_correct - ] - - mean_accuracy = np.mean(accuracies) - std_dev_accuracy = np.std(accuracies) - - category_accuracies = [] - for i in range(num_judgments): - cat_acc = {} - for cat, total in category_total.items(): - correct = category_correct[i][cat] - cat_acc[cat] = (correct / total) * 100 if total > 0 else 0 - category_accuracies.append(cat_acc) - - return ( - accuracies, - mean_accuracy, - std_dev_accuracy, - category_accuracies, - category_correct, - category_total, - ) - - -if __name__ == "__main__": - # The path to the results file is hardcoded here. - # You can change this path if you need to point to a different file. - results_file_path = "/Users/admin/Documents/Projects/b001-memsys_/evaluation/locomo_evaluation/results/locomo_evaluation_nemori/nemori_locomo_judged.json" - - try: - ( - accuracies, - mean_acc, - std_dev_acc, - category_accs, - category_correct, - category_total, - ) = compute_accuracy_from_judgments(results_file_path) - - print("--- Overall Accuracy ---") - for i, acc in enumerate(accuracies): - print(f"Trial {i+1} Accuracy: {acc:.2f}%") - - print(f"\nMean Accuracy: {mean_acc:.2f}%") - print(f"Standard Deviation of Accuracy: {std_dev_acc:.2f}") - - print("\n--- Accuracy by Category ---") - for i, cat_acc in enumerate(category_accs): - print(f"\nTrial {i+1}:") - for category, acc in sorted(cat_acc.items()): - total = category_total.get(category, 0) - correct = category_correct[i].get(category, 0) - print(f" Category {category}: {acc:.2f}% ({correct}/{total})") - - except FileNotFoundError: - print(f"Error: The file '{results_file_path}' was not found.") - except json.JSONDecodeError: - print(f"Error: Could not decode JSON from the file '{results_file_path}'.") - except Exception as e: - print(f"An unexpected error occurred: {e}") diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/tools/in_memory_cluster_storage.py b/methods/EverCore/evaluation/src/adapters/evermemos/tools/in_memory_cluster_storage.py deleted file mode 100644 index b27cf203..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/tools/in_memory_cluster_storage.py +++ /dev/null @@ -1,140 +0,0 @@ -"""In-memory cluster storage for evaluation. - -This storage implementation is used by the evaluation framework for clustering -during MemCell extraction. It keeps mem scene states in memory with optional -file persistence for checkpointing. -""" - -from typing import Any, Dict, Optional -from pathlib import Path -import json -import numpy as np - -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class InMemoryClusterStorage: - """In-memory cluster storage with optional file persistence.""" - - def __init__( - self, enable_persistence: bool = False, persist_dir: Optional[Path] = None - ): - """Initialize in-memory storage. - - Args: - enable_persistence: Whether to persist to disk - persist_dir: Directory for JSON files (required if enable_persistence=True) - """ - self._states: Dict[str, Dict[str, Any]] = {} - self._enable_persistence = enable_persistence - self._persist_dir = Path(persist_dir) if persist_dir else None - - if enable_persistence and not persist_dir: - raise ValueError("persist_dir is required when enable_persistence=True") - - if enable_persistence and self._persist_dir: - self._persist_dir.mkdir(parents=True, exist_ok=True) - self._load_from_disk() - - async def save_mem_scene(self, group_id: str, state: Dict[str, Any]) -> bool: - """Save mem scene state for a group.""" - try: - serializable_state = self._make_serializable(state) - self._states[group_id] = serializable_state - - if self._enable_persistence: - self._persist_to_disk(group_id, serializable_state) - - return True - except Exception as e: - logger.error(f"Failed to save mem scene for group {group_id}: {e}") - return False - - async def load_mem_scene(self, group_id: str) -> Optional[Dict[str, Any]]: - """Load mem scene state for a group.""" - return self._states.get(group_id) - - async def get_cluster_assignments(self, group_id: str) -> Dict[str, str]: - """Get event_id -> cluster_id mapping for a group. - - Supports both new and old serialization formats: - - New: { "memcell_info": { memcell_id: { "memscene": cluster_id, ... } } } - - Old: { "eventid_to_cluster": { event_id: cluster_id } } - """ - state = self._states.get(group_id, {}) - if "memcell_info" in state: - return { - memcell_id: str(info["memscene"]) - for memcell_id, info in state["memcell_info"].items() - if "memscene" in info - } - return state.get("eventid_to_cluster", {}) - - async def clear(self, group_id: Optional[str] = None) -> bool: - """Clear mem scene state.""" - try: - if group_id is None: - self._states.clear() - elif group_id in self._states: - del self._states[group_id] - return True - except Exception as e: - logger.error(f"Failed to clear mem scene state: {e}") - return False - - def _make_serializable(self, obj: Any) -> Any: - """Convert numpy arrays and other non-serializable objects to JSON-compatible types.""" - if isinstance(obj, np.ndarray): - return obj.tolist() - elif isinstance(obj, dict): - return {k: self._make_serializable(v) for k, v in obj.items()} - elif isinstance(obj, (list, tuple)): - return [self._make_serializable(item) for item in obj] - elif isinstance(obj, (np.integer, np.floating)): - return float(obj) - else: - return obj - - def _persist_to_disk(self, group_id: str, state: Dict[str, Any]) -> None: - """Persist mem scene state to JSON file.""" - if not self._persist_dir: - return - - try: - state_file = self._persist_dir / f"mem_scene_{group_id}.json" - with open(state_file, "w", encoding="utf-8") as f: - json.dump(state, f, ensure_ascii=False, indent=2, default=str) - - assignments_file = self._persist_dir / f"cluster_map_{group_id}.json" - if "memcell_info" in state: - assignments = { - memcell_id: str(info["memscene"]) - for memcell_id, info in state["memcell_info"].items() - if "memscene" in info - } - else: - assignments = state.get("eventid_to_cluster", {}) - with open(assignments_file, "w", encoding="utf-8") as f: - json.dump({"assignments": assignments}, f, ensure_ascii=False, indent=2) - except Exception as e: - logger.warning(f"Failed to persist mem scene for group {group_id}: {e}") - - def _load_from_disk(self) -> None: - """Load mem scene states from disk on initialization.""" - if not self._persist_dir or not self._persist_dir.exists(): - return - - try: - for state_file in self._persist_dir.glob("mem_scene_*.json"): - try: - group_id = state_file.stem.replace("mem_scene_", "") - with open(state_file, "r", encoding="utf-8") as f: - state = json.load(f) - self._states[group_id] = state - logger.info(f"Loaded mem scene for group {group_id} from disk") - except Exception as e: - logger.warning(f"Failed to load mem scene from {state_file}: {e}") - except Exception as e: - logger.error(f"Failed to load mem scenes from disk: {e}") diff --git a/methods/EverCore/evaluation/src/adapters/evermemos/tools/in_memory_profile_storage.py b/methods/EverCore/evaluation/src/adapters/evermemos/tools/in_memory_profile_storage.py deleted file mode 100644 index 64b0f437..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos/tools/in_memory_profile_storage.py +++ /dev/null @@ -1,229 +0,0 @@ -"""In-memory profile storage for evaluation. - -This storage implementation is used by the evaluation framework for profile -management during MemCell extraction. It keeps profiles in memory with optional -file persistence for checkpointing. -""" - -from typing import Any, Dict, List, Optional -from pathlib import Path -import json - -from common_utils.datetime_utils import get_now_with_timezone, to_iso_format -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class InMemoryProfileStorage: - """In-memory profile storage with optional file persistence.""" - - def __init__( - self, - enable_persistence: bool = False, - persist_dir: Optional[Path] = None, - enable_versioning: bool = True - ): - """Initialize in-memory storage. - - Args: - enable_persistence: Whether to persist to disk - persist_dir: Directory for JSON files (required if enable_persistence=True) - enable_versioning: Whether to keep version history - """ - self._profiles: Dict[str, Any] = {} - self._history: Dict[str, List[Dict[str, Any]]] = {} - self._enable_persistence = enable_persistence - self._persist_dir = persist_dir - self._enable_versioning = enable_versioning - - if enable_persistence and not persist_dir: - raise ValueError("persist_dir is required when enable_persistence=True") - - if enable_persistence and persist_dir: - persist_dir.mkdir(parents=True, exist_ok=True) - self._load_from_disk() - - async def save_profile( - self, - user_id: str, - profile: Any, - metadata: Optional[Dict[str, Any]] = None - ) -> bool: - """Save or update a user profile.""" - try: - self._profiles[user_id] = profile - - if self._enable_versioning: - history_entry = { - "profile": profile, - "metadata": metadata or {}, - "timestamp": to_iso_format(get_now_with_timezone()), - } - if user_id not in self._history: - self._history[user_id] = [] - self._history[user_id].append(history_entry) - - if self._enable_persistence: - self._persist_to_disk(user_id, profile, metadata) - - return True - except Exception as e: - logger.error(f"Failed to save profile for user {user_id}: {e}") - return False - - async def get_profile(self, user_id: str) -> Optional[Any]: - """Retrieve the latest profile for a user.""" - return self._profiles.get(user_id) - - async def get_all_profiles(self) -> Dict[str, Any]: - """Retrieve all user profiles.""" - return dict(self._profiles) - - async def get_profile_history( - self, - user_id: str, - limit: Optional[int] = None - ) -> List[Dict[str, Any]]: - """Retrieve profile version history for a user.""" - history = self._history.get(user_id, []) - history_reversed = list(reversed(history)) - if limit is not None and limit > 0: - return history_reversed[:limit] - return history_reversed - - async def clear(self) -> bool: - """Clear all stored profiles.""" - try: - self._profiles.clear() - self._history.clear() - return True - except Exception as e: - logger.error(f"Failed to clear profiles: {e}") - return False - - def _serialize_for_json(self, obj: Any) -> Any: - """Recursively serialize object to JSON-compatible format.""" - import datetime as dt - - if isinstance(obj, dt.datetime): - return to_iso_format(obj) - elif isinstance(obj, (dt.date, dt.time)): - return obj.isoformat() - elif isinstance(obj, dict): - return {k: self._serialize_for_json(v) for k, v in obj.items()} - elif isinstance(obj, (list, tuple)): - return [self._serialize_for_json(item) for item in obj] - elif hasattr(obj, 'value'): # Enum - return obj.value - elif hasattr(obj, 'to_dict'): - return self._serialize_for_json(obj.to_dict()) - elif hasattr(obj, '__dict__'): - return self._serialize_for_json(obj.__dict__) - else: - return obj - - def _persist_to_disk( - self, - user_id: str, - profile: Any, - metadata: Optional[Dict[str, Any]] - ) -> None: - """Persist profile to JSON file.""" - if not self._persist_dir: - return - - try: - if hasattr(profile, "to_dict"): - try: - payload = profile.to_dict() - except (AttributeError, TypeError) as e: - error_msg = str(e).lower() - if 'tzinfo' in error_msg or 'isoformat' in error_msg: - payload = profile.__dict__.copy() - if hasattr(payload.get('memory_type'), 'value'): - payload['memory_type'] = payload['memory_type'].value - ts = payload.get('timestamp') - if ts is not None: - if hasattr(ts, 'isoformat'): - payload['timestamp'] = ts.isoformat() - elif not isinstance(ts, str): - payload['timestamp'] = str(ts) - else: - raise - elif hasattr(profile, "__dict__"): - payload = profile.__dict__.copy() - if hasattr(payload.get('memory_type'), 'value'): - payload['memory_type'] = payload['memory_type'].value - ts = payload.get('timestamp') - if ts is not None: - if hasattr(ts, 'isoformat'): - payload['timestamp'] = ts.isoformat() - elif not isinstance(ts, str): - payload['timestamp'] = str(ts) - else: - payload = profile - - payload = self._serialize_for_json(payload) - - latest_file = self._persist_dir / f"profile_{user_id}.json" - with open(latest_file, "w", encoding="utf-8") as f: - json.dump(payload, f, ensure_ascii=False, indent=2, default=str) - - if self._enable_versioning: - history_dir = self._persist_dir / "history" / user_id - history_dir.mkdir(parents=True, exist_ok=True) - - now = get_now_with_timezone() - timestamp_str = to_iso_format(now).replace(":", "-").replace("+", "_") - version_file = history_dir / f"profile_{user_id}_{timestamp_str}.json" - with open(version_file, "w", encoding="utf-8") as f: - json.dump(payload, f, ensure_ascii=False, indent=2, default=str) - except Exception as e: - logger.warning(f"Failed to persist profile for user {user_id}: {e}") - - def _load_from_disk(self) -> None: - """Load profiles from disk on initialization.""" - if not self._persist_dir or not self._persist_dir.exists(): - return - - try: - for profile_file in self._persist_dir.glob("profile_*.json"): - if profile_file.stem.startswith("profile_") and not profile_file.parent.name == "history": - try: - with open(profile_file, "r", encoding="utf-8") as f: - data = json.load(f) - - user_id = data.get("user_id") - profile = data.get("profile") - - if user_id and profile: - self._profiles[user_id] = profile - logger.info(f"Loaded profile for user {user_id} from disk") - except Exception as e: - logger.warning(f"Failed to load profile from {profile_file}: {e}") - - if self._enable_versioning: - history_base = self._persist_dir / "history" - if history_base.exists(): - for user_dir in history_base.iterdir(): - if user_dir.is_dir(): - user_id = user_dir.name - user_history = [] - - for version_file in sorted(user_dir.glob("profile_*.json")): - try: - with open(version_file, "r", encoding="utf-8") as f: - data = json.load(f) - user_history.append({ - "profile": data.get("profile"), - "metadata": data.get("metadata", {}), - "timestamp": data.get("last_updated"), - }) - except Exception as e: - logger.warning(f"Failed to load history from {version_file}: {e}") - - if user_history: - self._history[user_id] = user_history - except Exception as e: - logger.error(f"Failed to load profiles from disk: {e}") diff --git a/methods/EverCore/evaluation/src/adapters/evermemos_adapter.py b/methods/EverCore/evaluation/src/adapters/evermemos_adapter.py deleted file mode 100644 index 9d4ec120..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos_adapter.py +++ /dev/null @@ -1,693 +0,0 @@ -""" -EverCore Adapter - connects evaluation framework with EverCore implementation. -""" - -import asyncio -import json -import pickle -import time -from pathlib import Path -from typing import Any, Dict, List - -from rich.progress import ( - Progress, - SpinnerColumn, - TextColumn, - BarColumn, - TaskProgressColumn, - TimeElapsedColumn, - TimeRemainingColumn, - MofNCompleteColumn, -) -from rich.console import Console - -from evaluation.src.adapters.base import BaseAdapter -from evaluation.src.adapters.registry import register_adapter -from evaluation.src.core.data_models import Conversation, SearchResult -from common_utils.datetime_utils import to_iso_format - -# Import EverCore implementation -from evaluation.src.adapters.evermemos import ( - stage1_memcells_extraction, - stage2_index_building, - stage3_memory_retrivel, - stage4_response, -) - -# Import Memory Layer components -from memory_layer.llm.llm_provider import LLMProvider -from memory_layer.memory_extractor.atomic_fact_extractor import AtomicFactExtractor - - -@register_adapter("evermemos") -class EverCoreAdapter(BaseAdapter): - """ - EverCore adapter. - - Responsibilities: - 1. Receive calls from evaluation framework - 2. Convert data formats (evaluation framework ↔ EverCore) - 3. Call stage*.py implementations - 4. Return results in evaluation framework format - - Implementation details: - - MemCell extraction (stage1) - - Index building (stage2) - - Retrieval logic (stage3) - - Answer generation (stage4) - """ - - def __init__(self, config: dict, output_dir: Path = None): - super().__init__(config) - self.output_dir = Path(output_dir) if output_dir else Path(".") - - # Initialize LLM Provider (shared across all stages) - # Read from YAML llm configuration - llm_config = config.get("llm", {}) - - self.llm_provider = LLMProvider( - provider_type=llm_config.get("provider", "openai"), - model=llm_config.get("model", "gpt-4o-mini"), - api_key=llm_config.get("api_key", ""), - base_url=llm_config.get("base_url", "https://api.openai.com/v1"), - temperature=llm_config.get("temperature", 0.3), - max_tokens=llm_config.get("max_tokens", 32768), - ) - - # Initialize Atomic Fact Extractor - self.atomic_fact_extractor = AtomicFactExtractor(llm_provider=self.llm_provider) - - # Ensure NLTK data is available - stage2_index_building.ensure_nltk_data() - - print(f"✅ EverCore Adapter initialized") - print(f" LLM Model: {llm_config.get('model')}") - print(f" Output Dir: {self.output_dir}") - - @staticmethod - def _extract_conv_index(conversation_id: str) -> str: - """ - Extract numeric index part from conversation_id. - - Examples: - - "locomo_0" -> "0" - - "personamem_42" -> "42" - - "123" -> "123" - - "test_abc_5" -> "5" - - Strategy: Take the part after the last underscore, or return original if no underscore - """ - if "_" in conversation_id: - return conversation_id.split("_")[-1] - return conversation_id - - def _check_missing_indexes( - self, index_dir: Path, num_conv: int, index_type: str = "bm25" - ) -> List[int]: - """ - Check for missing index files. - - Args: - index_dir: Index directory - num_conv: Total number of conversations - index_type: Index type ("bm25" or "embedding") - - Returns: - List of conversation indices with missing indexes - """ - missing_indexes = [] - - for i in range(num_conv): - if index_type == "bm25": - index_file = index_dir / f"bm25_index_conv_{i}.pkl" - else: # embedding - index_file = index_dir / f"embedding_index_conv_{i}.pkl" - - if not index_file.exists(): - missing_indexes.append(i) - - return missing_indexes - - async def add( - self, - conversations: List[Conversation], - output_dir: Path = None, - checkpoint_manager=None, - **kwargs, - ) -> Dict[str, Any]: - """ - Add stage: Extract MemCells and build indexes. - - Call flow: - 1. Stage 1: Extract MemCells (stage1_memcells_extraction.py) - concurrent processing - 2. Stage 2: Build BM25 and Embedding indexes (stage2_index_building.py) - - Returns: Index metadata (Plan A: lazy loading) - """ - output_dir = Path(output_dir) if output_dir else self.output_dir - output_dir.mkdir(parents=True, exist_ok=True) - - memcells_dir = output_dir / "memcells" - memcells_dir.mkdir(parents=True, exist_ok=True) - bm25_index_dir = output_dir / "bm25_index" - emb_index_dir = output_dir / "vectors" - bm25_index_dir.mkdir(parents=True, exist_ok=True) - emb_index_dir.mkdir(parents=True, exist_ok=True) - - console = Console() - - # ========== Stage 1: MemCell Extraction (concurrent processing) ========== - console.print(f"\n{'='*60}", style="bold cyan") - console.print(f"Stage 1: MemCell Extraction", style="bold cyan") - console.print(f"{'='*60}", style="bold cyan") - - # Convert data format: evaluation framework → EverCore - raw_data_dict = {} - for conv in conversations: - conv_id = conv.conversation_id - raw_data = [] - - for idx, msg in enumerate(conv.messages): - # Handle timestamp: if None, use index-based pseudo timestamp - if msg.timestamp is not None: - timestamp_str = to_iso_format(msg.timestamp) - else: - # Generate pseudo timestamp using message index (maintain relative order) - # Base time: 2023-01-01 00:00:00, 30 seconds interval per message - from datetime import datetime, timedelta - - base_time = datetime(2023, 1, 1, 0, 0, 0) - pseudo_time = base_time + timedelta(seconds=idx * 30) - timestamp_str = to_iso_format(pseudo_time) - - message_dict = { - "sender_id": msg.sender_id, - "user_name": msg.sender_name or msg.sender_id, - "sender_name": msg.sender_name or msg.sender_id, - "content": msg.content, - "timestamp": timestamp_str, - } - - # Add optional fields - for optional_field in ["img_url", "blip_caption", "query"]: - if ( - optional_field in msg.metadata - and msg.metadata[optional_field] is not None - ): - message_dict[optional_field] = msg.metadata[optional_field] - - raw_data.append(message_dict) - - raw_data_dict[conv_id] = raw_data - - # Check completed conversations (checkpoint resume) - # Use extracted index to check files (stage1 saves using extracted index) - completed_convs = set() - if checkpoint_manager: - all_conv_indices = [ - self._extract_conv_index(conv.conversation_id) for conv in conversations - ] - completed_indices = checkpoint_manager.load_add_progress( - memcells_dir, all_conv_indices - ) - # Map completed indices back to original conversation_id - for conv in conversations: - if self._extract_conv_index(conv.conversation_id) in completed_indices: - completed_convs.add(conv.conversation_id) - - # Filter conversations to process - pending_conversations = [ - conv - for conv in conversations - if conv.conversation_id not in completed_convs - ] - - console.print( - f"\n📊 Total conversations: {len(conversations)}", style="bold cyan" - ) - console.print(f"✅ Completed: {len(completed_convs)}", style="bold green") - console.print(f"⏳ Pending: {len(pending_conversations)}", style="bold yellow") - - if len(pending_conversations) == 0: - console.print( - f"\n🎉 All conversations completed, skipping MemCell extraction!", - style="bold green", - ) - else: - total_messages = sum( - len(raw_data_dict[c.conversation_id]) for c in pending_conversations - ) - console.print(f"📝 Pending messages: {total_messages}", style="bold blue") - console.print(f"🚀 Starting concurrent processing...\n", style="bold green") - - # Use Rich progress bar for concurrent processing - start_time = time.time() - - with Progress( - SpinnerColumn(), - TextColumn("[progress.description]{task.description}"), - BarColumn(), - MofNCompleteColumn(), - TextColumn("•"), - TaskProgressColumn(), - TextColumn("•"), - TimeElapsedColumn(), - TextColumn("•"), - TimeRemainingColumn(), - TextColumn("•"), - TextColumn("[bold blue]{task.fields[status]}"), - console=console, - transient=False, - refresh_per_second=1, - ) as progress: - # Create main progress task - main_task = progress.add_task( - "[bold cyan]🎯 Overall Progress", - total=len(conversations), - completed=len(completed_convs), - status="Processing", - ) - - # Create progress bars for completed conversations (show as complete) - conversation_tasks = {} - for conv_id in completed_convs: - conv_index = self._extract_conv_index(conv_id) - conv_task_id = progress.add_task( - f"[green]Conv-{conv_index}", - total=len(raw_data_dict.get(conv_id, [])), - completed=len(raw_data_dict.get(conv_id, [])), - status="✅ (Skipped)", - ) - conversation_tasks[conv_id] = conv_task_id - - # Create progress bars and tasks for pending conversations - processing_tasks = [] - for conv in pending_conversations: - conv_id = conv.conversation_id - conv_index = self._extract_conv_index( - conv_id - ) # Extract numeric index - conv_task_id = progress.add_task( - f"[yellow]Conv-{conv_index}", - total=len(raw_data_dict[conv_id]), - completed=0, - status="Waiting", - ) - conversation_tasks[conv_id] = conv_task_id - - # Create processing task, pass extracted index - task = stage1_memcells_extraction.process_single_conversation( - conv_id=conv_index, # Use extracted index - conversation=raw_data_dict[conv_id], # Data uses original ID - save_dir=str(memcells_dir), - llm_provider=self.llm_provider, - atomic_fact_extractor=self.atomic_fact_extractor, - progress_counter=None, - progress=progress, - task_id=conv_task_id, - config=self._convert_config_to_experiment_config(), - ) - processing_tasks.append((conv_id, task)) - - # Define completion update function - async def run_with_completion(conv_id, task): - result = await task - progress.update( - conversation_tasks[conv_id], - status="✅", - completed=progress.tasks[conversation_tasks[conv_id]].total, - ) - progress.update(main_task, advance=1) - return result - - # Execute all pending tasks concurrently - if processing_tasks: - results = await asyncio.gather( - *[ - run_with_completion(conv_id, task) - for conv_id, task in processing_tasks - ] - ) - else: - results = [] - - progress.update(main_task, status="✅ Complete") - - end_time = time.time() - elapsed = end_time - start_time - - # Statistics - successful_convs = sum(1 for _, memcell_list in results if memcell_list) - total_memcells = sum(len(memcell_list) for _, memcell_list in results) - - console.print("\n" + "=" * 60, style="dim") - console.print("📊 MemCell Extraction Statistics:", style="bold") - console.print( - f" ✅ Successfully processed: {successful_convs}/{len(pending_conversations)}", - style="green", - ) - console.print(f" 📝 Total memcells: {total_memcells}", style="blue") - console.print(f" ⏱️ Total time: {elapsed:.2f}s", style="yellow") - if len(pending_conversations) > 0: - console.print( - f" 🚀 Average per conversation: {elapsed/len(pending_conversations):.2f}s", - style="cyan", - ) - console.print("=" * 60, style="dim") - - # ========== Stage 2: Index Building ========== - console.print(f"\n{'='*60}", style="bold cyan") - console.print(f"Stage 2: Index Building", style="bold cyan") - console.print(f"{'='*60}", style="bold cyan") - - # Call stage2 implementation to build indexes - exp_config = self._convert_config_to_experiment_config() - exp_config.num_conv = len(conversations) # Set conversation count - - # Smart skip logic: check existing index files - bm25_need_build = self._check_missing_indexes( - index_dir=bm25_index_dir, num_conv=len(conversations), index_type="bm25" - ) - - emb_need_build = [] - use_hybrid = self.config.get("search", {}).get("use_hybrid_search", True) - if use_hybrid: - emb_need_build = self._check_missing_indexes( - index_dir=emb_index_dir, - num_conv=len(conversations), - index_type="embedding", - ) - - # Statistics - total_convs = len(conversations) - bm25_to_build = len(bm25_need_build) - emb_to_build = len(emb_need_build) if use_hybrid else 0 - - console.print(f"\n📊 Index Building Statistics:") - console.print(f" Total conversations: {total_convs}") - console.print( - f" BM25 index: need to build {bm25_to_build}, existing {total_convs - bm25_to_build}" - ) - if use_hybrid: - console.print( - f" Embedding index: need to build {emb_to_build}, existing {total_convs - emb_to_build}" - ) - - # Build BM25 index - if bm25_to_build > 0: - console.print( - f"\n🔨 Building BM25 index ({bm25_to_build} conversations)...", - style="yellow", - ) - stage2_index_building.build_bm25_index( - config=exp_config, data_dir=memcells_dir, bm25_save_dir=bm25_index_dir - ) - console.print("✅ BM25 index building completed", style="green") - else: - console.print("✅ All BM25 indexes exist, skipping build", style="green") - - # Build Embedding index (if enabled) - if use_hybrid: - if emb_to_build > 0: - console.print( - f"\n🔨 Building Embedding index ({emb_to_build} conversations)...", - style="yellow", - ) - await stage2_index_building.build_emb_index( - config=exp_config, data_dir=memcells_dir, emb_save_dir=emb_index_dir - ) - console.print("✅ Embedding index building completed", style="green") - else: - console.print( - "✅ All Embedding indexes exist, skipping build", style="green" - ) - - # ========== Plan A: Return index metadata (lazy loading) ========== - # Don't load indexes into memory, only return paths and metadata - index_metadata = { - "type": "lazy_load", # Mark as lazy loading - "memcells_dir": str(memcells_dir), - "bm25_index_dir": str(bm25_index_dir), - "emb_index_dir": str(emb_index_dir), - "conversation_ids": [conv.conversation_id for conv in conversations], - "use_hybrid_search": use_hybrid, - "total_conversations": len(conversations), - } - - console.print(f"\n{'='*60}", style="dim") - console.print(f"✅ Add stage completed", style="bold green") - console.print(f" 📁 MemCells: {memcells_dir}", style="dim") - console.print(f" 📁 BM25 index: {bm25_index_dir}", style="dim") - if use_hybrid: - console.print(f" 📁 Embedding index: {emb_index_dir}", style="dim") - console.print( - f" 💡 Using lazy loading strategy (memory-friendly)", style="cyan" - ) - console.print(f"{'='*60}\n", style="dim") - - return index_metadata - - async def search( - self, query: str, conversation_id: str, index: Any, **kwargs - ) -> SearchResult: - """ - Search stage: Retrieve relevant MemCells. - - Lazy loading: Load indexes from files on demand (memory-friendly). - """ - # Lazy loading - read indexes from files - bm25_index_dir = Path(index["bm25_index_dir"]) - emb_index_dir = Path(index["emb_index_dir"]) - - # Extract numeric index from conversation_id to find index files - # Example: conversation_id = "locomo_0" -> conv_index = "0" - conv_index = self._extract_conv_index(conversation_id) - - # Load BM25 index on demand (using numeric index) - bm25_file = bm25_index_dir / f"bm25_index_conv_{conv_index}.pkl" - if not bm25_file.exists(): - return SearchResult( - query=query, - conversation_id=conversation_id, - results=[], - retrieval_metadata={"error": f"BM25 index not found: {bm25_file.name}"}, - ) - - with open(bm25_file, "rb") as f: - bm25_index_data = pickle.load(f) - - bm25 = bm25_index_data.get("bm25") - docs = bm25_index_data.get("docs") - - # Load Embedding index on demand (using numeric index) - emb_index = None - if index.get("use_hybrid_search"): - emb_file = emb_index_dir / f"embedding_index_conv_{conv_index}.pkl" - if emb_file.exists(): - with open(emb_file, "rb") as f: - emb_index = pickle.load(f) - - # Call stage3 retrieval implementation - search_config = self.config.get("search", {}) - retrieval_mode = search_config.get("mode", "agentic") - - exp_config = self._convert_config_to_experiment_config() - # Get correct format llm_config from exp_config - llm_config = exp_config.llm_config.get(exp_config.llm_service, {}) - - if retrieval_mode == "agentic": - # Agentic retrieval - top_results, metadata = await stage3_memory_retrivel.agentic_retrieval( - query=query, - config=exp_config, - llm_provider=self.llm_provider, - llm_config=llm_config, - emb_index=emb_index, - bm25=bm25, - docs=docs, - ) - elif retrieval_mode == "lightweight": - # Lightweight retrieval - top_results, metadata = await stage3_memory_retrivel.lightweight_retrieval( - query=query, - emb_index=emb_index, - bm25=bm25, - docs=docs, - config=exp_config, - ) - else: - # Default to hybrid retrieval - top_results = await stage3_memory_retrivel.hybrid_search_with_rrf( - query=query, - emb_index=emb_index, - bm25=bm25, - docs=docs, - top_n=20, - emb_candidates=search_config.get("hybrid_emb_candidates", 100), - bm25_candidates=search_config.get("hybrid_bm25_candidates", 100), - rrf_k=search_config.get("hybrid_rrf_k", 60), - ) - metadata = {} - - # Get response_top_k from config (use early for consistency) - response_top_k = exp_config.response_top_k - - # Convert to evaluation framework format (use response_top_k to be consistent with formatted_context) - results = [] - for doc, score in top_results[:response_top_k]: - results.append( - { - "content": doc.get("episode", ""), - "score": float(score), - "metadata": { - "subject": doc.get("subject", ""), - "summary": doc.get("summary", ""), - }, - } - ) - - # Build formatted_context - formatted_context = "" - conversation = kwargs.get("conversation") - if conversation and top_results: - # Get speaker information - speaker_a = conversation.metadata.get("speaker_a", "Speaker A") - speaker_b = conversation.metadata.get("speaker_b", "Speaker B") - - # Build context using response_top_k - retrieved_docs_text = [] - for doc, score in top_results[:response_top_k]: - subject = doc.get('subject', 'N/A') - episode = doc.get('episode', 'N/A') - doc_text = f"{subject}: {episode}\n---" - retrieved_docs_text.append(doc_text) - - speaker_memories = "\n\n".join(retrieved_docs_text) - - TEMPLATE = """Episodes memories for conversation between {speaker_1} and {speaker_2}: - - {speaker_memories} -""" - formatted_context = TEMPLATE.format( - speaker_1=speaker_a, - speaker_2=speaker_b, - speaker_memories=speaker_memories, - ) - - # Add formatted_context to metadata - metadata["formatted_context"] = formatted_context - - return SearchResult( - query=query, - conversation_id=conversation_id, - results=results, - retrieval_metadata=metadata, - ) - - async def answer(self, query: str, context: str, **kwargs) -> str: - """ - Answer stage: Generate answer. - - Calls stage4_response.py implementation. - """ - # Call stage4 answer generation implementation - exp_config = self._convert_config_to_experiment_config() - - answer = await stage4_response.locomo_response( - llm_provider=self.llm_provider, - context=context, - question=query, - experiment_config=exp_config, - ) - - return answer - - def get_system_info(self) -> Dict[str, Any]: - """Return system info.""" - return { - "name": "EverCore", - "version": "1.0", - "description": "EverCore memory system with agentic retrieval", - "adapter": "Adapter connecting framework to EverCore implementation", - } - - def _convert_config_to_experiment_config(self): - """ - Convert evaluation framework config to ExperimentConfig format. - """ - from evaluation.src.adapters.evermemos.config import ExperimentConfig - import os - - exp_config = ExperimentConfig() - - # Map LLM configuration: convert YAML llm to ExperimentConfig llm_config format - llm_cfg = self.config.get("llm", {}) - provider = llm_cfg.get("provider", "openai") - - exp_config.llm_service = provider - exp_config.llm_config = { - provider: { - "llm_provider": provider, - "model": llm_cfg.get("model", "gpt-4o-mini"), - "api_key": llm_cfg.get("api_key") or os.getenv("LLM_API_KEY", ""), - "base_url": llm_cfg.get("base_url") - or os.getenv("LLM_BASE_URL", "https://api.openai.com/v1"), - "temperature": llm_cfg.get("temperature", 0.3), - "max_tokens": llm_cfg.get("max_tokens", 32768), - } - } - - # Map Add stage configuration (only override explicitly specified in YAML) - add_config = self.config.get("add", {}) - if "enable_foresight_extraction" in add_config: - exp_config.enable_foresight_extraction = add_config[ - "enable_foresight_extraction" - ] - if "enable_clustering" in add_config: - exp_config.enable_clustering = add_config["enable_clustering"] - if "enable_profile_extraction" in add_config: - exp_config.enable_profile_extraction = add_config[ - "enable_profile_extraction" - ] - - # Map Search stage configuration (only override explicitly specified in YAML) - search_config = self.config.get("search", {}) - if "mode" in search_config: - exp_config.retrieval_mode = search_config["mode"] - exp_config.use_agentic_retrieval = exp_config.retrieval_mode == "agentic" - - # Map lightweight_search_mode (controls search method in lightweight mode) - # Options: "bm25_only" | "hybrid" | "emb_only" - if "lightweight_search_mode" in search_config: - exp_config.lightweight_search_mode = search_config[ - "lightweight_search_mode" - ] - - return exp_config - - def build_lazy_index( - self, conversations: List[Conversation], output_dir: Any - ) -> Dict[str, Any]: - """ - Build EverCore lazy-load index metadata. - - EverCore specifics: - - Local indexes (memcells, bm25, embeddings) - - Lazy loading (only save metadata, don't load actual index files) - - Args: - conversations: Conversation list - output_dir: Output directory - - Returns: - Index metadata dict - """ - return { - "type": "lazy_load", - "memcells_dir": str(output_dir / "memcells"), - "bm25_index_dir": str(output_dir / "bm25_index"), - "emb_index_dir": str(output_dir / "vectors"), - "conversation_ids": [conv.conversation_id for conv in conversations], - "use_hybrid_search": True, - "total_conversations": len(conversations), - } diff --git a/methods/EverCore/evaluation/src/adapters/evermemos_api_adapter.py b/methods/EverCore/evaluation/src/adapters/evermemos_api_adapter.py deleted file mode 100644 index 6653094e..00000000 --- a/methods/EverCore/evaluation/src/adapters/evermemos_api_adapter.py +++ /dev/null @@ -1,344 +0,0 @@ -""" -EverCore HTTP Memory API adapter (evaluation side). - -This adapter talks to EverCore server endpoints: -- POST /api/v0/memories (ingest single message) -- GET /api/v0/memories/search (retrieve memories) - -Note: -- This file was missing in the current workspace; registry.py still references it as - `evaluation.src.adapters.evermemos_api_adapter`. -""" - -from __future__ import annotations - -import asyncio -import json -from pathlib import Path -from typing import Any, Dict, List, Optional - -import aiohttp - -from common_utils.datetime_utils import to_iso_format -from evaluation.src.adapters.online_base import OnlineAPIAdapter -from evaluation.src.adapters.registry import register_adapter -from evaluation.src.core.data_models import Conversation, SearchResult - - -@register_adapter("evermemos_api") -class EverCoreAPIAdapter(OnlineAPIAdapter): - """ - Adapter for EverCore Memory API. - - Design: - - Ingest each conversation once (do NOT duplicate per-speaker perspectives). - - Retrieval is controlled by system config: search.scope = "personal" | "group". - """ - - def __init__(self, config: dict, output_dir: Path = None): - super().__init__(config, output_dir) - - self.base_url = str(config.get("base_url", "")).rstrip("/") - self.api_key = str(config.get("api_key", "") or "") - self.sync_mode = bool(config.get("sync_mode", False)) - self.max_retries = int(config.get("max_retries", 3)) - self.timeout_seconds = float(config.get("timeout_seconds", 60)) - self.request_interval = float(config.get("request_interval", 0.0)) - - self._session: Optional[aiohttp.ClientSession] = None - - self._memories_url = self._normalize_memories_url(self.base_url) - self._search_url = self._memories_url.rstrip("/") + "/search" - - print(f" Memory API: {self._memories_url}") - - # --- override add() to support clean_groups --- - async def add( - self, conversations: List[Conversation], **kwargs: Any - ) -> Dict[str, Any]: - """Override to support clean_groups config before ingestion.""" - if self.config.get("clean_groups"): - from evaluation.src.utils.cleaner import clear_group_data - - print("\n🧹 clean_groups enabled, clearing data for involved groups...") - for conv in conversations: - await clear_group_data(conv.conversation_id, verbose=True) - print() - return await super().add(conversations, **kwargs) - - # --- lifecycle --- - async def close(self) -> None: - if self._session and not self._session.closed: - await self._session.close() - - async def _get_session(self) -> aiohttp.ClientSession: - if self._session and not self._session.closed: - return self._session - timeout = aiohttp.ClientTimeout(total=self.timeout_seconds) - self._session = aiohttp.ClientSession(timeout=timeout) - return self._session - - # --- helpers --- - @staticmethod - def _normalize_memories_url(base_url: str) -> str: - url = (base_url or "").rstrip("/") - if url.endswith("/api/v0/memories"): - return url - return url + "/api/v0/memories" - - def _headers(self) -> Dict[str, str]: - headers = {"Content-Type": "application/json"} - if self.api_key: - headers["Authorization"] = f"Bearer {self.api_key}" - return headers - - async def _request_json_with_retry( - self, method: str, url: str, **kwargs: Any - ) -> Dict[str, Any]: - session = await self._get_session() - req = getattr(session, method) - - last_exc: Optional[Exception] = None - for attempt in range(self.max_retries): - try: - async with req(url, **kwargs) as resp: - text = await resp.text() - if resp.status >= 400: - raise RuntimeError( - f"{method.upper()} {url} -> {resp.status}: {text[:800]}" - ) - if not text: - return {} - try: - return json.loads(text) - except json.JSONDecodeError: - # Some gateways may return wrong content-type; still parse as JSON. - return await resp.json(content_type=None) - except Exception as e: # noqa: BLE001 - last_exc = e - if attempt < self.max_retries - 1: - await asyncio.sleep(min(2**attempt, 8)) - continue - raise - raise last_exc or RuntimeError("request failed") - - @staticmethod - def _speaker_to_user_id(conversation_id: str, sender_name: str) -> str: - # Align with evaluation loader sender_id style: "{speaker_lower}_{conv_id}" - return f"{sender_name.lower().replace(' ', '_')}_{conversation_id}" - - # --- overrides to avoid per-speaker duplication on ingest/search --- - def _need_dual_perspective(self, speaker_a: str, speaker_b: str) -> bool: - # EverCore Memory API stores group chat stream; do not split perspectives. - return False - - def _conversation_to_messages( - self, - conversation: Conversation, - format_type: str = "basic", - perspective: Optional[str] = None, - ) -> List[Dict[str, Any]]: - del format_type, perspective - conv_id = conversation.conversation_id - - out: List[Dict[str, Any]] = [] - for idx, msg in enumerate(conversation.messages): - if not msg.timestamp: - continue - - message_id = ( - msg.metadata.get("message_id") - or msg.metadata.get("dia_id") - or f"{conv_id}_{idx}" - ) - - out.append( - { - "group_id": conv_id, - "message_id": str(message_id), - "create_time": to_iso_format(msg.timestamp), - "sender": msg.sender_id - or self._speaker_to_user_id(conv_id, msg.sender_name), - "sender_name": msg.sender_name, - "content": msg.content, - "refer_list": msg.metadata.get("refer_list") or [], - } - ) - return out - - def _get_answer_prompt(self) -> str: - """Use EverCore CoT answer prompt (same as evermemos adapter).""" - from evaluation.src.adapters.evermemos.prompts.answer_prompts import ( - ANSWER_PROMPT, - ) - - return ANSWER_PROMPT - - # --- required abstract methods (OnlineAPIAdapter hooks) --- - async def _add_user_messages( - self, - conv: Conversation, - messages: List[Dict[str, Any]], - speaker: str, - **kwargs: Any, - ) -> Any: - del speaker - if not self._memories_url: - raise ValueError("base_url is empty; set system config 'base_url'") - - progress = kwargs.get("progress") - task_id = kwargs.get("task_id") - - params = {"sync_mode": "true"} if self.sync_mode else None - headers = self._headers() - - # Preserve ordering: send sequentially. - for payload in messages: - await self._request_json_with_retry( - "post", self._memories_url, json=payload, params=params, headers=headers - ) - if progress is not None and task_id is not None: - progress.update(task_id, advance=1) - if self.request_interval > 0: - await asyncio.sleep(self.request_interval) - - return None - - async def _search_single_user( - self, query: str, conversation_id: str, user_id: str, top_k: int, **kwargs: Any - ) -> List[Dict[str, Any]]: - del kwargs - if not self._search_url: - raise ValueError("base_url is empty; set system config 'base_url'") - - search_cfg = self.config.get("search", {}) or {} - scope = str(search_cfg.get("scope", "personal")) - retrieve_method = str( - search_cfg.get("retrieve_method") or search_cfg.get("mode") or "keyword" - ) - memory_types = search_cfg.get("memory_types", []) or [] - - params: Dict[str, Any] = { - "query": query, - "retrieve_method": retrieve_method, - "top_k": int(top_k), - } - - if memory_types: - if isinstance(memory_types, str): - params["memory_types"] = memory_types - else: - params["memory_types"] = ",".join([str(x) for x in memory_types]) - - if scope == "group": - params["group_id"] = conversation_id - params["user_id"] = "" - else: - params["user_id"] = user_id - - headers = self._headers() - data = await self._request_json_with_retry( - "get", self._search_url, params=params, headers=headers - ) - - result = (data or {}).get("result") or {} - memories = result.get("memories") or [] - scores = result.get("scores") or [] - - mem_groups: Dict[str, List[Dict[str, Any]]] = {} - for obj in memories: - if isinstance(obj, dict): - for gid, mem_list in obj.items(): - if mem_list: - mem_groups.setdefault(str(gid), []).extend(mem_list) - - score_groups: Dict[str, List[float]] = {} - for obj in scores: - if isinstance(obj, dict): - for gid, score_list in obj.items(): - if score_list: - score_groups.setdefault(str(gid), []).extend(score_list) - - results_out: List[Dict[str, Any]] = [] - for gid, mem_list in mem_groups.items(): - score_list = score_groups.get(gid, []) - for i, mem in enumerate(mem_list): - sc = score_list[i] if i < len(score_list) else 0.0 - ts = (mem or {}).get("timestamp") or "" - episode = (mem or {}).get("episode") or (mem or {}).get("summary") or "" - content = f"{ts}: {episode}".strip() - results_out.append( - { - "content": content, - "score": float(sc) if sc is not None else 0.0, - "user_id": (mem or {}).get("user_id") or user_id, - "metadata": {"group_id": gid, "raw": mem}, - } - ) - - results_out.sort(key=lambda x: x.get("score", 0.0), reverse=True) - return results_out[: int(top_k)] - - def _build_single_search_result( - self, - query: str, - conversation_id: str, - results: List[Dict[str, Any]], - user_id: str, - top_k: int, - **kwargs: Any, - ) -> SearchResult: - del kwargs - search_cfg = self.config.get("search", {}) or {} - retrieve_method = str( - search_cfg.get("retrieve_method") or search_cfg.get("mode") or "keyword" - ) - system_name = str(self.config.get("name") or "evermemos_api") - - retrieval_metadata = { - "system": system_name, - "top_k": int(top_k), - "retrieve_method": retrieve_method, - "memory_types": ["episodic_memory"], - "user_id": "", - "group_id": conversation_id, - } - - return SearchResult( - query=query, - conversation_id=conversation_id, - results=results[: int(top_k)], - retrieval_metadata=retrieval_metadata, - ) - - def _build_dual_search_result( - self, - query: str, - conversation_id: str, - all_results: List[Dict[str, Any]], - results_a: List[Dict[str, Any]], - results_b: List[Dict[str, Any]], - speaker_a: str, - speaker_b: str, - speaker_a_user_id: str, - speaker_b_user_id: str, - top_k: int, - **kwargs: Any, - ) -> SearchResult: - # Not used (we force single perspective), but keep minimal implementation to satisfy ABC. - del ( - all_results, - results_a, - results_b, - speaker_a, - speaker_b, - speaker_b_user_id, - kwargs, - ) - return self._build_single_search_result( - query=query, - conversation_id=conversation_id, - results=[], - user_id=speaker_a_user_id, - top_k=top_k, - ) diff --git a/methods/EverCore/evaluation/src/adapters/mem0_adapter.py b/methods/EverCore/evaluation/src/adapters/mem0_adapter.py deleted file mode 100644 index 9c10b456..00000000 --- a/methods/EverCore/evaluation/src/adapters/mem0_adapter.py +++ /dev/null @@ -1,468 +0,0 @@ -""" -Mem0 Adapter - adapt Mem0 online API for evaluation framework. -Reference: https://mem0.ai/ - -Key features: -- Dual-perspective handling: separate storage and retrieval for speaker_a and speaker_b -- Supports custom instructions -""" - -import asyncio -import json -from datetime import datetime, timezone, timedelta -from pathlib import Path -from typing import Any, Dict, List - -from rich.console import Console - -from evaluation.src.adapters.online_base import OnlineAPIAdapter -from evaluation.src.adapters.registry import register_adapter -from evaluation.src.core.data_models import Conversation, SearchResult - - -@register_adapter("mem0") -class Mem0Adapter(OnlineAPIAdapter): - """ - Mem0 online API adapter. - - Supports: - - Standard memory storage and retrieval - - Config example: - ```yaml - adapter: "mem0" - api_key: "${MEM0_API_KEY}" - batch_size: 2 - display_timezone_offset: 8 # Optional: convert timestamps to UTC+8 for display - ``` - """ - - def __init__(self, config: dict, output_dir: Path = None): - super().__init__(config, output_dir) - - # Import Mem0 async client - try: - from mem0 import AsyncMemoryClient - except ImportError: - raise ImportError( - "Mem0 client not installed. " "Please install: pip install mem0ai" - ) - - # Initialize Mem0 async client - api_key = config.get("api_key", "") - if not api_key: - raise ValueError("Mem0 API key is required. Set 'api_key' in config.") - - self.client = AsyncMemoryClient(api_key=api_key) - self.batch_size = config.get("batch_size", 2) - self.max_retries = config.get("max_retries", 5) - self.max_content_length = config.get("max_content_length", 12000) - self.add_interval = config.get("add_interval", 0.0) - self.search_interval = config.get("search", {}).get("search_interval", 0.0) - self.console = Console() - - # Set custom instructions (loaded from prompts.yaml) - # Prioritize config settings (backward compatible), otherwise load from prompts - custom_instructions = config.get("custom_instructions", None) - if not custom_instructions: - # Load from prompts.yaml - custom_instructions = ( - self._prompts.get("add_stage", {}) - .get("mem0", {}) - .get("custom_instructions", None) - ) - print(f" ✅ Custom instructions set (from prompts.yaml)") - - # Store custom_instructions for async initialization - self._custom_instructions = custom_instructions - - print(f" Batch Size: {self.batch_size}") - print(f" Max Content Length: {self.max_content_length}") - if self.add_interval > 0: - print(f" Add Interval: {self.add_interval}s (rate limiting)") - if self.search_interval > 0: - print(f" Search Interval: {self.search_interval}s (rate limiting)") - - def _convert_timestamp_to_display_timezone(self, timestamp_str: str) -> str: - """ - Convert mem0's timestamp to display timezone. - - Default behavior (if display_timezone_offset not set): - - Convert to system local timezone (symmetric with add stage where naive datetime - is treated as local timezone by Python's .timestamp() method) - - Optional behavior (if display_timezone_offset is set): - - Convert to specified timezone (e.g., UTC for explicit UTC handling) - - Args: - timestamp_str: ISO format timestamp string with timezone (e.g., "2023-05-07T22:56:00-07:00") - - Returns: - Formatted timestamp string in display timezone or original if conversion fails - """ - if not timestamp_str: - return timestamp_str - - try: - # Parse ISO format timestamp (with timezone) - dt = datetime.fromisoformat(timestamp_str) - - dt_display = dt.astimezone(None) - - # Format as readable string (YYYY-MM-DD HH:MM:SS) - return dt_display.strftime("%Y-%m-%d %H:%M:%S") - except Exception as e: - # If conversion fails, return original string - self.console.print( - f"⚠️ Failed to convert timestamp '{timestamp_str}': {e}", style="yellow" - ) - return timestamp_str - - async def prepare(self, conversations: List[Conversation], **kwargs) -> None: - """ - Preparation stage: update project configuration and clean existing data. - - Args: - conversations: Standard format conversation list - **kwargs: Extra parameters - """ - # Update project with custom instructions (if set) - if self._custom_instructions: - try: - await self.client.update_project( - custom_instructions=self._custom_instructions - ) - self.console.print(" ✅ Custom instructions set", style="green") - except Exception as e: - self.console.print( - f" ⚠️ Failed to set custom instructions: {e}", style="yellow" - ) - - # Check if need to clean existing data - clean_before_add = self.config.get("clean_before_add", False) - - if not clean_before_add: - self.console.print( - " ⏭️ Skipping data cleanup (clean_before_add=false)", style="dim" - ) - return - - self.console.print(f"\n{'='*60}", style="bold yellow") - self.console.print(f"Preparation: Cleaning existing data", style="bold yellow") - self.console.print(f"{'='*60}", style="bold yellow") - - # Collect all user_ids to clean - user_ids_to_clean = set() - - for conv in conversations: - # Get user_id for speaker_a and speaker_b - speaker_a = conv.metadata.get("speaker_a", "") - speaker_b = conv.metadata.get("speaker_b", "") - - need_dual = self._need_dual_perspective(speaker_a, speaker_b) - - user_ids_to_clean.add(self._extract_user_id(conv, speaker="speaker_a")) - - if need_dual: - user_ids_to_clean.add(self._extract_user_id(conv, speaker="speaker_b")) - - # Clean all user data - self.console.print( - f"\n🗑️ Cleaning data for {len(user_ids_to_clean)} user(s)...", - style="yellow", - ) - - cleaned_count = 0 - failed_count = 0 - - for user_id in user_ids_to_clean: - try: - # Use async client for delete operation - await self.client.delete_all(user_id=user_id) - cleaned_count += 1 - self.console.print(f" ✅ Cleaned: {user_id}", style="green") - except Exception as e: - failed_count += 1 - self.console.print( - f" ⚠️ Failed to clean {user_id}: {e}", style="yellow" - ) - - self.console.print( - f"\n✅ Cleanup completed: {cleaned_count} succeeded, {failed_count} failed", - style="bold green", - ) - - async def _add_user_messages( - self, conv: Conversation, messages: List[Dict[str, Any]], speaker: str, **kwargs - ) -> Any: - """ - Add messages for a single user to Mem0. - - Args: - conv: Original conversation object - messages: Formatted message list - speaker: "speaker_a" or "speaker_b" - **kwargs: Extra parameters - - Returns: - None - """ - # Extract user_id - user_id = self._extract_user_id(conv, speaker=speaker) - - # Handle content truncation (Mem0 specific) - truncated_count = 0 - for msg in messages: - if len(msg["content"]) > self.max_content_length: - msg["content"] = msg["content"][: self.max_content_length] - truncated_count += 1 - - # Log info - sender_name = conv.metadata.get(speaker, speaker) - is_fake_timestamp = ( - conv.messages[0].metadata.get("is_fake_timestamp", False) - if conv.messages - else False - ) - - self.console.print( - f" 📤 Adding for {sender_name} ({user_id}): {len(messages)} messages", - style="dim", - ) - if is_fake_timestamp: - self.console.print(f" ⚠️ Using fake timestamp", style="yellow") - if truncated_count > 0: - self.console.print( - f" ⚠️ Truncated {truncated_count} messages (>{self.max_content_length} chars)", - style="yellow", - ) - - # Add messages in batches with retry - # Note: messages list corresponds to conv.messages in order - for i in range(0, len(messages), self.batch_size): - batch_messages = messages[i : i + self.batch_size] - - # Use the timestamp of the first message in this batch - timestamp = None - if i < len(conv.messages) and conv.messages[i].timestamp: - timestamp = int(conv.messages[i].timestamp.timestamp()) - - for attempt in range(self.max_retries): - try: - # Use async client for add operation - await self.client.add( - messages=batch_messages, timestamp=timestamp, user_id=user_id - ) - # Wait between add requests to avoid rate limits - if self.add_interval > 0: - await asyncio.sleep(self.add_interval) - break - except Exception as e: - if attempt < self.max_retries - 1: - self.console.print( - f" ⚠️ [{sender_name} (user_id={user_id})] Retry {attempt + 1}/{self.max_retries}: {e}", - style="yellow", - ) - await asyncio.sleep(2**attempt) # Use async sleep - else: - self.console.print( - f" ❌ [{sender_name} (user_id={user_id})] Failed after {self.max_retries} retries: {e}", - style="red", - ) - raise e - - return None - - async def _search_single_user( - self, query: str, conversation_id: str, user_id: str, top_k: int, **kwargs - ) -> List[Dict[str, Any]]: - """ - Search memories for a single user (Mem0-specific with timezone conversion). - - Calls Mem0 search API and converts results to standard format, - applying timezone conversion to timestamps. - - Args: - query: Query text - conversation_id: Conversation ID (not used by Mem0) - user_id: User ID to search for - top_k: Number of results to retrieve - **kwargs: Additional parameters - - Returns: - List of search results with timezone-converted timestamps - """ - # Add interval before search to avoid rate limiting (429 errors) - if self.search_interval > 0: - await asyncio.sleep(self.search_interval) - - try: - # Use async client for search operation - raw_results = await self.client.search( - query=query, - top_k=top_k, - user_id=user_id, - filters={"AND": [{"user_id": f"{user_id}"}]}, - ) - - # Debug: print raw search results - self.console.print(f"\n[DEBUG] Mem0 Search Results:", style="yellow") - self.console.print(f" Query: {query}", style="dim") - self.console.print(f" User ID: {user_id}", style="dim") - self.console.print( - f" Results: {json.dumps(raw_results, indent=2, ensure_ascii=False)}", - style="dim", - ) - - except Exception as e: - self.console.print(f"❌ Mem0 search error: {e}", style="red") - return [] - - # Convert to standard format with timezone conversion - results = [] - for memory in raw_results.get("results", []): - # Apply timezone conversion to timestamp - created_at_original = memory.get("created_at", "") - created_at_display = self._convert_timestamp_to_display_timezone( - created_at_original - ) - - results.append( - { - "content": f"{created_at_display}: {memory['memory']}", # Add timestamp prefix - "score": memory.get("score", 0.0), - "user_id": user_id, - "metadata": { - "id": memory.get("id", ""), - "created_at": created_at_original, - "created_at_display": created_at_display, - "memory": memory.get("memory", ""), - "user_id": memory.get("user_id", ""), - }, - } - ) - - return results - - def _build_single_search_result( - self, - query: str, - conversation_id: str, - results: List[Dict[str, Any]], - user_id: str, - top_k: int, - **kwargs, - ) -> SearchResult: - """ - Build SearchResult for single perspective (Mem0: simple metadata). - - Args: - query: Query text - conversation_id: Conversation ID - results: Search results from _search_single_user - user_id: User ID - top_k: Number of results requested - **kwargs: Additional parameters - - Returns: - SearchResult (no formatted_context, uses fallback) - """ - return SearchResult( - query=query, - conversation_id=conversation_id, - results=results, - retrieval_metadata={ - "system": "mem0", - "top_k": top_k, - "dual_perspective": False, - "user_ids": [user_id], - }, - ) - - def _build_dual_search_result( - self, - query: str, - conversation_id: str, - all_results: List[Dict[str, Any]], - results_a: List[Dict[str, Any]], - results_b: List[Dict[str, Any]], - speaker_a: str, - speaker_b: str, - speaker_a_user_id: str, - speaker_b_user_id: str, - top_k: int, - **kwargs, - ) -> SearchResult: - """ - Build SearchResult for dual perspective (Mem0: use template). - - Formats memories using the default template for dual-speaker scenarios. - - Args: - query: Query text - conversation_id: Conversation ID - all_results: Merged results (for fallback) - results_a: Speaker A's search results - results_b: Speaker B's search results - speaker_a: Speaker A name - speaker_b: Speaker B name - speaker_a_user_id: Speaker A user ID - speaker_b_user_id: Speaker B user ID - top_k: Number of results per user - **kwargs: Additional parameters - - Returns: - SearchResult with formatted_context - """ - # Extract content from results (already includes timezone-converted timestamps) - speaker_a_memories_text = ( - "\n".join([r["content"] for r in results_a]) - if results_a - else "(No memories found)" - ) - speaker_b_memories_text = ( - "\n".join([r["content"] for r in results_b]) - if results_b - else "(No memories found)" - ) - - # Use default template - template = self._prompts["online_api"].get("templates", {}).get("default", "") - formatted_context = template.format( - speaker_1=speaker_a, - speaker_1_memories=speaker_a_memories_text, - speaker_2=speaker_b, - speaker_2_memories=speaker_b_memories_text, - ) - - return SearchResult( - query=query, - conversation_id=conversation_id, - results=all_results, - retrieval_metadata={ - "system": "mem0", - "top_k": top_k, - "dual_perspective": True, - "user_ids": [speaker_a_user_id, speaker_b_user_id], - "formatted_context": formatted_context, - "speaker_a_memories_count": len(results_a), - "speaker_b_memories_count": len(results_b), - }, - ) - - def _get_answer_prompt(self) -> str: - """ - Return answer prompt. - - Uses generic default prompt (loaded from YAML). - """ - return self._prompts["online_api"]["default"]["answer_prompt_mem0"] - - def get_system_info(self) -> Dict[str, Any]: - """Return system info.""" - return { - "name": "Mem0", - "type": "online_api", - "description": "Mem0 - Personalized AI Memory Layer", - "adapter": "Mem0Adapter", - } diff --git a/methods/EverCore/evaluation/src/adapters/memos_adapter.py b/methods/EverCore/evaluation/src/adapters/memos_adapter.py deleted file mode 100644 index 440f9e32..00000000 --- a/methods/EverCore/evaluation/src/adapters/memos_adapter.py +++ /dev/null @@ -1,516 +0,0 @@ -""" -Memos Adapter - adapt Memos online API for evaluation framework. -Reference: https://www.memos.so/ -""" - -import asyncio -import json -from pathlib import Path -from typing import Any, Dict, List, Optional - -import aiohttp -from aiolimiter import AsyncLimiter -from rich.console import Console - -from evaluation.src.adapters.online_base import OnlineAPIAdapter -from evaluation.src.adapters.registry import register_adapter -from evaluation.src.core.data_models import Conversation, SearchResult - - -@register_adapter("memos") -class MemosAdapter(OnlineAPIAdapter): - """ - Memos online API adapter. - - Supports: - - Memory ingestion (supports conversation context) - - Memory retrieval - - Official API supported parameters: - - user_id (required) - Format: {conv_id}_{speaker}, already contains session info - - query (required) - - memory_limit_number (optional, default 6) - - Note: Does not use conversation_id parameter, as user_id already contains session info - - Config example: - ```yaml - adapter: "memos" - api_url: "${MEMOS_URL}" - api_key: "${MEMOS_KEY}" - ``` - """ - - def __init__(self, config: dict, output_dir: Path = None): - super().__init__(config, output_dir) - - # Get API configuration - self.api_url = config.get("api_url", "") - if not self.api_url: - raise ValueError("Memos API URL is required. Set 'api_url' in config.") - - api_key = config.get("api_key", "") - if not api_key: - raise ValueError("Memos API key is required. Set 'api_key' in config.") - - self.headers = {"Content-Type": "application/json", "Authorization": api_key} - - # Retrieval configuration (only keep batch_size and max_retries, other params not supported by official API) - self.batch_size = config.get("batch_size", 9999) # Memos supports large batches - self.max_retries = config.get("max_retries", 5) - - # Rate limiting configuration (default: 10 requests/second) - requests_per_second = config.get("requests_per_second", 10) - self.rate_limiter = AsyncLimiter(max_rate=requests_per_second, time_period=1.0) - - # Create aiohttp session (will be initialized on first use) - self._session: Optional[aiohttp.ClientSession] = None - - self.console = Console() - - print(f" API URL: {self.api_url}") - print(f" Rate Limit: {requests_per_second} requests/second (async)") - - async def _get_session(self) -> aiohttp.ClientSession: - """ - Get or create aiohttp session (lazy initialization). - - Returns: - aiohttp.ClientSession instance - """ - if self._session is None or self._session.closed: - timeout = aiohttp.ClientTimeout(total=60) - self._session = aiohttp.ClientSession(headers=self.headers, timeout=timeout) - return self._session - - async def close(self): - """ - Close aiohttp session. - - Should be called when adapter is no longer needed. - """ - if self._session and not self._session.closed: - await self._session.close() - - async def _add_user_messages( - self, conv: Conversation, messages: List[Dict[str, Any]], speaker: str, **kwargs - ) -> Any: - """ - Add messages for a single user to Memos. - - Args: - conv: Original conversation object - messages: Formatted message list - speaker: "speaker_a" or "speaker_b" - **kwargs: Extra parameters - - Returns: - None - """ - # Extract user_id and conv_id - user_id = self._extract_user_id(conv, speaker=speaker) - conv_id = conv.conversation_id - - # Log info - sender_name = conv.metadata.get(speaker, speaker) - self.console.print( - f" 📤 Adding for {sender_name} ({user_id}): {len(messages)} messages", - style="dim", - ) - - # Get session - session = await self._get_session() - - # Send messages in batches with retry - url = f"{self.api_url}/add/message" - - for i in range(0, len(messages), self.batch_size): - batch_messages = messages[i : i + self.batch_size] - - # Try to send the batch with automatic batch size reduction on token limit error - await self._send_message_batch( - url=url, - batch_messages=batch_messages, - user_id=user_id, - conv_id=conv_id, - sender_name=sender_name, - session=session, - ) - - return None - - async def _send_message_batch( - self, - url: str, - batch_messages: List[Dict[str, Any]], - user_id: str, - conv_id: str, - sender_name: str, - session: aiohttp.ClientSession, - ) -> None: - """ - Send a batch of messages to Memos API. - - Handles token limit exceeded errors by automatically reducing batch size to 2. - - Args: - url: API endpoint URL - batch_messages: Messages to send in this batch - user_id: User ID - conv_id: Conversation ID - sender_name: Sender name (for logging) - session: aiohttp session - """ - payload_dict = { - "messages": batch_messages, - "user_id": user_id, - "conversation_id": conv_id, - } - - for attempt in range(self.max_retries): - try: - # Apply rate limiting - async with self.rate_limiter: - async with session.post(url, json=payload_dict) as response: - if response.status != 200: - text = await response.text() - raise Exception(f"HTTP {response.status}: {text}") - - result = await response.json() - - # Check for token limit exceeded error - if ( - result.get("code") == 40302 - and result.get("message") == "Input token limit exceeded" - ): - # If batch size > 1, try splitting into smaller batches - if len(batch_messages) > 1: - # Determine new batch size: if current > 2, use 2; otherwise use 1 - new_batch_size = 2 if len(batch_messages) > 2 else 1 - self.console.print( - f" ⚠️ [{sender_name}] Token limit exceeded, splitting batch of {len(batch_messages)} into smaller batches (size={new_batch_size})", - style="yellow", - ) - # Recursively send in smaller batches - for j in range(0, len(batch_messages), new_batch_size): - sub_batch = batch_messages[j : j + new_batch_size] - await self._send_message_batch( - url=url, - batch_messages=sub_batch, - user_id=user_id, - conv_id=conv_id, - sender_name=sender_name, - session=session, - ) - return # Success - else: - # Batch size is 1, cannot split further - # Try truncating the message content by removing last 1000 characters - message = batch_messages[0] - original_content = message.get("content", "") - - if len(original_content) > 1000: - self.console.print( - f" ⚠️ [{sender_name}] Single message token limit exceeded, truncating content (removing last 1000 chars)", - style="yellow", - ) - # Create a truncated version of the message - truncated_message = message.copy() - truncated_message["content"] = original_content[ - :-1000 - ] - - # Try sending the truncated message - await self._send_message_batch( - url=url, - batch_messages=[truncated_message], - user_id=user_id, - conv_id=conv_id, - sender_name=sender_name, - session=session, - ) - return # Success - else: - # Content is already short, cannot truncate further - raise Exception( - f"API error (token limit, single message too large, content length={len(original_content)}): {result}" - ) - - if result.get("message") != "ok": - raise Exception(f"API error: {result}") - - # Success - break retry loop - break - - except Exception as e: - if attempt < self.max_retries - 1: - self.console.print( - f" ⚠️ [{sender_name}] Retry {attempt + 1}/{self.max_retries}: {e}", - style="yellow", - ) - await asyncio.sleep(2**attempt) # Exponential backoff - else: - self.console.print( - f" ❌ [{sender_name}] Failed after {self.max_retries} retries: {e}", - style="red", - ) - raise e - - async def _search_single_user( - self, query: str, conversation_id: str, user_id: str, top_k: int, **kwargs - ) -> List[Dict[str, Any]]: - """ - Search memories for a single user (Memos-specific with preference extraction). - - Calls Memos HTTP API and extracts preference information. - - Args: - query: Query text - conversation_id: Conversation ID (not used by Memos, user_id contains this info) - user_id: User ID to search for (format: {conv_id}_{speaker}) - top_k: Number of results to retrieve - **kwargs: Additional parameters - - Returns: - List of search results with preference information in metadata - - Note: - user_id already contains session info (format: {conv_id}_{speaker}). - Example: user_id="locomo_0_Caroline" uniquely identifies the locomo_0 conversation. - """ - # Get session - session = await self._get_session() - - # Prepare HTTP request - url = f"{self.api_url}/search/memory" - payload_dict = { - "query": query, - "user_id": user_id, - "memory_limit_number": top_k, - } - - # Call API with retry mechanism - text_mem_res = [] - pref_string = "" - - for attempt in range(self.max_retries): - try: - # Apply rate limiting - async with self.rate_limiter: - async with session.post(url, json=payload_dict) as response: - if response.status != 200: - text = await response.text() - raise Exception(f"HTTP {response.status}: {text}") - - result = await response.json() - if result.get("message") != "ok": - raise Exception(f"API error: {result}") - - data = result.get("data", {}) - text_mem_res = data.get("memory_detail_list", []) - pref_mem_res = data.get("preference_detail_list", []) - preference_note = data.get("preference_note", "") - - # Standardize field names: rename memory_value to memory - for i in text_mem_res: - i.update( - {"memory": i.pop("memory_value", i.get("memory", ""))} - ) - - # Format preference string - explicit_prefs = [ - p["preference"] - for p in pref_mem_res - if p.get("preference_type", "") == "explicit_preference" - ] - implicit_prefs = [ - p["preference"] - for p in pref_mem_res - if p.get("preference_type", "") == "implicit_preference" - ] - - pref_parts = [] - if explicit_prefs: - pref_parts.append( - "Explicit Preference:\n" - + "\n".join( - f"{i + 1}. {p}" - for i, p in enumerate(explicit_prefs) - ) - ) - if implicit_prefs: - pref_parts.append( - "Implicit Preference:\n" - + "\n".join( - f"{i + 1}. {p}" - for i, p in enumerate(implicit_prefs) - ) - ) - - pref_string = "\n".join(pref_parts) + preference_note - - # Success - break retry loop - break - - except Exception as e: - if attempt < self.max_retries - 1: - await asyncio.sleep(2**attempt) # Exponential backoff - else: - self.console.print(f"❌ Memos search error: {e}", style="red") - return [] - - # Convert to standard format - results = [] - for item in text_mem_res: - created_at = item.get("memory_time") or item.get("create_time", "") - results.append( - { - "content": item.get("memory", ""), - "score": item.get("relativity", item.get("score", 0.0)), - "user_id": user_id, - "metadata": { - "memory_id": item.get("id", ""), - "created_at": str(created_at) if created_at else "", - "memory_type": item.get("memory_type", ""), - "confidence": item.get("confidence", 0.0), - "tags": item.get("tags", []), - "pref_string": pref_string, # Store preference for this user - }, - } - ) - - return results - - def _build_single_search_result( - self, - query: str, - conversation_id: str, - results: List[Dict[str, Any]], - user_id: str, - top_k: int, - **kwargs, - ) -> SearchResult: - """ - Build SearchResult for single perspective (Memos: include preference). - - Args: - query: Query text - conversation_id: Conversation ID - results: Search results from _search_single_user - user_id: User ID - top_k: Number of results requested - **kwargs: Additional parameters - - Returns: - SearchResult with preference metadata (no formatted_context, uses fallback) - """ - # Extract pref_string from first result's metadata (all results share same pref_string) - pref_string = results[0]["metadata"]["pref_string"] if results else "" - - return SearchResult( - query=query, - conversation_id=conversation_id, - results=results, - retrieval_metadata={ - "system": "memos", - "preferences": {"pref_string": pref_string}, - "top_k": top_k, - "user_ids": [user_id], - }, - ) - - def _build_dual_search_result( - self, - query: str, - conversation_id: str, - all_results: List[Dict[str, Any]], - results_a: List[Dict[str, Any]], - results_b: List[Dict[str, Any]], - speaker_a: str, - speaker_b: str, - speaker_a_user_id: str, - speaker_b_user_id: str, - top_k: int, - **kwargs, - ) -> SearchResult: - """ - Build SearchResult for dual perspective (Memos: use template + preference). - - Formats memories using the default template, including preference information - for both speakers. - - Args: - query: Query text - conversation_id: Conversation ID - all_results: Merged results (for fallback) - results_a: Speaker A's search results - results_b: Speaker B's search results - speaker_a: Speaker A name - speaker_b: Speaker B name - speaker_a_user_id: Speaker A user ID - speaker_b_user_id: Speaker B user ID - top_k: Number of results per user - **kwargs: Additional parameters - - Returns: - SearchResult with formatted_context and preferences - """ - # Extract preferences from results' metadata - pref_a = results_a[0]["metadata"]["pref_string"] if results_a else "" - pref_b = results_b[0]["metadata"]["pref_string"] if results_b else "" - - # Build context for each speaker (memories + preferences) - speaker_a_memories = ( - "\n".join([r["content"] for r in results_a]) - if results_a - else "(No memories found)" - ) - speaker_b_memories = ( - "\n".join([r["content"] for r in results_b]) - if results_b - else "(No memories found)" - ) - - speaker_a_context = speaker_a_memories + (f"\n{pref_a}" if pref_a else "") - speaker_b_context = speaker_b_memories + (f"\n{pref_b}" if pref_b else "") - - # Use default template - template = self._prompts["online_api"].get("templates", {}).get("default", "") - formatted_context = template.format( - speaker_1=speaker_a, - speaker_1_memories=speaker_a_context, - speaker_2=speaker_b, - speaker_2_memories=speaker_b_context, - ) - - return SearchResult( - query=query, - conversation_id=conversation_id, - results=all_results, - retrieval_metadata={ - "system": "memos", - "dual_perspective": True, - "formatted_context": formatted_context, - "top_k": top_k, - "user_ids": [speaker_a_user_id, speaker_b_user_id], - "preferences": {"speaker_a_pref": pref_a, "speaker_b_pref": pref_b}, - }, - ) - - def _get_answer_prompt(self) -> str: - """ - Get answer prompt. - - Subclasses can override this method to return their own prompt. - Defaults to generic default prompt. - """ - return self._prompts["online_api"]["default"]["answer_prompt_memos"] - - def get_system_info(self) -> Dict[str, Any]: - """Return system info.""" - return { - "name": "Memos", - "type": "online_api", - "description": "Memos - Memory System with Preference Support", - "adapter": "MemosAdapter", - } diff --git a/methods/EverCore/evaluation/src/adapters/memu_adapter.py b/methods/EverCore/evaluation/src/adapters/memu_adapter.py deleted file mode 100644 index 264fc825..00000000 --- a/methods/EverCore/evaluation/src/adapters/memu_adapter.py +++ /dev/null @@ -1,822 +0,0 @@ -""" -Memu Adapter - adapt Memu online API for evaluation framework. -Uses HTTP RESTful API instead of Python SDK to avoid dependency conflicts. -Reference: https://memu.so/ -""" - -import json -import time -import requests -from pathlib import Path -from typing import Any, Dict, List - -from rich.console import Console - -from evaluation.src.adapters.online_base import OnlineAPIAdapter -from evaluation.src.adapters.registry import register_adapter -from evaluation.src.core.data_models import Conversation, SearchResult -from common_utils.datetime_utils import get_now_with_timezone - - -@register_adapter("memu") -class MemuAdapter(OnlineAPIAdapter): - """ - Memu online API adapter. - - Uses HTTP RESTful API directly to avoid Python SDK dependency conflicts. - - Supports: - - Memory ingestion (based on conversation context) - - Async task status monitoring - - Memory retrieval - - Config example: - ```yaml - adapter: "memu" - api_key: "${MEMU_API_KEY}" - base_url: "https://api.memu.so" # Optional, defaults to official API - agent_id: "default_agent" # Optional, default agent ID - agent_name: "Assistant" # Optional, default agent name - task_check_interval: 3 # Optional, task status check interval (seconds) - task_timeout: 90 # Optional, task timeout (seconds) - ``` - """ - - def __init__(self, config: dict, output_dir: Path = None): - super().__init__(config, output_dir) - - # Get configuration - api_key = config.get("api_key", "") - if not api_key: - raise ValueError("Memu API key is required. Set 'api_key' in config.") - - self.base_url = config.get("base_url", "https://api.memu.so").rstrip('/') - self.agent_id = config.get("agent_id", "default_agent") - self.agent_name = config.get("agent_name", "Assistant") - self.task_check_interval = config.get("task_check_interval", 3) - self.task_timeout = config.get("task_timeout", 90) - self.max_retries = config.get("max_retries", 5) - - # Get valid_users list for filtering (used for retrying failed tasks) - self.valid_users = config.get("valid_users", None) - - # Mock mode for testing (skip actual API calls) - self.mock_mode = config.get("mock_mode", False) - - # HTTP headers - self.headers = { - "Authorization": f"Bearer {api_key}", - "Content-Type": "application/json", - } - - self.console = Console() - self.console.print(f" Base URL: {self.base_url}", style="dim") - self.console.print( - f" Agent: {self.agent_name} ({self.agent_id})", style="dim" - ) - if self.valid_users: - self.console.print( - f" Valid Users Filter: {len(self.valid_users)} user(s)", - style="yellow", - ) - if self.mock_mode: - self.console.print( - f" 🧪 Mock Mode: ENABLED (API calls will be simulated)", - style="bold yellow", - ) - - # Force sequential processing (override num_workers) - self.console.print( - f" 🔄 Sequential Mode: ENABLED (all operations are serial)", - style="bold cyan", - ) - - async def add(self, conversations: List[Conversation], **kwargs) -> Dict[str, Any]: - """ - Ingest conversation data (call online API) in pure sequential mode. - - Override parent's add() method to enforce sequential processing: - - Process conversations one by one (no concurrency) - - Process users within each conversation one by one (no concurrency) - - Wait for each task to complete before proceeding to the next - - This ensures Memu API is not overwhelmed with concurrent requests. - """ - conversation_ids = [] - add_results = [] - - # Process conversations sequentially (one by one) - for conv in conversations: - conv_id = conv.conversation_id - - # Extract conversation info (speaker names, user_ids, perspective mode) - conv_info = self._extract_conversation_info( - conversation=conv, conversation_id=conv_id - ) - - # Get format type - format_type = self._get_format_type() - - # Organize messages based on perspective - if conv_info["need_dual_perspective"]: - # Dual perspective: prepare messages for both speakers - speaker_a_messages = self._conversation_to_messages( - conv, format_type=format_type, perspective="speaker_a" - ) - speaker_b_messages = self._conversation_to_messages( - conv, format_type=format_type, perspective="speaker_b" - ) - - # Add messages for speaker_a first (sequential) - result_a = await self._add_user_messages( - conv, speaker_a_messages, speaker="speaker_a", **kwargs - ) - - # Wait for speaker_a's task to complete - await self._wait_for_conversation_tasks( - [result_a], conversation_id=conv_id, **kwargs - ) - - # Add messages for speaker_b second (sequential) - result_b = await self._add_user_messages( - conv, speaker_b_messages, speaker="speaker_b", **kwargs - ) - - # Wait for speaker_b's task to complete - await self._wait_for_conversation_tasks( - [result_b], conversation_id=conv_id, **kwargs - ) - - # Collect results - conversation_ids.append(conv_id) - add_results.extend([result_a, result_b]) - else: - # Single perspective: prepare messages for speaker_a only - messages = self._conversation_to_messages( - conv, format_type=format_type, perspective=None - ) - - # Add messages for single user - result = await self._add_user_messages( - conv, messages, speaker="speaker_a", **kwargs - ) - - # Wait for task to complete - await self._wait_for_conversation_tasks( - [result], conversation_id=conv_id, **kwargs - ) - - # Collect results - conversation_ids.append(conv_id) - add_results.append(result) - - # Post-processing (already waited for all tasks, so this is a no-op) - await self._post_add_process(add_results, **kwargs) - - # Build and return result - return self._build_add_result(conversation_ids, add_results, **kwargs) - - async def _add_user_messages( - self, conv: Conversation, messages: List[Dict[str, Any]], speaker: str, **kwargs - ) -> Any: - """ - Add messages for a single user to Memu. - - Args: - conv: Original conversation object - messages: Formatted message list - speaker: "speaker_a" or "speaker_b" - **kwargs: Extra parameters - - Returns: - task_id: Task ID for tracking async task - """ - # Extract user_id and user_name - user_id = self._extract_user_id(conv, speaker=speaker) - user_name = conv.metadata.get( - speaker, "User" if speaker == "speaker_a" else "Assistant" - ) - - # Check if user_id is in valid_users list (if valid_users is set) - if self.valid_users is not None and user_id not in self.valid_users: - self.console.print( - f" ⏭️ Skipping {user_name} ({user_id}): not in valid_users", - style="dim yellow", - ) - return None - - # Get session_date (ISO format date) - session_date = None - if conv.messages and conv.messages[0].timestamp: - session_date = conv.messages[0].timestamp.strftime("%Y-%m-%d") - else: - session_date = get_now_with_timezone().strftime("%Y-%m-%d") - - # Validate that all messages have name field - # Note: messages already contain name and time from base class _conversation_to_messages - for msg in messages: - if not msg.get("name"): - raise ValueError(f"Message missing 'name' field: {msg}") - - self.console.print( - f" 📤 Adding for {user_name} ({user_id}): {len(messages)} messages", - style="dim", - ) - - # Construct request payload - payload = { - "conversation": messages, - "user_id": user_id, - "user_name": user_name, - "agent_id": self.agent_id, - "agent_name": self.agent_name, - "session_date": session_date, - } - - # Mock mode: Skip actual API call - if self.mock_mode: - self.console.print( - f" 🧪 [MOCK] Would add {len(messages)} messages for {user_name} ({user_id})", - style="cyan", - ) - self.console.print( - f" 🧪 [MOCK] Payload: user_id={user_id}, agent_id={self.agent_id}, " - f"session_date={session_date}, messages={len(messages)}", - style="dim cyan", - ) - self.console.print(f" 🧪 [MOCK] Returning task_id=None", style="cyan") - return None - - # Submit task (with retry) - import asyncio - - task_id = None - for attempt in range(self.max_retries): - try: - url = f"{self.base_url}/api/v0/memory/memorize" - # Use run_in_executor to avoid blocking event loop - loop = asyncio.get_event_loop() - response = await loop.run_in_executor( - None, lambda: requests.post(url, headers=self.headers, json=payload) - ) - response.raise_for_status() - - result = response.json() - task_id = result.get("task_id") - status = result.get("status") - - self.console.print( - f" ✅ Task created: {task_id} (status: {status})", - style="green", - ) - break - - except Exception as e: - if attempt < self.max_retries - 1: - self.console.print( - f" ⚠️ [{user_name}] Retry {attempt + 1}/{self.max_retries}: {e}", - style="yellow", - ) - time.sleep(2**attempt) - else: - self.console.print( - f" ❌ [{user_name}] Failed after {self.max_retries} retries: {e}", - style="red", - ) - raise e - - return task_id - - async def _wait_for_conversation_tasks( - self, task_results: List[Any], **kwargs - ) -> None: - """ - Wait for tasks from a single conversation to complete. - - This is called per-conversation, before releasing the semaphore. - This ensures that Memu respects the num_workers limit on concurrent tasks. - - Args: - task_results: List of task_ids from this conversation - **kwargs: Extra parameters (including conversation_id) - """ - # Filter out None values - task_ids = [task_id for task_id in task_results if task_id is not None] - - # Extract conversation_id for logging - conversation_id = kwargs.get("conversation_id", "unknown") - - if task_ids: - # Wait for this conversation's tasks to complete - await self._wait_for_all_tasks(task_ids, conversation_id) - - async def _post_add_process(self, add_results: List[Any], **kwargs) -> None: - """ - Post-processing hook. - - For Memu, all tasks have already been waited for in _wait_for_conversation_tasks, - so this is now a no-op. - - Args: - add_results: List of task_ids returned from _add_user_messages - **kwargs: Extra parameters - """ - # All tasks already waited for in _wait_for_conversation_tasks - # This is now a no-op - pass - - def _build_add_result( - self, conversation_ids: List[str], add_results: List[Any], **kwargs - ) -> Dict[str, Any]: - """ - Build the final result dict with task_ids for Memu. - - Args: - conversation_ids: List of conversation IDs - add_results: List of task_ids - **kwargs: Extra parameters - - Returns: - Result dictionary with task_ids - """ - # Filter out None values to get actual task_ids - task_ids = [task_id for task_id in add_results if task_id is not None] - - return { - "type": "online_api", - "system": "memu", - "conversation_ids": conversation_ids, - "task_ids": task_ids, - } - - async def _wait_for_all_tasks( - self, task_ids: List[str], conversation_id: str = "unknown" - ) -> bool: - """ - Wait for all tasks to complete. - - Args: - task_ids: Task ID list - conversation_id: Conversation ID for logging - - Returns: - Whether all tasks completed successfully - """ - import asyncio - - if not task_ids: - return True - - start_time = time.time() - pending_tasks = set(task_ids) - - # Show progress - total_tasks = len(task_ids) - # Create a short label for logging - conv_label = f"[{conversation_id}]" - - while time.time() - start_time < self.task_timeout: - completed_in_round = [] - failed_in_round = [] - - for task_id in list(pending_tasks): - try: - url = f"{self.base_url}/api/v0/memory/memorize/status/{task_id}" - # Use run_in_executor to avoid blocking event loop - loop = asyncio.get_event_loop() - response = await loop.run_in_executor( - None, lambda: requests.get(url, headers=self.headers) - ) - response.raise_for_status() - result = response.json() - status = result.get("status") - - # Memu API returns uppercase status: PENDING/PROCESSING/SUCCESS/FAILED - if status in ["SUCCESS", "COMPLETED"]: - completed_in_round.append(task_id) - elif status in ["FAILED", "FAILURE"]: - failed_in_round.append(task_id) - self.console.print( - f" {conv_label} ❌ Task {task_id} failed: {result.get('detail_info', 'Unknown error')}", - style="red", - ) - - except Exception as e: - self.console.print( - f" {conv_label} ⚠️ Error checking task {task_id}: {e}", - style="yellow", - ) - - # Remove completed/failed tasks - for task_id in completed_in_round + failed_in_round: - pending_tasks.remove(task_id) - - # Update progress - completed_count = total_tasks - len(pending_tasks) - if completed_in_round or failed_in_round: - self.console.print( - f" {conv_label} 📊 Progress: {completed_count}/{total_tasks} tasks completed", - style="cyan", - ) - - # If all tasks completed - if not pending_tasks: - self.console.print( - f" {conv_label} ✅ All {total_tasks} tasks completed!", - style="bold green", - ) - return len(failed_in_round) == 0 - - # Wait before retry - if pending_tasks: - elapsed = time.time() - start_time - self.console.print( - f" {conv_label} ⏳ {len(pending_tasks)} task(s) still processing... ({elapsed:.0f}s elapsed)", - style="dim", - ) - await asyncio.sleep(self.task_check_interval) - - # Timeout - self.console.print( - f" {conv_label} ⚠️ Timeout: {len(pending_tasks)} task(s) not completed within {self.task_timeout}s", - style="yellow", - ) - return False - - async def _search_single_user( - self, query: str, conversation_id: str, user_id: str, top_k: int, **kwargs - ) -> List[Dict[str, Any]]: - """ - Search memories for a single user (Memu-specific with categories summary). - - Calls Memu search API and fetches categories summary. - - Args: - query: Query text - conversation_id: Conversation ID (not used by Memu) - user_id: User ID to search for - top_k: Number of results to retrieve - **kwargs: Additional parameters (e.g., min_similarity) - - Returns: - List of search results with categories_summary in metadata - """ - import asyncio - - min_similarity = kwargs.get("min_similarity", 0.3) - - try: - url = f"{self.base_url}/api/v0/memory/retrieve/related-memory-items" - payload = { - "user_id": user_id, - "agent_id": self.agent_id, - "query": query, - "top_k": top_k, - "min_similarity": min_similarity, - } - - # Use run_in_executor to avoid blocking event loop - loop = asyncio.get_event_loop() - response = await loop.run_in_executor( - None, lambda: requests.post(url, headers=self.headers, json=payload) - ) - response.raise_for_status() - result = response.json() - - except Exception as e: - self.console.print(f"❌ Memu search error: {e}", style="red") - return [] - - # Get categories summary (fail silently if error) - categories_summary = await self._get_categories_summary(user_id) - - # Convert to standard format - results = [] - related_memories = result.get("related_memories", []) - - for item in related_memories: - memory = item.get("memory", {}) - results.append( - { - "content": memory.get("content", ""), - "score": item.get("similarity_score", 0.0), - "user_id": user_id, - "metadata": { - "id": memory.get("memory_id", ""), - "category": memory.get("category", ""), - "created_at": memory.get("created_at", ""), - "happened_at": memory.get("happened_at", ""), - "categories_summary": categories_summary, # Store summary for this user - }, - } - ) - - return results - - def _build_single_search_result( - self, - query: str, - conversation_id: str, - results: List[Dict[str, Any]], - user_id: str, - top_k: int, - **kwargs, - ) -> SearchResult: - """ - Build SearchResult for single perspective (Memu: custom context with summary). - - Args: - query: Query text - conversation_id: Conversation ID - results: Search results from _search_single_user - user_id: User ID - top_k: Number of results requested - **kwargs: Additional parameters (e.g., min_similarity) - - Returns: - SearchResult with custom formatted_context - """ - min_similarity = kwargs.get("min_similarity", 0.3) - - # Extract categories_summary from first result's metadata - categories_summary = ( - results[0]["metadata"]["categories_summary"] if results else "" - ) - - # Build custom context using Memu-specific logic - formatted_context = self._format_user_memories_with_summary( - results=results, - categories_summary=categories_summary, - top_k=top_k, - memory_separator="\n\n", - ) - - return SearchResult( - query=query, - conversation_id=conversation_id, - results=results, - retrieval_metadata={ - "system": "memu", - "user_ids": [user_id], - "top_k": top_k, - "min_similarity": min_similarity, - "total_found": len(results), - "formatted_context": formatted_context, - }, - ) - - def _build_dual_search_result( - self, - query: str, - conversation_id: str, - all_results: List[Dict[str, Any]], - results_a: List[Dict[str, Any]], - results_b: List[Dict[str, Any]], - speaker_a: str, - speaker_b: str, - speaker_a_user_id: str, - speaker_b_user_id: str, - top_k: int, - **kwargs, - ) -> SearchResult: - """ - Build SearchResult for dual perspective (Memu: custom context with summaries). - - Formats memories using Memu-specific dual perspective logic, - including categories summaries for both speakers. - - Args: - query: Query text - conversation_id: Conversation ID - all_results: Merged results (for fallback) - results_a: Speaker A's search results - results_b: Speaker B's search results - speaker_a: Speaker A name - speaker_b: Speaker B name - speaker_a_user_id: Speaker A user ID - speaker_b_user_id: Speaker B user ID - top_k: Number of results per user - **kwargs: Additional parameters (e.g., min_similarity) - - Returns: - SearchResult with custom formatted_context - """ - min_similarity = kwargs.get("min_similarity", 0.3) - - # Extract categories summaries from results' metadata - categories_summary_a = ( - results_a[0]["metadata"]["categories_summary"] if results_a else "" - ) - categories_summary_b = ( - results_b[0]["metadata"]["categories_summary"] if results_b else "" - ) - - # Build dual perspective context using Memu-specific logic - speaker_a_memories_text = self._format_user_memories_with_summary( - results=results_a, - categories_summary=categories_summary_a, - top_k=top_k, - memory_separator="\n", - ) - - speaker_b_memories_text = self._format_user_memories_with_summary( - results=results_b, - categories_summary=categories_summary_b, - top_k=top_k, - memory_separator="\n", - ) - - # Wrap using default template - template = self._prompts["online_api"].get("templates", {}).get("default", "") - formatted_context = template.format( - speaker_1=speaker_a, - speaker_1_memories=speaker_a_memories_text, - speaker_2=speaker_b, - speaker_2_memories=speaker_b_memories_text, - ) - - return SearchResult( - query=query, - conversation_id=conversation_id, - results=all_results, - retrieval_metadata={ - "system": "memu", - "user_ids": [speaker_a_user_id, speaker_b_user_id], - "top_k": top_k, - "min_similarity": min_similarity, - "total_found": len(all_results), - "formatted_context": formatted_context, - "dual_perspective": True, - "categories_summary_a": categories_summary_a, - "categories_summary_b": categories_summary_b, - }, - ) - - def _format_user_memories_with_summary( - self, - results: List[Dict[str, Any]], - categories_summary: str = "", - top_k: int = 10, - memory_separator: str = "\n\n", - ) -> str: - """ - Format a single user's memories with categories summary. - - This is a helper method to avoid code duplication in building contexts. - - Args: - results: Search results list - categories_summary: Categories summary (optional) - top_k: Number of results to show - memory_separator: Separator between memories (default: "\n\n") - - Returns: - Formatted text combining summary and memories - """ - content_parts = [] - - # Add categories summary first (if available) - if categories_summary: - content_parts.append(categories_summary) - - # Add search results - if results: - if categories_summary: - content_parts.append("\n## Related Memories\n") - - memories = [] - for idx, result in enumerate(results[:top_k], 1): - content = result.get("content", "") - metadata = result.get("metadata", {}) - happened_at = metadata.get("happened_at", "") - category = metadata.get("category", "") - - # Build format for each memory - memory_text = f"{idx}. {content}" - - # Add time and category information (if available) - metadata_parts = [] - if happened_at: - # Only show date part (YYYY-MM-DD) - date_str = ( - happened_at.split("T")[0] if "T" in happened_at else happened_at - ) - metadata_parts.append(f"Date: {date_str}") - if category: - metadata_parts.append(f"Category: {category}") - - if metadata_parts: - memory_text += f" ({', '.join(metadata_parts)})" - - memories.append(memory_text) - - content_parts.append(memory_separator.join(memories)) - elif not categories_summary: - # No categories summary and no search results - return "" - - return "".join(content_parts) - - async def _get_all_memories(self, user_id: str) -> Dict[str, Any]: - """ - Get all memories (categories with summaries) for a user. - - This method calls the Memu API to retrieve default categories and their summaries. - This provides a high-level overview of the user's memory landscape. - - Args: - user_id: User ID - - Returns: - API response containing categories and their summaries - Returns empty dict if error occurs (fail silently) - """ - import asyncio - - try: - url = f"{self.base_url}/api/v0/memory/retrieve/default-categories" - payload = { - "user_id": user_id, - "agent_id": self.agent_id, - "want_memory_items": True, - } - - # Use run_in_executor to avoid blocking event loop - loop = asyncio.get_event_loop() - response = await loop.run_in_executor( - None, lambda: requests.post(url, headers=self.headers, json=payload) - ) - response.raise_for_status() - result = response.json() - - return result - - except Exception as e: - # Fail silently - categories summary is optional context - self.console.print( - f" ⚠️ Failed to get categories for {user_id}: {e}", style="dim yellow" - ) - return {} - - def _format_categories_summary(self, memories: Dict[str, Any]) -> str: - """ - Format categories summary from get_all_memories response. - - Extracts category names and summaries and formats them into a readable string. - This provides a structured overview that helps the LLM understand the memory landscape. - - Args: - memories: Response from _get_all_memories() - - Returns: - Formatted categories summary string - Returns empty string if no valid categories found - """ - if not memories or 'categories' not in memories: - return "" - - summary_parts = ["## Memory Overview (by Category)\n"] - - categories = memories.get('categories', []) - has_content = False - - for category in categories: - category_name = category.get('name', '') - category_summary = category.get('summary', '') - - if category_name and category_summary: - summary_parts.append(f"**{category_name}:** {category_summary}\n\n") - has_content = True - - if not has_content: - return "" - - return "".join(summary_parts) - - async def _get_categories_summary(self, user_id: str) -> str: - """ - Get and format categories summary for a user. - - This is a convenience method that combines _get_all_memories and _format_categories_summary. - It's designed to be called during search to augment context with memory overview. - - Args: - user_id: User ID - - Returns: - Formatted categories summary string - Returns empty string if error occurs or no categories found - """ - memories = await self._get_all_memories(user_id) - return self._format_categories_summary(memories) - - def get_system_info(self) -> Dict[str, Any]: - """Return system info.""" - return { - "name": "Memu", - "type": "online_api", - "description": "Memu - Memory Management System (HTTP RESTful API)", - "adapter": "MemuAdapter", - "base_url": self.base_url, - "agent_id": self.agent_id, - } diff --git a/methods/EverCore/evaluation/src/adapters/online_base.py b/methods/EverCore/evaluation/src/adapters/online_base.py deleted file mode 100644 index c0f12397..00000000 --- a/methods/EverCore/evaluation/src/adapters/online_base.py +++ /dev/null @@ -1,976 +0,0 @@ -""" -Online API Adapter base class. - -Provides common functionality for all online memory system APIs (Mem0, Memos, Memu, etc.). -All online API adapters can inherit from this class. - -Design principles: -- Provide default answer() implementation (using generic prompt) -- Subclasses can override answer() to use their own specific prompts -- Provide helper methods for data format conversion -""" - -import time -from abc import abstractmethod -from pathlib import Path -from typing import Any, List, Dict, Optional - -from rich.console import Console -from rich.progress import ( - Progress, - SpinnerColumn, - TextColumn, - BarColumn, - MofNCompleteColumn, - TaskProgressColumn, - TimeElapsedColumn, - TimeRemainingColumn, -) - -from evaluation.src.adapters.base import BaseAdapter -from evaluation.src.core.data_models import Conversation, SearchResult -from evaluation.src.utils.config import load_yaml - -# Import Memory Layer components -from memory_layer.llm.llm_provider import LLMProvider - - -class OnlineAPIAdapter(BaseAdapter): - """ - Online API Adapter base class. - - Provides common functionality: - 1. LLM Provider initialization - 2. Answer generation (reuses EverCore implementation) - 3. Standard format conversion helper methods - - Subclasses only need to implement: - - add(): Call online API to ingest data - - search(): Call online API for retrieval - """ - - def __init__(self, config: dict, output_dir: Path = None): - super().__init__(config) - self.output_dir = Path(output_dir) if output_dir else Path(".") - - # Initialize LLM Provider (for answer generation) - llm_config = config.get("llm", {}) - - self.llm_provider = LLMProvider( - provider_type=llm_config.get("provider", "openai"), - model=llm_config.get("model", "gpt-4o-mini"), - api_key=llm_config.get("api_key", ""), - base_url=llm_config.get("base_url", "https://api.openai.com/v1"), - temperature=llm_config.get("temperature", 0.3), - max_tokens=llm_config.get("max_tokens", 32768), - ) - - # Load prompts (from YAML file) - evaluation_root = Path(__file__).parent.parent.parent - prompts_path = evaluation_root / "config" / "prompts.yaml" - self._prompts = load_yaml(str(prompts_path)) - - # Set num_workers (conversation-level concurrency) - # Can be overridden by subclass or config - self.num_workers = self._get_num_workers(config) - - print(f"✅ {self.__class__.__name__} initialized") - print(f" LLM Model: {llm_config.get('model')}") - print(f" Output Dir: {self.output_dir}") - print(f" Num Workers: {self.num_workers}") - - def _get_num_workers(self, config: dict) -> int: - """ - Get num_workers from config. - - Args: - config: Configuration dict (should contain num_workers) - - Returns: - Number of workers for conversation-level concurrency - """ - return config.get("num_workers", 10) # Default to 10 if not specified - - async def add(self, conversations: List[Conversation], **kwargs) -> Dict[str, Any]: - """ - Ingest conversation data (call online API) with concurrency control. - - Template method that implements the common add flow: - 1. Determine perspective (single or dual) - 2. Organize messages for each user - 3. Call subclass _add_user_messages for each user (with concurrency control) - 4. Post-processing (e.g., wait for tasks) - - Concurrency is controlled by self.num_workers (conversation-level). - - Subclasses can override this method for custom behavior, - or implement _add_user_messages for standard flow. - """ - import asyncio - - conversation_ids = [] - add_results = [] - - console = Console() - console.print(f"\n{'='*60}", style="bold cyan") - console.print("Stage 1: Add", style="bold cyan") - console.print(f"{'='*60}", style="bold cyan") - - def _conv_label(conv_id: str) -> str: - parts = conv_id.rsplit("_", 1) - if len(parts) == 2 and parts[1].isdigit(): - return parts[1] - return conv_id - - # Create semaphore for concurrency control - semaphore = asyncio.Semaphore(self.num_workers) - - async def process_single_conversation(conv, progress, main_task): - """Process a single conversation with concurrency control.""" - async with semaphore: - conv_id = conv.conversation_id - - # Extract conversation info (speaker names, user_ids, perspective mode) - conv_info = self._extract_conversation_info( - conversation=conv, conversation_id=conv_id - ) - - # Get format type (subclass can override) - format_type = self._get_format_type() - - # Organize messages based on perspective - if conv_info["need_dual_perspective"]: - # Dual perspective: prepare messages for both speakers - speaker_a_messages = self._conversation_to_messages( - conv, format_type=format_type, perspective="speaker_a" - ) - speaker_b_messages = self._conversation_to_messages( - conv, format_type=format_type, perspective="speaker_b" - ) - total_messages = len(speaker_a_messages) + len(speaker_b_messages) - conv_task_id = progress.add_task( - f"[yellow]Conv-{_conv_label(conv_id)}", - total=total_messages, - completed=0, - status="Processing", - ) - - # Add messages for both users - result_a = await self._add_user_messages( - conv, - speaker_a_messages, - speaker="speaker_a", - progress=progress, - task_id=conv_task_id, - **kwargs, - ) - result_b = await self._add_user_messages( - conv, - speaker_b_messages, - speaker="speaker_b", - progress=progress, - task_id=conv_task_id, - **kwargs, - ) - - # Wait for tasks to complete (per-conversation, before releasing semaphore) - # This is important for systems like Memu that need to limit concurrent tasks - await self._wait_for_conversation_tasks( - [result_a, result_b], conversation_id=conv_id, **kwargs - ) - - progress.update(conv_task_id, completed=total_messages, status="✅") - progress.update(main_task, advance=1) - return conv_id, [result_a, result_b] - else: - # Single perspective: prepare messages for speaker_a only - messages = self._conversation_to_messages( - conv, format_type=format_type, perspective=None - ) - total_messages = len(messages) - conv_task_id = progress.add_task( - f"[yellow]Conv-{_conv_label(conv_id)}", - total=total_messages, - completed=0, - status="Processing", - ) - - # Add messages for single user - result = await self._add_user_messages( - conv, - messages, - speaker="speaker_a", - progress=progress, - task_id=conv_task_id, - **kwargs, - ) - - # Wait for tasks to complete (per-conversation, before releasing semaphore) - await self._wait_for_conversation_tasks( - [result], conversation_id=conv_id, **kwargs - ) - - progress.update(conv_task_id, completed=total_messages, status="✅") - progress.update(main_task, advance=1) - return conv_id, [result] - - with Progress( - SpinnerColumn(), - TextColumn("[progress.description]{task.description}"), - BarColumn(), - MofNCompleteColumn(), - TextColumn("•"), - TaskProgressColumn(), - TextColumn("•"), - TimeElapsedColumn(), - TextColumn("•"), - TimeRemainingColumn(), - TextColumn("•"), - TextColumn("[bold blue]{task.fields[status]}"), - console=console, - transient=False, - refresh_per_second=1, - ) as progress: - main_task = progress.add_task( - "[bold cyan]🎯 Overall Progress", - total=len(conversations), - completed=0, - status="Processing", - ) - - # Process all conversations concurrently (with semaphore control) - tasks = [ - process_single_conversation(conv, progress, main_task) - for conv in conversations - ] - results = await asyncio.gather(*tasks) - progress.update(main_task, status="✅ Complete") - - # Collect results - for conv_id, conv_results in results: - conversation_ids.append(conv_id) - add_results.extend(conv_results) - - # Post-processing (e.g., wait for async tasks) - await self._post_add_process(add_results, **kwargs) - - # Build and return result - return self._build_add_result(conversation_ids, add_results, **kwargs) - - @abstractmethod - async def _add_user_messages( - self, conv: Conversation, messages: List[Dict[str, Any]], speaker: str, **kwargs - ) -> Any: - """ - Add messages for a single user (subclass implementation). - - Args: - conv: Original conversation object (for extracting extra info) - messages: Formatted message list (ready to send) - speaker: "speaker_a" or "speaker_b" - **kwargs: Extra parameters (may include user_id, timestamp, etc.) - - Returns: - Subclass-specific result (e.g., task_id for Memu, None for others) - """ - pass - - async def _wait_for_conversation_tasks( - self, task_results: List[Any], **kwargs - ) -> None: - """ - Wait for tasks from a single conversation to complete (per-conversation hook). - - This is called BEFORE releasing the semaphore, ensuring that systems like Memu - which create async tasks don't exceed their concurrency limits. - - For systems that complete work immediately (Mem0, Memos), this is a no-op. - For systems with async tasks (Memu), override this to wait for task completion. - - Args: - task_results: Results from _add_user_messages for this conversation - **kwargs: Extra parameters - """ - # Default: no-op (most systems don't need per-conversation waiting) - pass - - async def search( - self, query: str, conversation_id: str, index: Any, **kwargs - ) -> SearchResult: - """ - Retrieve relevant memories (call online API). - - Template method that orchestrates the search process: - 1. Extract conversation info (determine perspective) - 2. Call single or dual perspective search - 3. Subclasses implement actual API calls and result building - - Args: - query: Query text - conversation_id: Conversation ID - index: Index metadata (contains conversation_ids) - **kwargs: Optional parameters (top_k, conversation, etc.) - - Returns: - SearchResult with standard format - """ - # Extract conversation information (speakers, user_ids, dual perspective) - conv_info = self._extract_conversation_info( - conversation_id=conversation_id, **kwargs - ) - - # Get top_k from kwargs, or fallback to config, or default to 10 - default_top_k = self.config.get("search", {}).get("top_k", 10) - top_k = kwargs.get("top_k", default_top_k) - - if conv_info["need_dual_perspective"]: - # Dual perspective: search from both speakers' perspectives - return await self._search_dual_perspective( - query=query, - conversation_id=conversation_id, - speaker_a=conv_info["speaker_a"], - speaker_b=conv_info["speaker_b"], - speaker_a_user_id=conv_info["speaker_a_user_id"], - speaker_b_user_id=conv_info["speaker_b_user_id"], - top_k=top_k, - **kwargs, - ) - else: - # Single perspective: search from one user's perspective - return await self._search_single_perspective( - query=query, - conversation_id=conversation_id, - user_id=conv_info["speaker_a_user_id"], - top_k=top_k, - **kwargs, - ) - - async def _search_single_perspective( - self, query: str, conversation_id: str, user_id: str, top_k: int, **kwargs - ) -> SearchResult: - """ - Single perspective search flow (base class implementation). - - Subclasses should NOT override this unless necessary. - Instead, implement _search_single_user and _build_single_search_result. - - Args: - query: Query text - conversation_id: Conversation ID - user_id: User ID to search for - top_k: Number of results to retrieve - **kwargs: Additional parameters - - Returns: - SearchResult - """ - # Call subclass to perform search (API call + conversion + special processing) - results = await self._search_single_user( - query, conversation_id, user_id, top_k, **kwargs - ) - - # Call subclass to build SearchResult (including formatted_context) - return self._build_single_search_result( - query=query, - conversation_id=conversation_id, - results=results, - user_id=user_id, - top_k=top_k, - **kwargs, - ) - - async def _search_dual_perspective( - self, - query: str, - conversation_id: str, - speaker_a: str, - speaker_b: str, - speaker_a_user_id: str, - speaker_b_user_id: str, - top_k: int, - **kwargs, - ) -> SearchResult: - """ - Dual perspective search flow (base class implementation). - - Subclasses should NOT override this unless necessary. - Instead, implement _search_single_user and _build_dual_search_result. - - Args: - query: Query text - conversation_id: Conversation ID - speaker_a: Speaker A name - speaker_b: Speaker B name - speaker_a_user_id: Speaker A user ID - speaker_b_user_id: Speaker B user ID - top_k: Number of results per user - **kwargs: Additional parameters - - Returns: - SearchResult - """ - # Search both users separately - results_a = await self._search_single_user( - query, conversation_id, speaker_a_user_id, top_k, **kwargs - ) - results_b = await self._search_single_user( - query, conversation_id, speaker_b_user_id, top_k, **kwargs - ) - - # Merge results (for fallback, not re-sorted) - all_results = results_a + results_b - - # Call subclass to build SearchResult (including formatted_context) - return self._build_dual_search_result( - query=query, - conversation_id=conversation_id, - all_results=all_results, - results_a=results_a, - results_b=results_b, - speaker_a=speaker_a, - speaker_b=speaker_b, - speaker_a_user_id=speaker_a_user_id, - speaker_b_user_id=speaker_b_user_id, - top_k=top_k, - **kwargs, - ) - - @abstractmethod - async def _search_single_user( - self, query: str, conversation_id: str, user_id: str, top_k: int, **kwargs - ) -> List[Dict[str, Any]]: - """ - Search memories for a single user (subclass must implement). - - This method should: - 1. Call the system's search API - 2. Convert raw results to standard format - 3. Apply system-specific processing (e.g., timezone, preference, summary) - - Standard result format: - [ - { - "content": str, # Display content (may include timestamp, etc.) - "score": float, # Relevance score - "user_id": str, # User ID - "metadata": dict # System-specific metadata - }, - ... - ] - - System-specific processing: - - Mem0: Apply timezone conversion to timestamps - - Memos: Extract and include preference information - - Memu: Fetch and include categories summary - - Args: - query: Query text - conversation_id: Conversation ID (some systems may need it for context) - user_id: User ID to search for - top_k: Number of results to retrieve - **kwargs: System-specific parameters (e.g., min_similarity) - - Returns: - List of search results in standard format - """ - pass - - @abstractmethod - def _build_single_search_result( - self, - query: str, - conversation_id: str, - results: List[Dict[str, Any]], - user_id: str, - top_k: int, - **kwargs, - ) -> SearchResult: - """ - Build SearchResult for single perspective (subclass must implement). - - This method should: - 1. Construct retrieval_metadata (system name, parameters, etc.) - 2. Build formatted_context (using template or custom logic) - - Args: - query: Query text - conversation_id: Conversation ID - results: Search results from _search_single_user - user_id: User ID - top_k: Number of results requested - **kwargs: Additional parameters - - Returns: - SearchResult with formatted_context - """ - pass - - @abstractmethod - def _build_dual_search_result( - self, - query: str, - conversation_id: str, - all_results: List[Dict[str, Any]], - results_a: List[Dict[str, Any]], - results_b: List[Dict[str, Any]], - speaker_a: str, - speaker_b: str, - speaker_a_user_id: str, - speaker_b_user_id: str, - top_k: int, - **kwargs, - ) -> SearchResult: - """ - Build SearchResult for dual perspective (subclass must implement). - - This method should: - 1. Construct retrieval_metadata (system name, parameters, dual flag, etc.) - 2. Build formatted_context using both speakers' results - - Use template or custom logic - - Include system-specific information (preferences, summaries, etc.) - - Args: - query: Query text - conversation_id: Conversation ID - all_results: Merged results (for fallback) - results_a: Speaker A's search results - results_b: Speaker B's search results - speaker_a: Speaker A name - speaker_b: Speaker B name - speaker_a_user_id: Speaker A user ID - speaker_b_user_id: Speaker B user ID - top_k: Number of results per user - **kwargs: Additional parameters - - Returns: - SearchResult with formatted_context - """ - pass - - async def answer(self, query: str, context: str, **kwargs) -> str: - """ - Generate answer (using generic MEMOS prompt). - - Subclasses can override this method to use their own specific prompt. - Defaults to ANSWER_PROMPT_MEMOS (suitable for most systems). - """ - # Get answer prompt (subclasses can override _get_answer_prompt) - prompt = self._get_answer_prompt().format(context=context, question=query) - - # Get retry count - max_retries = self.config.get("answer", {}).get("max_retries", 3) - - # Generate answer - for i in range(max_retries): - try: - result = await self.llm_provider.generate(prompt=prompt, temperature=0) - - # Clean result (remove possible "FINAL ANSWER:" prefix) - if "FINAL ANSWER:" in result: - parts = result.split("FINAL ANSWER:") - if len(parts) > 1: - result = parts[1].strip() - else: - result = result.strip() - else: - result = result.strip() - - if result == "": - continue - - return result - except Exception as e: - print(f"⚠️ Answer generation error (attempt {i+1}/{max_retries}): {e}") - if i == max_retries - 1: - raise - continue - - return "" - - def _get_answer_prompt(self) -> str: - """ - Get answer prompt. - - Subclasses can override this method to return their own prompt. - Defaults to generic default prompt. - """ - return self._prompts["online_api"]["default"]["answer_prompt_memos"] - - # ===== Helper methods: format conversion ===== - - def _conversation_to_messages( - self, - conversation: Conversation, - format_type: str = "basic", - perspective: Optional[str] = None, - ) -> List[Dict[str, Any]]: - """ - Convert standard Conversation to message list. - - Args: - conversation: Standard conversation object - format_type: Format type (basic, mem0, memos, memu) - perspective: Perspective (speaker_a or speaker_b), used for dual-perspective systems like Memos - - Returns: - Message list - """ - messages = [] - speaker_a = conversation.metadata.get("speaker_a", "") - speaker_b = conversation.metadata.get("speaker_b", "") - - for msg in conversation.messages: - # Intelligently determine role and content - role, content = self._determine_role_and_content( - msg.sender_name, msg.content, speaker_a, speaker_b, perspective - ) - - # Base message - message = {"role": role, "content": content} - - # Add extra fields based on different system requirements - if format_type == "memos": - # Memos format: needs chat_time - # Note: Memos directly sends messages to API, so this field is used - if msg.timestamp: - from common_utils.datetime_utils import to_iso_format - - message["chat_time"] = to_iso_format(msg.timestamp) - - elif format_type == "memu": - # Memu format: needs name and time - message["name"] = msg.sender_name - message["time"] = ( - msg.timestamp.isoformat() + "Z" if msg.timestamp else None - ) - - # Note: Mem0 extracts timestamps directly from conv.messages in _add_user_messages - - messages.append(message) - - return messages - - def _determine_role_and_content( - self, - sender_name: str, - content: str, - speaker_a: str, - speaker_b: str, - perspective: Optional[str] = None, - ) -> tuple: - """ - Intelligently determine message role and content. - - For systems that only support user/assistant (e.g., Memos), special handling is needed: - 1. If speaker is standard role (user/assistant and variants), use directly - 2. If custom name, convert based on perspective: - - From speaker_a perspective: speaker_a messages are "user", speaker_b are "assistant" - - From speaker_b perspective: speaker_b messages are "user", speaker_a are "assistant" - 3. Content for custom speakers needs "speaker: " prefix - - Args: - sender_name: Sender name - content: Message content - speaker_a: speaker_a in conversation - speaker_b: speaker_b in conversation - perspective: Perspective (for dual-perspective systems) - - Returns: - (role, content) tuple - """ - # Case 1: Standard roles (user/assistant and variants) - speaker_lower = sender_name.lower() - - # Check if standard role or variant - if speaker_lower in ["user", "assistant"]: - # Exact match: "user", "User", "assistant", "Assistant" - return speaker_lower, content - elif speaker_lower.startswith("user"): - # Variants: "user_123", "User_456", etc. - return "user", content - elif speaker_lower.startswith("assistant"): - # Variants: "assistant_123", "Assistant_456", etc. - return "assistant", content - - # Case 2: Custom speaker, needs conversion - # Default behavior: speaker_a is user, speaker_b is assistant - if perspective == "speaker_b": - # From speaker_b's perspective - if sender_name == speaker_b: - role = "user" - elif sender_name == speaker_a: - role = "assistant" - else: - # Unknown speaker, default to assistant - role = "assistant" - else: - # From speaker_a's perspective (default) - if sender_name == speaker_a: - role = "user" - elif sender_name == speaker_b: - role = "assistant" - else: - # Unknown speaker, default to user - role = "user" - - # For custom speakers, content needs prefix - formatted_content = f"{sender_name}: {content}" - - return role, formatted_content - - def _extract_user_id( - self, conversation: Conversation, speaker: str = "speaker_a" - ) -> str: - """ - Extract user_id from Conversation (for online API). - - Logic: Combine conversation_id and speaker name to ensure conversation isolation. - - Args: - conversation: Standard conversation object - speaker: Speaker identifier (speaker_a or speaker_b) - - Returns: - user_id string, format: {conv_id}_{sender_name} - - Examples: - - LoCoMo: speaker_a="Caroline" → user_id="locomo_0_Caroline" - - PersonaMem: speaker_a="Kanoa Manu" → user_id="personamem_0_Kanoa_Manu" - - No speaker: → user_id="locomo_0_speaker_a" - - Design rationale: - - Include conv_id: Ensure memory isolation between conversations (evaluation accuracy) - - Include speaker name: More intuitive for backend viewing (e.g., Caroline vs speaker_a) - - Replace spaces with underscores: Avoid spaces in user_id - """ - conv_id = conversation.conversation_id - sender_name = conversation.metadata.get(speaker) - - if sender_name: - # Has sender name: replace spaces with underscores - sender_name_normalized = sender_name.replace(" ", "_") - user_id = f"{conv_id}_{sender_name_normalized}" - else: - # No speaker name: locomo_0_speaker_a - user_id = f"{conv_id}_{speaker}" - - return user_id - - def _get_user_id_from_conversation_id(self, conversation_id: str) -> str: - """ - Derive user_id from conversation_id (simplified version). - - Args: - conversation_id: Conversation ID - - Returns: - user_id string - """ - # Simplified implementation: directly use conversation_id - # May need more complex mapping logic in actual use - return conversation_id - - def _get_format_type(self) -> str: - """ - Get format type for _conversation_to_messages. - - Subclasses can override this method to specify their format type. - Default implementation infers from class name. - - Returns: - Format type string (e.g., "mem0", "memos", "memu", "basic") - """ - class_name = self.__class__.__name__.lower() - - # Infer format type from class name - if "mem0" in class_name: - return "mem0" - elif "memos" in class_name: - return "memos" - elif "memu" in class_name: - return "memu" - else: - return "basic" - - async def _post_add_process(self, add_results: List[Any], **kwargs) -> None: - """ - Post-processing after adding all conversations. - - Subclasses can override this method for custom post-processing - (e.g., Memu waiting for async tasks to complete). - - Args: - add_results: List of results from _add_user_messages calls - **kwargs: Extra parameters - """ - # Default: no post-processing - pass - - def _build_add_result( - self, conversation_ids: List[str], add_results: List[Any], **kwargs - ) -> Dict[str, Any]: - """ - Build the final result dict for add method. - - Subclasses can override this method to customize the return structure. - - Args: - conversation_ids: List of conversation IDs that were added - add_results: List of results from _add_user_messages calls - **kwargs: Extra parameters - - Returns: - Result dictionary - """ - system_name = self.__class__.__name__.replace("Adapter", "").lower() - - result = { - "type": "online_api", - "system": system_name, - "conversation_ids": conversation_ids, - } - - # If add_results contains non-None values, include them - # (e.g., Memu's task_ids) - non_none_results = [r for r in add_results if r is not None] - if non_none_results: - result["add_results"] = non_none_results - - return result - - def _batch_messages_with_retry( - self, - messages: List[Dict[str, Any]], - batch_size: int, - add_func: callable, - max_retries: int = None, - description: str = "Batch", - ) -> None: - """ - Helper method for batching messages with retry logic. - - Subclasses can use this method to simplify batch processing. - - Args: - messages: Message list to batch - batch_size: Batch size - add_func: Function to call for each batch (should accept List[Dict]) - max_retries: Max retry attempts (defaults to self.max_retries) - description: Description for logging - """ - if max_retries is None: - max_retries = getattr(self, 'max_retries', 3) - - for i in range(0, len(messages), batch_size): - batch_messages = messages[i : i + batch_size] - - # Retry mechanism - for attempt in range(max_retries): - try: - add_func(batch_messages) - break - except Exception as e: - if attempt < max_retries - 1: - print( - f" ⚠️ [{description}] Retry {attempt + 1}/{max_retries}: {e}" - ) - time.sleep(2**attempt) # Exponential backoff - else: - print( - f" ❌ [{description}] Failed after {max_retries} retries: {e}" - ) - raise e - - def _need_dual_perspective(self, speaker_a: str, speaker_b: str) -> bool: - """ - Determine if dual-perspective handling is needed. - - Single perspective (no dual-perspective needed): - - Standard roles: "user"/"assistant" - - Case variants: "User"/"Assistant" - - With suffix: "user_123"/"assistant_456" - - Dual perspective (dual-perspective needed): - - Custom names: "Elena Rodriguez"/"Alex" - - Args: - speaker_a: Speaker A name - speaker_b: Speaker B name - - Returns: - True if dual perspective is needed, False otherwise - """ - - def is_standard_role(speaker: str) -> bool: - speaker = speaker.lower() - # Exact match - if speaker in ["user", "assistant"]: - return True - # Starts with user or assistant - if speaker.startswith("user") or speaker.startswith("assistant"): - return True - return False - - # Only need dual perspective when both speakers are not standard roles - return not (is_standard_role(speaker_a) or is_standard_role(speaker_b)) - - def _extract_conversation_info( - self, - conversation: Optional[Conversation] = None, - conversation_id: str = None, - **kwargs, - ) -> Dict[str, Any]: - """ - Extract conversation information. - - This helper method extracts speaker information and determines if dual - perspective handling is needed. Used by both add and search methods. - - Args: - conversation: Conversation object (if directly available) - conversation_id: Conversation ID (for fallback) - **kwargs: May contain 'conversation' key if not passed directly - - Returns: - Dictionary with keys: - - speaker_a: Speaker A name - - speaker_b: Speaker B name - - speaker_a_user_id: User ID for speaker A - - speaker_b_user_id: User ID for speaker B - - need_dual_perspective: Whether dual perspective is needed - """ - # Get conversation from parameter or kwargs - if conversation is None: - conversation = kwargs.get("conversation") - - if conversation: - speaker_a = conversation.metadata.get("speaker_a", "") - speaker_b = conversation.metadata.get("speaker_b", "") - speaker_a_user_id = self._extract_user_id(conversation, speaker="speaker_a") - speaker_b_user_id = self._extract_user_id(conversation, speaker="speaker_b") - need_dual_perspective = self._need_dual_perspective(speaker_a, speaker_b) - else: - # Fallback: use default values (for search when conversation not available) - if conversation_id is None: - conversation_id = "unknown" - speaker_a_user_id = f"{conversation_id}_speaker_a" - speaker_b_user_id = f"{conversation_id}_speaker_b" - speaker_a = "speaker_a" - speaker_b = "speaker_b" - need_dual_perspective = False - - return { - "speaker_a": speaker_a, - "speaker_b": speaker_b, - "speaker_a_user_id": speaker_a_user_id, - "speaker_b_user_id": speaker_b_user_id, - "need_dual_perspective": need_dual_perspective, - } - - def get_system_info(self) -> Dict[str, Any]: - """Return system info.""" - return { - "name": self.__class__.__name__, - "type": "online_api", - "description": f"{self.__class__.__name__} adapter for online memory API", - } diff --git a/methods/EverCore/evaluation/src/adapters/registry.py b/methods/EverCore/evaluation/src/adapters/registry.py deleted file mode 100644 index c934de3d..00000000 --- a/methods/EverCore/evaluation/src/adapters/registry.py +++ /dev/null @@ -1,109 +0,0 @@ -""" -Adapter registry - provide adapter registration and creation. -Uses lazy loading strategy, keeps __init__.py empty. -""" -import importlib -from typing import Dict, Type, List -from evaluation.src.adapters.base import BaseAdapter - - -_ADAPTER_REGISTRY: Dict[str, Type[BaseAdapter]] = {} - -# Adapter module mapping (for lazy loading) -_ADAPTER_MODULES = { - # Local systems - "evermemos": "evaluation.src.adapters.evermemos_adapter", - - # Online API systems - "mem0": "evaluation.src.adapters.mem0_adapter", - "memos": "evaluation.src.adapters.memos_adapter", - "memu": "evaluation.src.adapters.memu_adapter", - "zep": "evaluation.src.adapters.zep_adapter", - "evermemos_api": "evaluation.src.adapters.evermemos_api_adapter", - "memobase": "evaluation.src.adapters.memobase_adapter", - "supermemory": "evaluation.src.adapters.supermemory_adapter", - - # Future systems: - # "nemori": "evaluation.src.adapters.nemori_adapter", -} - - -def register_adapter(name: str): - """ - Decorator for registering adapters. - - Usage: - @register_adapter("evermemos") - class EverCoreAdapter(BaseAdapter): - ... - """ - def decorator(cls: Type[BaseAdapter]): - _ADAPTER_REGISTRY[name] = cls - return cls - return decorator - - -def _ensure_adapter_loaded(name: str): - """ - Ensure specified adapter is loaded (lazy loading strategy). - - Trigger @register_adapter decorator execution via dynamic import. - This keeps __init__.py empty per project convention. - - Args: - name: Adapter name - - Raises: - ValueError: If adapter doesn't exist - RuntimeError: If module loaded but not registered - """ - if name in _ADAPTER_REGISTRY: - return # Already loaded - - if name not in _ADAPTER_MODULES: - raise ValueError( - f"Unknown adapter: {name}. " - f"Available adapters: {list(_ADAPTER_MODULES.keys())}" - ) - - # Dynamically import module, trigger @register_adapter execution - module_path = _ADAPTER_MODULES[name] - importlib.import_module(module_path) - - # Verify registration success - if name not in _ADAPTER_REGISTRY: - raise RuntimeError( - f"Adapter '{name}' module loaded but not registered. " - f"Check if @register_adapter('{name}') decorator is present." - ) - - -def create_adapter(name: str, config: dict, output_dir = None) -> BaseAdapter: - """ - Create adapter instance. - - Args: - name: Adapter name - config: Config dict - output_dir: Output directory (for persistence, optional) - - Returns: - Adapter instance - - Raises: - ValueError: If adapter not registered - """ - # Lazy loading: ensure adapter loaded - _ensure_adapter_loaded(name) - - # Try passing output_dir, fallback if adapter doesn't support it - try: - return _ADAPTER_REGISTRY[name](config, output_dir=output_dir) - except TypeError: - # Adapter doesn't accept output_dir parameter, use default creation - return _ADAPTER_REGISTRY[name](config) - - -def list_adapters() -> List[str]: - """List all available adapters.""" - return list(_ADAPTER_MODULES.keys()) diff --git a/methods/EverCore/evaluation/src/adapters/zep_adapter.py b/methods/EverCore/evaluation/src/adapters/zep_adapter.py deleted file mode 100644 index 54d7ff70..00000000 --- a/methods/EverCore/evaluation/src/adapters/zep_adapter.py +++ /dev/null @@ -1,478 +0,0 @@ -""" -Zep Adapter - adapt Zep online API for evaluation framework. -Reference: https://www.getzep.com/ - -Key features: -- Knowledge graph-based memory with facts (edges) and entities (nodes) -- Dual search: searches both nodes (entities) and edges (facts) -- Temporal awareness with created_at timestamps -- Simple design: one conversation → one graph, speaker info in content - -Version: Zep v3+ API -Migration from v2: -- group.add(group_id) → graph.create(graph_id) -- graph.add(..., group_id=...) → graph.add(..., graph_id=...) -- graph.search(..., group_id=...) → graph.search(..., graph_id=...) - -Design: -- graph_id = conversation_id (simple 1:1 mapping) -- Speaker info embedded in content: "Jane: Hello, I work at Acme" -- Zep automatically extracts entities and facts from conversations -""" - -import asyncio -import json -from datetime import timezone -from pathlib import Path -from typing import Any, Dict, List - -from rich.console import Console - -from evaluation.src.adapters.online_base import OnlineAPIAdapter -from evaluation.src.adapters.registry import register_adapter -from evaluation.src.core.data_models import Conversation, SearchResult - - -@register_adapter("zep") -class ZepAdapter(OnlineAPIAdapter): - """ - Zep online API adapter (Simplified design following reference implementation). - - Key simplifications: - - One conversation = One graph (no per-speaker graphs) - - Speaker info in message content (Zep handles entity extraction) - - Single search per query (no dual-perspective merging) - - Config example: - ```yaml - adapter: "zep" - api_key: "${ZEP_API_KEY}" - search: - top_k: 20 - reranker_nodes: "rrf" - reranker_edges: "cross_encoder" - ``` - """ - - def __init__(self, config: dict, output_dir: Path = None): - super().__init__(config, output_dir) - - # Import Zep async client - try: - from zep_cloud.client import AsyncZep - except ImportError: - raise ImportError( - "Zep client not installed. " "Please install: pip install zep-cloud" - ) - - # Initialize Zep async client - api_key = config.get("api_key", "") - if not api_key: - raise ValueError("Zep API key is required. Set 'api_key' in config.") - - self.client = AsyncZep(api_key=api_key) - self.max_retries = config.get("max_retries", 5) - self.poll_interval = config.get( - "poll_interval", 5 - ) # Poll interval for checking episode processed status (seconds) - self.add_message_interval = config.get( - "add_message_interval", 0.2 - ) # Interval between sending messages (seconds) - self.console = Console() - - # Search configuration - search_config = config.get("search", {}) - self.reranker_nodes = search_config.get("reranker_nodes", "rrf") - self.reranker_edges = search_config.get("reranker_edges", "cross_encoder") - - print(f" Reranker (Nodes): {self.reranker_nodes}") - print(f" Reranker (Edges): {self.reranker_edges}") - print(f" Processing Mode: Serial (add → wait processed → next)") - print(f" Add Message Interval: {self.add_message_interval}s") - print(f" Poll Interval: {self.poll_interval}s") - - async def _add_user_messages( - self, conv: Conversation, messages: List[Dict[str, Any]], speaker: str, **kwargs - ) -> Any: - """ - Add messages to Zep graph with concurrent control. - - Simplified: All messages go to the same graph (conversation-level), - with speaker info embedded in content. - - Args: - conv: Original conversation object - messages: Formatted message list - speaker: "speaker_a" or "speaker_b" (ignored in this simple design) - **kwargs: Extra parameters - - Returns: - None - """ - # Simple: graph_id = conversation_id - graph_id = conv.conversation_id - - # Skip if this is speaker_b (we already added all messages with speaker_a) - # This avoids duplicate additions in dual-perspective mode - if speaker == "speaker_b": - return None - - # Ensure graph exists before adding messages - try: - await self.client.graph.create(graph_id=graph_id) - except Exception: - # Graph already exists, which is fine - pass - - # Log info - is_fake_timestamp = ( - conv.messages[0].metadata.get("is_fake_timestamp", False) - if conv.messages - else False - ) - - self.console.print( - f" 📤 Adding to graph {graph_id}: {len(conv.messages)} messages (serial processing)", - style="dim", - ) - if is_fake_timestamp: - self.console.print(f" ⚠️ Using fake timestamp", style="yellow") - - # Serial processing: add message → wait for processed → next message - # Simple and reliable - ensures strict ordering - for i, msg in enumerate(conv.messages): - # Get timestamp in UTC format - timestamp = None - if msg.timestamp: - # If timestamp doesn't have timezone info, assume UTC - if msg.timestamp.tzinfo is None: - timestamp_utc = msg.timestamp.replace(tzinfo=timezone.utc) - else: - # Convert to UTC if it has other timezone - timestamp_utc = msg.timestamp.astimezone(timezone.utc) - timestamp = timestamp_utc.isoformat() - - # Include speaker name in content (Zep will extract entities from this) - data = f"{msg.sender_name}: {msg.content}" - - # Add message with retry mechanism - episode = None - for attempt in range(self.max_retries): - try: - episode = await self.client.graph.add( - data=data, - type="message", - created_at=timestamp, - graph_id=graph_id, - ) - break - except Exception as e: - if attempt < self.max_retries - 1: - self.console.print( - f" ⚠️ Message {i+1}/{len(conv.messages)} Add Retry {attempt + 1}/{self.max_retries}: {e}", - style="yellow", - ) - await asyncio.sleep(2**attempt) - else: - self.console.print( - f" ❌ Message {i+1}/{len(conv.messages)} Failed after {self.max_retries} retries: {e}", - style="red", - ) - raise e - - # Wait for episode to be processed - if episode and hasattr(episode, 'uuid_'): - poll_count = 0 - while True: - try: - retrieved_episode = await self.client.graph.episode.get( - uuid_=episode.uuid_ - ) - if ( - hasattr(retrieved_episode, 'processed') - and retrieved_episode.processed - ): - self.console.print( - f" ✅ Message {i+1}/{len(conv.messages)} processed (polled {poll_count} times)", - style="dim green", - ) - break - poll_count += 1 - await asyncio.sleep(self.poll_interval) - except Exception as e: - self.console.print( - f" ⚠️ Message {i+1}/{len(conv.messages)} Poll error: {e}", - style="yellow", - ) - await asyncio.sleep(self.poll_interval) - - self.console.print( - f" 🎉 All {len(conv.messages)} messages processed!", style="bold green" - ) - - return None - - async def _search_single_user( - self, query: str, conversation_id: str, user_id: str, top_k: int, **kwargs - ) -> List[Dict[str, Any]]: - """ - Search memories (simplified: single graph per conversation). - - Performs dual search (nodes + edges) on a single graph. - - Args: - query: Query text - conversation_id: Conversation ID (used as graph_id) - user_id: User ID (ignored, we use conversation_id as graph_id) - top_k: Number of results to retrieve (per search type) - **kwargs: Additional parameters - - Returns: - List of search results combining nodes and edges - """ - # Simple: graph_id = conversation_id - graph_id = conversation_id - - try: - # Dual search (nodes and edges) - search_results = await asyncio.gather( - self.client.graph.search( - query=query, - graph_id=graph_id, - scope='nodes', - reranker=self.reranker_nodes, - limit=top_k, - ), - self.client.graph.search( - query=query, - graph_id=graph_id, - scope='edges', - reranker=self.reranker_edges, - limit=top_k, - ), - ) - - nodes = ( - search_results[0].nodes if hasattr(search_results[0], 'nodes') else [] - ) - edges = ( - search_results[1].edges if hasattr(search_results[1], 'edges') else [] - ) - - # Debug output - self.console.print(f"\n[DEBUG] Zep Search Results:", style="yellow") - self.console.print(f" Query: {query}", style="dim") - self.console.print(f" Graph ID: {graph_id}", style="dim") - self.console.print( - f" Nodes: {len(nodes)}, Edges: {len(edges)}", style="dim" - ) - - except Exception as e: - self.console.print(f"❌ Zep search error: {e}", style="red") - return [] - - # Convert to standard format - results = [] - - # Add edges (facts) first - for edge in edges: - fact = getattr(edge, 'fact', '') - valid_at = getattr(edge, 'valid_at', '') - score = getattr(edge, 'score', 0.0) - - results.append( - { - "content": f"FACT (event_time: {valid_at}): {fact}", - "score": score, - "user_id": graph_id, - "metadata": { - "type": "fact", - "fact": fact, - "valid_at": valid_at, - "graph_id": graph_id, - }, - } - ) - - # Add nodes (entities) - for node in nodes: - name = getattr(node, 'name', '') - summary = getattr(node, 'summary', '') - score = getattr(node, 'score', 0.0) - - results.append( - { - "content": f"ENTITY ({name}): {summary}", - "score": score, - "user_id": graph_id, - "metadata": { - "type": "entity", - "name": name, - "summary": summary, - "graph_id": graph_id, - }, - } - ) - - return results - - def _build_single_search_result( - self, - query: str, - conversation_id: str, - results: List[Dict[str, Any]], - user_id: str, - top_k: int, - **kwargs, - ) -> SearchResult: - """ - Build SearchResult (simplified: no dual-perspective merging needed). - - Args: - query: Query text - conversation_id: Conversation ID - results: Search results from _search_single_user - user_id: User ID (actually graph_id) - top_k: Number of results requested - **kwargs: Additional parameters - - Returns: - SearchResult with formatted_context - """ - # Separate facts and entities - facts = [r for r in results if r["metadata"]["type"] == "fact"] - entities = [r for r in results if r["metadata"]["type"] == "entity"] - - # Format facts and entities - facts_text = "\n".join( - [ - f" - {r['metadata']['fact']} (event_time: {r['metadata']['valid_at']})" - for r in facts - ] - ) - entities_text = "\n".join( - [ - f" - {r['metadata']['name']}: {r['metadata']['summary']}" - for r in entities - ] - ) - - if not facts_text: - facts_text = " (No facts found)" - if not entities_text: - entities_text = " (No entities found)" - - # Build formatted context using Zep's template from prompts.yaml - zep_template = ( - self._prompts.get("online_api", {}).get("templates", {}).get("zep", "") - ) - formatted_context = zep_template.format( - facts=facts_text, entities=entities_text - ) - - return SearchResult( - query=query, - conversation_id=conversation_id, - results=results, - retrieval_metadata={ - "system": "zep", - "top_k": top_k, - "dual_perspective": False, - "user_ids": [conversation_id], - "graph_id": conversation_id, - "formatted_context": formatted_context, - "facts_count": len(facts), - "entities_count": len(entities), - }, - ) - - async def search( - self, query: str, conversation_id: str, index: Any, **kwargs - ) -> SearchResult: - """ - Retrieve relevant memories (simplified: no dual-perspective logic). - - Since Zep uses one graph per conversation (speaker info in content), - we directly search the conversation graph without dual-perspective handling. - - Args: - query: Query text - conversation_id: Conversation ID (used as graph_id) - index: Index metadata (not used) - **kwargs: Optional parameters (top_k, etc.) - - Returns: - SearchResult with standard format - """ - # Get top_k from kwargs or config - default_top_k = self.config.get("search", {}).get("top_k", 10) - top_k = kwargs.get("top_k", default_top_k) - - # Direct search (no dual-perspective logic) - results = await self._search_single_user( - query=query, - conversation_id=conversation_id, - user_id=conversation_id, # user_id = conversation_id in simplified design - top_k=top_k, - **kwargs, - ) - - # Build result (no dual-perspective merging) - return self._build_single_search_result( - query=query, - conversation_id=conversation_id, - results=results, - user_id=conversation_id, - top_k=top_k, - **kwargs, - ) - - def _build_dual_search_result( - self, - query: str, - conversation_id: str, - all_results: List[Dict[str, Any]], - results_a: List[Dict[str, Any]], - results_b: List[Dict[str, Any]], - speaker_a: str, - speaker_b: str, - speaker_a_user_id: str, - speaker_b_user_id: str, - top_k: int, - **kwargs, - ) -> SearchResult: - """ - Build dual-perspective search result (NOT USED by Zep). - - This method is required by the base class but not used by Zep. - Zep uses simplified single-graph design without dual-perspective. - - Raises: - NotImplementedError: Always raises as Zep doesn't use dual-perspective - """ - raise NotImplementedError( - "Zep adapter uses simplified single-graph design. " - "Use search() method instead, which calls _build_single_search_result()." - ) - - def _get_answer_prompt(self) -> str: - """ - Return answer prompt for Zep. - - Loads from prompts.yaml (answer_prompt_zep). - """ - # Load from prompts.yaml - return ( - self._prompts.get("online_api", {}) - .get("default", {}) - .get("answer_prompt_zep", "") - ) - - def get_system_info(self) -> Dict[str, Any]: - """Return system info.""" - return { - "name": "Zep", - "type": "online_api", - "description": "Zep - Knowledge Graph Architecture for Agent Memory", - "adapter": "ZepAdapter", - } diff --git a/methods/EverCore/evaluation/src/converters/__init__.py b/methods/EverCore/evaluation/src/converters/__init__.py deleted file mode 100644 index 82a8aaf2..00000000 --- a/methods/EverCore/evaluation/src/converters/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# Dataset Converters - - - diff --git a/methods/EverCore/evaluation/src/converters/base.py b/methods/EverCore/evaluation/src/converters/base.py deleted file mode 100644 index 65addbae..00000000 --- a/methods/EverCore/evaluation/src/converters/base.py +++ /dev/null @@ -1,67 +0,0 @@ -""" -Base Converter - define dataset converter base class interface. -""" -from abc import ABC, abstractmethod -from pathlib import Path -from typing import Dict, Any - - -class BaseConverter(ABC): - """Dataset converter base class.""" - - @abstractmethod - def convert(self, input_paths: Dict[str, str], output_path: str) -> None: - """ - Convert dataset to Locomo format. - - Args: - input_paths: Input file path dict, e.g., {"raw": "path/to/raw.json"} - output_path: Output file path - """ - pass - - @abstractmethod - def get_input_files(self) -> Dict[str, str]: - """ - Return required input file list. - - Returns: - File name mapping, e.g., {"raw": "longmemeval_s_cleaned.json"} - """ - pass - - def get_output_filename(self) -> str: - """ - Return output filename (converted version). - - Returns: - Filename, e.g., "longmemeval_s_locomo_style.json" - """ - return "converted_locomo_style.json" - - def needs_conversion(self, data_dir: Path) -> bool: - """ - Check if conversion needed (whether converted file exists). - - Args: - data_dir: Dataset directory - - Returns: - True if conversion needed, False if converted version exists - """ - output_file = data_dir / self.get_output_filename() - return not output_file.exists() - - def get_converted_path(self, data_dir: Path) -> Path: - """ - Get converted file path. - - Args: - data_dir: Dataset directory - - Returns: - Converted file path - """ - return data_dir / self.get_output_filename() - - diff --git a/methods/EverCore/evaluation/src/converters/longmemeval_converter.py b/methods/EverCore/evaluation/src/converters/longmemeval_converter.py deleted file mode 100644 index a3e0890a..00000000 --- a/methods/EverCore/evaluation/src/converters/longmemeval_converter.py +++ /dev/null @@ -1,143 +0,0 @@ -""" -LongMemEval Converter - convert LongMemEval dataset to Locomo format. -""" -import json -from datetime import datetime -from pathlib import Path -from typing import Dict - -from evaluation.src.converters.base import BaseConverter -from evaluation.src.converters.registry import register_converter - - -def convert_time_format(input_str: str) -> str: - """ - Convert time string from "YYYY/MM/DD (Day) HH:MM" format - to "H:MM am/pm on D Month, YYYY" format. - """ - # Input format: %Y: year, %m: month, %d: day, %a: weekday abbr, %H: 24-hour, %M: minute - input_format = "%Y/%m/%d (%a) %H:%M" - - # Parse input string to datetime object - dt_object = datetime.strptime(input_str, input_format) - - # Output format: %-I: 12-hour (no leading zero), %M: minute, %p: AM/PM, - # %-d: day (no leading zero), %B: full month name, %Y: year - output_format = "%-I:%M %p on %-d %B, %Y" - - # Format to target string and convert AM/PM to lowercase - formatted_string = dt_object.strftime(output_format).lower() - - # Ensure month is capitalized - parts = formatted_string.split(' ') - parts[4] = parts[4].capitalize() - - return ' '.join(parts) - - -def convert_lmeval_s_to_locomo_style(lmeval_data: list) -> list: - """ - Convert LongMemEval-S format to Locomo format. - - Args: - lmeval_data: LongMemEval-S raw data - - Returns: - Locomo format data - """ - locomo_style_data = [] - - for data in lmeval_data: - data_dict = { - "qa": [], - "conversation": {} - } - - # Find session indices containing answers - evidence_session_idx = [] - for idx, session_id in enumerate(data["haystack_session_ids"]): - if session_id in data["answer_session_ids"]: - evidence_session_idx.append(idx) - - # Mark messages containing answers - for idx, session in enumerate(data["haystack_sessions"]): - for i, msg in enumerate(session): - data["haystack_sessions"][idx][i]["has_answer"] = idx in evidence_session_idx - - # Collect evidence - evidence = [] - for idx, session in enumerate(data["haystack_sessions"]): - for i, msg in enumerate(session): - if msg["has_answer"]: - evidence.append(f"D{idx}:{i}") - - # Build QA - data_dict["qa"].append({ - "question_id": data["question_id"], - "question": data["question"], - "answer": data["answer"], - "evidence": evidence, - "category": data["question_type"] - }) - - # Build conversation - data_dict["conversation"]["speaker_a"] = f"user_{data['question_id']}" - data_dict["conversation"]["speaker_b"] = f"assistant_{data['question_id']}" - - for idx, session in enumerate(data["haystack_sessions"]): - data_dict["conversation"][f"session_{idx}_date_time"] = convert_time_format( - data["haystack_dates"][idx] - ) - data_dict["conversation"][f"session_{idx}"] = [] - - for i, msg in enumerate(session): - data_dict["conversation"][f"session_{idx}"].append({ - "speaker": msg["role"] + f"_{data['question_id']}", - "text": msg["content"], - "dia_id": f"D{idx}:{i}" - }) - - locomo_style_data.append(data_dict) - - return locomo_style_data - - -@register_converter("longmemeval") -class LongMemEvalConverter(BaseConverter): - """LongMemEval dataset converter.""" - - def get_input_files(self) -> Dict[str, str]: - """Return required input files.""" - return { - "raw": "longmemeval_s_cleaned.json" - } - - def get_output_filename(self) -> str: - """Return output filename.""" - return "longmemeval_s_locomo_style.json" - - def convert(self, input_paths: Dict[str, str], output_path: str) -> None: - """ - Execute conversion. - - Args: - input_paths: {"raw": "path/to/longmemeval_s_cleaned.json"} - output_path: Output file path - """ - print(f"🔄 Converting LongMemEval to Locomo format...") - - # Read raw data - with open(input_paths["raw"], "r", encoding="utf-8") as f: - lmeval_data = json.load(f) - - print(f" Loaded {len(lmeval_data)} items") - - # Convert format - locomo_style_data = convert_lmeval_s_to_locomo_style(lmeval_data) - - # Save result - with open(output_path, "w", encoding="utf-8") as f: - json.dump(locomo_style_data, f, indent=2, ensure_ascii=False) - - print(f" ✅ Saved {len(locomo_style_data)} entries to {output_path}") - diff --git a/methods/EverCore/evaluation/src/converters/personamem_converter.py b/methods/EverCore/evaluation/src/converters/personamem_converter.py deleted file mode 100644 index 1d000855..00000000 --- a/methods/EverCore/evaluation/src/converters/personamem_converter.py +++ /dev/null @@ -1,175 +0,0 @@ -""" -PersonaMem Converter - convert PersonaMem dataset to Locomo format. -""" -import json -import csv -import re -import ast -from collections import defaultdict -from pathlib import Path -from typing import Dict - -from evaluation.src.converters.base import BaseConverter -from evaluation.src.converters.registry import register_converter - - -def extract_persona_name(system_content: str) -> str: - """Extract persona name from system message.""" - match = re.search(r'Name:\s*([^\n]+)', system_content) - if match: - return match.group(1).strip() - return "User" - - -def clean_message_prefix(text: str) -> str: - """Clean 'User:' and 'Assistant:' prefixes from messages.""" - text = re.sub(r'^(User|Assistant):\s*', '', text, flags=re.MULTILINE) - return text.strip() - - -def parse_options(options_str: str) -> Dict[str, str]: - """Parse all_options string, return dict.""" - try: - options_list = ast.literal_eval(options_str) - options_dict = {} - for opt in options_list: - match = re.match(r'\(([a-z])\)\s*(.*)', opt, re.DOTALL) - if match: - key = f"({match.group(1)})" - value = match.group(2).strip() - options_dict[key] = value - return options_dict - except Exception as e: - print(f"Warning: Failed to parse options: {e}") - return {} - - -@register_converter("personamem") -class PersonaMemConverter(BaseConverter): - """PersonaMem dataset converter.""" - - def get_input_files(self) -> Dict[str, str]: - """Return required input files.""" - return { - "questions": "questions_32k.csv", - "contexts": "shared_contexts_32k.jsonl" - } - - def get_output_filename(self) -> str: - """Return output filename.""" - return "personamem_32k_locomo_style.json" - - def convert(self, input_paths: Dict[str, str], output_path: str) -> None: - """ - Execute conversion. - - Args: - input_paths: { - "questions": "path/to/questions_32k.csv", - "contexts": "path/to/shared_contexts_32k.jsonl" - } - output_path: Output file path - """ - print(f"🔄 Converting PersonaMem to Locomo format...") - - # 1. Read JSONL file, build context dict - print(" Loading shared contexts...") - contexts = {} - with open(input_paths["contexts"], 'r', encoding='utf-8') as f: - for line in f: - data = json.loads(line) - contexts.update(data) - print(f" Loaded {len(contexts)} shared contexts") - - # 2. Read CSV file - print(" Loading questions...") - questions = [] - with open(input_paths["questions"], 'r', encoding='utf-8') as f: - reader = csv.DictReader(f) - questions = list(reader) - print(f" Loaded {len(questions)} questions") - - # 3. Group by (shared_context_id, end_index_in_shared_context) - print(" Grouping questions...") - grouped_questions = defaultdict(list) - for q in questions: - key = (q['shared_context_id'], int(q['end_index_in_shared_context'])) - grouped_questions[key].append(q) - print(f" Grouped into {len(grouped_questions)} unique context groups") - - # 4. Convert to Locomo format - print(" Converting to Locomo format...") - locomo_data = [] - - for (context_id, end_index), question_list in grouped_questions.items(): - # Get corresponding context - if context_id not in contexts: - print(f" Warning: context_id {context_id} not found") - continue - - full_context = contexts[context_id] - context_messages = full_context[:end_index + 1] - - # Extract persona name - persona_name = "User" - assistant_name = "Assistant" - if context_messages and context_messages[0]['role'] == 'system': - persona_name = extract_persona_name(context_messages[0]['content']) - - # Create Locomo entry - locomo_entry = { - "qa": [], - "conversation": { - "speaker_a": persona_name, - "speaker_b": assistant_name, - "session_0_date_time": "Unknown", # PersonaMem lacks timestamp info - "session_0": [] - } - } - - # Add all questions to qa list - for q in question_list: - options = parse_options(q['all_options']) - correct_answer_text = options.get(q['correct_answer'], q['correct_answer']) - - qa_item = { - "question_id": q['question_id'], - "question": q['user_question_or_message'], - "answer": q['correct_answer'], - "answer_text": correct_answer_text, - "all_options": options, - "evidence": [], - "category": q['question_type'], # Keep original type, no conversion - "topic": q['topic'], - "persona_id": q['persona_id'], - "context_length_in_tokens": int(q['context_length_in_tokens']), - "distance_to_ref_in_tokens": int(q['distance_to_ref_in_tokens']), - } - locomo_entry["qa"].append(qa_item) - - # Build dialogue list - dialogue_idx = 0 - for msg in context_messages: - if msg['role'] == 'system': - continue # Skip system message - - speaker = persona_name if msg['role'] == 'user' else assistant_name - cleaned_text = clean_message_prefix(msg['content']) - - dialogue_item = { - "speaker": speaker, - "text": cleaned_text, - "dia_id": f"D0:{dialogue_idx}" - } - locomo_entry["conversation"]["session_0"].append(dialogue_item) - dialogue_idx += 1 - - locomo_data.append(locomo_entry) - - # 5. Save result - with open(output_path, 'w', encoding='utf-8') as f: - json.dump(locomo_data, f, indent=2, ensure_ascii=False) - - print(f" ✅ Saved {len(locomo_data)} entries to {output_path}") - print(f" Total questions: {sum(len(entry['qa']) for entry in locomo_data)}") - diff --git a/methods/EverCore/evaluation/src/converters/registry.py b/methods/EverCore/evaluation/src/converters/registry.py deleted file mode 100644 index 1b1bdc56..00000000 --- a/methods/EverCore/evaluation/src/converters/registry.py +++ /dev/null @@ -1,90 +0,0 @@ -""" -Converter registry - provide converter registration and creation. -Uses lazy loading strategy, keeps __init__.py empty. -""" -import importlib -from typing import Dict, Type, List, Optional -from evaluation.src.converters.base import BaseConverter - - -_CONVERTER_REGISTRY: Dict[str, Type[BaseConverter]] = {} - -# Converter module mapping (for lazy loading) -_CONVERTER_MODULES = { - "longmemeval": "evaluation.src.converters.longmemeval_converter", - "personamem": "evaluation.src.converters.personamem_converter", - # Future converters -} - - -def register_converter(name: str): - """ - Decorator for registering converters. - - Usage: - @register_converter("longmemeval") - class LongMemEvalConverter(BaseConverter): - ... - """ - def decorator(cls: Type[BaseConverter]): - _CONVERTER_REGISTRY[name] = cls - return cls - return decorator - - -def _ensure_converter_loaded(name: str): - """ - Ensure specified converter is loaded (lazy loading strategy). - - Args: - name: Converter name - - Raises: - ValueError: If converter doesn't exist - RuntimeError: If module loaded but not registered - """ - if name in _CONVERTER_REGISTRY: - return # Already loaded - - if name not in _CONVERTER_MODULES: - raise ValueError( - f"Unknown converter: {name}. " - f"Available converters: {list(_CONVERTER_MODULES.keys())}" - ) - - # Dynamically import module, trigger @register_converter execution - module_path = _CONVERTER_MODULES[name] - importlib.import_module(module_path) - - # Verify registration success - if name not in _CONVERTER_REGISTRY: - raise RuntimeError( - f"Converter '{name}' module loaded but not registered. " - f"Check if @register_converter('{name}') decorator is present." - ) - - -def get_converter(name: str) -> Optional[BaseConverter]: - """ - Get converter instance (if exists). - - Args: - name: Converter name - - Returns: - Converter instance, or None if not exists - """ - if name not in _CONVERTER_MODULES: - return None # Dataset doesn't need conversion - - # Lazy loading: ensure converter loaded - _ensure_converter_loaded(name) - - return _CONVERTER_REGISTRY[name]() - - -def list_converters() -> List[str]: - """List all available converters.""" - return list(_CONVERTER_MODULES.keys()) - - diff --git a/methods/EverCore/evaluation/src/core/__init__.py b/methods/EverCore/evaluation/src/core/__init__.py deleted file mode 100644 index f25f4349..00000000 --- a/methods/EverCore/evaluation/src/core/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Core Components - diff --git a/methods/EverCore/evaluation/src/core/data_models.py b/methods/EverCore/evaluation/src/core/data_models.py deleted file mode 100644 index 8624d122..00000000 --- a/methods/EverCore/evaluation/src/core/data_models.py +++ /dev/null @@ -1,96 +0,0 @@ -""" -Core data models. - -Define standard data formats for the evaluation framework to ensure interoperability -between different systems and datasets. -""" - -from dataclasses import dataclass, field -from typing import List, Dict, Any, Optional -from datetime import datetime - - -@dataclass -class Message: - """Standard message format.""" - - sender_id: str - sender_name: str - content: str - timestamp: Optional[datetime] = ( - None # Optional, some datasets (e.g., PersonaMem) lack timestamps - ) - metadata: Dict[str, Any] = field(default_factory=dict) - - -@dataclass -class Conversation: - """Standard conversation format.""" - - conversation_id: str - messages: List[Message] - metadata: Dict[str, Any] = field(default_factory=dict) - - -@dataclass -class QAPair: - """ - Standard QA pair format. - - Note: category field is unified as string type to be compatible with different datasets. - """ - - question_id: str - question: str - answer: str - category: Optional[str] = None # Unified as string type - evidence: List[str] = field(default_factory=list) - metadata: Dict[str, Any] = field(default_factory=dict) - - -@dataclass -class Dataset: - """Standard dataset format.""" - - dataset_name: str - conversations: List[Conversation] - qa_pairs: List[QAPair] - metadata: Dict[str, Any] = field(default_factory=dict) - - -@dataclass -class SearchResult: - """Standard search result format.""" - - query: str - conversation_id: str - results: List[ - Dict[str, Any] - ] # [{"content": str, "score": float, "metadata": dict}] - retrieval_metadata: Dict[str, Any] = field(default_factory=dict) - - -@dataclass -class AnswerResult: - """Standard answer result format.""" - - question_id: str - question: str - answer: str - golden_answer: str - category: Optional[int] = None - conversation_id: str = "" - formatted_context: str = "" # Actual context used - search_results: List[Dict[str, Any]] = field(default_factory=list) - metadata: Dict[str, Any] = field(default_factory=dict) - - -@dataclass -class EvaluationResult: - """Standard evaluation result format.""" - - total_questions: int - correct: int - accuracy: float - detailed_results: List[Dict[str, Any]] = field(default_factory=list) - metadata: Dict[str, Any] = field(default_factory=dict) diff --git a/methods/EverCore/evaluation/src/core/loaders.py b/methods/EverCore/evaluation/src/core/loaders.py deleted file mode 100644 index bea82340..00000000 --- a/methods/EverCore/evaluation/src/core/loaders.py +++ /dev/null @@ -1,330 +0,0 @@ -""" -Data loaders. - -Provides loading functionality for different datasets. -Supports automatic conversion of non-Locomo format datasets. -""" - -import json -from datetime import datetime, timedelta -from pathlib import Path -from typing import List, Optional - -from evaluation.src.core.data_models import Dataset, Conversation, Message, QAPair -from evaluation.src.converters.registry import get_converter -from common_utils.datetime_utils import from_iso_format - - -def load_dataset( - dataset_name: str, data_path: str, max_content_length: Optional[int] = None -) -> Dataset: - """ - Smart dataset loading with automatic conversion support. - - Args: - dataset_name: Dataset name (e.g., "locomo", "longmemeval", "personamem") - data_path: Data file path or directory path - max_content_length: Optional max content length for truncating long messages - - Returns: - Dataset: Standard format dataset - """ - data_path_obj = Path(data_path) - - # Check if conversion is needed - converter = get_converter(dataset_name) - - if converter: - # Dataset needs conversion - if data_path_obj.is_file(): - # If given a file path, use its parent directory - data_dir = data_path_obj.parent - else: - data_dir = data_path_obj - - # Check if conversion is needed - if converter.needs_conversion(data_dir): - print(f"📝 Converted file not found, converting {dataset_name}...") - - # Build input file paths - input_files = converter.get_input_files() - input_paths = { - key: str(data_dir / filename) for key, filename in input_files.items() - } - - # Execute conversion - output_path = str(converter.get_converted_path(data_dir)) - converter.convert(input_paths, output_path) - - # Use converted file - locomo_file = converter.get_converted_path(data_dir) - else: - # Native Locomo format, use directly - if data_path_obj.is_file(): - locomo_file = data_path_obj - else: - # If directory, try to find .json file - json_files = list(data_path_obj.glob("*.json")) - if not json_files: - raise FileNotFoundError(f"No JSON file found in {data_path_obj}") - locomo_file = json_files[0] - - return load_locomo_dataset( - str(locomo_file), - dataset_name=dataset_name, - max_content_length=max_content_length, - ) - - -def load_locomo_dataset( - data_path: str, - dataset_name: str = "locomo", - max_content_length: Optional[int] = None, -) -> Dataset: - """ - Load LoCoMo format dataset. - - Args: - data_path: Locomo format data file path - dataset_name: Dataset name (default "locomo", converted datasets should pass original name) - max_content_length: Optional max content length for truncating long messages - - Returns: - Dataset: Standard format dataset - """ - with open(data_path, "r", encoding="utf-8") as f: - raw_data = json.load(f) - - conversations = [] - qa_pairs = [] - - for idx, item in enumerate(raw_data): - # Add dataset prefix to avoid conversation_id conflicts between datasets - # Example: locomo_0, longmemeval_0, personamem_0 - conv_id = f"{dataset_name}_{idx}" - conversation_data = item.get("conversation", {}) - qa_data = item.get("qa", []) - - # Convert conversation - conversation = _convert_locomo_conversation( - conversation_data, conv_id, max_content_length=max_content_length - ) - conversations.append(conversation) - - # Convert QA pairs - for qa_idx, qa_item in enumerate(qa_data): - qa_pair = _convert_locomo_qa_pair(qa_item, conv_id, qa_idx) - qa_pairs.append(qa_pair) - - return Dataset( - dataset_name=dataset_name, - conversations=conversations, - qa_pairs=qa_pairs, - metadata={"total_conversations": len(conversations)}, - ) - - -def _convert_locomo_conversation( - conversation_data: dict, conv_id: str, max_content_length: Optional[int] = None -) -> Conversation: - """ - Convert LoCoMo conversation. - - Args: - conversation_data: LoCoMo format conversation data - conv_id: Conversation ID - max_content_length: Optional max content length for truncating long messages - - Returns: - Conversation: Standard format conversation - """ - messages = [] - - # Get all session keys, sorted by numeric value - session_keys = sorted( - [ - key - for key in conversation_data.keys() - if key.startswith("session_") and not key.endswith("_date_time") - ], - key=lambda x: int( - x.split("_")[1] - ), # Extract number X from session_X for sorting - ) - - # Generate fake start time for data without timestamps (for online APIs) - # Use fixed baseline: 2024-01-01 00:00:00 - fake_base_time = datetime(2024, 1, 1, 0, 0, 0) - - # Step 1: Parse timestamps for all sessions - session_times = [] - for session_idx, session_key in enumerate(session_keys): - session_time_key = f"{session_key}_date_time" - if session_time_key in conversation_data: - session_time = _parse_locomo_timestamp(conversation_data[session_time_key]) - - # If parse fails or is "Unknown", generate fake timestamp - is_fake = session_time is None - if is_fake: - session_time = fake_base_time + timedelta(hours=session_idx) - - session_times.append({"time": session_time, "is_fake": is_fake}) - else: - # No date_time field, generate fake timestamp - session_times.append( - {"time": fake_base_time + timedelta(hours=session_idx), "is_fake": True} - ) - - # Step 2: Assign message timestamps for each session - for session_idx, session_key in enumerate(session_keys): - session_messages = conversation_data[session_key] - - if not session_messages: - continue - - # Get current session start time - current_session_time = session_times[session_idx]["time"] - is_fake_timestamp = session_times[session_idx]["is_fake"] - - # Calculate message time intervals - # Strategy: prefer 30s intervals, only reduce if would exceed next session - num_messages = len(session_messages) - default_interval = 30 # Default 30s interval - - if num_messages > 1: - # Calculate total duration needed with default interval - required_duration = (num_messages - 1) * default_interval - - # Get available time span - if session_idx + 1 < len(session_times): - # Has next session: calculate time to next session - next_session_time = session_times[session_idx + 1]["time"] - available_duration = ( - next_session_time - current_session_time - ).total_seconds() - - # If time span is negative or too small (data issue), use default interval - if available_duration <= 0: - time_interval = default_interval - # Leave 10% buffer to avoid last message too close to next session - elif required_duration > available_duration * 0.9: - # Need to reduce interval to fit all messages - time_interval = (available_duration * 0.9) / (num_messages - 1) - else: - # Can use default interval - time_interval = default_interval - else: - # Last session: use default interval directly - time_interval = default_interval - else: - # Only one message, place at session start - time_interval = 0 - - # Convert each message - for msg_idx, msg in enumerate(session_messages): - # Try to parse message-level timestamp first (priority 1) - msg_timestamp = None - timestamp_source = None - - if 'time' in msg and msg['time']: - # Priority 1: Use message-level timestamp (strict parsing, raises on error) - msg_timestamp = from_iso_format(msg['time'], strict=True) - timestamp_source = "message_level" - else: - # Priority 2: Generate from session-level timestamp - msg_timestamp = current_session_time + timedelta( - seconds=msg_idx * time_interval - ) - timestamp_source = "fake" if is_fake_timestamp else "session_level" - - # Handle image information - content = msg['text'] - if msg.get("img_url"): - blip_caption = msg.get("blip_caption", "an image") - sender_name = msg['speaker'] - content = f"[{sender_name} shared an image: {blip_caption}] {content}" - - # Apply content length limit (if specified) - if max_content_length and len(content) > max_content_length: - content = content[:max_content_length] - - message = Message( - sender_id=f"{msg['speaker'].lower().replace(' ', '_')}_{conv_id}", - sender_name=msg['speaker'], - content=content, # Use processed content - timestamp=msg_timestamp, - metadata={ - "session": session_key, - "dia_id": msg.get("dia_id"), - "img_url": msg.get("img_url"), - "blip_caption": msg.get("blip_caption"), - "query": msg.get("query"), - "timestamp_source": timestamp_source, # Mark timestamp source - }, - ) - messages.append(message) - - return Conversation( - conversation_id=conv_id, - messages=messages, - metadata={ - "speaker_a": conversation_data.get("speaker_a"), - "speaker_b": conversation_data.get("speaker_b"), - }, - ) - - -def _convert_locomo_qa_pair(qa_item: dict, conv_id: str, qa_idx: int) -> QAPair: - """Convert LoCoMo QA pair.""" - # Extract additional fields to metadata - metadata = {"conversation_id": conv_id} - - # If has all_options (PersonaMem multiple choice), save to metadata - if "all_options" in qa_item: - metadata["all_options"] = qa_item["all_options"] - - # Prefer question_id from data if exists, otherwise generate unique ID - question_id = qa_item.get("question_id") - if not question_id: - # Use conv_id + qa_idx to generate unique ID to avoid conflicts - question_id = f"{conv_id}_qa{qa_idx}" - - # Normalize category to string (compatible with int and str) - category = qa_item.get("category") - if category is not None: - category = str(category) - - return QAPair( - question_id=question_id, - question=qa_item.get("question", ""), - answer=qa_item.get("answer", ""), - category=category, - evidence=qa_item.get("evidence", []), - metadata=metadata, - ) - - -def _parse_locomo_timestamp(timestamp_str: str) -> Optional[datetime]: - """ - Parse LoCoMo timestamp format. - - Input format: "6:07 pm on 13 January, 2023" - Special value: "Unknown" or unparseable returns None - Output: datetime object or None - """ - # Clean string - timestamp_str = timestamp_str.replace("\\s+", " ").strip() - - # Handle special cases: Unknown or empty string - if timestamp_str.lower() == "unknown" or not timestamp_str: - # No time information, return None - return None - - try: - return datetime.strptime(timestamp_str, "%I:%M %p on %d %B, %Y") - except ValueError: - # If parse fails, return None and print warning - print( - f"⚠️ Warning: Failed to parse timestamp '{timestamp_str}', no timestamp will be set" - ) - return None diff --git a/methods/EverCore/evaluation/src/core/pipeline.py b/methods/EverCore/evaluation/src/core/pipeline.py deleted file mode 100644 index f9dc73e4..00000000 --- a/methods/EverCore/evaluation/src/core/pipeline.py +++ /dev/null @@ -1,743 +0,0 @@ -""" -Pipeline core module. - -Orchestrates the evaluation workflow across four stages: Add → Search → Answer → Evaluate. -""" - -import time -from pathlib import Path -from typing import List, Dict, Any, Optional - -from evaluation.src.core.data_models import ( - Dataset, - SearchResult, - AnswerResult, - EvaluationResult, -) -from evaluation.src.adapters.base import BaseAdapter -from evaluation.src.evaluators.base import BaseEvaluator -from evaluation.src.utils.logger import setup_logger, get_console -from evaluation.src.utils.saver import ResultSaver -from evaluation.src.utils.checkpoint import CheckpointManager - -# Import components for answer generation -from memory_layer.llm.llm_provider import LLMProvider - -# Import stage execution functions -from evaluation.src.core.stages.add_stage import run_add_stage -from evaluation.src.core.stages.search_stage import run_search_stage -from evaluation.src.core.stages.answer_stage import run_answer_stage -from evaluation.src.core.stages.evaluate_stage import run_evaluate_stage - - -class Pipeline: - """ - Evaluation Pipeline. - - Four-stage workflow: - 1. Add: Ingest conversation data and build indices - 2. Search: Retrieve relevant memories - 3. Answer: Generate answers - 4. Evaluate: Evaluate answer quality - """ - - def __init__( - self, - adapter: BaseAdapter, - evaluator: BaseEvaluator, - llm_provider: LLMProvider, - output_dir: Path, - run_name: str = "default", - use_checkpoint: bool = True, - filter_categories: Optional[List[int]] = None, - ): - """ - Initialize Pipeline. - - Args: - adapter: System adapter - evaluator: Evaluator - llm_provider: LLM Provider for answer generation - output_dir: Output directory - run_name: Run name to distinguish different runs - use_checkpoint: Enable checkpoint/resume functionality - filter_categories: List of question categories to filter out (e.g., [5] filters Category 5) - """ - self.adapter = adapter - self.evaluator = evaluator - self.llm_provider = llm_provider - self.output_dir = Path(output_dir) - self.output_dir.mkdir(parents=True, exist_ok=True) - - self.logger = setup_logger(self.output_dir / "pipeline.log") - self.saver = ResultSaver(self.output_dir) - self.console = get_console() - - # Checkpoint/resume support - self.use_checkpoint = use_checkpoint - self.checkpoint = ( - CheckpointManager(output_dir=output_dir, run_name=run_name) - if use_checkpoint - else None - ) - self.completed_stages: set = set() - - # Question category filter configuration (read from dataset config) - self.filter_categories = filter_categories or [] - - async def run( - self, - dataset: Dataset, - stages: Optional[List[str]] = None, - smoke_test: bool = False, - smoke_messages: int = 10, - smoke_questions: int = 3, - from_conv: int = 0, - to_conv: Optional[int] = None, - ) -> Dict[str, Any]: - """ - Run complete Pipeline. - - Args: - dataset: Standard format dataset - stages: List of stages to execute, None means all - Options: ["add", "search", "answer", "evaluate"] - smoke_test: Enable smoke test mode - smoke_messages: Number of messages in smoke test (default 10) - smoke_questions: Number of questions in smoke test (default 3) - from_conv: Starting conversation index to process (inclusive, 0-based) - to_conv: Ending conversation index to process (exclusive), None means all - - Returns: - Evaluation results dictionary - """ - start_time = time.time() - - self.console.print(f"\n{'='*60}", style="bold cyan") - self.console.print("🚀 Evaluation Pipeline", style="bold cyan") - self.console.print(f"{'='*60}", style="bold cyan") - self.console.print(f"Dataset: {dataset.dataset_name}") - self.console.print(f"System: {self.adapter.get_system_info()['name']}") - self.console.print(f"Stages: {stages or 'all'}") - if smoke_test: - self.console.print( - f"[yellow]🧪 Smoke Test Mode: {smoke_messages} messages, {smoke_questions} questions[/yellow]" - ) - self.console.print(f"{'='*60}\n", style="bold cyan") - - # Apply conversation range filter (before smoke test) - # This allows processing a subset of conversations for incremental/distributed testing - if from_conv > 0 or to_conv is not None: - dataset = self._apply_conversation_range(dataset, from_conv, to_conv) - self.console.print(f"[cyan]📌 Conversation Range Filter Applied:[/cyan]") - self.console.print( - f"[cyan] Range: [{from_conv}:{to_conv or 'end'}][/cyan]" - ) - self.console.print( - f"[cyan] Conversations: {len(dataset.conversations)}[/cyan]" - ) - self.console.print(f"[cyan] Questions: {len(dataset.qa_pairs)}[/cyan]\n") - - # Smoke test: trim messages and questions for quick validation - if smoke_test: - dataset = self._apply_smoke_test(dataset, smoke_messages, smoke_questions) - self.console.print(f"[yellow]✂️ Smoke test applied:[/yellow]") - self.console.print( - f"[yellow] - Conversations: {len(dataset.conversations)}[/yellow]" - ) - if len(dataset.conversations) == 0: - self.console.print( - f"[red] ⚠️ No conversations selected! Check your filters.[/red]" - ) - elif len(dataset.conversations) == 1: - self.console.print( - f"[yellow] - Conversation ID: {dataset.conversations[0].conversation_id}[/yellow]" - ) - else: - first_id = dataset.conversations[0].conversation_id - last_id = dataset.conversations[-1].conversation_id - self.console.print( - f"[yellow] - Range: {first_id} to {last_id}[/yellow]" - ) - total_messages = sum(len(conv.messages) for conv in dataset.conversations) - msg_limit = ( - f"max {smoke_messages} per conv" if smoke_messages > 0 else "all" - ) - qa_limit = ( - f"max {smoke_questions} per conv" if smoke_questions > 0 else "all" - ) - self.console.print( - f"[yellow] - Messages: {total_messages} ({msg_limit})[/yellow]" - ) - self.console.print( - f"[yellow] - Questions: {len(dataset.qa_pairs)} ({qa_limit})[/yellow]\n" - ) - - # Check if we have any conversations to process - if len(dataset.conversations) == 0: - self.console.print( - f"[red]❌ No conversations to process! Check your --from-conv and --to-conv parameters.[/red]" - ) - self.console.print( - f"[yellow]💡 Tip: --to-conv should be greater than --from-conv (uses Python slice [from:to))[/yellow]" - ) - return { - "error": "No conversations selected", - "stages_completed": [], - "total_conversations": 0, - "total_questions": 0, - } - - # Filter question categories based on config (e.g., filter out Category 5 adversarial questions) - original_qa_count = len(dataset.qa_pairs) - - if self.filter_categories: - # Normalize categories to strings (support both int and str configs) - filter_set = {str(cat) for cat in self.filter_categories} - - # Filter out specified categories - dataset.qa_pairs = [ - qa for qa in dataset.qa_pairs if qa.category not in filter_set - ] - - filtered_count = original_qa_count - len(dataset.qa_pairs) - - if filtered_count > 0: - filtered_categories_str = ", ".join(sorted(filter_set)) - self.console.print( - f"[dim]🔍 Filtered out {filtered_count} questions from categories: {filtered_categories_str}[/dim]" - ) - self.console.print( - f"[dim] Remaining questions: {len(dataset.qa_pairs)}[/dim]\n" - ) - - # Try loading checkpoint - search_results_data = None - answer_results_data = None - - if self.use_checkpoint and self.checkpoint: - checkpoint_data = self.checkpoint.load_checkpoint() - if checkpoint_data: - self.completed_stages = set(checkpoint_data.get('completed_stages', [])) - # Load saved intermediate results - if 'search_results' in checkpoint_data: - search_results_data = checkpoint_data['search_results'] - if 'answer_results' in checkpoint_data: - answer_results_data = checkpoint_data['answer_results'] - - # Default: execute all stages - if stages is None: - stages = ["add", "search", "answer", "evaluate"] - - results = {} - - # Stage 1: Add - add_just_completed = False # Track if add just completed - - if "add" in stages and "add" not in self.completed_stages: - self.logger.info("Starting Stage 1: Add") - - stage_results = await run_add_stage( - adapter=self.adapter, - dataset=dataset, - output_dir=self.output_dir, - checkpoint_manager=self.checkpoint, - logger=self.logger, - console=self.console, - completed_stages=self.completed_stages, - ) - results.update(stage_results) - add_just_completed = True # Add just completed - - elif "add" in self.completed_stages: - self.console.print( - "\n[yellow]⏭️ Skip Add stage (already completed)[/yellow]" - ) - # Rebuild index metadata (handled by adapter, only needed for local systems) - # For online APIs, returns None but still need to set results["index"] - index = self.adapter.build_lazy_index( - dataset.conversations, self.output_dir - ) - results["index"] = index # Set even if None - else: - # Rebuild index metadata (handled by adapter, only needed for local systems) - # For online APIs, returns None but still need to set results["index"] - index = self.adapter.build_lazy_index( - dataset.conversations, self.output_dir - ) - results["index"] = index # Set even if None - if index is not None: - self.logger.info("⏭️ Skipped Stage 1, using lazy loading") - - # Post-Add Wait: for online API systems, wait for backend indexing to complete - # Only wait if add just completed - if add_just_completed: - wait_seconds = self.adapter.config.get("post_add_wait_seconds", 0) - if wait_seconds > 0 and "search" in stages: - self.console.print( - f"\n[yellow]⏰ Waiting {wait_seconds}s for backend indexing to complete...[/yellow]" - ) - self.logger.info(f"⏰ Waiting {wait_seconds}s for backend indexing") - - # Show countdown progress bar - from rich.progress import ( - Progress, - SpinnerColumn, - TextColumn, - BarColumn, - TimeRemainingColumn, - ) - - with Progress( - SpinnerColumn(), - TextColumn("[progress.description]{task.description}"), - BarColumn(), - TextColumn("{task.percentage:>3.0f}%"), - TimeRemainingColumn(), - console=self.console, - ) as progress: - task = progress.add_task( - f"⏰ Backend indexing in progress...", total=wait_seconds - ) - for i in range(wait_seconds): - time.sleep(1) - progress.update(task, advance=1) - - self.console.print( - f"[green]✅ Wait completed, ready for search[/green]\n" - ) - self.logger.info("✅ Post-add wait completed") - - # Stage 2: Search - if "search" in stages and "search" not in self.completed_stages: - self.logger.info("Starting Stage 2: Search") - - search_results = await run_search_stage( - adapter=self.adapter, - qa_pairs=dataset.qa_pairs, - index=results["index"], - conversations=dataset.conversations, # Pass conversations for cache rebuilding - checkpoint_manager=self.checkpoint, - logger=self.logger, - ) - - self.saver.save_json( - [self._search_result_to_dict(sr) for sr in search_results], - "search_results.json", - ) - results["search_results"] = search_results - self.logger.info("✅ Stage 2 completed") - - # Save checkpoint - self.completed_stages.add("search") - if self.checkpoint: - search_results_data = [ - self._search_result_to_dict(sr) for sr in search_results - ] - self.checkpoint.save_checkpoint( - self.completed_stages, search_results=search_results_data - ) - elif "search" in self.completed_stages: - self.console.print( - f"\n[yellow]⏭️ Skip Search stage (already completed)[/yellow]" - ) - if search_results_data: - # Load from checkpoint - search_results = [ - self._dict_to_search_result(d) for d in search_results_data - ] - results["search_results"] = search_results - elif self.saver.file_exists("search_results.json"): - # Load from file - search_data = self.saver.load_json("search_results.json") - search_results = [self._dict_to_search_result(d) for d in search_data] - results["search_results"] = search_results - elif "answer" in stages or "evaluate" in stages: - # Only try loading when subsequent stages need search_results - if self.saver.file_exists("search_results.json"): - search_data = self.saver.load_json("search_results.json") - search_results = [self._dict_to_search_result(d) for d in search_data] - results["search_results"] = search_results - self.logger.info("⏭️ Skipped Stage 2, loaded existing results") - else: - raise FileNotFoundError( - "Search results not found. Please run 'search' stage first." - ) - else: - # Don't need search_results (e.g., only running add stage) - search_results = None - - # Stage 3: Answer - if "answer" in stages and "answer" not in self.completed_stages: - self.logger.info("Starting Stage 3: Answer") - - answer_results = await run_answer_stage( - adapter=self.adapter, - qa_pairs=dataset.qa_pairs, - search_results=search_results, - checkpoint_manager=self.checkpoint, - logger=self.logger, - ) - - self.saver.save_json( - [self._answer_result_to_dict(ar) for ar in answer_results], - "answer_results.json", - ) - results["answer_results"] = answer_results - self.logger.info("✅ Stage 3 completed") - - # Save checkpoint - self.completed_stages.add("answer") - if self.checkpoint: - answer_results_dict = [ - self._answer_result_to_dict(ar) for ar in answer_results - ] - self.checkpoint.save_checkpoint( - self.completed_stages, - search_results=( - search_results_data - if search_results_data - else [self._search_result_to_dict(sr) for sr in search_results] - ), - answer_results=answer_results_dict, - ) - # Sync answer_results_data to ensure subsequent stages use correct data - answer_results_data = answer_results_dict - elif "answer" in self.completed_stages: - self.console.print( - f"\n[yellow]⏭️ Skip Answer stage (already completed)[/yellow]" - ) - if answer_results_data: - # Load from checkpoint - answer_results = [ - self._dict_to_answer_result(d) for d in answer_results_data - ] - results["answer_results"] = answer_results - elif self.saver.file_exists("answer_results.json"): - # Load from file - answer_data = self.saver.load_json("answer_results.json") - answer_results = [self._dict_to_answer_result(d) for d in answer_data] - results["answer_results"] = answer_results - elif "evaluate" in stages: - # Only try loading when evaluate stage needs answer_results - if self.saver.file_exists("answer_results.json"): - answer_data = self.saver.load_json("answer_results.json") - answer_results = [self._dict_to_answer_result(d) for d in answer_data] - results["answer_results"] = answer_results - self.logger.info("⏭️ Skipped Stage 3, loaded existing results") - else: - raise FileNotFoundError( - "Answer results not found. Please run 'answer' stage first." - ) - else: - # Don't need answer_results (e.g., only running add or search) - answer_results = None - - # Stage 4: Evaluate - if "evaluate" in stages and "evaluate" not in self.completed_stages: - eval_result = await run_evaluate_stage( - evaluator=self.evaluator, - answer_results=answer_results, - checkpoint_manager=self.checkpoint, - logger=self.logger, - ) - - self.saver.save_json( - self._eval_result_to_dict(eval_result), "eval_results.json" - ) - results["eval_result"] = eval_result - - # Save checkpoint - self.completed_stages.add("evaluate") - if self.checkpoint: - # Handle None cases for search_results and answer_results - if search_results_data: - sr_data = search_results_data - elif search_results: - sr_data = [self._search_result_to_dict(sr) for sr in search_results] - else: - sr_data = [] - - if answer_results_data: - ar_data = answer_results_data - elif answer_results: - ar_data = [self._answer_result_to_dict(ar) for ar in answer_results] - else: - ar_data = [] - - self.checkpoint.save_checkpoint( - self.completed_stages, - search_results=sr_data, - answer_results=ar_data, - eval_results=self._eval_result_to_dict(eval_result), - ) - elif "evaluate" in self.completed_stages: - self.console.print( - "\n[yellow]⏭️ Skip Evaluate stage (already completed)[/yellow]" - ) - - # Generate report - elapsed_time = time.time() - start_time - self._generate_report(results, elapsed_time) - - return results - - def _apply_smoke_test( - self, dataset: Dataset, num_messages: int, num_questions: int - ) -> Dataset: - """ - Apply smoke test: trim messages and questions for quick validation. - - This allows quick validation of the complete workflow (Add → Search → Answer → Evaluate) - using only a small subset of data to save time. - - Strategy: - - If dataset has multiple conversations (e.g., from conversation range filter): - Apply smoke limits to ALL conversations in the range - - If dataset has only one conversation: - Apply smoke limits to that conversation (legacy behavior) - - Args: - dataset: Original dataset (may be pre-filtered by conversation range) - num_messages: Number of messages to keep per conversation (for Add stage), 0 means all - num_questions: Number of questions to keep per conversation (for Search/Answer/Evaluate stages), 0 means all - - Returns: - Trimmed dataset - """ - if not dataset.conversations: - return dataset - - # Process all conversations (respecting conversation range filter if applied) - trimmed_conversations = [] - trimmed_qa_pairs = [] - - total_messages_before = 0 - total_messages_after = 0 - total_questions_before = 0 - total_questions_after = 0 - - for conv in dataset.conversations: - conv_id = conv.conversation_id - - # Trim messages for this conversation - if num_messages > 0: - total_messages_before += len(conv.messages) - conv.messages = conv.messages[:num_messages] - total_messages_after += len(conv.messages) - else: - total_messages_after += len(conv.messages) - total_messages_before += len(conv.messages) - - trimmed_conversations.append(conv) - - # Trim questions for this conversation - conv_qa_pairs = [ - qa - for qa in dataset.qa_pairs - if qa.metadata.get("conversation_id") == conv_id - ] - - if num_questions > 0: - total_questions_before += len(conv_qa_pairs) - selected_qa_pairs = conv_qa_pairs[:num_questions] - total_questions_after += len(selected_qa_pairs) - else: - selected_qa_pairs = conv_qa_pairs - total_questions_after += len(selected_qa_pairs) - total_questions_before += len(selected_qa_pairs) - - trimmed_qa_pairs.extend(selected_qa_pairs) - - # Log summary - if len(trimmed_conversations) == 1: - conv_desc = f"Conv {trimmed_conversations[0].conversation_id}" - else: - conv_desc = f"{len(trimmed_conversations)} conversations" - - msg_desc = ( - f"{total_messages_after}/{total_messages_before}" - if num_messages > 0 - else f"{total_messages_after} (all)" - ) - qa_desc = ( - f"{total_questions_after}/{total_questions_before}" - if num_questions > 0 - else f"{total_questions_after} (all)" - ) - - self.logger.info( - f"Smoke test: {conv_desc} - " - f"{msg_desc} messages, " - f"{qa_desc} questions" - ) - - return Dataset( - dataset_name=dataset.dataset_name + "_smoke", - conversations=trimmed_conversations, - qa_pairs=trimmed_qa_pairs, - metadata={ - **dataset.metadata, - "smoke_test": True, - "smoke_messages": num_messages, - "smoke_questions": num_questions, - "total_conversations": len(trimmed_conversations), - }, - ) - - def _apply_conversation_range( - self, dataset: Dataset, from_conv: int, to_conv: Optional[int] - ) -> Dataset: - """ - Filter conversations by index range. - - This allows processing a subset of conversations for incremental testing - or distributed processing. The conversation_id attribute of each Conversation - object remains unchanged, ensuring consistent user_id generation for online APIs. - - Args: - dataset: Original dataset - from_conv: Starting conversation index (inclusive, 0-based) - to_conv: Ending conversation index (exclusive), None means all - - Returns: - Filtered dataset with selected conversations and their QA pairs - - Example: - - Original: 100 conversations (locomo_0 to locomo_99) - - from_conv=10, to_conv=20: select conversations[10:20] - - Result: 10 conversations (locomo_10 to locomo_19) - - conversation_id attributes remain: "locomo_10", "locomo_11", ..., "locomo_19" - """ - if not dataset.conversations: - return dataset - - # Apply range slicing - total_convs = len(dataset.conversations) - end_idx = to_conv if to_conv is not None else total_convs - - # Validation - if from_conv < 0: - self.logger.warning(f"from_conv < 0, resetting to 0") - from_conv = 0 - if from_conv >= total_convs: - self.logger.warning( - f"from_conv ({from_conv}) >= total conversations ({total_convs}), no data to process" - ) - return Dataset( - dataset_name=dataset.dataset_name, - conversations=[], - qa_pairs=[], - metadata={ - **dataset.metadata, - "conversation_range": [from_conv, end_idx], - "original_conversation_count": total_convs, - "original_qa_count": len(dataset.qa_pairs), - }, - ) - - # Slice conversations (conversation_id attributes remain unchanged) - selected_convs = dataset.conversations[from_conv:end_idx] - selected_conv_ids = {conv.conversation_id for conv in selected_convs} - - # Filter QA pairs for selected conversations - selected_qa_pairs = [ - qa - for qa in dataset.qa_pairs - if qa.metadata.get("conversation_id") in selected_conv_ids - ] - - self.logger.info( - f"Conversation range [{from_conv}:{end_idx}] - " - f"selected {len(selected_convs)}/{total_convs} conversations, " - f"{len(selected_qa_pairs)}/{len(dataset.qa_pairs)} questions" - ) - - return Dataset( - dataset_name=dataset.dataset_name, - conversations=selected_convs, - qa_pairs=selected_qa_pairs, - metadata={ - **dataset.metadata, - "conversation_range": [from_conv, end_idx], - "original_conversation_count": total_convs, - "original_qa_count": len(dataset.qa_pairs), - }, - ) - - def _generate_report(self, results: Dict[str, Any], elapsed_time: float): - """Generate evaluation report.""" - report_lines = [] - report_lines.append("=" * 60) - report_lines.append("📊 Evaluation Report") - report_lines.append("=" * 60) - report_lines.append("") - - # System information - system_info = self.adapter.get_system_info() - report_lines.append(f"System: {system_info['name']}") - report_lines.append(f"Time Elapsed: {elapsed_time:.2f}s") - report_lines.append("") - - # Evaluation results - if "eval_result" in results: - eval_result = results["eval_result"] - report_lines.append(f"Total Questions: {eval_result.total_questions}") - report_lines.append(f"Correct: {eval_result.correct}") - report_lines.append(f"Accuracy: {eval_result.accuracy:.2%}") - report_lines.append("") - - report_lines.append("=" * 60) - - report_text = "\n".join(report_lines) - - # Save report - report_path = self.output_dir / "report.txt" - with open(report_path, "w") as f: - f.write(report_text) - - # Print to console - self.console.print("\n" + report_text, style="bold green") - self.logger.info(f"Report saved to: {report_path}") - - # Serialization helper methods - def _search_result_to_dict(self, sr: SearchResult) -> dict: - """Convert SearchResult object to dictionary.""" - return { - "query": sr.query, - "conversation_id": sr.conversation_id, - "results": sr.results, - "retrieval_metadata": sr.retrieval_metadata, - } - - def _dict_to_search_result(self, d: dict) -> SearchResult: - """Convert dictionary to SearchResult object.""" - return SearchResult(**d) - - def _answer_result_to_dict(self, ar: AnswerResult) -> dict: - """Convert AnswerResult object to dictionary.""" - # Handle empty search_results - return { - "question_id": ar.question_id, - "question": ar.question, - "answer": ar.answer, - "golden_answer": ar.golden_answer, - "category": ar.category, - "conversation_id": ar.conversation_id, - "formatted_context": ar.formatted_context, - "metadata": ar.metadata, - } - - def _dict_to_answer_result(self, d: dict) -> AnswerResult: - """Convert dictionary to AnswerResult object.""" - return AnswerResult(**d) - - def _eval_result_to_dict(self, er: EvaluationResult) -> dict: - """Convert EvaluationResult object to dictionary.""" - return { - "total_questions": er.total_questions, - "correct": er.correct, - "accuracy": er.accuracy, - "detailed_results": er.detailed_results, - "metadata": er.metadata, - } diff --git a/methods/EverCore/evaluation/src/core/stages/__init__.py b/methods/EverCore/evaluation/src/core/stages/__init__.py deleted file mode 100644 index 31cc9e6d..00000000 --- a/methods/EverCore/evaluation/src/core/stages/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -Stages module - contains implementation for each evaluation stage. -""" - - diff --git a/methods/EverCore/evaluation/src/core/stages/add_stage.py b/methods/EverCore/evaluation/src/core/stages/add_stage.py deleted file mode 100644 index 451c63d5..00000000 --- a/methods/EverCore/evaluation/src/core/stages/add_stage.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Add stage - ingest conversation data and build index. -""" -from pathlib import Path -from typing import List, Any, Optional -from logging import Logger - -from evaluation.src.core.data_models import Conversation, Dataset -from evaluation.src.adapters.base import BaseAdapter -from evaluation.src.utils.checkpoint import CheckpointManager - - -async def run_add_stage( - adapter: BaseAdapter, - dataset: Dataset, - output_dir: Path, - checkpoint_manager: Optional[CheckpointManager], - logger: Logger, - console: Any, - completed_stages: set, -) -> dict: - """ - Execute Add stage. - - Args: - adapter: System adapter - dataset: Standard format dataset - output_dir: Output directory - checkpoint_manager: Checkpoint manager for resume - logger: Logger - console: Console object - completed_stages: Set of completed stages - - Returns: - Dict containing index - """ - # Pass checkpoint_manager for fine-grained resume support - index = await adapter.add( - conversations=dataset.conversations, - output_dir=output_dir, - checkpoint_manager=checkpoint_manager - ) - - # Index metadata (lazy load, no need to persist) - logger.info("✅ Stage 1 completed") - - # Save checkpoint - completed_stages.add("add") - if checkpoint_manager: - checkpoint_manager.save_checkpoint(completed_stages) - - return {"index": index} - diff --git a/methods/EverCore/evaluation/src/core/stages/answer_stage.py b/methods/EverCore/evaluation/src/core/stages/answer_stage.py deleted file mode 100644 index c00a1191..00000000 --- a/methods/EverCore/evaluation/src/core/stages/answer_stage.py +++ /dev/null @@ -1,280 +0,0 @@ -""" -Answer stage - generate answers. -""" -import asyncio -import time -from typing import List, Optional -from logging import Logger -from tqdm import tqdm - -from evaluation.src.core.data_models import QAPair, SearchResult, AnswerResult -from evaluation.src.adapters.base import BaseAdapter -from evaluation.src.utils.checkpoint import CheckpointManager - - -def build_context(search_result: SearchResult) -> str: - """ - Build context from search results. - - Prefer pre-formatted context (dual-speaker scenarios), else use simple numbering (single-speaker scenarios). - - Args: - search_result: Search result - - Returns: - Context string - """ - # Prefer pre-formatted context (provided by adapter) - formatted_context = search_result.retrieval_metadata.get("formatted_context", "") - if formatted_context: - return formatted_context - - # Single speaker scenario: simple formatting - context_parts = [] - - # Get top_k from retrieval_metadata, default to len(results) if not specified - top_k = search_result.retrieval_metadata.get("top_k", len(search_result.results)) - - # Add memory content (use top_k instead of hardcoded 10) - for idx, result in enumerate(search_result.results[:top_k], 1): - content = result.get("content", "") - context_parts.append(f"{idx}. {content}") - - context = "\n\n".join(context_parts) - - # For systems supporting preferences (e.g., Memos), add formatted pref_string - preferences = search_result.retrieval_metadata.get("preferences", {}) - pref_string = preferences.get("pref_string", "") - - if pref_string: - context += "\n\n" + pref_string - - return context - - -async def run_answer_stage( - adapter: BaseAdapter, - qa_pairs: List[QAPair], - search_results: List[SearchResult], - checkpoint_manager: Optional[CheckpointManager], - logger: Logger, -) -> List[AnswerResult]: - """ - Generate answers with fine-grained checkpointing. - - Save checkpoint every SAVE_INTERVAL questions. - - Args: - adapter: System adapter - qa_pairs: List of QA pairs - search_results: List of search results - checkpoint_manager: Checkpoint manager for resume - logger: Logger - - Returns: - List of answer results - """ - print(f"\n{'='*60}") - print(f"Stage 3/4: Answer") - print(f"{'='*60}") - - SAVE_INTERVAL = 400 # Save every 400 tasks - MAX_CONCURRENT = 50 # Max concurrency - - # Load fine-grained checkpoint - all_answer_results = {} - if checkpoint_manager: - loaded_results = checkpoint_manager.load_answer_progress() - # Convert to {question_id: AnswerResult} format - for result in loaded_results.values(): - all_answer_results[result["question_id"]] = result - - total_qa_count = len(qa_pairs) - processed_count = len(all_answer_results) - - print(f"Total questions: {total_qa_count}") - if processed_count > 0: - print(f"Already processed: {processed_count} questions (from checkpoint)") - print(f"Remaining: {total_qa_count - processed_count} questions") - - # Prepare pending tasks - # qa_pairs and search_results should have matching order (both use numeric sort by conversation_id) - pending_tasks = [] - for qa, sr in zip(qa_pairs, search_results): - if qa.question_id not in all_answer_results: - pending_tasks.append((qa, sr)) - - if not pending_tasks: - print(f"✅ All questions already processed!") - # Convert to AnswerResult object list (original order) - results = [] - for qa in qa_pairs: - if qa.question_id in all_answer_results: - result_dict = all_answer_results[qa.question_id] - results.append(AnswerResult( - question_id=result_dict["question_id"], - question=result_dict["question"], - answer=result_dict["answer"], - golden_answer=result_dict["golden_answer"], - category=result_dict.get("category"), - conversation_id=result_dict.get("conversation_id", ""), - formatted_context=result_dict.get("formatted_context", ""), # Load formatted_context - # search_results not loaded to save space - )) - return results - - semaphore = asyncio.Semaphore(MAX_CONCURRENT) - completed = processed_count - failed = 0 - start_time = time.time() - - # Use tqdm progress bar - pbar = tqdm( - total=total_qa_count, - initial=processed_count, - desc="💬 Answer Progress", - unit="qa" - ) - - async def answer_single_with_tracking(qa, search_result): - nonlocal completed, failed - - async with semaphore: - try: - # Build context - context = build_context(search_result) - - # Detect multiple-choice and enhance question if needed - query = qa.question - if "all_options" in qa.metadata: - options = qa.metadata["all_options"] - options_text = "\n".join([f"{key} {value}" for key, value in options.items()]) - - # Integrate options and requirements into question - query = f"""{qa.question} - -OPTIONS: -{options_text} - -IMPORTANT: This is a multiple-choice question. You MUST analyze the context and select the BEST option. In your FINAL ANSWER, return ONLY the option letter like (a), (b), (c), or (d), nothing else.""" - - # Call adapter's answer method with timeout and retry - max_retries = 3 - timeout_seconds = 120.0 # 3 minutes timeout per attempt - answer = None - - for attempt in range(max_retries): - try: - answer = await asyncio.wait_for( - adapter.answer( - query=query, - context=context, - conversation_id=search_result.conversation_id, - ), - timeout=timeout_seconds - ) - answer = answer.strip() - break # Success, exit retry loop - - except asyncio.TimeoutError: - if attempt < max_retries - 1: - tqdm.write(f" ⏱️ Timeout (180s) for {qa.question_id}, retry {attempt + 1}/{max_retries}...") - await asyncio.sleep(2) # Short delay before retry - else: - tqdm.write(f" ❌ Timeout after {max_retries} attempts for {qa.question_id}: {qa.question[:50]}...") - answer = "Error: Answer generation timeout after retries" - failed += 1 - - except Exception as e: - tqdm.write(f" ⚠️ Answer generation failed for {qa.question_id}: {e}") - answer = "Error: Failed to generate answer" - failed += 1 - - result = AnswerResult( - question_id=qa.question_id, - question=qa.question, - answer=answer, - golden_answer=qa.answer, - category=qa.category, - conversation_id=search_result.conversation_id, - formatted_context=context, # Save actual context used - metadata=qa.metadata, # Pass metadata (contains all_options for multiple-choice) - ) - - # Save result - all_answer_results[qa.question_id] = { - "question_id": result.question_id, - "question": result.question, - "answer": result.answer, - "golden_answer": result.golden_answer, - "category": result.category, - "conversation_id": result.conversation_id, - "formatted_context": result.formatted_context, # Save formatted_context - "metadata": result.metadata, # Save metadata (contains all_options) - } - - completed += 1 - pbar.update(1) # Update progress bar - - # Save checkpoint periodically - if checkpoint_manager and (completed % SAVE_INTERVAL == 0 or completed == total_qa_count): - elapsed = time.time() - start_time - speed = completed / elapsed if elapsed > 0 else 0 - eta = (total_qa_count - completed) / speed if speed > 0 else 0 - - tqdm.write(f"Progress: {completed}/{total_qa_count} ({completed/total_qa_count*100:.1f}%) | " - f"Speed: {speed:.1f} qa/s | Failed: {failed} | ETA: {eta/60:.1f} min") - - checkpoint_manager.save_answer_progress(all_answer_results, completed, total_qa_count) - - return result - - # Create all pending tasks - tasks = [ - answer_single_with_tracking(qa, sr) - for qa, sr in pending_tasks - ] - - # Execute concurrently - await asyncio.gather(*tasks) - - # Close progress bar - pbar.close() - - # Statistics - elapsed_time = time.time() - start_time - success_rate = (completed - failed) / completed * 100 if completed > 0 else 0 - - print(f"\n{'='*60}") - print(f"✅ All responses generated!") - print(f" - Total questions: {total_qa_count}") - print(f" - Successful: {completed - failed}") - print(f" - Failed: {failed}") - print(f" - Success rate: {success_rate:.1f}%") - print(f" - Time elapsed: {elapsed_time/60:.1f} minutes ({elapsed_time:.0f}s)") - print(f" - Average speed: {total_qa_count/elapsed_time:.1f} qa/s") - print(f"{'='*60}\n") - - # Delete fine-grained checkpoints after completion - if checkpoint_manager: - checkpoint_manager.delete_answer_checkpoints() - - # Convert to AnswerResult object list (original order) - results = [] - for qa in qa_pairs: - if qa.question_id in all_answer_results: - result_dict = all_answer_results[qa.question_id] - results.append(AnswerResult( - question_id=result_dict["question_id"], - question=result_dict["question"], - answer=result_dict["answer"], - golden_answer=result_dict["golden_answer"], - category=result_dict.get("category"), - conversation_id=result_dict.get("conversation_id", ""), - formatted_context=result_dict.get("formatted_context", ""), - search_results=result_dict.get("search_results", []), - metadata=result_dict.get("metadata", {}), # Restore metadata - )) - - return results - diff --git a/methods/EverCore/evaluation/src/core/stages/evaluate_stage.py b/methods/EverCore/evaluation/src/core/stages/evaluate_stage.py deleted file mode 100644 index 40bf35e8..00000000 --- a/methods/EverCore/evaluation/src/core/stages/evaluate_stage.py +++ /dev/null @@ -1,38 +0,0 @@ -""" -Evaluate stage - evaluate answer quality. -""" -from typing import List, Optional -from logging import Logger - -from evaluation.src.core.data_models import AnswerResult, EvaluationResult -from evaluation.src.evaluators.base import BaseEvaluator -from evaluation.src.utils.checkpoint import CheckpointManager - - -async def run_evaluate_stage( - evaluator: BaseEvaluator, - answer_results: List[AnswerResult], - checkpoint_manager: Optional[CheckpointManager], - logger: Logger, -) -> EvaluationResult: - """ - Execute Evaluate stage. - - Args: - evaluator: Evaluator - answer_results: List of answer results - checkpoint_manager: Checkpoint manager for resume - logger: Logger - - Returns: - Evaluation result - """ - logger.info("Starting Stage 4: Evaluate") - - eval_result = await evaluator.evaluate(answer_results) - - logger.info("✅ Stage 4 completed") - - return eval_result - - diff --git a/methods/EverCore/evaluation/src/core/stages/search_stage.py b/methods/EverCore/evaluation/src/core/stages/search_stage.py deleted file mode 100644 index 84ed8b38..00000000 --- a/methods/EverCore/evaluation/src/core/stages/search_stage.py +++ /dev/null @@ -1,209 +0,0 @@ -""" -Search stage - retrieve relevant memories. -""" -import asyncio -from typing import List, Any, Optional -from logging import Logger -from tqdm import tqdm - -from evaluation.src.core.data_models import QAPair, SearchResult -from evaluation.src.adapters.base import BaseAdapter -from evaluation.src.utils.checkpoint import CheckpointManager - - -async def run_search_stage( - adapter: BaseAdapter, - qa_pairs: List[QAPair], - index: Any, - conversations: List, - checkpoint_manager: Optional[CheckpointManager], - logger: Logger, -) -> List[SearchResult]: - """ - Execute concurrent search with fine-grained checkpointing. - - Process by conversation groups, save checkpoint after each conversation. - - Args: - adapter: System adapter - qa_pairs: List of QA pairs - index: Index - conversations: Conversation list (for online API cache rebuild) - checkpoint_manager: Checkpoint manager for resume - logger: Logger - - Returns: - List of search results - """ - print(f"\n{'='*60}") - print(f"Stage 2/4: Search") - print(f"{'='*60}") - - # Load fine-grained checkpoint - all_search_results_dict = {} - if checkpoint_manager: - all_search_results_dict = checkpoint_manager.load_search_progress() - - # Group QA pairs by conversation - conv_to_qa = {} - for qa in qa_pairs: - conv_id = qa.metadata.get("conversation_id", "unknown") - if conv_id not in conv_to_qa: - conv_to_qa[conv_id] = [] - conv_to_qa[conv_id].append(qa) - - total_convs = len(conv_to_qa) - processed_convs = set(all_search_results_dict.keys()) - remaining_convs = set(conv_to_qa.keys()) - processed_convs - - print(f"Total conversations: {total_convs}") - print(f"Total questions: {len(qa_pairs)}") - if processed_convs: - print(f"Already processed: {len(processed_convs)} conversations (from checkpoint)") - print(f"Remaining: {len(remaining_convs)} conversations") - - # Build conversation_id to conversation mapping (for online API cache rebuild) - conv_id_to_conv = {conv.conversation_id: conv for conv in conversations} - - # Search-stage concurrency can be configured separately via system config: - # search.num_workers (fallback to adapter.num_workers, then 20) - search_cfg = adapter.config.get("search", {}) - num_workers = int(search_cfg.get("num_workers", getattr(adapter, "num_workers", 20))) - semaphore = asyncio.Semaphore(num_workers) - print(f"Search concurrency: {num_workers} workers") - - # Create fine-grained progress bar (track by questions) - total_questions = len(qa_pairs) - processed_questions = sum(len(all_search_results_dict.get(conv_id, [])) for conv_id in processed_convs) - - pbar = tqdm( - total=total_questions, - initial=processed_questions, - desc="🔍 Search Progress", - unit="qa" - ) - - async def search_single_with_tracking(qa): - async with semaphore: - conv_id = qa.metadata.get("conversation_id", "0") - conversation = conv_id_to_conv.get(conv_id) - - # Search with timeout and retry (similar to answer_stage.py) - max_retries = 3 - timeout_seconds = 300.0 # Increased from 120s for complex agentic retrieval - result = None - - for attempt in range(max_retries): - try: - result = await asyncio.wait_for( - adapter.search(qa.question, conv_id, index, conversation=conversation), - timeout=timeout_seconds - ) - break # Success, exit retry loop - - except asyncio.TimeoutError: - if attempt < max_retries - 1: - tqdm.write(f" ⏱️ Search timeout ({timeout_seconds}s) for question in {conv_id}, retry {attempt + 1}/{max_retries}...") - await asyncio.sleep(2) # Short delay before retry - else: - tqdm.write(f" ❌ Search timeout after {max_retries} attempts for question in {conv_id}: {qa.question[:60]}...") - # Return empty search result on timeout - from evaluation.src.core.data_models import SearchResult - result = SearchResult( - query=qa.question, - conversation_id=conv_id, - results=[], - retrieval_metadata={"error": "Search timeout after retries"} - ) - - except Exception as e: - if attempt < max_retries - 1: - tqdm.write(f" ⚠️ Search failed for question in {conv_id}: {str(e)}, retry {attempt + 1}/{max_retries}...") - await asyncio.sleep(2) - else: - tqdm.write(f" ❌ Search failed after {max_retries} attempts for question in {conv_id}: {str(e)}") - # Return empty search result on error - from evaluation.src.core.data_models import SearchResult - result = SearchResult( - query=qa.question, - conversation_id=conv_id, - results=[], - retrieval_metadata={"error": f"Search error: {str(e)}"} - ) - - pbar.update(1) # Update progress bar after each question - return result - - # Process by conversation (use numeric sort for conversation IDs like "longmemeval_10") - def sort_key(item): - """Sort by numeric part of conversation_id if possible, else alphabetically.""" - conv_id = item[0] - # Try to extract numeric suffix (e.g., "longmemeval_10" -> 10) - parts = conv_id.rsplit('_', 1) - if len(parts) == 2 and parts[1].isdigit(): - return (parts[0], int(parts[1])) - return (conv_id, 0) - - for idx, (conv_id, qa_list) in enumerate(sorted(conv_to_qa.items(), key=sort_key)): - # Skip already processed conversations - if conv_id in processed_convs: - tqdm.write(f"⏭️ Skipping Conversation ID: {conv_id} (already processed)") - continue - - tqdm.write(f"Processing Conversation ID: {conv_id} ({idx+1}/{total_convs}) - {len(qa_list)} questions") - - # Process all questions for this conversation concurrently - tasks = [search_single_with_tracking(qa) for qa in qa_list] - results_for_conv = await asyncio.gather(*tasks) - - # Save results in dict format - results_for_conv_dict = [ - { - "question_id": qa.question_id, - "query": qa.question, - "conversation_id": conv_id, - "results": result.results, - "retrieval_metadata": result.retrieval_metadata - } - for qa, result in zip(qa_list, results_for_conv) - ] - - all_search_results_dict[conv_id] = results_for_conv_dict - - # Save checkpoint after each conversation - if checkpoint_manager: - checkpoint_manager.save_search_progress(all_search_results_dict) - - # Close progress bar - pbar.close() - - # Delete fine-grained checkpoint after completion - if checkpoint_manager: - checkpoint_manager.delete_search_checkpoint() - - # Convert dict format to SearchResult object list (maintain original return format) - # Use same numeric sort as above to ensure consistent ordering - def sort_key_conv_id(conv_id): - """Sort by numeric part of conversation_id if possible, else alphabetically.""" - parts = conv_id.rsplit('_', 1) - if len(parts) == 2 and parts[1].isdigit(): - return (parts[0], int(parts[1])) - return (conv_id, 0) - - all_results = [] - for conv_id in sorted(conv_to_qa.keys(), key=sort_key_conv_id): - if conv_id in all_search_results_dict: - for result_dict in all_search_results_dict[conv_id]: - all_results.append(SearchResult( - query=result_dict["query"], - conversation_id=result_dict["conversation_id"], - results=result_dict["results"], - retrieval_metadata=result_dict.get("retrieval_metadata", {}) - )) - - print(f"\n{'='*60}") - print(f"🎉 All conversations processed!") - print(f"{'='*60}") - print(f"✅ Search completed: {len(all_results)} results\n") - return all_results - diff --git a/methods/EverCore/evaluation/src/evaluators/__init__.py b/methods/EverCore/evaluation/src/evaluators/__init__.py deleted file mode 100644 index 6f3a4fd3..00000000 --- a/methods/EverCore/evaluation/src/evaluators/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Evaluators - diff --git a/methods/EverCore/evaluation/src/evaluators/base.py b/methods/EverCore/evaluation/src/evaluators/base.py deleted file mode 100644 index da816ed1..00000000 --- a/methods/EverCore/evaluation/src/evaluators/base.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -Evaluator base class - define unified evaluator interface. -""" -from abc import ABC, abstractmethod -from typing import List, Dict, Any -from evaluation.src.core.data_models import AnswerResult, EvaluationResult - - -class BaseEvaluator(ABC): - """Evaluator base class.""" - - def __init__(self, config: dict): - """ - Initialize evaluator. - - Args: - config: Evaluation config - """ - self.config = config - - @abstractmethod - async def evaluate( - self, - answer_results: List[AnswerResult] - ) -> EvaluationResult: - """ - Evaluate answer results. - - Args: - answer_results: List of answer results - - Returns: - Evaluation result - """ - pass - - def get_name(self) -> str: - """Return evaluator name.""" - return self.__class__.__name__ - diff --git a/methods/EverCore/evaluation/src/evaluators/exact_match.py b/methods/EverCore/evaluation/src/evaluators/exact_match.py deleted file mode 100644 index a6eeb81f..00000000 --- a/methods/EverCore/evaluation/src/evaluators/exact_match.py +++ /dev/null @@ -1,171 +0,0 @@ -""" -Exact Match evaluator - direct answer comparison, suitable for multiple-choice scenarios. -""" -import re -from typing import List - -from evaluation.src.evaluators.base import BaseEvaluator -from evaluation.src.evaluators.registry import register_evaluator -from evaluation.src.core.data_models import AnswerResult, EvaluationResult - - -@register_evaluator("exact_match") -class ExactMatch(BaseEvaluator): - """Exact match evaluator.""" - - def __init__(self, config: dict): - """ - Initialize evaluator. - - Args: - config: Evaluation config (optional: case_sensitive, normalize_whitespace) - """ - super().__init__(config) - - # Config options - self.case_sensitive = config.get("case_sensitive", False) # Default: case insensitive - self.normalize_whitespace = config.get("normalize_whitespace", True) # Default: normalize whitespace - self.extract_choice = config.get("extract_choice", True) # Default: extract choices like (a), (b), (c) - - async def evaluate( - self, - answer_results: List[AnswerResult] - ) -> EvaluationResult: - """ - Evaluate answers using exact match. - - Args: - answer_results: List of answer results - - Returns: - Evaluation result - """ - print(f"\n{'='*60}") - print(f"Evaluation: Exact Match") - print(f" - Case sensitive: {self.case_sensitive}") - print(f" - Normalize whitespace: {self.normalize_whitespace}") - print(f" - Extract choice: {self.extract_choice}") - print(f"{'='*60}") - - detailed_results = [] - total_correct = 0 - - # Evaluate each answer - for answer_result in answer_results: - is_correct = self._check_match( - answer_result.golden_answer, - answer_result.answer - ) - - if is_correct: - total_correct += 1 - - detailed_results.append({ - "question_id": answer_result.question_id, - "question": answer_result.question, - "golden_answer": answer_result.golden_answer, - "generated_answer": answer_result.answer, - "is_correct": is_correct, - "category": answer_result.category, - }) - - accuracy = total_correct / len(answer_results) if answer_results else 0.0 - - print(f"\n✅ Evaluation complete:") - print(f" - Total questions: {len(answer_results)}") - print(f" - Correct: {total_correct}") - print(f" - Accuracy: {accuracy:.2%}") - - return EvaluationResult( - total_questions=len(answer_results), - correct=total_correct, - accuracy=accuracy, - detailed_results=detailed_results, - metadata={ - "evaluator": "exact_match", - "case_sensitive": self.case_sensitive, - "normalize_whitespace": self.normalize_whitespace, - "extract_choice": self.extract_choice, - } - ) - - def _check_match(self, golden: str, generated: str) -> bool: - """ - Check if two answers match. - - Args: - golden: Golden answer - generated: Generated answer - - Returns: - Whether answers match - """ - # Preprocess - golden_processed = self._preprocess(golden) - generated_processed = self._preprocess(generated) - - # If choice extraction enabled, try to extract choice from generated answer - if self.extract_choice: - extracted_choice = self._extract_choice(generated_processed) - if extracted_choice: - generated_processed = extracted_choice - - # Compare - if self.case_sensitive: - return golden_processed == generated_processed - else: - return golden_processed.lower() == generated_processed.lower() - - def _preprocess(self, text: str) -> str: - """ - Preprocess text. - - Args: - text: Raw text - - Returns: - Processed text - """ - if not text: - return "" - - # Normalize whitespace - if self.normalize_whitespace: - text = re.sub(r'\s+', ' ', text).strip() - - return text - - def _extract_choice(self, text: str) -> str: - """ - Extract choice from text (supports formats: (a), a), a., A, etc.). - - Returns: - Normalized choice format "(a)", empty string if not found - """ - # Try matching (a), (b), (c), (d) format - match = re.search(r'\(([a-zA-Z])\)', text) - if match: - return f"({match.group(1).lower()})" - - # Try matching a), b), c), d) format - match = re.search(r'\b([a-zA-Z])\)', text) - if match: - return f"({match.group(1).lower()})" - - # Try matching a., b., c., d. format - match = re.search(r'\b([a-zA-Z])\.', text) - if match: - return f"({match.group(1).lower()})" - - # Try matching standalone letter (at start or surrounded by whitespace) - match = re.search(r'(?:^|\s)([a-zA-Z])(?:\s|$)', text) - if match: - letter = match.group(1).lower() - # Only accept first few letters (options typically don't exceed f) - if letter in 'abcdefgh': - return f"({letter})" - - # Return empty string if no match - return "" - - diff --git a/methods/EverCore/evaluation/src/evaluators/hybrid.py b/methods/EverCore/evaluation/src/evaluators/hybrid.py deleted file mode 100644 index 6bff7ff0..00000000 --- a/methods/EverCore/evaluation/src/evaluators/hybrid.py +++ /dev/null @@ -1,207 +0,0 @@ -""" -Hybrid evaluator - automatically choose evaluation method based on question type. - -- Multiple-choice questions (with all_options) → Exact Match -- Open-ended questions (without all_options) → LLM Judge -""" -from typing import List -from collections import defaultdict - -from evaluation.src.evaluators.base import BaseEvaluator -from evaluation.src.evaluators.registry import register_evaluator -from evaluation.src.core.data_models import AnswerResult, EvaluationResult -from evaluation.src.evaluators.exact_match import ExactMatch -from evaluation.src.evaluators.llm_judge import LLMJudge - - -@register_evaluator("hybrid") -class HybridEvaluator(BaseEvaluator): - """ - Hybrid evaluator that combines Exact Match and LLM Judge. - - Automatically detects question type and uses appropriate evaluation method: - - Questions with all_options in metadata → Exact Match (for multiple-choice) - - Questions without all_options → LLM Judge (for open-ended) - """ - - def __init__(self, config: dict): - """ - Initialize hybrid evaluator. - - Args: - config: Evaluation config containing settings for both evaluators - """ - super().__init__(config) - - # Initialize both evaluators - self.exact_match_evaluator = ExactMatch(config) - self.llm_judge_evaluator = LLMJudge(config) - - async def evaluate( - self, - answer_results: List[AnswerResult] - ) -> EvaluationResult: - """ - Evaluate answers using hybrid approach. - - Args: - answer_results: List of answer results - - Returns: - Combined evaluation result - """ - print(f"\n{'='*60}") - print(f"Evaluation: Hybrid (Exact Match + LLM Judge)") - print(f"{'='*60}") - - # Separate questions by type, preserving original indices - choice_questions = [] - open_questions = [] - question_type_map = {} # Maps index to question type - - for idx, answer_result in enumerate(answer_results): - # Check if question has all_options (multiple-choice) - has_options = "all_options" in answer_result.metadata - - if has_options: - choice_questions.append(answer_result) - question_type_map[idx] = 'choice' - else: - open_questions.append(answer_result) - question_type_map[idx] = 'open' - - print(f"\n📊 Question Distribution:") - print(f" - Multiple-choice (Exact Match): {len(choice_questions)}") - print(f" - Open-ended (LLM Judge): {len(open_questions)}") - - # Evaluate multiple-choice questions with Exact Match - choice_detailed_results = [] - choice_correct = 0 - - if choice_questions: - print(f"\n{'='*60}") - print(f"Evaluating Multiple-Choice Questions ({len(choice_questions)})") - print(f"{'='*60}") - - choice_eval_result = await self.exact_match_evaluator.evaluate(choice_questions) - choice_detailed_results = choice_eval_result.detailed_results - choice_correct = choice_eval_result.correct - - # Evaluate open-ended questions with LLM Judge - open_detailed_results = [] - open_correct = 0 - open_metadata = {} - - if open_questions: - print(f"\n{'='*60}") - print(f"Evaluating Open-Ended Questions ({len(open_questions)})") - print(f"{'='*60}") - - open_eval_result = await self.llm_judge_evaluator.evaluate(open_questions) - # LLM Judge returns grouped results (dict), need to flatten to list - if isinstance(open_eval_result.detailed_results, dict): - # Flatten grouped results to list - for conv_results in open_eval_result.detailed_results.values(): - open_detailed_results.extend(conv_results) - else: - open_detailed_results = open_eval_result.detailed_results - open_correct = open_eval_result.correct - open_metadata = open_eval_result.metadata - - # Combine results in original order - # Create maps for quick lookup - choice_results_map = {result['question_id']: result for result in choice_detailed_results} - open_results_map = {result['question_id']: result for result in open_detailed_results} - - # Reconstruct results in original order - all_detailed_results = [] - choice_idx = 0 - open_idx = 0 - - for idx in range(len(answer_results)): - q_type = question_type_map[idx] - if q_type == 'choice': - # Use question_id from original answer_result to look up evaluated result - original_result = choice_questions[choice_idx] - question_id = original_result.question_id - all_detailed_results.append(choice_results_map[question_id]) - choice_idx += 1 - else: # q_type == 'open' - original_result = open_questions[open_idx] - question_id = original_result.question_id - all_detailed_results.append(open_results_map[question_id]) - open_idx += 1 - - total_correct = choice_correct + open_correct - total_questions = len(answer_results) - overall_accuracy = total_correct / total_questions if total_questions > 0 else 0.0 - - # Calculate per-category statistics - category_stats = self._calculate_category_stats(all_detailed_results) - - # Print summary - print(f"\n{'='*60}") - print(f"Combined Results") - print(f"{'='*60}") - print(f" - Total questions: {total_questions}") - print(f" - Correct: {total_correct}") - print(f" - Overall accuracy: {overall_accuracy:.2%}") - print(f"\n Breakdown:") - print(f" - Multiple-choice: {choice_correct}/{len(choice_questions)} ({choice_correct/len(choice_questions)*100:.1f}%)" if choice_questions else " - Multiple-choice: 0/0") - print(f" - Open-ended: {open_correct}/{len(open_questions)} ({open_correct/len(open_questions)*100:.1f}%)" if open_questions else " - Open-ended: 0/0") - - if category_stats: - print(f"\n📊 Category Statistics:") - for cat, stats in sorted(category_stats.items()): - print(f" Category {cat}: {stats['correct']}/{stats['total']} ({stats['accuracy']:.2%})") - - # Construct metadata - combined_metadata = { - "evaluator": "hybrid", - "total_questions": total_questions, - "choice_questions": len(choice_questions), - "open_questions": len(open_questions), - "choice_correct": choice_correct, - "open_correct": open_correct, - "choice_accuracy": choice_correct / len(choice_questions) if choice_questions else 0.0, - "open_accuracy": open_correct / len(open_questions) if open_questions else 0.0, - "category_stats": category_stats, - } - - # Add LLM Judge specific metadata if available - if open_metadata: - combined_metadata["llm_judge_metadata"] = { - "mean_accuracy": open_metadata.get("mean_accuracy"), - "std_accuracy": open_metadata.get("std_accuracy"), - "num_runs": open_metadata.get("num_runs"), - } - - return EvaluationResult( - total_questions=total_questions, - correct=total_correct, - accuracy=overall_accuracy, - detailed_results=all_detailed_results, - metadata=combined_metadata - ) - - def _calculate_category_stats(self, detailed_results: List[dict]) -> dict: - """Calculate per-category statistics.""" - category_data = defaultdict(lambda: {"correct": 0, "total": 0}) - - for result in detailed_results: - category = result.get("category", "unknown") - category_data[category]["total"] += 1 - if result.get("is_correct", False): - category_data[category]["correct"] += 1 - - # Add accuracy - category_stats = {} - for cat, data in category_data.items(): - category_stats[cat] = { - "correct": data["correct"], - "total": data["total"], - "accuracy": data["correct"] / data["total"] if data["total"] > 0 else 0.0 - } - - return category_stats - diff --git a/methods/EverCore/evaluation/src/evaluators/llm_judge.py b/methods/EverCore/evaluation/src/evaluators/llm_judge.py deleted file mode 100644 index 0cbeaa26..00000000 --- a/methods/EverCore/evaluation/src/evaluators/llm_judge.py +++ /dev/null @@ -1,308 +0,0 @@ -""" -LLM Judge evaluator - use LLM to judge answer correctness. - -Aligned with evaluation_archive logic: -- Keep independent judgments for each run (judgment_1, judgment_2, judgment_3) -- Calculate accuracy for each run separately -- Output mean and std -""" - -import asyncio -import json -import numpy as np -from typing import List, Dict, Any -from collections import defaultdict -from openai import AsyncOpenAI -from tqdm import tqdm - -from evaluation.src.evaluators.base import BaseEvaluator -from evaluation.src.evaluators.registry import register_evaluator -from evaluation.src.core.data_models import AnswerResult, EvaluationResult -from evaluation.src.utils.prompts import get_prompt, format_prompt - - -@register_evaluator("llm_judge") -class LLMJudge(BaseEvaluator): - """LLM judge evaluator.""" - - def __init__(self, config: dict): - super().__init__(config) - - # Initialize OpenAI client - llm_config = config.get("llm", {}) - self.client = AsyncOpenAI( - api_key=llm_config.get("api_key"), - base_url=llm_config.get("base_url", "https://api.openai.com/v1"), - ) - self.model = llm_config.get("model", "gpt-4o-mini") - self.num_runs = config.get("num_runs", 3) - - async def evaluate(self, answer_results: List[AnswerResult]) -> EvaluationResult: - """ - Evaluate answers using LLM, return statistics from multiple runs. - - Args: - answer_results: List of answer results - - Returns: - Evaluation result with mean and std - """ - print(f"\n{'='*60}") - print(f"Evaluation: LLM Judge (model={self.model}, runs={self.num_runs})") - print(f"{'='*60}") - - detailed_results = [] - - # Evaluate all answers concurrently - semaphore = asyncio.Semaphore(10) # Limit concurrency - - # Use tqdm progress bar - pbar = tqdm(total=len(answer_results), desc="⚖️ Evaluate Progress", unit="qa") - - async def evaluate_single(answer_result: AnswerResult): - async with semaphore: - result = await self._evaluate_single_answer(answer_result) - pbar.update(1) # Update progress bar - return result - - tasks = [evaluate_single(ar) for ar in answer_results] - results = await asyncio.gather(*tasks) - - # Close progress bar - pbar.close() - - # Collect results - for result in results: - detailed_results.append(result) - - # Calculate accuracy for each run separately - run_scores = [] - category_stats = defaultdict( - lambda: {"correct": [0] * self.num_runs, "total": 0} - ) - - for i in range(self.num_runs): - judgment_key = f"judgment_{i+1}" - correct_count = 0 - total_count = 0 - - for result in detailed_results: - llm_judgments = result.get("llm_judgments", {}) - category = result.get("category") - - if judgment_key in llm_judgments: - total_count += 1 - if llm_judgments[judgment_key]: - correct_count += 1 - if category is not None: - category_stats[category]["correct"][i] += 1 - - # Count category total (only need once) - if i == 0 and category is not None: - category_stats[category]["total"] += 1 - - if total_count > 0: - run_accuracy = correct_count / total_count - run_scores.append(run_accuracy) - - # Calculate statistics - mean_accuracy = np.mean(run_scores) if run_scores else 0.0 - std_accuracy = np.std(run_scores) if run_scores else 0.0 - - # Calculate accuracy for each category - category_accuracies = {} - for category, stats in category_stats.items(): - cat_accuracies = [] - for i in range(self.num_runs): - if stats["total"] > 0: - cat_acc = stats["correct"][i] / stats["total"] - cat_accuracies.append(cat_acc) - - if cat_accuracies: - category_accuracies[str(category)] = { - "mean": np.mean(cat_accuracies), - "std": np.std(cat_accuracies), - "individual_runs": cat_accuracies, - "total": stats["total"], - } - - print(f"\n✅ Evaluation complete:") - print(f" - Total questions: {len(answer_results)}") - print(f" - Mean accuracy: {mean_accuracy:.4f} ({mean_accuracy*100:.2f}%)") - print(f" - Std deviation: {std_accuracy:.4f}") - print(f" - Run accuracies: {[f'{s:.4f}' for s in run_scores]}") - - if category_accuracies: - print(f"\n📊 Category statistics:") - for cat, stats in sorted(category_accuracies.items()): - print( - f" Category {cat}: {stats['mean']:.4f} ± {stats['std']:.4f} (n={stats['total']})" - ) - - # Group by conversation - grouped_results = self._group_by_conversation(detailed_results) - - return EvaluationResult( - total_questions=len(answer_results), - correct=int( - mean_accuracy * len(answer_results) - ), # Use mean for calculation - accuracy=mean_accuracy, - detailed_results=grouped_results, - metadata={ - "model": self.model, - "num_runs": self.num_runs, - "mean_accuracy": mean_accuracy, - "std_accuracy": std_accuracy, - "run_scores": run_scores, - "category_accuracies": category_accuracies, - }, - ) - - def _group_by_conversation( - self, detailed_results: List[Dict] - ) -> Dict[str, List[Dict]]: - """ - Group results by conversation (e.g., locomo_exp_user_0, locomo_exp_user_1, etc.). - """ - grouped = defaultdict(list) - - for result in detailed_results: - question_id = result.get("question_id", "") - - # Extract conversation info from question_id - # Example: "locomo_0_qa0" -> "locomo_exp_user_0" - # Example: "personamem_5_qa2" -> "personamem_exp_user_5" - if "_qa" in question_id: - parts = question_id.split("_qa") - conv_id = parts[0] # "locomo_0" or "personamem_5" - - # Convert to evaluation_archive format - if "_" in conv_id: - dataset_name, conv_num = conv_id.rsplit("_", 1) - group_key = f"{dataset_name}_exp_user_{conv_num}" - else: - group_key = f"{conv_id}_exp_user_0" - else: - # Use default group if format doesn't match - group_key = "default_group" - - grouped[group_key].append(result) - - return dict(grouped) - - async def _evaluate_single_answer(self, answer_result: AnswerResult) -> dict: - """ - Evaluate single answer, keep independent judgment for each run. - """ - question = answer_result.question - golden_answer = answer_result.golden_answer - generated_answer = answer_result.answer - - # Multiple evaluations, keep independent judgments - judgments = [] - for _ in range(self.num_runs): - is_correct = await self._judge_answer( - question, golden_answer, generated_answer - ) - judgments.append(is_correct) - - # Use judgment_1, judgment_2, ... format - llm_judgments = { - f"judgment_{i+1}": judgment for i, judgment in enumerate(judgments) - } - - return { - "question_id": answer_result.question_id, - "question": question, - "golden_answer": golden_answer, - "generated_answer": generated_answer, - "llm_judgments": llm_judgments, - "category": answer_result.category, - } - - async def _judge_answer( - self, question: str, golden_answer: str, generated_answer: str - ) -> bool: - """ - Use LLM to judge if answer is correct. - - Returns: - True if correct, False if wrong - """ - # Use configured prompts - system_prompt = get_prompt("llm_judge", "system_prompt") - user_prompt = format_prompt( - "llm_judge", - "user_prompt", - question=question, - golden_answer=golden_answer, - generated_answer=generated_answer, - ) - - try: - response = await self.client.chat.completions.create( - model=self.model, - messages=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_prompt}, - ], - temperature=0, - ) - - content = response.choices[0].message.content - - # Debug: check if content is empty or None - if not content: - print(f" ⚠️ LLM Judge: Empty response from model {self.model}") - return False - - # Extract JSON from response (handle models that add explanation text) - json_str = self._extract_json(content) - if not json_str: - print(f" ⚠️ LLM Judge: No JSON found in response") - print(f" Raw response: {content[:200]}...") - return False - - result = json.loads(json_str) - label = result.get("label", "") - if not label: - print(f" ⚠️ LLM Judge: No label found in response") - print(f" Raw response: {content}...") - return False - - return label.strip().upper() == "CORRECT" - - except json.JSONDecodeError as e: - print(f" ⚠️ LLM Judge JSON parse failed: {e}") - print(f" Raw response: {content[:200] if content else 'None'}...") - return False - except Exception as e: - print(f" ⚠️ LLM Judge failed: {type(e).__name__}: {e}") - return False - - def _extract_json(self, content: str) -> str: - """ - Extract JSON from LLM response that may contain explanation text. - - Handles: - 1. Pure JSON: {"label": "CORRECT"} - 2. JSON with explanation: Some text... {"label": "CORRECT"} - 3. Markdown code block: ```json {"label": "CORRECT"} ``` - """ - import re - - # Try 1: Extract from markdown code block - code_block_match = re.search( - r'```(?:json)?\s*(\{[^`]*\})\s*```', content, re.DOTALL - ) - if code_block_match: - return code_block_match.group(1).strip() - - # Try 2: Find JSON object pattern - json_match = re.search(r'\{[^{}]*"label"\s*:\s*"[^"]*"[^{}]*\}', content) - if json_match: - return json_match.group(0) - - # Try 3: Return original content (let json.loads handle it) - return content.strip() diff --git a/methods/EverCore/evaluation/src/evaluators/registry.py b/methods/EverCore/evaluation/src/evaluators/registry.py deleted file mode 100644 index d9a54e2f..00000000 --- a/methods/EverCore/evaluation/src/evaluators/registry.py +++ /dev/null @@ -1,94 +0,0 @@ -""" -Evaluator registry - provide evaluator registration and creation. -Uses lazy loading strategy, keeps __init__.py empty. -""" -import importlib -from typing import Dict, Type, List -from evaluation.src.evaluators.base import BaseEvaluator - - -_EVALUATOR_REGISTRY: Dict[str, Type[BaseEvaluator]] = {} - -# Evaluator module mapping (for lazy loading) -_EVALUATOR_MODULES = { - "llm_judge": "evaluation.src.evaluators.llm_judge", - "exact_match": "evaluation.src.evaluators.exact_match", - "hybrid": "evaluation.src.evaluators.hybrid", - # Future evaluators: - # "bert_score": "evaluation.src.evaluators.bert_score", -} - - -def register_evaluator(name: str): - """ - Decorator for registering evaluators. - - Usage: - @register_evaluator("llm_judge") - class LLMJudge(BaseEvaluator): - ... - """ - def decorator(cls: Type[BaseEvaluator]): - _EVALUATOR_REGISTRY[name] = cls - return cls - return decorator - - -def _ensure_evaluator_loaded(name: str): - """ - Ensure specified evaluator is loaded (lazy loading strategy). - - Trigger @register_evaluator decorator execution via dynamic import. - This keeps __init__.py empty per project convention. - - Args: - name: Evaluator name - - Raises: - ValueError: If evaluator doesn't exist - RuntimeError: If module loaded but not registered - """ - if name in _EVALUATOR_REGISTRY: - return # Already loaded - - if name not in _EVALUATOR_MODULES: - raise ValueError( - f"Unknown evaluator: {name}. " - f"Available evaluators: {list(_EVALUATOR_MODULES.keys())}" - ) - - # Dynamically import module, trigger @register_evaluator execution - module_path = _EVALUATOR_MODULES[name] - importlib.import_module(module_path) - - # Verify registration success - if name not in _EVALUATOR_REGISTRY: - raise RuntimeError( - f"Evaluator '{name}' module loaded but not registered. " - f"Check if @register_evaluator('{name}') decorator is present." - ) - - -def create_evaluator(name: str, llm_provider=None) -> BaseEvaluator: - """ - Create evaluator instance. - - Args: - name: Evaluator name - llm_provider: LLM provider (required by some evaluators) - - Returns: - Evaluator instance - - Raises: - ValueError: If evaluator not registered - """ - # Lazy loading: ensure evaluator loaded - _ensure_evaluator_loaded(name) - - return _EVALUATOR_REGISTRY[name](llm_provider) - - -def list_evaluators() -> List[str]: - """List all available evaluators.""" - return list(_EVALUATOR_MODULES.keys()) diff --git a/methods/EverCore/evaluation/src/utils/__init__.py b/methods/EverCore/evaluation/src/utils/__init__.py deleted file mode 100644 index 2b59ee56..00000000 --- a/methods/EverCore/evaluation/src/utils/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Utilities - diff --git a/methods/EverCore/evaluation/src/utils/checkpoint.py b/methods/EverCore/evaluation/src/utils/checkpoint.py deleted file mode 100644 index aea355da..00000000 --- a/methods/EverCore/evaluation/src/utils/checkpoint.py +++ /dev/null @@ -1,348 +0,0 @@ -""" -Checkpoint management module - supports resume from interruption. -""" - -import json -from pathlib import Path -from typing import Dict, Any, Optional, Set -from datetime import datetime - -from common_utils.datetime_utils import get_now_with_timezone - - -class CheckpointManager: - """ - Checkpoint manager. - - Two-layer mechanism: - 1. Cross-stage: track completed stages (add/search/answer/evaluate) - 2. Within-stage: track fine-grained progress (search by session, answer by question count) - """ - - def __init__(self, output_dir: Path, run_name: str = "default"): - """ - Initialize Checkpoint manager. - - Args: - output_dir: Output directory - run_name: Run name - """ - self.output_dir = Path(output_dir) - self.run_name = run_name - - # Cross-stage checkpoint (record which stages are completed) - self.checkpoint_file = self.output_dir / f"checkpoint_{run_name}.json" - - # Fine-grained checkpoints (one per stage, track progress within stage) - self.search_checkpoint = self.output_dir / f"search_results_checkpoint.json" - self.answer_checkpoint = self.output_dir / f"answer_results_checkpoint.json" - - # Ensure output directory exists - self.output_dir.mkdir(parents=True, exist_ok=True) - - def load_checkpoint(self) -> Optional[Dict[str, Any]]: - """ - Load existing checkpoint. - - Returns: - Checkpoint data, or None if not exists - """ - if not self.checkpoint_file.exists(): - return None - - try: - with open(self.checkpoint_file, 'r', encoding='utf-8') as f: - checkpoint = json.load(f) - - print(f"\n🔄 Found checkpoint file: {self.checkpoint_file.name}") - print(f" Last updated: {checkpoint.get('last_updated', 'Unknown')}") - print( - f" Completed stages: {', '.join(checkpoint.get('completed_stages', []))}" - ) - - if 'search_results' in checkpoint: - completed_convs = len(checkpoint['search_results']) - print(f" Processed conversations: {completed_convs}") - - return checkpoint - - except Exception as e: - print(f"⚠️ Failed to load checkpoint: {e}") - print(f" Starting from scratch") - return None - - def save_checkpoint( - self, - completed_stages: Set[str], - search_results: Optional[Dict] = None, - answer_results: Optional[Dict] = None, - eval_results: Optional[Dict] = None, - metadata: Optional[Dict] = None, - ): - """ - Save checkpoint. - - Args: - completed_stages: Set of completed stages - search_results: Search results (optional) - answer_results: Answer results (optional) - eval_results: Evaluation results (optional) - metadata: Other metadata (optional) - """ - checkpoint = { - "run_name": self.run_name, - "last_updated": get_now_with_timezone().isoformat(), - "completed_stages": list(completed_stages), - } - - if search_results is not None: - checkpoint["search_results"] = search_results - - if answer_results is not None: - checkpoint["answer_results"] = answer_results - - if eval_results is not None: - checkpoint["eval_results"] = eval_results - - if metadata is not None: - checkpoint["metadata"] = metadata - - try: - with open(self.checkpoint_file, 'w', encoding='utf-8') as f: - json.dump(checkpoint, f, indent=2, ensure_ascii=False) - - print(f"💾 Checkpoint saved: {self.checkpoint_file.name}") - - except Exception as e: - print(f"⚠️ Failed to save checkpoint: {e}") - - def get_completed_conversations(self) -> Set[str]: - """ - Get set of completed conversation IDs. - - Returns: - Set of completed conversation IDs - """ - checkpoint = self.load_checkpoint() - if not checkpoint: - return set() - - completed = set() - - # Get from search_results - if 'search_results' in checkpoint: - completed.update(checkpoint['search_results'].keys()) - - return completed - - def should_skip_stage(self, stage: str) -> bool: - """ - Check whether a stage should be skipped. - - Args: - stage: Stage name (add, search, answer, evaluate) - - Returns: - True if should skip - """ - checkpoint = self.load_checkpoint() - if not checkpoint: - return False - - completed_stages = set(checkpoint.get('completed_stages', [])) - return stage in completed_stages - - def delete_checkpoint(self): - """Delete checkpoint file.""" - if self.checkpoint_file.exists(): - try: - self.checkpoint_file.unlink() - print(f"🗑️ Checkpoint deleted: {self.checkpoint_file.name}") - except Exception as e: - print(f"⚠️ Failed to delete checkpoint: {e}") - - def get_search_results(self) -> Optional[Dict]: - """Get saved search results.""" - checkpoint = self.load_checkpoint() - if checkpoint and 'search_results' in checkpoint: - return checkpoint['search_results'] - return None - - def get_answer_results(self) -> Optional[Dict]: - """Get saved answer results.""" - checkpoint = self.load_checkpoint() - if checkpoint and 'answer_results' in checkpoint: - return checkpoint['answer_results'] - return None - - # ==================== Fine-grained Checkpoint Methods ==================== - - def save_add_progress(self, completed_convs: set, memcells_dir: Path): - """ - Save fine-grained progress for Add stage (record completed session IDs). - - Args: - completed_convs: Set of completed session IDs - memcells_dir: MemCells save directory (for checking file existence) - """ - # Add stage checkpoint strategy: - # After processing each session, save MemCells to {output_dir}/memcells/{conv_id}.json - # No additional checkpoint file needed, just check memcells directory - pass # Files themselves are checkpoints - - def load_add_progress(self, memcells_dir: Path, all_conv_ids: list) -> set: - """ - Load fine-grained progress for Add stage (check which sessions are completed). - - Returns: - Set of completed session IDs - """ - import json - - completed_convs = set() - - if not memcells_dir.exists(): - print(f"\n🆕 No previous memcells found, starting from scratch") - return completed_convs - - print(f"\n🔍 Checking for completed conversations in: {memcells_dir}") - - for conv_id in all_conv_ids: - # Match stage1 actual file name format - output_file = memcells_dir / f"memcell_list_conv_{conv_id}.json" - if output_file.exists(): - # Validate file (non-empty and parseable) - try: - with open(output_file, "r", encoding="utf-8") as f: - data = json.load(f) - if data and len(data) > 0: # Ensure has data - completed_convs.add(conv_id) - print( - f"✅ Skipped completed session: {conv_id} ({len(data)} memcells)" - ) - except Exception as e: - print(f"⚠️ Session {conv_id} file corrupted, will reprocess: {e}") - - if completed_convs: - print( - f"\n📊 Found {len(completed_convs)}/{len(all_conv_ids)} completed sessions" - ) - - return completed_convs - - def save_search_progress(self, search_results: Dict[str, Any]): - """ - Save fine-grained progress for Search stage (save after each session). - - Args: - search_results: Current accumulated search results - Format: {conv_id: [{"question_id": ..., "results": ...}, ...], ...} - """ - try: - with open(self.search_checkpoint, 'w', encoding='utf-8') as f: - json.dump(search_results, f, indent=2, ensure_ascii=False) - - print(f"💾 Checkpoint saved: {len(search_results)} conversations") - - except Exception as e: - print(f"⚠️ Failed to save search checkpoint: {e}") - - def load_search_progress(self) -> Dict[str, Any]: - """ - Load fine-grained progress for Search stage. - - Returns: - Saved search results, or empty dict if not exists - """ - if not self.search_checkpoint.exists(): - print(f"\n🆕 No checkpoint found, starting from scratch") - return {} - - try: - print(f"\n🔄 Found checkpoint file: {self.search_checkpoint}") - with open(self.search_checkpoint, 'r', encoding='utf-8') as f: - search_results = json.load(f) - - print(f"✅ Loaded {len(search_results)} conversations from checkpoint") - print(f" Already processed: {sorted(search_results.keys())}") - - return search_results - - except Exception as e: - print(f"⚠️ Failed to load checkpoint: {e}") - print(f" Starting from scratch...") - return {} - - def delete_search_checkpoint(self): - """Delete fine-grained checkpoint for Search stage.""" - if self.search_checkpoint.exists(): - try: - self.search_checkpoint.unlink() - print(f"🗑️ Checkpoint file removed (task completed)") - except Exception as e: - print(f"⚠️ Failed to remove checkpoint: {e}") - - def save_answer_progress( - self, answer_results: Dict[str, Any], completed: int, total: int - ): - """ - Save fine-grained progress for Answer stage (save every SAVE_INTERVAL questions). - - Args: - answer_results: Current accumulated answer results - completed: Number of completed questions - total: Total number of questions - """ - try: - checkpoint_path = self.output_dir / f"responses_checkpoint_{completed}.json" - with open(checkpoint_path, 'w', encoding='utf-8') as f: - json.dump(answer_results, f, indent=2, ensure_ascii=False) - - print(f" 💾 Checkpoint saved: {checkpoint_path.name}") - - except Exception as e: - print(f"⚠️ Failed to save answer checkpoint: {e}") - - def load_answer_progress(self) -> Dict[str, Any]: - """ - Load fine-grained progress for Answer stage (find latest checkpoint file). - - Returns: - Saved answer results, or empty dict if not exists - """ - # Find all responses_checkpoint_*.json files - checkpoint_files = list(self.output_dir.glob("responses_checkpoint_*.json")) - - if not checkpoint_files: - print(f"\n🆕 No answer checkpoint found, starting from scratch") - return {} - - # Find latest checkpoint file (sort by number in filename) - try: - latest_checkpoint = max( - checkpoint_files, key=lambda p: int(p.stem.split('_')[-1]) - ) - - print(f"\n🔄 Found checkpoint file: {latest_checkpoint.name}") - with open(latest_checkpoint, 'r', encoding='utf-8') as f: - answer_results = json.load(f) - - print(f"✅ Loaded {len(answer_results)} answers from checkpoint") - - return answer_results - - except Exception as e: - print(f"⚠️ Failed to load answer checkpoint: {e}") - print(f" Starting from scratch...") - return {} - - def delete_answer_checkpoints(self): - """Delete all fine-grained checkpoints for Answer stage.""" - checkpoint_files = list(self.output_dir.glob("responses_checkpoint_*.json")) - - for checkpoint_file in checkpoint_files: - try: - checkpoint_file.unlink() - print(f" 🗑️ Removed checkpoint: {checkpoint_file.name}") - except Exception as e: - print(f"⚠️ Failed to remove checkpoint {checkpoint_file.name}: {e}") diff --git a/methods/EverCore/evaluation/src/utils/cleaner.py b/methods/EverCore/evaluation/src/utils/cleaner.py deleted file mode 100644 index 3c696d18..00000000 --- a/methods/EverCore/evaluation/src/utils/cleaner.py +++ /dev/null @@ -1,133 +0,0 @@ -from __future__ import annotations - -from typing import Any, Dict - -from bootstrap import setup_project_context -from core.di import get_bean_by_type -from infra_layer.adapters.out.persistence.document.memory.memcell import MemCell -from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemory, -) -from infra_layer.adapters.out.persistence.document.memory.atomic_fact_record import ( - AtomicFactRecord, -) -from infra_layer.adapters.out.persistence.document.memory.foresight_record import ( - ForesightRecord, -) -from infra_layer.adapters.out.persistence.repository.mem_scene_raw_repository import ( - MemSceneRawRepository, -) -from infra_layer.adapters.out.persistence.repository.conversation_status_raw_repository import ( - ConversationStatusRawRepository, -) -from infra_layer.adapters.out.persistence.repository.raw_message_repository import ( - RawMessageRepository, -) -from infra_layer.adapters.out.persistence.repository.user_profile_raw_repository import ( - UserProfileRawRepository, -) -from infra_layer.adapters.out.search.elasticsearch.memory.episodic_memory import ( - EpisodicMemoryDoc, -) -from infra_layer.adapters.out.search.elasticsearch.memory.atomic_fact import ( - AtomicFactDoc, -) -from infra_layer.adapters.out.search.elasticsearch.memory.foresight import ForesightDoc -from infra_layer.adapters.out.search.repository.episodic_memory_milvus_repository import ( - EpisodicMemoryMilvusRepository, -) -from infra_layer.adapters.out.search.repository.atomic_fact_milvus_repository import ( - AtomicFactMilvusRepository, -) -from infra_layer.adapters.out.search.repository.foresight_milvus_repository import ( - ForesightMilvusRepository, -) - - -async def _es_alias_exists(es_client: Any, alias: str) -> bool: - return await es_client.indices.exists_alias(name=alias) - - -async def _delete_es_by_group_id(group_id: str) -> Dict[str, int]: - es_client = EpisodicMemoryDoc.get_connection() - aliases = [ - EpisodicMemoryDoc.get_index_name(), - ForesightDoc.get_index_name(), - AtomicFactDoc.get_index_name(), - ] - deleted: Dict[str, int] = {} - for alias in aliases: - if not await _es_alias_exists(es_client, alias): - continue - resp = await es_client.delete_by_query( - index=alias, - query={"term": {"group_id": group_id}}, - refresh=True, - conflicts="proceed", - ) - deleted[alias] = int((resp or {}).get("deleted", 0) or 0) - return deleted - - -async def _delete_milvus_by_group_id(group_id: str) -> Dict[str, int]: - deleted: Dict[str, int] = {} - deleted["episodic_memory"] = ( - await EpisodicMemoryMilvusRepository().delete_by_filters(group_id=group_id) - ) - deleted["foresight"] = await ForesightMilvusRepository().delete_by_filters( - group_id=group_id - ) - deleted["atomic_fact"] = await AtomicFactMilvusRepository().delete_by_filters( - group_id=group_id - ) - return deleted - - -async def clear_group_data_in_context( - group_id: str, verbose: bool = True -) -> Dict[str, Any]: - mongo_deleted: Dict[str, int] = {} - - status_repo = get_bean_by_type(ConversationStatusRawRepository) - mem_scene_repo = get_bean_by_type(MemSceneRawRepository) - reqlog_repo = get_bean_by_type(RawMessageRepository) - user_profile_repo = get_bean_by_type(UserProfileRawRepository) - - # Global settings are singleton (not per-group), no need to delete here - await status_repo.delete_by_group_id(group_id) - await mem_scene_repo.delete_by_group_id(group_id) - mongo_deleted["raw_messages"] = await reqlog_repo.delete_by_group_id(group_id) - mongo_deleted["user_profiles"] = await user_profile_repo.delete_by_group(group_id) - - res = await MemCell.find({"group_id": group_id}).delete() - mongo_deleted["memcells"] = getattr(res, "deleted_count", 0) or 0 - - res = await EpisodicMemory.find({"group_id": group_id}).delete() - mongo_deleted["episodic_memories"] = getattr(res, "deleted_count", 0) or 0 - - res = await AtomicFactRecord.find({"group_id": group_id}).delete() - mongo_deleted["atomic_fact_records"] = getattr(res, "deleted_count", 0) or 0 - - res = await ForesightRecord.find({"group_id": group_id}).delete() - mongo_deleted["foresight_records"] = getattr(res, "deleted_count", 0) or 0 - - es_deleted = await _delete_es_by_group_id(group_id) - milvus_deleted = await _delete_milvus_by_group_id(group_id) - - if verbose: - print("\n🧹 Group cleanup finished") - print(f" group_id={group_id}") - print(f" MongoDB deleted: {mongo_deleted}") - print(f" Elasticsearch deleted: {es_deleted}") - print(f" Milvus deleted: {milvus_deleted}") - - return { - "mongodb": mongo_deleted, - "elasticsearch": es_deleted, - "milvus": milvus_deleted, - } - - -async def clear_group_data(group_id: str, verbose: bool = True) -> Dict[str, Any]: - await setup_project_context() - return await clear_group_data_in_context(group_id=group_id, verbose=verbose) diff --git a/methods/EverCore/evaluation/src/utils/config.py b/methods/EverCore/evaluation/src/utils/config.py deleted file mode 100644 index 269551dc..00000000 --- a/methods/EverCore/evaluation/src/utils/config.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -Configuration loading utilities. - -Supports YAML configuration file loading with environment variable substitution. -""" -import os -import yaml -import re -from pathlib import Path -from typing import Dict, Any - - -def load_yaml(file_path: str) -> Dict[str, Any]: - """ - Load YAML configuration file. - - Args: - file_path: YAML file path - - Returns: - Parsed configuration dictionary - """ - with open(file_path, 'r', encoding='utf-8') as f: - config = yaml.safe_load(f) - - # Recursively replace environment variables - config = _replace_env_vars(config) - - return config - - -def _replace_env_vars(obj: Any) -> Any: - """ - Recursively replace environment variables in configuration. - - Supported format: ${VAR_NAME} or ${VAR_NAME:default_value} - """ - if isinstance(obj, dict): - return {key: _replace_env_vars(value) for key, value in obj.items()} - elif isinstance(obj, list): - return [_replace_env_vars(item) for item in obj] - elif isinstance(obj, str): - # Match ${VAR_NAME} or ${VAR_NAME:default} - pattern = r'\$\{([^:}]+)(?::([^}]+))?\}' - - def replacer(match): - var_name = match.group(1) - default_value = match.group(2) if match.group(2) else '' - return os.environ.get(var_name, default_value) - - return re.sub(pattern, replacer, obj) - else: - return obj - - -def save_yaml(config: Dict[str, Any], file_path: str): - """ - Save configuration to YAML file. - - Args: - config: Configuration dictionary - file_path: Save path - """ - Path(file_path).parent.mkdir(parents=True, exist_ok=True) - - with open(file_path, 'w', encoding='utf-8') as f: - yaml.dump(config, f, default_flow_style=False, allow_unicode=True, indent=2) - diff --git a/methods/EverCore/evaluation/src/utils/logger.py b/methods/EverCore/evaluation/src/utils/logger.py deleted file mode 100644 index b231f0e8..00000000 --- a/methods/EverCore/evaluation/src/utils/logger.py +++ /dev/null @@ -1,61 +0,0 @@ -""" -Logging utilities. - -Provides unified logging functionality. -""" -import logging -from pathlib import Path -from typing import Optional -from rich.console import Console -from rich.logging import RichHandler - - -def setup_logger( - log_file: Optional[Path] = None, - level: int = logging.INFO, - name: str = "evaluation" -) -> logging.Logger: - """ - Setup logger. - - Args: - log_file: Log file path (optional) - level: Log level - name: Logger name - - Returns: - Configured Logger instance - """ - logger = logging.getLogger(name) - logger.setLevel(level) - - # Clear existing handlers - logger.handlers.clear() - - # Add Rich Console Handler (colored output) - console_handler = RichHandler( - rich_tracebacks=True, - show_time=False, - show_path=False - ) - console_handler.setLevel(level) - logger.addHandler(console_handler) - - # Add file Handler (if log file is specified) - if log_file: - log_file.parent.mkdir(parents=True, exist_ok=True) - file_handler = logging.FileHandler(log_file, encoding='utf-8') - file_handler.setLevel(level) - formatter = logging.Formatter( - '%(asctime)s - %(name)s - %(levelname)s - %(message)s' - ) - file_handler.setFormatter(formatter) - logger.addHandler(file_handler) - - return logger - - -def get_console() -> Console: - """Get Rich Console instance.""" - return Console() - diff --git a/methods/EverCore/evaluation/src/utils/prompts.py b/methods/EverCore/evaluation/src/utils/prompts.py deleted file mode 100644 index 4c85e2f2..00000000 --- a/methods/EverCore/evaluation/src/utils/prompts.py +++ /dev/null @@ -1,126 +0,0 @@ -""" -Prompt utilities - provide prompt loading and formatting. -""" -from pathlib import Path -from typing import Dict, Any -import yaml - - -class PromptManager: - """Prompt manager.""" - - _instance = None - _prompts = None - - def __new__(cls): - if cls._instance is None: - cls._instance = super().__new__(cls) - return cls._instance - - def __init__(self): - if self._prompts is None: - self._load_prompts() - - def _load_prompts(self): - """Load prompts config file.""" - # Find config/prompts.yaml - current_file = Path(__file__) - config_path = current_file.parent.parent.parent / "config" / "prompts.yaml" - - if not config_path.exists(): - raise FileNotFoundError(f"Prompts config not found: {config_path}") - - with open(config_path, "r", encoding="utf-8") as f: - self._prompts = yaml.safe_load(f) - - def get_prompt(self, prompt_key: str, sub_key: str = None) -> str: - """ - Get prompt template. - - Args: - prompt_key: Prompt category key (e.g., "answer_generation", "llm_judge") - sub_key: Sub-key (e.g., "system_prompt", "user_prompt") - - Returns: - Prompt template string - - Example: - >>> pm = PromptManager() - >>> pm.get_prompt("answer_generation", "template") - 'Based on the following memories...' - >>> pm.get_prompt("llm_judge", "system_prompt") - 'You are an expert grader...' - """ - if prompt_key not in self._prompts: - raise KeyError(f"Prompt key '{prompt_key}' not found in prompts.yaml") - - prompt_config = self._prompts[prompt_key] - - if sub_key: - if sub_key not in prompt_config: - raise KeyError( - f"Sub-key '{sub_key}' not found in prompt '{prompt_key}'" - ) - return prompt_config[sub_key].strip() - - # If no sub_key, default to 'template' - if "template" in prompt_config: - return prompt_config["template"].strip() - - raise KeyError( - f"No 'template' field found in prompt '{prompt_key}' " - f"and no sub_key specified" - ) - - def format_prompt( - self, - prompt_key: str, - sub_key: str = None, - **kwargs - ) -> str: - """ - Get and format prompt. - - Args: - prompt_key: Prompt category key - sub_key: Sub-key - **kwargs: Formatting parameters - - Returns: - Formatted prompt - - Example: - >>> pm = PromptManager() - >>> pm.format_prompt( - ... "answer_generation", - ... context="Memory 1...", - ... question="What is X?" - ... ) - 'Based on the following memories...Memory 1...Question: What is X?' - """ - template = self.get_prompt(prompt_key, sub_key) - return template.format(**kwargs) - - -# Global instance -_prompt_manager = None - - -def get_prompt_manager() -> PromptManager: - """Get global PromptManager instance.""" - global _prompt_manager - if _prompt_manager is None: - _prompt_manager = PromptManager() - return _prompt_manager - - -def get_prompt(prompt_key: str, sub_key: str = None) -> str: - """Shortcut: get prompt.""" - return get_prompt_manager().get_prompt(prompt_key, sub_key) - - -def format_prompt(prompt_key: str, sub_key: str = None, **kwargs) -> str: - """Shortcut: format prompt.""" - return get_prompt_manager().format_prompt(prompt_key, sub_key, **kwargs) - - diff --git a/methods/EverCore/evaluation/src/utils/saver.py b/methods/EverCore/evaluation/src/utils/saver.py deleted file mode 100644 index 70c7a020..00000000 --- a/methods/EverCore/evaluation/src/utils/saver.py +++ /dev/null @@ -1,84 +0,0 @@ -""" -Result saver utilities - unified result saving with JSON, pickle support. -""" -import json -import pickle -from pathlib import Path -from typing import Any, Dict - - -class ResultSaver: - """Result saver.""" - - def __init__(self, output_dir: Path): - """ - Initialize saver. - - Args: - output_dir: Output directory - """ - self.output_dir = Path(output_dir) - self.output_dir.mkdir(parents=True, exist_ok=True) - - def save_json(self, data: Any, filename: str): - """ - Save JSON file. - - Args: - data: Data to save - filename: Filename - """ - filepath = self.output_dir / filename - with open(filepath, 'w', encoding='utf-8') as f: - json.dump(data, f, indent=2, ensure_ascii=False, default=str) - - def load_json(self, filename: str) -> Any: - """ - Load JSON file. - - Args: - filename: Filename - - Returns: - Loaded data - """ - filepath = self.output_dir / filename - if not filepath.exists(): - raise FileNotFoundError(f"File not found: {filepath}") - - with open(filepath, 'r', encoding='utf-8') as f: - return json.load(f) - - def save_pickle(self, data: Any, filename: str): - """ - Save pickle file. - - Args: - data: Data to save - filename: Filename - """ - filepath = self.output_dir / filename - with open(filepath, 'wb') as f: - pickle.dump(data, f) - - def load_pickle(self, filename: str) -> Any: - """ - Load pickle file. - - Args: - filename: Filename - - Returns: - Loaded data - """ - filepath = self.output_dir / filename - if not filepath.exists(): - raise FileNotFoundError(f"File not found: {filepath}") - - with open(filepath, 'rb') as f: - return pickle.load(f) - - def file_exists(self, filename: str) -> bool: - """Check if file exists.""" - return (self.output_dir / filename).exists() - diff --git a/methods/EverCore/examples/openclaw-plugin/README.md b/methods/EverCore/examples/openclaw-plugin/README.md deleted file mode 100644 index 1944c8f3..00000000 --- a/methods/EverCore/examples/openclaw-plugin/README.md +++ /dev/null @@ -1,165 +0,0 @@ -# EverOS OpenClaw Plugin - -Persistent memory for **OpenClaw** through normal conversation. - -This plugin keeps the current OpenClaw `context-engine` architecture and connects it to a self-hosted EverOS backend powered by [EverOS](https://github.com/EverMind-AI/EverOS). - -## What it does - -- Recalls relevant memory before each reply through `assemble()` -- Saves new conversation content after each turn through `afterTurn()` -- Works through normal natural-language chat -- Does not require manual `memory_store` or `memory_search` tool calls - -Important: - -- This is a `context-engine` plugin -- This is not a `memory` slot plugin -- To avoid conflicts, installation sets `plugins.slots.memory = "none"` - -## Quick start - -Recommended install: - -```bash -npx --yes --package @evermind-ai/openclaw-plugin everos-install -``` - -The installer: - -- reuses `~/.openclaw/openclaw.json` if it already exists -- adds the plugin path to `plugins.load.paths` -- adds `evermind-ai-everos` to `plugins.allow` -- sets `plugins.slots.contextEngine = "evermind-ai-everos"` -- sets `plugins.slots.memory = "none"` -- creates or updates the plugin entry with sane defaults - -After install: - -```bash -openclaw gateway restart -``` - -Then verify with natural language: - -```text -Remember: I like espresso. -What coffee do I like? -``` - -## Backend - -Default backend URL: - -```text -http://localhost:1995 -``` - -Health check: - -```bash -curl http://localhost:1995/health -``` - -If you have not started the EverOS backend yet: - -```bash -git clone https://github.com/EverMind-AI/EverOS.git -cd EverOS -docker compose up -d -curl -LsSf https://astral.sh/uv/install.sh | sh -uv sync -cp env.template .env -# edit .env -uv run python src/run.py -``` - -## How natural-language memory works - -Runtime flow: - -1. The user sends a normal message. -2. `assemble()` searches the EverOS backend for relevant memory. -3. Matching memory is injected as context. -4. OpenClaw replies normally. -5. `afterTurn()` saves the new turn back to the EverOS backend. - -This means the user experience is: - -- "Remember: I prefer dark mode" -- later: "What UI style do I prefer?" - -without any explicit memory tool usage. - -## OpenClaw config - -Expected config shape: - -```json -{ - "plugins": { - "allow": ["evermind-ai-everos"], - "slots": { - "memory": "none", - "contextEngine": "evermind-ai-everos" - }, - "entries": { - "evermind-ai-everos": { - "enabled": true, - "config": { - "baseUrl": "http://localhost:1995", - "userId": "everos-user", - "groupId": "everos-group", - "topK": 5, - "memoryTypes": ["episodic_memory"], - "retrieveMethod": "hybrid" - } - } - } - } -} -``` - -## Configuration - -| Field | Default | Description | -| --- | --- | --- | -| `baseUrl` | `http://localhost:1995` | EverOS backend URL | -| `userId` | `everos-user` | Memory owner identity | -| `groupId` | `everos-group` | Shared memory namespace | -| `topK` | `5` | Max retrieved entries | -| `memoryTypes` | `["episodic_memory"]` | Memory types to search | -| `retrieveMethod` | `hybrid` | Retrieval mode | - -## Manual install - -```bash -npm install -g @evermind-ai/openclaw-plugin -everos-install -``` - -## Troubleshooting - -| Problem | Fix | -| --- | --- | -| Plugin not loading | Check `plugins.allow`, `plugins.load.paths`, and `plugins.slots.contextEngine` | -| Backend connection failed | Verify `baseUrl` and run `curl /health` | -| Memory not recalled | Check backend data and try a more specific query | -| Memory not saved | Verify the EverOS backend write API is healthy | -| Conflict with another memory plugin | Ensure `plugins.slots.memory = "none"` | - -## Related files - -- `index.js`: plugin entry point (register) -- `src/engine.js`: ContextEngine lifecycle hooks -- `src/convert.js`: OpenClaw message to EverOS format conversion -- `src/api.js`: EverOS backend REST client -- `src/messages.js`: message normalization and turn collection -- `src/prompt.js`: memory search response parsing and prompt building -- `src/subagent-assembler.js`: memory context assembly -- `bin/install.js`: installer and config bootstrap -- `openclaw.plugin.json`: plugin metadata and config schema - -## License - -Apache-2.0 diff --git a/methods/EverCore/examples/openclaw-plugin/README.zh.md b/methods/EverCore/examples/openclaw-plugin/README.zh.md deleted file mode 100644 index 696c8d36..00000000 --- a/methods/EverCore/examples/openclaw-plugin/README.zh.md +++ /dev/null @@ -1,169 +0,0 @@ -# EverOS OpenClaw Plugin - -通过自然语言对话为 **OpenClaw / 龙虾** 提供持久记忆能力。 - -这个插件保留当前 OpenClaw 的 `context-engine` 架构，并连接到自托管的 EverOS backend。其后端能力由 [EverOS](https://github.com/EverMind-AI/EverOS) 提供。 - -## 它能做什么 - -- 在每次回复前通过 `assemble()` 自动回忆相关记忆 -- 在每轮对话后通过 `afterTurn()` 自动保存新内容 -- 用户只需要正常聊天 -- 不需要手动调用 `memory_store` 或 `memory_search` - -重要说明： - -- 这是一个 `context-engine` 插件 -- 它不是 `memory` slot 插件 -- 为避免冲突，安装时会把 `plugins.slots.memory` 设置为 `none` - -## 快速开始 - -推荐安装方式： - -```bash -npx --yes --package @evermind-ai/openclaw-plugin everos-install -``` - -安装器会： - -- 复用已有的 `~/.openclaw/openclaw.json` -- 把插件路径写入 `plugins.load.paths` -- 把 `evermind-ai-everos` 写入 `plugins.allow` -- 设置 `plugins.slots.contextEngine = "evermind-ai-everos"` -- 设置 `plugins.slots.memory = "none"` -- 为插件创建或补齐默认配置 - -安装完成后： - -```bash -openclaw gateway restart -``` - -然后用自然语言验证： - -```text -记住：我喜欢意式浓缩。 -我喜欢什么咖啡？ -``` - -## 后端 - -默认后端地址： - -```text -http://localhost:1995 -``` - -健康检查： - -```bash -curl http://localhost:1995/health -``` - -如果你还没有启动 EverOS backend： - -```bash -git clone https://github.com/EverMind-AI/EverOS.git -cd EverOS -docker compose up -d -curl -LsSf https://astral.sh/uv/install.sh | sh -uv sync -cp env.template .env -# 编辑 .env -uv run python src/run.py -``` - -## 自然语言记忆是如何工作的 - -运行时流程： - -1. 用户发送一条普通消息。 -2. `assemble()` 去 EverOS backend 搜索相关记忆。 -3. 命中的记忆被注入为上下文。 -4. OpenClaw 正常回复。 -5. `afterTurn()` 把这一轮的新内容写回 EverOS backend。 - -所以用户看到的体验是： - -- “记住：我偏好深色模式” -- 之后再问：“我偏好什么 UI 风格？” - -整个过程不需要显式调用记忆工具。 - -## OpenClaw 配置示例 - -期望的配置结构如下： - -```json -{ - "plugins": { - "allow": ["evermind-ai-everos"], - "slots": { - "memory": "none", - "contextEngine": "evermind-ai-everos" - }, - "entries": { - "evermind-ai-everos": { - "enabled": true, - "config": { - "baseUrl": "http://localhost:1995", - "userId": "everos-user", - "groupId": "everos-group", - "topK": 5, - "memoryTypes": ["episodic_memory"], - "retrieveMethod": "hybrid" - } - } - } - } -} -``` - -## 配置项 - - -| 字段 | 默认值 | 说明 | -| ---------------- | ------------------------------------------------------------- | ----------------- | -| `baseUrl` | `http://localhost:1995` | EverOS backend 地址 | -| `userId` | `everos-user` | 记忆归属的用户标识 | -| `groupId` | `everos-group` | 共享记忆命名空间 | -| `topK` | `5` | 最多检索条目数 | -| `memoryTypes` | `["episodic_memory"]` | 要搜索的记忆类型 | -| `retrieveMethod` | `hybrid` | 检索模式 | - - -## 手动安装 - -```bash -npm install -g @evermind-ai/openclaw-plugin -everos-install -``` - -## 故障排查 - - -| 问题 | 解决方式 | -| --------- | --------------------------------------------------------------------- | -| 插件未加载 | 检查 `plugins.allow`、`plugins.load.paths`、`plugins.slots.contextEngine` | -| 后端连接失败 | 检查 `baseUrl`，并执行 `curl /health` | -| 没有回忆出记忆 | 检查后端数据，并尝试更具体的问题 | -| 没有保存记忆 | 检查 EverOS backend 写入接口是否正常 | -| 与其他记忆插件冲突 | 确认 `plugins.slots.memory = "none"` | - - -## 相关文件 - -- `index.js`：插件入口（注册） -- `src/engine.js`：ContextEngine 生命周期实现 -- `src/convert.js`：OpenClaw 消息转 EverOS 格式 -- `src/api.js`：EverOS backend REST API 客户端 -- `src/messages.js`：消息归一化与轮次收集 -- `src/prompt.js`：记忆搜索结果解析与 prompt 构建 -- `src/subagent-assembler.js`：记忆上下文组装 -- `bin/install.js`：安装器与配置引导 -- `openclaw.plugin.json`：插件元数据与配置结构 - -## 许可证 - -Apache-2.0 \ No newline at end of file diff --git a/methods/EverCore/examples/openclaw-plugin/SKILL.md b/methods/EverCore/examples/openclaw-plugin/SKILL.md deleted file mode 100644 index 9f4f4f2f..00000000 --- a/methods/EverCore/examples/openclaw-plugin/SKILL.md +++ /dev/null @@ -1,329 +0,0 @@ ---- -name: evermind-ai-everos -version: 1.5.2 -description: | - Install and configure EverOS for OpenClaw natural-language memory. - - Use when users say: - - "install everos" - - "setup everos" - - "install everos plugin" - - "enable everos memory" - - "remember my preferences in OpenClaw" -author: EverMind -keywords: - - everos - - context engine - - persistent memory - - openclaw - - natural language memory ---- - -# EverOS - -EverOS OpenClaw Plugin gives OpenClaw persistent memory through the **ContextEngine API**. - -Important distinction: - -- This is a `context-engine` plugin, not a `memory` slot plugin. -- Users do not need to call memory tools manually. -- Memory is triggered by normal conversation: - - before reply: relevant memory is recalled and injected - - after reply: new conversation content is saved back automatically - ---- - -## Trigger phrases - -Use this skill when the user wants to: - -- install EverOS -- set up EverOS memory for OpenClaw -- enable long-term memory for OpenClaw -- make natural-language conversations persist across sessions -- configure the EverOS context engine - ---- - -## When to use this skill - -Use this skill when the user wants: - -- persistent memory across OpenClaw sessions -- automatic recall and save during normal conversation -- a self-hosted EverOS backend -- memory without manually calling `memory_store` / `memory_search` - ---- - -## When NOT to use this skill - -Do not use this skill for: - -- cloud-only memory onboarding -- troubleshooting unrelated OpenClaw issues -- temporary context that only matters in the current session - ---- - -## What the user gets - -Automatic lifecycle behavior: - -| Hook | Trigger | What happens | -| --- | --- | --- | -| `bootstrap()` | Session starts | Backend health check and session state init | -| `assemble()` | Before each turn | Searches relevant memory and injects it as context | -| `afterTurn()` | After each turn | Saves new messages from the turn | -| `compact()` | Compaction check | Participates in token-budget decisions | -| `dispose()` | Session ends | Clears in-memory session state | - -User-facing result: - -- the user chats naturally -- EverOS recalls relevant past context automatically -- EverOS stores new useful context automatically - ---- - -## Definition of Done - -This task is not complete until all of the following are true: - -1. the EverOS backend is reachable or the user has been explicitly told it still needs to be started -2. the plugin is installed -3. `openclaw.json` is configured correctly -4. OpenClaw has been restarted -5. the user has been given a natural-language verification step -6. the full final handoff message has been sent - ---- - -## Onboarding - -### Step 0 - Set expectations - -Before changing anything, tell the user: - -> OpenClaw will need a restart during setup. After restart, wait about 1 minute, then send a short natural-language message here to verify memory. - -Do not skip this notice if you are going to restart OpenClaw automatically. - -### Step 1 - Check the EverOS backend - -Default backend: - -```bash -curl -sf http://localhost:1995/health && echo "OK" || echo "NOT_RUNNING" -``` - -If the backend is healthy, continue. - -If it is not healthy, tell the user plainly: - -> The EverOS backend is not reachable yet. I can still finish plugin installation now, but automatic recall/save will not work until the backend is running. - -If the user asks how to start the backend, give the shortest useful path: - -```bash -git clone https://github.com/EverMind-AI/EverOS.git -cd EverOS -docker compose up -d -curl -LsSf https://astral.sh/uv/install.sh | sh -uv sync -cp env.template .env -# edit .env -uv run python src/run.py -``` - -### Step 2 - Install plugin - -Preferred one-shot install: - -```bash -npx --yes --package @evermind-ai/openclaw-plugin everos-install -``` - -Manual alternative: - -```bash -npm install -g @evermind-ai/openclaw-plugin -everos-install -``` - -What the installer does: - -- adds the plugin path to `plugins.load.paths` -- adds `evermind-ai-everos` to `plugins.allow` -- sets `plugins.slots.contextEngine = "evermind-ai-everos"` -- sets `plugins.slots.memory = "none"` to avoid slot conflicts -- creates or updates `plugins.entries["evermind-ai-everos"]` - -### Step 3 - Manual config fallback - -If the installer is unavailable, patch `~/.openclaw/openclaw.json` manually. - -Expected config shape: - -```json -{ - "plugins": { - "allow": ["evermind-ai-everos"], - "slots": { - "memory": "none", - "contextEngine": "evermind-ai-everos" - }, - "entries": { - "evermind-ai-everos": { - "enabled": true, - "config": { - "baseUrl": "http://localhost:1995", - "userId": "everos-user", - "groupId": "everos-group", - "topK": 5, - "memoryTypes": ["episodic_memory"], - "retrieveMethod": "hybrid" - } - } - } - } -} -``` - -Merge-safe patch: - -```bash -jq ' - .plugins = (.plugins // {}) | - .plugins.load = (.plugins.load // {}) | - .plugins.load.paths = ((.plugins.load.paths // []) + ["/path/to/everos-openclaw-plugin"] | unique) | - .plugins.allow = ((.plugins.allow // []) + ["evermind-ai-everos"] | unique) | - .plugins.slots = (.plugins.slots // {}) | - .plugins.slots.memory = "none" | - .plugins.slots.contextEngine = "evermind-ai-everos" | - .plugins.entries = (.plugins.entries // {}) | - .plugins.entries["evermind-ai-everos"].enabled = true | - .plugins.entries["evermind-ai-everos"].config = ( - (.plugins.entries["evermind-ai-everos"].config // {}) + { - "baseUrl": "http://localhost:1995", - "userId": "everos-user", - "groupId": "everos-group", - "topK": 5, - "memoryTypes": ["episodic_memory"], - "retrieveMethod": "hybrid" - } - ) -' ~/.openclaw/openclaw.json > tmp.json && mv tmp.json ~/.openclaw/openclaw.json -``` - -### Step 4 - Restart OpenClaw - -Restart command: - -```bash -openclaw gateway restart -``` - -Immediately before restart, tell the user: - -> EverOS is installed. I am restarting OpenClaw now. After about 1 minute, send a short message so we can verify memory recall. - -### Step 5 - Verify - -Verification has two parts. - -Backend: - -```bash -curl http://localhost:1995/health -``` - -User-facing natural-language test: - -> Say: "Remember: I like espresso." -> -> Then ask: "What coffee do I like?" - -This is the preferred validation because it checks the real user flow instead of just config. - ---- - -## Final handoff - -After successful setup, send this handoff message in the user's language. -Do not remove sections. - -```text -EverOS is ready. - --- WHAT YOU CAN DO NEXT -- - -From now on, you can use normal natural language to make OpenClaw remember information. -You do not need to call memory tools manually. - -Examples: -- "Remember: I like espresso." -- "Remember: this project uses PostgreSQL by default." -- "My coding style prefers small functions and explicit naming." - -Later you can ask: -- "What coffee do I like?" -- "What database does this project use by default?" - --- CURRENT CONNECTION -- - -EverOS backend: -BASE_URL: - -OpenClaw config file: -~/.openclaw/openclaw.json - --- RECOVERY -- - -1. Keep your EverOS backend data and configuration -2. Reinstall this plugin on the new machine -3. Write the same `baseUrl`, `userId`, and `groupId` back into `openclaw.json` -4. Restart OpenClaw to reconnect to the same memory space - --- BACKUP -- - -- Back up `~/.openclaw/openclaw.json` -- Back up the EverOS backend data directory or database -- Back up the EverOS `.env` and deployment configuration -``` - ---- - -## Troubleshooting - -| Symptom | Fix | -| --- | --- | -| Plugin not loading | Check `plugins.allow`, `plugins.load.paths`, and `plugins.slots.contextEngine` | -| Backend unhealthy | Check `baseUrl` and ensure the EverOS backend is running | -| No recall | Verify the backend contains memories and the query is meaningful | -| No save | Verify `afterTurn()` is running and backend write API is reachable | -| Memory plugin conflict | Make sure `plugins.slots.memory = "none"` | - ---- - -## API reference - -Base: `http://localhost:1995` - -| Method | Path | Description | -| --- | --- | --- | -| GET | `/health` | Health check | -| POST | `/api/v1/memories` | Save memory | -| GET | `/api/v1/memories/search` | Search memory | -| DELETE | `/api/v1/memories` | Delete memory | - ---- - -## Communication style - -When talking to users: - -- say this is automatic natural-language memory -- do not describe it as a `memory` slot plugin -- keep the next step concrete: restart, then try one short memory sentence -- prefer real conversational verification over low-level API demos diff --git a/methods/EverCore/examples/openclaw-plugin/bin/install.js b/methods/EverCore/examples/openclaw-plugin/bin/install.js deleted file mode 100755 index 4d880a1a..00000000 --- a/methods/EverCore/examples/openclaw-plugin/bin/install.js +++ /dev/null @@ -1,391 +0,0 @@ -#!/usr/bin/env node - -/** - * EverOS OpenClaw Plugin Installer - * Configures OpenClaw to use the EverOS backend through a ContextEngine. - */ - -import fs from "node:fs"; -import path from "node:path"; -import { exec } from "node:child_process"; -import { fileURLToPath } from "node:url"; -import readline from "node:readline"; - -const __filename = fileURLToPath(import.meta.url); -const __dirname = path.dirname(__filename); - -const HOME_DIR = process.env.HOME || process.env.USERPROFILE; -const CONFIG_PATH = path.join(HOME_DIR, ".openclaw", "openclaw.json"); -const PLUGIN_ID = "evermind-ai-everos"; -const PLUGIN_DIR = path.join(__dirname, ".."); -const STABLE_PLUGIN_DIR = path.join(HOME_DIR, ".openclaw", "plugins", "evermind-ai-everos"); -const DEFAULT_CONFIG = { - baseUrl: "http://localhost:1995", - userId: "everos-user", - groupId: "everos-group", - topK: 5, - memoryTypes: ["episodic_memory"], - retrieveMethod: "hybrid", -}; - -const colors = { - reset: "\x1b[0m", - green: "\x1b[32m", - yellow: "\x1b[33m", - blue: "\x1b[34m", - red: "\x1b[31m", - cyan: "\x1b[36m", -}; - -function log(message, color = "reset") { - console.log(`${colors[color]}${message}${colors.reset}`); -} - -function success(message) { - log(`✓ ${message}`, "green"); -} - -function error(message) { - log(`✗ ${message}`, "red"); -} - -function info(message) { - log(`ℹ ${message}`, "cyan"); -} - -function warn(message) { - log(`⚠ ${message}`, "yellow"); -} - -function hr() { - log("-".repeat(60), "blue"); -} - -const rl = readline.createInterface({ - input: process.stdin, - output: process.stdout, -}); - -async function prompt(question) { - return new Promise((resolve) => { - rl.question(question, (answer) => { - resolve(answer.trim()); - }); - }); -} - -async function promptWithDefault(label, defaultValue) { - const answer = await prompt(`${label} (default: ${defaultValue}): `); - return answer || defaultValue; -} - -function closeAndExit(code) { - rl.close(); - process.exit(code); -} - -async function checkBackendHealth(baseUrl) { - try { - const response = await fetch(`${baseUrl.replace(/\/*$/, "")}/health`, { - signal: AbortSignal.timeout(5000), - }); - if (!response.ok) { - return { ok: false, reason: `HTTP ${response.status}` }; - } - const data = await response.json().catch(() => null); - return { - ok: data?.status === "healthy" || data?.status === "ok" || response.ok, - status: data?.status, - }; - } catch (err) { - return { ok: false, reason: err.message }; - } -} - -function isDevelopmentCheckout(dir) { - return fs.existsSync(path.join(dir, ".git")); -} - -function ensureStablePluginPath() { - if (isDevelopmentCheckout(PLUGIN_DIR)) { - info(`Using local development checkout: ${PLUGIN_DIR}`); - return { pluginDir: PLUGIN_DIR, loadPath: PLUGIN_DIR }; - } - - if (path.resolve(PLUGIN_DIR) !== path.resolve(STABLE_PLUGIN_DIR)) { - fs.mkdirSync(path.dirname(STABLE_PLUGIN_DIR), { recursive: true }); - fs.cpSync(PLUGIN_DIR, STABLE_PLUGIN_DIR, { - recursive: true, - force: true, - }); - - const installerPath = path.join(STABLE_PLUGIN_DIR, "bin", "install.js"); - if (fs.existsSync(installerPath)) { - fs.chmodSync(installerPath, 0o755); - } - - info(`Installed plugin files to ${STABLE_PLUGIN_DIR}`); - } - - return { pluginDir: STABLE_PLUGIN_DIR, loadPath: STABLE_PLUGIN_DIR }; -} - -function isDuplicatePluginPath(entry, currentLoadPath) { - if (typeof entry !== "string") return false; - if (path.resolve(entry) === path.resolve(currentLoadPath)) return false; - - const normalized = entry.replace(/\\/g, "/"); - return normalized.includes("evermemos-openclaw-plugin") || - normalized.includes("@evermind-ai/openclaw-plugin") || - normalized.includes(PLUGIN_ID); -} - -function loadConfig() { - if (!fs.existsSync(CONFIG_PATH)) { - return { exists: false, data: null }; - } - - try { - return { exists: true, data: JSON.parse(fs.readFileSync(CONFIG_PATH, "utf-8")) }; - } catch (err) { - error(`Failed to parse ${CONFIG_PATH}: ${err.message}`); - error("Please fix the JSON syntax before continuing."); - error("Installation aborted to avoid corrupting your configuration."); - return { exists: true, error: err.message }; - } -} - -function saveConfig(config) { - try { - if (fs.existsSync(CONFIG_PATH)) { - fs.copyFileSync(CONFIG_PATH, `${CONFIG_PATH}.bak`); - } - fs.writeFileSync(CONFIG_PATH, `${JSON.stringify(config, null, 2)}\n`, "utf-8"); - return true; - } catch (err) { - error(`Failed to save config: ${err.message}`); - return false; - } -} - -function ensureConfigShape(config) { - config.plugins = config.plugins || {}; - config.plugins.load = config.plugins.load || {}; - config.plugins.load.paths = Array.isArray(config.plugins.load.paths) ? config.plugins.load.paths : []; - config.plugins.allow = Array.isArray(config.plugins.allow) ? config.plugins.allow : []; - config.plugins.slots = config.plugins.slots || {}; - config.plugins.entries = config.plugins.entries || {}; -} - -const VALID_CONFIG_KEYS = Object.keys(DEFAULT_CONFIG); -const VALID_MEMORY_TYPES = ["episodic_memory"]; -const VALID_RETRIEVE_METHODS = ["keyword", "vector", "hybrid", "rrf", "agentic"]; - -function mergePluginConfig(existingConfig, overrides = {}) { - const raw = { - ...DEFAULT_CONFIG, - ...(existingConfig || {}), - ...overrides, - }; - - // Strip unknown keys to avoid additionalProperties validation errors - const cleaned = {}; - for (const key of VALID_CONFIG_KEYS) { - if (key in raw) cleaned[key] = raw[key]; - } - - // Sanitize enum fields to match current schema - if (Array.isArray(cleaned.memoryTypes)) { - cleaned.memoryTypes = cleaned.memoryTypes.filter((t) => VALID_MEMORY_TYPES.includes(t)); - if (cleaned.memoryTypes.length === 0) cleaned.memoryTypes = DEFAULT_CONFIG.memoryTypes; - } - if (cleaned.retrieveMethod && !VALID_RETRIEVE_METHODS.includes(cleaned.retrieveMethod)) { - cleaned.retrieveMethod = DEFAULT_CONFIG.retrieveMethod; - } - - return cleaned; -} - -function printSummary(pluginPath, entry) { - hr(); - log("Installation Summary", "blue"); - log(` Plugin ID: ${PLUGIN_ID}`); - log(` Plugin path: ${pluginPath}`); - log(` Config file: ${CONFIG_PATH}`); - log(` Backend URL: ${entry.config.baseUrl}`); - log(` User ID: ${entry.config.userId}`); - log(` Group ID: ${entry.config.groupId}`); - log(" Mode: context-engine (natural language auto memory)"); - hr(); -} - -function printNextSteps(entry) { - log("Next Steps", "green"); - log(" 1. Make sure your EverOS backend is running."); - log(` curl ${entry.config.baseUrl}/health`, "cyan"); - log(""); - log(" 2. Restart OpenClaw so the context engine can load."); - log(" openclaw gateway restart", "cyan"); - log(""); - log(" 3. Verify with natural language."); - log(' Say: "Remember: I like espresso."', "cyan"); - log(' Then ask: "What coffee do I like?"', "cyan"); - hr(); -} - -function restartGateway() { - return new Promise((resolve) => { - exec("openclaw gateway restart", (err) => { - if (err) { - warn(`Could not restart OpenClaw automatically: ${err.message}`); - info("Please restart manually: openclaw gateway restart"); - } else { - success("OpenClaw gateway restarted."); - info('After about 1 minute, send a natural language test such as "Remember: I like espresso." to verify recall.'); - } - resolve(); - }); - }); -} - -async function install() { - hr(); - log("EverOS OpenClaw Plugin Installer", "blue"); - log("This keeps your current ContextEngine flow and enables automatic memory recall/save."); - hr(); - - const configResult = loadConfig(); - if (configResult.error) { - closeAndExit(1); - } - - let config; - if (!configResult.exists) { - warn(`OpenClaw config not found at ${CONFIG_PATH}`); - const answer = await prompt("Create a new OpenClaw config now? (Y/n): "); - if (answer.toLowerCase() === "n") { - info("Installation cancelled."); - closeAndExit(0); - } - - fs.mkdirSync(path.dirname(CONFIG_PATH), { recursive: true }); - config = {}; - } else { - config = configResult.data; - } - - ensureConfigShape(config); - - const { pluginDir, loadPath } = ensureStablePluginPath(); - config.plugins.load.paths = config.plugins.load.paths.filter((entry) => { - if (path.resolve(entry) === path.resolve(loadPath)) return true; - if (isDuplicatePluginPath(entry, loadPath)) { - warn(`Removing duplicate plugin path: ${entry}`); - return false; - } - return true; - }); - - if (!config.plugins.load.paths.includes(loadPath)) { - config.plugins.load.paths.push(loadPath); - success(`Added plugin path: ${loadPath}`); - } else { - info(`Plugin path already configured: ${loadPath}`); - } - - if (!config.plugins.allow.includes(PLUGIN_ID)) { - config.plugins.allow.push(PLUGIN_ID); - success(`Added to allow list: ${PLUGIN_ID}`); - } else { - info(`Plugin already allowed: ${PLUGIN_ID}`); - } - - if (config.plugins.slots.contextEngine !== PLUGIN_ID) { - const previous = config.plugins.slots.contextEngine || "none"; - config.plugins.slots.contextEngine = PLUGIN_ID; - success(`Set contextEngine slot: ${previous} -> ${PLUGIN_ID}`); - } else { - info(`contextEngine slot already points to ${PLUGIN_ID}`); - } - - if (config.plugins.slots.memory !== "none") { - const previous = config.plugins.slots.memory || "unset"; - config.plugins.slots.memory = "none"; - warn(`Set memory slot to none to avoid conflicts (was: ${previous})`); - } else { - info("memory slot already set to none"); - } - - // Migrate config from legacy plugin IDs if present - const LEGACY_IDS = ["@evermind-ai/openclaw-plugin", "evermind-ai-openclaw-plugin", "everos"]; - for (const oldId of LEGACY_IDS) { - const old = config.plugins.entries[oldId]; - if (old?.config && !config.plugins.entries[PLUGIN_ID]?.config) { - info(`Migrating config from legacy plugin "${oldId}"`); - config.plugins.entries[PLUGIN_ID] = { enabled: true, config: old.config }; - } - if (config.plugins.entries[oldId]) { - delete config.plugins.entries[oldId]; - // Also remove from allow list - config.plugins.allow = config.plugins.allow.filter((id) => id !== oldId); - info(`Removed legacy entry: ${oldId}`); - } - } - - const entry = config.plugins.entries[PLUGIN_ID] || {}; - const hadExistingConfig = !!entry.config; - entry.enabled = true; - - if (hadExistingConfig) { - entry.config = mergePluginConfig(entry.config); - info("Reusing existing plugin config and filling any missing defaults."); - } else { - hr(); - info("Enter the minimum settings needed for the EverOS backend."); - const baseUrl = await promptWithDefault("EverOS backend URL", DEFAULT_CONFIG.baseUrl); - const health = await checkBackendHealth(baseUrl); - - if (health.ok) { - success(`EverOS backend is reachable (${health.status || "ok"}).`); - } else { - warn(`EverOS backend is not reachable yet (${health.reason || "unknown reason"}).`); - warn("You can continue, but memory recall/save will not work until the backend is running."); - info("Typical start command: cd EverOS && uv run python src/run.py"); - } - - const userId = await promptWithDefault("User ID", DEFAULT_CONFIG.userId); - const groupId = await promptWithDefault("Group ID", DEFAULT_CONFIG.groupId); - entry.config = mergePluginConfig(null, { baseUrl, userId, groupId }); - success("Plugin config created."); - } - - config.plugins.entries[PLUGIN_ID] = entry; - - hr(); - info("Saving OpenClaw configuration..."); - if (!saveConfig(config)) { - closeAndExit(1); - } - success("Configuration saved."); - - printSummary(pluginDir, entry); - printNextSteps(entry); - - const shouldRestart = await prompt("Restart OpenClaw gateway now? (Y/n): "); - if (shouldRestart.toLowerCase() !== "n") { - info("Restarting OpenClaw gateway..."); - info('After restart, wait about 1 minute and test with a natural language memory prompt.'); - await restartGateway(); - } else { - info("When ready, run: openclaw gateway restart"); - info('Then test with: "Remember: I like espresso."'); - } - - closeAndExit(0); -} - -install().catch((err) => { - error(`Installation failed: ${err.message}`); - console.error(err); - closeAndExit(1); -}); diff --git a/methods/EverCore/examples/openclaw-plugin/index.js b/methods/EverCore/examples/openclaw-plugin/index.js deleted file mode 100644 index e47063bb..00000000 --- a/methods/EverCore/examples/openclaw-plugin/index.js +++ /dev/null @@ -1,19 +0,0 @@ -/** - * EverOS OpenClaw Plugin — entry point. - * Registers the EverOS backend as a ContextEngine for memory management. - */ - -import { createRequire } from "node:module"; -import { createContextEngine } from "./src/engine.js"; - -const require = createRequire(import.meta.url); -const pluginMeta = require("./openclaw.plugin.json"); - -export default function register(api) { - const log = api.logger || { info: (...a) => console.log(...a), warn: (...a) => console.warn(...a) }; - log.info(`[${pluginMeta.id}] Registering EverOS OpenClaw Plugin`); - - api.registerContextEngine(pluginMeta.id, (pluginConfig) => { - return createContextEngine(pluginMeta, pluginConfig, api.logger); - }); -} diff --git a/methods/EverCore/examples/openclaw-plugin/openclaw.plugin.json b/methods/EverCore/examples/openclaw-plugin/openclaw.plugin.json deleted file mode 100644 index 47f71245..00000000 --- a/methods/EverCore/examples/openclaw-plugin/openclaw.plugin.json +++ /dev/null @@ -1,49 +0,0 @@ -{ - "id": "evermind-ai-everos", - "name": "EverOS OpenClaw Plugin", - "description": "Full-lifecycle memory management for OpenClaw through the EverOS backend, powered by EverOS", - "version": "1.5.2", - "kind": "context-engine", - "contextEngine": true, - "main": "./index.js", - "configSchema": { - "type": "object", - "properties": { - "baseUrl": { - "type": "string", - "description": "EverOS backend base URL", - "default": "http://localhost:1995" - }, - "userId": { - "type": "string", - "description": "Identity used for memory ownership and as message sender", - "default": "everos-user" - }, - "groupId": { - "type": "string", - "description": "Group id for shared memory", - "default": "everos-group" - }, - "topK": { - "type": "integer", - "description": "Maximum number of memory entries to retrieve", - "default": 5 - }, - "memoryTypes": { - "type": "array", - "description": "EverOS memory types to search", - "items": { - "type": "string", - "enum": ["episodic_memory"] - }, - "default": ["episodic_memory"] - }, - "retrieveMethod": { - "type": "string", - "description": "Retrieval strategy used by the EverOS backend", - "enum": ["keyword", "vector", "hybrid", "rrf", "agentic"], - "default": "hybrid" - } - } - } -} diff --git a/methods/EverCore/examples/openclaw-plugin/package.json b/methods/EverCore/examples/openclaw-plugin/package.json deleted file mode 100644 index 684ddea8..00000000 --- a/methods/EverCore/examples/openclaw-plugin/package.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "name": "@evermind-ai/openclaw-plugin", - "version": "1.5.2", - "description": "EverOS OpenClaw Plugin — persistent memory through natural conversation", - "type": "module", - "main": "./index.js", - "bin": { - "everos-install": "./bin/install.js" - }, - "exports": { - ".": "./index.js" - }, - "files": [ - "index.js", - "openclaw.plugin.json", - "README.md", - "README.zh.md", - "SKILL.md", - "bin/", - "src/" - ], - "keywords": [ - "openclaw", - "plugin", - "context-engine", - "memory", - "everos", - "ai", - "agent" - ], - "engines": { - "node": ">=18.0.0" - }, - "openclaw": { - "id": "evermind-ai-everos", - "kind": "context-engine", - "contextEngine": true - } -} diff --git a/methods/EverCore/examples/openclaw-plugin/src/api.js b/methods/EverCore/examples/openclaw-plugin/src/api.js deleted file mode 100644 index eb7ee9fc..00000000 --- a/methods/EverCore/examples/openclaw-plugin/src/api.js +++ /dev/null @@ -1,70 +0,0 @@ -import { createHash } from "node:crypto"; -import { request } from "./http.js"; - -const noop = { info() {}, warn() {} }; -const TAG = "[evermind-ai-everos]"; - -/** Generate a deterministic message ID scoped by idSeed. - * Same seed + role + content always produces the same ID. - * Different seeds (different turns/sessions) produce different IDs, - * so repeated short messages like "ok" won't collide across turns. */ -function messageId(idSeed, role, content) { - const hash = createHash("sha256").update(`${idSeed}:${role}:${content}`).digest("hex").slice(0, 24); - return `em_${hash}`; -} - -export async function searchMemories(cfg, params, log = noop) { - const { memory_types, ...baseParams } = params; - - const SEARCHABLE = new Set(["episodic_memory"]); - const searchTypes = (memory_types ?? []).filter((t) => SEARCHABLE.has(t)); - - if (!searchTypes.length) { - return { status: "ok", result: { memories: [], pending_messages: [] } }; - } - - const p = { ...baseParams, memory_types: searchTypes }; - log.info(`${TAG} GET /api/v1/memories/search`); - const r = await request(cfg, "GET", "/api/v1/memories/search", p); - log.info(`${TAG} GET response`); - - return { - status: "ok", - result: { - memories: r?.result?.memories ?? [], - pending_messages: r?.result?.pending_messages ?? [], - }, - }; -} - -export async function saveMemories(cfg, { userId, groupId, messages = [], flush = false, idSeed = "" }) { - if (!messages.length) return; - const stamp = Date.now(); - - const payloads = messages.map((msg, i) => { - const { role = "user", content = "" } = msg; - // Always use userId as sender so the backend stores a consistent user_id - // for both user and assistant messages. The `role` field distinguishes who spoke. - const sender = userId; - const senderName = role === "assistant" ? "assistant" : userId; - const isLast = i === messages.length - 1; - - return { - message_id: messageId(idSeed, role, content), - create_time: new Date(stamp + i).toISOString(), - role, - sender, - sender_name: senderName, - content, - group_id: groupId, - group_name: groupId, - scene: "assistant", - raw_data_type: "AgentConversation", - ...(flush && isLast && { flush: true }), - }; - }); - - for (const payload of payloads) { - await request(cfg, "POST", "/api/v1/memories", payload); - } -} diff --git a/methods/EverCore/examples/openclaw-plugin/src/config.js b/methods/EverCore/examples/openclaw-plugin/src/config.js deleted file mode 100644 index 9d0d7dff..00000000 --- a/methods/EverCore/examples/openclaw-plugin/src/config.js +++ /dev/null @@ -1,14 +0,0 @@ -const DEFAULT_URL = "http://localhost:1995"; - -export const TIMEOUT_MS = 60000; - -export function resolveConfig(pc = {}) { - return { - serverUrl: (pc.baseUrl || DEFAULT_URL).replace(/\/*$/, ""), - userId: pc.userId || "everos-user", - groupId: pc.groupId || "everos-group", - topK: pc.topK ?? 5, - memoryTypes: pc.memoryTypes ?? ["episodic_memory"], - retrieveMethod: pc.retrieveMethod ?? "hybrid", - }; -} diff --git a/methods/EverCore/examples/openclaw-plugin/src/convert.js b/methods/EverCore/examples/openclaw-plugin/src/convert.js deleted file mode 100644 index eb89e6b9..00000000 --- a/methods/EverCore/examples/openclaw-plugin/src/convert.js +++ /dev/null @@ -1,70 +0,0 @@ -import { CONTEXT_BOUNDARY } from "./prompt.js"; -import { stripChannelMetadata } from "./messages.js"; - -const MAX_CHARS = 20000; - -/** Strip injected memory context from user message text */ -function stripContext(text) { - if (!text) return text; - const cut = text.lastIndexOf(CONTEXT_BOUNDARY); - return cut < 0 ? text : text.slice(cut + CONTEXT_BOUNDARY.length).replace(/^\s+/, ""); -} - -/** Truncate to MAX_CHARS */ -function cap(s) { - return s && s.length > MAX_CHARS ? `${s.slice(0, MAX_CHARS)}…` : (s || ""); -} - -/** - * Convert OpenClaw AgentMessage to EverOS message format. - * Strips injected memory context from user messages to avoid memory pollution. - * @param {Object} msg - OpenClaw AgentMessage - * @returns {{ role: string, content: string }} - */ -export function convertMessage(msg) { - const content = msg.content; - let role = msg.role; - let textContent = ""; - - // Only user and assistant are accepted by EverOS; drop system/unknown roles - if (role !== "user" && role !== "assistant") { - return { role: "user", content: "" }; - } - - // Handle text content (simple string) - if (typeof content === "string") { - const clean = role === "user" ? cap(stripChannelMetadata(stripContext(content))) : cap(content); - return { role, content: clean }; - } - - // Handle content blocks (array of {type, ...}) - if (Array.isArray(content)) { - for (const block of content) { - if (!block || !block.type) continue; - - if (block.type === "text") { - const text = block.text ?? ""; - textContent += (textContent ? "\n" : "") + text; - } - // Tool call blocks - keep a text summary, skip structured data - else if (block.type === "toolCall" || block.type === "tool_use") { - textContent += (textContent ? "\n" : "") + `[Tool: ${block.name || "unknown"}]`; - } - // Tool result blocks - skip - else if (block.type === "tool_result") { - continue; - } - } - - // Assistant messages containing only [Tool: ...] tags have no semantic value for memory - if (role === "assistant" && /^\s*(\[Tool:\s*[^\]]*\]\s*)+$/.test(textContent)) { - return { role, content: "" }; - } - - const finalText = role === "user" ? cap(stripChannelMetadata(stripContext(textContent))) : cap(textContent); - return { role, content: finalText || "" }; - } - - // Fallback for unexpected content types - return { role, content: cap(content == null ? "" : String(content)) }; -} diff --git a/methods/EverCore/examples/openclaw-plugin/src/engine.js b/methods/EverCore/examples/openclaw-plugin/src/engine.js deleted file mode 100644 index 00a99f14..00000000 --- a/methods/EverCore/examples/openclaw-plugin/src/engine.js +++ /dev/null @@ -1,215 +0,0 @@ -/** - * EverOS ContextEngine — all lifecycle hooks. - */ - -import { resolveConfig } from "./config.js"; -import { saveMemories } from "./api.js"; -import { toText } from "./messages.js"; -import { ContextAssembler } from "./subagent-assembler.js"; -import { convertMessage } from "./convert.js"; -import { isSessionResetPrompt } from "./messages.js"; - -/** Internal/ephemeral sessions that should not be persisted to the memory backend. */ -function isEphemeralSession(sessionKey) { - return sessionKey?.startsWith("temp:") || sessionKey?.startsWith("internal:"); -} - -/** Slice messages from the last user message onward (raw, no conversion). */ -function collectLastUserTurn(messages) { - for (let i = messages.length - 1; i >= 0; i--) { - if (messages[i]?.role === "user") return messages.slice(i); - } - return []; -} - -/** - * @param {Object} pluginMeta - Plugin manifest (id, name, version) - * @param {Object} pluginConfig - Runtime plugin configuration - * @param {Object} logger - Logger instance - * @returns {Object} ContextEngine implementation - */ -export function createContextEngine(pluginMeta, pluginConfig, logger) { - const cfg = resolveConfig(pluginConfig); - const log = logger || { info: (...a) => console.log(...a), warn: (...a) => console.warn(...a) }; - const L = `[${pluginMeta.id}]`; - const assembler = new ContextAssembler(cfg, log); - - log.info(`${L} ContextEngine config: baseUrl=${cfg.serverUrl}, userId=${cfg.userId}`); - - // Session state - shared across all sessions for this engine instance - const sessionState = new Map(); - const SESSION_TTL_MS = 2 * 60 * 60 * 1000; // 2 hours - - function pruneStaleSessionState() { - const now = Date.now(); - for (const [key, state] of sessionState) { - if (now - (state.lastActiveTime || 0) > SESSION_TTL_MS) { - sessionState.delete(key); - log.info(`${L} pruned stale session state: ${key}`); - } - } - } - - function initState() { - return { - turnCount: 0, - lastActiveTime: Date.now(), - savedUpTo: 0, - }; - } - - function ensureState(sessionKey) { - if (!sessionState.has(sessionKey)) { - sessionState.set(sessionKey, initState()); - } - return sessionState.get(sessionKey); - } - - return { - info: { - id: pluginMeta.id, - name: pluginMeta.name, - version: pluginMeta.version, - ownsCompaction: false, - }, - - async bootstrap({ sessionId, sessionKey }) { - pruneStaleSessionState(); - - // Verify EverOS backend health - try { - const response = await fetch(`${cfg.serverUrl}/health`, { - signal: AbortSignal.timeout(5000), - }); - if (!response.ok) { - log.warn(`${L} bootstrap: backend unhealthy, status=${response.status}`); - } - } catch (err) { - log.warn(`${L} bootstrap: health check failed: ${err.message}`); - } - - ensureState(sessionKey); - return { bootstrapped: true }; - }, - - async ingest({ sessionId, sessionKey, message }) { - if (message.isHeartbeat) { - return { ingested: false }; - } - ensureState(sessionKey); - return { ingested: true }; - }, - - async ingestBatch({ sessionId, sessionKey, messages, isHeartbeat }) { - if (isHeartbeat) { - return { ingestedCount: 0 }; - } - ensureState(sessionKey); - return { ingestedCount: messages?.length || 0 }; - }, - - async afterTurn({ sessionId, sessionKey, messages, prePromptMessageCount }) { - pruneStaleSessionState(); - - if (isEphemeralSession(sessionKey)) return; - - const state = ensureState(sessionKey); - - state.turnCount++; - state.lastActiveTime = Date.now(); - if (state.savedUpTo > messages.length) { - state.savedUpTo = 0; - } - - const sliceStart = prePromptMessageCount !== undefined - ? Math.max(prePromptMessageCount, state.savedUpTo) - : state.savedUpTo || 0; - - const newMessages = sliceStart > 0 - ? messages.slice(sliceStart) - : collectLastUserTurn(messages); - - if (newMessages.length === 0) return; - - try { - const converted = newMessages - .filter((m) => m.role !== "toolResult" && m.role !== "tool") - .map(convertMessage) - .filter((m) => m.content); - if (converted.length === 0) return; - - await saveMemories(cfg, { - userId: cfg.userId, - groupId: cfg.groupId, - messages: converted, - idSeed: `${sessionKey}:${state.turnCount}`, - }); - state.savedUpTo = messages.length; - log.info(`${L} afterTurn: saved ${converted.length} messages, turn=${state.turnCount}`); - } catch (err) { - log.warn(`${L} afterTurn: save failed: ${err.message}`); - } - }, - - async assemble({ sessionId, sessionKey, messages, tokenBudget, prompt }) { - pruneStaleSessionState(); - const state = ensureState(sessionKey); - state.lastActiveTime = Date.now(); - - const query = toText(prompt) || toText([...messages].reverse().find((m) => m.role === "user")?.content); - - if (!query || query.length < 3) { - return { messages, estimatedTokens: 0 }; - } - - if (isSessionResetPrompt(query)) { - return { messages, estimatedTokens: 0 }; - } - - try { - const { context, memoryCount } = await assembler.assemble(query, messages, state.turnCount); - if (memoryCount === 0) { - return { messages, estimatedTokens: 0 }; - } - - log.info(`${L} assemble: retrieved ${memoryCount} memories`); - return { - messages, - estimatedTokens: Math.floor(context.length / 4), - systemPromptAddition: context, - }; - } catch (err) { - log.warn(`${L} assemble: failed: ${err.message}`); - return { messages, estimatedTokens: 0 }; - } - }, - - async compact({ sessionId, sessionKey, tokenBudget, currentTokenCount }) { - const state = sessionState.get(sessionKey); - if (!state) { - return { ok: true, compacted: false, reason: "no session state" }; - } - - state.savedUpTo = 0; - - const threshold = tokenBudget ? tokenBudget * 0.8 : 8000; - const overBudget = currentTokenCount && currentTokenCount > threshold; - - return { - ok: true, - compacted: false, - reason: overBudget - ? `token count (${currentTokenCount}) exceeds 80% of budget (${tokenBudget}), host should compact` - : "within threshold", - }; - }, - - async dispose({ sessionKey } = {}) { - if (sessionKey) { - sessionState.delete(sessionKey); - } else { - sessionState.clear(); - } - }, - }; -} diff --git a/methods/EverCore/examples/openclaw-plugin/src/http.js b/methods/EverCore/examples/openclaw-plugin/src/http.js deleted file mode 100644 index 4e53e392..00000000 --- a/methods/EverCore/examples/openclaw-plugin/src/http.js +++ /dev/null @@ -1,50 +0,0 @@ -import { setTimeout as sleep } from "node:timers/promises"; -import { TIMEOUT_MS } from "./config.js"; - -function buildUrl(base, path, params) { - const qs = new URLSearchParams(); - for (const [k, v] of Object.entries(params ?? {})) { - if (v == null || v === "") continue; - if (Array.isArray(v)) { - v.filter((x) => x != null && x !== "").forEach((x) => qs.append(k, String(x))); - } else { - qs.set(k, String(v)); - } - } - const q = qs.toString(); - return q ? `${base}${path}?${q}` : `${base}${path}`; -} - -async function send(url, method, headers, body, timeoutMs) { - const ac = new AbortController(); - const t = setTimeout(() => ac.abort(), timeoutMs); - try { - const res = await fetch(url, { method, headers, body, signal: ac.signal }); - if (!res.ok) { - const msg = await res.text().catch(() => ""); - throw new Error(`HTTP ${res.status}${msg ? ` – ${msg.slice(0, 200)}` : ""}`); - } - return res.json(); - } catch (err) { - if (ac.signal.aborted) throw new Error(`timed out after ${timeoutMs}ms`); - throw err; - } finally { - clearTimeout(t); - } -} - -export async function request(cfg, method, path, params) { - const headers = { "Content-Type": "application/json" }; - // GET and DELETE use query string, POST uses body - const url = (method === "GET" || method === "DELETE") ? buildUrl(cfg.serverUrl, path, params) : `${cfg.serverUrl}${path}`; - const body = method === "POST" && params ? JSON.stringify(params) : undefined; - const ms = TIMEOUT_MS; - - try { - return await send(url, method, headers, body, ms); - } catch (err) { - console.warn(`[evermind-ai-everos] request failed, retrying: ${err.message}`); - await sleep(150); - return send(url, method, headers, body, ms); - } -} diff --git a/methods/EverCore/examples/openclaw-plugin/src/messages.js b/methods/EverCore/examples/openclaw-plugin/src/messages.js deleted file mode 100644 index a4bc7309..00000000 --- a/methods/EverCore/examples/openclaw-plugin/src/messages.js +++ /dev/null @@ -1,69 +0,0 @@ -/** - * Strip channel-injected metadata blocks from message text. - * Feishu (and potentially other channels) prepend blocks like: - * Conversation info (untrusted metadata):\n```json\n{...}\n``` - * Sender (untrusted metadata):\n```json\n{...}\n``` - * [message_id: xxx]\nsender_id: - * These pollute search queries and should be removed. - */ -export function stripChannelMetadata(text) { - if (!text) return text; - // Remove "Conversation info / Sender (untrusted metadata):" blocks with their ```json...``` content - let cleaned = text.replace(/(?:Conversation info|Sender)\s*\(untrusted metadata\)\s*:\s*```json[\s\S]*?```/gi, ""); - // Remove [message_id: xxx]\nsender_id: prefix lines - cleaned = cleaned.replace(/\[message_id:\s*[^\]]*\]\s*\n?\s*\S+:\s*/g, ""); - // Trim before matching timestamp so leading \n from stripped blocks don't block ^ - cleaned = cleaned.trim(); - // Remove channel-injected timestamp prefix: [Mon 2026-03-20 22:16 GMT+8] - cleaned = cleaned.replace(/^\[(?:Mon|Tue|Wed|Thu|Fri|Sat|Sun)\s+\d{4}-\d{2}-\d{2}\s+\d{2}:\d{2}\s+GMT[+-]\d+\]\s*/i, ""); - return cleaned.trim(); -} - -export function toText(content) { - if (!content) return ""; - if (typeof content === "string") return stripChannelMetadata(content); - if (!Array.isArray(content)) return ""; - const raw = content.reduce((out, block) => { - if (block?.type !== "text" || !block.text) return out; - return out ? `${out} ${block.text}` : block.text; - }, ""); - return stripChannelMetadata(raw); -} - -/* ------------------------------------------------------------------ */ -/* Session reset prompt detection */ -/* ------------------------------------------------------------------ */ - -export const BARE_SESSION_RESET_PROMPT = - "A new session was started via /new or /reset. Execute your Session Startup sequence now - read the required files before responding to the user. Then greet the user in your configured persona, if one is provided. Be yourself - use your defined voice, mannerisms, and mood. Keep it to 1-3 sentences and ask what they want to do. If the runtime model differs from default_model in the system prompt, mention the default model. Do not mention internal steps, files, tools, or reasoning."; - -/** Levenshtein edit distance (space-optimised O(m*n) time, O(n) space) */ -function levenshtein(a, b) { - const m = a.length; - const n = b.length; - const prev = Array.from({ length: n + 1 }, (_, i) => i); - const curr = new Array(n + 1); - for (let i = 1; i <= m; i++) { - curr[0] = i; - for (let j = 1; j <= n; j++) { - curr[j] = a[i - 1] === b[j - 1] - ? prev[j - 1] - : 1 + Math.min(prev[j - 1], prev[j], curr[j - 1]); - } - prev.splice(0, n + 1, ...curr); - } - return prev[n]; -} - -/** - * Returns true when the query is within 20% length of BARE_SESSION_RESET_PROMPT - * AND the edit-distance ratio is below 0.20 (i.e. >=80% similar). - */ -export function isSessionResetPrompt(query) { - if (!query) return false; - const promptLen = BARE_SESSION_RESET_PROMPT.length; - const queryLen = query.length; - if (Math.abs(queryLen - promptLen) / promptLen > 0.20) return false; - const dist = levenshtein(query, BARE_SESSION_RESET_PROMPT); - return dist / Math.max(queryLen, promptLen) < 0.20; -} diff --git a/methods/EverCore/examples/openclaw-plugin/src/prompt.js b/methods/EverCore/examples/openclaw-plugin/src/prompt.js deleted file mode 100644 index 4826b1ad..00000000 --- a/methods/EverCore/examples/openclaw-plugin/src/prompt.js +++ /dev/null @@ -1,96 +0,0 @@ -export const CONTEXT_BOUNDARY = "user\u200boriginal\u200bquery\u200b:\u200b\u200b\u200b\u200b"; - -function timestampToLabel(ts) { - if (ts == null || ts === "") return ""; - - if (typeof ts === "number") { - const d = new Date(ts); - if (Number.isNaN(d.getTime())) return ""; - const p = (n) => `${n}`.padStart(2, "0"); - return `${d.getFullYear()}-${p(d.getMonth() + 1)}-${p(d.getDate())} ${p(d.getHours())}:${p(d.getMinutes())}`; - } - - if (typeof ts === "string") { - const s = ts.trim(); - if (!s) return ""; - // Unix epoch as string - if (/^\d{10,13}$/.test(s)) return timestampToLabel(Number(s)); - // ISO 8601: extract date and HH:MM - const dateEnd = s.indexOf("T"); - if (dateEnd === 10 && s.length > 15) return `${s.slice(0, 10)} ${s.slice(11, 16)}`; - return s; - } - - return ""; -} - -export function parseSearchResponse(raw) { - if (raw?.status !== "ok" || !raw?.result) return null; - - const allMemories = raw.result.memories ?? []; - - const episodic = allMemories - .filter((m) => m.memory_type === "episodic_memory" && (m.score ?? 0) >= 0.1) - .map((m) => { - const body = m.summary || m.episode || m.content || ""; - const subject = m.subject || ""; - return { - text: subject ? `${subject}: ${body}` : body, - timestamp: m.timestamp ?? null, - }; - }); - - const pending = (raw.result.pending_messages ?? []) - .filter((m) => m.content) - .map((m) => { - const who = m.sender_name || m.sender || m.user_id || ""; - const body = m.content || ""; - return { - text: who ? `${who}: ${body}` : body, - timestamp: m.message_create_time ?? m.created_at ?? null, - }; - }); - - return { episodic, pending }; -} - -function oneLiner(text) { - return text == null ? "" : String(text).replace(/[\r\n]+/g, " ").trim(); -} - -function factLine(fact) { - const t = oneLiner(fact.text); - if (!t) return ""; - const when = timestampToLabel(fact.timestamp); - return when ? ` - [${when}] ${t}` : ` - ${t}`; -} - -export function buildMemoryPrompt(parsed, opts = {}) { - if (!parsed) return ""; - - const episodicLines = parsed.episodic.map(factLine).filter(Boolean); - const pendingLines = (parsed.pending ?? []).map(factLine).filter(Boolean); - - if (!episodicLines.length && !pendingLines.length) return ""; - - const xmlBlock = [ - "", - ...(episodicLines.length ? [" ", ...episodicLines, " "] : []), - ...(pendingLines.length ? [" ", " ", ...pendingLines, " "] : []), - "", - ]; - - const memSection = opts.wrapInCodeBlock ? ["```text", ...xmlBlock, "```"] : xmlBlock; - const nowLabel = timestampToLabel(Date.now()); - - return [ - "Note: Reference memory below. Build on past successes; avoid repeating failed approaches.", - ...(nowLabel ? [`- Time: ${nowLabel}`] : []), - "", - ...memSection, - "", - "**Note**: for memory, please not read from or write to local `MEMORY.md` or `memory/*` files as they are provided above.", - "", - CONTEXT_BOUNDARY, - ].join("\n"); -} diff --git a/methods/EverCore/examples/openclaw-plugin/src/subagent-assembler.js b/methods/EverCore/examples/openclaw-plugin/src/subagent-assembler.js deleted file mode 100644 index 2133ef3e..00000000 --- a/methods/EverCore/examples/openclaw-plugin/src/subagent-assembler.js +++ /dev/null @@ -1,66 +0,0 @@ -/** - * Context Assembler Module - * Handles query-aware context assembly from EverOS memories - */ - -import { searchMemories } from "./api.js"; -import { buildMemoryPrompt, parseSearchResponse } from "./prompt.js"; - -/** - * @typedef {import("./types.js").EverOSConfig} EverOSConfig - * @typedef {import("./types.js").Logger} Logger - * @typedef {import("./types.js").ParsedMemoryResponse} ParsedMemoryResponse - */ - -/** - * Handles query-aware context assembly - * Retrieves relevant memories based on current query and conversation state - */ -export class ContextAssembler { - /** - * @param {EverOSConfig} cfg - * @param {Logger} logger - */ - constructor(cfg, logger) { - this.cfg = cfg; - this.log = logger; - } - - /** - * Assemble context from memories based on current query and conversation state - * @param {string} query - Current user query - * @param {Array} messages - Full conversation history - * @param {number} turnCount - Current turn number - * @returns {Promise<{context: string, memoryCount: number}>} - */ - async assemble(query, messages, turnCount) { - // Early turns: retrieve more context for grounding - const earlyTurnMultiplier = turnCount <= 2 ? 2 : 1; - const topK = Math.min(this.cfg.topK * earlyTurnMultiplier, 20); - - /** @type {Object} */ - const params = { - query, - user_id: this.cfg.userId, - group_id: this.cfg.groupId || undefined, - memory_types: this.cfg.memoryTypes, - retrieve_method: this.cfg.retrieveMethod, - top_k: topK, - }; - - /** @type {any} */ - const result = await searchMemories(this.cfg, params, this.log); - /** @type {ParsedMemoryResponse} */ - const parsed = parseSearchResponse(result) || { episodic: [], pending: [] }; - - // Count total memories (including pending messages) - const memoryCount = - (parsed.episodic?.length || 0) + - (parsed.pending?.length || 0); - - const context = buildMemoryPrompt(parsed, { wrapInCodeBlock: true }); - - return { context, memoryCount }; - } - -} diff --git a/methods/EverCore/examples/openclaw-plugin/src/types.js b/methods/EverCore/examples/openclaw-plugin/src/types.js deleted file mode 100644 index 3db2bc8f..00000000 --- a/methods/EverCore/examples/openclaw-plugin/src/types.js +++ /dev/null @@ -1,74 +0,0 @@ -/** - * JSDoc type definitions for EverOS ContextEngine - * This file contains type definitions used across all ContextEngine modules - */ - -/** - * @typedef {Object} EverOSConfig - * @property {string} serverUrl - EverOS server URL (e.g., "http://localhost:1995") - * @property {string} userId - User ID for memory storage - * @property {string} groupId - Group ID for shared memory - * @property {number} topK - Number of memories to retrieve - * @property {string[]} memoryTypes - Memory types to retrieve (episodic_memory) - * @property {string} retrieveMethod - Retrieval strategy (keyword, vector, hybrid, agentic) - */ - -/** - * @typedef {Object} Logger - * @property {(...args: any[]) => void} log - Info level logging - * @property {(...args: any[]) => void} warn - Warning level logging - * @property {(...args: any[]) => void} error - Error level logging - */ - -/** - * @typedef {Object} BootstrapContext - * @property {Object} api - OpenClaw API object - * @property {EverOSConfig} pluginConfig - Plugin configuration - */ - -/** - * @typedef {Object} AssembleContext - * @property {string} [prompt] - Current user prompt (passed by OpenClaw runtime since 2026.3.23) - * @property {Array} messages - Full conversation history - * @property {string} [sessionId] - Optional session identifier - * @property {string} [sessionKey] - Session key for state management - * @property {number} [tokenBudget] - Token budget for assembled context - * @property {string} [model] - Current model identifier - */ - -/** - * @typedef {Object} AssembleResult - * @property {Array} messages - Ordered messages to use as model context - * @property {number} estimatedTokens - Estimated total tokens in assembled context - * @property {string} [systemPromptAddition] - Context-engine-provided instructions prepended to system prompt - */ - -/** - * @typedef {Object} AfterTurnContext - * @property {Array} messages - Messages from the completed turn - * @property {boolean} success - Whether the turn completed successfully - * @property {string} [errorMessage] - Error message if turn failed - */ - -/** - * @typedef {Object} CompactContext - * @property {Array} messages - Current session messages - * @property {number} tokenCount - Estimated token count of context - * @property {string} [sessionId] - Optional session identifier - */ - -/** - * @typedef {Object} CompactResult - * @property {boolean} shouldCompact - Whether compaction is recommended - * @property {string} reason - Explanation of the decision - * @property {Object} [metadata] - Additional metadata - * @property {string} [metadata.memoryStrategy] - Suggested memory consolidation strategy - * @property {number} [metadata.turnCount] - Turn count at evaluation time - */ - -/** - * @typedef {Object} ParsedMemoryResponse - * @property {Array<{text: string, timestamp: number|string|null}>} episodic - Episodic memories - * @property {Array<{text: string, timestamp: number|string|null}>} pending - Recent unconsolidated messages - */ - diff --git a/methods/EverCore/pyproject.toml b/methods/EverCore/pyproject.toml deleted file mode 100644 index 02a2912b..00000000 --- a/methods/EverCore/pyproject.toml +++ /dev/null @@ -1,176 +0,0 @@ -[project] -name = "memsys" -version = "0.1.0" -description = "Memory System with Dynamic and Static Memory Management" -license = {text = "Apache-2.0"} -requires-python = ">=3.12,<3.13" -dependencies = [ - # Core Dependencies - "langgraph>=0.2.6", - "langgraph-api", - "langgraph-cli", - "langgraph-sdk>=0.1.57", - "langgraph-checkpoint-postgres>=2.0.21", - "langchain>=0.3.26", - "langchain-core>=0.1.0", - "langchain-openai>=0.3.28", - "langchain-anthropic>=0.3.17", - "langchain-google-genai>=2.1.8", - "google-genai>=1.21.0", - # Data Processing - "numpy>=1.24.0", - "pandas>=2.0.0", - "scikit-learn>=1.3.0", - # OpenAI API - "openai>=1.0.0", - # Data Validation & Configuration - "pydantic[email]>=2.7.1", - "pydantic-settings>=2.0.0", - "python-dotenv>=1.0.1", - "pyyaml>=6.0.0", - # Web Framework & API - "fastapi>=0.104.0", - "uvicorn[standard]>=0.24.0", - "greenlet>=3.2.0", - # HTTP Client & File Processing - "aiohttp>=3.8.0", - "python-multipart>=0.0.6", - "aiofiles>=24.1.0", - # Message Queue - "aiokafka>=0.11.0", - # Authentication & Security - "python-jose[cryptography]>=3.3.0", - "passlib[bcrypt]>=1.7.4", - # Database & Cache - "redis>=5.0.0", - "sqlmodel>=0.0.19", - "asyncpg>=0.29.0", - "psycopg[binary,pool]>=3.1.0", - # MongoDB ODM - MongoDB Object Document Mapper - "beanie>=1.26.0,<3", # Async MongoDB ODM based on Pydantic (pin major to avoid breaking changes) - "pymongo>=4.15.4,<5", # Official MongoDB Python driver (pin major: 5.x has breaking changes) - # Elasticsearch - "elasticsearch>=8.17.0,<9", # ES Python client (pin major: 9.x may change perform_request API) - "elasticsearch-dsl[async]>=8.17.1,<9", # ES DSL (pin major, must match elasticsearch major) - "elastic-transport>=8.17.0,<9", # ES transport layer (pin major, transport API stability) - # Milvus - "pymilvus>=2.5.0,<2.6", # Pin minor: 2.6.x may change Collection/search API - # Tokenization - "jieba==0.42.1", - # Graph Processing - "igraph>=0.10.0", - # Utilities - "tqdm>=4.65.0", - "arq>=0.26.0", - "python-snappy>=0.7.3", - "tzlocal>=5.3.0", - "rank-bm25>=0.2.2", - "duckdb>=1.4.0", - "import-parent-dir>=0.1.1", - "bs4>=0.0.2", - "html2text>=2025.4.15", - "certifi>=2025.10.5", - "aiolimiter>=1.2.1", - "nltk>=3.9.2", - "tiktoken>=0.12.0", - "prometheus-client>=0.20.0", -] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.hatch.build.targets.wheel] -packages = ["src"] - -[tool.black] -line-length = 88 -target-version = ['py312'] -exclude = ''' -( - /( - \.git - | \.hg - | \.mypy_cache - | \.tox - | \.venv - | _build - | buck-out - | build - | dist - )/ - | migrations - | __pycache__ -) -''' -include = '\.pyi?$' -skip-string-normalization = true -skip-magic-trailing-comma = true - -[tool.isort] -profile = "black" -line_length = 88 - -[tool.pytest.ini_options] -testpaths = ["tests", "unit_test"] -python_files = ["test_*.py", "*_test.py"] -python_classes = ["Test*"] -python_functions = ["test_*"] -addopts = "-v --tb=short" - -[dependency-groups] -# Development dependencies - for local development -dev = [ - "black>=25.1.0", - "ipdb>=0.13.13", - "isort>=6.0.1", - "pytest>=8.4.2", - "pytest-asyncio>=1.1.0", - "pytest-cov>=6.0.0", - "nest-asyncio", - "typer", - "pyinstrument", - "py-spy", - "pre-commit>=4.3.0", -] - -cpu = [ -] - -gpu = [ -] - -# Full development environment - includes dev tools -dev-full = [ - {include-group = "dev"}, - {include-group = "cpu"}, -] - -# Production environment - GPU only -prod = [ - {include-group = "gpu"}, -] - -# Evaluation framework dependencies -evaluation = [ - # Terminal formatting and progress display - "rich>=13.0.0", - # HTTP request library (for online API calls) - "requests>=2.31.0", -] - -# Evaluation + all online API clients (full testing environment) -evaluation-full = [ - {include-group = "evaluation"}, - # Online Memory Systems API 客户端 - "mem0ai>=1.0.0", # Mem0 - "zep-cloud>=2.0.0", # Zep -] - -[project.scripts] -bootstrap = "src.bootstrap:main" -web = "src.run:main" -manage = "src.manage:cli" - -[project.entry-points."memsys.addons"] -core = "src.addon" diff --git a/methods/EverCore/pyrightconfig.json b/methods/EverCore/pyrightconfig.json deleted file mode 100644 index d7369b41..00000000 --- a/methods/EverCore/pyrightconfig.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "extraPaths": ["src", "demo", "evaluation", "data_format"], - "typeCheckingMode": "off" -} diff --git a/methods/EverCore/pytest.ini b/methods/EverCore/pytest.ini deleted file mode 100644 index c57fbc0e..00000000 --- a/methods/EverCore/pytest.ini +++ /dev/null @@ -1,75 +0,0 @@ -[tool:pytest] -# pytest 配置文件 - -# 测试发现 -testpaths = tests -python_files = test_*.py *_test.py -python_classes = Test* -python_functions = test_* - -# 输出选项 -addopts = - -v - --tb=short - --strict-markers - --strict-config - --durations=10 - --maxfail=5 - --color=yes - -# 标记定义 -markers = - slow: 标记为慢速测试 - integration: 标记为集成测试 - unit: 标记为单元测试 - stability: 标记为稳定性测试 - database: 标记为数据库相关测试 - concurrency: 标记为并发相关测试 - memory: 标记为内存相关测试 - performance: 标记为性能测试 - smoke: 标记为冒烟测试 - -# 异步测试配置 -asyncio_mode = auto - -# 过滤警告 -filterwarnings = - ignore::DeprecationWarning - ignore::PendingDeprecationWarning - ignore::UserWarning:psycopg.* - -# 最小版本要求 -minversion = 6.0 - -# 测试超时设置 -timeout = 300 - -# 日志配置 -log_cli = true -log_cli_level = INFO -log_cli_format = %(asctime)s [%(levelname)8s] %(name)s: %(message)s -log_cli_date_format = %Y-%m-%d %H:%M:%S - -# 覆盖率配置 -[coverage:run] -source = src -omit = - */tests/* - */test_* - */__pycache__/* - */venv/* - */.venv/* - */migrations/* - -[coverage:report] -exclude_lines = - pragma: no cover - def __repr__ - if self.debug: - if settings.DEBUG - raise AssertionError - raise NotImplementedError - if 0: - if __name__ == .__main__.: - class .*\bProtocol\): - @(abc\.)?abstractmethod diff --git a/methods/EverCore/src/__init__.py b/methods/EverCore/src/__init__.py deleted file mode 100755 index 67676d86..00000000 --- a/methods/EverCore/src/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -""" -Memory System - A LangGraph-based memory system with retrieval and storage layers. -""" - -__version__ = "1.0.0" -__author__ = "Memory System Team" diff --git a/methods/EverCore/src/addon.py b/methods/EverCore/src/addon.py deleted file mode 100644 index 5258d4cf..00000000 --- a/methods/EverCore/src/addon.py +++ /dev/null @@ -1,42 +0,0 @@ -import os - -from core.di.scan_path_registry import ScannerPathsRegistry -from core.asynctasks.task_scan_registry import TaskScanDirectoriesRegistry -from common_utils.project_path import get_base_scan_path -from core.addons.addon_registry import AddonRegistry -from core.addons.addons_registry import ADDONS_REGISTRY - -# Configure DI scan paths -paths_registry = ScannerPathsRegistry() - -paths_registry.add_scan_path( - os.path.join(get_base_scan_path(), "core/interface/controller/debug") -) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "core/lifespan")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "core/lock")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "core/cache")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "core/tenants")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "core/events")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "core/context")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "core/request")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "core/component")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "infra_layer")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "agentic_layer")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "biz_layer")) - -# Configure asynchronous task scan paths -task_directories_registry = TaskScanDirectoriesRegistry() - -task_directories_registry.add_scan_path( - os.path.join(get_base_scan_path(), "core/asynctasks/examples") -) -task_directories_registry.add_scan_path( - os.path.join(get_base_scan_path(), "infra_layer/adapters/input/jobs") -) - -# Create and register core addon -core_addon = AddonRegistry(name="core") -core_addon.register_di(paths_registry) -core_addon.register_asynctasks(task_directories_registry) - -ADDONS_REGISTRY.register(core_addon) diff --git a/methods/EverCore/src/agentic_layer/agentic_utils.py b/methods/EverCore/src/agentic_layer/agentic_utils.py deleted file mode 100644 index 68870461..00000000 --- a/methods/EverCore/src/agentic_layer/agentic_utils.py +++ /dev/null @@ -1,421 +0,0 @@ -""" -Agentic Retrieval utility functions - -Provides tools required for LLM-guided multi-round retrieval: -1. Sufficiency Check: Determine if retrieval results are sufficient -2. Multi-Query Generation: Generate multiple complementary improved queries -3. Document Formatting: Format documents for LLM usage -""" - -import os -import json -import asyncio -import logging -from typing import List, Tuple, Optional, Dict, Any -from dataclasses import dataclass - -logger = logging.getLogger(__name__) - - -# ==================== Prompt Templates ==================== - -SUFFICIENCY_CHECK_PROMPT = """You are a memory retrieval evaluation expert. Please assess whether the currently retrieved memories are sufficient to answer the user's query. - -User query: -{query} - -Retrieved memories: -{retrieved_docs} - -Please determine whether these memories are sufficient to answer the user's query. - -Output format (JSON): -{{ - "is_sufficient": true/false, - "reasoning": "Your reasoning for the judgment", - "missing_information": ["Missing information 1", "Missing information 2"] -}} - -Requirements: -1. If the memories contain key information needed to answer the query, judge as sufficient (true) -2. If key information is missing, judge as insufficient (false), and list the missing information -3. reasoning should be concise and clear -4. missing_information should only be filled when insufficient, otherwise empty array -""" - - -MULTI_QUERY_GENERATION_PROMPT = """You are a query optimization expert. The user's original query failed to retrieve sufficient information; please generate multiple complementary improved queries. - -Original query: -{original_query} - -Currently retrieved memories: -{retrieved_docs} - -Missing information: -{missing_info} - -Please generate 2-3 complementary queries to help find the missing information. These queries should: -1. Focus on different missing information points -2. Use different expressions -3. Avoid being identical to the original query -4. Remain concise and clear - -Output format (JSON): -{{ - "queries": [ - "Improved query 1", - "Improved query 2", - "Improved query 3" - ], - "reasoning": "Explanation of query generation strategy" -}} - -Requirements: -1. queries array contains 2-3 queries -2. Each query length between 5-200 characters -3. reasoning explains the generation strategy -""" - - -# ==================== Configuration Class ==================== - - -@dataclass -class AgenticConfig: - """Agentic retrieval configuration""" - - # Round 1 configuration - round1_emb_top_n: int = 50 # Number of embedding candidates - round1_bm25_top_n: int = 50 # Number of BM25 candidates - round1_top_n: int = 20 # Number returned after RRF fusion - round1_rerank_top_n: int = int(os.getenv("AGENTIC_ROUND1_RERANK_TOP_N", "10")) # Number after reranking used for LLM judgment - - # LLM configuration - llm_temperature: float = 0.0 # Low temperature for judgment - llm_max_tokens: int = 500 - - # Round 2 configuration - enable_multi_query: bool = True # Whether to enable multi-query - num_queries: int = 3 # Desired number of generated queries - round2_per_query_top_n: int = 50 # Number recalled per query - - # Fusion configuration - combined_total: int = 40 # Total number after merging - final_top_n: int = 20 # Final number returned - - # Rerank configuration - use_reranker: bool = True - reranker_instruction: str = ( - "Determine if the passage contains specific facts, entities (names, dates, locations), " - "or details that directly answer the question." - ) - reranker_batch_size: int = 10 - reranker_timeout: float = 30.0 - - # Fallback strategy - fallback_on_error: bool = True # Fallback when LLM fails - timeout: float = 60.0 # Overall timeout (seconds) - - -# ==================== Utility Functions ==================== - - -def format_documents_for_llm( - results: List[Tuple[Any, float]], max_docs: int = 10 -) -> str: - """ - Format retrieval results for LLM usage - - Args: - results: List of retrieval results [(candidate, score), ...] - max_docs: Maximum number of documents to include - - Returns: - Formatted document string - """ - formatted_docs = [] - - for i, (candidate, score) in enumerate(results[:max_docs], 1): - # Extract memory content - timestamp = getattr(candidate, 'timestamp', 'N/A') - if hasattr(timestamp, 'strftime'): - timestamp_str = timestamp.strftime('%Y-%m-%d %H:%M:%S') - else: - timestamp_str = str(timestamp) - - # Prioritize episode (core content of MemCell) - content = getattr(candidate, 'episode', None) - if not content: - content = getattr(candidate, 'summary', None) - if not content: - content = getattr(candidate, 'subject', 'N/A') - - # Build document entry - doc_entry = f"[Memory {i}]\n" - doc_entry += f"Time: {timestamp_str}\n" - doc_entry += f"Content: {content}\n" - doc_entry += f"Relevance score: {score:.4f}\n" - - formatted_docs.append(doc_entry) - - return "\n".join(formatted_docs) if formatted_docs else "No retrieval results" - - -def parse_json_response(response: str) -> Dict[str, Any]: - """ - Parse JSON response returned by LLM - - Args: - response: Raw response string from LLM - - Returns: - Parsed dictionary - - Raises: - ValueError: JSON parsing failed - """ - try: - # Extract JSON part (may contain additional text) - start_idx = response.find("{") - end_idx = response.rfind("}") + 1 - - if start_idx == -1 or end_idx == 0: - raise ValueError("No JSON object found in response") - - json_str = response[start_idx:end_idx] - result = json.loads(json_str) - - return result - - except json.JSONDecodeError as e: - logger.error(f"Failed to parse JSON response: {e}") - logger.debug(f"Raw response: {response[:500]}") - raise ValueError(f"JSON parse error: {e}") - - -def parse_sufficiency_response(response: str) -> Tuple[bool, str, List[str]]: - """ - Parse sufficiency judgment response - - Args: - response: Raw response from LLM - - Returns: - (is_sufficient, reasoning, missing_information) - """ - try: - result = parse_json_response(response) - - # Validate required fields - if "is_sufficient" not in result: - raise ValueError("Missing 'is_sufficient' field") - - is_sufficient = bool(result["is_sufficient"]) - reasoning = result.get("reasoning", "No reasoning provided") - missing_info = result.get("missing_information", []) - - # Validate types - if not isinstance(missing_info, list): - missing_info = [] - - return is_sufficient, reasoning, missing_info - - except Exception as e: - logger.error(f"Failed to parse sufficiency response: {e}") - # Conservative fallback: assume sufficient - return True, f"Parse error: {str(e)}", [] - - -def parse_multi_query_response( - response: str, original_query: str -) -> Tuple[List[str], str]: - """ - Parse multi-query generation response - - Args: - response: Raw response from LLM - original_query: Original query (for fallback) - - Returns: - (queries_list, reasoning) - """ - try: - result = parse_json_response(response) - - # Validate required fields - if "queries" not in result or not isinstance(result["queries"], list): - raise ValueError("Missing or invalid 'queries' field") - - queries = result["queries"] - reasoning = result.get("reasoning", "No reasoning provided") - - # Filter and validate queries - valid_queries = [] - for q in queries: - if isinstance(q, str) and 5 <= len(q) <= 300: - # Avoid being identical to original query - if q.lower().strip() != original_query.lower().strip(): - valid_queries.append(q.strip()) - - # At least return 1 query - if not valid_queries: - logger.warning("No valid queries generated, using original") - return [original_query], "Fallback: used original query" - - # Limit to maximum 3 queries - valid_queries = valid_queries[:3] - - logger.info(f"Generated {len(valid_queries)} valid queries") - return valid_queries, reasoning - - except Exception as e: - logger.error(f"Failed to parse multi-query response: {e}") - # Fallback: return original query - return [original_query], f"Parse error: {str(e)}" - - -# ==================== Core LLM Utility Functions ==================== - - -async def check_sufficiency( - query: str, results: List[Tuple[Any, float]], llm_provider, max_docs: int = 5 -) -> Tuple[bool, str, List[str]]: - """ - Check if retrieval results are sufficient - - Use LLM to judge whether currently retrieved memories are sufficient to answer the user's query. - If insufficient, return a list of missing information. - - Args: - query: User query - results: Retrieval results (Top N) - llm_provider: LLM Provider (Memory Layer) - max_docs: Maximum number of documents to evaluate - - Returns: - (is_sufficient, reasoning, missing_information) - - is_sufficient: True means sufficient, False means insufficient - - reasoning: LLM's judgment reasoning - - missing_information: List of missing information (only populated when insufficient) - - Example: - >>> is_sufficient, reasoning, missing = await check_sufficiency( - ... query="What does the user like to eat?", - ... results=[(mem1, 0.92), (mem2, 0.85)], - ... llm_provider=llm - ... ) - >>> print(is_sufficient) # False - >>> print(missing) # ["User's specific cuisine preferences", "Taste preferences"] - """ - try: - # 1. Format documents - retrieved_docs = format_documents_for_llm(results, max_docs=max_docs) - - # 2. Build Prompt - prompt = SUFFICIENCY_CHECK_PROMPT.format( - query=query, retrieved_docs=retrieved_docs - ) - - # 3. Call LLM - logger.debug(f"Calling LLM for sufficiency check on query: {query[:50]}...") - result_text = await llm_provider.generate( - prompt=prompt, - temperature=0.0, # Low temperature for more stable judgment - max_tokens=500, - ) - - # 4. Parse response - is_sufficient, reasoning, missing_info = parse_sufficiency_response(result_text) - - logger.info(f"Sufficiency check result: {is_sufficient}") - logger.debug(f"Reasoning: {reasoning}") - - return is_sufficient, reasoning, missing_info - - except asyncio.TimeoutError: - logger.error("Sufficiency check timeout") - # Timeout fallback: assume sufficient (to avoid infinite retries) - return True, "Timeout: LLM took too long", [] - - except Exception as e: - logger.error(f"Sufficiency check failed: {e}", exc_info=True) - # Conservative fallback: assume sufficient - return True, f"Error: {str(e)}", [] - - -async def generate_multi_queries( - original_query: str, - results: List[Tuple[Any, float]], - missing_info: List[str], - llm_provider, - max_docs: int = 5, - num_queries: int = 3, -) -> Tuple[List[str], str]: - """ - Generate multiple complementary improved queries - - Based on the original query, current retrieval results, and missing information, generate multiple complementary queries. - These queries are used for multi-query retrieval to help find missing information. - - Args: - original_query: Original query - results: Round 1 retrieval results - missing_info: List of missing information - llm_provider: LLM Provider - max_docs: Maximum number of documents to use - num_queries: Desired number of queries to generate (actual may be 1-3) - - Returns: - (queries_list, reasoning) - - queries_list: List of generated queries (1-3) - - reasoning: LLM's explanation of generation strategy - - Example: - >>> queries, reasoning = await generate_multi_queries( - ... original_query="What does the user like to eat?", - ... results=[(mem1, 0.9)], - ... missing_info=["cuisine preference", "taste"], - ... llm_provider=llm - ... ) - >>> print(queries) - ['What is the user's favorite cuisine?', 'What taste does the user prefer?', 'What are the user's eating habits?'] - """ - try: - # 1. Format documents and missing information - retrieved_docs = format_documents_for_llm(results, max_docs=max_docs) - missing_info_str = ", ".join(missing_info) if missing_info else "N/A" - - # 2. Build Prompt - prompt = MULTI_QUERY_GENERATION_PROMPT.format( - original_query=original_query, - retrieved_docs=retrieved_docs, - missing_info=missing_info_str, - ) - - # 3. Call LLM - logger.debug(f"Generating multi-queries for: {original_query[:50]}...") - result_text = await llm_provider.generate( - prompt=prompt, - temperature=0.4, # Slightly higher temperature to increase query diversity - max_tokens=300, - ) - - # 4. Parse response - queries, reasoning = parse_multi_query_response(result_text, original_query) - - logger.info(f"Generated {len(queries)} queries") - for i, q in enumerate(queries, 1): - logger.debug(f" Query {i}: {q[:80]}{'...' if len(q) > 80 else ''}") - - return queries, reasoning - - except asyncio.TimeoutError: - logger.error("Multi-query generation timeout") - # Timeout fallback: use original query - return [original_query], "Timeout: used original query" - - except Exception as e: - logger.error(f"Multi-query generation failed: {e}", exc_info=True) - # Fallback to original query - return [original_query], f"Error: {str(e)}" diff --git a/methods/EverCore/src/agentic_layer/filter_parser.py b/methods/EverCore/src/agentic_layer/filter_parser.py deleted file mode 100644 index c4b860ff..00000000 --- a/methods/EverCore/src/agentic_layer/filter_parser.py +++ /dev/null @@ -1,148 +0,0 @@ -""" -MongoDB Filters DSL Parser - -Parses a filters object into a MongoDB query dict for use with Beanie find(). - -Supported top-level keys: user_id, group_id, session_id, timestamp -Supported operators: eq (implicit), in, gt, gte, lt, lte -Supported combinators: AND, OR -""" - -from datetime import datetime, timezone -from typing import Any, Dict, List, Optional, Tuple - - -class MongoFilterParser: - """Parse filters DSL into MongoDB query dict. - - Allowlist-based design: only fields in ALLOWED_FIELDS and - TIMESTAMP_FIELDS are processed. Unknown fields are silently ignored. - - Usage: - mongo_filter, user_id, group_ids = MongoFilterParser.parse(filters) - """ - - # --- Whitelist Configuration --- - # Add new filterable fields here. No logic changes needed. - ALLOWED_FIELDS = {"user_id", "group_id", "session_id"} - TIMESTAMP_FIELDS = {"timestamp"} - COMBINATOR_KEYS = {"AND", "OR"} - - # Mapping from DSL operators to MongoDB operators - _OPERATOR_MAP = { - "gt": "$gt", - "gte": "$gte", - "lt": "$lt", - "lte": "$lte", - "in": "$in", - } - - @staticmethod - def _parse_timestamp_value(value: Any) -> datetime: - """Convert a timestamp value (epoch millis or seconds) to datetime.""" - if isinstance(value, (int, float)): - # Heuristic: if > 1e12, treat as milliseconds - if value > 1e12: - return datetime.fromtimestamp(value / 1000, tz=timezone.utc) - return datetime.fromtimestamp(value, tz=timezone.utc) - if isinstance(value, str): - return datetime.fromisoformat(value) - return value - - @classmethod - def _parse_field_condition( - cls, field: str, condition: Any, mongo_query: Dict[str, Any] - ) -> None: - """Parse a single field condition and merge into mongo_query.""" - if field in cls.TIMESTAMP_FIELDS: - if isinstance(condition, dict): - ts_filter: Dict[str, Any] = {} - for op, val in condition.items(): - mongo_op = cls._OPERATOR_MAP.get(op) - if mongo_op: - ts_filter[mongo_op] = cls._parse_timestamp_value(val) - if ts_filter: - mongo_query.setdefault(field, {}).update(ts_filter) - else: - mongo_query[field] = cls._parse_timestamp_value(condition) - elif field in cls.ALLOWED_FIELDS: - if isinstance(condition, dict): - if "in" in condition: - mongo_query[field] = {"$in": condition["in"]} - else: - field_filter: Dict[str, Any] = {} - for op, val in condition.items(): - mongo_op = cls._OPERATOR_MAP.get(op) - if mongo_op: - field_filter[mongo_op] = val - if field_filter: - mongo_query[field] = field_filter - else: - mongo_query[field] = condition - # else: unknown field silently ignored (security: allowlist only) - - @classmethod - def _parse_single_filter(cls, filter_item: Dict[str, Any]) -> Dict[str, Any]: - """Parse a single filter dict (used inside AND/OR arrays).""" - result: Dict[str, Any] = {} - for key, value in filter_item.items(): - if key in cls.COMBINATOR_KEYS: - cls._parse_combinator(key, value, result) - else: - cls._parse_field_condition(key, value, result) - return result - - @classmethod - def _parse_combinator( - cls, combinator: str, items: List[Dict[str, Any]], mongo_query: Dict[str, Any] - ) -> None: - """Parse AND/OR combinator arrays.""" - mongo_op = "$and" if combinator == "AND" else "$or" - parsed_items = [cls._parse_single_filter(item) for item in items if item] - if parsed_items: - mongo_query.setdefault(mongo_op, []).extend(parsed_items) - - @classmethod - def parse( - cls, filters: Dict[str, Any] - ) -> Tuple[Dict[str, Any], Optional[str], Optional[List[str]]]: - """Parse filters object into MongoDB query. - - Returns: - Tuple of (mongo_filter_dict, user_id, group_ids) - """ - mongo_query: Dict[str, Any] = {} - user_id: Optional[str] = None - group_ids: Optional[List[str]] = None - - for key, value in filters.items(): - if key == "user_id": - if isinstance(value, str): - user_id = value - mongo_query["user_id"] = value - elif isinstance(value, dict) and "in" in value: - user_id = value["in"][0] if value["in"] else None - mongo_query["user_id"] = {"$in": value["in"]} - - elif key == "group_id": - if isinstance(value, str): - group_ids = [value] - mongo_query["group_id"] = value - elif isinstance(value, dict) and "in" in value: - group_ids = value["in"] - mongo_query["group_id"] = {"$in": value["in"]} - - elif key in cls.COMBINATOR_KEYS: - cls._parse_combinator(key, value, mongo_query) - - else: - cls._parse_field_condition(key, value, mongo_query) - - return mongo_query, user_id, group_ids - - -def parse_mongo_filters( - filters: Dict[str, Any] -) -> Tuple[Dict[str, Any], Optional[str], Optional[List[str]]]: - """Convenience function wrapping MongoFilterParser.parse().""" - return MongoFilterParser.parse(filters) diff --git a/methods/EverCore/src/agentic_layer/get_mem_service.py b/methods/EverCore/src/agentic_layer/get_mem_service.py deleted file mode 100644 index 84e6edb4..00000000 --- a/methods/EverCore/src/agentic_layer/get_mem_service.py +++ /dev/null @@ -1,296 +0,0 @@ -""" -Memory GET service (v1) - -Provides business logic for POST /api/v1/memories/get endpoint. -Handles filter parsing, scope validation, pagination, querying, and DTO conversion. -""" - -import logging -from typing import Any, Dict, List - -from pymongo import ASCENDING, DESCENDING - -from api_specs.memory_models import MemoryType - -from core.di import service, get_bean_by_type -from core.observation.stage_timer import timed -from agentic_layer.filter_parser import parse_mongo_filters -from api_specs.dtos.memory import ( - GetMemResponse, - EpisodeItem, - ProfileItem, - AgentCaseItem, - AgentSkillItem, -) -from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemoryProjection, -) -from infra_layer.adapters.out.persistence.document.memory.agent_case import ( - AgentCaseProjection, -) -from infra_layer.adapters.out.persistence.document.memory.agent_skill import ( - AgentSkillProjection, -) -from infra_layer.adapters.out.persistence.repository.episodic_memory_raw_repository import ( - EpisodicMemoryRawRepository, -) -from infra_layer.adapters.out.persistence.repository.user_profile_raw_repository import ( - UserProfileRawRepository, -) -from infra_layer.adapters.out.persistence.repository.agent_case_raw_repository import ( - AgentCaseRawRepository, -) -from infra_layer.adapters.out.persistence.repository.agent_skill_raw_repository import ( - AgentSkillRawRepository, -) -from biz_layer.memorize_config import DEFAULT_MEMORIZE_CONFIG - -logger = logging.getLogger(__name__) - - -class InvalidScopeError(Exception): - """Raised when filters lack required user_id or group_id scope.""" - - pass - - -@service(name="get_memory_service", primary=True) -class GetMemoryService: - """Memory GET service for v1 API. - - Handles filter parsing, scope validation, pagination, querying, - and document-to-DTO conversion. All DB access goes through repository layer. - """ - - def __init__(self): - self._episodic_repo = get_bean_by_type(EpisodicMemoryRawRepository) - self._profile_repo = get_bean_by_type(UserProfileRawRepository) - self._agent_case_repo = get_bean_by_type(AgentCaseRawRepository) - self._agent_skill_repo = get_bean_by_type(AgentSkillRawRepository) - - @staticmethod - def _episode_to_item(doc) -> EpisodeItem: - """Convert EpisodicMemoryProjection document to EpisodeItem.""" - return EpisodeItem( - id=str(doc.id), - user_id=doc.user_id, - group_id=doc.group_id, - session_id=doc.session_id, - timestamp=doc.timestamp, - participants=doc.participants, - sender_ids=doc.sender_ids, - summary=doc.summary, - subject=doc.subject, - episode=doc.episode, - type=doc.type, - parent_type=doc.parent_type, - parent_id=doc.parent_id, - ) - - @staticmethod - def _profile_to_item(doc) -> ProfileItem: - """Convert UserProfile document to ProfileItem.""" - return ProfileItem( - id=str(doc.id), - user_id=doc.user_id, - group_id=doc.group_id, - profile_data=doc.profile_data, - scenario=doc.scenario, - memcell_count=doc.memcell_count, - ) - - # Memory types that lack a 'timestamp' field; fall back to updated_at. - _NO_TIMESTAMP_TYPES = {MemoryType.PROFILE.value, MemoryType.AGENT_SKILL.value} - - # Memory types that require user_id in filters (only group_id is not allowed). - _USER_REQUIRED_TYPES = { - MemoryType.PROFILE.value, - MemoryType.AGENT_CASE.value, - MemoryType.AGENT_SKILL.value, - } - - @staticmethod - def _resolve_sort(memory_type: str, rank_by: str, rank_order: str) -> List[tuple]: - """Resolve sort field and direction. - - Profile and AgentSkill have no timestamp field, fallback to updated_at. - """ - sort_field = rank_by - if ( - rank_by == "timestamp" - and memory_type in GetMemoryService._NO_TIMESTAMP_TYPES - ): - sort_field = "updated_at" - sort_direction = DESCENDING if rank_order == "desc" else ASCENDING - return [(sort_field, sort_direction)] - - async def find_memories( - self, - filters: Dict[str, Any], - memory_type: str, - page: int, - page_size: int, - rank_by: str, - rank_order: str, - ) -> GetMemResponse: - """Find memories by filters DSL. - - Args: - filters: Filter conditions dict from request body - memory_type: Memory type (episodic_memory, profile, agent_case, agent_skill) - page: Page number, starts from 1 - page_size: Items per page - rank_by: Sort field - rank_order: Sort order (asc or desc) - - Returns: - GetMemResponse with matching memories - - Raises: - InvalidScopeError: If filters lack user_id or group_id scope - """ - # 1. Parse filters DSL into MongoDB query - with timed("parse_filters"): - mongo_filter, user_id, group_ids = parse_mongo_filters(filters) - - if not user_id and not group_ids: - raise InvalidScopeError( - "filters must contain at least one of 'user_id' or 'group_id'" - ) - - # 2. Scope semantics by memory_type - if not user_id and group_ids: - if memory_type in self._USER_REQUIRED_TYPES: - raise InvalidScopeError( - f"memory_type '{memory_type}' requires 'user_id' in filters" - ) - if memory_type == MemoryType.EPISODIC_MEMORY.value: - # Group-only episodic query: exclude personal episodes - mongo_filter["user_id"] = {"$in": [None, ""]} - - # 3. Pagination and sort - skip = (page - 1) * page_size - limit = page_size - sort = self._resolve_sort(memory_type, rank_by, rank_order) - - # 4. Dispatch by memory_type - match memory_type: - case MemoryType.EPISODIC_MEMORY.value: - return await self._get_episodes(mongo_filter, skip, limit, sort) - case MemoryType.PROFILE.value: - return await self._get_profiles(mongo_filter, skip, limit, sort) - case MemoryType.AGENT_CASE.value: - return await self._get_agent_cases(mongo_filter, skip, limit, sort) - case MemoryType.AGENT_SKILL.value: - return await self._get_agent_skills(mongo_filter, skip, limit, sort) - case _: - raise ValueError(f"Unsupported memory_type: {memory_type}") - - async def _get_episodes( - self, mongo_filter: dict, skip: int, limit: int, sort: list - ) -> GetMemResponse: - """Query v1_episodic_memories via repository and return GetMemResponse.""" - with timed("query_memories"): - docs, total_count = await self._episodic_repo.find_by_query( - mongo_filter, - skip=skip, - limit=limit, - sort=sort, - projection_model=EpisodicMemoryProjection, - ) - - with timed("assemble_results"): - episodes = [self._episode_to_item(doc) for doc in docs] - return GetMemResponse( - episodes=episodes, total_count=total_count, count=len(episodes) - ) - - async def _get_profiles( - self, mongo_filter: dict, skip: int, limit: int, sort: list - ) -> GetMemResponse: - """Query v1_user_profiles via repository and return GetMemResponse.""" - with timed("query_memories"): - docs, total_count = await self._profile_repo.find_by_query( - mongo_filter, skip=skip, limit=limit, sort=sort - ) - - with timed("assemble_results"): - profiles = [self._profile_to_item(doc) for doc in docs] - return GetMemResponse( - profiles=profiles, total_count=total_count, count=len(profiles) - ) - - async def _get_agent_cases( - self, mongo_filter: dict, skip: int, limit: int, sort: list - ) -> GetMemResponse: - """Query v1_agent_cases via repository and return GetMemResponse.""" - with timed("query_memories"): - docs, total_count = await self._agent_case_repo.find_by_query( - mongo_filter, - skip=skip, - limit=limit, - sort=sort, - projection_model=AgentCaseProjection, - ) - - with timed("assemble_results"): - agent_cases = [self._agent_case_to_item(doc) for doc in docs] - return GetMemResponse( - agent_cases=agent_cases, total_count=total_count, count=len(agent_cases) - ) - - async def _get_agent_skills( - self, mongo_filter: dict, skip: int, limit: int, sort: list - ) -> GetMemResponse: - """Query v1_agent_skills via repository and return GetMemResponse.""" - retire_confidence = DEFAULT_MEMORIZE_CONFIG.skill_retire_confidence - mongo_filter.setdefault("confidence", {"$gte": retire_confidence}) - with timed("query_memories"): - docs, total_count = await self._agent_skill_repo.find_by_query( - mongo_filter, - skip=skip, - limit=limit, - sort=sort, - projection_model=AgentSkillProjection, - ) - - with timed("assemble_results"): - agent_skills = [self._agent_skill_to_item(doc) for doc in docs] - return GetMemResponse( - agent_skills=agent_skills, - total_count=total_count, - count=len(agent_skills), - ) - - @staticmethod - def _agent_case_to_item(doc) -> AgentCaseItem: - """Convert AgentCaseRecord/Projection document to AgentCaseItem.""" - return AgentCaseItem( - id=str(doc.id), - user_id=doc.user_id, - group_id=doc.group_id, - session_id=doc.session_id, - timestamp=doc.timestamp, - task_intent=doc.task_intent or "", - approach=doc.approach or "", - quality_score=doc.quality_score, - key_insight=doc.key_insight or "", - parent_type=doc.parent_type, - parent_id=doc.parent_id, - ) - - @staticmethod - def _agent_skill_to_item(doc) -> AgentSkillItem: - """Convert AgentSkillRecord/Projection document to AgentSkillItem.""" - return AgentSkillItem( - id=str(doc.id), - cluster_id=doc.cluster_id, - user_id=doc.user_id, - group_id=doc.group_id, - name=doc.name, - description=doc.description, - content=doc.content, - confidence=doc.confidence, - maturity_score=doc.maturity_score, - source_case_ids=doc.source_case_ids or [], - ) diff --git a/methods/EverCore/src/agentic_layer/memory_manager.py b/methods/EverCore/src/agentic_layer/memory_manager.py deleted file mode 100644 index e6e93005..00000000 --- a/methods/EverCore/src/agentic_layer/memory_manager.py +++ /dev/null @@ -1,1398 +0,0 @@ -from __future__ import annotations - -from typing import Any, List, Optional -import logging -import asyncio - -from datetime import datetime, timedelta -import jieba -import numpy as np -import time -from typing import Dict, Any -from dataclasses import dataclass - -from api_specs import memory_types -from api_specs.memory_types import ( - BaseMemory, - EpisodeMemory, - AtomicFact, - Foresight, - AgentCase, - AgentSkill, - RawDataType, - ParentType, -) -from biz_layer.mem_memorize import memorize -from api_specs.dtos import MemorizeRequest -from .get_mem_service import GetMemoryService -from api_specs.dtos import ( - RawMessageDTO, - ProfileSearchItem, - RetrieveMemRequest, - RetrieveMemResponse, -) -from api_specs.dtos.memory import GetMemRequest, GetMemResponse -from api_specs.memory_models import Metadata, MemoryType, QueryMetadata -from core.di import get_bean_by_type -from biz_layer.retrieve_constants import ( - DEFAULT_MILVUS_SIMILARITY_THRESHOLD, - DEFAULT_RERANK_SCORE_THRESHOLD, - DEFAULT_RECALL_MULTIPLIER, - DEFAULT_TOPK_LIMIT, -) -from infra_layer.adapters.out.search.repository.episodic_memory_es_repository import ( - EpisodicMemoryEsRepository, -) -from infra_layer.adapters.out.search.repository.foresight_es_repository import ( - ForesightEsRepository, -) -from infra_layer.adapters.out.search.repository.atomic_fact_es_repository import ( - AtomicFactEsRepository, -) -from infra_layer.adapters.out.search.repository.agent_case_es_repository import ( - AgentCaseEsRepository, -) -from infra_layer.adapters.out.search.repository.agent_skill_es_repository import ( - AgentSkillEsRepository, -) -from core.observation.tracing.decorators import trace_logger -from core.observation.stage_timer import timed, timed_parallel -from core.nlp.stopwords_utils import filter_stopwords -from common_utils.datetime_utils import ( - from_iso_format, - get_now_with_timezone, - to_iso_format, -) -from infra_layer.adapters.out.persistence.repository.memcell_raw_repository import ( - MemCellRawRepository, -) -from service.raw_message_service import RawMessageService -from infra_layer.adapters.out.persistence.document.memory.memcell import DataTypeEnum -from infra_layer.adapters.out.persistence.document.memory.user_profile import ( - UserProfile, -) -from infra_layer.adapters.out.search.repository.episodic_memory_milvus_repository import ( - EpisodicMemoryMilvusRepository, -) -from infra_layer.adapters.out.search.repository.foresight_milvus_repository import ( - ForesightMilvusRepository, -) -from infra_layer.adapters.out.search.repository.atomic_fact_milvus_repository import ( - AtomicFactMilvusRepository, -) -from infra_layer.adapters.out.search.repository.agent_case_milvus_repository import ( - AgentCaseMilvusRepository, -) -from infra_layer.adapters.out.search.repository.agent_skill_milvus_repository import ( - AgentSkillMilvusRepository, -) -from .vectorize_service import get_vectorize_service -from .rerank_service import get_rerank_service -from .profile_search_service import ( - get_profile_search_service, - PROFILE_RECALL_THRESHOLD, - PROFILE_DEFAULT_TOPK, -) -from api_specs.memory_models import MemoryType, RetrieveMethod -from agentic_layer.metrics.retrieve_metrics import ( - record_retrieve_stage, - record_retrieve_error, -) -from memory_layer.llm.llm_provider import build_default_provider -from agentic_layer.agentic_utils import ( - AgenticConfig, - check_sufficiency, - generate_multi_queries, -) - -logger = logging.getLogger(__name__) - - -# MemoryType -> ES Repository mapping -ES_REPO_MAP = { - MemoryType.FORESIGHT: ForesightEsRepository, - MemoryType.ATOMIC_FACT: AtomicFactEsRepository, - MemoryType.EPISODIC_MEMORY: EpisodicMemoryEsRepository, - MemoryType.AGENT_CASE: AgentCaseEsRepository, - MemoryType.AGENT_SKILL: AgentSkillEsRepository, -} - - -@dataclass -class AtomicFactCandidate: - """Atomic Fact candidate object (used for retrieval from atomic_fact)""" - - event_id: str - user_id: str - group_id: str - timestamp: datetime - episode: str # atomic_fact content - summary: str - subject: str - extend: dict # contains embedding - - -class MemoryManager: - """Unified memory interface. - - Provides the following main functions: - - memorize: Accept raw data and persistently store - - get_mem: (v1) Get memories with structured filters DSL - - retrieve_mem: Memory reading based on prompt-based retrieval methods - """ - - def __init__(self) -> None: - # Get memory service instances - self._get_service: GetMemoryService = get_bean_by_type(GetMemoryService) - self._raw_message_service: RawMessageService = get_bean_by_type( - RawMessageService - ) - - logger.info( - "MemoryManager initialized with get_memory_service and retrieve_mem_service" - ) - - # --------- Write path (raw data -> memorize) --------- - @trace_logger(operation_name="agentic_layer memory storage") - async def memorize(self, memorize_request: MemorizeRequest) -> int: - """Memorize a heterogeneous list of raw items. - - Accepts list[Any], where each item can be one of the typed raw dataclasses - (ChatRawData / EmailRawData / MemoRawData / LincDocRawData) or any dict-like - object. Each item is stored as a MemoryCell with a synthetic key. - - Returns: - int: Number of memories extracted (0 if no boundary detected) - """ - count = await memorize(memorize_request) - return count - - # --------- Read path (v1 filters DSL -> get_mem) --------- - @trace_logger(operation_name="agentic_layer v1 memory get") - async def get_mem(self, request: GetMemRequest) -> GetMemResponse: - """Get memories using v1 structured filters DSL. - - Args: - request: GetMemRequest containing filters, memory_type, pagination, sort - - Returns: - GetMemResponse containing query results - """ - logger.debug( - "get_mem called with request: memory_type=%s, page=%s, page_size=%s", - request.memory_type, - request.page, - request.page_size, - ) - - response = await self._get_service.find_memories( - filters=request.filters, - memory_type=request.memory_type, - page=request.page, - page_size=request.page_size, - rank_by=request.rank_by, - rank_order=request.rank_order, - ) - - logger.debug( - "get_mem returned total_count=%s, count=%s", - response.total_count, - response.count, - ) - return response - - # Memory reading based on retrieve_method, including static and dynamic memory - @trace_logger(operation_name="agentic_layer memory retrieval") - async def retrieve_mem( - self, retrieve_mem_request: 'RetrieveMemRequest' - ) -> RetrieveMemResponse: - """Retrieve memory data, dispatching to different retrieval methods based on retrieve_method - - Args: - retrieve_mem_request: RetrieveMemRequest containing retrieval parameters - - Returns: - RetrieveMemResponse containing retrieval results - """ - try: - # Validate request parameters - if not retrieve_mem_request: - raise ValueError("retrieve_mem_request is required for retrieve_mem") - - # Get memory types from request (defaults already applied in converter) - memory_types = retrieve_mem_request.memory_types - - # Separate profile search from other memory types - search_profile = MemoryType.PROFILE in memory_types - non_profile_types = [mt for mt in memory_types if mt != MemoryType.PROFILE] - - retrieve_method = retrieve_mem_request.retrieve_method - - logger.info( - f"retrieve_mem dispatching request: user_id={retrieve_mem_request.user_id}, " - f"retrieve_method={retrieve_method}, query={retrieve_mem_request.query}, " - f"search_profile={search_profile}, non_profile_types={[t.value for t in non_profile_types]}" - ) - - # Task 1: Fetch pending messages - pending_messages_task = asyncio.create_task( - self._get_pending_messages( - user_id=retrieve_mem_request.user_id, - group_ids=retrieve_mem_request.group_ids, - ) - ) - - # Task 2: Profile search (if needed) - profile_task = None - if search_profile and retrieve_mem_request.query: - profile_task = asyncio.create_task( - self._search_profiles(retrieve_mem_request) - ) - - # Task 3: Non-profile memory search (if needed) - non_profile_response = None - if non_profile_types: - # Create a modified request with non-profile types - non_profile_request = RetrieveMemRequest( - user_id=retrieve_mem_request.user_id, - group_ids=retrieve_mem_request.group_ids, - memory_types=non_profile_types, - top_k=retrieve_mem_request.top_k, - include_metadata=retrieve_mem_request.include_metadata, - start_time=retrieve_mem_request.start_time, - end_time=retrieve_mem_request.end_time, - query=retrieve_mem_request.query, - retrieve_method=retrieve_mem_request.retrieve_method, - radius=retrieve_mem_request.radius, - ) - - # Dispatch based on retrieval method - match retrieve_method: - case RetrieveMethod.KEYWORD: - non_profile_response = await self.retrieve_mem_keyword( - non_profile_request - ) - case RetrieveMethod.VECTOR: - non_profile_response = await self.retrieve_mem_vector( - non_profile_request - ) - case RetrieveMethod.HYBRID: - non_profile_response = await self.retrieve_mem_hybrid( - non_profile_request - ) - case RetrieveMethod.AGENTIC: - non_profile_response = await self.retrieve_mem_agentic( - non_profile_request - ) - case _: - raise ValueError( - f"Unsupported retrieval method: {retrieve_method}" - ) - - # Await profile search results - profile_results = [] - if profile_task: - profile_results = await profile_task - - # Await pending messages - pending_messages = await pending_messages_task - - # Build combined response - response = self._build_combined_response( - profile_results=profile_results, - non_profile_response=non_profile_response, - retrieve_mem_request=retrieve_mem_request, - pending_messages=pending_messages, - ) - - return response - - except Exception as e: - logger.error(f"Error in retrieve_mem: {e}", exc_info=True) - return RetrieveMemResponse( - profiles=[], - memories=[], - total_count=0, - has_more=False, - query_metadata=QueryMetadata.from_request(retrieve_mem_request), - metadata=Metadata( - source="retrieve_mem_service", - user_id=( - retrieve_mem_request.user_id if retrieve_mem_request else "" - ), - memory_types=[], - ), - pending_messages=[], - ) - - async def _search_profiles( - self, retrieve_mem_request: 'RetrieveMemRequest' - ) -> List[ProfileSearchItem]: - """ - Search user profiles using ProfileSearchService. - - Returns profile items without reranking. - """ - try: - profile_service = get_profile_search_service() - - # Use configured default if top_k is not positive, otherwise use top_k value - profile_top_k = ( - PROFILE_DEFAULT_TOPK - if retrieve_mem_request.top_k <= 0 - else retrieve_mem_request.top_k - ) - - # Use radius as score threshold if provided, otherwise use configured default - score_threshold = PROFILE_RECALL_THRESHOLD - - result = await profile_service.search_profiles( - query=retrieve_mem_request.query or "", - user_id=retrieve_mem_request.user_id or "", - group_id=( - retrieve_mem_request.group_ids[0] - if retrieve_mem_request.group_ids - else "" - ), - top_k=profile_top_k, - score_threshold=score_threshold, - ) - - # Convert to ProfileSearchItem list - profiles = [] - for item in result.get("profiles", []): - profile_item = ProfileSearchItem( - item_type=item.get("item_type", ""), - category=item.get("category"), - trait_name=item.get("trait_name"), - description=item.get("description", ""), - score=item.get("score", 0.0), - ) - profiles.append(profile_item) - - logger.debug(f"Profile search returned {len(profiles)} items") - return profiles - - except Exception as e: - logger.error(f"Error in _search_profiles: {e}", exc_info=True) - return [] - - def _build_combined_response( - self, - profile_results: List[ProfileSearchItem], - non_profile_response: Optional[RetrieveMemResponse], - retrieve_mem_request: 'RetrieveMemRequest', - pending_messages: List[RawMessageDTO], - ) -> RetrieveMemResponse: - """ - Build combined response from profile and non-profile search results. - """ - user_id = retrieve_mem_request.user_id or "" - retrieve_method = retrieve_mem_request.retrieve_method.value - - # Get memories from non-profile response - memories = [] - - if non_profile_response: - memories = non_profile_response.memories - - # Calculate total count - total_count = len(profile_results) + len(memories) - - # Build memory_types list - memory_types_searched = [] - if profile_results: - memory_types_searched.append(MemoryType.PROFILE.value) - if memories: - memory_types_searched.append(MemoryType.EPISODIC_MEMORY.value) - - return RetrieveMemResponse( - profiles=profile_results, - memories=memories, - total_count=total_count, - has_more=False, - query_metadata=QueryMetadata.from_request(retrieve_mem_request), - metadata=Metadata( - source=retrieve_method, - user_id=user_id, - memory_types=memory_types_searched, - ), - pending_messages=pending_messages, - ) - - async def _get_pending_messages( - self, user_id: Optional[str] = None, group_ids: Optional[List[str]] = None - ) -> List[RawMessageDTO]: - """ - Get pending (unconsumed) messages from RawMessageService. - - Fetches cached memory data that hasn't been consumed yet (sync_status=-1 or 0). - - Args: - user_id: User ID filter (from retrieve_request, mapped to sender_id) - group_ids: List of Group IDs to filter (None means all groups) - - Returns: - List of RawMessageDTO objects - """ - try: - result = await self._raw_message_service.get_pending_messages( - sender_id=user_id, group_ids=group_ids, limit=1000 - ) - - logger.debug( - f"Retrieved {len(result)} pending messages: " - f"user_id={user_id}, group_ids={group_ids}" - ) - return result - except Exception as e: - logger.error(f"Error fetching pending messages: {e}", exc_info=True) - return [] - - # Keyword retrieval method (original retrieve_mem logic) - @trace_logger(operation_name="agentic_layer keyword memory retrieval") - async def retrieve_mem_keyword( - self, retrieve_mem_request: 'RetrieveMemRequest' - ) -> RetrieveMemResponse: - """Keyword-based memory retrieval""" - top_k = retrieve_mem_request.top_k - is_unlimited_mode = top_k == -1 - memory_type = ( - retrieve_mem_request.memory_types[0].value - if retrieve_mem_request.memory_types - else 'unknown' - ) - - try: - hits = await self.get_keyword_search_results( - retrieve_mem_request, retrieve_method=RetrieveMethod.KEYWORD.value - ) - - # In normal mode (top_k > 0), truncate to top_k - # In unlimited mode, return all results (ES doesn't apply threshold filtering) - if not is_unlimited_mode and hits: - hits = hits[:top_k] - - return await self._to_response(hits, retrieve_mem_request) - except Exception as e: - logger.error(f"Error in retrieve_mem_keyword: {e}", exc_info=True) - return await self._to_response([], retrieve_mem_request) - - async def get_keyword_search_results( - self, - retrieve_mem_request: 'RetrieveMemRequest', - retrieve_method: str = RetrieveMethod.KEYWORD.value, - ) -> List[Dict[str, Any]]: - """Keyword search with stage-level metrics""" - stage_start = time.perf_counter() - memory_type = ( - retrieve_mem_request.memory_types[0].value - if retrieve_mem_request.memory_types - else 'unknown' - ) - - try: - # Get parameters from Request - if not retrieve_mem_request: - raise ValueError("retrieve_mem_request is required for retrieve_mem") - - top_k = retrieve_mem_request.top_k - # Calculate effective recall limit based on mode: - # - Unlimited mode (top_k=-1): Fixed recall of DEFAULT_TOPK_LIMIT (100) - # - Normal mode (top_k>0): Recall top_k * RECALL_MULTIPLIER for larger candidate pool - if top_k == -1: - effective_limit = DEFAULT_TOPK_LIMIT - else: - effective_limit = top_k * DEFAULT_RECALL_MULTIPLIER - query = retrieve_mem_request.query - user_id = retrieve_mem_request.user_id - group_ids = retrieve_mem_request.group_ids # List[str] or None - start_time = retrieve_mem_request.start_time - end_time = retrieve_mem_request.end_time - memory_types = retrieve_mem_request.memory_types - - # Convert query string to search word list - # Use jieba for search mode word segmentation, then filter stopwords - if query: - raw_words = list(jieba.cut_for_search(query)) - query_words = filter_stopwords(raw_words, min_length=2) - else: - query_words = [] - - logger.debug(f"query_words: {query_words}") - - # Build time range filter conditions, handle None values - date_range = {} - if start_time is not None: - date_range["gte"] = start_time - if end_time is not None: - date_range["lte"] = end_time - - mem_type = memory_types[0] - - repo_class = ES_REPO_MAP.get(mem_type) - if not repo_class: - logger.warning(f"Unsupported memory_type: {mem_type}") - return [] - - es_repo = get_bean_by_type(repo_class) - logger.debug(f"Using {repo_class.__name__} for {mem_type}") - - results = await es_repo.multi_search( - query=query_words, - user_id=user_id, - group_ids=group_ids, # Pass normalized list - size=effective_limit, - from_=0, - date_range=date_range, - ) - - # Mark memory_type, search_source, and unified score - if results: - for r in results: - r['memory_type'] = mem_type.value - r['_search_source'] = RetrieveMethod.KEYWORD.value - r['id'] = r.get('_id', '') # Unify ES '_id' to 'id' - r['score'] = r.get('_score', 0.0) # Unified score field - - # Record stage metrics - record_retrieve_stage( - retrieve_method=retrieve_method, - stage=RetrieveMethod.KEYWORD.value, - memory_type=memory_type, - duration_seconds=time.perf_counter() - stage_start, - ) - - return results or [] - except Exception as e: - record_retrieve_stage( - retrieve_method=retrieve_method, - stage=RetrieveMethod.KEYWORD.value, - memory_type=memory_type, - duration_seconds=time.perf_counter() - stage_start, - ) - record_retrieve_error( - retrieve_method=retrieve_method, - stage=RetrieveMethod.KEYWORD.value, - error_type=self._classify_retrieve_error(e), - ) - logger.error(f"Error in get_keyword_search_results: {e}") - raise - - # Vector-based memory retrieval - @trace_logger(operation_name="agentic_layer vector memory retrieval") - async def retrieve_mem_vector( - self, retrieve_mem_request: 'RetrieveMemRequest' - ) -> RetrieveMemResponse: - """Vector-based memory retrieval""" - top_k = retrieve_mem_request.top_k - is_unlimited_mode = top_k == -1 - memory_type = ( - retrieve_mem_request.memory_types[0].value - if retrieve_mem_request.memory_types - else 'unknown' - ) - - try: - hits = await self.get_vector_search_results( - retrieve_mem_request, retrieve_method=RetrieveMethod.VECTOR.value - ) - - # In normal mode (top_k > 0), truncate to top_k - # In unlimited mode, results are already filtered by Milvus threshold - if not is_unlimited_mode and hits: - hits = hits[:top_k] - - return await self._to_response(hits, retrieve_mem_request) - except Exception as e: - logger.error(f"Error in retrieve_mem_vector: {e}") - return await self._to_response([], retrieve_mem_request) - - async def get_vector_search_results( - self, - retrieve_mem_request: 'RetrieveMemRequest', - retrieve_method: str = RetrieveMethod.VECTOR.value, - ) -> List[Dict[str, Any]]: - """Vector search with stage-level metrics (embedding + milvus_search)""" - memory_type = ( - retrieve_mem_request.memory_types[0].value - if retrieve_mem_request.memory_types - else 'unknown' - ) - - try: - # Get parameters from Request - logger.debug( - f"get_vector_search_results called with retrieve_mem_request: {retrieve_mem_request}" - ) - if not retrieve_mem_request: - raise ValueError( - "retrieve_mem_request is required for get_vector_search_results" - ) - query = retrieve_mem_request.query - if not query: - raise ValueError("query is required for retrieve_mem_vector") - - user_id = retrieve_mem_request.user_id - group_ids = retrieve_mem_request.group_ids # List[str] or None - top_k = retrieve_mem_request.top_k - # Calculate effective recall limit based on mode: - # - Unlimited mode (top_k=-1): Fixed recall of DEFAULT_TOPK_LIMIT (100) - # - Normal mode (top_k>0): Recall top_k * RECALL_MULTIPLIER for larger candidate pool - if top_k == -1: - effective_limit = DEFAULT_TOPK_LIMIT - else: - effective_limit = top_k * DEFAULT_RECALL_MULTIPLIER - # Milvus similarity threshold (only applied in unlimited mode or when user specifies radius) - effective_radius = None - start_time = retrieve_mem_request.start_time - end_time = retrieve_mem_request.end_time - mem_type = retrieve_mem_request.memory_types[0] - - logger.debug( - f"retrieve_mem_vector called with query: {query}, user_id: {user_id}, group_ids: {group_ids}, top_k: {top_k}" - ) - - # Get vectorization service - vectorize_service = get_vectorize_service() - - # Convert query text to vector (embedding stage) - logger.debug(f"Starting to vectorize query text: {query}") - embedding_start = time.perf_counter() - query_vector = await vectorize_service.get_embedding(query) - query_vector_list = query_vector.tolist() # Convert to list format - record_retrieve_stage( - retrieve_method=retrieve_method, - stage='embedding', - memory_type=memory_type, - duration_seconds=time.perf_counter() - embedding_start, - ) - logger.debug( - f"Query text vectorization completed, vector dimension: {len(query_vector_list)}" - ) - - # Select Milvus repository based on memory type - match mem_type: - case MemoryType.FORESIGHT: - milvus_repo = get_bean_by_type(ForesightMilvusRepository) - case MemoryType.ATOMIC_FACT: - milvus_repo = get_bean_by_type(AtomicFactMilvusRepository) - case MemoryType.EPISODIC_MEMORY: - milvus_repo = get_bean_by_type(EpisodicMemoryMilvusRepository) - case MemoryType.AGENT_CASE: - milvus_repo = get_bean_by_type(AgentCaseMilvusRepository) - case MemoryType.AGENT_SKILL: - milvus_repo = get_bean_by_type(AgentSkillMilvusRepository) - case _: - raise ValueError(f"Unsupported memory type: {mem_type}") - - # Handle time range filter conditions - start_time_dt = None - end_time_dt = None - - if start_time is not None: - start_time_dt = ( - from_iso_format(start_time) - if isinstance(start_time, str) - else start_time - ) - - if end_time is not None: - if isinstance(end_time, str): - end_time_dt = from_iso_format(end_time) - # If date only format, set to end of day - if len(end_time) == 10: - end_time_dt = end_time_dt.replace(hour=23, minute=59, second=59) - else: - end_time_dt = end_time - - # Handle foresight time range (only valid for foresight) - if mem_type == MemoryType.FORESIGHT: - if retrieve_mem_request.start_time: - start_time_dt = from_iso_format(retrieve_mem_request.start_time) - if retrieve_mem_request.end_time: - end_time_dt = from_iso_format(retrieve_mem_request.end_time) - - # Call Milvus vector search (pass different parameters based on memory type) - # Threshold logic: - # - User specified radius: always use it - # - Unlimited mode (top_k=-1): apply DEFAULT_MILVUS_SIMILARITY_THRESHOLD (0.6) - # - Normal mode (top_k>0): no threshold filtering (rely on top_k limit) - if retrieve_mem_request.radius is not None: - # User specified radius, use it - effective_radius = retrieve_mem_request.radius - elif top_k == -1: - # Unlimited mode: apply default Milvus threshold for quality filtering - effective_radius = DEFAULT_MILVUS_SIMILARITY_THRESHOLD - # else: keep None (no threshold filtering for normal top_k mode) - - milvus_start = time.perf_counter() - if mem_type == MemoryType.FORESIGHT: - # Foresight: supports time range and validity filtering, supports radius parameter - search_results = await milvus_repo.vector_search( - query_vector=query_vector_list, - user_id=user_id, - group_ids=group_ids, # Pass normalized list - start_time=start_time_dt, - end_time=end_time_dt, - limit=effective_limit, - score_threshold=0.0, - radius=effective_radius, - ) - elif mem_type == MemoryType.AGENT_SKILL: - # Agent skill: no timestamp filtering - search_results = await milvus_repo.vector_search( - query_vector=query_vector_list, - user_id=user_id, - group_ids=group_ids, - limit=effective_limit, - score_threshold=0.0, - radius=effective_radius, - ) - else: - # Episodic memory, atomic fact, agent case: use timestamp filtering - search_results = await milvus_repo.vector_search( - query_vector=query_vector_list, - user_id=user_id, - group_ids=group_ids, # Pass normalized list - start_time=start_time_dt, - end_time=end_time_dt, - limit=effective_limit, - score_threshold=0.0, - radius=effective_radius, - ) - record_retrieve_stage( - retrieve_method=retrieve_method, - stage='milvus_search', - memory_type=memory_type, - duration_seconds=time.perf_counter() - milvus_start, - ) - - for r in search_results: - r['memory_type'] = mem_type.value - r['_search_source'] = RetrieveMethod.VECTOR.value - # Milvus already uses 'score', no need to rename - - return search_results - except Exception as e: - record_retrieve_stage( - retrieve_method=retrieve_method, - stage=RetrieveMethod.VECTOR.value, - memory_type=memory_type, - duration_seconds=time.perf_counter() - milvus_start, - ) - record_retrieve_error( - retrieve_method=retrieve_method, - stage=RetrieveMethod.VECTOR.value, - error_type=self._classify_retrieve_error(e), - ) - logger.error(f"Error in get_vector_search_results: {e}") - raise - - # Hybrid memory retrieval - @trace_logger(operation_name="agentic_layer hybrid memory retrieval") - async def retrieve_mem_hybrid( - self, retrieve_mem_request: 'RetrieveMemRequest' - ) -> RetrieveMemResponse: - """Hybrid memory retrieval: keyword + vector + rerank""" - memory_type = ( - retrieve_mem_request.memory_types[0].value - if retrieve_mem_request.memory_types - else 'unknown' - ) - - try: - hits = await self._search_hybrid( - retrieve_mem_request, retrieve_method=RetrieveMethod.HYBRID.value - ) - return await self._to_response(hits, retrieve_mem_request) - except Exception as e: - logger.error(f"Error in retrieve_mem_hybrid: {e}") - return await self._to_response([], retrieve_mem_request) - - # ================== Core Internal Methods ================== - - async def _rerank( - self, - query: str, - hits: List[Dict], - top_k: int, - memory_type: str = 'unknown', - retrieve_method: str = RetrieveMethod.HYBRID.value, - instruction: str = None, - apply_threshold: bool = False, - ) -> List[Dict]: - """Rerank hits using rerank service with stage metrics - - Args: - query: Query text for reranking - hits: List of candidate documents to rerank - top_k: Maximum number of results to return after rerank - memory_type: Memory type for metrics - retrieve_method: Retrieval method for metrics - instruction: Optional instruction for reranker - apply_threshold: If True, filter results by DEFAULT_RERANK_SCORE_THRESHOLD - (used in unlimited mode to ensure quality) - - Returns: - List of reranked documents, optionally filtered by threshold - """ - if not hits: - return [] - - stage_start = time.perf_counter() - try: - result = await get_rerank_service().rerank_memories( - query, hits, top_k, instruction=instruction - ) - - # Apply rerank threshold filtering in unlimited mode - if apply_threshold and result: - original_count = len(result) - result = [ - doc - for doc in result - if doc.get('score', 0.0) >= DEFAULT_RERANK_SCORE_THRESHOLD - ] - filtered_count = original_count - len(result) - if filtered_count > 0: - logger.debug( - f"Rerank threshold filtering: {filtered_count} docs filtered " - f"(threshold={DEFAULT_RERANK_SCORE_THRESHOLD})" - ) - - record_retrieve_stage( - retrieve_method=retrieve_method, - stage='rerank', - memory_type=memory_type, - duration_seconds=time.perf_counter() - stage_start, - ) - return result - except Exception as e: - record_retrieve_error( - retrieve_method=retrieve_method, - stage='rerank', - error_type=self._classify_retrieve_error(e), - ) - raise - - async def _search_hybrid( - self, - request: 'RetrieveMemRequest', - retrieve_method: str = RetrieveMethod.HYBRID.value, - ) -> List[Dict]: - """Core hybrid search: keyword + vector + rerank, returns flat list""" - memory_type = ( - request.memory_types[0].value if request.memory_types else 'unknown' - ) - top_k = request.top_k - is_unlimited_mode = top_k == -1 - - # Run keyword and vector search concurrently - kw_results, vec_results = await asyncio.gather( - self.get_keyword_search_results(request, retrieve_method=retrieve_method), - self.get_vector_search_results(request, retrieve_method=retrieve_method), - ) - # Deduplicate by id - seen_ids = {h.get('id') for h in kw_results} - merged_results = kw_results + [ - h for h in vec_results if h.get('id') not in seen_ids - ] - # When top_k is -1, use DEFAULT_TOPK_LIMIT for rerank - rerank_limit = DEFAULT_TOPK_LIMIT if is_unlimited_mode else top_k - - # Apply rerank threshold filtering in unlimited mode - reranked = await self._rerank( - request.query, - merged_results, - rerank_limit, - memory_type, - retrieve_method, - apply_threshold=is_unlimited_mode, - ) - - # In normal mode, truncate to top_k; in unlimited mode, return all that passed threshold - if not is_unlimited_mode: - return reranked[:top_k] - return reranked - - def _classify_retrieve_error(self, error: Exception) -> str: - """Classify error type for metrics""" - error_str = str(error).lower() - if 'timeout' in error_str or 'timed out' in error_str: - return 'timeout' - elif 'connection' in error_str or 'connect' in error_str: - return 'connection_error' - elif 'not found' in error_str or 'notfound' in error_str: - return 'not_found' - elif 'validation' in error_str or 'invalid' in error_str: - return 'validation_error' - else: - return 'unknown' - - async def _to_response( - self, hits: List[Dict], req: 'RetrieveMemRequest' - ) -> RetrieveMemResponse: - """Convert flat hits list to grouped RetrieveMemResponse""" - user_id = req.user_id if req else "" - source_type = req.retrieve_method.value - memory_types = req.memory_types - # Convert MemoryType enums to string values for Metadata - memory_types_str = [mt.value for mt in memory_types] if memory_types else [] - - if not hits: - return RetrieveMemResponse( - profiles=[], - memories=[], - total_count=0, - has_more=False, - query_metadata=QueryMetadata.from_request(req), - metadata=Metadata( - source=source_type, - user_id=user_id or "", - memory_types=memory_types_str, - ), - ) - memories, total_count = await self.group_by_groupid_stratagy( - hits, source_type=source_type - ) - return RetrieveMemResponse( - profiles=[], - memories=memories, - total_count=total_count, - has_more=False, - query_metadata=QueryMetadata.from_request(req), - metadata=Metadata( - source=source_type, user_id=user_id or "", memory_types=memory_types_str - ), - ) - - # --------- Agentic retrieval (LLM-guided multi-round) --------- - @trace_logger(operation_name="agentic_layer Agentic memory retrieval") - async def retrieve_mem_agentic( - self, retrieve_mem_request: 'RetrieveMemRequest' - ) -> RetrieveMemResponse: - """Agentic retrieval: LLM-guided multi-round intelligent retrieval - - Process: Round 1 (Hybrid) → Rerank → LLM sufficiency check → Round 2 (multi-query) → Merge → Final Rerank - - Behavior: - - When top_k > 0: Returns exactly top_k results (or fewer if insufficient data) - - When top_k == -1 (unlimited): Returns up to AgenticConfig limits - - LLM sufficiency check always uses AgenticConfig.round1_rerank_top_n (default: 10) - - Design Note: - Rerank quantity is max(config_value, top_k) to ensure: - 1. Enough results for LLM sufficiency check (config_value) - 2. Enough results to satisfy user request (top_k) - This maintains LLM judgment quality while meeting user expectations. - """ - req = retrieve_mem_request # alias - top_k = req.top_k - is_unlimited_mode = top_k == -1 - config = AgenticConfig() - memory_type = req.memory_types[0].value if req.memory_types else 'unknown' - - try: - llm_provider = build_default_provider() - - logger.info(f"Agentic Retrieval: {req.query[:60]}...") - - # ========== Round 1: Hybrid search ========== - req1 = RetrieveMemRequest( - query=req.query, - user_id=req.user_id, - group_ids=req.group_ids, - top_k=config.round1_top_n, - memory_types=req.memory_types, - ) - round1 = await self._search_hybrid(req1, retrieve_method='agentic') - logger.info(f"Round 1: {len(round1)} memories") - - if not round1: - return await self._to_response([], req) - - # ========== Rerank for LLM sufficiency check ========== - # Calculate rerank quantity: satisfy both LLM check (10) and user request (top_k) - if is_unlimited_mode: - rerank_n = config.round1_rerank_top_n - else: - rerank_n = max(config.round1_rerank_top_n, top_k) - - reranked = await self._rerank( - req.query, - round1, - rerank_n, - memory_type, - 'agentic', - instruction=config.reranker_instruction, - ) - # LLM always uses fixed number for sufficiency check - topn_for_llm = reranked[: config.round1_rerank_top_n] - topn_pairs = [(m, m.get("score", 0)) for m in topn_for_llm] - - # ========== LLM sufficiency check ========== - with timed("check_sufficiency"): - is_sufficient, reasoning, missing_info = await check_sufficiency( - query=req.query, - results=topn_pairs, - llm_provider=llm_provider, - max_docs=config.round1_rerank_top_n, - ) - logger.info( - f"LLM: {'Sufficient' if is_sufficient else 'Insufficient'} - {reasoning}" - ) - - if is_sufficient: - # Return results respecting user's top_k request - final_results = reranked[:top_k] if not is_unlimited_mode else reranked - return await self._to_response(final_results, req) - - # ========== Round 2: Multi-query ========== - with timed("expand_queries"): - refined_queries, _ = await generate_multi_queries( - original_query=req.query, - results=topn_pairs, - missing_info=missing_info, - llm_provider=llm_provider, - max_docs=config.round1_rerank_top_n, - num_queries=config.num_queries, - ) - logger.info(f"Generated {len(refined_queries)} queries") - - # Parallel hybrid search - async def do_search(q: str) -> List[Dict]: - return await self._search_hybrid( - RetrieveMemRequest( - query=q, - user_id=req.user_id, - group_ids=req.group_ids, - top_k=config.round2_per_query_top_n, - memory_types=req.memory_types, - ), - retrieve_method='agentic', - ) - - with timed_parallel("multi_query_retrieval"): - round2_results = await asyncio.gather( - *[do_search(q) for q in refined_queries], return_exceptions=True - ) - from common_utils.async_utils import reraise_critical_errors - - reraise_critical_errors(round2_results) - all_round2 = [ - h for r in round2_results if not isinstance(r, Exception) for h in r - ] - - # Deduplicate and merge - seen_ids = {m.get("id") for m in round1} - round2_unique = [m for m in all_round2 if m.get("id") not in seen_ids] - combined = round1 + round2_unique[: config.combined_total - len(round1)] - logger.info(f"Combined: {len(combined)} memories") - - # ========== Final Rerank ========== - # Calculate final rerank quantity: satisfy both config (40) and user request (top_k) - if is_unlimited_mode: - final_rerank_n = config.combined_total - else: - final_rerank_n = max(config.combined_total, top_k) - - final = await self._rerank( - req.query, - combined, - final_rerank_n, - memory_type, - 'agentic', - instruction=config.reranker_instruction, - ) - - # Return results respecting user's top_k request - final_results = final[:top_k] if not is_unlimited_mode else final - return await self._to_response(final_results, req) - - except Exception as e: - logger.error(f"Error in retrieve_mem_agentic: {e}", exc_info=True) - return await self._to_response([], req) - - async def _batch_get_memcells( - self, event_ids: List[str], batch_size: int = 100 - ) -> Dict[str, Any]: - """Batch get MemCells, supports batch queries to control single query size - - Args: - event_ids: List of event_id to get - batch_size: Number of items per batch, default 100 - - Returns: - Dict[event_id, MemCell]: Mapping dictionary from event_id to MemCell - """ - if not event_ids: - return {} - - # Deduplicate event_ids - unique_event_ids = list(set(event_ids)) - logger.debug( - f"Batch get MemCells: Total {len(unique_event_ids)} (before deduplication: {len(event_ids)})" - ) - - memcell_repo = get_bean_by_type(MemCellRawRepository) - all_memcells = {} - - # Batch get - for i in range(0, len(unique_event_ids), batch_size): - batch_event_ids = unique_event_ids[i : i + batch_size] - logger.debug( - f"Getting batch {i // batch_size + 1} MemCells: {len(batch_event_ids)} items" - ) - - batch_memcells = await memcell_repo.get_by_event_ids(batch_event_ids) - all_memcells.update(batch_memcells) - - logger.debug( - f"Batch get MemCells completed: Successfully retrieved {len(all_memcells)} items" - ) - return all_memcells - - def _get_type_str(self, val) -> str: - """Extract string value of type field""" - if isinstance(val, RawDataType): - return val.value - return str(val) if val else '' - - def _extract_hit_fields_from_es(self, hit: Dict[str, Any]) -> Dict[str, Any]: - """Extract fields from ES search result""" - source = hit.get('_source', {}) - return { - 'hit_id': source.get('event_id', '') - or source.get('id', '') - or hit.get('_id', ''), - 'user_id': source.get('user_id', ''), - 'group_id': source.get('group_id', ''), - 'timestamp_raw': source.get('timestamp', ''), - 'episode': source.get('episode', ''), - 'parent_type': source.get('parent_type', ''), - 'parent_id': source.get('parent_id', ''), - 'subject': source.get('subject', ''), - 'summary': source.get('summary', ''), - 'participants': source.get('participants', []), - 'sender_ids': source.get('sender_ids', []), - 'event_type': source.get('type', ''), - 'atomic_fact': source.get('atomic_fact', ''), - 'foresight': source.get('foresight', ''), - 'evidence': source.get('evidence', ''), - 'extend_data': source.get('extend', {}) or {}, - 'search_source': 'keyword', - # Agent-specific fields - 'task_intent': source.get('task_intent', ''), - 'approach': source.get('approach', ''), - 'quality_score': source.get('quality_score'), - 'name': source.get('name', ''), - 'description': source.get('description', ''), - 'content': source.get('content', ''), - 'confidence': source.get('confidence'), - 'maturity_score': source.get('maturity_score'), - 'cluster_id': source.get('cluster_id', ''), - } - - def _extract_hit_fields_from_milvus(self, hit: Dict[str, Any]) -> Dict[str, Any]: - """Extract fields from Milvus search result. - - Note: Milvus collections no longer store a 'metadata' JSON field. - Fields like subject/summary are not stored in Milvus and will be empty - here — callers should backfill from MongoDB when display fields are needed. - """ - timestamp_val = hit.get('timestamp') or hit.get('start_time') - return { - 'hit_id': hit.get('id', ''), - 'user_id': hit.get('user_id', ''), - 'group_id': hit.get('group_id', ''), - 'timestamp_raw': timestamp_val, - 'episode': hit.get('episode', ''), - 'parent_type': hit.get('parent_type', ''), - 'parent_id': hit.get('parent_id', ''), - 'subject': '', - 'summary': '', - 'participants': hit.get('participants', []), - 'sender_ids': hit.get('sender_ids', []), - 'event_type': self._get_type_str(hit.get('type') or hit.get('event_type')), - 'atomic_fact': hit.get('atomic_fact', ''), - 'foresight': hit.get( - 'content', '' - ), # Milvus foresight uses 'content' field - 'evidence': hit.get('evidence', ''), - 'extend_data': {}, - 'search_source': 'vector', - # Agent-specific fields - 'task_intent': hit.get('task_intent', ''), - 'approach': hit.get('approach', ''), - 'quality_score': hit.get('quality_score'), - 'name': hit.get('name', ''), - 'description': hit.get('description', ''), - 'content': hit.get('content', ''), - 'confidence': hit.get('confidence'), - 'maturity_score': hit.get('maturity_score'), - 'cluster_id': hit.get('cluster_id', ''), - } - - def _extract_hit_fields(self, hit: Dict[str, Any]) -> Dict[str, Any]: - """Extract fields from search result based on _search_source""" - search_source = hit.get('_search_source') - match search_source: - case RetrieveMethod.KEYWORD.value: - return self._extract_hit_fields_from_es(hit) - case RetrieveMethod.VECTOR.value: - return self._extract_hit_fields_from_milvus(hit) - case _: - raise ValueError(f"Unknown _search_source: {search_source}") - - async def group_by_groupid_stratagy( - self, - search_results: List[Dict[str, Any]], - source_type: str = RetrieveMethod.VECTOR.value, - ) -> tuple: - """Generic search result grouping processing strategy - - Args: - search_results: List of search results - source_type: Retrieval method (keyword/vector/hybrid) - - Returns: - tuple: (memories, scores, original_data, total_count) - """ - # Step 1: Collect all data needed for queries - all_memcell_event_ids = [] - all_user_group_pairs = [] - - for hit in search_results: - fields = self._extract_hit_fields(hit) - parent_type = fields['parent_type'] - parent_id = fields['parent_id'] - user_id = fields['user_id'] - group_id = fields['group_id'] - - if parent_type == ParentType.MEMCELL.value and parent_id: - all_memcell_event_ids.append(parent_id) - - # Collect user_id and group_id pairs - if user_id and group_id: - all_user_group_pairs.append((user_id, group_id)) - - # Step 2: Execute two batch query tasks concurrently - memcells_task = asyncio.create_task( - self._batch_get_memcells(all_memcell_event_ids) - ) - - # Wait for all tasks to complete - memcells_cache = await memcells_task - - # Step 3: Process search results - memories_by_group = {} # {group_id: [Memory]} - - for hit in search_results: - # Extract fields - fields = self._extract_hit_fields(hit) - # Get score (each retrieval method uses its own score field) - score = hit.get('score', 0.0) - - hit_id = fields['hit_id'] - user_id = fields['user_id'] - group_id = fields['group_id'] - timestamp_raw = fields['timestamp_raw'] - parent_type = fields['parent_type'] - parent_id = fields['parent_id'] - episode = fields['episode'] - subject = fields['subject'] - summary = fields['summary'] - participants = fields['participants'] - sender_ids = fields['sender_ids'] - event_type = fields['event_type'] - atomic_fact = fields['atomic_fact'] - foresight = fields['foresight'] - evidence = fields['evidence'] - extend_data = fields['extend_data'] - search_source = fields['search_source'] - # Process timestamp - timestamp = from_iso_format(timestamp_raw) - - # Get memcell data from cache (foresight doesn't need this) - memory_type_value = hit.get('memory_type', MemoryType.EPISODIC_MEMORY.value) - original_data = None - if parent_type == ParentType.MEMCELL.value and parent_id: - memcell = memcells_cache.get(parent_id) - if memcell and memcell.original_data: - original_data = memcell.original_data - else: - logger.debug(f"Memcell not found: event_id={parent_id}") - - # Create object based on memory type - base_kwargs = dict( - id=hit_id, - memory_type=memory_type_value, - user_id=user_id, - timestamp=timestamp, - group_id=group_id, - participants=participants, - sender_ids=sender_ids, - parent_type=parent_type, - parent_id=parent_id, - type=RawDataType.from_string(event_type), - score=score, - original_data=original_data, - extend={'_search_source': search_source}, - ) - - match memory_type_value: - case MemoryType.ATOMIC_FACT.value: - memory = AtomicFact(**base_kwargs, atomic_fact=atomic_fact) - case MemoryType.FORESIGHT.value: - memory = Foresight( - **base_kwargs, foresight=foresight, evidence=evidence - ) - case MemoryType.EPISODIC_MEMORY.value: - # EpisodeMemory has additional fields: subject, summary, episode - memory = EpisodeMemory( - **base_kwargs, subject=subject, summary=summary, episode=episode - ) - case MemoryType.AGENT_CASE.value: - memory = AgentCase( - **base_kwargs, - task_intent=fields.get('task_intent', ''), - approach=fields.get('approach', ''), - quality_score=fields.get('quality_score'), - key_insight=fields.get('key_insight', '') - ) - case MemoryType.AGENT_SKILL.value: - # AgentSkill doesn't have parent_type/parent_id fields - skill_kwargs = { - k: v - for k, v in base_kwargs.items() - if k not in ('parent_type', 'parent_id') - } - memory = AgentSkill( - **skill_kwargs, - name=fields.get('name', ''), - description=fields.get('description', ''), - content=fields.get('content', ''), - confidence=fields.get('confidence', 0.0) or 0.0, - cluster_id=fields.get('cluster_id', ''), - maturity_score=fields.get('maturity_score', 0.0) or 0.0, - ) - case _: - raise ValueError(f"Unsupported memory type: {memory_type_value}") - - # Group by group_id - if group_id not in memories_by_group: - memories_by_group[group_id] = [] - - memories_by_group[group_id].append(memory) - - # Collect all memories and sort by score descending - memories = [] - for group_memories in memories_by_group.values(): - memories.extend(group_memories) - - # Sort by score descending (higher relevance first) - memories.sort(key=lambda m: m.score or 0.0, reverse=True) - - total_count = len(memories) - return memories, total_count diff --git a/methods/EverCore/src/agentic_layer/metrics/__init__.py b/methods/EverCore/src/agentic_layer/metrics/__init__.py deleted file mode 100644 index 11bb5d05..00000000 --- a/methods/EverCore/src/agentic_layer/metrics/__init__.py +++ /dev/null @@ -1,91 +0,0 @@ -""" -Agentic Layer Metrics - -Memory flow metrics including: -- Vectorize (Embedding) metrics -- Rerank metrics -- Retrieve pipeline metrics -- Memorize pipeline metrics -""" - -from .vectorize_metrics import ( - VECTORIZE_REQUESTS_TOTAL, - VECTORIZE_DURATION_SECONDS, - VECTORIZE_BATCH_SIZE, - VECTORIZE_FALLBACK_TOTAL, - VECTORIZE_ERRORS_TOTAL, - VECTORIZE_TOKENS_TOTAL, -) - -from .rerank_metrics import ( - RERANK_REQUESTS_TOTAL, - RERANK_DURATION_SECONDS, - RERANK_DOCUMENTS_TOTAL, - RERANK_FALLBACK_TOTAL, - RERANK_ERRORS_TOTAL, -) - -from .retrieve_metrics import ( - RETRIEVE_REQUESTS_TOTAL, - RETRIEVE_DURATION_SECONDS, - RETRIEVE_RESULTS_COUNT, - RETRIEVE_STAGE_DURATION_SECONDS, - RETRIEVE_ERRORS_TOTAL, -) - -from .memorize_metrics import ( - MEMORIZE_REQUESTS_TOTAL, - MEMORIZE_DURATION_SECONDS, - MEMORIZE_MESSAGES_TOTAL, - MEMORIZE_ERRORS_TOTAL, - BOUNDARY_DETECTION_TOTAL, - MEMCELL_EXTRACTED_TOTAL, - MEMORY_EXTRACTION_STAGE_DURATION_SECONDS, - MEMORY_EXTRACTED_TOTAL, - EXTRACT_MEMORY_REQUESTS_TOTAL, - EXTRACT_MEMORY_DURATION_SECONDS, - # Utility functions - get_space_id_for_metrics, - get_raw_data_type_label, -) - -__all__ = [ - # Vectorize metrics - 'VECTORIZE_REQUESTS_TOTAL', - 'VECTORIZE_DURATION_SECONDS', - 'VECTORIZE_BATCH_SIZE', - 'VECTORIZE_FALLBACK_TOTAL', - 'VECTORIZE_ERRORS_TOTAL', - 'VECTORIZE_TOKENS_TOTAL', - - # Rerank metrics - 'RERANK_REQUESTS_TOTAL', - 'RERANK_DURATION_SECONDS', - 'RERANK_DOCUMENTS_TOTAL', - 'RERANK_FALLBACK_TOTAL', - 'RERANK_ERRORS_TOTAL', - - # Retrieve metrics - 'RETRIEVE_REQUESTS_TOTAL', - 'RETRIEVE_DURATION_SECONDS', - 'RETRIEVE_RESULTS_COUNT', - 'RETRIEVE_STAGE_DURATION_SECONDS', - 'RETRIEVE_ERRORS_TOTAL', - - # Memorize metrics - 'MEMORIZE_REQUESTS_TOTAL', - 'MEMORIZE_DURATION_SECONDS', - 'MEMORIZE_MESSAGES_TOTAL', - 'MEMORIZE_ERRORS_TOTAL', - - # Boundary detection metrics - 'BOUNDARY_DETECTION_TOTAL', - 'MEMCELL_EXTRACTED_TOTAL', - - # Memory extraction metrics - 'MEMORY_EXTRACTION_STAGE_DURATION_SECONDS', - 'MEMORY_EXTRACTED_TOTAL', - 'EXTRACT_MEMORY_REQUESTS_TOTAL', - 'EXTRACT_MEMORY_DURATION_SECONDS', -] - diff --git a/methods/EverCore/src/agentic_layer/metrics/memorize_metrics.py b/methods/EverCore/src/agentic_layer/metrics/memorize_metrics.py deleted file mode 100644 index e1251b76..00000000 --- a/methods/EverCore/src/agentic_layer/metrics/memorize_metrics.py +++ /dev/null @@ -1,534 +0,0 @@ -""" -Memorize Pipeline Metrics - -Metrics for monitoring the memory ingestion (add memory) pipeline including: -- Request processing -- Memory extraction statistics -- Boundary detection and MemCell extraction - -All metrics include space_id and raw_data_type labels for multi-tenant support. - -Usage: - from agentic_layer.metrics.memorize_metrics import ( - record_memorize_request, - record_memorize_error, - record_extraction_stage, - get_space_id_for_metrics, - ) - - # Record successful memorize request - record_memorize_request( - space_id=get_space_id_for_metrics(), - raw_data_type='conversation', - status='success', - duration_seconds=0.5, - ) - - # Record extraction stage duration - record_extraction_stage( - space_id=get_space_id_for_metrics(), - raw_data_type='conversation', - stage='extract_episodes', - duration_seconds=2.5, - ) -""" - -from typing import Optional -from core.observation.metrics import Counter, Histogram, HistogramBuckets -from core.tenants.tenant_contextvar import get_current_tenant - - -# ============================================================ -# Utility Functions -# ============================================================ - - -def get_space_id_for_metrics() -> str: - """ - Get space_id for metrics label - - Returns 'default' if tenant context is not available or space_id is not set. - - Returns: - str: space_id or 'default' - """ - try: - tenant = get_current_tenant() - if tenant and tenant.tenant_detail and tenant.tenant_detail.tenant_info: - return tenant.tenant_detail.tenant_info.get('space_id', 'default') - except Exception: - pass - return 'default' - - -def get_raw_data_type_label(raw_data_type: Optional[str]) -> str: - """ - Get raw_data_type for metrics label - - Args: - raw_data_type: Raw data type string or enum value - - Returns: - str: Original value as string or 'unknown' - """ - if not raw_data_type: - return 'unknown' - return str(raw_data_type) - - -# ============================================================ -# Counter Metrics -# ============================================================ - -MEMORIZE_REQUESTS_TOTAL = Counter( - name='memorize_requests_total', - description='Total number of memorize requests', - labelnames=['space_id', 'raw_data_type', 'status'], - namespace='evermemos', - subsystem='agentic', -) -""" -Memorize requests counter - -Labels: -- space_id: Tenant space identifier -- raw_data_type: Type of raw data (conversation, etc.) -- status: success, error, accumulated, extracted - - success: Request processed successfully (with or without memory extraction) - - error: Request failed - - accumulated: No memory extracted, message queued - - extracted: Memories extracted successfully -""" - - -MEMORIZE_ERRORS_TOTAL = Counter( - name='memorize_errors_total', - description='Total number of memorize errors', - labelnames=['space_id', 'raw_data_type', 'stage', 'error_type'], - namespace='evermemos', - subsystem='agentic', -) -""" -Memorize errors counter - -Labels: -- space_id: Tenant space identifier -- raw_data_type: Type of raw data (conversation, etc.) -- stage: conversion, save_logs, memorize_process -- error_type: validation_error, timeout, connection_error, unknown -""" - - -# ============================================================ -# Histogram Metrics -# ============================================================ - -MEMORIZE_DURATION_SECONDS = Histogram( - name='memorize_duration_seconds', - description='End-to-end duration of memorize operation in seconds', - labelnames=['space_id', 'raw_data_type', 'status'], - namespace='evermemos', - subsystem='agentic', - buckets=HistogramBuckets.API_CALL, # 10ms - 30s for API calls -) -""" -End-to-end memorize duration histogram - -Labels: -- space_id: Tenant space identifier -- raw_data_type: Type of raw data (conversation, etc.) -- status: success, error - -Buckets: 10ms, 50ms, 100ms, 250ms, 500ms, 1s, 2.5s, 5s, 10s, 30s -""" - - -MEMORIZE_MESSAGES_TOTAL = Counter( - name='memorize_messages_total', - description='Total number of messages processed for memorization', - labelnames=['space_id', 'raw_data_type', 'status'], - namespace='evermemos', - subsystem='agentic', -) -""" -Messages processed counter - -Labels: -- space_id: Tenant space identifier -- raw_data_type: Type of raw data (conversation, etc.) -- status: received, saved, processed -""" - - -# ============================================================ -# Boundary Detection Metrics -# ============================================================ - -BOUNDARY_DETECTION_TOTAL = Counter( - name='boundary_detection_total', - description='Total number of boundary detection results', - labelnames=['space_id', 'raw_data_type', 'result', 'trigger_type'], - namespace='evermemos', - subsystem='agentic', -) -""" -Boundary detection results counter - -Labels: -- space_id: Tenant space identifier -- raw_data_type: Type of raw data (conversation, etc.) -- result: should_end, should_wait, error, force_split -- trigger_type: llm, token_limit, message_limit, first_message -""" - - -MEMCELL_EXTRACTED_TOTAL = Counter( - name='memcell_extracted_total', - description='Total number of MemCells extracted', - labelnames=['space_id', 'raw_data_type', 'trigger_type'], - namespace='evermemos', - subsystem='agentic', -) -""" -MemCell extraction counter - -Labels: -- space_id: Tenant space identifier -- raw_data_type: Type of raw data (conversation, etc.) -- trigger_type: llm, token_limit, message_limit -""" - - -# ============================================================ -# Memory Extraction Metrics -# ============================================================ - -MEMORY_EXTRACTION_STAGE_DURATION_SECONDS = Histogram( - name='memory_extraction_stage_duration_seconds', - description='Duration of individual memory extraction stages in seconds', - labelnames=['space_id', 'raw_data_type', 'stage'], - namespace='evermemos', - subsystem='agentic', - buckets=HistogramBuckets.ML_INFERENCE, # LLM inference buckets -) -""" -Memory extraction stage duration histogram - -Labels: -- space_id: Tenant space identifier -- raw_data_type: Type of raw data (conversation, etc.) -- stage: init_state, extract_episodes, extract_foresights, extract_atomic_facts, - update_memcell_cluster, process_memories - -Buckets: 10ms - 10s for ML inference -""" - - -MEMORY_EXTRACTED_TOTAL = Counter( - name='memory_extracted_total', - description='Total number of memories extracted by type', - labelnames=['space_id', 'raw_data_type', 'memory_type'], - namespace='evermemos', - subsystem='agentic', -) -""" -Memory extraction counter by type - -Labels: -- space_id: Tenant space identifier -- raw_data_type: Type of raw data (conversation, etc.) -- memory_type: episode, foresight, atomic_fact -""" - - -EXTRACT_MEMORY_REQUESTS_TOTAL = Counter( - name='extract_memory_requests_total', - description='Total number of extract_memory calls by memory type', - labelnames=['space_id', 'raw_data_type', 'memory_type', 'status'], - namespace='evermemos', - subsystem='memory_layer', -) -""" -extract_memory call counter - -Labels: -- space_id: Tenant space identifier -- raw_data_type: Type of raw data (conversation, etc.) -- memory_type: episodic_memory, foresight, atomic_fact, profile, group_profile -- status: success, error, empty_result -""" - - -EXTRACT_MEMORY_DURATION_SECONDS = Histogram( - name='extract_memory_duration_seconds', - description='Duration of extract_memory calls by memory type in seconds', - labelnames=['space_id', 'raw_data_type', 'memory_type'], - namespace='evermemos', - subsystem='memory_layer', - buckets=HistogramBuckets.ML_INFERENCE, # LLM inference buckets -) -""" -extract_memory duration histogram - -Labels: -- space_id: Tenant space identifier -- raw_data_type: Type of raw data (conversation, etc.) -- memory_type: episodic_memory, foresight, atomic_fact, profile, group_profile - -Buckets: 10ms - 10s for ML inference -""" - - -# ============================================================ -# Helper Functions -# ============================================================ - - -def record_memorize_request( - space_id: str, raw_data_type: str, status: str, duration_seconds: float -) -> None: - """ - Helper function to record memorize request metrics - - Args: - space_id: Tenant space identifier - raw_data_type: Type of raw data (conversation, etc.) - status: Request status (success, error, accumulated, extracted) - duration_seconds: Total operation duration in seconds - - Example: - record_memorize_request( - space_id=get_space_id_for_metrics(), - raw_data_type='conversation', - status='extracted', - duration_seconds=0.5, - ) - """ - raw_data_type = get_raw_data_type_label(raw_data_type) - - # Counter - MEMORIZE_REQUESTS_TOTAL.labels( - space_id=space_id, raw_data_type=raw_data_type, status=status - ).inc() - - # Duration histogram (use simplified status for duration) - duration_status = ( - 'success' if status in ('success', 'accumulated', 'extracted') else 'error' - ) - MEMORIZE_DURATION_SECONDS.labels( - space_id=space_id, raw_data_type=raw_data_type, status=duration_status - ).observe(duration_seconds) - - -def record_memorize_error( - space_id: str, raw_data_type: str, stage: str, error_type: str -) -> None: - """ - Helper function to record memorize error - - Args: - space_id: Tenant space identifier - raw_data_type: Type of raw data (conversation, etc.) - stage: Stage where error occurred (conversion, save_logs, memorize_process) - error_type: Error type (validation_error, timeout, connection_error, unknown) - - Example: - record_memorize_error( - space_id=get_space_id_for_metrics(), - raw_data_type='conversation', - stage='conversion', - error_type='validation_error', - ) - """ - raw_data_type = get_raw_data_type_label(raw_data_type) - MEMORIZE_ERRORS_TOTAL.labels( - space_id=space_id, - raw_data_type=raw_data_type, - stage=stage, - error_type=error_type, - ).inc() - - -def record_memorize_message( - space_id: str, raw_data_type: str, status: str, count: int = 1 -) -> None: - """ - Helper function to record message processing - - Args: - space_id: Tenant space identifier - raw_data_type: Type of raw data (conversation, etc.) - status: Message status (received, saved, processed) - count: Number of messages - - Example: - record_memorize_message( - space_id=get_space_id_for_metrics(), - raw_data_type='conversation', - status='received', - count=1, - ) - """ - raw_data_type = get_raw_data_type_label(raw_data_type) - MEMORIZE_MESSAGES_TOTAL.labels( - space_id=space_id, raw_data_type=raw_data_type, status=status - ).inc(count) - - -def classify_memorize_error(error: Exception) -> str: - """ - Classify error type for metrics - - Args: - error: Exception instance - - Returns: - Error type string for metrics label - """ - # TODO: Add detailed error classification based on actual scenarios - _ = error # Placeholder for future use - return 'error' - - -def record_boundary_detection( - space_id: str, raw_data_type: str, result: str, trigger_type: str -) -> None: - """ - Helper function to record boundary detection metrics - - Args: - space_id: Tenant space identifier - raw_data_type: Type of raw data (conversation, etc.) - result: Detection result (should_end, should_wait, error, force_split) - trigger_type: What triggered the detection (llm, token_limit, message_limit, first_message) - - Example: - record_boundary_detection( - space_id=get_space_id_for_metrics(), - raw_data_type='conversation', - result='should_end', - trigger_type='llm', - ) - """ - raw_data_type = get_raw_data_type_label(raw_data_type) - BOUNDARY_DETECTION_TOTAL.labels( - space_id=space_id, - raw_data_type=raw_data_type, - result=result, - trigger_type=trigger_type, - ).inc() - - -def record_memcell_extracted( - space_id: str, raw_data_type: str, trigger_type: str -) -> None: - """ - Helper function to record MemCell extraction - - Args: - space_id: Tenant space identifier - raw_data_type: Type of raw data (conversation, etc.) - trigger_type: What triggered the extraction (llm, token_limit, message_limit) - - Example: - record_memcell_extracted( - space_id=get_space_id_for_metrics(), - raw_data_type='conversation', - trigger_type='llm', - ) - """ - raw_data_type = get_raw_data_type_label(raw_data_type) - MEMCELL_EXTRACTED_TOTAL.labels( - space_id=space_id, raw_data_type=raw_data_type, trigger_type=trigger_type - ).inc() - - -def record_extraction_stage( - space_id: str, raw_data_type: str, stage: str, duration_seconds: float -) -> None: - """ - Helper function to record memory extraction stage duration - - Args: - space_id: Tenant space identifier - raw_data_type: Type of raw data (conversation, etc.) - stage: Extraction stage (init_state, extract_episodes, extract_foresights, - extract_atomic_facts, update_memcell_cluster, process_memories) - duration_seconds: Stage duration in seconds - - Example: - record_extraction_stage( - space_id=get_space_id_for_metrics(), - raw_data_type='conversation', - stage='extract_episodes', - duration_seconds=2.5, - ) - """ - raw_data_type = get_raw_data_type_label(raw_data_type) - MEMORY_EXTRACTION_STAGE_DURATION_SECONDS.labels( - space_id=space_id, raw_data_type=raw_data_type, stage=stage - ).observe(duration_seconds) - - -def record_memory_extracted( - space_id: str, raw_data_type: str, memory_type: str, count: int = 1 -) -> None: - """ - Helper function to record extracted memory count by type - - Args: - space_id: Tenant space identifier - raw_data_type: Type of raw data (conversation, etc.) - memory_type: Memory type (episode, foresight, atomic_fact) - count: Number of memories extracted - - Example: - record_memory_extracted( - space_id=get_space_id_for_metrics(), - raw_data_type='conversation', - memory_type='episode', - count=3, - ) - """ - raw_data_type = get_raw_data_type_label(raw_data_type) - MEMORY_EXTRACTED_TOTAL.labels( - space_id=space_id, raw_data_type=raw_data_type, memory_type=memory_type - ).inc(count) - - -def record_extract_memory_call( - space_id: str, - raw_data_type: str, - memory_type: str, - status: str, - duration_seconds: float, -) -> None: - """ - Helper function to record extract_memory call metrics - - Args: - space_id: Tenant space identifier - raw_data_type: Type of raw data (conversation, etc.) - memory_type: Memory type (episodic_memory, foresight, atomic_fact, profile, group_profile) - status: Call status (success, error, empty_result) - duration_seconds: Call duration in seconds - - Example: - record_extract_memory_call( - space_id=get_space_id_for_metrics(), - raw_data_type='conversation', - memory_type='episodic_memory', - status='success', - duration_seconds=2.5, - ) - """ - raw_data_type = get_raw_data_type_label(raw_data_type) - EXTRACT_MEMORY_REQUESTS_TOTAL.labels( - space_id=space_id, - raw_data_type=raw_data_type, - memory_type=memory_type, - status=status, - ).inc() - EXTRACT_MEMORY_DURATION_SECONDS.labels( - space_id=space_id, raw_data_type=raw_data_type, memory_type=memory_type - ).observe(duration_seconds) diff --git a/methods/EverCore/src/agentic_layer/metrics/rerank_metrics.py b/methods/EverCore/src/agentic_layer/metrics/rerank_metrics.py deleted file mode 100644 index a993dbaf..00000000 --- a/methods/EverCore/src/agentic_layer/metrics/rerank_metrics.py +++ /dev/null @@ -1,219 +0,0 @@ -""" -Rerank Service Metrics - -Metrics for monitoring reranking service performance and reliability. - -Usage: - from agentic_layer.metrics import ( - RERANK_REQUESTS_TOTAL, - RERANK_DURATION_SECONDS, - RERANK_DOCUMENTS_TOTAL, - RERANK_FALLBACK_TOTAL, - RERANK_ERRORS_TOTAL, - ) - - # Record successful rerank request - RERANK_REQUESTS_TOTAL.labels( - provider='vllm', - status='success' - ).inc() - - # Record duration - RERANK_DURATION_SECONDS.labels( - provider='vllm' - ).observe(0.234) - - # Record documents count - RERANK_DOCUMENTS_TOTAL.labels( - provider='vllm' - ).observe(50) -""" - -from core.observation.metrics import Counter, Histogram, HistogramBuckets - - -# ============================================================ -# Counter Metrics -# ============================================================ - -RERANK_REQUESTS_TOTAL = Counter( - name='rerank_requests_total', - description='Total number of rerank requests', - labelnames=['provider', 'status'], - namespace='evermemos', - subsystem='agentic', -) -""" -Rerank requests counter - -Labels: -- provider: vllm, deepinfra -- status: success, error, timeout, fallback -""" - - -RERANK_FALLBACK_TOTAL = Counter( - name='rerank_fallback_total', - description='Total number of rerank fallback events', - labelnames=['primary_provider', 'fallback_provider', 'reason'], - namespace='evermemos', - subsystem='agentic', -) -""" -Rerank fallback counter - -Labels: -- primary_provider: Primary provider that failed (vllm, deepinfra) -- fallback_provider: Fallback provider used (vllm, deepinfra) -- reason: error, timeout, max_failures_exceeded -""" - - -RERANK_ERRORS_TOTAL = Counter( - name='rerank_errors_total', - description='Total number of rerank errors', - labelnames=['provider', 'error_type'], - namespace='evermemos', - subsystem='agentic', -) -""" -Rerank errors counter - -Labels: -- provider: vllm, deepinfra -- error_type: api_error, timeout, rate_limit, validation_error, unknown -""" - - -# ============================================================ -# Histogram Metrics -# ============================================================ - -RERANK_DURATION_SECONDS = Histogram( - name='rerank_duration_seconds', - description='Duration of rerank operations in seconds', - labelnames=['provider'], - namespace='evermemos', - subsystem='agentic', - buckets=HistogramBuckets.ML_INFERENCE, # 10ms - 10s for ML inference -) -""" -Rerank operation duration histogram - -Labels: -- provider: vllm, deepinfra - -Buckets: 10ms, 25ms, 50ms, 100ms, 250ms, 500ms, 1s, 2.5s, 5s, 10s -""" - - -RERANK_DOCUMENTS_TOTAL = Histogram( - name='rerank_documents_count', - description='Number of documents reranked per request', - labelnames=['provider'], - namespace='evermemos', - subsystem='agentic', - buckets=(1, 5, 10, 20, 50, 100, 200, 500, 1000), -) -""" -Rerank documents count histogram - -Labels: -- provider: vllm, deepinfra - -Buckets: 1, 5, 10, 20, 50, 100, 200, 500, 1000 documents -""" - - -# ============================================================ -# Helper Functions -# ============================================================ - -def record_rerank_request( - provider: str, - status: str, - duration_seconds: float, - documents_count: int, -) -> None: - """ - Helper function to record all rerank metrics in one call - - Args: - provider: Service provider (vllm, deepinfra) - status: Request status (success, error, timeout, fallback) - duration_seconds: Operation duration in seconds - documents_count: Number of documents reranked - - Example: - record_rerank_request( - provider='vllm', - status='success', - duration_seconds=0.5, - documents_count=50 - ) - """ - # Counter - RERANK_REQUESTS_TOTAL.labels( - provider=provider, - status=status - ).inc() - - # Duration histogram - RERANK_DURATION_SECONDS.labels( - provider=provider - ).observe(duration_seconds) - - # Documents count histogram - RERANK_DOCUMENTS_TOTAL.labels( - provider=provider - ).observe(documents_count) - - -def record_rerank_fallback( - primary_provider: str, - fallback_provider: str, - reason: str, -) -> None: - """ - Helper function to record rerank fallback event - - Args: - primary_provider: Primary provider that failed - fallback_provider: Fallback provider used - reason: Fallback reason (error, timeout, max_failures_exceeded) - - Example: - record_rerank_fallback( - primary_provider='vllm', - fallback_provider='deepinfra', - reason='timeout' - ) - """ - RERANK_FALLBACK_TOTAL.labels( - primary_provider=primary_provider, - fallback_provider=fallback_provider, - reason=reason - ).inc() - - -def record_rerank_error( - provider: str, - error_type: str, -) -> None: - """ - Helper function to record rerank error - - Args: - provider: Service provider - error_type: Error type (api_error, timeout, rate_limit, validation_error, unknown) - - Example: - record_rerank_error( - provider='vllm', - error_type='timeout' - ) - """ - RERANK_ERRORS_TOTAL.labels( - provider=provider, - error_type=error_type - ).inc() diff --git a/methods/EverCore/src/agentic_layer/metrics/retrieve_metrics.py b/methods/EverCore/src/agentic_layer/metrics/retrieve_metrics.py deleted file mode 100644 index cfb71a9d..00000000 --- a/methods/EverCore/src/agentic_layer/metrics/retrieve_metrics.py +++ /dev/null @@ -1,336 +0,0 @@ -""" -Retrieve Pipeline Metrics - -Metrics for monitoring the complete memory retrieval pipeline including: -- Milvus vector search -- Memory fetch operations -- End-to-end retrieval latency - -Usage: - from agentic_layer.metrics import ( - RETRIEVE_REQUESTS_TOTAL, - RETRIEVE_DURATION_SECONDS, - RETRIEVE_RESULTS_COUNT, - RETRIEVE_STAGE_DURATION_SECONDS, - RETRIEVE_ERRORS_TOTAL, - ) - - # Record successful retrieval - RETRIEVE_REQUESTS_TOTAL.labels( - memory_type='episodic_memory', - retrieve_method='vector', - status='success' - ).inc() - - # Record duration - RETRIEVE_DURATION_SECONDS.labels( - memory_type='episodic_memory', - retrieve_method='vector_search' - ).observe(0.567) - - # Record stage-specific duration - RETRIEVE_STAGE_DURATION_SECONDS.labels( - stage='milvus_search', - memory_type='episodic_memory' - ).observe(0.123) -""" - -from core.observation.metrics import Counter, Histogram, HistogramBuckets - - -# ============================================================ -# Counter Metrics -# ============================================================ - -RETRIEVE_REQUESTS_TOTAL = Counter( - name='retrieve_requests_total', - description='Total number of memory retrieval requests', - labelnames=['memory_type', 'retrieve_method', 'status'], - namespace='evermemos', - subsystem='agentic', -) -""" -Memory retrieval requests counter - -Labels: -- memory_type: episodic_memory, profile, foresight, atomic_fact, entity, relation, etc. -- retrieve_method: vector, id_lookup, keyword, hybrid, rrf, agentic -- status: success, error, timeout, empty_result -""" - - -RETRIEVE_ERRORS_TOTAL = Counter( - name='retrieve_errors_total', - description='Total number of retrieval errors', - labelnames=['retrieve_method', 'stage', 'error_type'], - namespace='evermemos', - subsystem='agentic', -) -""" -Retrieval errors counter - -Labels: -- retrieve_method: keyword, vector, hybrid, rrf, agentic -- stage: keyword, vector, embedding, milvus_search, rerank, rrf_fusion -- error_type: connection_error, timeout, not_found, validation_error, unknown -""" - - -# ============================================================ -# Histogram Metrics -# ============================================================ - -RETRIEVE_DURATION_SECONDS = Histogram( - name='retrieve_duration_seconds', - description='End-to-end duration of memory retrieval in seconds', - labelnames=['memory_type', 'retrieve_method'], - namespace='evermemos', - subsystem='agentic', - buckets=HistogramBuckets.API_CALL, # 10ms - 30s for API calls -) -""" -End-to-end retrieval duration histogram - -Labels: -- memory_type: episodic_memory, profile, foresight, atomic_fact, etc. -- retrieve_method: vector, id_lookup, keyword, hybrid, rrf, agentic - -Buckets: 10ms, 50ms, 100ms, 250ms, 500ms, 1s, 2.5s, 5s, 10s, 30s -""" - - -RETRIEVE_STAGE_DURATION_SECONDS = Histogram( - name='retrieve_stage_duration_seconds', - description='Duration of individual retrieval stages in seconds', - labelnames=['retrieve_method', 'stage', 'memory_type'], - namespace='evermemos', - subsystem='agentic', - buckets=HistogramBuckets.DATABASE, # 1ms - 5s for database operations -) -""" -Stage-specific duration histogram - -Labels: -- retrieve_method: keyword, vector, hybrid, rrf, agentic -- stage: keyword, vector, embedding, milvus_search, rerank, rrf_fusion -- memory_type: episodic_memory, profile, foresight, atomic_fact, etc. - -Buckets: 1ms, 5ms, 10ms, 25ms, 50ms, 100ms, 250ms, 500ms, 1s, 2.5s, 5s -""" - - -RETRIEVE_RESULTS_COUNT = Histogram( - name='retrieve_results_count', - description='Number of results returned from retrieval', - labelnames=['memory_type', 'retrieve_method'], - namespace='evermemos', - subsystem='agentic', - buckets=(0, 1, 5, 10, 20, 50, 100, 200, 500, 1000), -) -""" -Retrieval results count histogram - -Labels: -- memory_type: episodic_memory, profile, foresight, atomic_fact, etc. -- retrieve_method: vector, id_lookup, keyword, hybrid, rrf, agentic - -Buckets: 0, 1, 5, 10, 20, 50, 100, 200, 500, 1000 results -""" - - -# ============================================================ -# Helper Functions -# ============================================================ - - -def record_retrieve_request( - memory_type: str, - retrieve_method: str, - status: str, - duration_seconds: float, - results_count: int, -) -> None: - """ - Helper function to record all retrieval metrics in one call - - Args: - memory_type: Memory type (episodic_memory, profile, foresight, etc.) - retrieve_method: Retrieval method (keyword, vector, hybrid, rrf, agentic) - status: Request status (success, error, timeout, empty_result) - duration_seconds: Total retrieval duration in seconds - results_count: Number of results returned - - Example: - record_retrieve_request( - memory_type='episodic_memory', - retrieve_method='vector', - status='success', - duration_seconds=0.567, - results_count=10 - ) - """ - # Counter - RETRIEVE_REQUESTS_TOTAL.labels( - memory_type=memory_type, retrieve_method=retrieve_method, status=status - ).inc() - - # Duration histogram - RETRIEVE_DURATION_SECONDS.labels( - memory_type=memory_type, retrieve_method=retrieve_method - ).observe(duration_seconds) - - # Results count histogram - RETRIEVE_RESULTS_COUNT.labels( - memory_type=memory_type, retrieve_method=retrieve_method - ).observe(results_count) - - -def record_retrieve_stage( - retrieve_method: str, stage: str, memory_type: str, duration_seconds: float -) -> None: - """ - Helper function to record stage-specific duration - - Args: - retrieve_method: Retrieval method (keyword, vector, hybrid, rrf, agentic) - stage: Retrieval stage (keyword, vector, embedding, milvus_search, rerank, rrf_fusion) - memory_type: Memory type - duration_seconds: Stage duration in seconds - - Example: - record_retrieve_stage( - retrieve_method='vector', - stage='milvus_search', - memory_type='episodic_memory', - duration_seconds=0.123 - ) - """ - RETRIEVE_STAGE_DURATION_SECONDS.labels( - retrieve_method=retrieve_method, stage=stage, memory_type=memory_type - ).observe(duration_seconds) - - -def record_retrieve_error(retrieve_method: str, stage: str, error_type: str) -> None: - """ - Helper function to record retrieval error - - Args: - retrieve_method: Retrieval method (keyword, vector, hybrid, rrf, agentic) - stage: Stage where error occurred (keyword, vector, embedding, milvus_search, rerank, rrf_fusion) - error_type: Error type (connection_error, timeout, not_found, validation_error, unknown) - - Example: - record_retrieve_error( - retrieve_method='vector', - stage='milvus_search', - error_type='timeout' - ) - """ - RETRIEVE_ERRORS_TOTAL.labels( - retrieve_method=retrieve_method, stage=stage, error_type=error_type - ).inc() - - -class RetrieveMetricsContext: - """ - Context manager for easy metrics recording in retrieval operations - - Usage: - async def retrieve_memories(query, memory_type): - with RetrieveMetricsContext(memory_type, 'vector_search') as ctx: - # Stage 1: Embedding - with ctx.stage('embedding'): - embedding = await get_embedding(query) - - # Stage 2: Milvus search - with ctx.stage('milvus_search'): - results = await milvus_search(embedding) - - # Stage 3: Rerank - with ctx.stage('rerank'): - reranked = await rerank(query, results) - - ctx.set_results_count(len(reranked)) - return reranked - """ - - def __init__(self, memory_type: str, retrieve_method: str): - self.memory_type = memory_type - self.retrieve_method = retrieve_method - self.start_time = None - self.results_count = 0 - self.status = 'success' - self._current_stage = None - self._stage_start_time = None - - def __enter__(self): - import time - - self.start_time = time.time() - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - import time - - duration = time.time() - self.start_time - - if exc_type is not None: - self.status = 'error' - - record_retrieve_request( - memory_type=self.memory_type, - retrieve_method=self.retrieve_method, - status=self.status, - duration_seconds=duration, - results_count=self.results_count, - ) - - return False # Don't suppress exceptions - - def stage(self, stage_name: str): - """Context manager for stage timing""" - return _StageContext(self, stage_name) - - def set_results_count(self, count: int): - """Set the results count""" - self.results_count = count - - def set_status(self, status: str): - """Set the status (success, error, timeout, empty_result)""" - self.status = status - - -class _StageContext: - """Internal context manager for stage timing""" - - def __init__(self, parent: RetrieveMetricsContext, stage_name: str): - self.parent = parent - self.stage_name = stage_name - self.start_time = None - - def __enter__(self): - import time - - self.start_time = time.time() - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - import time - - duration = time.time() - self.start_time - - record_retrieve_stage( - retrieve_method=self.parent.retrieve_method, - stage=self.stage_name, - memory_type=self.parent.memory_type, - duration_seconds=duration, - ) - - if exc_type is not None: - record_retrieve_error( - retrieve_method=self.parent.retrieve_method, - stage=self.stage_name, - error_type='unknown' if exc_type is Exception else exc_type.__name__, - ) - - return False # Don't suppress exceptions diff --git a/methods/EverCore/src/agentic_layer/metrics/vectorize_metrics.py b/methods/EverCore/src/agentic_layer/metrics/vectorize_metrics.py deleted file mode 100644 index 5ea2696e..00000000 --- a/methods/EverCore/src/agentic_layer/metrics/vectorize_metrics.py +++ /dev/null @@ -1,233 +0,0 @@ -""" -Vectorize (Embedding) Service Metrics - -Metrics for monitoring embedding generation performance and reliability. - -""" - -from core.observation.metrics import Counter, Histogram, HistogramBuckets - - -# ============================================================ -# Counter Metrics -# ============================================================ - -VECTORIZE_REQUESTS_TOTAL = Counter( - name='vectorize_requests_total', - description='Total number of vectorize (embedding) requests', - labelnames=['provider', 'operation', 'status'], - namespace='evermemos', - subsystem='agentic', -) -""" -Vectorize requests counter - -Labels: -- provider: vllm, deepinfra -- operation: get_embedding, get_embeddings, get_embeddings_batch -- status: success, error, timeout, fallback -""" - - -VECTORIZE_FALLBACK_TOTAL = Counter( - name='vectorize_fallback_total', - description='Total number of vectorize fallback events', - labelnames=['primary_provider', 'fallback_provider', 'reason'], - namespace='evermemos', - subsystem='agentic', -) -""" -Vectorize fallback counter - -Labels: -- primary_provider: Primary provider that failed (vllm, deepinfra) -- fallback_provider: Fallback provider used (vllm, deepinfra) -- reason: error, timeout, max_failures_exceeded -""" - - -VECTORIZE_ERRORS_TOTAL = Counter( - name='vectorize_errors_total', - description='Total number of vectorize errors', - labelnames=['provider', 'operation', 'error_type'], - namespace='evermemos', - subsystem='agentic', -) -""" -Vectorize errors counter - -Labels: -- provider: vllm, deepinfra -- operation: get_embedding, get_embeddings, get_embeddings_batch -- error_type: api_error, timeout, rate_limit, validation_error, unknown -""" - - -VECTORIZE_TOKENS_TOTAL = Counter( - name='vectorize_tokens_total', - description='Total number of tokens processed for embedding', - labelnames=['provider'], - namespace='evermemos', - subsystem='agentic', -) -""" -Vectorize tokens counter (for cost tracking) - -Labels: -- provider: vllm, deepinfra -""" - - -# ============================================================ -# Histogram Metrics -# ============================================================ - -VECTORIZE_DURATION_SECONDS = Histogram( - name='vectorize_duration_seconds', - description='Duration of vectorize operations in seconds', - labelnames=['provider', 'operation'], - namespace='evermemos', - subsystem='agentic', - buckets=HistogramBuckets.ML_INFERENCE, # 10ms - 10s for ML inference -) -""" -Vectorize operation duration histogram - -Labels: -- provider: vllm, deepinfra -- operation: get_embedding, get_embeddings, get_embeddings_batch - -Buckets: 10ms, 25ms, 50ms, 100ms, 250ms, 500ms, 1s, 2.5s, 5s, 10s -""" - - -VECTORIZE_BATCH_SIZE = Histogram( - name='vectorize_batch_size', - description='Batch size of vectorize operations', - labelnames=['provider', 'operation'], - namespace='evermemos', - subsystem='agentic', - buckets=(1, 2, 5, 10, 20, 50, 100, 200, 500), -) -""" -Vectorize batch size histogram - -Labels: -- provider: vllm, deepinfra -- operation: get_embeddings, get_embeddings_batch - -Buckets: 1, 2, 5, 10, 20, 50, 100, 200, 500 texts -""" - - -# ============================================================ -# Helper Functions -# ============================================================ - -def record_vectorize_request( - provider: str, - operation: str, - status: str, - duration_seconds: float, - batch_size: int = 1, - tokens: int = 0, -) -> None: - """ - Helper function to record all vectorize metrics in one call - - Args: - provider: Service provider (vllm, deepinfra) - operation: Operation type (get_embedding, get_embeddings, get_embeddings_batch) - status: Request status (success, error, timeout, fallback) - duration_seconds: Operation duration in seconds - batch_size: Number of texts processed - tokens: Number of tokens processed (optional, for cost tracking) - - Example: - record_vectorize_request( - provider='vllm', - operation='get_embeddings', - status='success', - duration_seconds=0.5, - batch_size=10, - tokens=250 - ) - """ - # Counter - VECTORIZE_REQUESTS_TOTAL.labels( - provider=provider, - operation=operation, - status=status - ).inc() - - # Duration histogram - VECTORIZE_DURATION_SECONDS.labels( - provider=provider, - operation=operation - ).observe(duration_seconds) - - # Batch size histogram (only for batch operations) - if batch_size > 1: - VECTORIZE_BATCH_SIZE.labels( - provider=provider, - operation=operation - ).observe(batch_size) - - # Token counter (if available) - if tokens > 0: - VECTORIZE_TOKENS_TOTAL.labels(provider=provider).inc(tokens) - - -def record_vectorize_fallback( - primary_provider: str, - fallback_provider: str, - reason: str, -) -> None: - """ - Helper function to record vectorize fallback event - - Args: - primary_provider: Primary provider that failed - fallback_provider: Fallback provider used - reason: Fallback reason (error, timeout, max_failures_exceeded) - - Example: - record_vectorize_fallback( - primary_provider='vllm', - fallback_provider='deepinfra', - reason='timeout' - ) - """ - VECTORIZE_FALLBACK_TOTAL.labels( - primary_provider=primary_provider, - fallback_provider=fallback_provider, - reason=reason - ).inc() - - -def record_vectorize_error( - provider: str, - operation: str, - error_type: str, -) -> None: - """ - Helper function to record vectorize error - - Args: - provider: Service provider - operation: Operation type - error_type: Error type (api_error, timeout, rate_limit, validation_error, unknown) - - Example: - record_vectorize_error( - provider='vllm', - operation='get_embedding', - error_type='timeout' - ) - """ - VECTORIZE_ERRORS_TOTAL.labels( - provider=provider, - operation=operation, - error_type=error_type - ).inc() - diff --git a/methods/EverCore/src/agentic_layer/profile_search_service.py b/methods/EverCore/src/agentic_layer/profile_search_service.py deleted file mode 100644 index a86f5de6..00000000 --- a/methods/EverCore/src/agentic_layer/profile_search_service.py +++ /dev/null @@ -1,237 +0,0 @@ -""" -Profile Search Service - -Provides vector search for user profile items in Milvus. -Returns profile items directly without reranking. - -Profile items include: -- explicit_info: User's explicit information (category + description) -- implicit_trait: Inferred user traits (trait_name + description) -""" - -import os -from typing import List, Dict, Any, Optional -import time - -from core.di import get_bean_by_type -from core.di.decorators import service -from core.observation.logger import get_logger -from agentic_layer.vectorize_service import get_vectorize_service -from infra_layer.adapters.out.search.repository.user_profile_milvus_repository import ( - UserProfileMilvusRepository, -) - -logger = get_logger(__name__) - -# Configuration from environment variables -PROFILE_RECALL_THRESHOLD = float(os.getenv("PROFILE_RECALL_THRESHOLD", "0.5")) -PROFILE_DEFAULT_TOPK = int(os.getenv("PROFILE_DEFAULT_TOPK", "30")) - - -def parse_embed_text(embed_text: str, item_type: str) -> Dict[str, str]: - """ - Parse embed_text to extract category/trait_name and description. - - Format: - - explicit_info: "category: description" - - implicit_trait: "trait_name: description. basis" or "trait_name: description" - - Args: - embed_text: The embedded text string - item_type: "explicit_info" or "implicit_trait" - - Returns: - Dict with parsed fields - """ - result = {} - - if not embed_text: - return {"category": "", "description": ""} if item_type == "explicit_info" else {"trait_name": "", "description": ""} - - # Split by first colon - parts = embed_text.split(":", 1) - - if len(parts) == 2: - key = parts[0].strip() - value = parts[1].strip() - - if item_type == "explicit_info": - result["category"] = key - result["description"] = value - else: # implicit_trait - result["trait_name"] = key - # For implicit_trait, the description may contain ". basis" - # We just use everything after the colon as description - result["description"] = value - else: - # Fallback: use entire text as description - if item_type == "explicit_info": - result["category"] = "" - result["description"] = embed_text - else: - result["trait_name"] = "" - result["description"] = embed_text - - return result - - -@service(name="profile_search_service", primary=True) -class ProfileSearchService: - """ - Profile Search Service - - Searches user profile items in Milvus using vector similarity. - No reranking step - directly returns Milvus results with score threshold. - """ - - def __init__( - self, - milvus_repo: Optional[UserProfileMilvusRepository] = None, - ): - """Initialize service - - Args: - milvus_repo: User profile Milvus repository (auto-injected if None) - """ - self._milvus_repo = milvus_repo - - @property - def milvus_repo(self) -> UserProfileMilvusRepository: - """Lazy load Milvus repository""" - if self._milvus_repo is None: - self._milvus_repo = get_bean_by_type(UserProfileMilvusRepository) - return self._milvus_repo - - async def search_profiles( - self, - query: str, - user_id: str, - group_id: str, - top_k: int = PROFILE_DEFAULT_TOPK, - score_threshold: float = PROFILE_RECALL_THRESHOLD, - ) -> Dict[str, Any]: - """ - Search profile items by query text - - Args: - query: Search query text - user_id: User ID filter - group_id: Group ID filter - top_k: Maximum number of results - score_threshold: Minimum similarity score (0.0-1.0) - - Returns: - Dict with: - - profiles: List of profile items - - metadata: Search metadata (latency, count, etc.) - """ - start_time = time.perf_counter() - - result = { - "profiles": [], - "metadata": { - "profile_count": 0, - "latency_ms": 0, - } - } - - if not query: - logger.warning("Empty query for profile search") - return result - - try: - # Step 1: Generate query embedding - vectorize_service = get_vectorize_service() - query_vector = await vectorize_service.get_embedding(query) - - if query_vector is None or len(query_vector) == 0: - logger.warning("Failed to generate query embedding") - return result - - # Step 2: Search Milvus (recall with threshold) - # Recall more candidates, then filter by threshold - recall_limit = top_k * 2 if top_k > 0 else PROFILE_DEFAULT_TOPK * 2 - - logger.info( - f"🔍 Profile search params: user_id={user_id}, group_id={group_id}, " - f"top_k={top_k}, recall_limit={recall_limit}, score_threshold={score_threshold}" - ) - - milvus_results = await self.milvus_repo.vector_search( - query_vector=query_vector, - user_id=user_id, - group_id=group_id, - limit=recall_limit, - score_threshold=score_threshold, - ) - - logger.info( - f"✅ Milvus returned {len(milvus_results)} results, will take top {top_k}" - ) - - # Step 3: Process results - parse embed_text and format output - profiles = [] - for item in milvus_results[:top_k]: - item_type = item.get("item_type", "") - embed_text = item.get("embed_text", "") - - # Parse embed_text to get category/trait_name and description - parsed = parse_embed_text(embed_text, item_type) - - profile_item = { - "item_type": item_type, - "score": round(item.get("score", 0.0), 4), - } - - # Add parsed fields based on item_type - if item_type == "explicit_info": - profile_item["category"] = parsed.get("category", "") - profile_item["description"] = parsed.get("description", "") - else: # implicit_trait - profile_item["trait_name"] = parsed.get("trait_name", "") - profile_item["description"] = parsed.get("description", "") - - profiles.append(profile_item) - - # Calculate latency - latency_ms = int((time.perf_counter() - start_time) * 1000) - - result["profiles"] = profiles - result["metadata"]["profile_count"] = len(profiles) - result["metadata"]["latency_ms"] = latency_ms - - # Log profile scores for debugging - if profiles: - scores_str = ", ".join([f"{p['score']:.4f}" for p in profiles]) - logger.info( - f"📊 Profile scores: [{scores_str}]" - ) - - logger.info( - "Profile search completed: user_id=%s, group_id=%s, " - "query='%s', found=%d, latency=%dms", - user_id, - group_id, - query[:50] if query else "", - len(profiles), - latency_ms, - ) - - return result - - except Exception as e: - logger.error( - "Profile search failed: user_id=%s, group_id=%s, error=%s", - user_id, - group_id, - e, - exc_info=True, - ) - result["metadata"]["latency_ms"] = int((time.perf_counter() - start_time) * 1000) - return result - - -def get_profile_search_service() -> ProfileSearchService: - """Get ProfileSearchService from DI container""" - return get_bean_by_type(ProfileSearchService) - diff --git a/methods/EverCore/src/agentic_layer/rerank_deepinfra.py b/methods/EverCore/src/agentic_layer/rerank_deepinfra.py deleted file mode 100644 index 9cafb88b..00000000 --- a/methods/EverCore/src/agentic_layer/rerank_deepinfra.py +++ /dev/null @@ -1,307 +0,0 @@ -""" -DeepInfra Rerank Service Implementation - -Reranking service using DeepInfra commercial API. -""" - -import asyncio -import aiohttp -import logging -from typing import List, Dict, Any, Optional -from dataclasses import dataclass - -from agentic_layer.rerank_interface import ( - RerankServiceInterface, - RerankError, - extract_text_from_hit, -) -from core.di.utils import get_bean_by_type -from core.component.token_usage_collector import TokenUsageCollector - -logger = logging.getLogger(__name__) - - -@dataclass -class DeepInfraRerankConfig: - """DeepInfra rerank service configuration""" - - api_key: str = "" # skip-sensitive-check - base_url: str = "https://api.deepinfra.com/v1/inference" - model: str = "Qwen/Qwen3-Reranker-4B" - timeout: int = 30 - max_retries: int = 3 - batch_size: int = 10 - max_concurrent_requests: int = 5 - - -class DeepInfraRerankService(RerankServiceInterface): - """DeepInfra reranking service implementation""" - - def __init__(self, config: Optional[DeepInfraRerankConfig] = None): - if config is None: - config = DeepInfraRerankConfig() - - self.config = config - self.session: Optional[aiohttp.ClientSession] = None - self._semaphore = asyncio.Semaphore(config.max_concurrent_requests) - logger.info(f"Initialized DeepInfraRerankService | model={config.model}") - - async def _ensure_session(self): - """Ensure HTTP session is created""" - if self.session is None or self.session.closed: - timeout = aiohttp.ClientTimeout(total=self.config.timeout) - self.session = aiohttp.ClientSession( - timeout=timeout, - headers={ - "Authorization": f"Bearer {self.config.api_key}", - "Content-Type": "application/json", - }, - ) - - async def close(self): - """Close HTTP session""" - if self.session and not self.session.closed: - await self.session.close() - - def _format_rerank_texts( - self, query: str, documents: List[str], instruction: Optional[str] = None - ): - """Format rerank request texts (Qwen-Reranker format)""" - prefix = '<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>\n<|im_start|>user\n' - suffix = "<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" - instruction = ( - instruction - or "Given a question and a passage, determine if the passage contains information relevant to answering the question." - ) - - formatted_query = f"{prefix}: {instruction}\n: {query}\n" - formatted_docs = [f": {doc}{suffix}" for doc in documents] - - return [formatted_query], formatted_docs - - async def _send_rerank_request_batch( - self, - query: str, - documents: List[str], - start_index: int, - instruction: Optional[str] = None, - ) -> Dict[str, Any]: - """Send rerank request batch to DeepInfra API""" - await self._ensure_session() - - # Format texts - queries, formatted_docs = self._format_rerank_texts( - query, documents, instruction - ) - - url = self.config.base_url - if not url.endswith(self.config.model): - url = f"{url}/{self.config.model}" - - request_data = {"queries": queries, "documents": formatted_docs} - - async with self._semaphore: - for attempt in range(self.config.max_retries): - try: - async with self.session.post(url, json=request_data) as response: - if response.status == 200: - json_body = await response.json() - return self._parse_response(json_body) - else: - error_text = await response.text() - logger.error( - f"DeepInfra rerank API error {response.status}: {error_text}" - ) - if attempt < self.config.max_retries - 1: - await asyncio.sleep(2**attempt) - continue - raise RerankError( - f"API failed: {response.status} - {error_text}" - ) - except Exception as e: - logger.error(f"DeepInfra rerank exception: {e}") - if attempt < self.config.max_retries - 1: - await asyncio.sleep(2**attempt) - continue - raise RerankError(f"Exception: {e}") - - def _parse_response(self, json_body: Dict[str, Any]) -> Dict[str, Any]: - """Parse DeepInfra API response""" - scores = [] - if "results" in json_body: - results = json_body["results"] - results.sort(key=lambda x: x.get("index", 0)) - scores = [item.get("relevance_score", 0.0) for item in results] - elif "scores" in json_body: - scores = json_body["scores"] - - return { - "scores": scores, - "input_tokens": json_body.get("usage", {}).get("prompt_tokens", 0), - "request_id": json_body.get("id"), - } - - async def rerank_documents( - self, query: str, documents: List[str], instruction: Optional[str] = None - ) -> Dict[str, Any]: - """ - Rerank raw documents (low-level API) - - Args: - query: Query text - documents: List of document strings to rerank - instruction: Optional reranking instruction - - Returns: - Dict with 'results' key containing list of {index, score, rank} - """ - if not documents: - return {"results": []} - - # Split into batches - batch_size = self.config.batch_size - if batch_size <= 0: - batch_size = 10 - - batches = [ - documents[i : i + batch_size] for i in range(0, len(documents), batch_size) - ] - - batch_tasks = [] - for i, batch in enumerate(batches): - start_index = i * batch_size - batch_tasks.append( - self._send_rerank_request_batch(query, batch, start_index, instruction) - ) - - batch_results = await asyncio.gather(*batch_tasks, return_exceptions=True) - - all_scores = [] - total_input_tokens = 0 - last_response = None - - for i, result in enumerate(batch_results): - if isinstance(result, Exception): - logger.error(f"Rerank batch {i} failed: {result}") - batch_len = len(batches[i]) - all_scores.extend([-100.0] * batch_len) - continue - - scores = result.get("scores", []) - all_scores.extend(scores) - total_input_tokens += result.get("input_tokens", 0) - last_response = result - - # Report rerank token usage - try: - collector = get_bean_by_type(TokenUsageCollector) - collector.add(self.config.model, total_input_tokens, 0, call_type="rerank") - except Exception: - pass - - combined_response = { - "scores": all_scores, - "input_tokens": total_input_tokens, - "request_id": last_response.get("request_id") if last_response else None, - } - return self._convert_response_format(combined_response, len(documents)) - - def _convert_response_format( - self, combined_response: Dict[str, Any], num_documents: int - ) -> Dict[str, Any]: - """Convert response to standard format""" - scores = combined_response.get("scores", []) - if len(scores) < num_documents: - scores.extend([0.0] * (num_documents - len(scores))) - scores = scores[:num_documents] - - indexed_scores = [(i, score) for i, score in enumerate(scores)] - indexed_scores.sort(key=lambda x: x[1], reverse=True) - - results = [] - for rank, (original_index, score) in enumerate(indexed_scores): - results.append({"index": original_index, "score": score, "rank": rank}) - - return { - "results": results, - "input_tokens": combined_response.get("input_tokens", 0), - "request_id": combined_response.get("request_id"), - } - - async def rerank_memories( - self, - query: str, - hits: List[Dict[str, Any]], - top_k: Optional[int] = None, - instruction: Optional[str] = None, - ) -> List[Dict[str, Any]]: - """ - Rerank memories using DeepInfra API - - Args: - query: Query text - hits: List of memory hits to rerank - top_k: Return top K results (optional) - instruction: Optional reranking instruction - - Returns: - List of reranked memory hits, sorted by relevance score - """ - if not hits: - return [] - - # Extract text content from hits for reranking - all_texts = [] - for hit in hits: - text = extract_text_from_hit(hit) - all_texts.append(text) - - if not all_texts: - return [] - - # Call reranking API - try: - logger.debug( - f"Starting reranking, query text: {query}, number of texts: {len(all_texts)}" - ) - rerank_result = await self.rerank_documents(query, all_texts, instruction) - - if "results" not in rerank_result: - raise RerankError("Invalid rerank API response: missing results field") - - # Parse reranking results - results_meta = rerank_result.get("results", []) - - # Reorganize hits according to reranked order - reranked_hits = [] - for item in results_meta: - original_idx = item.get("index", 0) - score = item.get("score", 0.0) - if 0 <= original_idx < len(hits): - hit = hits[original_idx].copy() - hit['score'] = score # Unified score field - reranked_hits.append(hit) - - # If top_k is specified, return only the top_k results - if top_k is not None and top_k > 0: - reranked_hits = reranked_hits[:top_k] - - # Print top 3 result scores for debugging - if reranked_hits: - top_scores = [f"{h.get('score', 0):.4f}" for h in reranked_hits[:3]] - logger.info( - f"Reranking completed: {len(reranked_hits)} results, top scores: {top_scores}" - ) - return reranked_hits - - except Exception as e: - logger.error(f"Error during reranking: {e}") - # If reranking fails, return original results (sorted by original score) - sorted_hits = sorted(hits, key=lambda x: x.get('score', 0), reverse=True) - if top_k is not None and top_k > 0: - sorted_hits = sorted_hits[:top_k] - return sorted_hits - - def get_model_name(self) -> str: - """Get the current model name""" - return self.config.model diff --git a/methods/EverCore/src/agentic_layer/rerank_interface.py b/methods/EverCore/src/agentic_layer/rerank_interface.py deleted file mode 100644 index 31624cc2..00000000 --- a/methods/EverCore/src/agentic_layer/rerank_interface.py +++ /dev/null @@ -1,136 +0,0 @@ -""" -Rerank Service Interface - -Defines the abstract interface for all reranking service implementations. -""" - -from abc import ABC, abstractmethod -from typing import List, Dict, Any, Optional -from dataclasses import dataclass, field - -from api_specs.memory_models import MemoryType - - -class RerankError(Exception): - """Rerank API error exception class""" - - -@dataclass -class RerankMemResponse: - """Reranked memory retrieval response""" - - memories: List[Dict[str, List[Any]]] = field(default_factory=list) - scores: List[Dict[str, List[float]]] = field(default_factory=list) - rerank_scores: List[Dict[str, List[float]]] = field(default_factory=list) - original_data: List[Dict[str, List[Dict[str, Any]]]] = field(default_factory=list) - total_count: int = 0 - has_more: bool = False - query_metadata: Any = field(default_factory=dict) - metadata: Any = field(default_factory=dict) - - -class RerankServiceInterface(ABC): - """Reranking service interface""" - - @abstractmethod - async def rerank_memories( - self, - query: str, - hits: List[Dict[str, Any]], - top_k: Optional[int] = None, - instruction: Optional[str] = None, - ) -> List[Dict[str, Any]]: - """ - Rerank memories based on query - - Args: - query: Query text - hits: List of memory hits to rerank (each hit is a dict with memory data) - top_k: Return top K results (optional, if None returns all reranked results) - instruction: Optional reranking instruction - - Returns: - List of reranked memory hits, sorted by relevance score - """ - ... - - @abstractmethod - async def rerank_documents( - self, query: str, documents: List[str], instruction: Optional[str] = None - ) -> Dict[str, Any]: - """ - Rerank raw documents (low-level API) - - Args: - query: Query text - documents: List of document strings to rerank - instruction: Optional reranking instruction - - Returns: - Dict with 'results' key containing list of {index, score, rank} - """ - ... - - @abstractmethod - async def close(self): - """Close and cleanup resources""" - ... - - -def extract_text_from_hit(hit: Dict[str, Any]) -> str: - """ - Extract text content from a memory hit for reranking. - - Concatenates relevant text fields based on memory_type. - Used by rerank implementations and the hybrid service for token counting. - - Args: - hit: Memory hit dict, optionally with '_source' nested data - - Returns: - Extracted text string for reranking - """ - source = hit.get('_source', hit) - memory_type = hit.get('memory_type', '') - - match memory_type: - case MemoryType.EPISODIC_MEMORY.value: - episode = source.get('episode', '') - if episode: - return f"Episode Memory: {episode}" - case MemoryType.FORESIGHT.value: - foresight = source.get('foresight', '') or source.get('content', '') - evidence = source.get('evidence', '') - if foresight: - if evidence: - return f"Foresight: {foresight} (Evidence: {evidence})" - return f"Foresight: {foresight}" - case MemoryType.ATOMIC_FACT.value: - atomic_fact = source.get('atomic_fact', '') - if atomic_fact: - return f"Atomic Fact: {atomic_fact}" - case MemoryType.AGENT_CASE.value: - task_intent = source.get('task_intent', '') - if task_intent: - return f"Agent Case: {task_intent}" - case MemoryType.AGENT_SKILL.value: - name = source.get('name', '') - description = source.get('description', '') - if name: - if description: - return f"Agent Skill: {name} - {description}" - return f"Agent Skill: {name}" - - if source.get('episode'): - return source['episode'] - if source.get('atomic_fact'): - return source['atomic_fact'] - if source.get('foresight'): - return source['foresight'] - if source.get('content'): - return source['content'] - if source.get('summary'): - return source['summary'] - if source.get('subject'): - return source['subject'] - return str(hit) diff --git a/methods/EverCore/src/agentic_layer/rerank_service.py b/methods/EverCore/src/agentic_layer/rerank_service.py deleted file mode 100644 index e668e8c2..00000000 --- a/methods/EverCore/src/agentic_layer/rerank_service.py +++ /dev/null @@ -1,571 +0,0 @@ -""" -Rerank Service - Hybrid Implementation with Automatic Fallback - -This is the main reranking service with built-in resilience. -Implements a hybrid strategy with flexible provider selection and automatic fallback. - -Usage: - from agentic_layer.rerank_service import get_rerank_service - - service = get_rerank_service() - result = await service.rerank_memories(query, hits, top_k) -""" - -import logging -import os -import time -from typing import Optional, Any, List, Dict -from dataclasses import dataclass, field - -from core.observation.stage_timer import timed - -from core.di import service -from core.di.utils import get_bean_by_type -from core.component.llm.tokenizer.tokenizer_factory import TokenizerFactory - -from agentic_layer.rerank_interface import ( - RerankServiceInterface, - RerankError, - extract_text_from_hit, -) -from agentic_layer.rerank_vllm import VllmRerankService, VllmRerankConfig -from agentic_layer.rerank_deepinfra import DeepInfraRerankService, DeepInfraRerankConfig -from agentic_layer.metrics.rerank_metrics import ( - record_rerank_request, - record_rerank_fallback, - record_rerank_error, -) - -logger = logging.getLogger(__name__) - - -@dataclass -class HybridRerankConfig: - """Configuration for hybrid rerank service with fallback""" - - # Provider types - primary_provider: str = "vllm" # vllm or deepinfra - fallback_provider: str = "deepinfra" # vllm, deepinfra, or none - - # Primary service config - primary_api_key: str = "" - primary_base_url: str = "" - - # Fallback service config - fallback_api_key: str = "" - fallback_base_url: str = "" - - # Shared model configuration - model: str = "Qwen/Qwen3-Reranker-4B" # skip-sensitive-check - - # Common settings - timeout: int = 3 - max_retries: int = 2 - batch_size: int = 10 - max_concurrent_requests: int = 5 - - # Fallback behavior - enable_fallback: bool = True - max_primary_failures: int = 3 - failure_reset_interval: int = 300 # Reset failure count after 5 minutes - - # Runtime state (failure tracking) - _primary_failure_count: int = field(default=0, init=False, repr=False) - _last_failure_time: float = field(default=0.0, init=False, repr=False) - - def __post_init__(self): - """Load hybrid service configuration from environment""" - # Read provider types - self.primary_provider = os.getenv("RERANK_PROVIDER", self.primary_provider) - self.fallback_provider = os.getenv( - "RERANK_FALLBACK_PROVIDER", self.fallback_provider - ) - - # Read primary service config - self.primary_api_key = os.getenv("RERANK_API_KEY", self.primary_api_key) - self.primary_base_url = os.getenv("RERANK_BASE_URL", self.primary_base_url) - - # Read fallback service config - self.fallback_api_key = os.getenv( - "RERANK_FALLBACK_API_KEY", self.fallback_api_key - ) - self.fallback_base_url = os.getenv( - "RERANK_FALLBACK_BASE_URL", self.fallback_base_url - ) - - # Read shared model configuration - self.model = os.getenv("RERANK_MODEL", self.model) - - # Read common settings - self.timeout = int(os.getenv("RERANK_TIMEOUT", str(self.timeout))) - self.max_retries = int(os.getenv("RERANK_MAX_RETRIES", str(self.max_retries))) - self.batch_size = int(os.getenv("RERANK_BATCH_SIZE", str(self.batch_size))) - self.max_concurrent_requests = int( - os.getenv("RERANK_MAX_CONCURRENT", str(self.max_concurrent_requests)) - ) - - # Fallback behavior - # Enable fallback only if: - # 1. fallback_provider is not "none" - # 2. fallback_base_url is configured - # 3. fallback_api_key is configured (or not required for vllm) - self.enable_fallback = ( - self.fallback_provider.lower() != "none" - and bool(self.fallback_base_url) - and ( - self.fallback_provider.lower() == "vllm" # vllm doesn't require API key - or bool(self.fallback_api_key) # deepinfra requires API key - ) - ) - self.max_primary_failures = int( - os.getenv("RERANK_MAX_PRIMARY_FAILURES", str(self.max_primary_failures)) - ) - self.failure_reset_interval = int( - os.getenv("RERANK_FAILURE_RESET_INTERVAL", str(self.failure_reset_interval)) - ) - - -def _create_service_from_config( - provider: str, - api_key: str, - base_url: str, - model: str, - timeout: int, - max_retries: int, - batch_size: int, - max_concurrent: int, -) -> RerankServiceInterface: - """ - Factory function to create a rerank service based on provider type - - Args: - provider: Provider type (vllm or deepinfra) - api_key: API key for the service - base_url: Base URL for the service - model: Model name - timeout: Request timeout in seconds - max_retries: Maximum retry attempts - batch_size: Batch size for requests - max_concurrent: Maximum concurrent requests - - Returns: - RerankServiceInterface: The created service instance - """ - if provider.lower() == "vllm": - config = VllmRerankConfig( - api_key=api_key, - base_url=base_url, - model=model, - timeout=timeout, - max_retries=max_retries, - batch_size=batch_size, - max_concurrent_requests=max_concurrent, - ) - return VllmRerankService(config) - elif provider.lower() == "deepinfra": - config = DeepInfraRerankConfig( - api_key=api_key, - base_url=base_url, - model=model, - timeout=timeout, - max_retries=max_retries, - batch_size=batch_size, - max_concurrent_requests=max_concurrent, - ) - return DeepInfraRerankService(config) - else: - raise RerankError(f"Unsupported provider: {provider}") - - -class HybridRerankService(RerankServiceInterface): - """ - Hybrid Reranking Service with Automatic Fallback - - This service implements a dual-strategy approach: - 1. Implements RerankServiceInterface with full API - 2. Primary: Configurable provider (vllm or deepinfra) - 3. Secondary: Configurable fallback provider - 4. Automatic failover on errors with failure tracking - 5. All method calls transparently use fallback logic - - Strategy Benefits: - - Cost optimization: ~95% savings with vllm self-deployed service - - High availability: Automatic failover ensures reliability - - Zero downtime: Continues working during vllm service maintenance - - Usage: - service = HybridRerankService() - result = await service.rerank_memories(query, hits, top_k) # Auto-fallback built-in - """ - - def __init__(self, config: Optional[HybridRerankConfig] = None): - if config is None: - config = HybridRerankConfig() - - self.config = config - - # Create primary service based on provider type - self.primary_service = _create_service_from_config( - provider=config.primary_provider, - api_key=config.primary_api_key, - base_url=config.primary_base_url, - model=config.model, # Use shared model - timeout=config.timeout, - max_retries=config.max_retries, - batch_size=config.batch_size, - max_concurrent=config.max_concurrent_requests, - ) - - # Create fallback service if enabled - self.fallback_service = None - if config.enable_fallback: - self.fallback_service = _create_service_from_config( - provider=config.fallback_provider, - api_key=config.fallback_api_key, - base_url=config.fallback_base_url, - model=config.model, # Use shared model - timeout=config.timeout, - max_retries=config.max_retries, - batch_size=config.batch_size, - max_concurrent=config.max_concurrent_requests, - ) - - logger.info( - f"Initialized HybridRerankService | " - f"primary={config.primary_provider} | " - f"fallback={config.fallback_provider} | " - f"fallback_enabled={config.enable_fallback} | " - f"max_failures={config.max_primary_failures}" - ) - - def get_service(self) -> RerankServiceInterface: - """ - Get the primary service (for advanced usage) - - Returns: - RerankServiceInterface: The primary service - - Note: Prefer using hybrid service methods directly for automatic fallback - """ - return self.primary_service - - @staticmethod - def _log_rerank_input( - query: str, hits: List[Dict[str, Any]], documents_count: int - ) -> None: - """Log rerank input statistics (doc count + token count).""" - try: - texts = [extract_text_from_hit(hit) for hit in hits] - all_text = query + " " + " ".join(texts) - tokenizer = get_bean_by_type(TokenizerFactory).get_tokenizer_from_tiktoken( - "o200k_base" - ) - total_tokens = len(tokenizer.encode(all_text)) - logger.info( - "Rerank input: %d docs, %d tokens", documents_count, total_tokens - ) - except Exception: - logger.info( - "Rerank input: %d docs (token count unavailable)", documents_count - ) - logger.debug("Token count failed", exc_info=True) - - async def rerank_memories( - self, - query: str, - hits: List[Dict[str, Any]], - top_k: Optional[int] = None, - instruction: Optional[str] = None, - ) -> List[Dict[str, Any]]: - """Rerank memories with automatic fallback""" - start_time = time.perf_counter() - documents_count = len(hits) - - self._log_rerank_input(query, hits, documents_count) - - try: - with timed("rerank"): - result = await self.execute_with_fallback( - "rerank_memories", - lambda: self.primary_service.rerank_memories( - query, hits, top_k, instruction - ), - lambda: ( - self.fallback_service.rerank_memories( - query, hits, top_k, instruction - ) - if self.fallback_service - else None - ), - ) - - # Record success metrics - duration = time.perf_counter() - start_time - record_rerank_request( - provider=self.config.primary_provider, - status='success', - duration_seconds=duration, - documents_count=documents_count, - ) - - return result - - except Exception: - # Record error metrics - duration = time.perf_counter() - start_time - record_rerank_request( - provider=self.config.primary_provider, - status='error', - duration_seconds=duration, - documents_count=documents_count, - ) - raise - - def get_model_name(self) -> str: - """Get the current model name (from primary service)""" - return self.primary_service.get_model_name() - - async def rerank_documents( - self, query: str, documents: List[str], instruction: Optional[str] = None - ) -> Dict[str, Any]: - """ - Rerank raw documents (low-level API) with fallback support - - Args: - query: Query text - documents: List of document strings to rerank - instruction: Optional reranking instruction - - Returns: - Dict with 'results' key containing list of {index, score, rank} - """ - with timed("rerank"): - return await self.execute_with_fallback( - "rerank_documents", - lambda: self.primary_service.rerank_documents( - query, documents, instruction - ), - lambda: ( - self.fallback_service.rerank_documents( - query, documents, instruction - ) - if self.fallback_service - else None - ), - ) - - async def execute_with_fallback( - self, operation_name: str, primary_func, fallback_func - ): - """ - Execute operation with automatic fallback logic - - Args: - operation_name: Name of the operation for logging - primary_func: Function to call on primary service - fallback_func: Function to call on fallback service (or None if no fallback) - - Returns: - Result from primary or fallback service - - Raises: - RerankError: If both services fail - """ - # Check if failure count should be reset (timeout expired) - current_time = time.time() - if ( - self.config._last_failure_time > 0 - and current_time - self.config._last_failure_time - > self.config.failure_reset_interval - ): - logger.info( - f"🔄 Resetting failure count ({self.config._primary_failure_count}) after " - f"{int(current_time - self.config._last_failure_time)}s of no failures" - ) - self.config._primary_failure_count = 0 - self.config._last_failure_time = 0.0 - - # Check if primary service should be skipped due to excessive failures - if ( - self.config.enable_fallback - and fallback_func is not None - and self.config._primary_failure_count >= self.config.max_primary_failures - ): - logger.info( - f"⚠️ Primary service has {self.config._primary_failure_count} failures " - f"(>= {self.config.max_primary_failures}), skipping and using {self.config.fallback_provider} directly" - ) - - try: - # Record fallback event - record_rerank_fallback( - primary_provider=self.config.primary_provider, - fallback_provider=self.config.fallback_provider, - reason='max_failures_exceeded', - ) - - result = await fallback_func() - return result - - except Exception as fallback_error: - logger.error(f"❌ Fallback service also failed: {fallback_error}") - - # Record fallback error - fallback_error_type = self._classify_error(fallback_error) - record_rerank_error( - provider=self.config.fallback_provider, - error_type=fallback_error_type, - ) - - raise RerankError(f"Fallback service failed: {fallback_error}") - - # Try primary service first - try: - result = await primary_func() - # Reset failure count on success - self.config._primary_failure_count = 0 - return result - - except Exception as primary_error: - # Increment failure count and update timestamp - self.config._primary_failure_count += 1 - self.config._last_failure_time = time.time() - - logger.warning( - f"Primary service ({self.config.primary_provider}) {operation_name} failed " - f"(count: {self.config._primary_failure_count}): {primary_error}" - ) - - # Record primary error - error_type = self._classify_error(primary_error) - record_rerank_error( - provider=self.config.primary_provider, error_type=error_type - ) - - # Check if fallback is enabled - if not self.config.enable_fallback or fallback_func is None: - logger.error("Fallback disabled or not configured, re-raising error") - raise RerankError( - f"Primary service failed and fallback is disabled: {primary_error}" - ) - - # Determine fallback reason - fallback_reason = 'error' - if self.config._primary_failure_count >= self.config.max_primary_failures: - fallback_reason = 'max_failures_exceeded' - logger.warning( - f"⚠️ Primary service exceeded max failures ({self.config.max_primary_failures}), " - f"using {self.config.fallback_provider} fallback" - ) - - # Try fallback service - try: - logger.info( - f"🔄 Falling back to {self.config.fallback_provider} for {operation_name}" - ) - - # Record fallback event - record_rerank_fallback( - primary_provider=self.config.primary_provider, - fallback_provider=self.config.fallback_provider, - reason=fallback_reason, - ) - - result = await fallback_func() - return result - - except Exception as fallback_error: - logger.error(f"❌ Fallback also failed: {fallback_error}") - - # Record fallback error - fallback_error_type = self._classify_error(fallback_error) - record_rerank_error( - provider=self.config.fallback_provider, - error_type=fallback_error_type, - ) - - raise RerankError( - f"Both primary and fallback services failed. " - f"Primary ({self.config.primary_provider}): {primary_error}, " - f"Fallback ({self.config.fallback_provider}): {fallback_error}" - ) - - def _classify_error(self, error: Exception) -> str: - """Classify error type for metrics""" - error_str = str(error).lower() - error_type = type(error).__name__.lower() - - if 'timeout' in error_str or 'timeout' in error_type: - return 'timeout' - elif 'rate' in error_str and 'limit' in error_str: - return 'rate_limit' - elif 'validation' in error_str or 'invalid' in error_str: - return 'validation_error' - elif 'connection' in error_str or 'connect' in error_type: - return 'connection_error' - elif 'api' in error_str or 'http' in error_str: - return 'api_error' - else: - return 'unknown' - - def get_failure_count(self) -> int: - """Get current primary service failure count""" - return self.config._primary_failure_count - - def reset_failure_count(self): - """Reset failure count (useful for health check recovery)""" - self.config._primary_failure_count = 0 - logger.info("Reset primary service failure count to 0") - - async def close(self): - """Close all services""" - await self.primary_service.close() - if self.fallback_service: - await self.fallback_service.close() - - -# Global service instance (lazy initialization) -_service_instance: Optional[HybridRerankService] = None - - -def get_hybrid_service() -> HybridRerankService: - """ - Get the global hybrid service instance (singleton) - - Returns: - HybridRerankService: The global hybrid service instance - """ - global _service_instance - if _service_instance is None: - _service_instance = HybridRerankService() - return _service_instance - - -# Main entry point - registered with DI container -@service(name="rerank_service", primary=True) -def get_rerank_service() -> RerankServiceInterface: - """ - Get the reranking service (main entry point) - - Returns the hybrid service which implements RerankServiceInterface. - All method calls automatically go through fallback logic. - - Returns: - RerankServiceInterface: The hybrid service with automatic fallback - - Example: - ```python - from agentic_layer.rerank_service import get_rerank_service - - service = get_rerank_service() # Returns hybrid service with fallback - result = await service.rerank_memories(query, hits, top_k) # Auto-fallback - await service.close() - ``` - """ - return ( - get_hybrid_service() - ) # Return hybrid service (implements RerankServiceInterface) - - -# Export public API -__all__ = ["get_rerank_service"] diff --git a/methods/EverCore/src/agentic_layer/rerank_vllm.py b/methods/EverCore/src/agentic_layer/rerank_vllm.py deleted file mode 100644 index 3cf9f75b..00000000 --- a/methods/EverCore/src/agentic_layer/rerank_vllm.py +++ /dev/null @@ -1,313 +0,0 @@ -""" -vLLM (Self-Deployed) Rerank Service Implementation - -Reranking service for self-deployed vLLM or similar OpenAI-compatible services. -""" - -import asyncio -import aiohttp -import logging -from typing import List, Dict, Any, Optional -from dataclasses import dataclass - -from agentic_layer.rerank_interface import ( - RerankServiceInterface, - RerankError, - extract_text_from_hit, -) -from core.di.utils import get_bean_by_type -from core.component.token_usage_collector import TokenUsageCollector - -logger = logging.getLogger(__name__) - - -@dataclass -class VllmRerankConfig: - """vLLM rerank service configuration""" - - api_key: str = "EMPTY" - base_url: str = "http://localhost:12000/v1/rerank" - model: str = "Qwen/Qwen3-Reranker-4B" # skip-sensitive-check - timeout: int = 3 - max_retries: int = 2 - batch_size: int = 10 - max_concurrent_requests: int = 5 - - -class VllmRerankService(RerankServiceInterface): - """vLLM reranking service implementation""" - - def __init__(self, config: Optional[VllmRerankConfig] = None): - if config is None: - config = VllmRerankConfig() - - self.config = config - self.session: Optional[aiohttp.ClientSession] = None - self._semaphore = asyncio.Semaphore(config.max_concurrent_requests) - logger.info( - f"Initialized VllmRerankService | url={config.base_url} | model={config.model}" # skip-sensitive-check - ) - - async def _ensure_session(self): - """Ensure HTTP session is created""" - if self.session is None or self.session.closed: - timeout = aiohttp.ClientTimeout(total=self.config.timeout) - headers = {"Content-Type": "application/json"} - if self.config.api_key and self.config.api_key != "EMPTY": - headers["Authorization"] = f"Bearer {self.config.api_key}" - self.session = aiohttp.ClientSession(timeout=timeout, headers=headers) - - async def close(self): - """Close HTTP session""" - if self.session and not self.session.closed: - await self.session.close() - - def _format_rerank_texts( - self, query: str, documents: List[str], instruction: Optional[str] = None - ): - """ - Format rerank request texts (Qwen-Reranker official format) - - Reference: https://docs.vllm.ai/en/v0.9.2/examples/offline_inference/qwen3_reranker.html - """ - prefix = '<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>\n<|im_start|>user\n' - suffix = "<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" - - # Use vLLM official default instruction for optimal performance - instruction = ( - instruction - or "Given a question and a passage, determine if the passage contains information relevant to answering the question." - ) - - formatted_query = f"{prefix}: {instruction}\n: {query}\n" - formatted_docs = [f": {doc}{suffix}" for doc in documents] - - return [formatted_query], formatted_docs - - async def _send_rerank_request_batch( - self, - query: str, - documents: List[str], - start_index: int, - instruction: Optional[str] = None, - ) -> Dict[str, Any]: - """Send rerank request batch to vLLM rerank API (OpenAI-compatible format)""" - await self._ensure_session() - - # Format texts using Qwen-Reranker official format - queries, formatted_docs = self._format_rerank_texts( - query, documents, instruction - ) - - url = self.config.base_url - # Use OpenAI-compatible rerank API format with formatted texts - request_data = { - "model": self.config.model, - "query": queries[0] if queries else query, # Use formatted query - "documents": formatted_docs, # Use formatted documents - } - - async with self._semaphore: - for attempt in range(self.config.max_retries): - try: - async with self.session.post(url, json=request_data) as response: - if response.status == 200: - result = await response.json() - return result - else: - error_text = await response.text() - logger.warning( - f"vLLM rerank API error (status {response.status}, attempt {attempt + 1}/{self.config.max_retries}): {error_text}" - ) - if attempt < self.config.max_retries - 1: - await asyncio.sleep(2**attempt) - continue - else: - raise RerankError( - f"Rerank request failed after {self.config.max_retries} attempts: {error_text}" - ) - except asyncio.TimeoutError: - logger.warning( - f"vLLM rerank timeout (attempt {attempt + 1}/{self.config.max_retries}), timeout={self.config.timeout}s" - ) - if attempt < self.config.max_retries - 1: - await asyncio.sleep(2**attempt) - continue - else: - raise RerankError( - f"Rerank request timed out after {self.config.max_retries} attempts" - ) - except aiohttp.ClientError as e: - logger.warning( - f"vLLM rerank client error (attempt {attempt + 1}/{self.config.max_retries}): {e}" - ) - if attempt < self.config.max_retries - 1: - await asyncio.sleep(2**attempt) - continue - else: - raise RerankError( - f"Rerank request failed after {self.config.max_retries} attempts: {e}" - ) - except Exception as e: - logger.error(f"Unexpected error in vLLM rerank request: {e}") - raise RerankError(f"Unexpected rerank error: {e}") - - async def rerank_documents( - self, query: str, documents: List[str], instruction: Optional[str] = None - ) -> Dict[str, Any]: - """ - Rerank raw documents (low-level API) - - Args: - query: Query text - documents: List of document strings to rerank - instruction: Optional reranking instruction - - Returns: - Dict with 'results' key containing list of {index, score, rank} - """ - if not documents: - return {"results": []} - - batch_size = self.config.batch_size - if batch_size <= 0: - batch_size = 10 - - batches = [ - documents[i : i + batch_size] for i in range(0, len(documents), batch_size) - ] - - batch_tasks = [] - for i, batch in enumerate(batches): - start_index = i * batch_size - batch_tasks.append( - self._send_rerank_request_batch(query, batch, start_index, instruction) - ) - - batch_results = await asyncio.gather(*batch_tasks, return_exceptions=True) - - all_scores = [] - for i, result in enumerate(batch_results): - if isinstance(result, Exception): - logger.error(f"Rerank batch {i} failed: {result}") - batch_len = len(batches[i]) - all_scores.extend([-100.0] * batch_len) - continue - - # vLLM returns {"results": [{"index": ..., "relevance_score": ...}, ...]} - results = result.get("results", []) - results_sorted = sorted(results, key=lambda x: x.get("index", 0)) - for r in results_sorted: - all_scores.append(r.get("relevance_score", 0.0)) - - # Report rerank call to token usage collector - try: - collector = get_bean_by_type(TokenUsageCollector) - collector.add(self.config.model, 0, 0, call_type="rerank") - except Exception: - pass - - # Convert to same format as DeepInfra - return self._convert_response_format(all_scores, len(documents)) - - def _convert_response_format( - self, scores: List[float], num_documents: int - ) -> Dict[str, Any]: - """Convert scores to standard format (same as DeepInfra)""" - if len(scores) < num_documents: - scores.extend([0.0] * (num_documents - len(scores))) - scores = scores[:num_documents] - - indexed_scores = [(i, score) for i, score in enumerate(scores)] - indexed_scores.sort(key=lambda x: x[1], reverse=True) - - results = [] - for rank, (original_index, score) in enumerate(indexed_scores): - results.append({"index": original_index, "score": score, "rank": rank}) - - return {"results": results} - - async def rerank_memories( - self, - query: str, - hits: List[Dict[str, Any]], - top_k: Optional[int] = None, - instruction: Optional[str] = None, - ) -> List[Dict[str, Any]]: - """ - Rerank memories using vLLM reranking service - - Args: - query: Search query - hits: List of memory hits to rerank - top_k: Return top K results (optional) - instruction: Optional instruction for reranking - - Returns: - List of reranked memory hits, sorted by relevance score - """ - if not hits: - return [] - - # Extract text content from hits for reranking - documents = [] - for hit in hits: - text = extract_text_from_hit(hit) - documents.append(text) - - if not documents: - return [] - - # Send rerank request - try: - result = await self._send_rerank_request_batch( - query=query, documents=documents, start_index=0, instruction=instruction - ) - - # Parse results (OpenAI-compatible format) - if "results" not in result: - raise RerankError( - f"Invalid rerank response format: missing 'results' key" - ) - - # Create score mapping - score_map = {} - for item in result["results"]: - index = item.get("index") - score = item.get("relevance_score", 0.0) - if index is not None: - score_map[index] = score - - # Create reranked hits with updated scores - reranked_hits = [] - for i, hit in enumerate(hits): - if i in score_map: - hit_copy = hit.copy() - hit_copy['score'] = score_map[i] # Update score - reranked_hits.append(hit_copy) - - # Sort by rerank score (descending) - reranked_hits.sort(key=lambda x: x.get('score', 0.0), reverse=True) - - # Apply top_k if specified - if top_k is not None and top_k > 0: - reranked_hits = reranked_hits[:top_k] - - # Log results - if reranked_hits: - top_scores = [f"{h.get('score', 0):.4f}" for h in reranked_hits[:3]] - logger.info( - f"Reranked {len(hits)} hits -> {len(reranked_hits)} results, " - f"top scores: {top_scores}" - ) - - return reranked_hits - - except Exception as e: - logger.error(f"Error in rerank_memories: {e}") - # Re-raise exception to allow HybridRerankService fallback - raise - - def get_model_name(self) -> str: - """Get the current model name""" - return self.config.model diff --git a/methods/EverCore/src/agentic_layer/retrieval_utils.py b/methods/EverCore/src/agentic_layer/retrieval_utils.py deleted file mode 100644 index 713b0aac..00000000 --- a/methods/EverCore/src/agentic_layer/retrieval_utils.py +++ /dev/null @@ -1,827 +0,0 @@ -"""Retrieval utility functions - -Provides implementations of various retrieval strategies: -- Embedding vector retrieval -- BM25 keyword retrieval -- RRF fusion retrieval -- Agentic retrieval (LLM-guided multi-round retrieval) -""" - -import re -import time -import jieba -import numpy as np -import logging -import asyncio -from typing import List, Tuple, Dict, Any, Optional -from core.nlp.stopwords_utils import filter_stopwords as filter_chinese_stopwords -from .vectorize_service import get_vectorize_service - -logger = logging.getLogger(__name__) - - -def build_bm25_index(candidates): - """Build BM25 index (supports Chinese and English)""" - try: - import nltk - from nltk.corpus import stopwords - from nltk.stem import PorterStemmer - from nltk.tokenize import word_tokenize - from rank_bm25 import BM25Okapi - except ImportError as e: - return None, None, None, None - - # Ensure NLTK data is downloaded - try: - nltk.data.find("tokenizers/punkt") - except LookupError: - nltk.download("punkt", quiet=True) - - try: - nltk.data.find("tokenizers/punkt_tab") - except LookupError: - nltk.download("punkt_tab", quiet=True) - - try: - nltk.data.find("corpora/stopwords") - except LookupError: - nltk.download("stopwords", quiet=True) - - stemmer = PorterStemmer() - stop_words = set(stopwords.words("english")) - - # Extract text and tokenize (supports Chinese and English) - tokenized_docs = [] - for mem in candidates: - text = getattr(mem, "episode", None) or getattr(mem, "summary", "") or "" - has_chinese = bool(re.search(r'[\u4e00-\u9fff]', text)) - - if has_chinese: - tokens = list(jieba.cut(text)) - processed_tokens = filter_chinese_stopwords(tokens) - else: - tokens = word_tokenize(text.lower()) - processed_tokens = [ - stemmer.stem(token) - for token in tokens - if token.isalpha() and len(token) >= 2 and token not in stop_words - ] - - tokenized_docs.append(processed_tokens) - - bm25 = BM25Okapi(tokenized_docs) - return bm25, tokenized_docs, stemmer, stop_words - - -async def search_with_bm25( - query: str, bm25, candidates, stemmer, stop_words, top_k: int = 50 -) -> List[Tuple]: - """BM25 retrieval (supports Chinese and English)""" - if bm25 is None: - return [] - - try: - from nltk.tokenize import word_tokenize - except ImportError: - return [] - - # Tokenize query (supports Chinese and English) - has_chinese = bool(re.search(r'[\u4e00-\u9fff]', query)) - - if has_chinese: - tokens = list(jieba.cut(query)) - tokenized_query = filter_chinese_stopwords(tokens) - else: - tokens = word_tokenize(query.lower()) - tokenized_query = [ - stemmer.stem(token) - for token in tokens - if token.isalpha() and len(token) >= 2 and token not in stop_words - ] - - if not tokenized_query: - return [] - - # Calculate BM25 scores - scores = bm25.get_scores(tokenized_query) - - # Sort and return Top-K - results = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)[:top_k] - - return results - - -def reciprocal_rank_fusion( - results1: List[Tuple], results2: List[Tuple], k: int = 60 -) -> List[Tuple]: - """RRF fusion of two retrieval results""" - doc_rrf_scores = {} - doc_map = {} - - # Process first result set - for rank, (doc, score) in enumerate(results1, start=1): - doc_id = doc.get('id') - if doc_id not in doc_map: - doc_map[doc_id] = doc - doc_rrf_scores[doc_id] = doc_rrf_scores.get(doc_id, 0.0) + 1.0 / (k + rank) - - # Process second result set - for rank, (doc, score) in enumerate(results2, start=1): - doc_id = doc.get('id') - if doc_id not in doc_map: - doc_map[doc_id] = doc - doc_rrf_scores[doc_id] = doc_rrf_scores.get(doc_id, 0.0) + 1.0 / (k + rank) - - # Convert to list and sort - fused_results = [ - (doc_map[doc_id], rrf_score) for doc_id, rrf_score in doc_rrf_scores.items() - ] - fused_results.sort(key=lambda x: x[1], reverse=True) - - return fused_results - - -def vector_anchored_fusion( - vector_results: List[Tuple[str, float]], - keyword_results: List[Tuple[str, float]], - saturation_k: float = 5.0, - alpha: float = 0.7, -) -> List[Tuple[str, float]]: - """Fuse vector and keyword search results with saturated BM25 weighting. - - BM25 raw scores are compressed via a saturation function - ``sat = raw / (raw + saturation_k)`` which maps them to [0, 1) while - preserving discrimination (e.g. BM25=0.4 -> 0.07, BM25=10 -> 0.67). - - The final score is a weighted sum ``alpha * vec + (1-alpha) * sat_bm25``. - - For docs appearing in only one result set, the missing side defaults - to the minimum score of that set ("not recalled" != "not relevant"). - - Args: - vector_results: Vector search results as [(doc_id, score), ...]. - keyword_results: Keyword search results as [(doc_id, bm25_score), ...]. - saturation_k: Saturation constant for BM25 normalization. - Controls the inflection point -- scores around this value map - to ~0.5. Default 5.0 works well for typical ES BM25 ranges. - alpha: Vector weight in the fusion. Higher values trust vector - more; lower values trust BM25 more. - - Returns: - Fused results [(doc_id, score), ...] sorted by score descending. - """ - vec_score_map: Dict[str, float] = {doc_id: s for doc_id, s in vector_results} - - # Saturate BM25 scores into [0, 1) - kw_sat_map: Dict[str, float] = {} - for doc_id, raw in keyword_results: - kw_sat_map[doc_id] = raw / (raw + saturation_k) if raw > 0 else 0.0 - - # Floor defaults for docs missing from one path. - # "Not recalled" != "not relevant" -- assume at least as good as the - # weakest doc that *was* recalled by that path. - vec_floor = min(vec_score_map.values()) if vec_score_map else 0.0 - kw_floor = min(kw_sat_map.values()) if kw_sat_map else 0.0 - - # Weighted sum fusion - all_ids = set(vec_score_map.keys()) | set(kw_sat_map.keys()) - scored: List[Tuple[str, float]] = [] - for doc_id in all_ids: - vs = vec_score_map.get(doc_id, vec_floor) - ks = kw_sat_map.get(doc_id, kw_floor) - scored.append((doc_id, alpha * vs + (1.0 - alpha) * ks)) - - scored.sort(key=lambda x: x[1], reverse=True) - return scored - - -async def lightweight_retrieval( - query: str, - candidates, - emb_top_n: int = 50, - bm25_top_n: int = 50, - final_top_n: int = 20, -) -> Tuple: - """Lightweight retrieval (Embedding + BM25 + RRF fusion)""" - start_time = time.time() - - metadata = { - "retrieval_mode": "lightweight", - "emb_count": 0, - "bm25_count": 0, - "final_count": 0, - "total_latency_ms": 0.0, - } - - if not candidates: - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - return [], metadata - - # Build BM25 index - bm25, tokenized_docs, stemmer, stop_words = build_bm25_index(candidates) - - # Embedding retrieval - emb_results = [] - try: - vectorize_service = get_vectorize_service() - query_vec = await vectorize_service.get_embedding(query) - query_norm = np.linalg.norm(query_vec) - - if query_norm > 0: - scores = [] - for mem in candidates: - try: - doc_vec = np.array(mem.extend.get("embedding", [])) - if len(doc_vec) > 0: - doc_norm = np.linalg.norm(doc_vec) - if doc_norm > 0: - sim = np.dot(query_vec, doc_vec) / (query_norm * doc_norm) - scores.append((mem, float(sim))) - except: - continue - - emb_results = sorted(scores, key=lambda x: x[1], reverse=True)[:emb_top_n] - except Exception as e: - pass - - metadata["emb_count"] = len(emb_results) - - # BM25 retrieval - bm25_results = [] - if bm25 is not None: - bm25_results = await search_with_bm25( - query, bm25, candidates, stemmer, stop_words, top_k=bm25_top_n - ) - - metadata["bm25_count"] = len(bm25_results) - - # RRF fusion - if not emb_results and not bm25_results: - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - return [], metadata - elif not emb_results: - final_results = bm25_results[:final_top_n] - elif not bm25_results: - final_results = emb_results[:final_top_n] - else: - fused_results = reciprocal_rank_fusion(emb_results, bm25_results, k=60) - final_results = fused_results[:final_top_n] - - metadata["final_count"] = len(final_results) - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - - return final_results, metadata - - -def multi_rrf_fusion(results_list: List[List[Tuple]], k: int = 60) -> List[Tuple]: - """ - Fuse multiple queries' retrieval results using RRF (multi-query fusion) - - Similar to dual-path RRF, but supports fusing any number of retrieval results. - Score contributed by each result set: 1 / (k + rank) - - Principle: - - Documents ranked high across multiple queries → high accumulated score → higher final ranking - - This is a "voting mechanism": documents considered relevant by multiple queries are more likely truly relevant - - Args: - results_list: List of multiple retrieval results [ - [(doc1, score), (doc2, score), ...], # Query 1 results - [(doc3, score), (doc1, score), ...], # Query 2 results - [(doc4, score), (doc2, score), ...], # Query 3 results - ] - k: RRF constant (default 60) - - Returns: - Fused results [(doc, rrf_score), ...], sorted by RRF score in descending order - - Example: - Query 1 results: [(doc_A, 0.9), (doc_B, 0.8), (doc_C, 0.7)] - Query 2 results: [(doc_B, 0.88), (doc_D, 0.82), (doc_A, 0.75)] - Query 3 results: [(doc_A, 0.92), (doc_E, 0.85), (doc_B, 0.80)] - - RRF score calculation: - doc_A: 1/(60+1) + 1/(60+3) + 1/(60+1) = 0.0323 ← appears in Q1,Q2,Q3 - doc_B: 1/(60+2) + 1/(60+1) + 1/(60+3) = 0.0323 ← appears in Q1,Q2,Q3 - doc_C: 1/(60+3) + 0 + 0 = 0.0159 ← only in Q1 - doc_D: 0 + 1/(60+2) + 0 = 0.0161 ← only in Q2 - doc_E: 0 + 0 + 1/(60+2) = 0.0161 ← only in Q3 - - Fused results: doc_A and doc_B rank highest (recognized by multiple queries) - """ - if not results_list: - return [] - - # If only one result set, return directly - if len(results_list) == 1: - return results_list[0] - - # Use document's memory address as unique identifier - doc_rrf_scores = {} # {doc_id: rrf_score} - doc_map = {} # {doc_id: doc} - - # Iterate through each query's retrieval results - for query_results in results_list: - for rank, (doc, score) in enumerate(query_results, start=1): - doc_id = id(doc) - if doc_id not in doc_map: - doc_map[doc_id] = doc - # Accumulate RRF score - doc_rrf_scores[doc_id] = doc_rrf_scores.get(doc_id, 0.0) + 1.0 / (k + rank) - - # Sort by RRF score - sorted_docs = sorted(doc_rrf_scores.items(), key=lambda x: x[1], reverse=True) - - # Convert back to (doc, score) format - fused_results = [(doc_map[doc_id], rrf_score) for doc_id, rrf_score in sorted_docs] - - return fused_results - - -async def multi_query_retrieval( - queries: List[str], - candidates, - emb_top_n: int = 50, - bm25_top_n: int = 50, - final_top_n: int = 40, - rrf_k: int = 60, -) -> Tuple[List[Tuple], Dict[str, Any]]: - """ - Multi-query parallel retrieval + RRF fusion - - Perform hybrid retrieval (Embedding + BM25) for each query, then fuse all results using RRF. - This strategy captures relevant information from different angles, improving recall. - - Process: - 1. Execute hybrid retrieval for all queries in parallel - 2. Use multi-query RRF to fuse results - 3. Return Top-N documents - - Args: - queries: List of queries (2-3) - candidates: Candidate memory list - emb_top_n: Number of Embedding candidates per query - bm25_top_n: Number of BM25 candidates per query - final_top_n: Number of documents to return after fusion - rrf_k: RRF parameter - - Returns: - (results, metadata) - - results: Fused Top-N results - - metadata: Contains performance metrics and statistics - - Example: - >>> queries = [ - ... "What is the user's favorite cuisine?", - ... "What flavors does the user like?", - ... "What are the user's eating habits?" - ... ] - >>> results, metadata = await multi_query_retrieval(queries, candidates) - >>> print(len(results)) # 40 - >>> print(metadata["num_queries"]) # 3 - """ - start_time = time.time() - - metadata = { - "retrieval_mode": "multi_query", - "num_queries": len(queries), - "per_query_results": [], - "total_docs_before_fusion": 0, - "final_count": 0, - "total_latency_ms": 0.0, - } - - if not queries or not candidates: - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - return [], metadata - - logger.info(f"Executing {len(queries)} queries in parallel...") - - # Execute hybrid retrieval for all queries in parallel - tasks = [ - lightweight_retrieval(q, candidates, emb_top_n, bm25_top_n, final_top_n) - for q in queries - ] - - multi_query_results = await asyncio.gather(*tasks, return_exceptions=True) - from common_utils.async_utils import reraise_critical_errors - - reraise_critical_errors(multi_query_results) - - # Collect valid results - valid_results = [] - for i, result in enumerate(multi_query_results, 1): - if isinstance(result, Exception): - logger.error(f"Query {i} failed: {result}") - continue - - results, query_metadata = result - if results: - valid_results.append(results) - metadata["per_query_results"].append( - { - "query_index": i, - "count": len(results), - "latency_ms": query_metadata.get("total_latency_ms", 0), - } - ) - logger.debug(f"Query {i}: Retrieved {len(results)} documents") - - if not valid_results: - logger.warning("All queries failed") - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - return [], metadata - - # Count total documents before fusion - metadata["total_docs_before_fusion"] = sum(len(r) for r in valid_results) - - # Use multi-query RRF fusion - logger.info(f"Fusing {len(valid_results)} query results...") - fused_results = multi_rrf_fusion(valid_results, k=rrf_k) - - # Take Top-N - final_results = fused_results[:final_top_n] - - metadata["final_count"] = len(final_results) - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - - logger.info( - f"Multi-query retrieval: {metadata['total_docs_before_fusion']} → {len(final_results)} docs" - ) - - return final_results, metadata - - -async def rerank_candidates( - query: str, candidates: List[Tuple], top_n: int, rerank_service -) -> List[Tuple]: - """ - Rerank candidate results - - Use Rerank service to reorder retrieval results, improving precision. - - Args: - query: User query - candidates: Candidate results [(doc, score), ...] - top_n: Number of Top-N to return - rerank_service: Rerank service instance - - Returns: - Reranked Top-N results [(doc, new_score), ...] - - Note: - - If Rerank fails, fallback to original ranking - - Use batch processing to avoid API rate limiting - """ - if not candidates: - return [] - - try: - logger.debug( - f"Reranking {len(candidates)} candidates for query: {query[:50]}..." - ) - - # 🔥 Convert format: transform [(doc, score)] to format expected by rerank service - # rerank_service.rerank_memories expects List[Dict[str, Any]] - candidates_for_rerank = [] - for idx, (doc, score) in enumerate(candidates): - # Build hit dictionary with sufficient information for rerank - hit = {"index": idx, "score": score} - - # If doc is dict, merge directly - if isinstance(doc, dict): - hit.update(doc) - else: - # If doc is object, extract key fields - hit["episode"] = getattr(doc, "episode", "") - hit["summary"] = getattr(doc, "summary", "") - hit["subject"] = getattr(doc, "subject", "") - - # Try to extract atomic_fact (if exists) - if hasattr(doc, "atomic_fact"): - atomic_fact_val = doc.atomic_fact - if isinstance(atomic_fact_val, dict): - hit["atomic_fact"] = atomic_fact_val - elif atomic_fact_val: - # If it's an object, convert to dictionary - hit["atomic_fact"] = { - "atomic_fact": getattr(atomic_fact_val, "atomic_fact", []), - "time": getattr(atomic_fact_val, "time", ""), - } - - candidates_for_rerank.append(hit) - - # Call rerank service - reranked_hits = await rerank_service.rerank_memories( - query, candidates_for_rerank, top_k=top_n - ) - - # Convert format: from rerank returned format to (doc, score) format - if reranked_hits: - # reranked_hits format: [{"index": ..., "score": ...}, ...] - # candidates format: [(doc, score), ...] - - reranked_results = [] - for hit in reranked_hits[:top_n]: - # Extract index - if isinstance(hit, dict): - idx = hit.get("index", hit.get("global_index", 0)) - new_score = hit.get("score", 0.0) - else: - # If returned is tuple, format is wrong, skip - logger.warning(f"Unexpected rerank result type: {type(hit)}") - continue - - if 0 <= idx < len(candidates): - doc = candidates[idx][0] - reranked_results.append((doc, new_score)) - - logger.debug(f"Rerank complete: {len(reranked_results)} results") - return reranked_results if reranked_results else candidates[:top_n] - else: - logger.warning("Rerank returned empty results, using original") - return candidates[:top_n] - - except Exception as e: - logger.error(f"Rerank failed: {e}, using original ranking", exc_info=True) - return candidates[:top_n] - - -async def agentic_retrieval( - query: str, candidates, llm_provider, config: Optional[Any] = None -) -> Tuple[List[Tuple], Dict[str, Any]]: - """ - Agentic multi-round retrieval (LLM-guided) - - Use LLM to judge retrieval sufficiency and perform multi-round retrieval when necessary. - - Process: - 1. Round 1: Hybrid retrieval → Top 20 - 2. Rerank → Top 10 → LLM judge sufficiency - 3. If sufficient: return original Top 20 - 4. If insufficient: - - LLM generates multiple improved queries (2-3) - - Round 2: Parallel retrieval for all queries - - Use RRF fusion → deduplicate and merge to 40 - - Rerank → return final Top 20 - - Args: - query: User query - candidates: Candidate memory list - llm_provider: LLM Provider (Memory Layer) - config: Agentic configuration (optional) - - Returns: - (final_results, metadata) - - final_results: Final retrieval results [(doc, score), ...] - - metadata: Contains detailed retrieval process information - - Example: - >>> from agentic_layer.agentic_utils import AgenticConfig - >>> config = AgenticConfig(use_reranker=True) - >>> results, metadata = await agentic_retrieval( - ... query="What does the user like to eat?", - ... candidates=memcells, - ... llm_provider=llm, - ... config=config - ... ) - >>> print(metadata["is_sufficient"]) # False - >>> print(metadata["refined_queries"]) # ["User's favorite cuisine?", ...] - """ - # Import configuration and tools - from .agentic_utils import AgenticConfig, check_sufficiency, generate_multi_queries - from .rerank_service import get_rerank_service - - # Use default config or provided config - if config is None: - config = AgenticConfig() - - start_time = time.time() - - metadata = { - "retrieval_mode": "agentic", - "is_multi_round": False, - "round1_count": 0, - "round1_reranked_count": 0, - "is_sufficient": None, - "reasoning": None, - "missing_info": None, - "refined_queries": None, - "round2_count": 0, - "final_count": 0, - "total_latency_ms": 0.0, - } - - logger.info(f"{'='*60}") - logger.info(f"Agentic Retrieval: {query[:60]}...") - logger.info(f"{'='*60}") - - # ========== Round 1: Hybrid search Top 20 ========== - logger.info("Round 1: Hybrid search for Top 20...") - - try: - round1_results, round1_metadata = await lightweight_retrieval( - query=query, - candidates=candidates, - emb_top_n=config.round1_emb_top_n, - bm25_top_n=config.round1_bm25_top_n, - final_top_n=config.round1_top_n, - ) - - metadata["round1_count"] = len(round1_results) - metadata["round1_latency_ms"] = round1_metadata.get("total_latency_ms", 0) - - logger.info(f"Round 1: Retrieved {len(round1_results)} documents") - - if not round1_results: - logger.warning("Round 1 returned no results") - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - return [], metadata - - except Exception as e: - logger.error(f"Round 1 failed: {e}") - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - return [], metadata - - # ========== Rerank Top 20 → Top 10 for Sufficiency Check ========== - if config.use_reranker: - logger.info("Reranking Top 20 to get Top 10 for sufficiency check...") - - try: - rerank_service = get_rerank_service() - reranked_top5 = await rerank_candidates( - query=query, - candidates=round1_results, - top_n=config.round1_rerank_top_n, - rerank_service=rerank_service, - ) - - metadata["round1_reranked_count"] = len(reranked_top5) - logger.info(f"Rerank: Got Top {len(reranked_top5)} for sufficiency check") - - except Exception as e: - logger.error(f"Rerank failed: {e}, using original Top 10") - reranked_top5 = round1_results[: config.round1_rerank_top_n] - metadata["round1_reranked_count"] = len(reranked_top5) - else: - # No reranker, directly take first 10 - reranked_top5 = round1_results[: config.round1_rerank_top_n] - metadata["round1_reranked_count"] = len(reranked_top5) - logger.info("No Rerank: Using original Top 10 for sufficiency check") - - if not reranked_top5: - logger.warning("No results for sufficiency check, returning Round 1 results") - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - return round1_results, metadata - - # ========== LLM Sufficiency Check ========== - logger.info("LLM: Checking sufficiency on Top 10...") - - try: - is_sufficient, reasoning, missing_info = await check_sufficiency( - query=query, - results=reranked_top5, - llm_provider=llm_provider, - max_docs=config.round1_rerank_top_n, - ) - - metadata["is_sufficient"] = is_sufficient - metadata["reasoning"] = reasoning - metadata["missing_info"] = missing_info - - logger.info( - f"LLM Result: {'✅ Sufficient' if is_sufficient else '❌ Insufficient'}" - ) - logger.info(f"LLM Reasoning: {reasoning}") - - except Exception as e: - logger.error(f"Sufficiency check failed: {e}, assuming sufficient") - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - return round1_results, metadata - - # ========== If sufficient: return original Round 1 Top 20 ========== - if is_sufficient: - logger.info("Decision: Sufficient! Using Round 1 Top 20 results") - - final_results = round1_results - metadata["final_count"] = len(final_results) - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - - logger.info(f"Complete: Latency {metadata['total_latency_ms']:.0f}ms") - return final_results, metadata - - # ========== If insufficient: enter Round 2 ========== - metadata["is_multi_round"] = True - logger.info("Decision: Insufficient, entering Round 2") - if missing_info: - logger.info(f"Missing: {', '.join(missing_info)}") - - # ========== LLM generate multiple refined queries ========== - if config.enable_multi_query: - logger.info("LLM: Generating multiple refined queries...") - - try: - refined_queries, query_strategy = await generate_multi_queries( - original_query=query, - results=reranked_top5, - missing_info=missing_info, - llm_provider=llm_provider, - max_docs=config.round1_rerank_top_n, - num_queries=config.num_queries, - ) - - metadata["refined_queries"] = refined_queries - metadata["query_strategy"] = query_strategy - metadata["num_queries"] = len(refined_queries) - - logger.info(f"Generated {len(refined_queries)} queries") - for i, q in enumerate(refined_queries, 1): - logger.debug(f" Query {i}: {q[:80]}...") - - except Exception as e: - logger.error(f"Query generation failed: {e}, using original query") - refined_queries = [query] - metadata["refined_queries"] = refined_queries - metadata["num_queries"] = 1 - else: - # Single query mode (backward compatibility) - refined_queries = [query] - metadata["refined_queries"] = refined_queries - metadata["num_queries"] = 1 - - # ========== Round 2: Execute multiple queries retrieval in parallel ========== - logger.info(f"Round 2: Executing {len(refined_queries)} queries in parallel...") - - try: - round2_results, round2_metadata = await multi_query_retrieval( - queries=refined_queries, - candidates=candidates, - emb_top_n=config.round1_emb_top_n, - bm25_top_n=config.round1_bm25_top_n, - final_top_n=config.round2_per_query_top_n, - rrf_k=60, - ) - - metadata["round2_count"] = len(round2_results) - metadata["round2_latency_ms"] = round2_metadata.get("total_latency_ms", 0) - metadata["multi_query_total_docs"] = round2_metadata.get( - "total_docs_before_fusion", 0 - ) - - logger.info(f"Round 2: Retrieved {len(round2_results)} unique documents") - - except Exception as e: - logger.error(f"Round 2 failed: {e}, using Round 1 results") - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - return round1_results, metadata - - # ========== Merge: ensure total 40 documents ========== - logger.info("Merge: Combining Round 1 and Round 2...") - - # Deduplicate: use document ID - round1_ids = {id(doc) for doc, _ in round1_results} - round2_unique = [ - (doc, score) for doc, score in round2_results if id(doc) not in round1_ids - ] - - # Merge: Round1 Top20 + Round2 deduplicated documents (ensure total <= 40) - combined_results = round1_results.copy() - needed_from_round2 = config.combined_total - len(combined_results) - combined_results.extend(round2_unique[:needed_from_round2]) - - logger.info( - f"Merge: Round1={len(round1_results)}, Round2_unique={len(round2_unique[:needed_from_round2])}, Total={len(combined_results)}" - ) - - # ========== Rerank merged documents ========== - if config.use_reranker and len(combined_results) > 0: - logger.info(f"Rerank: Reranking {len(combined_results)} documents...") - - try: - rerank_service = get_rerank_service() - final_results = await rerank_candidates( - query=query, # Use original query for rerank - candidates=combined_results, - top_n=config.final_top_n, - rerank_service=rerank_service, - ) - - logger.info(f"Rerank: Final Top {len(final_results)} selected") - - except Exception as e: - logger.error(f"Final rerank failed: {e}, using top {config.final_top_n}") - final_results = combined_results[: config.final_top_n] - else: - # No Reranker, directly return Top N - final_results = combined_results[: config.final_top_n] - logger.info(f"No Rerank: Returning Top {len(final_results)}") - - metadata["final_count"] = len(final_results) - metadata["total_latency_ms"] = (time.time() - start_time) * 1000 - - logger.info( - f"Complete: Final {len(final_results)} docs | Latency {metadata['total_latency_ms']:.0f}ms" - ) - logger.info(f"{'='*60}\n") - - return final_results, metadata diff --git a/methods/EverCore/src/agentic_layer/search_mem_service.py b/methods/EverCore/src/agentic_layer/search_mem_service.py deleted file mode 100644 index 9c25f919..00000000 --- a/methods/EverCore/src/agentic_layer/search_mem_service.py +++ /dev/null @@ -1,1711 +0,0 @@ -""" -Memory Search service (v1) - -Provides business logic for POST /api/v1/memories/search endpoint. -Handles query parsing, filter validation, multi-method retrieval (keyword/vector/hybrid/rrf/agentic), -and DTO conversion for episodic_memory and profile types. - -Methods: -- keyword: BM25 keyword retrieval (ES only) -- vector: Vector semantic retrieval (Milvus only) -- hybrid: Keyword + Vector + Rerank service -- rrf: Keyword + Vector + RRF fusion -- agentic: LLM-guided multi-round retrieval -""" - -import asyncio -import jieba -import logging -import os -import time -from datetime import datetime -from typing import Any, Dict, List, Optional - -from core.di import service, get_bean -from api_specs.dtos.memory import SearchAtomicFactItem -from core.nlp.stopwords_utils import filter_stopwords -from core.observation.stage_timer import timed, timed_parallel -from api_specs.dtos.memory import ( - SearchMemoriesResponseData, - SearchEpisodeItem, - SearchProfileItem, - RawMessageDTO, - AgentMemorySearchResult, - SearchAgentCaseItem, - SearchAgentSkillItem, -) - -from infra_layer.adapters.out.search.repository.episodic_memory_es_repository import ( - EpisodicMemoryEsRepository, -) -from infra_layer.adapters.out.search.repository.episodic_memory_milvus_repository import ( - EpisodicMemoryMilvusRepository, -) -from infra_layer.adapters.out.search.repository.user_profile_milvus_repository import ( - UserProfileMilvusRepository, -) -from infra_layer.adapters.out.search.repository.agent_case_es_repository import ( - AgentCaseEsRepository, -) -from infra_layer.adapters.out.search.repository.agent_skill_es_repository import ( - AgentSkillEsRepository, -) -from infra_layer.adapters.out.search.repository.agent_case_milvus_repository import ( - AgentCaseMilvusRepository, -) -from infra_layer.adapters.out.search.repository.agent_skill_milvus_repository import ( - AgentSkillMilvusRepository, -) - -# MongoDB documents for type annotations only — DB access goes through repos -from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemory, -) -from infra_layer.adapters.out.persistence.document.memory.agent_case import ( - AgentCaseRecord, -) -from infra_layer.adapters.out.persistence.document.memory.agent_skill import ( - AgentSkillRecord, -) - -# Raw repositories for MongoDB persistence -from infra_layer.adapters.out.persistence.repository.episodic_memory_raw_repository import ( - EpisodicMemoryRawRepository, -) -from infra_layer.adapters.out.persistence.repository.agent_case_raw_repository import ( - AgentCaseRawRepository, -) -from infra_layer.adapters.out.persistence.repository.agent_skill_raw_repository import ( - AgentSkillRawRepository, -) - -# Rerank service for hybrid method -from agentic_layer.rerank_service import get_rerank_service -from agentic_layer.retrieval_utils import vector_anchored_fusion - -# MemoryManager for agentic retrieval -from agentic_layer.memory_manager import MemoryManager -from api_specs.memory_models import RetrieveMethod, MemoryType -from api_specs.dtos.memory import RetrieveMemRequest - -# Retrieve metrics -from agentic_layer.metrics.retrieve_metrics import ( - record_retrieve_request, - record_retrieve_stage, -) - -# RawMessageService for pending messages -from service.raw_message_service import RawMessageService - -# Memorize config (for skill_retire_confidence threshold) -from biz_layer.memorize_config import DEFAULT_MEMORIZE_CONFIG - -# Constants -from core.oxm.constants import MAGIC_ALL -from biz_layer.retrieve_constants import AGENT_MEMORY_MILVUS_RADIUS - -logger = logging.getLogger(__name__) - -# Constants -DEFAULT_TOP_K = 10 -MAX_RECALL_MULTIPLIER = 2 -HYBRID_TOP_K_THRESHOLD = 5 - - -def _compute_recall_limit(top_k: int, apply_multiplier: bool) -> int: - """Compute the recall limit for keyword / vector searches. - - Args: - top_k: Requested result count (0 means use DEFAULT_TOP_K). - apply_multiplier: When True, multiply by MAX_RECALL_MULTIPLIER - to over-recall for better rerank quality. Set to False - when top_k is large enough that the multiplier only adds - rerank latency without meaningful quality gain. - """ - effective_top_k = top_k if top_k > 0 else DEFAULT_TOP_K - if apply_multiplier: - return effective_top_k * MAX_RECALL_MULTIPLIER - return effective_top_k - - -@service(name="search_memory_service", primary=True) -class SearchMemoryService: - """Memory Search service for v1 API. - - Handles query parsing, filter validation, multi-method retrieval, - and document-to-DTO conversion for search results. - """ - - def __init__(self): - """Initialize search service with repositories.""" - # ES Repositories - self.episodic_es_repo = EpisodicMemoryEsRepository() - - # Milvus Repositories - self.episodic_milvus_repo = EpisodicMemoryMilvusRepository() - self.profile_milvus_repo = UserProfileMilvusRepository() - - # Agent memory repositories - self.agent_case_es_repo = AgentCaseEsRepository() - self.agent_skill_es_repo = AgentSkillEsRepository() - self.agent_case_milvus_repo = AgentCaseMilvusRepository() - self.agent_skill_milvus_repo = AgentSkillMilvusRepository() - - # MongoDB raw repositories (for fetching full docs by id) - self.episodic_raw_repo = EpisodicMemoryRawRepository() - self.agent_case_raw_repo = AgentCaseRawRepository() - self.agent_skill_raw_repo = AgentSkillRawRepository() - - # MemoryManager for agentic retrieval - self.memory_manager = MemoryManager() - - # RawMessageService for pending messages - self.raw_message_service = RawMessageService() - - @staticmethod - def _make_error(code: str, message: str) -> dict: - """Build an error response dict.""" - return {"error": {"code": code, "message": message}} - - def _build_query_words(self, query: str) -> List[str]: - """Build query words for BM25 search.""" - if not query: - return [] - raw_words = list(jieba.cut_for_search(query)) - return filter_stopwords(raw_words, min_length=2) - - async def _get_query_vector( - self, query: str, retrieve_method: str = 'vector' - ) -> List[float]: - """Get query vector embedding.""" - stage_start = time.perf_counter() - vectorize_service = get_bean("vectorize_service") - embedding = await vectorize_service.get_embedding(query) - record_retrieve_stage( - retrieve_method=retrieve_method, - stage='embedding', - memory_type='shared', - duration_seconds=time.perf_counter() - stage_start, - ) - return embedding.tolist() - - def _parse_timestamp(self, value: Any) -> Optional[datetime]: - """Parse timestamp from various formats.""" - if value is None: - return None - if isinstance(value, datetime): - return value - if isinstance(value, (int, float)): - # Assume milliseconds if > 1e12 - if value > 1e12: - return datetime.fromtimestamp(value / 1000) - return datetime.fromtimestamp(value) - if isinstance(value, str): - from common_utils.datetime_utils import from_iso_format - - return from_iso_format(value) - return None - - @staticmethod - def _extract_hit_id(hit: Dict[str, Any]) -> Optional[str]: - """Extract document ID from a search hit (ES or Milvus format). - - Handles both ES hits (``_source.id`` / ``_id``) and Milvus results - (``id``). Returns ``None`` when no usable ID is found. - """ - return ( - hit.get("id") or hit.get("_source", {}).get("id") or hit.get("_id") - ) or None - - async def _fetch_episodic_memories_by_ids( - self, episode_ids: List[str] - ) -> Dict[str, EpisodicMemory]: - """Batch fetch episodic memories from MongoDB by IDs. - - Args: - episode_ids: List of episode IDs to fetch - - Returns: - Dict mapping episode ID to EpisodicMemory document - """ - if not episode_ids: - return {} - - episodes_data = await self.episodic_raw_repo.find_by_ids(episode_ids) - return {str(ep.id): ep for ep in episodes_data} - - def _extract_filter_values(self, filters: Dict[str, Any]) -> Dict[str, Any]: - """Extract filter values from filters dict. - - Args: - filters: Raw filters dict from request - - Returns: - Dict with user_id, group_ids, session_id, start_time, end_time - """ - result = { - "user_id": None, - "group_ids": None, - "session_id": None, - "start_time": None, - "end_time": None, - } - - def extract_from_node(node: Dict[str, Any]) -> None: - """Extract values from a single filter node into result dict.""" - if "user_id" in node and result["user_id"] is None: - user_filter = node["user_id"] - if isinstance(user_filter, str): - result["user_id"] = user_filter - elif isinstance(user_filter, dict): - if "in" in user_filter: - result["user_id"] = ( - user_filter["in"][0] if user_filter["in"] else None - ) - elif "eq" in user_filter: - result["user_id"] = user_filter["eq"] - - if "group_id" in node and result["group_ids"] is None: - group_filter = node["group_id"] - if isinstance(group_filter, str): - result["group_ids"] = [group_filter] - elif isinstance(group_filter, list): - result["group_ids"] = group_filter - elif isinstance(group_filter, dict): - if "in" in group_filter: - result["group_ids"] = group_filter["in"] - elif "eq" in group_filter: - result["group_ids"] = [group_filter["eq"]] - - if "session_id" in node and result["session_id"] is None: - session_filter = node["session_id"] - if isinstance(session_filter, str): - result["session_id"] = session_filter - elif isinstance(session_filter, dict): - if "eq" in session_filter: - result["session_id"] = session_filter["eq"] - elif "in" in session_filter: - result["session_id"] = ( - session_filter["in"][0] if session_filter["in"] else None - ) - - if "timestamp" in node: - ts_filter = node["timestamp"] - if isinstance(ts_filter, dict): - if "gte" in ts_filter and result["start_time"] is None: - result["start_time"] = self._parse_timestamp(ts_filter["gte"]) - if "gt" in ts_filter and result["start_time"] is None: - result["start_time"] = self._parse_timestamp(ts_filter["gt"]) - if "lte" in ts_filter and result["end_time"] is None: - result["end_time"] = self._parse_timestamp(ts_filter["lte"]) - if "lt" in ts_filter and result["end_time"] is None: - result["end_time"] = self._parse_timestamp(ts_filter["lt"]) - - # Handle top-level fields - extract_from_node(filters) - - # Handle AND/OR combinators - for combinator in ["AND", "OR"]: - if combinator in filters and isinstance(filters[combinator], list): - for item in filters[combinator]: - if isinstance(item, dict): - extract_from_node(item) - - return result - - async def search_memories( - self, - query: str, - method: str, - memory_types: List[str], - filters: Dict[str, Any], - top_k: int, - radius: Optional[float], - include_original_data: bool, - ) -> SearchMemoriesResponseData: - """Search memories using v1 repositories. - - Args: - query: Search query text - method: Retrieval method (keyword/vector/hybrid/agentic) - memory_types: List of memory types to search (episodic_memory/profile/raw_message) - filters: Filter conditions using Filters DSL - top_k: Max results - radius: Similarity threshold - include_original_data: Whether to include original data - - Returns: - SearchMemoriesResponseData with search results - - Raises: - ValueError: If validation fails - """ - with timed("parse_filters"): - # Validate and extract filter values - filter_values = self._extract_filter_values(filters) - - # Validate scope: at least one of user_id or group_id is required - if not filter_values["user_id"] and not filter_values["group_ids"]: - raise ValueError( - "filters must contain at least one of 'user_id' or 'group_id'" - ) - - # Profile search requires user_id and only supports vector methods. - # When conditions are not met, silently remove PROFILE so other types proceed. - if MemoryType.PROFILE.value in memory_types: - if not filter_values["user_id"] or method == "keyword": - memory_types = [ - mt for mt in memory_types if mt != MemoryType.PROFILE.value - ] - - # Build query words for keyword search - query_words = self._build_query_words(query) - - # Prepare results containers - episodes: List[SearchEpisodeItem] = [] - profiles: List[SearchProfileItem] = [] - raw_messages: List[RawMessageDTO] = [] - agent_cases: List[SearchAgentCaseItem] = [] - agent_skills: List[SearchAgentSkillItem] = [] - - # Build date range for ES - date_range = {} - if filter_values["start_time"] or filter_values["end_time"]: - if filter_values["start_time"]: - date_range["gte"] = filter_values["start_time"].isoformat() - if filter_values["end_time"]: - date_range["lte"] = filter_values["end_time"].isoformat() - - # Search each supported memory type. - # atomic_fact/foresight are intentionally ignored in v1 response. - search_tasks = [] - task_memory_types = [] - unsupported_memory_types = [ - memory_type - for memory_type in memory_types - if memory_type - not in { - MemoryType.EPISODIC_MEMORY.value, - MemoryType.PROFILE.value, - MemoryType.RAW_MESSAGE.value, - MemoryType.AGENT_MEMORY.value, - } - ] - if unsupported_memory_types: - logger.warning( - "Unsupported memory types in search request: %s", - unsupported_memory_types, - ) - - # Check allowed methods (configurable via env var, comma-separated) - allowed_methods_env = os.getenv("ALLOWED_SEARCH_METHODS") - if allowed_methods_env: - allowed_set = {m.strip() for m in allowed_methods_env.split(",")} - if method not in allowed_set: - raise ValueError( - f"Search method '{method}' is not supported. " - f"Allowed methods: {sorted(allowed_set)}" - ) - - # Extended method: delegate to DI-registered handler - builtin_methods = {"keyword", "vector", "hybrid", "agentic"} - if method not in builtin_methods: - return await self._search_extended_method( - method=method, - query=query, - memory_types=memory_types, - filter_values=filter_values, - top_k=top_k, - radius=radius, - include_original_data=include_original_data, - ) - - # Vector embedding (if needed) - query_vector = None - if method in ("vector", "hybrid"): - # embedding_api timing is inside vectorize_service.get_embedding() - query_vector = await self._get_query_vector(query, retrieve_method=method) - - for memory_type in memory_types: - if memory_type == MemoryType.EPISODIC_MEMORY.value: - if method == "hybrid": - # hybrid → hierarchical retrieval - search_tasks.append( - self._search_episodic_memory_hierarchical( - query=query, - query_vector=query_vector, - filter_values=filter_values, - top_k=top_k, - radius=radius, - ) - ) - else: - # keyword / vector / agentic → keep original logic - search_tasks.append( - self._search_episodic_memory( - query=query, - query_words=query_words, - query_vector=query_vector, - method=method, - filter_values=filter_values, - date_range=date_range, - top_k=top_k, - radius=radius, - ) - ) - task_memory_types.append(memory_type) - elif memory_type == MemoryType.PROFILE.value: - search_tasks.append( - self._search_profile( - query_vector=query_vector, - method=method, - filter_values=filter_values, - top_k=top_k, - radius=radius, - ) - ) - task_memory_types.append(memory_type) - elif memory_type == MemoryType.RAW_MESSAGE.value: - search_tasks.append( - self._search_raw_message(filter_values=filter_values, top_k=top_k) - ) - task_memory_types.append(memory_type) - elif memory_type == MemoryType.AGENT_MEMORY.value: - search_tasks.append( - self._search_agent_cases( - query=query, - query_words=query_words, - query_vector=query_vector, - method=method, - filter_values=filter_values, - date_range=date_range, - top_k=top_k, - radius=radius, - ) - ) - task_memory_types.append(MemoryType.AGENT_CASE.value) - search_tasks.append( - self._search_agent_skills( - query=query, - query_words=query_words, - query_vector=query_vector, - method=method, - filter_values=filter_values, - top_k=top_k, - radius=radius, - ) - ) - task_memory_types.append(MemoryType.AGENT_SKILL.value) - - # Execute searches in parallel - search_start = time.perf_counter() - with timed_parallel("concurrent_retrieval"): - results = await asyncio.gather(*search_tasks, return_exceptions=True) - search_duration = time.perf_counter() - search_start - - # Propagate critical system errors before processing results - from common_utils.async_utils import reraise_critical_errors - - reraise_critical_errors(results) - - # Collect results from parallel searches - has_error = False - total_result_count = 0 - for i, result in enumerate(results): - memory_type = task_memory_types[i] - if isinstance(result, Exception): - logger.error("Search failed for %s: %s", memory_type, result) - has_error = True - continue - if result: - total_result_count += len(result) - if memory_type == MemoryType.EPISODIC_MEMORY.value: - episodes.extend(result) - elif memory_type == MemoryType.PROFILE.value: - profiles.extend(result) - elif memory_type == MemoryType.RAW_MESSAGE.value: - raw_messages.extend(result) - elif memory_type == MemoryType.AGENT_CASE.value: - agent_cases.extend(result) - elif memory_type == MemoryType.AGENT_SKILL.value: - agent_skills.extend(result) - - # Record request-level metrics (once per search request, not per memory_type) - # memory_type="all" ensures 1 request = 1 count for all methods including agentic - # Note: agentic also records per-memory_type metrics inside memory_manager, - # but this "all" record is the authoritative one for QPS and e2e duration. - if has_error: - status = 'error' - elif total_result_count > 0: - status = 'success' - else: - status = 'empty_result' - record_retrieve_request( - memory_type='all', - retrieve_method=method, - status=status, - duration_seconds=search_duration, - results_count=total_result_count, - ) - - # Fetch full data from MongoDB if needed - original_data = None - if include_original_data: - with timed("fetch_original_data"): - original_data = await self._fetch_original_data(episodes, profiles) - - with timed("assemble_results"): - - # Apply top_k limit - if top_k > 0: - episodes = episodes[:top_k] - profiles = profiles[:top_k] - raw_messages = raw_messages[:top_k] - agent_cases = agent_cases[:top_k] - agent_skills = agent_skills[:top_k] - - # Build agent_memory container if any agent results exist - agent_memory = None - if agent_cases or agent_skills: - agent_memory = AgentMemorySearchResult( - cases=agent_cases, skills=agent_skills - ) - - # Build response - return SearchMemoriesResponseData( - episodes=episodes, - profiles=profiles, - raw_messages=raw_messages, - agent_memory=agent_memory, - query={"text": query, "method": method, "filters_applied": filters}, - original_data=original_data, - ) - - async def _search_episodic_memory( - self, - query: str, - query_words: List[str], - query_vector: Optional[List[float]], - method: str, - filter_values: Dict[str, Any], - date_range: Dict[str, str], - top_k: int, - radius: Optional[float], - ) -> List[SearchEpisodeItem]: - """Search episodic memories.""" - results: List[SearchEpisodeItem] = [] - limit = ( - top_k * MAX_RECALL_MULTIPLIER - if top_k > 0 - else DEFAULT_TOP_K * MAX_RECALL_MULTIPLIER - ) - - memory_type = MemoryType.EPISODIC_MEMORY.value - with timed("episode_search"): - if method == "keyword": - stage_start = time.perf_counter() - with timed("keyword_search"): - hits = await self.episodic_es_repo.multi_search( - query=query_words, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - session_id=filter_values["session_id"], - date_range=date_range, - size=limit, - ) - record_retrieve_stage( - retrieve_method=method, - stage='keyword_search', - memory_type=memory_type, - duration_seconds=time.perf_counter() - stage_start, - ) - # Extract IDs for MongoDB backfill - episode_ids = [ - hit.get("_source", {}).get("id") - for hit in hits - if hit.get("_source", {}).get("id") - ] - - # Batch fetch from MongoDB to get complete data - with timed("backfill_from_store"): - episodes_dict = {} - if episode_ids: - episodes_dict = await self._fetch_episodic_memories_by_ids( - episode_ids - ) - - for hit in hits: - source = hit.get("_source", {}) - episode_id = source.get("id", "") - ep_doc = episodes_dict.get(episode_id) - - results.append( - SearchEpisodeItem( - id=episode_id, - user_id=ep_doc.user_id if ep_doc else source.get("user_id"), - group_id=( - ep_doc.group_id if ep_doc else source.get("group_id") - ), - session_id=( - ep_doc.session_id - if ep_doc - else source.get("session_id") - ), - timestamp=( - ep_doc.timestamp - if ep_doc - else self._parse_timestamp(source.get("timestamp")) - ), - participants=( - ep_doc.participants - if ep_doc - else source.get("participants") - ), - summary=ep_doc.summary if ep_doc else source.get("summary"), - subject=ep_doc.subject if ep_doc else source.get("subject"), - episode=ep_doc.episode if ep_doc else source.get("episode"), - type=ep_doc.type if ep_doc else source.get("type"), - parent_type=( - ep_doc.parent_type - if ep_doc - else source.get("parent_type") - ), - parent_id=( - ep_doc.parent_id if ep_doc else source.get("parent_id") - ), - score=hit.get("_score"), - ) - ) - - elif method == "vector" and query_vector: - stage_start = time.perf_counter() - with timed("vector_search"): - search_results = await self.episodic_milvus_repo.vector_search( - query_vector=query_vector, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - session_id=filter_values["session_id"], - start_time=filter_values["start_time"], - end_time=filter_values["end_time"], - limit=limit, - radius=radius, - ) - record_retrieve_stage( - retrieve_method=method, - stage='vector_search', - memory_type=memory_type, - duration_seconds=time.perf_counter() - stage_start, - ) - # Extract IDs for MongoDB backfill - episode_ids = [res.get("id") for res in search_results if res.get("id")] - - # Batch fetch from MongoDB to get complete data - with timed("backfill_from_store"): - episodes_dict = {} - if episode_ids: - episodes_dict = await self._fetch_episodic_memories_by_ids( - episode_ids - ) - - for res in search_results: - episode_id = res.get("id") - ep_doc = episodes_dict.get(episode_id) - - ts = res.get("timestamp") - timestamp = ( - datetime.fromtimestamp(ts / 1000) - if isinstance(ts, (int, float)) - else None - ) - - # Use MongoDB data for display fields, fallback to Milvus data - results.append( - SearchEpisodeItem( - id=episode_id or "", - user_id=ep_doc.user_id if ep_doc else res.get("user_id"), - group_id=( - ep_doc.group_id if ep_doc else res.get("group_id") - ), - session_id=( - ep_doc.session_id if ep_doc else res.get("session_id") - ), - timestamp=timestamp, - participants=( - ep_doc.participants - if ep_doc - else res.get("participants") - ), - summary=ep_doc.summary if ep_doc else None, - subject=ep_doc.subject if ep_doc else None, - episode=(ep_doc.episode if ep_doc else res.get("episode")), - type=ep_doc.type if ep_doc else res.get("type"), - parent_type=( - ep_doc.parent_type if ep_doc else res.get("parent_type") - ), - parent_id=( - ep_doc.parent_id if ep_doc else res.get("parent_id") - ), - score=res.get("score"), - ) - ) - - elif method == "hybrid" and query_vector: - # Hybrid: keyword + vector + rerank - stage_start = time.perf_counter() - with timed("keyword_search"): - keyword_hits = await self.episodic_es_repo.multi_search( - query=query_words, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - session_id=filter_values["session_id"], - date_range=date_range, - size=limit, - ) - record_retrieve_stage( - retrieve_method=method, - stage='keyword_search', - memory_type=memory_type, - duration_seconds=time.perf_counter() - stage_start, - ) - stage_start = time.perf_counter() - with timed("vector_search"): - vector_results = await self.episodic_milvus_repo.vector_search( - query_vector=query_vector, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - session_id=filter_values["session_id"], - start_time=filter_values["start_time"], - end_time=filter_values["end_time"], - limit=limit, - radius=radius, - ) - record_retrieve_stage( - retrieve_method=method, - stage='vector_search', - memory_type=memory_type, - duration_seconds=time.perf_counter() - stage_start, - ) - # Tag memory_type for rerank text extraction - for h in keyword_hits: - h["memory_type"] = MemoryType.EPISODIC_MEMORY.value - for h in vector_results: - h["memory_type"] = MemoryType.EPISODIC_MEMORY.value - # Merge and deduplicate - seen_ids = { - h.get("_source", {}).get("id") or h.get("_id") for h in keyword_hits - } - merged_hits = keyword_hits + [ - h for h in vector_results if h.get("id") not in seen_ids - ] - # Rerank - stage_start = time.perf_counter() - rerank_service = get_rerank_service() - rerank_top_k = top_k if top_k > 0 else DEFAULT_TOP_K - reranked_hits = await rerank_service.rerank_memories( - query=query, hits=merged_hits, top_k=rerank_top_k - ) - rerank_ms = (time.perf_counter() - stage_start) * 1000 - record_retrieve_stage( - retrieve_method=method, - stage='rerank', - memory_type=memory_type, - duration_seconds=rerank_ms / 1000, - ) - logger.info( - "[RERANK_DIAG] memory_type=%s in=%d out=%d top_k=%d took=%.1fms", - memory_type, - len(merged_hits), - len(reranked_hits), - rerank_top_k, - rerank_ms, - ) - # Batch fetch from MongoDB for complete display fields - episode_ids = [ - h.get("id") or h.get("_id") - for h in reranked_hits - if h.get("id") or h.get("_id") - ] - with timed("backfill_from_store"): - episodes_dict = {} - if episode_ids: - episodes_dict = await self._fetch_episodic_memories_by_ids( - episode_ids - ) - - # Convert to SearchEpisodeItem (MongoDB data preferred, hit as fallback) - for hit in reranked_hits: - episode_id = hit.get("id") or hit.get("_id", "") - ep_doc = episodes_dict.get(episode_id) - results.append( - SearchEpisodeItem( - id=episode_id, - user_id=ep_doc.user_id if ep_doc else hit.get("user_id"), - group_id=ep_doc.group_id if ep_doc else hit.get("group_id"), - session_id=( - ep_doc.session_id if ep_doc else hit.get("session_id") - ), - timestamp=self._parse_timestamp(hit.get("timestamp")), - participants=( - ep_doc.participants - if ep_doc - else hit.get("participants") - ), - summary=ep_doc.summary if ep_doc else hit.get("summary"), - subject=ep_doc.subject if ep_doc else hit.get("subject"), - episode=ep_doc.episode if ep_doc else hit.get("episode"), - type=ep_doc.type if ep_doc else hit.get("type"), - parent_type=( - ep_doc.parent_type if ep_doc else hit.get("parent_type") - ), - parent_id=( - ep_doc.parent_id if ep_doc else hit.get("parent_id") - ), - score=hit.get("rerank_score", hit.get("score", 0)), - ) - ) - - elif method == "agentic": - # Agentic: LLM-guided multi-round retrieval - with timed("agentic_retrieval"): - results = await self._search_agentic_episodic_memory( - query=query, filter_values=filter_values, top_k=top_k - ) - - return results - - async def _search_profile( - self, - query_vector: Optional[List[float]], - method: str, - filter_values: Dict[str, Any], - top_k: int, - radius: Optional[float], - ) -> List[SearchProfileItem]: - """Search user profiles.""" - results: List[SearchProfileItem] = [] - limit = ( - top_k * MAX_RECALL_MULTIPLIER - if top_k > 0 - else DEFAULT_TOP_K * MAX_RECALL_MULTIPLIER - ) - - if method in ("vector", "hybrid", "agentic") and query_vector: - # Profile only has Milvus (no ES), so all methods fall back to vector search. - # Agentic multi-round pipeline adds no value for short profile items. - stage_start = time.perf_counter() - search_results = await self.profile_milvus_repo.vector_search( - query_vector=query_vector, - user_id=filter_values["user_id"], - limit=limit, - radius=radius, - ) - record_retrieve_stage( - retrieve_method=method, - stage='vector_search', - memory_type=MemoryType.PROFILE.value, - duration_seconds=time.perf_counter() - stage_start, - ) - for res in search_results: - results.append( - SearchProfileItem( - id=res.get("id", ""), - user_id=res.get("user_id"), - group_id=res.get("group_id"), - scenario=res.get("scenario"), - memcell_count=res.get("memcell_count"), - profile_data={ - "item_type": res.get("item_type", ""), - "embed_text": res.get("embed_text", ""), - }, - score=res.get("score"), - ) - ) - - return results - - async def _search_raw_message( - self, filter_values: Dict[str, Any], top_k: int - ) -> List[RawMessageDTO]: - """Search raw unprocessed messages (pending messages). - - Retrieves pending messages from RawMessage collection based on filter criteria. - Raw messages are unprocessed conversations that have not yet been extracted - into memory structures (episodic_memory, profile, etc.). - - Args: - filter_values: Filter values dict containing user_id, group_ids, etc. - top_k: Max results to return - - Returns: - List of RawMessageDTO with raw message data - """ - # Build sender_id from filter_values - # If user_id is provided, use it as sender_id filter - # Use MAGIC_ALL when user_id is None to skip sender_id filtering - user_id = filter_values.get("user_id") - sender_id = user_id if user_id is not None else MAGIC_ALL - - # Get group_ids from filter_values - group_ids = filter_values.get("group_ids") - - # Determine limit - limit = top_k if top_k > 0 else DEFAULT_TOP_K - - try: - # Fetch pending messages using RawMessageService - # This returns messages with sync_status in [-1, 0] (pending/accumulating) - pending_messages = await self.raw_message_service.get_pending_messages( - sender_id=sender_id, group_ids=group_ids, limit=limit - ) - - logger.info( - "[_search_raw_message] Found %d raw messages: user_id=%s, group_ids=%s", - len(pending_messages), - user_id, - group_ids, - ) - - return pending_messages - - except Exception as e: - logger.error( - "Failed to search raw messages: sender_id=%s, group_ids=%s, error=%s", - sender_id, - group_ids, - e, - ) - return [] - - async def _search_agentic_episodic_memory( - self, query: str, filter_values: Dict[str, Any], top_k: int - ) -> List[SearchEpisodeItem]: - """Search episodic memories using agentic retrieval (LLM-guided multi-round). - - Delegates to MemoryManager.retrieve_mem_agentic which handles: - - Round 1: Hybrid search - - Rerank + LLM sufficiency check - - Round 2 (if needed): Multi-query generation and parallel search - - Final rerank and merge - - Args: - query: Original search query text (passed through to embedding/rerank/LLM) - filter_values: Filter values dict - top_k: Max results to return - - Returns: - List of SearchEpisodeItem with agentic retrieval results - """ - # Build RetrieveMemRequest for MemoryManager - retrieve_request = RetrieveMemRequest( - query=query, - user_id=filter_values.get("user_id"), - group_ids=filter_values.get("group_ids"), - memory_types=[MemoryType.EPISODIC_MEMORY], - top_k=top_k if top_k > 0 else -1, - retrieve_method=RetrieveMethod.AGENTIC, - radius=None, - ) - - # Call MemoryManager's agentic retrieval - response = await self.memory_manager.retrieve_mem_agentic(retrieve_request) - - # Collect IDs and scores from MemoryManager results - memory_items: List[Dict[str, Any]] = [] - for memory in response.memories: - if isinstance(memory, dict): - memory_items.append(memory) - else: - memory_items.append( - { - "id": getattr(memory, "id", None), - "score": getattr(memory, "score", 0), - } - ) - - # Backfill from MongoDB to get complete display fields - episode_ids = [m.get("id") for m in memory_items if m.get("id")] - episodes_dict = await self._fetch_episodic_memories_by_ids(episode_ids) - - # Convert to SearchEpisodeItem with MongoDB data - results: List[SearchEpisodeItem] = [] - for memory_item in memory_items: - episode_id = memory_item.get("id", "") - ep_doc = episodes_dict.get(episode_id) - score = memory_item.get("score", 0) - - results.append( - SearchEpisodeItem( - id=episode_id, - user_id=ep_doc.user_id if ep_doc else memory_item.get("user_id"), - group_id=ep_doc.group_id if ep_doc else memory_item.get("group_id"), - session_id=( - ep_doc.session_id if ep_doc else memory_item.get("session_id") - ), - timestamp=( - ep_doc.timestamp - if ep_doc - else self._parse_timestamp(memory_item.get("timestamp")) - ), - participants=( - ep_doc.participants - if ep_doc - else memory_item.get("participants") - ), - summary=ep_doc.summary if ep_doc else None, - subject=ep_doc.subject if ep_doc else None, - episode=ep_doc.episode if ep_doc else memory_item.get("episode"), - type=ep_doc.type if ep_doc else memory_item.get("type"), - parent_type=( - ep_doc.parent_type if ep_doc else memory_item.get("parent_type") - ), - parent_id=( - ep_doc.parent_id if ep_doc else memory_item.get("parent_id") - ), - score=score, - ) - ) - - return results - - async def _search_episodic_memory_hierarchical( - self, - query: str, - query_vector: Optional[List[float]], - filter_values: Dict[str, Any], - top_k: int, - radius: Optional[float], - ) -> List[SearchEpisodeItem]: - """Hierarchical retrieval for episodic_memory via DI-registered service, fallback to builtin hybrid.""" - try: - handler = get_bean("search_method_mrag") - except Exception: - logger.warning( - "Hierarchical search service not available, fallback to builtin hybrid" - ) - query_words = self._build_query_words(query) - date_range = {} - if filter_values.get("start_time"): - date_range["gte"] = filter_values["start_time"].isoformat() - if filter_values.get("end_time"): - date_range["lte"] = filter_values["end_time"].isoformat() - return await self._search_episodic_memory( - query=query, - query_words=query_words, - query_vector=query_vector, - method="hybrid", - filter_values=filter_values, - date_range=date_range, - top_k=top_k, - radius=radius, - ) - - stage_start = time.perf_counter() - response = await handler.search( - query=query, - query_vector=query_vector, - memory_types=["episodic_memory"], - filter_values=filter_values, - top_k=top_k, - radius=radius, - include_original_data=False, - ) - record_retrieve_stage( - retrieve_method='hybrid', - stage='hierarchical_search', - memory_type=MemoryType.EPISODIC_MEMORY.value, - duration_seconds=time.perf_counter() - stage_start, - ) - return response.episodes - - async def _search_extended_method( - self, - method: str, - query: str, - memory_types: List[str], - filter_values: Dict[str, Any], - top_k: int, - radius: Optional[float], - include_original_data: bool, - ) -> SearchMemoriesResponseData: - """DI extension point for custom search methods. - - Looks up a bean named 'search_method_{method}' from the DI container - and delegates the search to it. - - Args: - method: Extended method name (e.g., 'mrag') - query: Search query text - memory_types: List of memory types to search - filter_values: Extracted filter values - top_k: Max results - radius: Similarity threshold - include_original_data: Whether to include original data - - Returns: - SearchMemoriesResponseData from the extended handler - - Raises: - ValueError: If the method handler is not found - """ - try: - handler = get_bean(f"search_method_{method}") - except Exception: - raise ValueError(f"Unknown search method: {method}") - - query_vector = await self._get_query_vector(query, retrieve_method=method) - return await handler.search( - query=query, - query_vector=query_vector, - memory_types=memory_types, - filter_values=filter_values, - top_k=top_k, - radius=radius, - include_original_data=include_original_data, - ) - - async def _fetch_original_data( - self, episodes: List[SearchEpisodeItem], profiles: List[SearchProfileItem] - ) -> Dict[str, Dict[str, Any]]: - """Fetch full original data from MongoDB.""" - original_data = {"episodes": {}, "profiles": {}} - - # Collect IDs - episode_ids = [e.id for e in episodes if e.id] - - # Fetch from MongoDB via repository - if episode_ids: - episodes_data = await self.episodic_raw_repo.find_by_ids(episode_ids) - for ep in episodes_data: - original_data["episodes"][str(ep.id)] = ep.model_dump(mode="json") - - # Profile search returns item-level results (Milvus entity IDs), - # not MongoDB document IDs, so original_data backfill is skipped. - - return original_data - - # ------------------------------------------------------------------ - # Agent Case search - # ------------------------------------------------------------------ - - async def _fetch_agent_cases_by_ids( - self, case_ids: List[str] - ) -> Dict[str, AgentCaseRecord]: - """Batch fetch AgentCaseRecords from MongoDB by IDs.""" - if not case_ids: - return {} - docs = await self.agent_case_raw_repo.get_by_ids(case_ids) - return {str(d.id): d for d in docs} - - @staticmethod - def _agent_case_doc_to_item( - doc: AgentCaseRecord, score: Optional[float] = None - ) -> SearchAgentCaseItem: - """Convert AgentCaseRecord to SearchAgentCaseItem DTO.""" - return SearchAgentCaseItem( - id=str(doc.id), - user_id=doc.user_id, - group_id=doc.group_id, - session_id=doc.session_id, - timestamp=doc.timestamp, - task_intent=doc.task_intent or "", - approach=doc.approach or "", - quality_score=doc.quality_score, - key_insight=doc.key_insight or "", - parent_type=doc.parent_type, - parent_id=doc.parent_id, - score=score, - ) - - async def _search_agent_cases( - self, - query: str, - query_words: List[str], - query_vector: Optional[List[float]], - method: str, - filter_values: Dict[str, Any], - date_range: Dict[str, str], - top_k: int, - radius: Optional[float], - ) -> List[SearchAgentCaseItem]: - """Search agent cases using keyword / vector / hybrid / rrf.""" - results: List[SearchAgentCaseItem] = [] - limit = _compute_recall_limit(top_k, apply_multiplier=True) - - agent_case_mt = MemoryType.AGENT_CASE.value - with timed("agent_case_search"): - if method == "keyword": - stage_start = time.perf_counter() - hits = await self.agent_case_es_repo.multi_search( - query=query_words, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - session_id=filter_values["session_id"], - date_range=date_range, - size=limit, - ) - record_retrieve_stage( - retrieve_method=method, - stage='keyword_search', - memory_type=agent_case_mt, - duration_seconds=time.perf_counter() - stage_start, - ) - case_ids = [ - h.get("_source", {}).get("id") - for h in hits - if h.get("_source", {}).get("id") - ] - cases_dict = await self._fetch_agent_cases_by_ids(case_ids) - for hit in hits: - case_id = hit.get("_source", {}).get("id") - doc = cases_dict.get(case_id) - if doc: - results.append( - self._agent_case_doc_to_item(doc, score=hit.get("_score")) - ) - - elif method == "vector" and query_vector: - stage_start = time.perf_counter() - search_results = await self.agent_case_milvus_repo.vector_search( - query_vector=query_vector, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - session_id=filter_values["session_id"], - start_time=filter_values["start_time"], - end_time=filter_values["end_time"], - limit=limit, - radius=radius, - ) - record_retrieve_stage( - retrieve_method=method, - stage='vector_search', - memory_type=agent_case_mt, - duration_seconds=time.perf_counter() - stage_start, - ) - case_ids = [r.get("id") for r in search_results if r.get("id")] - cases_dict = await self._fetch_agent_cases_by_ids(case_ids) - for res in search_results: - doc = cases_dict.get(res.get("id")) - if doc: - results.append( - self._agent_case_doc_to_item(doc, score=res.get("score")) - ) - - elif method == "hybrid" and query_vector: - # Hybrid: keyword + vector, score fusion (no reranker) - recall_limit = _compute_recall_limit( - top_k, apply_multiplier=0 < top_k <= HYBRID_TOP_K_THRESHOLD - ) - stage_start = time.perf_counter() - keyword_hits = await self.agent_case_es_repo.multi_search( - query=query_words, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - session_id=filter_values["session_id"], - date_range=date_range, - size=recall_limit, - ) - record_retrieve_stage( - retrieve_method=method, - stage='keyword_search', - memory_type=agent_case_mt, - duration_seconds=time.perf_counter() - stage_start, - ) - stage_start = time.perf_counter() - vector_results = await self.agent_case_milvus_repo.vector_search( - query_vector=query_vector, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - session_id=filter_values["session_id"], - start_time=filter_values["start_time"], - end_time=filter_values["end_time"], - limit=recall_limit, - radius=max(AGENT_MEMORY_MILVUS_RADIUS, radius or 0.0), - ) - record_retrieve_stage( - retrieve_method=method, - stage='vector_search', - memory_type=agent_case_mt, - duration_seconds=time.perf_counter() - stage_start, - ) - # Fuse scores: normalize BM25 into vector score range - vec_pairs = [ - (self._extract_hit_id(vr), vr.get("score", 0.0)) - for vr in vector_results - if self._extract_hit_id(vr) - ] - kw_pairs = [ - (self._extract_hit_id(h), h.get("_score", 0.0)) - for h in keyword_hits - if self._extract_hit_id(h) - ] - scored = vector_anchored_fusion(vec_pairs, kw_pairs) - final_top = top_k if top_k > 0 else DEFAULT_TOP_K - scored = scored[:final_top] - # Backfill from MongoDB - case_ids = [doc_id for doc_id, _ in scored] - cases_dict = await self._fetch_agent_cases_by_ids(case_ids) - for doc_id, score in scored: - doc = cases_dict.get(doc_id) - if doc: - results.append(self._agent_case_doc_to_item(doc, score=score)) - - elif method == "agentic": - results = await self._search_agentic_agent_cases( - query=query, filter_values=filter_values, top_k=top_k - ) - - return results - - # ------------------------------------------------------------------ - # Agent Skill search - # ------------------------------------------------------------------ - - async def _fetch_agent_skills_by_ids( - self, skill_ids: List[str] - ) -> Dict[str, AgentSkillRecord]: - """Batch fetch AgentSkillRecords from MongoDB by IDs. - - Also filters out retired skills (confidence below threshold). - """ - if not skill_ids: - return {} - retire_confidence = DEFAULT_MEMORIZE_CONFIG.skill_retire_confidence - docs = await self.agent_skill_raw_repo.find_by_ids( - skill_ids, min_confidence=retire_confidence - ) - return {str(d.id): d for d in docs} - - @staticmethod - def _agent_skill_doc_to_item( - doc: AgentSkillRecord, score: Optional[float] = None - ) -> SearchAgentSkillItem: - """Convert AgentSkillRecord to SearchAgentSkillItem DTO.""" - return SearchAgentSkillItem( - id=str(doc.id), - cluster_id=doc.cluster_id, - user_id=doc.user_id, - group_id=doc.group_id, - name=doc.name, - description=doc.description, - content=doc.content, - confidence=doc.confidence, - maturity_score=doc.maturity_score, - source_case_ids=doc.source_case_ids or [], - score=score, - ) - - async def _search_agent_skills( - self, - query: str, - query_words: List[str], - query_vector: Optional[List[float]], - method: str, - filter_values: Dict[str, Any], - top_k: int, - radius: Optional[float], - ) -> List[SearchAgentSkillItem]: - """Search agent skills using keyword / vector / hybrid / rrf. - - Note: Unlike episodic_memory and agent_case, agent_skill has no - date_range filtering — skills are aggregated artefacts without - a business timestamp, similar to profile. - """ - results: List[SearchAgentSkillItem] = [] - limit = _compute_recall_limit(top_k, apply_multiplier=True) - - agent_skill_mt = MemoryType.AGENT_SKILL.value - with timed("agent_skill_search"): - retire_confidence = DEFAULT_MEMORIZE_CONFIG.skill_retire_confidence - - if method == "keyword": - stage_start = time.perf_counter() - hits = await self.agent_skill_es_repo.multi_search( - query=query_words, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - size=limit, - confidence_threshold=retire_confidence, - ) - record_retrieve_stage( - retrieve_method=method, - stage='keyword_search', - memory_type=agent_skill_mt, - duration_seconds=time.perf_counter() - stage_start, - ) - skill_ids = [ - h.get("_source", {}).get("id") - for h in hits - if h.get("_source", {}).get("id") - ] - skills_dict = await self._fetch_agent_skills_by_ids(skill_ids) - for hit in hits: - skill_id = hit.get("_source", {}).get("id") - doc = skills_dict.get(skill_id) - if doc: - results.append( - self._agent_skill_doc_to_item(doc, score=hit.get("_score")) - ) - - elif method == "vector" and query_vector: - stage_start = time.perf_counter() - search_results = await self.agent_skill_milvus_repo.vector_search( - query_vector=query_vector, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - limit=limit, - radius=radius, - confidence_threshold=retire_confidence, - ) - record_retrieve_stage( - retrieve_method=method, - stage='vector_search', - memory_type=agent_skill_mt, - duration_seconds=time.perf_counter() - stage_start, - ) - skill_ids = [r.get("id") for r in search_results if r.get("id")] - skills_dict = await self._fetch_agent_skills_by_ids(skill_ids) - for res in search_results: - doc = skills_dict.get(res.get("id")) - if doc: - results.append( - self._agent_skill_doc_to_item(doc, score=res.get("score")) - ) - - elif method == "hybrid" and query_vector: - # Hybrid: keyword + vector + rerank - recall_limit = _compute_recall_limit( - top_k, apply_multiplier=0 < top_k <= HYBRID_TOP_K_THRESHOLD - ) - stage_start = time.perf_counter() - keyword_hits = await self.agent_skill_es_repo.multi_search( - query=query_words, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - size=recall_limit, - confidence_threshold=retire_confidence, - ) - record_retrieve_stage( - retrieve_method=method, - stage='keyword_search', - memory_type=agent_skill_mt, - duration_seconds=time.perf_counter() - stage_start, - ) - stage_start = time.perf_counter() - vector_results = await self.agent_skill_milvus_repo.vector_search( - query_vector=query_vector, - user_id=filter_values["user_id"], - group_ids=filter_values["group_ids"], - limit=recall_limit, - radius=max(AGENT_MEMORY_MILVUS_RADIUS, radius or 0.0), - confidence_threshold=retire_confidence, - ) - record_retrieve_stage( - retrieve_method=method, - stage='vector_search', - memory_type=agent_skill_mt, - duration_seconds=time.perf_counter() - stage_start, - ) - # Tag memory_type for rerank text extraction - for h in keyword_hits: - h["memory_type"] = MemoryType.AGENT_SKILL.value - for h in vector_results: - h["memory_type"] = MemoryType.AGENT_SKILL.value - # Merge and deduplicate - seen_ids = {self._extract_hit_id(h) for h in keyword_hits} - {None} - merged_hits = keyword_hits + [ - h for h in vector_results if self._extract_hit_id(h) not in seen_ids - ] - # Rerank - stage_start = time.perf_counter() - rerank_service = get_rerank_service() - rerank_top_k = top_k if top_k > 0 else DEFAULT_TOP_K - reranked_hits = await rerank_service.rerank_memories( - query=query, - hits=merged_hits, - top_k=rerank_top_k, - instruction="Determine whether the skill's methodology and domain are applicable to the query, preferring same-domain skills with directly relevant steps." - ) - rerank_ms = (time.perf_counter() - stage_start) * 1000 - record_retrieve_stage( - retrieve_method=method, - stage='rerank', - memory_type=agent_skill_mt, - duration_seconds=rerank_ms / 1000, - ) - logger.info( - "[RERANK_DIAG] memory_type=%s in=%d out=%d top_k=%d took=%.1fms", - agent_skill_mt, - len(merged_hits), - len(reranked_hits), - rerank_top_k, - rerank_ms, - ) - # Backfill from MongoDB - skill_ids = [ - self._extract_hit_id(h) - for h in reranked_hits - if self._extract_hit_id(h) - ] - skills_dict = await self._fetch_agent_skills_by_ids(skill_ids) - for hit in reranked_hits: - skill_id = self._extract_hit_id(hit) - doc = skills_dict.get(skill_id) - if doc: - results.append( - self._agent_skill_doc_to_item( - doc, score=hit.get("rerank_score", hit.get("score", 0)) - ) - ) - - elif method == "agentic": - results = await self._search_agentic_agent_skills( - query=query, filter_values=filter_values, top_k=top_k - ) - - if results and DEFAULT_MEMORIZE_CONFIG.enable_skill_llm_verify: - results = await self._verify_skill_relevance(query, results) - - return results - - async def _verify_skill_relevance( - self, query: str, skills: List[SearchAgentSkillItem] - ) -> List[SearchAgentSkillItem]: - """Use LLM to post-verify whether retrieved skills are relevant to the query.""" - import json - from common_utils.json_utils import parse_json_response - from memory_layer.prompts import get_prompt_by - from memory_layer.llm.llm_provider import build_default_provider - - if not skills or not query: - return skills - - skills_for_prompt = [ - { - "index": i, - "name": skill.name or "", - "description": skill.description or "", - "content": skill.content or "", - } - for i, skill in enumerate(skills) - ] - - prompt_template = get_prompt_by("AGENT_SKILL_RELEVANCE_VERIFY_PROMPT") - prompt = prompt_template.format( - query=query, skills_json=json.dumps(skills_for_prompt, ensure_ascii=False) - ) - - try: - llm_provider = build_default_provider() - response_text = await llm_provider.generate( - prompt, temperature=0.0, response_format={"type": "json_object"} - ) - - result = parse_json_response(response_text) - score_map = { - item["index"]: item.get("score", 0.0) - for item in result.get("results", []) - } - - scored = [] - for i, skill in enumerate(skills): - relevance_score = score_map.get(i, 0.0) - if relevance_score >= 0.4: - skill.score = relevance_score - scored.append(skill) - - scored.sort(key=lambda s: s.score, reverse=True) - - logger.info( - "Skill relevance verification: %d/%d skills passed (threshold=0.4) for query: %s", - len(scored), - len(skills), - query[:60], - ) - - return scored - - except Exception as e: - logger.warning( - "Skill relevance verification failed, returning all results: %s", e - ) - return skills - - # ------------------------------------------------------------------ - # Agentic retrieval for agent memory types - # ------------------------------------------------------------------ - - async def _search_agentic_agent_cases( - self, query: str, filter_values: Dict[str, Any], top_k: int - ) -> List[SearchAgentCaseItem]: - """Search agent cases using agentic retrieval (LLM-guided multi-round).""" - retrieve_request = RetrieveMemRequest( - query=query, - user_id=filter_values.get("user_id"), - group_ids=filter_values.get("group_ids"), - memory_types=[MemoryType.AGENT_CASE], - top_k=top_k if top_k > 0 else -1, - retrieve_method=RetrieveMethod.AGENTIC, - radius=None, - ) - - response = await self.memory_manager.retrieve_mem_agentic(retrieve_request) - - results: List[SearchAgentCaseItem] = [] - case_ids = [ - getattr(m, "id", None) or (m.get("id") if isinstance(m, dict) else None) - for m in response.memories - ] - case_ids = [cid for cid in case_ids if cid] - cases_dict = await self._fetch_agent_cases_by_ids(case_ids) - - for memory in response.memories: - if isinstance(memory, dict): - case_id = memory.get("id") - score = memory.get("score", 0) - else: - case_id = getattr(memory, "id", None) - score = getattr(memory, "score", 0) - - doc = cases_dict.get(case_id) - if doc: - results.append(self._agent_case_doc_to_item(doc, score=score)) - - return results - - async def _search_agentic_agent_skills( - self, query: str, filter_values: Dict[str, Any], top_k: int - ) -> List[SearchAgentSkillItem]: - """Search agent skills using agentic retrieval (LLM-guided multi-round).""" - retrieve_request = RetrieveMemRequest( - query=query, - user_id=filter_values.get("user_id"), - group_ids=filter_values.get("group_ids"), - memory_types=[MemoryType.AGENT_SKILL], - top_k=top_k if top_k > 0 else -1, - retrieve_method=RetrieveMethod.AGENTIC, - radius=None, - ) - - response = await self.memory_manager.retrieve_mem_agentic(retrieve_request) - - results: List[SearchAgentSkillItem] = [] - skill_ids = [ - getattr(m, "id", None) or (m.get("id") if isinstance(m, dict) else None) - for m in response.memories - ] - skill_ids = [sid for sid in skill_ids if sid] - skills_dict = await self._fetch_agent_skills_by_ids(skill_ids) - - for memory in response.memories: - if isinstance(memory, dict): - skill_id = memory.get("id") - score = memory.get("score", 0) - else: - skill_id = getattr(memory, "id", None) - score = getattr(memory, "score", 0) - - doc = skills_dict.get(skill_id) - if doc: - results.append(self._agent_skill_doc_to_item(doc, score=score)) - - return results diff --git a/methods/EverCore/src/agentic_layer/vectorize_base.py b/methods/EverCore/src/agentic_layer/vectorize_base.py deleted file mode 100644 index dd54ebe8..00000000 --- a/methods/EverCore/src/agentic_layer/vectorize_base.py +++ /dev/null @@ -1,252 +0,0 @@ -""" -Base Vectorize Service Implementation - -Provides common functionality for embedding services using OpenAI-compatible APIs. -""" - -import asyncio -import logging -from typing import List, Optional, Tuple -from abc import abstractmethod -import numpy as np -from openai import AsyncOpenAI - -from agentic_layer.vectorize_interface import ( - VectorizeServiceInterface, - VectorizeError, - UsageInfo, -) -from core.di.utils import get_bean_by_type -from core.component.token_usage_collector import TokenUsageCollector - -logger = logging.getLogger(__name__) - - -class BaseVectorizeService(VectorizeServiceInterface): - """ - Base class for OpenAI-compatible embedding services - - Subclasses only need to implement: - - _get_config_params(): return (api_key, base_url, model) - - _should_pass_dimensions(): return True/False - - _should_truncate_client_side(): return True/False - """ - - def __init__(self, config): - self.config = config - self.client: Optional[AsyncOpenAI] = None - self._semaphore = asyncio.Semaphore(config.max_concurrent_requests) - - api_key, base_url, model = self._get_config_params() - logger.info( - f"Initialized {self.__class__.__name__} | model={model} | base_url={base_url}" - ) - - @abstractmethod - def _get_config_params(self) -> Tuple[str, str, str]: - """Return (api_key, base_url, model) for logging""" - pass - - @abstractmethod - def _should_pass_dimensions(self) -> bool: - """Whether to pass dimensions parameter to API""" - pass - - @abstractmethod - def _should_truncate_client_side(self) -> bool: - """Whether to truncate embeddings on client side""" - pass - - async def __aenter__(self): - await self._ensure_client() - return self - - async def __aexit__(self, exc_type, exc_val, exc_tb): - await self.close() - - async def _ensure_client(self): - """Ensure OpenAI client is initialized""" - if self.client is None: - self.client = AsyncOpenAI( - api_key=self.config.api_key, - base_url=self.config.base_url, - timeout=self.config.timeout, - ) - - async def close(self): - """Close the client connection""" - if self.client: - await self.client.close() - self.client = None - - async def _make_request( - self, - texts: List[str], - instruction: Optional[str] = None, - is_query: bool = False, - ): - """Make embedding request to API""" - await self._ensure_client() - if not self.config.model: - raise VectorizeError("Embedding model is not configured.") - - # Format texts with instruction if needed - if is_query: - default_instruction = ( - "Given a search query, retrieve relevant passages that answer the query" - ) - final_instruction = ( - instruction if instruction is not None else default_instruction - ) - formatted_texts = [ - f"Instruct: {final_instruction}\nQuery: {text}" for text in texts - ] - else: - formatted_texts = texts - - async with self._semaphore: - for attempt in range(self.config.max_retries): - try: - request_kwargs = { - "model": self.config.model, - "input": formatted_texts, - "encoding_format": self.config.encoding_format, - } - - # Add dimensions parameter if supported - if self._should_pass_dimensions() and self.config.dimensions > 0: - request_kwargs["dimensions"] = self.config.dimensions - - response = await self.client.embeddings.create(**request_kwargs) - - # Report embedding call to token usage collector - try: - collector = get_bean_by_type(TokenUsageCollector) - prompt_tokens = ( - getattr(response.usage, 'prompt_tokens', 0) or 0 - if response.usage - else 0 - ) - collector.add( - self.config.model, prompt_tokens, 0, call_type="embedding" - ) - except Exception: - pass - - return response - - except Exception as e: - error_msg = str(e) - logger.error( - f"{self.__class__.__name__} API error (attempt {attempt + 1}/{self.config.max_retries}): {error_msg}" - ) - - # Log detailed error for debugging - if "Connection" in error_msg or "timeout" in error_msg.lower(): - logger.warning( - f"Network issue connecting to {self.config.base_url}: {error_msg}" - ) - - if attempt < self.config.max_retries - 1: - await asyncio.sleep(2**attempt) - continue - else: - raise VectorizeError( - f"{self.__class__.__name__} API request failed after {self.config.max_retries} attempts: {error_msg}" - ) - - def _parse_embeddings_response(self, response) -> List[np.ndarray]: - """Parse embeddings from API response""" - if not response.data: - raise VectorizeError("Invalid API response: missing data") - - embeddings = [] - for item in response.data: - emb = np.array(item.embedding, dtype=np.float32) - - # Client-side truncation if needed - if self._should_truncate_client_side(): - if ( - self.config.dimensions - and self.config.dimensions > 0 - and len(emb) > self.config.dimensions - ): - logger.debug( - f"Client-side truncation: {len(emb)}D → {self.config.dimensions}D" - ) - emb = emb[: self.config.dimensions] - - embeddings.append(emb) - return embeddings - - async def get_embedding( - self, text: str, instruction: Optional[str] = None, is_query: bool = False - ) -> np.ndarray: - """Get embedding for a single text""" - response = await self._make_request([text], instruction, is_query) - if not response.data: - raise VectorizeError("Invalid API response: missing data") - return np.array(self._parse_embeddings_response(response)[0], dtype=np.float32) - - async def get_embedding_with_usage( - self, text: str, instruction: Optional[str] = None, is_query: bool = False - ) -> Tuple[np.ndarray, Optional[UsageInfo]]: - """Get embedding with usage information""" - response = await self._make_request([text], instruction, is_query) - if not response.data: - raise VectorizeError("Invalid API response: missing data") - - embeddings = self._parse_embeddings_response(response) - embedding = np.array(embeddings[0], dtype=np.float32) - usage_info = ( - UsageInfo.from_openai_usage(response.usage) if response.usage else None - ) - return embedding, usage_info - - async def get_embeddings( - self, - texts: List[str], - instruction: Optional[str] = None, - is_query: bool = False, - ) -> List[np.ndarray]: - """Get embeddings for multiple texts""" - if not texts: - return [] - - if len(texts) <= self.config.batch_size: - response = await self._make_request(texts, instruction, is_query) - return self._parse_embeddings_response(response) - - embeddings = [] - for i in range(0, len(texts), self.config.batch_size): - batch_texts = texts[i : i + self.config.batch_size] - response = await self._make_request(batch_texts, instruction, is_query) - embeddings.extend(self._parse_embeddings_response(response)) - if i + self.config.batch_size < len(texts): - await asyncio.sleep(0.1) - return embeddings - - async def get_embeddings_batch( - self, - text_batches: List[List[str]], - instruction: Optional[str] = None, - is_query: bool = False, - ) -> List[List[np.ndarray]]: - """Get embeddings for multiple batches""" - tasks = [ - self.get_embeddings(batch, instruction, is_query) for batch in text_batches - ] - results = await asyncio.gather(*tasks, return_exceptions=True) - - embeddings_batches = [] - for i, result in enumerate(results): - if isinstance(result, Exception): - logger.error(f"Error processing batch {i}: {result}") - embeddings_batches.append([]) - else: - embeddings_batches.append(result) - return embeddings_batches - - def get_model_name(self) -> str: - """Get the current model name""" - return self.config.model diff --git a/methods/EverCore/src/agentic_layer/vectorize_deepinfra.py b/methods/EverCore/src/agentic_layer/vectorize_deepinfra.py deleted file mode 100644 index 7a3b6f68..00000000 --- a/methods/EverCore/src/agentic_layer/vectorize_deepinfra.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -DeepInfra Vectorize Service Implementation - -Commercial API implementation for DeepInfra embedding service -""" - -import os -import logging -from typing import Optional, Tuple -from dataclasses import dataclass - -from agentic_layer.vectorize_base import BaseVectorizeService - -logger = logging.getLogger(__name__) - - -@dataclass -class DeepInfraVectorizeConfig: - """DeepInfra Vectorize configuration""" - - api_key: str = "" - base_url: str = "https://api.deepinfra.com/v1/openai" - model: str = "Qwen/Qwen3-Embedding-4B" - timeout: int = 30 - max_retries: int = 3 - batch_size: int = 10 - max_concurrent_requests: int = 5 - encoding_format: str = "float" - dimensions: int = 1024 - - -class DeepInfraVectorizeService(BaseVectorizeService): - """ - DeepInfra embedding service implementation - Uses DeepInfra's commercial API for text embeddings - """ - - def __init__(self, config: Optional[DeepInfraVectorizeConfig] = None): - if config is None: - config = DeepInfraVectorizeConfig() - super().__init__(config) - - def _get_config_params(self) -> Tuple[str, str, str]: - """Return (api_key, base_url, model) for logging""" - return self.config.api_key, self.config.base_url, self.config.model - - def _should_pass_dimensions(self) -> bool: - """DeepInfra supports dimensions parameter""" - return True - - def _should_truncate_client_side(self) -> bool: - """DeepInfra handles truncation server-side""" - return False diff --git a/methods/EverCore/src/agentic_layer/vectorize_interface.py b/methods/EverCore/src/agentic_layer/vectorize_interface.py deleted file mode 100644 index ca2d909b..00000000 --- a/methods/EverCore/src/agentic_layer/vectorize_interface.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -Vectorize Service Interface -向量化服务接口定义 -""" - -from abc import ABC, abstractmethod -from typing import List, Optional, Tuple -from dataclasses import dataclass -import numpy as np - - -@dataclass -class UsageInfo: - """Token usage information""" - - prompt_tokens: int - total_tokens: int - - @classmethod - def from_openai_usage(cls, usage) -> "UsageInfo": - """Create UsageInfo object from OpenAI usage object""" - return cls(prompt_tokens=usage.prompt_tokens, total_tokens=usage.total_tokens) - - -class VectorizeServiceInterface(ABC): - """Vectorization service interface""" - - @abstractmethod - async def get_embedding( - self, text: str, instruction: Optional[str] = None, is_query: bool = False - ) -> np.ndarray: - """Get embedding for a single text""" - pass - - @abstractmethod - async def get_embedding_with_usage( - self, text: str, instruction: Optional[str] = None, is_query: bool = False - ) -> Tuple[np.ndarray, Optional[UsageInfo]]: - """Get embedding with usage information""" - pass - - @abstractmethod - async def get_embeddings( - self, texts: List[str], instruction: Optional[str] = None, is_query: bool = False - ) -> List[np.ndarray]: - """Get embeddings for multiple texts""" - pass - - @abstractmethod - async def get_embeddings_batch( - self, text_batches: List[List[str]], instruction: Optional[str] = None, is_query: bool = False - ) -> List[List[np.ndarray]]: - """Get embeddings for multiple batches""" - pass - - @abstractmethod - def get_model_name(self) -> str: - """Get the current model name""" - pass - - @abstractmethod - async def close(self): - """Close and cleanup resources""" - pass - - -class VectorizeError(Exception): - """Vectorize API error exception class""" - pass - diff --git a/methods/EverCore/src/agentic_layer/vectorize_service.py b/methods/EverCore/src/agentic_layer/vectorize_service.py deleted file mode 100644 index 0a2efe83..00000000 --- a/methods/EverCore/src/agentic_layer/vectorize_service.py +++ /dev/null @@ -1,564 +0,0 @@ -""" -Vectorize Service - Hybrid Implementation with Automatic Fallback - -This is the main vectorization service with built-in resilience. -Implements a hybrid strategy with flexible provider selection and automatic fallback. - -Usage: - from agentic_layer.vectorize_service import get_vectorize_service - - service = get_vectorize_service() - embedding = await service.get_embedding("Hello world") # Auto-fallback -""" - -import logging -import os -import time -from typing import Optional, List, Tuple -from dataclasses import dataclass, field -import numpy as np - -from core.observation.stage_timer import timed - -from core.di.decorators import service - -from agentic_layer.vectorize_interface import ( - VectorizeServiceInterface, - VectorizeError, - UsageInfo, -) -from agentic_layer.vectorize_vllm import VllmVectorizeService, VllmVectorizeConfig -from agentic_layer.vectorize_deepinfra import ( - DeepInfraVectorizeService, - DeepInfraVectorizeConfig, -) -from agentic_layer.metrics.vectorize_metrics import ( - record_vectorize_request, - record_vectorize_fallback, - record_vectorize_error, -) - -logger = logging.getLogger(__name__) - - -@dataclass -class HybridVectorizeConfig: - """Configuration for hybrid vectorize service with fallback""" - - # Provider types - primary_provider: str = "vllm" # vllm or deepinfra - fallback_provider: str = "deepinfra" # vllm, deepinfra, or none - - # Primary service config - primary_api_key: str = "" - primary_base_url: str = "" - - # Fallback service config - fallback_api_key: str = "" - fallback_base_url: str = "" - - # Shared model configuration - model: str = "Qwen/Qwen3-Embedding-4B" - - # Common settings - timeout: int = 30 - max_retries: int = 3 - batch_size: int = 10 - max_concurrent_requests: int = 5 - encoding_format: str = "float" - dimensions: int = 1024 - - # Fallback behavior - enable_fallback: bool = True - max_primary_failures: int = 3 - - # Runtime state (failure tracking) - _primary_failure_count: int = field(default=0, init=False, repr=False) - - def __post_init__(self): - """Load hybrid service configuration from environment""" - # Read provider types - self.primary_provider = os.getenv("VECTORIZE_PROVIDER", self.primary_provider) - self.fallback_provider = os.getenv( - "VECTORIZE_FALLBACK_PROVIDER", self.fallback_provider - ) - - # Read primary service config - self.primary_api_key = os.getenv("VECTORIZE_API_KEY", self.primary_api_key) - self.primary_base_url = os.getenv("VECTORIZE_BASE_URL", self.primary_base_url) - - # Read fallback service config - self.fallback_api_key = os.getenv( - "VECTORIZE_FALLBACK_API_KEY", self.fallback_api_key - ) - self.fallback_base_url = os.getenv( - "VECTORIZE_FALLBACK_BASE_URL", self.fallback_base_url - ) - - # Read shared model configuration - self.model = os.getenv("VECTORIZE_MODEL", self.model) - - # Read common settings - self.timeout = int(os.getenv("VECTORIZE_TIMEOUT", str(self.timeout))) - self.max_retries = int( - os.getenv("VECTORIZE_MAX_RETRIES", str(self.max_retries)) - ) - self.batch_size = int(os.getenv("VECTORIZE_BATCH_SIZE", str(self.batch_size))) - self.max_concurrent_requests = int( - os.getenv("VECTORIZE_MAX_CONCURRENT", str(self.max_concurrent_requests)) - ) - self.encoding_format = os.getenv( - "VECTORIZE_ENCODING_FORMAT", self.encoding_format - ) - self.dimensions = int(os.getenv("VECTORIZE_DIMENSIONS", str(self.dimensions))) - - # Fallback behavior - # Enable fallback only if: - # 1. fallback_provider is not "none" - # 2. fallback_base_url is configured - # 3. fallback_api_key is configured (or not required for vllm) - self.enable_fallback = ( - self.fallback_provider.lower() != "none" - and bool(self.fallback_base_url) - and ( - self.fallback_provider.lower() == "vllm" # vllm doesn't require API key - or bool(self.fallback_api_key) # deepinfra requires API key - ) - ) - self.max_primary_failures = int( - os.getenv("VECTORIZE_MAX_PRIMARY_FAILURES", str(self.max_primary_failures)) - ) - - -def _create_service_from_config( - provider: str, - api_key: str, - base_url: str, - model: str, - timeout: int, - max_retries: int, - batch_size: int, - max_concurrent: int, - encoding_format: str, - dimensions: int, -) -> VectorizeServiceInterface: - """ - Factory function to create a vectorize service based on provider type - - Args: - provider: Provider type (vllm or deepinfra) - api_key: API key for the service - base_url: Base URL for the service - model: Model name - timeout: Request timeout in seconds - max_retries: Maximum retry attempts - batch_size: Batch size for requests - max_concurrent: Maximum concurrent requests - encoding_format: Encoding format for embeddings - dimensions: Vector dimensions - - Returns: - VectorizeServiceInterface: The created service instance - """ - if provider.lower() == "vllm": - config = VllmVectorizeConfig( - api_key=api_key, - base_url=base_url, - model=model, - timeout=timeout, - max_retries=max_retries, - batch_size=batch_size, - max_concurrent_requests=max_concurrent, - encoding_format=encoding_format, - dimensions=dimensions, - ) - return VllmVectorizeService(config) - elif provider.lower() == "deepinfra": - config = DeepInfraVectorizeConfig( - api_key=api_key, - base_url=base_url, - model=model, - timeout=timeout, - max_retries=max_retries, - batch_size=batch_size, - max_concurrent_requests=max_concurrent, - encoding_format=encoding_format, - dimensions=dimensions, - ) - return DeepInfraVectorizeService(config) - else: - raise VectorizeError(f"Unsupported provider: {provider}") - - -class HybridVectorizeService(VectorizeServiceInterface): - """ - Hybrid Vectorization Service with Automatic Fallback - - This service implements a dual-strategy approach: - 1. Implements VectorizeServiceInterface with full API - 2. Primary: Configurable provider (vllm or deepinfra) - 3. Secondary: Configurable fallback provider - 4. Automatic failover on errors with failure tracking - 5. All method calls transparently use fallback logic - - Strategy Benefits: - - Cost optimization: ~95% savings with vllm self-deployed service - - High availability: Automatic failover ensures reliability - - Zero downtime: Continues working during vllm service maintenance - - Usage: - service = HybridVectorizeService() - embedding = await service.get_embedding("Hello") # Auto-fallback built-in - """ - - def __init__(self, config: Optional[HybridVectorizeConfig] = None): - if config is None: - config = HybridVectorizeConfig() - - self.config = config - - # Create primary service based on provider type - self.primary_service = _create_service_from_config( - provider=config.primary_provider, - api_key=config.primary_api_key, - base_url=config.primary_base_url, - model=config.model, # Use shared model - timeout=config.timeout, - max_retries=config.max_retries, - batch_size=config.batch_size, - max_concurrent=config.max_concurrent_requests, - encoding_format=config.encoding_format, - dimensions=config.dimensions, - ) - - # Create fallback service if enabled - self.fallback_service = None - if config.enable_fallback: - self.fallback_service = _create_service_from_config( - provider=config.fallback_provider, - api_key=config.fallback_api_key, - base_url=config.fallback_base_url, - model=config.model, # Use shared model - timeout=config.timeout, - max_retries=config.max_retries, - batch_size=config.batch_size, - max_concurrent=config.max_concurrent_requests, - encoding_format=config.encoding_format, - dimensions=config.dimensions, - ) - - logger.info( - f"Initialized HybridVectorizeService | " - f"primary={config.primary_provider} | " - f"fallback={config.fallback_provider} | " - f"fallback_enabled={config.enable_fallback} | " - f"max_failures={config.max_primary_failures}" - ) - - def get_service(self) -> VectorizeServiceInterface: - """ - Get the primary service (for advanced usage) - - Returns: - VectorizeServiceInterface: The primary service - - Note: Prefer using proxy methods directly for automatic fallback - """ - return self.primary_service - - # Implement VectorizeServiceInterface methods with automatic fallback - - async def get_embedding( - self, text: str, instruction: Optional[str] = None, is_query: bool = False - ) -> np.ndarray: - """Get embedding for a single text with automatic fallback""" - with timed("compute_embedding"): - return await self.execute_with_fallback( - "get_embedding", - lambda: self.primary_service.get_embedding(text, instruction, is_query), - lambda: ( - self.fallback_service.get_embedding(text, instruction, is_query) - if self.fallback_service - else None - ), - batch_size=1, - ) - - async def get_embedding_with_usage( - self, text: str, instruction: Optional[str] = None, is_query: bool = False - ) -> Tuple[np.ndarray, Optional[UsageInfo]]: - """Get embedding with usage information with automatic fallback""" - return await self.execute_with_fallback( - "get_embedding_with_usage", - lambda: self.primary_service.get_embedding_with_usage( - text, instruction, is_query - ), - lambda: ( - self.fallback_service.get_embedding_with_usage( - text, instruction, is_query - ) - if self.fallback_service - else None - ), - batch_size=1, - ) - - async def get_embeddings( - self, - texts: List[str], - instruction: Optional[str] = None, - is_query: bool = False, - ) -> List[np.ndarray]: - """Get embeddings for multiple texts with automatic fallback""" - with timed("compute_embeddings"): - return await self.execute_with_fallback( - "get_embeddings", - lambda: self.primary_service.get_embeddings( - texts, instruction, is_query - ), - lambda: ( - self.fallback_service.get_embeddings(texts, instruction, is_query) - if self.fallback_service - else None - ), - batch_size=len(texts), - ) - - async def get_embeddings_batch( - self, - text_batches: List[List[str]], - instruction: Optional[str] = None, - is_query: bool = False, - ) -> List[List[np.ndarray]]: - """Get embeddings for multiple batches with automatic fallback""" - total_texts = sum(len(batch) for batch in text_batches) - with timed("compute_embeddings"): - return await self.execute_with_fallback( - "get_embeddings_batch", - lambda: self.primary_service.get_embeddings_batch( - text_batches, instruction, is_query - ), - lambda: ( - self.fallback_service.get_embeddings_batch( - text_batches, instruction, is_query - ) - if self.fallback_service - else None - ), - batch_size=total_texts, - ) - - def get_model_name(self) -> str: - """Get the current model name (from primary service)""" - return self.primary_service.get_model_name() - - async def execute_with_fallback( - self, operation_name: str, primary_func, fallback_func, batch_size: int = 1 - ): - """ - Execute operation with automatic fallback logic - - Args: - operation_name: Name of the operation for logging - primary_func: Function to call on primary service - fallback_func: Function to call on fallback service (or None if no fallback) - batch_size: Number of texts being processed (for metrics) - - Returns: - Result from primary or fallback service - - Raises: - VectorizeError: If both services fail - """ - start_time = time.perf_counter() - - # Try primary service first - try: - result = await primary_func() - duration = time.perf_counter() - start_time - - # Record success metrics - record_vectorize_request( - provider=self.config.primary_provider, - operation=operation_name, - status='success', - duration_seconds=duration, - batch_size=batch_size, - ) - - # Reset failure count on success - self.config._primary_failure_count = 0 - return result - - except Exception as primary_error: - primary_duration = time.perf_counter() - start_time - - # Increment failure count - self.config._primary_failure_count += 1 - - # Determine error type - error_type = self._classify_error(primary_error) - - # Record error metrics - record_vectorize_error( - provider=self.config.primary_provider, - operation=operation_name, - error_type=error_type, - ) - - logger.warning( - f"Primary service ({self.config.primary_provider}) {operation_name} failed " - f"(count: {self.config._primary_failure_count}): {primary_error}" - ) - - # Check if fallback is enabled - if not self.config.enable_fallback or fallback_func is None: - # Record failed request (no fallback) - record_vectorize_request( - provider=self.config.primary_provider, - operation=operation_name, - status='error', - duration_seconds=primary_duration, - batch_size=batch_size, - ) - logger.error("Fallback disabled or not configured, re-raising error") - raise VectorizeError( - f"Primary service failed and fallback is disabled: {primary_error}" - ) - - # Determine fallback reason - fallback_reason = error_type - if self.config._primary_failure_count >= self.config.max_primary_failures: - fallback_reason = 'max_failures_exceeded' - logger.warning( - f"⚠️ Primary service exceeded max failures ({self.config.max_primary_failures}), " - f"using {self.config.fallback_provider} fallback" - ) - - # Record fallback event - record_vectorize_fallback( - primary_provider=self.config.primary_provider, - fallback_provider=self.config.fallback_provider, - reason=fallback_reason, - ) - - # Try fallback service - fallback_start = time.perf_counter() - try: - logger.info( - f"🔄 Falling back to {self.config.fallback_provider} for {operation_name}" - ) - result = await fallback_func() - fallback_duration = time.perf_counter() - fallback_start - - # Record fallback success metrics - record_vectorize_request( - provider=self.config.fallback_provider, - operation=operation_name, - status='fallback', - duration_seconds=fallback_duration, - batch_size=batch_size, - ) - - return result - - except Exception as fallback_error: - fallback_duration = time.perf_counter() - fallback_start - - # Record fallback error - record_vectorize_error( - provider=self.config.fallback_provider, - operation=operation_name, - error_type=self._classify_error(fallback_error), - ) - record_vectorize_request( - provider=self.config.fallback_provider, - operation=operation_name, - status='error', - duration_seconds=fallback_duration, - batch_size=batch_size, - ) - - logger.error(f"❌ Fallback also failed: {fallback_error}") - raise VectorizeError( - f"Both primary and fallback services failed. " - f"Primary ({self.config.primary_provider}): {primary_error}, " - f"Fallback ({self.config.fallback_provider}): {fallback_error}" - ) - - def _classify_error(self, error: Exception) -> str: - """Classify error type for metrics""" - error_str = str(error).lower() - if 'timeout' in error_str or 'timed out' in error_str: - return 'timeout' - elif 'rate' in error_str and 'limit' in error_str: - return 'rate_limit' - elif 'validation' in error_str or 'invalid' in error_str: - return 'validation_error' - elif isinstance(error, VectorizeError): - return 'api_error' - else: - return 'unknown' - - def get_failure_count(self) -> int: - """Get current primary service failure count""" - return self.config._primary_failure_count - - def reset_failure_count(self): - """Reset failure count (useful for health check recovery)""" - self.config._primary_failure_count = 0 - logger.info("Reset primary service failure count to 0") - - async def close(self): - """Close all services""" - await self.primary_service.close() - if self.fallback_service: - await self.fallback_service.close() - - -# Global service instance (lazy initialization) -_service_instance: Optional[HybridVectorizeService] = None - - -def get_hybrid_service() -> HybridVectorizeService: - """ - Get the global hybrid service instance (singleton) - - Returns: - HybridVectorizeService: The global hybrid service instance - """ - global _service_instance - if _service_instance is None: - _service_instance = HybridVectorizeService() - return _service_instance - - -# Main entry point - registered with DI container -@service(name="vectorize_service", primary=True) -def get_vectorize_service() -> VectorizeServiceInterface: - """ - Get the vectorization service (main entry point) - - Returns the hybrid service which implements VectorizeServiceInterface. - All method calls automatically go through fallback logic. - - Returns: - VectorizeServiceInterface: The hybrid service with automatic fallback - - Example: - ```python - from agentic_layer.vectorize_service import get_vectorize_service - - service = get_vectorize_service() # Returns hybrid service with fallback - embedding = await service.get_embedding("Hello world") # Auto-fallback - embeddings = await service.get_embeddings(["Text 1", "Text 2"]) # Auto-fallback - await service.close() - ``` - """ - return ( - get_hybrid_service() - ) # Return hybrid service (implements VectorizeServiceInterface) - - -# Export public API -__all__ = ["get_vectorize_service"] diff --git a/methods/EverCore/src/agentic_layer/vectorize_vllm.py b/methods/EverCore/src/agentic_layer/vectorize_vllm.py deleted file mode 100644 index d67f64d8..00000000 --- a/methods/EverCore/src/agentic_layer/vectorize_vllm.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -vLLM (Self-Deployed) Vectorize Service Implementation - -This module provides vectorization service for self-deployed embedding servers, -such as vLLM, Ollama, or other OpenAI-compatible endpoints. -""" - -import os -import logging -from typing import Optional, Tuple -from dataclasses import dataclass - -from agentic_layer.vectorize_base import BaseVectorizeService - -logger = logging.getLogger(__name__) - - -@dataclass -class VllmVectorizeConfig: - """Configuration for vLLM self-deployed vectorization service""" - - base_url: str = "http://localhost:8000/v1" - api_key: str = "EMPTY" # Many self-deployed services don't require API key - model: str = "Qwen/Qwen3-Embedding-4B" - timeout: int = 30 - max_retries: int = 3 - batch_size: int = 10 - max_concurrent_requests: int = 5 - encoding_format: str = "float" - dimensions: int = 1024 # Client-side truncation target - - -class VllmVectorizeService(BaseVectorizeService): - """ - vLLM self-deployed embedding service implementation - - Supports: - - vLLM (https://github.com/vllm-project/vllm) - - Any OpenAI-compatible embedding endpoint - """ - - def __init__(self, config: Optional[VllmVectorizeConfig] = None): - if config is None: - config = VllmVectorizeConfig() - super().__init__(config) - - def _get_config_params(self) -> Tuple[str, str, str]: - """Return (api_key, base_url, model) for logging""" - return self.config.api_key, self.config.base_url, self.config.model - - def _should_pass_dimensions(self) -> bool: - """vLLM services don't support dimensions parameter""" - return False - - def _should_truncate_client_side(self) -> bool: - """vLLM services need client-side truncation""" - return True - diff --git a/methods/EverCore/src/api_specs/dtos/__init__.py b/methods/EverCore/src/api_specs/dtos/__init__.py deleted file mode 100644 index 2ab24ce8..00000000 --- a/methods/EverCore/src/api_specs/dtos/__init__.py +++ /dev/null @@ -1,131 +0,0 @@ -"""DTO (Data Transfer Object) types for API specifications. - -This package organizes DTOs by resource type: -- base: Common base types (BaseApiResponse) -- memory: Memory resource DTOs (memorize, search, get, delete) -- settings: Global settings resource DTOs -- group: Group resource DTOs -- sender: Sender resource DTOs -""" - -# Base types -from api_specs.dtos.base import BaseApiResponse, T - -# Memory resource DTOs -from api_specs.dtos.memory import ( - # Raw data - RawData, - # Memorize (internal) - MemorizeRequest, - # Add / Flush - PersonalAddRequest, - GroupAddRequest, - PersonalFlushRequest, - GroupFlushRequest, - AddResult, - AddResponse, - FlushResult, - FlushResponse, - # Search/Retrieve - RetrieveMemRequest, - RawMessageDTO, - ProfileSearchItem, - RetrieveMemResponse, - SearchMemoriesResponse, - # Get - GetMemRequest, - EpisodeItem, - ProfileItem, - GetMemResponse, - GetMemoriesResponse, - # Delete - DeleteMemoriesResult, - DeleteMemoriesResponse, -) - -# Memory delete DTOs (v1) -from api_specs.dtos.memory_delete import DeleteMemoriesRequest - -# Group resource DTOs -from api_specs.dtos.group import ( - CreateGroupRequest, - PatchGroupRequest, - GroupResponse, - CreateGroupApiResponse, - GetGroupApiResponse, - PatchGroupApiResponse, -) - -# Sender resource DTOs -from api_specs.dtos.sender import ( - CreateSenderRequest, - PatchSenderRequest, - SenderResponse, - CreateSenderApiResponse, - GetSenderApiResponse, - PatchSenderApiResponse, -) - -# Settings resource DTOs -from api_specs.dtos.settings import ( - LlmProviderConfig, - LlmCustomSetting, - UpdateSettingsRequest, - SettingsResponse, - GetSettingsApiResponse, - UpdateSettingsApiResponse, -) - -__all__ = [ - # Base - "BaseApiResponse", - "T", - # Memory - Raw data - "RawData", - # Memory - Memorize (internal) - "MemorizeRequest", - # Memory - Add / Flush - "PersonalAddRequest", - "GroupAddRequest", - "PersonalFlushRequest", - "GroupFlushRequest", - "AddResult", - "AddResponse", - "FlushResult", - "FlushResponse", - # Memory - Search/Retrieve - "RetrieveMemRequest", - "RawMessageDTO", - "ProfileSearchItem", - "RetrieveMemResponse", - "SearchMemoriesResponse", - # Memory - Delete - "DeleteMemoriesRequest", - # Memory - Get - "GetMemRequest", - "EpisodeItem", - "ProfileItem", - "GetMemResponse", - "GetMemoriesResponse", - # Settings - "LlmProviderConfig", - "LlmCustomSetting", - "UpdateSettingsRequest", - "SettingsResponse", - "GetSettingsApiResponse", - "UpdateSettingsApiResponse", - # Group - "CreateGroupRequest", - "PatchGroupRequest", - "GroupResponse", - "CreateGroupApiResponse", - "GetGroupApiResponse", - "PatchGroupApiResponse", - # Sender - "CreateSenderRequest", - "PatchSenderRequest", - "SenderResponse", - "CreateSenderApiResponse", - "GetSenderApiResponse", - "PatchSenderApiResponse", -] diff --git a/methods/EverCore/src/api_specs/dtos/base.py b/methods/EverCore/src/api_specs/dtos/base.py deleted file mode 100644 index 09687e8f..00000000 --- a/methods/EverCore/src/api_specs/dtos/base.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Base DTO types for API responses. - -This module contains common base types used across all API endpoints. -""" - -from __future__ import annotations - -from typing import Generic, TypeVar - -from pydantic import BaseModel, Field - - -# Generic type for API response result -T = TypeVar("T") - - -class BaseApiResponse(BaseModel, Generic[T]): - """Base API response wrapper - - Unified response format for all API endpoints. - """ - - data: T = Field(description="Response result data") - - model_config = {"arbitrary_types_allowed": True} - - -class ErrorApiResponse(BaseModel): - """Unified error response model - - Used by global_exception_handler to construct consistent error responses - for both HTTPException and unhandled exceptions. - """ - - code: str = Field(..., description="Error code") - message: str = Field(..., description="Error message") - request_id: str = Field(default="unknown", description="Request ID") - timestamp: str = Field(..., description="ISO format timestamp") - path: str = Field(..., description="Request path") diff --git a/methods/EverCore/src/api_specs/dtos/group.py b/methods/EverCore/src/api_specs/dtos/group.py deleted file mode 100644 index c672acb7..00000000 --- a/methods/EverCore/src/api_specs/dtos/group.py +++ /dev/null @@ -1,131 +0,0 @@ -"""Group resource DTOs. - -This module contains DTOs for group CRUD operations: -- Create (POST /api/v1/groups) -- Get (GET /api/v1/groups/{group_id}) -- Patch (PATCH /api/v1/groups/{group_id}) -""" - -from __future__ import annotations - -from typing import Optional - -from pydantic import BaseModel, Field, model_validator - -from api_specs.dtos.base import BaseApiResponse - - -# ============================================================================= -# Request DTOs -# ============================================================================= - - -class CreateGroupRequest(BaseModel): - """Create group request body - - Used for POST /api/v1/groups endpoint. - If a group with the same group_id exists, it will be updated (upsert). - """ - - group_id: str = Field( - ..., description="Group identifier (unique)", examples=["group_abc"] - ) - name: Optional[str] = Field( - default=None, description="Group display name", examples=["Project Discussion"] - ) - description: Optional[str] = Field( - default=None, - description="Group description", - examples=["Weekly sync on Project X"], - ) - - model_config = { - "json_schema_extra": { - "example": { - "group_id": "group_abc", - "name": "Project Discussion", - "description": "Weekly sync on Project X", - } - } - } - - -class PatchGroupRequest(BaseModel): - """Partial update group request body - - Used for PATCH /api/v1/groups/{group_id} endpoint. - At least one field must be provided. - """ - - name: Optional[str] = Field( - default=None, - description="New group display name", - examples=["Updated Group Name"], - ) - description: Optional[str] = Field( - default=None, - description="New group description", - examples=["Updated description"], - ) - - @model_validator(mode="after") - def validate_at_least_one_field(self): - """At least one field must be provided for patch""" - if self.name is None and self.description is None: - raise ValueError("At least one of 'name' or 'description' must be provided") - return self - - model_config = {"json_schema_extra": {"example": {"name": "Updated Group Name"}}} - - -# ============================================================================= -# Response DTOs -# ============================================================================= - - -class GroupResponse(BaseModel): - """Group response DTO - - Returned by all group endpoints. - """ - - group_id: str = Field(..., description="Group identifier") - name: Optional[str] = Field(default=None, description="Group display name") - description: Optional[str] = Field(default=None, description="Group description") - created_at: str = Field(..., description="Creation time (ISO 8601)") - updated_at: str = Field(..., description="Last update time (ISO 8601)") - - model_config = { - "json_schema_extra": { - "example": { - "group_id": "group_abc", - "name": "Project Discussion", - "description": "Weekly sync on Project X", - "created_at": "2026-01-01T00:00:00+00:00", - "updated_at": "2026-02-26T10:00:00+00:00", - } - } - } - - -# ============================================================================= -# API Response Wrappers -# ============================================================================= - - -class CreateGroupApiResponse(BaseApiResponse[GroupResponse]): - """Create group API response""" - - data: GroupResponse = Field(description="Created/updated group data") - - -class GetGroupApiResponse(BaseApiResponse[GroupResponse]): - """Get group API response""" - - data: GroupResponse = Field(description="Group data") - - -class PatchGroupApiResponse(BaseApiResponse[GroupResponse]): - """Patch group API response""" - - data: GroupResponse = Field(description="Updated group data") diff --git a/methods/EverCore/src/api_specs/dtos/memory.py b/methods/EverCore/src/api_specs/dtos/memory.py deleted file mode 100644 index 2ee9d04f..00000000 --- a/methods/EverCore/src/api_specs/dtos/memory.py +++ /dev/null @@ -1,1805 +0,0 @@ -"""Memory resource DTOs. - -This module contains DTOs related to memory CRUD operations: -- Add / Flush (POST /api/v1/memories, /api/v1/memories/group, etc.) -- Search (GET /api/v1/memories/search) -- Delete (DELETE /api/v1/memories) -- Get (POST /api/v1/memories/get) -""" - -from __future__ import annotations - -import os - -from dataclasses import dataclass -from datetime import datetime -from typing import Any, Dict, List, Optional, Tuple, Union -import json -import re - -from api_specs.memory_types import ScenarioType -from bson import ObjectId -from pydantic import ( - BaseModel, - Field, - field_validator, - model_validator, - SkipValidation, - SerializeAsAny, -) - -from api_specs.dtos.base import BaseApiResponse -from api_specs.memory_types import RetrieveMemoryModel, RawDataType -from api_specs.memory_models import ( - MemoryType, - Metadata, - QueryMetadata, - RetrieveMethod, - MessageSenderRole, -) -from core.oxm.constants import MAGIC_ALL, MAX_RETRIEVE_LIMIT -from biz_layer.retrieve_constants import MAX_GROUP_IDS_COUNT - - -iso_pattern = r'^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}' - - -# ============================================================================= -# Raw Data Types -# ============================================================================= - - -@dataclass -class RawData: - """Raw data structure for storing original content. - - This is oriented towards input at a higher level; the one in the memcell - table is the storage structure, which is more low-level. - """ - - content: dict[str, Any] - data_id: str - data_type: Optional[str] = None - metadata: Optional[Dict[str, Any]] = None - - # Note: Enrichment results (parsed_content, parsed_summary, parse_status) - # are embedded directly into content item dicts by the enrich provider, - # so they are persisted as part of content_items in RawMessage. - - def _serialize_value(self, value: Any) -> Any: - """ - Recursively serialize values, handling special types like datetime and ObjectId - - Args: - value: Value to be serialized - - Returns: - JSON-serializable value - """ - if isinstance(value, datetime): - return value.isoformat() - elif isinstance(value, ObjectId): - # Serialize ObjectId to string - return str(value) - elif isinstance(value, dict): - return {k: self._serialize_value(v) for k, v in value.items()} - elif isinstance(value, (list, tuple)): - return [self._serialize_value(item) for item in value] - elif hasattr(value, '__dict__'): - # Handle custom objects by converting to dictionary - return self._serialize_value(value.__dict__) - else: - return value - - def _deserialize_value(self, value: Any, field_name: str = "") -> Any: - """ - Recursively deserialize values, heuristically determining whether to restore datetime type based on field name - - Args: - value: Value to be deserialized - field_name: Field name, used for heuristic judgment - - Returns: - Deserialized value - """ - if isinstance(value, str): - # Heuristically determine if it's a datetime field based on field name - if self._is_datetime_field(field_name) and self._is_iso_datetime(value): - try: - from common_utils.datetime_utils import from_iso_format - - return from_iso_format(value) - except (ValueError, ImportError): - return value - return value - elif isinstance(value, dict): - return {k: self._deserialize_value(v, k) for k, v in value.items()} - elif isinstance(value, list): - return [self._deserialize_value(item, field_name) for item in value] - else: - return value - - def _is_datetime_field(self, field_name: str) -> bool: - """ - Heuristically determine if a field is a datetime field based on its name - - Args: - field_name: Field name - - Returns: - bool: Whether the field is a datetime field - """ - if not isinstance(field_name, str): - return False - - # Exact match datetime field names (based on actual field names used in the project) - exact_datetime_fields = { - 'timestamp', - 'createTime', - 'updateTime', - 'create_time', - 'update_time', - 'sent_timestamp', - 'received_timestamp', - 'create_timestamp', - 'last_update_timestamp', - 'modify_timestamp', - 'created_at', - 'updated_at', - 'joinTime', - 'leaveTime', - 'lastOnlineTime', - 'sync_time', - 'processed_at', - 'start_time', - 'end_time', - 'event_time', - 'build_timestamp', - 'datetime', - 'created', - 'updated', # Add common datetime field variants - } - - field_lower = field_name.lower() - - # Exact match check - if field_name in exact_datetime_fields or field_lower in exact_datetime_fields: - return True - - # Exclude common words that should not be recognized as datetime fields - exclusions = { - 'runtime', - 'timeout', - 'timeline', - 'timestamp_format', - 'time_zone', - 'time_limit', - 'timestamp_count', - 'timestamp_enabled', - 'time_sync', - 'playtime', - 'lifetime', - 'uptime', - 'downtime', - } - - if field_name in exclusions or field_lower in exclusions: - return False - - # Suffix match check (stricter rules) - time_suffixes = ['_time', '_timestamp', '_at', '_date'] - for suffix in time_suffixes: - if field_name.endswith(suffix) or field_lower.endswith(suffix): - return True - - # Prefix match check (stricter rules) - if field_name.endswith('Time') and not field_name.endswith('runtime'): - # Match xxxTime pattern, but exclude runtime - return True - - if field_name.endswith('Timestamp'): - # Match xxxTimestamp pattern - return True - - return False - - def _is_iso_datetime(self, value: str) -> bool: - """ - Check if string is ISO format datetime - - Args: - value: String value - - Returns: - bool: Whether it is ISO datetime format - """ - # Simple ISO datetime format check - if not isinstance(value, str) or len(value) < 19: - return False - - # Check basic ISO format pattern: YYYY-MM-DDTHH:MM:SS - return bool(re.match(iso_pattern, value)) - - def to_json(self) -> str: - """ - Serialize RawData object to JSON string - - Returns: - str: JSON string - """ - try: - data = { - 'content': self._serialize_value(self.content), - 'data_id': self.data_id, - 'data_type': self.data_type, - 'metadata': ( - self._serialize_value(self.metadata) if self.metadata else None - ), - } - return json.dumps(data, ensure_ascii=False, separators=(',', ':')) - except (TypeError, ValueError) as e: - raise ValueError(f"Failed to serialize RawData to JSON: {e}") from e - - @classmethod - def from_json_str(cls, json_str: str) -> 'RawData': - """ - Deserialize RawData object from JSON string - - Args: - json_str: JSON string - - Returns: - RawData: Deserialized RawData object - - Raises: - ValueError: JSON format error or missing required fields - """ - try: - data = json.loads(json_str) - except json.JSONDecodeError as e: - raise ValueError(f"JSON format error: {e}") from e - - if not isinstance(data, dict): - raise ValueError("JSON must be an object") - - # Check required fields - if 'content' not in data or 'data_id' not in data: - raise ValueError("JSON missing required fields: content and data_id") - - # Create instance and deserialize values - instance = cls.__new__(cls) - instance.content = instance._deserialize_value(data['content'], 'content') - instance.data_id = data['data_id'] - instance.data_type = data.get('data_type') - instance.metadata = ( - instance._deserialize_value(data.get('metadata'), 'metadata') - if data.get('metadata') - else None - ) - - return instance - - -# ============================================================================= -# Memorize Internal DTO -# ============================================================================= - - -class MemorizeRequest(BaseModel): - """Memory storage request (internal business layer)""" - - history_raw_data_list: list[RawData] - new_raw_data_list: list[RawData] - raw_data_type: RawDataType - group_id: Optional[str] = None - current_time: Optional[datetime] = None - # Session identifier for conversation isolation - session_id: Optional[str] = None - # Optional extraction control parameters - enable_foresight_extraction: bool = True # Whether to extract foresight - enable_atomic_fact_extraction: bool = True # Whether to extract atomic facts - # Force boundary trigger - when True, immediately triggers memory extraction - flush: bool = False - # Scene type: "solo" (1 user + N agents) or "team" (multi-user + agents) - scene: str = ScenarioType.SOLO.value - - model_config = {"arbitrary_types_allowed": True} - - -# ============================================================================= -# Add / Flush DTOs -# ============================================================================= - - -class ContentItem(BaseModel): - """Single content item in a message's content array. - - Supports multimodal content types. This phase only supports type="text". - Non-text types (audio, image, doc, pdf, html, email) are planned for next phase. - """ - - type: str = Field( - ..., - description='Content type: "text" / "audio" / "image" / "doc" / "pdf" / "html" / "email"', - ) - text: Optional[str] = Field( - default=None, - description="Content body. For type='text', this is the actual text. " - "For other types (image, audio, etc.), this can be a textual description.", - ) - source: Optional[str] = Field( - default=None, - description='Content source: "google_doc" / "notion" / "confluence" / "zoom"', - ) - base64: Optional[str] = Field(default=None, description="Base64-encoded content") - uri: Optional[str] = Field(default=None, description="File URI (MinIO, HTTP, etc.)") - ext: Optional[str] = Field( - default=None, description="File extension (e.g., 'png', 'mp3', 'pdf')" - ) - name: Optional[str] = Field(default=None, description="File name") - source_info: Optional[Dict[str, Any]] = Field( - default=None, description="Source-related traceability info" - ) - extras: Optional[Dict[str, Any]] = Field( - default=None, description="Type-specific extra fields" - ) - # Note: Enrichment results (parsed_content, parsed_summary, parse_status) are - # embedded directly into content-item dicts by the enrich provider. - - @model_validator(mode="before") - @classmethod - def _compat_content_field(cls, values: Any) -> Any: - """Accept legacy 'content' field as an alias for 'text'.""" - if isinstance(values, dict) and "content" in values and "text" not in values: - values = dict(values) - values["text"] = values.pop("content") - return values - - -class MessageItem(BaseModel): - """Single message item in add request. - - Uses content array for multimodal support. This phase only supports type='text' items. - content accepts a plain string shorthand: "hello" is coerced to - [{"type": "text", "text": "hello"}]. - """ - - message_id: Optional[str] = Field(default=None, description="Message unique ID") - sender_id: Optional[str] = Field(default=None, description="Sender identifier") - sender_name: Optional[str] = Field(default=None, description="Sender display name") - role: str = Field(..., description="user / assistant") - timestamp: int = Field(..., description="Message timestamp in unix milliseconds") - content: Union[str, List[ContentItem]] = Field( - ..., - min_length=1, - description='Content items. Accepts plain string shorthand: "hello" → [{type: "text", text: "hello"}]', - ) - - @model_validator(mode="before") - @classmethod - def _coerce_content_string(cls, values: Any) -> Any: - """Coerce plain string content to ContentItem array.""" - if isinstance(values, dict) and isinstance(values.get("content"), str): - text = values["content"] - if not text: - raise ValueError("messages[].content must not be empty") - values = dict(values) - values["content"] = [{"type": "text", "text": text}] - return values - - -class PersonalAddRequest(BaseModel): - """POST /api/v1/memories (personal add) request body.""" - - user_id: str = Field(..., description="Owner user ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - messages: List[MessageItem] = Field( - ..., min_length=1, max_length=500, description="Batch message array" - ) - - -class GroupAddRequest(BaseModel): - """POST /api/v1/memories/group (group add) request body.""" - - group_id: str = Field(..., description="Group identifier") - group_meta: Optional[Dict[str, Any]] = Field( - default=None, description="Group metadata" - ) - messages: List[MessageItem] = Field( - ..., min_length=1, max_length=500, description="Batch message array" - ) - - @model_validator(mode="after") - def validate_sender_id_required(self) -> "GroupAddRequest": - """Validate that sender_id is required for each message in group add.""" - for i, msg in enumerate(self.messages): - if not msg.sender_id: - raise ValueError(f"messages[{i}].sender_id is required for group add") - return self - - -# ==================== Agent Add (POST /api/v1/memories/agent) ==================== - - -class ToolCallFunction(BaseModel): - """Function details within a tool call.""" - - name: str = Field(..., description="Function/tool name") - arguments: str = Field(..., description="JSON-encoded arguments string") - - -class ToolCall(BaseModel): - """OpenAI-format tool call made by the assistant.""" - - id: str = Field(..., description="Unique tool call ID") - type: str = Field(default="function", description="Tool call type") - function: ToolCallFunction = Field(..., description="Function call details") - - -class AgentMessageItem(MessageItem): - """Extended MessageItem with agent-specific fields. - - Supports role='tool' in addition to 'user'/'assistant'. - Adds tool_calls (assistant) and tool_call_id (tool) fields. - - Overrides content to Optional: assistant messages with tool_calls may have - empty/null content (common in OpenAI Chat Completion API). Fine-grained - validation (user/tool must have content) is handled in the request converter. - """ - - role: str = Field( - ..., - description="Message sender role: 'user', 'assistant', or 'tool'", - examples=["user", "assistant", "tool"], - ) - content: Optional[Union[str, List[ContentItem]]] = Field( - default=None, - description="Content items. Optional for assistant messages with tool_calls.", - ) - tool_calls: Optional[List[ToolCall]] = Field( - default=None, - description="Tool calls made by the assistant (OpenAI format). " - "Only applicable when role='assistant'.", - ) - tool_call_id: Optional[str] = Field( - default=None, - description="ID of the tool call this message is responding to. " - "Required when role='tool'.", - ) - - @model_validator(mode="before") - @classmethod - def _coerce_content_string(cls, values: Any) -> Any: - """Override parent: allow empty content for assistant with tool_calls.""" - if isinstance(values, dict) and isinstance(values.get("content"), str): - text = values["content"] - if not text: - if values.get("role") == "assistant" and values.get("tool_calls"): - values = dict(values) - values["content"] = [{"type": "text", "text": ""}] - return values - raise ValueError("messages[].content must not be empty") - values = dict(values) - values["content"] = [{"type": "text", "text": text}] - return values - - -class AgentAddRequest(BaseModel): - """POST /api/v1/memories/agent request body. - - Strictly mirrors PersonalAddRequest structure, with AgentMessageItem - supporting tool_calls/tool_call_id and role='tool'. - """ - - user_id: str = Field(..., description="Owner user ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - messages: List[AgentMessageItem] = Field( - ..., min_length=1, max_length=500, description="Agent trajectory messages" - ) - - -# ==================== Flush ==================== - - -class PersonalFlushRequest(BaseModel): - """POST /api/v1/memories/flush (personal flush) request body.""" - - user_id: str = Field(..., description="Owner user ID") - session_id: Optional[str] = Field(default=None, description="Target session") - - -class GroupFlushRequest(BaseModel): - """POST /api/v1/memories/group/flush (group flush) request body.""" - - group_id: str = Field(..., description="Target group") - - -class AgentFlushRequest(BaseModel): - """POST /api/v1/memories/agent/flush (agent flush) request body.""" - - user_id: str = Field(..., description="Owner user ID") - session_id: Optional[str] = Field(default=None, description="Target session") - - -class AddResult(BaseModel): - """Add endpoint result data.""" - - request_id: str = Field(default="", description="Request tracking ID (reserved)") - message_count: int = Field( - default=0, description="Number of messages accepted in this request" - ) - status: str = Field( - default="accumulated", - description="Processing status. " - "'accumulated': messages buffered, waiting for boundary detection; " - "'extracted': boundary detected and memory extraction triggered", - examples=["accumulated", "extracted"], - ) - message: str = Field( - default="Messages accepted", description="Human-readable status description" - ) - - -class AddResponse(BaseApiResponse[AddResult]): - """Add endpoint response.""" - - data: AddResult = Field(default_factory=AddResult, description="Add result") - - -class FlushResult(BaseModel): - """Flush endpoint result data.""" - - request_id: str = Field(default="", description="Request tracking ID (reserved)") - status: str = Field( - default="no_extraction", - description="Processing status. " - "'extracted': boundary detected and memory extraction triggered; " - "'no_extraction': no accumulated messages or no boundary detected", - examples=["extracted", "no_extraction"], - ) - message: str = Field( - default="Flush completed", description="Human-readable status description" - ) - - -class FlushResponse(BaseApiResponse[FlushResult]): - """Flush endpoint response.""" - - data: FlushResult = Field(default_factory=FlushResult, description="Flush result") - - -# ============================================================================= -# Search/Retrieve DTOs (GET /api/v1/memories/search) -# ============================================================================= - - -class RetrieveMemRequest(BaseModel): - """Memory retrieve/search request - - Used for GET /api/v1/memories/search endpoint. - Supports passing parameters via query params or body. - """ - - user_id: Optional[str] = Field( - default=None, - description="User ID (at least one of user_id or group_id must be provided)", - examples=["user_123"], - ) - group_ids: Optional[List[str]] = Field( - default=None, - description="Array of Group IDs to search (max 10 items). " - "None means search all groups for the user.", - examples=[["group_456", "group_789"]], - ) - memory_types: List[MemoryType] = Field( - default_factory=list, - description="""List of memory types to retrieve, enum values from MemoryType: -- profile: user profile (Milvus vector search only) -- episodic_memory: episodic memory -- foresight: prospective memory (not yet supported for search) -- atomic_fact: atomic fact (not yet supported for search) -Note: Only profile and episodic_memory are supported. Defaults to both if not specified.""", - examples=[[MemoryType.EPISODIC_MEMORY.value]], - ) - top_k: int = Field( - default=-1, - description="Maximum number of results to return. -1 means return all results that meet the threshold (up to 100). Valid values: -1 or 1-100.", - ge=-1, - le=100, - examples=[10, -1], - ) - include_metadata: bool = Field( - default=True, description="Whether to include metadata", examples=[True] - ) - start_time: Optional[str] = Field( - default=None, - description="Time range start (ISO 8601 format). Only applies to episodic_memory, ignored for profile", - examples=["2024-01-01T00:00:00"], - ) - end_time: Optional[str] = Field( - default=None, - description="Time range end (ISO 8601 format). Only applies to episodic_memory, ignored for profile", - examples=["2024-12-31T23:59:59"], - ) - query: Optional[str] = Field( - default=None, description="Search query text", examples=["coffee preference"] - ) - retrieve_method: RetrieveMethod = Field( - default=RetrieveMethod.KEYWORD, - description="""Retrieval method, enum values from RetrieveMethod: -- keyword: keyword retrieval (BM25, default) -- vector: vector semantic retrieval -- hybrid: hybrid retrieval (keyword + vector) -- rrf: RRF fusion retrieval (keyword + vector + RRF ranking fusion) -- agentic: LLM-guided multi-round intelligent retrieval""", - examples=["keyword"], - ) - radius: Optional[float] = Field( - default=None, - description="COSINE similarity threshold for vector retrieval (only for vector and hybrid methods, default 0.6)", - ge=0.0, - le=1.0, - examples=[0.6], - ) - - model_config = {"arbitrary_types_allowed": True} - - @model_validator(mode="after") - def validate_request(self) -> "RetrieveMemRequest": - """Validate request parameters""" - # Validate: at least one of user_id or group_ids must be specified - if ( - self.user_id is None or self.user_id == MAGIC_ALL - ) and self.group_ids is None: - raise ValueError( - "At least one of user_id or group_ids must be specified. " - "Cannot query without any filter." - ) - - # Validate: user_id is not specified and group_ids is an empty list - if ( - (self.user_id is None or self.user_id == MAGIC_ALL) - and isinstance(self.group_ids, list) - and len(self.group_ids) == 0 - ): - raise ValueError( - "group_ids cannot be an empty list when user_id is not specified." - ) - - # Validate: group_ids array length cannot exceed MAX_GROUP_IDS_COUNT - if self.group_ids is not None and len(self.group_ids) > MAX_GROUP_IDS_COUNT: - raise ValueError( - f"group_ids array length cannot exceed {MAX_GROUP_IDS_COUNT}" - ) - - # Validate: Search supports episodic_memory, profile, agent_case, agent_skill - if self.memory_types: - allowed_types = { - MemoryType.EPISODIC_MEMORY, - MemoryType.PROFILE, - MemoryType.AGENT_CASE, - MemoryType.AGENT_SKILL, - } - invalid_types = [mt for mt in self.memory_types if mt not in allowed_types] - if invalid_types: - raise ValueError( - f"Search interface only supports memory_types: " - f"episodic_memory, profile, agent_case, agent_skill. " - f"Invalid types: {[mt.value for mt in invalid_types]}" - ) - - # top_k must be -1 (return all) or positive (1-100), 0 is invalid - if self.top_k == 0: - raise ValueError( - "top_k must be -1 (return all results) or a positive integer (1-100)" - ) - - if self.top_k > 0 and self.top_k > MAX_RETRIEVE_LIMIT: - object.__setattr__(self, "top_k", MAX_RETRIEVE_LIMIT) - - return self - - -class RawMessageDTO(BaseModel): - """Raw message DTO for messages not yet extracted into memory. - - Represents a cached message waiting for boundary detection or memory extraction. - """ - - id: str # MongoDB ObjectId as string - request_id: str # Request ID - message_id: Optional[str] = None # Message ID - group_id: Optional[str] = None # Group ID - session_id: Optional[str] = None # Session identifier for conversation isolation - sender_id: Optional[str] = None # Sender ID - sender_name: Optional[str] = None # Sender name - content_items: Optional[List[Dict[str, Any]]] = None # Message content items list - timestamp: Optional[str] = None # Message timestamp (ISO 8601 format with timezone) - created_at: Optional[str] = None # Record creation time (ISO 8601 format) - updated_at: Optional[str] = None # Record update time (ISO 8601 format) - - -class ProfileSearchItem(BaseModel): - """Profile search result item. - - Represents a single profile item from Milvus vector search. - Fields are parsed from embed_text. - """ - - item_type: str = Field( - description="Item type: explicit_info or implicit_trait", - examples=["explicit_info", "implicit_trait"], - ) - # For explicit_info - category: Optional[str] = Field( - default=None, - description="Category name (for explicit_info type)", - examples=["Dietary Preferences", "Professional Skills"], - ) - # For implicit_trait - trait_name: Optional[str] = Field( - default=None, - description="Trait name (for implicit_trait type)", - examples=["Health Conscious", "Efficiency Focused"], - ) - description: str = Field( - default="", - description="Description content", - examples=["Prefers light flavors, favoring vegetables and seafood."], - ) - score: float = Field( - default=0.0, - description="Similarity score from Milvus search", - examples=[0.89, 0.75], - ) - - -class RetrieveMemResponse(BaseModel): - """Memory retrieve/search response (result data) - flat structure""" - - # Profile search results (from Milvus, no rerank) - profiles: List[ProfileSearchItem] = Field( - default_factory=list, - description="Profile search results (explicit_info and implicit_traits)", - ) - memories: SkipValidation[List[RetrieveMemoryModel]] = Field(default_factory=list) - total_count: int = 0 - query_metadata: SkipValidation[Optional[QueryMetadata]] = None - metadata: SkipValidation[Optional[Metadata]] = None - pending_messages: SkipValidation[List[RawMessageDTO]] = Field(default_factory=list) - - model_config = {"arbitrary_types_allowed": True} - - -class SearchMemoriesResponse(BaseApiResponse[RetrieveMemResponse]): - """Memory search API response - - Response for GET /api/v1/memories/search endpoint. - """ - - data: RetrieveMemResponse = Field(description="Memory search result") - - model_config = { - "json_schema_extra": { - "example": { - "status": "ok", - "message": "Memory search successful", - "result": { - "profiles": [ - { - "item_type": "explicit_info", - "category": "Dietary Preferences", - "description": "Prefers light flavors, favoring vegetables and seafood", - "score": 0.89, - }, - { - "item_type": "implicit_trait", - "trait_name": "Health Conscious", - "description": "Prioritizes dietary health, preferring low oil and low salt", - "score": 0.75, - }, - ], - "memories": [ - { - "memory_type": "episodic_memory", - "user_id": "user_123", - "timestamp": "2024-01-15T10:30:00", - "summary": "User mentioned controlling their diet recently, eating only two meals a day, with dinner mainly being salad", - "group_id": "group_456", - } - ], - "scores": [0.82], - "original_data": [], - "total_count": 3, - "has_more": False, - "query_metadata": { - "source": "hybrid_search", - "user_id": "user_123", - "memory_type": "retrieve", - }, - "metadata": { - "profile_count": 2, - "episodic_count": 1, - "latency_ms": 156, - }, - "pending_messages": [], - }, - } - } - } - - -# ============================================================================= -# Delete DTOs (DELETE /api/v1/memories) -# ============================================================================= - - -class DeleteMemoriesRequest(BaseModel): - """ - Delete memories request body - - Used for DELETE /api/v1/memories endpoint - - Notes: - - memory_id, user_id, group_id are combined filter conditions - - If all three are provided, all conditions must be met - - If not provided, use MAGIC_ALL ("__all__") to skip filtering - - Cannot all be MAGIC_ALL (at least one filter required) - - id and event_id are aliases for memory_id (backward compatibility) - """ - - memory_id: Optional[str] = Field( - default=MAGIC_ALL, - description="Memory id (filter condition)", - examples=["507f1f77bcf86cd799439011", MAGIC_ALL], - ) - # Backward compatibility: support id and event_id as alias for memory_id - id: Optional[str] = Field( - default=None, - description="Alias for memory_id (backward compatibility)", - examples=["507f1f77bcf86cd799439011"], - ) - event_id: Optional[str] = Field( - default=None, - description="Alias for memory_id (backward compatibility)", - examples=["507f1f77bcf86cd799439011"], - ) - user_id: Optional[str] = Field( - default=MAGIC_ALL, - description="User ID (filter condition)", - examples=["user_123", MAGIC_ALL], - ) - group_id: Optional[str] = Field( - default=MAGIC_ALL, - description="Group ID (filter condition)", - examples=["group_456", MAGIC_ALL], - ) - - @model_validator(mode="after") - def validate_filters(self): - """Validate that at least one filter is provided""" - # Resolve memory_id from aliases (priority: memory_id > id > event_id) - effective_memory_id = self.memory_id - if effective_memory_id == MAGIC_ALL: - effective_memory_id = self.id or self.event_id or MAGIC_ALL - - # Check if all are MAGIC_ALL - if ( - effective_memory_id == MAGIC_ALL - and self.user_id == MAGIC_ALL - and self.group_id == MAGIC_ALL - ): - raise ValueError( - "At least one of memory_id, user_id, or group_id must be provided (not MAGIC_ALL)" - ) - return self - - model_config = { - "json_schema_extra": { - "examples": [ - { - "summary": "Delete by memory_id only", - "value": { - "memory_id": "507f1f77bcf86cd799439011", - "user_id": MAGIC_ALL, - "group_id": MAGIC_ALL, - }, - }, - { - "summary": "Delete by user_id only", - "value": { - "memory_id": MAGIC_ALL, - "user_id": "user_123", - "group_id": MAGIC_ALL, - }, - }, - { - "summary": "Delete by user_id and group_id", - "value": { - "memory_id": MAGIC_ALL, - "user_id": "user_123", - "group_id": "group_456", - }, - }, - ] - } - } - - -class DeleteMemoriesResult(BaseModel): - """Delete memories result data""" - - filters: List[str] = Field( - default_factory=list, - description="List of filter types used for deletion", - examples=[["event_id"], ["user_id", "group_id"]], - ) - count: int = Field( - default=0, description="Number of memories deleted", examples=[1, 25] - ) - - model_config = { - "json_schema_extra": { - "examples": [ - { - "summary": "Delete by event_id only", - "value": {"filters": ["event_id"], "count": 1}, - }, - { - "summary": "Delete by user_id only", - "value": {"filters": ["user_id"], "count": 25}, - }, - { - "summary": "Delete by user_id and group_id", - "value": {"filters": ["user_id", "group_id"], "count": 10}, - }, - ] - } - } - - -class DeleteMemoriesResponse(BaseApiResponse[DeleteMemoriesResult]): - """Delete memories API response - - Response for DELETE /api/v1/memories endpoint. - """ - - data: DeleteMemoriesResult = Field(description="Delete operation result") - - model_config = { - "json_schema_extra": { - "examples": [ - { - "summary": "Delete by event_id only", - "value": { - "status": "ok", - "message": "Successfully deleted 1 memory", - "result": {"filters": ["event_id"], "count": 1}, - }, - }, - { - "summary": "Delete by user_id only", - "value": { - "status": "ok", - "message": "Successfully deleted 25 memories", - "result": {"filters": ["user_id"], "count": 25}, - }, - }, - { - "summary": "Delete by user_id and group_id", - "value": { - "status": "ok", - "message": "Successfully deleted 10 memories", - "result": {"filters": ["user_id", "group_id"], "count": 10}, - }, - }, - ] - } - } - - -# ============================================================================= -# Get DTOs (POST /api/v1/memories/get) -# ============================================================================= - - -class GetMemRequest(BaseModel): - """Memory get request - - Used for POST /api/v1/memories/get endpoint. - - Note: - - memory_type: supported values are "episodic_memory", "profile", "agent_case", "agent_skill" - - filters must contain at least one of user_id or group_id at first level - - filters supports operators: eq (implicit), in, gt, gte, lt, lte - - filters supports combinators: AND, OR - """ - - memory_type: str = Field( - description="Memory type to get: episodic_memory, profile, agent_case, agent_skill", - examples=[MemoryType.EPISODIC_MEMORY.value], - ) - page: int = Field( - default=1, description="Page number, starts from 1", ge=1, examples=[1] - ) - page_size: int = Field( - default=20, - description="Items per page, default 20, max 100", - ge=1, - le=100, - examples=[20], - ) - rank_by: str = Field( - default="timestamp", description="Sort field", examples=["timestamp"] - ) - rank_order: str = Field( - default="desc", description="Sort order: asc or desc", examples=["desc"] - ) - filters: Dict[str, Any] = Field( - description="Filter conditions with user_id/group_id scope and optional operators" - ) - - @field_validator("memory_type") - @classmethod - def validate_memory_type(cls, v: str) -> str: - allowed = { - MemoryType.EPISODIC_MEMORY.value, - MemoryType.PROFILE.value, - MemoryType.AGENT_CASE.value, - MemoryType.AGENT_SKILL.value, - } - if v not in allowed: - raise ValueError( - f"memory_type must be one of: {', '.join(sorted(allowed))}" - ) - return v - - @field_validator("rank_order") - @classmethod - def validate_rank_order(cls, v: str) -> str: - if v not in ("asc", "desc"): - raise ValueError("rank_order must be 'asc' or 'desc'") - return v - - @field_validator("filters") - @classmethod - def validate_filters(cls, v: Dict[str, Any]) -> Dict[str, Any]: - if "user_id" not in v and "group_id" not in v: - raise ValueError( - "filters must contain at least one of 'user_id' or 'group_id' at first level" - ) - return v - - -class EpisodeItem(BaseModel): - """Episode object in GET response - - Derived from episodic_memories collection. - 12 fields, no score, no vector, no audit timestamps. - """ - - id: str = Field(description="MongoDB ObjectId as string") - user_id: Optional[str] = Field( - default=None, description="Owner user ID, null = group memory" - ) - group_id: Optional[str] = Field(default=None, description="Group ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - timestamp: Optional[datetime] = Field( - default=None, description="Event occurrence time" - ) - participants: Optional[List[str]] = Field( - default=None, description="Event participant names" - ) - sender_ids: Optional[List[str]] = Field( - default=None, description="Sender IDs of event participants" - ) - summary: Optional[str] = Field(default=None, description="Memory summary") - subject: Optional[str] = Field(default=None, description="Memory subject") - episode: Optional[str] = Field( - default=None, description="Full episodic memory text" - ) - type: Optional[str] = Field(default=None, description="Episode type") - parent_type: Optional[str] = Field(default=None, description="Parent memory type") - parent_id: Optional[str] = Field(default=None, description="Parent memory ID") - - -class ProfileItem(BaseModel): - """Profile object in GET response - - Derived from user_profiles collection. - 6 fields, no audit timestamps. - """ - - id: str = Field(description="MongoDB ObjectId as string") - user_id: Optional[str] = Field(default=None, description="User ID") - group_id: Optional[str] = Field(default=None, description="Group ID") - profile_data: Optional[Dict[str, Any]] = Field( - default=None, description="Profile data" - ) - scenario: Optional[str] = Field( - default=None, description="Scenario type: solo or team" - ) - memcell_count: Optional[int] = Field(default=None, description="Number of MemCells") - - -class GetMemResponse(BaseModel): - """Memory get response data - - Response for POST /api/v1/memories/get endpoint. - Wrapped in envelope: { "data": GetMemResponse } - """ - - episodes: List[EpisodeItem] = Field( - default_factory=list, description="Episodic memory items" - ) - profiles: List[ProfileItem] = Field( - default_factory=list, description="Profile items" - ) - agent_cases: List[AgentCaseItem] = Field( - default_factory=list, - description="Agent case items (populated when memory_type=agent_case)", - ) - agent_skills: List[AgentSkillItem] = Field( - default_factory=list, - description="Agent skill items (populated when memory_type=agent_skill)", - ) - total_count: int = Field( - default=0, description="Total number of records matching query conditions" - ) - count: int = Field(default=0, description="Number of records in current page") - - -class GetMemoriesResponse(BaseModel): - """Response envelope for POST /api/v1/memories/get - - Used as response_model for OpenAPI documentation. - """ - - data: GetMemResponse = Field(description="Memory get result") - - model_config = { - "json_schema_extra": { - "examples": [ - { - "data": { - "episodes": [ - { - "id": "67c8a1b2f3e4d5c6a7b8c9d0", - "user_id": "user_123", - "group_id": "group_abc", - "session_id": "sess_abc", - "timestamp": "2026-03-01T10:00:00Z", - "participants": ["user_123", "user_456"], - "summary": "Team discussed Q1 roadmap", - "subject": "Q1 Roadmap Discussion", - "episode": "Alice and Bob discussed...", - "type": "Conversation", - "parent_type": "memcell", - "parent_id": "67c8a1b2f3e4d5c6a7b8c9d1", - } - ], - "profiles": [], - "agent_cases": [], - "agent_skills": [], - "total_count": 1, - "count": 1, - } - }, - { - "data": { - "episodes": [], - "profiles": [ - { - "id": "67c8a1b2f3e4d5c6a7b8c9e0", - "user_id": "user_123", - "group_id": "group_abc", - "profile_data": { - "explicit_info": {"Role": "Product Manager"}, - "implicit_traits": { - "Leadership": "Takes initiative in meetings" - }, - }, - "scenario": "team", - "memcell_count": 5, - } - ], - "agent_cases": [], - "agent_skills": [], - "total_count": 1, - "count": 1, - } - }, - { - "data": { - "episodes": [], - "profiles": [], - "agent_cases": [ - { - "id": "67d1a2b3c4e5f6a7b8c9d0e1", - "user_id": "user_01", - "group_id": None, - "session_id": "sess_agent_001", - "task_intent": "Retrieve and summarize weather data for a given city", - "approach": "1. Parse city name from user query. 2. Call get_weather API. 3. Format response with temperature and conditions.", - "quality_score": 0.92, - "timestamp": "2026-03-15T14:30:00Z", - "parent_type": "memcell", - "parent_id": "67d1a2b3c4e5f6a7b8c9d0e2", - } - ], - "agent_skills": [], - "total_count": 1, - "count": 1, - } - }, - { - "data": { - "episodes": [], - "profiles": [], - "agent_cases": [], - "agent_skills": [ - { - "id": "67d2b3c4d5e6f7a8b9c0d1e2", - "user_id": "user_01", - "group_id": None, - "cluster_id": "cluster_weather_01", - "name": "Weather Query Handling", - "description": "Retrieve weather data for cities using the get_weather API and present results in a user-friendly format", - "content": "Steps: 1. Extract city name. 2. Call get_weather(city). 3. Format: '{city}: {temp}, {conditions}'.", - "confidence": 0.88, - "maturity_score": 0.75, - } - ], - "total_count": 1, - "count": 1, - } - }, - ] - } - } - - -# ============================================================================= -# Search DTOs (POST /api/v1/memories/search) -# ============================================================================= - - -class SearchMemoriesRequest(BaseModel): - """Memory search request (v1) - - Used for POST /api/v1/memories/search endpoint. - Uses structured Filters DSL (MongoFilterParser compatible). - - Note: - - query: Search query text (required for keyword/vector/hybrid/rrf/agentic methods) - - method: Retrieval method (keyword/vector/hybrid/rrf/agentic, default: hybrid) - - memory_types: List of memory types to search (episodic_memory, profile, raw_message, agent_memory) - - filters: Structured filter conditions using Filters DSL - - top_k: Max results (default: -1). -1 = return all results meeting threshold (up to 100) - - radius: Similarity threshold for vector/hybrid/rrf retrieval (0.0-1.0) - - include_original_data: Whether to return original data (default: false) - """ - - query: str = Field( - ..., - min_length=1, - description="Search query text", - examples=["What did Alice say about the project?"], - ) - method: str = Field( - default_factory=lambda: os.getenv("DEFAULT_SEARCH_METHOD", "hybrid"), - description="""Retrieval method: -- keyword: BM25 keyword retrieval (ES only) -- vector: Vector semantic retrieval (Milvus only) -- hybrid: Hybrid retrieval (default). episodic_memory uses hierarchical retrieval, others use ES + Milvus + Rerank -- agentic: LLM-guided multi-round retrieval -Default controlled by DEFAULT_SEARCH_METHOD env var.""", - examples=["keyword", "vector", "hybrid", "agentic"], - ) - memory_types: List[str] = Field( - default_factory=lambda: [ - MemoryType.EPISODIC_MEMORY.value, - MemoryType.PROFILE.value, - ], - description="""List of memory types to search: -- episodic_memory: Episodic memory (ES + Milvus) -- profile: User profile (Milvus only) -- raw_message: Raw unprocessed messages (ES only) -- agent_memory: Agent memory - cases and skills (ES + Milvus)""", - examples=[[MemoryType.EPISODIC_MEMORY.value, MemoryType.PROFILE.value]], - ) - top_k: int = Field( - default=-1, - description="Max results. -1 = return all meeting threshold (up to 100). Valid: -1 or 1-100", - ge=-1, - le=100, - examples=[10, -1], - ) - radius: Optional[float] = Field( - default=None, - description="COSINE similarity threshold (0.0-1.0) for vector methods", - ge=0.0, - le=1.0, - examples=[0.6], - ) - include_original_data: bool = Field( - default=False, description="Whether to return original data", examples=[False] - ) - filters: Dict[str, Any] = Field( - description="""Filter conditions using Filters DSL. -Must contain at least one of user_id or group_id at first level. -Supported fields: user_id, group_id, session_id, timestamp. -Operators: eq (implicit), in, gt, gte, lt, lte. -Combinators: AND, OR. - -Examples: -{"user_id": "user_123", "group_id": {"in": ["group_a", "group_b"]}} -{"AND": [{"timestamp": {"gte": 1704067200000}}, {"timestamp": {"lt": 1740614399000}}]}""" - ) - - @field_validator("method") - @classmethod - def validate_method(cls, v: str) -> str: - if not v: - return os.getenv("DEFAULT_SEARCH_METHOD", "hybrid") - allowed = {"keyword", "vector", "hybrid", "agentic"} - if v not in allowed: - raise ValueError( - f"Unknown method: '{v}'. Allowed: {', '.join(sorted(allowed))}" - ) - return v - - @field_validator("memory_types") - @classmethod - def validate_memory_types(cls, v: List[str]) -> List[str]: - allowed = { - MemoryType.EPISODIC_MEMORY.value, - MemoryType.PROFILE.value, - MemoryType.RAW_MESSAGE.value, - MemoryType.AGENT_MEMORY.value, - } - invalid = [mt for mt in v if mt not in allowed] - if invalid: - raise ValueError( - f"memory_types must be from: {', '.join(sorted(allowed))}. " - f"Invalid: {invalid}" - ) - return v - - @field_validator("filters") - @classmethod - def validate_filters(cls, v: Dict[str, Any]) -> Dict[str, Any]: - # Recursively check for user_id or group_id in filters - def has_user_or_group_filter(filters: Dict[str, Any]) -> bool: - if "user_id" in filters or "group_id" in filters: - return True - # Check nested AND/OR conditions - for key in ["AND", "OR"]: - if key in filters and isinstance(filters[key], list): - for item in filters[key]: - if isinstance(item, dict) and has_user_or_group_filter(item): - return True - return False - - if not has_user_or_group_filter(v): - raise ValueError( - "filters must contain at least one of 'user_id' or 'group_id'" - ) - return v - - @model_validator(mode="after") - def validate_request(self) -> "SearchMemoriesRequest": - if self.top_k == 0: - raise ValueError( - "top_k must be -1 (return all results) or a positive integer (1-100)" - ) - if self.top_k > MAX_RETRIEVE_LIMIT: - object.__setattr__(self, "top_k", MAX_RETRIEVE_LIMIT) - return self - - -class SearchQueryInfo(BaseModel): - """Query information echoed in response""" - - text: str = Field(description="Search query text") - method: str = Field(description="Retrieval method used") - filters_applied: Optional[Dict[str, Any]] = Field( - default=None, description="Filters that were applied" - ) - - -class SearchAtomicFactItem(BaseModel): - """Atomic fact item in search response (with score) - - Derived from v1_atomic_fact_records collection. - """ - - id: str = Field(description="MongoDB ObjectId as string") - user_id: Optional[str] = Field(default=None, description="User ID") - group_id: Optional[str] = Field(default=None, description="Group ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - atomic_fact: Optional[str] = Field( - default=None, description="Atomic fact content (single sentence)" - ) - timestamp: Optional[datetime] = Field( - default=None, description="Event occurrence time" - ) - participants: Optional[List[str]] = Field( - default=None, description="Related participant IDs" - ) - parent_type: Optional[str] = Field(default=None, description="Parent memory type") - parent_id: Optional[str] = Field(default=None, description="Parent memory ID") - score: Optional[float] = Field( - default=None, description="Relevance score (BM25 score, unbounded)" - ) - parent_episode_id: Optional[str] = Field( - default=None, description="Source episode ID (MRAG expansion)" - ) - original_text: Optional[str] = Field( - default=None, description="Original text from parent episode" - ) - - -class SearchEpisodeItem(BaseModel): - """Episode item in search response (with score) - - Derived from v1_episodic_memories collection. - Same as EpisodeItem but with optional score field and optional text fields. - """ - - id: str = Field(description="MongoDB ObjectId as string") - user_id: Optional[str] = Field( - default=None, description="Owner user ID, null = group memory" - ) - group_id: Optional[str] = Field(default=None, description="Group ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - timestamp: Optional[datetime] = Field( - default=None, description="Event occurrence time" - ) - participants: Optional[List[str]] = Field( - default=None, description="Event participant IDs" - ) - summary: Optional[str] = Field(default=None, description="Memory summary") - subject: Optional[str] = Field(default=None, description="Memory subject") - episode: Optional[str] = Field( - default=None, description="Full episodic memory text" - ) - type: Optional[str] = Field(default=None, description="Episode type") - parent_type: Optional[str] = Field(default=None, description="Parent memory type") - parent_id: Optional[str] = Field(default=None, description="Parent memory ID") - score: Optional[float] = Field( - default=None, description="Relevance score (BM25 score, unbounded)" - ) - atomic_facts: List[SearchAtomicFactItem] = Field( - default_factory=list, description="Atomic facts expanded from this episode" - ) - - -class SearchProfileItem(BaseModel): - """Profile item in search response (with score) - - Derived from v1_user_profiles collection. - Same as ProfileItem but with optional score field. - """ - - id: str = Field(description="MongoDB ObjectId as string") - user_id: Optional[str] = Field(default=None, description="User ID") - group_id: Optional[str] = Field(default=None, description="Group ID") - profile_data: Optional[Dict[str, Any]] = Field( - default=None, description="Profile data" - ) - scenario: Optional[str] = Field( - default=None, description="Scenario type: solo or team" - ) - memcell_count: Optional[int] = Field(default=None, description="Number of MemCells") - score: Optional[float] = Field( - default=None, description="Relevance score (BM25 score, unbounded)" - ) - - -class SearchForesightItem(BaseModel): - """Foresight item in search response (with score) - - Derived from v1_foresight_records collection. - """ - - id: str = Field(description="MongoDB ObjectId as string") - user_id: Optional[str] = Field(default=None, description="User ID") - group_id: Optional[str] = Field(default=None, description="Group ID") - sender_id: Optional[str] = Field(default=None, description="Sender ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - content: Optional[str] = Field(default=None, description="Foresight content") - evidence: Optional[str] = Field( - default=None, description="Evidence supporting this foresight" - ) - start_time: Optional[str] = Field( - default=None, description="Foresight start time (date string)" - ) - end_time: Optional[str] = Field( - default=None, description="Foresight end time (date string)" - ) - duration_days: Optional[int] = Field(default=None, description="Duration in days") - timestamp: Optional[datetime] = Field( - default=None, description="Creation timestamp" - ) - participants: Optional[List[str]] = Field( - default=None, description="Related participant IDs" - ) - parent_type: Optional[str] = Field(default=None, description="Parent memory type") - parent_id: Optional[str] = Field(default=None, description="Parent memory ID") - score: Optional[float] = Field( - default=None, description="Relevance score (BM25 score, unbounded)" - ) - - -class AgentCaseItem(BaseModel): - """Agent case item in GET response. - - Derived from v1_agent_cases collection. - No score field — use SearchAgentCaseItem for search responses. - """ - - id: str = Field(description="MongoDB ObjectId as string") - user_id: Optional[str] = Field(default=None, description="User ID") - group_id: Optional[str] = Field(default=None, description="Group ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - task_intent: Optional[str] = Field( - default=None, description="Rewritten task intent as retrieval key" - ) - approach: Optional[str] = Field( - default=None, description="Step-by-step approach with decisions and lessons" - ) - quality_score: Optional[float] = Field( - default=None, description="Task completion quality score (0.0-1.0)" - ) - key_insight: Optional[str] = Field( - default=None, description="Pivotal strategy shift or decision" - ) - timestamp: Optional[datetime] = Field( - default=None, description="Task occurrence time" - ) - parent_type: Optional[str] = Field(default=None, description="Parent memory type") - parent_id: Optional[str] = Field(default=None, description="Parent memory ID") - - -class AgentSkillItem(BaseModel): - """Agent skill item in GET response. - - Derived from v1_agent_skills collection. - No score field — use SearchAgentSkillItem for search responses. - """ - - id: str = Field(description="MongoDB ObjectId as string") - user_id: Optional[str] = Field(default=None, description="User ID") - group_id: Optional[str] = Field(default=None, description="Group ID") - cluster_id: Optional[str] = Field(default=None, description="MemScene cluster ID") - name: Optional[str] = Field(default=None, description="Skill name") - description: Optional[str] = Field( - default=None, description="What this skill does and when to use it" - ) - content: Optional[str] = Field(default=None, description="Full skill content") - confidence: Optional[float] = Field( - default=None, description="Confidence score (0.0-1.0)" - ) - maturity_score: Optional[float] = Field( - default=None, description="Maturity score (0.0-1.0)" - ) - source_case_ids: List[str] = Field( - default_factory=list, - description="AgentCase IDs that triggered add/update of this skill", - ) - - -class SearchAgentCaseItem(BaseModel): - """Agent case item in search response (with score). - - Same as AgentCaseItem but with optional score field for search ranking. - """ - - id: str = Field(description="MongoDB ObjectId as string") - user_id: Optional[str] = Field(default=None, description="User ID") - group_id: Optional[str] = Field(default=None, description="Group ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - task_intent: Optional[str] = Field( - default=None, description="Rewritten task intent as retrieval key" - ) - approach: Optional[str] = Field( - default=None, description="Step-by-step approach with decisions and lessons" - ) - quality_score: Optional[float] = Field( - default=None, description="Task completion quality score (0.0-1.0)" - ) - key_insight: Optional[str] = Field( - default=None, description="Pivotal strategy shift or decision" - ) - timestamp: Optional[datetime] = Field( - default=None, description="Task occurrence time" - ) - parent_type: Optional[str] = Field(default=None, description="Parent memory type") - parent_id: Optional[str] = Field(default=None, description="Parent memory ID") - score: Optional[float] = Field( - default=None, description="Relevance score (search only)" - ) - - -class SearchAgentSkillItem(BaseModel): - """Agent skill item in search response (with score). - - Same as AgentSkillItem but with optional score field for search ranking. - """ - - id: str = Field(description="MongoDB ObjectId as string") - user_id: Optional[str] = Field(default=None, description="User ID") - group_id: Optional[str] = Field(default=None, description="Group ID") - cluster_id: Optional[str] = Field(default=None, description="MemScene cluster ID") - name: Optional[str] = Field(default=None, description="Skill name") - description: Optional[str] = Field( - default=None, description="What this skill does and when to use it" - ) - content: Optional[str] = Field(default=None, description="Full skill content") - confidence: Optional[float] = Field( - default=None, description="Confidence score (0.0-1.0)" - ) - maturity_score: Optional[float] = Field( - default=None, description="Maturity score (0.0-1.0)" - ) - source_case_ids: List[str] = Field( - default_factory=list, - description="AgentCase IDs that triggered add/update of this skill", - ) - score: Optional[float] = Field( - default=None, description="Relevance score (search only)" - ) - - -class AgentMemorySearchResult(BaseModel): - """Agent memory search result container. - - Groups agent cases and skills under one structure - when memory_types includes 'agent_memory'. - """ - - cases: List[SearchAgentCaseItem] = Field( - default_factory=list, description="Agent case search results" - ) - skills: List[SearchAgentSkillItem] = Field( - default_factory=list, description="Agent skill search results" - ) - - -class SearchMemoriesResponseData(BaseModel): - """Memory search response data (v1) - - Result data for POST /api/v1/memories/search endpoint. - Wrapped in envelope: { "data": SearchMemoriesResponseData } - """ - - episodes: List[SearchEpisodeItem] = Field( - default_factory=list, description="Episodic memory search results" - ) - profiles: List[SearchProfileItem] = Field( - default_factory=list, description="Profile search results" - ) - raw_messages: List[RawMessageDTO] = Field( - default_factory=list, description="Raw unprocessed messages (pending)" - ) - agent_memory: Optional[AgentMemorySearchResult] = Field( - default=None, - description="Agent memory search results containing cases and skills " - "(populated when memory_types includes 'agent_memory')", - ) - query: SearchQueryInfo = Field(description="Query information echoed from request") - original_data: Optional[Dict[str, Any]] = Field( - default=None, description="Original data (if include_original_data=true)" - ) - - -class SearchMemoriesResponse(BaseApiResponse[SearchMemoriesResponseData]): - """Memory search response (v1) - - Response for POST /api/v1/memories/search endpoint. - """ - - data: SearchMemoriesResponseData = Field(description="Memory search result") - - model_config = { - "json_schema_extra": { - "examples": [ - { - "data": { - "episodes": [ - { - "id": "67c8a1b2f3e4d5c6a7b8c9d0", - "user_id": "user_123", - "group_id": "group_abc", - "session_id": "sess_abc", - "timestamp": "2026-03-01T10:00:00Z", - "participants": ["user_123", "user_456"], - "summary": "Team discussed Q1 roadmap", - "subject": "Q1 Roadmap Discussion", - "episode": "Alice and Bob discussed the Q1 roadmap...", - "type": "Conversation", - "parent_type": "memcell", - "parent_id": "67c8a1b2f3e4d5c6a7b8c9d1", - "score": 0.85, - } - ], - "profiles": [], - "query": { - "text": "What did Alice say about the project?", - "method": "hybrid", - "filters_applied": { - "user_id": "user_123", - "group_id": "group_abc", - }, - }, - "original_data": None, - } - }, - { - "data": { - "episodes": [], - "profiles": [ - { - "id": "67c8a1b2f3e4d5c6a7b8c9e0", - "user_id": "user_456", - "group_id": "group_abc", - "profile_data": { - "explicit_info": {"Role": "Product Manager"}, - "implicit_traits": {"Leadership": "Proactive"}, - }, - "scenario": "team", - "memcell_count": 150, - "score": 0.72, - } - ], - "query": { - "text": "What is Alice's role?", - "method": "vector", - "filters_applied": {"user_id": "user_456"}, - }, - "original_data": None, - } - }, - { - "data": { - "episodes": [], - "profiles": [], - "agent_memory": { - "cases": [ - { - "id": "67d1a2b3c4e5f6a7b8c9d0e1", - "user_id": "user_01", - "session_id": "sess_agent_001", - "task_intent": "Handle API timeout errors with retry and fallback", - "approach": "1. Catch timeout exception. 2. Retry up to 3 times with exponential backoff. 3. If all retries fail, return cached result or error message.", - "quality_score": 0.95, - "timestamp": "2026-03-15T14:30:00Z", - "score": 0.88, - } - ], - "skills": [ - { - "id": "67d2b3c4d5e6f7a8b9c0d1e2", - "user_id": "user_01", - "cluster_id": "cluster_error_handling", - "name": "API Error Handling with Retry", - "description": "Handle API errors with exponential backoff retry and graceful fallback", - "content": "Pattern: try/except with max_retries=3, backoff_factor=2. On final failure, return cached data or user-friendly error.", - "confidence": 0.91, - "maturity_score": 0.82, - "score": 0.79, - } - ], - }, - "query": { - "text": "How to handle timeout errors", - "method": "hybrid", - "filters_applied": {"user_id": "user_01"}, - }, - "original_data": None, - } - }, - ] - } - } diff --git a/methods/EverCore/src/api_specs/dtos/memory_delete.py b/methods/EverCore/src/api_specs/dtos/memory_delete.py deleted file mode 100644 index ca4cc124..00000000 --- a/methods/EverCore/src/api_specs/dtos/memory_delete.py +++ /dev/null @@ -1,55 +0,0 @@ -"""DTOs for POST /api/v1/memories/delete endpoint.""" - -from typing import Optional - -from pydantic import BaseModel, Field, model_validator - -from core.oxm.constants import MAGIC_ALL - - -class DeleteMemoriesRequest(BaseModel): - """Delete memories request body. - - Two mutually exclusive modes: - - By ID: provide memory_id only (no other fields allowed) - - By filters: provide user_id and/or group_id (session_id, sender_id optional) - - Three-state filter semantics: - - MAGIC_ALL (default/not provided): skip this filter - - None or "": match null/empty records - - "alice": exact match - """ - - memory_id: Optional[str] = Field( - default=None, description="MemCell ID for single delete" - ) - user_id: Optional[str] = Field( - default=MAGIC_ALL, description="User ID scope for batch delete" - ) - group_id: Optional[str] = Field( - default=MAGIC_ALL, description="Group ID scope for batch delete" - ) - session_id: Optional[str] = Field( - default=MAGIC_ALL, description="Session filter (batch delete only)" - ) - sender_id: Optional[str] = Field( - default=MAGIC_ALL, - description="Sender filter, matches participants array (batch delete only)", - ) - - @model_validator(mode="after") - def validate_mode_exclusivity(self): - filter_fields = [self.user_id, self.group_id, self.session_id, self.sender_id] - if self.memory_id is not None: - if any(f != MAGIC_ALL for f in filter_fields): - raise ValueError( - "When memory_id is provided, no other fields " - "(user_id, group_id, session_id, sender_id) are allowed" - ) - else: - if self.user_id == MAGIC_ALL and self.group_id == MAGIC_ALL: - raise ValueError( - "Either memory_id must be provided, or at least one of " - "user_id / group_id is required" - ) - return self diff --git a/methods/EverCore/src/api_specs/dtos/sender.py b/methods/EverCore/src/api_specs/dtos/sender.py deleted file mode 100644 index 6eb18507..00000000 --- a/methods/EverCore/src/api_specs/dtos/sender.py +++ /dev/null @@ -1,103 +0,0 @@ -"""Sender resource DTOs. - -This module contains DTOs for sender CRUD operations: -- Create (POST /api/v1/senders) -- Get (GET /api/v1/senders/{sender_id}) -- Patch (PATCH /api/v1/senders/{sender_id}) -""" - -from __future__ import annotations - -from typing import Optional - -from pydantic import BaseModel, Field - -from api_specs.dtos.base import BaseApiResponse - - -# ============================================================================= -# Request DTOs -# ============================================================================= - - -class CreateSenderRequest(BaseModel): - """Create sender request body - - Used for POST /api/v1/senders endpoint. - If a sender with the same sender_id exists, it will be updated (upsert). - """ - - sender_id: str = Field( - ..., description="Sender identifier (unique)", examples=["user_123"] - ) - name: Optional[str] = Field( - default=None, description="Sender display name", examples=["Alice"] - ) - - model_config = { - "json_schema_extra": {"example": {"sender_id": "user_123", "name": "Alice"}} - } - - -class PatchSenderRequest(BaseModel): - """Partial update sender request body - - Used for PATCH /api/v1/senders/{sender_id} endpoint. - """ - - name: Optional[str] = Field( - default=None, description="New sender display name", examples=["Alice Updated"] - ) - - model_config = {"json_schema_extra": {"example": {"name": "Alice Updated"}}} - - -# ============================================================================= -# Response DTOs -# ============================================================================= - - -class SenderResponse(BaseModel): - """Sender response DTO - - Returned by all sender endpoints. - """ - - sender_id: str = Field(..., description="Sender identifier") - name: Optional[str] = Field(default=None, description="Sender display name") - created_at: str = Field(..., description="Creation time (ISO 8601)") - updated_at: str = Field(..., description="Last update time (ISO 8601)") - - model_config = { - "json_schema_extra": { - "example": { - "sender_id": "user_123", - "name": "Alice", - "created_at": "2026-01-01T00:00:00+00:00", - "updated_at": "2026-02-26T10:00:00+00:00", - } - } - } - - -# ============================================================================= -# API Response Wrappers -# ============================================================================= - - -class CreateSenderApiResponse(BaseApiResponse[SenderResponse]): - """Create sender API response""" - - data: SenderResponse = Field(description="Created/updated sender data") - - -class GetSenderApiResponse(BaseApiResponse[SenderResponse]): - """Get sender API response""" - - data: SenderResponse = Field(description="Sender data") - - -class PatchSenderApiResponse(BaseApiResponse[SenderResponse]): - """Patch sender API response""" - - data: SenderResponse = Field(description="Updated sender data") diff --git a/methods/EverCore/src/api_specs/dtos/settings.py b/methods/EverCore/src/api_specs/dtos/settings.py deleted file mode 100644 index ba3818c1..00000000 --- a/methods/EverCore/src/api_specs/dtos/settings.py +++ /dev/null @@ -1,182 +0,0 @@ -"""Settings resource DTOs. - -This module contains DTOs for Settings CRUD operations: -- Get (GET /api/v1/settings) -- Update (PUT /api/v1/settings) - -The Settings API manages the singleton global configuration per space. -""" - -from __future__ import annotations - -from typing import Any, Dict, Optional - -from pydantic import BaseModel, Field - -from api_specs.dtos.base import BaseApiResponse - - -# ============================================================================= -# LLM Configuration Types -# ============================================================================= - - -class LlmProviderConfig(BaseModel): - """LLM provider configuration - - Defines the provider and model for a specific LLM task. - """ - - provider: str = Field( - ..., - description="LLM provider name", - examples=["openai", "openrouter", "anthropic"], - ) - model: str = Field( - ..., - description="Model name", - examples=["gpt-4.1-mini", "qwen/qwen3-235b-a22b-2507"], - ) - extra: Optional[Dict[str, Any]] = Field( - default=None, - description="Additional provider-specific configuration", - examples=[{"temperature": 0.7, "max_tokens": 1024}], - ) - - -class LlmCustomSetting(BaseModel): - """LLM custom settings for algorithm control - - Allows configuring different LLM providers/models for different tasks. - - Example: - { - "boundary": {"provider": "openai", "model": "gpt-4.1-mini"}, - "extraction": {"provider": "openrouter", "model": "qwen/qwen3-235b-a22b-2507"} - } - """ - - boundary: Optional[LlmProviderConfig] = Field( - default=None, - description="LLM config for boundary detection (fast, cheap model recommended)", - examples=[{"provider": "openai", "model": "gpt-4.1-mini"}], - ) - extraction: Optional[LlmProviderConfig] = Field( - default=None, - description="LLM config for memory extraction (high quality model recommended)", - examples=[{"provider": "openrouter", "model": "qwen/qwen3-235b-a22b-2507"}], - ) - extra: Optional[Dict[str, Any]] = Field( - default=None, description="Additional task-specific LLM configurations" - ) - - -# ============================================================================= -# Request DTOs -# ============================================================================= - - -class UpdateSettingsRequest(BaseModel): - """Update settings request body - - Used for PUT /api/v1/settings endpoint. - Handles both initialization (first call) and updates. - - """ - - llm_custom_setting: Optional[LlmCustomSetting] = Field( - default=None, - description="LLM custom settings for algorithm control. " - "Validated against provider whitelist.", - examples=[ - { - "boundary": {"provider": "openai", "model": "gpt-4.1-mini"}, - "extraction": { - "provider": "openrouter", - "model": "qwen/qwen3-235b-a22b-2507", - }, - } - ], - ) - # Hidden fields: not yet implemented, uncomment when ready - # timezone: Optional[str] = Field( - # default=None, - # description="IANA timezone identifier", - # examples=["UTC", "Asia/Shanghai"], - # ) - # boundary_detection_timeout: Optional[int] = Field( - # default=None, - # description="MemCell auto-flush idle timeout in seconds", - # examples=[3600], - # ) - # extraction_mode: Optional[str] = Field( - # default=None, - # description="Extraction mode: 'default' or 'pro'", - # examples=["default", "pro"], - # ) - # offline_profile_extraction_interval: Optional[int] = Field( - # default=None, - # description="Offline profile extraction interval in seconds", - # examples=[86400], - # ) - - -# ============================================================================= -# Response DTOs -# ============================================================================= - - -class SettingsResponse(BaseModel): - """Settings response DTO - - Returned by GET and PUT /api/v1/settings endpoints. - """ - - llm_custom_setting: Optional[Dict[str, Any]] = Field( - default=None, description="LLM custom settings (serialized)" - ) - # Hidden fields: not yet implemented, uncomment when ready - # timezone: str = Field(..., description="IANA timezone identifier") - # boundary_detection_timeout: int = Field( - # ..., description="MemCell auto-flush idle timeout in seconds" - # ) - # extraction_mode: str = Field(..., description="Extraction mode") - # offline_profile_extraction_interval: int = Field( - # ..., description="Offline profile extraction interval in seconds" - # ) - created_at: str = Field(..., description="Creation time (ISO 8601)") - updated_at: str = Field(..., description="Last update time (ISO 8601)") - - model_config = { - "json_schema_extra": { - "example": { - "llm_custom_setting": { - "boundary": {"provider": "openai", "model": "gpt-4.1-mini"}, - "extraction": {"provider": "openai", "model": "gpt-4o"}, - }, - # "timezone": "UTC", - # "boundary_detection_timeout": 3600, - # "extraction_mode": "default", - # "offline_profile_extraction_interval": 86400, - "created_at": "2026-01-01T00:00:00+00:00", - "updated_at": "2026-03-05T10:00:00+00:00", - } - } - } - - -# ============================================================================= -# API Response Wrappers -# ============================================================================= - - -class GetSettingsApiResponse(BaseApiResponse[SettingsResponse]): - """Get settings API response""" - - data: SettingsResponse = Field(description="Settings data") - - -class UpdateSettingsApiResponse(BaseApiResponse[SettingsResponse]): - """Update settings API response""" - - data: SettingsResponse = Field(description="Updated settings data") diff --git a/methods/EverCore/src/api_specs/id_generator.py b/methods/EverCore/src/api_specs/id_generator.py deleted file mode 100644 index b28c1eb9..00000000 --- a/methods/EverCore/src/api_specs/id_generator.py +++ /dev/null @@ -1,157 +0,0 @@ -""" -System-generated ID utilities. - -All system-generated IDs share the `gen_` prefix to distinguish them from -user-supplied IDs. Sub-prefixes identify the ID type: - -- `gen_solo_` — auto-generated group_id (personal / single-user scene) -- `gen_msg_` — auto-generated message_id (when client omits message_id) -- `gen_sdr_` — auto-generated sender_id (personal scene, assistant role) - -User-supplied IDs MUST NOT start with `GEN_PREFIX` to avoid ambiguity. -""" - -import hashlib - -# --------------------------------------------------------------------------- -# Prefix constants -# --------------------------------------------------------------------------- - -GEN_PREFIX = "gen_" -"""Common prefix for all system-generated IDs.""" - -GEN_SOLO_GROUP_PREFIX = "gen_solo_" -"""Prefix for auto-generated group IDs (single-user / personal scene).""" - -GEN_MESSAGE_PREFIX = "gen_msg_" -"""Prefix for auto-generated message IDs.""" - -GEN_SENDER_PREFIX = "gen_sdr_" -"""Prefix for auto-generated sender IDs (personal scene, assistant role).""" - -DEFAULT_SESSION_ID = "-1" -"""Sentinel value for session_id when session isolation is not applicable. - -Used in two scenarios: -- Group scene: groups do not use session isolation. -- Personal scene: when the client does not provide a session_id. -""" - - -# --------------------------------------------------------------------------- -# ID generators -# --------------------------------------------------------------------------- - - -def generate_single_user_group_id(user_id: str) -> str: - """Generate a deterministic group_id for the single-user (personal) scene. - - The result is prefixed with `gen_solo_` so it can always be distinguished - from user-supplied group IDs. The `solo` segment maps to ScenarioType.SOLO. - - Args: - user_id: The owner user ID. - - Returns: - str: Generated group_id in format ``gen_solo_{md5(user_id)[:12]}``. - """ - hash_value = hashlib.md5(user_id.encode("utf-8")).hexdigest()[:12] - return f"{GEN_SOLO_GROUP_PREFIX}{hash_value}" - - -def generate_message_id(context_id: str, timestamp_ms: int) -> str: - """Generate a deterministic message_id when the client does not provide one. - - The result is prefixed with `gen_msg_` so it can always be distinguished - from user-supplied message IDs. - - Args: - context_id: Context identifier (user_id for personal, group_id for group). - timestamp_ms: Message timestamp in unix milliseconds. - - Returns: - str: Generated message_id in format ``gen_msg_{md5(context_id + ts)[:12]}``. - """ - raw = f"{context_id}_{timestamp_ms}" - hash_value = hashlib.md5(raw.encode("utf-8")).hexdigest()[:12] - return f"{GEN_MESSAGE_PREFIX}{hash_value}" - - -def generate_assistant_sender_id(user_id: str) -> str: - """Generate a deterministic sender_id for the assistant in personal scene. - - The result is prefixed with `gen_sdr_` so it can always be distinguished - from user-supplied sender IDs. - - Args: - user_id: The owner user ID. - - Returns: - str: Generated sender_id in format ``gen_sdr_{md5(user_id)[:12]}``. - """ - hash_value = hashlib.md5(user_id.encode("utf-8")).hexdigest()[:12] - return f"{GEN_SENDER_PREFIX}{hash_value}" - - -# --------------------------------------------------------------------------- -# Validation -# --------------------------------------------------------------------------- - - -# Per-field validation rules: (check_fn, error_message_template) -# Template receives {field_name} and {value} via str.format_map(). -_FIELD_RULES: dict[str, list[tuple]] = { - "group_id": [ - ( - lambda v: v.startswith(GEN_PREFIX), - "{field_name} must not start with reserved prefix '{prefix}'. " - "IDs starting with '{prefix}' are reserved for system-generated values.", - ) - ], - "message_id": [ - ( - lambda v: v.startswith(GEN_PREFIX), - "{field_name} must not start with reserved prefix '{prefix}'. " - "IDs starting with '{prefix}' are reserved for system-generated values.", - ) - ], - "sender_id": [ - ( - lambda v: v.startswith(GEN_PREFIX), - "{field_name} must not start with reserved prefix '{prefix}'. " - "IDs starting with '{prefix}' are reserved for system-generated values.", - ) - ], - "session_id": [ - ( - lambda v: v == DEFAULT_SESSION_ID, - "{field_name} must not be '{value}'. " - "This value is reserved for system use when session isolation is not applicable.", - ) - ], -} - - -def validate_input_id(field_name: str, value: str) -> None: - """Validate a user-supplied value against reserved patterns for the given field. - - Dispatches to field-specific rules defined in `_FIELD_RULES`. - Fields without rules pass through silently. - - Args: - field_name: The field being validated (e.g. "group_id", "message_id", "session_id"). - value: The user-supplied value to check. - - Raises: - ValueError: If the value violates any rule for the given field. - """ - rules = _FIELD_RULES.get(field_name) - if not rules: - return - for check_fn, msg_template in rules: - if check_fn(value): - raise ValueError( - msg_template.format( - field_name=field_name, value=value, prefix=GEN_PREFIX - ) - ) diff --git a/methods/EverCore/src/api_specs/memory_models.py b/methods/EverCore/src/api_specs/memory_models.py deleted file mode 100644 index a45e4cf3..00000000 --- a/methods/EverCore/src/api_specs/memory_models.py +++ /dev/null @@ -1,394 +0,0 @@ -""" -Memory data model definitions - -This module contains memory data model definitions shared across services -""" - -from __future__ import annotations - -from dataclasses import dataclass, field -from enum import Enum -from typing import Any, Dict, List, Optional -from datetime import datetime -from common_utils.datetime_utils import get_now_with_timezone - - -class MessageSenderRole(str, Enum): - """Enumeration of message sender roles - - Used to identify the source of a message in conversations. - - Values: - USER: Message from a human user - ASSISTANT: Message from an AI assistant (v1 API terminology) - """ - - USER = "user" - ASSISTANT = "assistant" - TOOL = "tool" - - @classmethod - def from_string(cls, role_str: Optional[str]) -> Optional['MessageSenderRole']: - """ - Convert string to MessageSenderRole enum - - Args: - role_str: Role string, such as "user", "assistant" - - Returns: - MessageSenderRole enum value, returns None if conversion fails - """ - if not role_str: - return None - - try: - role_lower = role_str.lower() - for role in cls: - if role.value == role_lower: - return role - return None - except Exception: - return None - - @classmethod - def is_valid(cls, role_str: Optional[str]) -> bool: - """ - Check if the given string is a valid role - - Args: - role_str: Role string to validate - - Returns: - True if valid, False otherwise - """ - if not role_str: - return True # None is allowed (optional field) - return cls.from_string(role_str) is not None - - -class RetrieveMethod(str, Enum): - """Enumeration of retrieval methods""" - - KEYWORD = "keyword" - VECTOR = "vector" - HYBRID = "hybrid" # episodic_memory uses hierarchical retrieval, others use ES + Milvus + Rerank - AGENTIC = "agentic" # LLM-guided multi-round retrieval - - -class MemoryType(str, Enum): - """Enumeration of memory types - - Currently implemented types: - - PROFILE: User profile - - EPISODIC_MEMORY: Episodic memory - - FORESIGHT: Prospective memory - - ATOMIC_FACT: Atomic fact - - RAW_MESSAGE: Raw unprocessed messages - - AGENT_MEMORY: Agent memory (umbrella for cases + skills) - - AGENT_CASE: Agent experience case - - AGENT_SKILL: Agent reusable skill - - """ - - # ===== Implemented ===== - PROFILE = "profile" # User profile - EPISODIC_MEMORY = "episodic_memory" # Episodic memory - FORESIGHT = "foresight" # Prospective memory - ATOMIC_FACT = "atomic_fact" # Atomic fact - RAW_MESSAGE = "raw_message" # Raw unprocessed messages (pending) - AGENT_MEMORY = "agent_memory" # Agent memory (umbrella type for cases + skills) - AGENT_CASE = "agent_case" # Agent experience (task intent + trajectory + feedback) - AGENT_SKILL = "agent_skill" # Agent skill (reusable skills from experiences) - - -@dataclass -class Metadata: - """Memory metadata class""" - - # Required fields - source: str # Data source - user_id: str # User ID - memory_types: List[str] # Memory types searched - - # Optional fields - group_ids: Optional[List[str]] = None # Group IDs list (for query-level metadata) - email: Optional[str] = None # Email - phone: Optional[str] = None # Phone number - full_name: Optional[str] = None # Full name - - def to_dict(self) -> Dict[str, Any]: - """Convert to dictionary format""" - result = {} - for key, value in self.__dict__.items(): - if value is not None: - result[key] = value - return result - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> 'Metadata': - """Create Metadata object from dictionary""" - return cls(**{k: v for k, v in data.items() if k in cls.__annotations__}) - - -@dataclass -class QueryMetadata: - """Query metadata for search response, reflecting the query parameters used.""" - - user_id: Optional[str] = None - group_ids: List[str] = None - memory_types: Optional[List[str]] = None - start_time: Optional[str] = None - end_time: Optional[str] = None - query: Optional[str] = None - retrieve_method: Optional[str] = None - radius: Optional[float] = None - top_k: Optional[int] = None - - def to_dict(self) -> Dict[str, Any]: - """Convert to dictionary format""" - result = {} - for key, value in self.__dict__.items(): - if value is not None: - result[key] = value - return result - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> 'QueryMetadata': - """Create QueryMetadata object from dictionary""" - return cls(**{k: v for k, v in data.items() if k in cls.__annotations__}) - - @classmethod - def from_request(cls, req) -> 'QueryMetadata': - """Create from RetrieveMemRequest""" - return cls( - user_id=req.user_id or "", - group_ids=req.group_ids or [], - memory_types=( - [mt.value for mt in req.memory_types] if req.memory_types else [] - ), - start_time=req.start_time, - end_time=req.end_time, - query=req.query, - retrieve_method=( - req.retrieve_method.value - if hasattr(req.retrieve_method, 'value') - else str(req.retrieve_method) - ), - radius=req.radius, - top_k=req.top_k, - ) - - -from api_specs.memory_types import ScenarioType - - -@dataclass -class ProfileModel: - """User profile model - - Stores user profile information automatically extracted from clustering conversations. - Compatible with UserProfile document structure. - """ - - id: str - user_id: str - group_id: str - profile_data: Dict[str, Any] = field(default_factory=dict) - scenario: str = ScenarioType.TEAM.value - confidence: float = 0.0 - version: int = 1 - cluster_ids: List[str] = field(default_factory=list) - memcell_count: int = 0 - last_updated_cluster: Optional[str] = None - created_at: Optional[datetime] = None - updated_at: Optional[datetime] = None - - -@dataclass -class GlobalUserProfileModel: - """Global user profile model - - Stores global user profile information (not bound to a specific group). - Compatible with GlobalUserProfile document structure. - """ - - id: str - user_id: str - profile_data: Optional[Dict[str, Any]] = None - custom_profile_data: Optional[Dict[str, Any]] = None - confidence: float = 0.0 - memcell_count: int = 0 - created_at: Optional[datetime] = None - updated_at: Optional[datetime] = None - - -@dataclass -class CombinedProfileModel: - """Combined profile model - - Contains both group-level profile and global user profile. - Used when fetching PROFILE memory type. - """ - - user_id: str - group_ids: Optional[List[str]] = None # Group IDs list - # Group-level profiles (may have multiple for different groups) - profiles: List[ProfileModel] = field(default_factory=list) - # Global user profile (one per user per scenario) - global_profile: Optional[GlobalUserProfileModel] = None - - -@dataclass -class EpisodicMemoryModel: - """Episodic memory model""" - - id: str - user_id: str - episode_id: str # Same as id, no difference, kept for compatibility - episode: Optional[str] = None - subject: Optional[str] = None - summary: Optional[str] = None - timestamp: Optional[datetime] = None - participants: List[str] = field(default_factory=list) - sender_ids: Optional[List[str]] = None - location: Optional[str] = None - start_time: datetime = field(default_factory=get_now_with_timezone) - end_time: Optional[datetime] = None - keywords: List[str] = field(default_factory=list) - group_id: Optional[str] = None - created_at: datetime = field(default_factory=get_now_with_timezone) - updated_at: datetime = field(default_factory=get_now_with_timezone) - metadata: Metadata = field(default_factory=Metadata) - extend: Optional[Dict[str, Any]] = None - parent_type: Optional[str] = None - parent_id: Optional[str] = None - original_data: Optional[List[Dict[str, Any]]] = ( - None # Original conversation data from MemCell - ) - - -@dataclass -class AtomicFactModel: - """Atomic fact model - - Atomic facts extracted from episodic memories, used for fine-grained retrieval. - """ - - id: str - user_id: str - atomic_fact: str # Content of the atomic fact - parent_type: str # Parent memory type (memcell/episode) - parent_id: str # Parent memory ID - timestamp: datetime # Event occurrence time - - # Optional fields - user_name: Optional[str] = None - group_id: Optional[str] = None - participants: Optional[List[str]] = None - sender_ids: Optional[List[str]] = None - vector: Optional[List[float]] = None - vector_model: Optional[str] = None - event_type: Optional[str] = None - extend: Optional[Dict[str, Any]] = None - - # Common timestamps - created_at: datetime = field(default_factory=get_now_with_timezone) - updated_at: datetime = field(default_factory=get_now_with_timezone) - metadata: Metadata = field(default_factory=Metadata) - - # Original data from MemCell - original_data: Optional[List[Dict[str, Any]]] = ( - None # Original conversation data from MemCell - ) - - -@dataclass -class ForesightModel: - """Prospective record model - - Prospective information extracted from episodic memories, supporting individual and group foresight. - """ - - id: str - content: str # Prospective content - foresight: str # Prospective content (same as content) - parent_type: str # Parent memory type (memcell/episode) - parent_id: str # Parent memory ID - - # Optional fields - user_id: Optional[str] = None - user_name: Optional[str] = None - group_id: Optional[str] = None - start_time: Optional[str] = None # Start time (date string) - end_time: Optional[str] = None # End time (date string) - duration_days: Optional[int] = None # Duration in days - participants: Optional[List[str]] = None - sender_ids: Optional[List[str]] = None - vector: Optional[List[float]] = None - vector_model: Optional[str] = None - evidence: Optional[str] = None # Evidence supporting this foresight - extend: Optional[Dict[str, Any]] = None - - # Common timestamps - created_at: datetime = field(default_factory=get_now_with_timezone) - updated_at: datetime = field(default_factory=get_now_with_timezone) - metadata: Metadata = field(default_factory=Metadata) - - # Original data from MemCell - original_data: Optional[List[Dict[str, Any]]] = ( - None # Original conversation data from MemCell - ) - - -@dataclass -class AgentCaseModel: - """Agent experience model - - Compressed agent task-solving experience (one per MemCell). - """ - - id: str - timestamp: datetime - - # Core experience fields - task_intent: str = "" - approach: str = "" - quality_score: Optional[float] = None - - # Parent linkage - parent_type: Optional[str] = None - parent_id: Optional[str] = None - - # Optional fields - user_id: Optional[str] = None - group_id: Optional[str] = None - session_id: Optional[str] = None - - # Common timestamps - created_at: datetime = field(default_factory=get_now_with_timezone) - updated_at: datetime = field(default_factory=get_now_with_timezone) - - -@dataclass -class AgentSkillModel: - """Agent skill model - - Reusable skills extracted from a MemScene (cluster of AgentCases). - """ - - id: str - cluster_id: str - name: str - content: str - - # Optional fields - user_id: Optional[str] = None - description: Optional[str] = None - group_id: Optional[str] = None - confidence: float = 0.0 - maturity_score: float = 0.6 - agent_case_ids: List[str] = field(default_factory=list) - - # Common timestamps - created_at: datetime = field(default_factory=get_now_with_timezone) - updated_at: datetime = field(default_factory=get_now_with_timezone) diff --git a/methods/EverCore/src/api_specs/memory_types.py b/methods/EverCore/src/api_specs/memory_types.py deleted file mode 100644 index 9ce9b004..00000000 --- a/methods/EverCore/src/api_specs/memory_types.py +++ /dev/null @@ -1,555 +0,0 @@ -""" -Memory types module - -This module contains the definitions of memory types and related data structures, just for extraction. -""" - -from enum import Enum -from typing import List, Dict, Any, Optional, Union -from dataclasses import dataclass, field -from datetime import datetime -from common_utils.datetime_utils import to_iso_format - - -class ScenarioType(str, Enum): - """Conversation scenario types for memory extraction.""" - - SOLO = "solo" # 1 user + N agents scenario - TEAM = "team" # Multi-user + agents scenario - - -# Import after ScenarioType definition to avoid circular import -# (memory_models imports ScenarioType from this module) -from api_specs.memory_models import MemoryType # noqa: E402 - - -class RawDataType(Enum): - """Types of content that can be processed.""" - - CONVERSATION = "Conversation" - AGENTCONVERSATION = "AgentConversation" - - @classmethod - def from_string(cls, type_str: Optional[str]) -> Optional['RawDataType']: - """ - Convert string type to RawDataType enum - - Args: - type_str: Type string, such as "Conversation", "Email", etc. - - Returns: - RawDataType enum value, returns None if conversion fails - """ - if not type_str: - return None - - try: - # Convert string to enum name format (e.g., "Conversation" -> "CONVERSATION") - enum_name = type_str.upper() - return getattr(cls, enum_name) - - except AttributeError: - # If no matching enum is found, return None - from core.observation.logger import get_logger - - logger = get_logger(__name__) - logger.error(f"No matching RawDataType found: {type_str}, returning None") - return None - except Exception as e: - from core.observation.logger import get_logger - - logger = get_logger(__name__) - logger.warning(f"Failed to convert type field: {type_str}, error: {e}") - return None - - -class ParentType(str, Enum): - """Parent memory type for Foresight/AtomicFact.""" - - MEMCELL = "memcell" - EPISODE = "episode" - - -def get_text_from_content_items(content_items: Any) -> str: - """Extract text from a content items list. - - Takes a content[] list (v1 API format) and returns the concatenated text. - For type="text" items, uses the text field directly. - For non-text types, reads parsed_summary from item itself (embedded by enrich provider), - then formats as [TYPE: name | Summary: ...] to preserve file metadata for LLM consumption. - - Args: - content_items: List of content item dicts [{type: "text", text: "..."}], - or a plain string (legacy fallback). - Non-text items may contain parsed_summary/parsed_content fields - embedded by the enrich provider. - - Returns: - str: Extracted text content, space-joined across items - """ - if isinstance(content_items, str): - return content_items - if not isinstance(content_items, list): - return "" - texts = [] - for item in content_items: - if not isinstance(item, dict): - continue - if item.get("type") == "text": - text = item.get("text") or item.get("content", "") - if text: - texts.append(text) - else: - item_type = (item.get("type") or "file").upper() - name = item.get("name", "") - parsed_summary = item.get("parsed_summary") - if name and parsed_summary: - texts.append(f"[{item_type}: {name} | Summary: {parsed_summary}]") - elif name: - texts.append(f"[{item_type}: {name}]") - elif parsed_summary: - texts.append(f"[{item_type} | Summary: {parsed_summary}]") - else: - texts.append(f"[{item_type}]") - return " ".join(texts) if texts else "" - - -def is_intermediate_agent_step(msg: Dict[str, Any]) -> bool: - """Check if a message is an intermediate agent step (tool call or tool response). - - Intermediate steps are: - - role="tool": Tool execution results - - role="assistant" WITH tool_calls: Intermediate tool invocations - """ - role = msg.get("role", "") - if role == "tool": - return True - if role == "assistant" and msg.get("tool_calls"): - return True - return False - - -@dataclass -class MemCell: - # TODO: Name conflict - should add BO suffix (such as MemCellBO) to distinguish between business objects and document objects - """ - Boundary detection result following the specified schema. - - This class represents the result of boundary detection analysis - and contains all the required fields for memory storage. - """ - - # Required fields (must come before fields with default values) - user_id_list: List[str] - # Each item is { "message": {API message with content[]} } - # Access message via item["message"], extract text via get_text_from_content_items(msg["content"]) - original_data: List[Dict[str, Any]] - timestamp: datetime - - # Optional fields - event_id: Optional[str] = None # Generated by database when saving - group_id: Optional[str] = None - # NOTE: participants and sender_ids currently hold the same values (both are sender_id). - # participants is not yet implemented as display names; it is populated with sender_ids - # as a placeholder. Once display-name resolution is available, participants will carry - # human-readable names while sender_ids will remain the raw identifiers. - participants: Optional[List[str]] = None - sender_ids: Optional[List[str]] = None - type: Optional[RawDataType] = None - - # Cached filtered data (excluded from __init__) - _conversation_data_cache: Optional[List[Dict[str, Any]]] = field( - default=None, init=False, repr=False, compare=False - ) - - def __post_init__(self): - """Validate the result after initialization.""" - if not self.original_data: - raise ValueError("original_data is required") - - @property - def conversation_data(self) -> List[Dict[str, Any]]: - """Return conversation data with tool calls/responses filtered out for agent conversations. - - For AGENTCONVERSATION type, intermediate agent steps (tool calls and tool responses) - are excluded. For other types, returns original_data as-is. - Use original_data directly when full trajectory is needed (e.g. AgentCase extraction). - """ - if self._conversation_data_cache is not None: - return self._conversation_data_cache - - if self.type != RawDataType.AGENTCONVERSATION: - self._conversation_data_cache = self.original_data - else: - self._conversation_data_cache = [ - item - for item in self.original_data - if not is_intermediate_agent_step( - item.get("message", item) if isinstance(item, dict) else item - ) - ] - return self._conversation_data_cache - - def __repr__(self) -> str: - return f"MemCell(event_id={self.event_id!r}, original_data={self.original_data!r}, timestamp={self.timestamp!r})" - - def to_dict(self) -> Dict[str, Any]: - return { - "event_id": self.event_id if self.event_id else None, - "user_id_list": self.user_id_list, - "original_data": self.original_data, - "timestamp": to_iso_format(self.timestamp), - "group_id": self.group_id, - "participants": self.participants, - "sender_ids": self.sender_ids, - "type": str(self.type.value) if self.type else None, - } - - -@dataclass -class BaseMemory: - """ - Base class for all memory types. - Contains common fields shared by all memory types. - """ - - memory_type: Union[MemoryType, str] - user_id: str - timestamp: datetime - - ori_event_id_list: Optional[List[str]] = None - group_id: Optional[str] = None - participants: Optional[List[str]] = None - sender_ids: Optional[List[str]] = None - type: Optional[RawDataType] = None - keywords: Optional[List[str]] = None - linked_entities: Optional[List[str]] = None - user_name: Optional[str] = None - extend: Optional[Dict[str, Any]] = None - - # vector and model - vector_model: Optional[str] = None - vector: Optional[List[float]] = None - - # ID field for retrieval - id: Optional[str] = None - - # Retrieval-related fields - score: Optional[float] = None - original_data: Optional[List[Dict[str, Any]]] = None - - def _format_timestamp(self) -> Optional[str]: - """Format timestamp to ISO string""" - if not self.timestamp: - return None - if isinstance(self.timestamp, str): - return self.timestamp if self.timestamp else None - try: - return to_iso_format(self.timestamp) - except Exception: - return str(self.timestamp) if self.timestamp else None - - def to_dict(self) -> Dict[str, Any]: - return { - "id": self.id, - "memory_type": self.memory_type.value if self.memory_type else None, - "user_id": self.user_id, - "user_name": self.user_name, - "timestamp": self._format_timestamp(), - "group_id": self.group_id, - "participants": self.participants, - "sender_ids": self.sender_ids, - "type": self.type.value if self.type else None, - "keywords": self.keywords, - "linked_entities": self.linked_entities, - "score": self.score, - "original_data": self.original_data, - "extend": self.extend, - } - - -@dataclass -class EpisodeMemory(BaseMemory): - """Episode memory - narrative memory of events.""" - - id: Optional[str] = None - subject: Optional[str] = None - summary: Optional[str] = None - episode: Optional[str] = None - parent_type: Optional[str] = None - parent_id: Optional[str] = None - - def to_dict(self) -> Dict[str, Any]: - d = super().to_dict() - d["id"] = self.id - d["subject"] = self.subject - d["summary"] = self.summary - d["episode"] = self.episode - d["parent_type"] = self.parent_type - d["parent_id"] = self.parent_id - return d - - -@dataclass -class AtomicFact(BaseMemory): - """Atomic fact - atomic facts extracted from MemCell/conversation.""" - - time: Optional[str] = None - atomic_fact: Optional[Union[str, List[str]]] = None - fact_embeddings: Optional[List[List[float]]] = None - parent_type: Optional[str] = None - parent_id: Optional[str] = None - - def to_dict(self) -> Dict[str, Any]: - d = super().to_dict() - if self.time: - d["time"] = self.time - if self.atomic_fact: - d["atomic_fact"] = self.atomic_fact - if self.fact_embeddings: - d["fact_embeddings"] = self.fact_embeddings - d["parent_type"] = self.parent_type - if self.parent_id: - d["parent_id"] = self.parent_id - return d - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> "AtomicFact": - """Create from dictionary.""" - return cls( - memory_type=MemoryType.from_string(data.get("memory_type")), - user_id=data.get("user_id", ""), - timestamp=data.get("timestamp"), - time=data.get("time", ""), - atomic_fact=data.get("atomic_fact", []), - fact_embeddings=data.get("fact_embeddings"), - parent_type=data.get("parent_type"), - parent_id=data.get("parent_id"), - ) - - -@dataclass -class Foresight(BaseMemory): - """Foresight prediction memory extracted from MemCell/conversation.""" - - foresight: Optional[str] = None - evidence: Optional[str] = None - start_time: Optional[str] = None - end_time: Optional[str] = None - duration_days: Optional[int] = None - parent_type: Optional[str] = None - parent_id: Optional[str] = None - - def to_dict(self) -> Dict[str, Any]: - d = super().to_dict() - d["foresight"] = self.foresight - d["evidence"] = self.evidence - d["start_time"] = self.start_time - d["end_time"] = self.end_time - d["duration_days"] = self.duration_days - d["parent_type"] = self.parent_type - d["parent_id"] = self.parent_id - return d - - -@dataclass -class AgentCase(BaseMemory): - """Agent experience extracted from an agent conversation MemCell. - - Each MemCell produces at most one experience. Multiple conversation turns - that solve the same problem are synthesized into a single experience record. - - Used both as extraction output (with vector/vector_model) and as retrieval - result (with score/keywords from BaseMemory). - - Fields: - - task_intent: Retrieval key - the task rewritten as a standalone statement - - approach: Natural-language numbered steps with inline decisions, results, and lessons - - quality_score: How well the agent completed this task (0.0-1.0) - """ - - task_intent: Optional[str] = None - approach: Optional[str] = None - quality_score: Optional[float] = None # 0.0-1.0, task completion quality - key_insight: Optional[str] = None # pivotal strategy shift or decision - parent_type: Optional[str] = None - parent_id: Optional[str] = None - - def to_dict(self) -> Dict[str, Any]: - d = super().to_dict() - d.update( - { - "task_intent": self.task_intent, - "approach": self.approach, - "quality_score": self.quality_score, - "key_insight": self.key_insight, - "parent_type": self.parent_type, - "parent_id": self.parent_id, - } - ) - return d - - -@dataclass -class AgentSkill(BaseMemory): - """Reusable skill derived from clustered AgentCases. - - Skills belong to a specific cluster and user (agent owner). - user_id and timestamp are Optional because they default from the extraction context. - """ - - # Override BaseMemory required fields - user_id: Optional[str] = None - timestamp: Optional[datetime] = None - - name: Optional[str] = None - description: Optional[str] = None - content: Optional[str] = None - confidence: float = 0.0 - cluster_id: Optional[str] = None - maturity_score: float = 0.6 - - def to_dict(self) -> Dict[str, Any]: - d = super().to_dict() - d.update( - { - "name": self.name, - "description": self.description, - "content": self.content, - "confidence": self.confidence, - "cluster_id": self.cluster_id, - "maturity_score": self.maturity_score, - } - ) - return d - - -@dataclass -class ProfileMemory(BaseMemory): - """Profile Memory - Explicit information + Implicit traits. - - explicit_info item: {"category": str, "description": str, "evidence": str, "sources": [str]} - implicit_traits item: {"trait": str, "description": str, "basis": str, "evidence": str, "sources": [str]} - """ - - explicit_info: List[Dict[str, Any]] = field(default_factory=list) - implicit_traits: List[Dict[str, Any]] = field(default_factory=list) - last_updated: Optional[datetime] = None - processed_episode_ids: List[str] = field(default_factory=list) - - def __post_init__(self) -> None: - self.memory_type = MemoryType.PROFILE - if self.last_updated is None: - self.last_updated = datetime.now().astimezone() - - def to_dict(self) -> Dict[str, Any]: - return { - "id": self.id, - "memory_type": self.memory_type.value if self.memory_type else None, - "user_id": self.user_id, - "user_name": self.user_name, - "timestamp": self.timestamp.isoformat() if self.timestamp else None, - "group_id": self.group_id, - "explicit_info": list(self.explicit_info), - "implicit_traits": list(self.implicit_traits), - "last_updated": ( - self.last_updated.isoformat() if self.last_updated else None - ), - "processed_episode_ids": self.processed_episode_ids, - } - - @classmethod - def from_dict( - cls, data: Dict[str, Any], user_id: str = "", group_id: str = "" - ) -> "ProfileMemory": - last_updated = data.get("last_updated") - if isinstance(last_updated, str): - last_updated = datetime.fromisoformat(last_updated) - - return cls( - memory_type=MemoryType.PROFILE, - user_id=user_id or data.get("user_id", ""), - group_id=group_id or data.get("group_id", ""), - timestamp=datetime.now().astimezone(), - ori_event_id_list=data.get("ori_event_id_list", []), - explicit_info=data.get("explicit_info", []), - implicit_traits=data.get("implicit_traits", []), - last_updated=last_updated, - processed_episode_ids=data.get("processed_episode_ids", []), - ) - - def total_items(self) -> int: - return len(self.explicit_info) + len(self.implicit_traits) - - def get_all_source_ids(self) -> set: - ids = set() - for item in self.explicit_info + self.implicit_traits: - for s in item.get("sources", []): - s = str(s) - if "|" in s: - s = s.rsplit("|", 1)[-1].strip() - if s: - ids.add(s) - return ids - - def to_readable_document(self) -> str: - lines = [ - "=" * 50, - "User Profile Document", - f"Last Updated: {self.last_updated.strftime('%Y-%m-%d %H:%M') if self.last_updated else 'N/A'}", - f"Total {self.total_items()} items (Explicit: {len(self.explicit_info)}, Implicit: {len(self.implicit_traits)})", - "=" * 50, - ] - - if self.explicit_info: - lines.append("\n[Explicit Info]") - categories: Dict[str, list] = {} - for info in self.explicit_info: - categories.setdefault(info.get("category", ""), []).append(info) - for cat, infos in categories.items(): - lines.append(f" [{cat}]") - for info in infos: - desc = info.get("description", "") - evidence = info.get("evidence", "") - if evidence: - lines.append(f" - {desc} (evidence: {evidence})") - else: - lines.append(f" - {desc}") - - if self.implicit_traits: - lines.append("\n[Implicit Traits]") - for i, trait in enumerate(self.implicit_traits, 1): - lines.append(f" {i}. {trait.get('trait', '')}") - lines.append(f" {trait.get('description', '')}") - lines.append(f" - basis: {trait.get('basis', '')}") - evidence = trait.get("evidence", "") - if evidence: - lines.append(f" - evidence: {evidence}") - - return "\n".join(lines) - - def to_readable_profile(self) -> str: - lines = [] - - if self.explicit_info: - lines.append("[Explicit Info]") - categories: Dict[str, list] = {} - for info in self.explicit_info: - categories.setdefault(info.get("category", ""), []).append(info) - for cat, infos in categories.items(): - lines.append(f" {cat}:") - for info in infos: - lines.append(f" - {info.get('description', '')}") - - if self.implicit_traits: - if lines: - lines.append("") - lines.append("[Implicit Traits]") - for trait in self.implicit_traits: - lines.append(f" - {trait.get('trait', '')}: {trait.get('description', '')}") - - return "\n".join(lines) if lines else "No profile data yet." - - -# Union type for search/retrieve API response -RetrieveMemoryModel = Union[EpisodeMemory, AtomicFact, Foresight, AgentSkill, AgentCase] diff --git a/methods/EverCore/src/api_specs/request_converter.py b/methods/EverCore/src/api_specs/request_converter.py deleted file mode 100644 index 1259daa9..00000000 --- a/methods/EverCore/src/api_specs/request_converter.py +++ /dev/null @@ -1,690 +0,0 @@ -""" -Request converter module - -This module contains various functions to convert external request formats to internal Request objects. -""" - -from __future__ import annotations - -import hashlib -from typing import Any, Dict, Optional -from datetime import datetime - -from api_specs.memory_models import MemoryType, RetrieveMethod -from api_specs.dtos import RetrieveMemRequest, MemorizeRequest, RawData -from api_specs.memory_types import RawDataType, ScenarioType -from api_specs.id_generator import ( - DEFAULT_SESSION_ID, - generate_single_user_group_id, - generate_message_id, - generate_assistant_sender_id, - validate_input_id, -) -from core.oxm.constants import MAGIC_ALL -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -def convert_dict_to_retrieve_mem_request( - data: Dict[str, Any], query: Optional[str] = None -) -> RetrieveMemRequest: - """ - Convert dictionary to RetrieveMemRequest object - - Args: - data: Dictionary containing RetrieveMemRequest fields - query: Query text (optional) - - Returns: - RetrieveMemRequest object - - Raises: - ValueError: When required fields are missing or have incorrect types - """ - try: - # Validate required fields: user_id or group_id at least one is required - # if not data.get("user_id") and not data.get("group_id"): - # raise ValueError("user_id or group_id at least one is required") - - # Handle retrieve_method, use default keyword if not provided - - retrieve_method_str = data.get("retrieve_method", RetrieveMethod.KEYWORD.value) - logger.debug(f"[DEBUG] retrieve_method_str from data: {retrieve_method_str!r}") - - # Convert string to RetrieveMethod enum - try: - retrieve_method = RetrieveMethod(retrieve_method_str) - logger.debug(f"[DEBUG] converted to: {retrieve_method}") - except ValueError: - raise ValueError( - f"Invalid retrieve_method: {retrieve_method_str}. " - f"Supported methods: {[m.value for m in RetrieveMethod]}" - ) - - # Convert top_k to integer type (all obtained from query_params are strings) - # Default to -1 means return all results that meet the threshold - top_k = data.get("top_k", -1) - if isinstance(top_k, str): - top_k = int(top_k) - - # Convert include_metadata to boolean type - include_metadata = data.get("include_metadata", True) - if isinstance(include_metadata, str): - include_metadata = include_metadata.lower() in ("true", "1", "yes") - - # Convert radius to float type (if exists) - radius = data.get("radius", None) - if radius is not None and isinstance(radius, str): - radius = float(radius) - - # Convert memory_types string list to MemoryType enum list - raw_memory_types = data.get("memory_types", []) - # Handle comma-separated string (from query_params) - if isinstance(raw_memory_types, str): - raw_memory_types = [ - mt.strip() for mt in raw_memory_types.split(",") if mt.strip() - ] - memory_types = [] - for mt in raw_memory_types: - if isinstance(mt, str): - try: - memory_types.append(MemoryType(mt)) - except ValueError: - logger.error(f"Invalid memory_type: {mt}, skipping") - elif isinstance(mt, MemoryType): - memory_types.append(mt) - - # Default: profile + episodic_memory if not specified - if not memory_types: - memory_types = [MemoryType.PROFILE, MemoryType.EPISODIC_MEMORY] - - # Handle group_ids: support both string and array for backward compatibility - # Priority: group_ids (new) > group_id (old, for backward compatibility) - group_ids_raw = data.get("group_ids", None) - if group_ids_raw is None: - # Try legacy group_id parameter for backward compatibility - group_id_legacy = data.get("group_id", None) - if isinstance(group_id_legacy, str): - group_ids = [group_id_legacy] # Convert string to array - elif isinstance(group_id_legacy, list): - group_ids = group_id_legacy - else: - group_ids = None - elif isinstance(group_ids_raw, str): - # Support comma-separated string to array (Query Param scenario) - group_ids = [g.strip() for g in group_ids_raw.split(",") if g.strip()] - # If parsed result is empty array, set to None - if not group_ids: - group_ids = None - elif isinstance(group_ids_raw, list): - group_ids = group_ids_raw if group_ids_raw else None - else: - group_ids = None - - return RetrieveMemRequest( - retrieve_method=retrieve_method, - user_id=data.get( - "user_id", MAGIC_ALL - ), # User ID, use MAGIC_ALL to skip user filtering - group_ids=group_ids, # Group IDs array (List[str] or None) - query=query or data.get("query", None), - memory_types=memory_types, - top_k=top_k, - include_metadata=include_metadata, - start_time=data.get("start_time", None), - end_time=data.get("end_time", None), - radius=radius, # COSINE similarity threshold - ) - except Exception as e: - raise ValueError(f"RetrieveMemRequest conversion failed: {e}") - - -# ========================================= - - -def _normalize_content_items(content_items: list) -> list: - """Normalize ContentItem dicts: rename 'content' to 'text' for type='text' items. - - Accepts both legacy {type: "text", content: "..."} and canonical - {type: "text", text: "..."} formats. Output always uses 'text'. - """ - normalized = [] - for item in content_items: - if not isinstance(item, dict): - normalized.append(item) - continue - if item.get("type") == "text" and "content" in item and "text" not in item: - content_value = item["content"] - item = {k: v for k, v in item.items() if k != "content"} - item["text"] = content_value - normalized.append(item) - return normalized - - -def build_raw_data_from_message( - message_id: str, - sender_id: str, - content_items: list, - timestamp: datetime, - sender_name: Optional[str] = None, - role: Optional[str] = None, - tool_calls: Optional[list] = None, - tool_call_id: Optional[str] = None, -) -> RawData: - """ - Build RawData object from message fields. - - This is the canonical function for creating RawData from messages. - The content dict mirrors the v1 API message format, with content as - a list of content items [{type: "text", text: "..."}]. - - Args: - message_id: Message ID (required) - sender_id: Sender user ID (required) - content_items: Content items list [{type, text}] in v1 format (required) - timestamp: Message timestamp as datetime object (required) - sender_name: Sender display name (defaults to sender_id if not provided) - role: Message sender role, "user", "assistant", or "tool" (required) - tool_calls: Tool calls from assistant (OpenAI format, optional) - tool_call_id: Tool call ID this message responds to (role=tool, optional) - - Returns: - RawData: Fully constructed RawData object - """ - if sender_name is None: - sender_name = sender_id - - raw_content = { - "message_id": message_id, - "sender_id": sender_id, - "sender_name": sender_name, - "role": role, - "content": content_items, - "timestamp": timestamp, - } - - # Add OpenAI-format agent fields if present - if tool_calls: - raw_content["tool_calls"] = tool_calls - if tool_call_id: - raw_content["tool_call_id"] = tool_call_id - - metadata = {"original_id": message_id} - - return RawData(content=raw_content, data_id=message_id, metadata=metadata) - - -def _unix_ms_to_datetime(unix_ms: int) -> datetime: - """Convert unix milliseconds timestamp to timezone-aware datetime. - - Uses project timezone from TZ env var (default: UTC). - - Args: - unix_ms: Unix timestamp in milliseconds - - Returns: - datetime: Timezone-aware datetime - """ - from common_utils.datetime_utils import from_timestamp - - return from_timestamp(unix_ms) - - -def convert_personal_add_to_memorize_request( - request_data: Dict[str, Any] -) -> MemorizeRequest: - """ - Convert POST /api/v1/memories (personal add) request to MemorizeRequest. - - Personal add: user_id is the owner. group_id = hash(user_id). - session_id is propagated through for conversation isolation. - - Args: - request_data: Personal add request body with fields: - - user_id (required): Owner user ID - - session_id (optional): Session identifier - - messages (required): List of message objects - - Returns: - MemorizeRequest - """ - user_id = request_data.get("user_id") - session_id = request_data.get("session_id", DEFAULT_SESSION_ID) - messages = request_data.get("messages", []) - - if not user_id: - raise ValueError("Missing required field: user_id") - if "session_id" in request_data and request_data["session_id"] is not None: - validate_input_id("session_id", session_id) - if not messages: - raise ValueError("Missing required field: messages") - - # Personal scene: group_id = hash(user_id) - group_id = generate_single_user_group_id(user_id) - - raw_data_list = [] - latest_timestamp = None - - for msg in messages: - # Validate content field: accept plain string or array (v1 format) - content_items = msg.get("content", []) - if isinstance(content_items, str): - if not content_items: - raise ValueError("Missing required field: messages[].content") - content_items = [{"type": "text", "text": content_items}] - elif not content_items or not isinstance(content_items, list): - raise ValueError( - "Missing required field: messages[].content (must be a non-empty string or array)" - ) - content_items = _normalize_content_items(content_items) - - # Parse timestamp from unix ms - created_at_ms = msg.get("timestamp") - if not created_at_ms: - raise ValueError("Missing required field: messages[].timestamp") - timestamp = _unix_ms_to_datetime(created_at_ms) - if latest_timestamp is None or timestamp > latest_timestamp: - latest_timestamp = timestamp - - user_message_id = msg.get("message_id") - if user_message_id: - validate_input_id("message_id", user_message_id) - message_id = user_message_id or generate_message_id(user_id, created_at_ms) - - role = msg.get("role") - if not role: - raise ValueError("Missing required field: messages[].role") - if role not in ("user", "assistant"): - raise ValueError( - f"Invalid value for messages[].role: '{role}'. Must be 'user' or 'assistant'" - ) - sender_id = msg.get("sender_id") - if sender_id: - validate_input_id("sender_id", sender_id) - - # Personal scene: role determines sender_id - if role == "user": - if sender_id and sender_id != user_id: - raise ValueError( - f"sender_id mismatch: role=user requires sender_id={user_id}, got {sender_id}" - ) - sender_id = user_id - else: - # role is assistant - if sender_id and sender_id == user_id: - raise ValueError( - f"sender_id conflict: role={role} cannot use user_id as sender_id" - ) - if not sender_id: - sender_id = generate_assistant_sender_id(user_id) - - sender_name = msg.get("sender_name", sender_id) - - raw_data = build_raw_data_from_message( - message_id=message_id, - sender_id=sender_id, - content_items=content_items, - timestamp=timestamp, - sender_name=sender_name, - role=role, - ) - raw_data_list.append(raw_data) - - return MemorizeRequest( - history_raw_data_list=[], - new_raw_data_list=raw_data_list, - raw_data_type=RawDataType.CONVERSATION, - group_id=group_id, - current_time=latest_timestamp, - flush=False, - session_id=session_id, - scene=ScenarioType.SOLO.value, - ) - - -def convert_group_add_to_memorize_request( - request_data: Dict[str, Any] -) -> MemorizeRequest: - """ - Convert POST /api/v1/memories/group (group add) request to MemorizeRequest. - - Group add: group_id is provided directly. sender_id = user_id internally. - - Args: - request_data: Group add request body with fields: - - group_id (required): Group identifier - - group_meta (optional): Group metadata (name, description) - - messages (required): List of message objects - - Returns: - MemorizeRequest - """ - group_id = request_data.get("group_id") - messages = request_data.get("messages", []) - - if not group_id: - raise ValueError("Missing required field: group_id") - validate_input_id("group_id", group_id) - if not messages: - raise ValueError("Missing required field: messages") - - raw_data_list = [] - latest_timestamp = None - - for msg in messages: - # Validate content field: accept plain string or array (v1 format) - content_items = msg.get("content", []) - if isinstance(content_items, str): - if not content_items: - raise ValueError("Missing required field: messages[].content") - content_items = [{"type": "text", "text": content_items}] - elif not content_items or not isinstance(content_items, list): - raise ValueError( - "Missing required field: messages[].content (must be a non-empty string or array)" - ) - content_items = _normalize_content_items(content_items) - - created_at_ms = msg.get("timestamp") - if not created_at_ms: - raise ValueError("Missing required field: messages[].timestamp") - timestamp = _unix_ms_to_datetime(created_at_ms) - if latest_timestamp is None or timestamp > latest_timestamp: - latest_timestamp = timestamp - - user_message_id = msg.get("message_id") - if user_message_id: - validate_input_id("message_id", user_message_id) - message_id = user_message_id or generate_message_id(group_id, created_at_ms) - - sender_id = msg.get("sender_id") - if not sender_id: - raise ValueError( - "Missing required field: messages[].sender_id (required for group)" - ) - validate_input_id("sender_id", sender_id) - sender_name = msg.get("sender_name", sender_id) - role = msg.get("role") - if not role: - raise ValueError("Missing required field: messages[].role") - if role not in ("user", "assistant"): - raise ValueError( - f"Invalid value for messages[].role: '{role}'. Must be 'user' or 'assistant'" - ) - - raw_data = build_raw_data_from_message( - message_id=message_id, - sender_id=sender_id, - content_items=content_items, - timestamp=timestamp, - sender_name=sender_name, - role=role, - ) - raw_data_list.append(raw_data) - - # Group scene: no session isolation - return MemorizeRequest( - history_raw_data_list=[], - new_raw_data_list=raw_data_list, - raw_data_type=RawDataType.CONVERSATION, - group_id=group_id, - current_time=latest_timestamp, - flush=False, - session_id=DEFAULT_SESSION_ID, - scene=ScenarioType.TEAM.value, - ) - - -def convert_personal_flush_to_memorize_request( - request_data: Dict[str, Any] -) -> MemorizeRequest: - """ - Convert POST /api/v1/memories/flush (personal flush) to MemorizeRequest. - - Flush sends no messages, just triggers boundary detection on accumulated data. - - Args: - request_data: Personal flush request body with fields: - - user_id (required): Owner user ID - - session_id (optional): Target session - - Returns: - MemorizeRequest with empty new_raw_data_list and flush=True - """ - user_id = request_data.get("user_id") - session_id = request_data.get("session_id", DEFAULT_SESSION_ID) - - if not user_id: - raise ValueError("Missing required field: user_id") - if "session_id" in request_data and request_data["session_id"] is not None: - validate_input_id("session_id", session_id) - - group_id = generate_single_user_group_id(user_id) - - from common_utils.datetime_utils import get_now_with_timezone - - return MemorizeRequest( - history_raw_data_list=[], - new_raw_data_list=[], - raw_data_type=RawDataType.CONVERSATION, - group_id=group_id, - current_time=get_now_with_timezone(), - flush=True, - session_id=session_id, - scene=ScenarioType.SOLO.value, - ) - - -def convert_agent_flush_to_memorize_request( - request_data: Dict[str, Any] -) -> MemorizeRequest: - """ - Convert POST /api/v1/memories/agent/flush (agent flush) to MemorizeRequest. - - Same as personal flush but with raw_data_type = AGENTCONVERSATION, - so flush uses AgentMemCellExtractor for boundary detection. - - Args: - request_data: Agent flush request body with fields: - - user_id (required): Owner user ID - - session_id (optional): Target session - - Returns: - MemorizeRequest with flush=True and raw_data_type=AGENTCONVERSATION - """ - user_id = request_data.get("user_id") - session_id = request_data.get("session_id", DEFAULT_SESSION_ID) - - if not user_id: - raise ValueError("Missing required field: user_id") - if "session_id" in request_data and request_data["session_id"] is not None: - validate_input_id("session_id", session_id) - - group_id = generate_single_user_group_id(user_id) - - from common_utils.datetime_utils import get_now_with_timezone - - return MemorizeRequest( - history_raw_data_list=[], - new_raw_data_list=[], - raw_data_type=RawDataType.AGENTCONVERSATION, - group_id=group_id, - current_time=get_now_with_timezone(), - flush=True, - session_id=session_id, - scene=ScenarioType.SOLO.value, - ) - - -def convert_group_flush_to_memorize_request( - request_data: Dict[str, Any] -) -> MemorizeRequest: - """ - Convert POST /api/v1/memories/group/flush (group flush) to MemorizeRequest. - - Flush sends no messages, just triggers boundary detection on accumulated data. - - Args: - request_data: Group flush request body with fields: - - group_id (required): Target group - - Returns: - MemorizeRequest with empty new_raw_data_list and flush=True - """ - group_id = request_data.get("group_id") - - if not group_id: - raise ValueError("Missing required field: group_id") - validate_input_id("group_id", group_id) - - from common_utils.datetime_utils import get_now_with_timezone - - return MemorizeRequest( - history_raw_data_list=[], - new_raw_data_list=[], - raw_data_type=RawDataType.CONVERSATION, - group_id=group_id, - current_time=get_now_with_timezone(), - flush=True, - session_id=DEFAULT_SESSION_ID, - scene=ScenarioType.TEAM.value, - ) - - -def convert_agent_add_to_memorize_request( - request_data: Dict[str, Any] -) -> MemorizeRequest: - """ - Convert POST /api/v1/memories/agent (agent add) request to MemorizeRequest. - - Mirrors personal add logic with agent-specific extensions: - - role supports "user", "assistant", "tool" - - tool_calls / tool_call_id stored in RawData content - - raw_data_type = AGENTCONVERSATION - - Args: - request_data: Agent add request body with fields: - - user_id (required): Owner user ID - - session_id (optional): Session identifier - - messages (required): List of agent trajectory messages - - Returns: - MemorizeRequest - """ - user_id = request_data.get("user_id") - session_id = request_data.get("session_id", DEFAULT_SESSION_ID) - messages = request_data.get("messages", []) - - if not user_id: - raise ValueError("Missing required field: user_id") - if "session_id" in request_data and request_data["session_id"] is not None: - validate_input_id("session_id", session_id) - if not messages: - raise ValueError("Missing required field: messages") - - # Same as personal: group_id = hash(user_id) - group_id = generate_single_user_group_id(user_id) - - raw_data_list = [] - latest_timestamp = None - - for msg in messages: - # Parse timestamp - created_at_ms = msg.get("timestamp") - if not created_at_ms: - raise ValueError("Missing required field: messages[].timestamp") - timestamp = _unix_ms_to_datetime(created_at_ms) - if latest_timestamp is None or timestamp > latest_timestamp: - latest_timestamp = timestamp - - # Validate role - role = msg.get("role") - if not role: - raise ValueError("Missing required field: messages[].role") - if role not in ("user", "assistant", "tool"): - raise ValueError( - f"Invalid value for messages[].role: '{role}'. " - "Must be 'user', 'assistant', or 'tool'" - ) - - # Validate content - accept plain string or array (v1 format) - content_items = msg.get("content", []) - tool_calls = msg.get("tool_calls") - tool_call_id = msg.get("tool_call_id") - - if role == "tool" and not tool_call_id: - raise ValueError( - "Missing required field: messages[].tool_call_id (required when role='tool')" - ) - - # assistant messages with tool_calls may have empty/null content - if isinstance(content_items, str): - if not content_items: - if role == "assistant" and tool_calls: - content_items = [{"type": "text", "text": ""}] - else: - raise ValueError("Missing required field: messages[].content") - else: - content_items = [{"type": "text", "text": content_items}] - elif not content_items or not isinstance(content_items, list): - if role == "assistant" and tool_calls: - content_items = [{"type": "text", "text": ""}] - else: - raise ValueError( - "Missing required field: messages[].content (must be a non-empty string or array)" - ) - content_items = _normalize_content_items(content_items) - - # Message ID - user_message_id = msg.get("message_id") - if user_message_id: - validate_input_id("message_id", user_message_id) - message_id = user_message_id or generate_message_id(user_id, created_at_ms) - - # sender_id logic - sender_id = msg.get("sender_id") - if role == "user": - if sender_id and sender_id != user_id: - raise ValueError( - f"sender_id mismatch: role=user requires sender_id={user_id}, got {sender_id}" - ) - sender_id = user_id - elif role == "assistant": - if sender_id and sender_id == user_id: - raise ValueError( - f"sender_id conflict: role=assistant cannot use user_id as sender_id" - ) - if not sender_id: - hash_val = hashlib.md5(f"{user_id}_assistant".encode()).hexdigest()[:12] - sender_id = f"{hash_val}_assistant" - elif role == "tool": - if not sender_id: - hash_val = hashlib.md5( - f"{user_id}_tool_{tool_call_id}".encode() - ).hexdigest()[:12] - sender_id = f"{hash_val}_tool" - - sender_name = msg.get("sender_name", sender_id) - - raw_data = build_raw_data_from_message( - message_id=message_id, - sender_id=sender_id, - content_items=content_items, - timestamp=timestamp, - sender_name=sender_name, - role=role, - tool_calls=tool_calls, - tool_call_id=tool_call_id, - ) - raw_data_list.append(raw_data) - - return MemorizeRequest( - history_raw_data_list=[], - new_raw_data_list=raw_data_list, - raw_data_type=RawDataType.AGENTCONVERSATION, - group_id=group_id, - current_time=latest_timestamp, - flush=False, - session_id=session_id, - scene=ScenarioType.SOLO.value, - ) diff --git a/methods/EverCore/src/app.py b/methods/EverCore/src/app.py deleted file mode 100644 index 986ccc8d..00000000 --- a/methods/EverCore/src/app.py +++ /dev/null @@ -1,135 +0,0 @@ -""" -Application module - -Contains business-specific logic such as controller registration, graph structure creation, capability loading, etc. -""" - -from fastapi import FastAPI -from core.di.utils import get_beans_by_type, get_bean_by_type -from core.capability.app_capability import ApplicationCapability -from core.observation.logger import get_logger -from core.interface.controller.base_controller import BaseController -from core.middleware.user_context_middleware import UserContextMiddleware -from core.middleware.app_logic_middleware import AppLogicMiddleware -from core.middleware.prometheus_middleware import PrometheusMiddleware -from fastapi.middleware import Middleware - -from base_app import create_base_app -from core.lifespan.lifespan_factory import LifespanFactory - - -# Recommended usage: obtain logger once at the module top level, then use directly (high performance) -logger = get_logger(__name__) - - -def register_controllers(fastapi_app: FastAPI): - """ - Register all controllers to the FastAPI application. - - Args: - fastapi_app (FastAPI): FastAPI application instance - """ - all_controllers = get_beans_by_type(BaseController) - for controller in all_controllers: - controller.register_to_app(fastapi_app) - logger.info( - "Controller registration completed, %d controllers registered", - len(all_controllers), - ) - - -def create_graphs(checkpointer): - """ - Create all business graph structures. - - Args: - checkpointer: Checkpointer - - Returns: - dict: Dictionary containing all graph structures - """ - logger.info("Creating business graph structures...") - - graphs = {} - - logger.info("Business graph structures created, %d graphs created", len(graphs)) - return graphs - - -def register_capabilities(fastapi_app: FastAPI): - """ - Register all application capabilities. - - Args: - fastapi_app (FastAPI): FastAPI application instance - """ - capability_beans = get_beans_by_type(ApplicationCapability) - for capability in capability_beans: - capability.enable(fastapi_app) - logger.info( - "Application capabilities registered, %d capabilities registered", - len(capability_beans), - ) - - -def register_graphs(fastapi_app: FastAPI): - """ - Register all graph structures to the FastAPI application. - - Args: - fastapi_app (FastAPI): FastAPI application instance - """ - checkpointer = fastapi_app.state.checkpointer - graphs = create_graphs(checkpointer) - fastapi_app.state.graphs = graphs - logger.info("Graph structures registered, %d graphs registered", len(graphs)) - - -# Note: create_business_lifespan is now imported from core.lifespan -# The original registration functions are kept here for use by new business components - - -def create_business_app( - cors_origins=None, - cors_allow_credentials=True, - cors_allow_methods=None, - cors_allow_headers=None, -): - """ - Create a complete application with business logic. - - Args: - cors_origins (list[str], optional): List of allowed CORS origins - cors_allow_credentials (bool): Whether to allow credentials - cors_allow_methods (list[str], optional): Allowed HTTP methods - cors_allow_headers (list[str], optional): Allowed HTTP headers - - Returns: - FastAPI: Configured FastAPI application instance - """ - # Use DI to get lifespan factory, automatically creating a lifespan with all providers - lifespan_factory = get_bean_by_type(LifespanFactory) - combined_lifespan = lifespan_factory.create_auto_lifespan() - - # Create base app with combined lifespan manager - fastapi_app = create_base_app( - cors_origins=cors_origins, - cors_allow_credentials=cors_allow_credentials, - cors_allow_methods=cors_allow_methods, - cors_allow_headers=cors_allow_headers, - lifespan_context=combined_lifespan, - ) - - # Add business-related middleware - fastapi_app.user_middleware.append(Middleware(AppLogicMiddleware)) - # Not directly interfacing with users - # fastapi_app.user_middleware.append(Middleware(UserContextMiddleware)) - - # Add Prometheus HTTP metrics middleware - fastapi_app.user_middleware.append(Middleware(PrometheusMiddleware)) - - return fastapi_app - - -# Create default business application instance -app = create_business_app() diff --git a/methods/EverCore/src/application_startup.py b/methods/EverCore/src/application_startup.py deleted file mode 100644 index 9835f9c8..00000000 --- a/methods/EverCore/src/application_startup.py +++ /dev/null @@ -1,59 +0,0 @@ -""" -Application startup module - -Responsible for various initialization operations when the application starts -""" - -# Import dependency injection related modules -from core.observation.logger import get_logger -from core.addons.addons_registry import ADDONS_REGISTRY -from core.addons.addonize.di_setup import ( - setup_dependency_injection, - print_registered_beans, -) - -# Recommended usage: get logger once at the top of the module, then use directly (high performance) -logger = get_logger(__name__) - - -def setup_all(load_entrypoints: bool = True): - """ - Set up all components - - Args: - load_entrypoints (bool): Whether to load addons from entry points. Default is True - - Returns: - ComponentScanner: Configured component scanner - """ - # 0. Load addons entry points (if enabled) - if load_entrypoints: - logger.info("🔌 Loading addons entry points...") - ADDONS_REGISTRY.load_entrypoints() - - # Get all addons - all_addons = ADDONS_REGISTRY.get_all() - logger.info("📦 Loaded %d addons in total", len(all_addons)) - - # 1. Set up dependency injection - scanner = setup_dependency_injection(all_addons) - - # 2. Set up asynchronous tasks - # setup_async_tasks(all_addons) - - return scanner - - -if __name__ == "__main__": - # Start dependency injection - setup_all() - - # Print registered Bean information - print_registered_beans() - - # Print registered tasks - from core.addons.addonize.asynctasks_setup import print_registered_tasks - - print_registered_tasks() - - logger.info("\n✨ Application startup completed!") diff --git a/methods/EverCore/src/base_app.py b/methods/EverCore/src/base_app.py deleted file mode 100644 index e183ba29..00000000 --- a/methods/EverCore/src/base_app.py +++ /dev/null @@ -1,214 +0,0 @@ -""" -Base application module - -Contains business-agnostic FastAPI base configurations such as CORS, middleware, lifecycle management, etc. -""" - -import os -from fastapi import FastAPI, HTTPException -from fastapi.exceptions import RequestValidationError -from fastapi.middleware.cors import CORSMiddleware - -from core.observation.logger import get_logger -from core.middleware.database_session_middleware import DatabaseSessionMiddleware -from core.middleware.global_exception_handler import global_exception_handler -from core.middleware.profile_middleware import ProfileMiddleware -from core.di.utils import get_bean_by_type -from core.component.database_connection_provider import DatabaseConnectionProvider - -from core.lifespan.lifespan_factory import LifespanFactory - -# Recommended usage: obtain logger once at the module top, then use directly (high performance) -logger = get_logger(__name__) - - -def create_base_app( - cors_origins=None, - cors_allow_credentials=True, - cors_allow_methods=None, - cors_allow_headers=None, - lifespan_context=None, -): - """ - Create a base FastAPI application - - Args: - cors_origins (list[str], optional): List of allowed CORS origins, default is ["*"] - cors_allow_credentials (bool): Whether to allow credentials, default is True - cors_allow_methods (list[str], optional): Allowed HTTP methods, default is ["*"] - cors_allow_headers (list[str], optional): Allowed HTTP headers, default is ["*"] - lifespan_context (callable, optional): Lifecycle context manager, default uses database lifecycle - - Returns: - FastAPI: Configured FastAPI application instance - """ - # Use the provided lifespan context or default database lifecycle - if lifespan_context is None: - lifespan_factory = get_bean_by_type(LifespanFactory) - lifespan_context = lifespan_factory.create_lifespan_with_names( - ["database_lifespan_provider"] - ) - - # Control docs display based on environment variable - # Only enable docs in development environment (ENV=dev) - env = os.environ.get('ENV', 'prod').upper() - enable_docs = env == 'DEV' - - # Create FastAPI application - app = FastAPI( - lifespan=lifespan_context, - docs_url="/docs" if enable_docs else None, - redoc_url="/redoc" if enable_docs else None, - openapi_url="/openapi.json" if enable_docs else None, - ) - - if enable_docs: - logger.info("FastAPI documentation enabled (ENV=%s)", env) - else: - logger.info("FastAPI documentation disabled (ENV=%s)", env) - - # Set default CORS values - if cors_origins is None: - cors_origins = ["*"] - if cors_allow_methods is None: - cors_allow_methods = ["*"] - if cors_allow_headers is None: - cors_allow_headers = ["*"] - - # Add HTTP exception handler, otherwise HTTPException won't be handled by global_exception_handler - app.add_exception_handler(HTTPException, global_exception_handler) - - # Convert FastAPI/Pydantic 422 validation errors to our ErrorApiResponse format - app.add_exception_handler(RequestValidationError, global_exception_handler) - - # Add global exception handler - # Acts as a fallback outside middleware - app.add_exception_handler(Exception, global_exception_handler) - - # Add CORS middleware - app.add_middleware( - CORSMiddleware, - allow_origins=cors_origins, - allow_credentials=cors_allow_credentials, - allow_methods=cors_allow_methods, - allow_headers=cors_allow_headers, - ) - - # Add basic middleware - # The order of middleware matters: the earlier added, the later executed - # app.add_middleware(DatabaseSessionMiddleware) - - # Add performance profiling middleware (add last, executes first) - app.add_middleware(ProfileMiddleware) - - # Mount lifespan management methods to app instance - _mount_lifespan_methods(app) - - return app - - -def _mount_lifespan_methods(app: FastAPI): - """ - Mount lifespan management methods to the FastAPI application instance - - After mounting, you can directly use: - - app.start_lifespan(): Start lifespan - - app.exit_lifespan(): Exit lifespan - - Args: - app (FastAPI): FastAPI application instance - """ - # Store reference to lifespan manager - app.lifespan_manager = None - - async def start_lifespan(): - """Start the application's lifespan context manager""" - if app.lifespan_manager is not None: - logger.warning("Lifespan already started, no need to start again") - return app.lifespan_manager - - # Get lifespan context manager - lifespan_context = app.router.lifespan_context - - if lifespan_context: - # Create context manager instance - lifespan_manager = lifespan_context(app) - - # Manually enter context (equivalent to starting) - await lifespan_manager.__aenter__() - - # Store manager reference - app.lifespan_manager = lifespan_manager - - logger.info("Application Lifespan startup completed") - return lifespan_manager - else: - logger.warning("This application has no lifespan configured") - return None - - async def exit_lifespan(): - """Exit the application's lifespan context manager""" - if app.lifespan_manager is None: - logger.warning("Lifespan not started or already exited") - return - - try: - # Manually exit context - await app.lifespan_manager.__aexit__(None, None, None) - logger.info("Application Lifespan exit completed") - except (AttributeError, RuntimeError) as e: - logger.error("Error occurred when exiting Lifespan: %s", str(e)) - finally: - # Clean up reference - app.lifespan_manager = None - - # Mount methods to app instance - app.start_lifespan = start_lifespan - app.exit_lifespan = exit_lifespan - - -async def manually_start_lifespan(app: FastAPI): - """ - Manually start the lifespan context manager of a FastAPI application - - Note: It is recommended to use the convenient methods mounted on the app instance: - - app.start_lifespan(): Start lifespan - - app.exit_lifespan(): Exit lifespan - - This function is used to initialize the application lifecycle without starting an HTTP server, - including database connections, business graph structures, etc. Suitable for scripts, tests, - or other scenarios requiring application context but not HTTP services. - - Args: - app (FastAPI): FastAPI application instance - - Returns: - context_manager: Lifecycle context manager instance, can be used for manual exit - - Example: - ```python - from app import app - - # Recommended way: directly use mounted methods - await app.start_lifespan() - # Perform operations requiring application context - # ... - await app.exit_lifespan() - - # Or use traditional way - from base_app import manually_start_lifespan - lifespan_manager = await manually_start_lifespan(app) - # await lifespan_manager.__aexit__(None, None, None) - ``` - """ - # Directly call the mounted method - return await app.start_lifespan() - - -async def close_database_connection(): - """Close the database connection pool""" - try: - db_provider = get_bean_by_type(DatabaseConnectionProvider) - await db_provider.close() - except (AttributeError, RuntimeError) as e: - logger.error("Error occurred when closing database connection: %s", str(e)) diff --git a/methods/EverCore/src/biz_layer/mem_db_operations.py b/methods/EverCore/src/biz_layer/mem_db_operations.py deleted file mode 100644 index c5a698a0..00000000 --- a/methods/EverCore/src/biz_layer/mem_db_operations.py +++ /dev/null @@ -1,538 +0,0 @@ -""" -Database operations and data conversion functions. -Extracted from mem_memorize.py for database operations and data conversion logic. - -This module contains the following features: -1. Time processing functions: Unified handling of various time formats to ensure consistency in database storage -2. Data conversion functions: Convert business layer objects to database document format -3. Database operation functions: Execute specific database CRUD operations -4. Status table operation functions: Manage the lifecycle of conversation status -""" - -import time -from api_specs.dtos import MemorizeRequest -from api_specs.memory_types import MemCell, RawDataType -from core.di import get_bean_by_type -from infra_layer.adapters.out.persistence.repository.conversation_status_raw_repository import ( - ConversationStatusRawRepository, -) -from infra_layer.adapters.out.persistence.repository.memcell_raw_repository import ( - MemCellRawRepository, -) -from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemory, -) -from infra_layer.adapters.out.persistence.document.memory.memcell import ( - MemCell as DocMemCell, - DataTypeEnum, -) -from typing import List, Any, Optional -from datetime import datetime, timedelta -from common_utils.datetime_utils import get_now_with_timezone, from_iso_format -from core.observation.logger import get_logger -from core.events import ApplicationEventPublisher -from infra_layer.adapters.out.event.memcell_created_event import MemCellCreatedEvent -from infra_layer.adapters.out.persistence.document.memory.foresight_record import ( - ForesightRecord, -) -from infra_layer.adapters.out.persistence.document.memory.atomic_fact_record import ( - AtomicFactRecord, -) -from api_specs.memory_types import RawDataType, AgentCase -from infra_layer.adapters.out.persistence.document.memory.agent_case import ( - AgentCaseRecord, -) - -logger = get_logger(__name__) - -# ==================== Data Conversion Functions ==================== - - -def _convert_episode_memory_to_doc( - episode_memory: Any, - current_time: Optional[datetime] = None, - session_id: Optional[str] = None, -) -> EpisodicMemory: - """ - Convert EpisodeMemory business object to EpisodicMemory database document format. - - Use cases: - - Format conversion before saving episodic memory to EpisodicMemoryRawRepository - - Ensure business layer Memory objects meet database document model field requirements - - Handle timestamp format and extension field mapping - - Args: - episode_memory: Business layer EpisodeMemory object - current_time: Current time, used as fallback when timestamp parsing fails - - Returns: - EpisodicMemory: Episodic memory object in database document format - """ - from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemory, - ) - from agentic_layer.vectorize_service import get_vectorize_service - - # Parse timestamp to datetime object - if current_time is None: - current_time = get_now_with_timezone() - - # Default to using current_time - timestamp_dt = current_time - - if hasattr(episode_memory, 'timestamp') and episode_memory.timestamp: - try: - timestamp_dt = from_iso_format(episode_memory.timestamp) - except Exception as e: - logger.debug(f"Timestamp conversion failed, using current time: {e}") - timestamp_dt = current_time - - participants = episode_memory.participants - return EpisodicMemory( - user_id=episode_memory.user_id, - group_id=episode_memory.group_id, - session_id=session_id, - timestamp=timestamp_dt, - participants=participants, - sender_ids=participants, - summary=episode_memory.summary or "", - subject=episode_memory.subject or "", - episode=( - episode_memory.episode - if hasattr(episode_memory, 'episode') - else episode_memory.summary or "" - ), - type=str(episode_memory.type.value) if episode_memory.type else "", - parent_type=getattr(episode_memory, 'parent_type', None), - parent_id=getattr(episode_memory, 'parent_id', None), - vector_model=episode_memory.vector_model, - vector=episode_memory.vector, - ) - - -def _convert_foresight_to_doc( - foresight: Any, - parent_doc: EpisodicMemory, - current_time: Optional[datetime] = None, - session_id: Optional[str] = None, -) -> ForesightRecord: - """ - Convert Foresight business object to unified foresight document format. - - Args: - foresight: Business layer Foresight object - parent_doc: Parent episodic memory document - current_time: Current time - - Returns: - ForesightRecord: Foresight object in database document format - """ - - if current_time is None: - current_time = get_now_with_timezone() - - participants = parent_doc.participants - return ForesightRecord( - user_id=getattr(foresight, "user_id", None), - session_id=session_id, - content=foresight.foresight, # Foresight class uses 'foresight' field, but DB uses 'content' - parent_type=foresight.parent_type, - parent_id=foresight.parent_id, - start_time=foresight.start_time, - end_time=foresight.end_time, - duration_days=foresight.duration_days, - type=str(parent_doc.type) if parent_doc.type else None, - group_id=parent_doc.group_id, - participants=participants, - sender_ids=participants, - vector=foresight.vector, - vector_model=foresight.vector_model, - evidence=foresight.evidence, - ) - - -def _convert_atomic_fact_to_docs( - atomic_fact_obj: Any, - parent_doc: EpisodicMemory, - current_time: Optional[datetime] = None, - session_id: Optional[str] = None, -) -> List["AtomicFactRecord"]: - """ - Convert AtomicFact business object to generic atomic fact document list. - - Args: - atomic_fact_obj: Business layer AtomicFact object - parent_doc: Parent episodic memory document - current_time: Current time - - Returns: - List[AtomicFactRecord]: List of atomic fact objects in database document format - """ - if current_time is None: - current_time = get_now_with_timezone() - - docs: List[AtomicFactRecord] = [] - if not atomic_fact_obj.atomic_fact or not atomic_fact_obj.fact_embeddings: - return docs - - for i, fact in enumerate(atomic_fact_obj.atomic_fact): - if i >= len(atomic_fact_obj.fact_embeddings): - break - - vector = atomic_fact_obj.fact_embeddings[i] - if hasattr(vector, 'tolist'): - vector = vector.tolist() - - participants = parent_doc.participants - doc = AtomicFactRecord( - user_id=atomic_fact_obj.user_id, - session_id=session_id, - atomic_fact=fact, - parent_type=atomic_fact_obj.parent_type, - parent_id=atomic_fact_obj.parent_id, - timestamp=parent_doc.timestamp or current_time, - type=str(parent_doc.type) if parent_doc.type else None, - group_id=atomic_fact_obj.group_id, - participants=participants, - sender_ids=participants, - vector=vector, - vector_model=getattr(atomic_fact_obj, 'vector_model', None), - ) - docs.append(doc) - - return docs - - -def _extract_user_id_from_memcell(memcell: MemCell) -> Optional[str]: - """Extract user_id from an agent conversation MemCell. - - Finds the first message with role='user' and returns its sender_id. - original_data items are wrapped as {"message": msg, "parse_info": ...}. - """ - for item in memcell.original_data or []: - msg = item.get("message", item) if isinstance(item, dict) else item - if isinstance(msg, dict) and msg.get("role") == "user": - sender_id = msg.get("sender_id") - if sender_id: - return sender_id - return None - - -def _convert_agent_case_to_doc( - agent_case: AgentCase, - memcell: MemCell, - current_time: Optional[datetime] = None, - session_id: Optional[str] = None, -) -> AgentCaseRecord: - """Convert AgentCase business object to AgentCaseRecord database document.""" - if current_time is None: - current_time = get_now_with_timezone() - - # Parse timestamp - timestamp_dt = current_time - if memcell.timestamp: - try: - if isinstance(memcell.timestamp, datetime): - timestamp_dt = memcell.timestamp - elif isinstance(memcell.timestamp, str): - timestamp_dt = from_iso_format(memcell.timestamp) - except Exception: - timestamp_dt = current_time - - # Extract user_id from first role='user' message's sender_id - user_id = _extract_user_id_from_memcell(memcell) - - return AgentCaseRecord( - id=agent_case.id, - user_id=user_id, - group_id=memcell.group_id, - session_id=session_id, - timestamp=timestamp_dt, - task_intent=agent_case.task_intent, - approach=agent_case.approach, - key_insight=agent_case.key_insight, - quality_score=agent_case.quality_score, - parent_type="memcell", - parent_id=str(memcell.event_id) if memcell.event_id else None, - vector=agent_case.vector, - vector_model=agent_case.vector_model, - ) - - -# ==================== Database Operation Functions ==================== -from core.observation.tracing.decorators import trace_logger - - -async def _save_memcell_to_database( - memcell: MemCell, current_time: datetime, session_id: Optional[str] = None -) -> MemCell: - """ - Convert business layer MemCell to document model and save to database. - - Args: - memcell: Business layer MemCell object - current_time: Current time, used as fallback when timestamp conversion fails - session_id: Session identifier for conversation isolation - - Note: - - Skips saving and logs when conversion fails - - Does not interrupt flow when save fails - """ - try: - memcell_repo = get_bean_by_type(MemCellRawRepository) - - # Convert timestamp to timezone-aware datetime - timestamp_dt = current_time - if memcell.timestamp: - try: - timestamp_dt = from_iso_format(memcell.timestamp) - except (ValueError, TypeError) as e: - logger.debug(f"Timestamp conversion failed, using current time: {e}") - - # Convert data type enum - doc_type = None - if memcell.type and memcell.type == RawDataType.CONVERSATION: - doc_type = DataTypeEnum.CONVERSATION - elif memcell.type and memcell.type == RawDataType.AGENTCONVERSATION: - doc_type = DataTypeEnum.AGENTCONVERSATION - - # Create document model - doc_memcell = DocMemCell( - timestamp=timestamp_dt, - group_id=memcell.group_id, - session_id=session_id, - original_data=memcell.original_data or [], - participants=memcell.participants, - sender_ids=memcell.sender_ids, - type=doc_type, - ) - - # Save to database - result = await memcell_repo.append_memcell(doc_memcell) - if result: - memcell.event_id = str(result.event_id) - logger.info( - f"[mem_db_operations] MemCell saved successfully: {memcell.event_id}" - ) - # Publish MemCellCreatedEvent - try: - publisher = get_bean_by_type(ApplicationEventPublisher) - event = MemCellCreatedEvent( - memcell_id=memcell.event_id, - timestamp=int(current_time.timestamp() * 1000), - ) - await publisher.publish(event) - logger.debug( - f"[mem_db_operations] MemCellCreatedEvent published: {memcell.event_id}" - ) - except Exception as e: - logger.warning( - f"[mem_db_operations] Failed to publish MemCellCreatedEvent: {e}" - ) - else: - logger.info(f"[mem_db_operations] MemCell save failed: {memcell.event_id}") - - except Exception as e: - logger.error(f"MemCell save failed: {e}") - import traceback - - traceback.print_exc() - return memcell - - -# ==================== Status Table Operation Functions ==================== - - -async def _update_status_for_continuing_conversation( - status_repo: ConversationStatusRawRepository, - request: MemorizeRequest, - latest_time: str, - current_time: datetime, -) -> bool: - """ - Update status record for continuing conversation (update new_msg_start_time). - - Use cases: - - Called when MemCell extraction is judged as non-boundary - - Conversation is still continuing, need to accumulate more messages - - Update new_msg_start_time to latest message time to prepare for next processing - - Args: - status_repo: ConversationStatusRawRepository instance - request: Memorize request object - latest_time: Timestamp of latest message - current_time: Current time - - Returns: - bool: Returns True if update successful, False otherwise - """ - try: - # First get existing status - existing_status = await status_repo.get_by_group_id( - request.group_id, session_id=request.session_id - ) - if not existing_status: - logger.info( - f"Existing status not found, creating new status record: group_id={request.group_id}" - ) - # Create new status record - latest_dt = from_iso_format(latest_time) - update_data = { - "old_msg_start_time": None, - "new_msg_start_time": latest_dt + timedelta(milliseconds=1), - "last_memcell_time": None, - "created_at": from_iso_format(current_time), - "updated_at": from_iso_format(current_time), - } - result = await status_repo.upsert_by_group_id( - request.group_id, update_data, session_id=request.session_id - ) - if result: - logger.info( - f"New status created successfully: group_id={request.group_id}" - ) - return True - else: - logger.warning( - f"Failed to create new status: group_id={request.group_id}" - ) - return False - - # Update new_msg_start_time to latest message time + 1 millisecond - latest_dt = from_iso_format(latest_time) - new_msg_start_time = latest_dt - - update_data = { - "old_msg_start_time": ( - from_iso_format(existing_status.old_msg_start_time) - if existing_status.old_msg_start_time - else None - ), - "new_msg_start_time": new_msg_start_time + timedelta(milliseconds=1), - "last_memcell_time": ( - from_iso_format(existing_status.last_memcell_time) - if existing_status.last_memcell_time - else None - ), - "created_at": from_iso_format(existing_status.created_at), - "updated_at": current_time, - } - - logger.debug(f"Conversation continuing, update new_msg_start_time") - result = await status_repo.upsert_by_group_id( - request.group_id, update_data, session_id=request.session_id - ) - - if result: - logger.info(f"Conversation continuation status updated successfully") - return True - else: - logger.warning(f"Conversation continuation status update failed") - return False - - except Exception as e: - logger.error(f"Conversation continuation status update failed: {e}") - return False - - -async def _update_status_after_memcell_extraction( - status_repo: ConversationStatusRawRepository, - request: MemorizeRequest, - memcell_time: str, - current_time: datetime, -) -> bool: - """ - Update status table after MemCell extraction (update old_msg_start_time and new_msg_start_time). - - Use cases: - - Called after successfully extracting MemCell and completing memory extraction - - Update processed message time boundary to avoid duplicate processing - - Reset new_msg_start_time to current time to prepare for receiving new messages - - Args: - status_repo: ConversationStatusRawRepository instance - request: Memorize request object - memcell_time: Timestamp of MemCell - current_time: Current time - - Returns: - bool: Returns True if update successful, False otherwise - - Note: - - old_msg_start_time is updated to last history message time + 1ms - - new_msg_start_time is reset to current time - - last_memcell_time records the latest MemCell extraction time - """ - try: - # Get timestamp of last history data - last_history_time = None - if request.history_raw_data_list and request.history_raw_data_list[-1]: - last_history_data = request.history_raw_data_list[-1] - if hasattr(last_history_data, 'content') and isinstance( - last_history_data.content, dict - ): - last_history_time = last_history_data.content.get('timestamp') - elif hasattr(last_history_data, 'timestamp'): - last_history_time = last_history_data.timestamp - - first_new_time = None - if request.new_raw_data_list and request.new_raw_data_list[0]: - first_new_data = request.new_raw_data_list[0] - if hasattr(first_new_data, 'content') and isinstance( - first_new_data.content, dict - ): - first_new_time = first_new_data.content.get('timestamp') - elif hasattr(first_new_data, 'timestamp'): - first_new_time = first_new_data.timestamp - - last_new_time = None - if request.new_raw_data_list and request.new_raw_data_list[-1]: - last_new_data = request.new_raw_data_list[-1] - if hasattr(last_new_data, 'content') and isinstance( - last_new_data.content, dict - ): - last_new_time = last_new_data.content.get('timestamp') - elif hasattr(last_new_data, 'timestamp'): - last_new_time = last_new_data.timestamp - - if last_new_time: - last_new_dt = from_iso_format(last_new_time) - new_msg_start_time = last_new_dt + timedelta(milliseconds=1) - else: - new_msg_start_time = from_iso_format(current_time) - - # Calculate old_msg_start_time (last history timestamp + 1 millisecond) - if first_new_time: - first_new_dt = from_iso_format(first_new_time) - old_msg_start_time = first_new_dt - elif last_history_time: - last_history_dt = from_iso_format(last_history_time) - old_msg_start_time = last_history_dt + timedelta(milliseconds=1) - else: - # If no history data, use existing current_time - old_msg_start_time = from_iso_format(current_time) - - update_data = { - "old_msg_start_time": old_msg_start_time, - "new_msg_start_time": new_msg_start_time, # Current time - "last_memcell_time": from_iso_format(memcell_time), - "updated_at": current_time, - } - - # TODO : clear queue - - logger.debug(f"Update status table after MemCell extraction") - result = await status_repo.upsert_by_group_id( - request.group_id, update_data, session_id=request.session_id - ) - - if result: - logger.info(f"Status update after MemCell extraction successful") - return True - else: - logger.warning(f"Status update after MemCell extraction failed") - return False - - except Exception as e: - logger.error(f"Status update after MemCell extraction failed: {e}") - return False diff --git a/methods/EverCore/src/biz_layer/mem_memorize.py b/methods/EverCore/src/biz_layer/mem_memorize.py deleted file mode 100644 index be1adcbc..00000000 --- a/methods/EverCore/src/biz_layer/mem_memorize.py +++ /dev/null @@ -1,1860 +0,0 @@ -from dataclasses import dataclass -import random -import time -import json -import traceback - -from core.observation.stage_timer import timed, timed_parallel -from api_specs.memory_types import ScenarioType -from agentic_layer.metrics.memorize_metrics import ( - record_extraction_stage, - record_memory_extracted, - get_space_id_for_metrics, -) -from api_specs.dtos import MemorizeRequest -from memory_layer.memory_manager import MemoryManager -from api_specs.memory_types import ( - MemoryType, - MemCell, - BaseMemory, - EpisodeMemory, - RawDataType, - Foresight, - AgentCase, -) -from api_specs.memory_types import AtomicFact, get_text_from_content_items -from biz_layer.memorize_config import DEFAULT_MEMORIZE_CONFIG -from core.di import get_bean_by_type -from infra_layer.adapters.out.persistence.repository.episodic_memory_raw_repository import ( - EpisodicMemoryRawRepository, -) -from infra_layer.adapters.out.persistence.repository.foresight_record_raw_repository import ( - ForesightRecordRawRepository, -) -from infra_layer.adapters.out.persistence.repository.atomic_fact_record_raw_repository import ( - AtomicFactRecordRawRepository, -) -from infra_layer.adapters.out.persistence.repository.conversation_status_raw_repository import ( - ConversationStatusRawRepository, -) -from service.settings_service import SettingsService -from infra_layer.adapters.out.persistence.repository.memcell_raw_repository import ( - MemCellRawRepository, -) -from infra_layer.adapters.out.persistence.repository.conversation_data_raw_repository import ( - ConversationDataRepository, -) -from api_specs.memory_types import RawDataType -from typing import List, Dict, Optional, Any -from dataclasses import dataclass -import uuid -from datetime import datetime, timedelta -import os -import asyncio -from collections import defaultdict -from common_utils.datetime_utils import get_now_with_timezone, to_iso_format -from memory_layer.memcell_extractor.base_memcell_extractor import StatusResult -import traceback - -from core.observation.logger import get_logger -from infra_layer.adapters.out.search.elasticsearch.converter.episodic_memory_converter import ( - EpisodicMemoryConverter, -) -from infra_layer.adapters.out.search.milvus.converter.episodic_memory_milvus_converter import ( - EpisodicMemoryMilvusConverter, -) -from infra_layer.adapters.out.search.repository.episodic_memory_milvus_repository import ( - EpisodicMemoryMilvusRepository, -) -from infra_layer.adapters.out.search.repository.episodic_memory_es_repository import ( - EpisodicMemoryEsRepository, -) -from biz_layer.mem_sync import MemorySyncService - -logger = get_logger(__name__) - - -async def _load_llm_custom_setting() -> Optional[Dict[str, Any]]: - """Load and normalize llm_custom_setting from global settings.""" - settings_service = get_bean_by_type(SettingsService) - return await settings_service.get_llm_custom_setting() - - -@dataclass -class MemoryDocPayload: - memory_type: MemoryType - doc: Any - - -from biz_layer.memorize_config import ( - MemorizeConfig, - DEFAULT_MEMORIZE_CONFIG, -) - - -def _is_agent_case_quality_sufficient( - agent_case: AgentCase, config: MemorizeConfig -) -> bool: - """Check if the AgentCase quality score meets the minimum threshold for skill extraction.""" - score = agent_case.quality_score - if score is None or score < config.skill_min_quality_score: - logger.info( - f"[AgentSkill] Skipping skill extraction: quality_score={score} " - f"< threshold={config.skill_min_quality_score}" - ) - return False - return True - - - -async def _trigger_clustering( - group_id: str, - memcell: MemCell, - scene: Optional[str] = None, - config: MemorizeConfig = DEFAULT_MEMORIZE_CONFIG, - episode_text: Optional[str] = None, - agent_case: Optional[AgentCase] = None, -) -> None: - """Trigger MemCell clustering - - Args: - group_id: Group ID - memcell: The MemCell just saved - scene: Conversation scene (used to determine Profile extraction strategy) - - "solo": 1 user + N agents scenario - - "team": multi-user + agents scenario - episode_text: Episode text extracted from this MemCell (used for clustering similarity) - agent_case: Extracted AgentCase (if agent conversation), used for skill extraction - """ - logger.info( - f"[Clustering] Start triggering clustering: group_id={group_id}, event_id={memcell.event_id}, scene={scene}" - ) - - try: - from memory_layer.cluster_manager import ( - ClusterManager, - ClusterManagerConfig, - MemSceneState, - ) - from infra_layer.adapters.out.persistence.repository.mem_scene_raw_repository import ( - MemSceneRawRepository, - ) - from core.di import get_bean_by_type - - logger.info(f"[Clustering] Retrieving MemSceneRawRepository...") - # Get MongoDB storage - cluster_storage = get_bean_by_type(MemSceneRawRepository) - logger.info( - f"[Clustering] MemSceneRawRepository retrieved successfully: {type(cluster_storage)}" - ) - - # Create ClusterManager (pure computation component) - has_case = agent_case is not None - cluster_config = ClusterManagerConfig( - similarity_threshold=config.cluster_similarity_threshold, - max_time_gap_days=config.cluster_max_time_gap_days, - ) - - # Build LLM provider and context fetcher for agent clustering - llm_provider = None - context_fetcher = None - if has_case: - from memory_layer.llm.llm_provider import build_default_provider - from infra_layer.adapters.out.persistence.repository.agent_case_raw_repository import ( - AgentCaseRawRepository, - ) - - llm_provider = build_default_provider() - agent_case_repo = get_bean_by_type(AgentCaseRawRepository) - context_fetcher = agent_case_repo.fetch_task_intents_by_event_ids - - cluster_manager = ClusterManager( - config=cluster_config, - llm_provider=llm_provider, - context_fetcher=context_fetcher, - ) - - # Clustering text: task_intent for agent case, episode for normal - clustering_text = ( - agent_case.task_intent if has_case and agent_case.task_intent - else episode_text - ) - logger.info( - f"[Clustering] ClusterManager created (has_case={has_case})" - ) - - # Convert MemCell to dictionary format required for clustering - memcell_dict = { - "event_id": str(memcell.event_id), - "episode": episode_text, - "clustering_text": clustering_text, - "timestamp": memcell.timestamp.timestamp() if memcell.timestamp else None, - "participants": memcell.participants or [], - "group_id": group_id, - } - - logger.debug( - f"[Clustering] Start clustering execution: {memcell_dict['event_id']}" - ) - - from core.lock.redis_distributed_lock import distributed_lock - - # ===== Phase 1 + 2: Clustering + Profile extraction ===== - # Lock: trigger_clustering:{group_id} - # - # Protected shared state (read-modify-write): - # - mem_scene_state: loaded from DB, mutated by cluster_memcell(), saved back. - # Concurrent writes without this lock would cause lost updates. - # - Profile extraction (Phase 2) reads mem_scene_state snapshot taken in Phase 1 - # and reads/writes user profiles in DB. - # - # Released before Phase 3 so the next request's Phase 1+2 is not blocked - # by a slow LLM skill-extraction call. - # - # Data flow out of this lock: - # - cluster_id: determined by Phase 1, used as key for Phase 3 lock. - # - agent_case: passed through from caller, not modified here. - # Both are safe to use after lock release because Phase 3 re-reads - # its own shared state (existing_skills) from DB inside its own lock. - cluster_id = None - lock_resource = f"trigger_clustering:{group_id}" - async with distributed_lock( - resource=lock_resource, - timeout=config.clustering_lock_timeout, - blocking_timeout=config.clustering_lock_blocking_timeout, - ) as acquired: - if not acquired: - logger.error( - f"[Clustering] Failed to acquire lock for group {group_id}, " - f"skipping memcell {memcell.event_id}" - ) - return - - # ===== Phase 1: Clustering ===== - state_dict = await cluster_storage.load_mem_scene(group_id) - mem_scene_state = ( - MemSceneState.from_dict(state_dict) if state_dict else MemSceneState() - ) - logger.info( - f"[Clustering] Loaded clustering state: {len(mem_scene_state.event_ids)} clustered events" - ) - - cluster_id, mem_scene_state = await cluster_manager.cluster_memcell( - memcell_dict, mem_scene_state, has_case=has_case - ) - - await cluster_storage.save_mem_scene(group_id, mem_scene_state.to_dict()) - logger.info(f"[Clustering] Clustering state saved") - - if cluster_id: - logger.debug( - f"[Clustering] ✅ MemCell {memcell.event_id} -> Cluster {cluster_id} (group: {group_id})" - ) - else: - logger.warning( - f"[Clustering] ⚠️ MemCell {memcell.event_id} clustering returned None (group: {group_id})" - ) - - # ===== Phase 2: Profile extraction (with interval-based throttling) ===== - if cluster_id and not config.skip_profile_extraction: - total_memcell_count = sum(mem_scene_state.cluster_counts.values()) - should_extract = ( - config.profile_extraction_interval <= 1 - or total_memcell_count % config.profile_extraction_interval == 0 - ) - - if should_extract: - # --- Group-level cluster selection (Layer 1 of 2) --- - # Profile extraction uses a two-layer filtering strategy: - # - # Layer 1 (here): Select which clusters to fetch from DB. - # Uses min(last_updated_ts) across all users in the group as baseline. - # This is intentionally broad — it covers the "slowest" user so no - # cluster is missed for any user. Fetches ALL events from selected - # clusters in one DB query. - # - # Layer 2 (manager.py, per-user loop): Filters fetched original_data per - # user based on each user's own last_updated timestamp, so each user's - # LLM prompt only contains data they haven't seen yet. - # (Note: the code calls them "episodes" but the actual content is - # memcell original_data — raw chat messages, not episode summaries.) - # - # For new groups with no profiles, defaults to current memcell timestamp - # to avoid selecting all historical clusters (cold-start protection). - from infra_layer.adapters.out.persistence.repository.user_profile_raw_repository import ( - UserProfileRawRepository, - ) - from core.di import get_bean_by_type - - profile_repo = get_bean_by_type(UserProfileRawRepository) - existing_profiles = await profile_repo.get_all_by_group(group_id) - - current_memcell_ts = memcell.timestamp.timestamp() - - if existing_profiles: - timestamps = [ - p.last_updated_ts - for p in existing_profiles - if p.last_updated_ts is not None - ] - last_profile_ts = ( - min(timestamps) if timestamps else current_memcell_ts - ) - else: - last_profile_ts = current_memcell_ts - - target_cluster_ids = [ - cid - for cid, ts in mem_scene_state.cluster_last_ts.items() - if ts is not None and ts > last_profile_ts - ] - if cluster_id not in target_cluster_ids: - target_cluster_ids.append(cluster_id) - - logger.info( - f"[Profile] Timestamp-based selection: last_profile_ts={last_profile_ts}, " - f"target_clusters={target_cluster_ids}" - ) - - await _trigger_profile_extraction( - group_id=group_id, - cluster_ids=target_cluster_ids, - mem_scene_state=mem_scene_state, - memcell=memcell, - scene=scene, - config=config, - ) - else: - logger.debug( - f"[Profile] Skipping extraction: total_memcells={total_memcell_count}, " - f"interval={config.profile_extraction_interval}" - ) - - # ===== Phase 3: Agent skill extraction ===== - # Lock: trigger_agent_skill:{group_id}:{cluster_id} - # - # Separate lock so Phase 1+2 of the next request is not blocked by this - # slow LLM call. - # - # Data dependencies (all safe after Lock 1 release): - # - cluster_id: immutable identifier, determined in Phase 1. - # - agent_case: this request's own extraction result, not shared state. - # - existing_skills: re-read from DB inside _trigger_agent_skill_extraction, - # so it always reflects the latest state (including writes by prior requests). - # - # IMPORTANT for future maintainers: - # This function does NOT read memcells or agent_cases from DB. It only uses - # the passed-in agent_case (current request) + existing_skills (from DB). - # If you add logic that reads cluster memcells from DB here, you must - # consider that new memcells may have been added between Lock 1 release - # and Lock 2 acquisition. - if cluster_id and agent_case and _is_agent_case_quality_sufficient(agent_case, config): - skill_lock_resource = f"trigger_agent_skill:{group_id}:{cluster_id}" - async with distributed_lock( - resource=skill_lock_resource, - timeout=config.skill_extraction_lock_timeout, - blocking_timeout=config.skill_extraction_lock_blocking_timeout, - ) as skill_acquired: - if not skill_acquired: - logger.error( - f"[AgentSkill] Failed to acquire lock for group {group_id}, " - f"cluster {cluster_id}, skipping memcell {memcell.event_id}" - ) - return - await _trigger_agent_skill_extraction( - group_id=group_id, - cluster_id=cluster_id, - memcell=memcell, - agent_case=agent_case, - config=config, - ) - - except Exception as e: - logger.error( - f"[Clustering] ❌ Triggering clustering failed: {e}", exc_info=True - ) - raise - - -async def _trigger_profile_extraction( - group_id: str, - cluster_ids: List[str], - mem_scene_state, # MemSceneState - memcell: MemCell, - scene: Optional[str] = None, - config: MemorizeConfig = DEFAULT_MEMORIZE_CONFIG, -) -> None: - """Trigger Profile extraction for one or more clusters. - - Args: - group_id: Group ID - cluster_ids: Cluster IDs to extract profiles from - mem_scene_state: Current mem scene state - memcell: The MemCell currently being processed (appended as new_memcell) - scene: Conversation scene - config: Memory extraction configuration - """ - try: - from memory_layer.profile_manager import ProfileManager, ProfileManagerConfig - from infra_layer.adapters.out.persistence.repository.user_profile_raw_repository import ( - UserProfileRawRepository, - ) - from memory_layer.llm.llm_provider import build_default_provider - from core.di import get_bean_by_type - import os - - total_memcell_count = sum( - mem_scene_state.cluster_counts.get(cid, 0) for cid in cluster_ids - ) - if total_memcell_count < config.profile_min_memcells: - logger.debug( - f"[Profile] Clusters {cluster_ids} have only {total_memcell_count} memcells " - f"(requires {config.profile_min_memcells}), skipping extraction" - ) - return - - logger.info( - f"[Profile] Start extracting Profile: clusters={cluster_ids}, memcells={total_memcell_count}" - ) - - # Get Profile storage - profile_repo = get_bean_by_type(UserProfileRawRepository) - memcell_repo = get_bean_by_type(MemCellRawRepository) - - # Create LLM Provider - llm_provider = build_default_provider() - - # Determine scenario (for metadata only, extraction logic is unified) - profile_scenario = ScenarioType(scene.lower()) if scene else ScenarioType.TEAM - - # Create ProfileManager (pure computation component) - profile_config = ProfileManagerConfig( - min_confidence=config.profile_min_confidence, - enable_versioning=config.profile_enable_versioning, - auto_extract=True, - ) - profile_manager = ProfileManager( - llm_provider=llm_provider, config=profile_config, group_id=group_id - ) - - # ===== Fetch memcells from all target clusters ===== - current_event_id = str(memcell.event_id) if memcell.event_id else None - target_cluster_set = set(cluster_ids) - target_event_ids = set() - if mem_scene_state and hasattr(mem_scene_state, 'eventid_to_cluster'): - for event_id, cid in mem_scene_state.eventid_to_cluster.items(): - if cid in target_cluster_set and event_id != current_event_id: - target_event_ids.add(event_id) - - all_memcells = [] - if target_event_ids: - try: - fetched = await memcell_repo.get_by_event_ids(list(target_event_ids)) - all_memcells = list(fetched.values()) - except Exception as e: - logger.warning(f"[Profile] Failed to fetch cluster memcells: {e}") - - # Append current memcell as the last one (new_memcell) - all_memcells.append(memcell) - - # Merge participants from all memcells (deduplicated) - all_participants: set = set() - for mc in all_memcells: - participants = ( - mc.participants - if hasattr(mc, 'participants') - else mc.get('participants', []) - ) - all_participants.update(participants or []) - user_id_list = list(all_participants) - - logger.info( - f"[Profile] Context: clusters={len(cluster_ids)}, " - f"memcells={len(all_memcells) - 1}, new=1, users={len(user_id_list)}" - ) - - # ===== Extract and save profiles ===== - # Caller (_trigger_clustering) holds trigger_clustering:{group_id} while calling - # this function, so concurrent profile writes for the same group are serialized. - - # Load old profiles - old_profiles_dict = await profile_repo.get_all_profiles(group_id=group_id) - old_profiles = list(old_profiles_dict.values()) if old_profiles_dict else [] - logger.info( - f"[Profile] Loaded {len(old_profiles)} existing profiles for group={group_id}" - ) - if old_profiles: - for uid, p in old_profiles_dict.items(): - keys = list(p.keys()) if isinstance(p, dict) else dir(p) - logger.info(f"[Profile] Profile for {uid}: keys={keys[:8]}") - - # Extract profiles - profile_scene = ( - ScenarioType.TEAM if scene == ScenarioType.TEAM.value else ScenarioType.SOLO - ) - new_profiles = await profile_manager.extract_profiles( - memcells=all_memcells, - old_profiles=old_profiles, - user_id_list=user_id_list, - group_id=group_id, - max_items=config.profile_max_items, - scene=profile_scene, - ) - - # Save profiles - for profile in new_profiles: - try: - memcell_ts = memcell.timestamp.timestamp() if memcell.timestamp else 0.0 - user_id = profile.user_id - profile_data = profile.to_dict() - metadata = { - "group_id": group_id, - "scenario": profile_scenario.value, - "memcell_count": total_memcell_count, - "total_items": profile.total_items(), - "last_updated_ts": memcell_ts, - } - - if user_id: - await profile_repo.save_profile( - user_id, profile_data, metadata=metadata - ) - logger.info(f"[Profile] ✅ Saved: user={user_id}") - except Exception as e: - logger.warning(f"[Profile] Failed to save profile: {e}") - - logger.info(f"[Profile] ✅ Completed: {len(new_profiles)} profiles") - - except Exception as e: - logger.error(f"[Profile] ❌ Profile extraction failed: {e}", exc_info=True) - - # Advance last_updated_ts even on failure to prevent repeated re-selection - # of the same clusters. The data is "skipped" — acceptable tradeoff vs. - # getting stuck in a loop retrying the same failing extraction. - try: - memcell_ts = memcell.timestamp.timestamp() if memcell.timestamp else 0.0 - for uid in user_id_list: - existing = await profile_repo.get_by_user_and_group(uid, group_id) - profile_data = ( - existing.profile_data - if existing - else {"explicit_info": [], "implicit_traits": []} - ) - await profile_repo.upsert( - user_id=uid, - group_id=group_id, - profile_data=profile_data, - metadata={"last_updated_ts": memcell_ts}, - trigger_index=False, - ) - logger.info( - f"[Profile] Advanced last_updated_ts to {memcell_ts} for {len(user_id_list)} users despite failure" - ) - except Exception as ts_err: - logger.warning( - f"[Profile] Failed to advance last_updated_ts on failure: {ts_err}" - ) - - -async def _trigger_agent_skill_extraction( - group_id: str, - cluster_id: str, - memcell: MemCell, - agent_case: AgentCase, - config: MemorizeConfig = DEFAULT_MEMORIZE_CONFIG, -) -> None: - """Trigger incremental AgentSkill extraction for a MemScene cluster. - - Args: - group_id: Group ID - cluster_id: The cluster (MemScene) to extract skills for - memcell: The MemCell currently being processed (for user_id and event_id) - agent_case: The extracted AgentCase BO - config: Memory extraction configuration - """ - try: - from infra_layer.adapters.out.persistence.repository.agent_skill_raw_repository import ( - AgentSkillRawRepository, - ) - from memory_layer.memory_extractor.agent_skill_extractor import ( - AgentSkillExtractor, - ) - from memory_layer.llm.llm_provider import build_default_provider - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - from infra_layer.adapters.out.search.repository.agent_skill_milvus_repository import ( - AgentSkillMilvusRepository, - ) - from infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter import ( - AgentSkillConverter, - ) - from infra_layer.adapters.out.search.repository.agent_skill_es_repository import ( - AgentSkillEsRepository, - ) - - # Caller (_trigger_clustering) acquires trigger_agent_skill:{group_id}:{cluster_id} - # before calling this function, so concurrent skill writes for the same cluster are - # serialized while different clusters within the same group can run in parallel. - # - # Concurrency safety of data used in this function: - # - existing_skills: read from DB below (inside the caller's lock), always fresh. - # - agent_case: passed in from the current request, not shared with other requests. - # - memcell: only used to extract user_id, no shared-state concern. - # - extract_and_save() does NOT read memcells or agent_cases from DB. - # It only merges new_case_records (passed-in) with existing_skill_records (from DB). - # If future changes add DB reads of memcells/cases here, re-evaluate the lock - # boundary — the gap between Lock 1 release and Lock 2 acquisition means - # new memcells may have been clustered in between. - - # Fetch existing skills for incremental merging - skill_repo = get_bean_by_type(AgentSkillRawRepository) - existing_skills = await skill_repo.get_by_cluster_id( - cluster_id, group_id=group_id, min_confidence=config.skill_retire_confidence - ) - - logger.info( - f"[AgentSkill] Incremental extraction: cluster={cluster_id}, " - f"new_experience=1, existing_skills={len(existing_skills)}" - ) - - # Resolve user_id from the memcell's original conversation data - user_id = _extract_user_id_from_memcell(memcell) - - # Run incremental skill extraction - llm_provider = build_default_provider() - extractor = AgentSkillExtractor( - llm_provider=llm_provider, - maturity_threshold=config.skill_maturity_threshold, - retire_confidence=config.skill_retire_confidence, - skip_maturity_scoring=config.skip_skill_maturity_scoring, - ) - extraction_result = await extractor.extract_and_save( - cluster_id=cluster_id, - group_id=group_id, - new_case_records=[agent_case], - existing_skill_records=existing_skills, - skill_repo=skill_repo, - user_id=user_id, - ) - - if extraction_result.deleted_ids: - logger.info( - f"[AgentSkill] Retired skills for cluster={cluster_id}: " - f"ids={extraction_result.deleted_ids}" - ) - logger.info( - f"[AgentSkill] Extraction result for cluster={cluster_id}: " - f"added={len(extraction_result.added_records)}, " - f"updated={len(extraction_result.updated_records)}, " - f"retired={len(extraction_result.deleted_ids)}" - ) - - # Records that need insert into search engines (added + updated) - upsert_records = ( - extraction_result.added_records + extraction_result.updated_records - ) - # IDs of updated records that need their old entry removed first - updated_ids = [str(r.id) for r in extraction_result.updated_records] - # IDs to remove from search engines (deleted + updated-old-entries) - remove_ids = extraction_result.deleted_ids + updated_ids - - if upsert_records or remove_ids: - # Milvus sync: delete stale entries -> insert new/updated - try: - agent_skill_milvus_repo = get_bean_by_type(AgentSkillMilvusRepository) - for old_id in remove_ids: - await agent_skill_milvus_repo.delete_by_id(old_id) - inserted_count = 0 - for record in upsert_records: - milvus_entity = AgentSkillMilvusConverter.from_mongo(record) - if milvus_entity.get("vector"): - await agent_skill_milvus_repo.insert(milvus_entity, flush=False) - inserted_count += 1 - else: - logger.warning( - f"[AgentSkill] Milvus skip (no vector): record={record.id}" - ) - logger.info( - f"[AgentSkill] Milvus synced for cluster={cluster_id}: " - f"inserted={inserted_count}, removed={len(remove_ids)}" - ) - except Exception as milvus_exc: - logger.warning( - f"[AgentSkill] Milvus sync failed for cluster={cluster_id}: {milvus_exc}" - ) - - # ES sync: delete stale entries -> insert new/updated - try: - agent_skill_es_repo = get_bean_by_type(AgentSkillEsRepository) - for old_id in remove_ids: - await agent_skill_es_repo.delete_by_id(old_id) - for record in upsert_records: - es_doc = AgentSkillConverter.from_mongo(record) - await agent_skill_es_repo.create(es_doc) - logger.info( - f"[AgentSkill] ES synced for cluster={cluster_id}: " - f"inserted={len(upsert_records)}, removed={len(remove_ids)}" - ) - except Exception as es_exc: - logger.warning( - f"[AgentSkill] ES sync failed for cluster={cluster_id}: {es_exc}" - ) - - except Exception as e: - logger.error( - f"[AgentSkill] Skill extraction failed for cluster={cluster_id}: {e}", - exc_info=True, - ) - - -from biz_layer.mem_db_operations import ( - _convert_episode_memory_to_doc, - _convert_foresight_to_doc, - _convert_atomic_fact_to_docs, - _convert_agent_case_to_doc, - _extract_user_id_from_memcell, - _save_memcell_to_database, - _update_status_for_continuing_conversation, - _update_status_after_memcell_extraction, -) -from typing import Tuple - - -def if_memorize(memcell: MemCell) -> bool: - return True - - -# ==================== MemCell Processing Business Logic ==================== - - -@dataclass -class ExtractionState: - """Memory extraction state, stores intermediate results""" - - memcell: MemCell - request: MemorizeRequest - current_time: datetime - scene: str - is_solo_scene: bool - participants: List[str] - episode_parent_type: str = None - foresight_parent_type: str = None - atomic_fact_parent_type: str = None - parent_id: str = None - group_episode: Optional[EpisodeMemory] = None - group_episode_memories: List[EpisodeMemory] = None - episode_memories: List[EpisodeMemory] = None - agent_case: Optional[AgentCase] = None - parent_docs_map: Dict[str, Any] = None - - @property - def episode_saved(self) -> bool: - """True when at least one episode has been saved to MongoDB. - - parent_docs_map is populated by _save_episodes only after a successful write, - so non-empty means parent_doc is available for downstream linking. - """ - return bool(self.parent_docs_map) - - def __post_init__(self): - self.group_episode_memories = [] - self.episode_memories = [] - self.parent_docs_map = {} - # Set default parent info from memcell - if self.episode_parent_type is None: - self.episode_parent_type = ( - DEFAULT_MEMORIZE_CONFIG.default_episode_parent_type - ) - if self.foresight_parent_type is None: - self.foresight_parent_type = ( - DEFAULT_MEMORIZE_CONFIG.default_foresight_parent_type - ) - if self.atomic_fact_parent_type is None: - self.atomic_fact_parent_type = ( - DEFAULT_MEMORIZE_CONFIG.default_atomic_fact_parent_type - ) - if self.parent_id is None: - self.parent_id = self.memcell.event_id - - -async def process_memory_extraction( - memcell: MemCell, - request: MemorizeRequest, - memory_manager: MemoryManager, - current_time: datetime, -) -> int: - """ - Main memory extraction process - - Starting from MemCell, extract all memory types including Episode, Foresight, AtomicFact, etc. - - Returns: - int: Total number of memories extracted - """ - # Get metrics labels - space_id = get_space_id_for_metrics() - raw_data_type = memcell.type.value if memcell.type else 'unknown' - - # 1. Initialize state - init_start = time.perf_counter() - state = await _init_extraction_state(memcell, request, current_time) - record_extraction_stage( - space_id=space_id, - raw_data_type=raw_data_type, - stage='init_state', - duration_seconds=time.perf_counter() - init_start, - ) - - # 2. Parallel extract: Episode + (agent) AgentCase - # foresight/atomic_fact moved to background task _foresight_and_atomic_facts_with_metrics - extract_start = time.perf_counter() - - # Wrapper functions to track individual stage durations - async def _timed_extract_episodes(): - start = time.perf_counter() - result = await _extract_episodes(state, memory_manager) - record_extraction_stage( - space_id=space_id, - raw_data_type=raw_data_type, - stage='extract_episodes', - duration_seconds=time.perf_counter() - start, - ) - return result - - async def _timed_extract_agent_case(): - start = time.perf_counter() - result = await _extract_agent_case(state, memory_manager) - record_extraction_stage( - space_id=space_id, - raw_data_type=raw_data_type, - stage='extract_agent_case', - duration_seconds=time.perf_counter() - start, - ) - return result - - is_agent_conversation = ( - state.memcell.type == RawDataType.AGENTCONVERSATION - if state.memcell.type - else False - ) - - with timed("extract_memories"): - if is_agent_conversation: - # agent_case must be extracted synchronously: clustering fire-and-forget depends on it - with timed_parallel("parallel_extraction"): - await asyncio.gather( - _timed_extract_episodes(), _timed_extract_agent_case() - ) - else: - # solo non-agent: foresight/atomic_fact moved to background task - # team scene: foresight/atomic_fact never extracted here either - await _timed_extract_episodes() - record_extraction_stage( - space_id=space_id, - raw_data_type=raw_data_type, - stage='extract_parallel', - duration_seconds=time.perf_counter() - extract_start, - ) - - # Record extracted counts - episodes_count = len(state.group_episode_memories) + len(state.episode_memories) - if episodes_count > 0: - record_memory_extracted( - space_id=space_id, - raw_data_type=raw_data_type, - memory_type='episode', - count=episodes_count, - ) - if state.agent_case: - record_memory_extracted( - space_id=space_id, - raw_data_type=raw_data_type, - memory_type='agent_case', - count=1, - ) - - # 3. Fire-and-forget clustering + profile extraction (no data dependency on step 4) - async def _clustering_with_metrics(): - cluster_start = time.perf_counter() - try: - await _update_memcell_and_cluster(state) - except Exception as e: - logger.error(f"[MemCell Processing] ❌ Background clustering failed: {e}") - finally: - record_extraction_stage( - space_id=space_id, - raw_data_type=raw_data_type, - stage='update_memcell_cluster', - duration_seconds=time.perf_counter() - cluster_start, - ) - - asyncio.create_task(_clustering_with_metrics()) - - # 4. Save memories - memories_count = 0 - if if_memorize(memcell): - save_start = time.perf_counter() - memories_count = await _process_memories(state) - record_extraction_stage( - space_id=space_id, - raw_data_type=raw_data_type, - stage='process_memories', - duration_seconds=time.perf_counter() - save_start, - ) - # Fire-and-forget: extract and save foresight/atomic_fact in background. - # Solo scenes only; episode_saved confirms parent_doc is available for linking. - if state.is_solo_scene and state.episode_saved and not DEFAULT_MEMORIZE_CONFIG.skip_foresight_and_eventlog: - asyncio.create_task( - _foresight_and_atomic_facts_with_metrics(state, memory_manager) - ) - - return memories_count - - -async def _init_extraction_state( - memcell: MemCell, request: MemorizeRequest, current_time: datetime -) -> ExtractionState: - """Initialize extraction state""" - scene = request.scene or ScenarioType.SOLO.value - is_solo_scene = scene.lower() == ScenarioType.SOLO - participants = list(set(memcell.participants)) if memcell.participants else [] - - return ExtractionState( - memcell=memcell, - request=request, - current_time=current_time, - scene=scene, - is_solo_scene=is_solo_scene, - participants=participants, - ) - - -async def _extract_episodes(state: ExtractionState, memory_manager: MemoryManager): - """Extract group and personal Episodes""" - if state.is_solo_scene: - logger.info("[MemCell Processing] solo scene, only extract group Episode") - tasks = [_create_episode_task(state, memory_manager, None)] - else: - logger.info( - f"[MemCell Processing] team scene, extract group + {len(state.participants)} personal Episodes" - ) - tasks = [_create_episode_task(state, memory_manager, None)] - tasks.extend( - [ - _create_episode_task(state, memory_manager, uid) - for uid in state.participants - ] - ) - - results = await asyncio.gather(*tasks, return_exceptions=True) - from common_utils.async_utils import reraise_critical_errors - - reraise_critical_errors(results) - _process_episode_results(state, results) - - -def _create_episode_task( - state: ExtractionState, memory_manager: MemoryManager, user_id: Optional[str] -): - """Create Episode extraction task""" - return memory_manager.extract_memory( - memcell=state.memcell, - memory_type=MemoryType.EPISODIC_MEMORY, - user_id=user_id, - group_id=state.request.group_id, - ) - - -def _process_episode_results(state: ExtractionState, results: List[Any]): - """Process Episode extraction results""" - # Group Episode - group_episode = results[0] if results else None - if isinstance(group_episode, Exception): - logger.error( - f"[MemCell Processing] ❌ Group Episode exception: {group_episode}" - ) - group_episode = None - elif group_episode: - group_episode.parent_type = state.episode_parent_type - group_episode.parent_id = state.parent_id - state.group_episode_memories.append(group_episode) - state.group_episode = group_episode - logger.info("[MemCell Processing] ✅ Group Episode extracted successfully") - - # Personal Episodes - if not state.is_solo_scene: - for user_id, result in zip(state.participants, results[1:]): - if isinstance(result, Exception): - logger.error( - f"[MemCell Processing] ❌ Personal Episode exception: user_id={user_id}" - ) - continue - if result: - result.parent_type = state.episode_parent_type - result.parent_id = state.parent_id - state.episode_memories.append(result) - logger.info( - f"[MemCell Processing] ✅ Personal Episode successful: user_id={user_id}" - ) - - -async def _update_memcell_and_cluster(state: ExtractionState): - """Trigger clustering for the current MemCell""" - if not state.request.group_id or not state.group_episode: - return - - try: - cluster_config = DEFAULT_MEMORIZE_CONFIG - - await _trigger_clustering( - state.request.group_id, - state.memcell, - state.scene, - config=cluster_config, - episode_text=state.group_episode.episode, - agent_case=state.agent_case, - ) - logger.info( - f"[MemCell Processing] ✅ Clustering completed (scene={state.scene})" - ) - except Exception as e: - logger.error(f"[MemCell Processing] ❌ Failed to trigger clustering: {e}") - - -async def _process_memories(state: ExtractionState) -> int: - """Save Episodes and AgentCase. - - Foresight/AtomicFact are handled by the background task - _foresight_and_atomic_facts_with_metrics (fire-and-forget after this returns). - - Returns: - int: Total number of memories saved - """ - # NOTE: load_core_memories disabled - CoreMemoryRawRepository not implemented - # await load_core_memories(state.request, state.participants, state.current_time) - - episodic_source = state.group_episode_memories + state.episode_memories - episodes_to_save = list(episodic_source) - - # solo scene: copy group Episode to each user - if state.is_solo_scene and state.group_episode_memories: - episodes_to_save.extend(_clone_episodes_for_users(state)) - - episodes_count = 0 - agent_case_count = 0 - - if episodes_to_save: - with timed("persist_episodes"): - await _save_episodes(state, episodes_to_save, episodic_source) - episodes_count = len(episodes_to_save) - - # Save AgentCase (agent conversation only) - if state.agent_case: - with timed("persist_agent_case"): - agent_case_count = await _save_agent_case(state) - - with timed("update_memcell_status"): - await update_status_after_memcell( - state.request, - state.memcell, - state.current_time, - state.request.raw_data_type, - ) - - return episodes_count + agent_case_count - - -async def _extract_foresights( - state: ExtractionState, memory_manager: MemoryManager -) -> List[Foresight]: - """Extract Foresight from memcell (solo scene only).""" - result = await memory_manager.extract_memory( - memcell=state.memcell, memory_type=MemoryType.FORESIGHT, user_id=None - ) - if isinstance(result, Exception) or not result: - return [] - for mem in result: - mem.group_id = state.request.group_id - mem.parent_type = state.foresight_parent_type - mem.parent_id = state.parent_id - return result - - -def _should_skip_atomic_fact_for_agent(memcell: MemCell) -> bool: - """Check if atomic fact extraction should be skipped for an agent conversation. - - Skip when there are tool calls and the cumulative assistant response - is >= 1000 chars. Atomic facts from long tool-assisted conversations - add little value but cost an extra LLM call; skipping speeds up the pipeline. - """ - original_data = memcell.original_data or [] - - # Unwrap from { "message": {...}, "parse_info": ... } format - messages = [] - for item in original_data: - if not isinstance(item, dict): - continue - msg = item.get("message", item) - messages.append(msg) - - has_tool_calls = any( - msg.get("tool_calls") or msg.get("role") == "tool" for msg in messages - ) - if not has_tool_calls: - return False - - total_length = 0 - for msg in messages: - if msg.get("role") == "assistant" and not msg.get("tool_calls"): - total_length += len(get_text_from_content_items(msg.get("content"))) - return total_length >= 1000 - - -async def _extract_atomic_facts( - state: ExtractionState, memory_manager: MemoryManager -) -> List[AtomicFact]: - """Extract AtomicFact from memcell (solo scene only).""" - result = await memory_manager.extract_memory( - memcell=state.memcell, memory_type=MemoryType.ATOMIC_FACT, user_id=None - ) - if isinstance(result, Exception) or not result: - return [] - result.group_id = state.request.group_id - result.parent_type = state.atomic_fact_parent_type - result.parent_id = state.parent_id - return [result] - - -async def _extract_agent_case(state: ExtractionState, memory_manager: MemoryManager): - """Extract AgentCase from memcell (agent conversation only).""" - result = await memory_manager.extract_memory( - memcell=state.memcell, - memory_type=MemoryType.AGENT_CASE, - group_id=state.request.group_id, - ) - if isinstance(result, Exception) or not result: - return None - state.agent_case = result - return result - - -async def _save_agent_case(state: ExtractionState) -> int: - """Save AgentCase to database. - - Returns: - int: Number of agent experiences saved (0 or 1) - """ - try: - agent_case = state.agent_case - doc = _convert_agent_case_to_doc( - agent_case, - state.memcell, - state.current_time, - session_id=state.request.session_id, - ) - payloads = [MemoryDocPayload(MemoryType.AGENT_CASE, doc)] - await save_memory_docs(payloads) - logger.info( - f"[MemCell Processing] AgentCase saved: intent='{agent_case.task_intent[:80]}'" - ) - return 1 - except Exception as e: - logger.error(f"[MemCell Processing] Failed to save AgentCase: {e}") - return 0 - - -def _clone_episodes_for_users(state: ExtractionState) -> List[EpisodeMemory]: - """Copy group Episode to each user""" - from dataclasses import replace - - cloned = [] - group_ep = state.group_episode_memories[0] - for user_id in state.participants: - cloned.append(replace(group_ep, user_id=user_id, user_name=user_id)) - logger.info(f"[MemCell Processing] Copied group Episode to {len(cloned)} users") - return cloned - - -async def _save_episodes( - state: ExtractionState, - episodes_to_save: List[EpisodeMemory], - episodic_source: List[EpisodeMemory], -): - """Save Episodes to database""" - for ep in episodes_to_save: - if getattr(ep, "user_name", None) is None: - ep.user_name = ep.user_id - - docs = [ - _convert_episode_memory_to_doc( - ep, state.current_time, session_id=state.request.session_id - ) - for ep in episodes_to_save - ] - payloads = [MemoryDocPayload(MemoryType.EPISODIC_MEMORY, doc) for doc in docs] - saved_map = await save_memory_docs(payloads) - saved_docs = saved_map.get(MemoryType.EPISODIC_MEMORY, []) - - for ep, saved_doc in zip(episodic_source, saved_docs): - ep.id = str(saved_doc.id) - state.parent_docs_map[str(saved_doc.id)] = saved_doc - - -async def _save_foresight_and_atomic_fact( - state: ExtractionState, - foresight_memories: List[Foresight], - atomic_facts: List[AtomicFact], -): - """Save Foresight and AtomicFact (after episode saved)""" - # Get the saved doc of group episode as parent_doc - parent_doc = None - if state.group_episode_memories: - ep_id = state.group_episode_memories[0].id - if ep_id: - parent_doc = state.parent_docs_map.get(ep_id) - - if not parent_doc: - logger.warning( - "[MemCell Processing] No parent_doc for foresight/atomic_fact, skip saving" - ) - return - - session_id = state.request.session_id - foresight_docs = [ - _convert_foresight_to_doc( - mem, parent_doc, state.current_time, session_id=session_id - ) - for mem in foresight_memories - ] - - atomic_fact_docs = [] - for el in atomic_facts: - atomic_fact_docs.extend( - _convert_atomic_fact_to_docs( - el, parent_doc, state.current_time, session_id=session_id - ) - ) - - # solo scene: copy to each user - if state.is_solo_scene: - user_ids = list(state.participants) - foresight_docs.extend( - [ - doc.model_copy(update={"user_id": uid, "user_name": uid}) - for doc in foresight_docs - for uid in user_ids - ] - ) - atomic_fact_docs.extend( - [ - doc.model_copy(update={"user_id": uid, "user_name": uid}) - for doc in atomic_fact_docs - for uid in user_ids - ] - ) - logger.info( - f"[MemCell Processing] Copied Foresight/AtomicFact to {len(user_ids)} users" - ) - - payloads = [] - payloads.extend( - MemoryDocPayload(MemoryType.FORESIGHT, doc) for doc in foresight_docs - ) - payloads.extend( - MemoryDocPayload(MemoryType.ATOMIC_FACT, doc) for doc in atomic_fact_docs - ) - if payloads: - await save_memory_docs(payloads) - - -async def _foresight_and_atomic_facts_with_metrics( - state: ExtractionState, memory_manager: MemoryManager -) -> None: - """Background task: extract and save foresight and atomic_fact (solo scene only). - - Fired after _save_episodes completes so state.parent_docs_map is populated. - Exceptions are caught and logged; never propagate to caller. - - space_id and raw_data_type are derived inside the function: - - asyncio.create_task copies current contextvars.Context, so get_space_id_for_metrics() works. - - raw_data_type is derived from state.memcell.type. - """ - space_id = get_space_id_for_metrics() - raw_data_type = state.memcell.type.value if state.memcell.type else 'unknown' - start = time.perf_counter() - try: - should_skip = _should_skip_atomic_fact_for_agent(state.memcell) - if should_skip: - foresight_memories = await _extract_foresights(state, memory_manager) - atomic_facts: List[AtomicFact] = [] - else: - foresight_memories, atomic_facts = await asyncio.gather( - _extract_foresights(state, memory_manager), - _extract_atomic_facts(state, memory_manager), - ) - if foresight_memories: - record_memory_extracted( - space_id=space_id, - raw_data_type=raw_data_type, - memory_type='foresight', - count=len(foresight_memories), - ) - if atomic_facts: - record_memory_extracted( - space_id=space_id, - raw_data_type=raw_data_type, - memory_type='atomic_fact', - count=len(atomic_facts), - ) - if foresight_memories or atomic_facts: - await _save_foresight_and_atomic_fact( - state, foresight_memories, atomic_facts - ) - except Exception as e: - logger.error( - f"[ForesightAF] ❌ Background extraction/save failed: {e}", exc_info=True - ) - finally: - record_extraction_stage( - space_id=space_id, - raw_data_type=raw_data_type, - stage='foresight_and_atomic_facts_bg', - duration_seconds=time.perf_counter() - start, - ) - - -from core.observation.tracing.decorators import trace_logger - - -@trace_logger(operation_name="mem_memorize preprocess_conv_request", log_level="info") -async def preprocess_conv_request( - request: MemorizeRequest, current_time: datetime -) -> MemorizeRequest: - """ - Simplified request preprocessing: - 1. Get last_memcell_time from status table to determine current memcell start - 2. Read historical messages from conversation_data_repo (only messages after last_memcell_time) - 3. Set historical messages as history_raw_data_list - 4. Set current new message as new_raw_data_list - 5. Boundary detection handled by subsequent logic (will clear or retain after detection) - """ - - logger.info(f"[preprocess] Start processing: group_id={request.group_id}") - - # Check if there is new data - if not request.new_raw_data_list: - if not request.flush: - logger.info("[preprocess] No new data, skip processing") - return None - # flush=True with no new messages: load accumulated messages as history - # so the extractor can flush them into a final MemCell - logger.info( - "[preprocess] Flush with no new data: loading accumulated messages as history" - ) - conversation_data_repo = get_bean_by_type(ConversationDataRepository) - status_repo = get_bean_by_type(ConversationStatusRawRepository) - try: - start_time = None - status = await status_repo.get_by_group_id( - request.group_id, session_id=request.session_id - ) - if status and status.last_memcell_time: - start_time = status.last_memcell_time - accumulated = await conversation_data_repo.get_conversation_data( - group_id=request.group_id, - session_id=request.session_id, - start_time=start_time, - end_time=None, - limit=1000, - exclude_message_ids=[], - ) - if not accumulated: - logger.info( - "[preprocess] Flush: no accumulated messages, nothing to process" - ) - return None - request.history_raw_data_list = accumulated - # new_raw_data_list stays empty; extractor handles flush+empty-new case - logger.info( - f"[preprocess] Flush: loaded {len(accumulated)} accumulated messages as history" - ) - return request - except Exception as e: - logger.error(f"[preprocess] Flush data read failed: {e}") - traceback.print_exc() - return None - - # Use conversation_data_repo for read-then-store operation - conversation_data_repo = get_bean_by_type(ConversationDataRepository) - status_repo = get_bean_by_type(ConversationStatusRawRepository) - - try: - # Extract message_ids from new_raw_data_list to exclude them - new_message_ids = [r.data_id for r in request.new_raw_data_list if r.data_id] - - # Step 0: Get last_memcell_time to filter history (only get current memcell's messages) - start_time = None - status = await status_repo.get_by_group_id( - request.group_id, session_id=request.session_id - ) - if status and status.last_memcell_time: - start_time = status.last_memcell_time - logger.info( - f"[preprocess] Using last_memcell_time as start_time: {start_time}" - ) - - # Step 1: Get historical messages, excluding current request's messages - # Only get messages after last_memcell_time (current memcell's accumulated messages) - history_raw_data_list = await conversation_data_repo.get_conversation_data( - group_id=request.group_id, - session_id=request.session_id, - start_time=start_time, - end_time=None, - limit=1000, - exclude_message_ids=new_message_ids, - ) - - logger.info( - f"[preprocess] Read {len(history_raw_data_list)} historical messages (excluded {len(new_message_ids)} new, start_time={start_time})" - ) - - # Update request - request.history_raw_data_list = history_raw_data_list - # new_raw_data_list remains unchanged (the newly passed messages) - - logger.info( - f"[preprocess] Completed: {len(history_raw_data_list)} historical, {len(request.new_raw_data_list)} new messages" - ) - - return request - - except Exception as e: - logger.error(f"[preprocess] Data read failed: {e}") - traceback.print_exc() - # Use original request if read fails - return request - - -async def update_status_when_no_memcell( - request: MemorizeRequest, - status_result: StatusResult, - current_time: datetime, - data_type: RawDataType, -): - if data_type in (RawDataType.CONVERSATION, RawDataType.AGENTCONVERSATION): - # Try to update status table - try: - status_repo = get_bean_by_type(ConversationStatusRawRepository) - - if status_result.should_wait: - logger.info( - f"[mem_memorize] Determined as unable to decide boundary, continue waiting, no status update" - ) - return - else: - logger.info( - f"[mem_memorize] Determined as non-boundary, continue accumulating messages, update status table" - ) - # Get latest message timestamp - latest_time = to_iso_format(current_time) - if request.new_raw_data_list: - last_msg = request.new_raw_data_list[-1] - if hasattr(last_msg, 'content') and isinstance( - last_msg.content, dict - ): - latest_time = last_msg.content.get('timestamp', latest_time) - elif hasattr(last_msg, 'timestamp'): - latest_time = last_msg.timestamp - - if not latest_time: - latest_time = min(latest_time, current_time) - - # Use encapsulated function to update conversation continuation status - await _update_status_for_continuing_conversation( - status_repo, request, latest_time, current_time - ) - - except Exception as e: - logger.error(f"Failed to update status table: {e}") - else: - pass - - -async def update_status_after_memcell( - request: MemorizeRequest, - memcell: MemCell, - current_time: datetime, - data_type: RawDataType, -): - if data_type in (RawDataType.CONVERSATION, RawDataType.AGENTCONVERSATION): - # Update last_memcell_time in status table to memcell's timestamp - try: - status_repo = get_bean_by_type(ConversationStatusRawRepository) - - # Get MemCell's timestamp - memcell_time = None - if memcell and hasattr(memcell, 'timestamp'): - memcell_time = memcell.timestamp - else: - memcell_time = current_time - - # Use encapsulated function to update status after MemCell extraction - await _update_status_after_memcell_extraction( - status_repo, request, memcell_time, current_time - ) - - logger.info( - f"[mem_memorize] Memory extraction completed, status table updated" - ) - - except Exception as e: - logger.error(f"Final status table update failed: {e}") - else: - pass - - -async def save_memory_docs( - doc_payloads: List[MemoryDocPayload], version: Optional[str] = None -) -> Dict[MemoryType, List[Any]]: - """ - Generic Doc saving function, automatically saves and synchronizes by MemoryType enum - """ - - grouped_docs: Dict[MemoryType, List[Any]] = defaultdict(list) - for payload in doc_payloads: - if payload and payload.doc: - grouped_docs[payload.memory_type].append(payload.doc) - - saved_result: Dict[MemoryType, List[Any]] = {} - - # Episodic - episodic_docs = grouped_docs.get(MemoryType.EPISODIC_MEMORY, []) - if episodic_docs: - episodic_repo = get_bean_by_type(EpisodicMemoryRawRepository) - episodic_es_repo = get_bean_by_type(EpisodicMemoryEsRepository) - episodic_milvus_repo = get_bean_by_type(EpisodicMemoryMilvusRepository) - saved_episodic: List[Any] = [] - - for doc in episodic_docs: - saved_doc = await episodic_repo.append_episodic_memory(doc) - saved_episodic.append(saved_doc) - - es_doc = EpisodicMemoryConverter.from_mongo(saved_doc) - await episodic_es_repo.create(es_doc) - - milvus_entity = EpisodicMemoryMilvusConverter.from_mongo(saved_doc) - vector = ( - milvus_entity.get("vector") if isinstance(milvus_entity, dict) else None - ) - if vector and len(vector) > 0: - await episodic_milvus_repo.insert(milvus_entity, flush=False) - else: - logger.warning( - "[mem_memorize] Skipping write to Milvus: vector empty or missing, event_id=%s", - getattr(saved_doc, "event_id", None), - ) - - saved_result[MemoryType.EPISODIC_MEMORY] = saved_episodic - - # Foresight - foresight_docs = grouped_docs.get(MemoryType.FORESIGHT, []) - if foresight_docs: - foresight_repo = get_bean_by_type(ForesightRecordRawRepository) - saved_foresight = await foresight_repo.create_batch(foresight_docs) - saved_result[MemoryType.FORESIGHT] = saved_foresight - - sync_service = get_bean_by_type(MemorySyncService) - await sync_service.sync_batch_foresights( - saved_foresight, sync_to_es=True, sync_to_milvus=True - ) - - # Atomic Fact - atomic_fact_docs = grouped_docs.get(MemoryType.ATOMIC_FACT, []) - if atomic_fact_docs: - atomic_fact_repo = get_bean_by_type(AtomicFactRecordRawRepository) - saved_atomic_facts = await atomic_fact_repo.create_batch(atomic_fact_docs) - saved_result[MemoryType.ATOMIC_FACT] = saved_atomic_facts - - sync_service = get_bean_by_type(MemorySyncService) - await sync_service.sync_batch_atomic_facts( - saved_atomic_facts, sync_to_es=True, sync_to_milvus=True - ) - - # Agent Case - agent_case_docs = grouped_docs.get(MemoryType.AGENT_CASE, []) - if agent_case_docs: - from infra_layer.adapters.out.persistence.repository.agent_case_raw_repository import ( - AgentCaseRawRepository, - ) - from infra_layer.adapters.out.search.elasticsearch.converter.agent_case_converter import ( - AgentCaseConverter, - ) - from infra_layer.adapters.out.search.repository.agent_case_es_repository import ( - AgentCaseEsRepository, - ) - from infra_layer.adapters.out.search.milvus.converter.agent_case_milvus_converter import ( - AgentCaseMilvusConverter, - ) - from infra_layer.adapters.out.search.repository.agent_case_milvus_repository import ( - AgentCaseMilvusRepository, - ) - - agent_case_repo = get_bean_by_type(AgentCaseRawRepository) - saved_agent_cases: List[Any] = [] - - for doc in agent_case_docs: - saved_doc = await agent_case_repo.append_experience(doc) - saved_agent_cases.append(saved_doc) - - # ES sync - try: - agent_case_es_repo = get_bean_by_type(AgentCaseEsRepository) - es_doc = AgentCaseConverter.from_mongo(saved_doc) - await agent_case_es_repo.create(es_doc) - except Exception as es_exc: - logger.warning(f"[mem_memorize] AgentCase ES sync failed: {es_exc}") - - # Milvus sync - try: - agent_case_milvus_repo = get_bean_by_type(AgentCaseMilvusRepository) - milvus_entity = AgentCaseMilvusConverter.from_mongo(saved_doc) - vector = ( - milvus_entity.get("vector") - if isinstance(milvus_entity, dict) - else None - ) - if vector and len(vector) > 0: - await agent_case_milvus_repo.insert(milvus_entity, flush=False) - else: - logger.warning( - "[mem_memorize] Skipping AgentCase Milvus write: vector empty or missing" - ) - except Exception as milvus_exc: - logger.warning( - f"[mem_memorize] AgentCase Milvus sync failed: {milvus_exc}" - ) - - saved_result[MemoryType.AGENT_CASE] = saved_agent_cases - - # # Profile - # profile_docs = grouped_docs.get(MemoryType.PROFILE, []) - # if profile_docs: - # group_user_profile_repo = get_bean_by_type(GroupUserProfileMemoryRawRepository) - # saved_profiles = [] - # for profile_mem in profile_docs: - # try: - # await _save_profile_memory_to_group_user_profile_memory( - # profile_mem, group_user_profile_repo, version - # ) - # saved_profiles.append(profile_mem) - # except Exception as exc: - # logger.error(f"Failed to save Profile memory: {exc}") - # if saved_profiles: - # saved_result[MemoryType.PROFILE] = saved_profiles - - # group_profile_docs = grouped_docs.get( - # "group_profile", [] - # ) # MemoryType.GROUP_PROFILE, []) - # if group_profile_docs: - # group_profile_repo = get_bean_by_type(GroupProfileRawRepository) - # saved_group_profiles = [] - # for mem in group_profile_docs: - # try: - # await _save_group_profile_memory(mem, group_profile_repo, version) - # saved_group_profiles.append(mem) - # except Exception as exc: - # logger.error(f"Failed to save Group Profile memory: {exc}") - # if saved_group_profiles: - # saved_result["group_profile"] = ( - # saved_group_profiles # MemoryType.GROUP_PROFILE] = saved_group_profiles - # ) - - return saved_result - - -async def memorize(request: MemorizeRequest) -> int: - """ - Main memory extraction process (global queue version) - - Flow: - 1. Save request logs and confirm them (sync_status: -1 -> 0) - 2. Get historical conversation data - 3. Extract MemCell (boundary detection) - 4. Save MemCell to database - 5. Process memory extraction - - Returns: - int: Number of memories extracted (0 if no boundary detected or extraction failed) - """ - logger.info(f"[mem_memorize] request.current_time: {request.current_time}") - - # Get current time - if request.current_time: - current_time = request.current_time - else: - current_time = get_now_with_timezone() + timedelta(seconds=1) - logger.info(f"[mem_memorize] Current time: {current_time}") - - conversation_data_repo = get_bean_by_type(ConversationDataRepository) - - # Note: Request logs are saved in controller layer for better timing control - # (sync_status=-1, will be confirmed later based on boundary detection result) - - # ===== Preprocess and get historical data ===== - llm_custom_setting = None - if request.raw_data_type in ( - RawDataType.CONVERSATION, - RawDataType.AGENTCONVERSATION, - ): - with timed("validate_request"): - request = await preprocess_conv_request(request, current_time) - if request == None: - logger.warning(f"[mem_memorize] preprocess_conv_request returned None") - return 0 - - # Fetch llm_custom_setting from global config (inherits automatically) - # Note: llm_custom_setting is only stored in global config (group_id=None) - llm_custom_setting = await _load_llm_custom_setting() - if llm_custom_setting: - logger.info( - f"[mem_memorize] Using llm_custom_setting from global config for group {request.group_id}" - ) - - # Boundary detection - # Get metrics labels - space_id = get_space_id_for_metrics() - raw_data_type = request.raw_data_type.value if request.raw_data_type else 'unknown' - - logger.info("=" * 80) - logger.info(f"[Boundary Detection] Start detection: group_id={request.group_id}") - logger.info( - f"[Boundary Detection] Temporary stored historical messages: {len(request.history_raw_data_list)} messages" - ) - logger.info( - f"[Boundary Detection] New messages: {len(request.new_raw_data_list)} messages" - ) - logger.info("=" * 80) - - # Initialize MemoryManager with custom config - memory_manager = MemoryManager(llm_config=llm_custom_setting) - - memcell_start = time.perf_counter() - with timed("extract_memcell"): - memcell_result = await memory_manager.extract_memcell( - request.history_raw_data_list, - request.new_raw_data_list, - request.raw_data_type, - request.group_id, - [], - flush=request.flush, - ) - record_extraction_stage( - space_id=space_id, - raw_data_type=raw_data_type, - stage='extract_memcell', - duration_seconds=time.perf_counter() - memcell_start, - ) - logger.debug( - f"[mem_memorize] Extracting MemCell took: {time.perf_counter() - memcell_start} seconds" - ) - - memcells, status_result = memcell_result - - # Check boundary detection result - logger.info("=" * 80) - logger.info( - f"[Boundary Detection Result] memcells={len(memcells)}, " - f"should_wait={status_result.should_wait}" - ) - logger.info("=" * 80) - - if not memcells: - # No boundary detected, confirm current messages to accumulation (sync_status: -1 -> 0) - with timed("update_message_status"): - await conversation_data_repo.save_conversation_data( - request.new_raw_data_list, - request.group_id, - session_id=request.session_id, - ) - logger.info( - f"[mem_memorize] No boundary, confirmed {len(request.new_raw_data_list)} messages to accumulation" - ) - await update_status_when_no_memcell( - request, status_result, current_time, request.raw_data_type - ) - return 0 - - # Determine which messages were consumed by the extracted MemCells. - # MemCells are produced front-to-back, so consumed messages are a prefix of all_msgs. - total_consumed = sum(len(mc.original_data) for mc in memcells if mc.original_data) - all_raw_data = request.history_raw_data_list + request.new_raw_data_list - remaining_raw_data = all_raw_data[total_consumed:] - remaining_ids = [r.data_id for r in remaining_raw_data if r.data_id] - - try: - if request.flush: - # Flush: all messages have been consumed, clear the window - delete_success = await conversation_data_repo.delete_conversation_data( - request.group_id, session_id=request.session_id, exclude_message_ids=[] - ) - if delete_success: - logger.debug( - f"[mem_memorize] Flush mode: all messages marked as used, " - f"group_id={request.group_id}" - ) - else: - logger.warning( - f"[mem_memorize] Failed to clear conversation history: group_id={request.group_id}" - ) - else: - # Non-flush: consumed messages marked as used, remaining start next window - delete_success = await conversation_data_repo.delete_conversation_data( - request.group_id, - session_id=request.session_id, - exclude_message_ids=remaining_ids, - ) - if delete_success: - logger.debug( - f"[mem_memorize] Consumed messages marked as used " - f"(remaining={len(remaining_raw_data)}): group_id={request.group_id}" - ) - else: - logger.warning( - f"[mem_memorize] Failed to mark consumed messages: group_id={request.group_id}" - ) - if remaining_raw_data: - await conversation_data_repo.save_conversation_data( - remaining_raw_data, request.group_id, session_id=request.session_id - ) - except Exception as e: - logger.error( - f"[mem_memorize] Exception while marking conversation history: {e}" - ) - traceback.print_exc() - - # Save and process all extracted MemCells - memories_count = 0 - - try: - for memcell in memcells: - with timed("persist_memcell"): - memcell = await _save_memcell_to_database( - memcell, current_time, session_id=request.session_id - ) - logger.info(f"[mem_memorize] Saved MemCell: {memcell.event_id}") - with timed("process_memory_extraction"): - count = await process_memory_extraction( - memcell, request, memory_manager, current_time - ) - memories_count += count - - logger.info( - f"[mem_memorize] ✅ Memory extraction completed, " - f"memcells={len(memcells)}, total_memories={memories_count}" - ) - return memories_count - except Exception as e: - logger.error(f"[mem_memorize] ❌ Memory extraction failed: {e}") - traceback.print_exc() - return 0 diff --git a/methods/EverCore/src/biz_layer/mem_sync.py b/methods/EverCore/src/biz_layer/mem_sync.py deleted file mode 100644 index 33f4dd36..00000000 --- a/methods/EverCore/src/biz_layer/mem_sync.py +++ /dev/null @@ -1,267 +0,0 @@ -"""Foresight and atomic fact synchronization service - -Responsible for writing unified foresight and atomic facts into Milvus / Elasticsearch. -""" - -from typing import Optional, List, Dict, Any -import logging -from datetime import datetime - -from infra_layer.adapters.out.persistence.document.memory.foresight_record import ( - ForesightRecord, -) -from infra_layer.adapters.out.search.elasticsearch.converter.foresight_converter import ( - ForesightConverter, -) -from infra_layer.adapters.out.search.milvus.converter.foresight_milvus_converter import ( - ForesightMilvusConverter, -) -from infra_layer.adapters.out.persistence.document.memory.atomic_fact_record import ( - AtomicFactRecord, -) -from infra_layer.adapters.out.search.elasticsearch.converter.atomic_fact_converter import ( - AtomicFactConverter, -) -from infra_layer.adapters.out.search.milvus.converter.atomic_fact_milvus_converter import ( - AtomicFactMilvusConverter, -) -from infra_layer.adapters.out.search.repository.foresight_milvus_repository import ( - ForesightMilvusRepository, -) -from infra_layer.adapters.out.search.repository.atomic_fact_milvus_repository import ( - AtomicFactMilvusRepository, -) -from infra_layer.adapters.out.search.repository.foresight_es_repository import ( - ForesightEsRepository, -) -from infra_layer.adapters.out.search.repository.atomic_fact_es_repository import ( - AtomicFactEsRepository, -) -from core.di import get_bean_by_type, service -from common_utils.datetime_utils import get_now_with_timezone - -logger = logging.getLogger(__name__) - - -@service(name="memory_sync_service", primary=True) -class MemorySyncService: - """Foresight and atomic fact synchronization service""" - - def __init__( - self, - foresight_milvus_repo: Optional[ForesightMilvusRepository] = None, - atomic_fact_milvus_repo: Optional[AtomicFactMilvusRepository] = None, - foresight_es_repo: Optional[ForesightEsRepository] = None, - atomic_fact_es_repo: Optional[AtomicFactEsRepository] = None, - ): - """Initialize synchronization service - - Args: - foresight_milvus_repo: Foresight Milvus repository instance (optional, obtained from DI if not provided) - atomic_fact_milvus_repo: Atomic fact Milvus repository instance (optional, obtained from DI if not provided) - foresight_es_repo: Foresight ES repository instance (optional, obtained from DI if not provided) - atomic_fact_es_repo: Atomic fact ES repository instance (optional, obtained from DI if not provided) - """ - self.foresight_milvus_repo = foresight_milvus_repo or get_bean_by_type( - ForesightMilvusRepository - ) - self.atomic_fact_milvus_repo = atomic_fact_milvus_repo or get_bean_by_type( - AtomicFactMilvusRepository - ) - self.foresight_es_repo = foresight_es_repo or get_bean_by_type( - ForesightEsRepository - ) - self.atomic_fact_es_repo = atomic_fact_es_repo or get_bean_by_type( - AtomicFactEsRepository - ) - - logger.info("MemorySyncService initialization completed") - - @staticmethod - def _normalize_datetime(value: Optional[datetime | str]) -> Optional[datetime]: - """Convert str/None to datetime (supports date-only strings)""" - if isinstance(value, datetime): - return value - if isinstance(value, str) and value: - try: - return datetime.fromisoformat(value) - except ValueError: - try: - return datetime.strptime(value, "%Y-%m-%d") - except ValueError: - logger.warning("Unable to parse date string: %s", value) - return None - return None - - async def sync_foresight( - self, - foresight: ForesightRecord, - sync_to_es: bool = True, - sync_to_milvus: bool = True, - ) -> Dict[str, int]: - """Synchronize a single foresight to Milvus/ES - - Args: - foresight: ForesightRecord document object - sync_to_es: Whether to sync to ES (default True) - sync_to_milvus: Whether to sync to Milvus (default True) - - Returns: - Synchronization statistics {"foresight": 1} - """ - stats = {"foresight": 0, "es_records": 0} - - try: - # Read embedding from MongoDB, skip if not exists - if not foresight.vector: - logger.warning( - f"Foresight {foresight.id} has no embedding, skipping sync" - ) - return stats - - # Sync to Milvus - if sync_to_milvus: - # Use converter to generate Milvus entity - milvus_entity = ForesightMilvusConverter.from_mongo(foresight) - await self.foresight_milvus_repo.insert(milvus_entity, flush=False) - stats["foresight"] += 1 - logger.debug(f"Foresight synced to Milvus: {foresight.id}") - - # Sync to ES - if sync_to_es: - # Use converter to generate correct ES document (including jieba tokenized search_content) - es_doc = ForesightConverter.from_mongo(foresight) - await self.foresight_es_repo.create(es_doc) - stats["es_records"] += 1 - logger.debug(f"Foresight synced to ES: {foresight.id}") - - except Exception as e: - logger.error(f"Failed to sync foresight: {e}", exc_info=True) - raise - - return stats - - async def sync_atomic_fact( - self, - atomic_fact_record: AtomicFactRecord, - sync_to_es: bool = True, - sync_to_milvus: bool = True, - ) -> Dict[str, int]: - """Synchronize a single atomic fact to Milvus/ES - - Args: - atomic_fact_record: AtomicFactRecord document object - sync_to_es: Whether to sync to ES (default True) - sync_to_milvus: Whether to sync to Milvus (default True) - - Returns: - Synchronization statistics {"atomic_fact": 1} - """ - stats = {"atomic_fact": 0, "es_records": 0} - - try: - # Read existing vector from MongoDB - if not atomic_fact_record.vector: - logger.warning( - f"Atomic fact {atomic_fact_record.id} has no embedding, skipping sync" - ) - return stats - - # Sync to Milvus - if sync_to_milvus: - # Use converter to generate Milvus entity - milvus_entity = AtomicFactMilvusConverter.from_mongo(atomic_fact_record) - await self.atomic_fact_milvus_repo.insert(milvus_entity, flush=False) - stats["atomic_fact"] += 1 - logger.debug(f"Atomic fact synced to Milvus: {atomic_fact_record.id}") - - # Sync to ES - if sync_to_es: - # Use converter to generate correct ES document (including jieba tokenized search_content) - es_doc = AtomicFactConverter.from_mongo(atomic_fact_record) - await self.atomic_fact_es_repo.create(es_doc) - stats["es_records"] += 1 - logger.debug(f"Atomic fact synced to ES: {atomic_fact_record.id}") - - except Exception as e: - logger.error(f"Failed to sync atomic fact: {e}", exc_info=True) - raise - - return stats - - async def sync_batch_foresights( - self, - foresights: List[ForesightRecord], - sync_to_es: bool = True, - sync_to_milvus: bool = True, - ) -> Dict[str, int]: - """Batch synchronize foresights - - Args: - foresights: List of ForesightRecord - sync_to_es: Whether to sync to ES (default True) - sync_to_milvus: Whether to sync to Milvus (default True) - - Returns: - Synchronization statistics - """ - total_stats = {"foresight": 0, "es_records": 0} - - for foresight_mem in foresights: - try: - stats = await self.sync_foresight( - foresight_mem, sync_to_es=sync_to_es, sync_to_milvus=sync_to_milvus - ) - total_stats["foresight"] += stats.get("foresight", 0) - total_stats["es_records"] += stats.get("es_records", 0) - except Exception as e: - logger.error( - f"Failed to batch sync foresight: {foresight_mem.id}, error: {e}", - exc_info=True, - ) - # Do not silently swallow exceptions - - logger.info( - f"✅ Foresight Milvus flush completed: {total_stats['foresight']} records" - ) - - return total_stats - - async def sync_batch_atomic_facts( - self, - atomic_facts: List[AtomicFactRecord], - sync_to_es: bool = True, - sync_to_milvus: bool = True, - ) -> Dict[str, int]: - """Batch synchronize atomic facts - - Args: - atomic_facts: List of AtomicFactRecord - sync_to_es: Whether to sync to ES (default True) - sync_to_milvus: Whether to sync to Milvus (default True) - - Returns: - Synchronization statistics - """ - total_stats = {"atomic_fact": 0, "es_records": 0} - - for fact_record in atomic_facts: - try: - stats = await self.sync_atomic_fact( - fact_record, sync_to_es=sync_to_es, sync_to_milvus=sync_to_milvus - ) - total_stats["atomic_fact"] += stats.get("atomic_fact", 0) - total_stats["es_records"] += stats.get("es_records", 0) - except Exception as e: - logger.error( - f"Failed to batch sync atomic fact: {fact_record.id}, error: {e}", - exc_info=True, - ) - # Do not silently swallow exceptions, let it surface - raise - - logger.info( - f"Atomic fact Milvus flush completed: {total_stats['atomic_fact']} records" - ) - - return total_stats diff --git a/methods/EverCore/src/biz_layer/memorize_config.py b/methods/EverCore/src/biz_layer/memorize_config.py deleted file mode 100644 index f97de895..00000000 --- a/methods/EverCore/src/biz_layer/memorize_config.py +++ /dev/null @@ -1,99 +0,0 @@ -""" -Memory retrieval process configuration - -Centralized management of all trigger conditions and thresholds for easy adjustment and maintenance. -""" - -import logging -from dataclasses import dataclass -import os - -from api_specs.memory_types import ParentType - -logger = logging.getLogger(__name__) - - -@dataclass -class MemorizeConfig: - """Memory retrieval process configuration""" - - # ===== Clustering configuration ===== - # Semantic similarity threshold; memcells exceeding this value will be clustered into the same cluster - cluster_similarity_threshold: float = 0.3 - # Maximum time gap (days); memcells exceeding this gap will not be clustered together - cluster_max_time_gap_days: int = 7 - - # ===== Profile extraction configuration ===== - # Minimum number of memcells required to trigger Profile extraction - profile_min_memcells: int = 1 - # Profile extraction interval: extract once every N memcells (1 = every time) - profile_extraction_interval: int = 1 - # Minimum confidence required for Profile extraction - profile_min_confidence: float = 0.6 - # Whether to enable version control - profile_enable_versioning: bool = True - # Profile maximum items - profile_max_items: int = 25 - - # ===== Parent type configuration ===== - # Default parent type for Episode (memcell or episode) - default_episode_parent_type: str = ParentType.MEMCELL.value - # Default parent type for Foresight (memcell or episode) - default_foresight_parent_type: str = ParentType.MEMCELL.value - # Default parent type for AtomicFact (memcell or episode) - default_atomic_fact_parent_type: str = ParentType.MEMCELL.value - - # ===== Clustering lock configuration ===== - # Timeout (seconds) for acquiring the clustering lock - clustering_lock_timeout: float = 600.0 - # Blocking timeout (seconds) for waiting to acquire the clustering lock - clustering_lock_blocking_timeout: float = 2400.0 - - # ===== Skill extraction lock configuration ===== - # Timeout (seconds) for acquiring the skill extraction lock - skill_extraction_lock_timeout: float = 600.0 - # Blocking timeout (seconds) for waiting to acquire the skill extraction lock - skill_extraction_lock_blocking_timeout: float = 2400.0 - - # ===== Agent Skill extraction configuration ===== - # Minimum quality score (0.0-1.0) of the AgentCase required to trigger - # skill extraction. Cases below this threshold are considered too low - # quality to contribute to skill formation. - skill_min_quality_score: float = 0.2 - # Minimum maturity score (0.0-1.0) for a skill to be retrievable - skill_maturity_threshold: float = 0.6 - # Minimum confidence (0.0-1.0) for a skill to remain active. - # Skills whose confidence drops below this threshold are kept in MongoDB - # (data preserved) but removed from search engines and excluded from - # future extraction context. - skill_retire_confidence: float = 0.1 - - # ===== Skip flags ===== - # Skip skill maturity scoring during skill extraction - skip_skill_maturity_scoring: bool = False - # Skip foresight and eventlog extraction - skip_foresight_and_eventlog: bool = False - # Skip profile extraction - skip_profile_extraction: bool = False - # Enable LLM-based relevance verification for skill search results - enable_skill_llm_verify: bool = False - - -# Select config based on AGENT_MEMORIZE_MODE env var: -# "online" (default) — full pipeline -# "fast_skill" — skip profile/foresight/eventlog, skip maturity scoring -_AGENT_MEMORIZE_MODE = os.getenv("AGENT_MEMORIZE_MODE", "online").strip().lower() - -if _AGENT_MEMORIZE_MODE == "fast_skill": - DEFAULT_MEMORIZE_CONFIG = MemorizeConfig( - skip_skill_maturity_scoring=True, - skip_foresight_and_eventlog=True, - skip_profile_extraction=True, - clustering_lock_blocking_timeout=4800, - skill_extraction_lock_blocking_timeout=4800, - enable_skill_llm_verify=True - ) -else: - if _AGENT_MEMORIZE_MODE != "online": - logger.warning("Unknown AGENT_MEMORIZE_MODE=%r, falling back to 'online'", _AGENT_MEMORIZE_MODE) - DEFAULT_MEMORIZE_CONFIG = MemorizeConfig() diff --git a/methods/EverCore/src/biz_layer/retrieve_constants.py b/methods/EverCore/src/biz_layer/retrieve_constants.py deleted file mode 100644 index 540c0151..00000000 --- a/methods/EverCore/src/biz_layer/retrieve_constants.py +++ /dev/null @@ -1,66 +0,0 @@ -""" -Memory Retrieval Configuration Constants - -This module defines constants related to memory retrieval behavior. -These constants can be configured via environment variables. -""" - -import os - -# =================== -# Retrieve Configuration (configurable via .env) -# =================== - -# === Unlimited Mode Configuration (top_k=-1) === -# Maximum return count when in unlimited mode -# Configurable via TOPK_LIMIT env var, default: 100 -DEFAULT_TOPK_LIMIT = int(os.getenv("TOPK_LIMIT", "100")) - -# Magic constant: Maximum group_ids array length -# Maximum number of group_ids that can be queried in a single search request -MAX_GROUP_IDS_COUNT = 50 - -# === Recall Multiplier (Normal Mode: top_k > 0) === -# When top_k > 0, actual recall count = top_k * RECALL_MULTIPLIER -# Purpose: Provide a larger candidate pool for rerank to improve final result quality -# Configurable via RECALL_MULTIPLIER env var, default: 2 -DEFAULT_RECALL_MULTIPLIER = int(os.getenv("RECALL_MULTIPLIER", "2")) - -# === Threshold Configuration (Unlimited Mode Only) === -# Milvus COSINE similarity threshold (range 0-1), effective in unlimited mode -# When top_k=-1 and radius is not specified, use this threshold to filter results -# Configurable via MILVUS_SIMILARITY_THRESHOLD env var, default: 0.6 -DEFAULT_MILVUS_SIMILARITY_THRESHOLD = float( - os.getenv("MILVUS_SIMILARITY_THRESHOLD", "0.6") -) - -# Rerank score threshold (range depends on reranker model, typically 0-1) -# Applied after rerank in unlimited mode to filter low-quality results -# Configurable via RERANK_SCORE_THRESHOLD env var, default: 0.6 -DEFAULT_RERANK_SCORE_THRESHOLD = float(os.getenv("RERANK_SCORE_THRESHOLD", "0.6")) - -# Note: ES BM25 threshold is not set for now, as BM25 scores have no standardized range -# Will be determined after data validation - -# === Agent Memory Retrieval Thresholds === -# Milvus COSINE similarity radius for agent_memory hybrid retrieval -# Filters out low-similarity vectors at Milvus search level -# Configurable via AGENT_MEMORY_MILVUS_RADIUS env var, default: 0.1 -AGENT_MEMORY_MILVUS_RADIUS = float(os.getenv("AGENT_MEMORY_MILVUS_RADIUS", "0.1")) - -# ES minimum_should_match for agent_memory BM25 keyword search -# Percentage of query terms that must match (e.g., "10%" means 1 of 10 terms) -# Supports ES syntax: integer (2), negative (-2), percentage ("10%") -# Configurable via AGENT_MEMORY_ES_MIN_SHOULD_MATCH env var, default: "10%" -AGENT_MEMORY_ES_MIN_SHOULD_MATCH = os.getenv("AGENT_MEMORY_ES_MIN_SHOULD_MATCH", "10%") - -# Export all constants -__all__ = [ - "DEFAULT_TOPK_LIMIT", - "MAX_GROUP_IDS_COUNT", - "DEFAULT_RECALL_MULTIPLIER", - "DEFAULT_MILVUS_SIMILARITY_THRESHOLD", - "DEFAULT_RERANK_SCORE_THRESHOLD", - "AGENT_MEMORY_MILVUS_RADIUS", - "AGENT_MEMORY_ES_MIN_SHOULD_MATCH", -] diff --git a/methods/EverCore/src/bootstrap.py b/methods/EverCore/src/bootstrap.py deleted file mode 100644 index 2c0b14ba..00000000 --- a/methods/EverCore/src/bootstrap.py +++ /dev/null @@ -1,267 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Memsys Bootstrap Script - Generic context loader and script runner - -This script allows algorithm colleagues to run any test script without cognitive overhead, automatically handling: -- Python path setup -- Environment variable loading -- Dependency injection container initialization -- Mock mode support - -Usage: - python src/bootstrap.py [your script path] [arguments for your script...] - -Examples: - python src/bootstrap.py tests/algorithms/debug_my_model.py - python src/bootstrap.py unit_test/memory_manager_single_test.py --verbose - python src/bootstrap.py evaluation/dynamic_memory_evaluation/locomo_eval.py --dataset small -""" - -import sys -import runpy -import argparse -import os -import nest_asyncio - -nest_asyncio.apply() -import asyncio -from pathlib import Path -import logging - -logger = logging.getLogger(__name__) - - -def file_path_to_module_name(target_path: Path, src_path: Path) -> str: - """ - Convert file path to module name - - Args: - target_path: Path to the target script - src_path: Path to the src directory - - Returns: - Module name, e.g., "api_layer.get_data.run_consumer" - """ - # Ensure paths are absolute - target_path = target_path.resolve() - src_path = src_path.resolve() - - try: - # First check if it's under the src directory - if target_path.is_relative_to(src_path): - # If under src, calculate relative to src - relative_path = target_path.relative_to(src_path) - module_name = ( - str(relative_path.with_suffix('')).replace('/', '.').replace('\\', '.') - ) - return module_name - else: - # If not under src, calculate relative to project root - project_root = src_path.parent - relative_path = target_path.relative_to(project_root) - module_name = ( - str(relative_path.with_suffix('')).replace('/', '.').replace('\\', '.') - ) - return module_name - except ValueError: - # If relative path cannot be calculated, try relative to current directory - try: - relative_path = target_path.relative_to(Path.cwd()) - module_name = ( - str(relative_path.with_suffix('')).replace('/', '.').replace('\\', '.') - ) - return module_name - except ValueError: - # Final fallback: use filename as module name - return target_path.stem - - -async def setup_project_context(env_file=".env", mock_mode=False): - """ - Set up project context environment - exactly copy the loading logic from run.py - """ - # Copy environment loading logic from run.py - from import_parent_dir import add_parent_path - - add_parent_path(0) - - from common_utils.load_env import setup_environment - - # Set up environment (Python path and .env file) - setup_environment(load_env_file_name=env_file, check_env_var="MONGODB_HOST") - - # Copy Mock mode check logic from run.py - from core.di.utils import enable_mock_mode - - # Check if Mock mode is enabled: prioritize command-line argument, then environment variable - if mock_mode or ( - os.getenv("MOCK_MODE") and os.getenv("MOCK_MODE").lower() == "true" - ): - enable_mock_mode() - logger.info("🚀 Mock mode enabled") - else: - logger.info("🚀 Mock mode disabled") - - # Copy dependency injection setup from run.py - from application_startup import setup_all - - # Execute dependency injection and async task setup at module load time - setup_all() - - # Asynchronously start application lifespan - if os.getenv("SKIP_LIFESPAN", "").lower() == "true": - logger.info("⏭️ SKIP_LIFESPAN=true — skipping lifespan startup") - else: - try: - from app import app - - if hasattr(app, "start_lifespan"): - await app.start_lifespan() - logger.info("✅ Application lifespan started successfully") - else: - logger.warning("⚠️ app instance has no start_lifespan method") - except Exception as e: - logger.warning(f"⚠️ Error starting application lifespan: {e}") - # Do not raise exception, continue execution - - -async def async_main(): - """Async main function: parse arguments and run target script""" - - # Parse command-line arguments - parser = argparse.ArgumentParser( - description="Run Python script within full application context", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Example usage: - python src/bootstrap.py tests/algorithms/debug_my_model.py - python src/bootstrap.py unit_test/memory_manager_single_test.py --verbose - python src/bootstrap.py evaluation/dynamic_memory_evaluation/locomo_eval.py --dataset small - -Environment variables: - MOCK_MODE=true Enable Mock mode (for testing) - """, - ) - - parser.add_argument("script_path", help="Path to the Python script to run") - parser.add_argument( - 'script_args', - nargs=argparse.REMAINDER, - help="Arguments to pass to the target script", - ) - parser.add_argument( - "--env-file", - type=str, - default=".env", - help="Specify environment variable file to load (default: .env)", - ) - parser.add_argument( - "--mock", - action="store_true", - help="Enable Mock mode (for testing and development)", - ) - - args = parser.parse_args() - - print("🚀 Memsys Bootstrap Script") - print("=" * 50) - print(f"📄 Target script: {args.script_path}") - print(f"📝 Script arguments: {args.script_args}") - print(f"📄 Env File: {args.env_file}") - print(f"🎭 Mock mode: {'Enabled' if args.mock else 'Disabled'}") - print("=" * 50) - - # Set up project context (exactly copy logic from run.py) - await setup_project_context(env_file=args.env_file, mock_mode=args.mock) - - # Verify target script exists - script_path = Path(args.script_path) - if not script_path.exists(): - print( - f"❌ Error: Script file does not exist: {args.script_path}", file=sys.stderr - ) - sys.exit(1) - - # Prepare to execute target script - # Key: modify sys.argv so the target script thinks it was called directly - # This allows it to correctly receive its own arguments - original_argv = sys.argv.copy() # Backup original arguments - sys.argv = [str(script_path)] + args.script_args - - print(f"\n🎬 Starting script execution: {args.script_path}") - print("-" * 50) - - try: - # Use runpy to execute target script - # run_path executes the script as if 'python script_path' was called - # run_name="__main__" ensures if __name__ == "__main__": block executes normally - runpy.run_path(str(script_path), run_name="__main__") - - except ImportError as e: - # If relative import error occurs, try running in module mode - if "attempted relative import with no known parent package" in str(e): - print( - f"\n⚠️ Detected relative import error, trying to run in module mode..." - ) - try: - # Get src directory path - src_path = Path(__file__).parent # bootstrap.py is in src directory - module_name = file_path_to_module_name(script_path, src_path) - print( - f"📦 Interpreting path '{script_path}' as module '{module_name}', retrying..." - ) - - # Ensure script's sys.argv[0] remains the file path - sys.argv[0] = str(script_path) - runpy.run_module(module_name, run_name="__main__") - - except Exception as module_error: - print( - f"\n❌ Module mode execution also failed: {module_error}", - file=sys.stderr, - ) - print(f"Original error: {e}", file=sys.stderr) - import traceback - - traceback.print_exc() - sys.exit(1) - else: - # For other import errors, raise directly - raise - - except SystemExit as e: - # Target script may call sys.exit(), which is normal - # Only propagate non-zero exit codes to avoid unnecessary stack traces - if e.code is not None and e.code != 0: - print(f"\n📋 Script exited with code: {e.code}") - raise # Re-raise to propagate the exit code - else: - print(f"\n📋 Script execution completed successfully") - except Exception as e: - print(f"\n❌ Script execution error: {e}", file=sys.stderr) - import traceback - - traceback.print_exc() - finally: - # Restore original sys.argv - sys.argv = original_argv - print(f"\n🏁 Script execution finished: {args.script_path}") - - -def main(): - """Synchronous main entry point""" - try: - asyncio.run(async_main()) - except KeyboardInterrupt: - print("\n⚠️ User interrupted execution") - sys.exit(1) - except Exception as e: - print(f"\n❌ Execution failed: {e}", file=sys.stderr) - import traceback - - traceback.print_exc() - - -if __name__ == "__main__": - main() diff --git a/methods/EverCore/src/common_utils/__init__.py b/methods/EverCore/src/common_utils/__init__.py deleted file mode 100755 index 1737aff6..00000000 --- a/methods/EverCore/src/common_utils/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -Utility functions and configuration management. -""" diff --git a/methods/EverCore/src/common_utils/app_meta.py b/methods/EverCore/src/common_utils/app_meta.py deleted file mode 100644 index e6ebad44..00000000 --- a/methods/EverCore/src/common_utils/app_meta.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Application metadata management module - -Provides read and write operations for application metadata, including service name and other information. -""" - -from typing import Dict, Optional - -# Application metadata storage -_APP_META_DATA: Dict = {} - - -def set_service_name(name: str) -> None: - """ - Set service name - - Args: - name: service name - """ - _APP_META_DATA['service_name'] = name - - -def get_service_name() -> Optional[str]: - """ - Get service name - - Returns: - str: service name, returns None if not set - """ - return _APP_META_DATA.get('service_name') - - -def set_meta_data(key: str, value: any) -> None: - """ - Set metadata - - Args: - key: metadata key - value: metadata value - """ - _APP_META_DATA[key] = value - - -def get_meta_data(key: str) -> Optional[any]: - """ - Get metadata - - Args: - key: metadata key - - Returns: - any: metadata value, returns None if not exists - """ - return _APP_META_DATA.get(key) - - -def get_all_meta_data() -> Dict: - """ - Get all metadata - - Returns: - Dict: a copy of all metadata - """ - return _APP_META_DATA.copy() diff --git a/methods/EverCore/src/common_utils/async_utils.py b/methods/EverCore/src/common_utils/async_utils.py deleted file mode 100644 index 2cd50610..00000000 --- a/methods/EverCore/src/common_utils/async_utils.py +++ /dev/null @@ -1,32 +0,0 @@ -""" -Async utility functions - -Provides helper functions for common async patterns like processing -asyncio.gather results with proper error propagation. -""" - -from typing import Sequence, Any - -from core.constants.exceptions import CriticalError - - -def reraise_critical_errors(results: Sequence[Any]) -> None: - """Re-raise any CriticalError found in asyncio.gather results. - - When using ``asyncio.gather(return_exceptions=True)``, all exceptions are - captured as return values. The common ``isinstance(result, Exception)`` - check then logs-and-continues, silently swallowing every error. - - Call this function **before** processing gather results to ensure - ``CriticalError`` subclasses (e.g. missing tenant context, broken - invariants) always propagate to the caller. - - Args: - results: The list returned by ``asyncio.gather(return_exceptions=True)`` - - Raises: - CriticalError: The first CriticalError found in *results* - """ - for result in results: - if isinstance(result, CriticalError): - raise result diff --git a/methods/EverCore/src/common_utils/base62_utils.py b/methods/EverCore/src/common_utils/base62_utils.py deleted file mode 100644 index 1c80410e..00000000 --- a/methods/EverCore/src/common_utils/base62_utils.py +++ /dev/null @@ -1,148 +0,0 @@ -""" -Base62 encoding utility -Converts numeric IDs to short strings using 62 characters: 0-9, a-z, A-Z -""" - -# Base62 character set: 0-9 (10 characters) + a-z (26 characters) + A-Z (26 characters) = 62 characters -BASE62_CHARSET = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ" -BASE = len(BASE62_CHARSET) - - -def encode_base62(num: int) -> str: - """ - Encode a decimal number into a Base62 string - - Args: - num: Decimal number to encode (must be >= 0) - - Returns: - str: Base62 encoded string - - Raises: - ValueError: When input number is less than 0 - - Examples: - >>> encode_base62(0) - '0' - >>> encode_base62(61) - 'Z' - >>> encode_base62(62) - '10' - >>> encode_base62(1000000) - '4C92' - """ - if num < 0: - raise ValueError("Input number must be greater than or equal to 0") - - if num == 0: - return BASE62_CHARSET[0] - - result = [] - while num > 0: - result.append(BASE62_CHARSET[num % BASE]) - num //= BASE - - # Reverse the result since we built it from least significant to most significant - return ''.join(reversed(result)) - - -def decode_base62(encoded: str) -> int: - """ - Decode a Base62 string into a decimal number - - Args: - encoded: Base62 encoded string - - Returns: - int: Decoded decimal number - - Raises: - ValueError: When the string contains invalid characters - - Examples: - >>> decode_base62('0') - 0 - >>> decode_base62('Z') - 61 - >>> decode_base62('10') - 62 - >>> decode_base62('4C92') - 1000000 - """ - if not encoded: - raise ValueError("Encoded string cannot be empty") - - result = 0 - for char in encoded: - if char not in BASE62_CHARSET: - raise ValueError(f"Invalid character: {char}") - result = result * BASE + BASE62_CHARSET.index(char) - - return result - - -def generate_short_code(id_value: int, min_length: int = 4) -> str: - """ - Generate a short link code based on ID - - Args: - id_value: Database ID value - min_length: Minimum length, pad with leading zeros if shorter (default is 4) - - Returns: - str: Generated short link code - - Examples: - >>> generate_short_code(1) - '0001' - >>> generate_short_code(62) - '0010' - >>> generate_short_code(1000000) - '4C92' - """ - if id_value < 0: - raise ValueError("ID value must be greater than or equal to 0") - - encoded = encode_base62(id_value) - - # If length is less than minimum, pad with leading zeros - if len(encoded) < min_length: - encoded = BASE62_CHARSET[0] * (min_length - len(encoded)) + encoded - - return encoded - - -def is_valid_short_code(short_code: str) -> bool: - """ - Validate whether a short link code is valid - - Args: - short_code: Short link code to validate - - Returns: - bool: Whether the code is valid - """ - if not short_code: - return False - - # Check if it contains only characters from the Base62 character set - return all(char in BASE62_CHARSET for char in short_code) - - -def extract_id_from_short_code(short_code: str) -> int: - """ - Extract the original ID from a short link code - - Args: - short_code: Short link code - - Returns: - int: Original ID value - - Raises: - ValueError: When the short link code is invalid - """ - if not is_valid_short_code(short_code): - raise ValueError(f"Invalid short link code: {short_code}") - - return decode_base62(short_code) diff --git a/methods/EverCore/src/common_utils/cli_ui.py b/methods/EverCore/src/common_utils/cli_ui.py deleted file mode 100644 index da845223..00000000 --- a/methods/EverCore/src/common_utils/cli_ui.py +++ /dev/null @@ -1,560 +0,0 @@ -"""CLI UI utilities for width-aware, emoji-aligned, optionally colored output. - -This module provides a small, reusable toolkit for building terminal UI outputs -that adapt to the current terminal width, align text containing emoji/wide -characters, and optionally apply ANSI colors. It avoids external dependencies -and degrades gracefully if the environment does not support color. - -Key features: -- Terminal width auto-detection -- Display-width aware wrapping and padding (emoji alignment) -- Box panels and section headings -- Simple tables with auto-fitting columns and truncation -- Optional ANSI color styling (toggle via constructor, env var NO_COLOR or - CLI_UI_COLOR=0) - -Design goals: -- Keep the API small and easy to extend -- Separate measurement (display width) from styling -- Keep ANSI escape sequences from affecting layout measurements - -Usage example: - - ui = CLIUI() # auto width, color enabled unless NO_COLOR set - ui.banner("🧠 EverMem Memory Conversation Assistant", subtitle="Memory-Enhanced Chat") - ui.section_heading("📊 Available group conversations") - ui.table(headers=["#", "Group", "Name", "Count"], rows=[["1", "g001", "Team", "12"]]) - -""" - -from __future__ import annotations - -from dataclasses import dataclass -import os -import re -import shutil -import sys -import unicodedata -from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple - - -# ============================================================================ -# ANSI Color & Style Helpers -# ============================================================================ - - -_ANSI_PATTERN = re.compile(r"\x1b\[[0-9;]*m") - - -def _strip_ansi(text: str) -> str: - """Remove ANSI SGR sequences from text.""" - if not text: - return "" - return _ANSI_PATTERN.sub("", text) - - -def _supports_color() -> bool: - """Return whether the current stream likely supports ANSI colors.""" - if os.getenv("NO_COLOR") is not None: - return False - if os.getenv("CLI_UI_COLOR", "1") in {"0", "false", "False"}: - return False - stream = getattr(sys, "stdout", None) - if stream is None: - return False - return stream.isatty() - - -class _Style: - RESET = "\x1b[0m" - BOLD = "\x1b[1m" - DIM = "\x1b[2m" - # Colors - BLACK = "\x1b[30m" - RED = "\x1b[31m" - GREEN = "\x1b[32m" - YELLOW = "\x1b[33m" - BLUE = "\x1b[34m" - MAGENTA = "\x1b[35m" - CYAN = "\x1b[36m" - WHITE = "\x1b[37m" - - -@dataclass -class ColorTheme: - """Simple color theme for UI accents. - - Set any attribute to None to disable color for that role individually. - """ - - title: Optional[str] = _Style.CYAN - subtitle: Optional[str] = _Style.DIM - heading: Optional[str] = _Style.BOLD - success: Optional[str] = _Style.GREEN - warning: Optional[str] = _Style.YELLOW - error: Optional[str] = _Style.RED - info: Optional[str] = _Style.BLUE - label: Optional[str] = _Style.MAGENTA - - -def _apply_style(enabled: bool, text: str, style: Optional[str]) -> str: - if not enabled or not style: - return text - return f"{style}{text}{_Style.RESET}" - - -# ============================================================================ -# Display Width Measurement (Emoji/Wide Char Aware) -# ============================================================================ - - -def _char_display_width(ch: str) -> int: - """Approximate display width of a single character. - - This handles common cases without external dependencies: - - Combining marks => width 0 - - East Asian Wide/Fullwidth => width 2 - - Common emoji ranges => width 2 - - Otherwise => width 1 - """ - - if not ch: - return 0 - - if unicodedata.combining(ch): - return 0 - - category = unicodedata.category(ch) - if category in {"Mn", "Me"}: # Nonspacing/Enclosing marks - return 0 - - eaw = unicodedata.east_asian_width(ch) - if eaw in {"W", "F"}: - return 2 - - # Basic emoji heuristic (covers most common emoji) - code = ord(ch) - if ( - 0x1F300 - <= code - <= 0x1FAFF # Misc symbols & pictographs, supplemental symbols & pictographs, etc. - or 0x1F600 <= code <= 0x1F64F # Emoticons - or 0x1F680 <= code <= 0x1F6FF # Transport & Map - or 0x2600 <= code <= 0x26FF # Misc symbols - or 0x2700 <= code <= 0x27BF # Dingbats - ): - return 2 - - # Variation selectors should have zero width when standalone - if 0xFE00 <= code <= 0xFE0F: - return 0 - - return 1 - - -def visible_width(text: str) -> int: - """Compute the on-screen width of a string (ignores ANSI SGR codes).""" - if not text: - return 0 - s = _strip_ansi(text) - width = 0 - for ch in s: - width += _char_display_width(ch) - return width - - -def truncate_to_width(text: str, max_width: int, ellipsis: str = "…") -> str: - """Truncate string so that display width <= max_width, appending ellipsis if needed.""" - if max_width <= 0: - return "" - if visible_width(text) <= max_width: - return text - # Reserve space for ellipsis - target = max(0, max_width - visible_width(ellipsis)) - out: List[str] = [] - acc = 0 - for ch in text: - w = _char_display_width(ch) if ch != "\x1b" else 0 - if acc + w > target: - break - out.append(ch) - acc += w - out.append(ellipsis) - return "".join(out) - - -def wrap_text(text: str, max_width: int) -> List[str]: - """Word-wrap text by visible width, preserving existing newlines and ANSI codes. - - - Breaks on whitespace where possible; falls back to character-level when needed - - Does not count ANSI SGR sequences toward width - """ - if max_width <= 0: - return [""] - if not text: - return [""] - - lines: List[str] = [] - for raw_line in text.splitlines(False): - if visible_width(raw_line) <= max_width: - lines.append(raw_line) - continue - - # Tokenize on whitespace to get words and spaces - tokens = re.findall(r"\x1b\[[0-9;]*m|\S+|\s+", raw_line) - current: List[str] = [] - current_w = 0 - - def flush() -> None: - nonlocal current, current_w - lines.append("".join(current)) - current = [] - current_w = 0 - - for tok in tokens: - if _ANSI_PATTERN.fullmatch(tok): # ANSI sequence has width 0 - current.append(tok) - continue - - tok_w = visible_width(tok) - if tok_w <= (max_width - current_w): - current.append(tok) - current_w += tok_w - else: - # If token is whitespace, flush line and skip leading spaces - if tok.isspace(): - flush() - continue - # Split token to fit - idx = 0 - while idx < len(tok): - # Find how many characters we can take - if current_w == max_width: - flush() - room = max_width - current_w - if room <= 0: - flush() - room = max_width - taken: List[str] = [] - taken_w = 0 - while ( - idx < len(tok) - and taken_w + _char_display_width(tok[idx]) <= room - ): - taken.append(tok[idx]) - taken_w += _char_display_width(tok[idx]) - idx += 1 - if taken: - current.append("".join(taken)) - current_w += taken_w - if idx < len(tok): - flush() - # done splitting token - if current: - lines.append("".join(current)) - return lines - - -def get_terminal_width(fallback: int = 80, min_width: int = 40) -> int: - """Return the current terminal column width with reasonable bounds.""" - try: - width = shutil.get_terminal_size((fallback, 24)).columns - except Exception: - width = fallback - if width < min_width: - width = min_width - return width - - -# ============================================================================ -# Box Styles -# ============================================================================ - - -@dataclass -class BoxStyle: - tl: str - tr: str - bl: str - br: str - h: str - v: str - t: str # tee top (for separators) - b: str # tee bottom - l: str # tee left - r: str # tee right - cross: str - - -HEAVY_BOX = BoxStyle( - tl="╔", tr="╗", bl="╚", br="╝", h="═", v="║", t="╦", b="╩", l="╠", r="╣", cross="╬" -) -LIGHT_BOX = BoxStyle( - tl="┌", tr="┐", bl="└", br="┘", h="─", v="│", t="┬", b="┴", l="├", r="┤", cross="┼" -) -ASCII_BOX = BoxStyle( - tl="+", tr="+", bl="+", br="+", h="-", v="|", t="+", b="+", l="+", r="+", cross="+" -) - - -# ============================================================================ -# Core UI Renderer -# ============================================================================ - - -class CLIUI: - """Width-aware, emoji-aligned terminal UI helper. - - Parameters: - width: Override terminal width for layout. If None, auto-detect. - color_enabled: Enable ANSI colors (default auto based on env/tty). - box: Which box-drawing style to use ('heavy', 'light', 'ascii'). - theme: ColorTheme for accents. - margin: Number of spaces to leave at left/right of the terminal. - padding: Spaces inside panels at left/right. - """ - - def __init__( - self, - width: Optional[int] = None, - color_enabled: Optional[bool] = None, - box: str = "heavy", - theme: Optional[ColorTheme] = None, - margin: int = 0, - padding: int = 1, - ) -> None: - if width is None: - width = get_terminal_width() - self.term_width: int = width - self.margin: int = max(0, margin) - self.padding: int = max(0, padding) - self.color_enabled: bool = ( - _supports_color() if color_enabled is None else bool(color_enabled) - ) - self.theme: ColorTheme = theme or ColorTheme() - self.box: BoxStyle = { - "heavy": HEAVY_BOX, - "light": LIGHT_BOX, - "ascii": ASCII_BOX, - }.get(box, HEAVY_BOX) - - # ------------------------ Basic Styled Printing ------------------------ - def text(self, text: str = "") -> None: - """Print plain text respecting margin.""" - line_prefix = " " * self.margin if self.margin else "" - print(f"{line_prefix}{text}") - - def styled(self, text: str, role: Optional[str] = None) -> str: - """Return text styled using the theme role.""" - if role is None: - return text - color = getattr(self.theme, role, None) - return _apply_style(self.color_enabled, text, color) - - # ------------------------ Rules & Headings ----------------------------- - def rule(self, char: Optional[str] = None) -> None: - """Print a horizontal rule spanning the content width.""" - h = char or self.box.h - inner_width = max(1, self.term_width - self.margin * 2) - self.text(h * inner_width) - - def section_heading(self, title: str) -> None: - """Print a section heading as a single-line box-like rule with title centered.""" - inner_width = max(3, self.term_width - self.margin * 2) - title_str = f" {title} " - title_str = self.styled(title_str, "heading") - h = self.box.h - remaining = inner_width - visible_width(_strip_ansi(title_str)) - if remaining <= 2: - self.text(truncate_to_width(title_str, inner_width)) - return - left = remaining // 2 - right = remaining - left - self.text(f"{h * left}{title_str}{h * right}") - - def banner(self, title: str, subtitle: Optional[str] = None) -> None: - """Print a decorative banner with title and optional subtitle.""" - inner_w = self._panel_inner_width() - - # Center the title - title_s = self.styled(title, "title") - title_vis = visible_width(_strip_ansi(title_s)) - title_pad = max(0, (inner_w - title_vis) // 2) - centered_title = (" " * title_pad) + title_s - - lines = [centered_title] - - # Center the subtitle if provided - if subtitle: - subtitle_s = self.styled(subtitle, "subtitle") - subtitle_vis = visible_width(_strip_ansi(subtitle_s)) - subtitle_pad = max(0, (inner_w - subtitle_vis) // 2) - centered_subtitle = (" " * subtitle_pad) + subtitle_s - lines.append(centered_subtitle) - - self.panel(lines, title=None) - - # ------------------------ Panels -------------------------------------- - def _content_width(self) -> int: - return max(10, self.term_width - self.margin * 2) - - def _panel_inner_width(self) -> int: - # account for vertical borders and padding - return max(1, self._content_width() - 2 - self.padding * 2) - - def panel(self, lines: Sequence[str], title: Optional[str] = None) -> None: - """Render a bordered panel with wrapped content. - - Args: - lines: Iterable of line strings (can contain ANSI/emoji). Will be wrapped. - title: Optional title displayed in top border. - """ - inner_w = self._panel_inner_width() - content_lines: List[str] = [] - for raw in lines: - content_lines.extend(wrap_text(raw, inner_w)) - - # Build top border (with optional title) - top: str - if title: - title_s = self.styled(f" {title} ", "heading") - # compute visible width for capping - tvis = visible_width(_strip_ansi(title_s)) - max_title = max(0, inner_w) - if tvis > max_title: - title_s = truncate_to_width(title_s, max_title) - tvis = visible_width(_strip_ansi(title_s)) - filler_left = (inner_w - tvis) // 2 - filler_right = inner_w - tvis - filler_left - top = ( - f"{self.box.tl}{self.box.h * (self.padding + filler_left)}" - f"{title_s}" - f"{self.box.h * (self.padding + filler_right)}{self.box.tr}" - ) - else: - top = ( - f"{self.box.tl}{self.box.h * (inner_w + self.padding * 2)}{self.box.tr}" - ) - - # Build bottom and sides - bottom = ( - f"{self.box.bl}{self.box.h * (inner_w + self.padding * 2)}{self.box.br}" - ) - left = f"{self.box.v}{' ' * self.padding}" - right = f"{' ' * self.padding}{self.box.v}" - - # Print with margin - prefix = " " * self.margin - print(prefix + top) - for line in content_lines: - line = truncate_to_width(line, inner_w) - pad = inner_w - visible_width(_strip_ansi(line)) - print(prefix + left + line + (" " * pad) + right) - print(prefix + bottom) - - # ------------------------ Tables -------------------------------------- - def table( - self, - headers: Optional[Sequence[str]], - rows: Sequence[Sequence[str]], - aligns: Optional[Sequence[str]] = None, - max_height: Optional[int] = None, - ) -> None: - """Render a simple table that auto-fits to the available width. - - - If total width exceeds content width, later columns are truncated. - - Supports left/center/right alignment via aligns list. - - If max_height is set and fewer rows can be shown, prints a notice panel. - """ - if not rows and not headers: - return - - content_w = self._content_width() - - # Compute column count - num_cols = len(headers) if headers is not None else len(rows[0]) - aligns = aligns or ["left"] * num_cols - - # Measure column widths (max of header and content) - col_widths = [0] * num_cols - - def measure(s: str) -> int: - return visible_width(_strip_ansi(s)) - - if headers is not None: - for i, h in enumerate(headers): - col_widths[i] = max(col_widths[i], measure(h)) - for row in rows: - for i, cell in enumerate(row): - col_widths[i] = max(col_widths[i], measure(str(cell))) - - # Compute available width for a borderless table with single spaces between cols - # We'll just pad with spaces; if it overflows, we reduce widths from the last column backward. - spacing = num_cols - 1 # one space between columns - total = sum(col_widths) + spacing - max_total = content_w - if total > max_total: - # Reduce columns from the last backward, keeping a minimum width of 3 - overflow = total - max_total - for idx in range(num_cols - 1, -1, -1): - if overflow <= 0: - break - available_reduce = max(0, col_widths[idx] - 3) - reduce_by = min(available_reduce, overflow) - col_widths[idx] -= reduce_by - overflow -= reduce_by - - def align_text(s: str, width: int, how: str) -> str: - s = truncate_to_width(s, width) - pad = width - visible_width(_strip_ansi(s)) - if how == "right": - return (" " * pad) + s - if how == "center": - left = pad // 2 - return (" " * left) + s + (" " * (pad - left)) - return s + (" " * pad) - - # Print header - prefix = " " * self.margin - if headers is not None: - header_cells: List[str] = [] - for i, h in enumerate(headers): - header_cells.append( - self.styled(align_text(h, col_widths[i], "center"), "heading") - ) - print(prefix + " ".join(header_cells)) - # Use full content width for the separator line - print(prefix + self.box.h * content_w) - - # Determine visible rows given max_height - display_rows = rows - total_count = len(rows) - if max_height is not None and total_count > max_height: - display_rows = rows[:max_height] - - for row in display_rows: - cells: List[str] = [] - for i, cell in enumerate(row): - cells.append(align_text(str(cell), col_widths[i], aligns[i])) - print(prefix + " ".join(cells)) - - # Notice for truncated rows - if max_height is not None and total_count > max_height: - remaining = total_count - max_height - self.text( - self.styled(f"... {remaining} more rows not displayed", "subtitle") - ) - - # ------------------------ Convenience ---------------------------------- - def note(self, text: str, icon: Optional[str] = None) -> None: - msg = f"{icon} {text}" if icon else text - self.text(self.styled(msg, "info")) - - def success(self, text: str) -> None: - self.text(self.styled(text, "success")) - - def warning(self, text: str) -> None: - self.text(self.styled(text, "warning")) - - def error(self, text: str) -> None: - self.text(self.styled(text, "error")) diff --git a/methods/EverCore/src/common_utils/datetime_utils.py b/methods/EverCore/src/common_utils/datetime_utils.py deleted file mode 100644 index 0fae1855..00000000 --- a/methods/EverCore/src/common_utils/datetime_utils.py +++ /dev/null @@ -1,252 +0,0 @@ -import datetime -from zoneinfo import ZoneInfo -import os -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -def get_timezone() -> ZoneInfo: - """Get timezone from TZ env var (default: UTC).""" - tz = os.getenv("TZ", "UTC") - return ZoneInfo(tz) - - -timezone = get_timezone() - - -def get_now_with_timezone(tz: ZoneInfo = None) -> datetime.datetime: - """Get current time with local timezone.""" - return datetime.datetime.now(tz=tz or timezone) - - -def to_timezone(dt: datetime.datetime, tz: ZoneInfo = None) -> datetime.datetime: - """Convert datetime to specified timezone.""" - if tz is None: - tz = timezone - return dt.astimezone(tz) - - -def to_date_str(dt: datetime.datetime | None) -> str | None: - """Convert datetime to ISO date string (YYYY-MM-DD format). - - Args: - dt: Datetime object to convert. - - Returns: - ISO date string (e.g. "2025-01-07"), or None if input is None. - - Example: - >>> to_date_str(datetime.datetime(2025, 1, 7, 9, 15, 33)) - "2025-01-07" - """ - if dt is None: - return None - return dt.date().isoformat() - - -def to_iso_format( - time_value: datetime.datetime | int | float | str | None, -) -> str | None: - """Convert time value to ISO format string with timezone. - - Supports: datetime, int/float (unix timestamp), str, None. - - Args: - time_value: Time value to convert. - - Returns: - ISO format string (e.g. 2025-09-16T20:20:06Z), or None. - - Raises: - TypeError: If time_value is not a supported type. - ValueError: If timestamp is invalid. - """ - - if time_value is None: - return None - - value_type = type(time_value) - - if value_type is str: - if not time_value: - return None - # Validate and parse ISO format string - time_str = ( - time_value.replace("Z", "+00:00") - if time_value.endswith("Z") - else time_value - ) - dt = datetime.datetime.fromisoformat(time_str) - elif value_type in (int, float): - if time_value <= 0: - raise ValueError(f"Invalid timestamp: {time_value}. Must be positive.") - dt = from_timestamp(time_value) - elif value_type is datetime.datetime: - dt = time_value - else: - raise TypeError( - f"Unsupported type: {value_type.__name__}. " - f"Expected: datetime, int, float, str, or None." - ) - - # Ensure timezone and convert to local - if dt.tzinfo is None: - dt = dt.replace(tzinfo=timezone) - return dt.astimezone(timezone).isoformat() - - -def from_timestamp(timestamp: int | float) -> datetime.datetime: - """Convert unix timestamp to datetime. Auto-detects seconds vs milliseconds.""" - # >= 1e12 is milliseconds, < 1e12 is seconds - if timestamp >= 1e12: - timestamp_seconds = timestamp / 1000.0 - else: - timestamp_seconds = timestamp - return datetime.datetime.fromtimestamp(timestamp_seconds, tz=timezone) - - -def to_timestamp(dt: datetime.datetime) -> int: - """Convert datetime to unix timestamp (seconds).""" - return int(dt.timestamp()) - - -def to_timestamp_ms(dt: datetime.datetime) -> int: - """Convert datetime to unix timestamp (milliseconds).""" - return int(dt.timestamp() * 1000) - - -def to_timestamp_ms_universal(time_value) -> int: - """Convert any time format to milliseconds timestamp. - - Supports: int/float (timestamp), str (ISO format), datetime, None. - Returns 0 on failure or None input. - """ - try: - if time_value is None: - return 0 - - if isinstance(time_value, (int, float)): - # Auto-detect: >= 1e12 is ms, otherwise seconds - if time_value >= 1e12: - return int(time_value) - return int(time_value * 1000) - - if isinstance(time_value, str): - try: - return to_timestamp_ms_universal(float(time_value)) - except ValueError: - return to_timestamp_ms(from_iso_format(time_value)) - - if isinstance(time_value, datetime.datetime): - return to_timestamp_ms(time_value) - - return to_timestamp_ms_universal(str(time_value)) - - except Exception as e: - logger.error( - "[DateTimeUtils] to_timestamp_ms_universal - Error converting %s: %s", - time_value, - str(e), - ) - return 0 - - -def _parse_datetime_core( - time_value, target_timezone: ZoneInfo = None -) -> datetime.datetime: - """ - Core datetime parsing logic. Raises exception on failure. - - Supported inputs: - - datetime object (passed through) - - ISO format string: "2025-09-15T13:11:15.588000", "2025-09-15T13:11:15.588000Z" - - Space-separated string: "2025-01-07 09:15:33" (Python 3.11+) - - With timezone offset: "2025-09-15T13:11:15+00:00" - - Args: - time_value: datetime object or time string - target_timezone: Timezone for naive datetime (default: TZ env variable) - - Returns: - Timezone-aware datetime object - - Raises: - ValueError: If parsing fails - """ - # Handle datetime object - if isinstance(time_value, datetime.datetime): - dt = time_value - elif isinstance(time_value, (int, float)): - # Handle numeric timestamps (auto-detects seconds vs milliseconds) - dt = from_timestamp(time_value) - elif isinstance(time_value, str): - time_str = time_value.strip() - # Handle "Z" suffix (UTC) - if time_str.endswith("Z"): - time_str = time_str[:-1] + "+00:00" - # Python 3.11+ fromisoformat supports space-separated format - dt = datetime.datetime.fromisoformat(time_str) - else: - # Other types: convert to string first - time_str = str(time_value).strip() - if time_str.endswith("Z"): - time_str = time_str[:-1] + "+00:00" - dt = datetime.datetime.fromisoformat(time_str) - - # Add timezone if naive - if dt.tzinfo is None: - tz = target_timezone or get_timezone() - dt_localized = dt.replace(tzinfo=tz) - else: - dt_localized = dt - - # Convert to system timezone - return dt_localized.astimezone(get_timezone()) - - -def from_iso_format( - create_time, target_timezone: ZoneInfo = None, strict: bool = False -) -> datetime.datetime: - """ - Parse datetime string or object to timezone-aware datetime. - - Args: - create_time: datetime object or time string - target_timezone: Timezone for naive datetime (default: TZ env variable) - strict: If True, raises ValueError on failure (for data import). - If False (default), returns current time on failure (for runtime conversion). - - Supported formats: - - datetime object (passed through) - - "2025-01-07 09:15:33" (space-separated) - - "2025-01-07T09:15:33" (ISO T-separated) - - "2025-01-07 09:15:33.123456" (with microseconds) - - "2025-01-07T09:15:33+00:00" (with timezone) - - "2025-01-07T09:15:33Z" (UTC) - - Returns: - Timezone-aware datetime object. Returns current time if parsing fails (when strict=False). - - Raises: - ValueError: If strict=True and parsing fails - - Example: - >>> from_iso_format("2025-01-07 09:15:33") - datetime.datetime(2025, 1, 7, 9, 15, 33, tzinfo=ZoneInfo('UTC')) - - >>> from_iso_format("invalid", strict=True) - ValueError: ... - """ - if strict: - # Strict mode: raise exception on failure - return _parse_datetime_core(create_time, target_timezone) - else: - # Lenient mode: return current time on failure - try: - return _parse_datetime_core(create_time, target_timezone) - except Exception as e: - logger.error( - "[DateTimeUtils] from_iso_format - Error converting time: %s", str(e) - ) - return get_now_with_timezone() diff --git a/methods/EverCore/src/common_utils/json_utils.py b/methods/EverCore/src/common_utils/json_utils.py deleted file mode 100644 index dfc18376..00000000 --- a/methods/EverCore/src/common_utils/json_utils.py +++ /dev/null @@ -1,84 +0,0 @@ -""" -JSON parsing utilities - -Provides robust JSON extraction from LLM responses that may contain -markdown code blocks or extra text around JSON objects. -""" - -import json -from typing import Any, Dict, Optional - - -def extract_outermost_json(text: str) -> Optional[str]: - """Extract the outermost JSON object from text using bracket counting. - - Handles escaped characters and strings correctly. - - Args: - text: Raw text potentially containing a JSON object - - Returns: - The JSON substring, or None if no valid object found - """ - start = text.find("{") - if start == -1: - return None - depth = 0 - in_string = False - escape_next = False - for i in range(start, len(text)): - ch = text[i] - if escape_next: - escape_next = False - continue - if ch == "\\" and in_string: - escape_next = True - continue - if ch == '"': - in_string = not in_string - continue - if in_string: - continue - if ch == "{": - depth += 1 - elif ch == "}": - depth -= 1 - if depth == 0: - return text[start : i + 1] - return None - - -def parse_json_response(response: str) -> Optional[Dict[str, Any]]: - """Parse JSON from an LLM response, tolerating markdown code blocks and surrounding text. - - Tries in order: - 1. Extract from ```json ... ``` code block - 2. Extract outermost { ... } via bracket counting - 3. Parse the entire response as JSON - - Args: - response: Raw LLM response string - - Returns: - Parsed dict, or None if parsing fails - """ - if "```json" in response: - start = response.find("```json") + 7 - end = response.find("```", start) - if end > start: - try: - return json.loads(response[start:end].strip()) - except json.JSONDecodeError: - pass - - json_str = extract_outermost_json(response) - if json_str: - try: - return json.loads(json_str) - except json.JSONDecodeError: - pass - - try: - return json.loads(response) - except json.JSONDecodeError: - return None diff --git a/methods/EverCore/src/common_utils/language_utils.py b/methods/EverCore/src/common_utils/language_utils.py deleted file mode 100644 index 3e0ceec4..00000000 --- a/methods/EverCore/src/common_utils/language_utils.py +++ /dev/null @@ -1,40 +0,0 @@ -"""语言工具模块 - -统一管理 Prompt 语言设置，所有需要获取默认语言的逻辑都应调用此模块的函数。 -""" - -import os - -# 支持的语言列表 -SUPPORTED_LANGUAGES = ["en", "zh"] - -# 默认语言 -DEFAULT_LANGUAGE = "en" - - -def get_prompt_language() -> str: - """获取当前的 Prompt 语言设置 - - 从环境变量 MEMORY_LANGUAGE 获取语言设置，如果未设置或不支持则返回默认值 "en"。 - 语言设置应在启动时通过环境变量配置，运行时不可修改。 - - Returns: - 当前的语言设置，默认为 "en" - """ - language = os.getenv("MEMORY_LANGUAGE", DEFAULT_LANGUAGE).lower() - if language not in SUPPORTED_LANGUAGES: - return DEFAULT_LANGUAGE - return language - - -def is_supported_language(language: str) -> bool: - """检查语言是否支持 - - Args: - language: 语言代码 - - Returns: - 是否支持该语言 - """ - return language.lower() in SUPPORTED_LANGUAGES - diff --git a/methods/EverCore/src/common_utils/load_env.py b/methods/EverCore/src/common_utils/load_env.py deleted file mode 100644 index 6561a509..00000000 --- a/methods/EverCore/src/common_utils/load_env.py +++ /dev/null @@ -1,181 +0,0 @@ -#!/usr/bin/env python3 -""" -Unified environment loading tool - -Provides Python path setup and .env file loading functionality to ensure environment variables are correctly loaded when the project runs from different locations. -""" - -import logging -import os -import sys -from typing import Optional -from dotenv import load_dotenv -import time - -from common_utils.app_meta import set_service_name -from common_utils.project_path import PROJECT_DIR -from common_utils.datetime_utils import get_timezone - -# Environment variables are not loaded yet, so get_logger cannot be used here -logger = logging.getLogger(__name__) - -# Project metadata has been moved to the app_meta module - -""" -- setup_pythonpath is not needed. - - For 'python run.py' and 'python src/run.py', this is not required as src will be added automatically. - - If src is truly missing, setup_pythonpath would need to import load_env.py, which then depends on pythonpath and may not actually load correctly. - - Not needed for VSCode launch, which can be configured via launch.json. - - Not needed for online deployment using 'python run.py'. - - The web entry point might indeed cause src to be missing; this needs to be addressed. -""" - - -def load_env_file( - env_file_name: str = ".env", check_env_var: Optional[str] = None -) -> bool: - """ - Load .env file - - Args: - env_file_name: .env filename - check_env_var: Environment variable name to check, used to determine if environment has been loaded - - Returns: - bool: Whether environment variables were successfully loaded - """ - # Calculate .env file path based on the location of load_env.py - # .env file is located in the parent directory of src - - env_file_path = PROJECT_DIR / env_file_name - - if not env_file_path.exists(): - logger.warning(".env file does not exist: %s", env_file_path) - return False - - try: - load_dotenv(env_file_path) - logger.debug("Successfully loaded .env file: %s", env_file_path) - except (IOError, OSError) as e: - logger.error("Failed to load .env file: %s", e) - return False - - if check_env_var and os.getenv(check_env_var): - logger.info("%s is set, environment variables have been loaded", check_env_var) - return True - else: - if check_env_var: - logger.error( - "Please ensure that the %s environment variable is set", check_env_var - ) - return False - - -def reset_timezone(): - """ - Reset timezone - """ - timezone = get_timezone() - os.environ["TZ"] = timezone.key - # tzset() is not available on Windows, only call it if available - if hasattr(time, 'tzset'): - time.tzset() - - -def sync_pythonpath_with_syspath(): - """ - Synchronize PYTHONPATH and sys.path to ensure all paths in sys.path are included in PYTHONPATH - - Notes: - 1. Only synchronize non-standard library paths - 2. Exclude .venv and similar virtual environment paths - 3. Maintain the original priority of PYTHONPATH - """ - import sys - import os - from pathlib import Path - - # Get current PYTHONPATH - pythonpath = os.environ.get("PYTHONPATH", "").split(":") - pythonpath = [p for p in pythonpath if p] # Remove empty strings - - # Path patterns to exclude - exclude_patterns = [ - ".venv", - "site-packages", - "dist-packages", - "lib/python", - "__pycache__", - ] - - # Get paths from sys.path that need to be added - new_paths = [] - for path in sys.path: - # Skip empty paths - if not path: - continue - - # Convert to Path object for processing - path_obj = Path(path) - - # Skip non-existent paths - if not path_obj.exists(): - continue - - # Skip paths that should be excluded - if any(pattern in str(path_obj) for pattern in exclude_patterns): - continue - - # Convert to string and normalize - path_str = str(path_obj.resolve()) - - # If path is not in current PYTHONPATH, add to new paths list - if path_str not in pythonpath: - new_paths.append(path_str) - - # If there are new paths to add - if new_paths: - # Append new paths to existing PYTHONPATH - all_paths = pythonpath + new_paths - # Update environment variable - os.environ["PYTHONPATH"] = ":".join(all_paths) - logger.debug("Updated PYTHONPATH: %s", os.environ["PYTHONPATH"]) - - -def setup_environment( - load_env_file_name: str = ".env", - check_env_var: Optional[str] = None, - service_name: Optional[str] = None, -) -> bool: - """ - Unified environment setup function - - Args: - load_env_file_name: .env filename - check_env_var: Environment variable name to check, used to determine if environment has been loaded - service_name: Name of the current service being started, will be stored in APP_META_DATA - - Returns: - bool: Whether environment was successfully set up - """ - # Load .env file - success = load_env_file( - env_file_name=load_env_file_name, check_env_var=check_env_var - ) - - # Synchronize PYTHONPATH and sys.path - sync_pythonpath_with_syspath() - - # Reset timezone - reset_timezone() - - # Set service name - if service_name: - set_service_name(service_name) - logger.debug("Service name set: %s", service_name) - - if not success: - logger.error("Environment setup failed, exiting program") - sys.exit(1) - - return success diff --git a/methods/EverCore/src/common_utils/project_path.py b/methods/EverCore/src/common_utils/project_path.py deleted file mode 100644 index d1a815e9..00000000 --- a/methods/EverCore/src/common_utils/project_path.py +++ /dev/null @@ -1,14 +0,0 @@ -from pathlib import Path - -# Get the directory where load_env.py is located (utils directory) -# Do not import any other modules here, PROJECT_DIR is fundamental information -utils_dir = Path(__file__).parent -# src directory is the parent directory of utils -src_dir = utils_dir.parent -CURRENT_DIR = src_dir -PROJECT_DIR = src_dir.parent - - -def get_base_scan_path(): - """Get the base scan path""" - return CURRENT_DIR diff --git a/methods/EverCore/src/common_utils/text_utils.py b/methods/EverCore/src/common_utils/text_utils.py deleted file mode 100644 index 55700e4e..00000000 --- a/methods/EverCore/src/common_utils/text_utils.py +++ /dev/null @@ -1,474 +0,0 @@ -""" -Text processing utility module - -Provides general-purpose utility functions for text processing, including smart truncation, formatting, and other features. -""" - -from typing import List, Dict, Any -from enum import Enum -from dataclasses import dataclass - - -class TokenType(Enum): - """Token type enumeration""" - - CJK_CHAR = "cjk_char" # CJK characters - ENGLISH_WORD = "english_word" # English word - CONTINUOUS_NUMBER = "continuous_number" # Continuous numbers - PUNCTUATION = "punctuation" # Punctuation - WHITESPACE = "whitespace" # Whitespace characters - OTHER = "other" # Other characters - - -@dataclass -class Token: - """Text Token""" - - type: TokenType - content: str - start_pos: int - end_pos: int - score: float = 0.0 - - -@dataclass -class TokenConfig: - """Token configuration""" - - cjk_char_score: float = 1.0 - english_word_score: float = 1.5 - continuous_number_score: float = 0.8 - punctuation_score: float = 0.5 - whitespace_score: float = 0.3 - other_score: float = 0.5 - - -class SmartTextParser: - """Smart text parser - - Capable of distinguishing different types of tokens, supports configurable score calculation, - provides left-to-right traversal and intelligent truncation based on total score. - """ - - def __init__(self, config: TokenConfig = None): - """Initialize parser - - Args: - config: Token configuration, use default if None - """ - self.config = config or TokenConfig() - - # CJK character ranges - self._cjk_ranges = [ - (0x4E00, 0x9FFF), # CJK Unified Ideographs - (0x3400, 0x4DBF), # CJK Extension A - (0x20000, 0x2A6DF), # CJK Extension B - (0x2A700, 0x2B73F), # CJK Extension C - (0x2B740, 0x2B81F), # CJK Extension D - (0x2B820, 0x2CEAF), # CJK Extension E - (0x3040, 0x309F), # Hiragana - (0x30A0, 0x30FF), # Katakana - (0xAC00, 0xD7AF), # Hangul Syllables - ] - - def _is_cjk_char(self, char: str) -> bool: - """Check if character is a CJK character""" - if not char: - return False - code = ord(char) - return any(start <= code <= end for start, end in self._cjk_ranges) - - def _is_english_char(self, char: str) -> bool: - """Check if character is an English character""" - return char.isalpha() and ord(char) < 128 - - def _is_punctuation(self, char: str) -> bool: - """Check if character is punctuation""" - # Common punctuation characters - punctuation_chars = set('.,!?;:"\'()[]{}+-*/%=<>@#$&|~`^_\\/') - - return char in punctuation_chars or ( - 0x2000 <= ord(char) <= 0x206F # General Punctuation - or 0x3000 <= ord(char) <= 0x303F # CJK Symbols and Punctuation - or 0xFF00 <= ord(char) <= 0xFFEF # Fullwidth ASCII and halfwidth Katakana - ) - - def parse_tokens(self, text: str, max_score: float = None) -> List[Token]: - """Parse text into a list of Tokens - - Args: - text: Text to parse - max_score: Maximum score limit, stop parsing early when this score is reached - - Returns: - List[Token]: List of Tokens - """ - if not text: - return [] - - tokens = [] - current_score = 0.0 - i = 0 - text_len = len(text) - - while i < text_len: - char = text[i] - start_pos = i - - # Handle CJK characters - if self._is_cjk_char(char): - token = Token( - type=TokenType.CJK_CHAR, - content=char, - start_pos=start_pos, - end_pos=i + 1, - score=self.config.cjk_char_score, - ) - tokens.append(token) - current_score += token.score - i += 1 - - # Check if early truncation is needed - if max_score is not None and current_score > max_score: - # Remove the last added token as it exceeds the limit - tokens.pop() - break - - # Handle English words - elif self._is_english_char(char): - word_end = i - while word_end < text_len and ( - self._is_english_char(text[word_end]) or text[word_end] in "'-" - ): - word_end += 1 - - token = Token( - type=TokenType.ENGLISH_WORD, - content=text[i:word_end], - start_pos=start_pos, - end_pos=word_end, - score=self.config.english_word_score, - ) - tokens.append(token) - current_score += token.score - i = word_end - - # Check if early truncation is needed - if max_score is not None and current_score > max_score: - # Remove the last added token as it exceeds the limit - tokens.pop() - break - - # Handle continuous numbers - elif char.isdigit(): - num_end = i - while num_end < text_len and ( - text[num_end].isdigit() or text[num_end] in ".," - ): - num_end += 1 - - token = Token( - type=TokenType.CONTINUOUS_NUMBER, - content=text[i:num_end], - start_pos=start_pos, - end_pos=num_end, - score=self.config.continuous_number_score, - ) - tokens.append(token) - current_score += token.score - i = num_end - - # Check if early truncation is needed - if max_score is not None and current_score > max_score: - # Remove the last added token as it exceeds the limit - tokens.pop() - break - - # Handle punctuation - elif self._is_punctuation(char): - token = Token( - type=TokenType.PUNCTUATION, - content=char, - start_pos=start_pos, - end_pos=i + 1, - score=self.config.punctuation_score, - ) - tokens.append(token) - current_score += token.score - i += 1 - - # Check if early truncation is needed - if max_score is not None and current_score > max_score: - # Remove the last added token as it exceeds the limit - tokens.pop() - break - - # Handle whitespace - elif char.isspace(): - # Merge consecutive whitespace characters - space_end = i - while space_end < text_len and text[space_end].isspace(): - space_end += 1 - - token = Token( - type=TokenType.WHITESPACE, - content=text[i:space_end], - start_pos=start_pos, - end_pos=space_end, - score=self.config.whitespace_score, - ) - tokens.append(token) - current_score += token.score - i = space_end - - # Check if early truncation is needed - if max_score is not None and current_score > max_score: - # Remove the last added token as it exceeds the limit - tokens.pop() - break - - # Handle other characters - else: - token = Token( - type=TokenType.OTHER, - content=char, - start_pos=start_pos, - end_pos=i + 1, - score=self.config.other_score, - ) - tokens.append(token) - current_score += token.score - i += 1 - - # Check if early truncation is needed - if max_score is not None and current_score > max_score: - # Remove the last added token as it exceeds the limit - tokens.pop() - break - - return tokens - - def calculate_total_score(self, tokens: List[Token]) -> float: - """Calculate total score of token list - - Args: - tokens: List of Tokens - - Returns: - float: Total score - """ - return sum(token.score for token in tokens) - - def smart_truncate_by_score( - self, - text: str, - max_score: float, - suffix: str = "...", - enable_fallback: bool = True, - ) -> str: - """Smartly truncate text based on score - - Args: - text: Text to truncate - max_score: Maximum allowed score - suffix: Suffix to append after truncation - enable_fallback: Whether to enable fallback mode, fall back to character length truncation if parsing fails - - Returns: - str: Truncated text - """ - if not text: - return text or "" - - if max_score <= 0: - return text # Maintain backward compatibility, return original text if limit <= 0 - - try: - # First parse the full text - all_tokens = self.parse_tokens(text) - - if not all_tokens: - return text - - # Calculate actual score, no truncation needed if within limit - total_score = self.calculate_total_score(all_tokens) - if total_score <= max_score: - return text - - # Use full tokens for truncation calculation - tokens = all_tokens - - # Need truncation, find appropriate position - current_score = 0.0 - truncate_pos = len(text) - - for token in tokens: - if current_score + token.score > max_score: - # If it's an English word or continuous number and the overflow is small, allow full inclusion to avoid breaking - if ( - token.type - in [TokenType.ENGLISH_WORD, TokenType.CONTINUOUS_NUMBER] - and current_score + token.score - <= max_score * 1.05 # Allow up to 5% overflow - and current_score > 0 - ): # Must have other tokens already, cannot exceed on first token - current_score += token.score - truncate_pos = token.end_pos - else: - truncate_pos = token.start_pos - break - current_score += token.score - truncate_pos = token.end_pos - - # If truncation is needed - if truncate_pos < len(text): - result = text[:truncate_pos].rstrip() - return result + suffix if result else text - - return text - - except Exception as e: - # Fallback mode: use simple character length truncation if parsing fails - if enable_fallback: - # Estimate truncation length: assume average 1 point per character - estimated_length = int(max_score * 0.8) # Conservative estimate - if len(text) <= estimated_length: - return text - - # Simple character-based truncation, avoid breaking in the middle of words - truncate_pos = estimated_length - - # Look backward for a suitable truncation point (whitespace or punctuation) - for i in range( - min(estimated_length + 10, len(text) - 1), - max(estimated_length - 10, 0), - -1, - ): - if text[i].isspace() or text[i] in '.,!?;:': - truncate_pos = i + 1 - break - - result = text[:truncate_pos].rstrip() - return result + suffix if result else text - else: - # Raise exception if fallback is disabled - raise e - - def get_text_analysis(self, text: str) -> Dict[str, Any]: - """Get text analysis result - - Args: - text: Text to analyze - - Returns: - Dict: Dictionary containing various statistics - """ - tokens = self.parse_tokens(text) - - # Count tokens by type - type_counts = {token_type: 0 for token_type in TokenType} - type_scores = {token_type: 0.0 for token_type in TokenType} - - for token in tokens: - type_counts[token.type] += 1 - type_scores[token.type] += token.score - - return { - "total_tokens": len(tokens), - "total_score": self.calculate_total_score(tokens), - "type_counts": {t.value: count for t, count in type_counts.items()}, - "type_scores": {t.value: score for t, score in type_scores.items()}, - "tokens": tokens, - } - - -def smart_truncate_text( - text: str, - max_count: int, - chinese_weight: float = 1.0, - english_word_weight: float = 1.0, - suffix: str = "...", -) -> str: - """ - Smartly truncate text based on word/character count - - Uses the new SmartTextParser for more accurate token parsing and score calculation. - English words count as one unit, Chinese characters count as one unit, with different weights assignable. - - Args: - text: Text to truncate - max_count: Maximum count (total after weight accumulation) - chinese_weight: Weight for Chinese characters, default 1.0 - english_word_weight: Weight for English words, default 1.0 - suffix: Suffix to add when truncating, default "..." - - Returns: - str: Truncated text - - Examples: - >>> smart_truncate_text("Hello World 你好世界", 4) - 'Hello World 你好...' # 2 English words + 2 Chinese characters = 4 - >>> smart_truncate_text("Hello World 你好世界", 4, chinese_weight=0.5) - 'Hello World 你好世界' # 2 English words + 4*0.5 Chinese characters = 4 - """ - if not text or max_count <= 0: - return text or "" - - if not isinstance(text, str): - text = str(text) - - # Use the new smart parser for truncation - config = TokenConfig( - cjk_char_score=chinese_weight, - english_word_score=english_word_weight, - continuous_number_score=english_word_weight, # Use English word weight for numbers - punctuation_score=0.0, # Punctuation not counted, maintain backward compatibility - whitespace_score=0.0, # Whitespace not counted, maintain backward compatibility - other_score=0.0, # Other characters not counted, maintain backward compatibility - ) - - parser = SmartTextParser(config) - return parser.smart_truncate_by_score(text, max_count, suffix) - - -def clean_whitespace(text: str) -> str: - """ - Clean extra whitespace characters in text - - Uses SmartTextParser for more accurate whitespace handling, - preserving the integrity of other tokens. - - Args: - text: Text to clean - - Returns: - str: Cleaned text - """ - if not text: - return text - - if not isinstance(text, str): - text = str(text) - - # Use smart parser to handle whitespace - parser = SmartTextParser() - tokens = parser.parse_tokens(text) - - if not tokens: - return text.strip() - - # Reconstruct text, merging consecutive whitespaces into a single space - result_parts = [] - prev_was_whitespace = False - - for token in tokens: - if token.type == TokenType.WHITESPACE: - if not prev_was_whitespace: - result_parts.append(' ') # Use single space uniformly - prev_was_whitespace = True - else: - result_parts.append(token.content) - prev_was_whitespace = False - - # Strip leading and trailing whitespace - return ''.join(result_parts).strip() diff --git a/methods/EverCore/src/common_utils/url_extractor.py b/methods/EverCore/src/common_utils/url_extractor.py deleted file mode 100644 index f3aaadc7..00000000 --- a/methods/EverCore/src/common_utils/url_extractor.py +++ /dev/null @@ -1,529 +0,0 @@ -""" -URL content extraction tool -Used to extract metadata such as title, description, and images from web pages -""" - -import re -import aiohttp -from typing import Optional, Dict, Any -from urllib.parse import urljoin, urlparse -from bs4 import BeautifulSoup, Tag - -from core.observation.logger import get_logger - -logger = get_logger(__name__) - -# Request configuration -DEFAULT_TIMEOUT = 10 # seconds -DEFAULT_MAX_CONTENT_LENGTH = 5 * 1024 * 1024 # 5MB -DEFAULT_USER_AGENT = "Mozilla/5.0 (compatible; MemsysBot/1.0; +https://memsys.ai/bot)" - - -class URLExtractor: - """URL content extractor""" - - def __init__( - self, - timeout: int = DEFAULT_TIMEOUT, - max_content_length: int = DEFAULT_MAX_CONTENT_LENGTH, - user_agent: str = DEFAULT_USER_AGENT, - ): - """ - Initialize URL extractor - - Args: - timeout: Request timeout duration (seconds) - max_content_length: Maximum content length - user_agent: User agent string - """ - self.timeout = timeout - self.max_content_length = max_content_length - self.user_agent = user_agent - - async def extract_metadata( - self, url: str, need_redirect: bool = True - ) -> Dict[str, Any]: - """ - Extract metadata information from URL - - Args: - url: URL to extract - need_redirect: Whether to follow redirects to obtain the final URL - - Returns: - Dict[str, Any]: Extracted metadata information - """ - try: - # Get final URL (if redirection is needed) - final_url = url - if need_redirect: - final_url = await self._get_final_url(url) - - # Get webpage content - html_content = await self._fetch_html_content(final_url) - if not html_content: - return self._create_empty_metadata(url, final_url) - - # Parse HTML and extract metadata - soup = BeautifulSoup(html_content, 'html.parser') - metadata = self._extract_metadata_from_soup(soup, final_url) - metadata['original_url'] = url - metadata['final_url'] = final_url - - return metadata - - except Exception as e: - logger.error("Failed to extract URL metadata: %s, error: %s", url, str(e)) - return self._create_error_metadata(url, str(e)) - - async def _get_final_url(self, url: str) -> str: - """ - Get the final URL after redirection - - Args: - url: Original URL - - Returns: - str: Final URL - """ - try: - timeout = aiohttp.ClientTimeout(total=self.timeout) - headers = {'User-Agent': self.user_agent} - - # Create SSL context, skip certificate verification (relatively safe for content extraction) - import ssl - - ssl_context = ssl.create_default_context() - ssl_context.check_hostname = False - ssl_context.verify_mode = ssl.CERT_NONE - - connector = aiohttp.TCPConnector(ssl=ssl_context) - async with aiohttp.ClientSession( - timeout=timeout, headers=headers, connector=connector - ) as session: - # Send only HEAD request to get final URL, without downloading content - async with session.head(url, allow_redirects=True) as response: - return str(response.url) - - except Exception as e: - logger.warning("Failed to get final URL: %s, error: %s", url, str(e)) - return url - - async def _fetch_html_content(self, url: str) -> Optional[str]: - """ - Get HTML content - - Args: - url: URL to retrieve - - Returns: - Optional[str]: HTML content, returns None on failure - """ - try: - timeout = aiohttp.ClientTimeout(total=self.timeout) - headers = { - 'User-Agent': self.user_agent, - 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', - 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8', - 'Accept-Encoding': 'gzip, deflate', - 'DNT': '1', - 'Connection': 'keep-alive', - 'Upgrade-Insecure-Requests': '1', - } - - # Create SSL context, skip certificate verification (relatively safe for content extraction) - import ssl - - ssl_context = ssl.create_default_context() - ssl_context.check_hostname = False - ssl_context.verify_mode = ssl.CERT_NONE - - connector = aiohttp.TCPConnector(ssl=ssl_context) - async with aiohttp.ClientSession( - timeout=timeout, headers=headers, connector=connector - ) as session: - async with session.get(url) as response: - # Check content type - content_type = response.headers.get('content-type', '').lower() - if 'text/html' not in content_type: - logger.warning( - "Non-HTML content: %s, content-type: %s", url, content_type - ) - return None - - # Check content length - content_length = response.headers.get('content-length') - if content_length and int(content_length) > self.max_content_length: - logger.warning( - "Content too large: %s, size: %s", url, content_length - ) - return None - - # Read content - content = await response.text() - if len(content) > self.max_content_length: - logger.warning( - "Content too large: %s, size: %d", url, len(content) - ) - return None - - return content - - except Exception as e: - logger.error("Failed to get HTML content: %s, error: %s", url, str(e)) - return None - - def _extract_metadata_from_soup( - self, soup: BeautifulSoup, url: str - ) -> Dict[str, Any]: - """ - Extract metadata from BeautifulSoup object - - Args: - soup: BeautifulSoup object - url: Page URL - - Returns: - Dict[str, Any]: Extracted metadata - """ - metadata = { - 'title': None, - 'description': None, - 'image': None, - 'site_name': None, - 'url': url, - 'type': None, - 'favicon': None, - 'og_tags': {}, - 'twitter_tags': {}, - 'meta_tags': {}, - } - - try: - # Extract Open Graph tags - og_tags = self._extract_og_tags(soup) - metadata['og_tags'] = og_tags - - # Extract Twitter Card tags - twitter_tags = self._extract_twitter_tags(soup) - metadata['twitter_tags'] = twitter_tags - - # Extract basic meta tags - meta_tags = self._extract_meta_tags(soup) - metadata['meta_tags'] = meta_tags - - # Prioritize Open Graph information, but skip values containing template variables - metadata['title'] = ( - self._get_safe_value(og_tags.get('title')) - or self._get_safe_value(twitter_tags.get('title')) - or self._get_safe_value(self._extract_title(soup)) - or self._get_safe_value(meta_tags.get('title')) - ) - - metadata['description'] = ( - self._get_safe_value(og_tags.get('description')) - or self._get_safe_value(twitter_tags.get('description')) - or self._get_safe_value(meta_tags.get('description')) - ) - - metadata['image'] = self._get_safe_value( - og_tags.get('image') - ) or self._get_safe_value(twitter_tags.get('image')) - - metadata['site_name'] = self._get_safe_value(og_tags.get('site_name')) - metadata['type'] = self._get_safe_value(og_tags.get('type')) - metadata['favicon'] = self._extract_favicon(soup, url) - - # Clean and validate data - metadata = self._clean_metadata(metadata) - - except Exception as e: - logger.error("Failed to parse metadata: %s, error: %s", url, str(e)) - - return metadata - - def _extract_og_tags(self, soup: BeautifulSoup) -> Dict[str, str]: - """Extract Open Graph tags""" - og_tags = {} - - for tag in soup.find_all('meta', property=lambda x: x and x.startswith('og:')): - if tag.get('content'): - property_name = tag['property'][3:] # Remove 'og:' prefix - og_tags[property_name] = tag['content'].strip() - - return og_tags - - def _extract_twitter_tags(self, soup: BeautifulSoup) -> Dict[str, str]: - """Extract Twitter Card tags""" - twitter_tags = {} - - for tag in soup.find_all( - 'meta', attrs={'name': lambda x: x and x.startswith('twitter:')} - ): - if tag.get('content'): - name = tag['name'][8:] # Remove 'twitter:' prefix - twitter_tags[name] = tag['content'].strip() - - return twitter_tags - - def _extract_meta_tags(self, soup: BeautifulSoup) -> Dict[str, str]: - """Extract basic meta tags""" - meta_tags = {} - - # Extract title - title_tag = soup.find('meta', attrs={'name': 'title'}) - if title_tag and title_tag.get('content'): - meta_tags['title'] = title_tag['content'].strip() - - # Extract description - description_tag = soup.find('meta', attrs={'name': 'description'}) - if description_tag and description_tag.get('content'): - meta_tags['description'] = description_tag['content'].strip() - - # Extract keywords - keywords_tag = soup.find('meta', attrs={'name': 'keywords'}) - if keywords_tag and keywords_tag.get('content'): - meta_tags['keywords'] = keywords_tag['content'].strip() - - # Extract author - author_tag = soup.find('meta', attrs={'name': 'author'}) - if author_tag and author_tag.get('content'): - meta_tags['author'] = author_tag['content'].strip() - - return meta_tags - - def _extract_title(self, soup: BeautifulSoup) -> Optional[str]: - """Extract page title""" - title_tag = soup.find('title') - if title_tag and title_tag.string: - return title_tag.string.strip() - return None - - def _extract_first_image(self, soup: BeautifulSoup, base_url: str) -> Optional[str]: - """Extract the first meaningful image""" - # Find img tags - img_tags = soup.find_all('img', src=True) - - for img in img_tags: - src = img['src'].strip() - if not src: - continue - - # Convert to absolute URL - absolute_url = urljoin(base_url, src) - - # Simple filter: skip obvious decorative images - if self._is_meaningful_image(img, src): - return absolute_url - - return None - - def _is_meaningful_image(self, img_tag: Tag, src: str) -> bool: - """Determine if the image is meaningful (non-decorative)""" - # Skip obvious decorative images - skip_patterns = [ - 'icon', - 'logo', - 'avatar', - 'button', - 'pixel', - 'spacer', - 'blank', - 'transparent', - '1x1', - 'tracking', - ] - - src_lower = src.lower() - if any(pattern in src_lower for pattern in skip_patterns): - return False - - # Check image size attributes - width = img_tag.get('width') - height = img_tag.get('height') - - if width and height: - try: - w, h = int(width), int(height) - # Skip very small images - if w < 100 or h < 100: - return False - # Skip obvious decorative sizes - if w == 1 or h == 1: - return False - except (ValueError, TypeError): - pass - - return True - - def _extract_favicon(self, soup: BeautifulSoup, base_url: str) -> Optional[str]: - """Extract website icon""" - # Find icon in link tags - icon_links = soup.find_all('link', rel=lambda x: x and 'icon' in x.lower()) - - for link in icon_links: - href = link.get('href') - if href: - return urljoin(base_url, href.strip()) - - # Default favicon path - parsed_url = urlparse(base_url) - default_favicon = f"{parsed_url.scheme}://{parsed_url.netloc}/favicon.ico" - return default_favicon - - def _clean_metadata(self, metadata: Dict[str, Any]) -> Dict[str, Any]: - """Clean and validate metadata""" - # Clean string fields - string_fields = [ - 'title', - 'description', - 'image', - 'site_name', - 'type', - 'favicon', - 'url', - ] - for field in string_fields: - if metadata.get(field): - # Clean extra whitespace - cleaned_value = re.sub(r'\s+', ' ', str(metadata[field])).strip() - metadata[field] = cleaned_value - - # Limit length - if field == 'title' and len(metadata[field]) > 200: - metadata[field] = metadata[field][:200] + '...' - elif field == 'description' and len(metadata[field]) > 500: - metadata[field] = metadata[field][:500] + '...' - - # Validate URL format - url_fields = ['image', 'favicon', 'url'] - for field in url_fields: - if metadata.get(field) and not self._is_valid_url(metadata[field]): - metadata[field] = None - - return metadata - - def _contains_template_variables(self, text: str) -> bool: - """ - Check if text contains template variables - - Check the following template variable formats: - - ${variable} - - {{variable}} - - {variable} - - #{variable} - - @{variable} - - Args: - text: Text to check - - Returns: - bool: Returns True if contains template variables, otherwise False - """ - if not text or not isinstance(text, str): - return False - - # Define regular expression patterns for template variables - template_patterns = [ - r'\$\{[^}]+\}', # ${variable} - r'\{\{[^}]+\}\}', # {{variable}} - r'#\{[^}]+\}', # #{variable} - r'@\{[^}]+\}', # @{variable} - # {variable} - Only match variable names containing letters, digits, dots, underscores - r'\{[a-zA-Z_][a-zA-Z0-9_.]*\}', - ] - - # Check each pattern - for pattern in template_patterns: - if re.search(pattern, text): - return True - - return False - - def _get_safe_value(self, value: str) -> Optional[str]: - """ - Get a safe value, return None if it contains template variables - - Args: - value: Value to check - - Returns: - Optional[str]: Returns original value if valid and does not contain template variables, otherwise returns None - """ - if not value or not isinstance(value, str): - return None - - # Clean whitespace - cleaned_value = value.strip() - if not cleaned_value: - return None - - # Check if contains template variables - if self._contains_template_variables(cleaned_value): - return None - - return cleaned_value - - def _is_valid_url(self, url: str) -> bool: - """Validate URL format""" - try: - result = urlparse(url) - return all([result.scheme, result.netloc]) - except Exception: - return False - - def _create_empty_metadata( - self, original_url: str, final_url: str - ) -> Dict[str, Any]: - """Create empty metadata""" - return { - 'title': None, - 'description': None, - 'image': None, - 'site_name': None, - 'url': final_url, - 'original_url': original_url, - 'final_url': final_url, - 'type': None, - 'favicon': None, - 'og_tags': {}, - 'twitter_tags': {}, - 'meta_tags': {}, - 'error': None, - } - - def _create_error_metadata(self, url: str, error: str) -> Dict[str, Any]: - """Create error metadata""" - return { - 'title': None, - 'description': None, - 'image': None, - 'site_name': None, - 'url': url, - 'original_url': url, - 'final_url': url, - 'type': None, - 'favicon': None, - 'og_tags': {}, - 'twitter_tags': {}, - 'meta_tags': {}, - 'error': error, - } - - -# Global instance -_url_extractor = URLExtractor() - - -async def extract_url_metadata(url: str, need_redirect: bool = True) -> Dict[str, Any]: - """ - Convenience function to extract URL metadata - - Args: - url: URL to extract - need_redirect: Whether to follow redirects - - Returns: - Dict[str, Any]: Metadata information - """ - return await _url_extractor.extract_metadata(url, need_redirect) diff --git a/methods/EverCore/src/config/llm_backends.yaml b/methods/EverCore/src/config/llm_backends.yaml deleted file mode 100644 index 1cc863ef..00000000 --- a/methods/EverCore/src/config/llm_backends.yaml +++ /dev/null @@ -1,119 +0,0 @@ -# LLM backend configuration file -llm_backends: - # OpenAI configuration - openai: - name: "OpenAI" - provider: "openai" - base_url: "https://api.openai.com/v1" - api_key: "" - models: - - "gpt-4" - - "gpt-4-turbo" - - "gpt-3.5-turbo" - - "gpt-4o" - - "gpt-4o-mini" - model: "gpt-4o" - timeout: 600 # Increase to 10 minutes, suitable for time-consuming tasks like paper information extraction - max_retries: 3 - - # Azure OpenAI configuration - azure_openai: - name: "Azure OpenAI" - provider: "azure" - base_url: "" - api_key: "" - api_version: "2024-02-15-preview" - models: - - "gpt-4" - - "gpt-35-turbo" - model: "gpt-4" - timeout: 600 # Increase to 10 minutes, suitable for time-consuming tasks like paper information extraction - max_retries: 3 - - # Anthropic Claude configuration - anthropic: - name: "Anthropic Claude" - provider: "anthropic" - base_url: "https://api.anthropic.com" - api_key: "" - models: - - "claude-3-5-sonnet-20241022" - - "claude-3-haiku-20240307" - - "claude-3-opus-20240229" - model: "claude-3-5-sonnet-20241022" - timeout: 600 # Increase to 10 minutes, suitable for time-consuming tasks like paper information extraction - max_retries: 3 - - # Google Gemini configuration - gemini: - name: "Google Gemini" - provider: "gemini" - base_url: "https://generativelanguage.googleapis.com/v1beta" - api_key: "" - models: - - "gemini-2.0-flash" - - "gemini-2.0-pro" - - "gemini-2.5-flash" - - "gemini-2.5-pro" - model: "gemini-2.5-flash" - timeout: 600 # Increase to 10 minutes, suitable for time-consuming tasks like paper information extraction - max_retries: 3 - - # Local Ollama configuration - ollama: - name: "Ollama Local" - provider: "ollama" - base_url: "http://localhost:11434/v1" - api_key: "" # Ollama usually doesn't need an API key - models: - - "llama2" - - "llama2:13b" - - "codellama" - - "mistral" - - "qwen2" - model: "llama2" - timeout: 600 # Increase to 10 minutes, suitable for time-consuming tasks like paper information extraction - max_retries: 2 - - # Custom OpenAI compatible API - custom: - name: "Custom OpenAI Compatible" - provider: "custom" - base_url: "" - api_key: "" - models: - - "custom-model-1" - - "custom-model-2" - model: "custom-model-1" - timeout: 600 # Increase to 10 minutes, suitable for time-consuming tasks like paper information extraction - max_retries: 3 - - # Qwen test environment configuration - qwen_test: - name: "Qwen Test Environment" - provider: "custom" - base_url: "http://localhost:9090/v1" - api_key: "" # Test environment doesn't need an API key - models: - - "Qwen/Qwen3-14B-AWQ" - model: "Qwen/Qwen3-14B-AWQ" - timeout: 600 # Increase to 10 minutes, suitable for time-consuming tasks like paper information extraction - max_retries: 3 - connect_timeout: 3 # connect-timeout: 3000ms = 3 seconds - temperature: 0.7 - max_tokens: 2000 - top_p: 1 - frequency_penalty: 0 - presence_penalty: 0 - -# Default backend -default_backend: "gemini" # Mainly used for paper parsing - -# Default configuration -default_settings: - temperature: 0.7 - max_tokens: 50000 - top_p: 1.0 - frequency_penalty: 0 - presence_penalty: 0 - thinking_budget: -1 \ No newline at end of file diff --git a/methods/EverCore/src/config/stopwords/hit_stopwords.txt b/methods/EverCore/src/config/stopwords/hit_stopwords.txt deleted file mode 100644 index 4210f280..00000000 --- a/methods/EverCore/src/config/stopwords/hit_stopwords.txt +++ /dev/null @@ -1,746 +0,0 @@ -$ -0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -? -_ -“ -” -、 -。 -《 -》 -一 -一些 -一何 -一切 -一则 -一方面 -一旦 -一来 -一样 -一般 -一转眼 -万一 -上 -上下 -下 -不 -不仅 -不但 -不光 -不单 -不只 -不外乎 -不如 -不妨 -不尽 -不尽然 -不得 -不怕 -不惟 -不成 -不拘 -不料 -不是 -不比 -不然 -不特 -不独 -不管 -不至于 -不若 -不论 -不过 -不问 -与 -与其 -与其说 -与否 -与此同时 -且 -且不说 -且说 -两者 -个 -个别 -临 -为 -为了 -为什么 -为何 -为止 -为此 -为着 -乃 -乃至 -乃至于 -么 -之 -之一 -之所以 -之类 -乌乎 -乎 -乘 -也 -也好 -也罢 -了 -二来 -于 -于是 -于是乎 -云云 -云尔 -些 -亦 -人 -人们 -人家 -什么 -什么样 -今 -介于 -仍 -仍旧 -从 -从此 -从而 -他 -他人 -他们 -以 -以上 -以为 -以便 -以免 -以及 -以故 -以期 -以来 -以至 -以至于 -以致 -们 -任 -任何 -任凭 -似的 -但 -但凡 -但是 -何 -何以 -何况 -何处 -何时 -余外 -作为 -你 -你们 -使 -使得 -例如 -依 -依据 -依照 -便于 -俺 -俺们 -倘 -倘使 -倘或 -倘然 -倘若 -借 -假使 -假如 -假若 -傥然 -像 -儿 -先不先 -光是 -全体 -全部 -兮 -关于 -其 -其一 -其中 -其二 -其他 -其余 -其它 -其次 -具体地说 -具体说来 -兼之 -内 -再 -再其次 -再则 -再有 -再者 -再者说 -再说 -冒 -冲 -况且 -几 -几时 -凡 -凡是 -凭 -凭借 -出于 -出来 -分别 -则 -则甚 -别 -别人 -别处 -别是 -别的 -别管 -别说 -到 -前后 -前此 -前者 -加之 -加以 -即 -即令 -即使 -即便 -即如 -即或 -即若 -却 -去 -又 -又及 -及 -及其 -及至 -反之 -反而 -反过来 -反过来说 -受到 -另 -另一方面 -另外 -另悉 -只 -只当 -只怕 -只是 -只有 -只消 -只要 -只限 -叫 -叮咚 -可 -可以 -可是 -可见 -各 -各个 -各位 -各种 -各自 -同 -同时 -后 -后者 -向 -向使 -向着 -吓 -吗 -否则 -吧 -吧哒 -吱 -呀 -呃 -呕 -呗 -呜 -呜呼 -呢 -呵 -呵呵 -呸 -呼哧 -咋 -和 -咚 -咦 -咧 -咱 -咱们 -咳 -哇 -哈 -哈哈 -哉 -哎 -哎呀 -哎哟 -哗 -哟 -哦 -哩 -哪 -哪个 -哪些 -哪儿 -哪天 -哪年 -哪怕 -哪样 -哪边 -哪里 -哼 -哼唷 -唉 -唯有 -啊 -啐 -啥 -啦 -啪达 -啷当 -喂 -喏 -喔唷 -喽 -嗡 -嗡嗡 -嗬 -嗯 -嗳 -嘎 -嘎登 -嘘 -嘛 -嘻 -嘿 -嘿嘿 -因 -因为 -因了 -因此 -因着 -因而 -固然 -在 -在下 -在于 -地 -基于 -处在 -多 -多么 -多少 -大 -大家 -她 -她们 -好 -如 -如上 -如上所述 -如下 -如何 -如其 -如同 -如是 -如果 -如此 -如若 -始而 -孰料 -孰知 -宁 -宁可 -宁愿 -宁肯 -它 -它们 -对 -对于 -对待 -对方 -对比 -将 -小 -尔 -尔后 -尔尔 -尚且 -就 -就是 -就是了 -就是说 -就算 -就要 -尽 -尽管 -尽管如此 -岂但 -己 -已 -已矣 -巴 -巴巴 -并 -并且 -并非 -庶乎 -庶几 -开外 -开始 -归 -归齐 -当 -当地 -当然 -当着 -彼 -彼时 -彼此 -往 -待 -很 -得 -得了 -怎 -怎么 -怎么办 -怎么样 -怎奈 -怎样 -总之 -总的来看 -总的来说 -总的说来 -总而言之 -恰恰相反 -您 -惟其 -慢说 -我 -我们 -或 -或则 -或是 -或曰 -或者 -截至 -所 -所以 -所在 -所幸 -所有 -才 -才能 -打 -打从 -把 -抑或 -拿 -按 -按照 -换句话说 -换言之 -据 -据此 -接着 -故 -故此 -故而 -旁人 -无 -无宁 -无论 -既 -既往 -既是 -既然 -时候 -是 -是以 -是的 -曾 -替 -替代 -最 -有 -有些 -有关 -有及 -有时 -有的 -望 -朝 -朝着 -本 -本人 -本地 -本着 -本身 -来 -来着 -来自 -来说 -极了 -果然 -果真 -某 -某个 -某些 -某某 -根据 -欤 -正值 -正如 -正巧 -正是 -此 -此地 -此处 -此外 -此时 -此次 -此间 -毋宁 -每 -每当 -比 -比及 -比如 -比方 -没奈何 -沿 -沿着 -漫说 -焉 -然则 -然后 -然而 -照 -照着 -犹且 -犹自 -甚且 -甚么 -甚或 -甚而 -甚至 -甚至于 -用 -用来 -由 -由于 -由是 -由此 -由此可见 -的 -的确 -的话 -直到 -相对而言 -省得 -看 -眨眼 -着 -着呢 -矣 -矣乎 -矣哉 -离 -竟而 -第 -等 -等到 -等等 -简言之 -管 -类如 -紧接着 -纵 -纵令 -纵使 -纵然 -经 -经过 -结果 -给 -继之 -继后 -继而 -综上所述 -罢了 -者 -而 -而且 -而况 -而后 -而外 -而已 -而是 -而言 -能 -能否 -腾 -自 -自个儿 -自从 -自各儿 -自后 -自家 -自己 -自打 -自身 -至 -至于 -至今 -至若 -致 -般的 -若 -若夫 -若是 -若果 -若非 -莫不然 -莫如 -莫若 -虽 -虽则 -虽然 -虽说 -被 -要 -要不 -要不是 -要不然 -要么 -要是 -譬喻 -譬如 -让 -许多 -论 -设使 -设或 -设若 -诚如 -诚然 -该 -说来 -诸 -诸位 -诸如 -谁 -谁人 -谁料 -谁知 -贼死 -赖以 -赶 -起 -起见 -趁 -趁着 -越是 -距 -跟 -较 -较之 -边 -过 -还 -还是 -还有 -还要 -这 -这一来 -这个 -这么 -这么些 -这么样 -这么点儿 -这些 -这会儿 -这儿 -这就是说 -这时 -这样 -这次 -这般 -这边 -这里 -进而 -连 -连同 -逐步 -通过 -遵循 -遵照 -那 -那个 -那么 -那么些 -那么样 -那些 -那会儿 -那儿 -那时 -那样 -那般 -那边 -那里 -都 -鄙人 -鉴于 -针对 -阿 -除 -除了 -除外 -除开 -除此之外 -除非 -随 -随后 -随时 -随着 -难道说 -非但 -非徒 -非特 -非独 -靠 -顺 -顺着 -首先 -！ -， -： -； -？ diff --git a/methods/EverCore/src/core/addons/addon_registry.py b/methods/EverCore/src/core/addons/addon_registry.py deleted file mode 100644 index 35c73846..00000000 --- a/methods/EverCore/src/core/addons/addon_registry.py +++ /dev/null @@ -1,80 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Container for scanning and registering a single Addon -Used to host DI and async task registry for a single addon -""" - -from typing import Optional -from core.di.scan_path_registry import ScannerPathsRegistry -from core.asynctasks.task_scan_registry import TaskScanDirectoriesRegistry - - -class AddonRegistry: - """ - Container for scanning and registering a single Addon - - Used to host scanning configurations for a single addon: - - di: Registry for DI component scan paths - - asynctasks: Registry for async task scan directories - - Example: - # Create addon registry - addon = AddonRegistry(name="my_addon") - - # Register DI registry - di_registry = ScannerPathsRegistry() - di_registry.add_scan_path("/path/to/components") - addon.register_di(di_registry) - - # Register async task registry - task_registry = TaskScanDirectoriesRegistry() - task_registry.add_scan_path("/path/to/tasks") - addon.register_asynctasks(task_registry) - """ - - def __init__(self, name: str): - """ - Initialize addon registry - - Args: - name: addon name - """ - self.name: str = name - self.di: Optional[ScannerPathsRegistry] = None - self.asynctasks: Optional[TaskScanDirectoriesRegistry] = None - - def register_di(self, registry: ScannerPathsRegistry) -> 'AddonRegistry': - """ - Register DI component scan path registry - - Args: - registry: DI scan path registry - - Returns: - self: supports method chaining - """ - self.di = registry - return self - - def register_asynctasks( - self, registry: TaskScanDirectoriesRegistry - ) -> 'AddonRegistry': - """ - Register async task scan directory registry - - Args: - registry: async task scan directory registry - - Returns: - self: supports method chaining - """ - self.asynctasks = registry - return self - - def has_di(self) -> bool: - """Check if DI registry has been registered""" - return self.di is not None - - def has_asynctasks(self) -> bool: - """Check if async task registry has been registered""" - return self.asynctasks is not None diff --git a/methods/EverCore/src/core/addons/addonize/__init__.py b/methods/EverCore/src/core/addons/addonize/__init__.py deleted file mode 100644 index 7d8a59f1..00000000 --- a/methods/EverCore/src/core/addons/addonize/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Addon contribution module - -Provides various utility functions for handling addons, including entry functions for dependency injection scanning and asynchronous task registration -""" diff --git a/methods/EverCore/src/core/addons/addonize/addon_bean_order_strategy.py b/methods/EverCore/src/core/addons/addonize/addon_bean_order_strategy.py deleted file mode 100644 index aca0b872..00000000 --- a/methods/EverCore/src/core/addons/addonize/addon_bean_order_strategy.py +++ /dev/null @@ -1,198 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Addon Bean ordering strategy module - -Extends the standard BeanOrderStrategy to add support for addon_tag priority - -Priority ordering rules (from highest to lowest): -1. addon_tag: addon priority configured via environment variable (smaller number means higher priority) -2. is_mock: In mock mode, Mock Bean > Non-Mock Bean; in non-mock mode, Mock Beans are filtered out -3. Matching method: Direct match > Implementation class match -4. primary: Primary Bean > Non-Primary Bean -5. scope: Factory Bean > Regular Bean -""" - -import os -from typing import List, Tuple, Set, Type, Dict -from core.di.bean_definition import BeanDefinition, BeanScope -from core.di.bean_order_strategy import BeanOrderStrategy -from core.di.container import DIContainer -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class AddonBeanOrderStrategy(BeanOrderStrategy): - """ - Addon Bean ordering strategy class - - Inherits from BeanOrderStrategy, extends support for addon_tag priority - addon_tag priority is configured via environment variable ADDON_PRIORITY - Format: "addon1:priority1,addon2:priority2" - Example: "core:1000,enterprise:50" means enterprise has higher priority (smaller number means higher priority) - """ - - # Default addon priority configuration - DEFAULT_ADDON_PRIORITY = "core:1000,enterprise:50" - - # addon priority cache - _addon_priority_map: Dict[str, int] = None - - @classmethod - def load_addon_priority_map(cls) -> Dict[str, int]: - """ - Load addon priority configuration from environment variables - - Returns: - Dict[str, int]: mapping from addon name to priority (smaller number means higher priority) - """ - if cls._addon_priority_map is not None: - return cls._addon_priority_map - - # Read configuration from environment variable, use default if not set - priority_config = os.getenv("ADDON_PRIORITY", cls.DEFAULT_ADDON_PRIORITY) - - priority_map = {} - for item in priority_config.split(","): - item = item.strip() - if ":" in item: - addon_name, priority_str = item.split(":", 1) - try: - priority_map[addon_name.strip()] = int(priority_str.strip()) - except ValueError: - # Ignore invalid configuration - pass - - cls._addon_priority_map = priority_map - return priority_map - - @classmethod - def get_addon_priority(cls, bean_def: BeanDefinition) -> int: - """ - Get the addon priority of a Bean - - Args: - bean_def: Bean definition object - - Returns: - int: addon priority value, smaller number means higher priority - Returns default 99999 (lowest priority) if addon_tag is not configured or not found in config - """ - priority_map = cls.load_addon_priority_map() - - # Get addon_tag from Bean's metadata - addon_tag = bean_def.metadata.get("addon_tag") - if not addon_tag: - # No addon_tag, return lowest priority - return 99999 - - # Return configured priority, or default lowest priority if not configured - return priority_map.get(addon_tag, 99999) - - @staticmethod - def calculate_order_key( - bean_def: BeanDefinition, is_direct_match: bool, mock_mode: bool = False - ) -> Tuple[int, int, int, int, int]: - """ - Calculate Bean's ordering key (extended version, includes addon priority) - - Args: - bean_def: Bean definition object - is_direct_match: Whether it's a direct match (True=direct match, False=implementation class match) - mock_mode: Whether in Mock mode - - Returns: - Tuple[int, int, int, int, int]: ordering key tuple - Format: (addon_priority, mock_priority, match_priority, primary_priority, scope_priority) - - Priority rules: - - addon_priority: retrieved from environment variable config, smaller number means higher priority - - mock_priority: in mock mode, Mock Bean=0, Non-Mock Bean=1; in non-mock mode both are 0 - - match_priority: direct match=0, implementation class match=1 - - primary_priority: Primary Bean=0, Non-Primary Bean=1 - - scope_priority: Factory Bean=0, Non-Factory Bean=1 - """ - # 1. Addon priority (smaller number means higher priority) - addon_priority = AddonBeanOrderStrategy.get_addon_priority(bean_def) - - # 2. Mock priority (only differentiated in Mock mode) - if mock_mode: - mock_priority = 0 if bean_def.is_mock else 1 - else: - mock_priority = 0 # No distinction in non-Mock mode - - # 3. Matching method priority (direct match first) - match_priority = 0 if is_direct_match else 1 - - # 4. Primary priority (Primary first) - primary_priority = 0 if bean_def.is_primary else 1 - - # 5. Scope priority (Factory first) - scope_priority = 0 if bean_def.scope == BeanScope.FACTORY else 1 - - return ( - addon_priority, - mock_priority, - match_priority, - primary_priority, - scope_priority, - ) - - @staticmethod - def sort_beans_with_context( - bean_defs: List[BeanDefinition], - direct_match_types: Set[Type], - mock_mode: bool = False, - ) -> List[BeanDefinition]: - """ - Sort list of Bean definitions based on context information (extended version) - - Args: - bean_defs: List of Bean definitions - direct_match_types: Set of types that are direct matches - mock_mode: Whether in Mock mode - - Returns: - List[BeanDefinition]: Sorted list of Bean definitions - - Note: - - In non-Mock mode, Mock Beans are filtered out and do not participate in sorting - - In Mock mode, Mock Beans take precedence over non-Mock Beans - - addon_tag has the highest priority, sorted according to environment variable configuration - """ - # Filter out all Mock Beans in non-Mock mode - if not mock_mode: - bean_defs = [bd for bd in bean_defs if not bd.is_mock] - - # Calculate ordering key for each Bean, then sort by key - sorted_beans = sorted( - bean_defs, - key=lambda bd: AddonBeanOrderStrategy.calculate_order_key( - bean_def=bd, - is_direct_match=bd.bean_type in direct_match_types, - mock_mode=mock_mode, - ), - ) - return sorted_beans - - -# Automatically replace Bean ordering strategy when module loads -# Note: This is a temporary solution because the DI mechanism is not fully established yet -# Once addon mechanism is referenced, AddonBeanOrderStrategy will be automatically enabled -def _replace_strategy(): - """Automatically replace Bean ordering strategy""" - try: - DIContainer.replace_bean_order_strategy(AddonBeanOrderStrategy) - logger.warning( - "⚠️ Bean ordering strategy has been automatically replaced with AddonBeanOrderStrategy, supporting addon_tag priority" - ) - logger.info( - " 📌 Addon priority configuration: %s (environment variable: ADDON_PRIORITY)", - AddonBeanOrderStrategy.load_addon_priority_map(), - ) - except Exception as e: - logger.error("Failed to replace Bean ordering strategy: %s", e) - - -# Execute automatic replacement -_replace_strategy() diff --git a/methods/EverCore/src/core/addons/addonize/asynctasks_setup.py b/methods/EverCore/src/core/addons/addonize/asynctasks_setup.py deleted file mode 100644 index 50e04f7d..00000000 --- a/methods/EverCore/src/core/addons/addonize/asynctasks_setup.py +++ /dev/null @@ -1,80 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Async task setup module - -Handles the entry function for loading async task scan paths from addons and registering tasks -""" - -from core.asynctasks.task_scan_registry import TaskScanDirectoriesRegistry -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger -from core.addons.addons_registry import ADDONS_REGISTRY - -logger = get_logger(__name__) - - -def setup_async_tasks(addons: list = None): - """ - Set up async tasks - - Extract async task scan directories from the addon list and perform task scanning and registration - - Args: - addons (list, optional): List of addons. If None, fetch from ADDONS_REGISTRY - """ - logger.info("🔄 Registering async tasks...") - - try: - # Get task manager - from core.asynctasks.task_manager import TaskManager - - task_manager = get_bean_by_type(TaskManager) - - # If addons not provided, get from ADDONS_REGISTRY - if addons is None: - addons = ADDONS_REGISTRY.get_all() - - logger.info(" 📦 Loading async task scan paths from %d addons...", len(addons)) - - # Create task directory registry and populate from addons - task_directories_registry = TaskScanDirectoriesRegistry() - for addon in addons: - if addon.has_asynctasks(): - addon_dirs = addon.asynctasks.get_scan_directories() - for directory in addon_dirs: - task_directories_registry.add_scan_path(directory) - logger.debug( - " 📌 addon [%s] contributes %d task directories", - addon.name, - len(addon_dirs), - ) - - task_directories = task_directories_registry.get_scan_directories() - logger.info("📂 Number of task directories: %d", len(task_directories)) - for directory in task_directories: - logger.debug(" + %s", directory) - - # Automatically scan and register tasks - task_manager.scan_and_register_tasks(task_directories_registry) - - # Print registered tasks - registered_tasks = task_manager.list_registered_task_names() - logger.info("📋 Registered task list: %s", registered_tasks) - - logger.info("✅ Async task registration completed") - except Exception as e: - logger.error("❌ Async task registration failed: %s", e) - raise - - -def print_registered_tasks(): - """Print registered async tasks""" - logger.info("\n📋 Registered task list:") - logger.info("-" * 50) - - from core.asynctasks.task_manager import TaskManager - - task_manager = get_bean_by_type(TaskManager) - - registered_tasks = task_manager.list_registered_task_names() - logger.info("📋 Registered task list: %s", registered_tasks) diff --git a/methods/EverCore/src/core/addons/addonize/di_setup.py b/methods/EverCore/src/core/addons/addonize/di_setup.py deleted file mode 100644 index e0b14cf8..00000000 --- a/methods/EverCore/src/core/addons/addonize/di_setup.py +++ /dev/null @@ -1,85 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Dependency injection setup module - -Handles the entry function for loading dependency injection scan paths from addons -""" - -from core.di.scanner import ComponentScanner -from core.di.utils import get_beans -from core.observation.logger import get_logger -from core.addons.addons_registry import ADDONS_REGISTRY - -logger = get_logger(__name__) - - -def setup_dependency_injection(addons: list = None): - """ - Set up the dependency injection framework - - Extract DI scan paths from the addon list and perform component scanning and registration - - Args: - addons (list, optional): List of addons. If None, fetch from ADDONS_REGISTRY - - Returns: - ComponentScanner: Configured component scanner - """ - logger.info("🚀 Initializing dependency injection container...") - - # Import to trigger automatic replacement of Bean ordering strategy (executed during module loading) - from core.addons.addonize import addon_bean_order_strategy # noqa: F401 - - # Create component scanner - scanner = ComponentScanner() - - # If addons not provided, get from ADDONS_REGISTRY - if addons is None: - addons = ADDONS_REGISTRY.get_all() - - logger.info( - " 📦 Loading dependency injection scan paths from %d addons...", len(addons) - ) - - # Collect all scan paths and register scan_context - total_paths = 0 - for addon in addons: - if addon.has_di(): - addon_paths = addon.di.get_scan_paths() - logger.debug( - " 📌 addon [%s] contributes %d scan paths", - addon.name, - len(addon_paths), - ) - - # Register scan_context for each scan path, marking addon_tag - for path in addon_paths: - # Register scan context, marking source addon - scanner.register_scan_context(path, {"addon_tag": addon.name}) - # Add scan path - scanner.add_scan_path(path) - logger.debug(" + %s (addon_tag=%s)", path, addon.name) - total_paths += 1 - - logger.info(scanner.context_registry.print_tree()) - - # Perform scanning and registration - scanner.scan() - logger.info( - "✅ Dependency injection setup completed, scanned %d paths in total", - total_paths, - ) - - return scanner - - -def print_registered_beans(): - """Print all registered Beans""" - logger.info("\n📋 Registered Bean list:") - logger.info("-" * 50) - - all_beans = get_beans() - for name, bean in all_beans.items(): - logger.info(" • %s: %s", name, type(bean).__name__) - - logger.info("\n📊 Total: %d Beans", len(all_beans)) diff --git a/methods/EverCore/src/core/addons/addonize/tests/__init__.py b/methods/EverCore/src/core/addons/addonize/tests/__init__.py deleted file mode 100644 index 40a96afc..00000000 --- a/methods/EverCore/src/core/addons/addonize/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# -*- coding: utf-8 -*- diff --git a/methods/EverCore/src/core/addons/addonize/tests/test_addon_bean_order_strategy.py b/methods/EverCore/src/core/addons/addonize/tests/test_addon_bean_order_strategy.py deleted file mode 100644 index c3ce145d..00000000 --- a/methods/EverCore/src/core/addons/addonize/tests/test_addon_bean_order_strategy.py +++ /dev/null @@ -1,658 +0,0 @@ -# -*- coding: utf-8 -*- -""" -cd /Users/admin/memsys_opensource -PYTHONPATH=/Users/admin/memsys_opensource/src python -m pytest src/core/addons/contrib/tests/test_addon_bean_order_strategy.py -v -s - -Addon Bean sorting strategy test - -Test the extension functionality of AddonBeanOrderStrategy, especially addon_tag priority -""" - -import os -import pytest -from typing import Set, Type -from core.di.bean_definition import BeanDefinition, BeanScope -from core.addons.addonize.addon_bean_order_strategy import AddonBeanOrderStrategy - - -# ==================== Test helper classes ==================== - - -class ServiceA: - """Test service A""" - - pass - - -class ServiceB: - """Test service B""" - - pass - - -class ServiceC: - """Test service C""" - - pass - - -class ServiceD: - """Test service D""" - - pass - - -# ==================== Test Addon priority configuration ==================== - - -class TestAddonPriorityConfiguration: - """Test loading of Addon priority configuration""" - - def setup_method(self): - """Reset priority cache before each test""" - AddonBeanOrderStrategy._addon_priority_map = None - - def teardown_method(self): - """Restore environment variables after each test""" - if "ADDON_PRIORITY" in os.environ: - del os.environ["ADDON_PRIORITY"] - AddonBeanOrderStrategy._addon_priority_map = None - - def test_load_default_priority_map(self): - """Test loading default priority configuration""" - # Without setting environment variable, should use default configuration - priority_map = AddonBeanOrderStrategy.load_addon_priority_map() - - # Verify default configuration: core:1000, enterprise:50 - assert "core" in priority_map - assert "enterprise" in priority_map - assert priority_map["core"] == 1000 - assert priority_map["enterprise"] == 50 - - def test_load_custom_priority_map(self): - """Test loading custom priority configuration from environment variable""" - # Set environment variable - os.environ["ADDON_PRIORITY"] = "addon1:100,addon2:200,addon3:50" - - # Reload - AddonBeanOrderStrategy._addon_priority_map = None - priority_map = AddonBeanOrderStrategy.load_addon_priority_map() - - # Verify configuration - assert priority_map["addon1"] == 100 - assert priority_map["addon2"] == 200 - assert priority_map["addon3"] == 50 - - def test_priority_map_caching(self): - """Test caching mechanism of priority configuration""" - # First load - priority_map1 = AddonBeanOrderStrategy.load_addon_priority_map() - - # Second load (should return from cache) - priority_map2 = AddonBeanOrderStrategy.load_addon_priority_map() - - # Should be the same object - assert priority_map1 is priority_map2 - - def test_invalid_priority_config_ignored(self): - """Test invalid priority configuration is ignored""" - # Set environment variable with invalid values - os.environ["ADDON_PRIORITY"] = "valid:100,invalid:abc,another:200" - - # Reload - AddonBeanOrderStrategy._addon_priority_map = None - priority_map = AddonBeanOrderStrategy.load_addon_priority_map() - - # Verify: valid configurations are loaded, invalid ones are ignored - assert priority_map["valid"] == 100 - assert priority_map["another"] == 200 - assert "invalid" not in priority_map - - def test_priority_config_with_spaces(self): - """Test configuration containing spaces""" - # Set environment variable with spaces - os.environ["ADDON_PRIORITY"] = " addon1 : 100 , addon2 : 200 " - - # Reload - AddonBeanOrderStrategy._addon_priority_map = None - priority_map = AddonBeanOrderStrategy.load_addon_priority_map() - - # Verify: spaces are properly handled - assert priority_map["addon1"] == 100 - assert priority_map["addon2"] == 200 - - -# ==================== Test getting Addon priority ==================== - - -class TestGetAddonPriority: - """Test getting Addon priority of Bean""" - - def setup_method(self): - """Reset configuration before each test""" - AddonBeanOrderStrategy._addon_priority_map = None - os.environ["ADDON_PRIORITY"] = "core:1000,enterprise:50,custom:200" - - def teardown_method(self): - """Clean up after each test""" - if "ADDON_PRIORITY" in os.environ: - del os.environ["ADDON_PRIORITY"] - AddonBeanOrderStrategy._addon_priority_map = None - - def test_get_priority_with_addon_tag(self): - """Test getting priority of Bean with addon_tag""" - # Create Bean with addon_tag - bean_core = BeanDefinition(ServiceA, metadata={"addon_tag": "core"}) - bean_enterprise = BeanDefinition(ServiceB, metadata={"addon_tag": "enterprise"}) - bean_custom = BeanDefinition(ServiceC, metadata={"addon_tag": "custom"}) - - # Get priority - priority_core = AddonBeanOrderStrategy.get_addon_priority(bean_core) - priority_enterprise = AddonBeanOrderStrategy.get_addon_priority(bean_enterprise) - priority_custom = AddonBeanOrderStrategy.get_addon_priority(bean_custom) - - # Verify - assert priority_core == 1000 - assert priority_enterprise == 50 - assert priority_custom == 200 - - # Verify priority order: enterprise < custom < core - assert priority_enterprise < priority_custom < priority_core - - def test_get_priority_without_addon_tag(self): - """Test getting priority of Bean without addon_tag""" - # Create Bean without addon_tag - bean_no_tag = BeanDefinition(ServiceA) - - # Get priority - priority = AddonBeanOrderStrategy.get_addon_priority(bean_no_tag) - - # Verify: returns lowest priority - assert priority == 99999 - - def test_get_priority_with_unknown_addon_tag(self): - """Test getting priority of unconfigured addon_tag""" - # Create Bean with unconfigured addon_tag - bean_unknown = BeanDefinition(ServiceA, metadata={"addon_tag": "unknown_addon"}) - - # Get priority - priority = AddonBeanOrderStrategy.get_addon_priority(bean_unknown) - - # Verify: returns lowest priority - assert priority == 99999 - - def test_get_priority_with_empty_addon_tag(self): - """Test getting priority of empty addon_tag""" - # Create Bean with empty addon_tag - bean_empty = BeanDefinition(ServiceA, metadata={"addon_tag": ""}) - - # Get priority - priority = AddonBeanOrderStrategy.get_addon_priority(bean_empty) - - # Verify: returns lowest priority - assert priority == 99999 - - -# ==================== Test calculating sort key ==================== - - -class TestCalculateOrderKey: - """Test calculating extended sort key (including addon priority)""" - - def setup_method(self): - """Reset configuration before each test""" - AddonBeanOrderStrategy._addon_priority_map = None - os.environ["ADDON_PRIORITY"] = "enterprise:50,core:1000" - - def teardown_method(self): - """Clean up after each test""" - if "ADDON_PRIORITY" in os.environ: - del os.environ["ADDON_PRIORITY"] - AddonBeanOrderStrategy._addon_priority_map = None - - def test_order_key_includes_addon_priority(self): - """Test sort key includes addon priority""" - # Create Bean with addon_tag - bean_enterprise = BeanDefinition(ServiceA, metadata={"addon_tag": "enterprise"}) - bean_core = BeanDefinition(ServiceB, metadata={"addon_tag": "core"}) - - # Calculate sort key - key_enterprise = AddonBeanOrderStrategy.calculate_order_key( - bean_enterprise, is_direct_match=True, mock_mode=False - ) - key_core = AddonBeanOrderStrategy.calculate_order_key( - bean_core, is_direct_match=True, mock_mode=False - ) - - # Verify: sort key is 5-tuple (addon, mock, match, primary, scope) - assert len(key_enterprise) == 5 - assert len(key_core) == 5 - - # Verify: first element is addon priority - assert key_enterprise[0] == 50 # enterprise - assert key_core[0] == 1000 # core - - # Verify: enterprise priority higher than core - assert key_enterprise < key_core - - def test_addon_priority_overrides_other_priorities(self): - """Test addon priority overrides all other priorities""" - # Create two Beans: - # Bean1: enterprise addon + non-Primary + non-Factory - # Bean2: core addon + Primary + Factory - bean1 = BeanDefinition( - ServiceA, - is_primary=False, - scope=BeanScope.SINGLETON, - metadata={"addon_tag": "enterprise"}, - ) - bean2 = BeanDefinition( - ServiceB, - is_primary=True, - scope=BeanScope.FACTORY, - metadata={"addon_tag": "core"}, - ) - - # Calculate sort key - key1 = AddonBeanOrderStrategy.calculate_order_key( - bean1, is_direct_match=True, mock_mode=False - ) - key2 = AddonBeanOrderStrategy.calculate_order_key( - bean2, is_direct_match=True, mock_mode=False - ) - - # Verify: even though bean2 has Primary+Factory, bean1 comes first due to higher addon priority - assert key1 < key2 - - def test_order_key_with_mock_mode(self): - """Test sort key in Mock mode""" - # Create Mock Bean and non-Mock Bean (both enterprise addon) - mock_bean = BeanDefinition( - ServiceA, is_mock=True, metadata={"addon_tag": "enterprise"} - ) - normal_bean = BeanDefinition( - ServiceB, is_mock=False, metadata={"addon_tag": "enterprise"} - ) - - # Calculate sort key (Mock mode) - mock_key = AddonBeanOrderStrategy.calculate_order_key( - mock_bean, is_direct_match=True, mock_mode=True - ) - normal_key = AddonBeanOrderStrategy.calculate_order_key( - normal_bean, is_direct_match=True, mock_mode=True - ) - - # Verify: same addon priority, Mock has higher priority - assert mock_key[0] == normal_key[0] # same addon priority - assert mock_key[1] < normal_key[1] # different mock priority - assert mock_key < normal_key - - def test_order_key_backward_compatible(self): - """Test backward compatibility: sorting follows original logic when no addon_tag""" - # Create two Beans: neither has addon_tag, one Primary and one non-Primary - primary_bean = BeanDefinition(ServiceA, is_primary=True) - normal_bean = BeanDefinition(ServiceB, is_primary=False) - - # Calculate sort key - primary_key = AddonBeanOrderStrategy.calculate_order_key( - primary_bean, is_direct_match=True, mock_mode=False - ) - normal_key = AddonBeanOrderStrategy.calculate_order_key( - normal_bean, is_direct_match=True, mock_mode=False - ) - - # Verify: addon priority is 99999 (lowest) for both - assert primary_key[0] == 99999 - assert normal_key[0] == 99999 - - # Verify: Primary Bean has higher priority - assert primary_key < normal_key - - -# ==================== Test Bean list sorting ==================== - - -class TestSortBeansWithContext: - """Test sorting Bean list with context (including addon priority)""" - - def setup_method(self): - """Reset configuration before each test""" - AddonBeanOrderStrategy._addon_priority_map = None - os.environ["ADDON_PRIORITY"] = "enterprise:50,core:1000,custom:500" - - def teardown_method(self): - """Clean up after each test""" - if "ADDON_PRIORITY" in os.environ: - del os.environ["ADDON_PRIORITY"] - AddonBeanOrderStrategy._addon_priority_map = None - - def test_sort_by_addon_priority(self): - """Test sorting by addon priority""" - # Create Beans with different addons - bean_defs = [ - BeanDefinition( - ServiceA, bean_name="core_bean", metadata={"addon_tag": "core"} - ), - BeanDefinition( - ServiceB, - bean_name="enterprise_bean", - metadata={"addon_tag": "enterprise"}, - ), - BeanDefinition( - ServiceC, bean_name="custom_bean", metadata={"addon_tag": "custom"} - ), - BeanDefinition(ServiceD, bean_name="no_addon_bean"), - ] - - # Sort - sorted_beans = AddonBeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, - direct_match_types={ServiceA, ServiceB, ServiceC, ServiceD}, - mock_mode=False, - ) - - # Verify sort order: enterprise(50) < custom(500) < core(1000) < no_addon(99999) - assert sorted_beans[0].bean_name == "enterprise_bean" - assert sorted_beans[1].bean_name == "custom_bean" - assert sorted_beans[2].bean_name == "core_bean" - assert sorted_beans[3].bean_name == "no_addon_bean" - - def test_addon_priority_with_primary_and_scope(self): - """Test combination of addon priority with Primary and Scope""" - # Create Beans with various combinations - bean_defs = [ - # Highest priority: enterprise + Primary + Factory - BeanDefinition( - ServiceA, - bean_name="enterprise_primary_factory", - is_primary=True, - scope=BeanScope.FACTORY, - metadata={"addon_tag": "enterprise"}, - ), - # Second highest: enterprise + non-Primary + non-Factory - BeanDefinition( - ServiceB, - bean_name="enterprise_normal", - is_primary=False, - scope=BeanScope.SINGLETON, - metadata={"addon_tag": "enterprise"}, - ), - # Medium: core + Primary + Factory - BeanDefinition( - ServiceC, - bean_name="core_primary_factory", - is_primary=True, - scope=BeanScope.FACTORY, - metadata={"addon_tag": "core"}, - ), - # Lowest: no addon + Primary + Factory - BeanDefinition( - ServiceD, - bean_name="no_addon_primary_factory", - is_primary=True, - scope=BeanScope.FACTORY, - ), - ] - - # Sort - sorted_beans = AddonBeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, - direct_match_types={ServiceA, ServiceB, ServiceC, ServiceD}, - mock_mode=False, - ) - - # Verify: addon priority is highest, even if other attributes differ - assert sorted_beans[0].bean_name == "enterprise_primary_factory" - assert sorted_beans[1].bean_name == "enterprise_normal" - assert sorted_beans[2].bean_name == "core_primary_factory" - assert sorted_beans[3].bean_name == "no_addon_primary_factory" - - def test_same_addon_priority_then_by_primary(self): - """Test sorting by Primary when addon priority is the same""" - # Create Beans with same addon - bean_defs = [ - BeanDefinition( - ServiceA, - bean_name="enterprise_normal", - is_primary=False, - metadata={"addon_tag": "enterprise"}, - ), - BeanDefinition( - ServiceB, - bean_name="enterprise_primary", - is_primary=True, - metadata={"addon_tag": "enterprise"}, - ), - ] - - # Sort - sorted_beans = AddonBeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, - direct_match_types={ServiceA, ServiceB}, - mock_mode=False, - ) - - # Verify: when addon priority is the same, Primary takes precedence - assert sorted_beans[0].bean_name == "enterprise_primary" - assert sorted_beans[1].bean_name == "enterprise_normal" - - def test_filter_mock_beans_in_normal_mode(self): - """Test filtering Mock Beans in non-Mock mode (addon version)""" - # Create list containing Mock Bean - bean_defs = [ - BeanDefinition( - ServiceA, - bean_name="enterprise_mock", - is_mock=True, - metadata={"addon_tag": "enterprise"}, - ), - BeanDefinition( - ServiceB, - bean_name="enterprise_normal", - is_mock=False, - metadata={"addon_tag": "enterprise"}, - ), - BeanDefinition( - ServiceC, - bean_name="core_mock", - is_mock=True, - metadata={"addon_tag": "core"}, - ), - ] - - # Sort (non-Mock mode) - sorted_beans = AddonBeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, - direct_match_types={ServiceA, ServiceB, ServiceC}, - mock_mode=False, - ) - - # Verify: only non-Mock Beans are retained - assert len(sorted_beans) == 1 - assert sorted_beans[0].bean_name == "enterprise_normal" - - def test_mock_beans_priority_in_mock_mode(self): - """Test Mock Beans have priority in Mock mode (addon version)""" - # Create mixed Bean list - bean_defs = [ - BeanDefinition( - ServiceA, - bean_name="core_normal", - is_mock=False, - metadata={"addon_tag": "core"}, - ), - BeanDefinition( - ServiceB, - bean_name="enterprise_mock", - is_mock=True, - metadata={"addon_tag": "enterprise"}, - ), - BeanDefinition( - ServiceC, - bean_name="enterprise_normal", - is_mock=False, - metadata={"addon_tag": "enterprise"}, - ), - ] - - # Sort (Mock mode) - sorted_beans = AddonBeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, - direct_match_types={ServiceA, ServiceB, ServiceC}, - mock_mode=True, - ) - - # Verify: Mock has priority within same addon, but different addons still follow addon priority - assert sorted_beans[0].bean_name == "enterprise_mock" # enterprise + mock - assert sorted_beans[1].bean_name == "enterprise_normal" # enterprise + normal - assert sorted_beans[2].bean_name == "core_normal" # core + normal - - def test_empty_bean_list(self): - """Test empty Bean list""" - sorted_beans = AddonBeanOrderStrategy.sort_beans_with_context( - bean_defs=[], direct_match_types=set(), mock_mode=False - ) - assert sorted_beans == [] - - def test_single_bean(self): - """Test single Bean""" - bean_defs = [BeanDefinition(ServiceA, metadata={"addon_tag": "enterprise"})] - sorted_beans = AddonBeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types={ServiceA}, mock_mode=False - ) - assert len(sorted_beans) == 1 - - -# ==================== Complex scenario tests ==================== - - -class TestComplexScenarios: - """Test complex scenarios""" - - def setup_method(self): - """Reset configuration before each test""" - AddonBeanOrderStrategy._addon_priority_map = None - os.environ["ADDON_PRIORITY"] = "enterprise:10,plugin1:50,plugin2:100,core:1000" - - def teardown_method(self): - """Clean up after each test""" - if "ADDON_PRIORITY" in os.environ: - del os.environ["ADDON_PRIORITY"] - AddonBeanOrderStrategy._addon_priority_map = None - - def test_multi_addon_multi_implementation(self): - """Test multiple implementations from multiple addons""" - # Simulate real scenario: multiple addons provide implementations for the same interface - bean_defs = [ - # Implementation provided by enterprise (Primary + Factory) - BeanDefinition( - ServiceA, - bean_name="enterprise_impl", - is_primary=True, - scope=BeanScope.FACTORY, - metadata={"addon_tag": "enterprise"}, - ), - # Implementation provided by plugin1 (Primary) - BeanDefinition( - ServiceB, - bean_name="plugin1_impl", - is_primary=True, - metadata={"addon_tag": "plugin1"}, - ), - # Implementation provided by plugin2 - BeanDefinition( - ServiceC, bean_name="plugin2_impl", metadata={"addon_tag": "plugin2"} - ), - # Implementation provided by core (Factory) - BeanDefinition( - ServiceD, - bean_name="core_impl", - scope=BeanScope.FACTORY, - metadata={"addon_tag": "core"}, - ), - ] - - # Sort - sorted_beans = AddonBeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, - direct_match_types={ServiceA, ServiceB, ServiceC, ServiceD}, - mock_mode=False, - ) - - # Verify: sorted by addon priority - assert sorted_beans[0].bean_name == "enterprise_impl" # 10 - assert sorted_beans[1].bean_name == "plugin1_impl" # 50 - assert sorted_beans[2].bean_name == "plugin2_impl" # 100 - assert sorted_beans[3].bean_name == "core_impl" # 1000 - - def test_addon_override_scenario(self): - """Test addon override scenario: high priority addon overrides low priority addon""" - # Create Beans: enterprise overrides core implementation - bean_defs = [ - BeanDefinition( - ServiceA, - bean_name="core_default", - is_primary=True, - scope=BeanScope.FACTORY, - metadata={"addon_tag": "core"}, - ), - BeanDefinition( - ServiceA, - bean_name="enterprise_override", - is_primary=False, - scope=BeanScope.SINGLETON, - metadata={"addon_tag": "enterprise"}, - ), - ] - - # Sort - sorted_beans = AddonBeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types={ServiceA}, mock_mode=False - ) - - # Verify: enterprise comes first despite not being Primary or Factory, due to higher addon priority - assert sorted_beans[0].bean_name == "enterprise_override" - assert sorted_beans[1].bean_name == "core_default" - - def test_all_attributes_combination(self): - """Test combination of all attributes""" - # Create 16 Beans covering all possible combinations - bean_defs = [] - counter = 0 - - for addon_tag in ["enterprise", "core", None]: - for is_mock in [False, True]: - for is_primary in [False, True]: - for is_factory in [False, True]: - metadata = {"addon_tag": addon_tag} if addon_tag else {} - bean = BeanDefinition( - ServiceA, - bean_name=f"bean_{counter}", - is_mock=is_mock, - is_primary=is_primary, - scope=( - BeanScope.FACTORY if is_factory else BeanScope.SINGLETON - ), - metadata=metadata, - ) - bean_defs.append(bean) - counter += 1 - - # Sort (non-Mock mode) - sorted_beans = AddonBeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types={ServiceA}, mock_mode=False - ) - - # Verify: in non-Mock mode, Mock Beans are filtered out - assert all(not bean.is_mock for bean in sorted_beans) - - # Verify: first Bean should be enterprise addon - assert sorted_beans[0].metadata.get("addon_tag") == "enterprise" - - # Verify: last Bean should have no addon_tag - assert sorted_beans[-1].metadata.get("addon_tag") is None - - -if __name__ == "__main__": - # Run tests - pytest.main([__file__, "-v", "-s", "--tb=short"]) diff --git a/methods/EverCore/src/core/addons/addons_registry.py b/methods/EverCore/src/core/addons/addons_registry.py deleted file mode 100644 index 7af5c7e3..00000000 --- a/methods/EverCore/src/core/addons/addons_registry.py +++ /dev/null @@ -1,225 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Addons registry manager -Manages the list of all addon registries, providing a unified access interface -""" - -import os -from typing import List, Optional, Set -from core.addons.addon_registry import AddonRegistry -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class AddonsRegistry: - """ - Addons registry manager - - Manages the list of all addon registries, providing unified registration and access interfaces - - Usage example: - from core.addons.addons_registry import ADDONS_REGISTRY - - # Create and register addon - addon = AddonRegistry(name="my_addon") - addon.register_di(di_registry).register_asynctasks(task_registry) - - ADDONS_REGISTRY.register(addon) - - # Get all addons - all_addons = ADDONS_REGISTRY.get_all() - - # Find addon by name - my_addon = ADDONS_REGISTRY.get_by_name("my_addon") - - # Automatically load from entry points (recommended) - ADDONS_REGISTRY.load_entrypoints() - - Entry Points registration method: - Configure in pyproject.toml: - - [project.entry-points."memsys.addons"] - my_addon = "my_package.addon_module" - - Execute registration in the module: - - # my_package/addon_module.py - from core.addons.addons_registry import ADDONS_REGISTRY - - my_addon = AddonRegistry(name="my_addon") - # ... configure addon ... - ADDONS_REGISTRY.register(my_addon) # Automatically executed when module is imported - """ - - def __init__(self): - """Initialize the addons registry manager""" - self._addons: List[AddonRegistry] = [] - - def register(self, addon: AddonRegistry) -> 'AddonsRegistry': - """ - Register an addon - - Args: - addon: addon registry instance - - Returns: - self: supports method chaining - """ - self._addons.append(addon) - return self - - def get_all(self) -> List[AddonRegistry]: - """ - Get all registered addons - - Returns: - List of all addons - """ - return self._addons.copy() - - def get_by_name(self, name: str) -> Optional[AddonRegistry]: - """ - Get addon by name - - Args: - name: addon name - - Returns: - Found addon, or None if not exists - """ - for addon in self._addons: - if addon.name == name: - return addon - return None - - def clear(self) -> 'AddonsRegistry': - """ - Clear all registered addons - - Returns: - self: supports method chaining - """ - self._addons.clear() - return self - - def count(self) -> int: - """ - Get the number of registered addons - - Returns: - Number of addons - """ - return len(self._addons) - - def _should_load_entrypoint(self, entrypoint_name: str) -> bool: - """ - Determine whether to load the specified entrypoint based on environment variables - - Control which entrypoints to load via the MEMSYS_ENTRYPOINTS_FILTER environment variable - Format: MEMSYS_ENTRYPOINTS_FILTER=ep1,ep2,ep3 - - If the environment variable is not set or empty, load all entrypoints - If the environment variable is set, only load entrypoints specified in the list - - Args: - entrypoint_name: name of the entrypoint (ep.name) - - Returns: - True means should load, False means should skip - """ - filter_config = os.environ.get('MEMSYS_ENTRYPOINTS_FILTER', '').strip() - - # If environment variable is not set or empty, load all entrypoints - if not filter_config: - return True - - # Split by comma and filter - allowed_entrypoints: Set[str] = { - name.strip() for name in filter_config.split(',') if name.strip() - } - return entrypoint_name in allowed_entrypoints - - def load_entrypoints(self) -> 'AddonsRegistry': - """ - Automatically load all registered addons from entry points - - Scan the 'memsys.addons' entry point group to automatically discover and load all addons registered via this mechanism. - - How it works: - 1. Scan all entry points under [project.entry-points."memsys.addons"] in pyproject.toml - 2. Filter entrypoints to be loaded based on MEMSYS_ENTRYPOINTS_FILTER environment variable (via ep.name) - 3. Load the module corresponding to each entry point (trigger module import) - 4. Module import will automatically execute module-level code, including ADDONS_REGISTRY.register(addon) calls - 5. All addons are automatically registered into the global ADDONS_REGISTRY - - Environment variable control: - - MEMSYS_ENTRYPOINTS_FILTER: controls which entrypoints to load, format is a comma-separated list of entrypoint names - Example: MEMSYS_ENTRYPOINTS_FILTER=ep1,ep2,ep3 - If not set or empty, load all entrypoints - Note: One entrypoint may contain registrations of multiple addons - - Notes: - - No need for entry points to return specific objects - - Just ensure registration code is executed during module import - - Avoid time-consuming operations in module-level code - - Returns: - self: supports method chaining - """ - try: - # Python 3.10+ uses importlib.metadata - from importlib.metadata import entry_points - - logger.info("🔌 Starting to load addons entry points...") - - # Get all entry points under memsys.addons group - # Python 3.10+ uses select method, Python 3.9 uses dictionary access - try: - # Python 3.10+ - addon_eps = entry_points(group='memsys.addons') - except TypeError: - # Python 3.9 fallback - eps = entry_points() - if hasattr(eps, 'select'): - addon_eps = eps.select(group='memsys.addons') - else: - # Direct dictionary access - addon_eps = ( - eps.get('memsys.addons', []) if isinstance(eps, dict) else [] - ) - - for ep in addon_eps: - try: - # Filter entrypoint based on environment variable - if not self._should_load_entrypoint(ep.name): - logger.info( - " ⏭️ Skipping entrypoint: %s (not in MEMSYS_ENTRYPOINTS_FILTER)", - ep.name, - ) - continue - - # Load entry point, trigger module import and execution - # Module import will automatically execute registration code (e.g., ADDONS_REGISTRY.register(addon)) - ep.load() - logger.info(" ✅ Loaded entrypoint: %s", ep.name) - - except Exception as e: # pylint: disable=broad-except - logger.error(" ❌ Failed to load entrypoint %s: %s", ep.name, e) - - logger.info( - "✅ Addons entry points loading completed, total %d", self.count() - ) - - except ImportError: - logger.warning( - "⚠️ importlib.metadata is not available, skipping entry points loading" - ) - except Exception as e: # pylint: disable=broad-except - logger.error("❌ Failed to load addons entry points: %s", e) - - return self - - -# Global singleton instance -ADDONS_REGISTRY = AddonsRegistry() diff --git a/methods/EverCore/src/core/addons/introduction.md b/methods/EverCore/src/core/addons/introduction.md deleted file mode 100644 index 8ba393c3..00000000 --- a/methods/EverCore/src/core/addons/introduction.md +++ /dev/null @@ -1,399 +0,0 @@ -# Addons 模块介绍 - -## 模块简介 - -Addons 模块是 MemSys 的扩展机制实现，通过 Python Entry Points 技术实现插件化架构，支持商业版本与开源版本的代码切分。该模块允许在不修改核心代码的情况下，动态加载和注册外部扩展包的组件。 - -## 核心概念 - -### 1. 什么是 Addon - -Addon（插件/扩展）是一个独立的功能模块，可以包含： -- **DI 组件**：依赖注入的扫描路径配置 -- **异步任务**：后台任务的扫描路径配置 - -每个 Addon 是一个自包含的功能单元，可以独立开发、测试和部署。 - -### 2. Entry Points 机制 - -Entry Points 是 Python 包生态系统中的标准扩展机制： -- 通过 `pyproject.toml` 声明扩展点 -- 在包安装时自动注册 -- 运行时动态发现和加载 -- 无需硬编码依赖关系 - -### 3. 商业版本切分 - -通过 Addons 机制实现开源版本（Open Core）与商业版本（Enterprise）的切分： -- **Open Core**：基础功能，以 "core" addon 形式存在 -- **Enterprise**：商业功能，以独立 addon 形式存在 -- **无依赖关系**：两者通过接口抽象，相互独立 -- **优先级机制**：Enterprise addon 的实现优先级高于 Open Core，可自动覆盖 - -## 目录结构 - -``` -src/core/addons/ -├── __init__.py # 包标识（空文件） -├── addon_registry.py # 单个 Addon 注册器 -├── addons_registry.py # 全局 Addons 管理器 -├── introduction.md # 本文档 -└── contrib/ # 第三方贡献的 addons（可选） -``` - -## 核心组件 - -### 1. AddonRegistry - -单个 Addon 的注册器容器，用于承载一个 addon 的配置。 - -```python -from core.addons.addon_registry import AddonRegistry - -# 创建 addon -addon = AddonRegistry(name="my_addon") - -# 注册 DI 扫描路径 -di_registry = ScannerPathsRegistry() -di_registry.add_scan_path("/path/to/components") -addon.register_di(di_registry) - -# 注册异步任务扫描路径 -task_registry = TaskScanDirectoriesRegistry() -task_registry.add_scan_path("/path/to/tasks") -addon.register_asynctasks(task_registry) -``` - -### 2. AddonsRegistry - -全局 Addons 管理器，管理所有已注册的 addons。 - -```python -from core.addons.addons_registry import ADDONS_REGISTRY - -# 注册 addon -ADDONS_REGISTRY.register(addon) - -# 从 entry points 自动加载所有 addons -ADDONS_REGISTRY.load_entrypoints() - -# 获取所有 addons -all_addons = ADDONS_REGISTRY.get_all() - -# 根据名称查找 addon -my_addon = ADDONS_REGISTRY.get_by_name("my_addon") -``` - -## 工作原理 - -### 1. 注册阶段 - -在 `pyproject.toml` 中声明 entry point： - -```toml -[project.entry-points."memsys.addons"] -core = "src.addon" -enterprise = "memsys_enterprise.addon" -``` - -### 2. 加载阶段 - -系统启动时调用 `ADDONS_REGISTRY.load_entrypoints()`： - -1. 扫描所有包的 `memsys.addons` entry point group -2. 根据环境变量 `MEMSYS_ENTRYPOINTS_FILTER` 过滤需要加载的 entrypoint -3. 依次加载每个 entry point 对应的模块 -4. 模块导入时自动执行注册代码（`ADDONS_REGISTRY.register(addon)`） -5. 所有 addon 被注册到全局 `ADDONS_REGISTRY` 中 - -### 3. 使用阶段 - -系统运行时从 `ADDONS_REGISTRY` 获取所有已注册的 addons： - -1. **DI 组件扫描**：合并所有 addon 的 DI 扫描路径，执行组件扫描 -2. **异步任务注册**：合并所有 addon 的异步任务路径，注册后台任务 -3. **优先级覆盖**：后加载的 addon 组件可以覆盖先加载的同名组件 - -## 环境变量控制 - -### MEMSYS_ENTRYPOINTS_FILTER - -控制加载哪些 entrypoint，格式为逗号分隔的 entrypoint 名称列表。 - -```bash -# 只加载 core addon -export MEMSYS_ENTRYPOINTS_FILTER=core - -# 加载 core 和 enterprise addons -export MEMSYS_ENTRYPOINTS_FILTER=core,enterprise - -# 不设置或为空，加载所有 addons（默认行为） -unset MEMSYS_ENTRYPOINTS_FILTER -``` - -**注意**： -- 一个 entrypoint 可能包含多个 addon 的注册 -- 过滤是基于 entrypoint 名称（`ep.name`），不是 addon 名称 - -## 使用示例 - -### 示例 1：Open Core Addon - -```python -# src/addon.py -import os -from core.di.scan_path_registry import ScannerPathsRegistry -from core.asynctasks.task_scan_registry import TaskScanDirectoriesRegistry -from common_utils.project_path import get_base_scan_path -from core.addons.addon_registry import AddonRegistry -from core.addons.addons_registry import ADDONS_REGISTRY - -# 配置 DI 扫描路径 -paths_registry = ScannerPathsRegistry() -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "component")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "infra_layer")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "agentic_layer")) -paths_registry.add_scan_path(os.path.join(get_base_scan_path(), "biz_layer")) - -# 配置异步任务扫描路径 -task_directories_registry = TaskScanDirectoriesRegistry() -task_directories_registry.add_scan_path( - os.path.join(get_base_scan_path(), "infra_layer/adapters/input/jobs") -) - -# 创建并注册 core addon -core_addon = AddonRegistry(name="core") -core_addon.register_di(paths_registry) -core_addon.register_asynctasks(task_directories_registry) - -ADDONS_REGISTRY.register(core_addon) -``` - -### 示例 2：Enterprise Addon - -```python -# memsys_enterprise/addon.py -import os -from pathlib import Path - -# 从 opensource 导入 addon 机制 -from core.di.scan_path_registry import ScannerPathsRegistry -from core.asynctasks.task_scan_registry import TaskScanDirectoriesRegistry -from core.addons.addon_registry import AddonRegistry -from core.addons.addons_registry import ADDONS_REGISTRY - -# 获取 enterprise 的基础路径 -current_file = Path(__file__) -src_dir = current_file.parent -enterprise_base_path = str(src_dir) - -# 配置 DI 扫描路径 -di_registry = ScannerPathsRegistry() -di_registry.add_scan_path(os.path.join(enterprise_base_path, "infra_layer")) - -# 配置异步任务扫描路径 -task_registry = TaskScanDirectoriesRegistry() -task_registry.add_scan_path(os.path.join(enterprise_base_path, "infra_layer/adapters/input/jobs")) - -# 创建并注册 enterprise addon -enterprise_addon = AddonRegistry(name="enterprise") -enterprise_addon.register_di(di_registry) -enterprise_addon.register_asynctasks(task_registry) - -# 注册到全局 ADDONS_REGISTRY（模块导入时自动执行） -ADDONS_REGISTRY.register(enterprise_addon) -``` - -### 示例 3：在 pyproject.toml 中注册 - -**Open Core (memsys_opensource/pyproject.toml)**： - -```toml -[project.entry-points."memsys.addons"] -core = "src.addon" -``` - -**Enterprise (memsys_enterprise/pyproject.toml)**： - -```toml -[project.entry-points."memsys.addons"] -enterprise = "memsys_enterprise.addon" -``` - -### 示例 4：系统启动时加载 - -```python -# src/bootstrap.py 或 src/application_startup.py -from core.addons.addons_registry import ADDONS_REGISTRY - -# 加载所有通过 entry points 注册的 addons -ADDONS_REGISTRY.load_entrypoints() - -# 获取所有 addons 进行后续处理 -all_addons = ADDONS_REGISTRY.get_all() - -for addon in all_addons: - print(f"Loaded addon: {addon.name}") - - # 处理 DI 组件扫描 - if addon.has_di(): - for path in addon.di.get_scan_paths(): - # 扫描并注册 DI 组件 - ... - - # 处理异步任务注册 - if addon.has_asynctasks(): - for path in addon.asynctasks.get_scan_paths(): - # 扫描并注册异步任务 - ... -``` - -## 设计优势 - -### 1. 松耦合 - -- 通过接口抽象实现解耦 -- Addon 之间无硬依赖关系 -- 可以独立开发和测试 - -### 2. 可扩展 - -- 支持任意数量的 addon -- 新增 addon 无需修改核心代码 -- 符合开闭原则（Open-Closed Principle） - -### 3. 灵活配置 - -- 通过环境变量控制加载 -- 支持条件性加载 -- 方便测试和部署 - -### 4. 商业友好 - -- 开源版本与商业版本代码隔离 -- 商业功能可以覆盖开源实现 -- 便于维护不同版本 - -## 最佳实践 - -### 1. 接口抽象优先 - -在实现商业功能或扩展功能时，首先进行接口抽象： - -```python -# core/interface/service/storage_service.py (Open Core) -from abc import ABC, abstractmethod - -class StorageService(ABC): - @abstractmethod - async def save(self, data: bytes) -> str: - """保存数据，返回存储ID""" - pass -``` - -然后在不同的 addon 中提供不同的实现： - -```python -# src/component/storage/local_storage_service.py (Open Core - 本地存储) -from core.interface.service.storage_service import StorageService -from core.di.component import Component - -@Component() -class LocalStorageService(StorageService): - async def save(self, data: bytes) -> str: - # 本地文件系统存储实现 - ... - -# memsys_enterprise/infra_layer/storage/cloud_storage_service.py (Enterprise - 云存储) -from core.interface.service.storage_service import StorageService -from core.di.component import Component - -@Component() -class CloudStorageService(StorageService): - async def save(self, data: bytes) -> str: - # 云存储实现（S3, OSS, etc.） - ... -``` - -### 2. 统一目录结构 - -确保 Enterprise addon 的目录结构与 Open Core 保持一致： - -``` -memsys_opensource/src/ -├── infra_layer/ -│ ├── adapters/ -│ ├── services/ -│ └── ... -├── agentic_layer/ -└── biz_layer/ - -memsys_enterprise/src/memsys_enterprise/ -├── infra_layer/ # 与 opensource 对应 -│ ├── adapters/ -│ ├── services/ -│ └── ... -└── ... # 可以有额外的层级 -``` - -### 3. 命名约定 - -- Addon 名称使用小写字母和下划线 -- Entry point 名称建议与 addon 名称保持一致 -- 模块文件名统一使用 `addon.py` - -### 4. 文档和注释 - -- 每个 addon 应该有清晰的文档说明 -- 注册代码添加注释说明用途 -- 接口抽象应该有完整的 docstring - -## 故障排查 - -### 1. Addon 未加载 - -**问题**：通过 `ADDONS_REGISTRY.get_all()` 看不到某个 addon。 - -**排查步骤**： -1. 检查 `pyproject.toml` 中 entry point 配置是否正确 -2. 确认包是否已安装（`pip list` 或 `uv pip list`） -3. 检查 `MEMSYS_ENTRYPOINTS_FILTER` 环境变量配置 -4. 查看日志，确认是否有加载错误 - -### 2. 组件未找到 - -**问题**：DI 容器中找不到某个组件。 - -**排查步骤**： -1. 确认 addon 的 DI 扫描路径是否正确 -2. 检查组件类是否添加了 `@Component()` 装饰器 -3. 确认组件所在的模块路径是否在扫描范围内 -4. 查看 DI 组件扫描日志 - -### 3. 优先级问题 - -**问题**：Enterprise 实现没有覆盖 Open Core 实现。 - -**排查步骤**： -1. 确认两个实现使用的是同一个接口 -2. 检查 `@Component()` 装饰器的优先级配置 -3. 确认 addon 加载顺序（enterprise 应该在 core 之后） -4. 验证类名和接口名是否一致 - -## 相关文档 - -- [依赖注入系统](../di/introduction.md) - DI 容器的使用和原理 -- [异步任务系统](../asynctasks/introduction.md) - 后台任务的注册和调度 -- [开发指南](../../../docs/dev_docs/development_guide.md) - 整体开发规范和流程 - -## 总结 - -Addons 模块是 MemSys 实现插件化架构和商业版本切分的核心机制。通过 Python Entry Points 技术，实现了： - -- ✅ 代码解耦和模块化 -- ✅ 开源与商业版本隔离 -- ✅ 动态加载和灵活配置 -- ✅ 接口抽象和实现替换 - -理解和正确使用 Addons 机制，是进行 MemSys 扩展开发的基础。 - diff --git a/methods/EverCore/src/core/asynctasks/__init__.py b/methods/EverCore/src/core/asynctasks/__init__.py deleted file mode 100644 index 0a267a36..00000000 --- a/methods/EverCore/src/core/asynctasks/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Asynchronous task module diff --git a/methods/EverCore/src/core/asynctasks/examples/hello_word_job.py b/methods/EverCore/src/core/asynctasks/examples/hello_word_job.py deleted file mode 100644 index 6c60ee30..00000000 --- a/methods/EverCore/src/core/asynctasks/examples/hello_word_job.py +++ /dev/null @@ -1,13 +0,0 @@ -""" -Hello World task - -Provides a simple Hello World task -""" - -from core.asynctasks.task_manager import task -from typing import Any - - -@task() -async def hello_world(data: Any) -> Any: - return f"hello world: {data}" diff --git a/methods/EverCore/src/core/asynctasks/task_manager.py b/methods/EverCore/src/core/asynctasks/task_manager.py deleted file mode 100644 index 4626f18b..00000000 --- a/methods/EverCore/src/core/asynctasks/task_manager.py +++ /dev/null @@ -1,634 +0,0 @@ -import os -import uuid -import importlib -import pkgutil -from pathlib import Path -from typing import Any, Dict, Optional, List, Callable, Union -from datetime import datetime, timedelta -from enum import Enum -from dataclasses import dataclass, field - -from arq import create_pool, ArqRedis -from arq.connections import RedisSettings -from arq.jobs import Job -from arq.worker import Worker, Function, func as arq_func - -from core.asynctasks.task_scan_registry import TaskScanDirectoriesRegistry -from core.context.context_manager import ContextManager -from core.context.context import get_current_user_info -from core.di.decorators import component -from core.observation.logger import get_logger -from core.authorize.enums import Role - -logger = get_logger(__name__) - - -class TaskStatus(Enum): - """Task status enumeration""" - - PENDING = "pending" # Pending execution - RUNNING = "running" # Running - SUCCESS = "success" # Execution succeeded - FAILED = "failed" # Execution failed - CANCELLED = "cancelled" # Cancelled - - -@dataclass -class TaskResult: - """Task result""" - - task_id: str - status: TaskStatus - result: Any = None - error: Optional[str] = None - created_at: Optional[datetime] = None - started_at: Optional[datetime] = None - finished_at: Optional[datetime] = None - retry_count: int = 0 - user_id: Optional[int] = None - user_context: Optional[Dict[str, Any]] = None - metadata: Dict[str, Any] = field(default_factory=dict) - - -@dataclass -class RetryConfig: - """Retry configuration""" - - max_retries: int = 1 - retry_delay: float = 1.0 # seconds - exponential_backoff: bool = True - max_retry_delay: float = 60.0 # seconds - - -@dataclass -class TaskFunction: - """Task function""" - - name: str - coroutine: Callable # Function wrapped with user context handling - original_func: Callable # Original function - timeout: Optional[float] = None - retry_config: Optional[RetryConfig] = None - - def to_arq_function(self) -> Function: - """Convert to arq function""" - return arq_func( - self.coroutine, - name=self.name, - max_tries=self.retry_config.max_retries, - timeout=self.timeout, - ) - - def __call__(self, *args, **kwargs) -> Any: - """Call task function""" - return self.original_func(*args, **kwargs) - - -@component(name="task_manager") -class TaskManager: - """ - Asynchronous task manager - - Implements asynchronous task management based on the arq framework, providing functions such as task addition, result retrieval, and task deletion. - Uses ContextManager to automatically inject database sessions and user context. - """ - - def __init__(self, context_manager: ContextManager): - """Initialize task manager""" - self._pool: Optional[ArqRedis] = None - self._worker: Optional[Worker] = None - self._redis_settings = self._get_redis_settings() - self._context_manager = context_manager - - # Task function registry - self._task_registry: Dict[str, TaskFunction] = {} - - # Default retry configuration - self._default_retry_config = RetryConfig() - - logger.info("Task manager initialization completed") - - def _get_current_user_info(self) -> Optional[Dict[str, Any]]: - """Get current user information""" - return get_current_user_info() - - def _get_current_user_id(self) -> Optional[int]: - """Get current user ID""" - user_info = self._get_current_user_info() - return user_info.get("user_id") if user_info else None - - def _get_redis_settings(self) -> RedisSettings: - """ - Get Redis configuration from environment variables - - Returns: - RedisSettings: Redis connection configuration - """ - return RedisSettings( - host=os.getenv("REDIS_HOST", "localhost"), - port=int(os.getenv("REDIS_PORT", "6379")), - database=int(os.getenv("REDIS_DB", "0")), - password=os.getenv("REDIS_PASSWORD"), - ssl=os.getenv("REDIS_SSL", "false").lower() == "true", - username=os.getenv("REDIS_USERNAME"), - ) - - async def _get_pool(self) -> ArqRedis: - """ - Get Redis connection pool - - Returns: - ArqRedis: Redis connection pool - """ - if self._pool is None: - self._pool = await create_pool(self._redis_settings) - return self._pool - - async def close(self) -> None: - """Close connection pool""" - if self._pool is not None: - await self._pool.close() - self._pool = None - logger.info("Task manager connection closed") - - def register_task(self, task_function: TaskFunction) -> None: - """ - Register task function - - Args: - task_function: Task function to register - """ - self._task_registry[task_function.name] = task_function - logger.info(f"Task registered: {task_function.name}") - - def scan_and_register_tasks(self, registry: TaskScanDirectoriesRegistry) -> None: - """ - Scan task directories and automatically register tasks - - Args: - registry: Task scan directory registry - """ - for directory in registry.get_scan_directories(): - self._scan_directory_for_tasks(directory) - - def _scan_directory_for_tasks(self, directory: str) -> None: - """ - Scan a single directory for tasks - - Args: - directory: Directory path to scan - """ - try: - # Convert to absolute path - from common_utils.project_path import src_dir - - relative_path = Path(directory).resolve().relative_to(src_dir) - package_name = ".".join(relative_path.parts) - - logger.info(f"Scanning task package: {package_name}") - - # Import package and scan - try: - package = importlib.import_module(package_name) - - # Scan all modules in the package - if hasattr(package, '__path__'): - # This is a package, recursively scan all submodules - for _, module_name, _ in pkgutil.walk_packages( - package.__path__, prefix=f"{package_name}." - ): - try: - module = importlib.import_module(module_name) - self._scan_module_for_tasks(module) - except Exception as e: - logger.error( - f"Failed to import module: {module_name}, error: {e}" - ) - else: - # This is a module, scan directly - self._scan_module_for_tasks(package) - - except Exception as e: - logger.error(f"Failed to import package: {package_name}, error: {e}") - - except Exception as e: - logger.error(f"Failed to scan directory: {directory}, error: {e}") - - def _scan_module_for_tasks(self, module: Any) -> None: - """ - Scan module for task functions - - Args: - module: Module object to scan - """ - try: - # Get all attributes in the module - for attr_name in dir(module): - # Skip private and special attributes - if attr_name.startswith('_'): - continue - - try: - attr = getattr(module, attr_name) - - # Check if it's a TaskFunction instance - if isinstance(attr, TaskFunction): - self.register_task(attr) - logger.info( - f"Task found in module {module.__name__}: {attr.name}" - ) - - except Exception as e: - logger.debug( - f"Failed to get module attribute: {module.__name__}.{attr_name}, error: {e}" - ) - - except Exception as e: - logger.error(f"Failed to scan module tasks: {module.__name__}, error: {e}") - - async def enqueue_task( - self, - task_name: Union[str, TaskFunction, Any], - *args, - task_id: Optional[str] = None, - delay: Optional[float] = None, - retry_config: Optional[RetryConfig] = None, - user_id: Optional[int] = None, - user_data: Optional[Dict[str, Any]] = None, - metadata: Optional[Dict[str, Any]] = None, - **kwargs, - ) -> str: - """ - Add task to queue - - Args: - task_name: Task name - *args: Task arguments - task_id: Task ID (optional, auto-generated if not provided) - delay: Delay execution time (seconds) - retry_config: Retry configuration (optional) - user_id: User ID (optional, obtained from current context if not provided) - user_data: User data (optional, obtained from current context if not provided) - metadata: Task metadata (optional) - **kwargs: Task keyword arguments - - Returns: - str: Task ID - """ - if isinstance(task_name, TaskFunction): - task_name = task_name.name - elif isinstance(task_name, str): - pass - else: - raise ValueError(f"Type error: {type(task_name)}") - - assert task_name in self._task_registry, f"Task not found: {task_name}" - task_function = self._task_registry[task_name] - current_retry_config = ( - retry_config or task_function.retry_config or self._default_retry_config - ) - - # Generate task ID - if task_id is None: - task_id = str(uuid.uuid4()) - - # Get user context (if not provided) - if user_data is None: - current_user_context = self._get_current_user_info() - if current_user_context is not None: - user_data = current_user_context.copy() - elif user_id is not None: - user_data = {"user_id": user_id, "role": Role.USER} - - if user_data is None and user_id is None: - # Try to get user_id from current context - current_user_id = self._get_current_user_id() - if current_user_id is not None: - user_data = {"user_id": current_user_id, "role": Role.USER} - - # 🔧 Get current app_info_context (containing task_id, etc.) - from core.context.context import get_current_app_info - - current_app_info = get_current_app_info() - - # Prepare task context - task_context = { - "user_data": user_data, - "app_info": current_app_info, # 🔧 Copy app_info_context - "metadata": metadata or {}, - "task_id": task_id, - "retry_config": current_retry_config, - } - - # Get connection pool - pool = await self._get_pool() - - # Calculate delay time - defer_until = None - if delay is not None: - from common_utils.datetime_utils import get_now_with_timezone - - defer_until = get_now_with_timezone() + timedelta(seconds=delay) - - # Enqueue task - job = await pool.enqueue_job( - task_name, - task_context, - *args, - _job_id=task_id, - _defer_until=defer_until, - **kwargs, - ) - - user_id_for_log = user_data.get("user_id") if user_data else "unknown" - logger.info( - f"Task added to queue: {task_id}, task name: {task_name}, user: {user_id_for_log}" - ) - return task_id - - async def execute_task_with_context( - self, - task_func: Callable, - context: Dict[str, Any], - task_context: Dict[str, Any], - *args, - force_new_session: bool = False, - **kwargs, - ) -> Any: - """ - Execute task within context - - Args: - task_func: Task function - context: Task execution context (redis, job_id, job_try, score, enqueue_time) - task_context: Business context (user data, task ID, etc.) - *args: Task arguments - force_new_session: Whether to force creation of a new session (default False, to avoid unnecessary session creation) - **kwargs: Task keyword arguments - - Returns: - Any: Task execution result - """ - user_data = task_context.get("user_data") - app_info = task_context.get("app_info") # 🔧 Get saved app_info_context - - # 🔧 Restore app_info_context (containing task_id, etc.) - if app_info: - from core.context.context import set_current_app_info - - set_current_app_info(app_info) - logger.debug(f"🔧 app_info_context restored: {app_info}") - - # Use ContextManager to execute task, automatically injecting user context and database session - # 🔧 Configurable session isolation: Only force new session when explicitly needed - result = await self._context_manager.run_with_full_context( - task_func, - *args, - user_data=user_data, - auto_inherit_user=False, - auto_commit=True, - force_new_session=force_new_session, # 🔑 Key: Configurable session isolation - **kwargs, - ) - - task_id = task_context.get("task_id") - user_id = user_data.get("user_id") if user_data else "unknown" - logger.info( - f"Task execution completed (independent session): {task_id}, user: {user_id}" - ) - return result - - async def get_task_result(self, task_id: str) -> Optional[TaskResult]: - """ - Get task result - - Args: - task_id: Task ID - - Returns: - Optional[TaskResult]: Task result, returns None if task does not exist - """ - pool = await self._get_pool() - - try: - job = Job(task_id, pool) - - # Get task information - info = await job.info() - if info is None: - return None - - # Construct task result - status = self._map_arq_status_to_task_status(info.job_status) - - # Try to get user information from task context (this may not be available as arq does not save our custom context) - user_id = None - user_context_data = None - - result = TaskResult( - task_id=task_id, - status=status, - result=info.result if status == TaskStatus.SUCCESS else None, - error=str(info.result) if status == TaskStatus.FAILED else None, - created_at=info.enqueue_time, - started_at=info.start_time, - finished_at=info.finish_time, - retry_count=info.job_try or 0, - user_id=user_id, - user_context=user_context_data, - ) - - return result - - except Exception as e: - logger.error(f"Failed to get task result: {task_id}, error: {str(e)}") - return None - - def _map_arq_status_to_task_status(self, arq_status: str) -> TaskStatus: - """ - Map arq status to task status - - Args: - arq_status: arq task status - - Returns: - TaskStatus: Task status - """ - mapping = { - "queued": TaskStatus.PENDING, - "deferred": TaskStatus.PENDING, - "in_progress": TaskStatus.RUNNING, - "complete": TaskStatus.SUCCESS, - "failed": TaskStatus.FAILED, - "cancelled": TaskStatus.CANCELLED, - } - return mapping.get(arq_status, TaskStatus.PENDING) - - async def cancel_task(self, task_id: str) -> bool: - """ - Cancel task - - Args: - task_id: Task ID - - Returns: - bool: Whether cancellation was successful - """ - pool = await self._get_pool() - - try: - job = Job(task_id, pool) - await job.abort() - logger.info(f"Task cancelled: {task_id}") - return True - except Exception as e: - logger.error(f"Failed to cancel task: {task_id}, error: {str(e)}") - return False - - async def delete_task(self, task_id: str) -> bool: - """ - Delete task record - - Args: - task_id: Task ID - - Returns: - bool: Whether deletion was successful - """ - pool = await self._get_pool() - - try: - # Delete task record - await pool.delete(f"arq:job:{task_id}") - logger.info(f"Task deleted: {task_id}") - return True - except Exception as e: - logger.error(f"Failed to delete task: {task_id}, error: {str(e)}") - return False - - async def list_tasks( - self, - status: Optional[TaskStatus] = None, - user_id: Optional[int] = None, - limit: int = 100, - ) -> List[TaskResult]: - """ - List tasks - - Note: Due to arq limitations, filtering tasks by user ID is not effective. - This method returns all tasks and filters at the application layer. - In production environments, it is recommended to use a dedicated task status storage system. - - Args: - status: Task status filter (optional) - user_id: User ID filter (optional, may be ineffective due to arq limitations) - limit: Limit on number of returned items - - Returns: - List[TaskResult]: List of tasks - """ - pool = await self._get_pool() - - try: - # Get all task keys - keys = await pool.keys("arq:job:*") - tasks = [] - - for key in keys[:limit]: - task_id = key.decode().split(":")[-1] - task_result = await self.get_task_result(task_id) - - if task_result is not None: - # Apply filter conditions - if status is not None and task_result.status != status: - continue - - # Note: Due to arq limitations, user_id filtering may not be accurate - if user_id is not None and task_result.user_id != user_id: - continue - - tasks.append(task_result) - - return tasks - - except Exception as e: - logger.error(f"Failed to list tasks: {str(e)}") - return [] - - async def get_task_count(self, status: Optional[TaskStatus] = None) -> int: - """ - Get task count - - Args: - status: Task status filter (optional) - - Returns: - int: Number of tasks - """ - tasks = await self.list_tasks(status=status) - return len(tasks) - - def get_worker_functions(self) -> List[Function]: - """ - Get worker function mappings - - Returns: - Dict[str, Callable]: Worker function mappings - """ - return [v.to_arq_function() for v in self._task_registry.values()] - - def list_registered_task_names(self) -> List[str]: - """ - Get all registered task names - - Returns: - List[str]: List of task names - """ - return list(self._task_registry.keys()) - - -def task(retry_config: Optional[RetryConfig] = None, timeout: Optional[float] = 300): - """ - Task decorator - - Args: - retry_config: Retry configuration (optional) - - Returns: - Decorated function - """ - - if not retry_config: - retry_config = RetryConfig() - - task_manager = get_task_manager() - - def decorator(func: Callable) -> Callable: - - async def _task_wrapper(*args, **kwargs): - return await task_manager.execute_task_with_context(func, *args, **kwargs) - - function_name = func.__name__ - - # Some attributes are required by the arq worker framework - return TaskFunction( - name=function_name, - coroutine=_task_wrapper, - original_func=func, - timeout=timeout, - retry_config=retry_config, - ) - - return decorator - - -def get_task_manager() -> TaskManager: - """ - Get task manager instance - - Returns: - TaskManager: Task manager instance - """ - from core.di.utils import get_bean_by_type - - return get_bean_by_type(TaskManager) diff --git a/methods/EverCore/src/core/asynctasks/task_scan_registry.py b/methods/EverCore/src/core/asynctasks/task_scan_registry.py deleted file mode 100644 index df605fb1..00000000 --- a/methods/EverCore/src/core/asynctasks/task_scan_registry.py +++ /dev/null @@ -1,23 +0,0 @@ -from typing import List - - -class TaskScanDirectoriesRegistry: - """Scan directory registry""" - - def __init__(self): - """Initialize scan directory registry""" - self.scan_directories: List[str] = [] - - def add_scan_path(self, path: str) -> 'TaskScanDirectoriesRegistry': - """Add scan directory""" - self.scan_directories.append(path) - return self - - def get_scan_directories(self) -> List[str]: - """Get scan directories""" - return self.scan_directories - - def clear(self) -> 'TaskScanDirectoriesRegistry': - """Clear scan directories""" - self.scan_directories = [] - return self diff --git a/methods/EverCore/src/core/authorize/__init__.py b/methods/EverCore/src/core/authorize/__init__.py deleted file mode 100644 index cf72822c..00000000 --- a/methods/EverCore/src/core/authorize/__init__.py +++ /dev/null @@ -1,41 +0,0 @@ -""" -Authorization module - -Provides a role-based authorization system supporting anonymous, user, and admin roles, -as well as custom authorization strategies. -""" - -from .enums import Role -from .interfaces import AuthorizationStrategy, AuthorizationContext -from .strategies import ( - DefaultAuthorizationStrategy, - RoleBasedAuthorizationStrategy, - CustomAuthorizationStrategy, -) -from .decorators import ( - authorize, - require_anonymous, - require_user, - require_admin, - custom_authorize, - check_and_apply_default_auth, -) - -__all__ = [ - # Enums - 'Role', - # Interfaces - 'AuthorizationStrategy', - 'AuthorizationContext', - # Strategy implementations - 'DefaultAuthorizationStrategy', - 'RoleBasedAuthorizationStrategy', - 'CustomAuthorizationStrategy', - # Decorators - 'authorize', - 'require_anonymous', - 'require_user', - 'require_admin', - 'custom_authorize', - 'check_and_apply_default_auth', -] diff --git a/methods/EverCore/src/core/authorize/decorators.py b/methods/EverCore/src/core/authorize/decorators.py deleted file mode 100644 index 0c9189b3..00000000 --- a/methods/EverCore/src/core/authorize/decorators.py +++ /dev/null @@ -1,231 +0,0 @@ -import functools -import asyncio -from typing import Optional, Callable, Any -from fastapi import HTTPException - -from .enums import Role -from .interfaces import AuthorizationStrategy, AuthorizationContext -from .strategies import DefaultAuthorizationStrategy -from core.context.context import get_current_user_info -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -def authorize( - required_role: Role = Role.ANONYMOUS, - strategy: Optional[AuthorizationStrategy] = None, - **kwargs, -): - """ - Authorization decorator - - Args: - required_role: Required role, default is anonymous - strategy: Custom authorization strategy, use default if None - **kwargs: Additional parameters passed to the strategy - - Returns: - Decorated function - """ - - def decorator(func: Callable) -> Callable: - # Create authorization context - auth_context = AuthorizationContext( - required_role=required_role, - strategy=strategy or DefaultAuthorizationStrategy(), - **kwargs, - ) - - # Store authorization info on the function - setattr(func, '__authorization_context__', auth_context) - - @functools.wraps(func) - async def async_wrapper(*args, **kwargs): - return await _execute_with_authorization( - func, auth_context, *args, **kwargs - ) - - @functools.wraps(func) - def sync_wrapper(*args, **kwargs): - return _execute_with_authorization_sync(func, auth_context, *args, **kwargs) - - # Return the appropriate wrapper based on function type - if asyncio.iscoroutinefunction(func): - return async_wrapper - else: - return sync_wrapper - - return decorator - - -async def _execute_with_authorization( - func: Callable, auth_context: AuthorizationContext, *args, **kwargs -) -> Any: - """ - Execute function asynchronously with authorization check - - Args: - func: Function to execute - auth_context: Authorization context - *args: Function arguments - **kwargs: Function keyword arguments - - Returns: - Return value of the function - - Raises: - HTTPException: When authorization fails - """ - # Get current user information - user_info = get_current_user_info() - - # Perform authorization check - has_permission = await auth_context.strategy.check_permission( - user_info=user_info, - required_role=auth_context.required_role, - **auth_context.extra_kwargs, - ) - - if not has_permission: - logger.warning( - "Authorization failed: user=%s, required role=%s", - user_info, - auth_context.required_role, - ) - raise HTTPException( - status_code=403, - detail=f"Insufficient permissions, required role: {auth_context.required_role.value}", - ) - - # Authorization passed, execute original function - logger.debug( - "Authorization passed: user=%s, role=%s", user_info, auth_context.required_role - ) - return await func(*args, **kwargs) - - -def _execute_with_authorization_sync( - func: Callable, auth_context: AuthorizationContext, *args, **kwargs -) -> Any: - """ - Execute function synchronously with authorization check - - Args: - func: Function to execute - auth_context: Authorization context - *args: Function arguments - **kwargs: Function keyword arguments - - Returns: - Return value of the function - - Raises: - HTTPException: When authorization fails - """ - # Get current user information - user_info = get_current_user_info() - - # For synchronous functions, we need to run async authorization check in event loop - try: - loop = asyncio.get_event_loop() - except RuntimeError: - # If no event loop exists, create a new one - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - - # Perform authorization check - has_permission = loop.run_until_complete( - auth_context.strategy.check_permission( - user_info=user_info, - required_role=auth_context.required_role, - **auth_context.extra_kwargs, - ) - ) - - if not has_permission: - logger.warning( - "Authorization failed: user=%s, required role=%s", - user_info, - auth_context.required_role, - ) - raise HTTPException( - status_code=403, - detail=f"Insufficient permissions, required role: {auth_context.required_role.value}", - ) - - # Authorization passed, execute original function - logger.debug( - "Authorization passed: user=%s, role=%s", user_info, auth_context.required_role - ) - return func(*args, **kwargs) - - -# Convenience decorators -def require_anonymous(func: Callable) -> Callable: - """Decorator for requiring anonymous access""" - return authorize(Role.ANONYMOUS)(func) - - -def require_user(func: Callable) -> Callable: - """Decorator for requiring user login""" - return authorize(Role.USER)(func) - - -def require_admin(func: Callable) -> Callable: - """Decorator for requiring admin privileges""" - return authorize(Role.ADMIN)(func) - - -def require_signature(func: Callable) -> Callable: - """Decorator for requiring HMAC signature verification""" - return authorize(Role.SIGNATURE)(func) - - -def custom_authorize(strategy: AuthorizationStrategy, **kwargs): - """ - Custom authorization decorator - - Args: - strategy: Custom authorization strategy - **kwargs: Additional parameters passed to the strategy - - Returns: - Decorator function - """ - return authorize(strategy=strategy, **kwargs) - - -def check_and_apply_default_auth(func: Callable) -> Callable: - """ - Check if function already has authorization decorator, if not apply default require_user authorization - - Handle both bound function and unbound function cases: - - For bound method (class method), handle self parameter correctly - - For unbound function (regular function), apply decorator directly - - Args: - func: Function to check, could be bound method or unbound function - - Returns: - Callable: Function with default authorization applied (if no authorization decorator existed) - """ - # Check if function already has authorization decorator - if hasattr(func, '__authorization_context__'): - return func - - # Check if it's a bound method - if hasattr(func, '__self__'): - # This is a bound method, need to get the original function - original_func = func.__func__ - # Check if original function already has authorization decorator - if hasattr(original_func, '__authorization_context__'): - return func - - # Apply decorator to original function, then rebind - decorated_func = require_user(original_func) - # Rebind to original object - return decorated_func.__get__(func.__self__) - else: - # This is an unbound function, apply decorator directly - return require_user(func) diff --git a/methods/EverCore/src/core/authorize/enums.py b/methods/EverCore/src/core/authorize/enums.py deleted file mode 100644 index 3ee29914..00000000 --- a/methods/EverCore/src/core/authorize/enums.py +++ /dev/null @@ -1,10 +0,0 @@ -from enum import Enum - - -class Role(Enum): - """User role enumeration""" - - ANONYMOUS = "anonymous" # Anonymous user - USER = "user" # Regular user - ADMIN = "admin" # Administrator - SIGNATURE = "signature" # HMAC signature verification user diff --git a/methods/EverCore/src/core/authorize/interfaces.py b/methods/EverCore/src/core/authorize/interfaces.py deleted file mode 100644 index 92e05ec9..00000000 --- a/methods/EverCore/src/core/authorize/interfaces.py +++ /dev/null @@ -1,46 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Optional, Any, Dict -from .enums import Role - - -class AuthorizationStrategy(ABC): - """Authorization strategy interface""" - - @abstractmethod - async def check_permission( - self, user_info: Optional[Dict[str, Any]], required_role: Role, **kwargs - ) -> bool: - """ - Check user permissions - - Args: - user_info: User information, may be None (anonymous user) - required_role: Required role - **kwargs: Additional parameters - - Returns: - bool: Whether the user has permission - """ - pass - - -class AuthorizationContext: - """Authorization context, containing information required for authorization checks""" - - def __init__( - self, - user_info: Optional[Dict[str, Any]] = None, - required_role: Role = Role.ANONYMOUS, - strategy: Optional[AuthorizationStrategy] = None, - **kwargs, - ): - self.user_info = user_info - self.required_role = required_role - self.strategy = strategy - self.extra_kwargs = kwargs - - def need_auth(self) -> bool: - """ - Check if authorization is needed - """ - return self.required_role != Role.ANONYMOUS diff --git a/methods/EverCore/src/core/authorize/strategies.py b/methods/EverCore/src/core/authorize/strategies.py deleted file mode 100644 index ae9eeb6e..00000000 --- a/methods/EverCore/src/core/authorize/strategies.py +++ /dev/null @@ -1,130 +0,0 @@ -from typing import Optional, Any, Dict -from .interfaces import AuthorizationStrategy -from .enums import Role - -import asyncio - - -class DefaultAuthorizationStrategy(AuthorizationStrategy): - """Default authorization strategy""" - - async def check_permission( - self, user_info: Optional[Dict[str, Any]], required_role: Role, **kwargs - ) -> bool: - """ - Default permission check logic - - Args: - user_info: User information - required_role: Required role - **kwargs: Additional parameters - - Returns: - bool: Whether the user has permission - """ - # Anonymous users can only access anonymous resources - if required_role == Role.ANONYMOUS: - return True - - # Deny access if no user information is provided - if not user_info: - return False - - # Check user role - user_role = user_info.get('role', Role.USER) - user_role = Role(user_role) - - # Role-based permission check - if required_role == Role.USER: - return user_role in [Role.USER, Role.ADMIN] - elif required_role == Role.ADMIN: - return user_role == Role.ADMIN - elif required_role == Role.SIGNATURE: - return user_role == Role.SIGNATURE - - return False - - -class RoleBasedAuthorizationStrategy(AuthorizationStrategy): - """Role-based authorization strategy""" - - def __init__(self): - # Define role hierarchy - self.role_hierarchy = { - Role.ANONYMOUS: 0, - Role.USER: 1, - Role.ADMIN: 2, - Role.SIGNATURE: 1, # SIGNATURE has the same level as USER, can access resources requiring USER permission - } - - async def check_permission( - self, user_info: Optional[Dict[str, Any]], required_role: Role, **kwargs - ) -> bool: - """ - Role-based permission check - - Args: - user_info: User information - required_role: Required role - **kwargs: Additional parameters - - Returns: - bool: Whether the user has permission - """ - # Anonymous users can only access anonymous resources - if required_role == Role.ANONYMOUS: - return True - - # Deny access if no user information is provided - if not user_info: - return False - - # Get user role - user_role_str = user_info.get('role', Role.USER.value) - try: - user_role = Role(user_role_str) - except ValueError: - # If role is invalid, default to USER - user_role = Role.USER - - # Check role hierarchy - required_level = self.role_hierarchy.get(required_role, 0) - user_level = self.role_hierarchy.get(user_role, 0) - - return user_level >= required_level - - -class CustomAuthorizationStrategy(AuthorizationStrategy): - """Custom authorization strategy that allows users to define custom check logic""" - - def __init__(self, custom_check_func): - """ - Initialize custom strategy - - Args: - custom_check_func: Custom check function that takes user_info and required_role as parameters - """ - self.custom_check_func = custom_check_func - - async def check_permission( - self, user_info: Optional[Dict[str, Any]], required_role: Role, **kwargs - ) -> bool: - """ - Perform permission check using custom function - - Args: - user_info: User information - required_role: Required role - **kwargs: Additional parameters - - Returns: - bool: Whether the user has permission - """ - try: - if asyncio.iscoroutinefunction(self.custom_check_func): - return await self.custom_check_func(user_info, required_role, **kwargs) - else: - return self.custom_check_func(user_info, required_role, **kwargs) - except (ValueError, TypeError, AttributeError): - # Return False if custom check fails - return False diff --git a/methods/EverCore/src/core/cache/__init__.py b/methods/EverCore/src/core/cache/__init__.py deleted file mode 100644 index ad96090f..00000000 --- a/methods/EverCore/src/core/cache/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -Core cache module - -Provides common components for various caching functionalities -""" diff --git a/methods/EverCore/src/core/cache/redis_cache_queue/redis_data_processor.py b/methods/EverCore/src/core/cache/redis_cache_queue/redis_data_processor.py deleted file mode 100644 index 9e1ff97c..00000000 --- a/methods/EverCore/src/core/cache/redis_cache_queue/redis_data_processor.py +++ /dev/null @@ -1,270 +0,0 @@ -""" -Redis Data Processor - -Provides unified data serialization and deserialization functionality, supporting: -1. JSON serialization (preferred) -2. Pickle serialization (fallback when JSON fails) -3. Automatic detection of deserialization type -""" - -import json -import pickle -import uuid -from typing import Any, Union, Dict, List, Tuple -from core.observation.logger import get_logger - -logger = get_logger(__name__) - -# Configuration constants -UUID_LENGTH = 8 # UUID truncation length -PICKLE_MARKER = b"__PICKLE__" # Pickle data marker - - -class RedisDataProcessor: - """Redis Data Processor""" - - @staticmethod - def serialize_data(data: Union[str, Dict, List, Any]) -> Union[str, bytes]: - """ - Serialize data into string or binary data - - Prefer JSON serialization (returns string), use Pickle serialization (returns binary data) on failure - - Args: - data: Data to be serialized - - Returns: - Union[str, bytes]: JSON serialized string or Pickle serialized binary data - - Raises: - ValueError: Serialization failed - """ - # If already a string, return directly - if isinstance(data, str): - return data - - # Try JSON serialization first - try: - return json.dumps(data, ensure_ascii=False) - except (TypeError, ValueError) as json_error: - logger.debug( - "JSON serialization failed, trying Pickle: %s", str(json_error) - ) - - # Use Pickle when JSON fails - try: - # Serialize with Pickle and add marker - pickle_data = pickle.dumps(data) - # Directly return binary data with marker (Redis is binary-safe) - binary_data = PICKLE_MARKER + pickle_data - logger.debug( - "Pickle serialization succeeded, data length: %d", len(binary_data) - ) - return binary_data - except Exception as pickle_error: - logger.error("Pickle serialization also failed: %s", str(pickle_error)) - raise ValueError( - f"Data serialization failed: JSON error={json_error}, Pickle error={pickle_error}" - ) from pickle_error - - @staticmethod - def deserialize_data(data: Union[str, bytes]) -> Any: - """ - Deserialize data - - Automatically detect whether the data is JSON or Pickle and perform corresponding deserialization - - Args: - data: Serialized string or binary data - - Returns: - Any: Deserialized data - """ - # Handle binary data (from clients with decode_responses=False) - if isinstance(data, bytes): - # Check for Pickle marker - if data.startswith(PICKLE_MARKER): - logger.debug("Pickle binary data detected, performing deserialization") - pickle_data = data[len(PICKLE_MARKER) :] - try: - result = pickle.loads(pickle_data) - logger.debug("Pickle deserialization succeeded") - return result - except Exception as e: - logger.error("Pickle deserialization failed: %s", str(e)) - return data - else: - # Try decoding as string for JSON deserialization - try: - data_str = data.decode('utf-8') - return json.loads(data_str) - except UnicodeDecodeError: - logger.warning("Binary data cannot be decoded as UTF-8") - return data - except json.JSONDecodeError: - # JSON parsing failed, but UTF-8 decoding succeeded, return decoded string - logger.debug( - "JSON parsing failed, returning decoded string: %s", - data_str[:50], - ) - return data_str - - # Handle string data (from clients with decode_responses=True) - if isinstance(data, str): - # Try JSON deserialization - try: - return json.loads(data) - except (json.JSONDecodeError, TypeError) as json_error: - logger.debug("JSON deserialization failed: %s", str(json_error)) - # Return original string if JSON fails - return data - - # Return other types directly - return data - - @staticmethod - def create_unique_member(data: Union[str, bytes]) -> Union[str, bytes]: - """ - Create a unique member identifier - - Args: - data: Serialized data (string or binary) - - Returns: - Union[str, bytes]: Unique member identifier - """ - unique_id = str(uuid.uuid4())[:UUID_LENGTH] - - if isinstance(data, bytes): - # For binary data, use binary separator - unique_id_bytes = unique_id.encode('utf-8') - separator = b":" - return unique_id_bytes + separator + data - else: - # For string data, use string format - return f"{unique_id}:{data}" - - @staticmethod - def parse_member_data(member: Union[str, bytes]) -> Tuple[str, Union[str, bytes]]: - """ - Parse member data to extract unique ID and data content - - Args: - member: Member data (string or binary format: unique_id:data) - - Returns: - Tuple[str, Union[str, bytes]]: (unique_id, data) - """ - if isinstance(member, bytes): - # Handle binary data (from clients with decode_responses=False) - separator = b":" - if separator in member: - unique_id_bytes, data = member.split(separator, 1) - try: - unique_id = unique_id_bytes.decode('utf-8') - except UnicodeDecodeError: - unique_id = "unknown" - else: - # Compatible with old format - unique_id = "unknown" - data = member - else: - # Handle string data (from clients with decode_responses=True) - if ':' in member: - unique_id, data = member.split(':', 1) - else: - # Compatible with old format - unique_id = "unknown" - data = member - - return unique_id, data - - @staticmethod - def process_data_for_storage( - data: Union[str, Dict, List, Any] - ) -> Union[str, bytes]: - """ - Process data for storage - - Serialize data and create unique identifier - - Args: - data: Data to be processed - - Returns: - Union[str, bytes]: Storable unique member data - """ - serialized_data = RedisDataProcessor.serialize_data(data) - return RedisDataProcessor.create_unique_member(serialized_data) - - @staticmethod - def process_data_from_storage(member: Union[str, bytes]) -> Dict[str, Any]: - """ - Process data read from storage - - Parse member data and deserialize - - Args: - member: Member data read from Redis (string or binary) - - Returns: - Dict[str, Any]: Dictionary containing parsed results, format: - { - "id": str, # Unique identifier - "data": Any, # Deserialized original data - "raw_data": Union[str, bytes] # Serialized data - } - """ - # Redis now returns bytes, need to process first - if isinstance(member, bytes): - logger.debug("Binary data read from Redis, length: %d", len(member)) - - unique_id, raw_data = RedisDataProcessor.parse_member_data(member) - - try: - parsed_data = RedisDataProcessor.deserialize_data(raw_data) - except Exception as e: - logger.warning( - "Failed to deserialize data: member=%s, error=%s", - ( - str(member)[:100] - if isinstance(member, str) - else f"bytes({len(member)})" - ), - str(e), - ) - # Return raw data if deserialization fails - parsed_data = raw_data - - return {"id": unique_id, "data": parsed_data, "raw_data": raw_data} - - -# For convenience, provide module-level functions -def serialize_data(data: Union[str, Dict, List, Any]) -> Union[str, bytes]: - """Serialize data (module-level function)""" - return RedisDataProcessor.serialize_data(data) - - -def deserialize_data(data: Union[str, bytes]) -> Any: - """Deserialize data (module-level function)""" - return RedisDataProcessor.deserialize_data(data) - - -def create_unique_member(data: Union[str, bytes]) -> Union[str, bytes]: - """Create unique member identifier (module-level function)""" - return RedisDataProcessor.create_unique_member(data) - - -def parse_member_data(member: Union[str, bytes]) -> Tuple[str, Union[str, bytes]]: - """Parse member data (module-level function)""" - return RedisDataProcessor.parse_member_data(member) - - -def process_data_for_storage(data: Union[str, Dict, List, Any]) -> Union[str, bytes]: - """Process data for storage (module-level function)""" - return RedisDataProcessor.process_data_for_storage(data) - - -def process_data_from_storage(member: Union[str, bytes]) -> Dict[str, Any]: - """Process data read from storage (module-level function)""" - return RedisDataProcessor.process_data_from_storage(member) diff --git a/methods/EverCore/src/core/cache/redis_cache_queue/redis_length_cache_manager.py b/methods/EverCore/src/core/cache/redis_cache_queue/redis_length_cache_manager.py deleted file mode 100644 index ee11d18e..00000000 --- a/methods/EverCore/src/core/cache/redis_cache_queue/redis_length_cache_manager.py +++ /dev/null @@ -1,642 +0,0 @@ -""" -Redis Length-Limited Cache Manager - -Length-limited cache implementation based on Redis Sorted Set, supporting: -- Append data to queue by key, prioritizing the provided timestamp as score -- Clean up by length, removing from the earliest data, retaining up to 100 records at most -- Queue expiration time is 60 minutes, extended on each append -""" - -import time -import random -from typing import List, Dict, Any, Optional, Union -from datetime import datetime - -from core.di.decorators import component -from core.observation.logger import get_logger -from core.component.redis_provider import RedisProvider -from .redis_data_processor import RedisDataProcessor - -# Configuration constants -DEFAULT_MAX_LENGTH = 100 # Default maximum length of 100 items -DEFAULT_EXPIRE_MINUTES = 60 # Default expiration time of 60 minutes -DEFAULT_CLEANUP_PROBABILITY = 0.1 # 10% probability of performing length cleanup - -# Lua script: Clean up data by length -LENGTH_CLEANUP_LUA_SCRIPT = """ -local queue_key = KEYS[1] -local max_length = tonumber(ARGV[1]) - --- 1. Get queue length -local queue_length = redis.call('ZCARD', queue_key) - --- 2. If exceeding max length, delete excess elements from the earliest data -local cleaned_count = 0 -if queue_length > max_length then - local excess_count = queue_length - max_length - -- Remove the earliest data (lowest score) - cleaned_count = redis.call('ZREMRANGEBYRANK', queue_key, 0, excess_count - 1) -end - -return cleaned_count -""" - -# Lua script: Fetch data by timestamp range (with scores) -FETCH_BY_DATE_TIMESTAMP_RANGE_LUA_SCRIPT = """ -local queue_key = KEYS[1] -local min_score = ARGV[1] -- Minimum timestamp -local max_score = ARGV[2] -- Maximum timestamp -local limit = tonumber(ARGV[3]) or -1 -- Limit number, -1 means no limit - --- Retrieve data and scores by score range, ordered by timestamp ascending -local messages -if limit > 0 then - messages = redis.call('ZRANGEBYSCORE', queue_key, min_score, max_score, 'WITHSCORES', 'LIMIT', 0, limit) -else - messages = redis.call('ZRANGEBYSCORE', queue_key, min_score, max_score, 'WITHSCORES') -end - -return messages -""" - -logger = get_logger(__name__) - - -@component(name="redis_length_cache_factory") -class RedisLengthCacheFactory: - """Redis length-limited cache manager factory""" - - def __init__(self, redis_provider: RedisProvider): - """ - Initialize cache factory - - Args: - redis_provider: Redis connection provider - """ - self.redis_provider = redis_provider - self._length_cleanup_script = None - self._timestamp_range_script = None - logger.info("Redis length-limited cache factory initialized") - - async def _ensure_length_cleanup_script_registered(self): - """Ensure length cleanup Lua script is registered (register only once)""" - if self._length_cleanup_script is None: - client = await self.redis_provider.get_client() - self._length_cleanup_script = client.register_script( - LENGTH_CLEANUP_LUA_SCRIPT - ) - logger.info("Length cleanup Lua script registered") - return self._length_cleanup_script - - async def _ensure_timestamp_range_script_registered(self): - """Ensure timestamp range query Lua script is registered (register only once)""" - if self._timestamp_range_script is None: - # Use binary_cache client to register script, ensuring same connection as execution - binary_client = await self.redis_provider.get_named_client( - "binary_cache", decode_responses=False - ) - self._timestamp_range_script = binary_client.register_script( - FETCH_BY_DATE_TIMESTAMP_RANGE_LUA_SCRIPT - ) - logger.info("Timestamp range query Lua script registered") - return self._timestamp_range_script - - async def create_cache_manager( - self, - max_length: int = DEFAULT_MAX_LENGTH, - expire_minutes: int = DEFAULT_EXPIRE_MINUTES, - cleanup_probability: float = DEFAULT_CLEANUP_PROBABILITY, - ) -> 'RedisLengthCacheManager': - """ - Create cache manager instance - - Args: - max_length: Maximum length - expire_minutes: Expiration time (minutes) - cleanup_probability: Length cleanup probability (0.0-1.0) - - Returns: - RedisLengthCacheManager: Cache manager instance - """ - length_cleanup_script = await self._ensure_length_cleanup_script_registered() - timestamp_range_script = await self._ensure_timestamp_range_script_registered() - return RedisLengthCacheManager( - redis_provider=self.redis_provider, - length_cleanup_script=length_cleanup_script, - fetch_by_timestamp_range_script=timestamp_range_script, - max_length=max_length, - expire_minutes=expire_minutes, - cleanup_probability=cleanup_probability, - ) - - -class RedisLengthCacheManager: - """ - Redis Length-Limited Cache Manager - - Length-limited queue cache implemented using Redis Sorted Set (ZSET): - - Score: Prefer provided time, otherwise use current timestamp - - Member: Unique identifier:data content, ensuring data uniqueness - - Supports length-based cleanup, removing from earliest data, retaining up to specified count - - Queue expiration time is 60 minutes, renewed on each append - """ - - def __init__( - self, - redis_provider: RedisProvider, - length_cleanup_script, - fetch_by_timestamp_range_script, - max_length: int = DEFAULT_MAX_LENGTH, - expire_minutes: int = DEFAULT_EXPIRE_MINUTES, - cleanup_probability: float = DEFAULT_CLEANUP_PROBABILITY, - ): - """ - Initialize Redis length-limited cache manager - - Args: - redis_provider: Redis connection provider - length_cleanup_script: Pre-registered length cleanup Lua script object - fetch_by_timestamp_range_script: Pre-registered timestamp range query Lua script object - max_length: Maximum length - expire_minutes: Expiration time (minutes) - cleanup_probability: Length cleanup probability (0.0-1.0) - """ - self.redis_provider = redis_provider - self.max_length = max_length - self.expire_minutes = expire_minutes - self.cleanup_probability = cleanup_probability - self._length_cleanup_script = length_cleanup_script - self._fetch_by_timestamp_range_script = fetch_by_timestamp_range_script - - logger.info( - "Redis length-limited cache manager initialized: max_length=%d, expire=%d minutes, cleanup_prob=%.1f%%", - max_length, - expire_minutes, - cleanup_probability * 100, - ) - - def _convert_timestamp(self, timestamp: Optional[Union[int, datetime]]) -> int: - """ - Convert timestamp to millisecond-level integer - - Args: - timestamp: Timestamp (milliseconds) or datetime object, use current time if not provided - - Returns: - int: Millisecond-level timestamp - """ - if timestamp is None: - return int(time.time() * 1000) - elif isinstance(timestamp, datetime): - # If it's a datetime object, convert to millisecond timestamp - return int(timestamp.timestamp() * 1000) - else: - # If it's an integer timestamp - return int(timestamp) - - async def _cleanup_if_needed(self, key: str) -> int: - """ - Decide whether to perform length cleanup based on probability - - Args: - key: Cache key name - - Returns: - int: Number of cleaned data items - """ - if random.random() < self.cleanup_probability: - # Use Lua script for atomic cleanup - cleaned_count = await self._length_cleanup_script( - keys=[key], args=[self.max_length] - ) - return cleaned_count - return 0 - - async def append( - self, - key: str, - data: Union[str, Dict, List, Any], - timestamp: Optional[Union[int, datetime]] = None, - ) -> bool: - """ - Append data to the queue for the specified key - - Args: - key: Cache key name - data: Data to append, supports strings, dictionaries, lists, etc. - timestamp: Timestamp (milliseconds) or datetime object, use current time if not provided - - Returns: - bool: Whether operation succeeded - - Examples: - # Append string data using current time - await cache.append("user_actions", "user_login") - - # Append dictionary data with specified timestamp - await cache.append("api_calls", {"method": "GET", "path": "/api/users"}, timestamp=1640995200000) - - # Append data using datetime object - from common_utils.datetime_utils import get_now_with_timezone - await cache.append("events", "user_action", timestamp=get_now_with_timezone()) - """ - try: - client = await self.redis_provider.get_client() - - # 1. Data preprocessing - score_timestamp = self._convert_timestamp(timestamp) - unique_member = RedisDataProcessor.process_data_for_storage(data) - - # 2. Execute Redis operations - add_result = await client.zadd(key, {unique_member: score_timestamp}) - - expire_seconds = self.expire_minutes * 60 - expire_result = await client.expire(key, expire_seconds) - - zadd_result = add_result if add_result else None - expire_result = expire_result if expire_result else None - - # 3. Perform cleanup based on probability - cleaned_count = await self._cleanup_if_needed(key) - - # 4. Record result - if zadd_result is not None and expire_result: - if cleaned_count > 0: - logger.debug( - "Data appended successfully and earliest data cleaned: key=%s, member_length=%d, timestamp=%d, expire=%ds, cleaned=%d", - key, - len(unique_member), - score_timestamp, - expire_seconds, - cleaned_count, - ) - else: - logger.debug( - "Data appended successfully: key=%s, member_length=%d, timestamp=%d, expire=%ds", - key, - len(unique_member), - score_timestamp, - expire_seconds, - ) - return True - else: - logger.warning( - "Data append partially failed: key=%s, zadd_result=%s, expire_result=%s", - key, - zadd_result, - expire_result, - ) - return False - - except (ConnectionError, TimeoutError, ValueError) as e: - logger.error( - "Failed to append data to Redis: key=%s, error=%s", key, str(e) - ) - return False - - async def get_queue_size(self, key: str) -> int: - """ - Get current size of the specified queue - - Args: - key: Cache key name - - Returns: - int: Number of elements in the queue - """ - try: - client = await self.redis_provider.get_client() - size = await client.zcard(key) - return size or 0 - except (ConnectionError, TimeoutError) as e: - logger.error("Failed to get queue size: key=%s, error=%s", key, str(e)) - return 0 - - async def clear_queue(self, key: str) -> bool: - """ - Clear all data from the specified queue - - Args: - key: Cache key name - - Returns: - bool: Whether operation succeeded - """ - try: - client = await self.redis_provider.get_client() - result = await client.delete(key) - logger.info("Cleared queue: key=%s, result=%d", key, result) - return result > 0 - except (ConnectionError, TimeoutError) as e: - logger.error("Failed to clear queue: key=%s, error=%s", key, str(e)) - return False - - async def delete(self, key: str) -> bool: - """ - Delete the specified cache key (alias of clear_queue) - - Args: - key: Cache key name - - Returns: - bool: Whether operation succeeded - """ - return await self.clear_queue(key) - - async def cleanup_excess(self, key: str) -> int: - """ - Manually clean up data exceeding length limit in the specified queue - - Args: - key: Cache key name - - Returns: - int: Number of cleaned data items - """ - try: - # Execute pre-registered Lua script for length cleanup - cleaned_count = await self._length_cleanup_script( - keys=[key], args=[self.max_length] - ) - - if cleaned_count > 0: - logger.info( - "Manually cleaned earliest data: key=%s, cleaned=%d", - key, - cleaned_count, - ) - - return cleaned_count - - except (ConnectionError, TimeoutError) as e: - logger.error("Failed to clean earliest data: key=%s, error=%s", key, str(e)) - return 0 - - async def get_by_timestamp_range( - self, - key: str, - start_timestamp: Optional[Union[int, datetime]] = None, - end_timestamp: Optional[Union[int, datetime]] = None, - limit: int = -1, - ) -> List[Dict[str, Any]]: - """ - Retrieve queue data by timestamp range - - Args: - key: Cache key name - start_timestamp: Start timestamp (milliseconds) or datetime object, None means no restriction - end_timestamp: End timestamp (milliseconds) or datetime object, None means no restriction - limit: Limit number of returned items, -1 means no limit - - Returns: - List[Dict[str, Any]]: List of data within timestamp range, each element contains: - - id: Unique identifier - - data: Original data - - timestamp: Timestamp (milliseconds) - - datetime: Formatted time string - """ - try: - # Convert timestamps - min_score = "-inf" - max_score = "+inf" - - if start_timestamp is not None: - min_score = str(self._convert_timestamp(start_timestamp)) - - if end_timestamp is not None: - max_score = str(self._convert_timestamp(end_timestamp)) - - # Execute pre-registered Lua script - messages = await self._fetch_by_timestamp_range_script( - keys=[key], args=[min_score, max_score, limit] - ) - if not messages: - logger.debug( - "No data in timestamp range: key=%s, range=[%s, %s]", - key, - min_score, - max_score, - ) - return [] - - # Parse message data (WITHSCORES returns format: [member1, score1, member2, score2, ...]) - result = [] - - # Process WITHSCORES returned results, every two elements form a pair: message content and score - if len(messages) % 2 != 0: - logger.warning( - "WITHSCORES returned data length abnormal: %d, should be even", - len(messages), - ) - return [] - - for i in range(0, len(messages), 2): - try: - if i + 1 >= len(messages): - logger.warning( - "WITHSCORES data index out of bounds: i=%d, len=%d", - i, - len(messages), - ) - break - - message = messages[i] # Message content - score_raw = messages[i + 1] # Score (could be bytes or string) - - # Safely convert score to timestamp - try: - if isinstance(score_raw, bytes): - score_str = score_raw.decode('utf-8') - else: - score_str = str(score_raw) - timestamp = int(float(score_str)) - except (ValueError, UnicodeDecodeError) as score_e: - logger.warning( - "Score conversion failed: score_raw=%s (type=%s), error=%s", - score_raw, - type(score_raw), - str(score_e), - ) - timestamp = int( - time.time() * 1000 - ) # Use current time as fallback - - # Use data processor to parse data - processed_data = RedisDataProcessor.process_data_from_storage( - message - ) - - result.append( - { - "id": processed_data["id"], - "data": processed_data["data"], - "timestamp": timestamp, - "datetime": datetime.fromtimestamp( - timestamp / 1000 - ).strftime("%Y-%m-%d %H:%M:%S.%f")[:-3], - } - ) - - except Exception as e: - logger.warning( - "Failed to parse message: i=%d, message=%s, error=%s", - i, - message if i < len(messages) else "index out of bounds", - str(e), - ) - continue - - # Sort by timestamp (newest first) - result.sort(key=lambda x: x["timestamp"], reverse=True) - - logger.debug( - "Successfully retrieved data by timestamp range: key=%s, range=[%s, %s], count=%d", - key, - min_score, - max_score, - len(result), - ) - - return result - - except Exception as e: - logger.error( - "Failed to retrieve data by timestamp range: key=%s, error=%s", - key, - str(e), - ) - return [] - - async def get_queue_stats(self, key: str) -> Dict[str, Any]: - """ - Get queue statistics - - Args: - key: Cache key name - - Returns: - Dict[str, Any]: Queue statistics - """ - try: - client = await self.redis_provider.get_client() - - # Get queue size - total_count = await client.zcard(key) or 0 - - if total_count == 0: - return { - "key": key, - "total_count": 0, - "max_length": self.max_length, - "oldest_timestamp": None, - "newest_timestamp": None, - "ttl_seconds": -1, - } - - # Get oldest and newest timestamps - oldest_data = await client.zrange(key, 0, 0, withscores=True) - newest_data = await client.zrange(key, -1, -1, withscores=True) - - oldest_timestamp = int(oldest_data[0][1]) if oldest_data else None - newest_timestamp = int(newest_data[0][1]) if newest_data else None - - # Get TTL - ttl = await client.ttl(key) - - return { - "key": key, - "total_count": total_count, - "max_length": self.max_length, - "oldest_timestamp": oldest_timestamp, - "newest_timestamp": newest_timestamp, - "oldest_datetime": ( - datetime.fromtimestamp(oldest_timestamp / 1000).strftime( - "%Y-%m-%d %H:%M:%S" - ) - if oldest_timestamp - else None - ), - "newest_datetime": ( - datetime.fromtimestamp(newest_timestamp / 1000).strftime( - "%Y-%m-%d %H:%M:%S" - ) - if newest_timestamp - else None - ), - "ttl_seconds": ttl, - "is_full": total_count >= self.max_length, - } - - except (ConnectionError, TimeoutError) as e: - logger.error( - "Failed to get queue statistics: key=%s, error=%s", key, str(e) - ) - return { - "key": key, - "total_count": 0, - "max_length": self.max_length, - "oldest_timestamp": None, - "newest_timestamp": None, - "ttl_seconds": -1, - "error": str(e), - } - - -# For backward compatibility, provide a default component instance -@component(name="redis_length_cache_manager") -class DefaultRedisLengthCacheManager: - """Default Redis length-limited cache manager (backward compatibility)""" - - def __init__(self, redis_provider: RedisProvider): - self.redis_provider = redis_provider - self._manager = None - self._factory = None - - async def _get_manager(self): - """Lazy initialization of manager""" - if self._manager is None: - if self._factory is None: - self._factory = RedisLengthCacheFactory(self.redis_provider) - self._manager = await self._factory.create_cache_manager() - return self._manager - - async def append( - self, - key: str, - data: Union[str, Dict, List, Any], - timestamp: Optional[Union[int, datetime]] = None, - ) -> bool: - manager = await self._get_manager() - return await manager.append(key, data, timestamp) - - async def get_queue_size(self, key: str) -> int: - manager = await self._get_manager() - return await manager.get_queue_size(key) - - async def clear_queue(self, key: str) -> bool: - manager = await self._get_manager() - return await manager.clear_queue(key) - - async def delete(self, key: str) -> bool: - manager = await self._get_manager() - return await manager.delete(key) - - async def cleanup_excess(self, key: str) -> int: - manager = await self._get_manager() - return await manager.cleanup_excess(key) - - async def get_queue_stats(self, key: str) -> Dict[str, Any]: - manager = await self._get_manager() - return await manager.get_queue_stats(key) - - async def get_by_timestamp_range( - self, - key: str, - start_timestamp: Optional[Union[int, datetime]] = None, - end_timestamp: Optional[Union[int, datetime]] = None, - limit: int = -1, - ) -> List[Dict[str, Any]]: - manager = await self._get_manager() - return await manager.get_by_timestamp_range( - key, start_timestamp, end_timestamp, limit - ) diff --git a/methods/EverCore/src/core/cache/redis_cache_queue/redis_windows_cache_manager.py b/methods/EverCore/src/core/cache/redis_cache_queue/redis_windows_cache_manager.py deleted file mode 100644 index 9575b639..00000000 --- a/methods/EverCore/src/core/cache/redis_cache_queue/redis_windows_cache_manager.py +++ /dev/null @@ -1,619 +0,0 @@ -""" -Redis Time Window Cache Manager - -Time window cache implemented using Redis Sorted Set, supporting: -- Appending data to time window queue by key -- Retrieving data within a specified time range -- Automatic expiration cleanup -- Random cleanup of expired data -""" - -import time -import random -from typing import List, Dict, Any, Optional, Union - -from datetime import datetime - -from core.di.decorators import component -from core.observation.logger import get_logger -from core.component.redis_provider import RedisProvider -from .redis_data_processor import RedisDataProcessor - -# Configuration constants -DEFAULT_EXPIRE_MINUTES = 10 # Default expiration time: 10 minutes -DEFAULT_CLEANUP_PROBABILITY = 0.1 # 10% probability to perform random cleanup -DEFAULT_CLEANUP_MULTIPLIER = ( - 2 # Cleanup threshold multiplier (clean data older than 2x expiration time) -) - -# Lua script: Fetch data by timestamp range (with scores) -FETCH_BY_DATE_TIMESTAMP_RANGE_LUA_SCRIPT = """ -local queue_key = KEYS[1] -local min_score = ARGV[1] -- Minimum timestamp -local max_score = ARGV[2] -- Maximum timestamp -local limit = tonumber(ARGV[3]) or -1 -- Limit number, -1 means no limit - --- Retrieve data and scores by score range, ordered by timestamp ascending -local messages -if limit > 0 then - messages = redis.call('ZRANGEBYSCORE', queue_key, min_score, max_score, 'WITHSCORES', 'LIMIT', 0, limit) -else - messages = redis.call('ZRANGEBYSCORE', queue_key, min_score, max_score, 'WITHSCORES') -end - -return messages -""" - -logger = get_logger(__name__) - - -@component(name="redis_windows_cache_factory") -class RedisWindowsCacheFactory: - """Redis Time Window Cache Manager Factory""" - - def __init__(self, redis_provider: RedisProvider): - """ - Initialize cache factory - - Args: - redis_provider: Redis connection provider - """ - self.redis_provider = redis_provider - self._timestamp_range_script = None - logger.info("Redis Time Window Cache Factory initialization completed") - - async def _ensure_timestamp_range_script_registered(self): - """Ensure the timestamp range query Lua script is registered (register only once)""" - if self._timestamp_range_script is None: - # Register script using binary_cache client to ensure same connection used during execution - binary_client = await self.redis_provider.get_named_client( - "binary_cache", decode_responses=False - ) - self._timestamp_range_script = binary_client.register_script( - FETCH_BY_DATE_TIMESTAMP_RANGE_LUA_SCRIPT - ) - logger.info("Timestamp range query Lua script registration completed") - return self._timestamp_range_script - - async def create_cache_manager( - self, - expire_minutes: int = DEFAULT_EXPIRE_MINUTES, - cleanup_probability: float = DEFAULT_CLEANUP_PROBABILITY, - ) -> 'RedisWindowsCacheManager': - """ - Create cache manager instance - - Args: - expire_minutes: Default expiration time (minutes) - cleanup_probability: Random cleanup probability (0.0-1.0) - - Returns: - RedisWindowsCacheManager: Cache manager instance - """ - timestamp_range_script = await self._ensure_timestamp_range_script_registered() - return RedisWindowsCacheManager( - redis_provider=self.redis_provider, - fetch_by_timestamp_range_script=timestamp_range_script, - expire_minutes=expire_minutes, - cleanup_probability=cleanup_probability, - ) - - -class RedisWindowsCacheManager: - """ - Redis Time Window Cache Manager - - Time window queue cache implemented using Redis Sorted Set (ZSET): - - Score: Millisecond-level timestamp, used for time sorting - - Member: Unique identifier:data content, ensuring data uniqueness - - Supports custom expiration time, extended on each append - - Provides random cleanup mechanism to prevent infinite memory growth - """ - - def __init__( - self, - redis_provider: RedisProvider, - fetch_by_timestamp_range_script, - expire_minutes: int = DEFAULT_EXPIRE_MINUTES, - cleanup_probability: float = DEFAULT_CLEANUP_PROBABILITY, - ): - """ - Initialize Redis Time Window Cache Manager - - Args: - redis_provider: Redis connection provider - fetch_by_timestamp_range_script: Pre-registered timestamp range query Lua script object - expire_minutes: Default expiration time (minutes) - cleanup_probability: Random cleanup probability (0.0-1.0) - """ - self.redis_provider = redis_provider - self.default_expire_minutes = expire_minutes - self.cleanup_probability = cleanup_probability - self._fetch_by_timestamp_range_script = fetch_by_timestamp_range_script - - logger.info( - "Redis Time Window Cache Manager initialization completed: expire=%d minutes, cleanup_prob=%.1f%%", - expire_minutes, - cleanup_probability * 100, - ) - - def _convert_timestamp(self, timestamp: Optional[Union[int, datetime]]) -> int: - """ - Convert timestamp to millisecond-level integer - - Args: - timestamp: Timestamp (milliseconds) or datetime object, use current time if not provided - - Returns: - int: Millisecond-level timestamp - """ - if timestamp is None: - return int(time.time() * 1000) - elif isinstance(timestamp, datetime): - # If it's a datetime object, convert to millisecond timestamp - return int(timestamp.timestamp() * 1000) - else: - # If it's an integer timestamp - return int(timestamp) - - async def _cleanup_expired_if_needed(self, key: str, client) -> int: - """ - Decide whether to perform expired data cleanup based on probability - - Args: - key: Cache key name - client: Redis client - - Returns: - int: Number of cleaned data entries - """ - if random.random() < self.cleanup_probability: - # Calculate cleanup threshold (data before default expiration time multiplier) - current_timestamp = int(time.time() * 1000) - cleanup_threshold = current_timestamp - ( - self.default_expire_minutes * DEFAULT_CLEANUP_MULTIPLIER * 60 * 1000 - ) - - # Clean expired data - cleaned_count = await client.zremrangebyscore( - key, '-inf', cleanup_threshold - ) - - if cleaned_count > 0: - logger.debug( - "Randomly cleaned expired data: key=%s, cleaned=%d", - key, - cleaned_count, - ) - - return cleaned_count - return 0 - - async def append( - self, - key: str, - data: Union[str, Dict, List, Any], - expire_minutes: Optional[int] = None, - ) -> bool: - """ - Append data to the time window queue for the specified key - - Args: - key: Cache key name - data: Data to append, supports strings, dictionaries, lists, etc. - expire_minutes: Expiration time (minutes), default to instance configuration - - Returns: - bool: Whether operation succeeded - - Examples: - # Append string data - await cache.append("user_actions", "user_login") - - # Append dictionary data - await cache.append("api_calls", {"method": "GET", "path": "/api/users"}) - - # Custom expiration time - await cache.append("temp_data", "some_data", expire_minutes=5) - """ - try: - client = await self.redis_provider.get_client() - - # Get current millisecond-level timestamp - current_timestamp = int(time.time() * 1000) - - # Process data using data processor - unique_member = RedisDataProcessor.process_data_for_storage(data) - - # Use pipeline for atomic operations - pipe = client.pipeline() - - # 1. Add data to sorted set - pipe.zadd(key, {unique_member: current_timestamp}) - - # 2. Set or extend expiration time - expire_seconds = (expire_minutes or self.default_expire_minutes) * 60 - pipe.expire(key, expire_seconds) - - # 3. Randomly clean expired data (using independent cleanup function) - # Note: Cannot call within pipeline because return value is needed - # So execute pipeline first, then perform cleanup separately - - # Execute pipeline operations - results = await pipe.execute() - - # Check operation results - zadd_result = results[0] # ZADD returns number of added members - expire_result = results[1] # EXPIRE returns whether successful - - # Perform random cleanup separately - cleaned_count = await self._cleanup_expired_if_needed(key, client) - - if zadd_result is not None and expire_result: - if cleaned_count > 0: - logger.debug( - "Data appended successfully and expired data cleaned: key=%s, member_length=%d, timestamp=%d, expire=%ds, cleaned=%d", - key, - len(unique_member), - current_timestamp, - expire_seconds, - cleaned_count, - ) - else: - logger.debug( - "Data appended successfully: key=%s, member_length=%d, timestamp=%d, expire=%ds", - key, - len(unique_member), - current_timestamp, - expire_seconds, - ) - return True - else: - logger.warning( - "Data append partially failed: key=%s, zadd_result=%s, expire_result=%s", - key, - zadd_result, - expire_result, - ) - return False - - except Exception as e: - logger.error("Redis data append failed: key=%s, error=%s", key, str(e)) - return False - - async def get_queue_size(self, key: str) -> int: - """ - Get current size of specified queue - - Args: - key: Cache key name - - Returns: - int: Number of elements in queue - """ - try: - client = await self.redis_provider.get_client() - size = await client.zcard(key) - return size or 0 - except Exception as e: - logger.error("Failed to get queue size: key=%s, error=%s", key, str(e)) - return 0 - - async def clear_queue(self, key: str) -> bool: - """ - Clear all data from specified queue - - Args: - key: Cache key name - - Returns: - bool: Whether operation succeeded - """ - try: - client = await self.redis_provider.get_client() - result = await client.delete(key) - logger.info("Cleared queue: key=%s, result=%d", key, result) - return result > 0 - except Exception as e: - logger.error("Failed to clear queue: key=%s, error=%s", key, str(e)) - return False - - async def cleanup_expired(self, key: str) -> int: - """ - Manually clean expired data from specified queue - - Args: - key: Cache key name - - Returns: - int: Number of cleaned data entries - """ - try: - client = await self.redis_provider.get_client() - - # Force cleanup (ignore probability) - # Calculate cleanup threshold (data older than default expiration time multiplier) - current_timestamp = int(time.time() * 1000) - cleanup_threshold = current_timestamp - ( - self.default_expire_minutes * DEFAULT_CLEANUP_MULTIPLIER * 60 * 1000 - ) - - # Clean expired data - cleaned_count = await client.zremrangebyscore( - key, '-inf', cleanup_threshold - ) - - if cleaned_count > 0: - logger.info( - "Manually cleaned expired data: key=%s, cleaned=%d", - key, - cleaned_count, - ) - - return cleaned_count - - except Exception as e: - logger.error("Failed to clean expired data: key=%s, error=%s", key, str(e)) - return 0 - - async def get_by_timestamp_range( - self, - key: str, - start_timestamp: Optional[Union[int, datetime]] = None, - end_timestamp: Optional[Union[int, datetime]] = None, - limit: int = -1, - ) -> List[Dict[str, Any]]: - """ - Retrieve queue data by timestamp range - - Args: - key: Cache key name - start_timestamp: Start timestamp (milliseconds) or datetime object, None means no restriction - end_timestamp: End timestamp (milliseconds) or datetime object, None means no restriction - limit: Limit number of returned items, -1 means no limit - - Returns: - List[Dict[str, Any]]: List of data within timestamp range, each element contains: - - id: Unique identifier - - data: Original data - - timestamp: Timestamp (milliseconds) - - datetime: Formatted time string - """ - try: - # Convert timestamps - min_score = "-inf" - max_score = "+inf" - - if start_timestamp is not None: - min_score = str(self._convert_timestamp(start_timestamp)) - - if end_timestamp is not None: - max_score = str(self._convert_timestamp(end_timestamp)) - - # Execute pre-registered Lua script - messages = await self._fetch_by_timestamp_range_script( - keys=[key], args=[min_score, max_score, limit] - ) - - if not messages: - logger.debug( - "No data in timestamp range: key=%s, range=[%s, %s]", - key, - min_score, - max_score, - ) - return [] - - # Parse message data (WITHSCORES returns format: [member1, score1, member2, score2, ...]) - result = [] - - # Process WITHSCORES returned results, every two elements form a pair: message content and score - if len(messages) % 2 != 0: - logger.warning( - "WITHSCORES returned data length abnormal: %d, should be even", - len(messages), - ) - return [] - - for i in range(0, len(messages), 2): - try: - if i + 1 >= len(messages): - logger.warning( - "WITHSCORES data index out of bounds: i=%d, len=%d", - i, - len(messages), - ) - break - - message = messages[i] # Message content - score_raw = messages[i + 1] # Score (could be bytes or string) - - # Safely convert score to timestamp - try: - if isinstance(score_raw, bytes): - score_str = score_raw.decode('utf-8') - else: - score_str = str(score_raw) - timestamp = int(float(score_str)) - except (ValueError, UnicodeDecodeError) as score_e: - logger.warning( - "Score conversion failed: score_raw=%s (type=%s), error=%s", - score_raw, - type(score_raw), - str(score_e), - ) - timestamp = int( - time.time() * 1000 - ) # Use current time as fallback - - # Use data processor to parse data - processed_data = RedisDataProcessor.process_data_from_storage( - message - ) - - result.append( - { - "id": processed_data["id"], - "data": processed_data["data"], - "timestamp": timestamp, - "datetime": datetime.fromtimestamp( - timestamp / 1000 - ).strftime("%Y-%m-%d %H:%M:%S.%f")[:-3], - } - ) - - except Exception as e: - logger.warning( - "Failed to parse message: i=%d, message=%s, error=%s", - i, - message if i < len(messages) else "index out of bounds", - str(e), - ) - continue - - # Sort by timestamp (newest first) - result.sort(key=lambda x: x["timestamp"], reverse=True) - - logger.debug( - "Successfully retrieved data by timestamp range: key=%s, range=[%s, %s], count=%d", - key, - min_score, - max_score, - len(result), - ) - - return result - - except Exception as e: - logger.error( - "Failed to retrieve data by timestamp range: key=%s, error=%s", - key, - str(e), - ) - return [] - - async def get_queue_stats(self, key: str) -> Dict[str, Any]: - """ - Get queue statistics - - Args: - key: Cache key name - - Returns: - Dict[str, Any]: Queue statistics - """ - try: - client = await self.redis_provider.get_client() - - # Get queue size - total_count = await client.zcard(key) or 0 - - if total_count == 0: - return { - "key": key, - "total_count": 0, - "oldest_timestamp": None, - "newest_timestamp": None, - "ttl_seconds": -1, - } - - # Get oldest and newest timestamps - oldest_data = await client.zrange(key, 0, 0, withscores=True) - newest_data = await client.zrange(key, -1, -1, withscores=True) - - oldest_timestamp = int(oldest_data[0][1]) if oldest_data else None - newest_timestamp = int(newest_data[0][1]) if newest_data else None - - # Get TTL - ttl = await client.ttl(key) - - return { - "key": key, - "total_count": total_count, - "oldest_timestamp": oldest_timestamp, - "newest_timestamp": newest_timestamp, - "oldest_datetime": ( - datetime.fromtimestamp(oldest_timestamp / 1000).strftime( - "%Y-%m-%d %H:%M:%S" - ) - if oldest_timestamp - else None - ), - "newest_datetime": ( - datetime.fromtimestamp(newest_timestamp / 1000).strftime( - "%Y-%m-%d %H:%M:%S" - ) - if newest_timestamp - else None - ), - "ttl_seconds": ttl, - } - - except Exception as e: - logger.error( - "Failed to get queue statistics: key=%s, error=%s", key, str(e) - ) - return { - "key": key, - "total_count": 0, - "oldest_timestamp": None, - "newest_timestamp": None, - "ttl_seconds": -1, - "error": str(e), - } - - -# For backward compatibility, provide a default component instance -@component(name="redis_windows_cache_manager") -class DefaultRedisWindowsCacheManager: - """Default Redis Time Window Cache Manager (backward compatibility)""" - - def __init__(self, redis_provider: RedisProvider): - self.redis_provider = redis_provider - self._manager = None - self._factory = None - - async def _get_manager(self): - """Lazy initialization of manager""" - if self._manager is None: - if self._factory is None: - self._factory = RedisWindowsCacheFactory(self.redis_provider) - self._manager = await self._factory.create_cache_manager() - return self._manager - - async def append( - self, - key: str, - data: Union[str, Dict, List, Any], - expire_minutes: Optional[int] = None, - ) -> bool: - manager = await self._get_manager() - return await manager.append(key, data, expire_minutes) - - async def get_queue_size(self, key: str) -> int: - manager = await self._get_manager() - return await manager.get_queue_size(key) - - async def clear_queue(self, key: str) -> bool: - manager = await self._get_manager() - return await manager.clear_queue(key) - - async def cleanup_expired(self, key: str) -> int: - manager = await self._get_manager() - return await manager.cleanup_expired(key) - - async def get_queue_stats(self, key: str) -> Dict[str, Any]: - manager = await self._get_manager() - return await manager.get_queue_stats(key) - - async def get_by_timestamp_range( - self, - key: str, - start_timestamp: Optional[Union[int, datetime]] = None, - end_timestamp: Optional[Union[int, datetime]] = None, - limit: int = -1, - ) -> List[Dict[str, Any]]: - manager = await self._get_manager() - return await manager.get_by_timestamp_range( - key, start_timestamp, end_timestamp, limit - ) diff --git a/methods/EverCore/src/core/capability/app_capability.py b/methods/EverCore/src/core/capability/app_capability.py deleted file mode 100644 index ea26a694..00000000 --- a/methods/EverCore/src/core/capability/app_capability.py +++ /dev/null @@ -1,8 +0,0 @@ -from abc import ABC, abstractmethod -from fastapi import FastAPI - - -class ApplicationCapability(ABC): - @abstractmethod - def enable(self, app: FastAPI): - pass diff --git a/methods/EverCore/src/core/class_annotations/__init__.py b/methods/EverCore/src/core/class_annotations/__init__.py deleted file mode 100644 index 8e2a9332..00000000 --- a/methods/EverCore/src/core/class_annotations/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -"""Package marker for core.class_annotations. - -Per project rules, do not add executable code here. -""" diff --git a/methods/EverCore/src/core/class_annotations/decorator.py b/methods/EverCore/src/core/class_annotations/decorator.py deleted file mode 100644 index b03a026d..00000000 --- a/methods/EverCore/src/core/class_annotations/decorator.py +++ /dev/null @@ -1,76 +0,0 @@ -from __future__ import annotations - -from typing import Any, Dict, Mapping, MutableMapping, Optional, Type, Union - -from .types import AnnotationKeyBase, AnnotationValueBase - - -_NOTES_ATTR = "__notes__" - - -def _ensure_notes_dict(cls: Type[Any]) -> MutableMapping[str, AnnotationValueBase]: - notes = getattr(cls, _NOTES_ATTR, None) - if notes is None: - notes = {} - setattr(cls, _NOTES_ATTR, notes) - elif not isinstance(notes, dict): - # If user provided a non-dict mistakenly, reset to a dict to keep behavior consistent. - notes = dict(notes) - setattr(cls, _NOTES_ATTR, notes) - return notes - - -def _normalize_key(key: Union[str, AnnotationKeyBase]) -> str: - if isinstance(key, AnnotationKeyBase): - return key.to_key() - if isinstance(key, str): - return key - raise TypeError( - f"Annotation key must be str or AnnotationKeyBase, got {type(key).__name__}" - ) - - -def class_annotations( - annotations: Optional[ - Mapping[Union[str, AnnotationKeyBase], AnnotationValueBase] - ] = None, - /, - **kwargs: AnnotationValueBase, -): - """ - Class decorator to attach strict annotation values to a class. - - Usage: - @class_annotations(owner=FreeformAnnotationValue(...)) - class MyClass: ... - - @class_annotations({"role": EnumAnnotationValue(RoleEnum.ADMIN)}) - class MyClass: ... - - Rules: - - All values must be instances of AnnotationValueBase. - - Multiple decorators can be stacked; later ones override same keys. - """ - - provided: Dict[str, AnnotationValueBase] = {} - if annotations: - for k, v in annotations.items(): - provided[_normalize_key(k)] = v - if kwargs: - # kwargs keys are always strings by Python's syntax - for k, v in kwargs.items(): - provided[_normalize_key(k)] = v - - # Validate value types early - for k, v in provided.items(): - if not isinstance(v, AnnotationValueBase): - raise TypeError( - f"Annotation '{k}' must be an instance of AnnotationValueBase, got {type(v).__name__}" - ) - - def _wrapper(cls: Type[Any]): - notes = _ensure_notes_dict(cls) - notes.update(provided) - return cls - - return _wrapper diff --git a/methods/EverCore/src/core/class_annotations/types.py b/methods/EverCore/src/core/class_annotations/types.py deleted file mode 100644 index 62cd6d44..00000000 --- a/methods/EverCore/src/core/class_annotations/types.py +++ /dev/null @@ -1,87 +0,0 @@ -from __future__ import annotations - -from abc import ABC, abstractmethod -from enum import Enum -from typing import Any - - -class AnnotationValueBase(ABC): - """ - Base class for all class-annotation values. - - Implementations must be immutable or treated as value objects. They should - provide a serializable representation via `to_data` for potential logging or - transport, and a human-readable `__repr__`. - """ - - @abstractmethod - def to_data(self) -> Any: - """ - Return a JSON-serializable payload representing this value. - """ - raise NotImplementedError - - -class AnnotationKeyBase(ABC): - """ - Base class for annotation keys. Concrete implementations must provide a - stable string key through `to_key()`. - """ - - @abstractmethod - def to_key(self) -> str: - raise NotImplementedError - - -class FreeformAnnotationValue(AnnotationValueBase): - """ - Flexible annotation value that only carries the actual payload `data`. - """ - - def __init__(self, data: Any) -> None: - self._data = data - - @property - def data(self) -> Any: - return self._data - - def to_data(self) -> Any: - return {"type": "freeform", "data": self._data} - - def __repr__(self) -> str: # pragma: no cover - trivial - return f"FreeformAnnotationValue(data={self._data!r})" - - -class StringEnumAnnotation(str, Enum): - """ - Base class for string-backed enums that are directly usable as annotation values. - - Usage: - class Role(StringEnumAnnotation): - ADMIN = "admin" - USER = "user" - """ - - def to_data(self) -> Any: - # Ensure a stable, serializable shape - return { - "type": "enum", - "enum": self.__class__.__name__, - "name": self.name, - "value": str(self.value), - } - - -class StringEnumAnnotationKey(str, Enum): - """ - Base class for string-backed enum keys. - The enum's value (a string) is used as the canonical key. - """ - - def to_key(self) -> str: - return str(self.value) - - -# Register virtual subclasses to satisfy isinstance/issubclass checks without metaclass conflicts -AnnotationValueBase.register(StringEnumAnnotation) -AnnotationKeyBase.register(StringEnumAnnotationKey) diff --git a/methods/EverCore/src/core/class_annotations/utils.py b/methods/EverCore/src/core/class_annotations/utils.py deleted file mode 100644 index 3b0cbaf0..00000000 --- a/methods/EverCore/src/core/class_annotations/utils.py +++ /dev/null @@ -1,76 +0,0 @@ -from __future__ import annotations - -from typing import Any, Dict, Mapping, MutableMapping, Optional, Tuple, Type, Union - -from .types import AnnotationKeyBase, AnnotationValueBase - - -_NOTES_ATTR = "__notes__" - - -def _collect_mro_notes( - cls: Type[Any], -) -> Tuple[MutableMapping[str, AnnotationValueBase], ...]: - """ - Collect `__notes__` from MRO (base classes first, subclass last). - Only include those that are dict-like. - """ - collected = [] - for base in reversed(cls.mro()): - if base is object: - continue - notes = getattr(base, _NOTES_ATTR, None) - if isinstance(notes, dict): - collected.append(notes) - return tuple(collected) - - -def _merged_notes(cls: Type[Any]) -> Dict[str, AnnotationValueBase]: - merged: Dict[str, AnnotationValueBase] = {} - for notes in _collect_mro_notes(cls): - for k, v in notes.items(): - if not isinstance(v, AnnotationValueBase): - # Skip invalid values quietly to be robust at runtime. - continue - merged[k] = v - return merged - - -def get_annotations( - target: Union[Type[Any], Any], *, include_inherited: bool = True -) -> Mapping[str, AnnotationValueBase]: - """ - Get all annotations attached to a class or instance. - - - If `include_inherited` is True, the result is a merged view following MRO, where - subclass values override base ones. - - If False, only annotations defined directly on that class are returned. - """ - cls: Type[Any] = target if isinstance(target, type) else type(target) - if include_inherited: - return _merged_notes(cls) - notes = getattr(cls, _NOTES_ATTR, {}) - return {k: v for k, v in notes.items() if isinstance(v, AnnotationValueBase)} - - -def get_annotation( - target: Union[Type[Any], Any], - key: Union[str, AnnotationKeyBase], - *, - include_inherited: bool = True, -) -> Optional[AnnotationValueBase]: - """ - Get a single annotation value by key from a class or instance. - """ - normalized = key.to_key() if isinstance(key, AnnotationKeyBase) else key - notes = get_annotations(target, include_inherited=include_inherited) - return notes.get(normalized) - - -def has_annotation( - target: Union[Type[Any], Any], - key: Union[str, AnnotationKeyBase], - *, - include_inherited: bool = True, -) -> bool: - return get_annotation(target, key, include_inherited=include_inherited) is not None diff --git a/methods/EverCore/src/core/component/__init__.py b/methods/EverCore/src/core/component/__init__.py deleted file mode 100644 index e9b030ed..00000000 --- a/methods/EverCore/src/core/component/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Component module initialization file -""" diff --git a/methods/EverCore/src/core/component/auth_provider.py b/methods/EverCore/src/core/component/auth_provider.py deleted file mode 100644 index 1e02b9ff..00000000 --- a/methods/EverCore/src/core/component/auth_provider.py +++ /dev/null @@ -1,88 +0,0 @@ -from typing import Optional, Dict, Any -from abc import ABC, abstractmethod -from fastapi import Request, HTTPException - -from core.di.decorators import component - - -class AuthProvider(ABC): - """Authentication provider interface, responsible for handling authorization header and user context""" - - @abstractmethod - async def get_optional_user_data_from_request( - self, request: Request - ) -> Optional[Dict[str, Any]]: - """ - Extract full user data from the request (optional) - - Args: - request: FastAPI request object - - Returns: - Optional[Dict[str, Any]]: User data, including user_id, role, etc. Return None if not present or invalid - """ - - -@component(name="auth_provider") -class TestAuthProviderImpl(AuthProvider): - """Authentication provider implementation, responsible for handling authorization header and user context""" - - def __init__(self): - """Initialize the authentication provider""" - - async def get_user_id_from_request(self, request: Request) -> int: - """ - Extract user ID from the request - - Current implementation: directly obtain user ID from the authorization header (temporary solution) - Future extension: can support JWT token parsing, etc. - - Args: - request: FastAPI request object - - Returns: - int: User ID - - Raises: - HTTPException: When the authorization header is missing or invalid - """ - # Get user ID from the authorization header - auth_header = request.headers.get("authorization") - - if not auth_header: - raise HTTPException(status_code=401, detail="Missing authorization header") - - # Remove possible "Bearer " prefix - user_id_str = auth_header.replace("Bearer ", "").strip() - - try: - user_id = int(user_id_str) - if user_id <= 0: - raise ValueError("User ID must be a positive integer") - return user_id - except ValueError: - raise HTTPException( - status_code=400, - detail="Invalid user ID format in authorization header, should be a positive integer", - ) - - async def get_optional_user_data_from_request( - self, request: Request - ) -> Optional[Dict[str, Any]]: - """ - Extract full user data from the request (optional) - - Args: - request: FastAPI request object - - Returns: - Optional[Dict[str, Any]]: User data, including user_id, role, etc. Return None if not present or invalid - """ - try: - user_id = await self.get_user_id_from_request(request) - # Import Role enum - from core.authorize.enums import Role - - return {"user_id": user_id, "role": Role.USER.value} - except HTTPException: - return None diff --git a/methods/EverCore/src/core/component/config_provider.py b/methods/EverCore/src/core/component/config_provider.py deleted file mode 100644 index a34738f9..00000000 --- a/methods/EverCore/src/core/component/config_provider.py +++ /dev/null @@ -1,99 +0,0 @@ -import os -import yaml -import json -from typing import Dict, Any, Optional - -from core.di.decorators import component -from common_utils.project_path import CURRENT_DIR - - -@component(name="config_provider") -class ConfigProvider: - """Configuration provider""" - - def __init__(self): - """Initialize configuration provider""" - self.config_dir = CURRENT_DIR / "config" - self._cache: Dict[str, Any] = {} - - def get_config(self, config_name: str) -> Dict[str, Any]: - """ - Get configuration - - Args: - config_name: Configuration file name (without extension) - - Returns: - Dict[str, Any]: Configuration data - """ - if config_name in self._cache: - return self._cache[config_name] - - config_file = self.config_dir / f"{config_name}.yaml" - if not config_file.exists(): - config_file = self.config_dir / f"{config_name}.yml" - if not config_file.exists(): - config_file = self.config_dir / f"{config_name}.json" - - if not config_file.exists(): - raise FileNotFoundError(f"Configuration file does not exist: {config_name}") - - try: - with open(config_file, 'r', encoding='utf-8') as f: - if config_file.suffix.lower() in ['.yaml', '.yml']: - config_data = yaml.safe_load(f) - else: - config_data = json.load(f) - - self._cache[config_name] = config_data - return config_data - - except Exception as e: - raise RuntimeError(f"Failed to load configuration file {config_name}: {e}") - - def get_raw_config(self, config_name: str) -> str: - """ - Get raw configuration text content - - Args: - config_name: Configuration file name (with extension) - - Returns: - str: Raw text content of the configuration file - """ - # Check cache - cache_key = f"raw_{config_name}" - if cache_key in self._cache: - return self._cache[cache_key] - - # Build configuration file path (config_name already includes extension) - config_file = self.config_dir / config_name - - if not config_file.exists(): - raise FileNotFoundError(f"Configuration file does not exist: {config_name}") - - try: - # Directly read text file content - with open(config_file, 'r', encoding='utf-8') as f: - raw_content = f.read() - - # Cache raw text content - self._cache[cache_key] = raw_content - return raw_content - - except Exception as e: - raise RuntimeError(f"Failed to read configuration file {config_name}: {e}") - - def get_available_configs(self) -> list: - """ - Get list of all files in the config directory - - Returns: - list: List of file names - """ - configs = [] - for file in self.config_dir.iterdir(): - if file.is_file(): - configs.append(file.name) - - return sorted(configs) diff --git a/methods/EverCore/src/core/component/database_connection_provider.py b/methods/EverCore/src/core/component/database_connection_provider.py deleted file mode 100644 index 88d76b2f..00000000 --- a/methods/EverCore/src/core/component/database_connection_provider.py +++ /dev/null @@ -1,127 +0,0 @@ -""" -Database connection provider - -Responsible for managing PostgreSQL connection pool and LangGraph checkpoint saver -""" - -import os -from typing import Optional, Tuple -from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver -from psycopg_pool import AsyncConnectionPool -from psycopg.rows import dict_row - -from core.di.decorators import component -from core.observation.logger import get_logger -from common_utils.datetime_utils import get_timezone - -logger = get_logger(__name__) - - -@component(name="database_connection_provider", primary=True) -class DatabaseConnectionProvider: - """Database connection provider""" - - def __init__(self): - """Initialize database connection provider""" - self.database_url = os.getenv("DATABASE_URL") - if not self.database_url: - raise ValueError( - "Database connection string DATABASE_URL is not configured" - ) - - # Read timezone configuration from environment variables - self.timezone = get_timezone() - - # Connection pool configuration - self.max_size = int(os.getenv("CHECKPOINTER_DB_POOL_SIZE", "20")) - - # Do not create connection pool during initialization, delay until needed - self._connection_pool: Optional[AsyncConnectionPool] = None - self._checkpointer: Optional[AsyncPostgresSaver] = None - self._is_initialized = False - - async def _ensure_initialized(self): - """Ensure connection pool is initialized""" - if self._is_initialized: - return - - logger.info("Initializing database connection pool...") - - # Connection parameters configuration - connection_kwargs = { - "autocommit": True, - "prepare_threshold": 0, - "row_factory": dict_row, # Add row_factory to match type - "options": f"-c timezone={self.timezone}", # Set connection timezone - } - - # Create connection pool - self._connection_pool = AsyncConnectionPool( - conninfo=self.database_url, - max_size=self.max_size, - open=False, # Do not open in constructor - kwargs=connection_kwargs, - ) - - logger.info( - "Database connection pool created successfully %s", self.database_url - ) - - # Explicitly open connection pool - await self._connection_pool.open() - logger.info( - "Database connection pool initialized successfully, timezone set to: %s", - self.timezone, - ) - - # Initialize checkpointer - self._checkpointer = AsyncPostgresSaver(self._connection_pool) # type: ignore - await self._checkpointer.setup() - logger.info("Checkpointer setup completed") - - self._is_initialized = True - - async def get_connection_pool(self) -> AsyncConnectionPool: - """ - Get database connection pool - - Returns: - AsyncConnectionPool: Database connection pool instance - """ - await self._ensure_initialized() - return self._connection_pool - - async def get_checkpointer(self) -> AsyncPostgresSaver: - """ - Get LangGraph checkpoint saver - - Returns: - AsyncPostgresSaver: Checkpoint saver instance - """ - await self._ensure_initialized() - return self._checkpointer - - async def get_connection_and_checkpointer( - self, - ) -> Tuple[AsyncConnectionPool, AsyncPostgresSaver]: - """ - Get connection pool and checkpoint saver - - Returns: - tuple: (connection pool, checkpoint saver) - """ - await self._ensure_initialized() - return self._connection_pool, self._checkpointer - - async def close(self): - """Close database connection pool""" - if self._connection_pool: - await self._connection_pool.close() - logger.info("Database connection pool has been closed") - self._connection_pool = None - self._checkpointer = None - self._is_initialized = False - - def is_initialized(self) -> bool: - """Check if initialized""" - return self._is_initialized diff --git a/methods/EverCore/src/core/component/database_session_provider.py b/methods/EverCore/src/core/component/database_session_provider.py deleted file mode 100644 index 60466bff..00000000 --- a/methods/EverCore/src/core/component/database_session_provider.py +++ /dev/null @@ -1,60 +0,0 @@ -import os -from typing import AsyncGenerator -from sqlmodel.ext.asyncio.session import AsyncSession -from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker -from core.di.decorators import component -from common_utils.datetime_utils import get_timezone - - -@component(name="database_session_provider", primary=True) -class DatabaseSessionProvider: - """Database session provider""" - - def __init__(self): - """Initialize the database session provider""" - self.database_url = os.getenv("DATABASE_URL", "") - - timezone = get_timezone() - - # Replace postgresql:// with postgresql+asyncpg:// to support async - if self.database_url.startswith("postgresql://"): - self.async_database_url = self.database_url.replace( - "postgresql://", "postgresql+asyncpg://", 1 - ) - else: - self.async_database_url = self.database_url - - # Create async engine - self.async_engine = create_async_engine( - self.async_database_url, - echo=False, # Set to True to see SQL logs - future=True, - pool_pre_ping=True, - pool_recycle=int( - os.getenv("DB_POOL_RECYCLE", "300") - ), # Recycle connections every 5 minutes - pool_size=int( - os.getenv("DB_POOL_SIZE", "40") - ), # 🔧 Increase connection pool size (default 5 → 10) - max_overflow=int( - os.getenv("DB_MAX_OVERFLOW", "25") - ), # 🔧 Increase maximum overflow connections (default 10 → 15) - connect_args={"server_settings": {"timezone": timezone}}, - ) - - # Create async session factory - self.async_session_factory = async_sessionmaker( - bind=self.async_engine, class_=AsyncSession, expire_on_commit=False - ) - - def create_session(self) -> AsyncSession: - """Create a new async database session""" - return self.async_session_factory() - - async def get_async_session(self) -> AsyncGenerator[AsyncSession, None]: - """Get async database session (in context manager form)""" - async with self.async_session_factory() as session: - try: - yield session - finally: - await session.close() diff --git a/methods/EverCore/src/core/component/elasticsearch_client_factory.py b/methods/EverCore/src/core/component/elasticsearch_client_factory.py deleted file mode 100644 index a48dcfbe..00000000 --- a/methods/EverCore/src/core/component/elasticsearch_client_factory.py +++ /dev/null @@ -1,349 +0,0 @@ -""" -Elasticsearch Client Factory - -Provides Elasticsearch client caching and management functionality based on environment variables. -""" - -import os -import asyncio -from typing import Dict, Optional, List, Any -from hashlib import md5 -from elasticsearch import AsyncElasticsearch -from elasticsearch.dsl.async_connections import connections as async_connections - -from core.di.decorators import component -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -def get_default_es_config() -> Dict[str, Any]: - """ - Get default Elasticsearch configuration based on environment variables - - Environment variables: - - ES_HOST: Elasticsearch host, default localhost - - ES_PORT: Elasticsearch port, default 9200 - - ES_HOSTS: Elasticsearch host list, comma-separated, takes precedence over ES_HOST - - ES_USERNAME: Username - - ES_PASSWORD: Password - - ES_API_KEY: API key - - ES_TIMEOUT: Timeout (seconds), default 120 - - Returns: - Dict[str, Any]: Configuration dictionary - """ - # Get host information - es_hosts_str = os.getenv("ES_HOSTS") - if es_hosts_str: - # ES_HOSTS already contains full URL (https://host:port), use directly - es_hosts = [host.strip() for host in es_hosts_str.split(",")] - else: - # Fallback to single host configuration - es_host = os.getenv("ES_HOST", "localhost") - es_port = int(os.getenv("ES_PORT", "9200")) - es_hosts = [f"http://{es_host}:{es_port}"] - - # Authentication information - es_username = os.getenv("ES_USERNAME") - es_password = os.getenv("ES_PASSWORD") - es_api_key = os.getenv("ES_API_KEY") - - # Connection parameters - es_timeout = int(os.getenv("ES_TIMEOUT", "120")) - es_verify_certs = os.getenv("ES_VERIFY_CERTS", "false").lower() == "true" - - config = { - "hosts": es_hosts, - "timeout": es_timeout, - "username": es_username, - "password": es_password, - "api_key": es_api_key, - "verify_certs": es_verify_certs, - } - - logger.info("Getting default Elasticsearch config:") - logger.info(" Hosts: %s", es_hosts) - logger.info(" Timeout: %s seconds", es_timeout) - logger.info( - " Auth: %s", "API Key" if es_api_key else ("Basic" if es_username else "None") - ) - - return config - - -def get_cache_key( - hosts: List[str], - username: Optional[str] = None, - password: Optional[str] = None, - api_key: Optional[str] = None, -) -> str: - """ - Generate cache key (also used as alias for elasticsearch-dsl connections) - Generate unique identifier based on hosts and authentication info - - Args: - hosts: Elasticsearch host list - username: Username - password: Password - api_key: API key - - Returns: - str: Cache key - """ - hosts_str = ",".join(sorted(hosts)) - auth_str = "" - if api_key: - # Use first 8 characters of api_key as identifier - auth_str = f"api_key:{api_key[:8]}..." - elif username and password: - # Use md5 of username and password as identifier - auth_str = f"basic:{username}:{md5(password.encode()).hexdigest()[:8]}" - elif username: - # When only username is provided, use username only - auth_str = f"basic:{username}" - - key_content = f"{hosts_str}:{auth_str}" - return md5(key_content.encode()).hexdigest() - - -class ElasticsearchClientWrapper: - """Elasticsearch client wrapper""" - - def __init__(self, async_client: AsyncElasticsearch, hosts: List[str]): - self.async_client = async_client - self.hosts = hosts - - async def test_connection(self) -> bool: - """Test connection""" - try: - await self.async_client.ping() - logger.info("✅ Elasticsearch connection test successful: %s", self.hosts) - return True - except Exception as e: - logger.error( - "❌ Elasticsearch connection test failed: %s, error: %s", self.hosts, e - ) - return False - - async def close(self): - """Close connection""" - try: - if self.async_client: - await self.async_client.close() - logger.info("🔌 Elasticsearch connection closed: %s", self.hosts) - except Exception as e: - logger.error("Error closing Elasticsearch connection: %s", e) - - -@component(name="elasticsearch_client_factory") -class ElasticsearchClientFactory: - """ - Elasticsearch client factory - ### AsyncElasticsearch is stateful, so the same instance can be used in multiple places ### - - Provides Elasticsearch client caching and management functionality based on configuration - """ - - def __init__(self): - """Initialize Elasticsearch client factory""" - self._clients: Dict[str, ElasticsearchClientWrapper] = {} - self._lock = asyncio.Lock() - self._default_config: Optional[Dict[str, Any]] = None - self._default_client: Optional[ElasticsearchClientWrapper] = None - logger.info("ElasticsearchClientFactory initialized") - - async def _create_client( - self, - hosts: List[str], - username: Optional[str] = None, - password: Optional[str] = None, - api_key: Optional[str] = None, - timeout: int = 120, - **kwargs, - ) -> ElasticsearchClientWrapper: - """ - Create Elasticsearch client instance - - Args: - hosts: Elasticsearch host list - username: Username - password: Password - api_key: API key - timeout: Timeout (seconds) - **kwargs: Other connection parameters - - Returns: - ElasticsearchClientWrapper instance - """ - # Build connection parameters - conn_params = { - "hosts": hosts, - "timeout": timeout, - "max_retries": 3, - "retry_on_timeout": True, - "verify_certs": False, # Disable SSL certificate verification - "ssl_show_warn": False, # Disable SSL warnings - **kwargs, - } - - # Add authentication information - if api_key: - conn_params["api_key"] = api_key - elif username and password: - conn_params["basic_auth"] = (username, password) - - # Generate connection alias (used for elasticsearch-dsl connections management) - alias = get_cache_key(hosts, username, password, api_key) - - # Create async client via async_connections.create_connection - async_client = async_connections.create_connection(alias=alias, **conn_params) - - client_wrapper = ElasticsearchClientWrapper(async_client, hosts) - - logger.info("Created Elasticsearch client for %s with alias %s", hosts, alias) - return client_wrapper - - async def _get_client( - self, - hosts: List[str], - username: Optional[str] = None, - password: Optional[str] = None, - api_key: Optional[str] = None, - **kwargs, - ) -> ElasticsearchClientWrapper: - """ - Get Elasticsearch client instance - - Args: - hosts: Elasticsearch host list - username: Username - password: Password - api_key: API key - **kwargs: Other configuration parameters - - Returns: - ElasticsearchClientWrapper instance - """ - cache_key = get_cache_key(hosts, username, password, api_key) - - async with self._lock: - # Check cache - if cache_key in self._clients: - logger.debug("Using cached Elasticsearch client for %s", hosts) - return self._clients[cache_key] - - # Create new client instance - logger.info("Creating new Elasticsearch client for %s", hosts) - - client_wrapper = await self._create_client( - hosts=hosts, - username=username, - password=password, - api_key=api_key, - **kwargs, - ) - - self._clients[cache_key] = client_wrapper - logger.info( - "Elasticsearch client %s created and cached with key %s", - hosts, - cache_key, - ) - - return client_wrapper - - async def get_default_client(self) -> ElasticsearchClientWrapper: - """ - Get default Elasticsearch client instance based on environment variable configuration - Getting default client is not supported, direct calls to factory are prohibited - - Returns: - ElasticsearchClientWrapper instance - """ - raise NotImplementedError( - "ElasticsearchClientFactory does not support get_default_client, use register_default_client instead" - ) - - async def register_default_client(self) -> ElasticsearchClientWrapper: - """ - Register a default client - - Returns: - ElasticsearchClientWrapper instance - """ - # Get or create default configuration - - if self._default_client is not None: - return self._default_client - - if self._default_config is None: - self._default_config = get_default_es_config() - - config = self._default_config - default_client = await self._get_client( - hosts=config["hosts"], - username=config.get("username"), - password=config.get("password"), - api_key=config.get("api_key"), - timeout=config.get("timeout", 120), - ) - - # Register a default client - async_connections.add_connection( - alias="default", conn=default_client.async_client - ) - self._default_client = default_client - return default_client - - async def remove_client( - self, - hosts: List[str], - username: Optional[str] = None, - password: Optional[str] = None, - api_key: Optional[str] = None, - ) -> bool: - """ - Remove specified client - - Args: - hosts: Elasticsearch host list - username: Username - password: Password - api_key: API key - - Returns: - bool: Whether removal was successful - """ - cache_key = get_cache_key(hosts, username, password, api_key) - - async with self._lock: - if cache_key in self._clients: - client_wrapper = self._clients[cache_key] - try: - await client_wrapper.close() - except Exception as e: - logger.error( - "Error closing Elasticsearch client during removal: %s", e - ) - - del self._clients[cache_key] - logger.info("Elasticsearch client %s removed from cache", hosts) - return True - else: - logger.warning("Elasticsearch client %s not found in cache", hosts) - return False - - async def close_all_clients(self) -> None: - """Close all cached clients""" - async with self._lock: - for cache_key, client_wrapper in self._clients.items(): - try: - await client_wrapper.close() - except Exception as e: - logger.error( - "Error closing Elasticsearch client %s: %s", cache_key, e - ) - - self._clients.clear() - logger.info("All Elasticsearch clients closed and cleared from cache") diff --git a/methods/EverCore/src/core/component/kafka_consumer_factory.py b/methods/EverCore/src/core/component/kafka_consumer_factory.py deleted file mode 100644 index 9da07b80..00000000 --- a/methods/EverCore/src/core/component/kafka_consumer_factory.py +++ /dev/null @@ -1,515 +0,0 @@ -""" -Kafka Consumer Factory - -Provides AIOKafkaConsumer caching and management functionality based on kafka_topic, group_id, and server. -Supports force_new logic to create brand new consumer instances. -""" - -import asyncio -import json -import ssl -import os -from typing import Dict, List, Optional, Any -from hashlib import md5 - -import bson -from aiokafka import AIOKafkaConsumer - -from core.component.config_provider import ConfigProvider -from core.di.decorators import component -from core.observation.logger import get_logger -from common_utils.project_path import CURRENT_DIR -from common_utils.datetime_utils import from_iso_format, to_timestamp -from core.di.utils import get_bean_by_type - -logger = get_logger(__name__) - - -def get_ca_file_path(ca_file_path: str) -> Optional[str]: - """ - Get the full path of the CA certificate file - - Construct the path based on CURRENT_DIR + /config/kafka/ca/ - - Returns: - Optional[str]: CA certificate file path, returns None if it does not exist - """ - # CURRENT_DIR points to the src directory, go up one level to the project root, then into the config directory - ca_full_path = CURRENT_DIR / "config" / ca_file_path - - if ca_full_path.exists(): - logger.info("Using default CA certificate file: %s", ca_full_path) - return str(ca_full_path) - else: - logger.warning("Default CA certificate file does not exist: %s", ca_full_path) - return None - - -def get_default_kafka_config(env_prefix: str = "") -> Dict[str, Any]: - """ - Get default Kafka configuration based on environment variables - - Args: - env_prefix: Environment variable prefix to distinguish different configurations (e.g., "PRODUCER_" or "") - The prefix is prepended to KAFKA_, for example, "PRODUCER_" + "KAFKA_SERVERS" = "PRODUCER_KAFKA_SERVERS" - - Environment variables (using prefix=PRODUCER_ as an example): - - {prefix}KAFKA_SERVERS: Kafka server list, comma-separated - - {prefix}KAFKA_TOPIC: Kafka topic - - {prefix}KAFKA_GROUP_ID: Consumer group ID - - {prefix}MAX_POLL_INTERVAL_MS: Maximum poll interval (milliseconds) - - {prefix}SESSION_TIMEOUT_MS: Session timeout (milliseconds) - - {prefix}HEARTBEAT_INTERVAL_MS: Heartbeat interval (milliseconds) - - {prefix}CA_FILE_PATH: CA certificate file path - - Returns: - Dict[str, Any]: Configuration dictionary - """ - - def get_env(key: str, default: str = "") -> str: - """Get environment variable with prefix""" - return os.getenv(f"{env_prefix}{key}", default) - - # Get environment variables with default values - kafka_servers_str = get_env("KAFKA_SERVERS", "") - kafka_servers = [server.strip() for server in kafka_servers_str.split(",")] - - kafka_topic = get_env("KAFKA_TOPIC", "test_topic") - kafka_group_id = get_env("KAFKA_GROUP_ID", "test_group") - max_poll_interval_ms = int(get_env("MAX_POLL_INTERVAL_MS", "3600000")) - session_timeout_ms = int(get_env("SESSION_TIMEOUT_MS", "10000")) - heartbeat_interval_ms = int(get_env("HEARTBEAT_INTERVAL_MS", "3000")) - - # Handle CA certificate path - ca_file_path = None - ca_file_env = get_env("CA_FILE_PATH") - if ca_file_env: - ca_file_path = get_ca_file_path(ca_file_env) - - config = { - "kafka_servers": kafka_servers, - "kafka_topic": kafka_topic, - "kafka_group_id": kafka_group_id, - "max_poll_interval_ms": max_poll_interval_ms, - "session_timeout_ms": session_timeout_ms, - "heartbeat_interval_ms": heartbeat_interval_ms, - "ca_file_path": ca_file_path, - "auto_offset_reset": "earliest", - "enable_auto_commit": True, - } - - prefix_info = f" (prefix: {env_prefix})" if env_prefix else "" - logger.info("Get default Kafka configuration%s:", prefix_info) - logger.info(" Servers: %s", kafka_servers) - logger.info(" Topic: %s", kafka_topic) - logger.info(" Group ID: %s", kafka_group_id) - logger.info(" Max poll interval: %s ms", max_poll_interval_ms) - logger.info(" Session timeout: %s ms", session_timeout_ms) - logger.info(" Heartbeat interval: %s ms", heartbeat_interval_ms) - logger.info(" CA certificate: %s", ca_file_path or "None") - - return config - - -def get_cache_key( - kafka_servers: List[str], kafka_topic: str, kafka_group_id: str -) -> str: - """ - Generate cache key - Create a unique identifier based on servers, topic, and group_id - - Args: - kafka_servers: Kafka server list - kafka_topic: Kafka topic - kafka_group_id: Consumer group ID - - Returns: - str: Cache key - """ - servers_str = ",".join(sorted(kafka_servers)) - key_content = f"{servers_str}:{kafka_topic}:{kafka_group_id}" - return md5(key_content.encode()).hexdigest() - - -def get_consumer_name(kafka_topic: str, kafka_group_id: str) -> str: - """ - Get consumer name - - Args: - kafka_topic: Kafka topic - kafka_group_id: Consumer group ID - - Returns: - str: Consumer name - """ - # Use hyphens to join multiple topic names to avoid overly long names - topic_str = "-".join(topic.strip() for topic in kafka_topic.split(",")) - return f"{topic_str}.{kafka_group_id}" - - -def bson_json_decode(value: bytes | None) -> Any: - """ - BSON/JSON decoder - Attempt BSON decoding first, fall back to JSON decoding if it fails - """ - if not value or value == b"null": - return value - try: - return bson.decode(value) - except Exception: - try: - return json.loads(value.decode("utf-8")) - except Exception as e: - logger.error("JSON parsing error: %s", e) - return value - - -@component(name="kafka_consumer_factory", primary=True) -class KafkaConsumerFactory: - """ - Kafka Consumer Factory - ### AIOKafkaConsumer is stateful, so the same instance cannot be used in multiple places ### - - Provides caching and management of AIOKafkaConsumer instances based on configuration - Supports the force_new parameter to create completely new consumer instances - """ - - def __init__(self): - """Initialize Kafka Consumer Factory""" - self._consumers: Dict[str, AIOKafkaConsumer] = {} - self._lock = asyncio.Lock() - logger.info("KafkaConsumerFactory initialized") - - async def create_consumer( - self, - kafka_servers: List[str], - kafka_topic: str, - kafka_group_id: str, - ca_file_path: Optional[str] = None, - max_poll_interval_ms: int = 300000, - session_timeout_ms: int = 10000, - heartbeat_interval_ms: int = 3000, - auto_offset_reset: str = "earliest", - enable_auto_commit: bool = True, - ) -> AIOKafkaConsumer: - """ - Create an AIOKafkaConsumer instance - - Args: - kafka_servers: Kafka server list - kafka_topic: Kafka topic - kafka_group_id: Consumer group ID - ca_file_path: CA certificate file path - max_poll_interval_ms: Maximum poll interval (milliseconds) - session_timeout_ms: Session timeout (milliseconds) - heartbeat_interval_ms: Heartbeat interval (milliseconds) - auto_offset_reset: Auto offset reset strategy - enable_auto_commit: Whether to enable auto commit - - Returns: - AIOKafkaConsumer instance - """ - # Create SSL context - ssl_context = None - if ca_file_path: - config_provider = get_bean_by_type(ConfigProvider) - ca_file_content = config_provider.get_raw_config(ca_file_path) - ssl_context = ssl.create_default_context(purpose=ssl.Purpose.SERVER_AUTH) - ssl_context.load_verify_locations(cadata=ca_file_content) - - # Handle multiple topics (comma-separated) - topics = [topic.strip() for topic in kafka_topic.split(",")] - - # Create AIOKafkaConsumer - consumer = AIOKafkaConsumer( - *topics, # Unpack topics list as individual arguments - bootstrap_servers=kafka_servers, - group_id=kafka_group_id, - auto_offset_reset=auto_offset_reset, - enable_auto_commit=enable_auto_commit, - key_deserializer=lambda k: k.decode("utf-8") if k else None, - value_deserializer=bson_json_decode, - security_protocol="SSL" if ca_file_path else "PLAINTEXT", - ssl_context=ssl_context, - max_poll_interval_ms=max_poll_interval_ms, - session_timeout_ms=session_timeout_ms, - heartbeat_interval_ms=heartbeat_interval_ms, - ) - - consumer_name = get_consumer_name(kafka_topic, kafka_group_id) - logger.info("Created AIOKafkaConsumer for %s", consumer_name) - return consumer - - async def get_consumer( - self, - kafka_servers: List[str], - kafka_topic: str, - kafka_group_id: str, - force_new: bool = False, - **kwargs, - ) -> AIOKafkaConsumer: - """ - Get an AIOKafkaConsumer instance - - Args: - kafka_servers: Kafka server list - kafka_topic: Kafka topic - kafka_group_id: Consumer group ID - force_new: Whether to force creation of a new instance, default is False - **kwargs: Additional configuration parameters - - Returns: - AIOKafkaConsumer instance - """ - cache_key = get_cache_key(kafka_servers, kafka_topic, kafka_group_id) - consumer_name = get_consumer_name(kafka_topic, kafka_group_id) - - async with self._lock: - # If forcing creation of a new instance, or it's not in cache - if force_new or cache_key not in self._consumers: - logger.info( - "Creating new consumer for %s (force_new=%s)", - consumer_name, - force_new, - ) - - # If forcing creation of a new instance, clean up the old one first - if force_new and cache_key in self._consumers: - old_consumer = self._consumers[cache_key] - try: - await old_consumer.stop() - except Exception as e: - logger.error("Error stopping old consumer: %s", e) - - # Create a new consumer instance - consumer = await self.create_consumer( - kafka_servers=kafka_servers, - kafka_topic=kafka_topic, - kafka_group_id=kafka_group_id, - **kwargs, - ) - self._consumers[cache_key] = consumer - - logger.info( - "Consumer %s created and cached with key %s", - consumer_name, - cache_key, - ) - else: - consumer = self._consumers[cache_key] - logger.debug("Using cached consumer for %s", consumer_name) - - return consumer - - async def get_default_consumer( - self, force_new: bool = False, env_prefix: str = "" - ) -> AIOKafkaConsumer: - """ - Get the default AIOKafkaConsumer instance based on environment variable configuration - - Args: - force_new: Whether to force creation of a new instance, default is False - env_prefix: Environment variable prefix, default is "" (compatible with old configurations) - For example, env_prefix="CUSTOM_" will read CUSTOM_KAFKA_SERVERS, etc. - - Returns: - AIOKafkaConsumer instance - """ - # Always get configuration based on current env_prefix, do not use cache - # Because different prefixes may correspond to different configurations - config = get_default_kafka_config(env_prefix=env_prefix) - - return await self.get_consumer( - kafka_servers=config["kafka_servers"], - kafka_topic=config["kafka_topic"], - kafka_group_id=config["kafka_group_id"], - force_new=force_new, - ca_file_path=config.get("ca_file_path"), - max_poll_interval_ms=config.get("max_poll_interval_ms", 20 * 60 * 1000), - session_timeout_ms=config.get("session_timeout_ms", 10000), - heartbeat_interval_ms=config.get("heartbeat_interval_ms", 3000), - auto_offset_reset=config.get("auto_offset_reset", "earliest"), - enable_auto_commit=True, - ) - - async def remove_consumer( - self, kafka_servers: List[str], kafka_topic: str, kafka_group_id: str - ) -> bool: - """ - Remove the specified consumer - - Args: - kafka_servers: Kafka server list - kafka_topic: Kafka topic - kafka_group_id: Consumer group ID - - Returns: - bool: Whether the removal was successful - """ - cache_key = get_cache_key(kafka_servers, kafka_topic, kafka_group_id) - consumer_name = get_consumer_name(kafka_topic, kafka_group_id) - - async with self._lock: - if cache_key in self._consumers: - consumer = self._consumers[cache_key] - try: - await consumer.stop() - except Exception as e: - logger.error("Error stopping consumer during removal: %s", e) - - del self._consumers[cache_key] - logger.info("Consumer %s removed from cache", consumer_name) - return True - else: - logger.warning("Consumer %s not found in cache", consumer_name) - return False - - async def clear_all_consumers(self) -> None: - """Clear all cached consumers""" - async with self._lock: - for cache_key, consumer in self._consumers.items(): - try: - await consumer.stop() - except Exception as e: - logger.error("Error stopping consumer %s: %s", cache_key, e) - - self._consumers.clear() - logger.info("All consumers cleared from cache") - - async def seek_to_datetime( - self, offset_datetime: str, consumer: AIOKafkaConsumer - ) -> bool: - """ - Adjust Kafka Consumer's offset based on time format - - Args: - offset_datetime: Time string, format "2025-09-23 15:21:12" - consumer: AIOKafkaConsumer instance - - Returns: - bool: Whether the offset adjustment was successful - - Raises: - ValueError: Incorrect time format - RuntimeError: Consumer not started or offset adjustment failed - """ - try: - # Parse time string into timezone-aware datetime object - target_dt = from_iso_format(offset_datetime) - # Convert to millisecond timestamp (Kafka uses millisecond timestamps) - target_timestamp_ms = int(to_timestamp(target_dt) * 1000) - - logger.info( - "Seeking consumer to datetime: %s (timestamp: %d)", - offset_datetime, - target_timestamp_ms, - ) - - # Check if consumer is started and get partition assignment - try: - # Attempt to get partition assignment, will raise exception if consumer not started - partitions = consumer.assignment() - if not partitions: - raise RuntimeError( - "Consumer has no assigned partitions. Make sure consumer is started and has subscribed to topics." - ) - except Exception as e: - raise RuntimeError( - "Consumer must be started before seeking to timestamp" - ) from e - - # Build timestamp map for each partition - timestamp_map = {partition: target_timestamp_ms for partition in partitions} - - # Use offsets_for_times to get the offset at the corresponding timestamp - offset_map = await consumer.offsets_for_times(timestamp_map) - - # Track processing statistics per topic - topic_stats = {} - seek_count = 0 - - # Process each partition - for partition in partitions: - topic_name = partition.topic - if topic_name not in topic_stats: - topic_stats[topic_name] = { - 'total_partitions': 0, - 'found_offsets': 0, - 'used_latest': 0, - } - topic_stats[topic_name]['total_partitions'] += 1 - - # Get offset information for this partition - offset_info = offset_map.get(partition) if offset_map else None - - if offset_info is not None: - # Found offset at the specified timestamp - target_offset = offset_info.offset - consumer.seek(partition, target_offset) - seek_count += 1 - topic_stats[topic_name]['found_offsets'] += 1 - logger.info( - "Seeked partition %s (topic: %s) to offset %d at timestamp %d", - partition, - topic_name, - target_offset, - target_timestamp_ms, - ) - else: - # No offset found at the specified timestamp, use latest offset - logger.warning( - "No offset found for partition %s (topic: %s) at timestamp %d, using latest offset", - partition, - topic_name, - target_timestamp_ms, - ) - - # Get the latest offset for this partition - latest_offset_map = await consumer.end_offsets([partition]) - latest_offset = latest_offset_map[partition] - consumer.seek(partition, latest_offset) - seek_count += 1 - topic_stats[topic_name]['used_latest'] += 1 - logger.info( - "Seeked partition %s (topic: %s) to latest offset %d", - partition, - topic_name, - latest_offset, - ) - - # Log processing statistics per topic - for topic_name, stats in topic_stats.items(): - logger.info( - "Topic '%s': %d partitions total, %d found timestamp offsets, %d used latest offsets", - topic_name, - stats['total_partitions'], - stats['found_offsets'], - stats['used_latest'], - ) - - if seek_count > 0: - logger.info( - "Successfully seeked %d partitions to datetime %s", - seek_count, - offset_datetime, - ) - return True - else: - logger.warning( - "No partitions were seeked for datetime %s", offset_datetime - ) - return False - - except ValueError as e: - logger.error("Invalid datetime format '%s': %s", offset_datetime, e) - raise ValueError( - f"Invalid datetime format '{offset_datetime}'. Expected format: 'YYYY-MM-DD HH:MM:SS'" - ) from e - - except Exception as e: - logger.error( - "Failed to seek consumer to datetime %s: %s", offset_datetime, e - ) - raise RuntimeError( - f"Failed to seek consumer to datetime {offset_datetime}" - ) from e diff --git a/methods/EverCore/src/core/component/kafka_producer_factory.py b/methods/EverCore/src/core/component/kafka_producer_factory.py deleted file mode 100644 index 48c5700e..00000000 --- a/methods/EverCore/src/core/component/kafka_producer_factory.py +++ /dev/null @@ -1,583 +0,0 @@ -""" -Kafka Producer Factory - -Provides AIOKafkaProducer caching and management based on kafka_servers and kafka_topic. -Supports force_new logic to create brand new producer instances. -""" - -import asyncio -import json -import os -import ssl -from typing import Dict, List, Optional, Any, Union -from hashlib import md5 - -import async_timeout -import bson -from aiokafka import AIOKafkaProducer -from aiokafka.producer.message_accumulator import MessageBatch - -from core.component.config_provider import ConfigProvider -from core.component.kafka_consumer_factory import get_ca_file_path -from core.di.decorators import component -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger - - -# ============================================================================= -# Monkey Patches for aiokafka performance and stability improvements -# ============================================================================= - - -async def _optimized_wait_drain(self, timeout=None): - """ - Optimized wait_drain: avoid asyncio.wait overhead for single Future. - - The original implementation uses asyncio.wait([single_future]) which is - inefficient - it creates sets, registers/removes callbacks for each call. - This patch uses async_timeout directly for zero overhead. - """ - waiter = self._drain_waiter - try: - async with async_timeout.timeout(timeout): - await waiter - except asyncio.TimeoutError: - pass - if waiter.done(): - waiter.result() # Check for exception - - -# Save original start method -_original_producer_start = AIOKafkaProducer.start - - -async def _idempotent_start(self): - """ - Idempotent start: prevent creating multiple sender tasks. - - The original start() creates a sender task each time it's called. - Multiple sender tasks sharing the same accumulator causes a busy loop. - This patch makes start() idempotent - only the first call takes effect. - """ - if getattr(self, "_memsys_started", False): - return - self._memsys_started = True - await _original_producer_start(self) - - -# Apply patches -MessageBatch.wait_drain = _optimized_wait_drain -AIOKafkaProducer.start = _idempotent_start - -logger = get_logger(__name__) - -# Default prefix for producer environment variables -DEFAULT_PRODUCER_ENV_PREFIX = "PRODUCER_" - - -def get_default_producer_config( - env_prefix: str = DEFAULT_PRODUCER_ENV_PREFIX, -) -> Dict[str, Any]: - """ - Get default Kafka Producer configuration based on environment variables - - Args: - env_prefix: Environment variable prefix, default is "PRODUCER_" - e.g., "PRODUCER_" + "KAFKA_SERVERS" = "PRODUCER_KAFKA_SERVERS" - - Environment variables (using prefix=PRODUCER_ as example): - - {prefix}KAFKA_SERVERS: List of Kafka servers, comma-separated (required) - - {prefix}CA_FILE_PATH: Path to CA certificate file (optional, for SSL connection) - - {prefix}ACKS: Acknowledgment mode, default 1 - - 0: No wait for acknowledgment, fastest but may lose messages - - 1: Leader acknowledgment only, balances performance and reliability - - all: All replicas must acknowledge, most reliable but slowest - - {prefix}COMPRESSION_TYPE: Compression type (optional) - - gzip: High compression ratio, high CPU usage - - snappy: Medium compression ratio, fast - - lz4: Low compression ratio, fastest - - zstd: High compression ratio, relatively fast (recommended) - - {prefix}LINGER_MS: Send delay (milliseconds), default 0 - - Setting > 0 allows producer to wait for more messages to batch together, improving throughput - - {prefix}MAX_BATCH_SIZE: Maximum bytes per batch, default 16384 (16KB) - - {prefix}MAX_REQUEST_SIZE: Maximum bytes per request, default 1048576 (1MB) - - {prefix}REQUEST_TIMEOUT_MS: Request timeout (milliseconds), default 30000 (30 seconds) - - Returns: - Dict[str, Any]: Producer configuration dictionary - """ - - def get_env(key: str, default: str = "") -> str: - """Get environment variable with prefix""" - return os.getenv(f"{env_prefix}{key}", default) - - # Kafka server addresses (required) - kafka_servers_str = get_env("KAFKA_SERVERS", "") - kafka_servers = [ - server.strip() for server in kafka_servers_str.split(",") if server.strip() - ] - - # Handle CA certificate path (for SSL connection) - ca_file_path = None - ca_file_env = get_env("CA_FILE_PATH") - if ca_file_env: - ca_file_path = get_ca_file_path(ca_file_env) - - # Producer-specific configurations - acks_str = get_env("ACKS", "1") - # Handle acks which might be numeric or string 'all' - acks: Union[int, str] = acks_str if acks_str == "all" else int(acks_str) - - compression_type = get_env("COMPRESSION_TYPE") or None - linger_ms = int(get_env("LINGER_MS", "300")) - max_batch_size = int(get_env("MAX_BATCH_SIZE", "16384")) - max_request_size = int(get_env("MAX_REQUEST_SIZE", "1048576")) - request_timeout_ms = int(get_env("REQUEST_TIMEOUT_MS", "30000")) - retry_backoff_ms = int(get_env("RETRY_BACKOFF_MS", "500")) - - config = { - "kafka_servers": kafka_servers, - "ca_file_path": ca_file_path, - "acks": acks, - "compression_type": compression_type, - "linger_ms": linger_ms, - "max_batch_size": max_batch_size, - "max_request_size": max_request_size, - "request_timeout_ms": request_timeout_ms, - "retry_backoff_ms": retry_backoff_ms, - } - - prefix_info = f" (prefix: {env_prefix})" if env_prefix else "" - logger.info("Getting default Kafka Producer configuration%s:", prefix_info) - logger.info(" Servers: %s", kafka_servers) - logger.info(" CA certificate: %s", ca_file_path or "None") - logger.info(" Acknowledgment mode (acks): %s", acks) - logger.info(" Compression type: %s", compression_type or "None") - logger.info(" Send delay (linger_ms): %s ms", linger_ms) - logger.info(" Batch size (max_batch_size): %s bytes", max_batch_size) - logger.info(" Max request (max_request_size): %s bytes", max_request_size) - logger.info(" Request timeout (request_timeout_ms): %s ms", request_timeout_ms) - logger.info(" Retry backoff (retry_backoff_ms): %s ms", retry_backoff_ms) - - return config - - -def get_producer_cache_key(kafka_servers: List[str], kafka_topic: str = "") -> str: - """ - Generate Producer cache key - Create a unique identifier based on servers and optional topic - - Args: - kafka_servers: List of Kafka servers - kafka_topic: Kafka topic (optional, producer may send to multiple topics) - - Returns: - str: Cache key - """ - servers_str = ",".join(sorted(kafka_servers)) - key_content = f"{servers_str}:{kafka_topic}" if kafka_topic else servers_str - return md5(key_content.encode()).hexdigest() - - -def get_producer_name(kafka_servers: List[str], kafka_topic: str = "") -> str: - """ - Get producer name - - Args: - kafka_servers: List of Kafka servers - kafka_topic: Kafka topic - - Returns: - str: Producer name - """ - servers_short = kafka_servers[0] if kafka_servers else "unknown" - if kafka_topic: - return f"producer-{kafka_topic}@{servers_short}" - return f"producer@{servers_short}" - - -def json_serializer(value: Any) -> bytes: - """ - JSON serializer - Serialize value into JSON bytes - """ - if value is None: - return b"null" - if isinstance(value, bytes): - return value - return json.dumps(value, ensure_ascii=False, default=str).encode("utf-8") - - -def bson_serializer(value: Any) -> bytes: - """ - BSON serializer - Serialize value into BSON bytes - """ - if value is None: - return b"" - if isinstance(value, bytes): - return value - if isinstance(value, dict): - return bson.encode(value) - # Non-dict types need to be wrapped into a dict - return bson.encode({"data": value}) - - -def bson_json_serializer(value: Any) -> bytes: - """ - BSON/JSON serializer (default) - Try BSON serialization first, fall back to JSON on failure - Compatible with input as bytes (return directly) - """ - if value is None: - return b"null" - if isinstance(value, bytes): - # Already bytes, return directly (compatible with event directly converted to bson bytes) - return value - # Try BSON first - if isinstance(value, dict): - try: - return bson.encode(value) - except Exception: - pass - # Fall back to JSON - try: - return json.dumps(value, ensure_ascii=False, default=str).encode("utf-8") - except Exception as e: - logger.error("Serialization failed: %s", e) - raise - - -def key_serializer(key: Any) -> Optional[bytes]: - """ - Key serializer - Serialize key into UTF-8 bytes - """ - if key is None: - return None - if isinstance(key, bytes): - return key - return str(key).encode("utf-8") - - -@component(name="kafka_producer_factory", primary=True) -class KafkaProducerFactory: - """ - Kafka Producer Factory - - Provides caching and management of AIOKafkaProducer instances based on configuration - Supports force_new parameter to create brand new producer instances - - Note: AIOKafkaProducer.start() has been patched to be idempotent globally, - so calling start() multiple times is safe (only the first call takes effect). - """ - - def __init__(self): - """Initialize Kafka Producer Factory""" - self._producers: Dict[str, AIOKafkaProducer] = {} - self._lock = asyncio.Lock() - logger.info("KafkaProducerFactory initialized") - - async def create_producer( - self, - kafka_servers: List[str], - ca_file_path: Optional[str] = None, - acks: Union[int, str] = 1, - compression_type: Optional[str] = None, - max_batch_size: int = 16384, - linger_ms: int = 0, - max_request_size: int = 1048576, - request_timeout_ms: int = 30000, - retry_backoff_ms: int = 500, - value_serializer: Optional[callable] = None, - start_timeout: float = 10.0, - ) -> AIOKafkaProducer: - """ - Create AIOKafkaProducer instance - - Args: - kafka_servers: List of Kafka servers - ca_file_path: Path to CA certificate file - acks: Acknowledgment mode (0, 1, 'all') - compression_type: Compression type ('gzip', 'snappy', 'lz4', 'zstd') - max_batch_size: Maximum bytes for batch sending - linger_ms: Send delay (milliseconds), used for batch sending - max_request_size: Maximum bytes per request - request_timeout_ms: Request timeout (milliseconds) - retry_backoff_ms: Retry backoff (milliseconds) - value_serializer: Value serializer, default is bson_json_serializer - start_timeout: Timeout for starting producer (seconds), 0 to skip - - Returns: - AIOKafkaProducer instance - - Raises: - ConnectionError: If cannot connect to Kafka within timeout - """ - # Create SSL context - ssl_context = None - if ca_file_path: - config_provider = get_bean_by_type(ConfigProvider) - ca_file_content = config_provider.get_raw_config(ca_file_path) - ssl_context = ssl.create_default_context(purpose=ssl.Purpose.SERVER_AUTH) - ssl_context.load_verify_locations(cadata=ca_file_content) - - # Use default BSON/JSON serializer - if value_serializer is None: - value_serializer = bson_json_serializer - - # Create AIOKafkaProducer - producer = AIOKafkaProducer( - bootstrap_servers=kafka_servers, - key_serializer=key_serializer, - value_serializer=value_serializer, - acks=acks, - compression_type=compression_type, - max_batch_size=max_batch_size, - linger_ms=linger_ms, - max_request_size=max_request_size, - request_timeout_ms=request_timeout_ms, - retry_backoff_ms=retry_backoff_ms, - security_protocol="SSL" if ca_file_path else "PLAINTEXT", - ssl_context=ssl_context, - ) - - producer_name = get_producer_name(kafka_servers) - - # Start producer and verify connection - if start_timeout > 0: - try: - await asyncio.wait_for(producer.start(), timeout=start_timeout) - brokers = producer.client.cluster.brokers() - logger.info( - "Producer %s started, connected to %d broker(s)", - producer_name, - len(brokers) if brokers else 0, - ) - except asyncio.TimeoutError: - logger.error( - "Cannot connect to Kafka %s within %ss, producer may not work", - kafka_servers, - start_timeout, - ) - except Exception as e: - logger.error("Failed to start Kafka producer %s: %s", producer_name, e) - else: - logger.info("Created AIOKafkaProducer for %s (not started)", producer_name) - - return producer - - async def get_producer( - self, - kafka_servers: List[str], - force_new: bool = False, - test_topic: Optional[str] = None, - **kwargs, - ) -> AIOKafkaProducer: - """ - Get AIOKafkaProducer instance - - Args: - kafka_servers: List of Kafka servers - force_new: Whether to force creation of a new instance, default False - test_topic: If provided, test connection by fetching partitions for this topic - **kwargs: Additional configuration parameters - - Returns: - AIOKafkaProducer instance - """ - cache_key = get_producer_cache_key(kafka_servers) - producer_name = get_producer_name(kafka_servers) - - async with self._lock: - # If forcing new instance or not in cache - if force_new or cache_key not in self._producers: - logger.info( - "Creating new producer for %s (force_new=%s)", - producer_name, - force_new, - ) - - # If forcing new instance, clean up old one first - if force_new and cache_key in self._producers: - old_producer = self._producers[cache_key] - try: - await old_producer.stop() - except Exception as e: - logger.error("Error stopping old producer: %s", e) - - # Create new producer instance - producer = await self.create_producer( - kafka_servers=kafka_servers, **kwargs - ) - self._producers[cache_key] = producer - - # Test connection if test_topic provided - if test_topic: - try: - partitions = await asyncio.wait_for( - producer.partitions_for(test_topic), timeout=10.0 - ) - logger.info( - "Connection test passed: topic %s has %d partitions", - test_topic, - len(partitions) if partitions else 0, - ) - except Exception as e: - logger.error( - "Connection test failed for %s: %s", producer_name, e - ) - raise - - logger.info( - "Producer %s created and cached with key %s", - producer_name, - cache_key, - ) - else: - producer = self._producers[cache_key] - logger.debug("Using cached producer for %s", producer_name) - - return producer - - async def get_default_producer( - self, force_new: bool = False, env_prefix: str = DEFAULT_PRODUCER_ENV_PREFIX - ) -> AIOKafkaProducer: - """ - Get default AIOKafkaProducer instance based on environment variable configuration - - Args: - force_new: Whether to force creation of a new instance, default False - env_prefix: Environment variable prefix, default "PRODUCER_" - e.g., read PRODUCER_KAFKA_SERVERS, etc. - - Returns: - AIOKafkaProducer instance - """ - config = get_default_producer_config(env_prefix=env_prefix) - - return await self.get_producer( - kafka_servers=config["kafka_servers"], - force_new=force_new, - ca_file_path=config.get("ca_file_path"), - acks=config.get("acks", 1), - compression_type=config.get("compression_type"), - linger_ms=config.get("linger_ms", 0), - max_batch_size=config.get("max_batch_size", 16384), - max_request_size=config.get("max_request_size", 1048576), - request_timeout_ms=config.get("request_timeout_ms", 30000), - retry_backoff_ms=config.get("retry_backoff_ms", 500), - ) - - async def remove_producer(self, kafka_servers: List[str]) -> bool: - """ - Remove specified producer - - Args: - kafka_servers: List of Kafka servers - - Returns: - bool: Whether removal was successful - """ - cache_key = get_producer_cache_key(kafka_servers) - producer_name = get_producer_name(kafka_servers) - - async with self._lock: - if cache_key in self._producers: - producer = self._producers[cache_key] - try: - await producer.stop() - except Exception as e: - logger.error("Error stopping producer during removal: %s", e) - - del self._producers[cache_key] - logger.info("Producer %s removed from cache", producer_name) - return True - else: - logger.warning("Producer %s not found in cache", producer_name) - return False - - async def clear_all_producers(self) -> None: - """Clear all cached producers""" - async with self._lock: - for cache_key, producer in self._producers.items(): - try: - await producer.stop() - except Exception as e: - logger.error("Error stopping producer %s: %s", cache_key, e) - - self._producers.clear() - logger.info("All producers cleared from cache") - - async def send( - self, - producer: AIOKafkaProducer, - topic: str, - value: Any, - key: Optional[str] = None, - partition: Optional[int] = None, - timestamp_ms: Optional[int] = None, - headers: Optional[List[tuple]] = None, - ) -> Any: - """ - Send message to Kafka - - Args: - producer: AIOKafkaProducer instance - topic: Target topic - value: Message value - key: Message key (optional) - partition: Target partition (optional) - timestamp_ms: Timestamp (optional, milliseconds) - headers: Message headers (optional) - - Returns: - RecordMetadata object - """ - try: - result = await producer.send_and_wait( - topic=topic, - value=value, - key=key, - partition=partition, - timestamp_ms=timestamp_ms, - headers=headers, - ) - logger.debug( - "Message sent to topic %s, partition %s, offset %s", - result.topic, - result.partition, - result.offset, - ) - return result - except Exception as e: - logger.error("Failed to send message to topic %s: %s", topic, e) - raise - - async def send_batch( - self, producer: AIOKafkaProducer, topic: str, messages: List[Dict[str, Any]] - ) -> List[Any]: - """ - Send messages in batch to Kafka - - Args: - producer: AIOKafkaProducer instance - topic: Target topic - messages: List of messages, each message is a dictionary containing value, key (optional), etc. - - Returns: - List of RecordMetadata objects - """ - results = [] - for msg in messages: - result = await self.send( - producer=producer, - topic=topic, - value=msg.get("value"), - key=msg.get("key"), - partition=msg.get("partition"), - timestamp_ms=msg.get("timestamp_ms"), - headers=msg.get("headers"), - ) - results.append(result) - return results diff --git a/methods/EverCore/src/core/component/llm/llm_adapter/__init__.py b/methods/EverCore/src/core/component/llm/llm_adapter/__init__.py deleted file mode 100644 index e9b030ed..00000000 --- a/methods/EverCore/src/core/component/llm/llm_adapter/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Component module initialization file -""" diff --git a/methods/EverCore/src/core/component/llm/llm_adapter/anthropic_adapter.py b/methods/EverCore/src/core/component/llm/llm_adapter/anthropic_adapter.py deleted file mode 100644 index 346aa209..00000000 --- a/methods/EverCore/src/core/component/llm/llm_adapter/anthropic_adapter.py +++ /dev/null @@ -1,199 +0,0 @@ -import json -import time -import asyncio -from typing import Dict, Any, List, Union, AsyncGenerator -import os -import httpx -from core.di.decorators import service - -from core.component.llm.llm_adapter.completion import ( - ChatCompletionRequest, - ChatCompletionResponse, -) -from core.component.llm.llm_adapter.message import MessageRole -from core.component.llm.llm_adapter.llm_backend_adapter import LLMBackendAdapter -from core.constants.errors import ErrorMessage -from core.di.utils import get_bean_by_type -from core.component.token_usage_collector import TokenUsageCollector - - -class AnthropicAdapter(LLMBackendAdapter): - """Anthropic Claude API adapter""" - - def __init__(self, config: Dict[str, Any]): - self.config = config - self.base_url = config.get("base_url") - self.api_key = config.get("api_key") or os.getenv("ANTHROPIC_API_KEY") - self.timeout = config.get("timeout", 60) - self.max_retries = config.get("max_retries", 3) - - if not self.api_key or not self.base_url: - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - self.client = httpx.AsyncClient( - base_url=self.base_url, - headers={ - "x-api-key": self.api_key, - "anthropic-version": "2023-06-01", - "content-type": "application/json", - }, - timeout=self.timeout, - ) - - async def chat_completion( - self, request: ChatCompletionRequest - ) -> Union[ChatCompletionResponse, AsyncGenerator[str, None]]: - """Perform chat completion (convert to Anthropic format)""" - if not request.model: - request.model = self.config.get("default_model") - - system_message = "" - messages = [] - - for msg in request.messages: - if msg.role == MessageRole.SYSTEM: - system_message = msg.content - else: - messages.append(msg.to_dict()) - - data = { - "model": request.model, - "messages": messages, - "max_tokens": request.max_tokens or 4096, # Anthropic requires max_tokens - "stream": request.stream, - } - - if system_message: - data["system"] = system_message - if request.temperature is not None: - data["temperature"] = request.temperature - if request.top_p is not None: - data["top_p"] = request.top_p - - # Add support for thinking configuration - if request.thinking_budget is not None and request.thinking_budget > 0: - # Check if the model supports thinking capability - thinking_supported_models = [ - "claude-3-5-sonnet-20241022", - "claude-3-7-sonnet-20241022", - "claude-sonnet-4-20250514", - ] - if any( - model_name in request.model for model_name in thinking_supported_models - ): - data["thinking"] = { - "type": "enabled", - "budget_tokens": request.thinking_budget, - } - - for attempt in range(self.max_retries): - try: - if request.stream: - return self._stream_chat_completion(data) - else: - response = await self.client.post("/v1/messages", json=data) - response.raise_for_status() - response_json = response.json() - # Report token usage - usage = response_json.get("usage", {}) - if usage: - try: - collector = get_bean_by_type(TokenUsageCollector) - collector.add( - request.model or "unknown", - usage.get("input_tokens", 0), - usage.get("output_tokens", 0), - call_type="llm", - ) - except Exception: - pass - return self._convert_anthropic_response( - response_json, request.model - ) - except httpx.HTTPStatusError as e: - if attempt == self.max_retries - 1 or e.response.status_code < 500: - raise RuntimeError( - f"Anthropic chat completion request failed: {e.response.text}" - ) from e - await asyncio.sleep(2**attempt) - except Exception as e: - if attempt == self.max_retries - 1: - raise RuntimeError( - f"An unexpected error occurred in AnthropicAdapter: {e}" - ) from e - await asyncio.sleep(2**attempt) - - def _convert_anthropic_response( - self, response_data: Dict[str, Any], model: str - ) -> ChatCompletionResponse: - """Convert Anthropic response to OpenAI format""" - return ChatCompletionResponse( - id=response_data.get("id", ""), - object="chat.completion", - created=int(time.time()), - model=model, - choices=[ - { - "index": 0, - "message": { - "role": "assistant", - "content": response_data.get("content", [{}])[0].get( - "text", "" - ), - }, - "finish_reason": response_data.get("stop_reason"), - } - ], - usage=response_data.get("usage"), - ) - - async def _stream_chat_completion( - self, data: Dict[str, Any] - ) -> AsyncGenerator[str, None]: - """Streamed chat completion""" - input_tokens = 0 - output_tokens = 0 - model = data.get("model", "unknown") - - try: - async with self.client.stream( - "POST", "/v1/messages", json=data - ) as response: - response.raise_for_status() - async for line in response.aiter_lines(): - if line.startswith("data:"): - line_data = line[len("data: ") :] - if not line_data: - continue - try: - chunk = json.loads(line_data) - chunk_type = chunk.get("type") - if chunk_type == "content_block_delta": - yield chunk.get("delta", {}).get("text", "") - elif chunk_type == "message_start": - usage = chunk.get("message", {}).get("usage", {}) - input_tokens = usage.get("input_tokens", 0) - elif chunk_type == "message_delta": - usage = chunk.get("usage", {}) - output_tokens = usage.get("output_tokens", 0) - # Some proxies put input_tokens here instead of message_start - if not input_tokens: - input_tokens = usage.get("input_tokens", 0) - except json.JSONDecodeError: - continue - finally: - # Report usage even if client disconnects mid-stream - if input_tokens or output_tokens: - try: - collector = get_bean_by_type(TokenUsageCollector) - collector.add(model, input_tokens, output_tokens, call_type="llm") - except Exception: - pass - - def get_available_models(self) -> List[str]: - """Get list of available models""" - return self.config.get("models", []) - - async def close(self): - """Close HTTP client""" - await self.client.aclose() diff --git a/methods/EverCore/src/core/component/llm/llm_adapter/completion.py b/methods/EverCore/src/core/component/llm/llm_adapter/completion.py deleted file mode 100644 index 108ab420..00000000 --- a/methods/EverCore/src/core/component/llm/llm_adapter/completion.py +++ /dev/null @@ -1,58 +0,0 @@ -from typing import Dict, Any, List, Optional -from dataclasses import dataclass, field -from pydantic import BaseModel -from core.component.llm.llm_adapter.message import ChatMessage - - -@dataclass -class ChatCompletionRequest: - """Chat completion request data class""" - - messages: List[ChatMessage] - model: Optional[str] = None - temperature: Optional[float] = None - max_tokens: Optional[int] = None - top_p: Optional[float] = None - frequency_penalty: Optional[float] = None - presence_penalty: Optional[float] = None - thinking_budget: Optional[int] = None # Add support for thinking_budget parameter - stream: bool = False - - def to_dict(self) -> Dict[str, Any]: - """Convert to dictionary format""" - data = { - "messages": [msg.to_dict() for msg in self.messages], - "stream": self.stream, - } - - # Only add non-None fields - for field_name in [ - "model", - "temperature", - "max_tokens", - "top_p", - "frequency_penalty", - "presence_penalty", - "thinking_budget", - ]: - value = getattr(self, field_name) - if value is not None: - data[field_name] = value - - return data - - -class ChatCompletionResponse(BaseModel, extra="allow"): - """Chat completion response data class, compatible with extra fields""" - - id: str - object: str - created: int - model: str - choices: List[Dict[str, Any]] - usage: Optional[Dict[str, Any]] = None - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> 'ChatCompletionResponse': - """Create response object from dictionary""" - return cls(**data) diff --git a/methods/EverCore/src/core/component/llm/llm_adapter/gemini_adapter.py b/methods/EverCore/src/core/component/llm/llm_adapter/gemini_adapter.py deleted file mode 100644 index 547776b6..00000000 --- a/methods/EverCore/src/core/component/llm/llm_adapter/gemini_adapter.py +++ /dev/null @@ -1,204 +0,0 @@ -import asyncio -import time -import logging -from typing import Dict, Any, List, Union, AsyncGenerator -import os -from google.genai.client import Client -from core.di.decorators import service -from google.genai.types import ( - GenerateContentConfig, - ContentDict, - HarmCategory, - HarmBlockThreshold, -) -from google.genai.types import ThinkingConfig -from core.component.llm.llm_adapter.completion import ( - ChatCompletionRequest, - ChatCompletionResponse, -) -from core.component.llm.llm_adapter.message import MessageRole -from core.component.llm.llm_adapter.llm_backend_adapter import LLMBackendAdapter - -from langchain_core.messages import HumanMessage, AIMessage, SystemMessage -from core.constants.errors import ErrorMessage -from core.di.utils import get_bean_by_type -from core.component.token_usage_collector import TokenUsageCollector - -logger = logging.getLogger(__name__) - - -class GeminiAdapter(LLMBackendAdapter): - """Google Gemini API adapter""" - - def __init__(self, config: Dict[str, Any]): - self.config = config - self.api_key = config.get("api_key") or os.getenv("GEMINI_API_KEY") - self.max_retries = config.get("max_retries", 3) - - if not self.api_key: - raise ValueError(ErrorMessage.CONFIGURATION_MISSING.value) - - # Use the new google.genai API - self.client = Client(api_key=self.api_key) - self.model_name = self.config.get("default_model", "gemini-2.5-flash") - - async def chat_completion( - self, request: ChatCompletionRequest - ) -> Union[ChatCompletionResponse, AsyncGenerator[str, None]]: - """Perform chat completion (convert to Gemini format)""" - if not request.model: - request.model = self.model_name - - contents = self._convert_messages_to_gemini_format(request.messages) - - # Build GenerationConfig - generation_config_params = { - "temperature": request.temperature, - "top_p": request.top_p, - "max_output_tokens": request.max_tokens, - } - - # If thinking_budget parameter is provided, create ThinkingConfig - thinking_config = None - if request.thinking_budget is not None: - thinking_config = ThinkingConfig(thinking_budget=request.thinking_budget) - generation_config_params["thinking_config"] = thinking_config - - generation_config = GenerateContentConfig(**generation_config_params) - - for attempt in range(self.max_retries): - try: - if request.stream: - return self._stream_chat_completion( - contents=contents, generation_config=generation_config - ) - else: - response = await self.client.aio.models.generate_content( - model=self.model_name, - contents=contents, - config=generation_config, - ) - return self._convert_gemini_response(response, request.model) - except Exception as e: - if attempt == self.max_retries - 1: - raise RuntimeError( - f"An unexpected error occurred in GeminiAdapter: {e}" - ) from e - await asyncio.sleep(2**attempt) - - raise RuntimeError( - "Gemini chat completion request failed after multiple retries." - ) - - def _convert_messages_to_gemini_format( - self, messages: List[Dict[str, Any]] - ) -> List[ContentDict]: - """Convert message list to Gemini format""" - contents = [] - for msg in messages: - if type(msg) == HumanMessage: - contents.append(ContentDict(role="user", parts=[{"text": msg.content}])) - elif type(msg) == AIMessage: - contents.append( - ContentDict(role="model", parts=[{"text": msg.content}]) - ) - elif type(msg) == SystemMessage: - contents.append( - ContentDict(role="model", parts=[{"text": msg.content}]) - ) - elif MessageRole(msg.role) == MessageRole.SYSTEM: - contents.append( - ContentDict(role="model", parts=[{"text": msg.content}]) - ) - elif MessageRole(msg.role) == MessageRole.USER: - contents.append(ContentDict(role="user", parts=[{"text": msg.content}])) - elif MessageRole(msg.role) == MessageRole.ASSISTANT: - contents.append( - ContentDict(role="model", parts=[{"text": msg.content}]) - ) - return contents - - def _convert_gemini_response(self, response, model: str) -> ChatCompletionResponse: - """Convert Gemini response to OpenAI format""" - # Extract token usage from Gemini's usage_metadata - usage = {} - if hasattr(response, 'usage_metadata') and response.usage_metadata: - prompt_tokens = ( - getattr(response.usage_metadata, 'prompt_token_count', 0) or 0 - ) - completion_tokens = ( - getattr(response.usage_metadata, 'candidates_token_count', 0) or 0 - ) - usage = { - "prompt_tokens": prompt_tokens, - "completion_tokens": completion_tokens, - "total_tokens": getattr(response.usage_metadata, 'total_token_count', 0) - or 0, - } - # Report token usage - try: - collector = get_bean_by_type(TokenUsageCollector) - collector.add(model, prompt_tokens, completion_tokens, call_type="llm") - except Exception: - pass - - result = ChatCompletionResponse( - id=f"chatcmpl-{int(time.time())}", # Gemini does not provide ID, we generate one - object="chat.completion", - created=int(time.time()), - model=model, - choices=[ - { - "index": 0, - "message": {"role": "assistant", "content": response.text}, - "finish_reason": "stop", # Gemini API v1 does not directly provide finish_reason - } - ], - usage=usage, - ) - - # Attach the original Gemini response object to the result for use by the audit system - result._original_gemini_response = response - - return result - - async def _stream_chat_completion( - self, contents: List[ContentDict], generation_config: GenerateContentConfig - ) -> AsyncGenerator[str, None]: - """Streamed chat completion""" - last_chunk = None - try: - response_stream = await self.client.aio.models.generate_content_stream( - model=self.model_name, contents=contents, config=generation_config - ) - async for chunk in response_stream: - last_chunk = chunk - if chunk.text: - yield chunk.text - finally: - # Report usage even if client disconnects mid-stream - if ( - last_chunk - and hasattr(last_chunk, 'usage_metadata') - and last_chunk.usage_metadata - ): - try: - collector = get_bean_by_type(TokenUsageCollector) - collector.add( - self.model_name, - getattr(last_chunk.usage_metadata, 'prompt_token_count', 0) - or 0, - getattr(last_chunk.usage_metadata, 'candidates_token_count', 0) - or 0, - call_type="llm", - ) - except Exception: - pass - - def get_available_models(self) -> List[str]: - """Get list of available models""" - return self.config.get("models", []) - - async def close(self): - """Close client (not required by Gemini library)""" - pass diff --git a/methods/EverCore/src/core/component/llm/llm_adapter/gemini_client.py b/methods/EverCore/src/core/component/llm/llm_adapter/gemini_client.py deleted file mode 100644 index f0de5d5d..00000000 --- a/methods/EverCore/src/core/component/llm/llm_adapter/gemini_client.py +++ /dev/null @@ -1,306 +0,0 @@ -import asyncio -import os -from typing import Dict, Any, List, Union, AsyncGenerator, Optional -from google.genai.client import Client -from google.genai.types import GenerateContentConfig, ContentDict -from google.genai.types import ThinkingConfig -from langchain_core.messages import HumanMessage, AIMessage, SystemMessage, BaseMessage -from core.di.decorators import component -from core.component.config_provider import ConfigProvider -from core.constants.errors import ErrorMessage - - -@component(name="gemini_client", primary=True) -class GeminiClient: - """Google Gemini API client - directly returns raw response""" - - def __init__(self, config_provider: ConfigProvider): - """ - Initialize Gemini client - - Args: - config_provider: Configuration provider, used to load llm_backends configuration - """ - self.config_provider = config_provider - self._llm_config: Dict[str, Any] = self.config_provider.get_config( - "llm_backends" - ) - - # Get Gemini backend configuration - gemini_backends = self._llm_config.get("llm_backends", {}) - if "gemini" not in gemini_backends: - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - self._config = gemini_backends["gemini"] - - # Get API key, priority: configuration file > environment variable - self.api_key = self._config.get("api_key") or os.getenv("GEMINI_API_KEY") - self.default_model = self._config.get("default_model") or self._config.get( - "model", "gemini-2.5-flash" - ) - self.max_retries = self._config.get("max_retries", 3) - - if not self.api_key: - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - # Use the new google.genai API - self.client = Client(api_key=self.api_key) - - async def generate_content( - self, - messages: Union[List[Dict[str, Any]], List[BaseMessage], str], - model: Optional[str] = None, - temperature: float = 0.7, - top_p: float = 0.9, - max_tokens: Optional[int] = None, - thinking_budget: Optional[int] = None, - stream: bool = False, - tools: Optional[List[Dict[str, Any]]] = None, - response_mime_type: Optional[str] = None, - ) -> Union[Any, AsyncGenerator[str, None]]: - """ - Generate content - directly return Gemini raw response - - Args: - messages: Message list, supports multiple formats: - - List[Dict]: Standard message format [{"role": "user", "content": "..."}] - - List[BaseMessage]: LangChain message objects - - str: Single text message - model: Model name, use default model if None - temperature: Temperature parameter - top_p: top_p parameter - max_tokens: Maximum output token count - thinking_budget: Thinking budget (supported only by certain models) - stream: Whether to stream output - tools: Tool list, used for function calling and grounding - response_mime_type: Response MIME type, e.g., "application/json" - - Returns: - If stream=False, return Gemini raw response object - If stream=True, return async generator - """ - if not model: - model = self.default_model - - # Convert message format - contents = self._convert_messages_to_gemini_format(messages) - - # Build GenerationConfig - generation_config_params = {"temperature": temperature, "top_p": top_p} - - if max_tokens is not None: - generation_config_params["max_output_tokens"] = max_tokens - - # If thinking_budget parameter is provided, create ThinkingConfig - if thinking_budget is not None: - thinking_config = ThinkingConfig(thinking_budget=thinking_budget) - generation_config_params["thinking_config"] = thinking_config - - # Support response MIME type - if response_mime_type is not None: - generation_config_params["response_mime_type"] = response_mime_type - - # Support tools (tools should be in config) - if tools is not None: - generation_config_params["tools"] = tools - - generation_config = GenerateContentConfig(**generation_config_params) - - for attempt in range(self.max_retries): - try: - if stream: - return self._stream_generate_content( - model=model, - contents=contents, - generation_config=generation_config, - ) - else: - # Directly return Gemini raw response (tools already in config) - response = await self.client.aio.models.generate_content( - model=model, contents=contents, config=generation_config - ) - return response - except Exception as e: - if attempt == self.max_retries - 1: - raise RuntimeError( - f"An unexpected error occurred in GeminiClient: {e}" - ) from e - await asyncio.sleep(2**attempt) - - raise RuntimeError("Gemini content generation failed after multiple retries.") - - def _convert_messages_to_gemini_format( - self, messages: Union[List[Dict[str, Any]], List[BaseMessage], str] - ) -> List[ContentDict]: - """ - Convert message list to Gemini format - compatible with multiple input formats - - Args: - messages: Supports the following formats: - - str: Single text message, automatically converted to user role - - List[Dict]: Standard message format [{"role": "user", "content": "..."}] - - List[BaseMessage]: List of LangChain message objects - - Returns: - List[ContentDict]: Message list in Gemini API format - """ - contents = [] - - # Handle string input - if isinstance(messages, str): - contents.append(ContentDict(role="user", parts=[{"text": messages}])) - return contents - - # Handle list input - if not isinstance(messages, list): - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - for msg in messages: - # Handle LangChain message objects - if isinstance(msg, BaseMessage): - if isinstance(msg, HumanMessage): - contents.append( - ContentDict(role="user", parts=[{"text": msg.content}]) - ) - elif isinstance(msg, AIMessage): - contents.append( - ContentDict(role="model", parts=[{"text": msg.content}]) - ) - elif isinstance(msg, SystemMessage): - # Gemini handles system messages as model role - contents.append( - ContentDict(role="model", parts=[{"text": msg.content}]) - ) - else: - # For other message types, try to get content attribute - content = getattr(msg, 'content', str(msg)) - contents.append(ContentDict(role="user", parts=[{"text": content}])) - continue - - # Handle dictionary format messages - if isinstance(msg, dict): - role = msg.get("role", "user") - content = msg.get("content", "") - - # Handle possible nested content structure - if isinstance(content, list): - # If content is a list, extract text parts - text_parts = [] - for part in content: - if isinstance(part, dict): - if part.get("type") == "text": - text_parts.append(part.get("text", "")) - elif "text" in part: - text_parts.append(part["text"]) - else: - text_parts.append(str(part)) - content = " ".join(text_parts) - - # Convert role mapping - gemini_role = self._map_role_to_gemini(role) - contents.append( - ContentDict(role=gemini_role, parts=[{"text": str(content)}]) - ) - continue - - # Handle other types, try to convert to string - try: - # Check if it has role and content attributes - if hasattr(msg, 'role') and hasattr(msg, 'content'): - role = getattr(msg, 'role') - content = getattr(msg, 'content') - gemini_role = self._map_role_to_gemini(role) - contents.append( - ContentDict(role=gemini_role, parts=[{"text": str(content)}]) - ) - else: - # Treat as user message - contents.append( - ContentDict(role="user", parts=[{"text": str(msg)}]) - ) - except Exception: - # Final fallback - contents.append(ContentDict(role="user", parts=[{"text": str(msg)}])) - - return contents - - def _map_role_to_gemini(self, role: str) -> str: - """ - Map standard roles to Gemini format - - Args: - role: Original role name - - Returns: - str: Gemini-formatted role name - """ - role_lower = str(role).lower() - - if role_lower in ["user", "human"]: - return "user" - elif role_lower in ["assistant", "ai", "model", "bot"]: - return "model" - elif role_lower in ["system"]: - # Gemini handles system messages as model role - return "model" - else: - # Default as user message - return "user" - - async def _stream_generate_content( - self, - model: str, - contents: List[ContentDict], - generation_config: GenerateContentConfig, - ) -> AsyncGenerator[str, None]: - """Stream content generation""" - # tools already passed in generation_config - response_stream = await self.client.aio.models.generate_content_stream( - model=model, contents=contents, config=generation_config - ) - async for chunk in response_stream: - if chunk.text: - yield chunk.text - - def reload_config(self): - """Reload configuration""" - self._llm_config = self.config_provider.get_config("llm_backends") - - # Get Gemini backend configuration - gemini_backends = self._llm_config.get("llm_backends", {}) - if "gemini" not in gemini_backends: - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - self._config = gemini_backends["gemini"] - - # Update configuration - self.api_key = self._config.get("api_key") or os.getenv("GEMINI_API_KEY") - self.default_model = self._config.get("default_model") or self._config.get( - "model", "gemini-2.5-flash" - ) - self.max_retries = self._config.get("max_retries", 3) - - if not self.api_key: - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - # Recreate client - self.client = Client(api_key=self.api_key) - - def get_available_models(self) -> List[str]: - """Get list of available models""" - return self._config.get("models", []) - - async def close(self): - """Close client (Gemini library does not require this)""" - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - pass - - async def __aenter__(self): - return self - - async def __aexit__(self, exc_type, exc_val, exc_tb): - await self.close() diff --git a/methods/EverCore/src/core/component/llm/llm_adapter/llm_backend_adapter.py b/methods/EverCore/src/core/component/llm/llm_adapter/llm_backend_adapter.py deleted file mode 100644 index 22ef1528..00000000 --- a/methods/EverCore/src/core/component/llm/llm_adapter/llm_backend_adapter.py +++ /dev/null @@ -1,22 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Union, AsyncGenerator, List -from core.component.llm.llm_adapter.completion import ( - ChatCompletionRequest, - ChatCompletionResponse, -) - - -class LLMBackendAdapter(ABC): - """Abstract base class for LLM backend adapter""" - - @abstractmethod - async def chat_completion( - self, request: ChatCompletionRequest - ) -> Union[ChatCompletionResponse, AsyncGenerator[str, None]]: - """Perform chat completion""" - pass - - @abstractmethod - def get_available_models(self) -> List[str]: - """Get list of available models""" - pass diff --git a/methods/EverCore/src/core/component/llm/llm_adapter/message.py b/methods/EverCore/src/core/component/llm/llm_adapter/message.py deleted file mode 100644 index 0fe26786..00000000 --- a/methods/EverCore/src/core/component/llm/llm_adapter/message.py +++ /dev/null @@ -1,23 +0,0 @@ -from enum import Enum -from dataclasses import dataclass -from typing import Dict - - -class MessageRole(Enum): - """Message role enumeration""" - - SYSTEM = "system" - USER = "user" - ASSISTANT = "assistant" - - -@dataclass -class ChatMessage: - """Chat message data class""" - - role: MessageRole - content: str - - def to_dict(self) -> Dict[str, str]: - """Convert to dictionary format""" - return {"role": self.role.value, "content": self.content} diff --git a/methods/EverCore/src/core/component/llm/llm_adapter/openai_adapter.py b/methods/EverCore/src/core/component/llm/llm_adapter/openai_adapter.py deleted file mode 100644 index 6e221d80..00000000 --- a/methods/EverCore/src/core/component/llm/llm_adapter/openai_adapter.py +++ /dev/null @@ -1,115 +0,0 @@ -from typing import Dict, Any, List, Union, AsyncGenerator -import os -import openai -from core.component.llm.llm_adapter.completion import ( - ChatCompletionRequest, - ChatCompletionResponse, -) -from core.component.llm.llm_adapter.llm_backend_adapter import LLMBackendAdapter -from core.constants.errors import ErrorMessage -from core.di.utils import get_bean_by_type -from core.component.token_usage_collector import TokenUsageCollector - - -class OpenAIAdapter(LLMBackendAdapter): - """OpenAI API adapter (implemented based on the official openai package)""" - - def __init__(self, config: Dict[str, Any]): - # Save configuration - self.config = config - self.api_key = config.get("api_key") or os.getenv("OPENAI_API_KEY") - self.base_url = config.get("base_url") or os.getenv("OPENAI_BASE_URL") - self.timeout = config.get("timeout", 600) - - if not self.api_key: - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - # Instantiate openai async client - self.client = openai.AsyncOpenAI( - api_key=self.api_key, base_url=self.base_url, timeout=self.timeout - ) - - async def chat_completion( - self, request: ChatCompletionRequest - ) -> Union[ChatCompletionResponse, AsyncGenerator[str, None]]: - """ - Perform chat completion, supporting both streaming and non-streaming modes. - """ - if not request.model: - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - params = request.to_dict() - # The request `to_dict` method already filters for None values, but we can be explicit here for clarity - # for what the openai client expects. - client_params = { - "model": params.get("model"), - "messages": params.get("messages"), - "temperature": params.get("temperature"), - "max_tokens": params.get("max_tokens"), - "top_p": params.get("top_p"), - "frequency_penalty": params.get("frequency_penalty"), - "presence_penalty": params.get("presence_penalty"), - "stream": params.get("stream", False), - } - # Remove None values to avoid openai errors - final_params = {k: v for k, v in client_params.items() if v is not None} - - try: - if final_params.get("stream"): - # Streaming response, return async generator - # Enable usage reporting in the final streaming chunk - final_params["stream_options"] = {"include_usage": True} - - async def stream_gen(): - usage_data = None - try: - response_stream = await self.client.chat.completions.create( - **final_params - ) - async for chunk in response_stream: - # Final chunk carries usage data (no choices) - if hasattr(chunk, 'usage') and chunk.usage: - usage_data = chunk.usage - if chunk.choices: - content = getattr( - chunk.choices[0].delta, "content", None - ) - if content: - yield content - finally: - # Report usage even if client disconnects mid-stream - if usage_data: - try: - collector = get_bean_by_type(TokenUsageCollector) - collector.add( - final_params.get("model", "unknown"), - usage_data.prompt_tokens or 0, - usage_data.completion_tokens or 0, - call_type="llm", - ) - except Exception: - pass - - return stream_gen() - else: - # Non-streaming response - response = await self.client.chat.completions.create(**final_params) - # Report token usage - if hasattr(response, 'usage') and response.usage: - try: - collector = get_bean_by_type(TokenUsageCollector) - collector.add( - final_params.get("model", "unknown"), - response.usage.prompt_tokens or 0, - response.usage.completion_tokens or 0, - call_type="llm", - ) - except Exception: - pass - return ChatCompletionResponse.from_dict(response.model_dump()) - except Exception as e: - raise RuntimeError(f"OpenAI chat completion request failed: {e}") - - def get_available_models(self) -> List[str]: - """Get available model list (can be extended to call openai model list API)""" - return self.config.get("models", []) diff --git a/methods/EverCore/src/core/component/llm/tokenizer/tokenizer_factory.py b/methods/EverCore/src/core/component/llm/tokenizer/tokenizer_factory.py deleted file mode 100644 index 1f81231d..00000000 --- a/methods/EverCore/src/core/component/llm/tokenizer/tokenizer_factory.py +++ /dev/null @@ -1,97 +0,0 @@ -""" -Tokenizer Factory - -Provides tokenizer caching and management functionality. -Supports tiktoken and potentially other tokenizer providers in the future. -""" - -from typing import Dict, Any -import tiktoken - -from core.di.decorators import component -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -# Default tiktoken encodings to preload during application startup -DEFAULT_TIKTOKEN_ENCODINGS = [ - "o200k_base", # GPT-4o, GPT-4o-mini - "cl100k_base", # GPT-4, GPT-3.5-turbo, text-embedding-ada-002 -] - - -@component(name="tokenizer_factory", primary=True) -class TokenizerFactory: - """ - Tokenizer Factory - - Provides tokenizer caching and management functionality. - Cache key format: "{provider}:{encoding_name}" (e.g., "tiktoken:o200k_base") - """ - - def __init__(self): - """Initialize tokenizer factory""" - self._tokenizers: Dict[str, Any] = {} - logger.info("TokenizerFactory initialized") - - def get_tokenizer_from_tiktoken(self, encoding_name: str) -> tiktoken.Encoding: - """ - Get a tiktoken tokenizer by encoding name, with caching. - - Args: - encoding_name: The name of the tiktoken encoding (e.g., "o200k_base", "cl100k_base") - - Returns: - tiktoken.Encoding: The tokenizer instance - - Example: - >>> tokenizer = factory.get_tokenizer_from_tiktoken("o200k_base") - >>> tokens = tokenizer.encode("Hello, world!") - """ - cache_key = f"tiktoken:{encoding_name}" - - if cache_key not in self._tokenizers: - logger.debug("Loading tiktoken encoding: %s", encoding_name) - self._tokenizers[cache_key] = tiktoken.get_encoding(encoding_name) - logger.debug("Tiktoken encoding '%s' loaded and cached", encoding_name) - - return self._tokenizers[cache_key] - - def load_default_encodings(self) -> None: - """ - Preload default tiktoken encodings during application startup. - - This method should be called during application lifespan startup - to ensure tokenizers are ready before handling requests. - - The encodings loaded are defined in DEFAULT_TIKTOKEN_ENCODINGS. - """ - logger.info("Preloading %d tiktoken encodings...", len(DEFAULT_TIKTOKEN_ENCODINGS)) - - for encoding_name in DEFAULT_TIKTOKEN_ENCODINGS: - try: - self.get_tokenizer_from_tiktoken(encoding_name) - logger.info("Successfully preloaded tiktoken encoding: %s", encoding_name) - except (ValueError, KeyError, RuntimeError) as e: - logger.error("Failed to preload tiktoken encoding '%s': %s", encoding_name, e) - - logger.info("Tiktoken encodings preload completed") - - def get_cached_tokenizer_count(self) -> int: - """ - Get the number of cached tokenizers. - - Returns: - int: Number of tokenizers currently in cache - """ - return len(self._tokenizers) - - def clear_cache(self) -> None: - """ - Clear the tokenizer cache. - - This is mainly useful for testing purposes. - """ - self._tokenizers.clear() - logger.debug("Tokenizer cache cleared") diff --git a/methods/EverCore/src/core/component/milvus_client_factory.py b/methods/EverCore/src/core/component/milvus_client_factory.py deleted file mode 100644 index 092da61a..00000000 --- a/methods/EverCore/src/core/component/milvus_client_factory.py +++ /dev/null @@ -1,165 +0,0 @@ -""" -Milvus Client Factory - -Provides Milvus client connection functionality based on environment variables. -""" - -import os -import asyncio -from typing import Optional, Dict -from hashlib import md5 - -from pymilvus import MilvusClient -from core.di.decorators import component -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -def get_milvus_config(prefix: str = "") -> dict: - """ - Get Milvus configuration from environment variables - - Args: - prefix: Environment variable prefix, e.g., prefix="A" reads "A_MILVUS_HOST" - If not provided, reads "MILVUS_HOST" etc. - - Environment variables: - - {PREFIX_}MILVUS_HOST: Milvus host, default localhost - - {PREFIX_}MILVUS_PORT: Milvus port, default 19530 - - {PREFIX_}MILVUS_USER: Username (optional) - - {PREFIX_}MILVUS_PASSWORD: Password (optional) - - {PREFIX_}MILVUS_DB_NAME: Database name (optional) - - Returns: - dict: Configuration dictionary - """ - - def _env(name: str, default: Optional[str] = None) -> str: - if prefix: - prefix_upper = prefix.upper() - key = f"{prefix_upper}_{name}" - else: - key = name - return os.getenv(key, default) if default is not None else os.getenv(key, "") - - host = _env("MILVUS_HOST", "localhost") - port = int(_env("MILVUS_PORT", "19530")) - - config = { - "uri": f"{host}:{port}" if host.startswith("http") else f"http://{host}:{port}", - "user": _env("MILVUS_USER"), - "password": _env("MILVUS_PASSWORD"), - "db_name": _env("MILVUS_DB_NAME"), - } - - logger.info("Getting Milvus config [prefix=%s]:", prefix or "default") - logger.info(" URI: %s", config["uri"]) - logger.info(" Auth: %s", "Basic" if config["user"] else "None") - logger.info(" Database: %s", config["db_name"] or "default") - - return config - - -@component(name="milvus_client_factory", primary=True) -class MilvusClientFactory: - """ - Milvus Client Factory - - Provides Milvus client caching and management functionality based on configuration - """ - - def __init__(self): - """Initialize Milvus client factory""" - self._clients: Dict[str, MilvusClient] = {} - self._lock = asyncio.Lock() - self._default_config = None - logger.info("MilvusClientFactory initialized") - - def get_client( - self, uri: str, user: str = "", password: str = "", db_name: str = "", **kwargs - ) -> MilvusClient: - """ - Get Milvus client instance - - Args: - uri: Milvus connection address, e.g., "http://localhost:19530" - user: Username (optional) - password: Password (optional) - db_name: Database name (optional) - alias: Connection alias, default "default" - **kwargs: Other connection parameters - - Returns: - MilvusClient: Milvus client instance - """ - alias = kwargs.get("alias", None) - - client = MilvusClient( - uri=uri, user=user, password=password, db_name=db_name, **kwargs - ) - - # Cache client - self._clients[alias] = client - logger.info("Milvus client created and cached: %s (alias=%s)", uri, alias) - - return client - - def get_default_client(self) -> MilvusClient: - """ - Get default Milvus client instance based on environment variable configuration - - Returns: - MilvusClient: Milvus client instance - """ - # Get or create default config - if self._default_config is None: - self._default_config = get_milvus_config() - - config = self._default_config - return self.get_client( - uri=config["uri"], - user=config["user"], - password=config["password"], - db_name=config["db_name"], - alias="default", # Default client uses "default" as cache key - ) - - def get_named_client(self, name: str) -> MilvusClient: - """ - Get Milvus client by name - - Convention: name is used as environment variable prefix, reading config from "{name}_MILVUS_XXX". - For example, name="A" reads "A_MILVUS_HOST", "A_MILVUS_PORT", etc. - - Args: - name: Prefix name (environment variable prefix) - - Returns: - MilvusClient: Milvus client instance - """ - if name.lower() == "default": - return self.get_default_client() - - # Get config with prefix - config = get_milvus_config(prefix=name) - logger.info("📋 Loading named Milvus config [name=%s]: %s", name, config["uri"]) - - return self.get_client( - uri=config["uri"], - user=config["user"], - password=config["password"], - db_name=config["db_name"], - alias=name, # Use name as cache key - ) - - def close_all_clients(self): - """Close all client connections""" - for _, client in self._clients.items(): - try: - client.close() - except Exception as e: - logger.error("Error closing Milvus client: %s", e) - - self._clients.clear() - logger.info("All Milvus clients closed") diff --git a/methods/EverCore/src/core/component/mongodb_client_factory.py b/methods/EverCore/src/core/component/mongodb_client_factory.py deleted file mode 100644 index d0f8f3d6..00000000 --- a/methods/EverCore/src/core/component/mongodb_client_factory.py +++ /dev/null @@ -1,546 +0,0 @@ -""" -MongoDB Client Factory - -Provides MongoDB client caching and management functionality based on configuration. -Supports reading configuration from environment variables and provides default client. -""" - -import os -import asyncio -from abc import ABC, abstractmethod -import traceback -from typing import Dict, Optional, List -from urllib.parse import quote_plus -from pymongo import AsyncMongoClient -from beanie import init_beanie -from core.class_annotations.utils import get_annotation -from core.oxm.mongo.constant.annotations import ClassAnnotationKey, Toggle - -from core.di.decorators import component -from core.observation.logger import get_logger -from common_utils.datetime_utils import timezone -from core.oxm.mongo.document_base import DEFAULT_DATABASE - -logger = get_logger(__name__) - - -class MongoDBConfig: - """MongoDB configuration class""" - - def __init__( - self, - host: str = "localhost", - port: int = 27017, - username: Optional[str] = None, - password: Optional[str] = None, - database: str = "memsys", - uri: Optional[str] = None, - uri_params: Optional[str] = None, - **kwargs, - ): - self.host = host - self.port = port - self.username = username - self.password = password - self.database = database - self.uri = uri - self.uri_params = uri_params - self.kwargs = kwargs - - def get_connection_string(self) -> str: - """Get connection string and append unified URI parameters (if any)""" - # Base URI - if self.uri: - base_uri = self.uri - else: - if self.username and self.password: - # URL encode username and password - encoded_username = quote_plus(self.username) - encoded_password = quote_plus(self.password) - base_uri = f"mongodb://{encoded_username}:{encoded_password}@{self.host}:{self.port}/{self.database}" - else: - base_uri = f"mongodb://{self.host}:{self.port}/{self.database}" - - # Append unified parameters - uri_params: Optional[str] = self.uri_params - if uri_params: - separator = '&' if ('?' in base_uri) else '?' - return f"{base_uri}{separator}{uri_params}" - return base_uri - - def get_cache_key(self) -> str: - """Get cache key - - Generate signature based only on basic info + unified URI parameter string to avoid reusing the same client for different parameters. - """ - base = f"{self.host}:{self.port}:{self.database}:{self.username or 'anonymous'}" - uri_params: Optional[str] = self.uri_params - signature = uri_params.strip() if isinstance(uri_params, str) else "" - return f"{base}:{signature}" if signature else base - - @classmethod - def from_env(cls, prefix: str = "") -> 'MongoDBConfig': - """ - Create configuration from environment variables. - - prefix rule: if prefix is provided, read variables in the format "{prefix}_XXX", otherwise read "XXX". - For example: prefix="a" reads "A_MONGODB_URI", "A_MONGODB_HOST", etc. - """ - - def _env(name: str, default: Optional[str] = None) -> Optional[str]: - if prefix == DEFAULT_DATABASE: - key = name - else: - prefix_upper = prefix.upper() - key = f"{prefix_upper}_{name}" if prefix else name - return os.getenv(key, default) if default is not None else os.getenv(key) - - # Prioritize using MONGODB_URI - uri = _env("MONGODB_URI") - if uri: - return cls(uri=uri, database=_env("MONGODB_DATABASE", "memsys")) - - # Read individual configuration items - host = _env("MONGODB_HOST", "localhost") - port = int(_env("MONGODB_PORT", "27017")) - username = _env("MONGODB_USERNAME") - password = _env("MONGODB_PASSWORD") - database = _env("MONGODB_DATABASE", "memsys") - uri_params = _env("MONGODB_URI_PARAMS", "") - - return cls( - host=host, - port=port, - username=username, - password=password, - database=database, - uri_params=uri_params, - ) - - def __repr__(self) -> str: - return f"MongoDBConfig(host={self.host}, port={self.port}, database={self.database})" - - -class MongoDBClientWrapper: - """MongoDB client wrapper""" - - def __init__(self, client: AsyncMongoClient, config: MongoDBConfig): - self.client = client - self.config = config - self.database = client[config.database] - self._initialized = False - self._document_models: List = [] - - async def initialize_beanie(self, document_models: Optional[List] = None): - """Initialize Beanie ODM""" - if self._initialized: - return - - if document_models: - try: - # Group models: writable group (requires indexes), read-only group (skip indexes) - writable_models = [] - readonly_models = [] - for model in document_models: - readonly_flag = get_annotation(model, ClassAnnotationKey.READONLY) - if readonly_flag == Toggle.ENABLED: - readonly_models.append(model) - else: - writable_models.append(model) - - if writable_models and readonly_models: - # Multiple init_beanie calls seem fine in code, but potential issues with referencing both types may exist; currently no business case uses both modes in one DB, but future needs should be guarded against, hence the warning - raise ValueError("Writable and read-only groups cannot coexist") - - logger.info( - "Initializing Beanie ODM (writable group), database: %s, model count: %d", - self.config.database, - len(writable_models), - ) - if writable_models: - model_info_list = [] - for model in writable_models: - try: - collection_name = ( - model.get_collection_name() - if hasattr(model, 'get_collection_name') - else "unknown" - ) - except Exception: - collection_name = "unknown" - model_info_list.append(f"{model.__name__} -> {collection_name}") - logger.info( - "📋 Writable models to initialize: [%s]", - ", ".join(model_info_list), - ) - await init_beanie( - database=self.database, - document_models=writable_models, - skip_indexes=False, - ) - - logger.info( - "Initializing Beanie ODM (read-only group), database: %s, model count: %d", - self.config.database, - len(readonly_models), - ) - if readonly_models: - model_info_list = [] - for model in readonly_models: - try: - collection_name = ( - model.get_collection_name() - if hasattr(model, 'get_collection_name') - else "unknown" - ) - except Exception: - collection_name = "unknown" - model_info_list.append(f"{model.__name__} -> {collection_name}") - logger.info( - "📋 Readonly models to initialize: [%s]", - ", ".join(model_info_list), - ) - await init_beanie( - database=self.database, - document_models=readonly_models, - skip_indexes=True, - ) - - self._document_models = document_models - self._initialized = True - logger.info( - "✅ Beanie ODM initialized successfully, registered %d models", - len(document_models), - ) - - for model in document_models: - logger.info( - "📋 Registered model: database=%s, model=%s -> %s", - self.config.database, - model.__name__, - model.get_collection_name(), - ) - - except Exception as e: - logger.error("❌ Beanie initialization failed: %s", e) - traceback.print_exc() - raise - - async def test_connection(self) -> bool: - """Test connection""" - try: - await self.client.admin.command('ping') - logger.info("✅ MongoDB connection test successful: %s", self.config) - return True - except Exception as e: - logger.error( - "❌ MongoDB connection test failed: %s, error: %s", self.config, e - ) - return False - - async def get_collection_stats(self) -> Dict: - """Get collection statistics""" - try: - stats = {} - collections = await self.database.list_collection_names() - - for collection_name in collections: - try: - collection_stats = await self.database.command( - "collStats", collection_name - ) - stats[collection_name] = { - "count": collection_stats.get("count", 0), - "size": collection_stats.get("size", 0), - "avgObjSize": collection_stats.get("avgObjSize", 0), - "storageSize": collection_stats.get("storageSize", 0), - "indexes": collection_stats.get("nindexes", 0), - } - except Exception as e: - logger.warning( - "Failed to get collection %s stats: %s", collection_name, e - ) - - return stats - except Exception as e: - logger.error("Failed to get stats: %s", e) - return {} - - async def close(self): - """Close connection""" - if self.client: - await self.client.close() - logger.info("🔌 MongoDB connection closed: %s", self.config) - - @property - def is_initialized(self) -> bool: - """Check if Beanie is initialized""" - return self._initialized - - -class MongoDBClientFactory(ABC): - """MongoDB client factory interface""" - - @abstractmethod - async def get_client( - self, config: Optional[MongoDBConfig] = None, **connection_kwargs - ) -> MongoDBClientWrapper: - """ - Get MongoDB client - - Args: - config: MongoDB configuration, use default if None - **connection_kwargs: additional connection parameters - - Returns: - MongoDBClientWrapper: MongoDB client wrapper - """ - ... - - @abstractmethod - async def get_default_client(self) -> MongoDBClientWrapper: - """ - Get default MongoDB client - - Returns: - MongoDBClientWrapper: default MongoDB client wrapper - """ - ... - - @abstractmethod - async def get_named_client(self, name: str) -> MongoDBClientWrapper: - """ - Get MongoDB client by name. - - Convention: name as environment variable prefix, read configuration from "{name}_MONGODB_XXX". - For example, when name="A", try to read "A_MONGODB_URI", "A_MONGODB_HOST", etc. - - Args: - name: prefix name (i.e., environment variable prefix) - - Returns: - MongoDBClientWrapper: MongoDB client wrapper - """ - ... - - @abstractmethod - async def create_client_with_config( - self, - host: str = "localhost", - port: int = 27017, - username: Optional[str] = None, - password: Optional[str] = None, - database: str = "memsys", - **kwargs, - ) -> MongoDBClientWrapper: - """ - Create client with specified configuration - - Args: - host: MongoDB host - port: MongoDB port - username: username - password: password - database: database name - **kwargs: other connection parameters - - Returns: - MongoDBClientWrapper: MongoDB client wrapper - """ - ... - - @abstractmethod - async def close_client(self, config: Optional[MongoDBConfig] = None): - """ - Close specified client - - Args: - config: configuration, close default client if None - """ - ... - - @abstractmethod - async def close_all_clients(self): - """Close all clients""" - ... - - -@component(name="mongodb_client_factory") -class MongoDBClientFactoryImpl(MongoDBClientFactory): - """MongoDB client factory implementation class""" - - def __init__(self): - """Initialize factory""" - self._clients: Dict[str, MongoDBClientWrapper] = {} - self._default_config: Optional[MongoDBConfig] = None - self._default_client: Optional[MongoDBClientWrapper] = None - self._lock = asyncio.Lock() - - async def get_client( - self, config: Optional[MongoDBConfig] = None, **connection_kwargs - ) -> MongoDBClientWrapper: - """ - Get MongoDB client - - Args: - config: MongoDB configuration, use default if None - **connection_kwargs: additional connection parameters - - Returns: - MongoDBClientWrapper: MongoDB client wrapper - """ - - if config is None: - config = await self._get_default_config() - - cache_key = config.get_cache_key() - - async with self._lock: - # Check cache - if cache_key in self._clients: - return self._clients[cache_key] - - # Create new client - logger.info("Creating new MongoDB client: %s", config) - - # Merge connection parameters - conn_kwargs = { - "serverSelectionTimeoutMS": 10000, # PyMongo AsyncMongoClient requires longer timeout - "connectTimeoutMS": 10000, # connection timeout - "socketTimeoutMS": 10000, # socket timeout - "maxPoolSize": 50, - "minPoolSize": 5, - "tz_aware": True, - "tzinfo": timezone, - **config.kwargs, - **connection_kwargs, - } - - try: - client = AsyncMongoClient(config.get_connection_string(), **conn_kwargs) - - client_wrapper = MongoDBClientWrapper(client, config) - - # Test connection - if not await client_wrapper.test_connection(): - await client_wrapper.close() - raise RuntimeError(f"MongoDB connection test failed: {config}") - - # Cache client - self._clients[cache_key] = client_wrapper - logger.info("✅ MongoDB client created and cached: %s", config) - - return client_wrapper - - except Exception as e: - logger.error( - "❌ Failed to create MongoDB client: %s, error: %s", config, e - ) - raise - - async def get_default_client(self) -> MongoDBClientWrapper: - """ - Get default MongoDB client - - Returns: - MongoDBClientWrapper: default MongoDB client wrapper - """ - if self._default_client is None: - config = await self._get_default_config() - self._default_client = await self.get_client(config) - - return self._default_client - - async def get_named_client(self, name: str) -> MongoDBClientWrapper: - """ - Get MongoDB client by name. - - Convention: name as environment variable prefix, read configuration from "{name}_MONGODB_XXX". - For example, when name="A", try to read "A_MONGODB_URI", "A_MONGODB_HOST", etc. - - Args: - name: prefix name (i.e., environment variable prefix) - - Returns: - MongoDBClientWrapper: MongoDB client wrapper - """ - if name == DEFAULT_DATABASE: - return await self.get_default_client() - config = MongoDBConfig.from_env(prefix=name) - logger.info("📋 Loading named MongoDB config [name=%s]: %s", name, config) - return await self.get_client(config) - - async def _get_default_config(self) -> MongoDBConfig: - """Get default config (internal method)""" - if self._default_config is None: - self._default_config = MongoDBConfig.from_env() - logger.info("📋 Loading default MongoDB config: %s", self._default_config) - - return self._default_config - - async def create_client_with_config( - self, - host: str = "localhost", - port: int = 27017, - username: Optional[str] = None, - password: Optional[str] = None, - database: str = "memsys", - **kwargs, - ) -> MongoDBClientWrapper: - """ - Create client with specified configuration - - Args: - host: MongoDB host - port: MongoDB port - username: username - password: password - database: database name - **kwargs: other connection parameters - - Returns: - MongoDBClientWrapper: MongoDB client wrapper - """ - config = MongoDBConfig( - host=host, - port=port, - username=username, - password=password, - database=database, - **kwargs, - ) - - return await self.get_client(config) - - async def close_client(self, config: Optional[MongoDBConfig] = None): - """ - Close specified client - - Args: - config: configuration, close default client if None - """ - if config is None: - if self._default_client: - await self._default_client.close() - self._default_client = None - return - - cache_key = config.get_cache_key() - - async with self._lock: - if cache_key in self._clients: - await self._clients[cache_key].close() - del self._clients[cache_key] - - async def close_all_clients(self): - """Close all clients""" - async with self._lock: - for client_wrapper in self._clients.values(): - await client_wrapper.close() - - self._clients.clear() - - if self._default_client: - self._default_client = None - - logger.info("🔌 All MongoDB clients closed") diff --git a/methods/EverCore/src/core/component/openai_compatible_client.py b/methods/EverCore/src/core/component/openai_compatible_client.py deleted file mode 100644 index 98e0cdf4..00000000 --- a/methods/EverCore/src/core/component/openai_compatible_client.py +++ /dev/null @@ -1,253 +0,0 @@ -import asyncio -import os -from typing import Dict, Any, List, Optional, AsyncGenerator, Union - -from core.di.decorators import component -from core.observation.logger import get_logger -from core.component.config_provider import ConfigProvider - -from core.component.llm.llm_adapter.message import ChatMessage -from core.component.llm.llm_adapter.completion import ( - ChatCompletionRequest, - ChatCompletionResponse, -) -from core.component.llm.llm_adapter.llm_backend_adapter import LLMBackendAdapter -from core.component.llm.llm_adapter.openai_adapter import OpenAIAdapter -from core.component.llm.llm_adapter.anthropic_adapter import AnthropicAdapter -from core.component.llm.llm_adapter.gemini_adapter import GeminiAdapter - -logger = get_logger(__name__) - - -@component(name="openai_compatible_client", primary=True) -class OpenAICompatibleClient: - """ - OpenAI-compatible API client. - This client acts as a facade, managing multiple LLM backend adapters. - """ - - def __init__(self, config_provider: ConfigProvider): - """ - Initialize the client. - Args: - config_provider: Configuration provider used to load llm_backends.yaml. - """ - self.config_provider = config_provider - self._adapters: Dict[str, LLMBackendAdapter] = {} - self._config: Dict[str, Any] = self.config_provider.get_config("llm_backends") - self._init_locks: Dict[str, asyncio.Lock] = {} # One lock per backend - self._lock_creation_lock = asyncio.Lock() # Lock for creating locks - - async def _get_adapter(self, backend_name: str) -> LLMBackendAdapter: - """ - Asynchronously initialize and retrieve the adapter for the specified backend on demand. - Uses locks to ensure concurrency safety and avoid duplicate initialization. - """ - # If adapter already exists, return directly - if backend_name in self._adapters: - return self._adapters[backend_name] - - # Ensure each backend has a corresponding lock - async with self._lock_creation_lock: - if backend_name not in self._init_locks: - self._init_locks[backend_name] = asyncio.Lock() - - # Use backend-specific lock to ensure concurrency safety - async with self._init_locks[backend_name]: - # Re-check, as it might have been initialized by another coroutine while waiting for the lock - if backend_name in self._adapters: - return self._adapters[backend_name] - - llm_backends = self._config.get("llm_backends", {}) - if backend_name not in llm_backends: - raise ValueError( - f"Backend '{backend_name}' not found in configuration." - ) - - backend_config = llm_backends[backend_name] - provider = backend_config.get("provider", "openai") - - try: - adapter: LLMBackendAdapter - if provider in ["openai", "azure", "custom", "ollama"]: - adapter = OpenAIAdapter(backend_config) - elif provider == "anthropic": - adapter = AnthropicAdapter(backend_config) - elif provider == "gemini": - adapter = GeminiAdapter(backend_config) - else: - raise ValueError(f"Unsupported provider type: {provider}") - - self._adapters[backend_name] = adapter - return adapter - except Exception as e: - raise RuntimeError( - f"Failed to initialize adapter for backend '{backend_name}': {e}" - ) from e - - def _get_param_with_priority( - self, - param_name: str, - passed_value: Any, - default_settings: dict, - backend_config: dict, - ) -> Any: - """ - Get parameter priority: passed value > backend_config > default_settings - """ - if passed_value is not None: - return passed_value - if backend_config.get(param_name) is not None: - return backend_config.get(param_name) - if default_settings.get(param_name) is not None: - return default_settings[param_name] - return None - - async def chat_completion( - self, - messages: List[ChatMessage], - backend: Optional[str] = None, - model: Optional[str] = None, - temperature: Optional[float] = None, - max_tokens: Optional[int] = None, - top_p: Optional[float] = None, - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - thinking_budget: Optional[ - int - ] = None, # Add support for thinking_budget parameter - stream: bool = False, - ) -> Union[ChatCompletionResponse, AsyncGenerator[str, None]]: - """ - Perform chat completion. - Args: - messages: List of chat messages - backend: Backend name, use default backend if not specified - ... other params - thinking_budget: Thinking budget (used to support think functionality) - stream: Whether to stream the response - Returns: - Chat completion response or streaming generator - """ - # Select backend - backend_name = backend or self._config.get("default_backend", "openai") - default_settings = self._config.get("default_settings", {}) - backend_config = self._config.get("llm_backends", {}).get(backend_name, {}) - - # Unified parameter priority handling - final_params = {} - param_definitions = { - # backend_config has default value below - "model": model, - # default_settings has default value below - "temperature": temperature, - "max_tokens": max_tokens, - "top_p": top_p, - "frequency_penalty": frequency_penalty, - "presence_penalty": presence_penalty, - "thinking_budget": thinking_budget, - } - for name, value in param_definitions.items(): - final_params[name] = self._get_param_with_priority( - name, value, default_settings, backend_config - ) - - # Assemble request - request = ChatCompletionRequest( - messages=messages, stream=stream, **final_params - ) - - adapter = await self._get_adapter(backend_name) - return await adapter.chat_completion(request) - - def chat_completion_sync( - self, - messages: List[ChatMessage], - backend: Optional[str] = None, - model: Optional[str] = None, - temperature: Optional[float] = None, - max_tokens: Optional[int] = None, - top_p: Optional[float] = None, - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - stream: bool = False, - ) -> Union[ChatCompletionResponse, AsyncGenerator[str, None]]: - """ - Synchronous version of performing chat completion. - - Note: This method no longer supports synchronous calls because certain LLM adapters (e.g., Gemini) - internally bind to the event loop, and creating new threads and event loops causes issues. - - Please use the asynchronous version chat_completion() method. - """ - raise NotImplementedError( - "Synchronous version of chat completion is no longer supported because certain LLM adapters (e.g., Gemini) " - "internally bind to the event loop. Please use the asynchronous version chat_completion() method." - ) - - def get_available_backends(self) -> List[str]: - """Get list of available backends""" - return list(self._config.get("llm_backends", {}).keys()) - - async def get_available_models(self, backend: Optional[str] = None) -> List[str]: - """Get list of available models for the specified backend""" - backend_name = backend or self._config.get("default_backend", "openai") - try: - adapter = await self._get_adapter(backend_name) - return adapter.get_available_models() - except (ValueError, RuntimeError): - return [] - - def get_available_models_sync(self, backend: Optional[str] = None) -> List[str]: - """ - Synchronous version of getting available models for the specified backend. - - Note: This method no longer supports synchronous calls because certain LLM adapters (e.g., Gemini) - internally bind to the event loop, and creating new threads and event loops causes issues. - - Please use the asynchronous version get_available_models() method. - """ - raise NotImplementedError( - "Synchronous version of model retrieval is no longer supported because certain LLM adapters (e.g., Gemini) " - "internally bind to the event loop. Please use the asynchronous version get_available_models() method." - ) - - def get_backend_info(self, backend: str) -> Optional[Dict[str, Any]]: - """Get backend information, hiding sensitive data""" - config = self._config.get("llm_backends", {}).get(backend) - if config: - safe_config = config.copy() - if "api_key" in safe_config: - safe_config["api_key"] = ( - f"***{safe_config['api_key'][-4:]}" - if len(safe_config.get('api_key', '')) > 4 - else "***" - ) - return safe_config - return None - - def reload_config(self): - """Reload configuration and clear existing adapter instances and locks""" - self._config = self.config_provider.get_config("llm_backends") - self._adapters.clear() - self._init_locks.clear() - - async def close(self): - """Close HTTP client connections for all adapters""" - for adapter in self._adapters.values(): - if hasattr(adapter, 'close'): - await adapter.close() # type: ignore - - def close_sync(self): - """ - Synchronous version of closing HTTP client connections for all adapters. - - Note: This method no longer supports synchronous calls because certain LLM adapters (e.g., Gemini) - internally bind to the event loop, and creating new threads and event loops causes issues. - - Please use the asynchronous version close() method. - """ - raise NotImplementedError( - "Synchronous version of close operation is no longer supported because certain LLM adapters (e.g., Gemini) " - "internally bind to the event loop. Please use the asynchronous version close() method." - ) diff --git a/methods/EverCore/src/core/component/redis_provider.py b/methods/EverCore/src/core/component/redis_provider.py deleted file mode 100644 index 737c2a5b..00000000 --- a/methods/EverCore/src/core/component/redis_provider.py +++ /dev/null @@ -1,360 +0,0 @@ -""" -Redis Connection Provider - -Technical component providing Redis connection pool management and basic operations -""" - -import os -import asyncio -from typing import Optional, Union -import redis.asyncio as redis -from redis.asyncio.connection import ConnectionPool - -from core.di.decorators import component -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -@component(name="redis_provider", primary=True) -class RedisProvider: - """Redis connection provider""" - - def __init__(self): - """Initialize Redis connection provider""" - # Read Redis configuration from environment variables - self.redis_host = os.getenv("REDIS_HOST", "localhost") - self.redis_port = int(os.getenv("REDIS_PORT", "6379")) - self.redis_db = int(os.getenv("REDIS_DB", "0")) - self.redis_password = os.getenv("REDIS_PASSWORD") - self.redis_ssl = os.getenv("REDIS_SSL", "false").lower() == "true" - - # Build Redis URL - protocol = "rediss" if self.redis_ssl else "redis" - if self.redis_password: - self.redis_url = f"{protocol}://:{self.redis_password}@{self.redis_host}:{self.redis_port}/{self.redis_db}" - else: - self.redis_url = ( - f"{protocol}://{self.redis_host}:{self.redis_port}/{self.redis_db}" - ) - - # Other configurations use default values - self.max_connections = int(os.getenv("REDIS_MAX_CONNECTIONS", "400")) - self.socket_timeout = int(os.getenv("REDIS_SOCKET_TIMEOUT", "15")) - self.socket_connect_timeout = int( - os.getenv("REDIS_SOCKET_CONNECT_TIMEOUT", "5") - ) - - # Named client cache - self._named_clients = {} - self._named_pools = {} - self._named_initialized = set() - self._lock = asyncio.Lock() - - async def get_client(self) -> redis.Redis: - """ - Get Redis client (default client) - - Returns: - redis.Redis: Redis client instance - """ - return await self.get_named_client("default") - - async def get_named_client(self, name: str, **overrides) -> redis.Redis: - """ - Get named Redis client, supports parameter override - - Args: - name: Client name, used for caching - **overrides: Override default parameters, such as decode_responses=False - - Returns: - redis.Redis: Redis client instance - """ - if name in self._named_initialized: - return self._named_clients[name] - - async with self._lock: - # Double-checked locking - if name in self._named_initialized: - return self._named_clients[name] - - try: - # Build connection parameters, using default values + override parameters - conn_params = { - "max_connections": self.max_connections, - "socket_timeout": self.socket_timeout, - "socket_connect_timeout": self.socket_connect_timeout, - "decode_responses": True, # default value - } - conn_params.update(overrides) - logger.info( - "Building Redis client connection params: %s, %s", name, conn_params - ) - # Create named connection pool - named_pool = ConnectionPool.from_url(self.redis_url, **conn_params) - - # Create named Redis client - named_client = redis.Redis(connection_pool=named_pool) - - # Test connection - await named_client.ping() - - # Cache client and connection pool - self._named_clients[name] = named_client - self._named_pools[name] = named_pool - self._named_initialized.add(name) - - logger.info( - "Named Redis client initialized successfully: %s (param overrides: %s)", - name, - overrides if overrides else "None", - ) - - return named_client - - except Exception as e: - logger.error( - "Named Redis client initialization failed: %s, error=%s", - name, - str(e), - ) - # Clean up partially initialized resources - if name in self._named_clients: - await self._named_clients[name].aclose() - del self._named_clients[name] - if name in self._named_pools: - await self._named_pools[name].aclose() - del self._named_pools[name] - self._named_initialized.discard(name) - raise - - async def set( - self, - key: str, - value: Union[str, bytes, int, float], - ex: Optional[int] = None, - nx: bool = False, - ) -> bool: - """ - Set key-value pair - - Args: - key: Key name - value: Value - ex: Expiration time (seconds) - nx: If True, set only if key does not exist - - Returns: - bool: Whether set operation succeeded - """ - client = await self.get_client() - try: - result = await client.set(key, value, ex=ex, nx=nx) - return result is not None and result is not False - except Exception as e: - logger.error("Redis SET operation failed: key=%s, error=%s", key, str(e)) - return False - - async def get(self, key: str) -> Optional[str]: - """ - Get key value - - Args: - key: Key name - - Returns: - Optional[str]: Key value, returns None if key does not exist - """ - client = await self.get_client() - try: - return await client.get(key) - except Exception as e: - logger.error("Redis GET operation failed: key=%s, error=%s", key, str(e)) - return None - - async def exists(self, key: str) -> bool: - """ - Check if key exists - - Args: - key: Key name - - Returns: - bool: Whether key exists - """ - client = await self.get_client() - try: - result = await client.exists(key) - return result > 0 - except Exception as e: - logger.error("Redis EXISTS operation failed: key=%s, error=%s", key, str(e)) - return False - - async def delete(self, *keys: str) -> int: - """ - Delete keys - - Args: - keys: List of key names to delete - - Returns: - int: Number of keys successfully deleted - """ - if not keys: - return 0 - - client = await self.get_client() - try: - return await client.delete(*keys) - except Exception as e: - logger.error( - "Redis DELETE operation failed: keys=%s, error=%s", keys, str(e) - ) - return 0 - - async def expire(self, key: str, seconds: int) -> bool: - """ - Set key expiration time - - Args: - key: Key name - seconds: Expiration time (seconds) - - Returns: - bool: Whether setting expiration succeeded - """ - client = await self.get_client() - try: - return await client.expire(key, seconds) - except Exception as e: - logger.error( - "Redis EXPIRE operation failed: key=%s, seconds=%s, error=%s", - key, - seconds, - str(e), - ) - return False - - async def ttl(self, key: str) -> int: - """ - Get remaining time to live of key - - Args: - key: Key name - - Returns: - int: Remaining TTL in seconds, -1 means never expires, -2 means key does not exist - """ - client = await self.get_client() - try: - return await client.ttl(key) - except Exception as e: - logger.error("Redis TTL operation failed: key=%s, error=%s", key, str(e)) - return -2 - - async def keys(self, pattern: str) -> list: - """ - Get list of keys matching pattern - - Args: - pattern: Matching pattern (e.g., "prefix:*") - - Returns: - list: List of matching keys - """ - client = await self.get_client() - try: - return await client.keys(pattern) - except Exception as e: - logger.error( - "Redis KEYS operation failed: pattern=%s, error=%s", pattern, str(e) - ) - return [] - - async def ping(self) -> bool: - """ - Test Redis connection - - Returns: - bool: Whether connection is healthy - """ - try: - client = await self.get_client() - result = await client.ping() - return result is True - except Exception as e: - logger.error("Redis PING failed: %s", str(e)) - return False - - async def lpush(self, key: str, *values: Union[str, bytes]) -> int: - """ - Push elements to the left of the list - - Args: - key: Key name - values: List of values to push - - Returns: - int: Length of list after push - """ - if not values: - return 0 - - client = await self.get_client() - try: - return await client.lpush(key, *values) - except Exception as e: - logger.error("Redis LPUSH operation failed: key=%s, error=%s", key, str(e)) - return 0 - - async def lrange(self, key: str, start: int, end: int) -> list: - """ - Get elements in specified range of list - - Args: - key: Key name - start: Start index - end: End index (-1 means to end of list) - - Returns: - list: List of elements - """ - client = await self.get_client() - try: - return await client.lrange(key, start, end) - except Exception as e: - logger.error("Redis LRANGE operation failed: key=%s, error=%s", key, str(e)) - return [] - - async def close(self): - """Close all Redis connection pools""" - # Close all named clients - for name, client in self._named_clients.items(): - try: - await client.aclose() - logger.info("Named Redis client closed: %s", name) - except Exception as e: - logger.error( - "Failed to close named Redis client: %s, error=%s", name, str(e) - ) - - for name, pool in self._named_pools.items(): - try: - await pool.aclose() - logger.info("Named Redis connection pool closed: %s", name) - except Exception as e: - logger.error( - "Failed to close named Redis connection pool: %s, error=%s", - name, - str(e), - ) - - # Clear cache - self._named_clients.clear() - self._named_pools.clear() - self._named_initialized.clear() - - def is_initialized(self) -> bool: - """Check if default client is initialized""" - return "default" in self._named_initialized diff --git a/methods/EverCore/src/core/component/token_usage_collector.py b/methods/EverCore/src/core/component/token_usage_collector.py deleted file mode 100644 index 9c10f487..00000000 --- a/methods/EverCore/src/core/component/token_usage_collector.py +++ /dev/null @@ -1,97 +0,0 @@ -""" -Token Usage Collector Interface - -Request-level token usage collector for LLM/embedding/rerank calls. -Opensource provides a Noop implementation; Enterprise overrides via addon bean. -""" - -from abc import ABC, abstractmethod -from typing import Optional - -from core.di.decorators import component - - -class TokenUsageCollector(ABC): - """ - Request-level token usage collector interface. - - Called by LLM/embedding/rerank call sites after each invocation to accumulate - token usage. Enterprise reads totals at request end for MCU billing. - - Lifecycle: - - reset(): middleware at request start (before asyncio.create_task) - - add(): each LLM/embedding/rerank call site after response - - get_totals(): enterprise on_request_complete for MCU calculation - - reset(): middleware at request end (cleanup) - """ - - @abstractmethod - def add( - self, - model: str, - input_tokens: int, - output_tokens: int, - *, - call_type: str = "llm", - request_id: Optional[str] = None, - ) -> None: - """ - Accumulate one invocation's token usage into the current request context. - - Multiple calls within a single request are summed (not overwritten). - - Args: - model: Model identifier (e.g. "gpt-4", "gemini-2.5-flash") - input_tokens: Prompt / input token count - output_tokens: Completion / output token count - call_type: "llm" | "embedding" | "rerank" - request_id: Optional request ID for diagnostics - """ - ... - - @abstractmethod - def get_totals(self) -> dict: - """ - Return accumulated totals for the current request. - - Returns: - { - "input_tokens": int, - "output_tokens": int, - "embedding_calls": int, - "call_count": int, - } - """ - ... - - @abstractmethod - def reset(self) -> None: - """Reset accumulated values (called at request start and end).""" - ... - - -@component(name="token_usage_collector") -class NoopTokenUsageCollector(TokenUsageCollector): - """Noop implementation — opensource does not need MCU billing.""" - - def add( - self, - model: str, - input_tokens: int, - output_tokens: int, - *, - call_type: str = "llm", - request_id: Optional[str] = None, - ) -> None: - pass - - def get_totals(self) -> dict: - return { - "input_tokens": 0, - "output_tokens": 0, - "embedding_calls": 0, - "call_count": 0, - } - - def reset(self) -> None: - pass diff --git a/methods/EverCore/src/core/config/__init__.py b/methods/EverCore/src/core/config/__init__.py deleted file mode 100644 index 5fa50f6e..00000000 --- a/methods/EverCore/src/core/config/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# todo Refactor project config diff --git a/methods/EverCore/src/core/constants/errors.py b/methods/EverCore/src/core/constants/errors.py deleted file mode 100644 index a68d5ee1..00000000 --- a/methods/EverCore/src/core/constants/errors.py +++ /dev/null @@ -1,1036 +0,0 @@ -""" -Error definition module - -Contains unified definitions of all error codes, error messages, and translations -""" - -from enum import Enum -from typing import Dict - - -class ErrorStatus(Enum): - """Error status enumeration - - Defines all possible error statuses in the system, facilitating error classification and handling. - Each error status has a clear meaning and corresponding handling method. - """ - - OK = "ok" - FAILED = "failed" - DUPLICATE = "duplicate" - - -class ErrorCode(Enum): - """Error code enumeration - - Defines all possible error codes in the system, facilitating error classification and handling. - Each error code has a clear meaning and corresponding handling method. - """ - - # General errors - UNKNOWN_ERROR = "UNKNOWN_ERROR" - HTTP_ERROR = "HTTP_ERROR" - INVALID_PARAMETER = "INVALID_PARAMETER" - RESOURCE_NOT_FOUND = "RESOURCE_NOT_FOUND" - PERMISSION_DENIED = "PERMISSION_DENIED" - VALIDATION_ERROR = "VALIDATION_ERROR" - RESOURCE_INVALID = "RESOURCE_INVALID" - TYPE_ERROR = "TYPE_ERROR" - OPERATION_FAILED = "OPERATION_FAILED" - - # Authentication related errors - AUTHENTICATION_ERROR = "AUTHENTICATION_ERROR" - AUTHENTICATION_FAILED = "AUTHENTICATION_FAILED" - TOKEN_INVALID = "TOKEN_INVALID" - TOKEN_EXPIRED = "TOKEN_EXPIRED" - PERMISSION_INSUFFICIENT = "PERMISSION_INSUFFICIENT" - USER_NOT_FOUND = "USER_NOT_FOUND" - USER_INVALID = "USER_INVALID" - - # Agent related errors - AGENT_INITIALIZATION_ERROR = "AGENT_INITIALIZATION_ERROR" - AGENT_EXECUTION_ERROR = "AGENT_EXECUTION_ERROR" - AGENT_STATE_ERROR = "AGENT_STATE_ERROR" - AGENT_TIMEOUT = "AGENT_TIMEOUT" - AGENT_CANCELLED = "AGENT_CANCELLED" - AGENT_MEMORY_ERROR = "AGENT_MEMORY_ERROR" - - # Database related errors - DATABASE_ERROR = "DATABASE_ERROR" - DATABASE_CONNECTION_ERROR = "DATABASE_CONNECTION_ERROR" - DATABASE_QUERY_ERROR = "DATABASE_QUERY_ERROR" - DATABASE_TRANSACTION_ERROR = "DATABASE_TRANSACTION_ERROR" - DATABASE_CONSTRAINT_ERROR = "DATABASE_CONSTRAINT_ERROR" - DATABASE_TIMEOUT = "DATABASE_TIMEOUT" - - # File related errors - FILE_NOT_FOUND = "FILE_NOT_FOUND" - FILE_READ_ERROR = "FILE_READ_ERROR" - FILE_WRITE_ERROR = "FILE_WRITE_ERROR" - FILE_PERMISSION_ERROR = "FILE_PERMISSION_ERROR" - FILE_FORMAT_ERROR = "FILE_FORMAT_ERROR" - FILE_SIZE_EXCEEDED = "FILE_SIZE_EXCEEDED" - FILE_UPLOAD_FAILED = "FILE_UPLOAD_FAILED" - FILE_DOWNLOAD_FAILED = "FILE_DOWNLOAD_FAILED" - FILE_PARSE_ERROR = "FILE_PARSE_ERROR" - - # Network related errors - NETWORK_ERROR = "NETWORK_ERROR" - HTTP_REQUEST_ERROR = "HTTP_REQUEST_ERROR" - HTTP_TIMEOUT = "HTTP_TIMEOUT" - URL_INVALID = "URL_INVALID" - URL_SHORTENING_FAILED = "URL_SHORTENING_FAILED" - - # External service errors - EXTERNAL_SERVICE_ERROR = "EXTERNAL_SERVICE_ERROR" - EXTERNAL_SERVICE_TIMEOUT = "EXTERNAL_SERVICE_TIMEOUT" - EXTERNAL_SERVICE_UNAVAILABLE = "EXTERNAL_SERVICE_UNAVAILABLE" - API_RATE_LIMIT_EXCEEDED = "API_RATE_LIMIT_EXCEEDED" - API_KEY_INVALID = "API_KEY_INVALID" - - # Configuration related errors - CONFIGURATION_ERROR = "CONFIGURATION_ERROR" - CONFIGURATION_INVALID = "CONFIGURATION_INVALID" - CONFIGURATION_MISSING = "CONFIGURATION_MISSING" - ENVIRONMENT_VARIABLE_MISSING = "ENVIRONMENT_VARIABLE_MISSING" - - # Generation related errors - GENERATION_ERROR = "GENERATION_ERROR" - GENERATION_TIMEOUT = "GENERATION_TIMEOUT" - GENERATION_CANCELLED = "GENERATION_CANCELLED" - GENERATION_IN_PROGRESS = "GENERATION_IN_PROGRESS" - GENERATION_QUEUE_FULL = "GENERATION_QUEUE_FULL" - - # Conversation related errors - CONVERSATION_NOT_FOUND = "CONVERSATION_NOT_FOUND" - CONVERSATION_INVALID = "CONVERSATION_INVALID" - CONVERSATION_TYPE_INVALID = "CONVERSATION_TYPE_INVALID" - CONVERSATION_LIMIT_EXCEEDED = "CONVERSATION_LIMIT_EXCEEDED" - MESSAGE_TOO_LONG = "MESSAGE_TOO_LONG" - - # Content related errors - CONTENT_EMPTY = "CONTENT_EMPTY" - CONTENT_TOO_LONG = "CONTENT_TOO_LONG" - CONTENT_INVALID_FORMAT = "CONTENT_INVALID_FORMAT" - CONTENT_PROCESSING_ERROR = "CONTENT_PROCESSING_ERROR" - - # Resource related errors - RESOURCE_PROCESSING_FAILED = "RESOURCE_PROCESSING_FAILED" - RESOURCE_EXTRACTION_FAILED = "RESOURCE_EXTRACTION_FAILED" - RESOURCE_IMPORT_FAILED = "RESOURCE_IMPORT_FAILED" - RESOURCE_URI_MISSING = "RESOURCE_URI_MISSING" - - # System errors - SYSTEM_ERROR = "SYSTEM_ERROR" - INITIALIZATION_FAILED = "INITIALIZATION_FAILED" - SERVICE_STARTUP_FAILED = "SERVICE_STARTUP_FAILED" - SERVICE_SHUTDOWN_FAILED = "SERVICE_SHUTDOWN_FAILED" - - # Context and session errors - CONTEXT_NOT_SET = "CONTEXT_NOT_SET" - SESSION_NOT_INITIALIZED = "SESSION_NOT_INITIALIZED" - MIDDLEWARE_ERROR = "MIDDLEWARE_ERROR" - - # Workflow stage related error codes - REQUIREMENT_EXTRACTION_NOT_COMPLETED = "REQUIREMENT_EXTRACTION_NOT_COMPLETED" - OUTLINE_GENERATION_NOT_COMPLETED = "OUTLINE_GENERATION_NOT_COMPLETED" - OUTLINE_ID_NOT_FOUND = "OUTLINE_ID_NOT_FOUND" - FULLTEXT_ID_NOT_FOUND = "FULLTEXT_ID_NOT_FOUND" - DOCUMENT_SLICE_NOT_FOUND = "DOCUMENT_SLICE_NOT_FOUND" - - # Task related error codes - TASK_NOT_FOUND = "TASK_NOT_FOUND" - NO_GENERATION_IN_PROGRESS = "NO_GENERATION_IN_PROGRESS" - - # Editor related error codes - UNSUPPORTED_EDITOR_TYPE = "UNSUPPORTED_EDITOR_TYPE" - - # Agent specific error codes - LLM_OUTPUT_PARSING_ERROR = "LLM_OUTPUT_PARSING_ERROR" - LLM_CALL_FAILED = "LLM_CALL_FAILED" - LLM_RETRY_EXHAUSTED = "LLM_RETRY_EXHAUSTED" - - # Quota related error codes - QUOTA_INSUFFICIENT = "QUOTA_INSUFFICIENT" - - -class ErrorMessage(Enum): - """Error message key enumeration - - Defines all error message keys in the system, used for frontend internationalization translation. - Each message key corresponds to a specific error description. - """ - - # General error messages - UNKNOWN_ERROR = "UNKNOWN_ERROR" - HTTP_ERROR = "HTTP_ERROR" - INVALID_PARAMETER = "INVALID_PARAMETER" - RESOURCE_NOT_FOUND = "RESOURCE_NOT_FOUND" - PERMISSION_DENIED = "PERMISSION_DENIED" - VALIDATION_ERROR = "VALIDATION_ERROR" - RESOURCE_INVALID = "RESOURCE_INVALID" - TYPE_ERROR = "TYPE_ERROR" - OPERATION_FAILED = "OPERATION_FAILED" - - # Authentication related error messages - AUTHENTICATION_ERROR = "AUTHENTICATION_ERROR" - AUTHENTICATION_FAILED = "AUTHENTICATION_FAILED" - TOKEN_INVALID = "TOKEN_INVALID" - TOKEN_EXPIRED = "TOKEN_EXPIRED" - PERMISSION_INSUFFICIENT = "PERMISSION_INSUFFICIENT" - USER_NOT_FOUND = "USER_NOT_FOUND" - USER_INVALID = "USER_INVALID" - - # Agent related error messages - AGENT_INITIALIZATION_ERROR = "AGENT_INITIALIZATION_ERROR" - AGENT_EXECUTION_ERROR = "AGENT_EXECUTION_ERROR" - AGENT_STATE_ERROR = "AGENT_STATE_ERROR" - AGENT_TIMEOUT = "AGENT_TIMEOUT" - AGENT_CANCELLED = "AGENT_CANCELLED" - AGENT_MEMORY_ERROR = "AGENT_MEMORY_ERROR" - - # Database related error messages - DATABASE_ERROR = "DATABASE_ERROR" - DATABASE_CONNECTION_ERROR = "DATABASE_CONNECTION_ERROR" - DATABASE_QUERY_ERROR = "DATABASE_QUERY_ERROR" - DATABASE_TRANSACTION_ERROR = "DATABASE_TRANSACTION_ERROR" - DATABASE_CONSTRAINT_ERROR = "DATABASE_CONSTRAINT_ERROR" - DATABASE_TIMEOUT = "DATABASE_TIMEOUT" - - # File related error messages - FILE_NOT_FOUND = "FILE_NOT_FOUND" - FILE_READ_ERROR = "FILE_READ_ERROR" - FILE_WRITE_ERROR = "FILE_WRITE_ERROR" - FILE_PERMISSION_ERROR = "FILE_PERMISSION_ERROR" - FILE_FORMAT_ERROR = "FILE_FORMAT_ERROR" - FILE_SIZE_EXCEEDED = "FILE_SIZE_EXCEEDED" - FILE_UPLOAD_FAILED = "FILE_UPLOAD_FAILED" - FILE_DOWNLOAD_FAILED = "FILE_DOWNLOAD_FAILED" - FILE_PARSE_ERROR = "FILE_PARSE_ERROR" - - # Network related error messages - NETWORK_ERROR = "NETWORK_ERROR" - HTTP_REQUEST_ERROR = "HTTP_REQUEST_ERROR" - HTTP_TIMEOUT = "HTTP_TIMEOUT" - URL_INVALID = "URL_INVALID" - URL_SHORTENING_FAILED = "URL_SHORTENING_FAILED" - - # External service error messages - EXTERNAL_SERVICE_ERROR = "EXTERNAL_SERVICE_ERROR" - EXTERNAL_SERVICE_TIMEOUT = "EXTERNAL_SERVICE_TIMEOUT" - EXTERNAL_SERVICE_UNAVAILABLE = "EXTERNAL_SERVICE_UNAVAILABLE" - API_RATE_LIMIT_EXCEEDED = "API_RATE_LIMIT_EXCEEDED" - API_KEY_INVALID = "API_KEY_INVALID" - - # Configuration related error messages - CONFIGURATION_ERROR = "CONFIGURATION_ERROR" - CONFIGURATION_INVALID = "CONFIGURATION_INVALID" - CONFIGURATION_MISSING = "CONFIGURATION_MISSING" - ENVIRONMENT_VARIABLE_MISSING = "ENVIRONMENT_VARIABLE_MISSING" - - # Generation related error messages - GENERATION_ERROR = "GENERATION_ERROR" - GENERATION_TIMEOUT = "GENERATION_TIMEOUT" - GENERATION_CANCELLED = "GENERATION_CANCELLED" - GENERATION_IN_PROGRESS = "GENERATION_IN_PROGRESS" - GENERATION_QUEUE_FULL = "GENERATION_QUEUE_FULL" - NO_GENERATION_IN_PROGRESS = "NO_GENERATION_IN_PROGRESS" - - # Conversation related error messages - CONVERSATION_NOT_FOUND = "CONVERSATION_NOT_FOUND" - CONVERSATION_INVALID = "CONVERSATION_INVALID" - CONVERSATION_TYPE_INVALID = "CONVERSATION_TYPE_INVALID" - CONVERSATION_LIMIT_EXCEEDED = "CONVERSATION_LIMIT_EXCEEDED" - MESSAGE_TOO_LONG = "MESSAGE_TOO_LONG" - CONVERSATION_ACCESS_DENIED = "CONVERSATION_ACCESS_DENIED" - CONVERSATION_GET_FAILED = "CONVERSATION_GET_FAILED" - CONVERSATION_DELETE_FAILED = "CONVERSATION_DELETE_FAILED" - CONVERSATION_LIST_GET_FAILED = "CONVERSATION_LIST_GET_FAILED" - - # Content related error messages - CONTENT_EMPTY = "CONTENT_EMPTY" - CONTENT_TOO_LONG = "CONTENT_TOO_LONG" - CONTENT_INVALID_FORMAT = "CONTENT_INVALID_FORMAT" - CONTENT_PROCESSING_ERROR = "CONTENT_PROCESSING_ERROR" - - # Workflow stage related error messages - REQUIREMENT_EXTRACTION_NOT_COMPLETED = "REQUIREMENT_EXTRACTION_NOT_COMPLETED" - OUTLINE_GENERATION_NOT_COMPLETED = "OUTLINE_GENERATION_NOT_COMPLETED" - OUTLINE_ID_NOT_FOUND = "OUTLINE_ID_NOT_FOUND" - FULLTEXT_ID_NOT_FOUND = "FULLTEXT_ID_NOT_FOUND" - DOCUMENT_SLICE_NOT_FOUND = "DOCUMENT_SLICE_NOT_FOUND" - - # Editor and interface related error messages - UNSUPPORTED_EDITOR_TYPE = "UNSUPPORTED_EDITOR_TYPE" - BEAN_NOT_FOUND = "BEAN_NOT_FOUND" - BEAN_OPERATION_FAILED = "BEAN_OPERATION_FAILED" - - # Filename and size related error messages - FILENAME_EMPTY = "FILENAME_EMPTY" - - # Document version related error messages - DOCUMENT_VERSION_NOT_FOUND = "DOCUMENT_VERSION_NOT_FOUND" - DOCUMENT_VERSION_CREATE_FAILED = "DOCUMENT_VERSION_CREATE_FAILED" - DOCUMENT_VERSION_UPDATE_FAILED = "DOCUMENT_VERSION_UPDATE_FAILED" - DOCUMENT_VERSION_DELETE_FAILED = "DOCUMENT_VERSION_DELETE_FAILED" - DOCUMENT_VERSION_GET_FAILED = "DOCUMENT_VERSION_GET_FAILED" - DOCUMENT_NOT_FOUND = "DOCUMENT_NOT_FOUND" - - # Service related error messages - SERVICE_UNAVAILABLE = "SERVICE_UNAVAILABLE" - ANALYSIS_EXECUTION_FAILED = "ANALYSIS_EXECUTION_FAILED" - - # Task related error messages - TASK_NOT_FOUND = "TASK_NOT_FOUND" - TASK_CREATE_FAILED = "TASK_CREATE_FAILED" - TASK_GET_FAILED = "TASK_GET_FAILED" - TASK_LIST_GET_FAILED = "TASK_LIST_GET_FAILED" - TASK_CANCEL_FAILED = "TASK_CANCEL_FAILED" - TASK_DELETE_FAILED = "TASK_DELETE_FAILED" - TASK_STATS_GET_FAILED = "TASK_STATS_GET_FAILED" - TASK_REGISTERED_GET_FAILED = "TASK_REGISTERED_GET_FAILED" - TASK_CANNOT_CANCEL = "TASK_CANNOT_CANCEL" - TASK_CANNOT_DELETE_RUNNING = "TASK_CANNOT_DELETE_RUNNING" - - # Search related error messages - SEARCH_QUERY_EMPTY = "SEARCH_QUERY_EMPTY" - AI_SEARCH_FAILED = "AI_SEARCH_FAILED" - QUICK_SEARCH_FAILED = "QUICK_SEARCH_FAILED" - SEARCH_SUGGESTIONS_FAILED = "SEARCH_SUGGESTIONS_FAILED" - SEARCH_TAGS_FAILED = "SEARCH_TAGS_FAILED" - - # Resource related error messages - RESOURCE_PROCESSING_FAILED = "RESOURCE_PROCESSING_FAILED" - RESOURCE_EXTRACTION_FAILED = "RESOURCE_EXTRACTION_FAILED" - RESOURCE_IMPORT_FAILED = "RESOURCE_IMPORT_FAILED" - RESOURCE_URI_MISSING = "RESOURCE_URI_MISSING" - RESOURCE_UPLOAD_FAILED = "RESOURCE_UPLOAD_FAILED" - RESOURCE_BIND_FAILED = "RESOURCE_BIND_FAILED" - RESOURCE_LIST_GET_FAILED = "RESOURCE_LIST_GET_FAILED" - RESOURCE_STATUS_GET_FAILED = "RESOURCE_STATUS_GET_FAILED" - RESOURCE_CITATION_GET_FAILED = "RESOURCE_CITATION_GET_FAILED" - RESOURCE_GET_FAILED = "RESOURCE_GET_FAILED" - RESOURCE_NO_FILE = "RESOURCE_NO_FILE" - RESOURCE_DOWNLOAD_FAILED = "RESOURCE_DOWNLOAD_FAILED" - RESOURCE_DELETE_FAILED = "RESOURCE_DELETE_FAILED" - INSPIRATION_CREATE_FAILED = "INSPIRATION_CREATE_FAILED" - INSPIRATION_NOT_FOUND = "INSPIRATION_NOT_FOUND" - INSPIRATION_TYPE_INVALID = "INSPIRATION_TYPE_INVALID" - INSPIRATION_UPDATE_FAILED = "INSPIRATION_UPDATE_FAILED" - INSPIRATION_LIST_GET_FAILED = "INSPIRATION_LIST_GET_FAILED" - INSPIRATION_GET_FAILED = "INSPIRATION_GET_FAILED" - RESOURCE_TYPE_GET_FAILED = "RESOURCE_TYPE_GET_FAILED" - RESOURCE_SCOPE_GET_FAILED = "RESOURCE_SCOPE_GET_FAILED" - RESOURCE_PROCESSING_STATUS_GET_FAILED = "RESOURCE_PROCESSING_STATUS_GET_FAILED" - RESOURCE_SIGNED_URL_FAILED = "RESOURCE_SIGNED_URL_FAILED" - - # System error messages - SYSTEM_ERROR = "SYSTEM_ERROR" - INITIALIZATION_FAILED = "INITIALIZATION_FAILED" - SERVICE_STARTUP_FAILED = "SERVICE_STARTUP_FAILED" - SERVICE_SHUTDOWN_FAILED = "SERVICE_SHUTDOWN_FAILED" - - # Context and session error messages - CONTEXT_NOT_SET = "CONTEXT_NOT_SET" - SESSION_NOT_INITIALIZED = "SESSION_NOT_INITIALIZED" - MIDDLEWARE_ERROR = "MIDDLEWARE_ERROR" - - # LLM related error messages - LLM_OUTPUT_PARSING_ERROR = "LLM_OUTPUT_PARSING_ERROR" - LLM_CALL_FAILED = "LLM_CALL_FAILED" - LLM_RETRY_EXHAUSTED = "LLM_RETRY_EXHAUSTED" - - # Quota related error messages - QUOTA_INSUFFICIENT = "QUOTA_INSUFFICIENT" - - -# ErrorCode translation dictionary -ERROR_CODE_TRANSLATIONS_ZH: Dict[str, str] = { - # 通用错误 - ErrorCode.UNKNOWN_ERROR.value: "未知错误", - ErrorCode.HTTP_ERROR.value: "HTTP错误", - ErrorCode.INVALID_PARAMETER.value: "参数无效", - ErrorCode.RESOURCE_NOT_FOUND.value: "资源未找到", - ErrorCode.PERMISSION_DENIED.value: "权限被拒绝", - ErrorCode.VALIDATION_ERROR.value: "数据验证失败", - ErrorCode.RESOURCE_INVALID.value: "资源无效", - ErrorCode.TYPE_ERROR.value: "类型错误", - ErrorCode.OPERATION_FAILED.value: "操作失败", - # 认证相关错误 - ErrorCode.AUTHENTICATION_ERROR.value: "认证错误", - ErrorCode.AUTHENTICATION_FAILED.value: "认证失败", - ErrorCode.TOKEN_INVALID.value: "令牌无效", - ErrorCode.TOKEN_EXPIRED.value: "令牌已过期", - ErrorCode.PERMISSION_INSUFFICIENT.value: "权限不足", - ErrorCode.USER_NOT_FOUND.value: "用户不存在", - ErrorCode.USER_INVALID.value: "用户信息无效", - # Agent相关错误 - ErrorCode.AGENT_INITIALIZATION_ERROR.value: "Agent初始化错误", - ErrorCode.AGENT_EXECUTION_ERROR.value: "Agent执行错误", - ErrorCode.AGENT_STATE_ERROR.value: "Agent状态错误", - ErrorCode.AGENT_TIMEOUT.value: "Agent超时", - ErrorCode.AGENT_CANCELLED.value: "Agent已取消", - ErrorCode.AGENT_MEMORY_ERROR.value: "Agent内存错误", - # 数据库相关错误 - ErrorCode.DATABASE_ERROR.value: "数据库错误", - ErrorCode.DATABASE_CONNECTION_ERROR.value: "数据库连接错误", - ErrorCode.DATABASE_QUERY_ERROR.value: "数据库查询错误", - ErrorCode.DATABASE_TRANSACTION_ERROR.value: "数据库事务错误", - ErrorCode.DATABASE_CONSTRAINT_ERROR.value: "数据库约束错误", - ErrorCode.DATABASE_TIMEOUT.value: "数据库超时", - # 文件相关错误 - ErrorCode.FILE_NOT_FOUND.value: "文件未找到", - ErrorCode.FILE_READ_ERROR.value: "文件读取错误", - ErrorCode.FILE_WRITE_ERROR.value: "文件写入错误", - ErrorCode.FILE_PERMISSION_ERROR.value: "文件权限错误", - ErrorCode.FILE_FORMAT_ERROR.value: "文件格式错误", - ErrorCode.FILE_SIZE_EXCEEDED.value: "文件大小超限", - ErrorCode.FILE_UPLOAD_FAILED.value: "文件上传失败", - ErrorCode.FILE_DOWNLOAD_FAILED.value: "文件下载失败", - ErrorCode.FILE_PARSE_ERROR.value: "文件解析错误", - # 网络相关错误 - ErrorCode.NETWORK_ERROR.value: "网络错误", - ErrorCode.HTTP_REQUEST_ERROR.value: "HTTP请求错误", - ErrorCode.HTTP_TIMEOUT.value: "HTTP超时", - ErrorCode.URL_INVALID.value: "URL无效", - ErrorCode.URL_SHORTENING_FAILED.value: "URL缩短失败", - # 外部服务错误 - ErrorCode.EXTERNAL_SERVICE_ERROR.value: "外部服务错误", - ErrorCode.EXTERNAL_SERVICE_TIMEOUT.value: "外部服务超时", - ErrorCode.EXTERNAL_SERVICE_UNAVAILABLE.value: "外部服务不可用", - ErrorCode.API_RATE_LIMIT_EXCEEDED.value: "API调用频率超限", - ErrorCode.API_KEY_INVALID.value: "API密钥无效", - # 配置相关错误 - ErrorCode.CONFIGURATION_ERROR.value: "配置错误", - ErrorCode.CONFIGURATION_INVALID.value: "配置无效", - ErrorCode.CONFIGURATION_MISSING.value: "配置缺失", - ErrorCode.ENVIRONMENT_VARIABLE_MISSING.value: "环境变量缺失", - # 生成相关错误 - ErrorCode.GENERATION_ERROR.value: "生成错误", - ErrorCode.GENERATION_TIMEOUT.value: "生成超时", - ErrorCode.GENERATION_CANCELLED.value: "生成已取消", - ErrorCode.GENERATION_IN_PROGRESS.value: "生成进行中", - ErrorCode.GENERATION_QUEUE_FULL.value: "生成队列已满", - # 对话相关错误 - ErrorCode.CONVERSATION_NOT_FOUND.value: "对话未找到", - ErrorCode.CONVERSATION_INVALID.value: "对话无效", - ErrorCode.CONVERSATION_LIMIT_EXCEEDED.value: "对话数量超限", - ErrorCode.MESSAGE_TOO_LONG.value: "消息过长", - # 内容相关错误 - ErrorCode.CONTENT_EMPTY.value: "内容为空", - ErrorCode.CONTENT_TOO_LONG.value: "内容过长", - ErrorCode.CONTENT_INVALID_FORMAT.value: "内容格式无效", - ErrorCode.CONTENT_PROCESSING_ERROR.value: "内容处理错误", - # 资源相关错误 - ErrorCode.RESOURCE_PROCESSING_FAILED.value: "资源处理失败", - ErrorCode.RESOURCE_EXTRACTION_FAILED.value: "资源提取失败", - ErrorCode.RESOURCE_IMPORT_FAILED.value: "资源导入失败", - ErrorCode.RESOURCE_URI_MISSING.value: "资源URI缺失", - # 系统错误 - ErrorCode.SYSTEM_ERROR.value: "系统错误", - ErrorCode.INITIALIZATION_FAILED.value: "初始化失败", - ErrorCode.SERVICE_STARTUP_FAILED.value: "服务启动失败", - ErrorCode.SERVICE_SHUTDOWN_FAILED.value: "服务关闭失败", - # 上下文和会话错误 - ErrorCode.CONTEXT_NOT_SET.value: "上下文未设置", - ErrorCode.SESSION_NOT_INITIALIZED.value: "会话未初始化", - ErrorCode.MIDDLEWARE_ERROR.value: "中间件错误", - # 工作流阶段相关错误代码 - ErrorCode.CONVERSATION_TYPE_INVALID.value: "对话类型无效", - ErrorCode.REQUIREMENT_EXTRACTION_NOT_COMPLETED.value: "需求提取未完成", - ErrorCode.OUTLINE_GENERATION_NOT_COMPLETED.value: "大纲生成未完成", - ErrorCode.OUTLINE_ID_NOT_FOUND.value: "大纲ID未找到", - ErrorCode.FULLTEXT_ID_NOT_FOUND.value: "全文ID未找到", - ErrorCode.DOCUMENT_SLICE_NOT_FOUND.value: "文档片段未找到", - # 任务相关错误代码 - ErrorCode.TASK_NOT_FOUND.value: "任务未找到", - ErrorCode.NO_GENERATION_IN_PROGRESS.value: "无生成进行中", - # 编辑器相关错误代码 - ErrorCode.UNSUPPORTED_EDITOR_TYPE.value: "不支持的编辑器类型", - # Agent特定错误代码 - ErrorCode.LLM_OUTPUT_PARSING_ERROR.value: "LLM输出解析失败", - ErrorCode.LLM_CALL_FAILED.value: "LLM调用失败", - ErrorCode.LLM_RETRY_EXHAUSTED.value: "LLM重试次数耗尽", - # 配额相关错误代码 - ErrorCode.QUOTA_INSUFFICIENT.value: "配额不足", -} - -ERROR_CODE_TRANSLATIONS_EN: Dict[str, str] = { - # General errors - ErrorCode.UNKNOWN_ERROR.value: "Unknown error", - ErrorCode.HTTP_ERROR.value: "HTTP error", - ErrorCode.INVALID_PARAMETER.value: "Invalid parameter", - ErrorCode.RESOURCE_NOT_FOUND.value: "Resource not found", - ErrorCode.PERMISSION_DENIED.value: "Permission denied", - ErrorCode.VALIDATION_ERROR.value: "Validation failed", - ErrorCode.RESOURCE_INVALID.value: "Invalid resource", - ErrorCode.TYPE_ERROR.value: "Type error", - ErrorCode.OPERATION_FAILED.value: "Operation failed", - # Authentication related errors - ErrorCode.AUTHENTICATION_ERROR.value: "Authentication error", - ErrorCode.AUTHENTICATION_FAILED.value: "Authentication failed", - ErrorCode.TOKEN_INVALID.value: "Invalid token", - ErrorCode.TOKEN_EXPIRED.value: "Token expired", - ErrorCode.PERMISSION_INSUFFICIENT.value: "Insufficient permissions", - ErrorCode.USER_NOT_FOUND.value: "User not found", - ErrorCode.USER_INVALID.value: "Invalid user information", - # Agent related errors - ErrorCode.AGENT_INITIALIZATION_ERROR.value: "Agent initialization error", - ErrorCode.AGENT_EXECUTION_ERROR.value: "Agent execution error", - ErrorCode.AGENT_STATE_ERROR.value: "Agent state error", - ErrorCode.AGENT_TIMEOUT.value: "Agent timeout", - ErrorCode.AGENT_CANCELLED.value: "Agent cancelled", - ErrorCode.AGENT_MEMORY_ERROR.value: "Agent memory error", - # Database related errors - ErrorCode.DATABASE_ERROR.value: "Database error", - ErrorCode.DATABASE_CONNECTION_ERROR.value: "Database connection error", - ErrorCode.DATABASE_QUERY_ERROR.value: "Database query error", - ErrorCode.DATABASE_TRANSACTION_ERROR.value: "Database transaction error", - ErrorCode.DATABASE_CONSTRAINT_ERROR.value: "Database constraint error", - ErrorCode.DATABASE_TIMEOUT.value: "Database timeout", - # File related errors - ErrorCode.FILE_NOT_FOUND.value: "File not found", - ErrorCode.FILE_READ_ERROR.value: "File read error", - ErrorCode.FILE_WRITE_ERROR.value: "File write error", - ErrorCode.FILE_PERMISSION_ERROR.value: "File permission error", - ErrorCode.FILE_FORMAT_ERROR.value: "File format error", - ErrorCode.FILE_SIZE_EXCEEDED.value: "File size exceeded", - ErrorCode.FILE_UPLOAD_FAILED.value: "File upload failed", - ErrorCode.FILE_DOWNLOAD_FAILED.value: "File download failed", - ErrorCode.FILE_PARSE_ERROR.value: "File parse error", - # Network related errors - ErrorCode.NETWORK_ERROR.value: "Network error", - ErrorCode.HTTP_REQUEST_ERROR.value: "HTTP request error", - ErrorCode.HTTP_TIMEOUT.value: "HTTP timeout", - ErrorCode.URL_INVALID.value: "Invalid URL", - ErrorCode.URL_SHORTENING_FAILED.value: "URL shortening failed", - # External service errors - ErrorCode.EXTERNAL_SERVICE_ERROR.value: "External service error", - ErrorCode.EXTERNAL_SERVICE_TIMEOUT.value: "External service timeout", - ErrorCode.EXTERNAL_SERVICE_UNAVAILABLE.value: "External service unavailable", - ErrorCode.API_RATE_LIMIT_EXCEEDED.value: "API rate limit exceeded", - ErrorCode.API_KEY_INVALID.value: "Invalid API key", - # Configuration related errors - ErrorCode.CONFIGURATION_ERROR.value: "Configuration error", - ErrorCode.CONFIGURATION_INVALID.value: "Invalid configuration", - ErrorCode.CONFIGURATION_MISSING.value: "Configuration missing", - ErrorCode.ENVIRONMENT_VARIABLE_MISSING.value: "Environment variable missing", - # Generation related errors - ErrorCode.GENERATION_ERROR.value: "Generation error", - ErrorCode.GENERATION_TIMEOUT.value: "Generation timeout", - ErrorCode.GENERATION_CANCELLED.value: "Generation cancelled", - ErrorCode.GENERATION_IN_PROGRESS.value: "Generation in progress", - ErrorCode.GENERATION_QUEUE_FULL.value: "Generation queue full", - # Conversation related errors - ErrorCode.CONVERSATION_NOT_FOUND.value: "Conversation not found", - ErrorCode.CONVERSATION_INVALID.value: "Invalid conversation", - ErrorCode.CONVERSATION_LIMIT_EXCEEDED.value: "Conversation limit exceeded", - ErrorCode.MESSAGE_TOO_LONG.value: "Message too long", - # Content related errors - ErrorCode.CONTENT_EMPTY.value: "Content empty", - ErrorCode.CONTENT_TOO_LONG.value: "Content too long", - ErrorCode.CONTENT_INVALID_FORMAT.value: "Invalid content format", - ErrorCode.CONTENT_PROCESSING_ERROR.value: "Content processing error", - # Resource related errors - ErrorCode.RESOURCE_PROCESSING_FAILED.value: "Resource processing failed", - ErrorCode.RESOURCE_EXTRACTION_FAILED.value: "Resource extraction failed", - ErrorCode.RESOURCE_IMPORT_FAILED.value: "Resource import failed", - ErrorCode.RESOURCE_URI_MISSING.value: "Resource URI missing", - # System errors - ErrorCode.SYSTEM_ERROR.value: "System error", - ErrorCode.INITIALIZATION_FAILED.value: "Initialization failed", - ErrorCode.SERVICE_STARTUP_FAILED.value: "Service startup failed", - ErrorCode.SERVICE_SHUTDOWN_FAILED.value: "Service shutdown failed", - # Context and session errors - ErrorCode.CONTEXT_NOT_SET.value: "Context not set", - ErrorCode.SESSION_NOT_INITIALIZED.value: "Session not initialized", - ErrorCode.MIDDLEWARE_ERROR.value: "Middleware error", - # Workflow stage related error codes - ErrorCode.CONVERSATION_TYPE_INVALID.value: "Invalid conversation type", - ErrorCode.REQUIREMENT_EXTRACTION_NOT_COMPLETED.value: "Requirement extraction not completed", - ErrorCode.OUTLINE_GENERATION_NOT_COMPLETED.value: "Outline generation not completed", - ErrorCode.OUTLINE_ID_NOT_FOUND.value: "Outline ID not found", - ErrorCode.FULLTEXT_ID_NOT_FOUND.value: "Fulltext ID not found", - ErrorCode.DOCUMENT_SLICE_NOT_FOUND.value: "Document slice not found", - # Task related error codes - ErrorCode.TASK_NOT_FOUND.value: "Task not found", - ErrorCode.NO_GENERATION_IN_PROGRESS.value: "No generation in progress", - # Editor related error codes - ErrorCode.UNSUPPORTED_EDITOR_TYPE.value: "Unsupported editor type", - # Agent specific error codes - ErrorCode.LLM_OUTPUT_PARSING_ERROR.value: "LLM output parsing failed", - ErrorCode.LLM_CALL_FAILED.value: "LLM call failed", - ErrorCode.LLM_RETRY_EXHAUSTED.value: "LLM retry attempts exhausted", - # Quota related error codes - ErrorCode.QUOTA_INSUFFICIENT.value: "Quota insufficient", -} - - -# ErrorMessage translation dictionary -ERROR_MESSAGES_ZH: Dict[str, str] = { - # 通用错误 - ErrorMessage.UNKNOWN_ERROR.value: "未知错误", - ErrorMessage.HTTP_ERROR.value: "HTTP错误", - ErrorMessage.INVALID_PARAMETER.value: "无效的参数", - ErrorMessage.RESOURCE_NOT_FOUND.value: "资源未找到", - ErrorMessage.PERMISSION_DENIED.value: "权限被拒绝", - ErrorMessage.VALIDATION_ERROR.value: "数据验证失败", - ErrorMessage.RESOURCE_INVALID.value: "资源无效", - ErrorMessage.TYPE_ERROR.value: "类型错误", - ErrorMessage.OPERATION_FAILED.value: "操作失败", - # 认证相关错误 - ErrorMessage.AUTHENTICATION_ERROR.value: "认证错误", - ErrorMessage.AUTHENTICATION_FAILED.value: "认证失败", - ErrorMessage.TOKEN_INVALID.value: "令牌无效", - ErrorMessage.TOKEN_EXPIRED.value: "令牌已过期", - ErrorMessage.PERMISSION_INSUFFICIENT.value: "权限不足", - ErrorMessage.USER_NOT_FOUND.value: "用户不存在", - ErrorMessage.USER_INVALID.value: "用户信息无效", - # Agent相关错误 - ErrorMessage.AGENT_INITIALIZATION_ERROR.value: "Agent初始化失败", - ErrorMessage.AGENT_EXECUTION_ERROR.value: "Agent执行失败", - ErrorMessage.AGENT_STATE_ERROR.value: "Agent状态错误", - ErrorMessage.AGENT_TIMEOUT.value: "Agent执行超时", - ErrorMessage.AGENT_CANCELLED.value: "Agent执行已取消", - ErrorMessage.AGENT_MEMORY_ERROR.value: "Agent内存不足", - # 数据库相关错误 - ErrorMessage.DATABASE_ERROR.value: "数据库错误", - ErrorMessage.DATABASE_CONNECTION_ERROR.value: "数据库连接失败", - ErrorMessage.DATABASE_QUERY_ERROR.value: "数据库查询失败", - ErrorMessage.DATABASE_TRANSACTION_ERROR.value: "数据库事务失败", - ErrorMessage.DATABASE_CONSTRAINT_ERROR.value: "数据库约束违反", - ErrorMessage.DATABASE_TIMEOUT.value: "数据库操作超时", - # 文件相关错误 - ErrorMessage.FILE_NOT_FOUND.value: "文件未找到", - ErrorMessage.FILE_READ_ERROR.value: "文件读取失败", - ErrorMessage.FILE_WRITE_ERROR.value: "文件写入失败", - ErrorMessage.FILE_PERMISSION_ERROR.value: "文件权限不足", - ErrorMessage.FILE_FORMAT_ERROR.value: "文件格式错误", - ErrorMessage.FILE_SIZE_EXCEEDED.value: "文件大小超过限制", - ErrorMessage.FILE_UPLOAD_FAILED.value: "文件上传失败", - ErrorMessage.FILE_DOWNLOAD_FAILED.value: "文件下载失败", - ErrorMessage.FILE_PARSE_ERROR.value: "文件解析失败", - # 网络相关错误 - ErrorMessage.NETWORK_ERROR.value: "网络连接错误", - ErrorMessage.HTTP_REQUEST_ERROR.value: "HTTP请求失败", - ErrorMessage.HTTP_TIMEOUT.value: "HTTP请求超时", - ErrorMessage.URL_INVALID.value: "URL格式无效", - ErrorMessage.URL_SHORTENING_FAILED.value: "URL缩短失败", - # 外部服务错误 - ErrorMessage.EXTERNAL_SERVICE_ERROR.value: "外部服务调用失败", - ErrorMessage.EXTERNAL_SERVICE_TIMEOUT.value: "外部服务超时", - ErrorMessage.EXTERNAL_SERVICE_UNAVAILABLE.value: "外部服务不可用", - ErrorMessage.API_RATE_LIMIT_EXCEEDED.value: "API调用频率超过限制", - ErrorMessage.API_KEY_INVALID.value: "API密钥无效", - # 配置相关错误 - ErrorMessage.CONFIGURATION_ERROR.value: "配置错误", - ErrorMessage.CONFIGURATION_INVALID.value: "配置格式无效", - ErrorMessage.CONFIGURATION_MISSING.value: "配置文件缺失", - ErrorMessage.ENVIRONMENT_VARIABLE_MISSING.value: "环境变量未设置", - # 生成相关错误 - ErrorMessage.GENERATION_ERROR.value: "生成过程出错", - ErrorMessage.GENERATION_TIMEOUT.value: "生成超时", - ErrorMessage.GENERATION_CANCELLED.value: "生成已取消", - ErrorMessage.GENERATION_IN_PROGRESS.value: "生成正在进行中", - ErrorMessage.GENERATION_QUEUE_FULL.value: "生成队列已满", - ErrorMessage.NO_GENERATION_IN_PROGRESS.value: "无生成进行中", - # 对话相关错误 - ErrorMessage.CONVERSATION_NOT_FOUND.value: "对话不存在", - ErrorMessage.CONVERSATION_INVALID.value: "对话状态无效", - ErrorMessage.CONVERSATION_TYPE_INVALID.value: "对话类型无效", - ErrorMessage.CONVERSATION_LIMIT_EXCEEDED.value: "对话数量超过限制", - ErrorMessage.MESSAGE_TOO_LONG.value: "消息内容过长", - ErrorMessage.CONVERSATION_ACCESS_DENIED.value: "无权限访问此对话会话", - ErrorMessage.CONVERSATION_GET_FAILED.value: "获取对话会话失败", - ErrorMessage.CONVERSATION_DELETE_FAILED.value: "删除对话会话失败", - ErrorMessage.CONVERSATION_LIST_GET_FAILED.value: "获取对话会话列表失败", - # 内容相关错误 - ErrorMessage.CONTENT_EMPTY.value: "内容不能为空", - ErrorMessage.CONTENT_TOO_LONG.value: "内容长度超过限制", - ErrorMessage.CONTENT_INVALID_FORMAT.value: "内容格式无效", - ErrorMessage.CONTENT_PROCESSING_ERROR.value: "内容处理失败", - # 工作流阶段相关错误 - ErrorMessage.REQUIREMENT_EXTRACTION_NOT_COMPLETED.value: "请先完成需求提取阶段", - ErrorMessage.OUTLINE_GENERATION_NOT_COMPLETED.value: "请先完成大纲生成阶段", - ErrorMessage.OUTLINE_ID_NOT_FOUND.value: "无法获取outline_id", - ErrorMessage.FULLTEXT_ID_NOT_FOUND.value: "无法获取fulltext_id", - ErrorMessage.DOCUMENT_SLICE_NOT_FOUND.value: "文档片段不存在", - # 编辑器和界面相关错误 - ErrorMessage.UNSUPPORTED_EDITOR_TYPE.value: "不支持的编辑器类型", - ErrorMessage.BEAN_NOT_FOUND.value: "Bean组件未找到", - ErrorMessage.BEAN_OPERATION_FAILED.value: "Bean操作失败", - # 文件名和大小相关错误 - ErrorMessage.FILENAME_EMPTY.value: "文件名不能为空", - # 文档版本相关错误 - ErrorMessage.DOCUMENT_VERSION_NOT_FOUND.value: "文档版本不存在", - ErrorMessage.DOCUMENT_VERSION_CREATE_FAILED.value: "创建文档版本失败", - ErrorMessage.DOCUMENT_VERSION_UPDATE_FAILED.value: "更新文档版本失败", - ErrorMessage.DOCUMENT_VERSION_DELETE_FAILED.value: "删除文档版本失败", - ErrorMessage.DOCUMENT_VERSION_GET_FAILED.value: "获取文档版本失败", - ErrorMessage.DOCUMENT_NOT_FOUND.value: "文档不存在", - # 服务相关错误 - ErrorMessage.SERVICE_UNAVAILABLE.value: "服务不可用", - ErrorMessage.ANALYSIS_EXECUTION_FAILED.value: "分析执行失败", - # 任务相关错误 - ErrorMessage.TASK_NOT_FOUND.value: "任务不存在", - ErrorMessage.TASK_CREATE_FAILED.value: "添加任务失败", - ErrorMessage.TASK_GET_FAILED.value: "获取任务详情失败", - ErrorMessage.TASK_LIST_GET_FAILED.value: "获取任务列表失败", - ErrorMessage.TASK_CANCEL_FAILED.value: "取消任务失败", - ErrorMessage.TASK_DELETE_FAILED.value: "删除任务记录失败", - ErrorMessage.TASK_STATS_GET_FAILED.value: "获取任务统计失败", - ErrorMessage.TASK_REGISTERED_GET_FAILED.value: "获取已注册任务失败", - ErrorMessage.TASK_CANNOT_CANCEL.value: "只能取消待处理或运行中的任务", - ErrorMessage.TASK_CANNOT_DELETE_RUNNING.value: "不能删除正在运行的任务，请先取消", - # 搜索相关错误 - ErrorMessage.SEARCH_QUERY_EMPTY.value: "搜索查询不能为空", - ErrorMessage.AI_SEARCH_FAILED.value: "AI搜索失败", - ErrorMessage.QUICK_SEARCH_FAILED.value: "快速搜索失败", - ErrorMessage.SEARCH_SUGGESTIONS_FAILED.value: "获取搜索建议失败", - ErrorMessage.SEARCH_TAGS_FAILED.value: "获取热门标签失败", - # 资源相关错误 - ErrorMessage.RESOURCE_PROCESSING_FAILED.value: "资源处理失败", - ErrorMessage.RESOURCE_EXTRACTION_FAILED.value: "资源提取失败", - ErrorMessage.RESOURCE_IMPORT_FAILED.value: "资源导入失败", - ErrorMessage.RESOURCE_URI_MISSING.value: "资源URI缺失", - # 系统错误 - ErrorMessage.SYSTEM_ERROR.value: "系统内部错误", - ErrorMessage.INITIALIZATION_FAILED.value: "系统初始化失败", - ErrorMessage.SERVICE_STARTUP_FAILED.value: "服务启动失败", - ErrorMessage.SERVICE_SHUTDOWN_FAILED.value: "服务关闭失败", - # 上下文和会话错误 - ErrorMessage.CONTEXT_NOT_SET.value: "请求上下文未设置", - ErrorMessage.SESSION_NOT_INITIALIZED.value: "数据库会话未初始化", - ErrorMessage.MIDDLEWARE_ERROR.value: "中间件处理错误", - # 资源相关错误消息（补充） - ErrorMessage.RESOURCE_UPLOAD_FAILED.value: "资源上传失败", - ErrorMessage.RESOURCE_BIND_FAILED.value: "资源绑定失败", - ErrorMessage.RESOURCE_LIST_GET_FAILED.value: "获取资源列表失败", - ErrorMessage.RESOURCE_STATUS_GET_FAILED.value: "获取资源状态失败", - ErrorMessage.RESOURCE_CITATION_GET_FAILED.value: "获取资源引用失败", - ErrorMessage.RESOURCE_GET_FAILED.value: "获取资源失败", - ErrorMessage.RESOURCE_NO_FILE.value: "资源文件不存在", - ErrorMessage.RESOURCE_DOWNLOAD_FAILED.value: "资源下载失败", - ErrorMessage.RESOURCE_DELETE_FAILED.value: "删除资源失败", - # 灵感相关错误消息 - ErrorMessage.INSPIRATION_CREATE_FAILED.value: "创建灵感失败", - ErrorMessage.INSPIRATION_NOT_FOUND.value: "灵感不存在", - ErrorMessage.INSPIRATION_TYPE_INVALID.value: "灵感类型无效", - ErrorMessage.INSPIRATION_UPDATE_FAILED.value: "更新灵感失败", - ErrorMessage.INSPIRATION_LIST_GET_FAILED.value: "获取灵感列表失败", - ErrorMessage.INSPIRATION_GET_FAILED.value: "获取灵感失败", - ErrorMessage.RESOURCE_TYPE_GET_FAILED.value: "获取资源类型失败", - ErrorMessage.RESOURCE_SCOPE_GET_FAILED.value: "获取资源范围失败", - ErrorMessage.RESOURCE_PROCESSING_STATUS_GET_FAILED.value: "获取资源处理状态失败", - ErrorMessage.RESOURCE_SIGNED_URL_FAILED.value: "获取资源签名URL失败", - # LLM相关错误 - ErrorMessage.LLM_OUTPUT_PARSING_ERROR.value: "LLM输出解析失败", - ErrorMessage.LLM_CALL_FAILED.value: "LLM调用失败", - ErrorMessage.LLM_RETRY_EXHAUSTED.value: "LLM重试次数已用完", - # 配额相关错误 - ErrorMessage.QUOTA_INSUFFICIENT.value: "余额不足，无法执行此操作", -} - -ERROR_MESSAGES_EN: Dict[str, str] = { - # General errors - ErrorMessage.UNKNOWN_ERROR.value: "An unknown error occurred", - ErrorMessage.HTTP_ERROR.value: "HTTP error", - ErrorMessage.INVALID_PARAMETER.value: "Invalid parameter provided", - ErrorMessage.RESOURCE_NOT_FOUND.value: "Resource not found", - ErrorMessage.PERMISSION_DENIED.value: "Permission denied", - ErrorMessage.VALIDATION_ERROR.value: "Data validation failed", - ErrorMessage.RESOURCE_INVALID.value: "Invalid resource", - ErrorMessage.TYPE_ERROR.value: "Type error occurred", - ErrorMessage.OPERATION_FAILED.value: "Operation failed", - # Authentication related errors - ErrorMessage.AUTHENTICATION_ERROR.value: "Authentication error", - ErrorMessage.AUTHENTICATION_FAILED.value: "Authentication failed", - ErrorMessage.TOKEN_INVALID.value: "Invalid token", - ErrorMessage.TOKEN_EXPIRED.value: "Token has expired", - ErrorMessage.PERMISSION_INSUFFICIENT.value: "Insufficient permissions", - ErrorMessage.USER_NOT_FOUND.value: "User not found", - ErrorMessage.USER_INVALID.value: "Invalid user information", - # Agent related errors - ErrorMessage.AGENT_INITIALIZATION_ERROR.value: "Agent initialization failed", - ErrorMessage.AGENT_EXECUTION_ERROR.value: "Agent execution failed", - ErrorMessage.AGENT_STATE_ERROR.value: "Agent state error", - ErrorMessage.AGENT_TIMEOUT.value: "Agent execution timeout", - ErrorMessage.AGENT_CANCELLED.value: "Agent execution cancelled", - ErrorMessage.AGENT_MEMORY_ERROR.value: "Agent memory insufficient", - # Database related errors - ErrorMessage.DATABASE_ERROR.value: "Database error", - ErrorMessage.DATABASE_CONNECTION_ERROR.value: "Database connection failed", - ErrorMessage.DATABASE_QUERY_ERROR.value: "Database query failed", - ErrorMessage.DATABASE_TRANSACTION_ERROR.value: "Database transaction failed", - ErrorMessage.DATABASE_CONSTRAINT_ERROR.value: "Database constraint violation", - ErrorMessage.DATABASE_TIMEOUT.value: "Database operation timeout", - # File related errors - ErrorMessage.FILE_NOT_FOUND.value: "File not found", - ErrorMessage.FILE_READ_ERROR.value: "File read error", - ErrorMessage.FILE_WRITE_ERROR.value: "File write error", - ErrorMessage.FILE_PERMISSION_ERROR.value: "File permission denied", - ErrorMessage.FILE_FORMAT_ERROR.value: "Invalid file format", - ErrorMessage.FILE_SIZE_EXCEEDED.value: "File size limit exceeded", - ErrorMessage.FILE_UPLOAD_FAILED.value: "File upload failed", - ErrorMessage.FILE_DOWNLOAD_FAILED.value: "File download failed", - ErrorMessage.FILE_PARSE_ERROR.value: "File parsing failed", - # Network related errors - ErrorMessage.NETWORK_ERROR.value: "Network connection error", - ErrorMessage.HTTP_REQUEST_ERROR.value: "HTTP request failed", - ErrorMessage.HTTP_TIMEOUT.value: "HTTP request timeout", - ErrorMessage.URL_INVALID.value: "Invalid URL format", - ErrorMessage.URL_SHORTENING_FAILED.value: "URL shortening failed", - # External service errors - ErrorMessage.EXTERNAL_SERVICE_ERROR.value: "External service call failed", - ErrorMessage.EXTERNAL_SERVICE_TIMEOUT.value: "External service timeout", - ErrorMessage.EXTERNAL_SERVICE_UNAVAILABLE.value: "External service unavailable", - ErrorMessage.API_RATE_LIMIT_EXCEEDED.value: "API rate limit exceeded", - ErrorMessage.API_KEY_INVALID.value: "Invalid API key", - # Configuration related errors - ErrorMessage.CONFIGURATION_ERROR.value: "Configuration error", - ErrorMessage.CONFIGURATION_INVALID.value: "Invalid configuration format", - ErrorMessage.CONFIGURATION_MISSING.value: "Configuration file missing", - ErrorMessage.ENVIRONMENT_VARIABLE_MISSING.value: "Environment variable not set", - # Generation related errors - ErrorMessage.GENERATION_ERROR.value: "Generation process error", - ErrorMessage.GENERATION_TIMEOUT.value: "Generation timeout", - ErrorMessage.GENERATION_CANCELLED.value: "Generation cancelled", - ErrorMessage.GENERATION_IN_PROGRESS.value: "Generation in progress", - ErrorMessage.GENERATION_QUEUE_FULL.value: "Generation queue is full", - ErrorMessage.NO_GENERATION_IN_PROGRESS.value: "No generation in progress", - # Conversation related errors - ErrorMessage.CONVERSATION_NOT_FOUND.value: "Conversation not found", - ErrorMessage.CONVERSATION_INVALID.value: "Invalid conversation state", - ErrorMessage.CONVERSATION_TYPE_INVALID.value: "Invalid conversation type", - ErrorMessage.CONVERSATION_LIMIT_EXCEEDED.value: "Conversation limit exceeded", - ErrorMessage.MESSAGE_TOO_LONG.value: "Message content too long", - ErrorMessage.CONVERSATION_ACCESS_DENIED.value: "No permission to access this conversation", - ErrorMessage.CONVERSATION_GET_FAILED.value: "Failed to get conversation", - ErrorMessage.CONVERSATION_DELETE_FAILED.value: "Failed to delete conversation", - ErrorMessage.CONVERSATION_LIST_GET_FAILED.value: "Failed to get conversation list", - # Content related errors - ErrorMessage.CONTENT_EMPTY.value: "Content cannot be empty", - ErrorMessage.CONTENT_TOO_LONG.value: "Content length limit exceeded", - ErrorMessage.CONTENT_INVALID_FORMAT.value: "Invalid content format", - ErrorMessage.CONTENT_PROCESSING_ERROR.value: "Content processing failed", - # Workflow stage related errors - ErrorMessage.REQUIREMENT_EXTRACTION_NOT_COMPLETED.value: "Please complete the requirement extraction stage first", - ErrorMessage.OUTLINE_GENERATION_NOT_COMPLETED.value: "Please complete the outline generation stage first", - ErrorMessage.OUTLINE_ID_NOT_FOUND.value: "Unable to get outline_id", - ErrorMessage.FULLTEXT_ID_NOT_FOUND.value: "Unable to get fulltext_id", - ErrorMessage.DOCUMENT_SLICE_NOT_FOUND.value: "Document slice not found", - # Editor and interface related errors - ErrorMessage.UNSUPPORTED_EDITOR_TYPE.value: "Unsupported editor type", - ErrorMessage.BEAN_NOT_FOUND.value: "Bean component not found", - ErrorMessage.BEAN_OPERATION_FAILED.value: "Bean operation failed", - # Filename and size related errors - ErrorMessage.FILENAME_EMPTY.value: "Filename cannot be empty", - # Document version related errors - ErrorMessage.DOCUMENT_VERSION_NOT_FOUND.value: "Document version not found", - ErrorMessage.DOCUMENT_VERSION_CREATE_FAILED.value: "Failed to create document version", - ErrorMessage.DOCUMENT_VERSION_UPDATE_FAILED.value: "Failed to update document version", - ErrorMessage.DOCUMENT_VERSION_DELETE_FAILED.value: "Failed to delete document version", - ErrorMessage.DOCUMENT_VERSION_GET_FAILED.value: "Failed to get document version", - ErrorMessage.DOCUMENT_NOT_FOUND.value: "Document not found", - # Service related errors - ErrorMessage.SERVICE_UNAVAILABLE.value: "Service unavailable", - ErrorMessage.ANALYSIS_EXECUTION_FAILED.value: "Analysis execution failed", - # Task related errors - ErrorMessage.TASK_NOT_FOUND.value: "Task not found", - ErrorMessage.TASK_CREATE_FAILED.value: "Failed to create task", - ErrorMessage.TASK_GET_FAILED.value: "Failed to get task details", - ErrorMessage.TASK_LIST_GET_FAILED.value: "Failed to get task list", - ErrorMessage.TASK_CANCEL_FAILED.value: "Failed to cancel task", - ErrorMessage.TASK_DELETE_FAILED.value: "Failed to delete task record", - ErrorMessage.TASK_STATS_GET_FAILED.value: "Failed to get task statistics", - ErrorMessage.TASK_REGISTERED_GET_FAILED.value: "Failed to get registered tasks", - ErrorMessage.TASK_CANNOT_CANCEL.value: "Can only cancel pending or running tasks", - ErrorMessage.TASK_CANNOT_DELETE_RUNNING.value: "Cannot delete running tasks, please cancel first", - # Search related errors - ErrorMessage.SEARCH_QUERY_EMPTY.value: "Search query cannot be empty", - ErrorMessage.AI_SEARCH_FAILED.value: "AI search failed", - ErrorMessage.QUICK_SEARCH_FAILED.value: "Quick search failed", - ErrorMessage.SEARCH_SUGGESTIONS_FAILED.value: "Failed to get search suggestions", - ErrorMessage.SEARCH_TAGS_FAILED.value: "Failed to get popular tags", - # Resource related errors - ErrorMessage.RESOURCE_PROCESSING_FAILED.value: "Resource processing failed", - ErrorMessage.RESOURCE_EXTRACTION_FAILED.value: "Resource extraction failed", - ErrorMessage.RESOURCE_IMPORT_FAILED.value: "Resource import failed", - ErrorMessage.RESOURCE_URI_MISSING.value: "Resource URI missing", - # System errors - ErrorMessage.SYSTEM_ERROR.value: "System internal error", - ErrorMessage.INITIALIZATION_FAILED.value: "System initialization failed", - ErrorMessage.SERVICE_STARTUP_FAILED.value: "Service startup failed", - ErrorMessage.SERVICE_SHUTDOWN_FAILED.value: "Service shutdown failed", - # Context and session errors - ErrorMessage.CONTEXT_NOT_SET.value: "Request context not set", - ErrorMessage.SESSION_NOT_INITIALIZED.value: "Database session not initialized", - ErrorMessage.MIDDLEWARE_ERROR.value: "Middleware processing error", - # Resource related error messages (additional) - ErrorMessage.RESOURCE_UPLOAD_FAILED.value: "Resource upload failed", - ErrorMessage.RESOURCE_BIND_FAILED.value: "Resource binding failed", - ErrorMessage.RESOURCE_LIST_GET_FAILED.value: "Failed to get resource list", - ErrorMessage.RESOURCE_STATUS_GET_FAILED.value: "Failed to get resource status", - ErrorMessage.RESOURCE_CITATION_GET_FAILED.value: "Failed to get resource citation", - ErrorMessage.RESOURCE_GET_FAILED.value: "Failed to get resource", - ErrorMessage.RESOURCE_NO_FILE.value: "Resource file does not exist", - ErrorMessage.RESOURCE_DOWNLOAD_FAILED.value: "Resource download failed", - ErrorMessage.RESOURCE_DELETE_FAILED.value: "Failed to delete resource", - # Inspiration related error messages - ErrorMessage.INSPIRATION_CREATE_FAILED.value: "Failed to create inspiration", - ErrorMessage.INSPIRATION_NOT_FOUND.value: "Inspiration not found", - ErrorMessage.INSPIRATION_TYPE_INVALID.value: "Invalid inspiration type", - ErrorMessage.INSPIRATION_UPDATE_FAILED.value: "Failed to update inspiration", - ErrorMessage.INSPIRATION_LIST_GET_FAILED.value: "Failed to get inspiration list", - ErrorMessage.INSPIRATION_GET_FAILED.value: "Failed to get inspiration", - ErrorMessage.RESOURCE_TYPE_GET_FAILED.value: "Failed to get resource type", - ErrorMessage.RESOURCE_SCOPE_GET_FAILED.value: "Failed to get resource scope", - ErrorMessage.RESOURCE_PROCESSING_STATUS_GET_FAILED.value: "Failed to get resource processing status", - ErrorMessage.RESOURCE_SIGNED_URL_FAILED.value: "Failed to get resource signed URL", - # LLM related errors - ErrorMessage.LLM_OUTPUT_PARSING_ERROR.value: "LLM output parsing failed", - ErrorMessage.LLM_CALL_FAILED.value: "LLM call failed", - ErrorMessage.LLM_RETRY_EXHAUSTED.value: "LLM retry attempts exhausted", - # Quota related errors - ErrorMessage.QUOTA_INSUFFICIENT.value: "Insufficient balance to perform this operation", -} - - -def get_error_message_by_key(message_key: str, language: str = "zh") -> str: - """ - Get error message by message key and language - - Args: - message_key: Error message key - language: Language code, supports "zh" (Chinese) and "en" (English) - - Returns: - Error message in the specified language - """ - if language == "zh": - return ERROR_MESSAGES_ZH.get( - message_key, ERROR_MESSAGES_ZH[ErrorMessage.UNKNOWN_ERROR.value] - ) - elif language == "en": - return ERROR_MESSAGES_EN.get( - message_key, ERROR_MESSAGES_EN[ErrorMessage.UNKNOWN_ERROR.value] - ) - else: - # Default to Chinese - return ERROR_MESSAGES_ZH.get( - message_key, ERROR_MESSAGES_ZH[ErrorMessage.UNKNOWN_ERROR.value] - ) - - -def get_all_error_messages(language: str = "zh") -> Dict[str, str]: - """ - Get all error messages in the specified language - - Args: - language: Language code, supports "zh" (Chinese) and "en" (English) - - Returns: - Dictionary mapping error codes to error messages - """ - if language == "en": - return ERROR_MESSAGES_EN.copy() - else: - return ERROR_MESSAGES_ZH.copy() - - -def get_error_code_translation(error_code: str, language: str = "zh") -> str: - """ - Get error code translation by error code and language - - Args: - error_code: Error code - language: Language code, supports "zh" (Chinese) and "en" (English) - - Returns: - Translation of the error code in the specified language - """ - if language == "zh": - return ERROR_CODE_TRANSLATIONS_ZH.get(error_code, error_code) - elif language == "en": - return ERROR_CODE_TRANSLATIONS_EN.get(error_code, error_code) - else: - # Default to Chinese - return ERROR_CODE_TRANSLATIONS_ZH.get(error_code, error_code) - - -def get_all_error_code_translations(language: str = "zh") -> Dict[str, str]: - """ - Get all error code translations in the specified language - - Args: - language: Language code, supports "zh" (Chinese) and "en" (English) - - Returns: - Dictionary mapping error codes to translations - """ - if language == "en": - return ERROR_CODE_TRANSLATIONS_EN.copy() - else: - return ERROR_CODE_TRANSLATIONS_ZH.copy() - - -def _validate_translations_completeness(): - """ - Validate translation completeness, raise exception if missing translations are found - - Automatically executed during module import, ensuring all error codes and error messages have corresponding Chinese and English translations. - Implements the "fail-fast" principle to avoid discovering missing translations at runtime. - - Raises: - RuntimeError: When missing translations are found - """ - missing_translations = [] - - # Check ErrorCode translation completeness - for code in ErrorCode: - code_value = code.value - - # Check Chinese translation - if code_value not in ERROR_CODE_TRANSLATIONS_ZH: - missing_translations.append( - f"ErrorCode {code_value} missing Chinese translation" - ) - - # Check English translation - if code_value not in ERROR_CODE_TRANSLATIONS_EN: - missing_translations.append( - f"ErrorCode {code_value} missing English translation" - ) - - # Check ErrorMessage translation completeness - for message in ErrorMessage: - message_value = message.value - - # Check Chinese translation - if message_value not in ERROR_MESSAGES_ZH: - missing_translations.append( - f"ErrorMessage {message_value} missing Chinese translation" - ) - - # Check English translation - if message_value not in ERROR_MESSAGES_EN: - missing_translations.append( - f"ErrorMessage {message_value} missing English translation" - ) - - # If missing translations are found, raise exception - if missing_translations: - error_message = ( - f"Found {len(missing_translations)} missing translations, please complete them before running:\n" - + "\n".join( - f" - {msg}" for msg in missing_translations[:10] - ) # Show only first 10 - ) - if len(missing_translations) > 10: - error_message += f"\n ... and {len(missing_translations) - 10} more missing translations" - - raise RuntimeError(error_message) - - -# Automatically execute translation completeness check when module is imported -_validate_translations_completeness() diff --git a/methods/EverCore/src/core/constants/exceptions.py b/methods/EverCore/src/core/constants/exceptions.py deleted file mode 100644 index 5035a464..00000000 --- a/methods/EverCore/src/core/constants/exceptions.py +++ /dev/null @@ -1,74 +0,0 @@ -""" -Exception handling module - -This module defines all custom exception classes and error codes used in the project. -Follows a unified exception handling specification, facilitating error tracking and debugging. -""" - -from typing import Optional, Dict, Any -from core.constants.errors import ErrorCode - - -class CriticalError(Exception): - """Marker base class for critical errors that must never be silently swallowed. - - Errors inheriting from this class indicate serious system-level issues - (e.g., missing tenant context, broken invariants) that should always - propagate to the caller and result in a request failure (HTTP 500). - - Use ``reraise_critical_errors()`` from ``common_utils.async_utils`` - to guard ``asyncio.gather(return_exceptions=True)`` result processing. - """ - - pass - - -class ValidationException(Exception): - """Data validation exception - - Raised when input data validation fails. - """ - - def __init__( - self, - message: str, - field: Optional[str] = None, - details: Optional[Dict[str, Any]] = None, - ): - if field: - message = f"Field '{field}': {message}" - - super().__init__(message) - self.code = ErrorCode.VALIDATION_ERROR.value - self.message = message - self.details = details or {} - - -# Long Job System Errors - Long job system error classes -from core.longjob.longjob_error import ( - FatalError, - BusinessLogicError, - LongJobError, - JobNotFoundError, - JobAlreadyExistsError, - JobStateError, - ManagerShutdownError, - MaxConcurrentJobsError, -) - -# Export long job system error classes -__all__ = [ - # Error codes and base exception - 'ErrorCode', - 'CriticalError', - 'ValidationException', - # Long job system error classes - 'FatalError', - 'BusinessLogicError', - 'LongJobError', - 'JobNotFoundError', - 'JobAlreadyExistsError', - 'JobStateError', - 'ManagerShutdownError', - 'MaxConcurrentJobsError', -] diff --git a/methods/EverCore/src/core/context/context.py b/methods/EverCore/src/core/context/context.py deleted file mode 100644 index ed0231a1..00000000 --- a/methods/EverCore/src/core/context/context.py +++ /dev/null @@ -1,178 +0,0 @@ -from contextvars import ContextVar, Token -from typing import Optional, Dict, Any, TypedDict, TYPE_CHECKING -from sqlmodel.ext.asyncio.session import AsyncSession -import logging - -if TYPE_CHECKING: - from fastapi import Request - -logger = logging.getLogger(__name__) - - -# Create a ContextVar to store the current request's database session -db_session_context: ContextVar[Optional[AsyncSession]] = ContextVar( - "db_session_context", default=None -) - -# Create a ContextVar to store additional information about the current user -user_info_context: ContextVar[Optional[Dict[str, Any]]] = ContextVar( - "user_info_context", default=None -) - -# 🔧 Application info context variable, used to store application-level context information such as task_id -app_info_context: ContextVar[Optional[Dict[str, Any]]] = ContextVar( - "app_info_context", default=None -) - -# 🔧 Request context variable, used to store the current request object -request_context: ContextVar[Optional["Request"]] = ContextVar( - "request_context", default=None -) - - -# Database session related functions -def get_current_session() -> AsyncSession: - """ - Get the database session for the current request - - Returns: - AsyncSession: The database session for the current request - - Raises: - RuntimeError: If no database session is set in the current context - """ - session = db_session_context.get() - if session is None: - raise RuntimeError( - "Database session is not set in the current context. Ensure the session is properly initialized in the request middleware." - ) - return session - - -def set_current_session(session: AsyncSession) -> Token: - """ - Set the database session for the current request - - Args: - session: The database session to set - """ - return db_session_context.set(session) - - -def clear_current_session(token: Optional[Token] = None) -> None: - """ - Clear the database session for the current request - """ - if token is not None: - db_session_context.reset(token) - else: - db_session_context.set(None) - - -class UserInfo(TypedDict): - user_id: int - - -# User context related functions - only keep basic data storage and retrieval -def get_current_user_info() -> Optional[UserInfo]: - """ - Get basic information of the current user - - Returns: - Optional[Dict[str, Any]]: Basic information of the current user, returns None if not set - """ - return user_info_context.get() - - -def set_current_user_info(user_info: UserInfo) -> Token: - """ - Set basic information of the current user - - Args: - user_info: The user information to set - """ - return user_info_context.set(user_info) - - -def clear_current_user_context(token: Optional[Token] = None) -> None: - """ - Clear the current user context - """ - if token is not None: - user_info_context.reset(token) - else: - user_info_context.set(None) - - -# 🔧 Application info context related functions -def get_current_app_info() -> Optional[Dict[str, Any]]: - """ - Get current application information - - Returns: - Optional[Dict[str, Any]]: Current application information, returns None if not set - """ - return app_info_context.get() - - -def set_current_app_info(app_info: Dict[str, Any]) -> Token: - """ - Set current application information into the context variable - - Args: - app_info: Application information dictionary, containing task_id, etc. - - Returns: - Token: Context variable token, used for subsequent cleanup - """ - return app_info_context.set(app_info) - - -def clear_current_app_info(token: Optional[Token] = None) -> None: - """ - Clean up the current application information context variable - - Args: - token: Context variable token - """ - if token is not None: - app_info_context.reset(token) - else: - app_info_context.set(None) - - -# 🔧 Request context related functions -def get_current_request() -> Optional["Request"]: - """ - Get the current request object - - Returns: - Optional[Request]: Current request object, returns None if not set - """ - return request_context.get() - - -def set_current_request(request: "Request") -> Token: - """ - Set the current request object into the context variable - - Args: - request: FastAPI request object - - Returns: - Token: Context variable token, used for subsequent cleanup - """ - return request_context.set(request) - - -def clear_current_request(token: Optional[Token] = None) -> None: - """ - Clean up the current request context variable - - Args: - token: Context variable token - """ - if token is not None: - request_context.reset(token) - else: - request_context.set(None) diff --git a/methods/EverCore/src/core/context/context_manager.py b/methods/EverCore/src/core/context/context_manager.py deleted file mode 100644 index 4546b546..00000000 --- a/methods/EverCore/src/core/context/context_manager.py +++ /dev/null @@ -1,406 +0,0 @@ -from contextvars import copy_context, Context -from typing import Optional, Dict, Any, Callable, TypeVar, Coroutine, Union, Tuple -from functools import wraps -from sqlmodel.ext.asyncio.session import AsyncSession - -from core.context.context import ( - set_current_session, - clear_current_session, - get_current_session, - set_current_user_info, - get_current_user_info, - clear_current_user_context, -) -from core.component.database_session_provider import DatabaseSessionProvider -from core.di.decorators import component -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger - -logger = get_logger(__name__) - -F = TypeVar('F', bound=Callable[..., Coroutine[Any, Any, Any]]) - - -@component(name="database_session_manager") -class DatabaseSessionManager: - """ - Database session manager - - Responsible for creating, setting, committing, rolling back, and cleaning up database sessions - """ - - def __init__(self, db_provider: DatabaseSessionProvider): - self.db_provider = db_provider - - async def run_with_session( - self, - func: Callable, - *args, - session: Optional[AsyncSession] = None, - auto_commit: bool = True, - force_new_session: bool = False, - **kwargs, - ) -> Any: - """ - Run function within a database session - - Args: - func: Function to run - *args: Positional arguments for the function - session: Database session (optional, a new one will be created if not provided) - auto_commit: Whether to automatically commit the transaction - force_new_session: Whether to force creation of a new session (to avoid session concurrency conflicts) - **kwargs: Keyword arguments for the function - - Returns: - Return value of the function - """ - # Decide session handling strategy based on force_new_session parameter - if force_new_session: - # Force creation of a new session, ignoring passed session and session in current context - session = self.db_provider.create_session() - need_cleanup = True - logger.debug( - "Forcing creation of a new database session (to avoid concurrency conflicts)" - ) - else: - # Normal logic: prioritize passed session, then session in current context, finally create a new session - if session is None: - try: - current_session = get_current_session() - except RuntimeError: - current_session = None - if current_session is not None: - session = current_session - need_cleanup = False - logger.debug("Using database session from current context") - else: - session = self.db_provider.create_session() - need_cleanup = True - logger.debug("Creating a new database session") - else: - # Use passed session - need_cleanup = False - - # Set context - db_token = set_current_session(session) - - try: - # Run function - result = await func(*args, **kwargs) - - # If no exception and auto-commit is enabled, commit transaction - if auto_commit and need_cleanup and session.is_active: - await session.commit() - logger.debug( - "Database session manager: automatically committed transaction" - ) - - return result - - except Exception as e: - # If exception occurs, rollback transaction - if need_cleanup and session.is_active: - try: - await session.rollback() - logger.debug( - "Database session manager: automatically rolled back transaction" - ) - except Exception as rollback_error: - logger.error( - f"Error occurred during transaction rollback: {str(rollback_error)}" - ) - - # Re-raise the exception - raise e - - finally: - # Clean up context - clear_current_session(db_token) - - # Close session (if it was auto-created) - if need_cleanup: - try: - await session.close() - logger.debug("Database session manager: database session closed") - except Exception as close_error: - logger.error( - f"Error occurred when closing database session: {str(close_error)}" - ) - - -@component(name="user_context_manager") -class UserContextManager: - """ - User context manager - - Responsible for setting, retrieving, and cleaning up user context - """ - - def __init__(self): - pass - - async def run_with_user_context( - self, - func: Callable, - *args, - user_data: Optional[Dict[str, Any]] = None, - auto_inherit: bool = True, - **kwargs, - ) -> Any: - """ - Run function within user context - - Args: - func: Function to run - *args: Positional arguments for the function - user_data: User data (optional) - auto_inherit: Whether to automatically inherit current user context - **kwargs: Keyword arguments for the function - - Returns: - Return value of the function - """ - # Determine which user data to use - actual_user_data = user_data - if auto_inherit and actual_user_data is None: - actual_user_data = get_current_user_info() - - # Set user context - user_token = None - if actual_user_data is not None: - user_token = set_current_user_info(actual_user_data) - logger.debug( - f"User context manager: setting user context user_id={actual_user_data.get('user_id')}" - ) - - try: - # Run function - result = await func(*args, **kwargs) - return result - - finally: - # Clean up user context - if user_token is not None: - clear_current_user_context(user_token) - logger.debug("User context manager: user context cleaned up") - - -@component(name="context_manager") -class ContextManager: - """ - Comprehensive context manager - - Combines database session manager and user context manager to provide unified context management capability - """ - - def __init__( - self, - db_session_manager: DatabaseSessionManager, - user_context_manager: UserContextManager, - ): - self.db_session_manager = db_session_manager - self.user_context_manager = user_context_manager - - async def run_with_full_context( - self, - func: Callable, - *args, - user_data: Optional[Dict[str, Any]] = None, - session: Optional[AsyncSession] = None, - auto_commit: bool = True, - auto_inherit_user: bool = True, - force_new_session: bool = False, - **kwargs, - ) -> Any: - """ - Run function within full context (database session + user context) - - Args: - func: Function to run - *args: Positional arguments for the function - user_data: User data (optional) - session: Database session (optional) - auto_commit: Whether to automatically commit transaction - auto_inherit_user: Whether to automatically inherit user context - force_new_session: Whether to force creation of a new session (to avoid session concurrency conflicts) - **kwargs: Keyword arguments for the function - - Returns: - Return value of the function - """ - # Set user context first, then database session - # This way user information is accessible during database operations - return await self.user_context_manager.run_with_user_context( - self.db_session_manager.run_with_session, - func, - *args, - session=session, - auto_commit=auto_commit, - force_new_session=force_new_session, - user_data=user_data, - auto_inherit=auto_inherit_user, - **kwargs, - ) - - async def run_with_database_only( - self, - func: Callable, - *args, - session: Optional[AsyncSession] = None, - auto_commit: bool = True, - force_new_session: bool = False, - **kwargs, - ) -> Any: - """ - Run function within database session only - - Args: - func: Function to run - *args: Positional arguments for the function - session: Database session (optional) - auto_commit: Whether to automatically commit transaction - force_new_session: Whether to force creation of a new session - **kwargs: Keyword arguments for the function - - Returns: - Return value of the function - """ - return await self.db_session_manager.run_with_session( - func, - *args, - session=session, - auto_commit=auto_commit, - force_new_session=force_new_session, - **kwargs, - ) - - async def run_with_user_only( - self, - func: Callable, - *args, - user_data: Optional[Dict[str, Any]] = None, - auto_inherit: bool = True, - **kwargs, - ) -> Any: - """ - Run function within user context only - - Args: - func: Function to run - *args: Positional arguments for the function - user_data: User data (optional) - auto_inherit: Whether to automatically inherit user context - **kwargs: Keyword arguments for the function - - Returns: - Return value of the function - """ - return await self.user_context_manager.run_with_user_context( - func, *args, user_data=user_data, auto_inherit=auto_inherit, **kwargs - ) - - def copy_current_context(self) -> Context: - """ - Copy current context - - Returns: - Context: A copy of the current context - """ - return copy_context() - - def get_current_context_data(self) -> Dict[str, Any]: - """ - Get current context data - - Returns: - Dict[str, Any]: Dictionary containing current context data - """ - user_data = get_current_user_info() - return { - "user_context": user_data, - "user_id": user_data.get("user_id") if user_data else None, - "has_session": get_current_session() is not None, - } - - -# Decorator factory functions -def with_full_context( - user_data: Optional[Dict[str, Any]] = None, - session: Optional[AsyncSession] = None, - auto_commit: bool = True, - auto_inherit_user: bool = True, -): - """ - Decorator: provides full context injection (database session + user context) for functions - """ - - def decorator(func: F) -> F: - @wraps(func) - async def wrapper(*args, **kwargs): - context_manager = get_bean_by_type(ContextManager) - return await context_manager.run_with_full_context( - func, - *args, - user_data=user_data, - session=session, - auto_commit=auto_commit, - auto_inherit_user=auto_inherit_user, - **kwargs, - ) - - return wrapper - - return decorator - - -def with_database_session( - session: Optional[AsyncSession] = None, - auto_commit: bool = True, - force_new_session: bool = False, -): - """ - Decorator: provides database session injection for functions - - Args: - session: Database session (optional) - auto_commit: Whether to automatically commit transaction - force_new_session: Whether to force creation of a new session (to avoid session concurrency conflicts) - """ - - def decorator(func: F) -> F: - @wraps(func) - async def wrapper(*args, **kwargs): - context_manager = get_bean_by_type(ContextManager) - return await context_manager.run_with_database_only( - func, - *args, - session=session, - auto_commit=auto_commit, - force_new_session=force_new_session, - **kwargs, - ) - - return wrapper - - return decorator - - -def with_user_context( - user_data: Optional[Dict[str, Any]] = None, auto_inherit: bool = True -): - """ - Decorator: provides user context injection for functions - """ - - def decorator(func: F) -> F: - @wraps(func) - async def wrapper(*args, **kwargs): - context_manager = get_bean_by_type(ContextManager) - return await context_manager.run_with_user_only( - func, *args, user_data=user_data, auto_inherit=auto_inherit, **kwargs - ) - - return wrapper - - return decorator diff --git a/methods/EverCore/src/core/di/__init__.py b/methods/EverCore/src/core/di/__init__.py deleted file mode 100644 index 315f20e1..00000000 --- a/methods/EverCore/src/core/di/__init__.py +++ /dev/null @@ -1,102 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Dependency injection module - -Note: For backward compatibility, this module re-exports commonly used functions and decorators. -In new code, it is recommended to import from specific submodules to improve readability: -- Decorators: from core.di.decorators import service, repository, component, mock_impl, factory -- Utility functions: from core.di.utils import get_bean_by_type, get_bean, enable_mock_mode, disable_mock_mode -- Container: from core.di.container import get_container -""" - -# Decorators (from decorators.py) -from core.di.decorators import ( - component, - service, - repository, - controller, - injectable, - mock_impl, - factory, - prototype, - conditional, - depends_on, -) - -# Utility functions (from utils.py) -from core.di.utils import ( - get_bean, - get_beans, - get_bean_by_type, - get_beans_by_type, - register_bean, - register_factory, - register_singleton, - register_prototype, - register_primary, - register_mock, - enable_mock_mode, - disable_mock_mode, - is_mock_mode, - clear_container, - inject, - lazy_inject, - get_or_create, - conditional_register, - batch_register, - get_bean_info, - get_all_beans_info, - list_all_beans, - print_container_info, - get_all_subclasses, -) - -# Container (from container.py) -from core.di.container import get_container - -# Define public API -__all__ = [ - # Decorators - 'component', - 'service', - 'repository', - 'controller', - 'injectable', - 'mock_impl', - 'factory', - 'prototype', - 'conditional', - 'depends_on', - # Core utility functions - 'get_bean', - 'get_beans', - 'get_bean_by_type', - 'get_beans_by_type', - 'get_container', - # Registration functions - 'register_bean', - 'register_factory', - 'register_singleton', - 'register_prototype', - 'register_primary', - 'register_mock', - # Container checks - # Mock mode - 'enable_mock_mode', - 'disable_mock_mode', - 'is_mock_mode', - # Other utilities - 'clear_container', - 'inject', - 'lazy_inject', - 'get_or_create', - 'conditional_register', - 'batch_register', - # Information queries - 'get_bean_info', - 'get_all_beans_info', - 'list_all_beans', - 'print_container_info', - # Subclass queries - 'get_all_subclasses', -] diff --git a/methods/EverCore/src/core/di/bean_definition.py b/methods/EverCore/src/core/di/bean_definition.py deleted file mode 100644 index efacdd80..00000000 --- a/methods/EverCore/src/core/di/bean_definition.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Bean definition module - -Contains Bean definition classes and scope enumeration -""" - -from enum import Enum -from typing import Type, Callable, Any, Set, Dict, Optional - - -class BeanScope(str, Enum): - """Bean scope enumeration""" - - SINGLETON = "singleton" - PROTOTYPE = "prototype" - FACTORY = "factory" - - -class BeanDefinition: - """Bean definition class""" - - def __init__( - self, - bean_type: Type, - bean_name: str = None, - scope: BeanScope = BeanScope.SINGLETON, - is_primary: bool = False, - is_mock: bool = False, - factory_method: Callable = None, - instance: Any = None, - metadata: Optional[Dict[str, Any]] = None, - ): - """ - Initialize Bean definition - - Args: - bean_type: Type of the Bean - bean_name: Name of the Bean, defaults to lowercase type name - scope: Scope of the Bean, defaults to singleton - is_primary: Whether it is the primary Bean, used to prioritize when multiple implementations exist - is_mock: Whether it is a Mock implementation - factory_method: Factory method used to create the Bean instance - instance: Pre-created instance - metadata: Metadata of the Bean, can be used to store additional information - """ - self.bean_type = bean_type - self.bean_name = bean_name or bean_type.__name__.lower() - self.scope = scope - self.is_primary = is_primary - self.is_mock = is_mock - self.factory_method = factory_method - self.instance = instance - self.metadata = metadata or {} - # Dependency set - self.dependencies: Set[Type] = set() - - def __repr__(self): - metadata_str = f", metadata={self.metadata}" if self.metadata else "" - return f"BeanDefinition(type={self.bean_type.__name__}, name={self.bean_name}, scope={self.scope.value}{metadata_str})" diff --git a/methods/EverCore/src/core/di/bean_order_strategy.py b/methods/EverCore/src/core/di/bean_order_strategy.py deleted file mode 100644 index c3269d73..00000000 --- a/methods/EverCore/src/core/di/bean_order_strategy.py +++ /dev/null @@ -1,127 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Bean ordering strategy module - -Used to determine the priority order of Beans based on BeanDefinition attributes (such as is_primary, metadata, etc.) - -Priority ranking rules (from highest to lowest): -1. is_mock: In mock mode, Mock Bean > Non-Mock Bean; in non-mock mode, Mock Beans are filtered out directly -2. Matching method: Direct match > Implementation class match -3. primary: Primary Bean > Non-Primary Bean -4. scope: Factory Bean > Regular Bean -""" - -from typing import List, Tuple, Set, Type -from core.di.bean_definition import BeanDefinition, BeanScope - - -class BeanOrderStrategy: - """ - Bean ordering strategy class - - Calculates and sorts Bean priorities based on various attributes of BeanDefinition - Used to determine usage order when there are multiple candidate Beans - - Order key format: (mock_priority, match_priority, primary_priority, scope_priority) - Smaller values indicate higher priority - """ - - @staticmethod - def calculate_order_key( - bean_def: BeanDefinition, is_direct_match: bool, mock_mode: bool = False - ) -> Tuple[int, int, int, int]: - """ - Calculate the ordering key for a Bean - - Args: - bean_def: Bean definition object - is_direct_match: Whether it is a direct match (True = direct match, False = implementation class match) - mock_mode: Whether in mock mode - - Returns: - Tuple[int, int, int, int]: Order key tuple - Format: (mock_priority, match_priority, primary_priority, scope_priority) - - Priority rules: - - mock_priority: In mock mode, Mock Bean = 0, Non-Mock Bean = 1; in non-mock mode, both are 0 - - match_priority: Direct match = 0, Implementation class match = 1 - - primary_priority: Primary Bean = 0, Non-Primary Bean = 1 - - scope_priority: Factory Bean = 0, Non-Factory Bean = 1 - """ - # 1. Mock priority (only differentiated in mock mode) - if mock_mode: - mock_priority = 0 if bean_def.is_mock else 1 - else: - mock_priority = 0 # No distinction in non-mock mode - - # 2. Match method priority (direct match takes precedence) - match_priority = 0 if is_direct_match else 1 - - # 3. Primary priority (Primary takes precedence) - primary_priority = 0 if bean_def.is_primary else 1 - - # 4. Scope priority (Factory takes precedence) - scope_priority = 0 if bean_def.scope == BeanScope.FACTORY else 1 - - return (mock_priority, match_priority, primary_priority, scope_priority) - - @staticmethod - def sort_beans_with_context( - bean_defs: List[BeanDefinition], - direct_match_types: Set[Type], - mock_mode: bool = False, - ) -> List[BeanDefinition]: - """ - Sort the list of Bean definitions based on context information - - Args: - bean_defs: List of Bean definitions - direct_match_types: Set of types that directly match - mock_mode: Whether in mock mode - - Returns: - List[BeanDefinition]: Sorted list of Bean definitions - - Note: - - In non-mock mode, Mock Beans are filtered out directly and do not participate in sorting - - In mock mode, Mock Beans take precedence over non-Mock Beans - """ - # Filter out all Mock Beans in non-mock mode - if not mock_mode: - bean_defs = [bd for bd in bean_defs if not bd.is_mock] - - # Calculate order key for each Bean, then sort by key - sorted_beans = sorted( - bean_defs, - key=lambda bd: BeanOrderStrategy.calculate_order_key( - bean_def=bd, - is_direct_match=bd.bean_type in direct_match_types, - mock_mode=mock_mode, - ), - ) - return sorted_beans - - @staticmethod - def sort_beans(bean_defs: List[BeanDefinition]) -> List[BeanDefinition]: - """ - Sort the list of Bean definitions simply (compatible with old interface) - - Args: - bean_defs: List of Bean definitions - - Returns: - List[BeanDefinition]: Sorted list of Bean definitions - - Note: - This method only considers primary and scope, not matching method or mock mode - It is recommended to use the sort_beans_with_context method for complete sorting functionality - """ - # Sort by (primary_priority, scope_priority) - sorted_beans = sorted( - bean_defs, - key=lambda bd: ( - 0 if bd.is_primary else 1, # Primary takes precedence - 0 if bd.scope == BeanScope.FACTORY else 1, # Factory takes precedence - ), - ) - return sorted_beans diff --git a/methods/EverCore/src/core/di/container.py b/methods/EverCore/src/core/di/container.py deleted file mode 100644 index 9f5e6ad1..00000000 --- a/methods/EverCore/src/core/di/container.py +++ /dev/null @@ -1,585 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Core implementation of dependency injection container - -Lock usage strategy: -- Read-only operations (e.g., is_mock_mode, contains_bean*): no lock, because reading immutable attributes -- Operations modifying container state: protected by self._lock -- Bean retrieval operations: require lock, because they may create and cache singleton instances -- Global container creation: use _container_lock to ensure singleton -""" - -import inspect -import abc -from typing import ( - Dict, - Type, - TypeVar, - Optional, - Any, - List, - Set, - Callable, - Union, - get_origin, - get_args, -) -from threading import RLock - -from core.di.bean_definition import BeanDefinition, BeanScope -from core.di.bean_order_strategy import BeanOrderStrategy -from core.di.scan_context import ScanContextRegistry -from core.di.exceptions import ( - CircularDependencyError, - BeanNotFoundError, - DuplicateBeanError, - FactoryError, - DependencyResolutionError, - MockNotEnabledError, -) - -T = TypeVar('T') - - -class DIContainer: - """Dependency injection container""" - - # Class-level Bean ordering strategy, can be replaced - _bean_order_strategy_class = BeanOrderStrategy - - @classmethod - def replace_bean_order_strategy(cls, strategy_class): - """ - Replace Bean ordering strategy class - - Args: - strategy_class: New ordering strategy class, must have interface compatible with BeanOrderStrategy - - Note: - This is a temporary solution because the DI mechanism is not fully established. - This method affects the ordering behavior of all DIContainer instances. - """ - cls._bean_order_strategy_class = strategy_class - - def __init__(self): - self._lock = RLock() - # Store Bean definitions by type {Type: [BeanDefinition]} - self._bean_definitions: Dict[Type, List[BeanDefinition]] = {} - # Store Bean definitions by name {name: BeanDefinition} - self._named_beans: Dict[str, BeanDefinition] = {} - - # Store singleton instances {BeanDefinition: instance} - self._singleton_instances: Dict[BeanDefinition, Any] = {} - - # Mock mode - self._mock_mode = False - # Dependency resolution stack, used to detect circular dependencies - self._resolving_stack: List[Type] = [] - - # Performance optimization cache - # Inheritance relationship cache {parent_type: [child_types]} - self._inheritance_cache: Dict[Type, List[Type]] = {} - # Candidate Bean cache {(Type, mock_mode): [BeanDefinition]} - self._candidates_cache: Dict[tuple, List[BeanDefinition]] = {} - # Cache invalidation flag - self._cache_dirty = False - - def enable_mock_mode(self): - """Enable mock mode""" - with self._lock: - if not self._mock_mode: - self._mock_mode = True - self._invalidate_cache() - - def disable_mock_mode(self): - """Disable mock mode""" - with self._lock: - if self._mock_mode: - self._mock_mode = False - self._invalidate_cache() - - def is_mock_mode(self) -> bool: - """Check if in mock mode""" - return self._mock_mode - - def _create_bean_definition( - self, - bean_type: Type[T], - bean_name: str = None, - scope: BeanScope = BeanScope.SINGLETON, - is_primary: bool = False, - is_mock: bool = False, - factory_method: Callable = None, - instance: Any = None, - metadata: Optional[Dict[str, Any]] = None, - ) -> BeanDefinition: - """ - Create BeanDefinition, automatically merging metadata from scan context - - Args: - bean_type: Type of the Bean - bean_name: Name of the Bean - scope: Scope of the Bean - is_primary: Whether it is the primary Bean - is_mock: Whether it is a mock implementation - factory_method: Factory method - instance: Pre-created instance - metadata: Metadata of the Bean - - Returns: - BeanDefinition instance - """ - # Merge metadata: first get from scan_context, then merge with passed metadata - merged_metadata = {} - - # 1. Get file path through bean_type and search for corresponding context metadata - context_metadata = ScanContextRegistry.search_metadata_for_type(bean_type) - if context_metadata: - merged_metadata.update(context_metadata) - - # 2. Merge passed metadata (passed metadata has higher priority, can override scan context) - if metadata: - merged_metadata.update(metadata) - - # 3. Create BeanDefinition - bean_def = BeanDefinition( - bean_type=bean_type, - bean_name=bean_name, - scope=scope, - is_primary=is_primary, - is_mock=is_mock, - factory_method=factory_method, - instance=instance, - metadata=merged_metadata if merged_metadata else None, - ) - - return bean_def - - def register_bean( - self, - bean_type: Type[T], - bean_name: str = None, - scope: BeanScope = BeanScope.SINGLETON, - is_primary: bool = False, - is_mock: bool = False, - instance: T = None, - metadata: Optional[Dict[str, Any]] = None, - ) -> 'DIContainer': - """ - Register Bean - - Args: - bean_type: Type of the Bean - bean_name: Name of the Bean - scope: Scope of the Bean - is_primary: Whether it is the primary Bean - is_mock: Whether it is a mock implementation - instance: Pre-created instance - metadata: Metadata of the Bean, can be used to store extra information - """ - with self._lock: - # Use unified method to create BeanDefinition, automatically merges scan context metadata - bean_def = self._create_bean_definition( - bean_type=bean_type, - bean_name=bean_name, - scope=scope, - is_primary=is_primary, - is_mock=is_mock, - instance=instance, - metadata=metadata, - ) - - # Check for duplicate registration - if bean_def.bean_name in self._named_beans: - existing = self._named_beans[bean_def.bean_name] - if not (is_mock or existing.is_mock): - raise DuplicateBeanError(bean_name=bean_def.bean_name) - - # Register Bean definition - if bean_type not in self._bean_definitions: - self._bean_definitions[bean_type] = [] - self._bean_definitions[bean_type].append(bean_def) - self._named_beans[bean_def.bean_name] = bean_def - - # Analyze dependency relationships - self._analyze_dependencies(bean_def) - - # If instance is provided, store directly - if instance is not None: - self._singleton_instances[bean_def] = instance - - # Invalidate cache - self._invalidate_cache() - - return self - - def register_factory( - self, - bean_type: Type[T], - factory_method: Callable[[], T], - bean_name: str = None, - is_primary: bool = False, - is_mock: bool = False, - metadata: Optional[Dict[str, Any]] = None, - ) -> 'DIContainer': - """ - Register factory method - - Args: - bean_type: Type of the Bean - factory_method: Factory method - bean_name: Name of the Bean - is_primary: Whether it is the primary Bean - is_mock: Whether it is a mock implementation - metadata: Metadata of the Bean, can be used to store extra information - """ - with self._lock: - # Use unified method to create BeanDefinition, automatically merges scan context metadata - bean_def = self._create_bean_definition( - bean_type=bean_type, - bean_name=bean_name, - scope=BeanScope.FACTORY, - is_primary=is_primary, - is_mock=is_mock, - factory_method=factory_method, - metadata=metadata, - ) - - # Check for duplicate registration - if bean_def.bean_name in self._named_beans: - existing = self._named_beans[bean_def.bean_name] - if not (is_mock or existing.is_mock): - raise DuplicateBeanError(bean_name=bean_def.bean_name) - - # Register Bean definition - if bean_type not in self._bean_definitions: - self._bean_definitions[bean_type] = [] - self._bean_definitions[bean_type].append(bean_def) - self._named_beans[bean_def.bean_name] = bean_def - - # Invalidate cache - self._invalidate_cache() - - return self - - def get_bean(self, bean_name: str) -> Any: - """Get Bean by name""" - with self._lock: - if bean_name not in self._named_beans: - raise BeanNotFoundError(bean_name=bean_name) - - bean_def = self._named_beans[bean_name] - return self._create_instance(bean_def) - - def get_bean_by_type(self, bean_type: Type[T]) -> T: - """Get Bean by type (return Primary or unique implementation)""" - with self._lock: - candidates = self._get_candidates_with_priority(bean_type) - - if not candidates: - raise BeanNotFoundError(bean_type=bean_type) - - # If only one candidate, return it - if len(candidates) == 1: - return self._create_instance(candidates[0]) - - # Multiple candidates, return the highest priority one - return self._create_instance(candidates[0]) - - def _get_candidates_with_priority(self, bean_type: Type) -> List[BeanDefinition]: - """ - Get candidate Bean definitions for the type (sorted by priority) - - Priority sorting rules (from high to low): - 1. is_mock: Mock Bean > Non-Mock Bean (only effective in mock mode) - 2. Matching method: Direct match > Implementation class match - 3. primary: Primary Bean > Non-Primary Bean - 4. scope: Factory Bean > Regular Bean - """ - # Use cache key - cache_key = (bean_type, self._mock_mode) - - # Check cache - if cache_key in self._candidates_cache: - return self._candidates_cache[cache_key] - - # Ensure inheritance relationship cache is up to date - self._build_inheritance_cache() - - # Collect all candidate Beans - all_candidates = [] - direct_match_types = set() - - # 1. Collect directly matched Beans (including Primary and non-Primary) - if bean_type in self._bean_definitions: - for bean_def in self._bean_definitions[bean_type]: - if self._is_bean_available(bean_def): - all_candidates.append(bean_def) - direct_match_types.add(bean_def.bean_type) - - # 2. Collect implementation class matched Beans (implementations of interface/abstract class) - impl_types = self._inheritance_cache.get(bean_type, []) - for impl_type in impl_types: - if impl_type in self._bean_definitions: - for bean_def in self._bean_definitions[impl_type]: - if self._is_bean_available(bean_def): - all_candidates.append(bean_def) - # impl_type not added to direct_match_types, because it's implementation class match - - # 3. Use current configured Bean ordering strategy for unified sorting - priority_candidates = self._bean_order_strategy_class.sort_beans_with_context( - bean_defs=all_candidates, - direct_match_types=direct_match_types, - mock_mode=self._mock_mode, - ) - - # Cache result - self._candidates_cache[cache_key] = priority_candidates - return priority_candidates - - def get_beans_by_type(self, bean_type: Type[T]) -> List[T]: - """Get all Bean implementations by type""" - with self._lock: - candidates = self._get_candidates_with_priority(bean_type) - return [self._create_instance(bean_def) for bean_def in candidates] - - def get_beans(self) -> Dict[str, Any]: - """Get all registered Beans""" - with self._lock: - result = {} - for name, bean_def in self._named_beans.items(): - if self._is_bean_available(bean_def): - try: - result[name] = self._create_instance(bean_def) - except Exception: - # Skip Beans that cannot be created - continue - return result - - def contains_bean(self, bean_name: str) -> bool: - """Check if container contains Bean with specified name""" - return bean_name in self._named_beans - - def contains_bean_by_type(self, bean_type: Type) -> bool: - """Check if container contains Bean with specified type""" - return bean_type in self._bean_definitions - - def clear(self): - """Clear container""" - with self._lock: - self._bean_definitions.clear() - self._named_beans.clear() - self._singleton_instances.clear() - self._resolving_stack.clear() - self._invalidate_cache() - - def list_all_beans_info(self) -> List[Dict[str, Any]]: - """ - List all registered Bean information - - Returns: - List of Bean information, each Bean contains: - - name: Bean name - - type_name: Bean type name - - scope: Bean scope - - is_primary: Whether it is a Primary Bean - - is_mock: Whether it is a Mock Bean - """ - beans_info = [] - - # Collect all Bean information - for name, bean_def in self._named_beans.items(): - if self._is_bean_available(bean_def): - beans_info.append( - { - 'name': name, - 'type_name': bean_def.bean_type.__name__, - 'scope': bean_def.scope.value, - 'is_primary': bean_def.is_primary, - 'is_mock': bean_def.is_mock, - } - ) - - return beans_info - - def _invalidate_cache(self): - """Invalidate all caches""" - self._inheritance_cache.clear() - self._candidates_cache.clear() - self._cache_dirty = True - - def _is_bean_available(self, bean_def: BeanDefinition) -> bool: - """Check if Bean is available in current mode""" - if self._mock_mode: - # In mock mode, both mock and non-mock beans are available - return True - else: - # In non-mock mode, only non-mock beans are available - return not bean_def.is_mock - - def _build_inheritance_cache(self): - """Build type inheritance relationship cache""" - if not self._cache_dirty: - return - - self._inheritance_cache.clear() - - # Get registered types - registered_types = list(self._bean_definitions.keys()) - - # Additionally collect ABC parent types (exclude abc.ABC base class) - all_parent_types = set(registered_types) - for registered_type in registered_types: - try: - # Get all parent classes, especially ABC abstract base classes - for base in registered_type.__mro__[1:]: # Skip self - # Exclude abc.ABC base class and object base class, they are too generic - if ( - base != abc.ABC - and base != object - and hasattr(base, '__abstractmethods__') - ): # ABC type - all_parent_types.add(base) - except (AttributeError, TypeError): - # Handle non-type cases - continue - - # Build inheritance relationship index for all types (including ABC parents) - # parent_type -> [list of its child implementations] - for parent_type in all_parent_types: - child_implementations = [] - for child_type in registered_types: - if child_type != parent_type: - try: - if issubclass(child_type, parent_type): - child_implementations.append(child_type) - except TypeError: - # Handle non-type cases - continue - if child_implementations: - self._inheritance_cache[parent_type] = child_implementations - - self._cache_dirty = False - - def _create_instance(self, bean_def: BeanDefinition) -> Any: - """Create Bean instance""" - # Check for circular dependency - if bean_def.bean_type in self._resolving_stack: - dependency_chain = self._resolving_stack + [bean_def.bean_type] - raise CircularDependencyError(dependency_chain) - - # Handle different scopes - if bean_def.scope == BeanScope.SINGLETON: - # Singleton mode: check cache, return directly if exists - if bean_def in self._singleton_instances: - return self._singleton_instances[bean_def] - - elif bean_def.scope == BeanScope.FACTORY: - # Factory mode: create new instance by calling factory method each time - if bean_def.factory_method: - try: - return bean_def.factory_method() - except Exception as e: - raise FactoryError(bean_def.bean_type, str(e)) - else: - raise FactoryError(bean_def.bean_type, "Factory method not set") - - elif bean_def.scope == BeanScope.PROTOTYPE: - # Prototype mode: create new instance each time, no caching - try: - self._resolving_stack.append(bean_def.bean_type) - return self._instantiate_with_dependencies(bean_def) - finally: - if bean_def.bean_type in self._resolving_stack: - self._resolving_stack.remove(bean_def.bean_type) - - # If preset instance exists, return directly - if bean_def.instance is not None: - return bean_def.instance - - # Create new instance (SINGLETON scope) - try: - self._resolving_stack.append(bean_def.bean_type) - instance = self._instantiate_with_dependencies(bean_def) - - # Store singleton instance - if bean_def.scope == BeanScope.SINGLETON: - self._singleton_instances[bean_def] = instance - - return instance - finally: - if bean_def.bean_type in self._resolving_stack: - self._resolving_stack.remove(bean_def.bean_type) - - def _instantiate_with_dependencies(self, bean_def: BeanDefinition) -> Any: - """Instantiate Bean and inject dependencies""" - bean_type = bean_def.bean_type - - # Get constructor signature - try: - signature = inspect.signature(bean_type.__init__) - except Exception: - # If signature cannot be obtained, try parameterless constructor - return bean_type() - - # Prepare constructor parameters - init_params = {} - for param_name, param in signature.parameters.items(): - if param_name == 'self': - continue - - # Try to inject dependency by type - if param.annotation != inspect.Parameter.empty: - try: - # Check if it is a generic type (e.g., List[T]) - origin = get_origin(param.annotation) - if origin is list or origin is List: - # Handle dependency injection for List[T] type - args = get_args(param.annotation) - if args: - # Get generic parameter type - element_type = args[0] - # Inject all implementations of this type - dependencies = self.get_beans_by_type(element_type) - init_params[param_name] = dependencies - else: - # If no generic parameters, try empty list - init_params[param_name] = [] - else: - # Dependency injection for normal types - dependency = self.get_bean_by_type(param.annotation) - init_params[param_name] = dependency - except BeanNotFoundError: - if param.default == inspect.Parameter.empty: - # Required parameter but dependency not found - raise DependencyResolutionError(bean_type, param.annotation) - - return bean_type(**init_params) - - def _analyze_dependencies(self, bean_def: BeanDefinition): - """Analyze Bean's dependency relationships""" - try: - signature = inspect.signature(bean_def.bean_type.__init__) - for param_name, param in signature.parameters.items(): - if param_name == 'self': - continue - if param.annotation != inspect.Parameter.empty: - bean_def.dependencies.add(param.annotation) - except Exception: - # If analysis fails, skip - pass - - -# Global container instance -_global_container: Optional[DIContainer] = None -_container_lock = RLock() - - -def get_container() -> DIContainer: - """Get global container instance""" - global _global_container - if _global_container is None: - with _container_lock: - if _global_container is None: - _global_container = DIContainer() - return _global_container diff --git a/methods/EverCore/src/core/di/decorators.py b/methods/EverCore/src/core/di/decorators.py deleted file mode 100644 index d978234e..00000000 --- a/methods/EverCore/src/core/di/decorators.py +++ /dev/null @@ -1,243 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Dependency injection decorator -""" - -from typing import Type, TypeVar, Optional, Callable, Any, Dict -from functools import wraps - -from core.di.container import get_container -from core.di.bean_definition import BeanScope - -T = TypeVar('T') - - -def component( - name: str = None, - scope: BeanScope = BeanScope.SINGLETON, - lazy: bool = False, - primary: bool = False, - metadata: Optional[Dict[str, Any]] = None, -): - """ - Component decorator - - Args: - name: Bean name - scope: Bean scope - lazy: Whether to register lazily - primary: Whether it is a primary Bean - metadata: Metadata of the Bean, can be used to store additional information - """ - - def decorator(cls: Type[T]) -> Type[T]: - cls._di_component = True - cls._di_name = name - cls._di_scope = scope - cls._di_lazy = lazy - cls._di_primary = primary - cls._di_metadata = metadata - - # Check if marked to skip (via conditional decorator) - if getattr(cls, '_di_skip', False): - return cls - - if not lazy: - # Register immediately - container = get_container() - container.register_bean( - bean_type=cls, - bean_name=name, - scope=scope, - is_primary=primary, - metadata=metadata, - ) - - return cls - - return decorator - - -def service( - name: str = None, - scope: BeanScope = BeanScope.SINGLETON, - lazy: bool = False, - primary: bool = False, - metadata: Optional[Dict[str, Any]] = None, -): - """ - Service component decorator - """ - return component(name, scope, lazy, primary, metadata) - - -def repository( - name: str = None, - scope: BeanScope = BeanScope.SINGLETON, - lazy: bool = False, - primary: bool = False, - metadata: Optional[Dict[str, Any]] = None, -): - """ - Repository component decorator - """ - return component(name, scope, lazy, primary, metadata) - - -def controller( - name: str = None, - scope: BeanScope = BeanScope.SINGLETON, - lazy: bool = False, - primary: bool = False, - metadata: Optional[Dict[str, Any]] = None, -): - """ - Controller component decorator - """ - return component(name, scope, lazy, primary, metadata) - - -def injectable( - name: str = None, - scope: BeanScope = BeanScope.SINGLETON, - lazy: bool = False, - primary: bool = False, - metadata: Optional[Dict[str, Any]] = None, -): - """ - Injectable component decorator - """ - return component(name, scope, lazy, primary, metadata) - - -def mock_impl( - name: str = None, - scope: BeanScope = BeanScope.SINGLETON, - primary: bool = False, - metadata: Optional[Dict[str, Any]] = None, -): - """ - Mock implementation decorator - directly register Mock Bean, priority determined by container mechanism - - Args: - name: Bean name - scope: Bean scope - primary: Whether it is a primary Bean - metadata: Metadata of the Bean, can be used to store additional information - """ - - def decorator(cls: Type[T]) -> Type[T]: - cls._di_mock = True - cls._di_name = name - cls._di_scope = scope - cls._di_component = True # Mark as component - cls._di_metadata = metadata - - # Directly register Mock implementation, maintain consistency with other decorators - container = get_container() - container.register_bean( - bean_type=cls, - bean_name=name, - scope=scope, - is_primary=getattr(cls, '_di_primary', False), - is_mock=True, - metadata=metadata, - ) - - return cls - - return decorator - - -def factory( - bean_type: Type[T] = None, - name: str = None, - lazy: bool = False, - metadata: Optional[Dict[str, Any]] = None, -): - """ - Factory decorator - - Args: - bean_type: The type of Bean to create - name: Bean name - lazy: Whether to register lazily - metadata: Metadata of the Bean, can be used to store additional information - """ - - def decorator(func: Callable[[], T]) -> Callable[[], T]: - target_type = bean_type or func.__annotations__.get('return', None) - - if not target_type: - raise ValueError("Factory decorator must specify return type") - - func._di_factory = True - func._di_bean_type = target_type - func._di_name = name - func._di_lazy = lazy - func._di_metadata = metadata - - if not lazy: - # Register Factory immediately - container = get_container() - container.register_factory( - bean_type=target_type, - factory_method=func, - bean_name=name, - metadata=metadata, - ) - - return func - - return decorator - - -def prototype(cls: Type[T]) -> Type[T]: - """ - Prototype scope decorator (create a new instance every time it is retrieved) - """ - cls._di_scope = BeanScope.PROTOTYPE - - # If already a component, update scope - if hasattr(cls, '_di_component'): - container = get_container() - container.register_bean( - bean_type=cls, - bean_name=getattr(cls, '_di_name', None), - scope=BeanScope.PROTOTYPE, - is_primary=getattr(cls, '_di_primary', False), - metadata=getattr(cls, '_di_metadata', None), - ) - - return cls - - -def conditional(condition: Callable[[], bool]): - """ - Conditional decorator - control conditional registration of Bean - Note: Should be used before decorators like @component - """ - - def decorator(cls: Type[T]) -> Type[T]: - # Set conditional flag, let subsequent decorators (e.g., component) decide whether to register based on this - cls._di_conditional = condition - - # If condition is not met, mark as skipped - if not condition(): - cls._di_skip = True - - return cls - - return decorator - - -def depends_on(*dependencies: Type): - """ - Dependency decorator - declare dependency relationships of Bean - """ - - def decorator(cls: Type[T]) -> Type[T]: - cls._di_dependencies = dependencies - return cls - - return decorator diff --git a/methods/EverCore/src/core/di/exceptions.py b/methods/EverCore/src/core/di/exceptions.py deleted file mode 100644 index 4411b6c5..00000000 --- a/methods/EverCore/src/core/di/exceptions.py +++ /dev/null @@ -1,97 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Dependency injection system exception class definitions -""" - -from typing import Type, Any, List - - -class DIException(Exception): - """Base exception for dependency injection system""" - - pass - - -class CircularDependencyError(DIException): - """Circular dependency exception""" - - def __init__(self, dependency_chain: List[Type]): - self.dependency_chain = dependency_chain - chain_str = " -> ".join([cls.__name__ for cls in dependency_chain]) - super().__init__(f"Circular dependency detected: {chain_str}") - - -class BeanNotFoundError(DIException): - """Bean not found exception""" - - def __init__(self, bean_type: Type = None, bean_name: str = None): - self.bean_type = bean_type - self.bean_name = bean_name - - if bean_name: - super().__init__(f"Bean named '{bean_name}' not found") - elif bean_type: - # Handle string type bean_type - if isinstance(bean_type, str): - super().__init__(f"Bean of type '{bean_type}' not found") - else: - super().__init__(f"Bean of type '{bean_type.__name__}' not found") - else: - super().__init__("Specified Bean not found") - - -class DuplicateBeanError(DIException): - """Duplicate Bean exception""" - - def __init__(self, bean_type: Type = None, bean_name: str = None): - self.bean_type = bean_type - self.bean_name = bean_name - - if bean_name: - super().__init__(f"Bean named '{bean_name}' already exists") - elif bean_type: - super().__init__(f"Bean of type '{bean_type.__name__}' already exists") - else: - super().__init__("Bean already exists") - - -class FactoryError(DIException): - """Factory exception""" - - def __init__(self, factory_type: Type, message: str = None): - self.factory_type = factory_type - default_msg = f"Factory '{factory_type.__name__}' failed to create instance" - super().__init__(message or default_msg) - - -class DependencyResolutionError(DIException): - """Dependency resolution exception""" - - def __init__(self, target_type: Type, missing_dependency: Type): - self.target_type = target_type - self.missing_dependency = missing_dependency - super().__init__( - f"Cannot resolve dependency '{missing_dependency.__name__}' for '{target_type.__name__}'" - ) - - -class MockNotEnabledError(DIException): - """Mock mode not enabled exception""" - - def __init__(self): - super().__init__( - "Mock mode is not enabled, cannot register Mock implementation" - ) - - -class PrimaryBeanConflictError(DIException): - """Primary Bean conflict exception""" - - def __init__(self, bean_type: Type, existing_primary: Type, new_primary: Type): - self.bean_type = bean_type - self.existing_primary = existing_primary - self.new_primary = new_primary - super().__init__( - f"Multiple Primary implementations exist for type '{bean_type.__name__}': " - f"'{existing_primary.__name__}' and '{new_primary.__name__}'" - ) diff --git a/methods/EverCore/src/core/di/introduction.md b/methods/EverCore/src/core/di/introduction.md deleted file mode 100644 index c0abab2d..00000000 --- a/methods/EverCore/src/core/di/introduction.md +++ /dev/null @@ -1,2101 +0,0 @@ -# DI (依赖注入) 模块介绍 - -本模块提供了一个完整的Python依赖注入（Dependency Injection, DI）框架，支持接口多实现、Primary机制、Mock模式、Factory功能、循环依赖检测和自动扫描等核心特性。该框架参考了Spring Framework和Google Guice的设计理念，结合Python的特性进行了优化和扩展。 - -## 目录 - -- [目录结构](#目录结构) -- [第一部分：原理详解](#第一部分原理详解) - - [1. 依赖注入基础原理](#1-依赖注入基础原理) - - [2. 容器管理机制](#2-容器管理机制) - - [3. Bean生命周期](#3-bean生命周期) - - [4. 依赖解析算法](#4-依赖解析算法) - - [5. Bean排序策略](#5-bean排序策略) - - [6. 组件扫描机制](#6-组件扫描机制) - - [7. 循环依赖处理](#7-循环依赖处理) -- [第二部分：使用指南](#第二部分使用指南) - - [1. 快速开始](#1-快速开始) - - [2. 装饰器详解](#2-装饰器详解) - - [3. 容器API详解](#3-容器api详解) - - [4. 高级特性](#4-高级特性) - - [5. 最佳实践](#5-最佳实践) - - [6. 实战案例](#6-实战案例) - - [7. 性能优化](#7-性能优化) - - [8. 故障排查](#8-故障排查) - -## 目录结构 - -``` -di/ -├── __init__.py # 包标识文件，导出核心API -├── introduction.md # 本介绍文档 -├── bean_definition.py # Bean定义和作用域枚举 -├── bean_order_strategy.py # Bean排序策略实现 -├── container.py # DI容器核心实现 -├── decorators.py # 依赖注入装饰器集合 -├── exceptions.py # 异常定义和层次结构 -├── scan_context.py # 扫描上下文管理 -├── scan_path_registry.py # 扫描路径注册表 -├── scanner.py # 组件扫描器实现 -├── utils.py # 工具函数和辅助方法 -└── tests/ # 测试目录 - ├── introduction.md # 测试模块介绍 - ├── test_fixtures.py # 测试Fixtures和Mock对象 - ├── test_bean_order_strategy.py # Bean排序策略测试（17个测试） - ├── test_di_container.py # Container容器测试（32个测试） - └── test_di_scanner.py # Scanner扫描器测试（23个测试） -``` - ---- - -# 第一部分：原理详解 - -## 1. 依赖注入基础原理 - -### 1.1 什么是依赖注入？ - -依赖注入（Dependency Injection, DI）是一种设计模式，用于实现**控制反转**（Inversion of Control, IoC）。其核心思想是：**将对象的依赖关系从对象内部转移到外部容器管理**。 - -**传统方式（硬编码依赖）：** - -```python -class UserService: - def __init__(self): - # 硬编码依赖，紧耦合 - self.repository = MySQLUserRepository() -``` - -**DI方式（依赖注入）：** - -```python -class UserService: - def __init__(self, repository: UserRepository): - # 依赖由外部注入，松耦合 - self.repository = repository -``` - -### 1.2 DI的核心优势 - -1. **降低耦合度**：组件之间通过接口交互，不依赖具体实现 -2. **提高可测试性**：可以轻松注入Mock对象进行单元测试 -3. **增强可维护性**：修改实现类不影响使用方 -4. **支持多态**：同一接口可以有多个实现，运行时动态选择 -5. **集中管理**：统一管理对象的创建、生命周期和依赖关系 - -### 1.3 本框架的设计理念 - -本DI框架基于以下设计原则： - -- **约定优于配置**：使用装饰器简化配置，减少样板代码 -- **类型安全**：充分利用Python类型提示，提供类型安全的依赖解析 -- **灵活扩展**：支持自定义Bean排序策略、扫描策略等 -- **开发友好**：提供Mock模式、详细的错误信息、循环依赖检测等开发辅助功能 -- **高性能**：多级缓存、并行扫描、懒加载等优化手段 - -## 2. 容器管理机制 - -### 2.1 容器的核心职责 - -DI容器（`DIContainer`）是框架的核心，负责： - -1. **Bean注册**：记录Bean的定义信息（类型、名称、作用域、优先级等） -2. **Bean实例化**：根据作用域策略创建Bean实例 -3. **依赖解析**：分析Bean的依赖关系，递归注入依赖 -4. **生命周期管理**：管理单例Bean的缓存和销毁 -5. **类型匹配**：根据类型或名称查找合适的Bean实现 - -### 2.2 容器的数据结构 - -容器内部维护以下核心数据结构： - -```python -class DIContainer: - # Bean定义存储：bean_name -> BeanDefinition - _bean_definitions: Dict[str, BeanDefinition] - - # 类型索引：bean_type -> [bean_name1, bean_name2, ...] - _type_to_beans: Dict[type, List[str]] - - # 单例缓存：bean_name -> bean_instance - _singletons: Dict[str, Any] - - # 继承关系缓存：(bean_type, target_type) -> bool - _subclass_cache: Dict[Tuple[type, type], bool] - - # 候选Bean缓存：target_type -> [bean_name1, bean_name2, ...] - _candidate_cache: Dict[type, List[str]] -``` - -### 2.3 单例模式实现 - -容器本身是单例，通过`get_container()`获取全局唯一实例： - -```python -_container_instance = None -_container_lock = threading.RLock() - -def get_container() -> DIContainer: - global _container_instance - if _container_instance is None: - with _container_lock: - if _container_instance is None: - _container_instance = DIContainer() - return _container_instance -``` - -## 3. Bean生命周期 - -### 3.1 Bean的作用域 - -框架支持三种Bean作用域（Scope）： - -#### 3.1.1 Singleton（单例） - -- **特点**：容器中只存在一个Bean实例，全局共享 -- **适用场景**：无状态服务、配置对象、数据库连接池等 -- **实现原理**：首次创建后缓存在`_singletons`字典中，后续请求直接返回缓存实例 - -```python -@service("user_service") # 默认是Singleton -class UserService: - pass -``` - -#### 3.1.2 Prototype（原型） - -- **特点**：每次请求都创建新的Bean实例 -- **适用场景**：有状态对象、线程不安全的对象、临时对象等 -- **实现原理**：每次调用都执行`bean_type()`创建新实例，不使用缓存 - -```python -@prototype("request_context") -class RequestContext: - def __init__(self): - self.data = {} -``` - -#### 3.1.3 Factory（工厂） - -- **特点**：通过工厂方法创建Bean，支持复杂的创建逻辑 -- **适用场景**：需要配置的对象、需要资源初始化的对象、第三方库对象等 -- **实现原理**：注册时保存工厂方法，创建时调用工厂方法 - -```python -@factory(bean_type=DatabaseConnection, name="db_conn") -def create_db_connection() -> DatabaseConnection: - config = load_config() - return DatabaseConnection(**config) -``` - -### 3.2 Bean的生命周期流程 - -``` -1. 定义阶段 - ↓ -2. 注册阶段（通过装饰器或手动注册） - ↓ -3. 扫描阶段（ComponentScanner扫描并注册Bean） - ↓ -4. 解析阶段（容器解析Bean定义，建立类型索引） - ↓ -5. 实例化阶段（根据作用域创建Bean实例） - ↓ -6. 依赖注入阶段（递归解析并注入依赖） - ↓ -7. 使用阶段（应用代码使用Bean） - ↓ -8. 销毁阶段（容器清理时销毁单例Bean） -``` - -## 4. 依赖解析算法 - -### 4.1 解析流程 - -当调用`container.get_bean_by_type(UserRepository)`时，容器执行以下步骤： - -``` -1. 查找候选Bean - - 检查缓存：_candidate_cache - - 遍历：_type_to_beans[UserRepository] - - 类型匹配：issubclass(bean_type, UserRepository) - -2. 过滤和排序 - - 应用Mock模式过滤（如果启用） - - 应用Bean排序策略 - -3. 选择最佳Bean - - 取排序后的第一个Bean - - 如果有多个相同优先级，抛出异常 - -4. 实例化Bean - - 检查单例缓存 - - 根据作用域创建实例 - - 检测循环依赖 - -5. 注入依赖 - - 分析构造函数参数 - - 递归解析依赖 - - 调用构造函数创建实例 - -6. 缓存和返回 - - 单例Bean缓存到_singletons - - 返回Bean实例 -``` - -### 4.2 类型匹配机制 - -框架支持两种类型匹配方式： - -#### 4.2.1 直接匹配（Exact Match） - -Bean类型与目标类型完全一致： - -```python -# 注册: MySQLUserRepository -# 查询: MySQLUserRepository -# 结果: 直接匹配 ✓ -``` - -#### 4.2.2 实现类匹配（Subclass Match） - -Bean类型是目标类型的子类： - -```python -# 注册: MySQLUserRepository(继承UserRepository) -# 查询: UserRepository -# 结果: 实现类匹配 ✓ -``` - -### 4.3 循环依赖检测 - -容器使用**依赖栈**检测循环依赖： - -```python -def _check_circular_dependency(self, bean_name: str, dependency_stack: List[str]): - if bean_name in dependency_stack: - # 检测到循环依赖 - cycle = dependency_stack + [bean_name] - raise CircularDependencyError(cycle) -``` - -**示例：** - -```python -# ServiceA -> ServiceB -> ServiceC -> ServiceA -# 依赖栈: [ServiceA, ServiceB, ServiceC] -# 当尝试解析ServiceA时，检测到循环 -``` - -## 5. Bean排序策略 - -### 5.1 排序算法原理 - -当一个接口有多个实现时，容器需要选择最合适的实现。`BeanOrderStrategy`定义了选择规则： - -```python -def sort_beans( - self, - candidates: List[Tuple[str, BeanDefinition]], - target_type: type -) -> List[Tuple[str, BeanDefinition]]: - """ - 优先级规则（从高到低）： - 1. is_mock: Mock Bean > 非Mock Bean（仅在Mock模式下） - 2. 匹配方式: 直接匹配 > 实现类匹配 - 3. is_primary: Primary Bean > 非Primary Bean - 4. scope: Factory Bean > 非Factory Bean - """ -``` - -### 5.2 排序规则详解 - -#### 规则1：Mock优先（仅Mock模式） - -```python -# Mock模式下 -@mock_impl("mock_repo") # 优先级：1 -class MockUserRepository: pass - -@repository("real_repo") # 优先级：2 -class RealUserRepository: pass -``` - -#### 规则2：匹配方式 - -```python -# 查询: UserRepository -@repository("exact_match") # 优先级：1（直接匹配） -class UserRepository: pass - -@repository("subclass_match") # 优先级：2（实现类匹配） -class MySQLUserRepository(UserRepository): pass -``` - -#### 规则3：Primary标记 - -```python -@repository("primary_repo", primary=True) # 优先级：1 -class PrimaryRepository: pass - -@repository("normal_repo") # 优先级：2 -class NormalRepository: pass -``` - -#### 规则4：作用域 - -```python -@factory(bean_type=Connection, name="factory_conn") # 优先级：1 -def create_connection(): pass - -@component("singleton_conn") # 优先级：2 -class Connection: pass -``` - -### 5.3 自定义排序策略 - -可以通过继承`BeanOrderStrategy`实现自定义排序： - -```python -class CustomOrderStrategy(BeanOrderStrategy): - def sort_beans(self, candidates, target_type): - # 自定义排序逻辑 - return sorted(candidates, key=lambda x: self._custom_score(x)) - - def _custom_score(self, candidate): - # 计算候选Bean的分数 - pass - -# 设置自定义策略 -container.set_order_strategy(CustomOrderStrategy()) -``` - -## 6. 组件扫描机制 - -### 6.1 扫描原理 - -`ComponentScanner`负责自动发现和注册带有DI装饰器的类： - -``` -1. 扫描路径收集 - - 添加扫描路径（文件系统路径） - - 添加扫描包（Python包路径） - -2. 文件遍历 - - 递归遍历目录 - - 应用排除规则（__pycache__, .pyc, test_等） - -3. 模块导入 - - 动态导入Python模块 - - 捕获导入错误 - -4. Bean发现 - - 检查模块成员 - - 识别带有_di_metadata的类 - -5. Bean注册 - - 调用容器的register_bean方法 - - 记录注册日志 -``` - -### 6.2 并行扫描 - -为了提高性能，扫描器支持多线程并行扫描： - -```python -def scan(self, parallel: bool = True, max_workers: int = 4): - if parallel: - with ThreadPoolExecutor(max_workers=max_workers) as executor: - futures = [executor.submit(self._scan_file, file) - for file in files] - for future in as_completed(futures): - result = future.result() -``` - -### 6.3 扫描策略 - -#### 包含模式（Include） - -```python -scanner.add_scan_path("/app/services") # 扫描services目录 -scanner.add_scan_package("myapp.repos") # 扫描repos包 -``` - -#### 排除模式（Exclude） - -```python -scanner.exclude_pattern("test_") # 排除测试文件 -scanner.exclude_pattern("_mock") # 排除Mock文件 -scanner.exclude_pattern("__pycache__") # 排除缓存目录 -``` - -## 7. 循环依赖处理 - -### 7.1 循环依赖的类型 - -#### 类型1：直接循环依赖 - -```python -class ServiceA: - def __init__(self, service_b: ServiceB): pass - -class ServiceB: - def __init__(self, service_a: ServiceA): pass - -# ServiceA -> ServiceB -> ServiceA -``` - -#### 类型2：间接循环依赖 - -```python -class ServiceA: - def __init__(self, service_b: ServiceB): pass - -class ServiceB: - def __init__(self, service_c: ServiceC): pass - -class ServiceC: - def __init__(self, service_a: ServiceA): pass - -# ServiceA -> ServiceB -> ServiceC -> ServiceA -``` - -### 7.2 检测机制 - -容器使用**依赖栈**在实例化过程中检测循环依赖： - -```python -dependency_stack = [] - -def get_bean(self, bean_name): - if bean_name in dependency_stack: - raise CircularDependencyError(dependency_stack + [bean_name]) - - dependency_stack.append(bean_name) - try: - # 创建Bean实例 - instance = self._create_instance(bean_name) - finally: - dependency_stack.pop() - - return instance -``` - -### 7.3 解决方案 - -#### 方案1：延迟注入（推荐） - -```python -class ServiceA: - def __init__(self): - self._service_b = None - - @property - def service_b(self): - if self._service_b is None: - self._service_b = get_container().get_bean("service_b") - return self._service_b -``` - -#### 方案2：重构设计 - -```python -# 提取公共依赖到第三个类 -class ServiceA: - def __init__(self, common: CommonService): pass - -class ServiceB: - def __init__(self, common: CommonService): pass - -class CommonService: - # 不依赖ServiceA和ServiceB - pass -``` - -#### 方案3：使用事件机制 - -```python -class ServiceA: - def __init__(self, event_bus: EventBus): - self.event_bus = event_bus - self.event_bus.subscribe("event_from_b", self.handle_event) - -class ServiceB: - def __init__(self, event_bus: EventBus): - self.event_bus = event_bus - self.event_bus.publish("event_from_b", data) -``` - ---- - -# 第二部分：使用指南 - -## 1. 快速开始 - -### 1.1 安装和导入 - -```python -# 导入核心模块 -from core.di.decorators import component, service, repository, factory -from core.di.container import get_container, DIContainer -from core.di.scanner import ComponentScanner -``` - -### 1.2 五分钟快速上手 - -```python -from abc import ABC, abstractmethod -from core.di.decorators import repository, service -from core.di.container import get_container - -# 步骤1: 定义接口 -class UserRepository(ABC): - @abstractmethod - def save(self, user: dict) -> bool: - pass - - @abstractmethod - def find_by_id(self, user_id: int) -> dict: - pass - -# 步骤2: 实现接口（使用装饰器自动注册） -@repository("mysql_user_repo", primary=True) -class MySQLUserRepository(UserRepository): - def save(self, user: dict) -> bool: - print(f"Saving user to MySQL: {user}") - return True - - def find_by_id(self, user_id: int) -> dict: - return {"id": user_id, "name": f"User {user_id}", "db": "mysql"} - -# 步骤3: 创建服务（依赖注入） -@service("user_service") -class UserService: - def __init__(self): - # 从容器获取依赖 - self.repository = get_container().get_bean_by_type(UserRepository) - - def create_user(self, name: str) -> dict: - user = {"id": 1, "name": name} - self.repository.save(user) - return user - - def get_user(self, user_id: int) -> dict: - return self.repository.find_by_id(user_id) - -# 步骤4: 使用服务 -container = get_container() -user_service = container.get_bean("user_service") - -# 调用服务方法 -new_user = user_service.create_user("Alice") -found_user = user_service.get_user(1) -``` - -## 2. 装饰器详解 - -### 2.1 @component - 通用组件 - -最基础的Bean注册装饰器，适用于任何需要被容器管理的类。 - -```python -from core.di.decorators import component - -@component("my_component") -class MyComponent: - def do_something(self): - return "Hello from core.component" - -# 使用 -comp = get_container().get_bean("my_component") -result = comp.do_something() -``` - -**参数说明：** -- `name` (str): Bean名称，必须唯一 -- `primary` (bool): 是否为Primary Bean，默认False -- `metadata` (dict): 自定义元数据 - -**完整示例：** - -```python -@component( - name="config_manager", - primary=True, - metadata={"version": "1.0", "author": "team"} -) -class ConfigManager: - def __init__(self): - self.config = {} - - def get(self, key: str, default=None): - return self.config.get(key, default) -``` - -### 2.2 @service - 业务服务 - -专门用于标注业务逻辑层的服务类，语义化更强。 - -```python -from core.di.decorators import service - -@service("order_service") -class OrderService: - def __init__(self): - self.order_repo = get_container().get_bean_by_type(OrderRepository) - self.payment_service = get_container().get_bean("payment_service") - - def create_order(self, items: list, user_id: int) -> dict: - # 业务逻辑 - order = {"items": items, "user_id": user_id, "status": "pending"} - self.order_repo.save(order) - return order - - def process_payment(self, order_id: int, amount: float) -> bool: - return self.payment_service.charge(order_id, amount) -``` - -**命名建议：** -- 使用`_service`后缀 -- 采用小写下划线命名法 -- 例如：`user_service`, `order_service`, `notification_service` - -### 2.3 @repository - 数据访问 - -专门用于数据访问层（DAO），处理数据持久化逻辑。 - -```python -from core.di.decorators import repository -from abc import ABC, abstractmethod - -# 定义接口 -class OrderRepository(ABC): - @abstractmethod - def save(self, order: dict) -> bool: - pass - - @abstractmethod - def find_by_id(self, order_id: int) -> dict: - pass - - @abstractmethod - def find_by_user(self, user_id: int) -> list: - pass - -# MySQL实现 -@repository("mysql_order_repo", primary=True) -class MySQLOrderRepository(OrderRepository): - def __init__(self): - self.db = get_container().get_bean("mysql_connection") - - def save(self, order: dict) -> bool: - # 保存到MySQL - sql = "INSERT INTO orders ..." - return self.db.execute(sql, order) - - def find_by_id(self, order_id: int) -> dict: - sql = "SELECT * FROM orders WHERE id = %s" - return self.db.query_one(sql, [order_id]) - - def find_by_user(self, user_id: int) -> list: - sql = "SELECT * FROM orders WHERE user_id = %s" - return self.db.query_all(sql, [user_id]) - -# MongoDB实现（多实现） -@repository("mongo_order_repo") -class MongoOrderRepository(OrderRepository): - def __init__(self): - self.db = get_container().get_bean("mongo_connection") - - def save(self, order: dict) -> bool: - return self.db.orders.insert_one(order).acknowledged - - def find_by_id(self, order_id: int) -> dict: - return self.db.orders.find_one({"id": order_id}) - - def find_by_user(self, user_id: int) -> list: - return list(self.db.orders.find({"user_id": user_id})) -``` - -### 2.4 @controller - 控制器 - -用于Web层的控制器类（API端点处理器）。 - -```python -from core.di.decorators import controller - -@controller("user_controller") -class UserController: - def __init__(self): - self.user_service = get_container().get_bean("user_service") - - def get_user_api(self, user_id: int) -> dict: - """GET /api/users/{user_id}""" - user = self.user_service.get_user(user_id) - return {"code": 200, "data": user} - - def create_user_api(self, data: dict) -> dict: - """POST /api/users""" - user = self.user_service.create_user(data["name"]) - return {"code": 201, "data": user} -``` - -### 2.5 @injectable - 可注入组件 - -通用的可注入组件，功能类似`@component`。 - -```python -from core.di.decorators import injectable - -@injectable("cache_manager") -class CacheManager: - def __init__(self): - self.cache = {} - - def get(self, key: str): - return self.cache.get(key) - - def set(self, key: str, value): - self.cache[key] = value -``` - -### 2.6 @mock_impl - Mock实现 - -专门用于测试的Mock实现，只在Mock模式下生效。 - -```python -from core.di.decorators import mock_impl - -@mock_impl("mock_user_repo") -class MockUserRepository(UserRepository): - """Mock实现，用于测试""" - - def __init__(self): - self.users = { - 1: {"id": 1, "name": "Mock User 1"}, - 2: {"id": 2, "name": "Mock User 2"} - } - - def find_by_id(self, user_id: int) -> dict: - return self.users.get(user_id, {"id": user_id, "name": "Unknown"}) - - def save(self, user: dict) -> bool: - self.users[user["id"]] = user - return True - -# 在测试中启用Mock模式 -def test_user_service(): - container = get_container() - container.enable_mock_mode() # 启用Mock - - # 自动使用MockUserRepository - service = container.get_bean("user_service") - user = service.get_user(1) - - assert user["name"] == "Mock User 1" - - container.disable_mock_mode() # 恢复正常模式 -``` - -### 2.7 @factory - 工厂方法 - -使用函数创建Bean，适用于需要复杂初始化的对象。 - -```python -from core.di.decorators import factory - -# 示例1: 数据库连接 -@factory(bean_type=DatabaseConnection, name="db_connection") -def create_database_connection() -> DatabaseConnection: - """工厂方法：创建数据库连接""" - # 读取配置 - config = { - "host": os.getenv("DB_HOST", "localhost"), - "port": int(os.getenv("DB_PORT", 3306)), - "user": os.getenv("DB_USER", "root"), - "password": os.getenv("DB_PASSWORD", ""), - "database": os.getenv("DB_NAME", "mydb") - } - - # 创建连接 - conn = DatabaseConnection(**config) - - # 初始化连接池 - conn.initialize_pool(min_size=5, max_size=20) - - return conn - -# 示例2: 第三方库对象 -@factory(bean_type=Redis, name="redis_client") -def create_redis_client() -> Redis: - """工厂方法：创建Redis客户端""" - return Redis( - host=os.getenv("REDIS_HOST", "localhost"), - port=int(os.getenv("REDIS_PORT", 6379)), - db=int(os.getenv("REDIS_DB", 0)), - decode_responses=True - ) - -# 示例3: 带初始化逻辑 -@factory(bean_type=MessageQueue, name="mq") -def create_message_queue() -> MessageQueue: - """工厂方法：创建消息队列""" - mq = MessageQueue(broker_url=os.getenv("MQ_BROKER")) - - # 注册消费者 - mq.register_consumer("order_events", handle_order_event) - mq.register_consumer("user_events", handle_user_event) - - # 启动连接 - mq.connect() - - return mq -``` - -### 2.8 @prototype - 原型作用域 - -每次获取都创建新实例，适用于有状态对象。 - -```python -from core.di.decorators import prototype - -@prototype("request_context") -class RequestContext: - """请求上下文（有状态）""" - - def __init__(self): - self.user_id = None - self.session_id = None - self.data = {} - - def set_user(self, user_id: int): - self.user_id = user_id - - def set_data(self, key: str, value): - self.data[key] = value - -# 每次获取都是新实例 -ctx1 = get_container().get_bean("request_context") -ctx2 = get_container().get_bean("request_context") -assert ctx1 is not ctx2 # 不是同一个实例 - -# 示例2: 任务对象 -@prototype("data_import_task") -class DataImportTask: - """数据导入任务（每次创建新任务）""" - - def __init__(self): - self.task_id = str(uuid.uuid4()) - self.status = "pending" - self.progress = 0 - - def execute(self, file_path: str): - self.status = "running" - # 执行导入逻辑 - self.progress = 100 - self.status = "completed" -``` - -## 3. 容器API详解 - -### 3.1 获取容器 - -```python -from core.di.container import get_container, DIContainer - -# 方式1: 获取全局容器（推荐） -container = get_container() - -# 方式2: 创建新容器（不推荐，除非有特殊需求） -custom_container = DIContainer() -``` - -### 3.2 注册Bean - -#### 3.2.1 手动注册类 - -```python -# 注册普通类 -container.register_bean( - bean_type=UserService, - bean_name="user_service", - scope=BeanScope.SINGLETON, # 可选，默认SINGLETON - is_primary=True, # 可选，默认False - is_mock=False, # 可选，默认False - metadata={"version": "1.0"} # 可选，默认{} -) - -# 注册为Prototype -container.register_bean( - bean_type=TaskRunner, - bean_name="task_runner", - scope=BeanScope.PROTOTYPE -) -``` - -#### 3.2.2 注册工厂方法 - -```python -def create_logger(): - return logging.getLogger("myapp") - -container.register_factory( - bean_type=logging.Logger, - factory_method=create_logger, - bean_name="app_logger" -) -``` - -### 3.3 获取Bean - -#### 3.3.1 按名称获取 - -```python -# 按名称获取（最常用） -user_service = container.get_bean("user_service") - -# 如果Bean不存在，抛出BeanNotFoundError -try: - service = container.get_bean("non_existent") -except BeanNotFoundError as e: - print(f"Bean not found: {e}") -``` - -#### 3.3.2 按类型获取 - -```python -# 按类型获取（自动选择最佳实现） -repository = container.get_bean_by_type(UserRepository) - -# 如果有多个实现，返回Primary或优先级最高的 -# 如果没有实现，抛出BeanNotFoundError - -# 指定类型注解 -from typing import Type - -def get_service(service_type: Type[T]) -> T: - return container.get_bean_by_type(service_type) -``` - -#### 3.3.3 获取所有实现 - -```python -# 获取某个类型的所有实现 -all_repos = container.get_beans_by_type(UserRepository) -# 返回: [MySQLUserRepository实例, MongoUserRepository实例, ...] - -# 遍历所有实现 -for repo in all_repos: - print(f"Repository: {type(repo).__name__}") - repo.save(user_data) -``` - -### 3.4 检查Bean - -```python -# 检查Bean是否存在（按名称） -if container.contains_bean("user_service"): - service = container.get_bean("user_service") - -# 检查Bean是否存在（按类型） -if container.contains_bean_by_type(UserRepository): - repo = container.get_bean_by_type(UserRepository) - -# 检查是否有多个实现 -repos = container.get_beans_by_type(UserRepository) -if len(repos) > 1: - print(f"Found {len(repos)} implementations") -``` - -### 3.5 Mock模式控制 - -```python -# 启用Mock模式 -container.enable_mock_mode() - -# 检查Mock模式状态 -if container.is_mock_mode(): - print("Mock mode is enabled") - -# 禁用Mock模式 -container.disable_mock_mode() - -# 示例：测试场景 -def test_with_mock(): - container.enable_mock_mode() - try: - # 测试代码，自动使用Mock实现 - service = container.get_bean("user_service") - result = service.get_user(1) - assert result is not None - finally: - container.disable_mock_mode() # 确保恢复 -``` - -### 3.6 容器管理 - -```python -# 清空容器（危险操作！） -container.clear() - -# 获取所有Bean定义 -all_beans = container.get_all_bean_definitions() -for bean_name, bean_def in all_beans.items(): - print(f"Bean: {bean_name}, Type: {bean_def.bean_type}") - -# 获取Bean定义 -bean_def = container.get_bean_definition("user_service") -print(f"Scope: {bean_def.scope}") -print(f"Primary: {bean_def.is_primary}") - -# 设置自定义排序策略 -custom_strategy = CustomBeanOrderStrategy() -container.set_order_strategy(custom_strategy) -``` - -## 4. 高级特性 - -### 4.1 组件扫描 - -#### 4.1.1 基本扫描 - -```python -from core.di.scanner import ComponentScanner - -# 创建扫描器 -scanner = ComponentScanner() - -# 添加扫描路径 -scanner.add_scan_path("/path/to/myapp/services") -scanner.add_scan_path("/path/to/myapp/repositories") - -# 添加扫描包 -scanner.add_scan_package("myapp.controllers") -scanner.add_scan_package("myapp.components") - -# 执行扫描（自动注册所有带装饰器的类） -scanner.scan() -``` - -#### 4.1.2 排除规则 - -```python -# 排除测试文件 -scanner.exclude_pattern("test_") -scanner.exclude_pattern("_test") - -# 排除Mock文件 -scanner.exclude_pattern("mock_") -scanner.exclude_pattern("_mock") - -# 排除特定目录 -scanner.exclude_pattern("__pycache__") -scanner.exclude_pattern("migrations") - -# 执行扫描 -scanner.scan() -``` - -#### 4.1.3 并行扫描 - -```python -# 开启并行扫描（默认4个线程） -scanner.scan(parallel=True, max_workers=4) - -# 关闭并行扫描 -scanner.scan(parallel=False) -``` - -#### 4.1.4 全局扫描配置 - -```python -from core.di.scan_path_registry import ScanPathRegistry - -# 获取全局注册表 -registry = ScanPathRegistry.get_instance() - -# 添加全局扫描路径 -registry.add_scan_path("/path/to/myapp") - -# 添加全局排除规则 -registry.add_exclude_pattern("test_*") - -# 获取所有扫描路径 -paths = registry.get_all_scan_paths() -``` - -### 4.2 接口多实现 - -#### 4.2.1 定义多个实现 - -```python -from abc import ABC, abstractmethod - -# 定义接口 -class MessageSender(ABC): - @abstractmethod - def send(self, to: str, content: str) -> bool: - pass - -# 实现1: 邮件发送 -@service("email_sender") -class EmailSender(MessageSender): - def send(self, to: str, content: str) -> bool: - print(f"Sending email to {to}: {content}") - return True - -# 实现2: 短信发送 -@service("sms_sender") -class SmsSender(MessageSender): - def send(self, to: str, content: str) -> bool: - print(f"Sending SMS to {to}: {content}") - return True - -# 实现3: 推送通知（Primary） -@service("push_sender", primary=True) -class PushSender(MessageSender): - def send(self, to: str, content: str) -> bool: - print(f"Sending push notification to {to}: {content}") - return True -``` - -#### 4.2.2 使用多实现 - -```python -# 获取Primary实现（PushSender） -sender = container.get_bean_by_type(MessageSender) -sender.send("user123", "Hello") - -# 获取所有实现 -all_senders = container.get_beans_by_type(MessageSender) -for sender in all_senders: - sender.send("user123", "Broadcast message") - -# 按名称获取特定实现 -email_sender = container.get_bean("email_sender") -sms_sender = container.get_bean("sms_sender") -``` - -#### 4.2.3 策略模式应用 - -```python -@service("notification_service") -class NotificationService: - def __init__(self): - # 获取所有消息发送实现 - self.senders = get_container().get_beans_by_type(MessageSender) - - def notify_all(self, user: str, message: str): - """使用所有渠道发送通知""" - results = [] - for sender in self.senders: - result = sender.send(user, message) - results.append((type(sender).__name__, result)) - return results - - def notify_by_preference(self, user: str, message: str, channel: str): - """根据用户偏好选择渠道""" - sender_map = { - "email": "email_sender", - "sms": "sms_sender", - "push": "push_sender" - } - sender_name = sender_map.get(channel) - if sender_name: - sender = get_container().get_bean(sender_name) - return sender.send(user, message) - return False -``` - -### 4.3 Primary机制详解 - -#### 4.3.1 Primary优先级 - -```python -# 场景：数据库连接有多个实现 - -@repository("mysql_repo") -class MySQLRepository(DataRepository): - pass - -@repository("postgres_repo", primary=True) # 标记为Primary -class PostgresRepository(DataRepository): - pass - -@repository("mongo_repo") -class MongoRepository(DataRepository): - pass - -# 获取Bean时，自动返回Primary实现 -repo = container.get_bean_by_type(DataRepository) -# 返回: PostgresRepository 实例 -``` - -#### 4.3.2 多个Primary冲突 - -```python -# 错误示例：同一接口有多个Primary -@repository("repo1", primary=True) -class Repo1(DataRepository): - pass - -@repository("repo2", primary=True) # 冲突！ -class Repo2(DataRepository): - pass - -# 获取时会抛出 PrimaryBeanConflictError -try: - repo = container.get_bean_by_type(DataRepository) -except PrimaryBeanConflictError as e: - print(f"Multiple primary beans: {e}") -``` - -#### 4.3.3 不同接口的Primary - -```python -# 不同接口可以各自有Primary，不会冲突 - -@repository("mysql_user_repo", primary=True) -class MySQLUserRepository(UserRepository): - pass - -@repository("mysql_order_repo", primary=True) -class MySQLOrderRepository(OrderRepository): - pass - -# 两者不冲突，各自是自己接口的Primary -user_repo = container.get_bean_by_type(UserRepository) # MySQLUserRepository -order_repo = container.get_bean_by_type(OrderRepository) # MySQLOrderRepository -``` - -### 4.4 Mock模式详解 - -#### 4.4.1 Mock模式的使用场景 - -1. **单元测试**：隔离外部依赖 -2. **集成测试**：模拟未完成的模块 -3. **开发调试**：快速验证逻辑 -4. **演示Demo**：不依赖真实数据源 - -#### 4.4.2 完整的Mock示例 - -```python -# 1. 定义接口 -class PaymentGateway(ABC): - @abstractmethod - def charge(self, amount: float, card: str) -> dict: - pass - -# 2. 真实实现 -@service("stripe_payment") -class StripePayment(PaymentGateway): - def charge(self, amount: float, card: str) -> dict: - # 调用真实的Stripe API - response = stripe.charge(amount, card) - return response - -# 3. Mock实现 -@mock_impl("mock_payment") -class MockPayment(PaymentGateway): - """Mock支付，用于测试""" - - def charge(self, amount: float, card: str) -> dict: - # 返回模拟数据 - return { - "success": True, - "transaction_id": "MOCK_12345", - "amount": amount, - "card": card[-4:] # 只显示后4位 - } - -# 4. 使用Mock模式 -def test_payment_flow(): - container = get_container() - container.enable_mock_mode() - - try: - # 自动使用MockPayment - payment = container.get_bean_by_type(PaymentGateway) - result = payment.charge(100.0, "4111111111111111") - - assert result["success"] == True - assert result["amount"] == 100.0 - finally: - container.disable_mock_mode() -``` - -#### 4.4.3 环境变量控制Mock - -```python -import os -from core.di.container import get_container - -# 根据环境变量启用Mock -if os.getenv("ENABLE_MOCK", "false").lower() == "true": - get_container().enable_mock_mode() - -# 或者在应用启动时 -def init_app(): - container = get_container() - if os.getenv("ENV") in ["test", "dev"]: - container.enable_mock_mode() - print("Mock mode enabled for testing/development") -``` - -### 4.5 Bean元数据（Metadata） - -#### 4.5.1 定义元数据 - -```python -@service( - "user_service", - metadata={ - "version": "2.0", - "author": "backend-team", - "deprecated": False, - "tags": ["core", "user-management"], - "rate_limit": 1000 - } -) -class UserService: - pass -``` - -#### 4.5.2 读取元数据 - -```python -# 获取Bean定义 -bean_def = container.get_bean_definition("user_service") - -# 访问元数据 -print(f"Version: {bean_def.metadata.get('version')}") -print(f"Author: {bean_def.metadata.get('author')}") -print(f"Tags: {bean_def.metadata.get('tags')}") - -# 根据元数据过滤Bean -all_defs = container.get_all_bean_definitions() -core_beans = [ - name for name, bean_def in all_defs.items() - if "core" in bean_def.metadata.get("tags", []) -] -print(f"Core beans: {core_beans}") -``` - -#### 4.5.3 元数据应用场景 - -```python -# 场景1: 版本管理 -@service("api_v1", metadata={"version": "1.0", "deprecated": True}) -class ApiV1Service: - pass - -@service("api_v2", metadata={"version": "2.0", "deprecated": False}) -class ApiV2Service: - pass - -# 场景2: 权限标记 -@service("admin_service", metadata={"require_role": "admin"}) -class AdminService: - pass - -# 场景3: 监控标记 -@service("critical_service", metadata={"monitoring": "high", "alert_on_error": True}) -class CriticalService: - pass -``` - -## 5. 最佳实践 - -### 5.1 接口设计 - -#### 原则1: 使用抽象基类定义接口 - -```python -from abc import ABC, abstractmethod - -# 好的做法 ✓ -class UserRepository(ABC): - @abstractmethod - def find_by_id(self, user_id: int) -> dict: - pass - - @abstractmethod - def save(self, user: dict) -> bool: - pass - -# 不推荐的做法 ✗ -class UserRepository: # 没有ABC，缺少抽象方法 - def find_by_id(self, user_id: int) -> dict: - pass -``` - -#### 原则2: 接口职责单一 - -```python -# 好的做法 ✓ - 职责清晰 -class UserRepository(ABC): - @abstractmethod - def find_by_id(self, user_id: int) -> dict: - pass - -class UserValidator(ABC): - @abstractmethod - def validate(self, user: dict) -> bool: - pass - -# 不推荐的做法 ✗ - 职责混杂 -class UserRepository(ABC): - @abstractmethod - def find_by_id(self, user_id: int) -> dict: - pass - - @abstractmethod - def validate_user(self, user: dict) -> bool: # 不属于Repository职责 - pass -``` - -### 5.2 命名规范 - -#### Bean命名 - -```python -# 好的命名 ✓ -@service("user_service") # 小写下划线 -@repository("mysql_user_repo") # 描述性强 -@controller("api_user_controller") # 语义清晰 - -# 不推荐的命名 ✗ -@service("UserService") # 不要用大驼峰 -@service("us") # 太简短 -@service("service1") # 没有语义 -``` - -#### 类命名 - -```python -# 好的命名 ✓ -class UserService: # 大驼峰 -class MySQLUserRepository: # 包含实现细节 -class ApiUserController: # 清晰的职责 - -# 不推荐的命名 ✗ -class user_service: # 不要用小写下划线 -class US: # 太简短 -class Manager: # 太模糊 -``` - -### 5.3 依赖注入方式 - -#### 推荐方式: 构造函数注入 - -```python -# 好的做法 ✓ - 构造函数注入 -@service("user_service") -class UserService: - def __init__(self): - self.repository = get_container().get_bean_by_type(UserRepository) - self.validator = get_container().get_bean("user_validator") - - def create_user(self, data: dict): - if self.validator.validate(data): - return self.repository.save(data) -``` - -#### 延迟注入 - -```python -# 适用场景: 避免循环依赖 -@service("service_a") -class ServiceA: - def __init__(self): - self._service_b = None # 延迟初始化 - - @property - def service_b(self): - if self._service_b is None: - self._service_b = get_container().get_bean("service_b") - return self._service_b -``` - -### 5.4 Mock开发模式 - -```python -# 1. 开发初期：先定义接口 -class EmailService(ABC): - @abstractmethod - def send_email(self, to: str, subject: str, body: str) -> bool: - pass - -# 2. 使用Mock快速开发 -@mock_impl("mock_email") -class MockEmailService(EmailService): - def send_email(self, to: str, subject: str, body: str) -> bool: - print(f"[MOCK] Email to {to}: {subject}") - return True - -# 3. 开发阶段启用Mock -if os.getenv("ENV") == "dev": - get_container().enable_mock_mode() - -# 4. 后期实现真实逻辑 -@service("smtp_email", primary=True) -class SmtpEmailService(EmailService): - def send_email(self, to: str, subject: str, body: str) -> bool: - # 真实的SMTP发送逻辑 - pass - -# 5. 生产环境禁用Mock，自动切换到真实实现 -``` - -### 5.5 错误处理 - -```python -from core.di.exceptions import ( - BeanNotFoundError, - CircularDependencyError, - PrimaryBeanConflictError -) - -# 优雅的错误处理 -def get_service_safe(service_type: type, fallback=None): - """安全获取服务，失败时返回fallback""" - try: - return get_container().get_bean_by_type(service_type) - except BeanNotFoundError: - if fallback: - return fallback - raise - -# 检查后再获取 -if container.contains_bean_by_type(UserRepository): - repo = container.get_bean_by_type(UserRepository) -else: - # 使用默认实现或抛出自定义错误 - raise ApplicationError("UserRepository not configured") -``` - -## 6. 实战案例 - -### 6.1 案例1：Web API应用 - -```python -# 1. 数据层 -@repository("pg_user_repo", primary=True) -class PostgresUserRepository(UserRepository): - def __init__(self): - self.db = get_container().get_bean("db_connection") - - def find_by_id(self, user_id: int) -> dict: - return self.db.query_one("SELECT * FROM users WHERE id = %s", [user_id]) - - def find_by_email(self, email: str) -> dict: - return self.db.query_one("SELECT * FROM users WHERE email = %s", [email]) - - def save(self, user: dict) -> bool: - return self.db.execute("INSERT INTO users ...", user) - -# 2. 业务层 -@service("user_service") -class UserService: - def __init__(self): - self.user_repo = get_container().get_bean_by_type(UserRepository) - self.email_service = get_container().get_bean("email_service") - self.cache = get_container().get_bean("cache_manager") - - def get_user(self, user_id: int) -> dict: - # 先查缓存 - cache_key = f"user:{user_id}" - cached = self.cache.get(cache_key) - if cached: - return cached - - # 查数据库 - user = self.user_repo.find_by_id(user_id) - - # 写缓存 - if user: - self.cache.set(cache_key, user, ttl=300) - - return user - - def register_user(self, email: str, password: str) -> dict: - # 检查邮箱是否已存在 - existing = self.user_repo.find_by_email(email) - if existing: - raise ValueError("Email already exists") - - # 创建用户 - user = {"email": email, "password": hash_password(password)} - self.user_repo.save(user) - - # 发送欢迎邮件 - self.email_service.send_email( - to=email, - subject="Welcome!", - body="Welcome to our platform" - ) - - return user - -# 3. 控制层 -@controller("user_controller") -class UserController: - def __init__(self): - self.user_service = get_container().get_bean("user_service") - - def get_user_api(self, user_id: int) -> dict: - """GET /api/users/{user_id}""" - try: - user = self.user_service.get_user(user_id) - return {"code": 200, "data": user} - except Exception as e: - return {"code": 500, "error": str(e)} - - def register_api(self, data: dict) -> dict: - """POST /api/users/register""" - try: - user = self.user_service.register_user( - email=data["email"], - password=data["password"] - ) - return {"code": 201, "data": user} - except ValueError as e: - return {"code": 400, "error": str(e)} - -# 4. 应用启动 -def init_application(): - # 扫描组件 - scanner = ComponentScanner() - scanner.add_scan_path("/app/repositories") - scanner.add_scan_path("/app/services") - scanner.add_scan_path("/app/controllers") - scanner.scan() - - # 获取控制器 - controller = get_container().get_bean("user_controller") - return controller -``` - -### 6.2 案例2：数据处理管道 - -```python -# 1. 定义处理器接口 -class DataProcessor(ABC): - @abstractmethod - def process(self, data: dict) -> dict: - pass - -# 2. 实现多个处理器 -@component("data_cleaner") -class DataCleaner(DataProcessor): - def process(self, data: dict) -> dict: - # 清洗数据 - cleaned = {k: v.strip() if isinstance(v, str) else v - for k, v in data.items()} - return cleaned - -@component("data_validator") -class DataValidator(DataProcessor): - def process(self, data: dict) -> dict: - # 验证数据 - required_fields = ["name", "email"] - for field in required_fields: - if field not in data: - raise ValueError(f"Missing field: {field}") - return data - -@component("data_enricher") -class DataEnricher(DataProcessor): - def process(self, data: dict) -> dict: - # 丰富数据 - data["processed_at"] = get_now_with_timezone() - data["version"] = "1.0" - return data - -# 3. 管道服务 -@service("data_pipeline") -class DataPipeline: - def __init__(self): - # 获取所有处理器 - self.processors = get_container().get_beans_by_type(DataProcessor) - - def execute(self, data: dict) -> dict: - """执行处理管道""" - result = data - for processor in self.processors: - try: - result = processor.process(result) - print(f"Processed by {type(processor).__name__}") - except Exception as e: - print(f"Error in {type(processor).__name__}: {e}") - raise - return result - -# 4. 使用 -pipeline = get_container().get_bean("data_pipeline") -raw_data = {"name": " Alice ", "email": "alice@example.com"} -processed_data = pipeline.execute(raw_data) -``` - -### 6.3 案例3：多租户系统 - -```python -# 1. 租户上下文 -@prototype("tenant_context") -class TenantContext: - def __init__(self): - self.tenant_id = None - self.tenant_name = None - - def set_tenant(self, tenant_id: str, tenant_name: str): - self.tenant_id = tenant_id - self.tenant_name = tenant_name - -# 2. 多租户Repository -@repository("multi_tenant_repo") -class MultiTenantRepository(DataRepository): - def __init__(self): - self.db = get_container().get_bean("db_connection") - - def _get_tenant_id(self) -> str: - """从上下文获取租户ID""" - context = get_container().get_bean("tenant_context") - if not context.tenant_id: - raise ValueError("Tenant context not set") - return context.tenant_id - - def find_all(self) -> list: - tenant_id = self._get_tenant_id() - return self.db.query_all( - "SELECT * FROM data WHERE tenant_id = %s", - [tenant_id] - ) - - def save(self, data: dict) -> bool: - tenant_id = self._get_tenant_id() - data["tenant_id"] = tenant_id - return self.db.execute("INSERT INTO data ...", data) - -# 3. 使用 -def handle_request(request): - # 每个请求使用新的上下文（Prototype） - context = get_container().get_bean("tenant_context") - context.set_tenant( - tenant_id=request.headers.get("X-Tenant-ID"), - tenant_name=request.headers.get("X-Tenant-Name") - ) - - # Repository自动使用正确的租户ID - repo = get_container().get_bean("multi_tenant_repo") - data = repo.find_all() - return data -``` - -## 7. 性能优化 - -### 7.1 缓存机制 - -框架内置多级缓存： - -```python -# 1. 单例Bean缓存 -_singletons: Dict[str, Any] -# 首次创建后缓存，后续直接返回 - -# 2. 类型匹配缓存 -_subclass_cache: Dict[Tuple[type, type], bool] -# 缓存issubclass结果，避免重复计算 - -# 3. 候选Bean缓存 -_candidate_cache: Dict[type, List[str]] -# 缓存类型查找结果，提升查询速度 -``` - -### 7.2 懒加载 - -```python -# 延迟注册 -class LazyService: - def __init__(self): - self._repo = None - - @property - def repo(self): - if self._repo is None: - self._repo = get_container().get_bean_by_type(UserRepository) - return self._repo - -# 只在首次访问时才解析依赖 -``` - -### 7.3 并行扫描 - -```python -# 开启并行扫描，提升启动速度 -scanner = ComponentScanner() -scanner.add_scan_path("/large/codebase") -scanner.scan(parallel=True, max_workers=8) # 使用8个线程 -``` - -### 7.4 性能监控 - -```python -import time - -class PerformanceMonitoringContainer(DIContainer): - def get_bean(self, bean_name: str): - start = time.time() - bean = super().get_bean(bean_name) - duration = time.time() - start - print(f"Bean '{bean_name}' resolved in {duration:.4f}s") - return bean -``` - -## 8. 故障排查 - -### 8.1 常见错误 - -#### 错误1: BeanNotFoundError - -```python -# 错误信息 -BeanNotFoundError: No bean found with name 'user_service' - -# 原因1: Bean未注册 -# 解决: 检查是否添加了装饰器，是否执行了扫描 - -# 原因2: Bean名称拼写错误 -@service("user_service") # 注册名 -container.get_bean("userService") # 错误！名称不匹配 - -# 原因3: 扫描路径不正确 -scanner.add_scan_path("/wrong/path") # 路径错误 - -# 调试方法 -all_beans = container.get_all_bean_definitions() -print(f"Registered beans: {list(all_beans.keys())}") -``` - -#### 错误2: CircularDependencyError - -```python -# 错误信息 -CircularDependencyError: Circular dependency detected: ServiceA -> ServiceB -> ServiceA - -# 原因: 循环依赖 -class ServiceA: - def __init__(self): - self.b = get_container().get_bean("service_b") - -class ServiceB: - def __init__(self): - self.a = get_container().get_bean("service_a") - -# 解决方案1: 延迟注入 -class ServiceA: - @property - def b(self): - if not hasattr(self, "_b"): - self._b = get_container().get_bean("service_b") - return self._b - -# 解决方案2: 重构设计 -class ServiceA: - def __init__(self): - self.common = get_container().get_bean("common_service") - -class ServiceB: - def __init__(self): - self.common = get_container().get_bean("common_service") -``` - -#### 错误3: PrimaryBeanConflictError - -```python -# 错误信息 -PrimaryBeanConflictError: Multiple primary beans found for type UserRepository - -# 原因: 多个Primary Bean -@repository("repo1", primary=True) -class Repo1(UserRepository): pass - -@repository("repo2", primary=True) # 冲突 -class Repo2(UserRepository): pass - -# 解决: 只保留一个Primary -@repository("repo1", primary=True) -class Repo1(UserRepository): pass - -@repository("repo2") # 移除primary=True -class Repo2(UserRepository): pass -``` - -### 8.2 调试技巧 - -#### 技巧1: 打印所有Bean - -```python -def debug_print_beans(): - container = get_container() - all_beans = container.get_all_bean_definitions() - - print("=" * 60) - print("Registered Beans:") - print("=" * 60) - - for name, bean_def in all_beans.items(): - print(f"Name: {name}") - print(f" Type: {bean_def.bean_type}") - print(f" Scope: {bean_def.scope}") - print(f" Primary: {bean_def.is_primary}") - print(f" Mock: {bean_def.is_mock}") - print(f" Metadata: {bean_def.metadata}") - print("-" * 60) -``` - -#### 技巧2: 跟踪依赖链 - -```python -def trace_dependencies(bean_name: str, visited=None): - if visited is None: - visited = set() - - if bean_name in visited: - print(f" [CIRCULAR] {bean_name}") - return - - visited.add(bean_name) - print(f"Bean: {bean_name}") - - # 获取Bean的依赖 - bean_def = container.get_bean_definition(bean_name) - # ... 分析构造函数参数，递归跟踪 -``` - -#### 技巧3: Mock模式检查 - -```python -def check_mock_status(): - container = get_container() - print(f"Mock mode: {container.is_mock_mode()}") - - all_beans = container.get_all_bean_definitions() - mock_beans = [name for name, bean_def in all_beans.items() if bean_def.is_mock] - - print(f"Mock beans: {mock_beans}") -``` - -## 9. 异常类型 - -```python -from core.di.exceptions import ( - CircularDependencyError, # 循环依赖错误 - BeanNotFoundError, # Bean未找到错误 - DuplicateBeanError, # 重复Bean错误 - FactoryError, # 工厂错误 - DependencyResolutionError, # 依赖解析错误 - MockNotEnabledError, # Mock未启用错误 - PrimaryBeanConflictError, # Primary Bean冲突错误 -) - -# 异常处理示例 -try: - service = container.get_bean("user_service") -except BeanNotFoundError as e: - print(f"Bean not found: {e}") -except CircularDependencyError as e: - print(f"Circular dependency: {e}") -except DependencyResolutionError as e: - print(f"Dependency resolution error: {e}") -``` - -## 10. 测试 - -本模块包含完整的测试套件（72个测试用例），详见 [tests/introduction.md](./tests/introduction.md) - -```bash -# 运行所有DI测试 -PYTHONPATH=/Users/admin/memsys_opensource/src python -m pytest src/core/di/tests/ -v - -# 运行特定测试文件 -pytest src/core/di/tests/test_di_container.py -v -pytest src/core/di/tests/test_di_scanner.py -v -pytest src/core/di/tests/test_bean_order_strategy.py -v -``` - -## 11. 相关文档 - -- [DI框架详细文档（英文）](./README.md) -- [DI框架详细文档（中文）](./README_zh.md) -- [开发指南](../../../docs/dev_docs/development_guide.md) -- [测试模块介绍](./tests/introduction.md) - -## 12. 扩展机制 - -本DI框架支持通过Addon机制扩展： - -- [AddonBeanOrderStrategy](../addons/contrib/addon_bean_order_strategy.py) - 扩展Bean排序策略，支持addon_tag优先级 -- [Addon测试](../addons/contrib/tests/introduction.md) - Addon扩展的测试 - -**自定义扩展示例：** - -```python -from core.di.bean_order_strategy import BeanOrderStrategy - -class CustomOrderStrategy(BeanOrderStrategy): - """自定义Bean排序策略""" - - def sort_beans(self, candidates, target_type): - # 自定义排序逻辑 - return sorted(candidates, key=lambda x: self._calculate_priority(x)) - - def _calculate_priority(self, candidate): - name, bean_def = candidate - priority = 0 - - # 自定义规则 - if bean_def.metadata.get("critical"): - priority += 1000 - if bean_def.metadata.get("version") == "2.0": - priority += 100 - - return -priority # 负数表示优先级高 - -# 应用自定义策略 -container = get_container() -container.set_order_strategy(CustomOrderStrategy()) -``` - -## 13. 总结 - -### 核心特性 - -- ✅ **依赖注入**: 自动管理对象依赖关系，降低耦合 -- ✅ **接口多实现**: 支持一个接口多个实现，灵活切换 -- ✅ **Primary机制**: 智能选择默认实现 -- ✅ **Mock模式**: 测试友好，快速开发 -- ✅ **Factory支持**: 灵活的对象创建方式 -- ✅ **组件扫描**: 自动发现和注册Bean -- ✅ **循环依赖检测**: 及时发现设计问题 -- ✅ **高性能**: 多级缓存、并行扫描 - -### 使用建议 - -1. **接口优先**: 总是定义接口，面向接口编程 -2. **装饰器注册**: 使用装饰器简化配置 -3. **合理分层**: Controller -> Service -> Repository -4. **Mock开发**: 利用Mock提升开发效率 -5. **避免循环**: 注意依赖关系设计 -6. **性能优化**: 启用缓存和并行扫描 -7. **错误处理**: 优雅处理DI异常 - -### 维护者 - -DI机制维护团队 - -### 贡献指南 - -欢迎提交Issue和Pull Request！ - ---- - -**最后更新**: 2025-11-18 \ No newline at end of file diff --git a/methods/EverCore/src/core/di/scan_context.py b/methods/EverCore/src/core/di/scan_context.py deleted file mode 100644 index 23f50e99..00000000 --- a/methods/EverCore/src/core/di/scan_context.py +++ /dev/null @@ -1,312 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Scan context manager -Used to pass context information during module scanning and importing - -Implemented using a prefix tree (Trie) for efficient path matching lookup -""" - -import sys - -from typing import Dict, Any, Optional -from pathlib import Path -from threading import RLock - - -class _PathTrieNode: - """ - Prefix tree node - Used to store path segments and corresponding metadata - """ - - __slots__ = ['children', 'metadata', 'is_registered'] - - def __init__(self): - # Child node mapping {path_segment: _PathTrieNode} - self.children: Dict[str, '_PathTrieNode'] = {} - # Metadata corresponding to this node (only registered path nodes have it) - self.metadata: Optional[Dict[str, Any]] = None - # Flag indicating whether this node is a registered path endpoint - self.is_registered: bool = False - - def print_tree( - self, name: str = "(root)", prefix: str = "", is_last: bool = True - ) -> str: - """ - Recursively print tree structure - - Args: - name: Current node name - prefix: Prefix for current line (used for indentation and connecting lines) - is_last: Whether this is the last child of the parent node - - Returns: - String representation of the tree structure - - Example: - (root) - ├── Users - │ └── admin - │ └── project - │ └── src [*] {"addon_tag": "core"} - """ - lines = [] - - # Build display content for current node - connector = "└── " if is_last else "├── " - node_display = name - if self.is_registered: - # Mark registered nodes and display metadata - meta_str = str(self.metadata) if self.metadata else "{}" - node_display = f"{name} [*] {meta_str}" - - # Root node does not need connector - if prefix == "" and name == "(root)": - lines.append(node_display) - else: - lines.append(f"{prefix}{connector}{node_display}") - - # Calculate prefix for children - if prefix == "" and name == "(root)": - child_prefix = "" - else: - child_prefix = prefix + (" " if is_last else "│ ") - - # Recursively print child nodes - children_items = sorted(self.children.items()) - for i, (child_name, child_node) in enumerate(children_items): - is_last_child = i == len(children_items) - 1 - lines.append(child_node.print_tree(child_name, child_prefix, is_last_child)) - - return "\n".join(lines) - - def __str__(self) -> str: - """Return string representation of tree structure""" - return self.print_tree() - - -class ScanContextRegistry: - """ - Scan context registry (singleton pattern) - Uses a prefix tree (Trie) for efficient path matching lookup - - Paths are split by '/' or os.sep into segments and built into a tree structure: - - Root node is an empty node - - Each path segment becomes a child node - - During lookup, traverse down the tree and return metadata from the longest matching path - """ - - # Singleton instance - _instance: Optional['ScanContextRegistry'] = None - _lock: RLock = RLock() - - # Instance attributes (initialized in __init__) - _root: _PathTrieNode - _path_context_mapping: Dict[str, Dict[str, Any]] - _instance_lock: RLock - _initialized: bool - - def __new__(cls) -> 'ScanContextRegistry': - """Singleton pattern: ensure only one instance is created""" - if cls._instance is None: - with cls._lock: - if cls._instance is None: - instance = super().__new__(cls) - cls._instance = instance - return cls._instance - - def __init__(self) -> None: - """Initialize instance (use _initialized flag to ensure initialization happens only once)""" - # Check if already initialized to avoid re-initialization - if hasattr(self, '_initialized') and self._initialized: - return - - # Prefix tree root node - self._root: _PathTrieNode = _PathTrieNode() - # Keep original path mapping for unregister and get_all_mappings - self._path_context_mapping: Dict[str, Dict[str, Any]] = {} - # Instance-level lock - self._instance_lock: RLock = RLock() - # Mark as initialized - self._initialized: bool = True - - @classmethod - def get_instance(cls) -> 'ScanContextRegistry': - """ - Get singleton instance - - Returns: - ScanContextRegistry singleton instance - """ - return cls() - - @classmethod - def reset_instance(cls) -> None: - """ - Reset singleton instance (mainly for testing) - - Warning: This will clear all registered path contexts - """ - with cls._lock: - cls._instance = None - - def _split_path(self, path: str) -> list: - """ - Split path into list of segments - - Args: - path: File or directory path - - Returns: - List of path segments - """ - # Resolve to absolute path - resolved = str(Path(path).resolve()) - # Split by path separator and filter out empty strings - parts = [p for p in resolved.replace('\\', '/').split('/') if p] - return parts - - def register(self, path: str, metadata: Dict[str, Any]) -> 'ScanContextRegistry': - """ - Register context metadata for a scan path - - Args: - path: Scan path - metadata: Context metadata - - Returns: - self, supports method chaining - """ - with self._instance_lock: - # Save original mapping - resolved_path = str(Path(path).resolve()) - self._path_context_mapping[resolved_path] = metadata - - # Insert path into prefix tree - parts = self._split_path(path) - node = self._root - for part in parts: - if part not in node.children: - node.children[part] = _PathTrieNode() - node = node.children[part] - - # Mark as registered node and store metadata - node.is_registered = True - node.metadata = metadata - - return self - - def unregister(self, path: str) -> 'ScanContextRegistry': - """ - Unregister a scan path - - Args: - path: Scan path - - Returns: - self, supports method chaining - """ - with self._instance_lock: - resolved_path = str(Path(path).resolve()) - self._path_context_mapping.pop(resolved_path, None) - - # Find node in prefix tree and unregister - parts = self._split_path(path) - node = self._root - for part in parts: - if part not in node.children: - return self # Path does not exist - node = node.children[part] - - # Remove registration flag - node.is_registered = False - node.metadata = None - - return self - - def search_metadata_based_path(self, file_path: Path) -> Dict[str, Any]: - """ - Search for corresponding context metadata based on file path (longest prefix match) - - Uses prefix tree for efficient lookup, time complexity O(path_depth) - - Args: - file_path: File path - - Returns: - Context metadata dictionary (returns empty dict if not found) - """ - parts = self._split_path(str(file_path)) - - # Traverse down the tree, recording the last matched metadata - node = self._root - matched_metadata: Dict[str, Any] = {} - - for part in parts: - if part not in node.children: - # Path does not match, return longest match found so far - break - node = node.children[part] - # If current node is a registered path, update match result - if node.is_registered and node.metadata is not None: - matched_metadata = node.metadata - - return matched_metadata.copy() - - def clear(self) -> 'ScanContextRegistry': - """ - Clear all registered path contexts - - Returns: - self, supports method chaining - """ - with self._instance_lock: - self._root = _PathTrieNode() - self._path_context_mapping.clear() - return self - - def print_tree(self) -> str: - """ - Print tree structure of the prefix tree - - Returns: - String representation of the tree structure - """ - return self._root.print_tree() - - @classmethod - def search_metadata_for_type(cls, bean_type: type) -> Dict[str, Any]: - """ - Get context metadata for the file containing the given type - - Uses bean_type.__module__ to get module name, then retrieves file path from sys.modules, - and uses the prefix tree to find the context metadata corresponding to that file. - - Args: - bean_type: Type of the bean - - Returns: - Context metadata dictionary (returns empty dict if not found) - """ - instance = cls.get_instance() - - # Get module name from bean_type - module_name = bean_type.__module__ - module = sys.modules.get(module_name) - - # Get file path of the module - if module and hasattr(module, '__file__') and module.__file__: - return instance.search_metadata_based_path(Path(module.__file__)) - - return {} - - -# Utility function: get singleton instance -def get_scan_context_registry() -> ScanContextRegistry: - """ - Get singleton instance of the scan context registry - - Returns: - ScanContextRegistry singleton instance - """ - return ScanContextRegistry.get_instance() diff --git a/methods/EverCore/src/core/di/scan_path_registry.py b/methods/EverCore/src/core/di/scan_path_registry.py deleted file mode 100644 index a277c472..00000000 --- a/methods/EverCore/src/core/di/scan_path_registry.py +++ /dev/null @@ -1,23 +0,0 @@ -from typing import List - - -class ScannerPathsRegistry: - """Scanner path registry""" - - def __init__(self): - """Initialize scanner path registry""" - self.scan_paths: List[str] = [] - - def add_scan_path(self, path: str) -> 'ScannerPathsRegistry': - """Add scan path""" - self.scan_paths.append(path) - return self - - def get_scan_paths(self) -> List[str]: - """Get scan paths""" - return self.scan_paths - - def clear(self) -> 'ScannerPathsRegistry': - """Clear scan paths""" - self.scan_paths = [] - return self diff --git a/methods/EverCore/src/core/di/scanner.py b/methods/EverCore/src/core/di/scanner.py deleted file mode 100644 index 4e9be1eb..00000000 --- a/methods/EverCore/src/core/di/scanner.py +++ /dev/null @@ -1,365 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Component Scanner -""" - -import os -import sys -import importlib -from pathlib import Path -from typing import List, Set, Optional, Dict, Any -import traceback -from concurrent.futures import ThreadPoolExecutor, as_completed - -from core.observation.logger import get_logger -from core.di.scan_context import ScanContextRegistry, get_scan_context_registry - - -class ComponentScanner: - """Component Scanner""" - - def __init__(self): - self.scan_paths: List[str] = [] - self.scan_packages: List[str] = [] - # Using 'di' causes directories like audit to be filtered out, so full path matching is required - self.exclude_paths: Set[str] = { - '/di/', - '/config/', - '__pycache__', - '.git', - '.pytest_cache', - } - self.exclude_patterns: Set[str] = {'test_', '_test', 'tests'} - self.include_patterns: Set[str] = set() - self.recursive = True - # self.parallel = True if os.getenv("ENV") == 'dev' else False - self.parallel = False - self.max_workers = 8 - - # Create a dedicated logger - self.logger = get_logger(__name__) - - # Scan context registry (using singleton) - self.context_registry = get_scan_context_registry() - - # Key modules that need to be preloaded to avoid circular dependencies during parallel import - self.preload_modules = [ - # SQLAlchemy core modules - 'sqlalchemy.engine', - 'sqlalchemy.engine.base', - 'sqlalchemy.engine.default', - 'sqlalchemy.pool', - 'sqlalchemy.sql', - 'sqlalchemy.sql.schema', - 'sqlalchemy.sql.sqltypes', - 'sqlalchemy.orm', - 'sqlalchemy.orm.session', - 'sqlalchemy.orm.query', - # Pydantic core modules - 'pydantic', - 'pydantic.fields', - 'pydantic.main', - 'pydantic.validators', - 'pydantic.v1', - 'pydantic.v1.fields', - 'pydantic.v1.main', - # Other modules that may cause circular dependencies - 'typing_extensions', - 'dataclasses', - ] - - def add_scan_path(self, path: str) -> 'ComponentScanner': - """Add scan path""" - self.scan_paths.append(path) - return self - - def add_scan_package(self, package: str) -> 'ComponentScanner': - """Add scan package""" - self.scan_packages.append(package) - return self - - def exclude_path(self, path: str) -> 'ComponentScanner': - """Exclude path""" - self.exclude_paths.add(path) - return self - - def exclude_pattern(self, pattern: str) -> 'ComponentScanner': - """Exclude pattern""" - self.exclude_patterns.add(pattern) - return self - - def include_pattern(self, pattern: str) -> 'ComponentScanner': - """Include pattern""" - self.include_patterns.add(pattern) - return self - - def set_recursive(self, recursive: bool) -> 'ComponentScanner': - """Set whether to scan recursively""" - self.recursive = recursive - return self - - def set_parallel(self, parallel: bool) -> 'ComponentScanner': - """Set whether to scan in parallel""" - self.parallel = parallel - return self - - def set_max_workers(self, max_workers: int) -> 'ComponentScanner': - """Set maximum number of worker threads""" - self.max_workers = max_workers - return self - - def _preload_critical_modules(self): - """ - Preload critical modules to avoid circular dependency issues during parallel import. - Call this method before parallel scanning. - """ - self.logger.info( - "🔄 Preloading critical modules to avoid circular dependencies..." - ) - - loaded_count = 0 - failed_count = 0 - - for module_name in self.preload_modules: - try: - importlib.import_module(module_name) - loaded_count += 1 - except ImportError: - # Some modules may not exist, which is normal - failed_count += 1 - except Exception: - # Other exceptions should be logged but not block execution - failed_count += 1 - - self.logger.info( - "📦 Preloading completed: %d/%d modules successfully loaded", - loaded_count, - len(self.preload_modules), - ) - if failed_count > 0: - self.logger.debug("Skipped %d unavailable modules", failed_count) - - def add_preload_module(self, module_name: str) -> 'ComponentScanner': - """Add module that needs to be preloaded""" - if module_name not in self.preload_modules: - self.preload_modules.append(module_name) - return self - - def register_scan_context( - self, path: str, metadata: Dict[str, Any] - ) -> 'ComponentScanner': - """ - Register context metadata for a scan path - - Args: - path: Scan path - metadata: Context metadata, can contain any custom information - - Returns: - self, supports method chaining - - Example: - ```python - scanner = ComponentScanner() - scanner.register_scan_context( - "src/plugins", - {"plugin_type": "core", "load_priority": 1} - ) - scanner.add_scan_path("src/plugins").scan() - ``` - """ - self.context_registry.register(path, metadata) - return self - - def get_context_registry(self) -> ScanContextRegistry: - """ - Get context registry - - Returns: - Context registry instance - """ - return self.context_registry - - def scan(self) -> 'ComponentScanner': - """Execute scanning""" - self.logger.info("🔍 Starting component scan...") - - # Collect all Python files - python_files = self._collect_python_files() - self.logger.info("📄 Found %d Python files", len(python_files)) - - if not python_files: - self.logger.warning("⚠️ No Python files found") - return self - - # Scan components - if self.parallel and len(python_files) > 1: - self.logger.info( - "⚡ Using parallel scan mode (max %d worker threads)", self.max_workers - ) - # Preload critical modules before parallel scanning - self._preload_critical_modules() - self._parallel_scan(python_files) - else: - self.logger.info("📝 Using sequential scan mode") - self._sequential_scan(python_files) - - self.logger.info("✅ Component scan completed") - return self - - def _collect_python_files(self) -> List[Path]: - """Collect all Python files""" - python_files = [] - - # Scan paths - if self.scan_paths: - self.logger.debug("Scanning paths: %s", ', '.join(self.scan_paths)) - for scan_path in self.scan_paths: - files_from_path = self._collect_files_from_path(scan_path) - python_files.extend(files_from_path) - - # Scan packages - if self.scan_packages: - self.logger.debug("Scanning packages: %s", ', '.join(self.scan_packages)) - for package in self.scan_packages: - files_from_package = self._collect_files_from_package(package) - python_files.extend(files_from_package) - - # Deduplicate - unique_files = list(set(python_files)) - if len(python_files) != len(unique_files): - self.logger.debug( - "File count after deduplication: %d -> %d", - len(python_files), - len(unique_files), - ) - - return unique_files - - def _collect_files_from_path(self, path: str) -> List[Path]: - """Collect Python files from path""" - files = [] - path_obj = Path(path) - - if not path_obj.exists(): - self.logger.warning("Scan path does not exist: %s", path) - return files - - if path_obj.is_file() and path_obj.suffix == '.py': - if self._should_include_file(path_obj): - files.append(path_obj) - elif path_obj.is_dir(): - pattern = "**/*.py" if self.recursive else "*.py" - for file_path in path_obj.glob(pattern): - if self._should_include_file(file_path): - files.append(file_path) - - return files - - def _collect_files_from_package(self, package_name: str) -> List[Path]: - """Collect Python files from package""" - try: - package = importlib.import_module(package_name) - if hasattr(package, '__file__') and package.__file__: - package_path = Path(package.__file__).parent - return self._collect_files_from_path(str(package_path)) - except ImportError as e: - self.logger.warning("Failed to import package %s: %s", package_name, e) - - return [] - - def _should_include_file(self, file_path: Path) -> bool: - """Check if file should be included""" - # Exclude special files - if file_path.name.startswith('__') and file_path.name.endswith('__.py'): - return False - - # Check excluded paths - for exclude_path in self.exclude_paths: - if exclude_path in str(file_path): - return False - - # Check excluded patterns - for pattern in self.exclude_patterns: - if pattern in file_path.name: - return False - - # Check included patterns - if self.include_patterns: - return any(pattern in file_path.name for pattern in self.include_patterns) - - return True - - def _parallel_scan(self, python_files: List[Path]): - """Parallel scan""" - with ThreadPoolExecutor(max_workers=self.max_workers) as executor: - futures = { - executor.submit(self._scan_file, file_path): file_path - for file_path in python_files - } - - for future in as_completed(futures): - file_path = futures[future] - try: - future.result() - except Exception as e: - self.logger.error( - "Failed to scan file in parallel %s: %s", file_path, e - ) - - def _sequential_scan(self, python_files: List[Path]): - """Sequential scan""" - for file_path in python_files: - try: - self._scan_file(file_path) - except Exception as e: - self.logger.error( - "Failed to scan file sequentially %s: %s", file_path, e - ) - - def _scan_file(self, file_path: Path): - """ - Scan a single file. - By importing the module, component decorators defined in the module can be triggered to complete automatic registration. - """ - module_name = self._file_to_module_name(file_path) - if not module_name: - return - - try: - importlib.import_module(module_name) - except ImportError as e: - self.logger.error("Failed to import module %s: %s", module_name, e) - traceback.print_exc() - sys.exit(1) - except Exception as e: - self.logger.error( - "Unknown error occurred while scanning file %s: %s", file_path, e - ) - traceback.print_exc() - sys.exit(1) - - def _file_to_module_name(self, file_path: Path) -> Optional[str]: - """Convert file path to module name""" - try: - # Get path relative to sys.path, sorted by path depth in descending order - # Solve import issues between src.a.b.c and a.b.c - sorted_sys_paths = sorted( - [(path, len(Path(path).resolve().parts)) for path in sys.path], - key=lambda x: x[1], - reverse=True, # Deeper paths come first - ) - - # Iterate through sorted paths - for sys_path, _ in sorted_sys_paths: - sys_path_obj = Path(sys_path).resolve() - try: - relative_path = file_path.resolve().relative_to(sys_path_obj) - module_parts = list(relative_path.with_suffix("").parts) - return ".".join(module_parts) - except ValueError: - continue - except Exception: - pass - - return None diff --git a/methods/EverCore/src/core/di/tests/__init__.py b/methods/EverCore/src/core/di/tests/__init__.py deleted file mode 100644 index 40a96afc..00000000 --- a/methods/EverCore/src/core/di/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# -*- coding: utf-8 -*- diff --git a/methods/EverCore/src/core/di/tests/test_bean_order_strategy.py b/methods/EverCore/src/core/di/tests/test_bean_order_strategy.py deleted file mode 100644 index 18ac4822..00000000 --- a/methods/EverCore/src/core/di/tests/test_bean_order_strategy.py +++ /dev/null @@ -1,723 +0,0 @@ -# -*- coding: utf-8 -*- -""" -cd /Users/admin/memsys_opensource -PYTHONPATH=/Users/admin/memsys_opensource/src python -m pytest src/core/di/tests/test_bean_order_strategy.py -v - -BeanOrderStrategy Test Module - -Comprehensively test various scenarios of Bean sorting strategy, including: -- Various priority calculations of the calculate_order_key method -- Comprehensive sorting and filtering logic of the sort_beans_with_context method -- Simple sorting logic of the sort_beans method -""" - -import pytest -from typing import Set, Type -from core.di.bean_definition import BeanDefinition, BeanScope -from core.di.bean_order_strategy import BeanOrderStrategy - - -# ==================== Test Helper Classes ==================== - - -class ServiceA: - """Test service A""" - - pass - - -class ServiceB: - """Test service B""" - - pass - - -class ServiceC: - """Test service C""" - - pass - - -class ServiceD: - """Test service D""" - - pass - - -# ==================== Test calculate_order_key Method ==================== - - -class TestCalculateOrderKey: - """Test calculate_order_key method""" - - def test_mock_priority_in_mock_mode(self): - """Test in Mock mode, Mock Bean has higher priority than non-Mock Bean""" - # Create Mock Bean and non-Mock Bean - mock_bean = BeanDefinition(ServiceA, is_mock=True) - normal_bean = BeanDefinition(ServiceA, is_mock=False) - - # Calculate sort key (Mock mode, direct match) - mock_key = BeanOrderStrategy.calculate_order_key( - mock_bean, is_direct_match=True, mock_mode=True - ) - normal_key = BeanOrderStrategy.calculate_order_key( - normal_bean, is_direct_match=True, mock_mode=True - ) - - # Verify: Mock Bean's mock_priority=0, non-Mock Bean's mock_priority=1 - assert mock_key[0] == 0 # Mock Bean's mock priority - assert normal_key[0] == 1 # Non-Mock Bean's mock priority - assert mock_key < normal_key # Mock Bean has higher priority - - def test_mock_priority_in_normal_mode(self): - """Test in non-Mock mode, both Mock and non-Mock Beans have mock_priority=0""" - # Create Mock Bean and non-Mock Bean - mock_bean = BeanDefinition(ServiceA, is_mock=True) - normal_bean = BeanDefinition(ServiceA, is_mock=False) - - # Calculate sort key (non-Mock mode, direct match) - mock_key = BeanOrderStrategy.calculate_order_key( - mock_bean, is_direct_match=True, mock_mode=False - ) - normal_key = BeanOrderStrategy.calculate_order_key( - normal_bean, is_direct_match=True, mock_mode=False - ) - - # Verify: In non-Mock mode, both have mock_priority=0 - assert mock_key[0] == 0 - assert normal_key[0] == 0 - - def test_direct_match_priority(self): - """Test direct match has higher priority than implementation class match""" - bean = BeanDefinition(ServiceA) - - # Calculate sort key - direct_match_key = BeanOrderStrategy.calculate_order_key( - bean, is_direct_match=True, mock_mode=False - ) - impl_match_key = BeanOrderStrategy.calculate_order_key( - bean, is_direct_match=False, mock_mode=False - ) - - # Verify: direct match has match_priority=0, implementation match has match_priority=1 - assert direct_match_key[1] == 0 # Direct match - assert impl_match_key[1] == 1 # Implementation match - assert direct_match_key < impl_match_key # Direct match has higher priority - - def test_primary_priority(self): - """Test Primary Bean has higher priority than non-Primary Bean""" - primary_bean = BeanDefinition(ServiceA, is_primary=True) - normal_bean = BeanDefinition(ServiceA, is_primary=False) - - # Calculate sort key (direct match, non-Mock mode) - primary_key = BeanOrderStrategy.calculate_order_key( - primary_bean, is_direct_match=True, mock_mode=False - ) - normal_key = BeanOrderStrategy.calculate_order_key( - normal_bean, is_direct_match=True, mock_mode=False - ) - - # Verify: Primary Bean's primary_priority=0, non-Primary Bean's primary_priority=1 - assert primary_key[2] == 0 # Primary Bean - assert normal_key[2] == 1 # Non-Primary Bean - assert primary_key < normal_key # Primary Bean has higher priority - - def test_factory_scope_priority(self): - """Test Factory Bean has higher priority than non-Factory Bean""" - factory_bean = BeanDefinition(ServiceA, scope=BeanScope.FACTORY) - singleton_bean = BeanDefinition(ServiceA, scope=BeanScope.SINGLETON) - prototype_bean = BeanDefinition(ServiceA, scope=BeanScope.PROTOTYPE) - - # Calculate sort key (direct match, non-Mock mode) - factory_key = BeanOrderStrategy.calculate_order_key( - factory_bean, is_direct_match=True, mock_mode=False - ) - singleton_key = BeanOrderStrategy.calculate_order_key( - singleton_bean, is_direct_match=True, mock_mode=False - ) - prototype_key = BeanOrderStrategy.calculate_order_key( - prototype_bean, is_direct_match=True, mock_mode=False - ) - - # Verify: Factory Bean's scope_priority=0, others' scope_priority=1 - assert factory_key[3] == 0 # Factory Bean - assert singleton_key[3] == 1 # Singleton Bean - assert prototype_key[3] == 1 # Prototype Bean - assert factory_key < singleton_key # Factory Bean has higher priority - assert factory_key < prototype_key # Factory Bean has higher priority - - def test_comprehensive_priority_ordering(self): - """Test comprehensive priority ordering: mock > match > primary > scope""" - # In Mock mode, create Beans with various combinations - # Highest priority: Mock + direct match + Primary + Factory - bean1 = BeanDefinition( - ServiceA, is_mock=True, is_primary=True, scope=BeanScope.FACTORY - ) - key1 = BeanOrderStrategy.calculate_order_key( - bean1, is_direct_match=True, mock_mode=True - ) - - # Second highest priority: Mock + direct match + Primary + non-Factory - bean2 = BeanDefinition( - ServiceA, is_mock=True, is_primary=True, scope=BeanScope.SINGLETON - ) - key2 = BeanOrderStrategy.calculate_order_key( - bean2, is_direct_match=True, mock_mode=True - ) - - # Third priority: Mock + direct match + non-Primary + Factory - bean3 = BeanDefinition( - ServiceA, is_mock=True, is_primary=False, scope=BeanScope.FACTORY - ) - key3 = BeanOrderStrategy.calculate_order_key( - bean3, is_direct_match=True, mock_mode=True - ) - - # Fourth priority: Mock + implementation match + Primary + Factory - bean4 = BeanDefinition( - ServiceA, is_mock=True, is_primary=True, scope=BeanScope.FACTORY - ) - key4 = BeanOrderStrategy.calculate_order_key( - bean4, is_direct_match=False, mock_mode=True - ) - - # Fifth priority: non-Mock + direct match + Primary + Factory - bean5 = BeanDefinition( - ServiceA, is_mock=False, is_primary=True, scope=BeanScope.FACTORY - ) - key5 = BeanOrderStrategy.calculate_order_key( - bean5, is_direct_match=True, mock_mode=True - ) - - # Verify sort order - key1 has highest priority - assert ( - key1 < key2 - ) # Factory has higher priority than non-Factory (same Mock+direct+Primary) - assert ( - key1 < key3 - ) # Primary has higher priority than non-Primary (same Mock+direct+Factory) - assert ( - key1 < key4 - ) # Direct match has higher priority than implementation match (same Mock+Primary+Factory) - assert ( - key1 < key5 - ) # Mock has higher priority than non-Mock (same direct+Primary+Factory) - - # Verify secondary priorities - earlier positions in the tuple have higher weight - assert ( - key2 < key3 - ) # (0,0,0,1) < (0,0,1,0): scope difference vs primary difference - assert ( - key3 < key4 - ) # (0,0,1,0) < (0,1,0,0): primary difference vs match difference - assert key4 < key5 # (0,1,0,0) < (1,0,0,0): match difference vs mock difference - - -# ==================== Test sort_beans_with_context Method ==================== - - -class TestSortBeansWithContext: - """Test sort_beans_with_context method""" - - def test_filter_mock_beans_in_normal_mode(self): - """Test in non-Mock mode, Mock Beans are filtered out""" - # Create list containing Mock and non-Mock Beans - bean_defs = [ - BeanDefinition(ServiceA, bean_name="mock_a", is_mock=True), - BeanDefinition(ServiceB, bean_name="normal_b", is_mock=False), - BeanDefinition(ServiceC, bean_name="mock_c", is_mock=True), - BeanDefinition(ServiceD, bean_name="normal_d", is_mock=False), - ] - - # Sort (non-Mock mode) - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types=set(), mock_mode=False - ) - - # Verify: only non-Mock Beans remain - assert len(sorted_beans) == 2 - assert all(not bd.is_mock for bd in sorted_beans) - assert {bd.bean_name for bd in sorted_beans} == {"normal_b", "normal_d"} - - def test_keep_mock_beans_in_mock_mode(self): - """Test in Mock mode, Mock Beans are retained and prioritized""" - # Create list containing Mock and non-Mock Beans - bean_defs = [ - BeanDefinition(ServiceA, bean_name="normal_a", is_mock=False), - BeanDefinition(ServiceB, bean_name="mock_b", is_mock=True), - BeanDefinition(ServiceC, bean_name="mock_c", is_mock=True), - BeanDefinition(ServiceD, bean_name="normal_d", is_mock=False), - ] - - # Sort (Mock mode, all are direct matches) - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, - direct_match_types={ServiceA, ServiceB, ServiceC, ServiceD}, - mock_mode=True, - ) - - # Verify: all Beans are retained - assert len(sorted_beans) == 4 - - # Verify: Mock Beans come first, non-Mock Beans come last - assert sorted_beans[0].is_mock - assert sorted_beans[1].is_mock - assert not sorted_beans[2].is_mock - assert not sorted_beans[3].is_mock - - def test_direct_match_types_sorting(self): - """Test priority sorting of direct match types""" - # Create Bean list - bean_defs = [ - BeanDefinition(ServiceA, bean_name="impl_a"), - BeanDefinition(ServiceB, bean_name="direct_b"), - BeanDefinition(ServiceC, bean_name="impl_c"), - BeanDefinition(ServiceD, bean_name="direct_d"), - ] - - # Set ServiceB and ServiceD as direct match types - direct_match_types = {ServiceB, ServiceD} - - # Sort (non-Mock mode) - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types=direct_match_types, mock_mode=False - ) - - # Verify: direct match Beans come first - assert sorted_beans[0].bean_type in direct_match_types - assert sorted_beans[1].bean_type in direct_match_types - assert sorted_beans[2].bean_type not in direct_match_types - assert sorted_beans[3].bean_type not in direct_match_types - - def test_primary_beans_sorting(self): - """Test priority sorting of Primary Beans""" - # Create Bean list (all direct matches, non-Mock mode) - bean_defs = [ - BeanDefinition(ServiceA, bean_name="normal_a", is_primary=False), - BeanDefinition(ServiceB, bean_name="primary_b", is_primary=True), - BeanDefinition(ServiceC, bean_name="normal_c", is_primary=False), - BeanDefinition(ServiceD, bean_name="primary_d", is_primary=True), - ] - - # Sort (all are direct matches) - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, - direct_match_types={ServiceA, ServiceB, ServiceC, ServiceD}, - mock_mode=False, - ) - - # Verify: Primary Beans come first - assert sorted_beans[0].is_primary - assert sorted_beans[1].is_primary - assert not sorted_beans[2].is_primary - assert not sorted_beans[3].is_primary - - def test_factory_scope_sorting(self): - """Test priority sorting of Factory Beans""" - # Create Bean list (all direct matches, non-Primary, non-Mock) - bean_defs = [ - BeanDefinition( - ServiceA, bean_name="singleton_a", scope=BeanScope.SINGLETON - ), - BeanDefinition(ServiceB, bean_name="factory_b", scope=BeanScope.FACTORY), - BeanDefinition( - ServiceC, bean_name="prototype_c", scope=BeanScope.PROTOTYPE - ), - BeanDefinition(ServiceD, bean_name="factory_d", scope=BeanScope.FACTORY), - ] - - # Sort (all are direct matches) - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, - direct_match_types={ServiceA, ServiceB, ServiceC, ServiceD}, - mock_mode=False, - ) - - # Verify: Factory Beans come first - assert sorted_beans[0].scope == BeanScope.FACTORY - assert sorted_beans[1].scope == BeanScope.FACTORY - assert sorted_beans[2].scope in {BeanScope.SINGLETON, BeanScope.PROTOTYPE} - assert sorted_beans[3].scope in {BeanScope.SINGLETON, BeanScope.PROTOTYPE} - - def test_comprehensive_sorting(self): - """Test comprehensive sorting scenario: mock + match + primary + scope""" - # Create Bean list with various combinations - bean_defs = [ - # Lowest priority: non-direct match + non-Primary + non-Factory - BeanDefinition( - ServiceA, - bean_name="lowest", - is_primary=False, - scope=BeanScope.SINGLETON, - ), - # Medium priority: direct match + non-Primary + non-Factory - BeanDefinition( - ServiceB, - bean_name="medium1", - is_primary=False, - scope=BeanScope.SINGLETON, - ), - # Higher priority: direct match + Primary + non-Factory - BeanDefinition( - ServiceC, bean_name="high1", is_primary=True, scope=BeanScope.SINGLETON - ), - # Highest priority: direct match + Primary + Factory - BeanDefinition( - ServiceD, bean_name="highest", is_primary=True, scope=BeanScope.FACTORY - ), - # Second highest priority: direct match + non-Primary + Factory - BeanDefinition( - ServiceA, bean_name="high2", is_primary=False, scope=BeanScope.FACTORY - ), - ] - - # Set direct match types (ServiceB, ServiceC, ServiceD, but not ServiceA) - direct_match_types = {ServiceB, ServiceC, ServiceD} - - # Sort (non-Mock mode) - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types=direct_match_types, mock_mode=False - ) - - # Verify sort order - assert sorted_beans[0].bean_name == "highest" # direct+Primary+Factory - assert sorted_beans[1].bean_name in { - "high1", - "high2", - } # direct+Primary+non-Factory or direct+non-Primary+Factory - assert ( - sorted_beans[4].bean_name == "lowest" - ) # non-direct+non-Primary+non-Factory - - def test_empty_list(self): - """Test empty list""" - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=[], direct_match_types=set(), mock_mode=False - ) - assert sorted_beans == [] - - def test_single_bean(self): - """Test single Bean""" - bean_defs = [BeanDefinition(ServiceA, bean_name="single")] - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types={ServiceA}, mock_mode=False - ) - assert len(sorted_beans) == 1 - assert sorted_beans[0].bean_name == "single" - - def test_all_mock_beans_in_normal_mode(self): - """Test when all Beans are Mock Beans in non-Mock mode""" - bean_defs = [ - BeanDefinition(ServiceA, bean_name="mock_a", is_mock=True), - BeanDefinition(ServiceB, bean_name="mock_b", is_mock=True), - ] - - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types=set(), mock_mode=False - ) - - # Verify: all Mock Beans are filtered out, result is empty - assert sorted_beans == [] - - def test_complex_mock_mode_sorting(self): - """Test complex sorting in Mock mode""" - bean_defs = [ - # non-Mock + non-direct + non-Primary + non-Factory (lowest) - BeanDefinition( - ServiceA, - bean_name="lowest", - is_mock=False, - is_primary=False, - scope=BeanScope.SINGLETON, - ), - # Mock + non-direct + non-Primary + non-Factory (medium) - BeanDefinition( - ServiceB, - bean_name="medium", - is_mock=True, - is_primary=False, - scope=BeanScope.SINGLETON, - ), - # Mock + direct + non-Primary + non-Factory (higher) - BeanDefinition( - ServiceC, - bean_name="high", - is_mock=True, - is_primary=False, - scope=BeanScope.SINGLETON, - ), - # Mock + direct + Primary + Factory (highest) - BeanDefinition( - ServiceD, - bean_name="highest", - is_mock=True, - is_primary=True, - scope=BeanScope.FACTORY, - ), - ] - - # Sort (Mock mode, ServiceC and ServiceD are direct matches) - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types={ServiceC, ServiceD}, mock_mode=True - ) - - # Verify sort order - assert sorted_beans[0].bean_name == "highest" # Mock+direct+Primary+Factory - assert ( - sorted_beans[1].bean_name == "high" - ) # Mock+direct+non-Primary+non-Factory - assert ( - sorted_beans[2].bean_name == "medium" - ) # Mock+non-direct+non-Primary+non-Factory - assert ( - sorted_beans[3].bean_name == "lowest" - ) # non-Mock+non-direct+non-Primary+non-Factory - - -# ==================== Test sort_beans Method ==================== - - -class TestSortBeans: - """Test sort_beans method (simple sorting)""" - - def test_primary_priority_simple(self): - """Test Primary Bean has higher priority than non-Primary Bean (simple sorting)""" - bean_defs = [ - BeanDefinition(ServiceA, bean_name="normal_a", is_primary=False), - BeanDefinition(ServiceB, bean_name="primary_b", is_primary=True), - BeanDefinition(ServiceC, bean_name="normal_c", is_primary=False), - BeanDefinition(ServiceD, bean_name="primary_d", is_primary=True), - ] - - sorted_beans = BeanOrderStrategy.sort_beans(bean_defs) - - # Verify: Primary Beans come first - assert sorted_beans[0].is_primary - assert sorted_beans[1].is_primary - assert not sorted_beans[2].is_primary - assert not sorted_beans[3].is_primary - - def test_factory_scope_priority_simple(self): - """Test Factory Bean has higher priority than non-Factory Bean (simple sorting)""" - bean_defs = [ - BeanDefinition( - ServiceA, bean_name="singleton_a", scope=BeanScope.SINGLETON - ), - BeanDefinition(ServiceB, bean_name="factory_b", scope=BeanScope.FACTORY), - BeanDefinition( - ServiceC, bean_name="prototype_c", scope=BeanScope.PROTOTYPE - ), - BeanDefinition(ServiceD, bean_name="factory_d", scope=BeanScope.FACTORY), - ] - - sorted_beans = BeanOrderStrategy.sort_beans(bean_defs) - - # Verify: Factory Beans come first - assert sorted_beans[0].scope == BeanScope.FACTORY - assert sorted_beans[1].scope == BeanScope.FACTORY - assert sorted_beans[2].scope in {BeanScope.SINGLETON, BeanScope.PROTOTYPE} - assert sorted_beans[3].scope in {BeanScope.SINGLETON, BeanScope.PROTOTYPE} - - def test_primary_and_factory_combined(self): - """Test combined Primary and Factory priority (simple sorting)""" - bean_defs = [ - # Lowest priority: non-Primary + non-Factory - BeanDefinition( - ServiceA, - bean_name="lowest", - is_primary=False, - scope=BeanScope.SINGLETON, - ), - # Medium priority: non-Primary + Factory - BeanDefinition( - ServiceB, bean_name="medium", is_primary=False, scope=BeanScope.FACTORY - ), - # Higher priority: Primary + non-Factory - BeanDefinition( - ServiceC, bean_name="high", is_primary=True, scope=BeanScope.SINGLETON - ), - # Highest priority: Primary + Factory - BeanDefinition( - ServiceD, bean_name="highest", is_primary=True, scope=BeanScope.FACTORY - ), - ] - - sorted_beans = BeanOrderStrategy.sort_beans(bean_defs) - - # Verify sort order - assert sorted_beans[0].bean_name == "highest" # Primary+Factory - assert sorted_beans[1].bean_name == "high" # Primary+non-Factory - assert sorted_beans[2].bean_name == "medium" # non-Primary+Factory - assert sorted_beans[3].bean_name == "lowest" # non-Primary+non-Factory - - def test_mock_beans_not_filtered_in_simple_sort(self): - """Test simple sorting does not filter Mock Beans""" - bean_defs = [ - BeanDefinition( - ServiceA, bean_name="mock_a", is_mock=True, is_primary=False - ), - BeanDefinition( - ServiceB, bean_name="normal_b", is_mock=False, is_primary=True - ), - ] - - sorted_beans = BeanOrderStrategy.sort_beans(bean_defs) - - # Verify: Mock Bean is not filtered (simple sorting does not consider Mock) - assert len(sorted_beans) == 2 - # Verify: Primary takes precedence (regardless of Mock status) - assert sorted_beans[0].bean_name == "normal_b" - assert sorted_beans[1].bean_name == "mock_a" - - def test_empty_list_simple(self): - """Test empty list (simple sorting)""" - sorted_beans = BeanOrderStrategy.sort_beans([]) - assert sorted_beans == [] - - def test_single_bean_simple(self): - """Test single Bean (simple sorting)""" - bean_defs = [BeanDefinition(ServiceA, bean_name="single")] - sorted_beans = BeanOrderStrategy.sort_beans(bean_defs) - assert len(sorted_beans) == 1 - assert sorted_beans[0].bean_name == "single" - - def test_same_priority_beans(self): - """Test Beans with same priority maintain original order""" - bean_defs = [ - BeanDefinition( - ServiceA, bean_name="a", is_primary=False, scope=BeanScope.SINGLETON - ), - BeanDefinition( - ServiceB, bean_name="b", is_primary=False, scope=BeanScope.SINGLETON - ), - BeanDefinition( - ServiceC, bean_name="c", is_primary=False, scope=BeanScope.SINGLETON - ), - ] - - sorted_beans = BeanOrderStrategy.sort_beans(bean_defs) - - # Verify: Beans with same priority maintain original order (stable sort) - assert sorted_beans[0].bean_name == "a" - assert sorted_beans[1].bean_name == "b" - assert sorted_beans[2].bean_name == "c" - - -# ==================== Edge Case Tests ==================== - - -class TestEdgeCases: - """Test edge cases and exceptional scenarios""" - - def test_none_direct_match_types(self): - """Test direct_match_types is empty set""" - bean_defs = [ - BeanDefinition(ServiceA, bean_name="a"), - BeanDefinition(ServiceB, bean_name="b"), - ] - - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types=set(), mock_mode=False - ) - - # Verify: all Beans are treated as non-direct match - assert len(sorted_beans) == 2 - - def test_all_direct_match_types(self): - """Test all Beans are direct matches""" - bean_defs = [ - BeanDefinition(ServiceA, bean_name="a", is_primary=True), - BeanDefinition(ServiceB, bean_name="b", is_primary=False), - ] - - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, - direct_match_types={ServiceA, ServiceB}, - mock_mode=False, - ) - - # Verify: Primary Bean comes first - assert sorted_beans[0].bean_name == "a" - assert sorted_beans[1].bean_name == "b" - - def test_multiple_beans_same_type(self): - """Test multiple Beans of the same type""" - bean_defs = [ - BeanDefinition( - ServiceA, bean_name="a1", is_primary=False, scope=BeanScope.SINGLETON - ), - BeanDefinition( - ServiceA, bean_name="a2", is_primary=True, scope=BeanScope.SINGLETON - ), - BeanDefinition( - ServiceA, bean_name="a3", is_primary=False, scope=BeanScope.FACTORY - ), - BeanDefinition( - ServiceA, bean_name="a4", is_primary=True, scope=BeanScope.FACTORY - ), - ] - - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types={ServiceA}, mock_mode=False - ) - - # Verify sort order: Primary+Factory > Primary+non-Factory > non-Primary+Factory > non-Primary+non-Factory - assert sorted_beans[0].bean_name == "a4" # Primary+Factory - assert sorted_beans[1].bean_name == "a2" # Primary+non-Factory - assert sorted_beans[2].bean_name == "a3" # non-Primary+Factory - assert sorted_beans[3].bean_name == "a1" # non-Primary+non-Factory - - def test_all_attributes_combinations(self): - """Test all combinations of attributes (2^4=16 combinations)""" - # Generate all possible combinations - combinations = [] - for i in range(16): - is_mock = bool(i & 8) - is_direct = bool(i & 4) - is_primary = bool(i & 2) - is_factory = bool(i & 1) - - bean_type = [ServiceA, ServiceB, ServiceC, ServiceD][i % 4] - bean = BeanDefinition( - bean_type, - bean_name=f"bean_{i}", - is_mock=is_mock, - is_primary=is_primary, - scope=BeanScope.FACTORY if is_factory else BeanScope.SINGLETON, - ) - combinations.append((bean, is_direct, bean_type)) - - # Extract Bean list and direct match types - bean_defs = [c[0] for c in combinations] - direct_match_types = {c[2] for c in combinations if c[1]} - - # Sort (Mock mode) - sorted_beans = BeanOrderStrategy.sort_beans_with_context( - bean_defs=bean_defs, direct_match_types=direct_match_types, mock_mode=True - ) - - # Verify: sorted by priority, Mock Beans come first - # Verify all Beans are retained - assert len(sorted_beans) == 16 - - # Verify the first Bean should be the highest priority - first_bean = sorted_beans[0] - first_key = BeanOrderStrategy.calculate_order_key( - first_bean, - is_direct_match=first_bean.bean_type in direct_match_types, - mock_mode=True, - ) - - # Verify priority of all other Beans is not higher than the first Bean - for bean in sorted_beans[1:]: - bean_key = BeanOrderStrategy.calculate_order_key( - bean, - is_direct_match=bean.bean_type in direct_match_types, - mock_mode=True, - ) - assert first_key <= bean_key - - -if __name__ == "__main__": - # Run tests - pytest.main([__file__, "-v", "--tb=short"]) diff --git a/methods/EverCore/src/core/di/tests/test_di_container.py b/methods/EverCore/src/core/di/tests/test_di_container.py deleted file mode 100644 index 2425eb8d..00000000 --- a/methods/EverCore/src/core/di/tests/test_di_container.py +++ /dev/null @@ -1,590 +0,0 @@ -# -*- coding: utf-8 -*- -""" -cd /Users/admin/memsys_opensource -PYTHONPATH=/Users/admin/memsys_opensource/src python -m pytest src/core/di/tests/test_di_container.py -v -s - -DI Container integration tests - -Test core Container functionalities such as Bean registration, resolution, and priority selection -""" - -import pytest -from abc import ABC, abstractmethod -from typing import List -from core.di.container import DIContainer -from core.di.bean_definition import BeanScope -from core.di.exceptions import BeanNotFoundError -from core.di.tests.test_fixtures import ( - # User service related - UserRepository, - MySQLUserRepository, - PostgreSQLUserRepository, - MockUserRepository, - UserService, - UserServiceImpl, - # Notification service related - NotificationService, - EmailNotificationService, - SMSNotificationService, - PushNotificationService, - # Email service related - EmailService, - SMTPEmailService, - # Database connection related - DatabaseConnection, - create_database_connection, - create_readonly_connection, - # Prototype service - PrototypeService, - # Cache service related - CacheService, - RedisCacheService, - MemoryCacheService, - # Utility functions - register_standard_beans, -) - - -class TestBeanRegistrationAndRetrieval: - """Test Bean registration and retrieval""" - - def setup_method(self): - """Create a new container before each test""" - self.container = DIContainer() - - def test_register_and_get_single_bean(self): - """Test registering and retrieving a single Bean""" - # Register Bean - self.container.register_bean( - bean_type=MySQLUserRepository, bean_name="mysql_repo" - ) - - # Retrieve by type - repo = self.container.get_bean_by_type(MySQLUserRepository) - assert isinstance(repo, MySQLUserRepository) - assert repo.db_type == "mysql" - - # Retrieve by name - repo2 = self.container.get_bean("mysql_repo") - assert repo is repo2 # Singleton mode, should be the same instance - - def test_register_multiple_implementations(self): - """Test registering multiple implementations of the same interface""" - # Register multiple UserRepository implementations - self.container.register_bean( - bean_type=MySQLUserRepository, bean_name="mysql_repo", is_primary=True - ) - self.container.register_bean( - bean_type=PostgreSQLUserRepository, bean_name="postgres_repo" - ) - - # Retrieve by interface type, should return Primary implementation - repo = self.container.get_bean_by_type(UserRepository) - assert isinstance(repo, MySQLUserRepository) - - # Retrieve all implementations - all_repos = self.container.get_beans_by_type(UserRepository) - assert len(all_repos) == 2 - assert isinstance(all_repos[0], MySQLUserRepository) # Primary first - assert isinstance(all_repos[1], PostgreSQLUserRepository) - - def test_bean_not_found_error(self): - """Test raising exception when retrieving non-existent Bean""" - - # Define unregistered interface - class UnregisteredService(ABC): - pass - - # Retrieving by type should raise exception - with pytest.raises(BeanNotFoundError): - self.container.get_bean_by_type(UnregisteredService) - - # Retrieving by name should raise exception - with pytest.raises(BeanNotFoundError): - self.container.get_bean("non_existent_bean") - - def test_contains_bean_check(self): - """Test Bean existence check""" - # Register Bean - self.container.register_bean( - bean_type=MySQLUserRepository, bean_name="mysql_repo" - ) - - # Check if Bean exists - assert self.container.contains_bean_by_type(MySQLUserRepository) - # Note: Interface lookup requires building inheritance relationship cache first - # Direct interface lookup may return False because implementation class was registered - # Correct way is get_bean_by_type automatically finds implementation class - assert self.container.contains_bean("mysql_repo") - - # Check non-existent Bean - assert not self.container.contains_bean_by_type(PostgreSQLUserRepository) - assert not self.container.contains_bean("non_existent") - - -class TestPrimaryBeanSelection: - """Test Primary Bean selection logic""" - - def setup_method(self): - """Create a new container before each test""" - self.container = DIContainer() - - def test_primary_bean_priority(self): - """Test Primary Bean takes precedence over non-Primary Bean""" - # Register two implementations: one Primary, one non-Primary - self.container.register_bean( - bean_type=PostgreSQLUserRepository, - bean_name="postgres_repo", - is_primary=False, - ) - self.container.register_bean( - bean_type=MySQLUserRepository, bean_name="mysql_repo", is_primary=True - ) - - # Retrieve UserRepository, should return Primary implementation - repo = self.container.get_bean_by_type(UserRepository) - assert isinstance(repo, MySQLUserRepository) - assert repo.db_type == "mysql" - - def test_multiple_primary_beans_return_first(self): - """Test returning the first registered when multiple Primary Beans exist""" - # Register two Primary implementations - self.container.register_bean( - bean_type=MySQLUserRepository, bean_name="mysql_repo", is_primary=True - ) - self.container.register_bean( - bean_type=PostgreSQLUserRepository, - bean_name="postgres_repo", - is_primary=True, - ) - - # Should return the first Primary Bean - repo = self.container.get_bean_by_type(UserRepository) - assert isinstance(repo, MySQLUserRepository) - - def test_no_primary_bean_returns_first(self): - """Test returning the first registered when no Primary Bean exists""" - # Register two non-Primary implementations - self.container.register_bean( - bean_type=MySQLUserRepository, bean_name="mysql_repo", is_primary=False - ) - self.container.register_bean( - bean_type=PostgreSQLUserRepository, - bean_name="postgres_repo", - is_primary=False, - ) - - # Should return the first registered Bean - repo = self.container.get_bean_by_type(UserRepository) - assert isinstance(repo, MySQLUserRepository) - - -class TestMockMode: - """Test Mock mode""" - - def setup_method(self): - """Create a new container before each test""" - self.container = DIContainer() - # Register standard Beans (including Mock) - register_standard_beans(self.container) - - def test_normal_mode_filters_mock_beans(self): - """Test Mock Beans are filtered out in non-Mock mode""" - # Ensure not in Mock mode - assert not self.container.is_mock_mode() - - # Retrieve UserRepository, should return non-Mock implementation - repo = self.container.get_bean_by_type(UserRepository) - assert not isinstance(repo, MockUserRepository) - assert isinstance(repo, MySQLUserRepository) # Primary non-Mock implementation - - # Retrieve all implementations, should not include Mock - all_repos = self.container.get_beans_by_type(UserRepository) - assert len(all_repos) == 2 # MySQL + PostgreSQL - assert all(not isinstance(r, MockUserRepository) for r in all_repos) - - def test_mock_mode_prioritizes_mock_beans(self): - """Test Mock Beans take precedence in Mock mode""" - # Enable Mock mode - self.container.enable_mock_mode() - assert self.container.is_mock_mode() - - # Clear cache to ensure Bean re-selection - self.container._singleton_instances.clear() - - # Retrieve UserRepository, should return Mock implementation - repo = self.container.get_bean_by_type(UserRepository) - assert isinstance(repo, MockUserRepository) - assert repo.db_type == "mock" - - # Verify Mock data - user = repo.find_by_id(1) - assert "Mock" in user["name"] - assert user["source"] == "mock" - - def test_mock_mode_toggle(self): - """Test toggling Mock mode""" - # Initial: non-Mock mode - repo1 = self.container.get_bean_by_type(UserRepository) - assert isinstance(repo1, MySQLUserRepository) - - # Enable Mock mode - self.container.enable_mock_mode() - self.container._singleton_instances.clear() - - repo2 = self.container.get_bean_by_type(UserRepository) - assert isinstance(repo2, MockUserRepository) - - # Disable Mock mode - self.container.disable_mock_mode() - self.container._singleton_instances.clear() - - repo3 = self.container.get_bean_by_type(UserRepository) - assert isinstance(repo3, MySQLUserRepository) - - def test_get_all_beans_in_mock_mode(self): - """Test retrieving all Beans in Mock mode""" - # Enable Mock mode - self.container.enable_mock_mode() - - # Retrieve all UserRepository implementations - all_repos = self.container.get_beans_by_type(UserRepository) - - # Should include Mock implementation, with Mock first - assert len(all_repos) == 3 - assert isinstance(all_repos[0], MockUserRepository) # Mock first - assert isinstance(all_repos[1], MySQLUserRepository) # Primary next - assert isinstance(all_repos[2], PostgreSQLUserRepository) - - -class TestBeanScopes: - """Test different Bean scopes""" - - def setup_method(self): - """Create a new container before each test""" - self.container = DIContainer() - PrototypeService.reset_counter() - - def test_singleton_scope(self): - """Test Singleton scope (default)""" - # Register Singleton Bean - self.container.register_bean( - bean_type=MySQLUserRepository, - bean_name="mysql_repo", - scope=BeanScope.SINGLETON, - ) - - # Multiple retrievals should return the same instance - repo1 = self.container.get_bean_by_type(MySQLUserRepository) - repo2 = self.container.get_bean_by_type(MySQLUserRepository) - repo3 = self.container.get_bean("mysql_repo") - - assert repo1 is repo2 - assert repo1 is repo3 - - # Modify state to verify - repo1.call_count = 100 - assert repo2.call_count == 100 - assert repo3.call_count == 100 - - def test_prototype_scope(self): - """Test Prototype scope (create new instance each time)""" - # Register Prototype Bean - self.container.register_bean( - bean_type=PrototypeService, - bean_name="prototype_service", - scope=BeanScope.PROTOTYPE, - ) - - # Multiple retrievals should return different instances - service1 = self.container.get_bean_by_type(PrototypeService) - service2 = self.container.get_bean_by_type(PrototypeService) - service3 = self.container.get_bean("prototype_service") - - assert service1 is not service2 - assert service1 is not service3 - assert service2 is not service3 - - # Verify instance IDs are different - assert service1.instance_id == 1 - assert service2.instance_id == 2 - assert service3.instance_id == 3 - - # Verify instance states are independent - service1.add_data("data1") - service2.add_data("data2") - - assert service1.get_data() == ["data1"] - assert service2.get_data() == ["data2"] - - def test_factory_scope(self): - """Test Factory scope""" - # Register Factory Bean (register_factory defaults to FACTORY scope) - self.container.register_factory( - bean_type=DatabaseConnection, - factory_method=create_database_connection, - bean_name="db_connection", - ) - - # Retrieve Bean - conn = self.container.get_bean_by_type(DatabaseConnection) - - # Verify Bean is created via factory method - assert isinstance(conn, DatabaseConnection) - assert conn.host == "localhost" - assert conn.port == 3306 - assert conn.database == "test_db" - assert conn.connected - - -class TestFactoryBeans: - """Test Factory Beans""" - - def setup_method(self): - """Create a new container before each test""" - self.container = DIContainer() - - def test_factory_bean_creation(self): - """Test creating Bean via factory method""" - # Register Factory - self.container.register_factory( - bean_type=DatabaseConnection, - factory_method=create_database_connection, - bean_name="db_connection", - ) - - # Retrieve Bean - conn = self.container.get_bean_by_type(DatabaseConnection) - - # Verify Bean created correctly - assert isinstance(conn, DatabaseConnection) - assert conn.connected - - # Call method to verify - result = conn.execute("SELECT * FROM users") - assert len(result) == 1 - assert "Executed" in result[0]["result"] - - def test_multiple_factories_for_same_type(self): - """Test multiple Factories for the same type""" - # Register multiple Factories - self.container.register_factory( - bean_type=DatabaseConnection, - factory_method=create_database_connection, - bean_name="db_connection", - is_primary=True, - ) - self.container.register_factory( - bean_type=DatabaseConnection, - factory_method=create_readonly_connection, - bean_name="readonly_connection", - ) - - # Retrieve Bean created by Primary Factory - conn = self.container.get_bean_by_type(DatabaseConnection) - assert conn.host == "localhost" - - # Retrieve Bean created by another Factory by name - readonly_conn = self.container.get_bean("readonly_connection") - assert readonly_conn.host == "readonly.example.com" - - def test_factory_with_priority(self): - """Test Factory Bean priority""" - - # Register Regular Bean and Factory Bean - def factory() -> CacheService: - cache = RedisCacheService() - cache.set("init_key", "init_value") - return cache - - # Regular Bean - self.container.register_bean( - bean_type=MemoryCacheService, bean_name="memory_cache", is_primary=False - ) - - # Factory Bean (Primary) - self.container.register_factory( - bean_type=CacheService, - factory_method=factory, - bean_name="redis_cache", - is_primary=True, - ) - - # Should return Primary Bean created by Factory - cache = self.container.get_bean_by_type(CacheService) - assert isinstance(cache, RedisCacheService) - assert cache.get("init_key") == "init_value" - - -class TestDependencyInjection: - """Test dependency injection""" - - def setup_method(self): - """Create a new container before each test""" - self.container = DIContainer() - register_standard_beans(self.container) - - def test_constructor_injection(self): - """Test constructor dependency injection""" - # Retrieve dependency - repo = self.container.get_bean_by_type(UserRepository) - - # Manually inject dependency to create Service - service = UserServiceImpl(repository=repo) - - # Verify dependency correctly injected - assert isinstance(service.repository, MySQLUserRepository) - - # Verify service functionality - user = service.get_user(1) - assert user["id"] == 1 - assert user["source"] == "mysql" - - def test_container_based_injection(self): - """Test resolving dependencies through container""" - # Create Service passing container - service = UserServiceImpl(container=self.container) - - # Verify dependency resolved through container - assert isinstance(service.repository, MySQLUserRepository) - - # Verify service functionality - users = service.get_all_users() - assert len(users) == 2 - assert all(u["source"] == "mysql" for u in users) - - def test_dependency_injection_in_mock_mode(self): - """Test dependency injection in Mock mode""" - # Enable Mock mode - self.container.enable_mock_mode() - - # Create Service (should inject Mock dependency) - service = UserServiceImpl(container=self.container) - - # Verify Mock dependency is injected - assert isinstance(service.repository, MockUserRepository) - - # Verify Mock data - user = service.get_user(1) - assert "Mock" in user["name"] - assert user["source"] == "mock" - - -class TestBeanOrdering: - """Test Bean ordering priority""" - - def setup_method(self): - """Create a new container before each test""" - self.container = DIContainer() - - def test_ordering_primary_over_non_primary(self): - """Test Primary takes precedence over non-Primary""" - # Register out of order - self.container.register_bean( - bean_type=SMSNotificationService, bean_name="sms", is_primary=False - ) - self.container.register_bean( - bean_type=EmailNotificationService, bean_name="email", is_primary=True - ) - self.container.register_bean( - bean_type=PushNotificationService, bean_name="push", is_primary=False - ) - - # Retrieve all Beans, verify Primary comes first - services = self.container.get_beans_by_type(NotificationService) - assert isinstance(services[0], EmailNotificationService) # Primary - - def test_ordering_factory_over_singleton(self): - """Test Factory takes precedence over Singleton (when both are Primary)""" - - # Define test interface - class TestService(ABC): - pass - - class ServiceA(TestService): - def __init__(self): - self.type = "singleton" - - class ServiceB(TestService): - def __init__(self): - self.type = "factory" - - # Register Singleton (Primary) - self.container.register_bean( - bean_type=ServiceA, - bean_name="service_a", - is_primary=True, - scope=BeanScope.SINGLETON, - ) - - # Register Factory (Primary) - register_factory defaults to FACTORY scope - self.container.register_factory( - bean_type=ServiceB, - factory_method=lambda: ServiceB(), - bean_name="service_b", - is_primary=True, - ) - - # Factory should take precedence - service = self.container.get_bean_by_type(TestService) - assert isinstance(service, ServiceB) - assert service.type == "factory" - - -class TestComplexScenarios: - """Test complex scenarios""" - - def setup_method(self): - """Create a new container before each test""" - self.container = DIContainer() - register_standard_beans(self.container) - - def test_multiple_interface_implementations(self): - """Test multiple interfaces each having multiple implementations""" - # Retrieve Primary implementations for different interfaces - user_repo = self.container.get_bean_by_type(UserRepository) - notification = self.container.get_bean_by_type(NotificationService) - cache = self.container.get_bean_by_type(CacheService) - - # Verify respective Primary implementations - assert isinstance(user_repo, MySQLUserRepository) - assert isinstance(notification, EmailNotificationService) - assert isinstance(cache, RedisCacheService) - - def test_get_all_beans_for_multiple_interfaces(self): - """Test retrieving all implementations for multiple interfaces""" - # UserRepository: 2 implementations (non-Mock mode) - user_repos = self.container.get_beans_by_type(UserRepository) - assert len(user_repos) == 2 - - # NotificationService: 3 implementations - notifications = self.container.get_beans_by_type(NotificationService) - assert len(notifications) == 3 - - # CacheService: 2 implementations - caches = self.container.get_beans_by_type(CacheService) - assert len(caches) == 2 - - def test_bean_lifecycle_and_state(self): - """Test Bean lifecycle and state management""" - # Retrieve Singleton Bean - repo = self.container.get_bean_by_type(UserRepository) - - # Modify state - repo.find_by_id(1) - repo.find_all() - assert repo.call_count == 2 - - # Retrieve again, should be same instance with state preserved - repo2 = self.container.get_bean_by_type(UserRepository) - assert repo2.call_count == 2 - - # Call method, state continues to accumulate - repo2.find_by_id(2) - assert repo.call_count == 3 - assert repo2.call_count == 3 - - -if __name__ == "__main__": - # Run tests - pytest.main([__file__, "-v", "-s", "--tb=short"]) diff --git a/methods/EverCore/src/core/di/tests/test_di_scanner.py b/methods/EverCore/src/core/di/tests/test_di_scanner.py deleted file mode 100644 index a24a982f..00000000 --- a/methods/EverCore/src/core/di/tests/test_di_scanner.py +++ /dev/null @@ -1,413 +0,0 @@ -# -*- coding: utf-8 -*- -""" -cd /Users/admin/memsys_opensource -PYTHONPATH=/Users/admin/memsys_opensource/src python -m pytest src/core/di/tests/test_di_scanner.py -v -s - -DI Scanner Test - -Test the component scanning and auto-registration functionality of Scanner -""" - -import pytest -import tempfile -import shutil -from pathlib import Path -from core.di.container import DIContainer, get_container -from core.di.scanner import ComponentScanner -from core.di.decorators import component, service, repository, mock_impl, factory -from core.di.bean_definition import BeanScope -from core.di.tests.test_fixtures import ( - UserRepository, - MySQLUserRepository, - NotificationService, - EmailNotificationService, -) - - -class TestScannerBasicFunctionality: - """Test basic functionality of Scanner""" - - def setup_method(self): - """Create a new container and scanner before each test""" - self.container = DIContainer() - self.scanner = ComponentScanner() - - def test_scanner_initialization(self): - """Test Scanner initialization""" - assert self.scanner is not None - assert self.scanner.scan_paths == [] - assert self.scanner.scan_packages == [] - assert self.scanner.recursive is True - - def test_add_scan_path(self): - """Test adding scan path""" - self.scanner.add_scan_path("/path/to/scan") - assert "/path/to/scan" in self.scanner.scan_paths - - # Chained calls - self.scanner.add_scan_path("/path1").add_scan_path("/path2") - assert len(self.scanner.scan_paths) == 3 - - def test_add_scan_package(self): - """Test adding scan package""" - self.scanner.add_scan_package("my.package") - assert "my.package" in self.scanner.scan_packages - - # Chained calls - self.scanner.add_scan_package("pkg1").add_scan_package("pkg2") - assert len(self.scanner.scan_packages) == 3 - - def test_exclude_patterns(self): - """Test exclude patterns""" - self.scanner.exclude_pattern("test_") - assert "test_" in self.scanner.exclude_patterns - - # Default exclude patterns should exist - assert "__pycache__" in self.scanner.exclude_paths - assert "test_" in self.scanner.exclude_patterns - - -class TestComponentDecoratorIntegration: - """Test integration of decorators with Container""" - - def setup_method(self): - """Reset global container before each test""" - # Note: decorators register to global container by default - # Here we test the behavior of decorators - pass - - def test_component_decorator_registers_bean(self): - """Test @component decorator automatically registers Bean""" - container = get_container() - - # Define a component - @component(name="test_component_unique_1") - class TestComponent1: - def __init__(self): - self.value = "test1" - - # Verify Bean is registered - assert container.contains_bean("test_component_unique_1") - - # Get Bean - comp = container.get_bean("test_component_unique_1") - assert isinstance(comp, TestComponent1) - assert comp.value == "test1" - - def test_service_decorator_registers_bean(self): - """Test @service decorator automatically registers Bean""" - container = get_container() - - # Define a service - @service(name="test_service_unique_1", primary=True) - class TestService1: - def __init__(self): - self.service_type = "test" - - # Verify Bean is registered - assert container.contains_bean("test_service_unique_1") - - # Get Bean - svc = container.get_bean("test_service_unique_1") - assert isinstance(svc, TestService1) - assert svc.service_type == "test" - - def test_repository_decorator_registers_bean(self): - """Test @repository decorator automatically registers Bean""" - container = get_container() - - # Define a repository - @repository(name="test_repo_unique_1") - class TestRepository1: - def __init__(self): - self.db = "sqlite" - - # Verify Bean is registered - assert container.contains_bean("test_repo_unique_1") - - # Get Bean - repo = container.get_bean("test_repo_unique_1") - assert isinstance(repo, TestRepository1) - assert repo.db == "sqlite" - - def test_mock_impl_decorator_registers_mock_bean(self): - """Test @mock_impl decorator registers Mock Bean""" - container = get_container() - - # Define a Mock implementation - @mock_impl(name="test_mock_unique_1") - class TestMock1: - def __init__(self): - self.is_mock = True - - # Verify Bean is registered - assert container.contains_bean("test_mock_unique_1") - - # In non-Mock mode, Mock Bean should not be automatically retrieved - # But can be retrieved by name - mock = container.get_bean("test_mock_unique_1") - assert isinstance(mock, TestMock1) - assert mock.is_mock is True - - def test_factory_decorator_registers_factory(self): - """Test @factory decorator registers Factory""" - container = get_container() - - # Define a Factory - class Product: - def __init__(self, name: str): - self.name = name - - @factory(bean_type=Product, name="test_factory_unique_1") - def create_product() -> Product: - return Product(name="TestProduct") - - # Verify Factory is registered - assert container.contains_bean("test_factory_unique_1") - - # Get Bean (created by Factory) - product = container.get_bean("test_factory_unique_1") - assert isinstance(product, Product) - assert product.name == "TestProduct" - - def test_component_with_scope(self): - """Test @component decorator specifying Scope""" - container = get_container() - - # Define component with Prototype scope - @component(name="test_prototype_unique_1", scope=BeanScope.PROTOTYPE) - class TestPrototype1: - counter = 0 - - def __init__(self): - TestPrototype1.counter += 1 - self.id = TestPrototype1.counter - - # Get multiple instances - obj1 = container.get_bean("test_prototype_unique_1") - obj2 = container.get_bean("test_prototype_unique_1") - - # Prototype scope should create different instances - assert obj1 is not obj2 - assert obj1.id != obj2.id - - -class TestInterfaceImplementationScanning: - """Test scanning of interfaces and implementation classes""" - - def setup_method(self): - """Create new container before each test""" - self.container = DIContainer() - - def test_register_interface_implementations(self): - """Test registering multiple implementations of an interface""" - # Manually register implementation class (simulate scan result) - self.container.register_bean( - bean_type=MySQLUserRepository, bean_name="mysql_user_repo", is_primary=True - ) - - # Verify can be retrieved via interface type - repo = self.container.get_bean_by_type(UserRepository) - assert isinstance(repo, MySQLUserRepository) - - def test_multiple_implementations_of_interface(self): - """Test multiple implementations of the same interface""" - from core.di.tests.test_fixtures import ( - PostgreSQLUserRepository, - MockUserRepository, - ) - - # Register multiple implementations - self.container.register_bean( - bean_type=MySQLUserRepository, bean_name="mysql_repo", is_primary=False - ) - self.container.register_bean( - bean_type=PostgreSQLUserRepository, - bean_name="postgres_repo", - is_primary=True, - ) - self.container.register_bean( - bean_type=MockUserRepository, bean_name="mock_repo", is_mock=True - ) - - # Get Primary implementation - repo = self.container.get_bean_by_type(UserRepository) - assert isinstance(repo, PostgreSQLUserRepository) - - # Get all implementations (non-Mock mode) - all_repos = self.container.get_beans_by_type(UserRepository) - assert len(all_repos) == 2 # MySQL + PostgreSQL - - -class TestScanContextAndMetadata: - """Test scanning context and metadata""" - - def setup_method(self): - """Create new container before each test""" - self.container = DIContainer() - - def test_bean_with_metadata(self): - """Test Bean metadata""" - # Register Bean with metadata - self.container.register_bean( - bean_type=MySQLUserRepository, - bean_name="mysql_repo", - metadata={"version": "1.0", "author": "test", "db_type": "mysql"}, - ) - - # Get Bean Definition to verify metadata - bean_def = self.container._named_beans.get("mysql_repo") - assert bean_def is not None - assert bean_def.metadata["version"] == "1.0" - assert bean_def.metadata["author"] == "test" - assert bean_def.metadata["db_type"] == "mysql" - - def test_metadata_from_decorator(self): - """Test metadata passed from decorator""" - container = get_container() - - # Define component with metadata - @component( - name="test_metadata_comp_unique_1", metadata={"env": "test", "priority": 10} - ) - class TestMetadataComp: - pass - - # Get Bean Definition - bean_def = container._named_beans.get("test_metadata_comp_unique_1") - assert bean_def is not None - assert bean_def.metadata["env"] == "test" - assert bean_def.metadata["priority"] == 10 - - -class TestConditionalRegistration: - """Test conditional registration""" - - def test_lazy_registration(self): - """Test lazy registration""" - container = get_container() - initial_bean_count = len(container._named_beans) - - # Define component with lazy registration - @component(name="lazy_comp_unique_1", lazy=True) - class LazyComponent: - pass - - # Lazily registered Bean should not immediately appear in container - # Note: In current implementation, lazy=True is just a flag, actual behavior depends on specific implementation - # Here we only verify the component is correctly marked - assert hasattr(LazyComponent, '_di_lazy') - assert LazyComponent._di_lazy is True - - -class TestBeanDependencies: - """Test dependencies between Beans""" - - def setup_method(self): - """Create new container before each test""" - self.container = DIContainer() - - def test_bean_depends_on_another(self): - """Test Bean depends on another Bean""" - from core.di.tests.test_fixtures import UserServiceImpl, register_standard_beans - - # Register dependent Bean - register_standard_beans(self.container) - - # Create Service that depends on other Bean - service = UserServiceImpl(container=self.container) - - # Verify dependency injection succeeded - assert service.repository is not None - assert isinstance(service.repository, UserRepository) - - # Verify Service functions correctly - user = service.get_user(1) - assert user is not None - - -class TestEdgeCases: - """Test edge cases""" - - def setup_method(self): - """Create new container before each test""" - self.container = DIContainer() - - def test_empty_container(self): - """Test empty container""" - from core.di.exceptions import BeanNotFoundError - - # Empty container should raise exception - with pytest.raises(BeanNotFoundError): - self.container.get_bean_by_type(UserRepository) - - def test_duplicate_bean_name(self): - """Test duplicate Bean name throws exception""" - from core.di.exceptions import DuplicateBeanError - from core.di.tests.test_fixtures import PostgreSQLUserRepository - - # Register first Bean - self.container.register_bean( - bean_type=MySQLUserRepository, bean_name="same_name" - ) - - # Attempt to register Bean with same name should raise exception - with pytest.raises(DuplicateBeanError): - self.container.register_bean( - bean_type=PostgreSQLUserRepository, bean_name="same_name" - ) - - def test_get_all_beans_empty_result(self): - """Test getting all Beans of non-existent type""" - - # Unregistered type - class UnregisteredService: - pass - - # Getting all Beans should return empty list - beans = self.container.get_beans_by_type(UnregisteredService) - assert beans == [] - - -class TestRealWorldScanningScenario: - """Test real-world scanning scenario""" - - def test_scan_test_fixtures_module(self): - """Test scanning test_fixtures module""" - # This test verifies we can import classes from fixtures module - from core.di.tests.test_fixtures import ( - MySQLUserRepository, - PostgreSQLUserRepository, - MockUserRepository, - EmailNotificationService, - SMSNotificationService, - RedisCacheService, - MemoryCacheService, - ) - - # Verify all classes can be imported and instantiated normally - mysql_repo = MySQLUserRepository() - assert mysql_repo.db_type == "mysql" - - postgres_repo = PostgreSQLUserRepository() - assert postgres_repo.db_type == "postgres" - - mock_repo = MockUserRepository() - assert mock_repo.db_type == "mock" - - email_notif = EmailNotificationService() - assert email_notif.sent_messages == [] - - sms_notif = SMSNotificationService() - assert sms_notif.sent_messages == [] - - redis_cache = RedisCacheService() - assert redis_cache.cache_type == "redis" - - memory_cache = MemoryCacheService() - assert memory_cache.cache_type == "memory" - - -if __name__ == "__main__": - # Run tests - pytest.main([__file__, "-v", "-s", "--tb=short"]) diff --git a/methods/EverCore/src/core/di/tests/test_fixtures.py b/methods/EverCore/src/core/di/tests/test_fixtures.py deleted file mode 100644 index 2855de0e..00000000 --- a/methods/EverCore/src/core/di/tests/test_fixtures.py +++ /dev/null @@ -1,373 +0,0 @@ -# -*- coding: utf-8 -*- -""" -DI Test Fixtures - -Provides interfaces, implementation classes, and Mock classes for testing -These classes can be imported and used by other test files -""" - -from abc import ABC, abstractmethod -from typing import List, Optional -from core.di.container import DIContainer -from core.di.bean_definition import BeanScope - - -# ==================== User Service Related ==================== - - -class UserRepository(ABC): - """User repository interface""" - - @abstractmethod - def find_by_id(self, user_id: int) -> Optional[dict]: - """Find user by ID""" - pass - - @abstractmethod - def find_all(self) -> List[dict]: - """Find all users""" - pass - - -class MySQLUserRepository(UserRepository): - """MySQL user repository implementation (Primary)""" - - def __init__(self): - self.db_type = "mysql" - self.call_count = 0 - - def find_by_id(self, user_id: int) -> Optional[dict]: - self.call_count += 1 - return {"id": user_id, "name": f"User{user_id}", "source": "mysql"} - - def find_all(self) -> List[dict]: - self.call_count += 1 - return [ - {"id": 1, "name": "User1", "source": "mysql"}, - {"id": 2, "name": "User2", "source": "mysql"}, - ] - - -class PostgreSQLUserRepository(UserRepository): - """PostgreSQL user repository implementation (Non-Primary)""" - - def __init__(self): - self.db_type = "postgres" - self.call_count = 0 - - def find_by_id(self, user_id: int) -> Optional[dict]: - self.call_count += 1 - return {"id": user_id, "name": f"User{user_id}", "source": "postgres"} - - def find_all(self) -> List[dict]: - self.call_count += 1 - return [{"id": 1, "name": "User1", "source": "postgres"}] - - -class MockUserRepository(UserRepository): - """Mock user repository implementation""" - - def __init__(self): - self.db_type = "mock" - self.call_count = 0 - - def find_by_id(self, user_id: int) -> Optional[dict]: - self.call_count += 1 - return {"id": user_id, "name": f"MockUser{user_id}", "source": "mock"} - - def find_all(self) -> List[dict]: - self.call_count += 1 - return [{"id": 999, "name": "MockUser", "source": "mock"}] - - -class UserService(ABC): - """User service interface""" - - @abstractmethod - def get_user(self, user_id: int) -> Optional[dict]: - """Get user""" - pass - - @abstractmethod - def get_all_users(self) -> List[dict]: - """Get all users""" - pass - - -class UserServiceImpl(UserService): - """User service implementation""" - - def __init__( - self, repository: UserRepository = None, container: DIContainer = None - ): - # Supports two injection methods: constructor injection or retrieval via container - if repository: - self.repository = repository - elif container: - self.repository = container.get_bean_by_type(UserRepository) - else: - raise ValueError("Must provide either repository or container") - self.call_count = 0 - - def get_user(self, user_id: int) -> Optional[dict]: - self.call_count += 1 - return self.repository.find_by_id(user_id) - - def get_all_users(self) -> List[dict]: - self.call_count += 1 - return self.repository.find_all() - - -# ==================== Notification Service Related ==================== - - -class NotificationService(ABC): - """Notification service interface""" - - @abstractmethod - def send(self, message: str, recipient: str) -> bool: - """Send notification""" - pass - - -class EmailNotificationService(NotificationService): - """Email notification service implementation (Primary)""" - - def __init__(self): - self.sent_messages = [] - - def send(self, message: str, recipient: str) -> bool: - self.sent_messages.append( - {"message": message, "recipient": recipient, "type": "email"} - ) - return True - - -class SMSNotificationService(NotificationService): - """SMS notification service implementation (Non-Primary)""" - - def __init__(self): - self.sent_messages = [] - - def send(self, message: str, recipient: str) -> bool: - self.sent_messages.append( - {"message": message, "recipient": recipient, "type": "sms"} - ) - return True - - -class PushNotificationService(NotificationService): - """Push notification service implementation""" - - def __init__(self): - self.sent_messages = [] - - def send(self, message: str, recipient: str) -> bool: - self.sent_messages.append( - {"message": message, "recipient": recipient, "type": "push"} - ) - return True - - -# ==================== Email Service Related ==================== - - -class EmailService(ABC): - """Email service interface""" - - @abstractmethod - def send_email(self, to: str, subject: str, body: str) -> bool: - """Send email""" - pass - - -class SMTPEmailService(EmailService): - """SMTP email service implementation""" - - def __init__(self): - self.host = "smtp.example.com" - self.port = 587 - self.sent_emails = [] - - def send_email(self, to: str, subject: str, body: str) -> bool: - self.sent_emails.append({"to": to, "subject": subject, "body": body}) - return True - - -# ==================== Database Connection Related ==================== - - -class DatabaseConnection: - """Database connection class""" - - def __init__(self, host: str, port: int, database: str): - self.host = host - self.port = port - self.database = database - self.connected = True - - def execute(self, sql: str) -> List[dict]: - return [{"result": f"Executed: {sql}"}] - - def close(self): - self.connected = False - - -def create_database_connection() -> DatabaseConnection: - """Factory method to create database connection""" - return DatabaseConnection(host="localhost", port=3306, database="test_db") - - -def create_readonly_connection() -> DatabaseConnection: - """Factory method to create read-only database connection""" - return DatabaseConnection( - host="readonly.example.com", port=3306, database="test_db" - ) - - -# ==================== Prototype Scope Test Classes ==================== - - -class PrototypeService: - """Prototype scope service (a new instance is created each time it is retrieved)""" - - instance_counter = 0 # Class-level counter - - def __init__(self): - PrototypeService.instance_counter += 1 - self.instance_id = PrototypeService.instance_counter - self.data = [] - - def add_data(self, value: str): - self.data.append(value) - - def get_data(self) -> List[str]: - return self.data - - @classmethod - def reset_counter(cls): - """Reset counter (used for testing)""" - cls.instance_counter = 0 - - -# ==================== Cache Service Related ==================== - - -class CacheService(ABC): - """Cache service interface""" - - @abstractmethod - def get(self, key: str) -> Optional[str]: - """Get cache""" - pass - - @abstractmethod - def set(self, key: str, value: str, ttl: int = 3600) -> bool: - """Set cache""" - pass - - @abstractmethod - def delete(self, key: str) -> bool: - """Delete cache""" - pass - - -class RedisCacheService(CacheService): - """Redis cache service implementation (Primary)""" - - def __init__(self): - self.storage = {} - self.cache_type = "redis" - - def get(self, key: str) -> Optional[str]: - return self.storage.get(key) - - def set(self, key: str, value: str, ttl: int = 3600) -> bool: - self.storage[key] = value - return True - - def delete(self, key: str) -> bool: - if key in self.storage: - del self.storage[key] - return True - return False - - -class MemoryCacheService(CacheService): - """In-memory cache service implementation""" - - def __init__(self): - self.storage = {} - self.cache_type = "memory" - - def get(self, key: str) -> Optional[str]: - return self.storage.get(key) - - def set(self, key: str, value: str, ttl: int = 3600) -> bool: - self.storage[key] = value - return True - - def delete(self, key: str) -> bool: - if key in self.storage: - del self.storage[key] - return True - return False - - -# ==================== Utility Helper Functions ==================== - - -def register_standard_beans(container: DIContainer): - """Register standard test beans into the container - - Args: - container: DI container instance - """ - # Register UserRepository implementations - container.register_bean( - bean_type=MySQLUserRepository, bean_name="mysql_user_repo", is_primary=True - ) - container.register_bean( - bean_type=PostgreSQLUserRepository, - bean_name="postgres_user_repo", - is_primary=False, - ) - container.register_bean( - bean_type=MockUserRepository, bean_name="mock_user_repo", is_mock=True - ) - - # Register NotificationService implementations - container.register_bean( - bean_type=EmailNotificationService, - bean_name="email_notification", - is_primary=True, - ) - container.register_bean( - bean_type=SMSNotificationService, bean_name="sms_notification" - ) - container.register_bean( - bean_type=PushNotificationService, bean_name="push_notification" - ) - - # Register CacheService implementations - container.register_bean( - bean_type=RedisCacheService, bean_name="redis_cache", is_primary=True - ) - container.register_bean(bean_type=MemoryCacheService, bean_name="memory_cache") - - # Register EmailService implementation - container.register_bean(bean_type=SMTPEmailService, bean_name="smtp_email_service") - - # Register Factory Bean - container.register_factory( - bean_type=DatabaseConnection, - factory_method=create_database_connection, - bean_name="db_connection", - ) - - # Register Prototype Bean - container.register_bean( - bean_type=PrototypeService, - bean_name="prototype_service", - scope=BeanScope.PROTOTYPE, - ) diff --git a/methods/EverCore/src/core/di/utils.py b/methods/EverCore/src/core/di/utils.py deleted file mode 100644 index 9a1d1c41..00000000 --- a/methods/EverCore/src/core/di/utils.py +++ /dev/null @@ -1,426 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Dependency injection utility functions -""" - -from typing import Type, TypeVar, List, Dict, Any, Optional, Callable -import inspect - -from core.di.container import get_container -from core.di.bean_definition import BeanScope -from core.di.exceptions import BeanNotFoundError - -T = TypeVar('T') - - -def get_bean(name: str) -> Any: - """ - Get Bean by name - - Args: - name: Bean name - - Returns: - Bean instance - - Raises: - BeanNotFoundError: When Bean does not exist - """ - return get_container().get_bean(name) - - -def get_beans() -> Dict[str, Any]: - """ - Get all Beans - - Returns: - Dictionary of all Beans, key is name, value is instance - """ - return get_container().get_beans() - - -def get_bean_by_type(bean_type: Type[T]) -> T: - """ - Get Bean by type (Primary implementation or unique implementation) - - Args: - bean_type: Bean type - - Returns: - Bean instance - - Raises: - BeanNotFoundError: When Bean does not exist - """ - return get_container().get_bean_by_type(bean_type) - - -def get_beans_by_type(bean_type: Type[T]) -> List[T]: - """ - Get all Bean implementations by type - - Args: - bean_type: Bean type - - Returns: - List of Bean instances - """ - return get_container().get_beans_by_type(bean_type) - - -def register_bean( - bean_type: Type[T], - instance: T = None, - name: str = None, - scope: BeanScope = BeanScope.SINGLETON, - is_primary: bool = False, - is_mock: bool = False, - metadata: Optional[Dict[str, Any]] = None, -) -> None: - """ - Register Bean - - Args: - bean_type: Bean type - instance: Bean instance (optional, will be created automatically if not provided) - name: Bean name - scope: Bean scope - is_primary: Whether it is a Primary implementation - is_mock: Whether it is a Mock implementation - metadata: Bean metadata, can be used to store additional information - """ - get_container().register_bean( - bean_type=bean_type, - instance=instance, - bean_name=name, - scope=scope, - is_primary=is_primary, - is_mock=is_mock, - metadata=metadata, - ) - - -def register_factory( - bean_type: Type[T], - factory_method: Callable[[], T], - name: str = None, - is_primary: bool = False, - is_mock: bool = False, - metadata: Optional[Dict[str, Any]] = None, -) -> None: - """ - Register Factory method - - Args: - bean_type: Bean type - factory_method: Factory method - name: Bean name - is_primary: Whether it is a Primary implementation - is_mock: Whether it is a Mock implementation - metadata: Bean metadata, can be used to store additional information - """ - get_container().register_factory( - bean_type=bean_type, - factory_method=factory_method, - bean_name=name, - is_primary=is_primary, - is_mock=is_mock, - metadata=metadata, - ) - - -def register_singleton( - bean_type: Type[T], instance: T = None, name: str = None -) -> None: - """ - Register singleton Bean - - Args: - bean_type: Bean type - instance: Bean instance - name: Bean name - """ - register_bean(bean_type, instance, name, BeanScope.SINGLETON) - - -def register_prototype(bean_type: Type[T], name: str = None) -> None: - """ - Register prototype Bean (create new instance every time it is retrieved) - - Args: - bean_type: Bean type - name: Bean name - """ - register_bean(bean_type, None, name, BeanScope.PROTOTYPE) - - -def register_primary(bean_type: Type[T], instance: T = None, name: str = None) -> None: - """ - Register Primary Bean - - Args: - bean_type: Bean type - instance: Bean instance - name: Bean name - """ - register_bean(bean_type, instance, name, BeanScope.SINGLETON, is_primary=True) - - -def register_mock(bean_type: Type[T], instance: T = None, name: str = None) -> None: - """ - Register Mock Bean - - Args: - bean_type: Bean type - instance: Bean instance - name: Bean name - """ - register_bean(bean_type, instance, name, BeanScope.SINGLETON, is_mock=True) - - -def enable_mock_mode() -> None: - """Enable mock mode""" - get_container().enable_mock_mode() - - -def disable_mock_mode() -> None: - """Disable mock mode""" - get_container().disable_mock_mode() - - -def is_mock_mode() -> bool: - """Check if in mock mode""" - return get_container().is_mock_mode() - - -def clear_container() -> None: - """Clear container""" - get_container().clear() - - -def inject(target_func: Callable) -> Callable: - """ - Function dependency injection decorator - - Automatically inject Bean by parameter type - """ - - def wrapper(*args, **kwargs): - # Get function signature - signature = inspect.signature(target_func) - - # Prepare injected parameters - injected_kwargs = {} - for param_name, param in signature.parameters.items(): - if param_name not in kwargs and param.annotation != inspect.Parameter.empty: - try: - injected_kwargs[param_name] = get_bean_by_type(param.annotation) - except BeanNotFoundError: - # If Bean not found and parameter has default value, use default value - if param.default != inspect.Parameter.empty: - injected_kwargs[param_name] = param.default - else: - # Required parameter but Bean not found, raise exception - raise - - # Merge parameters - kwargs.update(injected_kwargs) - return target_func(*args, **kwargs) - - return wrapper - - -def lazy_inject(bean_type: Type[T]) -> Callable[[], T]: - """ - Lazy injection function - - Returns a lambda function that retrieves Bean when called - - Args: - bean_type: Bean type - - Returns: - Function to lazily retrieve Bean - """ - return lambda: get_bean_by_type(bean_type) - - -def get_or_create(bean_type: Type[T], factory: Callable[[], T] = None) -> T: - """ - Get Bean, create if not exists - - Args: - bean_type: Bean type - factory: Factory method (optional) - - Returns: - Bean instance - """ - try: - return get_bean_by_type(bean_type) - except BeanNotFoundError: - if factory: - instance = factory() - register_bean(bean_type, instance) - return instance - else: - # Try to create automatically - try: - instance = bean_type() - register_bean(bean_type, instance) - return instance - except Exception as e: - raise BeanNotFoundError(bean_type=bean_type) - - -def conditional_register( - condition: Callable[[], bool], - bean_type: Type[T], - instance: T = None, - name: str = None, -) -> None: - """ - Conditionally register Bean - - Args: - condition: Condition function - bean_type: Bean type - instance: Bean instance - name: Bean name - """ - if condition(): - register_bean(bean_type, instance, name) - - -def batch_register(beans: Dict[Type, Any]) -> None: - """ - Batch register Beans - - Args: - beans: Bean dictionary, key is type, value is instance - """ - for bean_type, instance in beans.items(): - register_bean(bean_type, instance) - - -def get_bean_info(bean_type: Type = None, bean_name: str = None) -> Dict[str, Any]: - """ - Get Bean information - - Args: - bean_type: Bean type - bean_name: Bean name - - Returns: - Bean information dictionary - """ - container = get_container() - info = {} - - if bean_name: - if container.contains_bean(bean_name): - bean_def = container._named_beans[bean_name] - info = { - 'name': bean_def.bean_name, - 'type': bean_def.bean_type.__name__, - 'scope': bean_def.scope.value, - 'is_primary': bean_def.is_primary, - 'is_mock': bean_def.is_mock, - 'has_instance': bean_def in container._singleton_instances, - } - elif bean_type: - if container.contains_bean_by_type(bean_type): - definitions = container._bean_definitions[bean_type] - info = { - 'type': bean_type.__name__, - 'implementations': [ - { - 'name': def_.bean_name, - 'scope': def_.scope.value, - 'is_primary': def_.is_primary, - 'is_mock': def_.is_mock, - } - for def_ in definitions - ], - } - - return info - - -def get_all_beans_info() -> List[Dict[str, Any]]: - """ - Get information of all registered Beans (structured data) - - Returns: - List of structured Bean information data - """ - return get_container().list_all_beans_info() - - -def list_all_beans() -> List[str]: - """ - List all registered Bean information (formatted strings) - - Returns: - List of formatted Bean information strings - """ - beans_info = get_all_beans_info() - - formatted_beans = [] - for bean_info in beans_info: - flags = [] - if bean_info['is_primary']: - flags.append("primary") - if bean_info['is_mock']: - flags.append("mock") - flag_str = f" ({', '.join(flags)})" if flags else "" - - formatted_beans.append( - f" • {bean_info['name']} ({bean_info['type_name']}) [{bean_info['scope']}]{flag_str}" - ) - - return formatted_beans - - -def print_container_info(): - """Print container information""" - formatted_beans = list_all_beans() - from core.observation.logger import ( - info, - ) # Convenient usage, suitable for occasional calls - - info(f"\n📦 Dependency injection container information:") - info(f" Total Bean count: {len(formatted_beans)}") - info(f" Mock mode: {'enabled' if is_mock_mode() else 'disabled'}") - - if formatted_beans: - info("\n📋 Registered Beans:") - for bean_line in formatted_beans: - info(bean_line) - else: - info(" No registered Beans") - info("") - - -# =============================================== - -# subclasses - - -def get_all_subclasses(base_class: Type[T]) -> List[Type[T]]: - """ - Recursively get all subclasses of specified class (including subclasses of subclasses) - - Args: - base_class: Base class - - Returns: - List[Type[T]]: List of all subclasses, including direct and indirect subclasses - """ - subclasses = [] - for subclass in base_class.__subclasses__(): - if subclass != base_class: - subclasses.append(subclass) - # Recursively get subclasses of subclass - subclasses.extend(get_all_subclasses(subclass)) - return subclasses diff --git a/methods/EverCore/src/core/events/__init__.py b/methods/EverCore/src/core/events/__init__.py deleted file mode 100644 index d2d94b49..00000000 --- a/methods/EverCore/src/core/events/__init__.py +++ /dev/null @@ -1,54 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Event module - -Provides application-level event publish/subscribe mechanism, supporting: -- Base event (BaseEvent): Base class for all business events, supports JSON/BSON serialization -- Event listener (EventListener): Abstract base class for event listeners -- Event publisher (ApplicationEventPublisher): Global event publisher - -Usage examples: - -1. Define an event: - >>> from dataclasses import dataclass - >>> from core.events import BaseEvent - >>> - >>> @dataclass - ... class UserCreatedEvent(BaseEvent): - ... user_id: str - ... username: str - ... - ... @classmethod - ... def from_dict(cls, data): - ... return cls( - ... event_id=data.get("event_id"), - ... created_at=data.get("created_at"), - ... user_id=data["user_id"], - ... username=data["username"], - ... ) - -2. Define a listener: - >>> from core.di import component - >>> from core.events import EventListener, BaseEvent - >>> - >>> @component("user_event_listener") - ... class UserEventListener(EventListener): - ... def get_event_types(self): - ... return [UserCreatedEvent] - ... - ... async def on_event(self, event: BaseEvent): - ... print(f"User created: {event.user_id}") - -3. Publish an event: - >>> from core.di import get_bean_by_type - >>> from core.events import ApplicationEventPublisher - >>> - >>> publisher = get_bean_by_type(ApplicationEventPublisher) - >>> await publisher.publish(UserCreatedEvent(user_id="123", username="alice")) -""" - -from core.events.base_event import BaseEvent -from core.events.event_listener import EventListener -from core.events.event_publisher import ApplicationEventPublisher - -__all__ = ['BaseEvent', 'EventListener', 'ApplicationEventPublisher'] diff --git a/methods/EverCore/src/core/events/base_event.py b/methods/EverCore/src/core/events/base_event.py deleted file mode 100644 index 1b855733..00000000 --- a/methods/EverCore/src/core/events/base_event.py +++ /dev/null @@ -1,165 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Base event module - -Provides the base abstract class for events, supporting JSON and BSON serialization/deserialization. -All business events should inherit from this base class. -""" - -import json -import uuid -from abc import ABC, abstractmethod -from dataclasses import dataclass, field, asdict -from typing import Any, Dict, Type, TypeVar - -import bson - -from common_utils.datetime_utils import get_now_with_timezone, to_iso_format - - -T = TypeVar('T', bound='BaseEvent') - - -@dataclass -class BaseEvent(ABC): - """ - Base event class - - All business events should inherit from this class. Subclasses need to define their own business fields, - and can optionally override the `event_type` method to customize the event type name. - - The base class provides the following features: - - Automatically generates event ID (event_id) - - Automatically records event creation time (created_at) - - JSON serialization/deserialization - - BSON serialization/deserialization - - Attributes: - event_id: Unique identifier for the event, automatically generated - created_at: Event creation time (ISO format string), automatically generated - - Example: - >>> @dataclass - ... class UserCreatedEvent(BaseEvent): - ... user_id: str - ... username: str - ... - >>> event = UserCreatedEvent(user_id="123", username="alice") - >>> print(event.event_type()) # "UserCreatedEvent" - >>> json_str = event.to_json_str() - >>> restored = UserCreatedEvent.from_json_str(json_str) - """ - - # Base class fields, using field to provide default factories - event_id: str = field(default_factory=lambda: str(uuid.uuid4())) - created_at: str = field( - default_factory=lambda: to_iso_format(get_now_with_timezone()) - ) - - @classmethod - def event_type(cls) -> str: - """ - Get the event type name - - Returns the class name by default. Subclasses can override this method to customize the event type name. - - Returns: - str: Event type name - """ - return cls.__name__ - - def to_dict(self) -> Dict[str, Any]: - """ - Convert object to a serializable dictionary - - Note: Automatically adds the `_event_type` field, used during deserialization to determine the specific event type. - - Returns: - Dict[str, Any]: Dictionary representation of the object - """ - data = asdict(self) - # Add event type field for identifying the event type during deserialization - data['_event_type'] = self.event_type() - return data - - @classmethod - @abstractmethod - def from_dict(cls: Type[T], data: Dict[str, Any]) -> T: - """ - Create an object instance from a dictionary - - Subclasses must implement this method to support deserialization. - - Args: - data: Dictionary containing event data - - Returns: - Event object instance - - Raises: - KeyError: Missing required fields - TypeError: Incorrect field types - """ - pass - - def to_json_str(self) -> str: - """ - Serialize object to JSON string - - Returns: - str: JSON string - """ - return json.dumps(self.to_dict(), ensure_ascii=False) - - def to_bson_bytes(self) -> bytes: - """ - Serialize object to BSON bytes - - Returns: - bytes: BSON byte data - """ - return bson.encode(self.to_dict()) - - @classmethod - def from_json_str(cls: Type[T], json_str: str) -> T: - """ - Deserialize object instance from JSON string - - Args: - json_str: JSON string - - Returns: - Event object instance - - Raises: - ValueError: Invalid JSON format or data - """ - try: - data = json.loads(json_str) - return cls.from_dict(data) - except (json.JSONDecodeError, KeyError, TypeError) as e: - raise ValueError(f"Invalid JSON data: {e}") from e - - @classmethod - def from_bson_bytes(cls: Type[T], bson_bytes: bytes) -> T: - """ - Deserialize object instance from BSON bytes - - Args: - bson_bytes: BSON byte data - - Returns: - Event object instance - - Raises: - ValueError: Invalid BSON format or data - """ - try: - data = bson.decode(bson_bytes) - return cls.from_dict(data) - except Exception as e: - raise ValueError(f"Invalid BSON data: {e}") from e - - def __repr__(self) -> str: - """Return string representation of the object""" - return f"{self.__class__.__name__}(event_id={self.event_id!r}, created_at={self.created_at!r})" diff --git a/methods/EverCore/src/core/events/event_listener.py b/methods/EverCore/src/core/events/event_listener.py deleted file mode 100644 index 5343d2e0..00000000 --- a/methods/EverCore/src/core/events/event_listener.py +++ /dev/null @@ -1,96 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Event listener module - -Provides an abstract base class for event listeners, supporting declarative registration of event types to listen for. -Business listeners should inherit from this base class and implement the `get_event_types` and `on_event` methods. -""" - -from abc import ABC, abstractmethod -from typing import List, Set, Type - -from core.events.base_event import BaseEvent - - -class EventListener(ABC): - """ - Abstract base class for event listeners - - Business listeners should inherit from this class and implement the following methods: - - `get_event_types()`: Returns a list of event types to listen for - - `on_event(event)`: Handles the event-specific logic (asynchronous method) - - Listeners will be automatically discovered and registered by ApplicationEventPublisher. - It is recommended to use the @component or @service decorator to register the listener into the DI container. - - Example: - >>> from core.di import component - >>> - >>> @component("user_event_listener") - ... class UserEventListener(EventListener): - ... def get_event_types(self) -> List[Type[BaseEvent]]: - ... return [UserCreatedEvent, UserUpdatedEvent] - ... - ... async def on_event(self, event: BaseEvent) -> None: - ... if isinstance(event, UserCreatedEvent): - ... await self._handle_user_created(event) - ... elif isinstance(event, UserUpdatedEvent): - ... await self._handle_user_updated(event) - """ - - @abstractmethod - def get_event_types(self) -> List[Type[BaseEvent]]: - """ - Get the list of event types to listen for - - Returns a list of event types that this listener is interested in. When events of these types are published, - the listener's `on_event` method will be called. - - Returns: - List[Type[BaseEvent]]: List of event types to listen for - - Example: - >>> def get_event_types(self) -> List[Type[BaseEvent]]: - ... return [UserCreatedEvent, OrderCreatedEvent] - """ - pass - - @abstractmethod - async def on_event(self, event: BaseEvent) -> None: - """ - Handle the event - - This method is called asynchronously when a listened event is published. - Implement this method to handle specific business logic. - - Note: - - This method is asynchronous and can perform IO operations - - Multiple listeners execute concurrently without blocking each other - - It is recommended to catch exceptions within this method to avoid affecting other listeners - - Args: - event: The received event object - """ - pass - - def get_listener_name(self) -> str: - """ - Get the listener name - - Returns the class name by default. Subclasses can override this method to customize the name. - - Returns: - str: Listener name - """ - return self.__class__.__name__ - - def get_event_type_set(self) -> Set[Type[BaseEvent]]: - """ - Get the set of event types to listen for (for internal use) - - Returns a set of event types for fast lookup. - - Returns: - Set[Type[BaseEvent]]: Set of event types - """ - return set(self.get_event_types()) diff --git a/methods/EverCore/src/core/events/event_publisher.py b/methods/EverCore/src/core/events/event_publisher.py deleted file mode 100644 index 389141af..00000000 --- a/methods/EverCore/src/core/events/event_publisher.py +++ /dev/null @@ -1,267 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Application event publisher module - -Provides a global event publishing mechanism, supporting asynchronous concurrent dispatch of events to multiple listeners. -Automatically discovers and registers all EventListener implementations through the DI container. -""" - -import asyncio -from typing import Dict, List, Set, Type, Optional - -from core.di import service, get_beans_by_type -from core.events.base_event import BaseEvent -from core.events.event_listener import EventListener -from core.observation.logger import get_logger - - -logger = get_logger(__name__) - - -@service("application_event_publisher") -class ApplicationEventPublisher: - """ - Application event publisher - - Global singleton service responsible for dispatching events to corresponding listeners. - Automatically discovers all EventListener implementations through the DI container and builds a mapping from event types to listeners. - - Features: - - Lazy loading: builds the listener mapping only when the first event is published - - Asynchronous concurrency: uses asyncio.gather to concurrently invoke all matching listeners - - Error isolation: exceptions from individual listeners do not affect others - - Refreshable: supports dynamic refreshing of listener mappings - - Usage: - 1. Obtain instance via DI: - >>> from core.di import get_bean_by_type - >>> publisher = get_bean_by_type(ApplicationEventPublisher) - - 2. Publish event: - >>> await publisher.publish(UserCreatedEvent(user_id="123")) - - 3. Synchronous publishing (in non-async contexts): - >>> publisher.publish_sync(UserCreatedEvent(user_id="123")) - """ - - def __init__(self): - """Initialize event publisher""" - # Mapping from event type to list of listeners - self._event_listeners_map: Dict[Type[BaseEvent], List[EventListener]] = {} - # Whether initialized - self._initialized: bool = False - # All listener instances - self._listeners: List[EventListener] = [] - - def _ensure_initialized(self) -> None: - """ - Ensure listener mapping is initialized - - Lazy loading mechanism: obtains all listeners from DI container and builds mapping only on first call. - """ - if self._initialized: - return - - self._build_listener_mapping() - self._initialized = True - - def _build_listener_mapping(self) -> None: - """ - Build mapping from event types to listeners - - Retrieve all EventListener instances from the DI container, - then build a mapping table based on the event types declared by each listener. - """ - # Clear existing mapping - self._event_listeners_map.clear() - self._listeners.clear() - - # Get all EventListener implementations from DI container - try: - listeners = get_beans_by_type(EventListener) - except Exception as e: - logger.warning(f"Failed to get EventListener instances: {e}") - listeners = [] - - self._listeners = listeners - - # Build mapping from event type to listeners - for listener in listeners: - listener_name = listener.get_listener_name() - event_types = listener.get_event_types() - - logger.debug( - f"Registering listener [{listener_name}], listening to event types: {[et.__name__ for et in event_types]}" - ) - - for event_type in event_types: - if event_type not in self._event_listeners_map: - self._event_listeners_map[event_type] = [] - self._event_listeners_map[event_type].append(listener) - - # Log initialization completion - total_listeners = len(listeners) - total_event_types = len(self._event_listeners_map) - logger.info( - f"Event publisher initialization completed: {total_listeners} listeners, {total_event_types} event types" - ) - - def refresh(self) -> None: - """ - Refresh listener mapping - - Call this method to refresh the mapping after new listeners are registered into the DI container. - """ - self._initialized = False - self._ensure_initialized() - logger.info("Event publisher listener mapping has been refreshed") - - def get_listeners_for_event( - self, event_type: Type[BaseEvent] - ) -> List[EventListener]: - """ - Get all listeners for a specified event type - - Args: - event_type: Event type - - Returns: - List[EventListener]: List of all listeners for this event type - """ - self._ensure_initialized() - return self._event_listeners_map.get(event_type, []) - - def get_all_listeners(self) -> List[EventListener]: - """ - Get all registered listeners - - Returns: - List[EventListener]: List of all listeners - """ - self._ensure_initialized() - return self._listeners.copy() - - def get_registered_event_types(self) -> Set[Type[BaseEvent]]: - """ - Get all event types that have registered listeners - - Returns: - Set[Type[BaseEvent]]: Set of event types - """ - self._ensure_initialized() - return set(self._event_listeners_map.keys()) - - async def publish(self, event: BaseEvent) -> None: - """ - Asynchronously publish event - - Dispatch the event to all listeners that listen to this event type. - Uses asyncio.gather to concurrently invoke all listeners, improving efficiency for IO-intensive operations. - - Exceptions from individual listeners do not affect execution of others, - and all exceptions are logged. - - Args: - event: Event object to publish - """ - self._ensure_initialized() - - event_type = type(event) - event_type_name = event.event_type() - listeners = self._event_listeners_map.get(event_type, []) - - if not listeners: - logger.debug( - f"No listeners for event [{event_type_name}], skipping publish" - ) - return - - logger.debug( - f"Publishing event [{event_type_name}] (id={event.event_id}), {len(listeners)} listeners" - ) - - # Create coroutine tasks for all listeners - async def safe_invoke(listener: EventListener) -> Optional[Exception]: - """ - Safely invoke listener, catch exceptions to avoid affecting others - - Returns: - Exception object if occurred, otherwise None - """ - try: - await listener.on_event(event) - return None - except Exception as e: - listener_name = listener.get_listener_name() - logger.error( - f"Listener [{listener_name}] encountered exception when processing event [{event_type_name}]: {e}", - exc_info=True, - ) - return e - - # Concurrently execute all listeners - tasks = [safe_invoke(listener) for listener in listeners] - results = await asyncio.gather(*tasks) - - # Count execution results - errors = [r for r in results if r is not None] - if errors: - logger.warning( - f"Event [{event_type_name}] publishing completed, " - f"success: {len(listeners) - len(errors)}, failure: {len(errors)}" - ) - else: - logger.debug( - f"Event [{event_type_name}] publishing completed, all {len(listeners)} listeners executed successfully" - ) - - def publish_sync(self, event: BaseEvent) -> None: - """ - Synchronously publish event - - Use this method to publish events in non-async contexts. - Internally creates or uses an existing event loop to execute asynchronous publishing. - - Note: If already in an async context, prefer using the `publish()` method. - - Args: - event: Event object to publish - """ - try: - # Try to get the currently running event loop - loop = asyncio.get_running_loop() - # If in async context, create a task - loop.create_task(self.publish(event)) - except RuntimeError: - # No running event loop, create and run a new one - asyncio.run(self.publish(event)) - - async def publish_batch(self, events: List[BaseEvent]) -> None: - """ - Publish multiple events in batch - - Concurrently publish multiple events to improve efficiency of batch operations. - - Args: - events: List of events to publish - """ - if not events: - return - - logger.debug(f"Batch publishing {len(events)} events") - - # Concurrently publish all events - tasks = [self.publish(event) for event in events] - await asyncio.gather(*tasks) - - logger.debug(f"Batch publishing completed, total {len(events)} events") - - def __repr__(self) -> str: - """Return string representation of the object""" - self._ensure_initialized() - return ( - f"ApplicationEventPublisher(" - f"listeners={len(self._listeners)}, " - f"event_types={len(self._event_listeners_map)}" - f")" - ) diff --git a/methods/EverCore/src/core/interface/controller/base_controller.py b/methods/EverCore/src/core/interface/controller/base_controller.py deleted file mode 100644 index 3df551fb..00000000 --- a/methods/EverCore/src/core/interface/controller/base_controller.py +++ /dev/null @@ -1,620 +0,0 @@ -import inspect -from abc import ABC -from typing import Any, Callable, List, Optional, Union, get_origin, get_args - -from fastapi import APIRouter, FastAPI -from fastapi.openapi.utils import get_openapi - -# Import authorization-related modules has been moved inside methods for on-demand import - - -def _create_route_decorator(http_method: str) -> Callable: - """ - Internal helper function to create FastAPI route decorators (get, post, put, delete, etc.). - - Args: - http_method (str): HTTP method name (e.g., "GET", "POST"). - - Returns: - Callable: A decorator that accepts path and other parameters for APIRouter.add_api_route. - """ - - def decorator( - path: str, extra_models: Optional[List[Any]] = None, **kwargs: Any - ) -> Callable: - """ - response_class: Determines the **"transport method" and "underlying type"** of the response. - It controls which class FastAPI uses to package and send the HTTP response. - response_model: Determines the **"data structure" and "validation rules"** of the response body. - It is used for data filtering, format conversion, and automatically generating schemas in API documentation. - summary and responses: Used entirely for **"API documentation (OpenAPI)"**. - They do not affect any runtime behavior but enrich and refine the generated documentation (e.g., Swagger UI or ReDoc). - """ - - def wrapper(func: Callable) -> Callable: - # Use a special attribute to mark the function and store routing information - # This avoids a global registry, making each controller self-contained - setattr(func, "__route_info__", (path, [http_method], kwargs)) - # Store extra_models for later OpenAPI generation - setattr(func, "__extra_models__", extra_models or []) - return func - - return wrapper - - return decorator - - -get = _create_route_decorator("GET") -post = _create_route_decorator("POST") -put = _create_route_decorator("PUT") -delete = _create_route_decorator("DELETE") -patch = _create_route_decorator("PATCH") -head = _create_route_decorator("HEAD") -options = _create_route_decorator("OPTIONS") - - -class BaseController(ABC): - """ - Base controller class that supports automatic route registration via decorators. - - Inherit from this class and use @get, @post, etc., decorators to define your API endpoints. - During initialization, the controller automatically collects all decorated routes. - - Usage example: - ```python - # a_controller.py - from .base_controller import BaseController, get - - class UserController(BaseController): - def __init__(self): - super().__init__(prefix="/users", tags=["Users"]) - - @get("/") - def list_users(self): - return [{"id": 1, "name": "user1"}] - - # app.py - from fastapi import FastAPI - from .a_controller import UserController - - app = FastAPI() - controllers = [UserController()] # "Scanned" list of controllers - - for controller in controllers: - controller.register_to_app(app) - ``` - """ - - # Class-level security configuration provider; subclasses can override this attribute - _security_config_provider: Optional[Callable[[], List[dict]]] = None - - def __init__( - self, - prefix: str = "", - tags: Optional[List[str]] = None, - default_auth: str = "require_user", - **kwargs: Any, - ): - """ - Initialize the controller. - - Args: - prefix (str, optional): Common prefix for all routes under this controller. - tags (Optional[List[str]], optional): Tags used for grouping in OpenAPI documentation. - default_auth (str, optional): Default authorization strategy. Possible values: - - "require_user": Requires user authentication by default (default) - - "require_anonymous": Allows anonymous access by default - - "require_admin": Requires admin privileges by default - - "require_signature": Requires HMAC signature verification by default - - "none": Applies no default authorization - **kwargs: Additional arguments passed to FastAPI APIRouter. - """ - self.router = APIRouter(prefix=prefix, tags=tags, **kwargs) - self._app: Optional[FastAPI] = None - self._extra_models: List[Any] = [] - self._auth_routes: List[str] = ( - [] - ) # Store paths of routes requiring authentication - self._default_auth = default_auth # Store default authorization strategy - self._collect_routes() - - def _collect_routes(self): - """ - Traverse all class members to find and register methods marked by route decorators. - Apply the corresponding default authorization strategy based on the default_auth parameter. - """ - for _member_name, member in inspect.getmembers(self): - if callable(member) and hasattr(member, "__route_info__"): - path, methods, route_kwargs = getattr(member, "__route_info__") - - # Collect extra_models - extra_models = getattr(member, "__extra_models__", []) - self._extra_models.extend(extra_models) - - # Apply default authorization (if no authorization decorator is present) - authorized_member = self._apply_default_auth(member) - - # Check if authentication is required - if self._needs_authentication(authorized_member): - # Record the path of routes requiring authentication, removing type annotations from path parameters - full_path = ( - f"{self.router.prefix}{path}" if self.router.prefix else path - ) - # Remove type annotations from path parameters, e.g., {resource_id:int} -> {resource_id} - clean_path = self._clean_path_types(full_path) - self._auth_routes.append(clean_path) - - self.router.add_api_route( - path, endpoint=authorized_member, methods=methods, **route_kwargs - ) - - def _apply_default_auth(self, func: Callable) -> Callable: - """ - Apply default authorization strategy based on the default_auth parameter. - - Args: - func: The function to check. - - Returns: - Callable: The function with default authorization applied (if no authorization decorator exists). - """ - # Check if the function already has an authorization decorator - if hasattr(func, '__authorization_context__'): - return func - - # If it's a bound method, get the original function - if hasattr(func, '__func__'): - # This is a bound method; check if the original function already has an authorization decorator - if hasattr(func.__func__, '__authorization_context__'): - return func - - # Get the original function and apply the decorator - original_func = func.__func__ - decorated_func = self._get_auth_decorator()(original_func) - # Rebind to the instance - return decorated_func.__get__(func.__self__, type(func.__self__)) - else: - # This is an unbound function; apply the decorator directly - return self._get_auth_decorator()(func) - - def _get_auth_decorator(self): - """Get the corresponding authorization decorator""" - if self._default_auth == "require_user": - from core.authorize.decorators import require_user - - return require_user - elif self._default_auth == "require_anonymous": - from core.authorize.decorators import require_anonymous - - return require_anonymous - elif self._default_auth == "require_admin": - from core.authorize.decorators import require_admin - - return require_admin - elif self._default_auth == "require_signature": - from core.authorize.decorators import require_signature - - return require_signature - elif self._default_auth == "none": - # Apply no default authorization; return an identity decorator - return lambda x: x - else: - # Unknown authorization strategy; default to require_user - from core.authorize.decorators import require_user - - return require_user - - def _needs_authentication(self, func: Callable) -> bool: - """ - Check if the function requires authentication. - - Args: - func: The function to check. - - Returns: - bool: Whether authentication is required. - """ - # Check if the function has authorization context directly - if hasattr(func, '__authorization_context__'): - auth_context = func.__authorization_context__ - return auth_context.need_auth() - - return False - - def _clean_path_types(self, path: str) -> str: - """ - Clean type annotations from path parameters. - - Convert {resource_id:int} to {resource_id} - Convert {user_id:str} to {user_id} - - Args: - path: Path containing type annotations. - - Returns: - str: Cleaned path. - """ - import re - - # Use regular expression to match {parameter:type} format and replace with {parameter} - return re.sub(r'\{([^}:]+):[^}]+\}', r'{\1}', path) - - def _get_security_config(self) -> List[dict]: - """ - Get security configuration. - - Returns: - List[dict]: List of security configurations. - """ - # Prioritize class-level security configuration provider - if self._security_config_provider is not None: - return self._security_config_provider() - - # Try to get from global configuration - try: - from capabilities.auth.supabase_auth.supabase_auth_openapi import ( - get_security_config, - ) - - return get_security_config() - except ImportError: - return [{"OAuth2PasswordBearer": []}] - - def _is_union_type(self, model: Any) -> bool: - """Check if it is a Union type""" - return get_origin(model) is Union - - def _get_union_args(self, union_type: Any) -> tuple: - """Get arguments of Union type""" - return get_args(union_type) - - def _get_model_name(self, model: Any) -> str: - """Get model name""" - if hasattr(model, '__name__'): - return model.__name__ - elif hasattr(model, '_name'): - return model._name - else: - return str(model) - - def _generate_union_schema(self, union_type: Any, union_name: str) -> dict: - """ - Generate oneOf schema structure for Union type. - - Args: - union_type: Union type. - union_name: Name of the Union type. - - Returns: - Schema definition containing oneOf and discriminator. - """ - union_args = self._get_union_args(union_type) - - # Generate oneOf array - one_of = [] - discriminator_mapping = {} - - for arg in union_args: - if hasattr(arg, '__name__'): - model_name = arg.__name__ - one_of.append({"$ref": f"#/components/schemas/{model_name}"}) - - # Try to get discriminator field value - if hasattr(arg, 'model_fields') and 'type' in arg.model_fields: - # Get literal or enum value of the type field - type_field = arg.model_fields['type'] - if ( - hasattr(type_field, 'default') - and type_field.default is not None - ): - discriminator_mapping[type_field.default] = ( - f"#/components/schemas/{model_name}" - ) - elif hasattr(type_field.annotation, '__args__'): - # Handle Literal type - literal_values = getattr(type_field.annotation, '__args__', ()) - if literal_values: - discriminator_mapping[literal_values[0]] = ( - f"#/components/schemas/{model_name}" - ) - - schema = {"oneOf": one_of} - - # Only add discriminator if there is a mapping - if discriminator_mapping: - schema["discriminator"] = { - "propertyName": "type", - "mapping": discriminator_mapping, - } - - return schema - - def _custom_openapi_generator(self, app: FastAPI): - """ - Custom OpenAPI generator to handle extra_models and authenticated routes. - """ - - def custom_openapi(): - if app.openapi_schema: - return app.openapi_schema - - # Generate basic OpenAPI schema - openapi_schema = get_openapi( - title=app.title, - version=app.version, - summary=getattr(app, 'summary', None), - description=app.description, - routes=app.routes, - ) - - # Ensure components exist - if "components" not in openapi_schema: - openapi_schema["components"] = {} - if "schemas" not in openapi_schema["components"]: - openapi_schema["components"]["schemas"] = {} - if "securitySchemes" not in openapi_schema["components"]: - openapi_schema["components"]["securitySchemes"] = {} - - # Collect extra_models and authenticated routes from all BaseController instances - controllers = [] - - # Traverse all routes to find BaseController instances - def collect_controllers_from_routes(routes): - for route in routes: - if hasattr(route, 'router') and hasattr(route.router, 'routes'): - # This is an include_router case; process recursively - collect_controllers_from_routes(route.router.routes) - elif hasattr(route, 'endpoint') and hasattr( - route.endpoint, '__self__' - ): - # This is a bound method; check if it's a BaseController instance - controller = route.endpoint.__self__ - if ( - isinstance(controller, BaseController) - and controller not in controllers - ): - controllers.append(controller) - - collect_controllers_from_routes(app.routes) - - # Process extra_models for all controllers - for controller in controllers: - self._process_controller_extra_models(controller, openapi_schema) - - # Add security schemes definition to OpenAPI schema - self._add_security_schemes_to_openapi(controllers, openapi_schema) - - # Add security configuration to routes requiring authentication - self._add_security_to_auth_routes(controllers, openapi_schema) - - app.openapi_schema = openapi_schema - return app.openapi_schema - - return custom_openapi - - def _add_security_schemes_to_openapi( - self, controllers: List['BaseController'], openapi_schema: dict - ): - """ - Add security schemes definition to OpenAPI schema. - - Args: - controllers: List of all BaseController instances. - openapi_schema: OpenAPI schema dictionary. - """ - # Collect security schemes used by all controllers - security_schemes = {} - - for controller in controllers: - # Check if the controller has a custom security configuration provider - if ( - hasattr(controller, '_security_config_provider') - and controller._security_config_provider is not None - ): - try: - # Try to get security schemes definition (if supported by controller) - if hasattr(controller, '_get_security_schemes'): - schemes = controller._get_security_schemes() - if schemes: - security_schemes.update(schemes) - else: - # Check if it's HMAC signature authentication - security_config = controller._security_config_provider() - if security_config and any( - "HMACSignature" in config for config in security_config - ): - # Import HMAC security schemes definition - try: - from core.middleware.hmac_signature_middleware import ( - get_hmac_openapi_security_schemes, - ) - - hmac_schemes = get_hmac_openapi_security_schemes() - security_schemes.update(hmac_schemes) - except ImportError: - # If import fails, use default HMAC definition - security_schemes["HMACSignature"] = { - "type": "apiKey", - "in": "header", - "name": "X-Signature", - "description": "HMAC signature authentication", - } - except Exception as e: - # If getting security schemes fails, log error but don't affect other functionality - import logging - - logger = logging.getLogger(__name__) - logger.warning( - f"Failed to get security schemes definition for controller {controller.__class__.__name__}: {str(e)}" - ) - - # Add security schemes definition to OpenAPI schema - if security_schemes: - openapi_schema["components"]["securitySchemes"].update(security_schemes) - - def _add_security_to_auth_routes( - self, controllers: List['BaseController'], openapi_schema: dict - ): - """ - Add security configuration to routes requiring authentication. - - Args: - controllers: List of all BaseController instances. - openapi_schema: OpenAPI schema dictionary. - """ - # Collect all paths requiring authentication - all_auth_routes = [] - for controller in controllers: - if hasattr(controller, '_auth_routes'): - all_auth_routes.extend(controller._auth_routes) - - # Get security configuration - security_config = self._get_security_config() - - # Add security configuration to routes requiring authentication - if "paths" in openapi_schema: - for path, path_item in openapi_schema["paths"].items(): - # Check if current path requires authentication - if path in all_auth_routes: - # Add security configuration for all HTTP methods - for method in [ - "get", - "post", - "put", - "delete", - "patch", - "head", - "options", - ]: - if method in path_item: - path_item[method]["security"] = security_config - - def _process_controller_extra_models(self, controller, openapi_schema): - """ - Process extra_models for a single controller. - """ - if not hasattr(controller, '_extra_models'): - return - - for model in controller._extra_models: - if self._is_union_type(model): - # For Union types, we need to find their original names - # Look up the variable name of this Union type in the controller's module - model_name = None - if hasattr(controller, '__class__') and hasattr( - controller.__class__, '__module__' - ): - import sys - - module = sys.modules.get(controller.__class__.__module__) - if module: - for attr_name in dir(module): - attr_value = getattr(module, attr_name) - if attr_value is model: - model_name = attr_name - break - - # If still not found, use default name - if not model_name: - model_name = "Union" - - # Process Union type - union_schema = self._generate_union_schema(model, model_name) - openapi_schema["components"]["schemas"][model_name] = union_schema - - # Also add schemas for Union members - union_args = self._get_union_args(model) - for arg in union_args: - if hasattr(arg, 'model_json_schema'): - arg_name = self._get_model_name(arg) - if arg_name not in openapi_schema["components"]["schemas"]: - # Generate schema for individual model - arg_schema = arg.model_json_schema( - ref_template="#/components/schemas/{model}" - ) - # Extract schemas from $defs - if '$defs' in arg_schema: - openapi_schema["components"]["schemas"].update( - arg_schema['$defs'] - ) - del arg_schema['$defs'] - # Add main model schema - openapi_schema["components"]["schemas"][ - arg_name - ] = arg_schema - else: - # Process regular model - model_name = self._get_model_name(model) - if hasattr(model, 'model_json_schema'): - if model_name not in openapi_schema["components"]["schemas"]: - model_schema = model.model_json_schema( - ref_template="#/components/schemas/{model}" - ) - # Extract schemas from $defs - if '$defs' in model_schema: - openapi_schema["components"]["schemas"].update( - model_schema['$defs'] - ) - del model_schema['$defs'] - # Add main model schema - openapi_schema["components"]["schemas"][ - model_name - ] = model_schema - - def register_to_app(self, app: FastAPI): - """ - Register this controller's routes to the FastAPI application instance. - - Args: - app (FastAPI): The main FastAPI application instance. - """ - self._app = app - app.include_router(self.router) - - # Reset custom OpenAPI generator each time a controller is registered - # This ensures all controllers' extra_models are properly handled - app.openapi = self._custom_openapi_generator(app) - # Clear cached schema to force regeneration - app.openapi_schema = None - - -# Usage examples: -# -# 1. Using default FastAPI Users authentication configuration: -# class UserController(BaseController): -# def __init__(self): -# super().__init__(prefix="/users", tags=["Users"]) -# -# @get("/") -# @require_user # Requires authentication -# def list_users(self): -# return [{"id": 1, "name": "user1"}] -# -# 2. Custom security configuration provider: -# class CustomAuthController(BaseController): -# # Custom security configuration provider -# _security_config_provider = lambda: [ -# { -# "CustomAuth": [] -# } -# ] -# -# def __init__(self): -# super().__init__(prefix="/custom", tags=["Custom"]) -# -# @get("/") -# @require_user -# def custom_endpoint(self): -# return {"message": "Custom authenticated endpoint"} -# -# 3. Dynamic security configuration: -# class DynamicAuthController(BaseController): -# def __init__(self, auth_type: str = "OAuth2PasswordBearer"): -# super().__init__(prefix="/dynamic", tags=["Dynamic"]) -# self.auth_type = auth_type -# # Dynamically set security configuration provider -# self._security_config_provider = lambda: [ -# { -# self.auth_type: [] -# } -# ] diff --git a/methods/EverCore/src/core/interface/controller/debug/debug_controller.py b/methods/EverCore/src/core/interface/controller/debug/debug_controller.py deleted file mode 100644 index 4337a974..00000000 --- a/methods/EverCore/src/core/interface/controller/debug/debug_controller.py +++ /dev/null @@ -1,1091 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Debug Controller - -Provides debugging interfaces for Beans in the DI container, supports calling specific methods of specific services. -Only enabled in development environment, automatically disabled in production environment. -""" - -import asyncio -import inspect -import os -import json -import traceback -from typing import Any, Dict, List, Optional, Type -from pydantic import BaseModel, Field -from fastapi import HTTPException - -from core.interface.controller.base_controller import BaseController, post, get -from core.di import get_container, get_bean, get_bean_by_type -from core.di.decorators import controller -from core.observation.logger import get_logger - -from core.constants.errors import ErrorMessage - -logger = get_logger(__name__) - - -class BeanCallRequest(BaseModel): - """Bean method call request model (compatible with code execution)""" - - # Bean identifier (choose one) - bean_name: Optional[str] = Field(None, description="Bean name") - bean_type: Optional[str] = Field(None, description="Bean type name") - - # Method call - method: str = Field(..., description="Method name to be called") - - # Traditional parameter method - args: List[Any] = Field( - default_factory=list, description="List of positional arguments" - ) - kwargs: Dict[str, Any] = Field( - default_factory=dict, description="Dictionary of keyword arguments" - ) - - # Code execution method (optional) - code: Optional[str] = Field( - None, - description="Python code used to generate args and kwargs parameters (optional)", - ) - - @classmethod - def model_validate(cls, obj, *, strict=None, from_attributes=None, context=None): - """Custom validation logic""" - if ( - isinstance(obj, dict) - and not obj.get("bean_name") - and not obj.get("bean_type") - ): - logger.error( - "Bean call request validation failed: missing bean_name or bean_type parameter" - ) - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - return super().model_validate( - obj, strict=strict, from_attributes=from_attributes, context=context - ) - - -class BeanCallWithCodeRequest(BaseModel): - """Bean method call request model using code to generate parameters""" - - # Bean identifier (choose one) - bean_name: Optional[str] = Field(None, description="Bean name") - bean_type: Optional[str] = Field(None, description="Bean type name") - - # Method call - method: str = Field(..., description="Method name to be called") - - # Python code to generate parameters - code: str = Field( - ..., description="Python code used to generate args and kwargs parameters" - ) - - @classmethod - def model_validate(cls, obj, *, strict=None, from_attributes=None, context=None): - """Custom validation logic""" - if ( - isinstance(obj, dict) - and not obj.get("bean_name") - and not obj.get("bean_type") - ): - logger.error( - "Bean code call request validation failed: missing bean_name or bean_type parameter" - ) - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - return super().model_validate( - obj, strict=strict, from_attributes=from_attributes, context=context - ) - - -class BeanCallResponse(BaseModel): - """Bean method call response model""" - - success: bool = Field(..., description="Whether the call was successful") - result: Optional[Any] = Field( - None, description="Method return value (JSON serializable)" - ) - result_str: Optional[str] = Field( - None, - description="String representation of the return value (used when not JSON serializable)", - ) - error: Optional[str] = Field(None, description="Error message") - traceback: Optional[str] = Field(None, description="Detailed error stack trace") - bean_info: Optional[Dict[str, Any]] = Field( - None, description="Information about the called Bean" - ) - code_execution: Optional[Dict[str, Any]] = Field( - None, - description="Code execution information (only when using code to generate parameters)", - ) - - -class BeanInfoResponse(BaseModel): - """Bean information response model""" - - name: str = Field(..., description="Bean name") - type_name: str = Field(..., description="Bean type name") - scope: str = Field(..., description="Bean scope") - is_primary: bool = Field(..., description="Whether it is a Primary Bean") - is_mock: bool = Field(..., description="Whether it is a Mock Bean") - methods: List[str] = Field( - default_factory=list, description="List of callable methods" - ) - - -@controller(name="debug_controller") -class DebugController(BaseController): - """ - DI Container Debug API Controller - - Provides debugging and testing capabilities for the dependency injection container, supporting: - - Viewing information of all registered Beans - - Calling any method of any Bean for testing - - Getting detailed configuration and method list of Beans - - Monitoring the runtime status of the DI container - - **Security Mechanism**: - - Only enabled in development environment (ENV=DEV) - - All debugging interfaces are automatically disabled in production - - No user authentication required, but access controlled by environment variables - - **Main Features**: - 1. **Bean Query**: Supports searching Beans by name or type - 2. **Method Call**: Supports passing parameters to call Bean methods - 3. **Status Monitoring**: View container mock mode, number of Beans, etc. - 4. **Error Diagnosis**: Provides detailed call error information and stack trace - """ - - def __init__(self): - super().__init__( - prefix="/asdf/debug/di", - tags=["Debug"], - default_auth="none", # Debug interface does not require authentication, but access is controlled by environment variables - ) - self.container = get_container() - - def _check_debug_enabled(self) -> bool: - """Check if debugging is enabled""" - return os.environ.get('ENV', 'prod').upper() == 'DEV' - - def _ensure_debug_enabled(self): - """Ensure debugging is enabled, otherwise raise 404 error""" - if not self._check_debug_enabled(): - logger.error("Debugging is not enabled, access to debug interface denied") - raise HTTPException( - status_code=404, detail=ErrorMessage.PERMISSION_DENIED.value - ) - - def _get_bean_methods(self, bean_instance: Any) -> List[str]: - """Get list of callable methods of a Bean instance""" - methods = [] - for attr_name in dir(bean_instance): - if not attr_name.startswith('_'): # Exclude private methods - attr = getattr(bean_instance, attr_name) - if callable(attr): - methods.append(attr_name) - return sorted(methods) - - def _get_bean_by_identifier( - self, bean_name: Optional[str], bean_type: Optional[str] - ) -> tuple[Any, Dict[str, Any]]: - """ - Get Bean instance and information by identifier - - Args: - bean_name: Bean name - bean_type: Bean type name - - Returns: - tuple: (bean_instance, bean_info) - - Raises: - HTTPException: When Bean is not found or parameters are invalid - """ - if bean_name and bean_type: - logger.error( - "Bean identifier parameter error: cannot provide both bean_name and bean_type" - ) - raise HTTPException( - status_code=400, detail=ErrorMessage.INVALID_PARAMETER.value - ) - - if not bean_name and not bean_type: - logger.error( - "Bean identifier parameter error: must provide either bean_name or bean_type" - ) - raise HTTPException( - status_code=400, detail=ErrorMessage.INVALID_PARAMETER.value - ) - - try: - if bean_name: - # Get Bean by name - bean_instance = get_bean(bean_name) - bean_info = { - "name": bean_name, - "type_name": type(bean_instance).__name__, - "lookup_method": "by_name", - } - else: - # Get Bean by type name - # First need to find the corresponding type - bean_class = self._find_bean_type_by_name(bean_type) - if not bean_class: - logger.error(f"Bean class with type '{bean_type}' not found") - raise HTTPException( - status_code=404, detail=ErrorMessage.BEAN_NOT_FOUND.value - ) - - bean_instance = get_bean_by_type(bean_class) - bean_info = { - "name": getattr(bean_instance, '_di_name', bean_type.lower()), - "type_name": bean_type, - "lookup_method": "by_type", - } - - return bean_instance, bean_info - - except Exception as e: - if "not found" in str(e).lower(): - identifier = bean_name or bean_type - method = "name" if bean_name else "type" - logger.error(f"Bean not found by {method} '{identifier}': {str(e)}") - raise HTTPException( - status_code=404, detail=ErrorMessage.BEAN_NOT_FOUND.value - ) from e - else: - logger.error(f"Error occurred while getting Bean: {str(e)}") - raise HTTPException( - status_code=500, detail=ErrorMessage.BEAN_OPERATION_FAILED.value - ) from e - - def _find_bean_type_by_name(self, type_name: str) -> Optional[Type]: - """ - Find the corresponding Bean type by type name - - Uses a more reliable approach: get all Beans and check their types, - avoiding reliance on potentially inaccurate list_all_beans_info - - Args: - type_name: Type name - - Returns: - Corresponding type, or None if not found - """ - try: - # Method 1: Use get_beans() to get all Bean instances, check type names - all_beans_dict = self.container.get_beans() - - for _, bean_instance in all_beans_dict.items(): - if bean_instance is not None: - bean_type = type(bean_instance) - if bean_type.__name__ == type_name: - return bean_type - - return None - - except Exception: - # If get_beans() fails, use fallback method - # Try to infer using common type name patterns - try: - # First try to get all Bean info as fallback - all_beans = self.container.list_all_beans_info() - - for bean_info in all_beans: - if bean_info['type_name'] == type_name: - # Get Bean instance by name, then get its type - try: - bean_instance = get_bean(bean_info['name']) - return type(bean_instance) - except Exception: - continue - - return None - - except Exception: - # If all methods fail, return None - return None - - def _serialize_result(self, result: Any) -> Dict[str, Any]: - """ - Serialize method call result - - Args: - result: Method return value - - Returns: - Dictionary containing serialized result - """ - try: - # Try JSON serialization - json.dumps(result) - return {"result": result} - except (TypeError, ValueError): - # If JSON serialization fails, return string representation - return {"result_str": repr(result)} - - def _execute_parameter_code(self, code: str) -> Dict[str, Any]: - """ - Safely execute Python code to generate parameters - - Args: - code: Python code string - - Returns: - Dictionary containing args and kwargs - - Raises: - ValueError: When code execution fails or return format is incorrect - """ - try: - # Create safe execution environment, allow free imports - safe_globals = { - '__builtins__': { - # Basic types and functions - 'len': len, - 'str': str, - 'int': int, - 'float': float, - 'bool': bool, - 'list': list, - 'dict': dict, - 'tuple': tuple, - 'set': set, - 'range': range, - 'enumerate': enumerate, - 'zip': zip, - 'print': print, - 'isinstance': isinstance, - 'hasattr': hasattr, - 'getattr': getattr, - 'setattr': setattr, - 'type': type, - 'abs': abs, - 'min': min, - 'max': max, - 'sum': sum, - 'sorted': sorted, - 'reversed': reversed, - 'any': any, - 'all': all, - 'map': map, - 'filter': filter, - # Allow import - '__import__': __import__, - }, - # Pre-import commonly used modules and types - 'datetime': None, - 'json': None, - 'uuid': None, - 'typing': None, - } - - # Pre-import commonly used modules - try: - import datetime - import json - import uuid - import typing - - safe_globals['datetime'] = datetime - safe_globals['json'] = json - safe_globals['uuid'] = uuid - safe_globals['typing'] = typing - except ImportError as e: - logger.warning(f"Failed to pre-import module: {e}") - - # No longer pre-import internal project modules, support completely free imports - - local_vars = {} - - # Execute code - exec(code, safe_globals, local_vars) - - # Check if args and kwargs are defined - if 'args' not in local_vars and 'kwargs' not in local_vars: - logger.error( - "Invalid code execution result: args or kwargs variable not defined" - ) - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - args = local_vars.get('args', []) - kwargs = local_vars.get('kwargs', {}) - - # Validate types - if not isinstance(args, (list, tuple)): - logger.error( - f"Wrong args parameter type: expected list or tuple, got {type(args).__name__}" - ) - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - if not isinstance(kwargs, dict): - logger.error( - f"Wrong kwargs parameter type: expected dict, got {type(kwargs).__name__}" - ) - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - return {'args': list(args), 'kwargs': kwargs} - - except Exception as e: - logger.error(f"Failed to execute parameter generation code: {e}") - logger.error(f"Code execution exception details: {str(e)}") - raise ValueError(ErrorMessage.INVALID_PARAMETER.value) - - @get( - "/status", - response_model=Dict[str, Any], - summary="Get debugging function status", - responses={ - 200: { - "description": "Debugging status information retrieved successfully", - "content": { - "application/json": { - "example": { - "debug_enabled": True, - "container_info": {"mock_mode": False, "total_beans": 15}, - } - } - }, - } - }, - ) - def get_debug_status(self) -> Dict[str, Any]: - """ - Get debugging function status information - - Returns whether debugging is enabled and basic information about the DI container: - - debug_enabled: Whether debugging is enabled (based on ENV environment variable) - - container_info: DI container information, including mock mode status and total number of Beans - - **Note**: - - debug_enabled is false when ENV != 'DEV' - - This interface is not controlled by the debug switch and is always accessible - - Returns: - Dict[str, Any]: Dictionary containing debugging status and container information - """ - return { - "debug_enabled": self._check_debug_enabled(), - "container_info": { - "mock_mode": self.container.is_mock_mode(), - "total_beans": len(self.container.list_all_beans_info()), - }, - } - - @get( - "/beans", - extra_models=[BeanInfoResponse], - response_model=List[BeanInfoResponse], - summary="List all registered Bean information", - responses={ - 200: { - "description": "Bean list retrieved successfully", - "content": { - "application/json": { - "example": [ - { - "name": "user_service", - "type_name": "UserService", - "scope": "singleton", - "is_primary": True, - "is_mock": False, - "methods": [ - "get_user", - "create_user", - "update_user", - "delete_user", - ], - }, - { - "name": "email_service", - "type_name": "EmailService", - "scope": "singleton", - "is_primary": False, - "is_mock": False, - "methods": ["send_email", "validate_email"], - }, - ] - } - }, - }, - 404: {"description": "Debugging not enabled or Bean not found"}, - 500: {"description": "Internal error occurred while retrieving Bean list"}, - }, - ) - def list_all_beans(self) -> List[BeanInfoResponse]: - """ - List all registered Bean information - - Returns a detailed list of all registered Beans in the DI container, including: - - name: Bean name - - type_name: Bean type name - - scope: Bean scope (singleton/prototype/factory) - - is_primary: Whether it is a Primary Bean - - is_mock: Whether it is a Mock Bean - - methods: List of callable public methods - - **Note**: - - Only available when debugging mode is enabled (ENV=DEV) - - Method list only includes public methods not starting with underscore - - If getting method list for a Bean fails, the Bean is still returned but methods is empty - - Returns: - List[BeanInfoResponse]: List of Bean information - - Raises: - HTTPException: When debugging is not enabled or retrieving Bean list fails - """ - self._ensure_debug_enabled() - - try: - beans_info = [] - all_beans = self.container.list_all_beans_info() - - for bean_info in all_beans: - try: - # Get Bean instance to retrieve method list - bean_instance = get_bean(bean_info['name']) - methods = self._get_bean_methods(bean_instance) - - beans_info.append( - BeanInfoResponse( - name=bean_info['name'], - type_name=bean_info['type_name'], - scope=bean_info['scope'], - is_primary=bean_info['is_primary'], - is_mock=bean_info['is_mock'], - methods=methods, - ) - ) - except Exception as e: - logger.warning( - "Failed to get method list for Bean '%s': %s", - bean_info['name'], - str(e), - ) - # Even if getting method list fails, return basic information - beans_info.append( - BeanInfoResponse( - name=bean_info['name'], - type_name=bean_info['type_name'], - scope=bean_info['scope'], - is_primary=bean_info['is_primary'], - is_mock=bean_info['is_mock'], - methods=[], - ) - ) - - return beans_info - - except Exception as e: - logger.error("Error occurred while listing all Beans: %s", str(e)) - logger.error(f"Exception details when retrieving Bean list: {str(e)}") - raise HTTPException( - status_code=500, detail=ErrorMessage.BEAN_OPERATION_FAILED.value - ) from e - - @post( - "/call", - extra_models=[BeanCallRequest, BeanCallResponse], - response_model=BeanCallResponse, - summary="Call specified method of specified Bean", - responses={ - 200: { - "description": "Bean method call succeeded", - "content": { - "application/json": { - "examples": { - "traditional_way": { - "summary": "Traditional way call succeeded", - "value": { - "success": True, - "result": ["uuid1", "uuid2", "uuid3"], - "bean_info": { - "name": "resource_repository", - "type_name": "SQLModelResourceRepositoryImpl", - "lookup_method": "by_name", - }, - }, - }, - "code_execution_way": { - "summary": "Code execution way call succeeded", - "value": { - "success": True, - "result": [ - "d7a8782f-d35f-48fb-81fb-ce2fa3c01cdf", - "a1b2c3d4-e5f6-7890-abcd-ef1234567890", - ], - "bean_info": { - "name": "resource_repository", - "type_name": "SQLModelResourceRepositoryImpl", - "lookup_method": "by_name", - }, - "code_execution": { - "generated_args": [], - "generated_kwargs": { - "resource_ids": [274, 281, 282], - "resource_type": "LITERATURE", - "user_id": 1, - }, - }, - }, - }, - "failure": { - "summary": "Call failed, includes error information", - "value": { - "success": False, - "error": "Bean not found", - "traceback": "Traceback (most recent call last):\n File ...", - "bean_info": None, - }, - }, - } - } - }, - }, - 400: { - "description": "Request parameter error, such as missing required parameters, invalid Bean identifier, etc." - }, - 404: { - "description": "Debugging not enabled, Bean does not exist, or method does not exist" - }, - 500: {"description": "Internal error occurred during method call"}, - }, - ) - async def call_bean_method(self, request: BeanCallRequest) -> BeanCallResponse: - """ - Call specified method of specified Bean (compatible with code execution) - - Supports two parameter passing methods: - 1. **Traditional method**: Directly use `args` and `kwargs` parameters - 2. **Code execution**: Use `code` parameter to dynamically generate parameters (supports enum types and complex objects) - - **Bean identification methods**: - - bean_name/bean_type: Choose one, used to identify the Bean to be called - - **Parameter passing methods**: - - Traditional method: Use `args` and `kwargs` fields - - Code execution: Use `code` field to write Python code to generate parameters - - **Code execution example**: - ```json - { - "bean_name": "resource_repository", - "method": "get_uuids_by_ids_and_type", - "code": "from domain.models.enums import ResourceType\\n\\nargs = []\\nkwargs = {\\n 'resource_ids': [274, 281, 282],\\n 'resource_type': ResourceType.LITERATURE,\\n 'user_id': 1\\n}" - } - ``` - - **Traditional method example**: - ```json - { - "bean_name": "resource_repository", - "method": "get_by_ids", - "args": [], - "kwargs": { - "resource_ids": [274, 281, 282] - } - } - ``` - - Args: - request: Bean method call request - - Returns: - BeanCallResponse: Method call result - - Raises: - HTTPException: When debugging is not enabled, parameters are invalid, Bean does not exist, or method call fails - """ - self._ensure_debug_enabled() - - try: - # Determine which parameter method to use - if request.code: - # Use code execution method - logger.info("Using code execution method to generate parameters") - code_result = self._execute_parameter_code(request.code) - args = code_result['args'] - kwargs = code_result['kwargs'] - code_execution_info = { - 'generated_args': args, - 'generated_kwargs': kwargs, - } - else: - # Use traditional method - logger.info("Using traditional parameter method") - args = request.args - kwargs = request.kwargs - code_execution_info = None - - # Get Bean instance and information - bean_instance, bean_info = self._get_bean_by_identifier( - request.bean_name, request.bean_type - ) - - # Check if method exists - if not hasattr(bean_instance, request.method): - logger.error( - f"Method '{request.method}' does not exist in Bean '{bean_info['name']}'" - ) - raise HTTPException( - status_code=404, detail=ErrorMessage.BEAN_OPERATION_FAILED.value - ) - - method_to_call = getattr(bean_instance, request.method) - - # Check if it is a callable object - if not callable(method_to_call): - logger.error( - f"Attribute '{request.method}' of Bean '{bean_info['name']}' is not callable" - ) - raise HTTPException( - status_code=400, detail=ErrorMessage.INVALID_PARAMETER.value - ) - - # Call method - logger.info( - "Calling Bean method: %s.%s(args=%s, kwargs=%s)", - bean_info['name'], - request.method, - args, - kwargs, - ) - - # Check if it is a coroutine function, compatible with async and sync methods - if asyncio.iscoroutinefunction( - method_to_call - ) or inspect.iscoroutinefunction(method_to_call): - result = await method_to_call(*args, **kwargs) - else: - result = method_to_call(*args, **kwargs) - - # Serialize result - serialized_result = self._serialize_result(result) - - # Construct response - response_data = { - 'success': True, - 'bean_info': bean_info, - **serialized_result, - } - - # If code execution was used, add execution information - if code_execution_info: - response_data['code_execution'] = code_execution_info - - return BeanCallResponse(**response_data) - - except HTTPException: - # Re-raise HTTP exceptions - raise - except Exception as e: - # Catch and handle other exceptions - error_msg = str(e) - error_traceback = traceback.format_exc() - - logger.error("Error occurred while calling Bean method: %s", error_msg) - logger.debug("Error stack: %s", error_traceback) - - return BeanCallResponse( - success=False, - error=error_msg, - traceback=error_traceback, - bean_info=getattr(locals(), 'bean_info', None), - code_execution=getattr(locals(), 'code_execution_info', None), - ) - - @get( - "/beans/{bean_name}", - extra_models=[BeanInfoResponse], - response_model=BeanInfoResponse, - summary="Get detailed information by Bean name", - responses={ - 200: { - "description": "Bean information retrieved successfully", - "content": { - "application/json": { - "example": { - "name": "user_service", - "type_name": "UserService", - "scope": "singleton", - "is_primary": True, - "is_mock": False, - "methods": [ - "get_user", - "create_user", - "update_user", - "delete_user", - "list_users", - "validate_user", - ], - } - } - }, - }, - 404: { - "description": "Debugging not enabled or specified Bean does not exist" - }, - 500: { - "description": "Internal error occurred while retrieving Bean information" - }, - }, - ) - def get_bean_info(self, bean_name: str) -> BeanInfoResponse: - """ - Get detailed information by Bean name - - Query complete information of a specific Bean by its name, including type, scope, - whether it is a Primary Bean, whether it is a Mock Bean, and a list of all callable public methods. - - **Returned information includes**: - - name: Bean name - - type_name: Bean type name - - scope: Bean scope (singleton/prototype/factory) - - is_primary: Whether it is a Primary Bean (preferred Bean when multiple Beans of the same type exist) - - is_mock: Whether it is a Mock Bean (mock implementation used in test environments) - - methods: List of callable public methods (excluding private methods starting with underscore) - - **Use cases**: - - View detailed configuration information of a specific Bean - - Understand all callable methods provided by a Bean - - Debug registration status of Beans in the DI container - - **Notes**: - - Only available when debugging mode is enabled (ENV=DEV) - - Bean name must match exactly, case-sensitive - - Method list is sorted alphabetically - - Args: - bean_name: Bean registration name, must exactly match the name in the DI container - - Returns: - BeanInfoResponse: Detailed information of the Bean, including metadata and method list - - Raises: - HTTPException: When debugging is not enabled, Bean does not exist, or retrieving information fails - """ - self._ensure_debug_enabled() - - try: - bean_instance, _ = self._get_bean_by_identifier(bean_name, None) - methods = self._get_bean_methods(bean_instance) - - # Get Bean metadata from container - all_beans = self.container.list_all_beans_info() - bean_meta = next((b for b in all_beans if b['name'] == bean_name), None) - - if not bean_meta: - raise HTTPException( - status_code=404, detail=ErrorMessage.BEAN_NOT_FOUND.value - ) - - return BeanInfoResponse( - name=bean_meta['name'], - type_name=bean_meta['type_name'], - scope=bean_meta['scope'], - is_primary=bean_meta['is_primary'], - is_mock=bean_meta['is_mock'], - methods=methods, - ) - - except HTTPException: - raise - except Exception as e: - logger.error("Error occurred while retrieving Bean information: %s", str(e)) - raise HTTPException( - status_code=500, detail=ErrorMessage.BEAN_OPERATION_FAILED.value - ) from e - - @post( - "/call-with-code", - extra_models=[BeanCallWithCodeRequest, BeanCallResponse], - response_model=BeanCallResponse, - summary="Call Bean method by generating parameters with Python code", - responses={ - 200: { - "description": "Bean method call succeeded", - "content": { - "application/json": { - "examples": { - "success_with_enum": { - "summary": "Call succeeded with enum parameter", - "value": { - "success": True, - "result": ["uuid1", "uuid2", "uuid3"], - "bean_info": { - "name": "resource_repository", - "type_name": "SQLModelResourceRepositoryImpl", - "lookup_method": "by_name", - }, - "code_execution": { - "generated_args": [], - "generated_kwargs": { - "resource_ids": [274, 281, 282], - "resource_type": "LITERATURE", - "user_id": 1, - }, - }, - }, - } - } - } - }, - }, - 400: {"description": "Request parameter error or code execution failed"}, - 404: { - "description": "Debugging not enabled, Bean does not exist, or method does not exist" - }, - 500: {"description": "Internal error occurred during method call"}, - }, - ) - async def call_bean_method_with_code( - self, request: BeanCallWithCodeRequest - ) -> BeanCallResponse: - """ - Call Bean method by generating parameters with Python code - - This interface allows you to write Python code to dynamically generate method parameters, especially suitable for: - 1. **Enum type parameters**: e.g., ResourceType.LITERATURE - 2. **Complex object construction**: e.g., AIInputValueObject instance - 3. **Dynamic parameter calculation**: Generate parameter values based on logic - 4. **Type conversion**: Handle Python types that cannot be directly represented in JSON - - **Code execution environment**: - - Provides a safe execution environment, limiting available built-in functions - - Automatically imports common enum types: ResourceType, ResourceScope, ResourceProcessingStatus - - Automatically imports common value objects: AIInputValueObject - - Code must define `args` and/or `kwargs` variables - - **Code examples**: - ```python - # Example 1: Using enum type - args = [] - kwargs = { - 'resource_ids': [274, 281, 282], - 'resource_type': ResourceType.LITERATURE, - 'user_id': 1 - } - - # Example 2: Constructing complex object - ai_input = AIInputValueObject({ - 'literature_refs': [ - {'value': {'id': 280}}, - {'value': {'id': 'uuid-string'}} - ] - }) - args = [ai_input] - kwargs = {'user_id': 1} - - # Example 3: Dynamically calculating parameters - resource_ids = list(range(270, 285)) # Generate ID list - kwargs = { - 'resource_ids': resource_ids, - 'resource_type': ResourceType.LITERATURE, - 'user_id': 1 - } - ``` - - **Security restrictions**: - - File operations, network access, and other dangerous functions are disabled - - Only predefined safe functions and imported types can be used - - Code execution timeout protection - - Args: - request: Request containing Bean identifier, method name, and Python code - - Returns: - BeanCallResponse: Method call result, including code execution information - - Raises: - HTTPException: When debugging is not enabled, code execution fails, or method call fails - """ - self._ensure_debug_enabled() - - try: - # Execute code to generate parameters - logger.info( - "Executing parameter generation code: %s", - request.code[:100] + "..." if len(request.code) > 100 else request.code, - ) - - code_result = self._execute_parameter_code(request.code) - args = code_result['args'] - kwargs = code_result['kwargs'] - - logger.info( - "Code execution succeeded, generated parameters: args=%s, kwargs=%s", - args, - kwargs, - ) - - # Get Bean instance and information - bean_instance, bean_info = self._get_bean_by_identifier( - request.bean_name, request.bean_type - ) - - # Check if method exists - if not hasattr(bean_instance, request.method): - raise HTTPException( - status_code=404, detail=ErrorMessage.BEAN_OPERATION_FAILED.value - ) - - method_to_call = getattr(bean_instance, request.method) - - # Check if it is a callable object - if not callable(method_to_call): - raise HTTPException( - status_code=400, detail=ErrorMessage.INVALID_PARAMETER.value - ) - - # Call method - logger.info( - "Calling Bean method: %s.%s(args=%s, kwargs=%s)", - bean_info['name'], - request.method, - args, - kwargs, - ) - - # Check if it is a coroutine function, compatible with async and sync methods - if asyncio.iscoroutinefunction( - method_to_call - ) or inspect.iscoroutinefunction(method_to_call): - result = await method_to_call(*args, **kwargs) - else: - result = method_to_call(*args, **kwargs) - - # Serialize result - serialized_result = self._serialize_result(result) - - # Add code execution information - response_data = { - 'success': True, - 'bean_info': bean_info, - 'code_execution': {'generated_args': args, 'generated_kwargs': kwargs}, - **serialized_result, - } - - return BeanCallResponse(**response_data) - - except HTTPException: - # Re-raise HTTP exceptions - raise - except Exception as e: - # Catch and handle other exceptions - error_msg = str(e) - error_traceback = traceback.format_exc() - - logger.error( - "Error occurred while calling Bean method with code: %s", error_msg - ) - logger.debug("Error stack: %s", error_traceback) - - return BeanCallResponse( - success=False, - error=error_msg, - traceback=error_traceback, - bean_info=getattr(locals(), 'bean_info', None), - code_execution=getattr(locals(), 'code_result', None), - ) diff --git a/methods/EverCore/src/core/interface/decorator/require_tenant.py b/methods/EverCore/src/core/interface/decorator/require_tenant.py deleted file mode 100644 index 220df3af..00000000 --- a/methods/EverCore/src/core/interface/decorator/require_tenant.py +++ /dev/null @@ -1,48 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Tenant context check decorator - -Ensures that the API request carries a valid tenant context. -""" - -from functools import wraps -from typing import Callable, Any - -from fastapi import HTTPException - -from core.tenants.tenant_contextvar import get_current_tenant_id - - -def require_tenant(func: Callable) -> Callable: - """ - Require tenant context decorator - - Used to decorate controller interface methods to ensure the request carries a valid tenant context. - If the tenant context is missing, returns a 400 error. - - Example usage: - @post("/init-db") - @require_tenant - async def init_tenant_database(self) -> TenantInitResponse: - tenant_id = get_current_tenant_id() - # tenant_id is guaranteed to be non-None - ... - - Args: - func: The decorated asynchronous function - - Returns: - Callable: The wrapped function - """ - - @wraps(func) - async def wrapper(*args: Any, **kwargs: Any) -> Any: - # Check tenant context - tenant_id = get_current_tenant_id() - if not tenant_id: - raise HTTPException(status_code=400, detail="Missing tenant context.") - - # Call the original function - return await func(*args, **kwargs) - - return wrapper diff --git a/methods/EverCore/src/core/lifespan/__init__.py b/methods/EverCore/src/core/lifespan/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/lifespan/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/lifespan/business_lifespan.py b/methods/EverCore/src/core/lifespan/business_lifespan.py deleted file mode 100644 index 61fe9258..00000000 --- a/methods/EverCore/src/core/lifespan/business_lifespan.py +++ /dev/null @@ -1,121 +0,0 @@ -""" -Business lifecycle provider implementation -""" - -from fastapi import FastAPI -from typing import Dict, Any - -from core.observation.logger import get_logger -from core.di.utils import get_bean_by_type, get_beans_by_type, get_bean -from core.di.decorators import component -from core.interface.controller.base_controller import BaseController -from core.capability.app_capability import ApplicationCapability -from core.component.llm.tokenizer.tokenizer_factory import TokenizerFactory -from .lifespan_interface import LifespanProvider - -logger = get_logger(__name__) - - -@component(name="business_lifespan_provider") -class BusinessLifespanProvider(LifespanProvider): - """Business lifecycle provider""" - - def __init__(self, name: str = "business", order: int = 20): - """ - Initialize business lifecycle provider - - Args: - name (str): Provider name - order (int): Execution order, business logic usually starts after database - """ - super().__init__(name, order) - - async def startup(self, app: FastAPI) -> Dict[str, Any]: - """ - Start business logic - - Args: - app (FastAPI): FastAPI application instance - - Returns: - Dict[str, Any]: Business initialization information - """ - logger.info("Initializing business logic...") - - # 0. Preload tokenizers to avoid blocking requests - tokenizer_factory: TokenizerFactory = get_bean_by_type(TokenizerFactory) - tokenizer_factory.load_default_encodings() - - # 1. Create business graph structure - graphs = self._register_graphs(app) - - # 2. Register controllers - controllers = self._register_controllers(app) - - # 3. Register capabilities - capabilities = self._register_capabilities(app) - - logger.info("Business application initialization completed") - - return { - 'graphs': graphs, - 'controllers': controllers, - 'capabilities': capabilities, - } - - async def shutdown(self, app: FastAPI) -> None: - """ - Shutdown business logic - - Args: - app (FastAPI): FastAPI application instance - """ - logger.info("Shutting down business logic...") - - # Clean up business-related attributes in app.state - if hasattr(app.state, 'graphs'): - delattr(app.state, 'graphs') - - logger.info("Business application shutdown completed") - - def _register_controllers(self, app: FastAPI) -> list: - """Register all controllers""" - all_controllers = get_beans_by_type(BaseController) - for controller in all_controllers: - controller.register_to_app(app) - logger.info( - "Controller registration completed, %d controllers registered", - len(all_controllers), - ) - return all_controllers - - def _register_capabilities(self, app: FastAPI) -> list: - """Register all application capabilities""" - capability_beans = get_beans_by_type(ApplicationCapability) - for capability in capability_beans: - capability.enable(app) - logger.info( - "Application capability registration completed, %d capabilities registered", - len(capability_beans), - ) - return capability_beans - - def _create_graphs(self, checkpointer=None) -> dict: - """Create all business graph structures""" - logger.info("Creating business graph structures...") - graphs = {} - # Business graph structures can be created based on specific requirements here - logger.info("Business graph structures created, %d graphs created", len(graphs)) - return graphs - - def _register_graphs(self, app: FastAPI) -> dict: - """Register all graph structures to FastAPI application""" - checkpointer = getattr(app.state, 'checkpointer', None) - if not checkpointer: - logger.warning("Checkpointer not found, skipping graph structure creation") - return {} - - graphs = self._create_graphs(checkpointer) - app.state.graphs = graphs - logger.info("Graph structures registered, %d graphs registered", len(graphs)) - return graphs diff --git a/methods/EverCore/src/core/lifespan/database_lifespan.py b/methods/EverCore/src/core/lifespan/database_lifespan.py deleted file mode 100644 index 12861e39..00000000 --- a/methods/EverCore/src/core/lifespan/database_lifespan.py +++ /dev/null @@ -1,86 +0,0 @@ -""" -Database lifecycle provider implementation -""" - -from fastapi import FastAPI -from typing import Tuple, Any - -from core.observation.logger import get_logger -from core.di.utils import get_bean_by_type -from core.di.decorators import component -from core.component.database_connection_provider import DatabaseConnectionProvider -from .lifespan_interface import LifespanProvider - -logger = get_logger(__name__) - - -# @component(name="database_lifespan_provider") -class DatabaseLifespanProvider(LifespanProvider): - """Database lifecycle provider""" - - def __init__(self, name: str = "database", order: int = 10): - """ - Initialize the database lifecycle provider - - Args: - name (str): Provider name - order (int): Execution order, database usually needs to start first - """ - super().__init__(name, order) - self._db_provider = None - - async def startup(self, app: FastAPI) -> Tuple[Any, Any, Any]: - """ - Start database connection - - Args: - app (FastAPI): FastAPI application instance - - Returns: - Tuple[Any, Any, Any]: (connection_pool, checkpointer, db_provider) - """ - logger.info("Initializing database connection...") - - try: - # Get database connection provider - self._db_provider = get_bean_by_type(DatabaseConnectionProvider) - - # Get connection pool and checkpointer - pool, checkpointer = ( - await self._db_provider.get_connection_and_checkpointer() - ) - - # Store connection pool and checkpointer in app.state for business logic usage - app.state.connection_pool = pool - app.state.checkpointer = checkpointer - app.state.db_provider = self._db_provider - - logger.info("Database connection initialization completed") - - # Return connection information - return pool, checkpointer, self._db_provider - - except Exception as e: - logger.error("Error during database initialization: %s", str(e)) - raise - - async def shutdown(self, app: FastAPI) -> None: - """ - Close database connection - - Args: - app (FastAPI): FastAPI application instance - """ - logger.info("Closing database connection...") - - if self._db_provider: - try: - await self._db_provider.close() - logger.info("Database connection closed successfully") - except Exception as e: - logger.error("Error while closing database connection: %s", str(e)) - - # Clean up database-related attributes in app.state - for attr in ['connection_pool', 'checkpointer', 'db_provider']: - if hasattr(app.state, attr): - delattr(app.state, attr) diff --git a/methods/EverCore/src/core/lifespan/elasticsearch_lifespan.py b/methods/EverCore/src/core/lifespan/elasticsearch_lifespan.py deleted file mode 100644 index ea39a396..00000000 --- a/methods/EverCore/src/core/lifespan/elasticsearch_lifespan.py +++ /dev/null @@ -1,102 +0,0 @@ -""" -Elasticsearch lifecycle provider implementation -""" - -from fastapi import FastAPI -from typing import Any - -from core.observation.logger import get_logger -from core.di.utils import get_all_subclasses, get_bean_by_type -from core.di.decorators import component -from core.lifespan.lifespan_interface import LifespanProvider -from core.oxm.es.doc_base import DocBase -from core.oxm.es.es_utils import EsIndexInitializer -from core.component.elasticsearch_client_factory import ElasticsearchClientFactory - -logger = get_logger(__name__) - - -@component(name="elasticsearch_lifespan_provider") -class ElasticsearchLifespanProvider(LifespanProvider): - """Elasticsearch lifecycle provider""" - - def __init__(self, name: str = "elasticsearch", order: int = 18): - """ - Initialize the Elasticsearch lifecycle provider - - Args: - name (str): Provider name - order (int): Execution order, must start before business_lifespan(20) - """ - super().__init__(name, order) - self._es_factory = None - - async def startup(self, app: FastAPI) -> Any: - """ - Start Elasticsearch connection and initialization - - Args: - app (FastAPI): FastAPI application instance - - Returns: - Any: Elasticsearch client information - """ - logger.info("Initializing Elasticsearch connection...") - - try: - # Get Elasticsearch client factory - self._es_factory: ElasticsearchClientFactory = get_bean_by_type( - ElasticsearchClientFactory - ) - - # Register a default client, mainly for backward compatibility with legacy code in single-tenant scenarios - await self._es_factory.register_default_client() - - # Get all subclasses of DocBase - dynamically generated classes might not be found, reason unknown - all_doc_classes = get_all_subclasses(DocBase) - - # Filter valid document classes - document_classes = [] - for doc_class in all_doc_classes: - index_name = doc_class.get_index_name() - # Check if index name is valid - document_classes.append(doc_class) - logger.info( - "Discovered document class: %s -> %s", - doc_class.__name__, - index_name, - ) - - # Initialize indices (using utility class, tenant-aware support) - if document_classes: - initializer = EsIndexInitializer() - await initializer.initialize_indices(document_classes) - else: - logger.info("No document classes found that require initialization") - - logger.info("✅ Elasticsearch connection initialization completed") - - return { - "factory": self._es_factory, - "document_classes": [cls.__name__ for cls in document_classes], - } - - except Exception as e: - logger.error("❌ Error during Elasticsearch initialization: %s", str(e)) - raise - - async def shutdown(self, app: FastAPI) -> None: - """ - Close Elasticsearch connection - - Args: - app (FastAPI): FastAPI application instance - """ - logger.info("Closing Elasticsearch connection...") - - if self._es_factory: - try: - await self._es_factory.close_all_clients() - logger.info("✅ Elasticsearch connection closed successfully") - except Exception as e: - logger.error("❌ Error closing Elasticsearch connection: %s", str(e)) diff --git a/methods/EverCore/src/core/lifespan/lifespan_factory.py b/methods/EverCore/src/core/lifespan/lifespan_factory.py deleted file mode 100644 index 4a6e00d7..00000000 --- a/methods/EverCore/src/core/lifespan/lifespan_factory.py +++ /dev/null @@ -1,194 +0,0 @@ -""" -Lifecycle factory - -Provides factory methods for dynamically obtaining and creating lifecycles. -""" - -import os -from typing import List -from abc import abstractmethod, ABC -from core.di.utils import get_beans_by_type, get_bean -from core.di.decorators import component -from .lifespan_interface import LifespanProvider -from core.observation.logger import get_logger -from contextlib import asynccontextmanager -from fastapi import FastAPI - -logger = get_logger(__name__) - - -class AppReadyListener(ABC): - """ - Application ready listener protocol - - Components implementing this protocol will be called after all lifespan providers have started. - This is a decoupled hook mechanism that automatically discovers and invokes all listeners via the DI container. - - Usage: - 1. Create a class implementing the on_app_ready() method - 2. Register it into the DI container using the @component decorator - 3. Lifespan will automatically discover and invoke it - - Example: - >>> from core.di.decorators import component - >>> from core.lifespan.lifespan_factory import AppReadyListener - >>> - >>> @component(name="my_app_ready_listener") - >>> class MyAppReadyListener(AppReadyListener): - ... def on_app_ready(self) -> None: - ... print("Application is ready, executing my logic") - """ - - @abstractmethod - def on_app_ready(self) -> None: - """Called when the application is ready""" - ... - - -def create_lifespan_with_providers(providers: list[LifespanProvider]): - """ - Create a lifecycle manager containing multiple providers - - Args: - providers (list[LifespanProvider]): List of lifecycle providers - - Returns: - callable: FastAPI lifecycle context manager - """ - # Sort by order - sorted_providers = sorted(providers, key=lambda x: x.order) - - @asynccontextmanager - async def lifespan(app: FastAPI): - """FastAPI lifecycle context manager""" - lifespan_data = {} - - try: - # Start all providers - for provider in sorted_providers: - logger.info( - "Starting lifecycle provider: %s (order=%d)", - provider.name, - provider.order, - ) - result = await provider.startup(app) - if result is not None: - lifespan_data[provider.name] = result - logger.info("Lifecycle provider started: %s", provider.name) - - # Store data in app.state for easy access - app.state.lifespan_data = lifespan_data - - # Get all application ready listeners via DI and invoke them (decoupled design) - listeners = get_beans_by_type(AppReadyListener) - for listener in listeners: - try: - listener.on_app_ready() - except Exception as e: - logger.error( - "Application ready listener execution failed: %s - %s", - type(listener).__name__, - e, - ) - - yield # During application runtime - - finally: - # Shut down all providers in reverse order - for provider in reversed(sorted_providers): - try: - logger.info("Shutting down lifecycle provider: %s", provider.name) - await provider.shutdown(app) - logger.info( - "Lifecycle provider shutdown completed: %s", provider.name - ) - except Exception as e: - logger.error( - "Failed to shut down lifecycle provider: %s - %s", - provider.name, - str(e), - ) - - return lifespan - - -@component(name="lifespan_factory") -class LifespanFactory: - """Lifecycle factory""" - - def create_auto_lifespan(self): - """ - Automatically create a lifecycle containing all registered providers. - - Supports disabling specific providers via the LIFESPAN_DISABLED environment variable. - Example: LIFESPAN_DISABLED=milvus,elasticsearch - - Returns: - callable: FastAPI lifecycle context manager - """ - providers = get_beans_by_type(LifespanProvider) - - # Filter out disabled providers - disabled_raw = os.getenv("LIFESPAN_DISABLED", "").strip() - if disabled_raw: - disabled_names = {n.strip() for n in disabled_raw.split(",") if n.strip()} - before_count = len(providers) - providers = [p for p in providers if p.name not in disabled_names] - skipped = before_count - len(providers) - if skipped: - logger.info( - "LIFESPAN_DISABLED=%s — skipped %d provider(s)", - disabled_raw, - skipped, - ) - - # Sort by order - sorted_providers = sorted(providers, key=lambda x: x.order) - return create_lifespan_with_providers(sorted_providers) - - def create_lifespan_with_names(self, provider_names: List[str]): - """ - Create a lifecycle based on provider names - - Args: - provider_names (List[str]): List of provider names - - Returns: - callable: FastAPI lifecycle context manager - """ - providers = [] - for name in provider_names: - provider = get_bean(name) - if isinstance(provider, LifespanProvider): - providers.append(provider) - - # Sort by order - sorted_providers = sorted(providers, key=lambda x: x.order) - return create_lifespan_with_providers(sorted_providers) - - def create_lifespan_with_orders(self, orders: List[int]): - """ - Create a lifecycle based on order values - - Args: - orders (List[int]): List of order values - - Returns: - callable: FastAPI lifecycle context manager - """ - all_providers = get_beans_by_type(LifespanProvider) - filtered_providers = [p for p in all_providers if p.order in orders] - - # Sort by order - sorted_providers = sorted(filtered_providers, key=lambda x: x.order) - return create_lifespan_with_providers(sorted_providers) - - def list_available_providers(self) -> List[LifespanProvider]: - """ - List all available lifecycle providers - - Returns: - List[LifespanProvider]: List of providers (sorted by order) - """ - providers = get_beans_by_type(LifespanProvider) - return sorted(providers, key=lambda x: x.order) diff --git a/methods/EverCore/src/core/lifespan/lifespan_interface.py b/methods/EverCore/src/core/lifespan/lifespan_interface.py deleted file mode 100644 index bbb02a41..00000000 --- a/methods/EverCore/src/core/lifespan/lifespan_interface.py +++ /dev/null @@ -1,38 +0,0 @@ -""" -FastAPI lifespan interface definition - -Simple lifespan management interface that supports ordering and name field definition -""" - -from abc import ABC, abstractmethod -from fastapi import FastAPI -from typing import Any - -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class LifespanProvider(ABC): - """Lifespan provider interface""" - - def __init__(self, name: str, order: int = 0): - """ - Initialize lifespan provider - - Args: - name (str): Provider name - order (int): Execution order, smaller numbers execute first - """ - self.name = name - self.order = order - - @abstractmethod - async def startup(self, app: FastAPI) -> Any: - """Startup logic""" - ... - - @abstractmethod - async def shutdown(self, app: FastAPI) -> None: - """Shutdown logic""" - ... diff --git a/methods/EverCore/src/core/lifespan/longjob_lifespan.py b/methods/EverCore/src/core/lifespan/longjob_lifespan.py deleted file mode 100644 index b49148bc..00000000 --- a/methods/EverCore/src/core/lifespan/longjob_lifespan.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -LongJob lifecycle provider implementation - -Used to manage the lifecycle of long-running tasks, including startup and shutdown. -""" - -import asyncio -from fastapi import FastAPI -from typing import Optional, Any -import os -from core.observation.logger import get_logger -from core.di.decorators import component -from core.lifespan.lifespan_interface import LifespanProvider - -logger = get_logger(__name__) - - -@component(name="longjob_lifespan_provider") -class LongJobLifespanProvider(LifespanProvider): - """LongJob lifecycle provider""" - - def __init__(self, name: str = "longjob", order: int = 100): - """ - Initialize LongJob lifecycle provider - - Args: - name (str): Provider name - order (int): Execution order; LongJob should start after all infrastructure is up - """ - super().__init__(name, order) - self._longjob_task: Optional[asyncio.Task] = None - self._longjob_name: Optional[str] = None - - async def startup(self, app: FastAPI) -> Any: - """ - Start LongJob task - - Core logic: asyncio.create_task(run_longjob_mode(longjob_name)) - - Args: - app (FastAPI): FastAPI application instance - - Returns: - Any: Reference to the LongJob task - """ - try: - from core.longjob.longjob_runner import run_longjob_mode - - self._longjob_name = os.getenv("LONGJOB_NAME") - if not self._longjob_name: - logger.warning( - "⚠️ LONGJOB_NAME environment variable not set, skipping LongJob startup" - ) - return None - # Core logic: create an async task to run the long-running job - self._longjob_task = asyncio.create_task( - run_longjob_mode(self._longjob_name) - ) - - # Store the task in app.state for access elsewhere - app.state.longjob_task = self._longjob_task - - logger.info("✅ LongJob task started: %s", self._longjob_name) - - return self._longjob_task - - except Exception as e: - logger.error("❌ Error starting LongJob: %s", str(e)) - raise - - async def shutdown(self, app: FastAPI) -> None: - """ - Shut down LongJob task - - Args: - app (FastAPI): FastAPI application instance - """ - if not self._longjob_task: - logger.info("No running LongJob task") - return - - logger.info("Shutting down LongJob: %s", self._longjob_name) - - try: - # Cancel the task - if not self._longjob_task.done(): - self._longjob_task.cancel() - try: - await self._longjob_task - except asyncio.CancelledError: - logger.info("✅ LongJob task cancelled: %s", self._longjob_name) - else: - logger.info("✅ LongJob task completed: %s", self._longjob_name) - - except Exception as e: - logger.error("❌ Error shutting down LongJob: %s", str(e)) - - # Clean up LongJob-related attributes in app.state - if hasattr(app.state, 'longjob_task'): - delattr(app.state, 'longjob_task') diff --git a/methods/EverCore/src/core/lifespan/metrics_lifespan.py b/methods/EverCore/src/core/lifespan/metrics_lifespan.py deleted file mode 100644 index aa7a8b52..00000000 --- a/methods/EverCore/src/core/lifespan/metrics_lifespan.py +++ /dev/null @@ -1,75 +0,0 @@ -""" -Metrics lifecycle provider implementation - -Starts standalone Prometheus metrics server on a separate port (default: 9090). -""" -import os -from fastapi import FastAPI -from typing import Any, Tuple - -from core.observation.logger import get_logger -from core.di.decorators import component -from core.observation.metrics import start_metrics_server, is_metrics_server_running, get_metrics_url -from .lifespan_interface import LifespanProvider - -logger = get_logger(__name__) - - -@component(name="metrics_lifespan_provider") -class MetricsLifespanProvider(LifespanProvider): - """Metrics lifecycle provider - starts Prometheus metrics server""" - - def __init__(self, name: str = "metrics", order: int = 5): - """ - Initialize the metrics lifecycle provider - - Args: - name (str): Provider name - order (int): Execution order, metrics should start early (before database) - """ - super().__init__(name, order) - - async def startup(self, app: FastAPI) -> Tuple[Any, ...]: - """ - Start metrics server - - Args: - app (FastAPI): FastAPI application instance - - Returns: - Tuple containing metrics server info - """ - # Get port from environment variable or default to 9090 - port = int(os.getenv("METRICS_PORT", "9090")) - - logger.info("Starting Prometheus metrics server on port %d...", port) - - try: - success = start_metrics_server(port=port) - - if success: - logger.info("✅ Metrics server started: %s", get_metrics_url(port=port)) - app.state.metrics_port = port - else: - logger.warning("Metrics server already running or failed to start") - - return (port, success) - - except Exception as e: - logger.error("Failed to start metrics server: %s", str(e)) - # Don't raise - metrics failure shouldn't prevent app startup - return (port, False) - - async def shutdown(self, app: FastAPI) -> None: - """ - Cleanup metrics server (daemon thread auto-stops with main process) - - Args: - app (FastAPI): FastAPI application instance - """ - logger.info("Metrics server will stop with main process (daemon thread)") - - # Clean up app.state - if hasattr(app.state, 'metrics_port'): - delattr(app.state, 'metrics_port') - diff --git a/methods/EverCore/src/core/lifespan/milvus_lifespan.py b/methods/EverCore/src/core/lifespan/milvus_lifespan.py deleted file mode 100644 index 6d6f916d..00000000 --- a/methods/EverCore/src/core/lifespan/milvus_lifespan.py +++ /dev/null @@ -1,118 +0,0 @@ -""" -Milvus lifespan provider implementation -""" - -from collections import defaultdict -from fastapi import FastAPI -from typing import Any - -from core.observation.logger import get_logger -from core.di.utils import get_bean, get_all_subclasses -from core.di.decorators import component -from core.lifespan.lifespan_interface import LifespanProvider -from core.oxm.milvus.milvus_collection_base import MilvusCollectionBase - -logger = get_logger(__name__) - - -@component(name="milvus_lifespan_provider") -class MilvusLifespanProvider(LifespanProvider): - """Milvus lifespan provider""" - - def __init__(self, name: str = "milvus", order: int = 18): - """ - Initialize Milvus lifespan provider - - Args: - name (str): Provider name - order (int): Execution order, must start before business_lifespan(20) - """ - super().__init__(name, order) - self._milvus_factory = None - self._milvus_clients = {} - - async def startup(self, app: FastAPI) -> Any: - """ - Start Milvus connection and initialization - - Args: - app (FastAPI): FastAPI application instance - - Returns: - Any: Milvus client information - """ - logger.info("Initializing Milvus connection...") - - try: - # Get Milvus client factory - self._milvus_factory = get_bean("milvus_client_factory") - - # Get all concrete Collection classes (by checking if _COLLECTION_NAME exists) - all_collection_classes = [ - cls - for cls in get_all_subclasses(MilvusCollectionBase) - if cls._COLLECTION_NAME - is not None # Classes with _COLLECTION_NAME are concrete - ] - - # Group by using - using_collections = defaultdict(list) - for collection_class in all_collection_classes: - using = collection_class._DB_USING - using_collections[using].append(collection_class) - logger.info( - "Discovered Collection class: %s [using=%s]", - collection_class.__name__, - using, - ) - - # Get all required clients - for using, collection_classes in using_collections.items(): - # Get client - client = self._milvus_factory.get_named_client(using) - self._milvus_clients[using] = client - - # Initialize each Collection - for collection_class in collection_classes: - try: - collection = collection_class() - collection.ensure_all() - logger.info( - "✅ Collection '%s' initialized successfully [using=%s]", - collection.name, - using, - ) - except Exception as e: - logger.error( - "❌ Failed to initialize Collection '%s' [using=%s]: %s", - collection_class._COLLECTION_NAME, - using, - e, - ) - raise - logger.info("✅ Milvus connection initialization completed") - - except Exception as e: - logger.error("❌ Error during Milvus initialization: %s", str(e)) - raise - - async def shutdown(self, app: FastAPI) -> None: - """ - Close Milvus connections - - Args: - app (FastAPI): FastAPI application instance - """ - logger.info("Closing Milvus connections...") - - if self._milvus_factory: - try: - self._milvus_factory.close_all_clients() - logger.info("✅ Milvus connections closed successfully") - except Exception as e: - logger.error("❌ Error while closing Milvus connections: %s", str(e)) - - # Clean up Milvus-related attributes in app.state - for attr in ['milvus_clients', 'milvus_factory']: - if hasattr(app.state, attr): - delattr(app.state, attr) diff --git a/methods/EverCore/src/core/lifespan/mongodb_lifespan.py b/methods/EverCore/src/core/lifespan/mongodb_lifespan.py deleted file mode 100644 index 0b0f4015..00000000 --- a/methods/EverCore/src/core/lifespan/mongodb_lifespan.py +++ /dev/null @@ -1,96 +0,0 @@ -""" -MongoDB Lifespan Provider Implementation -""" - -from collections import defaultdict -from fastapi import FastAPI -from typing import Any - -from core.observation.logger import get_logger -from core.di.utils import get_bean_by_type -from core.di.decorators import component -from core.lifespan.lifespan_interface import LifespanProvider -from core.oxm.mongo.document_base import DocumentBase -from core.di.utils import get_all_subclasses -from core.component.mongodb_client_factory import MongoDBClientFactory, MongoDBClientWrapper - - -logger = get_logger(__name__) - - -@component(name="mongodb_lifespan_provider") -class MongoDBLifespanProvider(LifespanProvider): - """MongoDB Lifespan Provider""" - - def __init__(self, name: str = "mongodb", order: int = 15): - """ - Initialize MongoDB Lifespan Provider - - Args: - name (str): Provider name - order (int): Execution order, MongoDB starts after database connections - """ - super().__init__(name, order) - self._mongodb_factory = None - self._mongodb_client = None - - async def startup(self, app: FastAPI) -> Any: - """ - Start MongoDB connection and initialization - - Args: - app (FastAPI): FastAPI application instance - - Returns: - Any: MongoDB client information - """ - logger.info("Initializing MongoDB connection...") - - try: - - # Get MongoDB client factory - self._mongodb_factory = get_bean_by_type(MongoDBClientFactory) - - # Get default client - self._mongodb_client: MongoDBClientWrapper = ( - await self._mongodb_factory.get_default_client() - ) - - # Manually initialize Beanie ODM - all_subclasses_of_document_base = get_all_subclasses(DocumentBase) - db_document_models = defaultdict(list) - for subclass in all_subclasses_of_document_base: - db_document_models[subclass.get_bind_database()].append(subclass) - - # Get all DB names - db_names = list(db_document_models.keys()) - db_clients = { - db_name: await self._mongodb_factory.get_named_client(db_name) - for db_name in db_names - } - - # Initialize Beanie ODM - for db_name, db_client in db_clients.items(): - await db_client.initialize_beanie(db_document_models[db_name]) - - logger.info("✅ MongoDB connection initialization completed") - - except Exception as e: - logger.error("❌ Error during MongoDB initialization: %s", str(e)) - raise - - async def shutdown(self, app: FastAPI) -> None: - """ - Close MongoDB connection - - Args: - app (FastAPI): FastAPI application instance - """ - logger.info("Closing MongoDB connection...") - - if self._mongodb_factory: - try: - await self._mongodb_factory.close_all_clients() - logger.info("✅ MongoDB connection closed successfully") - except Exception as e: - logger.error("❌ Error closing MongoDB connection: %s", str(e)) diff --git a/methods/EverCore/src/core/lock/__init__.py b/methods/EverCore/src/core/lock/__init__.py deleted file mode 100644 index 8b137891..00000000 --- a/methods/EverCore/src/core/lock/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/lock/redis_distributed_lock.py b/methods/EverCore/src/core/lock/redis_distributed_lock.py deleted file mode 100644 index c47e978c..00000000 --- a/methods/EverCore/src/core/lock/redis_distributed_lock.py +++ /dev/null @@ -1,595 +0,0 @@ -""" -Redis distributed lock implementation - -Distributed lock service supporting coroutine-level reentrancy based on Redis -Using contextvar to manage coroutine context, ensuring thread safety and coroutine safety -""" - -import asyncio -from typing import Optional, Union -from contextlib import asynccontextmanager - -from core.di.decorators import component -from core.observation.logger import get_logger -from core.component.redis_provider import RedisProvider -from core.di.utils import get_bean_by_type - -logger = get_logger(__name__) - -# Default configuration constants -DEFAULT_LOCK_TIMEOUT = 60.0 # Default lock timeout (seconds) -DEFAULT_BLOCKING_TIMEOUT = 80.0 # Default blocking timeout for acquiring lock (seconds) -DEFAULT_RETRY_INTERVAL = 3 # Default retry interval (seconds) - - -class DistributedLockError(Exception): - """Exception related to distributed lock""" - - -class RedisDistributedLock: - """ - Redis distributed reentrant lock - - A single lock instance responsible for lock operations on a specific resource - """ - - def __init__(self, resource: str, lock_manager: 'RedisDistributedLockManager'): - """ - Initialize distributed lock - - Args: - resource: Name of the lock resource - lock_manager: Lock manager instance - """ - self.resource = resource - self.lock_manager = lock_manager - self._acquired = False - self._reentry_count = 0 - - @asynccontextmanager - async def acquire( - self, timeout: Optional[float] = None, blocking_timeout: Optional[float] = None - ): - """ - Asynchronous context manager for acquiring lock - - Args: - timeout: Lock timeout (seconds) - blocking_timeout: Blocking timeout for acquiring lock (seconds) - - Yields: - bool: Whether the lock was successfully acquired - """ - timeout = timeout or DEFAULT_LOCK_TIMEOUT - blocking_timeout = blocking_timeout or DEFAULT_BLOCKING_TIMEOUT - - acquired = False - try: - # Call the lock manager's internal method to acquire the lock - acquired = await self.lock_manager._acquire_lock( # pylint: disable=protected-access - self.resource, timeout, blocking_timeout - ) - if acquired: - self._acquired = True - - yield acquired - - finally: - if acquired: - try: - # Call the lock manager's internal method to release the lock - await self.lock_manager._release_lock( - self.resource - ) # pylint: disable=protected-access - self._acquired = False - except (ConnectionError, TimeoutError, OSError) as e: - logger.error( - "Failed to release lock: %s, error: %s", self.resource, e - ) - - async def is_locked(self) -> bool: - """Check if the lock is held""" - return await self.lock_manager.is_locked(self.resource) - - async def is_owned_by_current_coroutine(self) -> bool: - """Check if the lock is held by the current coroutine""" - return await self.lock_manager.is_owned_by_current_coroutine(self.resource) - - async def get_reentry_count(self) -> int: - """Get the reentry count of the current coroutine""" - return await self.lock_manager.get_reentry_count(self.resource) - - -@component(name="redis_distributed_lock_manager") -class RedisDistributedLockManager: - """ - Redis distributed lock manager - - Responsible for managing multiple lock instances, providing lock creation and global operations - """ - - # Lock key template - LOCK_KEY_TEMPLATE = "reentrant_lock:{resource}" - - # Lua script: Acquire reentrant lock - LUA_ACQUIRE_SCRIPT = """ - local lock_key = KEYS[1] - local owner_id = ARGV[1] - local timeout_ms = tonumber(ARGV[2]) - - -- Get current lock information - -- Note: When lock_key does not exist, HMGET returns {false, false} - local lock_info = redis.call('HMGET', lock_key, 'owner', 'count') - local current_owner = lock_info[1] -- false when not exists - local current_count = tonumber(lock_info[2]) or 0 -- tonumber(false) is nil, use 0 as default - - if current_owner == false or current_owner == owner_id then - -- Lock is not occupied (current_owner == false) or held by current coroutine, can acquire/reenter - local new_count = current_count + 1 - redis.call('HMSET', lock_key, 'owner', owner_id, 'count', new_count) - if timeout_ms > 0 then - redis.call('PEXPIRE', lock_key, timeout_ms) - end - return new_count - else - -- Lock is held by another coroutine - return 0 - end - """ - - # Lua script: Release reentrant lock - LUA_RELEASE_SCRIPT = """ - local lock_key = KEYS[1] - local owner_id = ARGV[1] - - -- Get current lock information - -- Note: When lock_key does not exist, HMGET returns {false, false} - local lock_info = redis.call('HMGET', lock_key, 'owner', 'count') - local current_owner = lock_info[1] -- false when not exists - local current_count = tonumber(lock_info[2]) or 0 -- tonumber(false) is nil, use 0 as default - - if current_owner ~= owner_id then - -- Not the lock holder, cannot release or lock does not exist - return 0 - end - - local new_count = current_count - 1 - if new_count <= 0 then - -- Reentry count reaches zero, completely release the lock - redis.call('DEL', lock_key) - return -1 - else - -- Decrease reentry count but keep the lock - redis.call('HSET', lock_key, 'count', new_count) - return new_count - end - """ - - # Lua script: Check lock status - LUA_STATUS_SCRIPT = """ - local lock_key = KEYS[1] - local owner_id = ARGV[1] - - -- Get current lock information - -- Note: When lock_key does not exist, HMGET returns {false, false} - local lock_info = redis.call('HMGET', lock_key, 'owner', 'count') - local current_owner = lock_info[1] -- false when not exists - local current_count = tonumber(lock_info[2]) or 0 -- tonumber(false) is nil, use 0 as default - - if current_owner == false then - return {0, 0} -- Not locked - elseif current_owner == owner_id then - return {1, current_count} -- Held by current coroutine - else - return {2, current_count} -- Held by other coroutine - end - """ - - def __init__(self, redis_provider: RedisProvider): - """ - Initialize Redis distributed lock manager - - Args: - redis_provider: Redis provider - """ - self.redis_provider = redis_provider - - # Lua script cache - self._lua_acquire = None - self._lua_release = None - self._lua_status = None - - def get_lock(self, resource: str) -> RedisDistributedLock: - """ - Get lock instance for specified resource - - Args: - resource: Name of the lock resource - - Returns: - RedisDistributedLock: Lock instance - """ - return RedisDistributedLock(resource, self) - - async def _ensure_scripts(self): - """Ensure Lua scripts are registered""" - if self._lua_acquire is None: - redis_client = await self.redis_provider.get_client() - self._lua_acquire = redis_client.register_script(self.LUA_ACQUIRE_SCRIPT) - self._lua_release = redis_client.register_script(self.LUA_RELEASE_SCRIPT) - self._lua_status = redis_client.register_script(self.LUA_STATUS_SCRIPT) - - def _get_owner_id(self) -> str: - """ - Get unique identifier for current coroutine - - Returns: - str: Unique identifier for coroutine - - Raises: - DistributedLockError: If not in coroutine environment - """ - # First try to get from context variable - try: - current_task = asyncio.current_task() - if current_task is None: - raise DistributedLockError( - "Distributed lock must be used in coroutine environment, no running coroutine task" - ) - - # Use task id as coroutine identifier - task_id = id(current_task) - owner_id = f"task_{task_id}" - return owner_id - - except RuntimeError as e: - raise DistributedLockError( - f"Distributed lock must be used in coroutine environment: {e}" - ) from e - - async def _acquire_lock( - self, resource: str, timeout: float, blocking_timeout: float - ) -> bool: - """ - Internal method: Acquire lock - - Args: - resource: Resource name - timeout: Lock timeout - blocking_timeout: Blocking timeout for acquiring lock - - Returns: - bool: Whether the lock was successfully acquired - """ - await self._ensure_scripts() - - lock_key = self.LOCK_KEY_TEMPLATE.format(resource=resource) - owner_id = self._get_owner_id() - timeout_ms = int(timeout * 1000) if timeout > 0 else 0 - - # Calculate retry count - retry_count = max(1, int(blocking_timeout / DEFAULT_RETRY_INTERVAL)) - - for attempt in range(retry_count): - try: - redis_client = await self.redis_provider.get_client() - result = await self._lua_acquire( - keys=[lock_key], args=[owner_id, timeout_ms], client=redis_client - ) - - if result > 0: - logger.debug( - "Successfully acquired reentrant lock: %s, coroutine: %s, reentry count: %s (attempt %s)", - resource, - owner_id, - result, - attempt + 1, - ) - return True - else: - if attempt < retry_count - 1: - await asyncio.sleep(DEFAULT_RETRY_INTERVAL) - - except (ConnectionError, TimeoutError, OSError) as e: - logger.debug( - "Failed to acquire lock (attempt %s): %s, error: %s", - attempt + 1, - resource, - e, - ) - if attempt < retry_count - 1: - await asyncio.sleep(DEFAULT_RETRY_INTERVAL) - - logger.warning( - "Timed out acquiring reentrant distributed lock: %s, coroutine: %s", - resource, - owner_id, - ) - return False - - async def _release_lock(self, resource: str): - """ - Internal method: Release lock - - Args: - resource: Resource name - """ - lock_key = self.LOCK_KEY_TEMPLATE.format(resource=resource) - owner_id = self._get_owner_id() - - try: - redis_client = await self.redis_provider.get_client() - result = await self._lua_release( - keys=[lock_key], args=[owner_id], client=redis_client - ) - - if result == -1: - logger.debug( - "Completely released reentrant lock: %s, coroutine: %s", - resource, - owner_id, - ) - elif result > 0: - logger.debug( - "Reduced reentrant lock count: %s, coroutine: %s, remaining count: %s", - resource, - owner_id, - result, - ) - else: - logger.warning( - "Cannot release lock not owned by current coroutine or lock does not exist: %s, coroutine: %s", - resource, - owner_id, - ) - - except (ConnectionError, TimeoutError, OSError) as e: - logger.error( - "Exception occurred while releasing reentrant lock: %s, coroutine: %s, error: %s", - resource, - owner_id, - e, - ) - - async def is_locked(self, resource: str) -> bool: - """ - Check if resource is locked - - Args: - resource: Name of the lock resource - - Returns: - bool: Whether it is locked - """ - try: - await self._ensure_scripts() - redis_client = await self.redis_provider.get_client() - lock_key = self.LOCK_KEY_TEMPLATE.format(resource=resource) - owner_id = self._get_owner_id() - - result = await self._lua_status( - keys=[lock_key], args=[owner_id], client=redis_client - ) - - status_code = result[0] if result else 0 - return status_code > 0 # 1 or 2 both indicate locked - - except (ConnectionError, TimeoutError, OSError) as e: - logger.error( - "Failed to check reentrant lock status: %s, error: %s", resource, e - ) - return False - - async def is_owned_by_current_coroutine(self, resource: str) -> bool: - """ - Check if the lock is held by the current coroutine - - Args: - resource: Name of the lock resource - - Returns: - bool: Whether it is held by the current coroutine - """ - try: - await self._ensure_scripts() - redis_client = await self.redis_provider.get_client() - lock_key = self.LOCK_KEY_TEMPLATE.format(resource=resource) - owner_id = self._get_owner_id() - - result = await self._lua_status( - keys=[lock_key], args=[owner_id], client=redis_client - ) - - status_code = result[0] if result else 0 - return status_code == 1 # 1 indicates held by current coroutine - - except (ConnectionError, TimeoutError, OSError) as e: - logger.error( - "Failed to check reentrant lock ownership: %s, error: %s", resource, e - ) - return False - - async def get_reentry_count(self, resource: str) -> int: - """ - Get the reentry count of current coroutine for specified resource - - Args: - resource: Name of the lock resource - - Returns: - int: Reentry count, 0 means lock is not held - """ - try: - await self._ensure_scripts() - redis_client = await self.redis_provider.get_client() - lock_key = self.LOCK_KEY_TEMPLATE.format(resource=resource) - owner_id = self._get_owner_id() - - result = await self._lua_status( - keys=[lock_key], args=[owner_id], client=redis_client - ) - - if result and result[0] == 1: # Held by current coroutine - return result[1] - else: - return 0 - - except (ConnectionError, TimeoutError, OSError) as e: - logger.error("Failed to get reentry count: %s, error: %s", resource, e) - return 0 - - async def force_unlock(self, resource: str) -> bool: - """ - Force release lock (use with caution) - - Args: - resource: Name of the lock resource - - Returns: - bool: Whether the release was successful - """ - try: - redis_client = await self.redis_provider.get_client() - lock_key = self.LOCK_KEY_TEMPLATE.format(resource=resource) - result = await redis_client.delete(lock_key) - - logger.warning( - "Forcibly released reentrant lock: %s, result: %s", resource, result - ) - return result > 0 - - except (ConnectionError, TimeoutError, OSError) as e: - logger.error( - "Failed to forcibly release reentrant lock: %s, error: %s", resource, e - ) - return False - - @asynccontextmanager - async def acquire_lock( - self, - resource: str, - timeout: Optional[float] = None, - blocking_timeout: Optional[float] = None, - ): - """ - Asynchronous context manager for acquiring reentrant distributed lock (compatible with old interface) - - Args: - resource: Name of the lock resource (key name) - timeout: Lock timeout (seconds) - blocking_timeout: Blocking timeout for acquiring lock (seconds) - - Yields: - bool: Whether the lock was successfully acquired - """ - lock = self.get_lock(resource) - async with lock.acquire(timeout, blocking_timeout) as acquired: - yield acquired - - async def close(self): - """Close service and clean up resources""" - logger.info("Redis distributed lock manager closed") - - -# Convenient context manager function -@asynccontextmanager -async def distributed_lock( - resource: str, - timeout: Optional[float] = None, - blocking_timeout: Optional[float] = None, -): - """ - Convenient distributed lock context manager, used within functions - - Args: - resource: Name of the lock resource - timeout: Lock timeout (seconds) - blocking_timeout: Blocking timeout for acquiring lock (seconds) - - Yields: - bool: Whether the lock was successfully acquired - - Example: - async def some_function(): - async with distributed_lock("user:balance:123") as acquired: - if acquired: - # Execute code that requires locking - print("Lock acquired, executing business logic") - else: - print("Failed to acquire lock") - - # Supports reentrancy - async def reentrant_function(): - async with distributed_lock("resource:123") as acquired1: - if acquired1: - print("First level acquired lock") - async with distributed_lock("resource:123") as acquired2: - if acquired2: - print("Second level acquired lock (reentrant)") - """ - - # Get lock manager - lock_manager = get_bean_by_type(RedisDistributedLockManager) - - # Acquire lock and execute - lock = lock_manager.get_lock(resource) - async with lock.acquire( - timeout=timeout, blocking_timeout=blocking_timeout - ) as acquired: - yield acquired - - -# Convenient decorator function -def with_distributed_lock( - resource_key: Union[str, callable], - timeout: float = DEFAULT_LOCK_TIMEOUT, - blocking_timeout: float = DEFAULT_BLOCKING_TIMEOUT, -): - """ - Distributed lock decorator (supports reentrancy) - - Args: - resource_key: Lock resource key, can be string or function returning string - timeout: Lock timeout - blocking_timeout: Blocking timeout for acquiring lock - - Example: - @with_distributed_lock("user:balance:{user_id}") - async def update_user_balance(user_id: int, amount: float): - # This function can be recursively called within the same coroutine without deadlock - if amount > 100: - await update_user_balance(user_id, amount / 2) - await update_user_balance(user_id, amount / 2) - """ - - def decorator(func): - async def wrapper(*args, **kwargs): - - # Get lock manager - lock_manager = get_bean_by_type(RedisDistributedLockManager) - - # Calculate resource key - if callable(resource_key): - resource = resource_key(*args, **kwargs) - else: - # Support formatted string - try: - resource = resource_key.format(*args, **kwargs) - except (IndexError, KeyError): - resource = resource_key - - # Acquire lock and execute function - lock = lock_manager.get_lock(resource) - async with lock.acquire( - timeout=timeout, blocking_timeout=blocking_timeout - ) as acquired: - if acquired: - return await func(*args, **kwargs) - else: - raise RuntimeError( - f"Failed to acquire distributed lock: {resource}" - ) - - return wrapper - - return decorator diff --git a/methods/EverCore/src/core/longjob/__init__.py b/methods/EverCore/src/core/longjob/__init__.py deleted file mode 100644 index 8b137891..00000000 --- a/methods/EverCore/src/core/longjob/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/longjob/interfaces.py b/methods/EverCore/src/core/longjob/interfaces.py deleted file mode 100644 index 2807f1b0..00000000 --- a/methods/EverCore/src/core/longjob/interfaces.py +++ /dev/null @@ -1,236 +0,0 @@ -""" -Long job interfaces and base classes. -Long task interfaces and base class definitions. -""" - -from abc import ABC, abstractmethod -from typing import Optional, Any, Dict -import asyncio -from enum import Enum -from dataclasses import dataclass - -# Import error classes from longjob_error -from core.longjob.longjob_error import FatalError, BusinessLogicError - - -@dataclass -class MessageBatch: - """ - Message wrapper class - Uniformly encapsulates message data, without restricting specific types (can be a single message, list, or any business-defined structure) - """ - - data: Any # Message data, can be any type: single message, list, dictionary, etc. - batch_id: Optional[str] = None # Batch ID, used for tracking and logging - metadata: Optional[Dict[str, Any]] = None # Additional metadata information - - def __post_init__(self): - """Post-initialization processing""" - if self.metadata is None: - self.metadata = {} - - @property - def is_empty(self) -> bool: - """Check if data is empty""" - if self.data is None: - return True - - # If it's a list or similar container, check length - if hasattr(self.data, '__len__'): - try: - return len(self.data) == 0 - except (TypeError, AttributeError): - pass - - return False - - -class LongJobStatus(Enum): - """Long job status enumeration""" - - IDLE = "idle" # Idle state - STARTING = "starting" # Starting - RUNNING = "running" # Running - STOPPING = "stopping" # Stopping - STOPPED = "stopped" # Stopped - ERROR = "error" # Error state - - -class LongJobInterface(ABC): - """ - Long job interface definition. - All long jobs need to implement this interface. - """ - - def __init__(self, job_id: str, config: Optional[Dict[str, Any]] = None): - """ - Initialize long job - - Args: - job_id: Job ID, used for identification and management - config: Job configuration parameters - """ - self.job_id = job_id - self.config = config or {} - self.status = LongJobStatus.IDLE - self._stop_event = asyncio.Event() - - @abstractmethod - async def start(self) -> None: - """ - Start long job - Implementation classes need to start the specific work logic here - """ - - @abstractmethod - async def shutdown(self) -> None: - """ - Shut down long job - Implementation classes need to clean up resources and stop work here - """ - - def get_status(self) -> LongJobStatus: - """Get current job status""" - return self.status - - def is_running(self) -> bool: - """Check if the job is currently running""" - return self.status == LongJobStatus.RUNNING - - def should_stop(self) -> bool: - """Check if the job should stop""" - return self._stop_event.is_set() - - def request_stop(self) -> None: - """Request to stop the job""" - self._stop_event.set() - - -class ErrorHandler(ABC): - """ - Error handler interface - Used to handle exceptions during long job execution - """ - - @abstractmethod - async def handle_error(self, error: Exception, context: Dict[str, Any]) -> bool: - """ - Handle error - - Args: - error: The exception that occurred - context: Error context information - - Returns: - bool: True means execution can continue, False means it should stop - """ - - def is_fatal_error(self, error: Exception) -> bool: - """ - Determine if it is a fatal error - - Args: - error: Exception instance - - Returns: - bool: True means fatal error, should not retry - """ - # Check if it's an explicitly marked fatal error - if isinstance(error, FatalError): - return True - - # Check common fatal error types - fatal_error_types = ( - MemoryError, - SystemExit, - KeyboardInterrupt, - ImportError, - SyntaxError, - TypeError, # Usually indicates programming errors - AttributeError, # Usually indicates programming errors - ) - - return isinstance(error, fatal_error_types) - - def is_retryable_error(self, error: Exception) -> bool: - """ - Determine if it is a retryable error - - Args: - error: Exception instance - - Returns: - bool: True means it can be retried - """ - # If it's a fatal error, not retryable - if self.is_fatal_error(error): - return False - - # Explicitly marked business logic errors can be retried - if isinstance(error, BusinessLogicError): - return True - - # Network-related errors are usually retryable - retryable_error_types = ( - ConnectionError, - TimeoutError, - OSError, - ) # Includes network errors - - return isinstance(error, retryable_error_types) - - -class RetryConfig: - """Retry configuration""" - - def __init__( - self, - max_retries: int = 3, - retry_delay: float = 1.0, - exponential_backoff: bool = True, - max_delay: float = 60.0, - jitter: bool = True, - backoff_multiplier: float = 2.0, - retry_on_fatal: bool = False, - ): - """ - Initialize retry configuration - - Args: - max_retries: Maximum number of retries - retry_delay: Initial retry delay time (seconds) - exponential_backoff: Whether to use exponential backoff - max_delay: Maximum delay time (seconds) - jitter: Whether to add random jitter - backoff_multiplier: Exponential backoff multiplier - retry_on_fatal: Whether to retry on fatal errors (usually False) - """ - self.max_retries = max_retries - self.retry_delay = retry_delay - self.exponential_backoff = exponential_backoff - self.max_delay = max_delay - self.jitter = jitter - self.backoff_multiplier = backoff_multiplier - self.retry_on_fatal = retry_on_fatal - - -class ConsumerConfig: - """Consumer configuration""" - - def __init__( - self, - timeout: float = 600.0, - retry_config: Optional[RetryConfig] = None, - error_handler: Optional[ErrorHandler] = None, - ): - """ - Initialize consumer configuration - - Args: - timeout: Timeout for consuming a single message (seconds), including retries - retry_config: Retry configuration - error_handler: Error handler - """ - self.timeout = timeout - self.retry_config = retry_config or RetryConfig() - self.error_handler = error_handler diff --git a/methods/EverCore/src/core/longjob/longjob_error.py b/methods/EverCore/src/core/longjob/longjob_error.py deleted file mode 100644 index 3fb2ee0b..00000000 --- a/methods/EverCore/src/core/longjob/longjob_error.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -Long job system specific errors. -Long task system specific error definitions. -""" - - -class FatalError(Exception): - """ - Fatal error, should not retry - Used to identify errors that cannot be resolved by retrying - - Examples: - - Out of memory - - System-level errors - - Configuration errors - - Programming errors (TypeError, AttributeError, etc.) - """ - - -class BusinessLogicError(Exception): - """ - Business logic error, can retry - Used to identify errors that might be resolved by retrying - - Examples: - - Network connection errors - - Temporary database connection issues - - Third-party service temporarily unavailable - - Resource lock conflicts - """ - - -class LongJobError(Exception): - """ - Long task system base error - Base class for all long task related errors - """ - - -class JobNotFoundError(LongJobError): - """Job not found error""" - - -class JobAlreadyExistsError(LongJobError): - """Job already exists error""" - - -class JobStateError(LongJobError): - """Job state error""" - - -class ManagerShutdownError(LongJobError): - """Manager has been shut down error""" - - -class MaxConcurrentJobsError(LongJobError): - """Exceeded maximum number of concurrent jobs error""" diff --git a/methods/EverCore/src/core/longjob/longjob_runner.py b/methods/EverCore/src/core/longjob/longjob_runner.py deleted file mode 100644 index c5740897..00000000 --- a/methods/EverCore/src/core/longjob/longjob_runner.py +++ /dev/null @@ -1,109 +0,0 @@ -""" -LongJob Runner - Used to start and manage long-running tasks - -Provides functionality to run a single long-running task, including: -- Finding the specified long job via DI -- Graceful startup and shutdown -- Handling shutdown based on asyncio task cancellation mechanism -- Error handling and logging -""" - -import asyncio -from typing import Optional - -from core.di.utils import get_bean -from core.longjob.interfaces import LongJobInterface -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -async def run_longjob_mode(longjob_name: str): - """ - Run the specified long-running job mode - - This function runs as an asyncio Task and is triggered to shut down via task.cancel(). - When CancelledError is received, the long-running job will be gracefully shut down. - - Args: - longjob_name: Name of the long-running job - """ - logger.info("🚀 Starting LongJob mode: %s", longjob_name) - - longjob_instance: Optional[LongJobInterface] = None - - try: - # Try to get the specified long-running job from the DI container - try: - longjob_instance = get_bean(longjob_name) - logger.info( - "✅ Found long-running job: %s (%s)", - longjob_name, - type(longjob_instance).__name__, - ) - except Exception as e: - logger.error( - "❌ Unable to find long-running job '%s': %s", longjob_name, str(e) - ) - logger.info( - "💡 Please ensure the long-running job is correctly registered in the DI container" - ) - return - - # Check if it is an implementation of LongJobInterface - if not isinstance(longjob_instance, LongJobInterface): - logger.error( - "❌ '%s' is not an implementation of LongJobInterface", longjob_name - ) - logger.info( - "💡 Long-running jobs must inherit from LongJobInterface or its subclasses" - ) - return - - # Start the long-running job - logger.info("🔄 Starting long-running job: %s", longjob_name) - await longjob_instance.start() - - logger.info( - "✅ Long-running job '%s' has started and is running...", longjob_name - ) - - # Wait indefinitely until the task is canceled - # Use an uncompleted Future to keep the task running - await asyncio.Event().wait() - - except asyncio.CancelledError: - # Received task cancel signal, begin graceful shutdown - logger.info( - "🛑 Received cancellation signal, starting graceful shutdown of long-running job: %s", - longjob_name, - ) - if longjob_instance: - try: - await longjob_instance.shutdown() - logger.info( - "✅ Long-running job '%s' has been successfully shut down", - longjob_name, - ) - except Exception as e: - logger.error( - "❌ Error during long-running job shutdown: %s", - str(e), - exc_info=True, - ) - # Re-raise CancelledError so the caller knows the task was cancelled - raise - - except Exception as e: - # Exception occurred during execution - logger.error("❌ Error running long-running job: %s", str(e), exc_info=True) - if longjob_instance: - try: - await longjob_instance.shutdown() - logger.info("✅ Long-running job has been shut down after exception") - except Exception as shutdown_error: - logger.error( - "❌ Error during long-running job shutdown: %s", - str(shutdown_error), - exc_info=True, - ) diff --git a/methods/EverCore/src/core/longjob/recycle_consumer_base.py b/methods/EverCore/src/core/longjob/recycle_consumer_base.py deleted file mode 100644 index 89453b1a..00000000 --- a/methods/EverCore/src/core/longjob/recycle_consumer_base.py +++ /dev/null @@ -1,451 +0,0 @@ -""" -Recycle consumer base implementation. -Base implementation of recycle consumer. -""" - -import asyncio -import logging -import random -from abc import ABC, abstractmethod -from typing import Optional, Any, Dict -from datetime import datetime - -from core.longjob.interfaces import ( - LongJobInterface, - LongJobStatus, - ConsumerConfig, - ErrorHandler, - MessageBatch, -) -from common_utils.datetime_utils import get_now_with_timezone - - -class DefaultErrorHandler(ErrorHandler): - """Default error handler""" - - def __init__(self, logger: Optional[logging.Logger] = None): - self.logger = logger or logging.getLogger(__name__) - - async def handle_error(self, error: Exception, context: Dict[str, Any]) -> bool: - """ - Default error handling: log error and return True to continue execution - - Args: - error: The exception occurred - context: Error context information - - Returns: - bool: Whether to continue execution - """ - self.logger.error( - f"Error in consumer {context.get('job_id', 'unknown')}: {str(error)}", - exc_info=True, - extra=context, - ) - return True - - -class RecycleConsumerBase(LongJobInterface, ABC): - """ - Base implementation of recycle consumer - Provides a basic framework for continuous consumption, including error handling, retry logic, timeout handling, etc. - """ - - def __init__( - self, - job_id: str, - config: Optional[Dict[str, Any]] = None, - consumer_config: Optional[ConsumerConfig] = None, - ): - """ - Initialize recycle consumer - - Args: - job_id: Job ID - config: Base configuration - consumer_config: Consumer-specific configuration - """ - super().__init__(job_id, config) - self.consumer_config = consumer_config or ConsumerConfig() - self.logger = logging.getLogger(f"{__name__}.{job_id}") - self._task: Optional[asyncio.Task] = None - self._error_handler = self.consumer_config.error_handler or DefaultErrorHandler( - self.logger - ) - - # Statistics - self.stats = { - 'total_processed': 0, - 'total_errors': 0, - 'total_timeouts': 0, - 'start_time': None, - 'last_processed_time': None, - } - - async def start(self) -> None: - """Start consumer""" - if self.status in [LongJobStatus.RUNNING, LongJobStatus.STARTING]: - self.logger.warning( - "Consumer %s is already running or starting", self.job_id - ) - return - - self.logger.info("Starting consumer %s", self.job_id) - self.status = LongJobStatus.STARTING - - try: - # Initialize resources - await self._initialize() - - # Start consumption loop - self._task = asyncio.create_task(self._consume_loop()) - self.status = LongJobStatus.RUNNING - self.stats['start_time'] = get_now_with_timezone() - - self.logger.info("Consumer %s started successfully", self.job_id) - - except Exception as e: - self.status = LongJobStatus.ERROR - self.logger.error( - "Failed to start consumer %s: %s", self.job_id, str(e), exc_info=True - ) - raise - - async def shutdown( - self, timeout: float = 30.0, wait_for_current_task: bool = True - ) -> None: - """ - Gracefully shutdown consumer - - Args: - timeout: Shutdown timeout in seconds - wait_for_current_task: Whether to wait for current task to complete - """ - if self.status in [LongJobStatus.STOPPED, LongJobStatus.STOPPING]: - self.logger.warning( - "Consumer %s is already stopped or stopping", self.job_id - ) - return - - self.logger.info("Gracefully shutting down consumer %s", self.job_id) - self.status = LongJobStatus.STOPPING - - # Request stop - self.request_stop() - - # Wait for task completion - if self._task and not self._task.done(): - if wait_for_current_task: - try: - # Wait for current message processing to complete - self.logger.info( - "Waiting for current task to complete in consumer %s", - self.job_id, - ) - await asyncio.wait_for(self._task, timeout=timeout) - self.logger.info( - "Current task completed gracefully in consumer %s", self.job_id - ) - except asyncio.TimeoutError: - self.logger.warning( - "Consumer %s shutdown timeout after %ss, cancelling task", - self.job_id, - timeout, - ) - self._task.cancel() - try: - await self._task - except asyncio.CancelledError: - self.logger.info("Task cancelled in consumer %s", self.job_id) - else: - # Cancel task immediately - self.logger.info( - "Immediately cancelling task in consumer %s", self.job_id - ) - self._task.cancel() - try: - await self._task - except asyncio.CancelledError: - pass - - # Cleanup resources - try: - await self._cleanup() - except Exception as cleanup_error: - self.logger.error( - "Error during cleanup: %s", str(cleanup_error), exc_info=True - ) - - self.status = LongJobStatus.STOPPED - self.logger.info("Consumer %s shutdown completed", self.job_id) - - async def _consume_loop(self) -> None: - """Main consumption loop""" - self.logger.info("Consumer %s entering consume loop", self.job_id) - - while not self.should_stop(): - try: - # Check if there are messages to consume - if not await self._has_messages(): - await asyncio.sleep(0.1) # Brief sleep to avoid high CPU usage - continue - - # Consume messages - await self._consume_messages() - - except Exception as e: - # Error handling - context = { - 'job_id': self.job_id, - 'timestamp': get_now_with_timezone().isoformat(), - 'stats': self.stats.copy(), - } - - self.stats['total_errors'] += 1 - - try: - should_continue = await self._error_handler.handle_error(e, context) - if not should_continue: - self.logger.error( - "Error handler requested stop for consumer %s", self.job_id - ) - break - except Exception as handler_error: - self.logger.error( - "Error in error handler for consumer %s: %s", - self.job_id, - str(handler_error), - exc_info=True, - ) - # If error handler itself fails, sleep briefly and continue - await asyncio.sleep(1.0) - - self.logger.info("Consumer %s exiting consume loop", self.job_id) - - async def _consume_messages(self) -> None: - """Core logic for consuming messages""" - timeout = self.consumer_config.timeout - - # Business logic handles batch processing itself; here only process single message - if self.should_stop(): - return - - try: - # Use timeout to control processing time for a single message - await asyncio.wait_for(self._process_single_message(), timeout=timeout) - - self.stats['total_processed'] += 1 - self.stats['last_processed_time'] = get_now_with_timezone() - - except asyncio.TimeoutError: - self.stats['total_timeouts'] += 1 - self.logger.warning( - "Message processing timeout in consumer %s (timeout: %ss)", - self.job_id, - timeout, - ) - # Timeout is also treated as an error, handled by error handler - timeout_error = TimeoutError(f"Message processing timeout ({timeout}s)") - context = { - 'job_id': self.job_id, - 'error_type': 'timeout', - 'timeout': timeout, - 'timestamp': get_now_with_timezone().isoformat(), - } - - try: - should_continue = await self._error_handler.handle_error( - timeout_error, context - ) - if not should_continue: - raise timeout_error - except Exception as handler_error: - self.logger.error( - "Error in timeout error handler: %s", - str(handler_error), - exc_info=True, - ) - - except Exception as e: - # Other exceptions will be caught and handled in the outer loop - raise - - async def _process_single_message(self) -> None: - """ - Process a single message with enhanced retry logic - First fetch the message, then pass the same message batch during retries - """ - retry_config = self.consumer_config.retry_config - last_error = None - message_batch = None - - for attempt in range(retry_config.max_retries + 1): - try: - # Fetch message on first attempt, use same batch on retries - if attempt == 0: - raw_message = await self._fetch_message() - if raw_message is None: - return # No message to process - - # If not MessageBatch, wrap automatically - if isinstance(raw_message, MessageBatch): - message_batch = raw_message - else: - message_batch = MessageBatch( - data=raw_message, - batch_id=f"auto_wrapped_{id(raw_message)}", - metadata={'auto_wrapped': True}, - ) - - if message_batch.is_empty: - return # No message to process - - # Call subclass's specific message handling logic, passing message batch - await self._handle_message(message_batch) - return # Successfully processed, return directly - - except Exception as e: - last_error = e - - # Check if it's a fatal error and retry on fatal is disabled - if ( - self._error_handler.is_fatal_error(e) - and not retry_config.retry_on_fatal - ): - self.logger.error( - "Fatal error in consumer %s, not retrying: %s", - self.job_id, - str(e), - ) - raise - - # Check if it's a non-retryable error - if ( - not self._error_handler.is_retryable_error(e) - and not retry_config.retry_on_fatal - ): - self.logger.error( - "Non-retryable error in consumer %s: %s", self.job_id, str(e) - ) - raise - - if attempt < retry_config.max_retries: - # Calculate retry delay - delay = self._calculate_retry_delay(attempt, retry_config) - - self.logger.warning( - "Message processing failed (attempt %d/%d) in consumer %s, retrying in %ss: %s", - attempt + 1, - retry_config.max_retries + 1, - self.job_id, - delay, - str(e), - ) - - await asyncio.sleep(delay) - else: - # Maximum retries reached, raise exception - self.logger.error( - "Message processing failed after %d attempts in consumer %s: %s", - retry_config.max_retries + 1, - self.job_id, - str(e), - ) - raise last_error - - # Theoretically unreachable, but for safety - if last_error: - raise last_error from None - - def _calculate_retry_delay(self, attempt: int, retry_config) -> float: - """ - Calculate retry delay with support for exponential backoff and jitter - - Args: - attempt: Current retry count (starting from 0) - retry_config: Retry configuration - - Returns: - float: Delay time in seconds - """ - if retry_config.exponential_backoff: - # Exponential backoff - delay = retry_config.retry_delay * ( - retry_config.backoff_multiplier**attempt - ) - else: - # Fixed delay - delay = retry_config.retry_delay - - # Limit maximum delay - delay = min(delay, retry_config.max_delay) - - # Add random jitter - if retry_config.jitter: - # Random between 50% to 150% - jitter_factor = 0.5 + random.random() - delay = delay * jitter_factor - - return delay - - def get_stats(self) -> Dict[str, Any]: - """Get consumer statistics""" - stats = self.stats.copy() - stats['status'] = self.status.value - stats['uptime'] = None - - if stats['start_time']: - uptime = get_now_with_timezone() - stats['start_time'] - stats['uptime'] = uptime.total_seconds() - - return stats - - @abstractmethod - async def _initialize(self) -> None: - """ - Initialize resources - Subclasses need to implement this method to initialize specific resources (e.g., connections, queues, etc.) - """ - - @abstractmethod - async def _cleanup(self) -> None: - """ - Cleanup resources - Subclasses need to implement this method to clean up specific resources - """ - - @abstractmethod - async def _has_messages(self) -> bool: - """ - Check if there are messages available for consumption - Subclasses need to implement this method to check if the message source has new messages - - Returns: - bool: Whether there are messages available for consumption - """ - - @abstractmethod - async def _fetch_message(self) -> Optional[Any]: - """ - Fetch message data - Subclasses need to implement this method to retrieve messages from the message source, can return any type of data - The framework will automatically determine the type and wrap it if it's not a MessageBatch - - Returns: - Optional[Any]: Retrieved message data, can be any type, return None if no message - """ - - @abstractmethod - async def _handle_message(self, message_batch: MessageBatch) -> None: - """ - Specific logic to handle message batch - Subclasses need to implement this method to define the specific message processing logic - - Args: - message_batch: Message batch to be processed, returned by _fetch_message - - Note: - This method should process the incoming message batch, and throw an exception if processing fails - Retry logic is handled by the base class, and the same message batch will be passed during retries - Subclasses can access all messages via message_batch.messages and decide how to process them (individually or in batch) - """ diff --git a/methods/EverCore/src/core/middleware/app_logic_middleware.py b/methods/EverCore/src/core/middleware/app_logic_middleware.py deleted file mode 100644 index 2f8b2379..00000000 --- a/methods/EverCore/src/core/middleware/app_logic_middleware.py +++ /dev/null @@ -1,97 +0,0 @@ -""" -Application logic middleware -Responsible for extracting and setting application-level context information, and handling application-related logic (e.g., reporting) -""" - -from typing import Callable, Dict, Any, Optional - -from fastapi import Request, HTTPException -from starlette.responses import Response -from starlette.middleware.base import BaseHTTPMiddleware -from starlette.types import ASGIApp - -from core.observation.logger import get_logger -from core.context.context import set_current_app_info, set_current_request -from core.di.utils import get_bean_by_type -from core.request.app_logic_provider import AppLogicProvider - -logger = get_logger(__name__) - - -class AppLogicMiddleware(BaseHTTPMiddleware): - """ - Application logic middleware - - Responsible for managing the request lifecycle and invoking callback methods of AppLogicProvider: - - setup_app_context(): Extract and set application context (called on every request) - - on_request_begin(): Called when request begins (controlled by should_process_request) - - on_request_complete(): Called when request ends (controlled by should_process_request) - """ - - def __init__(self, app: ASGIApp): - super().__init__(app) - self._app_logic_provider = get_bean_by_type(AppLogicProvider) - - async def dispatch(self, request: Request, call_next: Callable) -> Response: - # ========== Extract and set application context (called on every request) ========== - app_info = self._app_logic_provider.setup_app_context(request) - - # Set context - set_current_request(request) - # _CachedRequest is a subclass of Request, it caches the request body in memory - await request.body() - if app_info: - set_current_app_info(app_info) - - # ========== Check whether to process business logic for this request ========== - should_process = self._app_logic_provider.should_process_request(request) - if not should_process: - # Skip business logic processing, directly call the next middleware - return await call_next(request) - - response: Optional[Response] = None - error_message: Optional[str] = None - exception_status_code: Optional[int] = None - - try: - # ========== Validate request: call validate_request ========== - await self._app_logic_provider.validate_request(request) - # If validation fails (e.g., quota exceeded), HTTPException will be raised - # FastAPI will catch HTTPException and return appropriate response (e.g., 429) - - # ========== Request begins: call on_request_begin ========== - await self._app_logic_provider.on_request_begin(request) - - # ========== Call next layer processing ========== - response = await call_next(request) - return response - - except HTTPException as e: - logger.error("HTTPException in application logic middleware: %s", e) - error_message = str(e.detail) if e.detail else str(e) - exception_status_code = e.status_code - raise - - except Exception as e: - logger.error("Exception in application logic middleware: %s", e) - error_message = str(e) - raise - - finally: - # ========== Request ends: call on_request_complete ========== - # Determine HTTP status code - if response is not None: - http_code = response.status_code - elif exception_status_code is not None: - http_code = exception_status_code - else: - http_code = 500 - - try: - await self._app_logic_provider.on_request_complete( - request=request, http_code=http_code, error_message=error_message - ) - except Exception as callback_error: - logger.warning( - "on_request_complete execution failed: %s", callback_error - ) diff --git a/methods/EverCore/src/core/middleware/database_session_middleware.py b/methods/EverCore/src/core/middleware/database_session_middleware.py deleted file mode 100644 index 4d0273c7..00000000 --- a/methods/EverCore/src/core/middleware/database_session_middleware.py +++ /dev/null @@ -1,237 +0,0 @@ -from fastapi import Request, Response -from starlette.responses import StreamingResponse -from starlette.middleware.base import _StreamingResponse -from starlette.middleware.base import BaseHTTPMiddleware -from starlette.types import ASGIApp -from typing import Callable, AsyncGenerator -from sqlmodel.ext.asyncio.session import AsyncSession - -from core.context.context import ( - set_current_session, - clear_current_session, - get_current_session, -) -from core.component.database_session_provider import DatabaseSessionProvider -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class DatabaseSessionMiddleware(BaseHTTPMiddleware): - """ - Simplified database session middleware - - Provides a database session for each HTTP request and intelligently handles cleanup: - - Checks session state, automatically rolls back on error - - Automatically rolls back on request failure - - Automatically commits if there are uncommitted changes on successful request - - Gives the application maximum freedom in transaction control - """ - - def __init__(self, app: ASGIApp): - super().__init__(app) - self.db_provider = get_bean_by_type(DatabaseSessionProvider) - - async def dispatch(self, request: Request, call_next: Callable) -> Response: - """ - Provide a database session for each request and intelligently handle transactions - - Args: - request: FastAPI request object - call_next: Next middleware or route handler - - Returns: - Response: Response object - """ - # Create a new database session - session = self.db_provider.create_session() - token = set_current_session(session) - response = None - is_streaming = False - - try: - # Execute request handling - response = await call_next(request) - - # Check if it's a streaming response, which requires special handling - if isinstance(response, StreamingResponse) or isinstance( - response, _StreamingResponse - ): - is_streaming = True - # Streaming response: wrap the generator to extend session lifetime - wrapped_generator = self._wrap_streaming_generator( - response.body_iterator, session - ) - return StreamingResponse( - wrapped_generator, - status_code=response.status_code, - headers=response.headers, - media_type=response.media_type, - background=response.background, - ) - else: - # Non-streaming response: use original logic - await self._handle_successful_request(session) - return response - - except Exception as e: - # Request handling failed, rollback session - await self._handle_failed_request(session, e) - raise - - finally: - # Clean up the original context token - # For non-streaming responses: directly clean up session - # For streaming responses: only reset token, session cleanup is handled by the wrapped generator - if not is_streaming: - clear_current_session(token) - await self._close_session_safely(session) - else: - # Streaming response: reset the original context token, but do not close session - # Session closing is handled by the streaming generator - try: - clear_current_session(token) - logger.debug( - "Original context token for streaming response has been reset" - ) - except Exception as reset_error: - logger.warning( - f"Failed to reset original context token for streaming response: {str(reset_error)}" - ) - # Token reset failure should not affect the response - - async def _handle_successful_request(self, session: AsyncSession) -> None: - """ - Handle successful request - intelligently decide whether to commit transaction - - Args: - session: Database session - """ - try: - # Check if session is active - if not session.is_active: - logger.debug("Session is not active, skipping processing") - return - - # Commit transaction - simple & safe, AI don't mess this up - await session.commit() - - except Exception as e: - logger.error(f"Error while handling successful request: {str(e)}") - # If processing fails, attempt rollback - await self._rollback_safely(session) - - async def _handle_failed_request( - self, session: AsyncSession, original_exception: Exception - ) -> None: - """ - Handle failed request - rollback transaction - - Args: - session: Database session - original_exception: Original exception - """ - try: - # Request failed, rollback directly - await self._rollback_safely(session) - logger.info( - f"Request failed, transaction rollback executed: {str(original_exception)}" - ) - - except Exception as rollback_error: - logger.error(f"Error during rollback: {str(rollback_error)}") - # Rollback failed, but do not mask the original exception - - async def _rollback_safely(self, session: AsyncSession) -> None: - """ - Safely rollback session - - Args: - session: Database session - """ - try: - await session.rollback() - logger.debug("Session successfully rolled back") - except Exception as rollback_error: - logger.error(f"Rollback failed: {str(rollback_error)}") - - async def _close_session_safely(self, session: AsyncSession) -> None: - """ - Safely close session - - Based on test results, session.close() behavior: - 1. Automatically rolls back uncommitted transactions - 2. Cleans up transaction objects - 3. Returns connection to connection pool - 4. Idempotent operation, can be called multiple times - 5. Session can still be reused - - Args: - session: Database session - """ - try: - await session.close() - logger.debug("Session safely closed") - except Exception as e: - logger.error(f"Error while closing session: {str(e)}") - # Even if closing fails, do not raise exception to avoid masking original error - - async def _wrap_streaming_generator( - self, original_generator: AsyncGenerator[bytes, None], session: AsyncSession - ) -> AsyncGenerator[bytes, None]: - """ - Wrap streaming response generator to extend the database session's lifetime - - This method ensures: - 1. Database session remains active throughout the streaming transmission - 2. Intelligently handles session after successful streaming (commits uncommitted changes) - 3. Rolls back session if an exception occurs during streaming - 4. Cleans up session resources regardless of success or failure - - To avoid ContextVar issues across contexts, we re-set the session into the context variable - within the streaming generator and clean it up afterward. - - Args: - original_generator: Original streaming data generator - session: Database session - token: Original context variable token (not used here) - - Yields: - bytes: Streaming data chunks - """ - # Re-set session within the streaming generator's context - # This avoids issues with cross-context token reset - local_token = set_current_session(session) - - try: - # Yield streaming data chunks one by one - async for chunk in original_generator: - yield chunk - - # Streaming transmission completed successfully, handle session intelligently - await self._handle_successful_request(session) - logger.debug( - "Streaming response transmission completed, database session handled" - ) - - except Exception as e: - # Exception during streaming transmission, rollback session - await self._handle_failed_request(session, e) - logger.error( - f"Streaming response transmission failed, database session rolled back: {str(e)}" - ) - # Re-raise exception for upper layers to handle - raise - - finally: - # Cleanup: reset current context token and close session - try: - clear_current_session(local_token) - await self._close_session_safely(session) - logger.debug("Streaming response session resources cleaned up") - except Exception as cleanup_error: - logger.error( - f"Error while cleaning up streaming response session resources: {str(cleanup_error)}" - ) - # Cleanup failure should not affect response stream diff --git a/methods/EverCore/src/core/middleware/global_exception_handler.py b/methods/EverCore/src/core/middleware/global_exception_handler.py deleted file mode 100644 index e941b990..00000000 --- a/methods/EverCore/src/core/middleware/global_exception_handler.py +++ /dev/null @@ -1,105 +0,0 @@ -""" -Global exception handler - -Provides a unified exception handling mechanism for FastAPI applications, ensuring all HTTP exceptions -(including exceptions raised by middleware) are properly handled and returned to the client. -""" - -from fastapi import Request, HTTPException -from fastapi.exceptions import RequestValidationError -from fastapi.responses import JSONResponse -from starlette.status import ( - HTTP_422_UNPROCESSABLE_CONTENT, - HTTP_500_INTERNAL_SERVER_ERROR, -) - -from core.observation.logger import get_logger -from common_utils.datetime_utils import to_iso_format, get_now_with_timezone -from core.constants.errors import ErrorCode -from core.di.utils import get_bean_by_type -from core.request.app_logic_provider import AppLogicProvider -from api_specs.dtos.base import ErrorApiResponse - -logger = get_logger(__name__) - - -async def global_exception_handler(request: Request, exc: Exception) -> JSONResponse: - """ - Global exception handler - - Handles all exceptions uniformly, including HTTPException and other exceptions, - ensuring they are properly formatted and returned to the client. - - Args: - request: FastAPI request object - exc: Exception object - - Returns: - JSONResponse: Formatted error response - """ - request_id = get_bean_by_type(AppLogicProvider).get_current_request_id() - - # Handle Pydantic/FastAPI validation errors (422) — convert to ErrorApiResponse format - if isinstance(exc, RequestValidationError): - errors = exc.errors() - if errors: - first = errors[0] - loc = " -> ".join(str(p) for p in first.get("loc", []) if p != "body") - msg = first.get("msg", "Validation error") - message = f"{msg}: {loc}" if loc else msg - else: - message = "Request validation error" - - logger.warning( - "Validation Failed: %s %s - %s", request.method, str(request.url), message - ) - error = ErrorApiResponse( - code=ErrorCode.HTTP_ERROR.value, - message=message, - request_id=request_id, - timestamp=to_iso_format(get_now_with_timezone()), - path=str(request.url.path), - ) - return JSONResponse( - status_code=HTTP_422_UNPROCESSABLE_CONTENT, content=error.model_dump() - ) - - # Handle HTTP exceptions - if isinstance(exc, HTTPException): - logger.warning( - "HTTP exception: %s %s - Status code: %d, Detail: %s", - request.method, - str(request.url), - exc.status_code, - exc.detail, - ) - - error = ErrorApiResponse( - code=ErrorCode.HTTP_ERROR.value, - message=exc.detail, - request_id=request_id, - timestamp=to_iso_format(get_now_with_timezone()), - path=str(request.url.path), - ) - return JSONResponse(status_code=exc.status_code, content=error.model_dump()) - - # Handle other exceptions - logger.error( - "Unhandled exception: %s %s - Exception type: %s, Detail: %s", - request.method, - str(request.url), - type(exc).__name__, - str(exc), - exc_info=True, - ) - - error = ErrorApiResponse( - code=ErrorCode.SYSTEM_ERROR.value, - message="Internal server error", - request_id=request_id, - timestamp=to_iso_format(get_now_with_timezone()), - path=str(request.url.path), - ) - return JSONResponse( - status_code=HTTP_500_INTERNAL_SERVER_ERROR, content=error.model_dump() - ) diff --git a/methods/EverCore/src/core/middleware/hmac_signature_middleware.py b/methods/EverCore/src/core/middleware/hmac_signature_middleware.py deleted file mode 100644 index cef0b361..00000000 --- a/methods/EverCore/src/core/middleware/hmac_signature_middleware.py +++ /dev/null @@ -1,422 +0,0 @@ -import hmac -import hashlib -import time -import os -from typing import Callable, Optional - -from fastapi import Request, Response -from starlette.middleware.base import BaseHTTPMiddleware -from starlette.types import ASGIApp - -from core.authorize.enums import Role -from core.context.context import set_current_user_info, clear_current_user_context -from core.observation.logger import get_logger -from core.component.redis_provider import RedisProvider -from core.di import get_bean_by_type - -logger = get_logger(__name__) - - -class HMACSignatureMiddleware(BaseHTTPMiddleware): - """ - HMAC signature verification middleware - - Verifies the HMAC signature of requests to ensure request integrity and authenticity. - Uses HTTP method, URL path, and timestamp as signing data. - The time window is 5 minutes; requests exceeding this window will be rejected. - """ - - def __init__( - self, - app: ASGIApp, - secret_key: str, - time_window_minutes: int = 5, - redis_provider: Optional[RedisProvider] = None, - ): - """ - Initialize HMAC signature middleware - - Args: - app: ASGI application instance - secret_key: Secret key for HMAC signature - time_window_minutes: Time window (in minutes), default is 5 minutes - redis_provider: Redis provider, used for replay attack prevention - """ - super().__init__(app) - self.secret_key = secret_key.encode('utf-8') - self.time_window_seconds = time_window_minutes * 60 - self._redis_provider = redis_provider - - @property - def redis_provider(self) -> RedisProvider: - return self._redis_provider or get_bean_by_type(RedisProvider) - - async def dispatch(self, request: Request, call_next: Callable) -> Response: - """ - Handle HMAC signature verification and set user context - - Expected request headers: - - X-Timestamp: Unix timestamp (seconds) - - X-Nonce: Nonce (for replay attack prevention) - - X-Signature: HMAC-SHA256 signature (hexadecimal format) - - Signature data format: {METHOD}|{URL_PATH}|{TIMESTAMP}|{NONCE} - - Args: - request: FastAPI request object - call_next: Next middleware or route handler - - Returns: - Response: Response object - """ - # Clear any existing user context - clear_current_user_context() - - # Set user context token - token = None - - # Step 1: Attempt HMAC signature verification and set user context - try: - # Get timestamp, nonce, and signature from request headers - timestamp_header = request.headers.get("X-Timestamp") - nonce_header = request.headers.get("X-Nonce") - signature_header = request.headers.get("X-Signature") - - # If there are signature-related headers, perform verification - if ( - timestamp_header and nonce_header and signature_header - ) or signature_header == "1234567890": - # Verify HMAC signature - is_valid_signature = await self._verify_hmac_signature( - request, timestamp_header, nonce_header, signature_header - ) - - if is_valid_signature: - # Signature verification succeeded, set user context with SIGNATURE role - user_data = {"user_id": -1, "role": Role.SIGNATURE.value} - token = set_current_user_info(user_data) - logger.info("HMAC signature user context set: role=SIGNATURE") - else: - logger.info( - "HMAC signature verification failed, user context not set" - ) - else: - logger.info( - "HMAC signature headers not found, skipping signature verification" - ) - - except Exception as e: - logger.error( - "Exception during HMAC signature verification: %s, " - "timestamp_header='%s', nonce_header='%s', signature_header='%s', " - "method='%s', url_path='%s'", - str(e), - timestamp_header, - nonce_header, - signature_header, - request.method, - request.url.path, - ) - # Signature verification failure does not affect request processing - # Specific permission checks are handled by individual endpoints - - # Step 2: Execute business logic - try: - response = await call_next(request) - return response - - except Exception as e: - logger.error(f"Exception in business logic processing: {str(e)}") - # Re-raise business logic exceptions for upstream handling - raise - - finally: - # Clean up user context - if token is not None: - try: - clear_current_user_context(token) - logger.debug("HMAC signature user context cleaned up") - except Exception as reset_error: - logger.warning( - f"Error occurred while cleaning up HMAC signature user context: {str(reset_error)}" - ) - - async def _verify_hmac_signature( - self, - request: Request, - timestamp_header: str, - nonce_header: str, - signature_header: str, - ) -> bool: - """ - Verify HMAC signature - - Args: - request: FastAPI request object - timestamp_header: Timestamp header value - nonce_header: Nonce header value - signature_header: Signature header value - - Returns: - bool: Whether the signature is valid - """ - - if signature_header == "1234567890" and os.getenv("ENV") == "dev": - return True - - try: - # Parse timestamp - request_timestamp = int(timestamp_header) - except ValueError: - logger.error( - "HMAC signature verification failed - invalid timestamp format: " - "timestamp_header='%s', nonce_header='%s', signature_header='%s', " - "method='%s', url_path='%s'", - timestamp_header, - nonce_header, - signature_header, - request.method, - request.url.path, - ) - return False - - # Validate nonce is not empty - if not nonce_header or not nonce_header.strip(): - logger.error( - "HMAC signature verification failed - X-Nonce header is empty or invalid: " - "timestamp_header='%s', nonce_header='%s', signature_header='%s', " - "method='%s', url_path='%s'", - timestamp_header, - nonce_header, - signature_header, - request.method, - request.url.path, - ) - return False - - # Replay attack prevention: Use atomic operation to check and store nonce - nonce_key = f"nonce:{nonce_header}" - expire_seconds = self.time_window_seconds * 2 - - if self.redis_provider: - try: - # Use Redis SET NX EX command for atomic operation: set if key does not exist, otherwise return False - # This completes the check and storage in one atomic operation, avoiding race conditions - nonce_stored = await self.redis_provider.set( - nonce_key, str(request_timestamp), ex=expire_seconds, nx=True - ) - if not nonce_stored: - logger.error( - "HMAC signature verification failed - replay attack detected, nonce has been used: " - "timestamp_header='%s', nonce_header='%s', signature_header='%s', " - "method='%s', url_path='%s', request_timestamp=%d, current_time=%d", - timestamp_header, - nonce_header, - signature_header, - request.method, - request.url.path, - request_timestamp, - int(time.time()), - ) - return False - logger.debug( - "Nonce stored in Redis: %s, expiration time=%d seconds", - nonce_header, - expire_seconds, - ) - except Exception as e: - logger.error( - "Error occurred while checking and storing nonce: %s", str(e) - ) - # If Redis is unavailable, log a warning but do not block the request (degraded mode) - logger.warning("Redis unavailable, skipping nonce replay check") - - # Validate time window - current_time = int(time.time()) - time_diff = abs(current_time - request_timestamp) - - if time_diff > self.time_window_seconds: - logger.error( - "HMAC signature verification failed - request exceeds time window: " - "timestamp_header='%s', nonce_header='%s', signature_header='%s', " - "method='%s', url_path='%s', current_time=%d, request_timestamp=%d, " - "time_diff=%d seconds, time_window=%d seconds", - timestamp_header, - nonce_header, - signature_header, - request.method, - request.url.path, - current_time, - request_timestamp, - time_diff, - self.time_window_seconds, - ) - return False - - # Build signature data - method = request.method - url_path = request.url.path - signature_data = f"{method}|{url_path}|{request_timestamp}|{nonce_header}" - - # Calculate expected signature - expected_signature = hmac.new( - self.secret_key, signature_data.encode('utf-8'), hashlib.sha256 - ).hexdigest() - - # Verify signature - if not hmac.compare_digest(signature_header, expected_signature): - logger.error( - "HMAC signature verification failed - signature mismatch: " - "timestamp_header='%s', nonce_header='%s', signature_header='%s', " - "method='%s', url_path='%s', signature_data='%s', " - "expected_signature='%s', secret_key_length=%d", - timestamp_header, - nonce_header, - signature_header, - request.method, - request.url.path, - signature_data, - expected_signature, - len(self.secret_key), - ) - return False - - logger.debug("HMAC signature verification succeeded: %s %s", method, url_path) - return True - - -def get_hmac_security_config(): - """ - Get OpenAPI security configuration for HMAC signature authentication - - Returns: - List[dict]: List of OpenAPI security configurations defining requirements for HMAC signature authentication - """ - return [{"HMACSignature": []}] - - -def get_hmac_openapi_security_schemes(): - """ - Get OpenAPI security scheme definitions for HMAC signature authentication - - This function returns the configuration used in OpenAPI components.securitySchemes, - defining the specific implementation and parameters of HMAC signature authentication. - - Returns: - dict: OpenAPI securitySchemes configuration - """ - return { - "HMACSignature": { - "type": "apiKey", - "in": "header", - "name": "X-Signature", - "description": """**HMAC Signature Authentication** - -Uses the HMAC-SHA256 algorithm to sign and verify requests, ensuring request integrity and authenticity. - -**Signing Algorithm:** -1. Construct signature data: `{HTTP_METHOD}|{URL_PATH}|{TIMESTAMP}|{NONCE}` - - Example: `POST|/finance/storage/sign/download|1755572417|7fe6a3edabb9c1383b6d75a72ffce2e5` -2. Compute signature using HMAC-SHA256 algorithm and shared secret key -3. Convert signature to hexadecimal string - -**Required Request Headers:** -- `X-Timestamp`: Unix timestamp (seconds), used for replay attack prevention -- `X-Nonce`: Nonce (used for replay attack prevention, must be unique per request) -- `X-Signature`: HMAC-SHA256 signature (hexadecimal format) - -**Replay Attack Prevention Mechanism:** -- The difference between request timestamp and server time must not exceed 5 minutes -- Each nonce can only be used once; the server records used nonces in Redis -- The nonce expiration time in Redis is twice the time window (10 minutes), ensuring security -- Requests reusing the same nonce will be rejected - -**Signature Example:** -```bash -# Generate signature using HMAC signature generator tool -python tests/hmac_signature_generator.py -m POST -p "/finance/storage/sign/download" -k "abc-12345" - -# Or generate signature manually (assuming key is "abc-12345") -TIMESTAMP=$(date +%s) -NONCE=$(openssl rand -hex 16) -SIGNATURE_DATA="POST|/finance/storage/sign/download|${TIMESTAMP}|${NONCE}" -SIGNATURE=$(echo -n "$SIGNATURE_DATA" | openssl dgst -sha256 -hmac "abc-12345" | cut -d' ' -f2) - -curl -X POST "https://api.example.com/finance/storage/sign/download" \\ - -H "X-Timestamp: ${TIMESTAMP}" \\ - -H "X-Nonce: ${NONCE}" \\ - -H "X-Signature: ${SIGNATURE}" -``` - -**Python Code Example:** -```python -import hmac -import hashlib -import secrets -import time - -# Complete example of generating a signature -method = "POST" -url_path = "/finance/storage/sign/download" -timestamp = int(time.time()) -nonce = secrets.token_hex(16) -secret_key = "abc-12345" - -# Construct signature data -signature_data = f"{method}|{url_path}|{timestamp}|{nonce}" -signature = hmac.new( - secret_key.encode('utf-8'), - signature_data.encode('utf-8'), - hashlib.sha256 -).hexdigest() - -# Request headers -headers = { - "X-Timestamp": str(timestamp), - "X-Nonce": nonce, - "X-Signature": signature -} -``` - -**Development Environment Shortcut:** -- When environment variable `ENV=dev`, you can use `X-Signature: 1234567890` as a test signature -- Production environment must use correct HMAC signature - -**Security Notes:** -- The secret key must be kept confidential and not leaked -- It is recommended to rotate keys regularly -- Ensure client clock is synchronized with server clock -- Nonce should be generated using a cryptographically secure random number generator -- Redis is used to store nonces; ensure Redis security and availability""", - "x-example": { - "X-Timestamp": "1755572417", - "X-Nonce": "7fe6a3edabb9c1383b6d75a72ffce2e5", - "X-Signature": "6c17b2d568d42b9e0a9df422133f3e84bf4c3aa9bed04400843822586f25e4cd", - }, - } - } - - -def create_hmac_middleware( - secret_key: str, - time_window_minutes: int = 5, - redis_provider: Optional[RedisProvider] = None, -): - """ - Factory function to create HMAC signature middleware - - Args: - secret_key: Secret key for HMAC signature - time_window_minutes: Time window (in minutes), default is 5 minutes - redis_provider: Redis provider, used for replay attack prevention - - Returns: - Callable: Middleware constructor - """ - - def middleware_factory(app: ASGIApp) -> HMACSignatureMiddleware: - return HMACSignatureMiddleware( - app, secret_key, time_window_minutes, redis_provider - ) - - return middleware_factory diff --git a/methods/EverCore/src/core/middleware/profile_middleware.py b/methods/EverCore/src/core/middleware/profile_middleware.py deleted file mode 100644 index 0eeb426a..00000000 --- a/methods/EverCore/src/core/middleware/profile_middleware.py +++ /dev/null @@ -1,139 +0,0 @@ -""" -Performance Profiling Middleware - -Provides performance profiling functionality for HTTP requests based on the pyinstrument library. - -Features: -1. URL parameter trigger: enable profiling by adding ?profile=true to the request URL -2. HTML report: returns a visual performance analysis HTML report -3. Environment variable control: enable/disable via the PROFILING_ENABLED environment variable -4. Graceful degradation: automatically disabled if pyinstrument is not installed, without affecting normal requests - -Environment Variables: -- PROFILING_ENABLED: whether to enable profiling (default: false) -- PROFILING: same as PROFILING_ENABLED (alternative environment variable name) - -Usage: -1. Set environment variable: export PROFILING_ENABLED=true -2. Install dependency: uv add pyinstrument -3. Add parameter when accessing endpoint: http://localhost:8000/api/endpoint?profile=true -""" - -import os -from typing import Callable, Optional - -from fastapi import Request -from fastapi.responses import HTMLResponse -from starlette.responses import Response -from starlette.middleware.base import BaseHTTPMiddleware -from starlette.types import ASGIApp - -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class ProfileMiddleware(BaseHTTPMiddleware): - """ - Performance profiling middleware - - Enables performance profiling when the request URL contains the ?profile=true parameter and returns an HTML-formatted analysis report - """ - - def __init__(self, app: ASGIApp): - """ - Initialize the performance profiling middleware - - Args: - app: ASGI application instance - """ - super().__init__(app) - - # Read from environment variable whether profiling is enabled - profiling_env = os.getenv( - 'PROFILING_ENABLED', os.getenv('PROFILING', 'true') - ).lower() - self._profiling_enabled = profiling_env in ('true', '1', 'yes') - - # Check if pyinstrument is available - self._profiler_available = False - if self._profiling_enabled: - try: - import pyinstrument - - self._profiler_available = True - logger.info("✅ Performance profiling middleware enabled") - logger.info( - "Tip: Add ?profile=true parameter to the request URL to enable profiling" - ) - except ImportError: - logger.warning( - "⚠️ pyinstrument not installed, profiling feature will be disabled" - ) - logger.warning("Please run: uv add pyinstrument") - self._profiling_enabled = False - else: - logger.debug( - "Performance profiling is not enabled (set environment variable PROFILING_ENABLED=true to enable)" - ) - - async def dispatch(self, request: Request, call_next: Callable) -> Response: - """ - Handle HTTP requests and perform performance profiling when needed - - Args: - request: FastAPI request object - call_next: next middleware or route handler - - Returns: - Response: response object (normal response or profiling report) - """ - # If feature is not enabled, pass through directly - if not self._profiling_enabled or not self._profiler_available: - return await call_next(request) - - # Check if profiling is required - profiling = request.query_params.get("profile", "").lower() in ( - "true", - "1", - "yes", - ) - - if not profiling: - # No profiling needed, process request normally - return await call_next(request) - - # Profiling is needed - try: - # Dynamic import (although availability has been checked, import when used for type safety) - from pyinstrument import Profiler - - # Create and start profiler - profiler = Profiler() - profiler.start() - - logger.info("Profiling started: %s %s", request.method, request.url.path) - - try: - # Execute request (note: original response will be discarded and replaced with profiler report) - await call_next(request) - except Exception as e: - # Even if the request fails, stop profiler and return profiling report - logger.error("Request failed during profiling: %s", str(e)) - # Continue generating profiling report - - # Stop profiler - profiler.stop() - - # Generate HTML report - html_output = profiler.output_html() - - logger.info("Profiling completed: %s %s", request.method, request.url.path) - - # Return HTML-formatted profiling report - return HTMLResponse(content=html_output, status_code=200) - - except Exception as e: - logger.error("Error occurred during profiling: %s", str(e)) - # If profiling fails, re-execute normal request - return await call_next(request) diff --git a/methods/EverCore/src/core/middleware/prometheus_middleware.py b/methods/EverCore/src/core/middleware/prometheus_middleware.py deleted file mode 100644 index 1763d6bf..00000000 --- a/methods/EverCore/src/core/middleware/prometheus_middleware.py +++ /dev/null @@ -1,193 +0,0 @@ -""" -Prometheus HTTP Metrics Middleware - -Auto-instrumentation middleware for HTTP request metrics. -Inspired by Kratos, Hertz, and other microservice frameworks. - -Usage: - from fastapi import FastAPI - from core.middleware.prometheus_middleware import PrometheusMiddleware - - app = FastAPI() - app.add_middleware(PrometheusMiddleware) -""" -from core.observation.logger import get_logger -import time -from typing import Callable -from starlette.middleware.base import BaseHTTPMiddleware -from starlette.requests import Request -from starlette.responses import Response -from prometheus_client import Counter, Histogram -from core.observation.metrics.registry import get_metrics_registry - -logger = get_logger(__name__) - - -# Pre-defined HTTP metrics (following Prometheus naming conventions) -_http_requests_total = Counter( - name='http_requests_total', - documentation='Total number of HTTP requests', - labelnames=['method', 'path', 'status'], - namespace='evermemos', - registry=get_metrics_registry(), -) - -_http_request_duration_seconds = Histogram( - name='http_request_duration_seconds', - documentation='HTTP request duration in seconds', - labelnames=['method', 'path'], - namespace='evermemos', - buckets=(0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0), - registry=get_metrics_registry(), -) - -_http_request_size_bytes = Histogram( - name='http_request_size_bytes', - documentation='HTTP request size in bytes', - labelnames=['method', 'path'], - namespace='evermemos', - buckets=(100, 1000, 10000, 100000, 1000000, 10000000), - registry=get_metrics_registry(), -) - -_http_response_size_bytes = Histogram( - name='http_response_size_bytes', - documentation='HTTP response size in bytes', - labelnames=['method', 'path'], - namespace='evermemos', - buckets=(100, 1000, 10000, 100000, 1000000, 10000000), - registry=get_metrics_registry(), -) - - -def _get_fastapi_route_template(request: Request) -> str: - """ - Get the actual route template from FastAPI request. - - Args: - request: FastAPI request object - - Returns: - str: Route template string, empty string if not available - """ - try: - # Get route info from request.scope (available after request processing) - if hasattr(request, 'scope') and 'route' in request.scope: - route = request.scope['route'] - if hasattr(route, 'path'): - return route.path - - # If no route in scope, try to infer from path_params - if hasattr(request, 'path_params') and request.path_params: - path = request.url.path - for param_name, param_value in request.path_params.items(): - if str(param_value) in path: - path = path.replace(str(param_value), f"{{{param_name}}}") - return path - - except Exception as e: - logger.debug("Failed to get FastAPI route template: %s", str(e)) - - return "" - - -def _normalize_path(request: Request) -> str: - """ - Get normalized path label, prefer FastAPI route template. - - Strategy: - 1. Try to get actual route template from FastAPI route info - 2. Mark unmatched paths as {unmatched} - - Examples: - - /api/users/123 -> /api/users/{user_id} (FastAPI route template) - - /unknown/path -> {unmatched} (unmatched path) - - Args: - request: FastAPI request object - - Returns: - str: Normalized path - """ - route_template = _get_fastapi_route_template(request) - if route_template: - return route_template - - return '{unmatched}' - - - -class PrometheusMiddleware(BaseHTTPMiddleware): - """ - Prometheus HTTP Metrics Middleware - - Automatically records: - - http_requests_total (Counter): Total requests by method, path, status - - http_request_duration_seconds (Histogram): Request latency - - http_request_size_bytes (Histogram): Request body size - - http_response_size_bytes (Histogram): Response body size - - Design inspired by: - - Kratos (Bilibili): middleware.Middleware pattern - - Hertz (ByteDance): promhttp.InstrumentHandler pattern - - Go kit: endpoint.Middleware pattern - - Usage: - app = FastAPI() - app.add_middleware(PrometheusMiddleware) - """ - - # Paths to skip metrics collection - SKIP_PATHS = {'/metrics', '/health', '/healthz', '/ready', '/favicon.ico'} - - async def dispatch(self, request: Request, call_next: Callable) -> Response: - # Skip metrics for certain paths - if request.url.path in self.SKIP_PATHS: - return await call_next(request) - - method = request.method - - # Record request size (before processing) - request_size = 0 - if request.headers.get('content-length'): - request_size = int(request.headers.get('content-length', 0)) - - # Time the request - start_time = time.perf_counter() - status = '500' # Default to 500 in case of unhandled exception - response = None - - try: - response = await call_next(request) - status = str(response.status_code) - except Exception: - raise - finally: - # Get path AFTER call_next - route info is now available - path = _normalize_path(request) - - # Record metrics - duration = time.perf_counter() - start_time - - _http_requests_total.labels( - method=method, - path=path, - status=status, - ).inc() - - _http_request_duration_seconds.labels( - method=method, - path=path, - ).observe(duration) - - # Record request size - if request_size > 0: - _http_request_size_bytes.labels(method=method, path=path).observe(request_size) - - # Record response size - if response and hasattr(response, 'headers') and response.headers.get('content-length'): - response_size = int(response.headers.get('content-length', 0)) - _http_response_size_bytes.labels(method=method, path=path).observe(response_size) - - return response - diff --git a/methods/EverCore/src/core/middleware/sse_exception_middleware.py b/methods/EverCore/src/core/middleware/sse_exception_middleware.py deleted file mode 100644 index 0563a5a3..00000000 --- a/methods/EverCore/src/core/middleware/sse_exception_middleware.py +++ /dev/null @@ -1,85 +0,0 @@ -""" -SSE (Server-Sent Events) exception handling middleware - -Provides a decorator to convert HTTP exceptions and other exceptions into SSE event format, -ensuring that exceptions in streaming responses are returned to the client in a standard format. - -This middleware belongs to the infrastructure layer and handles technical details related to the HTTP protocol. -""" - -import json -import logging -from typing import Any, AsyncGenerator, Callable -from functools import wraps - -from fastapi import HTTPException - -logger = logging.getLogger(__name__) - - -def yield_sse_data(data: Any) -> str: - """ - Format data into SSE format - - Args: - data: Data to be sent - - Returns: - str: Data string in SSE format - """ - return f"data: {json.dumps(data, ensure_ascii=False)}\n\n" - - -def sse_exception_handler( - func: Callable[..., AsyncGenerator[str, None]] -) -> Callable[..., AsyncGenerator[str, None]]: - """ - SSE stream exception handling decorator - - Converts HTTPException and other exceptions into SSE event format, ensuring the client can - handle errors in streaming responses in a consistent manner. - - Exception conversion rules: - - HTTPException -> {"type": "error", "data": {"code": status_code, "message": detail}} - - Other exceptions -> {"type": "error", "data": {"code": 500, "message": "Internal server error: {error}"}} - - Usage: - @sse_exception_handler - async def my_sse_generator() -> AsyncGenerator[str, None]: - # Generate SSE events - yield yield_sse_data({"type": "message", "content": "hello"}) - - Args: - func: Asynchronous generator function returning AsyncGenerator[str, None] - - Returns: - Decorated asynchronous generator function - """ - - @wraps(func) - async def wrapper(*args, **kwargs) -> AsyncGenerator[str, None]: - try: - async for event in func(*args, **kwargs): - yield event - except HTTPException as e: - # Convert HTTPException into SSE error event - error_data = { - "type": "error", - "data": {"code": e.status_code, "message": e.detail}, - } - logger.error( - f"HTTP exception occurred in SSE stream: {e.status_code} - {e.detail}" - ) - yield yield_sse_data(error_data) - except Exception as e: - # Convert other exceptions into SSE error event - error_data = { - "type": "error", - "data": {"code": 500, "message": f"Internal server error: {str(e)}"}, - } - logger.error( - f"Unknown exception occurred in SSE stream: {e}", exc_info=True - ) - yield yield_sse_data(error_data) - - return wrapper diff --git a/methods/EverCore/src/core/middleware/user_context_middleware.py b/methods/EverCore/src/core/middleware/user_context_middleware.py deleted file mode 100644 index 2f6aa1d7..00000000 --- a/methods/EverCore/src/core/middleware/user_context_middleware.py +++ /dev/null @@ -1,110 +0,0 @@ -from core.authorize.enums import Role -from fastapi import Request, Response, HTTPException -from starlette.middleware.base import BaseHTTPMiddleware -from starlette.types import ASGIApp -from typing import Callable - -from core.context.context import set_current_user_info, clear_current_user_context -from core.component.auth_provider import AuthProvider -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class UserContextMiddleware(BaseHTTPMiddleware): - """ - User context middleware - - Extract user information from each HTTP request and set it into the context variable, - so that user information can be accessed via context throughout the entire request processing, - without explicitly passing the request parameter. - """ - - def __init__(self, app: ASGIApp): - super().__init__(app) - self.auth_provider = get_bean_by_type(AuthProvider) - - async def dispatch(self, request: Request, call_next: Callable) -> Response: - """ - Set user context for each request - - Args: - request: FastAPI request object - call_next: Next middleware or route handler - - Returns: - Response: Response object - """ - # Clear any existing user context - clear_current_user_context() - - # Set user context token - token = None - - # Step 1: Try to get and set user context - try: - # Attempt to get full user data from the request - # This method now: - # 1. No authentication data -> returns anonymous user info - # 2. Authentication failure -> raises HTTPException(401) - user_data = await self.auth_provider.get_optional_user_data_from_request( - request - ) - - if user_data is not None: - # Set user context (including anonymous users) - token = set_current_user_info(user_data) - if user_data.get("role") == Role.ANONYMOUS.value: - logger.debug("Anonymous user context set") - else: - logger.debug( - "User context set: User ID=%s, Role=%s", - user_data.get("user_id"), - user_data.get("role"), - ) - else: - user_data = {"user_id": None, "role": Role.ANONYMOUS.value} - token = set_current_user_info(user_data) - logger.debug("No user data obtained, set anonymous user context") - - except HTTPException as e: - # If it's a 401 authentication failure, re-raise it directly, don't swallow - if e.status_code == 401: - logger.debug( - "Authentication failed, returning 401 error directly: %s", e.detail - ) - raise e - else: - logger.error( - "HTTP exception occurred while setting user context: %s - %s", - e.status_code, - e.detail, - ) - # Other HTTP exceptions do not affect request processing continuation - except Exception as e: - logger.error("Exception occurred while setting user context: %s", str(e)) - # User context setup failure does not affect request processing continuation - # Specific authentication checks are handled by individual endpoints - - # Step 2: Execute business logic - try: - response = await call_next(request) - return response - - except Exception as e: - logger.error("Business logic processing exception: %s", str(e)) - # Re-raise business logic exceptions for upper layers to handle - raise - - finally: - # Clean up user context - if token is not None: - try: - clear_current_user_context(token) - logger.debug("User context cleaned up") - except Exception as reset_error: - logger.warning( - "Error occurred while cleaning up user context: %s", - str(reset_error), - ) diff --git a/methods/EverCore/src/core/nlp/stopwords_utils.py b/methods/EverCore/src/core/nlp/stopwords_utils.py deleted file mode 100644 index c7e3c741..00000000 --- a/methods/EverCore/src/core/nlp/stopwords_utils.py +++ /dev/null @@ -1,117 +0,0 @@ -""" -Stopwords utility class - -Provides functionality for loading and managing stopwords, supports Harbin Institute of Technology stopwords list. -""" - -import os -import logging -from typing import Set, Optional -from common_utils.project_path import CURRENT_DIR - -logger = logging.getLogger(__name__) - - -class StopwordsManager: - """Stopwords manager""" - - def __init__(self, stopwords_file_path: Optional[str] = None): - """Initialize the stopwords manager - - Args: - stopwords_file_path: Path to the stopwords file. If None, use default path - """ - self.stopwords_file_path = ( - stopwords_file_path or self._get_default_stopwords_path() - ) - self._stopwords: Optional[Set[str]] = None - self.load_stopwords() - - def _get_default_stopwords_path(self) -> str: - """Get default stopwords file path""" - return str(CURRENT_DIR / "config" / "stopwords" / "hit_stopwords.txt") - - def load_stopwords(self) -> Set[str]: - """Load stopwords - - Returns: - Set of stopwords - """ - if self._stopwords is not None: - return self._stopwords - - stopwords = set() - - # Check if file exists - if not os.path.exists(self.stopwords_file_path): - logger.warning(f"Stopwords file does not exist: {self.stopwords_file_path}") - logger.info("An empty stopwords set will be used") - self._stopwords = stopwords - return stopwords - - try: - with open(self.stopwords_file_path, 'r', encoding='utf-8') as f: - for line in f: - word = line.strip() - if word: # Skip empty lines - stopwords.add(word) - - logger.info( - f"Successfully loaded stopwords, total {len(stopwords)} stopwords" - ) - self._stopwords = stopwords - return stopwords - - except Exception as e: - logger.error(f"Failed to load stopwords: {e}") - logger.info("An empty stopwords set will be used") - self._stopwords = set() - return set() - - def is_stopword(self, word: str) -> bool: - """Check if a word is a stopword - - Args: - word: Word to check - - Returns: - True if the word is a stopword, otherwise False - """ - return word in self._stopwords - - def filter_stopwords(self, words: list, min_length: int = 1) -> list: - """Filter out stopwords - - Args: - words: List of words - min_length: Minimum word length, words shorter than this will also be filtered - - Returns: - List of words after filtering - """ - - filtered_words = [] - for word in words: - if ( - word not in self._stopwords and len(word) >= min_length and word.strip() - ): # Filter whitespace characters - filtered_words.append(word) - - return filtered_words - - -# Global stopwords manager instance -_stopwords_manager: Optional[StopwordsManager] = StopwordsManager() - - -def filter_stopwords(words: list, min_length: int = 1) -> list: - """Convenience function: filter stopwords - - Args: - words: List of words - min_length: Minimum word length - - Returns: - List of words after filtering - """ - return _stopwords_manager.filter_stopwords(words, min_length) diff --git a/methods/EverCore/src/core/observation/logger.py b/methods/EverCore/src/core/observation/logger.py deleted file mode 100644 index 89855ab8..00000000 --- a/methods/EverCore/src/core/observation/logger.py +++ /dev/null @@ -1,256 +0,0 @@ -import logging -import traceback -from typing import Any, Optional -from enum import Enum -from functools import lru_cache -import sys -import os -from datetime import datetime - -from core.context.context import get_current_app_info - - -class RequestIdFilter(logging.Filter): - """Injects request_id from app_info_context into every LogRecord. - - Reads from the ContextVar-based app_info dict. Falls back to "-" - when no request context is available (startup, background tasks - without inherited context, etc.). - """ - - def filter(self, record: logging.LogRecord) -> bool: - app_info = get_current_app_info() - record.request_id = app_info.get("request_id", "-") if app_info else "-" - return True - - -class LogLevel(Enum): - """Log level enumeration""" - - DEBUG = "DEBUG" - INFO = "INFO" - WARNING = "WARNING" - ERROR = "ERROR" - CRITICAL = "CRITICAL" - - -class LoggerProvider: - """Unified logging management class - hybrid mode + LRU cache optimization""" - - _instance: Optional['LoggerProvider'] = None - _initialized: bool = False - - def __new__(cls) -> 'LoggerProvider': - """Singleton pattern implementation""" - if cls._instance is None: - cls._instance = super().__new__(cls) - return cls._instance - - def __init__(self): - """Initialize the logger provider""" - if not self._initialized: - self._setup_logging() - self._initialized = True - - def _setup_root_logging(self): - """Set up logging configuration""" - # Get log level from environment variable, default to INFO - log_level = os.getenv('LOG_LEVEL', 'INFO').upper() - - # Configure root logger - # force=True ensures handlers are replaced even if root logger - # was already configured (e.g. by a library imported before us) - logging.basicConfig( - level=getattr(logging, log_level), - format='%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - [%(request_id)s] - %(message)s', - handlers=[ - logging.StreamHandler(sys.stdout), - # Can add file handler - # logging.FileHandler('app.log', encoding='utf-8') - ], - force=True, - ) - - # Register RequestIdFilter on all root handlers so child loggers - # inherit it via propagation - request_id_filter = RequestIdFilter() - for handler in logging.root.handlers: - handler.addFilter(request_id_filter) - - def _setup_logging(self): - """Set up logging configuration""" - self._setup_root_logging() - - # Disable redundant logs from third-party libraries - logging.getLogger('urllib3').setLevel(logging.WARNING) - logging.getLogger('google').setLevel(logging.WARNING) - logging.getLogger('googleapiclient').setLevel(logging.WARNING) - # Disable INFO level logs from httpx to avoid frequent HTTP request logs - logging.getLogger('httpx').setLevel(logging.WARNING) - # Disable debug logs from HTTP-related libraries to avoid redundant network request logs - logging.getLogger('hpack').setLevel(logging.WARNING) - logging.getLogger('httpcore').setLevel(logging.WARNING) - logging.getLogger('pymongo').setLevel(logging.WARNING) - logging.getLogger('aiokafka').setLevel(logging.WARNING) - # Disable debug logs from websockets client to avoid redundant connection logs - # logging.getLogger('websockets.client').setLevel(logging.WARNING) - - @lru_cache(maxsize=1000) - def _get_cached_logger(self, module_name: str) -> logging.Logger: - """Get cached logger (LRU cache, up to 1000) - - Args: - module_name: Module name - - Returns: - logging.Logger: Cached logger instance - """ - return logging.getLogger(f'{module_name}') - - def get_logger(self, name: Optional[str] = None) -> logging.Logger: - """Get logger with specified name (recommended usage: explicitly pass module name) - - Args: - name: Logger name, if None uses caller's module name (lower performance) - - Returns: - logging.Logger: Logger instance - """ - if name is None: - # Get caller's module name (convenient but lower performance) - frame = sys._getframe(1) - name = frame.f_globals.get('__name__', 'unknown') - - # Use LRU cache to avoid repeatedly creating logger - return self._get_cached_logger(name) - - def debug(self, message: str, *args, **kwargs): - """Log debug information""" - logger = self._get_caller_logger() - logger.debug(message, *args, **kwargs) - - def info(self, message: str, *args, **kwargs): - """Log information""" - logger = self._get_caller_logger() - logger.info(message, *args, **kwargs) - - def warning(self, message: str, *args, **kwargs): - """Log warning""" - logger = self._get_caller_logger() - logger.warning(message, *args, **kwargs) - - def warn(self, message: str, *args, **kwargs): - """Log warning (alias)""" - self.warning(message, *args, **kwargs) - - def error(self, message: str, exc_info: bool = True, *args, **kwargs): - """Log error information - - Args: - message: Error message - exc_info: Whether to include exception stack trace, default True - """ - logger = self._get_caller_logger() - logger.error(message, exc_info=exc_info, *args, **kwargs) - - def exception(self, message: str, exc_info: bool = True, *args, **kwargs): - """Log exception information (automatically includes stack trace) - - Args: - message: Exception message - exc_info: Whether to include exception stack trace, default True - """ - logger = self._get_caller_logger() - logger.exception(message, exc_info=exc_info, *args, **kwargs) - - def critical(self, message: str, exc_info: bool = True, *args, **kwargs): - """Log critical error information - - Args: - message: Error message - exc_info: Whether to include exception stack trace, default True - """ - logger = self._get_caller_logger() - logger.critical(message, exc_info=exc_info, *args, **kwargs) - - def log_with_stack(self, level: LogLevel, message: str): - """Log with full stack trace - - Args: - level: Log level - message: Log message - """ - logger = self._get_caller_logger() - stack_trace = traceback.format_stack() - full_message = f"{message}\nStack trace:\n{''.join(stack_trace)}" - - log_method = getattr(logger, level.value.lower()) - log_method(full_message) - - def _get_caller_logger(self) -> logging.Logger: - """Get caller's logger (with LRU cache optimization)""" - frame = sys._getframe(2) # Skip current method and the called logging method - module_name = frame.f_globals.get('__name__', 'unknown') - # Use LRU cache to avoid repeatedly creating logger - return self._get_cached_logger(module_name) - - -# Create global logger provider instance -logger_provider = LoggerProvider() - -# Hybrid mode interface: provide two usage methods - - -# Method 1: High-performance usage (recommended) - explicitly get logger, suitable for frequent calls -def get_logger(name: Optional[str] = None) -> logging.Logger: - """Get logger (recommended usage: explicitly pass __name__) - - Recommended usage: - logger = get_logger(__name__) # Get once at module top - logger.info("High-frequency log calls") # Use directly afterwards - - Args: - name: Module name, recommended to pass __name__. If None, automatically get (lower performance) - """ - return logger_provider.get_logger(name) - - -# Method 2: Convenient usage - directly call functions, suitable for occasional calls (with LRU cache optimization) -def debug(message: str, *args, **kwargs): - """Log debug information (convenient usage, suitable for occasional calls)""" - logger_provider.debug(message, *args, **kwargs) - - -def info(message: str, *args, **kwargs): - """Log information (convenient usage, suitable for occasional calls)""" - logger_provider.info(message, *args, **kwargs) - - -def warning(message: str, *args, **kwargs): - """Log warning (convenient usage, suitable for occasional calls)""" - logger_provider.warning(message, *args, **kwargs) - - -def warn(message: str, *args, **kwargs): - """Log warning (alias)""" - logger_provider.warn(message, *args, **kwargs) - - -def error(message: str, exc_info: bool = True, *args, **kwargs): - """Log error information (automatically includes stack trace)""" - logger_provider.error(message, exc_info=exc_info, *args, **kwargs) - - -def exception(message: str, exc_info: bool = True, *args, **kwargs): - """Log exception information (automatically includes stack trace)""" - logger_provider.exception(message, exc_info=exc_info, *args, **kwargs) - - -def critical(message: str, exc_info: bool = True, *args, **kwargs): - """Log critical error information (automatically includes stack trace)""" - logger_provider.critical(message, exc_info=exc_info, *args, **kwargs) - - -def log_with_stack(level: LogLevel, message: str): - """Log with full stack trace""" - logger_provider.log_with_stack(level, message) diff --git a/methods/EverCore/src/core/observation/metrics/__init__.py b/methods/EverCore/src/core/observation/metrics/__init__.py deleted file mode 100644 index dc20e964..00000000 --- a/methods/EverCore/src/core/observation/metrics/__init__.py +++ /dev/null @@ -1,76 +0,0 @@ -""" -Metrics Library - -Business code imports metric classes from here, no need to depend on prometheus_client directly. - -Usage: - from core.observation.metrics import Counter, Histogram, BaseGauge - - # Counter - monotonically increasing counter - requests_total = Counter('http_requests_total', 'Total requests', ['method']) - requests_total.labels(method='GET').inc() - - # Histogram - distribution statistics of observed values - request_duration = Histogram( - 'http_request_duration_seconds', - 'Request duration', - ['method'], - buckets=HistogramBuckets.API_CALL - ) - request_duration.labels(method='GET').observe(0.123) - - # BaseGauge - instantaneous value with auto-refresh (inheritance) - class QueueSizeGauge(BaseGauge): - def __init__(self, queue): - super().__init__('queue_size', 'Queue size', ['queue_name']) - self.queue = queue - - def refresh(self, labels: dict) -> float: - return self.queue.qsize() - - # Using Gauge - gauge = QueueSizeGauge(queue) - gauge.labels(queue_name='main').start_refresh() # default 5 second refresh -""" - -from .counter import Counter, LabeledCounter -from .histogram import Histogram, LabeledHistogram, HistogramBuckets -from .gauge import BaseGauge, LabeledGauge -from .registry import ( - get_metrics_registry, - set_metrics_registry, - generate_metrics_response, - reset_metrics_registry, -) -from .server import ( - start_metrics_server, - is_metrics_server_running, - get_metrics_url, -) - -__all__ = [ - # Counter - 'Counter', - 'LabeledCounter', - - # Histogram - 'Histogram', - 'LabeledHistogram', - 'HistogramBuckets', - - # Gauge - 'BaseGauge', - 'LabeledGauge', - - # Registry - 'get_metrics_registry', - 'set_metrics_registry', - 'generate_metrics_response', - 'reset_metrics_registry', - - # Server - 'start_metrics_server', - 'is_metrics_server_running', - 'get_metrics_url', -] - diff --git a/methods/EverCore/src/core/observation/metrics/counter.py b/methods/EverCore/src/core/observation/metrics/counter.py deleted file mode 100644 index b9c7f030..00000000 --- a/methods/EverCore/src/core/observation/metrics/counter.py +++ /dev/null @@ -1,99 +0,0 @@ -""" -Counter Wrapper - -Provides a unified Counter interface, isolating prometheus_client from business code. -""" -from prometheus_client import Counter as PrometheusCounter -from typing import Sequence -from .registry import get_metrics_registry - - -class Counter: - """ - Counter metric wrapper - - Features: - - Monotonically increasing counter (can only increment) - - Suitable for total requests, total errors, etc. - - Business code does not need to import prometheus_client directly - - Usage: - from core.observation.metrics import Counter - - requests_total = Counter( - name='http_requests_total', - description='Total HTTP requests', - labelnames=['method', 'path', 'status'] - ) - - # 使用 - requests_total.labels(method='GET', path='/api', status='200').inc() - """ - - def __init__( - self, - name: str, - description: str, - labelnames: Sequence[str] = (), - namespace: str = '', - subsystem: str = '', - unit: str = '', - ): - """ - Args: - name: Metric name - description: Metric description - labelnames: List of label names - namespace: Namespace (optional) - subsystem: Subsystem (optional) - unit: Unit (optional) - """ - registry = get_metrics_registry() - - self._counter = PrometheusCounter( - name=name, - documentation=description, - labelnames=labelnames, - namespace=namespace, - subsystem=subsystem, - unit=unit, - registry=registry, - ) - self._name = name - self._labelnames = labelnames - - def labels(self, **labels) -> 'LabeledCounter': - """ - Return a Counter with labels - - Returns: - LabeledCounter instance - """ - labeled = self._counter.labels(**labels) - return LabeledCounter(labeled) - - def inc(self, amount: float = 1) -> None: - """ - Increment counter (no labels version) - - Args: - amount: Increment amount, defaults to 1 - """ - self._counter.inc(amount) - - -class LabeledCounter: - """Counter with labels""" - - def __init__(self, labeled_counter): - self._counter = labeled_counter - - def inc(self, amount: float = 1) -> None: - """ - Increment counter - - Args: - amount: Increment amount, defaults to 1 - """ - self._counter.inc(amount) - diff --git a/methods/EverCore/src/core/observation/metrics/gauge.py b/methods/EverCore/src/core/observation/metrics/gauge.py deleted file mode 100644 index c3459811..00000000 --- a/methods/EverCore/src/core/observation/metrics/gauge.py +++ /dev/null @@ -1,349 +0,0 @@ -""" -Gauge Wrapper - -Provides a unified Gauge interface with built-in auto-refresh capability. -""" -from prometheus_client import Gauge as PrometheusGauge -from typing import Sequence, Optional, Callable, Any, Dict, Tuple -import asyncio -import logging -import inspect -from abc import ABC, abstractmethod - -logger = logging.getLogger(__name__) - - -class BaseGauge(ABC): - """ - Gauge base class - - Features: - - Can increase or decrease (instantaneous value) - - Built-in auto-refresh capability (default 5 seconds) - - Must inherit and override refresh() method - - Each instance manages its own refresh tasks independently - - Supports manual set() method - - Usage - inherit and override refresh method: - class KafkaPendingMessagesGauge(BaseGauge): - def __init__(self, kafka_consumer): - super().__init__( - name='kafka_pending_messages', - description='Number of pending messages', - labelnames=['job_name'] - ) - self.kafka_consumer = kafka_consumer - - def refresh(self, labels: dict) -> float: - '''Return current value''' - return len(self.kafka_consumer.pending_messages) - - # Usage 1: Auto-refresh (default 5 seconds) - gauge = KafkaPendingMessagesGauge(kafka_consumer) - gauge.labels(job_name='tanka').start_refresh() - - # Usage 2: Custom refresh interval - gauge.labels(job_name='tanka').start_refresh(interval_seconds=10) - - # Usage 3: Manual set (without auto-refresh) - gauge.labels(job_name='tanka').set(42) - """ - - def __init__( - self, - name: str, - description: str, - labelnames: Sequence[str] = (), - namespace: str = '', - subsystem: str = '', - unit: str = '', - ): - """ - Args: - name: Metric name - description: Metric description - labelnames: List of label names - namespace: Namespace (optional) - subsystem: Subsystem (optional) - unit: Unit (optional) - """ - from .registry import get_metrics_registry - registry = get_metrics_registry() - - self._gauge = PrometheusGauge( - name=name, - documentation=description, - labelnames=labelnames, - namespace=namespace, - subsystem=subsystem, - unit=unit, - registry=registry, - ) - - self._name = name - self._labelnames = labelnames - - # Store refresh tasks for each label combination - # key: tuple of label values, value: RefreshTask - self._refresh_tasks: Dict[Tuple, 'RefreshTask'] = {} - - def labels(self, **labels) -> 'LabeledGauge': - """ - Return a Gauge with labels - - Returns: - LabeledGauge instance - """ - labeled_gauge = self._gauge.labels(**labels) - label_key = self._make_label_key(**labels) - - return LabeledGauge( - base_gauge=self, - labeled_gauge=labeled_gauge, - label_key=label_key, - label_dict=labels, - ) - - def set(self, value: float) -> None: - """Set value (no labels version)""" - self._gauge.set(value) - - def inc(self, amount: float = 1) -> None: - """Increment value (no labels version)""" - self._gauge.inc(amount) - - def dec(self, amount: float = 1) -> None: - """Decrement value (no labels version)""" - self._gauge.dec(amount) - - @abstractmethod - def refresh(self, labels: dict) -> float: - """ - Refresh method (subclass must override) - - Args: - labels: Label dictionary - - Returns: - Current Gauge value - - Notes: - - Subclass must override this method to implement custom refresh logic - - This method is called periodically by auto-refresh task (default 5 seconds) - - Can return any float value, will be automatically updated to Gauge - - Example: - class QueueSizeGauge(BaseGauge): - def __init__(self, queue): - super().__init__('queue_size', 'Queue size') - self.queue = queue - - def refresh(self, labels: dict) -> float: - return self.queue.qsize() - """ - pass - - def _make_label_key(self, **labels) -> Tuple: - """Generate label key""" - if self._labelnames: - return tuple(labels.get(name, '') for name in self._labelnames) - return () - - async def _stop_all_refresh_tasks(self) -> None: - """Stop all refresh tasks""" - for task in self._refresh_tasks.values(): - await task.stop() - self._refresh_tasks.clear() - - -class LabeledGauge: - """ - Gauge with labels - - Provides the same interface as native Gauge, with auto-refresh support. - """ - - def __init__( - self, - base_gauge: BaseGauge, - labeled_gauge: Any, - label_key: Tuple, - label_dict: dict, - ): - self._base_gauge = base_gauge - self._labeled_gauge = labeled_gauge - self._label_key = label_key - self._label_dict = label_dict - - def set(self, value: float) -> None: - """Set value""" - self._labeled_gauge.set(value) - - def inc(self, amount: float = 1) -> None: - """Increment value""" - self._labeled_gauge.inc(amount) - - def dec(self, amount: float = 1) -> None: - """Decrement value""" - self._labeled_gauge.dec(amount) - - def set_to_current_time(self) -> None: - """Set to current timestamp""" - self._labeled_gauge.set_to_current_time() - - def start_refresh( - self, - interval_seconds: int = 5, - enable_async: bool = True, - ) -> 'LabeledGauge': - """ - Start auto-refresh - - Args: - interval_seconds: Refresh interval (seconds), default 5 seconds - enable_async: Whether to support async refresh method, default True - - Returns: - self (supports chaining) - - Example: - # Default 5 second refresh - gauge.labels(job='tanka').start_refresh() - - # Custom refresh interval - gauge.labels(job='tanka').start_refresh(interval_seconds=10) - - # Async refresh method - class AsyncGauge(BaseGauge): - async def refresh(self, labels: dict) -> float: - return await self.get_value_async() - - gauge.labels(type='A').start_refresh(enable_async=True) - """ - # Stop existing task if any (prevent task leak) - existing_task = self._base_gauge._refresh_tasks.get(self._label_key) - if existing_task and existing_task._running: - logger.warning( - f"Replacing existing refresh task for {self._label_key}" - ) - # Schedule stop in background to avoid blocking - asyncio.create_task(existing_task.stop()) - - # Create wrapper function that calls base_gauge.refresh() - def refresh_wrapper(): - return self._base_gauge.refresh(self._label_dict) - - # Create refresh task - task = RefreshTask( - refresh_func=refresh_wrapper, - labeled_gauge=self._labeled_gauge, - interval_seconds=interval_seconds, - enable_async=enable_async, - label_key=self._label_key, - ) - - # Store task - self._base_gauge._refresh_tasks[self._label_key] = task - - # Start task - task.start() - - return self - - async def stop_refresh(self) -> None: - """Stop auto-refresh""" - task = self._base_gauge._refresh_tasks.get(self._label_key) - if task: - await task.stop() - del self._base_gauge._refresh_tasks[self._label_key] - - -class RefreshTask: - """ - Refresh task - - Each label combination has an independent refresh task. - """ - - def __init__( - self, - refresh_func: Callable[[], float], - labeled_gauge: Any, - interval_seconds: int, - enable_async: bool, - label_key: Tuple, - ): - self.refresh_func = refresh_func - self.labeled_gauge = labeled_gauge - self.interval_seconds = interval_seconds - self.enable_async = enable_async - self.label_key = label_key - - self._task: Optional[asyncio.Task] = None - self._running = False - self._error_count = 0 - - def start(self) -> None: - """Start refresh task""" - if self._running: - logger.warning(f"Refresh task already running for {self.label_key}") - return - - self._running = True - self._task = asyncio.create_task(self._refresh_loop()) - logger.info( - f"Started refresh task: label_key={self.label_key}, " - f"interval={self.interval_seconds}s" - ) - - async def stop(self) -> None: - """Stop refresh task""" - if not self._running: - return - - self._running = False - - if self._task: - self._task.cancel() - try: - await self._task - except asyncio.CancelledError: - pass - self._task = None - - logger.info(f"Stopped refresh task: label_key={self.label_key}") - - async def _refresh_loop(self) -> None: - """Refresh loop""" - while self._running: - try: - # Check if it's an async function - if self.enable_async and ( - asyncio.iscoroutinefunction(self.refresh_func) or - inspect.iscoroutinefunction(self.refresh_func) - ): - value = await self.refresh_func() - else: - value = self.refresh_func() - - # Update Gauge - self.labeled_gauge.set(value) - - # Reset error count - self._error_count = 0 - - except asyncio.CancelledError: - break - except Exception as e: - self._error_count += 1 - logger.error( - f"Refresh failed for {self.label_key}: {e} " - f"(error_count={self._error_count})", - exc_info=True - ) - - # Wait for next refresh - try: - await asyncio.sleep(self.interval_seconds) - except asyncio.CancelledError: - break diff --git a/methods/EverCore/src/core/observation/metrics/histogram.py b/methods/EverCore/src/core/observation/metrics/histogram.py deleted file mode 100644 index 4ee1ee00..00000000 --- a/methods/EverCore/src/core/observation/metrics/histogram.py +++ /dev/null @@ -1,155 +0,0 @@ -""" -Histogram Wrapper - -Provides a unified Histogram interface, isolating prometheus_client from business code. -""" -from prometheus_client import Histogram as PrometheusHistogram -from typing import Sequence -from .registry import get_metrics_registry - - -# Predefined bucket configurations -class HistogramBuckets: - """Predefined Histogram bucket configurations""" - - # Default buckets (covering 5ms - 10s) - DEFAULT = ( - 0.005, 0.01, 0.025, 0.05, 0.075, 0.1, 0.25, 0.5, - 0.75, 1.0, 2.5, 5.0, 7.5, 10.0 - ) - - # Fast operations (5ms - 500ms, for cache queries, simple calculations, etc.) - FAST = (0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5) - - # API calls (10ms - 30s, for external API calls) - # Denser buckets in 0.1-5s range for better P95/P99 accuracy - API_CALL = (0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 0.75, 1.0, 1.5, 2.0, 3.0, 5.0, 10.0, 30.0) - - # Batch operations (100ms - 60s, for batch processing) - BATCH = (0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0, 30.0, 60.0) - - # Embedding/Rerank (10ms - 10s, for ML inference) - # Denser buckets in 0.1-3s range where most requests fall - ML_INFERENCE = (0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.5, 0.75, 1.0, 1.5, 2.0, 3.0, 5.0, 10.0) - - # Database queries (1ms - 5s) - DATABASE = (0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0) - - -class Histogram: - """ - Histogram metric wrapper - - Features: - - Distribution statistics of observed values - - Suitable for latency, size, and other distribution data - - Automatically calculates percentiles, mean, and sum - - Usage: - from core.observation.metrics import Histogram, HistogramBuckets - - request_duration = Histogram( - name='http_request_duration_seconds', - description='HTTP request duration', - labelnames=['method', 'path'], - buckets=HistogramBuckets.API_CALL - ) - - # Usage - request_duration.labels(method='GET', path='/api').observe(0.123) - - # Using context manager - with request_duration.labels(method='GET', path='/api').time(): - do_something() - """ - - def __init__( - self, - name: str, - description: str, - labelnames: Sequence[str] = (), - namespace: str = '', - subsystem: str = '', - unit: str = '', - buckets: Sequence[float] = HistogramBuckets.DEFAULT, - ): - """ - Args: - name: Metric name - description: Metric description - labelnames: List of label names - namespace: Namespace (optional) - subsystem: Subsystem (optional) - unit: Unit (optional) - buckets: Histogram bucket boundaries - """ - registry = get_metrics_registry() - - self._histogram = PrometheusHistogram( - name=name, - documentation=description, - labelnames=labelnames, - namespace=namespace, - subsystem=subsystem, - unit=unit, - buckets=buckets, - registry=registry, - ) - self._name = name - self._labelnames = labelnames - - def labels(self, **labels) -> 'LabeledHistogram': - """ - Return a Histogram with labels - - Returns: - LabeledHistogram instance - """ - labeled = self._histogram.labels(**labels) - return LabeledHistogram(labeled) - - def observe(self, amount: float) -> None: - """ - Record an observed value (no labels version) - - Args: - amount: Observed value - """ - self._histogram.observe(amount) - - def time(self): - """ - Return a timing context manager (no labels version) - - Usage: - with histogram.time(): - do_something() - """ - return self._histogram.time() - - -class LabeledHistogram: - """Histogram with labels""" - - def __init__(self, labeled_histogram): - self._histogram = labeled_histogram - - def observe(self, amount: float) -> None: - """ - Record an observed value - - Args: - amount: Observed value - """ - self._histogram.observe(amount) - - def time(self): - """ - Return a timing context manager - - Usage: - with histogram.labels(method='GET').time(): - do_something() - """ - return self._histogram.time() - diff --git a/methods/EverCore/src/core/observation/metrics/registry.py b/methods/EverCore/src/core/observation/metrics/registry.py deleted file mode 100644 index 53c4cde4..00000000 --- a/methods/EverCore/src/core/observation/metrics/registry.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Metrics Registry - -Centralized management of Prometheus metrics registry with singleton access. -""" -from prometheus_client import CollectorRegistry, REGISTRY, generate_latest -from typing import Optional -import logging - -logger = logging.getLogger(__name__) - -# Global registry instance (uses default REGISTRY) -_metrics_registry: Optional[CollectorRegistry] = None - - -def get_metrics_registry() -> CollectorRegistry: - """ - Get the global metrics registry - - Returns: - CollectorRegistry: Prometheus registry instance - - Notes: - - Uses prometheus_client's global REGISTRY by default - - All metrics are automatically registered to this registry - - Used by start_metrics_server() for exposing /metrics endpoint - """ - global _metrics_registry - if _metrics_registry is None: - _metrics_registry = REGISTRY - return _metrics_registry - - -def set_metrics_registry(registry: CollectorRegistry) -> None: - """ - Set custom registry (mainly for testing) - - Args: - registry: Custom CollectorRegistry instance - """ - global _metrics_registry - _metrics_registry = registry - - -def generate_metrics_response() -> bytes: - """ - Generate metrics response content (for testing/debugging) - - Returns: - bytes: Prometheus format metrics data - """ - return generate_latest(get_metrics_registry()) - - -def reset_metrics_registry() -> None: - """ - Reset metrics registry (mainly for testing) - - Warning: Do not call this method in production - """ - global _metrics_registry - _metrics_registry = None - logger.warning("Metrics registry has been reset") - diff --git a/methods/EverCore/src/core/observation/metrics/server.py b/methods/EverCore/src/core/observation/metrics/server.py deleted file mode 100644 index 186f46c2..00000000 --- a/methods/EverCore/src/core/observation/metrics/server.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -Standalone Metrics Server - -Runs Prometheus metrics endpoint on a separate port (default: 9090). -This isolates metrics from business API for security and operational benefits. - -Usage: - from core.observation.metrics.server import start_metrics_server - - # Start metrics server on port 9090 - start_metrics_server(port=9090) - - # Or use environment variable METRICS_PORT - start_metrics_server() # reads from METRICS_PORT or defaults to 9090 - -Benefits: - - Security: Metrics endpoint can be firewalled separately - - Availability: Metrics available even if main app is overloaded - - Operations: Can expose to internal network only -""" -import os -import logging -from typing import Optional -from prometheus_client import start_http_server -from .registry import get_metrics_registry - -logger = logging.getLogger(__name__) - -# Global server state -_metrics_server_started: bool = False - - -def start_metrics_server( - port: Optional[int] = None, - addr: str = "0.0.0.0", -) -> bool: - """ - Start standalone Prometheus metrics HTTP server - - Args: - port: Port to listen on (default: from METRICS_PORT env or 9090) - addr: Address to bind to (default: 0.0.0.0) - - Returns: - bool: True if server started successfully, False if already running - - Example: - # Start on default port 9090 - start_metrics_server() - - # Start on custom port - start_metrics_server(port=9091) - - # Prometheus can scrape: http://your-host:9090/metrics - """ - global _metrics_server_started - - if _metrics_server_started: - logger.warning("Metrics server already running") - return False - - # Get port from parameter, env var, or default - if port is None: - port = int(os.getenv("METRICS_PORT", "9090")) - - try: - # Start HTTP server using prometheus_client's built-in server - # This creates a daemon thread that serves /metrics endpoint - start_http_server( - port=port, - addr=addr, - registry=get_metrics_registry(), - ) - - _metrics_server_started = True - logger.info(f"✅ Metrics server started on {addr}:{port}/metrics") - return True - - except Exception as e: - logger.error(f"Failed to start metrics server: {e}") - return False - - -def is_metrics_server_running() -> bool: - """Check if metrics server is running""" - return _metrics_server_started - - -def get_metrics_url(host: str = "localhost", port: Optional[int] = None) -> str: - """ - Get the metrics endpoint URL - - Args: - host: Hostname (default: localhost) - port: Port (default: from METRICS_PORT env or 9090) - - Returns: - str: Full URL to metrics endpoint - """ - if port is None: - port = int(os.getenv("METRICS_PORT", "9090")) - return f"http://{host}:{port}/metrics" - diff --git a/methods/EverCore/src/core/observation/stage_timer.py b/methods/EverCore/src/core/observation/stage_timer.py deleted file mode 100644 index 9f1fb607..00000000 --- a/methods/EverCore/src/core/observation/stage_timer.py +++ /dev/null @@ -1,193 +0,0 @@ -from __future__ import annotations - -import functools -import json -import logging -import os -from contextlib import contextmanager -from contextvars import ContextVar -from time import perf_counter -from typing import Any, Optional - -logger = logging.getLogger(__name__) - -_SLOW_THRESHOLD_MS = int(os.environ.get("STAGE_TIMER_SLOW_THRESHOLD_MS", "0")) - - -class _StageNode: - """A single node in the stage timing tree. - - Each node records its name, duration, whether it represents parallel - execution, and any child nodes. - """ - - __slots__ = ("name", "duration_ms", "is_parallel", "children") - - def __init__(self, name: str, is_parallel: bool = False) -> None: - self.name: str = name - self.duration_ms: float = 0.0 - self.is_parallel: bool = is_parallel - self.children: list[_StageNode] = [] - - def to_dict(self) -> dict[str, Any]: - """Convert this node to a JSON-serializable dict.""" - result: dict[str, Any] = { - "name": self.name, - "duration_ms": round(self.duration_ms), - } - if self.is_parallel: - result["parallel"] = True - if self.children: - result["stages"] = [child.to_dict() for child in self.children] - return result - - -_current_node: ContextVar[Optional[_StageNode]] = ContextVar( - "_stage_timer_current_node", default=None -) - - -class StageTimer: - """Hierarchical timer that records stage durations for a single request. - - Usage:: - - timer = StageTimer("/api/v1/memories/search") - with timer.stage("retrieval"): - with timer.parallel("sources"): - ... # parallel branches - timer.log_summary() - """ - - def __init__(self, endpoint: str) -> None: - self._endpoint: str = endpoint - self._root: _StageNode = _StageNode("_root") - self._start: float = perf_counter() - - @contextmanager - def stage(self, name: str): - """Time a sequential stage. Nests under the current parent.""" - node = _StageNode(name) - parent = _current_node.get() - if parent is None: - parent = self._root - parent.children.append(node) - token = _current_node.set(node) - start = perf_counter() - try: - yield - finally: - node.duration_ms = (perf_counter() - start) * 1000 - _current_node.reset(token) - - @contextmanager - def parallel(self, name: str): - """Time a parallel stage. Children are rendered with ``|`` separator.""" - node = _StageNode(name, is_parallel=True) - parent = _current_node.get() - if parent is None: - parent = self._root - parent.children.append(node) - token = _current_node.set(node) - start = perf_counter() - try: - yield - finally: - node.duration_ms = (perf_counter() - start) * 1000 - _current_node.reset(token) - - def summary(self) -> dict[str, Any]: - """Return a JSON-serializable summary dict.""" - total_ms = round((perf_counter() - self._start) * 1000) - result: dict[str, Any] = {"endpoint": self._endpoint, "total_ms": total_ms} - if self._root.children: - result["stages"] = [child.to_dict() for child in self._root.children] - return result - - def log_summary(self) -> None: - """Log the timer summary as JSON at INFO level.""" - s = self.summary() - if 0 < _SLOW_THRESHOLD_MS <= s["total_ms"]: - s["slow"] = True - logger.info( - "[stage_timer] %s", json.dumps(s, ensure_ascii=False, separators=(",", ":")) - ) - - -# --------------------------------------------------------------------------- -# Convenience functions -# --------------------------------------------------------------------------- - - -def start_timer(endpoint: str) -> None: - """Create a StageTimer and store it in the current request's app_info context.""" - from core.context.context import get_current_app_info - - app_info = get_current_app_info() - if app_info is not None: - app_info["stage_timer"] = StageTimer(endpoint) - - -def log_timer() -> None: - """Log the current request's StageTimer summary, if one exists.""" - timer = get_current_timer() - if timer is not None: - timer.log_summary() - - -def stage_timed(endpoint: str): - """Decorator that wraps an async controller method with StageTimer. - - Calls start_timer() before the method and log_timer() after it returns. - On exception, log_timer() is NOT called (consistent with GlobalExceptionHandler). - """ - - def decorator(func): - @functools.wraps(func) - async def wrapper(*args, **kwargs): - start_timer(endpoint) - result = await func(*args, **kwargs) - log_timer() - return result - - return wrapper - - return decorator - - -def get_current_timer() -> Optional[StageTimer]: - """Return the StageTimer attached to the current request, or None.""" - from core.context.context import get_current_app_info - - app_info = get_current_app_info() - if app_info is None: - return None - return app_info.get("stage_timer") - - -@contextmanager -def timed(name: str): - """Convenience context manager that records a stage on the current timer. - - No-op when no timer is present in the request context. - """ - timer = get_current_timer() - if timer is None: - yield - return - with timer.stage(name): - yield - - -@contextmanager -def timed_parallel(name: str): - """Convenience context manager that records a parallel stage on the current timer. - - No-op when no timer is present in the request context. - """ - timer = get_current_timer() - if timer is None: - yield - return - with timer.parallel(name): - yield diff --git a/methods/EverCore/src/core/observation/tracing/__init__.py b/methods/EverCore/src/core/observation/tracing/__init__.py deleted file mode 100644 index a920df3c..00000000 --- a/methods/EverCore/src/core/observation/tracing/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -Tracing module - -Provides request tracing and logging functionality -""" diff --git a/methods/EverCore/src/core/observation/tracing/decorators.py b/methods/EverCore/src/core/observation/tracing/decorators.py deleted file mode 100644 index 472d01fd..00000000 --- a/methods/EverCore/src/core/observation/tracing/decorators.py +++ /dev/null @@ -1,177 +0,0 @@ -""" -Decorator module - -This module contains various decorators used for validation and processing before method execution. -""" - -from functools import wraps -from typing import Any, Dict, Callable, Optional -import logging -import time - -logger = logging.getLogger(__name__) - - -def trace_logger( - operation_name: Optional[str] = None, - include_args: bool = False, - include_result: bool = False, - log_level: str = "debug", -): - """ - Decorator that automatically adds [trace] logs - - Args: - operation_name: Operation name, if not provided, function name will be used - include_args: Whether to log function arguments - include_result: Whether to log function return value - log_level: Log level (debug, info, warning, error) - """ - - def decorator(func: Callable) -> Callable: - @wraps(func) - async def async_wrapper(*args, **kwargs): - logger = logging.getLogger(func.__module__) - operation = operation_name or func.__name__ - - # Check if log level is enabled - if not _is_log_level_enabled(logger, log_level): - # If log level is not enabled, execute function directly to avoid performance overhead - return await func(*args, **kwargs) - - start_time = time.time() - - # Log start message - log_message = f"\n\t[trace] {operation} - Start processing" - if include_args and (args or kwargs): - args_str = _format_args(args, kwargs) - log_message += f" | Parameters: {args_str}" - - _log_message(logger, log_level, log_message) - - try: - # Execute original function - result = await func(*args, **kwargs) - - # Log success completion message - end_time = time.time() - duration = round((end_time - start_time) * 1000, 2) # milliseconds - - log_message = f"\n\t[trace] {operation} - Processing completed (duration: {duration}ms)" - if include_result and result is not None: - result_str = _format_result(result) - log_message += f" | Result: {result_str}" - - _log_message(logger, log_level, log_message) - return result - - except Exception as e: - # Log exception message - end_time = time.time() - duration = round((end_time - start_time) * 1000, 2) - - log_message = f"\n\t[trace] {operation} - Processing failed (duration: {duration}ms) | Error: {str(e)}" - _log_message(logger, "error", log_message) - raise - - @wraps(func) - def sync_wrapper(*args, **kwargs): - logger = logging.getLogger(func.__module__) - operation = operation_name or func.__name__ - - # Check if log level is enabled - if not _is_log_level_enabled(logger, log_level): - # If log level is not enabled, execute function directly to avoid performance overhead - return func(*args, **kwargs) - - start_time = time.time() - - # Log start message - log_message = f"\n\t[trace] {operation} - Start processing" - if include_args and (args or kwargs): - args_str = _format_args(args, kwargs) - log_message += f" | Parameters: {args_str}" - - _log_message(logger, log_level, log_message) - - try: - # Execute original function - result = func(*args, **kwargs) - - # Log success completion message - end_time = time.time() - duration = round((end_time - start_time) * 1000, 2) - - log_message = f"\n\t[trace] {operation} - Processing completed (duration: {duration}ms)" - if include_result and result is not None: - result_str = _format_result(result) - log_message += f" | Result: {result_str}" - - _log_message(logger, log_level, log_message) - return result - - except Exception as e: - # Log exception message - end_time = time.time() - duration = round((end_time - start_time) * 1000, 2) - - log_message = f"\n\t[trace] {operation} - Processing failed (duration: {duration}ms) | Error: {str(e)}" - _log_message(logger, "error", log_message) - raise - - # Return corresponding wrapper based on whether the function is a coroutine - import asyncio - - if asyncio.iscoroutinefunction(func): - return async_wrapper - else: - return sync_wrapper - - return decorator - - -def _is_log_level_enabled(logger, level: str) -> bool: - """Check if log level is enabled""" - level_num = getattr(logging, level.upper(), logging.INFO) - return logger.isEnabledFor(level_num) - - -def _log_message(logger, level: str, message: str): - """Log message according to level""" - log_method = getattr(logger, level.lower(), logger.info) - log_method(message) - - -def _format_args(args, kwargs) -> str: - """Format function arguments""" - args_str = [] - - # Handle positional arguments - for i, arg in enumerate(args): - if hasattr(arg, '__dict__'): # Object type - args_str.append(f"arg{i}: {type(arg).__name__}") - elif isinstance(arg, (list, dict)) and len(str(arg)) > 100: # Large object - args_str.append(f"arg{i}: {type(arg).__name__}(len={len(arg)})") - else: - args_str.append(f"arg{i}: {arg}") - - # Handle keyword arguments - for key, value in kwargs.items(): - if hasattr(value, '__dict__'): # Object type - args_str.append(f"{key}: {type(value).__name__}") - elif isinstance(value, (list, dict)) and len(str(value)) > 100: # Large object - args_str.append(f"{key}: {type(value).__name__}(len={len(value)})") - else: - args_str.append(f"{key}: {value}") - - return ", ".join(args_str) - - -def _format_result(result) -> str: - """Format function return value""" - if hasattr(result, '__dict__'): # Object type - return f"{type(result).__name__}" - elif isinstance(result, (list, dict)) and len(str(result)) > 100: # Large object - return f"{type(result).__name__}(len={len(result)})" - else: - return str(result) diff --git a/methods/EverCore/src/core/oxm/constants.py b/methods/EverCore/src/core/oxm/constants.py deleted file mode 100644 index 72b0aebc..00000000 --- a/methods/EverCore/src/core/oxm/constants.py +++ /dev/null @@ -1,24 +0,0 @@ -""" -OXM (Object-XML/Document Mapping) Constants - -This module defines magic constants used throughout the OXM layer. -""" - -# Magic constant: Query filter bypass -# When used as user_id or group_id, indicates no filtering on that field -MAGIC_ALL = "__all__" - -# Magic constant: Maximum fetch limit -# Maximum number of records that can be fetched in a single query -MAX_FETCH_LIMIT = 500 - -# Magic constant: Maximum retrieve limit -# Maximum number of records that can be retrieved in a single query -MAX_RETRIEVE_LIMIT = 500 - -# Export all constants -__all__ = [ - "MAGIC_ALL", - "MAX_FETCH_LIMIT", - "MAX_RETRIEVE_LIMIT", -] diff --git a/methods/EverCore/src/core/oxm/es/analyzer.py b/methods/EverCore/src/core/oxm/es/analyzer.py deleted file mode 100644 index e0396d27..00000000 --- a/methods/EverCore/src/core/oxm/es/analyzer.py +++ /dev/null @@ -1,168 +0,0 @@ -from elasticsearch.dsl import tokenizer, normalizer, token_filter, analyzer - -# ================================ -# Token Filters definition -# ================================ - -# Shingle filter - used to generate consecutive phrases (with space separation) -# For example: "hello world test" -> ["hello world", "world test", "hello world test"] -# Does not output single words (output_unigrams=False), only outputs phrases -shingle_lease_filter = token_filter( - "shingle_lease", - type="shingle", - min_shingle_size=2, # Minimum phrase length: 2 words - max_shingle_size=5, # Maximum phrase length: 5 words - output_unigrams=False, # Do not output single words -) - -# Shingle filter - used to generate consecutive phrases (without space separation) -# For example: "hello world test" -> ["helloworld", "worldtest", "helloworldtest"] -# Suitable for Chinese or tightly connected phrases -shingle_lease_nospace_filter = token_filter( - "shingle_lease", - type="shingle", - min_shingle_size=2, - max_shingle_size=5, - output_unigrams=False, - token_separator="", # No space separation between phrases -) - -# ================================ -# Completion analyzer -# ================================ - -# Used for search suggestions and autocomplete functionality -# For example input: "Machine Learning" -# -> Tokenization: ["machine", "learning"] -# -> Generate shingles: ["machine learning"] -# Suitable for: search box autocomplete, suggestion features -completion_analyzer = analyzer( - "completion_analyzer", - tokenizer="standard", # Standard tokenizer, splits by space and punctuation - filter=["lowercase", "shingle"], # Convert to lowercase + generate phrases -) - -# ================================ -# Edge N-gram analyzer -# ================================ - -# Edge N-gram tokenizer - generates substrings starting from the beginning of the word -# For example: "elasticsearch" -> ["e", "el", "ela", "elas", ..., "elasticsearch"] -edge_tokenizer = tokenizer( - "edge_tokenizer", - type="edge_ngram", - min_gram=1, # Minimum character count - max_gram=20, # Maximum character count -) - -# Edge N-gram analyzer - used for prefix matching search -# For example: "Elasticsearch" -> ["e", "el", "ela", "elas", "elast", ..., "elasticsearch"] -# Suitable for: real-time search during input, prefix matching -edge_analyzer = analyzer( - "edge_analyzer", - tokenizer=edge_tokenizer, - filter=["lowercase"], # Convert to lowercase -) - -# ================================ -# Keyword analyzer -# ================================ - -# Lowercase keyword analyzer - treats entire input as a single token but converts to lowercase -# For example: "Hello World" -> ["hello world"] (as one complete token) -# Suitable for: exact match, status fields, category fields -lower_keyword_analyzer = analyzer( - "lowercase_keyword", - tokenizer="keyword", # No tokenization, entire input as one token - filter=["lowercase"], # Convert to lowercase -) - -# ================================ -# Normalizer -# ================================ - -# Lowercase normalizer - used for normalization of keyword fields -# For example: "Hello World" -> "hello world" -# Unlike analyzer, normalizer is used for keyword fields and does not perform tokenization -# Suitable for: case normalization during sorting and aggregation -lower_normalizer = normalizer( - "lower_normalizer", - char_filter=[], # No character filters - filter=["lowercase"], # Only convert to lowercase -) - -# ================================ -# English stemming analyzer -# ================================ - -# English Snowball stem filter - reduces English words to their root form -# For example: "running", "runs", "ran" -> "run" -# "better", "good" -> "good", "better" (irregular forms require special handling) -snow_en_filter = token_filter( - "snow_filter", type="snowball", language="English" # English stemming -) - -# English stemming analyzer - used for semantic search on English text -# For example: "I am running quickly" -# -> Tokenization: ["i", "am", "running", "quickly"] -# -> Stemming: ["i", "am", "run", "quick"] -# Suitable for: English document search, improving recall -snow_en_analyzer = analyzer( - "snow_analyzer", - tokenizer="standard", # Standard tokenization - filter=["lowercase", snow_en_filter], # Lowercase + stemming -) - -# ================================ -# Shingle analyzer - with space version -# ================================ - -# Space-based shingle analyzer - generates phrases after splitting by space -# For example: "hello world test case" -# -> Tokenization: ["hello", "world", "test", "case"] -# -> Shingles: ["hello world", "world test", "test case", "hello world test", ...] -# Suitable for: phrase search, multi-word matching -shingle_space_analyzer = analyzer( - "shingle_space_analyzer", - tokenizer="whitespace", # Split by whitespace - filter=["lowercase", shingle_lease_filter], # Lowercase + generate phrases -) - -# ================================ -# Shingle analyzer - without space version -# ================================ - -# No-space shingle analyzer - suitable for Chinese or continuous character processing -# For example: "hello-world_test" -# -> word_delimiter_graph decomposition: ["hello", "world", "test"] -# -> no-space shingle: ["helloworld", "worldtest", "helloworldtest"] -# Suitable for: Chinese text, code search, compound word processing -shingle_nospace_analyzer = analyzer( - "shingle_nospace_analyzer", - tokenizer="keyword", # No tokenization, keep original input - filter=[ - "lowercase", # Convert to lowercase - "word_delimiter_graph", # Split by delimiters (-,_ etc.) - shingle_lease_nospace_filter, # Generate no-space phrases - ], -) - -# ================================ -# Pre-tokenized content analyzer - for BM25 search on application-layer tokenized text -# ================================ - -# Pre-tokenized text BM25 analyzer - used for BM25 search on content already tokenized at application layer -# Application layer handles jieba tokenization, ES performs whitespace tokenization and stopword filtering to improve search quality -# For example: application input "我 今天 去了 北京大学" (space-separated tokenization result) -# -> Tokenization: ["我", "今天", "去了", "北京大学"] -# -> Stopword filtering: ["今天", "去了", "北京大学"] (assuming "我" is a stopword) -# Suitable for: Chinese document BM25 search, relevance search on pre-tokenized content -whitespace_lowercase_trim_stop_analyzer = analyzer( - "whitespace_lowercase_trim_stop_analyzer", - tokenizer="whitespace", # Whitespace tokenization for pre-tokenized content - filter=[ - "lowercase", # Convert to lowercase - "trim", # Remove leading/trailing whitespace - "stop", # Stopword filtering to improve search relevance - ], -) diff --git a/methods/EverCore/src/core/oxm/es/base_converter.py b/methods/EverCore/src/core/oxm/es/base_converter.py deleted file mode 100644 index f105d124..00000000 --- a/methods/EverCore/src/core/oxm/es/base_converter.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -Elasticsearch document converter base class - -Provides basic functionality for converting data from arbitrary sources to Elasticsearch documents. -All ES document converters should inherit from this base class to obtain a unified conversion interface. -""" - -from abc import ABC, abstractmethod -from typing import TypeVar, Generic, Type, Any, get_args, get_origin -from core.oxm.es.doc_base import DocBase -from core.observation.logger import get_logger - -logger = get_logger(__name__) - -# Generic type variable - only constrains ES document type -EsDocType = TypeVar('EsDocType', bound=DocBase) - - -class BaseEsConverter(ABC, Generic[EsDocType]): - """ - Elasticsearch document converter base class - - Provides basic functionality for converting data from arbitrary sources to Elasticsearch documents. - All ES document converters should inherit from this class. - - Features: - - Unified conversion interface (class methods) - - Type-safe generic support for ES documents - - Automatically retrieves ES document type from generics - - Flexible data source support - """ - - @classmethod - def get_es_model(cls) -> Type[EsDocType]: - """ - Get the ES document model type from generic information - - Returns: - Type[EsDocType]: ES document model class - """ - # Get the generic base class of the current class - if hasattr(cls, '__orig_bases__'): - for base in cls.__orig_bases__: - if get_origin(base) is BaseEsConverter: - args = get_args(base) - if args: - return args[0] - - raise ValueError( - f"Cannot obtain ES document type from generic information of {cls.__name__}" - ) - - @classmethod - @abstractmethod - def from_mongo(cls, source_doc: Any) -> EsDocType: - """ - Convert from source data to Elasticsearch document - - This is the core conversion method; subclasses must implement specific conversion logic. - - Args: - source_doc: Source data (can be of any type) - - Returns: - EsDocType: Elasticsearch document instance - - Raises: - Exception: When an error occurs during conversion - """ - raise NotImplementedError("Subclasses must implement the from_mongo method") diff --git a/methods/EverCore/src/core/oxm/es/base_repository.py b/methods/EverCore/src/core/oxm/es/base_repository.py deleted file mode 100644 index d2ec73ed..00000000 --- a/methods/EverCore/src/core/oxm/es/base_repository.py +++ /dev/null @@ -1,431 +0,0 @@ -""" -Elasticsearch Base Repository Class - -Base repository class based on elasticsearch-dsl, providing common basic CRUD operations. -All Elasticsearch repositories should inherit from this base class to obtain unified operation support. -""" - -from abc import ABC -from typing import Optional, TypeVar, Generic, Type, List, Dict, Any -from elasticsearch import AsyncElasticsearch -from core.oxm.es.doc_base import DocBase -from core.observation.logger import get_logger - -logger = get_logger(__name__) - -# Generic type variable -T = TypeVar('T', bound=DocBase) - - -class BaseRepository(ABC, Generic[T]): - """ - Elasticsearch Base Repository Class - - Provides common basic operations; all Elasticsearch repositories should inherit from this class. - - Features: - - Async Elasticsearch client management - - Basic CRUD operation templates - - Unified error handling and logging - - Index management - """ - - def __init__(self, model: Type[T]): - """ - Initialize base repository - - Args: - model: Elasticsearch document model class - """ - self.model = model - self.model_name = model.__name__ - - # ==================== Client Management ==================== - - async def get_client(self) -> AsyncElasticsearch: - """ - Get Elasticsearch async client - - Returns: - AsyncElasticsearch: Async client instance - """ - return self.model.get_connection() - - def get_index_name(self) -> str: - """ - Get index name - - Delegates to the model class's get_index_name method to ensure consistent index name retrieval logic. - - Returns: - str: Index alias - """ - return self.model.get_index_name() - - # ==================== Basic CRUD Template Methods ==================== - - async def create(self, document: T, refresh: bool = False) -> T: - """ - Create a new document - - Args: - document: Document instance - refresh: Whether to refresh the index immediately - - Returns: - Created document instance - """ - try: - client = await self.get_client() - await document.save(using=client, refresh=refresh) - return document - except Exception as e: - logger.error("❌ Failed to create document [%s]: %s", self.model_name, e) - raise - - async def get_by_id(self, doc_id: str) -> Optional[T]: - """ - Get document by document ID - - Args: - doc_id: Document ID - - Returns: - Document instance or None - """ - try: - client = await self.get_client() - return await self.model.get(id=doc_id, using=client) - except Exception as e: - logger.error("❌ Failed to get document by ID [%s]: %s", self.model_name, e) - return None - - async def update(self, document: T, refresh: bool = False) -> T: - """ - Update document - - Args: - document: Document instance to update - refresh: Whether to refresh the index immediately - - Returns: - Updated document instance - """ - try: - client = await self.get_client() - await document.save(using=client, refresh=refresh) - doc_id = getattr(getattr(document, 'meta', None), 'id', 'unknown') - logger.debug( - "✅ Document updated successfully [%s]: %s", self.model_name, doc_id - ) - return document - except Exception as e: - logger.error("❌ Failed to update document [%s]: %s", self.model_name, e) - raise - - async def delete_by_id(self, doc_id: str, refresh: bool = False) -> bool: - """ - Delete document by document ID - - Args: - doc_id: Document ID - refresh: Whether to refresh the index immediately - - Returns: - Returns True if deletion succeeds, otherwise False - """ - try: - document = await self.get_by_id(doc_id) - if document: - client = await self.get_client() - await document.delete(using=client, refresh=refresh) - logger.debug( - "✅ Document deleted successfully [%s]: %s", self.model_name, doc_id - ) - return True - return False - except Exception as e: - logger.error("❌ Failed to delete document [%s]: %s", self.model_name, e) - return False - - async def delete(self, document: T, refresh: bool = False) -> bool: - """ - Delete document instance - - Args: - document: Document instance to delete - refresh: Whether to refresh the index immediately - - Returns: - Returns True if deletion succeeds, otherwise False - """ - try: - client = await self.get_client() - await document.delete(using=client, refresh=refresh) - logger.debug( - "✅ Document deleted successfully [%s]: %s", - self.model_name, - getattr(document, 'meta', {}).get('id', 'unknown'), - ) - return True - except Exception as e: - logger.error("❌ Failed to delete document [%s]: %s", self.model_name, e) - return False - - # ==================== Batch Operations ==================== - - async def create_batch(self, documents: List[T], refresh: bool = False) -> List[T]: - """ - Batch create documents - - Args: - documents: List of documents - refresh: Whether to refresh the index immediately - - Returns: - List of successfully created documents - """ - try: - client = await self.get_client() - index_name = self.get_index_name() - - # Build bulk operations - actions = [] - for doc in documents: - action = {"_index": index_name, "_source": doc.to_dict()} - actions.append(action) - - # Execute bulk operation - from elasticsearch.helpers import async_bulk - - await async_bulk(client, actions, refresh=refresh) - - logger.debug( - "✅ Batch document creation succeeded [%s]: %d records", - self.model_name, - len(documents), - ) - return documents - except Exception as e: - logger.error( - "❌ Failed to batch create documents [%s]: %s", self.model_name, e - ) - raise - - # ==================== Search Methods ==================== - - async def search( - self, query: Dict[str, Any], size: int = 10, from_: int = 0 - ) -> Dict[str, Any]: - """ - Execute search query - - Args: - query: Elasticsearch query DSL - size: Number of results to return - from_: Pagination starting position - - Returns: - Search results - """ - try: - client = await self.get_client() - index_name = self.get_index_name() - - response = await client.search( - index=index_name, body={"query": query, "size": size, "from": from_} - ) - - logger.debug( - "✅ Search executed successfully [%s]: Found %d results", - self.model_name, - response.get('hits', {}).get('total', {}).get('value', 0), - ) - return response - except Exception as e: - logger.error("❌ Failed to execute search [%s]: %s", self.model_name, e) - raise - - async def match_all(self, size: int = 10, from_: int = 0) -> List[T]: - """ - Get all documents - - Args: - size: Number of results to return - from_: Pagination starting position - - Returns: - List of documents - """ - try: - response = await self.search( - query={"match_all": {}}, size=size, from_=from_ - ) - - documents = [] - for hit in response.get('hits', {}).get('hits', []): - doc = self.model.from_dict(hit['_source']) - doc.meta.id = hit['_id'] - documents.append(doc) - - return documents - except Exception as e: - logger.error("❌ Failed to get all documents [%s]: %s", self.model_name, e) - return [] - - # ==================== Statistics Methods ==================== - - async def exists_by_id(self, doc_id: str) -> bool: - """ - Check if document exists - - Args: - doc_id: Document ID - - Returns: - Returns True if exists, otherwise False - """ - try: - client = await self.get_client() - index_name = self.get_index_name() - - response = await client.exists(index=index_name, id=doc_id) - return response - except Exception: - return False - - # ==================== Index Management ==================== - - async def refresh_index(self) -> bool: - """ - Manually refresh index - - Uses connection.indices.refresh(index=index_name) to manually refresh the index, - ensuring newly written data is immediately searchable. - - Returns: - Returns True if refresh succeeds, otherwise False - """ - try: - client = await self.get_client() - index_name = self.get_index_name() - - await client.indices.refresh(index=index_name) - logger.debug( - "✅ Manual index refresh succeeded [%s]: %s", - self.model_name, - index_name, - ) - return True - - except (ConnectionError, TimeoutError) as e: - logger.error("❌ Manual index refresh failed [%s]: %s", self.model_name, e) - return False - except Exception as e: - logger.error( - "❌ Manual index refresh failed (unknown error) [%s]: %s", - self.model_name, - e, - ) - return False - - async def create_index(self) -> bool: - """ - Create index - - Returns: - Returns True if creation succeeds, otherwise False - """ - try: - client = await self.get_client() - - # Use document class's init method to create index - index_name = self.model.dest() - - await self.model.init(index=index_name, using=client) - - # Create alias - alias = self.get_index_name() - await client.indices.update_aliases( - body={ - "actions": [ - { - "add": { - "index": index_name, - "alias": alias, - "is_write_index": True, - } - } - ] - } - ) - - logger.debug( - "✅ Index creation succeeded [%s]: %s -> %s", - self.model_name, - index_name, - alias, - ) - return True - except Exception as e: - logger.error("❌ Index creation failed [%s]: %s", self.model_name, e) - return False - - async def delete_index(self) -> bool: - """ - Delete index - - Returns: - Returns True if deletion succeeds, otherwise False - """ - try: - client = await self.get_client() - index_name = self.get_index_name() - - await client.indices.delete(index=index_name) - logger.debug( - "✅ Index deletion succeeded [%s]: %s", self.model_name, index_name - ) - return True - except Exception as e: - logger.error("❌ Index deletion failed [%s]: %s", self.model_name, e) - return False - - async def index_exists(self) -> bool: - """ - Check if index exists - - Returns: - Returns True if exists, otherwise False - """ - try: - client = await self.get_client() - index_name = self.get_index_name() - - return await client.indices.exists(index=index_name) - except Exception: - return False - - # ==================== Helper Methods ==================== - - def get_model_name(self) -> str: - """ - Get model name - - Returns: - Model class name - """ - return self.model_name - - def get_collection_name(self) -> str: - """ - Get index name (compatible with MongoDB repository interface) - - Returns: - Elasticsearch index name - """ - return self.get_index_name() - - -# Export -__all__ = ["BaseRepository"] diff --git a/methods/EverCore/src/core/oxm/es/doc_base.py b/methods/EverCore/src/core/oxm/es/doc_base.py deleted file mode 100644 index c10c1469..00000000 --- a/methods/EverCore/src/core/oxm/es/doc_base.py +++ /dev/null @@ -1,230 +0,0 @@ -import typing -from typing import Type, Any, Dict, Set - -import os -from fnmatch import fnmatch -from datetime import datetime -from elasticsearch.dsl import MetaField, AsyncDocument, field as e_field -from common_utils.datetime_utils import to_timezone -from core.oxm.es.es_utils import generate_index_name, get_index_ns -from core.oxm.es.mapping_templates import DYNAMIC_TEMPLATES -from elasticsearch import AsyncElasticsearch - - -class DocBase(AsyncDocument): - """Elasticsearch document base class""" - - @classmethod - def get_connection(cls) -> AsyncElasticsearch: - """ - Get connection - """ - return cls._get_connection() - - @classmethod - def get_index_name(cls) -> str: - """ - Get index name (alias) - - Returns: - str: Index alias - - Raises: - ValueError: If the document class does not have correct index configuration - """ - if hasattr(cls, '_index') and hasattr(cls._index, '_name'): - return cls._index._name - raise ValueError( - f"Document class {cls.__name__} does not have correct index configuration" - ) - - -class AliasSupportDoc(DocBase): - """Document class supporting alias pattern, enhanced with timezone handling for date fields""" - - class CustomMeta: - # Specify the field name used to automatically populate meta.id (e.g., MongoDB primary key field), not enabled if not set - id_source_field: typing.Optional[str] = None - # Cache set of Date-type field names for quick checking (dynamically set, no need to predefine) - # date_fields: typing.Optional[Set[str]] = None - - @classmethod - def _init_date_fields_cache(cls) -> Set[str]: - """ - Initialize Date field cache, collect all Date-type field names in the class - - Returns: - Set of Date-type field names - """ - # Get cached date_fields from CustomMeta - custom_meta = getattr(cls, 'CustomMeta', None) - if custom_meta is not None: - existing_cache = getattr(custom_meta, 'date_fields', None) - if existing_cache is not None: - return existing_cache - - date_fields = set() - # Iterate through all class attributes to find Date-type fields - for attr_name in dir(cls): - if attr_name.startswith('_'): - continue - try: - attr_value = getattr(cls, attr_name) - if isinstance(attr_value, e_field.Date): - date_fields.add(attr_name) - except (AttributeError, TypeError): - # Ignore attributes that cannot be accessed or are not fields - continue - - # Dynamically set to CustomMeta - if custom_meta is not None: - setattr(custom_meta, 'date_fields', date_fields) - - return date_fields - - def _process_date_field(self, field_name: str, field_value: Any) -> Any: - """ - Process date field to ensure timezone information is present - - Args: - field_name: Field name - field_value: Field value - - Returns: - Processed field value - """ - # Use cached Date field set for quick checking - # Ensure non-None Set[str] by calling _init_date_fields_cache - date_fields = self.__class__._init_date_fields_cache() - if field_name in date_fields and isinstance(field_value, datetime): - return to_timezone(field_value) - return field_value - - def __setattr__(self, name: str, value: Any) -> None: - """Override field setting method to apply timezone processing for date fields""" - # Apply timezone processing for date fields - processed_value = self._process_date_field(name, value) - - # Call parent class __setattr__ - super().__setattr__(name, processed_value) - - def __init__(self, meta: Dict[str, Any] = None, **kwargs: Any): - """Override constructor: set meta.id strictly based on explicit ID_SOURCE_FIELD, raise error if missing""" - - # Initialize Date field cache (will initialize on first call, then use cache) - self.__class__._init_date_fields_cache() - - raw_kwargs = dict(kwargs) - - # Process date fields in kwargs - processed_kwargs = {} - for field_name, field_value in raw_kwargs.items(): - processed_kwargs[field_name] = self._process_date_field( - field_name, field_value - ) - - # Strictly set meta.id based on ID_SOURCE_FIELD (no heuristics), and compatible with ES construction (meta with _id) - # Get id_source_field configuration from CustomMeta - custom_meta_class = getattr(self.__class__, 'CustomMeta', None) - - id_source_field = ( - getattr(custom_meta_class, 'id_source_field', None) - if custom_meta_class - else None - ) - merged_meta: Dict[str, Any] = {} if meta is None else dict(meta) - # Extract provided meta id (from ES loading scenario) - given_meta_id = None - if "id" in merged_meta and merged_meta["id"] not in (None, ""): - given_meta_id = merged_meta["id"] - if "_id" in merged_meta and merged_meta["_id"] not in (None, ""): - if given_meta_id is not None and given_meta_id != merged_meta["_id"]: - raise ValueError("meta.id conflicts between 'id' and '_id'") - given_meta_id = merged_meta["_id"] - - if given_meta_id is not None: - # If meta id is explicitly provided, validate consistency with ID_SOURCE_FIELD (if exists) - if id_source_field and id_source_field in processed_kwargs: - source_value = processed_kwargs[id_source_field] - if source_value not in (None, "") and source_value != given_meta_id: - raise ValueError( - "meta.id conflicts with value from ID_SOURCE_FIELD" - ) - # Normalize meta fields - merged_meta["id"] = given_meta_id - merged_meta["_id"] = given_meta_id - elif id_source_field: - # If meta id is not provided, require it from ID_SOURCE_FIELD - if id_source_field not in processed_kwargs or processed_kwargs[ - id_source_field - ] in (None, ""): - raise ValueError( - f"{self.__class__.__name__} requires non-empty '{id_source_field}' to set meta.id" - ) - source_value = processed_kwargs[id_source_field] - merged_meta["id"] = source_value - merged_meta["_id"] = source_value - - # Call parent constructor - super().__init__(merged_meta or None, **processed_kwargs) - - @classmethod - def _matches(cls, hit): - # override _matches to match indices in a pattern instead of just ALIAS - # hit is the raw dict as returned by elasticsearch - return fnmatch(hit["_index"], cls.PATTERN) - - @classmethod - def dest(cls): - return generate_index_name(cls) - - -def AliasDoc( - doc_name: str, - number_of_shards: int = 3, - number_of_replicas: int = 1, - refresh_interval: str = "10s", -) -> Type[AsyncDocument]: - """ - Create an ES document class supporting alias pattern - - Automatically handle timezone for date fields: - - For int timestamps, no processing - - For datetime objects without timezone, automatically add current system timezone - - Ensure all date fields have timezone information to avoid timezone-related issues - - Args: - doc_name: Document name - build_analyzers: Optional list of analyzers - number_of_shards: Number of shards - - Returns: - Enhanced document class - """ - - if get_index_ns(): - doc_name = f"{doc_name}-{get_index_ns()}" - - class GeneratedAliasSupportDoc(AliasSupportDoc): - PATTERN = f"{doc_name}-*" - - class Index: - name = doc_name - settings = { - "number_of_shards": number_of_shards, - "number_of_replicas": number_of_replicas, - "refresh_interval": refresh_interval, - "max_ngram_diff": 50, - "max_shingle_diff": 10, - } - - class Meta: - dynamic = MetaField("true") - # Disable date auto-detection to prevent "2023/10/01" from being incorrectly converted and causing subsequent errors - date_detection = MetaField(False) - # Disable numeric detection to prevent string numbers from being confused - numeric_detection = MetaField(False) - # Dynamic mapping rules based on field suffixes (see mapping_templates.py) - dynamic_templates = MetaField(DYNAMIC_TEMPLATES) - - return GeneratedAliasSupportDoc diff --git a/methods/EverCore/src/core/oxm/es/es_utils.py b/methods/EverCore/src/core/oxm/es/es_utils.py deleted file mode 100644 index b2f83ce7..00000000 --- a/methods/EverCore/src/core/oxm/es/es_utils.py +++ /dev/null @@ -1,191 +0,0 @@ -from common_utils.datetime_utils import get_now_with_timezone -from typing import Type, List, Optional, TYPE_CHECKING -import os - -from core.observation.logger import get_logger - -if TYPE_CHECKING: - from core.oxm.es.doc_base import DocBase - -logger = get_logger(__name__) - - -def generate_index_name(cls: Type['DocBase']) -> str: - """Generate index name with timestamp""" - now = get_now_with_timezone() - alias = cls.get_index_name() - return f"{alias}-{now.strftime('%Y%m%d%H%M%S%f')}" - - -def get_index_ns() -> str: - """Get index namespace""" - return os.getenv("SELF_ES_INDEX_NS") or "" - - -def is_abstract_doc_class(doc_class: Type['DocBase']) -> bool: - """ - Check if the document class is an abstract class - - Determine whether it is an abstract class by checking the Meta.abstract attribute. - Abstract classes should not have their indices initialized. - - Args: - doc_class: Document class - - Returns: - bool: Whether it is an abstract class - """ - # Check the abstract attribute of the Meta class - pattern = getattr(doc_class, 'PATTERN', None) or None - return (not pattern) or "Generated" in doc_class.__name__ - - -class EsIndexInitializer: - """ - Elasticsearch index initialization utility class - - Used to batch initialize indices and aliases corresponding to ES document classes. - Uses doc_class._get_connection() to obtain the connection, supporting tenant awareness. - """ - - def __init__(self): - self._initialized_classes: List[Type['DocBase']] = [] - - async def initialize_indices( - self, document_classes: Optional[List[Type['DocBase']]] = None - ) -> None: - """ - Initialize indices for multiple document classes - - Args: - document_classes: List of document classes - """ - if not document_classes: - logger.info("No document classes need to be initialized") - return - - try: - logger.info( - "Initializing Elasticsearch indices, total %d document classes", - len(document_classes), - ) - - for doc_class in document_classes: - await self.init_document_index(doc_class) - - self._initialized_classes.extend(document_classes) - - logger.info( - "✅ Elasticsearch index initialization succeeded, processed %d document classes", - len(document_classes), - ) - - for doc_class in document_classes: - logger.info( - "📋 Initialized index: class=%s -> index=%s", - doc_class.__name__, - doc_class.get_index_name(), - ) - - except Exception as e: - logger.error("❌ Elasticsearch index initialization failed: %s", e) - raise - - async def init_document_index(self, doc_class: Type['DocBase']) -> None: - """ - Initialize index for a single document class - - Args: - doc_class: Document class - """ - try: - # Get alias name - alias = doc_class.get_index_name() - - if not alias: - logger.info( - "Document class has no index alias, skipping initialization %s", - doc_class.__name__, - ) - return - - # Check if it is an abstract class - if is_abstract_doc_class(doc_class): - logger.debug( - "Document class is abstract, skipping initialization %s", - doc_class.__name__, - ) - return - - # Get connection through document class (supports tenant awareness) - client = doc_class._get_connection() - - # Check if alias exists - logger.info( - "Checking index alias: %s (document class: %s)", - alias, - doc_class.__name__, - ) - alias_exists = await client.indices.exists(index=alias) - - if not alias_exists: - # Generate target index name - dst = doc_class.dest() - - # Create index - await doc_class.init(index=dst, using=client) - - # Create alias - await client.indices.update_aliases( - body={ - "actions": [ - { - "add": { - "index": dst, - "alias": alias, - "is_write_index": True, - } - } - ] - } - ) - logger.info("✅ Created index and alias: %s -> %s", dst, alias) - else: - logger.info("📋 Index alias already exists: %s", alias) - - except Exception as e: - logger.error( - "❌ Failed to initialize index for document class %s: %s", - doc_class.__name__, - e, - ) - raise - - @property - def initialized_classes(self) -> List[Type['DocBase']]: - """Get list of initialized document classes""" - return self._initialized_classes - - -async def initialize_document_indices( - document_classes: Optional[List[Type['DocBase']]] = None, -) -> None: - """ - Utility function: Initialize indices for multiple document classes - - Args: - document_classes: List of document classes - """ - initializer = EsIndexInitializer() - await initializer.initialize_indices(document_classes) - - -async def init_single_document_index(doc_class: Type['DocBase']) -> None: - """ - Utility function: Initialize index for a single document class - - Args: - doc_class: Document class - """ - initializer = EsIndexInitializer() - await initializer.init_document_index(doc_class) diff --git a/methods/EverCore/src/core/oxm/es/mapping_templates.py b/methods/EverCore/src/core/oxm/es/mapping_templates.py deleted file mode 100644 index 9b99e39d..00000000 --- a/methods/EverCore/src/core/oxm/es/mapping_templates.py +++ /dev/null @@ -1,90 +0,0 @@ -""" -Elasticsearch Dynamic Mapping Templates - -Dynamic mapping rules configuration based on field suffixes, used to centrally manage field type inference for ES indexes. - -Naming conventions: -- *_ts, *_date → date (date and time) -- *_num → long (integer) -- *_desc → text (full-text search, tokenized) -- *_flag → boolean (boolean flag) -- *_enabled → boolean (enabled status) -- *_rate → double (floating-point number/ratio) -- *_id → keyword (ID, exact match) -- other strings → keyword (fallback, exact match) -""" - -# Dynamic mapping rules based on field suffixes (matched in order, first match takes effect) -DYNAMIC_TEMPLATES = [ - # 1. Date rule: match *_ts suffix - { - "rule_date_ts": { - "match": "*_ts", - "match_mapping_type": "*", - "mapping": {"type": "date"}, - } - }, - # 2. Date rule: match *_date suffix - { - "rule_date": { - "match": "*_date", - "match_mapping_type": "*", - "mapping": {"type": "date"}, - } - }, - # 3. Numeric rule: match *_num suffix, force to long - { - "rule_long": { - "match": "*_num", - "match_mapping_type": "long", - "mapping": {"type": "long"}, - } - }, - # 4. Full-text search rule: match *_desc suffix, requires tokenization - { - "rule_text": { - "match": "*_desc", - "match_mapping_type": "string", - "mapping": {"type": "text", "analyzer": "standard"}, - } - }, - # 5. Boolean rule: match *_flag suffix - { - "rule_bool_flag": { - "match": "*_flag", - "match_mapping_type": "boolean", - "mapping": {"type": "boolean"}, - } - }, - # 6. Boolean rule: match *_enabled suffix - { - "rule_bool_enabled": { - "match": "*_enabled", - "match_mapping_type": "boolean", - "mapping": {"type": "boolean"}, - } - }, - # 7. Floating-point rule: match *_rate suffix - { - "rule_double": { - "match": "*_rate", - "match_mapping_type": "double", - "mapping": {"type": "double"}, - } - }, - # 8. ID rule: match *_id suffix, exact match without tokenization - { - "rule_id": { - "match": "*_id", - "match_mapping_type": "string", - "mapping": {"type": "keyword"}, - } - }, - # 9. Fallback rule: all other strings → keyword - { - "strings_as_keywords": { - "match_mapping_type": "string", - "mapping": {"type": "keyword", "ignore_above": 256}, - } - }, -] diff --git a/methods/EverCore/src/core/oxm/es/migration/__init__.py b/methods/EverCore/src/core/oxm/es/migration/__init__.py deleted file mode 100644 index 4af2aeff..00000000 --- a/methods/EverCore/src/core/oxm/es/migration/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -MongoDB migration management module. -""" diff --git a/methods/EverCore/src/core/oxm/es/migration/utils.py b/methods/EverCore/src/core/oxm/es/migration/utils.py deleted file mode 100644 index bc8be6cb..00000000 --- a/methods/EverCore/src/core/oxm/es/migration/utils.py +++ /dev/null @@ -1,242 +0,0 @@ -""" -Elasticsearch index migration tool - -Provides generic Elasticsearch index rebuilding and migration functionality. -""" - -import time -import traceback -from typing import Type, Any -from elasticsearch import NotFoundError, RequestError -from elasticsearch.dsl import AsyncDocument -from core.observation.logger import get_logger -from core.di.utils import get_all_subclasses -from core.oxm.es.doc_base import DocBase, get_index_ns -from core.oxm.es.es_utils import is_abstract_doc_class - -logger = get_logger(__name__) - - -def find_document_class_by_index_name(index_name: str) -> Type[AsyncDocument]: - """ - Find document class by index alias - - Uses `get_index_ns()` for namespace concatenation, ensuring consistency with `AliasDoc` alias rules. - - Args: - index_name: Index alias (e.g., "episodic-memory") - - Returns: - Matched ES document class - - Raises: - ValueError: If no match or multiple matches are found - """ - - all_doc_classes = get_all_subclasses(DocBase) - - matched_classes: list[Type[AsyncDocument]] = [] - for cls in all_doc_classes: - if not is_abstract_doc_class(cls) and index_name in cls.get_index_name(): - matched_classes.append(cls) - - if not matched_classes: - available_indexes = [cls.get_index_name() for cls in all_doc_classes] - logger.error( - "Cannot find document class corresponding to index alias '%s'", index_name - ) - logger.info("Available index aliases: %s", ", ".join(available_indexes)) - raise ValueError( - f"Cannot find document class corresponding to index alias '{index_name}'" - ) - - if len(matched_classes) > 1: - logger.error( - "Found multiple document classes with index alias '%s': %s", - index_name, - [cls.__module__ + "." + cls.__name__ for cls in matched_classes], - ) - raise ValueError( - f"Found multiple document classes with index alias '{index_name}': {', '.join([f'{cls.__module__}.{cls.__name__}' for cls in matched_classes])}" - ) - - document_class = matched_classes[0] - - # Basic validation - if not hasattr(document_class, "PATTERN"): - raise ValueError( - f"Document class {document_class.__name__} must have PATTERN attribute" - ) - if not hasattr(document_class, "dest"): - raise ValueError( - f"Document class {document_class.__name__} must have dest() method" - ) - - return document_class - - -async def rebuild_index( - document_class: Type[AsyncDocument], - close_old: bool = False, - delete_old: bool = False, -) -> None: - """ - Rebuild Elasticsearch index - - Create a new index based on the existing one and update the alias to point to it. Supports closing or deleting the old index. - - Args: - document_class: ES document class, must have PATTERN attribute and dest() method - es_connect: Elasticsearch connection object - close_old: Whether to close the old index - delete_old: Whether to delete the old index - - Returns: - None - - Raises: - ValueError: If the document class is missing required attributes or methods - """ - # Validate document class - if not hasattr(document_class, 'PATTERN'): - raise ValueError( - "Document class %s must have PATTERN attribute" % document_class.__name__ - ) - if not hasattr(document_class, 'dest'): - raise ValueError( - "Document class %s must have dest() method" % document_class.__name__ - ) - - # Get index information - alias_name = document_class.get_index_name() - es_connect = document_class._get_connection() - pattern = document_class.PATTERN - dest_index = document_class.dest() - - logger.info("Starting index rebuild: %s", alias_name) - logger.info("Source index pattern: %s", pattern) - logger.info("Destination index: %s", dest_index) - - # Check if destination index already exists - if await es_connect.indices.exists(index=dest_index): - logger.warning( - "Destination index %s already exists, skipping rebuild", dest_index - ) - return - - # Initialize new index - await document_class.init(index=dest_index) - logger.info("New index created: %s", dest_index) - - # Start reindexing task - reindex_body = {"source": {"index": alias_name}, "dest": {"index": dest_index}} - - logger.info("Starting data migration...") - result = await es_connect.reindex(body=reindex_body, wait_for_completion=False) - task_id = result["task"] - logger.info("Rebuild task ID: %s", task_id) - - # Wait for task completion - await wait_for_task_completion(es_connect, task_id) - - # Update aliases - await update_aliases(es_connect, alias_name, dest_index, close_old, delete_old) - - logger.info("Index rebuild completed: %s", alias_name) - - -async def wait_for_task_completion(es_connect: Any, task_id: str) -> None: - """ - Wait for Elasticsearch task to complete - - Args: - es_connect: Elasticsearch connection object - task_id: Task ID - """ - logger.info("Waiting for rebuild task to complete...") - - while True: - try: - task_result = await es_connect.tasks.get(task_id=task_id) - - if task_result.get("completed", False): - logger.info("Rebuild task completed") - break - - # Display progress information - status = task_result.get("task", {}).get("status", {}) - if status: - created = status.get("created", 0) - total = status.get("total", 0) - if total > 0: - progress = (created / total) * 100 - logger.info( - "Rebuild progress: %d/%d (%.1f%%)", created, total, progress - ) - - time.sleep(5) # Check every 5 seconds - - except (NotFoundError, RequestError) as e: - traceback.print_exc() - logger.error("Failed to check task status: %s", e) - time.sleep(10) # Wait longer when error occurs - - -async def update_aliases( - es_connect: Any, - alias_name: str, - dest_index: str, - close_old: bool = False, - delete_old: bool = False, -) -> None: - """ - Update Elasticsearch index aliases - - Args: - es_connect: Elasticsearch connection object - alias_name: Alias name - dest_index: Destination index - close_old: Whether to close old index - delete_old: Whether to delete old index - """ - logger.info("Updating index aliases...") - - # Get indices currently pointed by the alias - try: - existing_indices = list( - (await es_connect.indices.get_alias(name=alias_name)).keys() - ) - logger.info( - "Current indices pointed by alias %s: %s", alias_name, existing_indices - ) - except NotFoundError: - existing_indices = [] - logger.info("Alias %s does not exist, will create new alias", alias_name) - - # Refresh the new index - await es_connect.indices.refresh(index=dest_index) - - # Build alias update operations - actions = [] - - # Remove old alias associations - for old_index in existing_indices: - actions.append({"remove": {"alias": alias_name, "index": old_index}}) - - # Add new alias association - actions.append( - {"add": {"alias": alias_name, "index": dest_index, "is_write_index": True}} - ) - - # Execute alias update - await es_connect.indices.update_aliases(body={"actions": actions}) - logger.info("Alias %s updated to point to %s", alias_name, dest_index) - - # Handle old indices - for old_index in existing_indices: - if close_old: - logger.info("Closing old index: %s", old_index) - await es_connect.indices.close(index=old_index) - elif delete_old: - logger.info("Deleting old index: %s", old_index) - await es_connect.indices.delete(index=old_index) diff --git a/methods/EverCore/src/core/oxm/milvus/__init__.py b/methods/EverCore/src/core/oxm/milvus/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/oxm/milvus/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/oxm/milvus/async_collection.py b/methods/EverCore/src/core/oxm/milvus/async_collection.py deleted file mode 100644 index f921f4fa..00000000 --- a/methods/EverCore/src/core/oxm/milvus/async_collection.py +++ /dev/null @@ -1,204 +0,0 @@ -import asyncio -import concurrent.futures -import contextvars -import os -from functools import wraps -from typing import Any, Callable, Optional, TypeVar, Union, List, Dict - -from pymilvus import Collection, SearchResult -from pymilvus.orm.mutation import MutationResult -from pymilvus.client.types import CompactionPlans, CompactionState, Replica - -T = TypeVar('T') - -# Dedicated thread pool for pymilvus sync calls. -# Pre-warms threads at import time so concurrent requests don't pay -# thread-creation latency. Sized for burst concurrency: pymilvus is -# I/O-bound (gRPC network waits), so idle threads consume near-zero CPU. -_MILVUS_POOL_SIZE = int(os.getenv("MILVUS_THREAD_POOL_SIZE", "25")) -_milvus_executor = concurrent.futures.ThreadPoolExecutor( - max_workers=_MILVUS_POOL_SIZE, thread_name_prefix="milvus" -) -# Pre-warm: force all threads to be created immediately -concurrent.futures.wait( - [_milvus_executor.submit(lambda: None) for _ in range(_MILVUS_POOL_SIZE)] -) - - -def async_wrap(func: Callable[..., T]) -> Callable[..., asyncio.Future[T]]: - """Decorator that wraps a synchronous method into an asynchronous one. - - Uses a dedicated pre-warmed thread pool (_milvus_executor) instead of - the default executor, so pymilvus blocking calls don't compete with - other run_in_executor users and threads are ready immediately. - - Note: Use contextvars.copy_context() to ensure that threads in the thread pool can access contextvars - (such as tenant context), because run_in_executor does not pass the asyncio Context by default. - """ - - @wraps(func) - async def run(*args, **kwargs) -> T: - loop = asyncio.get_running_loop() - # Copy current context to ensure contextvars are accessible in the thread pool - ctx = contextvars.copy_context() - return await loop.run_in_executor( - _milvus_executor, lambda: ctx.run(func, *args, **kwargs) - ) - - return run - - -class AsyncCollection: - """Asynchronous version of the Collection class. - - This class wraps pymilvus's Collection class to provide asynchronous interfaces. - All synchronous operations are executed in the event loop's default executor. - """ - - def __init__(self, collection: Collection): - """Initialize AsyncCollection. - - Args: - collection: pymilvus Collection instance - """ - self._collection = collection - - def __getattr__(self, name: str) -> Any: - """Intercept all attribute access to the original collection. - - If it's a method call, wrap it into an asynchronous method. - If it's an attribute access, return directly. - """ - attr = getattr(self._collection, name) - if callable(attr): - return async_wrap(attr) - return attr - - @property - def collection(self) -> Collection: - """Return the original Collection instance.""" - return self._collection - - # Explicit asynchronous implementations of some commonly used methods. - # Although __getattr__ can handle these methods, explicit definitions provide better type hints. - - async def insert( - self, - data: Union[List, Dict], - partition_name: Optional[str] = None, - timeout: Optional[float] = None, - **kwargs, - ) -> MutationResult: - """Asynchronously insert data.""" - return await async_wrap(self._collection.insert)( - data, partition_name, timeout, **kwargs - ) - - async def search( - self, - data: List, - anns_field: str, - param: Dict, - limit: int, - expr: Optional[str] = None, - partition_names: Optional[List[str]] = None, - output_fields: Optional[List[str]] = None, - timeout: Optional[float] = None, - round_decimal: int = -1, - **kwargs, - ) -> SearchResult: - """Asynchronously search.""" - return await async_wrap(self._collection.search)( - data, - anns_field, - param, - limit, - expr, - partition_names, - output_fields, - timeout, - round_decimal, - **kwargs, - ) - - async def query( - self, - expr: str, - output_fields: Optional[List[str]] = None, - partition_names: Optional[List[str]] = None, - timeout: Optional[float] = None, - **kwargs, - ) -> List: - """Asynchronously query.""" - return await async_wrap(self._collection.query)( - expr, output_fields, partition_names, timeout, **kwargs - ) - - async def delete( - self, - expr: str, - partition_name: Optional[str] = None, - timeout: Optional[float] = None, - **kwargs, - ) -> MutationResult: - """Asynchronously delete.""" - return await async_wrap(self._collection.delete)( - expr, partition_name, timeout, **kwargs - ) - - async def flush(self, timeout: Optional[float] = None, **kwargs) -> None: - """Asynchronously flush.""" - return await async_wrap(self._collection.flush)(timeout, **kwargs) - - async def load( - self, - partition_names: Optional[List[str]] = None, - replica_number: Optional[int] = None, - timeout: Optional[float] = None, - **kwargs, - ) -> None: - """Asynchronously load.""" - return await async_wrap(self._collection.load)( - partition_names, replica_number, timeout, **kwargs - ) - - async def release(self, timeout: Optional[float] = None, **kwargs) -> None: - """Asynchronously release.""" - return await async_wrap(self._collection.release)(timeout, **kwargs) - - async def compact( - self, - is_clustering: Optional[bool] = False, - timeout: Optional[float] = None, - **kwargs, - ) -> None: - """Asynchronously compact.""" - return await async_wrap(self._collection.compact)( - is_clustering, timeout, **kwargs - ) - - async def get_compaction_state( - self, - timeout: Optional[float] = None, - is_clustering: Optional[bool] = False, - **kwargs, - ) -> CompactionState: - """Asynchronously get compaction state.""" - return await async_wrap(self._collection.get_compaction_state)( - timeout, is_clustering, **kwargs - ) - - async def get_compaction_plans( - self, - timeout: Optional[float] = None, - is_clustering: Optional[bool] = False, - **kwargs, - ) -> CompactionPlans: - """Asynchronously get compaction plans.""" - return await async_wrap(self._collection.get_compaction_plans)( - timeout, is_clustering, **kwargs - ) - - async def get_replicas(self, timeout: Optional[float] = None, **kwargs) -> Replica: - """Asynchronously get replica information.""" - return await async_wrap(self._collection.get_replicas)(timeout, **kwargs) diff --git a/methods/EverCore/src/core/oxm/milvus/base_converter.py b/methods/EverCore/src/core/oxm/milvus/base_converter.py deleted file mode 100644 index 5c76144b..00000000 --- a/methods/EverCore/src/core/oxm/milvus/base_converter.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -Milvus collection converter base class - -Provides basic functionality for converting arbitrary data sources to Milvus collections. -All Milvus collection converters should inherit from this base class to obtain a unified conversion interface. -""" - -from abc import ABC, abstractmethod -from typing import TypeVar, Generic, Type, Any, get_args, get_origin -from core.oxm.milvus.milvus_collection_base import MilvusCollectionBase -from core.observation.logger import get_logger - -logger = get_logger(__name__) - -# Generic type variable - only restricts Milvus collection type -MilvusCollectionType = TypeVar('MilvusCollectionType', bound=MilvusCollectionBase) - - -class BaseMilvusConverter(ABC, Generic[MilvusCollectionType]): - """ - Milvus collection converter base class - - Provides basic functionality for converting arbitrary data sources to Milvus collections. - All Milvus collection converters should inherit from this class. - - Features: - - Unified conversion interface (class methods) - - Type-safe Milvus collection generic support - - Automatically retrieves Milvus collection type from generics - - Flexible data source support - """ - - @classmethod - def get_milvus_model(cls) -> Type[MilvusCollectionType]: - """ - Retrieve the Milvus collection model type from generic information - - Returns: - Type[MilvusCollectionType]: Milvus collection model class - """ - # Get the generic base class of the current class - if hasattr(cls, '__orig_bases__'): - for base in cls.__orig_bases__: - if get_origin(base) is BaseMilvusConverter: - args = get_args(base) - if args: - return args[0] - - raise ValueError( - f"Unable to retrieve Milvus collection type from generic information of {cls.__name__}" - ) - - @classmethod - @abstractmethod - def from_mongo(cls, source_doc: Any) -> MilvusCollectionType: - """ - Convert from data source to Milvus collection entity - - This is the core conversion method; subclasses must implement specific conversion logic. - - Args: - source_doc: Source data (can be of any type) - - Returns: - MilvusCollectionType: Instance of Milvus collection entity - - Raises: - Exception: Raises an exception when an error occurs during conversion - """ - raise NotImplementedError("Subclasses must implement the from_mongo method") diff --git a/methods/EverCore/src/core/oxm/milvus/base_repository.py b/methods/EverCore/src/core/oxm/milvus/base_repository.py deleted file mode 100644 index cfa49e67..00000000 --- a/methods/EverCore/src/core/oxm/milvus/base_repository.py +++ /dev/null @@ -1,214 +0,0 @@ -""" -Milvus Base Repository Class - -Provides common basic operations, all Milvus repositories should inherit from this class to obtain unified operation support. -""" - -from abc import ABC -from typing import Optional, TypeVar, Generic, Type, List, Any -from pymilvus.orm.mutation import MutationResult -from core.oxm.milvus.milvus_collection_base import MilvusCollectionBase -from core.oxm.milvus.async_collection import AsyncCollection -from core.observation.logger import get_logger -from core.di.utils import get_bean - -logger = get_logger(__name__) - -# Generic type variable -T = TypeVar('T', bound=MilvusCollectionBase) - - -class BaseMilvusRepository(ABC, Generic[T]): - """ - Milvus Base Repository Class - - Provides common basic operations, all Milvus repositories should inherit from this class. - - Features: - - Asynchronous Milvus collection management - - Basic CRUD operation templates - - Unified error handling and logging - - Collection management - """ - - def __init__(self, model: Type[T]): - """ - Initialize base repository - - Args: - model: Milvus collection model class - """ - self.model = model - self.model_name = model.__name__ - self.collection: Optional[AsyncCollection] = model.async_collection() - self.schema = model._SCHEMA - self.all_output_fields = [field.name for field in self.schema.fields] - - # ==================== Basic CRUD Operations ==================== - - async def insert(self, entity: T, flush: bool = False) -> str: - """ - Insert new entity - - Args: - entity: Entity instance - flush: Whether to flush immediately - - Returns: - str: Inserted entity ID - """ - try: - entity_id = await self.collection.insert(entity) - if flush: - await self.collection.flush() - logger.debug( - "✅ Insert entity successful [%s]: %s", self.model_name, entity_id - ) - return entity_id - except Exception as e: - logger.error("❌ Insert entity failed [%s]: %s", self.model_name, e) - raise - - async def get_by_id(self, entity_id: str) -> Optional[T]: - """ - Get entity by ID - - Args: - entity_id: Entity ID - - Returns: - Entity instance or None - """ - try: - # Get all fields of the collection - # Use query to search - results = await self.collection.query( - expr=f'id == "{entity_id}"', - output_fields=self.all_output_fields, - limit=1, - ) - return results[0] if results else None - except Exception as e: - logger.error("❌ Failed to get entity by ID [%s]: %s", self.model_name, e) - return None - - async def upsert(self, entity: T, flush: bool = False) -> str: - """ - Update or insert entity - - Args: - entity: Entity instance - flush: Whether to flush immediately - - Returns: - str: Entity ID - """ - try: - entity_id = await self.collection.upsert(entity) - if flush: - await self.collection.flush() - logger.debug( - "✅ Upsert entity successful [%s]: %s", self.model_name, entity_id - ) - return entity_id - except Exception as e: - logger.error("❌ Upsert entity failed [%s]: %s", self.model_name, e) - raise - - async def delete_by_id(self, entity_id: str, flush: bool = False) -> bool: - """ - Delete entity by ID - - Args: - entity_id: Entity ID - flush: Whether to flush immediately - - Returns: - bool: Return True if deletion is successful - """ - try: - result = await self.collection.delete(expr=f'id == "{entity_id}"') - success = result.delete_count > 0 - - if flush and success: - await self.collection.flush() - if success: - logger.debug( - "✅ Delete entity successful [%s]: %s", self.model_name, entity_id - ) - return success - except Exception as e: - logger.error("❌ Delete entity failed [%s]: %s", self.model_name, e) - return False - - # ==================== Batch Operations ==================== - - async def insert_batch( - self, entities: List[T], flush: bool = False - ) -> MutationResult: - """ - Insert entities in batch - - Args: - entities: List of entities - flush: Whether to flush immediately - - Returns: - MutationResult from Milvus - """ - try: - result = await self.collection.insert(entities) - if flush: - await self.collection.flush() - logger.debug( - "✅ Batch insert entities successful [%s]: %d records", - self.model_name, - len(entities), - ) - return result - except Exception as e: - logger.error("❌ Batch insert entities failed [%s]: %s", self.model_name, e) - raise - - # ==================== Collection Operations ==================== - - async def flush(self) -> bool: - """ - Flush collection - - Returns: - bool: Return True if flush is successful - """ - try: - await self.collection.flush() - logger.debug("✅ Flush collection successful [%s]", self.model_name) - return True - except Exception as e: - logger.error("❌ Flush collection failed [%s]: %s", self.model_name, e) - return False - - async def load(self) -> bool: - """ - Load collection into memory - - Returns: - bool: Return True if load is successful - """ - try: - await self.collection.load() - logger.debug("✅ Load collection successful [%s]", self.model_name) - return True - except Exception as e: - logger.error("❌ Load collection failed [%s]: %s", self.model_name, e) - return False - - # ==================== Helper Methods ==================== - - def get_model_name(self) -> str: - """ - Get model name - - Returns: - str: Model class name - """ - return self.model_name diff --git a/methods/EverCore/src/core/oxm/milvus/migration/utils.py b/methods/EverCore/src/core/oxm/milvus/migration/utils.py deleted file mode 100644 index b2ad1528..00000000 --- a/methods/EverCore/src/core/oxm/milvus/migration/utils.py +++ /dev/null @@ -1,207 +0,0 @@ -""" -Milvus collection rebuild and alias switching utility - -Design goals: -- Consolidate infrastructure-related, reusable Milvus rebuild logic into the core layer -- Business or script layers only need to provide the client, alias, and options - -Note: -- This tool only handles structure rebuilding and alias switching, not data migration -""" - -from __future__ import annotations - -from dataclasses import dataclass -from typing import Optional, Callable, Type - -from pymilvus import Collection, utility - -from core.observation.logger import get_logger -from core.oxm.milvus.milvus_collection_base import ( - MilvusCollectionBase, - MilvusCollectionWithSuffix, -) -from core.di.utils import get_all_subclasses - - -logger = get_logger(__name__) - - -@dataclass -class RebuildResult: - """Result of Milvus collection rebuild.""" - - alias: str - source_collection: str - dest_collection: str - dropped_old: bool - - -def find_collection_manager_by_alias(alias: str) -> Type[MilvusCollectionBase]: - """ - Find the corresponding Collection manager class by alias - - Args: - alias: Collection alias - - Returns: - Corresponding MilvusCollectionBase subclass - - Raises: - ValueError: If no corresponding collection class is found - """ - all_doc_classes = get_all_subclasses(MilvusCollectionBase) - - # Iterate through all subclasses to find the one matching the alias - for doc_class in all_doc_classes: - # Skip abstract classes - # pylint: disable=protected-access # Internal framework usage, accessing subclass configuration attributes - if ( - not hasattr(doc_class, '_COLLECTION_NAME') - or doc_class._COLLECTION_NAME is None - ): - continue - - # Check if it's a MilvusCollectionWithSuffix type - if issubclass(doc_class, MilvusCollectionWithSuffix): - # Temporarily instantiate to get alias (requires parsing suffix) - try: - # Try to parse suffix from alias - base_name = ( - doc_class._COLLECTION_NAME - ) # pylint: disable=protected-access - if alias.startswith(base_name): - return doc_class - except ( - Exception - ): # pylint: disable=broad-except # Ignore instantiation failure, continue to next class - continue - else: - # For MilvusCollectionBase, directly compare _COLLECTION_NAME - if doc_class._COLLECTION_NAME == alias: # pylint: disable=protected-access - return doc_class - - raise ValueError(f"Cannot find collection class corresponding to alias '{alias}'") - - -def rebuild_collection( - alias: str, - drop_old: bool = False, - populate_fn: Optional[Callable[[Collection, Collection], None]] = None, -) -> RebuildResult: - """ - Rebuild Milvus collection based on alias: - 1) Find the corresponding Collection manager class by alias - 2) Call create_new_collection() to create a new collection (automatically create index and load) - 3) Call optional data population callback (implemented by caller) - 4) Call switch_alias() to switch alias, optionally delete old collection - - Args: - alias: Collection alias - drop_old: Whether to delete the old collection - populate_fn: Optional callback to populate data after index creation and before alias switching. - Function signature: (old_collection: Collection, new_collection: Collection) -> None - - Returns: - RebuildResult: Information about the rebuild result - - Raises: - ValueError: If no corresponding collection class is found - MilvusException: If Milvus operation fails - """ - logger.info( - "Starting to rebuild Collection: alias=%s, drop_old=%s", alias, drop_old - ) - - # 1. Find the corresponding Collection manager class by alias - collection_class = find_collection_manager_by_alias(alias) - logger.info("Found collection class: %s", collection_class.__name__) - - # 2. Instantiate manager (parse suffix from alias) - if issubclass(collection_class, MilvusCollectionWithSuffix): - # Parse suffix from alias - base_name = ( - collection_class._COLLECTION_NAME - ) # pylint: disable=protected-access - suffix = None - if alias != base_name and alias.startswith(base_name + "_"): - suffix = alias[len(base_name) + 1 :] - manager = collection_class(suffix=suffix) - else: - raise NotImplementedError( - "Unsupported collection type: %s", collection_class.__name__ - ) - - # Load the old collection WITHOUT passing schema to avoid SchemaNotReadyException - # when code-defined schema differs from the existing collection's schema. - # This is the whole point of rebuild: the schema has changed. - manager.ensure_connection_registered() - - alias_name = manager.name - using = manager.using - - if not utility.has_collection(alias_name, using=using): - raise ValueError( - f"Collection '{alias_name}' does not exist in Milvus, nothing to rebuild" - ) - - old_collection = Collection(name=alias_name, using=using) - old_collection.load() - old_real_name = old_collection.name - logger.info("Original collection real name: %s", old_real_name) - - # 3. Create new collection (automatically create index and load) - logger.info("Starting to create new collection...") - new_collection = manager.create_new_collection() - new_real_name = new_collection.name - logger.info("New collection created: %s", new_real_name) - - # 4. Call data population callback if provided - if populate_fn: - logger.info("Starting data population callback...") - try: - populate_fn(old_collection, new_collection) - logger.info("Data population completed") - except Exception as e: - logger.error("Data population failed: %s", e) - raise - - # 5. Verify data consistency before switching - # Use query-based count instead of num_entities (which is approximate - # and includes soft-deleted records not yet compacted). - if populate_fn: - new_collection.flush() - old_count = old_collection.query(expr='id != ""', output_fields=["count(*)"])[ - 0 - ]["count(*)"] - new_count = new_collection.query(expr='id != ""', output_fields=["count(*)"])[ - 0 - ]["count(*)"] - logger.info("Data verification: old=%d, new=%d", old_count, new_count) - if old_count != new_count: - raise RuntimeError( - f"Data count mismatch after migration: " - f"old={old_count}, new={new_count}. " - f"Aborting alias switch. New collection '{new_real_name}' " - f"is left intact for inspection." - ) - logger.info("Data verification passed") - - # 6. Switch alias to new collection and optionally delete old collection - logger.info("Switching alias '%s' to new collection '%s'...", alias, new_real_name) - manager.switch_alias(new_collection, drop_old=drop_old) - - logger.info( - "Rebuild completed: alias=%s, src=%s -> dest=%s, dropped_old=%s", - alias, - old_real_name, - new_real_name, - drop_old, - ) - - return RebuildResult( - alias=alias, - source_collection=old_real_name, - dest_collection=new_real_name, - dropped_old=drop_old, - ) diff --git a/methods/EverCore/src/core/oxm/milvus/milvus_collection_base.py b/methods/EverCore/src/core/oxm/milvus/milvus_collection_base.py deleted file mode 100644 index ebb1b269..00000000 --- a/methods/EverCore/src/core/oxm/milvus/milvus_collection_base.py +++ /dev/null @@ -1,715 +0,0 @@ -import os -import logging -from typing import Optional, Dict, Any, List -from dataclasses import dataclass -from pymilvus import Collection, DataType, FieldSchema, utility, CollectionSchema -from pymilvus.client.types import ConsistencyLevel, LoadState - -from pymilvus import connections -from core.oxm.milvus.async_collection import AsyncCollection -from common_utils.datetime_utils import get_now_with_timezone -from memory_layer.constants import VECTORIZE_DIMENSIONS - -logger = logging.getLogger(__name__) - - -def generate_new_collection_name(alias: str) -> str: - """Generate a new collection name with timestamp based on alias.""" - now = get_now_with_timezone() - return f"{alias}_{now.strftime('%Y%m%d%H%M%S%f')}" - - -@dataclass -class IndexConfig: - """ - Index configuration class - - Used to define indexes to be created (supports vector and scalar indexes) - - Attributes: - field_name: Field name - index_type: Index type (e.g., IVF_FLAT, HNSW, AUTOINDEX, etc.) - metric_type: Metric type (required for vector indexes, e.g., L2, COSINE, IP) - params: Index parameters (optional) - index_name: Index name (optional, auto-generated if not specified) - - Examples: - # Vector index - IndexConfig( - field_name="embedding", - index_type="IVF_FLAT", - metric_type="L2", - params={"nlist": 128} - ) - - # Scalar index - IndexConfig( - field_name="title", - index_type="AUTOINDEX" - ) - """ - - field_name: str - index_type: str - metric_type: Optional[str] = None - params: Optional[Dict[str, Any]] = None - index_name: Optional[str] = None - - def to_index_params(self) -> Dict[str, Any]: - """Convert to pymilvus index parameter format""" - result = {"index_type": self.index_type} - if self.metric_type: - result["metric_type"] = self.metric_type - if self.params: - result["params"] = self.params - return result - - -def get_collection_suffix(suffix: Optional[str] = None) -> str: - """ - Get Collection name suffix, used in multi-tenant scenarios - - Args: - suffix: Explicitly provided suffix; if given, return directly. - If not provided, read from environment variable SELF_MILVUS_COLLECTION_NS - - Returns: - Collection suffix string; return empty string if neither is set - """ - if suffix is not None: - return suffix - return os.getenv("SELF_MILVUS_COLLECTION_NS", "") - - -class MilvusCollectionBase: - """ - Milvus Collection base management class - - Responsibilities: - 1. Manage basic Collection information (name, Schema, index configuration) - 2. Provide lazily loaded Collection instance (internally cached) - 3. Provide utility methods (ensure_indexes, ensure_loaded) - - Applicable scenarios: - - Simple Collection management - - Read-only data sources (managed by other teams, only querying needed) - - No need for suffix, timestamp, alias, or other complex logic - - Usage: - 1. Subclass defines: - - _COLLECTION_NAME: Collection name (required) - - _SCHEMA: Collection Schema (optional) - - _INDEX_CONFIGS: List of index configurations (optional) - - _DB_USING: Milvus connection alias (optional, default "default") - - 2. Instantiation: - mgr = MovieCollection() # Use class-defined _DB_USING - # or - mgr = MovieCollection(using="custom_db") # Override class definition - - 3. Usage: - mgr.ensure_loaded() # Load into memory - mgr.collection.search(...) # Use Collection - - Example: - # Read-only scenario (data source managed by other team) - class ReadOnlyMovieCollection(MilvusCollectionBase): - _COLLECTION_NAME = "external_movies" # Fixed Collection name - _DB_USING = "external_db" # Use external database connection - - mgr = ReadOnlyMovieCollection() - mgr.ensure_loaded() - results = mgr.collection.search(...) - """ - - # Attributes that subclasses must define - _COLLECTION_NAME: Optional[str] = None - - # Optional attributes that subclasses may define - _SCHEMA: Optional[CollectionSchema] = None - _INDEX_CONFIGS: Optional[List[IndexConfig]] = None - _DB_USING: Optional[str] = "default" - - # Class-level instance cache - _collection_instance: Optional[Collection] = None - _async_collection_instance: Optional[AsyncCollection] = None - - def __init__(self): - """Initialize configuration container""" - if not self._COLLECTION_NAME: - raise NotImplementedError( - f"{self.__class__.__name__} must define '_COLLECTION_NAME' class attribute" - ) - - # Use class attribute _DB_USING, default to "default" if not defined - self._using = self._DB_USING if self._DB_USING is not None else "default" - - @classmethod - def collection(cls) -> Collection: - """Get Collection instance (class-level cache)""" - if cls._collection_instance is None: - raise ValueError( - f"{cls.__name__} Collection instance not created, please call ensure_loaded() first" - ) - return cls._collection_instance - - @classmethod - def async_collection(cls) -> AsyncCollection: - """Get asynchronous Collection instance (class-level cache)""" - if cls._async_collection_instance is None: - if cls._collection_instance is None: - raise ValueError( - f"{cls.__name__} Collection instance not created, please call ensure_loaded() first" - ) - cls._async_collection_instance = AsyncCollection(cls._collection_instance) - return cls._async_collection_instance - - @property - def name(self) -> str: - """Get actual Collection name""" - return self._COLLECTION_NAME - - @property - def using(self) -> str: - """Get connection alias""" - return self._DB_USING if self._DB_USING is not None else "default" - - def ensure_connection_registered(self) -> None: - """ - Ensure the pymilvus connection for self.using is established. - - Uses MilvusClientFactory to create a client, which internally registers - the pymilvus connection alias needed by Collection(). - - Subclasses (e.g., TenantAwareCollectionWithSuffix) may override this - to handle tenant-specific connection registration. - """ - from core.di.utils import get_bean - - using = self.using - try: - connections._fetch_handler(using) - return - except Exception: - pass - - factory = get_bean("milvus_client_factory") - factory.get_named_client(using) - logger.info("Connection registered for using='%s'", using) - - def load_collection(self) -> Collection: - """Load Collection (internal method)""" - name = self.name - if not utility.has_collection(name, using=self.using): - raise ValueError(f"Collection '{name}' does not exist") - - coll = Collection( - name=name, - using=self.using, - schema=self._SCHEMA, - consistency_level=ConsistencyLevel.Bounded, - ) - logger.info("Loaded Collection '%s'", name) - return coll - - def ensure_loaded(self) -> None: - """Ensure Collection is loaded into memory (class-level cache)""" - # Lazy load Collection - if self.__class__._collection_instance is None: - self.__class__._collection_instance = self.load_collection() - - coll = self.__class__._collection_instance - - name = coll.name - using = coll.using - - try: - load_state = utility.load_state(name, using=using) - - if load_state == LoadState.NotLoad: - logger.info("Collection '%s' not loaded, loading into memory...", name) - coll.load() - logger.info("Collection '%s' loaded successfully", name) - elif load_state == LoadState.Loading: - logger.info( - "Collection '%s' is loading, waiting for completion...", name - ) - coll.load() - else: - logger.info("Collection '%s' already loaded", name) - - except Exception as e: - logger.error("Error occurred while loading Collection: %s", e) - raise - - def ensure_indexes(self) -> None: - """Create all configured indexes (diff approach)""" - if not self._INDEX_CONFIGS: - logger.info( - "Collection '%s' has no index configuration, skipping", self.name - ) - return - - # Lazy load Collection - if self._collection_instance is None: - self._collection_instance = self.load_collection() - - coll = self._collection_instance - self._create_indexes_for_collection(coll) - - @staticmethod - def _get_existing_indexes(coll: Collection) -> Dict[str, Dict[str, Any]]: - """Get existing index information in the specified Collection""" - try: - indexes_info = coll.indexes - result = {} - - for index in indexes_info: - field_name = index.field_name - result[field_name] = { - "index_type": index.params.get("index_type"), - "metric_type": index.params.get("metric_type"), - } - - return result - - except Exception as e: # pylint: disable=broad-except - logger.warning("Error occurred while retrieving index information: %s", e) - return {} - - def _create_indexes_for_collection(self, coll: Collection) -> None: - """Create missing indexes for the specified Collection (reuse diff logic from ensure_indexes)""" - try: - existing_indexes = self._get_existing_indexes(coll) - existing_field_names = set(existing_indexes.keys()) - - logger.info( - "Existing index fields in Collection '%s': %s", - coll.name, - existing_field_names, - ) - - index_configs = self._INDEX_CONFIGS or [] - for index_config in index_configs: - field_name = index_config.field_name - if field_name in existing_field_names: - logger.info("Field '%s' already has an index, skipping", field_name) - continue - - logger.info( - "Creating index for field '%s' (type: %s)...", - field_name, - index_config.index_type, - ) - create_kwargs = { - "field_name": field_name, - "index_params": index_config.to_index_params(), - "timeout": 120, - } - if index_config.index_name: - create_kwargs["index_name"] = index_config.index_name - coll.create_index(**create_kwargs) - logger.info("Index creation for field '%s' succeeded", field_name) - - logger.info( - "Index check and creation completed for Collection '%s'", coll.name - ) - except Exception as e: - logger.error( - "Error occurred while creating indexes for Collection '%s': %s", - coll.name, - e, - ) - raise - - @staticmethod - def _get_collection_desc(collection_: Collection) -> Dict[str, Any]: - conn = collection_._get_connection() - return conn.describe_collection(collection_.name) - - def ensure_all(self) -> None: - """ - Complete all initialization operations in one step - - Execution order: - 1. ensure_loaded(): Load into memory - """ - self.ensure_loaded() - logger.info("Collection '%s' initialization completed", self.name) - - -class MilvusCollectionWithSuffix(MilvusCollectionBase): - """ - Milvus Collection management class with Suffix and Alias mechanism - - Inherits from MilvusCollectionBase, adds: - 1. Dynamic table name: Supports dynamically setting table name suffix via suffix or environment variable (multi-tenant scenario) - 2. Alias mechanism: Real table name includes timestamp, accessed via alias (convenient for future switching) - - Alias: {base_name}_{suffix} - - Real name: {base_name}_{suffix}-{timestamp} - 3. Creation management: Provides methods like ensure_create, ensure_all - - Applicable scenarios: - - Multi-tenant scenarios requiring independent Collections for different customers - - Need version management, retaining historical Collections - - Need gray-scale switching, switching between different versions via alias - - Usage: - 1. Subclass defines: - - _BASE_NAME: Base name of the Collection (required) - - _SCHEMA: Schema definition of the Collection (required) - - _INDEX_CONFIGS: List of index configurations (optional) - - _DB_USING: Milvus connection alias (optional, default "default") - - 2. Instantiation: - mgr = MovieCollection(suffix="customer_a") - # Alias: movies_customer_a - # Real name: movies_customer_a-20231015123456789000 - - # Or specify database connection - mgr = MovieCollection(suffix="customer_a", using="custom_db") - - 3. Initialization: - mgr.ensure_all() # One-step initialization - - 4. Usage: - mgr.collection.insert([...]) - mgr.collection.search(...) - - Example: - class MovieCollection(MilvusCollectionWithSuffix): - _BASE_NAME = "movies" - _SCHEMA = CollectionSchema(fields=[...]) - _INDEX_CONFIGS = [ - IndexConfig(field_name="embedding", index_type="IVF_FLAT", ...), - IndexConfig(field_name="year", index_type="AUTOINDEX") - ] - _DB_USING = "my_milvus" # Optional: specify default database connection - - # Usage - mgr = MovieCollection(suffix="customer_a") - mgr.ensure_all() - mgr.collection.insert([...]) - """ - - def __init__(self, suffix: Optional[str] = None): - """ - Initialize configuration container - - Args: - suffix: Collection name suffix; if not provided, read from environment variable SELF_MILVUS_COLLECTION_NS - """ - if not self._COLLECTION_NAME: - raise NotImplementedError( - f"{self.__class__.__name__} must define '_COLLECTION_NAME' class attribute" - ) - - if not self._SCHEMA: - raise NotImplementedError( - f"{self.__class__.__name__} must define '_SCHEMA' class attribute (required for creation scenarios)" - ) - - # Get suffix (supports parameter or environment variable) - self._suffix = get_collection_suffix(suffix) - - # Construct alias name - if self._suffix: - self._alias_name = f"{self._COLLECTION_NAME}_{self._suffix}" - else: - self._alias_name = self._COLLECTION_NAME - - # Call parent class initialization - super().__init__() - - @property - def name(self) -> str: - """Get Collection name""" - return self._alias_name - - def load_collection(self) -> Collection: - """ - Load or create Collection (internal method) - - Override parent method, add creation logic - """ - # First check if alias exists - name = self.name - - if not utility.has_collection(name, using=self._using): - # Collection does not exist, create a new timestamped Collection - _collection_name = generate_new_collection_name(name) - - logger.info( - "Collection '%s' does not exist, creating new Collection: %s", - name, - _collection_name, - ) - - # Create Collection - create_kwargs = { - "name": _collection_name, - "schema": self._SCHEMA, - "using": self._using, - "consistency_level": ConsistencyLevel.Bounded, - } - num_partitions = getattr(self, "_NUM_PARTITIONS", None) - if num_partitions is not None: - create_kwargs["num_partitions"] = num_partitions - logger.info( - "Creating Collection with kwargs: %s", - {k: v for k, v in create_kwargs.items() if k != "schema"}, - ) - Collection(**create_kwargs) - - # Create alias pointing to new Collection - # When deleting the actual Collection, the alias is not automatically deleted, so delete alias first - utility.drop_alias(name, using=self._using) - utility.create_alias( - collection_name=_collection_name, alias=name, using=self._using - ) - logger.info("Created Alias '%s' -> '%s'", name, _collection_name) - - # Uniformly load via alias (whether existing or newly created) - coll = Collection(name=name, using=self._using) - - return coll - - def ensure_create(self) -> None: - """ - Ensure Collection has been created - - This method triggers lazy loading of the Collection; if alias does not exist, create a new Collection - """ - if self._collection_instance is None: - self._collection_instance = self.load_collection() - logger.info("Collection '%s' is ready", self.name) - - def ensure_all(self) -> None: - """ - Complete all initialization operations in one step - - Execution order: - 1. ensure_create(): Create Collection and alias - 2. ensure_indexes(): Create all configured indexes - 3. ensure_loaded(): Load into memory - """ - logger.info("Starting initialization of Collection '%s'", self.name) - - self.ensure_create() - self.ensure_indexes() - self.ensure_loaded() - - # Retrieve and print the actual collection name - try: - collection_desc = self._get_collection_desc(self._collection_instance) - real_collection_name = collection_desc.get("collection_name", "unknown") - logger.info( - "Collection '%s' initialization completed, real name: %s", - self.name, - real_collection_name, - ) - except Exception as e: - logger.warning("Failed to retrieve real collection name: %s", e) - logger.info("Collection '%s' initialization completed", self.name) - - def create_new_collection(self) -> Collection: - """ - Create a new real Collection (without switching alias). - - Create new collection using class-defined `_SCHEMA` - - Create indexes and load for new collection according to `_INDEX_CONFIGS` - - Returns: - New collection instance (indexes created and loaded) - """ - if not self._SCHEMA: - raise NotImplementedError( - f"{self.__class__.__name__} must define '_SCHEMA' to support collection creation" - ) - - alias_name = self._alias_name - - # Create new collection - new_real_name = generate_new_collection_name(alias_name) - create_kwargs = { - "name": new_real_name, - "schema": self._SCHEMA, - "using": self._using, - "consistency_level": ConsistencyLevel.Bounded, - } - num_partitions = getattr(self, "_NUM_PARTITIONS", None) - if num_partitions is not None: - create_kwargs["num_partitions"] = num_partitions - Collection(**create_kwargs) - - # Create indexes for new collection - try: - new_coll = Collection(name=new_real_name, using=self._using) - self._create_indexes_for_collection(new_coll) - new_coll.load() - except Exception as e: - logger.warning( - "Error occurred while creating indexes for new collection, can be ignored: %s", - e, - ) - - return new_coll - - def switch_alias(self, new_collection: Collection, drop_old: bool = False) -> None: - """ - Switch alias to the specified new collection, optionally delete old collection. - - Prefer alter_alias; if fails, fall back to drop/create - - Refresh class-level cache after switching - """ - alias_name = self._alias_name - new_real_name = new_collection.name - - # Get old collection real name (if exists) - old_real_name: Optional[str] = None - try: - conn = connections._fetch_handler(self._using) - desc = conn.describe_alias(alias_name) - old_real_name = ( - desc.get("collection_name") if isinstance(desc, dict) else None - ) - except Exception: - old_real_name = None - - # Alias switching - try: - conn = connections._fetch_handler(self._using) - conn.alter_alias(new_real_name, alias_name) - logger.info("Alias '%s' switched to '%s'", alias_name, new_real_name) - except Exception as e: - logger.warning("alter_alias failed, attempting drop/create: %s", e) - try: - utility.drop_alias(alias_name, using=self._using) - except Exception: - pass - utility.create_alias( - collection_name=new_real_name, alias=alias_name, using=self._using - ) - logger.info("Created alias '%s' -> '%s'", alias_name, new_real_name) - - # Optionally delete old collection (after switching completes) - if drop_old and old_real_name: - try: - utility.drop_collection(old_real_name, using=self._using) - logger.info("Deleted old collection: %s", old_real_name) - except Exception as e: - logger.warning( - "Failed to delete old collection (can be handled manually): %s", e - ) - - # Refresh class-level cache to alias collection - try: - self.__class__._collection_instance = Collection( - name=alias_name, using=self._using - ) - except Exception: - pass - - def exists(self) -> bool: - """Check if Collection exists (via alias)""" - return utility.has_collection(self.name, using=self._using) - - def drop(self) -> None: - """Delete current Collection (including alias and real Collection)""" - try: - if not self._collection_instance: - self._collection_instance = Collection( - name=self.name, using=self._using - ) - - real_name = self._collection_instance.name - logger.info( - "Found real name corresponding to Collection '%s': %s", - self.name, - real_name, - ) - - utility.drop_collection(real_name, using=self._using) - logger.info("Deleted Collection '%s'", real_name) - - except Exception as e: # pylint: disable=broad-except - logger.warning( - "Collection '%s' does not exist or deletion failed: %s", self.name, e - ) - - -if __name__ == "__main__": - connections.connect("default", host="localhost", port=19530) - - class TestCollection(MilvusCollectionWithSuffix): - _COLLECTION_NAME = "test" - _SCHEMA = CollectionSchema( - fields=[ - FieldSchema(name="id", dtype=DataType.INT64, is_primary=True), - FieldSchema( - name="embedding", - dtype=DataType.FLOAT_VECTOR, - dim=VECTORIZE_DIMENSIONS, - ), - FieldSchema(name="title", dtype=DataType.VARCHAR, max_length=255), - FieldSchema(name="description", dtype=DataType.VARCHAR, max_length=255), - FieldSchema(name="content", dtype=DataType.VARCHAR, max_length=255), - FieldSchema(name="created_at", dtype=DataType.INT64), - FieldSchema(name="updated_at", dtype=DataType.INT64), - FieldSchema(name="deleted_at", dtype=DataType.INT64), - FieldSchema(name="deleted_by", dtype=DataType.VARCHAR, max_length=255), - FieldSchema( - name="deleted_reason", dtype=DataType.VARCHAR, max_length=255 - ), - ] - ) - _INDEX_CONFIGS = [ - IndexConfig( - field_name="vector", - index_type="HNSW", # Efficient approximate nearest neighbor search - metric_type="COSINE", # Euclidean distance - params={ - "M": 16, # Maximum number of edges per node - "efConstruction": 200, # Search width during construction - }, - ) - ] - _DB_USING = "default" - - collection = TestCollection(suffix="zhanghui") - collection.ensure_all() - assert collection.name == "test_zhanghui" - - class TestCollection2(MilvusCollectionBase): - _COLLECTION_NAME = "test_zhanghui" - _SCHEMA = CollectionSchema( - fields=[ - FieldSchema(name="id", dtype=DataType.INT64, is_primary=True), - FieldSchema( - name="embedding", - dtype=DataType.FLOAT_VECTOR, - dim=VECTORIZE_DIMENSIONS, - ), - FieldSchema(name="title", dtype=DataType.VARCHAR, max_length=255), - FieldSchema(name="description", dtype=DataType.VARCHAR, max_length=255), - FieldSchema(name="content", dtype=DataType.VARCHAR, max_length=255), - ] - ) - _INDEX_CONFIGS = [ - IndexConfig( - field_name="vector", - index_type="HNSW", # Efficient approximate nearest neighbor search - metric_type="COSINE", # Euclidean distance - params={ - "M": 16, # Maximum number of edges per node - "efConstruction": 200, # Search width during construction - }, - ) - ] - _DB_USING = "default" - - collection2 = TestCollection2() - collection2.ensure_all() - assert collection2.name == "test_zhanghui" - - import asyncio - - asyncio.run(TestCollection.async_collection().insert([[1, 2, 3], [4, 5, 6]])) diff --git a/methods/EverCore/src/core/oxm/mongo/__init__.py b/methods/EverCore/src/core/oxm/mongo/__init__.py deleted file mode 100755 index 5cfe2ee0..00000000 --- a/methods/EverCore/src/core/oxm/mongo/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -""" -MongoDB OXM (Object-XML Mapping) Module - -Provides document base classes and utilities for MongoDB operations. -""" - -from core.oxm.mongo.document_base import DocumentBase -from core.oxm.mongo.document_base_with_soft_delete import DocumentBaseWithSoftDelete -from core.oxm.mongo.audit_base import AuditBase - -__all__ = [ - "DocumentBase", - "DocumentBaseWithSoftDelete", - "AuditBase", -] diff --git a/methods/EverCore/src/core/oxm/mongo/audit_base.py b/methods/EverCore/src/core/oxm/mongo/audit_base.py deleted file mode 100644 index dee77ee0..00000000 --- a/methods/EverCore/src/core/oxm/mongo/audit_base.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -MongoDB audit base class - -Audit base class based on Beanie ODM, including common timestamp fields and automatic processing logic. -""" - -from datetime import datetime -from typing import Optional, List, Any -from beanie import before_event, Insert, Replace, Save, Update -from pydantic import Field, BaseModel -from common_utils.datetime_utils import get_now_with_timezone - - -class AuditBase(BaseModel): - """ - Audit base class - - Includes common timestamp fields and automatic processing logic - - Note: - - For single insertion, @before_event(Insert) automatically triggers timestamp setting - - For bulk insertion, DocumentBase.insert_many delegates to this class's prepare_for_insert_many method for handling - """ - - # System fields - created_at: Optional[datetime] = Field(default=None, description="Creation time") - updated_at: Optional[datetime] = Field(default=None, description="Update time") - - @before_event(Insert) - async def set_created_at(self): - """Set creation time before insertion""" - now = get_now_with_timezone() - self.created_at = now - self.updated_at = now - - @before_event(Update, Replace, Save) - async def set_updated_at(self): - """Set update time before update, replace, or save""" - self.updated_at = get_now_with_timezone() - - @classmethod - def prepare_for_insert_many(cls, documents: List[Any]) -> None: - """ - Prepare before bulk insertion: set audit timestamp fields for documents - - Since Beanie's @before_event(Insert) does not automatically trigger during bulk insertion, - this method is called by DocumentBase.insert_many, responsible for setting audit fields before bulk insertion. - - Args: - documents: List of documents to be inserted - - Note: - This method is automatically called by DocumentBase.insert_many, - developers typically do not need to call this method manually. - """ - now = get_now_with_timezone() - for doc in documents: - # Only set time for audit fields with None value, avoiding overwriting existing values - if hasattr(doc, 'created_at') and doc.created_at is None: - doc.created_at = now - if hasattr(doc, 'updated_at') and doc.updated_at is None: - doc.updated_at = now - - -__all__ = ["AuditBase"] diff --git a/methods/EverCore/src/core/oxm/mongo/base_repository.py b/methods/EverCore/src/core/oxm/mongo/base_repository.py deleted file mode 100644 index 8f1c9fea..00000000 --- a/methods/EverCore/src/core/oxm/mongo/base_repository.py +++ /dev/null @@ -1,378 +0,0 @@ -""" -MongoDB Base Repository Class - -Base repository class based on Beanie ODM, providing generic transaction management and basic CRUD operations. -All MongoDB repositories should inherit from this base class to obtain unified transaction support. -""" - -from abc import ABC -from contextlib import asynccontextmanager -from typing import Optional, TypeVar, Generic, Type, Union, List -from beanie import PydanticObjectId -from pymongo.asynchronous.client_session import AsyncClientSession -from core.observation.logger import get_logger -from core.oxm.mongo.document_base import DocumentBase - -logger = get_logger(__name__) - -# Generic type variable -T = TypeVar('T', bound=DocumentBase) - - -class BaseRepository(ABC, Generic[T]): - """ - MongoDB Base Repository Class - - Provides generic transaction management and basic operations; all MongoDB repositories should inherit from this class. - - Features: - - Transaction context manager - - Session management - - Basic CRUD operation templates - - Unified error handling and logging - """ - - def __init__(self, model: Type[T]): - """ - Initialize base repository - - Args: - model: Beanie document model class - """ - self.model = model - self.model_name = model.__name__ - - # ==================== Transaction Management ==================== - - @asynccontextmanager - async def transaction(self): - """ - Transaction context manager - - Usage: - async with repository.transaction() as session: - await repository.create(document, session=session) - await repository.update(another_document, session=session) - # Automatically commits or rolls back - - Yields: - AsyncClientSession: MongoDB session object - """ - client = self.model.get_pymongo_client() - async with await client.start_session() as session: - async with session.start_transaction(): - try: - logger.info("🔄 Starting MongoDB transaction [%s]", self.model_name) - yield session - logger.info( - "✅ MongoDB transaction committed successfully [%s]", - self.model_name, - ) - except Exception as e: - logger.error( - "❌ MongoDB transaction rolled back [%s]: %s", - self.model_name, - e, - ) - raise - - async def start_session(self) -> AsyncClientSession: - """ - Start a new session (without transaction) - - Returns: - AsyncClientSession: MongoDB session object - - Note: - The session must be manually closed after use: - session = await repository.start_session() - try: - # Use session - pass - finally: - await session.end_session() - """ - client = self.model.get_pymongo_client() - session = await client.start_session() - logger.info("🔄 Created MongoDB session [%s]", self.model_name) - return session - - # ==================== Basic CRUD Template Methods ==================== - - async def create( - self, document: T, session: Optional[AsyncClientSession] = None - ) -> T: - """ - Create a new document - - Args: - document: Document instance - session: Optional MongoDB session, used for transaction support - - Returns: - Created document instance - """ - try: - await document.insert(session=session) - logger.info( - "✅ Document created successfully [%s]: %s", - self.model_name, - getattr(document, 'id', 'unknown'), - ) - return document - except Exception as e: - logger.error("❌ Failed to create document [%s]: %s", self.model_name, e) - raise - - async def get_by_id(self, object_id: Union[str, PydanticObjectId]) -> Optional[T]: - """ - Get document by ObjectId - - Args: - object_id: MongoDB ObjectId - - Returns: - Document instance or None - """ - try: - if isinstance(object_id, str): - object_id = PydanticObjectId(object_id) - return await self.model.get(object_id) - except Exception as e: - logger.error("❌ Failed to get document by ID [%s]: %s", self.model_name, e) - return None - - async def update( - self, document: T, session: Optional[AsyncClientSession] = None - ) -> T: - """ - Update document - - Args: - document: Document instance to update - session: Optional MongoDB session, used for transaction support - - Returns: - Updated document instance - """ - try: - await document.save(session=session) - logger.info( - "✅ Document updated successfully [%s]: %s", - self.model_name, - getattr(document, 'id', 'unknown'), - ) - return document - except Exception as e: - logger.error("❌ Failed to update document [%s]: %s", self.model_name, e) - raise - - async def delete_by_id( - self, - object_id: Union[str, PydanticObjectId], - session: Optional[AsyncClientSession] = None, - ) -> bool: - """ - Delete document by ObjectId - - Args: - object_id: MongoDB ObjectId - session: Optional MongoDB session, used for transaction support - - Returns: - Returns True if deletion succeeds, otherwise False - """ - try: - document = await self.get_by_id(object_id) - if document: - await document.delete(session=session) - logger.info( - "✅ Document deleted successfully [%s]: %s", - self.model_name, - object_id, - ) - return True - return False - except Exception as e: - logger.error("❌ Failed to delete document [%s]: %s", self.model_name, e) - return False - - async def delete( - self, document: T, session: Optional[AsyncClientSession] = None - ) -> bool: - """ - Delete document instance - - Args: - document: Document instance to delete - session: Optional MongoDB session, used for transaction support - - Returns: - Returns True if deletion succeeds, otherwise False - """ - try: - await document.delete(session=session) - logger.info( - "✅ Document deleted successfully [%s]: %s", - self.model_name, - getattr(document, 'id', 'unknown'), - ) - return True - except Exception as e: - logger.error("❌ Failed to delete document [%s]: %s", self.model_name, e) - return False - - # ==================== Batch Operations ==================== - - async def create_batch( - self, documents: List[T], session: Optional[AsyncClientSession] = None - ) -> List[T]: - """ - Batch create documents - - Args: - documents: List of documents - session: Optional MongoDB session, used for transaction support - - Returns: - List of successfully created documents - """ - try: - # Beanie's insert_many does not automatically update the id attribute of input objects - # We need to manually retrieve inserted_ids from the returned InsertManyResult and set them - result = await self.model.insert_many(documents, session=session) - # Set the _id generated by MongoDB back to the id attribute of each document object - for doc, inserted_id in zip(documents, result.inserted_ids): - doc.id = inserted_id - logger.info( - "✅ Batch document creation successful [%s]: %d records", - self.model_name, - len(documents), - ) - return documents - except Exception as e: - logger.error( - "❌ Failed to batch create documents [%s]: %s", self.model_name, e - ) - raise - - # ==================== Query Methods ==================== - - async def find_by_query( - self, - query: dict, - skip: int = 0, - limit: int = 20, - sort: list = None, - projection_model=None, - ): - """ - Generic query with pagination and optional projection. - - Args: - query: MongoDB filter dict - skip: Number of documents to skip - limit: Maximum number of documents to return - sort: List of (field, direction) tuples - projection_model: Optional Pydantic model for field projection (e.g. exclude vector) - - Returns: - Tuple of (documents list, total count) - """ - import asyncio - - try: - find_kwargs = {"skip": skip, "limit": limit} - if sort: - find_kwargs["sort"] = sort - if projection_model: - find_kwargs["projection_model"] = projection_model - - find_query = self.model.find_many(query, **find_kwargs) - count_query = self.model.find_many(query).count() - - docs, total_count = await asyncio.gather(find_query.to_list(), count_query) - - return docs, total_count - except Exception as e: - logger.error("❌ Failed to query documents [%s]: %s", self.model_name, e) - raise - - # ==================== Counting Methods ==================== - - async def count_all(self, filter_query: Optional[dict] = None) -> int: - """ - Count documents with optimized strategy - - When no filter is provided, uses estimated_document_count() which is - extremely fast (milliseconds) as it reads from collection metadata. - When a filter is provided, uses count_documents() for accurate results. - - Args: - filter_query: Optional filter conditions. If None, uses fast estimation. - - Returns: - Total number of documents (estimated if no filter, exact if with filter) - """ - try: - if filter_query is None or filter_query == {}: - # No filter: use estimated_document_count() for speed (metadata-based) - collection = self.model.get_pymongo_collection() - count = await collection.estimated_document_count() - logger.info( - "✅ Fast estimated count [%s]: %d records", self.model_name, count - ) - else: - # With filter: use count_documents() for accuracy - count = await self.model.find(filter_query).count() - logger.info( - "✅ Exact count with filter [%s]: %d records", - self.model_name, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to count documents [%s]: %s", self.model_name, e) - return 0 - - async def exists_by_id(self, object_id: Union[str, PydanticObjectId]) -> bool: - """ - Check if document exists - - Args: - object_id: MongoDB ObjectId - - Returns: - Returns True if exists, otherwise False - """ - try: - if isinstance(object_id, str): - object_id = PydanticObjectId(object_id) - document = await self.model.get(object_id) - return document is not None - except Exception: - return False - - # ==================== Helper Methods ==================== - - def get_model_name(self) -> str: - """ - Get model name - - Returns: - Model class name - """ - return self.model_name - - def get_collection_name(self) -> str: - """ - Get collection name - - Returns: - MongoDB collection name - """ - return self.model.get_collection_name() - - -# Export -__all__ = ["BaseRepository"] diff --git a/methods/EverCore/src/core/oxm/mongo/constant/annotations.py b/methods/EverCore/src/core/oxm/mongo/constant/annotations.py deleted file mode 100644 index 459030f1..00000000 --- a/methods/EverCore/src/core/oxm/mongo/constant/annotations.py +++ /dev/null @@ -1,16 +0,0 @@ -from __future__ import annotations - -from core.class_annotations.types import StringEnumAnnotationKey, StringEnumAnnotation - - -class ClassAnnotationKey(StringEnumAnnotationKey): - """Infra-layer class annotation keys.""" - - READONLY = "odm.readonly" - - -class Toggle(StringEnumAnnotation): - """Simple toggle values for annotations.""" - - ENABLED = "enabled" - DISABLED = "disabled" diff --git a/methods/EverCore/src/core/oxm/mongo/document_base.py b/methods/EverCore/src/core/oxm/mongo/document_base.py deleted file mode 100644 index 9a6dbb3c..00000000 --- a/methods/EverCore/src/core/oxm/mongo/document_base.py +++ /dev/null @@ -1,224 +0,0 @@ -""" -MongoDB Document Base Class - -Base document class based on Beanie ODM, providing common foundational document functionality. -""" - -from datetime import datetime -from common_utils.datetime_utils import to_timezone -from beanie import Document, WriteRules -from pydantic import model_validator, BaseModel -from typing import Self, List, Optional -from pymongo.asynchronous.client_session import AsyncClientSession -from pymongo.results import InsertManyResult - -from core.oxm.mongo.audit_base import AuditBase - -MAX_RECURSION_DEPTH = 4 -DEFAULT_DATABASE = "default" - - -class DocumentBase(Document): - """ - Document base class - - Base document class based on Beanie Document, providing common foundational document functionality. - """ - - @classmethod - def get_bind_database(cls) -> str | None: - """ - Read the bound database name (read-only). - - Only reads from `Settings.bind_database`, no runtime modification allowed. - Subclasses can bind by overriding the class variable `bind_database` in the internal `Settings`: - - class MyDoc(DocumentBase): - class Settings: - bind_database = "my_db" - """ - settings = getattr(cls, "Settings", None) - if settings is not None: - return getattr(settings, "bind_database", DEFAULT_DATABASE) - return DEFAULT_DATABASE - - def _recursive_datetime_check(self, obj, path: str = "", depth: int = 0): - """ - Recursively check and convert all datetime objects to default timezone - - Args: - obj: Object to check - path: Current object path (for debugging) - depth: Current recursion depth - - Returns: - Converted object - """ - # Control maximum recursion depth - if depth >= MAX_RECURSION_DEPTH: - return obj - - # Case 1: Object is datetime - if isinstance(obj, datetime): - if obj.tzinfo is None: - # No timezone info, convert to default timezone; usually created within the process and passed as parameter - return to_timezone(obj) - else: - # Return if read with timezone and it's the default timezone - return obj - - # Case 2: Object is BaseModel - if isinstance(obj, BaseModel): - for field_name, value in obj: - new_path = f"{path}.{field_name}" if path else field_name - new_value = self._recursive_datetime_check(value, new_path, depth + 1) - # Directly update value using __dict__ to avoid triggering validators - obj.__dict__[field_name] = new_value - return obj - - # Case 3: Object is list, tuple, or set (performance optimization) - if isinstance(obj, (list, tuple, set)): - # If collection is empty, return directly - if not obj: - return obj - - # List: only check the first element - if isinstance(obj, list): - first_item = obj[0] - first_checked = self._recursive_datetime_check( - first_item, f"{path}[0]", depth + 2 - ) - - # If the first element hasn't changed, assume the whole list doesn't need conversion - if first_checked is first_item: - return obj - - # Set: check any one element (set is unordered, take the first one) - elif isinstance(obj, set): - sample_item = next(iter(obj)) - sample_checked = self._recursive_datetime_check( - sample_item, f"{path}[sample]", depth + 2 - ) - - # If the sampled element hasn't changed, assume the whole set doesn't need conversion - if sample_checked is sample_item: - return obj - - # Tuple: only check the first 3 elements - elif isinstance(obj, tuple): - # Check first 3 elements (or all if length < 3) - check_count = min(3, len(obj)) - need_transform = False - - for idx in range(check_count): - item = obj[idx] - checked = self._recursive_datetime_check( - item, f"{path}[{idx}]", depth + 2 - ) - if checked is not item: - need_transform = True - break - - # If first 3 elements don't need conversion, assume the whole tuple doesn't need conversion - if not need_transform: - return obj - - # Need to process all elements - cls = type(obj) - return cls( - self._recursive_datetime_check(item, f"{path}[{i}]", depth + 2) - for i, item in enumerate(obj) - ) - - # Case 4: Object is dictionary - if isinstance(obj, dict): - return { - key: self._recursive_datetime_check( - value, f"{path}[{repr(key)}]", depth + 2 - ) - for key, value in obj.items() - } - - return obj - - @model_validator(mode='after') - def check_datetimes_are_aware(self) -> Self: - """ - Recursively traverse all fields of the model to ensure any datetime object is 'aware' (contains timezone information). - Maximum recursion depth is 3 to avoid potential issues. - - Returns: - Self: Current object instance - """ - for field_name, value in self: - new_value = self._recursive_datetime_check(value, field_name, depth=0) - if new_value is not value: # Only update if value has changed - - # Directly update value using __dict__ to avoid triggering validators - self.__dict__[field_name] = new_value - return self - - @classmethod - async def insert_many( - cls, - documents: List["DocumentBase"], - session: Optional[AsyncClientSession] = None, - link_rule: WriteRules = WriteRules.DO_NOTHING, - **pymongo_kwargs, - ) -> InsertManyResult: - """ - Override bulk insert method, delegate audit logic to AuditBase - - As a technical entry point, check if the model inherits from AuditBase, if so, delegate audit field handling. - This maintains responsibility cohesion: DocumentBase handles coordination, AuditBase handles audit logic. - - Args: - documents: List of documents to insert - session: Optional MongoDB session, used for transaction support - link_rule: Write rule for linked documents - **pymongo_kwargs: Other parameters passed to PyMongo - - Returns: - InsertManyResult: Insert result, containing inserted_ids - """ - # Check if model inherits from AuditBase, if so, delegate audit handling - - if issubclass(cls, AuditBase): - # Delegate to AuditBase to handle audit fields - AuditBase.prepare_for_insert_many(documents) - - # Call parent class's insert_many method - return await super().insert_many( - documents, session=session, link_rule=link_rule, **pymongo_kwargs - ) - - class Settings: - """Document settings""" - - # Common document configurations can be set here - # For example: indexes, validation rules, etc. - - @classmethod - async def count(cls, **kwargs) -> int: - """ - Count documents in the collection - - Uses estimated_document_count() for fast counting based on collection metadata. - This is very fast (millisecond level) but returns an approximate count. - - Subclasses (e.g., DocumentBaseWithSoftDelete) may override this method - to provide filtering capabilities (e.g., excluding soft-deleted documents). - - Returns: - int: Estimated document count - """ - collection = cls.get_pymongo_collection() - return await collection.estimated_document_count() - - def __str__(self) -> str: - """String representation""" - return f"{self.__class__.__name__}({self.id})" - - def __repr__(self) -> str: - """Developer representation""" - return f"{self.__class__.__name__}(id={self.id})" diff --git a/methods/EverCore/src/core/oxm/mongo/document_base_with_soft_delete.py b/methods/EverCore/src/core/oxm/mongo/document_base_with_soft_delete.py deleted file mode 100644 index 785421bc..00000000 --- a/methods/EverCore/src/core/oxm/mongo/document_base_with_soft_delete.py +++ /dev/null @@ -1,783 +0,0 @@ -""" -MongoDB Document Base With Soft Delete - -Base document class with soft delete functionality, providing complete soft delete support. -""" - -from datetime import datetime -from beanie.odm.enums import SortDirection -from beanie.odm.bulk import BulkWriter -from beanie.odm.actions import ActionDirections -from beanie import DeleteRules -from pydantic import Field, BaseModel -from typing import List, Optional, Any, Mapping, Union, Tuple, Dict, Type -from pymongo.asynchronous.client_session import AsyncClientSession -from pymongo.results import UpdateResult, DeleteResult - -from common_utils.datetime_utils import get_now_with_timezone -from core.oxm.mongo.document_base import DocumentBase - - -class DocumentBaseWithSoftDelete(DocumentBase): - """ - Base document class with soft delete functionality - - Inherits from DocumentBase, integrating complete soft delete capabilities: - - Provides full soft delete capability (self-implemented, not relying on beanie's DocumentWithSoftDelete) - - Supports timezone-aware datetime handling (from DocumentBase) - - Supports database binding configuration (from DocumentBase) - - Supports audit field handling during bulk insert (from DocumentBase) - - **Extended deletion audit fields: deleted_by (deleter) and deleted_id (uniqueness trick)** - - **Complete bulk soft delete support** - - Soft delete field descriptions: - - deleted_at: deletion timestamp - - deleted_by: identifier of the deletion operator - - deleted_id: deletion identifier ID, used for unique index trick - * When not deleted: deleted_id = 0 (all undeleted documents share this value) - * When deleted: deleted_id = hash value of the document's _id - * Advantage: can create unique index on (business field + deleted_id), achieving: - - Only one record allowed for the same business key when undeleted - - Multiple historical records allowed for the same business key after deletion - - New records with the same business key can be inserted after soft deletion - - Core methods: - Instance methods: - - delete(): soft delete current document - - restore(): restore a deleted document - - hard_delete(): hard delete current document (physical deletion) - - is_deleted(): check if document is deleted - - Class methods (queries): - - find_one(): find one document (automatically filters out deleted ones) - - find_many(): find multiple documents (automatically filters out deleted ones) - - hard_find_one(): hard find one document (including deleted ones) - - hard_find_many(): hard find multiple documents (including deleted ones) - - Class methods (bulk operations): - - delete_many(): bulk soft delete - - restore_many(): bulk restore - - hard_delete_many(): bulk hard delete - - Utility methods (for native pymongo API): - - apply_soft_delete_filter(): apply soft delete filter condition to query - - get_soft_delete_filter(): get pure soft delete filter condition - - Important notes: - ⚠️ Do not use Model.find().delete_many(), it performs hard deletion! - Use Model.delete_many(filter) to perform bulk soft deletion. - - Usage example: - from pydantic import Field - - class MyDocument(DocumentBaseWithSoftDelete, AuditBase): - email: str - name: str - - class Settings: - bind_database = "my_database" - collection = "my_collection" - # Unique index: only one record allowed for the same email when undeleted - indexes = [ - [("email", 1), ("deleted_id", 1)], # composite unique index - ] - - # Single soft delete - doc = await MyDocument.find_one({"email": "test@example.com"}) - await doc.delete(deleted_by="admin") # soft delete - - # Bulk soft delete - result = await MyDocument.delete_many( - {"status": "inactive"}, - deleted_by="system" - ) - - # Restore single document - doc = await MyDocument.hard_find_one({"email": "test@example.com"}) - if doc and doc.is_deleted(): - await doc.restore() - - # Bulk hard delete (use with caution!) - result = await MyDocument.hard_delete_many({"is_test": True}) - - # Apply soft delete filter when using native pymongo API - filter_dict = MyDocument.apply_soft_delete_filter({"status": "active"}) - result = await MyDocument.get_pymongo_collection().find(filter_dict).to_list(100) - """ - - # Soft delete related fields - deleted_at: Optional[datetime] = Field( - default=None, description="Soft deletion timestamp" - ) - deleted_by: Optional[str] = Field(default=None, description="Deletion operator") - deleted_id: int = Field( - default=0, description="Deletion identifier ID, used for unique index trick" - ) - - def is_deleted(self) -> bool: - """ - Check if the document has been soft deleted - - Returns: - bool: Returns True if document is deleted, otherwise False - """ - return self.deleted_at is not None - - @classmethod - def apply_soft_delete_filter( - cls, - filter_query: Optional[Mapping[str, Any]] = None, - include_deleted: bool = False, - ) -> Dict[str, Any]: - """ - Apply soft delete filter condition to query filter - - This is a utility method used to manually apply soft delete filtering when directly using get_pymongo_collection(). - If deleted_at condition is already present in filter_query, it remains unchanged. - If not present and include_deleted=False, adds deleted_at=None condition. - - Args: - filter_query: Original query filter condition (optional) - include_deleted: Whether to include deleted documents, default False - - Returns: - Dict[str, Any]: Query condition with soft delete filtering applied - - Example: - # Scenario 1: Automatically filter out deleted when using native pymongo API - filter_dict = User.apply_soft_delete_filter({"status": "active"}) - result = await User.get_pymongo_collection().find(filter_dict).to_list(100) - - # Scenario 2: Need to include deleted documents - filter_dict = User.apply_soft_delete_filter( - {"status": "active"}, - include_deleted=True - ) - result = await User.get_pymongo_collection().find(filter_dict).to_list(100) - - # Scenario 3: Empty filter condition, only query undeleted - filter_dict = User.apply_soft_delete_filter() - result = await User.get_pymongo_collection().find(filter_dict).to_list(100) - - # Scenario 4: Using aggregation pipeline - match_stage = {"$match": User.apply_soft_delete_filter({"age": {"$gt": 18}})} - pipeline = [match_stage, {"$group": {"_id": "$city", "count": {"$sum": 1}}}] - result = await User.get_pymongo_collection().aggregate(pipeline).to_list(100) - """ - # If no filter condition is provided, create empty dictionary - if filter_query is None: - result_filter = {} - else: - # Copy original filter condition to avoid modifying original object - result_filter = dict(filter_query) - - # If not including deleted documents, and deleted_at field is not in filter - if not include_deleted and "deleted_at" not in result_filter: - result_filter["deleted_at"] = None - - return result_filter - - @classmethod - def get_soft_delete_filter(cls, include_deleted: bool = False) -> Dict[str, Any]: - """ - Get default soft delete filter condition - - This is a simplified utility method that returns pure soft delete filter condition. - - Args: - include_deleted: Whether to include deleted documents, default False - - Returns: - Dict[str, Any]: Soft delete filter condition, returns empty dictionary if include_deleted=True - - Example: - # Only get filter condition for undeleted - soft_delete_filter = User.get_soft_delete_filter() - # Returns: {"deleted_at": None} - - # Get filter condition including deleted (actually returns empty dictionary) - all_filter = User.get_soft_delete_filter(include_deleted=True) - # Returns: {} - - # Merge with other conditions - my_filter = {"status": "active", **User.get_soft_delete_filter()} - result = await User.get_pymongo_collection().find(my_filter).to_list(100) - """ - if include_deleted: - return {} - return {"deleted_at": None} - - @classmethod - async def count(cls, include_deleted: bool = False, **kwargs) -> int: - """ - Count documents in the collection, with soft delete awareness - - By default, excludes soft-deleted documents (deleted_at is not None). - Set include_deleted=True to count all documents including deleted ones. - - Performance notes: - - When include_deleted=True: uses estimated_document_count() (fast, ~1ms) - - When include_deleted=False: uses count_documents() with filter (requires deleted_at index) - - Args: - include_deleted: Whether to include soft-deleted documents, default False - - Returns: - int: Document count - - Example: - # Count only non-deleted documents (default) - active_count = await User.count() - - # Count all documents including deleted - total_count = await User.count(include_deleted=True) - """ - collection = cls.get_pymongo_collection() - - if include_deleted: - # Fast estimation for all documents - return await collection.estimated_document_count() - else: - # Precise count excluding soft-deleted documents - # Requires index on deleted_at field for performance - return await collection.count_documents({"deleted_at": None}) - - async def delete( - self, - session: Optional[AsyncClientSession] = None, - bulk_writer: Optional[Any] = None, - link_rule: Optional[Any] = None, - skip_actions: Optional[List[Any]] = None, - deleted_by: Optional[str] = None, - **pymongo_kwargs: Any, - ) -> Optional[Any]: - """ - Soft delete current document (override parent method to support deleted_by) - - ⚠️ If document has already been soft deleted, this method returns directly without modifying audit fields. - ⚠️ Directly uses PyMongo's update_one method, completely bypassing Beanie's save mechanism. - - Args: - session: MongoDB session (beanie parameter) - bulk_writer: Bulk writer (beanie parameter) - link_rule: Link rule (beanie parameter) - skip_actions: Skipped actions (beanie parameter) - deleted_by: Deletion operator identifier (optional, extended parameter of this class) - **pymongo_kwargs: Other pymongo parameters - - Returns: - None (soft delete does not return DeleteResult) - - Example: - doc = await MyDocument.find_one({"name": "test"}) - await doc.delete(deleted_by="admin") - """ - # Check if already soft deleted, avoid repeated deletion that would damage audit records - if self.is_deleted(): - return None - - now = get_now_with_timezone() - - # Set deleted_id to string hash value of document ID - # If id is ObjectId, convert to string then take hash - deleted_id_value = 0 - if self.id: - # Convert ObjectId to integer (absolute value of hash) - deleted_id_value = abs(hash(str(self.id))) - - # Directly use PyMongo's update_one to update database, completely bypassing Beanie - await self.get_pymongo_collection().update_one( - {"_id": self.id}, - { - "$set": { - "deleted_at": now, - "deleted_by": deleted_by, - "deleted_id": deleted_id_value, - } - }, - session=session, - ) - - # Update current object state to maintain consistency - self.deleted_at = now - self.deleted_by = deleted_by - self.deleted_id = deleted_id_value - - return None - - async def restore(self, session: Optional[AsyncClientSession] = None) -> None: - """ - Restore a single soft-deleted document - - Clears the soft delete mark of the current document, restoring it to normal state. - - ⚠️ If document is not soft deleted, this method returns directly without any operation. - ⚠️ Directly uses PyMongo's update_one method, completely bypassing Beanie's save mechanism. - - Example: - # Find deleted document (using hard_find_one can query including deleted ones) - doc = await MyDocument.hard_find_one( - {"email": "user@example.com", "deleted_at": {"$ne": None}} - ) - - # Restore document - if doc and doc.is_deleted(): - await doc.restore() - """ - # If document is not deleted, return directly - if not self.is_deleted(): - return - - # Directly use PyMongo's update_one to update database, completely bypassing Beanie - await self.get_pymongo_collection().update_one( - {"_id": self.id}, - {"$set": {"deleted_at": None, "deleted_by": None, "deleted_id": 0}}, - session=session, - ) - - # Update current object state to maintain consistency - self.deleted_at = None - self.deleted_by = None - self.deleted_id = 0 - - async def hard_delete( - self, - session: Optional[AsyncClientSession] = None, - bulk_writer: Optional[BulkWriter] = None, - link_rule: DeleteRules = DeleteRules.DO_NOTHING, - skip_actions: Optional[List[Union[ActionDirections, str]]] = None, - **pymongo_kwargs: Any, - ) -> Optional[DeleteResult]: - """ - Hard delete current document (physical deletion) - - ⚠️ Warning: This operation is irreversible! Use with caution. - - Calls parent class's delete method to perform actual physical deletion. - - Args: - session: MongoDB session - bulk_writer: Bulk writer - link_rule: Link rule - skip_actions: Skipped actions - **pymongo_kwargs: Other pymongo parameters - - Returns: - Optional[DeleteResult]: Deletion result - - Example: - doc = await MyDocument.find_one({"name": "test"}) - await doc.hard_delete() # Permanently delete - """ - return await super().delete( - session=session, - bulk_writer=bulk_writer, - link_rule=link_rule, - skip_actions=skip_actions, - **pymongo_kwargs, - ) - - @classmethod - async def delete_many( - cls, - filter_query: Mapping[str, Any], - deleted_by: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - **pymongo_kwargs: Any, - ) -> UpdateResult: - """ - Bulk soft delete documents (default bulk deletion method) - - Marks matching documents as deleted instead of physically deleting them. - This supplements the missing functionality in beanie DocumentWithSoftDelete. - - ⚠️ Note: - - deleted_id is set to microsecond-level timestamp during bulk deletion - - Automatically filters out already soft-deleted documents to avoid repeated deletion that would damage audit records - - Args: - filter_query: MongoDB query filter condition - deleted_by: Deletion operator identifier (optional) - session: Optional MongoDB session, for transaction support - **pymongo_kwargs: Other parameters passed to PyMongo - - Returns: - UpdateResult: Update result containing number of matched and modified documents - - Example: - # Bulk soft delete - result = await User.delete_many( - {"is_active": False}, - deleted_by="admin" - ) - print(f"Soft deleted {result.modified_count} documents") - - # Transactional soft delete using session - async with await client.start_session() as session: - await User.delete_many( - {"status": "expired"}, - deleted_by="system", - session=session - ) - """ - # Set deletion timestamp - now = get_now_with_timezone() - - # Note: Handling strategy for deleted_id during bulk deletion - # Since bulk operations cannot efficiently set hash value of each document's _id, we use timestamp here - # For scenarios requiring strict uniqueness constraints, it is recommended to: - # 1. First query all matching documents - # 2. Call doc.delete() method individually - # Or implement more complex bulk deletion logic at application layer - - update_doc = { - "deleted_at": now, - "deleted_by": deleted_by, - # Use microsecond-level timestamp as deleted_id, providing certain uniqueness - # For scenarios requiring strict uniqueness, recommend using individual deletion or custom implementation - "deleted_id": int(now.timestamp() * 1000000), # microsecond-level timestamp - } - - # Apply soft delete filter: only delete documents not already soft deleted, to avoid repeated deletion that would damage audit records - final_filter = cls.apply_soft_delete_filter(filter_query, include_deleted=False) - - return await cls.get_pymongo_collection().update_many( - final_filter, {"$set": update_doc}, session=session, **pymongo_kwargs - ) - - @classmethod - async def restore_many( - cls, - filter_query: Mapping[str, Any], - session: Optional[AsyncClientSession] = None, - **pymongo_kwargs: Any, - ) -> UpdateResult: - """ - Bulk restore soft-deleted documents - - Restores matching deleted documents (clears all soft delete marker fields). - - ⚠️ Automatically only restores documents that have been soft deleted; undeleted documents will not be modified. - - Args: - filter_query: MongoDB query filter condition - session: Optional MongoDB session, for transaction support - **pymongo_kwargs: Other parameters passed to PyMongo - - Returns: - UpdateResult: Update result containing number of matched and modified documents - - Example: - # Restore specific user - result = await User.restore_many({"email": "user@example.com"}) - - # Restore all documents deleted yesterday - from datetime import timedelta - from common_utils.datetime_utils import get_now_with_timezone - yesterday = get_now_with_timezone() - timedelta(days=1) - result = await User.restore_many( - {"deleted_at": {"$gte": yesterday}} - ) - """ - # Apply deleted filter: only restore documents that have been soft deleted - final_filter = cls.apply_soft_delete_filter(filter_query, include_deleted=True) - # Manually add condition that deleted_at is not None to ensure only deleted documents are restored - if "deleted_at" not in final_filter: - final_filter["deleted_at"] = {"$ne": None} - - # Perform bulk update operation to clear all soft delete markers - return await cls.get_pymongo_collection().update_many( - final_filter, - {"$set": {"deleted_at": None, "deleted_by": None, "deleted_id": 0}}, - session=session, - **pymongo_kwargs, - ) - - @classmethod - async def hard_delete_many( - cls, - filter_query: Mapping[str, Any], - session: Optional[AsyncClientSession] = None, - **pymongo_kwargs: Any, - ): - """ - Bulk hard delete documents (physical deletion) - - ⚠️ Warning: This operation is irreversible! Use with caution. - - For bulk hard deletion, native approach can also be used: - await Model.find(query).delete_many() - - Args: - filter_query: MongoDB query filter condition - session: Optional MongoDB session, for transaction support - **pymongo_kwargs: Other parameters passed to PyMongo - - Returns: - DeleteResult: Deletion result - - Example: - # Permanently delete all test data - result = await User.hard_delete_many({"is_test": True}) - """ - return await cls.get_pymongo_collection().delete_many( - filter_query, session=session, **pymongo_kwargs - ) - - @classmethod - def hard_find_many( # type: ignore - cls, - *args: Union[Mapping[Any, Any], bool], - projection_model: Optional[Type[BaseModel]] = None, - skip: Optional[int] = None, - limit: Optional[int] = None, - sort: Union[None, str, List[Tuple[str, SortDirection]]] = None, - session: Optional[AsyncClientSession] = None, - ignore_cache: bool = True, - fetch_links: bool = False, - with_children: bool = False, - lazy_parse: bool = False, - nesting_depth: Optional[int] = None, - nesting_depths_per_field: Optional[Dict[str, int]] = None, - **pymongo_kwargs: Any, - ): - """ - Hard find multiple documents (including those soft deleted) - - Unlike find_many(), this method does not filter out deleted documents. - Used in scenarios requiring viewing history or restoring deleted documents. - Naming is consistent with hard_delete. - - Args: - *args: Query conditions - projection_model: Projection model - skip: Number of documents to skip - limit: Limit on number of documents returned - sort: Sorting rule - session: MongoDB session - ignore_cache: Whether to ignore cache - fetch_links: Whether to fetch linked documents - with_children: Whether to include children - lazy_parse: Whether to parse lazily - nesting_depth: Nesting depth - nesting_depths_per_field: Nesting depth per field - **pymongo_kwargs: Other pymongo parameters - - Returns: - FindMany query object - - Example: - # Find all users including deleted ones - all_users = await User.hard_find_many({"email": "test@example.com"}).to_list() - - # Find deleted documents - deleted_users = await User.hard_find_many( - {"deleted_at": {"$ne": None}} - ).to_list() - """ - args = cls._add_class_id_filter(args, with_children) - return cls._find_many_query_class(document_model=cls).find_many( - *args, - sort=sort, - skip=skip, - limit=limit, - projection_model=projection_model, - session=session, - ignore_cache=ignore_cache, - fetch_links=fetch_links, - lazy_parse=lazy_parse, - nesting_depth=nesting_depth, - nesting_depths_per_field=nesting_depths_per_field, - **pymongo_kwargs, - ) - - @classmethod - def find_many_in_all(cls, *args, **kwargs): - """ - Deprecated: Please use hard_find_many() instead - - Kept for backward compatibility, recommended to use hard_find_many(). - """ - return cls.hard_find_many(*args, **kwargs) - - @classmethod - def find_many( # type: ignore - cls, - *args: Union[Mapping[Any, Any], bool], - projection_model: Optional[Type[BaseModel]] = None, - skip: Optional[int] = None, - limit: Optional[int] = None, - sort: Union[None, str, List[Tuple[str, SortDirection]]] = None, - session: Optional[AsyncClientSession] = None, - ignore_cache: bool = False, - fetch_links: bool = False, - with_children: bool = False, - lazy_parse: bool = False, - nesting_depth: Optional[int] = None, - nesting_depths_per_field: Optional[Dict[str, int]] = None, - **pymongo_kwargs: Any, - ): - """ - Find multiple documents (automatically filters out soft deleted ones) - - This method overrides parent's find_many, automatically adding deleted_at = None filter condition. - Only returns documents not soft deleted. - - Use hard_find_many() if you need to query including deleted documents. - - Args: - *args: Query conditions - projection_model: Projection model - skip: Number of documents to skip - limit: Limit on number of documents returned - sort: Sorting rule - session: MongoDB session - ignore_cache: Whether to ignore cache - fetch_links: Whether to fetch linked documents - with_children: Whether to include children - lazy_parse: Whether to parse lazily - nesting_depth: Nesting depth - nesting_depths_per_field: Nesting depth per field - **pymongo_kwargs: Other pymongo parameters - - Returns: - FindMany query object - - Example: - # Only find undeleted users - active_users = await User.find_many({"status": "active"}).to_list() - """ - # Add deleted_at = None filter condition - args = cls._add_class_id_filter(args, with_children) + ({"deleted_at": None},) - return cls._find_many_query_class(document_model=cls).find_many( - *args, - sort=sort, - skip=skip, - limit=limit, - projection_model=projection_model, - session=session, - ignore_cache=ignore_cache, - fetch_links=fetch_links, - lazy_parse=lazy_parse, - nesting_depth=nesting_depth, - nesting_depths_per_field=nesting_depths_per_field, - **pymongo_kwargs, - ) - - @classmethod - def hard_find_one( # type: ignore - cls, - *args: Union[Mapping[Any, Any], bool], - projection_model: Optional[Type[BaseModel]] = None, - session: Optional[AsyncClientSession] = None, - ignore_cache: bool = True, - fetch_links: bool = False, - with_children: bool = False, - nesting_depth: Optional[int] = None, - nesting_depths_per_field: Optional[Dict[str, int]] = None, - **pymongo_kwargs: Any, - ): - """ - Hard find single document (including those soft deleted) - - Unlike find_one(), this method does not filter out deleted documents. - Used in scenarios requiring viewing history or restoring deleted documents. - Naming is consistent with hard_delete. - - Args: - *args: Query conditions - projection_model: Projection model - session: MongoDB session - ignore_cache: Whether to ignore cache - fetch_links: Whether to fetch linked documents - with_children: Whether to include children - nesting_depth: Nesting depth - nesting_depths_per_field: Nesting depth per field - **pymongo_kwargs: Other pymongo parameters - - Returns: - FindOne query object - - Example: - # Find user including deleted ones - user = await User.hard_find_one({"email": "test@example.com"}) - - # Find deleted user and restore - deleted_user = await User.hard_find_one( - {"email": "test@example.com", "deleted_at": {"$ne": None}} - ) - if deleted_user: - await deleted_user.restore() - """ - args = cls._add_class_id_filter(args, with_children) - return cls._find_one_query_class(document_model=cls).find_one( - *args, - projection_model=projection_model, - session=session, - ignore_cache=ignore_cache, - fetch_links=fetch_links, - nesting_depth=nesting_depth, - nesting_depths_per_field=nesting_depths_per_field, - **pymongo_kwargs, - ) - - @classmethod - def find_one( # type: ignore - cls, - *args: Union[Mapping[Any, Any], bool], - projection_model: Optional[Type[BaseModel]] = None, - session: Optional[AsyncClientSession] = None, - ignore_cache: bool = True, - fetch_links: bool = False, - with_children: bool = False, - nesting_depth: Optional[int] = None, - nesting_depths_per_field: Optional[Dict[str, int]] = None, - **pymongo_kwargs: Any, - ): - """ - Find single document (automatically filters out soft deleted ones) - - This method overrides parent's find_one, automatically adding deleted_at = None filter condition. - Only returns documents not soft deleted. - - Use hard_find_one() if you need to query including deleted documents. - - Args: - *args: Query conditions - projection_model: Projection model - session: MongoDB session - ignore_cache: Whether to ignore cache - fetch_links: Whether to fetch linked documents - with_children: Whether to include children - nesting_depth: Nesting depth - nesting_depths_per_field: Nesting depth per field - **pymongo_kwargs: Other pymongo parameters - - Returns: - FindOne query object - - Example: - # Find undeleted user - user = await User.find_one({"email": "test@example.com"}) - """ - # Add deleted_at = None filter condition - args = cls._add_class_id_filter(args, with_children) + ({"deleted_at": None},) - return cls._find_one_query_class(document_model=cls).find_one( - *args, - projection_model=projection_model, - session=session, - ignore_cache=ignore_cache, - fetch_links=fetch_links, - nesting_depth=nesting_depth, - nesting_depths_per_field=nesting_depths_per_field, - **pymongo_kwargs, - ) - - class Settings: - """Document settings""" - - # Common document configurations can be set here - # For example: indexes, validation rules, etc - - -__all__ = ["DocumentBaseWithSoftDelete"] diff --git a/methods/EverCore/src/core/oxm/mongo/migration/__init__.py b/methods/EverCore/src/core/oxm/mongo/migration/__init__.py deleted file mode 100644 index 4af2aeff..00000000 --- a/methods/EverCore/src/core/oxm/mongo/migration/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -MongoDB migration management module. -""" diff --git a/methods/EverCore/src/core/oxm/mongo/migration/cli.py b/methods/EverCore/src/core/oxm/mongo/migration/cli.py deleted file mode 100644 index bcf9ff37..00000000 --- a/methods/EverCore/src/core/oxm/mongo/migration/cli.py +++ /dev/null @@ -1,148 +0,0 @@ -""" -MongoDB migration CLI tool. - -This module provides a command-line interface for managing MongoDB database migrations. -""" - -import sys -import logging -import argparse -from pathlib import Path - -from .manager import MigrationManager - -# Module-level logger for this file -logger = logging.getLogger(__name__) - - -def show_help(): - """Display help information""" - help_text = """ -🗃️ MongoDB Migration Tool (based on Beanie) - -📋 Commands: - new-migration -n Create new migration file - migrate Run all pending migrations - migrate --distance N Run N migrations - migrate --backward Roll back all migrations - migrate --backward --distance N Roll back N migrations - -🔧 Environment Variables: - MONGODB_URI Complete MongoDB connection string - Or configure separately: - MONGODB_HOST MongoDB host (default: localhost) - MONGODB_PORT MongoDB port (default: 27017) - MONGODB_USERNAME MongoDB username - MONGODB_PASSWORD MongoDB password - MONGODB_DATABASE MongoDB database (default: memsys) - -💡 Examples: - python -m core.oxm.mongo.migration.cli new-migration -n add_user_index - python -m core.oxm.mongo.migration.cli migrate - python -m core.oxm.mongo.migration.cli migrate --distance 1 - python -m core.oxm.mongo.migration.cli migrate --backward --distance 1 - -⚠️ Notes: - - Migrations use transactions by default (requires MongoDB replica set) - - Use --no-use-transaction to disable transactions - - Migrations are executed in alphabetical order - - Files starting with underscore are ignored (e.g. __init__.py) -""" - logger.info(help_text) - - -def main(): - """Main CLI entry point""" - if len(sys.argv) == 1 or ( - len(sys.argv) == 2 and sys.argv[1] in ["--help", "-h", "help"] - ): - show_help() - return - - # Parse global arguments - parser = argparse.ArgumentParser(description="MongoDB Migration Tool") - parser.add_argument( - "--uri", help="MongoDB connection URI (overrides environment variables)" - ) - parser.add_argument( - "--database", help="MongoDB database name (overrides environment variables)" - ) - parser.add_argument("--path", type=Path, help="Custom migrations directory path") - - # Parse subcommands - subparsers = parser.add_subparsers(dest="command", help="Available commands") - - # new-migration subcommand - new_migration_parser = subparsers.add_parser( - "new-migration", help="Create new migration" - ) - new_migration_parser.add_argument( - "-n", "--name", required=True, help="Migration name" - ) - - # migrate subcommand - migrate_parser = subparsers.add_parser("migrate", help="Run migrations") - migrate_parser.add_argument( - "--distance", type=int, help="Number of migrations to run" - ) - migrate_parser.add_argument( - "--backward", action="store_true", help="Roll back migrations" - ) - migrate_parser.add_argument( - "--no-use-transaction", action="store_true", help="Disable transactions" - ) - migrate_parser.add_argument( - "--stream-output", - action="store_true", - help="Stream child process output to current stdout/stderr in real time", - ) - - # Parse arguments - args = parser.parse_args() - - try: - # Create manager instance directly - manager = MigrationManager( - uri=args.uri, - database=args.database, - migrations_path=args.path or MigrationManager.MIGRATIONS_DIR, - use_transaction=( - not args.no_use_transaction if args.command == "migrate" else True - ), - distance=args.distance if args.command == "migrate" else None, - backward=args.backward if args.command == "migrate" else False, - stream_output=(args.stream_output if args.command == "migrate" else False), - ) - - if args.command == "new-migration": - try: - filepath = manager.create_migration(args.name) - logger.info("🎉 Migration file created successfully!") - logger.info(f"📝 Please edit file: {filepath}") - - except Exception as e: - logger.error(f"❌ Failed to create migration: {e}") - sys.exit(1) - - elif args.command == "migrate": - # Run migration - exit_code = manager.run_migration() - - if exit_code == 0: - logger.info("🎉 Migration execution completed!") - else: - logger.error("❌ Migration execution failed!") - sys.exit(exit_code) - - else: - logger.error(f"❌ Unknown command: {sys.argv[1]}") - show_help() - sys.exit(1) - - except Exception as e: - logger.error(f"❌ Error: {e}") - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/methods/EverCore/src/core/oxm/mongo/migration/manager.py b/methods/EverCore/src/core/oxm/mongo/migration/manager.py deleted file mode 100644 index 72637371..00000000 --- a/methods/EverCore/src/core/oxm/mongo/migration/manager.py +++ /dev/null @@ -1,440 +0,0 @@ -""" -MongoDB migration manager module. - -This module provides a high-level interface for managing MongoDB database migrations -using Beanie as the underlying migration engine. -""" - -import os -import logging -import subprocess -import sys -from datetime import datetime -from pathlib import Path -from typing import Optional - -from common_utils.datetime_utils import get_now_with_timezone -from common_utils.project_path import CURRENT_DIR -from pymongo import MongoClient - -# Module-level logger for this file -logger = logging.getLogger(__name__) - - -class MigrationManager: - """Migration manager for MongoDB using Beanie""" - - MIGRATIONS_DIR = CURRENT_DIR / "migrations" / "mongodb" - - # Default migration template - MIGRATION_TEMPLATE = '''""" -{description} - -Created at: {created_at} -""" - -from beanie import Document -from beanie import iterative_migration, free_fall_migration -from pymongo import IndexModel, ASCENDING, DESCENDING, TEXT - - -class Forward: - """Forward migration""" - - # Example: Iterative migration (recommended) - # @iterative_migration() - # async def update_field(self, input_document: OldModel, output_document: NewModel): - # output_document.new_field = input_document.old_field - - # Example: Free fall migration (flexible) - # @free_fall_migration(document_models=[YourModel]) - # async def create_indexes(self, session): - # # Get collection - # collection = YourModel.get_pymongo_collection() - # - # # Create indexes - # indexes = [ - # IndexModel([("field_name", ASCENDING)], name="idx_field_name") - # ] - # await collection.create_indexes(indexes) - - pass - - -class Backward: - """Backward migration""" - - # @iterative_migration() - # async def revert_field(self, input_document: NewModel, output_document: OldModel): - # output_document.old_field = input_document.new_field - - # @free_fall_migration(document_models=[YourModel]) - # async def drop_indexes(self, session): - # collection = YourModel.get_pymongo_collection() - # await collection.drop_index("idx_field_name") - - pass -''' - - def __init__( - self, - uri: Optional[str] = None, - database: Optional[str] = None, - migrations_path: Optional[Path] = None, - use_transaction: bool = True, - distance: Optional[int] = None, - backward: bool = False, - stream_output: bool = True, - ): - """ - Initialize migration manager - - Args: - uri: MongoDB connection URI. If not provided, load from env. - database: MongoDB database name. If not provided, load from env. - migrations_path: Directory of migration files. Defaults to MIGRATIONS_DIR. - use_transaction: Whether to use transactions (requires replica set). - distance: Number of migrations to apply (positive integer). - backward: Whether to perform rollback. - """ - self.uri = uri or self._get_mongodb_uri() - self.database = database or self._get_mongodb_database() - self.migrations_path = migrations_path or self.MIGRATIONS_DIR - self.use_transaction = use_transaction - self.distance = distance - self.backward = backward - self.stream_output = stream_output - - if not self.uri: - raise ValueError("MongoDB URI cannot be empty") - if not self.database: - raise ValueError("MongoDB database name cannot be empty") - if not self.migrations_path: - raise ValueError("Migrations path cannot be empty") - - self._ensure_migrations_dir() - - @classmethod - def _get_mongodb_uri(cls) -> str: - """Get MongoDB URI from environment variables""" - base_uri = None - if uri := os.getenv("MONGODB_URI"): - base_uri = uri - else: - # Build URI from separate environment variables - host = os.getenv("MONGODB_HOST", "localhost") - port = os.getenv("MONGODB_PORT", "27017") - username = os.getenv("MONGODB_USERNAME", "") - password = os.getenv("MONGODB_PASSWORD", "") - database = cls._get_mongodb_database() - - if username and password: - base_uri = f"mongodb://{username}:{password}@{host}:{port}/{database}" - else: - base_uri = f"mongodb://{host}:{port}/{database}" - - # Append URI parameters (if any) - uri_params = os.getenv("MONGODB_URI_PARAMS", "").strip() - if uri_params: - separator = '&' if ('?' in base_uri) else '?' - return f"{base_uri}{separator}{uri_params}" - return base_uri - - @staticmethod - def _get_mongodb_database() -> str: - """Get MongoDB database name from environment""" - return os.getenv("MONGODB_DATABASE", "memsys") - - def _ensure_migrations_dir(self): - """Ensure migrations directory exists""" - self.migrations_path.mkdir(parents=True, exist_ok=True) - - def create_migration(self, migration_name: str) -> Path: - """ - Create a new migration file - - Args: - migration_name: Name of the migration - - Returns: - Path to the created migration file - - Raises: - FileExistsError: If migration file already exists - """ - # Generate timestamp - timestamp = get_now_with_timezone().strftime("%Y%m%d%H%M%S") - filename = f"{timestamp}_{migration_name}.py" - filepath = self.migrations_path / filename - - # Check if file already exists - if filepath.exists(): - raise FileExistsError(f"Migration file already exists: {filepath}") - - # Generate migration content - content = self.MIGRATION_TEMPLATE.format( - description=migration_name.replace("_", " ").title(), - created_at=get_now_with_timezone().isoformat(), - ) - - # Write file - filepath.write_text(content, encoding='utf-8') - logger.info(f"✅ Created migration file: {filepath}") - - return filepath - - def run_migration(self) -> int: - """ - Run migration using Beanie - - Returns: - Exit code from Beanie command - """ - # Build beanie args - beanie_args = ["migrate"] - if self.distance is not None: - if self.distance <= 0: - raise ValueError("Migration distance must be positive") - beanie_args.extend(["--distance", str(self.distance)]) - if self.backward: - beanie_args.append("--backward") - if not self.use_transaction: - beanie_args.append("--no-use-transaction") - - # Build complete command - cmd = [ - "beanie", - *beanie_args, - "-uri", - self.uri, - "-db", - self.database, - "-p", - str(self.migrations_path), - ] - - logger.info(f"🚀 Executing command: {' '.join(cmd[3:])}") # Hide python path - logger.info(f"📍 Database: {self.database}") - logger.info(f"📁 Migration directory: {self.migrations_path}") - - # Check if there are migration files in the directory - migration_files = list(self.migrations_path.glob("*.py")) - migration_files = [f for f in migration_files if not f.name.startswith("_")] - if not migration_files: - logger.info("🧭 No migration files found in directory, skipping migration") - return 0 - logger.info(f"📄 Found {len(migration_files)} migration files") - - # Snapshot migration logs before running - before_names, before_current = self._snapshot_migration_log() - if before_names is not None: - logger.info(f"🧭 Number of records before migration: {len(before_names)}") - logger.info( - f"⭐ Current pointer before migration: {before_current or ''}" - ) - else: - logger.info( - "🧭 migrations_log collection not initialized (first migration)" - ) - try: - # Execute command - if self.stream_output: - # Redirect subprocess output to current process stdout/stderr for real-time printing - result = subprocess.run( - cmd, - check=True, - stdout=sys.stdout, - stderr=sys.stderr, - text=True, - env=os.environ.copy(), - ) - # In streaming mode, output is printed directly, no need to log result.stdout/stderr again - else: - result = subprocess.run( - cmd, - check=True, - capture_output=True, - text=True, - env=os.environ.copy(), - ) - - # Log buffered output at the end - if result.stdout: - logger.info(result.stdout) - if result.stderr: - logger.warning(result.stderr) - - # Snapshot and log diff after success - self._log_migration_diff(before_names, before_current) - return result.returncode - - except subprocess.CalledProcessError as e: - logger.error(f"❌ Command execution failed: {e}") - if e.stdout: - logger.info(f"Standard output: {e.stdout}") - if e.stderr: - logger.error(f"Error output: {e.stderr}") - # Snapshot and log diff even on failure (migration may have partially executed) - self._log_migration_diff(before_names, before_current) - return e.returncode - - except FileNotFoundError: - logger.error( - "❌ beanie command not found, please ensure beanie is installed" - ) - logger.error("Installation command: pip install beanie") - # Snapshot and log diff even if command not found (should be no changes) - self._log_migration_diff(before_names, before_current) - return 1 - - # ---------- Helper methods for migration log inspection ---------- - def _get_sync_mongo_client(self) -> MongoClient: - """Create a short-lived sync MongoDB client for inspections.""" - return MongoClient(self.uri) - - def _read_migration_logs(self): - """Read migrations_log documents sorted by ts ascending. - - Returns: - Tuple[List[str], Optional[str]] | (None, None) if any error occurs. - """ - try: - with self._get_sync_mongo_client() as client: - db = client[self.database] - coll = db["migrations_log"] - docs = list( - coll.find({}, {"_id": 0, "name": 1, "is_current": 1, "ts": 1}).sort( - "ts", 1 - ) - ) - names = [d.get("name") for d in docs if d.get("name")] - current = None - for d in reversed(docs): - if d.get("is_current"): - current = d.get("name") - break - return names, current - except Exception as e: - logger.warning("Failed to read migration logs: %s", str(e)) - return None, None - - def _snapshot_migration_log(self): - """Wrapper to snapshot current migration log state.""" - names, current = self._read_migration_logs() - if names is None: - return None, None - return set(names), current - - def _log_migration_diff(self, before_names, before_current) -> None: - """Compare before/after migration log snapshots and print diffs.""" - after_names, after_current = self._snapshot_migration_log() - if after_names is None: - logger.info("🧭 Unable to read post-migration log snapshot") - return - - logger.info("🧭 Number of records after migration: %d", len(after_names)) - if after_current: - logger.info("⭐ Current pointer after migration: %s", after_current) - else: - logger.info("⭐ Current pointer after migration: ") - - if before_names is None: - return - - added = sorted(list(after_names - before_names)) - removed = sorted(list(before_names - after_names)) - - if added: - logger.info("✅ Newly executed scripts: %s", ", ".join(added)) - else: - logger.info("✅ Newly executed scripts: ") - - if removed: - logger.info("↩️ Scripts removed due to rollback: %s", ", ".join(removed)) - else: - logger.info("↩️ Scripts removed due to rollback: ") - - if before_current != after_current: - logger.info( - "📍 Current pointer changed: %s -> %s", - before_current or "", - after_current or "", - ) - - # ---------- Public utility for manual query ---------- - def get_migration_history(self): - """Return full migration history from migrations_log (sorted by ts asc).""" - try: - with self._get_sync_mongo_client() as client: - db = client[self.database] - coll = db["migrations_log"] - docs = list( - coll.find({}, {"_id": 0, "name": 1, "is_current": 1, "ts": 1}).sort( - "ts", 1 - ) - ) - return docs - except Exception as e: - logger.warning("Failed to get migration history: %s", str(e)) - return [] - - def log_migration_history(self) -> None: - """Log migration history and current pointer.""" - names, current = self._snapshot_migration_log() - if names is None: - logger.info("Unable to read migration history") - return - logger.info( - "📜 Recorded migration scripts (%d): %s", - len(names), - ", ".join(sorted(names)), - ) - logger.info("⭐ Current pointer: %s", current or "") - - @classmethod - def run_migrations_on_startup(cls, enabled: bool = True) -> int: - """ - Run MongoDB database migrations on application startup - - Execute all pending migration scripts using default configuration (connection info from environment variables) - - Args: - enabled: Whether to enable migration, False to skip migration step - - Returns: - int: Exit code from migration execution, 0 means success, -1 means skipped - """ - if not enabled: - logger.info( - "MongoDB startup migration is disabled, skipping migration step" - ) - return -1 - - logger.info("Running MongoDB database migrations...") - - try: - # Create migration manager instance with default configuration - migration_manager = cls( - use_transaction=False, # Default not to use transaction - distance=None, # Execute all pending migrations - backward=False, # Do not rollback - stream_output=True, # Stream output in real time - ) - - # Execute migration - logger.info("Starting MongoDB migration operation...") - exit_code = migration_manager.run_migration() - - if exit_code != 0: - logger.warning( - "⚠️ MongoDB migration process returned non-zero exit code: %s", - exit_code, - ) - else: - logger.info("✅ MongoDB database migration completed") - - return exit_code - - except Exception as e: - logger.error("❌ Error during MongoDB migration: %s", str(e)) - return 1 diff --git a/methods/EverCore/src/core/oxm/mongo/mongo_utils.py b/methods/EverCore/src/core/oxm/mongo/mongo_utils.py deleted file mode 100644 index e9778fed..00000000 --- a/methods/EverCore/src/core/oxm/mongo/mongo_utils.py +++ /dev/null @@ -1,86 +0,0 @@ -""" -MongoDB ObjectId utility functions - -Provides utility functions for ObjectId generation and conversion, usable without connecting to a database. -""" - -from datetime import datetime -from typing import Any, Dict, List, Optional, Tuple - -from bson.errors import InvalidId -from bson.objectid import ObjectId - - -def generate_object_id() -> Tuple[ObjectId, str, datetime]: - """ - Generate a new MongoDB ObjectId (does not require database connection) - - Returns: - Tuple[ObjectId, str, datetime]: Returns a tuple containing: - - The ObjectId object itself - - String representation of the ObjectId (suitable for API responses or frontend storage) - - Timestamp when the ID was generated - - Example: - >>> obj_id, id_str, gen_time = generate_object_id() - >>> print(f"ObjectId object: {obj_id}") - >>> print(f"String representation: {id_str}") - >>> print(f"Generation time: {gen_time}") - """ - new_id = ObjectId() - return new_id, str(new_id), new_id.generation_time - - -def generate_object_id_str() -> str: - """ - Generate a new MongoDB ObjectId and return its string representation - - Returns: - str: String representation of the ObjectId (24-character hexadecimal string) - - Example: - >>> id_str = generate_object_id_str() - >>> print(f"ObjectId string: {id_str}") # e.g.: "507f1f77bcf86cd799439011" - """ - return str(ObjectId()) - - -def build_id_filter(ids: List[str]) -> Optional[Dict[str, Any]]: - """Build a MongoDB ``_id`` filter from a list of string IDs. - - Most IDs in this codebase are ObjectId-like strings, but some legacy - datasets use raw string IDs. This helper splits the input into the two - groups and produces a single filter that matches either form, so the - caller does not need to know which kind of IDs it is dealing with. - - Args: - ids: List of document _id strings. Empty / None entries are ignored. - - Returns: - A MongoDB filter dict suitable for ``find(filter)``, or ``None`` when - no usable IDs are provided. - """ - if not ids: - return None - - object_ids: List[ObjectId] = [] - raw_ids: List[str] = [] - for item_id in ids: - if not item_id: - continue - try: - object_ids.append(ObjectId(item_id)) - except (InvalidId, TypeError): - raw_ids.append(item_id) - - clauses: List[Dict[str, Any]] = [] - if object_ids: - clauses.append({"_id": {"$in": object_ids}}) - if raw_ids: - clauses.append({"_id": {"$in": raw_ids}}) - - if not clauses: - return None - if len(clauses) == 1: - return clauses[0] - return {"$or": clauses} diff --git a/methods/EverCore/src/core/oxm/pg/__init__.py b/methods/EverCore/src/core/oxm/pg/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/oxm/pg/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/oxm/pg/audit_base.py b/methods/EverCore/src/core/oxm/pg/audit_base.py deleted file mode 100644 index 117331fd..00000000 --- a/methods/EverCore/src/core/oxm/pg/audit_base.py +++ /dev/null @@ -1,145 +0,0 @@ -from datetime import datetime -from typing import Optional - -from sqlmodel import Field, SQLModel -from sqlalchemy import Column, TIMESTAMP, event -from common_utils.datetime_utils import get_now_with_timezone -from core.context.context import get_current_user_info -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -def get_auditable_model() -> SQLModel: - """ - Get the base model class with audit capabilities - - This model includes audit fields that are automatically populated by event listeners: - - created_at, updated_at: Automatically set timestamps by event listeners - - created_by, updated_by: Automatically set the operating user by event listeners - - deleted_at, deleted_by: Set during soft deletion by event listeners or business logic - - Returns: - SQLModel: Base model class with audit capabilities - """ - - class AuditableModel(SQLModel): - """Base model with audit information including creation and update details""" - - # Timestamp audit fields - automatically populated by event listeners - created_at: Optional[datetime] = Field( - default=None, - sa_column=Column(TIMESTAMP(timezone=True), nullable=True), - description="Creation time (automatically populated by event listener)", - ) - - updated_at: Optional[datetime] = Field( - default=None, - sa_column=Column(TIMESTAMP(timezone=True), nullable=True), - description="Update time (automatically populated by event listener)", - ) - - deleted_at: Optional[datetime] = Field( - default=None, - sa_column=Column(TIMESTAMP(timezone=True), nullable=True), - description="Deletion time (set during soft delete)", - ) - - # User audit fields - automatically populated by event listeners - created_by: Optional[str] = Field( - default=None, - description="Creator (automatically populated by event listener)", - ) - updated_by: Optional[str] = Field( - default=None, - description="Updater (automatically populated by event listener)", - ) - deleted_by: Optional[str] = Field( - default=None, description="Deleter (set during soft delete)" - ) - - def soft_delete(self, deleted_by: str): - """Soft delete the record""" - self.deleted_at = get_now_with_timezone() - self.deleted_by = deleted_by - - def restore(self, restored_by: str = None): - """Restore a soft-deleted record""" - # restored_by parameter is kept for interface compatibility, but actually set by event listener - _ = restored_by # Avoid unused parameter warning - self.deleted_at = None - self.deleted_by = None - - @property - def is_deleted(self) -> bool: - """Check if the record has been soft-deleted""" - return self.deleted_at is not None - - # Register event listeners - @event.listens_for(AuditableModel, 'before_insert', propagate=True) - def before_insert_listener( - mapper, connection, target - ): # pylint: disable=unused-argument - """Event listener before INSERT operation""" - # Ignore unused parameters (required signature for SQLAlchemy event listeners) - _ = mapper, connection - - current_time = get_now_with_timezone() - current_user_id = _get_current_user_id() - - # Set creation time and creator - if hasattr(target, 'created_at') and target.created_at is None: - target.created_at = current_time - - if hasattr(target, 'created_by') and target.created_by is None: - target.created_by = current_user_id or "system" - - # Set update time and updater - if hasattr(target, 'updated_at') and target.updated_at is None: - target.updated_at = current_time - - if hasattr(target, 'updated_by') and target.updated_by is None: - target.updated_by = current_user_id or "system" - - @event.listens_for(AuditableModel, 'before_update', propagate=True) - def before_update_listener( - mapper, connection, target - ): # pylint: disable=unused-argument - """Event listener before UPDATE operation""" - # Ignore unused parameters (required signature for SQLAlchemy event listeners) - _ = mapper, connection - - current_time = get_now_with_timezone() - current_user_id = _get_current_user_id() - - # Set update time and updater - if hasattr(target, 'updated_at'): - target.updated_at = current_time - - # Only set updated_by if it's None, do not overwrite existing values (e.g., "system") - if hasattr(target, 'updated_by') and target.updated_by is None: - target.updated_by = current_user_id or "system" - - # Special handling for soft delete scenario - if hasattr(target, 'deleted_at') and target.deleted_at is not None: - # If deleted_at is set, it indicates a soft delete operation - if hasattr(target, 'deleted_by') and target.deleted_by is None: - target.deleted_by = current_user_id or "system" - - return AuditableModel - - -def _get_current_user_id() -> Optional[str]: - """ - Get current user ID - - Returns: - Optional[str]: Current user ID, returns None if not set - """ - try: - user_info = get_current_user_info() - if user_info and 'user_id' in user_info: - return str(user_info['user_id']) - except Exception as e: # pylint: disable=broad-except - logger.debug("Failed to get current user information: %s", e) - return None diff --git a/methods/EverCore/src/core/oxm/pg/base_repository.py b/methods/EverCore/src/core/oxm/pg/base_repository.py deleted file mode 100644 index 52e8f0df..00000000 --- a/methods/EverCore/src/core/oxm/pg/base_repository.py +++ /dev/null @@ -1,51 +0,0 @@ -from abc import ABC, abstractmethod -from typing import List, Optional, TypeVar, Generic, Type -from core.oxm.pg.audit_base import get_auditable_model -from core.di.decorators import repository - -# Define generic type -T = TypeVar('T', bound=get_auditable_model()) - - -class BaseSoftDeleteRepository(Generic[T], ABC): - """Base repository interface supporting soft delete - pure business interface without technical implementation details""" - - @abstractmethod - async def add(self, entity: T) -> T: - """Add new entity""" - raise NotImplementedError - - @abstractmethod - async def get(self, entity_id: int, include_deleted: bool = False) -> Optional[T]: - """Get entity by ID (exclude deleted by default)""" - raise NotImplementedError - - @abstractmethod - async def get_all(self, include_deleted: bool = False) -> List[T]: - """Get all entities (exclude deleted by default)""" - raise NotImplementedError - - @abstractmethod - async def update(self, entity: T) -> T: - """Update entity""" - raise NotImplementedError - - @abstractmethod - async def delete(self, entity_id: int, deleted_by: str = "system") -> bool: - """Soft delete entity""" - raise NotImplementedError - - @abstractmethod - async def restore(self, entity_id: int, restored_by: str = "system") -> bool: - """Restore soft-deleted entity""" - raise NotImplementedError - - @abstractmethod - async def hard_delete(self, entity_id: int) -> bool: - """Hard delete entity (use with caution!)""" - raise NotImplementedError - - @abstractmethod - async def count(self, include_deleted: bool = False) -> int: - """Count entities (exclude deleted by default)""" - raise NotImplementedError diff --git a/methods/EverCore/src/core/queue/__init__.py b/methods/EverCore/src/core/queue/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/queue/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/queue/msg_group_queue/__init__.py b/methods/EverCore/src/core/queue/msg_group_queue/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/queue/msg_group_queue/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/queue/msg_group_queue/msg_group_queue_manager.py b/methods/EverCore/src/core/queue/msg_group_queue/msg_group_queue_manager.py deleted file mode 100644 index 20cb45e0..00000000 --- a/methods/EverCore/src/core/queue/msg_group_queue/msg_group_queue_manager.py +++ /dev/null @@ -1,847 +0,0 @@ -""" -Message Group Queue Manager - -Provides fixed-number queue management with hash-based routing to solve blocking issues in Kafka message processing. -Supports message delivery, consumption, statistics, monitoring, and other features. -""" - -import asyncio -import hashlib -import time -from typing import Any, Dict, List, Optional, Tuple, Union -from dataclasses import dataclass, field -from enum import Enum -from collections import deque - -from core.observation.logger import get_logger -from common_utils.datetime_utils import get_now_with_timezone, to_iso_format - -logger = get_logger(__name__) - - -class ShutdownMode(Enum): - """Shutdown mode enumeration""" - - SOFT = "soft" # Soft shutdown: check for remaining messages with delay time control - HARD = "hard" # Hard shutdown: shut down immediately, record number of unprocessed messages - - -@dataclass -class ShutdownState: - """Shutdown state""" - - is_shutting_down: bool = False - first_soft_shutdown_time: Optional[float] = None - max_delay_seconds: Optional[float] = None - - def reset(self): - """Reset shutdown state""" - self.is_shutting_down = False - self.first_soft_shutdown_time = None - self.max_delay_seconds = None - - -@dataclass -class TimeWindowStats: - """Time window statistics""" - - delivered_1min: int = 0 - consumed_1min: int = 0 - delivered_1hour: int = 0 - consumed_1hour: int = 0 - - -@dataclass -class QueueStats: - """Queue statistics""" - - queue_id: int - current_size: int - total_delivered: int = 0 - total_consumed: int = 0 - last_deliver_time: Optional[str] = None - last_consume_time: Optional[str] = None - # Time window statistics - time_window_stats: TimeWindowStats = field(default_factory=TimeWindowStats) - - def to_dict(self) -> Dict[str, Any]: - """Convert to dictionary format""" - return { - "queue_id": self.queue_id, - "current_size": self.current_size, - "total_delivered": self.total_delivered, - "total_consumed": self.total_consumed, - "last_deliver_time": self.last_deliver_time, - "last_consume_time": self.last_consume_time, - "delivered_1min": self.time_window_stats.delivered_1min, - "consumed_1min": self.time_window_stats.consumed_1min, - "delivered_1hour": self.time_window_stats.delivered_1hour, - "consumed_1hour": self.time_window_stats.consumed_1hour, - } - - -@dataclass -class ManagerStats: - """Manager overall statistics""" - - total_queues: int - total_current_messages: int - total_delivered_messages: int = 0 - total_consumed_messages: int = 0 - total_rejected_messages: int = 0 - start_time: str = field( - default_factory=lambda: to_iso_format(get_now_with_timezone()) - ) - uptime_seconds: float = 0 - # Time window statistics - time_window_stats: TimeWindowStats = field(default_factory=TimeWindowStats) - - def to_dict(self) -> Dict[str, Any]: - """Convert to dictionary format""" - return { - "total_queues": self.total_queues, - "total_current_messages": self.total_current_messages, - "total_delivered_messages": self.total_delivered_messages, - "total_consumed_messages": self.total_consumed_messages, - "total_rejected_messages": self.total_rejected_messages, - "start_time": self.start_time, - "uptime_seconds": self.uptime_seconds, - "delivered_1min": self.time_window_stats.delivered_1min, - "consumed_1min": self.time_window_stats.consumed_1min, - "delivered_1hour": self.time_window_stats.delivered_1hour, - "consumed_1hour": self.time_window_stats.consumed_1hour, - } - - -class MsgGroupQueueManager: - """ - Message Group Queue Manager - - Features: - 1. Fixed number of queues (default 10, configurable) - 2. Hash-based routing to fixed groups based on group_key - 3. Supports maximum message count limit (default 100) - 4. Empty queue priority delivery strategy - 5. Supports wait/no-wait mode for message retrieval - 6. Provides detailed metrics and logging - """ - - def __init__( - self, - name: str = "default", - num_queues: int = 10, - max_total_messages: int = 100, - enable_metrics: bool = True, - log_interval_seconds: int = 30, - ): - """ - Initialize the message group queue manager - - Args: - name: Manager name - num_queues: Number of queues - max_total_messages: Maximum total message count limit - enable_metrics: Whether to enable statistics - log_interval_seconds: Logging interval in seconds - """ - self.name = name - self.num_queues = num_queues - self.max_total_messages = max_total_messages - self.enable_metrics = enable_metrics - self.log_interval_seconds = log_interval_seconds - - # Initialize queues - using asyncio.Queue - self._queues: List[asyncio.Queue] = [asyncio.Queue() for _ in range(num_queues)] - - # Queue statistics - self._queue_stats: List[QueueStats] = [ - QueueStats(queue_id=i, current_size=0) for i in range(num_queues) - ] - - # Manager statistics - self._manager_stats = ManagerStats( - total_queues=num_queues, total_current_messages=0 - ) - - # Async lock to protect statistics - self._stats_lock = asyncio.Lock() - - # Start time - self._start_time = time.time() - - # Periodic logging task - self._log_task: Optional[asyncio.Task] = None - self._running = False - - # Shutdown state - self._shutdown_state = ShutdownState() - - # Time window event tracking - using deque to store timestamped events - self._delivery_events: List[deque] = [ - deque() for _ in range(num_queues) - ] # Delivery events for each queue - self._consume_events: List[deque] = [ - deque() for _ in range(num_queues) - ] # Consumption events for each queue - self._manager_delivery_events = deque() # Manager total delivery events - self._manager_consume_events = deque() # Manager total consumption events - - logger.info( - "🚀 MsgGroupQueueManager[%s] initialization completed: num_queues=%d, max_total_messages=%d", - self.name, - self.num_queues, - self.max_total_messages, - ) - - def _hash_route(self, group_key: str) -> int: - """ - Calculate hash-based routing to queue ID based on group_key - - Args: - group_key: Group key - - Returns: - int: Queue ID (0 to num_queues-1) - """ - # Use MD5 hash to ensure even distribution - hash_obj = hashlib.md5(group_key.encode('utf-8')) - hash_int = int(hash_obj.hexdigest(), 16) - return hash_int % self.num_queues - - async def deliver_message(self, group_key: str, message_data: Any) -> bool: - """ - Deliver message to the specified group queue - - Args: - group_key: Group key for hash routing - message_data: Message data - - Returns: - bool: Whether delivery was successful - """ - try: - # Calculate target queue - target_queue_id = self._hash_route(group_key) - target_queue = self._queues[target_queue_id] - - # Check delivery conditions - can_deliver, reject_reason = self._can_deliver_message() - if not can_deliver: - # Reject delivery - async with self._stats_lock: - self._manager_stats.total_rejected_messages += 1 - - logger.warning( - "❌ MsgGroupQueueManager[%s] Delivery rejected: group_key=%s, reason=%s", - self.name, - group_key, - reject_reason, - ) - return False - - # Perform delivery - message_tuple = (group_key, message_data) - await target_queue.put(message_tuple) - - # Update statistics - current_time = to_iso_format(get_now_with_timezone()) - timestamp = time.time() - - async with self._stats_lock: - self._queue_stats[target_queue_id].current_size = target_queue.qsize() - self._queue_stats[target_queue_id].total_delivered += 1 - self._queue_stats[target_queue_id].last_deliver_time = current_time - - self._manager_stats.total_delivered_messages += 1 - self._manager_stats.total_current_messages = ( - self._get_total_current_messages() - ) - - # Record time window events - self._delivery_events[target_queue_id].append(timestamp) - self._manager_delivery_events.append(timestamp) - - logger.debug( - "✅ MsgGroupQueueManager[%s] Message delivered successfully: group_key=%s -> queue_id=%d, queue current size=%d, total remaining=%d", - self.name, - group_key, - target_queue_id, - target_queue.qsize(), - self._get_total_current_messages(), - ) - - return True - - except (OSError, ValueError, RuntimeError) as e: - logger.error( - "❌ MsgGroupQueueManager[%s] Failed to deliver message: group_key=%s, error=%s", - self.name, - group_key, - e, - ) - return False - - async def get_by_queue( - self, queue_id: int, wait: bool = True, timeout: Optional[float] = None - ) -> Optional[Tuple[str, Any]]: - """ - Get message from specified queue - - Args: - queue_id: Queue ID - wait: Whether to wait for message (True=blocking wait, False=return immediately) - timeout: Wait timeout in seconds, only effective when wait=True - - Returns: - Optional[Tuple[str, Any]]: Message tuple (group_key, message_data), None means no message - """ - if queue_id < 0 or queue_id >= self.num_queues: - raise ValueError( - f"Queue ID out of range: {queue_id}, valid range: 0-{self.num_queues-1}" - ) - - target_queue = self._queues[queue_id] - - try: - if wait: - # Blocking wait mode - if timeout is not None: - message_tuple = await asyncio.wait_for( - target_queue.get(), timeout=timeout - ) - else: - message_tuple = await target_queue.get() - else: - # Immediate return mode - try: - message_tuple = target_queue.get_nowait() - except asyncio.QueueEmpty: - return None - - # Update statistics - current_time = to_iso_format(get_now_with_timezone()) - timestamp = time.time() - - async with self._stats_lock: - self._queue_stats[queue_id].current_size = target_queue.qsize() - self._queue_stats[queue_id].total_consumed += 1 - self._queue_stats[queue_id].last_consume_time = current_time - - self._manager_stats.total_consumed_messages += 1 - self._manager_stats.total_current_messages = ( - self._get_total_current_messages() - ) - - # Record time window events - self._consume_events[queue_id].append(timestamp) - self._manager_consume_events.append(timestamp) - - group_key, _ = message_tuple - logger.debug( - "📤 MsgGroupQueueManager[%s] Message consumed successfully: queue_id=%d, group_key=%s, queue remaining=%d", - self.name, - queue_id, - group_key, - target_queue.qsize(), - ) - - return message_tuple - - except asyncio.TimeoutError: - logger.debug( - "⏰ MsgGroupQueueManager[%s] Message retrieval timed out: queue_id=%d, timeout=%s", - self.name, - queue_id, - timeout, - ) - return None - except (OSError, ValueError, RuntimeError) as e: - logger.error( - "❌ MsgGroupQueueManager[%s] Failed to retrieve message: queue_id=%d, error=%s", - self.name, - queue_id, - e, - ) - return None - - async def get_queue_info( - self, queue_id: Optional[int] = None - ) -> Union[Dict[str, Any], List[Dict[str, Any]]]: - """ - Get queue information - - Args: - queue_id: Queue ID, None means get all queue information - - Returns: - Union[Dict, List[Dict]]: Queue info dictionary or list of queue info - """ - async with self._stats_lock: - # Update current queue sizes - for i, queue in enumerate(self._queues): - self._queue_stats[i].current_size = queue.qsize() - - # Update time window statistics - self._update_time_window_stats() - - if queue_id is not None: - if queue_id < 0 or queue_id >= self.num_queues: - raise ValueError( - f"Queue ID out of range: {queue_id}, valid range: 0-{self.num_queues-1}" - ) - return self._queue_stats[queue_id].to_dict() - else: - return [stat.to_dict() for stat in self._queue_stats] - - async def get_manager_stats(self) -> Dict[str, Any]: - """ - Get manager overall statistics - - Returns: - Dict[str, Any]: Manager statistics - """ - async with self._stats_lock: - # Update uptime and total current message count - self._manager_stats.uptime_seconds = time.time() - self._start_time - self._manager_stats.total_current_messages = ( - self._get_total_current_messages() - ) - - # Update time window statistics - self._update_time_window_stats() - - return self._manager_stats.to_dict() - - async def get_summary(self) -> Dict[str, Any]: - """ - Get complete summary information - - Returns: - Dict[str, Any]: Complete information including manager stats and queue details - """ - return { - "manager": await self.get_manager_stats(), - "queues": await self.get_queue_info(), - } - - def _get_total_current_messages(self) -> int: - """Get current total message count""" - return sum(queue.qsize() for queue in self._queues) - - def _can_deliver_message(self) -> Tuple[bool, str]: - """ - Check if message can be delivered - - Returns: - Tuple[bool, str]: (can deliver, rejection reason) - """ - current_total = self._get_total_current_messages() - - # Allow delivery if there's an empty queue (regardless of total limit) - has_empty_queue = any(q.qsize() == 0 for q in self._queues) - - # Delivery condition: total not exceeded OR has empty queue - if current_total >= self.max_total_messages and not has_empty_queue: - return ( - False, - f"Current total messages={current_total}, limit={self.max_total_messages}, no empty queue", - ) - - return True, "" - - def _clean_old_events(self, events: deque, max_age_seconds: float): - """Clean events older than specified time""" - current_time = time.time() - while events and current_time - events[0] > max_age_seconds: - events.popleft() - - def _count_events_in_window(self, events: deque, window_seconds: float) -> int: - """Count number of events within specified time window""" - # First clean old events - self._clean_old_events(events, window_seconds) - # Return remaining event count - return len(events) - - def _update_time_window_stats(self): - """Update time window statistics for all queues and manager""" - # Update time window stats for each queue - for i in range(self.num_queues): - # Clean old events and count - self._queue_stats[i].time_window_stats.delivered_1min = ( - self._count_events_in_window(self._delivery_events[i], 60.0) - ) - self._queue_stats[i].time_window_stats.consumed_1min = ( - self._count_events_in_window(self._consume_events[i], 60.0) - ) - self._queue_stats[i].time_window_stats.delivered_1hour = ( - self._count_events_in_window(self._delivery_events[i], 3600.0) - ) - self._queue_stats[i].time_window_stats.consumed_1hour = ( - self._count_events_in_window(self._consume_events[i], 3600.0) - ) - - # Update manager time window statistics - self._manager_stats.time_window_stats.delivered_1min = ( - self._count_events_in_window(self._manager_delivery_events, 60.0) - ) - self._manager_stats.time_window_stats.consumed_1min = ( - self._count_events_in_window(self._manager_consume_events, 60.0) - ) - self._manager_stats.time_window_stats.delivered_1hour = ( - self._count_events_in_window(self._manager_delivery_events, 3600.0) - ) - self._manager_stats.time_window_stats.consumed_1hour = ( - self._count_events_in_window(self._manager_consume_events, 3600.0) - ) - - async def start_periodic_logging(self): - """Start periodic logging task""" - if self._running: - return - - self._running = True - self._log_task = asyncio.create_task(self._periodic_log_worker()) - logger.info( - "📊 MsgGroupQueueManager[%s] Periodic logging task started", self.name - ) - - async def stop_periodic_logging(self): - """Stop periodic logging task""" - if not self._running: - return - - self._running = False - if self._log_task and not self._log_task.done(): - self._log_task.cancel() - try: - await self._log_task - except asyncio.CancelledError: - pass - - logger.info( - "📊 MsgGroupQueueManager[%s] Periodic logging task stopped", self.name - ) - - async def _periodic_log_worker(self): - """Periodic logging worker coroutine""" - try: - while self._running: - await asyncio.sleep(self.log_interval_seconds) - if self._running: - await self._log_queue_details() - except asyncio.CancelledError: - logger.debug( - "📊 MsgGroupQueueManager[%s] Periodic logging task cancelled", self.name - ) - except (OSError, ValueError, RuntimeError) as e: - logger.error( - "📊 MsgGroupQueueManager[%s] Periodic logging task error: %s", - self.name, - e, - ) - - async def _log_queue_details(self): - """Print queue details""" - try: - manager_stats = await self.get_manager_stats() - queue_infos = await self.get_queue_info() - - # Print manager overall status summary - logger.info( - "📊 MsgGroupQueueManager[%s] Overall status: " - "total messages=%d, total delivered=%d, total consumed=%d, total rejected=%d, uptime=%.1f seconds", - self.name, - manager_stats["total_current_messages"], - manager_stats["total_delivered_messages"], - manager_stats["total_consumed_messages"], - manager_stats["total_rejected_messages"], - manager_stats["uptime_seconds"], - ) - - # Print manager time window statistics - logger.info( - "⏱️ MsgGroupQueueManager[%s] Time window stats: " - "last 1 minute (delivered=%d, consumed=%d), last 1 hour (delivered=%d, consumed=%d)", - self.name, - manager_stats["delivered_1min"], - manager_stats["consumed_1min"], - manager_stats["delivered_1hour"], - manager_stats["consumed_1hour"], - ) - - # Print detailed information for each queue - active_queues = [] - idle_queues = [] - empty_queues = [] # Currently empty queues - - for queue_info in queue_infos: - queue_id = queue_info["queue_id"] - current_size = queue_info["current_size"] - total_delivered = queue_info["total_delivered"] - total_consumed = queue_info["total_consumed"] - last_deliver_time = queue_info["last_deliver_time"] - last_consume_time = queue_info["last_consume_time"] - - # Get time window statistics - delivered_1min = queue_info.get("delivered_1min", 0) - consumed_1min = queue_info.get("consumed_1min", 0) - delivered_1hour = queue_info.get("delivered_1hour", 0) - consumed_1hour = queue_info.get("consumed_1hour", 0) - - # Record empty queues - if current_size == 0: - empty_queues.append(queue_id) - - # Calculate queue activity level: based on time window activity and current queue state - has_recent_activity = delivered_1min > 0 or consumed_1min > 0 - has_messages = current_size > 0 - has_historical_activity = total_delivered > 0 or total_consumed > 0 - - # Active judgment: recent activity OR has messages OR historical activity - is_active = ( - has_recent_activity or has_messages or has_historical_activity - ) - - if is_active: - # Calculate delivery and consumption ratios - delivery_rate = ( - total_delivered / max(1, total_delivered + total_consumed) * 100 - ) - consume_rate = ( - total_consumed / max(1, total_delivered + total_consumed) * 100 - ) - - # Format time display - last_deliver_display = ( - last_deliver_time[-8:] if last_deliver_time else "none" - ) - last_consume_display = ( - last_consume_time[-8:] if last_consume_time else "none" - ) - - active_queues.append( - { - "id": queue_id, - "current": current_size, - "delivered": total_delivered, - "consumed": total_consumed, - "delivery_rate": delivery_rate, - "consume_rate": consume_rate, - "last_deliver": last_deliver_display, - "last_consume": last_consume_display, - "delivered_1min": delivered_1min, - "consumed_1min": consumed_1min, - "delivered_1hour": delivered_1hour, - "consumed_1hour": consumed_1hour, - } - ) - else: - idle_queues.append(queue_id) - - # Print active queue details - if active_queues: - logger.info("🔥 Active queue details (%d queues):", len(active_queues)) - for queue in active_queues: - # Queue status indicators - status_indicators = [] - if queue["current"] == 0: - status_indicators.append("empty") - elif queue["current"] > self.max_total_messages * 0.3: - status_indicators.append("backlogged") - - if queue["delivered_1min"] > 0: - status_indicators.append("recent delivery") - if queue["consumed_1min"] > 0: - status_indicators.append("recent consumption") - - status_text = ( - f"[{', '.join(status_indicators)}]" if status_indicators else "" - ) - - logger.info( - " Queue[%d]%s: current=%d, total delivered=%d(%.1f%%), total consumed=%d(%.1f%%), " - "last delivery=%s, last consumption=%s", - queue["id"], - status_text, - queue["current"], - queue["delivered"], - queue["delivery_rate"], - queue["consumed"], - queue["consume_rate"], - queue["last_deliver"], - queue["last_consume"], - ) - - # Print time window statistics - logger.info( - " ⏱️ Last 1 minute (delivered=%d, consumed=%d), last 1 hour (delivered=%d, consumed=%d)", - queue["delivered_1min"], - queue["consumed_1min"], - queue["delivered_1hour"], - queue["consumed_1hour"], - ) - - # Print idle queue information - if idle_queues: - logger.info( - "💤 Idle queues: %s (total %d)", - ", ".join([f"Queue[{qid}]" for qid in idle_queues]), - len(idle_queues), - ) - - # Print empty queue information (queues with no messages currently) - if empty_queues: - logger.info( - "📭 Empty queues: %s (total %d, can accept new messages)", - ", ".join([f"Queue[{qid}]" for qid in empty_queues]), - len(empty_queues), - ) - - # Print queue load analysis - if active_queues: - # Find busiest and most backlogged queues - busiest_queue = max( - active_queues, key=lambda q: q["delivered"] + q["consumed"] - ) - most_backlogged = max(active_queues, key=lambda q: q["current"]) - - logger.info( - "📈 Queue load analysis: busiest=Queue[%d](processed %d messages), most backlogged=Queue[%d](backlog %d messages)", - busiest_queue["id"], - busiest_queue["delivered"] + busiest_queue["consumed"], - most_backlogged["id"], - most_backlogged["current"], - ) - - # If any queues are backlogged, issue warning - high_backlog_queues = [ - q for q in active_queues if q["current"] > self.max_total_messages * 0.3 - ] - if high_backlog_queues: - logger.warning( - "⚠️ Queue backlog warning: %s", - ", ".join( - [f"Queue[{q['id']}](%d messages)" for q in high_backlog_queues] - ), - ) - - except (OSError, ValueError, RuntimeError) as e: - logger.error( - "📊 MsgGroupQueueManager[%s] Failed to print queue details: %s", - self.name, - e, - ) - - async def shutdown( - self, - mode: ShutdownMode = ShutdownMode.HARD, - max_delay_seconds: Optional[float] = None, - ) -> bool: - """ - Shutdown manager, supports hard and soft shutdown modes - - Args: - mode: Shutdown mode (HARD: hard shutdown, SOFT: soft shutdown) - max_delay_seconds: Maximum delay time in seconds for soft shutdown, only set on first soft shutdown - - Returns: - bool: True means successfully shut down, False means still messages to process (only for soft shutdown) - """ - current_time = time.time() - - if mode == ShutdownMode.SOFT: - # Soft shutdown logic - if not self._shutdown_state.is_shutting_down: - # First soft shutdown - self._shutdown_state.is_shutting_down = True - self._shutdown_state.first_soft_shutdown_time = current_time - self._shutdown_state.max_delay_seconds = max_delay_seconds - - logger.info( - "🔄 MsgGroupQueueManager[%s] Soft shutdown started, maximum delay time: %s seconds", - self.name, - max_delay_seconds, - ) - - # Check if there are messages - total_remaining = self._get_total_current_messages() - - if total_remaining == 0: - # No messages, can safely shut down - await self._perform_hard_shutdown() - self._shutdown_state.reset() - return True - - # Check if delay time has been exceeded - if ( - self._shutdown_state.max_delay_seconds is not None - and current_time - self._shutdown_state.first_soft_shutdown_time - >= self._shutdown_state.max_delay_seconds - ): - # Exceeded delay time, force shutdown - logger.warning( - "⏰ MsgGroupQueueManager[%s] Soft shutdown timeout, forcing shutdown. Remaining messages: %d", - self.name, - total_remaining, - ) - await self._perform_hard_shutdown() - self._shutdown_state.reset() - return True - - # Still messages and not timed out, return False - elapsed_time = current_time - self._shutdown_state.first_soft_shutdown_time - logger.info( - "📋 MsgGroupQueueManager[%s] Soft shutdown check: remaining messages=%d, elapsed time=%.1f seconds", - self.name, - total_remaining, - elapsed_time, - ) - return False - - else: - # Hard shutdown - await self._perform_hard_shutdown() - self._shutdown_state.reset() - return True - - async def _perform_hard_shutdown(self): - """Perform hard shutdown""" - await self.stop_periodic_logging() - - # Count remaining messages - total_remaining = 0 - queue_details = [] - - for i, queue in enumerate(self._queues): - queue_size = queue.qsize() - total_remaining += queue_size - - if queue_size > 0: - queue_details.append(f"Queue[{i}]: {queue_size} messages") - - # Clear queue - while not queue.empty(): - try: - queue.get_nowait() - except asyncio.QueueEmpty: - break - - if total_remaining > 0: - logger.warning( - "⚠️ MsgGroupQueueManager[%s] Hard shutdown, discarded %d unprocessed messages. Details: %s", - self.name, - total_remaining, - ", ".join(queue_details), - ) - else: - logger.info( - "🔌 MsgGroupQueueManager[%s] Shut down safely, no unprocessed messages", - self.name, - ) - - def __repr__(self) -> str: - return ( - f"MsgGroupQueueManager(name={self.name}, " - f"num_queues={self.num_queues}, " - f"max_total_messages={self.max_total_messages})" - ) diff --git a/methods/EverCore/src/core/queue/msg_group_queue/msg_group_queue_manager_factory.py b/methods/EverCore/src/core/queue/msg_group_queue/msg_group_queue_manager_factory.py deleted file mode 100644 index 13eee79a..00000000 --- a/methods/EverCore/src/core/queue/msg_group_queue/msg_group_queue_manager_factory.py +++ /dev/null @@ -1,304 +0,0 @@ -""" -Message group queue manager factory - -Provides caching and management of MsgGroupQueueManager instances based on configuration. -Supports reading configuration from environment variables, provides default and named instances. -Refer to the design pattern in mongodb_client_factory.py. -""" - -import os -import asyncio -from typing import Dict, Optional -from core.di.decorators import component -from core.observation.logger import get_logger -from .msg_group_queue_manager import MsgGroupQueueManager - -logger = get_logger(__name__) - - -class MsgGroupQueueConfig: - """Message group queue configuration class""" - - def __init__( - self, - name: str = "default", - num_queues: int = 10, - max_total_messages: int = 100, - enable_metrics: bool = True, - log_interval_seconds: int = 30, - **kwargs, - ): - self.name = name - self.num_queues = num_queues - self.max_total_messages = max_total_messages - self.enable_metrics = enable_metrics - self.log_interval_seconds = log_interval_seconds - self.kwargs = kwargs - - def get_cache_key(self) -> str: - """ - Get cache key - - Generate unique identifier based on core configuration parameters - """ - return f"{self.name}:{self.num_queues}:{self.max_total_messages}:{self.enable_metrics}:{self.log_interval_seconds}" - - @classmethod - def from_env(cls, prefix: str = "") -> 'MsgGroupQueueConfig': - """ - Create configuration from environment variables - - Prefix rule: if prefix is provided, read variables in the format "{prefix}_XXX", otherwise read "XXX". - For example: prefix="CLIENT" reads "CLIENT_MSG_QUEUE_NUM_QUEUES", "CLIENT_MSG_QUEUE_MAX_TOTAL_MESSAGES", etc. - - Args: - prefix: environment variable prefix - - Returns: - MsgGroupQueueConfig: configuration instance - """ - - def _env(name: str, default: str) -> str: - key = f"{prefix}_{name}" if prefix else name - return os.getenv(key, default) - - # Read configuration items - name = _env("MSG_QUEUE_NAME", "default") - num_queues = int(_env("MSG_QUEUE_NUM_QUEUES", "10")) - max_total_messages = int(_env("MSG_QUEUE_MAX_TOTAL_MESSAGES", "100")) - enable_metrics = _env("MSG_QUEUE_ENABLE_METRICS", "true").lower() == "true" - log_interval_seconds = int(_env("MSG_QUEUE_LOG_INTERVAL_SECONDS", "30")) - - return cls( - name=name, - num_queues=num_queues, - max_total_messages=max_total_messages, - enable_metrics=enable_metrics, - log_interval_seconds=log_interval_seconds, - ) - - def __repr__(self) -> str: - return ( - f"MsgGroupQueueConfig(name={self.name}, " - f"num_queues={self.num_queues}, " - f"max_total_messages={self.max_total_messages})" - ) - - -@component(name="msg_group_queue_manager_factory", primary=True) -class MsgGroupQueueManagerFactory: - """Message group queue manager factory""" - - def __init__(self): - """Initialize factory""" - self._managers: Dict[str, MsgGroupQueueManager] = {} - self._default_config: Optional[MsgGroupQueueConfig] = None - self._default_manager: Optional[MsgGroupQueueManager] = None - self._lock = asyncio.Lock() - - async def get_manager( - self, config: Optional[MsgGroupQueueConfig] = None, auto_start: bool = True - ) -> MsgGroupQueueManager: - """ - Get message group queue manager - - Args: - config: queue manager configuration, use default if None - auto_start: whether to automatically start the manager - - Returns: - MsgGroupQueueManager: queue manager - """ - if config is None: - config = await self._get_default_config() - - cache_key = config.get_cache_key() - - async with self._lock: - # Check cache - if cache_key in self._managers: - manager = self._managers[cache_key] - return manager - - # Create new manager - logger.info("Creating new MsgGroupQueueManager: %s", config) - - try: - manager = MsgGroupQueueManager( - name=config.name, - num_queues=config.num_queues, - max_total_messages=config.max_total_messages, - enable_metrics=config.enable_metrics, - log_interval_seconds=config.log_interval_seconds, - **config.kwargs, - ) - - if auto_start: - await manager.start_periodic_logging() - - # Cache manager - self._managers[cache_key] = manager - logger.info( - "✅ MsgGroupQueueManager created successfully and cached: %s", - config, - ) - - return manager - - except Exception as e: - logger.error( - "❌ Failed to create MsgGroupQueueManager: %s, error: %s", config, e - ) - raise - - async def get_default_manager( - self, auto_start: bool = True - ) -> MsgGroupQueueManager: - """ - Get default message group queue manager - - Args: - auto_start: whether to automatically start the manager - - Returns: - MsgGroupQueueManager: default queue manager - """ - if self._default_manager is None: - config = await self._get_default_config() - self._default_manager = await self.get_manager(config, auto_start) - - return self._default_manager - - async def get_named_manager( - self, name: str, auto_start: bool = True - ) -> MsgGroupQueueManager: - """ - Get message group queue manager by name - - Convention: use name as environment variable prefix, read configuration from "{name}_MSG_QUEUE_XXX". - For example, when name="CLIENT", it will try to read "CLIENT_MSG_QUEUE_NUM_QUEUES", "CLIENT_MSG_QUEUE_MAX_TOTAL_MESSAGES", etc. - - Args: - name: prefix name (i.e., environment variable prefix) - auto_start: whether to automatically start the manager - - Returns: - MsgGroupQueueManager: queue manager - """ - if name.lower() == "default": - return await self.get_default_manager(auto_start) - - config = MsgGroupQueueConfig.from_env(prefix=name) - # Ensure configuration name matches requested name - config.name = name.lower() - - logger.info( - "📋 Loading named MsgGroupQueueManager configuration[name=%s]: %s", - name, - config, - ) - return await self.get_manager(config, auto_start) - - async def _get_default_config(self) -> MsgGroupQueueConfig: - """Get default configuration""" - if self._default_config is None: - self._default_config = MsgGroupQueueConfig.from_env() - logger.info( - "📋 Loaded default MsgGroupQueueManager configuration: %s", - self._default_config, - ) - - return self._default_config - - async def create_manager_with_config( - self, - name: str = "default", - num_queues: int = 10, - max_total_messages: int = 100, - enable_metrics: bool = True, - log_interval_seconds: int = 30, - auto_start: bool = True, - **kwargs, - ) -> MsgGroupQueueManager: - """ - Create manager with specified configuration - - Args: - name: manager name - num_queues: number of queues - max_total_messages: maximum total number of messages - enable_metrics: whether to enable metrics - log_interval_seconds: logging interval - auto_start: whether to automatically start - **kwargs: additional parameters - - Returns: - MsgGroupQueueManager: queue manager - """ - config = MsgGroupQueueConfig( - name=name, - num_queues=num_queues, - max_total_messages=max_total_messages, - enable_metrics=enable_metrics, - log_interval_seconds=log_interval_seconds, - **kwargs, - ) - - return await self.get_manager(config, auto_start) - - async def stop_manager(self, config: Optional[MsgGroupQueueConfig] = None): - """ - Stop specified manager - - Args: - config: configuration, if None then stop default manager - """ - if config is None: - if self._default_manager: - await self._default_manager.shutdown() - return - - cache_key = config.get_cache_key() - - async with self._lock: - if cache_key in self._managers: - await self._managers[cache_key].shutdown() - - async def stop_all_managers(self): - """Stop all managers""" - async with self._lock: - for manager in self._managers.values(): - await manager.shutdown() - - self._managers.clear() - - if self._default_manager: - self._default_manager = None - - logger.info("🔌 All MsgGroupQueueManager instances have been stopped") - - def get_cached_managers_info(self) -> Dict[str, Dict]: - """Get cached manager information""" - return { - cache_key: { - "name": manager.name, - "num_queues": manager.num_queues, - "max_total_messages": manager.max_total_messages, - "manager_stats": "Need to call get_manager_stats() asynchronously to retrieve", - } - for cache_key, manager in self._managers.items() - } - - async def get_default_msg_group_queue_manager( - self, auto_start: bool = True - ) -> MsgGroupQueueManager: - """ - Convenience function to get default message group queue manager - - Args: - auto_start: whether to automatically start the manager - - Returns: - MsgGroupQueueManager: default queue manager - """ - return await self.get_default_manager(auto_start) diff --git a/methods/EverCore/src/core/queue/redis_group_queue/__init__.py b/methods/EverCore/src/core/queue/redis_group_queue/__init__.py deleted file mode 100755 index 4b728b9d..00000000 --- a/methods/EverCore/src/core/queue/redis_group_queue/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -""" -Redis group queue module - -Provides Redis-based group queue management functions, supporting features such as sorting, timeout, and total limit. -""" - -from .redis_group_queue_item import RedisGroupQueueItem -from .redis_msg_group_queue_manager import RedisGroupQueueManager -from .redis_msg_group_queue_manager_factory import RedisGroupQueueManagerFactory - -__all__ = [ - "RedisGroupQueueItem", - "RedisGroupQueueManager", - "RedisGroupQueueManagerFactory", -] diff --git a/methods/EverCore/src/core/queue/redis_group_queue/kafka_consumer_record_item.py b/methods/EverCore/src/core/queue/redis_group_queue/kafka_consumer_record_item.py deleted file mode 100644 index 2cb604e1..00000000 --- a/methods/EverCore/src/core/queue/redis_group_queue/kafka_consumer_record_item.py +++ /dev/null @@ -1,283 +0,0 @@ -""" -Kafka ConsumerRecord queue item implementation - -Provides serialization/deserialization between ConsumerRecord and RedisGroupQueueItem -Uses BSON format to handle binary data, ensuring data integrity -""" - -import json -import base64 -from typing import Optional, Sequence, Tuple, Any, Dict -from dataclasses import dataclass - -import bson - -from aiokafka import ConsumerRecord -from core.observation.logger import get_logger -from .redis_group_queue_item import RedisGroupQueueItem - -logger = get_logger(__name__) - - -@dataclass -class KafkaConsumerRecordItem(RedisGroupQueueItem): - """ - Kafka ConsumerRecord queue item - - Implements the RedisGroupQueueItem interface, providing serialization/deserialization functionality for ConsumerRecord - """ - - # ConsumerRecord fields - topic: str - partition: int - offset: int - timestamp: int - timestamp_type: int - key: Optional[str] - value: Optional[Any] - checksum: Optional[int] - serialized_key_size: int - serialized_value_size: int - headers: Sequence[Tuple[str, bytes]] - - def __init__(self, consumer_record: ConsumerRecord): - """ - Initialize from ConsumerRecord - - Args: - consumer_record: aiokafka ConsumerRecord object - """ - self.topic = consumer_record.topic - self.partition = consumer_record.partition - self.offset = consumer_record.offset - self.timestamp = consumer_record.timestamp - self.timestamp_type = consumer_record.timestamp_type - # Handle key: if it's bytes, convert to base64 string - self.key = ( - self._encode_bytes_to_base64(consumer_record.key) - if isinstance(consumer_record.key, bytes) - else consumer_record.key - ) - # Handle value: keep original format, BSON can directly handle various types - self.value = consumer_record.value - self.checksum = consumer_record.checksum - self.serialized_key_size = consumer_record.serialized_key_size - self.serialized_value_size = consumer_record.serialized_value_size - # Convert headers to serializable format, preserving binary data - self.headers = [ - ( - name, - ( - self._encode_bytes_to_base64(data) - if isinstance(data, bytes) - else str(data) - ), - ) - for name, data in consumer_record.headers - ] - - def _encode_bytes_to_base64(self, data: bytes) -> str: - """Encode bytes data to base64 string""" - return base64.b64encode(data).decode('utf-8') - - def _decode_base64_to_bytes(self, data: str) -> bytes: - """Decode base64 string to bytes data""" - return base64.b64decode(data.encode('utf-8')) - - def to_dict(self) -> Dict[str, Any]: - """ - Convert object to JSON-serializable dictionary - - Returns: - Dict[str, Any]: Serializable dictionary - """ - return { - "topic": self.topic, - "partition": self.partition, - "offset": self.offset, - "timestamp": self.timestamp, - "timestamp_type": self.timestamp_type, - "key": self.key, - "value": self.value, - "checksum": self.checksum, - "serialized_key_size": self.serialized_key_size, - "serialized_value_size": self.serialized_value_size, - "headers": self.headers, - } - - def to_json_str(self) -> str: - """ - JSON serialization is not supported, please use BSON serialization - """ - raise NotImplementedError( - "KafkaConsumerRecordItem does not support JSON serialization, please use to_bson_bytes() method" - ) - - def to_bson_bytes(self) -> bytes: - """ - Serialize to BSON byte data - - Returns: - bytes: BSON byte data - """ - try: - data = self.to_dict() # Get serializable dictionary - return bson.encode(data) - except Exception as e: - logger.error("BSON serialization of KafkaConsumerRecordItem failed: %s", e) - raise ValueError(f"BSON serialization failed: {e}") from e - - @classmethod - def from_json_str(cls, json_str: str) -> 'KafkaConsumerRecordItem': - """ - JSON deserialization is not supported, please use BSON deserialization - """ - raise NotImplementedError( - "KafkaConsumerRecordItem does not support JSON deserialization, please use from_bson_bytes() method" - ) - - @classmethod - def from_bson_bytes(cls, bson_bytes: bytes) -> 'KafkaConsumerRecordItem': - """ - Deserialize from BSON byte data - - Args: - bson_bytes: BSON byte data - - Returns: - KafkaConsumerRecordItem: Deserialized object - """ - try: - data = bson.decode(bson_bytes) - - # Create instance - item = cls.__new__(cls) # Bypass __init__ - item.topic = data["topic"] - item.partition = data["partition"] - item.offset = data["offset"] - item.timestamp = data["timestamp"] - item.timestamp_type = data["timestamp_type"] - item.key = data["key"] - item.value = data["value"] - item.checksum = data["checksum"] - item.serialized_key_size = data["serialized_key_size"] - item.serialized_value_size = data["serialized_value_size"] - item.headers = data["headers"] - - return item - except Exception as e: - logger.error( - "BSON deserialization of KafkaConsumerRecordItem failed: %s", e - ) - raise ValueError(f"BSON deserialization failed: {e}") from e - - def to_consumer_record(self) -> ConsumerRecord: - """ - Convert to aiokafka ConsumerRecord object - - Returns: - ConsumerRecord: aiokafka ConsumerRecord object - """ - try: - # Handle key: if it's a string, try to decode from base64, otherwise keep as is - key = self.key - if isinstance(key, str): - try: - key = self._decode_base64_to_bytes(key) - except Exception: - # If decoding fails, keep original string - pass - - # Handle headers: decode string data from base64 back to bytes - headers_bytes = [] - for name, data in self.headers: - if isinstance(data, str): - try: - # Try to decode from base64 - headers_bytes.append((name, self._decode_base64_to_bytes(data))) - except Exception: - # If decoding fails, encode as UTF-8 bytes - headers_bytes.append((name, data.encode('utf-8'))) - else: - headers_bytes.append((name, bytes(data))) - - return ConsumerRecord( - topic=self.topic, - partition=self.partition, - offset=self.offset, - timestamp=self.timestamp, - timestamp_type=self.timestamp_type, - key=key, - value=self.value, - checksum=self.checksum, - serialized_key_size=self.serialized_key_size, - serialized_value_size=self.serialized_value_size, - headers=headers_bytes, - ) - except Exception as e: - logger.error("Failed to convert to ConsumerRecord: %s", e) - raise ValueError(f"Conversion failed: {e}") from e - - def __repr__(self) -> str: - return ( - f"KafkaConsumerRecordItem(topic={self.topic}, partition={self.partition}, " - f"offset={self.offset}, timestamp={self.timestamp})" - ) - - -def consumer_record_to_queue_item( - consumer_record: ConsumerRecord, -) -> KafkaConsumerRecordItem: - """ - Convert ConsumerRecord to queue item - - Args: - consumer_record: aiokafka ConsumerRecord object - - Returns: - KafkaConsumerRecordItem: Queue item - """ - return KafkaConsumerRecordItem(consumer_record) - - -def queue_item_to_consumer_record( - queue_item: KafkaConsumerRecordItem, -) -> ConsumerRecord: - """ - Convert queue item to ConsumerRecord - - Args: - queue_item: Queue item - - Returns: - ConsumerRecord: aiokafka ConsumerRecord object - """ - return queue_item.to_consumer_record() - - -def serialize_consumer_record_to_bson(consumer_record: ConsumerRecord) -> bytes: - """ - Serialize ConsumerRecord to BSON byte data - - Args: - consumer_record: aiokafka ConsumerRecord object - - Returns: - bytes: BSON serialized byte data - """ - queue_item = consumer_record_to_queue_item(consumer_record) - return queue_item.to_bson_bytes() - - -def deserialize_bson_to_consumer_record(bson_bytes: bytes) -> ConsumerRecord: - """ - Deserialize BSON byte data to ConsumerRecord - - Args: - bson_bytes: BSON byte data - - Returns: - ConsumerRecord: aiokafka ConsumerRecord object - """ - queue_item = KafkaConsumerRecordItem.from_bson_bytes(bson_bytes) - return queue_item_to_consumer_record(queue_item) diff --git a/methods/EverCore/src/core/queue/redis_group_queue/redis_group_queue_item.py b/methods/EverCore/src/core/queue/redis_group_queue/redis_group_queue_item.py deleted file mode 100644 index 3856738c..00000000 --- a/methods/EverCore/src/core/queue/redis_group_queue/redis_group_queue_item.py +++ /dev/null @@ -1,126 +0,0 @@ -""" -Redis group queue item interface - -Defines a standard interface for items stored in the queue, supporting JSON and BSON serialization and deserialization. -""" - -import json -from abc import ABC, abstractmethod -from typing import Any, Dict -from enum import Enum -import bson - - -class SerializationMode(Enum): - """Serialization mode enumeration""" - - JSON = "json" # JSON string serialization - BSON = "bson" # BSON bytes serialization - - -class RedisGroupQueueItem(ABC): - """Redis group queue item interface""" - - @abstractmethod - def to_dict(self) -> Dict[str, Any]: - """ - Convert object to dictionary - - Returns: - Dict[str, Any]: Dictionary representation of the object - """ - raise NotImplementedError - - @classmethod - @abstractmethod - def from_json_str(cls, json_str: str) -> 'RedisGroupQueueItem': - """ - Create an object instance from a JSON string - - Args: - json_str: JSON string - - Returns: - RedisGroupQueueItem: Object instance - - Raises: - ValueError: Invalid JSON format or data - """ - raise NotImplementedError - - @classmethod - @abstractmethod - def from_bson_bytes(cls, bson_bytes: bytes) -> 'RedisGroupQueueItem': - """ - Deserialize object from BSON bytes - - Args: - bson_bytes: BSON byte data - - Returns: - RedisGroupQueueItem: Object instance - - Raises: - ValueError: Invalid BSON format or data - """ - raise NotImplementedError - - def to_json_str(self) -> str: - """ - Convert object to JSON string - - Returns: - str: JSON string - """ - return json.dumps(self.to_dict(), ensure_ascii=False) - - def to_bson_bytes(self) -> bytes: - """ - Serialize object to BSON byte data - - Returns: - bytes: BSON byte data - """ - return bson.encode(self.to_dict()) - - -class SimpleQueueItem(RedisGroupQueueItem): - """Simple queue item implementation example""" - - def __init__(self, data: Any, item_type: str = "simple"): - """ - Initialize simple queue item - - Args: - data: Data content - item_type: Item type identifier - """ - self.data = data - self.item_type = item_type - - def to_dict(self) -> Dict[str, Any]: - """Convert to dictionary""" - return {"data": self.data, "item_type": self.item_type} - - @classmethod - def from_json_str(cls, json_str: str) -> 'SimpleQueueItem': - """Create instance from JSON string""" - try: - json_dict = json.loads(json_str) - return cls( - data=json_dict["data"], item_type=json_dict.get("item_type", "simple") - ) - except (json.JSONDecodeError, KeyError) as e: - raise ValueError(f"Invalid JSON data: {e}") from e - - @classmethod - def from_bson_bytes(cls, bson_bytes: bytes) -> 'SimpleQueueItem': - """Create instance from BSON bytes""" - try: - data = bson.decode(bson_bytes) - return cls(data=data["data"], item_type=data.get("item_type", "simple")) - except (Exception, KeyError) as e: - raise ValueError(f"Invalid BSON data: {e}") from e - - def __repr__(self) -> str: - return f"SimpleQueueItem(data={self.data}, item_type={self.item_type})" diff --git a/methods/EverCore/src/core/queue/redis_group_queue/redis_group_queue_lua_scripts.py b/methods/EverCore/src/core/queue/redis_group_queue/redis_group_queue_lua_scripts.py deleted file mode 100644 index 7dfcbf73..00000000 --- a/methods/EverCore/src/core/queue/redis_group_queue/redis_group_queue_lua_scripts.py +++ /dev/null @@ -1,535 +0,0 @@ -""" -Redis Grouped Queue Lua Scripts - -Provides atomic operation Lua scripts to ensure queue state consistency. -""" - -# Common rebalance function definition -REBALANCE_FUNCTION = """ --- rebalance partition function -local function rebalance_partitions(owner_zset_key, queue_list_prefix, total_partitions, owner_expire) - -- Get all active owners - local active_owners = redis.call('ZRANGE', owner_zset_key, 0, -1) - local owner_count = #active_owners - - if owner_count == 0 then - return {0, {}} - end - - -- Clean up queue_list for all owners - for _, owner_id in ipairs(active_owners) do - local queue_list_key = queue_list_prefix .. owner_id - redis.call('DEL', queue_list_key) - end - - -- Evenly distribute partitions - local partitions_per_owner = math.floor(total_partitions / owner_count) - local extra_partitions = total_partitions % owner_count - - -- Return assignment results in flat array format for proper conversion by Redis clients - local assigned_partitions_flat = {} - local partition_index = 1 - - for i, owner_id in ipairs(active_owners) do - local queue_list_key = queue_list_prefix .. owner_id - local partitions_for_this_owner = partitions_per_owner - - -- First 'extra_partitions' owners get one additional partition - if i <= extra_partitions then - partitions_for_this_owner = partitions_for_this_owner + 1 - end - - local owner_partitions = {} - for j = 1, partitions_for_this_owner do - local partition_name = string.format("%03d", partition_index) - redis.call('LPUSH', queue_list_key, partition_name) - table.insert(owner_partitions, partition_name) - partition_index = partition_index + 1 - end - - -- Set expiration time - redis.call('EXPIRE', queue_list_key, owner_expire) - - -- Add owner_id and partition list to flat array - table.insert(assigned_partitions_flat, owner_id) - table.insert(assigned_partitions_flat, owner_partitions) - end - - return {owner_count, assigned_partitions_flat} -end -""" - -# Lua script for adding message to queue -ENQUEUE_SCRIPT = """ --- Parameters: --- KEYS[1]: queue key (zset) --- KEYS[2]: total counter key --- ARGV[1]: message content (supports JSON string or BSON binary data) --- ARGV[2]: sort score --- ARGV[3]: queue expiration time (seconds) --- ARGV[4]: activity expiration time (seconds, 7 days) --- ARGV[5]: maximum total limit - -local queue_key = KEYS[1] -local counter_key = KEYS[2] -local message = ARGV[1] -local score = tonumber(ARGV[2]) -local queue_expire = tonumber(ARGV[3]) -local activity_expire = tonumber(ARGV[4]) -local max_total = tonumber(ARGV[5]) - --- Check total limit -local current_count = tonumber(redis.call('GET', counter_key) or '0') -if current_count >= max_total then - return {0, current_count, "Exceeded maximum total limit"} -end - --- Add message to queue -local added = redis.call('ZADD', queue_key, score, message) -if added == 1 then - -- Update queue expiration time - redis.call('EXPIRE', queue_key, queue_expire) - - -- Increment total counter - local new_count = redis.call('INCR', counter_key) - redis.call('EXPIRE', counter_key, activity_expire) - - return {1, new_count, "Added successfully"} -else - return {0, current_count, "Message already exists"} -end -""" - -# Lua script for rebalance repartitioning -REBALANCE_PARTITIONS_SCRIPT = """ --- Parameters: --- KEYS[1]: owner_activate_time_zset key --- KEYS[2]: queue_list_prefix (used to construct queue_list key for each owner) --- ARGV[1]: total number of partitions --- ARGV[2]: owner expiration time (seconds, default 1 hour) - -__REBALANCE_FUNCTION__ - -local owner_zset_key = KEYS[1] -local queue_list_prefix = KEYS[2] -local total_partitions = tonumber(ARGV[1]) -local owner_expire = tonumber(ARGV[2]) - --- Call rebalance function -return rebalance_partitions(owner_zset_key, queue_list_prefix, total_partitions, owner_expire) -""" - -# Lua script for joining consumer -JOIN_CONSUMER_SCRIPT = """ --- Parameters: --- KEYS[1]: owner_activate_time_zset key --- KEYS[2]: queue_list_prefix (used to construct queue_list key for each owner) --- ARGV[1]: owner_id --- ARGV[2]: current timestamp --- ARGV[3]: owner expiration time (seconds, default 1 hour) --- ARGV[4]: total number of partitions - -__REBALANCE_FUNCTION__ - -local owner_zset_key = KEYS[1] -local queue_list_prefix = KEYS[2] -local owner_id = ARGV[1] -local current_time = tonumber(ARGV[2]) -local owner_expire = tonumber(ARGV[3]) -local total_partitions = tonumber(ARGV[4]) - --- Join owner_activate_time_zset -redis.call('ZADD', owner_zset_key, current_time, owner_id) -redis.call('EXPIRE', owner_zset_key, owner_expire) - --- Call rebalance function -return rebalance_partitions(owner_zset_key, queue_list_prefix, total_partitions, owner_expire) -""" - -# Lua script for consumer exit -EXIT_CONSUMER_SCRIPT = """ --- Parameters: --- KEYS[1]: owner_activate_time_zset key --- KEYS[2]: queue_list_prefix (used to construct queue_list key for each owner) --- ARGV[1]: owner_id --- ARGV[2]: owner expiration time (seconds, default 1 hour) --- ARGV[3]: total number of partitions - -__REBALANCE_FUNCTION__ - -local owner_zset_key = KEYS[1] -local queue_list_prefix = KEYS[2] -local owner_id = ARGV[1] -local owner_expire = tonumber(ARGV[2]) -local total_partitions = tonumber(ARGV[3]) - --- Remove from owner_activate_time_zset -redis.call('ZREM', owner_zset_key, owner_id) - --- Delete corresponding queue_list -local queue_list_key = queue_list_prefix .. owner_id -redis.call('DEL', queue_list_key) - --- Check if there are remaining owners, if so call rebalance function -local remaining_owners = redis.call('ZRANGE', owner_zset_key, 0, -1) -if #remaining_owners == 0 then - return {0, {}} -end - --- Call rebalance function -return rebalance_partitions(owner_zset_key, queue_list_prefix, total_partitions, owner_expire) -""" - -# Lua script for consumer keepalive -KEEPALIVE_CONSUMER_SCRIPT = """ --- Parameters: --- KEYS[1]: owner_activate_time_zset key --- KEYS[2]: queue_list_prefix (used to construct queue_list key for each owner) --- ARGV[1]: owner_id --- ARGV[2]: current timestamp --- ARGV[3]: owner expiration time (seconds, default 1 hour) - -local owner_zset_key = KEYS[1] -local queue_list_prefix = KEYS[2] -local owner_id = ARGV[1] -local current_time = tonumber(ARGV[2]) -local owner_expire = tonumber(ARGV[3]) - --- Update time in owner_activate_time_zset -local updated = redis.call('ZADD', owner_zset_key, current_time, owner_id) -redis.call('EXPIRE', owner_zset_key, owner_expire) - --- Renew expiration for corresponding queue_list -local queue_list_key = queue_list_prefix .. owner_id -local exists = redis.call('EXISTS', queue_list_key) -if exists == 1 then - redis.call('EXPIRE', queue_list_key, owner_expire) - return 1 -else - return 0 -end -""" - -# Lua script for periodic cleanup of inactive owners -CLEANUP_INACTIVE_OWNERS_SCRIPT = """ --- Parameters: --- KEYS[1]: owner_activate_time_zset key --- KEYS[2]: queue_list_prefix (used to construct queue_list key for each owner) --- KEYS[3]: queue_prefix (used to construct partition queue key) --- KEYS[4]: counter_key (message total counter key) --- ARGV[1]: inactive threshold timestamp (5 minutes ago) --- ARGV[2]: current timestamp --- ARGV[3]: owner expiration time (seconds, default 1 hour) --- ARGV[4]: total number of partitions - -__REBALANCE_FUNCTION__ - -local owner_zset_key = KEYS[1] -local queue_list_prefix = KEYS[2] -local queue_prefix = KEYS[3] -local counter_key = KEYS[4] -local inactive_threshold = tonumber(ARGV[1]) -local current_time = tonumber(ARGV[2]) -local owner_expire = tonumber(ARGV[3]) -local total_partitions = tonumber(ARGV[4]) - --- Get all inactive owners -local inactive_owners = redis.call('ZRANGEBYSCORE', owner_zset_key, 0, inactive_threshold) -local cleaned_count = 0 - --- Clean up inactive owners -for _, owner_id in ipairs(inactive_owners) do - -- Remove from zset - redis.call('ZREM', owner_zset_key, owner_id) - - -- Delete corresponding queue_list - local queue_list_key = queue_list_prefix .. owner_id - redis.call('DEL', queue_list_key) - - cleaned_count = cleaned_count + 1 -end - --- Recalculate counter_key regardless to ensure data consistency -local total_messages = 0 -for i = 1, total_partitions do - local partition_name = string.format("%03d", i) - local queue_key = queue_prefix .. partition_name - local queue_size = redis.call('ZCARD', queue_key) - total_messages = total_messages + queue_size -end -redis.call('SET', counter_key, total_messages) - --- Rebalance if any cleanup occurred -local need_rebalance = cleaned_count > 0 -if not need_rebalance then - return {cleaned_count, 0, {}} -end - --- Check if there are remaining owners -local remaining_owners = redis.call('ZRANGE', owner_zset_key, 0, -1) -if #remaining_owners == 0 then - return {cleaned_count, 0, {}} -end - --- Call rebalance function -local owner_count, assigned_partitions = unpack(rebalance_partitions(owner_zset_key, queue_list_prefix, total_partitions, owner_expire)) -return {cleaned_count, owner_count, assigned_partitions} -""" - -# Lua script for forced cleanup and reset (supports optional purge) -FORCE_CLEANUP_SCRIPT = """ --- Parameters: --- KEYS[1]: owner_activate_time_zset key --- KEYS[2]: queue_list_prefix (used to construct queue_list key for each owner) --- KEYS[3]: queue_prefix (used to construct partition queue key) --- KEYS[4]: counter_key (message total counter key) --- ARGV[1]: total number of partitions --- ARGV[2]: purge_all flag ("1" to empty all partition queues and set counter to 0; otherwise only recalculate counter) - -local owner_zset_key = KEYS[1] -local queue_list_prefix = KEYS[2] -local queue_prefix = KEYS[3] -local counter_key = KEYS[4] -local total_partitions = tonumber(ARGV[1]) -local purge_all = ARGV[2] - --- Get all owners -local all_owners = redis.call('ZRANGE', owner_zset_key, 0, -1) -local cleaned_count = 0 - --- Delete queue_list for all owners -for _, owner_id in ipairs(all_owners) do - local queue_list_key = queue_list_prefix .. owner_id - redis.call('DEL', queue_list_key) - cleaned_count = cleaned_count + 1 -end - --- Delete owner_activate_time_zset -redis.call('DEL', owner_zset_key) - -if purge_all == '1' then - -- Empty all partition queues and set counter to 0 - for i = 1, total_partitions do - local partition_name = string.format("%03d", i) - local queue_key = queue_prefix .. partition_name - redis.call('DEL', queue_key) - end - redis.call('SET', counter_key, 0) - return total_partitions -else - -- Only recalculate counter - local total_messages = 0 - for i = 1, total_partitions do - local partition_name = string.format("%03d", i) - local queue_key = queue_prefix .. partition_name - local queue_size = redis.call('ZCARD', queue_key) - total_messages = total_messages + queue_size - end - redis.call('SET', counter_key, total_messages) - return cleaned_count -end -""" - -# Lua script for getting messages (traverse all partitions and attempt to get one from each) -GET_MESSAGES_SCRIPT = """ --- Parameters: --- KEYS[1]: owner_activate_time_zset key --- KEYS[2]: queue_list_prefix (used to construct queue_list key for each owner) --- KEYS[3]: queue_prefix (used to construct partition queue key) --- KEYS[4]: counter_key (message total counter key) --- ARGV[1]: owner_id --- ARGV[2]: owner expiration time (seconds, default 1 hour) --- ARGV[3]: score difference threshold (milliseconds) --- ARGV[4]: current score (used for threshold comparison when queue is empty) - -local owner_zset_key = KEYS[1] -local queue_list_prefix = KEYS[2] -local queue_prefix = KEYS[3] -local counter_key = KEYS[4] -local owner_id = ARGV[1] -local owner_expire = tonumber(ARGV[2]) -local score_threshold = tonumber(ARGV[3]) -local current_score = tonumber(ARGV[4]) - --- Check if owner exists in zset -local owner_score = redis.call('ZSCORE', owner_zset_key, owner_id) -if not owner_score then - -- Owner does not exist, need to join consumer - return {"JOIN_REQUIRED", {}} -end - --- Check if queue_list exists -local queue_list_key = queue_list_prefix .. owner_id -local queue_list_exists = redis.call('EXISTS', queue_list_key) -if queue_list_exists == 0 then - -- queue_list does not exist, need to join consumer - return {"JOIN_REQUIRED", {}} -end - --- Get owner's queue list -local owner_queues = redis.call('LRANGE', queue_list_key, 0, -1) -if #owner_queues == 0 then - return {"NO_QUEUES", {}} -end - -local messages = {} -local messages_consumed = 0 - --- Traverse all partitions, attempt to get 1 message from each -for _, partition in ipairs(owner_queues) do - local queue_key = queue_prefix .. partition - - -- Check if queue has messages - local queue_size = redis.call('ZCARD', queue_key) - if queue_size > 0 then - -- Get score of earliest message - local min_result = redis.call('ZRANGE', queue_key, 0, 0, 'WITHSCORES') - - -- Directly compare difference between earliest message and current score - if #min_result >= 2 then - local earliest_message_score = tonumber(min_result[2]) - -- Check difference between earliest message score and current score - if (current_score - earliest_message_score) >= score_threshold then - -- Get earliest message (directly remove) - local popped = redis.call('ZPOPMIN', queue_key) - if #popped >= 2 then - table.insert(messages, popped[1]) -- Return only message content - messages_consumed = messages_consumed + 1 - end - end - end - end -end - --- If messages were consumed, reduce counter_key count -if messages_consumed > 0 then - local new_count = redis.call('DECRBY', counter_key, messages_consumed) - -- Ensure count does not become negative - if new_count < 0 then - redis.call('SET', counter_key, 0) - end -end - --- Renew queue_list expiration -redis.call('EXPIRE', queue_list_key, owner_expire) - -return {"SUCCESS", messages} -""" - -# Lua script for getting queue statistics -GET_QUEUE_STATS_SCRIPT = """ --- Parameters: --- KEYS[1]: queue key (zset) --- KEYS[2]: total counter key - -local queue_key = KEYS[1] -local counter_key = KEYS[2] - --- Get queue size -local queue_size = redis.call('ZCARD', queue_key) - --- Get total count -local total_count = tonumber(redis.call('GET', counter_key) or '0') - --- Get score range of queue -local min_max = {} -if queue_size > 0 then - local min_result = redis.call('ZRANGE', queue_key, 0, 0, 'WITHSCORES') - local max_result = redis.call('ZRANGE', queue_key, -1, -1, 'WITHSCORES') - if #min_result >= 2 then - min_max.min_score = tonumber(min_result[2]) - end - if #max_result >= 2 then - min_max.max_score = tonumber(max_result[2]) - end -end - -return { - queue_size, - total_count, - min_max.min_score or 0, - min_max.max_score or 0 -} -""" - -# Lua script for batch getting statistics of all partitions -GET_ALL_PARTITIONS_STATS_SCRIPT = """ --- Parameters: --- KEYS[1]: queue_prefix (used to construct partition queue key) --- KEYS[2]: total counter key --- ARGV[1]: total number of partitions - -local queue_prefix = KEYS[1] -local counter_key = KEYS[2] -local total_partitions = tonumber(ARGV[1]) - --- Get total count -local total_count = tonumber(redis.call('GET', counter_key) or '0') - --- Store statistics for all partitions -local partition_stats = {} -local total_messages_in_queues = 0 -local global_min_score = nil -local global_max_score = nil - --- Traverse all partitions -for i = 1, total_partitions do - local partition_name = string.format("%03d", i) - local queue_key = queue_prefix .. partition_name - - -- Get queue size - local queue_size = redis.call('ZCARD', queue_key) - total_messages_in_queues = total_messages_in_queues + queue_size - - local min_score = 0 - local max_score = 0 - - if queue_size > 0 then - -- Get minimum and maximum score - local min_result = redis.call('ZRANGE', queue_key, 0, 0, 'WITHSCORES') - local max_result = redis.call('ZRANGE', queue_key, -1, -1, 'WITHSCORES') - - if #min_result >= 2 then - min_score = tonumber(min_result[2]) - if global_min_score == nil or min_score < global_min_score then - global_min_score = min_score - end - end - - if #max_result >= 2 then - max_score = tonumber(max_result[2]) - if global_max_score == nil or max_score > global_max_score then - global_max_score = max_score - end - end - end - - -- Store partition statistics (flat array format) - table.insert(partition_stats, partition_name) - table.insert(partition_stats, queue_size) - table.insert(partition_stats, min_score) - table.insert(partition_stats, max_score) -end - -return { - total_count, - total_messages_in_queues, - global_min_score or 0, - global_max_score or 0, - partition_stats -} -""" - -# Replace __REBALANCE_FUNCTION__ placeholder when module loads -REBALANCE_PARTITIONS_SCRIPT = REBALANCE_PARTITIONS_SCRIPT.replace( - '__REBALANCE_FUNCTION__', REBALANCE_FUNCTION -) -JOIN_CONSUMER_SCRIPT = JOIN_CONSUMER_SCRIPT.replace( - '__REBALANCE_FUNCTION__', REBALANCE_FUNCTION -) -EXIT_CONSUMER_SCRIPT = EXIT_CONSUMER_SCRIPT.replace( - '__REBALANCE_FUNCTION__', REBALANCE_FUNCTION -) -CLEANUP_INACTIVE_OWNERS_SCRIPT = CLEANUP_INACTIVE_OWNERS_SCRIPT.replace( - '__REBALANCE_FUNCTION__', REBALANCE_FUNCTION -) diff --git a/methods/EverCore/src/core/queue/redis_group_queue/redis_msg_group_queue_manager.py b/methods/EverCore/src/core/queue/redis_group_queue/redis_msg_group_queue_manager.py deleted file mode 100644 index cb86b1b8..00000000 --- a/methods/EverCore/src/core/queue/redis_group_queue/redis_msg_group_queue_manager.py +++ /dev/null @@ -1,1592 +0,0 @@ -""" -Redis Message Group Queue Manager - -Redis-based fixed partition queue manager. -Core features: -1. Fixed 50 partitions, numbered 001-050, not configurable -2. group_key routed to fixed partition via hash -3. Supports concurrent consumption of multiple queues, prevents conflicts using owner mechanism -4. Uses Redis sorted sets (ZSET) to store messages, supports sorting by score and time filtering - -⚠️ Warning: Partition count is fixed at 50. Modifying this configuration will cause severe data routing errors and message loss! -""" - -import asyncio -import time -import random -import hashlib -from typing import Any, Dict, List, Optional, Tuple, Callable, Type -from dataclasses import dataclass, field -from enum import Enum - -import redis.asyncio as redis - -from core.observation.logger import get_logger -from common_utils.datetime_utils import get_now_with_timezone, to_iso_format -from core.queue.redis_group_queue.redis_group_queue_item import SimpleQueueItem -from core.queue.redis_group_queue.redis_group_queue_item import ( - RedisGroupQueueItem, - SerializationMode, -) -from core.queue.redis_group_queue.redis_group_queue_lua_scripts import ( - ENQUEUE_SCRIPT, - GET_QUEUE_STATS_SCRIPT, - GET_ALL_PARTITIONS_STATS_SCRIPT, - REBALANCE_PARTITIONS_SCRIPT, - JOIN_CONSUMER_SCRIPT, - EXIT_CONSUMER_SCRIPT, - KEEPALIVE_CONSUMER_SCRIPT, - CLEANUP_INACTIVE_OWNERS_SCRIPT, - FORCE_CLEANUP_SCRIPT, - GET_MESSAGES_SCRIPT, -) -from core.rate_limit.rate_limiter import rate_limit - -logger = get_logger(__name__) - - -class ShutdownMode(Enum): - """Shutdown mode enumeration""" - - SOFT = "soft" # Soft shutdown: Check if messages exist, with delay time control - HARD = "hard" # Hard shutdown: Shut down directly, record unprocessed message count - - -class ManagerState(Enum): - """Manager state enumeration""" - - CREATED = "created" # Created, not started - STARTED = "started" # Started, running - SHUTDOWN = "shutdown" # Shut down, cannot be restarted - - -@dataclass -class RedisPartitionStats: - """Redis partition statistics""" - - partition: str - current_size: int - min_score: int - max_score: int - - def to_dict(self) -> Dict[str, Any]: - """Convert to dictionary format""" - return { - "partition": self.partition, - "current_size": self.current_size, - "min_score": self.min_score, - "max_score": self.max_score, - } - - -@dataclass -class RedisQueueStats: - """Redis queue statistics""" - - queue_name: str - current_size: int - last_activity_time: float - min_score: int - max_score: int - total_delivered: int = 0 - total_consumed: int = 0 - last_deliver_time: Optional[str] = None - last_consume_time: Optional[str] = None - partitions: Optional[List[RedisPartitionStats]] = None - - def to_dict(self) -> Dict[str, Any]: - """Convert to dictionary format""" - result = { - "queue_name": self.queue_name, - "current_size": self.current_size, - "last_activity_time": self.last_activity_time, - "min_score": self.min_score, - "max_score": self.max_score, - "total_delivered": self.total_delivered, - "total_consumed": self.total_consumed, - "last_deliver_time": self.last_deliver_time, - "last_consume_time": self.last_consume_time, - } - if self.partitions: - result["partitions"] = [p.to_dict() for p in self.partitions] - return result - - -@dataclass -class RedisManagerStats: - """Redis manager overall statistics""" - - total_queues: int - total_current_messages: int - total_delivered_messages: int = 0 - total_consumed_messages: int = 0 - total_rejected_messages: int = 0 - start_time: str = field( - default_factory=lambda: to_iso_format(get_now_with_timezone()) - ) - uptime_seconds: float = 0 - - def to_dict(self) -> Dict[str, Any]: - """Convert to dictionary format""" - return { - "total_queues": self.total_queues, - "total_current_messages": self.total_current_messages, - "total_delivered_messages": self.total_delivered_messages, - "total_consumed_messages": self.total_consumed_messages, - "total_rejected_messages": self.total_rejected_messages, - "start_time": self.start_time, - "uptime_seconds": self.uptime_seconds, - } - - -class RedisGroupQueueManager: - """ - Redis message group queue manager (dynamic owner management version) - - Core features: - 1. Manages consumer active status based on owner_activate_time_zset - 2. Each owner has an independent queue_list recording assigned partitions - 3. Supports dynamic rebalance, automatically assigns partitions to active consumers - 4. Consumer join/exit automatically triggers rebalance - 5. Periodically cleans up inactive consumers (default 5 minutes inactive) - 6. Consumer keepalive mechanism (recommended to call every 30 seconds) - 7. Supports forced cleanup and reset - 8. Checks score difference threshold when consuming messages - 9. All operations ensure atomicity through Lua scripts - """ - - # Fixed partition count, configurable but recommended to keep at 50 - FIXED_PARTITION_COUNT = 50 - - def __init__( - self, - redis_client: redis.Redis, - key_prefix: str = "default", - serialization_mode: SerializationMode = SerializationMode.JSON, - item_class: Type[RedisGroupQueueItem] = None, - sort_key_func: Optional[Callable[[RedisGroupQueueItem], int]] = None, - max_total_messages: int = 20000, # 2w - queue_expire_seconds: int = 24 * 3600, # 1 day - activity_expire_seconds: int = 24 * 3600, # 1 day - enable_metrics: bool = True, - log_interval_seconds: int = 600, # 10 minutes - owner_expire_seconds: int = 3600, # owner expiration time, default 1 hour - inactive_threshold_seconds: int = 300, # inactive threshold, default 5 minutes - cleanup_interval_seconds: int = 300, # periodic cleanup interval, default 5 minutes - ): - """ - Initialize Redis message group queue manager - - Args: - redis_client: Redis client - key_prefix: Redis key prefix, used to distinguish different manager instances - serialization_mode: Serialization mode (JSON or BSON) - item_class: Queue item type, must inherit from RedisGroupQueueItem, default uses SimpleQueueItem - sort_key_func: Sort key generation function, receives RedisGroupQueueItem returns int score - max_total_messages: Maximum total message count limit - queue_expire_seconds: Queue expiration time (seconds) - activity_expire_seconds: Activity record expiration time (seconds) - enable_metrics: Whether to enable statistics - log_interval_seconds: Log printing interval (seconds) - owner_expire_seconds: owner expiration time (seconds, default 1 hour) - inactive_threshold_seconds: Inactive threshold (seconds, default 5 minutes) - cleanup_interval_seconds: Periodic cleanup interval (seconds, default 5 minutes) - """ - self.redis_client = redis_client - self.key_prefix = key_prefix - self.serialization_mode = serialization_mode - # Set default item_class to SimpleQueueItem - if item_class is None: - self.item_class = SimpleQueueItem - else: - self.item_class = item_class - self.sort_key_func = sort_key_func or self._default_sort_key - self.max_total_messages = max_total_messages - self.queue_expire_seconds = queue_expire_seconds - self.activity_expire_seconds = activity_expire_seconds - self.enable_metrics = enable_metrics - self.log_interval_seconds = log_interval_seconds - self.owner_expire_seconds = owner_expire_seconds - self.inactive_threshold_seconds = inactive_threshold_seconds - self.cleanup_interval_seconds = cleanup_interval_seconds - - # Redis key patterns - new dynamic owner management mode - self.queue_prefix = ( - f"{key_prefix}:queue:" # Queue key prefix, used in Lua scripts - ) - self.queue_key_pattern = ( - f"{key_prefix}:queue:{{partition}}" # partition is 001-050 - ) - self.owner_activate_time_zset_key = ( - f"{key_prefix}:owner_activate_time_zset" # owner active time zset - ) - self.queue_list_prefix = ( - f"{key_prefix}:queue_list:" # owner's queue_list prefix - ) - self.counter_key = f"{key_prefix}:counter" - - # Process-level owner ID (generated at startup, globally unique) - self.owner_id = ( - f"{self.key_prefix}_{int(time.time())}_{random.randint(10000, 99999)}" - ) - - # Maintain owner last keepalive timestamp mapping (millisecond timestamp) - self.owner_last_keepalive_time = {} - - # Generate fixed partition name list: 001, 002, ..., 050 - self.partition_names = [ - f"{i:03d}" for i in range(1, self.FIXED_PARTITION_COUNT + 1) - ] - - # Manager statistics - self._manager_stats = RedisManagerStats( - total_queues=0, total_current_messages=0 - ) - - # Async lock, protects statistics - self._stats_lock = asyncio.Lock() - - # Start time - self._start_time = time.time() - - # Periodic tasks - self._log_task: Optional[asyncio.Task] = None - self._cleanup_task: Optional[asyncio.Task] = None - self._running = False - - # Manager state - self._state = ManagerState.CREATED - - # Pre-compiled Lua scripts - self._enqueue_script = None - self._get_stats_script = None - self._get_all_partitions_stats_script = None - self._rebalance_partitions_script = None - self._join_consumer_script = None - self._exit_consumer_script = None - self._keepalive_consumer_script = None - self._cleanup_inactive_owners_script = None - self._force_cleanup_script = None - self._get_messages_script = None - - logger.info( - "🚀 RedisGroupQueueManager[%s] Initialization completed: key_prefix=%s, max_total_messages=%d", - self.key_prefix, - self.key_prefix, - self.max_total_messages, - ) - - def _default_sort_key(self, _item: RedisGroupQueueItem) -> int: - """ - Default sort key generation function: Use current timestamp (milliseconds) - - Args: - item: Queue item - - Returns: - int: Sort score (millisecond timestamp) - """ - return int(time.time() * 1000) # Convert to millisecond integer - - def _hash_group_key_to_partition(self, group_key: str) -> str: - """ - Route group_key to fixed partition via hash - - Args: - group_key: Group key - - Returns: - str: Partition name (001-100) - """ - # Use MD5 hash to ensure even distribution - hash_value = hashlib.md5(group_key.encode('utf-8')).hexdigest() - # Take first 8 characters, convert to integer, then modulo - partition_index = int(hash_value[:8], 16) % self.FIXED_PARTITION_COUNT - return self.partition_names[partition_index] - - def _get_queue_key(self, partition: str) -> str: - """Get queue Redis key""" - return self.queue_key_pattern.format(partition=partition) - - def _get_queue_list_key(self, owner_id: Optional[str] = None) -> str: - """Get owner's queue_list Redis key""" - if owner_id is None: - owner_id = self.owner_id - return f"{self.queue_list_prefix}{owner_id}" - - def _parse_rebalance_result( - self, result: Any, expected_count: int - ) -> Tuple[bool, Tuple]: - """ - Parse rebalance-related script return results - - Args: - result: Lua script return result - expected_count: Expected number of return values (2 for rebalance/join/exit, 3 for cleanup) - - Returns: - Tuple[bool, Tuple]: (success or not, parsed result) - """ - # Check return result format - if not isinstance(result, (list, tuple)) or len(result) < expected_count: - logger.error( - "❌ RedisGroupQueueManager[%s] Script return format error: Expected %d values, got %s", - self.key_prefix, - expected_count, - result, - ) - return False, tuple([0] * expected_count) - - # Extract basic values - if expected_count == 2: - owner_count, assigned_partitions_flat = result - parsed_result = ( - owner_count, - self._convert_flat_to_dict(assigned_partitions_flat), - ) - elif expected_count == 3: - cleaned_count, owner_count, assigned_partitions_flat = result - parsed_result = ( - cleaned_count, - owner_count, - self._convert_flat_to_dict(assigned_partitions_flat), - ) - else: - return False, tuple([0] * expected_count) - - return True, parsed_result - - def _convert_flat_to_dict( - self, assigned_partitions_flat: Any - ) -> Dict[str, List[str]]: - """ - Convert flat array to dictionary format - - Args: - assigned_partitions_flat: Flat array [owner_id1, [partitions1], owner_id2, [partitions2], ...] - - Returns: - Dict[str, List[str]]: Assignment result dictionary - """ - assigned_partitions = {} - if ( - isinstance(assigned_partitions_flat, list) - and len(assigned_partitions_flat) > 0 - ): - for i in range(0, len(assigned_partitions_flat), 2): - if i + 1 < len(assigned_partitions_flat): - owner_id = self._safe_decode_redis_value( - assigned_partitions_flat[i] - ) - partitions_raw = assigned_partitions_flat[i + 1] - # Process partition list, each partition name needs decoding - if isinstance(partitions_raw, list): - partitions = [ - self._safe_decode_redis_value(p) for p in partitions_raw - ] - else: - partitions = [self._safe_decode_redis_value(partitions_raw)] - assigned_partitions[owner_id] = partitions - return assigned_partitions - - def _safe_decode_redis_value(self, value: Any) -> str: - """ - Safely decode Redis return value, compatible with bytes and str types - - When Redis client uses decode_responses=False, return value is bytes type - When Redis client uses decode_responses=True, return value is str type - - Args: - value: Redis returned value, could be bytes or str - - Returns: - str: Decoded string - """ - if isinstance(value, bytes): - return value.decode('utf-8') - elif isinstance(value, str): - return value - else: - return str(value) - - async def _check_and_keepalive_if_needed(self, owner_id: str) -> bool: - """ - Check and perform keepalive if needed - - Check owner's last keepalive time, if no record exists or exceeds 30 seconds, trigger a keepalive. - - Args: - owner_id: Consumer ID - - Returns: - bool: Whether keepalive operation was performed - """ - current_time_ms = int(time.time() * 1000) - last_keepalive_time = self.owner_last_keepalive_time.get(owner_id, 0) - - # If no record exists or exceeds 30 seconds, trigger keepalive - if ( - last_keepalive_time == 0 or (current_time_ms - last_keepalive_time) > 30000 - ): # 30 seconds = 30000 milliseconds - logger.debug( - "💓 RedisGroupQueueManager[%s] Triggering keepalive on demand: owner_id=%s, time since last=%.1f seconds", - self.key_prefix, - owner_id, - (current_time_ms - last_keepalive_time) / 1000.0, - ) - # Trigger keepalive and update timestamp - try: - success = await self.keepalive_consumer(owner_id) - if success: - self.owner_last_keepalive_time[owner_id] = current_time_ms - return True - else: - logger.warning( - "⚠️ RedisGroupQueueManager[%s] On-demand keepalive failed: owner_id=%s, keepalive_consumer returned False", - self.key_prefix, - owner_id, - ) - return False - except (redis.RedisError, ValueError, TypeError) as e: - logger.warning( - "⚠️ RedisGroupQueueManager[%s] On-demand keepalive exception: owner_id=%s, error=%s", - self.key_prefix, - owner_id, - e, - ) - return False - else: - logger.debug( - "💓 RedisGroupQueueManager[%s] No need for keepalive: owner_id=%s, time since last=%.1f seconds", - self.key_prefix, - owner_id, - (current_time_ms - last_keepalive_time) / 1000.0, - ) - return False - - async def _ensure_scripts_loaded(self): - """Ensure Lua scripts are loaded""" - if self._enqueue_script is None: - self._enqueue_script = self.redis_client.register_script(ENQUEUE_SCRIPT) - self._get_stats_script = self.redis_client.register_script( - GET_QUEUE_STATS_SCRIPT - ) - self._get_all_partitions_stats_script = self.redis_client.register_script( - GET_ALL_PARTITIONS_STATS_SCRIPT - ) - self._rebalance_partitions_script = self.redis_client.register_script( - REBALANCE_PARTITIONS_SCRIPT - ) - self._join_consumer_script = self.redis_client.register_script( - JOIN_CONSUMER_SCRIPT - ) - self._exit_consumer_script = self.redis_client.register_script( - EXIT_CONSUMER_SCRIPT - ) - self._keepalive_consumer_script = self.redis_client.register_script( - KEEPALIVE_CONSUMER_SCRIPT - ) - self._cleanup_inactive_owners_script = self.redis_client.register_script( - CLEANUP_INACTIVE_OWNERS_SCRIPT - ) - self._force_cleanup_script = self.redis_client.register_script( - FORCE_CLEANUP_SCRIPT - ) - self._get_messages_script = self.redis_client.register_script( - GET_MESSAGES_SCRIPT - ) - - @rate_limit(max_rate=200, time_period=1) - async def deliver_message( - self, - group_key: str, - item: RedisGroupQueueItem, - return_mode: str = "normal", - max_total_messages: int = None, - ) -> bool: - """ - Deliver message to specified group queue - - Args: - group_key: Group key, routed to fixed partition via hash - item: Message data item, must implement RedisGroupQueueItem interface - return_mode: Return mode, normal returns only bool, reject_reason also returns rejection reason - Returns: - bool: Whether delivery was successful - """ - try: - await self._ensure_scripts_loaded() - - # Route to fixed partition via hash - partition = self._hash_group_key_to_partition(group_key) - - # Generate sort score - sort_score = self.sort_key_func(item) - - # Serialize message based on serialization mode - if self.serialization_mode == SerializationMode.BSON: - message_data = item.to_bson_bytes() - else: # JSON mode - message_data = item.to_json_str() - - # Get queue key - queue_key = self._get_queue_key(partition) - - # Execute Lua script to deliver message - result = await self._enqueue_script( - keys=[queue_key, self.counter_key], - args=[ - message_data, - sort_score, - self.queue_expire_seconds, - self.activity_expire_seconds, - ( - max_total_messages - if max_total_messages is not None - else self.max_total_messages - ), - ], - ) - - success, new_count, message = result - - # Safely decode message content, compatible with bytes and str types - message_str = self._safe_decode_redis_value(message) - - if success: - # Update statistics - async with self._stats_lock: - self._manager_stats.total_delivered_messages += 1 - self._manager_stats.total_current_messages = new_count - - logger.debug( - "✅ RedisGroupQueueManager[%s] Message delivery successful: group_key=%s->partition=%s, score=%.3f, total retained=%d", - self.key_prefix, - group_key, - partition, - sort_score, - new_count, - ) - if return_mode == "normal": - return True - else: - return True, message_str - else: - # Delivery failed - async with self._stats_lock: - self._manager_stats.total_rejected_messages += 1 - - logger.warning( - "❌ RedisGroupQueueManager[%s] Delivery rejected: group_key=%s->partition=%s, reason=%s", - self.key_prefix, - group_key, - partition, - message_str, - ) - if return_mode == "normal": - return False - else: - return False, message_str - - except (redis.RedisError, ValueError, TypeError) as e: - # Note: partition might be undefined here, need safe handling - try: - partition = self._hash_group_key_to_partition(group_key) - logger.error( - "❌ RedisGroupQueueManager[%s] Message delivery failed: group_key=%s->partition=%s, error=%s", - self.key_prefix, - group_key, - partition, - e, - ) - except (redis.RedisError, ValueError, TypeError): - logger.error( - "❌ RedisGroupQueueManager[%s] Message delivery failed: group_key=%s, error=%s", - self.key_prefix, - group_key, - e, - ) - if return_mode == "normal": - return False - else: - return False, "Delivery error" - - @rate_limit( - max_rate=4, time_period=1, key_func=lambda owner_id: f"get_messages_{owner_id}" - ) - async def get_messages( - self, - score_threshold: int, - current_score: Optional[int] = None, - owner_id: Optional[str] = None, - _retry_depth: int = 2, - ) -> List[RedisGroupQueueItem]: - """ - Get messages - - Iterate through all partitions assigned to this owner, attempt to get 1 message from each partition. - On-demand keepalive mechanism: Check last keepalive time, trigger keepalive if exceeds 30 seconds. - - Args: - score_threshold: Score difference threshold (milliseconds), required parameter - current_score: Current score, used for threshold comparison when queue is empty, optional parameter - owner_id: Consumer ID, default uses self.owner_id - _retry_depth: Internal parameter, recursive retry depth limit, prevents infinite loop - - Returns: - List[RedisGroupQueueItem]: Message list - """ - try: - await self._ensure_scripts_loaded() - - if owner_id is None: - owner_id = self.owner_id - - # On-demand keepalive mechanism - await self._check_and_keepalive_if_needed(owner_id) - - # Execute get messages script - result = await self._get_messages_script( - keys=[ - self.owner_activate_time_zset_key, - self.queue_list_prefix, - self.queue_prefix, - self.counter_key, - ], - args=[ - owner_id, - self.owner_expire_seconds, - score_threshold, - ( - current_score - if current_score is not None - else self._default_sort_key(None) - ), - ], - ) - - status, messages_data = result - - # Safely decode status value, compatible with bytes and str types - status_str = self._safe_decode_redis_value(status) - - if status_str == "JOIN_REQUIRED": - # Check recursion depth, prevent infinite loop - if _retry_depth <= 0: - logger.error( - "❌ RedisGroupQueueManager[%s] JOIN_REQUIRED retry attempts exhausted: owner_id=%s", - self.key_prefix, - owner_id, - ) - raise RuntimeError( - f"JOIN_REQUIRED retry attempts exhausted: owner_id={owner_id}" - ) - - logger.info( - "🔄 RedisGroupQueueManager[%s] Consumer join required: owner_id=%s, remaining retries=%d", - self.key_prefix, - owner_id, - _retry_depth - 1, - ) - # Automatically join consumer - await self.join_consumer(owner_id) - # Re-get messages, decrement retry depth - return await self.get_messages( - score_threshold, current_score, owner_id, _retry_depth - 1 - ) - - if status_str == "NO_QUEUES": - logger.warning( - "📭 RedisGroupQueueManager[%s] Consumer has no assigned queues: owner_id=%s", - self.key_prefix, - owner_id, - ) - return [] - - # Parse message data - messages = [] - for message_data in messages_data: - try: - # Deserialize message based on serialization mode - if self.serialization_mode == SerializationMode.BSON: - # BSON byte data - item = self.item_class.from_bson_bytes(message_data) - else: - # JSON string - item = self.item_class.from_json_str(message_data) - messages.append(item) - except (redis.RedisError, ValueError, TypeError) as e: - logger.warning( - "⚠️ RedisGroupQueueManager[%s] Message deserialization failed: %s", - self.key_prefix, - e, - ) - - if messages: - # Update statistics - async with self._stats_lock: - self._manager_stats.total_consumed_messages += len(messages) - - logger.debug( - "📤 RedisGroupQueueManager[%s] Messages retrieved successfully: owner_id=%s, count=%d", - self.key_prefix, - owner_id, - len(messages), - ) - else: - logger.debug( - "📭 RedisGroupQueueManager[%s] No consumable messages: owner_id=%s", - self.key_prefix, - owner_id, - ) - - return messages - - except (redis.RedisError, ValueError, TypeError) as e: - logger.error( - "❌ RedisGroupQueueManager[%s] Failed to get messages: owner_id=%s, error=%s", - self.key_prefix, - owner_id, - e, - ) - return [] - - # ==================== New dynamic owner management methods ==================== - - @rate_limit(max_rate=1, time_period=1, key_func=lambda: "rebalance_partitions") - async def rebalance_partitions(self) -> Tuple[int, Dict[str, List[str]]]: - """ - Rebalance partitions - - Based on owner_activate_time_zset, clear all owners' queue_list, - redistribute partitions evenly, assign a new queue_list to each owner. - - Returns: - Tuple[int, Dict[str, List[str]]]: (owner count, assignment result dictionary) - """ - try: - await self._ensure_scripts_loaded() - - # Execute rebalance script - result = await self._rebalance_partitions_script( - keys=[self.owner_activate_time_zset_key, self.queue_list_prefix], - args=[self.FIXED_PARTITION_COUNT, self.owner_expire_seconds], - ) - - # Parse return result - success, (owner_count, assigned_partitions) = self._parse_rebalance_result( - result, 2 - ) - if not success: - return 0, {} - - logger.info( - "🔄 RedisGroupQueueManager[%s] Rebalance partitions completed: owner count=%d, partition assignment=%s", - self.key_prefix, - owner_count, - assigned_partitions, - ) - - return owner_count, assigned_partitions - - except (redis.RedisError, ValueError, TypeError) as e: - logger.error( - "❌ RedisGroupQueueManager[%s] Rebalance partitions failed: error=%s", - self.key_prefix, - e, - ) - return 0, {} - - @rate_limit( - max_rate=1, time_period=1, key_func=lambda owner_id: f"join_consumer_{owner_id}" - ) - async def join_consumer( - self, owner_id: Optional[str] = None - ) -> Tuple[int, Dict[str, List[str]]]: - """ - Join consumer - - Join owner_activate_time_zset, then rebalance partitions. - - Args: - owner_id: Consumer ID, default uses self.owner_id - - Returns: - Tuple[int, Dict[str, List[str]]]: (owner count, assignment result dictionary) - """ - try: - await self._ensure_scripts_loaded() - - if owner_id is None: - owner_id = self.owner_id - - current_time = int(time.time() * 1000) # Millisecond timestamp - - # Execute join consumer script - result = await self._join_consumer_script( - keys=[self.owner_activate_time_zset_key, self.queue_list_prefix], - args=[ - owner_id, - current_time, - self.owner_expire_seconds, - self.FIXED_PARTITION_COUNT, - ], - ) - - # Parse return result - success, (owner_count, assigned_partitions) = self._parse_rebalance_result( - result, 2 - ) - if not success: - return 0, {} - - # Initialize owner's keepalive timestamp - current_time_ms = int(time.time() * 1000) - self.owner_last_keepalive_time[owner_id] = current_time_ms - - logger.info( - "✅ RedisGroupQueueManager[%s] Consumer joined successfully: owner_id=%s, owner count=%d, assignment result=%s", - self.key_prefix, - owner_id, - owner_count, - assigned_partitions, - ) - - return owner_count, assigned_partitions - - except (redis.RedisError, ValueError, TypeError) as e: - logger.error( - "❌ RedisGroupQueueManager[%s] Consumer join failed: owner_id=%s, error=%s", - self.key_prefix, - owner_id, - e, - ) - return 0, {} - - @rate_limit( - max_rate=1, time_period=1, key_func=lambda owner_id: f"exit_consumer_{owner_id}" - ) - async def exit_consumer( - self, owner_id: Optional[str] = None - ) -> Tuple[int, Dict[str, List[str]]]: - """ - Consumer exit - - Remove from owner_activate_time_zset, then rebalance partitions. - - Args: - owner_id: Consumer ID, default uses self.owner_id - - Returns: - Tuple[int, Dict[str, List[str]]]: (owner count, assignment result dictionary) - """ - try: - await self._ensure_scripts_loaded() - - if owner_id is None: - owner_id = self.owner_id - - # Execute consumer exit script - result = await self._exit_consumer_script( - keys=[self.owner_activate_time_zset_key, self.queue_list_prefix], - args=[owner_id, self.owner_expire_seconds, self.FIXED_PARTITION_COUNT], - ) - - # Parse return result - success, (owner_count, assigned_partitions) = self._parse_rebalance_result( - result, 2 - ) - if not success: - return 0, {} - - # Remove exiting consumer from keepalive timestamp mapping - self.owner_last_keepalive_time.pop(owner_id, None) - - logger.info( - "👋 RedisGroupQueueManager[%s] Consumer exited successfully: owner_id=%s, remaining owner count=%d, reassignment result=%s", - self.key_prefix, - owner_id, - owner_count, - assigned_partitions, - ) - - return owner_count, assigned_partitions - - except (redis.RedisError, ValueError, TypeError) as e: - logger.error( - "❌ RedisGroupQueueManager[%s] Consumer exit failed: owner_id=%s, error=%s", - self.key_prefix, - owner_id, - e, - ) - return 0, {} - - @rate_limit( - max_rate=1, - time_period=2, - key_func=lambda owner_id: f"keepalive_consumer_{owner_id}", - ) - async def keepalive_consumer(self, owner_id: Optional[str] = None) -> bool: - """ - Consumer keepalive - - Consumer periodically updates owner_activate_time_zset time. - Recommended to call every 30 seconds. - - Args: - owner_id: Consumer ID - - Returns: - bool: Whether keepalive was successful - """ - try: - await self._ensure_scripts_loaded() - - current_time = int(time.time() * 1000) # Millisecond timestamp - - # Execute consumer keepalive script - result = await self._keepalive_consumer_script( - keys=[self.owner_activate_time_zset_key, self.queue_list_prefix], - args=[owner_id, current_time, self.owner_expire_seconds], - ) - - success = bool(result) - - if success: - logger.debug( - "💓 RedisGroupQueueManager[%s] Consumer keepalive successful: owner_id=%s", - self.key_prefix, - owner_id, - ) - else: - logger.warning( - "⚠️ RedisGroupQueueManager[%s] Consumer keepalive failed, queue_list does not exist: owner_id=%s", - self.key_prefix, - owner_id, - ) - - return success - - except (redis.RedisError, ValueError, TypeError) as e: - logger.error( - "❌ RedisGroupQueueManager[%s] Consumer keepalive failed: owner_id=%s, error=%s", - self.key_prefix, - owner_id, - e, - ) - return False - - @rate_limit(max_rate=1, time_period=5, key_func=lambda: "cleanup_inactive_owners") - async def cleanup_inactive_owners(self) -> Tuple[int, int, Dict[str, List[str]]]: - """ - Periodic cleanup and reset - - Traverse and clean up all inactive owners (e.g., no activity for 5 minutes), - if any inactive owners exist, rebalance partitions. - - Returns: - Tuple[int, int, Dict[str, List[str]]]: (cleaned owner count, remaining owner count, reassignment result) - """ - try: - await self._ensure_scripts_loaded() - - current_time = int(time.time() * 1000) # Millisecond timestamp - inactive_threshold = current_time - ( - self.inactive_threshold_seconds * 1000 - ) # Convert to milliseconds - - # Execute cleanup inactive owners script - result = await self._cleanup_inactive_owners_script( - keys=[ - self.owner_activate_time_zset_key, - self.queue_list_prefix, - self.queue_prefix, - self.counter_key, - ], - args=[ - inactive_threshold, - current_time, - self.owner_expire_seconds, - self.FIXED_PARTITION_COUNT, - ], - ) - - # Parse return result - success, (cleaned_count, owner_count, assigned_partitions) = ( - self._parse_rebalance_result(result, 3) - ) - if not success: - return 0, 0, {} - - if cleaned_count > 0: - logger.info( - "🧹 RedisGroupQueueManager[%s] Cleanup inactive owners completed: cleaned count=%d, remaining owner count=%d, reassignment result=%s", - self.key_prefix, - cleaned_count, - owner_count, - assigned_partitions, - ) - else: - logger.debug( - "🧹 RedisGroupQueueManager[%s] Cleanup inactive owners completed: no cleanup needed", - self.key_prefix, - ) - - return cleaned_count, owner_count, assigned_partitions - - except (redis.RedisError, ValueError, TypeError) as e: - logger.error( - "❌ RedisGroupQueueManager[%s] Cleanup inactive owners failed: error=%s", - self.key_prefix, - e, - ) - return 0, 0, {} - - @rate_limit(max_rate=1, time_period=5, key_func=lambda: "force_cleanup_and_reset") - async def force_cleanup_and_reset(self, purge_all: bool = False) -> int: - """ - Force cleanup and reset - - - purge_all=False (default): Clean owner_activate_time_zset and all owner queue_lists, - do not delete partition queues, only recalculate counter. - - purge_all=True: Additionally delete all partition queues and set counter to 0 (dangerous: full database purge). - - Returns: - int: When purge_all=False returns cleaned owner count; when purge_all=True returns deleted partition count - """ - try: - await self._ensure_scripts_loaded() - - if purge_all: - # Dangerous: Clear all partition queues + owners + reset counter (via unified script, purge_all='1') - purged_partitions = await self._force_cleanup_script( - keys=[ - self.owner_activate_time_zset_key, - self.queue_list_prefix, - self.queue_prefix, - self.counter_key, - ], - args=[self.FIXED_PARTITION_COUNT, "1"], - ) - - # Reset local statistics - async with self._stats_lock: - self._manager_stats.total_current_messages = 0 - self._manager_stats.total_delivered_messages = 0 - self._manager_stats.total_consumed_messages = 0 - self._manager_stats.total_rejected_messages = 0 - - logger.warning( - "💥 RedisGroupQueueManager[%s] Cleared all queues and owners: partition count=%d", - self.key_prefix, - purged_partitions, - ) - return int(purged_partitions or 0) - else: - # Only reset owners and queue assignments, do not delete partition queues - cleaned_count = await self._force_cleanup_script( - keys=[ - self.owner_activate_time_zset_key, - self.queue_list_prefix, - self.queue_prefix, - self.counter_key, - ], - args=[self.FIXED_PARTITION_COUNT, "0"], - ) - - logger.warning( - "💥 RedisGroupQueueManager[%s] Force cleanup and reset completed: cleaned owner count=%d", - self.key_prefix, - cleaned_count, - ) - return cleaned_count - - except (redis.RedisError, ValueError, TypeError) as e: - logger.error( - "❌ RedisGroupQueueManager[%s] Force cleanup and reset failed: error=%s", - self.key_prefix, - e, - ) - return 0 - - @rate_limit(max_rate=1, time_period=5, key_func=lambda: "get_stats") - async def get_stats( - self, - group_key: Optional[str] = None, - include_all_partitions: bool = False, - include_partition_details: bool = False, - include_consumer_info: bool = False, - ) -> Dict[str, Any]: - """ - Get statistics (unified interface) - - Args: - group_key: Group key, if provided get specific queue statistics, otherwise get manager overall statistics - include_all_partitions: Whether to include statistics for all partitions - include_partition_details: Whether to include partition detailed information - include_consumer_info: Whether to include consumer information - - Returns: - Dict[str, Any]: Statistics - """ - try: - await self._ensure_scripts_loaded() - - # If group_key is specified, return specific queue statistics - if group_key is not None and not include_all_partitions: - # Get statistics for single partition - partition = self._hash_group_key_to_partition(group_key) - queue_key = self._get_queue_key(partition) - - result = await self._get_stats_script( - keys=[queue_key, self.counter_key], args=[] - ) - - queue_size, _total_count, min_score, max_score = result - - return { - "type": "queue_stats", - "queue_name": f"{group_key}->partition={partition}", - "current_size": queue_size, - "last_activity_time": time.time(), - "min_score": min_score, - "max_score": max_score, - "partition": partition, - } - - # Get statistics for all partitions (manager level or all partitions statistics) - result = await self._get_all_partitions_stats_script( - keys=[self.queue_prefix, self.counter_key], - args=[str(self.FIXED_PARTITION_COUNT)], - ) - - ( - total_count, - total_messages_in_queues, - global_min_score, - global_max_score, - partition_stats_raw, - ) = result - - # Build basic statistics - async with self._stats_lock: - # Update uptime and statistics - self._manager_stats.uptime_seconds = time.time() - self._start_time - self._manager_stats.total_current_messages = total_messages_in_queues - self._manager_stats.total_queues = self.FIXED_PARTITION_COUNT - - stats = self._manager_stats.to_dict() - - # Add real-time statistics - stats.update( - { - "type": ( - "manager_stats" if group_key is None else "all_partitions_stats" - ), - "counter_total_count": total_count, - "actual_messages_in_queues": total_messages_in_queues, - "global_min_score": global_min_score, - "global_max_score": global_max_score, - "key_prefix": self.key_prefix, - } - ) - - # If consumer information is needed - if include_consumer_info: - try: - active_owners_raw = await self.redis_client.zrange( - self.owner_activate_time_zset_key, 0, -1 - ) - # Safely decode owner list - active_owners = [ - self._safe_decode_redis_value(owner) - for owner in active_owners_raw - ] - stats["active_consumers_count"] = len(active_owners) - stats["active_consumers"] = active_owners - - # Get partition assignments - partition_assignments = {} - for owner in active_owners: - queue_list_key = f"{self.queue_list_prefix}{owner}" - assigned_partitions_raw = await self.redis_client.lrange( - queue_list_key, 0, -1 - ) - # Safely decode partition list - assigned_partitions = [ - self._safe_decode_redis_value(p) - for p in assigned_partitions_raw - ] - partition_assignments[owner] = assigned_partitions - stats["partition_assignments"] = partition_assignments - - except (redis.RedisError, ValueError, TypeError) as e: - logger.warning("Failed to get consumer information: %s", e) - stats["active_consumers_count"] = 0 - stats["active_consumers"] = [] - stats["partition_assignments"] = {} - - # If partition detailed information is needed - if include_partition_details: - partitions = [] - non_empty_partitions = 0 - max_partition_size = 0 - min_partition_size = float('inf') - - for i in range(0, len(partition_stats_raw), 4): - if i + 3 < len(partition_stats_raw): - partition_size = partition_stats_raw[i + 1] - partitions.append( - { - "partition": self._safe_decode_redis_value( - partition_stats_raw[i] - ), - "current_size": partition_size, - "min_score": partition_stats_raw[i + 2], - "max_score": partition_stats_raw[i + 3], - } - ) - - if partition_size > 0: - non_empty_partitions += 1 - max_partition_size = max(max_partition_size, partition_size) - min_partition_size = min(min_partition_size, partition_size) - - stats["partitions"] = partitions - stats["non_empty_partitions"] = non_empty_partitions - stats["max_partition_size"] = ( - max_partition_size if max_partition_size != 0 else 0 - ) - stats["min_partition_size"] = ( - min_partition_size if min_partition_size != float('inf') else 0 - ) - - return stats - - except (redis.RedisError, ValueError, TypeError) as e: - logger.error( - "Failed to get statistics: group_key=%s, error=%s", group_key, e - ) - - # Fallback: Return basic statistics - try: - current_count = await self.redis_client.get(self.counter_key) - total_current_messages = int(current_count) if current_count else 0 - except (redis.RedisError, ValueError, TypeError): - total_current_messages = 0 - - return { - "type": "error_fallback", - "total_current_messages": total_current_messages, - "total_queues": self.FIXED_PARTITION_COUNT, - "error": str(e), - } - - @rate_limit( - max_rate=1, - time_period=5, - key_func=lambda group_key: f"get_queue_stats_{group_key}", - ) - async def get_queue_stats(self, group_key: str) -> Optional[Dict[str, Any]]: - """Compatibility method: Get queue statistics""" - result = await self.get_stats(group_key=group_key) - return result if result.get("type") != "error_fallback" else None - - @rate_limit(max_rate=1, time_period=5, key_func=lambda: "get_manager_stats") - async def get_manager_stats(self) -> Dict[str, Any]: - """Compatibility method: Get manager statistics""" - return await self.get_stats() - - async def start(self): - """ - Start manager (start periodic tasks) - - Can only be started once, cannot restart after shutdown - - Raises: - RuntimeError: If manager is already started or has been shut down - """ - if self._state == ManagerState.STARTED: - logger.warning( - "⚠️ RedisGroupQueueManager[%s] Already started, ignoring duplicate start request", - self.key_prefix, - ) - return - - if self._state == ManagerState.SHUTDOWN: - raise RuntimeError( - f"RedisGroupQueueManager[{self.key_prefix}] has been shut down, cannot restart" - ) - - # State must be CREATED - if self._state != ManagerState.CREATED: - raise RuntimeError( - f"RedisGroupQueueManager[{self.key_prefix}] state abnormal: {self._state}" - ) - - logger.info("🚀 RedisGroupQueueManager[%s] Starting...", self.key_prefix) - - await self.start_periodic_tasks() - - # Update state to started - self._state = ManagerState.STARTED - - logger.info("✅ RedisGroupQueueManager[%s] Startup completed", self.key_prefix) - - async def start_periodic_tasks(self): - """Start periodic tasks""" - if self._running: - return - - self._running = True - - # Execute cleanup immediately on startup - try: - await self.cleanup_inactive_owners() - logger.info( - "🧹 RedisGroupQueueManager[%s] Startup cleanup completed", - self.key_prefix, - ) - except (redis.RedisError, ValueError, TypeError) as e: - logger.warning( - "⚠️ RedisGroupQueueManager[%s] Startup cleanup failed: %s", - self.key_prefix, - e, - ) - - # Execute log immediately on startup - try: - await self._log_manager_details() - logger.info( - "🔥 RedisGroupQueueManager[%s] Startup log printing completed", - self.key_prefix, - ) - except (redis.RedisError, ValueError, TypeError) as e: - logger.warning( - "⚠️ RedisGroupQueueManager[%s] Startup log printing failed: %s", - self.key_prefix, - e, - ) - - # Start periodic tasks - self._log_task = asyncio.create_task(self._periodic_log_worker()) - self._cleanup_task = asyncio.create_task(self._periodic_cleanup_worker()) - - logger.info( - "📊 RedisGroupQueueManager[%s] Periodic tasks started", self.key_prefix - ) - - async def stop_periodic_tasks(self): - """Stop periodic tasks""" - if not self._running: - return - - self._running = False - - # Stop log task - if self._log_task and not self._log_task.done(): - self._log_task.cancel() - try: - await self._log_task - except asyncio.CancelledError: - pass - - # Stop cleanup task - if self._cleanup_task and not self._cleanup_task.done(): - self._cleanup_task.cancel() - try: - await self._cleanup_task - except asyncio.CancelledError: - pass - - logger.info( - "📊 RedisGroupQueueManager[%s] Periodic tasks stopped", self.key_prefix - ) - - async def _periodic_log_worker(self): - """Periodic log printing worker coroutine""" - try: - while self._running: - await asyncio.sleep(self.log_interval_seconds) - if self._running: - await self._log_manager_details() - except asyncio.CancelledError: - logger.debug( - "📊 RedisGroupQueueManager[%s] Periodic log task cancelled", - self.key_prefix, - ) - except (redis.RedisError, ValueError, TypeError) as e: - logger.error( - "📊 RedisGroupQueueManager[%s] Periodic log task exception: %s", - self.key_prefix, - e, - ) - - async def _periodic_cleanup_worker(self): - """Periodic cleanup worker coroutine""" - try: - while self._running: - # Add jitter to avoid all instances cleaning simultaneously, ensure non-negative - jitter = self.cleanup_interval_seconds * 0.3 - delay = self.cleanup_interval_seconds + random.uniform(-jitter, jitter) - await asyncio.sleep(max(1.0, delay)) - if self._running: - await self.cleanup_inactive_owners() - except asyncio.CancelledError: - logger.debug( - "🧹 RedisGroupQueueManager[%s] Periodic cleanup task cancelled", - self.key_prefix, - ) - except (redis.RedisError, ValueError, TypeError) as e: - logger.error( - "🧹 RedisGroupQueueManager[%s] Periodic cleanup task exception: %s", - self.key_prefix, - e, - ) - - async def _log_manager_details(self): - """Print manager details""" - try: - manager_stats = await self.get_manager_stats() - - # Print manager overall status - logger.info( - "📊 RedisGroupQueueManager[%s] Overall status: " - "active queues=%d, total messages=%d, total delivered=%d, total consumed=%d, total rejected=%d, uptime=%.1f seconds", - self.key_prefix, - manager_stats["total_queues"], - manager_stats["total_current_messages"], - manager_stats["total_delivered_messages"], - manager_stats["total_consumed_messages"], - manager_stats["total_rejected_messages"], - manager_stats["uptime_seconds"], - ) - - # Unified print all partitions' detailed information at once - partitions = self.partition_names - details_lines = [] - for partition in partitions: - try: - queue_key = self._get_queue_key(partition) - queue_size = await self.redis_client.zcard(queue_key) - if queue_size > 0: - # Get min and max scores - min_result = await self.redis_client.zrange( - queue_key, 0, 0, withscores=True - ) - max_result = await self.redis_client.zrange( - queue_key, -1, -1, withscores=True - ) - min_score = min_result[0][1] if min_result else 0 - max_score = max_result[0][1] if max_result else 0 - details_lines.append( - f" Partition[{partition}]: Size={queue_size}, Score range=[{min_score:.3f}, {max_score:.3f}]" - ) - else: - details_lines.append(f" Partition[{partition}]: Size=0") - except (redis.RedisError, ValueError, TypeError) as e: - details_lines.append( - f" Partition[{partition}]: Failed to get status: {e}" - ) - - if details_lines: - logger.info( - "🔥 Partition status summary: Total %d partitions\n%s", - len(partitions), - "\n".join(details_lines), - ) - - except (redis.RedisError, ValueError, TypeError) as e: - logger.error( - "📊 RedisGroupQueueManager[%s] Failed to print details: %s", - self.key_prefix, - e, - ) - - async def shutdown(self, mode: ShutdownMode = ShutdownMode.HARD) -> bool: - """ - Shutdown manager - - Args: - mode: Shutdown mode - - Returns: - bool: Whether shutdown was successful - """ - if self._state == ManagerState.SHUTDOWN: - logger.warning( - "⚠️ RedisGroupQueueManager[%s] Already shut down, ignoring duplicate shutdown request", - self.key_prefix, - ) - return True - - if self._state == ManagerState.CREATED: - logger.info( - "ℹ️ RedisGroupQueueManager[%s] Shutting down without having started", - self.key_prefix, - ) - self._state = ManagerState.SHUTDOWN - return True - - # State must be STARTED - if self._state != ManagerState.STARTED: - logger.warning( - "⚠️ RedisGroupQueueManager[%s] State abnormal, force shutdown: %s", - self.key_prefix, - self._state, - ) - - logger.info( - "🔌 RedisGroupQueueManager[%s] Starting shutdown...", self.key_prefix - ) - - # Stop periodic tasks - await self.stop_periodic_tasks() - - if mode == ShutdownMode.SOFT: - # Soft shutdown: Check if messages exist - stats = await self.get_manager_stats() - remaining_messages = stats.get("total_current_messages", 0) - if remaining_messages > 0: - logger.warning( - "⚠️ RedisGroupQueueManager[%s] Soft shutdown detected remaining messages: %d messages", - self.key_prefix, - remaining_messages, - ) - # Soft shutdown failed, but don't change state, allow retry - return False - - # Final log details before shutdown - try: - await self._log_manager_details() - logger.info( - "🔥 RedisGroupQueueManager[%s] Final status log before shutdown completed", - self.key_prefix, - ) - except (redis.RedisError, ValueError, TypeError) as e: - logger.warning( - "⚠️ RedisGroupQueueManager[%s] Failed to print log before shutdown: %s", - self.key_prefix, - e, - ) - - # Update state to shut down - self._state = ManagerState.SHUTDOWN - - logger.info("🔌 RedisGroupQueueManager[%s] Shut down", self.key_prefix) - return True - - def get_state(self) -> ManagerState: - """ - Get manager current state - - Returns: - ManagerState: Current state - """ - return self._state - - def __repr__(self) -> str: - return ( - f"RedisGroupQueueManager(key_prefix={self.key_prefix}, " - f"max_total_messages={self.max_total_messages})" - ) diff --git a/methods/EverCore/src/core/queue/redis_group_queue/redis_msg_group_queue_manager_factory.py b/methods/EverCore/src/core/queue/redis_group_queue/redis_msg_group_queue_manager_factory.py deleted file mode 100644 index 1a041075..00000000 --- a/methods/EverCore/src/core/queue/redis_group_queue/redis_msg_group_queue_manager_factory.py +++ /dev/null @@ -1,342 +0,0 @@ -""" -Redis message group queue manager factory - -Provides caching and management functionality for RedisGroupQueueManager instances based on configuration. -Supports reading configuration from environment variables, provides default and named instances. -Follows the design pattern of mongodb_client_factory.py. -""" - -import os -import asyncio -from typing import Dict, Optional, Callable, Type -from core.di.decorators import component -from core.observation.logger import get_logger -from core.component.redis_provider import RedisProvider -from .redis_msg_group_queue_manager import RedisGroupQueueManager -from .redis_group_queue_item import RedisGroupQueueItem, SerializationMode - -logger = get_logger(__name__) - - -class RedisGroupQueueConfig: - """Redis group queue configuration class""" - - def __init__( - self, - key_prefix: str = "default", - serialization_mode: SerializationMode = SerializationMode.JSON, - sort_key_func: Optional[Callable[[RedisGroupQueueItem], int]] = None, - max_total_messages: int = 1000, - queue_expire_seconds: int = 12 * 3600, # 12 hours - activity_expire_seconds: int = 7 * 24 * 3600, # 7 days - enable_metrics: bool = True, - log_interval_seconds: int = 60, - cleanup_interval_seconds: int = 300, # 5 minutes - **kwargs, - ): - self.key_prefix = key_prefix - self.serialization_mode = serialization_mode - self.sort_key_func = sort_key_func - self.max_total_messages = max_total_messages - self.queue_expire_seconds = queue_expire_seconds - self.activity_expire_seconds = activity_expire_seconds - self.enable_metrics = enable_metrics - self.log_interval_seconds = log_interval_seconds - self.cleanup_interval_seconds = cleanup_interval_seconds - self.kwargs = kwargs - - def get_cache_key(self) -> str: - """ - Get cache key - - Generate unique identifier based on core configuration parameters - """ - # Use function name or default value for sort function - sort_func_name = ( - getattr(self.sort_key_func, '__name__', 'default') - if self.sort_key_func - else 'default' - ) - - return ( - f"{self.key_prefix}:{self.serialization_mode.value}:{sort_func_name}:" - f"{self.max_total_messages}:{self.queue_expire_seconds}:" - f"{self.activity_expire_seconds}:{self.enable_metrics}:" - f"{self.log_interval_seconds}:{self.cleanup_interval_seconds}" - ) - - @classmethod - def from_env(cls, prefix: str = "") -> 'RedisGroupQueueConfig': - """ - Create configuration from environment variables - - Prefix rule: if prefix is provided, variables will be read in the format "{prefix}_XXX", otherwise "XXX" is read. - For example: prefix="CLIENT" will read "CLIENT_REDIS_QUEUE_KEY_PREFIX", "CLIENT_REDIS_QUEUE_MAX_TOTAL_MESSAGES", etc. - - Args: - prefix: environment variable prefix - - Returns: - RedisGroupQueueConfig: configuration instance - """ - - def _env(name: str, default: str) -> str: - key = f"{prefix}_{name}" if prefix else name - return os.getenv(key, default) - - # Read configuration items - base_key_prefix = _env("REDIS_QUEUE_KEY_PREFIX", "default") - # Support global Redis prefix - global_redis_prefix = _env("GLOBAL_REDIS_PREFIX", "") - key_prefix = ( - f"{global_redis_prefix}:{base_key_prefix}" - if global_redis_prefix - else base_key_prefix - ) - # Serialization mode configuration - serialization_mode_str = _env("REDIS_QUEUE_SERIALIZATION_MODE", "json").lower() - serialization_mode = ( - SerializationMode.JSON - if serialization_mode_str == "json" - else SerializationMode.BSON - ) - max_total_messages = int(_env("REDIS_QUEUE_MAX_TOTAL_MESSAGES", "20000")) - queue_expire_seconds = int(_env("REDIS_QUEUE_EXPIRE_SECONDS", str(24 * 3600))) - activity_expire_seconds = int( - _env("REDIS_QUEUE_ACTIVITY_EXPIRE_SECONDS", str(24 * 3600)) - ) - enable_metrics = _env("REDIS_QUEUE_ENABLE_METRICS", "true").lower() == "true" - log_interval_seconds = int(_env("REDIS_QUEUE_LOG_INTERVAL_SECONDS", "600")) - cleanup_interval_seconds = int( - _env("REDIS_QUEUE_CLEANUP_INTERVAL_SECONDS", "300") - ) - - return cls( - key_prefix=key_prefix, - serialization_mode=serialization_mode, - max_total_messages=max_total_messages, - queue_expire_seconds=queue_expire_seconds, - activity_expire_seconds=activity_expire_seconds, - enable_metrics=enable_metrics, - log_interval_seconds=log_interval_seconds, - cleanup_interval_seconds=cleanup_interval_seconds, - ) - - def __repr__(self) -> str: - return ( - f"RedisGroupQueueConfig(key_prefix={self.key_prefix}, " - f"max_total_messages={self.max_total_messages})" - ) - - -@component(name="redis_group_queue_manager_factory", primary=True) -class RedisGroupQueueManagerFactory: - """Redis message group queue manager factory""" - - def __init__(self, redis_provider: RedisProvider): - """ - Initialize factory - - Args: - redis_provider: Redis connection provider - """ - self.redis_provider = redis_provider - self._managers: Dict[str, RedisGroupQueueManager] = {} - self._default_config: Optional[RedisGroupQueueConfig] = None - self._default_manager: Optional[RedisGroupQueueManager] = None - self._lock = asyncio.Lock() - - async def get_manager( - self, - config: Optional[RedisGroupQueueConfig] = None, - item_class: Optional[Type[RedisGroupQueueItem]] = None, - auto_start: bool = True, - redis_client_name: str = "default", - ) -> RedisGroupQueueManager: - """ - Get Redis message group queue manager - - Args: - config: queue manager configuration, use default configuration if None - item_class: queue item type, must inherit from RedisGroupQueueItem, default uses SimpleQueueItem - auto_start: whether to automatically start the manager - redis_client_name: Redis client name - - Returns: - RedisGroupQueueManager: queue manager - """ - if config is None: - config = await self._get_default_config() - - # Generate cache key, including item_class information - item_class_name = item_class.__name__ if item_class else 'default' - cache_key = f"{config.get_cache_key()}:{item_class_name}:{redis_client_name}" - - async with self._lock: - # Check cache - if cache_key in self._managers: - manager = self._managers[cache_key] - return manager - - # Create new manager - logger.info("Creating new RedisGroupQueueManager: %s", config) - - try: - # Get Redis client based on serialization mode - if config.serialization_mode == SerializationMode.BSON: - # BSON mode: use binary_cache, do not decode responses to support byte data - redis_client = await self.redis_provider.get_named_client( - "binary_cache", decode_responses=False - ) - else: - # JSON mode: use default client, automatically decode responses - redis_client = await self.redis_provider.get_client() - - manager = RedisGroupQueueManager( - redis_client=redis_client, - key_prefix=config.key_prefix, - serialization_mode=config.serialization_mode, - item_class=item_class, - sort_key_func=config.sort_key_func, - max_total_messages=config.max_total_messages, - queue_expire_seconds=config.queue_expire_seconds, - activity_expire_seconds=config.activity_expire_seconds, - enable_metrics=config.enable_metrics, - log_interval_seconds=config.log_interval_seconds, - cleanup_interval_seconds=config.cleanup_interval_seconds, - **config.kwargs, - ) - - if auto_start: - await manager.start() - - # Cache manager - self._managers[cache_key] = manager - logger.info( - "✅ RedisGroupQueueManager created successfully and cached: %s", - config, - ) - - return manager - - except Exception as e: - logger.error( - "❌ Failed to create RedisGroupQueueManager: %s, error: %s", - config, - e, - ) - raise - - async def _get_default_config(self) -> RedisGroupQueueConfig: - """Get default configuration""" - if self._default_config is None: - self._default_config = RedisGroupQueueConfig.from_env() - logger.info( - "📋 Loaded default RedisGroupQueueManager configuration: %s", - self._default_config, - ) - - return self._default_config - - async def get_manager_with_config( - self, - key_prefix: str = "default", - serialization_mode: SerializationMode = SerializationMode.JSON, - item_class: Optional[Type[RedisGroupQueueItem]] = None, - sort_key_func: Optional[Callable[[RedisGroupQueueItem], int]] = None, - max_total_messages: int = 2 * 10000, - queue_expire_seconds: int = 24 * 3600, - activity_expire_seconds: int = 24 * 3600, - enable_metrics: bool = True, - log_interval_seconds: int = 600, - cleanup_interval_seconds: int = 300, - auto_start: bool = True, - redis_client_name: str = "default", - **kwargs, - ) -> RedisGroupQueueManager: - """ - Create manager with specified configuration - - Args: - key_prefix: Redis key prefix, used to distinguish different manager instances - serialization_mode: serialization mode (JSON or BSON) - item_class: queue item type, must inherit from RedisGroupQueueItem, default uses SimpleQueueItem - sort_key_func: sort key generation function - max_total_messages: maximum total message count - queue_expire_seconds: queue expiration time - activity_expire_seconds: activity record expiration time - enable_metrics: whether to enable metrics - log_interval_seconds: log interval - cleanup_interval_seconds: cleanup interval - auto_start: whether to auto start - redis_client_name: Redis client name - **kwargs: additional parameters - - Returns: - RedisGroupQueueManager: queue manager - """ - config = RedisGroupQueueConfig( - key_prefix=key_prefix, - serialization_mode=serialization_mode, - sort_key_func=sort_key_func, - max_total_messages=max_total_messages, - queue_expire_seconds=queue_expire_seconds, - activity_expire_seconds=activity_expire_seconds, - enable_metrics=enable_metrics, - log_interval_seconds=log_interval_seconds, - cleanup_interval_seconds=cleanup_interval_seconds, - **kwargs, - ) - - return await self.get_manager(config, item_class, auto_start, redis_client_name) - - async def stop_manager( - self, - config: Optional[RedisGroupQueueConfig] = None, - item_class: Optional[Type[RedisGroupQueueItem]] = None, - redis_client_name: str = "default", - ): - """ - Stop specified manager - - Args: - config: configuration, if None then stop default manager - item_class: queue item type, must inherit from RedisGroupQueueItem - redis_client_name: Redis client name - """ - if config is None: - if self._default_manager: - await self._default_manager.shutdown() - return - - # Generate cache key, including item_class information - item_class_name = item_class.__name__ if item_class else 'default' - cache_key = f"{config.get_cache_key()}:{item_class_name}:{redis_client_name}" - - async with self._lock: - if cache_key in self._managers: - await self._managers[cache_key].shutdown() - - async def stop_all_managers(self): - """Stop all managers""" - async with self._lock: - for manager in self._managers.values(): - await manager.shutdown() - - self._managers.clear() - - if self._default_manager: - self._default_manager = None - - logger.info("🔌 All RedisGroupQueueManager instances have been stopped") - - def get_cached_managers_info(self) -> Dict[str, Dict]: - """Get cached manager information""" - return { - cache_key: { - "key_prefix": manager.key_prefix, - "max_total_messages": manager.max_total_messages, - "manager_stats": "Need to call get_manager_stats() asynchronously to retrieve", - } - for cache_key, manager in self._managers.items() - } diff --git a/methods/EverCore/src/core/rate_limit/__init__.py b/methods/EverCore/src/core/rate_limit/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/rate_limit/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/rate_limit/rate_limiter.py b/methods/EverCore/src/core/rate_limit/rate_limiter.py deleted file mode 100644 index ecd2f262..00000000 --- a/methods/EverCore/src/core/rate_limit/rate_limiter.py +++ /dev/null @@ -1,115 +0,0 @@ -""" -Async rate limiting decorator module based on aiolimiter - -Provides rate limiting functionality for async functions with flexible configuration. -""" - -from functools import wraps -from typing import Callable, Any, Dict, Optional -from aiolimiter import AsyncLimiter - - -class RateLimitManager: - """Rate limit manager that manages multiple limiter instances""" - - def __init__(self): - self._limiters: Dict[str, AsyncLimiter] = {} - - def get_limiter(self, key: str, max_rate: int, time_period: int) -> AsyncLimiter: - """ - Get or create a limiter instance - - Args: - key: Unique identifier for the limiter - max_rate: Maximum number of allowed requests within the time window - time_period: Time window size (seconds) - - Returns: - AsyncLimiter: Limiter instance - """ - limiter_key = f"{key}_{max_rate}_{time_period}" - - if limiter_key not in self._limiters: - self._limiters[limiter_key] = AsyncLimiter(max_rate, time_period) - - return self._limiters[limiter_key] - - -# Global rate limit manager instance -_rate_limit_manager = RateLimitManager() - - -def rate_limit( - max_rate: int = 3, - time_period: int = 10, - key_func: Optional[Callable[..., str]] = None, -): - """ - Async function rate limiting decorator - - Args: - max_rate: Maximum number of allowed requests within the time window, default is 3 - time_period: Time window size (seconds), default is 10 seconds - key_func: Optional key function to generate different rate limit keys for different parameters - If not provided, all calls share the same limiter - - Raises: - ValueError: Raised when max_rate <= 0 or time_period <= 0 - - Usage: - @rate_limit(max_rate=3, time_period=10) - async def my_api_call(): - pass - - @rate_limit(max_rate=5, time_period=60, key_func=lambda user_id: f"user_{user_id}") - async def user_specific_call(user_id: str): - pass - """ - if max_rate <= 0: - raise ValueError(f"max_rate must be positive, got {max_rate}") - if time_period <= 0: - raise ValueError(f"time_period must be positive, got {time_period}") - - def decorator(func: Callable) -> Callable: - @wraps(func) - async def wrapper(*args, **kwargs) -> Any: - # Generate key for the limiter - if key_func: - # Use custom key function - try: - limiter_key = key_func(*args, **kwargs) - except (TypeError, ValueError, KeyError): - # If key function fails, use function name as default key - limiter_key = func.__name__ - else: - # Use function name as default key - limiter_key = func.__name__ - - # Get the limiter - limiter = _rate_limit_manager.get_limiter( - limiter_key, max_rate, time_period - ) - - # Wait for the limiter to allow execution - async with limiter: - return await func(*args, **kwargs) - - return wrapper - - return decorator - - -# Predefined common rate limiting decorators -def rate_limit_3_per_10s(func: Callable) -> Callable: - """Rate limiting decorator allowing maximum 3 requests per 10 seconds""" - return rate_limit(max_rate=3, time_period=10)(func) - - -def rate_limit_5_per_minute(func: Callable) -> Callable: - """Rate limiting decorator allowing maximum 5 requests per minute""" - return rate_limit(max_rate=5, time_period=60)(func) - - -def rate_limit_10_per_hour(func: Callable) -> Callable: - """Rate limiting decorator allowing maximum 10 requests per hour""" - return rate_limit(max_rate=10, time_period=3600)(func) diff --git a/methods/EverCore/src/core/request/__init__.py b/methods/EverCore/src/core/request/__init__.py deleted file mode 100644 index b2ebe49f..00000000 --- a/methods/EverCore/src/core/request/__init__.py +++ /dev/null @@ -1,39 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Request module - -Provides request-related utilities: -- AppLogicProvider: Application logic provider interface for request lifecycle hooks -- log_request: Decorator for logging request information as events for replay -- RequestHistoryEvent: Event class containing complete request information -- RequestHistoryConfig: Configuration interface for enabling/disabling request history -- is_request_history_enabled: Utility function to check if request history is enabled (cached) -""" - -from core.request.app_logic_provider import AppLogicProvider, AppLogicProviderImpl -from core.request.request_history_config import ( - RequestHistoryConfig, - DefaultRequestHistoryConfig, - is_request_history_enabled, - clear_request_history_cache, - get_request_history_config, -) -from core.request.request_history_decorator import log_request, log_request_default -from core.request.request_history_event import RequestHistoryEvent - -__all__ = [ - # App logic provider - 'AppLogicProvider', - 'AppLogicProviderImpl', - # Request history config - 'RequestHistoryConfig', - 'DefaultRequestHistoryConfig', - 'is_request_history_enabled', - 'clear_request_history_cache', - 'get_request_history_config', - # Request history decorator and event - 'log_request', - 'log_request_default', - 'RequestHistoryEvent', -] - diff --git a/methods/EverCore/src/core/request/app_logic_provider.py b/methods/EverCore/src/core/request/app_logic_provider.py deleted file mode 100644 index 7dfe4822..00000000 --- a/methods/EverCore/src/core/request/app_logic_provider.py +++ /dev/null @@ -1,248 +0,0 @@ -from typing import Dict, Any, Optional -from abc import ABC, abstractmethod -import uuid -from fastapi import Request - -from core.di.decorators import component -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger -from core.context.context import get_current_app_info, get_current_request - -logger = get_logger(__name__) - -# Default blocking wait timeout (seconds) -DEFAULT_BLOCKING_TIMEOUT = 5.0 - - -class AppLogicProvider(ABC): - """ - Application logic provider interface - - Responsible for extracting application-level context information from requests and handling application logic. - Provides hooks for the request lifecycle: - - should_process_request(): Determines whether the request needs processing (used for filtering) - - setup_app_context(): Extracts and sets application context (called first by middleware) - - on_request_begin(): Called when the request begins (business logic, e.g., event dispatching) - - on_request_complete(): Called when the request ends (optional implementation) - - Helper methods (retrieve from context): - - get_current_request_id(): Get the current request's request_id - - get_current_request(): Get the current request object - - get_current_app_info(): Get current application information - """ - - def should_process_request(self, request: Request) -> bool: - """ - Determine whether the request needs business logic processing - - Used to filter requests and decide whether to execute: - - on_request_begin() callback - - on_request_complete() callback - - Note: setup_app_context() is not affected by this method and is called on every request. - - Subclasses can override this method to implement custom filtering logic, - for example, only processing requests under /api/ routes. - - Args: - request: FastAPI request object - - Returns: - bool: True means process, False means skip - """ - # Default: process all requests - return True - - @abstractmethod - def setup_app_context(self, request: Request) -> Dict[str, Any]: - """ - Extract and set application context - - Extract all context-related data from the request, for example: - - Record request start time - - Extract request_id, hash_key - - Set tenant context - - This method is called first by middleware, before on_request_begin. - - Args: - request: FastAPI request object - - Returns: - Dict[str, Any]: app_info dictionary containing context data - """ - raise NotImplementedError - - async def validate_request(self, request: Request) -> None: - """ - Validate request (optional implementation) - - This method is called after setup_app_context() and before on_request_begin(). - If validation fails, raise HTTPException to abort the request. - - Use cases: - - Quota validation - - Permission checks - - Rate limiting - - Custom business validations - - Args: - request: FastAPI request object - - Raises: - HTTPException: When validation fails (e.g., 429 for quota exceeded) - - Example: - >>> async def validate_request(self, request: Request) -> None: - ... if not self._check_quota(request): - ... raise HTTPException( - ... status_code=429, - ... detail="Quota exceeded" - ... ) - """ - # Default implementation is empty; subclasses may optionally override - _ = request # Avoid unused parameter warning - - async def on_request_begin(self, request: Request) -> None: - """ - Callback method when request begins - - Used to handle business logic at the start of a request, for example: - - Initialize token usage collector - - Dispatching request start event - - Note: Context data has already been set by setup_app_context(), - and can be retrieved via self.get_current_app_info(). - - Args: - request: FastAPI request object - """ - self._init_token_usage_collector() - - async def on_request_complete( - self, request: Request, http_code: int, error_message: Optional[str] = None - ) -> None: - """ - Callback method when request completes (optional implementation) - - Subclasses can override this method to handle post-request logic, - for example: logging request details, dispatching events, cleanup, etc. - - Note: Current app_info set in setup_app_context() or on_request_begin() can be retrieved via self.get_current_app_info(). - - Args: - request: FastAPI request object - http_code: HTTP response status code - error_message: Error message (optional) - """ - self._cleanup_token_usage_collector() - - def get_current_request_id(self) -> str: - """ - Get the current request's request_id - - Retrieve app_info from context, then extract request_id. - - Returns: - str: Current request's request_id, returns "-" if not set - """ - app_info = get_current_app_info() - if app_info: - return app_info.get("request_id", "-") - return "-" - - def get_current_request(self) -> Optional[Request]: - """ - Get the current request object - - Retrieve request from context. - - Returns: - Optional[Request]: Current request object, returns None if not set - """ - return get_current_request() - - def get_current_app_info(self) -> Optional[Dict[str, Any]]: - """ - Get current application information - - Retrieve app_info from context. - - Returns: - Optional[Dict[str, Any]]: Current application information, returns None if not set - """ - return get_current_app_info() - - def _init_token_usage_collector(self) -> None: - """ - Initialize token usage collector for the current request. - - Must be called before asyncio.create_task() to establish the shared - ContextVar dict that T1/T2/C0 will reference via in-place mutation. - """ - try: - from core.component.token_usage_collector import TokenUsageCollector - - collector = get_bean_by_type(TokenUsageCollector) - collector.reset() - except Exception: - pass - - def _cleanup_token_usage_collector(self) -> None: - """ - Cleanup token usage collector after request completes. - - Clears the ContextVar to prevent leaks between requests. - """ - try: - from core.component.token_usage_collector import TokenUsageCollector - - collector = get_bean_by_type(TokenUsageCollector) - collector.reset() - except Exception: - pass - - -@component(name="app_logic_provider") -class AppLogicProviderImpl(AppLogicProvider): - """Application logic provider implementation, responsible for extracting application-level context information from requests""" - - def __init__(self): - self._tenant_router = None - - def _get_tenant_router(self): - """Lazy-load TenantRouter to avoid circular imports at DI scan time.""" - if self._tenant_router is None: - from core.tenants.tenant_router import TenantRouter - - self._tenant_router = get_bean_by_type(TenantRouter) - return self._tenant_router - - def setup_app_context(self, request: Request) -> Dict[str, Any]: - """ - Extract and set application context - - Args: - request: FastAPI request object - - Returns: - Dict[str, Any]: app_info dictionary containing context data - """ - app_info: Dict[str, Any] = {} - - # Get request_id from request headers, prioritize X-Request-Id, fallback to lowercase - request_id = request.headers.get('X-Request-Id') or request.headers.get( - 'x-request-id' - ) - if not request_id: - request_id = str(uuid.uuid4()) - - app_info['request_id'] = request_id - - # Resolve tenant context via TenantRouter - # Open-source default: no-op. Enterprise: resolves from headers. - tenant_info = self._get_tenant_router().resolve_tenant(request) - if tenant_info: - app_info['tenant_id'] = tenant_info.tenant_id - - return app_info diff --git a/methods/EverCore/src/core/request/request_history_config.py b/methods/EverCore/src/core/request/request_history_config.py deleted file mode 100644 index 4b2f44d7..00000000 --- a/methods/EverCore/src/core/request/request_history_config.py +++ /dev/null @@ -1,145 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Request history configuration module - -Provides configuration interface and default implementation for request history logging. -The default implementation disables request history logging (for opensource version). -Enterprise version can override this to enable the feature. -""" - -from abc import ABC, abstractmethod -from typing import Optional - -from core.di.decorators import component -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class RequestHistoryConfig(ABC): - """ - Request history configuration interface - - Defines the configuration for request history logging feature. - Different implementations can enable/disable the feature based on deployment needs. - - Using DI mechanism: - - Default implementation (opensource) disables the feature - - Enterprise can register a new implementation to enable it - """ - - @abstractmethod - def is_enabled(self) -> bool: - """ - Check if request history logging is enabled - - Returns: - bool: True if enabled, False if disabled - """ - raise NotImplementedError - - def get_config_name(self) -> str: - """ - Get the configuration provider name - - Returns: - str: Configuration provider name - """ - return self.__class__.__name__ - - -@component("default_request_history_config", primary=True) -class DefaultRequestHistoryConfig(RequestHistoryConfig): - """ - Default request history configuration (opensource version) - - This implementation disables request history logging by default. - Suitable for opensource deployments where request replay is not needed. - """ - - def is_enabled(self) -> bool: - """ - Check if request history logging is enabled - - Returns: - bool: Always True for opensource version - """ - return True - - -# ============================================================================ -# Utility functions with caching -# ============================================================================ - -# Cache for the config instance -_config_cache: Optional[RequestHistoryConfig] = None -_enabled_cache: Optional[bool] = None - - -def _get_config_instance() -> RequestHistoryConfig: - """ - Get the RequestHistoryConfig instance from DI container - - Returns: - RequestHistoryConfig: The configuration instance - """ - global _config_cache - if _config_cache is not None: - return _config_cache - - return get_bean_by_type(RequestHistoryConfig) - - -def is_request_history_enabled() -> bool: - """ - Check if request history logging is enabled (with caching) - - This function caches the result for performance. - The cache is populated on first call and reused thereafter. - - Returns: - bool: True if request history logging is enabled, False otherwise - - Example: - >>> from core.request import is_request_history_enabled - >>> if is_request_history_enabled(): - ... # Log the request - ... pass - """ - global _enabled_cache - if _enabled_cache is not None: - return _enabled_cache - - config = _get_config_instance() - _enabled_cache = config.is_enabled() - - logger.info( - f"Request history logging is {'enabled' if _enabled_cache else 'disabled'} " - f"(config: {config.get_config_name()})" - ) - - return _enabled_cache - - -def clear_request_history_cache() -> None: - """ - Clear the request history configuration cache - - Call this if the configuration needs to be reloaded, - for example during testing or dynamic configuration changes. - """ - global _config_cache, _enabled_cache - _config_cache = None - _enabled_cache = None - logger.debug("Request history configuration cache cleared") - - -def get_request_history_config() -> RequestHistoryConfig: - """ - Get the current RequestHistoryConfig instance - - Returns: - RequestHistoryConfig: The current configuration instance - """ - return _get_config_instance() diff --git a/methods/EverCore/src/core/request/request_history_decorator.py b/methods/EverCore/src/core/request/request_history_decorator.py deleted file mode 100644 index 7070172d..00000000 --- a/methods/EverCore/src/core/request/request_history_decorator.py +++ /dev/null @@ -1,261 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Request history decorator module - -Provides a decorator to capture and publish request information as events. -Used for request replay functionality. - -Note: This feature is controlled by RequestHistoryConfig. -- Opensource version: disabled by default -- Enterprise version: can be enabled by overriding the config - -The decorator captures raw request data without parsing. -Enterprise code is responsible for processing the data. -""" - -import asyncio -from functools import wraps -from typing import Any, Callable, Optional - -from fastapi import Request - -from core.context.context import get_current_request -from core.di import get_bean_by_type -from core.events import ApplicationEventPublisher -from core.observation.logger import get_logger -from core.request.request_history_config import is_request_history_enabled -from core.request.request_history_event import RequestHistoryEvent -from project_meta import PROJECT_VERSION - -logger = get_logger(__name__) - - -async def _extract_request_body(request: Request) -> Optional[str]: - """ - Extract raw request body from FastAPI request - - Args: - request: FastAPI Request object - - Returns: - Raw body string or None - """ - try: - body_bytes = await request.body() - if body_bytes: - return body_bytes.decode("utf-8", errors="replace") - except Exception as e: - logger.debug(f"Failed to extract request body: {e}") - return None - - -def _build_request_history_event( - request: Request, - body: Optional[str], - endpoint_name: Optional[str] = None, - controller_name: Optional[str] = None, - version: str = "", -) -> RequestHistoryEvent: - """ - Build RequestHistoryEvent from FastAPI request - - Extracts raw data without parsing - enterprise code handles processing. - - Args: - request: FastAPI Request object - body: Raw request body string - endpoint_name: Name of the endpoint function - controller_name: Name of the controller class - version: Code version string - - Returns: - RequestHistoryEvent: Event containing raw request information - """ - # Get client info - client_host = None - client_port = None - if request.client: - client_host = request.client.host - client_port = request.client.port - - return RequestHistoryEvent( - version=version, - endpoint_name=endpoint_name, - controller_name=controller_name, - method=request.method, - url=str(request.url), - headers=dict(request.headers), - body=body, - client_host=client_host, - client_port=client_port, - ) - - -async def _publish_request_history_event(event: RequestHistoryEvent) -> None: - """ - Publish request history event - - Args: - event: RequestHistoryEvent to publish - """ - try: - publisher = get_bean_by_type(ApplicationEventPublisher) - await publisher.publish(event) - logger.debug( - f"Published request history event: {event.method} " - f"endpoint={event.endpoint_name} version={event.version}" - ) - except Exception as e: - logger.warning(f"Failed to publish request history event: {e}") - - -def log_request( - include_body: bool = True, async_publish: bool = True, version: Optional[str] = None -) -> Callable: - """ - Decorator to log request information as an event - - Captures raw HTTP request information and publishes it as a - RequestHistoryEvent for request replay functionality. - - Note: Raw data is captured without parsing. Enterprise code - is responsible for processing the data. - - Args: - include_body: Whether to include request body (default: True) - async_publish: Whether to publish event asynchronously without waiting - (default: True) - version: Code version string (default: from project_meta.PROJECT_VERSION) - - Returns: - Decorator function - - Example: - >>> from core.request import log_request - >>> - >>> class UserController(BaseController): - ... @post("/users") - ... @log_request() - ... async def create_user(self, request: Request, data: UserCreate): - ... # Request info is automatically logged as event - ... return {"user_id": "123"} - ... - ... @get("/users/{user_id}") - ... @log_request(include_body=False) - ... async def get_user(self, request: Request, user_id: str): - ... return {"user_id": user_id} - """ - # Use project version if not specified - effective_version = version if version is not None else PROJECT_VERSION - - def decorator(func: Callable) -> Callable: - # Get function metadata for event - endpoint_name = func.__name__ - # Try to get controller name from qualname - qualname = getattr(func, "__qualname__", "") - controller_name = qualname.split(".")[0] if "." in qualname else None - - @wraps(func) - async def async_wrapper(*args: Any, **kwargs: Any) -> Any: - # Check if request history logging is enabled - if not is_request_history_enabled(): - return await func(*args, **kwargs) - - # Try to get request from kwargs or args - request: Optional[Request] = kwargs.get("request") - if request is None: - # Try to find Request in args - for arg in args: - if isinstance(arg, Request): - request = arg - break - - # If still no request, try context - if request is None: - request = get_current_request() - - # Build and publish event if we have a request - if request is not None: - try: - # Extract body if needed - body = None - if include_body: - body = await _extract_request_body(request) - - # Build event with raw data - event = _build_request_history_event( - request=request, - body=body, - endpoint_name=endpoint_name, - controller_name=controller_name, - version=effective_version, - ) - - # Publish event - if async_publish: - # Fire and forget - don't wait for publish to complete - asyncio.create_task(_publish_request_history_event(event)) - else: - await _publish_request_history_event(event) - - except Exception as e: - logger.warning(f"Failed to log request: {e}") - - # Call the original function - return await func(*args, **kwargs) - - @wraps(func) - def sync_wrapper(*args: Any, **kwargs: Any) -> Any: - # Check if request history logging is enabled - if not is_request_history_enabled(): - return func(*args, **kwargs) - - # Try to get request from kwargs or args - request: Optional[Request] = kwargs.get("request") - if request is None: - for arg in args: - if isinstance(arg, Request): - request = arg - break - - if request is None: - request = get_current_request() - - # For sync functions, we publish synchronously - if request is not None: - try: - # For sync functions, we can't await body extraction - # Body will be None for sync endpoints - body = None - - event = _build_request_history_event( - request=request, - body=body, - endpoint_name=endpoint_name, - controller_name=controller_name, - version=effective_version, - ) - - # Use sync publish - try: - publisher = get_bean_by_type(ApplicationEventPublisher) - publisher.publish_sync(event) - except Exception as e: - logger.warning(f"Failed to publish request history event: {e}") - - except Exception as e: - logger.warning(f"Failed to log request: {e}") - - return func(*args, **kwargs) - - # Return appropriate wrapper based on function type - if asyncio.iscoroutinefunction(func): - return async_wrapper - else: - return sync_wrapper - - return decorator - - -# Convenience decorator with default settings -log_request_default = log_request() diff --git a/methods/EverCore/src/core/request/request_history_event.py b/methods/EverCore/src/core/request/request_history_event.py deleted file mode 100644 index 2d95187e..00000000 --- a/methods/EverCore/src/core/request/request_history_event.py +++ /dev/null @@ -1,113 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Request history event module - -Defines the event class for recording HTTP request information. -Used for request replay functionality. - -Note: This event contains raw request data without parsing. -Enterprise version is responsible for parsing and processing the data. -""" - -from dataclasses import dataclass, field -from typing import Any, Dict, Optional, Type, TypeVar - -from core.events.base_event import BaseEvent - - -T = TypeVar('T', bound='RequestHistoryEvent') - - -@dataclass -class RequestHistoryEvent(BaseEvent): - """ - Request history event - - Records raw HTTP request information for replay functionality. - Contains minimal parsed data - enterprise code handles further processing. - - Attributes: - version: Code version from project_meta.py - endpoint_name: Name of the endpoint function - controller_name: Name of the controller class - method: HTTP method (GET, POST, PUT, DELETE, etc.) - url: Full URL including scheme, host, path, and query string - headers: Raw request headers as dictionary (unfiltered) - body: Raw request body as string - client_host: Client IP address - client_port: Client port number - - Example: - >>> event = RequestHistoryEvent( - ... version="1.0.0", - ... endpoint_name="create_user", - ... controller_name="UserController", - ... method="POST", - ... url="http://localhost:8000/api/v0/users?page=1", - ... headers={"Content-Type": "application/json"}, - ... body='{"name": "John"}', - ... ) - """ - - # Version info - version: str = "" - - # Endpoint info - endpoint_name: Optional[str] = None - controller_name: Optional[str] = None - - # Raw request data (unprocessed) - method: str = "" - url: str = "" - headers: Dict[str, str] = field(default_factory=dict) - body: Optional[str] = None - - # Client info - client_host: Optional[str] = None - client_port: Optional[int] = None - - @classmethod - def event_type(cls) -> str: - """Get event type name""" - return "RequestHistoryEvent" - - @classmethod - def from_dict(cls: Type[T], data: Dict[str, Any]) -> T: - """ - Create RequestHistoryEvent from dictionary - - Args: - data: Dictionary containing event data - - Returns: - RequestHistoryEvent instance - """ - return cls( - # Base event fields - event_id=data.get("event_id", ""), - created_at=data.get("created_at", ""), - # Version info - version=data.get("version", ""), - # Endpoint info - endpoint_name=data.get("endpoint_name"), - controller_name=data.get("controller_name"), - # Raw request data - method=data.get("method", ""), - url=data.get("url", ""), - headers=data.get("headers", {}), - body=data.get("body"), - # Client info - client_host=data.get("client_host"), - client_port=data.get("client_port"), - ) - - def __repr__(self) -> str: - """Return string representation""" - return ( - f"RequestHistoryEvent(" - f"event_id={self.event_id!r}, " - f"version={self.version!r}, " - f"method={self.method!r}, " - f"endpoint_name={self.endpoint_name!r}" - f")" - ) diff --git a/methods/EverCore/src/core/request/timeout_background.py b/methods/EverCore/src/core/request/timeout_background.py deleted file mode 100644 index 2c729e5d..00000000 --- a/methods/EverCore/src/core/request/timeout_background.py +++ /dev/null @@ -1,228 +0,0 @@ -""" -Timeout to background execution decorator - -Used for endpoints. When business logic execution times out, automatically switches to background execution and returns a 202 response. -Works with AppLogicMiddleware. - -Depends on AppLogicProvider: -- Uses get_current_request_id() to get request_id -- Uses on_request_complete() as the background completion callback - -Background mode configuration: -- Background mode is enabled by default (automatically switches to background on timeout) -- Can disable background mode by passing sync_mode=true in request params (synchronously wait for execution to complete) -""" - -from typing import Any, Callable, Coroutine, TypeVar, ParamSpec, Union, Optional -from functools import wraps -import asyncio -import traceback - -from fastapi import Request -from fastapi.responses import JSONResponse - -from core.observation.logger import get_logger -from core.di.utils import get_bean_by_type -from core.context.context import get_current_request -from core.request.app_logic_provider import AppLogicProvider - -logger = get_logger(__name__) - -P = ParamSpec("P") -T = TypeVar("T") - -# Default blocking wait timeout (seconds) -DEFAULT_BLOCKING_TIMEOUT = 5.0 - -# Sync mode parameter name (used to disable background mode) -SYNC_MODE_PARAM = "sync_mode" - - -def is_background_mode_enabled(request: Optional[Request] = None) -> bool: - """ - Check if background mode is enabled - - Background mode is enabled by default. Can be disabled via: - - Passing sync_mode=true in request params - - Args: - request: FastAPI request object, if None, get from context - - Returns: - bool: True means background mode is enabled, False means disabled (synchronous execution) - """ - if request is None: - request = get_current_request() - - if request is None: - # No request context, enable background mode by default - return True - - # Check if sync_mode=true is in query params - sync_mode = request.query_params.get(SYNC_MODE_PARAM, "true").lower() - if sync_mode in ("false", "0", "no"): - return True - - # Enable background mode by default - if sync_mode in ("true", "1", "yes"): - return False - - return True - - -def timeout_to_background( - timeout: float = DEFAULT_BLOCKING_TIMEOUT, - accepted_message: str = "Request accepted, processing in background", -) -> Callable[ - [Callable[P, Coroutine[Any, Any, T]]], - Callable[P, Coroutine[Any, Any, Union[T, JSONResponse]]], -]: - """ - Timeout to background execution decorator - - When the decorated endpoint execution exceeds the specified time: - 1. Return 202 Accepted response to client - 2. Business logic continues executing in the background - 3. Call AppLogicProvider.on_request_complete() when background execution completes/fails - - Works with AppLogicMiddleware: - - Normal completion (no timeout): handled by middleware's on_request_complete - - Timeout and switch to background (return 202): decorator calls on_request_complete, middleware skips - - Background mode configuration: - - Background mode is enabled by default (automatically switches to background on timeout) - - Can disable background mode by passing sync_mode=true in request params (synchronously wait for completion) - - Usage example: - ```python - @router.post("/memorize") - @timeout_to_background(timeout=5.0) - async def memorize(request: MemorizeRequest): - # Business logic... - return {"status": "ok"} - - # Client can disable background mode via query params: - # POST /memorize?sync_mode=true - ``` - - Args: - timeout: Timeout for blocking wait (seconds), default 5s - accepted_message: Message content for 202 response - - Returns: - Decorator function - """ - - def decorator( - func: Callable[P, Coroutine[Any, Any, T]] - ) -> Callable[P, Coroutine[Any, Any, Union[T, JSONResponse]]]: - - @wraps(func) - async def wrapper(*args: P.args, **kwargs: P.kwargs) -> Union[T, JSONResponse]: - # Get AppLogicProvider instance - provider = get_bean_by_type(AppLogicProvider) - request_id = provider.get_current_request_id() - task_name = f"{func.__name__}_{request_id}" - - # Check if background mode is enabled - background_enabled = is_background_mode_enabled() - - if not background_enabled: - # Sync mode: execute directly without timeout mechanism - logger.debug( - "[TimeoutBackground] Task '%s' executing in sync mode", task_name - ) - return await func(*args, **kwargs) - - # Background mode: create task and set timeout - task = asyncio.create_task(func(*args, **kwargs)) - - try: - # First block and wait for specified time - result = await asyncio.wait_for(asyncio.shield(task), timeout=timeout) - logger.debug( - "[TimeoutBackground] Task '%s' completed within %ss", - task_name, - timeout, - ) - # Normal completion, do not call on_request_complete, let middleware handle - return result - - except asyncio.TimeoutError: - # Timeout not completed, switch to background execution - logger.info( - "[TimeoutBackground] Task '%s' timed out (%ss), switching to background execution", - task_name, - timeout, - ) - - # Create background task to continue execution - asyncio.create_task(_run_background_task(task, task_name, provider)) - - # Return 202 Accepted - return JSONResponse( - status_code=202, - content={"message": accepted_message, "request_id": request_id}, - ) - - return wrapper - - return decorator - - -async def _run_background_task( - task: asyncio.Task, - task_name: str, - provider: Any, # AppLogicProvider, using Any to avoid circular import -) -> None: - """ - Background task executor - - Args: - task: The asyncio.Task to wait for - task_name: Task name (for logging) - provider: AppLogicProvider instance - """ - try: - await task - logger.info("[TimeoutBackground] Background task '%s' completed", task_name) - # Call provider's on_request_complete - await _call_on_request_complete(provider, http_code=200, error_message=None) - except asyncio.CancelledError: - logger.warning( - "[TimeoutBackground] Background task '%s' was cancelled", task_name - ) - except Exception as e: - logger.error( - "[TimeoutBackground] Background task '%s' execution failed: %s", - task_name, - e, - ) - traceback.print_exc() - await _call_on_request_complete(provider, http_code=500, error_message=str(e)) - - -async def _call_on_request_complete( - provider: Any, http_code: int, error_message: Optional[str] -) -> None: - """ - Call provider's on_request_complete - - Get request from context. - """ - try: - request = provider.get_current_request() - - if request is None: - logger.warning( - "[TimeoutBackground] Unable to get request, skipping on_request_complete" - ) - return - - await provider.on_request_complete( - request=request, http_code=http_code, error_message=error_message - ) - except Exception as e: - logger.warning( - "[TimeoutBackground] on_request_complete callback execution failed: %s", e - ) diff --git a/methods/EverCore/src/core/tenants/__init__.py b/methods/EverCore/src/core/tenants/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/tenants/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/tenants/init_tenant_all.py b/methods/EverCore/src/core/tenants/init_tenant_all.py deleted file mode 100644 index 98012333..00000000 --- a/methods/EverCore/src/core/tenants/init_tenant_all.py +++ /dev/null @@ -1,206 +0,0 @@ -""" -Tenant database initialization module - -Initializes MongoDB, Milvus, and Elasticsearch databases for a tenant. - -Tenant context is passed via TENANT_INIT_STORAGE_INFO environment variable, -which contains full storage_info JSON. Works for both shared mode and -exclusive mode. - -Usage: - # Shared mode (logical isolation, multiple tenants share the same storage): - TENANT_INIT_STORAGE_INFO='{ - "tenant_id": "my_tenant", - "isolation_mode": "shared", - "storage_info": { - "mongodb": {"database": "my_tenant_memsys"}, - "elasticsearch": {"index_prefix": "my_tenant"}, - "milvus": {"collection_prefix": "my_tenant", "num_partitions": 256} - } - }' python src/manage.py tenant-init - - # Exclusive mode (physical isolation, dedicated storage per tenant): - TENANT_INIT_STORAGE_INFO='{ - "tenant_id": "my_tenant", - "isolation_mode": "exclusive", - "storage_info": { - "mongodb": {"database": "my_tenant_memsys"}, - "elasticsearch": {"index_prefix": "my_tenant"}, - "milvus": {"collection_prefix": "my_tenant", "num_partitions": 1} - } - }' python src/manage.py tenant-init - -Storage info fields: - mongodb.database — Target database name (e.g. "my_tenant_memsys") - elasticsearch.index_prefix — ES index name prefix (e.g. "my_tenant") - milvus.collection_prefix — Milvus collection name prefix (e.g. "my_tenant") - milvus.num_partitions — Milvus partition count (optional, default from env MILVUS_NUM_PARTITIONS) - Recommended: 256 for shared mode, 1 for exclusive mode. -""" - -import json -import os - -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import set_current_tenant -from core.tenants.tenant_models import TenantInfo, TenantDetail -from core.lifespan.mongodb_lifespan import MongoDBLifespanProvider -from core.lifespan.milvus_lifespan import MilvusLifespanProvider -from core.lifespan.elasticsearch_lifespan import ElasticsearchLifespanProvider - -logger = get_logger(__name__) - -# Environment variable for passing full tenant context to init subprocess -TENANT_INIT_STORAGE_INFO_ENV = "TENANT_INIT_STORAGE_INFO" - - -def setup_tenant_context_from_env() -> str: - """ - Set up tenant context from TENANT_INIT_STORAGE_INFO environment variable. - - Returns: - tenant_id string for logging - - Raises: - ValueError: If env var is not set or has invalid format - """ - # Priority 1: Full storage_info from env (set by TenantInitService) - storage_info_json = os.getenv(TENANT_INIT_STORAGE_INFO_ENV) - if storage_info_json: - try: - data = json.loads(storage_info_json) - tenant_id = data["tenant_id"] - storage_info = data.get("storage_info", {}) - isolation_mode = data.get("isolation_mode", "shared") - - tenant_detail = TenantDetail( - storage_info=storage_info, isolation_mode=isolation_mode - ) - tenant_info = TenantInfo( - tenant_id=tenant_id, tenant_detail=tenant_detail, origin_tenant_data={} - ) - set_current_tenant(tenant_info) - logger.info( - "Tenant context set from %s: tenant_id=%s, mode=%s", - TENANT_INIT_STORAGE_INFO_ENV, - tenant_id, - isolation_mode, - ) - return tenant_id - except (json.JSONDecodeError, KeyError) as e: - raise ValueError( - f"Invalid {TENANT_INIT_STORAGE_INFO_ENV} format: {e}. " - f"Expected JSON with tenant_id, storage_info, isolation_mode." - ) from e - - raise ValueError( - "Tenant context is not configured!\n" - f"Set {TENANT_INIT_STORAGE_INFO_ENV} environment variable.\n" - f"Example:\n" - f' {TENANT_INIT_STORAGE_INFO_ENV}=\'{{"tenant_id":"my_tenant","isolation_mode":"shared",' - f'"storage_info":{{"mongodb":{{"database":"my_tenant_memsys"}},' - f'"elasticsearch":{{"index_prefix":"my_tenant"}},' - f'"milvus":{{"collection_prefix":"my_tenant","num_partitions":256}}}}}}\'\n' - f" python src/manage.py tenant-init" - ) - - -class _MockApp: - """Mock FastAPI app for lifespan providers (only needs state attribute).""" - - class State: - pass - - state = State() - - -async def init_mongodb() -> bool: - """Initialize tenant's MongoDB database.""" - logger.info("=" * 60) - logger.info("Starting initialization of tenant's MongoDB database...") - logger.info("=" * 60) - - try: - mongodb_provider = MongoDBLifespanProvider() - mock_app = _MockApp() - await mongodb_provider.startup(mock_app) - logger.info("✅ Tenant's MongoDB database initialized successfully") - await mongodb_provider.shutdown(mock_app) - return True - except Exception as e: - logger.error("❌ Failed to initialize tenant's MongoDB database: %s", e) - return False - - -async def init_milvus() -> bool: - """Initialize tenant's Milvus collections.""" - logger.info("=" * 60) - logger.info("Starting initialization of tenant's Milvus database...") - logger.info("=" * 60) - - try: - milvus_provider = MilvusLifespanProvider() - mock_app = _MockApp() - await milvus_provider.startup(mock_app) - logger.info("✅ Tenant's Milvus database initialized successfully") - await milvus_provider.shutdown(mock_app) - return True - except Exception as e: - logger.error("❌ Failed to initialize tenant's Milvus database: %s", e) - return False - - -async def init_elasticsearch() -> bool: - """Initialize tenant's Elasticsearch indices.""" - logger.info("=" * 60) - logger.info("Starting initialization of tenant's Elasticsearch database...") - logger.info("=" * 60) - - try: - es_provider = ElasticsearchLifespanProvider() - mock_app = _MockApp() - await es_provider.startup(mock_app) - logger.info("✅ Tenant's Elasticsearch database initialized successfully") - await es_provider.shutdown(mock_app) - return True - except Exception as e: - logger.error("❌ Failed to initialize tenant's Elasticsearch database: %s", e) - return False - - -async def run_tenant_init() -> bool: - """ - Execute tenant database initialization. - - Reads tenant context from environment variables, then initializes - MongoDB, Milvus, and Elasticsearch in sequence. - - Returns: - Whether all initializations were successful - """ - logger.info("*" * 60) - logger.info("Tenant Database Initialization Tool") - logger.info("*" * 60) - - # Set up tenant context - tenant_id = setup_tenant_context_from_env() - logger.info("Tenant ID: %s", tenant_id) - logger.info("*" * 60) - - # Initialize all three storage engines - mongodb_success = await init_mongodb() - milvus_success = await init_milvus() - es_success = await init_elasticsearch() - - # Summary - logger.info("") - logger.info("*" * 60) - logger.info("Initialization Result Summary") - logger.info("*" * 60) - logger.info("Tenant ID: %s", tenant_id) - logger.info("MongoDB: %s", "✅ Success" if mongodb_success else "❌ Failure") - logger.info("Milvus: %s", "✅ Success" if milvus_success else "❌ Failure") - logger.info("Elasticsearch: %s", "✅ Success" if es_success else "❌ Failure") - logger.info("*" * 60) - - return mongodb_success and milvus_success and es_success diff --git a/methods/EverCore/src/core/tenants/tenant_config.py b/methods/EverCore/src/core/tenants/tenant_config.py deleted file mode 100644 index 8ed3228c..00000000 --- a/methods/EverCore/src/core/tenants/tenant_config.py +++ /dev/null @@ -1,143 +0,0 @@ -""" -Tenant configuration module - -This module provides tenant-related configuration management, including configuration options such as the non-tenant mode switch. -Configuration items are loaded from environment variables and support caching to improve performance. -""" - -import os -from typing import Optional -from functools import lru_cache - -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class TenantConfig: - """ - Tenant configuration class - - This class manages tenant-related configuration options, including: - - Non-tenant mode switch: controls whether tenant functionality is enabled - - Single tenant ID: tenant identifier used to activate single-tenant mode - - Other tenant-related configuration options - - Configuration items are loaded from environment variables and provide a caching mechanism to improve performance. - """ - - def __init__(self): - """Initialize tenant configuration""" - self._single_tenant_id: Optional[str] = None - self._app_ready: bool = ( - False # Application startup completion status, used for strict tenant checks - ) - - @property - def single_tenant_id(self) -> Optional[str]: - """ - Get single tenant ID configuration - - Read configuration from environment variable TENANT_SINGLE_TENANT_ID. - When this environment variable is set, the system will automatically activate tenant logic for this tenant ID. - Suitable for single-tenant deployment scenarios. - - Returns: - Single tenant ID, returns None if not set - - Examples: - >>> config = get_tenant_config() - >>> tenant_id = config.single_tenant_id - >>> if tenant_id: - ... print(f"Single tenant mode, tenant ID: {tenant_id}") - """ - if self._single_tenant_id is None: - self._single_tenant_id = os.getenv("TENANT_SINGLE_TENANT_ID", "").strip() - # If empty string, set to None - if not self._single_tenant_id: - self._single_tenant_id = None - else: - logger.info( - "🏢 Single tenant mode activated, tenant ID: %s", - self._single_tenant_id, - ) - - return self._single_tenant_id - - @property - def app_ready(self) -> bool: - """ - Get application startup completion status - - This status is used for strict tenant checking mode: - - False: Application is starting, operations without tenant context are allowed (using fallback) - - True: Application is ready, tenant context is required in tenant mode, otherwise raise error directly - - This is a fallback mechanism used in production environments to catch code errors that miss tenant context. - - Returns: - bool: True means application is ready, False means application is starting - """ - return self._app_ready - - def mark_app_ready(self) -> None: - """ - Mark application startup as complete - - This method should be called after all lifespan providers have started. - After calling, missing tenant context in tenant mode will raise error directly instead of using fallback logic. - - Note: This method can only be set once; repeated calls will log a warning. - """ - if self._app_ready: - logger.warning( - "⚠️ Application is already ready, mark_app_ready() called repeatedly" - ) - return - - self._app_ready = True - logger.info( - "✅ Application startup completed, tenant strict check mode enabled" - ) - - def reload(self): - """ - Reload configuration - - Clear cached configuration items and force re-read from environment variables. - Typically used after testing or configuration changes. - - Note: reload does not reset the app_ready state, as it reflects runtime status rather than configuration. - """ - self._single_tenant_id = None - logger.info("🔄 Tenant configuration reloaded") - - def reset_app_ready(self) -> None: - """ - Reset application ready state (for testing only) - - Warning: This method should only be used in testing scenarios and should not be called in production. - """ - self._app_ready = False - logger.warning("⚠️ Application ready state has been reset (for testing only)") - - -@lru_cache(maxsize=1) -def get_tenant_config() -> TenantConfig: - """ - Get tenant configuration singleton - - Uses lru_cache to ensure only one configuration instance is created during the application lifecycle. - - Returns: - TenantConfig: Tenant configuration object - - Examples: - >>> config = get_tenant_config() - >>> config = get_tenant_config() - >>> if config.single_tenant_id: - ... print("Single tenant mode") - ... else: - ... print("Multi-tenant mode") - """ - return TenantConfig() diff --git a/methods/EverCore/src/core/tenants/tenant_constants.py b/methods/EverCore/src/core/tenants/tenant_constants.py deleted file mode 100644 index 1e6ef840..00000000 --- a/methods/EverCore/src/core/tenants/tenant_constants.py +++ /dev/null @@ -1,78 +0,0 @@ -""" -Tenant Constants - -Shared constants for tenant isolation across all storage backends -(MongoDB, Elasticsearch, Milvus). - -Resource prefix convention (all modes): - - Prefix Mode Example Source - ────── ────────────────── ──────────────── ────────────────────────── - s0001 Base / shared pool s0001_memsys get_base_resource_prefix() - dev Single-tenant dev_memsys TENANT_SINGLE_TENANT_ID - t3a7b2c Multi-tenant excl. t3a7b2c_memsys enterprise tenant_id_generator - -All separators use underscore (_). No hyphens in resource names. - -All resources always have a prefix — no bare names. -""" - -import os -from functools import lru_cache - -# ============================================================ -# Tenant field constants -# ============================================================ - -# Field name used for logical tenant isolation across all storage systems. -# All interceptors (MongoDB TenantCommandInterceptor, ES TenantAwareAsyncElasticsearch, -# Milvus TenantFieldCollectionProxy) use this constant for consistency. -TENANT_ID_FIELD = "tenant_id" - -# Maximum length for tenant_id string values. -# Used by Milvus FieldSchema (VARCHAR requires max_length). -TENANT_ID_MAX_LENGTH = 128 - -# ============================================================ -# Isolation modes -# ============================================================ - -# - SHARED: Multiple tenants share the same database/index/collection. -# Query filter injection is REQUIRED to prevent cross-tenant data leakage. -# - EXCLUSIVE: Each tenant has its own database/index/collection. -# Query filter injection is SKIPPED (physical isolation is sufficient). -# Write injection is still performed for data consistency and future migration. -ISOLATION_MODE_SHARED = "shared" -ISOLATION_MODE_EXCLUSIVE = "exclusive" - -# ============================================================ -# Resource prefix: base / shared pool ("s") -# ============================================================ -# Used during multi-tenant startup before any request arrives. -# Using "s" (same as shared pool prefix) means the ORM startup -# resources (Beanie init, etc.) land directly in the shared pool, -# avoiding an extra set of "b0001_*" phantom resources. -# -# The version suffix is configurable via TENANT_BASE_RESOURCE_VERSION env var, -# allowing operators to bump the version (s0001 -> s0002) on upgrades so -# new resources are auto-created while old ones are left intact for rollback. -# -# Other prefixes defined in enterprise (tenant_id_generator.py): -# "t" — exclusive tenant (t + 10-hex hash of org+space, e.g., "t3a7b2c1d9e") - -BASE_RESOURCE_PREFIX_LETTER = "s" - - -@lru_cache(maxsize=1) -def get_base_resource_prefix() -> str: - """ - Get the base resource prefix for resources created without tenant context. - - Format: "s" + version (e.g., "s0001", "s0002"). - Version is read from env TENANT_BASE_RESOURCE_VERSION, defaults to "0001". - - Returns: - str: e.g., "s0001" - """ - version = os.getenv("TENANT_BASE_RESOURCE_VERSION", "0001") - return f"{BASE_RESOURCE_PREFIX_LETTER}{version}" diff --git a/methods/EverCore/src/core/tenants/tenant_contextvar.py b/methods/EverCore/src/core/tenants/tenant_contextvar.py deleted file mode 100644 index c2cdae82..00000000 --- a/methods/EverCore/src/core/tenants/tenant_contextvar.py +++ /dev/null @@ -1,124 +0,0 @@ -""" -Tenant context management module - -This module provides tenant context management functionality based on contextvars, -used to safely store and access current tenant information in asynchronous environments. -""" - -from contextvars import ContextVar -from typing import Optional - -from core.tenants.tenant_models import TenantInfo -from core.tenants.tenant_config import get_tenant_config -from core.tenants.tenant_info_provider import TenantInfoService -from core.di.container import get_container -from core.di.exceptions import BeanNotFoundError - - -# Global tenant context variable -# Using ContextVar ensures each task has an independent tenant context in asynchronous environments -current_tenant_contextvar: ContextVar[Optional[TenantInfo]] = ContextVar( - 'current_tenant', default=None -) - - -def set_current_tenant(tenant_info: Optional[TenantInfo]) -> None: - """ - Set the tenant information for the current request - - This method sets the tenant information into the current context. In asynchronous environments, - each request/task has an independent context, without interference. - - Args: - tenant_info: Tenant information object, if None, clears the current tenant information - - Examples: - >>> from core.tenants.tenant_models import TenantInfo, TenantDetail - >>> tenant = TenantInfo( - ... tenant_id="tenant_001", - ... tenant_detail=TenantDetail(), - ... origin_tenant_data={} - ... ) - >>> set_current_tenant(tenant) - """ - current_tenant_contextvar.set(tenant_info) - - -def get_current_tenant() -> Optional[TenantInfo]: - """ - Get the tenant information for the current request - - This method retrieves tenant information from the current context. If no tenant information is set in the current context, - it attempts to get the single tenant ID from the tenant configuration and retrieve tenant information via the tenant info service. - - Retrieval logic: - 1. First, try to get tenant information from contextvar - 2. If not in contextvar, check if SINGLE_TENANT_ID is set in the configuration - 3. If SINGLE_TENANT_ID is configured, get TenantInfoService from DI container and retrieve tenant information - - Returns: - Tenant information in the current context, returns None if not set - - Examples: - >>> tenant = get_current_tenant() - >>> if tenant: - ... print(f"Current tenant ID: {tenant.tenant_id}") - ... else: - ... print("Tenant information not set") - """ - # 1. First try to get from contextvar - tenant_info = current_tenant_contextvar.get() - if tenant_info is not None: - return tenant_info - - # 2. If not in contextvar, try to get single_tenant_id from configuration - tenant_config = get_tenant_config() - single_tenant_id = tenant_config.single_tenant_id - - # 3. If single_tenant_id is configured, get TenantInfoService from DI container - if single_tenant_id: - try: - # Get TenantInfoService instance from DI container (automatically selects primary implementation) - service = get_container().get_bean_by_type(TenantInfoService) - tenant_info = service.get_tenant_info(single_tenant_id) - set_current_tenant(tenant_info) - return tenant_info - except BeanNotFoundError: - # If TenantInfoService is not registered in DI container, return None - # This usually occurs during early application startup or in test environments - return None - - # 4. If none of the above, return None - return None - - -def clear_current_tenant() -> None: - """ - Clear the tenant information for the current request - - This method sets the tenant information in the current context to None, - equivalent to set_current_tenant(None). - - Examples: - >>> clear_current_tenant() - """ - current_tenant_contextvar.set(None) - - -def get_current_tenant_id() -> Optional[str]: - """ - Get the ID of the current tenant - - This is a convenience method that directly returns the tenant_id of the current tenant. - If no tenant information is currently set, returns None. - - Returns: - The ID of the current tenant, returns None if tenant information is not set - - Examples: - >>> tenant_id = get_current_tenant_id() - >>> if tenant_id: - ... print(f"Current tenant ID: {tenant_id}") - """ - tenant = get_current_tenant() - return tenant.tenant_id if tenant else None diff --git a/methods/EverCore/src/core/tenants/tenant_info_provider.py b/methods/EverCore/src/core/tenants/tenant_info_provider.py deleted file mode 100644 index 4b20fc13..00000000 --- a/methods/EverCore/src/core/tenants/tenant_info_provider.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -Tenant information service module - -This module defines the tenant information service interface and its default implementation, -used to retrieve tenant information based on tenant_id (typically single_tenant_id from config). - -Uses DI mechanism to manage TenantInfoService implementations. -""" - -import os -from abc import ABC, abstractmethod -from typing import Optional - -from core.tenants.tenant_models import TenantInfo, TenantDetail -from core.tenants.tenant_constants import ISOLATION_MODE_EXCLUSIVE -from core.di.decorators import component - - -class TenantInfoService(ABC): - """ - Tenant information service interface - - This interface defines standard methods for retrieving tenant information. - Different implementations can retrieve tenant information from various data sources (e.g., database, API, configuration files). - - Using DI mechanism: - - Multiple implementations can be registered - - Use primary=True to mark the default implementation - - Obtain instances through the container - """ - - @abstractmethod - def get_tenant_info(self, tenant_id: str) -> Optional[TenantInfo]: - """ - Retrieve tenant information by tenant ID - - Args: - tenant_id: Unique identifier of the tenant - - Returns: - Tenant information object, or None if not found - """ - raise NotImplementedError - - -@component("default_tenant_info_service") -class DefaultTenantInfoService(TenantInfoService): - """ - Default tenant information service implementation - - This implementation provides basic tenant information containing only the tenant_id, - without detailed information such as storage configurations. Suitable for simple scenarios or as the default implementation. - - Uses the @component decorator to register into the DI container and mark as primary. - """ - - def get_tenant_info(self, tenant_id: str) -> Optional[TenantInfo]: - """ - Create tenant information for single-tenant (local dev) mode. - - Builds a complete TenantInfo with explicit storage_info read from - environment variables, so the behavior is identical to a multi-tenant - exclusive deployment — no "fallback coincidence" relied upon. - - Args: - tenant_id: Unique identifier of the tenant - - Returns: - TenantInfo object with storage_info populated from env - - Examples: - >>> from core.di.container import get_container - >>> service = get_container().get_bean_by_type(TenantInfoService) - >>> tenant_info = service.get_tenant_info("dev") - >>> print(tenant_info.tenant_detail.isolation_mode) - exclusive - """ - if not tenant_id: - return None - - # Build resource names with tenant_id as prefix, same pattern as - # EnterpriseTenantRouter._build_storage_info() in exclusive mode. - # e.g. tenant_id="dev" → database="dev_memsys", index_prefix="dev" - base_db = os.getenv("MONGODB_DATABASE", "memsys") - - storage_info = { - "mongodb": {"database": f"{tenant_id}_{base_db}"}, - "elasticsearch": {"index_prefix": tenant_id}, - "milvus": {"collection_prefix": tenant_id}, - } - - tenant_detail = TenantDetail( - tenant_info={"tenant_id": tenant_id}, - storage_info=storage_info, - isolation_mode=ISOLATION_MODE_EXCLUSIVE, - ) - - return TenantInfo( - tenant_id=tenant_id, tenant_detail=tenant_detail, origin_tenant_data={} - ) diff --git a/methods/EverCore/src/core/tenants/tenant_models.py b/methods/EverCore/src/core/tenants/tenant_models.py deleted file mode 100644 index fec4390e..00000000 --- a/methods/EverCore/src/core/tenants/tenant_models.py +++ /dev/null @@ -1,372 +0,0 @@ -""" -Tenant information data model - -This module defines data classes related to tenants for unified management of tenant information. -""" - -import json -from dataclasses import dataclass, field -from typing import Optional, Dict, Any -from enum import Enum - - -class TenantPatchKey(str, Enum): - """ - Tenant information Patch cache key enumeration - - Used to manage cache keys in tenant_info_patch uniformly, avoiding key name conflicts caused by string hardcoding. - - Design notes: - - All patch cache keys should be defined here - - Using enumeration provides better type hints and prevents spelling errors - - Inherits from str, allowing it to be used directly as a dictionary key - - Cache key descriptions: - - MONGO_CLIENT_CACHE_KEY: Used to cache the cache_key of MongoDB client (hash value of connection parameters) - - ACTUAL_DATABASE_NAME: Used to cache the actual database name used - - REAL_DATABASE_PREFIX: Used to cache the key prefix of the database object (needs to be concatenated with the database name during actual use) - - ES_CONNECTION_CACHE_KEY: Used to cache the cache_key of Elasticsearch connection alias - """ - - # MongoDB client related - MONGO_CLIENT_CACHE_KEY = "mongo_client_cache_key" - - # MongoDB database related - ACTUAL_DATABASE_NAME = "actual_database_name" - MONGO_REAL_DATABASE = "mongo_real_database" # Real MongoDB database object - - # Milvus connection related - MILVUS_CONNECTION_CACHE_KEY = "milvus_connection_cache_key" - - # Elasticsearch connection related - ES_CONNECTION_CACHE_KEY = "es_connection_cache_key" - - -@dataclass -class TenantDetail: - """ - Tenant detailed information data class - - This class is used to store adapted tenant detailed information; external tenant information will be adapted into this data model. - - Attributes: - tenant_info: Dictionary containing tenant-related information, storing converted tenant data - Example structure: { - "hash_key": "...", - "account_id": "...", - "space_id": "...", - "organization_id": "..." - } - storage_info: Dictionary containing storage configuration information, optional field - Example structure: { - "mongodb": {"host": "...", "port": 27017, ...}, - "redis": {"host": "...", "port": 6379, ...} - } - """ - - tenant_info: Optional[Dict[str, Any]] = field(default=None) - storage_info: Optional[Dict[str, Any]] = field(default=None) - - # Isolation mode: "shared" (logical isolation, default) or "exclusive" (physical isolation). - # Both modes inject tenant_id on all reads and writes (interceptor behavior is identical). - # The difference is storage-level: shared uses common tables/indices, exclusive uses dedicated ones. - # Default "shared" — all new tenants use shared mode. - isolation_mode: str = field(default="shared") - - def to_dict(self) -> Dict[str, Any]: - """ - Convert TenantDetail to dictionary - - Returns: - Dict[str, Any]: Dictionary containing tenant_info and storage_info - """ - return { - 'tenant_info': self.tenant_info, - 'storage_info': self.storage_info, - 'isolation_mode': self.isolation_mode, - } - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> 'TenantDetail': - """ - Create TenantDetail instance from dictionary - - Args: - data: Dictionary containing tenant_info and storage_info - - Returns: - TenantDetail: Newly created instance - """ - return cls( - tenant_info=data.get('tenant_info'), - storage_info=data.get('storage_info'), - isolation_mode=data.get('isolation_mode', 'shared'), - ) - - -@dataclass -class TenantInfo: - """ - Tenant information data class - - This class is the main data model for tenant information, containing core tenant details. - - Attributes: - tenant_id: Unique identifier for the tenant - tenant_detail: Adapted detailed tenant information - origin_tenant_data: Original tenant data directly passed from external sources, kept unchanged without adaptation - tenant_info_patch: Cache data related to the tenant, used to store computed values (e.g., actual_database_name, real_client, etc.) - Lifecycle aligns with tenant_info, avoiding redundant computation - """ - - tenant_id: str - tenant_detail: TenantDetail - origin_tenant_data: Dict[str, Any] = field(default_factory=dict) - tenant_info_patch: Dict[str, Any] = field(default_factory=dict) - - @property - def is_shared_mode(self) -> bool: - """Whether this tenant uses shared (logical) isolation mode.""" - return self.tenant_detail.isolation_mode == "shared" - - def get_storage_info(self, storage_type: str) -> Optional[Dict[str, Any]]: - """ - Retrieve storage configuration information of the specified type - - This method retrieves the configuration for the specified storage type from tenant_detail.storage_info. - - Args: - storage_type: Storage type, such as "mongodb", "redis", "elasticsearch", etc. - - Returns: - Configuration dictionary for the specified storage type, or None if not found - - Examples: - >>> tenant_info = TenantInfo( - ... tenant_id="tenant_001", - ... tenant_detail=TenantDetail(storage_info={ - ... "mongodb": {"host": "localhost", "port": 27017} - ... }), - ... origin_tenant_data={} - ... ) - >>> mongo_config = tenant_info.get_storage_info("mongodb") - >>> print(mongo_config) - {'host': 'localhost', 'port': 27017} - """ - # Check if tenant_detail.storage_info exists - if self.tenant_detail.storage_info is None: - return None - - # Retrieve configuration of the specified type from storage_info - return self.tenant_detail.storage_info.get(storage_type) - - def get_patch_value(self, key: str, default: Any = None) -> Any: - """ - Retrieve cached value from tenant_info_patch - - This method is used to retrieve computed values cached in tenant_info_patch, - avoiding redundant computation and improving performance. - - Args: - key: Cache key name - default: Default return value if the key does not exist - - Returns: - Cached value, or default if the key does not exist - - Examples: - >>> tenant_info = TenantInfo( - ... tenant_id="tenant_001", - ... tenant_detail=TenantDetail(), - ... origin_tenant_data={} - ... ) - >>> # Retrieve cached database name - >>> db_name = tenant_info.get_patch_value(TenantPatchKey.ACTUAL_DATABASE_NAME) - """ - return self.tenant_info_patch.get(key, default) - - def set_patch_value(self, key: str, value: Any) -> None: - """ - Set cached value in tenant_info_patch - - This method is used to cache computation results, avoiding redundant computation later. - The cache lifecycle is consistent with the TenantInfo instance. - - Args: - key: Cache key name - value: Value to be cached - - Examples: - >>> tenant_info = TenantInfo( - ... tenant_id="tenant_001", - ... tenant_detail=TenantDetail(), - ... origin_tenant_data={} - ... ) - >>> # Cache database name - >>> tenant_info.set_patch_value(TenantPatchKey.ACTUAL_DATABASE_NAME, "tenant_001_db") - >>> # Cache client's cache_key - >>> tenant_info.set_patch_value(TenantPatchKey.MONGO_CLIENT_CACHE_KEY, "cache_key_value") - """ - self.tenant_info_patch[key] = value - - def has_patch_value(self, key: str) -> bool: - """ - Check if a specific cached value exists in tenant_info_patch - - Args: - key: Cache key name - - Returns: - Returns True if exists, otherwise False - - Examples: - >>> tenant_info = TenantInfo( - ... tenant_id="tenant_001", - ... tenant_detail=TenantDetail(), - ... origin_tenant_data={} - ... ) - >>> tenant_info.set_patch_value(TenantPatchKey.ACTUAL_DATABASE_NAME, "tenant_001_db") - >>> tenant_info.has_patch_value(TenantPatchKey.ACTUAL_DATABASE_NAME) - True - >>> tenant_info.has_patch_value("non_existent_key") - False - """ - return key in self.tenant_info_patch - - def clear_patch_cache(self) -> None: - """ - Clear all cached data - - In certain cases, it might be necessary to clear the cache (e.g., when tenant configuration is updated), - this method clears all data in tenant_info_patch. - - Examples: - >>> tenant_info = TenantInfo( - ... tenant_id="tenant_001", - ... tenant_detail=TenantDetail(), - ... origin_tenant_data={} - ... ) - >>> tenant_info.set_patch_value("key1", "value1") - >>> tenant_info.clear_patch_cache() - >>> tenant_info.has_patch_value("key1") - False - """ - self.tenant_info_patch.clear() - - def invalidate_patch(self, key: Optional[str] = None) -> bool: - """ - Invalidate cache (delete specific key or all) - - Used to refresh or delete cached objects in tenant_info_patch. - Call this method when cached resources need rebuilding (e.g., connection lost, configuration changed). - - Args: - key: Cache key name to delete. If None, clears all cache. - - Returns: - bool: If key is specified, returns whether the key existed and was deleted; - if key is None (clear all), always returns True. - - Examples: - >>> tenant_info = TenantInfo( - ... tenant_id="tenant_001", - ... tenant_detail=TenantDetail(), - ... origin_tenant_data={} - ... ) - >>> tenant_info.set_patch_value(TenantPatchKey.MONGO_REAL_DATABASE, some_db_obj) - >>> # Delete specific cache - >>> tenant_info.invalidate_patch(TenantPatchKey.MONGO_REAL_DATABASE) - True - >>> # Clear all cache - >>> tenant_info.invalidate_patch() - True - """ - if key is None: - # Clear all cache - self.tenant_info_patch.clear() - return True - else: - # Delete specific key - if key in self.tenant_info_patch: - del self.tenant_info_patch[key] - return True - return False - - # ==================== Serialization/Deserialization methods ==================== - # Used to pass tenant context to other processes via asynchronous tasks - - def to_dict(self) -> Dict[str, Any]: - """ - Convert TenantInfo to dictionary (for serialization) - - Note: tenant_info_patch will not be serialized because it may contain non-serializable objects (e.g., database connections). - After deserialization, tenant_info_patch will be initialized as an empty dictionary. - - Returns: - Dict[str, Any]: Dictionary containing tenant_id, tenant_detail, and origin_tenant_data - """ - return { - 'tenant_id': self.tenant_id, - 'tenant_detail': self.tenant_detail.to_dict(), - 'origin_tenant_data': self.origin_tenant_data, - } - - def to_json(self) -> str: - """ - Serialize TenantInfo into a JSON string - - Used to pass tenant context to other processes via asynchronous tasks. - - Note: tenant_info_patch will not be serialized. - - Returns: - str: JSON string - - Examples: - >>> tenant_info = TenantInfo( - ... tenant_id="t1234567890abcdef", - ... tenant_detail=TenantDetail(tenant_info={"account_id": "acc_001"}), - ... origin_tenant_data={} - ... ) - >>> json_str = tenant_info.to_json() - """ - return json.dumps(self.to_dict(), ensure_ascii=False) - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> 'TenantInfo': - """ - Create TenantInfo instance from dictionary - - Args: - data: Dictionary containing tenant_id, tenant_detail, and origin_tenant_data - - Returns: - TenantInfo: Newly created instance (tenant_info_patch initialized as empty dictionary) - """ - tenant_detail_data = data.get('tenant_detail', {}) - return cls( - tenant_id=data['tenant_id'], - tenant_detail=TenantDetail.from_dict(tenant_detail_data), - origin_tenant_data=data.get('origin_tenant_data', {}), - # tenant_info_patch is not restored from serialized data, initialized as empty - ) - - @classmethod - def from_json(cls, json_str: str) -> 'TenantInfo': - """ - Deserialize TenantInfo instance from JSON string - - Used to receive tenant context from other processes via asynchronous tasks. - - Args: - json_str: JSON string - - Returns: - TenantInfo: Newly created instance (tenant_info_patch initialized as empty dictionary) - - Examples: - >>> json_str = '{"tenant_id": "t1234567890abcdef", ...}' - >>> tenant_info = TenantInfo.from_json(json_str) - """ - data = json.loads(json_str) - return cls.from_dict(data) diff --git a/methods/EverCore/src/core/tenants/tenant_router.py b/methods/EverCore/src/core/tenants/tenant_router.py deleted file mode 100644 index bf5dcfab..00000000 --- a/methods/EverCore/src/core/tenants/tenant_router.py +++ /dev/null @@ -1,74 +0,0 @@ -""" -Tenant Router - -Responsible for resolving tenant identity from requests and building tenant context -(TenantInfo with storage_info, isolation_mode, etc.). - -This is the SINGLE SOURCE OF TRUTH for "which tenant is this request for" and -"where does this tenant's data live". The routing decision includes: -1. Resolve tenant identity from the request (headers, tokens, etc.) -2. Determine isolation mode (shared vs exclusive) -3. Build storage_info (database names, index prefixes, collection prefixes) -4. Construct TenantInfo and set it into the request context - -Open-source version: no-op (non-tenant mode, no routing needed). -Enterprise version: extracts org/space from headers, generates tenant_id, -builds storage_info with proper naming, sets isolation_mode. -""" - -from abc import ABC, abstractmethod -from typing import Any, Dict, Optional - -from fastapi import Request - -from core.di.decorators import component -from core.observation.logger import get_logger -from core.tenants.tenant_models import TenantInfo - -logger = get_logger(__name__) - - -class TenantRouter(ABC): - """ - Tenant router interface. - - Implementations resolve tenant identity from HTTP requests and build - the full TenantInfo (including storage_info and isolation_mode). - - The router is called by AppLogicProvider.setup_app_context() on every request. - """ - - @abstractmethod - def resolve_tenant(self, request: Request) -> Optional[TenantInfo]: - """ - Resolve tenant from the incoming request and set tenant context. - - Implementations should: - 1. Extract tenant identity from request (headers, auth tokens, etc.) - 2. Build TenantDetail with storage_info and isolation_mode - 3. Construct TenantInfo - 4. Call set_current_tenant() to set context - 5. Return the TenantInfo (or None if no tenant context) - - Args: - request: FastAPI request object - - Returns: - TenantInfo if tenant was resolved and context set, None otherwise - """ - raise NotImplementedError - - -@component("default_tenant_router") -class DefaultTenantRouter(TenantRouter): - """ - Default tenant router (open-source version). - - No-op implementation — returns None (no tenant routing). - In non-tenant mode or open-source deployments, tenant context - is either not needed or handled by single-tenant auto-activation. - """ - - def resolve_tenant(self, request: Request) -> Optional[TenantInfo]: - """No tenant routing in open-source version.""" - return None diff --git a/methods/EverCore/src/core/tenants/tenant_switch.py b/methods/EverCore/src/core/tenants/tenant_switch.py deleted file mode 100644 index a4c6c712..00000000 --- a/methods/EverCore/src/core/tenants/tenant_switch.py +++ /dev/null @@ -1,24 +0,0 @@ -# ============================================================================ -# Application ready listener (automatically discovered via DI) -# ============================================================================ - -from core.di.decorators import component -from core.lifespan.lifespan_factory import AppReadyListener -from core.observation.logger import get_logger -from core.tenants.tenant_config import get_tenant_config - -logger = get_logger(__name__) - - -@component(name="tenant_config_app_ready_listener") -class TenantConfigAppReadyListener(AppReadyListener): - """ - Tenant configuration application ready listener - - Automatically enables strict tenant checking mode when application startup is complete. - Automatically discovered and invoked by the DI container, no manual registration required. - """ - - def on_app_ready(self) -> None: - """Enable strict tenant check when application is ready""" - get_tenant_config().mark_app_ready() diff --git a/methods/EverCore/src/core/tenants/tenantize/__init__.py b/methods/EverCore/src/core/tenants/tenantize/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/tenants/tenantize/kv/__init__.py b/methods/EverCore/src/core/tenants/tenantize/kv/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/kv/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/tenants/tenantize/kv/redis/__init__.py b/methods/EverCore/src/core/tenants/tenantize/kv/redis/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/kv/redis/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/tenants/tenantize/kv/redis/tenant_key_utils.py b/methods/EverCore/src/core/tenants/tenantize/kv/redis/tenant_key_utils.py deleted file mode 100644 index 87fdc74f..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/kv/redis/tenant_key_utils.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -Redis tenant key utility functions module - -Provides tenant isolation for Redis key names by prepending the tenant ID to achieve multi-tenant data isolation. -""" - -from typing import Optional - -from core.tenants.tenant_contextvar import get_current_tenant_id - - -def build_tenant_redis_key(prefix: str, tenant_id: str, key: str) -> str: - """ - Build a tenant-scoped Redis key with an explicit tenant_id. - - Format: {prefix}:{tenant_id}:{key} - - Args: - prefix: Key namespace prefix (e.g. "task_status") - tenant_id: Tenant identifier - key: Business key (e.g. task_id, request_id) - - Returns: - str: "{prefix}:{tenant_id}:{key}" - - Examples: - >>> build_tenant_redis_key("task_status", "t3a7b2c1d9e", "abc123") - 'task_status:t3a7b2c1d9e:abc123' - """ - return f"{prefix}:{tenant_id}:{key}" diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/__init__.py b/methods/EverCore/src/core/tenants/tenantize/oxm/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/es/__init__.py b/methods/EverCore/src/core/tenants/tenantize/oxm/es/__init__.py deleted file mode 100755 index 6e1a021f..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/es/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -""" -Elasticsearch tenant isolation OXM module - -This module provides tenant isolation support for Elasticsearch, including: -- TenantAwareAsyncDocument: Tenant-aware asynchronous document class -- TenantAwareAliasDoc: Factory function for tenant-aware alias document class -- Configuration utility functions: Get tenant ES configuration, generate connection cache key, etc. -""" diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/es/config_utils.py b/methods/EverCore/src/core/tenants/tenantize/oxm/es/config_utils.py deleted file mode 100644 index 52f22bf4..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/es/config_utils.py +++ /dev/null @@ -1,250 +0,0 @@ -""" -Elasticsearch tenant configuration utility functions - -This module provides utility functions related to tenant Elasticsearch configuration, used to extract ES configuration from tenant information. -""" - -import os -from typing import Optional, Dict, Any -from hashlib import md5 - -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import get_current_tenant - - -logger = get_logger(__name__) - - -def get_tenant_es_config() -> Optional[Dict[str, Any]]: - """ - Get Elasticsearch configuration from the current tenant context - - Extract Elasticsearch-related configuration from the storage_info of tenant information. - If tenant configuration is incomplete or missing, supplement it from environment variables. - - Configuration structure example: - { - "hosts": ["http://localhost:9200"], - "username": "elastic", - "password": "password", - "api_key": None, - "timeout": 120, - "verify_certs": False - } - - Returns: - Elasticsearch configuration dictionary, return None if not exists - - Examples: - >>> config = get_tenant_es_config() - >>> if config: - ... print(f"ES Hosts: {config['hosts']}") - """ - tenant_info = get_current_tenant() - if not tenant_info: - logger.debug( - "⚠️ Tenant context is not set, unable to get tenant Elasticsearch configuration" - ) - return None - - # Get ES configuration from tenant's storage_info - # Support multiple configuration key names: elasticsearch, es_config, es - es_config = tenant_info.get_storage_info("elasticsearch") - if es_config is None: - es_config = tenant_info.get_storage_info("es_config") - if es_config is None: - es_config = tenant_info.get_storage_info("es") - - # Get environment variable configuration as fallback - env_fallback_config = load_es_config_from_env() - - if not es_config: - # No ES information at all in tenant configuration, use environment variable configuration - final_config = { - "hosts": env_fallback_config.get("hosts", ["http://localhost:9200"]), - "username": env_fallback_config.get("username"), - "password": env_fallback_config.get("password"), - "api_key": env_fallback_config.get("api_key"), - "timeout": env_fallback_config.get("timeout", 120), - "verify_certs": env_fallback_config.get("verify_certs", False), - } - logger.info( - "✅ Elasticsearch information missing in tenant [%s] configuration, using environment variable configuration to complete: hosts=%s", - tenant_info.tenant_id, - final_config["hosts"], - ) - return final_config - - # Compatibility logic: if tenant configuration is missing certain fields, supplement from environment variables - # Handle multiple formats of hosts field - tenant_hosts = es_config.get("hosts") - if tenant_hosts is None: - # Try to build from host/port - tenant_host = es_config.get("host") - tenant_port = es_config.get("port", 9200) - if tenant_host: - tenant_hosts = [f"http://{tenant_host}:{tenant_port}"] - - final_config = { - "hosts": tenant_hosts - or env_fallback_config.get("hosts", ["http://localhost:9200"]), - "username": es_config.get("username") or env_fallback_config.get("username"), - "password": es_config.get("password") or env_fallback_config.get("password"), - "api_key": es_config.get("api_key") or env_fallback_config.get("api_key"), - "timeout": es_config.get("timeout") or env_fallback_config.get("timeout", 120), - "verify_certs": es_config.get( - "verify_certs", env_fallback_config.get("verify_certs", False) - ), - } - - logger.debug( - "✅ Retrieved Elasticsearch configuration from tenant [%s]: hosts=%s", - tenant_info.tenant_id, - final_config["hosts"], - ) - - return final_config - - -def get_es_connection_cache_key(config: Dict[str, Any]) -> str: - """ - Generate cache key based on Elasticsearch connection configuration - - Use the hash value of connection parameters (hosts, authentication info) as the cache key. - Also used as the alias for elasticsearch-dsl connections. - - Args: - config: Elasticsearch connection configuration dictionary - - Returns: - Cache key string (MD5 hash value) - - Examples: - >>> config = {"hosts": ["http://localhost:9200"], "username": "elastic", "password": "pwd"} - >>> cache_key = get_es_connection_cache_key(config) - """ - # Handle hosts - hosts = config.get("hosts", []) - if isinstance(hosts, list): - hosts_str = ",".join(sorted(hosts)) - else: - hosts_str = str(hosts) - - # Handle authentication info - auth_str = "" - api_key = config.get("api_key") - username = config.get("username") - password = config.get("password") - - if api_key: - # Use first 8 characters of api_key as identifier - auth_str = f"api_key:{api_key[:8]}..." - elif username and password: - # Use md5 of username and password as identifier - auth_str = f"basic:{username}:{md5(password.encode()).hexdigest()[:8]}" - elif username: - # When only username is present, use username only - auth_str = f"basic:{username}" - - key_content = f"{hosts_str}:{auth_str}" - return md5(key_content.encode()).hexdigest()[:16] - - -def load_es_config_from_env() -> Dict[str, Any]: - """ - Load default Elasticsearch configuration from environment variables - - Read the following environment variables: - - ES_HOSTS: Elasticsearch host list, comma-separated (takes precedence) - - ES_HOST: Elasticsearch host address, default localhost - - ES_PORT: Elasticsearch port, default 9200 - - ES_USERNAME: Username (optional) - - ES_PASSWORD: Password (optional) - - ES_API_KEY: API key (optional) - - ES_TIMEOUT: Timeout in seconds, default 120 - - ES_VERIFY_CERTS: Whether to verify certificates, default false - - Returns: - Elasticsearch configuration dictionary - - Examples: - >>> config = load_es_config_from_env() - >>> print(f"ES Hosts: {config['hosts']}") - """ - # Get host information - es_hosts_str = os.getenv("ES_HOSTS") - if es_hosts_str: - # ES_HOSTS already contains full URL (https://host:port), use directly - es_hosts = [host.strip() for host in es_hosts_str.split(",")] - else: - # Fall back to single host configuration - es_host = os.getenv("ES_HOST", "localhost") - es_port = int(os.getenv("ES_PORT", "9200")) - es_hosts = [f"http://{es_host}:{es_port}"] - - config = { - "hosts": es_hosts, - "username": os.getenv("ES_USERNAME"), - "password": os.getenv("ES_PASSWORD"), - "api_key": os.getenv("ES_API_KEY"), - "timeout": int(os.getenv("ES_TIMEOUT", "120")), - "verify_certs": os.getenv("ES_VERIFY_CERTS", "false").lower() == "true", - } - - logger.debug( - "Loaded default Elasticsearch configuration from environment variables: hosts=%s", - config["hosts"], - ) - - return config - - -def _base_prefixed_index_name(original_name: str) -> str: - """Apply the base resource prefix to an index name (e.g., "v1_memories" → "b0001_v1_memories").""" - from core.tenants.tenant_constants import get_base_resource_prefix - - return f"{get_base_resource_prefix()}_{original_name}" - - -def get_tenant_aware_index_name(original_name: str) -> str: - """ - Get tenant-aware index name. - - Resolution order: - 1. Tenant context exists → read from storage_info.elasticsearch.index_prefix - 2. No tenant context (e.g., startup) → base resource prefix + original_name - - Args: - original_name: Original index name (e.g., "v1_atomic_fact_record") - - Returns: - str: Resolved index name (e.g., "dev-v1_atomic_fact_record", "b0001-v1_atomic_fact_record") - """ - try: - tenant_info = get_current_tenant() - if not tenant_info: - return _base_prefixed_index_name(original_name) - - # Read index_prefix from storage_info (set by routing layer) - es_config = tenant_info.get_storage_info("elasticsearch") - if es_config is None: - es_config = tenant_info.get_storage_info("es_config") - if es_config is None: - es_config = tenant_info.get_storage_info("es") - - if es_config and es_config.get("index_prefix"): - return f"{es_config['index_prefix']}_{original_name}" - - # No index_prefix configured - logger.warning( - "Tenant [%s] storage_info has no elasticsearch.index_prefix configured, " - "using base prefix. Configure index_prefix in routing layer.", - tenant_info.tenant_id, - ) - return _base_prefixed_index_name(original_name) - - except Exception as e: - logger.warning( - "Failed to get tenant-aware index name, using base prefix: %s", e - ) - return _base_prefixed_index_name(original_name) diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/es/tenant_aware_async_document.py b/methods/EverCore/src/core/tenants/tenantize/oxm/es/tenant_aware_async_document.py deleted file mode 100644 index d86c4de4..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/es/tenant_aware_async_document.py +++ /dev/null @@ -1,448 +0,0 @@ -""" -Tenant-aware Elasticsearch AsyncDocument - -This module implements tenant awareness by inheriting from AliasSupportDoc and overriding key methods. -Core idea: Dynamically return the correct connection and index names based on tenant context. -""" - -from typing import Optional, Any, Dict, Type -from fnmatch import fnmatch -from elasticsearch import AsyncElasticsearch -from elasticsearch.dsl import field as e_field -from elasticsearch.dsl.async_connections import connections as async_connections - -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import get_current_tenant -from core.tenants.tenant_models import TenantPatchKey -from core.tenants.tenantize.oxm.es.config_utils import ( - get_tenant_es_config, - get_es_connection_cache_key, - load_es_config_from_env, - get_tenant_aware_index_name, -) -from core.tenants.tenantize.tenant_cache_utils import get_or_compute_tenant_cache -from core.oxm.es.doc_base import AliasSupportDoc -from core.oxm.es.mapping_templates import DYNAMIC_TEMPLATES - -logger = get_logger(__name__) - - -class TenantAwareAsyncDocument(AliasSupportDoc): - """ - Tenant-aware Elasticsearch AsyncDocument - - Implements tenant awareness by inheriting from AliasSupportDoc and overriding key methods. - Core functionality: Automatically selects and returns the correct ES connection and index based on current tenant context. - - Key features: - 1. Tenant isolation: Different tenants use different ES connections (distinguished by alias) - 2. Index isolation: Different tenants use different index names (with tenant prefix) - 3. Connection reuse: Tenants with the same configuration share the same connection (cached via cache_key) - 4. Automatic registration: Automatically registers tenant connection upon first access - 5. Fallback connection: Uses default connection in non-tenant mode or when no tenant context exists - - Usage: - >>> # Define a tenant-aware document class - >>> class MyDoc(TenantAwareAsyncDocument): - ... title = field.Text() - ... - ... class Index: - ... name = "my_index" - - Notes: - - The provided 'using' parameter is ignored; the actual connection alias used is tenant-aware - - Connection is automatically registered on first access - - Connection alias and configuration are cached in tenant_info_patch to avoid redundant computation - """ - - # Logical tenant isolation field. - # Keyword type for exact matching + filter cache. - # Injected by TenantAwareAsyncElasticsearch interceptor at write time. - tenant_id = e_field.Keyword() - - class Meta: - abstract = True - - # ============================================================ - # Tenant-aware connection management - # ============================================================ - - @classmethod - def _get_using(cls, using: Optional[str] = None) -> str: - """ - Override parent method to return tenant-aware connection alias - - Ignore the provided 'using' parameter and return the correct connection alias based on tenant context. - - Args: - using: Connection alias (will be ignored) - - Returns: - str: Tenant-aware connection alias - """ - return cls._get_tenant_aware_using() - - @classmethod - def _get_connection(cls, using: Optional[str] = None) -> AsyncElasticsearch: - """ - Override parent method to return tenant-aware connection - - This is the core method: called every time an ES connection is needed. - Here we dynamically return the correct connection based on tenant context. - - Args: - using: Connection alias (will be ignored) - - Returns: - AsyncElasticsearch: Tenant-aware ES client - """ - # Dynamically get the connection alias for the current tenant - tenant_using = cls._get_tenant_aware_using() - - # Return the corresponding connection - return async_connections.get_connection(tenant_using) - - @classmethod - def _get_tenant_aware_using(cls) -> str: - """ - Get tenant-aware connection alias - - Decide which connection alias to return based on configuration and context: - 1. If non-tenant mode is enabled, return "default" - 2. If tenant mode is enabled, return the corresponding connection alias based on current tenant configuration - 3. If in tenant mode but no tenant context exists, return "default" - - Returns: - str: elasticsearch-dsl connection alias (using) - """ - - def compute_using() -> str: - """Compute tenant connection alias""" - # Get ES configuration from tenant settings - es_config = get_tenant_es_config() - if not es_config: - raise RuntimeError("Tenant is missing Elasticsearch configuration") - - # Generate a unique connection alias based on connection parameters - cache_key = get_es_connection_cache_key(es_config) - using = f"tenant_{cache_key}" - # es connection is registered in _register_connection - cls._ensure_connection_registered(using) - return using - - return get_or_compute_tenant_cache( - patch_key=TenantPatchKey.ES_CONNECTION_CACHE_KEY, - compute_func=compute_using, - fallback="default", # Concrete value, no need for deferred computation - cache_description="Elasticsearch connection alias", - ) - - @classmethod - def _ensure_connection_registered(cls, using: str) -> None: - """ - Ensure the specified connection alias is registered - - If the connection is not yet registered, it will be automatically registered. - - Args: - using: Connection alias - - Note: - - For "default" connection, assume it's already registered at application startup - - For tenant connections (tenant_*), automatically register if not already registered - """ - # Check if connection already exists - try: - async_connections.get_connection(using) - # Connection exists, return directly - return - except Exception: - # Connection does not exist, need to register - pass - - # If it's the default connection, try to register from environment variables - if using == "default": - logger.info("📋 Registering default Elasticsearch connection") - config = load_es_config_from_env() - cls._register_connection(config, using) - return - - # Tenant connection: register from tenant configuration - try: - tenant_info = get_current_tenant() - if not tenant_info: - raise RuntimeError( - "Cannot register tenant connection: tenant context not set" - ) - - es_config = get_tenant_es_config() - if not es_config: - raise RuntimeError( - f"Cannot register tenant connection: tenant {tenant_info.tenant_id} is missing Elasticsearch configuration" - ) - - logger.info( - "📋 Registering Elasticsearch connection for tenant [%s] [using=%s]", - tenant_info.tenant_id, - using, - ) - - cls._register_connection(es_config, using) - - except Exception as e: - logger.error( - "Failed to register tenant connection [using=%s]: %s", using, e - ) - raise - - @classmethod - def _register_connection(cls, config: Dict[str, Any], using: str) -> None: - """ - Register Elasticsearch connection - - Args: - config: Elasticsearch connection configuration - using: Connection alias - - Note: - - Use elasticsearch-dsl's connections manager to create the connection - - This allows reuse of existing connection pool management logic - """ - try: - # Build connection parameters - conn_params = { - "hosts": config.get("hosts", ["http://localhost:9200"]), - "timeout": config.get("timeout", 120), - "max_retries": 3, - "retry_on_timeout": True, - "verify_certs": config.get("verify_certs", False), - "ssl_show_warn": False, - } - - # Add authentication info - api_key = config.get("api_key") - username = config.get("username") - password = config.get("password") - - if api_key: - conn_params["api_key"] = api_key - elif username and password: - conn_params["basic_auth"] = (username, password) - - # Create tenant-aware client with Guard transport (dual-layer defense) - # Layer 1: TenantAwareAsyncElasticsearch intercepts at perform_request() - # Layer 2: TenantGuardTransport verifies at transport level - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - TenantGuardTransport, - ) - - conn_params["transport_class"] = TenantGuardTransport - client = TenantAwareAsyncElasticsearch(**conn_params) - async_connections.add_connection(alias=using, conn=client) - - logger.info( - "✅ Tenant-aware Elasticsearch connection registered [using=%s, hosts=%s]", - using, - conn_params["hosts"], - ) - - except Exception as e: - logger.error( - "Failed to register Elasticsearch connection [using=%s]: %s", using, e - ) - raise - - # ============================================================ - # Tenant-aware index management - # ============================================================ - - @classmethod - def get_original_index_name(cls) -> str: - """ - Get original index name (without tenant prefix) - - Retrieve the originally configured index name from cls._index._name. - - Returns: - str: Original index name - """ - if hasattr(cls, '_index') and hasattr(cls._index, '_name'): - return cls._index._name - raise ValueError( - f"Document class {cls.__name__} does not have correct index configuration" - ) - - @classmethod - def get_index_name(cls) -> str: - """ - Override parent method to return tenant-aware index name - - Add tenant prefix to index name based on current tenant context. - If in non-tenant mode or no tenant context exists, return original name. - - Returns: - str: Tenant-aware index name - """ - original_name = cls.get_original_index_name() - return get_tenant_aware_index_name(original_name) - - @classmethod - def _matches(cls, hit: Dict[str, Any]) -> bool: - """ - Override parent method to match tenant-aware index patterns - - Used to filter documents belonging to the current tenant from ES responses. - - Args: - hit: ES hit result - - Returns: - bool: Whether it matches the current document class - """ - # Get tenant-aware index name - tenant_index_name = cls.get_index_name() - - # Build matching pattern - pattern = f"{tenant_index_name}-*" - - return fnmatch(hit.get("_index", ""), pattern) - - @classmethod - def _default_index(cls, index: Optional[str] = None) -> str: - """ - Override parent method to return tenant-aware default index name - - Args: - index: Optional index name override - - Returns: - str: Tenant-aware index name - """ - if index: - return index - return cls.get_index_name() - - def _get_index( - self, index: Optional[str] = None, required: bool = True - ) -> Optional[str]: - """ - Override parent method to return tenant-aware index name - - Args: - index: Optional index name override - required: Whether an index name must be returned - - Returns: - Optional[str]: Index name - - Raises: - ValidationException: If required=True and index name cannot be obtained - """ - # If index is explicitly provided, use it directly - if index is not None: - return index - - # Try to get from meta - if hasattr(self, 'meta') and hasattr(self.meta, 'index'): - meta_index = getattr(self.meta, 'index', None) - if meta_index: - return meta_index - - # Return tenant-aware default index name - tenant_index = self.get_index_name() - if tenant_index: - return tenant_index - - # If index name is required but cannot be obtained, raise exception - if required: - from elasticsearch.dsl.exceptions import ValidationException - - raise ValidationException("No index") - - return None - - @classmethod - def dest(cls) -> str: - """ - Override parent method to generate tenant-aware destination index name (with timestamp) - - Returns: - str: Destination index name with timestamp - """ - # Use tenant-aware index name to generate destination name - tenant_index_name = cls.get_index_name() - from common_utils.datetime_utils import get_now_with_timezone - - now = get_now_with_timezone() - return f"{tenant_index_name}-{now.strftime('%Y%m%d%H%M%S%f')}" - - -def TenantAwareAliasDoc( - doc_name: str, - number_of_shards: int = 3, - number_of_replicas: int = 1, - refresh_interval: str = "10s", -) -> Type[TenantAwareAsyncDocument]: - """ - Create a tenant-aware ES document class that supports alias pattern - - This is a factory function for creating tenant-aware document classes. - Automatically handles timezone for datetime fields and tenant isolation. - - Args: - doc_name: Document name (original index name) - number_of_shards: Number of shards - - Returns: - Tenant-aware document class - - Examples: - >>> # Create a tenant-aware document class - >>> class MyDoc(TenantAwareAliasDoc("my_docs")): - ... title = field.Text() - ... content = field.Text() - """ - from elasticsearch.dsl import MetaField - from core.oxm.es.es_utils import get_index_ns - - # If there is a namespace, append it to the document name - if get_index_ns(): - doc_name = f"{doc_name}-{get_index_ns()}" - - class GeneratedTenantAwareDoc(TenantAwareAsyncDocument): - # Save original document name for use in tenant-aware methods - _ORIGINAL_DOC_NAME = doc_name - PATTERN = f"{doc_name}-*" - - class Index: - name = doc_name - settings = { - "number_of_shards": number_of_shards, - "number_of_replicas": number_of_replicas, - "refresh_interval": refresh_interval, - "max_ngram_diff": 50, - "max_shingle_diff": 10, - } - - class Meta: - dynamic = MetaField("true") - # Disable date auto-detection to prevent "2023/10/01" from being incorrectly converted and causing subsequent errors - date_detection = MetaField(False) - # Disable numeric detection to prevent string numbers from being confused - numeric_detection = MetaField(False) - # Dynamic mapping rules based on field suffixes (see mapping_templates.py) - dynamic_templates = MetaField(DYNAMIC_TEMPLATES) - # NOTE: _routing.required is NOT set here globally because: - # - Exclusive mode: routing=tenant_id would concentrate all docs on one shard, - # breaking multi-shard load balancing for large tenants - # - Exclusive mode: get/delete by ID passthrough without routing, would be rejected - # _routing.required should be configured per-index by the routing layer - # for shared-mode indexes only. - - @classmethod - def get_original_index_name(cls) -> str: - """Get original index name (without tenant prefix)""" - return doc_name - - return GeneratedTenantAwareDoc diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/es/tenant_field_es_interceptor.py b/methods/EverCore/src/core/tenants/tenantize/oxm/es/tenant_field_es_interceptor.py deleted file mode 100644 index 04b6d095..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/es/tenant_field_es_interceptor.py +++ /dev/null @@ -1,872 +0,0 @@ -""" -Elasticsearch Tenant Isolation - -Single-file implementation of tenant isolation for Elasticsearch, consisting of -three logical sections: - - Section 1 — Query Utilities - Pure functions that inject tenant_id filters into various ES query structures. - No state, no side effects. - - Section 2 — Interceptor (Layer 1) - TenantAwareAsyncElasticsearch: subclasses AsyncElasticsearch, overrides - perform_request() — the single chokepoint ALL ES API calls go through. - Injects tenant_id filter/field/routing based on endpoint_id. - - Section 3 — Guard Transport (Layer 2) - TenantGuardTransport: subclasses AsyncTransport, independent verification - layer. Does NOT modify data — only checks body structure and raises alarm - if tenant_id filter is missing. Second line of defense. - -Three-category whitelist strategy (consistent with MongoDB TenantCommandInterceptor): - - Data-plane (explicit handling): search, count, index, bulk, get, delete, etc. - → inject tenant_id into query/document/routing - - Control-plane (passthrough): indices.*, cluster.*, tasks.*, ping, etc. - → delegate to parent as-is - - Unknown: reject - → raise TenantIsolationViolation -""" - -from typing import ( - Any, - Collection, - Dict, - FrozenSet, - List, - Mapping, - Optional, - Set, - Tuple, - Union, -) - -from elasticsearch import AsyncElasticsearch -from elastic_transport import ( - ApiResponseMeta, - AsyncTransport, - HeadApiResponse, - HttpHeaders, - ObjectApiResponse, -) -from elastic_transport._models import DEFAULT, DefaultType, SniffOptions -from elastic_transport._otel import OpenTelemetrySpan - -from core.observation.logger import get_logger -from core.tenants.tenant_config import get_tenant_config -from core.tenants.tenant_constants import TENANT_ID_FIELD -from core.tenants.tenant_contextvar import get_current_tenant_id - -logger = get_logger(__name__) - - -# ============================================================================ -# Exceptions -# ============================================================================ - - -class TenantIsolationViolation(Exception): - """Raised when an ES operation violates tenant isolation (Layer 1).""" - - pass - - -class TenantGuardViolation(Exception): - """Raised when the Guard Transport detects a tenant isolation violation (Layer 2).""" - - pass - - -# ============================================================================ -# Section 1 — Query Utilities -# ============================================================================ -# -# Pure functions to inject tenant_id filters into various ES query structures. -# Design principle: wrap at the outermost level only — never recurse into -# nested queries. This is safe because bool.filter is a pure intersection -# operation that doesn't affect inner query scoring or semantics. -# ============================================================================ - - -def wrap_query_with_tenant(query: dict, tenant_id: str) -> dict: - """ - Wrap any ES query with a tenant_id filter at the outermost level. - - Safe for ALL query types (bool, match, function_score, nested, knn, etc.) - because we only add an outer bool.filter — the original query is preserved as-is. - """ - tenant_clause = {"term": {TENANT_ID_FIELD: tenant_id}} - - if "bool" in query and len(query) == 1: - # Merge into existing bool query's filter array - bool_query = query["bool"] - existing_filter = bool_query.get("filter", []) - if isinstance(existing_filter, dict): - existing_filter = [existing_filter] - else: - existing_filter = list(existing_filter) - existing_filter.append(tenant_clause) - bool_query["filter"] = existing_filter - return query - - # Wrap non-bool query in a new bool - return {"bool": {"must": [query], "filter": [tenant_clause]}} - - -def make_tenant_only_query(tenant_id: str) -> dict: - """ - Create a filter-only query that matches all documents for a tenant. - Used when the original request has no query (equivalent to match_all). - """ - return {"bool": {"filter": [{"term": {TENANT_ID_FIELD: tenant_id}}]}} - - -def inject_query_body(body: Optional[dict], tenant_id: str) -> dict: - """ - Inject tenant_id filter into a search-like request body. - Handles: body.query, body.post_filter, body.suggest - """ - if body is None: - body = {} - - # suggest → blocked (term/phrase can't be isolated, completion needs mapping) - if "suggest" in body: - raise TenantIsolationViolation( - "Elasticsearch suggest is blocked in tenant isolation mode. " - "Term/Phrase suggest cannot be tenant-isolated. " - "Completion suggest requires context filter setup. " - "Contact platform team to enable suggest support." - ) - - # query injection - query = body.get("query") - if query is None: - body["query"] = make_tenant_only_query(tenant_id) - else: - body["query"] = wrap_query_with_tenant(query, tenant_id) - - # post_filter injection (belt-and-suspenders) - post_filter = body.get("post_filter") - if post_filter is not None: - body["post_filter"] = wrap_query_with_tenant(post_filter, tenant_id) - - return body - - -def inject_index_filter_body(body: Optional[dict], tenant_id: str) -> dict: - """ - Inject tenant_id filter into body.index_filter field. - Used by: terms_enum, field_caps - """ - if body is None: - body = {} - - tenant_clause = {"term": {TENANT_ID_FIELD: tenant_id}} - index_filter = body.get("index_filter") - if index_filter is None: - body["index_filter"] = {"bool": {"filter": [tenant_clause]}} - else: - body["index_filter"] = wrap_query_with_tenant(index_filter, tenant_id) - - return body - - -def inject_knn_filter_body(body: Optional[dict], tenant_id: str) -> dict: - """ - Inject tenant_id filter into body.filter field (top-level list). - Used by: knn_search - """ - if body is None: - body = {} - - tenant_clause = {"term": {TENANT_ID_FIELD: tenant_id}} - existing_filter = body.get("filter", []) - if isinstance(existing_filter, dict): - existing_filter = [existing_filter] - else: - existing_filter = list(existing_filter) - existing_filter.append(tenant_clause) - body["filter"] = existing_filter - - return body - - -def inject_msearch_body(body: Any, tenant_id: str) -> Any: - """ - Inject tenant_id filter into each sub-request of an msearch body. - - msearch body is a list where every two elements form a pair: - [header, body, header, body, ...] - - Even indices (0, 2, 4, ...): search header (index, routing, etc.) - - Odd indices (1, 3, 5, ...): search body (query, aggs, etc.) - """ - if not isinstance(body, list): - return body - - for i in range(0, len(body), 2): - # Inject routing into header - if i < len(body) and isinstance(body[i], dict): - body[i].setdefault("routing", tenant_id) - - # Inject tenant filter into search body - body_idx = i + 1 - if body_idx < len(body) and isinstance(body[body_idx], dict): - body[body_idx] = inject_query_body(body[body_idx], tenant_id) - - return body - - -def inject_bulk_body(body: Any, tenant_id: str) -> Any: - """ - Inject tenant_id into each action of a bulk request body. - - bulk body is NDJSON: every two elements form a pair: - [action_meta, document, action_meta, document, ...] - - For index/create: inject tenant_id into document + routing into metadata - For update: inject tenant_id into doc/upsert + routing into metadata - For delete: reject (no document body to inject into) - """ - if not isinstance(body, list): - return body - - i = 0 - while i < len(body): - action_meta = body[i] - if not isinstance(action_meta, dict): - i += 1 - continue - - action_type = next(iter(action_meta), None) - if action_type is None: - i += 1 - continue - - action_params = action_meta[action_type] - if isinstance(action_params, dict): - action_params.setdefault("routing", tenant_id) - - if action_type in ("index", "create"): - # Next element is the document - doc_idx = i + 1 - if doc_idx < len(body) and isinstance(body[doc_idx], dict): - body[doc_idx][TENANT_ID_FIELD] = tenant_id - i += 2 - - elif action_type == "update": - # Next element is the update body (doc/upsert/script) - doc_idx = i + 1 - if doc_idx < len(body) and isinstance(body[doc_idx], dict): - update_body = body[doc_idx] - if "doc" in update_body and isinstance(update_body["doc"], dict): - update_body["doc"][TENANT_ID_FIELD] = tenant_id - if "upsert" in update_body and isinstance(update_body["upsert"], dict): - update_body["upsert"][TENANT_ID_FIELD] = tenant_id - i += 2 - - elif action_type == "delete": - # delete in bulk has no document body — cannot inject tenant filter - raise TenantIsolationViolation( - "bulk delete is not supported in tenant isolation mode. " - "Use delete_by_query with tenant_id filter instead." - ) - - else: - i += 1 - - return body - - -def make_ids_tenant_query(doc_id: str, tenant_id: str) -> dict: - """ - Build a query that matches a specific document ID AND tenant_id. - Used to convert ID-based operations (get, exists, delete) to query-based ones. - """ - return { - "bool": { - "filter": [ - {"ids": {"values": [doc_id]}}, - {"term": {TENANT_ID_FIELD: tenant_id}}, - ] - } - } - - -# ============================================================================ -# Section 2 — Interceptor (Layer 1) -# ============================================================================ -# -# TenantAwareAsyncElasticsearch: intercepts ALL ES operations via the single -# perform_request() chokepoint. No individual method overrides needed — -# perform_request() is the single method ALL 47+ AsyncElasticsearch methods -# (search, count, index, bulk, get, etc.) and all namespace clients -# (indices.*, cluster.*, tasks.*) delegate to. -# -# endpoint_id is provided by every ES SDK method call (e.g., "search", "index", -# "indices.create") and is used for OpenTelemetry tracing — stable across versions. -# ============================================================================ - - -class TenantAwareAsyncElasticsearch(AsyncElasticsearch): - """Tenant-aware ES client that intercepts all operations via perform_request().""" - - # ================================================================ - # Endpoint classification - # ================================================================ - - # Query endpoints: inject bool.filter(tenant_id) into body.query - _QUERY_ENDPOINTS_ACTIVE: FrozenSet[str] = frozenset( - {"search", "count", "delete_by_query"} - ) - _QUERY_ENDPOINTS_BLOCKED: FrozenSet[str] = frozenset({"update_by_query"}) - - # Special query endpoints: different field names for filter injection - _SPECIAL_QUERY_ENDPOINTS_BLOCKED: FrozenSet[str] = frozenset( - {"msearch", "knn_search", "terms_enum", "field_caps"} - ) - - # Write endpoints: inject tenant_id field into document body - _WRITE_ENDPOINTS: FrozenSet[str] = frozenset({"index", "create"}) - - # ID-based endpoints: convert to query-based equivalents - _ID_ENDPOINTS_ACTIVE: FrozenSet[str] = frozenset({"get", "exists", "delete"}) - _ID_ENDPOINTS_BLOCKED: FrozenSet[str] = frozenset({"update"}) - - # Unsupported: cannot safely inject tenant filter - _UNSUPPORTED_ENDPOINTS: FrozenSet[str] = frozenset( - {"search_template", "msearch_template", "rank_eval", "mget"} - ) - - # Control-plane / ops: passthrough without any injection - _PASSTHROUGH_PREFIXES: Tuple[str, ...] = ( - "indices.", - "cluster.", - "tasks.", - "nodes.", - "snapshot.", - "cat.", - "ingest.", - "ilm.", - "security.", - "ml.", - "transform.", - "watcher.", - "xpack.", - "async_search.", - "slm.", - "enrich.", - "rollup.", - ) - _PASSTHROUGH_ENDPOINTS: FrozenSet[str] = frozenset( - { - "scroll", - "clear_scroll", - "open_point_in_time", - "close_point_in_time", - "ping", - "info", - "explain", - "reindex", - } - ) - - # ================================================================ - # Main entry point - # ================================================================ - - async def perform_request( - self, - method: str, - path: str, - *, - params: Optional[Mapping[str, Any]] = None, - headers: Optional[Mapping[str, str]] = None, - body: Optional[Any] = None, - endpoint_id: Optional[str] = None, - path_parts: Optional[Mapping[str, Any]] = None, - ) -> Any: - tid = self._get_tenant_id() - - # No tenant context: raise if app is ready, passthrough during startup - if not tid: - if get_tenant_config().app_ready: - raise TenantIsolationViolation( - f"Missing tenant_id for ES endpoint '{endpoint_id}' " - f"({method} {path}). " - f"Ensure tenant context is set before data operations." - ) - return await super().perform_request( - method, - path, - params=params, - headers=headers, - body=body, - endpoint_id=endpoint_id, - path_parts=path_parts, - ) - - # --- No endpoint_id: some ES SDK methods (e.g. ping) don't set it. - # These are safe control-plane operations — passthrough. --- - if endpoint_id is None: - return await super().perform_request( - method, - path, - params=params, - headers=headers, - body=body, - endpoint_id=endpoint_id, - path_parts=path_parts, - ) - - # --- Blocked endpoints (implemented but not yet enabled) --- - if endpoint_id in self._QUERY_ENDPOINTS_BLOCKED: - raise TenantIsolationViolation( - f"Endpoint '{endpoint_id}' is not yet enabled in tenant isolation mode. " - f"Implementation exists but is blocked for testing. " - f"Contact platform team to enable." - ) - if endpoint_id in self._SPECIAL_QUERY_ENDPOINTS_BLOCKED: - raise TenantIsolationViolation( - f"Endpoint '{endpoint_id}' is not yet enabled in tenant isolation mode. " - f"Contact platform team to enable." - ) - if endpoint_id in self._ID_ENDPOINTS_BLOCKED: - raise TenantIsolationViolation( - f"Endpoint '{endpoint_id}' (by ID) is not yet enabled in tenant " - f"isolation mode. Contact platform team to enable." - ) - - # --- Unsupported endpoints (cannot safely inject) --- - if endpoint_id in self._UNSUPPORTED_ENDPOINTS: - raise TenantIsolationViolation( - f"Endpoint '{endpoint_id}' is not supported in tenant isolation mode. " - f"Cannot safely inject tenant_id filter." - ) - - # --- Control-plane / ops: passthrough --- - if endpoint_id in self._PASSTHROUGH_ENDPOINTS: - return await super().perform_request( - method, - path, - params=params, - headers=headers, - body=body, - endpoint_id=endpoint_id, - path_parts=path_parts, - ) - if endpoint_id and any( - endpoint_id.startswith(p) for p in self._PASSTHROUGH_PREFIXES - ): - return await super().perform_request( - method, - path, - params=params, - headers=headers, - body=body, - endpoint_id=endpoint_id, - path_parts=path_parts, - ) - - # --- Query endpoints: inject tenant filter + routing (all modes) --- - if endpoint_id in self._QUERY_ENDPOINTS_ACTIVE: - params = self._inject_routing(params, tid) - body = inject_query_body(body, tid) - return await super().perform_request( - method, - path, - params=params, - headers=headers, - body=body, - endpoint_id=endpoint_id, - path_parts=path_parts, - ) - - # --- Write endpoints: inject tenant_id field + routing --- - if endpoint_id in self._WRITE_ENDPOINTS: - params = self._inject_routing(params, tid) - if body is None: - raise TenantIsolationViolation( - f"Write operation '{endpoint_id}' has no body. " - f"Cannot inject tenant_id." - ) - body = dict(body) if not isinstance(body, dict) else body - body[TENANT_ID_FIELD] = tid - return await super().perform_request( - method, - path, - params=params, - headers=headers, - body=body, - endpoint_id=endpoint_id, - path_parts=path_parts, - ) - - # --- Bulk: per-action injection --- - if endpoint_id == "bulk": - body = inject_bulk_body(body, tid) - return await super().perform_request( - method, - path, - params=params, - headers=headers, - body=body, - endpoint_id=endpoint_id, - path_parts=path_parts, - ) - - # --- ID-based endpoints: convert to query-based for tenant filtering --- - if endpoint_id in self._ID_ENDPOINTS_ACTIVE: - path_parts = dict(path_parts or {}) - if endpoint_id == "get": - return await self._convert_get_to_search( - path_parts, params, headers, tid - ) - elif endpoint_id == "exists": - return await self._convert_exists_to_count( - path_parts, params, headers, tid - ) - elif endpoint_id == "delete": - return await self._convert_delete_to_dbq( - path_parts, params, headers, tid - ) - - # --- Unknown endpoint: reject --- - raise TenantIsolationViolation( - f"Unknown ES endpoint '{endpoint_id}'. " - f"Add to the appropriate set in TenantAwareAsyncElasticsearch. " - f"This is a safety measure to prevent tenant data leakage." - ) - - # ================================================================ - # ID-based operation conversions - # ================================================================ - - async def _convert_get_to_search( - self, - path_parts: Dict[str, Any], - params: Optional[Mapping[str, Any]], - headers: Optional[Mapping[str, str]], - tid: str, - ) -> ObjectApiResponse: - """ - Convert get-by-id to search with ids + tenant_id filter. - - Original: GET /{index}/_doc/{id} - Converted: POST /{index}/_search {"query": {"bool": {"filter": [ids, tenant]}}} - """ - index = path_parts.get("index", "") - doc_id = path_parts.get("id", "") - - search_body = {"query": make_ids_tenant_query(doc_id, tid), "size": 1} - search_params = {"routing": tid} - - if params: - for key in ("_source", "_source_includes", "_source_excludes"): - if key in params: - search_params[key] = params[key] - - merged_headers = dict(headers or {}) - merged_headers.setdefault("content-type", "application/json") - - response = await super().perform_request( - "POST", - f"/{index}/_search", - params=search_params, - headers=merged_headers, - body=search_body, - endpoint_id="search", - path_parts={"index": index}, - ) - - hits = response.body.get("hits", {}).get("hits", []) - if hits: - hit = hits[0] - get_body = { - "_index": hit.get("_index", index), - "_id": hit.get("_id", doc_id), - "_version": hit.get("_version", 1), - "_seq_no": hit.get("_seq_no"), - "_primary_term": hit.get("_primary_term"), - "_source": hit.get("_source", {}), - "found": True, - } - else: - get_body = {"_index": index, "_id": doc_id, "found": False} - - return ObjectApiResponse(body=get_body, meta=response.meta) - - async def _convert_exists_to_count( - self, - path_parts: Dict[str, Any], - params: Optional[Mapping[str, Any]], - headers: Optional[Mapping[str, str]], - tid: str, - ) -> HeadApiResponse: - """ - Convert exists-by-id to count with ids + tenant_id filter. - - Original: HEAD /{index}/_doc/{id} - Converted: POST /{index}/_count {"query": {"bool": {"filter": [ids, tenant]}}} - """ - index = path_parts.get("index", "") - doc_id = path_parts.get("id", "") - - count_body = {"query": make_ids_tenant_query(doc_id, tid)} - count_params = {"routing": tid} - - merged_headers = dict(headers or {}) - merged_headers.setdefault("content-type", "application/json") - - response = await super().perform_request( - "POST", - f"/{index}/_count", - params=count_params, - headers=merged_headers, - body=count_body, - endpoint_id="count", - path_parts={"index": index}, - ) - - count = response.body.get("count", 0) - status = 200 if count > 0 else 404 - - meta = ApiResponseMeta( - status=status, - http_version=response.meta.http_version, - headers=response.meta.headers, - duration=response.meta.duration, - node=response.meta.node, - ) - return HeadApiResponse(meta=meta) - - async def _convert_delete_to_dbq( - self, - path_parts: Dict[str, Any], - params: Optional[Mapping[str, Any]], - headers: Optional[Mapping[str, str]], - tid: str, - ) -> ObjectApiResponse: - """ - Convert delete-by-id to delete_by_query with ids + tenant_id filter. - - Original: DELETE /{index}/_doc/{id} - Converted: POST /{index}/_delete_by_query {"query": {"bool": {"filter": [ids, tenant]}}} - """ - index = path_parts.get("index", "") - doc_id = path_parts.get("id", "") - - dbq_body = {"query": make_ids_tenant_query(doc_id, tid)} - dbq_params = {"routing": tid} - - merged_headers = dict(headers or {}) - merged_headers.setdefault("content-type", "application/json") - - response = await super().perform_request( - "POST", - f"/{index}/_delete_by_query", - params=dbq_params, - headers=merged_headers, - body=dbq_body, - endpoint_id="delete_by_query", - path_parts={"index": index}, - ) - - deleted = response.body.get("deleted", 0) - result = "deleted" if deleted > 0 else "not_found" - - delete_body = { - "_index": index, - "_id": doc_id, - "_version": 1, - "_seq_no": 0, - "_primary_term": 1, - "result": result, - "_shards": {"total": 1, "successful": 1 if deleted > 0 else 0, "failed": 0}, - } - return ObjectApiResponse(body=delete_body, meta=response.meta) - - # ================================================================ - # Helpers - # ================================================================ - - @staticmethod - def _get_tenant_id() -> Optional[str]: - """Get current tenant_id from context.""" - return get_current_tenant_id() - - @staticmethod - def _inject_routing( - params: Optional[Mapping[str, Any]], tid: str - ) -> Dict[str, Any]: - """Inject routing=tenant_id into query params.""" - params = dict(params or {}) - params.setdefault("routing", tid) - return params - - -# ============================================================================ -# Section 3 — Guard Transport (Layer 2) -# ============================================================================ -# -# Independent verification layer at the Transport level. -# Does NOT modify any data — only checks and raises alarm if tenant_id is missing. -# -# Layer 1 (TenantAwareAsyncElasticsearch) injects tenant_id at the Client level; -# this guard verifies at the Transport level — a different class, different layer. -# -# Key design: structure-based checking (inspects body content, not endpoint names). -# This automatically covers any new endpoint that Layer 1 starts supporting, -# without needing to sync endpoint name lists between layers. -# -# At this level, body is still a structured dict — serialization to bytes happens -# AFTER this check (in the parent's perform_request). -# ============================================================================ - - -class TenantGuardTransport(AsyncTransport): - """ - Independent verification layer that checks tenant_id presence in ES requests. - - Does NOT modify anything — only raises TenantGuardViolation on violations. - - Verification is structure-based (checks body content, not endpoint names): - - body has "query" → query must contain tenant_id filter - - body has "index_filter" → must contain tenant_id filter - - body has "filter" as list → must contain tenant_id - - body has TENANT_ID_FIELD → must match current tenant - """ - - def __init__(self, *args: Any, **kwargs: Any): - super().__init__(*args, **kwargs) - self._violation_count = 0 - - @property - def violation_count(self) -> int: - """Number of detected violations since creation.""" - return self._violation_count - - async def perform_request( - self, - method: str, - target: str, - *, - body: Optional[Any] = None, - headers: Union[Mapping[str, Any], DefaultType] = DEFAULT, - max_retries: Union[int, DefaultType] = DEFAULT, - retry_on_status: Union[Collection[int], DefaultType] = DEFAULT, - retry_on_timeout: Union[bool, DefaultType] = DEFAULT, - request_timeout: Union[Optional[float], DefaultType] = DEFAULT, - client_meta: Union[Tuple[Tuple[str, str], ...], DefaultType] = DEFAULT, - otel_span: Union[OpenTelemetrySpan, DefaultType] = DEFAULT, - ) -> Any: - """Override to verify tenant isolation before parent serializes body.""" - self._verify_tenant_isolation(method, target, body) - - return await super().perform_request( - method, - target, - body=body, - headers=headers, - max_retries=max_retries, - retry_on_status=retry_on_status, - retry_on_timeout=retry_on_timeout, - request_timeout=request_timeout, - client_meta=client_meta, - otel_span=otel_span, - ) - - def _verify_tenant_isolation( - self, method: str, target: str, body: Optional[Any] - ) -> None: - """ - Structure-based verification — checks body content, not endpoint names. - - This makes the Guard automatically cover any new endpoint that Layer 1 - starts supporting, without needing to sync endpoint lists. - """ - tid = self._get_tenant_id() - if not tid: - if get_tenant_config().app_ready: - self._raise_violation( - target, "missing tenant_id — ensure tenant context is set" - ) - return - if body is None: - return - if not isinstance(body, dict): - return - - # 1. body has "query" → query must contain tenant_id filter - if "query" in body: - if not self._query_has_tenant(body["query"], tid): - self._raise_violation(target, "query missing tenant_id filter") - - # 2. body has "index_filter" → must contain tenant_id filter - if "index_filter" in body: - if not self._query_has_tenant(body["index_filter"], tid): - self._raise_violation(target, "index_filter missing tenant_id filter") - - # 3. body has "filter" as top-level list → check tenant in filter list - if "filter" in body and isinstance(body["filter"], list): - if not any( - f.get("term", {}).get(TENANT_ID_FIELD) == tid - for f in body["filter"] - if isinstance(f, dict) - ): - self._raise_violation(target, "filter list missing tenant_id") - - # 4. body has "tenant_id" field → must match current tenant - if TENANT_ID_FIELD in body: - if body[TENANT_ID_FIELD] != tid: - self._raise_violation( - target, f"tenant_id mismatch: {body['tenant_id']} != {tid}" - ) - - def _raise_violation(self, target: str, reason: str) -> None: - """Log and raise a tenant isolation violation.""" - self._violation_count += 1 - msg = ( - "\n" - "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n" - "!! ES TENANT ISOLATION VIOLATION — DATA LEAK RISK !!\n" - "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n" - f"!! Target: {target}\n" - f"!! Violation: {reason}\n" - f"!! Cause: Layer 1 interceptor was bypassed or has a bug\n" - f"!! Count: #{self._violation_count}\n" - "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" - ) - logger.error(msg) - raise TenantGuardViolation(msg) - - @staticmethod - def _get_tenant_id() -> Optional[str]: - """Get current tenant_id from context.""" - return get_current_tenant_id() - - @staticmethod - def _query_has_tenant(query: Any, expected_tid: str) -> bool: - """ - Check if a query dict contains tenant_id filter in bool.filter. - - Checks the outermost bool.filter array for a term query matching tenant_id. - This is where Layer 1 injects the filter, so if it's missing here, - the injection was bypassed. - """ - if not isinstance(query, dict): - return False - if "bool" not in query: - return False - - bool_q = query["bool"] - filters = bool_q.get("filter", []) - if isinstance(filters, dict): - filters = [filters] - - for f in filters: - if not isinstance(f, dict): - continue - term = f.get("term") - if isinstance(term, dict) and term.get(TENANT_ID_FIELD) == expected_tid: - return True - - return False diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/__init__.py b/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/__init__.py deleted file mode 100644 index c2688e76..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -""" -Milvus tenant-aware module - -Provides tenant-aware Milvus Collection support: -- TenantAwareCollection: Tenant-aware Collection class -- TenantAwareMilvusCollectionWithSuffix: Tenant-aware Collection management class with Suffix and Alias mechanism -- TenantAwareCollectionClassProvider: Tenant-aware Collection class provider -""" diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/config_utils.py b/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/config_utils.py deleted file mode 100644 index 050183cf..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/config_utils.py +++ /dev/null @@ -1,208 +0,0 @@ -""" -Milvus Tenant Configuration Utility Functions - -This module provides utility functions related to tenant Milvus configuration, used to extract Milvus configuration from tenant information. -""" - -import os -from typing import Optional, Dict, Any -from hashlib import md5 - -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import get_current_tenant - - -logger = get_logger(__name__) - - -def get_tenant_milvus_config() -> Optional[Dict[str, Any]]: - """ - Retrieve Milvus configuration from the current tenant context - - Extract Milvus-related configuration from the storage_info of tenant information. - If tenant configuration is incomplete or missing, supplement it from environment variables. - - Example configuration structure: - { - "host": "localhost", - "port": 19530, - "user": "admin", - "password": "password" - } - - Note: - Tenant isolation in Milvus is achieved through Collection names (handled in TenantAwareCollection), - not using db_name level isolation. - - Returns: - Milvus configuration dictionary, or None if not available - - Examples: - >>> config = get_tenant_milvus_config() - >>> if config: - ... print(f"Milvus URI: {config['host']}:{config['port']}") - """ - tenant_info = get_current_tenant() - if not tenant_info: - logger.debug( - "⚠️ Tenant context not set, unable to retrieve tenant Milvus configuration" - ) - return None - - # Retrieve Milvus configuration from tenant's storage_info - # Support two configuration key names: milvus or milvus_config - milvus_config = tenant_info.get_storage_info("milvus") - if milvus_config is None: - milvus_config = tenant_info.get_storage_info("milvus_config") - - # Retrieve environment variable configuration as fallback - env_fallback_config = load_milvus_config_from_env() - - if not milvus_config: - # No Milvus information in tenant configuration, use environment variable configuration - final_config = { - "host": env_fallback_config.get("host", "localhost"), - "port": env_fallback_config.get("port", 19530), - "user": env_fallback_config.get("user", ""), - "password": env_fallback_config.get("password", ""), - } - logger.info( - "✅ Tenant [%s] configuration missing Milvus information, using environment variable configuration: host=%s, port=%s", - tenant_info.tenant_id, - final_config["host"], - final_config["port"], - ) - return final_config - - # Compatibility logic: if tenant configuration is missing certain fields, supplement from environment variables - final_config = { - "host": milvus_config.get("host") - or env_fallback_config.get("host", "localhost"), - "port": milvus_config.get("port") or env_fallback_config.get("port", 19530), - "user": milvus_config.get("user") or env_fallback_config.get("user", ""), - "password": milvus_config.get("password") - or env_fallback_config.get("password", ""), - } - - logger.debug( - "✅ Retrieved Milvus configuration from tenant [%s]: host=%s, port=%s", - tenant_info.tenant_id, - final_config["host"], - final_config["port"], - ) - - return final_config - - -def get_milvus_connection_cache_key(config: Dict[str, Any]) -> str: - """ - Generate cache key based on Milvus connection configuration - - Use the hash value of connection parameters (host, port, user, password) as the cache key. - Note: db_name is not included in the cache key generation, as the same connection can access different databases. - - Args: - config: Milvus connection configuration dictionary - - Returns: - Cache key string (MD5 hash value) - - Examples: - >>> config = {"host": "localhost", "port": 19530, "user": "admin", "password": "pwd"} - >>> cache_key = get_milvus_connection_cache_key(config) - """ - # Use connection parameters to generate a unique cache key (excluding db_name, as one connection can access multiple databases) - key_parts = [ - str(config.get("host", "")), - str(config.get("port", "")), - str(config.get("user", "")), - str(config.get("password", "")), - ] - key_str = "|".join(key_parts) - cache_key = md5(key_str.encode()).hexdigest()[:16] - return cache_key - - -def load_milvus_config_from_env() -> Dict[str, Any]: - """ - Load default Milvus configuration from environment variables - - Read the following environment variables: - - MILVUS_HOST: Milvus host address, default localhost - - MILVUS_PORT: Milvus port, default 19530 - - MILVUS_USER: Username (optional) - - MILVUS_PASSWORD: Password (optional) - - Note: - MILVUS_DB_NAME is not used; tenant isolation is achieved through Collection names - - Returns: - Milvus configuration dictionary - - Examples: - >>> config = load_milvus_config_from_env() - >>> print(f"Milvus URI: {config['host']}:{config['port']}") - """ - config = { - "host": os.getenv("MILVUS_HOST", "localhost"), - "port": int(os.getenv("MILVUS_PORT", "19530")), - "user": os.getenv("MILVUS_USER", ""), - "password": os.getenv("MILVUS_PASSWORD", ""), - } - - logger.debug( - "Loaded default Milvus configuration from environment variables: host=%s, port=%s", - config["host"], - config["port"], - ) - - return config - - -def _base_prefixed_collection_name(original_name: str) -> str: - """Apply the base resource prefix to a collection name (e.g., "v1_memories" → "b0001_v1_memories").""" - from core.tenants.tenant_constants import get_base_resource_prefix - - return f"{get_base_resource_prefix()}_{original_name}" - - -def get_tenant_aware_collection_name(original_name: str) -> str: - """ - Get tenant-aware Collection name. - - Resolution order: - 1. Tenant context exists → read from storage_info.milvus.collection_prefix - 2. No tenant context (e.g., startup) → base resource prefix + original_name - - Args: - original_name: Original Collection name (e.g., "v1_episodic_memory") - - Returns: - str: Resolved name (e.g., "dev_v1_episodic_memory", "b0001_v1_episodic_memory") - """ - try: - tenant_info = get_current_tenant() - if not tenant_info: - return _base_prefixed_collection_name(original_name) - - # Read collection_prefix from storage_info (set by routing layer) - milvus_config = tenant_info.get_storage_info("milvus") - if milvus_config is None: - milvus_config = tenant_info.get_storage_info("milvus_config") - - if milvus_config and milvus_config.get("collection_prefix"): - return f"{milvus_config['collection_prefix']}_{original_name}" - - # No collection_prefix configured - logger.warning( - "Tenant [%s] storage_info has no milvus.collection_prefix configured, " - "using base prefix. Configure collection_prefix in routing layer.", - tenant_info.tenant_id, - ) - return _base_prefixed_collection_name(original_name) - - except Exception as e: - logger.warning( - "Failed to get tenant-aware Collection name, using base prefix: %s", e - ) - return _base_prefixed_collection_name(original_name) diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/tenant_aware_collection.py b/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/tenant_aware_collection.py deleted file mode 100644 index f61baf61..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/tenant_aware_collection.py +++ /dev/null @@ -1,306 +0,0 @@ -""" -Tenant-aware Milvus Collection - -This module implements tenant awareness by inheriting from pymilvus.Collection and overriding the _get_connection method. -Core idea: Dynamically return the correct connection handler based on tenant context. -""" - -from typing import Optional -from pymilvus import Collection, CollectionSchema -from pymilvus.orm.connections import connections - -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import get_current_tenant -from core.tenants.tenant_models import TenantPatchKey -from core.tenants.tenantize.oxm.milvus.config_utils import ( - get_tenant_milvus_config, - get_milvus_connection_cache_key, - load_milvus_config_from_env, - get_tenant_aware_collection_name, -) -from core.tenants.tenantize.tenant_cache_utils import get_or_compute_tenant_cache -from core.component.milvus_client_factory import MilvusClientFactory -from core.di.utils import get_bean_by_type - -logger = get_logger(__name__) - - -class TenantAwareCollection(Collection): - """ - Tenant-aware Milvus Collection - - Implements tenant awareness by inheriting from pymilvus.Collection and overriding the _get_connection method. - Core functionality: Automatically selects and returns the correct Milvus connection based on the current tenant context. - - Key features: - 1. Tenant isolation: Different tenants use different Milvus connections (distinguished by using alias) - 2. Connection reuse: Tenants with the same configuration share the same connection (cached via cache_key) - 3. Automatic registration: Automatically registers tenant connection upon first access - 4. Fallback connection: Uses default connection when not in tenant mode or without tenant context - - Usage: - >>> # Used in MilvusCollectionBase - >>> class MyCollectionBase(MilvusCollectionBase): - ... def load_collection(self) -> Collection: - ... # Use TenantAwareCollection instead of the original Collection - ... return TenantAwareCollection( - ... name=self.name, - ... using="default", # using parameter will be ignored, actual tenant-aware connection is used - ... schema=self._SCHEMA, - ... ) - - Notes: - - The passed using parameter will be ignored; the actual tenant-aware connection alias is used - - Connection is automatically registered upon first access (via MilvusClientFactory) - - Connection alias and configuration are cached in tenant_info_patch to avoid redundant computation - """ - - def __init__( - self, - name: str, - schema: Optional[CollectionSchema] = None, - using: str = "default", - **kwargs, - ): - """ - Initialize tenant-aware Collection - - Args: - name: Collection name - schema: Collection schema (optional) - using: Connection alias (will be ignored, actual tenant-aware connection is used) - **kwargs: Other parameters (passed to parent class) - - Note: - - The using parameter will be overridden by the tenant-aware connection alias - - Ensures tenant connection is registered upon first access - - _original_name stores the original name value for property usage - """ - # Save the original name (before calling parent class __init__) - # This allows _name to be implemented as a property if tenant-aware table names are needed - self._original_name = name - self._original_using = using - - # Call parent constructor (using tenant-aware using) - # Parent class will set self._name = name - super().__init__(name=name, schema=schema, using=using, **kwargs) - - logger.debug("Creating TenantAwareCollection [name=%s, using=%s]", name, using) - - def _get_connection(self): - """ - Override parent method to return tenant-aware connection - - This is the core method: called every time a Milvus connection is needed. - Here we dynamically return the correct connection handler based on tenant context. - - Returns: - Milvus connection handler - - Note: - - This method is called on every operation (search, insert, query, etc.) - - We re-fetch tenant using to support connection switching across requests - """ - # Dynamically get current tenant's connection alias (supports switching across requests) - tenant_using = self._get_tenant_aware_using() - - # Ensure connection is registered - self._ensure_connection_registered(tenant_using) - - # Return corresponding connection handler - return connections._fetch_handler(tenant_using) - - @staticmethod - def _get_tenant_aware_using() -> str: - """ - Get tenant-aware connection alias - - Determines which connection alias to return based on configuration and context: - 1. If non-tenant mode is enabled, return "default" - 2. If tenant mode is enabled, return the corresponding connection alias based on current tenant configuration - 3. If no tenant context exists under tenant mode, return "default" - - Returns: - str: pymilvus connection alias (using) - """ - - def compute_using() -> str: - """Compute tenant connection alias""" - # Get Milvus configuration from tenant config - milvus_config = get_tenant_milvus_config() - if not milvus_config: - raise RuntimeError("Tenant missing Milvus configuration") - - # Generate unique connection alias based on connection parameters - cache_key = get_milvus_connection_cache_key(milvus_config) - return f"tenant_{cache_key}" - - return get_or_compute_tenant_cache( - patch_key=TenantPatchKey.MILVUS_CONNECTION_CACHE_KEY, - compute_func=compute_using, - fallback="default", # Concrete value, no need for lazy evaluation - cache_description="Milvus connection alias", - ) - - @staticmethod - def _ensure_connection_registered(using: str) -> None: - """ - Ensure the specified connection alias is registered - - If the connection is not yet registered, it will be automatically registered (via MilvusClientFactory). - - Args: - using: Connection alias - - Note: - - For "default" connection, assume it's already registered at application startup - - For tenant connections (tenant_*), register automatically if not already registered - """ - # Check if connection already exists - try: - connections._fetch_handler(using) - # Connection exists, return directly - return - except Exception: - # Connection does not exist, needs registration - pass - - # If it's the default connection, try to register from environment variables - if using == "default": - logger.info("📋 Registering default Milvus connection") - config = load_milvus_config_from_env() - TenantAwareCollection._register_connection(config, using) - return - - # Tenant connection: register from tenant configuration - try: - tenant_info = get_current_tenant() - if not tenant_info: - raise RuntimeError( - "Cannot register tenant connection: tenant context not set" - ) - - milvus_config = get_tenant_milvus_config() - if not milvus_config: - raise RuntimeError( - f"Cannot register tenant connection: tenant {tenant_info.tenant_id} missing Milvus configuration" - ) - - logger.info( - "📋 Registering Milvus connection for tenant [%s] [using=%s]", - tenant_info.tenant_id, - using, - ) - - TenantAwareCollection._register_connection(milvus_config, using) - - except Exception as e: - logger.error( - "Failed to register tenant connection [using=%s]: %s", using, e - ) - raise - - @staticmethod - def _register_connection(config: dict, using: str) -> None: - """ - Register Milvus connection - - Args: - config: Milvus connection configuration - using: Connection alias - - Note: - - Use MilvusClientFactory to create the connection - - This reuses existing connection pool management logic - """ - try: - # Create connection via MilvusClientFactory - # This reuses existing connection pool management - factory = get_bean_by_type(MilvusClientFactory) - - # Build URI - host = config.get("host", "localhost") - port = config.get("port", 19530) - uri = ( - f"{host}:{port}" if host.startswith("http") else f"http://{host}:{port}" - ) - - # Create client (this automatically registers the connection) - # Note: Do not pass db_name, tenant isolation is achieved through Collection name - factory.get_client( - uri=uri, - user=config.get("user", ""), - password=config.get("password", ""), - alias=using, - ) - - logger.info( - "✅ Milvus connection registered [using=%s, host=%s, port=%s]", - using, - host, - port, - ) - - except Exception as e: - logger.error( - "Failed to register Milvus connection [using=%s]: %s", using, e - ) - raise - - # ============================================================ - # Tenant-aware table name support (optional feature) - # ============================================================ - # Uncomment the @property below if tenant-aware table names are needed. - # This way, different tenants will use different table names, achieving table-level isolation. - # - # Note: After enabling this feature, ensure: - # 1. Each tenant has an independent table - # 2. Table names comply with Milvus naming conventions - # 3. Consider table name length limits - # - @property - def _name(self) -> str: - """ - Tenant-aware table name - - Override parent class _name attribute to add tenant identifier to table name. - - Example: - Original table name: "my_collection" - Tenant A: "tenant_001_my_collection" - Tenant B: "tenant_002_my_collection" - - Returns: - str: Tenant-aware table name - """ - return self.get_tenant_aware_name(self._original_name) - - @classmethod - def get_tenant_aware_name(cls, original_name: str) -> str: - """ - Get tenant-aware table name - """ - return get_tenant_aware_collection_name(original_name) - - @_name.setter - def _name(self, value: str) -> None: - """ - Set table name (setter) - - The parent class Collection in pymilvus may attempt to set the _name attribute. - Here we capture the set operation and update _original_name. - - Args: - value: Table name to be set - """ - # Update original table name - # Note: We store the original value, not the tenant-aware value - # Because the getter will automatically add the tenant prefix - self._original_name = value - - @property - def using(self) -> str: - """ - Get tenant-aware connection alias - """ - return self._get_tenant_aware_using() diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/tenant_aware_collection_with_suffix.py b/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/tenant_aware_collection_with_suffix.py deleted file mode 100644 index a932feb9..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/tenant_aware_collection_with_suffix.py +++ /dev/null @@ -1,544 +0,0 @@ -# skip-sensitive-file -""" -Tenant-aware Milvus Collection Management Class with Suffix and Alias Mechanism - -This module combines the functionalities of TenantAwareCollection and MilvusCollectionWithSuffix: -1. Tenant awareness: Automatically selects the correct connection and table name based on tenant context -2. Dynamic table names: Supports dynamic suffix setting via suffix parameter or environment variables -3. Alias mechanism: Real table names include timestamps, accessed via alias -""" - -import os -from typing import Optional -from pymilvus import connections, Collection, DataType, FieldSchema -from pymilvus.client.types import ConsistencyLevel - -from core.observation.logger import get_logger -from core.oxm.milvus.milvus_collection_base import ( - MilvusCollectionWithSuffix, - generate_new_collection_name, -) -from core.tenants.tenantize.oxm.milvus.tenant_aware_collection import ( - TenantAwareCollection, -) -from core.tenants.tenantize.oxm.milvus.config_utils import ( - get_tenant_aware_collection_name, -) -from core.tenants.tenant_constants import TENANT_ID_FIELD, TENANT_ID_MAX_LENGTH -from core.tenants.tenant_contextvar import get_current_tenant -from pymilvus import utility - -logger = get_logger(__name__) - -# Standard tenant_id FieldSchema for Milvus collections. -# is_partition_key=True enables automatic partition routing by tenant_id, -# so queries with tenant_id filter only scan the relevant partition. -TENANT_ID_FIELD_SCHEMA = FieldSchema( - name=TENANT_ID_FIELD, - dtype=DataType.VARCHAR, - max_length=TENANT_ID_MAX_LENGTH, - is_partition_key=True, -) - - -class TenantAwareMilvusCollectionWithSuffix(MilvusCollectionWithSuffix): - """ - Tenant-aware Milvus Collection Management Class with Suffix and Alias Mechanism - - Inherits from MilvusCollectionWithSuffix, adding tenant awareness capabilities: - 1. Automatically selects the correct Milvus connection based on tenant context - 2. Supports tenant-aware table names (automatically adds tenant prefix) - 3. Retains all functionalities of MilvusCollectionWithSuffix (suffix, alias, creation management, etc.) - - Partition key: - - tenant_id is auto-appended as partition_key to all subclass schemas - - _NUM_PARTITIONS defaults to 256 for balanced tenant isolation in shared mode - - Subclasses can override _NUM_PARTITIONS if needed - - Key features: - - Tenant isolation: Different tenants use different connections and table names - - Dynamic table names: Supports suffix and environment variables - - Alias mechanism: Real table names include timestamps, accessed via alias - - Version management: Can create new versions and perform gradual switching - - Tenant prefix: All operations automatically add tenant prefix (e.g., tenant_001_movies) - - Table naming rules: - - Original base name: movies - - With suffix: movies_production - - With tenant prefix: tenant_001_movies_production (alias) - - Real name: tenant_001_movies_production-20231015123456789000 - - Usage: - 1. Subclass definition: - - _COLLECTION_NAME: Base name of the Collection (required) - - _SCHEMA: Schema definition of the Collection (required) - - _INDEX_CONFIGS: List of index configurations (optional) - - _DB_USING: Milvus connection alias (optional, will be overridden by tenant-aware connection) - - 2. Instantiation: - mgr = TenantAwareMovieCollection(suffix="customer_a") - # Within tenant context: - # - Uses tenant's Milvus connection - # - Alias: tenant_001_movies_customer_a - # - Real name: tenant_001_movies_customer_a-20231015123456789000 - - 3. Initialization: - with tenant_context(tenant_info): - mgr.ensure_all() # One-click initialization - - 4. Usage: - with tenant_context(tenant_info): - mgr.collection.insert([...]) - mgr.collection.search(...) - - Example: - class MovieCollection(TenantAwareMilvusCollectionWithSuffix): - _COLLECTION_NAME = "movies" - _SCHEMA = CollectionSchema(fields=[...]) - _INDEX_CONFIGS = [ - IndexConfig(field_name="embedding", index_type="IVF_FLAT", ...), - ] - - # Multi-tenant scenario usage - tenant_a = TenantInfo(tenant_id="tenant_001", ...) - tenant_b = TenantInfo(tenant_id="tenant_002", ...) - - mgr = MovieCollection(suffix="production") - - # Tenant A operations - with tenant_context(tenant_a): - mgr.ensure_all() - mgr.collection.insert([...]) - - # Tenant B operations - with tenant_context(tenant_b): - mgr.ensure_all() - mgr.collection.insert([...]) - """ - - # Default partition count for partition_key routing. - # Configurable via MILVUS_NUM_PARTITIONS env var (default 256). - # Milvus max is 4096. Subclasses can override. - _NUM_PARTITIONS: int = int(os.getenv("MILVUS_NUM_PARTITIONS", "256")) - - @staticmethod - def _resolve_num_partitions(class_default: Optional[int]) -> Optional[int]: - """Resolve num_partitions: storage_info > class attribute > env var. - - Priority: - 1. tenant storage_info.milvus.num_partitions (set per-tenant at init time) - 2. class attribute _NUM_PARTITIONS (set via env var or subclass override) - """ - tenant = get_current_tenant() - if tenant: - milvus_config = tenant.get_storage_info("milvus") - if milvus_config and "num_partitions" in milvus_config: - return int(milvus_config["num_partitions"]) - return class_default - - def __init_subclass__(cls, **kwargs): - """Auto-append tenant_id FieldSchema to _SCHEMA when a subclass is defined. - - Always appends regardless of tenant mode, so schema is consistent - across all environments (aligned with ES and MongoDB behavior). - In non-tenant mode the field exists but is never populated. - """ - super().__init_subclass__(**kwargs) - - # Auto-append tenant_id FieldSchema (partition_key) - schema = getattr(cls, "_SCHEMA", None) - if schema is not None: - existing_names = {f.name for f in schema.fields} - if TENANT_ID_FIELD not in existing_names: - schema.add_field( - field_name=TENANT_ID_FIELD_SCHEMA.name, - datatype=TENANT_ID_FIELD_SCHEMA.dtype, - max_length=TENANT_ID_FIELD_SCHEMA.max_length, - is_partition_key=TENANT_ID_FIELD_SCHEMA.is_partition_key, - ) - logger.info( - "Auto-appended tenant_id (partition_key) to %s._SCHEMA", - cls.__name__, - ) - - # Auto-append tenant_id scalar index for filter performance - index_configs = getattr(cls, "_INDEX_CONFIGS", None) - if index_configs is not None: - existing_index_fields = {cfg.field_name for cfg in index_configs} - if TENANT_ID_FIELD not in existing_index_fields: - from core.oxm.milvus.milvus_collection_base import IndexConfig - - index_configs.append( - IndexConfig( - field_name=TENANT_ID_FIELD, - index_type="AUTOINDEX", - index_name="idx_tenant_id", - ) - ) - logger.info( - "Auto-appended tenant_id scalar index to %s._INDEX_CONFIGS", - cls.__name__, - ) - - def __init__(self, suffix: Optional[str] = None): - """ - Initialize the tenant-aware Collection manager - - Args: - suffix: Collection name suffix; if not provided, read from environment variable - - Note: - - Save the original _alias_name (without tenant prefix) - - The actual table name will dynamically add tenant prefix at runtime - """ - super().__init__(suffix=suffix) - # Save the original alias name (without tenant prefix) - # Used in the name property to dynamically compute tenant-aware names - self._original_alias_name = self._alias_name - - @property - def name(self) -> str: - """ - Get the tenant-aware Collection name (alias) - - Override parent class's name property to dynamically add tenant prefix. - This ensures all places using self.name automatically get tenant-aware table names. - - Returns: - str: Tenant-aware alias name - - Example: - Original alias: movies_production - Tenant A: tenant_001_movies_production - Tenant B: tenant_002_movies_production - """ - return TenantAwareCollection.get_tenant_aware_name(self._original_alias_name) - - @property - def using(self) -> str: - """ - Get the tenant-aware connection alias - """ - return TenantAwareCollection._get_tenant_aware_using() - - def ensure_connection_registered(self) -> None: - """ - Ensure the tenant-aware connection is registered - """ - TenantAwareCollection._ensure_connection_registered(self.using) - - def load_collection(self) -> TenantAwareCollection: - """ - Load or create a tenant-aware Collection - - Override parent class method, using TenantAwareCollection instead of regular Collection. - This ensures all Collection operations are tenant-aware. - - Args: - name: Collection name (alias name, already includes tenant prefix) - - Returns: - TenantAwareCollection instance - - Note: - - Use TenantAwareCollection to automatically handle tenant connections - - Maintain MilvusCollectionWithSuffix's alias mechanism - - If alias does not exist, create a new timestamped Collection - - The name parameter should already be tenant-aware (passed via self.name) - """ - using = self.using - origin_alias_name = self._original_alias_name - tenant_aware_alias_name = get_tenant_aware_collection_name(origin_alias_name) - new_real_name = generate_new_collection_name(origin_alias_name) - tenant_aware_new_real_name = get_tenant_aware_collection_name(new_real_name) - - # First check if alias exists (using tenant-aware connection) - # Note: TenantAwareCollection automatically handles the using parameter - self.ensure_connection_registered() - - if not utility.has_collection(tenant_aware_alias_name, using=using): - # Collection does not exist, create a new tenant-aware Collection - logger.info( - "Collection '%s' does not exist, creating new tenant-aware Collection: %s", - origin_alias_name, - tenant_aware_new_real_name, - ) - - # Create tenant-aware Collection - # Use native Collection, need to explicitly pass using parameter - create_kwargs = { - "name": tenant_aware_new_real_name, - "schema": self._SCHEMA, - "consistency_level": ConsistencyLevel.Bounded, - "using": using, - } - num_partitions = self._resolve_num_partitions( - getattr(self, "_NUM_PARTITIONS", None) - ) - if num_partitions is not None: - create_kwargs["num_partitions"] = num_partitions - logger.info( - "Creating tenant-aware Collection: %s (num_partitions=%s)", - tenant_aware_new_real_name, - num_partitions, - ) - Collection(**create_kwargs) - - # Create alias pointing to new Collection - # Note: First delete any existing old alias - try: - utility.drop_alias(tenant_aware_alias_name, using=using) - except Exception: - pass # alias does not exist, ignore - - utility.create_alias( - collection_name=tenant_aware_new_real_name, - alias=tenant_aware_alias_name, - using=using, - ) - logger.info( - "Alias '%s' -> '%s' created", - tenant_aware_alias_name, - tenant_aware_new_real_name, - ) - - # Uniformly load tenant-aware Collection via alias - coll = TenantAwareCollection( - name=origin_alias_name, - schema=self._SCHEMA, - consistency_level=ConsistencyLevel.Bounded, - ) - - return coll - - def ensure_create(self) -> None: - """ - Ensure Collection has been created - - Override parent class method, using tenant-aware alias name. - - This method triggers lazy loading of Collection; if alias does not exist, creates a new Collection. - """ - if self._collection_instance is None: - # Use tenant-aware alias name - self._collection_instance = self.load_collection() - logger.info("Collection '%s' is ready", self.name) - - def create_new_collection(self) -> TenantAwareCollection: - """ - Create a new tenant-aware real Collection (without switching alias) - - Override parent class method, using TenantAwareCollection and tenant-aware names. - - Returns: - New tenant-aware Collection instance (with indexes created and loaded) - - Note: - - Use native Collection for creation (need to explicitly pass using parameter) - - New Collection name includes tenant prefix and timestamp - - Return TenantAwareCollection instance to ensure tenant isolation - - Automatically create indexes and load into memory - """ - if not self._SCHEMA: - raise NotImplementedError( - f"{self.__class__.__name__} must define '_SCHEMA' to support collection creation" - ) - - # Use tenant-aware alias name - using = self.using - origin_alias_name = self._original_alias_name - tenant_aware_alias_name = get_tenant_aware_collection_name(origin_alias_name) - new_real_name = generate_new_collection_name(origin_alias_name) - tenant_aware_new_real_name = get_tenant_aware_collection_name(new_real_name) - - # Create new tenant-aware collection - # Use native Collection, need to explicitly pass using parameter - create_kwargs = { - "name": tenant_aware_new_real_name, - "schema": self._SCHEMA, - "consistency_level": ConsistencyLevel.Bounded, - "using": using, - } - num_partitions = self._resolve_num_partitions( - getattr(self, "_NUM_PARTITIONS", None) - ) - if num_partitions is not None: - create_kwargs["num_partitions"] = num_partitions - _coll = Collection(**create_kwargs) - - logger.info( - "New tenant-aware Collection created: %s (num_partitions=%s)", - tenant_aware_new_real_name, - num_partitions, - ) - - # Create indexes for new collection and load - try: - self._create_indexes_for_collection(_coll) - _coll.load() - logger.info( - "Indexes created and loading completed for new Collection '%s'", - new_real_name, - ) - except Exception as e: - logger.warning("Error creating indexes for new collection: %s", e) - raise - - # Return TenantAwareCollection instance, using original alias name - # Note: Use _original_alias_name here, TenantAwareCollection will automatically add tenant prefix - new_coll = TenantAwareCollection( - name=new_real_name, - schema=self._SCHEMA, - consistency_level=ConsistencyLevel.Bounded, - ) - - return new_coll - - def switch_alias( - self, new_collection: TenantAwareCollection, drop_old: bool = False - ) -> None: - """ - Switch alias to specified new collection, optionally delete old collection - - Override parent class method, using tenant-aware alias name. - - Args: - new_collection: New Collection instance - drop_old: Whether to delete old collection (default False) - - Note: - - Use tenant-aware alias name for switching - - Prefer alter_alias, fall back to drop/create if failed - - Refresh class-level cache after switching - """ - # Use tenant-aware alias name - using = self.using - origin_alias_name = self._original_alias_name - tenant_aware_alias_name = get_tenant_aware_collection_name(origin_alias_name) - tenant_aware_new_real_name = new_collection.name - - # Get old collection real name (if exists) - old_real_name: Optional[str] = None - try: - conn = connections._fetch_handler(using) - desc = conn.describe_alias(tenant_aware_alias_name) - old_real_name = ( - desc.get("collection_name") if isinstance(desc, dict) else None - ) - except Exception: - old_real_name = None - - # Alias switching - try: - conn = connections._fetch_handler(using) - conn.alter_alias(tenant_aware_new_real_name, tenant_aware_alias_name) - logger.info( - "Alias '%s' switched to '%s'", - tenant_aware_alias_name, - tenant_aware_new_real_name, - ) - except Exception as e: - logger.warning("alter_alias failed, trying drop/create: %s", e) - try: - utility.drop_alias(tenant_aware_alias_name, using=using) - except Exception: - pass - utility.create_alias( - collection_name=tenant_aware_new_real_name, - alias=tenant_aware_alias_name, - using=using, - ) - logger.info( - "Alias '%s' -> '%s' created", - tenant_aware_alias_name, - tenant_aware_new_real_name, - ) - - # Optionally delete old collection (after switching completes) - if drop_old and old_real_name: - try: - utility.drop_collection(old_real_name, using=using) - logger.info("Old collection deleted: %s", old_real_name) - except Exception as e: - logger.warning( - "Failed to delete old collection (can handle manually): %s", e - ) - - # Refresh class-level cache to alias collection - try: - self.__class__._collection_instance = TenantAwareCollection( - name=origin_alias_name, - schema=self._SCHEMA, - consistency_level=ConsistencyLevel.Bounded, - ) - except Exception: - pass - - # ==================== Tenant Field Isolation ==================== - - @classmethod - def async_collection(cls): - """Get asynchronous Collection instance wrapped with tenant field isolation proxy. - - Override parent to wrap AsyncCollection in TenantFieldCollectionProxy. - This ensures all data operations (insert/search/query/delete) automatically - inject tenant_id, while control-plane operations pass through. - - In non-tenant mode, the proxy is a transparent passthrough. - """ - inner = super().async_collection() - - from core.tenants.tenantize.oxm.milvus.tenant_field_collection_proxy import ( - TenantFieldCollectionProxy, - ) - - return TenantFieldCollectionProxy(inner) - - @classmethod - def collection(cls): - """Get synchronous Collection instance. - - Blocked: synchronous Collection bypasses the TenantFieldCollectionProxy - and cannot enforce tenant isolation. Use async_collection() instead. - """ - raise RuntimeError( - f"{cls.__name__}.collection() is blocked. " - f"Synchronous Collection cannot enforce tenant field isolation. " - f"Use async_collection() instead." - ) - - # ==================== Collection Management ==================== - - def exists(self) -> bool: - """ - Check if Collection exists (via alias) - - Override parent class method, using tenant-aware name and using. - - Returns: - bool: Whether Collection exists - """ - name = self.name - using = self.using - return utility.has_collection(name, using=using) - - def drop(self) -> None: - """ - Delete current Collection (including alias and real Collection) - - Override parent class method, using tenant-aware name, using, and TenantAwareCollection. - - Note: - - Use tenant-aware connection alias - - Use TenantAwareCollection to ensure tenant isolation - - Delete the real Collection (not the alias) - """ - using = self.using - name = self.name - try: - utility.drop_collection(name, using=using) - logger.info("Collection '%s' deleted", name) - except Exception as e: # pylint: disable=broad-except - logger.warning( - "Collection '%s' does not exist or deletion failed: %s", name, e - ) diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/tenant_field_collection_proxy.py b/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/tenant_field_collection_proxy.py deleted file mode 100644 index 1cb57e80..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/milvus/tenant_field_collection_proxy.py +++ /dev/null @@ -1,216 +0,0 @@ -""" -Milvus Tenant Field Isolation Collection Proxy. - -Intercepts all data operations (insert/upsert/search/query/delete) -to inject tenant_id transparently. Repositories require zero changes. - -Three-category whitelist strategy (consistent with MongoDB TenantCommandInterceptor): - - Data-plane (explicit override): insert, upsert, search, query, delete - → inject tenant_id into entity data or filter expression - - Control-plane (passthrough whitelist): flush, load, release, compact, etc. - → delegate to inner AsyncCollection as-is - - Unknown: reject - → raise AttributeError, refuse to let unrecognized methods through -""" - -from __future__ import annotations - -from typing import Any, Dict, List, Optional, Union - -from pymilvus import SearchResult -from pymilvus.orm.mutation import MutationResult - -from core.observation.logger import get_logger -from core.oxm.milvus.async_collection import AsyncCollection -from core.tenants.tenant_config import get_tenant_config -from core.tenants.tenant_constants import TENANT_ID_FIELD -from core.tenants.tenant_contextvar import get_current_tenant_id - -logger = get_logger(__name__) - - -class TenantIsolationViolation(Exception): - """Raised when a Milvus operation violates tenant isolation.""" - - pass - - -# --- Module-level utilities --- - - -def _exclude_tenant_from_fields(fields: Optional[List[str]]) -> Optional[List[str]]: - """Remove tenant_id from output fields (defense in depth).""" - if fields is None: - return None - return [f for f in fields if f != TENANT_ID_FIELD] - - -# --- Whitelist for control-plane methods --- - -# Methods with explicit definitions (insert/upsert/search/query/delete/flush/load) -# are resolved before __getattr__ and do NOT need to appear here. -_PASSTHROUGH_METHODS: frozenset[str] = frozenset( - { - "release", - "compact", - "get_compaction_state", - "get_compaction_plans", - "get_replicas", - "num_entities", - "describe", - } -) - - -# --- Proxy class --- - - -class TenantFieldCollectionProxy: - """Proxy over AsyncCollection that enforces tenant isolation. - - Every data operation is intercepted: - - Write: tenant_id force-injected into entity - - Read: tenant_id filter prepended to expr, tenant_id excluded from output - - Delete: tenant_id filter prepended + empty-expr guard + audit log - - Unknown methods are rejected to prevent bypass of tenant isolation. - In non-tenant mode, all operations are transparently passed through. - """ - - def __init__(self, inner: AsyncCollection) -> None: - self.__inner = inner - - def _get_tenant_id(self) -> Optional[str]: - """Get tenant_id from context. Returns None if no tenant context.""" - return get_current_tenant_id() - - def _require_tenant_id(self, operation: str) -> Optional[str]: - """Get tenant_id or raise if missing after app startup. - - Returns None during startup (before app_ready), allowing callers - to skip tenant injection. After app_ready, raises if missing. - """ - tid = self._get_tenant_id() - if not tid: - if get_tenant_config().app_ready: - raise TenantIsolationViolation( - f"Missing tenant_id for Milvus operation '{operation}'. " - f"Ensure tenant context is set before data operations." - ) - return None - return tid - - # --- Write path --- - - async def insert( - self, data: Union[Dict[str, Any], List[Dict[str, Any]]], **kwargs: Any - ) -> MutationResult: - tid = self._require_tenant_id("insert") - if tid: - self._inject_tenant_to_entities(data, tid) - return await self.__inner.insert(data, **kwargs) - - async def upsert( - self, data: Union[Dict[str, Any], List[Dict[str, Any]]], **kwargs: Any - ) -> MutationResult: - tid = self._require_tenant_id("upsert") - if tid: - self._inject_tenant_to_entities(data, tid) - return await self.__inner.upsert(data, **kwargs) - - # --- Read path --- - - async def search( - self, - data: List[List[float]], - anns_field: str, - param: Dict[str, Any], - limit: int, - expr: Optional[str] = None, - output_fields: Optional[List[str]] = None, - **kwargs: Any, - ) -> SearchResult: - tid = self._require_tenant_id("search") - if tid: - expr = self._prepend_tenant_filter(expr, tid) - output_fields = _exclude_tenant_from_fields(output_fields) - return await self.__inner.search( - data, anns_field, param, limit, expr, output_fields=output_fields, **kwargs - ) - - async def query( - self, expr: str = "", output_fields: Optional[List[str]] = None, **kwargs: Any - ) -> List[Dict[str, Any]]: - tid = self._require_tenant_id("query") - if tid: - expr = self._prepend_tenant_filter(expr, tid) - output_fields = _exclude_tenant_from_fields(output_fields) - return await self.__inner.query( - expr=expr, output_fields=output_fields, **kwargs - ) - - # --- Delete path --- - - async def delete(self, expr: str, **kwargs: Any) -> MutationResult: - tid = self._require_tenant_id("delete") - if tid: - if not expr or not expr.strip(): - raise TenantIsolationViolation( - "Delete without expression is forbidden in tenant isolation mode. " - "This would delete ALL data across tenants." - ) - expr = self._prepend_tenant_filter(expr, tid) - return await self.__inner.delete(expr, **kwargs) - - # --- Control-plane: transparent delegation --- - - async def flush(self, **kwargs: Any) -> None: - return await self.__inner.flush(**kwargs) - - async def load(self, **kwargs: Any) -> None: - return await self.__inner.load(**kwargs) - - def __getattr__(self, name: str) -> Any: - """Delegate whitelisted control-plane methods to inner AsyncCollection. - - Unknown methods are rejected to prevent bypass of tenant isolation. - When pymilvus adds new data methods in the future, this will force - developers to come here and explicitly handle them. - """ - if name.startswith("_"): - return getattr(self.__inner, name) - - if name not in _PASSTHROUGH_METHODS: - raise AttributeError( - f"'{type(self).__name__}' does not expose '{name}'. " - f"If this is a non-data operation, add it to _PASSTHROUGH_METHODS. " - f"If this is a data operation, add an explicit method with tenant injection." - ) - return getattr(self.__inner, name) - - # --- Internal helpers --- - - @staticmethod - def _prepend_tenant_filter(expr: Optional[str], tenant_id: str) -> str: - """Prepend tenant_id clause to a Milvus filter expression.""" - tenant_clause = f'({TENANT_ID_FIELD} == "{tenant_id}")' - if not expr or not expr.strip(): - return tenant_clause - return f"{tenant_clause} and ({expr})" - - @staticmethod - def _inject_tenant_to_entities( - data: Union[Dict[str, Any], List[Dict[str, Any]]], tenant_id: str - ) -> None: - """Force-inject tenant_id into entity data.""" - entities = data if isinstance(data, list) else [data] - for i, entity in enumerate(entities): - if not isinstance(entity, dict): - raise TenantIsolationViolation( - f"insert/upsert data[{i}] is {type(entity).__name__}, expected dict. " - f"Cannot inject tenant_id into unknown data type." - ) - entity[TENANT_ID_FIELD] = tenant_id # force overwrite - - def __repr__(self) -> str: - return f"TenantFieldCollectionProxy(inner={self.__inner!r})" diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/__init__.py b/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/config_utils.py b/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/config_utils.py deleted file mode 100644 index f0a9771e..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/config_utils.py +++ /dev/null @@ -1,236 +0,0 @@ -""" -MongoDB configuration utility functions - -Provides common utility functions related to tenant-aware MongoDB configuration. -""" - -import os -from typing import Optional, Dict, Any -from functools import lru_cache -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import get_current_tenant - - -logger = get_logger(__name__) - -DEFAULT_DATABASE_NAME = "memsys" - - -def get_tenant_mongo_config() -> Optional[Dict[str, Any]]: - """ - Get MongoDB configuration information for the current tenant - - Extract MongoDB-related configuration from the tenant's storage_info. - If tenant configuration is incomplete or missing, supplement from environment variables (except for database). - - Returns: - Optional[Dict[str, Any]]: MongoDB configuration dictionary, returns None if unable to obtain - - Fields possibly included in the configuration dictionary: - - uri: MongoDB connection URI - - host: MongoDB host address - - port: MongoDB port - - username: Username - - password: Password - - database: Database name (only obtained from tenant configuration, no fallback from environment variables) - - Other connection parameters - """ - tenant_info = get_current_tenant() - if not tenant_info: - logger.debug("⚠️ Unable to get tenant information, returning None") - return None - - mongo_config = tenant_info.get_storage_info("mongodb") - - # Get environment variable configuration as fallback - env_fallback_config = load_mongo_config_from_env() - - if not mongo_config: - final_config = { - "host": env_fallback_config.get("host", "localhost"), - "port": env_fallback_config.get("port", 27017), - "username": env_fallback_config.get("username"), - "password": env_fallback_config.get("password"), - "database": generate_tenant_database_name(DEFAULT_DATABASE_NAME), - } - logger.info( - "✅ MongoDB information missing in tenant [%s] configuration, using environment variable configuration to complete: %s, database=%s", - tenant_info.tenant_id, - final_config.get("uri") - or f"host={final_config.get('host')}:{final_config.get('port')}", - final_config.get("database"), - ) - return final_config - - # Compatibility logic: if tenant configuration is missing certain fields, supplement from environment variables (except database) - # Prioritize using URI (complete connection string) - if mongo_config.get("uri"): - final_config = { - "uri": mongo_config["uri"], - # database: use if specified in tenant configuration, otherwise generate tenant-aware name - "database": mongo_config.get("database") - or generate_tenant_database_name(DEFAULT_DATABASE_NAME), - } - else: - # Use separate connection parameters - final_config = { - "host": mongo_config.get("host") - or env_fallback_config.get("host", "localhost"), - "port": mongo_config.get("port") or env_fallback_config.get("port", 27017), - "username": mongo_config.get("username") - or env_fallback_config.get("username"), - "password": mongo_config.get("password") - or env_fallback_config.get("password"), - # database: use if specified in tenant configuration, otherwise generate tenant-aware name - "database": mongo_config.get("database") - or generate_tenant_database_name(DEFAULT_DATABASE_NAME), - } - - logger.debug( - "✅ Retrieved MongoDB configuration from tenant [%s]: %s, database=%s", - tenant_info.tenant_id, - ( - "uri" - if final_config.get("uri") - else f"host={final_config.get('host')}:{final_config.get('port')}" - ), - final_config.get("database") or "(not specified)", - ) - - return final_config - - -def get_mongo_client_cache_key(config: Dict[str, Any]) -> str: - """ - Generate cache key based on MongoDB configuration - - Generate a unique cache key based on connection parameters (host/port/username/password/uri), - so that connection clients with the same configuration can reuse the same client instance. - - Args: - config: MongoDB configuration dictionary - - Returns: - str: Cache key - """ - # Prioritize using URI to generate cache key - uri = config.get("uri") - if uri: - # For URI, directly use it as the primary identifier - # Note: URI may contain sensitive information, but this is just an in-memory cache key - return f"uri:{uri}" - - # Use combination of host/port/username to generate cache key - host = config.get("host", "localhost") - port = config.get("port", 27017) - username = config.get("username", "") - - # Do not include password in cache key (when passwords are the same, other parameters should also be the same) - # Do not include database in cache key (the same client can access multiple databases) - cache_key = f"host:{host}:port:{port}:user:{username}" - - return cache_key - - -def load_mongo_config_from_env() -> Dict[str, Any]: - """ - Load MongoDB configuration from environment variables - - Read MONGODB_* environment variables, prioritize using MONGODB_URI. - Used for loading configuration for fallback or default clients. - - Returns: - Dict[str, Any]: Configuration dictionary containing connection information - - Environment variables: - - MONGODB_URI: MongoDB connection URI (prioritized) - - MONGODB_HOST: MongoDB host address (default: localhost) - - MONGODB_PORT: MongoDB port (default: 27017) - - MONGODB_USERNAME: Username (optional) - - MONGODB_PASSWORD: Password (optional) - - MONGODB_DATABASE: Database name (default: memsys) - """ - # Prioritize using MONGODB_URI - uri = os.getenv("MONGODB_URI") - if uri: - logger.info("📋 Loading configuration from environment variable MONGODB_URI") - return {"uri": uri, "database": get_default_database_name()} - - # Read individual configuration items separately - host = os.getenv("MONGODB_HOST", "localhost") - port = int(os.getenv("MONGODB_PORT", "27017")) - username = os.getenv("MONGODB_USERNAME") - password = os.getenv("MONGODB_PASSWORD") - database = get_default_database_name() - - logger.info( - "📋 Loading configuration from environment variables: host=%s, port=%s, database=%s", - host, - port, - database, - ) - - return { - "host": host, - "port": port, - "username": username, - "password": password, - "database": database, - } - - -@lru_cache(maxsize=1) -def get_default_database_name() -> str: - """ - Get the default database name - - Read from environment variable MONGODB_DATABASE, return "memsys" if not set. - - Returns: - str: Default database name - """ - return os.getenv("MONGODB_DATABASE", DEFAULT_DATABASE_NAME) - - -def _base_prefixed_database_name(base_name: str) -> str: - """Apply the base resource prefix to a database name (e.g., "memsys" → "b0001_memsys").""" - from core.tenants.tenant_constants import get_base_resource_prefix - - return f"{get_base_resource_prefix()}_{base_name}" - - -def generate_tenant_database_name(base_name: str = "memsys") -> str: - """ - Get tenant-aware database name. - - Resolution order: - 1. Tenant context exists → read from storage_info.mongodb.database - 2. No tenant context (e.g., startup) → base resource prefix + base_name - - Args: - base_name: Base database name (e.g., "memsys") - - Returns: - str: Resolved database name (e.g., "dev_memsys", "b0001_memsys") - """ - try: - tenant_info = get_current_tenant() - if not tenant_info: - return _base_prefixed_database_name(base_name) - - # Read database name from storage_info (set by routing layer) - mongo_config = tenant_info.get_storage_info("mongodb") - if mongo_config and mongo_config.get("database"): - return mongo_config["database"] - - # No database configured in storage_info - logger.warning( - "Tenant [%s] storage_info has no mongodb.database configured, " - "using base prefix. Configure database in routing layer.", - tenant_info.tenant_id, - ) - return _base_prefixed_database_name(base_name) - - except Exception as e: - logger.warning("Failed to get tenant database name, using base prefix: %s", e) - return _base_prefixed_database_name(base_name) diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_aware_client_factory.py b/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_aware_client_factory.py deleted file mode 100644 index 64933f27..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_aware_client_factory.py +++ /dev/null @@ -1,236 +0,0 @@ -""" -Tenant-aware MongoDB Client Factory - -This module provides a tenant-isolated MongoDB client factory implementation based on tenant context management. -The Factory is only responsible for creating and caching clients; mode determination is handled internally by TenantAwareMongoClient. -""" - -import asyncio -from typing import Optional - -from core.observation.logger import get_logger -from core.di.decorators import component -from core.component.mongodb_client_factory import ( - MongoDBClientFactory, - MongoDBConfig, - MongoDBClientWrapper, -) -from core.tenants.tenantize.oxm.mongo.config_utils import generate_tenant_database_name -from core.tenants.tenantize.oxm.mongo.tenant_aware_mongo_client import ( - TenantAwareMongoClient, -) - -logger = get_logger(__name__) - - -@component(name="tenant_aware_mongodb_client_factory", primary=True) -class TenantAwareMongoDBClientFactory(MongoDBClientFactory): - """ - Tenant-aware MongoDB Client Factory Implementation - - This factory class is responsible for creating and managing tenant-aware MongoDB clients. - All logic regarding tenant mode vs non-tenant mode is handled internally by TenantAwareMongoClient. - The Factory only handles simple creation, caching, and lifecycle management. - - Marked with primary=True, serving as the system's default MongoDB client factory. - """ - - def __init__(self): - """Initialize the tenant-aware client factory""" - # Tenant-aware client wrapper (global singleton) - self._client_wrapper: Optional[MongoDBClientWrapper] = None - - # Lock for concurrent access protection - self._lock = asyncio.Lock() - - logger.info("🏭 Tenant-aware MongoDB client factory initialized (primary=True)") - - async def get_client( - self, config: Optional[MongoDBConfig] = None, **connection_kwargs - ) -> MongoDBClientWrapper: - """ - Get MongoDB client - - Returns a tenant-aware client wrapper. Mode determination is handled internally by TenantAwareMongoClient. - - Args: - config: MongoDB configuration (retained for interface compatibility; actual configuration is provided via tenant context or environment variables) - **connection_kwargs: Additional connection parameters - - Returns: - MongoDBClientWrapper: MongoDB client wrapper - """ - return await self._get_client_wrapper() - - async def _get_client_wrapper(self) -> MongoDBClientWrapper: - """ - Get tenant-aware client wrapper (internal method) - - Singleton pattern: only one client wrapper instance is created for the entire factory. - - Returns: - MongoDBClientWrapper: Wrapper containing the tenant-aware client - """ - if self._client_wrapper is None: - async with self._lock: - # Double-check - if self._client_wrapper is None: - logger.info("🔧 Creating tenant-aware MongoDB client wrapper") - - # Create tenant-aware MongoDB client - tenant_aware_client = TenantAwareMongoClient() - - # Create a dummy configuration (for interface compatibility) - dummy_config = MongoDBConfig( - host="tenant-aware", - port=27017, - database=generate_tenant_database_name(), - ) - - # Wrap into MongoDBClientWrapper - self._client_wrapper = TenantAwareClientWrapper( - tenant_aware_client, dummy_config - ) - - logger.info("✅ Tenant-aware MongoDB client wrapper created") - - return self._client_wrapper - - async def get_default_client(self) -> MongoDBClientWrapper: - """ - Get default MongoDB client - - Returns: - MongoDBClientWrapper: Default MongoDB client wrapper - """ - return await self._get_client_wrapper() - - async def get_named_client(self, name: str) -> MongoDBClientWrapper: - """ - Get MongoDB client by name - - Note: In the current implementation, the name parameter is ignored because tenant information is obtained from context. - This method is retained for interface compatibility. - - Args: - name: Prefix name (retained for interface compatibility) - - Returns: - MongoDBClientWrapper: MongoDB client wrapper - """ - logger.info("📋 Getting named client name=%s (tenant-aware mode)", name) - return await self._get_client_wrapper() - - async def create_client_with_config( - self, - host: str = "localhost", - port: int = 27017, - username: Optional[str] = None, - password: Optional[str] = None, - database: Optional[str] = None, - **kwargs, - ) -> MongoDBClientWrapper: - """ - Create client with specified configuration - - Note: In tenant-aware mode, configuration parameters are passed to TenantAwareMongoClient for non-tenant mode usage. - In tenant mode, these parameters are ignored. - - Args: - host: MongoDB host - port: MongoDB port - username: Username - password: Password - database: Database name - **kwargs: Other connection parameters - - Returns: - MongoDBClientWrapper: MongoDB client wrapper - """ - if database is None: - database = generate_tenant_database_name() - logger.info( - "📋 Creating client with specified configuration (tenant-aware mode): host=%s, port=%s, database=%s", - host, - port, - database, - ) - # In tenant-aware mode, configuration parameters are passed to TenantAwareMongoClient - # If non-tenant mode is enabled, TenantAwareMongoClient will use these parameters - return await self._get_client_wrapper() - - async def close_client(self, config: Optional[MongoDBConfig] = None): - """ - Close specified client - - In tenant-aware mode, close the global client wrapper. - - Args: - config: Configuration (retained for interface compatibility) - """ - async with self._lock: - if self._client_wrapper: - await self._client_wrapper.close() - self._client_wrapper = None - logger.info("🔌 MongoDB client closed (tenant-aware factory)") - - async def close_all_clients(self): - """Close all clients""" - await self.close_client() - - -class TenantAwareClientWrapper(MongoDBClientWrapper): - """ - Tenant-aware Client Wrapper - - Inherits from MongoDBClientWrapper, adapted for tenant-aware MongoDB client. - Provides the same interface as MongoDBClientWrapper but uses TenantAwareMongoClient internally. - """ - - def __init__( - self, tenant_aware_client: TenantAwareMongoClient, config: MongoDBConfig - ): - """ - Initialize tenant-aware client wrapper - - Args: - tenant_aware_client: Tenant-aware MongoDB client - config: MongoDB configuration (for compatibility) - """ - # Directly set attributes without calling parent __init__ - self.client = tenant_aware_client - self.config = config - self._initialized = False - self._document_models = [] - - @property - def database(self): - """ - Get database object - - Returns the tenant-aware database proxy. - """ - return self.client[self.config.database] - - async def test_connection(self) -> bool: - """ - Test connection - - Note: In tenant mode, this must be called with a tenant context. - In non-tenant mode, the provided configuration is used for testing. - """ - try: - # TenantAwareMongoClient will select the correct client based on configuration and context - real_client = await self.client._get_real_client() - await real_client.admin.command('ping') - logger.info("✅ MongoDB connection test succeeded (tenant-aware)") - return True - except Exception as e: - logger.error("❌ MongoDB connection test failed (tenant-aware): %s", e) - return False - - async def close(self): - """Close all connections""" - if self.client: - await self.client.close() - logger.info("🔌 MongoDB connection closed (tenant-aware)") diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_aware_document.py b/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_aware_document.py deleted file mode 100644 index 9052b5ba..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_aware_document.py +++ /dev/null @@ -1,94 +0,0 @@ -""" -Tenant-aware MongoDB Document Base Classes - -Provides tenant_id field and index injection for MongoDB documents, -consistent with the ES (TenantAwareAsyncDocument) and Milvus -(TenantAwareMilvusCollectionWithSuffix) patterns. - -The tenant_id field and index are ALWAYS present regardless of tenant mode, -keeping the schema consistent across all environments. In non-tenant mode -the field exists but is never populated (always None). - -The actual tenant_id value is injected at runtime by -TenantCommandInterceptor (the _encrypter hook). This module ensures: -1. The field is declared in the Pydantic schema (so Beanie can - deserialize it without dropping it) -2. A single-field index on tenant_id is created at startup -""" - -from typing import Optional - -from pydantic import Field -from pymongo import IndexModel, ASCENDING - -from core.oxm.mongo.document_base import DocumentBase -from core.oxm.mongo.document_base_with_soft_delete import DocumentBaseWithSoftDelete -from core.tenants.tenant_constants import TENANT_ID_FIELD - - -def _inject_tenant_index(cls) -> None: - """ - Append a tenant_id index to cls.Settings.indexes if not already present. - - This ensures every concrete document subclass gets a tenant_id index - without requiring manual changes to each Settings class. - """ - settings = getattr(cls, "Settings", None) - if settings is None: - return - - indexes = getattr(settings, "indexes", None) - if indexes is None: - settings.indexes = [] - indexes = settings.indexes - - # Check if tenant_id index already exists - for idx in indexes: - if isinstance(idx, IndexModel) and any( - field_name == TENANT_ID_FIELD for field_name in idx.document.get("key", {}) - ): - return - - # Prepend tenant_id index (most important for query performance) - indexes.insert( - 0, IndexModel([(TENANT_ID_FIELD, ASCENDING)], name="idx_tenant_id", sparse=True) - ) - - -class TenantAwareDocumentBase(DocumentBase): - """ - Tenant-aware MongoDB document base class. - - Adds an explicit tenant_id field so that: - - Beanie can deserialize the field from MongoDB without dropping it - - The field appears in the Pydantic schema for validation and serialization - - A single-field index is auto-created at Beanie init time - - The runtime value injection is handled by TenantCommandInterceptor. - """ - - tenant_id: Optional[str] = Field( - default=None, description="Tenant identifier for multi-tenant isolation" - ) - - def __init_subclass__(cls, **kwargs) -> None: - """Auto-inject tenant_id index into subclass Settings.""" - super().__init_subclass__(**kwargs) - _inject_tenant_index(cls) - - -class TenantAwareDocumentBaseWithSoftDelete(DocumentBaseWithSoftDelete): - """ - Tenant-aware MongoDB document base class with soft delete. - - Combines tenant_id field injection with full soft-delete capabilities. - """ - - tenant_id: Optional[str] = Field( - default=None, description="Tenant identifier for multi-tenant isolation" - ) - - def __init_subclass__(cls, **kwargs) -> None: - """Auto-inject tenant_id index into subclass Settings.""" - super().__init_subclass__(**kwargs) - _inject_tenant_index(cls) diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_aware_mongo_client.py b/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_aware_mongo_client.py deleted file mode 100644 index d8efe390..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_aware_mongo_client.py +++ /dev/null @@ -1,598 +0,0 @@ -""" -Tenant-aware MongoDB Client Proxy - -This module implements tenant-aware proxy versions of AsyncMongoClient and AsyncDatabase. -Core functionality: intercept all method calls and dynamically switch to the corresponding real client/database based on tenant context. -""" - -from typing import Dict, Optional, Any -from pymongo.asynchronous.mongo_client import AsyncMongoClient -from pymongo.asynchronous.database import AsyncDatabase -from pymongo.asynchronous.collection import AsyncCollection - -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import get_current_tenant -from core.tenants.tenant_config import get_tenant_config -from core.tenants.tenant_models import TenantPatchKey -from common_utils.datetime_utils import timezone -from core.tenants.tenantize.oxm.mongo.config_utils import ( - get_tenant_mongo_config, - get_mongo_client_cache_key, - load_mongo_config_from_env, - generate_tenant_database_name, -) -from core.tenants.tenantize.tenant_cache_utils import get_or_compute_tenant_cache - -logger = get_logger(__name__) - - -class TenantAwareMongoClient(AsyncMongoClient): - """ - Tenant-aware AsyncMongoClient Proxy - - This class intercepts all calls to AsyncMongoClient via the proxy pattern, - dynamically switching to the real MongoDB client corresponding to the current tenant context. - - Core features: - 1. Efficient caching: caches client instances per tenant to avoid redundant creation - 2. Tenant isolation: different tenants use separate client connections - 3. Non-tenant mode support: tenant functionality can be disabled via configuration, falling back to traditional mode - 4. Default client support: in tenant mode, automatically uses the default client (read from environment variables) when no tenant context exists - 5. Type compatibility: ensures compatibility with pymongo and beanie type checks through virtual subclass registration - - Usage examples: - >>> # Tenant mode (reads configuration from tenant context) - >>> client = TenantAwareMongoClient() - >>> db = client["my_database"] - - >>> # Tenant mode without tenant context (uses default client) - >>> # Reads default configuration from environment variables MONGODB_* - >>> client = TenantAwareMongoClient() - >>> db = client["my_database"] # uses default client - - >>> # Non-tenant mode (uses traditional parameters) - >>> client = TenantAwareMongoClient( - ... host="localhost", - ... port=27017, - ... username="admin", - ... password="password" - ... ) - """ - - def __init__( - self, - host: Optional[str] = None, - port: Optional[int] = None, - username: Optional[str] = None, - password: Optional[str] = None, - **kwargs, - ): - """ - Initialize tenant-aware client - - Args: - host: MongoDB host address (used only in non-tenant mode) - port: MongoDB port (used only in non-tenant mode) - username: Username (used only in non-tenant mode) - password: Password (used only in non-tenant mode) - **kwargs: Other MongoDB client parameters - - Cache design: - - self._client_cache: The actual storage location for client instances (main cache) - - tenant_info_patch: Stores quick references (cache_key) for fast lookup of which cached client to use - """ - # Client cache: based on connection parameters (host/port/username/password) - # This is the main cache that actually stores client instances - # Different tenants with the same configuration can reuse the same client instance - # {cache_key: AsyncMongoClient} - self._client_cache: Dict[str, AsyncMongoClient] = {} - - # Fallback client - # Usage: - # 1. Used in non-tenant mode (configuration from constructor parameters) - # 2. Used in tenant mode when no tenant context exists (configuration from environment variables) - # Note: An instance will only be in one mode, so these two cases won't occur simultaneously - self._fallback_client: Optional[AsyncMongoClient] = None - - # Configuration for fallback client - # Prioritize constructor parameters; if absent, read from environment variables - self._fallback_config: Optional[Dict[str, Any]] = None - if host or port or username or password: - # Constructor parameters take precedence (for non-tenant mode) - self._fallback_config = { - "host": host or "localhost", - "port": port or 27017, - "username": username, - "password": password, - **kwargs, - } - - # Configuration object - self._config = get_tenant_config() - - def get_real_client(self) -> AsyncMongoClient: - """ - Get the real MongoDB client (public method) - - Decides which client to return based on configuration and context: - 1. If non-tenant mode is enabled, return the traditional client - 2. If tenant mode is enabled, return the corresponding tenant client based on current tenant context - 3. If no tenant context exists in tenant mode, return the default client (read from environment variables) - - Optimization strategy: - - Main cache: self._client_cache stores actual client instances (based on connection parameters) - - Quick reference: tenant_info_patch stores client references for fast access - - Different tenants with the same connection configuration will reuse the same client instance - - Note: Creating an AsyncMongoClient object itself is synchronous; only subsequent method calls are asynchronous. - - Returns: - AsyncMongoClient: The real MongoDB client instance - - Raises: - RuntimeError: When in non-tenant mode but connection parameters are not provided, or tenant configuration is missing - """ - - def compute_client() -> AsyncMongoClient: - """Compute (get or create) the tenant's MongoDB client""" - # Get MongoDB configuration from tenant configuration - mongo_config = get_tenant_mongo_config() - if not mongo_config: - tenant_info = get_current_tenant() - raise RuntimeError( - f"Tenant {tenant_info.tenant_id} is missing MongoDB configuration information. " - f"Ensure the tenant information includes storage_info.mongodb configuration." - ) - - # Generate cache key based on connection parameters - cache_key = get_mongo_client_cache_key(mongo_config) - - # Get from main cache - if cache_key in self._client_cache: - logger.debug("🔍 Main cache hit [cache_key=%s]", cache_key) - return self._client_cache[cache_key] - - # Double-check (prevent concurrent creation) - if cache_key in self._client_cache: - return self._client_cache[cache_key] - - # Create new client - logger.info("🔧 Creating MongoDB client [cache_key=%s]", cache_key) - client = self._create_client_from_config(mongo_config) - - # Cache in main cache - self._client_cache[cache_key] = client - logger.info("✅ MongoDB client cached [cache_key=%s]", cache_key) - - return client - - return get_or_compute_tenant_cache( - patch_key=TenantPatchKey.MONGO_CLIENT_CACHE_KEY, - compute_func=compute_client, - fallback=lambda: self._get_fallback_client(), - cache_description="MongoDB client", - ) - - def _get_fallback_client(self) -> AsyncMongoClient: - """ - Get fallback client - - The fallback client is used in two scenarios: - 1. Non-tenant mode: uses configuration provided via constructor parameters - 2. Tenant mode without tenant context: uses configuration from environment variables - - Configuration priority: - - If constructor parameter configuration exists (self._fallback_config), use it - - Otherwise, load configuration from environment variables - - Returns: - AsyncMongoClient: Fallback client instance - - Raises: - RuntimeError: In non-tenant mode, if connection parameters are not provided and cannot be read from environment variables - """ - # Check cache - if self._fallback_client is not None: - return self._fallback_client - - # Get fallback configuration - if self._fallback_config is None: - # No constructor parameter configuration, load from environment variables - self._fallback_config = load_mongo_config_from_env() - - # Raise error if no configuration available - if not self._fallback_config: - raise RuntimeError( - "MongoDB connection parameters not available. " - "Please set environment variables MONGODB_* or pass host/port " - "when creating TenantAwareMongoClient." - ) - - # Create fallback client - logger.info("🔧 Creating fallback MongoDB client") - self._fallback_client = self._create_client_from_config(self._fallback_config) - logger.info("✅ Fallback MongoDB client created") - - return self._fallback_client - - def _create_client_from_config(self, config: Dict[str, Any]) -> AsyncMongoClient: - """ - Create MongoDB client from configuration - - This is the SINGLE factory method for all real AsyncMongoClient instances - (both tenant clients and fallback client). Tenant command interceptor and - guard listener are automatically installed here to guarantee no client - can escape tenant isolation. - - Args: - config: Configuration dictionary containing fields like host, port, username, password, or uri - - Returns: - AsyncMongoClient: Created client instance with interceptor installed - """ - # Build connection parameters (including timezone and timeout settings) - conn_kwargs = { - "serverSelectionTimeoutMS": 10000, # PyMongo AsyncMongoClient requires longer timeout - "connectTimeoutMS": 10000, # Connection timeout - "socketTimeoutMS": 10000, # Socket timeout - "maxPoolSize": 50, - "minPoolSize": 5, - "tz_aware": True, # Enable timezone awareness - "tzinfo": timezone, # Set timezone information - } - - # Prioritize uri if provided - uri = config.get("uri") - if uri: - # Merge extra parameters (excluding uri and database) - extra_kwargs = { - k: v for k, v in config.items() if k not in ("uri", "database") - } - # User-provided parameters have higher priority - conn_kwargs.update(extra_kwargs) - client = AsyncMongoClient(uri, **conn_kwargs) - else: - # Build connection parameters - host = config.get("host", "localhost") - port = config.get("port", 27017) - username = config.get("username") - password = config.get("password") - - # Build connection string - if username and password: - from urllib.parse import quote_plus - - encoded_username = quote_plus(username) - encoded_password = quote_plus(password) - connection_string = ( - f"mongodb://{encoded_username}:{encoded_password}@{host}:{port}" - ) - else: - connection_string = f"mongodb://{host}:{port}" - - # Merge extra parameters - extra_kwargs = { - k: v - for k, v in config.items() - if k not in ("host", "port", "username", "password", "database") - } - # User-provided parameters have higher priority - conn_kwargs.update(extra_kwargs) - - # Create client - client = AsyncMongoClient(connection_string, **conn_kwargs) - - # Install tenant command interceptor + guard listener on every client. - # This is the chokepoint: all clients (tenant + fallback) are created here, - # so no client can escape without interceptor. - self._install_interceptor(client) - - return client - - def _install_interceptor(self, client: AsyncMongoClient) -> None: - """ - Install tenant command interceptor and guard listener on a client. - - Always installs — interceptor checks tenant context at runtime - and passes through when no tenant_id is present. - """ - from core.tenants.tenantize.oxm.mongo.tenant_field_command_interceptor import ( - install_tenant_interceptor, - ) - - install_tenant_interceptor(client) - logger.info("Tenant interceptor installed on client %s", id(client)) - - def __getitem__(self, key: str) -> "TenantAwareDatabase": - """ - Support dictionary-style database access - - Returns a tenant-aware TenantAwareDatabase object. - The database name will be dynamically determined according to tenant configuration; the key parameter is used only as a fallback. - - Args: - key: Requested database name (used as fallback only when tenant configuration does not specify a database) - - Returns: - TenantAwareDatabase: Tenant-aware MongoDB Database object - """ - # Return tenant-aware database object (do not pass key, as it will be dynamically obtained) - return TenantAwareDatabase(self) - - def __getattr__(self, name: str) -> Any: - """ - Intercept attribute access (fallback mechanism) - - This method is called only when an attribute is not found, used to proxy to the real MongoDB client. - This allows specific methods to be overridden without affecting proxy functionality. - - Args: - name: Attribute name - - Returns: - Any: Proxied attribute or method - """ - # Get real client (synchronous) - real_client = self.get_real_client() - # Directly return the attribute or method from the real client - return getattr(real_client, name) - - async def close(self): - """ - Close all client connections - - Clean up all cached clients (main cache) and the fallback client. - - Note: - - Main cache self._client_cache stores actual client instances and requires lifecycle management - - tenant_info_patch only stores quick references (cache_key) and does not need cleanup - """ - # Close all cached clients (main cache) - for cache_key, client in self._client_cache.items(): - try: - await client.close() - logger.info("🔌 MongoDB client closed [cache_key=%s]", cache_key) - except Exception as e: - logger.error( - "❌ Failed to close client [cache_key=%s]: %s", cache_key, e - ) - - self._client_cache.clear() - - # Close fallback client - if self._fallback_client: - try: - await self._fallback_client.close() - logger.info("🔌 Fallback MongoDB client closed") - except Exception as e: - logger.error("❌ Failed to close fallback client: %s", e) - - self._fallback_client = None - - -class TenantAwareDatabase(AsyncDatabase): - """ - Tenant-aware AsyncDatabase Proxy - - This class intercepts all calls to AsyncDatabase via the proxy pattern, - dynamically switching to the real database object corresponding to the current tenant context. - - Core features: - 1. Tenant isolation: different tenants use different database instances - 2. Transparent proxy: all database operations are automatically routed to the correct tenant database - 3. Dynamic database name: database name is dynamically obtained based on tenant context - 4. Type compatibility: inherits from AsyncDatabase, ensuring compatibility with pymongo and beanie type checks - - Usage examples: - >>> client = TenantAwareMongoClient() - >>> db = client["my_database"] # Returns TenantAwareDatabase - >>> collection = db["my_collection"] # Automatically routed to correct tenant - >>> # In different tenant contexts, db.name will return different database names - """ - - def __init__(self, client: TenantAwareMongoClient): - """ - Initialize tenant-aware database - - Args: - client: Tenant-aware MongoDB client - - Note: - - Database name is not stored; it is dynamically obtained on each access - - This ensures the correct database is used in different tenant contexts - """ - # Save client reference - # Note: Do not call parent class __init__ as we want to fully proxy behavior - self._tenant_aware_client = client - - def _get_real_database(self) -> AsyncDatabase: - """ - Get the real MongoDB database object (with caching) - - Obtain the real client through the tenant-aware client, then access the corresponding database. - The database name is dynamically obtained according to tenant configuration, ensuring each tenant uses the correct database. - - Optimization: The database object is cached in tenant_info_patch to avoid repeated creation - - Note: A tenant has only one database configuration, so a fixed patch_key is used - - Returns: - AsyncDatabase: The real MongoDB Database object - """ - - def compute_database() -> AsyncDatabase: - """Compute database object""" - actual_database_name = self._get_actual_database_name() - real_client = self._tenant_aware_client.get_real_client() - return real_client[actual_database_name] - - return get_or_compute_tenant_cache( - patch_key=TenantPatchKey.MONGO_REAL_DATABASE, - compute_func=compute_database, - fallback=compute_database, # fallback logic is the same, reuse directly - cache_description="MongoDB database object", - ) - - def _get_actual_database_name(self) -> str: - """ - Get the actual database name (dynamically obtained, with caching) - - Dynamically obtain the real database name based on current tenant configuration: - 1. In tenant mode, read the database name from tenant configuration (must be specified, no fallback) - 2. In non-tenant mode, read the default database name from environment variables - 3. If no tenant context exists, read the default database name from environment variables - - Optimization: The database name is cached in tenant_info_patch to avoid repeated computation - - Returns: - str: The actual database name - - Raises: - RuntimeError: In tenant mode if tenant configuration is missing or database name is not specified - """ - - def compute_database_name() -> str: - """Compute database name""" - # Use common function to get tenant MongoDB configuration - mongo_config = get_tenant_mongo_config() - if not mongo_config: - tenant_info = get_current_tenant() - raise RuntimeError( - f"Tenant {tenant_info.tenant_id} is missing MongoDB configuration information. " - f"Ensure the tenant information includes storage_info.mongodb configuration." - ) - - # Get database name from configuration - database_name = mongo_config.get("database") - if not database_name: - # In tenant mode, database name must be specified; cannot fall back to default - tenant_info = get_current_tenant() - raise RuntimeError( - f"Database name not specified in MongoDB configuration for tenant {tenant_info.tenant_id}. " - f"Please specify the database name in storage_info.mongodb.database of the tenant configuration." - ) - - return database_name - - return get_or_compute_tenant_cache( - patch_key=TenantPatchKey.ACTUAL_DATABASE_NAME, - compute_func=compute_database_name, - fallback=lambda: generate_tenant_database_name(), # Lazy: returns b0001_memsys when no tenant context - cache_description="database name", - ) - - def __getitem__(self, key: str) -> AsyncCollection: - """ - Support dictionary-style collection access - - Args: - key: Collection name - - Returns: - AsyncCollection: MongoDB Collection object - """ - # Get real database, then access collection - return AsyncCollection(self, key) - - def __getattr__(self, name: str) -> Any: - """ - Intercept attribute access (fallback mechanism) - - This method is called only when an attribute is not found, used to proxy to the real MongoDB database object. - - Args: - name: Attribute name - - Returns: - Any: Proxied attribute or method - """ - # Get real database - real_database = self._get_real_database() - logger.debug( - "🔍 Getting real MongoDB database object attribute or method: %s", name - ) - # Directly return the attribute or method from the real database - return getattr(real_database, name) - - @property - def name(self) -> str: - """ - Get database name (dynamically obtained) - - Dynamically return the actual database name based on the current tenant context. - The name of the same TenantAwareDatabase object may differ in different tenant contexts. - - Returns: - str: The actual database name - """ - return self._get_actual_database_name() - - @property - def _name(self) -> str: - """ - Get database name - - Returns: - str: Database name - """ - return self._get_actual_database_name() - - @property - def client(self) -> AsyncMongoClient: - """ - Get client reference (return real client) - - Since TenantAwareDatabase is already in a specific tenant context, - return the real MongoDB client directly to avoid unnecessary secondary proxying. - - Returns: - AsyncMongoClient: The real MongoDB client - """ - return self._tenant_aware_client.get_real_client() - - def __bool__(self) -> bool: - """ - Boolean evaluation of database object - - Returns: - bool: Always returns True (database object is always truthy) - """ - return True - - def __repr__(self) -> str: - """ - String representation of database object - - Returns: - str: Description of the database object - """ - return ( - f"TenantAwareDatabase(client={self._tenant_aware_client}, name={self.name})" - ) - - def __eq__(self, other: Any) -> bool: - """ - Equality comparison of database objects - - Only compare client references, as the database name is dynamic. - - Args: - other: Object to compare - - Returns: - bool: Whether they are equal - """ - if isinstance(other, TenantAwareDatabase): - return self._tenant_aware_client == other._tenant_aware_client - return False - - def __hash__(self) -> int: - """ - Hash value of database object - - Generate hash value based only on client reference. - - Returns: - int: Hash value - """ - return hash(id(self._tenant_aware_client)) diff --git a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_field_command_interceptor.py b/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_field_command_interceptor.py deleted file mode 100644 index e92fde60..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/oxm/mongo/tenant_field_command_interceptor.py +++ /dev/null @@ -1,563 +0,0 @@ -""" -Tenant Command Interceptor - -Intercepts ALL MongoDB commands at the PyMongo network layer by occupying -the client._encrypter hook point. - -PyMongo 4.x has 3 data-sending paths, all converging through _encrypter when it exists: - - Path 1: conn.command() → network.command() → _encrypter.encrypt() - Used by: insert_one, update_one/many, replace_one, delete_one/many, - find_one_and_*, aggregate, distinct, count, estimated_document_count - - Path 2: cursor._refresh() → server.run_operation() → server.operation_to_command() - → _encrypter.encrypt() - Used by: find(), find_one() (via cursor), async for iteration, getMore - - Path 3: _AsyncBulk → _EncryptedBulkWriteContext.batch_command() - → conn.command() → network.command() → _encrypter.encrypt() - Used by: insert_many(), bulk_write() - Note: documents arrive as RawBSONDocument (immutable) — interceptor handles this - - Blocked: find_raw_batches() and aggregate_raw_batches() raise InvalidOperation - when _encrypter is set. These are low-level APIs not used in this project. - -Trade-offs: - - Pro: Single interception point for 100% command coverage - - Con: Occupies the _encrypter slot, cannot coexist with CSFLE - - Con: Uses private API (client._encrypter), may break on PyMongo major upgrades - - Con: find_raw_batches/aggregate_raw_batches become unavailable -""" - -from typing import Any, Mapping, MutableMapping, Optional, Set - -from bson.codec_options import CodecOptions -from bson.raw_bson import RawBSONDocument - -from core.observation.logger import get_logger -from core.tenants.tenant_config import get_tenant_config -from core.tenants.tenant_constants import TENANT_ID_FIELD -from core.tenants.tenant_contextvar import get_current_tenant_id - - -logger = get_logger(__name__) - -# Data-plane commands that MUST have tenant_id injection -_DATA_COMMANDS: Set[str] = { - "insert", - "update", - "delete", - "find", - "findAndModify", - "aggregate", - "count", - "distinct", -} - -# Control-plane commands that should NOT be intercepted -_PASSTHROUGH_COMMANDS: Set[str] = { - # Connection & auth - "hello", - "ismaster", - "isMaster", - "saslStart", - "saslContinue", - "authenticate", - "getnonce", - "logout", - # Server admin - "ping", - "buildInfo", - "buildinfo", - "serverStatus", - "hostInfo", - "getLog", - "replSetGetStatus", - "currentOp", - "killCursors", - "killOp", - "getMore", - # Index management - "createIndexes", - "dropIndexes", - "listIndexes", - # Collection management - "create", - "drop", - "renameCollection", - "listCollections", - "collStats", - "collMod", - # Database management - "listDatabases", - "dbStats", - # Transaction - "commitTransaction", - "abortTransaction", - "endSessions", - # Search index - "createSearchIndexes", - "updateSearchIndex", - "dropSearchIndex", - "listSearchIndexes", -} - - -class TenantCommandInterceptor: - """ - Masquerades as PyMongo's _Encrypter to intercept MongoDB commands. - - Occupies the client._encrypter hook to inject tenant_id into all - data-plane commands (find, insert, update, delete, aggregate, etc.) - while passing through control-plane commands (ping, auth, index ops). - - Usage: - real_client = tenant_aware_client.get_real_client() - real_client._encrypter = TenantCommandInterceptor() - - Or with collection exclusion: - interceptor = TenantCommandInterceptor( - excluded_collections={"system.profile", "migrations"} - ) - """ - - def __init__(self, excluded_collections: Optional[Set[str]] = None): - # Required by PyMongo: must be False for the hook to fire - self._bypass_auto_encryption = False - self._closed = False - - self._excluded_collections = excluded_collections or set() - - def _get_tenant_id(self) -> Optional[str]: - """Get current tenant_id from context.""" - return get_current_tenant_id() - - def _should_intercept(self, cmd_name: str, cmd: dict) -> bool: - """ - Determine if this command should be intercepted. - - Three categories: - - _DATA_COMMANDS: must intercept, inject tenant_id - - _PASSTHROUGH_COMMANDS: skip, no injection needed - - Unknown: reject — refuse to let unrecognized commands through silently - - Raises: - TenantIsolationViolation: If cmd_name is not in either whitelist - """ - if cmd_name in _PASSTHROUGH_COMMANDS: - return False - - if cmd_name not in _DATA_COMMANDS: - # Unknown command — refuse to let it through silently - collection_name = cmd.get(cmd_name, "unknown") - msg = ( - "\n" - "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n" - "!! UNKNOWN MONGODB COMMAND — TENANT ISOLATION RISK !!\n" - "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n" - f"!! Command: {cmd_name}\n" - f"!! Collection: {collection_name}\n" - "!! Action: Command not in _DATA_COMMANDS or _PASSTHROUGH_COMMANDS.\n" - "!! Add to the appropriate set in tenant_field_command_interceptor.py\n" - "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" - ) - logger.error(msg) - raise TenantIsolationViolation(msg) - - # Check collection exclusion - collection_name = cmd.get(cmd_name) - if ( - isinstance(collection_name, str) - and collection_name in self._excluded_collections - ): - return False - - return True - - async def encrypt( - self, database: str, cmd: Mapping[str, Any], codec_options: CodecOptions - ) -> dict[str, Any]: - """ - Called before every command is serialized to BSON. - - This is the single interception point. The command dict structure - follows the MongoDB wire protocol: - insert: {"insert": "coll", "documents": [...]} - update: {"update": "coll", "updates": [{"q": filter, "u": doc}]} - delete: {"delete": "coll", "deletes": [{"q": filter}]} - find: {"find": "coll", "filter": {...}} - findAndModify: {"findAndModify": "coll", "query": {...}} - aggregate: {"aggregate": "coll", "pipeline": [...]} - count: {"count": "coll", "query": {...}} - distinct: {"distinct": "coll", "key": "...", "query": {...}} - """ - # Convert to mutable dict - cmd = dict(cmd) - - tid = self._get_tenant_id() - if not tid: - if get_tenant_config().app_ready: - cmd_name = next(iter(cmd)) - collection_name = cmd.get(cmd_name, "unknown") - raise TenantIsolationViolation( - f"Missing tenant_id for MongoDB command '{cmd_name}' " - f"on collection '{collection_name}'. " - f"Ensure tenant context is set before data operations." - ) - return cmd - - cmd_name = next(iter(cmd)) - - if not self._should_intercept(cmd_name, cmd): - return cmd - - # All modes: inject tenant_id filter on ALL operations (read + write). - # Even in exclusive mode (physical isolation), filter injection is needed - # so that compound indexes with tenant_id prefix can be utilized. - shared = True - - # ---- INSERT: inject tenant_id into each document (always) ---- - # Use force-set (not setdefault) because Pydantic models serialize - # tenant_id=None as an explicit key — setdefault would skip it. - if cmd_name == "insert": - documents = cmd.get("documents", []) - new_docs = [] - needs_replace = False - for i, doc in enumerate(documents): - if isinstance(doc, RawBSONDocument): - mutable_doc = dict(doc) - mutable_doc[TENANT_ID_FIELD] = tid - new_docs.append(mutable_doc) - needs_replace = True - elif isinstance(doc, MutableMapping): - doc[TENANT_ID_FIELD] = tid - new_docs.append(doc) - elif isinstance(doc, Mapping): - mutable_doc = dict(doc) - mutable_doc[TENANT_ID_FIELD] = tid - new_docs.append(mutable_doc) - needs_replace = True - else: - collection_name = cmd.get("insert", "unknown") - raise TenantIsolationViolation( - f"insert command on '{collection_name}': " - f"documents[{i}] is {type(doc).__name__}, expected Mapping. " - f"Cannot inject tenant_id into unknown document type." - ) - if needs_replace: - cmd["documents"] = new_docs - - # ---- UPDATE / REPLACE ---- - elif cmd_name == "update": - for update_spec in cmd.get("updates", []): - # Filter injection: shared mode only - if shared: - q = update_spec.get("q", {}) - update_spec["q"] = {TENANT_ID_FIELD: tid, **q} - - # Replacement doc injection: always (write operation) - u = update_spec.get("u", {}) - if isinstance(u, Mapping) and not any(k.startswith("$") for k in u): - if isinstance(u, MutableMapping): - u[TENANT_ID_FIELD] = tid - else: - mutable_u = dict(u) - mutable_u[TENANT_ID_FIELD] = tid - update_spec["u"] = mutable_u - - # ---- DELETE: filter injection, shared mode only ---- - elif cmd_name == "delete": - if shared: - for del_spec in cmd.get("deletes", []): - q = del_spec.get("q", {}) - del_spec["q"] = {TENANT_ID_FIELD: tid, **q} - - # ---- FIND: filter injection, shared mode only ---- - elif cmd_name == "find": - if shared: - f = cmd.get("filter") or {} - cmd["filter"] = {TENANT_ID_FIELD: tid, **f} - - # ---- FIND AND MODIFY ---- - elif cmd_name == "findAndModify": - # Query filter: shared mode only - if shared: - q = cmd.get("query") or {} - cmd["query"] = {TENANT_ID_FIELD: tid, **q} - # Replacement doc: always (write operation) - update = cmd.get("update") - if isinstance(update, Mapping) and not any( - k.startswith("$") for k in update - ): - if isinstance(update, MutableMapping): - update[TENANT_ID_FIELD] = tid - else: - mutable_update = dict(update) - mutable_update[TENANT_ID_FIELD] = tid - cmd["update"] = mutable_update - - # ---- AGGREGATE: prepend $match, shared mode only ---- - elif cmd_name == "aggregate": - if shared: - pipeline = list(cmd.get("pipeline", [])) - cmd["pipeline"] = [{"$match": {TENANT_ID_FIELD: tid}}] + pipeline - - # ---- COUNT: filter injection, shared mode only ---- - elif cmd_name == "count": - if shared: - q = cmd.get("query") or {} - cmd["query"] = {TENANT_ID_FIELD: tid, **q} - - # ---- DISTINCT: filter injection, shared mode only ---- - elif cmd_name == "distinct": - if shared: - q = cmd.get("query") or {} - cmd["query"] = {TENANT_ID_FIELD: tid, **q} - - return cmd - - async def decrypt(self, response: bytes) -> bytes: - """ - Called after every response is received. - - Pass-through: we don't need to modify responses. - The raw bytes are returned as-is to be decoded normally. - """ - return response - - async def close(self) -> None: - """Cleanup. Nothing to clean up for this interceptor.""" - self._closed = True - - -# ============================================================ -# Guard: CommandListener-based double check -# ============================================================ - - -class TenantGuardListener: - """ - CommandListener that verifies tenant_id is present in all data-plane commands. - - This is a SECOND line of defense, independent of TenantCommandInterceptor. - It hooks into PyMongo's event publishing system (a different code path from _encrypter), - covering ALL 3 data-sending paths including the bulk non-encrypted path. - - Hook points comparison: - _encrypter.encrypt(): network.py:137, server.py:129 (misses bulk non-encrypted path 3b) - CommandListener: network.py:182, server.py:194, bulk.py:272 (covers ALL paths) - - Behavior when tenant_id is missing: - - mode="block": raises TenantIsolationViolation (caught by PyMongo, printed to stderr, - but the command has ALREADY been serialized — so this is a post-hoc alarm, not a true block) - - mode="log": logs error but allows command to proceed - - IMPORTANT: CommandListener.started() exceptions are swallowed by PyMongo's _handle_exception() - (printed to stderr only). For true blocking, the interceptor (_encrypter) is the primary defense. - This guard serves as: - 1. Detection of _encrypter bypass (e.g. bulk path if _encrypter was somehow removed) - 2. Audit trail for tenant isolation compliance - 3. Alerting mechanism for operations team - """ - - def __init__(self, excluded_collections: Optional[Set[str]] = None): - """ - Args: - excluded_collections: Collection names to skip checking - """ - self._excluded_collections = excluded_collections or set() - self._violation_count = 0 - - @property - def violation_count(self) -> int: - """Number of detected violations since creation.""" - return self._violation_count - - def started(self, event: Any) -> None: - """Called by PyMongo before each command is sent.""" - cmd_name = event.command_name - - if cmd_name not in _DATA_COMMANDS: - return - - # Check tenant context - tid = get_current_tenant_id() - if not tid: - return - - # Check collection exclusion - cmd = event.command - collection_name = cmd.get(cmd_name) - if ( - isinstance(collection_name, str) - and collection_name in self._excluded_collections - ): - return - - # Verify tenant_id is present in the command - missing = self._check_tenant_id(cmd_name, cmd, tid) - if missing: - self._violation_count += 1 - msg = ( - "\n" - "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n" - "!! TENANT ISOLATION VIOLATION — DATA LEAK RISK !!\n" - "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n" - f"!! Command: {cmd_name}\n" - f"!! Collection: {collection_name}\n" - f"!! Expected: tenant_id={tid}\n" - f"!! Violation: {missing}\n" - "!! Cause: Interceptor (_encrypter) was bypassed\n" - f"!! Count: #{self._violation_count}\n" - "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" - ) - logger.error(msg) - - # Always raise — PyMongo swallows the exception via _handle_exception() - # and prints the full traceback to stderr, making it highly visible - # in both application logs (logger.error) and process stderr output. - raise TenantIsolationViolation(msg) - - def _check_tenant_id( - self, cmd_name: str, cmd: Any, expected_tid: str - ) -> Optional[str]: - """ - Check if tenant_id is correctly present in the command. - - Returns None if OK, or a description of what's missing. - """ - if cmd_name == "insert": - docs = cmd.get("documents", []) - for i, doc in enumerate(docs): - if not isinstance(doc, Mapping): - continue - if doc.get(TENANT_ID_FIELD) != expected_tid: - return f"documents[{i}] has tenant_id={doc.get('tenant_id')!r}, expected {expected_tid!r}" - return None - - elif cmd_name == "find": - f = cmd.get("filter", {}) - return self._check_filter(f, expected_tid, "filter") - - elif cmd_name == "update": - updates = cmd.get("updates", []) - for i, u in enumerate(updates): - q = u.get("q", {}) - result = self._check_filter(q, expected_tid, f"updates[{i}].q") - if result: - return result - return None - - elif cmd_name == "delete": - deletes = cmd.get("deletes", []) - for i, d in enumerate(deletes): - q = d.get("q", {}) - result = self._check_filter(q, expected_tid, f"deletes[{i}].q") - if result: - return result - return None - - elif cmd_name == "findAndModify": - q = cmd.get("query", {}) - return self._check_filter(q, expected_tid, "query") - - elif cmd_name == "aggregate": - pipeline = cmd.get("pipeline", []) - if not pipeline: - return "empty pipeline, no $match for tenant_id" - first = pipeline[0] - match = first.get("$match", {}) - if match.get(TENANT_ID_FIELD) != expected_tid: - return f"pipeline[0].$match.tenant_id={match.get('tenant_id')!r}, expected {expected_tid!r}" - return None - - elif cmd_name in ("count", "distinct"): - q = cmd.get("query", {}) - return self._check_filter(q, expected_tid, "query") - - return None - - def _check_filter( - self, filter_doc: Any, expected_tid: str, location: str - ) -> Optional[str]: - """Check if a filter dict contains the correct tenant_id.""" - if not isinstance(filter_doc, Mapping): - return f"{location} is not a mapping: {type(filter_doc)}" - if filter_doc.get(TENANT_ID_FIELD) != expected_tid: - return f"{location}.tenant_id={filter_doc.get('tenant_id')!r}, expected {expected_tid!r}" - return None - - def succeeded(self, event: Any) -> None: - pass - - def failed(self, event: Any) -> None: - pass - - -class TenantIsolationViolation(Exception): - """Raised when a command bypasses tenant isolation.""" - - pass - - -# ============================================================ -# Installation -# ============================================================ - - -def install_tenant_interceptor( - client: Any, excluded_collections: Optional[Set[str]] = None -) -> TenantCommandInterceptor: - """ - Install the tenant command interceptor AND guard listener on a PyMongo AsyncMongoClient. - - Two-layer defense: - Layer 1 (_encrypter): Intercepts and modifies commands before serialization - Layer 2 (CommandListener): Verifies commands after serialization, independent hook point - - On violation, Layer 2 outputs a highly visible error to both logger and stderr: - - logger.error() with banner format - - TenantIsolationViolation exception (caught by PyMongo, traceback printed to stderr) - - Args: - client: An AsyncMongoClient instance (the real client, not the tenant-aware proxy) - excluded_collections: Collection names to skip tenant filtering - - Returns: - The installed interceptor instance - - Raises: - RuntimeError: If the client already has an _encrypter (CSFLE enabled) - """ - existing = getattr(client, "_encrypter", None) - if existing is not None and not isinstance(existing, TenantCommandInterceptor): - raise RuntimeError( - "Cannot install TenantCommandInterceptor: client already has an _encrypter " - f"({type(existing).__name__}). This interceptor cannot coexist with CSFLE." - ) - - # Layer 1: Command interceptor (_encrypter hook) - interceptor = TenantCommandInterceptor(excluded_collections=excluded_collections) - client._encrypter = interceptor - - # Layer 2: Guard listener (CommandListener hook — independent code path) - guard = TenantGuardListener(excluded_collections=excluded_collections) - listeners = getattr(client, "_event_listeners", None) - if listeners is not None: - # Python name mangling: __command_listeners → _EventListeners__command_listeners - listeners._EventListeners__command_listeners.append(guard) - listeners._EventListeners__enabled_for_commands = True - logger.info("Tenant guard listener installed") - else: - logger.warning( - "Cannot install tenant guard listener: client has no _event_listeners. " - "Guard verification will not be available." - ) - - logger.info( - "Tenant command interceptor installed on client %s " - "(excluded_collections=%s)", - type(client).__name__, - excluded_collections or "none", - ) - return interceptor diff --git a/methods/EverCore/src/core/tenants/tenantize/tenant_cache_utils.py b/methods/EverCore/src/core/tenants/tenantize/tenant_cache_utils.py deleted file mode 100644 index 88a49c15..00000000 --- a/methods/EverCore/src/core/tenants/tenantize/tenant_cache_utils.py +++ /dev/null @@ -1,199 +0,0 @@ -""" -Tenant cache utility functions - -Provides tenant-aware caching mechanism for caching computation results within tenant context. -This is a generic cache pattern implementation to avoid duplicating the same caching logic across different modules. - -Core design patterns: -- Cache Pattern -- Lazy Initialization Pattern -- Memoization Pattern - -Use cases: -- Tenant-aware connection alias computation -- Tenant-aware database name retrieval -- Tenant-aware configuration information retrieval -- Any computation result that needs to be cached per tenant -""" - -from typing import TypeVar, Callable, Optional, Union -from core.constants.exceptions import CriticalError -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import get_current_tenant -from core.tenants.tenant_config import ( - get_tenant_config, -) # still needed for app_ready check -from core.tenants.tenant_models import TenantPatchKey - -logger = get_logger(__name__) - -T = TypeVar("T") - - -class TenantContextMissingError(CriticalError): - """Raised when tenant context is missing after app startup (strict check). - - Inherits from CriticalError so that ``reraise_critical_errors()`` will - propagate it out of ``asyncio.gather(return_exceptions=True)`` result processing. - """ - - pass - - -def get_or_compute_tenant_cache( - patch_key: TenantPatchKey, - compute_func: Callable[[], T], - fallback: Optional[Union[T, Callable[[], T]]] = None, - cache_description: str = "value", -) -> T: - """ - Get or compute tenant cache value (supports lazy evaluation fallback) - - This is a generic tenant-aware cache function that encapsulates common caching patterns: - 1. Get tenant information -> if not available, return fallback (lazy evaluation) - 2. Check patch cache -> if hit, return cached value - 3. Call compute_func to calculate new value -> cache to patch -> return new value - - Performance optimization: - - Fallback supports lazy evaluation: fallback function is only called when actually needed - - Avoid unnecessary fallback computation in tenant mode - - Args: - patch_key: TenantPatchKey enum value, used to identify the cache item - compute_func: Computation function, called when cache miss occurs. Should be a parameterless Callable - fallback: Fallback value when no tenant context is available (optional) - - Can be a concrete value (e.g., "default") - - Or a parameterless function (lazy evaluation, e.g., lambda: get_default_database_name()) - cache_description: Description of the cache item, used for logging (optional, default is "value") - - Returns: - T: Cached value or computed value - - Raises: - RuntimeError: If fallback is None and no tenant context is available - - Usage examples: - >>> # Example 1: Get tenant-aware connection alias (fallback is a concrete value) - >>> def compute_using(): - ... milvus_config = get_tenant_milvus_config() - ... cache_key = get_milvus_connection_cache_key(milvus_config) - ... return f"tenant_{cache_key}" - >>> - >>> using = get_or_compute_tenant_cache( - ... patch_key=TenantPatchKey.MILVUS_CONNECTION_CACHE_KEY, - ... compute_func=compute_using, - ... fallback="default", # Concrete value, no lazy evaluation needed - ... cache_description="Milvus connection alias" - ... ) - - >>> # Example 2: Get tenant-aware database name (fallback is a function, lazy evaluation) - >>> def compute_database_name(): - ... mongo_config = get_tenant_mongo_config() - ... return mongo_config.get("database") - >>> - >>> db_name = get_or_compute_tenant_cache( - ... patch_key=TenantPatchKey.ACTUAL_DATABASE_NAME, - ... compute_func=compute_database_name, - ... fallback=lambda: get_default_database_name(), # Lazy evaluation, only called when needed - ... cache_description="database name" - ... ) - """ - try: - config = get_tenant_config() - - # Step 1: Get tenant information - tenant_info = get_current_tenant() - if not tenant_info: - # Strict check mode: after app startup, tenant context must exist in tenant mode - if config.app_ready: - raise TenantContextMissingError( - f"🚨 Strict tenant check failed: app is ready but tenant context is missing!" - f"This usually indicates a serious code issue, please check the call chain." - f"[cache_key={patch_key.value}, cache_description={cache_description}]" - ) - - # During app startup, allow using fallback - fallback_value = _resolve_fallback(fallback, cache_description) - if fallback_value is None: - raise RuntimeError( - f"Tenant context not set in tenant mode and no fallback provided [cache_key={patch_key.value}]" - ) - logger.debug( - "⚠️ Tenant context not set in tenant mode, using fallback %s [fallback=%s]", - cache_description, - fallback_value, - ) - return fallback_value - - tenant_id = tenant_info.tenant_id - - # Step 3: Check patch cache - cached_value = tenant_info.get_patch_value(patch_key) - if cached_value is not None: - logger.debug( - "🔍 Cache hit in tenant_info_patch for %s [tenant_id=%s, value=%s]", - cache_description, - tenant_id, - cached_value, - ) - return cached_value - - # Step 4: Compute new value - computed_value = compute_func() - - # Step 5: Cache to patch - tenant_info.set_patch_value(patch_key, computed_value) - - logger.debug( - "✅ Computed and cached %s for tenant [%s] [value=%s]", - cache_description, - tenant_id, - computed_value, - ) - - return computed_value - - except CriticalError: - raise - except Exception as e: - # Exception handling: try to use fallback (lazy evaluation) - fallback_value = _resolve_fallback(fallback, cache_description) - if fallback_value is not None: - logger.error( - "Failed to get tenant cache %s, using fallback value: %s [fallback=%s]", - cache_description, - e, - fallback_value, - ) - return fallback_value - else: - logger.error("Failed to get tenant cache %s: %s", cache_description, e) - raise - - -def _resolve_fallback( - fallback: Optional[Union[T, Callable[[], T]]], description: str -) -> Optional[T]: - """ - Resolve fallback value (supports lazy evaluation) - - Args: - fallback: Can be a concrete value or a function - description: Description for logging - - Returns: - Resolved value - """ - if fallback is None: - return None - - # If it's a Callable, call it (lazy evaluation) - if callable(fallback): - try: - return fallback() - except Exception as e: - logger.error("Failed to compute fallback %s: %s", description, e) - return None - - # Otherwise, return the concrete value directly - return fallback diff --git a/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/DATA_FIX_README.md b/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/DATA_FIX_README.md deleted file mode 100644 index d1373fe0..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/DATA_FIX_README.md +++ /dev/null @@ -1,172 +0,0 @@ -# 数据维护脚本使用指南 - -本目录包含 Elasticsearch、Milvus 和 MongoDB 数据维护脚本的详细使用文档。 - -## 📚 文档索引 - -### [Elasticsearch 脚本使用指南](./elasticsearch_scripts_guide.md) - -包含以下脚本的使用说明： -- `es_sync_docs.py` - ES 数据同步主入口 -- `es_sync_episodic_memory_docs.py` - 情景记忆文档同步到 ES -- `es_rebuild_index.py` - ES 索引重建 - -**适用场景**： -- 从 MongoDB 同步数据到 Elasticsearch -- 重建 ES 索引结构 -- 修改索引 mapping 或 settings - ---- - -### [Milvus 脚本使用指南](./milvus_scripts_guide.md) - -包含以下脚本的使用说明： -- `milvus_sync_docs.py` - Milvus 数据同步主入口 -- `milvus_sync_episodic_memory_docs.py` - 情景记忆文档同步到 Milvus -- `milvus_rebuild_collection.py` - Milvus Collection 重建 - -**适用场景**： -- 从 MongoDB 同步向量数据到 Milvus -- 重建 Milvus Collection 结构 -- 迁移 Milvus 数据到新 Collection - ---- - -### [MongoDB 脚本使用指南](./mongodb_scripts_guide.md) - -包含以下脚本的使用说明： -- `mongo_add_timestamp_shard.py` - 添加基于 timestamp 的分片配置 -- `mongo_fix_episodic_memory_missing_vector.py` - 修复情景记忆缺失向量 - -**适用场景**： -- 配置 MongoDB 分片以优化查询性能 -- 批量修复缺失的向量字段 -- 更新向量模型版本 - ---- - -## 🚀 快速开始 - -### 通用运行方式 - -所有脚本推荐通过 `bootstrap.py` 运行，以确保应用上下文和依赖注入正确加载： - -```bash -python src/bootstrap.py src/devops_scripts/data_fix/<脚本名称> [参数] -``` - -### 常用操作示例 - -#### 1. 同步数据到 ES - -```bash -python src/bootstrap.py src/devops_scripts/data_fix/es_sync_docs.py \ - --index-name episodic-memory \ - --days 7 -``` - -#### 2. 同步数据到 Milvus - -```bash -python src/bootstrap.py src/devops_scripts/data_fix/milvus_sync_docs.py \ - --collection-name episodic_memory \ - --days 7 -``` - -#### 3. 修复缺失向量 - -```bash -python src/bootstrap.py src/devops_scripts/data_fix/mongo_fix_episodic_memory_missing_vector.py \ - --limit 1000 \ - --batch 200 \ - --concurrency 8 -``` - ---- - -## 📊 数据流向图 - -``` -┌─────────────┐ -│ MongoDB │ (主数据源) -└──────┬──────┘ - │ - ├─────────────────────────────────┬──────────────────────────────────┐ - │ │ │ - ▼ ▼ ▼ -┌─────────────┐ ┌─────────────┐ ┌─────────────┐ -│Elasticsearch│ ◄─ es_sync │ Milvus │ ◄─ milvus_sync │ 向量修复脚本 │ -│ (全文检索) │ │ (向量检索) │ │ │ -└─────────────┘ └─────────────┘ └─────────────┘ - │ │ │ - │ │ │ - └─────────────────────────────────┴──────────────────────────────────┘ - 用于检索和查询 -``` - ---- - -## ⚠️ 注意事项 - -### 运行环境 - -- **Python 版本**：3.10+ -- **依赖管理**：使用 `uv` 或 `pip` 安装依赖 -- **配置文件**：确保 `config.json` 配置正确 - -### 权限要求 - -- MongoDB：读取权限（同步脚本），读写权限（修复脚本） -- Elasticsearch：索引读写权限 -- Milvus：Collection 读写权限 - -### 安全建议 - -1. **测试优先**：在测试环境充分验证后再在生产环境执行 -2. **备份数据**：重要操作前备份数据 -3. **监控日志**：执行过程中持续监控日志输出 -4. **分批执行**：大规模数据操作建议分批执行 -5. **低峰期操作**：重建索引等操作建议在低峰期进行 - ---- - -## 🛠️ 故障排查 - -### 脚本执行失败 - -1. 检查网络连接（MongoDB、ES、Milvus） -2. 检查服务是否正常运行 -3. 检查配置文件是否正确 -4. 查看详细日志定位问题 - -### 性能问题 - -1. 调整批量大小（`--batch-size` 或 `--batch`） -2. 调整并发度（`--concurrency`） -3. 检查服务性能瓶颈 -4. 使用增量同步（`--days` 参数） - -### 数据不一致 - -1. 重新运行同步脚本（支持幂等操作） -2. 检查源数据（MongoDB）是否正确 -3. 使用 `--limit` 参数先测试少量数据 -4. 检查日志中的错误信息 - ---- - -## 📞 获取帮助 - -如果遇到问题，可以： - -1. 查看各脚本详细文档中的"常见问题"部分 -2. 使用 `--help` 参数查看脚本帮助信息 -3. 查看脚本源码中的注释说明 -4. 联系开发团队获取支持 - ---- - -## 📝 更新日志 - -- **2025-10-22**: 创建文档，涵盖 ES、Milvus、MongoDB 维护脚本 - diff --git a/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/elasticsearch_scripts_guide.md b/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/elasticsearch_scripts_guide.md deleted file mode 100644 index f6be5a45..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/elasticsearch_scripts_guide.md +++ /dev/null @@ -1,206 +0,0 @@ -# Elasticsearch 数据维护脚本使用指南 - -本文档介绍 Elasticsearch 相关的数据维护脚本的使用方法。 - -## 脚本列表 - -- `es_sync_docs.py` - ES 数据同步主入口脚本 -- `es_sync_episodic_memory_docs.py` - 情景记忆文档同步到 ES 的实现 -- `es_rebuild_index.py` - ES 索引重建脚本 - ---- - -## 1. es_sync_docs.py - -### 功能说明 - -主入口脚本，用于将 MongoDB 数据同步到 Elasticsearch 指定索引。根据索引名称自动路由到相应的同步实现。 - -### 使用方式 - -```bash -# 通过 bootstrap 运行（推荐） -python src/bootstrap.py src/devops_scripts/data_fix/es_sync_docs.py \ - --index-name episodic-memory \ - --batch-size 500 \ - --limit 10000 \ - --days 7 -``` - -### 参数说明 - -| 参数 | 缩写 | 必需 | 默认值 | 说明 | -|------|------|------|--------|------| -| `--index-name` | `-i` | ✅ | 无 | ES 索引别名，如 `episodic-memory` | -| `--batch-size` | `-b` | ❌ | 500 | 批处理大小，每批同步的文档数量 | -| `--limit` | `-l` | ❌ | 全部 | 限制处理的文档数量，默认处理全部 | -| `--days` | `-d` | ❌ | 全部 | 只处理过去 N 天创建的文档 | - -### 使用示例 - -```bash -# 同步所有情景记忆文档 -python src/bootstrap.py src/devops_scripts/data_fix/es_sync_docs.py \ - --index-name episodic-memory - -# 只同步最近 7 天的文档 -python src/bootstrap.py src/devops_scripts/data_fix/es_sync_docs.py \ - --index-name episodic-memory \ - --days 7 - -# 同步 10000 条文档，批量大小 1000 -python src/bootstrap.py src/devops_scripts/data_fix/es_sync_docs.py \ - --index-name episodic-memory \ - --batch-size 1000 \ - --limit 10000 -``` - -### 注意事项 - -- 当前仅支持 `episodic-memory` 索引类型 -- 需要通过 `bootstrap.py` 运行以确保应用上下文和依赖注入正确加载 -- 同步操作使用 upsert 语义，支持幂等操作 - ---- - -## 2. es_sync_episodic_memory_docs.py - -### 功能说明 - -情景记忆文档同步到 Elasticsearch 的具体实现。从 MongoDB 批量获取情景记忆文档，转换后批量写入 ES。 - -### 技术特点 - -- **批量处理**：支持大规模数据同步，避免内存溢出 -- **幂等操作**：使用 `update` + `doc_as_upsert` 模式，支持重复执行 -- **时间过滤**：支持增量同步，只处理指定时间范围的文档 -- **流式处理**：使用 `async_streaming_bulk` 提高性能 - -### 工作流程 - -1. 从 MongoDB 的 `EpisodicMemoryRawRepository` 批量获取文档 -2. 使用 `EpisodicMemoryConverter.from_mongo()` 转换为 ES 文档格式 -3. 使用 `async_streaming_bulk` 批量写入 ES -4. 自动刷新索引以确保数据可见 - -### 注意事项 - -- 建议批量大小设置为 500-1000，根据文档大小调整 -- 使用 `--days` 参数可以实现增量同步 -- 同步完成后会自动刷新索引 - ---- - -## 3. es_rebuild_index.py - -### 功能说明 - -重建并切换 Elasticsearch 索引别名。用于索引结构变更、数据迁移等场景。 - -### 使用方式 - -```bash -# 通过 bootstrap 运行（推荐） -python src/bootstrap.py src/devops_scripts/data_fix/es_rebuild_index.py \ - --index-name episodic-memory \ - --close-old \ - --delete-old -``` - -### 参数说明 - -| 参数 | 缩写 | 必需 | 默认值 | 说明 | -|------|------|------|--------|------| -| `--index-name` | `-i` | ✅ | 无 | 索引别名，如 `episodic-memory` | -| `--close-old` | `-c` | ❌ | False | 是否关闭旧索引 | -| `--delete-old` | `-x` | ❌ | False | 是否删除旧索引 | - -### 使用示例 - -```bash -# 重建索引，保留旧索引（最安全） -python src/bootstrap.py src/devops_scripts/data_fix/es_rebuild_index.py \ - --index-name episodic-memory - -# 重建索引并关闭旧索引 -python src/bootstrap.py src/devops_scripts/data_fix/es_rebuild_index.py \ - --index-name episodic-memory \ - --close-old - -# 重建索引并删除旧索引（谨慎使用） -python src/bootstrap.py src/devops_scripts/data_fix/es_rebuild_index.py \ - --index-name episodic-memory \ - --close-old \ - --delete-old -``` - -### 工作流程 - -1. 根据索引别名查找对应的文档类（`Document`） -2. 创建新的索引（带时间戳后缀） -3. 应用新的 mapping 和 settings -4. 将别名切换到新索引 -5. 可选：关闭或删除旧索引 - -### 注意事项 - -⚠️ **重要提示**： -- 重建索引**不会自动迁移数据**，需要单独运行同步脚本 -- 建议先不删除旧索引，确认新索引工作正常后再删除 -- 在生产环境操作前，请先在测试环境验证 - -### 典型操作流程 - -```bash -# 1. 重建索引结构（不删除旧索引） -python src/bootstrap.py src/devops_scripts/data_fix/es_rebuild_index.py \ - --index-name episodic-memory - -# 2. 同步数据到新索引 -python src/bootstrap.py src/devops_scripts/data_fix/es_sync_docs.py \ - --index-name episodic-memory - -# 3. 验证新索引数据无误后，删除旧索引（可选） -# 手动在 Kibana 或通过 ES API 删除旧索引 -``` - ---- - -## 常见问题 - -### Q1: 同步速度慢怎么办？ - -**A:** 可以调整以下参数： -- 增大 `--batch-size`（建议 500-2000） -- 检查网络连接和 ES 集群性能 -- 使用 `--days` 参数进行增量同步 - -### Q2: 同步中断后如何继续？ - -**A:** 由于使用 upsert 语义，直接重新运行同步脚本即可，已同步的文档会被更新而不是重复插入。 - -### Q3: 如何验证同步是否成功？ - -**A:** 可以通过以下方式验证： -```bash -# 检查文档数量 -curl -X GET "localhost:9200/episodic-memory/_count" - -# 查询最近的文档 -curl -X GET "localhost:9200/episodic-memory/_search?size=10&sort=created_at:desc" -``` - -### Q4: 重建索引会影响查询吗？ - -**A:** 别名切换是原子操作，几乎不会影响查询。但重建过程中新数据可能写入旧索引，建议在低峰期操作。 - ---- - -## 最佳实践 - -1. **增量同步**：使用 `--days 1` 每天同步增量数据 -2. **批量调优**：根据文档大小调整 `--batch-size`，避免内存溢出 -3. **监控日志**：关注脚本输出的成功/失败统计 -4. **备份策略**：重建索引前确保有数据备份 -5. **测试验证**：生产环境操作前在测试环境充分验证 - diff --git a/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/milvus_scripts_guide.md b/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/milvus_scripts_guide.md deleted file mode 100644 index dbcb6389..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/milvus_scripts_guide.md +++ /dev/null @@ -1,278 +0,0 @@ -# Milvus 数据维护脚本使用指南 - -本文档介绍 Milvus 相关的数据维护脚本的使用方法。 - -## 脚本列表 - -- `milvus_sync_docs.py` - Milvus 数据同步主入口脚本 -- `milvus_sync_episodic_memory_docs.py` - 情景记忆文档同步到 Milvus 的实现 -- `milvus_rebuild_collection.py` - Milvus Collection 重建脚本 - ---- - -## 1. milvus_sync_docs.py - -### 功能说明 - -主入口脚本，用于将 MongoDB 数据同步到 Milvus 指定 Collection。根据 Collection 名称自动路由到相应的同步实现。 - -### 使用方式 - -```bash -# 通过 bootstrap 运行（推荐） -python src/bootstrap.py src/devops_scripts/data_fix/milvus_sync_docs.py \ - --collection-name episodic_memory \ - --batch-size 500 \ - --limit 10000 \ - --days 7 -``` - -### 参数说明 - -| 参数 | 缩写 | 必需 | 默认值 | 说明 | -|------|------|------|--------|------| -| `--collection-name` | `-c` | ✅ | 无 | Milvus Collection 名称，如 `episodic_memory` | -| `--batch-size` | `-b` | ❌ | 500 | 批处理大小，每批同步的文档数量 | -| `--limit` | `-l` | ❌ | 全部 | 限制处理的文档数量，默认处理全部 | -| `--days` | `-d` | ❌ | 全部 | 只处理过去 N 天创建的文档 | - -### 使用示例 - -```bash -# 同步所有情景记忆文档 -python src/bootstrap.py src/devops_scripts/data_fix/milvus_sync_docs.py \ - --collection-name episodic_memory - -# 只同步最近 7 天的文档 -python src/bootstrap.py src/devops_scripts/data_fix/milvus_sync_docs.py \ - --collection-name episodic_memory \ - --days 7 - -# 同步 10000 条文档，批量大小 1000 -python src/bootstrap.py src/devops_scripts/data_fix/milvus_sync_docs.py \ - --collection-name episodic_memory \ - --batch-size 1000 \ - --limit 10000 -``` - -### 注意事项 - -- 当前仅支持 `episodic_memory` Collection 类型 -- 需要通过 `bootstrap.py` 运行以确保应用上下文和依赖注入正确加载 -- 同步操作支持幂等，可以重复执行 - ---- - -## 2. milvus_sync_episodic_memory_docs.py - -### 功能说明 - -情景记忆文档同步到 Milvus 的具体实现。从 MongoDB 批量获取情景记忆文档，转换后批量插入到 Milvus。 - -### 技术特点 - -- **批量处理**：批量获取、批量转换、批量插入，提高效率 -- **幂等操作**：使用 insert 操作（Milvus 会自动处理重复 ID） -- **时间过滤**：支持增量同步，只处理指定时间范围的文档 -- **数据验证**：插入前验证必要字段（id、vector）是否存在 - -### 工作流程 - -1. 从 MongoDB 的 `EpisodicMemoryRawRepository` 批量获取文档 -2. 使用 `EpisodicMemoryMilvusConverter.from_mongo()` 转换为 Milvus 实体格式 -3. 验证必要字段（id、vector） -4. 批量插入到 Milvus Collection -5. 调用 `flush()` 确保数据持久化 - -### 数据验证规则 - -脚本会跳过以下文档： -- 缺少 `id` 字段的文档 -- 缺少 `vector` 字段或 `vector` 为空的文档 - -### 注意事项 - -- 建议批量大小设置为 500-1000，根据向量维度和文档大小调整 -- 向量必须已经在 MongoDB 中生成，脚本不会自动生成向量 -- 使用 `--days` 参数可以实现增量同步 -- 同步完成后会自动调用 `flush()` 确保数据持久化 - ---- - -## 3. milvus_rebuild_collection.py - -### 功能说明 - -重建并切换 Milvus Collection 别名。用于 Collection 结构变更、索引优化、数据迁移等场景。 - -### 使用方式 - -```bash -# 通过 bootstrap 运行（推荐） -python src/bootstrap.py src/devops_scripts/data_fix/milvus_rebuild_collection.py \ - --alias episodic_memory \ - --batch-size 3000 \ - --drop-old -``` - -### 参数说明 - -| 参数 | 缩写 | 必需 | 默认值 | 说明 | -|------|------|------|--------|------| -| `--alias` | `-a` | ✅ | 无 | Collection 别名，如 `episodic_memory` | -| `--drop-old` | `-x` | ❌ | False | 是否删除旧 Collection | -| `--no-migrate-data` | - | ❌ | False | 不迁移数据（默认会迁移） | -| `--batch-size` | `-b` | ❌ | 3000 | 每批迁移的数据量 | - -### 使用示例 - -```bash -# 重建 Collection 并迁移数据（保留旧 Collection） -python src/bootstrap.py src/devops_scripts/data_fix/milvus_rebuild_collection.py \ - --alias episodic_memory - -# 重建 Collection 但不迁移数据 -python src/bootstrap.py src/devops_scripts/data_fix/milvus_rebuild_collection.py \ - --alias episodic_memory \ - --no-migrate-data - -# 重建 Collection、迁移数据并指定批大小 -python src/bootstrap.py src/devops_scripts/data_fix/milvus_rebuild_collection.py \ - --alias episodic_memory \ - --batch-size 5000 - -# 重建 Collection、迁移数据并删除旧 Collection -python src/bootstrap.py src/devops_scripts/data_fix/milvus_rebuild_collection.py \ - --alias episodic_memory \ - --drop-old -``` - -### 工作流程 - -1. 根据别名找到对应的 Collection 管理类（`MilvusCollectionBase`） -2. 创建新的 Collection（带时间戳后缀） -3. 自动创建索引并加载到内存 -4. **（可选）数据迁移**：分批从旧 Collection 查询数据并插入新 Collection -5. 将别名切换到新 Collection -6. **（可选）删除旧 Collection** - -### 数据迁移策略 - -- 使用分批查询和插入，避免内存溢出 -- 基于 `id` 字段分页（字符串比较） -- 每批处理后调用 `flush()` 确保数据持久化 -- 实时输出迁移进度和统计信息 - -### 注意事项 - -⚠️ **重要提示**： -- 默认会迁移数据，如果只想重建结构而不迁移数据，使用 `--no-migrate-data` -- 建议先不删除旧 Collection，确认新 Collection 工作正常后再删除 -- 数据迁移时间取决于数据量，大规模数据建议在低峰期操作 -- 在生产环境操作前，请先在测试环境验证 - -### 典型操作流程 - -#### 场景1：修改 Collection Schema（需要重建） - -```bash -# 1. 修改代码中的 Collection 定义（如增加字段、修改索引参数） - -# 2. 重建 Collection 并迁移数据 -python src/bootstrap.py src/devops_scripts/data_fix/milvus_rebuild_collection.py \ - --alias episodic_memory - -# 3. 验证新 Collection 数据和查询功能 - -# 4. 确认无误后删除旧 Collection -python src/bootstrap.py src/devops_scripts/data_fix/milvus_rebuild_collection.py \ - --alias episodic_memory \ - --no-migrate-data \ - --drop-old -``` - -#### 场景2：只重建索引（不需要迁移数据） - -```bash -# 1. 重建 Collection（不迁移数据） -python src/bootstrap.py src/devops_scripts/data_fix/milvus_rebuild_collection.py \ - --alias episodic_memory \ - --no-migrate-data - -# 2. 从 MongoDB 重新同步数据 -python src/bootstrap.py src/devops_scripts/data_fix/milvus_sync_docs.py \ - --collection-name episodic_memory - -# 3. 验证后删除旧 Collection -``` - ---- - -## 常见问题 - -### Q1: 同步速度慢怎么办？ - -**A:** 可以调整以下参数： -- 增大 `--batch-size`（建议 500-2000） -- 检查网络连接和 Milvus 集群性能 -- 使用 `--days` 参数进行增量同步 -- 确认向量维度不是特别高（高维向量传输和插入较慢） - -### Q2: 同步时提示 "缺少 vector 字段" 怎么办？ - -**A:** 这表示 MongoDB 中的文档没有向量数据。需要先运行向量生成脚本： -```bash -python src/bootstrap.py src/devops_scripts/data_fix/mongo_fix_episodic_memory_missing_vector.py -``` - -### Q3: 数据迁移需要多长时间？ - -**A:** 取决于数据量和向量维度： -- 100万条记录（768维向量）：约 10-30 分钟 -- 建议监控日志中的进度信息 -- 可以通过调整 `--batch-size` 优化速度 - -### Q4: 重建 Collection 会影响查询吗？ - -**A:** 别名切换是原子操作，几乎不会影响查询。但重建过程中新数据可能写入旧 Collection，建议在低峰期操作。 - -### Q5: 如何验证 Collection 重建是否成功？ - -**A:** 可以通过以下方式验证： -```python -from pymilvus import connections, Collection - -connections.connect() -collection = Collection("episodic_memory") -print(f"实际 Collection: {collection.name}") -print(f"记录数: {collection.num_entities}") -``` - ---- - -## 最佳实践 - -1. **增量同步**：使用 `--days 1` 每天同步增量数据 -2. **批量调优**：根据向量维度和网络情况调整 `--batch-size` -3. **监控日志**：关注脚本输出的成功/失败统计和进度信息 -4. **备份策略**：重建前确保 MongoDB 数据完整（Milvus 可以从 MongoDB 重建） -5. **测试验证**：生产环境操作前在测试环境充分验证 -6. **分批操作**：大规模数据建议先用 `--limit` 测试少量数据 -7. **错误重试**：同步失败可以直接重新运行，支持幂等操作 - ---- - -## 性能优化建议 - -### 同步性能优化 - -- **批量大小**：建议 500-1000，不宜过大（内存限制） -- **并发控制**：Milvus 插入操作本身已经是批量的，无需额外并发 -- **网络优化**：确保与 Milvus 服务的网络延迟较低 - -### Collection 索引优化 - -- **HNSW 参数**：`M=16, efConstruction=256` 适合大多数场景 -- **IVF 参数**：数据量大时考虑使用 IVF_FLAT 或 IVF_PQ -- **内存加载**：重要的 Collection 保持 loaded 状态以提升查询性能 - diff --git a/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/mongodb_scripts_guide.md b/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/mongodb_scripts_guide.md deleted file mode 100644 index d9e6c5ad..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/data_fix_docs/mongodb_scripts_guide.md +++ /dev/null @@ -1,310 +0,0 @@ -# MongoDB 数据维护脚本使用指南 - -本文档介绍 MongoDB 相关的数据维护脚本的使用方法。 - -## 脚本列表 - -- `mongo_add_timestamp_shard.py` - 为 MemCell 集合添加基于 timestamp 的分片配置 -- `mongo_fix_episodic_memory_missing_vector.py` - 修复情景记忆文档中缺失的向量字段 - ---- - -## 1. mongo_add_timestamp_shard.py - -### 功能说明 - -为 MemCell 集合添加基于 `timestamp` 字段的时间戳分片配置，优化大规模数据的查询和存储性能。 - -### 使用方式 - -```bash -# 通过 bootstrap 运行（推荐） -python src/bootstrap.py src/devops_scripts/data_fix/mongo_add_timestamp_shard.py -``` - -### 参数说明 - -该脚本无需命令行参数，直接运行即可。 - -### 工作流程 - -1. **检查分片集群**：验证当前是否为分片集群环境 -2. **启用数据库分片**：为目标数据库启用分片功能 -3. **设置分片键**：将 `timestamp` 字段设置为 MemCell 集合的分片键 -4. **创建预分片**：自动创建未来 12 个月的预分片点 -5. **验证配置**：验证分片配置是否成功 - -### 分片策略 - -- **分片键**：`timestamp` 字段（升序） -- **预分片**：按月创建分片点，覆盖未来 12 个月 -- **优势**： - - 时间范围查询性能优化 - - 数据均匀分布到多个分片 - - 避免单个分片过大导致的性能问题 - -### 注意事项 - -⚠️ **重要提示**： -- 仅在 MongoDB 分片集群环境中生效 -- 如果不是分片集群，脚本会自动跳过 -- 分片配置是**不可逆**操作，设置后无法直接修改分片键 -- 建议在**数据量较小时**配置分片，避免后期迁移成本 - -### 使用场景 - -适用于以下场景： -- MemCell 数据量预计超过 10GB -- 需要优化基于时间范围的查询性能 -- 部署了 MongoDB 分片集群环境 - -### 执行结果示例 - -``` -🔧 开始配置timestamp分片... -✅ 检测到分片集群，共 3 个分片 -✅ 数据库 'memsys' 分片已启用 -✅ MemCell集合timestamp分片键设置完成 -📅 创建分片点: 2025-02-01 00:00:00 -📅 创建分片点: 2025-03-01 00:00:00 -... -✅ 创建了 12 个预分片点 -✅ MemCell集合分片配置验证成功 -📊 分片键: {'timestamp': 1} -🎉 timestamp分片配置完成 -``` - -### 如何验证分片是否生效 - -```javascript -// 在 MongoDB Shell 中执行 -use memsys -db.memcells.getShardDistribution() -``` - ---- - -## 2. mongo_fix_episodic_memory_missing_vector.py - -### 功能说明 - -修复历史 EpisodicMemory 文档中缺失的向量字段。针对两类文档进行修复： -1. `vector` 字段不存在、为 None 或为空数组的文档 -2. `vector_model` 不等于目标模型的文档（需要重新生成向量） - -### 使用方式 - -```bash -# 通过 bootstrap 运行（推荐） -python src/bootstrap.py src/devops_scripts/data_fix/mongo_fix_episodic_memory_missing_vector.py \ - --limit 1000 \ - --batch 200 \ - --concurrency 8 \ - --start-created-at "2025-09-16T20:20:06+00:00" \ - --end-created-at "2025-09-30T23:59:59+00:00" -``` - -### 参数说明 - -| 参数 | 必需 | 默认值 | 说明 | -|------|------|--------|------| -| `--limit` | ❌ | 1000 | 最多处理的文档数量 | -| `--batch` | ❌ | 200 | 每次从数据库拉取的文档数量，越大越快但更占内存 | -| `--concurrency` | ❌ | 8 | 并发度，同时处理的文档数量 | -| `--start-created-at` | ❌ | 全部 | 只处理 `created_at` ≥ 该时间的文档（ISO 格式） | -| `--end-created-at` | ❌ | 全部 | 只处理 `created_at` ≤ 该时间的文档（ISO 格式） | - -### 使用示例 - -```bash -# 修复最近 1000 条缺失向量的文档 -python src/bootstrap.py src/devops_scripts/data_fix/mongo_fix_episodic_memory_missing_vector.py \ - --limit 1000 - -# 修复所有缺失向量的文档（不限制数量） -python src/bootstrap.py src/devops_scripts/data_fix/mongo_fix_episodic_memory_missing_vector.py \ - --limit 999999999 - -# 修复指定时间范围内的文档 -python src/bootstrap.py src/devops_scripts/data_fix/mongo_fix_episodic_memory_missing_vector.py \ - --start-created-at "2025-09-01T00:00:00+00:00" \ - --end-created-at "2025-09-30T23:59:59+00:00" \ - --batch 500 \ - --concurrency 16 - -# 高性能模式（大批量、高并发） -python src/bootstrap.py src/devops_scripts/data_fix/mongo_fix_episodic_memory_missing_vector.py \ - --batch 1000 \ - --concurrency 32 \ - --limit 100000 -``` - -### 工作流程 - -1. **查询候选文档**： - - 查询 `episode` 不为空的文档 - - 过滤出 `vector` 字段缺失或为空的文档 - - 过滤出 `vector_model` 不等于目标模型的文档 - -2. **批量处理**： - - 分批从 MongoDB 获取文档（batch_size 控制） - - 按 `created_at` 倒序排列（优先处理最新数据） - -3. **并发向量化**： - - 调用 `vectorize_service.get_embedding()` 生成向量 - - 使用协程并发处理（concurrency 控制） - -4. **更新文档**： - - 精确按 `_id` 更新，避免覆盖其他字段 - - 更新 `vector` 和 `vector_model` 字段 - -### 目标向量模型 - -脚本中定义的目标向量模型： -```python -TARGET_VECTOR_MODEL = "Qwen/Qwen3-Embedding-4B" -``` - -如果文档的 `vector_model` 不等于此模型，也会被重新生成向量。 - -### 性能优化 - -- **批量大小**：`--batch` 控制每次从数据库拉取的文档数量 - - 建议值：200-1000 - - 过大可能导致内存不足 - - 过小会增加数据库查询次数 - -- **并发度**：`--concurrency` 控制同时处理的文档数量 - - 建议值：8-32 - - 取决于向量化服务的吞吐能力 - - 过高可能导致向量化服务过载 - -### 注意事项 - -⚠️ **重要提示**： -- 向量化操作依赖外部服务（如 OpenAI、本地模型等），确保服务可用 -- 处理大量文档时注意控制并发度，避免服务过载 -- 脚本会跳过 `episode` 为空的文档 -- 建议先用小 `--limit` 测试，确认无误后再大规模执行 - -### 执行结果示例 - -``` -🔍 开始扫描需修复文档（limit=1000, batch=200, concurrency=8） -📦 拉取到候选 200 条（已累计处理=0/1000） -⏱️ 当前处理到 created_at=2025-09-25T15:30:45+00:00 -📦 拉取到候选 200 条（已累计处理=200/1000） -⏱️ 当前处理到 created_at=2025-09-24T10:20:30+00:00 -... -✅ 修复完成 | total=1000, succeeded=995, failed=5 -❌ 修复失败 doc=66f2a1b3c4d5e6f789012345, error=Timeout calling vectorize service -``` - -### 错误处理 - -- 单个文档处理失败不会中断整个流程 -- 失败的文档会被记录在日志中 -- 可以重新运行脚本处理失败的文档（基于查询条件自动重试） - ---- - -## 常见问题 - -### Q1: mongo_add_timestamp_shard.py 提示 "不是分片集群环境"？ - -**A:** 这表示当前 MongoDB 不是分片集群，分片功能不可用。分片仅在分片集群环境中有意义： -- 如果是单节点或副本集，可以忽略此脚本 -- 如果需要分片，请先搭建 MongoDB 分片集群 - -### Q2: 如何查看当前有多少文档缺失向量？ - -**A:** 可以在 MongoDB Shell 中执行： -```javascript -use memsys -db.episodic_memories.countDocuments({ - episode: { $exists: true, $ne: "" }, - $or: [ - { vector: { $exists: false } }, - { vector: null }, - { vector: [] }, - { vector_model: { $ne: "Qwen/Qwen3-Embedding-4B" } } - ] -}) -``` - -### Q3: 向量修复脚本运行很慢怎么办？ - -**A:** 可以从以下几个方面优化： -1. 增大并发度：`--concurrency 32` -2. 使用时间范围过滤：`--start-created-at` 和 `--end-created-at` -3. 检查向量化服务性能（如切换到更快的模型） -4. 分批次执行，每次处理一部分数据 - -### Q4: 向量修复失败了怎么办？ - -**A:** -- 查看日志中的错误信息 -- 常见原因：向量化服务不可用、网络超时、episode 内容过长 -- 可以直接重新运行脚本，已成功的文档会被跳过（因为已经有向量了） - -### Q5: 分片配置后如何回滚？ - -**A:** 分片配置是不可逆的，无法直接回滚。如果必须取消分片： -1. 停止均衡器：`sh.stopBalancer()` -2. 导出数据 -3. 删除分片集合 -4. 重新创建非分片集合 -5. 导入数据 - -⚠️ 这是高风险操作，建议在测试环境验证。 - ---- - -## 最佳实践 - -### 分片配置最佳实践 - -1. **提前规划**：在数据量较小时（< 1GB）配置分片 -2. **监控分片分布**：定期检查数据是否均匀分布 -3. **合理选择分片键**：`timestamp` 适合时间序列数据 -4. **预分片**：脚本自动创建预分片点，避免热点问题 - -### 向量修复最佳实践 - -1. **增量修复**：使用时间范围参数，每天修复当天的数据 -2. **监控日志**：关注成功率和失败原因 -3. **错误重试**：失败后可以直接重新运行（幂等操作） -4. **性能调优**：根据向量化服务能力调整并发度 -5. **定期检查**：建立定时任务，自动修复新产生的缺失向量 - -### 定时任务示例 - -```bash -# crontab 配置示例：每天凌晨 2 点修复最近 2 天的数据 -0 2 * * * cd /path/to/memsys && python src/bootstrap.py src/devops_scripts/data_fix/mongo_fix_episodic_memory_missing_vector.py --days 2 --batch 500 --concurrency 16 -``` - ---- - -## 性能基准参考 - -### 向量修复性能（参考） - -| 并发度 | 批量大小 | 处理速度 | 内存占用 | -|--------|---------|---------|---------| -| 8 | 200 | ~100 docs/min | ~500MB | -| 16 | 500 | ~200 docs/min | ~1GB | -| 32 | 1000 | ~300 docs/min | ~2GB | - -*注：实际性能取决于向量化服务、网络环境、文档大小等因素* - -### 分片效果（参考） - -| 数据量 | 分片前查询时间 | 分片后查询时间 | 改善比例 | -|--------|--------------|--------------|---------| -| 10GB | 5-10s | 1-2s | 5-10x | -| 100GB | 30-60s | 3-5s | 10-20x | -| 1TB | 5-10min | 10-30s | 20-40x | - -*注：实际效果取决于查询模式、分片数量、硬件配置等因素* - diff --git a/methods/EverCore/src/devops_scripts/data_fix/es_rebuild_index.py b/methods/EverCore/src/devops_scripts/data_fix/es_rebuild_index.py deleted file mode 100644 index 83bd3677..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/es_rebuild_index.py +++ /dev/null @@ -1,56 +0,0 @@ -import argparse -import asyncio -import traceback - -from elasticsearch.dsl import AsyncDocument - -from core.observation.logger import get_logger -from core.oxm.es.migration.utils import find_document_class_by_index_name, rebuild_index - - -logger = get_logger(__name__) - - -async def run(index_name: str, close_old: bool, delete_old: bool) -> None: - try: - document_class: type[AsyncDocument] = find_document_class_by_index_name( - index_name - ) - logger.info( - "Found document class: %s.%s", - document_class.__module__, - document_class.__name__, - ) - logger.info("Index alias: %s", document_class.get_index_name()) - - await rebuild_index(document_class, close_old=close_old, delete_old=delete_old) - except Exception as exc: # noqa: BLE001 - logger.error("Failed to rebuild index: %s", exc) - traceback.print_exc() - raise - - -def main(argv: list[str] | None = None) -> int: - parser = argparse.ArgumentParser( - description="Rebuild and switch Elasticsearch index alias" - ) - parser.add_argument( - "--index-name", "-i", required=True, help="Index alias, e.g.: episodic-memory" - ) - parser.add_argument( - "--close-old", "-c", action="store_true", help="Whether to close the old index" - ) - parser.add_argument( - "--delete-old", - "-x", - action="store_true", - help="Whether to delete the old index", - ) - args = parser.parse_args(argv) - - asyncio.run(run(args.index_name, args.close_old, args.delete_old)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/methods/EverCore/src/devops_scripts/data_fix/es_sync_docs.py b/methods/EverCore/src/devops_scripts/data_fix/es_sync_docs.py deleted file mode 100644 index fa420f8a..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/es_sync_docs.py +++ /dev/null @@ -1,77 +0,0 @@ -import argparse -import asyncio -import traceback -from elasticsearch.dsl import AsyncDocument - -from core.observation.logger import get_logger -from core.oxm.es.migration.utils import find_document_class_by_index_name - - -logger = get_logger(__name__) - - -async def run( - index_name: str, batch_size: int, limit_: int | None, days: int | None -) -> None: - """Synchronize MongoDB data to the specified Elasticsearch index.""" - try: - document_class: type[AsyncDocument] = find_document_class_by_index_name( - index_name - ) - logger.info( - "Found document class: %s.%s", - document_class.__module__, - document_class.__name__, - ) - - doc_alias = document_class.get_index_name() - logger.info("Index alias: %s", doc_alias) - - if "episodic-memory" in str(doc_alias): - from devops_scripts.data_fix.es_sync_episodic_memory_docs import ( - sync_episodic_memory_docs, - ) - - await sync_episodic_memory_docs( - batch_size=batch_size, limit=limit_, days=days - ) - else: - raise ValueError(f"Unsupported index type: {doc_alias}") - except Exception as exc: # noqa: BLE001 - logger.error("Failed to synchronize documents: %s", exc) - traceback.print_exc() - raise - - -def main(argv: list[str] | None = None) -> int: - parser = argparse.ArgumentParser( - description="Synchronize MongoDB data to Elasticsearch" - ) - parser.add_argument( - "--index-name", "-i", required=True, help="Index alias, e.g.: episodic-memory" - ) - parser.add_argument( - "--batch-size", "-b", type=int, default=500, help="Batch size, default 500" - ) - parser.add_argument( - "--limit", - "-l", - type=int, - default=None, - help="Limit the number of documents to process, default all", - ) - parser.add_argument( - "--days", - "-d", - type=int, - default=None, - help="Process only documents created in the last N days, default all", - ) - args = parser.parse_args(argv) - - asyncio.run(run(args.index_name, args.batch_size, args.limit, args.days)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/methods/EverCore/src/devops_scripts/data_fix/es_sync_episodic_memory_docs.py b/methods/EverCore/src/devops_scripts/data_fix/es_sync_episodic_memory_docs.py deleted file mode 100644 index 057f99b6..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/es_sync_episodic_memory_docs.py +++ /dev/null @@ -1,147 +0,0 @@ -import traceback -from datetime import timedelta -from typing import Optional, AsyncIterator, Dict, Any - -from core.observation.logger import get_logger -from core.di.utils import get_bean_by_type -from elasticsearch.helpers import async_streaming_bulk - - -logger = get_logger(__name__) - - -async def sync_episodic_memory_docs( - batch_size: int, limit: Optional[int], days: Optional[int] -) -> None: - """ - Sync episodic memory documents to Elasticsearch. - - Args: - batch_size: Batch size - limit: Maximum number of documents to process - days: Only process documents created in the last N days; None means process all - """ - from infra_layer.adapters.out.persistence.repository.episodic_memory_raw_repository import ( - EpisodicMemoryRawRepository, - ) - from infra_layer.adapters.out.search.elasticsearch.converter.episodic_memory_converter import ( - EpisodicMemoryConverter, - ) - from infra_layer.adapters.out.search.elasticsearch.memory.episodic_memory import ( - EpisodicMemoryDoc, - ) - - from common_utils.datetime_utils import get_now_with_timezone - - mongo_repo = get_bean_by_type(EpisodicMemoryRawRepository) - index_name = EpisodicMemoryDoc.get_index_name() - - query_filter = {} - if days is not None: - now = get_now_with_timezone() - start_time = now - timedelta(days=days) - query_filter["created_at"] = {"$gte": start_time} - logger.info( - "Only processing documents created in the past %s days (starting from %s)", - days, - start_time, - ) - - logger.info("Starting to sync episodic memory documents to ES...") - - total_processed = 0 - success_count = 0 - error_count = 0 - - # Get ES async client and index name - try: - async_client = EpisodicMemoryDoc.get_connection() - except Exception as e: # noqa: BLE001 - logger.error("Failed to get Elasticsearch client: %s", e) - raise - - async def generate_actions() -> AsyncIterator[Dict[str, Any]]: - nonlocal total_processed - skip = 0 - while True: - # Use repository method to query with pagination - mongo_docs = await mongo_repo.find_by_filter_paginated( - query_filter=query_filter, - skip=skip, - limit=batch_size, - sort_field="created_at", - sort_desc=False, - ) - - if not mongo_docs: - logger.info("No more documents to process") - break - - first_doc_time = ( - mongo_docs[0].created_at - if hasattr(mongo_docs[0], "created_at") - else "unknown" - ) - last_doc_time = ( - mongo_docs[-1].created_at - if hasattr(mongo_docs[-1], "created_at") - else "unknown" - ) - logger.info( - "Preparing to bulk write documents %s - %s, time range: %s ~ %s", - skip + 1, - skip + len(mongo_docs), - first_doc_time, - last_doc_time, - ) - - for mongo_doc in mongo_docs: - es_doc = EpisodicMemoryConverter.from_mongo(mongo_doc) - src = es_doc.to_dict() - doc_id = es_doc.meta.id - - yield { - "retry_on_conflict": 3, - "_op_type": "update", - "_index": index_name, - "doc_as_upsert": True, - "_id": doc_id, - "doc": src, - } - - total_processed += 1 - if limit and total_processed >= limit: - logger.info( - "Reached processing limit %s, stop generating actions", limit - ) - return - - skip += batch_size - if len(mongo_docs) < batch_size: - logger.info("All documents have been processed") - break - - try: - # Use streaming bulk to perform bulk upsert - async for ok, info in async_streaming_bulk( - async_client, generate_actions(), chunk_size=batch_size - ): - if ok: - success_count += 1 - else: - error_count += 1 - logger.error("Bulk write failed: %s", info) - - # Refresh index - await async_client.indices.refresh(index=index_name) - - logger.info( - "Sync completed! Total processed: %s, Success: %s, Failed: %s", - total_processed, - success_count, - error_count, - ) - except Exception as exc: # noqa: BLE001 - logger.error("An error occurred during sync: %s", exc) - traceback.print_exc() - raise diff --git a/methods/EverCore/src/devops_scripts/data_fix/milvus_rebuild_collection.py b/methods/EverCore/src/devops_scripts/data_fix/milvus_rebuild_collection.py deleted file mode 100644 index 841b2005..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/milvus_rebuild_collection.py +++ /dev/null @@ -1,425 +0,0 @@ -""" -Milvus rebuild script (calling core common tools) - -Implemented based on methods provided by MilvusCollectionBase: -- Find the corresponding Collection management class by alias -- Call create_new_collection() to create a new collection (automatically create index and load) -- Perform data migration (supports batch processing to avoid memory overflow) -- Call switch_alias() to switch the alias to the new collection -- Optionally delete the old Collection - -Supports: -- Single collection rebuild: -a -- All collections rebuild: --all - -Usage (via bootstrap with SKIP_LIFESPAN to avoid schema validation on startup): - SKIP_LIFESPAN=true TENANT_INIT_STORAGE_INFO='...' python src/bootstrap.py src/devops_scripts/data_fix/milvus_rebuild_collection.py --all - -Note: This script migrates data by default (in batches of 3000). -To disable data migration, use the --no-migrate-data option. -""" - -import argparse -import sys -import traceback -from typing import Optional, List - -from pymilvus import Collection - -from core.observation.logger import get_logger -from core.di.utils import get_all_subclasses -from core.oxm.milvus.migration.utils import rebuild_collection -from core.oxm.milvus.milvus_collection_base import ( - MilvusCollectionBase, - MilvusCollectionWithSuffix, -) -from devops_scripts.progress import ProgressReporter, StdoutProgressReporter - - -logger = get_logger(__name__) - - -# --------------------------------------------------------------------------- -# Data migration callback -# --------------------------------------------------------------------------- - - -def migrate_data_callback( - old_collection: Collection, - new_collection: Collection, - batch_size: int = 3000, - progress: Optional[ProgressReporter] = None, - alias: str = "", -) -> None: - """ - Data migration callback function (using offset pagination + sorting to ensure data integrity) - - Args: - old_collection: Old collection instance - new_collection: New collection instance - batch_size: Number of records processed per batch, default is 3000 - progress: Optional progress reporter for real-time status updates - alias: Collection alias (for progress reporting) - - Note: - Use offset + limit + order_by for paginated queries to avoid: - 1. Data loss (unordered queries may return in unpredictable order) - 2. Data duplication (pagination position may drift) - - Although queries with large offsets are less efficient, they are acceptable for one-time data migration, - and ensure data completeness and accuracy. - - When schema has changed (fields added/removed), only fields present in the - new collection schema are migrated. Removed fields are dropped automatically. - """ - # Collect new schema field names for filtering removed fields - new_field_names = {f.name for f in new_collection.schema.fields} - - logger.info( - "Start migrating data: %s -> %s (batch size: %d)", - old_collection.name, - new_collection.name, - batch_size, - ) - - # Query total record count before migration starts - total_records = old_collection.query(expr='id != ""', output_fields=["count(*)"])[ - 0 - ]["count(*)"] - logger.info("Total records to migrate: %d", total_records) - - if progress: - progress.emit( - {"event": "migrate_start", "alias": alias, "total_records": total_records} - ) - - total_migrated = 0 # Total number of records migrated - offset = 0 # Current query offset - batch_num = 0 # Current batch number - - try: - while True: - batch_num += 1 - logger.info( - "Querying batch %d, offset: %d, limit: %d", - batch_num, - offset, - batch_size, - ) - - # Use offset+limit for pagination, and sort by id - # Note: STL_SORT index on the id field is required to use order_by - # Without an index, it may raise an error or have poor performance - try: - # Try using the order_by parameter (pymilvus 2.4+) - query_result = old_collection.query( - expr="", # Query all data - output_fields=["*"], - limit=batch_size, - offset=offset, - order_by=[("id", "asc")], # Sort by id in ascending order - ) - except TypeError: - # If order_by is not supported (older version), fall back to unordered query - # In this case, there's still a risk of data loss or duplication - logger.warning( - "Current pymilvus version does not support order_by parameter, using unordered query" - ) - logger.warning( - "It is recommended to upgrade pymilvus to version 2.4+, or create an STL_SORT index on the id field" - ) - query_result = old_collection.query( - expr="", output_fields=["*"], limit=batch_size, offset=offset - ) - except Exception as e: - # If the error is due to missing index, prompt user to create one - if "index" in str(e).lower() or "sort" in str(e).lower(): - logger.error( - "Query failed, possibly because there is no STL_SORT index on the id field: %s", - e, - ) - logger.error( - "Please create an STL_SORT index on the id field of the old collection, or use unordered query" - ) - raise - - # If query result is empty, no more data to migrate - if not query_result: - logger.info("No more data, migration completed") - break - - # Filter each row to only include fields in new schema (handles field removal) - filtered_result = [ - {k: v for k, v in row.items() if k in new_field_names} - for row in query_result - ] - - batch_count = len(filtered_result) - logger.info( - "Retrieved %d records, starting to insert into new collection...", - batch_count, - ) - - # Insert into new collection - new_collection.insert(filtered_result) - new_collection.flush() - - # Update statistics - total_migrated += batch_count - offset += batch_count # Update offset - logger.info("Migrated %d records", total_migrated) - - # Report migration progress - if progress: - progress.emit( - { - "event": "migrate_progress", - "alias": alias, - "batch": batch_num, - "batch_count": batch_count, - "total_migrated": total_migrated, - "total_records": total_records, - } - ) - - # If the number of records retrieved is less than batch_size, it's the last batch - if batch_count < batch_size: - logger.info("Last batch, migration completed") - break - - except Exception as e: - logger.error("Error occurred during data migration: %s", e) - raise - - logger.info("Data migration completed: total %d records", total_migrated) - - -# --------------------------------------------------------------------------- -# Discovery & execution -# --------------------------------------------------------------------------- - - -def discover_all_aliases() -> List[str]: - """ - Discover all concrete collection aliases by scanning MilvusCollectionBase subclasses. - - Returns: - List of collection base names (e.g., ["v1_episodic_memory", "v1_user_profile"]) #skip-sensitive-check - """ - aliases = [] - for cls in get_all_subclasses(MilvusCollectionBase): - if cls._COLLECTION_NAME is None: - continue - if not issubclass(cls, MilvusCollectionWithSuffix): - continue - aliases.append(cls._COLLECTION_NAME) - return aliases - - -def run( - alias: str, - drop_old: bool, - migrate_data: bool, - batch_size: int, - progress: ProgressReporter, -) -> None: - """ - Execute rebuild logic for a single collection (delegated to core tools) - - Args: - alias: Collection alias - drop_old: Whether to delete the old collection - migrate_data: Whether to migrate data - batch_size: Number of records processed per batch - progress: Progress reporter - """ - progress.emit({"event": "collection_start", "alias": alias}) - - try: - # Determine whether to pass the callback function based on whether data migration is needed - if migrate_data: - populate_fn = lambda old_col, new_col: migrate_data_callback( - old_col, new_col, batch_size, progress=progress, alias=alias - ) - else: - populate_fn = None - - result = rebuild_collection( - alias=alias, drop_old=drop_old, populate_fn=populate_fn - ) - - progress.emit( - { - "event": "collection_done", - "alias": alias, - "status": "ok", - "source": result.source_collection, - "dest": result.dest_collection, - "dropped_old": result.dropped_old, - } - ) - except Exception as exc: - progress.emit( - { - "event": "collection_done", - "alias": alias, - "status": "fail", - "error": str(exc)[:500], - } - ) - logger.error("Milvus rebuild failed: %s", exc) - traceback.print_exc() - raise - - -# --------------------------------------------------------------------------- -# CLI entry point -# --------------------------------------------------------------------------- - - -def main(argv: Optional[List[str]] = None) -> int: - """ - Main function: parse command-line arguments and execute rebuild - - Args: - argv: List of command-line arguments - - Returns: - Exit code (0 indicates success) - """ - parser = argparse.ArgumentParser( - description="Rebuild and switch Milvus Collection alias", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Example usage: - # Rebuild a single collection - ... -a v1_episodic_memory - - # Rebuild multiple specific collections - ... -a v1_episodic_memory v1_user_profile - - # Rebuild ALL discovered collections - ... --all - - # With tenant context (recommended) - SKIP_LIFESPAN=true TENANT_INIT_STORAGE_INFO='{"tenant_id":"s0001","isolation_mode":"shared","storage_info":{"milvus":{"collection_prefix":"s0001"}}}' python src/bootstrap.py ... --all #skip-sensitive-check - - # Rebuild without migrating data - ... -a v1_episodic_memory --no-migrate-data - - # Rebuild with custom batch size and drop old collection - ... --all --batch-size 5000 --drop-old - """, - ) - - group = parser.add_mutually_exclusive_group(required=True) - group.add_argument( - "--alias", - "-a", - nargs="+", - help="Collection alias(es), e.g.: -a v1_episodic_memory v1_user_profile", - ) - group.add_argument( - "--all", - action="store_true", - dest="rebuild_all", - help="Rebuild all discovered collections", - ) - parser.add_argument( - "--drop-old", - "-x", - action="store_true", - help="Whether to delete old collection (default: keep)", - ) - parser.add_argument( - "--no-migrate-data", - action="store_true", - help="Do not migrate data (default: migrate data)", - ) - parser.add_argument( - "--batch-size", - "-b", - type=int, - default=3000, - help="Number of records per migration batch (default: 3000)", - ) - - args = parser.parse_args(argv) - migrate_data = not args.no_migrate_data - progress = StdoutProgressReporter() - - # Set up tenant context from TENANT_INIT_STORAGE_INFO if available - import os - - if os.getenv("TENANT_INIT_STORAGE_INFO"): - from core.tenants.init_tenant_all import setup_tenant_context_from_env - - tenant_id = setup_tenant_context_from_env() - logger.info("Rebuild running with tenant context: %s", tenant_id) - - # Determine which aliases to rebuild - if args.rebuild_all: - aliases = discover_all_aliases() - else: - aliases = args.alias # list from nargs="+" - - if not aliases: - logger.warning("No collections to rebuild") - return 0 - - progress.emit({"event": "start", "total": len(aliases), "aliases": aliases}) - - # Single alias: run directly (fail fast) - if len(aliases) == 1: - run( - alias=aliases[0], - drop_old=args.drop_old, - migrate_data=migrate_data, - batch_size=args.batch_size, - progress=progress, - ) - progress.emit({"event": "summary", "total": 1, "success": 1, "failed": 0}) - return 0 - - # Multiple aliases: run with summary - failed = 0 - for i, alias in enumerate(aliases, 1): - logger.info("=" * 60) - logger.info("[%d/%d] Rebuilding collection: %s", i, len(aliases), alias) - logger.info("=" * 60) - try: - run( - alias=alias, - drop_old=args.drop_old, - migrate_data=migrate_data, - batch_size=args.batch_size, - progress=progress, - ) - logger.info("[OK] %s", alias) - except Exception: - logger.error("[FAIL] %s", alias) - failed += 1 - - progress.emit( - { - "event": "summary", - "total": len(aliases), - "success": len(aliases) - failed, - "failed": failed, - } - ) - - logger.info("") - logger.info("=" * 60) - logger.info( - "Rebuild summary: total=%d, success=%d, failed=%d", - len(aliases), - len(aliases) - failed, - failed, - ) - logger.info("=" * 60) - return 1 if failed > 0 else 0 - - -if __name__ == "__main__": - sys.exit(main(sys.argv[1:])) diff --git a/methods/EverCore/src/devops_scripts/data_fix/milvus_sync_docs.py b/methods/EverCore/src/devops_scripts/data_fix/milvus_sync_docs.py deleted file mode 100644 index d42f2aaa..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/milvus_sync_docs.py +++ /dev/null @@ -1,131 +0,0 @@ -""" -Sync MongoDB data to Milvus - -Main entry script that calls the corresponding sync implementation based on Collection name. -Supports command-line arguments for batch size, processing limits, and time range. - -Usage (recommended to run via bootstrap, which automatically loads application context and dependencies): - python src/bootstrap.py src/devops_scripts/data_fix/milvus_sync_docs.py --collection-name episodic_memory --batch-size 500 - -Arguments: - --collection-name, -c Milvus Collection name (required), e.g.: episodic_memory - --batch-size, -b Batch size (default 500) - --limit, -l Limit the number of documents to process (default: all) - --days, -d Only process documents created in the past N days (default: all) -""" - -import argparse -import asyncio -import traceback - -from core.observation.logger import get_logger - - -logger = get_logger(__name__) - - -async def run( - collection_name: str, batch_size: int, limit_: int | None, days: int | None -) -> None: - """ - Sync MongoDB data to the specified Milvus Collection. - - Routes to the specific sync implementation based on the Collection name. - - Args: - collection_name: Milvus Collection name, e.g.: episodic_memory - batch_size: Batch size, default 500 - limit_: Limit the number of documents to process, None means process all - days: Only process documents created in the past N days, None means process all - - Raises: - ValueError: If the Collection name is not supported - Exception: If an error occurs during synchronization - """ - try: - logger.info("Starting sync to Milvus Collection: %s", collection_name) - - # Route to specific implementation based on Collection name - if collection_name == "episodic_memory": - from devops_scripts.data_fix.milvus_sync_episodic_memory_docs import ( - sync_episodic_memory_docs, - ) - - await sync_episodic_memory_docs( - batch_size=batch_size, limit=limit_, days=days - ) - else: - raise ValueError(f"Unsupported Collection type: {collection_name}") - - except Exception as exc: # noqa: BLE001 - logger.error("Failed to sync documents: %s", exc) - traceback.print_exc() - raise - - -def main(argv: list[str] | None = None) -> int: - """ - Command-line entry function. - - Parses command-line arguments and calls the sync function. - - Args: - argv: List of command-line arguments, None means use sys.argv - - Returns: - int: Exit code, 0 indicates success - - Examples: - # Sync all episodic_memory documents - python milvus_sync_docs.py --collection-name episodic_memory - - # Sync only documents from the last 7 days, with batch size 1000 - python milvus_sync_docs.py --collection-name episodic_memory --batch-size 1000 --days 7 - - # Limit processing to 10,000 documents - python milvus_sync_docs.py --collection-name episodic_memory --limit 10000 - """ - parser = argparse.ArgumentParser( - description="Sync MongoDB data to Milvus", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - %(prog)s --collection-name episodic_memory - %(prog)s --collection-name episodic_memory --batch-size 1000 --days 7 - %(prog)s --collection-name episodic_memory --limit 10000 - """, - ) - - parser.add_argument( - "--collection-name", - "-c", - required=True, - help="Milvus Collection name, e.g.: episodic_memory", - ) - parser.add_argument( - "--batch-size", "-b", type=int, default=500, help="Batch size, default 500" - ) - parser.add_argument( - "--limit", - "-l", - type=int, - default=None, - help="Limit the number of documents to process, default: all", - ) - parser.add_argument( - "--days", - "-d", - type=int, - default=None, - help="Only process documents created in the past N days, default: all", - ) - - args = parser.parse_args(argv) - - # Run async sync task - asyncio.run(run(args.collection_name, args.batch_size, args.limit, args.days)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/methods/EverCore/src/devops_scripts/data_fix/milvus_sync_episodic_memory_docs.py b/methods/EverCore/src/devops_scripts/data_fix/milvus_sync_episodic_memory_docs.py deleted file mode 100644 index 91e1d2cc..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/milvus_sync_episodic_memory_docs.py +++ /dev/null @@ -1,205 +0,0 @@ -""" -Sync episodic memory documents to Milvus - -Bulk retrieve episodic memory documents from MongoDB, convert them, and insert into Milvus. -Focuses on efficiency using a strategy of bulk retrieval, bulk conversion, and bulk insertion. - -Technical implementation: -- Bulk read documents from MongoDB (controlled by batch_size) -- Use EpisodicMemoryMilvusConverter for format conversion -- Bulk insert into Milvus Collection -- Supports incremental sync (based on days parameter) -- Supports idempotent operations (using upsert semantics) -""" - -import traceback -from datetime import timedelta -from typing import Optional, List, Dict, Any - -from core.observation.logger import get_logger -from core.di.utils import get_bean_by_type - - -logger = get_logger(__name__) - - -async def sync_episodic_memory_docs( - batch_size: int, limit: Optional[int], days: Optional[int] -) -> None: - """ - Sync episodic memory documents to Milvus. - - Implementation strategy: - 1. Bulk retrieve documents from MongoDB (batch_size per batch) - 2. Bulk convert to Milvus entity format - 3. Bulk insert into Milvus (using upsert semantics, supports idempotency) - 4. Loop until all documents are processed - - Args: - batch_size: Batch size, recommended 500-1000 - limit: Maximum number of documents to process, None means process all - days: Only process documents created in the last N days, None means process all - """ - from infra_layer.adapters.out.persistence.repository.episodic_memory_raw_repository import ( - EpisodicMemoryRawRepository, - ) - from infra_layer.adapters.out.search.milvus.converter.episodic_memory_milvus_converter import ( - EpisodicMemoryMilvusConverter, - ) - from infra_layer.adapters.out.search.milvus.memory.episodic_memory_collection import ( - EpisodicMemoryCollection, - ) - from common_utils.datetime_utils import get_now_with_timezone - - # Get MongoDB Repository - mongo_repo = get_bean_by_type(EpisodicMemoryRawRepository) - - # Build query filter - query_filter: Dict[str, Any] = {} - if days is not None: - now = get_now_with_timezone() - start_time = now - timedelta(days=days) - query_filter["created_at"] = {"$gte": start_time} - logger.info( - "Only processing documents created in the past %s days (starting from %s)", - days, - start_time, - ) - - logger.info("Starting to sync episodic memory documents to Milvus...") - - # Statistics counters - total_processed = 0 - success_count = 0 - error_count = 0 - - # Get Milvus Collection - try: - # Directly use the async_collection() method of EpisodicMemoryCollection - collection = EpisodicMemoryCollection.async_collection() - collection_name = collection.collection.name - logger.info("Using Milvus Collection: %s", collection_name) - except Exception as e: # noqa: BLE001 - logger.error("Failed to get Milvus Collection: %s", e) - raise - - # Main loop for batch processing - try: - skip = 0 - while True: - # Bulk retrieve documents from MongoDB - query = mongo_repo.model.find(query_filter).sort("created_at") - mongo_docs = await query.skip(skip).limit(batch_size).to_list() - - if not mongo_docs: - logger.info("No more documents to process") - break - - # Record time range of current batch - first_doc_time = ( - mongo_docs[0].created_at - if hasattr(mongo_docs[0], "created_at") - else "unknown" - ) - last_doc_time = ( - mongo_docs[-1].created_at - if hasattr(mongo_docs[-1], "created_at") - else "unknown" - ) - logger.info( - "Preparing to write batch %s - %s, time range: %s ~ %s", - skip + 1, - skip + len(mongo_docs), - first_doc_time, - last_doc_time, - ) - - # Bulk convert to Milvus entities - milvus_entities: List[Dict[str, Any]] = [] - batch_errors = 0 - - for mongo_doc in mongo_docs: - try: - # Convert individual document - milvus_entity = EpisodicMemoryMilvusConverter.from_mongo(mongo_doc) - - # Validate required fields - if not milvus_entity.get("id"): - logger.warning( - "Document missing id field, skipping: %s", mongo_doc.id - ) - batch_errors += 1 - continue - - if not milvus_entity.get("vector"): - logger.warning( - "Document missing vector field, skipping: id=%s", - milvus_entity.get("id"), - ) - batch_errors += 1 - continue - - milvus_entities.append(milvus_entity) - - except Exception as e: # noqa: BLE001 - logger.error( - "Failed to convert document: id=%s, error=%s", - getattr(mongo_doc, 'id', 'unknown'), - e, - ) - batch_errors += 1 - continue - - # Bulk insert into Milvus - if milvus_entities: - try: - # Milvus insert method accepts list format data - # Need to convert list of entity dictionaries to list of lists format - insert_data = milvus_entities - - _ = await collection.insert(insert_data) - - # Count successful insertions - inserted_count = len(milvus_entities) - success_count += inserted_count - logger.info("Bulk insert successful: %d records", inserted_count) - - except Exception as e: # noqa: BLE001 - logger.error("Bulk insert to Milvus failed: %s", e) - traceback.print_exc() - error_count += len(milvus_entities) - - # Update statistics - total_processed += len(mongo_docs) - error_count += batch_errors - - # Check if limit reached - if limit and total_processed >= limit: - logger.info("Processing limit %s reached, stopping", limit) - break - - # Move to next batch - skip += batch_size - if len(mongo_docs) < batch_size: - logger.info("All documents have been processed") - break - - # Flush Collection to ensure data persistence - try: - await collection.flush() - logger.info("Milvus Collection flush completed") - except Exception as e: # noqa: BLE001 - logger.warning("Milvus Collection flush failed: %s", e) - - # Output statistics - logger.info( - "Sync completed! Total processed: %s, Success: %s, Failed: %s", - total_processed, - success_count, - error_count, - ) - - except Exception as exc: # noqa: BLE001 - logger.error("Error occurred during sync: %s", exc) - traceback.print_exc() - raise diff --git a/methods/EverCore/src/devops_scripts/data_fix/mongo_add_timestamp_shard.py b/methods/EverCore/src/devops_scripts/data_fix/mongo_add_timestamp_shard.py deleted file mode 100644 index 1f26d622..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/mongo_add_timestamp_shard.py +++ /dev/null @@ -1,150 +0,0 @@ -#!/usr/bin/env python3 -""" -Add Timestamp Shard - -Add timestamp-based sharding configuration to the MemCell collection -Created: 2025-09-11T23:37:54.703305 -""" - -import asyncio -import logging -from common_utils.datetime_utils import get_now_with_timezone - -from pymongo.errors import OperationFailure - -from infra_layer.adapters.out.persistence.document.memory.memcell import MemCell - -logger = logging.getLogger(__name__) - - -async def enable_timestamp_sharding(session=None): - """ - Enable timestamp sharding for the MemCell collection - """ - try: - # Get MongoDB collection and client - collection = MemCell.get_pymongo_collection() - db = collection.database - client = db.client - admin_db = client.admin - - logger.info("🔧 Starting timestamp sharding configuration...") - - # 1. Check if it's a sharded cluster - try: - shard_status = await admin_db.command('listShards') - if not shard_status.get('shards'): - logger.warning( - "⚠️ Current environment is not a sharded cluster, skipping sharding configuration" - ) - return - logger.info( - f"✅ Sharded cluster detected, total {len(shard_status['shards'])} shards" - ) - except OperationFailure as e: - logger.warning( - f"⚠️ Unable to check sharding status: {e}, may not be a sharded environment" - ) - return - - # 2. Enable database sharding - try: - await admin_db.command('enableSharding', db.name) - logger.info(f"✅ Sharding enabled for database '{db.name}'") - except OperationFailure as e: - if "already enabled" in str(e).lower(): - logger.info(f"📝 Sharding already exists for database '{db.name}'") - else: - logger.error(f"❌ Failed to enable database sharding: {e}") - raise - - # 3. Set collection shard key - timestamp - collection_name = f"{db.name}.memcells" - try: - await admin_db.command( - 'shardCollection', collection_name, key={"timestamp": 1} - ) - logger.info("✅ Shard key configuration for MemCell collection completed") - except OperationFailure as e: - if "already sharded" in str(e).lower(): - logger.info("📝 Sharding already exists for MemCell collection") - else: - logger.error(f"❌ Failed to set collection sharding: {e}") - raise - - # 4. Create pre-split chunks (optional, improves initial performance) - try: - from datetime import timedelta - - # Create pre-split points for the next 12 months - base_date = get_now_with_timezone().replace( - day=1, hour=0, minute=0, second=0, microsecond=0 - ) - split_points = [] - - for i in range(1, 13): # Next 12 months - split_date = base_date + timedelta(days=30 * i) - split_points.append({"timestamp": split_date}) - - # Execute pre-splitting - for point in split_points: - try: - await admin_db.command('split', collection_name, middle=point) - logger.debug(f"📅 Created split point: {point['timestamp']}") - except OperationFailure as e: - if "already exists" not in str(e).lower(): - logger.debug(f"Failed to create pre-split point: {e}") - - logger.info(f"✅ Created {len(split_points)} pre-split points") - - except Exception as e: - logger.warning(f"⚠️ Pre-splitting creation failed: {e}") - - # 5. Verify sharding configuration - try: - shard_info = await db.command('collStats', 'memcells') - - if shard_info.get('sharded'): - logger.info( - "✅ MemCell collection sharding configuration verified successfully" - ) - logger.info(f"📊 Shard key: {shard_info.get('shardKey', {})}") - else: - logger.warning("⚠️ Sharding configuration verification failed") - - except Exception as e: - logger.warning(f"⚠️ Sharding verification failed: {e}") - - logger.info("🎉 Timestamp sharding configuration completed") - - except Exception as e: - logger.error(f"❌ Error occurred during sharding configuration: {e}") - raise - - -async def disable_timestamp_sharding(session=None): - """ - Warning: Disabling sharding is a dangerous operation, generally not recommended in production environments - """ - logger.warning( - "⚠️ Disabling sharding is a dangerous operation, requires manual handling by administrator" - ) - logger.info( - "📝 Please manually execute the following MongoDB commands to disable sharding:" - ) - logger.info(" 1. Stop balancer: sh.stopBalancer()") - logger.info(" 2. Wait for balancer to complete: sh.waitForBalancer()") - logger.info( - " 3. Removing sharding configuration requires recreating the collection" - ) - - -async def main(): - """Main function""" - # Execute sharding configuration - await enable_timestamp_sharding() - - -if __name__ == "__main__": - # Run main function - asyncio.run(main()) diff --git a/methods/EverCore/src/devops_scripts/data_fix/mongo_fix_episodic_memory_missing_vector.py b/methods/EverCore/src/devops_scripts/data_fix/mongo_fix_episodic_memory_missing_vector.py deleted file mode 100644 index cd9921d7..00000000 --- a/methods/EverCore/src/devops_scripts/data_fix/mongo_fix_episodic_memory_missing_vector.py +++ /dev/null @@ -1,283 +0,0 @@ -#!/usr/bin/env python3 -""" -Fix missing vector fields in historical EpisodicMemory documents. - -How to run (recommended to run via bootstrap, which automatically loads application context and dependency injection): - python src/bootstrap.py src/scripts/data_fix/fix_episodic_memory_missing_vector.py --limit 1000 --batch 200 --concurrency 8 - -Arguments: - --limit Maximum number of documents to process (default 1000) - --batch Number of documents to fetch from database each time (default 200, larger is faster but uses more memory) - --concurrency Concurrency level (default 8) -""" - -import argparse -import asyncio -from typing import Any, Dict, List, Optional, Tuple - -from core.observation.logger import get_logger -from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemory, -) -from agentic_layer.vectorize_service import get_vectorize_service -from common_utils.datetime_utils import from_iso_format, to_iso_format - - -logger = get_logger(__name__) - -# Target vector model: records not using this model also need to be re-processed -TARGET_VECTOR_MODEL = "Qwen/Qwen3-Embedding-4B" - - -async def _fetch_candidates( - size: int, - created_before: Optional[Any], - created_gte: Optional[Any], - created_lte: Optional[Any], -) -> List[EpisodicMemory]: - """ - Query candidate episodic memory documents missing vectors. - - Returns two types of documents: - 1) Documents where episode is not empty but vector is missing/None/empty array - 2) Documents where vector_model is not equal to the target model (TARGET_VECTOR_MODEL) (i.e., need re-processing) - """ - and_filters: List[Dict[str, Any]] = [ - {"episode": {"$exists": True, "$ne": ""}}, - { - "$or": [ - {"vector": {"$exists": False}}, - {"vector": None}, - {"vector": []}, - {"vector_model": {"$ne": TARGET_VECTOR_MODEL}}, - {"vector_model": {"$exists": False}}, - {"vector_model": None}, - {"vector_model": ""}, - ] - }, - ] - - # created_at filter conditions (range + pagination anchor) - created_at_filter: Dict[str, Any] = {} - if created_gte is not None: - created_at_filter["$gte"] = created_gte - if created_lte is not None: - created_at_filter["$lte"] = created_lte - # Pagination anchor: prioritize recently created data, then continue with earlier data - if created_before is not None: - created_at_filter["$lt"] = created_before - if created_at_filter: - and_filters.append({"created_at": created_at_filter}) - - query: Dict[str, Any] = {"$and": and_filters} - - cursor = EpisodicMemory.find(query).sort("-created_at").limit(size) # Recent first - - results = await cursor.to_list() - return results - - -async def _process_one( - document: EpisodicMemory, semaphore: asyncio.Semaphore -) -> Tuple[Optional[str], Optional[str]]: - """ - Process a single document: vectorize episode and write back vector and vector_model. - - Returns (doc_id, error); error is None on success. - """ - async with semaphore: - try: - if not document.episode: - return str(document.id), "episode is empty, skipping" - - vectorize_service = get_vectorize_service() - embedding = await vectorize_service.get_embedding(document.episode) - vector_list = embedding.tolist() # Consistent with repository logic - model_name = vectorize_service.get_model_name() - - # Update precisely by _id to avoid overwriting other fields - await EpisodicMemory.find({"_id": document.id}).update( - {"$set": {"vector": vector_list, "vector_model": model_name}} - ) - - return str(document.id), None - except Exception as exc: # noqa: BLE001 Non-critical error, log and continue - return str(document.id), str(exc) - - -async def run_fix( - limit: int = 1000, - batch: int = 200, - concurrency: int = 10, - start_created_at: Optional[Any] = None, - end_created_at: Optional[Any] = None, -) -> Dict[str, Any]: - """ - Execute the fix task. - - Args: - limit: Maximum number of documents to process - batch: Number of documents to fetch from database in each batch - concurrency: Concurrency level (coroutine concurrency) - - Returns: - Statistics dictionary - """ - if limit <= 0: - limit = 1 - if batch <= 0: - batch = 1 - if concurrency <= 0: - concurrency = 1 - - semaphore = asyncio.Semaphore(concurrency) - - processed_total = 0 - succeeded = 0 - errors: List[Tuple[str, str]] = [] - created_before: Optional[Any] = None - # Range filtering passed via function parameters - created_gte: Optional[Any] = start_created_at - created_lte: Optional[Any] = end_created_at - - logger.info( - "🔍 Starting scan for documents to fix (limit=%d, batch=%d, concurrency=%d)", - limit, - batch, - concurrency, - ) - - while processed_total < limit: - fetch_size = min(batch, limit - processed_total) - candidates = await _fetch_candidates( - size=fetch_size, - created_before=created_before, - created_gte=created_gte, - created_lte=created_lte, - ) - - if not candidates: - break - - # Next page anchor: earliest created_at in this batch - try: - created_before = candidates[-1].created_at - try: - logger.info( - "⏱️ Currently processing created_at=%s", - to_iso_format(created_before), - ) - except Exception: # noqa: BLE001 - logger.info("⏱️ Currently processing created_at=%s", str(created_before)) - except AttributeError: - # If model lacks this field or exception occurs, fall back to skip logic (do not update anchor) - pass - - logger.info( - "📦 Fetched %d candidates (cumulative processed=%d/%d)", - len(candidates), - processed_total, - limit, - ) - - tasks: List[asyncio.Task] = [] - for doc in candidates: - task = asyncio.create_task(_process_one(doc, semaphore)) - tasks.append(task) - - results = await asyncio.gather(*tasks, return_exceptions=False) - - for doc_id, err in results: - if err is None: - succeeded += 1 - else: - errors.append((doc_id or "unknown", err)) - - processed_total += len(candidates) - - failed = len(errors) - if failed: - for doc_id, err_msg in errors[:20]: # Avoid excessive logging - logger.error("❌ Fix failed doc=%s, error=%s", doc_id, err_msg) - if failed > 20: - logger.error("… %d more errors not printed individually", failed - 20) - - logger.info( - "✅ Fix completed | total=%d, succeeded=%d, failed=%d", - processed_total, - succeeded, - failed, - ) - return { - "total": processed_total, - "succeeded": succeeded, - "failed": failed, - "errors": errors, - } - - -def _parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser( - description="Fix missing vector data in historical EpisodicMemory" - ) - parser.add_argument( - "--limit", - type=int, - default=1000, - help="Maximum number of documents to process (default 1000)", - ) - parser.add_argument( - "--batch", - type=int, - default=200, - help="Number of documents to fetch from database each time (default 200)", - ) - parser.add_argument( - "--concurrency", type=int, default=8, help="Concurrency level (default 8)" - ) - parser.add_argument( - "--start-created-at", - dest="start_created_at", - type=str, - default=None, - help="Only process documents with created_at greater than or equal to this time (ISO format, e.g., 2025-09-16T20:20:06+00:00)", - ) - parser.add_argument( - "--end-created-at", - dest="end_created_at", - type=str, - default=None, - help="Only process documents with created_at less than or equal to this time (ISO format, e.g., 2025-09-30T23:59:59+00:00)", - ) - return parser.parse_args() - - -def main(): - args = _parse_args() - # When running via bootstrap, application context is already loaded; execute async task directly here - # Parse time range arguments (ISO -> timezone-aware datetime) - start_dt = from_iso_format(args.start_created_at) if args.start_created_at else None - end_dt = from_iso_format(args.end_created_at) if args.end_created_at else None - - if start_dt or end_dt: - try: - start_str = to_iso_format(start_dt) if start_dt else "(not specified)" - end_str = to_iso_format(end_dt) if end_dt else "(not specified)" - except Exception: # noqa: BLE001 - start_str = str(start_dt) if start_dt else "(not specified)" - end_str = str(end_dt) if end_dt else "(not specified)" - logger.info("⛳ Using created_at filter range: [%s, %s]", start_str, end_str) - - asyncio.run( - run_fix( - limit=args.limit, - batch=args.batch, - concurrency=args.concurrency, - start_created_at=start_dt, - end_created_at=end_dt, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/methods/EverCore/src/devops_scripts/i18n/i18n_tool.py b/methods/EverCore/src/devops_scripts/i18n/i18n_tool.py deleted file mode 100644 index 89c29c6d..00000000 --- a/methods/EverCore/src/devops_scripts/i18n/i18n_tool.py +++ /dev/null @@ -1,1682 +0,0 @@ -""" -I18N Tool - Chinese to English translation and review tool for Python files. - -This unified tool provides multiple functions for internationalization: -1. translate: Translate Chinese comments/logs to English in Python files -2. check: Check for remaining Chinese content in Python files -3. review: Review git commits to verify translation changes -4. hook: Pre-commit hook to check for Chinese characters in staged files/commit msg - -CRITICAL RULES FOR TRANSLATION: - 1. DO NOT modify any code logic - this is the most important rule - 2. Only translate Chinese comments and Chinese log messages - 3. Never change variable names, function names, class names, etc. - 4. Never change any code structure or behavior - 5. Violations of these rules are STRICTLY FORBIDDEN - -Usage: - # Translate commands - python -m src.devops_scripts.i18n.i18n_tool translate - python -m src/devops_scripts.i18n.i18n_tool translate --dry-run - python -m src/devops_scripts.i18n.i18n_tool translate --directory tests - python -m src/devops_scripts.i18n.i18n_tool translate --directory src tests - - # Check commands - python -m src/devops_scripts.i18n.i18n_tool check - python -m src/devops_scripts.i18n.i18n_tool check --directory tests - - # Review commands - python -m src/devops_scripts.i18n.i18n_tool review - python -m src/devops_scripts.i18n.i18n_tool review --commit abc123 - python -m src/devops_scripts.i18n.i18n_tool review --commit HEAD~3..HEAD - python -m src/devops_scripts.i18n.i18n_tool review --reset # Clear progress and start fresh - - # Hook commands (for pre-commit) - python -m src/devops_scripts.i18n.i18n_tool hook file1.py file2.py - python -m src/devops_scripts.i18n.i18n_tool hook --commit-msg .git/COMMIT_EDITMSG -""" - -from __future__ import annotations - -import os -import sys -import re -import asyncio -import json -import subprocess -from fnmatch import fnmatch -from pathlib import Path -from typing import Optional -from dataclasses import dataclass -from enum import Enum - -# ============================================================================== -# Path Configuration (no project dependencies needed) -# ============================================================================== - -# Add src to path for imports (only when needed by other commands) -SRC_DIR = Path(__file__).parent.parent.parent -PROJECT_DIR = SRC_DIR.parent - - -def _setup_project_imports(): - """Setup project imports when needed (lazy loading).""" - if str(SRC_DIR) not in sys.path: - sys.path.insert(0, str(SRC_DIR)) - - -# ============================================================================== -# Common Configuration -# ============================================================================== - -# Progress files to track which files have been processed -TRANSLATION_PROGRESS_FILE = Path(__file__).parent / ".translation_progress.json" -REVIEW_PROGRESS_FILE = Path(__file__).parent / ".review_progress.json" -# Maximum file size to process (in bytes) - about 100KB -MAX_FILE_SIZE = 100 * 1024 - -# Directories to skip (relative to SRC_DIR) -SKIP_DIRECTORIES = ["memory_layer/prompts"] - -# Files to skip (relative to SRC_DIR) -SKIP_FILES = [ - # "memory_layer/memory_extractor/profile_memory/conversation.py", - # "common_utils/text_utils.py", - # "core/oxm/es/analyzer.py", - # "memory_layer/memory_extractor/profile_memory/types.py", - # "memory_layer/memory_extractor/profile_memory/value_helpers.py", - # This tool itself contains Chinese examples in prompts, skip it - "devops_scripts/i18n/i18n_tool.py" -] - -# ============================================================================== -# Hook Configuration (for pre-commit) -# ============================================================================== - -# File patterns to skip in hook check (glob patterns, relative to project root) -# All files are checked by default, add patterns here to skip -HOOK_SKIP_PATTERNS = [ - # i18n tool itself contains CJK examples in prompts - "src/devops_scripts/i18n/i18n_tool.py", - # sensitive info tool contains CJK examples in AI prompts - "src/devops_scripts/sensitive_info/sensitive_info_tool.py", - # Prompt files may contain CJK - "src/memory_layer/prompts/*", - # Test files that specifically test i18n handling - "**/test_*i18n*.py", - # Documentation files (may contain CJK for localization) - "*.md", - "*.rst", - # Lock files and generated files - "*.lock", - "package-lock.json", - "yarn.lock", - # Data files - "*.json", - "*.yaml", - "*.yml", - "*.toml", - "*.xml", - "*.csv", - # Binary and media files - "*.png", - "*.jpg", - "*.jpeg", - "*.gif", - "*.svg", - "*.ico", - "*.pdf", - "*.zip", - "*.tar", - "*.gz", - # Compiled files - "*.pyc", - "*.pyo", - "*.so", - "*.dll", - "*.exe", - # Git files - ".git/*", - ".gitignore", - ".gitattributes", -] - -# Environment variable to skip hook checks -HOOK_SKIP_ENV_VAR = "SKIP_I18N_CHECK" - -# Keywords in commit message to skip check -HOOK_SKIP_COMMIT_MSG_KEYWORDS = [ - "skip-i18n-check", - "#skip-i18n-check", - "[skip-i18n]", - "[no-i18n-check]", -] - -# Inline comment to skip i18n check for a specific line -# Usage: code_with_chinese() #skip-i18n-check -HOOK_SKIP_LINE_COMMENT = "#skip-i18n-check" - -# File-level skip marker - add this at the top of a file to skip the entire file -# Usage: Add "# skip-i18n-file" or "#skip-i18n-file" in the first 10 lines -HOOK_SKIP_FILE_MARKERS = [ - "#skip-i18n-file", - "# skip-i18n-file", - "# skip-i18n-file", - "# -*- skip-i18n-file -*-", -] - -# CJK Unicode ranges (Chinese, Japanese, Korean) -CJK_PATTERN = re.compile( - r'[' - r'\u4e00-\u9fff' # CJK Unified Ideographs (Chinese) - r'\u3040-\u309f' # Hiragana (Japanese) - r'\u30a0-\u30ff' # Katakana (Japanese) - r'\uac00-\ud7af' # Hangul Syllables (Korean) - r'\u3400-\u4dbf' # CJK Unified Ideographs Extension A - r'\uf900-\ufaff' # CJK Compatibility Ideographs - r']' -) - - -# ============================================================================== -# LLM Prompts -# ============================================================================== - -TRANSLATION_PROMPT = '''You are a translation assistant. Your task is to translate Chinese comments and Chinese log messages in Python code to English. - -**CRITICAL RULES - MUST FOLLOW:** -1. **ABSOLUTELY DO NOT modify any code logic** - This is the most important rule. Violations are STRICTLY FORBIDDEN. -2. **ONLY translate Chinese text** in: - - Single-line comments (# ...) - - Multi-line docstrings (""" ... """ or \'\'\' ... \'\'\') - - String literals used in logging (logger.info(), logger.debug(), logger.warning(), logger.error(), print(), etc.) - - f-string literals used in logging -3. **DO NOT change:** - - Variable names, function names, class names - - Code structure, indentation, line breaks - - Any Python syntax or operators - - Non-Chinese text - - Import statements - - Type hints - - Any actual code behavior -4. Keep the original formatting and indentation exactly as is -5. If there is no Chinese text to translate, return the code unchanged -6. Return ONLY the translated code, no explanations - -**Example translations:** -- `# 初始化配置` → `# Initialize configuration` -- `logger.info("开始处理数据")` → `logger.info("Start processing data")` -- `"""这是一个测试函数"""` → `"""This is a test function"""` -- `print(f"处理完成，共 {{count}} 条")` → `print(f"Processing completed, total {{count}} items")` - -Now translate the following Python code: - -```python -{code} -``` - -Return the translated Python code:''' - -REVIEW_PROMPT = '''You are a code review assistant. Your task is to analyze a git diff and determine if the changes are ONLY translation-related (translating Chinese comments/logs to English) or if there are actual code logic changes. - -**Your task:** -Analyze the following git diff and classify it as one of: -1. **SAFE** - Changes are purely translation-related: - - Chinese comments translated to English (# 中文注释 → # English comment) - - Chinese log messages translated to English (logger.info("中文") → logger.info("English")) - - Chinese docstrings translated to English - - No actual code logic changes - -2. **NEEDS_REVIEW** - Changes may include code logic modifications: - - Variable names, function names, or class names changed - - Code structure modified - - Import statements added/removed/changed - - Logic conditions or return values changed - - Exception handling modified - - New code added or existing code removed (beyond comments/logs) - - Type hints changed - - Default parameter values changed - - Any behavioral changes - -**Important:** -- Whitespace-only changes (indentation, blank lines) are SAFE -- Formatting changes that don't affect behavior are SAFE -- If you see ANY potential code logic change, classify as NEEDS_REVIEW -- When in doubt, classify as NEEDS_REVIEW - -**Response format (MUST follow exactly):** -First line: SAFE or NEEDS_REVIEW -Second line onwards: Brief explanation of your reasoning (max 2-3 sentences) - -**Git diff to analyze:** -```diff -{diff} -``` - -**Your analysis:**''' - - -# ============================================================================== -# Review Result Types -# ============================================================================== - - -class ReviewResult(Enum): - """Review result status.""" - - SAFE = "safe" - NEEDS_REVIEW = "needs_review" - ERROR = "error" - - -@dataclass -class FileReviewResult: - """Result of reviewing a single file.""" - - file_path: str - result: ReviewResult - reason: str - diff_summary: str = "" - - -# ============================================================================== -# Common Utilities -# ============================================================================== - - -def contains_chinese(text: str) -> bool: - """Check if text contains Chinese characters.""" - chinese_pattern = re.compile(r'[\u4e00-\u9fff]') - return bool(chinese_pattern.search(text)) - - -def _load_env_and_get_llm_provider(): - """Load environment and create LLM provider (lazy loading).""" - _setup_project_imports() - - from dotenv import load_dotenv - from memory_layer.llm import OpenAIProvider - - env_file_path = PROJECT_DIR / ".env" - if env_file_path.exists(): - load_dotenv(env_file_path) - print(f"Loaded environment from {env_file_path}") - - return OpenAIProvider( - model=os.getenv("LLM_MODEL", "gpt-4.1-mini"), - api_key=os.getenv("LLM_API_KEY"), - base_url=os.getenv("LLM_BASE_URL"), - temperature=0.1, - ) - - -def resolve_directories(dir_names: list[str] | None) -> list[Path]: - """Resolve directory names to absolute paths.""" - if not dir_names: - return [SRC_DIR] - - directories = [] - for dir_name in dir_names: - dir_path = Path(dir_name) - if not dir_path.is_absolute(): - dir_path = PROJECT_DIR / dir_name - directories.append(dir_path) - return directories - - -def print_header(title: str): - """Print a section header.""" - print("=" * 70) - print(title) - print("=" * 70) - print() - - -def print_summary_header(): - """Print summary section header.""" - print() - print("=" * 70) - print("Summary") - print("=" * 70) - - -# ============================================================================== -# File Operations -# ============================================================================== - - -def should_skip_directory(dir_path: Path, src_dir: Path) -> bool: - """Check if a directory should be skipped based on SKIP_DIRECTORIES config.""" - try: - rel_path = dir_path.relative_to(src_dir) - rel_path_str = str(rel_path).replace('\\', '/') - for skip_dir in SKIP_DIRECTORIES: - if rel_path_str == skip_dir or rel_path_str.startswith(skip_dir + '/'): - return True - except ValueError: - pass - return False - - -def should_skip_file(file_path: Path, src_dir: Path) -> bool: - """Check if a file should be skipped based on SKIP_FILES config.""" - try: - rel_path = file_path.relative_to(src_dir) - rel_path_str = str(rel_path).replace('\\', '/') - return rel_path_str in SKIP_FILES - except ValueError: - pass - return False - - -def get_python_files(target_dir: Path) -> list[Path]: - """Get all Python files under the target directory.""" - python_files = [] - skipped_dirs = [] - skipped_files = [] - - for root, dirs, files in os.walk(target_dir): - root_path = Path(root) - - # Skip __pycache__ and hidden directories - dirs[:] = [d for d in dirs if not d.startswith('.') and d != '__pycache__'] - - # Skip configured directories (only for src directory) - if target_dir == SRC_DIR: - dirs_to_remove = [] - for d in dirs: - dir_path = root_path / d - if should_skip_directory(dir_path, SRC_DIR): - dirs_to_remove.append(d) - skipped_dirs.append(dir_path) - for d in dirs_to_remove: - dirs.remove(d) - - for file in files: - if file.endswith('.py'): - file_path = Path(root) / file - if target_dir == SRC_DIR and should_skip_file(file_path, SRC_DIR): - skipped_files.append(file_path) - else: - python_files.append(file_path) - - if skipped_dirs: - print(f"Skipped directories: {[str(d) for d in skipped_dirs]}") - if skipped_files: - print(f"Skipped files: {[str(f) for f in skipped_files]}") - - return python_files - - -def get_python_files_from_directories(directories: list[Path]) -> list[Path]: - """Get all Python files from multiple directories.""" - all_files = [] - for target_dir in directories: - if not target_dir.exists(): - print(f"Warning: Directory {target_dir} does not exist, skipping") - continue - print(f"Scanning directory: {target_dir}") - files = get_python_files(target_dir) - all_files.extend(files) - print(f" Found {len(files)} Python files") - return all_files - - -# ============================================================================== -# Progress Tracking - Translation -# ============================================================================== - - -def load_translation_progress() -> dict: - """Load translation progress from file.""" - if TRANSLATION_PROGRESS_FILE.exists(): - try: - with open(TRANSLATION_PROGRESS_FILE, 'r', encoding='utf-8') as f: - return json.load(f) - except Exception: - return {"processed": [], "errors": []} - return {"processed": [], "errors": []} - - -def save_translation_progress(progress: dict): - """Save translation progress to file.""" - with open(TRANSLATION_PROGRESS_FILE, 'w', encoding='utf-8') as f: - json.dump(progress, f, indent=2, ensure_ascii=False) - - -def clear_translation_progress(): - """Clear translation progress file.""" - if TRANSLATION_PROGRESS_FILE.exists(): - TRANSLATION_PROGRESS_FILE.unlink() - - -# ============================================================================== -# Progress Tracking - Review -# ============================================================================== - - -def load_review_progress() -> dict: - """Load review progress from file.""" - if REVIEW_PROGRESS_FILE.exists(): - try: - with open(REVIEW_PROGRESS_FILE, 'r', encoding='utf-8') as f: - return json.load(f) - except Exception: - return {"commit_range": "", "safe": [], "needs_review": [], "errors": []} - return {"commit_range": "", "safe": [], "needs_review": [], "errors": []} - - -def save_review_progress(progress: dict): - """Save review progress to file.""" - with open(REVIEW_PROGRESS_FILE, 'w', encoding='utf-8') as f: - json.dump(progress, f, indent=2, ensure_ascii=False) - - -def clear_review_progress(): - """Clear review progress file.""" - if REVIEW_PROGRESS_FILE.exists(): - REVIEW_PROGRESS_FILE.unlink() - - -# ============================================================================== -# Git Operations -# ============================================================================== - - -def run_git_command(args: list[str], cwd: Path | None = None) -> tuple[bool, str]: - """Run a git command and return success status and output.""" - try: - result = subprocess.run( - ["git"] + args, - cwd=cwd or PROJECT_DIR, - capture_output=True, - text=True, - timeout=60, - ) - if result.returncode == 0: - return True, result.stdout.strip() - else: - return False, result.stderr.strip() - except subprocess.TimeoutExpired: - return False, "Git command timed out" - except Exception as e: - return False, str(e) - - -def get_changed_files_from_git(commit_ref: str) -> tuple[bool, list[str]]: - """Get list of changed Python files in a commit or commit range. - - Args: - commit_ref: A single commit (e.g., "HEAD", "abc123") or a range (e.g., "HEAD~3..HEAD") - """ - if ".." in commit_ref: - # It's a range, use git diff - success, output = run_git_command( - ["diff", "--name-only", "--diff-filter=ACMR", commit_ref, "--", "*.py"] - ) - else: - # Single commit, use git show to get files changed in that specific commit - success, output = run_git_command( - ["show", "--name-only", "--format=", commit_ref, "--", "*.py"] - ) - if not success: - return False, [output] - files = [f.strip() for f in output.split("\n") if f.strip()] - return True, files - - -def get_file_diff( - commit_ref: str, file_path: str, context_lines: int = 3 -) -> tuple[bool, str]: - """Get the diff for a specific file in a commit or commit range. - - Args: - commit_ref: A single commit or a range - file_path: Path to the file - context_lines: Number of context lines around changes (default: 3, use 0 for minimal) - """ - if ".." in commit_ref: - # It's a range, use git diff - success, output = run_git_command( - ["diff", f"-U{context_lines}", commit_ref, "--", file_path] - ) - else: - # Single commit, use git show to get the diff for that specific commit - success, output = run_git_command( - ["show", f"-U{context_lines}", "--format=", commit_ref, "--", file_path] - ) - return success, output - - -def get_commit_info(commit_ref: str = "HEAD") -> tuple[bool, dict]: - """Get information about a commit.""" - success, hash_output = run_git_command(["rev-parse", "--short", commit_ref]) - if not success: - return False, {"error": hash_output} - - success, message = run_git_command(["log", "-1", "--format=%s", commit_ref]) - message = message if success else "Unknown" - - success, author = run_git_command(["log", "-1", "--format=%an <%ae>", commit_ref]) - author = author if success else "Unknown" - - success, date = run_git_command(["log", "-1", "--format=%ci", commit_ref]) - date = date if success else "Unknown" - - return True, { - "hash": hash_output, - "message": message, - "author": author, - "date": date, - } - - -# ============================================================================== -# Translation Functions -# ============================================================================== - - -def filter_files_with_chinese( - python_files: list[Path], progress: dict -) -> tuple[list[Path], int, int]: - """Pre-filter files to only include those with Chinese characters.""" - files_to_process = [] - skipped_no_chinese = 0 - skipped_already_done = 0 - - print("Pre-scanning files for Chinese content...") - for file_path in python_files: - file_str = str(file_path) - - try: - file_size = file_path.stat().st_size - if file_size > MAX_FILE_SIZE: - if file_str not in progress.get("processed", []): - files_to_process.append(file_path) - else: - skipped_already_done += 1 - continue - - with open(file_path, 'r', encoding='utf-8') as f: - content = f.read() - - if contains_chinese(content): - if file_str in progress.get("processed", []): - progress["processed"].remove(file_str) - print(f" [RE-PROCESS] {file_path} - Still has Chinese content") - files_to_process.append(file_path) - else: - skipped_no_chinese += 1 - if file_str not in progress.get("processed", []): - progress["processed"].append(file_str) - else: - skipped_already_done += 1 - except Exception as e: - print(f" Warning: Could not pre-scan {file_path}: {e}") - files_to_process.append(file_path) - - save_translation_progress(progress) - print( - f"Pre-scan complete: {len(files_to_process)} files with Chinese to translate, " - f"{skipped_no_chinese} without Chinese (skipped), {skipped_already_done} already done" - ) - - return files_to_process, skipped_no_chinese, skipped_already_done - - -async def translate_file( - provider: OpenAIProvider, - file_path: Path, - semaphore: asyncio.Semaphore, - progress: dict, - progress_lock: asyncio.Lock, - dry_run: bool = False, - index: int = 0, - total: int = 0, -) -> tuple[Path, bool, Optional[str]]: - """Translate a single Python file.""" - file_str = str(file_path) - progress_prefix = f"[{index}/{total}]" if total > 0 else "" - - if file_str in progress.get("processed", []): - print(f"{progress_prefix} [ALREADY-DONE] {file_path}") - return (file_path, True, None) - - async with semaphore: - try: - file_size = file_path.stat().st_size - if file_size > MAX_FILE_SIZE: - print( - f"{progress_prefix} [SKIP-LARGE] {file_path} - File too large ({file_size/1024:.1f}KB)" - ) - async with progress_lock: - progress["processed"].append(file_str) - save_translation_progress(progress) - return (file_path, True, f"Skipped: file too large") - - with open(file_path, 'r', encoding='utf-8') as f: - original_content = f.read() - - if not contains_chinese(original_content): - print(f"{progress_prefix} [SKIP] {file_path} - No Chinese text found") - async with progress_lock: - progress["processed"].append(file_str) - save_translation_progress(progress) - return (file_path, True, None) - - print( - f"{progress_prefix} [TRANSLATING] {file_path} ({file_size/1024:.1f}KB)" - ) - - prompt = TRANSLATION_PROMPT.format(code=original_content) - translated_content = await provider.generate(prompt, temperature=0.1) - - # Clean up response - translated_content = translated_content.strip() - if translated_content.startswith('```python'): - translated_content = translated_content[9:] - if translated_content.startswith('```'): - translated_content = translated_content[3:] - if translated_content.endswith('```'): - translated_content = translated_content[:-3] - translated_content = translated_content.strip() - - if ( - not translated_content - or len(translated_content) < len(original_content) * 0.5 - ): - error_msg = "Translation result seems too short or empty" - async with progress_lock: - progress["errors"].append({"file": file_str, "error": error_msg}) - save_translation_progress(progress) - return (file_path, False, error_msg) - - if not dry_run: - with open(file_path, 'w', encoding='utf-8') as f: - f.write(translated_content) - print(f"{progress_prefix} [DONE] {file_path}") - else: - print(f"{progress_prefix} [DRY-RUN] {file_path} - Would translate") - - async with progress_lock: - progress["processed"].append(file_str) - save_translation_progress(progress) - return (file_path, True, None) - - except Exception as e: - error_msg = str(e) - print(f"{progress_prefix} [ERROR] {file_path}: {error_msg}") - async with progress_lock: - progress["errors"].append({"file": file_str, "error": error_msg}) - save_translation_progress(progress) - return (file_path, False, error_msg) - - -# ============================================================================== -# Review Functions -# ============================================================================== - - -# Maximum diff size for LLM analysis (200KB should be fine for most models) -MAX_DIFF_SIZE = 200 * 1024 - - -async def review_file_diff( - provider: OpenAIProvider, - file_path: str, - diff: str, - semaphore: asyncio.Semaphore, - progress: dict, - progress_lock: asyncio.Lock, - verbose: bool = False, - index: int = 0, - total: int = 0, -) -> FileReviewResult: - """Review a single file diff using LLM.""" - progress_prefix = f"[{index}/{total}]" if total > 0 else "" - - async with semaphore: - try: - if len(diff) > MAX_DIFF_SIZE: - result = FileReviewResult( - file_path=file_path, - result=ReviewResult.NEEDS_REVIEW, - reason=f"Diff too large for automated analysis ({len(diff)/1024:.1f}KB > {MAX_DIFF_SIZE/1024:.0f}KB limit)", - diff_summary=f"Diff size: {len(diff) / 1024:.1f}KB", - ) - async with progress_lock: - progress["needs_review"].append( - {"file": file_path, "reason": result.reason} - ) - save_review_progress(progress) - print( - f"{progress_prefix} [NEEDS-REVIEW] {file_path} - Diff too large ({len(diff)/1024:.1f}KB)" - ) - return result - - if not diff.strip(): - result = FileReviewResult( - file_path=file_path, - result=ReviewResult.SAFE, - reason="No changes in diff", - diff_summary="Empty diff", - ) - async with progress_lock: - progress["safe"].append(file_path) - save_review_progress(progress) - print(f"{progress_prefix} [SAFE] {file_path} - Empty diff") - return result - - if verbose: - print(f"{progress_prefix} [ANALYZING] {file_path}") - else: - print(f"{progress_prefix} [ANALYZING] {file_path}") - - prompt = REVIEW_PROMPT.format(diff=diff) - response = await provider.generate(prompt, temperature=0.1) - response = response.strip() - - lines = response.split("\n", 1) - first_line = lines[0].strip().upper() - reason = lines[1].strip() if len(lines) > 1 else "No explanation provided" - - if "SAFE" in first_line and "NEEDS" not in first_line: - review_result = ReviewResult.SAFE - else: - review_result = ReviewResult.NEEDS_REVIEW - if "SAFE" not in first_line and "REVIEW" not in first_line: - reason = f"Unclear response: {first_line}. {reason}" - - diff_lines = diff.split("\n") - diff_summary = "\n".join(diff_lines[:10]) - if len(diff_lines) > 10: - diff_summary += f"\n... ({len(diff_lines) - 10} more lines)" - - result = FileReviewResult( - file_path=file_path, - result=review_result, - reason=reason, - diff_summary=diff_summary, - ) - - # Save progress - async with progress_lock: - if review_result == ReviewResult.SAFE: - progress["safe"].append(file_path) - print(f"{progress_prefix} [SAFE] {file_path}") - else: - progress["needs_review"].append( - {"file": file_path, "reason": reason} - ) - print(f"{progress_prefix} [NEEDS-REVIEW] {file_path}") - save_review_progress(progress) - - return result - - except Exception as e: - result = FileReviewResult( - file_path=file_path, - result=ReviewResult.ERROR, - reason=f"Error during analysis: {str(e)}", - ) - async with progress_lock: - progress["errors"].append({"file": file_path, "error": str(e)}) - save_review_progress(progress) - print(f"{progress_prefix} [ERROR] {file_path}: {e}") - return result - - -# ============================================================================== -# Command: translate -# ============================================================================== - - -async def cmd_translate( - directories: list[Path], - max_concurrency: int = 5, - dry_run: bool = False, - specific_files: list[str] | None = None, - reset_progress: bool = False, -): - """Execute the translate command.""" - print_header("Chinese to English Translation") - - print("CRITICAL RULES:") - print(" 1. DO NOT modify any code logic") - print(" 2. Only translate Chinese comments and log messages") - print(" 3. Preserve all code structure and behavior") - print() - print(f"Target directories: {[str(d) for d in directories]}") - print() - - if reset_progress: - clear_translation_progress() - print("Progress cleared, starting fresh") - progress = load_translation_progress() - if progress.get("processed"): - print( - f"Resuming from previous run: {len(progress['processed'])} files already processed" - ) - - provider = _load_env_and_get_llm_provider() - - if specific_files: - python_files = [Path(f) for f in specific_files] - else: - python_files = get_python_files_from_directories(directories) - - python_files.sort() - total_files = len(python_files) - print(f"Found {total_files} Python files in total") - print(f"Max concurrency: {max_concurrency}") - print(f"Max file size: {MAX_FILE_SIZE/1024:.1f}KB") - print(f"Dry run: {dry_run}") - print() - - files_to_process, skipped_no_chinese, skipped_already_done = ( - filter_files_with_chinese(python_files, progress) - ) - - if not files_to_process: - print("No files with Chinese content to process!") - return 0 - - print() - print(f"Files to translate: {len(files_to_process)}") - print() - - semaphore = asyncio.Semaphore(max_concurrency) - progress_lock = asyncio.Lock() - - tasks = [ - translate_file( - provider, - file_path, - semaphore, - progress, - progress_lock, - dry_run, - index=idx + 1, - total=len(files_to_process), - ) - for idx, file_path in enumerate(files_to_process) - ] - - results = await asyncio.gather(*tasks, return_exceptions=True) - - success_count = 0 - error_count = 0 - errors = [] - - for result in results: - if isinstance(result, Exception): - error_count += 1 - errors.append(str(result)) - else: - file_path, success, error_msg = result - if success: - success_count += 1 - else: - error_count += 1 - errors.append(f"{file_path}: {error_msg}") - - print_summary_header() - print(f"Total Python files found: {total_files}") - print(f"Files skipped (no Chinese): {skipped_no_chinese}") - print(f"Files skipped (already done): {skipped_already_done}") - print(f"Files translated this run: {len(files_to_process)}") - print(f"Successfully processed: {success_count}") - print(f"Errors: {error_count}") - print( - f"Total processed (including previous runs): {len(progress.get('processed', []))}" - ) - - if errors: - print() - print("Errors encountered:") - for error in errors[:20]: - print(f" - {error}") - if len(errors) > 20: - print(f" ... and {len(errors) - 20} more errors") - - print() - print(f"Progress saved to: {TRANSLATION_PROGRESS_FILE}") - print("Run with --reset to start fresh") - return 0 if error_count == 0 else 1 - - -# ============================================================================== -# Command: check -# ============================================================================== - - -def cmd_check(directories: list[Path], specific_files: list[str] | None = None) -> int: - """Execute the check command.""" - print_header("Chinese Content Check") - - if specific_files: - python_files = [Path(f) for f in specific_files] - else: - python_files = get_python_files_from_directories(directories) - - python_files.sort() - total_files = len(python_files) - print(f"Scanning {total_files} Python files for Chinese content...") - print() - - files_with_chinese = [] - files_checked = 0 - - for file_path in python_files: - try: - with open(file_path, 'r', encoding='utf-8') as f: - content = f.read() - - files_checked += 1 - - if contains_chinese(content): - lines_with_chinese = [] - for line_num, line in enumerate(content.split('\n'), 1): - if contains_chinese(line): - lines_with_chinese.append((line_num, line.strip()[:80])) - - files_with_chinese.append( - { - 'path': file_path, - 'lines': lines_with_chinese, - 'total_chinese_lines': len(lines_with_chinese), - } - ) - - except Exception as e: - print(f" [ERROR] Could not read {file_path}: {e}") - - print("=" * 70) - print("Check Results") - print("=" * 70) - print(f"Total files checked: {files_checked}") - print(f"Files with Chinese: {len(files_with_chinese)}") - print() - - if files_with_chinese: - print("Files containing Chinese characters:") - print("-" * 70) - for file_info in files_with_chinese: - print(f"\n📄 {file_info['path']}") - print(f" ({file_info['total_chinese_lines']} lines with Chinese)") - for line_num, line_content in file_info['lines'][:5]: - print(f" Line {line_num}: {line_content}...") - if len(file_info['lines']) > 5: - print(f" ... and {len(file_info['lines']) - 5} more lines") - print() - print("-" * 70) - print(f"❌ Found {len(files_with_chinese)} files with Chinese content") - print(" Run 'translate' command to translate them") - else: - print("✅ No Chinese content found in any Python files!") - - return len(files_with_chinese) - - -# ============================================================================== -# Command: review -# ============================================================================== - - -async def cmd_review( - commit_ref: str = "HEAD", - max_concurrency: int = 5, - verbose: bool = False, - dry_run: bool = False, - reset_progress: bool = False, -) -> int: - """Execute the review command.""" - print_header("Translation Changes Review") - - # Determine if it's a single commit or a range - is_range = ".." in commit_ref - - if is_range: - # It's a range like HEAD~3..HEAD - display_ref = commit_ref.split("..")[-1] - else: - # Single commit reference - display_ref = commit_ref - - success, commit_info = get_commit_info(display_ref) - if success: - print(f"Reviewing commit: {commit_info['hash']}") - print(f" Message: {commit_info['message']}") - print(f" Author: {commit_info['author']}") - print(f" Date: {commit_info['date']}") - - if is_range: - print(f"Commit range: {commit_ref}") - else: - print(f"Single commit: {commit_ref}") - print() - - # Handle progress - if reset_progress: - clear_review_progress() - print("Progress cleared, starting fresh") - - progress = load_review_progress() - - # Check if we're resuming a different commit - if progress.get("commit_ref") and progress.get("commit_ref") != commit_ref: - print(f"Previous review was for different commit: {progress['commit_ref']}") - print("Clearing progress and starting fresh for new commit") - clear_review_progress() - progress = load_review_progress() - - progress["commit_ref"] = commit_ref - - print("Getting changed Python files...") - success, files = get_changed_files_from_git(commit_ref) - if not success: - print(f"Error getting changed files: {files[0] if files else 'Unknown error'}") - return 1 - - if not files: - print("No Python files changed in this commit range.") - return 0 - - # Filter out already processed files - already_processed = set(progress.get("safe", [])) - already_processed.update(item["file"] for item in progress.get("needs_review", [])) - already_processed.update(item["file"] for item in progress.get("errors", [])) - - files_to_process = [f for f in files if f not in already_processed] - - print(f"Found {len(files)} changed Python file(s) total:") - for f in files: - status = "" - if f in progress.get("safe", []): - status = " [already: SAFE]" - elif f in [item["file"] for item in progress.get("needs_review", [])]: - status = " [already: NEEDS-REVIEW]" - elif f in [item["file"] for item in progress.get("errors", [])]: - status = " [already: ERROR]" - print(f" - {f}{status}") - print() - - if already_processed: - print( - f"Resuming from previous run: {len(already_processed)} files already processed" - ) - print(f"Files remaining to process: {len(files_to_process)}") - print() - - if dry_run: - print("[DRY-RUN] Skipping LLM analysis") - return 0 - - if not files_to_process: - print("All files already processed!") - else: - provider = _load_env_and_get_llm_provider() - - print("Analyzing changes with LLM...") - print() - semaphore = asyncio.Semaphore(max_concurrency) - progress_lock = asyncio.Lock() - tasks = [] - - for idx, file_path in enumerate(files_to_process): - # Try to get diff with default context (3 lines) - success, diff = get_file_diff(commit_ref, file_path, context_lines=3) - - # If diff is too large, try with minimal context (0 lines) - if success and len(diff) > MAX_DIFF_SIZE: - print( - f" [INFO] {file_path}: diff too large ({len(diff)/1024:.1f}KB), retrying with minimal context..." - ) - success, diff = get_file_diff(commit_ref, file_path, context_lines=0) - if success: - print(f" [INFO] {file_path}: reduced to {len(diff)/1024:.1f}KB") - - if not success: - - async def make_error_result( - fp=file_path, err=diff, prog=progress, lock=progress_lock - ): - async with lock: - prog["errors"].append( - {"file": fp, "error": f"Error getting diff: {err}"} - ) - save_review_progress(prog) - return FileReviewResult( - file_path=fp, - result=ReviewResult.ERROR, - reason=f"Error getting diff: {err}", - ) - - tasks.append(make_error_result()) - else: - tasks.append( - review_file_diff( - provider, - file_path, - diff, - semaphore, - progress, - progress_lock, - verbose, - index=idx + 1, - total=len(files_to_process), - ) - ) - - await asyncio.gather(*tasks, return_exceptions=True) - - # Reload progress to get final results - progress = load_review_progress() - - # Build result lists from progress - safe_files = [ - FileReviewResult(file_path=f, result=ReviewResult.SAFE, reason="") - for f in progress.get("safe", []) - ] - needs_review_files = [ - FileReviewResult( - file_path=item["file"], - result=ReviewResult.NEEDS_REVIEW, - reason=item["reason"], - ) - for item in progress.get("needs_review", []) - ] - error_files = [ - FileReviewResult( - file_path=item["file"], - result=ReviewResult.ERROR, - reason=item.get("error", "Unknown error"), - ) - for item in progress.get("errors", []) - ] - - print() - print("=" * 70) - print("Review Results") - print("=" * 70) - print() - - if needs_review_files: - print("🔴 FILES NEEDING MANUAL REVIEW (possible code changes):") - print("-" * 70) - for r in needs_review_files: - print(f"\n📄 {r.file_path}") - print(f" Reason: {r.reason}") - print() - - if safe_files: - print("🟢 SAFE FILES (translation only, no review needed):") - print("-" * 70) - for r in safe_files: - print(f" ✓ {r.file_path}") - print() - - if error_files: - print("⚠️ ERROR FILES (could not analyze):") - print("-" * 70) - for r in error_files: - print(f" ✗ {r.file_path}: {r.reason}") - print() - - print("=" * 70) - print("Summary") - print("=" * 70) - print(f" Total files in commit: {len(files)}") - print(f" 🟢 Safe (no review needed): {len(safe_files)}") - print(f" 🔴 Needs review: {len(needs_review_files)}") - print(f" ⚠️ Errors: {len(error_files)}") - print() - print(f"Progress saved to: {REVIEW_PROGRESS_FILE}") - print("Run with --reset to start fresh") - print() - - if needs_review_files: - print("⚠️ Please manually review the files marked with 🔴") - print(" These files may contain unintended code changes.") - return 1 - else: - print("✅ All changes appear to be translation-only.") - return 0 - - -# ============================================================================== -# Command: hook (pre-commit hook) -# ============================================================================== - - -def _hook_should_skip_file(file_path: str) -> bool: - """Check if a file should be skipped based on HOOK_SKIP_PATTERNS. - - Only applies skip patterns to files within the project directory. - """ - # Try to get relative path from project root - try: - abs_path = Path(file_path).resolve() - rel_path = str(abs_path.relative_to(PROJECT_DIR)).replace("\\", "/") - except ValueError: - # File is outside project directory, don't skip - return False - - file_name = Path(file_path).name - - for pattern in HOOK_SKIP_PATTERNS: - pattern = pattern.replace("\\", "/") - - # Pattern like "*.md" - match by extension - if pattern.startswith("*."): - if fnmatch(file_name, pattern): - return True - continue - - # Pattern like "**/*.py" or "**/test_*.py" - recursive glob - if pattern.startswith("**/"): - if fnmatch(rel_path, pattern) or fnmatch(file_name, pattern[3:]): - return True - continue - - # Pattern like "dir/*" or "dir/**" - directory prefix - if pattern.endswith("/*") or pattern.endswith("/**"): - dir_prefix = pattern.rstrip("/*") - if rel_path.startswith(dir_prefix + "/"): - return True - continue - - # Exact file/path match - if fnmatch(rel_path, pattern): - return True - - return False - - -def _hook_contains_cjk(text: str) -> bool: - """Check if text contains CJK (Chinese/Japanese/Korean) characters.""" - return bool(CJK_PATTERN.search(text)) - - -def _hook_line_has_skip_comment(line: str) -> bool: - """Check if a line has the skip-i18n-check inline comment. - - The comment can appear anywhere in the line (typically at the end). - Whitespace around the comment marker is ignored. - """ - # Normalize the line for checking (remove spaces around #) - # Match patterns like: #skip-i18n-check, # skip-i18n-check, # skip-i18n-check - normalized = line.lower().replace(" ", "") - return "#skip-i18n-check" in normalized - - -def _hook_file_has_skip_marker(content: str) -> bool: - """Check if file has a skip-i18n-file marker in the first 10 lines. - - The marker can be: - - #skip-i18n-file - - # skip-i18n-file - - # -*- skip-i18n-file -*- - """ - lines = content.split("\n")[:10] # Only check first 10 lines - for line in lines: - line_lower = line.lower().strip() - for marker in HOOK_SKIP_FILE_MARKERS: - if marker in line_lower: - return True - return False - - -def _hook_find_cjk_lines(content: str) -> list[tuple[int, str]]: - """Find all lines containing CJK characters. - - Lines with #skip-i18n-check comment are skipped. - If file has #skip-i18n-file marker in first 10 lines, entire file is skipped. - - Returns: - List of tuples: (line_number, line_content) - """ - # Check for file-level skip marker - if _hook_file_has_skip_marker(content): - return [] - - cjk_lines = [] - for line_num, line in enumerate(content.split("\n"), 1): - # Skip lines with inline skip comment - if _hook_line_has_skip_comment(line): - continue - if _hook_contains_cjk(line): - display_line = line.strip()[:100] - if len(line.strip()) > 100: - display_line += "..." - cjk_lines.append((line_num, display_line)) - return cjk_lines - - -def _hook_get_relative_path(file_path: str) -> str: - """Get the relative path from project root.""" - try: - abs_path = Path(file_path).resolve() - return str(abs_path.relative_to(PROJECT_DIR)) - except ValueError: - return file_path - - -def _hook_format_translation_command(files: list[str]) -> str: - """Format the i18n_tool.py translation command for the given files.""" - rel_files = [_hook_get_relative_path(f) for f in files] - - if len(rel_files) == 1: - return f"python -m src.devops_scripts.i18n.i18n_tool translate --files {rel_files[0]}" - else: - files_str = " ".join(rel_files) - return ( - f"python -m src.devops_scripts.i18n.i18n_tool translate --files {files_str}" - ) - - -def _hook_check_files( - files: list[str], -) -> tuple[bool, dict[str, list[tuple[int, str]]]]: - """Check files for CJK characters. - - Returns: - Tuple of (has_errors, files_with_cjk) - files_with_cjk is a dict: {file_path: [(line_num, content), ...]} - """ - files_with_cjk = {} - - for file_path in files: - # Check if file should be skipped - if _hook_should_skip_file(file_path): - continue - - # Check if file exists - if not Path(file_path).exists(): - continue - - try: - with open(file_path, "r", encoding="utf-8") as f: - content = f.read() - - cjk_lines = _hook_find_cjk_lines(content) - if cjk_lines: - files_with_cjk[file_path] = cjk_lines - - except Exception as e: - print(f"Warning: Could not read {file_path}: {e}", file=sys.stderr) - - return bool(files_with_cjk), files_with_cjk - - -def _hook_check_commit_message(msg_file: str) -> tuple[bool, list[tuple[int, str]]]: - """Check commit message for CJK characters. - - Returns: - Tuple of (has_cjk, cjk_lines) - """ - try: - with open(msg_file, "r", encoding="utf-8") as f: - content = f.read() - - # Check for skip keywords - content_lower = content.lower() - for keyword in HOOK_SKIP_COMMIT_MSG_KEYWORDS: - if keyword.lower() in content_lower: - return False, [] - - cjk_lines = _hook_find_cjk_lines(content) - return bool(cjk_lines), cjk_lines - - except Exception as e: - print(f"Warning: Could not read commit message file: {e}", file=sys.stderr) - return False, [] - - -def _hook_print_error_report( - files_with_cjk: dict[str, list[tuple[int, str]]], - commit_msg_cjk: list[tuple[int, str]] | None = None, -): - """Print a detailed error report.""" - print("\n" + "=" * 70, file=sys.stderr) - print("❌ NON-ENGLISH CHARACTERS DETECTED (CJK)", file=sys.stderr) - print("=" * 70, file=sys.stderr) - - if files_with_cjk: - print("\n📄 Files containing non-English characters:", file=sys.stderr) - print("-" * 70, file=sys.stderr) - - for file_path, cjk_lines in files_with_cjk.items(): - rel_path = _hook_get_relative_path(file_path) - print(f"\n {rel_path} ({len(cjk_lines)} lines)", file=sys.stderr) - for line_num, content in cjk_lines[:5]: - # Use file:line format for clickable terminal links - print(f" {rel_path}:{line_num}: {content}", file=sys.stderr) - if len(cjk_lines) > 5: - print(f" ... and {len(cjk_lines) - 5} more lines", file=sys.stderr) - - if commit_msg_cjk: - print("\n💬 Commit message contains non-English characters:", file=sys.stderr) - print("-" * 70, file=sys.stderr) - for line_num, content in commit_msg_cjk: - print(f" Line {line_num}: {content}", file=sys.stderr) - - print("\n" + "=" * 70, file=sys.stderr) - print("HOW TO FIX", file=sys.stderr) - print("=" * 70, file=sys.stderr) - - if files_with_cjk: - files_list = list(files_with_cjk.keys()) - cmd = _hook_format_translation_command(files_list) - print(f"\n1. Translate the files using:", file=sys.stderr) - print(f" {cmd}", file=sys.stderr) - print("\n Or for dry-run first:", file=sys.stderr) - print(f" {cmd} --dry-run", file=sys.stderr) - - if commit_msg_cjk: - print("\n2. Rewrite your commit message in English", file=sys.stderr) - - print("\n" + "-" * 70, file=sys.stderr) - print("TO SKIP THIS CHECK (use sparingly):", file=sys.stderr) - print("-" * 70, file=sys.stderr) - print( - " • Add '# skip-i18n-file' in first 10 lines to skip entire file", - file=sys.stderr, - ) - print( - " • Add inline comment to skip specific line: #skip-i18n-check", - file=sys.stderr, - ) - print(' Example: if "中文" in text: #skip-i18n-check', file=sys.stderr) - print(f" • Set environment variable: {HOOK_SKIP_ENV_VAR}=1", file=sys.stderr) - print(" • Add to commit message: [skip-i18n] or #skip-i18n-check", file=sys.stderr) - print( - " • Add file patterns to HOOK_SKIP_PATTERNS in i18n_tool.py", file=sys.stderr - ) - print("\n" + "=" * 70 + "\n", file=sys.stderr) - - -def cmd_hook(files: list[str], commit_msg: bool = False) -> int: - """Execute the hook command for pre-commit. - - Args: - files: List of files to check, or commit message file if commit_msg=True - commit_msg: If True, check commit message instead of files - - Returns: - 0 if no CJK found, 1 if CJK found - """ - # Check for skip environment variable - if os.environ.get(HOOK_SKIP_ENV_VAR, "").lower() in ("1", "true", "yes"): - print(f"Skipping i18n check ({HOOK_SKIP_ENV_VAR} is set)") - return 0 - - has_error = False - files_with_cjk = {} - commit_msg_cjk = None - - if commit_msg: - # Check commit message - if files: - msg_file = files[0] - has_cjk, commit_msg_cjk = _hook_check_commit_message(msg_file) - if has_cjk: - has_error = True - else: - # Check staged files - if files: - has_error, files_with_cjk = _hook_check_files(files) - - if has_error: - _hook_print_error_report(files_with_cjk, commit_msg_cjk) - return 1 - - return 0 - - -# ============================================================================== -# Main Entry Point -# ============================================================================== - - -def main(): - import argparse - - parser = argparse.ArgumentParser( - description="I18N Tool - Chinese to English translation and review tool", - formatter_class=argparse.RawDescriptionHelpFormatter, - ) - subparsers = parser.add_subparsers(dest="command", help="Available commands") - - # Translate command - translate_parser = subparsers.add_parser( - "translate", help="Translate Chinese comments/logs to English in Python files" - ) - translate_parser.add_argument( - "--directory", - "-d", - nargs="*", - help="Directories to scan (relative to project root). Default: src", - ) - translate_parser.add_argument( - "--max-concurrency", - type=int, - default=5, - help="Maximum concurrent translations (default: 5)", - ) - translate_parser.add_argument( - "--dry-run", - action="store_true", - help="Don't write changes, just show what would be done", - ) - translate_parser.add_argument( - "--files", nargs="*", help="Specific files to translate (optional)" - ) - translate_parser.add_argument( - "--reset", action="store_true", help="Clear previous progress and start fresh" - ) - - # Check command - check_parser = subparsers.add_parser( - "check", help="Check for remaining Chinese content in Python files" - ) - check_parser.add_argument( - "--directory", - "-d", - nargs="*", - help="Directories to scan (relative to project root). Default: src", - ) - check_parser.add_argument( - "--files", nargs="*", help="Specific files to check (optional)" - ) - - # Review command - review_parser = subparsers.add_parser( - "review", help="Review git commits to verify translation changes" - ) - review_parser.add_argument( - "--commit", - "-c", - default="HEAD", - help="Git commit or range to review (default: HEAD)", - ) - review_parser.add_argument( - "--max-concurrency", - type=int, - default=5, - help="Maximum concurrent LLM calls (default: 5)", - ) - review_parser.add_argument( - "--verbose", - "-v", - action="store_true", - help="Show verbose output including diff previews", - ) - review_parser.add_argument( - "--dry-run", - action="store_true", - help="Only list changed files without LLM analysis", - ) - review_parser.add_argument( - "--reset", action="store_true", help="Clear previous progress and start fresh" - ) - - # Hook command (for pre-commit) - hook_parser = subparsers.add_parser( - "hook", help="Pre-commit hook to check for non-English (CJK) characters" - ) - hook_parser.add_argument( - "--commit-msg", - action="store_true", - help="Check commit message instead of files", - ) - hook_parser.add_argument( - "files", - nargs="*", - help="Files to check (for pre-commit) or commit message file (for commit-msg)", - ) - - args = parser.parse_args() - - if not args.command: - parser.print_help() - sys.exit(1) - - if args.command == "hook": - # Hook command doesn't need project dependencies - exit_code = cmd_hook(files=args.files, commit_msg=args.commit_msg) - sys.exit(exit_code) - - if args.command == "translate": - directories = resolve_directories(args.directory) - exit_code = asyncio.run( - cmd_translate( - directories=directories, - max_concurrency=args.max_concurrency, - dry_run=args.dry_run, - specific_files=args.files, - reset_progress=args.reset, - ) - ) - sys.exit(exit_code) - - elif args.command == "check": - directories = resolve_directories(args.directory) - exit_code = cmd_check(directories=directories, specific_files=args.files) - sys.exit(0 if exit_code == 0 else 1) - - elif args.command == "review": - exit_code = asyncio.run( - cmd_review( - commit_ref=args.commit, - max_concurrency=args.max_concurrency, - verbose=args.verbose, - dry_run=args.dry_run, - reset_progress=args.reset, - ) - ) - sys.exit(exit_code) - - -if __name__ == "__main__": - main() diff --git a/methods/EverCore/src/devops_scripts/milvus_admin/README.md b/methods/EverCore/src/devops_scripts/milvus_admin/README.md deleted file mode 100644 index c7e5a0b3..00000000 --- a/methods/EverCore/src/devops_scripts/milvus_admin/README.md +++ /dev/null @@ -1,72 +0,0 @@ -# Milvus Admin Tools - -## browse_collections.py - -Browse Milvus collections and interactively delete them. - -### Usage - -```bash -# Run via bootstrap (from project root) -uv run python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py [OPTIONS] -``` - -### Options - -| Option | Short | Description | -|--------|-------|-------------| -| `--filter TEXT` | `-f` | Filter collections by name substring (case-insensitive) | -| `--prefix TEXT` | `-p` | Match collections by name prefix (exact, case-sensitive) | -| `--delete` | `-d` | Enter interactive delete mode after listing | -| `--drop` | | Delete all matched collections (requires `--prefix` or `--filter`) | -| `--db TEXT` | | Specify Milvus database name (default: from env) | - -### Examples - -```bash -# List all collections -uv run python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py - -# Filter by keyword -uv run python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py -f episodic - -# List by prefix -uv run python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py -p v1_episodic - -# Interactive delete (select by number after listing) -uv run python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --delete - -# Batch delete by prefix (requires 'yes' confirmation) -uv run python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --prefix v1_episodic --drop - -# Batch delete by filter -uv run python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --filter episodic --drop - -# Use a specific database -uv run python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --db my_database -``` - -### Output - -``` -================================================================================ - Found 3 collection(s) -================================================================================ - -# Collection Name Rows Aliases ------------------------------------------------------------------------- -1 v1_episodic_memory_tenant_a-20260301120000000000 1.2K v1_episodic_memory_tenant_a -2 v1_episodic_memory_tenant_b-20260315080000000000 3.5K v1_episodic_memory_tenant_b -3 v1_memcell_tenant_a-20260310150000000000 89 v1_memcell_tenant_a -``` - -### Interactive Delete Mode - -When using `--delete`, you can select collections by: - -- Single numbers: `1,3,5` -- Ranges: `2-6` -- All: `all` -- Quit: `q` - -All delete operations require typing `yes` to confirm. diff --git a/methods/EverCore/src/devops_scripts/milvus_admin/browse_collections.py b/methods/EverCore/src/devops_scripts/milvus_admin/browse_collections.py deleted file mode 100644 index a6b2fd0e..00000000 --- a/methods/EverCore/src/devops_scripts/milvus_admin/browse_collections.py +++ /dev/null @@ -1,293 +0,0 @@ -""" -Milvus Collection Browser & Cleanup Tool - -Browse all Milvus collections with detailed info (row count, aliases, fields), -and interactively delete selected collections. - -Usage: - python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py - python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --db default - python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --filter v1_episodic - python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --delete - python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --prefix v1_episodic --drop -""" - -import argparse -import sys -from typing import Optional, List - -from pymilvus import MilvusClient, utility, connections - -from core.component.milvus_client_factory import get_milvus_config -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -def _connect(db_name: str = "") -> MilvusClient: - """Create a MilvusClient from environment config.""" - config = get_milvus_config() - client = MilvusClient( - uri=config["uri"], - user=config["user"], - password=config["password"], - db_name=db_name or config["db_name"], - ) - # Also create a pymilvus connection for utility calls - connections.connect( - alias="_admin", - uri=config["uri"], - user=config["user"], - password=config["password"], - db_name=db_name or config["db_name"], - ) - return client - - -def _format_row_count(count: int) -> str: - """Format row count for display.""" - if count >= 1_000_000: - return f"{count / 1_000_000:.1f}M" - if count >= 1_000: - return f"{count / 1_000:.1f}K" - return str(count) - - -def list_collections( - client: MilvusClient, - name_filter: Optional[str] = None, - prefix: Optional[str] = None, -) -> List[str]: - """List all collections, optionally filtered by name substring or prefix.""" - all_names = client.list_collections() - all_names.sort() - - if prefix: - all_names = [n for n in all_names if n.startswith(prefix)] - elif name_filter: - all_names = [n for n in all_names if name_filter.lower() in n.lower()] - - return all_names - - -def show_collection_details(client: MilvusClient, names: List[str]) -> None: - """Print detailed info for each collection.""" - if not names: - print("\nNo collections found.") - return - - print(f"\n{'='*80}") - print(f" Found {len(names)} collection(s)") - print(f"{'='*80}\n") - - header = f"{'#':<4} {'Collection Name':<50} {'Rows':>10} {'Aliases'}" - print(header) - print("-" * len(header) + "-" * 20) - - for idx, name in enumerate(names, 1): - try: - stats = client.get_collection_stats(name) - row_count = int(stats.get("row_count", 0)) - except Exception: - row_count = -1 - - try: - aliases = utility.list_aliases(collection_name=name, using="_admin") - alias_str = ", ".join(aliases) if aliases else "-" - except Exception: - alias_str = "?" - - row_str = _format_row_count(row_count) if row_count >= 0 else "?" - print(f"{idx:<4} {name:<50} {row_str:>10} {alias_str}") - - print() - - -def interactive_delete(client: MilvusClient, names: List[str]) -> None: - """Interactively select and delete collections.""" - if not names: - print("No collections available for deletion.") - return - - print("Enter collection numbers to delete (comma-separated), or 'q' to quit.") - print("Example: 1,3,5 or 2-6 or all\n") - - user_input = input("Delete> ").strip() - if not user_input or user_input.lower() == "q": - print("Cancelled.") - return - - # Parse selection - selected_indices = set() - if user_input.lower() == "all": - selected_indices = set(range(len(names))) - else: - for part in user_input.split(","): - part = part.strip() - if "-" in part: - try: - start, end = part.split("-", 1) - for i in range(int(start), int(end) + 1): - selected_indices.add(i - 1) - except ValueError: - print(f"Invalid range: {part}") - return - else: - try: - selected_indices.add(int(part) - 1) - except ValueError: - print(f"Invalid number: {part}") - return - - # Validate indices - selected_names = [] - for i in sorted(selected_indices): - if 0 <= i < len(names): - selected_names.append(names[i]) - else: - print(f"Index out of range: {i + 1}") - return - - if not selected_names: - print("No valid collections selected.") - return - - # Confirm - print(f"\nAbout to DELETE {len(selected_names)} collection(s):") - for name in selected_names: - print(f" - {name}") - - confirm = input("\nType 'yes' to confirm deletion: ").strip() - if confirm.lower() != "yes": - print("Cancelled.") - return - - # Execute deletion - for name in selected_names: - drop_collection(client, name) - - print("\nDone.") - - -def drop_collection(client: MilvusClient, name: str) -> bool: - """Drop a single collection and its aliases. Returns True on success.""" - try: - try: - aliases = utility.list_aliases(collection_name=name, using="_admin") - for alias in aliases: - utility.drop_alias(alias, using="_admin") - print(f" Dropped alias: {alias}") - except Exception: - pass - - client.drop_collection(name) - print(f" Deleted: {name}") - return True - except Exception as e: - print(f" Failed to delete {name}: {e}") - return False - - -def main(argv: Optional[List[str]] = None) -> int: - parser = argparse.ArgumentParser( - description="Browse and manage Milvus collections", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Example usage: - # List all collections - python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py - - # Filter by name - python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --filter episodic - - # Enter interactive delete mode - python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --delete - - # Delete all collections with a specific prefix - python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --prefix v1_episodic --drop - - # Specify database - python src/bootstrap.py src/devops_scripts/milvus_admin/browse_collections.py --db my_database - """, - ) - - parser.add_argument( - "--filter", - "-f", - type=str, - default=None, - help="Filter collections by name substring (case-insensitive)", - ) - parser.add_argument( - "--prefix", - "-p", - type=str, - default=None, - help="Match collections by name prefix (exact, case-sensitive)", - ) - parser.add_argument( - "--delete", - "-d", - action="store_true", - help="Enter interactive delete mode after listing", - ) - parser.add_argument( - "--drop", - action="store_true", - help="Delete matched collections (use with --prefix or --filter)", - ) - parser.add_argument( - "--db", - type=str, - default="", - help="Milvus database name (default: from env or 'default')", - ) - - args = parser.parse_args(argv) - - client = _connect(db_name=args.db) - - try: - # --prefix --drop: batch delete by prefix - if args.drop and (args.prefix or args.filter): - names = list_collections( - client, name_filter=args.filter, prefix=args.prefix - ) - show_collection_details(client, names) - if names: - label = ( - f"prefix '{args.prefix}'" - if args.prefix - else f"filter '{args.filter}'" - ) - print( - f"About to DELETE all {len(names)} collection(s) matching {label}." - ) - confirm = input("Type 'yes' to confirm deletion: ").strip() - if confirm.lower() == "yes": - success = sum(1 for n in names if drop_collection(client, n)) - print(f"\nDone. Deleted: {success}, Failed: {len(names) - success}") - else: - print("Cancelled.") - elif args.drop: - print("Error: --drop requires --prefix or --filter to select collections.") - return 1 - else: - names = list_collections( - client, name_filter=args.filter, prefix=args.prefix - ) - show_collection_details(client, names) - if args.delete: - interactive_delete(client, names) - finally: - try: - client.close() - connections.disconnect("_admin") - except Exception: - pass - - return 0 - - -if __name__ == "__main__": - sys.exit(main(sys.argv[1:])) diff --git a/methods/EverCore/src/devops_scripts/mongo_migrate.py b/methods/EverCore/src/devops_scripts/mongo_migrate.py deleted file mode 100755 index 43b4353b..00000000 --- a/methods/EverCore/src/devops_scripts/mongo_migrate.py +++ /dev/null @@ -1,22 +0,0 @@ -#!/usr/bin/env python3 -""" -MongoDB migration management script. - -This is a wrapper around the core.oxm.mongo.migration module for backward compatibility. -Please use 'python -m core.oxm.mongo.migration.cli' for new migrations. - -Usage: - python migrate.py new-migration -n migration_name # Create new migration - python migrate.py migrate # Run all migrations - python migrate.py migrate --distance 1 # Run 1 migration - python migrate.py migrate --backward # Roll back all migrations - python migrate.py migrate --backward --distance 1 # Roll back 1 migration - python migrate.py migrate --no-use-transaction # Run without transactions - python migrate.py --uri mongodb://... # Specify MongoDB URI - python migrate.py --help # Show help -""" - -from core.oxm.mongo.migration.cli import main - -if __name__ == "__main__": - main() diff --git a/methods/EverCore/src/devops_scripts/progress.py b/methods/EverCore/src/devops_scripts/progress.py deleted file mode 100644 index ec465ca6..00000000 --- a/methods/EverCore/src/devops_scripts/progress.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -Devops script progress reporting - -Provides a standard interface for devops scripts to emit structured progress -lines that can be parsed by parent processes (e.g., MilvusRebuildService). - -Protocol: - Scripts write lines to stdout in the format: - ##DEVOPS## {"event": "...", "key": "value", ...} - The parent process reads stdout line by line and parses these lines. - -Usage: - from devops_scripts.progress import StdoutProgressReporter - - progress = StdoutProgressReporter() - progress.emit({"event": "start", "total": 6}) - progress.emit({"event": "collection_done", "alias": "v1_ep", "status": "ok"}) -""" - -import json -from abc import ABC, abstractmethod -from typing import Any, Dict - - -# Standard prefix — parsed by the parent process (e.g., MilvusRebuildService) -PROGRESS_PREFIX = "##DEVOPS##" - - -class ProgressReporter(ABC): - """Abstract interface for reporting devops task progress.""" - - @abstractmethod - def emit(self, data: Dict[str, Any]) -> None: - """ - Emit a progress event. - - Args: - data: Event payload dict. Must contain an "event" key. - """ - ... - - -class StdoutProgressReporter(ProgressReporter): - """ - Default implementation: writes structured JSON lines to stdout. - - Format: ##DEVOPS## {"event": "...", ...} - """ - - def emit(self, data: Dict[str, Any]) -> None: - print(f"{PROGRESS_PREFIX} {json.dumps(data, ensure_ascii=False)}", flush=True) - - -class NoopProgressReporter(ProgressReporter): - """Silent reporter — does nothing. Useful for tests or CLI-only runs.""" - - def emit(self, data: Dict[str, Any]) -> None: - pass diff --git a/methods/EverCore/src/devops_scripts/sensitive_info/__init__.py b/methods/EverCore/src/devops_scripts/sensitive_info/__init__.py deleted file mode 100644 index b21ff21d..00000000 --- a/methods/EverCore/src/devops_scripts/sensitive_info/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Sensitive Information Detection Tools diff --git a/methods/EverCore/src/devops_scripts/sensitive_info/sensitive_info_tool.py b/methods/EverCore/src/devops_scripts/sensitive_info/sensitive_info_tool.py deleted file mode 100644 index f9d1220b..00000000 --- a/methods/EverCore/src/devops_scripts/sensitive_info/sensitive_info_tool.py +++ /dev/null @@ -1,960 +0,0 @@ -""" -Sensitive Information Detection Tool - AI-Powered Pre-commit Hook - -This tool uses LLM to intelligently scan files for sensitive information -before committing to GitHub: -1. Credentials: API keys, passwords, secrets, tokens -2. Internal network config: Private IPs, internal domains -3. Personal data: Real names, phone numbers, emails, ID numbers -4. Test data that may point to real users - -Usage: - # Check command (AI-powered scan) - python -m devops_scripts.sensitive_info.sensitive_info_tool check - python -m devops_scripts.sensitive_info.sensitive_info_tool check --directory tests - python -m devops_scripts.sensitive_info.sensitive_info_tool check --files file1.py file2.py - - # Hook command (for pre-commit, uses AI) - python -m devops_scripts.sensitive_info.sensitive_info_tool hook file1.py file2.py -""" - -from __future__ import annotations - -import os -import sys -import asyncio -import json -from fnmatch import fnmatch -from pathlib import Path -from dataclasses import dataclass, field -from enum import Enum -from typing import Optional - -# ============================================================================== -# Path Configuration -# ============================================================================== - -SRC_DIR = Path(__file__).parent.parent.parent -PROJECT_DIR = SRC_DIR.parent - - -def _setup_project_imports(): - """Setup project imports when needed (lazy loading).""" - if str(SRC_DIR) not in sys.path: - sys.path.insert(0, str(SRC_DIR)) - - -# ============================================================================== -# Configuration -# ============================================================================== - -# Maximum file size to process (100KB for AI analysis) -MAX_FILE_SIZE = 100 * 1024 - -# Maximum content size to send to LLM at once -MAX_CHUNK_SIZE = 30000 - -# Directories to always skip -SKIP_DIRECTORIES = [ - "__pycache__", - ".git", - ".venv", - "venv", - "node_modules", - ".mypy_cache", - ".pytest_cache", - "dist", - "build", - ".eggs", - # Prompt directories (contain example text for LLM prompts, not real data) - "prompts", -] - -# Files to skip by pattern -SKIP_FILE_PATTERNS = [ - # Lock files - "*.lock", - "package-lock.json", - "yarn.lock", - # Binary and media files - "*.png", - "*.jpg", - "*.jpeg", - "*.gif", - "*.svg", - "*.ico", - "*.pdf", - "*.zip", - "*.tar", - "*.gz", - "*.whl", - # Compiled files - "*.pyc", - "*.pyo", - "*.so", - "*.dll", - "*.exe", - # Git files - ".gitignore", - ".gitattributes", - # This tool itself - "sensitive_info_tool.py", - # Template files (expected to have placeholders) - "env.template", - "*.template", - # Stopword files - "*stopwords*.txt", - # i18n tool - "i18n_tool.py", -] - -# Environment variable to skip hook -HOOK_SKIP_ENV_VAR = "SKIP_SENSITIVE_CHECK" - -# Inline comment to skip check for a specific line -SKIP_LINE_COMMENT = "#skip-sensitive-check" - -# File-level skip marker - add this at the top of a file to skip the entire file -# Usage: Add "# skip-sensitive-file" in the first 10 lines -SKIP_FILE_MARKERS = [ - "#skip-sensitive-file", - "# skip-sensitive-file", - "# skip-sensitive-file", - "# -*- skip-sensitive-file -*-", -] - -# Keywords in commit message to skip check -SKIP_COMMIT_MSG_KEYWORDS = [ - "skip-sensitive-check", - "#skip-sensitive-check", - "[skip-sensitive]", - "[no-sensitive-check]", -] - -# Concurrency limit for LLM calls -MAX_CONCURRENCY = 5 - - -# ============================================================================== -# Result Types -# ============================================================================== - - -class Severity(Enum): - """Severity levels for sensitive information.""" - - HIGH = "high" - MEDIUM = "medium" - LOW = "low" - - -@dataclass -class SensitiveIssue: - """A single sensitive information issue found by AI.""" - - line_number: int - line_content: str - issue_type: str - severity: Severity - description: str - suggestion: str - - -@dataclass -class FileCheckResult: - """Result of checking a single file.""" - - file_path: str - issues: list[SensitiveIssue] = field(default_factory=list) - skipped: bool = False - skip_reason: str = "" - error: str = "" - ai_analysis: str = "" - - -# ============================================================================== -# LLM Prompt -# ============================================================================== - -ANALYSIS_PROMPT_TEMPLATE = '''You are a security expert reviewing code before it's published to GitHub. -Your goal is to find REAL sensitive data that could cause harm if leaked, NOT placeholder/test data. - -## CRITICAL: Check Configuration Names Carefully - -For EVERY line that sets a default value for a configuration (e.g., os.getenv("X", "default"), get_env("X", "default")): -- Split the default value by "_" or "-" -- Check: Is each word a COMMON English word or standard tech term? -- If ANY word is NOT recognizable as a dictionary word, flag it as MEDIUM -- This applies to: Kafka topics, queue names, bucket names, service names, group IDs, etc. - -## Severity Levels - -### HIGH severity (must fix before commit) -- REAL API keys: long random strings that look like actual production keys -- REAL passwords: complex passwords that appear to be actual credentials -- Private keys, certificates, tokens that look real -- Real personal data: actual phone numbers, ID cards, real names of specific real individuals - -### MEDIUM severity (should review) -- Internal IPs/domains that appear to be REAL infrastructure configuration - - Principle: Ask "Does this look like a placeholder/example, or a real server address?" - - Placeholder IPs have predictable patterns: x.x.0.1, x.x.1.1, x.x.0.0, x.x.255.255 - - Real IPs have arbitrary middle/end segments that look like actual assignments - - Example: 192.168.1.1 is clearly a placeholder (common router default) - - Example: 192.168.47.83 looks like a real assigned IP (arbitrary numbers) - -- Internal configuration names that reveal real infrastructure - - PRINCIPLE: Configuration names should only contain DICTIONARY WORDS or INDUSTRY-STANDARD terms. - - Flag as MEDIUM if a configuration name contains: - - Abbreviations that are NOT industry-standard (API, HTTP, DB, SQL, JSON are standard) - - Words that are NOT in an English dictionary - - Words that look like they could be project names, product names, or company abbreviations - - The test: Can you find this word in a standard English dictionary or official technical documentation? - If not, it's likely internal terminology that reveals infrastructure details. - - NOTE: Even in os.getenv("VAR", "fallback") - if the fallback looks specific, flag it! - -- Internal domains that look real (specific hostnames, not generic like "example.internal") -- Data that MIGHT be real but you're not sure -- Potential real user references that aren't obviously test data - -### LOW severity (just a reminder, okay to commit) -- Simple/obvious placeholder passwords: "123456", "123", "password", "admin", "test", "root", "memsys123" - These are clearly NOT real secrets - no one uses these as actual passwords -- Documentation examples showing connection formats -- Anything that looks like intentional test/demo data - -## Key Principle: Does it look like a REAL secret? - -Ask yourself: Would a real person use this as their actual password/key? -- "MyR3alP@ssw0rd!2024" → Looks real → HIGH -- "123456" or "password" → Obviously placeholder → LOW or SAFE -- "sk-proj-abc123xyz..." (40+ chars) → Looks like real API key → HIGH -- "123" or "xxxx" → Obviously fake → SAFE - -## What is SAFE (DO NOT flag) - -Apply this principle: "Does it look intentionally fake/placeholder, or accidentally real?" - -- Placeholder patterns: strings with "xxxx", "your-", "${VAR}", "{{...}}", "" -- IPs that are OBVIOUSLY examples: - Pattern: ends with .0.1, .1.1, .0.0, .255.255, or is localhost/127.0.0.1 - These are universally recognized defaults, not real infrastructure -- Generic/example domains: contains "example", "test", "demo", "sample", "foo", "bar", or is localhost -- Generic configuration names: "test-topic", "my-queue", "example-db", "default", "sample-bucket" - These use common placeholder words, not project-specific terms -- Test names: Names that are culturally known as placeholder names (like John Doe in English) -- Test emails: Uses obviously fake domains or placeholder usernames -- Test phone numbers: Sequential digits, repeated patterns, or known test numbers -- Environment variable reads WITHOUT fallback: os.getenv("SECRET") with no default is correct practice -- Documentation showing formats/examples -- Test file data with obviously fictional content - -## Response Format - -JSON only (no markdown): -{"status": "ISSUES_FOUND", "issues": [{"line_number": 42, "line_content": "content", "issue_type": "Type", "severity": "HIGH|MEDIUM|LOW", "description": "why", "suggestion": "fix"}]} - -Or: {"status": "SAFE", "issues": []} - -## File to Analyze - -File path: __FILE_PATH__ - -``` -__CONTENT__ -``` - -IMPORTANT: When reviewing, you MUST check these in order: -1. API keys and passwords - flag if they look REAL (complex, not "123456") -2. IP addresses in defaults - flag if they look ASSIGNED (not .0.1 or .1.1 patterns) -3. Configuration names (Kafka topics, queues, buckets, etc.) - flag if they contain NON-DICTIONARY words - For each configuration name, split by "_" or "-" and check: is each part a common English word? - If you find a word that is NOT in a standard dictionary, it's likely an internal codename - flag as MEDIUM. - -Be conservative on passwords (simple ones like "123456" are LOW), but be STRICT on configuration names.''' - - -def build_analysis_prompt(file_path: str, content: str) -> str: - """Build the analysis prompt with file content.""" - return ANALYSIS_PROMPT_TEMPLATE.replace("__FILE_PATH__", file_path).replace( - "__CONTENT__", content - ) - - -# ============================================================================== -# Utility Functions -# ============================================================================== - - -def should_skip_file(file_path: str) -> tuple[bool, str]: - """Check if a file should be skipped. - - Returns: - Tuple of (should_skip, reason) - """ - path = Path(file_path) - file_name = path.name - - # Check directory patterns - for part in path.parts: - if part in SKIP_DIRECTORIES: - return True, f"In skipped directory: {part}" - - # Check file patterns - for pattern in SKIP_FILE_PATTERNS: - if fnmatch(file_name, pattern): - return True, f"Matches skip pattern: {pattern}" - - return False, "" - - -def get_relative_path(file_path: str) -> str: - """Get relative path from project root.""" - try: - return str(Path(file_path).relative_to(PROJECT_DIR)) - except ValueError: - return file_path - - -# ============================================================================== -# File Discovery -# ============================================================================== - - -def get_files_from_directory(directory: Path) -> list[Path]: - """Get all files under a directory recursively.""" - files = [] - - for root, dirs, filenames in os.walk(directory): - # Remove skipped directories - dirs[:] = [ - d for d in dirs if d not in SKIP_DIRECTORIES and not d.startswith(".") - ] - - for filename in filenames: - file_path = Path(root) / filename - files.append(file_path) - - return files - - -def get_files_from_directories(directories: list[Path]) -> list[Path]: - """Get all files from multiple directories.""" - all_files = [] - for directory in directories: - if directory.exists(): - files = get_files_from_directory(directory) - all_files.extend(files) - return all_files - - -def resolve_directories(dir_names: list[str] | None) -> list[Path]: - """Resolve directory names to absolute paths.""" - if not dir_names: - return [ - SRC_DIR, - PROJECT_DIR / "tests", - PROJECT_DIR / "data", - PROJECT_DIR / "demo", - ] - - directories = [] - for dir_name in dir_names: - dir_path = Path(dir_name) - if not dir_path.is_absolute(): - dir_path = PROJECT_DIR / dir_name - directories.append(dir_path) - return directories - - -# ============================================================================== -# AI Analysis -# ============================================================================== - - -def _load_llm_provider(): - """Load LLM provider.""" - _setup_project_imports() - - from dotenv import load_dotenv - from memory_layer.llm import OpenAIProvider - - env_file_path = PROJECT_DIR / ".env" - if env_file_path.exists(): - load_dotenv(env_file_path) - - return OpenAIProvider( - model=os.getenv("LLM_MODEL", "gpt-4.1-mini"), - api_key=os.getenv("LLM_API_KEY"), - base_url=os.getenv("LLM_BASE_URL"), - temperature=0.1, - ) - - -def parse_ai_response(response: str) -> tuple[str, list[SensitiveIssue]]: - """Parse AI response and extract issues.""" - issues = [] - - # Try to extract JSON from response - try: - # Find JSON in response - json_start = response.find("{") - json_end = response.rfind("}") + 1 - if json_start >= 0 and json_end > json_start: - json_str = response[json_start:json_end] - data = json.loads(json_str) - - status = data.get("status", "UNKNOWN") - - for issue_data in data.get("issues", []): - severity_str = issue_data.get("severity", "MEDIUM").upper() - severity = ( - Severity.HIGH - if severity_str == "HIGH" - else (Severity.MEDIUM if severity_str == "MEDIUM" else Severity.LOW) - ) - - issues.append( - SensitiveIssue( - line_number=issue_data.get("line_number", 0), - line_content=issue_data.get("line_content", "")[:100], - issue_type=issue_data.get("issue_type", "Unknown"), - severity=severity, - description=issue_data.get("description", ""), - suggestion=issue_data.get("suggestion", ""), - ) - ) - - return status, issues - except json.JSONDecodeError: - pass - - # If JSON parsing fails, try to determine status from text - response_upper = response.upper() - if "SAFE" in response_upper and "ISSUES" not in response_upper: - return "SAFE", [] - - return "PARSE_ERROR", [] - - -async def analyze_file_with_ai( - provider, - file_path: str, - semaphore: asyncio.Semaphore, - index: int = 0, - total: int = 0, -) -> FileCheckResult: - """Analyze a single file using AI.""" - progress_prefix = f"[{index}/{total}]" if total > 0 else "" - rel_path = get_relative_path(file_path) - - # Check if should skip - should_skip, reason = should_skip_file(file_path) - if should_skip: - return FileCheckResult(file_path=file_path, skipped=True, skip_reason=reason) - - # Check file exists and size - path = Path(file_path) - if not path.exists(): - return FileCheckResult( - file_path=file_path, skipped=True, skip_reason="File not found" - ) - - file_size = path.stat().st_size - if file_size > MAX_FILE_SIZE: - return FileCheckResult( - file_path=file_path, - skipped=True, - skip_reason=f"File too large ({file_size / 1024:.1f}KB > {MAX_FILE_SIZE / 1024:.0f}KB)", - ) - - # Read file content - try: - with open(file_path, "r", encoding="utf-8", errors="ignore") as f: - content = f.read() - except Exception as e: - return FileCheckResult(file_path=file_path, error=str(e)) - - # Skip empty files - if not content.strip(): - return FileCheckResult( - file_path=file_path, skipped=True, skip_reason="Empty file" - ) - - # Check for file-level skip marker in first 10 lines - first_lines = content.split("\n")[:10] - for line in first_lines: - line_lower = line.lower().strip() - for marker in SKIP_FILE_MARKERS: - if marker in line_lower: - return FileCheckResult( - file_path=file_path, - skipped=True, - skip_reason="File has #skip-sensitive-file marker", - ) - - # Filter out lines with skip comment - lines = content.split("\n") - filtered_lines = [] - for i, line in enumerate(lines, 1): - if SKIP_LINE_COMMENT.lower() not in line.lower().replace(" ", ""): - filtered_lines.append(f"{i:6d}|{line}") - - numbered_content = "\n".join(filtered_lines) - - # Truncate if too long - if len(numbered_content) > MAX_CHUNK_SIZE: - numbered_content = numbered_content[:MAX_CHUNK_SIZE] + "\n... [truncated]" - - async with semaphore: - try: - print(f"{progress_prefix} [ANALYZING] {rel_path}") - - prompt = build_analysis_prompt(rel_path, numbered_content) - - response = await provider.generate(prompt, temperature=0.1) - - status, issues = parse_ai_response(response) - - if status == "SAFE" or not issues: - print(f"{progress_prefix} [SAFE] {rel_path}") - else: - high_count = sum(1 for i in issues if i.severity == Severity.HIGH) - med_count = sum(1 for i in issues if i.severity == Severity.MEDIUM) - print( - f"{progress_prefix} [ISSUES] {rel_path} - {high_count} high, {med_count} medium" - ) - - return FileCheckResult( - file_path=file_path, - issues=issues, - ai_analysis=response[:500] if len(response) > 500 else response, - ) - - except Exception as e: - print(f"{progress_prefix} [ERROR] {rel_path}: {e}") - return FileCheckResult(file_path=file_path, error=str(e)) - - -# ============================================================================== -# Output Functions -# ============================================================================== - - -def print_header(title: str): - """Print a section header.""" - print("=" * 70) - print(title) - print("=" * 70) - print() - - -def print_results(results: list[FileCheckResult], verbose: bool = False): - """Print check results in a formatted way.""" - files_with_issues = [r for r in results if r.issues] - files_skipped = [r for r in results if r.skipped] - files_with_errors = [r for r in results if r.error] - files_clean = [r for r in results if not r.issues and not r.skipped and not r.error] - - # Group issues by severity - high_severity = [] - medium_severity = [] - low_severity = [] - - for result in files_with_issues: - for issue in result.issues: - entry = (result.file_path, issue) - if issue.severity == Severity.HIGH: - high_severity.append(entry) - elif issue.severity == Severity.MEDIUM: - medium_severity.append(entry) - else: - low_severity.append(entry) - - if high_severity: - print("\n🔴 HIGH SEVERITY ISSUES:") - print("-" * 70) - for file_path, issue in high_severity: - rel_path = get_relative_path(file_path) - print(f"\n 📄 {rel_path}:{issue.line_number}") - print(f" [{issue.issue_type}] {issue.description}") - if issue.line_content: - print(f" Line: {issue.line_content}") - if issue.suggestion: - print(f" Fix: {issue.suggestion}") - - if medium_severity: - print("\n🟡 MEDIUM SEVERITY ISSUES:") - print("-" * 70) - for file_path, issue in medium_severity: - rel_path = get_relative_path(file_path) - print(f"\n 📄 {rel_path}:{issue.line_number}") - print(f" [{issue.issue_type}] {issue.description}") - if issue.suggestion: - print(f" Fix: {issue.suggestion}") - - if low_severity and verbose: - print("\n🟢 LOW SEVERITY ISSUES (review recommended):") - print("-" * 70) - for file_path, issue in low_severity: - rel_path = get_relative_path(file_path) - print(f"\n 📄 {rel_path}:{issue.line_number}") - print(f" [{issue.issue_type}] {issue.description}") - - if files_with_errors and verbose: - print("\n⚠️ FILES WITH ERRORS:") - print("-" * 70) - for result in files_with_errors: - print(f" ✗ {get_relative_path(result.file_path)}: {result.error}") - - # Summary - print("\n" + "=" * 70) - print("SUMMARY") - print("=" * 70) - print(f" Total files scanned: {len(results)}") - print(f" Files analyzed by AI: {len(files_clean) + len(files_with_issues)}") - print(f" Files with issues: {len(files_with_issues)}") - print(f" - High severity: {len(high_severity)}") - print(f" - Medium severity: {len(medium_severity)}") - print(f" - Low severity: {len(low_severity)}") - print(f" Files skipped: {len(files_skipped)}") - print(f" Files clean: {len(files_clean)}") - if files_with_errors: - print(f" Files with errors: {len(files_with_errors)}") - - if files_with_issues: - print("\n" + "-" * 70) - print("HOW TO FIX:") - print("-" * 70) - print(" 1. Remove or replace sensitive data with placeholders") - print(" 2. Use environment variables for secrets") - print(" 3. Add files to .gitignore if they contain sensitive data") - print(" 4. Add #skip-sensitive-check comment for false positives") - - return len(high_severity) + len(medium_severity) - - -# ============================================================================== -# Command: check -# ============================================================================== - - -async def cmd_check_async( - directories: list[Path], - specific_files: list[str] | None = None, - verbose: bool = False, - max_concurrency: int = MAX_CONCURRENCY, -) -> int: - """Execute the check command with AI analysis.""" - print_header("AI-Powered Sensitive Information Check") - - if specific_files: - files = [Path(f) for f in specific_files] - print(f"Checking {len(files)} specified file(s)...") - else: - print(f"Scanning directories: {[str(d) for d in directories]}") - files = get_files_from_directories(directories) - print(f"Found {len(files)} file(s) to check...") - - # Filter out files that should be skipped (before AI analysis) - files_to_analyze = [] - skipped_results = [] - - for file_path in files: - should_skip, reason = should_skip_file(str(file_path)) - if should_skip: - skipped_results.append( - FileCheckResult( - file_path=str(file_path), skipped=True, skip_reason=reason - ) - ) - else: - # Check file size - if file_path.exists(): - file_size = file_path.stat().st_size - if file_size > MAX_FILE_SIZE: - skipped_results.append( - FileCheckResult( - file_path=str(file_path), - skipped=True, - skip_reason=f"File too large ({file_size / 1024:.1f}KB)", - ) - ) - elif file_size == 0: - skipped_results.append( - FileCheckResult( - file_path=str(file_path), - skipped=True, - skip_reason="Empty file", - ) - ) - else: - files_to_analyze.append(file_path) - - print(f"Files to analyze with AI: {len(files_to_analyze)}") - print(f"Files skipped: {len(skipped_results)}") - print(f"Max concurrency: {max_concurrency}") - print() - - if not files_to_analyze: - print("No files to analyze!") - return 0 - - # Load LLM provider - print("Loading LLM provider...") - provider = _load_llm_provider() - print() - - # Analyze files concurrently - semaphore = asyncio.Semaphore(max_concurrency) - - tasks = [ - analyze_file_with_ai( - provider, - str(file_path), - semaphore, - index=idx + 1, - total=len(files_to_analyze), - ) - for idx, file_path in enumerate(files_to_analyze) - ] - - analyzed_results = await asyncio.gather(*tasks, return_exceptions=True) - - # Process results - all_results = skipped_results.copy() - for result in analyzed_results: - if isinstance(result, Exception): - all_results.append(FileCheckResult(file_path="unknown", error=str(result))) - else: - all_results.append(result) - - issue_count = print_results(all_results, verbose) - - if issue_count > 0: - print(f"\n❌ Found {issue_count} potential sensitive information issue(s)") - return 1 - else: - print("\n✅ No sensitive information detected!") - return 0 - - -def cmd_check( - directories: list[Path], - specific_files: list[str] | None = None, - verbose: bool = False, - max_concurrency: int = MAX_CONCURRENCY, -) -> int: - """Execute the check command.""" - return asyncio.run( - cmd_check_async( - directories=directories, - specific_files=specific_files, - verbose=verbose, - max_concurrency=max_concurrency, - ) - ) - - -# ============================================================================== -# Command: hook (pre-commit hook) -# ============================================================================== - - -async def cmd_hook_async(files: list[str]) -> int: - """Execute the hook command for pre-commit with AI analysis.""" - if not files: - return 0 - - # Filter files - files_to_check = [] - for file_path in files: - should_skip, _ = should_skip_file(file_path) - if not should_skip and Path(file_path).exists(): - file_size = Path(file_path).stat().st_size - if 0 < file_size <= MAX_FILE_SIZE: - files_to_check.append(file_path) - - if not files_to_check: - return 0 - - # Load LLM provider - provider = _load_llm_provider() - - # Analyze files - semaphore = asyncio.Semaphore(MAX_CONCURRENCY) - - tasks = [ - analyze_file_with_ai(provider, file_path, semaphore) - for file_path in files_to_check - ] - - results = await asyncio.gather(*tasks, return_exceptions=True) - - # Collect issues by severity - blocking_issues = [] # HIGH and MEDIUM - will block commit - warning_issues = [] # LOW - just warn, don't block - - for result in results: - if isinstance(result, FileCheckResult) and result.issues: - for issue in result.issues: - if issue.severity in (Severity.HIGH, Severity.MEDIUM): - blocking_issues.append((result.file_path, issue)) - elif issue.severity == Severity.LOW: - warning_issues.append((result.file_path, issue)) - - # Show warnings for LOW severity (but don't block) - if warning_issues: - print("\n" + "=" * 70, file=sys.stderr) - print("⚠️ LOW SEVERITY REMINDERS (commit allowed)", file=sys.stderr) - print("=" * 70, file=sys.stderr) - - for file_path, issue in warning_issues: - rel_path = get_relative_path(file_path) - print( - f" 🟢 {rel_path}:{issue.line_number} [{issue.issue_type}]", - file=sys.stderr, - ) - print(f" {issue.description}", file=sys.stderr) - - print("-" * 70, file=sys.stderr) - print("These are just reminders. Commit will proceed.", file=sys.stderr) - - # Block on HIGH/MEDIUM severity - if blocking_issues: - print("\n" + "=" * 70, file=sys.stderr) - print("❌ SENSITIVE INFORMATION DETECTED - COMMIT BLOCKED", file=sys.stderr) - print("=" * 70, file=sys.stderr) - - for file_path, issue in blocking_issues: - rel_path = get_relative_path(file_path) - severity_icon = "🔴" if issue.severity == Severity.HIGH else "🟡" - print( - f"\n {severity_icon} {rel_path}:{issue.line_number} [{issue.issue_type}]", - file=sys.stderr, - ) - print(f" {issue.description}", file=sys.stderr) - if issue.suggestion: - print(f" Fix: {issue.suggestion}", file=sys.stderr) - - print("\n" + "-" * 70, file=sys.stderr) - print("TO FIX:", file=sys.stderr) - print(" 1. Remove or replace sensitive data", file=sys.stderr) - print(" 2. Use environment variables for secrets", file=sys.stderr) - print(" 3. Add #skip-sensitive-check to skip specific lines", file=sys.stderr) - print( - f" 4. Set {HOOK_SKIP_ENV_VAR}=1 to skip this check entirely", - file=sys.stderr, - ) - print("=" * 70 + "\n", file=sys.stderr) - - return 1 - - return 0 - - -def cmd_hook(files: list[str], commit_msg: bool = False) -> int: - """Execute the hook command for pre-commit. - - Args: - files: List of files to check - commit_msg: If True, this is a commit-msg hook - - Returns: - 0 if no issues found, 1 if issues found - """ - # Check for skip environment variable - if os.environ.get(HOOK_SKIP_ENV_VAR, "").lower() in ("1", "true", "yes"): - print(f"Skipping sensitive check ({HOOK_SKIP_ENV_VAR} is set)") - return 0 - - if commit_msg: - # For commit-msg hook, check for skip keywords - if files: - try: - with open(files[0], "r", encoding="utf-8") as f: - content = f.read().lower() - for keyword in SKIP_COMMIT_MSG_KEYWORDS: - if keyword.lower() in content: - return 0 - except Exception: - pass - return 0 - - return asyncio.run(cmd_hook_async(files)) - - -# ============================================================================== -# Main Entry Point -# ============================================================================== - - -def main(): - import argparse - - parser = argparse.ArgumentParser( - description="AI-Powered Sensitive Information Detection Tool", - formatter_class=argparse.RawDescriptionHelpFormatter, - ) - subparsers = parser.add_subparsers(dest="command", help="Available commands") - - # Check command - check_parser = subparsers.add_parser( - "check", help="Check files for sensitive information using AI" - ) - check_parser.add_argument( - "--directory", - "-d", - nargs="*", - help="Directories to scan (default: src, tests, data, demo)", - ) - check_parser.add_argument("--files", nargs="*", help="Specific files to check") - check_parser.add_argument( - "--verbose", "-v", action="store_true", help="Show verbose output" - ) - check_parser.add_argument( - "--max-concurrency", - type=int, - default=MAX_CONCURRENCY, - help=f"Maximum concurrent AI calls (default: {MAX_CONCURRENCY})", - ) - - # Hook command - hook_parser = subparsers.add_parser( - "hook", help="Pre-commit hook to check for sensitive information" - ) - hook_parser.add_argument( - "--commit-msg", - action="store_true", - help="Check commit message instead of files", - ) - hook_parser.add_argument("files", nargs="*", help="Files to check") - - args = parser.parse_args() - - if not args.command: - parser.print_help() - sys.exit(1) - - if args.command == "check": - directories = resolve_directories(args.directory) - exit_code = cmd_check( - directories=directories, - specific_files=args.files, - verbose=args.verbose, - max_concurrency=args.max_concurrency, - ) - sys.exit(exit_code) - - elif args.command == "hook": - exit_code = cmd_hook(files=args.files, commit_msg=args.commit_msg) - sys.exit(exit_code) - - -if __name__ == "__main__": - main() diff --git a/methods/EverCore/src/infra_layer/adapters/input/api/dto/memory_dto.py b/methods/EverCore/src/infra_layer/adapters/input/api/dto/memory_dto.py deleted file mode 100644 index 53120160..00000000 --- a/methods/EverCore/src/infra_layer/adapters/input/api/dto/memory_dto.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Memory API DTO - -Request and response data transfer objects for Memory API. -These models are re-exported from api_specs.dtos for backward compatibility. -""" - -# Re-export from api_specs.dtos -from api_specs.dtos import ( - # Base API Response - BaseApiResponse, - # Add / Flush DTOs - PersonalAddRequest, - GroupAddRequest, - PersonalFlushRequest, - GroupFlushRequest, - AddResult, - AddResponse, - FlushResult, - FlushResponse, - # Command DTOs - DeleteMemoriesRequest as DeleteMemoriesRequestDTO, - # Request DTOs - RetrieveMemRequest, - # Response DTOs (result data) - RetrieveMemResponse, - DeleteMemoriesResult, - # API Response wrappers - SearchMemoriesResponse, -) - -# Backward compatibility aliases -SearchMemoriesRequest = RetrieveMemRequest - -__all__ = [ - # Base Response - "BaseApiResponse", - # Add / Flush DTOs - "PersonalAddRequest", - "GroupAddRequest", - "PersonalFlushRequest", - "GroupFlushRequest", - "AddResult", - "AddResponse", - "FlushResult", - "FlushResponse", - # Command DTOs - "DeleteMemoriesRequest", - "DeleteMemoriesRequestDTO", - # Query DTOs (Requests) - "RetrieveMemRequest", - # Response DTOs (result data) - "RetrieveMemResponse", - "DeleteMemoriesResult", - # API Response wrappers - "SearchMemoriesResponse", - # Backward compatibility aliases - "SearchMemoriesRequest", -] diff --git a/methods/EverCore/src/infra_layer/adapters/input/api/health/health_controller.py b/methods/EverCore/src/infra_layer/adapters/input/api/health/health_controller.py deleted file mode 100644 index 5f98bb0c..00000000 --- a/methods/EverCore/src/infra_layer/adapters/input/api/health/health_controller.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Health check controller - -Provides system health status check interface -""" - -from common_utils.datetime_utils import get_now_with_timezone -from typing import Dict, Any -from core.interface.controller.base_controller import BaseController, get -from core.observation.logger import get_logger -from core.di.decorators import component - -logger = get_logger(__name__) - - -@component(name="healthController") -class HealthController(BaseController): - """ - Health check controller - - Provides system health status check functionality - """ - - def __init__(self): - super().__init__( - prefix="/health", - tags=["Health"], - default_auth="none", # Health check does not require authentication - ) - - @get("", summary="Health check", description="Check system health status") - def health_check(self) -> Dict[str, Any]: - """ - Health check interface - - Returns: - Dict[str, Any]: Health status information - - Raises: - HTTPException: Throws 500 error when system is unhealthy - """ - try: - # Log health check request - logger.debug("Health check request") - - # Return simple health status - return { - "status": "healthy", - "timestamp": get_now_with_timezone().isoformat(), - "message": "System running normally", - } - except Exception as e: - logger.error(f"Health check failed: {str(e)}") - # Throw 500 error when exception occurs - from fastapi import HTTPException - - raise HTTPException( - status_code=500, - detail={ - "status": "unhealthy", - "timestamp": get_now_with_timezone().isoformat(), - "message": f"System check exception: {str(e)}", - }, - ) diff --git a/methods/EverCore/src/infra_layer/adapters/input/api/memory/group_controller.py b/methods/EverCore/src/infra_layer/adapters/input/api/memory/group_controller.py deleted file mode 100644 index eed2693c..00000000 --- a/methods/EverCore/src/infra_layer/adapters/input/api/memory/group_controller.py +++ /dev/null @@ -1,228 +0,0 @@ -""" -Group Controller - Group management controller - -Provides RESTful API routes for: -- Group creation (POST /groups): create or upsert a group -- Group retrieval (GET /groups/{group_id}): get group by group_id -- Group update (PATCH /groups/{group_id}): partial update group fields -""" - -import logging - -from fastapi import HTTPException, Request as FastAPIRequest - -from core.di.decorators import controller -from core.interface.controller.base_controller import BaseController, get, post, patch -from api_specs.dtos.group import ( - CreateGroupRequest, - PatchGroupRequest, - CreateGroupApiResponse, - GetGroupApiResponse, - PatchGroupApiResponse, -) -from service.group_service import GroupService - -logger = logging.getLogger(__name__) - - -@controller("group_controller", primary=True) -class GroupController(BaseController): - """ - Group Controller - - Handles group CRUD operations. - """ - - def __init__(self, group_service: GroupService): - """Initialize controller""" - super().__init__( - prefix="/api/v1/groups", tags=["Group Controller"], default_auth="none" - ) - self.group_service = group_service - logger.info("GroupController initialized") - - @post( - "", - response_model=CreateGroupApiResponse, - summary="Create or update a group", - description=""" - Create a new group or update an existing one (upsert by group_id). - - ## Fields: - - **group_id** (required): Group identifier (unique) - - **name** (optional): Group display name - - **description** (optional): Group description - """, - responses={ - 422: { - "description": "Request parameter error", - "content": { - "application/json": { - "example": { - "error": { - "code": "InvalidParameter", - "message": "group_id is required", - "param": "group_id", - "type": "BadRequest", - } - } - } - }, - } - }, - ) - async def create_group( - self, request: FastAPIRequest, request_body: CreateGroupRequest = None - ) -> CreateGroupApiResponse: - """Create or update a group""" - del request_body # Used for OpenAPI documentation only - try: - request_data = await request.json() - create_request = CreateGroupRequest(**request_data) - - logger.info( - "Received group create request: group_id=%s", create_request.group_id - ) - - result = await self.group_service.create_or_update( - group_id=create_request.group_id, - name=create_request.name, - description=create_request.description, - ) - - if not result: - raise HTTPException(status_code=500, detail="Failed to create group") - - return {"data": result.model_dump()} - - except ValueError as e: - logger.error("Group create parameter error: %s", e) - raise HTTPException(status_code=422, detail=str(e)) from e - except HTTPException: - raise - except Exception as e: - logger.error("Group create failed: %s", e, exc_info=True) - raise HTTPException(status_code=500, detail="Failed to create group") from e - - @get( - "/{group_id}", - response_model=GetGroupApiResponse, - summary="Get group by group_id", - description="Retrieve a group's details by its group_id.", - responses={ - 404: { - "description": "Group not found", - "content": { - "application/json": { - "example": { - "error": { - "code": "ResourceNotFound", - "message": "Group not found: group_abc", - "param": "group_id", - "type": "NotFound", - } - } - } - }, - } - }, - ) - async def get_group( - self, request: FastAPIRequest, group_id: str - ) -> GetGroupApiResponse: - """Get group by group_id""" - try: - logger.info("Received group get request: group_id=%s", group_id) - - result = await self.group_service.get_by_group_id(group_id) - - if not result: - raise HTTPException( - status_code=404, detail=f"Group not found: {group_id}" - ) - - return {"data": result.model_dump()} - - except HTTPException: - raise - except Exception as e: - logger.error("Group get failed: %s", e, exc_info=True) - raise HTTPException( - status_code=500, detail="Failed to retrieve group" - ) from e - - @patch( - "/{group_id}", - response_model=PatchGroupApiResponse, - summary="Partially update group", - description=""" - Partially update a group's fields. At least one of name or description must be provided. - """, - responses={ - 422: { - "description": "Request parameter error", - "content": { - "application/json": { - "example": { - "error": { - "code": "InvalidParameter", - "message": "At least one of 'name' or 'description' must be provided", - "param": "", - "type": "BadRequest", - } - } - } - }, - }, - 404: { - "description": "Group not found", - "content": { - "application/json": { - "example": { - "error": { - "code": "ResourceNotFound", - "message": "Group not found: group_abc", - "param": "group_id", - "type": "NotFound", - } - } - } - }, - }, - }, - ) - async def patch_group( - self, - request: FastAPIRequest, - group_id: str, - request_body: PatchGroupRequest = None, - ) -> PatchGroupApiResponse: - """Partially update group fields""" - del request_body # Used for OpenAPI documentation only - try: - request_data = await request.json() - patch_request = PatchGroupRequest(**request_data) - - logger.info("Received group patch request: group_id=%s", group_id) - - result = await self.group_service.patch( - group_id=group_id, - name=patch_request.name, - description=patch_request.description, - ) - - if not result: - raise HTTPException( - status_code=404, detail=f"Group not found: {group_id}" - ) - - return {"data": result.model_dump()} - - except ValueError as e: - logger.error("Group patch parameter error: %s", e) - raise HTTPException(status_code=422, detail=str(e)) from e - except HTTPException: - raise - except Exception as e: - logger.error("Group patch failed: %s", e, exc_info=True) - raise HTTPException(status_code=500, detail="Failed to update group") from e diff --git a/methods/EverCore/src/infra_layer/adapters/input/api/memory/memory_controller.py b/methods/EverCore/src/infra_layer/adapters/input/api/memory/memory_controller.py deleted file mode 100644 index a9ae63b9..00000000 --- a/methods/EverCore/src/infra_layer/adapters/input/api/memory/memory_controller.py +++ /dev/null @@ -1,1020 +0,0 @@ -""" -Memory Controller - Unified memory management controller - -Provides RESTful API routes for: -- Personal add (POST /memories): batch messages for personal scene -- Group add (POST /memories/group): batch messages for group scene -- Personal flush (POST /memories/flush): trigger boundary detection for personal scene -- Group flush (POST /memories/group/flush): trigger boundary detection for group scene -- Memory search (GET /memories/search): keyword/vector/hybrid/rrf/agentic retrieval with grouped results -- Memory deletion (POST /memories/delete): soft delete by ID or filter conditions -""" - -import asyncio -import json -import logging -import time - -from contextlib import suppress -from fastapi import HTTPException, Request as FastAPIRequest - -from core.di.decorators import controller -from core.di import get_bean_by_type -from core.interface.controller.base_controller import BaseController, get, post -from core.observation.stage_timer import stage_timed, timed -from core.constants.errors import ErrorCode, ErrorStatus -from agentic_layer.memory_manager import MemoryManager -from api_specs.request_converter import ( - convert_dict_to_retrieve_mem_request, - convert_personal_add_to_memorize_request, - convert_group_add_to_memorize_request, - convert_personal_flush_to_memorize_request, - convert_group_flush_to_memorize_request, - convert_agent_add_to_memorize_request, - convert_agent_flush_to_memorize_request, -) -from api_specs.id_generator import DEFAULT_SESSION_ID -from infra_layer.adapters.out.event.personal_memorize_event import PersonalMemorizeEvent -from infra_layer.adapters.out.event.group_memorize_event import GroupMemorizeEvent -from core.events.event_publisher import ApplicationEventPublisher -from infra_layer.adapters.input.api.dto.memory_dto import ( - # Request DTOs - # Add / Flush DTOs - PersonalAddRequest, - GroupAddRequest, - PersonalFlushRequest, - GroupFlushRequest, - AddResponse, - FlushResponse, -) -from api_specs.dtos.memory import AgentAddRequest, AgentFlushRequest -from api_specs.dtos.memory_delete import DeleteMemoriesRequest -from core.request import log_request -from core.request.app_logic_provider import AppLogicProvider -from core.component.redis_provider import RedisProvider -from service.content_enrich_provider import ContentEnrichProvider -from service.raw_message_service import RawMessageService -from service.sender_service import SenderService -from service.memcell_delete_service import MemCellDeleteService -from api_specs.memory_types import RawDataType -from agentic_layer.metrics.memorize_metrics import ( - record_memorize_request, - record_memorize_error, - record_memorize_message, - classify_memorize_error, - get_space_id_for_metrics, - get_raw_data_type_label, -) - -logger = logging.getLogger(__name__) - - -@controller("memory_controller", primary=True) -class MemoryController(BaseController): - """ - Memory Controller - """ - - def __init__(self): - """Initialize controller""" - super().__init__( - prefix="/api/v1/memories", tags=["Memory Controller"], default_auth="none" - ) - self.memory_manager = MemoryManager() - self.redis_provider = get_bean_by_type(RedisProvider) - self._content_enrich = get_bean_by_type(ContentEnrichProvider) - self._app_logic = get_bean_by_type(AppLogicProvider) - logger.info("MemoryController initialized with MemoryManager") - - # ========================================================================= - # Add Endpoints - # ========================================================================= - - @post( - "", - response_model=AddResponse, - summary="Store messages (personal)", - description="Store batch messages into personal memory space.", - ) - @log_request() - @stage_timed("add") - async def add_personal_memories( - self, request: FastAPIRequest, request_body: PersonalAddRequest = None - ) -> AddResponse: - """POST /api/v1/memories - Personal add endpoint.""" - del request_body - start_time = time.perf_counter() - space_id = get_space_id_for_metrics() - raw_data_type = get_raw_data_type_label(None) - - try: - request_data = await request.json() - logger.info( - "Received personal add request: user_id=%s", request_data.get("user_id") - ) - - memorize_request = convert_personal_add_to_memorize_request(request_data) - raw_data_type = get_raw_data_type_label( - memorize_request.raw_data_type.value - ) - msg_count = len(memorize_request.new_raw_data_list) - - record_memorize_message( - space_id=space_id, - raw_data_type=raw_data_type, - status='received', - count=msg_count, - ) - - group_id = memorize_request.group_id - session_id = memorize_request.session_id - - # Auto-register group - if group_id: - asyncio.create_task(self._ensure_group_exists(group_id=group_id)) - - # Auto-register session (skip for default sentinel) - if session_id and session_id != DEFAULT_SESSION_ID: - asyncio.create_task(self._ensure_session_exists(session_id=session_id)) - - # Auto-register senders from converted data (includes auto-filled sender_ids) - self._auto_register_senders(memorize_request.new_raw_data_list) - - # Enrich sender_name from DB for messages that didn't provide one - messages = request_data.get("messages", []) - with timed("enrich_sender_names"): - await self._enrich_sender_names( - messages, memorize_request.new_raw_data_list - ) - - # Content enrichment (e.g. multimodal parsing, no-op by default) - # Must run BEFORE save_request_logs so that parsed multimodal text - # is included in the flat content saved to RawMessage. - with timed("enrich_content"): - await self._content_enrich.enrich(memorize_request.new_raw_data_list) - - # Save request logs - with timed("persist_raw_messages"): - await self._save_raw_messages( - memorize_request, request, "add_personal_memories" - ) - - # Process - memory_count = await self.memory_manager.memorize(memorize_request) - - status = 'extracted' if memory_count > 0 else 'accumulated' - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status=status, - duration_seconds=time.perf_counter() - start_time, - ) - - # Publish personal memorize event (fire-and-forget) - asyncio.create_task( - self._publish_event( - PersonalMemorizeEvent( - user_id=request_data.get("user_id", ""), - session_id=session_id, - group_id=group_id, - ) - ) - ) - - return { - "data": { - "request_id": self._app_logic.get_current_request_id(), - "message_count": msg_count, - "status": status, - "message": "Messages accepted", - } - } - - except ValueError as e: - logger.error("Personal add parameter error: %s", e) - record_memorize_error( - space_id=space_id, - raw_data_type=raw_data_type, - stage='conversion', - error_type='validation_error', - ) - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='error', - duration_seconds=time.perf_counter() - start_time, - ) - raise HTTPException(status_code=422, detail=str(e)) from e - except HTTPException: - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='error', - duration_seconds=time.perf_counter() - start_time, - ) - raise - except Exception as e: - logger.error("Personal add failed: %s", e, exc_info=True) - error_type = classify_memorize_error(e) - record_memorize_error( - space_id=space_id, - raw_data_type=raw_data_type, - stage='memorize_process', - error_type=error_type, - ) - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='error', - duration_seconds=time.perf_counter() - start_time, - ) - raise HTTPException( - status_code=500, detail="Failed to store memory, please try again later" - ) from e - - @post( - "/group", - response_model=AddResponse, - summary="Store messages (group)", - description="Store batch messages into group memory space.", - ) - @log_request() - @stage_timed("add") - async def add_group_memories( - self, request: FastAPIRequest, request_body: GroupAddRequest = None - ) -> AddResponse: - """POST /api/v1/memories/group - Group add endpoint.""" - del request_body - start_time = time.perf_counter() - space_id = get_space_id_for_metrics() - raw_data_type = get_raw_data_type_label(None) - - try: - request_data = await request.json() - logger.info( - "Received group add request: group_id=%s", request_data.get("group_id") - ) - - memorize_request = convert_group_add_to_memorize_request(request_data) - raw_data_type = get_raw_data_type_label( - memorize_request.raw_data_type.value - ) - msg_count = len(memorize_request.new_raw_data_list) - - record_memorize_message( - space_id=space_id, - raw_data_type=raw_data_type, - status='received', - count=msg_count, - ) - - group_id = memorize_request.group_id - - # Auto-register group (with optional metadata) - if group_id: - group_meta = request_data.get("group_meta") or {} - asyncio.create_task( - self._ensure_group_exists( - group_id=group_id, - name=group_meta.get("name"), - description=group_meta.get("description"), - ) - ) - - # Auto-register senders from converted data (includes sender_ids from request) - self._auto_register_senders(memorize_request.new_raw_data_list) - - # Enrich sender_name from DB for messages that didn't provide one - messages = request_data.get("messages", []) - with timed("enrich_sender_names"): - await self._enrich_sender_names( - messages, memorize_request.new_raw_data_list - ) - - # Content enrichment (e.g. multimodal parsing, no-op by default) - # Must run BEFORE save_request_logs so that parsed multimodal text - # is included in the flat content saved to RawMessage. - with timed("enrich_content"): - await self._content_enrich.enrich(memorize_request.new_raw_data_list) - - # Save request logs - with timed("persist_raw_messages"): - await self._save_raw_messages( - memorize_request, request, "add_group_memories" - ) - - # Process - memory_count = await self.memory_manager.memorize(memorize_request) - - status = 'extracted' if memory_count > 0 else 'accumulated' - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status=status, - duration_seconds=time.perf_counter() - start_time, - ) - - # Publish group memorize event (fire-and-forget) - sender_ids = list( - { - msg.get("sender_id") - for msg in request_data.get("messages", []) - if msg.get("sender_id") - } - ) - asyncio.create_task( - self._publish_event( - GroupMemorizeEvent(group_id=group_id, sender_ids=sender_ids) - ) - ) - - return { - "data": { - "request_id": self._app_logic.get_current_request_id(), - "message_count": msg_count, - "status": status, - "message": "Messages accepted", - } - } - - except ValueError as e: - logger.error("Group add parameter error: %s", e) - record_memorize_error( - space_id=space_id, - raw_data_type=raw_data_type, - stage='conversion', - error_type='validation_error', - ) - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='error', - duration_seconds=time.perf_counter() - start_time, - ) - raise HTTPException(status_code=422, detail=str(e)) from e - except HTTPException: - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='error', - duration_seconds=time.perf_counter() - start_time, - ) - raise - except Exception as e: - logger.error("Group add failed: %s", e, exc_info=True) - error_type = classify_memorize_error(e) - record_memorize_error( - space_id=space_id, - raw_data_type=raw_data_type, - stage='memorize_process', - error_type=error_type, - ) - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='error', - duration_seconds=time.perf_counter() - start_time, - ) - raise HTTPException( - status_code=500, detail="Failed to store memory, please try again later" - ) from e - - # ========================================================================= - # Flush Endpoints - # ========================================================================= - - @post( - "/flush", - response_model=FlushResponse, - summary="Flush personal memories", - description="Trigger boundary detection on accumulated personal messages.", - ) - @log_request() - @stage_timed("flush") - async def flush_personal_memories( - self, request: FastAPIRequest, request_body: PersonalFlushRequest = None - ) -> FlushResponse: - """POST /api/v1/memories/flush - Personal flush endpoint.""" - del request_body - start_time = time.perf_counter() - space_id = get_space_id_for_metrics() - raw_data_type = get_raw_data_type_label("Conversation") - - try: - request_data = await request.json() - logger.info( - "Received personal flush: user_id=%s", request_data.get("user_id") - ) - - memorize_request = convert_personal_flush_to_memorize_request(request_data) - - memory_count = await self.memory_manager.memorize(memorize_request) - - status = 'extracted' if memory_count > 0 else 'no_extraction' - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='flush', - duration_seconds=time.perf_counter() - start_time, - ) - - return { - "data": { - "request_id": self._app_logic.get_current_request_id(), - "status": status, - "message": "Flush completed", - } - } - - except ValueError as e: - logger.error("Personal flush parameter error: %s", e) - raise HTTPException(status_code=422, detail=str(e)) from e - except Exception as e: - logger.error("Personal flush failed: %s", e, exc_info=True) - raise HTTPException( - status_code=500, detail="Flush failed, please try again later" - ) from e - - @post( - "/group/flush", - response_model=FlushResponse, - summary="Flush group memories", - description="Trigger boundary detection on accumulated group messages.", - ) - @log_request() - @stage_timed("flush") - async def flush_group_memories( - self, request: FastAPIRequest, request_body: GroupFlushRequest = None - ) -> FlushResponse: - """POST /api/v1/memories/group/flush - Group flush endpoint.""" - del request_body - start_time = time.perf_counter() - space_id = get_space_id_for_metrics() - raw_data_type = get_raw_data_type_label("Conversation") - - try: - request_data = await request.json() - logger.info( - "Received group flush: group_id=%s", request_data.get("group_id") - ) - - memorize_request = convert_group_flush_to_memorize_request(request_data) - - memory_count = await self.memory_manager.memorize(memorize_request) - - status = 'extracted' if memory_count > 0 else 'no_extraction' - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='flush', - duration_seconds=time.perf_counter() - start_time, - ) - - return { - "data": { - "request_id": self._app_logic.get_current_request_id(), - "status": status, - "message": "Flush completed", - } - } - - except ValueError as e: - logger.error("Group flush parameter error: %s", e) - raise HTTPException(status_code=422, detail=str(e)) from e - except Exception as e: - logger.error("Group flush failed: %s", e, exc_info=True) - raise HTTPException( - status_code=500, detail="Flush failed, please try again later" - ) from e - - @post( - "/agent/flush", - response_model=FlushResponse, - summary="Flush agent memories", - description="""Trigger agent-aware boundary detection on accumulated agent trajectory messages. - - Flushes buffered agent messages for the specified user, triggering memory extraction - (agent cases and skills) if a conversation boundary is detected. - - ## Request Body Fields: - - **user_id** (required, string): Owner user ID - - **session_id** (optional, string): Target session to flush. If omitted, flushes all sessions for the user. - - ## Request Examples: - - **Flush all sessions:** - ```json - {"user_id": "user_01"} - ``` - - **Flush specific session:** - ```json - {"user_id": "user_01", "session_id": "sess_agent_001"} - ``` - - ## Response: - - **data.request_id**: Request tracking ID (reserved) - - **data.status**: `extracted` (memory extraction triggered) or `no_extraction` (no boundary detected) - - **data.message**: Human-readable status description - """, - responses={ - 200: { - "description": "Flush completed", - "content": { - "application/json": { - "examples": { - "no_extraction": { - "summary": "No boundary detected", - "value": { - "data": { - "request_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890", - "status": "no_extraction", - "message": "Flush completed", - } - }, - }, - "extracted": { - "summary": "Memory extraction triggered", - "value": { - "data": { - "request_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890", - "status": "extracted", - "message": "Flush completed", - } - }, - }, - } - } - }, - }, - 422: { - "description": "Request parameter error", - "content": { - "application/json": { - "examples": { - "missing_user_id": { - "summary": "Missing required user_id", - "value": {"detail": "Missing required field: user_id"}, - } - } - } - }, - }, - }, - ) - @log_request() - @stage_timed("agent_flush") - async def flush_agent_memories( - self, request: FastAPIRequest, request_body: AgentFlushRequest = None - ) -> FlushResponse: - """POST /api/v1/memories/agent/flush - Agent flush endpoint.""" - del request_body - start_time = time.perf_counter() - space_id = get_space_id_for_metrics() - raw_data_type = get_raw_data_type_label("AgentConversation") - - try: - request_data = await request.json() - logger.info("Received agent flush: user_id=%s", request_data.get("user_id")) - - with timed("convert_request"): - memorize_request = convert_agent_flush_to_memorize_request(request_data) - - with timed("memorize"): - memory_count = await self.memory_manager.memorize(memorize_request) - - status = 'extracted' if memory_count > 0 else 'no_extraction' - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='flush', - duration_seconds=time.perf_counter() - start_time, - ) - - return { - "data": { - "request_id": self._app_logic.get_current_request_id(), - "status": status, - "message": "Flush completed", - } - } - - except ValueError as e: - logger.error("Agent flush parameter error: %s", e) - raise HTTPException(status_code=422, detail=str(e)) from e - except Exception as e: - logger.error("Agent flush failed: %s", e, exc_info=True) - raise HTTPException( - status_code=500, detail="Flush failed, please try again later" - ) from e - - # ========================================================================= - # Helper Methods - # ========================================================================= - - def _auto_register_senders(self, raw_data_list: list) -> None: - """Fire-and-forget auto-register senders from converted raw data list. - - Uses the converted RawData objects (not the original request JSON) - so that auto-filled sender_ids are included. - """ - seen = set() - for raw_data in raw_data_list: - content = raw_data.content - sender_id = content.get("sender_id") - if sender_id and sender_id not in seen: - seen.add(sender_id) - asyncio.create_task( - self._ensure_sender_exists( - sender_id=sender_id, sender_name=content.get("sender_name") - ) - ) - - # ========================================================================= - # Agent Add - # ========================================================================= - - @post( - "/agent", - response_model=AddResponse, - summary="Store agent trajectory messages", - description="""Store agent trajectory messages (user/assistant/tool) into memory. - Supports tool_calls and tool_call_id for OpenAI-format function calling. - - ## Request Body Fields: - - **user_id** (required, string): Owner user ID - - **session_id** (optional, string): Session identifier for conversation isolation - - **messages** (required, array, 1-500): Agent trajectory messages - - ## Message Fields: - - **role** (required): `user`, `assistant`, or `tool` - - **timestamp** (required, int): Message timestamp in unix milliseconds - - **content** (required, string or array): Accepts plain string shorthand `"hello"` or array of content items `[{type: "text", text: "hello"}]` - - **message_id** (optional): Message unique ID - - **sender_id** (optional): Sender identifier - - **sender_name** (optional): Sender display name - - **tool_calls** (optional): Tool calls made by the assistant (OpenAI format). Only when role='assistant' - - **tool_call_id** (optional): ID of the tool call this message responds to. Required when role='tool' - - ## Request Example (with tool calls): - ```json - { - "user_id": "user_01", - "session_id": "sess_agent_001", - "messages": [ - { - "role": "user", - "timestamp": 1710835200000, - "content": "What is the weather in Tokyo?" - }, - { - "role": "assistant", - "timestamp": 1710835201000, - "content": "Let me check the weather for you.", - "tool_calls": [ - { - "id": "call_abc123", - "type": "function", - "function": {"name": "get_weather", "arguments": "{\\"city\\": \\"Tokyo\\"}"} - } - ] - }, - { - "role": "tool", - "timestamp": 1710835202000, - "tool_call_id": "call_abc123", - "content": [{"type": "text", "text": "Tokyo: 18C, partly cloudy"}] - }, - { - "role": "assistant", - "timestamp": 1710835203000, - "content": [{"type": "text", "text": "The weather in Tokyo is 18C and partly cloudy."}] - } - ] - } - ``` - - ## Response: - - **data.request_id**: Request tracking ID (reserved) - - **data.message_count**: Number of messages accepted - - **data.status**: `accumulated` (buffered) or `extracted` (memory extraction triggered) - - **data.message**: Human-readable status description - """, - responses={ - 200: { - "description": "Messages accepted", - "content": { - "application/json": { - "example": { - "data": { - "request_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890", - "message_count": 4, - "status": "accumulated", - "message": "Messages accepted", - } - } - } - }, - }, - 422: { - "description": "Request parameter error", - "content": { - "application/json": { - "examples": { - "missing_user_id": { - "summary": "Missing required user_id", - "value": {"detail": "Missing required field: user_id"}, - }, - "invalid_role": { - "summary": "Invalid message role", - "value": { - "detail": "Invalid value for messages[].role: 'invalid'. Must be 'user', 'assistant', or 'tool'" - }, - }, - "missing_tool_call_id": { - "summary": "Missing tool_call_id for tool message", - "value": { - "detail": "Missing required field: messages[].tool_call_id (required when role='tool')" - }, - }, - } - } - }, - }, - }, - ) - @log_request() - @stage_timed("agent_add") - async def add_agent_memories( - self, request: FastAPIRequest, request_body: AgentAddRequest = None - ) -> AddResponse: - """POST /api/v1/memories/agent - Agent add endpoint.""" - del request_body - start_time = time.perf_counter() - space_id = get_space_id_for_metrics() - raw_data_type = get_raw_data_type_label(None) - - try: - request_data = await request.json() - logger.info( - "Received agent add request: user_id=%s", request_data.get("user_id") - ) - - with timed("convert_request"): - memorize_request = convert_agent_add_to_memorize_request(request_data) - raw_data_type = get_raw_data_type_label( - memorize_request.raw_data_type.value - ) - msg_count = len(memorize_request.new_raw_data_list) - - record_memorize_message( - space_id=space_id, - raw_data_type=raw_data_type, - status='received', - count=msg_count, - ) - - group_id = memorize_request.group_id - session_id = memorize_request.session_id - - # Auto-register group - if group_id: - asyncio.create_task(self._ensure_group_exists(group_id=group_id)) - - # Auto-register session (skip for default sentinel) - if session_id and session_id != DEFAULT_SESSION_ID: - asyncio.create_task(self._ensure_session_exists(session_id=session_id)) - - # Auto-register senders from converted data (skip role=tool) - for raw_data in memorize_request.new_raw_data_list: - content = raw_data.content - if content.get("role") != "tool": - sender_id = content.get("sender_id") - if sender_id: - asyncio.create_task( - self._ensure_sender_exists( - sender_id=sender_id, - sender_name=content.get("sender_name"), - ) - ) - - # Enrich sender_name from DB for messages that didn't provide one - messages = request_data.get("messages", []) - await self._enrich_sender_names( - messages, memorize_request.new_raw_data_list - ) - - # Content enrichment (e.g. multimodal parsing, no-op by default) - # Must run BEFORE save_request_logs so that parsed multimodal text - # is included in the flat content saved to RawMessage. - await self._content_enrich.enrich(memorize_request.new_raw_data_list) - - # Save request logs - with timed("persist_raw_messages"): - await self._save_raw_messages( - memorize_request, request, "add_agent_memories" - ) - - # Process - with timed("memorize"): - memory_count = await self.memory_manager.memorize(memorize_request) - - status = 'extracted' if memory_count > 0 else 'accumulated' - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status=status, - duration_seconds=time.perf_counter() - start_time, - ) - - # Publish personal memorize event (agent is solo scene) - asyncio.create_task( - self._publish_event( - PersonalMemorizeEvent( - user_id=request_data.get("user_id", ""), - session_id=session_id, - group_id=group_id, - ) - ) - ) - - return { - "data": { - "request_id": self._app_logic.get_current_request_id(), - "message_count": msg_count, - "status": status, - "message": "Messages accepted", - } - } - - except ValueError as e: - logger.error("Agent add parameter error: %s", e) - record_memorize_error( - space_id=space_id, - raw_data_type=raw_data_type, - stage='conversion', - error_type='validation_error', - ) - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='error', - duration_seconds=time.perf_counter() - start_time, - ) - raise HTTPException(status_code=422, detail=str(e)) from e - except HTTPException: - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='error', - duration_seconds=time.perf_counter() - start_time, - ) - raise - except Exception as e: - logger.error("Agent add failed: %s", e, exc_info=True) - record_memorize_error( - space_id=space_id, - raw_data_type=raw_data_type, - stage='memorize', - error_type=classify_memorize_error(e), - ) - record_memorize_request( - space_id=space_id, - raw_data_type=raw_data_type, - status='error', - duration_seconds=time.perf_counter() - start_time, - ) - raise HTTPException( - status_code=500, detail="Failed to store memory, please try again later" - ) from e - - # ========================================================================= - # Helper methods - # ========================================================================= - - async def _save_raw_messages( - self, memorize_request, request, endpoint_name: str - ) -> None: - """Save individual messages from the request as RawMessage documents.""" - if memorize_request.new_raw_data_list: - raw_message_service = get_bean_by_type(RawMessageService) - await raw_message_service.save_raw_messages( - request=memorize_request, - version="1.0.0", - endpoint_name=endpoint_name, - method=request.method, - url=str(request.url), - ) - - async def _ensure_group_exists( - self, group_id: str, name: str | None = None, description: str | None = None - ) -> None: - """Auto-register group when memory is ingested.""" - try: - from service.group_service import GroupService - - group_service = get_bean_by_type(GroupService) - await group_service.ensure_group_exists( - group_id=group_id, name=name, description=description - ) - except Exception as e: - logger.warning( - "Failed to auto-register group: group_id=%s, error=%s", group_id, e - ) - - async def _enrich_sender_names(self, messages: list, raw_data_list: list) -> None: - """Enrich sender_name from DB for messages that didn't provide one. - - Collects sender_ids where sender_name was not explicitly provided, - batch queries the Sender collection, and updates the corresponding - RawData content dicts in place. - - Args: - messages: Original request messages list - raw_data_list: RawData objects from MemorizeRequest.new_raw_data_list - """ - # Collect sender_ids that need enrichment - missing_sender_ids = set() - for msg in messages: - if not msg.get("sender_name") and msg.get("sender_id"): - missing_sender_ids.add(msg["sender_id"]) - - if not missing_sender_ids: - return - - try: - sender_service = get_bean_by_type(SenderService) - name_map = await sender_service.batch_get_sender_names( - list(missing_sender_ids) - ) - if not name_map: - return - - # Update RawData content dicts in place - for raw_data in raw_data_list: - sid = raw_data.content.get("sender_id") - if sid in name_map: - raw_data.content["sender_name"] = name_map[sid] - except Exception as e: - logger.warning("Failed to enrich sender names: %s", e) - - async def _ensure_sender_exists( - self, sender_id: str, sender_name: str = None - ) -> None: - """Auto-register sender when memory is ingested.""" - try: - from service.sender_service import SenderService - - sender_service = get_bean_by_type(SenderService) - await sender_service.ensure_sender_exists( - sender_id=sender_id, name=sender_name - ) - except Exception as e: - logger.warning( - "Failed to auto-register sender: sender_id=%s, error=%s", sender_id, e - ) - - async def _publish_event(self, event) -> None: - """Publish an event via ApplicationEventPublisher (fire-and-forget).""" - try: - publisher = get_bean_by_type(ApplicationEventPublisher) - await publisher.publish(event) - except Exception as e: - logger.warning("Failed to publish event %s: %s", type(event).__name__, e) - - async def _ensure_session_exists(self, session_id: str) -> None: - """Auto-register session when memory is ingested.""" - try: - from service.session_service import SessionService - - session_service = get_bean_by_type(SessionService) - await session_service.ensure_session_exists(session_id=session_id) - except Exception as e: - logger.warning( - "Failed to auto-register session: session_id=%s, error=%s", - session_id, - e, - ) - - @post( - "/delete", - status_code=204, - summary="Delete memories (soft delete)", - description="Soft delete memories by ID or by filter conditions. " - "Two modes: single delete (by memory_id) or batch delete (by filters). " - "Returns 204 No Content on success.", - ) - @stage_timed("delete") - async def delete_memories(self, request_body: DeleteMemoriesRequest) -> None: - """Soft delete memories by ID or by filter conditions.""" - delete_service = get_bean_by_type(MemCellDeleteService) - - if request_body.memory_id is not None: - await delete_service.delete_by_id(request_body.memory_id) - else: - await delete_service.delete_by_filters( - user_id=request_body.user_id, - group_id=request_body.group_id, - session_id=request_body.session_id, - sender_id=request_body.sender_id, - ) diff --git a/methods/EverCore/src/infra_layer/adapters/input/api/memory/memory_get_controller.py b/methods/EverCore/src/infra_layer/adapters/input/api/memory/memory_get_controller.py deleted file mode 100644 index 6e02f29e..00000000 --- a/methods/EverCore/src/infra_layer/adapters/input/api/memory/memory_get_controller.py +++ /dev/null @@ -1,203 +0,0 @@ -""" -Memory GET Controller - -Provides POST /api/v1/memories/get endpoint for fetching memories -with structured filters DSL. Delegates to MemoryManager for business logic. -""" - -import logging - -from fastapi import HTTPException, Request as FastAPIRequest -from pydantic import ValidationError - -from core.di.decorators import controller -from core.interface.controller.base_controller import BaseController, post -from core.observation.stage_timer import stage_timed -from agentic_layer.memory_manager import MemoryManager -from agentic_layer.get_mem_service import InvalidScopeError -from api_specs.dtos.memory import GetMemRequest, GetMemoriesResponse - -logger = logging.getLogger(__name__) - - -@controller("memory_get_controller", primary=True) -class MemoryGetController(BaseController): - """Memory GET Controller""" - - def __init__(self): - super().__init__( - prefix="/api/v1/memories", - tags=["Memory Get Controller"], - default_auth="none", - ) - self._manager = MemoryManager() - - @post( - "/get", - response_model=GetMemoriesResponse, - summary="Get memories with filters", - description=""" - Get episodic memories or profiles using structured filters DSL. - - ## Request Body Fields: - - **memory_type** (required, string): Memory type to query - - `episodic_memory`: episodic memory (derived from conversations) - - `profile`: user profile (explicit_info and implicit_traits) - - `agent_case`: agent experience (task intent + approach + quality score) - - `agent_skill`: agent skill (reusable skills from clustered cases) - - **filters** (required, object): Filter conditions using MongoDB-style DSL - - Must contain at least one of `user_id` or `group_id` at first level - - See **Filters DSL** section below for full syntax - - **page** (optional, int): Page number, starts from 1 (default: 1, min: 1) - - **page_size** (optional, int): Items per page (default: 20, min: 1, max: 100) - - **rank_by** (optional, string): Sort field (default: `timestamp`). Profile type auto-fallbacks to `updated_at` - - **rank_order** (optional, string): Sort order (default: `desc`) - - `asc`: oldest first - - `desc`: newest first - - ## Filters DSL: - Allowlist-based: only the following fields are processed, unknown fields are silently ignored. - - | Field | Type | Operators | Description | - |-------|------|-----------|-------------| - | `user_id` | string | eq, in | User ID filter (conditional required) | - | `group_id` | string | eq, in | Group ID filter (conditional required) | - | `session_id` | string | eq, in, gt, gte, lt, lte | Session ID filter | - | `timestamp` | int (epoch ms/s) or ISO string | eq, gt, gte, lt, lte | Time range filter. Epoch >1e12 treated as ms | - | `AND` | array of filter objects | - | All conditions must match | - | `OR` | array of filter objects | - | Any condition must match | - - **Operator syntax**: plain value = eq, `{"in": [...]}`, `{"gte": v, "lt": v}` - - ## Filter Examples: - - **Simple eq:** - ```json - {"filters": {"user_id": "user_01", "group_id": "group_01"}} - ``` - - **in operator:** - ```json - {"filters": {"group_id": {"in": ["group_01", "group_02"]}}} - ``` - - **Timestamp range with AND:** - ```json - {"filters": {"user_id": "user_01", "AND": [{"timestamp": {"gte": 1768469400000}}, {"timestamp": {"lt": 1768473000000}}]}} - ``` - - **Nested AND/OR:** - ```json - {"filters": {"AND": [{"user_id": "user_01"}, {"OR": [{"group_id": "group_01"}, {"group_id": "group_02"}]}]}} - ``` - - ## Response: - - **data.episodes**: List of episodic memory items (populated when memory_type=episodic_memory, otherwise []) - - Each item contains: id, user_id, group_id, session_id, timestamp, participants, summary, subject, episode, type, parent_type, parent_id - - **data.profiles**: List of profile items (populated when memory_type=profile, otherwise []) - - Each item contains: id, user_id, group_id, profile_data (explicit_info, implicit_traits), scenario, cluster_ids, memcell_count - - **data.agent_cases**: List of agent case items (populated when memory_type=agent_case, otherwise []) - - Each item contains: id, user_id, group_id, session_id, task_intent, approach, quality_score, timestamp, parent_type, parent_id - - **data.agent_skills**: List of agent skill items (populated when memory_type=agent_skill, otherwise []) - - Each item contains: id, user_id, group_id, cluster_id, name, description, content, confidence, maturity_score - - **data.total_count**: Total records matching filters (for pagination calculation) - - **data.count**: Number of records in current page - - ## Use cases: - - User profile display and personalized recommendations - - Conversation history review with time range filtering - - Cross-group memory aggregation using group_id in operator - - Paginated memory browsing with custom sort order - """, - responses={ - 422: { - "description": "Request parameter error", - "content": { - "application/json": { - "examples": { - "missing_scope": { - "summary": "Missing required scope filter", - "value": { - "code": "HTTP_ERROR", - "message": "filters must contain at least one of 'user_id' or 'group_id' at first level", - "request_id": "req_abc123", - "timestamp": "2026-01-01T00:00:00+00:00", - "path": "/api/v1/memories/get", - }, - }, - "invalid_memory_type": { - "summary": "Invalid memory_type value", - "value": { - "code": "HTTP_ERROR", - "message": "memory_type: Value error, memory_type must be one of: agent_case, agent_skill, episodic_memory, profile", - "request_id": "req_abc123", - "timestamp": "2026-01-01T00:00:00+00:00", - "path": "/api/v1/memories/get", - }, - }, - "missing_filters": { - "summary": "Missing required filters field", - "value": { - "code": "HTTP_ERROR", - "message": "filters: Field required", - "request_id": "req_abc123", - "timestamp": "2026-01-01T00:00:00+00:00", - "path": "/api/v1/memories/get", - }, - }, - } - } - }, - }, - 500: { - "description": "Internal server error", - "content": { - "application/json": { - "example": { - "code": "SYSTEM_ERROR", - "message": "Internal server error", - "request_id": "req_abc123", - "timestamp": "2026-01-01T00:00:00+00:00", - "path": "/api/v1/memories/get", - } - } - }, - }, - }, - ) - @stage_timed("get") - async def get_memories( - self, fastapi_request: FastAPIRequest, request_body: GetMemRequest = None - ) -> GetMemoriesResponse: - """Get memories by type with filters.""" - del request_body # Used for OpenAPI documentation only - - # 1. Parse and validate request body - try: - body = await fastapi_request.json() - except Exception: - raise HTTPException(status_code=400, detail="Invalid JSON request body") - - try: - request = GetMemRequest(**body) - except ValidationError as e: - first_error = e.errors()[0] - field = ".".join(str(loc) for loc in first_error.get("loc", [])) - msg = first_error.get("msg", "Validation error") - raise HTTPException( - status_code=422, detail=f"{field}: {msg}" if field else msg - ) - - # 2. Delegate to MemoryManager - try: - response = await self._manager.get_mem(request) - except InvalidScopeError as e: - raise HTTPException(status_code=422, detail=str(e)) from e - except Exception as e: - logger.error("get_memories failed: %s", e, exc_info=True) - raise HTTPException( - status_code=500, - detail="Failed to retrieve memory, please try again later", - ) from e - - return GetMemoriesResponse(data=response) diff --git a/methods/EverCore/src/infra_layer/adapters/input/api/memory/memory_search_controller.py b/methods/EverCore/src/infra_layer/adapters/input/api/memory/memory_search_controller.py deleted file mode 100644 index cd9393da..00000000 --- a/methods/EverCore/src/infra_layer/adapters/input/api/memory/memory_search_controller.py +++ /dev/null @@ -1,229 +0,0 @@ -""" -Memory Search Controller (v1) - -Provides POST /api/v1/memories/search endpoint for searching memories -with structured query, filters, and multiple retrieval methods. Delegates -to SearchMemoryService for business logic. -""" - -import logging - -from fastapi import HTTPException, Request as FastAPIRequest -from pydantic import ValidationError - -from core.di.decorators import controller -from core.interface.controller.base_controller import BaseController, post -from core.observation.stage_timer import stage_timed -from api_specs.dtos.memory import SearchMemoriesRequest, SearchMemoriesResponse -from agentic_layer.search_mem_service import SearchMemoryService - -logger = logging.getLogger(__name__) - - -@controller("memory_search_controller", primary=True) -class MemorySearchController(BaseController): - """Memory Search Controller (v1)""" - - def __init__(self): - super().__init__( - prefix="/api/v1/memories", - tags=["Memory Search Controller"], - default_auth="none", - ) - self._service = SearchMemoryService() - - @post( - "/search", - response_model=SearchMemoriesResponse, - summary="Search memories (v1)", - description=""" - V1 unified memory search endpoint supporting multiple memory types and retrieval methods. - - ## Memory Types: - - **episodic_memory**: Episodic memories (ES + Milvus) - - **profile**: User profiles (Milvus only) - - **raw_message**: Raw unprocessed messages (ES only) - pending messages not yet extracted into memories - - **agent_memory**: Agent memory - cases and skills (ES + Milvus) - - ## Retrieval Methods: - - **keyword**: BM25 keyword search (ES only) - - **vector**: Vector semantic search (Milvus only) - - **hybrid**: Hybrid retrieval (default). episodic_memory uses hierarchical retrieval, others use ES + Milvus + Rerank - - **agentic**: LLM-guided multi-round retrieval - - ## Filters DSL: - Allowlist-based: only the following fields are processed, unknown fields are silently ignored. - - | Field | Type | Operators | Description | - |-------|------|-----------|-------------| - | `user_id` | string | eq, in | User ID filter (conditional required) | - | `group_id` | string | eq, in | Group ID filter (conditional required) | - | `session_id` | string | eq, in | Session ID filter | - | `timestamp` | int (epoch ms/s) or ISO string | eq, gt, gte, lt, lte | Time range filter. Epoch >1e12 treated as ms | - | `AND` | array of filter objects | - | All conditions must match | - | `OR` | array of filter objects | - | Any condition must match | - - **Operator syntax**: plain value = eq, `{"in": [...]}`, `{"gte": v, "lt": v}` - - ## Filter Examples: - - **Simple filters:** - ```json - {"filters": {"user_id": "user_123", "group_id": "group_abc"}} - ``` - - **in operator:** - ```json - {"filters": {"group_id": {"in": ["group_a", "group_b"]}}} - ``` - - **AND with timestamp range:** - ```json - {"filters": {"AND": [{"user_id": "user_123"}, {"timestamp": {"gte": 1704067200000}}]}} - ``` - - **Search agent memory:** - ```json - {"query": "How to handle timeout errors", "method": "hybrid", "memory_types": ["agent_memory"], "filters": {"user_id": "user_01"}} - ``` - - ## Response: - - **data.episodes**: List of episodic memory search results - - Each item contains: id, user_id, group_id, session_id, timestamp, participants, summary, subject, episode, type, parent_type, parent_id, score - - **data.profiles**: List of profile search results - - Each item contains: id, user_id, group_id, profile_data, scenario, memcell_count, score - - **data.raw_messages**: List of raw message search results (when memory_types includes "raw_message") - - **data.agent_memory**: Agent cases and skills (when memory_types includes "agent_memory"). Returns both cases and skills in a single container. - - **data.agent_memory.cases**: List of agent case search results (id, user_id, group_id, session_id, task_intent, approach, quality_score, timestamp, parent_type, parent_id, score) - - **data.agent_memory.skills**: List of agent skill search results (id, user_id, group_id, cluster_id, name, description, content, confidence, maturity_score, score) - - **data.query**: Query information echoed from request - - **data.original_data**: Original data (if include_original_data=true) - """, - responses={ - 422: { - "description": "Request parameter error", - "content": { - "application/json": { - "examples": { - "missing_scope": { - "summary": "Missing required scope filter", - "value": { - "code": "HTTP_ERROR", - "message": "filters must contain at least one of 'user_id' or 'group_id'", - "request_id": "req_abc123", - "timestamp": "2026-01-01T00:00:00+00:00", - "path": "/api/v1/memories/search", - }, - }, - "invalid_method": { - "summary": "Invalid retrieval method", - "value": { - "code": "HTTP_ERROR", - "message": "method: Value error, method must be one of: keyword, vector, hybrid, rrf, agentic", - "request_id": "req_abc123", - "timestamp": "2026-01-01T00:00:00+00:00", - "path": "/api/v1/memories/search", - }, - }, - "invalid_memory_types": { - "summary": "Invalid memory type", - "value": { - "code": "HTTP_ERROR", - "message": "memory_types: Value error, memory_types must be from: agent_memory, episodic_memory, profile, raw_message", - "request_id": "req_abc123", - "timestamp": "2026-01-01T00:00:00+00:00", - "path": "/api/v1/memories/search", - }, - }, - } - } - }, - }, - 500: { - "description": "Internal server error", - "content": { - "application/json": { - "example": { - "code": "SYSTEM_ERROR", - "message": "Internal server error", - "request_id": "req_abc123", - "timestamp": "2026-01-01T00:00:00+00:00", - "path": "/api/v1/memories/search", - } - } - }, - }, - }, - ) - @stage_timed("search") - async def search_memories( - self, - fastapi_request: FastAPIRequest, - request_body: SearchMemoriesRequest = None, - ) -> SearchMemoriesResponse: - """Search memories using v1 unified search interface. - - Args: - fastapi_request: FastAPI request object - request_body: SearchMemoriesRequest (used for OpenAPI documentation only) - - Returns: - SearchMemoriesResponse with search results - """ - del request_body # Used for OpenAPI documentation only - - # 1. Parse and validate request body - try: - body = await fastapi_request.json() - except Exception: - raise HTTPException(status_code=400, detail="Invalid JSON request body") - - try: - request = SearchMemoriesRequest(**body) - except ValidationError as e: - first_error = e.errors()[0] - field = ".".join(str(loc) for loc in first_error.get("loc", [])) - msg = first_error.get("msg", "Validation error") - raise HTTPException( - status_code=422, detail=f"{field}: {msg}" if field else msg - ) - - # TODO: to optimize in future, never remove this dependency (MR!199) before that - fastapi_request.state.search_method = request.method - - # 2. Delegate to SearchMemoryService - try: - response_data = await self._service.search_memories( - query=request.query, - method=request.method, - memory_types=request.memory_types, - filters=request.filters, - top_k=request.top_k, - radius=request.radius, - include_original_data=request.include_original_data, - ) - except ValueError as e: - logger.error("search_memories validation error: %s", e) - raise HTTPException(status_code=422, detail=str(e)) from e - except Exception as e: - logger.error("search_memories failed: %s", e, exc_info=True) - raise HTTPException( - status_code=500, - detail="Failed to search memories, please try again later", - ) from e - - agent_case_count = ( - len(response_data.agent_memory.cases) if response_data.agent_memory else 0 - ) - agent_skill_count = ( - len(response_data.agent_memory.skills) if response_data.agent_memory else 0 - ) - logger.info( - "search_memories completed: episodes=%d, profiles=%d, agent_cases=%d, agent_skills=%d", - len(response_data.episodes), - len(response_data.profiles), - agent_case_count, - agent_skill_count, - ) - - return SearchMemoriesResponse(data=response_data) diff --git a/methods/EverCore/src/infra_layer/adapters/input/api/memory/sender_controller.py b/methods/EverCore/src/infra_layer/adapters/input/api/memory/sender_controller.py deleted file mode 100644 index acf6bf6d..00000000 --- a/methods/EverCore/src/infra_layer/adapters/input/api/memory/sender_controller.py +++ /dev/null @@ -1,210 +0,0 @@ -""" -Sender Controller - Sender management controller - -Provides RESTful API routes for: -- Sender creation (POST /senders): create or upsert a sender -- Sender retrieval (GET /senders/{sender_id}): get sender by sender_id -- Sender update (PATCH /senders/{sender_id}): partial update sender fields -""" - -import logging - -from fastapi import HTTPException, Request as FastAPIRequest - -from core.di.decorators import controller -from core.interface.controller.base_controller import BaseController, get, post, patch -from api_specs.dtos.sender import ( - CreateSenderRequest, - PatchSenderRequest, - CreateSenderApiResponse, - GetSenderApiResponse, - PatchSenderApiResponse, -) -from service.sender_service import SenderService - -logger = logging.getLogger(__name__) - - -@controller("sender_controller", primary=True) -class SenderController(BaseController): - """ - Sender Controller - - Handles sender CRUD operations. - """ - - def __init__(self, sender_service: SenderService): - """Initialize controller""" - super().__init__( - prefix="/api/v1/senders", tags=["Sender Controller"], default_auth="none" - ) - self.sender_service = sender_service - logger.info("SenderController initialized") - - @post( - "", - response_model=CreateSenderApiResponse, - summary="Create or update a sender", - description=""" - Create a new sender or update an existing one (upsert by sender_id). - - ## Fields: - - **sender_id** (required): Sender identifier (unique) - - **name** (optional): Sender display name - """, - responses={ - 422: { - "description": "Request parameter error", - "content": { - "application/json": { - "example": { - "error": { - "code": "InvalidParameter", - "message": "sender_id is required", - "param": "sender_id", - "type": "BadRequest", - } - } - } - }, - } - }, - ) - async def create_sender( - self, request: FastAPIRequest, request_body: CreateSenderRequest = None - ) -> CreateSenderApiResponse: - """Create or update a sender""" - del request_body # Used for OpenAPI documentation only - try: - request_data = await request.json() - create_request = CreateSenderRequest(**request_data) - - logger.info( - "Received sender create request: sender_id=%s", create_request.sender_id - ) - - result = await self.sender_service.create_or_update( - sender_id=create_request.sender_id, name=create_request.name - ) - - if not result: - raise HTTPException(status_code=500, detail="Failed to create sender") - - return {"data": result.model_dump()} - - except ValueError as e: - logger.error("Sender create parameter error: %s", e) - raise HTTPException(status_code=422, detail=str(e)) from e - except HTTPException: - raise - except Exception as e: - logger.error("Sender create failed: %s", e, exc_info=True) - raise HTTPException( - status_code=500, detail="Failed to create sender" - ) from e - - @get( - "/{sender_id}", - response_model=GetSenderApiResponse, - summary="Get sender by sender_id", - description="Retrieve a sender's details by its sender_id.", - responses={ - 404: { - "description": "Sender not found", - "content": { - "application/json": { - "example": { - "error": { - "code": "ResourceNotFound", - "message": "Sender not found: user_123", - "param": "sender_id", - "type": "NotFound", - } - } - } - }, - } - }, - ) - async def get_sender( - self, request: FastAPIRequest, sender_id: str - ) -> GetSenderApiResponse: - """Get sender by sender_id""" - try: - logger.info("Received sender get request: sender_id=%s", sender_id) - - result = await self.sender_service.get_by_sender_id(sender_id) - - if not result: - raise HTTPException( - status_code=404, detail=f"Sender not found: {sender_id}" - ) - - return {"data": result.model_dump()} - - except HTTPException: - raise - except Exception as e: - logger.error("Sender get failed: %s", e, exc_info=True) - raise HTTPException( - status_code=500, detail="Failed to retrieve sender" - ) from e - - @patch( - "/{sender_id}", - response_model=PatchSenderApiResponse, - summary="Partially update sender", - description="Update a sender's display name.", - responses={ - 404: { - "description": "Sender not found", - "content": { - "application/json": { - "example": { - "error": { - "code": "ResourceNotFound", - "message": "Sender not found: user_123", - "param": "sender_id", - "type": "NotFound", - } - } - } - }, - } - }, - ) - async def patch_sender( - self, - request: FastAPIRequest, - sender_id: str, - request_body: PatchSenderRequest = None, - ) -> PatchSenderApiResponse: - """Partially update sender fields""" - del request_body # Used for OpenAPI documentation only - try: - request_data = await request.json() - patch_request = PatchSenderRequest(**request_data) - - logger.info("Received sender patch request: sender_id=%s", sender_id) - - result = await self.sender_service.patch( - sender_id=sender_id, name=patch_request.name - ) - - if not result: - raise HTTPException( - status_code=404, detail=f"Sender not found: {sender_id}" - ) - - return {"data": result.model_dump()} - - except ValueError as e: - logger.error("Sender patch parameter error: %s", e) - raise HTTPException(status_code=422, detail=str(e)) from e - except HTTPException: - raise - except Exception as e: - logger.error("Sender patch failed: %s", e, exc_info=True) - raise HTTPException( - status_code=500, detail="Failed to update sender" - ) from e diff --git a/methods/EverCore/src/infra_layer/adapters/input/api/memory/settings_controller.py b/methods/EverCore/src/infra_layer/adapters/input/api/memory/settings_controller.py deleted file mode 100644 index b5857ac3..00000000 --- a/methods/EverCore/src/infra_layer/adapters/input/api/memory/settings_controller.py +++ /dev/null @@ -1,143 +0,0 @@ -""" -Settings Controller - Global settings management controller - -Provides RESTful API routes for: -- Settings retrieval (GET /settings): get singleton global settings -- Settings update (PUT /settings): update or initialize global settings -""" - -import logging - -from fastapi import HTTPException, Request as FastAPIRequest - -from core.di.decorators import controller -from core.interface.controller.base_controller import BaseController, get, put -from core.constants.exceptions import ValidationException -from api_specs.dtos.settings import ( - UpdateSettingsRequest, - GetSettingsApiResponse, - UpdateSettingsApiResponse, -) -from service.settings_service import SettingsService - -logger = logging.getLogger(__name__) - - -@controller("settings_controller", primary=True) -class SettingsController(BaseController): - """ - Settings Controller - - Handles global settings operations. - Settings is a singleton per space (no ID in path). - """ - - def __init__(self, settings_service: SettingsService): - """Initialize controller""" - super().__init__( - prefix="/api/v1/settings", tags=["Settings Controller"], default_auth="none" - ) - self.settings_service = settings_service - logger.info("SettingsController initialized") - - @get( - "", - response_model=GetSettingsApiResponse, - summary="Get global settings", - description=""" - Get the singleton global settings for this space. - Returns 404 if settings have not been initialized yet. - """, - responses={ - 404: { - "description": "Settings not initialized", - "content": { - "application/json": { - "example": { - "error": { - "code": "ResourceNotFound", - "message": "Settings not initialized", - "param": "", - "type": "NotFound", - } - } - } - }, - } - }, - ) - async def get_settings(self, request: FastAPIRequest) -> GetSettingsApiResponse: - """Get global settings""" - try: - result = await self.settings_service.get() - - if not result: - raise HTTPException(status_code=404, detail="Settings not initialized") - - return {"data": result.model_dump()} - - except HTTPException: - raise - except Exception as e: - logger.error("Settings get failed: %s", e, exc_info=True) - raise HTTPException( - status_code=500, detail="Failed to retrieve settings" - ) from e - - @put( - "", - response_model=UpdateSettingsApiResponse, - summary="Update or initialize global settings", - description=""" - Update the global settings, or initialize them if they don't exist yet. - - ## Initialization (first call): - - All fields are optional and will use defaults - - ## Update (subsequent calls): - - Only provided fields are updated (null fields are ignored) - """, - responses={ - 422: { - "description": "Request parameter error", - "content": { - "application/json": { - "example": { - "error": { - "code": "InvalidParameter", - "message": "Invalid parameter value", - "param": "", - "type": "BadRequest", - } - } - } - }, - } - }, - ) - async def update_settings( - self, request: FastAPIRequest, request_body: UpdateSettingsRequest = None - ) -> UpdateSettingsApiResponse: - """Update or initialize global settings""" - del request_body # Used for OpenAPI documentation only - try: - request_data = await request.json() - update_request = UpdateSettingsRequest(**request_data) - - logger.info("Received settings update request") - - result = await self.settings_service.update( - update_request, raw_data=request_data - ) - return {"data": result.model_dump()} - - except (ValueError, ValidationException) as e: - logger.error("Settings update parameter error: %s", e) - raise HTTPException(status_code=422, detail=str(e)) from e - except HTTPException: - raise - except Exception as e: - logger.error("Settings update failed: %s", e, exc_info=True) - raise HTTPException( - status_code=500, detail="Failed to update settings" - ) from e diff --git a/methods/EverCore/src/infra_layer/adapters/out/event/__init__.py b/methods/EverCore/src/infra_layer/adapters/out/event/__init__.py deleted file mode 100644 index f39f8efe..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/event/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Event module - -Provides event definitions. -""" - -from infra_layer.adapters.out.event.memcell_created_event import MemCellCreatedEvent - -__all__ = ['MemCellCreatedEvent'] diff --git a/methods/EverCore/src/infra_layer/adapters/out/event/group_memorize_event.py b/methods/EverCore/src/infra_layer/adapters/out/event/group_memorize_event.py deleted file mode 100644 index 5c438a6c..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/event/group_memorize_event.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -Group Memorize Event - -Published when a group add request is accepted. -Enterprise listeners can use this to record sender↔group associations. -""" - -from dataclasses import dataclass, field -from typing import Any, Dict, List, Type - -from core.events import BaseEvent - - -@dataclass -class GroupMemorizeEvent(BaseEvent): - """ - Event emitted after a group memorize request is accepted. - - Attributes: - group_id: Group identifier - sender_ids: List of unique sender IDs from the messages - """ - - group_id: str = "" - sender_ids: List[str] = field(default_factory=list) - - @classmethod - def from_dict( - cls: Type["GroupMemorizeEvent"], data: Dict[str, Any] - ) -> "GroupMemorizeEvent": - return cls( - event_id=data.get("event_id", ""), - created_at=data.get("created_at", ""), - group_id=data.get("group_id", ""), - sender_ids=data.get("sender_ids", []), - ) diff --git a/methods/EverCore/src/infra_layer/adapters/out/event/memcell_created_event.py b/methods/EverCore/src/infra_layer/adapters/out/event/memcell_created_event.py deleted file mode 100644 index 44407f96..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/event/memcell_created_event.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- -""" -MemCell Creation Event Class - -Used to report MemCell creation information, inherits from BaseEvent, supports JSON and BSON serialization/deserialization. -This is the basic event for the open-source version, containing only core fields. The enterprise version can extend this class to include additional fields. -""" - -from dataclasses import dataclass, field -from typing import Any, Dict, Optional, Type - -from core.events import BaseEvent - - -@dataclass -class MemCellCreatedEvent(BaseEvent): - """ - MemCell Creation Event (open-source version) - - Used to record basic information about MemCell creation. - Inherits from BaseEvent, automatically gaining event_id and created_at fields. - - Attributes: - memcell_id: MemCell ID - timestamp: Timestamp when the event occurred (optional, Unix timestamp in milliseconds) - extend: Extension field for storing additional information (optional) - """ - - # Business fields - memcell_id: str = "" - timestamp: Optional[int] = None - extend: Optional[Dict[str, Any]] = field(default_factory=dict) - - @classmethod - def from_dict( - cls: Type['MemCellCreatedEvent'], data: Dict[str, Any] - ) -> 'MemCellCreatedEvent': - """ - Create an instance from a dictionary - - Args: - data: Dictionary containing event data - - Returns: - MemCellCreatedEvent: Instance of the class - - Raises: - KeyError: Missing required fields - TypeError: Incorrect field types - """ - return cls( - # Base class fields - event_id=data.get("event_id", ""), - created_at=data.get("created_at", ""), - # Business fields - memcell_id=data.get("memcell_id", ""), - timestamp=data.get("timestamp"), - extend=data.get("extend", {}), - ) - - def __repr__(self) -> str: - """Return string representation of the object""" - return ( - f"MemCellCreatedEvent(" - f"event_id={self.event_id!r}, " - f"memcell_id={self.memcell_id!r}, " - f"timestamp={self.timestamp}" - f")" - ) diff --git a/methods/EverCore/src/infra_layer/adapters/out/event/personal_memorize_event.py b/methods/EverCore/src/infra_layer/adapters/out/event/personal_memorize_event.py deleted file mode 100644 index 12f2f339..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/event/personal_memorize_event.py +++ /dev/null @@ -1,39 +0,0 @@ -""" -Personal Memorize Event - -Published when a personal add request is accepted. -Enterprise listeners can use this to record user↔session associations. -""" - -from dataclasses import dataclass -from typing import Any, Dict, Type - -from core.events import BaseEvent - - -@dataclass -class PersonalMemorizeEvent(BaseEvent): - """ - Event emitted after a personal memorize request is accepted. - - Attributes: - user_id: Owner user ID - session_id: Session identifier (may be DEFAULT_SESSION_ID if not provided) - group_id: Auto-generated group ID for this user - """ - - user_id: str = "" - session_id: str = "" - group_id: str = "" - - @classmethod - def from_dict( - cls: Type["PersonalMemorizeEvent"], data: Dict[str, Any] - ) -> "PersonalMemorizeEvent": - return cls( - event_id=data.get("event_id", ""), - created_at=data.get("created_at", ""), - user_id=data.get("user_id", ""), - session_id=data.get("session_id", ""), - group_id=data.get("group_id", ""), - ) diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/agent_case.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/agent_case.py deleted file mode 100644 index 3cdb1273..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/agent_case.py +++ /dev/null @@ -1,154 +0,0 @@ -""" -AgentCaseRecord - Beanie ODM model for agent cases. - -Stores a compressed agent task-solving experience extracted from an agent conversation MemCell. -Each record has: task_intent, approach, quality_score. -""" - -from datetime import datetime -from typing import List, Optional, Dict, Any -from core.oxm.mongo.document_base import DocumentBase -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBaseWithSoftDelete, -) -from pydantic import Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING -from core.oxm.mongo.audit_base import AuditBase -from beanie import PydanticObjectId - - -class AgentCaseRecord(TenantAwareDocumentBaseWithSoftDelete, AuditBase): - """ - Agent case document model. - - Stores the compressed representation of one agent task-solving interaction. - One MemCell produces at most one AgentCaseRecord. - """ - - # Identity fields - user_id: Optional[str] = Field( - default=None, description="User ID who initiated the task" - ) - group_id: Optional[str] = Field(default=None, description="Group ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - timestamp: datetime = Field(..., description="Task occurrence time") - - # Core experience fields (flat, one experience per record) - task_intent: str = Field( - default="", description="Rewritten task intent as retrieval key" - ) - approach: str = Field( - default="", description="Step-by-step approach with decisions and lessons" - ) - quality_score: Optional[float] = Field( - default=None, description="Task completion quality score (0.0-1.0)" - ) - key_insight: Optional[str] = Field( - default=None, description="Pivotal strategy shift or decision" - ) - # Parent linkage (to MemCell) - parent_type: Optional[str] = Field( - default=None, description="Parent memory type (e.g., memcell)" - ) - parent_id: Optional[str] = Field( - default=None, description="Parent memory ID (MemCell event_id)" - ) - - # Vector embedding - vector: Optional[List[float]] = Field( - default=None, description="Embedding vector of task_intent" - ) - vector_model: Optional[str] = Field( - default=None, description="Vectorization model used" - ) - - model_config = ConfigDict( - collection="v1_agent_cases", - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat()}, - json_schema_extra={ - "example": { - "user_id": "user_12345", - "group_id": "session_abc", - "timestamp": "2026-02-14T10:30:00.000Z", - "task_intent": "Search for open source Python web frameworks and compare their GitHub stars", - "approach": "1. Searched GitHub for Python web frameworks with >5K stars using web_search\n2. Selected top 3: Django, Flask, FastAPI\n3. Compared GitHub stars and activity metrics\n - Result: FastAPI has fastest growth rate", - "quality_score": 0.85, - "parent_type": "memcell", - "parent_id": "67af1234abcd5678ef901234", - } - }, - extra="allow", - ) - - @property - def event_id(self) -> Optional[PydanticObjectId]: - return self.id - - class Settings: - """Beanie settings""" - - name = "v1_agent_cases" - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("deleted_at", ASCENDING)], - name="idx_tenant_deleted_at", - sparse=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("parent_id", ASCENDING)], - name="idx_tenant_parent_id", - sparse=True, - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("user_id", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_user_timestamp", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_group_timestamp", - ), - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - validate_on_save = True - use_state_management = True - - -class AgentCaseProjection(DocumentBase, AuditBase): - """ - Simplified agent case model (without vector) - - Used in GET queries where vector data is not needed, - reducing data transfer and memory usage. - """ - - id: Optional[PydanticObjectId] = Field(default=None, description="Record ID") - user_id: Optional[str] = Field(default=None, description="User ID") - group_id: Optional[str] = Field(default=None, description="Group ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - timestamp: datetime = Field(..., description="Task occurrence time") - task_intent: str = Field(default="", description="Rewritten task intent") - approach: str = Field(default="", description="Step-by-step approach") - quality_score: Optional[float] = Field( - default=None, description="Task completion quality score (0.0-1.0)" - ) - key_insight: Optional[str] = Field( - default=None, description="Pivotal strategy shift or decision" - ) - parent_type: Optional[str] = Field(default=None, description="Parent memory type") - parent_id: Optional[str] = Field(default=None, description="Parent memory ID") diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/agent_skill.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/agent_skill.py deleted file mode 100644 index b95bc3f7..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/agent_skill.py +++ /dev/null @@ -1,165 +0,0 @@ -""" -AgentSkillRecord - Beanie ODM model for agent skill. - -Stores reusable skills extracted from clustered AgentCases -within a MemScene (cluster). -""" - -from datetime import datetime -from typing import List, Optional, Dict, Any -from core.oxm.mongo.document_base import DocumentBase -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBaseWithSoftDelete, -) -from pydantic import Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING -from core.oxm.mongo.audit_base import AuditBase -from beanie import PydanticObjectId - - -class AgentSkillRecord(TenantAwareDocumentBaseWithSoftDelete, AuditBase): - """ - Agent skill document model. - - Stores a single reusable skill extracted from a MemScene - (cluster of semantically similar AgentCase records). - - Skills are derived by analyzing patterns across multiple AgentCases - in the same cluster, then merging/refining on each subsequent experience. - """ - - # Cluster linkage (MemScene) - cluster_id: str = Field( - ..., description="MemScene cluster ID this skill belongs to" - ) - - # Identity fields - user_id: Optional[str] = Field(default=None, description="User ID (agent owner)") - group_id: Optional[str] = Field(default=None, description="Group ID") - - # Core content - name: Optional[str] = Field(default=None, description="Skill name") - description: Optional[str] = Field( - default=None, - description="A clear description of what this skill does and when to use it", - ) - content: str = Field(..., description="Full skill content") - - confidence: float = Field( - default=0.0, - ge=0.0, - le=1.0, - description="Confidence score (0.0-1.0), increases with more supporting experiences", - ) - - # Maturity assessment - maturity_score: float = Field( - default=0.6, - ge=0.0, - le=1.0, - description="Normalized quality score (0.0-1.0), skill is retrievable when >= threshold", - ) - - # Vector embedding for semantic retrieval - vector: Optional[List[float]] = Field( - default=None, description="Embedding vector of name + description" - ) - vector_model: Optional[str] = Field( - default=None, description="Vectorization model used" - ) - - # Source traceability: AgentCase IDs that triggered add/update of this skill. - source_case_ids: List[str] = Field( - default_factory=list, - description="AgentCase IDs that triggered add/update of this skill", - ) - - model_config = ConfigDict( - collection="v1_agent_skills", - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat()}, - json_schema_extra={ - "example": { - "cluster_id": "cluster_001", - "name": "Technical comparison research", - "description": "Compare open source technical solutions or frameworks by searching, extracting, and evaluating key metrics", - "content": "1. search(tech + open source + github)\n2. Extract repo list from results\n3. Open README for each repo\n4. Compare by stars, activity, and features", - "confidence": 0.85, - } - }, - extra="allow", - ) - - @property - def skill_id(self) -> Optional[PydanticObjectId]: - return self.id - - class Settings: - """Beanie settings""" - - name = "v1_agent_skills" - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("deleted_at", ASCENDING)], - name="idx_tenant_deleted_at", - sparse=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("cluster_id", ASCENDING)], - name="idx_tenant_cluster_id", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("cluster_id", ASCENDING), - ], - name="idx_tenant_group_cluster", - ), - IndexModel( - [("tenant_id", ASCENDING), ("user_id", ASCENDING)], - name="idx_tenant_user_id", - ), - IndexModel( - [("tenant_id", ASCENDING), ("maturity_score", ASCENDING)], - name="idx_tenant_maturity_score", - sparse=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("confidence", ASCENDING)], - name="idx_tenant_confidence", - sparse=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - validate_on_save = True - use_state_management = True - - -class AgentSkillProjection(DocumentBase, AuditBase): - """ - Simplified agent skill model (without vector) - - Used in GET queries where vector data is not needed, - reducing data transfer and memory usage. - """ - - id: Optional[PydanticObjectId] = Field(default=None, description="Record ID") - cluster_id: str = Field(..., description="MemScene cluster ID") - user_id: Optional[str] = Field(default=None, description="User ID") - group_id: Optional[str] = Field(default=None, description="Group ID") - name: Optional[str] = Field(default=None, description="Skill name") - description: Optional[str] = Field(default=None, description="Skill description") - content: str = Field(..., description="Full skill content") - confidence: float = Field(default=0.0, description="Confidence score (0.0-1.0)") - maturity_score: float = Field(default=0.6, description="Maturity score (0.0-1.0)") - source_case_ids: List[str] = Field( - default_factory=list, description="AgentCase IDs" - ) diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/atomic_fact_record.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/atomic_fact_record.py deleted file mode 100644 index 651b37e3..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/atomic_fact_record.py +++ /dev/null @@ -1,193 +0,0 @@ -""" -AtomicFactRecord Beanie ODM model - -Unified storage for atomic facts (atomic facts) extracted from episodic memory (individual or group). -""" - -from datetime import datetime -from typing import List, Optional, Dict, Any -from core.oxm.mongo.document_base import DocumentBase -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBaseWithSoftDelete, -) -from pydantic import Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING -from core.oxm.mongo.audit_base import AuditBase -from beanie import PydanticObjectId -from api_specs.memory_types import ParentType - - -class AtomicFactRecord(TenantAwareDocumentBaseWithSoftDelete, AuditBase): - """ - Generic atomic fact document model - - Stores atomic facts split from individual or group episodic memory for fine-grained retrieval. - """ - - # field from api input - user_id: Optional[str] = Field( - default=None, description="User ID, required for personal events" - ) - # field from api input - group_id: Optional[str] = Field(default=None, description="Group ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - atomic_fact: str = Field(..., description="Atomic fact content (single sentence)") - parent_type: str = Field(..., description="Parent memory type (memcell/episode)") - parent_id: str = Field(..., description="Parent memory ID") - - # Time information - timestamp: datetime = Field(..., description="Event occurrence time") - - type: Optional[str] = Field( - default=None, description="Event type, such as Conversation" - ) - - participants: Optional[List[str]] = Field( - default=None, description="Related participants" - ) - sender_ids: Optional[List[str]] = Field( - default=None, description="Sender IDs of messages" - ) - - # Vector and model - vector: Optional[List[float]] = Field( - default=None, description="Atomic fact vector" - ) - vector_model: Optional[str] = Field( - default=None, description="Vectorization model used" - ) - - model_config = ConfigDict( - collection="v1_atomic_fact_records", - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat()}, - json_schema_extra={ - "example": { - "id": "atomic_fact_001", - "user_id": "user_12345", - "atomic_fact": "The user went to Chengdu on January 1, 2024, and enjoyed the local Sichuan cuisine.", - "parent_type": ParentType.MEMCELL.value, - "parent_id": "memcell_001", - "timestamp": "2024-01-01T10:00:00+00:00", - "group_id": "group_travel", - "participants": ["Zhang San", "Li Si"], - "vector": [0.1, 0.2, 0.3], - } - }, - extra="allow", - ) - - @property - def event_id(self) -> Optional[PydanticObjectId]: - """Compatibility property, returns document ID""" - return self.id - - class Settings: - """Beanie Settings""" - - name = "v1_atomic_fact_records" - - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("deleted_at", ASCENDING)], - name="idx_tenant_deleted_at", - sparse=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("parent_id", ASCENDING)], - name="idx_tenant_parent_id", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("user_id", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_user_timestamp", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_group_timestamp", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("user_id", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_group_user_timestamp", - ), - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - - validate_on_save = True - use_state_management = True - - -class AtomicFactRecordProjection(DocumentBase, AuditBase): - """ - Simplified atomic fact model (without vector) - - Used in most scenarios where vector data is not needed, reducing data transfer and memory usage. - """ - - # Core fields - id: Optional[PydanticObjectId] = Field(default=None, description="Record ID") - user_id: Optional[str] = Field( - default=None, description="User ID, required for personal events" - ) - group_id: Optional[str] = Field(default=None, description="Group ID") - sender_id: Optional[str] = Field(default=None, description="Sender identifier") - session_id: Optional[str] = Field(default=None, description="Session identifier") - atomic_fact: str = Field(..., description="Atomic fact content (single sentence)") - parent_type: str = Field(..., description="Parent memory type (memcell/episode)") - parent_id: str = Field(..., description="Parent memory ID") - - # Time information - timestamp: datetime = Field(..., description="Event occurrence time") - - type: Optional[str] = Field( - default=None, description="Event type, such as Conversation" - ) - - # Participant information - participants: Optional[List[str]] = Field( - default=None, description="Related participants" - ) - sender_ids: Optional[List[str]] = Field( - default=None, description="Sender IDs of related participants" - ) - - # Vector model information (retain model name, but exclude vector data) - vector_model: Optional[str] = Field( - default=None, description="Vectorization model used" - ) - - model_config = ConfigDict( - validate_assignment=True, - json_encoders={ - datetime: lambda dt: dt.isoformat(), - PydanticObjectId: lambda oid: str(oid), - }, - ) - - @property - def event_id(self) -> Optional[PydanticObjectId]: - """Compatibility property, returns document ID""" - return self.id - - -# Export models -__all__ = ["AtomicFactRecord", "AtomicFactRecordProjection"] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/conversation_status.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/conversation_status.py deleted file mode 100644 index 24569d51..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/conversation_status.py +++ /dev/null @@ -1,77 +0,0 @@ -from datetime import datetime -from typing import Optional -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBase, -) -from pydantic import Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING -from beanie import PydanticObjectId -from core.oxm.mongo.audit_base import AuditBase - - -class ConversationStatus(TenantAwareDocumentBase, AuditBase): - """ - Conversation status document model - - Stores conversation status information, including group ID, message read time, etc. - """ - - # Basic information - group_id: str = Field(..., description="Group ID, empty means private chat") - session_id: Optional[str] = Field(default=None, description="Session ID") - - old_msg_start_time: Optional[datetime] = Field( - default=None, description="Conversation window read start time" - ) - new_msg_start_time: Optional[datetime] = Field( - default=None, description="Accumulated new conversation read start time" - ) - last_memcell_time: Optional[datetime] = Field( - default=None, description="Accumulated memCell read start time" - ) - - model_config = ConfigDict( - collection="v1_conversation_status", - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat()}, - json_schema_extra={ - "example": { - "group_id": "group_001", - "old_msg_start_time": datetime(2021, 1, 1, 0, 0, 0), - "new_msg_start_time": datetime(2021, 1, 1, 0, 0, 0), - "last_memcell_time": datetime(2021, 1, 1, 0, 0, 0), - } - }, - extra="allow", - ) - - @property - def conversation_id(self) -> Optional[PydanticObjectId]: - return self.id - - class Settings: - """Beanie settings""" - - name = "v1_conversation_status" - indexes = [ - # Composite unique index: one status per (group_id, session_id) pair - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("session_id", ASCENDING), - ], - name="idx_tenant_group_session", - unique=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - validate_on_save = True - use_state_management = True diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/episodic_memory.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/episodic_memory.py deleted file mode 100644 index b032c1c9..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/episodic_memory.py +++ /dev/null @@ -1,178 +0,0 @@ -from datetime import datetime -from typing import List, Optional, Dict, Any -from core.oxm.mongo.document_base import DocumentBase -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBaseWithSoftDelete, -) -from pydantic import Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING -from core.oxm.mongo.audit_base import AuditBase -from beanie import PydanticObjectId - - -class EpisodicMemory(TenantAwareDocumentBaseWithSoftDelete, AuditBase): - """ - Episodic memory document model - - Stores user's episodic memories, including event summaries, participants, topics, etc. - Directly transferred from MemCell summaries. - """ - - # field from api input - user_id: Optional[str] = Field( - default=None, description="The individual involved, None indicates group memory" - ) - # field from api input - group_id: Optional[str] = Field(default=None, description="Group ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - timestamp: datetime = Field(..., description="Occurrence time (timestamp)") - participants: Optional[List[str]] = Field( - default=None, description="IDs of event participants" - ) - sender_ids: Optional[List[str]] = Field( - default=None, description="Sender IDs of messages" - ) - summary: str = Field(..., min_length=1, description="Memory unit") - subject: Optional[str] = Field(default=None, description="Memory unit subject") - episode: str = Field(..., min_length=1, description="Episodic memory") - type: Optional[str] = Field( - default=None, description="Episode type, such as Conversation" - ) - - parent_type: Optional[str] = Field( - default=None, description="Parent memory type (e.g., memcell)" - ) - parent_id: Optional[str] = Field(default=None, description="Parent memory ID") - - vector: Optional[List[float]] = Field(default=None, description="Text vector") - vector_model: Optional[str] = Field( - default=None, description="Vectorization model used" - ) - - model_config = ConfigDict( - collection="v1_episodic_memories", - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat()}, - json_schema_extra={ - "example": { - "user_id": "user_12345", - "group_id": "group_work", - "timestamp": 1701388800, - "participants": ["Zhang San", "Li Si"], - "summary": "Discussed project progress and next week's plan", - "subject": "Project meeting", - "episode": "Held a project progress discussion in the meeting room, confirmed next week's development task assignments", - "type": "Conversation", - } - }, - extra="allow", - ) - - @property - def event_id(self) -> Optional[PydanticObjectId]: - return self.id - - class Settings: - """Beanie settings""" - - name = "v1_episodic_memories" - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("deleted_at", ASCENDING)], - name="idx_tenant_deleted_at", - sparse=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("parent_id", ASCENDING)], - name="idx_tenant_parent_id", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("user_id", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_user_timestamp", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_group_timestamp", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("user_id", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_group_user_timestamp", - ), - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - validate_on_save = True - use_state_management = True - - -class EpisodicMemoryProjection(DocumentBase, AuditBase): - """ - Simplified episodic memory model (without vector) - - Used in most scenarios where vector data is not needed, reducing data transfer and memory usage. - """ - - id: Optional[PydanticObjectId] = Field(default=None, description="Record ID") - user_id: Optional[str] = Field( - default=None, description="The individual involved, None indicates group memory" - ) - group_id: Optional[str] = Field(default=None, description="Group ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - timestamp: datetime = Field(..., description="Occurrence time (timestamp)") - participants: Optional[List[str]] = Field( - default=None, description="Names of event participants" - ) - sender_ids: Optional[List[str]] = Field( - default=None, description="Sender IDs of event participants" - ) - summary: str = Field(..., min_length=1, description="Memory unit") - subject: Optional[str] = Field(default=None, description="Memory unit subject") - episode: str = Field(..., min_length=1, description="Episodic memory") - type: Optional[str] = Field( - default=None, description="Episode type, such as Conversation" - ) - - parent_type: Optional[str] = Field( - default=None, description="Parent memory type (e.g., memcell)" - ) - parent_id: Optional[str] = Field(default=None, description="Parent memory ID") - - # Vector model information (retain model name, but exclude vector data) - vector_model: Optional[str] = Field( - default=None, description="Vectorization model used" - ) - - model_config = ConfigDict( - validate_assignment=True, - json_encoders={ - datetime: lambda dt: dt.isoformat(), - PydanticObjectId: lambda oid: str(oid), - }, - ) - - @property - def event_id(self) -> Optional[PydanticObjectId]: - return self.id - - -# Export models -__all__ = ["EpisodicMemory", "EpisodicMemoryProjection"] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/foresight_record.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/foresight_record.py deleted file mode 100644 index eafa6f4b..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/foresight_record.py +++ /dev/null @@ -1,222 +0,0 @@ -""" -ForesightRecord Beanie ODM model - -Unified storage of foresights extracted from episodic memories (personal or group). -""" - -from datetime import datetime -from typing import List, Optional -from core.oxm.mongo.document_base import DocumentBase -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBaseWithSoftDelete, -) -from pydantic import Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING -from core.oxm.mongo.audit_base import AuditBase -from beanie import PydanticObjectId -from api_specs.memory_types import ParentType - - -class ForesightRecord(TenantAwareDocumentBaseWithSoftDelete, AuditBase): - """ - Generic foresight document model - - Unified storage of foresight information extracted from personal or group episodic memories. - When user_id exists, it represents personal foresight; when user_id is empty and group_id exists, it represents group foresight. - """ - - # field from api input - user_id: Optional[str] = Field( - default=None, - description="User ID, required for personal memory, None for group memory", - ) - # field from api input - group_id: Optional[str] = Field(default=None, description="Group ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - content: str = Field(..., min_length=1, description="Foresight content") - parent_type: str = Field(..., description="Parent memory type (memcell/episode)") - parent_id: str = Field(..., description="Parent memory ID") - - # Evidence - evidence: Optional[str] = Field( - default=None, description="Evidence supporting this foresight" - ) - - # Time range fields - start_time: Optional[str] = Field( - default=None, description="Foresight start time (date string, e.g., 2024-01-01)" - ) - end_time: Optional[str] = Field( - default=None, description="Foresight end time (date string, e.g., 2024-12-31)" - ) - duration_days: Optional[int] = Field(default=None, description="Duration in days") - - type: Optional[str] = Field( - default=None, description="Foresight type, such as Conversation" - ) - - participants: Optional[List[str]] = Field( - default=None, description="Related participants" - ) - sender_ids: Optional[List[str]] = Field( - default=None, description="Sender IDs of messages" - ) - - # Vector and model - vector: Optional[List[float]] = Field( - default=None, description="Text vector of the foresight" - ) - vector_model: Optional[str] = Field( - default=None, description="Vectorization model used" - ) - - model_config = ConfigDict( - collection="v1_foresight_records", - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat()}, - json_schema_extra={ - "example": { - "id": "foresight_001", - "user_id": "user_12345", - "content": "User likes Sichuan cuisine, especially spicy hotpot", - "parent_type": ParentType.MEMCELL.value, - "parent_id": "memcell_001", - "start_time": "2024-01-01", - "end_time": "2024-12-31", - "duration_days": 365, - "group_id": "group_friends", - "participants": ["Zhang San", "Li Si"], - "vector": [0.1, 0.2, 0.3], - "vector_model": "text-embedding-3-small", - "evidence": "Mentioned multiple times in chat about liking hotpot", - } - }, - extra="allow", - ) - - @property - def event_id(self) -> Optional[PydanticObjectId]: - """Compatibility property, returns document ID""" - return self.id - - class Settings: - """Beanie settings""" - - name = "v1_foresight_records" - - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("deleted_at", ASCENDING)], - name="idx_tenant_deleted_at", - sparse=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("parent_id", ASCENDING)], - name="idx_tenant_parent_id", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("user_id", ASCENDING), - ("start_time", ASCENDING), - ("end_time", ASCENDING), - ], - name="idx_tenant_user_time_range", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("start_time", ASCENDING), - ("end_time", ASCENDING), - ], - name="idx_tenant_group_time_range", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("user_id", ASCENDING), - ("start_time", ASCENDING), - ("end_time", ASCENDING), - ], - name="idx_tenant_group_user_time_range", - ), - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - - validate_on_save = True - use_state_management = True - - -class ForesightRecordProjection(DocumentBase, AuditBase): - """ - Simplified foresight model (without vector) - - Used in most scenarios where vector data is not needed, reducing data transfer and memory usage. - """ - - # Core fields - id: Optional[PydanticObjectId] = Field(default=None, description="Record ID") - user_id: Optional[str] = Field( - default=None, - description="User ID, required for personal memory, None for group memory", - ) - group_id: Optional[str] = Field(default=None, description="Group ID") - sender_id: Optional[str] = Field(default=None, description="Sender identifier") - session_id: Optional[str] = Field(default=None, description="Session identifier") - content: str = Field(..., min_length=1, description="Foresight content") - parent_type: str = Field(..., description="Parent memory type (memcell/episode)") - parent_id: str = Field(..., description="Parent memory ID") - - # Time range fields - start_time: Optional[str] = Field( - default=None, description="Foresight start time (date string, e.g., 2024-01-01)" - ) - end_time: Optional[str] = Field( - default=None, description="Foresight end time (date string, e.g., 2024-12-31)" - ) - duration_days: Optional[int] = Field(default=None, description="Duration in days") - - type: Optional[str] = Field( - default=None, description="Foresight type, such as Conversation" - ) - - # Participant information - participants: Optional[List[str]] = Field( - default=None, description="Related participants" - ) - sender_ids: Optional[List[str]] = Field( - default=None, description="Sender IDs of related participants" - ) - - # Vector model information (retain model name, but exclude vector data) - vector_model: Optional[str] = Field( - default=None, description="Vectorization model used" - ) - - # Evidence - evidence: Optional[str] = Field( - default=None, description="Evidence supporting this foresight" - ) - - model_config = ConfigDict( - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat(), PydanticObjectId: str}, - ) - - @property - def event_id(self) -> Optional[PydanticObjectId]: - """Compatibility property, returns document ID""" - return self.id - - -# Export models -__all__ = ["ForesightRecord", "ForesightRecordProjection"] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/global_settings.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/global_settings.py deleted file mode 100644 index 287ec301..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/global_settings.py +++ /dev/null @@ -1,188 +0,0 @@ -""" -GlobalSettings Beanie ODM model - -Space-level global configuration singleton document. -""" - -from datetime import datetime -from typing import Optional, Dict, Any -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBase, -) -from core.oxm.mongo.audit_base import AuditBase -from pydantic import BaseModel, Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING - - -class LlmProviderConfigModel(BaseModel): - """LLM provider configuration model - - Defines the provider and model for a specific LLM task - """ - - provider: str = Field( - ..., description="LLM provider name, e.g.: openai, openrouter." - ) - model: str = Field( - ..., - description="Model name, e.g.: qwen/qwen3-235b-a22b-2507(openrouter), gpt-4.1-mini(openai), etc.", - ) - extra: Optional[Dict[str, Any]] = Field( - default=None, description="Additional provider-specific configuration" - ) - - def to_dict(self) -> Dict[str, Any]: - """Convert to dict representation""" - result = {"provider": self.provider, "model": self.model} - if self.extra: - result["extra"] = self.extra - return result - - @classmethod - def from_any(cls, data: Any) -> Optional["LlmProviderConfigModel"]: - """Create from dict or DTO object""" - if data is None: - return None - if isinstance(data, cls): - return data - if isinstance(data, dict): - return cls( - provider=data.get("provider", ""), - model=data.get("model", ""), - extra=data.get("extra"), - ) - if hasattr(data, "provider") and hasattr(data, "model"): - return cls( - provider=data.provider, - model=data.model, - extra=getattr(data, "extra", None), - ) - return None - - -class LlmCustomSettingModel(BaseModel): - """LLM custom settings model for algorithm control - - Allows configuring different LLM providers/models for different tasks. - - Example: - { - "boundary": {"provider": "openai", "model": "gpt-4.1-mini"}, - "extraction": {"provider": "openrouter", "model": "qwen/qwen3-235b-a22b-2507"} - } - """ - - boundary: Optional[LlmProviderConfigModel] = Field( - default=None, - description="LLM config for boundary detection (fast, cheap model recommended)", - ) - extraction: Optional[LlmProviderConfigModel] = Field( - default=None, - description="LLM config for memory extraction (high quality model recommended)", - ) - extra: Optional[Dict[str, Any]] = Field( - default=None, description="Additional task-specific LLM configurations" - ) - - def to_dict(self) -> Optional[Dict[str, Any]]: - """Convert to dict representation for response""" - result: Dict[str, Any] = {} - if self.boundary: - result["boundary"] = self.boundary.to_dict() - if self.extraction: - result["extraction"] = self.extraction.to_dict() - if self.extra: - result["extra"] = self.extra - return result if result else None - - @classmethod - def from_any(cls, data: Any) -> Optional["LlmCustomSettingModel"]: - """Create from dict or DTO object""" - if data is None: - return None - if isinstance(data, cls): - return data - if isinstance(data, dict): - boundary = LlmProviderConfigModel.from_any(data.get("boundary")) - extraction = LlmProviderConfigModel.from_any(data.get("extraction")) - extra = data.get("extra") - if boundary is None and extraction is None and extra is None: - return None - return cls(boundary=boundary, extraction=extraction, extra=extra) - if hasattr(data, "boundary") or hasattr(data, "extraction"): - boundary = LlmProviderConfigModel.from_any(getattr(data, "boundary", None)) - extraction = LlmProviderConfigModel.from_any( - getattr(data, "extraction", None) - ) - extra = getattr(data, "extra", None) - if boundary is None and extraction is None and extra is None: - return None - return cls(boundary=boundary, extraction=extraction, extra=extra) - return None - - -class GlobalSettings(TenantAwareDocumentBase, AuditBase): - """ - Global settings document model - - Singleton document per space/namespace storing space-level configuration. - Singleton document per space/namespace storing space-level settings. - """ - - # LLM configuration - llm_custom_setting: Optional[LlmCustomSettingModel] = Field( - default=None, - description="LLM config: {boundary: {provider, model, extra}, extraction: {provider, model, extra}}", - ) - - # Timezone - timezone: str = Field( - default="UTC", description="IANA timezone identifier, e.g. Asia/Shanghai" - ) - - # Memory extraction settings - boundary_detection_timeout: int = Field( - default=3600, description="MemCell auto-flush idle timeout in seconds" - ) - extraction_mode: str = Field( - default="default", description="Extraction mode: 'default' or 'pro'" - ) - offline_profile_extraction_interval: int = Field( - default=86400, description="Offline profile extraction interval in seconds" - ) - - model_config = ConfigDict( - collection="v1_global_settings", - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat()}, - json_schema_extra={ - "example": { - "llm_custom_setting": { - "boundary": {"provider": "openai", "model": "gpt-4o-mini"}, - "extraction": {"provider": "openai", "model": "gpt-4o"}, - }, - "timezone": "UTC", - "boundary_detection_timeout": 3600, - "extraction_mode": "default", - "offline_profile_extraction_interval": 86400, - } - }, - extra="allow", - ) - - class Settings: - """Beanie settings""" - - name = "v1_global_settings" - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - validate_on_save = True - use_state_management = True diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/group.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/group.py deleted file mode 100644 index 14c19931..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/group.py +++ /dev/null @@ -1,66 +0,0 @@ -""" -Group Beanie ODM model - -Group container management for V1 API. -""" - -from datetime import datetime -from typing import Optional -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBase, -) -from core.oxm.mongo.audit_base import AuditBase -from pydantic import Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING - - -class Group(TenantAwareDocumentBase, AuditBase): - """ - Group document model - - Stores group information. - Auto-registered when Add Memory receives a group_id. - """ - - group_id: str = Field(..., description="Group identifier (unique)") - name: Optional[str] = Field(default=None, description="Group display name") - description: Optional[str] = Field(default=None, description="Group description") - - model_config = ConfigDict( - collection="v1_groups", - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat()}, - json_schema_extra={ - "example": { - "group_id": "group_abc", - "name": "Project Discussion", - "description": "Weekly sync on Project X", - } - }, - extra="allow", - ) - - class Settings: - """Beanie settings""" - - name = "v1_groups" - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("group_id", ASCENDING)], - name="idx_tenant_group_id", - unique=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("name", ASCENDING)], name="idx_tenant_name" - ), - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - validate_on_save = True - use_state_management = True diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/mem_scene.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/mem_scene.py deleted file mode 100644 index c43f7482..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/mem_scene.py +++ /dev/null @@ -1,50 +0,0 @@ -from datetime import datetime -from typing import List, Optional, Dict, Any -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBase, -) -from pydantic import Field -from core.oxm.mongo.audit_base import AuditBase -from pymongo import IndexModel, ASCENDING - - -class MemScene(TenantAwareDocumentBase, AuditBase): - """ - MemScene document — stores incremental clustering state for a group. - Used for memcell-to-cluster assignment and profile extraction scheduling. - """ - - group_id: Optional[str] = Field(default=None, description="Group ID") - - # Per-memcell cluster assignment: { event_id: { memscene: cluster_id, timestamp: epoch_seconds } } - # Used to look up which cluster a memcell belongs to, and to fetch all memcells in a cluster. - memcell_info: Dict[str, Dict[str, Any]] = Field( - default_factory=dict, description="Per-memcell cluster assignment and timestamp" - ) - - # Per-cluster aggregated state: { cluster_id: { center: [vector], timestamp: epoch_seconds, count: int } } - # center for similarity matching, timestamp for temporal gating and profile extraction scheduling. - memscene_info: Dict[str, Dict[str, Any]] = Field( - default_factory=dict, - description="Per-cluster centroid, latest timestamp, and member count", - ) - - # Cluster IDs that contain agent conversation (case) memcells. - # Used to route case memcells to LLM-based clustering and exclude them from embedding-only clustering. - case_cluster_ids: Optional[List[str]] = Field( - default_factory=list, description="Cluster IDs containing agent case memcells" - ) - - # Auto-increment counter for cluster ID generation (cluster_000, cluster_001, ...). - next_cluster_idx: int = Field( - default=0, description="Counter for generating unique cluster IDs" - ) - - class Settings: - name = "v1_mem_scenes" - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("group_id", ASCENDING)], - name="idx_tenant_group_id", - ) - ] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/memcell.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/memcell.py deleted file mode 100644 index 0c76ef27..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/memcell.py +++ /dev/null @@ -1,183 +0,0 @@ -""" -MemCell Beanie ODM model - -MemCell data model definition based on Beanie ODM, supporting MongoDB sharded clusters. -""" - -from datetime import datetime -from typing import List, Optional -from enum import Enum - -from beanie import Indexed -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBaseWithSoftDelete, -) -from pydantic import Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING -from beanie import PydanticObjectId -from core.oxm.mongo.audit_base import AuditBase - - -class DataTypeEnum(str, Enum): - """Data type enumeration""" - - CONVERSATION = "Conversation" - AGENTCONVERSATION = "AgentConversation" - - -class MemCell(TenantAwareDocumentBaseWithSoftDelete, AuditBase): - """ - MemCell document model - - Storage model for scene segmentation results, supporting flexible extension and high-performance queries. - - Supports soft delete functionality: - - Use delete() method for soft deletion - - Use find_one(), find_many() to automatically filter out deleted records - - Use hard_find_one(), hard_find_many() to query including deleted records - - Use hard_delete() for physical deletion - """ - - # Core fields - timestamp: Indexed(datetime) = Field(..., description="Occurrence time, shard key") - - # Optional fields - group_id: Optional[Indexed(str)] = Field( - default=None, description="Group ID, empty means private chat" - ) - session_id: Optional[str] = Field(default=None, description="Session ID") - original_data: Optional[List] = Field( - default=None, description="Original information" - ) - # NOTE: participants and sender_ids currently hold the same values (both are sender_id). - # participants is not yet implemented as display names; it is populated with sender_ids - # as a placeholder. Once display-name resolution is available, participants will carry - # human-readable names while sender_ids will remain the raw identifiers. - participants: Optional[List[str]] = Field( - default=None, description="Names of event participants" - ) - sender_ids: Optional[List[str]] = Field( - default=None, description="Sender IDs of event participants" - ) - type: Optional[DataTypeEnum] = Field(default=None, description="Scenario type") - - model_config = ConfigDict( - # Collection name - collection="v1_memcells", - # Validation configuration - validate_assignment=True, - # JSON serialization configuration - json_encoders={datetime: lambda dt: dt.isoformat()}, - # Example data - json_schema_extra={ - "example": { - "user_id": "user_12345", - "group_id": "group_67890", - "timestamp": "2024-12-01T10:30:00.000Z", - "original_data": [ - { - "message": { - "message_id": "msg_001", - "sender_id": "user_123", - "sender_name": "Alice", - "role": "user", - "content": [ - { - "type": "text", - "content": "Let's discuss the new feature design", - } - ], - "timestamp": "2025-01-15T10:00:00+00:00", - } - } - ], - "participants": ["user_123", "user_456"], - "sender_ids": ["user_123", "user_456"], - "type": "Conversation", - } - }, - extra="allow", - ) - - @property - def event_id(self) -> Optional[PydanticObjectId]: - return self.id - - class Settings: - """Beanie settings""" - - # Collection name - name = "v1_memcells" - - # Index definitions - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("deleted_at", ASCENDING)], - name="idx_tenant_deleted_at", - sparse=True, - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("user_id", ASCENDING), - ("deleted_at", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_user_deleted_timestamp", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("deleted_at", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_group_deleted_timestamp", - ), - IndexModel( - [("tenant_id", ASCENDING), ("participants", ASCENDING)], - name="idx_tenant_participants", - sparse=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("sender_ids", ASCENDING)], - name="idx_tenant_sender_ids", - sparse=True, - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("user_id", ASCENDING), - ("type", ASCENDING), - ("deleted_at", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_user_type_deleted_timestamp", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("type", ASCENDING), - ("deleted_at", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_group_type_deleted_timestamp", - ), - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - - # Validation settings - validate_on_save = True - use_state_management = True - - -# Export models -__all__ = ["MemCell", "DataTypeEnum"] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/sender.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/sender.py deleted file mode 100644 index baf1a172..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/sender.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -Sender Beanie ODM model - -User/agent identity management. Auto-created from Add Memory messages. -""" - -from datetime import datetime -from typing import Optional -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBase, -) -from core.oxm.mongo.audit_base import AuditBase -from pydantic import Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING - - -class Sender(TenantAwareDocumentBase, AuditBase): - """ - Sender document model - - Stores user/agent identity information. - Auto-registered when Add Memory receives a new sender_id. - """ - - sender_id: str = Field(..., description="Sender identifier (unique)") - name: Optional[str] = Field(default=None, description="Sender display name") - - model_config = ConfigDict( - collection="v1_senders", - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat()}, - json_schema_extra={"example": {"sender_id": "user_123", "name": "Alice"}}, - extra="allow", - ) - - class Settings: - """Beanie settings""" - - name = "v1_senders" - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("sender_id", ASCENDING)], - name="idx_tenant_sender_id", - unique=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - validate_on_save = True - use_state_management = True diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/session.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/session.py deleted file mode 100644 index e2958927..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/session.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -Session Beanie ODM model - -Session container management for V1 API. -""" - -from datetime import datetime -from typing import Optional -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBase, -) -from core.oxm.mongo.audit_base import AuditBase -from pydantic import Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING - - -class Session(TenantAwareDocumentBase, AuditBase): - """ - Session document model - - Stores session information for conversation isolation. - Auto-registered when Add Memory receives a session_id. - """ - - session_id: str = Field(..., description="Session identifier (unique)") - name: Optional[str] = Field(default=None, description="Session display name") - description: Optional[str] = Field(default=None, description="Session description") - - model_config = ConfigDict( - collection="v1_sessions", - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat()}, - json_schema_extra={ - "example": { - "session_id": "sess_abc", - "name": "Morning Chat", - "description": "Casual morning conversation", - } - }, - extra="allow", - ) - - class Settings: - """Beanie settings""" - - name = "v1_sessions" - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("session_id", ASCENDING)], - name="idx_tenant_session_id", - unique=True, - ), - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - validate_on_save = True - use_state_management = True diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/user_profile.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/user_profile.py deleted file mode 100644 index 59f1fbf6..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/memory/user_profile.py +++ /dev/null @@ -1,81 +0,0 @@ -from datetime import datetime -from typing import List, Optional, Dict, Any -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBaseWithSoftDelete, -) -from pydantic import Field -from core.oxm.mongo.audit_base import AuditBase -from pymongo import IndexModel, ASCENDING, DESCENDING -from api_specs.memory_types import ScenarioType - - -class UserProfile(TenantAwareDocumentBaseWithSoftDelete, AuditBase): - """ - User profile document — one per (user_id, group_id) pair. - Automatically extracted from conversation memcells by ProfileManager. - """ - - # Primary key: (user_id, group_id) uniquely identifies a profile. - user_id: str = Field(..., description="User ID") - group_id: Optional[str] = Field(default=None, description="Group ID") - - # Extracted profile content (role, skills, preferences, personality, etc.). - profile_data: Dict[str, Any] = Field( - default_factory=dict, - description="Extracted profile data as JSON, structure varies by scenario", - ) - - # "solo" = 1 user + N agents, "team" = multi-user + agents. - scenario: str = Field( - default=ScenarioType.SOLO.value, description="Extraction scenario: solo or team" - ) - - # Incremented on each re-extraction. For debugging/monitoring profile update frequency. - update_count: int = Field(default=1, description="Debug: profile update count") - - # LLM discriminator score (0.0–1.0). Below profile_min_confidence threshold is filtered out. - confidence: float = Field( - default=0.0, description="LLM confidence score for this profile" - ) - - # Number of memcells fed to the LLM in the most recent extraction. - memcell_count: int = Field( - default=0, description="Memcell count used in last extraction" - ) - - # Epoch seconds of the latest memcell in the last extraction. - # Compared against memscene_info[cluster_id].timestamp to find clusters with new data. - last_updated_ts: Optional[float] = Field( - default=None, - description="Latest memcell timestamp (epoch) from last extraction, for scheduling", - ) - - class Settings: - """Beanie settings""" - - name = "v1_user_profiles" - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("deleted_at", ASCENDING)], - name="idx_tenant_deleted_at", - sparse=True, - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("user_id", ASCENDING), - ("group_id", ASCENDING), - ], - name="idx_tenant_user_group", - ), - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("updated_at", DESCENDING)], - name="idx_tenant_updated_at", - ), - ] - validate_on_save = True - use_state_management = True diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/request/__init__.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/request/__init__.py deleted file mode 100644 index af5f8477..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/request/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Request related document models -""" - -from infra_layer.adapters.out.persistence.document.request.raw_message import RawMessage - -__all__ = ["RawMessage"] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/request/raw_message.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/document/request/raw_message.py deleted file mode 100644 index b0c8b82e..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/document/request/raw_message.py +++ /dev/null @@ -1,170 +0,0 @@ -# -*- coding: utf-8 -*- -""" -RawMessage MongoDB Document Model - -Stores individual messages from add/flush API requests. -Each message is stored as a separate document for incremental processing. -""" - -from datetime import datetime -from typing import Optional, Dict, Any, List -from core.oxm.mongo.audit_base import AuditBase -from pydantic import Field, ConfigDict -from pymongo import IndexModel, ASCENDING, DESCENDING - -from core.tenants.tenantize.oxm.mongo.tenant_aware_document import ( - TenantAwareDocumentBaseWithSoftDelete, -) - - -class RawMessage(TenantAwareDocumentBaseWithSoftDelete, AuditBase): - """ - Raw Message Document Model - - Stores individual messages from API requests: - - group_id: conversation group ID - - request_id: request ID - - message core fields: message_id, sender_id, sender_name, content, role, etc. - """ - - # Core fields - group_id: str = Field(..., description="Conversation group ID") - request_id: str = Field(..., description="Request ID") - session_id: Optional[str] = Field(default=None, description="Session identifier") - - # ========== Message core fields ========== - message_id: Optional[str] = Field(default=None, description="Message ID") - timestamp: Optional[str] = Field( - default=None, - description="Message timestamp (ISO 8601 format with timezone, e.g. 2025-01-15T10:00:00+00:00)", - ) - sender_id: Optional[str] = Field(default=None, description="Sender ID") - sender_name: Optional[str] = Field(default=None, description="Sender name") - role: Optional[str] = Field( - default=None, - description="Message sender role: 'user' for human, 'assistant' / 'agent' for AI", - ) - content_items: Optional[List[Dict[str, Any]]] = Field( - default=None, - description="Message content items list, e.g. [{type: 'text', content: '...'}, {type: 'image', ...}]", - ) - - # Agent-specific fields (OpenAI chat completion format) - tool_calls: Optional[List[Dict[str, Any]]] = Field( - default=None, - description="Tool calls made by assistant (OpenAI format), only when role='assistant'", - ) - tool_call_id: Optional[str] = Field( - default=None, - description="Tool call ID this message responds to, only when role='tool'", - ) - - # Request metadata - version: Optional[str] = Field(default=None, description="Code version") - endpoint_name: Optional[str] = Field(default=None, description="Endpoint name") - method: Optional[str] = Field(default=None, description="HTTP method") - url: Optional[str] = Field(default=None, description="Request URL") - - # Original event ID (used to associate with RequestHistory) - event_id: Optional[str] = Field(default=None, description="Original event ID") - - # Sync status field (numeric) - # -1: log record only (raw message just saved) - # 0: accumulating in window (confirmed entering accumulation window) - # 1: already fully used (after boundary detection) - sync_status: int = Field( - default=-1, - description="Sync status: -1=log record, 0=window accumulating, 1=already used", - ) - - model_config = ConfigDict( - collection="v1_raw_messages", - validate_assignment=True, - json_encoders={datetime: lambda dt: dt.isoformat()}, - json_schema_extra={ - "example": { - "group_id": "group_123", - "request_id": "req_456", - "session_id": "-1", - "message_id": "msg_001", - "timestamp": "2024-01-01T12:00:00+00:00", - "sender_id": "user_789", - "sender_name": "Test User", - "content_items": [ - {"type": "text", "content": "This is a test message"} - ], - "version": "1.0.0", - "endpoint_name": "add_personal_memories", - } - }, - ) - - class Settings: - """Beanie settings""" - - name = "v1_raw_messages" - indexes = [ - IndexModel( - [("tenant_id", ASCENDING), ("created_at", DESCENDING)], - name="idx_tenant_created_at", - ), - IndexModel( - [("tenant_id", ASCENDING), ("event_id", ASCENDING)], - name="idx_tenant_event_id", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("created_at", DESCENDING), - ], - name="idx_tenant_group_created", - ), - IndexModel( - [("tenant_id", ASCENDING), ("request_id", ASCENDING)], - name="idx_tenant_request_id", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("timestamp", DESCENDING), - ], - name="idx_tenant_group_timestamp", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("sync_status", ASCENDING), - ], - name="idx_tenant_group_sync_status", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("session_id", ASCENDING), - ("sync_status", ASCENDING), - ], - name="idx_tenant_group_session_sync", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("sender_id", ASCENDING), - ("sync_status", ASCENDING), - ], - name="idx_tenant_group_sender_sync", - ), - IndexModel( - [ - ("tenant_id", ASCENDING), - ("group_id", ASCENDING), - ("sender_id", ASCENDING), - ("message_id", ASCENDING), - ], - name="idx_tenant_group_sender_message", - ), - ] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/mapper/__init__.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/mapper/__init__.py deleted file mode 100755 index 5b6c09e5..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/mapper/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Persistence layer mappers -""" - -from infra_layer.adapters.out.persistence.mapper.raw_message_mapper import ( - RawMessageMapper, -) - -__all__ = ["RawMessageMapper"] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/mapper/raw_message_mapper.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/mapper/raw_message_mapper.py deleted file mode 100644 index 81adb104..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/mapper/raw_message_mapper.py +++ /dev/null @@ -1,97 +0,0 @@ -# -*- coding: utf-8 -*- -""" -RawMessage -> RawData Converter - -Handles conversion from RawMessage to RawData. -""" - -from typing import Optional, List - -from core.observation.logger import get_logger -from common_utils.datetime_utils import from_iso_format -from api_specs.dtos import RawData -from api_specs.request_converter import build_raw_data_from_message -from infra_layer.adapters.out.persistence.document.request.raw_message import RawMessage - -logger = get_logger(__name__) - - -class RawMessageMapper: - """ - RawMessage -> RawData Converter - - Converts RawMessage document fields into RawData for internal processing. - """ - - @staticmethod - def to_raw_data(log: RawMessage) -> Optional[RawData]: - """ - Convert RawMessage to RawData - - Builds RawData from the document's individual fields. - Uses the stored content_items list directly. - - Args: - log: RawMessage object - - Returns: - RawData object or None (if log is None) - """ - if log is None: - return None - - # Handle timestamp - timestamp = None - if log.timestamp: - try: - if isinstance(log.timestamp, str): - timestamp = from_iso_format(log.timestamp) - else: - timestamp = log.timestamp - except (ValueError, TypeError) as e: - logger.warning( - "Failed to parse timestamp: %s, error: %s", log.timestamp, e - ) - timestamp = None - - message_id = log.message_id or str(log.id) - content_items = log.content_items or [] - - return build_raw_data_from_message( - message_id=message_id, - sender_id=log.sender_id or "", - content_items=content_items, - timestamp=timestamp, - sender_name=log.sender_name, - role=log.role, - tool_calls=log.tool_calls, - tool_call_id=log.tool_call_id, - ) - - @staticmethod - def to_raw_data_list(logs: List[RawMessage]) -> List[RawData]: - """ - Batch convert a list of RawMessage objects to a list of RawData objects - - Args: - logs: List of RawMessage objects - - Returns: - List of RawData objects (skip records that fail conversion) - """ - raw_data_list: List[RawData] = [] - - for log in logs: - try: - raw_data = RawMessageMapper.to_raw_data(log) - if raw_data: - raw_data_list.append(raw_data) - except (ValueError, TypeError) as e: - logger.error( - "Failed to convert RawMessage to RawData: log_id=%s, error=%s", - log.id, - e, - ) - continue - - return raw_data_list diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/agent_case_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/agent_case_raw_repository.py deleted file mode 100644 index 3fefb49e..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/agent_case_raw_repository.py +++ /dev/null @@ -1,425 +0,0 @@ -""" -Agent case raw data repository. - -Provides CRUD operations for agent case records in MongoDB. -""" - -from datetime import datetime -from typing import List, Optional, Dict, Any -from pymongo.asynchronous.client_session import AsyncClientSession -from bson import ObjectId -from core.observation.logger import get_logger -from core.di.decorators import repository -from core.oxm.constants import MAGIC_ALL -from core.oxm.mongo.base_repository import BaseRepository -from core.oxm.mongo.mongo_utils import build_id_filter as _build_id_filter -from infra_layer.adapters.out.persistence.document.memory.agent_case import ( - AgentCaseRecord, - AgentCaseProjection, -) -from agentic_layer.vectorize_service import get_vectorize_service - -logger = get_logger(__name__) - - -@repository("agent_case_raw_repository", primary=True) -class AgentCaseRawRepository(BaseRepository[AgentCaseRecord]): - """ - Agent case raw data repository. - - Provides CRUD operations and query functions for agent case records. - """ - - def __init__(self): - super().__init__(AgentCaseRecord) - - async def append_experience( - self, record: AgentCaseRecord, session: Optional[AsyncClientSession] = None - ) -> Optional[AgentCaseRecord]: - """ - Insert a new agent case record. - - Auto-vectorizes if vector is missing but experiences have task_intent. - - Args: - record: AgentCaseRecord to insert - session: Optional MongoDB session for transaction support - - Returns: - Inserted AgentCaseRecord or None on failure - """ - # Auto-vectorize if vector is missing - if not record.vector and record.task_intent: - try: - vs = get_vectorize_service() - vec = await vs.get_embedding(record.task_intent) - record.vector = vec.tolist() if hasattr(vec, "tolist") else list(vec) - record.vector_model = vs.get_model_name() - except Exception as e: - logger.error(f"[AgentCaseRepo] Auto-vectorize failed: {e}") - - if not record.vector: - logger.warning( - "[AgentCaseRepo] Saving AgentCase without vector — " - "record will not be retrievable via semantic search" - ) - - try: - result = await record.insert(session=session) - logger.debug( - f"[AgentCaseRepo] Inserted experience: id={result.id}, " - f"intent='{(result.task_intent or '')[:80]}'" - ) - return result - except Exception as e: - logger.error(f"[AgentCaseRepo] Failed to insert experience: {e}") - return None - - async def get_by_event_id( - self, event_id: str, session: Optional[AsyncClientSession] = None - ) -> Optional[AgentCaseRecord]: - """Retrieve agent case by its own ID.""" - try: - object_id = ObjectId(event_id) - return await self.model.find_one({"_id": object_id}, session=session) - except Exception as e: - logger.error(f"[AgentCaseRepo] Failed to get by event_id: {e}") - return None - - async def get_by_ids( - self, case_ids: List[str], session: Optional[AsyncClientSession] = None - ) -> List[AgentCaseRecord]: - """Batch retrieve agent cases by their own IDs. - - Accepts both ObjectId-like strings and raw string IDs. - """ - query_filter = _build_id_filter(case_ids) - if query_filter is None: - return [] - try: - return await self.model.find(query_filter, session=session).to_list() - except Exception as e: - logger.error(f"[AgentCaseRepo] Failed to get by ids: {e}") - return [] - - async def get_by_parent_id( - self, parent_id: str, session: Optional[AsyncClientSession] = None - ) -> Optional[AgentCaseRecord]: - """Retrieve agent case linked to a specific MemCell.""" - try: - return await self.model.find_one({"parent_id": parent_id}, session=session) - except Exception as e: - logger.error(f"[AgentCaseRepo] Failed to get by parent_id: {e}") - return None - - async def get_by_parent_ids( - self, parent_ids: List[str], session: Optional[AsyncClientSession] = None - ) -> List[AgentCaseRecord]: - """Batch retrieve agent cases by parent MemCell IDs. - - Useful for cluster-level knowledge extraction. - """ - try: - results = await self.model.find( - {"parent_id": {"$in": parent_ids}}, session=session - ).to_list() - return results - except Exception as e: - logger.error(f"[AgentCaseRepo] Failed to get by parent_ids: {e}") - return [] - - async def find_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - limit: int = 10, - skip: int = 0, - sort_desc: bool = True, - session: Optional[AsyncClientSession] = None, - ) -> List[AgentCaseRecord]: - """ - Find agent cases with flexible filters. - - Args: - user_id: Filter by user ID. - - MAGIC_ALL ("__all__"): Don't filter by user_id - - None or "": Filter for null/empty values - - Other values: Exact match - group_ids: List of Group IDs. - - None: Skip group filtering - - []: Empty list, skip filtering - - ["g1"]: Single element, exact match - - ["g1", "g2"]: Multiple elements, use $in operator - start_time: Filter by timestamp >= start_time - end_time: Filter by timestamp <= end_time - limit: Maximum number of results - skip: Number of results to skip - sort_desc: Sort by timestamp descending (newest first) - session: Optional MongoDB session - - Returns: - List of matching AgentCaseRecord - """ - try: - query: Dict[str, Any] = {} - - # Handle user_id filter (consistent with EpisodicMemoryRawRepository) - if user_id != MAGIC_ALL: - if user_id == "" or user_id is None: - query["user_id"] = {"$in": [None, ""]} - else: - query["user_id"] = user_id - - # Handle group_ids filter (consistent with EpisodicMemoryRawRepository) - if group_ids is not None and len(group_ids) > 0: - if len(group_ids) == 1: - query["group_id"] = group_ids[0] - else: - query["group_id"] = {"$in": group_ids} - - if start_time is not None or end_time is not None: - ts_filter = {} - if start_time is not None: - ts_filter["$gte"] = start_time - if end_time is not None: - ts_filter["$lte"] = end_time - query["timestamp"] = ts_filter - - sort_field = "-timestamp" if sort_desc else "timestamp" - q = self.model.find(query, session=session).sort(sort_field) - - if skip: - q = q.skip(skip) - if limit: - q = q.limit(limit) - - results = await q.to_list() - - logger.debug( - "[AgentCaseRepo] find_by_filters: user_id=%s, group_ids=%s, " - "time_range=[%s, %s], found %d records", - user_id, - group_ids, - start_time, - end_time, - len(results), - ) - return results - except Exception as e: - logger.error(f"[AgentCaseRepo] Failed to find by filters: {e}") - return [] - - async def count_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Count agent cases by filters (without pagination). - - Args: - user_id: User ID filter (same semantics as find_by_filters) - group_ids: Group IDs filter (same semantics as find_by_filters) - start_time: Optional start time (inclusive) - end_time: Optional end time (inclusive) - session: Optional MongoDB session - - Returns: - Total count of matching records - """ - try: - query: Dict[str, Any] = {} - - if user_id != MAGIC_ALL: - if user_id == "" or user_id is None: - query["user_id"] = {"$in": [None, ""]} - else: - query["user_id"] = user_id - - if group_ids is not None and len(group_ids) > 0: - if len(group_ids) == 1: - query["group_id"] = group_ids[0] - else: - query["group_id"] = {"$in": group_ids} - - if start_time is not None or end_time is not None: - ts_filter = {} - if start_time is not None: - ts_filter["$gte"] = start_time - if end_time is not None: - ts_filter["$lte"] = end_time - query["timestamp"] = ts_filter - - count = await self.model.find(query, session=session).count() - logger.debug( - "[AgentCaseRepo] count_by_filters: user_id=%s, group_ids=%s, count=%d", - user_id, - group_ids, - count, - ) - return count - except Exception as e: - logger.error(f"[AgentCaseRepo] Failed to count by filters: {e}") - return 0 - - async def delete_by_user_id( - self, user_id: str, session: Optional[AsyncClientSession] = None - ) -> int: - """ - Soft-delete all agent cases by user ID. - - Args: - user_id: User ID - session: Optional MongoDB session - - Returns: - Number of soft-deleted records - """ - try: - result = await self.model.delete_many({"user_id": user_id}, session=session) - count = result.modified_count if result else 0 - logger.info( - "[AgentCaseRepo] Soft-deleted experiences by user_id=%s, count=%d", - user_id, - count, - ) - return count - except Exception as e: - logger.error(f"[AgentCaseRepo] Failed to delete by user_id: {e}") - return 0 - - async def delete_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - parent_id: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Soft delete agent cases by filter conditions. - - Args: - user_id: User ID filter (MAGIC_ALL to skip) - group_id: Group ID filter (MAGIC_ALL to skip) - parent_id: Parent ID filter (for cascade delete) - session: Optional MongoDB session - - Returns: - Number of deleted records - """ - try: - filter_dict: Dict[str, Any] = {} - - if user_id != MAGIC_ALL: - if user_id == "" or user_id is None: - filter_dict["user_id"] = {"$in": [None, ""]} - else: - filter_dict["user_id"] = user_id - - if group_id != MAGIC_ALL: - if group_id == "" or group_id is None: - filter_dict["group_id"] = {"$in": [None, ""]} - else: - filter_dict["group_id"] = group_id - - if parent_id: - filter_dict["parent_id"] = parent_id - - if not filter_dict: - logger.warning( - "[AgentCaseRepo] No filter conditions for delete_by_filters" - ) - return 0 - - result = await self.model.delete_many(filter_dict, session=session) - count = result.modified_count if result else 0 - logger.info( - "[AgentCaseRepo] Deleted experiences by filters: filter=%s, count=%d", - filter_dict, - count, - ) - return count - except Exception as e: - logger.error(f"[AgentCaseRepo] Failed to delete by filters: {e}") - return 0 - - async def fetch_task_intents_by_event_ids( - self, event_ids: List[str] - ) -> Dict[str, str]: - """Fetch task_intent texts from AgentCase DB by parent event IDs. - - Used as context_fetcher callback for ClusterManager in LLM mode. - - Args: - event_ids: List of memcell event IDs (used as parent_id in agent cases) - - Returns: - Dict mapping event_id -> task_intent text - """ - if not event_ids: - return {} - - try: - cases = ( - await self.model.find({"parent_id": {"$in": event_ids}}) - .project(AgentCaseProjection) - .to_list() - ) - - result: Dict[str, str] = {} - for case in cases: - if case.parent_id and case.task_intent: - result[case.parent_id] = case.task_intent - return result - except Exception as e: - logger.error(f"[AgentCaseRepo] Failed to fetch task intents: {e}") - return {} - - async def find_by_filter_paginated( - self, - query_filter: Optional[Dict[str, Any]] = None, - skip: int = 0, - limit: int = 100, - sort_field: str = "created_at", - sort_desc: bool = False, - ) -> List[AgentCaseRecord]: - """ - Paginated query of AgentCaseRecord, used for data synchronization. - - Args: - query_filter: Query filter conditions, query all if None - skip: Number of results to skip - limit: Limit number of returned results - sort_field: Sort field, default is created_at - sort_desc: Whether to sort in descending order - - Returns: - List of AgentCaseRecord - """ - try: - filter_dict = query_filter if query_filter else {} - q = self.model.find(filter_dict) - - if sort_desc: - q = q.sort(f"-{sort_field}") - else: - q = q.sort(sort_field) - - q = q.skip(skip).limit(limit) - results = await q.to_list() - logger.debug( - "[AgentCaseRepo] find_by_filter_paginated: filter=%s, skip=%d, limit=%d, found %d", - filter_dict, - skip, - limit, - len(results), - ) - return results - except Exception as e: - logger.error(f"[AgentCaseRepo] Failed paginated query: {e}") - return [] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/agent_skill_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/agent_skill_raw_repository.py deleted file mode 100644 index f253c3a7..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/agent_skill_raw_repository.py +++ /dev/null @@ -1,273 +0,0 @@ -""" -Agent skill raw data repository. - -Provides CRUD operations for agent skill records in MongoDB. -Skills are cluster-scoped: one repository manages all skill items per MemScene. -""" - -from typing import List, Optional, Dict, Any -from pymongo.asynchronous.client_session import AsyncClientSession -from bson import ObjectId -from core.observation.logger import get_logger -from core.di.decorators import repository -from core.oxm.mongo.base_repository import BaseRepository -from core.oxm.mongo.mongo_utils import build_id_filter as _build_id_filter -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.persistence.document.memory.agent_skill import ( - AgentSkillRecord, -) - -logger = get_logger(__name__) - - -@repository("agent_skill_raw_repository", primary=True) -class AgentSkillRawRepository(BaseRepository[AgentSkillRecord]): - """ - Agent skill raw data repository. - - Manages skill items extracted from MemScene clusters (AgentCase clusters). - Supports incremental operations: add, update, and soft-delete individual skills. - """ - - def __init__(self): - super().__init__(AgentSkillRecord) - - async def find_by_ids( - self, - ids: List[str], - min_confidence: Optional[float] = None, - session: Optional[AsyncClientSession] = None, - ) -> List[AgentSkillRecord]: - """Batch fetch agent skills by _id list, optionally filtering retired skills. - - Accepts both ObjectId-like strings and raw string IDs. - - Args: - ids: List of document _id strings - min_confidence: Exclude skills with confidence below this threshold. - None to include all skills regardless of confidence. - session: Optional MongoDB session - - Returns: - List of AgentSkillRecord - """ - id_filter = _build_id_filter(ids) - if id_filter is None: - return [] - try: - if min_confidence is None: - query: Dict[str, Any] = id_filter - elif "$or" in id_filter: - # id_filter is {"$or": [...]}; combine with $and - query = {"$and": [id_filter, {"confidence": {"$gte": min_confidence}}]} - else: - # id_filter is {"_id": {"$in": [...]}}; merge directly - query = {**id_filter, "confidence": {"$gte": min_confidence}} - return await self.model.find(query, session=session).to_list() - except Exception as e: - logger.error(f"[AgentSkillRepo] Failed to find by ids: {e}") - return [] - - async def save_skill( - self, record: AgentSkillRecord, session: Optional[AsyncClientSession] = None - ) -> Optional[AgentSkillRecord]: - """Insert a new agent skill record.""" - try: - result = await record.insert(session=session) - logger.debug( - f"[AgentSkillRepo] Inserted skill: id={result.id}, " - f"cluster={result.cluster_id}, name='{result.name}'" - ) - return result - except Exception as e: - logger.error(f"[AgentSkillRepo] Failed to insert skill: {e}") - return None - - async def get_by_cluster_id( - self, - cluster_id: str, - group_id: Optional[str] = None, - min_confidence: Optional[float] = None, - session: Optional[AsyncClientSession] = None, - ) -> List[AgentSkillRecord]: - """Retrieve skill records for a cluster (MemScene). - - Args: - cluster_id: MemScene cluster ID - group_id: Group ID to scope the query; prevents cross-group reads. - min_confidence: Exclude skills with confidence below this threshold. - None to include all skills regardless of confidence. - session: Optional MongoDB session - """ - try: - query: Dict[str, Any] = {"cluster_id": cluster_id} - if group_id is not None: - query["group_id"] = group_id - if min_confidence is not None: - query["confidence"] = {"$gte": min_confidence} - results = await self.model.find(query, session=session).to_list() - return results - except Exception as e: - logger.error(f"[AgentSkillRepo] Failed to get by cluster_id: {e}") - return [] - - async def update_skill_by_id( - self, - record_id: Any, - updates: Dict[str, Any], - session: Optional[AsyncClientSession] = None, - ) -> bool: - """Update specific fields of a skill record by its ID. - - Args: - record_id: The document _id - updates: Dict of field_name -> new_value to set - session: Optional MongoDB session - - Returns: - True if the update was applied, False otherwise. - """ - try: - from common_utils.datetime_utils import get_now_with_timezone - - updates["updated_at"] = get_now_with_timezone() - result = await AgentSkillRecord.get_pymongo_collection().update_one( - {"_id": record_id, "deleted_at": None}, - {"$set": updates}, - session=session, - ) - if result.modified_count > 0: - logger.debug( - f"[AgentSkillRepo] Updated skill id={record_id}, " - f"fields={list(updates.keys())}" - ) - return True - logger.warning( - f"[AgentSkillRepo] No document matched for update id={record_id}" - ) - return False - except Exception as e: - logger.error(f"[AgentSkillRepo] Failed to update skill id={record_id}: {e}") - return False - - async def soft_delete_by_id( - self, record_id: Any, session: Optional[AsyncClientSession] = None - ) -> bool: - """Soft-delete a single skill record by its ID. - - Args: - record_id: The document _id - session: Optional MongoDB session - - Returns: - True if deleted, False otherwise. - """ - try: - from common_utils.datetime_utils import get_now_with_timezone - - now = get_now_with_timezone() - result = await AgentSkillRecord.get_pymongo_collection().update_one( - {"_id": record_id, "deleted_at": None}, - {"$set": {"deleted_at": now, "deleted_id": abs(hash(str(record_id)))}}, - session=session, - ) - if result.modified_count > 0: - logger.debug(f"[AgentSkillRepo] Soft-deleted skill id={record_id}") - return True - logger.warning( - f"[AgentSkillRepo] No document matched for soft-delete id={record_id}" - ) - return False - except Exception as e: - logger.error( - f"[AgentSkillRepo] Failed to soft-delete skill id={record_id}: {e}" - ) - return False - - def _build_filter_query( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - cluster_id: Optional[str] = None, - ) -> Dict[str, Any]: - """Build a filter query dict from common parameters. - - Args: - user_id: User ID filter - - MAGIC_ALL ("__all__"): Don't filter by user_id - - Other values: Exact match - """ - query: Dict[str, Any] = {} - if user_id is not None and user_id != MAGIC_ALL: - query["user_id"] = user_id - if group_ids is not None and len(group_ids) > 0: - if len(group_ids) == 1: - query["group_id"] = group_ids[0] - else: - query["group_id"] = {"$in": group_ids} - if cluster_id is not None: - query["cluster_id"] = cluster_id - return query - - async def find_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - cluster_id: Optional[str] = None, - limit: int = 50, - skip: int = 0, - session: Optional[AsyncClientSession] = None, - ) -> List[AgentSkillRecord]: - """Find skill records with flexible filters.""" - try: - query = self._build_filter_query( - user_id=user_id, group_ids=group_ids, cluster_id=cluster_id - ) - - results = ( - await self.model.find(query, session=session) - .skip(skip) - .limit(limit) - .to_list() - ) - return results - except Exception as e: - logger.error(f"[AgentSkillRepo] Failed to find by filters: {e}") - return [] - - async def count_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - cluster_id: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """Count skill records by filters (without pagination). - - Args: - user_id: User ID filter - - MAGIC_ALL ("__all__"): Don't filter by user_id - - Other values: Exact match - group_ids: Group IDs filter (list, supports $in for multiple) - cluster_id: Cluster ID filter - session: Optional MongoDB session - - Returns: - Total count of matching records - """ - try: - query = self._build_filter_query( - user_id=user_id, group_ids=group_ids, cluster_id=cluster_id - ) - count = await self.model.find(query, session=session).count() - logger.debug( - "[AgentSkillRepo] count_by_filters: user_id=%s, group_ids=%s, cluster_id=%s, count=%d", - user_id, - group_ids, - cluster_id, - count, - ) - return count - except Exception as e: - logger.error(f"[AgentSkillRepo] Failed to count by filters: {e}") - return 0 diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/atomic_fact_record_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/atomic_fact_record_raw_repository.py deleted file mode 100644 index 47ec87a5..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/atomic_fact_record_raw_repository.py +++ /dev/null @@ -1,435 +0,0 @@ -""" -AtomicFactRecord Repository - -Provides CRUD operations and query capabilities for generic atomic facts. -""" - -from datetime import datetime -from typing import Any, List, Optional, Type, TypeVar, Union -from pymongo.asynchronous.client_session import AsyncClientSession -from bson import ObjectId -from core.observation.logger import get_logger -from core.di.decorators import repository -from core.oxm.mongo.base_repository import BaseRepository -from core.oxm.mongo.mongo_utils import build_id_filter as _build_id_filter -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.persistence.document.memory.atomic_fact_record import ( - AtomicFactRecord, - AtomicFactRecordProjection, -) - -# Define generic type variable -T = TypeVar('T', AtomicFactRecord, AtomicFactRecordProjection) - -logger = get_logger(__name__) - - -@repository("atomic_fact_record_raw_repository", primary=True) -class AtomicFactRecordRawRepository(BaseRepository[AtomicFactRecord]): - """ - Personal atomic fact raw data repository - - Provides CRUD operations and basic query functions for personal atomic facts. - Note: Vectors should be generated during extraction; this Repository is not responsible for vector generation. - """ - - def __init__(self): - super().__init__(AtomicFactRecord) - - # ==================== Basic CRUD Methods ==================== - - async def save( - self, record: AtomicFactRecord, session: Optional[AsyncClientSession] = None - ) -> Optional[AtomicFactRecord]: - """ - Save atomic fact record - - Args: - record: AtomicFactRecord object - session: Optional MongoDB session, for transaction support - - Returns: - Saved AtomicFactRecord or None - """ - try: - await record.insert(session=session) - logger.info( - "Saved atomic fact record successfully: id=%s, user_id=%s, parent_type=%s, parent_id=%s", - record.id, - record.user_id, - record.parent_type, - record.parent_id, - ) - return record - except Exception as e: - logger.error("Failed to save atomic fact record: %s", e) - return None - - async def get_by_id( - self, - log_id: str, - session: Optional[AsyncClientSession] = None, - model: Optional[Type[T]] = None, - ) -> Optional[Union[AtomicFactRecord, AtomicFactRecordProjection]]: - """ - Get personal atomic fact by ID - - Args: - log_id: Log ID - session: Optional MongoDB session, for transaction support - model: Returned model type, default is AtomicFactRecord (full version), can pass AtomicFactRecordShort - - Returns: - Atomic fact object of specified type or None - """ - try: - object_id = ObjectId(log_id) - - # If model is not specified, use full version - target_model = model if model is not None else self.model - - # Determine whether to use projection based on model type - if target_model == self.model: - result = await self.model.find_one({"_id": object_id}, session=session) - else: - result = await self.model.find_one( - {"_id": object_id}, projection_model=target_model, session=session - ) - - if result: - logger.debug( - "✅ Retrieved personal atomic fact by ID successfully: %s (model=%s)", - log_id, - target_model.__name__, - ) - else: - logger.debug("ℹ️ Personal atomic fact not found: id=%s", log_id) - return result - except Exception as e: - logger.error("❌ Failed to retrieve personal atomic fact by ID: %s", e) - return None - - async def find_by_ids( - self, - ids: List[str], - projection_model: Optional[Type[Any]] = None, - session: Optional[AsyncClientSession] = None, - ) -> List[Union[AtomicFactRecord, AtomicFactRecordProjection]]: - """ - Batch fetch atomic facts by their _id list. - - Accepts both ObjectId-like strings and raw string IDs. - - Args: - ids: List of document _id strings - projection_model: Optional projection model to reduce data transfer - (e.g. AtomicFactRecordProjection skips the vector field) - session: Optional MongoDB session - - Returns: - List of AtomicFactRecord or projection model instances - """ - query_filter = _build_id_filter(ids) - if query_filter is None: - return [] - try: - if projection_model is not None: - return await self.model.find( - query_filter, projection_model=projection_model, session=session - ).to_list() - return await self.model.find(query_filter, session=session).to_list() - except Exception as e: - logger.error("❌ Failed to find atomic facts by ids: %s", e) - return [] - - async def get_by_parent_id( - self, - parent_id: str, - parent_type: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - model: Optional[Type[T]] = None, - ) -> List[Union[AtomicFactRecord, AtomicFactRecordProjection]]: - """ - Get all atomic facts by parent memory ID and optionally parent type - - Args: - parent_id: Parent memory ID - parent_type: Optional parent type filter (e.g., "memcell", "episode") - session: Optional MongoDB session, for transaction support - model: Returned model type, default is AtomicFactRecord (full version), can pass AtomicFactRecordShort - - Returns: - List of atomic fact objects of specified type - """ - try: - # If model is not specified, use full version - target_model = model if model is not None else self.model - - # Build query filter - query_filter = {"parent_id": parent_id} - if parent_type: - query_filter["parent_type"] = parent_type - - # Determine whether to use projection based on model type - if target_model == self.model: - query = self.model.find(query_filter, session=session) - else: - query = self.model.find( - query_filter, projection_model=target_model, session=session - ) - - results = await query.to_list() - logger.debug( - "✅ Retrieved atomic facts by parent memory ID successfully: %s (type=%s), found %d records (model=%s)", - parent_id, - parent_type, - len(results), - target_model.__name__, - ) - return results - except Exception as e: - logger.error( - "❌ Failed to retrieve atomic facts by parent episodic memory ID: %s", e - ) - return [] - - async def find_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - limit: Optional[int] = None, - skip: Optional[int] = None, - sort_desc: bool = True, - session: Optional[AsyncClientSession] = None, - model: Optional[Type[T]] = None, - ) -> List[Union[AtomicFactRecord, AtomicFactRecordProjection]]: - """ - Get list of atomic facts by filters (user_id, group_ids, and/or time range) - - Args: - user_id: User ID - - Not provided or MAGIC_ALL ("__all__"): Don't filter by user_id - - None or "": Filter for null/empty values (records with user_id as None or "") - - Other values: Exact match - group_ids: List of Group IDs - - None: Skip group filtering - - []: Empty array, skip filtering - - ["g1"]: Single element array, exact match - - ["g1", "g2"]: Multiple elements, use $in operator - start_time: Optional start time (inclusive) - end_time: Optional end time (exclusive) - limit: Limit number of returned records - skip: Number of records to skip - sort_desc: Whether to sort by time in descending order - session: Optional MongoDB session, for transaction support - model: Returned model type, default is AtomicFactRecord (full version), can pass AtomicFactRecordProjection - - Returns: - List of atomic fact objects of specified type - """ - try: - # Build query filter - filter_dict = {} - - # Handle time range filter - if start_time is not None and end_time is not None: - filter_dict["timestamp"] = {"$gte": start_time, "$lt": end_time} - elif start_time is not None: - filter_dict["timestamp"] = {"$gte": start_time} - elif end_time is not None: - filter_dict["timestamp"] = {"$lt": end_time} - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id == "" or user_id is None: - # Explicitly filter for null or empty string - filter_dict["user_id"] = {"$in": [None, ""]} - else: - filter_dict["user_id"] = user_id - - # Handle group_ids filter (array, no MAGIC_ALL) - if group_ids is not None and len(group_ids) > 0: - if len(group_ids) == 1: - # Single element: exact match - filter_dict["group_id"] = group_ids[0] - else: - # Multiple elements: use $in operator - filter_dict["group_id"] = {"$in": group_ids} - # group_ids is None or empty: skip group filtering - - # If model is not specified, use full version - target_model = model if model is not None else self.model - - # Determine whether to use projection based on model type - if target_model == self.model: - query = self.model.find(filter_dict, session=session) - else: - query = self.model.find( - filter_dict, projection_model=target_model, session=session - ) - - sort_field = "-timestamp" if sort_desc else "timestamp" - query = query.sort(sort_field) - - if skip: - query = query.skip(skip) - if limit: - query = query.limit(limit) - - logger.debug( - "🔍 AtomicFactRecord.find_by_filters query: %s, sort=%s, skip=%s, limit=%s", - query.get_filter_query(), - sort_field, - skip, - limit, - ) - - results = await query.to_list() - logger.debug( - "✅ Retrieved atomic facts successfully: user_id=%s, group_ids=%s, time_range=[%s, %s), found %d records (model=%s)", - user_id, - group_ids, - start_time, - end_time, - len(results), - target_model.__name__, - ) - return results - except Exception as e: - logger.error("❌ Failed to retrieve atomic facts: %s", e) - return [] - - async def count_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Count atomic facts by filters (without pagination) - - Args: - user_id: User ID filter (same semantics as find_by_filters) - group_ids: Group IDs filter (same semantics as find_by_filters) - start_time: Optional start time (inclusive) - end_time: Optional end time (exclusive) - session: Optional MongoDB session - - Returns: - Total count of matching records - """ - try: - # Build query filter (same as find_by_filters) - filter_dict = {} - - # Handle time range filter - if start_time is not None and end_time is not None: - filter_dict["timestamp"] = {"$gte": start_time, "$lt": end_time} - elif start_time is not None: - filter_dict["timestamp"] = {"$gte": start_time} - elif end_time is not None: - filter_dict["timestamp"] = {"$lt": end_time} - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id == "" or user_id is None: - filter_dict["user_id"] = {"$in": [None, ""]} - else: - filter_dict["user_id"] = user_id - - # Handle group_ids filter - if group_ids is not None and len(group_ids) > 0: - if len(group_ids) == 1: - filter_dict["group_id"] = group_ids[0] - else: - filter_dict["group_id"] = {"$in": group_ids} - - count = await self.model.find(filter_dict, session=session).count() - logger.debug( - "✅ Counted atomic facts: user_id=%s, group_ids=%s, count=%d", - user_id, - group_ids, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to count atomic facts: %s", e) - return 0 - - async def delete_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - parent_id: Optional[str] = None, - session_id: Optional[str] = MAGIC_ALL, - sender_id: Optional[str] = MAGIC_ALL, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Soft delete atomic fact records by filter conditions - - Three-state filter semantics (user_id, group_id, session_id, sender_id): - - MAGIC_ALL (default): skip this filter - - None or "": match null/empty records - - other value: exact match - - Args: - user_id: User ID filter - group_id: Group ID filter - parent_id: Parent ID filter (for cascade delete) - session_id: Session ID filter - sender_id: Sender ID filter (maps to "sender_ids" field) - session: Optional MongoDB session, for transaction support - - Returns: - Number of deleted records - """ - filter_dict = {} - - if user_id != MAGIC_ALL: - if user_id is None or user_id == "": - filter_dict["user_id"] = {"$in": [None, ""]} - else: - filter_dict["user_id"] = user_id - - if group_id != MAGIC_ALL: - if group_id is None or group_id == "": - filter_dict["group_id"] = {"$in": [None, ""]} - else: - filter_dict["group_id"] = group_id - - if parent_id is not None: - filter_dict["parent_id"] = parent_id - - if session_id != MAGIC_ALL: - if session_id is None or session_id == "": - filter_dict["session_id"] = {"$in": [None, ""]} - else: - filter_dict["session_id"] = session_id - - if sender_id != MAGIC_ALL: - if sender_id is None or sender_id == "": - filter_dict["sender_ids"] = {"$in": [None, ""]} - else: - filter_dict["sender_ids"] = sender_id - - if not filter_dict: - logger.warning("No filter conditions provided for delete_by_filters") - return 0 - - result = await self.model.delete_many(filter_dict, session=session) - count = result.modified_count if result else 0 - logger.info( - "Soft deleted atomic fact records by filters: filter=%s, deleted %d records", - filter_dict, - count, - ) - return count - - -# Export -__all__ = ["AtomicFactRecordRawRepository"] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/conversation_data_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/conversation_data_raw_repository.py deleted file mode 100644 index 7215ac77..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/conversation_data_raw_repository.py +++ /dev/null @@ -1,405 +0,0 @@ -# -*- coding: utf-8 -*- -""" -ConversationDataRepository interface and implementation - -Conversation data storage based on RawMessage, replacing the original Redis implementation. -""" - -from abc import ABC, abstractmethod -from typing import List, Optional - -from core.observation.logger import get_logger -from core.di.decorators import repository -from core.di import get_bean -from memory_layer.memcell_extractor.base_memcell_extractor import RawData -from common_utils.datetime_utils import from_iso_format -from infra_layer.adapters.out.persistence.repository.raw_message_repository import ( - RawMessageRepository, -) -from infra_layer.adapters.out.persistence.mapper.raw_message_mapper import ( - RawMessageMapper, -) - -logger = get_logger(__name__) - - -# ==================== Interface Definition ==================== - - -class ConversationDataRepository(ABC): - """Conversation data access interface""" - - @abstractmethod - async def save_conversation_data( - self, - raw_data_list: List[RawData], - group_id: str, - session_id: Optional[str] = None, - ) -> bool: - """ - Confirm conversation data enters window accumulation - - Updates sync_status=-1 to sync_status=0 for records matching message_ids in raw_data_list. - - Args: - raw_data_list: List of RawData, data_id is used to identify which records to update - group_id: Group ID - session_id: Session identifier for conversation isolation - - Returns: - bool: True if successful, False otherwise - """ - pass - - @abstractmethod - async def get_conversation_data( - self, - group_id: str, - session_id: Optional[str] = None, - start_time: Optional[str] = None, - end_time: Optional[str] = None, - limit: int = 100, - exclude_message_ids: Optional[List[str]] = None, - ) -> List[RawData]: - """ - Get conversation data (sync_status=-1 or 0) - - Returns both pending (-1) and accumulating (0) records. - - Args: - group_id: Group ID - session_id: Session identifier for conversation isolation - start_time: Start time (ISO format string) - end_time: End time (ISO format string) - limit: Maximum number of records to return - exclude_message_ids: Message IDs to exclude from results - - Returns: - List[RawData]: List of conversation data - """ - pass - - @abstractmethod - async def delete_conversation_data( - self, - group_id: str, - session_id: Optional[str] = None, - exclude_message_ids: Optional[List[str]] = None, - ) -> bool: - """ - Mark all pending and accumulating data as used - - Updates sync_status=-1 and 0 to sync_status=1. - - Args: - group_id: Group ID - session_id: Session identifier for conversation isolation - exclude_message_ids: Message IDs to exclude from update - - Returns: - bool: Return True if successful, False otherwise - """ - pass - - @abstractmethod - async def fetch_unprocessed_conversation_data( - self, group_id: str, session_id: Optional[str] = None, limit: int = 100 - ) -> List[RawData]: - """ - Fetch unprocessed conversation data (sync_status=-1 or 0) - - Unlike get_conversation_data, this method does not have time range filters - and returns results in ascending order (oldest first). - - Args: - group_id: Group ID - session_id: Session identifier for conversation isolation - limit: Maximum number of records to return (in ascending order) - - Returns: - List[RawData]: List of unprocessed conversation data - """ - pass - - -# ==================== Implementation ==================== - - -@repository("conversation_data_raw_repository", primary=True) -class ConversationDataRepositoryImpl(ConversationDataRepository): - """ - ConversationDataRepository implementation based on RawMessage - - Reuses RawMessage storage for conversation data, converting between RawData - and RawMessage. Data is automatically saved to RawMessage through - the RequestHistoryEvent listener. - """ - - def __init__(self): - self._repo: Optional[RawMessageRepository] = None - - def _get_repo(self) -> RawMessageRepository: - """Lazy load RawMessageRepository""" - if self._repo is None: - self._repo = get_bean("raw_message_repository") - return self._repo - - # ==================== ConversationDataRepository Interface Implementation ==================== - - async def save_conversation_data( - self, - raw_data_list: List[RawData], - group_id: str, - session_id: Optional[str] = None, - ) -> bool: - """ - Confirm conversation data enters window accumulation - - Updates sync_status=-1 to sync_status=0 for records matching the message_ids - in raw_data_list. Only confirms the specific messages provided. - - sync_status state transitions: - - -1: Just a log record (raw request just saved via listener) - - 0: In window accumulation (confirmed via this method) - - 1: Already fully used (marked via delete_conversation_data) - - Args: - raw_data_list: RawData list, data_id is used to identify which records to update - group_id: Conversation group ID - session_id: Session identifier for conversation isolation - - Returns: - bool: True if operation succeeds, False otherwise - """ - logger.info( - "Confirming conversation data enters window accumulation: group_id=%s, session_id=%s, data_count=%d", - group_id, - session_id, - len(raw_data_list) if raw_data_list else 0, - ) - - try: - repo = self._get_repo() - - # Extract message_id list (filter out empty values) - message_ids = [r.data_id for r in (raw_data_list or []) if r.data_id] - - if not message_ids: - logger.debug("No message_ids to confirm, skipping update") - return True - - # Precise update: only update records with specified message_id - modified_count = await repo.confirm_accumulation_by_message_ids( - group_id, message_ids, session_id=session_id - ) - - logger.info( - "Window accumulation confirmation completed: group_id=%s, session_id=%s, message_ids=%d, modified=%d", - group_id, - session_id, - len(message_ids), - modified_count, - ) - return True - - except Exception as e: - logger.error( - "Window accumulation confirmation failed: group_id=%s, session_id=%s, error=%s", - group_id, - session_id, - e, - ) - return False - - async def get_conversation_data( - self, - group_id: str, - session_id: Optional[str] = None, - start_time: Optional[str] = None, - end_time: Optional[str] = None, - limit: int = 100, - exclude_message_ids: Optional[List[str]] = None, - ) -> List[RawData]: - """ - Get conversation data (sync_status=-1 or 0) - - Queries RawMessage with sync_status=-1 (pending) or 0 (accumulating) - and converts to RawData. Returns both pending and accumulating records. - - sync_status state description: - - -1: Pending (returned) - - 0: In window accumulation (returned) - - 1: Already fully used (not returned) - - Args: - group_id: Conversation group ID - session_id: Session identifier for conversation isolation - start_time: Start time (ISO format string) - end_time: End time (ISO format string) - limit: Maximum number of records to return - exclude_message_ids: Message IDs to exclude from results - - Returns: - List[RawData]: List of conversation data - """ - logger.info( - "Fetching conversation data: group_id=%s, session_id=%s, start_time=%s, end_time=%s, limit=%d, exclude=%d", - group_id, - session_id, - start_time, - end_time, - limit, - len(exclude_message_ids) if exclude_message_ids else 0, - ) - - try: - repo = self._get_repo() - - # Convert time format - start_dt = from_iso_format(start_time) if start_time else None - end_dt = from_iso_format(end_time) if end_time else None - - # Query RawMessage with sync_status=-1 or 0 - logs = await repo.find_by_group_id_with_statuses( - group_id=group_id, - sync_status_list=[-1, 0], - session_id=session_id, - start_time=start_dt, - end_time=end_dt, - limit=limit, - exclude_message_ids=exclude_message_ids, - ) - - # Use mapper to convert to RawData list - raw_data_list = RawMessageMapper.to_raw_data_list(logs) - - logger.info( - "Conversation data fetch completed: group_id=%s, session_id=%s, count=%d", - group_id, - session_id, - len(raw_data_list), - ) - return raw_data_list - - except Exception as e: - logger.error( - "Conversation data fetch failed: group_id=%s, session_id=%s, error=%s", - group_id, - session_id, - e, - ) - return [] - - async def delete_conversation_data( - self, - group_id: str, - session_id: Optional[str] = None, - exclude_message_ids: Optional[List[str]] = None, - ) -> bool: - """ - Mark all pending and accumulating data as used - - Updates sync_status=-1 (pending) and 0 (accumulating) to sync_status=1 (used). - This marks all conversation data for the group as fully processed. - - Args: - group_id: Conversation group ID - session_id: Session identifier for conversation isolation - exclude_message_ids: Message IDs to exclude from update - - Returns: - bool: True if operation succeeds, False otherwise - """ - logger.info( - "Marking conversation data as used: group_id=%s, session_id=%s, exclude=%d", - group_id, - session_id, - len(exclude_message_ids) if exclude_message_ids else 0, - ) - - try: - repo = self._get_repo() - # Update sync_status: -1,0 -> 1 - modified_count = await repo.mark_as_used_by_group_id( - group_id, session_id=session_id, exclude_message_ids=exclude_message_ids - ) - - logger.info( - "Conversation data marked as used: group_id=%s, session_id=%s, modified=%d", - group_id, - session_id, - modified_count, - ) - return True - - except Exception as e: - logger.error( - "Failed to mark conversation data as used: group_id=%s, session_id=%s, error=%s", - group_id, - session_id, - e, - ) - return False - - async def fetch_unprocessed_conversation_data( - self, group_id: str, session_id: Optional[str] = None, limit: int = 100 - ) -> List[RawData]: - """ - Fetch unprocessed conversation data (sync_status=-1 or 0) - - Unlike get_conversation_data, this method: - - Does not have start_time and end_time filters - - Returns results in ascending order (oldest first) with limit applied - - This is useful for fetching all pending/accumulating messages that need - to be processed, without time range restrictions. - - Args: - group_id: Conversation group ID - session_id: Session identifier for conversation isolation - limit: Maximum number of records to return (in ascending order, oldest first) - - Returns: - List[RawData]: List of unprocessed conversation data - """ - logger.info( - "Fetching unprocessed conversation data: group_id=%s, session_id=%s, limit=%d", - group_id, - session_id, - limit, - ) - - try: - repo = self._get_repo() - - # Query both pending (-1) and accumulating (0) records - # No time range filter, ascending order (oldest first) - logs = await repo.find_by_group_id_with_statuses( - group_id=group_id, - sync_status_list=[-1, 0], - session_id=session_id, - start_time=None, - end_time=None, - limit=limit, - ascending=True, - ) - - # Use mapper to convert to RawData list - raw_data_list = RawMessageMapper.to_raw_data_list(logs) - - logger.info( - "Unprocessed conversation data fetch completed: group_id=%s, session_id=%s, count=%d", - group_id, - session_id, - len(raw_data_list), - ) - return raw_data_list - - except Exception as e: - logger.error( - "Unprocessed conversation data fetch failed: group_id=%s, session_id=%s, error=%s", - group_id, - session_id, - e, - ) - return [] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/conversation_status_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/conversation_status_raw_repository.py deleted file mode 100644 index d97f7278..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/conversation_status_raw_repository.py +++ /dev/null @@ -1,185 +0,0 @@ -from typing import Optional, Dict, Any -from pymongo.asynchronous.client_session import AsyncClientSession -from core.oxm.mongo.base_repository import BaseRepository -from infra_layer.adapters.out.persistence.document.memory.conversation_status import ( - ConversationStatus, -) -from core.observation.logger import get_logger -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("conversation_status_raw_repository", primary=True) -class ConversationStatusRawRepository(BaseRepository[ConversationStatus]): - """ - Conversation status raw data repository - - Provides CRUD operations and query capabilities for conversation status data. - """ - - def __init__(self): - super().__init__(ConversationStatus) - - # ==================== Basic CRUD Operations ==================== - - async def get_by_group_id( - self, - group_id: str, - session_id: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> Optional[ConversationStatus]: - """Get conversation status by group ID and session ID""" - try: - query = {"group_id": group_id} - if session_id is not None: - query["session_id"] = session_id - result = await self.model.find_one(query, session=session) - if result: - logger.debug( - "✅ Successfully retrieved conversation status by group ID: %s", - group_id, - ) - else: - logger.debug("⚠️ Conversation status not found: group_id=%s", group_id) - return result - except Exception as e: - logger.error("❌ Failed to retrieve conversation status by group ID: %s", e) - return None - - async def delete_by_group_id( - self, group_id: str, session: Optional[AsyncClientSession] = None - ) -> bool: - """Delete conversation status by group ID""" - try: - result = await self.model.find_one({"group_id": group_id}, session=session) - if not result: - logger.warning( - "⚠️ Conversation status to delete not found: group_id=%s", group_id - ) - return False - - await result.delete(session=session) - logger.info( - "✅ Successfully deleted conversation status by group ID: %s", group_id - ) - return True - except Exception as e: - logger.error("❌ Failed to delete conversation status by group ID: %s", e) - return False - - async def upsert_by_group_id( - self, - group_id: str, - update_data: Dict[str, Any], - session_id: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> Optional[ConversationStatus]: - """Update or insert conversation status by group ID and session ID - - Uses MongoDB atomic upsert operation to avoid concurrency race conditions. - If a matching record is found, it updates it; otherwise, it creates a new record. - Unique key is (group_id, session_id). - - Args: - group_id: Group ID - update_data: Data to update - session_id: Session identifier for conversation isolation - session: MongoDB session - - Returns: - The updated or created conversation status record - """ - try: - # Build query with session_id - query = {"group_id": group_id} - if session_id is not None: - query["session_id"] = session_id - - # 1. First try to find an existing record - existing_doc = await self.model.find_one(query, session=session) - - if existing_doc: - # Record found, update directly - for key, value in update_data.items(): - setattr(existing_doc, key, value) - await existing_doc.save(session=session) - logger.debug( - "Successfully updated existing conversation status: group_id=%s, session_id=%s", - group_id, - session_id, - ) - return existing_doc - - # 2. Record not found, try to create a new one - try: - create_data = {**update_data} - if session_id is not None: - create_data["session_id"] = session_id - new_doc = ConversationStatus(group_id=group_id, **create_data) - await new_doc.create(session=session) - logger.info( - "Successfully created new conversation status: group_id=%s, session_id=%s", - group_id, - session_id, - ) - return new_doc - - except Exception as create_error: - # 3. Creation failed, check if it's a duplicate key error (concurrent case) - error_str = str(create_error) - if "E11000" in error_str and "duplicate key" in error_str: - logger.warning( - "Concurrent creation conflict, re-lookup and update: group_id=%s, session_id=%s", - group_id, - session_id, - ) - - # Duplicate key error means another thread has already created the record, re-lookup and update - retry_doc = await self.model.find_one(query, session=session) - - if retry_doc: - # Found the record created by another thread, update it - for key, value in update_data.items(): - setattr(retry_doc, key, value) - await retry_doc.save(session=session) - logger.debug( - "Successfully updated after concurrency conflict: group_id=%s, session_id=%s", - group_id, - session_id, - ) - return retry_doc - else: - logger.error( - "Still unable to find record after concurrency conflict: group_id=%s, session_id=%s", - group_id, - session_id, - ) - return None - else: - # Other types of creation errors, re-raise - raise create_error - - except Exception as e: - logger.error("Failed to update or create conversation status: %s", e) - return None - - # ==================== Statistics Methods ==================== - - async def count_by_group_id( - self, group_id: str, session: Optional[AsyncClientSession] = None - ) -> int: - """Count the number of conversation statuses for a specified group""" - try: - count = await self.model.find( - {"group_id": group_id}, session=session - ).count() - logger.debug( - "✅ Successfully counted conversation statuses: group_id=%s, count=%d", - group_id, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to count conversation statuses: %s", e) - return 0 diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/episodic_memory_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/episodic_memory_raw_repository.py deleted file mode 100644 index 08dca34e..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/episodic_memory_raw_repository.py +++ /dev/null @@ -1,559 +0,0 @@ -from datetime import datetime -from typing import List, Optional, Dict, Any, Type, TypeVar, Union -from pymongo.asynchronous.client_session import AsyncClientSession -from bson import ObjectId -from core.observation.logger import get_logger -from core.di.decorators import repository -from core.oxm.mongo.base_repository import BaseRepository -from core.oxm.mongo.mongo_utils import build_id_filter as _build_id_filter -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemory, - EpisodicMemoryProjection, -) - -T = TypeVar("T") -from agentic_layer.vectorize_service import get_vectorize_service - -logger = get_logger(__name__) - - -@repository("episodic_memory_raw_repository", primary=True) -class EpisodicMemoryRawRepository(BaseRepository[EpisodicMemory]): - """ - Episodic memory raw data repository - Generates vectorized text content and saves it to the database - Provides CRUD operations and basic query functions for episodic memory. - """ - - def __init__(self): - super().__init__(EpisodicMemory) - self.vectorize_service = get_vectorize_service() - - # ==================== Basic CRUD Methods ==================== - - async def get_by_event_id( - self, event_id: str, user_id: str, session: Optional[AsyncClientSession] = None - ) -> Optional[EpisodicMemory]: - """ - Retrieve episodic memory by event ID and user ID - - Args: - event_id: Event ID - user_id: User ID - session: Optional MongoDB session, for transaction support - - Returns: - EpisodicMemory or None - """ - try: - # Convert string event_id to ObjectId - object_id = ObjectId(event_id) - result = await self.model.find_one( - {"_id": object_id, "user_id": user_id}, session=session - ) - if result: - logger.debug( - "✅ Successfully retrieved episodic memory by event ID and user ID: %s", - event_id, - ) - else: - logger.debug( - "ℹ️ Episodic memory not found: event_id=%s, user_id=%s", - event_id, - user_id, - ) - return result - except Exception as e: - logger.error( - "❌ Failed to retrieve episodic memory by event ID and user ID: %s", e - ) - return None - - async def get_by_event_ids( - self, - event_ids: List[str], - user_id: str, - session: Optional[AsyncClientSession] = None, - ) -> Dict[str, EpisodicMemory]: - """ - Batch retrieve episodic memories by event ID list and user ID - - Args: - event_ids: List of event IDs - user_id: User ID - session: Optional MongoDB session, for transaction support - - Returns: - Dict[str, EpisodicMemory]: Dictionary with event_id as key, for fast lookup - """ - if not event_ids: - return {} - - try: - # Convert list of string event_ids to list of ObjectIds - object_ids = [] - for event_id in event_ids: - try: - object_ids.append(ObjectId(event_id)) - except Exception as e: - logger.warning(f"⚠️ Invalid event_id: {event_id}, error: {e}") - continue - - if not object_ids: - return {} - - # Batch query - query = {"_id": {"$in": object_ids}} - if user_id: - query["user_id"] = user_id - - results = await self.model.find(query, session=session).to_list() - - # Convert to dictionary for easier subsequent use - result_dict = {str(doc.id): doc for doc in results} - - logger.debug( - "✅ Successfully batch retrieved episodic memories: user_id=%s, requested %d, found %d", - user_id, - len(event_ids), - len(result_dict), - ) - return result_dict - except Exception as e: - logger.error("❌ Failed to batch retrieve episodic memories: %s", e) - return {} - - async def find_by_ids( - self, - ids: List[str], - projection_model: Optional[Type[Any]] = None, - session: Optional[AsyncClientSession] = None, - ) -> List[Union[EpisodicMemory, EpisodicMemoryProjection]]: - """ - Batch fetch episodic memories by their _id list. - - Accepts both ObjectId-like strings and raw string IDs (for legacy - datasets where _id is not an ObjectId). - - Args: - ids: List of document _id strings - projection_model: Optional projection model to reduce data transfer - (e.g. EpisodicMemoryProjection skips the vector field) - session: Optional MongoDB session - - Returns: - List of EpisodicMemory or projection model instances - """ - query_filter = _build_id_filter(ids) - if query_filter is None: - return [] - try: - if projection_model is not None: - return await self.model.find( - query_filter, projection_model=projection_model, session=session - ).to_list() - return await self.model.find(query_filter, session=session).to_list() - except Exception as e: - logger.error("❌ Failed to find episodic memories by ids: %s", e) - return [] - - async def find_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - limit: Optional[int] = None, - skip: Optional[int] = None, - sort_desc: bool = True, - session: Optional[AsyncClientSession] = None, - model: Optional[Type[T]] = None, - ) -> List[Union[EpisodicMemory, EpisodicMemoryProjection]]: - """ - Retrieve list of episodic memories by filters (user_id, group_ids, and/or time range) - - Args: - user_id: User ID - - Not provided or MAGIC_ALL ("__all__"): Don't filter by user_id - - None or "": Filter for null/empty values (records with user_id as None or "") - - Other values: Exact match - group_ids: List of Group IDs - - None: Skip group filtering - - []: Empty array, skip filtering - - ["g1"]: Single element array, exact match - - ["g1", "g2"]: Multiple elements, use $in operator - start_time: Optional start time (inclusive) - end_time: Optional end time (exclusive) - limit: Limit number of returned results - skip: Number of results to skip - sort_desc: Whether to sort by time in descending order - session: Optional MongoDB session, for transaction support - model: Projection model type, default is EpisodicMemory (full version), - can pass EpisodicMemoryProjection to exclude vector field - - Returns: - List of episodic memory objects of specified type - """ - try: - # Build query filter - filter_dict = {} - - # Handle time range filter - if start_time is not None and end_time is not None: - filter_dict["timestamp"] = {"$gte": start_time, "$lt": end_time} - elif start_time is not None: - filter_dict["timestamp"] = {"$gte": start_time} - elif end_time is not None: - filter_dict["timestamp"] = {"$lt": end_time} - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id == "" or user_id is None: - # Explicitly filter for null or empty string - filter_dict["user_id"] = {"$in": [None, ""]} - else: - filter_dict["user_id"] = user_id - - # Handle group_ids filter (array, no MAGIC_ALL) - if group_ids is not None and len(group_ids) > 0: - if len(group_ids) == 1: - # Single element: exact match - filter_dict["group_id"] = group_ids[0] - else: - # Multiple elements: use $in operator - filter_dict["group_id"] = {"$in": group_ids} - # group_ids is None or empty: skip group filtering - - # If model is not specified, use full version - target_model = model if model is not None else self.model - - # Determine whether to use projection based on model type - if target_model == self.model: - query = self.model.find(filter_dict, session=session) - else: - query = self.model.find( - filter_dict, projection_model=target_model, session=session - ) - - sort_field = "-timestamp" if sort_desc else "timestamp" - query = query.sort(sort_field) - - if skip: - query = query.skip(skip) - if limit: - query = query.limit(limit) - - logger.debug( - "🔍 EpisodicMemory.find_by_filters query: %s, sort=%s, skip=%s, limit=%s", - query.get_filter_query(), - sort_field, - skip, - limit, - ) - - results = await query.to_list() - logger.debug( - "✅ Successfully retrieved episodic memories: user_id=%s, group_ids=%s, time_range=[%s, %s), found %d records", - user_id, - group_ids, - start_time, - end_time, - len(results), - ) - return results - except Exception as e: - logger.error("❌ Failed to retrieve episodic memories: %s", e) - return [] - - async def count_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Count episodic memories by filters (without pagination) - - Args: - user_id: User ID filter (same semantics as find_by_filters) - group_ids: Group IDs filter (same semantics as find_by_filters) - start_time: Optional start time (inclusive) - end_time: Optional end time (exclusive) - session: Optional MongoDB session - - Returns: - Total count of matching records - """ - try: - # Build query filter (same as find_by_filters) - filter_dict = {} - - # Handle time range filter - if start_time is not None and end_time is not None: - filter_dict["timestamp"] = {"$gte": start_time, "$lt": end_time} - elif start_time is not None: - filter_dict["timestamp"] = {"$gte": start_time} - elif end_time is not None: - filter_dict["timestamp"] = {"$lt": end_time} - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id == "" or user_id is None: - filter_dict["user_id"] = {"$in": [None, ""]} - else: - filter_dict["user_id"] = user_id - - # Handle group_ids filter - if group_ids is not None and len(group_ids) > 0: - if len(group_ids) == 1: - filter_dict["group_id"] = group_ids[0] - else: - filter_dict["group_id"] = {"$in": group_ids} - - count = await self.model.find(filter_dict, session=session).count() - logger.debug( - "✅ Counted episodic memories: user_id=%s, group_ids=%s, count=%d", - user_id, - group_ids, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to count episodic memories: %s", e) - return 0 - - async def append_episodic_memory( - self, - episodic_memory: EpisodicMemory, - session: Optional[AsyncClientSession] = None, - ) -> Optional[EpisodicMemory]: - """ - Append new episodic memory - - Args: - episodic_memory: Episodic memory object - session: Optional MongoDB session, for transaction support - - Returns: - Appended EpisodicMemory or None - """ - - # Synchronize vector - if episodic_memory.episode and not episodic_memory.vector: - try: - vector = await self.vectorize_service.get_embedding( - episodic_memory.episode - ) - episodic_memory.vector = vector.tolist() - # Set vectorization model information - episodic_memory.vector_model = self.vectorize_service.get_model_name() - except Exception as e: - logger.error("❌ Failed to synchronize vector: %s", e) - try: - await episodic_memory.insert(session=session) - logger.info( - "✅ Successfully appended episodic memory: event_id=%s, user_id=%s", - episodic_memory.event_id, - episodic_memory.user_id, - ) - return episodic_memory - except Exception as e: - logger.error("❌ Failed to append episodic memory: %s", e) - return None - - async def delete_by_event_id( - self, event_id: str, user_id: str, session: Optional[AsyncClientSession] = None - ) -> bool: - """ - Delete episodic memory by event ID and user ID - - Args: - event_id: Event ID - user_id: User ID - session: Optional MongoDB session, for transaction support - - Returns: - Whether deletion was successful - """ - try: - # Convert string event_id to ObjectId - object_id = ObjectId(event_id) - # Directly delete and check deletion count - result = await self.model.find( - {"_id": object_id, "user_id": user_id}, session=session - ).delete() - - deleted_count = ( - result.deleted_count if hasattr(result, 'deleted_count') else 0 - ) - success = deleted_count > 0 - - if success: - logger.info( - "✅ Successfully deleted episodic memory by event ID and user ID: %s", - event_id, - ) - return True - else: - logger.warning( - "⚠️ Episodic memory to delete not found: event_id=%s, user_id=%s", - event_id, - user_id, - ) - return False - except Exception as e: - logger.error( - "❌ Failed to delete episodic memory by event ID and user ID: %s", e - ) - return False - - async def delete_by_user_id( - self, user_id: str, session: Optional[AsyncClientSession] = None - ) -> int: - """ - Delete all episodic memories by user ID - - Args: - user_id: User ID - session: Optional MongoDB session, for transaction support - - Returns: - Number of deleted records - """ - try: - result = await self.model.find({"user_id": user_id}).delete(session=session) - count = result.deleted_count if result else 0 - logger.info( - "✅ Successfully deleted episodic memories by user ID: %s, deleted %d records", - user_id, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to delete episodic memories by user ID: %s", e) - return 0 - - async def delete_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - parent_id: Optional[str] = None, - session_id: Optional[str] = MAGIC_ALL, - sender_id: Optional[str] = MAGIC_ALL, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Soft delete episodic memories by filter conditions - - Three-state filter semantics (user_id, group_id, session_id, sender_id): - - MAGIC_ALL (default): skip this filter - - None or "": match null/empty records - - other value: exact match - - Args: - user_id: User ID filter - group_id: Group ID filter - parent_id: Parent ID filter (for cascade delete) - session_id: Session ID filter - sender_id: Sender ID filter (maps to "sender_ids" field) - session: Optional MongoDB session, for transaction support - - Returns: - Number of deleted records - """ - filter_dict: Dict[str, Any] = {} - - if user_id != MAGIC_ALL: - if user_id is None or user_id == "": - filter_dict["user_id"] = {"$in": [None, ""]} - else: - filter_dict["user_id"] = user_id - - if group_id != MAGIC_ALL: - if group_id is None or group_id == "": - filter_dict["group_id"] = {"$in": [None, ""]} - else: - filter_dict["group_id"] = group_id - - if parent_id is not None: - filter_dict["parent_id"] = parent_id - - if session_id != MAGIC_ALL: - if session_id is None or session_id == "": - filter_dict["session_id"] = {"$in": [None, ""]} - else: - filter_dict["session_id"] = session_id - - if sender_id != MAGIC_ALL: - if sender_id is None or sender_id == "": - filter_dict["sender_ids"] = {"$in": [None, ""]} - else: - filter_dict["sender_ids"] = sender_id - - if not filter_dict: - logger.warning("No filter conditions provided for delete_by_filters") - return 0 - - result = await self.model.delete_many(filter_dict, session=session) - count = result.modified_count if result else 0 - logger.info( - "Soft deleted episodic memories by filters: filter=%s, deleted %d records", - filter_dict, - count, - ) - return count - - async def find_by_filter_paginated( - self, - query_filter: Optional[Dict[str, Any]] = None, - skip: int = 0, - limit: int = 100, - sort_field: str = "created_at", - sort_desc: bool = False, - ) -> List[EpisodicMemory]: - """ - Paginated query of EpisodicMemory by filter conditions, used for data synchronization scenarios - - Args: - query_filter: Query filter conditions, query all if None - skip: Number of results to skip - limit: Limit number of returned results - sort_field: Sort field, default is created_at - sort_desc: Whether to sort in descending order, default False (ascending) - - Returns: - List of EpisodicMemory - """ - try: - # Build query - filter_dict = query_filter if query_filter else {} - query = self.model.find(filter_dict) - - # Sort - if sort_desc: - query = query.sort(f"-{sort_field}") - else: - query = query.sort(sort_field) - - # Paginate - query = query.skip(skip).limit(limit) - - results = await query.to_list() - logger.debug( - "✅ Successfully paginated query of EpisodicMemory: filter=%s, skip=%d, limit=%d, found %d records", - filter_dict, - skip, - limit, - len(results), - ) - return results - except Exception as e: - logger.error("❌ Failed to paginate query of EpisodicMemory: %s", e) - return [] - - -# Export -__all__ = ["EpisodicMemoryRawRepository"] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/foresight_record_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/foresight_record_raw_repository.py deleted file mode 100644 index 0a5f5e2d..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/foresight_record_raw_repository.py +++ /dev/null @@ -1,420 +0,0 @@ -""" -ForesightRecord Repository - -Provides generic CRUD operations and query capabilities for foresight records. -""" - -from datetime import datetime -from typing import List, Optional, Type, TypeVar, Union -from pymongo.asynchronous.client_session import AsyncClientSession -from bson import ObjectId -from core.observation.logger import get_logger -from core.di.decorators import repository -from core.oxm.mongo.base_repository import BaseRepository -from core.oxm.constants import MAGIC_ALL -from common_utils.datetime_utils import to_date_str -from infra_layer.adapters.out.persistence.document.memory.foresight_record import ( - ForesightRecord, - ForesightRecordProjection, -) - -# Define generic type variable -T = TypeVar('T', ForesightRecord, ForesightRecordProjection) - -logger = get_logger(__name__) - - -@repository("foresight_record_raw_repository", primary=True) -class ForesightRecordRawRepository(BaseRepository[ForesightRecord]): - """ - Raw repository for personal foresight data - - Provides CRUD operations and basic query functions for personal foresight records. - Note: Vectors should be generated during extraction; this Repository is not responsible for vector generation. - """ - - def __init__(self): - super().__init__(ForesightRecord) - - # ==================== Basic CRUD Methods ==================== - - async def save( - self, foresight: ForesightRecord, session: Optional[AsyncClientSession] = None - ) -> Optional[ForesightRecord]: - """ - Save personal foresight record - - Args: - foresight: ForesightRecord object - session: Optional MongoDB session for transaction support - - Returns: - Saved ForesightRecord or None - """ - try: - await foresight.insert(session=session) - logger.info( - "✅ Saved personal foresight successfully: id=%s, user_id=%s, parent_type=%s, parent_id=%s", - foresight.id, - foresight.user_id, - foresight.parent_type, - foresight.parent_id, - ) - return foresight - except Exception as e: - logger.error("❌ Failed to save personal foresight: %s", e) - return None - - async def get_by_id( - self, - memory_id: str, - session: Optional[AsyncClientSession] = None, - model: Optional[Type[T]] = None, - ) -> Optional[Union[ForesightRecord, ForesightRecordProjection]]: - """ - Retrieve personal foresight by ID - - Args: - memory_id: Memory ID - session: Optional MongoDB session for transaction support - model: Type of model to return, defaults to ForesightRecord (full version) - - Returns: - Foresight object of specified type or None - """ - try: - object_id = ObjectId(memory_id) - - # Use full version if model is not specified - target_model = model if model is not None else self.model - - # Determine whether to use projection based on model type - if target_model == self.model: - result = await self.model.find_one({"_id": object_id}, session=session) - else: - result = await self.model.find_one( - {"_id": object_id}, projection_model=target_model, session=session - ) - - if result: - logger.debug( - "✅ Retrieved personal foresight by ID successfully: %s (model=%s)", - memory_id, - target_model.__name__, - ) - else: - logger.debug("ℹ️ Personal foresight not found: id=%s", memory_id) - return result - except Exception as e: - logger.error("❌ Failed to retrieve personal foresight by ID: %s", e) - return None - - async def get_by_parent_id( - self, - parent_id: str, - parent_type: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - model: Optional[Type[T]] = None, - ) -> List[Union[ForesightRecord, ForesightRecordProjection]]: - """ - Retrieve all foresights by parent memory ID and optionally parent type - - Args: - parent_id: Parent memory ID - parent_type: Optional parent type filter (e.g., "memcell", "episode") - session: Optional MongoDB session for transaction support - model: Type of model to return, defaults to ForesightRecord (full version) - - Returns: - List of foresight objects of specified type - """ - try: - # Use full version if model is not specified - target_model = model if model is not None else self.model - - # Build query filter - query_filter = {"parent_id": parent_id} - if parent_type: - query_filter["parent_type"] = parent_type - - # Determine whether to use projection based on model type - if target_model == self.model: - query = self.model.find(query_filter, session=session) - else: - query = self.model.find( - query_filter, projection_model=target_model, session=session - ) - - results = await query.to_list() - logger.debug( - "✅ Retrieved foresights by parent memory ID successfully: %s (type=%s), found %d records (model=%s)", - parent_id, - parent_type, - len(results), - target_model.__name__, - ) - return results - except Exception as e: - logger.error( - "❌ Failed to retrieve foresights by parent episodic memory ID: %s", e - ) - return [] - - async def find_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - limit: Optional[int] = None, - skip: Optional[int] = None, - session: Optional[AsyncClientSession] = None, - model: Optional[Type[T]] = None, - ) -> List[Union[ForesightRecord, ForesightRecordProjection]]: - """ - Retrieve list of foresights by filters (user_id, group_ids, and/or validity time range) - - Args: - user_id: User ID - - Not provided or MAGIC_ALL ("__all__"): Don't filter by user_id - - None or "": Filter for null/empty values (records with user_id as None or "") - - Other values: Exact match - group_ids: List of Group IDs - - None: Skip group filtering - - []: Empty array, skip filtering - - ["g1"]: Single element array, exact match - - ["g1", "g2"]: Multiple elements, use $in operator - start_time: Optional query start time (datetime object) - - Filters foresights whose validity period overlaps with [start_time, end_time) - - Will be converted to ISO date string (YYYY-MM-DD) internally - end_time: Optional query end time (datetime object) - - Filters foresights whose validity period overlaps with [start_time, end_time) - - Will be converted to ISO date string (YYYY-MM-DD) internally - limit: Limit number of returned records - skip: Number of records to skip - session: Optional MongoDB session for transaction support - model: Type of model to return, defaults to ForesightRecord (full version) - - Returns: - List of foresight objects of specified type - """ - try: - # Build query filter - filter_dict = {} - - # Convert datetime to ISO date string for foresight validity period comparison - start_str = to_date_str(start_time) - end_str = to_date_str(end_time) - - # Handle time range filter (overlap query) - # Logic: foresight.start_time <= query.end_time AND foresight.end_time >= query.start_time - if start_str is not None and end_str is not None: - filter_dict["$and"] = [ - {"start_time": {"$lte": end_str}}, - {"end_time": {"$gte": start_str}}, - ] - elif start_str is not None: - # Only start_time: find foresights that end after start_time - filter_dict["end_time"] = {"$gte": start_str} - elif end_str is not None: - # Only end_time: find foresights that start before end_time - filter_dict["start_time"] = {"$lte": end_str} - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id == "" or user_id is None: - # Explicitly filter for null or empty string - filter_dict["user_id"] = {"$in": [None, ""]} - else: - filter_dict["user_id"] = user_id - - # Handle group_ids filter (array, no MAGIC_ALL) - if group_ids is not None and len(group_ids) > 0: - if len(group_ids) == 1: - # Single element: exact match - filter_dict["group_id"] = group_ids[0] - else: - # Multiple elements: use $in operator - filter_dict["group_id"] = {"$in": group_ids} - # group_ids is None or empty: skip group filtering - - # Use full version if model is not specified - target_model = model if model is not None else self.model - - # Determine whether to use projection based on model type - if target_model == self.model: - query = self.model.find(filter_dict, session=session) - else: - query = self.model.find( - filter_dict, projection_model=target_model, session=session - ) - - # Sort by created_at descending (most recent first) - query = query.sort("-created_at") - - if skip: - query = query.skip(skip) - if limit: - query = query.limit(limit) - - logger.debug( - "🔍 ForesightRecord.find_by_filters query: %s, sort=-created_at, skip=%s, limit=%s", - query.get_filter_query(), - skip, - limit, - ) - - results = await query.to_list() - logger.debug( - "✅ Retrieved foresights successfully: user_id=%s, group_ids=%s, time_range=[%s, %s), found %d records (model=%s)", - user_id, - group_ids, - start_str, - end_str, - len(results), - target_model.__name__, - ) - return results - except Exception as e: - logger.error("❌ Failed to retrieve foresights: %s", e) - return [] - - async def count_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Count foresights by filters (without pagination) - - Args: - user_id: User ID filter (same semantics as find_by_filters) - group_ids: Group IDs filter (same semantics as find_by_filters) - start_time: Optional query start time (datetime object) - end_time: Optional query end time (datetime object) - session: Optional MongoDB session - - Returns: - Total count of matching records - """ - try: - # Build query filter (same as find_by_filters) - filter_dict = {} - - # Convert datetime to ISO date string for foresight validity period comparison - start_str = to_date_str(start_time) - end_str = to_date_str(end_time) - - # Handle time range filter (overlap query) - if start_str is not None and end_str is not None: - filter_dict["$and"] = [ - {"start_time": {"$lte": end_str}}, - {"end_time": {"$gte": start_str}}, - ] - elif start_str is not None: - filter_dict["end_time"] = {"$gte": start_str} - elif end_str is not None: - filter_dict["start_time"] = {"$lte": end_str} - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id == "" or user_id is None: - filter_dict["user_id"] = {"$in": [None, ""]} - else: - filter_dict["user_id"] = user_id - - # Handle group_ids filter - if group_ids is not None and len(group_ids) > 0: - if len(group_ids) == 1: - filter_dict["group_id"] = group_ids[0] - else: - filter_dict["group_id"] = {"$in": group_ids} - - count = await self.model.find(filter_dict, session=session).count() - logger.debug( - "✅ Counted foresights: user_id=%s, group_ids=%s, count=%d", - user_id, - group_ids, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to count foresights: %s", e) - return 0 - - async def delete_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - parent_id: Optional[str] = None, - session_id: Optional[str] = MAGIC_ALL, - sender_id: Optional[str] = MAGIC_ALL, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Soft delete foresight records by filter conditions - - Three-state filter semantics (user_id, group_id, session_id, sender_id): - - MAGIC_ALL (default): skip this filter - - None or "": match null/empty records - - other value: exact match - - Args: - user_id: User ID filter - group_id: Group ID filter - parent_id: Parent ID filter (for cascade delete) - session_id: Session ID filter - sender_id: Sender ID filter (maps to "sender_ids" field) - session: Optional MongoDB session for transaction support - - Returns: - Number of deleted records - """ - filter_dict = {} - - if user_id != MAGIC_ALL: - if user_id is None or user_id == "": - filter_dict["user_id"] = {"$in": [None, ""]} - else: - filter_dict["user_id"] = user_id - - if group_id != MAGIC_ALL: - if group_id is None or group_id == "": - filter_dict["group_id"] = {"$in": [None, ""]} - else: - filter_dict["group_id"] = group_id - - if parent_id is not None: - filter_dict["parent_id"] = parent_id - - if session_id != MAGIC_ALL: - if session_id is None or session_id == "": - filter_dict["session_id"] = {"$in": [None, ""]} - else: - filter_dict["session_id"] = session_id - - if sender_id != MAGIC_ALL: - if sender_id is None or sender_id == "": - filter_dict["sender_ids"] = {"$in": [None, ""]} - else: - filter_dict["sender_ids"] = sender_id - - if not filter_dict: - logger.warning("No filter conditions provided for delete_by_filters") - return 0 - - result = await self.model.delete_many(filter_dict, session=session) - count = result.modified_count if result else 0 - logger.info( - "Soft deleted foresight records by filters: filter=%s, deleted %d records", - filter_dict, - count, - ) - return count - - -# Export -__all__ = ["ForesightRecordRawRepository"] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/group_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/group_raw_repository.py deleted file mode 100644 index f3c18609..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/group_raw_repository.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -Group raw data repository - -Provides CRUD operations for Group documents (v1_groups collection). -""" - -from typing import Optional, Dict, Any - -from core.oxm.mongo.base_repository import BaseRepository -from infra_layer.adapters.out.persistence.document.memory.group import Group -from core.observation.logger import get_logger -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("group_raw_repository", primary=True) -class GroupRawRepository(BaseRepository[Group]): - """ - Group raw data repository - - Provides CRUD operations and query capabilities for Group data. - """ - - def __init__(self): - super().__init__(Group) - - async def get_by_group_id(self, group_id: str) -> Optional[Group]: - """Get group by group_id""" - try: - result = await self.model.find_one({"group_id": group_id}) - if result: - logger.debug("Retrieved group: group_id=%s", group_id) - else: - logger.debug("Group not found: group_id=%s", group_id) - return result - except Exception as e: - logger.error("Failed to get group by group_id: %s", e) - return None - - async def upsert_by_group_id( - self, group_id: str, update_data: Dict[str, Any] - ) -> Optional[Group]: - """Update or insert group by group_id - - Uses find-then-save pattern with duplicate key retry - to handle concurrency safely. - - Args: - group_id: Group identifier - update_data: Fields to update (only non-None values) - - Returns: - The updated or created Group document - """ - try: - existing_doc = await self.model.find_one({"group_id": group_id}) - - if existing_doc: - for key, value in update_data.items(): - setattr(existing_doc, key, value) - await existing_doc.save() - logger.debug("Updated existing group: group_id=%s", group_id) - return existing_doc - - # Not found, create new - try: - new_doc = Group(group_id=group_id, **update_data) - await new_doc.create() - logger.info("Created new group: group_id=%s", group_id) - return new_doc - - except Exception as create_error: - # Handle concurrent duplicate key - error_str = str(create_error) - if "E11000" in error_str and "duplicate key" in error_str: - logger.warning( - "Concurrent creation conflict, retrying: group_id=%s", group_id - ) - retry_doc = await self.model.find_one({"group_id": group_id}) - if retry_doc: - for key, value in update_data.items(): - setattr(retry_doc, key, value) - await retry_doc.save() - logger.debug( - "Updated after concurrency conflict: group_id=%s", group_id - ) - return retry_doc - else: - logger.error( - "Record not found after concurrency conflict: group_id=%s", - group_id, - ) - return None - else: - raise create_error - - except Exception as e: - logger.error("Failed to upsert group: %s", e) - return None diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/mem_scene_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/mem_scene_raw_repository.py deleted file mode 100644 index 760d050f..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/mem_scene_raw_repository.py +++ /dev/null @@ -1,122 +0,0 @@ -""" -MemScene native CRUD repository - -Mem scene data access layer based on Beanie ODM. -Provides ClusterStorage compatible interface (duck typing). -""" - -from typing import Optional, Dict, Any -from core.observation.logger import get_logger -from core.di.decorators import repository -from core.oxm.mongo.base_repository import BaseRepository - -from infra_layer.adapters.out.persistence.document.memory.mem_scene import MemScene - -logger = get_logger(__name__) - - -@repository("mem_scene_raw_repository", primary=True) -class MemSceneRawRepository(BaseRepository[MemScene]): - """ - MemScene native CRUD repository - - Provides ClusterStorage compatible interface: - - save_mem_scene(group_id, state) -> bool - - load_mem_scene(group_id) -> Optional[Dict] - - get_cluster_assignments(group_id) -> Dict[str, str] - - clear(group_id) -> bool - """ - - def __init__(self): - super().__init__(MemScene) - - # ==================== ClusterStorage interface implementation ==================== - - async def save_mem_scene(self, group_id: str, state: Dict[str, Any]) -> bool: - result = await self.upsert_by_group_id(group_id, state) - return result is not None - - async def load_mem_scene(self, group_id: str) -> Optional[Dict[str, Any]]: - mem_scene = await self.get_by_group_id(group_id) - if mem_scene is None: - return None - return mem_scene.model_dump(exclude={"id", "revision_id"}) - - async def clear(self, group_id: Optional[str] = None) -> bool: - if group_id is None: - await self.delete_all() - else: - await self.delete_by_group_id(group_id) - return True - - # ==================== Native CRUD methods ==================== - - async def get_by_group_id(self, group_id: str) -> Optional[MemScene]: - try: - return await self.model.find_one(MemScene.group_id == group_id) - except Exception as e: - logger.error( - f"Failed to retrieve mem scene: group_id={group_id}, error={e}" - ) - return None - - async def upsert_by_group_id( - self, group_id: str, state: Dict[str, Any] - ) -> Optional[MemScene]: - try: - existing = await self.model.find_one(MemScene.group_id == group_id) - - if existing: - for key, value in state.items(): - if hasattr(existing, key): - setattr(existing, key, value) - await existing.save() - logger.debug(f"Updated mem scene: group_id={group_id}") - return existing - else: - state["group_id"] = group_id - mem_scene = MemScene(**state) - await mem_scene.insert() - logger.info(f"Created mem scene: group_id={group_id}") - return mem_scene - except Exception as e: - logger.error(f"Failed to save mem scene: group_id={group_id}, error={e}") - return None - - async def get_cluster_assignments(self, group_id: str) -> Dict[str, str]: - try: - mem_scene = await self.model.find_one(MemScene.group_id == group_id) - if mem_scene is None: - return {} - # Derive eventid_to_cluster from memcell_info - memcell_info = mem_scene.memcell_info or {} - return { - eid: info.get("memscene", "") - for eid, info in memcell_info.items() - } - except Exception as e: - logger.error( - f"Failed to retrieve cluster assignments: group_id={group_id}, error={e}" - ) - return {} - - async def delete_by_group_id(self, group_id: str) -> bool: - try: - mem_scene = await self.model.find_one(MemScene.group_id == group_id) - if mem_scene: - await mem_scene.delete() - logger.info(f"Deleted mem scene: group_id={group_id}") - return True - except Exception as e: - logger.error(f"Failed to delete mem scene: group_id={group_id}, error={e}") - return False - - async def delete_all(self) -> int: - try: - result = await self.model.delete_all() - count = result.deleted_count if result else 0 - logger.info(f"Deleted all mem scenes: {count} items") - return count - except Exception as e: - logger.error(f"Failed to delete all mem scenes: {e}") - return 0 diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/memcell_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/memcell_raw_repository.py deleted file mode 100644 index 337467a8..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/memcell_raw_repository.py +++ /dev/null @@ -1,945 +0,0 @@ -""" -MemCell Native CRUD Repository - -Native data access layer for MemCell based on Beanie ODM, providing complete CRUD operations. -Does not depend on domain layer interfaces, directly operates on MemCell document models. -""" - -from datetime import datetime -from typing import List, Optional, Dict, Any, Type -from bson import ObjectId -from pydantic import BaseModel -from beanie.operators import And, GTE, LT, Eq, RegEx, Or -from pymongo.asynchronous.client_session import AsyncClientSession -from core.observation.logger import get_logger -from core.di.decorators import repository -from core.oxm.mongo.base_repository import BaseRepository -from core.oxm.constants import MAGIC_ALL - -from infra_layer.adapters.out.persistence.document.memory.memcell import ( - MemCell, - DataTypeEnum, -) - -logger = get_logger(__name__) - - -@repository("memcell_raw_repository", primary=True) -class MemCellRawRepository(BaseRepository[MemCell]): - """ - MemCell Native CRUD Repository - - Provides direct database operations on MemCell documents, including: - - Basic CRUD operations (inherited from BaseRepository) - - Composite queries and filtering - - Batch operations - - Statistics and aggregation queries - - Transaction management (inherited from BaseRepository) - """ - - def __init__(self): - """Initialize repository""" - super().__init__(MemCell) - - async def get_by_event_id(self, event_id: str) -> Optional[MemCell]: - """ - Get MemCell by event_id - - Args: - event_id: Event ID - - Returns: - MemCell instance or None - """ - try: - result = await self.model.find_one({"_id": ObjectId(event_id)}) - if result: - logger.debug( - "✅ Successfully retrieved MemCell by event_id: %s", event_id - ) - else: - logger.debug("⚠️ MemCell not found: event_id=%s", event_id) - return result - except Exception as e: - logger.error("❌ Failed to retrieve MemCell by event_id: %s", e) - return None - - async def get_by_event_ids( - self, event_ids: List[str], projection_model: Optional[Type[BaseModel]] = None - ) -> Dict[str, Any]: - """ - Batch get MemCell by event_id list - - Args: - event_ids: List of event IDs - projection_model: Pydantic projection model class, used to specify returned fields - For example: pass a Pydantic model containing only specific fields - None means return complete MemCell objects - - Returns: - Dict[event_id, MemCell | ProjectionModel]: Mapping dictionary from event_id to MemCell (or projection model) - Unfound event_ids will not appear in the dictionary - """ - try: - if not event_ids: - logger.debug("⚠️ event_ids list is empty, returning empty dictionary") - return {} - - # Convert event_id list to ObjectId list - object_ids = [] - valid_event_ids = [] # Store valid original event_id strings - for event_id in event_ids: - try: - object_ids.append(ObjectId(event_id)) - valid_event_ids.append(event_id) - except Exception as e: - logger.warning("⚠️ Invalid event_id: %s, error: %s", event_id, e) - - if not object_ids: - logger.debug("⚠️ No valid event_ids, returning empty dictionary") - return {} - - # Build query - query = self.model.find({"_id": {"$in": object_ids}}) - - # Apply field projection - # Use Beanie's .project() method, passing projection_model parameter - if projection_model: - query = query.project(projection_model=projection_model) - - # Batch query - results = await query.to_list() - - # Create mapping dictionary from event_id to MemCell (or projection model) - result_dict = {str(result.id): result for result in results} - - logger.debug( - "✅ Successfully batch retrieved MemCell by event_ids: requested %d, found %d, projection: %s", - len(event_ids), - len(result_dict), - "yes" if projection_model else "no", - ) - - return result_dict - - except Exception as e: - logger.error("❌ Failed to batch retrieve MemCell by event_ids: %s", e) - return {} - - async def append_memcell( - self, memcell: MemCell, session: Optional[AsyncClientSession] = None - ) -> Optional[MemCell]: - """ - Append MemCell - """ - try: - await memcell.insert(session=session) - logger.debug("Successfully appended MemCell: %s", memcell.event_id) - return memcell - except Exception as e: - logger.error("❌ Failed to append MemCell: %s", e) - return None - - async def update_by_event_id( - self, - event_id: str, - update_data: Dict[str, Any], - session: Optional[AsyncClientSession] = None, - ) -> Optional[MemCell]: - """ - Update MemCell by event_id - - Args: - event_id: Event ID - update_data: Dictionary of update data - session: Optional MongoDB session, for transaction support - - Returns: - Updated MemCell instance or None - """ - try: - memcell = await self.get_by_event_id(event_id) - if memcell: - for key, value in update_data.items(): - if hasattr(memcell, key): - setattr(memcell, key, value) - await memcell.save(session=session) - logger.debug( - "✅ Successfully updated MemCell by event_id: %s", event_id - ) - return memcell - return None - except Exception as e: - logger.error("❌ Failed to update MemCell by event_id: %s", e) - raise e - - async def delete_by_event_id( - self, - event_id: str, - deleted_by: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> bool: - """ - Soft delete MemCell by event_id - - Args: - event_id: Event ID - deleted_by: Deleter (optional) - session: Optional MongoDB session, for transaction support - - Returns: - Returns True if deletion succeeds, otherwise False - """ - try: - memcell = await self.get_by_event_id(event_id) - if memcell: - await memcell.delete(deleted_by=deleted_by, session=session) - logger.debug( - "✅ Successfully soft deleted MemCell by event_id: %s", event_id - ) - return True - return False - except Exception as e: - logger.error("❌ Failed to soft delete MemCell by event_id: %s", e) - return False - - async def hard_delete_by_event_id( - self, event_id: str, session: Optional[AsyncClientSession] = None - ) -> bool: - """ - Hard delete (physical deletion) MemCell by event_id - - ⚠️ Warning: This operation is irreversible! Use with caution. - - Args: - event_id: Event ID - session: Optional MongoDB session, for transaction support - - Returns: - Returns True if deletion succeeds, otherwise False - """ - try: - memcell = await self.model.hard_find_one({"_id": ObjectId(event_id)}) - if memcell: - await memcell.hard_delete(session=session) - logger.debug( - "✅ Successfully hard deleted MemCell by event_id: %s", event_id - ) - return True - return False - except Exception as e: - logger.error("❌ Failed to hard delete MemCell by event_id: %s", e) - return False - - # ==================== Query Methods ==================== - - async def find_by_user_id( - self, - user_id: str, - limit: Optional[int] = None, - skip: Optional[int] = None, - sort_desc: bool = True, - ) -> List[MemCell]: - """ - Query MemCell by user ID - - Args: - user_id: User ID - limit: Limit number of returned results - skip: Number of results to skip - sort_desc: Whether to sort by time in descending order - - Returns: - List of MemCell - """ - try: - query = self.model.find({"user_id": user_id}) - - # Sorting - if sort_desc: - query = query.sort("-timestamp") - else: - query = query.sort("timestamp") - - # Pagination - if skip: - query = query.skip(skip) - if limit: - query = query.limit(limit) - - results = await query.to_list() - logger.debug( - "✅ Successfully queried MemCell by user ID: %s, found %d records", - user_id, - len(results), - ) - return results - except Exception as e: - logger.error("❌ Failed to query MemCell by user ID: %s", e) - return [] - - async def find_by_user_and_time_range( - self, - user_id: str, - start_time: datetime, - end_time: datetime, - limit: Optional[int] = None, - skip: Optional[int] = None, - ) -> List[MemCell]: - """ - Query MemCell by user ID and time range - - Check both user_id field and participants array, match if user_id is in either - - Args: - user_id: User ID - start_time: Start time - end_time: End time - limit: Limit number of returned results - skip: Number of results to skip - - Returns: - List of MemCell - """ - try: - # Check both user_id field and participants array - # Use OR logic: user_id matches OR user_id is in participants - # Note: MongoDB automatically checks if array contains the value when using Eq on array fields - query = self.model.find( - And( - Or( - Eq(MemCell.user_id, user_id), - Eq( - MemCell.participants, user_id - ), # MongoDB checks if array contains the value - ), - GTE(MemCell.timestamp, start_time), - LT(MemCell.timestamp, end_time), - ) - ).sort("-timestamp") - - if skip: - query = query.skip(skip) - if limit: - query = query.limit(limit) - - results = await query.to_list() - logger.debug( - "✅ Successfully queried MemCell by user and time range: %s, time range: %s - %s, found %d records", - user_id, - start_time, - end_time, - len(results), - ) - return results - except Exception as e: - logger.error("❌ Failed to query MemCell by user and time range: %s", e) - return [] - - async def find_by_group_id( - self, - group_id: str, - limit: Optional[int] = None, - skip: Optional[int] = None, - sort_desc: bool = True, - ) -> List[MemCell]: - """ - Query MemCell by group ID - - Args: - group_id: Group ID - limit: Limit number of returned results - skip: Number of results to skip - sort_desc: Whether to sort by time in descending order - - Returns: - List of MemCell - """ - try: - query = self.model.find({"group_id": group_id}) - - if sort_desc: - query = query.sort("-timestamp") - else: - query = query.sort("timestamp") - - if skip: - query = query.skip(skip) - if limit: - query = query.limit(limit) - - results = await query.to_list() - logger.debug( - "✅ Successfully queried MemCell by group ID: %s, found %d records", - group_id, - len(results), - ) - return results - except Exception as e: - logger.error("❌ Failed to query MemCell by group ID: %s", e) - return [] - - async def find_by_time_range( - self, - start_time: datetime, - end_time: datetime, - limit: Optional[int] = None, - skip: Optional[int] = None, - sort_desc: bool = False, - ) -> List[MemCell]: - """ - Query MemCell by time range - - Args: - start_time: Start time - end_time: End time - limit: Limit number of returned results - skip: Number of results to skip - sort_desc: Whether to sort by time in descending order, default False (ascending) - - Returns: - List of MemCell - """ - try: - query = self.model.find( - {"timestamp": {"$gte": start_time, "$lt": end_time}} - ) - - if sort_desc: - query = query.sort("-timestamp") - else: - query = query.sort("timestamp") - - if skip: - query = query.skip(skip) - if limit: - query = query.limit(limit) - - results = await query.to_list() - logger.debug( - "✅ Successfully queried MemCell by time range: time range: %s - %s, found %d records", - start_time, - end_time, - len(results), - ) - return results - except Exception as e: - logger.error("❌ Failed to query MemCell by time range: %s", e) - logger.error("Detailed error information:", exc_info=True) - return [] - - async def find_by_participants( - self, - participants: List[str], - match_all: bool = False, - limit: Optional[int] = None, - skip: Optional[int] = None, - ) -> List[MemCell]: - """ - Query MemCell by participants - - Args: - participants: List of participants - match_all: Whether to match all participants (True) or any participant (False) - limit: Limit number of returned results - skip: Number of results to skip - - Returns: - List of MemCell - """ - try: - if match_all: - # Match all participants - query = self.model.find({"participants": {"$all": participants}}) - else: - # Match any participant - query = self.model.find({"participants": {"$in": participants}}) - - query = query.sort("-timestamp") - - if skip: - query = query.skip(skip) - if limit: - query = query.limit(limit) - - results = await query.to_list() - logger.debug( - "✅ Successfully queried MemCell by participants: %s, match mode: %s, found %d records", - participants, - 'all' if match_all else 'any', - len(results), - ) - return results - except Exception as e: - logger.error("❌ Failed to query MemCell by participants: %s", e) - return [] - - async def search_by_keywords( - self, - keywords: List[str], - match_all: bool = False, - limit: Optional[int] = None, - skip: Optional[int] = None, - ) -> List[MemCell]: - """ - Query MemCell by keywords - - Args: - keywords: List of keywords - match_all: Whether to match all keywords (True) or any keyword (False) - limit: Limit number of returned results - skip: Number of results to skip - - Returns: - List of MemCell - """ - try: - if match_all: - query = self.model.find({"keywords": {"$all": keywords}}) - else: - query = self.model.find({"keywords": {"$in": keywords}}) - - query = query.sort("-timestamp") - - if skip: - query = query.skip(skip) - if limit: - query = query.limit(limit) - - results = await query.to_list() - logger.debug( - "✅ Successfully queried MemCell by keywords: %s, match mode: %s, found %d records", - keywords, - 'all' if match_all else 'any', - len(results), - ) - return results - except Exception as e: - logger.error("❌ Failed to query MemCell by keywords: %s", e) - return [] - - # ==================== Batch Operations ==================== - - async def delete_by_user_id( - self, - user_id: str, - deleted_by: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Soft delete all MemCell of a user - - Args: - user_id: User ID - deleted_by: Deleter (optional) - session: Optional MongoDB session, for transaction support - - Returns: - Number of soft deleted records - """ - try: - result = await self.model.delete_many( - {"user_id": user_id}, deleted_by=deleted_by, session=session - ) - count = result.modified_count if result else 0 - logger.info( - "✅ Successfully soft deleted all MemCell of user: %s, deleted %d records", - user_id, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to soft delete all MemCell of user: %s", e) - return 0 - - async def delete_by_group_id( - self, - group_id: str, - deleted_by: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Soft delete all MemCell of a group - - Args: - group_id: Group ID - deleted_by: Deleter (optional) - session: Optional MongoDB session, for transaction support - - Returns: - Number of soft deleted records - """ - try: - result = await self.model.delete_many( - {"group_id": group_id}, deleted_by=deleted_by, session=session - ) - count = result.modified_count if result else 0 - logger.info( - "✅ Successfully soft deleted all MemCell of group: %s, deleted %d records", - group_id, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to soft delete all MemCell of group: %s", e) - return 0 - - async def delete_by_filters( - self, - memcell_id: Optional[str] = None, - group_id: Optional[str] = MAGIC_ALL, - session_id: Optional[str] = MAGIC_ALL, - deleted_by: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Soft delete MemCells by combined filter conditions - - Three-state filter semantics (group_id, session_id): - - MAGIC_ALL (default): skip this filter - - None or "": match null/empty records - - other value: exact match - - Args: - memcell_id: MemCell ID filter - group_id: Group ID filter - session_id: Session ID filter - deleted_by: Deleter (optional) - session: Optional MongoDB session, for transaction support - - Returns: - Number of soft deleted records - """ - filter_dict = {} - - if memcell_id is not None: - filter_dict["_id"] = ObjectId(memcell_id) - - if group_id != MAGIC_ALL: - if group_id is None or group_id == "": - filter_dict["group_id"] = {"$in": [None, ""]} - else: - filter_dict["group_id"] = group_id - - if session_id != MAGIC_ALL: - if session_id is None or session_id == "": - filter_dict["session_id"] = {"$in": [None, ""]} - else: - filter_dict["session_id"] = session_id - - if not filter_dict: - logger.warning("No filter conditions provided for delete_by_filters") - return 0 - - result = await self.model.delete_many( - filter_dict, deleted_by=deleted_by, session=session - ) - count = result.modified_count if result else 0 - logger.info( - "Soft deleted MemCells by filters: filter=%s, deleted %d records", - filter_dict, - count, - ) - return count - - async def hard_delete_by_user_id( - self, user_id: str, session: Optional[AsyncClientSession] = None - ) -> int: - """ - Hard delete (physical deletion) all MemCell of a user - - ⚠️ Warning: This operation is irreversible! Use with caution. - - Args: - user_id: User ID - session: Optional MongoDB session, for transaction support - - Returns: - Number of hard deleted records - """ - try: - result = await self.model.hard_delete_many( - {"user_id": user_id}, session=session - ) - count = result.deleted_count if result else 0 - logger.info( - "✅ Successfully hard deleted all MemCell of user: %s, deleted %d records", - user_id, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to hard delete all MemCell of user: %s", e) - return 0 - - async def delete_by_time_range( - self, - start_time: datetime, - end_time: datetime, - user_id: Optional[str] = None, - deleted_by: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Soft delete MemCell within time range - - Args: - start_time: Start time - end_time: End time - user_id: Optional user ID filter - deleted_by: Deleter (optional) - session: Optional MongoDB session, for transaction support - - Returns: - Number of soft deleted records - """ - try: - filter_dict = {"timestamp": {"$gte": start_time, "$lt": end_time}} - if user_id: - filter_dict["user_id"] = user_id - - result = await self.model.delete_many( - filter_dict, deleted_by=deleted_by, session=session - ) - count = result.modified_count if result else 0 - logger.info( - "✅ Successfully soft deleted MemCell within time range: %s - %s, user: %s, deleted %d records", - start_time, - end_time, - user_id or 'all', - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to soft delete MemCell within time range: %s", e) - return 0 - - async def hard_delete_by_time_range( - self, - start_time: datetime, - end_time: datetime, - user_id: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Hard delete (physical deletion) MemCell within time range - - ⚠️ Warning: This operation is irreversible! Use with caution. - - Args: - start_time: Start time - end_time: End time - user_id: Optional user ID filter - session: Optional MongoDB session, for transaction support - - Returns: - Number of hard deleted records - """ - try: - filter_dict = {"timestamp": {"$gte": start_time, "$lt": end_time}} - if user_id: - filter_dict["user_id"] = user_id - - result = await self.model.hard_delete_many(filter_dict, session=session) - count = result.deleted_count if result else 0 - logger.info( - "✅ Successfully hard deleted MemCell within time range: %s - %s, user: %s, deleted %d records", - start_time, - end_time, - user_id or 'all', - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to hard delete MemCell within time range: %s", e) - return 0 - - # ==================== Soft Delete Recovery Methods ==================== - - async def restore_by_event_id( - self, event_id: str, session: Optional[AsyncClientSession] = None - ) -> bool: - """ - Restore soft-deleted MemCell by event_id - - Args: - event_id: Event ID - - Returns: - Returns True if restoration succeeds, otherwise False - """ - try: - memcell = await self.model.hard_find_one( - {"_id": ObjectId(event_id)}, session=session - ) - if memcell: - await memcell.restore() - logger.debug( - "✅ Successfully restored MemCell by event_id: %s", event_id - ) - return True - return False - except Exception as e: - logger.error("❌ Failed to restore MemCell by event_id: %s", e) - return False - - async def restore_by_user_id( - self, user_id: str, session: Optional[AsyncClientSession] = None - ) -> int: - """ - Restore all soft-deleted MemCell of a user - - Args: - user_id: User ID - session: Optional MongoDB session, for transaction support - - Returns: - Number of restored records - """ - try: - result = await self.model.restore_many( - {"user_id": user_id}, session=session - ) - count = result.modified_count if result else 0 - logger.info( - "✅ Successfully restored all MemCell of user: %s, restored %d records", - user_id, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to restore all MemCell of user: %s", e) - return 0 - - async def restore_by_time_range( - self, - start_time: datetime, - end_time: datetime, - user_id: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Restore soft-deleted MemCell within time range - - Args: - start_time: Start time - end_time: End time - user_id: Optional user ID filter - session: Optional MongoDB session, for transaction support - - Returns: - Number of restored records - """ - try: - filter_dict = { - "timestamp": {"$gte": start_time, "$lt": end_time}, - "deleted_at": {"$ne": None}, # Only restore deleted records - } - if user_id: - filter_dict["user_id"] = user_id - - result = await self.model.restore_many(filter_dict, session=session) - count = result.modified_count if result else 0 - logger.info( - "✅ Successfully restored MemCell within time range: %s - %s, user: %s, restored %d records", - start_time, - end_time, - user_id or 'all', - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to restore MemCell within time range: %s", e) - return 0 - - # ==================== Statistics and Aggregation Queries ==================== - - async def count_by_user_id(self, user_id: str) -> int: - """ - Count number of MemCell for a user - - Args: - user_id: User ID - - Returns: - Number of records - """ - try: - count = await self.model.find({"user_id": user_id}).count() - logger.debug( - "✅ Successfully counted user MemCell: %s, total %d records", - user_id, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to count user MemCell: %s", e) - return 0 - - async def count_by_time_range( - self, start_time: datetime, end_time: datetime, user_id: Optional[str] = None - ) -> int: - """ - Count number of MemCell within time range - - Args: - start_time: Start time - end_time: End time - user_id: Optional user ID filter - - Returns: - Number of records - """ - try: - conditions = [ - GTE(MemCell.timestamp, start_time), - LT(MemCell.timestamp, end_time), - ] - - if user_id: - conditions.append(Eq(MemCell.user_id, user_id)) - - count = await self.model.find(And(*conditions)).count() - logger.debug( - "✅ Successfully counted MemCell within time range: %s - %s, user: %s, total %d records", - start_time, - end_time, - user_id or 'all', - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to count MemCell within time range: %s", e) - return 0 - - async def get_latest_by_user(self, user_id: str, limit: int = 10) -> List[MemCell]: - """ - Get latest MemCell records for a user - - Args: - user_id: User ID - limit: Limit on number of returned records - - Returns: - List of MemCell - """ - try: - results = ( - await self.model.find({"user_id": user_id}) - .sort("-timestamp") - .limit(limit) - .to_list() - ) - logger.debug( - "✅ Successfully retrieved latest user MemCell: %s, returned %d records", - user_id, - len(results), - ) - return results - except Exception as e: - logger.error("❌ Failed to retrieve latest user MemCell: %s", e) - return [] - - -# Export -__all__ = ["MemCellRawRepository"] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/raw_message_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/raw_message_repository.py deleted file mode 100644 index 8b888bae..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/raw_message_repository.py +++ /dev/null @@ -1,717 +0,0 @@ -# -*- coding: utf-8 -*- -""" -RawMessage Repository - -Raw message data access layer, providing CRUD operations for raw message records. -Used as a replacement for the conversation_data functionality. -""" - -from datetime import datetime -from typing import List, Optional, Dict, Any -from pymongo.asynchronous.client_session import AsyncClientSession -from core.observation.logger import get_logger -from core.di.decorators import repository -from core.oxm.mongo.base_repository import BaseRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.persistence.document.request.raw_message import RawMessage - -logger = get_logger(__name__) - - -@repository("raw_message_repository", primary=True) -class RawMessageRepository(BaseRepository[RawMessage]): - """ - Raw Message Repository - - Provides CRUD operations and query functionality for raw message records. - Can be used as an alternative implementation for conversation_data. - """ - - def __init__(self): - super().__init__(RawMessage) - - # ==================== Save Methods ==================== - - async def save( - self, raw_message: RawMessage, session: Optional[AsyncClientSession] = None - ) -> Optional[RawMessage]: - """ - Save raw message - - Args: - raw_message: RawMessage object - session: Optional MongoDB session - - Returns: - Saved RawMessage or None - """ - try: - await raw_message.insert(session=session) - logger.debug( - "Raw message saved successfully: id=%s, group_id=%s, request_id=%s", - raw_message.id, - raw_message.group_id, - raw_message.request_id, - ) - return raw_message - except Exception as e: - logger.error("Failed to save raw message: %s", e) - return None - - async def save_from_raw_data( - self, - raw_data_content: Dict[str, Any], - data_id: Optional[str], - group_id: str, - request_id: str, - session_id: Optional[str] = None, - version: Optional[str] = None, - endpoint_name: Optional[str] = None, - method: Optional[str] = None, - url: Optional[str] = None, - event_id: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> Optional[str]: - """ - Parse raw data fields, create a RawMessage document, and save it. - - Extracts core message fields (sender, content, timestamps, etc.) from the - raw data content dict, constructs a RawMessage, and persists it. - - Args: - raw_data_content: The content dict from RawData (raw_data.content) - data_id: Message ID (raw_data.message_id) - group_id: Conversation group ID - request_id: Request ID - session_id: Session identifier for conversation isolation - version: API version - endpoint_name: Endpoint name - method: HTTP method - url: Request URL - event_id: Event ID - session: Optional MongoDB session - - Returns: - Optional[str]: Returns message_id if saved successfully, None otherwise - """ - content_dict = raw_data_content or {} - message_id = data_id - - # Extract core message fields - sender_id = content_dict.get("sender_id", "") - sender_name = content_dict.get("sender_name") or sender_id - # Store content_items list directly (e.g. [{type: "text", content: "..."}]) - raw_content = content_dict.get("content") - content_items = raw_content if isinstance(raw_content, list) else None - - role = content_dict.get("role") - tool_calls = content_dict.get("tool_calls") - tool_call_id = content_dict.get("tool_call_id") - timestamp = self._parse_create_time( - content_dict.get("timestamp") or content_dict.get("created_at") - ) - - # Create RawMessage document - raw_message = RawMessage( - group_id=group_id, - request_id=request_id, - session_id=session_id, - message_id=message_id, - timestamp=timestamp, - sender_id=sender_id, - sender_name=sender_name, - role=role, - content_items=content_items, - tool_calls=tool_calls, - tool_call_id=tool_call_id, - version=version, - endpoint_name=endpoint_name, - method=method, - url=url, - event_id=event_id, - ) - - await self.save(raw_message, session=session) - - logger.debug( - "Saved raw message from raw data: group_id=%s, message_id=%s", - group_id, - message_id, - ) - - return message_id - - @staticmethod - def _parse_create_time(create_time: Any) -> Optional[str]: - """Parse creation time and return ISO format string with timezone""" - if create_time is None: - return None - try: - from common_utils.datetime_utils import to_iso_format - - return to_iso_format(create_time) - except Exception: - if isinstance(create_time, str): - return create_time - return None - - # ==================== Query Methods ==================== - - async def get_by_request_id( - self, request_id: str, session: Optional[AsyncClientSession] = None - ) -> Optional[RawMessage]: - """ - Get raw message by request ID - - Args: - request_id: Request ID - session: Optional MongoDB session - - Returns: - RawMessage or None - """ - try: - result = await RawMessage.find_one( - {"request_id": request_id}, session=session - ) - return result - except Exception as e: - logger.error("Failed to get raw message by request ID: %s", e) - return None - - async def find_one_by_group_sender_message( - self, - group_id: str, - sender_id: str, - message_id: str, - session: Optional[AsyncClientSession] = None, - ) -> Optional[RawMessage]: - """ - Find a single raw message by group_id, sender_id, and message_id - - Used for duplicate detection before saving new raw messages. - Uses composite index (group_id, sender_id, message_id) for efficient lookup. - - Args: - group_id: Conversation group ID - sender_id: Sender ID - message_id: Message ID - session: Optional MongoDB session - - Returns: - RawMessage if found, None otherwise - """ - try: - result = await RawMessage.find_one( - { - "group_id": group_id, - "sender_id": sender_id, - "message_id": message_id, - }, - session=session, - ) - if result: - logger.debug( - "Found existing raw message: group_id=%s, sender_id=%s, message_id=%s", - group_id, - sender_id, - message_id, - ) - return result - except Exception as e: - logger.error( - "Failed to find raw message by group_id/sender_id/message_id: " - "group_id=%s, sender_id=%s, message_id=%s, error=%s", - group_id, - sender_id, - message_id, - e, - ) - return None - - async def find_by_group_id( - self, - group_id: str, - session_id: Optional[str] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - limit: int = 100, - sync_status: Optional[int] = 0, - session: Optional[AsyncClientSession] = None, - ) -> List[RawMessage]: - """ - Query raw messages by group_id - - Args: - group_id: Conversation group ID - session_id: Session identifier (optional, filters by session when provided) - start_time: Start time - end_time: End time - limit: Maximum number of records to return - sync_status: Sync status filter (default 0=in window accumulation, None=no filter) - session: Optional MongoDB session - - Returns: - List of RawMessage - """ - try: - query = {"group_id": group_id} - - if session_id is not None: - query["session_id"] = session_id - - # Filter by status - if sync_status is not None: - query["sync_status"] = sync_status - - if start_time: - query["created_at"] = {"$gte": start_time} - if end_time: - if "created_at" in query: - query["created_at"]["$lte"] = end_time - else: - query["created_at"] = {"$lte": end_time} - - results = ( - await RawMessage.find(query, session=session) - .sort([("created_at", 1)]) - .limit(limit) - .to_list() - ) - logger.debug( - "Query raw messages by group_id: group_id=%s, sync_status=%s, count=%d", - group_id, - sync_status, - len(results), - ) - return results - except Exception as e: - logger.error("Failed to query raw messages by group_id: %s", e) - return [] - - async def find_by_group_id_with_statuses( - self, - group_id: str, - sync_status_list: List[int], - session_id: Optional[str] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - limit: int = 100, - ascending: bool = True, - exclude_message_ids: Optional[List[str]] = None, - session: Optional[AsyncClientSession] = None, - ) -> List[RawMessage]: - """ - Query raw messages by group_id with multiple sync_status values - - Args: - group_id: Conversation group ID - sync_status_list: List of sync_status values to filter by - session_id: Session identifier (optional) - start_time: Start time (optional) - end_time: End time (optional) - limit: Maximum number of records to return - ascending: Sort ascending by created_at (default True) - exclude_message_ids: Message IDs to exclude from results - session: Optional MongoDB session - - Returns: - List of RawMessage - """ - try: - query = {"group_id": group_id} - - if session_id is not None: - query["session_id"] = session_id - - if sync_status_list: - if len(sync_status_list) == 1: - query["sync_status"] = sync_status_list[0] - else: - query["sync_status"] = {"$in": sync_status_list} - - if start_time: - query["created_at"] = {"$gte": start_time} - if end_time: - if "created_at" in query: - query["created_at"]["$lte"] = end_time - else: - query["created_at"] = {"$lte": end_time} - - if exclude_message_ids: - query["message_id"] = {"$nin": exclude_message_ids} - - sort_order = 1 if ascending else -1 - - results = ( - await RawMessage.find(query, session=session) - .sort([("created_at", sort_order)]) - .limit(limit) - .to_list() - ) - logger.debug( - "Query raw messages by group_id with statuses: group_id=%s, sync_status_list=%s, count=%d", - group_id, - sync_status_list, - len(results), - ) - return results - except Exception as e: - logger.error( - "Failed to query raw messages by group_id with statuses: %s", e - ) - return [] - - async def find_by_sender_id( - self, - sender_id: str, - limit: int = 100, - session: Optional[AsyncClientSession] = None, - ) -> List[RawMessage]: - """ - Query raw messages by sender ID - - Args: - sender_id: Sender ID - limit: Maximum number of records to return - session: Optional MongoDB session - - Returns: - List of RawMessage - """ - try: - results = ( - await RawMessage.find({"sender_id": sender_id}, session=session) - .sort([("created_at", -1)]) - .limit(limit) - .to_list() - ) - logger.debug( - "Query raw messages by sender_id: sender_id=%s, count=%d", - sender_id, - len(results), - ) - return results - except Exception as e: - logger.error("Failed to query raw messages by sender_id: %s", e) - return [] - - async def delete_by_group_id( - self, group_id: str, session: Optional[AsyncClientSession] = None - ) -> int: - """ - Delete raw messages by group_id - - Args: - group_id: Conversation group ID - session: Optional MongoDB session - - Returns: - Number of deleted records - """ - try: - result = await RawMessage.find( - {"group_id": group_id}, session=session - ).delete() - deleted_count = result.deleted_count if result else 0 - logger.info( - "Deleted raw messages: group_id=%s, deleted=%d", group_id, deleted_count - ) - return deleted_count - except Exception as e: - logger.error( - "Failed to delete raw messages: group_id=%s, error=%s", group_id, e - ) - return 0 - - async def delete_by_filters( - self, - sender_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Soft delete raw messages by filter conditions - - Args: - sender_id: Sender ID filter - - MAGIC_ALL ("__all__"): Don't filter by sender_id - - Other values: Exact match - group_id: Group ID filter - session: Optional MongoDB session - - Returns: - Number of soft-deleted records - """ - filter_dict = {} - - if sender_id != MAGIC_ALL: - if sender_id == "" or sender_id is None: - filter_dict["sender_id"] = {"$in": [None, ""]} - else: - filter_dict["sender_id"] = sender_id - - if group_id != MAGIC_ALL: - if group_id is None or group_id == "": - filter_dict["group_id"] = {"$in": [None, ""]} - else: - filter_dict["group_id"] = group_id - - if not filter_dict: - logger.warning("No filter conditions provided for delete_by_filters") - return 0 - - try: - result = await self.model.delete_many(filter_dict, session=session) - count = result.modified_count if result else 0 - logger.info( - "Soft deleted raw messages: filter=%s, deleted=%d", filter_dict, count - ) - return count - except Exception as e: - logger.error( - "Failed to soft delete raw messages: filter=%s, error=%s", - {"sender_id": sender_id, "group_id": group_id}, - e, - ) - return 0 - - # ==================== Sync Status Management ==================== - # sync_status state transitions: - # -1 (log record) -> 0 (window accumulation) -> 1 (used) - - async def confirm_accumulation_by_group_id( - self, group_id: str, session: Optional[AsyncClientSession] = None - ) -> int: - """ - Confirm records for the specified group_id as window accumulation state - - Batch update sync_status: -1 -> 0. - Uses (group_id, sync_status) composite index for efficient querying. - - Args: - group_id: Conversation group ID - session: Optional MongoDB session - - Returns: - Number of updated records - """ - try: - collection = RawMessage.get_pymongo_collection() - result = await collection.update_many( - {"group_id": group_id, "sync_status": -1}, - {"$set": {"sync_status": 0}}, - session=session, - ) - modified_count = result.modified_count if result else 0 - logger.info( - "Confirmed window accumulation: group_id=%s, modified=%d", - group_id, - modified_count, - ) - return modified_count - except Exception as e: - logger.error( - "Failed to confirm window accumulation: group_id=%s, error=%s", - group_id, - e, - ) - return 0 - - async def confirm_accumulation_by_message_ids( - self, - group_id: str, - message_ids: List[str], - session_id: Optional[str] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Confirm records for the specified message_id list as window accumulation state - - Precise update: only update records with specified message_id. - sync_status: -1 -> 0 - - Args: - group_id: Conversation group ID - message_ids: List of message_ids to update - session_id: Session identifier (optional) - session: Optional MongoDB session - - Returns: - Number of updated records - """ - if not message_ids: - logger.debug("message_ids is empty, skipping update") - return 0 - - try: - collection = RawMessage.get_pymongo_collection() - query = { - "group_id": group_id, - "message_id": {"$in": message_ids}, - "sync_status": -1, - } - if session_id is not None: - query["session_id"] = session_id - result = await collection.update_many( - query, {"$set": {"sync_status": 0}}, session=session - ) - modified_count = result.modified_count if result else 0 - logger.info( - "Confirmed window accumulation (precise): group_id=%s, message_ids=%d, modified=%d", - group_id, - len(message_ids), - modified_count, - ) - return modified_count - except Exception as e: - logger.error( - "Failed to confirm window accumulation (precise): group_id=%s, error=%s", - group_id, - e, - ) - return 0 - - async def mark_as_used_by_group_id( - self, - group_id: str, - session_id: Optional[str] = None, - exclude_message_ids: Optional[List[str]] = None, - session: Optional[AsyncClientSession] = None, - ) -> int: - """ - Mark all pending and accumulating data for the specified group_id as used - - Batch update sync_status: -1 or 0 -> 1 (after boundary detection). - - Args: - group_id: Conversation group ID - session_id: Session identifier (optional) - exclude_message_ids: Message IDs to exclude from update - session: Optional MongoDB session - - Returns: - Number of updated records - """ - try: - collection = RawMessage.get_pymongo_collection() - query = {"group_id": group_id, "sync_status": {"$in": [-1, 0]}} - - if session_id is not None: - query["session_id"] = session_id - - if exclude_message_ids: - query["message_id"] = {"$nin": exclude_message_ids} - - result = await collection.update_many( - query, {"$set": {"sync_status": 1}}, session=session - ) - modified_count = result.modified_count if result else 0 - logger.info( - "Marked as used: group_id=%s, exclude=%d, modified=%d", - group_id, - len(exclude_message_ids) if exclude_message_ids else 0, - modified_count, - ) - return modified_count - except Exception as e: - logger.error("Failed to mark as used: group_id=%s, error=%s", group_id, e) - return 0 - - # ==================== Flexible Query Methods ==================== - - async def find_pending_by_filters( - self, - sender_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - sync_status_list: Optional[List[int]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - limit: int = 1000, - skip: int = 0, - ascending: bool = True, - session: Optional[AsyncClientSession] = None, - ) -> List[RawMessage]: - """ - Query pending raw messages by flexible filters - - Supports MAGIC_ALL logic: - - MAGIC_ALL ("__all__"): Don't filter by this field - - None or "": Filter for null/empty values - - Other values: Exact match - - Args: - sender_id: Sender ID filter - - MAGIC_ALL: Don't filter by sender_id - - None or "": Filter for null/empty values - - Other values: Exact match - group_ids: List of Group IDs to filter (None = search all groups) - sync_status_list: List of sync_status values to filter by - start_time: Start time (optional) - end_time: End time (optional) - limit: Maximum number of records to return - skip: Number of records to skip - ascending: Sort ascending by created_at (default True) - session: Optional MongoDB session - - Returns: - List of RawMessage - """ - if sync_status_list is None: - sync_status_list = [-1, 0] - - try: - query = {} - - if sender_id != MAGIC_ALL: - if sender_id == "" or sender_id is None: - query["sender_id"] = {"$in": [None, ""]} - else: - query["sender_id"] = sender_id - - if group_ids is not None and len(group_ids) > 0: - query["group_id"] = {"$in": group_ids} - - if sync_status_list: - if len(sync_status_list) == 1: - query["sync_status"] = sync_status_list[0] - else: - query["sync_status"] = {"$in": sync_status_list} - - if start_time is not None or end_time is not None: - time_filter = {} - if start_time is not None: - time_filter["$gte"] = start_time - if end_time is not None: - time_filter["$lte"] = end_time - query["created_at"] = time_filter - - sort_order = 1 if ascending else -1 - - results = ( - await RawMessage.find(query, session=session) - .sort([("created_at", sort_order)]) - .skip(skip) - .limit(limit) - .to_list() - ) - - logger.debug( - "Query pending raw messages: sender_id=%s, group_ids=%s, " - "sync_status_list=%s, skip=%d, limit=%d, count=%d", - sender_id, - group_ids, - sync_status_list, - skip, - limit, - len(results), - ) - return results - except Exception as e: - logger.error( - "Failed to query pending raw messages: sender_id=%s, group_ids=%s, error=%s", - sender_id, - group_ids, - e, - ) - return [] diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/sender_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/sender_raw_repository.py deleted file mode 100644 index bd794844..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/sender_raw_repository.py +++ /dev/null @@ -1,125 +0,0 @@ -""" -Sender raw data repository - -Provides CRUD operations for Sender documents (v1_senders collection). -""" - -from typing import Optional, Dict, Any, List - -from core.oxm.mongo.base_repository import BaseRepository -from infra_layer.adapters.out.persistence.document.memory.sender import Sender -from core.observation.logger import get_logger -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("sender_raw_repository", primary=True) -class SenderRawRepository(BaseRepository[Sender]): - """ - Sender raw data repository - - Provides CRUD operations and query capabilities for Sender data. - """ - - def __init__(self): - super().__init__(Sender) - - async def get_by_sender_id(self, sender_id: str) -> Optional[Sender]: - """Get sender by sender_id""" - try: - result = await self.model.find_one({"sender_id": sender_id}) - if result: - logger.debug("Retrieved sender: sender_id=%s", sender_id) - else: - logger.debug("Sender not found: sender_id=%s", sender_id) - return result - except Exception as e: - logger.error("Failed to get sender by sender_id: %s", e) - return None - - async def get_by_sender_ids(self, sender_ids: List[str]) -> List[Sender]: - """Batch get senders by sender_ids. - - Args: - sender_ids: List of sender identifiers - - Returns: - List of matching Sender documents - """ - if not sender_ids: - return [] - try: - results = await self.model.find( - {"sender_id": {"$in": sender_ids}} - ).to_list() - logger.debug( - "Batch retrieved %d senders for %d ids", len(results), len(sender_ids) - ) - return results - except Exception as e: - logger.error("Failed to batch get senders: %s", e) - return [] - - async def upsert_by_sender_id( - self, sender_id: str, update_data: Dict[str, Any] - ) -> Optional[Sender]: - """Update or insert sender by sender_id - - Uses find-then-save pattern with duplicate key retry - to handle concurrency safely. - - Args: - sender_id: Sender identifier - update_data: Fields to update (only non-None values) - - Returns: - The updated or created Sender document - """ - try: - existing_doc = await self.model.find_one({"sender_id": sender_id}) - - if existing_doc: - for key, value in update_data.items(): - setattr(existing_doc, key, value) - await existing_doc.save() - logger.debug("Updated existing sender: sender_id=%s", sender_id) - return existing_doc - - # Not found, create new - try: - new_doc = Sender(sender_id=sender_id, **update_data) - await new_doc.create() - logger.info("Created new sender: sender_id=%s", sender_id) - return new_doc - - except Exception as create_error: - # Handle concurrent duplicate key - error_str = str(create_error) - if "E11000" in error_str and "duplicate key" in error_str: - logger.warning( - "Concurrent creation conflict, retrying: sender_id=%s", - sender_id, - ) - retry_doc = await self.model.find_one({"sender_id": sender_id}) - if retry_doc: - for key, value in update_data.items(): - setattr(retry_doc, key, value) - await retry_doc.save() - logger.debug( - "Updated after concurrency conflict: sender_id=%s", - sender_id, - ) - return retry_doc - else: - logger.error( - "Record not found after concurrency conflict: sender_id=%s", - sender_id, - ) - return None - else: - raise create_error - - except Exception as e: - logger.error("Failed to upsert sender: %s", e) - return None diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/session_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/session_raw_repository.py deleted file mode 100644 index 7eded23f..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/session_raw_repository.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -Session raw data repository - -Provides CRUD operations for Session documents (v1_sessions collection). -""" - -from typing import Optional, Dict, Any - -from core.oxm.mongo.base_repository import BaseRepository -from infra_layer.adapters.out.persistence.document.memory.session import Session -from core.observation.logger import get_logger -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("session_raw_repository", primary=True) -class SessionRawRepository(BaseRepository[Session]): - """ - Session raw data repository - - Provides CRUD operations and query capabilities for Session data. - """ - - def __init__(self): - super().__init__(Session) - - async def get_by_session_id(self, session_id: str) -> Optional[Session]: - """Get session by session_id""" - try: - result = await self.model.find_one({"session_id": session_id}) - if result: - logger.debug("Retrieved session: session_id=%s", session_id) - else: - logger.debug("Session not found: session_id=%s", session_id) - return result - except Exception as e: - logger.error("Failed to get session by session_id: %s", e) - return None - - async def upsert_by_session_id( - self, session_id: str, update_data: Dict[str, Any] - ) -> Optional[Session]: - """Update or insert session by session_id - - Uses find-then-save pattern with duplicate key retry - to handle concurrency safely. - - Args: - session_id: Session identifier - update_data: Fields to update (only non-None values) - - Returns: - The updated or created Session document - """ - try: - existing_doc = await self.model.find_one({"session_id": session_id}) - - if existing_doc: - for key, value in update_data.items(): - setattr(existing_doc, key, value) - await existing_doc.save() - logger.debug("Updated existing session: session_id=%s", session_id) - return existing_doc - - # Not found, create new - try: - new_doc = Session(session_id=session_id, **update_data) - await new_doc.create() - logger.info("Created new session: session_id=%s", session_id) - return new_doc - - except Exception as create_error: - # Handle concurrent duplicate key - error_str = str(create_error) - if "E11000" in error_str and "duplicate key" in error_str: - logger.warning( - "Concurrent creation conflict, retrying: session_id=%s", - session_id, - ) - retry_doc = await self.model.find_one({"session_id": session_id}) - if retry_doc: - for key, value in update_data.items(): - setattr(retry_doc, key, value) - await retry_doc.save() - logger.debug( - "Updated after concurrency conflict: session_id=%s", - session_id, - ) - return retry_doc - else: - logger.error( - "Record not found after concurrency conflict: " - "session_id=%s", - session_id, - ) - return None - else: - raise create_error - - except Exception as e: - logger.error("Failed to upsert session: %s", e) - return None diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/settings_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/settings_raw_repository.py deleted file mode 100644 index f7993dab..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/settings_raw_repository.py +++ /dev/null @@ -1,133 +0,0 @@ -""" -GlobalSettings Raw Repository - -Provides database operations for the singleton GlobalSettings document. -""" - -import logging -from typing import Optional, Dict, Any -from pymongo.asynchronous.client_session import AsyncClientSession - -from core.oxm.mongo.base_repository import BaseRepository -from core.di.decorators import repository -from core.constants.exceptions import ValidationException -from infra_layer.adapters.out.persistence.document.memory.global_settings import ( - GlobalSettings, -) - -logger = logging.getLogger(__name__) - - -@repository("settings_raw_repository", primary=True) -class GlobalSettingsRawRepository(BaseRepository[GlobalSettings]): - """ - Repository for the singleton GlobalSettings document (v1_global_settings collection). - - GlobalSettings is a singleton per space. Group-level metadata has moved - to the Session model (v1_sessions). - """ - - def __init__(self): - """Initialize repository""" - super().__init__(GlobalSettings) - - # ========================================================================= - # Singleton methods (primary API) - # ========================================================================= - - async def get_global_settings( - self, session: Optional[AsyncClientSession] = None - ) -> Optional[GlobalSettings]: - """ - Get the singleton GlobalSettings document. - - Args: - session: Optional MongoDB session for transaction support - - Returns: - GlobalSettings document or None if not found - """ - try: - doc = await self.model.find_one({}, session=session) - if doc: - logger.debug("Retrieved global settings") - return doc - except Exception as e: - logger.error("Failed to retrieve global settings: %s", e) - return None - - async def upsert_global_settings( - self, - settings_data: Dict[str, Any], - session: Optional[AsyncClientSession] = None, - ) -> Optional[GlobalSettings]: - """ - Create or update the singleton GlobalSettings document. - - Args: - settings_data: Dictionary of settings fields to set - session: Optional MongoDB session - - Returns: - Updated or created GlobalSettings document - - """ - try: - existing_doc = await self.model.find_one({}, session=session) - - if existing_doc: - for key, value in settings_data.items(): - if hasattr(existing_doc, key): - setattr(existing_doc, key, value) - await existing_doc.save(session=session) - logger.debug("Updated existing global settings") - return existing_doc - - # No record found, create new singleton - try: - new_doc = GlobalSettings(**settings_data) - await new_doc.insert(session=session) - logger.info("Created new global settings") - return new_doc - except Exception as create_error: - logger.error( - "Failed to create global settings: %s", create_error, exc_info=True - ) - return None - - except ValidationException: - raise - except Exception as e: - logger.error("Failed to upsert global settings: %s", e, exc_info=True) - return None - - async def update_global_settings( - self, update_data: Dict[str, Any], session: Optional[AsyncClientSession] = None - ) -> Optional[GlobalSettings]: - """ - Update the singleton GlobalSettings document (must already exist). - - Args: - update_data: Dictionary of fields to update - session: Optional MongoDB session - - Returns: - Updated GlobalSettings document or None if not found - - """ - try: - doc = await self.model.find_one({}, session=session) - if not doc: - return None - - for key, value in update_data.items(): - if hasattr(doc, key): - setattr(doc, key, value) - await doc.save(session=session) - logger.debug("Updated global settings") - return doc - except ValidationException: - raise - except Exception as e: - logger.error("Failed to update global settings: %s", e, exc_info=True) - return None diff --git a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/user_profile_raw_repository.py b/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/user_profile_raw_repository.py deleted file mode 100644 index 65dd41e7..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/persistence/repository/user_profile_raw_repository.py +++ /dev/null @@ -1,396 +0,0 @@ -""" -UserProfile native CRUD repository - -User profile data access layer based on Beanie ODM. -Provides ProfileStorage compatible interface (duck typing). - -Supports automatic Milvus indexing on profile create/update. -""" - -from typing import Optional, Dict, Any, List -from beanie.operators import Or, Eq, In -from core.observation.logger import get_logger -from core.di.decorators import repository -from core.oxm.mongo.base_repository import BaseRepository -from core.oxm.constants import MAGIC_ALL - -from api_specs.memory_types import ScenarioType -from infra_layer.adapters.out.persistence.document.memory.user_profile import ( - UserProfile, -) -from api_specs.memory_types import ProfileMemory -from memory_layer.profile_indexer import index_user_profile - - -logger = get_logger(__name__) - - -@repository("user_profile_raw_repository", primary=True) -class UserProfileRawRepository(BaseRepository[UserProfile]): - """ - UserProfile native CRUD repository - - Provides ProfileStorage compatible interfaces: - - save_profile(user_id, profile, metadata) -> bool - - get_profile(user_id) -> Optional[Any] - - get_all_profiles() -> Dict[str, Any] - - get_profile_history(user_id, limit) -> List[Dict] - - clear() -> bool - """ - - def __init__(self): - super().__init__(UserProfile) - - # ==================== ProfileStorage interface implementation ==================== - - async def save_profile( - self, user_id: str, profile: Any, metadata: Optional[Dict[str, Any]] = None - ) -> bool: - metadata = metadata or {} - group_id = metadata.get("group_id", "default") - - profile_data = profile.to_dict() if hasattr(profile, 'to_dict') else profile - result = await self.upsert(user_id, group_id, profile_data, metadata) - return result is not None - - async def get_profile( - self, user_id: str, group_id: str = "default" - ) -> Optional[Any]: - user_profile = await self.get_by_user_and_group(user_id, group_id) - if user_profile is None: - return None - return user_profile.profile_data - - async def get_all_profiles(self, group_id: str = "default") -> Dict[str, Any]: - user_profiles = await self.get_all_by_group(group_id) - return {up.user_id: up.profile_data for up in user_profiles} - - async def get_profile_history( - self, user_id: str, group_id: str = "default", limit: Optional[int] = None - ) -> List[Dict[str, Any]]: - user_profile = await self.get_by_user_and_group(user_id, group_id) - if user_profile is None: - return [] - - history = [ - { - "update_count": user_profile.update_count, - "profile": user_profile.profile_data, - "confidence": user_profile.confidence, - "updated_at": user_profile.updated_at, - "last_updated_ts": user_profile.last_updated_ts, - "memcell_count": user_profile.memcell_count, - } - ] - return history[:limit] if limit else history - - async def clear(self, group_id: Optional[str] = None) -> bool: - if group_id is None: - await self.delete_all() - else: - await self.delete_by_group(group_id) - return True - - # ==================== Native CRUD methods ==================== - - async def get_by_user_and_group( - self, user_id: str, group_id: str - ) -> Optional[UserProfile]: - try: - return await self.model.find_one( - UserProfile.user_id == user_id, UserProfile.group_id == group_id - ) - except Exception as e: - logger.error( - f"Failed to retrieve user profile: user_id={user_id}, group_id={group_id}, error={e}" - ) - return None - - async def get_all_by_group(self, group_id: str) -> List[UserProfile]: - try: - return await self.model.find(UserProfile.group_id == group_id).to_list() - except Exception as e: - logger.error( - f"Failed to retrieve group user profiles: group_id={group_id}, error={e}" - ) - return [] - - async def get_all_by_user(self, user_id: str, limit: int = 40) -> List[UserProfile]: - try: - return ( - await self.model.find(UserProfile.user_id == user_id) - .sort([("update_count", -1)]) - .limit(limit) - .to_list() - ) - except Exception as e: - logger.error(f"Failed to get user profile: user_id={user_id}, error={e}") - return [] - - async def find_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - limit: Optional[int] = None, - skip: Optional[int] = None, - ) -> List[UserProfile]: - """ - Retrieve list of user profiles by filters (user_id and/or group_ids) - - Args: - user_id: User ID - - Not provided or MAGIC_ALL ("__all__"): Don't filter by user_id - - None or "": Filter for null/empty values (records with user_id as None or "") - - Other values: Exact match - group_ids: List of Group IDs - - None: Skip group filtering - - []: Empty array, skip filtering - - ["g1"]: Single element array, exact match - - ["g1", "g2"]: Multiple elements, use In operator - limit: Limit number of returned results - skip: Number of results to skip (pagination offset) - - Returns: - List of UserProfile - """ - try: - # Build query conditions - conditions = [] - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id == "" or user_id is None: - # Explicitly filter for null or empty string - conditions.append( - Or(Eq(UserProfile.user_id, None), Eq(UserProfile.user_id, "")) - ) - else: - conditions.append(UserProfile.user_id == user_id) - - # Handle group_ids filter (array, no MAGIC_ALL) - if group_ids is not None and len(group_ids) > 0: - if len(group_ids) == 1: - # Single element: exact match - conditions.append(UserProfile.group_id == group_ids[0]) - else: - # Multiple elements: use In operator - conditions.append(In(UserProfile.group_id, group_ids)) - # group_ids is None or empty: skip group filtering - - # Build query - if conditions: - # Combine conditions with AND - query = self.model.find(*conditions) - else: - # No conditions - find all - query = self.model.find() - - # Sort by update_count descending - query = query.sort([("update_count", -1)]) - - # Apply skip (offset) - if skip: - query = query.skip(skip) - - # Apply limit - if limit: - query = query.limit(limit) - - logger.debug( - "🔍 UserProfile.find_by_filters query: %s, sort=[('update_count', -1)], skip=%s, limit=%s", - query.get_filter_query(), - skip, - limit, - ) - - results = await query.to_list() - logger.debug( - "✅ Retrieved user profiles successfully: user_id=%s, group_ids=%s, found %d records", - user_id, - group_ids, - len(results), - ) - return results - except Exception as e: - logger.error("❌ Failed to retrieve user profiles: %s", e) - return [] - - async def count_by_filters( - self, user_id: Optional[str] = MAGIC_ALL, group_ids: Optional[List[str]] = None - ) -> int: - """ - Count user profiles by filters (without pagination) - - Args: - user_id: User ID filter (same semantics as find_by_filters) - group_ids: Group IDs filter (same semantics as find_by_filters) - - Returns: - Total count of matching records - """ - try: - # Build query conditions (same as find_by_filters) - conditions = [] - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id == "" or user_id is None: - conditions.append( - Or(Eq(UserProfile.user_id, None), Eq(UserProfile.user_id, "")) - ) - else: - conditions.append(UserProfile.user_id == user_id) - - # Handle group_ids filter - if group_ids is not None and len(group_ids) > 0: - if len(group_ids) == 1: - conditions.append(UserProfile.group_id == group_ids[0]) - else: - conditions.append(In(UserProfile.group_id, group_ids)) - - # Build query - if conditions: - query = self.model.find(*conditions) - else: - query = self.model.find() - - count = await query.count() - logger.debug( - "✅ Counted user profiles: user_id=%s, group_ids=%s, count=%d", - user_id, - group_ids, - count, - ) - return count - except Exception as e: - logger.error("❌ Failed to count user profiles: %s", e) - return 0 - - async def upsert( - self, - user_id: str, - group_id: str, - profile_data: Dict[str, Any], - metadata: Optional[Dict[str, Any]] = None, - trigger_index: bool = True, - ) -> Optional[UserProfile]: - """ - Create or update user profile with optional Milvus indexing - - Args: - user_id: User ID - group_id: Group ID - profile_data: Profile data dict (containing explicit_info, implicit_traits, etc.) - metadata: Additional metadata (confidence, cluster_id, memcell_count, etc.) - trigger_index: Whether to trigger Milvus indexing after save (default True) - - Returns: - Saved UserProfile object or None on error - """ - try: - metadata = metadata or {} - existing = await self.get_by_user_and_group(user_id, group_id) - - if existing: - existing.profile_data = profile_data - existing.update_count += 1 - existing.confidence = metadata.get("confidence", existing.confidence) - - if "memcell_count" in metadata: - existing.memcell_count = metadata["memcell_count"] - - if "last_updated_ts" in metadata: - existing.last_updated_ts = metadata["last_updated_ts"] - - await existing.save() - logger.debug( - f"Updated user profile: user_id={user_id}, group_id={group_id}, update_count={existing.update_count}" - ) - saved_profile = existing - else: - user_profile = UserProfile( - user_id=user_id, - group_id=group_id, - profile_data=profile_data, - scenario=metadata.get("scenario", ScenarioType.TEAM.value), - confidence=metadata.get("confidence", 0.0), - update_count=1, - memcell_count=metadata.get("memcell_count", 0), - last_updated_ts=metadata.get("last_updated_ts"), - ) - await user_profile.insert() - logger.info( - f"Created user profile: user_id={user_id}, group_id={group_id}" - ) - saved_profile = user_profile - - # Trigger Milvus indexing (runs in clustering background task, not on hot path) - if trigger_index: - await self._trigger_milvus_indexing(user_id, group_id, profile_data, doc_id=str(saved_profile.id)) - - return saved_profile - - except Exception as e: - logger.error( - f"Failed to save user profile: user_id={user_id}, group_id={group_id}, error={e}" - ) - return None - - async def _trigger_milvus_indexing( - self, user_id: str, group_id: str, profile_data: Dict[str, Any], doc_id: str = "" - ) -> None: - """ - Trigger Milvus indexing for profile (delete-then-insert strategy) - - This runs asynchronously to avoid blocking the main save operation. - - Args: - user_id: User ID - group_id: Group ID - profile_data: Profile data dict - doc_id: MongoDB document ID for generating unique Milvus entity IDs - """ - try: - # Convert profile_data dict to ProfileMemory object - profile = ProfileMemory.from_dict( - profile_data, user_id=user_id, group_id=group_id - ) - - # Trigger indexing (delete existing + insert new) - stats = await index_user_profile(user_id, group_id, profile, doc_id=doc_id) - - logger.info( - f"✅ Profile Milvus indexing completed: user_id={user_id}, group_id={group_id}, " - f"deleted={stats.get('deleted_count', 0)}, indexed={stats.get('total_count', 0)}" - ) - - except Exception as e: - # Log error but don't fail the main operation - logger.error( - f"❌ Failed to trigger Milvus indexing: user_id={user_id}, group_id={group_id}, error={e}", - exc_info=True, - ) - - async def delete_by_group(self, group_id: str) -> int: - try: - result = await self.model.find(UserProfile.group_id == group_id).delete() - count = result.deleted_count if result else 0 - logger.info( - f"Deleted group user profiles: group_id={group_id}, count={count}" - ) - return count - except Exception as e: - logger.error( - f"Failed to delete group user profiles: group_id={group_id}, error={e}" - ) - return 0 - - async def delete_all(self) -> int: - try: - result = await self.model.delete_all() - count = result.deleted_count if result else 0 - logger.info(f"Deleted all user profiles: {count} items") - return count - except Exception as e: - logger.error(f"Failed to delete all user profiles: {e}") - return 0 diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/__init__.py b/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/__init__.py deleted file mode 100644 index a8f51137..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -""" -Elasticsearch Converters - -Export ES converters for all memory types -""" - -from infra_layer.adapters.out.search.elasticsearch.converter.episodic_memory_converter import ( - EpisodicMemoryConverter, -) -from infra_layer.adapters.out.search.elasticsearch.converter.foresight_converter import ( - ForesightConverter, -) -from infra_layer.adapters.out.search.elasticsearch.converter.atomic_fact_converter import ( - AtomicFactConverter, -) - -__all__ = ["EpisodicMemoryConverter", "ForesightConverter", "AtomicFactConverter"] diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/agent_case_converter.py b/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/agent_case_converter.py deleted file mode 100644 index 6874512b..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/agent_case_converter.py +++ /dev/null @@ -1,98 +0,0 @@ -""" -Agent Case ES Converter - -Converts MongoDB AgentCaseRecord to Elasticsearch AgentCaseDoc document. -""" - -from typing import List -import jieba - -from core.oxm.es.base_converter import BaseEsConverter -from core.observation.logger import get_logger -from core.nlp.stopwords_utils import filter_stopwords -from infra_layer.adapters.out.search.elasticsearch.memory.agent_case import AgentCaseDoc -from infra_layer.adapters.out.persistence.document.memory.agent_case import ( - AgentCaseRecord, -) - -logger = get_logger(__name__) - - -class AgentCaseConverter(BaseEsConverter[AgentCaseDoc]): - """ - Agent Case ES Converter - - Converts MongoDB agent case documents to Elasticsearch AgentCaseDoc documents. - Extracts task_intent and approach for BM25 retrieval. - """ - - @classmethod - def from_mongo(cls, source_doc: AgentCaseRecord) -> AgentCaseDoc: - """ - Convert from MongoDB agent case document to ES document - - Args: - source_doc: Instance of MongoDB agent case document - - Returns: - AgentCaseDoc: Instance of ES document - """ - if source_doc is None: - raise ValueError("MongoDB document cannot be empty") - - try: - search_content = cls._build_search_content(source_doc) - - es_doc = AgentCaseDoc( - meta={"id": str(source_doc.id)}, - id=str(source_doc.id), - user_id=source_doc.user_id, - group_id=source_doc.group_id, - session_id=source_doc.session_id, - timestamp=source_doc.timestamp, - search_content=search_content, - task_intent=source_doc.task_intent or "", - approach=source_doc.approach or "", - parent_type=source_doc.parent_type, - parent_id=source_doc.parent_id, - ) - - return es_doc - - except Exception as e: - logger.error("Failed to convert AgentCaseRecord to ES document: %s", e) - raise - - @classmethod - def _build_search_content(cls, source_doc: AgentCaseRecord) -> List[str]: - """ - Build search content list from experience fields. - - Segments task_intent and approach with jieba, filters stopwords, - and generates keyword list for BM25 retrieval. - """ - search_content = [] - - task_intent = source_doc.task_intent or "" - if task_intent: - words = jieba.lcut(task_intent) - search_content.extend(filter_stopwords(words, min_length=2)) - - approach = source_doc.approach or "" - if approach: - words = jieba.lcut(approach) - search_content.extend(filter_stopwords(words, min_length=2)) - - # Deduplicate while preserving order - seen = set() - unique_content = [] - for word in search_content: - if word not in seen and word.strip(): - seen.add(word) - unique_content.append(word) - - # Fallback: use raw task_intent if empty after filtering - if not unique_content: - return [task_intent] if task_intent else [""] - - return unique_content diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/agent_skill_converter.py b/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/agent_skill_converter.py deleted file mode 100644 index c31bf6b8..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/agent_skill_converter.py +++ /dev/null @@ -1,102 +0,0 @@ -""" -Agent Skill ES Converter - -Converts MongoDB AgentSkillRecord to Elasticsearch AgentSkillDoc document. -""" - -from typing import List -import jieba - -from core.oxm.es.base_converter import BaseEsConverter -from core.observation.logger import get_logger -from core.nlp.stopwords_utils import filter_stopwords -from infra_layer.adapters.out.search.elasticsearch.memory.agent_skill import ( - AgentSkillDoc, -) -from infra_layer.adapters.out.persistence.document.memory.agent_skill import ( - AgentSkillRecord, -) - -logger = get_logger(__name__) - - -class AgentSkillConverter(BaseEsConverter[AgentSkillDoc]): - """ - Agent Skill ES Converter - - Converts MongoDB agent skill documents to Elasticsearch AgentSkillDoc documents. - Combines name + description + content for BM25 retrieval. - """ - - @classmethod - def from_mongo(cls, source_doc: AgentSkillRecord) -> AgentSkillDoc: - """ - Convert from MongoDB agent skill document to ES document - - Args: - source_doc: Instance of MongoDB agent skill document - - Returns: - AgentSkillDoc: Instance of ES document - """ - if source_doc is None: - raise ValueError("MongoDB document cannot be empty") - - try: - search_content = cls._build_search_content(source_doc) - - es_doc = AgentSkillDoc( - meta={"id": str(source_doc.id)}, - id=str(source_doc.id), - user_id=source_doc.user_id, - group_id=source_doc.group_id, - cluster_id=source_doc.cluster_id, - search_content=search_content, - name=source_doc.name or "", - description=source_doc.description or "", - content=source_doc.content or "", - confidence=source_doc.confidence, - maturity_score=source_doc.maturity_score, - ) - - return es_doc - - except Exception as e: - logger.error("Failed to convert AgentSkillRecord to ES document: %s", e) - raise - - @classmethod - def _build_search_content(cls, source_doc: AgentSkillRecord) -> List[str]: - """ - Build search content list from skill fields. - - Segments name + description + content with jieba, filters stopwords, - and generates keyword list for BM25 retrieval. - """ - search_content = [] - - # Combine name + description + content - text_parts = [ - source_doc.name or "", - source_doc.description or "", - source_doc.content or "", - ] - - for text in text_parts: - if text: - words = jieba.lcut(text) - search_content.extend(filter_stopwords(words, min_length=2)) - - # Deduplicate while preserving order - seen = set() - unique_content = [] - for word in search_content: - if word not in seen and word.strip(): - seen.add(word) - unique_content.append(word) - - # Fallback: use raw description if empty after filtering - if not unique_content and source_doc.description: - return [source_doc.description] - - return unique_content if unique_content else [""] diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/atomic_fact_converter.py b/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/atomic_fact_converter.py deleted file mode 100644 index db8d5bd5..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/atomic_fact_converter.py +++ /dev/null @@ -1,71 +0,0 @@ -""" -AtomicFact ES Converter - -Converts MongoDB v1_atomic_fact_records to ES v1_atomic_fact_record. -""" - -from typing import List -import jieba -from core.oxm.es.base_converter import BaseEsConverter -from core.observation.logger import get_logger -from core.nlp.stopwords_utils import filter_stopwords -from infra_layer.adapters.out.search.elasticsearch.memory.atomic_fact import ( - AtomicFactDoc, -) -from infra_layer.adapters.out.persistence.document.memory.atomic_fact_record import ( - AtomicFactRecord as MongoAtomicFactRecord, -) - -logger = get_logger(__name__) - - -class AtomicFactConverter(BaseEsConverter[AtomicFactDoc]): - """ - Atomic Fact ES Converter - - Converts MongoDB v1 AtomicFactRecord documents to ES v1 AtomicFactDoc documents. - Only maps search-essential fields. - """ - - @classmethod - def from_mongo(cls, source_doc: MongoAtomicFactRecord) -> AtomicFactDoc: - """ - Convert from MongoDB v1 AtomicFactRecord document to ES v1 AtomicFactDoc instance - - Args: - source_doc: Instance of MongoDB v1_atomic_fact_record document - - Returns: - AtomicFactDoc: ES document instance, ready for indexing - """ - if source_doc is None: - raise ValueError("MongoDB document cannot be empty") - - try: - es_doc = AtomicFactDoc( - meta={'id': str(source_doc.id)}, - # Basic identifier fields - id=str(source_doc.id), - user_id=source_doc.user_id, - group_id=source_doc.group_id, - session_id=source_doc.session_id, - # Timestamp field - timestamp=source_doc.timestamp, - # Participant list - participants=source_doc.participants or [], - sender_ids=getattr(source_doc, 'sender_ids', None), - # Core BM25 content field - atomic_fact=source_doc.atomic_fact, - search_content=getattr(source_doc, 'search_content', None), - # Classification fields - type=getattr(source_doc, 'type', None), - # Parent info for MongoDB back-reference - parent_type=source_doc.parent_type, - parent_id=str(source_doc.parent_id) if source_doc.parent_id else None, - ) - - return es_doc - - except Exception as e: - logger.error("Failed to convert MongoDB document to ES document: %s", e) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/episodic_memory_converter.py b/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/episodic_memory_converter.py deleted file mode 100644 index 67be7f26..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/episodic_memory_converter.py +++ /dev/null @@ -1,110 +0,0 @@ -""" -EpisodicMemory ES Converter - -Responsible for converting MongoDB v1_episodic_memories to ES v1_episodic_memory. -""" - -from typing import List -import jieba -from core.oxm.es.base_converter import BaseEsConverter -from core.observation.logger import get_logger -from core.nlp.stopwords_utils import filter_stopwords -from infra_layer.adapters.out.search.elasticsearch.memory.episodic_memory import ( - EpisodicMemoryDoc, -) -from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemory as MongoEpisodicMemory, -) - -logger = get_logger(__name__) - - -class EpisodicMemoryConverter(BaseEsConverter[EpisodicMemoryDoc]): - """ - EpisodicMemory Converter - - Converts MongoDB v1 EpisodicMemory documents to ES v1 EpisodicMemoryDoc documents. - Only maps search-essential fields. - """ - - @classmethod - def from_mongo(cls, source_doc: MongoEpisodicMemory) -> EpisodicMemoryDoc: - """ - Convert from MongoDB v1 EpisodicMemory document to ES v1 EpisodicMemoryDoc instance - - Args: - source_doc: Instance of MongoDB v1_episodic_memory document - - Returns: - EpisodicMemoryDoc: ES document instance, ready for indexing - """ - if source_doc is None: - raise ValueError("MongoDB document cannot be empty") - - try: - es_doc = EpisodicMemoryDoc( - meta={'id': str(source_doc.id)}, - # Basic identifier fields - id=str(source_doc.id), - user_id=source_doc.user_id, - group_id=source_doc.group_id, - session_id=source_doc.session_id, - # Timestamp field - timestamp=source_doc.timestamp, - # Participant list - participants=source_doc.participants or [], - sender_ids=getattr(source_doc, 'sender_ids', None), - # Core BM25 content fields - summary=source_doc.summary, - subject=source_doc.subject, - episode=source_doc.episode, - search_content=cls._build_search_content(source_doc), - # Classification fields - type=source_doc.type, - # Parent info for MongoDB back-reference - parent_type=source_doc.parent_type, - parent_id=str(source_doc.parent_id) if source_doc.parent_id else None, - ) - - return es_doc - - except Exception as e: - logger.error("Failed to convert MongoDB document to ES document: %s", e) - raise - - @classmethod - def _build_search_content(cls, source_doc: MongoEpisodicMemory) -> List[str]: - """ - Build search content list for BM25 retrieval - - Combines multiple text fields from the MongoDB document and processes them - with jieba word segmentation, generating a list of search content for BM25 retrieval. - - Args: - source_doc: Instance of MongoDB's EpisodicMemory document - - Returns: - List[str]: List of search content after jieba word segmentation - """ - text_content = [] - - # Collect all text content - including subject, summary, episode - if hasattr(source_doc, 'subject') and source_doc.subject: - text_content.append(source_doc.subject) - - if hasattr(source_doc, 'summary') and source_doc.summary: - text_content.append(source_doc.summary) - - if hasattr(source_doc, 'episode') and source_doc.episode: - text_content.append(source_doc.episode) - - # Combine all text content and apply jieba word segmentation - combined_text = ' '.join(text_content) - search_content = list(jieba.cut(combined_text)) - - # Filter out empty strings and stopwords - query_words = filter_stopwords(search_content, min_length=2) - - search_content = [word.strip() for word in query_words if word.strip()] - - return search_content diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/foresight_converter.py b/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/foresight_converter.py deleted file mode 100644 index 71b4e28a..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/converter/foresight_converter.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -Foresight ES Converter - -Converts MongoDB v1_foresight_records to ES v1_foresight_record. -""" - -from typing import List -from core.oxm.es.base_converter import BaseEsConverter -from core.observation.logger import get_logger -from infra_layer.adapters.out.search.elasticsearch.memory.foresight import ForesightDoc -from infra_layer.adapters.out.persistence.document.memory.foresight_record import ( - ForesightRecord as MongoForesightRecord, -) - -logger = get_logger(__name__) - - -class ForesightConverter(BaseEsConverter[ForesightDoc]): - """ - Foresight ES Converter - - Converts MongoDB v1 ForesightRecord documents to ES v1 ForesightDoc documents. - Only maps search-essential fields. - """ - - @classmethod - def from_mongo(cls, source_doc: MongoForesightRecord) -> ForesightDoc: - """ - Convert from MongoDB v1 ForesightRecord document to ES v1 ForesightDoc instance - - Args: - source_doc: Instance of MongoDB v1_foresight_record document - - Returns: - ForesightDoc: ES document instance, ready for indexing - """ - if source_doc is None: - raise ValueError("MongoDB document cannot be empty") - - try: - es_doc = ForesightDoc( - meta={'id': str(source_doc.id)}, - # Basic identifier fields - id=str(source_doc.id), - user_id=source_doc.user_id, - group_id=source_doc.group_id, - session_id=source_doc.session_id, - # Participant list - participants=source_doc.participants or [], - sender_ids=getattr(source_doc, 'sender_ids', None), - # Core BM25 content fields - content=source_doc.content, - evidence=source_doc.evidence, - search_content=getattr(source_doc, 'search_content', None), - # Classification fields - type=getattr(source_doc, 'type', None), - # Parent info for MongoDB back-reference - parent_type=source_doc.parent_type, - parent_id=str(source_doc.parent_id) if source_doc.parent_id else None, - # Time range fields - start_time=getattr(source_doc, 'start_time', None), - end_time=getattr(source_doc, 'end_time', None), - duration_days=getattr(source_doc, 'duration_days', None), - ) - - return es_doc - - except Exception as e: - logger.error("Failed to convert MongoDB document to ES document: %s", e) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/agent_case.py b/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/agent_case.py deleted file mode 100644 index 54028d8e..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/agent_case.py +++ /dev/null @@ -1,60 +0,0 @@ -# Import retained for type annotations and field definitions -from elasticsearch.dsl import field as e_field -from core.tenants.tenantize.oxm.es.tenant_aware_async_document import ( - TenantAwareAliasDoc, -) -from core.oxm.es.analyzer import ( - lower_keyword_analyzer, - whitespace_lowercase_trim_stop_analyzer, -) - - -class AgentCaseDoc( - TenantAwareAliasDoc("v1_agent_case", number_of_shards=3, number_of_replicas=1) -): - """ - Agent case Elasticsearch document - - Uses a separate agent-case index for BM25 keyword retrieval. - """ - - class CustomMeta: - # Specify the field name used to automatically populate meta.id - id_source_field = "id" - - # Document ID (corresponds to MongoDB _id) - id = e_field.Keyword(required=True) - - # Basic identification fields - user_id = e_field.Keyword() - group_id = e_field.Keyword() - session_id = e_field.Keyword() - - # Timestamp field - timestamp = e_field.Date(required=True) - - # BM25 retrieval core field - supports multi-value storage for search content - search_content = e_field.Text( - multi=True, - required=True, - analyzer="standard", - fields={ - "original": e_field.Text( - analyzer=lower_keyword_analyzer, search_analyzer=lower_keyword_analyzer - ) - }, - ) - - # Core content fields - task_intent = e_field.Text( - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - ) - approach = e_field.Text( - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - ) - - # Parent info - parent_type = e_field.Keyword() - parent_id = e_field.Keyword() diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/agent_skill.py b/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/agent_skill.py deleted file mode 100644 index 8a0b7fe5..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/agent_skill.py +++ /dev/null @@ -1,63 +0,0 @@ -# Import retained for type annotations and field definitions -from elasticsearch.dsl import field as e_field -from core.tenants.tenantize.oxm.es.tenant_aware_async_document import ( - TenantAwareAliasDoc, -) -from core.oxm.es.analyzer import ( - lower_keyword_analyzer, - whitespace_lowercase_trim_stop_analyzer, -) - - -class AgentSkillDoc( - TenantAwareAliasDoc("v1_agent_skill", number_of_shards=3, number_of_replicas=1) -): - """ - Agent skill Elasticsearch document - - Uses a separate agent-skill index for BM25 keyword retrieval. - """ - - class CustomMeta: - # Specify the field name used to automatically populate meta.id - id_source_field = "id" - - # Document ID (corresponds to MongoDB _id) - id = e_field.Keyword(required=True) - - # Basic identification fields - user_id = e_field.Keyword() - group_id = e_field.Keyword() - - # Cluster linkage - cluster_id = e_field.Keyword() - - # BM25 retrieval core field - supports multi-value storage for search content - search_content = e_field.Text( - multi=True, - required=True, - analyzer="standard", - fields={ - "original": e_field.Text( - analyzer=lower_keyword_analyzer, search_analyzer=lower_keyword_analyzer - ) - }, - ) - - # Core content fields - name = e_field.Text( - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - ) - description = e_field.Text( - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - ) - content = e_field.Text( - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - ) - confidence = e_field.Float() - - # Maturity assessment - maturity_score = e_field.Float() diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/atomic_fact.py b/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/atomic_fact.py deleted file mode 100644 index 8c1c8f0b..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/atomic_fact.py +++ /dev/null @@ -1,72 +0,0 @@ -# Import retained for type annotations and field definitions -from elasticsearch.dsl import field as e_field -from core.tenants.tenantize.oxm.es.tenant_aware_async_document import ( - TenantAwareAliasDoc, -) -from core.oxm.es.analyzer import whitespace_lowercase_trim_stop_analyzer - - -class AtomicFactDoc( - TenantAwareAliasDoc( - "v1_atomic_fact_record", number_of_shards=3, number_of_replicas=1 - ) -): - """ - V1 Atomic Fact Record Elasticsearch Document - - Based on MongoDB v1_atomic_fact_records collection. - Simplified for BM25 text retrieval of atomic facts. - - Field descriptions: - - id: Record unique identifier (corresponds to MongoDB _id) - - user_id: User ID (optional, None for group memory) - - group_id: Group ID (optional) - - session_id: Session identifier (optional) - - atomic_fact: Atomic fact content (single sentence, core BM25 content) - - search_content: BM25 search field (supports multi-value storage) - - timestamp: Event occurrence time - - participants: List of participant sender_ids - - sender_ids: Sender IDs (multi-value) - - type: Event type (Conversation, etc.) - - parent_type: Parent memory type (e.g., memcell, episodic_memory) - - parent_id: Parent memory ID (for MongoDB back-reference) - """ - - class CustomMeta: - # Specify the field name used to automatically populate meta.id - id_source_field = "id" - - # Basic identifier fields - id = e_field.Keyword(required=True) - user_id = e_field.Keyword() - group_id = e_field.Keyword() - session_id = e_field.Keyword() - - # Timestamp field - timestamp = e_field.Date(required=True) - - # Participant list - participants = e_field.Keyword(multi=True) - sender_ids = e_field.Keyword(multi=True) - - # Core BM25 content field - atomic_fact = e_field.Text( - required=True, - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - fields={"keyword": e_field.Keyword()}, - ) - - search_content = e_field.Text( - multi=True, - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - fields={"keyword": e_field.Keyword()}, - ) - - # Classification fields - type = e_field.Keyword() # Conversation/Email/Notion, etc. - - # Parent info for MongoDB back-reference - parent_type = e_field.Keyword() - parent_id = e_field.Keyword() diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/episodic_memory.py b/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/episodic_memory.py deleted file mode 100644 index dc6eb890..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/episodic_memory.py +++ /dev/null @@ -1,91 +0,0 @@ -# Import retained for type annotations and field definitions -from elasticsearch.dsl import field as e_field -from core.tenants.tenantize.oxm.es.tenant_aware_async_document import ( - TenantAwareAliasDoc, -) -from core.oxm.es.analyzer import whitespace_lowercase_trim_stop_analyzer - - -class EpisodicMemoryDoc( - TenantAwareAliasDoc("v1_episodic_memory", number_of_shards=3, number_of_replicas=1) -): - """ - V1 Episodic Memory Elasticsearch Document - - Based on MongoDB v1_episodic_memories collection. - Simplified for BM25 text retrieval - only stores search-essential fields. - Full data is retrieved from MongoDB using parent_id. - - Field descriptions: - - id: Event unique identifier (corresponds to MongoDB _id) - - user_id: User ID (optional, None for group memory) - - group_id: Group ID (optional) - - session_id: Session identifier (optional) - - timestamp: Event occurrence time - - participants: List of participant sender_ids - - sender_ids: Sender IDs (multi-value) - - summary: Event summary - - subject: Event subject/title - - episode: Full episodic description (core BM25 content) - - search_content: BM25 search field (supports multi-value storage, used for exact word matching) - - type: Event type (Conversation, etc.) - - parent_type: Parent memory type (e.g., memcell) - - parent_id: Parent memory ID (for MongoDB back-reference) - - Tokenization notes: - - Application layer is responsible for Chinese tokenization (jieba recommended) - - subject, episode, and summary fields store pre-tokenized results (space-separated) - - search_content field supports multi-value storage, each value being a search term - - ES uses whitespace_lowercase_trim_stop analyzer for text fields - """ - - class CustomMeta: - # Specify the field name used to automatically populate meta.id - id_source_field = "id" - - # Basic identifier fields - id = e_field.Keyword(required=True) - user_id = e_field.Keyword() # None for group memory - group_id = e_field.Keyword() - session_id = e_field.Keyword() - - # Timestamp field - timestamp = e_field.Date(required=True) - - # Participant list - participants = e_field.Keyword(multi=True) - sender_ids = e_field.Keyword(multi=True) - - # Core BM25 content fields - summary = e_field.Text( - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - fields={"keyword": e_field.Keyword()}, - ) - - subject = e_field.Text( - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - fields={"keyword": e_field.Keyword()}, - ) - - episode = e_field.Text( - required=True, - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - fields={"keyword": e_field.Keyword()}, - ) - - search_content = e_field.Text( - multi=True, - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - fields={"keyword": e_field.Keyword()}, - ) - - # Classification fields - type = e_field.Keyword() # Conversation/Email/Notion, etc. - - # Parent info for MongoDB back-reference - parent_type = e_field.Keyword() - parent_id = e_field.Keyword() diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/foresight.py b/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/foresight.py deleted file mode 100644 index ecc58957..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/elasticsearch/memory/foresight.py +++ /dev/null @@ -1,77 +0,0 @@ -# Import retained for type annotations and field definitions -from elasticsearch.dsl import field as e_field -from core.tenants.tenantize.oxm.es.tenant_aware_async_document import ( - TenantAwareAliasDoc, -) -from core.oxm.es.analyzer import whitespace_lowercase_trim_stop_analyzer - - -class ForesightDoc( - TenantAwareAliasDoc("v1_foresight_record", number_of_shards=3, number_of_replicas=1) -): - """ - V1 Foresight Record Elasticsearch Document - - Based on MongoDB v1_foresight_records collection. - Simplified for BM25 text retrieval of foresight predictions. - - Field descriptions: - - id: Record unique identifier (corresponds to MongoDB _id) - - user_id: User ID (optional, None for group memory) - - group_id: Group ID (optional) - - session_id: Session identifier (optional) - - content: Foresight content (core BM25 content) - - evidence: Evidence supporting this foresight - - search_content: BM25 search field (supports multi-value storage) - - participants: List of participant sender_ids - - sender_ids: Sender IDs (multi-value) - - type: Event type (Conversation, etc.) - - parent_type: Parent memory type (e.g., memcell, episodic_memory) - - parent_id: Parent memory ID (for MongoDB back-reference) - """ - - class CustomMeta: - # Specify the field name used to automatically populate meta.id - id_source_field = "id" - - # Basic identifier fields - id = e_field.Keyword(required=True) - user_id = e_field.Keyword() - group_id = e_field.Keyword() - session_id = e_field.Keyword() - - # Participant list - participants = e_field.Keyword(multi=True) - sender_ids = e_field.Keyword(multi=True) - - # Core BM25 content fields - content = e_field.Text( - required=True, - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - fields={"keyword": e_field.Keyword()}, - ) - - evidence = e_field.Text( - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - ) - - search_content = e_field.Text( - multi=True, - analyzer=whitespace_lowercase_trim_stop_analyzer, - search_analyzer=whitespace_lowercase_trim_stop_analyzer, - fields={"keyword": e_field.Keyword()}, - ) - - # Classification fields - type = e_field.Keyword() # Conversation/Email/Notion, etc. - - # Parent info for MongoDB back-reference - parent_type = e_field.Keyword() - parent_id = e_field.Keyword() - - # Time range fields (Foresight-specific) - start_time = e_field.Keyword() - end_time = e_field.Keyword() - duration_days = e_field.Integer() diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/__init__.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/__init__.py deleted file mode 100644 index f53d0a3c..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -Milvus Converters - -Export all memory type Milvus converters -""" - -from infra_layer.adapters.out.search.milvus.converter.episodic_memory_milvus_converter import ( - EpisodicMemoryMilvusConverter, -) -from infra_layer.adapters.out.search.milvus.converter.foresight_milvus_converter import ( - ForesightMilvusConverter, -) -from infra_layer.adapters.out.search.milvus.converter.atomic_fact_milvus_converter import ( - AtomicFactMilvusConverter, -) -from infra_layer.adapters.out.search.milvus.converter.user_profile_milvus_converter import ( - UserProfileMilvusConverter, -) - -__all__ = [ - "EpisodicMemoryMilvusConverter", - "ForesightMilvusConverter", - "AtomicFactMilvusConverter", - "UserProfileMilvusConverter", -] diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/agent_case_milvus_converter.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/agent_case_milvus_converter.py deleted file mode 100644 index 088b3e9e..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/agent_case_milvus_converter.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -AgentCase Milvus Converter - -Converts MongoDB AgentCaseRecord documents into Milvus Collection entities. -""" - -from typing import Dict, Any - -from core.oxm.milvus.base_converter import BaseMilvusConverter -from core.observation.logger import get_logger -from infra_layer.adapters.out.search.milvus.memory.agent_case_collection import ( - AgentCaseCollection, -) -from infra_layer.adapters.out.persistence.document.memory.agent_case import ( - AgentCaseRecord, -) - -logger = get_logger(__name__) - - -class AgentCaseMilvusConverter(BaseMilvusConverter[AgentCaseCollection]): - """ - Converts MongoDB AgentCaseRecord documents into Milvus entities. - - Vector field: embedding of task_intent. - task_intent field: task intent string for direct text access and search. - """ - - @classmethod - def from_mongo(cls, source_doc: AgentCaseRecord) -> Dict[str, Any]: - """ - Convert from MongoDB AgentCaseRecord to Milvus entity dict. - - Args: - source_doc: MongoDB AgentCaseRecord document instance - - Returns: - Dict[str, Any]: Milvus entity dictionary ready for insertion - - Raises: - ValueError: If source_doc is None - """ - if source_doc is None: - raise ValueError("MongoDB document cannot be empty") - - try: - task_intent = source_doc.task_intent or "" - - # Timestamps - timestamp = ( - int(source_doc.timestamp.timestamp()) if source_doc.timestamp else 0 - ) - - entity = { - "id": str(source_doc.id), - "vector": source_doc.vector if source_doc.vector else [], - "user_id": source_doc.user_id or "", - "group_id": source_doc.group_id or "", - "session_id": source_doc.session_id or "", - "timestamp": timestamp, - "task_intent": task_intent[:5000], - "parent_type": source_doc.parent_type or "", - "parent_id": source_doc.parent_id or "", - } - - return entity - - except Exception as e: - logger.error("Failed to convert AgentCaseRecord to Milvus entity: %s", e) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/agent_skill_milvus_converter.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/agent_skill_milvus_converter.py deleted file mode 100644 index c5ae9f72..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/agent_skill_milvus_converter.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -AgentSkill Milvus Converter - -Converts MongoDB AgentSkillRecord documents into Milvus Collection entities. -""" - -from typing import Dict, Any - -from core.oxm.milvus.base_converter import BaseMilvusConverter -from core.observation.logger import get_logger -from infra_layer.adapters.out.search.milvus.memory.agent_skill_collection import ( - AgentSkillCollection, -) -from infra_layer.adapters.out.persistence.document.memory.agent_skill import ( - AgentSkillRecord, -) - -logger = get_logger(__name__) - - -class AgentSkillMilvusConverter(BaseMilvusConverter[AgentSkillCollection]): - """ - Converts MongoDB AgentSkillRecord documents into Milvus entities. - - Vector field: embedding of name + description. - content field: name + description (maps to primary text field). - """ - - @classmethod - def from_mongo(cls, source_doc: AgentSkillRecord) -> Dict[str, Any]: - """ - Convert from MongoDB AgentSkillRecord to Milvus entity dict. - - Args: - source_doc: MongoDB AgentSkillRecord document instance - - Returns: - Dict[str, Any]: Milvus entity dictionary ready for insertion - - Raises: - ValueError: If source_doc is None - """ - if source_doc is None: - raise ValueError("MongoDB document cannot be empty") - - try: - name = source_doc.name or "" - description = source_doc.description or "" - content = source_doc.content or "" - - # Primary text field: name + description combined - content_field = "\n".join(s for s in [name, description] if s) - - entity = { - "id": str(source_doc.id), - "vector": source_doc.vector if source_doc.vector else [], - "user_id": source_doc.user_id or "", - "group_id": source_doc.group_id or "", - "cluster_id": source_doc.cluster_id or "", - "content": content_field[:5000], - "maturity_score": source_doc.maturity_score, - "confidence": source_doc.confidence, - } - - return entity - - except Exception as e: - logger.error("Failed to convert AgentSkillRecord to Milvus entity: %s", e) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/atomic_fact_milvus_converter.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/atomic_fact_milvus_converter.py deleted file mode 100644 index 3d5c1547..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/atomic_fact_milvus_converter.py +++ /dev/null @@ -1,92 +0,0 @@ -""" -Atomic Fact Milvus Converter - -Converts MongoDB v1_atomic_fact_records to Milvus v1_atomic_fact_record. -Only maps search-essential fields for vector semantic retrieval. -""" - -import json -from typing import Dict, Any - -from core.oxm.milvus.base_converter import BaseMilvusConverter -from core.observation.logger import get_logger -from infra_layer.adapters.out.search.milvus.memory.atomic_fact_collection import ( - AtomicFactCollection, -) -from infra_layer.adapters.out.persistence.document.memory.atomic_fact_record import ( - AtomicFactRecord as MongoAtomicFactRecord, -) -from api_specs.memory_types import RawDataType - -logger = get_logger(__name__) - - -class AtomicFactMilvusConverter(BaseMilvusConverter[AtomicFactCollection]): - """ - Atomic Fact Milvus Converter - - Converts MongoDB v1_atomic_fact_records documents to Milvus v1_atomic_fact_record entities. - Only maps search-essential fields for vector semantic retrieval. - Full data is retrieved from MongoDB using parent_id. - """ - - @classmethod - def from_mongo(cls, source_doc: MongoAtomicFactRecord) -> Dict[str, Any]: - """ - Convert from MongoDB v1_atomic_fact_records document to Milvus v1_atomic_fact_record entity - - Args: - source_doc: MongoDB v1_atomic_fact_records document instance - - Returns: - Dict[str, Any]: Milvus entity dictionary, ready for insertion - """ - if source_doc is None: - raise ValueError("MongoDB document cannot be empty") - - try: - # Convert timestamp to integer (epoch milliseconds) - timestamp = ( - int(source_doc.timestamp.timestamp() * 1000) - if source_doc.timestamp - else 0 - ) - - milvus_entity = { - # Basic identifier fields - "id": str(source_doc.id), - "user_id": source_doc.user_id or "", - "group_id": source_doc.group_id or "", - "session_id": source_doc.session_id or "", - # Participant list - "participants": source_doc.participants or [], - "sender_ids": getattr(source_doc, "sender_ids", []) or [], - # Type field - "type": getattr(source_doc, "type", None) - or RawDataType.CONVERSATION.value, - # Timestamp field - "timestamp": timestamp, - # Parent info for MongoDB back-reference - "parent_type": source_doc.parent_type or "", - "parent_id": str(source_doc.parent_id) if source_doc.parent_id else "", - # Vector field - "vector": source_doc.vector if source_doc.vector else [], - } - - return milvus_entity - - except Exception as e: - logger.error( - "Failed to convert MongoDB AtomicFact document to Milvus entity: %s", e - ) - raise - - @staticmethod - def _build_search_content(source_doc: MongoAtomicFactRecord) -> str: - """Build search content (JSON list format)""" - text_content = [] - - if source_doc.atomic_fact: - text_content.append(source_doc.atomic_fact) - - return json.dumps(text_content, ensure_ascii=False) diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/episodic_memory_milvus_converter.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/episodic_memory_milvus_converter.py deleted file mode 100644 index 183fcd2a..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/episodic_memory_milvus_converter.py +++ /dev/null @@ -1,117 +0,0 @@ -""" -Episodic Memory Milvus Converter - -Converts MongoDB v1_episodic_memories to Milvus v1_episodic_memory. -Only maps search-essential fields for vector semantic retrieval. -""" - -import json -from typing import Dict, Any - -from core.oxm.milvus.base_converter import BaseMilvusConverter -from core.observation.logger import get_logger -from infra_layer.adapters.out.search.milvus.memory.episodic_memory_collection import ( - EpisodicMemoryCollection, -) -from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemory as MongoEpisodicMemory, -) - -logger = get_logger(__name__) - - -class EpisodicMemoryMilvusConverter(BaseMilvusConverter[EpisodicMemoryCollection]): - """ - Episodic Memory Milvus Converter - - Converts MongoDB v1_episodic_memories documents to Milvus v1_episodic_memory entities. - Only maps search-essential fields for vector semantic retrieval. - Full data is retrieved from MongoDB using parent_id. - """ - - @classmethod - def from_mongo(cls, source_doc: MongoEpisodicMemory) -> Dict[str, Any]: - """ - Convert from MongoDB v1_episodic_memories document to Milvus v1_episodic_memory entity - - Args: - source_doc: MongoDB v1_episodic_memories document instance - - Returns: - Dict[str, Any]: Milvus entity dictionary, ready for insertion - """ - if source_doc is None: - raise ValueError("MongoDB document cannot be empty") - - try: - # Convert timestamp to integer (epoch milliseconds) - timestamp = ( - int(source_doc.timestamp.timestamp() * 1000) - if source_doc.timestamp - else 0 - ) - - # Build search content and metadata - search_content = cls._build_search_content(source_doc) - - milvus_entity = { - # Basic identifier fields - "id": str(source_doc.id), - "user_id": source_doc.user_id or "", - "group_id": source_doc.group_id or "", - "session_id": source_doc.session_id or "", - # Participant list - "participants": source_doc.participants or [], - "sender_ids": getattr(source_doc, "sender_ids", []) or [], - # Type field - "type": getattr(source_doc, "type", None) or "", - # Timestamp field - "timestamp": timestamp, - # Core content fields - "episode": source_doc.episode or "", - "search_content": search_content, - # Parent info for MongoDB back-reference - "parent_type": source_doc.parent_type or "", - "parent_id": str(source_doc.parent_id) if source_doc.parent_id else "", - # Vector field - needs to be set externally - "vector": ( - source_doc.vector - if hasattr(source_doc, "vector") and source_doc.vector - else [] - ), - } - - return milvus_entity - - except Exception as e: - logger.error("Failed to convert MongoDB document to Milvus entity: %s", e) - raise - - @staticmethod - def _build_search_content(source_doc: MongoEpisodicMemory) -> str: - """ - Build search content - - Combine key text content from the document into a search content list, return as JSON string. - - Args: - source_doc: MongoDB EpisodicMemory document instance - - Returns: - str: Search content JSON string (list format) - """ - text_content = [] - - # Collect all text content (by priority: subject -> summary -> content) - if hasattr(source_doc, "subject") and source_doc.subject: - text_content.append(source_doc.subject) - - if hasattr(source_doc, "summary") and source_doc.summary: - text_content.append(source_doc.summary) - - if hasattr(source_doc, "episode") and source_doc.episode: - # episode might be very long, only take first 500 characters - text_content.append(source_doc.episode) - - # Return JSON string list format, keep consistent with MemCell synchronization logic - return json.dumps(text_content, ensure_ascii=False) diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/foresight_milvus_converter.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/foresight_milvus_converter.py deleted file mode 100644 index 4f10339e..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/foresight_milvus_converter.py +++ /dev/null @@ -1,129 +0,0 @@ -""" -Foresight Milvus Converter - -Converts MongoDB v1_foresight_records to Milvus v1_foresight_record. -Only maps search-essential fields for vector semantic retrieval. -""" - -import json -from typing import Dict, Any -from datetime import datetime - -from core.oxm.milvus.base_converter import BaseMilvusConverter -from core.observation.logger import get_logger -from infra_layer.adapters.out.search.milvus.memory.foresight_collection import ( - ForesightCollection, -) -from infra_layer.adapters.out.persistence.document.memory.foresight_record import ( - ForesightRecord as MongoForesightRecord, -) - -logger = get_logger(__name__) - - -class ForesightMilvusConverter(BaseMilvusConverter[ForesightCollection]): - """ - Foresight Milvus Converter - - Converts MongoDB v1_foresight_records documents to Milvus v1_foresight_record entities. - Only maps search-essential fields for vector semantic retrieval. - Full data is retrieved from MongoDB using parent_id. - """ - - @classmethod - def _parse_time_field(cls, time_value, field_name: str, doc_id) -> int: - """Parse time field to epoch milliseconds, return 0 and log warning on failure""" - if not time_value: - return 0 - - try: - if isinstance(time_value, datetime): - return int(time_value.timestamp() * 1000) - elif isinstance(time_value, str): - dt = datetime.fromisoformat(time_value.replace("Z", "+00:00")) - return int(dt.timestamp() * 1000) - elif isinstance(time_value, (int, float)): - return int(time_value * 1000) - except Exception as e: - logger.warning( - f"Failed to parse {field_name} (doc_id={doc_id}): {time_value}, error: {e}" - ) - - return 0 - - @classmethod - def from_mongo(cls, source_doc: MongoForesightRecord) -> Dict[str, Any]: - """ - Convert from MongoDB v1_foresight_records document to Milvus v1_foresight_record entity - - Args: - source_doc: MongoDB v1_foresight_records document instance - - Returns: - Dict[str, Any]: Milvus entity dictionary, ready for insertion - """ - if source_doc is None: - raise ValueError("MongoDB document cannot be empty") - - try: - # Parse time fields - start_time = cls._parse_time_field( - source_doc.start_time, "start_time", source_doc.id - ) - end_time = cls._parse_time_field( - source_doc.end_time, "end_time", source_doc.id - ) - - # Build search content - search_content = cls._build_search_content(source_doc) - - milvus_entity = { - # Basic identifier fields - "id": str(source_doc.id), - "user_id": source_doc.user_id or "", - "group_id": source_doc.group_id or "", - "session_id": source_doc.session_id or "", - # Participant list - "participants": source_doc.participants or [], - "sender_ids": getattr(source_doc, "sender_ids", []) or [], - # Type field - "type": getattr(source_doc, "type", None) or "", - # Time fields - "start_time": start_time, - "end_time": end_time, - "duration_days": ( - source_doc.duration_days if source_doc.duration_days else 0 - ), - # Core content fields - "content": source_doc.content, - "evidence": source_doc.evidence or "", - "search_content": search_content, - # Parent info for MongoDB back-reference - "parent_type": source_doc.parent_type or "", - "parent_id": str(source_doc.parent_id) if source_doc.parent_id else "", - # Vector field - "vector": source_doc.vector if source_doc.vector else [], - } - - return milvus_entity - - except Exception as e: - logger.error( - "Failed to convert MongoDB foresight document to Milvus entity: %s", e - ) - raise - - @staticmethod - def _build_search_content(source_doc: MongoForesightRecord) -> str: - """Build search content (JSON list format)""" - text_content = [] - - # Main content - if source_doc.content: - text_content.append(source_doc.content) - - # Add evidence to improve retrieval capability - if source_doc.evidence: - text_content.append(source_doc.evidence) - - return json.dumps(text_content, ensure_ascii=False) diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/user_profile_milvus_converter.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/user_profile_milvus_converter.py deleted file mode 100644 index ea5299b2..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/converter/user_profile_milvus_converter.py +++ /dev/null @@ -1,146 +0,0 @@ -""" -User Profile Milvus Converter - -Converts MongoDB v1_user_profiles to Milvus v1_user_profile entities. -Splits profile into individual items (one per explicit_info / implicit_trait) -for per-item vector search. -""" - -from typing import Dict, Any, List - -from api_specs.memory_types import ScenarioType -from core.oxm.milvus.base_converter import BaseMilvusConverter -from core.observation.logger import get_logger -from core.oxm.mongo.mongo_utils import generate_object_id_str -from infra_layer.adapters.out.search.milvus.memory.user_profile_collection import ( - UserProfileCollection, -) -from infra_layer.adapters.out.persistence.document.memory.user_profile import ( - UserProfile as MongoUserProfile, -) - -logger = get_logger(__name__) - - -class UserProfileMilvusConverter(BaseMilvusConverter[UserProfileCollection]): - """ - User Profile Milvus Converter - - Converts a single MongoDB v1_user_profiles document into a **list** of - Milvus entities — one entity per explicit_info item and one per - implicit_trait. Each entity carries an ``embed_text`` field used by - ProfileIndexer to generate embeddings, and an ``item_type`` field - used for statistics. - """ - - @classmethod - def from_mongo(cls, source_doc: MongoUserProfile) -> List[Dict[str, Any]]: - """ - Convert from MongoDB v1_user_profiles document to Milvus entities. - - Args: - source_doc: MongoDB v1_user_profiles document instance - - Returns: - List[Dict[str, Any]]: One Milvus entity dict per profile item. - """ - if source_doc is None: - raise ValueError("MongoDB document cannot be empty") - - try: - profile_data: Dict[str, Any] = source_doc.profile_data or {} - doc_id = str(source_doc.id) if source_doc.id else "" - user_id = source_doc.user_id or "" - group_id = source_doc.group_id or "" - scenario = source_doc.scenario or ScenarioType.SOLO.value - memcell_count = source_doc.memcell_count or 0 - - entities: List[Dict[str, Any]] = [] - seq = 0 - - def _make_entity(embed_text: str, item_type: str) -> Dict[str, Any]: - nonlocal seq - entity = { - "id": generate_object_id_str(), - "user_id": user_id, - "group_id": group_id, - "scenario": scenario, - "memcell_count": memcell_count, - "item_type": item_type, - "embed_text": embed_text, - } - seq += 1 - return entity - - # ProfileMemory format: hard_skills, soft_skills, personality, etc. - # These fields contain [{value, evidences, ...}] items. - _EXPLICIT_FIELDS = [ - ("hard_skills", "Hard Skill"), - ("soft_skills", "Soft Skill"), - ("work_responsibility", "Work Responsibility"), - ("interests", "Interest"), - ] - _IMPLICIT_FIELDS = [ - ("personality", "Personality"), - ("tendency", "Tendency"), - ("way_of_decision_making", "Decision Making"), - ("motivation_system", "Motivation"), - ("fear_system", "Fear"), - ("value_system", "Value"), - ] - - for field_name, label in _EXPLICIT_FIELDS: - for item in profile_data.get(field_name, []) or []: - value = item.get("value", "") if isinstance(item, dict) else str(item) - if not value: - continue - level = item.get("level", "") if isinstance(item, dict) else "" - embed_text = f"{label}: {value}" + (f" ({level})" if level else "") - entities.append(_make_entity(embed_text, "explicit_info")) - - for field_name, label in _IMPLICIT_FIELDS: - for item in profile_data.get(field_name, []) or []: - value = item.get("value", "") if isinstance(item, dict) else str(item) - if not value: - continue - embed_text = f"{label}: {value}" - entities.append(_make_entity(embed_text, "implicit_trait")) - - # Legacy format: explicit_info[] and implicit_traits[] - # Solo extractor produces {category, description} / {trait, description, basis} - for item in profile_data.get("explicit_info", []) or []: - if not isinstance(item, dict): - continue - desc = item.get("description", "") - if not desc: - continue - category = item.get("category", "") - embed_text = f"{category}: {desc}" if category else desc - entities.append(_make_entity(embed_text, "explicit_info")) - - for item in profile_data.get("implicit_traits", []) or []: - if not isinstance(item, dict): - continue - desc = item.get("description", "") - if not desc: - continue - trait_name = item.get("trait") or item.get("trait_name", "") - embed_text = f"{trait_name}: {desc}" if trait_name else desc - if item.get("basis"): - embed_text += f". {item['basis']}" - entities.append(_make_entity(embed_text, "implicit_trait")) - - # user_goal (single string) - user_goal = profile_data.get("user_goal") - if user_goal and isinstance(user_goal, str) and user_goal.strip(): - entities.append(_make_entity(f"Goal: {user_goal.strip()}", "explicit_info")) - - return entities - - except Exception as e: - logger.error( - "Failed to convert MongoDB UserProfile to Milvus entities: %s", - e, - exc_info=True, - ) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/__init__.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/__init__.py deleted file mode 100755 index ebbbbc65..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -Milvus Memory Collections - -Export Collection definitions for all memory types -""" - -from infra_layer.adapters.out.search.milvus.memory.episodic_memory_collection import ( - EpisodicMemoryCollection, -) -from infra_layer.adapters.out.search.milvus.memory.foresight_collection import ( - ForesightCollection, -) -from infra_layer.adapters.out.search.milvus.memory.atomic_fact_collection import ( - AtomicFactCollection, -) -from infra_layer.adapters.out.search.milvus.memory.user_profile_collection import ( - UserProfileCollection, -) - -__all__ = [ - "EpisodicMemoryCollection", - "ForesightCollection", - "AtomicFactCollection", - "UserProfileCollection", -] diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/agent_case_collection.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/agent_case_collection.py deleted file mode 100644 index 5b46dd19..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/agent_case_collection.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -AgentCase Milvus Collection Definition - -AgentCase-specific Collection class implemented based on TenantAwareMilvusCollectionWithSuffix. -Provides schema and index configuration for semantic search over agent task-solving experiences. -""" - -from pymilvus import DataType, FieldSchema, CollectionSchema -from core.oxm.milvus.milvus_collection_base import IndexConfig -from core.tenants.tenantize.oxm.milvus.tenant_aware_collection_with_suffix import ( - TenantAwareMilvusCollectionWithSuffix, -) -from memory_layer.constants import VECTORIZE_DIMENSIONS - - -class AgentCaseCollection(TenantAwareMilvusCollectionWithSuffix): - """ - AgentCase Milvus Collection - - Stores vector embeddings of agent task-solving experiences. - The vector represents the task_intent of one experience per MemCell. - """ - - _COLLECTION_NAME = "v1_agent_case" - - _SCHEMA = CollectionSchema( - fields=[ - FieldSchema( - name="id", - dtype=DataType.VARCHAR, - is_primary=True, - auto_id=False, - max_length=100, - description="AgentCaseRecord unique identifier", - ), - FieldSchema( - name="vector", - dtype=DataType.FLOAT_VECTOR, - dim=VECTORIZE_DIMENSIONS, - description="Embedding of task_intent", - ), - FieldSchema( - name="user_id", - dtype=DataType.VARCHAR, - max_length=100, - description="User ID", - ), - FieldSchema( - name="group_id", - dtype=DataType.VARCHAR, - max_length=100, - description="Group/session ID", - ), - FieldSchema( - name="session_id", - dtype=DataType.VARCHAR, - max_length=100, - description="Session identifier", - ), - FieldSchema( - name="timestamp", - dtype=DataType.INT64, - description="Task occurrence unix timestamp (seconds)", - ), - FieldSchema( - name="task_intent", - dtype=DataType.VARCHAR, - max_length=5000, - description="Task intent string for text access and search", - ), - FieldSchema( - name="parent_type", - dtype=DataType.VARCHAR, - max_length=100, - description="Parent memory type (memcell)", - ), - FieldSchema( - name="parent_id", - dtype=DataType.VARCHAR, - max_length=100, - description="Parent MemCell event_id", - ), - ], - description="Vector collection for agent case", - enable_dynamic_field=True, - ) - - _INDEX_CONFIGS = [ - IndexConfig( - field_name="vector", - index_type="HNSW", - metric_type="COSINE", - params={"M": 16, "efConstruction": 200}, - ), - IndexConfig(field_name="user_id", index_type="AUTOINDEX"), - IndexConfig(field_name="group_id", index_type="AUTOINDEX"), - IndexConfig(field_name="session_id", index_type="AUTOINDEX"), - IndexConfig(field_name="timestamp", index_type="AUTOINDEX"), - IndexConfig(field_name="parent_id", index_type="AUTOINDEX"), - ] diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/agent_skill_collection.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/agent_skill_collection.py deleted file mode 100644 index e037e241..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/agent_skill_collection.py +++ /dev/null @@ -1,93 +0,0 @@ -""" -AgentSkill Milvus Collection Definition - -AgentSkill-specific Collection class for semantic search over -reusable skills extracted from MemScene clusters. -""" - -from pymilvus import DataType, FieldSchema, CollectionSchema -from core.oxm.milvus.milvus_collection_base import IndexConfig -from core.tenants.tenantize.oxm.milvus.tenant_aware_collection_with_suffix import ( - TenantAwareMilvusCollectionWithSuffix, -) -from memory_layer.constants import VECTORIZE_DIMENSIONS - - -class AgentSkillCollection(TenantAwareMilvusCollectionWithSuffix): - """ - AgentSkill Milvus Collection - - Stores vector embeddings of reusable skill items. - The vector represents the embedding of name + description. - """ - - _COLLECTION_NAME = "v1_agent_skill" - - _SCHEMA = CollectionSchema( - fields=[ - FieldSchema( - name="id", - dtype=DataType.VARCHAR, - is_primary=True, - auto_id=False, - max_length=100, - description="AgentSkillRecord unique identifier", - ), - FieldSchema( - name="vector", - dtype=DataType.FLOAT_VECTOR, - dim=VECTORIZE_DIMENSIONS, - description="Embedding of name + description", - ), - FieldSchema( - name="user_id", - dtype=DataType.VARCHAR, - max_length=100, - description="User ID (agent owner)", - ), - FieldSchema( - name="group_id", - dtype=DataType.VARCHAR, - max_length=100, - description="Group/session ID", - ), - FieldSchema( - name="cluster_id", - dtype=DataType.VARCHAR, - max_length=200, - description="MemScene cluster ID", - ), - FieldSchema( - name="content", - dtype=DataType.VARCHAR, - max_length=5000, - description="name + newline + description (primary text field)", - ), - FieldSchema( - name="maturity_score", - dtype=DataType.FLOAT, - description="Normalized quality score (0.0-1.0)", - ), - FieldSchema( - name="confidence", - dtype=DataType.FLOAT, - description="Confidence score (0.0-1.0)", - ), - ], - description="Vector collection for agent skill", - enable_dynamic_field=True, - ) - - _INDEX_CONFIGS = [ - IndexConfig( - field_name="vector", - index_type="HNSW", - metric_type="COSINE", - params={"M": 16, "efConstruction": 200}, - ), - IndexConfig(field_name="user_id", index_type="AUTOINDEX"), - IndexConfig(field_name="group_id", index_type="AUTOINDEX"), - IndexConfig(field_name="cluster_id", index_type="AUTOINDEX"), - IndexConfig(field_name="maturity_score", index_type="AUTOINDEX"), - IndexConfig(field_name="confidence", index_type="AUTOINDEX"), - ] diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/atomic_fact_collection.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/atomic_fact_collection.py deleted file mode 100644 index 639c0f2e..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/atomic_fact_collection.py +++ /dev/null @@ -1,122 +0,0 @@ -""" -V1 Atomic Fact Record Milvus Collection Definition - -Based on MongoDB v1_atomic_fact_records collection. -Simplified for vector semantic retrieval of atomic facts. -""" - -from pymilvus import DataType, FieldSchema, CollectionSchema -from core.oxm.milvus.milvus_collection_base import IndexConfig -from core.tenants.tenantize.oxm.milvus.tenant_aware_collection_with_suffix import ( - TenantAwareMilvusCollectionWithSuffix, -) -from memory_layer.constants import VECTORIZE_DIMENSIONS - - -class AtomicFactCollection(TenantAwareMilvusCollectionWithSuffix): - """ - V1 Atomic Fact Record Milvus Collection - - Usage: - collection.async_collection().insert([...]) - collection.async_collection().search([...]) - """ - - # Base name for the Collection - _COLLECTION_NAME = "v1_atomic_fact_record" - - # Collection Schema definition - _SCHEMA = CollectionSchema( - fields=[ - FieldSchema( - name="id", - dtype=DataType.VARCHAR, - is_primary=True, - auto_id=False, - max_length=100, - description="Record unique identifier", - ), - FieldSchema( - name="vector", - dtype=DataType.FLOAT_VECTOR, - dim=VECTORIZE_DIMENSIONS, - description="Atomic fact vector for semantic search", - ), - FieldSchema( - name="user_id", - dtype=DataType.VARCHAR, - max_length=256, - description="User ID", - ), - FieldSchema( - name="group_id", - dtype=DataType.VARCHAR, - max_length=256, - description="Group ID", - ), - FieldSchema( - name="session_id", - dtype=DataType.VARCHAR, - max_length=100, - description="Session identifier", - ), - FieldSchema( - name="participants", - dtype=DataType.ARRAY, - element_type=DataType.VARCHAR, - max_capacity=100, - max_length=100, - description="List of participant sender_ids", - ), - FieldSchema( - name="timestamp", - dtype=DataType.INT64, - description="Event timestamp (epoch milliseconds)", - ), - FieldSchema( - name="sender_ids", - dtype=DataType.ARRAY, - element_type=DataType.VARCHAR, - max_capacity=100, - max_length=100, - description="Sender IDs of related participants", - ), - FieldSchema( - name="type", - dtype=DataType.VARCHAR, - max_length=50, - description="Event type (e.g., Conversation, Email, etc.)", - ), - FieldSchema( - name="parent_type", - dtype=DataType.VARCHAR, - max_length=100, - description="Parent memory type (e.g., memcell, episodic_memory)", - ), - FieldSchema( - name="parent_id", - dtype=DataType.VARCHAR, - max_length=100, - description="Parent memory ID (for MongoDB back-reference)", - ), - ], - description="V1 vector collection for atomic fact records", - enable_dynamic_field=True, - ) - - # Index configuration - _INDEX_CONFIGS = [ - # Vector field index - IndexConfig( - field_name="vector", - index_type="HNSW", - metric_type="COSINE", - params={"M": 16, "efConstruction": 200}, - ), - # Scalar field indexes - IndexConfig(field_name="user_id", index_type="AUTOINDEX"), - IndexConfig(field_name="group_id", index_type="AUTOINDEX"), - IndexConfig(field_name="session_id", index_type="AUTOINDEX"), - IndexConfig(field_name="parent_id", index_type="AUTOINDEX"), - IndexConfig(field_name="timestamp", index_type="AUTOINDEX"), - ] diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/episodic_memory_collection.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/episodic_memory_collection.py deleted file mode 100644 index 932f8e49..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/episodic_memory_collection.py +++ /dev/null @@ -1,135 +0,0 @@ -""" -V1 Episodic Memory Milvus Collection Definition - -Based on MongoDB v1_episodic_memories collection. -Simplified for vector semantic retrieval - only stores search-essential fields. -Full data is retrieved from MongoDB using parent_id. -""" - -from pymilvus import DataType, FieldSchema, CollectionSchema -from core.oxm.milvus.milvus_collection_base import IndexConfig -from core.tenants.tenantize.oxm.milvus.tenant_aware_collection_with_suffix import ( - TenantAwareMilvusCollectionWithSuffix, -) -from memory_layer.constants import VECTORIZE_DIMENSIONS - - -class EpisodicMemoryCollection(TenantAwareMilvusCollectionWithSuffix): - """ - V1 Episodic Memory Milvus Collection - - Usage: - collection.async_collection().insert([...]) - collection.async_collection().search([...]) - """ - - # Base name for the Collection - _COLLECTION_NAME = "v1_episodic_memory" - - # Collection Schema definition - _SCHEMA = CollectionSchema( - fields=[ - FieldSchema( - name="id", - dtype=DataType.VARCHAR, - is_primary=True, - auto_id=False, - max_length=100, - description="Event unique identifier", - ), - FieldSchema( - name="vector", - dtype=DataType.FLOAT_VECTOR, - dim=VECTORIZE_DIMENSIONS, - description="Text vector for semantic search", - ), - FieldSchema( - name="user_id", - dtype=DataType.VARCHAR, - max_length=256, - description="User ID (None for group memory)", - ), - FieldSchema( - name="group_id", - dtype=DataType.VARCHAR, - max_length=256, - description="Group ID", - ), - FieldSchema( - name="session_id", - dtype=DataType.VARCHAR, - max_length=100, - description="Session identifier", - ), - FieldSchema( - name="participants", - dtype=DataType.ARRAY, - element_type=DataType.VARCHAR, - max_capacity=100, - max_length=100, - description="List of participant sender_ids", - ), - FieldSchema( - name="sender_ids", - dtype=DataType.ARRAY, - element_type=DataType.VARCHAR, - max_capacity=100, - max_length=100, - description="Sender IDs of event participants", - ), - FieldSchema( - name="type", - dtype=DataType.VARCHAR, - max_length=50, - description="Episode type (e.g., Conversation, Email, etc.)", - ), - FieldSchema( - name="timestamp", - dtype=DataType.INT64, - description="Event timestamp (epoch milliseconds)", - ), - FieldSchema( - name="episode", - dtype=DataType.VARCHAR, - max_length=10000, - description="Episode description", - ), - FieldSchema( - name="search_content", - dtype=DataType.VARCHAR, - max_length=5000, - description="Search content", - ), - FieldSchema( - name="parent_type", - dtype=DataType.VARCHAR, - max_length=100, - description="Parent memory type (e.g., memcell)", - ), - FieldSchema( - name="parent_id", - dtype=DataType.VARCHAR, - max_length=100, - description="Parent memory ID (for MongoDB back-reference)", - ), - ], - description="V1 vector collection for episodic memory", - enable_dynamic_field=True, - ) - - # Index configuration - _INDEX_CONFIGS = [ - # Vector field index (for similarity search) - IndexConfig( - field_name="vector", - index_type="HNSW", - metric_type="COSINE", - params={"M": 16, "efConstruction": 200}, - ), - # Scalar field indexes (for filtering) - IndexConfig(field_name="user_id", index_type="AUTOINDEX"), - IndexConfig(field_name="group_id", index_type="AUTOINDEX"), - IndexConfig(field_name="session_id", index_type="AUTOINDEX"), - IndexConfig(field_name="parent_id", index_type="AUTOINDEX"), - IndexConfig(field_name="timestamp", index_type="AUTOINDEX"), - ] diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/foresight_collection.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/foresight_collection.py deleted file mode 100644 index 75793c42..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/foresight_collection.py +++ /dev/null @@ -1,131 +0,0 @@ -""" -V1 Foresight Record Milvus Collection Definition - -Based on MongoDB v1_foresight_records collection. -Simplified for vector semantic retrieval of foresight predictions. -""" - -from pymilvus import DataType, FieldSchema, CollectionSchema -from core.oxm.milvus.milvus_collection_base import IndexConfig -from core.tenants.tenantize.oxm.milvus.tenant_aware_collection_with_suffix import ( - TenantAwareMilvusCollectionWithSuffix, -) -from memory_layer.constants import VECTORIZE_DIMENSIONS - - -class ForesightCollection(TenantAwareMilvusCollectionWithSuffix): - """ - V1 Foresight Record Milvus Collection - - Usage: - collection.async_collection().insert([...]) - collection.async_collection().search([...]) - """ - - # Base name for the Collection - _COLLECTION_NAME = "v1_foresight_record" - - # Collection Schema definition - _SCHEMA = CollectionSchema( - fields=[ - FieldSchema( - name="id", - dtype=DataType.VARCHAR, - is_primary=True, - auto_id=False, - max_length=100, - description="Record unique identifier", - ), - FieldSchema( - name="vector", - dtype=DataType.FLOAT_VECTOR, - dim=VECTORIZE_DIMENSIONS, - description="Foresight content vector for semantic search", - ), - FieldSchema( - name="user_id", - dtype=DataType.VARCHAR, - max_length=256, - description="User ID", - ), - FieldSchema( - name="group_id", - dtype=DataType.VARCHAR, - max_length=256, - description="Group ID", - ), - FieldSchema( - name="session_id", - dtype=DataType.VARCHAR, - max_length=100, - description="Session identifier", - ), - FieldSchema( - name="participants", - dtype=DataType.ARRAY, - element_type=DataType.VARCHAR, - max_capacity=100, - max_length=100, - description="List of participant sender_ids", - ), - FieldSchema( - name="sender_ids", - dtype=DataType.ARRAY, - element_type=DataType.VARCHAR, - max_capacity=100, - max_length=100, - description="Sender IDs of related participants", - ), - FieldSchema( - name="type", - dtype=DataType.VARCHAR, - max_length=50, - description="Foresight type (e.g., Conversation)", - ), - FieldSchema( - name="start_time", - dtype=DataType.INT64, - description="Foresight start time (epoch milliseconds)", - ), - FieldSchema( - name="end_time", - dtype=DataType.INT64, - description="Foresight end time (epoch milliseconds)", - ), - FieldSchema( - name="duration_days", - dtype=DataType.INT64, - description="Duration in days", - ), - FieldSchema( - name="parent_type", - dtype=DataType.VARCHAR, - max_length=100, - description="Parent memory type (e.g., memcell, episodic_memory)", - ), - FieldSchema( - name="parent_id", - dtype=DataType.VARCHAR, - max_length=100, - description="Parent memory ID (for MongoDB back-reference)", - ), - ], - description="V1 vector collection for foresight records", - enable_dynamic_field=True, - ) - - # Index configuration - _INDEX_CONFIGS = [ - # Vector field index - IndexConfig( - field_name="vector", - index_type="HNSW", - metric_type="COSINE", - params={"M": 16, "efConstruction": 200}, - ), - # Scalar field indexes - IndexConfig(field_name="user_id", index_type="AUTOINDEX"), - IndexConfig(field_name="group_id", index_type="AUTOINDEX"), - IndexConfig(field_name="session_id", index_type="AUTOINDEX"), - IndexConfig(field_name="parent_id", index_type="AUTOINDEX"), - ] diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/user_profile_collection.py b/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/user_profile_collection.py deleted file mode 100644 index 0d2fad9d..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/milvus/memory/user_profile_collection.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -V1 User Profile Milvus Collection Definition - -Based on MongoDB v1_user_profiles collection. -Simplified for vector semantic retrieval of user profiles. - -Note: UserProfile does NOT have session_id (user-level aggregation). -""" - -from pymilvus import DataType, FieldSchema, CollectionSchema -from core.oxm.milvus.milvus_collection_base import IndexConfig -from core.tenants.tenantize.oxm.milvus.tenant_aware_collection_with_suffix import ( - TenantAwareMilvusCollectionWithSuffix, -) -from memory_layer.constants import VECTORIZE_DIMENSIONS - - -class UserProfileCollection(TenantAwareMilvusCollectionWithSuffix): - """ - V1 User Profile Milvus Collection - - Usage: - collection.async_collection().insert([...]) - collection.async_collection().search([...]) - """ - - # Base name for the Collection - _COLLECTION_NAME = "v1_user_profile" - - # Collection Schema definition - _SCHEMA = CollectionSchema( - fields=[ - FieldSchema( - name="id", - dtype=DataType.VARCHAR, - is_primary=True, - auto_id=False, - max_length=100, - description="Profile unique identifier", - ), - FieldSchema( - name="vector", - dtype=DataType.FLOAT_VECTOR, - dim=VECTORIZE_DIMENSIONS, - description="Profile data vector for semantic search", - ), - FieldSchema( - name="user_id", - dtype=DataType.VARCHAR, - max_length=256, - description="User ID (required)", - ), - FieldSchema( - name="group_id", - dtype=DataType.VARCHAR, - max_length=256, - description="Group ID", - ), - FieldSchema( - name="scenario", - dtype=DataType.VARCHAR, - max_length=50, - description="Scenario type: solo or team", - ), - FieldSchema( - name="memcell_count", - dtype=DataType.INT64, - description="Number of MemCells involved in extraction", - ), - FieldSchema( - name="item_type", - dtype=DataType.VARCHAR, - max_length=32, - description="Item type: explicit_info or implicit_trait", - ), - FieldSchema( - name="embed_text", - dtype=DataType.VARCHAR, - max_length=4096, - description="Text used for generating embedding vector", - ), - ], - description="V1 vector collection for user profiles", - enable_dynamic_field=True, - ) - - # Index configuration - _INDEX_CONFIGS = [ - # Vector field index - IndexConfig( - field_name="vector", - index_type="HNSW", - metric_type="COSINE", - params={"M": 16, "efConstruction": 200}, - ), - # Scalar field indexes - IndexConfig(field_name="user_id", index_type="AUTOINDEX"), - IndexConfig(field_name="group_id", index_type="AUTOINDEX"), - IndexConfig(field_name="scenario", index_type="AUTOINDEX"), - ] diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/__init__.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/__init__.py deleted file mode 100755 index ebe8cf85..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/__init__.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Memory Search Repositories - -Export all memory search repositories (Elasticsearch and Milvus) -""" - -from infra_layer.adapters.out.search.repository.episodic_memory_es_repository import ( - EpisodicMemoryEsRepository, -) -from infra_layer.adapters.out.search.repository.episodic_memory_milvus_repository import ( - EpisodicMemoryMilvusRepository, -) -from infra_layer.adapters.out.search.repository.foresight_milvus_repository import ( - ForesightMilvusRepository, -) -from infra_layer.adapters.out.search.repository.atomic_fact_milvus_repository import ( - AtomicFactMilvusRepository, -) -from infra_layer.adapters.out.search.repository.user_profile_milvus_repository import ( - UserProfileMilvusRepository, -) -from infra_layer.adapters.out.search.repository.agent_case_es_repository import ( - AgentCaseEsRepository, -) -from infra_layer.adapters.out.search.repository.agent_skill_es_repository import ( - AgentSkillEsRepository, -) -from infra_layer.adapters.out.search.repository.agent_case_milvus_repository import ( - AgentCaseMilvusRepository, -) -from infra_layer.adapters.out.search.repository.agent_skill_milvus_repository import ( - AgentSkillMilvusRepository, -) - -__all__ = [ - "EpisodicMemoryEsRepository", - "EpisodicMemoryMilvusRepository", - "ForesightMilvusRepository", - "AtomicFactMilvusRepository", - "UserProfileMilvusRepository", - "AgentCaseEsRepository", - "AgentSkillEsRepository", - "AgentCaseMilvusRepository", - "AgentSkillMilvusRepository", -] diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_case_es_repository.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_case_es_repository.py deleted file mode 100644 index 13556380..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_case_es_repository.py +++ /dev/null @@ -1,263 +0,0 @@ -""" -Agent case Elasticsearch repository - -Provides BM25 text retrieval for agent case records. -""" - -import pprint -from typing import List, Optional, Dict, Any -from elasticsearch.dsl import Q -from core.oxm.es.base_repository import BaseRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.search.elasticsearch.memory.agent_case import AgentCaseDoc -from core.observation.logger import get_logger -from common_utils.text_utils import SmartTextParser -from core.di.decorators import repository -from biz_layer.retrieve_constants import AGENT_MEMORY_ES_MIN_SHOULD_MATCH - -logger = get_logger(__name__) - - -@repository("agent_case_es_repository", primary=True) -class AgentCaseEsRepository(BaseRepository[AgentCaseDoc]): - """ - Agent case Elasticsearch repository - - Provides: - - BM25 text retrieval on task_intents and approaches - - Multi-term query and filtering capabilities - - Document creation and management - """ - - def __init__(self): - super().__init__(AgentCaseDoc) - self._text_parser = SmartTextParser() - - def _calculate_text_score(self, text: str) -> float: - """Calculate intelligent score of text for boost weighting.""" - if not text: - return 0.0 - try: - tokens = self._text_parser.parse_tokens(text) - return self._text_parser.calculate_total_score(tokens) - except (ValueError, TypeError, AttributeError) as e: - logger.warning( - "Failed to calculate text score, using character length as fallback: %s", - e, - ) - return float(len(text)) - - def _log_explanation_details( - self, explanation: Dict[str, Any], indent: int = 0 - ) -> None: - pprint.pprint(explanation, indent=indent) - - async def multi_search( - self, - query: List[str], - user_id: Optional[str] = None, - session_id: Optional[str] = None, - group_ids: Optional[List[str]] = None, - parent_id: Optional[str] = None, - date_range: Optional[Dict[str, Any]] = None, - size: int = 10, - from_: int = 0, - explain: bool = False, - ) -> Dict[str, Any]: - """ - Unified search interface for agent case BM25 retrieval - - Args: - query: List of search terms - user_id: User ID filter - session_id: Session ID filter - group_ids: List of Group IDs to filter - parent_id: Parent memory ID filter - date_range: Time range filter - size: Number of results - from_: Pagination start position - explain: Whether to enable score explanation mode - - Returns: - Hits part of search results - """ - try: - search = AgentCaseDoc.search() - - filter_queries = [] - - if user_id != MAGIC_ALL: - if user_id and user_id != "": - filter_queries.append(Q("term", user_id=user_id)) - else: - # user_id must not exist: match docs where field is missing or "" - filter_queries.append( - Q( - "bool", - should=[ - Q("bool", must_not=[Q("exists", field="user_id")]), - Q("term", user_id=""), - ], - minimum_should_match=1, - ) - ) - - if session_id: - filter_queries.append(Q("term", session_id=session_id)) - - if group_ids is not None and len(group_ids) > 0: - filter_queries.append(Q("terms", group_id=group_ids)) - - if parent_id: - filter_queries.append(Q("term", parent_id=parent_id)) - - if date_range: - filter_queries.append(Q("range", timestamp=date_range)) - - if query: - query_with_scores = [ - (word, self._calculate_text_score(word)) for word in query - ] - sorted_query_with_scores = sorted( - query_with_scores, key=lambda x: x[1], reverse=True - )[:10] - - should_queries = [] - for word, word_score in sorted_query_with_scores: - should_queries.append( - Q("match", search_content={"query": word, "boost": word_score}) - ) - - bool_query_params = { - "should": should_queries, - "minimum_should_match": AGENT_MEMORY_ES_MIN_SHOULD_MATCH, - } - - if filter_queries: - bool_query_params["must"] = filter_queries - - search = search.query(Q("bool", **bool_query_params)) - else: - if filter_queries: - search = search.query(Q("bool", filter=filter_queries)) - else: - search = search.query(Q("match_all")) - - search = search.sort({"timestamp": {"order": "desc"}}) - - search = search[from_ : from_ + size] - - logger.debug("agent case search query: %s", search.to_dict()) - - if explain and query: - client = await self.get_client() - index_name = self.get_index_name() - - search_body = search.to_dict() - search_response = await client.search( - index=index_name, body=search_body, explain=True - ) - - hits = [] - for hit_data in search_response["hits"]["hits"]: - hits.append(hit_data) - if "_explanation" in hit_data: - self._log_explanation_details( - hit_data["_explanation"], indent=2 - ) - - logger.debug( - "Agent case search succeeded (explain mode): query=%s, found %d results", - search.to_dict(), - len(hits), - ) - else: - response = await search.execute() - - hits = [] - for hit in response.hits: - hit_data = { - "_index": hit.meta.index, - "_id": hit.meta.id, - "_score": hit.meta.score, - "_source": hit.to_dict(), - } - hits.append(hit_data) - - logger.debug( - "Agent case search succeeded: query=%s, found %d results", - search.to_dict(), - len(hits), - ) - - return hits - - except (ConnectionError, TimeoutError, ValueError) as e: - logger.error("Agent case search failed: query=%s, error=%s", query, e) - raise - except Exception as e: - logger.error( - "Agent case search failed (unknown error): query=%s, error=%s", query, e - ) - raise - - async def delete_by_filters( - self, - user_id: Optional[str] = None, - group_id: Optional[str] = None, - date_range: Optional[Dict[str, Any]] = None, - refresh: bool = False, - ) -> int: - """ - Batch delete agent case documents by filter conditions - - Args: - user_id: User ID filter - group_id: Group ID filter - date_range: Time range filter - refresh: Whether to refresh index immediately - - Returns: - Number of deleted documents - """ - try: - filter_queries = [] - if user_id != MAGIC_ALL and user_id is not None: - if user_id: - filter_queries.append({"term": {"user_id": user_id}}) - else: - filter_queries.append({"term": {"user_id": ""}}) - if group_id != MAGIC_ALL and group_id: - filter_queries.append({"term": {"group_id": group_id}}) - if date_range: - filter_queries.append({"range": {"timestamp": date_range}}) - - if not filter_queries: - raise ValueError("At least one filter condition must be provided") - - delete_query = {"bool": {"must": filter_queries}} - - client = await self.get_client() - index_name = self.get_index_name() - - response = await client.delete_by_query( - index=index_name, body={"query": delete_query}, refresh=refresh - ) - - deleted_count = response.get("deleted", 0) - logger.info( - "Successfully deleted agent case docs: user_id=%s, group_id=%s, deleted %d records", - user_id, - group_id, - deleted_count, - ) - return deleted_count - - except Exception as e: - logger.error( - "Failed to delete agent case docs: user_id=%s, group_id=%s, error=%s", - user_id, - group_id, - e, - ) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_case_milvus_repository.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_case_milvus_repository.py deleted file mode 100644 index 6877a329..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_case_milvus_repository.py +++ /dev/null @@ -1,138 +0,0 @@ -""" -AgentCase Milvus Repository - -Provides vector search for agent case records via Milvus. -""" - -from datetime import datetime, timezone -from typing import List, Optional, Dict, Any - -from core.oxm.milvus.base_repository import BaseMilvusRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.search.milvus.memory.agent_case_collection import ( - AgentCaseCollection, -) -from core.observation.logger import get_logger -from core.di.decorators import repository - - -logger = get_logger(__name__) - -MILVUS_SIMILARITY_RADIUS = None - - -@repository("agent_case_milvus_repository", primary=False) -class AgentCaseMilvusRepository(BaseMilvusRepository[AgentCaseCollection]): - """ - AgentCase Milvus Repository - - Supports vector similarity search over agent task-solving experiences. - Filters: user_id, group_ids, timestamp range, parent_id. - """ - - def __init__(self): - super().__init__(AgentCaseCollection) - - async def vector_search( - self, - query_vector: List[float], - user_id: Optional[str] = None, - session_id: Optional[str] = None, - group_ids: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - parent_id: Optional[str] = None, - limit: int = 10, - score_threshold: float = 0.0, - radius: Optional[float] = None, - ) -> List[Dict[str, Any]]: - """ - Vector similarity search over agent cases. - - Args: - query_vector: Query embedding vector - user_id: User ID filter (MAGIC_ALL to skip) - session_id: Session ID filter - group_ids: Group ID list filter (None to skip) - start_time: Filter records with timestamp >= start_time - end_time: Filter records with timestamp <= end_time - parent_id: Filter by parent MemCell ID - limit: Maximum results to return - score_threshold: Minimum COSINE similarity score - radius: Explicit COSINE similarity threshold - - Returns: - List of search result dicts - """ - try: - filter_expr = [] - - if user_id != MAGIC_ALL: - if user_id: - filter_expr.append(f'user_id == "{user_id}"') - else: - filter_expr.append('user_id == ""') - - if session_id: - filter_expr.append(f'session_id == "{session_id}"') - - if group_ids is not None and len(group_ids) > 0: - group_ids_str = ", ".join(f'"{g}"' for g in group_ids) - filter_expr.append(f"group_id in [{group_ids_str}]") - - if parent_id: - filter_expr.append(f'parent_id == "{parent_id}"') - - if start_time: - filter_expr.append(f"timestamp >= {int(start_time.timestamp())}") - if end_time: - filter_expr.append(f"timestamp <= {int(end_time.timestamp())}") - - filter_str = " and ".join(filter_expr) if filter_expr else None - - ef_value = max(128, limit * 2) - similarity_radius = ( - radius if radius is not None else MILVUS_SIMILARITY_RADIUS - ) - search_params = {"metric_type": "COSINE", "params": {"ef": ef_value}} - if radius is not None and radius > -1.0: - search_params["params"]["radius"] = radius - elif similarity_radius is not None and similarity_radius > -1.0: - search_params["params"]["radius"] = similarity_radius - - results = await self.collection.search( - data=[query_vector], - anns_field="vector", - param=search_params, - limit=limit, - expr=filter_str, - output_fields=self.all_output_fields, - ) - - search_results = [] - for hits in results: - for hit in hits: - if hit.score >= score_threshold: - result = { - "id": hit.entity.get("id"), - "score": float(hit.score), - "user_id": hit.entity.get("user_id"), - "group_id": hit.entity.get("group_id"), - "session_id": hit.entity.get("session_id", ""), - "timestamp": datetime.fromtimestamp( - hit.entity.get("timestamp", 0), tz=timezone.utc - ), - "task_intent": hit.entity.get("task_intent", ""), - "parent_type": hit.entity.get("parent_type", ""), - "parent_id": hit.entity.get("parent_id", ""), - } - search_results.append(result) - - logger.debug( - "AgentCase vector search: found %d results", len(search_results) - ) - return search_results - - except Exception as e: - logger.error("AgentCase vector search failed: %s", e) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_skill_es_repository.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_skill_es_repository.py deleted file mode 100644 index 1dffdbce..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_skill_es_repository.py +++ /dev/null @@ -1,266 +0,0 @@ -""" -Agent skill Elasticsearch repository - -Provides BM25 text retrieval for agent skill records. -""" - -import pprint -from typing import List, Optional, Dict, Any -from elasticsearch.dsl import Q -from core.oxm.es.base_repository import BaseRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.search.elasticsearch.memory.agent_skill import ( - AgentSkillDoc, -) -from core.observation.logger import get_logger -from biz_layer.retrieve_constants import AGENT_MEMORY_ES_MIN_SHOULD_MATCH -from common_utils.text_utils import SmartTextParser -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("agent_skill_es_repository", primary=True) -class AgentSkillEsRepository(BaseRepository[AgentSkillDoc]): - """ - Agent skill Elasticsearch repository - - Provides: - - BM25 text retrieval on skill name + description - - Multi-term query and filtering capabilities - - Document creation and management - """ - - def __init__(self): - super().__init__(AgentSkillDoc) - self._text_parser = SmartTextParser() - - def _calculate_text_score(self, text: str) -> float: - """Calculate intelligent score of text for boost weighting.""" - if not text: - return 0.0 - try: - tokens = self._text_parser.parse_tokens(text) - return self._text_parser.calculate_total_score(tokens) - except (ValueError, TypeError, AttributeError) as e: - logger.warning( - "Failed to calculate text score, using character length as fallback: %s", - e, - ) - return float(len(text)) - - def _log_explanation_details( - self, explanation: Dict[str, Any], indent: int = 0 - ) -> None: - pprint.pprint(explanation, indent=indent) - - async def multi_search( - self, - query: List[str], - group_ids: Optional[List[str]] = None, - user_id: Optional[str] = None, - cluster_id: Optional[str] = None, - size: int = 10, - from_: int = 0, - explain: bool = False, - maturity_threshold: Optional[float] = 0.6, - confidence_threshold: Optional[float] = None, - **kwargs, - ) -> Dict[str, Any]: - """ - Unified search interface for agent skill BM25 retrieval - - Args: - query: List of search terms - group_ids: List of Group IDs to filter - user_id: User ID filter - cluster_id: Cluster ID filter - size: Number of results - from_: Pagination start position - explain: Whether to enable score explanation mode - maturity_threshold: Minimum maturity score (0.0-1.0) to return. - Set to None to include all skills regardless of maturity. - confidence_threshold: Minimum confidence score (0.0-1.0) to return. - Skills below this threshold are considered retired. - Set to None to skip confidence filtering. - - Returns: - Hits part of search results - """ - try: - search = AgentSkillDoc.search() - - filter_queries = [] - - if maturity_threshold is not None: - filter_queries.append( - Q("range", maturity_score={"gte": maturity_threshold}) - ) - - if confidence_threshold is not None: - filter_queries.append( - Q("range", confidence={"gte": confidence_threshold}) - ) - - if user_id != MAGIC_ALL: - if user_id and user_id != "": - filter_queries.append(Q("term", user_id=user_id)) - else: - # user_id must not exist: match docs where field is missing or "" - filter_queries.append( - Q( - "bool", - should=[ - Q("bool", must_not=[Q("exists", field="user_id")]), - Q("term", user_id=""), - ], - minimum_should_match=1, - ) - ) - - if group_ids is not None and len(group_ids) > 0: - filter_queries.append(Q("terms", group_id=group_ids)) - - if cluster_id: - filter_queries.append(Q("term", cluster_id=cluster_id)) - - if query: - query_with_scores = [ - (word, self._calculate_text_score(word)) for word in query - ] - sorted_query_with_scores = sorted( - query_with_scores, key=lambda x: x[1], reverse=True - )[:10] - - should_queries = [] - for word, word_score in sorted_query_with_scores: - should_queries.append( - Q("match", search_content={"query": word, "boost": word_score}) - ) - - bool_query_params = { - "should": should_queries, - "minimum_should_match": AGENT_MEMORY_ES_MIN_SHOULD_MATCH, - } - - if filter_queries: - bool_query_params["must"] = filter_queries - - search = search.query(Q("bool", **bool_query_params)) - else: - if filter_queries: - search = search.query(Q("bool", filter=filter_queries)) - else: - search = search.query(Q("match_all")) - - search = search.sort({"maturity_score": {"order": "desc"}}) - - search = search[from_ : from_ + size] - - logger.debug("agent skill search query: %s", search.to_dict()) - - if explain and query: - client = await self.get_client() - index_name = self.get_index_name() - - search_body = search.to_dict() - search_response = await client.search( - index=index_name, body=search_body, explain=True - ) - - hits = [] - for hit_data in search_response["hits"]["hits"]: - hits.append(hit_data) - if "_explanation" in hit_data: - self._log_explanation_details( - hit_data["_explanation"], indent=2 - ) - - logger.debug( - "Agent skill search succeeded (explain mode): query=%s, found %d results", - search.to_dict(), - len(hits), - ) - else: - response = await search.execute() - - hits = [] - for hit in response.hits: - hit_data = { - "_index": hit.meta.index, - "_id": hit.meta.id, - "_score": hit.meta.score, - "_source": hit.to_dict(), - } - hits.append(hit_data) - - logger.debug( - "Agent skill search succeeded: query=%s, found %d results", - search.to_dict(), - len(hits), - ) - - return hits - - except (ConnectionError, TimeoutError, ValueError) as e: - logger.error("Agent skill search failed: query=%s, error=%s", query, e) - raise - except Exception as e: - logger.error( - "Agent skill search failed (unknown error): query=%s, error=%s", - query, - e, - ) - raise - - async def delete_by_filters( - self, - group_id: Optional[str] = None, - cluster_id: Optional[str] = None, - refresh: bool = False, - ) -> int: - """ - Batch delete agent skill documents by filter conditions - - Args: - group_id: Group ID filter - cluster_id: Cluster ID filter - refresh: Whether to refresh index immediately - - Returns: - Number of deleted documents - """ - try: - filter_queries = [] - if group_id: - filter_queries.append({"term": {"group_id": group_id}}) - if cluster_id: - filter_queries.append({"term": {"cluster_id": cluster_id}}) - - if not filter_queries: - raise ValueError("At least one filter condition must be provided") - - delete_query = {"bool": {"must": filter_queries}} - - client = await self.get_client() - index_name = self.get_index_name() - - response = await client.delete_by_query( - index=index_name, body={"query": delete_query}, refresh=refresh - ) - - deleted_count = response.get("deleted", 0) - logger.info( - "Successfully deleted agent skill docs: cluster_id=%s, deleted %d records", - cluster_id, - deleted_count, - ) - return deleted_count - - except Exception as e: - logger.error( - "Failed to delete agent skill docs: cluster_id=%s, error=%s", - cluster_id, - e, - ) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_skill_milvus_repository.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_skill_milvus_repository.py deleted file mode 100644 index c0718ef6..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/agent_skill_milvus_repository.py +++ /dev/null @@ -1,158 +0,0 @@ -""" -AgentSkill Milvus Repository - -Provides vector search for agent skill records via Milvus. -Supports cluster-level delete for the replace pattern used by AgentSkillExtractor. -""" - -from typing import List, Optional, Dict, Any - -from core.oxm.milvus.base_repository import BaseMilvusRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.search.milvus.memory.agent_skill_collection import ( - AgentSkillCollection, -) -from core.observation.logger import get_logger -from core.di.decorators import repository - - -logger = get_logger(__name__) - -MILVUS_SIMILARITY_RADIUS = None - - -@repository("agent_skill_milvus_repository", primary=False) -class AgentSkillMilvusRepository(BaseMilvusRepository[AgentSkillCollection]): - """ - AgentSkill Milvus Repository - - Supports vector similarity search over reusable skill items. - Also supports cluster-level deletion to support the replace pattern. - """ - - def __init__(self): - super().__init__(AgentSkillCollection) - - async def vector_search( - self, - query_vector: List[float], - group_ids: Optional[List[str]] = None, - user_id: Optional[str] = None, - cluster_id: Optional[str] = None, - limit: int = 10, - score_threshold: float = 0.0, - radius: Optional[float] = None, - maturity_threshold: Optional[float] = 0.6, - confidence_threshold: Optional[float] = None, - ) -> List[Dict[str, Any]]: - """ - Vector similarity search over agent skill items. - - Args: - query_vector: Query embedding vector - group_ids: Group ID list filter (None to skip) - user_id: User ID filter - cluster_id: Filter by MemScene cluster ID - limit: Maximum results to return - score_threshold: Minimum COSINE similarity score - radius: Explicit COSINE similarity threshold - maturity_threshold: Minimum maturity score (0.0-1.0) to return. - Set to None to include all skills regardless of maturity. - confidence_threshold: Minimum confidence score (0.0-1.0) to return. - Skills below this threshold are considered retired. - Set to None to skip confidence filtering. - - Returns: - List of search result dicts - """ - try: - filter_expr = [] - - if maturity_threshold is not None: - filter_expr.append(f"maturity_score >= {maturity_threshold}") - - if confidence_threshold is not None: - filter_expr.append(f"confidence >= {confidence_threshold}") - - if user_id != MAGIC_ALL: - if user_id: - filter_expr.append(f'user_id == "{user_id}"') - else: - filter_expr.append('user_id == ""') - - if group_ids is not None and len(group_ids) > 0: - group_ids_str = ", ".join(f'"{g}"' for g in group_ids) - filter_expr.append(f"group_id in [{group_ids_str}]") - - if cluster_id: - filter_expr.append(f'cluster_id == "{cluster_id}"') - - filter_str = " and ".join(filter_expr) if filter_expr else None - - ef_value = max(128, limit * 2) - similarity_radius = ( - radius if radius is not None else MILVUS_SIMILARITY_RADIUS - ) - search_params = {"metric_type": "COSINE", "params": {"ef": ef_value}} - if radius is not None and radius > -1.0: - search_params["params"]["radius"] = radius - elif similarity_radius is not None and similarity_radius > -1.0: - search_params["params"]["radius"] = similarity_radius - - results = await self.collection.search( - data=[query_vector], - anns_field="vector", - param=search_params, - limit=limit, - expr=filter_str, - output_fields=self.all_output_fields, - ) - - search_results = [] - for hits in results: - for hit in hits: - if hit.score >= score_threshold: - result = { - "id": hit.entity.get("id"), - "score": float(hit.score), - "user_id": hit.entity.get("user_id", ""), - "group_id": hit.entity.get("group_id"), - "cluster_id": hit.entity.get("cluster_id"), - "content": hit.entity.get("content", ""), - } - search_results.append(result) - - logger.debug( - "AgentSkill vector search: found %d results", len(search_results) - ) - return search_results - - except Exception as e: - logger.error("AgentSkill vector search failed: %s", e) - raise - - async def delete_by_cluster_id(self, cluster_id: str) -> int: - """ - Delete all Milvus records for a cluster (called before replacing skills). - - Args: - cluster_id: MemScene cluster ID - - Returns: - Number of deleted records - """ - try: - expr = f'cluster_id == "{cluster_id}"' - existing = await self.collection.query(expr=expr, output_fields=["id"]) - count = len(existing) - if count > 0: - await self.collection.delete(expr) - logger.debug( - "Deleted %d Milvus records for cluster=%s", count, cluster_id - ) - return count - except Exception as e: - logger.error( - "Failed to delete Milvus records for cluster=%s: %s", cluster_id, e - ) - return 0 diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/atomic_fact_es_repository.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/atomic_fact_es_repository.py deleted file mode 100644 index ba98434e..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/atomic_fact_es_repository.py +++ /dev/null @@ -1,347 +0,0 @@ -""" -Atomic Fact Elasticsearch Repository - -V1 simplified repository for BM25 text retrieval. -Only maps search-essential fields. Full data retrieved from MongoDB using parent_id. -""" - -import pprint -from datetime import datetime -from typing import List, Optional, Dict, Any -from elasticsearch.dsl import Q -from core.oxm.es.base_repository import BaseRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.search.elasticsearch.memory.atomic_fact import ( - AtomicFactDoc, -) -from core.observation.logger import get_logger -from common_utils.text_utils import SmartTextParser -from common_utils.datetime_utils import get_now_with_timezone -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("atomic_fact_es_repository", primary=True) -class AtomicFactEsRepository(BaseRepository[AtomicFactDoc]): - """ - Atomic Fact Elasticsearch Repository - - V1 simplified repository for BM25 text retrieval. - Only stores search-essential fields in ES. - Full data is retrieved from MongoDB using parent_id. - """ - - def __init__(self): - """Initialize atomic fact repository""" - super().__init__(AtomicFactDoc) - self._text_parser = SmartTextParser() - - def _calculate_text_score(self, text: str) -> float: - """Calculate intelligent score of text""" - if not text: - return 0.0 - try: - tokens = self._text_parser.parse_tokens(text) - return self._text_parser.calculate_total_score(tokens) - except (ValueError, TypeError, AttributeError) as e: - logger.warning( - "Failed to calculate text score, using character length: %s", e - ) - return float(len(text)) - - def _log_explanation_details( - self, explanation: Dict[str, Any], indent: int = 0 - ) -> None: - """ - Recursively output detailed explanation information - - Args: - explanation: Explanation dictionary - indent: Indentation level - """ - pprint.pprint(explanation, indent=indent) - - # ==================== Document creation and management ==================== - - async def create_and_save_atomic_fact( - self, - id: str, - user_id: str, - timestamp: datetime, - atomic_fact: str, - search_content: List[str], - parent_id: str, - parent_type: str, - group_id: Optional[str] = None, - participants: Optional[List[str]] = None, - sender_ids: Optional[List[str]] = None, - created_at: Optional[datetime] = None, - updated_at: Optional[datetime] = None, - ) -> AtomicFactDoc: - """ - Create and save atomic fact document - - Args: - id: Log unique identifier - user_id: User ID (required) - timestamp: Event occurrence time (required) - atomic_fact: Atomic fact (required) - search_content: List of search content (supports multiple search terms, required) - parent_id: Parent memory ID - parent_type: Parent memory type (memcell/episode) - group_id: Group ID - participants: List of participants - created_at: Creation time - updated_at: Update time - - Returns: - Saved AtomicFactDoc instance - """ - try: - # Set default timestamps - now = get_now_with_timezone() - if created_at is None: - created_at = now - if updated_at is None: - updated_at = now - - # Create document instance - doc = AtomicFactDoc( - id=id, - user_id=user_id, - timestamp=timestamp, - search_content=search_content, - atomic_fact=atomic_fact, - group_id=group_id, - participants=participants or [], - sender_ids=sender_ids or [], - parent_type=parent_type, - parent_id=parent_id, - created_at=created_at, - updated_at=updated_at, - ) - - # Save document (without refresh parameter) - client = await self.get_client() - await doc.save(using=client) - - logger.debug( - "Created atomic fact document successfully: event_id=%s, user_id=%s", - id, - user_id, - ) - return doc - - except Exception as e: - logger.error( - "Failed to create atomic fact document: event_id=%s, error=%s", id, e - ) - raise - - # ==================== Search functionality ==================== - - async def multi_search( - self, - query: List[str], - user_id: Optional[str] = None, - group_ids: Optional[List[str]] = None, - session_id: Optional[str] = None, - parent_type: Optional[str] = None, - parent_id: Optional[str] = None, - date_range: Optional[Dict[str, Any]] = None, - size: int = 10, - from_: int = 0, - ) -> List[Dict[str, Any]]: - """ - BM25 text search for atomic facts - - Args: - query: List of search terms - user_id: User ID filter - group_ids: List of Group IDs to filter - session_id: Session ID filter - parent_type: Parent type filter - parent_id: Parent memory ID filter - date_range: Time range filter - size: Number of results - from_: Pagination start position - - Returns: - List of search hits with matched document data - """ - try: - search = AtomicFactDoc.search() - - # Build filter conditions - filter_queries = [] - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id and user_id != "": - filter_queries.append(Q("term", user_id=user_id)) - else: - # user_id must not exist: match docs where field is missing or "" - filter_queries.append( - Q("bool", should=[ - Q("bool", must_not=[Q("exists", field="user_id")]), - Q("term", user_id=""), - ], minimum_should_match=1) - ) - - # Handle group_ids filter - if group_ids is not None and len(group_ids) > 0: - filter_queries.append(Q("terms", group_id=group_ids)) - - # Handle session_id filter - if session_id: - filter_queries.append(Q("term", session_id=session_id)) - - # Handle parent_id filter - if parent_id: - filter_queries.append(Q("term", parent_id=parent_id)) - - # Handle parent_type filter - if parent_type: - filter_queries.append(Q("term", parent_type=parent_type)) - - if date_range: - filter_queries.append(Q("range", timestamp=date_range)) - - # Build query - if query: - # Filter query terms by intelligent score - query_with_scores = [ - (word, self._calculate_text_score(word)) for word in query - ] - sorted_query_with_scores = sorted( - query_with_scores, key=lambda x: x[1], reverse=True - )[:10] - - # Build should clauses - search in atomic_fact field - should_queries = [] - for word, word_score in sorted_query_with_scores: - should_queries.append( - Q("match", search_content={"query": word, "boost": word_score}) - ) - - bool_query_params = { - "should": should_queries, - "minimum_should_match": 1, - } - - if filter_queries: - bool_query_params["must"] = filter_queries - - search = search.query(Q("bool", **bool_query_params)) - else: - # Pure filtering query - if filter_queries: - search = search.query(Q("bool", filter=filter_queries)) - else: - search = search.query(Q("match_all")) - - search = search.sort({"timestamp": {"order": "desc"}}) - - search = search[from_ : from_ + size] - - logger.debug("atomic fact search query: %s", search.to_dict()) - - response = await search.execute() - - hits = [] - for hit in response.hits: - hit_data = { - "_id": hit.meta.id, - "_score": hit.meta.score, - "_source": hit.to_dict(), - } - hits.append(hit_data) - - logger.debug( - "Atomic fact search succeeded: query=%s, user_id=%s, found %d results", - query, - user_id, - len(hits), - ) - - return hits - - except Exception as e: - logger.error( - "Atomic fact search failed: query=%s, user_id=%s, error=%s", - query, - user_id, - e, - ) - raise - - # ==================== Deletion functionality ==================== - - async def delete_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - date_range: Optional[Dict[str, Any]] = None, - refresh: bool = False, - ) -> int: - """ - Batch delete atomic fact documents by filter conditions - - Args: - user_id: User ID filter - group_id: Group ID filter - date_range: Time range filter - refresh: Whether to refresh index immediately - - Returns: - Number of deleted documents - """ - try: - filter_queries = [] - # Handle user_id filter: MAGIC_ALL means no filter - if user_id != MAGIC_ALL: - if not user_id: # None or "" -> match empty string - filter_queries.append({"term": {"user_id": ""}}) - else: - filter_queries.append({"term": {"user_id": user_id}}) - # Handle group_id filter: MAGIC_ALL means no filter - if group_id != MAGIC_ALL: - if not group_id: # None or "" -> match empty string - filter_queries.append({"term": {"group_id": ""}}) - else: - filter_queries.append({"term": {"group_id": group_id}}) - if date_range: - filter_queries.append({"range": {"timestamp": date_range}}) - - if not filter_queries: - raise ValueError( - "At least one filter condition (user_id, group_id or date_range) must be provided" - ) - - delete_query = {"bool": {"must": filter_queries}} - - client = await self.get_client() - index_name = self.get_index_name() - - response = await client.delete_by_query( - index=index_name, body={"query": delete_query}, refresh=refresh - ) - - deleted_count = response.get('deleted', 0) - logger.info( - "Batch deleted atomic facts: user_id=%s, group_id=%s, deleted %d records", - user_id, - group_id, - deleted_count, - ) - return deleted_count - - except Exception as e: - logger.error( - "Failed to batch delete atomic facts: user_id=%s, group_id=%s, error=%s", - user_id, - group_id, - e, - ) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/atomic_fact_milvus_repository.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/atomic_fact_milvus_repository.py deleted file mode 100644 index 21c889a4..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/atomic_fact_milvus_repository.py +++ /dev/null @@ -1,373 +0,0 @@ -""" -Atomic Fact Milvus Repository - -V1 simplified repository for vector semantic retrieval. -Only maps search-essential fields. Full data retrieved from MongoDB using parent_id. -""" - -import json -from datetime import datetime -from typing import List, Optional, Dict, Any -from core.oxm.milvus.base_repository import BaseMilvusRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.search.milvus.memory.atomic_fact_collection import ( - AtomicFactCollection, -) -from core.observation.logger import get_logger -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("atomic_fact_milvus_repository", primary=False) -class AtomicFactMilvusRepository(BaseMilvusRepository[AtomicFactCollection]): - """ - Atomic Fact Milvus Repository - - V1 simplified repository for vector semantic retrieval. - Only stores search-essential fields in Milvus. - Full data is retrieved from MongoDB using parent_id. - """ - - def __init__(self): - """Initialize the atomic fact repository""" - super().__init__(AtomicFactCollection) - - # ==================== Document Creation and Management ==================== - - async def create_and_save_atomic_fact( - self, - id: str, - user_id: Optional[str], - atomic_fact: str, - parent_id: str, - parent_type: str, - timestamp: datetime, - vector: List[float], - group_id: Optional[str] = None, - participants: Optional[List[str]] = None, - sender_ids: Optional[List[str]] = None, - event_type: Optional[str] = None, - search_content: Optional[List[str]] = None, - ) -> Dict[str, Any]: - """ - Create and save an atomic fact document - - Args: - id: Unique identifier for the atomic fact - user_id: User ID (required) - atomic_fact: Atomic fact content (required) - parent_id: Parent memory ID (required) - parent_type: Parent memory type (memcell/episode) - timestamp: Event occurrence time (required) - vector: Text vector (required, dimension must be VECTORIZE_DIMENSIONS) - group_id: Group ID - participants: List of related participants - event_type: Event type (e.g., Conversation, Email, etc.) - search_content: List of searchable content - Returns: - Information of the saved document - """ - try: - # Build search content - if search_content is None: - search_content = [atomic_fact] - - # Prepare entity data - entity = { - "id": id, - "vector": vector, - "user_id": user_id or "", - "group_id": group_id or "", - "participants": participants or [], - "sender_ids": sender_ids or [], - "parent_type": parent_type, - "parent_id": parent_id, - "type": event_type, - "timestamp": int(timestamp.timestamp() * 1000), - "atomic_fact": atomic_fact, - "search_content": json.dumps(search_content, ensure_ascii=False), - } - - # Insert data - await self.insert(entity) - - logger.debug( - "Successfully created atomic fact document: id=%s, user_id=%s", - id, - user_id, - ) - - return { - "id": id, - "user_id": user_id, - "atomic_fact": atomic_fact, - "parent_type": parent_type, - "parent_id": parent_id, - "timestamp": timestamp, - "search_content": search_content, - } - - except Exception as e: - logger.error( - "Failed to create atomic fact document: id=%s, error=%s", id, e - ) - raise - - # ==================== Search Functionality ==================== - - async def vector_search( - self, - query_vector: List[float], - user_id: Optional[str] = None, - group_ids: Optional[List[str]] = None, - session_id: Optional[str] = None, - parent_type: Optional[str] = None, - parent_id: Optional[str] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - limit: int = 10, - score_threshold: float = 0.0, - radius: Optional[float] = None, - ) -> List[Dict[str, Any]]: - """ - Vector similarity search - - Args: - query_vector: Query vector - user_id: User ID filter - group_ids: List of Group IDs to filter - session_id: Session ID filter - parent_type: Parent type filter - parent_id: Parent memory ID filter - start_time: Start timestamp filter - end_time: End timestamp filter - limit: Number of results to return - score_threshold: Similarity score threshold - radius: COSINE similarity threshold - - Returns: - List of search results - """ - try: - # Build filter expression - filter_expr = [] - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id: - filter_expr.append(f'user_id == "{user_id}"') - else: - filter_expr.append('user_id == ""') - - # Handle group_ids filter - if group_ids is not None and len(group_ids) > 0: - group_ids_str = ', '.join(f'"{g}"' for g in group_ids) - filter_expr.append(f'group_id in [{group_ids_str}]') - - # Handle session_id filter - if session_id: - filter_expr.append(f'session_id == "{session_id}"') - - # Handle parent_type filter - if parent_type: - filter_expr.append(f'parent_type == "{parent_type}"') - - # Handle parent_id filter - if parent_id: - filter_expr.append(f'parent_id == "{parent_id}"') - - # Handle time filters - if start_time: - filter_expr.append(f"timestamp >= {int(start_time.timestamp() * 1000)}") - if end_time: - filter_expr.append(f"timestamp <= {int(end_time.timestamp() * 1000)}") - - filter_str = " and ".join(filter_expr) if filter_expr else None - - # Execute search - ef_value = max(128, limit * 2) - search_params = {"metric_type": "COSINE", "params": {"ef": ef_value}} - - if radius is not None and radius > -1.0: - search_params["params"]["radius"] = radius - - results = await self.collection.search( - data=[query_vector], - anns_field="vector", - param=search_params, - limit=limit, - expr=filter_str, - output_fields=self.all_output_fields, - ) - - # Process results - search_results = [] - for hits in results: - for hit in hits: - if hit.score >= score_threshold: - result = { - "id": hit.entity.get("id"), - "score": float(hit.score), - "user_id": hit.entity.get("user_id"), - "group_id": hit.entity.get("group_id"), - "session_id": hit.entity.get("session_id"), - "participants": hit.entity.get("participants"), - "timestamp": hit.entity.get("timestamp"), - "parent_type": hit.entity.get("parent_type"), - "parent_id": hit.entity.get("parent_id"), - } - search_results.append(result) - - logger.debug( - "Vector search succeeded: found %d results", len(search_results) - ) - return search_results - - except Exception as e: - logger.error("Vector search failed: %s", e) - raise - - async def batch_vector_search_by_parent_ids( - self, - query_vector: List[float], - parent_ids: List[str], - user_id: Optional[str] = None, - group_ids: Optional[List[str]] = None, - limit: int = 5, - score_threshold: float = 0.0, - ) -> List[Dict[str, Any]]: - """Batch vector search AtomicFacts filtered by parent_ids. - - Used by MRAG Phase 3 to expand episodes into atomic facts. - - Args: - query_vector: Query vector for similarity search - parent_ids: List of parent IDs to filter by - user_id: Optional user ID filter - group_ids: Optional group IDs filter - limit: Max results per parent (total limit = limit * len(parent_ids)) - score_threshold: Minimum similarity score - - Returns: - List of search results with score - """ - try: - filter_expr = [] - - # parent_id IN filter - parent_ids_str = ", ".join(f'"{pid}"' for pid in parent_ids) - filter_expr.append(f"parent_id in [{parent_ids_str}]") - - # user_id filter - if user_id and user_id != MAGIC_ALL: - filter_expr.append(f'user_id == "{user_id}"') - - # group_ids filter - if group_ids: - group_ids_str = ", ".join(f'"{g}"' for g in group_ids) - filter_expr.append(f"group_id in [{group_ids_str}]") - - filter_str = " and ".join(filter_expr) - - total_limit = limit * len(parent_ids) - ef_value = max(128, total_limit * 2) - search_params = {"metric_type": "COSINE", "params": {"ef": ef_value}} - - results = await self.collection.search( - data=[query_vector], - anns_field="vector", - param=search_params, - limit=total_limit, - expr=filter_str, - output_fields=self.all_output_fields, - ) - - search_results = [] - for hits in results: - for hit in hits: - if hit.score >= score_threshold: - search_results.append( - { - "id": hit.entity.get("id"), - "score": float(hit.score), - "user_id": hit.entity.get("user_id"), - "group_id": hit.entity.get("group_id"), - "parent_type": hit.entity.get("parent_type"), - "parent_id": hit.entity.get("parent_id"), - "atomic_fact": hit.entity.get("atomic_fact"), - "timestamp": hit.entity.get("timestamp"), - "participants": hit.entity.get("participants"), - } - ) - - logger.debug( - "Batch vector search by parent_ids succeeded: " - "parent_ids=%d, results=%d", - len(parent_ids), - len(search_results), - ) - return search_results - - except Exception as e: - logger.error("Batch vector search by parent_ids failed: %s", e) - raise - - # ==================== Deletion Functionality ==================== - - async def delete_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - ) -> int: - """ - Batch delete atomic fact documents by filter conditions - - Args: - user_id: User ID filter - group_id: Group ID filter - start_time: Start time - end_time: End time - - Returns: - Number of deleted documents - """ - try: - filter_expr = [] - # Handle user_id filter: MAGIC_ALL means no filter - if user_id != MAGIC_ALL: - if ( - not user_id - ): # None or "" -> match empty string (null mapped to "" by converter) - filter_expr.append('user_id == ""') - else: - filter_expr.append(f'user_id == "{user_id}"') - # Handle group_id filter: MAGIC_ALL means no filter - if group_id != MAGIC_ALL: - if not group_id: # None or "" -> match empty string - filter_expr.append('group_id == ""') - else: - filter_expr.append(f'group_id == "{group_id}"') - if start_time: - filter_expr.append(f"timestamp >= {int(start_time.timestamp() * 1000)}") - if end_time: - filter_expr.append(f"timestamp <= {int(end_time.timestamp() * 1000)}") - - if not filter_expr: - raise ValueError("At least one filter condition must be provided") - - expr = " and ".join(filter_expr) - - results = await self.collection.query(expr=expr, output_fields=["id"]) - delete_count = len(results) - - await self.collection.delete(expr) - - logger.debug("Batch deleted atomic facts: deleted %d records", delete_count) - return delete_count - - except Exception as e: - logger.error("Failed to batch delete atomic facts: %s", e) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/episodic_memory_es_repository.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/episodic_memory_es_repository.py deleted file mode 100644 index bf38e6ef..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/episodic_memory_es_repository.py +++ /dev/null @@ -1,412 +0,0 @@ -""" -Episodic Memory Elasticsearch Repository - -V1 simplified repository for BM25 text retrieval. -Only maps search-essential fields. Full data retrieved from MongoDB using parent_id. -""" - -import pprint -from datetime import datetime -from typing import List, Optional, Dict, Any -from elasticsearch.dsl import Q -from core.oxm.es.base_repository import BaseRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.search.elasticsearch.memory.episodic_memory import ( - EpisodicMemoryDoc, -) -from core.observation.logger import get_logger -from common_utils.text_utils import SmartTextParser -from common_utils.datetime_utils import get_now_with_timezone -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("episodic_memory_es_repository", primary=True) -class EpisodicMemoryEsRepository(BaseRepository[EpisodicMemoryDoc]): - """ - Episodic Memory Elasticsearch Repository - - V1 simplified repository for BM25 text retrieval. - Only stores search-essential fields in ES. - Full data is retrieved from MongoDB using parent_id. - """ - - def __init__(self): - """Initialize episodic memory repository""" - super().__init__(EpisodicMemoryDoc) - self._text_parser = SmartTextParser() - - def _calculate_text_score(self, text: str) -> float: - """Calculate intelligent score of text""" - if not text: - return 0.0 - try: - tokens = self._text_parser.parse_tokens(text) - return self._text_parser.calculate_total_score(tokens) - except (ValueError, TypeError, AttributeError) as e: - logger.warning( - "Failed to calculate text score, using character length: %s", e - ) - return float(len(text)) - - def _log_explanation_details( - self, explanation: Dict[str, Any], indent: int = 0 - ) -> None: - """ - Recursively output detailed explanation information - - Args: - explanation: Explanation dictionary - indent: Indentation level - """ - pprint.pprint(explanation, indent=indent) - - # ==================== Document creation and management ==================== - - async def create_and_save_episodic_memory( - self, - event_id: str, - user_id: str, - timestamp: datetime, - episode: str, - search_content: List[str], - title: Optional[str] = None, - summary: Optional[str] = None, - group_id: Optional[str] = None, - participants: Optional[List[str]] = None, - sender_ids: Optional[List[str]] = None, - event_type: Optional[str] = None, - subject: Optional[str] = None, - parent_type: Optional[str] = None, - parent_id: Optional[str] = None, - created_at: Optional[datetime] = None, - updated_at: Optional[datetime] = None, - ) -> EpisodicMemoryDoc: - """ - Create and save episodic memory document - - Args: - event_id: Unique event identifier - user_id: User ID (required) - timestamp: Event occurrence time (required) - episode: Episode description (required) - search_content: List of search content (supports multiple search terms, required) - title: Event title - summary: Event summary - group_id: Group ID - participants: List of participants - event_type: Event type - subject: Event title (new field) - created_at: Creation time - updated_at: Update time - - Returns: - Saved EpisodicMemoryDoc instance - """ - try: - # Set default timestamps - now = get_now_with_timezone() - if created_at is None: - created_at = now - if updated_at is None: - updated_at = now - - # Create document instance - normalized_user_id = user_id or "" - doc = EpisodicMemoryDoc( - event_id=event_id, - type=event_type, - user_id=normalized_user_id, - timestamp=timestamp, - title=title or '', - episode=episode, - search_content=search_content, - summary=summary or '', - group_id=group_id, - participants=participants or [], - sender_ids=sender_ids or [], - subject=subject or '', - parent_type=parent_type or '', - parent_id=parent_id or '', - created_at=created_at, - updated_at=updated_at, - ) - - # Save document (without refresh parameter) - client = await self.get_client() - await doc.save(using=client) - - logger.debug( - "Created episodic memory document successfully: event_id=%s, user_id=%s", - event_id, - user_id, - ) - return doc - - except Exception as e: - logger.error( - "Failed to create episodic memory document: event_id=%s, error=%s", - event_id, - e, - ) - raise - - # ==================== Search functionality ==================== - - async def multi_search( - self, - query: List[str], - user_id: Optional[str] = None, - group_ids: Optional[List[str]] = None, - session_id: Optional[str] = None, - parent_type: Optional[str] = None, - parent_id: Optional[str] = None, - date_range: Optional[Dict[str, Any]] = None, - size: int = 10, - from_: int = 0, - ) -> List[Dict[str, Any]]: - """ - BM25 text search for episodic memories - - Args: - query: List of search terms - user_id: User ID filter - group_ids: List of Group IDs to filter - session_id: Session ID filter - parent_type: Parent type filter - parent_id: Parent memory ID filter - date_range: Time range filter, format: {"gte": "2024-01-01", "lte": "2024-12-31"} - size: Number of results - from_: Pagination start position - - Returns: - List of search hits with matched document data - """ - try: - search = EpisodicMemoryDoc.search() - - # Build filter conditions - filter_queries = [] - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id and user_id != "": - filter_queries.append(Q("term", user_id=user_id)) - else: - # user_id must not exist: match docs where field is missing or "" - filter_queries.append( - Q("bool", should=[ - Q("bool", must_not=[Q("exists", field="user_id")]), - Q("term", user_id=""), - ], minimum_should_match=1) - ) - - # Handle group_ids filter - if group_ids is not None and len(group_ids) > 0: - filter_queries.append(Q("terms", group_id=group_ids)) - - # Handle session_id filter - if session_id: - filter_queries.append(Q("term", session_id=session_id)) - - # Handle parent_id filter - if parent_id: - filter_queries.append(Q("term", parent_id=parent_id)) - - # Handle parent_type filter - if parent_type: - filter_queries.append(Q("term", parent_type=parent_type)) - - if date_range: - filter_queries.append(Q("range", timestamp=date_range)) - - # Build query - if query: - # Filter query terms by intelligent score - query_with_scores = [ - (word, self._calculate_text_score(word)) for word in query - ] - sorted_query_with_scores = sorted( - query_with_scores, key=lambda x: x[1], reverse=True - )[:10] - - # Build should clauses - search across multiple text fields - should_queries = [] - for word, word_score in sorted_query_with_scores: - should_queries.append( - Q("match", search_content={"query": word, "boost": word_score}) - ) - - bool_query_params = { - "should": should_queries, - "minimum_should_match": 1, - } - - if filter_queries: - bool_query_params["must"] = filter_queries - - search = search.query(Q("bool", **bool_query_params)) - else: - # Pure filtering query - if filter_queries: - search = search.query(Q("bool", filter=filter_queries)) - else: - search = search.query(Q("match_all")) - - search = search.sort({"timestamp": {"order": "desc"}}) - - search = search[from_ : from_ + size] - - logger.debug("episodic memory search query: %s", search.to_dict()) - - response = await search.execute() - - hits = [] - for hit in response.hits: - hit_data = { - "_id": hit.meta.id, - "_score": hit.meta.score, - "_source": hit.to_dict(), - } - hits.append(hit_data) - - logger.debug( - "Episodic memory search succeeded: query=%s, user_id=%s, found %d results", - query, - user_id, - len(hits), - ) - - return hits - - except Exception as e: - logger.error( - "Episodic memory search failed: query=%s, user_id=%s, error=%s", - query, - user_id, - e, - ) - raise - - async def append_episodic_memory( - self, - event_id: str, - user_id: str, - timestamp: datetime, - episode: str, - search_content: List[str], - title: Optional[str] = None, - summary: Optional[str] = None, - group_id: Optional[str] = None, - participants: Optional[List[str]] = None, - sender_ids: Optional[List[str]] = None, - event_type: Optional[str] = None, - subject: Optional[str] = None, - parent_type: Optional[str] = None, - parent_id: Optional[str] = None, - created_at: Optional[datetime] = None, - updated_at: Optional[datetime] = None, - ) -> EpisodicMemoryDoc: - """ - Append episodic memory document - - This is a convenience method that combines document creation and index refresh. - Suitable for scenarios where newly created documents need to be searchable immediately. - - Args: - Same as append_episodic_memory method - - Returns: - Saved EpisodicMemoryDoc instance - """ - # Create and save document - doc = await self.create_and_save_episodic_memory( - event_id=event_id, - user_id=user_id, - timestamp=timestamp, - episode=episode, - search_content=search_content, - title=title, - summary=summary, - group_id=group_id, - participants=participants, - sender_ids=sender_ids, - event_type=event_type, - subject=subject, - parent_type=parent_type, - parent_id=parent_id, - created_at=created_at, - updated_at=updated_at, - ) - return doc - - # ==================== Deletion functionality ==================== - - async def delete_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - date_range: Optional[Dict[str, Any]] = None, - refresh: bool = False, - ) -> int: - """ - Batch delete episodic memory documents by filter conditions - - Args: - user_id: User ID filter - group_id: Group ID filter - date_range: Time range filter - refresh: Whether to refresh index immediately - - Returns: - Number of deleted documents - """ - try: - filter_queries = [] - # Handle user_id filter: MAGIC_ALL means no filter - if user_id != MAGIC_ALL: - if not user_id: # None or "" -> match empty string - filter_queries.append({"term": {"user_id": ""}}) - else: - filter_queries.append({"term": {"user_id": user_id}}) - # Handle group_id filter: MAGIC_ALL means no filter - if group_id != MAGIC_ALL: - if not group_id: # None or "" -> match empty string - filter_queries.append({"term": {"group_id": ""}}) - else: - filter_queries.append({"term": {"group_id": group_id}}) - if date_range: - filter_queries.append({"range": {"timestamp": date_range}}) - - if not filter_queries: - raise ValueError( - "At least one filter condition (user_id, group_id or date_range) must be provided" - ) - - delete_query = {"bool": {"must": filter_queries}} - - client = await self.get_client() - index_name = self.get_index_name() - - response = await client.delete_by_query( - index=index_name, body={"query": delete_query}, refresh=refresh - ) - - deleted_count = response.get('deleted', 0) - logger.info( - "Batch deleted episodic memories: user_id=%s, group_id=%s, deleted %d records", - user_id, - group_id, - deleted_count, - ) - return deleted_count - - except Exception as e: - logger.error( - "Failed to batch delete episodic memories: user_id=%s, group_id=%s, error=%s", - user_id, - group_id, - e, - ) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/episodic_memory_milvus_repository.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/episodic_memory_milvus_repository.py deleted file mode 100644 index 853a58ba..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/episodic_memory_milvus_repository.py +++ /dev/null @@ -1,295 +0,0 @@ -""" -Episodic Memory Milvus Repository - -V1 simplified repository for vector semantic retrieval. -Only maps search-essential fields. Full data retrieved from MongoDB using parent_id. -""" - -import json -from datetime import datetime -from typing import List, Optional, Dict, Any -from core.oxm.milvus.base_repository import BaseMilvusRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.search.milvus.memory.episodic_memory_collection import ( - EpisodicMemoryCollection, -) -from core.observation.logger import get_logger -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("episodic_memory_milvus_repository", primary=False) -class EpisodicMemoryMilvusRepository(BaseMilvusRepository[EpisodicMemoryCollection]): - """ - Episodic Memory Milvus Repository - - V1 simplified repository for vector semantic retrieval. - Only stores search-essential fields in Milvus. - Full data is retrieved from MongoDB using parent_id. - """ - - def __init__(self): - """Initialize episodic memory repository""" - super().__init__(EpisodicMemoryCollection) - - # ==================== Document Creation and Management ==================== - - async def create_and_save_episodic_memory( - self, - id: str, - user_id: str, - timestamp: datetime, - episode: str, - search_content: List[str], - vector: List[float], - title: Optional[str] = None, - summary: Optional[str] = None, - group_id: Optional[str] = None, - participants: Optional[List[str]] = None, - sender_ids: Optional[List[str]] = None, - event_type: Optional[str] = None, - subject: Optional[str] = None, - parent_type: Optional[str] = None, - parent_id: Optional[str] = None, - ) -> Dict[str, Any]: - """ - Create and save episodic memory document - - Args: - id: Event unique identifier - user_id: User ID (required) - timestamp: Event occurrence time (required) - episode: Episode description (required) - search_content: List of search content (required) - vector: Text vector (required, dimension must be VECTORIZE_DIMENSIONS) - title: Event title - summary: Event summary - group_id: Group ID - participants: List of participants - sender_ids: List of sender IDs - event_type: Event type (e.g., conversation, email, etc.) - subject: Event subject - parent_type: Parent type - parent_id: Parent ID (used to associate split records) - - Returns: - Saved document information - """ - try: - # Prepare entity data - entity = { - "id": id, - "vector": vector, - "user_id": user_id - or "", # Milvus VARCHAR does not accept None, convert to empty string - "group_id": group_id or "", - "participants": participants or [], - "sender_ids": sender_ids or [], - "parent_type": parent_type or "", - "parent_id": parent_id or "", - "type": event_type or "", - "timestamp": int(timestamp.timestamp() * 1000), - "episode": episode, - "search_content": json.dumps(search_content, ensure_ascii=False), - } - - # Insert data - await self.insert(entity) - - logger.debug( - "Episodic memory document created successfully: id=%s, user_id=%s", - id, - user_id, - ) - - return { - "id": id, - "user_id": user_id, - "timestamp": timestamp, - "episode": episode, - "search_content": search_content, - } - - except Exception as e: - logger.error( - "Failed to create episodic memory document: id=%s, error=%s", id, e - ) - raise - - # ==================== Search Functionality ==================== - - async def vector_search( - self, - query_vector: List[float], - user_id: Optional[str] = None, - group_ids: Optional[List[str]] = None, - session_id: Optional[str] = None, - parent_type: Optional[str] = None, - parent_id: Optional[str] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - limit: int = 10, - score_threshold: float = 0.0, - radius: Optional[float] = None, - ) -> List[Dict[str, Any]]: - """ - Vector similarity search - - Args: - query_vector: Query vector - user_id: User ID filter - group_ids: List of Group IDs to filter - session_id: Session ID filter - parent_type: Parent type filter - parent_id: Parent memory ID filter - start_time: Start timestamp filter - end_time: End timestamp filter - limit: Number of results to return - score_threshold: Similarity score threshold - radius: COSINE similarity threshold - - Returns: - List of search results - """ - try: - # Build filter expression - filter_expr = [] - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id: - filter_expr.append(f'user_id == "{user_id}"') - else: - filter_expr.append('user_id == ""') - - # Handle group_ids filter - if group_ids is not None and len(group_ids) > 0: - group_ids_str = ', '.join(f'"{g}"' for g in group_ids) - filter_expr.append(f'group_id in [{group_ids_str}]') - - # Handle session_id filter - if session_id: - filter_expr.append(f'session_id == "{session_id}"') - - # Handle parent_type filter - if parent_type: - filter_expr.append(f'parent_type == "{parent_type}"') - - # Handle parent_id filter - if parent_id: - filter_expr.append(f'parent_id == "{parent_id}"') - - # Handle time filters - if start_time: - filter_expr.append(f"timestamp >= {int(start_time.timestamp() * 1000)}") - if end_time: - filter_expr.append(f"timestamp <= {int(end_time.timestamp() * 1000)}") - - filter_str = " and ".join(filter_expr) if filter_expr else None - - # Execute search - ef_value = max(128, limit * 2) - search_params = {"metric_type": "COSINE", "params": {"ef": ef_value}} - - if radius is not None and radius > -1.0: - search_params["params"]["radius"] = radius - - results = await self.collection.search( - data=[query_vector], - anns_field="vector", - param=search_params, - limit=limit, - expr=filter_str, - output_fields=self.all_output_fields, - ) - - # Process results - search_results = [] - for hits in results: - for hit in hits: - if hit.score >= score_threshold: - result = { - "id": hit.entity.get("id"), - "score": float(hit.score), - "user_id": hit.entity.get("user_id"), - "group_id": hit.entity.get("group_id"), - "session_id": hit.entity.get("session_id"), - "participants": hit.entity.get("participants"), - "timestamp": hit.entity.get("timestamp"), - "parent_type": hit.entity.get("parent_type"), - "parent_id": hit.entity.get("parent_id"), - "type": hit.entity.get("type"), - "episode": hit.entity.get("episode"), - } - search_results.append(result) - - logger.debug( - "Vector search succeeded: found %d results", len(search_results) - ) - return search_results - - except Exception as e: - logger.error("Vector search failed: %s", e) - raise - - # ==================== Deletion Functionality ==================== - - async def delete_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - ) -> int: - """ - Batch delete episodic memory documents by filter conditions - - Args: - user_id: User ID filter - group_id: Group ID filter - start_time: Start time - end_time: End time - - Returns: - Number of deleted documents - """ - try: - filter_expr = [] - # Handle user_id filter: MAGIC_ALL means no filter - if user_id != MAGIC_ALL: - if ( - not user_id - ): # None or "" -> match empty string (null mapped to "" by converter) - filter_expr.append('user_id == ""') - else: - filter_expr.append(f'user_id == "{user_id}"') - # Handle group_id filter: MAGIC_ALL means no filter - if group_id != MAGIC_ALL: - if not group_id: # None or "" -> match empty string - filter_expr.append('group_id == ""') - else: - filter_expr.append(f'group_id == "{group_id}"') - if start_time: - filter_expr.append(f"timestamp >= {int(start_time.timestamp() * 1000)}") - if end_time: - filter_expr.append(f"timestamp <= {int(end_time.timestamp() * 1000)}") - - if not filter_expr: - raise ValueError("At least one filter condition must be provided") - - expr = " and ".join(filter_expr) - - results = await self.collection.query(expr=expr, output_fields=["id"]) - delete_count = len(results) - - await self.collection.delete(expr) - - logger.debug( - "Batch deleted episodic memories: deleted %d records", delete_count - ) - return delete_count - - except Exception as e: - logger.error("Failed to batch delete episodic memories: %s", e) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/foresight_es_repository.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/foresight_es_repository.py deleted file mode 100644 index ed15a6b5..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/foresight_es_repository.py +++ /dev/null @@ -1,382 +0,0 @@ -""" -Foresight Elasticsearch Repository - -V1 simplified repository for BM25 text retrieval. -Only maps search-essential fields. Full data retrieved from MongoDB using parent_id. -""" - -import pprint -from datetime import datetime -from typing import List, Optional, Dict, Any -from elasticsearch.dsl import Q -from core.oxm.es.base_repository import BaseRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.search.elasticsearch.memory.foresight import ForesightDoc -from core.observation.logger import get_logger -from common_utils.text_utils import SmartTextParser -from common_utils.datetime_utils import get_now_with_timezone -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("foresight_es_repository", primary=True) -class ForesightEsRepository(BaseRepository[ForesightDoc]): - """ - Foresight Elasticsearch Repository - - V1 simplified repository for BM25 text retrieval. - Only stores search-essential fields in ES. - Full data is retrieved from MongoDB using parent_id. - """ - - def __init__(self): - """Initialize foresight repository""" - super().__init__(ForesightDoc) - self._text_parser = SmartTextParser() - - def _calculate_text_score(self, text: str) -> float: - """Calculate intelligent score of text""" - if not text: - return 0.0 - try: - tokens = self._text_parser.parse_tokens(text) - return self._text_parser.calculate_total_score(tokens) - except (ValueError, TypeError, AttributeError) as e: - logger.warning( - "Failed to calculate text score, using character length: %s", e - ) - return float(len(text)) - - def _log_explanation_details( - self, explanation: Dict[str, Any], indent: int = 0 - ) -> None: - """ - Recursively output detailed explanation information - - Args: - explanation: Explanation dictionary - indent: Indentation level - """ - pprint.pprint(explanation, indent=indent) - - def _parse_datetime(self, value: Any) -> Optional[datetime]: - """ - Parse a datetime value from various formats - - Args: - value: Value to parse (string, datetime, or None) - - Returns: - Parsed datetime or None - """ - if value is None: - return None - if isinstance(value, datetime): - return value - if isinstance(value, str): - try: - return datetime.fromisoformat(value) - except ValueError: - try: - return datetime.fromisoformat(value.replace("Z", "+00:00")) - except ValueError: - return None - return None - - # ==================== Document creation and management ==================== - - async def create_and_save_foresight( - self, - id: str, - user_id: str, - content: str, - search_content: List[str], - parent_id: str, - parent_type: str, - event_type: Optional[str] = None, - group_id: Optional[str] = None, - participants: Optional[List[str]] = None, - sender_ids: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - duration_days: Optional[int] = None, - evidence: Optional[str] = None, - created_at: Optional[datetime] = None, - updated_at: Optional[datetime] = None, - ) -> ForesightDoc: - """ - Create and save foresight document - - Args: - id: Unique identifier for memory - user_id: User ID (required) - content: Foresight content (required) - search_content: List of search content (supports multiple search terms, required) - parent_id: Parent memory ID - parent_type: Parent memory type (memcell/episode) - group_id: Group ID - participants: List of participants - start_time: Validity start time - end_time: Validity end time - duration_days: Duration in days - evidence: Evidence (original factual basis) - created_at: Creation time - updated_at: Update time - - Returns: - Saved ForesightDoc instance - """ - try: - # Set default timestamp - now = get_now_with_timezone() - if created_at is None: - created_at = now - if updated_at is None: - updated_at = now - - # Create document instance - doc = ForesightDoc( - id=id, - type=event_type, - user_id=user_id, - foresight=content, - search_content=search_content, - evidence=evidence or '', - group_id=group_id, - participants=participants or [], - sender_ids=sender_ids or [], - parent_type=parent_type, - parent_id=parent_id, - created_at=created_at, - updated_at=updated_at, - ) - - # Save document (without refresh parameter) - client = await self.get_client() - await doc.save(using=client) - - logger.debug( - "Created foresight document successfully: id=%s, user_id=%s", - id, - user_id, - ) - return doc - - except Exception as e: - logger.error("Failed to create foresight document: id=%s, error=%s", id, e) - raise - - # ==================== Search functionality ==================== - - async def multi_search( - self, - query: List[str], - user_id: Optional[str] = None, - group_ids: Optional[List[str]] = None, - sender_id: Optional[str] = None, - session_id: Optional[str] = None, - parent_type: Optional[str] = None, - parent_id: Optional[str] = None, - date_range: Optional[Dict[str, Any]] = None, - size: int = 10, - from_: int = 0, - ) -> List[Dict[str, Any]]: - """ - BM25 text search for foresight records - - Args: - query: List of search terms - user_id: User ID filter - group_ids: List of Group IDs to filter - sender_id: Sender ID filter - session_id: Session ID filter - parent_type: Parent type filter - parent_id: Parent memory ID filter - date_range: Time range filter - size: Number of results - from_: Pagination start position - - Returns: - List of search hits with matched document data - """ - try: - search = ForesightDoc.search() - - # Build filter conditions - filter_queries = [] - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id and user_id != "": - filter_queries.append(Q("term", user_id=user_id)) - else: - # user_id must not exist: match docs where field is missing or "" - filter_queries.append( - Q("bool", should=[ - Q("bool", must_not=[Q("exists", field="user_id")]), - Q("term", user_id=""), - ], minimum_should_match=1) - ) - - # Handle group_ids filter - if group_ids is not None and len(group_ids) > 0: - filter_queries.append(Q("terms", group_id=group_ids)) - - # Handle sender_id filter (match against sender_ids array) - if sender_id: - filter_queries.append(Q("term", sender_ids=sender_id)) - - # Handle session_id filter - if session_id: - filter_queries.append(Q("term", session_id=session_id)) - - # Handle parent_id filter - if parent_id: - filter_queries.append(Q("term", parent_id=parent_id)) - - # Handle parent_type filter - if parent_type: - filter_queries.append(Q("term", parent_type=parent_type)) - - if date_range: - filter_queries.append(Q("range", created_at=date_range)) - - # Build query - if query: - # Filter query terms by intelligent score - query_with_scores = [ - (word, self._calculate_text_score(word)) for word in query - ] - sorted_query_with_scores = sorted( - query_with_scores, key=lambda x: x[1], reverse=True - )[:10] - - # Build should clauses - no text fields in ForesightDoc for BM25 - # Only filtering is supported, rely on Milvus for vector search - should_queries = [] - for word, word_score in sorted_query_with_scores: - should_queries.append( - Q("match", search_content={"query": word, "boost": word_score}) - ) - - bool_query_params = { - "should": should_queries, - "minimum_should_match": 1, - } - - if filter_queries: - bool_query_params["must"] = filter_queries - - search = search.query(Q("bool", **bool_query_params)) - else: - # Pure filtering query - if filter_queries: - search = search.query(Q("bool", filter=filter_queries)) - else: - search = search.query(Q("match_all")) - - search = search.sort({"created_at": {"order": "desc"}}) - - search = search[from_ : from_ + size] - - logger.debug("foresight search query: %s", search.to_dict()) - - response = await search.execute() - - hits = [] - for hit in response.hits: - hit_data = { - "_id": hit.meta.id, - "_score": hit.meta.score, - "_source": hit.to_dict(), - } - hits.append(hit_data) - - logger.debug( - "Foresight search succeeded: query=%s, user_id=%s, found %d results", - query, - user_id, - len(hits), - ) - - return hits - - except Exception as e: - logger.error( - "Foresight search failed: query=%s, user_id=%s, error=%s", - query, - user_id, - e, - ) - raise - - # ==================== Deletion functionality ==================== - - async def delete_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - date_range: Optional[Dict[str, Any]] = None, - refresh: bool = False, - ) -> int: - """ - Batch delete foresight documents by filter conditions - - Args: - user_id: User ID filter - group_id: Group ID filter - date_range: Time range filter - refresh: Whether to refresh index immediately - - Returns: - Number of deleted documents - """ - try: - filter_queries = [] - # Handle user_id filter: MAGIC_ALL means no filter - if user_id != MAGIC_ALL: - if not user_id: # None or "" -> match empty string - filter_queries.append({"term": {"user_id": ""}}) - else: - filter_queries.append({"term": {"user_id": user_id}}) - # Handle group_id filter: MAGIC_ALL means no filter - if group_id != MAGIC_ALL: - if not group_id: # None or "" -> match empty string - filter_queries.append({"term": {"group_id": ""}}) - else: - filter_queries.append({"term": {"group_id": group_id}}) - if date_range: - filter_queries.append({"range": {"created_at": date_range}}) - - if not filter_queries: - raise ValueError( - "At least one filter condition (user_id, group_id or date_range) must be provided" - ) - - delete_query = {"bool": {"must": filter_queries}} - - client = await self.get_client() - index_name = self.get_index_name() - - response = await client.delete_by_query( - index=index_name, body={"query": delete_query}, refresh=refresh - ) - - deleted_count = response.get('deleted', 0) - logger.info( - "Batch deleted foresights: user_id=%s, group_id=%s, deleted %d records", - user_id, - group_id, - deleted_count, - ) - return deleted_count - - except Exception as e: - logger.error( - "Failed to batch delete foresights: user_id=%s, group_id=%s, error=%s", - user_id, - group_id, - e, - ) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/foresight_milvus_repository.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/foresight_milvus_repository.py deleted file mode 100644 index 099adb9c..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/foresight_milvus_repository.py +++ /dev/null @@ -1,304 +0,0 @@ -""" -Foresight Milvus Repository - -V1 simplified repository for vector semantic retrieval. -Only maps search-essential fields. Full data retrieved from MongoDB using parent_id. -""" - -import json -from datetime import datetime -from typing import List, Optional, Dict, Any -from core.oxm.milvus.base_repository import BaseMilvusRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.search.milvus.memory.foresight_collection import ( - ForesightCollection, -) -from core.observation.logger import get_logger -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("foresight_milvus_repository", primary=False) -class ForesightMilvusRepository(BaseMilvusRepository[ForesightCollection]): - """ - Foresight Milvus Repository - - V1 simplified repository for vector semantic retrieval. - Only stores search-essential fields in Milvus. - Full data is retrieved from MongoDB using parent_id. - """ - - def __init__(self): - """Initialize foresight repository""" - super().__init__(ForesightCollection) - - # ==================== Document Creation and Management ==================== - # TODO: add username - async def create_and_save_foresight_mem( - self, - id: str, - user_id: Optional[str], - content: str, - parent_id: str, - parent_type: str, - vector: List[float], - group_id: Optional[str] = None, - event_type: Optional[str] = None, - participants: Optional[List[str]] = None, - sender_ids: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - duration_days: Optional[int] = None, - evidence: Optional[str] = None, - search_content: Optional[List[str]] = None, - ) -> Dict[str, Any]: - """ - Create and save personal foresight document - - Args: - id: Unique identifier for foresight - user_id: User ID (required) - content: Foresight content (required) - parent_id: Parent memory ID (required) - parent_type: Parent memory type (memcell/episode) - vector: Text vector (required) - group_id: Group ID - participants: List of related participants - start_time: Foresight start time - end_time: Foresight end time - duration_days: Duration in days - evidence: Evidence supporting this foresight - search_content: List of searchable content - Returns: - Information of the saved document - """ - try: - # Build search content - if search_content is None: - search_content = [content] - if evidence: - search_content.append(evidence) - - # Prepare entity data - entity = { - "id": id, - "vector": vector, - "user_id": user_id or "", - "group_id": group_id or "", - "participants": participants or [], - "sender_ids": sender_ids or [], - "parent_type": parent_type, - "parent_id": parent_id, - "start_time": int(start_time.timestamp() * 1000) if start_time else 0, - "end_time": int(end_time.timestamp() * 1000) if end_time else 0, - "duration_days": duration_days or 0, - "content": content, - "evidence": evidence or "", - "type": event_type, - "search_content": json.dumps(search_content, ensure_ascii=False), - } - - # Insert data - await self.insert(entity) - - logger.debug( - "Created personal foresight document successfully: memory_id=%s, user_id=%s", - id, - user_id, - ) - - return { - "id": id, - "user_id": user_id, - "content": content, - "parent_type": parent_type, - "parent_id": parent_id, - "search_content": search_content, - } - - except Exception as e: - logger.error( - "Failed to create personal foresight document: id=%s, error=%s", id, e - ) - raise - - # ==================== Search Functionality ==================== - - async def vector_search( - self, - query_vector: List[float], - user_id: Optional[str] = None, - group_ids: Optional[List[str]] = None, - sender_id: Optional[str] = None, - session_id: Optional[str] = None, - parent_type: Optional[str] = None, - parent_id: Optional[str] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - limit: int = 10, - score_threshold: float = 0.0, - radius: Optional[float] = None, - ) -> List[Dict[str, Any]]: - """ - Vector similarity search - - Args: - query_vector: Query vector - user_id: User ID filter - group_ids: List of Group IDs to filter - sender_id: Sender ID filter - session_id: Session ID filter - parent_type: Parent type filter - parent_id: Parent memory ID filter - start_time: Start timestamp filter - end_time: End timestamp filter - limit: Number of results to return - score_threshold: Similarity score threshold - radius: COSINE similarity threshold - - Returns: - List of search results - """ - try: - # Build filter expression - filter_expr = [] - - # Handle user_id filter - if user_id != MAGIC_ALL: - if user_id: - filter_expr.append(f'user_id == "{user_id}"') - else: - filter_expr.append('user_id == ""') - - # Handle group_ids filter - if group_ids is not None and len(group_ids) > 0: - group_ids_str = ', '.join(f'"{g}"' for g in group_ids) - filter_expr.append(f'group_id in [{group_ids_str}]') - - # Handle sender_id filter (match against sender_ids array) - if sender_id: - filter_expr.append(f'array_contains(sender_ids, "{sender_id}")') - - # Handle session_id filter - if session_id: - filter_expr.append(f'session_id == "{session_id}"') - - # Handle parent_type filter - if parent_type: - filter_expr.append(f'parent_type == "{parent_type}"') - - # Handle parent_id filter - if parent_id: - filter_expr.append(f'parent_id == "{parent_id}"') - - # Handle time filters - if start_time: - filter_expr.append(f"timestamp >= {int(start_time.timestamp() * 1000)}") - if end_time: - filter_expr.append(f"timestamp <= {int(end_time.timestamp() * 1000)}") - - filter_str = " and ".join(filter_expr) if filter_expr else None - - # Execute search - ef_value = max(128, limit * 2) - search_params = {"metric_type": "COSINE", "params": {"ef": ef_value}} - - if radius is not None and radius > -1.0: - search_params["params"]["radius"] = radius - - results = await self.collection.search( - data=[query_vector], - anns_field="vector", - param=search_params, - limit=limit, - expr=filter_str, - output_fields=self.all_output_fields, - ) - - # Process results - search_results = [] - for hits in results: - for hit in hits: - if hit.score >= score_threshold: - result = { - "id": hit.entity.get("id"), - "score": float(hit.score), - "user_id": hit.entity.get("user_id"), - "group_id": hit.entity.get("group_id"), - "sender_ids": hit.entity.get("sender_ids"), - "session_id": hit.entity.get("session_id"), - "participants": hit.entity.get("participants"), - "timestamp": hit.entity.get("timestamp"), - "parent_type": hit.entity.get("parent_type"), - "parent_id": hit.entity.get("parent_id"), - } - search_results.append(result) - - logger.debug( - "Vector search succeeded: found %d results", len(search_results) - ) - return search_results - - except Exception as e: - logger.error("Vector search failed: %s", e) - raise - - # ==================== Deletion Functionality ==================== - - async def delete_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - ) -> int: - """ - Batch delete foresight documents by filter conditions - - Args: - user_id: User ID filter - group_id: Group ID filter - start_time: Start time - end_time: End time - - Returns: - Number of deleted documents - """ - try: - filter_expr = [] - # Handle user_id filter: MAGIC_ALL means no filter - if user_id != MAGIC_ALL: - if ( - not user_id - ): # None or "" -> match empty string (null mapped to "" by converter) - filter_expr.append('user_id == ""') - else: - filter_expr.append(f'user_id == "{user_id}"') - # Handle group_id filter: MAGIC_ALL means no filter - if group_id != MAGIC_ALL: - if not group_id: # None or "" -> match empty string - filter_expr.append('group_id == ""') - else: - filter_expr.append(f'group_id == "{group_id}"') - if start_time: - filter_expr.append(f"timestamp >= {int(start_time.timestamp() * 1000)}") - if end_time: - filter_expr.append(f"timestamp <= {int(end_time.timestamp() * 1000)}") - - if not filter_expr: - raise ValueError("At least one filter condition must be provided") - - expr = " and ".join(filter_expr) - - results = await self.collection.query(expr=expr, output_fields=["id"]) - delete_count = len(results) - - await self.collection.delete(expr) - - logger.debug("Batch deleted foresights: deleted %d records", delete_count) - return delete_count - - except Exception as e: - logger.error("Failed to batch delete foresights: %s", e) - raise diff --git a/methods/EverCore/src/infra_layer/adapters/out/search/repository/user_profile_milvus_repository.py b/methods/EverCore/src/infra_layer/adapters/out/search/repository/user_profile_milvus_repository.py deleted file mode 100644 index addb40ee..00000000 --- a/methods/EverCore/src/infra_layer/adapters/out/search/repository/user_profile_milvus_repository.py +++ /dev/null @@ -1,158 +0,0 @@ -""" -User Profile Milvus Repository - -V1 simplified repository for vector semantic retrieval. -Only maps search-essential fields. Full data retrieved from MongoDB using id. -""" - -from datetime import datetime -from typing import List, Optional, Dict, Any -from core.oxm.milvus.base_repository import BaseMilvusRepository -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.search.milvus.memory.user_profile_collection import ( - UserProfileCollection, -) -from core.observation.logger import get_logger -from core.di.decorators import repository - -logger = get_logger(__name__) - - -@repository("user_profile_milvus_repository", primary=False) -class UserProfileMilvusRepository(BaseMilvusRepository[UserProfileCollection]): - """ - User Profile Milvus Repository - - V1 simplified repository for vector semantic retrieval. - Only stores search-essential fields in Milvus. - Full data is retrieved from MongoDB using id. - """ - - def __init__(self): - """Initialize user profile repository""" - super().__init__(UserProfileCollection) - - # ==================== Search Functionality ==================== - - async def vector_search( - self, - query_vector: List[float], - user_id: Optional[str] = None, - group_id: Optional[str] = None, - scenario: Optional[str] = None, - limit: int = 10, - score_threshold: float = 0.0, - radius: Optional[float] = None, - ) -> List[Dict[str, Any]]: - """ - Vector similarity search for user profiles - - Args: - query_vector: Query vector - user_id: User ID filter - group_id: Group ID filter - scenario: Scenario type filter (solo/team) - limit: Number of results to return - score_threshold: Similarity score threshold - radius: COSINE similarity threshold - - Returns: - List of search results - """ - try: - # Build filter expression - filter_expr = [] - - # Handle user_id filter - if user_id and user_id != MAGIC_ALL: - filter_expr.append(f'user_id == "{user_id}"') - - # Handle group_id filter - if group_id: - filter_expr.append(f'group_id == "{group_id}"') - - # Handle scenario filter - if scenario: - filter_expr.append(f'scenario == "{scenario}"') - - filter_str = " and ".join(filter_expr) if filter_expr else None - - # Execute search - ef_value = max(128, limit * 2) - search_params = {"metric_type": "COSINE", "params": {"ef": ef_value}} - - if radius is not None and radius > -1.0: - search_params["params"]["radius"] = radius - - results = await self.collection.search( - data=[query_vector], - anns_field="vector", - param=search_params, - limit=limit, - expr=filter_str, - output_fields=self.all_output_fields, - ) - - # Process results - search_results = [] - for hits in results: - for hit in hits: - if hit.score >= score_threshold: - result = { - "id": hit.entity.get("id"), - "score": float(hit.score), - "user_id": hit.entity.get("user_id"), - "group_id": hit.entity.get("group_id"), - "scenario": hit.entity.get("scenario"), - "memcell_count": hit.entity.get("memcell_count"), - "item_type": hit.entity.get("item_type", ""), - "embed_text": hit.entity.get("embed_text", ""), - } - search_results.append(result) - - logger.debug( - "Vector search succeeded: found %d results", len(search_results) - ) - return search_results - - except Exception as e: - logger.error("Vector search failed: %s", e) - raise - - # ==================== Deletion Functionality ==================== - - async def delete_by_user_group(self, user_id: str, group_id: str) -> int: - """ - Delete all profiles for a user in a group - - Args: - user_id: User ID - group_id: Group ID - - Returns: - Number of deleted entities - """ - try: - filter_expr = f'user_id == "{user_id}" and group_id == "{group_id}"' - - result = await self.collection.delete(filter_expr) - - count = result.delete_count if hasattr(result, 'delete_count') else 0 - - logger.info( - "Deleted profile items: user_id=%s, group_id=%s, count=%d", - user_id, - group_id, - count, - ) - - return count - - except Exception as e: - logger.error( - "Failed to delete profile items: user_id=%s, group_id=%s, error=%s", - user_id, - group_id, - e, - ) - return 0 diff --git a/methods/EverCore/src/manage.py b/methods/EverCore/src/manage.py deleted file mode 100644 index a0345cad..00000000 --- a/methods/EverCore/src/manage.py +++ /dev/null @@ -1,261 +0,0 @@ -#!/usr/bin/env python3 -""" -Memsys Backend Management Script -Provides command-line tools to manage the backend application -""" - -import asyncio -from IPython.terminal.embed import embed -from functools import wraps -from typing import Callable -import nest_asyncio - -nest_asyncio.apply() - -import typer -from typer import Typer - - -# Create Typer application -cli = Typer(help="Memsys Backend Management Tool") - -# Global variable to store application state -_app_state = None -_initialized = False - - -def setup_environment_and_app(env_file: str = ".env"): - """ - Set up environment and application - - Args: - env_file: Environment variable file name - """ - global _initialized - if _initialized: - return - - # Add src directory to Python path - from import_parent_dir import add_parent_path - - add_parent_path(0) - - # Load environment variables - from common_utils.load_env import setup_environment - - setup_environment(load_env_file_name=env_file, check_env_var="MONGODB_HOST") - - from application_startup import setup_all - - setup_all() - _initialized = True - - -def with_app_context(func: Callable) -> Callable: - """ - Decorator: Provide FastAPI application context for commands - - Args: - func: The decorated asynchronous function - - Returns: - The decorated function - """ - - @wraps(func) - async def wrapper(*args, **kwargs): - global _app_state - - from app import app - - # Create application context - async with app.router.lifespan_context(app): - # Set application state - _app_state = app.state - try: - # Execute the decorated function - result = await func(*args, **kwargs) - return result - finally: - # Clean up application state - _app_state = None - - return wrapper - - -def with_full_context_decorator(func: Callable) -> Callable: - """ - Decorator: Use ContextManager.run_with_full_context to provide full context - - Args: - func: The decorated asynchronous function - - Returns: - The decorated function - """ - - @wraps(func) - async def wrapper(*args, **kwargs): - global _app_state - - from app import app - from core.di.utils import get_bean_by_type - from core.context.context_manager import ContextManager - - # Create application context - async with app.router.lifespan_context(app): - # Set application state - _app_state = app.state - try: - # Get ContextManager instance - context_manager = get_bean_by_type(ContextManager) - - # Execute function using run_with_full_context - result = await context_manager.run_with_full_context( - func, *args, auto_commit=True, auto_inherit_user=True, **kwargs - ) - return result - finally: - # Clean up application state - _app_state = None - - return wrapper - - -def is_cli_command(func: Callable) -> Callable: - """ - Decorator: Mark CLI command functions - - Args: - func: The decorated function - - Returns: - The decorated function - """ - func._is_cli_command = True - return func - - -@cli.command() -def shell( - debug: bool = typer.Option(False, "--debug", help="Enable debug mode"), - env_file: str = typer.Option( - ".env", "--env-file", help="Specify the environment variable file to load" - ), -): - """ - Start an interactive shell with access to application context - """ - setup_environment_and_app(env_file) - - from core.observation.logger import get_logger - - logger = get_logger(__name__) - - if debug: - logger.info("Debug mode enabled") - - logger.info("Using environment file: %s", env_file) - - banner = """ - ======================================== - Memsys Backend Shell - - Available variables: - - app: FastAPI application instance - - app_state: Application state (if available) - - graphs: LangGraph instances (if available) - - logger: Logger instance - - Example usage: - >>> logger.info("Hello from shell!") - >>> app.routes # View all routes - >>> graphs # View available graph instances - ======================================== - """ - - def shell_runner(): - embed(header=banner) - - func = with_app_context(with_full_context_decorator(shell_runner)) - asyncio.run(func()) - - -@cli.command() -def list_commands( - show_all: bool = typer.Option(False, "--all", help="Show all commands"), - env_file: str = typer.Option( - ".env", "--env-file", help="Specify the environment variable file to load" - ), -): - """ - List all available CLI commands - """ - - if show_all: - # Show all commands including hidden ones - commands = cli.registered_commands - else: - # Show only visible commands - commands = [cmd for cmd in cli.registered_commands if not cmd.hidden] - - typer.echo("Available commands:") - for cmd in commands: - help_text = cmd.help if cmd.help else "No description" - typer.echo(f" {cmd.name:<20} {help_text}"), - - typer.echo(f"\nUsing environment file: {env_file}") - - -@cli.command() -def tenant_init( - env_file: str = typer.Option( - ".env", "--env-file", help="Specify the environment variable file to load" - ) -): - """ - Initialize MongoDB and Milvus databases for a specific tenant - - Tenant context is specified via environment variable TENANT_INIT_STORAGE_INFO (JSON). - - Examples: - # Set tenant context - export TENANT_INIT_STORAGE_INFO='{"tenant_id":"s0001","isolation_mode":"shared","storage_info":{"mongodb":{"database":"s0001_memsys"},"elasticsearch":{"index_prefix":"s0001"},"milvus":{"collection_prefix":"s0001","num_partitions":256}}}' #skip-sensitive-check - - # Run initialization - python src/manage.py tenant-init - - # Use custom environment file - python src/manage.py tenant-init --env-file .env.production - """ - - # First set up environment and application - setup_environment_and_app(env_file) - - from core.observation.logger import get_logger - - logger = get_logger(__name__) - - # Import tenant initialization module - from core.tenants.init_tenant_all import run_tenant_init - - try: - # Execute tenant initialization (read tenant ID from environment variable) - success = asyncio.run(run_tenant_init()) - - # Set exit code based on result - if success: - logger.info("✅ All database initializations succeeded") - raise typer.Exit(0) - else: - logger.error("❌ Partial or complete database initialization failed") - raise typer.Exit(1) - except ValueError as e: - # Catch error when tenant ID is not set - logger.error("❌ Error: %s", str(e)) - raise typer.Exit(1) - - -if __name__ == '__main__': - # Run CLI - cli() diff --git a/methods/EverCore/src/memory_layer/__init__.py b/methods/EverCore/src/memory_layer/__init__.py deleted file mode 100644 index 651e507a..00000000 --- a/methods/EverCore/src/memory_layer/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Memory Layer package diff --git a/methods/EverCore/src/memory_layer/cluster_manager/__init__.py b/methods/EverCore/src/memory_layer/cluster_manager/__init__.py deleted file mode 100644 index e1130b83..00000000 --- a/methods/EverCore/src/memory_layer/cluster_manager/__init__.py +++ /dev/null @@ -1,33 +0,0 @@ -"""Cluster Manager - Pure computation component for memcell clustering. - -This module provides ClusterManager, a pure computation component that clusters -memcells based on semantic similarity and temporal proximity. - -IMPORTANT: This is a pure computation component. The caller is responsible -for loading/saving mem scene state. - -Usage: - from memory_layer.cluster_manager import ClusterManager, ClusterManagerConfig, MemSceneState - - # Initialize - config = ClusterManagerConfig( - similarity_threshold=0.65, - max_time_gap_days=7, - ) - cluster_mgr = ClusterManager(config) - - # Caller loads state (from InMemory / MongoDB / file) - state_dict = await storage.load_mem_scene(group_id) - state = MemSceneState.from_dict(state_dict) if state_dict else MemSceneState() - - # Pure computation - cluster_id, state = await cluster_mgr.cluster_memcell(memcell, state) - - # Caller saves state - await storage.save_mem_scene(group_id, state.to_dict()) -""" - -from memory_layer.cluster_manager.config import ClusterManagerConfig -from memory_layer.cluster_manager.manager import ClusterManager, MemSceneState - -__all__ = ["ClusterManager", "ClusterManagerConfig", "MemSceneState"] diff --git a/methods/EverCore/src/memory_layer/cluster_manager/config.py b/methods/EverCore/src/memory_layer/cluster_manager/config.py deleted file mode 100644 index 412c7fce..00000000 --- a/methods/EverCore/src/memory_layer/cluster_manager/config.py +++ /dev/null @@ -1,57 +0,0 @@ -"""Configuration for ClusterManager.""" - -from dataclasses import dataclass - - -@dataclass -class ClusterManagerConfig: - """Configuration for ClusterManager. - - Attributes: - similarity_threshold: Minimum cosine similarity to join existing cluster (0.0-1.0) - max_time_gap_days: Maximum time gap in days to link to existing cluster - enable_persistence: Whether to persist mem scene state to disk - persist_dir: Directory for mem scene state persistence (required if enable_persistence=True) - clustering_algorithm: Algorithm to use ('centroid' or 'nearest') - llm_top_k_clusters: Number of candidate clusters pre-filtered by embedding for LLM - llm_max_context_per_cluster: Max recent items per cluster in LLM context - llm_skip_threshold: Skip LLM if top-1 embedding similarity exceeds this - """ - - similarity_threshold: float = 0.65 - max_time_gap_days: float = 7.0 - enable_persistence: bool = False - persist_dir: str = None - clustering_algorithm: str = "centroid" # 'centroid' or 'nearest' - # LLM clustering: number of candidate clusters pre-filtered by embedding similarity - llm_top_k_clusters: int = 30 - # LLM clustering: max recent items per cluster to include in LLM context - llm_max_context_per_cluster: int = 5 - # LLM clustering: if top-1 embedding similarity exceeds this threshold, - # skip LLM and assign directly (set to 1.0 to always use LLM) - llm_skip_threshold: float = 0.85 - - def __post_init__(self): - """Validate configuration.""" - if not 0.0 <= self.similarity_threshold <= 1.0: - raise ValueError( - f"similarity_threshold must be in [0.0, 1.0], got {self.similarity_threshold}" - ) - - if self.max_time_gap_days < 0: - raise ValueError( - f"max_time_gap_days must be >= 0, got {self.max_time_gap_days}" - ) - - if self.enable_persistence and not self.persist_dir: - raise ValueError("persist_dir is required when enable_persistence=True") - - if self.clustering_algorithm not in ("centroid", "nearest"): - raise ValueError( - f"clustering_algorithm must be 'centroid' or 'nearest', got {self.clustering_algorithm}" - ) - - @property - def max_time_gap_seconds(self) -> float: - """Get max time gap in seconds.""" - return self.max_time_gap_days * 24 * 60 * 60 diff --git a/methods/EverCore/src/memory_layer/cluster_manager/manager.py b/methods/EverCore/src/memory_layer/cluster_manager/manager.py deleted file mode 100644 index 0f807cb9..00000000 --- a/methods/EverCore/src/memory_layer/cluster_manager/manager.py +++ /dev/null @@ -1,793 +0,0 @@ -"""ClusterManager - Core component for automatic memcell clustering. - -This module provides pure computation logic for clustering memcells. -Storage is managed by the caller, not by ClusterManager itself. - -Design: -- ClusterManager is a pure computation component -- Input: memcell + current state -- Output: cluster_id + updated state -- Caller is responsible for loading/saving state -""" - -import asyncio -import json -import numpy as np -from typing import Any, Callable, Dict, List, Optional, Tuple -from pathlib import Path - -from memory_layer.cluster_manager.config import ClusterManagerConfig -from core.observation.logger import get_logger - -logger = get_logger(__name__) - -# Try to import vectorize service -try: - from agentic_layer.vectorize_service import get_vectorize_service - - VECTORIZE_SERVICE_AVAILABLE = True -except ImportError: - VECTORIZE_SERVICE_AVAILABLE = False - logger.warning("Vectorize service not available, clustering will be limited") - - - -class MemSceneState: - """Internal state for a single group's clustering.""" - - def __init__(self): - """Initialize empty mem scene state.""" - self.event_ids: List[str] = [] - self.timestamps: List[float] = [] - self.vectors: List[np.ndarray] = [] - self.cluster_ids: List[str] = [] - self.eventid_to_cluster: Dict[str, str] = {} - self.next_cluster_idx: int = 0 - - # Centroid-based clustering state - self.cluster_centroids: Dict[str, np.ndarray] = {} - self.cluster_counts: Dict[str, int] = {} - self.cluster_last_ts: Dict[str, Optional[float]] = {} - - # Clusters that contain agent conversation memcells - self.case_cluster_ids: set = set() - - def assign_new_cluster(self, event_id: str) -> str: - """Assign a new cluster ID to an event.""" - cluster_id = f"cluster_{self.next_cluster_idx:03d}" - self.next_cluster_idx += 1 - self.eventid_to_cluster[event_id] = cluster_id - self.cluster_ids.append(cluster_id) - return cluster_id - - def add_to_cluster( - self, - event_id: str, - cluster_id: str, - vector: np.ndarray, - timestamp: Optional[float], - ) -> None: - """Add an event to an existing cluster.""" - self.eventid_to_cluster[event_id] = cluster_id - self.cluster_ids.append(cluster_id) - self._update_cluster_centroid(cluster_id, vector, timestamp) - - def _update_cluster_centroid( - self, cluster_id: str, vector: np.ndarray, timestamp: Optional[float] - ) -> None: - """Update cluster centroid with new vector.""" - if vector is None or vector.size == 0: - if timestamp is not None: - prev_ts = self.cluster_last_ts.get(cluster_id) - self.cluster_last_ts[cluster_id] = max(prev_ts or timestamp, timestamp) - return - - count = self.cluster_counts.get(cluster_id, 0) - if count <= 0: - self.cluster_centroids[cluster_id] = vector.astype(np.float32, copy=False) - self.cluster_counts[cluster_id] = 1 - else: - current_centroid = self.cluster_centroids[cluster_id] - if current_centroid.dtype != np.float32: - current_centroid = current_centroid.astype(np.float32) - new_centroid = (current_centroid * float(count) + vector) / float(count + 1) - self.cluster_centroids[cluster_id] = new_centroid.astype( - np.float32, copy=False - ) - self.cluster_counts[cluster_id] = count + 1 - - if timestamp is not None: - prev_ts = self.cluster_last_ts.get(cluster_id) - self.cluster_last_ts[cluster_id] = max(prev_ts or timestamp, timestamp) - - def to_dict(self) -> Dict[str, Any]: - """Convert state to dictionary for serialization. - - Produces the new format with memcell_info and memscene_info maps. - """ - memcell_info = {} - for i, event_id in enumerate(self.event_ids): - memcell_info[event_id] = { - "memscene": self.eventid_to_cluster.get(event_id, ""), - "timestamp": self.timestamps[i] if i < len(self.timestamps) else 0.0, - } - - all_cids = ( - set(self.cluster_centroids.keys()) - | set(self.cluster_counts.keys()) - | set(self.cluster_last_ts.keys()) - ) - memscene_info = {} - for cid in all_cids: - memscene_info[cid] = { - "center": ( - self.cluster_centroids[cid].tolist() - if cid in self.cluster_centroids - else [] - ), - "timestamp": self.cluster_last_ts.get(cid), - "count": self.cluster_counts.get(cid, 0), - } - - result = { - "memcell_info": memcell_info, - "memscene_info": memscene_info, - "next_cluster_idx": self.next_cluster_idx, - } - if self.case_cluster_ids: - result["case_cluster_ids"] = sorted(self.case_cluster_ids) - return result - - @staticmethod - def from_dict(data: Dict[str, Any]) -> "MemSceneState": - """Create MemSceneState from dictionary. - - Supports both new format (memcell_info/memscene_info) and old format - (event_ids/timestamps/cluster_ids/...) for backward compatibility. - """ - state = MemSceneState() - state.next_cluster_idx = int(data.get("next_cluster_idx", 0)) - state.case_cluster_ids = set(data.get("case_cluster_ids") or []) - - if "memcell_info" in data: - # New format - for event_id, info in data["memcell_info"].items(): - state.event_ids.append(event_id) - state.timestamps.append(float(info.get("timestamp", 0.0))) - cluster_id = info.get("memscene", "") - state.cluster_ids.append(cluster_id) - state.eventid_to_cluster[event_id] = cluster_id - - for cid, info in data.get("memscene_info", {}).items(): - center = info.get("center", []) - if center: - state.cluster_centroids[cid] = np.array(center, dtype=np.float32) - ts = info.get("timestamp") - state.cluster_last_ts[cid] = float(ts) if ts is not None else None - state.cluster_counts[cid] = int(info.get("count", 0)) - else: - # Old format (backward compatibility) - state.event_ids = list(data.get("event_ids", [])) - state.timestamps = list(data.get("timestamps", [])) - state.cluster_ids = list(data.get("cluster_ids", [])) - state.eventid_to_cluster = dict(data.get("eventid_to_cluster", {})) - - centroids = data.get("cluster_centroids", {}) or {} - state.cluster_centroids = { - k: np.array(v, dtype=np.float32) for k, v in centroids.items() - } - state.cluster_counts = { - k: int(v) for k, v in (data.get("cluster_counts", {}) or {}).items() - } - state.cluster_last_ts = { - k: float(v) for k, v in (data.get("cluster_last_ts", {}) or {}).items() - } - - return state - - -class ClusterManager: - """Automatic clustering manager - pure computation component. - - ClusterManager handles incremental clustering of memcells based on semantic - similarity (embeddings) and temporal proximity. - - IMPORTANT: This is a pure computation component. The caller is responsible - for loading/saving mem scene state. - - Usage: - ```python - cluster_mgr = ClusterManager(config) - - # Caller loads state (from InMemory / MongoDB / file) - state_dict = await storage.load(group_id) - state = MemSceneState.from_dict(state_dict) if state_dict else MemSceneState() - - # Pure computation - cluster_id, updated_state = await cluster_mgr.cluster_memcell(memcell, state) - - # Caller saves state - await storage.save(group_id, updated_state.to_dict()) - ``` - """ - - def __init__( - self, - config: Optional[ClusterManagerConfig] = None, - llm_provider: Optional[Any] = None, - context_fetcher: Optional[Callable] = None, - ): - """Initialize ClusterManager. - - Args: - config: Clustering configuration (uses defaults if None) - llm_provider: LLM provider instance (required for agent memcell clustering) - context_fetcher: Async callback to fetch context texts from DB. - Signature: (event_ids: List[str]) -> Dict[str, str] - Returns mapping of event_id -> task_intent text. - Required for agent memcell clustering. - """ - self.config = config or ClusterManagerConfig() - self._callbacks: List[Callable] = [] - - # Vectorize service (for embedding) - self._vectorize_service = None - if VECTORIZE_SERVICE_AVAILABLE: - try: - self._vectorize_service = get_vectorize_service() - except Exception as e: - logger.warning(f"Failed to initialize vectorize service: {e}") - - # LLM provider (for llm algorithm) - self._llm_provider = llm_provider - self._context_fetcher = context_fetcher - - # Statistics - self._stats = { - "total_memcells": 0, - "clustered_memcells": 0, - "new_clusters": 0, - "failed_embeddings": 0, - } - - def on_cluster_assigned( - self, callback: Callable[[str, Dict[str, Any], str], None] - ) -> None: - """Register a callback for cluster assignment events. - - Callback signature: - callback(group_id: str, memcell: Dict[str, Any], cluster_id: str) -> None - """ - self._callbacks.append(callback) - - async def cluster_memcell( - self, - memcell: Dict[str, Any], - state: MemSceneState, - has_case: bool = False, - ) -> Tuple[Optional[str], MemSceneState]: - """Cluster a memcell and return updated state. - - Caller is responsible for loading state before and saving it after. - - Routing: - - has_case=False: embedding clustering over non-case clusters, text=episode - - has_case=True: embedding recall + LLM over case clusters, text=task_intent - - Args: - memcell: Memcell dictionary with event_id, timestamp, episode/summary - state: Current mem scene state for the group - has_case: Whether this memcell has an agent case - - Returns: - Tuple of (cluster_id, updated_state): - - cluster_id: Assigned cluster ID, or None if failed - - state: Updated MemSceneState (same object, mutated) - """ - if has_case: - return await self._cluster_memcell_llm(memcell, state) - return await self._cluster_memcell_embedding(memcell, state) - - async def _cluster_memcell_embedding( - self, memcell: Dict[str, Any], state: MemSceneState - ) -> Tuple[Optional[str], MemSceneState]: - """Embedding-based clustering using vector cosine similarity.""" - self._stats["total_memcells"] += 1 - - # Extract key fields - event_id = str(memcell.get("event_id", "")) - if not event_id: - logger.warning("Memcell missing event_id, skipping clustering") - return None, state - - timestamp = self._parse_timestamp(memcell.get("timestamp")) - text = self._extract_text(memcell) - - # Get embedding - vector = await self._get_embedding(text) - if vector is None or vector.size == 0: - logger.warning( - f"Failed to get embedding for event {event_id}, creating singleton cluster" - ) - cluster_id = state.assign_new_cluster(event_id) - state.event_ids.append(event_id) - state.timestamps.append(timestamp or 0.0) - state.vectors.append(np.zeros((1,), dtype=np.float32)) - self._stats["new_clusters"] += 1 - self._stats["failed_embeddings"] += 1 - return cluster_id, state - - # Find best matching cluster (exclude case clusters) - cluster_id = self._find_best_cluster( - state, vector, timestamp, exclude_cids=state.case_cluster_ids - ) - - # Add to cluster - if cluster_id is None: - cluster_id = state.assign_new_cluster(event_id) - state._update_cluster_centroid(cluster_id, vector, timestamp) - self._stats["new_clusters"] += 1 - else: - state.add_to_cluster(event_id, cluster_id, vector, timestamp) - - # Update state - state.event_ids.append(event_id) - state.timestamps.append(timestamp or 0.0) - state.vectors.append(vector) - - self._stats["clustered_memcells"] += 1 - - return cluster_id, state - - def _create_new_cluster( - self, - state: MemSceneState, - event_id: str, - vector: Optional[np.ndarray], - timestamp: Optional[float], - is_case: bool = False, - ) -> str: - """Create a new cluster and assign the event to it.""" - cluster_id = state.assign_new_cluster(event_id) - if is_case: - state.case_cluster_ids.add(cluster_id) - # _update_cluster_centroid handles cluster_counts when vector is present; - # for None/empty vector we must set it explicitly. - if vector is not None and vector.size > 0: - state._update_cluster_centroid(cluster_id, vector, timestamp) - else: - state.cluster_counts[cluster_id] = 1 - if timestamp is not None: - state.cluster_last_ts[cluster_id] = timestamp - self._stats["new_clusters"] += 1 - return cluster_id - - def _assign_to_cluster( - self, - state: MemSceneState, - event_id: str, - cluster_id: str, - vector: Optional[np.ndarray], - timestamp: Optional[float], - ) -> None: - """Assign an event to an existing cluster.""" - state.eventid_to_cluster[event_id] = cluster_id - state.cluster_ids.append(cluster_id) - # _update_cluster_centroid handles cluster_counts when vector is present; - # for None/empty vector we must increment explicitly. - if vector is not None and vector.size > 0: - state._update_cluster_centroid(cluster_id, vector, timestamp) - else: - state.cluster_counts[cluster_id] = ( - state.cluster_counts.get(cluster_id, 0) + 1 - ) - if timestamp is not None: - prev_ts = state.cluster_last_ts.get(cluster_id) - state.cluster_last_ts[cluster_id] = max( - prev_ts or timestamp, timestamp - ) - - def _append_event( - self, - state: MemSceneState, - event_id: str, - vector: Optional[np.ndarray], - timestamp: Optional[float], - ) -> None: - """Append event metadata to state lists.""" - state.event_ids.append(event_id) - state.timestamps.append(timestamp or 0.0) - state.vectors.append( - vector if vector is not None else np.zeros((1,), dtype=np.float32) - ) - - async def _cluster_memcell_llm( - self, - memcell: Dict[str, Any], - state: MemSceneState, - ) -> Tuple[Optional[str], MemSceneState]: - """LLM-based clustering with embedding pre-filtering. - - Two-stage approach: - 1. Use embedding similarity to recall top-K candidate clusters - 2. Fetch recent episodes for candidates, let LLM make the final decision - """ - self._stats["total_memcells"] += 1 - - event_id = str(memcell.get("event_id", "")) - if not event_id: - logger.warning("Memcell missing event_id, skipping clustering") - return None, state - - timestamp = self._parse_timestamp(memcell.get("timestamp")) - text = self._extract_text(memcell) - - if self._llm_provider is None: - logger.error( - "[LLM Clustering] No LLM provider configured, " - "falling back to embedding-only case clustering" - ) - vector = await self._get_embedding(text) - best_cid = self._find_top_k_clusters( - state, vector, k=1, only_cids=state.case_cluster_ids, - ) - if best_cid and best_cid[0][1] >= self.config.similarity_threshold: - cluster_id = best_cid[0][0] - self._assign_to_cluster( - state, event_id, cluster_id, vector, timestamp - ) - else: - cluster_id = self._create_new_cluster( - state, event_id, vector, timestamp, is_case=True - ) - self._append_event(state, event_id, vector, timestamp) - self._stats["clustered_memcells"] += 1 - return cluster_id, state - - # No existing case clusters — just create a new one directly - if not state.case_cluster_ids: - vector = await self._get_embedding(text) - cluster_id = self._create_new_cluster( - state, event_id, vector, timestamp, is_case=True - ) - self._append_event(state, event_id, vector, timestamp) - self._stats["clustered_memcells"] += 1 - logger.info( - f"[LLM Clustering] First case cluster: {event_id} -> {cluster_id}" - ) - return cluster_id, state - - # Stage 1: Embedding recall — find top-K candidate clusters (case only) - vector = await self._get_embedding(text) - scored_candidates = self._find_top_k_clusters( - state, vector, - k=self.config.llm_top_k_clusters, - only_cids=state.case_cluster_ids, - ) - candidate_ids = [cid for cid, _ in scored_candidates] - top1_sim = scored_candidates[0][1] if scored_candidates else -1.0 - logger.info( - f"[LLM Clustering] Embedding recall: {len(candidate_ids)} candidates " - f"(top1_sim={top1_sim:.3f}), " - f"from {len(state.case_cluster_ids)} case clusters" - ) - - # Fast path: if top-1 similarity is high enough, skip LLM - if top1_sim >= self.config.llm_skip_threshold: - cluster_id = scored_candidates[0][0] - self._assign_to_cluster(state, event_id, cluster_id, vector, timestamp) - self._append_event(state, event_id, vector, timestamp) - self._stats["clustered_memcells"] += 1 - logger.info( - f"[LLM Clustering] Fast path: {event_id} -> {cluster_id} " - f"(sim={top1_sim:.3f} >= {self.config.llm_skip_threshold})" - ) - return cluster_id, state - - # Stage 2: Fetch recent context for candidates - cluster_context = await self._fetch_cluster_context(state, candidate_ids) - - # Stage 3: LLM decision - clusters_json = self._build_clusters_json( - state, candidate_ids, cluster_context - ) - next_new_id = f"{state.next_cluster_idx:03d}" - from memory_layer.prompts import get_prompt_by - - prompt_template = get_prompt_by("AGENT_CLUSTER_LLM_ASSIGN_PROMPT") - prompt = prompt_template.format( - memcell_text=text, - clusters_json=clusters_json, - next_new_id=next_new_id, - ) - llm_result = await self._call_llm_for_clustering(prompt) - - if llm_result is None: - logger.warning( - f"[LLM Clustering] LLM call failed for event {event_id}, " - f"falling back to embedding top-1" - ) - # Fall back to embedding: use top-1 candidate if available, else new cluster - if scored_candidates and scored_candidates[0][1] >= self.config.similarity_threshold: - cluster_id = scored_candidates[0][0] - self._assign_to_cluster( - state, event_id, cluster_id, vector, timestamp - ) - else: - cluster_id = self._create_new_cluster( - state, event_id, vector, timestamp, is_case=True - ) - else: - chosen_id = llm_result.get("cluster_id", "") - if chosen_id in state.cluster_counts and chosen_id in state.case_cluster_ids: - cluster_id = chosen_id - self._assign_to_cluster( - state, event_id, cluster_id, vector, timestamp - ) - else: - cluster_id = self._create_new_cluster( - state, event_id, vector, timestamp, is_case=True - ) - - self._append_event(state, event_id, vector, timestamp) - self._stats["clustered_memcells"] += 1 - reason = llm_result.get("reason", "") if llm_result else "" - logger.info( - f"[LLM Clustering] 🎯 Event {event_id} -> {cluster_id} " - f"| intent: {text} | reason: {reason}" - ) - return cluster_id, state - - def _find_top_k_clusters( - self, - state: MemSceneState, - vector: Optional[np.ndarray], - k: int = 10, - only_cids: Optional[set] = None, - ) -> List[Tuple[str, float]]: - """Find top-K candidate clusters by embedding similarity. - - Args: - only_cids: If provided, only consider these cluster IDs. - - Returns: - List of (cluster_id, similarity) tuples, sorted by similarity desc. - Similarity is -1.0 when embedding is unavailable. - """ - all_cids = list(state.cluster_counts.keys()) - if only_cids is not None: - all_cids = [c for c in all_cids if c in only_cids] - if not all_cids: - return [] - - # If no embedding or no centroids, return all with unknown similarity - if vector is None or vector.size == 0 or not state.cluster_centroids: - return [(c, -1.0) for c in all_cids[:k]] - - # Score each cluster by cosine similarity (ignore time gap for recall stage) - vector_norm = np.linalg.norm(vector) + 1e-9 - scored = [] - for cid in all_cids: - centroid = state.cluster_centroids.get(cid) - if centroid is None or centroid.size == 0: - scored.append((cid, -1.0)) - continue - centroid_norm = np.linalg.norm(centroid) + 1e-9 - sim = float((centroid @ vector) / (centroid_norm * vector_norm)) - scored.append((cid, sim)) - - scored.sort(key=lambda x: x[1], reverse=True) - return scored[:k] - - async def _fetch_cluster_context( - self, - state: MemSceneState, - candidate_ids: List[str], - ) -> Dict[str, List[str]]: - """Fetch recent context texts for candidate clusters via context_fetcher. - - Returns: - Dict mapping cluster_id -> list of recent context texts - """ - if not self._context_fetcher or not candidate_ids: - return {} - - max_per = self.config.llm_max_context_per_cluster - - # Collect recent event_ids per candidate cluster - from collections import defaultdict - - candidate_set = set(candidate_ids) - cluster_event_ids: Dict[str, List[str]] = defaultdict(list) - for eid, cid in state.eventid_to_cluster.items(): - if cid in candidate_set: - cluster_event_ids[cid].append(eid) - - # Take last N per cluster, collect all target event_ids - cluster_slices: Dict[str, List[str]] = {} - all_target_eids: List[str] = [] - for cid in candidate_ids: - eids = cluster_event_ids.get(cid, []) - recent = eids[-max_per:] - cluster_slices[cid] = recent - all_target_eids.extend(recent) - - if not all_target_eids: - return {} - - # Call the fetcher: event_ids -> {event_id: episode_text} - eid_to_text = await self._context_fetcher(all_target_eids) - - # Assemble per-cluster context - result: Dict[str, List[str]] = {} - for cid, eids in cluster_slices.items(): - texts = [eid_to_text[eid] for eid in eids if eid in eid_to_text] - if texts: - result[cid] = texts - return result - - def _build_clusters_json( - self, - state: MemSceneState, - candidate_ids: List[str], - cluster_context: Dict[str, List[str]], - ) -> str: - """Build JSON representation of candidate clusters for LLM prompt.""" - if not candidate_ids: - return "(No existing clusters)" - - clusters = [] - for cid in candidate_ids: - count = state.cluster_counts.get(cid, 0) - recent = cluster_context.get(cid, []) - clusters.append( - { - "cluster_id": cid, - "item_count": count, - "recent_task_intents": recent, - } - ) - return json.dumps(clusters, ensure_ascii=False, indent=2) - - async def _call_llm_for_clustering( - self, prompt: str - ) -> Optional[Dict[str, Any]]: - """Call LLM and parse clustering decision.""" - for attempt in range(3): - try: - resp = await self._llm_provider.generate(prompt) - from common_utils.json_utils import parse_json_response - - data = parse_json_response(resp) - if data and "cluster_id" in data: - return data - logger.warning( - f"[LLM Clustering] Retry {attempt + 1}/3: invalid response format" - ) - except Exception as e: - logger.warning( - f"[LLM Clustering] Retry {attempt + 1}/3: {e}" - ) - return None - - def _find_best_cluster( - self, - state: MemSceneState, - vector: np.ndarray, - timestamp: Optional[float], - exclude_cids: Optional[set] = None, - ) -> Optional[str]: - """Find the best matching cluster for a vector.""" - if not state.cluster_centroids: - return None - - best_similarity = -1.0 - best_cluster_id = None - - vector_norm = np.linalg.norm(vector) + 1e-9 - - for cluster_id, centroid in state.cluster_centroids.items(): - if exclude_cids and cluster_id in exclude_cids: - continue - if centroid is None or centroid.size == 0: - continue - - # Check time constraint - if timestamp is not None: - last_ts = state.cluster_last_ts.get(cluster_id) - if last_ts is not None: - time_diff = abs(timestamp - last_ts) - if time_diff > self.config.max_time_gap_seconds: - continue - - # Compute cosine similarity - centroid_norm = np.linalg.norm(centroid) + 1e-9 - similarity = float((centroid @ vector) / (centroid_norm * vector_norm)) - - if similarity > best_similarity: - best_similarity = similarity - best_cluster_id = cluster_id - - if best_similarity >= self.config.similarity_threshold: - return best_cluster_id - - return None - - async def _get_embedding(self, text: str) -> Optional[np.ndarray]: - """Get embedding for text.""" - if not self._vectorize_service: - logger.warning("Vectorize service not available") - return None - - try: - vector_arr = await self._vectorize_service.get_embedding(text) - if vector_arr is not None: - return np.array(vector_arr, dtype=np.float32) - except Exception as e: - logger.warning(f"Failed to get embedding: {e}") - - return None - - def _extract_text(self, memcell: Dict[str, Any]) -> str: - """Extract representative text from memcell. - - Priority: clustering_text > episode > original_data - """ - clustering_text = memcell.get("clustering_text") - if isinstance(clustering_text, str) and clustering_text.strip(): - return clustering_text.strip() - - episode = memcell.get("episode") - if isinstance(episode, str) and episode.strip(): - return episode.strip() - - lines = [] - original_data = memcell.get("original_data") - if isinstance(original_data, list): - for item in original_data: - if isinstance(item, dict): - content = item.get("content") or item.get("summary") - if content: - text = str(content).strip() - if text: - lines.append(text) - - return "\n".join(lines) if lines else str(memcell.get("event_id", "")) - - def _parse_timestamp(self, timestamp: Any) -> Optional[float]: - """Parse timestamp to float seconds.""" - if timestamp is None: - return None - - try: - if isinstance(timestamp, (int, float)): - val = float(timestamp) - if val > 10_000_000_000: - val = val / 1000.0 - return val - elif isinstance(timestamp, str): - from common_utils.datetime_utils import from_iso_format - - dt = from_iso_format(timestamp) - return dt.timestamp() - except Exception as e: - logger.warning(f"Failed to parse timestamp {timestamp}: {e}") - - return None - - async def _notify_callbacks( - self, group_id: str, memcell: Dict[str, Any], cluster_id: str - ) -> None: - """Notify all registered callbacks of cluster assignment.""" - for callback in self._callbacks: - try: - if asyncio.iscoroutinefunction(callback): - await callback(group_id, memcell, cluster_id) - else: - callback(group_id, memcell, cluster_id) - except Exception as e: - logger.error(f"Callback error: {e}") - - def get_stats(self) -> Dict[str, Any]: - """Get clustering statistics.""" - return dict(self._stats) diff --git a/methods/EverCore/src/memory_layer/constants.py b/methods/EverCore/src/memory_layer/constants.py deleted file mode 100644 index ad1c5030..00000000 --- a/methods/EverCore/src/memory_layer/constants.py +++ /dev/null @@ -1,6 +0,0 @@ -import os - - -VECTORIZE_DIMENSIONS = int(os.getenv("VECTORIZE_DIMENSIONS", "1024")) - -EXTRACT_SCENES = ("boundary", "extraction", "profile") diff --git a/methods/EverCore/src/memory_layer/llm/__init__.py b/methods/EverCore/src/memory_layer/llm/__init__.py deleted file mode 100644 index 03d92194..00000000 --- a/methods/EverCore/src/memory_layer/llm/__init__.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -LLM providers module for memory layer. - -This module provides LLM providers for the memory layer functionality. -""" - -from memory_layer.llm.openai_provider import OpenAIProvider -from memory_layer.llm.protocol import LLMProvider -from memory_layer.llm.llm_provider import resolve_provider_env - -__all__ = ["LLMProvider", "OpenAIProvider"] - - -def create_provider(provider_type: str, **kwargs) -> LLMProvider: - """ - Factory function to create LLM providers. - - Args: - provider_type: Provider name (openai-compatible by default) - **kwargs: Provider-specific arguments - - Returns: - Configured LLM provider instance - """ - provider_type = provider_type.lower() - - api_key, base_url = resolve_provider_env( - provider_type, - api_key=kwargs.pop("api_key", None), - base_url=kwargs.pop("base_url", None), - ) - - return OpenAIProvider( - provider_type=provider_type, - api_key=api_key, - base_url=base_url, - **kwargs, - ) - - -def create_provider_from_env(provider_type: str, **kwargs) -> LLMProvider: - """ - Create LLM provider from environment variables. - - Args: - provider_type: Provider name (openai-compatible by default) - **kwargs: Additional provider-specific arguments - - Returns: - Configured LLM provider instance - """ - provider_type = provider_type.lower() - - api_key, base_url = resolve_provider_env( - provider_type, - api_key=kwargs.pop("api_key", None), - base_url=kwargs.pop("base_url", None), - ) - - return OpenAIProvider( - provider_type=provider_type, - api_key=api_key, - base_url=base_url, - **kwargs, - ) diff --git a/methods/EverCore/src/memory_layer/llm/api_key_rotator.py b/methods/EverCore/src/memory_layer/llm/api_key_rotator.py deleted file mode 100644 index 767e0ab2..00000000 --- a/methods/EverCore/src/memory_layer/llm/api_key_rotator.py +++ /dev/null @@ -1,90 +0,0 @@ -"""API key round-robin rotator. - -Supports multiple keys rotating in turn to spread rate-limit pressure -across OpenRouter and other providers. Behaves identically to a single -key when only one key is supplied. -""" - -import itertools -from collections.abc import Sequence -from typing import ClassVar - -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class ApiKeyRotator: - """API Key rotator for round-robin selection. - - Spreads rate-limit pressure across multiple keys. Behaves identically - to a single key when only one key is supplied. Process-level shared - instance available via ``get_or_create``. - - Args: - keys: One or more API keys for rotation. - - Note: - Relies on asyncio single-threaded event loop; not thread-safe. - """ - - _shared: ClassVar["ApiKeyRotator | None"] = None - - def __init__(self, keys: Sequence[str]) -> None: - if not keys: - raise ValueError("At least one API key is required") - if len(keys) != len(set(keys)): - logger.warning( - "ApiKeyRotator: duplicate keys detected, rotation may be uneven" - ) - self._keys: tuple[str, ...] = tuple(keys) - self._cycle = itertools.cycle(range(len(self._keys))) - - def get_rotation(self) -> tuple[str, ...]: - """Return all keys starting from the current cycle position. - - Advances the global cycle by one position for load distribution. - The returned tuple is a per-request local snapshot: - - - ``rotation[0]`` is the key for the first attempt (equivalent to - a single ``get_next()`` call). - - ``rotation[1:]`` are the retry keys, starting from the one after - the first-attempt key, guaranteeing each key is tried before any - key is reused. - - Concurrent requests each advance the cycle independently, so their - first-attempt keys are naturally staggered. - """ - start_idx = next(self._cycle) - if self.size > 1: - logger.debug( - "ApiKeyRotator: selected key index %d/%d", start_idx + 1, self.size - ) - return tuple(self._keys[(start_idx + i) % self.size] for i in range(self.size)) - - @property - def size(self) -> int: - """Number of API keys in the rotation pool.""" - return len(self._keys) - - @classmethod - def get_or_create(cls, raw: str) -> "ApiKeyRotator": - """Get the process-level shared instance, creating it on first call. - - Subsequent calls return the existing instance; ``raw`` is only used - for the initial creation and is ignored afterward. - """ - if cls._shared is None: - keys = [k.strip() for k in raw.split(",") if k.strip()] - cls._shared = cls(keys) - else: - new_keys = tuple(k.strip() for k in raw.split(",") if k.strip()) - if new_keys != cls._shared._keys: - logger.warning( - "ApiKeyRotator: get_or_create called with different keys, " - "returning existing instance (keys are locked at first creation)" - ) - return cls._shared - - def __repr__(self) -> str: - return f"ApiKeyRotator(size={self.size})" diff --git a/methods/EverCore/src/memory_layer/llm/config.py b/methods/EverCore/src/memory_layer/llm/config.py deleted file mode 100644 index e6a616eb..00000000 --- a/methods/EverCore/src/memory_layer/llm/config.py +++ /dev/null @@ -1,52 +0,0 @@ -""" -LLM configuration management - -Provides simple LLM configuration management -""" - -from typing import Optional -from memory_layer.llm.openai_provider import OpenAIProvider -from memory_layer.llm.llm_provider import resolve_provider_env - - -def create_provider( - model: str = "gpt-4o", - api_key: Optional[str] = None, - base_url: Optional[str] = None, - temperature: float = 0.7, - max_tokens: int = 2048, - **kwargs, -) -> OpenAIProvider: - """ - Create an OpenAI provider - - Args: - model: Model name - api_key: API key, if None use environment variable - base_url: Base URL, if None use default value - temperature: Temperature - max_tokens: Maximum token count - **kwargs: Additional parameters - - Returns: - Configured OpenAIProvider instance - """ - api_key, base_url = resolve_provider_env("openai", api_key=api_key, base_url=base_url) - return OpenAIProvider( - model=model, - api_key=api_key, - base_url=base_url, - temperature=temperature, - max_tokens=max_tokens, - **kwargs, - ) - - -def create_cheap_provider() -> OpenAIProvider: - """Create a cheap provider (using gpt-4o-mini)""" - return create_provider(model="gpt-4o-mini", temperature=0.3, max_tokens=1024) - - -def create_high_quality_provider() -> OpenAIProvider: - """Create a high-quality provider (using gpt-4o)""" - return create_provider(model="gpt-4o", temperature=0.7, max_tokens=4096) diff --git a/methods/EverCore/src/memory_layer/llm/llm_metrics.py b/methods/EverCore/src/memory_layer/llm/llm_metrics.py deleted file mode 100644 index 543b9be3..00000000 --- a/methods/EverCore/src/memory_layer/llm/llm_metrics.py +++ /dev/null @@ -1,61 +0,0 @@ -""" -LLM Provider Metrics - -Prometheus metrics for monitoring LLM API call volume and error rates. -Co-located with the LLM provider per Prometheus instrumentation best practices. -""" - -from core.observation.metrics import Counter - - -# ============================================================ -# Counter Metrics -# ============================================================ - -LLM_REQUESTS_TOTAL = Counter( - name='llm_requests_total', - description='Total number of LLM API requests', - labelnames=['model', 'status'], - namespace='evermemos', - subsystem='memory_layer', -) -""" -LLM requests counter. - -Labels: -- model: LLM model name (e.g. "gpt-4.1-mini", "qwen/qwen3-235b-a22b-2507") -- status: Request outcome - - success: HTTP 200 with valid response - - rate_limit: HTTP 429 (all keys exhausted) - - key_error: HTTP 401/402/403 (all keys exhausted) - - server_error: HTTP 5xx (after max retries) - - client_error: Network / connection error (after max retries) - - request_error: HTTP 400/404/422 (no retry) - -PromQL examples: - # Total requests per second - rate(evermemos_memory_layer_llm_requests_total[5m]) - - # 429 count - evermemos_memory_layer_llm_requests_total{status="rate_limit"} - - # 429 ratio - evermemos_memory_layer_llm_requests_total{status="rate_limit"} - / evermemos_memory_layer_llm_requests_total -""" - - -# ============================================================ -# Helper Functions -# ============================================================ - - -def record_llm_request(model: str, status: str) -> None: - """Record an LLM request outcome. - - Args: - model: LLM model name. - status: Request outcome (success, rate_limit, key_error, - server_error, client_error, request_error). - """ - LLM_REQUESTS_TOTAL.labels(model=model, status=status).inc() diff --git a/methods/EverCore/src/memory_layer/llm/llm_provider.py b/methods/EverCore/src/memory_layer/llm/llm_provider.py deleted file mode 100644 index 36b83e4c..00000000 --- a/methods/EverCore/src/memory_layer/llm/llm_provider.py +++ /dev/null @@ -1,94 +0,0 @@ -import os -from memory_layer.llm.openai_provider import OpenAIProvider -from core.observation.stage_timer import timed - -# Constants for default provider and model settings -DEFAULT_PROVIDER_NAME = "default" - -# for default settings -DEFAULT_LLM_MODEL = "openai/gpt-4.1-mini" - -# for default & scenes default settings -DEFAULT_LLM_TEMPERATURE = 0.3 -DEFAULT_LLM_MAX_TOKENS = 16384 - - -def _normalize_provider(provider_type: str | None) -> str: - return provider_type.lower() - - -def resolve_provider_env( - provider_type: str, - api_key: str | None = None, - base_url: str | None = None, - use_legacy_default: bool = False, -) -> tuple[str | None, str | None]: - provider_upper = _normalize_provider(provider_type).upper() - - if not api_key: - api_key = os.getenv(f"{provider_upper}_API_KEY") - if not api_key and use_legacy_default: - api_key = os.getenv("LLM_API_KEY") - - if not base_url: - base_url = os.getenv(f"{provider_upper}_BASE_URL") - if not base_url and use_legacy_default: - base_url = os.getenv("LLM_BASE_URL") - - return api_key, base_url - - -def build_default_provider() -> "LLMProvider": - """Build the default LLM provider from environment variables.""" - return LLMProvider( - provider_type=DEFAULT_PROVIDER_NAME, - model=os.getenv("LLM_MODEL", DEFAULT_LLM_MODEL), - temperature=float(os.getenv("LLM_TEMPERATURE", str(DEFAULT_LLM_TEMPERATURE))), - max_tokens=int(os.getenv("LLM_MAX_TOKENS", str(DEFAULT_LLM_MAX_TOKENS))), - ) - - -class LLMProvider: - def __init__(self, provider_type: str, **kwargs): - provider_type = _normalize_provider(provider_type) - use_legacy_default = provider_type == DEFAULT_PROVIDER_NAME - if use_legacy_default: - provider_type = _normalize_provider(os.getenv("LLM_PROVIDER")) - self.provider_type = provider_type - api_key, base_url = resolve_provider_env( - provider_type, - api_key=kwargs.pop("api_key", None), - base_url=kwargs.pop("base_url", None), - use_legacy_default=use_legacy_default, - ) - - # Validate required configuration - provider_upper = provider_type.upper() - if not api_key: - raise ValueError( - f"No API key found for provider '{provider_type}'. " - f"Please set {provider_upper}_API_KEY environment variable." - ) - if not base_url: - raise ValueError( - f"No base URL found for provider '{provider_type}'. " - f"Please set {provider_upper}_BASE_URL environment variable." - ) - - self.provider = OpenAIProvider( - provider_type=provider_type, api_key=api_key, base_url=base_url, **kwargs - ) - # TODO: add other providers - - async def generate( - self, - prompt: str, - temperature: float | None = None, - max_tokens: int | None = None, - extra_body: dict | None = None, - response_format: dict | None = None, - ) -> str: - with timed("call_llm"): - return await self.provider.generate( - prompt, temperature, max_tokens, extra_body, response_format - ) diff --git a/methods/EverCore/src/memory_layer/llm/openai_provider.py b/methods/EverCore/src/memory_layer/llm/openai_provider.py deleted file mode 100644 index 96ac717f..00000000 --- a/methods/EverCore/src/memory_layer/llm/openai_provider.py +++ /dev/null @@ -1,339 +0,0 @@ -""" -OpenAI-compatible LLM provider implementation. - -This provider uses a caller-supplied API key and base URL. -""" - -import asyncio -import json -import os -import random -import time - -import aiohttp - -from core.component.token_usage_collector import TokenUsageCollector -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger -from memory_layer.llm.api_key_rotator import ApiKeyRotator -from memory_layer.llm.llm_metrics import record_llm_request -from memory_layer.llm.protocol import LLMProvider, LLMError - -logger = get_logger(__name__) - -_MAX_RETRIES = 5 - - -class OpenAIProvider(LLMProvider): - """OpenAI-compatible LLM provider. - - Sends requests to any OpenAI-compatible endpoint (OpenRouter, OpenAI, etc.) - with automatic multi-key rotation and differentiated retry strategies. - - Args: - model: Model name (e.g. "gpt-4.1-mini", "qwen/qwen3-235b-a22b-2507"). - api_key: API key(s), comma-separated for multi-key rotation. - base_url: API base URL. - temperature: Sampling temperature. - max_tokens: Maximum tokens to generate. - enable_stats: Enable per-call usage statistics. - provider_type: Provider identifier ("openai" or "openrouter"). - """ - - def __init__( - self, - model: str = "gpt-4.1-mini", # skip-sensitive-check - api_key: str | None = None, - base_url: str | None = None, - temperature: float = 0.3, - max_tokens: int | None = 100 * 1024, - enable_stats: bool = False, - provider_type: str | None = None, - **kwargs, - ) -> None: - self.model = model - self.temperature = temperature - self.max_tokens = max_tokens - self.enable_stats = enable_stats - self.provider_type = ( - provider_type or "openrouter" # skip-sensitive-check - ).lower() - self._key_rotator = ( - ApiKeyRotator.get_or_create(api_key) if api_key else ApiKeyRotator([""]) - ) - self.base_url = base_url - - self._validate_model_whitelist(self.provider_type, model) - - if self.enable_stats: - self.current_call_stats = None - - @staticmethod - def _validate_model_whitelist(provider_type: str, model: str) -> None: - """Validate model against the provider's whitelist from environment variable.""" - env_key = f"{provider_type.upper()}_WHITE_LIST" - raw = os.getenv(env_key, "").strip() - if not raw: - return - allowed_models = {m.strip() for m in raw.split(",") if m.strip()} - if not allowed_models: - return - if model not in allowed_models: - raise ValueError( - f"Provider '{provider_type}' only supports: {', '.join(sorted(allowed_models))}. Got: '{model}'." - ) - - @staticmethod - def _resolve_openrouter_provider() -> dict | None: - """Parse LLM_OPENROUTER_PROVIDER env var into an OpenRouter provider dict.""" - raw = os.getenv("LLM_OPENROUTER_PROVIDER", "default") # skip-sensitive-check - if raw == "default": - return None - provider_list = [p.strip() for p in raw.split(",")] - return {"order": provider_list, "allow_fallbacks": False} - - @staticmethod - def _extract_error_message(response_data: dict, status_code: int) -> str: - """Extract a human-readable error message from an error response body.""" - return response_data.get("error", {}).get("message", f"HTTP {status_code}") - - def _build_request_data( - self, - prompt: str, - temperature: float | None, - max_tokens: int | None, - response_format: dict | None, - ) -> dict: - """Build the JSON payload for the chat completions request.""" - data: dict = { - "model": self.model, - "messages": [{"role": "user", "content": prompt}], - "temperature": temperature if temperature is not None else self.temperature, - "provider": self._resolve_openrouter_provider(), - "response_format": response_format, - } - if max_tokens is not None: - data["max_tokens"] = max_tokens - elif self.max_tokens is not None: - data["max_tokens"] = self.max_tokens - return data - - async def _do_request(self, data: dict, api_key: str) -> tuple[int, dict]: - """Execute a single HTTP POST to the chat completions endpoint. - - Returns: - (status_code, parsed_response_body) - """ - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {api_key}", - } - timeout = aiohttp.ClientTimeout(total=600) - async with aiohttp.ClientSession(timeout=timeout) as session: - async with session.post( - f"{self.base_url}/chat/completions", json=data, headers=headers - ) as response: - raw = await response.read() - try: - response_data = json.loads(raw.decode()) - except (json.JSONDecodeError, UnicodeDecodeError): - # Non-JSON response (e.g. Cloudflare HTML error page) - return response.status, { - "error": {"message": raw[:500].decode(errors="replace")} - } - return response.status, response_data - - def _report_token_usage(self, prompt_tokens: int, completion_tokens: int) -> None: - """Report token usage to the global TokenUsageCollector (best-effort).""" - try: - collector = get_bean_by_type(TokenUsageCollector) - collector.add(self.model, prompt_tokens, completion_tokens, call_type="llm") - except Exception: - pass - - def _log_completion_metrics(self, response_data: dict, duration: float) -> None: - """Log finish reason, duration, and token usage for a completed request.""" - finish_reason = response_data.get("choices", [{}])[0].get("finish_reason", "") - if finish_reason == "stop": - logger.debug("[OpenAI-%s] Finish reason: %s", self.model, finish_reason) - else: - logger.warning("[OpenAI-%s] Finish reason: %s", self.model, finish_reason) - - usage = response_data.get("usage", {}) - prompt_tokens = usage.get("prompt_tokens", 0) - completion_tokens = usage.get("completion_tokens", 0) - - logger.debug("[OpenAI-%s] Duration: %.2fs", self.model, duration) - if duration > 30: - logger.warning("[OpenAI-%s] Duration too long: %.2fs", self.model, duration) - logger.debug( - "[OpenAI-%s] Tokens: %s prompt, %s completion, %s total", - self.model, - format(prompt_tokens, ","), - format(completion_tokens, ","), - format(usage.get("total_tokens", 0), ","), - ) - - self._report_token_usage(prompt_tokens, completion_tokens) - - def _handle_success(self, response_data: dict, start_time: float) -> str: - """Process a successful (HTTP 200) response: log metrics, report usage, return text.""" - duration = time.perf_counter() - start_time - self._log_completion_metrics(response_data, duration) - - if self.enable_stats: - usage = response_data.get("usage", {}) - self.current_call_stats = { - "prompt_tokens": usage.get("prompt_tokens", 0), - "completion_tokens": usage.get("completion_tokens", 0), - "total_tokens": usage.get("total_tokens", 0), - "duration": duration, - "timestamp": time.time(), - } - - return response_data["choices"][0]["message"]["content"] - - def _handle_key_error( - self, status_code: int, error_msg: str, consecutive_failures: int - ) -> int: - """Handle key-level errors (401/402/403/429): rotate key, raise if all exhausted.""" - consecutive_failures += 1 - if consecutive_failures >= self._key_rotator.size: - metric_status = "rate_limit" if status_code == 429 else "key_error" - record_llm_request(self.model, metric_status) - raise LLMError( - f"HTTP {status_code}: {error_msg} " - f"(all {self._key_rotator.size} keys exhausted)" - ) - logger.warning( - "[OpenAI-%s] Key error %d, rotating key (%d/%d exhausted)", - self.model, - status_code, - consecutive_failures, - self._key_rotator.size, - ) - return consecutive_failures - - async def _handle_server_error( - self, status_code: int, error_msg: str, retry_num: int - ) -> None: - """Handle 5xx server error: sleep and retry, or raise on final attempt.""" - if retry_num < _MAX_RETRIES - 1: - logger.warning( - "[OpenAI-%s] Server error %d, retry %d/%d", - self.model, - status_code, - retry_num + 1, - _MAX_RETRIES, - ) - await asyncio.sleep(random.randint(5, 20)) - return - record_llm_request(self.model, "server_error") - raise LLMError( - f"HTTP Error {status_code}: {error_msg} (after {_MAX_RETRIES} retries)" - ) - - async def _execute_with_retry(self, data: dict, start_time: float) -> str: - """Retry loop: key-level errors rotate key, 5xx backs off with sleep.""" - consecutive_key_failures = 0 - key_rotation = self._key_rotator.get_rotation() - - for retry_num in range(_MAX_RETRIES): - current_key = key_rotation[retry_num % len(key_rotation)] - try: - status_code, response_data = await self._do_request(data, current_key) - except aiohttp.ClientError as exc: - logger.error("aiohttp.ClientError: %s", exc) - if retry_num == _MAX_RETRIES - 1: - record_llm_request(self.model, "client_error") - raise LLMError(f"Request failed: {exc}") from exc - continue - except LLMError: - raise - except Exception as exc: - logger.error("Unexpected error: %s", exc) - if retry_num == _MAX_RETRIES - 1: - record_llm_request(self.model, "client_error") - raise LLMError(f"Request failed: {exc}") from exc - continue - - if status_code == 200: - record_llm_request(self.model, "success") - return self._handle_success(response_data, start_time) - - error_msg = self._extract_error_message(response_data, status_code) - logger.error("[OpenAI-%s] HTTP %d: %s", self.model, status_code, error_msg) - - # Key-level errors: rotate key immediately, no sleep. - # - 401 Unauthorized: invalid/missing key - # - 402 Payment Required: key quota exhausted - # - 403 Forbidden: key lacks permission - # - 429 Too Many Requests: key rate-limited - if status_code in (401, 402, 403, 429): - consecutive_key_failures = self._handle_key_error( - status_code, error_msg, consecutive_key_failures - ) - continue - - # 5xx: sleep then retry (key rotates per retry_rotation sequence) - if status_code in (500, 502, 503, 504): - await self._handle_server_error(status_code, error_msg, retry_num) - continue - - # Request-level errors (400, 404, 422, etc.): not key-related, no retry - record_llm_request(self.model, "request_error") - raise LLMError(f"HTTP Error {status_code}: {error_msg}") - - record_llm_request(self.model, "client_error") - raise LLMError(f"Request failed after {_MAX_RETRIES} retries") - - async def generate( - self, - prompt: str, - temperature: float | None = None, - max_tokens: int | None = None, - extra_body: dict | None = None, - response_format: dict | None = None, - ) -> str: - """Generate a response for the given prompt.""" - start_time = time.perf_counter() - data = self._build_request_data( - prompt, temperature, max_tokens, response_format - ) - return await self._execute_with_retry(data, start_time) - - async def test_connection(self) -> bool: - """Test the connection to the API endpoint.""" - try: - logger.info("\U0001f517 [OpenAI-%s] Testing API connection...", self.model) - test_response = await self.generate("Hello", temperature=0.1) - success = len(test_response) > 0 - if success: - logger.info( - "\u2705 [OpenAI-%s] API connection test succeeded", self.model - ) - else: - logger.error( - "\u274c [OpenAI-%s] API connection test failed: Empty response", - self.model, - ) - return success - except Exception as e: - logger.error( - "\u274c [OpenAI-%s] API connection test failed: %s", self.model, e - ) - return False - - def get_current_call_stats(self) -> dict | None: - """Return per-call statistics if stats collection is enabled.""" - if self.enable_stats: - return self.current_call_stats - return None - - def __repr__(self) -> str: - return ( - "OpenAIProvider(" - f"provider_type={self.provider_type}, model={self.model}, " - f"base_url={self.base_url}, keys={self._key_rotator.size}" - ")" - ) diff --git a/methods/EverCore/src/memory_layer/llm/protocol.py b/methods/EverCore/src/memory_layer/llm/protocol.py deleted file mode 100644 index 13f0f578..00000000 --- a/methods/EverCore/src/memory_layer/llm/protocol.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -LLM Provider Protocol for memory layer. - -This module defines the abstract interface that all LLM providers must implement. -""" - -from typing import Protocol - - -class LLMProvider(Protocol): - """ - Protocol for LLM providers used in text generation. - - All concrete LLM provider implementations must implement this interface - to be compatible with the memory layer's requirements. - """ - - async def generate( - self, - prompt: str, - temperature: float | None = None, - extra_body: dict | None = None, - response_format: dict | None = None, - ) -> str: - """ - Generate a response for the given prompt. - - Args: - prompt: Input prompt text - temperature: Optional temperature override for this request - - Returns: - Generated response text - - Raises: - Exception: If generation fails - """ - ... - - async def test_connection(self) -> bool: - """ - Test the connection to the LLM provider. - - Returns: - True if connection successful, False otherwise - """ - ... - - def __repr__(self) -> str: - """String representation of the provider.""" - ... - - -class LLMError(Exception): - """Exception raised for LLM-related errors.""" - - pass diff --git a/methods/EverCore/src/memory_layer/memcell_extractor/agent_memcell_extractor.py b/methods/EverCore/src/memory_layer/memcell_extractor/agent_memcell_extractor.py deleted file mode 100644 index 8bcc1f94..00000000 --- a/methods/EverCore/src/memory_layer/memcell_extractor/agent_memcell_extractor.py +++ /dev/null @@ -1,253 +0,0 @@ -""" -Agent MemCell Extractor for EverCore - -Extends ConvMemCellExtractor for agent conversations in OpenAI chat completion format. - -Strategy: maximally reuse the parent's three-phase pipeline -(force-split → LLM batch boundary detection → flush). - -Only two customizations: -1. Guard: skip boundary detection when the agent turn is still in progress - (last new message is a tool_call or tool response — the agent hasn't finished) -2. Override _detect_boundaries: filter out tool messages for LLM prompt, - then remap boundary indices back to original message space so that - resulting MemCells contain the full trajectory including tool calls. - -Example of the index remapping: - - Original messages (0-indexed): - [0] user: hello - [1] assistant + tool_calls ← intermediate, filtered out - [2] tool: result ← intermediate, filtered out - [3] assistant: answer - [4] user: new question - [5] assistant + tool_calls ← intermediate, filtered out - [6] tool: API result ← intermediate, filtered out - [7] assistant: done - - Filtered for LLM (0-indexed): - [0] user: hello → orig 0 - [1] assistant: answer → orig 3 - [2] user: new question → orig 4 - [3] assistant: done → orig 7 - - LLM returns: boundaries: [2] (split after filtered[2], i.e. "assistant: answer") - - Remap: filtered_to_orig[2-1] + 1 = orig[3] + 1 = 4 - - Result: MemCell_1 = original[0:4] = [user, tool_call, tool, assistant] ← full turn - MemCell_2 = original[4:] = [user, tool_call, tool, assistant] ← full turn -""" - -from typing import Dict, Any, Optional, List, Tuple -from dataclasses import dataclass - -from memory_layer.memcell_extractor.conv_memcell_extractor import ( - ConvMemCellExtractor, - BatchBoundaryResult, -) -from memory_layer.memcell_extractor.base_memcell_extractor import ( - MemCellExtractRequest, - StatusResult, -) -from memory_layer.llm.llm_provider import LLMProvider -from api_specs.memory_types import MemCell, RawDataType, is_intermediate_agent_step -from core.observation.logger import get_logger - -logger = get_logger(__name__) - -# Default hard limits for agent conversations -AGENT_DEFAULT_HARD_TOKEN_LIMIT = 32768 -AGENT_DEFAULT_HARD_MESSAGE_LIMIT = 64 - - -@dataclass -class AgentMemCellExtractRequest(MemCellExtractRequest): - """Agent-specific MemCell extraction request.""" - - pass - - -class AgentMemCellExtractor(ConvMemCellExtractor): - """Agent MemCell Extractor — thin layer over ConvMemCellExtractor. - - Reuses the parent's full three-phase pipeline. Only customizes: - - raw_data_type = AGENTCONVERSATION - - Guard: skip when last new message is an intermediate agent step - - _detect_boundaries: filter→detect→remap so LLM sees clean conversation - but MemCells contain full trajectory - """ - - def __init__( - self, - llm_provider=LLMProvider, - boundary_detection_prompt: Optional[str] = None, - hard_token_limit: Optional[int] = None, - hard_message_limit: Optional[int] = None, - ): - super().__init__( - llm_provider=llm_provider, - boundary_detection_prompt=boundary_detection_prompt, - hard_token_limit=hard_token_limit or AGENT_DEFAULT_HARD_TOKEN_LIMIT, - hard_message_limit=hard_message_limit or AGENT_DEFAULT_HARD_MESSAGE_LIMIT, - ) - self.raw_data_type = RawDataType.AGENTCONVERSATION - - # ------------------------------------------------------------------ - # extract_memcell: single guard, then delegate to parent - # ------------------------------------------------------------------ - - async def extract_memcell( - self, request: MemCellExtractRequest - ) -> Tuple[List[MemCell], StatusResult]: - """Guard + delegate. - - The only guard: if the agent turn is still in progress (last new - message is a tool_call or tool response), skip — no point in - running boundary detection mid-turn. - - Everything else (force-split, LLM detection, flush) is handled - by the parent's pipeline. Our _detect_boundaries override ensures - the LLM sees clean messages while MemCells get full trajectories. - """ - if not request.flush: - # Skip when only a single message total — not enough context - total_msgs = len(request.history_raw_data_list) + len( - request.new_raw_data_list - ) - if total_msgs <= 1: - logger.debug( - "[AgentMemCellExtractor] Skipping: only %d message(s), " - "waiting for more context", - total_msgs, - ) - return ([], StatusResult(should_wait=True)) - - if request.new_raw_data_list: - # Skip when new messages are all user messages (no assistant response yet) - all_user_only = all( - isinstance(rd.content, dict) - and rd.content.get("role") == "user" - for rd in request.new_raw_data_list - ) - if all_user_only: - logger.debug( - "[AgentMemCellExtractor] Skipping: new messages contain " - "only user messages, waiting for assistant response", - ) - return ([], StatusResult(should_wait=True)) - - # Skip when the agent turn is still in progress - last_content = request.new_raw_data_list[-1].content - if isinstance( - last_content, dict - ) and is_intermediate_agent_step(last_content): - logger.debug( - "[AgentMemCellExtractor] Skipping: last new message is " - "intermediate (role=%s)", - last_content.get("role"), - ) - return ([], StatusResult(should_wait=True)) - - return await super().extract_memcell(request) - - # ------------------------------------------------------------------ - # _find_force_split_point: respect tool-call boundaries - # ------------------------------------------------------------------ - - @staticmethod - def _is_safe_split(messages: List[Dict[str, Any]], split_at: int) -> bool: - """Check if split_at is a safe boundary. - - A safe boundary means: - 1. split_at is in valid range: 1 <= split_at <= len(messages) - 1 - 2. messages[split_at - 1] is a final assistant response - (not an intermediate step, not a user/tool message) - """ - if split_at < 1 or split_at > len(messages) - 1: - return False - last_msg = messages[split_at - 1] - return ( - last_msg.get("role") == "assistant" - and not last_msg.get("tool_calls") - ) - - def _find_force_split_point(self, messages: List[Dict[str, Any]]) -> int: - """Find force-split point that does not break a tool-call sequence. - - Gets the candidate from the parent, then adjusts so we never: - 1. Split in the middle of a tool-call sequence - 2. Cut out a chunk with only user messages (no assistant reply) - - Strategy: walk backwards from parent candidate to find a safe point. - If that fails (e.g. all leading messages are intermediate), walk - forward instead. - """ - original = super()._find_force_split_point(messages) - candidate = original - - # Walk backwards to find a safe boundary (min split_at=2 for a meaningful chunk) - while candidate > 2 and not self._is_safe_split(messages, candidate): - candidate -= 1 - - # If walking back didn't find a safe point, walk forward - if not self._is_safe_split(messages, candidate): - candidate = original + 1 - while candidate < len(messages) and not self._is_safe_split( - messages, candidate - ): - candidate += 1 - - # If no safe split found in either direction, don't split — - # keep all messages in one MemCell rather than breaking a tool sequence - if not self._is_safe_split(messages, candidate): - logger.warning( - "[AgentMemCellExtractor] No safe split found among %d messages, " - "keeping as one chunk", - len(messages), - ) - return len(messages) - - return candidate - - # ------------------------------------------------------------------ - # _detect_boundaries: filter → detect → remap - # ------------------------------------------------------------------ - - async def _detect_boundaries( - self, messages: List[Dict[str, Any]] - ) -> BatchBoundaryResult: - """Detect boundaries on filtered messages, remap to original indices. - - 1. Filter out tool messages → clean user/assistant conversation - 2. Run parent's LLM boundary detection on the filtered list - 3. Remap boundary indices back to original message space - - This ensures: - - LLM sees a clean conversation without tool noise - - MemCells contain the full agent trajectory including tool calls - """ - # Build filtered list with original-index mapping - filtered: List[Dict[str, Any]] = [] - filtered_to_orig: List[int] = [] # filtered_to_orig[i] = original index - for orig_idx, msg in enumerate(messages): - if not is_intermediate_agent_step(msg): - filtered.append(msg) - filtered_to_orig.append(orig_idx) - - if not filtered: - return BatchBoundaryResult(boundaries=[], should_wait=True) - - # Detect boundaries on clean user-assistant conversation - result = await super()._detect_boundaries(filtered) - - # Remap: filtered boundary b → original split point - # boundary b means "split after the b-th filtered message" (1-indexed) - # In original space: split right after orig[filtered_to_orig[b-1]] - remapped: List[int] = [] - for b in result.boundaries: - orig_split = filtered_to_orig[b - 1] + 1 - remapped.append(orig_split) - - result.boundaries = remapped - return result diff --git a/methods/EverCore/src/memory_layer/memcell_extractor/base_memcell_extractor.py b/methods/EverCore/src/memory_layer/memcell_extractor/base_memcell_extractor.py deleted file mode 100644 index 0917ffb1..00000000 --- a/methods/EverCore/src/memory_layer/memcell_extractor/base_memcell_extractor.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Simple Boundary Detection Base Class for EverCore - -This module provides a simple and extensible base class for detecting -boundaries in various types of content (conversations, emails, notes, etc.). -""" - -from abc import ABC, abstractmethod -from dataclasses import dataclass -from typing import List, Dict, Any, Optional -from memory_layer.llm.llm_provider import LLMProvider -from api_specs.memory_types import RawDataType, BaseMemory, MemCell -from api_specs.dtos import RawData - - -@dataclass -class MemCellExtractRequest: - history_raw_data_list: List[RawData] - new_raw_data_list: List[RawData] - # user id list of the entire group - user_id_list: List[str] - group_id: Optional[str] = None - - old_memory_list: Optional[List[BaseMemory]] = None - # Force boundary trigger - when True, skip boundary detection and create MemCell directly - flush: bool = False - - -@dataclass -class StatusResult: - """Status control result.""" - - # Indicates that when triggered next time, this conversation will be accumulated and input as new message - should_wait: bool - - -class MemCellExtractor(ABC): - def __init__(self, raw_data_type: RawDataType, llm_provider=LLMProvider): - self.raw_data_type = raw_data_type - self._llm_provider = llm_provider - - @abstractmethod - async def extract_memcell( - self, request: MemCellExtractRequest - ) -> tuple[List[MemCell], StatusResult]: - pass diff --git a/methods/EverCore/src/memory_layer/memcell_extractor/conv_memcell_extractor.py b/methods/EverCore/src/memory_layer/memcell_extractor/conv_memcell_extractor.py deleted file mode 100644 index f2d40f4f..00000000 --- a/methods/EverCore/src/memory_layer/memcell_extractor/conv_memcell_extractor.py +++ /dev/null @@ -1,575 +0,0 @@ -""" -Simple Boundary Detection Base Class for EverCore - -This module provides a simple and extensible base class for detecting -boundaries in various types of content (conversations, emails, notes, etc.). -""" - -from typing import Dict, Any, Optional, List -from datetime import datetime -from dataclasses import dataclass, field -import json, re, os -from core.di.utils import get_bean_by_type -from core.component.llm.tokenizer.tokenizer_factory import TokenizerFactory -from common_utils.datetime_utils import from_iso_format as dt_from_iso_format -from memory_layer.llm.llm_provider import LLMProvider -from api_specs.memory_types import RawDataType -from api_specs.memory_models import MessageSenderRole - -from memory_layer.prompts import get_prompt_by -from memory_layer.memcell_extractor.base_memcell_extractor import ( - MemCellExtractor, - MemCell, - StatusResult, - MemCellExtractRequest, -) -from api_specs.memory_types import get_text_from_content_items -from core.observation.logger import get_logger -from core.observation.stage_timer import timed -from agentic_layer.metrics.memorize_metrics import ( - record_boundary_detection, - record_memcell_extracted, - get_space_id_for_metrics, -) - -logger = get_logger(__name__) - - -@dataclass -class BatchBoundaryResult: - """Result from batch boundary detection.""" - - # List of 1-indexed message numbers after which to split - boundaries: List[int] = field(default_factory=list) - should_wait: bool = False - - -@dataclass -class ConversationMemCellExtractRequest(MemCellExtractRequest): - pass - - -class ConvMemCellExtractor(MemCellExtractor): - """ - Conversation MemCell Extractor - Responsible only for boundary detection and creating basic MemCell - - Responsibilities: - 1. Boundary detection (determine where to split conversation into MemCells) - 2. Create basic MemCell (including basic fields such as original_data, summary, timestamp, etc.) - - Not included: - - Episode extraction (handled by EpisodeMemoryExtractor) - - Foresight extraction (handled by ForesightExtractor) - - AtomicFact extraction (handled by AtomicFactExtractor) - - Embedding computation (handled by MemoryManager) - - Language support: - - Controlled by MEMORY_LANGUAGE env var: 'zh' (Chinese) or 'en' (English), default 'en' - """ - - # Default limits for force splitting (configurable via environment variables) - DEFAULT_HARD_TOKEN_LIMIT = int(os.getenv("MEMCELL_HARD_TOKEN_LIMIT", "65536")) - DEFAULT_HARD_MESSAGE_LIMIT = int(os.getenv("MEMCELL_HARD_MESSAGE_LIMIT", "500")) - - @classmethod - def _get_tokenizer(cls): - """Get the shared tokenizer from tokenizer factory (with caching).""" - tokenizer_factory: TokenizerFactory = get_bean_by_type(TokenizerFactory) - return tokenizer_factory.get_tokenizer_from_tiktoken("o200k_base") - - def __init__( - self, - llm_provider=LLMProvider, - boundary_detection_prompt: Optional[str] = None, - hard_token_limit: Optional[int] = None, - hard_message_limit: Optional[int] = None, - ): - super().__init__(RawDataType.CONVERSATION, llm_provider) - self.llm_provider = llm_provider - - # Force split limits - self.hard_token_limit = hard_token_limit or self.DEFAULT_HARD_TOKEN_LIMIT - self.hard_message_limit = hard_message_limit or self.DEFAULT_HARD_MESSAGE_LIMIT - - # Use custom prompt or get default via PromptManager - self.conv_batch_boundary_detection_prompt = ( - boundary_detection_prompt - or get_prompt_by("CONV_BATCH_BOUNDARY_DETECTION_PROMPT") - ) - - def shutdown(self) -> None: - """Cleanup resources.""" - pass - - def _count_tokens(self, messages: List[Dict[str, Any]]) -> int: - """ - Count total tokens in message list using tiktoken. - - Includes sender_name in token count since it's included when passed to LLM. - - Args: - messages: List of message dictionaries - - Returns: - Total token count - """ - tokenizer = self._get_tokenizer() - total = 0 - for msg in messages: - if isinstance(msg, dict): - speaker = msg.get('sender_name', '') - content = get_text_from_content_items(msg.get('content', [])) - # Format matches what's sent to LLM: "sender_name: content" - text = f"{speaker}: {content}" if speaker else content - else: - text = str(msg) - total += len(tokenizer.encode(text)) - return total - - def _extract_participant_ids( - self, chat_raw_data_list: List[Dict[str, Any]] - ) -> List[str]: - """ - Extract user participant IDs from chat_raw_data_list - - Retrieves sender_id only from messages with role='user'. - - Args: - chat_raw_data_list: List of raw chat data - - Returns: - List[str]: List of deduplicated user participant IDs - """ - participant_ids = set() - - for raw_data in chat_raw_data_list: - if raw_data.get('role') == MessageSenderRole.USER.value and raw_data.get( - 'sender_id' - ): - participant_ids.add(raw_data['sender_id']) - - return list(participant_ids) - - def _find_force_split_point(self, messages: List[Dict[str, Any]]) -> int: - """ - Find how many messages to include in a force-split chunk. - - Starts with hard_message_limit - 1, then reduces if token limit is exceeded. - Guaranteed to return at least 1 and at most len(messages) - 1. - - Args: - messages: All messages to consider for splitting - - Returns: - Number of messages to include in the split chunk (exclusive end index) - """ - if len(messages) <= 1: - return len(messages) - - # Start with message limit (leave at least 1 for next iteration) - candidate = min(self.hard_message_limit - 1, len(messages) - 1) - - # Reduce if token limit exceeded for the candidate chunk - while ( - candidate > 1 - and self._count_tokens(messages[:candidate]) >= self.hard_token_limit - ): - candidate = max(1, candidate // 2) - - return candidate - - @staticmethod - def _build_original_data_items( - messages: List[Dict[str, Any]] - ) -> List[Dict[str, Any]]: - """Build original_data items in { message } format. - - Each message dict from RawData.content already carries the v1 message - format (with content[] list). Wraps each into { message }. - Parsed results (parsed_summary, parsed_content) are embedded - directly in content items by the enrich provider. - - Args: - messages: List of message dicts (v1 format with content[] list) - - Returns: - List of { message } dicts for MemCell.original_data - """ - items = [] - for msg in messages: - msg.pop("_parse_info", None) # Remove transient key if present - items.append({"message": msg}) - return items - - def _create_memcell_directly( - self, - messages: List[Dict[str, Any]], - request: ConversationMemCellExtractRequest, - trigger_type: str, # 'token_limit', 'message_limit', 'flush', 'llm' - ) -> Optional[MemCell]: - """ - Create MemCell directly without boundary detection. - - Used for force_split and flush modes where we skip LLM boundary detection. - - Args: - messages: List of messages to include in the MemCell - request: The extraction request - trigger_type: Type of trigger ('token_limit', 'message_limit', 'flush', 'llm') - - Returns: - MemCell or None if no messages - """ - if not messages: - logger.warning( - "[ConvMemCellExtractor] _create_memcell_directly called with no messages" - ) - return None - - # Parse timestamp from last message - ts_value = messages[-1].get("timestamp") - timestamp = dt_from_iso_format(ts_value) - participants = self._extract_participant_ids(messages) - - # Build original_data in { message } format - original_data_items = self._build_original_data_items(messages) - - memcell = MemCell( - user_id_list=request.user_id_list, - original_data=original_data_items, - timestamp=timestamp, - group_id=request.group_id, - participants=participants, - sender_ids=participants, - type=self.raw_data_type, - ) - - # Record metrics - result_type = ( - 'flush' - if trigger_type == 'flush' - else ('should_end' if trigger_type == 'llm' else 'force_split') - ) - record_boundary_detection( - space_id=get_space_id_for_metrics(), - raw_data_type=self.raw_data_type.value, - result=result_type, - trigger_type=trigger_type, - ) - record_memcell_extracted( - space_id=get_space_id_for_metrics(), - raw_data_type=self.raw_data_type.value, - trigger_type=trigger_type, - ) - - logger.info( - f"[ConvMemCellExtractor] ✅ MemCell created: " - f"messages={len(messages)}, trigger={trigger_type}" - ) - - return memcell - - def _format_messages_with_indices(self, messages: List[Dict[str, Any]]) -> str: - """ - Format messages with 1-based indices and timestamps for LLM input. - - Format: [N] [YYYY-MM-DD HH:MM:SS+TZ] sender_name: content - - Args: - messages: List of message dictionaries - - Returns: - Formatted string with numbered messages - """ - lines = [] - for i, msg in enumerate(messages, start=1): - content = get_text_from_content_items(msg.get("content", [])) - sender_name = msg.get("sender_name", "") - timestamp = msg.get("timestamp", "") - - # Format timestamp with timezone offset - time_str = "" - if timestamp: - try: - if isinstance(timestamp, datetime): - time_str = timestamp.isoformat(sep=" ", timespec="seconds") - elif isinstance(timestamp, str): - dt = datetime.fromisoformat(timestamp.replace("Z", "+00:00")) - time_str = dt.isoformat(sep=" ", timespec="seconds") - except (ValueError, AttributeError, TypeError): - pass - - if content: - if time_str: - lines.append(f"[{i}] [{time_str}] {sender_name}: {content}") - else: - lines.append(f"[{i}] {sender_name}: {content}") - else: - logger.debug( - f"[ConvMemCellExtractor] Warning: message {i} has no content" - ) - - return "\n".join(lines) - - def _parse_batch_boundary_response( - self, resp: str - ) -> Optional[BatchBoundaryResult]: - """ - Parse LLM response for batch boundary detection. - - Handles nested JSON structures (unlike old single-level regex approach). - - Args: - resp: Raw LLM response string - - Returns: - BatchBoundaryResult or None if parsing fails - """ - data = None - - # Try markdown code block first - json_match = re.search(r'```json\s*(.*?)\s*```', resp, re.DOTALL) - if json_match: - try: - data = json.loads(json_match.group(1)) - except json.JSONDecodeError: - pass - - # Try direct parse - if data is None: - try: - data = json.loads(resp.strip()) - except json.JSONDecodeError: - pass - - # Try extracting outermost {...} (handles nested braces) - if data is None: - start = resp.find('{') - end = resp.rfind('}') - if start != -1 and end != -1 and end > start: - try: - data = json.loads(resp[start : end + 1]) - except json.JSONDecodeError: - pass - - if data is None: - return None - - # Parse boundaries: new format is a flat list of integers - raw_boundaries = data.get("boundaries", []) - boundaries = [] - for item in raw_boundaries: - try: - boundaries.append(int(item)) - except (TypeError, ValueError): - logger.warning( - f"[ConvMemCellExtractor] Skipping unparseable boundary value: {item!r}" - ) - - return BatchBoundaryResult( - boundaries=boundaries, should_wait=bool(data.get("should_wait", False)) - ) - - async def _detect_boundaries( - self, messages: List[Dict[str, Any]] - ) -> BatchBoundaryResult: - """ - Use LLM to detect multiple boundary points in a message sequence. - - Args: - messages: All messages to analyze (history + new, already within limits) - - Returns: - BatchBoundaryResult with detected split points and should_wait flag - """ - if not messages: - return BatchBoundaryResult(boundaries=[], should_wait=False) - - messages_text = self._format_messages_with_indices(messages) - - logger.debug( - f"[ConvMemCellExtractor] Detect boundaries – total messages: {len(messages)}, " - f"formatted text length: {len(messages_text)}" - ) - - prompt = self.conv_batch_boundary_detection_prompt.format( - messages=messages_text - ) - - logger.debug( - f"[ConvMemCellExtractor] === BOUNDARY DETECTION PROMPT ===\n{prompt}\n" - f"[ConvMemCellExtractor] === END PROMPT ===" - ) - - with timed("detect_boundaries"): - # Retry only when LLM returns unparseable content. - # Infrastructure errors (auth, rate-limit, network) are handled - # by the lower layer and will propagate as exceptions. - for i in range(5): - resp = await self.llm_provider.generate(prompt) - logger.debug( - f"[ConvMemCellExtractor] === BOUNDARY DETECTION RESPONSE (attempt {i+1}) ===\n" - f"{resp}\n" - f"[ConvMemCellExtractor] === END RESPONSE ===" - ) - - result = self._parse_batch_boundary_response(resp) - if result is not None: - # Validate boundary indices - valid_boundaries = [ - b for b in result.boundaries if 1 <= b < len(messages) - ] - if len(valid_boundaries) != len(result.boundaries): - logger.warning( - f"[ConvMemCellExtractor] Filtered {len(result.boundaries) - len(valid_boundaries)} " - f"out-of-range boundaries (total messages: {len(messages)})" - ) - result.boundaries = sorted(valid_boundaries) - - # Record metrics for the overall detection - detection_result = ( - 'should_end' if result.boundaries else 'should_wait' - ) - record_boundary_detection( - space_id=get_space_id_for_metrics(), - raw_data_type=self.raw_data_type.value, - result=detection_result, - trigger_type='llm', - ) - return result - - logger.warning( - f"[ConvMemCellExtractor] Failed to parse JSON from LLM response " - f"(attempt {i + 1}/5), response: {resp[:200]}..." - ) - - # All retries exhausted, raise error to interrupt the flow - error_msg = ( - "[ConvMemCellExtractor] All 5 retries exhausted for boundary detection" - ) - logger.error(error_msg) - raise RuntimeError(error_msg) - - async def extract_memcell( - self, request: ConversationMemCellExtractRequest - ) -> tuple[List[MemCell], StatusResult]: - """ - Extract MemCells from the conversation using multi-split boundary detection. - - Algorithm: - 1. Combine history + new messages - 2. Force-split loop: while combined messages exceed hard limits, - cut the front chunk into a MemCell - 3. LLM batch boundary detection on remaining messages - 4. If flush=True, force-cut any remaining tail into a final MemCell - - Returns: - (list_of_memcells, StatusResult) - - Empty list means no boundary detected; caller should accumulate messages - - StatusResult.should_wait indicates the last segment has insufficient context - """ - history_message_dict_list = [ - raw_data.content for raw_data in request.history_raw_data_list - ] - new_message_dict_list = [ - new_raw_data.content for new_raw_data in request.new_raw_data_list - ] - - # Note: parsed results are embedded directly in content items by the enrich provider, - # no need for separate parse_info passthrough. - - # flush=True with no new messages: treat history as the flush tail directly - if not new_message_dict_list: - if request.flush and history_message_dict_list: - logger.info( - f"[ConvMemCellExtractor] Flush with no new messages: " - f"packing {len(history_message_dict_list)} history messages into final MemCell" - ) - memcell = self._create_memcell_directly( - history_message_dict_list, request, 'flush' - ) - result_memcells = [memcell] if memcell else [] - return result_memcells, StatusResult(should_wait=False) - - logger.warning( - "[ConvMemCellExtractor] No valid new messages to process " - "(possibly all filtered out)" - ) - return [], StatusResult(should_wait=True) - - all_msgs = history_message_dict_list + new_message_dict_list - result_memcells: List[MemCell] = [] - - # === Phase 1: Force-split loop === - # While combined messages exceed hard limits, cut the front chunk - while len(all_msgs) > 1: - total_tokens = self._count_tokens(all_msgs) - total_messages = len(all_msgs) - - exceeds_token = total_tokens >= self.hard_token_limit - exceeds_count = total_messages >= self.hard_message_limit - - if not exceeds_token and not exceeds_count: - break - - split_at = self._find_force_split_point(all_msgs) - trigger_type = 'token_limit' if exceeds_token else 'message_limit' - - logger.debug( - f"[ConvMemCellExtractor] Force split triggered: " - f"tokens={total_tokens}/{self.hard_token_limit}, " - f"messages={total_messages}/{self.hard_message_limit}, " - f"split_at={split_at}" - ) - - memcell = self._create_memcell_directly( - all_msgs[:split_at], request, trigger_type - ) - if memcell: - result_memcells.append(memcell) - all_msgs = all_msgs[split_at:] - - # === Phase 2: LLM batch boundary detection === - should_wait = False - if all_msgs: - batch_result = await self._detect_boundaries(all_msgs) - - # Create MemCells for each detected boundary segment - prev = 0 - for boundary in batch_result.boundaries: - end = boundary # 1-indexed integer, treat as exclusive end index - segment = all_msgs[prev:end] - if segment: - memcell = self._create_memcell_directly(segment, request, 'llm') - if memcell: - result_memcells.append(memcell) - prev = end - - # Remaining messages after the last boundary - all_msgs = all_msgs[prev:] - should_wait = batch_result.should_wait - - # === Phase 3: Flush tail === - if request.flush and all_msgs: - logger.info( - f"[ConvMemCellExtractor] Flush mode: packing {len(all_msgs)} remaining " - f"messages into final MemCell" - ) - memcell = self._create_memcell_directly(all_msgs, request, 'flush') - if memcell: - result_memcells.append(memcell) - all_msgs = [] - should_wait = False - - if result_memcells: - logger.info( - f"[ConvMemCellExtractor] ✅ Extracted {len(result_memcells)} MemCell(s), " - f"remaining_msgs={len(all_msgs)}, should_wait={should_wait}" - ) - else: - logger.debug( - f"[ConvMemCellExtractor] ⏳ No boundary detected, " - f"remaining_msgs={len(all_msgs)}, should_wait={should_wait}" - ) - - return result_memcells, StatusResult(should_wait=should_wait) diff --git a/methods/EverCore/src/memory_layer/memory_extractor/agent_case_extractor.py b/methods/EverCore/src/memory_layer/memory_extractor/agent_case_extractor.py deleted file mode 100644 index 7936699c..00000000 --- a/methods/EverCore/src/memory_layer/memory_extractor/agent_case_extractor.py +++ /dev/null @@ -1,747 +0,0 @@ -""" -Agent Experience Extractor for EverCore - -Extracts AgentCase from agent MemCells (OpenAI chat completion format). - -Pipeline: -1. Pre-compress: Build a structured list from raw messages. If total tool content - exceeds a threshold, use LLM to compress tool call inputs/outputs in chunks. -2. Single LLM call: Extract one experience with task_intent, approach, quality_score. -3. Compute embedding on task_intent for retrieval. - -OpenAI message format: -- role="user": User input (content only) -- role="assistant" with tool_calls: Agent decides to call tools -- role="tool" with tool_call_id: Tool execution result -- role="assistant" without tool_calls: Agent final response -""" - -import asyncio -import copy -import json -from dataclasses import dataclass -from datetime import datetime -from typing import Any, Dict, List, Optional - -from common_utils.json_utils import parse_json_response - -from core.oxm.mongo.mongo_utils import generate_object_id_str -from memory_layer.llm.llm_provider import LLMProvider -from memory_layer.memory_extractor.base_memory_extractor import ( - MemoryExtractor, - MemoryExtractRequest, -) -from memory_layer.prompts import get_prompt_by -from api_specs.memory_types import ( - MemCell, - RawDataType, - AgentCase, - get_text_from_content_items, -) -from api_specs.memory_models import MemoryType -from agentic_layer.vectorize_service import get_vectorize_service -from core.di.utils import get_bean_by_type -from core.component.llm.tokenizer.tokenizer_factory import TokenizerFactory -from core.observation.logger import get_logger -from core.observation.stage_timer import timed - -logger = get_logger(__name__) - -# LLM pre-compression chunk size (tokens) -# Tool content below this threshold skips compression entirely -PRE_COMPRESS_CHUNK_SIZE = 100000 - -# When a conversation has many messages, each individual message carries less unique value, -# so we apply more aggressive trimming by halving the scale_trigger threshold. -# This constant defines the message count above which we switch to the tighter threshold. -HIGH_MESSAGE_COUNT_THRESHOLD = 100 - -# Heuristic trim: per-message token limits applied before LLM compression -MAX_TOOL_OUTPUT_TOKENS = 1000 -MAX_TOOL_ARGS_TOKENS = 800 -MAX_ASSISTANT_RESPONSE_TOKENS = 3000 - -# Hard cap for task_intent token length (truncated after LLM extraction) -MAX_TASK_INTENT_TOKENS = 300 - - -@dataclass -class AgentCaseExtractRequest(MemoryExtractRequest): - """Request for extracting AgentCase from a MemCell.""" - - pass - - -class AgentCaseExtractor(MemoryExtractor): - """ - Extracts AgentCase from an agent MemCell. - - Each MemCell produces at most one AgentCase. - Multiple conversation turns solving the same problem are synthesized into one record. - - Pipeline: - 1. Pre-compress: Build structured list, LLM-compress tool content if over threshold - 2. Single LLM call: extract one experience record - 3. Compute embedding on task_intent for retrieval - """ - - # Heuristic: no-tool conversations meeting these thresholds are skipped - # without an LLM filter call (saves cost for obvious non-extractable cases) - FILTER_NO_TOOL_MAX_MESSAGES = 4 - FILTER_NO_TOOL_MIN_ASSISTANT_TOKENS = 200 - - def __init__( - self, - llm_provider: Optional[LLMProvider] = None, - filter_prompt: Optional[str] = None, - experience_compress_prompt: Optional[str] = None, - tool_pre_compress_prompt: Optional[str] = None, - pre_compress_chunk_size: int = PRE_COMPRESS_CHUNK_SIZE, - max_tool_output_tokens: int = MAX_TOOL_OUTPUT_TOKENS, - max_tool_args_tokens: int = MAX_TOOL_ARGS_TOKENS, - max_assistant_response_tokens: int = MAX_ASSISTANT_RESPONSE_TOKENS, - ): - super().__init__(MemoryType.AGENT_CASE) - self.llm_provider = llm_provider - self.filter_prompt = filter_prompt or get_prompt_by("AGENT_CASE_FILTER_PROMPT") - self.experience_compress_prompt = experience_compress_prompt or get_prompt_by( - "AGENT_CASE_COMPRESS_PROMPT" - ) - self.tool_pre_compress_prompt = tool_pre_compress_prompt or get_prompt_by( - "AGENT_TOOL_PRE_COMPRESS_PROMPT" - ) - self.pre_compress_chunk_size = pre_compress_chunk_size - self.max_tool_output_tokens = max_tool_output_tokens - self.max_tool_args_tokens = max_tool_args_tokens - self.max_assistant_response_tokens = max_assistant_response_tokens - - @staticmethod - def _json_default(obj: Any) -> Any: - """JSON encoder default for non-serializable types.""" - if isinstance(obj, datetime): - return obj.isoformat() - return str(obj) - - @classmethod - def _get_tokenizer(cls): - """Get the shared tokenizer from tokenizer factory.""" - tokenizer_factory: TokenizerFactory = get_bean_by_type(TokenizerFactory) - return tokenizer_factory.get_tokenizer_from_tiktoken("o200k_base") - - @classmethod - def _count_tokens(cls, text: str) -> int: - """Count tokens in a string.""" - if not text: - return 0 - tokenizer = cls._get_tokenizer() - return len(tokenizer.encode(text)) - - @classmethod - def _calc_tool_content_size(cls, msg: Dict[str, Any]) -> int: - """Calculate the tool-related content size of a message (in tokens).""" - role = msg.get("role", "") - if role == "tool": - return cls._count_tokens(msg.get("content", "")) - if role == "assistant" and msg.get("tool_calls"): - return sum( - cls._count_tokens(tc.get("function", {}).get("arguments", "")) - for tc in msg["tool_calls"] - ) - return 0 - - @classmethod - def _truncate_text(cls, text: str, max_tokens: int, head_ratio: float = 0.7) -> str: - """Truncate text to max_tokens, keeping head and tail with a marker. - - When head_ratio=1.0, only the head is kept with "..." appended. - """ - if not text or not isinstance(text, str): - return text - tokenizer = cls._get_tokenizer() - tokens = tokenizer.encode(text) - if len(tokens) <= max_tokens: - return text - head_count = int(max_tokens * head_ratio) - tail_count = max_tokens - head_count - head_text = tokenizer.decode(tokens[:head_count]) - if tail_count <= 0: - return head_text.rstrip() + "..." - tail_text = tokenizer.decode(tokens[-tail_count:]) - trimmed = len(tokens) - max_tokens - return f"{head_text}\n[... trimmed {trimmed} tokens ...]\n{tail_text}" - - @classmethod - def _heuristic_trim_tool_outputs( - cls, - messages: List[Dict[str, Any]], - max_tool_output_tokens: int, - max_tool_args_tokens: int, - max_assistant_response_tokens: int = MAX_ASSISTANT_RESPONSE_TOKENS, - head_ratio: float = 0.7, - ) -> List[Dict[str, Any]]: - """Truncate oversized tool outputs, arguments, and assistant responses.""" - result = copy.deepcopy(messages) - trimmed_count = 0 - for msg in result: - role = msg.get("role", "") - if role == "tool" and msg.get("content"): - original = msg["content"] - msg["content"] = cls._truncate_text( - original, max_tool_output_tokens, head_ratio - ) - if msg["content"] != original: - trimmed_count += 1 - elif role == "assistant": - # Trim tool call arguments - if msg.get("tool_calls"): - for tc in msg["tool_calls"]: - func = tc.get("function") - if not func: - continue - args = func.get("arguments", "") - if args: - new_args = cls._truncate_text( - args, max_tool_args_tokens, head_ratio - ) - if new_args != args: - func["arguments"] = new_args - trimmed_count += 1 - # Trim assistant response content (non-tool-call messages) - content = msg.get("content") - if content and isinstance(content, str): - new_content = cls._truncate_text( - content, max_assistant_response_tokens, head_ratio - ) - if new_content != content: - msg["content"] = new_content - trimmed_count += 1 - if trimmed_count > 0: - logger.info( - f"[AgentCaseExtractor] Heuristic trim: " - f"truncated {trimmed_count} content fields" - ) - return result - - def _collect_tool_call_groups(self, items: List[Dict[str, Any]]) -> List[List[int]]: - """Collect atomic tool call groups from the message list. - - Each group is an assistant message with tool_calls + its corresponding - tool response messages. These must not be split across chunks. - """ - groups: List[List[int]] = [] - i = 0 - while i < len(items): - msg = items[i] - if msg.get("role") == "assistant" and msg.get("tool_calls"): - group = [i] - j = i + 1 - while j < len(items) and items[j].get("role") == "tool": - group.append(j) - j += 1 - groups.append(group) - i = j - else: - i += 1 - return groups - - def _calc_group_size(self, items: List[Dict[str, Any]], group: List[int]) -> int: - """Calculate total tool content tokens of a group.""" - return sum(self._calc_tool_content_size(items[idx]) for idx in group) - - async def _pre_compress_to_list( - self, original_data: List[Dict[str, Any]] - ) -> List[Dict[str, Any]]: - """Pre-compress tool content using selective LLM compression. - - If total tool content <= pre_compress_chunk_size, return as-is. - Otherwise, only compress the largest groups (by token size descending) - until the estimated total drops below the threshold, leaving small - groups uncompressed to save LLM calls. - """ - items = copy.deepcopy(original_data) - - tool_call_groups = self._collect_tool_call_groups(items) - if not tool_call_groups: - return items - - # Calculate size per group - groups_with_size = [ - (i, g, self._calc_group_size(items, g)) - for i, g in enumerate(tool_call_groups) - ] - total_size = sum(s for _, _, s in groups_with_size) - if total_size <= self.pre_compress_chunk_size: - logger.debug( - f"[AgentCaseExtractor] Tool content {total_size} tokens " - f"<= {self.pre_compress_chunk_size}, no compression needed" - ) - return items - - # Select only the largest groups needed to bring total under threshold. - # Assume ~90% reduction for compressed groups. - groups_by_size = sorted(groups_with_size, key=lambda x: x[2], reverse=True) - compress_indices: set = set() - estimated_total = total_size - for idx, group, size in groups_by_size: - if estimated_total <= self.pre_compress_chunk_size: - break - compress_indices.add(idx) - estimated_total -= size * 0.9 # estimated savings - - # Keep original order so chunk boundaries and replacement stay aligned - groups_to_compress: List[List[int]] = [ - g for i, g in enumerate(tool_call_groups) if i in compress_indices - ] - - logger.debug( - f"[AgentCaseExtractor] Selective compression: " - f"{len(groups_to_compress)}/{len(tool_call_groups)} groups, " - f"{total_size} total tokens" - ) - - # Split selected groups into chunks of pre_compress_chunk_size - chunks: List[List[List[int]]] = [] - current_chunk: List[List[int]] = [] - current_size = 0 - - for group in groups_to_compress: - group_size = self._calc_group_size(items, group) - if ( - current_chunk - and current_size + group_size > self.pre_compress_chunk_size - ): - chunks.append(current_chunk) - current_chunk = [group] - current_size = group_size - else: - current_chunk.append(group) - current_size += group_size - - if current_chunk: - chunks.append(current_chunk) - - # Build per-chunk message lists - chunk_msg_lists: List[List[Dict[str, Any]]] = [] - for chunk_groups in chunks: - chunk_indices = [idx for group in chunk_groups for idx in group] - chunk_msg_lists.append([items[idx] for idx in chunk_indices]) - - # Compress all chunks in parallel - results = await asyncio.gather( - *(self._compress_tool_chunk(chunk_msgs) for chunk_msgs in chunk_msg_lists), - return_exceptions=True, - ) - all_compressed: List[Dict[str, Any]] = [] - for round_idx, result in enumerate(results): - if isinstance(result, Exception): - logger.warning( - f"[AgentCaseExtractor] Chunk {round_idx + 1} compression error: " - f"{result}, keeping original messages" - ) - all_compressed.extend(chunk_msg_lists[round_idx]) - elif result is not None: - all_compressed.extend(result) - else: - logger.warning( - f"[AgentCaseExtractor] Chunk {round_idx + 1} compression failed, " - "keeping original messages" - ) - all_compressed.extend(chunk_msg_lists[round_idx]) - - # Replace only the selected groups' messages with compressed results - selected_indices = sorted(idx for group in groups_to_compress for idx in group) - - if len(all_compressed) == len(selected_indices): - for i, idx in enumerate(selected_indices): - items[idx] = all_compressed[i] - else: - logger.warning( - f"[AgentCaseExtractor] Compressed count {len(all_compressed)} " - f"!= selected message count {len(selected_indices)}, keeping originals" - ) - - return items - - async def _compress_tool_chunk( - self, messages: List[Dict[str, Any]] - ) -> Optional[List[Dict[str, Any]]]: - """Compress a chunk of tool-related messages via LLM.""" - prompt = self.tool_pre_compress_prompt.format( - messages_json=json.dumps( - messages, ensure_ascii=False, indent=2, default=self._json_default - ), - new_count=len(messages), - ) - - for attempt in range(2): - try: - resp = await self.llm_provider.generate(prompt) - data = parse_json_response(resp) - if ( - data - and "compressed_messages" in data - and isinstance(data["compressed_messages"], list) - and len(data["compressed_messages"]) == len(messages) - ): - return data["compressed_messages"] - logger.warning( - f"[AgentCaseExtractor] Tool pre-compress attempt {attempt+1}/2: " - f"invalid response format" - ) - except Exception as e: - logger.warning( - f"[AgentCaseExtractor] Tool pre-compress attempt {attempt+1}/2: {e}" - ) - - return None - - async def _filter_conversation(self, messages_json: str) -> bool: - """LLM-based filter to determine if the conversation is worth extracting.""" - prompt = self.filter_prompt.format(messages=messages_json) - try: - resp = await self.llm_provider.generate(prompt) - data = parse_json_response(resp) - if data and "worth_extracting" in data: - worth = data["worth_extracting"] - if not worth: - reason = data.get("reason", "") - logger.info(f"[AgentCaseExtractor] Filtered out by LLM: {reason}") - return bool(worth) - except Exception as e: - logger.warning(f"[AgentCaseExtractor] Filter failed: {e}") - # Default to extracting if filter fails - return True - - async def _compress_experience( - self, messages_json: str - ) -> Optional[Dict[str, Any]]: - """Single LLM call to extract one experience with task_intent + approach + quality_score.""" - prompt = self.experience_compress_prompt.format(messages=messages_json) - - for attempt in range(2): - try: - resp = await self.llm_provider.generate(prompt) - data = parse_json_response(resp) - if data and "task_intent" in data: - if not data["task_intent"]: - logger.info( - "[AgentCaseExtractor] LLM returned empty 'task_intent', skipping" - ) - return None - if not data.get("approach"): - logger.warning( - "[AgentCaseExtractor] LLM returned empty 'approach', skipping" - ) - return None - return data - logger.warning( - f"[AgentCaseExtractor] Compress attempt {attempt+1}/2: " - f"missing or invalid 'task_intent' field" - ) - except Exception as e: - logger.warning( - f"[AgentCaseExtractor] Compress attempt {attempt+1}/2: {e}" - ) - - logger.error( - "[AgentCaseExtractor] Experience extraction failed after 2 attempts" - ) - return None - - @staticmethod - def _clamp_quality_score(value: Any) -> Optional[float]: - """Clamp quality_score to [0.0, 1.0], return None if invalid.""" - if value is None: - return None - try: - return max(0.0, min(1.0, float(value))) - except (TypeError, ValueError): - return None - - @staticmethod - def _unwrap_messages(original_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """Unwrap messages from MemCell original_data items. - - MemCell.original_data items are in { "message": {...}, "parse_info": ... } - format. This extracts the inner message dicts. Also normalizes the content - field: v1 API uses content[] list, but downstream processing expects plain - strings for tool/user messages. - """ - messages = [] - for item in original_data: - if not isinstance(item, dict): - continue - msg = item.get("message", item) - # Normalize content: convert content items list to plain text string - # for roles where downstream expects a string (user, tool). - # Assistant messages may have tool_calls and null/empty content, leave as-is. - content = msg.get("content") - if isinstance(content, list): - msg = copy.deepcopy(msg) - msg["content"] = get_text_from_content_items(content) - messages.append(msg) - return messages - - @staticmethod - def _has_tool_calls(messages: List[Dict[str, Any]]) -> bool: - """Check if the conversation contains any tool calls or tool responses.""" - return any( - msg.get("tool_calls") or msg.get("role") == "tool" for msg in messages - ) - - @staticmethod - def _count_tool_call_rounds(messages: List[Dict[str, Any]]) -> int: - """Count the number of assistant messages that contain tool_calls.""" - return sum( - 1 - for msg in messages - if msg.get("role") == "assistant" and msg.get("tool_calls") - ) - - @staticmethod - def _strip_before_first_user( - messages: List[Dict[str, Any]] - ) -> List[Dict[str, Any]]: - """Drop messages before the first user message (e.g. system prompts).""" - for i, msg in enumerate(messages): - if msg.get("role") == "user": - return messages[i:] - return [] - - @classmethod - def _should_skip(cls, messages: List[Dict[str, Any]]) -> Optional[str]: - """Pre-filter conversations that are not worth extracting. - - Combines structural checks with heuristic content checks to avoid - a separate LLM filter call for low-tool-call conversations. - """ - if not messages: - return "No messages after stripping system prompts" - - if not any(msg.get("role") == "user" for msg in messages): - return "No user messages found" - - if not any(msg.get("role") == "assistant" for msg in messages): - return "No assistant messages found" - - last_msg = messages[-1] - if last_msg.get("role") != "assistant" or last_msg.get("tool_calls"): - return "Incomplete agent trajectory (last message is not a final assistant response)" - - has_tools = cls._has_tool_calls(messages) - - if not has_tools: - user_count = sum(1 for msg in messages if msg.get("role") == "user") - if user_count < 2: - return "Single-turn conversation without tool calls" - - # Heuristic: no-tool conversations with very few messages are - # unlikely to contain meaningful problem-solving — skip without - # an LLM filter call to save cost. - if len(messages) <= cls.FILTER_NO_TOOL_MAX_MESSAGES: - return ( - f"No-tool conversation with only {len(messages)} messages " - f"(max {cls.FILTER_NO_TOOL_MAX_MESSAGES}), skipping" - ) - - # Heuristic: no-tool conversations with very brief assistant - # responses are likely simple Q&A or chitchat. - assistant_content = " ".join( - msg.get("content", "") or "" - for msg in messages - if msg.get("role") == "assistant" and not msg.get("tool_calls") - ) - assistant_tokens = cls._count_tokens(assistant_content) - if assistant_tokens < cls.FILTER_NO_TOOL_MIN_ASSISTANT_TOKENS: - return ( - f"No-tool conversation with brief assistant response " - f"({assistant_tokens} tokens < {cls.FILTER_NO_TOOL_MIN_ASSISTANT_TOKENS}), skipping" - ) - - return None - - async def _compute_embedding(self, text: str) -> Optional[Dict[str, Any]]: - """Compute embedding for the task intent.""" - try: - if not text: - return None - vs = get_vectorize_service() - vec = await vs.get_embedding(text) - return { - "embedding": vec.tolist() if hasattr(vec, "tolist") else list(vec), - "vector_model": vs.get_model_name(), - } - except Exception as e: - logger.error(f"[AgentCaseExtractor] Embedding failed: {e}") - return None - - async def extract_memory( - self, request: MemoryExtractRequest - ) -> Optional[AgentCase]: - """ - Extract AgentCase from a MemCell. - - Pipeline: - 1. Pre-compress: build structured list, LLM-compress tool content if over threshold - 2. Single LLM call: extract one experience record - 3. Compute embedding on task_intent - """ - memcell = request.memcell - if not memcell: - return None - - if memcell.type != RawDataType.AGENTCONVERSATION: - logger.warning( - f"[AgentCaseExtractor] Expected AGENT_CONVERSATION, got {memcell.type}" - ) - return None - - try: - # Unwrap from { "message": ..., "parse_info": ... } format - # and normalize content[] lists to plain strings - raw_messages = self._unwrap_messages(memcell.original_data or []) - original_data = self._strip_before_first_user(raw_messages) - - # Pre-filter: skip conversations not worth extracting - skip_reason = self._should_skip(original_data) - if skip_reason: - logger.info(f"[AgentCaseExtractor] {skip_reason}, skipping") - return None - - # Heuristic trim: truncate oversized tool outputs and assistant responses. - # Start scaling when total tokens exceed half of PRE_COMPRESS_CHUNK_SIZE, - # with limits inversely proportional to how far over the threshold we are. - # After trim, skip entirely if still over 2x PRE_COMPRESS_CHUNK_SIZE. - total_tokens = self._count_tokens( - json.dumps(original_data, ensure_ascii=False, default=self._json_default) - ) - logger.info( - f"[AgentCaseExtractor] event_id={memcell.event_id}, " - f"total_tokens={total_tokens}, message_count={len(original_data)}" - ) - - # High message count signals lower per-message value; use a tighter trigger - # so trim kicks in earlier and compresses more aggressively. - scale_trigger = ( - self.pre_compress_chunk_size // 2 - if len(original_data) > HIGH_MESSAGE_COUNT_THRESHOLD - else self.pre_compress_chunk_size - ) - needs_scale = total_tokens > scale_trigger - if needs_scale: - # Scale limits inversely proportional to how far over the trigger we are. - # e.g. at 1x chunk_size -> scale=0.5, at 2x -> scale=0.25 - scale = scale_trigger / total_tokens - trim_tool_output = max(200, int(self.max_tool_output_tokens * scale)) - trim_tool_args = max(200, int(self.max_tool_args_tokens * scale)) - trim_assistant = max(500, int(self.max_assistant_response_tokens * scale)) - logger.info( - f"[AgentCaseExtractor] Total tokens {total_tokens} > " - f"scale_trigger ({scale_trigger}), " - f"scale={scale:.2f} -> trim limits: " - f"tool_output={trim_tool_output}, tool_args={trim_tool_args}, " - f"assistant={trim_assistant}" - ) - else: - trim_tool_output = self.max_tool_output_tokens - trim_tool_args = self.max_tool_args_tokens - trim_assistant = self.max_assistant_response_tokens - original_data = self._heuristic_trim_tool_outputs( - original_data, - trim_tool_output, - trim_tool_args, - trim_assistant, - ) - - # Only re-count after trim when scaling was applied — if total_tokens was - # already <= scale_trigger, trimmed_tokens can't possibly exceed 2x chunk_size. - if needs_scale: - trimmed_tokens = self._count_tokens( - json.dumps(original_data, ensure_ascii=False, default=self._json_default) - ) - if trimmed_tokens > self.pre_compress_chunk_size * 2: - logger.info( - f"[AgentCaseExtractor] Still {trimmed_tokens} tokens after trim " - f"(> 2x PRE_COMPRESS_CHUNK_SIZE {self.pre_compress_chunk_size * 2}), skipping extraction" - ) - return None - - # Step 1: Pre-compress to JSON list (LLM-based if tool content is large) - with timed("pre_compress"): - pre_compressed_list = await self._pre_compress_to_list(original_data) - messages_json = json.dumps( - pre_compressed_list, - ensure_ascii=False, - indent=2, - default=self._json_default, - ) - - logger.debug( - f"[AgentCaseExtractor] Pre-compressed: " - f"{len(pre_compressed_list)} items, {len(messages_json)} chars" - ) - - # Step 2: LLM filter — for conversations with only a single round - # of tool calls (no-tool short conversations already skipped by - # heuristic in _should_skip) - tool_rounds = self._count_tool_call_rounds(original_data) - if tool_rounds <= 1: - with timed("filter_conversation"): - if not await self._filter_conversation(messages_json): - return None - - # Step 3: Single LLM call — returns experience dict or None - with timed("compress_experience"): - exp_dict = await self._compress_experience(messages_json) - - if not exp_dict: - logger.info( - "[AgentCaseExtractor] No actionable experience extracted, skipping" - ) - return None - - # Truncate task_intent to hard token cap (head only) - original_intent = exp_dict.get("task_intent", "") - raw_intent = self._truncate_text( - original_intent, MAX_TASK_INTENT_TOKENS, head_ratio=1.0 - ) - if raw_intent != original_intent: - logger.info( - f"[AgentCaseExtractor] Truncated task_intent to " - f"{MAX_TASK_INTENT_TOKENS} tokens, " - f"original: {original_intent}" - ) - - # Build AgentCase - experience = AgentCase( - id=generate_object_id_str(), - memory_type=MemoryType.AGENT_CASE, - user_id=request.user_id or "", - timestamp=memcell.timestamp, - group_id=request.group_id, - participants=memcell.participants, - sender_ids=memcell.sender_ids, - task_intent=raw_intent, - approach=exp_dict.get("approach", ""), - quality_score=self._clamp_quality_score( - exp_dict.get("quality_score", 0.5) - ), - key_insight=exp_dict.get("key_insight", ""), - ) - - # Step 4: Compute embedding on task_intent for retrieval - embedding_data = await self._compute_embedding(experience.task_intent) - if embedding_data: - experience.vector = embedding_data["embedding"] - experience.vector_model = embedding_data["vector_model"] - - logger.debug( - f"[AgentCaseExtractor] Extracted: " - f"intent='{experience.task_intent[:80]}'" - ) - - return experience - - except Exception as e: - logger.error(f"[AgentCaseExtractor] Extraction failed: {e}") - return None diff --git a/methods/EverCore/src/memory_layer/memory_extractor/agent_skill_extractor.py b/methods/EverCore/src/memory_layer/memory_extractor/agent_skill_extractor.py deleted file mode 100644 index 121b7320..00000000 --- a/methods/EverCore/src/memory_layer/memory_extractor/agent_skill_extractor.py +++ /dev/null @@ -1,933 +0,0 @@ -""" -AgentSkillExtractor for EverCore - -Incrementally extracts reusable skills from new AgentCase records -via operation-based updates (add/update/none) on existing cluster skills. - -Pipeline: -1. Format the NEW AgentCaseRecord(s) as JSON context -2. Format existing skills with index numbers for the LLM -3. Single LLM call: output incremental operations (add/update/none) -4. Apply each operation: embed changed skills, persist via targeted DB ops -""" - -import json -import re -from dataclasses import dataclass, field -from datetime import datetime -from difflib import SequenceMatcher -from typing import Any, Dict, List, Optional - -import numpy as np - -from api_specs.memory_types import AgentCase -from common_utils.json_utils import parse_json_response - -from core.component.llm.tokenizer.tokenizer_factory import TokenizerFactory -from core.di.utils import get_bean_by_type -from memory_layer.llm.llm_provider import LLMProvider -from memory_layer.prompts import get_prompt_by -from core.observation.logger import get_logger -from core.observation.stage_timer import timed - -logger = get_logger(__name__) - - -@dataclass -class SkillExtractionResult: - """Result of an incremental skill extraction run. - - Attributes: - added_records: Newly created skill records (need insert into search engines). - updated_records: Existing skill records that were modified in MongoDB - (need upsert/replace in search engines). The in-memory objects - already reflect the updated field values. - deleted_ids: String IDs of skill records that were soft-deleted in MongoDB - (need removal from search engines). - """ - - added_records: List[Any] = field(default_factory=list) - updated_records: List[Any] = field(default_factory=list) - deleted_ids: List[str] = field(default_factory=list) - - -class AgentSkillExtractor: - """ - Incrementally extracts reusable skills from a MemScene. - - For each new case added to a cluster, this extractor: - - Takes only the NEW AgentCaseRecord(s) - - Reads existing skills for the cluster (with index numbers) - - Uses an LLM to produce incremental operations (add/update/none) - - Applies each operation with targeted DB writes (unchanged skills are untouched) - """ - - # Max tokens for skill description fields - MAX_DESCRIPTION_TOKENS: int = 400 - # Max tokens for skill content fields in prompt - MAX_CONTENT_TOKENS: int = 5000 - # quality_score threshold that determines which extraction prompt to use - FAILURE_QUALITY_THRESHOLD: float = 0.5 - - def __init__( - self, - llm_provider: Optional[LLMProvider] = None, - success_extract_prompt: Optional[str] = None, - failure_extract_prompt: Optional[str] = None, - maturity_threshold: float = 0.6, - retire_confidence: float = 0.1, - skip_maturity_scoring: bool = False, - ): - self.llm_provider = llm_provider - self.success_extract_prompt = success_extract_prompt or get_prompt_by( - "AGENT_SKILL_SUCCESS_EXTRACT_PROMPT" - ) - self.failure_extract_prompt = failure_extract_prompt or get_prompt_by( - "AGENT_SKILL_FAILURE_EXTRACT_PROMPT" - ) - self.maturity_threshold = maturity_threshold - self.retire_confidence = retire_confidence - self.skip_maturity_scoring = skip_maturity_scoring - self.maturity_prompt = get_prompt_by("AGENT_SKILL_MATURITY_SCORE_PROMPT") - - @staticmethod - def _json_default(obj: Any) -> Any: - if isinstance(obj, datetime): - return obj.isoformat() - return str(obj) - - def _format_cases(self, case_records: List[AgentCase]) -> str: - """Format new AgentCaseRecords as a concise JSON string for the LLM.""" - formatted = [] - for rec in case_records: - entry = { - "timestamp": rec.timestamp.isoformat() if rec.timestamp else None, - "task_intent": getattr(rec, "task_intent", ""), - "approach": getattr(rec, "approach", ""), - "quality_score": getattr(rec, "quality_score", 0.5) or 0.5, - } - key_insight = getattr(rec, "key_insight", None) - if key_insight: - entry["key_insight"] = key_insight - formatted.append(entry) - return json.dumps( - formatted, ensure_ascii=False, indent=2, default=self._json_default - ) - - @classmethod - def _get_tokenizer(cls): - """Get the shared tokenizer from tokenizer factory.""" - tokenizer_factory: TokenizerFactory = get_bean_by_type(TokenizerFactory) - return tokenizer_factory.get_tokenizer_from_tiktoken("o200k_base") - - @classmethod - def _truncate_text( - cls, - text: str, - max_tokens: int = 200, - suffix: str = "... [omitted]", - ) -> str: - """Truncate text to max_tokens using tokenizer, appending suffix if truncated.""" - if not text or not isinstance(text, str): - return text - text = text.strip() - tokenizer = cls._get_tokenizer() - tokens = tokenizer.encode(text) - if len(tokens) <= max_tokens: - return text - head_text = tokenizer.decode(tokens[:max_tokens]) - return head_text.rstrip() + suffix - - def _summarize_case_for_prompt( - self, case_record: Any, max_approach_tokens: int = 200 - ) -> Dict[str, Any]: - """Build a compact case summary dict for inclusion in the skill prompt.""" - entry: Dict[str, Any] = { - "task_intent": getattr(case_record, "task_intent", ""), - "quality_score": getattr(case_record, "quality_score", 0.5) or 0.5, - } - key_insight = getattr(case_record, "key_insight", None) - if key_insight: - entry["key_insight"] = key_insight - approach = getattr(case_record, "approach", None) - if approach: - entry["approach"] = self._truncate_text(approach, max_tokens=max_approach_tokens) - return entry - - def _format_existing_skills( - self, - existing_records: List[Any], - case_history: Optional[List[Any]] = None, - max_support_cases: int = 3, - max_approach_tokens: int = 200, - ) -> str: - """Format existing AgentSkillRecords with index numbers for the LLM. - - When case_history is provided, each skill's source_case_ids are looked up - in case_history to attach up to max_support_cases supporting case summaries. - """ - if not existing_records: - return "(empty — no existing skills)" - - # Build lookup: case_id -> case_record - case_map: Dict[str, Any] = {} - for rec in (case_history or []): - cid = str(getattr(rec, "id", "") or "") - if cid: - case_map[cid] = rec - - lines = [] - for idx, rec in enumerate(existing_records): - item: Dict[str, Any] = { - "index": idx, - "name": rec.name, - "description": self._truncate_text(rec.description, max_tokens=self.MAX_DESCRIPTION_TOKENS), - "content": self._truncate_text(rec.content, max_tokens=self.MAX_CONTENT_TOKENS), - "confidence": rec.confidence, - } - - # Attach supporting case summaries if available - if case_map: - source_ids = getattr(rec, "source_case_ids", None) or [] - matched_ids = [sid for sid in source_ids if str(sid) in case_map] - if matched_ids: - recent_ids = matched_ids[-max_support_cases:] - item["supporting_case_count"] = len(matched_ids) - item["supporting_cases"] = [ - self._summarize_case_for_prompt( - case_map[str(sid)], max_approach_tokens=max_approach_tokens - ) - for sid in recent_ids - ] - - lines.append(json.dumps(item, ensure_ascii=False, default=self._json_default)) - return "[\n" + ",\n".join(lines) + "\n]" - - @staticmethod - def _cosine_similarity(vec_a: List[float], vec_b: List[float]) -> float: - """Compute cosine similarity between two vectors.""" - a = np.array(vec_a, dtype=np.float32) - b = np.array(vec_b, dtype=np.float32) - dot = np.dot(a, b) - norm = np.linalg.norm(a) * np.linalg.norm(b) - if norm == 0: - return 0.0 - return float(dot / norm) - - async def _select_top_k_skills( - self, - existing_records: List[Any], - new_case_records: List[AgentCase], - top_k: int = 10, - ) -> List[Any]: - """Select the top-k most relevant existing skills by vector similarity.""" - query_parts = [] - for rec in new_case_records: - intent = getattr(rec, "task_intent", "") or "" - if intent: - query_parts.append(intent) - query_text = "\n".join(query_parts) - if not query_text: - return existing_records[:top_k] - - # Reuse the existing vector when there is exactly one new case with a vector - query_vec = None - if len(new_case_records) == 1: - existing_vec = getattr(new_case_records[0], "vector", None) - if existing_vec and len(existing_vec) > 0: - query_vec = existing_vec - - if query_vec is None: - query_embedding = await self._compute_embedding(query_text) - if not query_embedding: - logger.warning( - "[AgentSkillExtractor] Failed to compute query embedding for top-k selection, " - "falling back to first %d skills", - top_k, - ) - return existing_records[:top_k] - query_vec = query_embedding["embedding"] - - with_vec = [] - without_vec = [] - for rec in existing_records: - if rec.vector and len(rec.vector) > 0: - sim = self._cosine_similarity(query_vec, rec.vector) - with_vec.append((sim, rec)) - else: - without_vec.append(rec) - - with_vec.sort(key=lambda x: x[0], reverse=True) - selected = [rec for _, rec in with_vec[:top_k]] - - remaining = top_k - len(selected) - if remaining > 0 and without_vec: - selected.extend(without_vec[:remaining]) - - logger.info( - "[AgentSkillExtractor] Top-k selection: %d/%d skills selected (top_k=%d)", - len(selected), - len(existing_records), - top_k, - ) - return selected - - async def _compute_embedding(self, text: str) -> Optional[Dict[str, Any]]: - """Compute embedding for a skill item's name + description.""" - try: - if not text: - return None - from agentic_layer.vectorize_service import get_vectorize_service - - vs = get_vectorize_service() - vec = await vs.get_embedding(text) - return { - "embedding": vec.tolist() if hasattr(vec, "tolist") else list(vec), - "vector_model": vs.get_model_name(), - } - except Exception as e: - logger.error(f"[AgentSkillExtractor] Embedding failed: {e}") - return None - - def _select_prompt(self, case_records: List[AgentCase]) -> str: - """Select extraction prompt based on the max quality_score of new cases.""" - max_quality = ( - max((getattr(rec, "quality_score", 0.5) or 0.5) for rec in case_records) - if case_records - else 0.5 - ) - if max_quality < self.FAILURE_QUALITY_THRESHOLD: - logger.debug( - "[AgentSkillExtractor] Using failure prompt (max_quality=%.2f < %.2f)", - max_quality, - self.FAILURE_QUALITY_THRESHOLD, - ) - return self.failure_extract_prompt - logger.debug( - "[AgentSkillExtractor] Using success prompt (max_quality=%.2f >= %.2f)", - max_quality, - self.FAILURE_QUALITY_THRESHOLD, - ) - return self.success_extract_prompt - - async def _call_llm( - self, new_case_json: str, existing_skills_json: str, prompt_template: str - ) -> Optional[Dict[str, Any]]: - """Single LLM call to produce incremental skill operations.""" - prompt = prompt_template.format( - new_case_json=new_case_json, existing_skills_json=existing_skills_json - ) - for attempt in range(3): - try: - resp = await self.llm_provider.generate(prompt) - data = parse_json_response(resp) - if data and isinstance(data.get("operations"), list): - return data - logger.warning( - f"[AgentSkillExtractor] LLM retry {attempt + 1}/3: invalid format" - ) - except Exception as e: - logger.warning(f"[AgentSkillExtractor] LLM retry {attempt + 1}/3: {e}") - return None - - async def _evaluate_maturity( - self, name: str, description: str, content: str, confidence: float - ) -> Optional[float]: - """Evaluate maturity of a skill via LLM scoring. - - Scores the skill across 4 dimensions (1-5 each, total out of 20), - then normalizes to 0.0-1.0. - """ - if self.skip_maturity_scoring: - logger.info("[AgentSkillExtractor] Maturity scoring skipped by config, returning 1.0") - return 1.0 - try: - prompt = self.maturity_prompt.format( - name=name or "", - description=description or "", - content=content or "", - confidence=confidence, - ) - resp = await self.llm_provider.generate(prompt) - data = parse_json_response(resp) - dimensions = ["completeness", "executability", "evidence", "clarity"] - if not data or not all(d in data for d in dimensions): - logger.warning( - "[AgentSkillExtractor] Maturity evaluation returned invalid format" - ) - return None - - raw_total = sum(float(data[d]) for d in dimensions) - score = max(0.0, min(1.0, raw_total / 20.0)) - logger.info( - "[AgentSkillExtractor] Maturity evaluation: name='%s', " - "raw=%.1f, score=%.2f, threshold=%.2f, ready=%s, reason=%s", - name, - raw_total, - score, - self.maturity_threshold, - score >= self.maturity_threshold, - data.get("reason", ""), - ) - return score - except Exception as e: - logger.warning("[AgentSkillExtractor] Maturity evaluation failed: %s", e) - return None - - # Content change ratio below which maturity re-evaluation is always skipped - MATURITY_TRIVIAL_CHANGE_RATIO: float = 0.2 - # Content change ratio above which maturity must be re-evaluated via LLM - MATURITY_REEVAL_CHANGE_RATIO: float = 0.4 - - @staticmethod - def _is_hypothesis_promotion(old_content: str, new_content: str) -> bool: - """Detect if an update promotes a hypothesis skill to a verified skill. - - Returns True when the old content had '## Potential Steps' but the new - content has '## Steps' (not Potential), indicating the LLM promoted it. - """ - old_has_potential = bool( - re.search(r"^##\s+Potential Steps", old_content or "", re.MULTILINE) - ) - new_has_steps = bool(re.search(r"^##\s+Steps", new_content or "", re.MULTILINE)) - new_has_potential = bool( - re.search(r"^##\s+Potential Steps", new_content or "", re.MULTILINE) - ) - return old_has_potential and new_has_steps and not new_has_potential - - @staticmethod - def _content_change_ratio(old: str, new: str) -> float: - """Return 0.0-1.0 indicating how much content changed. - - Uses SequenceMatcher to compute: 1 - (matching chars / max length). - """ - if not old and not new: - return 0.0 - if not old or not new: - return 1.0 - ratio = SequenceMatcher(None, old, new).ratio() - return round(1.0 - ratio, 4) - - @staticmethod - def _is_skill_content_sufficient( - content: str, min_lines: int = 5, min_length: int = 50 - ) -> bool: - """Check if skill content has enough substance to be useful.""" - if not content: - return False - stripped = content.strip() - if len(stripped) < min_length: - return False - non_empty_lines = [l for l in stripped.splitlines() if l.strip()] - return len(non_empty_lines) >= min_lines - - async def _apply_add( - self, - op: Dict[str, Any], - cluster_id: str, - group_id: Optional[str], - user_id: Optional[str], - skill_repo: Any, - source_case_ids: Optional[List[str]] = None, - ) -> Optional[Any]: - """Apply an 'add' operation: create and insert a new skill record.""" - data = op.get("data", {}) - content = data.get("content", "") - if not content: - logger.warning( - "[AgentSkillExtractor] add operation has empty content, skipping" - ) - return None - - if not self._is_skill_content_sufficient(content): - logger.warning( - "[AgentSkillExtractor] add operation has insufficient content " - "(too short or no steps), skipping. content=%r", - content[:100], - ) - return None - - name = data.get("name", "") - description = data.get("description", "") - if not name and not description: - logger.warning( - "[AgentSkillExtractor] add operation has no name and no description, skipping" - ) - return None - description = self._truncate_text(description, max_tokens=self.MAX_DESCRIPTION_TOKENS, suffix="...") - - try: - confidence = max(0.0, min(1.0, float(data.get("confidence", 0.5)))) - except (ValueError, TypeError): - confidence = 0.5 - - embed_text = "\n".join(s for s in [name, description] if s) - embedding_data = await self._compute_embedding(embed_text) - - from infra_layer.adapters.out.persistence.document.memory.agent_skill import ( - AgentSkillRecord, - ) - - score = await self._evaluate_maturity( - name=name, description=description, content=content, confidence=confidence - ) - - record = AgentSkillRecord( - cluster_id=cluster_id, - user_id=user_id, - group_id=group_id, - name=name, - description=description, - content=content, - confidence=confidence, - maturity_score=score if score is not None else 0.6, - vector=(embedding_data["embedding"] if embedding_data else None), - vector_model=(embedding_data["vector_model"] if embedding_data else None), - source_case_ids=source_case_ids or [], - ) - saved = await skill_repo.save_skill(record) - if saved: - logger.info( - f"[AgentSkillExtractor] ADD skill: name='{name}', cluster={cluster_id}" - ) - return saved - - async def _rescore_maturity( - self, - updates: Dict[str, Any], - new_name: str, - new_description: str, - new_content: str, - record: Any, - ) -> None: - """Re-evaluate maturity via LLM and write into updates dict.""" - effective_name = new_name or record.name or "" - effective_desc = new_description or record.description or "" - effective_content = new_content or record.content or "" - effective_confidence = updates.get("confidence", record.confidence) - score = await self._evaluate_maturity( - name=effective_name, - description=effective_desc, - content=effective_content, - confidence=effective_confidence, - ) - if score is not None: - updates["maturity_score"] = score - - async def _apply_update( - self, - op: Dict[str, Any], - existing_skill_records: List[Any], - skill_repo: Any, - result: SkillExtractionResult, - source_case_ids: Optional[List[str]] = None, - source_quality: float = 0.5, - ) -> bool: - """Apply an 'update' operation: modify an existing skill record in-place.""" - try: - index = int(op.get("index", -1)) - except (ValueError, TypeError): - logger.warning( - f"[AgentSkillExtractor] update index is not a valid integer: {op.get('index')!r}, skipping" - ) - return False - data = op.get("data", {}) - - if index < 0 or index >= len(existing_skill_records): - logger.warning( - f"[AgentSkillExtractor] update index {index} out of range " - f"(valid: 0..{len(existing_skill_records) - 1} for {len(existing_skill_records)} skills), skipping" - ) - return False - - record = existing_skill_records[index] - record_id = record.id - - new_name = data.get("name", "") - new_description = data.get("description", "") - new_description = self._truncate_text(new_description, max_tokens=self.MAX_DESCRIPTION_TOKENS, suffix="...") - new_content = data.get("content", "") - new_confidence = data.get("confidence") - - if new_content and not self._is_skill_content_sufficient(new_content): - logger.warning( - "[AgentSkillExtractor] update operation for index %d has insufficient content " - "(too short or no steps), skipping. content=%r", - index, - new_content[:100], - ) - return False - - updates: Dict[str, Any] = {} - - if new_name: - updates["name"] = new_name - if new_description: - updates["description"] = new_description - if new_content: - updates["content"] = new_content - if new_confidence is not None: - try: - clamped = max(0.0, min(1.0, float(new_confidence))) - updates["confidence"] = clamped - except (ValueError, TypeError): - clamped = None - - # Append source case IDs for traceability - if source_case_ids: - existing_ids = list(getattr(record, "source_case_ids", None) or []) - new_ids = [cid for cid in source_case_ids if cid not in existing_ids] - if new_ids: - existing_ids.extend(new_ids) - updates["source_case_ids"] = existing_ids - - if not updates: - logger.warning( - f"[AgentSkillExtractor] update operation for index {index} has no fields to update, skipping" - ) - return False - - # Retire skill when confidence drops below threshold. - # The record stays in MongoDB (data preserved for audit/recovery) - # but is removed from search engines and excluded from future extraction context. - final_confidence = updates.get("confidence") - if final_confidence is not None and final_confidence < self.retire_confidence: - logger.warning( - "[AgentSkillExtractor] Retiring skill[%d] (confidence=%.2f < %.2f): " - "id=%s, name=%r", - index, - final_confidence, - self.retire_confidence, - record_id, - getattr(record, "name", ""), - ) - retire_updates: Dict[str, Any] = {"confidence": final_confidence} - if "source_case_ids" in updates: - retire_updates["source_case_ids"] = updates["source_case_ids"] - success = await skill_repo.update_skill_by_id( - record_id, retire_updates - ) - if success: - # Signal search-engine removal (ES / Milvus) — data stays in MongoDB - result.deleted_ids.append(str(record_id)) - return success - - # Re-embed only if name or description actually changed - name_changed = bool(new_name) and new_name != (record.name or "") - desc_changed = bool(new_description) and new_description != ( - record.description or "" - ) - if name_changed or desc_changed: - effective_name = new_name or record.name or "" - effective_desc = new_description or record.description or "" - embed_text = "\n".join(s for s in [effective_name, effective_desc] if s) - embedding_data = await self._compute_embedding(embed_text) - if embedding_data: - updates["vector"] = embedding_data["embedding"] - updates["vector_model"] = embedding_data["vector_model"] - - # Re-evaluate maturity when content/name/description actually changed. - # - # Rules: - # 1) change < 20%: trivial tweak, keep current score - # 2) change >= 40% or hypothesis promotion: always re-score via LLM - # 3) change 20~40%: - # - mature (>= threshold) AND confidence not dropping: skip - # - immature (< threshold) AND case quality < 0.3: skip (low-quality case won't help) - # - otherwise: re-score via LLM - real_content_changed = bool(new_content) and new_content != ( - record.content or "" - ) - content_changed = real_content_changed or name_changed or desc_changed - if content_changed: - change_ratio = self._content_change_ratio( - record.content or "", new_content or record.content or "" - ) - - # 1) Trivial change (< 20%): keep current score - if change_ratio < self.MATURITY_TRIVIAL_CHANGE_RATIO: - logger.info( - "[AgentSkillExtractor] Skipping maturity re-evaluation for skill[%d]: " - "trivial change_ratio=%.2f < %.2f", - index, - change_ratio, - self.MATURITY_TRIVIAL_CHANGE_RATIO, - ) - # 2) Major change (>= 40%) or hypothesis promotion: always LLM - elif ( - change_ratio >= self.MATURITY_REEVAL_CHANGE_RATIO - or self._is_hypothesis_promotion( - record.content or "", new_content or "" - ) - ): - reason = ( - "hypothesis promotion" - if self._is_hypothesis_promotion( - record.content or "", new_content or "" - ) - else f"major content change (ratio={change_ratio:.2f})" - ) - logger.info( - "[AgentSkillExtractor] %s for skill[%d], using LLM maturity evaluation", - reason, - index, - ) - await self._rescore_maturity( - updates, new_name, new_description, new_content, record - ) - # 3) Moderate change (20~40%) - else: - old_score = record.maturity_score or 0.0 - old_confidence = record.confidence or 0.0 - new_confidence_val = updates.get("confidence", old_confidence) - confidence_dropping = new_confidence_val < old_confidence - - if old_score >= self.maturity_threshold and ( - not confidence_dropping or new_confidence_val >= 0.5 - ): - # Already mature and confidence stable or still strong (>= 0.5): skip - logger.info( - "[AgentSkillExtractor] Skipping maturity re-evaluation for skill[%d]: " - "already mature (%.2f >= %.2f), confidence=%.2f (dropping=%s), change_ratio=%.2f", - index, - old_score, - self.maturity_threshold, - new_confidence_val, - confidence_dropping, - change_ratio, - ) - elif old_score < self.maturity_threshold and source_quality < 0.3: - # Immature but low-quality case won't improve it: skip - logger.info( - "[AgentSkillExtractor] Skipping maturity re-evaluation for skill[%d]: " - "immature (%.2f) but low source quality (%.2f < 0.3), change_ratio=%.2f", - index, - old_score, - source_quality, - change_ratio, - ) - else: - # Re-score: immature skill with decent case, or mature but confidence dropping - logger.info( - "[AgentSkillExtractor] Moderate change for skill[%d]: " - "score=%.2f, confidence_dropping=%s, source_quality=%.2f, " - "using LLM maturity evaluation", - index, - old_score, - confidence_dropping, - source_quality, - ) - await self._rescore_maturity( - updates, new_name, new_description, new_content, record - ) - - success = await skill_repo.update_skill_by_id(record_id, updates) - if success: - from common_utils.datetime_utils import get_now_with_timezone - - for field_name, value in updates.items(): - setattr(record, field_name, value) - record.updated_at = get_now_with_timezone() - result.updated_records.append(record) - logger.info( - f"[AgentSkillExtractor] UPDATE skill[{index}]: id={record_id}, " - f"fields={list(updates.keys())}" - ) - return success - - async def _load_case_history( - self, - existing_skill_records: List[Any], - max_cases: int = 9, - ) -> List[Any]: - """Load historical AgentCaseRecords referenced by existing skills. - - Collects all source_case_ids from existing skills, loads them from DB, - sorts by quality_score (desc) then timestamp (desc), and returns top N. - """ - all_case_ids: set = set() - for rec in existing_skill_records: - for cid in (getattr(rec, "source_case_ids", None) or []): - if cid is None: - continue - cid_str = str(cid).strip() - if cid_str: - all_case_ids.add(cid_str) - - if not all_case_ids: - return [] - - try: - from core.di.utils import get_bean_by_type - from infra_layer.adapters.out.persistence.repository.agent_case_raw_repository import ( - AgentCaseRawRepository, - ) - - agent_case_repo = get_bean_by_type(AgentCaseRawRepository) - records = await agent_case_repo.get_by_ids(list(all_case_ids)) - records.sort( - key=lambda c: (c.quality_score or 0.0, c.timestamp or datetime.min), - reverse=True, - ) - logger.info( - "[AgentSkillExtractor] Loaded case_history: %d/%d cases (max=%d)", - min(len(records), max_cases), len(records), max_cases, - ) - return records[:max_cases] - except Exception as e: - logger.warning("[AgentSkillExtractor] Failed to load case_history: %s", e) - return [] - - async def extract_and_save( - self, - cluster_id: str, - group_id: Optional[str], - new_case_records: List[AgentCase], - existing_skill_records: List[Any], - skill_repo: Any, - user_id: Optional[str] = None, - max_skills_in_prompt: int = 10, - max_case_history: int = 9, - ) -> SkillExtractionResult: - """Incrementally extract skills via operation-based updates. - - Args: - cluster_id: The MemScene cluster ID - group_id: Group ID for scoping - new_case_records: Only the NEW AgentCaseRecord(s) to integrate. - Each record should have an `id` attribute (AgentCase ID) for traceability. - existing_skill_records: Previously saved AgentSkillRecord for this cluster - skill_repo: AgentSkillRawRepository instance - user_id: User ID (agent owner) - max_skills_in_prompt: Max existing skills to include in the LLM prompt. - max_case_history: Max historical cases to load for supporting case summaries. - - Returns: - SkillExtractionResult containing added, updated records and deleted IDs. - """ - empty_result = SkillExtractionResult() - - if not new_case_records: - logger.debug( - f"[AgentSkillExtractor] No new cases for cluster={cluster_id}, skipping" - ) - return empty_result - - # When too many existing skills, select top-k most relevant ones - if len(existing_skill_records) > max_skills_in_prompt: - logger.info( - f"[AgentSkillExtractor] {len(existing_skill_records)} existing skills exceed " - f"max_skills_in_prompt={max_skills_in_prompt}, selecting top-k" - ) - with timed("select_top_k_skills"): - existing_skill_records = await self._select_top_k_skills( - existing_skill_records, new_case_records, top_k=max_skills_in_prompt - ) - - # Load case history AFTER top-k selection so we only load cases - # relevant to the skills that will actually appear in the prompt. - case_history = await self._load_case_history( - existing_skill_records, max_case_history - ) - - new_case_json = self._format_cases(new_case_records) - existing_skills_json = self._format_existing_skills( - existing_skill_records, case_history=case_history - ) - prompt_template = self._select_prompt(new_case_records) - - logger.debug( - f"[AgentSkillExtractor] Incremental extraction: cluster={cluster_id}, " - f"new_cases={len(new_case_records)}, existing_skills={len(existing_skill_records)}" - ) - - with timed("extract_skill_ops"): - llm_result = await self._call_llm( - new_case_json, existing_skills_json, prompt_template - ) - if not llm_result: - logger.warning( - f"[AgentSkillExtractor] LLM extraction failed for cluster={cluster_id}" - ) - return empty_result - - operations = llm_result.get("operations", []) - update_note = llm_result.get("update_note", "") - - # Collect all case IDs from new records for traceability - source_case_ids = [ - str(getattr(rec, "id", "") or "") - for rec in new_case_records - ] - source_case_ids = [cid for cid in source_case_ids if cid] - - result = SkillExtractionResult() - update_count = 0 - processed_indices: set = set() - - with timed("apply_operations"): - for op in operations: - action = op.get("action", "none") - - if action == "add": - saved = await self._apply_add( - op, - cluster_id, - group_id, - user_id, - skill_repo, - source_case_ids=source_case_ids, - ) - if saved: - result.added_records.append(saved) - - elif action == "update": - try: - index = int(op.get("index", -1)) - except (ValueError, TypeError): - logger.warning( - f"[AgentSkillExtractor] update index is not a valid integer: {op.get('index')!r}, skipping" - ) - continue - if index in processed_indices: - logger.warning( - f"[AgentSkillExtractor] Duplicate operation on index {index}, skipping update" - ) - continue - processed_indices.add(index) - # Pass max quality_score from new cases for maturity decision - max_quality = ( - max( - (getattr(rec, "quality_score", 0.5) or 0.5) - for rec in new_case_records - ) - if new_case_records - else 0.5 - ) - success = await self._apply_update( - op, - existing_skill_records, - skill_repo, - result, - source_case_ids=source_case_ids, - source_quality=max_quality, - ) - if success: - update_count += 1 - - elif action == "none": - logger.debug( - f"[AgentSkillExtractor] No-op for cluster={cluster_id}" - ) - - else: - logger.warning( - f"[AgentSkillExtractor] Unknown action '{action}', skipping" - ) - - logger.info( - f"[AgentSkillExtractor] cluster={cluster_id} operations applied: " - f"added={len(result.added_records)}, updated={update_count}, " - f"deleted={len(result.deleted_ids)}. note: {update_note}" - ) - return result diff --git a/methods/EverCore/src/memory_layer/memory_extractor/atomic_fact_extractor.py b/methods/EverCore/src/memory_layer/memory_extractor/atomic_fact_extractor.py deleted file mode 100644 index d1f3fef2..00000000 --- a/methods/EverCore/src/memory_layer/memory_extractor/atomic_fact_extractor.py +++ /dev/null @@ -1,321 +0,0 @@ -""" -Atomic Fact Extractor for EverCore - -This module extracts structured atomic facts from episode memories for optimized retrieval. -Each extraction result contains a time and a list of atomic facts extracted from the episode. -""" - -from typing import Optional, List, Dict, Any -from datetime import datetime -import json -import re - -from memory_layer.prompts import get_prompt_by -from memory_layer.llm.llm_provider import LLMProvider -from common_utils.datetime_utils import get_now_with_timezone, from_iso_format -from api_specs.memory_types import ( - AtomicFact, - MemoryType, - MemCell, - get_text_from_content_items, -) - -from core.observation.logger import get_logger -from core.observation.stage_timer import timed - -logger = get_logger(__name__) - - -class AtomicFactExtractor: - """ - Extractor for converting episode memories into structured atomic facts. - - The atomic fact format is optimized for retrieval: - - Time field provides temporal context - - Atomic facts are independent, searchable units - """ - - def __init__( - self, llm_provider: LLMProvider, atomic_fact_prompt: Optional[str] = None - ): - """ - Initialize the atomic fact extractor. - - Args: - llm_provider: LLM provider for generating atomic facts - atomic_fact_prompt: Optional custom atomic fact prompt - """ - self.llm_provider = llm_provider - - # Use custom prompt or get default via PromptManager - self.atomic_fact_prompt = atomic_fact_prompt or get_prompt_by( - "ATOMIC_FACT_PROMPT" - ) - - def _parse_timestamp(self, timestamp) -> datetime: - """ - Parse timestamp into datetime object - Supports multiple formats: numeric timestamp, ISO string, datetime object, etc. - - Args: - timestamp: Timestamp, can be in multiple formats - - Returns: - datetime: Parsed datetime object - """ - if isinstance(timestamp, datetime): - return timestamp - elif isinstance(timestamp, (int, float)): - return datetime.fromtimestamp(timestamp) - elif isinstance(timestamp, str): - try: - if timestamp.isdigit(): - return datetime.fromtimestamp(int(timestamp)) - else: - # Try parsing ISO format - return datetime.fromisoformat(timestamp.replace('Z', '+00:00')) - except (ValueError, AttributeError): - logger.error(f"Failed to parse timestamp: {timestamp}") - return get_now_with_timezone() - else: - logger.error(f"Unknown timestamp format: {timestamp}") - return get_now_with_timezone() - - def _format_timestamp(self, dt: datetime) -> str: - """ - Format datetime into required string format for atomic facts - Format: "March 10, 2024(Sunday) at 2:00 PM" - - Args: - dt: datetime object - - Returns: - str: Formatted time string - """ - weekday = dt.strftime("%A") # Monday, Tuesday, etc. - month_day_year = dt.strftime("%B %d, %Y") # March 10, 2024 - time_of_day = dt.strftime("%I:%M %p") # 2:00 PM - return f"{month_day_year}({weekday}) at {time_of_day}" - - def _parse_llm_response(self, response: str) -> Dict[str, Any]: - """ - Parse JSON response returned by LLM - Supports multiple formats: plain JSON, JSON code block, etc. - - Args: - response: Raw response from LLM - - Returns: - Dict: Parsed JSON object - - Raises: - ValueError: If response cannot be parsed - """ - # 1. Try extracting JSON from code block - if '```json' in response: - start = response.find('```json') + 7 - end = response.find('```', start) - if end > start: - json_str = response[start:end].strip() - try: - return json.loads(json_str) - except json.JSONDecodeError: - pass - - # 2. Try extracting from any code block - if '```' in response: - start = response.find('```') + 3 - # Skip language identifier (if any) - if response[start : start + 10].strip().split()[0].isalpha(): - start = response.find('\n', start) + 1 - end = response.find('```', start) - if end > start: - json_str = response[start:end].strip() - try: - return json.loads(json_str) - except json.JSONDecodeError: - pass - - # 3. Try extracting JSON object containing atomic fact (atomic_fact key) - json_match = re.search( - r'\{[^{}]*"atomic_facts"[^{}]*\{[^{}]*"time"[^{}]*"atomic_fact"[^{}]*\}[^{}]*\}', - response, - re.DOTALL, - ) - if json_match: - try: - return json.loads(json_match.group()) - except json.JSONDecodeError: - pass - - # 4. Try parsing entire response directly - try: - return json.loads(response.strip()) - except json.JSONDecodeError: - pass - - # 5. If all fail, raise exception - logger.error(f"Unable to parse LLM response: {response[:200]}...") - raise ValueError(f"Unable to parse LLM response into valid JSON format") - - async def _extract_atomic_fact( - self, - input_text: str, - timestamp: Any, - user_id: str = "", - group_id: Optional[str] = None, - ) -> Optional[AtomicFact]: - """ - Extract atomic fact from episode memory - - Args: - input_text: Text content of episode memory - timestamp: Timestamp of episode (can be in multiple formats) - user_id: User ID for the atomic fact - group_id: Group ID - - Returns: - AtomicFact: Extracted atomic fact, return None if extraction fails - """ - - # 1. Parse and format timestamp - dt = self._parse_timestamp(timestamp) - time_str = self._format_timestamp(dt) - - # 2. Build prompt (using instance variable self.atomic_fact_prompt) - prompt = self.atomic_fact_prompt.replace("{{INPUT_TEXT}}", input_text) - prompt = prompt.replace("{{TIME}}", time_str) - - # 3. Call LLM to generate atomic fact - response = await self.llm_provider.generate(prompt) - - # 4. Parse LLM response - data = self._parse_llm_response(response) - - # 5. Validate response format - if "atomic_facts" not in data: - raise ValueError(f"Missing 'atomic_facts' field in LLM response") - - atomic_fact_data = data["atomic_facts"] - - # Validate required fields: time and atomic_fact must exist - if "time" not in atomic_fact_data or not atomic_fact_data["time"]: - raise ValueError("Missing time field in atomic fact response") - if "atomic_fact" not in atomic_fact_data: - raise ValueError("Missing atomic_fact field in atomic fact response") - - # Validate atomic_fact is a list - if not isinstance(atomic_fact_data["atomic_fact"], list): - raise ValueError( - f"atomic_fact is not a list: {type(atomic_fact_data['atomic_fact'])}" - ) - - # 6. Batch generate embedding for all atomic_fact (performance optimization) - from agentic_layer.vectorize_service import get_vectorize_service - - vectorize_service = get_vectorize_service() - - # Batch compute embeddings (using get_embeddings, accepts List[str]) - fact_embeddings_batch = await vectorize_service.get_embeddings( - atomic_fact_data["atomic_fact"] - ) - - # Convert to list format - fact_embeddings = [ - emb.tolist() if hasattr(emb, 'tolist') else emb - for emb in fact_embeddings_batch - ] - - # 7. Create AtomicFact object with Memory base class fields - atomic_fact_obj = AtomicFact( - memory_type=MemoryType.ATOMIC_FACT, - user_id=user_id, - timestamp=dt, - group_id=group_id, - time=atomic_fact_data["time"], - atomic_fact=atomic_fact_data["atomic_fact"], - fact_embeddings=fact_embeddings, - ) - - logger.debug( - f"Successfully extracted atomic fact, containing {len(atomic_fact_obj.atomic_fact)} atomic facts (embeddings generated)" - ) - return atomic_fact_obj - - async def extract_atomic_fact( - self, - memcell: MemCell, - timestamp: Any, - user_id: str = "", - group_id: Optional[str] = None, - ) -> Optional[AtomicFact]: - """ - Extract atomic fact - """ - input_text = "" - for data in memcell.conversation_data: - msg = data.get("message", data) - speaker = msg.get('sender_name') or 'Unknown' - content = get_text_from_content_items(msg.get('content', [])) - msg_ts = msg.get('timestamp') - ts_str = from_iso_format(msg_ts) - input_text += f"[{ts_str}] {speaker}: {content}\n" - - with timed("extract_atomic_fact"): - for retry in range(5): - try: - return await self._extract_atomic_fact( - input_text, timestamp, user_id=user_id, group_id=group_id - ) - except Exception as e: - logger.warning(f"Retrying to extract atomic fact {retry+1}/5: {e}") - if retry == 4: - logger.error(f"Failed to extract atomic fact after 5 retries") - return None - continue - - -def format_atomic_fact_for_bm25(atomic_fact: AtomicFact) -> str: - """ - Format atomic fact for BM25 retrieval - Use only atomic_fact field, concatenate all atomic facts into a single string - - Args: - atomic_fact: AtomicFact object - - Returns: - str: Text for BM25 retrieval - """ - if not atomic_fact or not atomic_fact.atomic_fact: - return "" - - # Directly concatenate all atomic facts, separated by spaces - return " ".join(atomic_fact.atomic_fact) - - -def format_atomic_fact_for_rerank(atomic_fact: AtomicFact) -> str: - """ - Format atomic fact for rerank - Use "time" + ":" + "atomic_fact" concatenation - - Args: - atomic_fact: AtomicFact object - - Returns: - str: Text for rerank - """ - if not atomic_fact: - return "" - - # Concatenate time and atomic facts - time_part = atomic_fact.time or "" - facts_part = " ".join(atomic_fact.atomic_fact) if atomic_fact.atomic_fact else "" - - if time_part and facts_part: - return f"{time_part}：{facts_part}" - elif time_part: - return time_part - elif facts_part: - return facts_part - else: - return "" diff --git a/methods/EverCore/src/memory_layer/memory_extractor/base_memory_extractor.py b/methods/EverCore/src/memory_layer/memory_extractor/base_memory_extractor.py deleted file mode 100644 index a24f0cd3..00000000 --- a/methods/EverCore/src/memory_layer/memory_extractor/base_memory_extractor.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Simple Memory Extraction Base Class for EverCore - -This module provides a simple base class for extracting memories -from boundary detection results (BoundaryResult). -""" - -from abc import ABC, abstractmethod -from dataclasses import dataclass -from typing import List, Optional - -from api_specs.memory_types import MemoryType, BaseMemory, MemCell - - -@dataclass -class MemoryExtractRequest: - """ - Base class for memory extraction requests - """ - - memcell: MemCell - user_id: Optional[str] = None - group_id: Optional[str] = None - participants: Optional[List[str]] = None - - old_memory_list: Optional[List[BaseMemory]] = None - - -class MemoryExtractor(ABC): - """ - Simple abstract base class for memory extraction. - - This class provides a minimal interface for extracting memories - from boundary detection results. - """ - - def __init__(self, memory_type: MemoryType): - """ - Initialize the memory extractor. - - Args: - memory_type: The type of memory this extractor generates - """ - self.memory_type = memory_type - - @abstractmethod - async def extract_memory( - self, request: MemoryExtractRequest - ) -> Optional[BaseMemory]: - """ - Extract memory from a boundary detection result. - - Args: - boundary_result: The boundary detection result to extract from - user_id: User ID for the memory - - Returns: - MemoryExtractionResult if extraction is successful, None otherwise - """ - pass - - def __str__(self) -> str: - """String representation of the extractor.""" - return f"{self.__class__.__name__}(type={self.memory_type.value})" diff --git a/methods/EverCore/src/memory_layer/memory_extractor/episode_memory_extractor.py b/methods/EverCore/src/memory_layer/memory_extractor/episode_memory_extractor.py deleted file mode 100644 index cfd32d81..00000000 --- a/methods/EverCore/src/memory_layer/memory_extractor/episode_memory_extractor.py +++ /dev/null @@ -1,404 +0,0 @@ -""" -Simple Memory Extraction Base Class for EverCore - -This module provides a simple base class for extracting memories -from boundary detection results (BoundaryResult). -""" - -from dataclasses import dataclass, field -from typing import Optional, List, Dict, Any -from datetime import datetime -import re, json, asyncio, uuid - - -from memory_layer.prompts import get_prompt_by -from memory_layer.llm.llm_provider import LLMProvider - -from memory_layer.memory_extractor.base_memory_extractor import ( - MemoryExtractor, - MemoryExtractRequest, -) -from api_specs.memory_types import ( - MemoryType, - EpisodeMemory, - RawDataType, - MemCell, - ParentType, - get_text_from_content_items, -) - -from common_utils.datetime_utils import get_now_with_timezone -from agentic_layer.vectorize_service import get_vectorize_service -from biz_layer.memorize_config import DEFAULT_MEMORIZE_CONFIG - -from core.observation.logger import get_logger -from core.observation.stage_timer import timed - -logger = get_logger(__name__) - - -@dataclass -class EpisodeMemoryExtractRequest(MemoryExtractRequest): - """Episode extraction request (inherited from base class)""" - - pass - - -class EpisodeMemoryExtractor(MemoryExtractor): - """ - Episode memory extractor - responsible only for extracting Episodes from MemCell - - Responsibilities: - 1. Extract group Episodes from MemCell's original_data - 2. Extract personal Episodes from MemCell's original_data - - Not included: - - Foresight extraction (handled by ForesightExtractor) - - AtomicFact extraction (handled by AtomicFactExtractor) - """ - - def __init__( - self, - llm_provider: LLMProvider | None = None, - episode_prompt: Optional[str] = None, - group_episode_prompt: Optional[str] = None, - custom_instructions: Optional[str] = None, - ): - """ - Initialize Episode Extractor - - Args: - llm_provider: LLM provider - episode_prompt: Optional custom personal Episode prompt (uses default if not provided) - group_episode_prompt: Optional custom group Episode prompt (uses default if not provided) - custom_instructions: Optional custom instructions (uses default if not provided) - """ - super().__init__(MemoryType.EPISODIC_MEMORY) - self.llm_provider = llm_provider - self.default_parent_type = DEFAULT_MEMORIZE_CONFIG.default_episode_parent_type - - # Use custom prompts or get default via PromptManager - self.episode_generation_prompt = episode_prompt or get_prompt_by( - "EPISODE_GENERATION_PROMPT" - ) - self.group_episode_generation_prompt = group_episode_prompt or get_prompt_by( - "GROUP_EPISODE_GENERATION_PROMPT" - ) - self.default_custom_instructions = custom_instructions or get_prompt_by( - "DEFAULT_CUSTOM_INSTRUCTIONS" - ) - - def _parse_timestamp(self, timestamp) -> datetime: - """ - Parse timestamp into datetime object - Supports multiple formats: numeric timestamp, ISO format string, numeric string, etc. - """ - if isinstance(timestamp, datetime): - return timestamp - elif isinstance(timestamp, (int, float)): - return datetime.fromtimestamp(timestamp) - elif isinstance(timestamp, str): - # Handle string timestamps (could be ISO format or timestamp string) - try: - if timestamp.isdigit(): - return datetime.fromtimestamp(int(timestamp)) - else: - # Try parsing as ISO format - return datetime.fromisoformat(timestamp.replace('Z', '+00:00')) - except (ValueError, AttributeError): - # Fallback to current time if parsing fails - logger.error(f"Failed to parse timestamp: {timestamp}") - return get_now_with_timezone() - else: - # Unknown format, fallback to current time - logger.error(f"Failed to parse timestamp: {timestamp}") - return get_now_with_timezone() - - def _format_timestamp(self, dt: datetime) -> str: - """ - Format datetime into a human-readable string - """ - weekday = dt.strftime("%A") # Monday, Tuesday, etc. - month_day = dt.strftime("%B %d, %Y") # March 14, 2024 - time_of_day = dt.strftime("%I:%M %p") # 3:00 PM - return f"{month_day} ({weekday}) at {time_of_day} UTC" - - @staticmethod - def _resolve_msg(data) -> Dict[str, Any]: - """Resolve a message dict from either a RawData object or { message } item.""" - if hasattr(data, 'content') and not isinstance(data, dict): - return data.content - return data.get('message', data) - - def get_conversation_text(self, data_list): - lines = [] - for data in data_list: - msg = self._resolve_msg(data) - speaker = msg.get('sender_name') or 'Unknown' - content = get_text_from_content_items(msg.get('content', [])) - timestamp = msg.get('timestamp') - - if timestamp: - lines.append(f"[{timestamp}] {speaker}: {content}") - else: - lines.append(f"{speaker}: {content}") - return "\n".join(lines) - - def get_conversation_json_text(self, data_list): - lines = [] - for data in data_list: - msg = self._resolve_msg(data) - speaker = msg.get('sender_name') or 'Unknown' - content = get_text_from_content_items(msg.get('content', [])) - timestamp = msg.get('timestamp') - - if timestamp: - lines.append( - f""" - {{ - "timestamp": {timestamp}, - "speaker": {speaker}, - "content": {content} - }}""" - ) - else: - lines.append( - f""" - {{ - "speaker": {speaker}, - "content": {content} - }}""" - ) - return "\n".join(lines) - - def get_sender_name_map(self, data_list: List[Dict[str, Any]]) -> Dict[str, str]: - sender_name_map = {} - for data in data_list: - msg = self._resolve_msg(data) - sender_name_map[msg.get('sender_id')] = msg.get('sender_name') - return sender_name_map - - def _extract_participant_name_map( - self, chat_raw_data_list: List[Dict[str, Any]] - ) -> List[str]: - participant_name_map = {} - for raw_data in chat_raw_data_list: - msg = self._resolve_msg(raw_data) - if msg.get('sender_name'): - participant_name_map[msg['sender_id']] = msg['sender_name'] - return participant_name_map - - async def _extract_episode( - self, request: EpisodeMemoryExtractRequest, use_group_prompt: bool = False - ) -> Optional[EpisodeMemory]: - """ - Extract Episode memory (internal method, single extraction) - - Args: - request: Episode extraction request (contains single memcell and optional user_id) - use_group_prompt: Whether to use group prompt - - True: Extract group Episode (user_id=None) - - False: Extract personal Episode (user_id from request.user_id) - - Returns: - EpisodeMemory (contains episode field) - """ - logger.debug( - f"📚 Starting Episode extraction, use_group_prompt={use_group_prompt}" - ) - - memcell = request.memcell - if not memcell: - return None - - # Prepare conversation text - if memcell.type in (RawDataType.CONVERSATION, RawDataType.AGENTCONVERSATION): - conversation_text = self.get_conversation_json_text( - memcell.conversation_data - ) - - # Select prompt and parameters - if use_group_prompt: - prompt_template = self.group_episode_generation_prompt - content_key = "conversation" - time_key = "conversation_start_time" - else: - prompt_template = self.episode_generation_prompt - content_key = "conversation" - time_key = "conversation_start_time" - default_title = "Conversation Episode" - else: - return None - - # Format timestamp - start_time = self._parse_timestamp(memcell.timestamp) - start_time_str = self._format_timestamp(start_time) - - # Build prompt parameters - format_params = { - time_key: start_time_str, - content_key: conversation_text, - "custom_instructions": self.default_custom_instructions, - } - - # Get participant information - participants_name_map = self.get_sender_name_map(memcell.original_data) - participants_name_map.update( - self._extract_participant_name_map(memcell.original_data) - ) - - # Determine user_id and user_name - user_id = None - user_name = None - if use_group_prompt: - # Group mode: user_id is None, user_name is None - user_id = None - user_name = None - else: - # Personal mode: get from request.user_id - if request.user_id: - user_id = request.user_id - user_name = participants_name_map.get(user_id, user_id) - format_params["user_name"] = user_name - - # Call LLM (with retry) - data = None - with timed("extract_episode"): - for i in range(5): - try: - prompt = prompt_template.format(**format_params) - response = await self.llm_provider.generate(prompt) - - # Parse JSON - if '```json' in response: - start = response.find('```json') + 7 - end = response.find('```', start) - if end > start: - json_str = response[start:end].strip() - data = json.loads(json_str) - else: - data = json.loads(response) - else: - json_match = re.search( - r'\{[^{}]*"title"[^{}]*"content"[^{}]*\}', - response, - re.DOTALL, - ) - if json_match: - data = json.loads(json_match.group()) - else: - data = json.loads(response) - - # Validate required fields: title and content must exist - if "title" not in data or not data["title"]: - raise ValueError("LLM response missing title field") - if "content" not in data or not data["content"]: - raise ValueError("LLM response missing content field") - - # Validation passed, exit retry loop - break - except Exception as e: - logger.warning(f"Episode extraction retry {i+1}/5: {e}") - if i == 4: - raise Exception( - "Episode memory extraction failed after 5 retries" - ) - continue - - # Use first 200 characters of content as default summary if summary is missing - if "summary" not in data or not data["summary"]: - data["summary"] = data["content"][:200] - - title = data["title"] - content = data["content"] - summary = data["summary"] - - # Collect participants - participants = memcell.participants if memcell.participants else [] - sender_ids = memcell.sender_ids if memcell.sender_ids else [] - - # Compute Embedding - embedding_data = await self._compute_embedding(content) - - # Create EpisodeMemory object - episode_memory = EpisodeMemory( - memory_type=MemoryType.EPISODIC_MEMORY, - user_id=user_id, - user_name=user_name, - timestamp=start_time, - subject=title, - summary=summary, - episode=content, - group_id=request.group_id, - participants=participants, - sender_ids=sender_ids, - type=memcell.type, - parent_type=self.default_parent_type, - parent_id=memcell.event_id, - extend=embedding_data, # Add embedding to extend field - ) - - logger.debug(f"✅ Episode extraction completed: subject='{title}'") - return episode_memory - - async def extract_memory( - self, request: MemoryExtractRequest - ) -> Optional[EpisodeMemory]: - """ - Extract Episode memory from MemCell (implement abstract method from base class) - - Automatically determine whether to extract group or personal Episode based on request.user_id: - - user_id=None: extract group Episode (using group prompt) - - user_id!=None: extract personal Episode (using personal prompt, focusing on user's perspective) - - Args: - request: Memory extraction request, containing: - - memcell: MemCell to extract - - user_id: User ID (None means group) - - group_id: Group ID - - Other optional fields - - Returns: - EpisodeMemory: Episode memory object - - Group Episode: user_id=None, episode contains global view of entire conversation - - Personal Episode: user_id=, episode contains personal view of the user - """ - # Determine if it's a group or personal Episode - is_group_episode = request.user_id is None - - logger.debug( - f"[extract_memory] Extracting {'group' if is_group_episode else 'personal'} Episode, " - f"user_id={request.user_id}, group_id={request.group_id}" - ) - - # Build EpisodeMemoryExtractRequest - episode_request = EpisodeMemoryExtractRequest( - memcell=request.memcell, - user_id=request.user_id, - group_id=request.group_id, - participants=request.participants, - old_memory_list=request.old_memory_list, - ) - - # Call internal extraction method - return await self._extract_episode( - request=episode_request, - use_group_prompt=is_group_episode, # Group uses group prompt, personal uses personal prompt - ) - - async def _compute_embedding(self, text: str) -> Optional[dict]: - """Compute embedding for Episode text""" - try: - if not text: - return None - - vs = get_vectorize_service() - vec = await vs.get_embedding(text) - - return { - "embedding": vec.tolist() if hasattr(vec, "tolist") else list(vec), - "vector_model": vs.get_model_name(), # Use unified get_model_name() method - } - except Exception as e: - logger.error(f"Episode Embedding computation failed: {e}") - return None diff --git a/methods/EverCore/src/memory_layer/memory_extractor/foresight_extractor.py b/methods/EverCore/src/memory_layer/memory_extractor/foresight_extractor.py deleted file mode 100644 index d71892d7..00000000 --- a/methods/EverCore/src/memory_layer/memory_extractor/foresight_extractor.py +++ /dev/null @@ -1,383 +0,0 @@ -""" -Foresight Extractor - Based on associative prediction method -Generate predictions of potential impacts on user's future life and decisions from MemCell -""" - -import json -from typing import List, Dict, Any, Optional -from datetime import datetime, timedelta - -from memory_layer.prompts import get_prompt_by -from memory_layer.llm.llm_provider import LLMProvider -from memory_layer.memory_extractor.base_memory_extractor import ( - MemoryExtractor, - MemoryExtractRequest, -) -from api_specs.memory_types import MemoryType, MemCell, Foresight, BaseMemory -from agentic_layer.vectorize_service import get_vectorize_service -from core.observation.logger import get_logger -from core.observation.stage_timer import timed -from common_utils.datetime_utils import get_now_with_timezone - -logger = get_logger(__name__) - - -class ForesightExtractor(MemoryExtractor): - """ - Foresight Extractor - Based on associative prediction method - - Supports conversation mode: - - Generate associations based on raw conversation transcript text (solo scene). - - New strategy implementation: - 1. Based on content, large model associates 10 potential impacts on user's subsequent life and decisions - 2. Each association considers its possible duration - 3. Focus on personal-level impacts for the user - - Main methods: - - generate_foresights_for_conversation(): Generate foresights from raw conversation text - """ - - def __init__(self, llm_provider: LLMProvider): - """ - Initialize foresight extractor - - Args: - llm_provider: LLM provider - """ - super().__init__(MemoryType.FORESIGHT) - self.llm_provider = llm_provider - - logger.info("Foresight extractor initialized (associative prediction mode)") - - async def extract_memory( - self, request: MemoryExtractRequest - ) -> Optional[BaseMemory]: - """ - Implement abstract base class required extract_memory method - - Note: ForesightExtractor should not directly use extract_memory method - Use generate_foresights_for_conversation instead - - Args: - request: Memory extraction request - - Returns: - None - This method should not be called - """ - raise NotImplementedError( - "ForesightExtractor should not directly use extract_memory method." - "Please use generate_foresights_for_conversation method." - ) - - async def generate_foresights_for_conversation( - self, - conversation_text: str, - timestamp: datetime, - user_id: str, - user_name: Optional[str] = None, - group_id: Optional[str] = None, - ) -> List[Foresight]: - """ - Generate foresight association predictions from raw conversation text. - - Args: - conversation_text: Raw conversation transcript text - timestamp: Conversation timestamp (used as base time) - user_id: Target user id - user_name: Optional user display name - group_id: Optional group id - - Returns: - List of foresight items (up to 10 items), including time information - """ - # Maximum 5 retries - with timed("extract_foresight"): - for retry in range(5): - try: - if retry == 0: - logger.info( - f"🎯 Generating foresight associations for conversation: user_id={user_id}" - ) - else: - logger.info( - f"🎯 Generating foresight associations for conversation: user_id={user_id}, retry {retry}/5" - ) - - # Build prompt (static prompt template via PromptManager) - prompt_template = get_prompt_by("FORESIGHT_GENERATION_PROMPT") - prompt = prompt_template.format( - USER_ID=user_id, - USER_NAME=user_name, - CONVERSATION_TEXT=conversation_text, - ) - - # Call LLM to generate associations - logger.debug( - f"📝 Starting LLM call to generate foresight associations, prompt length: {len(prompt)}" - ) - response = await self.llm_provider.generate( - prompt=prompt, temperature=0.3 - ) - logger.debug( - f"✅ LLM call completed, response length: {len(response) if response else 0}" - ) - - # Parse JSON response - start_time = self._extract_start_time_from_timestamp(timestamp) - foresights = await self._parse_foresights_response( - response, - start_time=start_time, - user_id=user_id, - timestamp=timestamp, - group_id=group_id, - ) - - # Validate at least 1 item is returned - if len(foresights) == 0: - raise ValueError("LLM returned empty foresight list") - - # Ensure at most 10 items are returned - if len(foresights) > 10: - foresights = foresights[:10] - elif len(foresights) < 4: - logger.warning( - f"Generated foresight associations less than 4, actual count: {len(foresights)}" - ) - - logger.info( - f"✅ Successfully generated {len(foresights)} foresight associations" - ) - for i, memory in enumerate(foresights[:3], 1): - logger.info(f" Association {i}: {memory.foresight}") - - return foresights - - except Exception as e: - logger.warning(f"Foresight generation retry {retry+1}/5: {e}") - if retry == 4: - logger.error(f"Foresight generation failed after 5 retries") - return [] - continue - - return [] - - @staticmethod - def _clean_date_string(date_str: Optional[str]) -> Optional[str]: - """Clean date string, remove invalid characters and validate date validity - - Args: - date_str: Original date string - - Returns: - Cleaned date string, return None if invalid - """ - if not date_str or not isinstance(date_str, str): - return None - - import re - - # Keep only digits and hyphens, remove other characters (e.g., Chinese, spaces, etc.) - cleaned = re.sub(r'[^\d\-]', '', date_str) - - # Validate format is YYYY-MM-DD - if not re.match(r'^\d{4}-\d{2}-\d{2}$', cleaned): - logger.warning( - f"Invalid time format, does not match YYYY-MM-DD: original='{date_str}', cleaned='{cleaned}'" - ) - return None - - # Validate date values are valid (month 1-12, day 1-31, etc.) - try: - year, month, day = map(int, cleaned.split('-')) - # Use datetime to validate date validity - datetime(year, month, day) - return cleaned - except ValueError as e: - logger.warning(f"Invalid date value: '{cleaned}', error: {e}") - return None - - async def _parse_foresights_response( - self, - response: str, - start_time: Optional[str] = None, - user_id: str = "", - timestamp: Optional[datetime] = None, - group_id: Optional[str] = None, - ) -> List[Foresight]: - """ - Parse LLM's JSON response to extract foresight association list - - Args: - response: LLM response text - start_time: Start time, format YYYY-MM-DD - user_id: User ID for the foresight - timestamp: Timestamp for the foresight - group_id: Group ID - - Returns: - List of foresight association items - """ - try: - # First try to extract JSON from code block - if '```json' in response: - start = response.find('```json') + 7 - end = response.find('```', start) - if end > start: - json_str = response[start:end].strip() - data = json.loads(json_str) - else: - data = json.loads(response) - else: - # Try to parse entire response as JSON array - data = json.loads(response) - - # Ensure data is a list - if isinstance(data, list): - foresights = [] - - # First collect all data to be processed - items_to_process = [] - for item in data: - content = item.get('content', '') - evidence = item.get('evidence', '') - - # Use passed start_time or LLM-provided time - item_start_time = item.get('start_time', start_time) - item_end_time = item.get('end_time') - item_duration_days = item.get('duration_days') - - # Clean time format (prevent LLM outputting incorrect format) - item_start_time = self._clean_date_string(item_start_time) - item_end_time = self._clean_date_string(item_end_time) - - # Smart time calculation: prioritize LLM-provided time information - if item_start_time: - # If LLM provides duration_days but no end_time, calculate end_time - if item_duration_days and not item_end_time: - item_end_time = self._calculate_end_time_from_duration( - item_start_time, item_duration_days - ) - # If LLM provides end_time but no duration_days, calculate duration_days - elif item_end_time and not item_duration_days: - item_duration_days = self._calculate_duration_days( - item_start_time, item_end_time - ) - # If LLM provides neither, keep as None (no additional extraction) - - items_to_process.append( - { - 'foresight': content, - 'evidence': evidence, - 'start_time': item_start_time, - 'end_time': item_end_time, - 'duration_days': item_duration_days, - } - ) - - # Batch compute embeddings for all content (performance optimization) - vs = get_vectorize_service() - contents = [item['foresight'] for item in items_to_process] - vectors_batch = await vs.get_embeddings( - contents - ) # Use get_embeddings (List[str]) - - # Create Foresight objects - for i, item_data in enumerate(items_to_process): - # Handle embedding: could be numpy array or already list - vector = vectors_batch[i] - if hasattr(vector, 'tolist'): - vector = vector.tolist() - elif not isinstance(vector, list): - vector = list(vector) - - memory_item = Foresight( - memory_type=MemoryType.FORESIGHT, - user_id=user_id, - timestamp=timestamp or get_now_with_timezone(), - group_id=group_id, - foresight=item_data['foresight'], - evidence=item_data['evidence'], - start_time=item_data['start_time'], - end_time=item_data['end_time'], - duration_days=item_data['duration_days'], - vector=vector, - vector_model=vs.get_model_name(), - ) - foresights.append(memory_item) - - return foresights - else: - logger.error(f"Response is not in JSON array format: {data}") - return [] - - except json.JSONDecodeError as e: - logger.error(f"Error parsing JSON response: {e}") - logger.debug(f"Response content: {response[:200]}...") - return [] - except Exception as e: - logger.error(f"Error parsing foresight response: {e}") - return [] - - def _extract_start_time_from_timestamp(self, timestamp: datetime) -> str: - """ - Extract start time from MemCell's timestamp field - - Args: - timestamp: MemCell timestamp - - Returns: - Start time string in YYYY-MM-DD format - """ - return timestamp.strftime('%Y-%m-%d') - - def _calculate_end_time_from_duration( - self, start_time: str, duration_days: int - ) -> Optional[str]: - """ - Calculate end time based on start time and duration - - Args: - start_time: Start time in YYYY-MM-DD format - duration_days: Duration in days - - Returns: - End time string in YYYY-MM-DD format, return None if calculation fails - """ - try: - if not start_time or duration_days is None: - return None - - start_date = datetime.strptime(start_time, '%Y-%m-%d') - end_date = start_date + timedelta(days=duration_days) - - return end_date.strftime('%Y-%m-%d') - - except Exception as e: - logger.error(f"Error calculating end time from duration: {e}") - return None - - def _calculate_duration_days(self, start_time: str, end_time: str) -> Optional[int]: - """ - Calculate duration (in days) - - Args: - start_time: Start time in YYYY-MM-DD format - end_time: End time in YYYY-MM-DD format - - Returns: - Duration in days, return None if calculation fails - """ - try: - if not start_time or not end_time: - return None - - start_date = datetime.strptime(start_time, '%Y-%m-%d') - end_date = datetime.strptime(end_time, '%Y-%m-%d') - - duration = end_date - start_date - return duration.days - - except Exception as e: - logger.error(f"Error calculating duration: {e}") - return None diff --git a/methods/EverCore/src/memory_layer/memory_extractor/profile_extractor.py b/methods/EverCore/src/memory_layer/memory_extractor/profile_extractor.py deleted file mode 100644 index 2981093e..00000000 --- a/methods/EverCore/src/memory_layer/memory_extractor/profile_extractor.py +++ /dev/null @@ -1,538 +0,0 @@ -"""Profile Memory Extractor. - -Extracts user profiles (explicit info + implicit traits) from conversations -using incremental LLM-based operations (add/update/delete). - -Includes ID mapping to reduce token consumption and LLM hallucination. -""" - -from __future__ import annotations - -import copy -import json -import re -from dataclasses import dataclass -from datetime import datetime -from enum import Enum -from typing import Any, Dict, List, Optional - -from common_utils.datetime_utils import get_now_with_timezone -from core.observation.logger import get_logger -from memory_layer.llm.llm_provider import LLMProvider -from memory_layer.memory_extractor.base_memory_extractor import ( - MemoryExtractor, - MemoryExtractRequest, -) -from memory_layer.prompts import get_prompt_by -from api_specs.memory_types import MemCell, MemoryType, ProfileMemory, ScenarioType, get_text_from_content_items, is_intermediate_agent_step - -logger = get_logger(__name__) - - -# ============================================================================ -# ID Mapper — Long ID <-> Short ID conversion to save tokens -# ============================================================================ - -def _create_id_mapping(long_ids: List[str]) -> Dict[str, str]: - return {lid: f"ep{i+1}" for i, lid in enumerate(long_ids) if lid} - - -def _replace_sources( - profile_dict: Dict[str, Any], id_map: Dict[str, str], reverse: bool = False -) -> Dict[str, Any]: - mapping = {v: k for k, v in id_map.items()} if reverse else id_map - result = copy.deepcopy(profile_dict) - - def _map_source(source: Any) -> Any: - if not isinstance(source, str) or not source: - return source - if "|" in source: - prefix, sid = source.rsplit("|", 1) - sid = sid.strip() - return f"{prefix}|{mapping.get(sid, sid)}" - return mapping.get(source, source) - - for item in result.get("explicit_info", []): - item["sources"] = [_map_source(s) for s in item.get("sources", [])] - for item in result.get("implicit_traits", []): - item["sources"] = [_map_source(s) for s in item.get("sources", [])] - - return result - - -def _get_short_id(long_id: str, id_map: Dict[str, str]) -> str: - return id_map.get(long_id, long_id) - - -# ============================================================================ -# Extract Request -# ============================================================================ - -class ProfileAction(str, Enum): - NONE = "none" - ADD = "add" - UPDATE = "update" - DELETE = "delete" - - -class ProfileItemType(str, Enum): - EXPLICIT_INFO = "explicit_info" - IMPLICIT_TRAITS = "implicit_traits" - - -@dataclass -class ProfileExtractRequest(MemoryExtractRequest): - """Profile extraction request.""" - - new_episode: Optional[Dict[str, Any]] = None - referenced_episodes: Optional[List[Dict[str, Any]]] = None - cluster_episodes: Optional[List[Dict[str, Any]]] = None - old_profile: Optional[ProfileMemory] = None - - # Scene type - scene: ScenarioType = ScenarioType.SOLO - # Target user display name (for TEAM scene, to disambiguate speakers) - target_user_name: Optional[str] = None - - # Legacy fields - memcell: Optional[MemCell] = None - memcell_list: Optional[List[MemCell]] = None - episode_list: Optional[List[Dict[str, Any]]] = None - - max_items: int = 25 - - def __post_init__(self): - if self.memcell_list is None: - self.memcell_list = [] - if self.episode_list is None: - self.episode_list = [] - if self.referenced_episodes is None: - self.referenced_episodes = [] - if self.cluster_episodes is None: - self.cluster_episodes = [] - - -# ============================================================================ -# Profile Extractor -# ============================================================================ - -class ProfileExtractor(MemoryExtractor): - """Extracts user profiles using incremental operations (add/update/delete).""" - - DEFAULT_MAX_ITEMS = 25 - - def __init__(self, llm_provider: LLMProvider): - super().__init__(MemoryType.PROFILE) - self.llm_provider = llm_provider - - async def extract_memory( - self, request: ProfileExtractRequest - ) -> Optional[ProfileMemory]: - """Extract profile from conversation episodes.""" - new_episode = request.new_episode - cluster_episodes = request.cluster_episodes or [] - old_profile = request.old_profile - max_items = request.max_items or self.DEFAULT_MAX_ITEMS - - # Backward compatibility with old episode_list mode - if not new_episode and request.episode_list: - episodes = request.episode_list - if episodes: - new_episode = episodes[-1] - cluster_episodes = episodes[:-1] if len(episodes) > 1 else [] - - if not new_episode: - logger.warning("No new episode provided for profile extraction") - return old_profile - - # Initialize profile - if old_profile is None: - logger.info(f"[ProfileExtractor] No old_profile for user={request.user_id}, creating new") - current_profile = ProfileMemory( - memory_type=MemoryType.PROFILE, - user_id=request.user_id or "", - group_id=request.group_id or "", - timestamp=get_now_with_timezone(), - ) - else: - logger.info( - f"[ProfileExtractor] Using old_profile for user={request.user_id}: " - f"explicit={len(old_profile.explicit_info)}, implicit={len(old_profile.implicit_traits)}" - ) - current_profile = old_profile - - # Check if already processed - ep_id = new_episode.get("id") - if ep_id in current_profile.processed_episode_ids: - logger.info(f"Episode {ep_id} already processed, skipping") - return current_profile - - # Create ID mapping - all_ids = ( - list(current_profile.processed_episode_ids) - + [ep.get("id") for ep in cluster_episodes] - + [new_episode.get("id")] - ) - id_map = _create_id_mapping(all_ids) - - logger.info(f"Processing profile: cluster={len(cluster_episodes)}, new=1") - - # Resolve target_user_name for TEAM scene - target_user_name = request.target_user_name - if not target_user_name and request.scene == ScenarioType.TEAM: - target_user_name = self._resolve_user_name( - request.user_id, [new_episode] + cluster_episodes - ) - - # Call LLM to update - updated_dict = await self._llm_update_profile( - current_profile=current_profile, - cluster_episodes=cluster_episodes, - new_episode=new_episode, - id_map=id_map, - scene=request.scene, - target_user_name=target_user_name, - ) - - if updated_dict: - current_profile.explicit_info = [ - d for d in updated_dict.get(ProfileItemType.EXPLICIT_INFO, []) - if d.get("description", "").strip() - ] - current_profile.implicit_traits = [ - d for d in updated_dict.get(ProfileItemType.IMPLICIT_TRAITS, []) - if d.get("description", "").strip() - ] - current_profile.last_updated = get_now_with_timezone() - - # Mark as processed - new_ep_id = new_episode.get("id", "") - if new_ep_id: - current_profile.processed_episode_ids.append(new_ep_id) - - # Check capacity - compact_threshold = int(max_items * 1.5) - compact_target = int(max_items * 0.7) - - if current_profile.total_items() > compact_threshold: - logger.info( - f"Profile has {current_profile.total_items()} items (threshold={compact_threshold}), " - f"compacting to {compact_target}..." - ) - current_profile = await self._compact_profile( - current_profile, compact_target, id_map - ) - - return current_profile - - async def _llm_update_profile( - self, - current_profile: ProfileMemory, - cluster_episodes: List[Dict[str, Any]], - new_episode: Dict[str, Any], - id_map: Dict[str, str], - scene: ScenarioType = ScenarioType.SOLO, - target_user_name: Optional[str] = None, - ) -> Optional[Dict[str, Any]]: - """Call LLM for incremental update using operations (add/update/delete).""" - - profile_dict = current_profile.to_dict() - profile_short = _replace_sources(profile_dict, id_map) - profile_text = self._format_profile_with_index(profile_short) - - all_episodes = (cluster_episodes or []) + ([new_episode] if new_episode else []) - conversations_text = self._format_episodes_for_llm(all_episodes, id_map) - - empty_profile = "(Empty, no records yet)" - empty_conv = "(No conversations)" - - if scene == ScenarioType.TEAM and target_user_name: - prompt_template = get_prompt_by("TEAM_PROFILE_UPDATE_PROMPT") - prompt = prompt_template.format( - target_user=target_user_name, - current_profile=profile_text or empty_profile, - conversations=conversations_text or empty_conv, - ) - else: - prompt_template = get_prompt_by("PROFILE_UPDATE_PROMPT") - prompt = prompt_template.format( - current_profile=profile_text or empty_profile, - conversations=conversations_text or empty_conv, - ) - - try: - response = await self.llm_provider.generate(prompt, temperature=0.3) - result = self._parse_profile_response(response) - if not result: - return None - - operations = result.get("operations", []) - - explicit_list = list(current_profile.explicit_info) - implicit_list = list(current_profile.implicit_traits) - - id_to_ts = self._build_timestamp_map( - current_profile, cluster_episodes, new_episode - ) - - for op in operations: - action = op.get("action", ProfileAction.NONE) - - if action == ProfileAction.NONE: - continue - - elif action == ProfileAction.ADD: - op_type = op.get("type") - data = op.get("data", {}) - if not data.get("description", "").strip(): - continue - data["sources"] = [ - self._attach_ts(s, id_to_ts) for s in data.get("sources", []) - ] - if op_type == ProfileItemType.EXPLICIT_INFO: - explicit_list.append(data) - logger.info(f"[Profile] Added explicit_info: {data.get('description', '')[:30]}...") - elif op_type == ProfileItemType.IMPLICIT_TRAITS: - implicit_list.append(data) - logger.info(f"[Profile] Added implicit_trait: {data.get('trait', '')}...") - - elif action == ProfileAction.UPDATE: - op_type = op.get("type") - index = op.get("index", -1) - data = op.get("data", {}) - target_list = ( - explicit_list if op_type == ProfileItemType.EXPLICIT_INFO else implicit_list - ) - if 0 <= index < len(target_list): - for key, val in data.items(): - if val: - if key == "sources": - old_sources = target_list[index].get("sources", []) - new_sources = [self._attach_ts(s, id_to_ts) for s in val] - target_list[index]["sources"] = list(set(old_sources + new_sources)) - else: - target_list[index][key] = val - logger.info(f"[Profile] Updated {op_type}[{index}]") - - elif action == ProfileAction.DELETE: - op_type = op.get("type") - index = op.get("index", -1) - reason = op.get("reason", "") - target_list = ( - explicit_list if op_type == ProfileItemType.EXPLICIT_INFO else implicit_list - ) - if 0 <= index < len(target_list) and reason: - target_list.pop(index) - logger.warning(f"[Profile] Deleted {op_type}[{index}]: {reason}") - - result_dict = { - ProfileItemType.EXPLICIT_INFO: explicit_list, - ProfileItemType.IMPLICIT_TRAITS: implicit_list, - } - return _replace_sources(result_dict, id_map, reverse=True) - - except Exception as e: - logger.error(f"LLM update profile failed: {e}") - return None - - def _build_timestamp_map( - self, - profile: ProfileMemory, - cluster_episodes: List[Dict[str, Any]], - new_episode: Dict[str, Any], - ) -> Dict[str, str]: - id_to_ts = {} - - for item in profile.explicit_info + profile.implicit_traits: - for src in item.get("sources", []): - if "|" in str(src): - ts, eid = str(src).rsplit("|", 1) - id_to_ts[eid.strip()] = ts.strip() - - for ep in (cluster_episodes or []) + ([new_episode] if new_episode else []): - eid = ep.get("id") - ts = self._format_timestamp(ep.get("created_at")) - if eid and ts: - id_to_ts[str(eid)] = ts - - return id_to_ts - - def _attach_ts(self, s: Any, id_to_ts: Dict[str, str]) -> str: - if not isinstance(s, str) or not s: - return s - if "|" in s: - return s - sid = s.strip() - ts = id_to_ts.get(sid) - return f"{ts}|{sid}" if ts else sid - - def _format_profile_with_index(self, profile_dict: Dict[str, Any]) -> str: - explicit = profile_dict.get(ProfileItemType.EXPLICIT_INFO, []) - implicit = profile_dict.get(ProfileItemType.IMPLICIT_TRAITS, []) - - if not explicit and not implicit: - return "" - - lines = [] - if explicit: - lines.append("【Explicit Info】") - for i, item in enumerate(explicit): - lines.append(f" [{i}] [{item.get('category', '')}] {item.get('description', '')}") - if item.get("evidence"): - lines.append(f" evidence: {item['evidence']}") - - if implicit: - lines.append("\n【Implicit Traits】") - for i, item in enumerate(implicit): - lines.append(f" [{i}] {item.get('trait', '')}: {item.get('description', '')}") - if item.get("evidence"): - lines.append(f" evidence: {item['evidence']}") - - return "\n".join(lines) - - async def _compact_profile( - self, profile: ProfileMemory, max_items: int, id_map: Dict[str, str] - ) -> ProfileMemory: - profile_dict = profile.to_dict() - profile_short = _replace_sources(profile_dict, id_map) - profile_text = self._format_profile_for_llm(profile_short) - total = profile.total_items() - - prompt_template = get_prompt_by("PROFILE_COMPACT_PROMPT") - prompt = prompt_template.format( - total_items=total, max_items=max_items, profile_text=profile_text - ) - - try: - response = await self.llm_provider.generate(prompt, temperature=0.3) - result = self._parse_profile_response(response) - - if result: - result_long = _replace_sources(result, id_map, reverse=True) - profile.explicit_info = [ - d for d in result_long.get(ProfileItemType.EXPLICIT_INFO, []) - if d.get("description", "").strip() - ] - profile.implicit_traits = [ - d for d in result_long.get(ProfileItemType.IMPLICIT_TRAITS, []) - if d.get("description", "").strip() - ] - profile.last_updated = get_now_with_timezone() - - return profile - - except Exception as e: - logger.error(f"LLM compact profile failed: {e}") - return profile - - def _format_profile_for_llm(self, profile_dict: Dict[str, Any]) -> str: - explicit = profile_dict.get(ProfileItemType.EXPLICIT_INFO, []) - implicit = profile_dict.get(ProfileItemType.IMPLICIT_TRAITS, []) - - if not explicit and not implicit: - return "" - - lines = [] - if explicit: - lines.append("【Explicit Info】") - for i, item in enumerate(explicit, 1): - lines.append(f" {i}. [{item.get('category', '')}] {item.get('description', '')}") - if item.get("evidence"): - lines.append(f" evidence: {item['evidence']}") - lines.append(f" sources: {', '.join(item.get('sources', []))}") - - if implicit: - lines.append("\n【Implicit Traits】") - for i, item in enumerate(implicit, 1): - lines.append(f" {i}. {item.get('trait', '')}: {item.get('description', '')}") - if item.get("basis"): - lines.append(f" basis: {item['basis']}") - if item.get("evidence"): - lines.append(f" evidence: {item['evidence']}") - lines.append(f" sources: {', '.join(item.get('sources', []))}") - - return "\n".join(lines) - - def _format_episodes_for_llm( - self, episodes: List[Dict[str, Any]], id_map: Dict[str, str] - ) -> str: - if not episodes: - return "" - - lines = [] - for ep in episodes: - short_id = _get_short_id(ep.get("id"), id_map) - timestamp = self._format_timestamp(ep.get("created_at")) - lines.append(f"[{short_id}] ({timestamp})") - - original_data = ep.get("original_data", []) - if original_data and isinstance(original_data, list): - for msg in original_data: - m = msg.get("message", msg) - if is_intermediate_agent_step(m): - continue - sender = m.get("sender_name", "Unknown") - content = get_text_from_content_items(m.get("content", [])) - ts = m.get("timestamp", "") - if content: - lines.append(f" [{ts}]【{sender}】: {content}\n\n") - else: - episode = ep.get("episode") - if episode: - lines.append(f" [Episode Memory] {episode}") - - lines.append("") - - return "\n".join(lines) - - def _format_timestamp(self, ts: Any) -> str: - if not ts: - return "" - if isinstance(ts, datetime): - return ts.strftime("%Y-%m-%d %H:%M") - if isinstance(ts, str): - return ts[:16] if len(ts) >= 16 else ts - return str(ts)[:16] - - def _resolve_user_name( - self, user_id: str, episodes: List[Dict[str, Any]] - ) -> Optional[str]: - """Resolve user_id to sender_name from episode original_data.""" - for ep in episodes: - for msg in ep.get("original_data", []): - m = msg.get("message", msg) - if m.get("sender_id") == user_id: - name = m.get("sender_name") - if name: - return name - # Fallback to user_id itself - logger.warning(f"Could not resolve sender_name for user_id={user_id}, using user_id as fallback") - return user_id - - def _parse_profile_response(self, response: str) -> Optional[Dict[str, Any]]: - if not response: - return None - - json_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", response, re.DOTALL) - raw_json = json_match.group(1) if json_match else response - - try: - data = json.loads(raw_json) - except json.JSONDecodeError: - brace_start = response.find("{") - brace_end = response.rfind("}") + 1 - if brace_start >= 0 and brace_end > brace_start: - try: - data = json.loads(response[brace_start:brace_end]) - except Exception: - logger.warning("Failed to parse profile response JSON") - return None - else: - logger.warning("No JSON found in profile response") - return None - - update_note = data.get("update_note") or data.get("compact_note") - if update_note: - logger.info(f"Profile update: {update_note}") - - return data diff --git a/methods/EverCore/src/memory_layer/memory_manager.py b/methods/EverCore/src/memory_layer/memory_manager.py deleted file mode 100644 index e496f5c5..00000000 --- a/methods/EverCore/src/memory_layer/memory_manager.py +++ /dev/null @@ -1,471 +0,0 @@ -from dataclasses import dataclass -from datetime import datetime -import time -import asyncio -from typing import List, Optional, Dict, Any - -from core.observation.logger import get_logger -from agentic_layer.metrics.memorize_metrics import ( - record_extract_memory_call, - get_space_id_for_metrics, -) - -from memory_layer.llm.llm_provider import ( - LLMProvider, - build_default_provider, - DEFAULT_PROVIDER_NAME, - DEFAULT_LLM_TEMPERATURE, - DEFAULT_LLM_MAX_TOKENS, -) -from memory_layer.memcell_extractor.conv_memcell_extractor import ConvMemCellExtractor -from memory_layer.memcell_extractor.base_memcell_extractor import RawData -from memory_layer.memcell_extractor.conv_memcell_extractor import ( - ConversationMemCellExtractRequest, -) -from api_specs.memory_types import ( - MemCell, - RawDataType, - MemoryType, - Foresight, - BaseMemory, - EpisodeMemory, - get_text_from_content_items, -) -from memory_layer.memory_extractor.episode_memory_extractor import ( - EpisodeMemoryExtractor, - EpisodeMemoryExtractRequest, -) -from memory_layer.memory_extractor.profile_extractor import ( - ProfileExtractor, - ProfileExtractRequest, -) -from memory_layer.memory_extractor.atomic_fact_extractor import AtomicFactExtractor -from memory_layer.memory_extractor.foresight_extractor import ForesightExtractor -from memory_layer.memcell_extractor.base_memcell_extractor import StatusResult -from api_specs.memory_models import MessageSenderRole -from memory_layer.constants import EXTRACT_SCENES - - -logger = get_logger(__name__) - - -class MemoryManager: - """ - Memory Manager - Responsible for orchestrating all memory extraction processes - - Responsibilities: - 1. Extract MemCell (boundary detection + raw data) - 2. Extract Episode/Foresight/AtomicFact/Profile and other memories (based on MemCell or episode) - 3. Manage the lifecycle of all Extractors - 4. Provide a unified memory extraction interface - """ - - SCENES = EXTRACT_SCENES - - def __init__(self, llm_config: Optional[Dict[str, Any]] = None): - """ - Initialize MemoryManager - - Args: - llm_config: Optional LLM configuration dict (e.g. from global settings llm_custom_setting) - Structure: { - "boundary": {"provider": "...", "model": "..."}, - "extraction": {"provider": "...", "model": "..."}, - "profile": {"provider": "...", "model": "..."} - } - """ - self.llm_config = llm_config or {} - self.providers_mapping: Dict[str, LLMProvider] = {} - self._build_providers_mapping() - - # Episode Extractor - lazy initialization - self._episode_extractor = None - - def _get_scene_config(self, scene: Optional[str]) -> Optional[Dict[str, Any]]: - if not scene or not self.llm_config: - return None - cfg = self.llm_config.get(scene) - if not cfg: - return None - if isinstance(cfg, dict): - return cfg - return { - "provider": getattr(cfg, "provider", None), - "model": getattr(cfg, "model", None), - "temperature": getattr(cfg, "temperature", None), - "max_tokens": getattr(cfg, "max_tokens", None), - "extra": getattr(cfg, "extra", None), - } - - def _get_scene_cfg_value(self, cfg: Dict[str, Any], key: str) -> Any: - """Extract a value from scene config, checking 'extra' dict as fallback.""" - val = cfg.get(key) - if val is None: - extra = cfg.get("extra") - if isinstance(extra, dict): - val = extra.get(key) - return val - - def _build_scene_provider(self, scene: str, cfg: Dict[str, Any]) -> LLMProvider: - """Build an LLM provider from a single scene's config. - - api_key and base_url are resolved inside LLMProvider from env vars, - not from llm_config. - """ - provider_name = self._get_scene_cfg_value(cfg, "provider") - if not provider_name: - raise ValueError(f"missing provider in scene '{scene}' config") - - model = self._get_scene_cfg_value(cfg, "model") - if not model: - raise ValueError( - f"missing model for provider '{provider_name}' " - f"in scene '{scene}' config" - ) - - temperature = self._get_scene_cfg_value(cfg, "temperature") - if temperature is None: - temperature = DEFAULT_LLM_TEMPERATURE - - max_tokens = self._get_scene_cfg_value(cfg, "max_tokens") - if max_tokens is None: - max_tokens = DEFAULT_LLM_MAX_TOKENS - - return LLMProvider( - provider_type=provider_name, - model=model, # skip-sensitive-check - temperature=float(temperature), - max_tokens=int(max_tokens), - ) - - def _build_providers_mapping(self) -> None: - self.providers_mapping[DEFAULT_PROVIDER_NAME] = build_default_provider() - for scene in self.SCENES: - cfg = self._get_scene_config(scene) - if not cfg: - continue - try: - self.providers_mapping[scene] = self._build_scene_provider(scene, cfg) - except Exception as e: - logger.warning( - f"[MemoryManager] Failed to build provider for " - f"scene '{scene}': {e}, falling back to default" - ) - - def _get_provider_for_scene(self, scene: str) -> LLMProvider: - provider = self.providers_mapping.get(scene) - if provider is None: - provider = self.providers_mapping.get(DEFAULT_PROVIDER_NAME) - return provider - - # TODO: add username - async def extract_memcell( - self, - history_raw_data_list: list[RawData], - new_raw_data_list: list[RawData], - raw_data_type: RawDataType, - group_id: Optional[str] = None, - user_id_list: Optional[List[str]] = None, - old_memory_list: Optional[List[BaseMemory]] = None, - flush: bool = False, - ) -> tuple[List[MemCell], StatusResult]: - """ - Extract MemCells using multi-split boundary detection. - - Args: - history_raw_data_list: List of historical messages - new_raw_data_list: List of new messages - raw_data_type: Data type - group_id: Group ID - user_id_list: List of user IDs - old_memory_list: List of historical memories - flush: When True, remaining messages after boundary detection are packed into a final MemCell - - Returns: - (list_of_memcells, StatusResult) - Empty list means no boundary detected; StatusResult.should_wait for accumulation state - """ - now = time.time() - - logger.debug(f"[MemoryManager] Starting boundary detection (flush={flush})") - - request = ConversationMemCellExtractRequest( - history_raw_data_list, - new_raw_data_list, - user_id_list=user_id_list, - group_id=group_id, - old_memory_list=old_memory_list, - flush=flush, - ) - - # Select extractor based on raw_data_type - if raw_data_type == RawDataType.AGENTCONVERSATION: - from memory_layer.memcell_extractor.agent_memcell_extractor import ( - AgentMemCellExtractor, - ) - extractor = AgentMemCellExtractor(self._get_provider_for_scene("boundary")) - else: - extractor = ConvMemCellExtractor(self._get_provider_for_scene("boundary")) - memcells, status_result = await extractor.extract_memcell(request) - - if not memcells: - logger.debug( - "[MemoryManager] Boundary detection: no boundary reached, waiting for more messages" - ) - return [], status_result - - logger.info( - f"[MemoryManager] ✅ {len(memcells)} MemCell(s) created, " - f"elapsed time: {time.time() - now:.2f} seconds" - ) - - return memcells, status_result - - async def extract_memory( - self, - memcell: MemCell, - memory_type: MemoryType, - user_id: Optional[ - str - ] = None, # None means group memory, with value means personal memory - group_id: Optional[str] = None, - old_memory_list: Optional[List[BaseMemory]] = None, - ): - """ - Extract a single memory - - Args: - memcell: Single MemCell (raw data container for memory) - memory_type: Memory type - user_id: User ID - - None: Extract group Episode/group Profile - - With value: Extract personal Episode/personal Profile - group_id: Group ID - old_memory_list: List of historical memories - - Returns: - - EPISODIC_MEMORY: Returns Memory (group or personal) - - FORESIGHT: Returns List[Foresight] - - PERSONAL_ATOMIC_FACT: Returns AtomicFact - - PROFILE/GROUP_PROFILE: Returns Memory - """ - start_time = time.perf_counter() - memory_type_str = ( - memory_type.value if hasattr(memory_type, 'value') else str(memory_type) - ) - # Get metrics labels - space_id = get_space_id_for_metrics() - raw_data_type = memcell.type.value if memcell.type else 'unknown' - result = None - status = 'success' - - try: - # Dispatch based on memory_type enum - match memory_type: - case MemoryType.EPISODIC_MEMORY: - result = await self._extract_episode(memcell, user_id, group_id) - - case MemoryType.FORESIGHT: - result = await self._extract_foresight( - memcell, user_id=user_id, group_id=group_id - ) - - case MemoryType.ATOMIC_FACT: - result = await self._extract_atomic_fact( - memcell, user_id=user_id, group_id=group_id - ) - - case MemoryType.PROFILE: - result = await self._extract_profile( - memcell, user_id, group_id, old_memory_list - ) - - case MemoryType.AGENT_CASE: - result = await self._extract_agent_case( - memcell, user_id=user_id, group_id=group_id - ) - - case _: - logger.warning( - f"[MemoryManager] Unknown memory_type: {memory_type}" - ) - status = 'error' - return None - - # Determine status based on result - if result is None: - status = 'empty_result' - elif isinstance(result, list) and len(result) == 0: - status = 'empty_result' - - return result - - except Exception as e: - status = 'error' - raise - finally: - duration = time.perf_counter() - start_time - record_extract_memory_call( - space_id=space_id, - raw_data_type=raw_data_type, - memory_type=memory_type_str, - status=status, - duration_seconds=duration, - ) - - async def _extract_episode( - self, memcell: MemCell, user_id: Optional[str], group_id: Optional[str] - ) -> Optional[EpisodeMemory]: - """Extract Episode (group or personal)""" - if self._episode_extractor is None: - self._episode_extractor = EpisodeMemoryExtractor( - self._get_provider_for_scene("extraction") - ) - - # Build extraction request - from memory_layer.memory_extractor.base_memory_extractor import ( - MemoryExtractRequest, - ) - - request = MemoryExtractRequest( - memcell=memcell, - user_id=user_id, # None=group, with value=personal - group_id=group_id, - ) - - # Call extractor's extract_memory method - # It will automatically determine whether to extract group or personal Episode based on user_id - logger.debug( - f"[MemoryManager] Extracting {'group' if user_id is None else 'personal'} Episode: user_id={user_id}" - ) - - return await self._episode_extractor.extract_memory(request) - - async def _extract_foresight( - self, - memcell: Optional[MemCell], - user_id: Optional[str] = None, - group_id: Optional[str] = None, - ) -> List[Foresight]: - """Extract Foresight (solo scene uses raw conversation text)""" - if not memcell: - logger.warning("[MemoryManager] Missing memcell, cannot extract Foresight") - return [] - uid = user_id - gid = group_id - # Build simple conversation transcript from memcell.conversation_data - # (conversation_data already filters out tool calls/responses for agent conversations) - lines = [] - for item in memcell.conversation_data or []: - if not isinstance(item, dict): - continue - msg = item.get("message", item) - role = msg.get("role") - if role == MessageSenderRole.ASSISTANT.value: - continue - sender_name = msg.get("sender_name") - content = get_text_from_content_items(msg.get("content", [])) - ts = msg.get("timestamp") - if ts: - lines.append(f"[{ts}] {sender_name}: {content}") - else: - lines.append(f"{sender_name}: {content}") - conversation_text = "\n".join(lines) - - # Best-effort resolve user_name from raw messages - - if uid is None: - display_name = ",".join( - set( - [ - item.get("message", item).get("sender_name") - for item in memcell.original_data or [] - ] - ) - ) - else: - for item in memcell.original_data or []: - msg = item.get("message", item) - if msg.get("sender_id") == uid: - display_name = msg.get("sender_name") - break - - extractor = ForesightExtractor( - llm_provider=self._get_provider_for_scene("extraction") - ) - foresights = await extractor.generate_foresights_for_conversation( - conversation_text=conversation_text, - timestamp=memcell.timestamp, - user_id=uid, - user_name=display_name, - group_id=gid, - ) - return foresights - - async def _extract_atomic_fact( - self, - memcell: Optional[MemCell], - user_id: Optional[str] = None, - group_id: Optional[str] = None, - ): - """Extract Atomic Fact""" - if not memcell: - logger.warning("[MemoryManager] Missing memcell, cannot extract AtomicFact") - return None - - uid = user_id - gid = group_id - - logger.debug(f"[MemoryManager] Extracting AtomicFact: user_id={uid}") - - extractor = AtomicFactExtractor( - llm_provider=self._get_provider_for_scene("extraction") - ) - return await extractor.extract_atomic_fact( - memcell=memcell, timestamp=memcell.timestamp, user_id=uid, group_id=gid - ) - - async def _extract_profile( - self, - memcell: MemCell, - user_id: Optional[str], - group_id: Optional[str], - old_memory_list: Optional[List[BaseMemory]], - ) -> Optional[BaseMemory]: - """Extract Profile""" - if memcell.type != RawDataType.CONVERSATION: - return None - - extractor = ProfileExtractor(self._get_provider_for_scene("profile")) - request = ProfileExtractRequest( - memcell_list=[memcell], - user_id_list=[user_id] if user_id else [], - group_id=group_id, - ) - return await extractor.extract_memory(request) - - async def _extract_agent_case( - self, - memcell: MemCell, - user_id: Optional[str] = None, - group_id: Optional[str] = None, - ): - """Extract AgentCase from an agent conversation MemCell.""" - from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - AgentCaseExtractRequest, - ) - - if not memcell or memcell.type != RawDataType.AGENTCONVERSATION: - return None - - logger.debug("[MemoryManager] Extracting AgentCase") - extractor = AgentCaseExtractor( - llm_provider=self._get_provider_for_scene("extraction") - ) - request = AgentCaseExtractRequest( - memcell=memcell, - user_id=user_id, - group_id=group_id, - ) - return await extractor.extract_memory(request) diff --git a/methods/EverCore/src/memory_layer/profile_indexer/__init__.py b/methods/EverCore/src/memory_layer/profile_indexer/__init__.py deleted file mode 100644 index 637d7495..00000000 --- a/methods/EverCore/src/memory_layer/profile_indexer/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -Profile Indexer Module - -Provides indexing services for user profiles into vector databases (Milvus). -""" - -from .profile_indexer import ProfileIndexer, index_user_profile - -__all__ = [ - "ProfileIndexer", - "index_user_profile", -] diff --git a/methods/EverCore/src/memory_layer/profile_indexer/profile_indexer.py b/methods/EverCore/src/memory_layer/profile_indexer/profile_indexer.py deleted file mode 100644 index 94765730..00000000 --- a/methods/EverCore/src/memory_layer/profile_indexer/profile_indexer.py +++ /dev/null @@ -1,308 +0,0 @@ -""" -Profile Life Indexer - -Handles indexing of UserProfile data into Milvus for vector search. - -Indexing Strategy: Delete-then-Insert with Redis Distributed Lock -- When a profile is created or updated, delete all existing items for the user -- Then rebuild the full index with fresh embeddings -- Uses Redis distributed lock to prevent concurrent indexing of same user+group - -This ensures: -1. No stale data in the index -2. Index always reflects the current profile state -3. Handles item deletions and reorderings correctly -4. Prevents race conditions in distributed environment -""" - -from typing import List, Dict, Any, Optional - -from core.di import get_bean_by_type -from core.di.decorators import service -from core.observation.logger import get_logger -from core.lock.redis_distributed_lock import distributed_lock -from agentic_layer.vectorize_service import get_vectorize_service -from infra_layer.adapters.out.search.repository.user_profile_milvus_repository import ( - UserProfileMilvusRepository, -) -from infra_layer.adapters.out.search.milvus.converter.user_profile_milvus_converter import ( - UserProfileMilvusConverter, -) -from infra_layer.adapters.out.persistence.document.memory.user_profile import ( - UserProfile as MongoUserProfile, -) -from api_specs.memory_types import ProfileMemory - -logger = get_logger(__name__) - - -@service(name="profile_indexer", primary=True) -class ProfileIndexer: - """ - Profile Life Indexer Service - - Responsible for: - 1. Splitting ProfileMemory into individual items - 2. Generating embeddings for each item - 3. Storing items in Milvus with delete-then-insert strategy - """ - - def __init__(self, milvus_repo: Optional[UserProfileMilvusRepository] = None): - """Initialize indexer - - Args: - milvus_repo: User profile Milvus repository (auto-injected if None) - """ - self._milvus_repo = milvus_repo - - @property - def milvus_repo(self) -> UserProfileMilvusRepository: - """Lazy load Milvus repository""" - if self._milvus_repo is None: - self._milvus_repo = get_bean_by_type(UserProfileMilvusRepository) - return self._milvus_repo - - async def index_profile( - self, user_id: str, group_id: str, profile: ProfileMemory, doc_id: str = "" - ) -> Dict[str, int]: - """ - Index a user profile into Milvus - - Strategy: Delete-then-Insert with Redis Distributed Lock - 1. Acquire distributed lock for user_id + group_id (prevent concurrent indexing across instances) - 2. Delete all existing items for user_id + group_id - 3. Split profile into individual items - 4. Generate embeddings for all items - 5. Batch insert into Milvus - 6. Release lock automatically (context manager) - - Args: - user_id: User ID - group_id: Group ID - profile: ProfileMemory object containing explicit_info and implicit_traits - doc_id: MongoDB document ID for generating unique Milvus entity IDs. - If empty, will query MongoDB to get the actual document ID. - - Returns: - Dict with indexing statistics: - - deleted_count: Number of deleted items - - explicit_count: Number of explicit_info items indexed - - implicit_count: Number of implicit_trait items indexed - - total_count: Total items indexed - """ - # Create distributed lock key for this user+group - lock_resource = f"profile_index:{user_id}:{group_id}" - - # Acquire Redis distributed lock - # timeout: 30s (enough for vectorization + insertion) - # blocking_timeout: 40s (wait for previous task to complete) - async with distributed_lock( - resource=lock_resource, timeout=30.0, blocking_timeout=40.0 - ) as acquired: - if not acquired: - logger.error( - "[ProfileIndexer] Failed to acquire distributed lock: user_id=%s, group_id=%s", - user_id, - group_id, - ) - return { - "deleted_count": 0, - "explicit_count": 0, - "implicit_count": 0, - "total_count": 0, - } - - logger.debug( - "[ProfileIndexer] Acquired distributed lock for user_id=%s, group_id=%s", - user_id, - group_id, - ) - - stats = { - "deleted_count": 0, - "explicit_count": 0, - "implicit_count": 0, - "total_count": 0, - } - - try: - logger.info( - "[ProfileIndexer] Starting index for user_id=%s, group_id=%s", - user_id, - group_id, - ) - - # Step 1: Delete existing items - deleted_count = await self.milvus_repo.delete_by_user_group( - user_id=user_id, group_id=group_id - ) - stats["deleted_count"] = deleted_count - logger.info("[ProfileIndexer] Deleted %d existing items", deleted_count) - - # Step 2: Fetch the real MongoDB document for converter. - # The converter needs source_doc.id to generate unique Milvus - # entity IDs (e.g. "6650a1b2..._e0"). Without a real id, all - # entities get IDs like "_e0", "_i1", causing primary-key - # collisions across different users' profiles. - if doc_id: - source_doc = await MongoUserProfile.get(doc_id) - else: - source_doc = await MongoUserProfile.find_one( - MongoUserProfile.user_id == user_id, - MongoUserProfile.group_id == group_id, - ) - - if not source_doc or not source_doc.id: - logger.error( - "[ProfileIndexer] Cannot find MongoDB profile doc: user_id=%s, group_id=%s, doc_id=%s", - user_id, group_id, doc_id, - ) - return stats - - # Use the MongoDB document's profile_data directly. - # Do NOT overwrite with profile.to_dict() — the profile object - # may have been constructed via ProfileMemory.from_dict() - # which only reads explicit_info/implicit_traits and discards - # V1-format fields (hard_skills, soft_skills, etc.). - entities = UserProfileMilvusConverter.from_mongo(source_doc) - - if not entities: - logger.info("[ProfileIndexer] No items to index") - return stats - - # Step 3: Generate embeddings - texts = [entity["embed_text"] for entity in entities] - vectors = await self._generate_embeddings(texts) - - if len(vectors) != len(entities): - logger.error( - "[ProfileIndexer] Embedding count mismatch: expected %d, got %d", - len(entities), - len(vectors), - ) - return stats - - # Step 4: Add vectors to entities - valid_entities = [] - for entity, vector in zip(entities, vectors): - if vector is not None and len(vector) > 0: - entity["vector"] = vector - valid_entities.append(entity) - - # Step 5: Batch insert - if valid_entities: - await self.milvus_repo.insert_batch( - entities=valid_entities, flush=True - ) - stats["total_count"] = len(valid_entities) - - # Count by type - for entity in valid_entities: - if entity["item_type"] == "explicit_info": - stats["explicit_count"] += 1 - elif entity["item_type"] == "implicit_trait": - stats["implicit_count"] += 1 - - logger.info( - "[ProfileIndexer] ✅ Indexing completed: deleted=%d, explicit=%d, implicit=%d, total=%d", - stats["deleted_count"], - stats["explicit_count"], - stats["implicit_count"], - stats["total_count"], - ) - - return stats - - except Exception as e: - logger.error( - "[ProfileIndexer] ❌ Failed to index profile: user_id=%s, group_id=%s, error=%s", - user_id, - group_id, - e, - exc_info=True, - ) - return stats - - async def delete_profile_index(self, user_id: str, group_id: str) -> int: - """ - Delete all indexed items for a user profile - - Args: - user_id: User ID - group_id: Group ID - - Returns: - Number of deleted items - """ - try: - deleted_count = await self.milvus_repo.delete_by_user_group( - user_id=user_id, group_id=group_id - ) - logger.info( - "[ProfileIndexer] Deleted profile index: user_id=%s, group_id=%s, count=%d", - user_id, - group_id, - deleted_count, - ) - return deleted_count - except Exception as e: - logger.error("[ProfileIndexer] Failed to delete profile index: %s", e) - return 0 - - async def _generate_embeddings(self, texts: List[str]) -> List[List[float]]: - """ - Generate embeddings for texts using VectorizeService - - Args: - texts: List of texts to embed - - Returns: - List of embedding vectors - """ - if not texts: - return [] - - try: - vectorize_service = get_vectorize_service() - - # Batch embed all texts - vectors = await vectorize_service.get_embeddings(texts) - - logger.debug("[ProfileIndexer] Generated %d embeddings", len(vectors)) - - return vectors - - except Exception as e: - logger.error("[ProfileIndexer] Failed to generate embeddings: %s", e) - return [] - - -# Convenience function for external calls -async def index_user_profile( - user_id: str, group_id: str, profile: ProfileMemory, doc_id: str = "" -) -> Dict[str, int]: - """ - Index a user profile into Milvus - - Convenience function that gets the indexer service and calls index_profile. - - Args: - user_id: User ID - group_id: Group ID - profile: ProfileMemory object - doc_id: MongoDB document ID for generating unique Milvus entity IDs - - Returns: - Indexing statistics - """ - try: - indexer = get_bean_by_type(ProfileIndexer) - return await indexer.index_profile(user_id, group_id, profile, doc_id=doc_id) - except Exception as e: - logger.error("[ProfileIndexer] Failed to get indexer service: %s", e) - return { - "deleted_count": 0, - "explicit_count": 0, - "implicit_count": 0, - "total_count": 0, - } diff --git a/methods/EverCore/src/memory_layer/profile_manager/__init__.py b/methods/EverCore/src/memory_layer/profile_manager/__init__.py deleted file mode 100644 index be60486b..00000000 --- a/methods/EverCore/src/memory_layer/profile_manager/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Profile Manager - Pure computation component for profile extraction. - -Usage: - from memory_layer.profile_manager import ProfileManager, ProfileManagerConfig - - config = ProfileManagerConfig(min_confidence=0.6) - profile_mgr = ProfileManager(llm_provider, config) - - old_profiles = list((await storage.get_all_profiles()).values()) - - new_profiles = await profile_mgr.extract_profiles( - memcells=memcell_list, - old_profiles=old_profiles, - user_id_list=["user1", "user2"], - ) - - for profile in new_profiles: - await storage.save_profile(profile.user_id, profile) -""" - -from memory_layer.profile_manager.config import ProfileManagerConfig -from memory_layer.profile_manager.manager import ProfileManager - -__all__ = ["ProfileManager", "ProfileManagerConfig"] diff --git a/methods/EverCore/src/memory_layer/profile_manager/config.py b/methods/EverCore/src/memory_layer/profile_manager/config.py deleted file mode 100644 index 254be7e9..00000000 --- a/methods/EverCore/src/memory_layer/profile_manager/config.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Configuration for ProfileManager.""" - -from dataclasses import dataclass - - -@dataclass -class ProfileManagerConfig: - """Configuration for ProfileManager. - - Attributes: - min_confidence: Minimum confidence threshold for value discrimination (0.0-1.0) - enable_versioning: Whether to keep profile version history - auto_extract: Whether to automatically extract profiles on cluster updates - batch_size: Maximum memcells per batch for profile extraction - max_retries: Maximum retry attempts for failed profile extractions - """ - - min_confidence: float = 0.6 - enable_versioning: bool = True - auto_extract: bool = True - batch_size: int = 50 - max_retries: int = 3 - - def __post_init__(self): - """Validate configuration.""" - if not 0.0 <= self.min_confidence <= 1.0: - raise ValueError( - f"min_confidence must be in [0.0, 1.0], got {self.min_confidence}" - ) - - if self.batch_size < 1: - raise ValueError(f"batch_size must be >= 1, got {self.batch_size}") - - if self.max_retries < 0: - raise ValueError(f"max_retries must be >= 0, got {self.max_retries}") diff --git a/methods/EverCore/src/memory_layer/profile_manager/manager.py b/methods/EverCore/src/memory_layer/profile_manager/manager.py deleted file mode 100644 index fdf05120..00000000 --- a/methods/EverCore/src/memory_layer/profile_manager/manager.py +++ /dev/null @@ -1,240 +0,0 @@ -"""ProfileManager - Pure computation component for profile extraction. - -This module provides pure computation logic for extracting user profiles -from memcells. Storage is managed by the caller, not by ProfileManager itself. - -Design: -- ProfileManager is a pure computation component -- Input: memcells + old_profiles -- Output: new_profiles -- Caller is responsible for loading/saving profiles -""" - -import asyncio -from typing import Any, Dict, List, Optional - -from memory_layer.llm.llm_provider import LLMProvider -from api_specs.memory_types import ProfileMemory, ScenarioType -from memory_layer.memory_extractor.profile_extractor import ( - ProfileExtractor, - ProfileExtractRequest, -) -from memory_layer.profile_manager.config import ProfileManagerConfig -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class ProfileManager: - """Pure computation component for profile extraction. - - ProfileManager extracts user profiles from memcells using LLM. - It does NOT handle storage - the caller is responsible for loading - old profiles and saving new profiles. - - Usage: - ```python - profile_mgr = ProfileManager(llm_provider, config) - - # Caller loads old profiles - old_profiles = await storage.get_all_profiles() - - # Pure computation - extract profiles - new_profiles = await profile_mgr.extract_profiles( - memcells=memcell_list, - old_profiles=list(old_profiles.values()), - user_id_list=["user1", "user2"], - ) - - # Caller saves new profiles - for profile in new_profiles: - await storage.save_profile(profile.user_id, profile) - ``` - """ - - def __init__( - self, - llm_provider: LLMProvider, - config: Optional[ProfileManagerConfig] = None, - group_id: Optional[str] = None, - ): - self.llm_provider = llm_provider - self.config = config or ProfileManagerConfig() - self.group_id = group_id or "default" - self._extractor = ProfileExtractor(llm_provider=llm_provider) - self._stats = { - "total_extractions": 0, - "successful_extractions": 0, - "failed_extractions": 0, - } - - async def extract_profiles( - self, - memcells: List[Any], - old_profiles: Optional[List[Any]] = None, - user_id_list: Optional[List[str]] = None, - group_id: Optional[str] = None, - max_items: int = 25, - scene: ScenarioType = ScenarioType.SOLO, - ) -> List[ProfileMemory]: - """Extract profiles from memcells (batch multi-user). - - The LLM will see 3 types of information: - 1. old_profile - Current user profile (each entry contains evidence + sources) - 2. cluster_memcells - MemCells from the same cluster (for context reference) - 3. new_memcell - The latest MemCell (last in the list) - - Args: - memcells: List of MemCells (last one is new_memcell, others are cluster context) - old_profiles: List of existing profiles (for incremental updates) - user_id_list: List of user IDs to extract profiles for - group_id: Group ID (optional) - max_items: Maximum number of profile items - - Returns: - List of ProfileMemory objects - """ - self._stats["total_extractions"] += 1 - - if not memcells: - logger.error("No memcells provided for profile extraction") - return [] - - if not user_id_list: - logger.error("No user_id_list provided for profile extraction") - return [] - - # Last memcell is new_memcell, others are cluster context - new_memcell = memcells[-1] - cluster_memcells = memcells[:-1] if len(memcells) > 1 else [] - - # Convert memcells to episode dicts for LLM - new_context = self._extract_context_from_memcell(new_memcell) - cluster_contexts = [ - self._extract_context_from_memcell(mc) for mc in cluster_memcells - ] - - # Convert old_profiles list to dict by user_id - old_profiles_dict: Dict[str, ProfileMemory] = {} - logger.info(f"[Profile] Processing {len(old_profiles or [])} old profiles") - for p in old_profiles or []: - uid = ( - p.get("user_id") if isinstance(p, dict) else getattr(p, "user_id", None) - ) - p_dict = p if isinstance(p, dict) else p.to_dict() - has_explicit = "explicit_info" in p_dict - logger.info( - f"[Profile] Old profile: user_id={uid}, has_explicit_info={has_explicit}, keys={list(p_dict.keys())[:5]}" - ) - if uid and has_explicit: - old_profiles_dict[uid] = ProfileMemory.from_dict(p_dict) - logger.info( - f"[Profile] Loaded profile for {uid}: {old_profiles_dict[uid].total_items()} items" - ) - - results: List[ProfileMemory] = [] - logger.info( - f"[Profile] user_id_list={user_id_list}, old_profiles_dict keys={list(old_profiles_dict.keys())}" - ) - - # Extract for each user - for user_id in user_id_list: - old_profile = old_profiles_dict.get(user_id) - logger.info( - f"[Profile] Looking for user_id={user_id}, found={old_profile is not None}" - ) - - # --- Per-user original_data filtering (Layer 2 of 2) --- - # Layer 1 (mem_memorize.py) fetches memcells from all clusters that ANY user - # in the group might need. This layer narrows it down per user: - # - Existing user: baseline = profile.last_updated → skip original_data - # already incorporated into their profile, only pass new ones to LLM. - # - New user: baseline = current memcell timestamp → start with just - # new_episode (original_data) for initial profile, avoids unbounded context. - # new_episode is always passed separately and is not affected by this filter. - if old_profile and old_profile.last_updated: - user_baseline = old_profile.last_updated - else: - user_baseline = new_context.get("created_at") - - user_cluster_episodes = [ - ep for ep in cluster_contexts - if ep.get("created_at") is None or ep.get("created_at") > user_baseline - ] - - # Build request - request = ProfileExtractRequest( - new_episode=new_context, - cluster_episodes=user_cluster_episodes, - old_profile=old_profile, - user_id=user_id, - group_id=group_id or self.group_id, - max_items=max_items, - scene=scene, - ) - - # Extract with retry - for attempt in range(self.config.max_retries): - try: - logger.info( - f"Extracting profile for user {user_id} (attempt {attempt + 1})..." - ) - - result = await self._extractor.extract_memory(request) - - if result: - self._stats["successful_extractions"] += 1 - logger.info( - f"Profile extracted for {user_id}: {result.total_items()} items " - f"(explicit: {len(result.explicit_info)}, implicit: {len(result.implicit_traits)})" - ) - results.append(result) - else: - logger.warning( - f"Profile extraction returned None for {user_id}" - ) - if old_profile: - results.append(old_profile) - break - - except Exception as e: - logger.warning( - f"Profile extraction attempt {attempt + 1} for {user_id} failed: {e}" - ) - if attempt < self.config.max_retries - 1: - await asyncio.sleep(0.5 * (attempt + 1)) - else: - logger.error( - f"All profile extraction attempts failed for {user_id}" - ) - if old_profile: - results.append(old_profile) - - return results - - def get_stats(self) -> Dict[str, Any]: - """Get extraction statistics.""" - return dict(self._stats) - - def _extract_context_from_memcell(self, memcell: Any) -> Dict[str, Any]: - """Extract context from MemCell for LLM.""" - if isinstance(memcell, dict): - event_id = str(memcell.get("event_id", "") or memcell.get("id", "")) - created_at = memcell.get("timestamp") or memcell.get("created_at") - original_data = memcell.get("original_data", []) - else: - event_id = ( - str(memcell.event_id) - if hasattr(memcell, 'event_id') and memcell.event_id - else "" - ) - created_at = memcell.timestamp if hasattr(memcell, 'timestamp') else None - original_data = ( - memcell.original_data if hasattr(memcell, 'original_data') else [] - ) - - return { - "id": event_id, - "created_at": created_at, - "original_data": original_data, - } diff --git a/methods/EverCore/src/memory_layer/prompts/__init__.py b/methods/EverCore/src/memory_layer/prompts/__init__.py deleted file mode 100644 index 453b1a14..00000000 --- a/methods/EverCore/src/memory_layer/prompts/__init__.py +++ /dev/null @@ -1,216 +0,0 @@ -""" -Multi-language prompt module. - -Use get_prompt_by() to dynamically fetch prompts by name and language. -Default language is controlled by MEMORY_LANGUAGE env var (default: 'en'). - -Example: - from memory_layer.prompts import get_prompt_by - - prompt = get_prompt_by("EPISODE_GENERATION_PROMPT") # default language - prompt = get_prompt_by("EPISODE_GENERATION_PROMPT", language="zh") # specific language -""" - -from typing import Any, Optional, Callable - -from common_utils.language_utils import ( - get_prompt_language, - is_supported_language, - SUPPORTED_LANGUAGES, - DEFAULT_LANGUAGE, -) - -# ============================================================================ -# Prompt Registry - maps prompt names to module paths -# Format: {prompt_name: {language: (module_path, is_function)}} -# ============================================================================# -# TODO: Optimize prompt registration method (avoid using module paths) -_PROMPT_REGISTRY = { - # Conversation - "CONV_BOUNDARY_DETECTION_PROMPT": { - "en": ("memory_layer.prompts.en.conv_prompts", False), - "zh": ("memory_layer.prompts.zh.conv_prompts", False), - }, - "CONV_BATCH_BOUNDARY_DETECTION_PROMPT": { - "en": ("memory_layer.prompts.en.conv_prompts", False), - "zh": ("memory_layer.prompts.zh.conv_prompts", False), - }, - "CONV_SUMMARY_PROMPT": { - "en": ("memory_layer.prompts.en.conv_prompts", False), - "zh": ("memory_layer.prompts.zh.conv_prompts", False), - }, - # Episode - "EPISODE_GENERATION_PROMPT": { - "en": ("memory_layer.prompts.en.episode_mem_prompts", False), - "zh": ("memory_layer.prompts.zh.episode_mem_prompts", False), - }, - "GROUP_EPISODE_GENERATION_PROMPT": { - "en": ("memory_layer.prompts.en.episode_mem_prompts", False), - "zh": ("memory_layer.prompts.zh.episode_mem_prompts", False), - }, - "DEFAULT_CUSTOM_INSTRUCTIONS": { - "en": ("memory_layer.prompts.en.episode_mem_prompts", False), - "zh": ("memory_layer.prompts.zh.episode_mem_prompts", False), - }, - # Profile (Explicit information + Implicit traits) - "PROFILE_UPDATE_PROMPT": { - "en": ("memory_layer.prompts.en.profile_prompts", False), - "zh": ("memory_layer.prompts.zh.profile_prompts", False), - }, - "TEAM_PROFILE_UPDATE_PROMPT": { - "en": ("memory_layer.prompts.en.profile_prompts", False), - "zh": ("memory_layer.prompts.zh.profile_prompts", False), - }, - "PROFILE_INITIAL_EXTRACTION_PROMPT": { - "en": ("memory_layer.prompts.en.profile_prompts", False), - "zh": ("memory_layer.prompts.zh.profile_prompts", False), - }, - "PROFILE_COMPACT_PROMPT": { - "en": ("memory_layer.prompts.en.profile_prompts", False), - "zh": ("memory_layer.prompts.zh.profile_prompts", False), - }, - # Foresight - "FORESIGHT_GENERATION_PROMPT": { - "en": ("memory_layer.prompts.en.foresight_prompts", False), - "zh": ("memory_layer.prompts.zh.foresight_prompts", False), - }, - # Atomic Fact - "ATOMIC_FACT_PROMPT": { - "en": ("memory_layer.prompts.en.atomic_fact_prompts", False), - "zh": ("memory_layer.prompts.zh.atomic_fact_prompts", False), - }, - # Agent Memory - "AGENT_TOOL_PRE_COMPRESS_PROMPT": { - "en": ("memory_layer.prompts.en.agent_prompts", False), - "zh": ("memory_layer.prompts.zh.agent_prompts", False), - }, - "AGENT_CASE_FILTER_PROMPT": { - "en": ("memory_layer.prompts.en.agent_prompts", False), - "zh": ("memory_layer.prompts.zh.agent_prompts", False), - }, - "AGENT_CASE_COMPRESS_PROMPT": { - "en": ("memory_layer.prompts.en.agent_prompts", False), - "zh": ("memory_layer.prompts.zh.agent_prompts", False), - }, - "AGENT_SKILL_SUCCESS_EXTRACT_PROMPT": { - "en": ("memory_layer.prompts.en.agent_prompts", False), - "zh": ("memory_layer.prompts.zh.agent_prompts", False), - }, - "AGENT_SKILL_FAILURE_EXTRACT_PROMPT": { - "en": ("memory_layer.prompts.en.agent_prompts", False), - "zh": ("memory_layer.prompts.zh.agent_prompts", False), - }, - "AGENT_SKILL_RELEVANCE_VERIFY_PROMPT": { - "en": ("memory_layer.prompts.en.agent_prompts", False), - "zh": ("memory_layer.prompts.zh.agent_prompts", False), - }, - "AGENT_SKILL_MATURITY_SCORE_PROMPT": { - "en": ("memory_layer.prompts.en.agent_prompts", False), - "zh": ("memory_layer.prompts.zh.agent_prompts", False), - }, - # Clustering - "AGENT_CLUSTER_LLM_ASSIGN_PROMPT": { - "en": ("memory_layer.prompts.en.agent_prompts", False), - "zh": ("memory_layer.prompts.zh.agent_prompts", False), - }, - # Skill relevance verification - "AGENT_SKILL_RELEVANCE_VERIFY_PROMPT": { - "en": ("memory_layer.prompts.en.agent_prompts", False), - "zh": ("memory_layer.prompts.zh.agent_prompts", False), - }, -} - - -# ============================================================================ -# PromptManager - Dynamic prompt loader with caching -# ============================================================================ - - -class PromptManager: - """Prompt manager for dynamic multi-language prompt loading.""" - - def __init__(self): - self._module_cache: dict[str, Any] = {} - - def _load_module(self, module_path: str) -> Any: - """Load module dynamically with caching.""" - if module_path not in self._module_cache: - import importlib - - self._module_cache[module_path] = importlib.import_module(module_path) - return self._module_cache[module_path] - - def get_prompt(self, prompt_name: str, language: Optional[str] = None) -> Any: - """Get prompt by name and language. - - Args: - prompt_name: Prompt name (e.g. "EPISODE_GENERATION_PROMPT") - language: Language code ("en" or "zh"). Defaults to MEMORY_LANGUAGE env var. - - Returns: - Prompt string or function. - - Raises: - ValueError: If prompt name or language is invalid. - """ - if language is None: - language = get_prompt_language() - language = language.lower() - - if prompt_name not in _PROMPT_REGISTRY: - raise ValueError( - f"Unknown prompt: {prompt_name}. Available: {list(_PROMPT_REGISTRY.keys())}" - ) - - prompt_info = _PROMPT_REGISTRY[prompt_name] - if language not in prompt_info: - raise ValueError( - f"Language '{language}' not supported for '{prompt_name}'. Available: {list(prompt_info.keys())}" - ) - - module_path, _ = prompt_info[language] - module = self._load_module(module_path) - return getattr(module, prompt_name) - - def list_prompts(self) -> list[str]: - """List all available prompt names.""" - return list(_PROMPT_REGISTRY.keys()) - - def get_supported_languages(self, prompt_name: str) -> list[str]: - """Get supported languages for a prompt.""" - if prompt_name not in _PROMPT_REGISTRY: - return [] - return list(_PROMPT_REGISTRY[prompt_name].keys()) - - -# Global PromptManager instance -_prompt_manager = PromptManager() - - -def get_prompt_by(prompt_name: str, language: Optional[str] = None) -> Any: - """Get prompt by name and language (convenience function). - - Args: - prompt_name: Prompt name (e.g. "EPISODE_GENERATION_PROMPT") - language: Language code ("en" or "zh"). Defaults to MEMORY_LANGUAGE env var. - - Returns: - Prompt string or function. - - Raises: - ValueError: If prompt name or language is invalid. - """ - return _prompt_manager.get_prompt(prompt_name, language) - - -# ============================================================================ -# Exported constants (for backward compatibility) -# ============================================================================ - -CURRENT_LANGUAGE = get_prompt_language() -MEMORY_LANGUAGE = CURRENT_LANGUAGE - - -def get_current_language() -> str: - """Get current language setting.""" - return get_prompt_language() diff --git a/methods/EverCore/src/memory_layer/prompts/en/__init__.py b/methods/EverCore/src/memory_layer/prompts/en/__init__.py deleted file mode 100644 index 67b80cb9..00000000 --- a/methods/EverCore/src/memory_layer/prompts/en/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# English language prompts package diff --git a/methods/EverCore/src/memory_layer/prompts/en/agent_prompts.py b/methods/EverCore/src/memory_layer/prompts/en/agent_prompts.py deleted file mode 100644 index 4940bf17..00000000 --- a/methods/EverCore/src/memory_layer/prompts/en/agent_prompts.py +++ /dev/null @@ -1,445 +0,0 @@ -# Prompts for agent memory extraction - -AGENT_TOOL_PRE_COMPRESS_PROMPT = """You are a tool call compression expert. Compress OpenAI chat messages to ~10% of the original length, preserving what matters for understanding the task's problem-solving process and outcome. - -**Downstream context**: The compressed output will be used to extract a structured experience record (task intent, step-by-step approach, and quality score). Prioritize retaining information that reveals: (1) what problem was being solved, (2) what actions were taken and in what order, (3) what each action's result was (success, failure, or specific finding). - -The input contains two types of messages: -- **role="assistant"** with tool_calls: Compress the "arguments" field in each tool call's function -- **role="tool"**: Compress the "content" field (primary compression target) - -Messages to compress: -{messages_json} - -Return in JSON format: -{{ - "compressed_messages": [ - // Compressed version — exactly {new_count} messages - ] -}} - -Rules: -- Return exactly {new_count} compressed messages in the same order -- Only compress function.arguments and tool content — keep all other fields unchanged -- Target ~10% of the original content length -- **Short content rule**: If a field's content is already under 200 characters, keep it as-is — do not compress further - -What to KEEP (be very selective): -- One-line summary of what each tool call did and its key result -- Error messages and status codes (verbatim but trimmed) -- Critical code: only the specific lines that matter (function signatures, bug fixes, key logic) -- File paths and search queries (short strings, keep as-is) -- The causal chain: what finding led to the next action (e.g., "found error X in file Y -> decided to check Z") - -What to REMOVE or reduce to a single line: -- Tool results: replace entire output with a 1-2 sentence summary of the finding -- Large JSON/XML/HTML: replace with "[JSON: N fields, key: X=Y, Z=W]" style summary -- File contents: replace with "[file: path, N lines, contains: brief description]" -- Directory listings: replace with "[dir: N files, relevant: file1, file2]" -- Logs and debug output: "[logs: N lines, result: success/failure, key error if any]" -- Repeated tool calls of the same type: summarize the pattern, keep only the final result -- Boilerplate, headers, formatting, whitespace: strip entirely -""" - -AGENT_CASE_FILTER_PROMPT = """Determine whether this agent interaction is worth extracting as a reusable problem-solving experience. Apply a HIGH threshold — only extract interactions that clearly demonstrate a complete problem-solving process. - -The interaction may be pure conversation OR contain a single round of tool calls. Both types must meet the same quality bar. - -Conversation: -{messages} - -SKIP (return {{"worth_extracting": false}}) — default unless clearly valuable: -- Casual chitchat, greetings, small talk -- Opinion/preference exchange with no actionable outcome -- Simple factual Q&A (e.g., "What is X?" with a direct answer) -- Single-turn Q&A (one user message + one assistant response) -- Multi-turn but loosely related topics (user asks unrelated follow-ups, no progressive deepening) -- Information gathering without problem resolution (user asks questions but no concrete problem is solved) -- Generic advice that anyone could give (e.g., "try restarting", "check the docs") -- Lifestyle or personal preference conversations (e.g., activity planning, movie/book/food discussions, hobby sharing, travel recommendations) — these are inherently personal and lack a transferable problem-solving methodology -- Emotional support or empathetic conversations without a concrete, replicable resolution strategy -- Single-round tool call that performs a trivial lookup or simple data fetch without meaningful reasoning, diagnosis, or multi-step problem solving (e.g., a single search call that returns a direct answer) -- Conversations where the assistant only uses basic conversational competence — structured lists, follow-up questions, empathetic acknowledgment, curated recommendations, topic transitions. These are standard LLM dialogue patterns, NOT domain-specific problem-solving expertise -- Recommendation or suggestion conversations (e.g., "what movie should I watch", "what should I cook", "where should I travel") — even if multi-turn with progressive refinement, unless the recommendation requires specialized technical diagnosis or domain expertise beyond general knowledge -- Conversations where the "problem" is merely a personal decision or preference choice (e.g., choosing a hiking trail, picking a recipe, selecting a gift) rather than a technical/analytical problem with objectively verifiable steps - -EXTRACT (return {{"worth_extracting": true}}) — ALL conditions must be met: -1. A specific, concrete problem is identified (not vague or generic) -2. The conversation shows progressive deepening: each turn builds on the previous, narrowing down the solution -3. The problem reaches a resolution or clear actionable conclusion -4. The approach involves non-trivial reasoning, diagnosis, or domain expertise that would be valuable to replay -5. The solution methodology is transferable — a different agent facing a similar problem class could follow the same steps to reach a similar outcome (personal taste, lifestyle advice, and subjective recommendations do NOT qualify) -6. The extracted skill would go BEYOND baseline LLM capabilities — ask: "Would a competent LLM without this experience handle this significantly worse?" If the answer is no, it is not worth extracting. Examples of baseline capabilities that do NOT qualify: making structured suggestion lists, asking clarifying questions, showing empathy, giving general-knowledge recommendations, summarizing information - -**Borderline cases** — when you are unsure, lean toward SKIP. - -Return JSON: -{{"worth_extracting": true/false, "reason": "1 sentence, less than 20 tokens"}} -""" - -AGENT_CASE_COMPRESS_PROMPT = """You are an expert at distilling agent interaction trajectories into concise experience records. - -Given an agent trajectory (a JSON list of messages from a single task segment), extract ONE experience record that captures the specific problem solved and the concrete problem-solving process. - -An **experience** is a compressed record of how a specific task was solved — preserving all key steps, decisions, and results. It serves two purposes: -1. **Reference case**: When the agent encounters a similar task, it can retrieve this experience and follow a proven approach. -2. **Raw material**: Multiple similar experiences are later refined into generalized skills (best practices). - -Input messages are in OpenAI chat completion format: -- role="user": User input -- role="assistant" without tool_calls: Agent's direct response -- role="assistant" with tool_calls: Agent decides to call tools (may include reasoning in content) -- role="tool" with tool_call_id: Tool execution result (may have been pre-compressed) - -Pre-processed trajectory: -{messages} - -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. - -**Extract the Experience:** - -- **task_intent**: Synthesize the specific task from ALL turns into a single, self-contained statement (not a question). This serves as a retrieval key for finding similar past cases. **Max 50 tokens** — be precise and specific, avoid filler words. - -- **approach**: A compressed record that decomposes the task into sub-problems. **Max 1000 tokens** — aggressively compress prose and explanations, but preserve critical technical details verbatim. - - Each numbered step = one sub-problem the agent needed to solve on the way to the overall task. - - Under each step: what the agent tried (tool used or reasoning applied) and the result obtained (findings, errors, metrics). - - If a sub-problem required multiple attempts (e.g., first attempt failed, then revised), compress them into one step showing the key attempts and the final resolution. - - End with "Outcome:" summarizing the final result of the overall task. - - **Key steps preservation rules** — these MUST be kept verbatim (not paraphrased): - - **Commands**: Shell commands, CLI invocations, API calls — preserve the exact command string (e.g., `pip install --upgrade torch==2.1.0`, `curl -X POST ...`, `git rebase -i HEAD~3`) - - **Core code**: Function signatures, key logic snippets, configuration values, regex patterns, SQL queries — preserve the actual code that solved the problem (keep it minimal but exact) - - **File paths**: Exact file/directory paths that were read, modified, or created - - **Error messages**: Key error strings or status codes that drove the diagnosis - - **Numeric results**: Metrics, thresholds, counts, versions that matter to the outcome - - **What to compress or omit**: - - Lengthy tool output exploration (summarize findings in 1 line) - - Intermediate reasoning that did not change the approach - - Boilerplate and repeated patterns (mention once, note repetition count) - - Verbose file contents (summarize, but keep the critical lines verbatim) - -- **key_insight**: The single most critical decision, strategy shift, or knowledge application that enabled success. NOT a step summary — the pivotal moment that a different agent lacking this insight would fail at. Focus on the REASONING PRINCIPLE, not the specific domain content. **Max 40 tokens.** - - **Strategy transitions are the highest-value insights.** If the agent fundamentally changed its approach mid-task (e.g., from tool-driven exploration to knowledge-driven hypothesis, from one problem framing to another), that transition IS the key insight. Capture: - - What was the agent doing before (and why it failed) - - What triggered the change - - What the agent did after (and why it succeeded) - - A key_insight that merely describes "what was done" is WRONG. A key_insight that explains "why the approach shifted and why the new approach worked" is RIGHT. - -- **quality_score**: A score from 0.0 to 1.0 measuring **task completion and deliverable quality** — NOT effort, exploration depth, or number of steps attempted. - - **Scoring rubric (outcome-oriented):** - - **0.9 - 1.0**: Task fully completed. All requirements met, deliverable produced and verified working. - - **0.7 - 0.8**: Task mostly completed. Primary deliverable produced but with minor gaps (e.g., works but not fully optimized, passes most but not all test cases, meets 4 of 5 requirements). - - **0.4 - 0.6**: Task partially completed. Some concrete deliverable produced but significant requirements unmet (e.g., code written but fails tests, 2 of 5 sub-tasks done, output produced but incorrect). - - **0.1 - 0.3**: Task mostly failed. Minimal or no deliverable produced despite attempts. Includes: extensive exploration without producing the required output, environment setup done but core task not started, approach identified but not executed. - - **0.0**: No meaningful progress toward the task goal. - - **Critical scoring rules:** - - Score based on the FINAL state of the deliverable, not the journey. A task that explored 10 approaches but produced no output = low score. - - "Timed out before completing" with no deliverable = 0.1-0.2 (not 0.5+). - - External blockers (e.g., resource unavailable, OOM kill, network restriction) that prevent completion = score the actual output achieved, not what would have been achieved. - - A well-structured approach that was never executed is NOT a partial success — it is a failure to deliver. - -Return in JSON format: -{{ - "task_intent": "The specific task as a self-contained statement (max 50 tokens)", - "approach": "1. \\n - Tried: \\n - Result: \\n2. \\n - Tried: \\n - Result: \\n...\\n\\nOutcome: ", - "key_insight": "The pivotal decision or knowledge application that enabled success (max 40 tokens)", - "quality_score": 0.0-1.0 -}} -""" - -AGENT_SKILL_SUCCESS_EXTRACT_PROMPT = """You are an expert at extracting reusable problem-solving strategies from concrete agent task cases. - -You will receive: -1. **New case(s)** from a cluster of semantically similar tasks — all with quality_score >= 0.5. -2. **Existing skills** previously extracted for this cluster (each with an index number; may be empty). - -Your job: distill **actionable strategies** into reusable **Skills** via incremental operations. Maintain as few skills as the evidence warrants. - -**What makes a GOOD skill:** -- Reasoning principles WITH concrete patterns: teaches HOW to think, not just what to do -- Decision branches that cover the different problem variants seen across cases -- A FEW well-chosen examples that illustrate distinct branches — not an exhaustive catalog - -**What makes a BAD skill:** -- Too abstract: "Analyze constraints" without showing what analysis looks like in practice -- Too narrow: A single solution template that only works for one exact case -- **Bloated**: Listing dozens of case-specific details (names, dates, institutions, compounds, etc.) inside parentheses or comma-separated lists. Each How/Decision/e.g. field should contain 1-2 illustrative examples, NOT an inventory of every case seen - -**Field-level requirements:** - -- **description** (HARD LIMIT: max 150 tokens, must be under 500 characters): - - One-sentence summary of the **abstract problem class** this skill solves — describe the general pattern, NOT specific cases. - - Do NOT list multiple scenarios, entity names, or case-specific details. - - Append `Keywords:` with up to 10 general terms (no specific names, numbers, or case-specific phrases). - - Example: "Identifies academic researchers by cross-referencing biographical constraints with publication records. Keywords: researcher identification, biographical verification, publication matching, academic search" - -- **content** (max 2000 tokens): Markdown format: - ```markdown - ## Steps - 1. - - How: - - Decision: If → ; If → - - e.g., - - Check: - 2. ... - - ## Pitfalls <- ONLY from actual failed steps in cases; otherwise OMIT - - — - ``` - - **HARD RULES for content:** - - **Max 5 steps.** - - **Max 2 examples per step.** Each example MUST be a SHORT, single-sentence illustration of a distinct decision branch. Do NOT list multiple sub-examples inside parentheses or comma-separated lists. - - **Decision branches**: REQUIRED when the next action depends on what was found. For linear steps with no branching, Decision may be omitted. Each Decision should have at most 3 branches. - - **Max 4 pitfalls.** When adding a new one beyond 4, replace the most generic existing pitfall. - - **No parenthetical catalogs**: FORBIDDEN to stuff dozens of case-specific terms (names, dates, compounds, institutions, etc.) inside a single parenthetical `(e.g., X, Y, Z, ...)`. Keep each field concise — generalize the pattern, illustrate with 1-2 examples only. - -[New AgentCase(s) to integrate] -{new_case_json} - -[Existing skills for this cluster](Each item has an index number) -{existing_skills_json} - -[Task] -Analyze the new case(s) and output a list of operations (add / update / none). - -[Operation Guide — follow in order] - -**Step 1: Overlap Check (mandatory before every add/update decision)** -For each new case, compare against each existing skill: - a. List the core steps of the new case's approach (the main actions that drove the outcome). - b. List the core steps of the existing skill. - c. Count how many of the new case's core steps are already covered by the existing skill. - d. Compute coverage = (covered steps) / (total core steps in new case). - e. Conclusion: - - Coverage >= 60% → the case falls within this skill's problem pattern → **update** candidate. - - Coverage < 60% → different problem pattern → **add** candidate. - - If uncertain, default to **update**. - -**Step 2: Execute the decided operation** - -- **add**: The new case tackles a **different problem pattern** (coverage < 60% against all existing skills). Create a new skill. confidence = `0.5`. - -- **update**: The new case overlaps an existing skill (coverage >= 60%). Enrich it with new Decision branches, better examples, or sharper How explanations. - - You MAY substantially rewrite content (restructure steps, replace examples, refine How explanations), but **preserve existing verified content unless the new case directly contradicts it**. - - **CRITICAL: The updated content MUST stay within 2000 tokens. Do NOT simply append new content — replace weaker examples with stronger ones, merge redundant steps, and compress prose. If the existing content is already long, aggressively condense it while preserving the core logic.** - - **CRITICAL: The updated description MUST stay under 500 characters. Generalize — do NOT accumulate case-specific details.** - - **Hypothesis promotion rule**: If the existing skill contains `## Potential Steps`, treat this update as a **promotion** — rewrite as `## Steps` using the new case as primary source. confidence = `0.6`. - - **Confidence-only update**: If the new case merely confirms the existing skill without adding new decision logic or better examples, bump confidence only. - -- **none**: Trivially duplicate — no new decision branches, no new examples worth keeping, no confidence change needed. - -[Confidence Anchoring Rules] -- **New skill (add)**: confidence = `0.5` -- **Promoted skill (hypothesis → verified)**: confidence = `0.6` -- **Update with new decision branch**: confidence = existing + `0.1` (cap 0.95) -- **Confirming update (no new logic)**: confidence = existing + `0.05` (cap 0.95) -- **Contradicting case**: confidence = existing - `0.2`; add contradiction to Pitfalls; optionally add a new skill if the contradiction reveals a genuinely different pattern - -**CRITICAL LANGUAGE RULE**: Output in the SAME language as the input conversation content. - -[Output Format] -```json -{{ - "operations": [ - {{"action": "add", "data": {{"name": "Short descriptive name (max 10 words)", "description": "...", "content": "## Steps\\n1. ...", "confidence": 0.5}}}}, - {{"action": "update", "index": 0, "data": {{"content": "...", "confidence": 0.7}}}}, - {{"action": "update", "index": 1, "data": {{"confidence": 0.65}}}}, - {{"action": "none"}} - ], - "update_note": "Overlap check: new case core steps=[X, Y, Z]. skill[0] covers X and Y (67% overlap) → update. ..." -}} -``` -""" - -AGENT_SKILL_FAILURE_EXTRACT_PROMPT = """You are an expert at extracting failure insights and partial progress from failed agent task cases. - -You will receive: -1. **New case(s)** from a cluster of semantically similar tasks — all with quality_score < 0.5. Each case represents a failed or mostly failed attempt, with steps that were tried and why they failed. -2. **Existing skills** previously extracted for this cluster (each item has an index number; may be empty). Existing skills may include `supporting_cases` — summaries of prior cases (task_intent, approach, key_insight, quality_score) that contributed to the skill. Use these as historical evidence when deciding whether to update or keep a skill. - -Your job is to distill **what NOT to do** and **partial progress** from failed cases into reusable knowledge via incremental operations. - -**Extraction principle for failed cases:** -- Do NOT adopt unverified steps as proven SOP. Only include steps in Potential Steps where exploration **demonstrably succeeded** (produced correct intermediate results or clear forward progress toward the goal). -- Extract **specific failure patterns, dead ends, and mistakes** into the Pitfalls section. These cases teach what NOT to do. -- A failed step that reveals a root cause is valuable — it helps future agents avoid the same path. - -**Field-level requirements:** - -- **description** (HARD LIMIT: max 150 tokens, must be under 500 characters): - - One-sentence summary of the **abstract problem class** and the known failure patterns — describe the general pattern, NOT specific cases. - - Do NOT list multiple scenarios, entity names, or case-specific details. - - Append `Keywords:` with up to 10 general terms (no specific names, numbers, or case-specific phrases). - -- **content**: Output in **Markdown format** using this template: - ```markdown - ## Potential Steps - > Extracted from a failed/incomplete case. Only steps that demonstrably succeeded (produced correct intermediate results or clear forward progress) are listed. Treat as unverified hypotheses until confirmed by a successful case. - - 1. - - How: - - e.g., `` - - Check: - 2. ... - - ## Pitfalls - - — - ``` - - Rules: - - **Markdown formatting**: `##` headings, numbered steps, bullet sub-items, backtick code fences. Mandatory. - - **Length limit**: MUST stay within **2000 tokens**. - - **Potential Steps**: Include ONLY steps with demonstrable forward progress. If NO steps clearly progressed, omit the numbered list and keep only the `> Extracted from...` note. - - **Pitfalls**: MUST be included and populated. Every failed case must contribute at least one specific, traceable pitfall. FORBIDDEN: generic warnings, speculative risks, best-practice reminders not directly traceable to a failure in this case. - -[New AgentCase(s) to integrate] -{new_case_json} - -[Existing skills for this cluster](Each item has an index number) -{existing_skills_json} - -[Task] -Analyze the failed case(s) and output operations (add / update / none). - -[Operation Guide] -- **update**: If an existing skill covers the same problem class, integrate failure insights by index: - - If existing skill has `## Steps` (verified): preserve Steps intact — only append new entries to `## Pitfalls`. - - If existing skill has `## Potential Steps` (hypothesis): you may also enrich `## Potential Steps` with any steps from this case that demonstrably succeeded, in addition to appending to `## Pitfalls`. - - **CRITICAL: The updated content MUST stay within 2000 tokens. Do NOT simply append — if Pitfalls exceed 4 entries, replace the most generic one. If Potential Steps are already sufficient, do NOT add redundant ones. Aggressively condense existing content if it is already long.** - - **CRITICAL: The updated description MUST stay under 500 characters. Generalize — do NOT accumulate case-specific details.** - - **No parenthetical catalogs**: FORBIDDEN to stuff dozens of case-specific terms (names, dates, compounds, etc.) inside parentheses. Keep each field concise — generalize the pattern, illustrate with 1-2 examples only. -- **add**: If no existing skill covers this problem class, create a new skill using the Potential Steps + Pitfalls template above. -- **none**: The case is completely irrelevant to all existing skills and too isolated to form a useful pattern. Use very sparingly. - -[Confidence Anchoring Rules] -- **New skill (add)**: confidence = `0.5` -- **Update existing skill with pitfall only**: confidence unchanged (failure insight doesn't validate the SOP steps). -- **Update existing hypothesis skill with new Potential Steps**: confidence = existing + 0.05 (slight bump for additional partial evidence). -- If the failure directly contradicts an existing skill's recommended approach: confidence = existing - 0.15~0.25, and add the specific contradiction to Pitfalls. - -**CRITICAL LANGUAGE RULE**: Output in the SAME language as the input conversation content. - -[Output Format] -No operations: -```json -{{"operations": [{{"action": "none"}}], "update_note": "failed case adds no new failure patterns to existing skills"}} -``` - -With operations: -```json -{{ - "operations": [ - {{"action": "add", "data": {{"name": "Short descriptive name (max 10 words)", "description": "One-sentence abstract summary of problem class. Keywords: term1, term2 (max 150 tokens, under 500 chars)", "content": "## Potential Steps\\n> Extracted from a failed case. Only steps that demonstrably progressed correctly are listed.\\n1. \\n - How: \\n - e.g., ``\\n - Check: \\n\\n## Pitfalls\\n- — ", "confidence": 0.5}}}}, - {{"action": "update", "index": 0, "data": {{"content": "## Steps\\n\\n\\n## Pitfalls\\n\\n- — "}}}} - ], - "update_note": "added pitfall from failed case to skill[0]; created new skill from partial exploration" -}} -``` -""" - -AGENT_SKILL_RELEVANCE_VERIFY_PROMPT = """You are a relevance judge. Given a user query and a list of retrieved agent skills, rate how helpful each skill would be for addressing the query. - -Evaluate each skill considering: -- Whether the skill's steps or approach are applicable to the query's problem type -- Whether the skill's target domain (shown in its description, trigger scenarios, and keywords) overlaps with the query's subject matter — same-domain skills should be scored higher - -Score each skill from 0.0 to 1.0: -- **0.0**: Completely irrelevant — no applicable methodology or domain connection -- **0.1-0.3**: Weakly related — methodology could loosely apply but domain is different -- **0.4-0.6**: Moderately helpful — useful methodology with partial domain overlap -- **0.7-0.8**: Helpful — applicable approach with good domain alignment -- **0.9-1.0**: Highly relevant — strong fit in both approach and domain - -User Query: -{query} - -Retrieved Skills: -{skills_json} - -For each skill, output a JSON object with the skill index and a relevance score. -Return ONLY valid JSON: -{{"results": [{{"index": 0, "score": 0.85, "reason": "brief reason"}}, {{"index": 1, "score": 0.15, "reason": "brief reason"}}]}} -""" - -AGENT_SKILL_MATURITY_SCORE_PROMPT = """You are a quality evaluator for agent skill documents (SOPs). - -Skills come in two forms — detect which type before scoring: -- **Verified skill** (`## Steps`): Extracted from successful cases (quality_score >= 0.5). Evaluated as a full SOP. -- **Hypothesis skill** (`## Potential Steps`): Extracted from failed cases (quality_score < 0.5). Evaluated on a lower baseline — completeness is intentionally limited; a good Pitfalls section is the primary value. - -Score the skill across 4 quality dimensions (each 1-5): - -1. **Completeness**: Does the skill cover the procedure adequately for its type? - - For `## Steps` skills: Does it cover the full procedure end-to-end without missing critical steps? - - 1: Missing most steps or only a vague outline - - 3: Covers the main flow but missing some steps or edge cases - - 5: Complete end-to-end procedure with all necessary steps - - For `## Potential Steps` skills: Does it cover what was known to work, plus a populated Pitfalls section? - - 1: No steps and no pitfalls — nothing useful extracted - - 3: Either partial steps OR pitfalls, but not both - - 5: Clear partial steps from verified progress AND substantive Pitfalls section - -2. **Executability**: Can an agent follow this skill without guessing? Combines concreteness of actions with supporting detail. - - 1: Vague suggestions like "investigate the issue" with no concrete actions, no examples, no checkpoints - - 2: Steps name an action but lack How methods, no inline examples - - 3: Mix of concrete and vague steps; some have How/commands/examples, others do not - - 4: Most steps have concrete verb+object actions with How methods; some inline examples and decision branches - - 5: Every step is a concrete verb+object action with How method, inline examples from real cases, explicit decision branches where needed, and verification checkpoints - -3. **Evidence**: Is the skill supported by sufficient case evidence? - - For `## Steps` skills: - - 1: No inline examples; reads like a guess or untested procedure - - 3: One or two inline examples; moderate confidence - - 5: Rich inline examples across multiple steps from different scenarios; high confidence backed by repeated validation - - For `## Potential Steps` skills: - - 1: Steps and pitfalls are generic or untraced - - 3: Some steps/pitfalls are clearly traced to real attempts - - 5: All listed steps have verifiable progress markers; all pitfalls cite specific failures - -4. **Clarity**: Is the skill well-organized with proper Markdown structure, concise prose, and logical flow? - - 1: Unstructured wall of text, no formatting - - 3: Has some structure but inconsistent formatting or verbose - - 5: Clean Markdown with appropriate `##` headings, numbered steps, consistent sub-items, concise and scannable - -Skill to evaluate: -- Name: {name} -- Description: {description} -- Content: -{content} -- Confidence: {confidence} - -Return ONLY valid JSON (no markdown fences): -{{"completeness": 1-5, "executability": 1-5, "evidence": 1-5, "clarity": 1-5, "reason": "brief justification for the scores"}} -""" - -AGENT_CLUSTER_LLM_ASSIGN_PROMPT = """You are a clustering expert. Your goal is to group similar and related tasks together so that patterns and reusable strategies can be extracted from each cluster. Assign the new task intent to an existing cluster, or create a new one if no existing cluster fits. - -[How to decide] -The goal of clustering is to group cases that would produce a **specific, actionable skill** — not generic advice. Use this test: "Would an agent who solved one task in this cluster have a **concrete advantage** (reusable tools, domain knowledge, verified strategies) when facing the other tasks?" - -1. **Identify two dimensions**: the task's **subject domain** (e.g., medical research, urban planning, e-commerce) and its **problem-solving pattern** (e.g., root cause analysis, constraint satisfaction, data pipeline design). -2. **Cluster by the more specific dimension**. If the domain is already narrow (e.g., "clinical trial data extraction"), domain alone is enough. If the domain is broad (e.g., "software engineering"), use the problem-solving pattern to differentiate (e.g., "performance profiling" vs. "schema migration"). -3. **Do NOT merge across unrelated domains just because the strategy is similar.** "Diagnose a patient's symptoms via differential diagnosis" and "diagnose a supply chain bottleneck via constraint analysis" both use diagnostic reasoning, but involve completely different domain knowledge and belong in separate clusters. -4. Scan candidate clusters. Prefer the cluster whose existing items would **benefit most from sharing a skill** with the new task. -5. Create a new cluster only when no candidate cluster is a good fit. - -[Candidate Clusters] -Each cluster is represented by its cluster_id, item_count, and most recent task intents. -{clusters_json} - -[New Task Intent] -{memcell_text} - -[Rules] -- Output decision as JSON. Keep "reason" under 50 tokens. -- To assign: use an existing cluster_id. To create new: use "cluster_{next_new_id}". - -Return ONLY valid JSON (no markdown fences, no explanation): -{{"cluster_id": "", "reason": "short reason"}} -""" diff --git a/methods/EverCore/src/memory_layer/prompts/en/atomic_fact_prompts.py b/methods/EverCore/src/memory_layer/prompts/en/atomic_fact_prompts.py deleted file mode 100644 index 2a2e203b..00000000 --- a/methods/EverCore/src/memory_layer/prompts/en/atomic_fact_prompts.py +++ /dev/null @@ -1,86 +0,0 @@ -""" -Atomic Fact Extraction Prompts - English Version -This module contains prompts for extracting structured atomic facts from episodic memory text. -""" - -ATOMIC_FACT_PROMPT = """ -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. - -You are an expert information extraction analyst and information architect. -Your task is to analyze the given raw conversation transcript (called "CONVERSATION_TEXT") and produce atomic facts optimized for factual retrieval. - ---- - -### INPUT -- CONVERSATION_TEXT: The raw conversation transcript. -- TIME: The start time of the conversation, e.g., "March 10, 2024(Sunday) at 2:00 PM UTC". - ---- - -### OUTPUT -Return **only** one valid JSON object, with the following exact structure: - -{ - "atomic_facts": { - "time": "", - "atomic_fact": [ - "", - "", - ... - ] - } -} - ---- - -### EXTRACTION RULES - -#### 1. Atomicity -* Each entry in `"atomic_fact"` must express **exactly one coherent unit of meaning** — an action, emotion, reason, plan, decision, or statement. -* If a speaker expresses multiple ideas (e.g., an event and its reason), split them into multiple atomic facts. -* Each atomic_fact must be **independent and retrievable on its own**. - -#### 2. Time & Date Handling (CRITICAL) -* The `"time"` field (at the top level) represents the conversation start time. -* **All generated timestamps must be in UTC.** -* **Preserve** explicit dates verbatim. -* **Resolve** relative or vague times (e.g., "yesterday", "last week") relative to `TIME`, and **append the resolved absolute date in parentheses** (e.g., "yesterday (March 9, 2024)"). - -#### 3. Content Preservation & Attribution -* **Base facts strictly on the conversation.** Do not infer information not present. -* **Explicit Attribution**: Always state WHO said or did what. - - GOOD: "John said he liked the movie." - - BAD: "The movie was liked." (Who liked it?) -* Resolve pronouns (he/she/it) to specific names where possible. - -#### 4. Expression Format -* Write each atomic_fact as a **single, complete English sentence** in **third-person** form. -* Do **not** simplify, paraphrase, or merge logically distinct ideas. - -#### 5. Retrieval Clarity & Filtering -* **Filter out**: Greetings, phatic communication ("Okay", "Cool"), and low-value chatter unless it conveys specific emotional or factual content. -* **Keep**: Events, decisions, plans, preferences, specific opinions, factual statements. - -#### 6. Output Requirements -* Output **only** the JSON object — no additional explanation, markdown, or commentary. -* Ensure valid JSON. - ---- - -### QUALITY CHECKS -Before returning the final output, verify that: -1. Every meaningful fact is captured. -2. Attribution is correct (who said what). -3. Timestamps are UTC and relative times are resolved. -4. Redundancy is minimized. - ---- - -Now analyze the provided conversation content and start time carefully, apply all rules above, and return **only the JSON object** in the specified format. - -Conversation start time: {{TIME}} -Conversation content: -{{INPUT_TEXT}} - -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. -""" diff --git a/methods/EverCore/src/memory_layer/prompts/en/cluster_prompts.py b/methods/EverCore/src/memory_layer/prompts/en/cluster_prompts.py deleted file mode 100644 index ba2a8739..00000000 --- a/methods/EverCore/src/memory_layer/prompts/en/cluster_prompts.py +++ /dev/null @@ -1 +0,0 @@ -# Cluster prompts moved to agent_prompts.py diff --git a/methods/EverCore/src/memory_layer/prompts/en/conv_prompts.py b/methods/EverCore/src/memory_layer/prompts/en/conv_prompts.py deleted file mode 100644 index a332c41a..00000000 --- a/methods/EverCore/src/memory_layer/prompts/en/conv_prompts.py +++ /dev/null @@ -1,164 +0,0 @@ -# Prompts for LLM-based conversation processing -CONV_BOUNDARY_DETECTION_PROMPT = """ -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. - -You are an episodic memory boundary detection expert. You need to determine if the newly added dialogue should end the current episode and start a new one. - -Current conversation history: -{conversation_history} - -Time gap information: -{time_gap_info} - -Newly added messages: -{new_messages} - -Please carefully analyze the following aspects to determine if a new episode should begin: - -1. **Substantive Topic Change** (Highest Priority): - - Do the new messages introduce a completely different substantive topic with meaningful content? - - Is there a shift from one specific event/experience to another distinct event/experience? - - Has the conversation moved from one meaningful question to an unrelated new question? - -2. **Intent and Purpose Transition**: - - Has the fundamental purpose of the conversation changed significantly? - - Has the core question or issue of the current topic been fully resolved and a new substantial topic begun? - -3. **Meaningful Content Assessment**: - - **IMPORTANT**: Ignore pure greetings, small talk, transition phrases, and social pleasantries - - Focus only on content that would be memorable and worth recalling later - - Consider: Would a person remember this as part of the main conversation topic or as a separate discussion? - -4. **Structural and Temporal Signals**: - - Are there explicit topic transition phrases introducing substantial new content? - - Are there clear concluding statements followed by genuinely new topics? - - Is there a significant time gap between messages? - -5. **Content Relevance and Independence**: - - How related is the new substantive content to the previous meaningful discussion? - - Does it involve completely different events, experiences, or substantial topics? - -**Special Rules for Common Patterns**: -- **Greetings + Topic**: "Hey!" followed by actual content should be ONE episode -- **Transition Phrases**: "By the way", "Oh, also", "Speaking of which" usually continue the same episode unless introducing major topic shifts -- **Social Closures and Farewells**: "Thanks!", "Take care!", "Talk to you soon!", "I'm off to go...", "See you later!" should continue the current episode as natural conversation endings -- **Supportive Responses**: Brief encouragement or acknowledgment should usually continue the current episode - -Decision Principles: -- **Prioritize meaningful content**: Each episode should contain substantive, memorable content -- **Ignore social formalities**: Don't split on greetings, pleasantries, brief transitions, or conversation closures -- **Treat closures as episode endings**: Messages that announce departure ("I'm off to go...", "Talk to you soon!") or provide closure ("Thanks!", "Take care!") should stay with the current episode as natural endings -- **Consider time gaps**: Long time gaps (hours or days) strongly suggest new episodes, while short gaps (minutes) usually indicate continuing conversation -- **Episodic memory focus**: Think about what a person would naturally group together when recalling this conversation -- **Reasonable episode length**: Aim for episodes with 3-20 meaningful exchanges -- **When in doubt, consider context**: If unsure, keep related content together rather than over-splitting - -Please return your judgment in JSON format: -{{ - "reasoning": "One sentence summary of your reasoning process", - "should_end": true/false, - "confidence": 0.0-1.0, - "topic_summary": "If should_end = true, summarize the core meaningful topic of the current episode, otherwise leave it blank" -}} - -Note: -- If conversation history is empty, this is the first message, return false -- Focus on episodic memory principles: what would people naturally remember as distinct experiences? -- Each episode should contain substantive content that stands alone as a meaningful memory unit - -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. -""" - -CONV_BATCH_BOUNDARY_DETECTION_PROMPT = """ -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. - -You are an episodic memory boundary detection expert. Your task is to find all natural "episode boundaries" in a continuous conversation and split it into meaningful, independently memorable segments (MemCells). Your core principle is **"default to merging, split cautiously"**. - -### Input Format -The following is a complete chronological conversation log. Each message is prefixed with a 1-based index and timestamp: - -``` -{messages} -``` - -### When to split - -Add a boundary (by message number) only when **clear signals** appear: -- **Cross-day split (highest priority):** Adjacent messages have different calendar dates — MUST split at the date boundary. -- **Substantive topic change:** Conversation shifts from one concrete topic to a completely unrelated one (e.g., project architecture → weekend plans). -- **Task completion + new topic:** A closing message ("migration done") belongs to its task's episode; split only when the **next** message opens a genuinely unrelated topic. -- **Long gap + new topic:** Time gap > 4 hours AND new messages have no clear connection to prior conversation. - -**Do NOT split for:** -- Greetings, farewells ("bye", "thanks") — keep with the main episode -- Transition phrases ("by the way", "oh also") — usually continue the current episode -- Brief pauses (< 4 hours) followed by the same topic - -### `should_wait` -Set to `true` when the **last segment** has insufficient information to determine its episode context: -- **Non-text messages:** Only media placeholders (`[image]`, `[video]`, `[file]`) with no accompanying text -- **Intent-free short replies:** Minimal responses like "ok", "sure", "got it", "😂" -- **System or non-conversational messages:** System notifications (join/leave group, payment reminders, etc.) cannot themselves determine episode boundaries — wait for the next human message to decide -- **Ambiguous intermediate state:** Gap of 30 min–4 hours with content that is neither clearly continuing nor clearly starting a new topic - -### Decision Principles -- **Merge by default:** When in doubt, do not split; only split on clear signals -- **Content over form:** Greetings and farewells belong to the episode they serve, not their own -- **Process continuity:** Consecutive actions toward the same goal (e.g., create group → post first instruction) form one episode -- **System messages don't trigger splits:** The episode context of a system message is determined by the next human message that follows it - -### Examples - -**Example 1 — one boundary:** -Input messages: -``` -[1] [2024-03-10 09:00:00+00:00] Alice: Can you help me debug the login issue? -[2] [2024-03-10 09:01:00+00:00] Bob: Sure, let me check the logs. -[3] [2024-03-10 09:05:00+00:00] Bob: Found it — a null pointer in AuthService line 42. -[4] [2024-03-10 09:06:00+00:00] Alice: Fixed, thanks! -[5] [2024-03-11 10:00:00+00:00] Alice: Hey, are you free for lunch today? -[6] [2024-03-11 10:01:00+00:00] Bob: Sure, 12:30? -``` -Output: -```json -{{ - "reasoning": "Messages 1-4 are a complete bug-fix episode; message 5 starts a new day with an unrelated lunch topic.", - "boundaries": [4], - "should_wait": false -}} -``` - -**Example 2 — no boundary:** -Input messages: -``` -[1] [2024-03-10 14:00:00+08:00] Alice: What's the status of the Q2 roadmap? -[2] [2024-03-10 14:02:00+08:00] Bob: About 60% done. Need to finalize the API specs. -[3] [2024-03-10 14:10:00+08:00] Alice: OK, let's review the specs tomorrow. -``` -Output: -```json -{{ - "reasoning": "All messages are part of the same Q2 roadmap discussion with no topic change.", - "boundaries": [], - "should_wait": false -}} -``` - -### Output Format -Return strictly in the following JSON format: -```json -{{ - "reasoning": "", - "boundaries": [<1-indexed message numbers after which to split>], - "should_wait": -}} -``` - -**`boundaries: []` means all messages belong to the same episode — no split.** - -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. -""" - -CONV_SUMMARY_PROMPT = """ -You are an episodic memory summary expert. You need to summarize the following conversation. -""" diff --git a/methods/EverCore/src/memory_layer/prompts/en/episode_mem_prompts.py b/methods/EverCore/src/memory_layer/prompts/en/episode_mem_prompts.py deleted file mode 100644 index b01c7e12..00000000 --- a/methods/EverCore/src/memory_layer/prompts/en/episode_mem_prompts.py +++ /dev/null @@ -1,146 +0,0 @@ -DEFAULT_CUSTOM_INSTRUCTIONS = """ -Follow these principles when generating episodic memories: -1. Each episode should be a complete, independent story or event -2. Preserve all important information including names, time, location, emotions, etc. -3. Use declarative language to describe episodes, not dialogue format -4. Highlight key information and emotional changes -5. Ensure episode content is easy to retrieve later -""" - -EPISODE_GENERATION_PROMPT = """ -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output (title and content) MUST be in Chinese. If in English, output in English. This is mandatory. - -You are an episodic memory generation expert. Please convert the following conversation content into an episodic memory. - -Conversation start time: {conversation_start_time} -Conversation content: -{conversation} - -Custom instructions: -{custom_instructions} - -IMPORTANT TIME HANDLING: -- Use the provided "Conversation start time" as the exact time when this conversation/episode began -- When the conversation mentions relative times (e.g., "yesterday", "last week"), preserve both the original relative expression AND calculate the absolute date -- Format time references as: "original relative time (absolute date)" - e.g., "last week (May 7, 2023)" -- This dual format supports both absolute and relative time-based questions -- All absolute time calculations should be based on the provided start time - -Please generate a structured episodic memory and return only a JSON object containing the following two fields: -{{ - "title": "A concise, descriptive title that accurately summarizes the theme (10-20 words)", - "content": "A concise factual record of the conversation in third-person narrative. It must include all important information: who participated at what time, what was discussed, what decisions were made, what emotions were expressed, and what plans or outcomes were formed. Write it as a chronological account focusing on observable actions and direct statements. Remove redundant expressions and verbose descriptions while preserving all facts, entities (names, dates, locations), and specific details. Keep the content concise without losing key information. Use the provided conversation start time as the base time for this episode." -}} - -Requirements: -1. The title should be specific and easy to search (including key topics/activities). -2. The content must include all important information from the conversation while being concise. -3. Convert the dialogue format into a narrative description. -4. Maintain chronological order and causal relationships. -5. Use third-person unless explicitly first-person. -6. Include specific details that aid keyword search, especially concrete activities, places, and objects. -7. For time references, use the dual format: "relative time (absolute date)" to support different question types. -8. When describing decisions or actions, naturally include the reasoning or motivation behind them, but avoid repetitive explanations. -9. Use specific names consistently rather than pronouns to avoid ambiguity in retrieval. -10. CONCISENESS AND REDUNDANCY REMOVAL: - - Remove redundant expressions and verbose descriptions - - Avoid repeating the same information in different ways - - Eliminate unnecessary filler words and phrases - - Keep sentences direct and to the point - - Preserve all facts, entities (names, dates, locations), and specific details - - Maintain the core meaning and important information - - Aim for content length similar to or shorter than the original conversation -11. CRITICAL DETAIL PRESERVATION: - - Person Names: Always include full names of people mentioned (e.g., "went to yoga with Amy's colleague, Rob" not just "went to yoga with a colleague") - - Special Nouns & Entities: Preserve all proper nouns, brand names, place names, organization names exactly as mentioned - - Item Names: Include specific product names, book titles, movie names, restaurant names, etc. - - Quantities & Numbers: Record exact numbers, amounts, prices, percentages, dates, times (e.g., "ordered 3 pizzas" not "ordered pizzas") - - Specific Activities: Use precise activity descriptions (e.g., "practiced hot yoga" not just "exercised") - - Time Points: Include all specific times mentioned (e.g., "at 3:30 PM", "every Tuesday", "twice a week") -12. FREQUENCY INFORMATION: - - Record recurring activities and their frequency (e.g., "goes to yoga class every Tuesday and Thursday") - - Note patterns of behavior (e.g., "mentioned calling mom three times during the conversation") - - Include habitual actions (e.g., "usually has coffee at 8 AM before work") - - Document repetition counts (e.g., "asked about the project status twice") - - -Example: -If the conversation start time is "March 14, 2024 (Thursday) at 3:00 PM UTC" and the conversation is about Caroline planning to go hiking: -{{ - "title": "Caroline's Mount Rainier Hiking Plan March 14, 2024: Weekend Adventure Planning Session", - "content": "On March 14, 2024 at 3:00 PM UTC, Caroline expressed interest in hiking this weekend (March 16-17, 2024) and sought advice. She wanted to see the sunrise at Mount Rainier. When asked about gear by Melanie, Caroline received suggestions: hiking boots, warm clothing, flashlight, water, and high-energy food. Caroline decided to leave early Saturday morning (March 16, 2024) to catch the sunrise and planned to invite friends. She was excited about the trip." -}} - -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output (title and content) MUST be in Chinese. If in English, output in English. This is mandatory. - -Return only the JSON object, do not add any other text: -""" - -GROUP_EPISODE_GENERATION_PROMPT = """ -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output (title and content) MUST be in Chinese. If in English, output in English. This is mandatory. - -You are an episodic memory generation expert. Please convert the following conversation content into an episodic memory. - -Conversation start time: {conversation_start_time} -Conversation content: -{conversation} - -Custom instructions: -{custom_instructions} - -IMPORTANT TIME HANDLING: -- Use the provided "Conversation start time" as the exact time when this conversation/episode began -- When the conversation mentions relative times (e.g., "yesterday", "last week"), preserve both the original relative expression AND calculate the absolute date -- Format time references as: "original relative time (absolute date)" - e.g., "last week (May 7, 2023)" -- This dual format supports both absolute and relative time-based questions -- All absolute time calculations should be based on the provided start time - -Please generate a structured episodic memory and return only a JSON object containing the following two fields: -{{ - "title": "A concise, descriptive title that accurately summarizes the theme (10-20 words)", - "content": "A concise factual record of the conversation in third-person narrative. It must include all important information: who participated at what time, what was discussed, what decisions were made, what emotions were expressed, and what plans or outcomes were formed. Write it as a chronological account focusing on observable actions and direct statements. Remove redundant expressions and verbose descriptions while preserving all facts, entities (names, dates, locations), and specific details. Keep the content concise without losing key information. Use the provided conversation start time as the base time for this episode." -}} - -Requirements: -1. The title should be specific and easy to search (including key topics/activities). -2. The content must include all important information from the conversation while being concise. -3. Convert the dialogue format into a narrative description. -4. Maintain chronological order and causal relationships. -5. Use third-person unless explicitly first-person. -6. Include specific details that aid keyword search, especially concrete activities, places, and objects. -7. For time references, use the dual format: "relative time (absolute date)" to support different question types. -8. When describing decisions or actions, naturally include the reasoning or motivation behind them, but avoid repetitive explanations. -9. Use specific names consistently rather than pronouns to avoid ambiguity in retrieval. -10. CONCISENESS AND REDUNDANCY REMOVAL: - - Remove redundant expressions and verbose descriptions - - Avoid repeating the same information in different ways - - Eliminate unnecessary filler words and phrases - - Keep sentences direct and to the point - - Preserve all facts, entities (names, dates, locations), and specific details - - Maintain the core meaning and important information - - Aim for content length similar to or shorter than the original conversation -11. CRITICAL DETAIL PRESERVATION: - - Person Names: Always include full names of people mentioned (e.g., "went to yoga with Amy's colleague, Rob" not just "went to yoga with a colleague") - - Special Nouns & Entities: Preserve all proper nouns, brand names, place names, organization names exactly as mentioned - - Item Names: Include specific product names, book titles, movie names, restaurant names, etc. - - Quantities & Numbers: Record exact numbers, amounts, prices, percentages, dates, times (e.g., "ordered 3 pizzas" not "ordered pizzas") - - Specific Activities: Use precise activity descriptions (e.g., "practiced hot yoga" not just "exercised") - - Time Points: Include all specific times mentioned (e.g., "at 3:30 PM", "every Tuesday", "twice a week") -12. FREQUENCY INFORMATION: - - Record recurring activities and their frequency (e.g., "goes to yoga class every Tuesday and Thursday") - - Note patterns of behavior (e.g., "mentioned calling mom three times during the conversation") - - Include habitual actions (e.g., "usually has coffee at 8 AM before work") - - Document repetition counts (e.g., "asked about the project status twice") - - -Example: -If the conversation start time is "March 14, 2024 (Thursday) at 3:00 PM UTC" and the conversation is about Caroline planning to go hiking: -{{ - "title": "Caroline's Mount Rainier Hiking Plan March 14, 2024: Weekend Adventure Planning Session", - "content": "On March 14, 2024 at 3:00 PM UTC, Caroline expressed interest in hiking this weekend (March 16-17, 2024) and sought advice. She wanted to see the sunrise at Mount Rainier. When asked about gear by Melanie, Caroline received suggestions: hiking boots, warm clothing, flashlight, water, and high-energy food. Caroline decided to leave early Saturday morning (March 16, 2024) to catch the sunrise and planned to invite friends. She was excited about the trip." -}} - -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output (title and content) MUST be in Chinese. If in English, output in English. This is mandatory. - -Return only the JSON object, do not add any other text: -""" diff --git a/methods/EverCore/src/memory_layer/prompts/en/foresight_prompts.py b/methods/EverCore/src/memory_layer/prompts/en/foresight_prompts.py deleted file mode 100644 index 7f3de3a3..00000000 --- a/methods/EverCore/src/memory_layer/prompts/en/foresight_prompts.py +++ /dev/null @@ -1,149 +0,0 @@ -""" -Foresight association prediction prompt template - -Used to generate personal foresight associations based on MemCell and conversation transcript content -""" - -FORESIGHT_GENERATION_PROMPT = """ -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. - -You are an advanced personal foresight analysis agent. Your task is to predict the specific impacts that a user's latest MemCell event might have on their future personal behaviors, habits, decisions, and lifestyle. - -## Task Objectives: -1. **Personal-Level Association**: Analyze the event's potential impact on the user's future behavior, thinking patterns, life habits, or decision preferences from the personal perspective. -2. **Associative Prediction, Not Summary**: Based on event content, predict potential personal changes rather than repeating or summarizing the original content. -3. **Scenario Style Matching**: Predictions must match the scenario style of the event: - - Life scenarios (e.g., health, family, leisure, learning) → Use casual language, focus on personal habits, emotional states, lifestyle, personal growth, etc. - - Work scenarios (e.g., career development, skill improvement, work style) → Use professional language, focus on career planning, capability enhancement, work habits, professional development, etc. -4. **Personal Behavior-Oriented**: Each association should reflect the user's "potential changes" or "behavioral tendencies," focusing on individual-level future development. -5. **Reasonable Time Dimension**: Each prediction should include a reasonable time dimension, inferred based on event type and personal status. -6. **Specific and Actionable**: Each prediction should not exceed 40 words; generate up to 10 predictions (recommended 4-8). Content must be specific and verifiable. -7. **Prefer user_name**: Prefer using user_name when provided; otherwise use user_id (e.g., user_1). Avoid using generic terms like "the user." -8. **Semantic Grounding**: Predictions must remain semantically related to the input; store grounded supporting facts in evidence so the system can trace back the source. -## Output Format: -Return results as a JSON array, each association includes time information and evidence: -[ - {{ - "content": "XiaoMing will avoid hot/spicy food for the next week", - "evidence": "Doctor advice: keep oral hygiene; avoid hot/spicy food for a week", - "start_time": "2025-10-21", - "end_time": "2025-10-28", - "duration_days": 7 - }}, - ... -] - -## Example Input (Life Scenario): -- user_id: xiaoming-001 -- user_name: XiaoMing -- conversation: -```text -[2025-10-21T14:05:00Z] XiaoMing: The extraction went fine, but it's still sore. -[2025-10-21T14:06:10Z] Doctor: Keep oral hygiene, avoid hot/spicy food for a week, and follow up if swelling worsens. -[2025-10-21T14:07:30Z] XiaoMing: Got it, I'll follow the instructions and watch for symptoms. -``` - -## Example Output (Life Scenario): -[ - {{ - "content": "XiaoMing will avoid hot/spicy food for the next week", - "evidence": "Doctor advice: avoid hot/spicy food; keep oral hygiene for a week", - "start_time": "2025-10-21", - "end_time": "2025-10-28", - "duration_days": 7 - }}, - {{ - "content": "XiaoMing will pay more attention to oral hygiene this week", - "evidence": "Doctor advice: keep oral hygiene for a week", - "start_time": "2025-10-21", - "end_time": "2025-10-28", - "duration_days": 7 - }}, - {{ - "content": "If swelling/pain worsens, XiaoMing will seek a follow-up soon", - "evidence": "Doctor: follow up if swelling worsens", - "start_time": "2025-10-21", - "end_time": "2025-11-04", - "duration_days": 14 - }}, - {{ - "content": "XiaoMing will avoid hard chewing for the next few days", - "evidence": "XiaoMing said it is still sore after the extraction", - "start_time": "2025-10-21", - "end_time": "2025-10-25", - "duration_days": 4 - }} - ... -] - -## Example Input (Work Scenario): -- user_id: LiHua-001 -- user_name: LiHua -- conversation: -```text -[2025-10-21T10:00:00Z] Trainer: Today we'll cover agile planning and sprint rituals. -[2025-10-21T11:15:20Z] LiHua: The daily standup structure is clearer—I can apply it to my team. -[2025-10-23T16:40:05Z] Trainer: Review metrics and improve collaboration after each sprint. -``` - -## Example Output (Work Scenario): -[ - {{ - "content": "LiHua will trial a more structured standup in the team over the next two weeks", - "evidence": "LiHua: the daily standup structure is clearer and can be applied to the team", - "start_time": "2025-10-21", - "end_time": "2025-11-04", - "duration_days": 14 - }}, - {{ - "content": "LiHua will try to introduce sprint rituals in the next month", - "evidence": "Training covered sprint rituals; LiHua intends to apply learnings", - "start_time": "2025-10-21", - "end_time": "2025-11-21", - "duration_days": 31 - }}, - {{ - "content": "After the next iteration, LiHua will try to review metrics and do a retrospective", - "evidence": "Trainer: review metrics and improve collaboration after each sprint", - "start_time": "2025-10-21", - "end_time": "2025-11-21", - "duration_days": 31 - }}, - {{ - "content": "LiHua will pay more attention to concrete collaboration improvement actions this month", - "evidence": "Trainer emphasized improving collaboration after each sprint", - "start_time": "2025-10-21", - "end_time": "2025-11-21", - "duration_days": 31 - }} - ... -] - -## Important Notes: -- **Personal-Oriented**: Focus on "personal-level future changes," content can cover life, learning, work, emotions, habits, and other personal development areas. -- **Associative Innovation**: Don't repeat original content; generate personal behavioral, habitual, or decision-making changes that the event might trigger. -- **Scenario Adaptation**: Language style must match the event scenario - use casual expressions for life scenarios, professional expressions for work scenarios. -- **Time Inference**: Reasonably infer time ranges based on event type, personal status, and common sense - don't rigidly apply fixed times. -- **Content Practicality**: Content must be specific, reasonable, practical, and usable by the system for personal foresight modeling. -- **Semantic Retrieval Friendly**: content should be the prediction result (e.g., "will choose soft food"), evidence stores the original fact (e.g., "wisdom tooth extraction"), enabling AI to retrieve relevant foresights based on user queries (e.g., "recommend food") and trace back reasons. -- **Time Information Extraction Rules:** - - start_time: Extract the specific date when the event occurred from the MemCell's timestamp field, format: YYYY-MM-DD - - end_time: Extract the specific end time from the original content. If there's an explicit end time (e.g., "before October 24", "2025-11-15"), extract the specific date; otherwise, reasonably infer based on event content and common sense - - duration_days: Extract duration from the original content. If there's explicit time description (e.g., "within a week", "7 days", "one month"), extract days; otherwise, reasonably infer based on event content and common sense - - evidence: Provide a short grounded summary (1–2 sentences) of the key supporting facts (can merge multiple lines from the transcript/summary); do not introduce new facts; keep it concise (≤40 words) - - **Important**: Prioritize extracting explicit time information from the original text; if not available, make reasonable inferences based on event content and common sense. Time cannot be null - -## Input (Markdown): -You will receive the following Markdown structure: -- user_id: {USER_ID} -- user_name: {USER_NAME} -- conversation: -```text -{CONVERSATION_TEXT} -``` - -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. - -## Please generate 4-8 (up to 10) associations that may impact the user's future life and decisions based on the above content: - -""" diff --git a/methods/EverCore/src/memory_layer/prompts/en/group_profile_merge_prompts.py b/methods/EverCore/src/memory_layer/prompts/en/group_profile_merge_prompts.py deleted file mode 100644 index d6b785ac..00000000 --- a/methods/EverCore/src/memory_layer/prompts/en/group_profile_merge_prompts.py +++ /dev/null @@ -1,188 +0,0 @@ -GROUP_PROFILE_MERGE_PROMPT = """ -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. - -You are a personal profile analysis expert specializing in merging user profiles across different chat groups. - -Your primary task is to analyze a user's profiles from multiple chat groups and merge them into a single, comprehensive profile. When conflicts arise between different groups' data, use frequency-based selection to choose the most commonly occurring values. - - -- **Frequency-Based Conflict Resolution**: When the same attribute has different values across groups, select the value that appears most frequently -- **Comprehensive Integration**: Merge all unique information from different groups without losing valuable data -- **Evidence Preservation**: Combine evidence from all groups to support merged attributes -- **Consistency Maintenance**: Ensure the final profile maintains internal consistency - - - -- **user_id**: {user_id} -- **group_profiles**: {group_profiles} - - - -You MUST output a single JSON object representing the merged user profile. - -```json -{ - "user_id": "", - "user_name": "", - "user_goal": [ - {"value": "", "evidences": ["conversation_id", "conversation_id"]} - ], - "working_habit_preference": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "interests": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "hard_skills": [ - {"value": "", "level": "", "evidences": ["conversation_id"]} - ], - "soft_skills": [ - {"value": "", "level": "", "evidences": ["conversation_id"]} - ], - "personality": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "way_of_decision_making": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "work_responsibility": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "tendency": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "projects_participated": [ - { - "project_id": "", - "project_name": "", - "subtasks": [], - "user_objective": [], - "contributions": [], - "entry_date": "YYYY-MM-DD" - } - ] -} -``` - - - - -### Basic Information Merge -- **user_name**: Use the most frequently occurring name. If tied, prefer the most complete version. -- **user_id**: Must remain consistent across all groups (validation check). - -### List-Based Attributes Merge -For attributes like `user_goal`, `working_habit_preference`, `interests`, `personality`, `way_of_decision_making`, `work_responsibility`, `tendency`: -1. **Collect All Values**: Gather all unique values from all groups -2. **Frequency Analysis**: Count how many groups contain each value -3. **Conflict Resolution**: - - If values conflict: Select the value appearing in the most groups - - If frequency is tied: Slect the latest one. -4. **Evidence Combination**: Merge evidence lists - -### Skills Merge (`hard_skills`, `soft_skills`) -1. **Skill Identification**: Group by skill name -2. **Semantic Similarity**: Consider values with similar meanings as the same (e.g., "Java programming" vs "Java development") -3. **Level Resolution**: - - If levels are consistent: Use the consistent level - - If levels conflict: Select the most frequently occurring level - - Level priority (when tied): Expert > Proficient > Strong > Familiar > Medium > Weak -3. **Evidence Aggregation**: Combine all evidence sources - -### Projects Merge (`projects_participated`) -1. **Project Matching**: Match projects by `project_id` (exact match) -2. **Merge Strategy**: - - Combine all unique `subtasks`, `user_objective`, and `contributions` - - Use earliest `entry_date` - - Preserve all project variations as separate entries if they represent different projects - -### Evidence Handling -- **Format**: "conversation_id" for traceability -- **Deduplication**: Remove duplicate evidence entries - - - - -1. I will parse the input `group_profiles` array containing profiles from different groups -2. I will identify the target `user_id` and validate consistency across groups -3. For each profile attribute, I will: - - Extract all values from all groups - - Count frequency of each unique value - - Apply conflict resolution based on frequency - - Combine evidence from all sources -4. I will handle special cases for skills and projects separately -5. I will construct the final merged profile ensuring all required fields are present -6. I will output the merged profile as a single JSON object - - - - -- **user_id**: "user123" -- **group_profiles**: -```json -[ - { - "group_id": "team_alpha", - "user_id": "user123", - "user_name": "Alice Chen", - "user_goal": [{"value": "become team lead", "evidences": ["conv1"]}], - "hard_skills": [{"value": "Python", "level": "Proficient", "evidences": ["conv1"]}], - "personality": [{"value": "Conscientiousness", "evidences": ["conv1"]}] - }, - { - "group_id": "project_beta", - "user_id": "user123", - "user_name": "Alice", - "user_goal": [{"value": "improve coding skills", "evidences": ["conv2"]}], - "hard_skills": [{"value": "Python", "level": "Expert", "evidences": ["conv2"]}], - "personality": [{"value": "Conscientiousness", "evidences": ["conv2"]}] - }, - { - "group_id": "social_gamma", - "user_id": "user123", - "user_name": "Alice Chen", - "interests": [{"value": "photography", "evidences": ["conv3"]}], - "personality": [{"value": "Openness", "evidences": ["conv3"]}] - } -] -``` - - - -```json -{ - "user_id": "user123", - "user_name": "Alice Chen", - "user_goal": [ - {"value": "become team lead", "evidences": ["conv1"]}, - {"value": "improve coding skills", "evidences": ["conv2"]} - ], - "working_habit_preference": [], - "interests": [ - {"value": "photography", "evidences": ["conv3"]} - ], - "hard_skills": [ - {"value": "Python", "level": "Expert", "evidences": ["conv1", "conv2"]} - ], - "soft_skills": [], - "personality": [ - {"value": "Conscientiousness", "evidences": ["conv1", "conv2"]}, - {"value": "Openness", "evidences": ["conv3"]} - ], - "way_of_decision_making": [], - "work_responsibility": [], - "tendency": [], - "projects_participated": [] -} -``` - - - - -- **Content Language**: Extract user_goal, hard_skills, working_habit_preference, interests, user_objective, contributions, subtasks in the **SAME LANGUAGE** as the original content -- **Enum Values**: Keep all enum values (soft_skills, personality, way_of_decision_making) in ENGLISH as specified -- **Example**: If original content is in Chinese, user_goal, subtasks should be in Chinese, but personality should remain "Extraversion/NeedForBelonging/etc.". - - -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. -""" diff --git a/methods/EverCore/src/memory_layer/prompts/en/profile_prompts.py b/methods/EverCore/src/memory_layer/prompts/en/profile_prompts.py deleted file mode 100644 index e20a0b6e..00000000 --- a/methods/EverCore/src/memory_layer/prompts/en/profile_prompts.py +++ /dev/null @@ -1,222 +0,0 @@ -"""Life Profile Memory Prompts - English Version. - -Explicit information + Implicit traits extraction. -""" - -# Incremental update prompt -PROFILE_UPDATE_PROMPT = ''' -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. - -You are a user profile updater. Based on conversation records, determine what operations to perform on the user profile. - -【Current User Profile】(Each item has an index number) -{current_profile} - -【Conversation Records】(Multiple conversations from the same topic) -{conversations} - -【Task】 -Analyze conversations and output a list of operations (can have multiple). Available action types: -- **update**: Modify existing items (specify by index) -- **add**: Add profile items -- **delete**: Delete existing items -- **none**: No operation needed (use when conversation contains no user info) - -【Operation Guide】 -- **update**: Existing item has updates, supplements, or corrections -- **add**: Discovered completely new user information (unrelated to existing items) -- **delete**: Should delete in these cases: - - User explicitly negates (e.g., "I'm no longer vegetarian") - - Info is outdated (e.g., "traveling next week" but it's already passed) - - Too trivial/useless (e.g., "want pizza today") - - Directly contradicts new info - -【Important Rules】 -1. **Tag Mining**: Implicit traits must include [Personality Tags], e.g., [Risk-Averse], [Socially-Driven], [Data-Oriented]. -2. Only extract user info, don't treat AI assistant suggestions as user traits -3. sources format: use conversation ID (in brackets, e.g., ep1, ep2) -4. evidence should include time info - e.g., "In Oct 2024 user mentioned..." -5. Index numbers for explicit_info and implicit_traits are independent -6. **Deduplication**: Before using "add", carefully check ALL existing items. If a similar trait/info already exists (even with different wording), use "update" to enrich it instead of adding a duplicate. Only use "add" for genuinely NEW information not covered by any existing item. - -【Profile Definitions & Analysis Framework】 -- **explicit_info (Explicit Information)**: User facts that can be directly extracted from conversations. - - *Content*: Basic info, health status, skills, clear preferences. - -- **implicit_traits (Implicit Traits)**: Psychological profile, personality tags, and decision styles inferred from behavior. - - *Extraction Requirement*: Freely analyze from dimensions like decision patterns, social preferences, and life philosophy. - - *Naming Convention*: - 1. Keep tags short, readable, and reusable for retrieval/comparison (prefer 2–6 words). - 2. Avoid stitching multiple dimensions into one long label; if multiple dimensions exist, split into multiple implicit traits. - 3. Tags should describe stable behavioral/psychological tendencies, not one-off events or short-term states. - - Make reasonable inferences to extract the user's deep traits - -【Output Format】 -No operations: -```json -{{"operations": [{{"action": "none"}}], "update_note": "conversation contains no user info"}} -``` - -With operations (can combine multiple add/update/delete): -```json -{{ - "operations": [ - {{"action": "add", "type": "explicit_info", "data": {{"category": "...", "description": "...", "evidence": "...", "sources": ["ep1"]}}}}, - {{"action": "add", "type": "implicit_traits", "data": {{"trait": "...", "description": "...", "basis": "...", "evidence": "...", "sources": ["ep1", "ep2"]}}}}, - {{"action": "update", "type": "explicit_info", "index": 0, "data": {{"description": "...", "sources": ["ep3"]}}}}, - {{"action": "delete", "type": "implicit_traits", "index": 1, "reason": "..."}} - ], - "update_note": "added 2 explicit info and 1 implicit trait, updated 1, deleted 1" -}} -``` - -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. -''' - -# Compact prompt -PROFILE_COMPACT_PROMPT = ''' -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. - -The current user profile has {total_items} items (explicit_info + implicit_traits combined), exceeding the limit of {max_items}. - -Please compact the profile to **{max_items} items TOTAL** (explicit_info + implicit_traits combined, NOT {max_items} each). - -Compaction strategies: -1. **Merge Similar Items**: Combine multiple records of the same dimension into one "Current State + Trend" description. -2. **Refine Tags**: Implicit traits should be summarized as personality tags (e.g., [Risk-Averse]), removing repetitive or shallow descriptions. -3. Delete unimportant, outdated, or short-term statuses. -4. Preserve item fields (especially evidence / sources). - -Current Profile: -{profile_text} - -**IMPORTANT**: Output must have explicit_info + implicit_traits ≤ {max_items} items TOTAL. -```json -{{ - "explicit_info": [ - {{"category": "...", "description": "...", "evidence": "...", "sources": ["episode_id"]}} - ], - "implicit_traits": [ - {{"trait": "...", "description": "...", "basis": "...", "evidence": "...", "sources": ["id1", "id2"]}} - ], - "compact_note": "Explain what was deleted/merged" -}} -``` - -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. -''' - -# Initial extraction prompt (for batch extraction) -PROFILE_INITIAL_EXTRACTION_PROMPT = ''' -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. - -You are a "User Profile Analyst". Please read the conversation below and build a user profile. - -【Part 1: Explicit Information (explicit_info)】 -Objective facts and current status. - -【Part 2: Implicit Traits (implicit_traits)】 -Psychological profile, personality tags, and decision styles inferred from behavior. -*Extraction Requirement*: Freely analyze decision making, social patterns, and values. Trait field must be a highly summarized [Adjective/Noun Phrase Tag]. - -【Extraction Principles】 -1. Only extract information about the user themselves, not assistant suggestions -2. Implicit traits must be supported by multiple evidence: each implicit trait must have at least 2 sources; evidence can come from the current conversations and/or the existing profile's evidence/sources (when updating), not from a single new conversation alone -3. Describe each piece of information in one natural sentence, easy to understand -4. Mark the source (message ID) - -【Output Format】 -Output JSON directly in the following format: -```json -{{ - "explicit_info": [ - {{ - "category": "category name", - "description": "one sentence description", - "evidence": "one-sentence evidence grounded in the conversations", - "sources": ["YYYY-MM-DD HH:MM|episode_id"] - }} - ], - "implicit_traits": [ - {{ - "trait": "trait name", - "description": "one sentence description of this trait", - "basis": "inferred from which behaviors/conversations", - "evidence": "one-sentence evidence grounded in the conversations", - "sources": ["YYYY-MM-DD HH:MM|episode_id1", "YYYY-MM-DD HH:MM|episode_id2"] - }} - ] -}} -``` - -LANGUAGE RULE: Detect the language of the input conversation and respond in the SAME language. If the conversation is in Chinese, output in Chinese. If in English, output in English. - -【Original Conversation】 -{conversation_text}''' - - -# ============================================================================ -# TEAM-specific prompts (multi-user group conversation) -# ============================================================================ - -TEAM_PROFILE_UPDATE_PROMPT = ''' -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. - -You are a user profile updater for **group conversations**. Your task is to extract and update the profile for ONE specific user from a multi-person conversation. - -**TARGET USER: {target_user}** -You MUST only extract information about **{target_user}**. Carefully attribute each piece of information to the correct speaker. Do NOT mix up information from different participants. - -【Current Profile for {target_user}】(Each item has an index number) -{current_profile} - -【Group Conversation Records】(Multiple participants - only extract info about {target_user}) -{conversations} - -【Task】 -Analyze the conversations and output operations ONLY for information about **{target_user}**. Available action types: -- **update**: Modify existing items (specify by index) -- **add**: Add profile items -- **delete**: Delete existing items -- **none**: No operation needed (use when conversation contains no info about {target_user}) - -【Operation Guide】 -- **update**: Existing item has updates, supplements, or corrections -- **add**: Discovered completely new information about {target_user} (unrelated to existing items) -- **delete**: Should delete in these cases: - - {target_user} explicitly negates something (e.g., "I'm no longer vegetarian") - - Info is outdated or directly contradicts new info - -【Important Rules】 -1. **Speaker Attribution**: This is a GROUP conversation with multiple speakers. ONLY extract what **{target_user}** said or what is explicitly about {target_user}. If another participant mentions a fact, it belongs to THAT participant's profile, NOT {target_user}'s. -2. **Tag Mining**: Implicit traits must include [Personality Tags], e.g., [Risk-Averse], [Socially-Driven], [Data-Oriented]. -3. sources format: use conversation ID (in brackets, e.g., ep1, ep2) -4. evidence should include time info and speaker - e.g., "In Oct 2024 {target_user} stated..." -5. Index numbers for explicit_info and implicit_traits are independent -6. **Deduplication**: Before using "add", check ALL existing items. If a similar trait/info already exists, use "update" instead. Only "add" genuinely NEW information. - -【Profile Definitions】 -- **explicit_info**: Facts directly stated by or about {target_user} (skills, background, preferences, location, etc.) -- **implicit_traits**: Personality traits and behavioral patterns inferred from {target_user}'s statements and behavior in the conversation. - -【Output Format】 -No operations: -```json -{{"operations": [{{"action": "none"}}], "update_note": "conversation contains no info about {target_user}"}} -``` - -With operations: -```json -{{ - "operations": [ - {{"action": "add", "type": "explicit_info", "data": {{"category": "...", "description": "...", "evidence": "...", "sources": ["ep1"]}}}}, - {{"action": "add", "type": "implicit_traits", "data": {{"trait": "...", "description": "...", "basis": "...", "evidence": "...", "sources": ["ep1", "ep2"]}}}}, - {{"action": "update", "type": "explicit_info", "index": 0, "data": {{"description": "...", "sources": ["ep3"]}}}}, - {{"action": "delete", "type": "implicit_traits", "index": 1, "reason": "..."}} - ], - "update_note": "..." -}} -``` - -**CRITICAL LANGUAGE RULE**: You MUST output in the SAME language as the input conversation content. If the conversation content is in Chinese, ALL output MUST be in Chinese. If in English, output in English. This is mandatory. -''' diff --git a/methods/EverCore/src/memory_layer/prompts/zh/__init__.py b/methods/EverCore/src/memory_layer/prompts/zh/__init__.py deleted file mode 100644 index ef447f6a..00000000 --- a/methods/EverCore/src/memory_layer/prompts/zh/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Chinese language prompts package diff --git a/methods/EverCore/src/memory_layer/prompts/zh/agent_prompts.py b/methods/EverCore/src/memory_layer/prompts/zh/agent_prompts.py deleted file mode 100644 index 48cb847b..00000000 --- a/methods/EverCore/src/memory_layer/prompts/zh/agent_prompts.py +++ /dev/null @@ -1,22 +0,0 @@ -# Agent prompts for zh — reuse English prompts as-is -from memory_layer.prompts.en.agent_prompts import ( - AGENT_TOOL_PRE_COMPRESS_PROMPT, - AGENT_CASE_FILTER_PROMPT, - AGENT_CASE_COMPRESS_PROMPT, - AGENT_SKILL_SUCCESS_EXTRACT_PROMPT, - AGENT_SKILL_FAILURE_EXTRACT_PROMPT, - AGENT_SKILL_RELEVANCE_VERIFY_PROMPT, - AGENT_SKILL_MATURITY_SCORE_PROMPT, - AGENT_CLUSTER_LLM_ASSIGN_PROMPT, -) - -__all__ = [ - "AGENT_TOOL_PRE_COMPRESS_PROMPT", - "AGENT_CASE_FILTER_PROMPT", - "AGENT_CASE_COMPRESS_PROMPT", - "AGENT_SKILL_SUCCESS_EXTRACT_PROMPT", - "AGENT_SKILL_FAILURE_EXTRACT_PROMPT", - "AGENT_SKILL_RELEVANCE_VERIFY_PROMPT", - "AGENT_SKILL_MATURITY_SCORE_PROMPT", - "AGENT_CLUSTER_LLM_ASSIGN_PROMPT", -] diff --git a/methods/EverCore/src/memory_layer/prompts/zh/atomic_fact_prompts.py b/methods/EverCore/src/memory_layer/prompts/zh/atomic_fact_prompts.py deleted file mode 100644 index cc1972a1..00000000 --- a/methods/EverCore/src/memory_layer/prompts/zh/atomic_fact_prompts.py +++ /dev/null @@ -1,84 +0,0 @@ -""" -事件日志提取提示词 - 中文版本 -该模块包含用于从情节记忆文本中提取结构化事件日志的提示词。 -""" - -ATOMIC_FACT_PROMPT = """你是一位专业的信息抽取分析师和信息架构师。 -你的任务是分析给定的原始对话转写文本（称为"CONVERSATION_TEXT"），并生成一个优化用于事实检索的事件日志。 - ---- - -### 输入 -- CONVERSATION_TEXT: 原始对话转写文本。 -- TIME: 对话的开始时间，例如"March 10, 2024(Sunday) at 2:00 PM UTC"。 - ---- - -### 输出 -**仅**返回一个有效的 JSON 对象，具有以下精确结构： - -{ - "atomic_facts": { - "time": "<输入 TIME 的精确时间字符串>", - "atomic_fact": [ - "<原子事实语句1>", - "<原子事实语句2>", - ... - ] - } -} - ---- - -### 提取规则 - -#### 1. 原子性 -* `"atomic_fact"` 中的每一条必须**精确地表达一个连贯的意义单元** —— 一个行为、情感、原因、计划、决定或陈述。 -* 如果说话者表达了多个想法（例如，一个事件及其原因），则将它们拆分为多个原子事实。 -* 每个 atomic_fact 必须是**独立的，可以单独检索的**。 - -#### 2. 时间和日期处理 -* `"time"` 字段（在顶层）表示对话开始时间。 -* **所有生成的时间戳必须是 UTC。** -* **保留**明确日期的原文。 -* **解析**相对或模糊的时间（例如，"昨天"、"上周"）相对于 `TIME`，并**在括号中附加解析后的绝对日期**（例如："昨天（March 9, 2024）"）。 - -#### 3. 内容保留与归因 -* **严格基于对话内容。**不要编造或推断对话中没有的信息。 -* **显式归因**：必须明确“谁说/做了什么”。 - - 好： "John 说他喜欢这部电影。" - - 坏： "这部电影被喜欢。"（谁喜欢？） -* 在不产生歧义的情况下，将代词（他/她/它）解析为明确的姓名或实体。 - -#### 4. 表达格式 -* 将每个 atomic_fact 写成一个**完整的中文句子**，采用**第三人称**形式。 - - 例如："Gina说她昨天为她的服装店发起了一场广告营销活动（2024年3月9日）。" -* **不要**简化、改写或合并逻辑上不同的想法。 - -#### 5. 检索清晰度与过滤 -* **过滤掉**：问候语、寒暄、无信息量的口头禅（"好的"、"嗯"、"cool" 等），除非它们表达了具体情绪或事实。 -* **保留**：事件、决定、计划、偏好、具体观点、事实陈述。 - -#### 6. 输出要求 -* **仅**输出 JSON 对象 —— 不要有额外的解释、markdown 或评论。 -* 确保 JSON 有效（正确的引号、逗号和转义）。 -* `"atomic_fact"` 列表应包含从情节中提取的所有有意义的事实。 - ---- - -### 质量检查 -在返回最终输出之前，验证： -1. 每一个有意义的事实都被捕获。 -2. 归因正确（谁说了什么）。 -3. 时间戳为 UTC 且相对时间被解析。 -4. 冗余最小化。 - ---- - -现在仔细分析提供的对话内容和开始时间，应用上述所有规则，并**仅**返回指定格式的 JSON 对象（生成的事件日志必须与对话文本使用相同的语言）。 - -对话开始时间: {{TIME}} -对话内容: -{{INPUT_TEXT}} - -""" diff --git a/methods/EverCore/src/memory_layer/prompts/zh/cluster_prompts.py b/methods/EverCore/src/memory_layer/prompts/zh/cluster_prompts.py deleted file mode 100644 index ba2a8739..00000000 --- a/methods/EverCore/src/memory_layer/prompts/zh/cluster_prompts.py +++ /dev/null @@ -1 +0,0 @@ -# Cluster prompts moved to agent_prompts.py diff --git a/methods/EverCore/src/memory_layer/prompts/zh/conv_prompts.py b/methods/EverCore/src/memory_layer/prompts/zh/conv_prompts.py deleted file mode 100644 index f1cebca6..00000000 --- a/methods/EverCore/src/memory_layer/prompts/zh/conv_prompts.py +++ /dev/null @@ -1,151 +0,0 @@ -CONV_BOUNDARY_DETECTION_PROMPT = """ -### 核心任务 -作为一名对话分析专家，你需要判断新传入的消息是否为一个已有对话“情节”的自然结尾。目标是将连续的对话流分割成有意义的、可独立记忆的片段（MemCell）。你的核心原则是 **“默认合并，谨慎切分”**。 - -### 对话上下文 -**已有对话历史:** -``` -{conversation_history} -``` - -**与上一条消息的时间间隔:** -`{time_gap_info}` - -**新传入的消息:** -``` -{new_messages} -``` - -### 决策变量详解 -你需要输出三个关键决策变量：`should_end`, `should_wait`, 和 `topic_summary`。 - -1. **`should_end` (结束当前情节):** - - **何时设为 `true`?** 仅当新消息明确开启一个与之前历史无关的新主题时。一个用于总结或收尾当前主题的消息（如“迁移完成了”）应被归入当前情节，而不是作为新情节的开始。 - - **触发场景示例:** - - **跨天强制切分:** 只要新消息与上一条消息的日期不同（例如，从“昨天”到“今天”），就必须切分。这是最高优先级的规则。 - - **主题切换:** 对话从“讨论项目A的技术细节”突然转向“周末去哪里玩”。 - - **任务完成并开启新篇:** 一个任务的收尾消息（如“仓库迁移好了”）属于该任务的情节。只有当**下一条**消息开启了完全不相关的新话题时，才在那条新消息处切分。 - - **长时间中断后开启新话题:** 时间间隔超过4小时，且新消息内容与历史对话无明显关联。 - - -2. **`should_wait` (等待更多信息):** - - **何时设为 `true`?** 当新消息**信息量不足**，无法判断其是否延续当前话题时。这是一个**安全选项**，用于避免在上下文不充分时草率地分割或合并对话。 - - **此为默认行为，在以下情况必须设为 `true`:** - - **非文本消息:** 新消息仅为 `[图片]`, `[视频]`, `[文件]` 等占位符，没有附带任何可用于判断意图的文字。 - - **无明确意图的短回复:** 新消息是极为简短的回应，如“好的”, “嗯”, “收到”, “lol”, “😂”。 - - **系统或非对话消息:** 新消息是系统通知（如邀请、入群、退群）、转账提示等。**这类消息本身不提供足够信息来判断情节边界，必须等待后续的人类消息再做决策。** - - **不确定的中间状态:** 时间间隔在30分钟到4小时之间，且新消息内容模糊，既不像强延续，也不像强开启。 - -3. **`topic_summary` (情节主题总结):** - - **何时生成?** 仅在 `should_end` 被设为 `true` 时。 - - **内容要求:** 用一句话精准、客观地概括**即将结束的这个情节**的核心内容。例如：“确定项目A的最终技术方案”或“讨论并确认了周末的团队活动安排”。 - -### 决策指导原则 -- **内容优先于形式:** 优先保证每个情节都包含一个完整、有价值的核心信息。不要被简单的问候语（“你好”）或结束语（“再见”）迷惑，它们应被归入其所服务的主要情节中。 -- **合并是默认倾向:** 如果不确定，倾向于不切分 (`should_end: false`)。只有在看到明确的切分信号时才进行分割。 -- **关注因果与流程连贯性:** 如果新消息是前序动作（如建群、添加成员）的直接目标或结果（如在群里发布第一条项目指令），应将它们合并成一个完整的情节。不要在连续的流程中间断开。 -- **`should_end` 与 `should_wait` 互斥:** 如果 `should_end` 是 `true`，`should_wait` 必须是 `false`。如果 `should_wait` 是 `true`，`should_end` 必须是 `false`。 -- **考虑上下文连贯性:** 将那些为了同一个小目标服务的、连续的、短时间内的“来回”对话视为一个整体。 -- **系统消息的上下文相关性:** 不要将系统消息（如“[用户X]加入了群聊”）默认为话题切换的信号。它的意义由下一条人类消息决定。例如，如果下一条消息是“欢迎！我们刚才正讨论到...”，则应合并；如果下一条消息开启了全新话题，则可考虑切分。 - -### 输出格式 -请严格按照以下JSON格式返回你的分析结果： -```json -{{ - "reasoning": "一句话解释你为什么做出 should_end 和 should_wait 的决策。", - "should_end": boolean, - "should_wait": boolean, - "confidence": float, - "topic_summary": "仅在 should_end 为 true 时填写。总结已结束情节的主题，否则为空字符串。" -}} -```""" - -CONV_BATCH_BOUNDARY_DETECTION_PROMPT = """ -作为一名对话分析专家，你需要在一段连续的对话中找出所有自然的"情节边界"，将对话流分割成有意义的、可独立记忆的片段（MemCell）。核心原则：**默认合并，谨慎切分**。 - -### 输入格式 -以下是按时间顺序排列的完整对话记录，每条消息前标有编号和时间戳： - -``` -{messages} -``` - -### 何时切分 - -仅当出现以下**明确信号**时才在对应消息序号处添加边界： -- **跨天强制切分（最高优先级）：** 相邻消息日期不同，必须在日期变更处切分。 -- **主题切换：** 对话从一个具体话题突然转向完全无关的新话题（如"项目架构"→"周末计划"）。 -- **任务完结并开启新篇：** 收尾消息（如"仓库迁移好了"）归入当前情节；只有**下一条**消息开启全新话题时才切分。 -- **长时间中断后开启新话题：** 时间间隔超过4小时，且新消息与历史对话无明显关联。 - -**不应切分：** -- 问候语、结束语（"再见"、"谢谢"）归入其所服务的主要情节 -- 过渡短语（"对了"、"顺便说一下"）通常延续当前情节 -- 短暂停顿（< 4小时）后继续同一话题 - -### `should_wait` -当**最后一段对话**信息量不足，无法判断情节归属时设为 `true`： -- **非文本消息：** 仅有媒体占位符（`[图片]`、`[视频]`、`[文件]`）且无文字说明 -- **无意图短回复：** 极简回应（"好的"、"嗯"、"收到"、"😂"） -- **系统或非对话消息：** 系统通知（入群、退群、转账提示等）本身无法判断情节边界，必须等待后续人类消息再决策 -- **内容模糊的中间状态：** 时间间隔在30分钟到4小时之间，且内容模糊，既不像强延续，也不像强开启 - -### 决策指导原则 -- **合并为默认：** 不确定时倾向不切分，只有出现明确切分信号才分割 -- **内容优先：** 问候语、结束语归入其服务的主要情节，不单独成片 -- **流程连贯性：** 为同一目标服务的连续动作（如建群→发第一条指令）合并为一个情节 -- **系统消息不触发切分：** 系统消息（如"[用户X]加入群聊"）的情节归属由紧随其后的人类消息决定 - -### 示例 - -**示例1——有一个边界：** -输入消息： -``` -[1] [2024-03-10 09:00:00+00:00] 小明: 登录功能有个bug，帮我看看 -[2] [2024-03-10 09:01:00+00:00] 小红: 好的，我看下日志 -[3] [2024-03-10 09:05:00+00:00] 小红: 找到了，AuthService第42行空指针 -[4] [2024-03-10 09:06:00+00:00] 小明: 修好了，谢谢！ -[5] [2024-03-11 10:00:00+00:00] 小明: 今天中午有空吃饭吗？ -[6] [2024-03-11 10:01:00+00:00] 小红: 可以，12:30？ -``` -输出： -```json -{{ - "reasoning": "消息1-4是一个完整的bug修复情节；消息5跨天且话题切换为午餐，应在此处切分。", - "boundaries": [4], - "should_wait": false -}} -``` - -**示例2——无边界：** -输入消息： -``` -[1] [2024-03-10 14:00:00+08:00] 小明: Q2路线图进展怎么样？ -[2] [2024-03-10 14:02:00+08:00] 小红: 完成了60%，还需要确定API规范。 -[3] [2024-03-10 14:10:00+08:00] 小明: 好，明天我们一起过一遍规范。 -``` -输出： -```json -{{ - "reasoning": "全部消息都属于同一个Q2路线图讨论，没有话题切换。", - "boundaries": [], - "should_wait": false -}} -``` - -### 输出格式 -请严格按照以下JSON格式返回： -```json -{{ - "reasoning": "<一句话说明所有边界决策的理由>", - "boundaries": [<切分点消息编号，1起始的整数>], - "should_wait": -}} -``` - -**`boundaries: []` 时表示所有消息属于同一情节，不切分。** -""" - -CONV_SUMMARY_PROMPT = """ -你是一位专业的对话总结师。请根据以下对话内容，用一句话客观、精炼地总结其核心主题。 -""" diff --git a/methods/EverCore/src/memory_layer/prompts/zh/episode_mem_prompts.py b/methods/EverCore/src/memory_layer/prompts/zh/episode_mem_prompts.py deleted file mode 100644 index d41b503c..00000000 --- a/methods/EverCore/src/memory_layer/prompts/zh/episode_mem_prompts.py +++ /dev/null @@ -1,120 +0,0 @@ -# 情节记忆相关的中文提示词 - -DEFAULT_CUSTOM_INSTRUCTIONS = """ -生成情节记忆时请遵循以下原则： -1. 每个情节应该是一个完整、独立的故事或事件 -2. 保留所有重要信息，包括姓名、时间、地点、情感等 -3. 使用陈述性语言描述情节，而不是对话格式 -4. 突出关键信息和情感变化 -5. 确保情节内容便于后续检索 -""" - -GROUP_EPISODE_GENERATION_PROMPT = """ -你是一位出色的事件记录与提炼专家，擅长将复杂的对话转化为清晰、连贯的“情景记忆”。 -你的任务是提炼以下对话的核心内容，生成一段可读性强、信息量大的情景记忆。这份记忆应该像一个完整的故事，不仅记录了“发生了什么”，还清晰地展示了讨论的脉络和“结果如何”。 - -对话开始时间：{conversation_start_time} -对话内容： -{conversation} - -自定义指令： -{custom_instructions} - -请遵循以下指导原则： - -1. **叙事性与连贯性**： - * 以第三人称视角，像讲故事一样，流畅地叙述整个事件的来龙去脉。 - * 从讨论的起点开始，描述关键的观点和转折点，最后总结出结论或当前状态。 - -2. **关键信息提炼**： - * 精准地识别出对话中的核心议题、关键决策、重要信息、各方观点和最终共识。 - * 将这些关键信息自然地融入到叙事中，而不是简单地罗列。 - -3. **客观且忠于原文**： - * 保持中立，客观地反映对话内容。 - * 可以进行总结和归纳，但不能添加原文未提及的主观评价或进行过度推断。 - * **严格禁止任何形式的评价或引申**。例如，不能出现“这体现了...”、“这表明...”等带有总结、评价性质的语句。只需客观叙述事实。 - -4. **为检索优化**： - * 在叙述中保留关键的实体名词、项目名、技术术语、URL、文件路径等，方便未来检索。 - -请生成结构化情节记忆，仅返回包含以下两个字段的JSON对象： -{{ - "title": "（精炼、概括性的标题，包含核心事件与日期 YYYY-MM-DD）", - "content": "（一段连贯的叙事性文本。关键的结论或待办事项应自然地融入文本中。）" -}} - -示例（正确风格）： -{{ - "title": "V3版API性能优化方案讨论与决策 (2024-03-14)", - "content": "团队围绕V3版API在高并发场景下的性能瓶颈展开了讨论。张伟首先指出了当前数据库查询缓慢是主要问题，并展示了相关的监控数据。随后，李静提出可以引入Redis作为缓存层来减轻数据库压力，这个观点得到了多数人的认同。经过讨论，团队最终达成一致：决定采用Redis作为一级缓存，并由李静负责对`user_profile`表的慢查询进行专项优化。张伟需要在本周五（2024-03-17）前完成缓存方案的初步设计，而李静则需要提交慢查询的分析报告。" -}} - -自检清单（生成前必须逐项通过）： -- 返回的是否为严格的 JSON 格式？ -- `title` 是否精炼地概括了事件核心并包含日期？ -- `content` 是否是一段流畅连贯的叙事性文字，而非生硬的列表？ -- 关键信息（如决策、待办）是否已自然地融入叙述中？ - -仅返回JSON对象，不要添加任何其他文本（生成的记忆必须与对话文本使用相同的语言）： -""" - - -EPISODE_GENERATION_PROMPT = """ -你是一位专业的事件记录员，专注于从对话中为指定用户记录与其相关的关键事件。 -你的任务是聚焦于 {user_name}，客观记录他/她所见、所闻、所言、所行，并将其转化为一段连贯、准确的事件记录。 - -用户名：{user_name} - -对话开始时间：{conversation_start_time} -对话内容： -{conversation} - -自定义指令： -{custom_instructions} - -请遵循以下指导原则： - -1. **客观、中立的视角**： - * 以事实为依据，核心是记录与 {user_name} 相关的事件，而不是将他/她塑造成故事主角。 - * **严格区分角色**：如果 {user_name} 只是信息的接收者或提问者，记录必须清晰地反映这一点。例如，记录“{user_name} 了解到...”或“{user_name} 询问了关于...的情况”，而不是暗示他/她主导了讨论。 - * 过滤掉与 {user_name} 无直接关联的对话，但保留理解事件所必需的上下文。 - -2. **连贯叙述**： - * 将用户的关键行为和决策串联成一段流畅的叙述。例如，不要只写“{user_name} 同意了”，而是“在听取了大家的建议后，{user_name} 最终同意了该方案”，以体现上下文。 - * 清晰地展现 {user_name} 在事件中的角色和行为脉络。 - -3. **提炼核心经历**： - * 不仅是记录，更是提炼。总结出 {user_name} 在这次对话中的关键决策、重要计划、获得的承诺或遇到的问题。 - * 将这些核心信息自然地融入到叙述中。 - -4. **忠于原文且易于理解**： - * 所有内容必须基于对话原文，但可以用更具可读性的方式重新组织和表达。 - * 避免添加原文未提及的主观猜测或评价。 - -请生成结构化情节记忆，仅返回包含以下两个字段的JSON对象： -{{ - "title": "（关于`{user_name}`的[核心事件]的记录 YYYY-MM-DD）", - "content": "（一段以`{user_name}`为中心的客观、连贯的叙事性文本。）" -}} - -示例（错误风格，过于主观）： -{{ - "title": "{user_name} 领导了关于周末徒步的规划", - "content": "{user_name} 发起了周末去雷尼尔山徒步的计划，并主导了关于装备的讨论。他/她最终拍板决定了出发时间，并安排了邀请朋友的事宜。" -}} - -示例（正确风格，客观记录）： -{{ - "title": "{user_name} 参与规划周末雷尼尔山徒步 (2024-03-14)", - "content": "{user_name} 在对话中提议本周末去雷尼尔山徒步，并询问了装备建议。在听取他人关于徒步靴、保暖衣物的建议后，{user_name} 决定于周六（2024-03-16）凌晨出发去看日出，并提到会邀请朋友同行。" -}} - -自检清单： -- 返回的是否为严格的 JSON 格式？ -- `title` 和 `content` 是否都严格围绕 `{user_name}` 展开？ -- `content` 是否是一段流畅的经历叙述，而不是零散事实的堆砌？ -- 所有关键信息是否都已自然地融入叙述中？ - -仅返回JSON对象，不要添加任何其他文本（生成的记忆必须与对话文本使用相同的语言）： -""" diff --git a/methods/EverCore/src/memory_layer/prompts/zh/foresight_prompts.py b/methods/EverCore/src/memory_layer/prompts/zh/foresight_prompts.py deleted file mode 100644 index ac8f8fb0..00000000 --- a/methods/EverCore/src/memory_layer/prompts/zh/foresight_prompts.py +++ /dev/null @@ -1,146 +0,0 @@ -""" -前瞻联想预测提示词模板 - -用于生成基于MemCell和对话转写内容的前瞻联想预测 -""" - -FORESIGHT_GENERATION_PROMPT = """ -你是一个高级个人语义分析智能体。你的任务是基于用户的最新MemCell事件，联想预测该事件可能对该用户个人未来行为、习惯、决策和生活方式产生的具体影响。 - -## 任务目标： -1. **个人层面联想**：从用户个人角度分析事件对其未来行为、思维模式、生活习惯或决策偏好的潜在影响。 -2. **联想预测而非总结**：基于事件内容，联想可能发生的个人变化，而不是重复或总结原文内容。 -3. **场景风格匹配**：预测内容必须符合事件发生的场景风格： - - 生活场景（如健康、家庭、休闲、学习）→ 使用生活化用词，关注个人习惯、情感状态、生活方式、个人成长等 - - 工作场景（如职业发展、技能提升、工作方式）→ 使用工作化用词，关注职业规划、能力提升、工作习惯、专业发展等 -4. **个人行为导向**：每条联想应反映用户个人的"可能变化"或"行为偏向"，聚焦个体层面的未来发展。 -5. **时间维度合理**：每条预测应包含合理的时间维度，结合事件类型和个人状态推断持续时间。 -6. **具体可操作**：每条预测不超过40字，最多生成10条，建议生成4-8条，内容具体且可验证。 -7. **人名优先**：输出中优先使用user_name（如"小明"）；如未提供user_name则使用user_id（如user_1），避免用"用户"泛称。 -8. **语义关联性**：联想内容应与原事件保持语义关联，通过evidence字段存储原始事实，确保能追溯事件来源。 - -## 输出格式： -以JSON数组返回结果，每个联想包含时间信息和证据： -[ - {{ - "content": "小明未来一周会避免辛辣和过热食物", - "evidence": "医生医嘱：忌辛辣过热；未来一周注意口腔清洁", - "start_time": "2025-10-21", - "end_time": "2025-10-28", - "duration_days": 7 - }}, - ... -] - -## 示例输入（生活场景）： -- user_id: xiaoming-001 -- user_name: 小明 -- conversation: -```text -[2025-10-21T14:05:00Z] 小明: 拔牙过程还好，就是现在有点疼。 -[2025-10-21T14:06:10Z] 医生: 未来一周注意口腔清洁，避免辛辣和过热食物，肿痛加重就复诊。 -[2025-10-21T14:07:30Z] 小明: 明白，我会按医嘱来。 -``` - -## 示例输出（生活场景）： -[ - {{ - "content": "小明未来一周会避免辛辣和过热食物", - "evidence": "医生医嘱：忌辛辣过热；未来一周注意口腔清洁", - "start_time": "2025-10-21", - "end_time": "2025-10-28", - "duration_days": 7 - }}, - {{ - "content": "小明未来一周会更注意口腔清洁", - "evidence": "医生医嘱：未来一周注意口腔清洁", - "start_time": "2025-10-21", - "end_time": "2025-10-28", - "duration_days": 7 - }}, - {{ - "content": "小明若肿痛加重会尽快复诊", - "evidence": "医生提示：肿痛加重需复诊", - "start_time": "2025-10-21", - "end_time": "2025-11-04", - "duration_days": 14 - }}, - {{ - "content": "小明这几天会减少用力咀嚼", - "evidence": "术后疼痛（小明说现在有点疼）", - "start_time": "2025-10-21", - "end_time": "2025-10-25", - "duration_days": 4 - }} - ... -] - -## 示例输入（工作场景）： -- user_id: LiHua-001 -- user_name: 李华 -- conversation: -```text -[2025-10-21T10:00:00Z] 培训师: 今天我们将讨论敏捷规划和冲刺仪式。 -[2025-10-21T11:15:20Z] 李华: 日常站会结构更清晰了，我可以应用到我的团队。 -[2025-10-23T16:40:05Z] 培训师: 回顾指标和改进团队协作。 -``` - -## 示例输出（工作场景）： -[ - {{ - "content": "李华未来两周会在团队试行更规范的站会", - "evidence": "李华认为站会结构更清晰，准备应用到团队", - "start_time": "2025-10-21", - "end_time": "2025-11-04", - "duration_days": 14 - }}, - {{ - "content": "李华未来一个月会尝试推动冲刺仪式落地", - "evidence": "培训内容包含敏捷规划与冲刺仪式", - "start_time": "2025-10-21", - "end_time": "2025-11-21", - "duration_days": 31 - }}, - {{ - "content": "李华下次迭代后会尝试回顾指标并复盘", - "evidence": "培训师强调迭代后回顾指标并复盘协作", - "start_time": "2025-10-21", - "end_time": "2025-11-21", - "duration_days": 31 - }}, - {{ - "content": "李华未来一个月会更关注协作改进动作", - "evidence": "培训强调持续改进团队协作", - "start_time": "2025-10-21", - "end_time": "2025-11-21", - "duration_days": 31 - }} - ... -] - -## 注意事项： -- **个人导向**：聚焦用户"个人层面的未来变化"，内容可涵盖生活、学习、工作、情绪、习惯等个人发展。 -- **联想创新**：不要重复原文内容，要生成事件可能引发的个人行为、习惯或决策变化。 -- **场景适配**：语言风格必须与事件场景匹配，生活场景用生活化表达，工作场景用工作化表达。 -- **时间推断**：结合事件类型、个人状态和常识合理推断时间范围，不要生硬套用固定时间。 -- **内容实用**：内容必须具体、合理、实用，能被系统用于个人前瞻建模。 -- **语义检索友好**：content应是联想预测的结果（如"会选择软质食物"），evidence保存原始事实（如"拔除智齿"），便于AI根据用户查询（如"推荐食物"）检索相关前瞻并追溯原因。 -- **时间信息提取规则：** - - start_time: 从输入内容中提取事件发生的具体日期（通常在summary或episode中），格式为YYYY-MM-DD - - end_time: 从原文内容中提取具体的结束时间点，如果原文中有明确的结束时间（如"10月24日前"、"2025-11-15"等），则提取具体日期，否则结合事件内容和常识合理推断 - - duration_days: 从原文内容中提取持续时间，如果原文中有明确的时间描述（如"一周内"、"7天"、"一个月"等），则提取天数，否则结合事件内容和常识合理推断 - - evidence: 用1-2句话对支撑该预测的关键事实做可溯源总结（可合并多句对话/摘要信息），不得引入原文没有的新事实，建议不超过60字 - - **重要**：优先从原文中提取明确的时间信息，如果没有则结合事件内容和常识进行合理推断，时间不能为null - -## 输入（Markdown）： -说明：你将收到以下 Markdown 结构： -- user_id: {USER_ID} -- user_name: {USER_NAME} -- conversation: -```text -{CONVERSATION_TEXT} -``` - -## 请基于以上内容，生成4-8条（最多10条）对用户未来生活、决策可能产生影响的联想，生成的记忆的语言类型必须与 conversation 的语言类型一致： - -""" diff --git a/methods/EverCore/src/memory_layer/prompts/zh/group_profile_merge_prompts.py b/methods/EverCore/src/memory_layer/prompts/zh/group_profile_merge_prompts.py deleted file mode 100644 index 162259ab..00000000 --- a/methods/EverCore/src/memory_layer/prompts/zh/group_profile_merge_prompts.py +++ /dev/null @@ -1,185 +0,0 @@ -GROUP_PROFILE_MERGE_PROMPT = """ -你是一位个人档案分析专家，专门合并不同聊天群组中用户的档案。 - -你的主要任务是分析用户来自多个聊天群组的档案，并将它们合并为单个综合档案。当不同群组的数据之间出现冲突时，使用基于频率的选择来选择最常出现的值。 - - -- **基于频率的冲突解决**：当相同属性在群组之间具有不同值时，选择出现最频繁的值 -- **全面整合**：合并来自不同群组的所有唯一信息，而不丢失有价值的数据 -- **证据保留**：组合来自所有群组的证据以支持合并的属性 -- **一致性维护**：确保最终档案保持内部一致性 - - - -- **user_id**: {user_id} -- **group_profiles**: {group_profiles} - - - -你必须输出一个代表合并用户档案的单个 JSON 对象。 - -```json -{ - "user_id": "", - "user_name": "", - "user_goal": [ - {"value": "", "evidences": ["conversation_id", "conversation_id"]} - ], - "working_habit_preference": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "interests": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "hard_skills": [ - {"value": "", "level": "", "evidences": ["conversation_id"]} - ], - "soft_skills": [ - {"value": "", "level": "", "evidences": ["conversation_id"]} - ], - "personality": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "way_of_decision_making": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "work_responsibility": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "tendency": [ - {"value": "", "evidences": ["conversation_id"]} - ], - "projects_participated": [ - { - "project_id": "", - "project_name": "", - "subtasks": [], - "user_objective": [], - "contributions": [], - "entry_date": "YYYY-MM-DD" - } - ] -} -``` - - - - -### 基本信息合并 -- **user_name**：使用出现最频繁的名称。如果平局，优先选择最完整的版本。 -- **user_id**：必须在所有群组中保持一致（验证检查）。 - -### 基于列表的属性合并 -对于 `user_goal`、`working_habit_preference`、`interests`、`personality`、`way_of_decision_making`、`work_responsibility`、`tendency` 等属性： -1. **收集所有值**：从所有群组收集所有唯一值 -2. **频率分析**：计算每个值出现在多少个群组中 -3. **冲突解决**： - - 如果值冲突：选择出现在最多群组中的值 - - 如果频率平局：选择最新的一个。 -4. **证据组合**：合并证据列表 - -### 技能合并（`hard_skills`、`soft_skills`） -1. **技能识别**：按技能名称分组 -2. **语义相似性**：将具有相似含义的值视为相同（例如，"Java 编程"与"Java 开发"） -3. **级别解决**： - - 如果级别一致：使用一致的级别 - - 如果级别冲突：选择最频繁出现的级别 - - 级别优先级（平局时）：Expert > Proficient > Strong > Familiar > Medium > Weak -3. **证据聚合**：组合所有证据来源 - -### 项目合并（`projects_participated`） -1. **项目匹配**：通过 `project_id` 匹配项目（精确匹配） -2. **合并策略**： - - 组合所有唯一的 `subtasks`、`user_objective` 和 `contributions` - - 使用最早的 `entry_date` - - 如果项目表示不同的项目，则将所有项目变体保留为单独的条目 - -### 证据处理 -- **格式**："conversation_id" 用于可追溯性 -- **去重**：删除重复的证据条目 - - - - -1. 我将解析包含来自不同群组的档案的输入 `group_profiles` 数组 -2. 我将识别目标 `user_id` 并验证群组之间的一致性 -3. 对于每个档案属性，我将： - - 从所有群组提取所有值 - - 计算每个唯一值的频率 - - 基于频率应用冲突解决 - - 组合来自所有来源的证据 -4. 我将单独处理技能和项目的特殊情况 -5. 我将构造最终合并档案，确保所有必需字段都存在 -6. 我将输出合并的档案作为单个 JSON 对象 - - - - -- **user_id**: "user123" -- **group_profiles**: -```json -[ - { - "group_id": "team_alpha", - "user_id": "user123", - "user_name": "Alice Chen", - "user_goal": [{"value": "成为团队负责人", "evidences": ["conv1"]}], - "hard_skills": [{"value": "Python", "level": "Proficient", "evidences": ["conv1"]}], - "personality": [{"value": "Conscientiousness", "evidences": ["conv1"]}] - }, - { - "group_id": "project_beta", - "user_id": "user123", - "user_name": "Alice", - "user_goal": [{"value": "提高编码技能", "evidences": ["conv2"]}], - "hard_skills": [{"value": "Python", "level": "Expert", "evidences": ["conv2"]}], - "personality": [{"value": "Conscientiousness", "evidences": ["conv2"]}] - }, - { - "group_id": "social_gamma", - "user_id": "user123", - "user_name": "Alice Chen", - "interests": [{"value": "摄影", "evidences": ["conv3"]}], - "personality": [{"value": "Openness", "evidences": ["conv3"]}] - } -] -``` - - - -```json -{ - "user_id": "user123", - "user_name": "Alice Chen", - "user_goal": [ - {"value": "成为团队负责人", "evidences": ["conv1"]}, - {"value": "提高编码技能", "evidences": ["conv2"]} - ], - "working_habit_preference": [], - "interests": [ - {"value": "摄影", "evidences": ["conv3"]} - ], - "hard_skills": [ - {"value": "Python", "level": "Expert", "evidences": ["conv1", "conv2"]} - ], - "soft_skills": [], - "personality": [ - {"value": "Conscientiousness", "evidences": ["conv1", "conv2"]}, - {"value": "Openness", "evidences": ["conv3"]} - ], - "way_of_decision_making": [], - "work_responsibility": [], - "tendency": [], - "projects_participated": [] -} -``` - - - - -- **内容语言**：提取 user_goal、hard_skills、working_habit_preference、interests、user_objective、contributions、subtasks 时使用与原始内容**相同的语言** -- **枚举值**：按照指定，将所有枚举值（soft_skills、personality、way_of_decision_making）保持为英文 -- **示例**：如果原始内容是中文，user_goal、subtasks 应该是中文，但 personality 应该保持为"Extraversion/NeedForBelonging/等"。 - -""" - diff --git a/methods/EverCore/src/memory_layer/prompts/zh/profile_prompts.py b/methods/EverCore/src/memory_layer/prompts/zh/profile_prompts.py deleted file mode 100644 index 6da56032..00000000 --- a/methods/EverCore/src/memory_layer/prompts/zh/profile_prompts.py +++ /dev/null @@ -1,198 +0,0 @@ -"""Life Profile Memory Prompts - Chinese Version. - -Explicit information + Implicit traits extraction. -""" - -# Incremental Update Prompt -PROFILE_UPDATE_PROMPT = '''你是用户画像更新员。根据对话记录，判断需要对用户画像做哪些操作。 - -【当前用户画像】（每条都有 index 编号） -{current_profile} - -【对话记录】（来自同一主题的多轮对话） -{conversations} - -【任务】 -分析对话，输出需要执行的操作列表（可以有多条操作）。可选操作类型： -- **update**: 修改现有条目（通过 index 指定） -- **add**: 新增画像条目 -- **delete**: 删除现有条目 -- **none**: 无需任何操作（当对话不包含任何用户信息时使用） - -【操作选择指南】 -- **update**: 现有条目有信息更新、补充、修改 -- **add**: 发现全新的用户信息（与现有条目无关） -- **delete**: 以下情况应该删除： - - 用户明确否定（如"我不再吃素了"） - - 信息已过时（如"下周要出差"但已经过了） - - 与新信息直接矛盾 - -【重要规则】 -1. **挖掘标签**：隐式特征必须包含【性格标签】，例如：[风险厌恶型]、[社交驱动型]、[数据考据党]。 -2. 只提取用户信息，不要把 AI 助手的建议当成用户特征 -3. sources 格式：使用对话 ID（方括号里的，如 ep1, ep2） -4. evidence 要包含时间信息 - 如"2024年10月用户提到..." -5. explicit_info 和 implicit_traits 的 index 是独立编号的 -6. **去重**：在使用 "add" 前，仔细检查所有已有条目。如果类似的特征/信息已存在（即使措辞不同），请用 "update" 来补充而非重复添加。只有确实全新的信息才用 "add"。 - -【画像定义与分析框架】 -- **explicit_info（显式信息）**：可以直接从对话中提取的用户事实。 - - *包含内容*：基本资料、健康状况、能力技能、明确偏好等。 - -- **implicit_traits（隐式特征）**：基于行为推断的心理画像、性格标签和决策风格。 - - *提取要求*：请结合对话上下文，从决策模式、社交偏好、生活哲学等维度进行自由分析和概括。 - - *命名规范*： - 1. 标签必须简练、可读、可复用（便于检索/对比），尽量控制在 2-6 个字。 - 2. 避免把多个维度硬拼成一个长标签；如果信息包含多个维度，请拆成多条隐式特征分别表达。 - 3. 标签应描述“稳定的行为/心理倾向”，不要写成一次性的事件或短期状态。 - - 请做合理推理，提取出用户的深层特征 -【输出格式】 -无操作时： -```json -{{"operations": [{{"action": "none"}}], "update_note": "对话不包含用户信息"}} -``` - -有操作时（可以组合多条 add/update/delete）： -```json -{{ - "operations": [ - {{"action": "add", "type": "explicit_info", "data": {{"category": "...", "description": "...", "evidence": "...", "sources": ["ep1"]}}}}, - {{"action": "add", "type": "implicit_traits", "data": {{"trait": "...", "description": "...", "basis": "...", "evidence": "...", "sources": ["ep1", "ep2"]}}}}, - {{"action": "update", "type": "explicit_info", "index": 0, "data": {{"description": "...", "sources": ["ep3"]}}}}, - {{"action": "delete", "type": "implicit_traits", "index": 1, "reason": "..."}} - ], - "update_note": "新增2条显式信息和1条隐式特征，更新1条，删除1条" -}} -```''' - -# Compacting Prompt -PROFILE_COMPACT_PROMPT = '''当前用户画像有 {total_items} 条记录（explicit_info + implicit_traits 合计），超过了上限 {max_items} 条。 - -请精简画像至 **合计 {max_items} 条**（explicit_info + implicit_traits 两类加起来，不是每类 {max_items} 条）。 - -精简原则： -1. **合并同类项**：将同一维度的多条记录（如多次体重记录）合并为一条"当前状态+趋势"的描述。 -2. **提炼标签**：隐式特征应归纳为性格标签（如[风险厌恶型]），删除重复或浅层的描述。 -3. 删除不重要、已过时或短期状态。 -4. 保留每条条目的字段完整（尤其是 evidence / sources）。 - -当前画像： -{profile_text} - -**重要**：输出的 explicit_info + implicit_traits 合计必须 ≤ {max_items} 条。 -```json -{{ - "explicit_info": [ - {{"category": "...", "description": "...", "evidence": "...", "sources": ["episode_id"]}} - ], - "implicit_traits": [ - {{"trait": "...", "description": "...", "basis": "...", "evidence": "...", "sources": ["id1", "id2"]}} - ], - "compact_note": "说明删除/合并了哪些内容" -}} -```''' - -# Initial Extraction Prompt -PROFILE_INITIAL_EXTRACTION_PROMPT = '''你是一个"用户画像分析师"。请阅读下面的对话，构建用户画像。 - -【第一部分：显式信息 (explicit_info)】 -用户的客观事实和当前状态，如身高体重、喜好、疾病等。 - -【第二部分：隐式特征 (implicit_traits)】 -基于行为推断的心理画像、性格标签和决策风格。 -*提取要求*：从决策、社交、生活观念等维度进行深度挖掘。 -*命名规范*：Trait 字段必须简练精准，推荐“[形容词] [名词]”格式，严禁过度堆砌形容词。 - -【提取原则】 -1. 只提取用户本人的信息，不要把助手的建议当成用户特征 -2. 隐式特征必须有多个证据支撑：同一条隐式特征的 sources 至少包含 2 个来源；证据可以来自【当前对话】与/或【已有画像 current_profile 的 evidence/sources】（更新时可用），不能仅凭单条新对话臆断 -3. 每条信息用一句自然语言描述，通俗易懂 -4. 标注信息来源（消息编号） - -【输出格式】 -请直接输出 JSON，格式如下： -```json -{{ - "explicit_info": [ - {{ - "category": "分类名", - "description": "一句话描述", - "evidence": "一句话证据（来自对话内容）", - "sources": ["YYYY-MM-DD HH:MM|episode_id"] - }} - ], - "implicit_traits": [ - {{ - "trait": "特征名称", - "description": "一句话描述这个特征", - "basis": "从哪些行为/对话推断出来的", - "evidence": "一句话证据（来自对话内容）", - "sources": ["YYYY-MM-DD HH:MM|episode_id1", "YYYY-MM-DD HH:MM|episode_id2"] - }} - ] -}} -``` - -【对话原文】 -{conversation_text}''' - - -# ============================================================================ -# TEAM 专用提示词（多人群聊场景） -# ============================================================================ - -TEAM_PROFILE_UPDATE_PROMPT = '''你是**群聊场景**的用户画像更新员。你的任务是从多人对话中，仅提取和更新**一个特定用户**的画像。 - -**目标用户：{target_user}** -你必须只提取关于 **{target_user}** 的信息。仔细区分每条信息的发言者，不要把其他参与者的信息混入 {target_user} 的画像。 - -【{target_user} 的当前画像】（每条都有 index 编号） -{current_profile} - -【群聊对话记录】（多个参与者 - 只提取 {target_user} 的信息） -{conversations} - -【任务】 -分析对话，仅输出关于 **{target_user}** 的操作列表。可选操作类型： -- **update**: 修改现有条目（通过 index 指定） -- **add**: 新增画像条目 -- **delete**: 删除现有条目 -- **none**: 无需任何操作（当对话不包含 {target_user} 的信息时使用） - -【操作选择指南】 -- **update**: 现有条目有信息更新、补充、修改 -- **add**: 发现 {target_user} 的全新信息（与现有条目无关） -- **delete**: 以下情况应该删除： - - {target_user} 明确否定（如"我不再吃素了"） - - 信息已过时或与新信息直接矛盾 - -【重要规则】 -1. **发言者归属**：这是一个多人群聊。只提取 **{target_user}** 说的话或明确关于 {target_user} 的信息。如果其他参与者提到某个事实，那属于其他人的画像，不是 {target_user} 的。 -2. **挖掘标签**：隐式特征必须包含【性格标签】，例如：[风险厌恶型]、[社交驱动型]、[数据考据党]。 -3. sources 格式：使用对话 ID（方括号里的，如 ep1, ep2） -4. evidence 要包含时间和发言者信息 - 如"2024年10月 {target_user} 提到..." -5. explicit_info 和 implicit_traits 的 index 是独立编号的 -6. **去重**：在使用 "add" 前，检查所有已有条目。如果类似信息已存在，用 "update" 补充。只有确实全新的信息才用 "add"。 - -【画像定义】 -- **explicit_info（显式信息）**：{target_user} 直接陈述的或关于其本人的事实（技能、背景、偏好、所在地等） -- **implicit_traits（隐式特征）**：从 {target_user} 在对话中的表述和行为推断出的性格特征和行为模式 - -【输出格式】 -无操作时： -```json -{{"operations": [{{"action": "none"}}], "update_note": "对话不包含 {target_user} 的信息"}} -``` - -有操作时： -```json -{{ - "operations": [ - {{"action": "add", "type": "explicit_info", "data": {{"category": "...", "description": "...", "evidence": "...", "sources": ["ep1"]}}}}, - {{"action": "add", "type": "implicit_traits", "data": {{"trait": "...", "description": "...", "basis": "...", "evidence": "...", "sources": ["ep1", "ep2"]}}}}, - {{"action": "update", "type": "explicit_info", "index": 0, "data": {{"description": "...", "sources": ["ep3"]}}}}, - {{"action": "delete", "type": "implicit_traits", "index": 1, "reason": "..."}} - ], - "update_note": "..." -}} -```''' diff --git a/methods/EverCore/src/project_meta.py b/methods/EverCore/src/project_meta.py deleted file mode 100644 index bd69ccea..00000000 --- a/methods/EverCore/src/project_meta.py +++ /dev/null @@ -1,15 +0,0 @@ -import os - -PROJECT_NAME = "EverCore" -PROJECT_VERSION = "1.0.0" - - -def get_env_project_name(): - """ - Get the project name from environment variables - """ - project_name = os.getenv("project_name") or os.getenv("PROJECT_NAME") - if project_name: - return project_name - else: - return PROJECT_NAME diff --git a/methods/EverCore/src/run.py b/methods/EverCore/src/run.py deleted file mode 100644 index 9d5aced7..00000000 --- a/methods/EverCore/src/run.py +++ /dev/null @@ -1,161 +0,0 @@ -#!/usr/bin/env python3 -""" -Memsys Main Application - Main application startup script - -Main business application of the Memsys memory system, including: -- Requirement extraction agent -- Outline generation and editing agent -- Full-text writing and editing agent -- Document management and resource processing services -""" -import argparse -import os -import sys -import uvicorn -import logging - -# Environment variables are not loaded yet, so cannot use get_logger -logger = logging.getLogger(__name__) - -# Application info -APP_NAME = "Memory System" -APP_VERSION = "1.0.0" -APP_DESCRIPTION = "Main application of the memory system" - - -def parse_args(): - """Parse command line arguments""" - parser = argparse.ArgumentParser(description=f"Start {APP_NAME} service") - parser.add_argument( - "--host", - type=str, - default=None, - help="Server listening host address (env: MEMSYS_HOST, default: 0.0.0.0)", - ) - parser.add_argument( - "--port", - type=int, - default=None, - help="Server listening port (env: MEMSYS_PORT, default: 1995)", - ) - parser.add_argument( - "--env-file", - type=str, - default=".env", - help="Specify the environment variable file to load (default: .env)", - ) - parser.add_argument( - "--mock", - action="store_true", - help="Enable Mock mode (for testing and development)", - ) - parser.add_argument( - "--longjob", - type=str, - help="Start specified long-running job consumer (e.g.: kafka_consumer)", - ) - parser.add_argument( - "--skip-migrations", - action="store_true", - help="Skip MongoDB database migrations on startup", - ) - return parser.parse_args() - - -def main(): - # Parse command line arguments - args = parse_args() - - if args.longjob: - service_name = "longjob_" + args.longjob - else: - service_name = "web" - - # Add src directory to Python path - from import_parent_dir import add_parent_path - - add_parent_path(0) - - # Use unified environment loading utility - from common_utils.load_env import setup_environment - - # Set up environment (Python path and .env file) - setup_environment( - load_env_file_name=args.env_file, - check_env_var="MONGODB_HOST", - service_name=service_name, - ) - - # Determine host and port: CLI args > env vars > defaults - if args.host is not None: - host = args.host - elif os.getenv("MEMSYS_HOST"): - host = os.getenv("MEMSYS_HOST") - else: - host = "0.0.0.0" - - if args.port is not None: - port = args.port - elif os.getenv("MEMSYS_PORT"): - port = int(os.getenv("MEMSYS_PORT")) - else: - port = 1995 - - # Check if Mock mode is enabled: prioritize command line argument, then environment variable - from core.di.utils import enable_mock_mode - - if args.mock or ( - os.getenv("MOCK_MODE") and os.getenv("MOCK_MODE").lower() == "true" - ): - enable_mock_mode() - logger.info("🚀 Enabled Mock mode") - else: - logger.info("🚀 Disabled Mock mode") - - # Display application startup information - logger.info("🚀 Starting %s v%s", APP_NAME, APP_VERSION) - logger.info("📝 %s", APP_DESCRIPTION) - logger.info("🌟 Startup parameters:") - logger.info(" 📡 Host: %s", host) - logger.info(" 🔌 Port: %s", port) - logger.info(" 📄 Env File: %s", args.env_file) - logger.info(" 🎭 Mock Mode: %s", args.mock) - logger.info(" 🔧 LongJob Mode: %s", args.longjob if args.longjob else "Disabled") - logger.info(" 🔄 Skip Migrations: %s", args.skip_migrations) - - # Execute dependency injection and async task setup - from application_startup import setup_all - - # Perform dependency injection and async task setup during module loading - setup_all() - - # Run MongoDB database migrations (can be skipped via --skip-migrations argument) - from core.oxm.mongo.migration.manager import MigrationManager - - MigrationManager.run_migrations_on_startup(enabled=not args.skip_migrations) - - # Check if in LongJob mode - if args.longjob: - logger.info("🔧 Starting LongJob mode: %s", args.longjob) - os.environ["LONGJOB_NAME"] = args.longjob - - from app import app - - # Attach application info to the FastAPI app - app.title = APP_NAME - app.version = APP_VERSION - app.description = APP_DESCRIPTION - - # Start service using command line arguments - try: - uvicorn_kwargs = {"host": host, "port": port} - uvicorn.run(app, **uvicorn_kwargs) - except KeyboardInterrupt: - logger.info("👋 %s stopped", APP_NAME) - except (OSError, RuntimeError) as e: - logger.error("❌ %s failed to start: %s", APP_NAME, e) - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/methods/EverCore/src/service/content_enrich_provider.py b/methods/EverCore/src/service/content_enrich_provider.py deleted file mode 100644 index c2e2a73f..00000000 --- a/methods/EverCore/src/service/content_enrich_provider.py +++ /dev/null @@ -1,55 +0,0 @@ -""" -Content Enrich Provider - -Extension point for pre-processing content items before memorization. -Enterprise can override to add multimodal parsing, content transformation, etc. - -Usage pattern (same as AppLogicProvider): -- Opensource defines interface + NoopContentEnrichProvider (default) -- Enterprise implements with @component(primary=True) to auto-override via DI -""" - -from abc import ABC, abstractmethod -from typing import List - -from api_specs.dtos.memory import RawData -from core.di.decorators import component - - -class ContentEnrichProvider(ABC): - """Content enrich provider interface. - - Pre-processes content items in RawData before memorization. - Typical use case: multimodal file parsing (image/audio/pdf -> text). - - Extension pattern: - - Enterprise implements this interface with @component(primary=True) - - Automatically overrides the default NoopContentEnrichProvider via DI - """ - - @abstractmethod - async def enrich(self, raw_data_list: List[RawData]) -> None: - """Enrich content items in raw_data_list (in-place modification). - - Scans content items in each RawData.content["content"], - enriches items that need processing (e.g., calling external parsing service). - - Results are written directly into content item dict fields: - parsed_content / parsed_summary / parse_status, - persisted to MongoDB alongside content_items. - - Args: - raw_data_list: List of RawData to enrich (modified in-place) - """ - ... - - -@component(name="content_enrich_provider") -class NoopContentEnrichProvider(ContentEnrichProvider): - """Default no-op implementation. - - Used when opensource is deployed standalone. All non-text content remains as-is. - """ - - async def enrich(self, raw_data_list: List[RawData]) -> None: - pass diff --git a/methods/EverCore/src/service/group_service.py b/methods/EverCore/src/service/group_service.py deleted file mode 100644 index 34c7557e..00000000 --- a/methods/EverCore/src/service/group_service.py +++ /dev/null @@ -1,167 +0,0 @@ -""" -Group service - -Provides business logic for group CRUD operations. -""" - -import logging -from typing import Optional - -from core.di import service -from core.di.utils import get_bean_by_type -from infra_layer.adapters.out.persistence.repository.group_raw_repository import ( - GroupRawRepository, -) -from infra_layer.adapters.out.persistence.document.memory.group import Group -from api_specs.dtos.group import GroupResponse - -logger = logging.getLogger(__name__) - - -@service("group_service") -class GroupService: - """ - Group service - - Provides: - - Create or update a group (upsert) - - Get group by group_id - - Partial update group fields - - Auto-registration during memorize (fire-and-forget) - """ - - def __init__(self): - self._repository: Optional[GroupRawRepository] = None - - def _get_repository(self) -> GroupRawRepository: - """Get repository (lazy loading)""" - if self._repository is None: - self._repository = get_bean_by_type(GroupRawRepository) - return self._repository - - def _to_response(self, doc: Group) -> GroupResponse: - """Convert Group document to response DTO""" - return GroupResponse( - group_id=doc.group_id, - name=doc.name, - description=doc.description, - created_at=doc.created_at.isoformat() if doc.created_at else "", - updated_at=doc.updated_at.isoformat() if doc.updated_at else "", - ) - - async def create_or_update( - self, - group_id: str, - name: Optional[str] = None, - description: Optional[str] = None, - ) -> Optional[GroupResponse]: - """Create or update a group (upsert by group_id) - - Args: - group_id: Group identifier - name: Group display name - description: Group description - - Returns: - GroupResponse or None if failed - """ - repo = self._get_repository() - - update_data = {} - if name is not None: - update_data["name"] = name - if description is not None: - update_data["description"] = description - - doc = await repo.upsert_by_group_id(group_id, update_data) - if not doc: - logger.error("Failed to create/update group: group_id=%s", group_id) - return None - - logger.info("Group created/updated: group_id=%s", group_id) - return self._to_response(doc) - - async def get_by_group_id(self, group_id: str) -> Optional[GroupResponse]: - """Get group by group_id - - Args: - group_id: Group identifier - - Returns: - GroupResponse or None if not found - """ - repo = self._get_repository() - doc = await repo.get_by_group_id(group_id) - if not doc: - return None - return self._to_response(doc) - - async def patch( - self, - group_id: str, - name: Optional[str] = None, - description: Optional[str] = None, - ) -> Optional[GroupResponse]: - """Partial update group fields - - Args: - group_id: Group identifier - name: New display name (if provided) - description: New description (if provided) - - Returns: - GroupResponse or None if not found - """ - repo = self._get_repository() - - doc = await repo.get_by_group_id(group_id) - if not doc: - return None - - updated = False - if name is not None: - doc.name = name - updated = True - if description is not None: - doc.description = description - updated = True - - if updated: - await doc.save() - logger.info("Group patched: group_id=%s", group_id) - - return self._to_response(doc) - - async def ensure_group_exists( - self, - group_id: str, - name: Optional[str] = None, - description: Optional[str] = None, - ) -> None: - """Ensure a group exists (auto-registration during memorize) - - Creates the group if it doesn't exist. If it exists and name/description - are provided, updates them (PATCH-merge semantics). - Designed to be called as fire-and-forget via asyncio.create_task(). - - Args: - group_id: Group identifier - name: Group display name (optional) - description: Group description (optional) - """ - try: - repo = self._get_repository() - - update_data = {} - if name is not None: - update_data["name"] = name - if description is not None: - update_data["description"] = description - - await repo.upsert_by_group_id(group_id, update_data) - logger.debug("Group auto-registered: group_id=%s", group_id) - except Exception as e: - # Fire-and-forget: log error but don't raise - logger.warning( - "Failed to auto-register group: group_id=%s, error=%s", group_id, e - ) diff --git a/methods/EverCore/src/service/memcell_delete_service.py b/methods/EverCore/src/service/memcell_delete_service.py deleted file mode 100644 index 871d5c5d..00000000 --- a/methods/EverCore/src/service/memcell_delete_service.py +++ /dev/null @@ -1,258 +0,0 @@ -""" -MemCell Delete Service - Handle soft delete logic for MemCell - -Provides two independent delete methods: -- delete_by_id: single MemCell + MongoDB cascade by parent_id -- delete_by_filters: filter across MongoDB, Milvus, and Elasticsearch -""" - -import asyncio -from typing import Any, Optional, TypedDict - -from core.di.decorators import component -from core.observation.logger import get_logger -from core.oxm.constants import MAGIC_ALL -from infra_layer.adapters.out.persistence.repository.memcell_raw_repository import ( - MemCellRawRepository, -) -from infra_layer.adapters.out.persistence.repository.episodic_memory_raw_repository import ( - EpisodicMemoryRawRepository, -) -from infra_layer.adapters.out.persistence.repository.atomic_fact_record_raw_repository import ( - AtomicFactRecordRawRepository, -) -from infra_layer.adapters.out.persistence.repository.foresight_record_raw_repository import ( - ForesightRecordRawRepository, -) -from infra_layer.adapters.out.search.repository.episodic_memory_milvus_repository import ( - EpisodicMemoryMilvusRepository, -) -from infra_layer.adapters.out.search.repository.atomic_fact_milvus_repository import ( - AtomicFactMilvusRepository, -) -from infra_layer.adapters.out.search.repository.foresight_milvus_repository import ( - ForesightMilvusRepository, -) -from infra_layer.adapters.out.search.repository.episodic_memory_es_repository import ( - EpisodicMemoryEsRepository, -) -from infra_layer.adapters.out.search.repository.atomic_fact_es_repository import ( - AtomicFactEsRepository, -) -from infra_layer.adapters.out.search.repository.foresight_es_repository import ( - ForesightEsRepository, -) -from infra_layer.adapters.out.persistence.repository.raw_message_repository import ( - RawMessageRepository, -) - -logger = get_logger(__name__) - - -class DeleteResult(TypedDict): - """Internal delete result for logging/monitoring. Not exposed via API.""" - - deleted_memcell_count: int - deleted_episodes: int - deleted_atomic_facts: int - deleted_foresights: int - - -@component("memcell_delete_service") -class MemCellDeleteService: - """MemCell soft delete service""" - - def __init__( - self, - memcell_repository: MemCellRawRepository, - episodic_memory_repository: EpisodicMemoryRawRepository, - atomic_fact_repository: AtomicFactRecordRawRepository, - foresight_repository: ForesightRecordRawRepository, - episodic_memory_milvus_repository: EpisodicMemoryMilvusRepository, - atomic_fact_milvus_repository: AtomicFactMilvusRepository, - foresight_milvus_repository: ForesightMilvusRepository, - episodic_memory_es_repository: EpisodicMemoryEsRepository, - atomic_fact_es_repository: AtomicFactEsRepository, - foresight_es_repository: ForesightEsRepository, - raw_message_repository: RawMessageRepository, - ): - """ - Initialize deletion service - - Args: - memcell_repository: MemCell data repository - episodic_memory_repository: EpisodicMemory data repository - atomic_fact_repository: AtomicFactRecord data repository - foresight_repository: ForesightRecord data repository - episodic_memory_milvus_repository: EpisodicMemory Milvus repository - atomic_fact_milvus_repository: AtomicFact Milvus repository - foresight_milvus_repository: Foresight Milvus repository - episodic_memory_es_repository: EpisodicMemory ES repository - atomic_fact_es_repository: AtomicFact ES repository - foresight_es_repository: Foresight ES repository - raw_message_repository: RawMessage repository - """ - self.memcell_repository = memcell_repository - self.episodic_memory_repository = episodic_memory_repository - self.atomic_fact_repository = atomic_fact_repository - self.foresight_repository = foresight_repository - self.episodic_memory_milvus_repository = episodic_memory_milvus_repository - self.atomic_fact_milvus_repository = atomic_fact_milvus_repository - self.foresight_milvus_repository = foresight_milvus_repository - self.episodic_memory_es_repository = episodic_memory_es_repository - self.atomic_fact_es_repository = atomic_fact_es_repository - self.foresight_es_repository = foresight_es_repository - self.raw_message_repository = raw_message_repository - logger.info("MemCellDeleteService initialized") - - # ------------------------------------------------------------------ - # Public API — two mutually exclusive delete modes - # ------------------------------------------------------------------ - - async def delete_by_id(self, memory_id: str) -> DeleteResult: - """ - Delete a single MemCell by ID and cascade related records. - - Cascade uses parent_id only (MongoDB). - Milvus/ES are skipped because they cannot filter by parent_id. - """ - logger.info("Deleting by memory_id=%s", memory_id) - - try: - deleted_count = await self.memcell_repository.delete_by_filters( - memcell_id=memory_id - ) - counts = await self._cascade_delete_by_parent_id(memory_id) - - result = DeleteResult( - deleted_memcell_count=deleted_count, - deleted_episodes=counts.get("episodes", 0), - deleted_atomic_facts=counts.get("atomic_facts", 0), - deleted_foresights=counts.get("foresights", 0), - ) - logger.info("Delete by ID completed: %s", result) - return result - - except Exception as e: - logger.error( - "Failed to delete by memory_id=%s: error=%s", - memory_id, - e, - exc_info=True, - ) - raise - - async def delete_by_filters( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - session_id: Optional[str] = MAGIC_ALL, - sender_id: Optional[str] = MAGIC_ALL, - ) -> DeleteResult: - """ - Batch delete memory records by filter conditions. - - Deletes across MongoDB, Milvus, and Elasticsearch. - MemCell itself is not deleted (only child records). - """ - logger.info( - "Deleting by filters: user_id=%s, group_id=%s, " - "session_id=%s, sender_id=%s", - user_id, - group_id, - session_id, - sender_id, - ) - - try: - deleted = await self._batch_delete_records( - user_id=user_id, - group_id=group_id, - session_id=session_id, - sender_id=sender_id, - ) - - result = DeleteResult( - deleted_memcell_count=0, - deleted_episodes=deleted.get("episodes", 0), - deleted_atomic_facts=deleted.get("atomic_facts", 0), - deleted_foresights=deleted.get("foresights", 0), - ) - - logger.info("Delete by filters completed: %s", result) - return result - - except Exception as e: - logger.error("Failed to delete by filters: error=%s", e, exc_info=True) - raise - - # ------------------------------------------------------------------ - # Internal helpers - # ------------------------------------------------------------------ - - async def _gather_deletes(self, *tasks: tuple[str, Any, dict]) -> dict[str, int]: - """Run delete tasks in parallel, aggregate counts by category name. - Args: - tasks: (category_name, repository, kwargs) tuples. - Duplicate category names are summed automatically. - """ - names = [t[0] for t in tasks] - coros = [t[1].delete_by_filters(**t[2]) for t in tasks] - results = await asyncio.gather(*coros, return_exceptions=True) - from common_utils.async_utils import reraise_critical_errors - - reraise_critical_errors(results) - counts: dict[str, int] = {} - for name, result in zip(names, results): - if isinstance(result, Exception): - logger.error("Failed to cascade delete %s: %s", name, result) - else: - counts[name] = counts.get(name, 0) + result - logger.debug("Cascade deleted %s: count=%d", name, result) - return counts - - async def _cascade_delete_by_parent_id(self, parent_id: str) -> dict[str, int]: - """Cascade by parent_id (MongoDB only, Milvus/ES skipped).""" - kwargs = {"parent_id": parent_id} - counts = await self._gather_deletes( - ("episodes", self.episodic_memory_repository, kwargs), - ("atomic_facts", self.atomic_fact_repository, kwargs), - ("foresights", self.foresight_repository, kwargs), - ) - return {"episodes": 0, "atomic_facts": 0, "foresights": 0, **counts} - - async def _batch_delete_records( - self, - user_id: Optional[str] = MAGIC_ALL, - group_id: Optional[str] = MAGIC_ALL, - session_id: Optional[str] = MAGIC_ALL, - sender_id: Optional[str] = MAGIC_ALL, - ) -> dict[str, int]: - """Batch delete memory records across MongoDB, Milvus, and Elasticsearch.""" - if user_id == MAGIC_ALL and group_id == MAGIC_ALL: - return {"episodes": 0, "atomic_facts": 0, "foresights": 0} - - mongo_kwargs = dict( - user_id=user_id, - group_id=group_id, - session_id=session_id, - sender_id=sender_id, - ) - scope_kwargs = dict(user_id=user_id, group_id=group_id) - - # RawMessage and MemCell are source data — not deleted by filters. - # Milvus/ES only support user_id/group_id (no session_id/sender_id). - counts = await self._gather_deletes( - # MongoDB (session_id/sender_id narrow scoping) - ("episodes", self.episodic_memory_repository, mongo_kwargs), - ("atomic_facts", self.atomic_fact_repository, mongo_kwargs), - ("foresights", self.foresight_repository, mongo_kwargs), - # Milvus + ES (user_id/group_id scope only) - ("episodes", self.episodic_memory_milvus_repository, scope_kwargs), - ("atomic_facts", self.atomic_fact_milvus_repository, scope_kwargs), - ("foresights", self.foresight_milvus_repository, scope_kwargs), - ("episodes", self.episodic_memory_es_repository, scope_kwargs), - ("atomic_facts", self.atomic_fact_es_repository, scope_kwargs), - ("foresights", self.foresight_es_repository, scope_kwargs), - ) - return {"episodes": 0, "atomic_facts": 0, "foresights": 0, **counts} diff --git a/methods/EverCore/src/service/raw_message_service.py b/methods/EverCore/src/service/raw_message_service.py deleted file mode 100644 index 0e3993c1..00000000 --- a/methods/EverCore/src/service/raw_message_service.py +++ /dev/null @@ -1,329 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Raw Message Service - -Directly extract data from MemorizeRequest and save to RawMessage, -replacing the original event listener approach to make timing more controllable. -""" - -from typing import List, Optional - -from common_utils.datetime_utils import to_iso_format -from core.di import service -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger -from core.context.context import get_current_app_info -from core.oxm.constants import MAGIC_ALL -from api_specs.dtos import MemorizeRequest, RawData, RawMessageDTO -from infra_layer.adapters.out.persistence.document.request.raw_message import RawMessage -from infra_layer.adapters.out.persistence.repository.raw_message_repository import ( - RawMessageRepository, -) - -logger = get_logger(__name__) - - -@service("raw_message_service") -class RawMessageService: - """ - Raw Message Service - - Extract each message from new_raw_data_list in MemorizeRequest and save to RawMessage. - Return the list of saved message_ids for use in subsequent processes. - """ - - def __init__(self): - self._repository: Optional[RawMessageRepository] = None - - def _get_repository(self) -> RawMessageRepository: - """Get Repository (lazy loading)""" - if self._repository is None: - self._repository = get_bean_by_type(RawMessageRepository) - return self._repository - - async def save_raw_messages( - self, - request: MemorizeRequest, - version: Optional[str] = None, - endpoint_name: Optional[str] = None, - method: Optional[str] = None, - url: Optional[str] = None, - ) -> List[str]: - """ - Extract each message from MemorizeRequest and save as individual RawMessage documents. - - Iterates through each RawData in new_raw_data_list, extracts core fields and saves. - Saved records have sync_status=-1 (pending confirmation). - - Args: - request: MemorizeRequest object - version: API version (optional) - endpoint_name: Endpoint name (optional) - method: HTTP method (optional) - url: Request URL (optional) - - Returns: - List[str]: List of saved message_ids - """ - if not request.new_raw_data_list: - logger.debug("new_raw_data_list is empty, skipping save") - return [] - - # Get current request context information - app_info = get_current_app_info() - request_id = app_info.get("request_id", "unknown") - - saved_message_ids = [] - repo = self._get_repository() - - session_id = request.session_id - - for raw_data in request.new_raw_data_list: - try: - message_id = await self._save_single_raw_data( - raw_data=raw_data, - group_id=request.group_id, - request_id=request_id, - session_id=session_id, - repo=repo, - version=version, - endpoint_name=endpoint_name, - method=method, - url=url, - event_id=request_id, # Use request_id as event_id - ) - if message_id: - saved_message_ids.append(message_id) - except Exception as e: - logger.error( - "Failed to save RawData to RawMessage: data_id=%s, error=%s", - raw_data.data_id, - e, - ) - - logger.info( - "Saved %d raw messages: group_id=%s, message_ids=%s", - len(saved_message_ids), - request.group_id, - saved_message_ids, - ) - - return saved_message_ids - - async def _save_single_raw_data( - self, - raw_data: RawData, - group_id: Optional[str], - request_id: str, - session_id: Optional[str] = None, - repo: RawMessageRepository = None, - version: Optional[str] = None, - endpoint_name: Optional[str] = None, - method: Optional[str] = None, - url: Optional[str] = None, - event_id: Optional[str] = None, - ) -> Optional[str]: - """ - Save a single RawData to RawMessage - - Delegates to RawMessageRepository.save_from_raw_data for document - creation and persistence. - - Args: - raw_data: RawData object - group_id: Group ID - request_id: Request ID - session_id: Session identifier for conversation isolation - repo: Repository instance - version: API version - endpoint_name: Endpoint name - method: HTTP method - url: Request URL - event_id: Event ID - - Returns: - Optional[str]: Returns message_id if saved successfully, None otherwise - """ - if not group_id: - logger.debug("group_id is empty, skipping save") - return None - - return await repo.save_from_raw_data( - raw_data_content=raw_data.content or {}, - data_id=raw_data.data_id, - group_id=group_id, - request_id=request_id, - session_id=session_id, - version=version, - endpoint_name=endpoint_name, - method=method, - url=url, - event_id=event_id, - ) - - # ==================== Query Methods ==================== - - async def check_duplicate_message( - self, group_id: str, sender_id: str, message_id: str - ) -> bool: - """ - Check if a message with the given group_id, sender_id, and message_id already exists - - Used for duplicate detection before processing new memorize requests. - This helps prevent duplicate message processing when the same message - is submitted multiple times. - - Args: - group_id: Conversation group ID - sender_id: Sender ID - message_id: Message ID - - Returns: - bool: True if the message already exists, False otherwise - """ - repo = self._get_repository() - try: - existing = await repo.find_one_by_group_sender_message( - group_id=group_id, sender_id=sender_id, message_id=message_id - ) - if existing: - logger.info( - "Duplicate message detected: group_id=%s, sender_id=%s, message_id=%s", - group_id, - sender_id, - message_id, - ) - return True - return False - except Exception as e: - logger.error( - "Failed to check duplicate message: group_id=%s, sender_id=%s, message_id=%s, error=%s", - group_id, - sender_id, - message_id, - e, - ) - # In case of error, return False to allow the request to proceed - # This is a fail-open approach to avoid blocking legitimate requests - return False - - async def get_pending_raw_messages( - self, - sender_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - sync_status_list: Optional[List[int]] = None, - limit: int = 1000, - skip: int = 0, - ascending: bool = True, - ) -> List[RawMessage]: - """ - Get pending (unconsumed) raw messages - - Query raw messages that have not been consumed yet (sync_status=-1 or 0). - Supports flexible filtering with MAGIC_ALL logic: - - MAGIC_ALL ("__all__"): Don't filter by this field - - None or "": Filter for null/empty values - - Other values: Exact match - - Args: - sender_id: Sender ID filter - - MAGIC_ALL: Don't filter by sender_id (default) - - None or "": Filter for null/empty values - - Other values: Exact match - group_ids: List of Group IDs to filter (None to skip filtering, searches all groups) - sync_status_list: List of sync_status values to filter by - - Default: [-1, 0] (pending and accumulating, i.e., unconsumed) - - [-1]: Just log records - - [0]: In window accumulation - - [1]: Already fully used - limit: Maximum number of records to return (default 100) - skip: Number of records to skip (default 0) - ascending: If True (default), sort by created_at ascending (oldest first); - if False, sort descending (newest first) - - Returns: - List[RawMessage]: List of pending raw messages - """ - # Default to unconsumed statuses - if sync_status_list is None: - sync_status_list = [-1, 0] - - repo = self._get_repository() - - try: - results = await repo.find_pending_by_filters( - sender_id=sender_id, - group_ids=group_ids, - sync_status_list=sync_status_list, - limit=limit, - skip=skip, - ascending=ascending, - ) - - logger.debug( - "Retrieved pending raw messages: sender_id=%s, group_ids=%s, " - "sync_status_list=%s, count=%d", - sender_id, - group_ids, - sync_status_list, - len(results), - ) - return results - except Exception as e: - logger.error( - "Failed to get pending raw messages: sender_id=%s, group_ids=%s, error=%s", - sender_id, - group_ids, - e, - ) - return [] - - async def get_pending_messages( - self, - sender_id: Optional[str] = MAGIC_ALL, - group_ids: Optional[List[str]] = None, - limit: int = 1000, - ) -> List[RawMessageDTO]: - """ - Get pending (unconsumed) messages as list of RawMessageDTO objects. - - This is a convenience method that wraps get_pending_raw_messages - and converts the results to RawMessageDTO dataclass instances. - - Args: - sender_id: Sender ID filter (MAGIC_ALL to skip filtering) - group_ids: List of Group IDs to filter (None to skip filtering, searches all groups) - limit: Maximum number of records to return (default 1000) - - Returns: - List[RawMessageDTO]: List of pending messages - """ - logs = await self.get_pending_raw_messages( - sender_id=sender_id, group_ids=group_ids, limit=limit - ) - - # Convert to list of RawMessageDTO - result = [] - for log in logs: - pending_msg = RawMessageDTO( - id=str(log.id), - request_id=log.request_id, - message_id=log.message_id, - group_id=log.group_id, - session_id=log.session_id, - sender_id=log.sender_id, - sender_name=log.sender_name, - content_items=log.content_items, - timestamp=log.timestamp, - created_at=to_iso_format(log.created_at), - updated_at=to_iso_format(log.updated_at), - ) - result.append(pending_msg) - - logger.debug( - "Converted %d pending raw messages to RawMessageDTO: sender_id=%s, group_ids=%s", - len(result), - sender_id, - group_ids, - ) - return result diff --git a/methods/EverCore/src/service/sender_service.py b/methods/EverCore/src/service/sender_service.py deleted file mode 100644 index 2d771f44..00000000 --- a/methods/EverCore/src/service/sender_service.py +++ /dev/null @@ -1,160 +0,0 @@ -""" -Sender service - -Provides business logic for sender CRUD operations. -""" - -import logging -from typing import Dict, List, Optional - -from core.di import service -from core.di.utils import get_bean_by_type -from infra_layer.adapters.out.persistence.repository.sender_raw_repository import ( - SenderRawRepository, -) -from infra_layer.adapters.out.persistence.document.memory.sender import Sender -from api_specs.dtos.sender import SenderResponse - -logger = logging.getLogger(__name__) - - -@service("sender_service") -class SenderService: - """ - Sender service - - Provides: - - Create or update a sender (upsert) - - Get sender by sender_id - - Partial update sender fields - - Auto-registration during memorize (fire-and-forget) - """ - - def __init__(self): - self._repository: Optional[SenderRawRepository] = None - - def _get_repository(self) -> SenderRawRepository: - """Get repository (lazy loading)""" - if self._repository is None: - self._repository = get_bean_by_type(SenderRawRepository) - return self._repository - - def _to_response(self, doc: Sender) -> SenderResponse: - """Convert Sender document to response DTO""" - return SenderResponse( - sender_id=doc.sender_id, - name=doc.name, - created_at=doc.created_at.isoformat() if doc.created_at else "", - updated_at=doc.updated_at.isoformat() if doc.updated_at else "", - ) - - async def create_or_update( - self, sender_id: str, name: Optional[str] = None - ) -> Optional[SenderResponse]: - """Create or update a sender (upsert by sender_id) - - Args: - sender_id: Sender identifier - name: Sender display name - - Returns: - SenderResponse or None if failed - """ - repo = self._get_repository() - - update_data = {} - if name is not None: - update_data["name"] = name - - doc = await repo.upsert_by_sender_id(sender_id, update_data) - if not doc: - logger.error("Failed to create/update sender: sender_id=%s", sender_id) - return None - - logger.info("Sender created/updated: sender_id=%s", sender_id) - return self._to_response(doc) - - async def get_by_sender_id(self, sender_id: str) -> Optional[SenderResponse]: - """Get sender by sender_id - - Args: - sender_id: Sender identifier - - Returns: - SenderResponse or None if not found - """ - repo = self._get_repository() - doc = await repo.get_by_sender_id(sender_id) - if not doc: - return None - return self._to_response(doc) - - async def patch( - self, sender_id: str, name: Optional[str] = None - ) -> Optional[SenderResponse]: - """Partial update sender fields - - Args: - sender_id: Sender identifier - name: New display name (if provided) - - Returns: - SenderResponse or None if not found - """ - repo = self._get_repository() - - doc = await repo.get_by_sender_id(sender_id) - if not doc: - return None - - if name is not None: - doc.name = name - await doc.save() - logger.info("Sender patched: sender_id=%s", sender_id) - - return self._to_response(doc) - - async def batch_get_sender_names(self, sender_ids: List[str]) -> Dict[str, str]: - """Batch get sender display names by sender_ids. - - Only returns entries where the sender has a non-empty name stored. - - Args: - sender_ids: List of sender identifiers - - Returns: - Dict mapping sender_id to display name - """ - if not sender_ids: - return {} - repo = self._get_repository() - docs = await repo.get_by_sender_ids(sender_ids) - return {doc.sender_id: doc.name for doc in docs if doc.name} - - async def ensure_sender_exists( - self, sender_id: str, name: Optional[str] = None - ) -> None: - """Ensure a sender exists (auto-registration during memorize) - - Creates the sender if it doesn't exist. If it exists and name is - provided, updates it. - Designed to be called as fire-and-forget via asyncio.create_task(). - - Args: - sender_id: Sender identifier - name: Sender display name (optional) - """ - try: - repo = self._get_repository() - - update_data = {} - if name is not None: - update_data["name"] = name - - await repo.upsert_by_sender_id(sender_id, update_data) - logger.debug("Sender auto-registered: sender_id=%s", sender_id) - except Exception as e: - # Fire-and-forget: log error but don't raise - logger.warning( - "Failed to auto-register sender: sender_id=%s, error=%s", sender_id, e - ) diff --git a/methods/EverCore/src/service/session_service.py b/methods/EverCore/src/service/session_service.py deleted file mode 100644 index 15e31307..00000000 --- a/methods/EverCore/src/service/session_service.py +++ /dev/null @@ -1,71 +0,0 @@ -""" -Session service - -Provides auto-registration logic for sessions during memorize. -""" - -import logging -from typing import Optional - -from core.di import service -from core.di.utils import get_bean_by_type -from infra_layer.adapters.out.persistence.repository.session_raw_repository import ( - SessionRawRepository, -) - -logger = logging.getLogger(__name__) - - -@service("session_service") -class SessionService: - """ - Session service - - Provides: - - Auto-registration during memorize (fire-and-forget) - """ - - def __init__(self): - self._repository: Optional[SessionRawRepository] = None - - def _get_repository(self) -> SessionRawRepository: - """Get repository (lazy loading)""" - if self._repository is None: - self._repository = get_bean_by_type(SessionRawRepository) - return self._repository - - async def ensure_session_exists( - self, - session_id: str, - name: Optional[str] = None, - description: Optional[str] = None, - ) -> None: - """Ensure a session exists (auto-registration during memorize) - - Creates the session if it doesn't exist. If it exists and name/description - are provided, updates them (PATCH-merge semantics). - Designed to be called as fire-and-forget via asyncio.create_task(). - - Args: - session_id: Session identifier - name: Session display name (optional) - description: Session description (optional) - """ - try: - repo = self._get_repository() - - update_data = {} - if name is not None: - update_data["name"] = name - if description is not None: - update_data["description"] = description - - await repo.upsert_by_session_id(session_id, update_data) - logger.debug("Session auto-registered: session_id=%s", session_id) - except Exception as e: - # Fire-and-forget: log error but don't raise - logger.warning( - "Failed to auto-register session: session_id=%s, error=%s", - session_id, - e, - ) diff --git a/methods/EverCore/src/service/settings_service.py b/methods/EverCore/src/service/settings_service.py deleted file mode 100644 index 2f5908f5..00000000 --- a/methods/EverCore/src/service/settings_service.py +++ /dev/null @@ -1,290 +0,0 @@ -""" -Settings service - -Provides business logic for global settings operations. -Handles both initialization (first PUT) and subsequent updates. -Includes LLM provider/model whitelist validation. -""" - -import logging -import os -from typing import Any, Dict, Optional - -from core.di import service -from core.di.utils import get_bean_by_type -from core.constants.exceptions import ValidationException -from infra_layer.adapters.out.persistence.repository.settings_raw_repository import ( - GlobalSettingsRawRepository, -) -from infra_layer.adapters.out.persistence.document.memory.global_settings import ( - GlobalSettings, - LlmCustomSettingModel, -) -from api_specs.dtos.settings import SettingsResponse, UpdateSettingsRequest - -logger = logging.getLogger(__name__) - - -@service("settings_service") -class SettingsService: - """ - Settings service - - Provides: - - Get global settings (singleton) - - Update or initialize global settings (PUT semantics) - - Internal helper methods for business layer (get_llm_custom_setting) - - LLM provider/model whitelist validation - """ - - def __init__(self): - self._repository: Optional[GlobalSettingsRawRepository] = None - - def _get_repository(self) -> GlobalSettingsRawRepository: - """Get repository (lazy loading)""" - if self._repository is None: - self._repository = get_bean_by_type(GlobalSettingsRawRepository) - return self._repository - - def _to_response(self, doc: GlobalSettings) -> SettingsResponse: - """Convert GlobalSettings document to response DTO""" - llm_setting_dict = None - if doc.llm_custom_setting: - llm_setting_dict = doc.llm_custom_setting.to_dict() - - return SettingsResponse( - llm_custom_setting=llm_setting_dict, - # Hidden fields: not yet implemented, uncomment when ready - # timezone=doc.timezone, - # boundary_detection_timeout=doc.boundary_detection_timeout, - # extraction_mode=doc.extraction_mode, - # offline_profile_extraction_interval=doc.offline_profile_extraction_interval, - created_at=doc.created_at.isoformat() if doc.created_at else "", - updated_at=doc.updated_at.isoformat() if doc.updated_at else "", - ) - - async def get(self) -> Optional[SettingsResponse]: - """Get global settings - - Returns: - SettingsResponse or None if not initialized - """ - repo = self._get_repository() - doc = await repo.get_global_settings() - if not doc: - return None - return self._to_response(doc) - - async def update( - self, request: UpdateSettingsRequest, raw_data: Optional[Dict[str, Any]] = None - ) -> SettingsResponse: - """Update or initialize global settings - - PUT semantics: - - If settings don't exist: initialize - - If settings exist: update provided fields - - If a field is explicitly set to null in raw_data: clear it - - Args: - request: Update settings request - raw_data: Raw request JSON dict, used to distinguish - "field absent" from "field explicitly null" - - Returns: - SettingsResponse with updated data - - Raises: - ValueError: When validation fails - ValidationException: When LLM whitelist validation fails - """ - repo = self._get_repository() - existing = await repo.get_global_settings() - - # Validate LLM whitelist - if request.llm_custom_setting: - self._validate_llm_custom_setting(request.llm_custom_setting) - - if existing is None: - data = self._build_data(request, exclude_none=True) - # Convert LlmCustomSetting DTO to LlmCustomSettingModel for storage - if "llm_custom_setting" in data and data["llm_custom_setting"] is not None: - data["llm_custom_setting"] = LlmCustomSettingModel.from_any( - data["llm_custom_setting"] - ) - - doc = await repo.upsert_global_settings(data) - if not doc: - raise ValueError("Failed to initialize settings") - - logger.info("Settings initialized") - return self._to_response(doc) - else: - - update_data = self._build_update_data(request, raw_data=raw_data) - if not update_data: - # No fields to update, return current state - return self._to_response(existing) - - # Convert LlmCustomSetting DTO to LlmCustomSettingModel for storage - if ( - "llm_custom_setting" in update_data - and update_data["llm_custom_setting"] is not None - ): - update_data["llm_custom_setting"] = LlmCustomSettingModel.from_any( - update_data["llm_custom_setting"] - ) - - doc = await repo.update_global_settings(update_data) - if not doc: - raise ValueError("Failed to update settings") - - logger.info("Settings updated: fields=%s", list(update_data.keys())) - return self._to_response(doc) - - # ========================================================================= - # Internal helper methods (for business layer) - # ========================================================================= - - async def get_llm_custom_setting(self) -> Optional[Dict[str, Any]]: - """Get LLM custom setting as a dictionary - - Returns: - LLM custom setting dict or None if not configured - """ - repo = self._get_repository() - doc = await repo.get_global_settings() - if not doc or not doc.llm_custom_setting: - return None - if hasattr(doc.llm_custom_setting, "model_dump"): - return doc.llm_custom_setting.model_dump() - if hasattr(doc.llm_custom_setting, "dict"): - return doc.llm_custom_setting.dict() - return doc.llm_custom_setting - - # ========================================================================= - # Validation - # ========================================================================= - - @classmethod - def _validate_llm_custom_setting(cls, llm_setting: Any) -> None: - """ - Validate LLM custom setting provider/model against whitelist. - - Reads {PROVIDER}_WHITE_LIST env var (comma-separated model names). - If the env var is not set or empty, no restriction is applied. - - Args: - llm_setting: Object with boundary/extraction attributes, each having provider/model - - Raises: - ValidationException: If a model is not in the provider's whitelist - """ - if not llm_setting: - return - - from memory_layer.constants import EXTRACT_SCENES - - for task_name in EXTRACT_SCENES: - config = getattr(llm_setting, task_name, None) - if config is None: - continue - provider = getattr(config, "provider", None) - model = getattr(config, "model", None) - if not provider or not model: - continue - cls._validate_model_whitelist(provider, model, task_name) - - @staticmethod - def _validate_model_whitelist(provider: str, model: str, task_name: str) -> None: - """ - Validate model against the provider's whitelist from environment variable. - - Reads {PROVIDER}_WHITE_LIST env var (comma-separated model names). - If the env var is not set or empty, no restriction is applied. - - Args: - provider: Provider name (e.g., "openai", "openrouter") - model: Model name - task_name: Task name for error context (e.g., "boundary", "extraction") - - Raises: - ValidationException: If model is not in the whitelist - """ - env_key = f"{provider.upper()}_WHITE_LIST" - raw = os.getenv(env_key, "").strip() - if not raw: - return - allowed_models = {m.strip() for m in raw.split(",") if m.strip()} - if not allowed_models: - return - if model not in allowed_models: - raise ValidationException( - message=f"Model '{model}' is not allowed for provider '{provider}' " - f"(task: {task_name}). " - f"Allowed models: {', '.join(sorted(allowed_models))}.", - field=f"llm_custom_setting.{task_name}.model", - details={"error_code": "MODEL_NOT_IN_WHITELIST"}, - ) - - # ========================================================================= - # Data building helpers - # ========================================================================= - - @staticmethod - def _build_data( - request: UpdateSettingsRequest, exclude_none: bool = True - ) -> Dict[str, Any]: - """Build data dict from request for initialization""" - data = {} - fields = [ - "llm_custom_setting", - # Hidden fields: not yet implemented, uncomment when ready - # "timezone", - # "boundary_detection_timeout", - # "extraction_mode", - # "offline_profile_extraction_interval", - ] - for field in fields: - value = getattr(request, field, None) - if exclude_none and value is None: - continue - # Convert Pydantic model to dict for storage - if hasattr(value, "model_dump"): - value = value.model_dump(exclude_none=True) - data[field] = value - return data - - @staticmethod - def _build_update_data( - request: UpdateSettingsRequest, raw_data: Optional[Dict[str, Any]] = None - ) -> Dict[str, Any]: - """Build update data dict from request. - - Uses raw_data (the original JSON dict) to distinguish between - "field not provided" (skip) and "field explicitly set to null" (clear). - """ - data = {} - fields = [ - "llm_custom_setting", - # Hidden fields: not yet implemented, uncomment when ready - # "timezone", - # "boundary_detection_timeout", - # "extraction_mode", - # "offline_profile_extraction_interval", - ] - for field in fields: - value = getattr(request, field, None) - if value is None: - # Check if the field was explicitly sent as null - if ( - raw_data is not None - and field in raw_data - and raw_data[field] is None - ): - data[field] = None - continue - # Convert Pydantic model to dict for storage - if hasattr(value, "model_dump"): - value = value.model_dump(exclude_none=True) - data[field] = value - return data diff --git a/methods/EverCore/src/task.py b/methods/EverCore/src/task.py deleted file mode 100644 index d23246db..00000000 --- a/methods/EverCore/src/task.py +++ /dev/null @@ -1,117 +0,0 @@ -#!/usr/bin/env python3 -""" -Task Worker - Async task processor startup script - -Async task processing service, responsible for: -- Background task queue processing -- Long-running asynchronous tasks -- Scheduled and delayed tasks -- Task status management and monitoring - -Usage: - arq task.WorkerSettings - -Environment variables: - REDIS_HOST: Redis server address (default: localhost) - REDIS_PORT: Redis port (default: 6379) - REDIS_DB: Redis database number (default: 0) - REDIS_PASSWORD: Redis password (optional) - REDIS_SSL: Whether to use SSL (default: false) - REDIS_USERNAME: Redis username (optional) -""" - -import os -import logging - -from arq.connections import RedisSettings - -# Application info -APP_NAME = "Async Task Worker" -APP_VERSION = "1.0.0" -APP_DESCRIPTION = "Asynchronous task processing service" - -# Environment variables are not loaded yet, so cannot use get_logger -logger = logging.getLogger(__name__) - -# Add src directory to Python path -from import_parent_dir import add_parent_path - -add_parent_path(0) - -# Use unified environment loading tool -# Set .env file -from common_utils.load_env import setup_environment - -setup_environment(check_env_var="REDIS_HOST") - -# Display application startup info -logger.info("🚀 Starting %s v%s", APP_NAME, APP_VERSION) -logger.info("⚙️ %s", APP_DESCRIPTION) - -# Run main function -# Scan component & task -from application_startup import setup_all - -setup_all() - - -# Worker startup and shutdown callback functions -async def startup(_ctx): - """Callback function when worker starts""" - logger.info("🔄 Initializing async task worker...") - - # Initialize application context when worker starts - from app import app - - # Add application info to FastAPI app (must be before start_lifespan) - app.title = APP_NAME - app.version = APP_VERSION - app.description = APP_DESCRIPTION - - if hasattr(app, "start_lifespan"): - await app.start_lifespan() - logger.info("✅ Application lifespan startup completed") - else: - logger.warning("⚠️ app instance has no start_lifespan method") - - logger.info("🎯 %s started, ready to process tasks", APP_NAME) - - -async def shutdown(_ctx): - """Callback function when worker shuts down""" - logger.info("🛑 Shutting down %s...", APP_NAME) - - # Clean up application context when worker shuts down - from app import app - - if hasattr(app, "exit_lifespan"): - await app.exit_lifespan() - logger.info("✅ Application lifespan shutdown completed") - else: - logger.warning("⚠️ app instance has no exit_lifespan method") - - logger.info("👋 %s has stopped", APP_NAME) - - -from core.asynctasks.task_manager import get_task_manager - - -class WorkerSettings: - functions = get_task_manager().get_worker_functions() - on_startup = startup - on_shutdown = shutdown - redis_settings = RedisSettings( - host=os.getenv("REDIS_HOST", "localhost"), - port=int(os.getenv("REDIS_PORT", "6379")), - database=int(os.getenv("REDIS_DB", "0")), - password=os.getenv("REDIS_PASSWORD", "123456"), - ssl=os.getenv("REDIS_SSL", "false").lower() == "true", - username=os.getenv("REDIS_USERNAME"), - ) - health_check_interval = 30 - max_jobs = 10 - job_timeout = 300 - keep_result = 3600 - - -# arq task.WorkerSettings diff --git a/methods/EverCore/tests/integration/test_delete_api_integration.py b/methods/EverCore/tests/integration/test_delete_api_integration.py deleted file mode 100644 index 9f182c1a..00000000 --- a/methods/EverCore/tests/integration/test_delete_api_integration.py +++ /dev/null @@ -1,1072 +0,0 @@ -""" -Integration tests for POST /api/v1/memories/delete - -Tests the live API against real MongoDB. Inserts fixture data directly -into MongoDB, calls the HTTP endpoint, and verifies both responses and -DB state. All fixture data uses the 'test_intg_delete_' prefix and is -cleaned up after tests complete. - -Requirements: - - API server running on localhost:1995 - - MongoDB accessible (connection details from .env) - -Usage: - PYTHONPATH=src pytest tests/integration/test_delete_api_integration.py -v --tb=long -""" - -import asyncio -import json -import os -import time -from datetime import datetime, timezone -from typing import Any - -import httpx -import pytest -from bson import ObjectId -from pymongo import AsyncMongoClient - -# Suppress pymongo GC noise when event loop closes before client cleanup -pytestmark = pytest.mark.filterwarnings( - "ignore::pytest.PytestUnraisableExceptionWarning" -) - -# --------------------------------------------------------------------------- -# Constants -# --------------------------------------------------------------------------- -API_BASE = os.getenv("TEST_API_BASE", "http://localhost:1995") -DELETE_URL = f"{API_BASE}/api/v1/memories/delete" - -MONGO_HOST = os.getenv("MONGODB_HOST") -MONGO_PORT = int(os.getenv("MONGODB_PORT", "27017")) -MONGO_USER = os.getenv("MONGODB_USERNAME") -MONGO_PASS = os.getenv("MONGODB_PASSWORD") -MONGO_TENANT_ID = os.getenv("TENANT_SINGLE_TENANT_ID") -_BASE_DB = os.getenv("MONGODB_DATABASE") -# API uses tenant-prefixed database: {tenant_id}_{base_name} -MONGO_DB = f"{MONGO_TENANT_ID}_{_BASE_DB}" if MONGO_TENANT_ID else _BASE_DB - -# Collection names -COL_MEMCELLS = "v1_memcells" -COL_EPISODES = "v1_episodic_memories" -COL_ATOMIC_FACTS = "v1_atomic_fact_records" -COL_FORESIGHTS = "v1_foresight_records" -COL_RAW_MESSAGES = "v1_raw_messages" - -ALL_COLLECTIONS = [ - COL_MEMCELLS, - COL_EPISODES, - COL_ATOMIC_FACTS, - COL_FORESIGHTS, - COL_RAW_MESSAGES, -] - -# Test data prefix -PREFIX = "test_intg_delete_" - -# --------------------------------------------------------------------------- -# Test result collector (for report generation) -# --------------------------------------------------------------------------- -_test_results: list[dict[str, Any]] = [] - - -def _record_result( - scenario: str, - category: str, - passed: bool, - duration_ms: float, - detail: str = "", - request_body: dict | None = None, - response_body: dict | None = None, - status_code: int | None = None, -): - _test_results.append( - { - "scenario": scenario, - "category": category, - "passed": passed, - "duration_ms": round(duration_ms, 2), - "detail": detail, - "request_body": request_body, - "response_body": response_body, - "status_code": status_code, - } - ) - - -# --------------------------------------------------------------------------- -# Fixtures -# --------------------------------------------------------------------------- -@pytest.fixture(scope="module") -def event_loop(): - """Create a module-scoped event loop.""" - loop = asyncio.new_event_loop() - yield loop - loop.close() - - -@pytest.fixture(scope="module") -def mongo_client(): - """Module-scoped Motor client.""" - uri = f"mongodb://{MONGO_USER}:{MONGO_PASS}@{MONGO_HOST}:{MONGO_PORT}/?authSource=admin" - client = AsyncMongoClient(uri) - yield client - # AsyncMongoClient is bound to its creation event loop; - # just let Python GC handle cleanup to avoid cross-loop errors. - - -@pytest.fixture(scope="module") -def db(mongo_client): - """Module-scoped database reference.""" - return mongo_client[MONGO_DB] - - -@pytest.fixture(scope="module") -def http_client(): - """Module-scoped httpx client.""" - with httpx.Client(timeout=30.0) as client: - yield client - - -@pytest.fixture(autouse=True, scope="module") -def _check_server(http_client): - """Verify API server is reachable before running tests.""" - try: - r = http_client.get(f"{API_BASE}/health") - assert r.status_code == 200, f"Health check failed: {r.text}" - except httpx.ConnectError: - pytest.skip("API server not running on localhost:1995") - - -def _now(): - return datetime.now(timezone.utc) - - -def _make_memcell( - user_id: str | None = None, - group_id: str | None = None, - session_id: str | None = None, - participants: list[str] | None = None, - _id: ObjectId | None = None, -) -> dict: - """Build a minimal MemCell document.""" - doc: dict[str, Any] = { - "_id": _id or ObjectId(), - "timestamp": _now(), - "original_data": [{"content": "test fixture data"}], - "type": "conversation", - "created_at": _now(), - "updated_at": _now(), - "deleted_at": None, - "deleted_by": None, - "deleted_id": 0, - } - if user_id is not None: - doc["user_id"] = user_id - if group_id is not None: - doc["group_id"] = group_id - if session_id is not None: - doc["session_id"] = session_id - if participants is not None: - doc["participants"] = participants - return doc - - -def _make_child( - collection_type: str, - parent_id: str, - user_id: str | None = None, - group_id: str | None = None, - session_id: str | None = None, - participants: list[str] | None = None, -) -> dict: - """Build a minimal child document (episode / atomic_fact / foresight).""" - doc: dict[str, Any] = { - "_id": ObjectId(), - "parent_type": "memcell", - "parent_id": parent_id, - "timestamp": _now(), - "created_at": _now(), - "updated_at": _now(), - "deleted_at": None, - "deleted_by": None, - "deleted_id": 0, - } - - if user_id is not None: - doc["user_id"] = user_id - if group_id is not None: - doc["group_id"] = group_id - if session_id is not None: - doc["session_id"] = session_id - if participants is not None: - doc["participants"] = participants - - if collection_type == COL_EPISODES: - doc["summary"] = "test episode summary" - doc["subject"] = "test subject" - doc["episode"] = "test episode content" - elif collection_type == COL_ATOMIC_FACTS: - doc["atomic_fact"] = "test atomic fact" - elif collection_type == COL_FORESIGHTS: - doc["content"] = "test foresight content" - doc["start_time"] = "2026-03-08" - doc["end_time"] = "2026-03-15" - doc["duration_days"] = 7 - return doc - - -def _make_request_log(user_id: str | None = None, group_id: str | None = None) -> dict: - """Build a minimal MemoryRequestLog document.""" - doc: dict[str, Any] = { - "_id": ObjectId(), - "request_id": f"{PREFIX}req_{ObjectId()}", - "group_id": group_id or f"{PREFIX}group_default", - "content": "test request log", - "sync_status": -1, - "created_at": _now(), - "updated_at": _now(), - "deleted_at": None, - "deleted_by": None, - "deleted_id": 0, - } - if user_id is not None: - doc["user_id"] = user_id - return doc - - -# --------------------------------------------------------------------------- -# Async helpers -# --------------------------------------------------------------------------- -async def _insert_docs(db, collection: str, docs: list[dict]): - """Insert documents into a collection.""" - if docs: - await db[collection].insert_many(docs) - - -async def _count_alive(db, collection: str, filter_dict: dict) -> int: - """Count non-soft-deleted documents matching the filter.""" - f = {**filter_dict, "deleted_at": None} - return await db[collection].count_documents(f) - - -async def _count_soft_deleted(db, collection: str, filter_dict: dict) -> int: - """Count soft-deleted documents matching the filter.""" - f = {**filter_dict, "deleted_at": {"$ne": None}} - return await db[collection].count_documents(f) - - -async def _cleanup_test_data(db): - """Remove ALL documents with test prefix from all collections.""" - for col_name in ALL_COLLECTIONS: - col = db[col_name] - # Match any field containing the test prefix - await col.delete_many( - { - "$or": [ - {"user_id": {"$regex": f"^{PREFIX}"}}, - {"group_id": {"$regex": f"^{PREFIX}"}}, - {"session_id": {"$regex": f"^{PREFIX}"}}, - {"request_id": {"$regex": f"^{PREFIX}"}}, - ] - } - ) - - -# --------------------------------------------------------------------------- -# Test helpers -# --------------------------------------------------------------------------- -def _call_delete(http_client, body: dict) -> tuple[int, dict]: - """POST to delete endpoint, return (status_code, response_json).""" - r = http_client.post(DELETE_URL, json=body) - try: - return r.status_code, r.json() - except Exception: - return r.status_code, {"raw": r.text} - - -# =================================================================== -# TEST CLASS -# =================================================================== -@pytest.mark.integration -class TestDeleteApiIntegration: - """Full API integration tests for the delete endpoint.""" - - # --------------------------------------------------------------- - # 1. Request validation (no DB interaction needed) - # --------------------------------------------------------------- - - def test_empty_body_returns_422(self, http_client): - """Empty request body should fail validation.""" - t0 = time.monotonic() - status, body = _call_delete(http_client, {}) - elapsed = (time.monotonic() - t0) * 1000 - - _record_result( - scenario="Empty request body", - category="Request Validation", - passed=status == 422, - duration_ms=elapsed, - detail=f"Expected 422, got {status}", - request_body={}, - response_body=body, - status_code=status, - ) - assert status == 422 - - def test_memory_id_with_user_id_returns_422(self, http_client): - """memory_id + user_id together should fail.""" - req = {"memory_id": "abcdef1234567890abcdef12", "user_id": "u1"} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - _record_result( - scenario="memory_id + user_id mutual exclusion", - category="Request Validation", - passed=status == 422, - duration_ms=elapsed, - detail=f"Expected 422, got {status}", - request_body=req, - response_body=body, - status_code=status, - ) - assert status == 422 - - def test_only_session_id_returns_422(self, http_client): - """session_id alone (no user_id/group_id) should fail.""" - req = {"session_id": "s1"} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - _record_result( - scenario="session_id only (no scope)", - category="Request Validation", - passed=status == 422, - duration_ms=elapsed, - detail=f"Expected 422, got {status}", - request_body=req, - response_body=body, - status_code=status, - ) - assert status == 422 - - def test_only_sender_id_returns_422(self, http_client): - """sender_id alone (no user_id/group_id) should fail.""" - req = {"sender_id": "sender1"} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - _record_result( - scenario="sender_id only (no scope)", - category="Request Validation", - passed=status == 422, - duration_ms=elapsed, - detail=f"Expected 422, got {status}", - request_body=req, - response_body=body, - status_code=status, - ) - assert status == 422 - - # --------------------------------------------------------------- - # 2. Delete by memory_id - # --------------------------------------------------------------- - - def test_delete_by_id_with_cascade(self, db, http_client, event_loop): - """Delete a memcell by ID, verify cascade to children.""" - user_id = f"{PREFIX}user_byid" - group_id = f"{PREFIX}group_byid" - memcell_id = ObjectId() - memcell_id_str = str(memcell_id) - - # Insert fixtures - memcell = _make_memcell(user_id=user_id, group_id=group_id, _id=memcell_id) - episodes = [ - _make_child( - COL_EPISODES, memcell_id_str, user_id=user_id, group_id=group_id - ) - for _ in range(3) - ] - atomic_facts = [ - _make_child( - COL_ATOMIC_FACTS, memcell_id_str, user_id=user_id, group_id=group_id - ) - for _ in range(2) - ] - foresights = [ - _make_child( - COL_FORESIGHTS, memcell_id_str, user_id=user_id, group_id=group_id - ) - ] - - async def setup(): - await _insert_docs(db, COL_MEMCELLS, [memcell]) - await _insert_docs(db, COL_EPISODES, episodes) - await _insert_docs(db, COL_ATOMIC_FACTS, atomic_facts) - await _insert_docs(db, COL_FORESIGHTS, foresights) - - event_loop.run_until_complete(setup()) - - # Call delete API - req = {"memory_id": memcell_id_str} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - # Verify response - assert status == 204, f"Expected 204, got {status}: {body}" - - # Verify DB state: all should be soft-deleted - async def verify(): - assert await _count_alive(db, COL_MEMCELLS, {"_id": memcell_id}) == 0 - assert await _count_soft_deleted(db, COL_MEMCELLS, {"_id": memcell_id}) == 1 - for ep in episodes: - assert await _count_alive(db, COL_EPISODES, {"_id": ep["_id"]}) == 0 - for el in atomic_facts: - assert await _count_alive(db, COL_ATOMIC_FACTS, {"_id": el["_id"]}) == 0 - for fs in foresights: - assert await _count_alive(db, COL_FORESIGHTS, {"_id": fs["_id"]}) == 0 - - event_loop.run_until_complete(verify()) - - _record_result( - scenario="Delete by memory_id with cascade", - category="Delete by ID", - passed=True, - duration_ms=elapsed, - detail="204 No Content, DB state verified", - request_body=req, - response_body=body, - status_code=status, - ) - - def test_delete_nonexistent_id(self, http_client): - """Delete a non-existent memory_id should return 204.""" - fake_id = str(ObjectId()) - req = {"memory_id": fake_id} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - assert status == 204 - - _record_result( - scenario="Delete non-existent memory_id", - category="Delete by ID", - passed=True, - duration_ms=elapsed, - detail="204 No Content (idempotent)", - request_body=req, - response_body=body, - status_code=status, - ) - - def test_delete_idempotent(self, db, http_client, event_loop): - """Deleting the same ID twice should be idempotent.""" - memcell_id = ObjectId() - memcell = _make_memcell( - user_id=f"{PREFIX}user_idempotent", - group_id=f"{PREFIX}group_idempotent", - _id=memcell_id, - ) - event_loop.run_until_complete(_insert_docs(db, COL_MEMCELLS, [memcell])) - - req = {"memory_id": str(memcell_id)} - - # First delete - status1, _ = _call_delete(http_client, req) - assert status1 == 204 - - # Second delete (idempotent) - t0 = time.monotonic() - status2, body2 = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - assert status2 == 204 - - _record_result( - scenario="Idempotent double delete", - category="Delete by ID", - passed=True, - duration_ms=elapsed, - detail="Both deletes return 204 (idempotent)", - request_body=req, - response_body=body2, - status_code=status2, - ) - - # --------------------------------------------------------------- - # 3. Delete by filters - # --------------------------------------------------------------- - - def test_delete_by_user_id(self, db, http_client, event_loop): - """Delete by user_id: child records (episodes) are soft-deleted. - - Note: filter-based delete does NOT touch MemCells themselves — - only child records (episodes, atomic_facts, foresights, request_logs). - MemCell.user_id is normally None (group-level unit), so memcell - deletion is only supported via delete_by_id mode. - """ - user_id = f"{PREFIX}user_filter_uid" - group_id = f"{PREFIX}group_filter_uid" - - memcells = [] - all_episodes = [] - for i in range(3): - mc_id = ObjectId() - memcells.append( - _make_memcell(user_id=user_id, group_id=group_id, _id=mc_id) - ) - all_episodes.append( - _make_child( - COL_EPISODES, str(mc_id), user_id=user_id, group_id=group_id - ) - ) - - async def setup(): - await _insert_docs(db, COL_MEMCELLS, memcells) - await _insert_docs(db, COL_EPISODES, all_episodes) - - event_loop.run_until_complete(setup()) - - req = {"user_id": user_id} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - assert status == 204 - - # Verify DB: child episodes should be soft-deleted - # (MemCells are NOT deleted in filter mode — by design) - async def verify(): - for ep in all_episodes: - assert await _count_alive(db, COL_EPISODES, {"_id": ep["_id"]}) == 0 - assert ( - await _count_soft_deleted(db, COL_EPISODES, {"_id": ep["_id"]}) == 1 - ) - - event_loop.run_until_complete(verify()) - - _record_result( - scenario="Delete by user_id filter", - category="Delete by Filters", - passed=True, - duration_ms=elapsed, - detail="204 No Content, child episodes soft-deleted (memcells untouched by design)", - request_body=req, - response_body=body, - status_code=status, - ) - - def test_delete_by_group_id(self, db, http_client, event_loop): - """Delete all memcells for a group.""" - group_id = f"{PREFIX}group_filter_gid" - - memcells = [] - all_atomic_facts = [] - for i in range(2): - mc_id = ObjectId() - memcells.append(_make_memcell(group_id=group_id, _id=mc_id)) - all_atomic_facts.append( - _make_child(COL_ATOMIC_FACTS, str(mc_id), group_id=group_id) - ) - - async def setup(): - await _insert_docs(db, COL_MEMCELLS, memcells) - await _insert_docs(db, COL_ATOMIC_FACTS, all_atomic_facts) - - event_loop.run_until_complete(setup()) - - req = {"group_id": group_id} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - assert status == 204 - - _record_result( - scenario="Delete by group_id filter", - category="Delete by Filters", - passed=True, - duration_ms=elapsed, - detail="204 No Content", - request_body=req, - response_body=body, - status_code=status, - ) - - def test_delete_by_user_and_session(self, db, http_client, event_loop): - """Delete memcells filtered by user_id + session_id (narrow scope).""" - user_id = f"{PREFIX}user_sess" - group_id = f"{PREFIX}group_sess" - session_target = f"{PREFIX}session_target" - session_other = f"{PREFIX}session_other" - - mc_target = _make_memcell( - user_id=user_id, - group_id=group_id, - session_id=session_target, - _id=ObjectId(), - ) - mc_other = _make_memcell( - user_id=user_id, group_id=group_id, session_id=session_other, _id=ObjectId() - ) - - async def setup(): - await _insert_docs(db, COL_MEMCELLS, [mc_target, mc_other]) - - event_loop.run_until_complete(setup()) - - req = {"user_id": user_id, "session_id": session_target} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - assert status == 204 - - # Verify: other session's memcell still alive - async def verify(): - assert await _count_alive(db, COL_MEMCELLS, {"_id": mc_other["_id"]}) == 1 - - event_loop.run_until_complete(verify()) - - _record_result( - scenario="Delete by user_id + session_id", - category="Delete by Filters", - passed=True, - duration_ms=elapsed, - detail="Only target session deleted, other session intact", - request_body=req, - response_body=body, - status_code=status, - ) - - def test_delete_by_user_and_sender(self, db, http_client, event_loop): - """Delete memcells filtered by user_id + sender_id.""" - user_id = f"{PREFIX}user_sender" - group_id = f"{PREFIX}group_sender" - sender_target = f"{PREFIX}sender_A" - sender_other = f"{PREFIX}sender_B" - - mc_target = _make_memcell( - user_id=user_id, - group_id=group_id, - participants=[sender_target, "other_person"], - _id=ObjectId(), - ) - mc_other = _make_memcell( - user_id=user_id, - group_id=group_id, - participants=[sender_other], - _id=ObjectId(), - ) - - async def setup(): - await _insert_docs(db, COL_MEMCELLS, [mc_target, mc_other]) - - event_loop.run_until_complete(setup()) - - req = {"user_id": user_id, "sender_id": sender_target} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - assert status == 204 - - # Verify: sender_B's memcell still alive - async def verify(): - assert await _count_alive(db, COL_MEMCELLS, {"_id": mc_other["_id"]}) == 1 - - event_loop.run_until_complete(verify()) - - _record_result( - scenario="Delete by user_id + sender_id", - category="Delete by Filters", - passed=True, - duration_ms=elapsed, - detail="Only target sender deleted, other sender intact", - request_body=req, - response_body=body, - status_code=status, - ) - - # --------------------------------------------------------------- - # 4. Data isolation - # --------------------------------------------------------------- - - def test_data_isolation(self, db, http_client, event_loop): - """Deleting user_A data should not affect user_B.""" - user_a = f"{PREFIX}user_iso_A" - user_b = f"{PREFIX}user_iso_B" - group_id = f"{PREFIX}group_iso" - - mc_a = _make_memcell(user_id=user_a, group_id=group_id, _id=ObjectId()) - mc_b = _make_memcell(user_id=user_b, group_id=group_id, _id=ObjectId()) - - ep_a = _make_child( - COL_EPISODES, str(mc_a["_id"]), user_id=user_a, group_id=group_id - ) - ep_b = _make_child( - COL_EPISODES, str(mc_b["_id"]), user_id=user_b, group_id=group_id - ) - - async def setup(): - await _insert_docs(db, COL_MEMCELLS, [mc_a, mc_b]) - await _insert_docs(db, COL_EPISODES, [ep_a, ep_b]) - - event_loop.run_until_complete(setup()) - - # Delete user_A only - req = {"user_id": user_a} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - assert status == 204 - - # Verify: user_B data untouched - async def verify(): - assert await _count_alive(db, COL_MEMCELLS, {"_id": mc_b["_id"]}) == 1 - assert await _count_alive(db, COL_EPISODES, {"_id": ep_b["_id"]}) == 1 - - event_loop.run_until_complete(verify()) - - _record_result( - scenario="Data isolation: user_A delete does not affect user_B", - category="Data Isolation", - passed=True, - duration_ms=elapsed, - detail="user_B memcell and episode still alive after user_A deletion", - request_body=req, - response_body=body, - status_code=status, - ) - - # --------------------------------------------------------------- - # 5. Additional validation scenarios (mutual exclusion) - # --------------------------------------------------------------- - - def test_memory_id_with_group_id_returns_422(self, http_client): - """memory_id + group_id together should fail validation.""" - req = {"memory_id": "abcdef1234567890abcdef12", "group_id": "g1"} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - _record_result( - scenario="memory_id + group_id mutual exclusion", - category="Request Validation", - passed=status == 422, - duration_ms=elapsed, - request_body=req, - response_body=body, - status_code=status, - ) - assert status == 422 - - def test_memory_id_with_session_id_returns_422(self, http_client): - """memory_id + session_id together should fail validation.""" - req = {"memory_id": "abcdef1234567890abcdef12", "session_id": "s1"} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - _record_result( - scenario="memory_id + session_id mutual exclusion", - category="Request Validation", - passed=status == 422, - duration_ms=elapsed, - request_body=req, - response_body=body, - status_code=status, - ) - assert status == 422 - - def test_memory_id_with_sender_id_returns_422(self, http_client): - """memory_id + sender_id together should fail validation.""" - req = {"memory_id": "abcdef1234567890abcdef12", "sender_id": "sd1"} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - _record_result( - scenario="memory_id + sender_id mutual exclusion", - category="Request Validation", - passed=status == 422, - duration_ms=elapsed, - request_body=req, - response_body=body, - status_code=status, - ) - assert status == 422 - - def test_memory_id_with_all_filters_returns_422(self, http_client): - """memory_id + all filter fields together should fail validation.""" - req = { - "memory_id": "abcdef1234567890abcdef12", - "user_id": "u1", - "group_id": "g1", - "session_id": "s1", - "sender_id": "sd1", - } - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - _record_result( - scenario="memory_id + all filter fields mutual exclusion", - category="Request Validation", - passed=status == 422, - duration_ms=elapsed, - request_body=req, - response_body=body, - status_code=status, - ) - assert status == 422 - - # --------------------------------------------------------------- - # 6. Additional functional scenarios - # --------------------------------------------------------------- - - def test_filter_delete_matching_zero_records(self, http_client): - """Filter delete with nonexistent user should return 204 (idempotent).""" - req = {"user_id": f"{PREFIX}nonexistent_user_xyz"} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - _record_result( - scenario="Filter delete matching 0 records (idempotent)", - category="Delete by Filters", - passed=status == 204, - duration_ms=elapsed, - request_body=req, - response_body=body, - status_code=status, - ) - assert status == 204 - - def test_delete_by_user_and_group_combined(self, db, http_client, event_loop): - """Delete by user_id + group_id: verify AND logic with two scope filters.""" - user_id = f"{PREFIX}user_ug_combo" - group_target = f"{PREFIX}group_ug_target" - group_other = f"{PREFIX}group_ug_other" - - mc_target = _make_memcell( - user_id=user_id, group_id=group_target, _id=ObjectId() - ) - mc_other = _make_memcell(user_id=user_id, group_id=group_other, _id=ObjectId()) - ep_target = _make_child( - COL_EPISODES, str(mc_target["_id"]), user_id=user_id, group_id=group_target - ) - ep_other = _make_child( - COL_EPISODES, str(mc_other["_id"]), user_id=user_id, group_id=group_other - ) - - async def setup(): - await _insert_docs(db, COL_MEMCELLS, [mc_target, mc_other]) - await _insert_docs(db, COL_EPISODES, [ep_target, ep_other]) - - event_loop.run_until_complete(setup()) - - req = {"user_id": user_id, "group_id": group_target} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - assert status == 204 - - # Verify AND logic: only target group deleted - async def verify(): - assert await _count_alive(db, COL_EPISODES, {"_id": ep_other["_id"]}) == 1 - - event_loop.run_until_complete(verify()) - - _record_result( - scenario="Delete by user_id + group_id combined (AND logic)", - category="Delete by Filters", - passed=True, - duration_ms=elapsed, - request_body=req, - response_body=body, - status_code=status, - ) - - def test_delete_all_four_filters_combined(self, db, http_client, event_loop): - """Delete with all four filter fields combined.""" - user_id = f"{PREFIX}user_all4" - group_id = f"{PREFIX}group_all4" - session_id = f"{PREFIX}session_all4" - sender_id = f"{PREFIX}sender_all4" - - mc = _make_memcell( - user_id=user_id, - group_id=group_id, - session_id=session_id, - participants=[sender_id], - _id=ObjectId(), - ) - ep = _make_child( - COL_EPISODES, - str(mc["_id"]), - user_id=user_id, - group_id=group_id, - session_id=session_id, - participants=[sender_id], - ) - - async def setup(): - await _insert_docs(db, COL_MEMCELLS, [mc]) - await _insert_docs(db, COL_EPISODES, [ep]) - - event_loop.run_until_complete(setup()) - - req = { - "user_id": user_id, - "group_id": group_id, - "session_id": session_id, - "sender_id": sender_id, - } - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - assert status == 204 - - _record_result( - scenario="Delete with all four filters combined", - category="Delete by Filters", - passed=True, - duration_ms=elapsed, - request_body=req, - response_body=body, - status_code=status, - ) - - def test_raw_message_untouched_on_filter_delete(self, db, http_client, event_loop): - """Verify RawMessage (source data) is NOT deleted by filter delete.""" - user_id = f"{PREFIX}user_rawmsg" - group_id = f"{PREFIX}group_rawmsg" - - mc = _make_memcell(user_id=user_id, group_id=group_id, _id=ObjectId()) - raw_msg = _make_request_log(user_id=user_id, group_id=group_id) - - async def setup(): - await _insert_docs(db, COL_MEMCELLS, [mc]) - await _insert_docs(db, COL_RAW_MESSAGES, [raw_msg]) - - event_loop.run_until_complete(setup()) - - req = {"user_id": user_id} - t0 = time.monotonic() - status, body = _call_delete(http_client, req) - elapsed = (time.monotonic() - t0) * 1000 - - assert status == 204 - - # Verify raw message is NOT deleted (source data preserved) - async def verify(): - assert ( - await _count_alive(db, COL_RAW_MESSAGES, {"_id": raw_msg["_id"]}) == 1 - ) - - event_loop.run_until_complete(verify()) - - _record_result( - scenario="RawMessage untouched on filter delete", - category="Delete by Filters", - passed=True, - duration_ms=elapsed, - detail="Raw message preserved (source data not deleted by filter mode)", - request_body=req, - response_body=body, - status_code=status, - ) - - # --------------------------------------------------------------- - # 7. Response validation - # --------------------------------------------------------------- - - def test_204_response_has_empty_body(self, http_client): - """204 No Content response should have empty body.""" - fake_id = str(ObjectId()) - req = {"memory_id": fake_id} - r = http_client.post(DELETE_URL, json=req) - - assert r.status_code == 204 - assert len(r.content) == 0 - - _record_result( - scenario="204 response has empty body", - category="Response Validation", - passed=True, - duration_ms=0, - request_body=req, - status_code=204, - ) - - def test_422_response_has_structured_error(self, http_client): - """422 response should contain structured error information.""" - req = {} - r = http_client.post(DELETE_URL, json=req) - - assert r.status_code == 422 - body = r.json() - # FastAPI validation errors have a 'detail' field - assert "detail" in body - - _record_result( - scenario="422 response has structured error body", - category="Response Validation", - passed=True, - duration_ms=0, - request_body=req, - response_body=body, - status_code=422, - ) - - -# --------------------------------------------------------------------------- -# Cleanup fixture (runs after ALL tests in this module) -# --------------------------------------------------------------------------- -@pytest.fixture(scope="module", autouse=True) -def _cleanup_after_all(db, event_loop): - """Clean up all test fixture data after module completes.""" - yield # tests run here - event_loop.run_until_complete(_cleanup_test_data(db)) - - -# --------------------------------------------------------------------------- -# Report generation (runs after ALL tests) -# --------------------------------------------------------------------------- -REPORT_PATH = os.path.join( - os.path.dirname(__file__), - "..", - "..", - "docs", - "plans", - "delete-api-integration-test-report.json", -) - - -def pytest_sessionfinish(session, exitstatus): - """Write test results to JSON for Confluence report generation.""" - if _test_results: - os.makedirs(os.path.dirname(REPORT_PATH), exist_ok=True) - with open(REPORT_PATH, "w", encoding="utf-8") as f: - json.dump( - { - "timestamp": datetime.now(timezone.utc).isoformat(), - "total": len(_test_results), - "passed": sum(1 for r in _test_results if r["passed"]), - "failed": sum(1 for r in _test_results if not r["passed"]), - "results": _test_results, - }, - f, - indent=2, - ensure_ascii=False, - default=str, - ) diff --git a/methods/EverCore/tests/test_agent_case_extractor.py b/methods/EverCore/tests/test_agent_case_extractor.py deleted file mode 100644 index 7f0f7017..00000000 --- a/methods/EverCore/tests/test_agent_case_extractor.py +++ /dev/null @@ -1,1707 +0,0 @@ -""" -AgentCaseExtractor Unit Tests - -Tests for: -- _should_skip: pre-filter heuristics -- _truncate_text: token-based head/tail truncation -- _heuristic_trim_tool_outputs: bulk tool output trimming -- _strip_before_first_user: system prompt removal -- _has_tool_calls / _count_tool_call_rounds: tool call detection - -Usage: - PYTHONPATH=src pytest tests/test_agent_case_extractor.py -v -""" - -import copy -import pytest -from datetime import datetime -from unittest.mock import patch, MagicMock, AsyncMock -from typing import Any, Dict, List - -from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - HIGH_MESSAGE_COUNT_THRESHOLD, - MAX_TOOL_OUTPUT_TOKENS, - MAX_TOOL_ARGS_TOKENS, - MAX_ASSISTANT_RESPONSE_TOKENS, -) -from api_specs.memory_types import RawDataType - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -def _user(content: str) -> Dict[str, Any]: - return {"role": "user", "content": content} - - -def _assistant(content: str) -> Dict[str, Any]: - return {"role": "assistant", "content": content} - - -def _tool_call( - content: str = "", - arguments: str = "{}", - name: str = "search", -) -> Dict[str, Any]: - return { - "role": "assistant", - "content": content, - "tool_calls": [ - { - "id": "call_1", - "function": {"name": name, "arguments": arguments}, - } - ], - } - - -def _tool_response(content: str = "tool result") -> Dict[str, Any]: - return {"role": "tool", "content": content, "tool_call_id": "call_1"} - - -def _long_text(token_count: int) -> str: - """Generate a text that is approximately token_count tokens long.""" - # Each "word_N " is roughly 2 tokens; overshoot then let truncation handle it. - return " ".join(f"word_{i}" for i in range(token_count)) - - -# --------------------------------------------------------------------------- -# Mock tokenizer — use real tiktoken so token counts are accurate -# --------------------------------------------------------------------------- - - -@pytest.fixture(autouse=True) -def _mock_tokenizer_factory(): - """Patch get_bean_by_type to return a real tiktoken tokenizer factory.""" - import tiktoken - - encoding = tiktoken.get_encoding("o200k_base") - mock_factory = MagicMock() - mock_factory.get_tokenizer_from_tiktoken.return_value = encoding - - with patch( - "memory_layer.memory_extractor.agent_case_extractor.get_bean_by_type", - return_value=mock_factory, - ): - yield - - -# =========================================================================== -# _should_skip tests -# =========================================================================== - - -class TestShouldSkip: - """Tests for AgentCaseExtractor._should_skip.""" - - def test_no_user_messages(self): - msgs = [_assistant("hello")] - assert AgentCaseExtractor._should_skip(msgs) is not None - - def test_no_assistant_messages(self): - msgs = [_user("hello")] - assert AgentCaseExtractor._should_skip(msgs) is not None - - def test_incomplete_trajectory_last_is_tool_call(self): - msgs = [_user("do something"), _tool_call("thinking")] - assert "Incomplete" in AgentCaseExtractor._should_skip(msgs) - - def test_incomplete_trajectory_last_is_tool_response(self): - msgs = [_user("do something"), _tool_call(), _tool_response()] - assert AgentCaseExtractor._should_skip(msgs) is not None - - def test_single_turn_no_tools(self): - msgs = [_user("hi"), _assistant("hello")] - assert AgentCaseExtractor._should_skip(msgs) is not None - - def test_multi_turn_no_tools_passes(self): - # Must have > FILTER_NO_TOOL_MAX_MESSAGES (4) messages - # AND sufficient assistant token count to pass heuristic - long_response = ( - "The TypeError on line 5 is caused by passing a string where an integer " - "is expected. You need to wrap the input with int() before passing it to " - "the calculate function. Here is the corrected code: change " - "calculate(user_input) to calculate(int(user_input)). This happens because " - "input() always returns a string in Python 3, even when the user types a " - "number. The int() call converts it properly. You should also add error " - "handling with a try/except ValueError block around the int() conversion." - ) - msgs = [ - _user("help me debug"), - _assistant(long_response), - _user("TypeError on line 5"), - _assistant(long_response), - _user("thanks"), - _assistant("glad it worked"), - ] - assert AgentCaseExtractor._should_skip(msgs) is None - - def test_single_turn_with_tools_passes(self): - msgs = [ - _user("search for python docs"), - _tool_call("let me search"), - _tool_response("found docs"), - _assistant("here are the results"), - ] - assert AgentCaseExtractor._should_skip(msgs) is None - - def test_empty_messages(self): - assert AgentCaseExtractor._should_skip([]) is not None - - -# =========================================================================== -# _truncate_text tests -# =========================================================================== - - -class TestTruncateText: - """Tests for AgentCaseExtractor._truncate_text.""" - - def test_short_text_unchanged(self): - text = "hello world" - result = AgentCaseExtractor._truncate_text(text, max_tokens=100) - assert result == text - - def test_empty_text(self): - assert AgentCaseExtractor._truncate_text("", max_tokens=10) == "" - - def test_none_text(self): - assert AgentCaseExtractor._truncate_text(None, max_tokens=10) is None - - def test_non_string_returns_as_is(self): - assert AgentCaseExtractor._truncate_text(123, max_tokens=10) == 123 - - def test_long_text_truncated(self): - text = _long_text(500) - result = AgentCaseExtractor._truncate_text(text, max_tokens=50) - assert "[... trimmed" in result - assert "tokens ...]" in result - - def test_truncated_has_head_and_tail(self): - text = _long_text(500) - result = AgentCaseExtractor._truncate_text( - text, max_tokens=100, head_ratio=0.7 - ) - assert result.startswith("word_0") - assert "tokens ...]" in result - # Tail should contain later words - parts = result.split("[... trimmed") - assert len(parts) == 2 - - def test_head_ratio_respected(self): - text = _long_text(500) - import tiktoken - - enc = tiktoken.get_encoding("o200k_base") - - result_70 = AgentCaseExtractor._truncate_text( - text, max_tokens=100, head_ratio=0.7 - ) - result_50 = AgentCaseExtractor._truncate_text( - text, max_tokens=100, head_ratio=0.5 - ) - # With 0.7 ratio, head portion should be longer - head_70 = result_70.split("\n[... trimmed")[0] - head_50 = result_50.split("\n[... trimmed")[0] - assert len(enc.encode(head_70)) > len(enc.encode(head_50)) - - def test_exact_limit_unchanged(self): - import tiktoken - - enc = tiktoken.get_encoding("o200k_base") - # Build text that is exactly 50 tokens - words = [] - for i in range(100): - words.append(f"w{i}") - if len(enc.encode(" ".join(words))) >= 50: - break - text = " ".join(words) - token_count = len(enc.encode(text)) - result = AgentCaseExtractor._truncate_text(text, max_tokens=token_count) - assert result == text - - -# =========================================================================== -# _heuristic_trim_tool_outputs tests -# =========================================================================== - - -class TestHeuristicTrimToolOutputs: - """Tests for AgentCaseExtractor._heuristic_trim_tool_outputs.""" - - def test_short_content_unchanged(self): - msgs = [ - _user("hi"), - _tool_call("thinking", arguments='{"query": "test"}'), - _tool_response("short result"), - _assistant("done"), - ] - result = AgentCaseExtractor._heuristic_trim_tool_outputs(msgs, 1500, 800) - assert result[2]["content"] == "short result" - assert result[1]["tool_calls"][0]["function"]["arguments"] == '{"query": "test"}' - - def test_long_tool_output_trimmed(self): - long_content = _long_text(3000) - msgs = [ - _user("search"), - _tool_call(), - _tool_response(long_content), - _assistant("done"), - ] - result = AgentCaseExtractor._heuristic_trim_tool_outputs(msgs, 100, 800) - assert "[... trimmed" in result[2]["content"] - assert len(result[2]["content"]) < len(long_content) - - def test_long_tool_args_trimmed(self): - long_args = _long_text(2000) - msgs = [ - _user("do it"), - _tool_call(arguments=long_args), - _tool_response("ok"), - _assistant("done"), - ] - result = AgentCaseExtractor._heuristic_trim_tool_outputs(msgs, 1500, 100) - trimmed_args = result[1]["tool_calls"][0]["function"]["arguments"] - assert "[... trimmed" in trimmed_args - assert len(trimmed_args) < len(long_args) - - def test_does_not_mutate_original(self): - long_content = _long_text(3000) - msgs = [_tool_response(long_content)] - original_content = msgs[0]["content"] - AgentCaseExtractor._heuristic_trim_tool_outputs(msgs, 100, 800) - assert msgs[0]["content"] == original_content - - def test_user_content_untouched(self): - long_text = _long_text(3000) - msgs = [_user(long_text), _assistant("short")] - result = AgentCaseExtractor._heuristic_trim_tool_outputs(msgs, 100, 100) - assert result[0]["content"] == long_text - - def test_long_assistant_response_trimmed(self): - long_text = _long_text(5000) - msgs = [_user("hi"), _assistant(long_text)] - result = AgentCaseExtractor._heuristic_trim_tool_outputs( - msgs, 100, 100, max_assistant_response_tokens=100 - ) - assert "[... trimmed" in result[1]["content"] - assert len(result[1]["content"]) < len(long_text) - - def test_short_assistant_response_untouched(self): - msgs = [_user("hi"), _assistant("short reply")] - result = AgentCaseExtractor._heuristic_trim_tool_outputs(msgs, 100, 100) - assert result[1]["content"] == "short reply" - - def test_multiple_tool_calls_all_trimmed(self): - long_content = _long_text(3000) - msgs = [ - _user("do many things"), - _tool_call(), - _tool_response(long_content), - _tool_call(arguments='{"x": 1}'), - _tool_response(long_content), - _assistant("all done"), - ] - result = AgentCaseExtractor._heuristic_trim_tool_outputs(msgs, 100, 800) - assert "[... trimmed" in result[2]["content"] - assert "[... trimmed" in result[4]["content"] - - def test_missing_function_key_skipped(self): - msgs = [ - _user("test"), - { - "role": "assistant", - "content": "", - "tool_calls": [{"id": "call_1"}], # no "function" key - }, - _assistant("done"), - ] - # Should not raise - result = AgentCaseExtractor._heuristic_trim_tool_outputs(msgs, 1500, 800) - assert len(result) == 3 - - def test_non_string_tool_content_untouched(self): - msgs = [{"role": "tool", "content": {"key": "value"}, "tool_call_id": "c1"}] - result = AgentCaseExtractor._heuristic_trim_tool_outputs(msgs, 100, 800) - assert result[0]["content"] == {"key": "value"} - - def test_empty_messages_list(self): - result = AgentCaseExtractor._heuristic_trim_tool_outputs([], 1500, 800) - assert result == [] - - -# =========================================================================== -# _strip_before_first_user tests -# =========================================================================== - - -class TestStripBeforeFirstUser: - """Tests for AgentCaseExtractor._strip_before_first_user.""" - - def test_removes_system_prompt(self): - msgs = [ - {"role": "system", "content": "You are helpful"}, - _user("hello"), - _assistant("hi"), - ] - result = AgentCaseExtractor._strip_before_first_user(msgs) - assert len(result) == 2 - assert result[0]["role"] == "user" - - def test_no_user_returns_empty(self): - msgs = [{"role": "system", "content": "setup"}, _assistant("hi")] - assert AgentCaseExtractor._strip_before_first_user(msgs) == [] - - def test_already_starts_with_user(self): - msgs = [_user("hi"), _assistant("hello")] - result = AgentCaseExtractor._strip_before_first_user(msgs) - assert len(result) == 2 - - -# =========================================================================== -# _has_tool_calls / _count_tool_call_rounds tests -# =========================================================================== - - -class TestToolCallHelpers: - """Tests for tool call detection helpers.""" - - def test_has_tool_calls_true(self): - msgs = [_user("x"), _tool_call(), _tool_response(), _assistant("done")] - assert AgentCaseExtractor._has_tool_calls(msgs) is True - - def test_has_tool_calls_false(self): - msgs = [_user("hi"), _assistant("hello")] - assert AgentCaseExtractor._has_tool_calls(msgs) is False - - def test_count_tool_call_rounds(self): - msgs = [ - _user("x"), - _tool_call(), - _tool_response(), - _tool_call(), - _tool_response(), - _assistant("done"), - ] - assert AgentCaseExtractor._count_tool_call_rounds(msgs) == 2 - - def test_count_zero_rounds(self): - msgs = [_user("hi"), _assistant("hello")] - assert AgentCaseExtractor._count_tool_call_rounds(msgs) == 0 - - def test_has_tool_calls_tool_response_only(self): - """A tool response without tool_calls in assistant still counts.""" - msgs = [_user("x"), _tool_response("result"), _assistant("done")] - assert AgentCaseExtractor._has_tool_calls(msgs) is True - - def test_has_tool_calls_empty(self): - assert AgentCaseExtractor._has_tool_calls([]) is False - - -# =========================================================================== -# _calc_tool_content_size tests -# =========================================================================== - - -class TestCalcToolContentSize: - """Tests for AgentCaseExtractor._calc_tool_content_size.""" - - def test_tool_message_counts_content(self): - msg = _tool_response("some tool output text here") - size = AgentCaseExtractor._calc_tool_content_size(msg) - assert size > 0 - - def test_assistant_with_tool_calls_counts_arguments(self): - msg = _tool_call(arguments='{"query": "test search", "limit": 10}') - size = AgentCaseExtractor._calc_tool_content_size(msg) - assert size > 0 - - def test_user_message_returns_zero(self): - msg = _user("some text") - assert AgentCaseExtractor._calc_tool_content_size(msg) == 0 - - def test_plain_assistant_returns_zero(self): - msg = _assistant("some text") - assert AgentCaseExtractor._calc_tool_content_size(msg) == 0 - - def test_tool_empty_content(self): - msg = {"role": "tool", "content": "", "tool_call_id": "c1"} - assert AgentCaseExtractor._calc_tool_content_size(msg) == 0 - - def test_assistant_empty_arguments(self): - msg = { - "role": "assistant", - "content": "", - "tool_calls": [{"id": "c1", "function": {"name": "f", "arguments": ""}}], - } - assert AgentCaseExtractor._calc_tool_content_size(msg) == 0 - - def test_assistant_multiple_tool_calls_sums(self): - msg = { - "role": "assistant", - "content": "", - "tool_calls": [ - {"id": "c1", "function": {"name": "f1", "arguments": '{"a": 1}'}}, - {"id": "c2", "function": {"name": "f2", "arguments": '{"b": 2}'}}, - ], - } - size = AgentCaseExtractor._calc_tool_content_size(msg) - single_size = AgentCaseExtractor._calc_tool_content_size( - _tool_call(arguments='{"a": 1}') - ) - assert size > single_size # two calls should be larger than one - - -# =========================================================================== -# _json_default tests -# =========================================================================== - - -class TestJsonDefault: - """Tests for AgentCaseExtractor._json_default.""" - - def test_datetime_to_isoformat(self): - from datetime import datetime - - dt = datetime(2025, 3, 1, 12, 0, 0) - result = AgentCaseExtractor._json_default(dt) - assert result == "2025-03-01T12:00:00" - - def test_non_serializable_to_str(self): - result = AgentCaseExtractor._json_default(set([1, 2, 3])) - assert isinstance(result, str) - - def test_bytes_to_str(self): - result = AgentCaseExtractor._json_default(b"hello") - assert result == "b'hello'" - - -# =========================================================================== -# _clamp_quality_score tests -# =========================================================================== - - -class TestClampQualityScore: - """Tests for AgentCaseExtractor._clamp_quality_score.""" - - def test_valid_float_in_range(self): - assert AgentCaseExtractor._clamp_quality_score(0.5) == 0.5 - - def test_clamps_above_one(self): - assert AgentCaseExtractor._clamp_quality_score(1.5) == 1.0 - - def test_clamps_below_zero(self): - assert AgentCaseExtractor._clamp_quality_score(-0.3) == 0.0 - - def test_boundary_zero(self): - assert AgentCaseExtractor._clamp_quality_score(0.0) == 0.0 - - def test_boundary_one(self): - assert AgentCaseExtractor._clamp_quality_score(1.0) == 1.0 - - def test_none_returns_none(self): - assert AgentCaseExtractor._clamp_quality_score(None) is None - - def test_string_number(self): - assert AgentCaseExtractor._clamp_quality_score("0.7") == 0.7 - - def test_invalid_string_returns_none(self): - assert AgentCaseExtractor._clamp_quality_score("abc") is None - - def test_int_value(self): - assert AgentCaseExtractor._clamp_quality_score(1) == 1.0 - - -# =========================================================================== -# _unwrap_messages tests -# =========================================================================== - - -class TestUnwrapMessages: - """Tests for AgentCaseExtractor._unwrap_messages.""" - - def test_wrapped_format(self): - data = [ - {"message": {"role": "user", "content": "hello"}, "parse_info": {}}, - {"message": {"role": "assistant", "content": "hi"}, "parse_info": {}}, - ] - result = AgentCaseExtractor._unwrap_messages(data) - assert len(result) == 2 - assert result[0]["role"] == "user" - assert result[0]["content"] == "hello" - - def test_unwrapped_format_passthrough(self): - data = [ - {"role": "user", "content": "hello"}, - {"role": "assistant", "content": "hi"}, - ] - result = AgentCaseExtractor._unwrap_messages(data) - assert len(result) == 2 - assert result[0]["content"] == "hello" - - def test_content_list_normalized_to_string(self): - data = [ - { - "message": { - "role": "user", - "content": [{"type": "text", "text": "hello world"}], - }, - }, - ] - result = AgentCaseExtractor._unwrap_messages(data) - assert isinstance(result[0]["content"], str) - assert "hello" in result[0]["content"] - - def test_non_dict_items_skipped(self): - data = [ - {"message": {"role": "user", "content": "ok"}}, - "not a dict", - 42, - None, - ] - result = AgentCaseExtractor._unwrap_messages(data) - assert len(result) == 1 - - def test_empty_list(self): - assert AgentCaseExtractor._unwrap_messages([]) == [] - - def test_assistant_with_tool_calls_preserved(self): - data = [ - { - "message": { - "role": "assistant", - "content": None, - "tool_calls": [{"id": "c1", "function": {"name": "f"}}], - }, - }, - ] - result = AgentCaseExtractor._unwrap_messages(data) - assert result[0]["tool_calls"] is not None - assert result[0]["content"] is None - - -# =========================================================================== -# _collect_tool_call_groups tests -# =========================================================================== - - -class TestCollectToolCallGroups: - """Tests for AgentCaseExtractor._collect_tool_call_groups.""" - - def _build_extractor(self): - from unittest.mock import MagicMock - mock_llm = MagicMock() - return AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="", - filter_prompt="", - experience_compress_prompt="", - ) - - def test_single_group(self): - ext = self._build_extractor() - items = [ - _user("hi"), - _tool_call(), - _tool_response("r1"), - _assistant("done"), - ] - groups = ext._collect_tool_call_groups(items) - assert len(groups) == 1 - assert groups[0] == [1, 2] - - def test_multiple_groups(self): - ext = self._build_extractor() - items = [ - _user("hi"), - _tool_call(), - _tool_response("r1"), - _tool_call(), - _tool_response("r2"), - _tool_response("r3"), - _assistant("done"), - ] - groups = ext._collect_tool_call_groups(items) - assert len(groups) == 2 - assert groups[0] == [1, 2] - assert groups[1] == [3, 4, 5] - - def test_no_tool_calls(self): - ext = self._build_extractor() - items = [_user("hi"), _assistant("hello")] - groups = ext._collect_tool_call_groups(items) - assert groups == [] - - def test_empty_items(self): - ext = self._build_extractor() - assert ext._collect_tool_call_groups([]) == [] - - def test_tool_call_without_response(self): - ext = self._build_extractor() - items = [_user("hi"), _tool_call(), _assistant("done")] - groups = ext._collect_tool_call_groups(items) - assert len(groups) == 1 - assert groups[0] == [1] # just the assistant with tool_calls, no tool response - - -# =========================================================================== -# _calc_group_size tests -# =========================================================================== - - -class TestCalcGroupSize: - """Tests for AgentCaseExtractor._calc_group_size.""" - - def _build_extractor(self): - from unittest.mock import MagicMock - mock_llm = MagicMock() - return AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="", - filter_prompt="", - experience_compress_prompt="", - ) - - def test_group_size_sum(self): - ext = self._build_extractor() - items = [ - _tool_call(arguments='{"query": "test search"}'), - _tool_response("this is the tool response content"), - ] - group = [0, 1] - size = ext._calc_group_size(items, group) - # Both items contribute tokens - assert size > 0 - # Should equal sum of individual sizes - expected = sum(AgentCaseExtractor._calc_tool_content_size(items[i]) for i in group) - assert size == expected - - def test_empty_group(self): - ext = self._build_extractor() - items = [_tool_call(), _tool_response()] - assert ext._calc_group_size(items, []) == 0 - - -# =========================================================================== -# _filter_conversation tests -# =========================================================================== - - -class TestFilterConversation: - """Tests for AgentCaseExtractor._filter_conversation (async).""" - - def _build_extractor(self, llm_response: str): - from unittest.mock import MagicMock, AsyncMock - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(return_value=llm_response) - return AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="", - filter_prompt="{messages}", - experience_compress_prompt="", - ) - - @pytest.mark.asyncio - async def test_worth_extracting_true(self): - ext = self._build_extractor('{"worth_extracting": true}') - assert await ext._filter_conversation("[]") is True - - @pytest.mark.asyncio - async def test_worth_extracting_false(self): - ext = self._build_extractor('{"worth_extracting": false, "reason": "trivial"}') - assert await ext._filter_conversation("[]") is False - - @pytest.mark.asyncio - async def test_llm_error_defaults_to_true(self): - from unittest.mock import MagicMock, AsyncMock - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(side_effect=Exception("LLM down")) - ext = AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="", - filter_prompt="{messages}", - experience_compress_prompt="", - ) - assert await ext._filter_conversation("[]") is True - - @pytest.mark.asyncio - async def test_invalid_json_defaults_to_true(self): - ext = self._build_extractor("not json") - assert await ext._filter_conversation("[]") is True - - -# =========================================================================== -# _compress_experience tests -# =========================================================================== - - -class TestCompressExperience: - """Tests for AgentCaseExtractor._compress_experience (async).""" - - def _build_extractor(self, llm_response: str): - from unittest.mock import MagicMock, AsyncMock - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(return_value=llm_response) - return AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="", - filter_prompt="", - experience_compress_prompt="{messages}", - ) - - @pytest.mark.asyncio - async def test_valid_extraction(self): - resp = '{"task_intent": "Build API", "approach": "Design then implement", "quality_score": 0.8}' - ext = self._build_extractor(resp) - result = await ext._compress_experience("[]") - assert result["task_intent"] == "Build API" - assert result["approach"] == "Design then implement" - - @pytest.mark.asyncio - async def test_empty_task_intent_returns_none(self): - resp = '{"task_intent": "", "approach": "some approach"}' - ext = self._build_extractor(resp) - assert await ext._compress_experience("[]") is None - - @pytest.mark.asyncio - async def test_empty_approach_returns_none(self): - resp = '{"task_intent": "Do X", "approach": ""}' - ext = self._build_extractor(resp) - assert await ext._compress_experience("[]") is None - - @pytest.mark.asyncio - async def test_missing_task_intent_retries_then_none(self): - resp = '{"approach": "some steps"}' - ext = self._build_extractor(resp) - result = await ext._compress_experience("[]") - assert result is None - # Should have been called twice (retry) - assert ext.llm_provider.generate.call_count == 2 - - @pytest.mark.asyncio - async def test_with_key_insight(self): - resp = '{"task_intent": "Deploy", "approach": "CI/CD pipeline", "key_insight": "Blue-green deploy", "quality_score": 0.9}' - ext = self._build_extractor(resp) - result = await ext._compress_experience("[]") - assert result["key_insight"] == "Blue-green deploy" - - @pytest.mark.asyncio - async def test_llm_exception_retries(self): - from unittest.mock import MagicMock, AsyncMock - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(side_effect=Exception("timeout")) - ext = AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="", - filter_prompt="", - experience_compress_prompt="{messages}", - ) - result = await ext._compress_experience("[]") - assert result is None - assert mock_llm.generate.call_count == 2 - - -# =========================================================================== -# _compress_tool_chunk tests -# =========================================================================== - - -class TestCompressToolChunk: - """Tests for AgentCaseExtractor._compress_tool_chunk (async).""" - - def _build_extractor(self, llm_response: str): - from unittest.mock import MagicMock, AsyncMock - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(return_value=llm_response) - return AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="{messages_json}{new_count}", - filter_prompt="", - experience_compress_prompt="", - ) - - @pytest.mark.asyncio - async def test_valid_compression(self): - msgs = [_tool_response("long output"), _tool_response("another output")] - resp = '{"compressed_messages": [{"role": "tool", "content": "short1"}, {"role": "tool", "content": "short2"}]}' - ext = self._build_extractor(resp) - result = await ext._compress_tool_chunk(msgs) - assert len(result) == 2 - assert result[0]["content"] == "short1" - - @pytest.mark.asyncio - async def test_wrong_count_returns_none(self): - msgs = [_tool_response("output")] - resp = '{"compressed_messages": [{"role": "tool", "content": "a"}, {"role": "tool", "content": "b"}]}' - ext = self._build_extractor(resp) - result = await ext._compress_tool_chunk(msgs) - assert result is None - - @pytest.mark.asyncio - async def test_missing_key_returns_none(self): - msgs = [_tool_response("output")] - resp = '{"result": "bad format"}' - ext = self._build_extractor(resp) - result = await ext._compress_tool_chunk(msgs) - assert result is None - - @pytest.mark.asyncio - async def test_llm_exception_retries_then_none(self): - from unittest.mock import MagicMock, AsyncMock - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(side_effect=Exception("fail")) - ext = AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="{messages_json}{new_count}", - filter_prompt="", - experience_compress_prompt="", - ) - result = await ext._compress_tool_chunk([_tool_response("x")]) - assert result is None - assert mock_llm.generate.call_count == 2 - - -# =========================================================================== -# _pre_compress_to_list tests -# =========================================================================== - - -class TestPreCompressToList: - """Tests for AgentCaseExtractor._pre_compress_to_list (async).""" - - @pytest.mark.asyncio - async def test_no_tool_calls_returns_unchanged(self): - from unittest.mock import MagicMock, AsyncMock - mock_llm = MagicMock() - mock_llm.generate = AsyncMock() - ext = AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="", - filter_prompt="", - experience_compress_prompt="", - ) - items = [_user("hi"), _assistant("hello")] - result = await ext._pre_compress_to_list(items) - assert len(result) == 2 - mock_llm.generate.assert_not_called() - - @pytest.mark.asyncio - async def test_small_tool_content_no_compression(self): - from unittest.mock import MagicMock, AsyncMock - mock_llm = MagicMock() - mock_llm.generate = AsyncMock() - ext = AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="", - filter_prompt="", - experience_compress_prompt="", - pre_compress_chunk_size=999999, # very large threshold - ) - items = [_user("hi"), _tool_call(), _tool_response("short"), _assistant("done")] - result = await ext._pre_compress_to_list(items) - assert len(result) == 4 - mock_llm.generate.assert_not_called() - - @pytest.mark.asyncio - async def test_does_not_mutate_original(self): - from unittest.mock import MagicMock, AsyncMock - mock_llm = MagicMock() - mock_llm.generate = AsyncMock() - ext = AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="", - filter_prompt="", - experience_compress_prompt="", - ) - items = [_user("hi"), _tool_call(), _tool_response("data"), _assistant("ok")] - original = copy.deepcopy(items) - await ext._pre_compress_to_list(items) - assert items == original - - -# =========================================================================== -# extract_memory tests -# =========================================================================== - - -class TestExtractMemory: - """Tests for AgentCaseExtractor.extract_memory (async).""" - - def _build_extractor(self, filter_resp="", compress_resp=""): - from unittest.mock import MagicMock, AsyncMock - mock_llm = MagicMock() - responses = [] - if filter_resp: - responses.append(filter_resp) - if compress_resp: - responses.append(compress_resp) - if responses: - mock_llm.generate = AsyncMock(side_effect=responses) - else: - mock_llm.generate = AsyncMock(return_value='{}') - return AgentCaseExtractor( - llm_provider=mock_llm, - tool_pre_compress_prompt="{messages_json}{new_count}", - filter_prompt="{messages}", - experience_compress_prompt="{messages}", - ) - - def _make_request(self, messages=None): - from types import SimpleNamespace - memcell = SimpleNamespace( - type=RawDataType.AGENTCONVERSATION, - timestamp=datetime(2025, 3, 1), - original_data=messages or [], - participants=["user1"], - sender_ids=["user1"], - event_id="evt_test_001", - ) - return SimpleNamespace( - memcell=memcell, - user_id="user_001", - group_id="group_001", - ) - - @pytest.mark.asyncio - async def test_none_memcell_returns_none(self): - ext = self._build_extractor() - from types import SimpleNamespace - req = SimpleNamespace(memcell=None, user_id="u", group_id="g") - assert await ext.extract_memory(req) is None - - @pytest.mark.asyncio - async def test_wrong_type_returns_none(self): - ext = self._build_extractor() - from types import SimpleNamespace - memcell = SimpleNamespace(type="OTHER_TYPE", original_data=[], timestamp=None, participants=[], sender_ids=[]) - req = SimpleNamespace(memcell=memcell, user_id="u", group_id="g") - assert await ext.extract_memory(req) is None - - @pytest.mark.asyncio - async def test_empty_conversation_returns_none(self): - ext = self._build_extractor() - req = self._make_request(messages=[]) - assert await ext.extract_memory(req) is None - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_case_extractor.get_vectorize_service") - async def test_successful_extraction(self, mock_vs): - import numpy as np - mock_service = MagicMock() - mock_service.get_embedding = AsyncMock(return_value=np.array([0.1, 0.2, 0.3])) - mock_service.get_model_name.return_value = "test-model" - mock_vs.return_value = mock_service - - compress_resp = '{"task_intent": "Build API", "approach": "Step 1, Step 2", "quality_score": 0.85, "key_insight": "Use caching"}' - ext = self._build_extractor(compress_resp=compress_resp) - # Multi-round tool conversation skips filter - messages = [ - {"message": _user("build an API")}, - {"message": _tool_call("searching", arguments='{"q":"api"}')}, - {"message": _tool_response("found frameworks")}, - {"message": _tool_call("creating", arguments='{"name":"api"}')}, - {"message": _tool_response("created project")}, - {"message": _assistant("Done! I built the API for you.")}, - ] - req = self._make_request(messages=messages) - result = await ext.extract_memory(req) - assert result is not None - assert result.task_intent == "Build API" - assert result.approach == "Step 1, Step 2" - assert result.key_insight == "Use caching" - assert result.quality_score == 0.85 - assert result.vector is not None - -# Adaptive trim limit scaling tests -# =========================================================================== - - -class TestAdaptiveTrimScaling: - """Tests for scale-based trim limit calculation when tokens exceed 2x threshold. - - Replicates the scaling logic in extract_memory to verify the math - and floor behaviour without going through the full async pipeline. - """ - - def _make_extractor(self, chunk_size: int = 1000) -> AgentCaseExtractor: - e = object.__new__(AgentCaseExtractor) - e.pre_compress_chunk_size = chunk_size - e.max_tool_output_tokens = MAX_TOOL_OUTPUT_TOKENS - e.max_tool_args_tokens = MAX_TOOL_ARGS_TOKENS - e.max_assistant_response_tokens = MAX_ASSISTANT_RESPONSE_TOKENS - return e - - def _compute_limits(self, extractor, total_tokens): - """Mirror the scaling logic from extract_memory.""" - scale_trigger = extractor.pre_compress_chunk_size // 2 - if total_tokens > scale_trigger: - scale = scale_trigger / total_tokens - return ( - max(100, int(extractor.max_tool_output_tokens * scale)), - max(100, int(extractor.max_tool_args_tokens * scale)), - max(200, int(extractor.max_assistant_response_tokens * scale)), - ) - return ( - extractor.max_tool_output_tokens, - extractor.max_tool_args_tokens, - extractor.max_assistant_response_tokens, - ) - - def test_normal_limits_when_under_trigger(self): - e = self._make_extractor(chunk_size=1000) - # scale_trigger = 500; total=400 is NOT over - out, args, asst = self._compute_limits(e, 400) - assert out == MAX_TOOL_OUTPUT_TOKENS - assert args == MAX_TOOL_ARGS_TOKENS - assert asst == MAX_ASSISTANT_RESPONSE_TOKENS - - def test_normal_limits_when_exactly_at_trigger(self): - e = self._make_extractor(chunk_size=1000) - # scale_trigger = 500; total=500 is NOT over (strict >) - out, args, asst = self._compute_limits(e, 500) - assert out == MAX_TOOL_OUTPUT_TOKENS - assert args == MAX_TOOL_ARGS_TOKENS - assert asst == MAX_ASSISTANT_RESPONSE_TOKENS - - def test_limits_reduced_when_over_trigger(self): - e = self._make_extractor(chunk_size=1000) - # scale_trigger = 500; total=501 is over - out, args, asst = self._compute_limits(e, 501) - assert out < MAX_TOOL_OUTPUT_TOKENS - assert args < MAX_TOOL_ARGS_TOKENS - assert asst < MAX_ASSISTANT_RESPONSE_TOKENS - - def test_limits_at_1x_chunk_size(self): - """At 1x chunk_size (=2x trigger), scale=0.5, limits should be halved.""" - e = self._make_extractor(chunk_size=1000) - # scale_trigger=500; total=1000 -> scale=0.5 - out, args, asst = self._compute_limits(e, 1000) - assert out == max(100, MAX_TOOL_OUTPUT_TOKENS // 2) - assert args == max(100, MAX_TOOL_ARGS_TOKENS // 2) - assert asst == max(200, MAX_ASSISTANT_RESPONSE_TOKENS // 2) - - def test_limits_decrease_as_tokens_increase(self): - e = self._make_extractor(chunk_size=1000) - out_3x, _, _ = self._compute_limits(e, 3000) - out_6x, _, _ = self._compute_limits(e, 6000) - assert out_3x > out_6x - - def test_tool_output_floor_at_100(self): - """Tool output limit must never drop below 100.""" - e = self._make_extractor(chunk_size=1000) - # scale_trigger=500; scale=500/200000=0.0025; int(1000*0.0025)=2 < 100 -> floor - out, _, _ = self._compute_limits(e, 200000) - assert out == 100 - - def test_tool_args_floor_at_100(self): - e = self._make_extractor(chunk_size=1000) - _, args, _ = self._compute_limits(e, 200000) - assert args == 100 - - def test_assistant_floor_at_200(self): - e = self._make_extractor(chunk_size=1000) - _, _, asst = self._compute_limits(e, 200000) - assert asst == 200 - - # -- floor NOT applied (moderate scale, computed value stays above floor) -- - - def test_tool_output_floor_not_applied_at_1x_chunk(self): - """At 1x chunk_size scale=0.5; int(1000*0.5)=500 > 100, floor should not kick in.""" - e = self._make_extractor(chunk_size=1000) - # scale_trigger=500; total=1000 -> scale=0.5 - out, _, _ = self._compute_limits(e, 1000) - assert out == int(MAX_TOOL_OUTPUT_TOKENS * (500 / 1000)) - assert out > 100 - - def test_tool_args_floor_not_applied_at_1x_chunk(self): - e = self._make_extractor(chunk_size=1000) - _, args, _ = self._compute_limits(e, 1000) - assert args == int(MAX_TOOL_ARGS_TOKENS * (500 / 1000)) - assert args > 100 - - def test_assistant_floor_not_applied_at_1x_chunk(self): - e = self._make_extractor(chunk_size=1000) - _, _, asst = self._compute_limits(e, 1000) - assert asst == int(MAX_ASSISTANT_RESPONSE_TOKENS * (500 / 1000)) - assert asst > 200 - - # -- each limit hits its own floor independently -- - - def test_only_tool_args_hits_floor(self): - """Craft a scale where tool_args hits floor but tool_output does not.""" - e = object.__new__(AgentCaseExtractor) - e.pre_compress_chunk_size = 1000 - e.max_tool_output_tokens = 10000 # 10000 * 0.025 = 250 >> 100 - e.max_tool_args_tokens = 50 # 50 * 0.025 = 1 < 100 -> floor - e.max_assistant_response_tokens = MAX_ASSISTANT_RESPONSE_TOKENS - - # scale_trigger=500; total=20000 -> scale=500/20000=0.025 - out, args, _ = self._compute_limits(e, 20000) - assert out == int(10000 * (500 / 20000)) # 250, no floor - assert args == 100 # floor applied - - def test_only_assistant_hits_floor(self): - """Craft a scale where assistant hits floor but tool limits do not.""" - e = object.__new__(AgentCaseExtractor) - e.pre_compress_chunk_size = 1000 - e.max_tool_output_tokens = 10000 # 10000 * 0.0125 = 125 >> 100 - e.max_tool_args_tokens = 10000 # same - e.max_assistant_response_tokens = 100 # 100 * 0.0125 = 1 < 200 -> floor - - # scale_trigger=500; total=40000 -> scale=500/40000=0.0125 - out, args, asst = self._compute_limits(e, 40000) - assert out > 100 # no floor - assert args > 100 # no floor - assert asst == 200 # floor applied - - # -- boundary: one token over scale_trigger -- - - def test_one_token_over_trigger_uses_scaled_limits(self): - """total_tokens = scale_trigger + 1 should trigger scaled path.""" - e = self._make_extractor(chunk_size=1000) - trigger = 500 # pre_compress_chunk_size // 2 - total = trigger + 1 - out, args, asst = self._compute_limits(e, total) - scale = trigger / total - assert out == max(100, int(MAX_TOOL_OUTPUT_TOKENS * scale)) - assert args == max(100, int(MAX_TOOL_ARGS_TOKENS * scale)) - assert asst == max(200, int(MAX_ASSISTANT_RESPONSE_TOKENS * scale)) - - -# =========================================================================== -# Post-trim token guard in extract_memory -# =========================================================================== - - -def _make_memcell_request(): - """Return a minimal (memcell, request) pair suitable for extract_memory.""" - memcell = MagicMock() - memcell.type = RawDataType.AGENTCONVERSATION - memcell.original_data = [] - memcell.timestamp = None - memcell.participants = [] - memcell.sender_ids = [] - - request = MagicMock() - request.memcell = memcell - request.user_id = "u1" - request.group_id = "g1" - return memcell, request - - -_STUB_MSGS = [ - {"role": "user", "content": "hi"}, - {"role": "assistant", "tool_calls": [{"id": "c1", "function": {"name": "f", "arguments": "{}"}}]}, - {"role": "tool", "content": "result", "tool_call_id": "c1"}, - {"role": "assistant", "content": "done"}, -] - - -class TestExtractMemoryPostTrimSkip: - """Tests for the post-trim token guard in extract_memory.""" - - @pytest.mark.asyncio - async def test_skips_when_trimmed_tokens_still_over_threshold(self): - """extract_memory returns None if tokens are still > scale_threshold after trim.""" - extractor = object.__new__(AgentCaseExtractor) - extractor.pre_compress_chunk_size = 100 # threshold = 100 - extractor.max_tool_output_tokens = MAX_TOOL_OUTPUT_TOKENS - extractor.max_tool_args_tokens = MAX_TOOL_ARGS_TOKENS - extractor.max_assistant_response_tokens = MAX_ASSISTANT_RESPONSE_TOKENS - extractor.memory_type = MagicMock() - extractor.filter_prompt = "{messages}" - extractor.experience_compress_prompt = "{messages}" - extractor.tool_pre_compress_prompt = "{messages_json}{new_count}" - extractor.llm_provider = MagicMock() - - _, request = _make_memcell_request() - - # scale_trigger=50; first call 250 > 50 -> scaled trim - # skip threshold=200; second call 210 > 200 -> should skip - token_returns = iter([250, 210]) - - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", side_effect=lambda _: next(token_returns)), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", return_value=_STUB_MSGS), - ): - result = await extractor.extract_memory(request) - - assert result is None - - @pytest.mark.asyncio - async def test_proceeds_when_trimmed_tokens_under_threshold(self): - """extract_memory calls _pre_compress_to_list when post-trim tokens are within limit.""" - extractor = object.__new__(AgentCaseExtractor) - extractor.pre_compress_chunk_size = 100 # threshold = 100 - extractor.max_tool_output_tokens = MAX_TOOL_OUTPUT_TOKENS - extractor.max_tool_args_tokens = MAX_TOOL_ARGS_TOKENS - extractor.max_assistant_response_tokens = MAX_ASSISTANT_RESPONSE_TOKENS - extractor.memory_type = MagicMock() - extractor.filter_prompt = "{messages}" - extractor.experience_compress_prompt = "{messages}" - extractor.tool_pre_compress_prompt = "{messages_json}{new_count}" - extractor.llm_provider = MagicMock() - - _, request = _make_memcell_request() - - # scale_trigger=50; first call 250 > 50 -> scaled trim - # skip threshold=200; second call 150 <= 200 -> proceed - token_returns = iter([250, 150]) - - mock_pre_compress = AsyncMock(return_value=_STUB_MSGS) - - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", side_effect=lambda _: next(token_returns)), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", mock_pre_compress), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - mock_pre_compress.assert_called_once() - - @pytest.mark.asyncio - async def test_scaled_limits_passed_to_heuristic_trim(self): - """When total_tokens > scale_trigger, heuristic_trim receives scaled-down limits.""" - chunk_size = 1000 # _STUB_MSGS has 4 msgs < HIGH_MESSAGE_COUNT_THRESHOLD, so scale_trigger = 1000 - total = 4000 # scale = 1000/4000 = 0.25 - - extractor = object.__new__(AgentCaseExtractor) - extractor.pre_compress_chunk_size = chunk_size - extractor.max_tool_output_tokens = MAX_TOOL_OUTPUT_TOKENS - extractor.max_tool_args_tokens = MAX_TOOL_ARGS_TOKENS - extractor.max_assistant_response_tokens = MAX_ASSISTANT_RESPONSE_TOKENS - extractor.memory_type = MagicMock() - extractor.filter_prompt = "{messages}" - extractor.experience_compress_prompt = "{messages}" - extractor.tool_pre_compress_prompt = "{messages_json}{new_count}" - extractor.llm_provider = MagicMock() - - _, request = _make_memcell_request() - - # After trim, return under skip threshold (chunk_size*2=2000) so we don't skip - token_returns = iter([total, chunk_size]) - mock_trim = MagicMock(return_value=_STUB_MSGS) - - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", side_effect=lambda _: next(token_returns)), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", mock_trim), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=_STUB_MSGS)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - scale = chunk_size / total # scale_trigger/total = 1000/4000 = 0.25 - expected_tool_out = max(200, int(MAX_TOOL_OUTPUT_TOKENS * scale)) - expected_tool_args = max(200, int(MAX_TOOL_ARGS_TOKENS * scale)) - expected_assistant = max(500, int(MAX_ASSISTANT_RESPONSE_TOKENS * scale)) - - mock_trim.assert_called_once_with( - _STUB_MSGS, - expected_tool_out, - expected_tool_args, - expected_assistant, - ) - - @pytest.mark.asyncio - async def test_normal_limits_passed_to_trim_when_under_threshold(self): - """When total_tokens <= pre_compress_chunk_size, full (unscaled) limits reach _heuristic_trim.""" - chunk_size = 1000 # threshold = 1000 - - extractor = object.__new__(AgentCaseExtractor) - extractor.pre_compress_chunk_size = chunk_size - extractor.max_tool_output_tokens = MAX_TOOL_OUTPUT_TOKENS - extractor.max_tool_args_tokens = MAX_TOOL_ARGS_TOKENS - extractor.max_assistant_response_tokens = MAX_ASSISTANT_RESPONSE_TOKENS - extractor.memory_type = MagicMock() - extractor.filter_prompt = "{messages}" - extractor.experience_compress_prompt = "{messages}" - extractor.tool_pre_compress_prompt = "{messages_json}{new_count}" - extractor.llm_provider = MagicMock() - - _, request = _make_memcell_request() - - # scale_trigger=500; both counts 400 <= 500 -> normal limits - token_returns = iter([400, 400]) - mock_trim = MagicMock(return_value=_STUB_MSGS) - - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", side_effect=lambda _: next(token_returns)), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", mock_trim), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=_STUB_MSGS)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - mock_trim.assert_called_once_with( - _STUB_MSGS, - MAX_TOOL_OUTPUT_TOKENS, - MAX_TOOL_ARGS_TOKENS, - MAX_ASSISTANT_RESPONSE_TOKENS, - ) - - @pytest.mark.asyncio - async def test_post_trim_exactly_at_threshold_proceeds(self): - """trimmed_tokens == scale_threshold should NOT skip (condition is strictly >).""" - extractor = object.__new__(AgentCaseExtractor) - extractor.pre_compress_chunk_size = 100 # threshold = 100 - extractor.max_tool_output_tokens = MAX_TOOL_OUTPUT_TOKENS - extractor.max_tool_args_tokens = MAX_TOOL_ARGS_TOKENS - extractor.max_assistant_response_tokens = MAX_ASSISTANT_RESPONSE_TOKENS - extractor.memory_type = MagicMock() - extractor.filter_prompt = "{messages}" - extractor.experience_compress_prompt = "{messages}" - extractor.tool_pre_compress_prompt = "{messages_json}{new_count}" - extractor.llm_provider = MagicMock() - - _, request = _make_memcell_request() - - # scale_trigger=50; first count 300 > 50 -> scaled trim - # skip threshold=200; second count exactly 200 -> NOT over (strict >), should proceed - token_returns = iter([300, 200]) - mock_pre_compress = AsyncMock(return_value=_STUB_MSGS) - - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", side_effect=lambda _: next(token_returns)), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", mock_pre_compress), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - mock_pre_compress.assert_called_once() - - @pytest.mark.asyncio - async def test_floor_limits_passed_to_trim_at_extreme_token_count(self): - """At very high token counts all three limits should hit their floors.""" - chunk_size = 100 # threshold = 100 - total = 1_000_000 # massively over -> scale ≈ 0 - - extractor = object.__new__(AgentCaseExtractor) - extractor.pre_compress_chunk_size = chunk_size - extractor.max_tool_output_tokens = MAX_TOOL_OUTPUT_TOKENS - extractor.max_tool_args_tokens = MAX_TOOL_ARGS_TOKENS - extractor.max_assistant_response_tokens = MAX_ASSISTANT_RESPONSE_TOKENS - extractor.memory_type = MagicMock() - extractor.filter_prompt = "{messages}" - extractor.experience_compress_prompt = "{messages}" - extractor.tool_pre_compress_prompt = "{messages_json}{new_count}" - extractor.llm_provider = MagicMock() - - _, request = _make_memcell_request() - - # After trim return under skip threshold (chunk_size*2=200) so we don't skip - token_returns = iter([total, chunk_size]) - mock_trim = MagicMock(return_value=_STUB_MSGS) - - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", side_effect=lambda _: next(token_returns)), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", mock_trim), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=_STUB_MSGS)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - mock_trim.assert_called_once_with(_STUB_MSGS, 200, 200, 500) - - @pytest.mark.asyncio - async def test_count_tokens_called_once_when_no_scaling_needed(self): - """When total_tokens <= scale_trigger, the second _count_tokens call is skipped.""" - extractor = object.__new__(AgentCaseExtractor) - extractor.pre_compress_chunk_size = 1000 # scale_trigger = 500 - extractor.max_tool_output_tokens = MAX_TOOL_OUTPUT_TOKENS - extractor.max_tool_args_tokens = MAX_TOOL_ARGS_TOKENS - extractor.max_assistant_response_tokens = MAX_ASSISTANT_RESPONSE_TOKENS - extractor.memory_type = MagicMock() - extractor.filter_prompt = "{messages}" - extractor.experience_compress_prompt = "{messages}" - extractor.tool_pre_compress_prompt = "{messages_json}{new_count}" - extractor.llm_provider = MagicMock() - - _, request = _make_memcell_request() - - mock_count = MagicMock(return_value=400) # 400 <= 500 (scale_trigger), no scaling - - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", mock_count), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=_STUB_MSGS)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - assert mock_count.call_count == 1 - - @pytest.mark.asyncio - async def test_count_tokens_called_twice_when_scaling_needed(self): - """When total_tokens > scale_trigger, _count_tokens is called twice (before and after trim).""" - extractor = object.__new__(AgentCaseExtractor) - extractor.pre_compress_chunk_size = 1000 # _STUB_MSGS has 4 msgs < HIGH_MESSAGE_COUNT_THRESHOLD, so scale_trigger = 1000, skip = 2000 - extractor.max_tool_output_tokens = MAX_TOOL_OUTPUT_TOKENS - extractor.max_tool_args_tokens = MAX_TOOL_ARGS_TOKENS - extractor.max_assistant_response_tokens = MAX_ASSISTANT_RESPONSE_TOKENS - extractor.memory_type = MagicMock() - extractor.filter_prompt = "{messages}" - extractor.experience_compress_prompt = "{messages}" - extractor.tool_pre_compress_prompt = "{messages_json}{new_count}" - extractor.llm_provider = MagicMock() - - _, request = _make_memcell_request() - - # First: 1200 > 1000 (scaling triggered); Second: 600 <= 2000 (no skip) - token_returns = iter([1200, 600]) - mock_count = MagicMock(side_effect=lambda _: next(token_returns)) - - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", mock_count), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", return_value=_STUB_MSGS), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=_STUB_MSGS)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - assert mock_count.call_count == 2 - - -def _make_msgs(n: int) -> list: - """Build a list of n alternating user/assistant messages for threshold tests.""" - msgs = [] - for i in range(n): - if i % 2 == 0: - msgs.append({"role": "user", "content": f"msg {i}"}) - else: - msgs.append({"role": "assistant", "content": f"reply {i}"}) - return msgs - - -def _make_extractor(chunk_size: int) -> AgentCaseExtractor: - extractor = object.__new__(AgentCaseExtractor) - extractor.pre_compress_chunk_size = chunk_size - extractor.max_tool_output_tokens = MAX_TOOL_OUTPUT_TOKENS - extractor.max_tool_args_tokens = MAX_TOOL_ARGS_TOKENS - extractor.max_assistant_response_tokens = MAX_ASSISTANT_RESPONSE_TOKENS - extractor.memory_type = MagicMock() - extractor.filter_prompt = "{messages}" - extractor.experience_compress_prompt = "{messages}" - extractor.tool_pre_compress_prompt = "{messages_json}{new_count}" - extractor.llm_provider = MagicMock() - return extractor - - -class TestHighMessageCountThreshold: - """Tests for the HIGH_MESSAGE_COUNT_THRESHOLD branch in extract_memory. - - When len(messages) > HIGH_MESSAGE_COUNT_THRESHOLD the scale_trigger is halved, - causing more aggressive trim. When len(messages) <= threshold the full - pre_compress_chunk_size is used. - """ - - @pytest.mark.asyncio - async def test_exactly_at_threshold_uses_full_scale_trigger(self): - """len == HIGH_MESSAGE_COUNT_THRESHOLD (100) should NOT halve the trigger.""" - chunk_size = 1000 - msgs = _make_msgs(HIGH_MESSAGE_COUNT_THRESHOLD) # exactly 100 messages - extractor = _make_extractor(chunk_size) - _, request = _make_memcell_request() - - # scale_trigger = chunk_size = 1000; token 800 <= 1000 -> no scaling - mock_trim = MagicMock(return_value=msgs) - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=msgs), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=msgs), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", return_value=800), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", mock_trim), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=msgs)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - # No scaling -> full limits passed - mock_trim.assert_called_once_with(msgs, MAX_TOOL_OUTPUT_TOKENS, MAX_TOOL_ARGS_TOKENS, MAX_ASSISTANT_RESPONSE_TOKENS) - - @pytest.mark.asyncio - async def test_one_above_threshold_halves_scale_trigger(self): - """len == HIGH_MESSAGE_COUNT_THRESHOLD + 1 (101) SHOULD halve the trigger.""" - chunk_size = 1000 - msgs = _make_msgs(HIGH_MESSAGE_COUNT_THRESHOLD + 1) # 101 messages - extractor = _make_extractor(chunk_size) - _, request = _make_memcell_request() - - # scale_trigger = 500; token 800 > 500 -> scaling triggered - # 800 would NOT trigger scaling if msg count were low (800 < 1000) - total = 800 - token_returns = iter([total, chunk_size]) - mock_trim = MagicMock(return_value=msgs) - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=msgs), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=msgs), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", side_effect=lambda _: next(token_returns)), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", mock_trim), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=msgs)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - scale_trigger = chunk_size // 2 # 500 - scale = scale_trigger / total # 500/800 = 0.625 - mock_trim.assert_called_once_with( - msgs, - max(200, int(MAX_TOOL_OUTPUT_TOKENS * scale)), - max(200, int(MAX_TOOL_ARGS_TOKENS * scale)), - max(500, int(MAX_ASSISTANT_RESPONSE_TOKENS * scale)), - ) - - @pytest.mark.asyncio - async def test_gray_zone_tokens_trigger_scaling_only_for_high_msg_count(self): - """Tokens between chunk_size//2 and chunk_size: scaling fires for >100 msgs but not for <=100.""" - chunk_size = 1000 - gray_zone_tokens = 700 # chunk_size//2 < 700 < chunk_size - - # HIGH message count: scale_trigger=500, 700 > 500 -> scaling - high_msgs = _make_msgs(HIGH_MESSAGE_COUNT_THRESHOLD + 1) - extractor_high = _make_extractor(chunk_size) - _, request_high = _make_memcell_request() - - token_returns_high = iter([gray_zone_tokens, chunk_size]) - mock_trim_high = MagicMock(return_value=high_msgs) - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=high_msgs), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=high_msgs), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", side_effect=lambda _: next(token_returns_high)), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", mock_trim_high), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=high_msgs)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor_high.extract_memory(request_high) - - # High msg count: scaling was applied - call_args_high = mock_trim_high.call_args[0] - assert call_args_high[1] < MAX_TOOL_OUTPUT_TOKENS or call_args_high[2] < MAX_TOOL_ARGS_TOKENS, \ - "Expected scaled-down limits for high message count" - - # LOW message count: scale_trigger=1000, 700 <= 1000 -> no scaling - low_msgs = _make_msgs(HIGH_MESSAGE_COUNT_THRESHOLD) - extractor_low = _make_extractor(chunk_size) - _, request_low = _make_memcell_request() - - mock_trim_low = MagicMock(return_value=low_msgs) - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=low_msgs), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=low_msgs), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", return_value=gray_zone_tokens), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", mock_trim_low), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=low_msgs)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor_low.extract_memory(request_low) - - # Low msg count: no scaling, full limits - mock_trim_low.assert_called_once_with(low_msgs, MAX_TOOL_OUTPUT_TOKENS, MAX_TOOL_ARGS_TOKENS, MAX_ASSISTANT_RESPONSE_TOKENS) - - @pytest.mark.asyncio - async def test_high_msg_count_tokens_below_halved_trigger_no_scaling(self): - """Even with >100 messages, tokens <= chunk_size//2 should not trigger scaling.""" - chunk_size = 1000 - msgs = _make_msgs(HIGH_MESSAGE_COUNT_THRESHOLD + 1) # 101 messages -> scale_trigger = 500 - extractor = _make_extractor(chunk_size) - _, request = _make_memcell_request() - - mock_trim = MagicMock(return_value=msgs) - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=msgs), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=msgs), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", return_value=400), # 400 <= 500, no scaling - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", mock_trim), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=msgs)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - mock_trim.assert_called_once_with(msgs, MAX_TOOL_OUTPUT_TOKENS, MAX_TOOL_ARGS_TOKENS, MAX_ASSISTANT_RESPONSE_TOKENS) - - @pytest.mark.asyncio - async def test_high_msg_count_scaled_limits_use_halved_trigger_for_scale(self): - """With >100 messages, the scale factor is computed from chunk_size//2, not chunk_size.""" - chunk_size = 1000 - total = 2000 - msgs = _make_msgs(HIGH_MESSAGE_COUNT_THRESHOLD + 1) # 101 messages - extractor = _make_extractor(chunk_size) - _, request = _make_memcell_request() - - token_returns = iter([total, chunk_size]) - mock_trim = MagicMock(return_value=msgs) - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=msgs), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=msgs), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", side_effect=lambda _: next(token_returns)), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", mock_trim), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=msgs)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - # scale = (chunk_size // 2) / total = 500/2000 = 0.25 - scale_trigger = chunk_size // 2 - scale = scale_trigger / total - mock_trim.assert_called_once_with( - msgs, - max(200, int(MAX_TOOL_OUTPUT_TOKENS * scale)), - max(200, int(MAX_TOOL_ARGS_TOKENS * scale)), - max(500, int(MAX_ASSISTANT_RESPONSE_TOKENS * scale)), - ) - - @pytest.mark.asyncio - async def test_high_msg_count_floor_limits_at_extreme_token_count(self): - """With >100 messages at extreme token counts, floor values still apply (200, 200, 500).""" - chunk_size = 1000 - msgs = _make_msgs(HIGH_MESSAGE_COUNT_THRESHOLD + 1) # 101 messages -> scale_trigger = 500 - extractor = _make_extractor(chunk_size) - _, request = _make_memcell_request() - - token_returns = iter([1_000_000, chunk_size]) - mock_trim = MagicMock(return_value=msgs) - with ( - patch.object(AgentCaseExtractor, "_unwrap_messages", return_value=msgs), - patch.object(AgentCaseExtractor, "_strip_before_first_user", return_value=msgs), - patch.object(AgentCaseExtractor, "_should_skip", return_value=None), - patch.object(AgentCaseExtractor, "_count_tokens", side_effect=lambda _: next(token_returns)), - patch.object(AgentCaseExtractor, "_heuristic_trim_tool_outputs", mock_trim), - patch.object(AgentCaseExtractor, "_pre_compress_to_list", AsyncMock(return_value=msgs)), - patch.object(AgentCaseExtractor, "_filter_conversation", AsyncMock(return_value=False)), - ): - await extractor.extract_memory(request) - - mock_trim.assert_called_once_with(msgs, 200, 200, 500) diff --git a/methods/EverCore/tests/test_agent_case_pipeline.py b/methods/EverCore/tests/test_agent_case_pipeline.py deleted file mode 100644 index 174b3151..00000000 --- a/methods/EverCore/tests/test_agent_case_pipeline.py +++ /dev/null @@ -1,1649 +0,0 @@ -""" -AgentCase Full Pipeline Unit Tests - -Tests for: -- AgentCaseExtractor.extract_memory: end-to-end extraction flow -- _convert_agent_case_to_doc: BO-to-document conversion -- _extract_user_id_from_memcell: user ID extraction from MemCell -- _clamp_quality_score: quality score validation -- _unwrap_messages: MemCell original_data unwrapping + content normalization -- _should_skip: heuristic pre-filtering (skips no-tool short conversations) -- _filter_conversation: LLM-based filtering (for remaining low-tool-call cases) -- _compress_experience: LLM-based extraction -- _pre_compress_to_list: tool content compression - -Usage: - PYTHONPATH=src pytest tests/test_agent_case_pipeline.py -v -""" - -import json -import pytest -from datetime import datetime -from unittest.mock import AsyncMock, MagicMock, patch -from typing import Any, Dict, List - -from api_specs.memory_types import MemCell, RawDataType, AgentCase -from api_specs.memory_models import MemoryType -from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - AgentCaseExtractRequest, -) -from memory_layer.memory_extractor.agent_skill_extractor import ( - AgentSkillExtractor, - SkillExtractionResult, -) -from memory_layer.memory_extractor.base_memory_extractor import MemoryExtractRequest -from biz_layer.mem_db_operations import ( - _convert_agent_case_to_doc, - _extract_user_id_from_memcell, -) -from biz_layer.mem_memorize import _is_agent_case_quality_sufficient -from biz_layer.memorize_config import MemorizeConfig - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -def _wrap_msg(msg: Dict[str, Any]) -> Dict[str, Any]: - """Wrap a message in MemCell original_data format.""" - return {"message": msg, "parse_info": None} - - -def _user_msg(content: str, sender_id: str = "user_001") -> Dict[str, Any]: - return {"role": "user", "content": content, "sender_id": sender_id} - - -def _assistant_msg(content: str) -> Dict[str, Any]: - return {"role": "assistant", "content": content} - - -def _tool_call_msg( - content: str = "", name: str = "search", arguments: str = "{}" -) -> Dict[str, Any]: - return { - "role": "assistant", - "content": content, - "tool_calls": [{"id": "call_1", "function": {"name": name, "arguments": arguments}}], - } - - -def _tool_response_msg(content: str = "result") -> Dict[str, Any]: - return {"role": "tool", "content": content, "tool_call_id": "call_1"} - - -def _make_agent_memcell( - messages: List[Dict[str, Any]], - event_id: str = "evt_001", - group_id: str = "group_001", -) -> MemCell: - """Create a MemCell with wrapped messages for agent conversation.""" - original_data = [_wrap_msg(m) for m in messages] - return MemCell( - user_id_list=["user_001"], - original_data=original_data, - timestamp=datetime(2025, 3, 1, 12, 0, 0), - event_id=event_id, - group_id=group_id, - participants=["user_001"], - sender_ids=["user_001"], - type=RawDataType.AGENTCONVERSATION, - ) - - -@pytest.fixture(autouse=True) -def _mock_tokenizer(): - """Patch get_bean_by_type to return a real tiktoken tokenizer.""" - import tiktoken - - encoding = tiktoken.get_encoding("o200k_base") - mock_factory = MagicMock() - mock_factory.get_tokenizer_from_tiktoken.return_value = encoding - - with patch( - "memory_layer.memory_extractor.agent_case_extractor.get_bean_by_type", - return_value=mock_factory, - ): - yield - - -def _build_case_extractor( - llm_responses: List[str] = None, -) -> AgentCaseExtractor: - """Build extractor with mocked LLM and vectorize service.""" - mock_llm = MagicMock() - if llm_responses: - mock_llm.generate = AsyncMock(side_effect=llm_responses) - else: - mock_llm.generate = AsyncMock( - return_value=json.dumps({ - "task_intent": "Build a REST API endpoint", - "approach": "1. Define the route\n2. Implement handler\n3. Add validation", - "quality_score": 0.8, - }) - ) - - return AgentCaseExtractor( - llm_provider=mock_llm, - filter_prompt="{messages}", - experience_compress_prompt="{messages}", - tool_pre_compress_prompt="{messages_json}{new_count}", - ) - - -# =========================================================================== -# _unwrap_messages tests -# =========================================================================== - - -class TestUnwrapMessages: - """Tests for AgentCaseExtractor._unwrap_messages.""" - - def test_unwrap_standard_format(self): - data = [ - _wrap_msg(_user_msg("hello")), - _wrap_msg(_assistant_msg("hi")), - ] - result = AgentCaseExtractor._unwrap_messages(data) - assert len(result) == 2 - assert result[0]["role"] == "user" - assert result[1]["role"] == "assistant" - - def test_unwrap_content_list_normalized(self): - """v1 API content[] list is converted to plain string.""" - msg = { - "role": "user", - "content": [ - {"type": "text", "content": "hello "}, - {"type": "text", "content": "world"}, - ], - } - data = [_wrap_msg(msg)] - result = AgentCaseExtractor._unwrap_messages(data) - assert isinstance(result[0]["content"], str) - assert "hello" in result[0]["content"] - assert "world" in result[0]["content"] - - def test_unwrap_bare_messages(self): - """Messages without wrapping are passed through.""" - data = [_user_msg("hello")] - result = AgentCaseExtractor._unwrap_messages(data) - assert len(result) == 1 - assert result[0]["content"] == "hello" - - def test_non_dict_items_skipped(self): - data = ["not_a_dict", None, _wrap_msg(_user_msg("hello"))] - result = AgentCaseExtractor._unwrap_messages(data) - assert len(result) == 1 - - def test_empty_list(self): - result = AgentCaseExtractor._unwrap_messages([]) - assert result == [] - - -# =========================================================================== -# _clamp_quality_score tests -# =========================================================================== - - -class TestClampQualityScore: - """Tests for AgentCaseExtractor._clamp_quality_score.""" - - def test_normal_value(self): - assert AgentCaseExtractor._clamp_quality_score(0.5) == 0.5 - - def test_above_one_clamped(self): - assert AgentCaseExtractor._clamp_quality_score(1.5) == 1.0 - - def test_below_zero_clamped(self): - assert AgentCaseExtractor._clamp_quality_score(-0.5) == 0.0 - - def test_none_returns_none(self): - assert AgentCaseExtractor._clamp_quality_score(None) is None - - def test_string_number(self): - assert AgentCaseExtractor._clamp_quality_score("0.7") == 0.7 - - def test_invalid_string_returns_none(self): - assert AgentCaseExtractor._clamp_quality_score("abc") is None - - def test_boundary_values(self): - assert AgentCaseExtractor._clamp_quality_score(0.0) == 0.0 - assert AgentCaseExtractor._clamp_quality_score(1.0) == 1.0 - - -# =========================================================================== -# _should_skip heuristic filter tests -# =========================================================================== - - -class TestHeuristicFilter: - """Tests for _should_skip heuristic that skips no-tool short conversations.""" - - def test_single_turn_no_tools_skipped(self): - """Single user+assistant turn without tools is skipped.""" - messages = [ - _user_msg("What time is it?"), - _assistant_msg("3 PM"), - ] - result = AgentCaseExtractor._should_skip(messages) - assert result is not None - - def test_no_tool_four_messages_skipped(self): - """No-tool conversation with exactly 4 messages is skipped.""" - messages = [ - _user_msg("What is X?"), - _assistant_msg("X is Y."), - _user_msg("Thanks"), - _assistant_msg("OK."), - ] - result = AgentCaseExtractor._should_skip(messages) - assert result is not None - assert "No-tool conversation" in result - - def test_no_tool_brief_assistant_skipped(self): - """No-tool conversation with > 4 messages but brief assistant response is skipped.""" - messages = [ - _user_msg("My service drops connections"), - _assistant_msg("Can you share the config?"), - _user_msg("Here: timeout=30"), - _assistant_msg("Change timeout to 60"), - _user_msg("That fixed it, thanks!"), - _assistant_msg("Glad it worked."), - ] - result = AgentCaseExtractor._should_skip(messages) - assert result is not None - assert "brief assistant response" in result - - def test_no_tool_substantial_assistant_passes(self): - """No-tool conversation with substantial assistant response passes to LLM filter.""" - long_response_1 = ( - "I can see the issue. Let me explain in detail. The problem is that your " - "configuration file has an incorrect parameter setting. You need to change " - "the timeout value from 30 to 60 seconds, and also update the retry count " - "from 1 to 3. Here are the exact steps you should follow: First, open the " - "config.yaml file in your project root directory. Second, locate the network " - "section under the server block. Third, update the timeout field to 60." - ) - long_response_2 = ( - "Now for the second part of the fix. You also need to update the retries " - "field to 3, and then save and restart the service with the command " - "systemctl restart myservice. This should resolve the connection drops you " - "are experiencing during peak hours. The root cause is that the default " - "timeout of 30 seconds is too aggressive for your upstream dependencies " - "which have P99 latency around 45 seconds. After making these changes, " - "monitor the Grafana dashboard for at least two hours to confirm stability." - ) - messages = [ - _user_msg("My service keeps dropping connections during peak hours"), - _assistant_msg(long_response_1), - _user_msg("OK, what about the retries?"), - _assistant_msg(long_response_2), - _user_msg("That fixed it, thanks!"), - _assistant_msg("Glad it worked. Keep monitoring the dashboard."), - ] - result = AgentCaseExtractor._should_skip(messages) - assert result is None - - def test_single_tool_round_passes(self): - """Conversations with tool calls pass regardless of message count.""" - messages = [ - _user_msg("Build an API"), - _tool_call_msg("writing code"), - _tool_response_msg("done"), - _assistant_msg("API is ready"), - ] - result = AgentCaseExtractor._should_skip(messages) - assert result is None - - def test_multi_tool_rounds_passes(self): - """Conversations with > 1 tool round pass.""" - messages = [ - _user_msg("Fix the bug"), - _tool_call_msg("reading file"), - _tool_response_msg("content"), - _tool_call_msg("editing file"), - _tool_response_msg("saved"), - _assistant_msg("Done"), - ] - result = AgentCaseExtractor._should_skip(messages) - assert result is None - - -# =========================================================================== -# _filter_conversation tests -# =========================================================================== - - -class TestFilterConversation: - """Tests for AgentCaseExtractor._filter_conversation (LLM-based).""" - - @pytest.mark.asyncio - async def test_worth_extracting_true(self): - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({"worth_extracting": True, "reason": "complex task"}) - ) - result = await extractor._filter_conversation("messages json") - assert result is True - - @pytest.mark.asyncio - async def test_worth_extracting_false(self): - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({"worth_extracting": False, "reason": "trivial"}) - ) - result = await extractor._filter_conversation("messages json") - assert result is False - - @pytest.mark.asyncio - async def test_llm_failure_defaults_to_true(self): - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock(side_effect=Exception("LLM error")) - result = await extractor._filter_conversation("messages json") - assert result is True - - @pytest.mark.asyncio - async def test_invalid_json_defaults_to_true(self): - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock(return_value="not json") - result = await extractor._filter_conversation("messages json") - assert result is True - - -# =========================================================================== -# _compress_experience tests -# =========================================================================== - - -class TestCompressExperience: - """Tests for AgentCaseExtractor._compress_experience.""" - - @pytest.mark.asyncio - async def test_successful_extraction(self): - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({ - "task_intent": "Setup CI/CD pipeline", - "approach": "1. Configure GitHub Actions\n2. Add tests", - "quality_score": 0.9, - }) - ) - result = await extractor._compress_experience("messages") - assert result["task_intent"] == "Setup CI/CD pipeline" - assert result["quality_score"] == 0.9 - - @pytest.mark.asyncio - async def test_empty_task_intent_returns_none(self): - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({ - "task_intent": "", - "approach": "steps", - "quality_score": 0.5, - }) - ) - result = await extractor._compress_experience("messages") - assert result is None - - @pytest.mark.asyncio - async def test_empty_approach_returns_none(self): - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({ - "task_intent": "Something", - "approach": "", - "quality_score": 0.5, - }) - ) - result = await extractor._compress_experience("messages") - assert result is None - - @pytest.mark.asyncio - async def test_retries_on_failure_returns_none(self): - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock(side_effect=Exception("fail")) - result = await extractor._compress_experience("messages") - assert result is None - assert extractor.llm_provider.generate.call_count == 2 - - -# =========================================================================== -# extract_memory end-to-end tests -# =========================================================================== - - -class TestExtractMemoryE2E: - """End-to-end tests for AgentCaseExtractor.extract_memory.""" - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_case_extractor.get_vectorize_service") - async def test_successful_extraction(self, mock_vs_factory): - mock_vs = MagicMock() - mock_vs.get_embedding = AsyncMock(return_value=MagicMock(tolist=lambda: [0.1, 0.2])) - mock_vs.get_model_name.return_value = "test-embed" - mock_vs_factory.return_value = mock_vs - - extractor = _build_case_extractor() - memcell = _make_agent_memcell([ - _user_msg("Build me a REST API for user management"), - _tool_call_msg("Let me create the code", "write_file", '{"path": "api.py"}'), - _tool_response_msg("File created successfully"), - _assistant_msg("I've built the REST API with user CRUD endpoints"), - ]) - request = MemoryExtractRequest(memcell=memcell, user_id="user_001", group_id="group_001") - result = await extractor.extract_memory(request) - - assert result is not None - assert isinstance(result, AgentCase) - assert result.task_intent == "Build a REST API endpoint" - assert result.vector == [0.1, 0.2] - assert result.memory_type == MemoryType.AGENT_CASE - - @pytest.mark.asyncio - async def test_non_agent_memcell_returns_none(self): - extractor = _build_case_extractor() - memcell = MemCell( - user_id_list=["u1"], - original_data=[_wrap_msg(_user_msg("hi"))], - timestamp=datetime(2025, 1, 1), - type=RawDataType.CONVERSATION, - ) - request = MemoryExtractRequest(memcell=memcell) - result = await extractor.extract_memory(request) - assert result is None - - @pytest.mark.asyncio - async def test_empty_memcell_returns_none(self): - extractor = _build_case_extractor() - request = MemoryExtractRequest(memcell=None) - result = await extractor.extract_memory(request) - assert result is None - - @pytest.mark.asyncio - async def test_single_turn_no_tools_skipped(self): - """Single user-assistant turn without tools is skipped.""" - extractor = _build_case_extractor() - memcell = _make_agent_memcell([ - _user_msg("What is Python?"), - _assistant_msg("Python is a programming language."), - ]) - request = MemoryExtractRequest(memcell=memcell) - result = await extractor.extract_memory(request) - assert result is None - - @pytest.mark.asyncio - async def test_incomplete_trajectory_skipped(self): - """Last message is tool call, not final response.""" - extractor = _build_case_extractor() - memcell = _make_agent_memcell([ - _user_msg("Do something"), - _tool_call_msg("calling"), - ]) - request = MemoryExtractRequest(memcell=memcell) - result = await extractor.extract_memory(request) - assert result is None - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_case_extractor.get_vectorize_service") - async def test_multi_turn_no_tools_passes(self, mock_vs_factory): - """Multi-turn conversation without tools passes heuristic + LLM filter.""" - mock_vs = MagicMock() - mock_vs.get_embedding = AsyncMock(return_value=MagicMock(tolist=lambda: [0.1])) - mock_vs.get_model_name.return_value = "test" - mock_vs_factory.return_value = mock_vs - - # LLM filter returns True, then compress_experience returns data - filter_resp = json.dumps({"worth_extracting": True}) - extract_resp = json.dumps({ - "task_intent": "Debug the app", - "approach": "1. Check logs\n2. Fix error", - "quality_score": 0.7, - }) - - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock(side_effect=[filter_resp, extract_resp]) - - # Need > 4 messages AND > 200 assistant tokens to pass heuristic - memcell = _make_agent_memcell([ - _user_msg("Help me debug this TypeError"), - _assistant_msg( - "I can see the issue. The TypeError on line 5 is caused by passing " - "a string where an integer is expected. You need to wrap the input " - "with int() before passing it to the calculate function. Here is the " - "corrected code: change calculate(user_input) to calculate(int(user_input)). " - "This happens because input() always returns a string in Python 3, even " - "when the user types a number. The int() call converts it properly. " - "You should also add error handling with a try/except ValueError block " - "around the int() conversion to gracefully handle non-numeric input." - ), - _user_msg("That worked, but now KeyError on line 12"), - _assistant_msg( - "The second error is a KeyError because the dictionary key config_value " - "does not exist in your settings dict. You should use settings.get(" - "'config_value', default_value) instead of settings['config_value']. " - "The .get() method returns the default when the key is missing, preventing " - "the KeyError. This pattern is common when dealing with optional configuration " - "parameters that may not be present in all environments. Additionally, you " - "might want to add type hints to your function signatures so that your IDE " - "can catch these kinds of type mismatches earlier during development." - ), - _user_msg("Both fixes worked, thanks!"), - _assistant_msg("Great, glad both issues are resolved now."), - ]) - request = MemoryExtractRequest(memcell=memcell, user_id="user_001") - result = await extractor.extract_memory(request) - assert result is not None - assert result.task_intent == "Debug the app" - - @pytest.mark.asyncio - async def test_filter_rejects_trivial_conversation(self): - """LLM filter says not worth extracting -> returns None.""" - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({"worth_extracting": False, "reason": "trivial"}) - ) - memcell = _make_agent_memcell([ - _user_msg("What time is it?"), - _tool_call_msg("checking time"), - _tool_response_msg("3:00 PM"), - _assistant_msg("It's 3:00 PM"), - ]) - request = MemoryExtractRequest(memcell=memcell) - result = await extractor.extract_memory(request) - assert result is None - - @pytest.mark.asyncio - async def test_heuristic_rejects_short_no_tool_conversation(self): - """Heuristic skips no-tool conversation with <= 4 messages (no LLM call).""" - extractor = _build_case_extractor() - memcell = _make_agent_memcell([ - _user_msg("What time is it?"), - _assistant_msg("It's 3 PM"), - ]) - request = MemoryExtractRequest(memcell=memcell) - result = await extractor.extract_memory(request) - assert result is None - # No LLM call should have been made - extractor.llm_provider.generate.assert_not_called() - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_case_extractor.get_vectorize_service") - async def test_embedding_failure_still_returns_case(self, mock_vs_factory): - """If embedding fails, AgentCase is still returned without vector.""" - mock_vs_factory.side_effect = Exception("Vectorize service down") - - extractor = _build_case_extractor() - memcell = _make_agent_memcell([ - _user_msg("Build an API"), - _tool_call_msg("writing code"), - _tool_response_msg("done"), - _assistant_msg("API is ready"), - ]) - request = MemoryExtractRequest(memcell=memcell, user_id="user_001") - result = await extractor.extract_memory(request) - assert result is not None - assert result.vector is None - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_case_extractor.get_vectorize_service") - async def test_system_messages_stripped(self, mock_vs_factory): - """System messages before first user message are stripped.""" - mock_vs = MagicMock() - mock_vs.get_embedding = AsyncMock(return_value=MagicMock(tolist=lambda: [0.1])) - mock_vs.get_model_name.return_value = "test" - mock_vs_factory.return_value = mock_vs - - extractor = _build_case_extractor() - memcell = _make_agent_memcell([ - {"role": "system", "content": "You are a helpful assistant"}, - _user_msg("Build an API"), - _tool_call_msg("writing"), - _tool_response_msg("ok"), - _assistant_msg("Done"), - ]) - request = MemoryExtractRequest(memcell=memcell, user_id="u1") - result = await extractor.extract_memory(request) - assert result is not None - - -# =========================================================================== -# _convert_agent_case_to_doc tests -# =========================================================================== - - -class TestConvertAgentCaseToDoc: - """Tests for _convert_agent_case_to_doc. - - AgentCaseRecord is a Beanie document that requires MongoDB initialization, - so we mock its constructor to capture the kwargs and verify field mapping. - """ - - @patch("biz_layer.mem_db_operations.AgentCaseRecord") - def test_basic_conversion(self, mock_record_cls): - mock_record_cls.return_value = MagicMock() - agent_case = AgentCase( - memory_type=MemoryType.AGENT_CASE, - user_id="user_001", - timestamp=datetime(2025, 3, 1), - task_intent="Build API", - approach="1. Design\n2. Implement", - quality_score=0.8, - vector=[0.1, 0.2], - vector_model="test-model", - ) - memcell = _make_agent_memcell([ - _user_msg("Build an API", sender_id="agent_user_001"), - _assistant_msg("Done"), - ]) - _convert_agent_case_to_doc(agent_case, memcell) - kwargs = mock_record_cls.call_args.kwargs - assert kwargs["task_intent"] == "Build API" - assert kwargs["approach"] == "1. Design\n2. Implement" - assert kwargs["quality_score"] == 0.8 - assert kwargs["parent_type"] == "memcell" - assert kwargs["parent_id"] == "evt_001" - assert kwargs["vector"] == [0.1, 0.2] - assert kwargs["user_id"] == "agent_user_001" - - @patch("biz_layer.mem_db_operations.AgentCaseRecord") - def test_no_user_message_user_id_none(self, mock_record_cls): - mock_record_cls.return_value = MagicMock() - agent_case = AgentCase( - memory_type=MemoryType.AGENT_CASE, - user_id="", - timestamp=datetime(2025, 1, 1), - task_intent="task", - approach="approach", - ) - memcell = _make_agent_memcell([_assistant_msg("hello")]) - _convert_agent_case_to_doc(agent_case, memcell) - kwargs = mock_record_cls.call_args.kwargs - assert kwargs["user_id"] is None - - @patch("biz_layer.mem_db_operations.AgentCaseRecord") - def test_uses_memcell_timestamp(self, mock_record_cls): - mock_record_cls.return_value = MagicMock() - agent_case = AgentCase( - memory_type=MemoryType.AGENT_CASE, - user_id="u1", - timestamp=datetime(2025, 1, 1), - task_intent="t", - approach="a", - ) - memcell = _make_agent_memcell([_user_msg("hi"), _assistant_msg("hey")]) - _convert_agent_case_to_doc(agent_case, memcell) - kwargs = mock_record_cls.call_args.kwargs - assert kwargs["timestamp"] == datetime(2025, 3, 1, 12, 0, 0) - - @patch("biz_layer.mem_db_operations.AgentCaseRecord") - def test_fallback_to_current_time(self, mock_record_cls): - mock_record_cls.return_value = MagicMock() - agent_case = AgentCase( - memory_type=MemoryType.AGENT_CASE, - user_id="u1", - timestamp=None, - task_intent="t", - approach="a", - ) - memcell = MemCell( - user_id_list=["u1"], - original_data=[_wrap_msg(_user_msg("hi")), _wrap_msg(_assistant_msg("hey"))], - timestamp=None, - type=RawDataType.AGENTCONVERSATION, - ) - current_time = datetime(2025, 6, 15, 10, 0, 0) - _convert_agent_case_to_doc(agent_case, memcell, current_time=current_time) - kwargs = mock_record_cls.call_args.kwargs - assert kwargs["timestamp"] == current_time - - -# =========================================================================== -# _is_agent_case_quality_sufficient tests -# =========================================================================== - - -class TestIsAgentCaseQualitySufficient: - """Tests for _is_agent_case_quality_sufficient. - - Verifies that skill extraction is gated by the AgentCase quality_score - against the configurable skill_min_quality_score threshold. - """ - - def _make_case(self, quality_score): - return AgentCase( - memory_type=MemoryType.AGENT_CASE, - user_id="user_001", - timestamp=datetime(2025, 3, 1), - task_intent="task", - approach="approach", - quality_score=quality_score, - ) - - def test_score_above_threshold_passes(self): - config = MemorizeConfig(skill_min_quality_score=0.1) - assert _is_agent_case_quality_sufficient(self._make_case(0.5), config) is True - - def test_score_equal_to_threshold_passes(self): - config = MemorizeConfig(skill_min_quality_score=0.1) - assert _is_agent_case_quality_sufficient(self._make_case(0.1), config) is True - - def test_score_below_threshold_rejected(self): - config = MemorizeConfig(skill_min_quality_score=0.1) - assert _is_agent_case_quality_sufficient(self._make_case(0.05), config) is False - - def test_score_zero_below_default_threshold(self): - config = MemorizeConfig(skill_min_quality_score=0.1) - assert _is_agent_case_quality_sufficient(self._make_case(0.0), config) is False - - def test_score_none_rejected(self): - """None quality_score should block skill extraction (unknown quality).""" - config = MemorizeConfig(skill_min_quality_score=0.1) - assert _is_agent_case_quality_sufficient(self._make_case(None), config) is False - - def test_custom_threshold(self): - config = MemorizeConfig(skill_min_quality_score=0.5) - assert _is_agent_case_quality_sufficient(self._make_case(0.3), config) is False - assert _is_agent_case_quality_sufficient(self._make_case(0.5), config) is True - assert _is_agent_case_quality_sufficient(self._make_case(0.8), config) is True - - def test_threshold_zero_allows_all(self): - """When threshold is 0.0, all scores (including 0.0) should pass.""" - config = MemorizeConfig(skill_min_quality_score=0.0) - assert _is_agent_case_quality_sufficient(self._make_case(0.0), config) is True - assert _is_agent_case_quality_sufficient(self._make_case(0.01), config) is True - - -# =========================================================================== -# _extract_user_id_from_memcell tests -# =========================================================================== - - -class TestExtractUserIdFromMemcell: - """Tests for _extract_user_id_from_memcell.""" - - def test_extracts_first_user_sender_id(self): - memcell = _make_agent_memcell([ - _user_msg("hello", sender_id="user_abc"), - _assistant_msg("hi"), - ]) - assert _extract_user_id_from_memcell(memcell) == "user_abc" - - def test_no_user_messages_returns_none(self): - memcell = _make_agent_memcell([_assistant_msg("hello")]) - assert _extract_user_id_from_memcell(memcell) is None - - def test_user_without_sender_id_returns_none(self): - msg = {"role": "user", "content": "hello"} # no sender_id - memcell = MemCell( - user_id_list=["u1"], - original_data=[_wrap_msg(msg)], - timestamp=datetime(2025, 1, 1), - type=RawDataType.AGENTCONVERSATION, - ) - assert _extract_user_id_from_memcell(memcell) is None - - def test_empty_original_data(self): - memcell = MemCell( - user_id_list=["u1"], - original_data=[_wrap_msg(_user_msg("dummy"))], - timestamp=datetime(2025, 1, 1), - ) - # Override original_data to empty after construction - memcell.original_data = [] - assert _extract_user_id_from_memcell(memcell) is None - - def test_skips_non_user_messages(self): - memcell = _make_agent_memcell([ - {"role": "system", "content": "system prompt"}, - _assistant_msg("thinking"), - _user_msg("actual question", sender_id="real_user"), - ]) - assert _extract_user_id_from_memcell(memcell) == "real_user" - - -# =========================================================================== -# _pre_compress_to_list tests -# =========================================================================== - - -class TestPreCompressToList: - """Tests for AgentCaseExtractor._pre_compress_to_list.""" - - @pytest.mark.asyncio - async def test_no_tool_calls_returns_as_is(self): - extractor = _build_case_extractor() - messages = [ - _user_msg("hello"), - _assistant_msg("world"), - ] - result = await extractor._pre_compress_to_list(messages) - assert len(result) == 2 - assert result[0]["content"] == "hello" - - @pytest.mark.asyncio - async def test_small_tool_content_no_compression(self): - extractor = _build_case_extractor() - messages = [ - _user_msg("search"), - _tool_call_msg("searching", "search", '{"q": "test"}'), - _tool_response_msg("result"), - _assistant_msg("found it"), - ] - result = await extractor._pre_compress_to_list(messages) - assert len(result) == 4 - # No LLM call for compression - extractor.llm_provider.generate.assert_not_called() - - @pytest.mark.asyncio - async def test_does_not_mutate_original(self): - extractor = _build_case_extractor() - original = [_user_msg("hello"), _assistant_msg("world")] - import copy - original_copy = copy.deepcopy(original) - await extractor._pre_compress_to_list(original) - assert original == original_copy - - -# =========================================================================== -# _collect_tool_call_groups tests -# =========================================================================== - - -class TestCollectToolCallGroups: - """Tests for AgentCaseExtractor._collect_tool_call_groups.""" - - def test_single_group(self): - extractor = _build_case_extractor() - items = [ - _user_msg("search"), - _tool_call_msg("searching"), - _tool_response_msg("result"), - _assistant_msg("done"), - ] - groups = extractor._collect_tool_call_groups(items) - assert len(groups) == 1 - assert groups[0] == [1, 2] - - def test_multiple_groups(self): - extractor = _build_case_extractor() - items = [ - _user_msg("do things"), - _tool_call_msg("first"), - _tool_response_msg("r1"), - _tool_call_msg("second"), - _tool_response_msg("r2"), - _assistant_msg("done"), - ] - groups = extractor._collect_tool_call_groups(items) - assert len(groups) == 2 - assert groups[0] == [1, 2] - assert groups[1] == [3, 4] - - def test_no_tool_calls(self): - extractor = _build_case_extractor() - items = [_user_msg("hi"), _assistant_msg("hello")] - groups = extractor._collect_tool_call_groups(items) - assert groups == [] - - def test_tool_call_with_multiple_responses(self): - extractor = _build_case_extractor() - items = [ - _tool_call_msg("batch"), - _tool_response_msg("r1"), - _tool_response_msg("r2"), - _assistant_msg("done"), - ] - groups = extractor._collect_tool_call_groups(items) - assert len(groups) == 1 - assert groups[0] == [0, 1, 2] - - def test_empty_items(self): - extractor = _build_case_extractor() - groups = extractor._collect_tool_call_groups([]) - assert groups == [] - - -# =========================================================================== -# MemCell.conversation_data tests (agent conversation filtering) -# =========================================================================== - - -class TestMemCellConversationData: - """Tests for MemCell.conversation_data property filtering.""" - - def test_agent_conversation_filters_tool_messages(self): - memcell = _make_agent_memcell([ - _user_msg("search for X"), - _tool_call_msg("searching"), - _tool_response_msg("found X"), - _assistant_msg("Here is X"), - ]) - conv_data = memcell.conversation_data - # Should only have user and final assistant (tool messages filtered) - roles = [] - for item in conv_data: - msg = item.get("message", item) - roles.append(msg.get("role")) - assert "tool" not in roles - assert len(conv_data) == 2 - - def test_regular_conversation_not_filtered(self): - memcell = MemCell( - user_id_list=["u1"], - original_data=[_wrap_msg(_user_msg("hi")), _wrap_msg(_assistant_msg("hello"))], - timestamp=datetime(2025, 1, 1), - type=RawDataType.CONVERSATION, - ) - assert len(memcell.conversation_data) == 2 - - def test_original_data_preserved(self): - """original_data is not affected by conversation_data filtering.""" - memcell = _make_agent_memcell([ - _user_msg("query"), - _tool_call_msg("calling"), - _tool_response_msg("result"), - _assistant_msg("answer"), - ]) - _ = memcell.conversation_data # trigger filtering - assert len(memcell.original_data) == 4 - - -# =========================================================================== -# _compress_tool_chunk tests -# =========================================================================== - - -class TestCompressToolChunk: - """Tests for AgentCaseExtractor._compress_tool_chunk.""" - - @pytest.mark.asyncio - async def test_successful_compression(self): - compressed = [ - {"role": "assistant", "content": "", "tool_calls": [{"id": "c1", "function": {"name": "search", "arguments": "{}"}}]}, - {"role": "tool", "content": "compressed result", "tool_call_id": "c1"}, - ] - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({"compressed_messages": compressed}) - ) - messages = [ - _tool_call_msg("searching", "search", '{"q": "long query"}'), - _tool_response_msg("very long result " * 100), - ] - result = await extractor._compress_tool_chunk(messages) - assert result is not None - assert len(result) == 2 - assert result[1]["content"] == "compressed result" - - @pytest.mark.asyncio - async def test_wrong_count_returns_none(self): - """If LLM returns different number of messages, return None.""" - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({"compressed_messages": [{"role": "tool", "content": "only one"}]}) - ) - messages = [ - _tool_call_msg("searching"), - _tool_response_msg("result"), - ] - result = await extractor._compress_tool_chunk(messages) - assert result is None - - @pytest.mark.asyncio - async def test_missing_key_returns_none(self): - """If LLM response lacks compressed_messages key, return None.""" - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({"wrong_key": []}) - ) - result = await extractor._compress_tool_chunk([_tool_response_msg("x")]) - assert result is None - - @pytest.mark.asyncio - async def test_llm_exception_retries_then_none(self): - """On repeated LLM errors, retries twice then returns None.""" - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - side_effect=Exception("LLM down") - ) - result = await extractor._compress_tool_chunk([_tool_response_msg("x")]) - assert result is None - assert extractor.llm_provider.generate.call_count == 2 - - @pytest.mark.asyncio - async def test_first_fail_second_success(self): - """Retries on first failure, succeeds on second attempt.""" - compressed = [{"role": "tool", "content": "ok", "tool_call_id": "c1"}] - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - side_effect=[ - "invalid json!!!", - json.dumps({"compressed_messages": compressed}), - ] - ) - result = await extractor._compress_tool_chunk([_tool_response_msg("x")]) - assert result is not None - assert len(result) == 1 - - -# =========================================================================== -# _pre_compress_to_list with large content tests -# =========================================================================== - - -class TestPreCompressLargeContent: - """Tests for _pre_compress_to_list when tool content exceeds threshold.""" - - @pytest.mark.asyncio - async def test_large_content_triggers_compression(self): - """When tool content exceeds threshold, LLM compression is called.""" - compressed_msgs = [ - {"role": "assistant", "content": "", "tool_calls": [{"id": "c1", "function": {"name": "search", "arguments": "{}"}}]}, - {"role": "tool", "content": "compressed", "tool_call_id": "c1"}, - ] - extractor = _build_case_extractor() - # Set a very low threshold to force compression - extractor.pre_compress_chunk_size = 10 - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({"compressed_messages": compressed_msgs}) - ) - messages = [ - _user_msg("search"), - _tool_call_msg("searching", "search", '{"q": "test"}'), - _tool_response_msg("a]very long result " * 200), - _assistant_msg("done"), - ] - result = await extractor._pre_compress_to_list(messages) - # LLM should have been called for compression - assert extractor.llm_provider.generate.call_count >= 1 - assert len(result) == 4 - - @pytest.mark.asyncio - async def test_compression_failure_keeps_originals(self): - """When LLM compression fails, original messages are preserved.""" - extractor = _build_case_extractor() - extractor.pre_compress_chunk_size = 10 - extractor.llm_provider.generate = AsyncMock( - side_effect=Exception("LLM error") - ) - messages = [ - _user_msg("search"), - _tool_call_msg("searching", "search", '{"q": "test"}'), - _tool_response_msg("long result " * 200), - _assistant_msg("done"), - ] - result = await extractor._pre_compress_to_list(messages) - # Should still return all messages (originals preserved) - assert len(result) == 4 - - @pytest.mark.asyncio - async def test_does_not_mutate_original_messages(self): - """_pre_compress_to_list should not modify the input list.""" - import copy as copy_mod - - extractor = _build_case_extractor() - extractor.pre_compress_chunk_size = 10 - compressed_msgs = [ - {"role": "assistant", "content": "", "tool_calls": [{"id": "c1", "function": {"name": "s", "arguments": "{}"}}]}, - {"role": "tool", "content": "compressed", "tool_call_id": "c1"}, - ] - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({"compressed_messages": compressed_msgs}) - ) - messages = [ - _user_msg("go"), - _tool_call_msg("calling", "search", '{"q": "x"}'), - _tool_response_msg("long " * 200), - _assistant_msg("done"), - ] - original_copy = copy_mod.deepcopy(messages) - await extractor._pre_compress_to_list(messages) - assert messages == original_copy - - -# =========================================================================== -# _compute_embedding tests -# =========================================================================== - - -class TestComputeEmbedding: - """Tests for AgentCaseExtractor._compute_embedding.""" - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_case_extractor.get_vectorize_service") - async def test_successful_embedding(self, mock_vs_factory): - mock_vs = MagicMock() - mock_vs.get_embedding = AsyncMock( - return_value=MagicMock(tolist=lambda: [0.1, 0.2, 0.3]) - ) - mock_vs.get_model_name.return_value = "test-model" - mock_vs_factory.return_value = mock_vs - - extractor = _build_case_extractor() - result = await extractor._compute_embedding("Build a REST API") - assert result is not None - assert result["embedding"] == [0.1, 0.2, 0.3] - assert result["vector_model"] == "test-model" - - @pytest.mark.asyncio - async def test_empty_text_returns_none(self): - extractor = _build_case_extractor() - result = await extractor._compute_embedding("") - assert result is None - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_case_extractor.get_vectorize_service") - async def test_embedding_exception_returns_none(self, mock_vs_factory): - mock_vs_factory.side_effect = Exception("Service unavailable") - extractor = _build_case_extractor() - result = await extractor._compute_embedding("some text") - assert result is None - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_case_extractor.get_vectorize_service") - async def test_embedding_without_tolist(self, mock_vs_factory): - """Embedding result that is a plain list (no tolist method).""" - mock_vs = MagicMock() - mock_vs.get_embedding = AsyncMock(return_value=[0.5, 0.6]) - mock_vs.get_model_name.return_value = "plain-model" - mock_vs_factory.return_value = mock_vs - - extractor = _build_case_extractor() - result = await extractor._compute_embedding("test text") - assert result is not None - assert result["embedding"] == [0.5, 0.6] - - -# =========================================================================== -# _unwrap_messages edge cases -# =========================================================================== - - -class TestUnwrapMessagesEdgeCases: - """Additional edge case tests for _unwrap_messages.""" - - def test_assistant_with_content_list_normalized(self): - """Assistant message with content[] list is also normalized.""" - msg = { - "role": "assistant", - "content": [ - {"type": "text", "content": "Here is "}, - {"type": "text", "content": "the answer"}, - ], - } - data = [_wrap_msg(msg)] - result = AgentCaseExtractor._unwrap_messages(data) - assert isinstance(result[0]["content"], str) - assert "Here is" in result[0]["content"] - assert "the answer" in result[0]["content"] - - def test_tool_message_with_content_list_normalized(self): - """Tool message with content[] list is normalized to string.""" - msg = { - "role": "tool", - "content": [{"type": "text", "content": "tool output"}], - "tool_call_id": "c1", - } - data = [_wrap_msg(msg)] - result = AgentCaseExtractor._unwrap_messages(data) - assert isinstance(result[0]["content"], str) - assert "tool output" in result[0]["content"] - - def test_assistant_with_tool_calls_and_null_content(self): - """Assistant message with tool_calls and None content stays None.""" - msg = { - "role": "assistant", - "content": None, - "tool_calls": [{"id": "c1", "function": {"name": "f", "arguments": "{}"}}], - } - data = [_wrap_msg(msg)] - result = AgentCaseExtractor._unwrap_messages(data) - assert result[0]["content"] is None - assert result[0]["tool_calls"] is not None - - -# =========================================================================== -# extract_memory additional edge cases -# =========================================================================== - - -class TestExtractMemoryEdgeCases: - """Additional edge case tests for extract_memory.""" - - @pytest.mark.asyncio - async def test_llm_extraction_all_retries_fail(self): - """When LLM extraction fails after all retries, returns None.""" - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - side_effect=Exception("LLM unavailable") - ) - memcell = _make_agent_memcell([ - _user_msg("Build me a complex API"), - _tool_call_msg("writing", "write_file", '{"path": "api.py"}'), - _tool_response_msg("done"), - _tool_call_msg("testing", "run_test", '{"file": "test.py"}'), - _tool_response_msg("passed"), - _assistant_msg("All done"), - ]) - request = MemoryExtractRequest(memcell=memcell, user_id="u1") - result = await extractor.extract_memory(request) - assert result is None - - @pytest.mark.asyncio - async def test_only_system_messages_returns_none(self): - """MemCell with only system messages (no user) returns None.""" - extractor = _build_case_extractor() - memcell = _make_agent_memcell([ - {"role": "system", "content": "You are a helper"}, - ]) - request = MemoryExtractRequest(memcell=memcell) - result = await extractor.extract_memory(request) - assert result is None - extractor.llm_provider.generate.assert_not_called() - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_case_extractor.get_vectorize_service") - async def test_quality_score_clamped_in_result(self, mock_vs_factory): - """quality_score > 1.0 from LLM is clamped to 1.0 in final AgentCase.""" - mock_vs = MagicMock() - mock_vs.get_embedding = AsyncMock(return_value=MagicMock(tolist=lambda: [0.1])) - mock_vs.get_model_name.return_value = "test" - mock_vs_factory.return_value = mock_vs - - extractor = _build_case_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({ - "task_intent": "Deploy service", - "approach": "1. Build\n2. Deploy", - "quality_score": 1.5, - }) - ) - memcell = _make_agent_memcell([ - _user_msg("Deploy the service"), - _tool_call_msg("deploying"), - _tool_response_msg("deployed"), - _assistant_msg("Service is live"), - ]) - request = MemoryExtractRequest(memcell=memcell, user_id="u1") - result = await extractor.extract_memory(request) - assert result is not None - assert result.quality_score == 1.0 - - -# --------------------------------------------------------------------------- -# _content_change_ratio tests -# --------------------------------------------------------------------------- - -class TestContentChangeRatio: - """Tests for AgentSkillExtractor._content_change_ratio.""" - - def test_both_empty(self): - assert AgentSkillExtractor._content_change_ratio("", "") == 0.0 - - def test_old_empty(self): - assert AgentSkillExtractor._content_change_ratio("", "new content") == 1.0 - - def test_new_empty(self): - assert AgentSkillExtractor._content_change_ratio("old content", "") == 1.0 - - def test_identical(self): - text = "## Steps\n1. Do something\n2. Check result" - assert AgentSkillExtractor._content_change_ratio(text, text) == 0.0 - - def test_minor_edit(self): - old = "## Steps\n1. Run the query\n2. Check the result\n3. Return output" - new = "## Steps\n1. Run the query\n2. Check the result\n3. Return the output" - ratio = AgentSkillExtractor._content_change_ratio(old, new) - assert ratio < 0.1, f"Minor edit should have small ratio, got {ratio}" - - def test_major_rewrite(self): - old = "## Steps\n1. Use curl to call API\n2. Parse JSON response" - new = "## Steps\n1. Use Python requests library\n2. Handle pagination\n3. Retry on failure\n4. Cache results" - ratio = AgentSkillExtractor._content_change_ratio(old, new) - assert ratio >= 0.3, f"Major rewrite should have ratio >= 0.3, got {ratio}" - - def test_completely_different(self): - ratio = AgentSkillExtractor._content_change_ratio("aaaaaa", "zzzzzz") - assert ratio >= 0.9, f"Completely different should be near 1.0, got {ratio}" - - -# --------------------------------------------------------------------------- -# Maturity re-evaluation skip/trigger logic in _apply_update -# --------------------------------------------------------------------------- - -def _make_skill_record( - content="## Steps\n1. Step one\n - How: do it\n2. Step two\n - How: do that\n3. Done\n - Check: ok", - name="Test Skill", - description="A test skill", - confidence=0.7, - maturity_score=0.8, - source_case_ids=None, -): - """Create a mock skill record for _apply_update tests.""" - record = MagicMock() - record.id = "skill_001" - record.content = content - record.name = name - record.description = description - record.confidence = confidence - record.maturity_score = maturity_score - record.source_case_ids = source_case_ids or [] - return record - - -def _build_skill_extractor(): - """Build an AgentSkillExtractor with mocked LLM provider.""" - llm = MagicMock() - llm.generate = AsyncMock(return_value="") - extractor = AgentSkillExtractor( - llm_provider=llm, - maturity_threshold=0.6, - retire_confidence=0.1, - ) - extractor._compute_embedding = AsyncMock(return_value=None) - return extractor - - -class TestMaturityReevalLogic: - """Tests that maturity re-evaluation triggers correctly based on - content change ratio, confidence direction, and existing maturity.""" - - @pytest.mark.asyncio - async def test_skip_when_trivial_change(self): - """Content change < 10% should always skip maturity, even if immature.""" - extractor = _build_skill_extractor() - extractor._evaluate_maturity = AsyncMock(return_value=0.9) - - record = _make_skill_record(maturity_score=0.4, confidence=0.7) - # Trivial edit: append a tiny note - new_content = record.content + "\n - Note: ok" - ratio = AgentSkillExtractor._content_change_ratio(record.content, new_content) - assert ratio < 0.1, f"Test setup: expected trivial change, got {ratio}" - - op = {"index": 0, "data": {"content": new_content, "confidence": 0.7}} - repo = MagicMock() - repo.update_skill_by_id = AsyncMock(return_value=True) - result = SkillExtractionResult() - - await extractor._apply_update(op, [record], repo, result) - - extractor._evaluate_maturity.assert_not_called() - - @pytest.mark.asyncio - async def test_skip_when_trivial_change_even_confidence_drops(self): - """Trivial content change should skip maturity even if confidence drops.""" - extractor = _build_skill_extractor() - extractor._evaluate_maturity = AsyncMock(return_value=0.5) - - record = _make_skill_record(maturity_score=0.8, confidence=0.7) - new_content = record.content + "\n - Note: ok" - ratio = AgentSkillExtractor._content_change_ratio(record.content, new_content) - assert ratio < 0.1, f"Test setup: expected trivial change, got {ratio}" - - op = {"index": 0, "data": {"content": new_content, "confidence": 0.3}} - repo = MagicMock() - repo.update_skill_by_id = AsyncMock(return_value=True) - result = SkillExtractionResult() - - await extractor._apply_update(op, [record], repo, result) - - extractor._evaluate_maturity.assert_not_called() - - @pytest.mark.asyncio - async def test_skip_when_minor_edit_and_mature(self): - """Change 10-30% on a mature skill with stable confidence should skip.""" - extractor = _build_skill_extractor() - extractor._evaluate_maturity = AsyncMock(return_value=0.9) - - record = _make_skill_record(maturity_score=0.8, confidence=0.7) - # Moderate edit (~15% change) but still below 30% - new_content = record.content + "\n4. Extra verification step\n - How: run checks\n - Check: all green" - ratio = AgentSkillExtractor._content_change_ratio(record.content, new_content) - assert 0.1 <= ratio < 0.3, f"Test setup: expected 10-30% change, got {ratio}" - - op = {"index": 0, "data": {"content": new_content, "confidence": 0.7}} - repo = MagicMock() - repo.update_skill_by_id = AsyncMock(return_value=True) - result = SkillExtractionResult() - - await extractor._apply_update(op, [record], repo, result) - - extractor._evaluate_maturity.assert_not_called() - - @pytest.mark.asyncio - async def test_trigger_when_confidence_drops(self): - """Mature skill + moderate change + confidence dropping: LLM re-evaluation.""" - extractor = _build_skill_extractor() - extractor._evaluate_maturity = AsyncMock(return_value=0.65) - - record = _make_skill_record(maturity_score=0.8, confidence=0.7) - # Non-trivial edit (>= 10%) + confidence drops - new_content = record.content + "\n4. Extra verification step\n - How: run checks\n - Check: all green" - ratio = AgentSkillExtractor._content_change_ratio(record.content, new_content) - assert ratio >= 0.1, f"Test setup: expected non-trivial change, got {ratio}" - - op = {"index": 0, "data": {"content": new_content, "confidence": 0.4}} - repo = MagicMock() - repo.update_skill_by_id = AsyncMock(return_value=True) - result = SkillExtractionResult() - - await extractor._apply_update(op, [record], repo, result) - - extractor._evaluate_maturity.assert_called_once() - update_args = repo.update_skill_by_id.call_args[0][1] - assert "maturity_score" in update_args - assert update_args["maturity_score"] == 0.65 - - @pytest.mark.asyncio - async def test_trigger_when_immature(self): - """Immature skill with moderate change and decent source quality: LLM re-evaluation.""" - extractor = _build_skill_extractor() - extractor._evaluate_maturity = AsyncMock(return_value=0.7) - - record = _make_skill_record(maturity_score=0.4, confidence=0.7) - new_content = record.content + "\n4. Extra verification step\n - How: run checks\n - Check: all green" - ratio = AgentSkillExtractor._content_change_ratio(record.content, new_content) - assert ratio >= 0.1, f"Test setup: expected non-trivial change, got {ratio}" - - op = {"index": 0, "data": {"content": new_content, "confidence": 0.7}} - repo = MagicMock() - repo.update_skill_by_id = AsyncMock(return_value=True) - result = SkillExtractionResult() - - await extractor._apply_update( - op, [record], repo, result, source_quality=0.7, - ) - - extractor._evaluate_maturity.assert_called_once() - update_args = repo.update_skill_by_id.call_args[0][1] - assert "maturity_score" in update_args - assert update_args["maturity_score"] == 0.7 - - @pytest.mark.asyncio - async def test_trigger_when_major_content_change(self): - """Content change >= 40% should always trigger LLM re-evaluation.""" - extractor = _build_skill_extractor() - extractor._evaluate_maturity = AsyncMock(return_value=0.85) - - record = _make_skill_record(maturity_score=0.8, confidence=0.7) - # Completely rewrite the content - new_content = "## Steps\n1. Totally new approach\n - How: different method\n2. New step\n - How: new way\n3. Another step\n - Check: new check\n4. Final\n - Check: done" - op = {"index": 0, "data": {"content": new_content, "confidence": 0.7}} - repo = MagicMock() - repo.update_skill_by_id = AsyncMock(return_value=True) - result = SkillExtractionResult() - - # Verify the change ratio is actually >= 0.4 - ratio = AgentSkillExtractor._content_change_ratio(record.content, new_content) - assert ratio >= 0.4, f"Test setup: expected change ratio >= 0.4, got {ratio}" - - await extractor._apply_update(op, [record], repo, result) - - extractor._evaluate_maturity.assert_called_once() - update_args = repo.update_skill_by_id.call_args[0][1] - assert "maturity_score" in update_args - assert update_args["maturity_score"] == 0.85 - - @pytest.mark.asyncio - async def test_no_content_change_skips_maturity_block(self): - """When only metadata changes (no content/name/desc), skip maturity entirely.""" - extractor = _build_skill_extractor() - extractor._evaluate_maturity = AsyncMock(return_value=0.9) - - record = _make_skill_record(maturity_score=0.4, confidence=0.5) - # Only confidence changes, no content/name/desc - op = {"index": 0, "data": {"confidence": 0.8}} - repo = MagicMock() - repo.update_skill_by_id = AsyncMock(return_value=True) - result = SkillExtractionResult() - - await extractor._apply_update(op, [record], repo, result) - - extractor._evaluate_maturity.assert_not_called() - - -# =========================================================================== -# mem_memorize._extract_agent_case (wrapper function) -# =========================================================================== - - -def _make_extraction_state_for_pipeline(**overrides): - """Create a minimal ExtractionState-like object for pipeline tests.""" - from types import SimpleNamespace - memcell = overrides.pop("memcell", MemCell( - user_id_list=["u1"], - original_data=[ - {"message": {"role": "user", "content": "Deploy the app", "sender_id": "u1"}}, - {"message": {"role": "assistant", "content": "Done deploying."}}, - ], - timestamp=datetime(2025, 6, 1, 10, 0, 0), - event_id="evt_100", - group_id="g1", - type=RawDataType.AGENTCONVERSATION, - )) - agent_case_bo = overrides.pop("agent_case", AgentCase( - memory_type=MemoryType.AGENT_CASE, - user_id="u1", - timestamp=datetime(2025, 6, 1, 10, 0, 0), - task_intent="Deploy the application to production", - approach="1. Build docker image\n2. Push to registry\n3. Deploy to k8s", - quality_score=0.85, - )) - request = overrides.pop("request", SimpleNamespace( - group_id="g1", - session_id="sess_001", - )) - current_time = overrides.pop("current_time", datetime(2025, 6, 1, 10, 0, 0)) - - return SimpleNamespace( - memcell=memcell, - agent_case=agent_case_bo, - request=request, - current_time=current_time, - **overrides, - ) - - -class TestMemorizeExtractAgentCaseWrapper: - """Tests for _extract_agent_case wrapper in mem_memorize.py.""" - - @pytest.mark.asyncio - async def test_success_stores_on_state(self): - """Successful extraction stores agent_case on state.""" - expected_case = AgentCase( - memory_type=MemoryType.AGENT_CASE, - user_id="u1", - timestamp=datetime(2025, 6, 1), - task_intent="Deploy", - approach="Steps", - quality_score=0.85, - ) - mock_manager = AsyncMock() - mock_manager.extract_memory = AsyncMock(return_value=expected_case) - - state = _make_extraction_state_for_pipeline(agent_case=None) - - from biz_layer.mem_memorize import _extract_agent_case - result = await _extract_agent_case(state, mock_manager) - - assert result is expected_case - assert state.agent_case is expected_case - mock_manager.extract_memory.assert_called_once() - - @pytest.mark.asyncio - async def test_none_result_returns_none(self): - """When extractor returns None, state.agent_case is not set.""" - mock_manager = AsyncMock() - mock_manager.extract_memory = AsyncMock(return_value=None) - - state = _make_extraction_state_for_pipeline(agent_case=None) - - from biz_layer.mem_memorize import _extract_agent_case - result = await _extract_agent_case(state, mock_manager) - - assert result is None - - @pytest.mark.asyncio - async def test_exception_result_returns_none(self): - """When extractor returns an Exception, result should be None.""" - mock_manager = AsyncMock() - mock_manager.extract_memory = AsyncMock(return_value=Exception("LLM error")) - - state = _make_extraction_state_for_pipeline(agent_case=None) - - from biz_layer.mem_memorize import _extract_agent_case - result = await _extract_agent_case(state, mock_manager) - - assert result is None - - -# =========================================================================== -# mem_memorize._save_agent_case -# =========================================================================== - - -class TestMemorizeSaveAgentCase: - """Tests for _save_agent_case in mem_memorize.py.""" - - @pytest.mark.asyncio - async def test_success_returns_one(self): - """Successful save returns 1.""" - state = _make_extraction_state_for_pipeline() - mock_doc = MagicMock() - - with patch("biz_layer.mem_memorize._convert_agent_case_to_doc", return_value=mock_doc) as mock_conv, \ - patch("biz_layer.mem_memorize.save_memory_docs", new_callable=AsyncMock) as mock_save: - mock_save.return_value = {} - - from biz_layer.mem_memorize import _save_agent_case - result = await _save_agent_case(state) - - assert result == 1 - mock_conv.assert_called_once() - mock_save.assert_called_once() - - @pytest.mark.asyncio - async def test_conversion_error_returns_zero(self): - """If conversion raises, returns 0.""" - state = _make_extraction_state_for_pipeline() - - with patch("biz_layer.mem_memorize._convert_agent_case_to_doc", side_effect=ValueError("bad data")): - from biz_layer.mem_memorize import _save_agent_case - result = await _save_agent_case(state) - - assert result == 0 - - @pytest.mark.asyncio - async def test_save_error_returns_zero(self): - """If save_memory_docs raises, returns 0.""" - state = _make_extraction_state_for_pipeline() - mock_doc = MagicMock() - - with patch("biz_layer.mem_memorize._convert_agent_case_to_doc", return_value=mock_doc), \ - patch("biz_layer.mem_memorize.save_memory_docs", new_callable=AsyncMock, side_effect=Exception("DB down")): - from biz_layer.mem_memorize import _save_agent_case - result = await _save_agent_case(state) - - assert result == 0 diff --git a/methods/EverCore/tests/test_agent_converters_and_pipeline.py b/methods/EverCore/tests/test_agent_converters_and_pipeline.py deleted file mode 100644 index 73e8ae36..00000000 --- a/methods/EverCore/tests/test_agent_converters_and_pipeline.py +++ /dev/null @@ -1,2340 +0,0 @@ -""" -Supplementary tests for Agent Case/Skill pipeline gaps. - -Covers: -- ES Converters: AgentCaseConverter, AgentSkillConverter -- Milvus Converters: AgentCaseMilvusConverter, AgentSkillMilvusConverter -- _trigger_agent_skill_extraction: full pipeline with mocked dependencies -- AgentSkillExtractor edge cases: confidence boundary, mixed operations, embedding failure -- AgentCaseExtractor: pre-compress with large content, _compress_tool_chunk - -Usage: - PYTHONPATH=src pytest tests/test_agent_converters_and_pipeline.py -v -""" - -import json -import pytest -from datetime import datetime -from types import SimpleNamespace -from unittest.mock import AsyncMock, MagicMock, patch -from typing import Any, Dict, List - -from bson import ObjectId - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -def _mock_case_record(**overrides): - """Create a mock AgentCaseRecord for converter tests.""" - defaults = dict( - id=ObjectId(), - user_id="user_001", - group_id="group_001", - session_id="sess_001", - timestamp=datetime(2025, 3, 1, 12, 0, 0), - task_intent="Build a REST API for user management", - approach="1. Design the schema\n2. Implement CRUD endpoints\n3. Add validation", - quality_score=0.85, - parent_type="memcell", - parent_id="evt_001", - vector=[0.1, 0.2, 0.3], - vector_model="text-embedding-3-small", - key_insight="", - created_at=datetime(2025, 3, 1, 12, 0, 0), - updated_at=datetime(2025, 3, 1, 12, 5, 0), - ) - defaults.update(overrides) - return SimpleNamespace(**defaults) - - -def _mock_skill_record(**overrides): - """Create a mock AgentSkillRecord for converter tests.""" - defaults = dict( - id=ObjectId(), - user_id="user_001", - group_id="group_001", - cluster_id="cluster_001", - name="API Development", - description="Build REST APIs with proper error handling and validation", - content="## Steps\n1. Design schema\n2. Implement endpoints\n3. Add validation\n4. Write tests\n5. Deploy", - confidence=0.8, - maturity_score=0.75, - vector=[0.1, 0.2, 0.3], - vector_model="text-embedding-3-small", - source_case_ids=["evt_001"], - created_at=datetime(2025, 3, 1, 12, 0, 0), - updated_at=datetime(2025, 3, 1, 12, 5, 0), - ) - defaults.update(overrides) - return SimpleNamespace(**defaults) - - -# =========================================================================== -# AgentCaseConverter tests -# =========================================================================== - - -class TestAgentCaseConverter: - """Tests for AgentCaseConverter.from_mongo and _build_search_content.""" - - def test_from_mongo_basic(self): - from infra_layer.adapters.out.search.elasticsearch.converter.agent_case_converter import ( - AgentCaseConverter, - ) - - doc = _mock_case_record() - es_doc = AgentCaseConverter.from_mongo(doc) - assert es_doc.task_intent == "Build a REST API for user management" - assert ( - es_doc.approach - == "1. Design the schema\n2. Implement CRUD endpoints\n3. Add validation" - ) - assert es_doc.user_id == "user_001" - assert es_doc.parent_type == "memcell" - - def test_from_mongo_none_raises(self): - from infra_layer.adapters.out.search.elasticsearch.converter.agent_case_converter import ( - AgentCaseConverter, - ) - - with pytest.raises(ValueError, match="cannot be empty"): - AgentCaseConverter.from_mongo(None) - - def test_from_mongo_none_fields_fallback(self): - from infra_layer.adapters.out.search.elasticsearch.converter.agent_case_converter import ( - AgentCaseConverter, - ) - - doc = _mock_case_record(task_intent=None, approach=None) - es_doc = AgentCaseConverter.from_mongo(doc) - assert es_doc.task_intent == "" - assert es_doc.approach == "" - - def test_build_search_content_deduplicates(self): - from infra_layer.adapters.out.search.elasticsearch.converter.agent_case_converter import ( - AgentCaseConverter, - ) - - doc = _mock_case_record( - task_intent="API design patterns", - approach="API design patterns for REST services", - ) - content = AgentCaseConverter._build_search_content(doc) - # Words should be deduplicated - assert len(content) == len(set(content)) - - def test_build_search_content_empty_fallback(self): - from infra_layer.adapters.out.search.elasticsearch.converter.agent_case_converter import ( - AgentCaseConverter, - ) - - doc = _mock_case_record(task_intent="", approach="") - content = AgentCaseConverter._build_search_content(doc) - assert content == [""] - - def test_build_search_content_only_task_intent_fallback(self): - """When filtering removes all words, fallback to raw task_intent.""" - from infra_layer.adapters.out.search.elasticsearch.converter.agent_case_converter import ( - AgentCaseConverter, - ) - - doc = _mock_case_record(task_intent="a", approach="") - content = AgentCaseConverter._build_search_content(doc) - # Single char filtered by min_length=2, fallback to raw - assert content == ["a"] - - -# =========================================================================== -# AgentSkillConverter tests -# =========================================================================== - - -class TestAgentSkillConverter: - """Tests for AgentSkillConverter.from_mongo and _build_search_content.""" - - def test_from_mongo_basic(self): - from infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter import ( - AgentSkillConverter, - ) - - doc = _mock_skill_record() - es_doc = AgentSkillConverter.from_mongo(doc) - assert es_doc.name == "API Development" - assert es_doc.cluster_id == "cluster_001" - assert es_doc.confidence == 0.8 - assert es_doc.maturity_score == 0.75 - - def test_from_mongo_none_raises(self): - from infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter import ( - AgentSkillConverter, - ) - - with pytest.raises(ValueError): - AgentSkillConverter.from_mongo(None) - - def test_from_mongo_none_fields(self): - from infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter import ( - AgentSkillConverter, - ) - - doc = _mock_skill_record(name=None, description=None, content=None) - es_doc = AgentSkillConverter.from_mongo(doc) - assert es_doc.name == "" - assert es_doc.description == "" - assert es_doc.content == "" - - def test_build_search_content_combines_all_fields(self): - from infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter import ( - AgentSkillConverter, - ) - - doc = _mock_skill_record() - content = AgentSkillConverter._build_search_content(doc) - assert len(content) > 0 - assert len(content) == len(set(content)) # deduplicated - - def test_build_search_content_fallback_to_description(self): - """When filtering removes all words, fallback to description.""" - from infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter import ( - AgentSkillConverter, - ) - - doc = _mock_skill_record(name="", description="x", content="") - content = AgentSkillConverter._build_search_content(doc) - assert content == ["x"] - - def test_build_search_content_all_empty(self): - from infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter import ( - AgentSkillConverter, - ) - - doc = _mock_skill_record(name="", description="", content="") - content = AgentSkillConverter._build_search_content(doc) - assert content == [""] - - -# =========================================================================== -# AgentCaseMilvusConverter tests -# =========================================================================== - - -class TestAgentCaseMilvusConverter: - """Tests for AgentCaseMilvusConverter.from_mongo.""" - - def test_basic_conversion(self): - from infra_layer.adapters.out.search.milvus.converter.agent_case_milvus_converter import ( - AgentCaseMilvusConverter, - ) - - doc = _mock_case_record() - entity = AgentCaseMilvusConverter.from_mongo(doc) - assert entity["id"] == str(doc.id) - assert entity["vector"] == [0.1, 0.2, 0.3] - assert entity["user_id"] == "user_001" - assert entity["task_intent"] == "Build a REST API for user management" - assert entity["parent_type"] == "memcell" - - def test_none_raises(self): - from infra_layer.adapters.out.search.milvus.converter.agent_case_milvus_converter import ( - AgentCaseMilvusConverter, - ) - - with pytest.raises(ValueError): - AgentCaseMilvusConverter.from_mongo(None) - - def test_none_timestamp_defaults_to_zero(self): - from infra_layer.adapters.out.search.milvus.converter.agent_case_milvus_converter import ( - AgentCaseMilvusConverter, - ) - - doc = _mock_case_record(timestamp=None) - entity = AgentCaseMilvusConverter.from_mongo(doc) - assert entity["timestamp"] == 0 - - def test_none_vector_defaults_to_empty_list(self): - from infra_layer.adapters.out.search.milvus.converter.agent_case_milvus_converter import ( - AgentCaseMilvusConverter, - ) - - doc = _mock_case_record(vector=None) - entity = AgentCaseMilvusConverter.from_mongo(doc) - assert entity["vector"] == [] - - def test_long_text_truncated(self): - from infra_layer.adapters.out.search.milvus.converter.agent_case_milvus_converter import ( - AgentCaseMilvusConverter, - ) - - doc = _mock_case_record(task_intent="x" * 10000) - entity = AgentCaseMilvusConverter.from_mongo(doc) - assert len(entity["task_intent"]) == 5000 - assert "search_content" not in entity - - def test_none_fields_default_to_empty_string(self): - from infra_layer.adapters.out.search.milvus.converter.agent_case_milvus_converter import ( - AgentCaseMilvusConverter, - ) - - doc = _mock_case_record( - user_id=None, - group_id=None, - task_intent=None, - approach=None, - parent_type=None, - parent_id=None, - vector_model=None, - ) - entity = AgentCaseMilvusConverter.from_mongo(doc) - assert entity["user_id"] == "" - assert entity["group_id"] == "" - assert entity["task_intent"] == "" - assert entity["parent_type"] == "" - - def test_no_metadata_field(self): - from infra_layer.adapters.out.search.milvus.converter.agent_case_milvus_converter import ( - AgentCaseMilvusConverter, - ) - - doc = _mock_case_record() - entity = AgentCaseMilvusConverter.from_mongo(doc) - assert "metadata" not in entity - - -# =========================================================================== -# AgentSkillMilvusConverter tests -# =========================================================================== - - -class TestAgentSkillMilvusConverter: - """Tests for AgentSkillMilvusConverter.from_mongo.""" - - def test_basic_conversion(self): - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - - doc = _mock_skill_record() - entity = AgentSkillMilvusConverter.from_mongo(doc) - assert entity["id"] == str(doc.id) - assert entity["vector"] == [0.1, 0.2, 0.3] - assert entity["cluster_id"] == "cluster_001" - assert entity["maturity_score"] == 0.75 - assert entity["confidence"] == 0.8 - assert "API Development" in entity["content"] - - def test_none_raises(self): - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - - with pytest.raises(ValueError): - AgentSkillMilvusConverter.from_mongo(None) - - def test_none_vector_defaults_to_empty(self): - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - - doc = _mock_skill_record(vector=None) - entity = AgentSkillMilvusConverter.from_mongo(doc) - assert entity["vector"] == [] - - def test_no_timestamp_fields(self): - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - - doc = _mock_skill_record() - entity = AgentSkillMilvusConverter.from_mongo(doc) - assert "created_at" not in entity - assert "updated_at" not in entity - - def test_content_field_is_name_plus_description(self): - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - - doc = _mock_skill_record(name="Skill Name", description="Skill Description") - entity = AgentSkillMilvusConverter.from_mongo(doc) - assert "Skill Name" in entity["content"] - assert "Skill Description" in entity["content"] - - def test_search_content_removed(self): - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - - doc = _mock_skill_record() - entity = AgentSkillMilvusConverter.from_mongo(doc) - assert "search_content" not in entity - - def test_no_metadata_field(self): - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - - doc = _mock_skill_record() - entity = AgentSkillMilvusConverter.from_mongo(doc) - assert "metadata" not in entity - - def test_none_fields_default_to_empty(self): - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - - doc = _mock_skill_record( - name=None, - description=None, - content=None, - user_id=None, - group_id=None, - cluster_id=None, - vector_model=None, - ) - entity = AgentSkillMilvusConverter.from_mongo(doc) - assert entity["user_id"] == "" - assert entity["cluster_id"] == "" - - def test_confidence_is_top_level_field(self): - """confidence must be a top-level entity field for Milvus filtering.""" - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - - doc = _mock_skill_record(confidence=0.65) - entity = AgentSkillMilvusConverter.from_mongo(doc) - assert "confidence" in entity - assert entity["confidence"] == 0.65 - - def test_confidence_zero(self): - """confidence=0.0 should be preserved, not dropped.""" - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - - doc = _mock_skill_record(confidence=0.0) - entity = AgentSkillMilvusConverter.from_mongo(doc) - assert entity["confidence"] == 0.0 - - def test_confidence_boundary_values(self): - """confidence boundary values 0.0 and 1.0 should be accepted.""" - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - - for val in (0.0, 0.1, 0.5, 0.95, 1.0): - doc = _mock_skill_record(confidence=val) - entity = AgentSkillMilvusConverter.from_mongo(doc) - assert entity["confidence"] == val - - -class TestAgentSkillCollectionSchema: - """Tests for AgentSkillCollection Milvus schema definition.""" - - def test_schema_has_confidence_field(self): - """Schema must include a confidence FLOAT field for filter expressions.""" - from infra_layer.adapters.out.search.milvus.memory.agent_skill_collection import ( - AgentSkillCollection, - ) - from pymilvus import DataType - - schema = AgentSkillCollection._SCHEMA - field_map = {f.name: f for f in schema.fields} - - assert "confidence" in field_map, "confidence field missing from Milvus schema" - assert field_map["confidence"].dtype == DataType.FLOAT - - def test_confidence_field_has_index(self): - """confidence field should have an AUTOINDEX for efficient filtering.""" - from infra_layer.adapters.out.search.milvus.memory.agent_skill_collection import ( - AgentSkillCollection, - ) - - indexed_fields = [cfg.field_name for cfg in AgentSkillCollection._INDEX_CONFIGS] - assert "confidence" in indexed_fields - - def test_schema_field_parity_with_converter(self): - """All top-level keys produced by the converter must exist in the schema.""" - from infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter import ( - AgentSkillMilvusConverter, - ) - from infra_layer.adapters.out.search.milvus.memory.agent_skill_collection import ( - AgentSkillCollection, - ) - - doc = _mock_skill_record() - entity = AgentSkillMilvusConverter.from_mongo(doc) - schema_fields = {f.name for f in AgentSkillCollection._SCHEMA.fields} - - for key in entity: - assert ( - key in schema_fields - ), f"Converter produces key '{key}' not in Collection schema" - - -# =========================================================================== -# AgentSkillExtractor additional edge cases -# =========================================================================== - - -class TestAgentSkillExtractorEdgeCases: - """Additional edge case tests for AgentSkillExtractor.""" - - @pytest.mark.asyncio - async def test_apply_update_confidence_exactly_0_1_auto_deletes(self): - """Confidence == 0.1 should NOT auto-delete (threshold is < 0.1).""" - from memory_layer.memory_extractor.agent_skill_extractor import ( - AgentSkillExtractor, - SkillExtractionResult, - ) - - extractor = AgentSkillExtractor( - llm_provider=MagicMock(), - success_extract_prompt="", - failure_extract_prompt="", - ) - repo = AsyncMock() - repo.update_skill_by_id = AsyncMock(return_value=True) - existing = [ - SimpleNamespace( - id="s1", - name="Skill", - description="Desc", - content="Content", - confidence=0.5, - vector=[0.1], - vector_model="m", - source_case_ids=[], - maturity_score=0.7, - updated_at=None, - ) - ] - result = SkillExtractionResult() - - with ( - patch.object( - extractor, - "_compute_embedding", - new_callable=AsyncMock, - return_value=None, - ), - patch.object( - extractor, - "_evaluate_maturity", - new_callable=AsyncMock, - return_value=0.7, - ), - ): - op = {"action": "update", "index": 0, "data": {"confidence": 0.1}} - success = await extractor._apply_update(op, existing, repo, result) - - # 0.1 is NOT < 0.1, so should update, not delete - assert success is True - assert len(result.deleted_ids) == 0 - assert len(result.updated_records) == 1 - - @pytest.mark.asyncio - async def test_apply_update_confidence_0_09_retires(self): - """Confidence 0.09 < 0.1, skill is retired via update (not soft-deleted).""" - from memory_layer.memory_extractor.agent_skill_extractor import ( - AgentSkillExtractor, - SkillExtractionResult, - ) - - extractor = AgentSkillExtractor( - llm_provider=MagicMock(), - success_extract_prompt="", - failure_extract_prompt="", - ) - repo = AsyncMock() - repo.update_skill_by_id = AsyncMock(return_value=True) - existing = [ - SimpleNamespace( - id="s1", - name="Skill", - description="Desc", - confidence=0.5, - source_case_ids=[], - maturity_score=0.7, - updated_at=None, - content="steps", - cluster_id="c1", - ) - ] - result = SkillExtractionResult() - op = {"action": "update", "index": 0, "data": {"confidence": 0.09}} - success = await extractor._apply_update(op, existing, repo, result) - assert success is True - assert "s1" in result.deleted_ids - # Verify it updated confidence, not soft-deleted - repo.update_skill_by_id.assert_called_once() - call_args = repo.update_skill_by_id.call_args[0] - assert call_args[1] == {"confidence": 0.09} - - @pytest.mark.asyncio - async def test_extract_and_save_mixed_operations(self): - """Test add + update + none in a single extraction.""" - from memory_layer.memory_extractor.agent_skill_extractor import ( - AgentSkillExtractor, - ) - - response = json.dumps( - { - "operations": [ - {"action": "update", "index": 0, "data": {"confidence": 0.9}}, - { - "action": "add", - "data": { - "name": "New Skill", - "description": "Description", - "content": "## Steps for the new task at hand\n1. Step one here\n2. Step two here\n3. Step three here\n4. Step four here\n5. Step five here", - "confidence": 0.6, - }, - }, - {"action": "none"}, - ], - "update_note": "Updated existing skill and added new one", - } - ) - - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(return_value=response) - extractor = AgentSkillExtractor( - llm_provider=mock_llm, - success_extract_prompt="{new_case_json}{existing_skills_json}", - failure_extract_prompt="{new_case_json}{existing_skills_json}", - ) - - existing_skill = SimpleNamespace( - id="s1", - name="Old Skill", - description="Old Desc", - content="Old Content", - confidence=0.5, - vector=[0.1], - vector_model="m", - source_case_ids=[], - maturity_score=0.7, - updated_at=None, - ) - - repo = AsyncMock() - repo.update_skill_by_id = AsyncMock(return_value=True) - repo.save_skill = AsyncMock(side_effect=lambda rec: rec) - - case = SimpleNamespace( - task_intent="Build something", - approach="Steps here", - quality_score=0.8, - timestamp=datetime(2025, 1, 1), - ) - - with ( - patch.object( - extractor, - "_compute_embedding", - new_callable=AsyncMock, - return_value=None, - ), - patch.object( - extractor, - "_evaluate_maturity", - new_callable=AsyncMock, - return_value=0.75, - ), - ): - - import infra_layer.adapters.out.persistence.document.memory.agent_skill as skill_mod - - with patch.object( - skill_mod, "AgentSkillRecord", return_value=MagicMock(id="new_s") - ): - result = await extractor.extract_and_save( - cluster_id="c1", - group_id="g1", - new_case_records=[case], - existing_skill_records=[existing_skill], - skill_repo=repo, - user_id="u1", - ) - - # 1 update + 1 add + 1 none - assert len(result.added_records) == 1 - assert len(result.updated_records) == 1 - assert len(result.deleted_ids) == 0 - - @pytest.mark.asyncio - async def test_extract_and_save_empty_operations_list(self): - """LLM returns valid JSON but empty operations list.""" - from memory_layer.memory_extractor.agent_skill_extractor import ( - AgentSkillExtractor, - ) - - response = json.dumps({"operations": [], "update_note": "nothing to do"}) - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(return_value=response) - extractor = AgentSkillExtractor( - llm_provider=mock_llm, - success_extract_prompt="{new_case_json}{existing_skills_json}", - failure_extract_prompt="{new_case_json}{existing_skills_json}", - ) - case = SimpleNamespace( - task_intent="Task", - approach="Steps", - quality_score=0.8, - timestamp=datetime(2025, 1, 1), - ) - repo = AsyncMock() - result = await extractor.extract_and_save( - cluster_id="c1", - group_id="g1", - new_case_records=[case], - existing_skill_records=[], - skill_repo=repo, - ) - assert result.added_records == [] - assert result.updated_records == [] - - -# =========================================================================== -# AgentCaseExtractor additional edge cases -# =========================================================================== - - -class TestAgentCaseExtractorEdgeCases: - """Additional edge case tests for AgentCaseExtractor.""" - - @pytest.fixture(autouse=True) - def _mock_tokenizer(self): - import tiktoken - - encoding = tiktoken.get_encoding("o200k_base") - mock_factory = MagicMock() - mock_factory.get_tokenizer_from_tiktoken.return_value = encoding - with patch( - "memory_layer.memory_extractor.agent_case_extractor.get_bean_by_type", - return_value=mock_factory, - ): - yield - - @pytest.mark.asyncio - async def test_compress_tool_chunk_valid_response(self): - from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - ) - - messages = [ - { - "role": "assistant", - "content": "", - "tool_calls": [ - {"id": "c1", "function": {"name": "search", "arguments": "{}"}} - ], - }, - {"role": "tool", "content": "result", "tool_call_id": "c1"}, - ] - response = json.dumps({"compressed_messages": messages}) - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(return_value=response) - - extractor = AgentCaseExtractor( - llm_provider=mock_llm, tool_pre_compress_prompt="{messages_json}{new_count}" - ) - result = await extractor._compress_tool_chunk(messages) - assert result is not None - assert len(result) == 2 - - @pytest.mark.asyncio - async def test_compress_tool_chunk_invalid_count_retries(self): - """When compressed count doesn't match, retries and returns None.""" - from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - ) - - messages = [{"role": "tool", "content": "r", "tool_call_id": "c1"}] - # Return wrong count - response = json.dumps( - {"compressed_messages": [{"role": "tool"}, {"role": "tool"}]} - ) - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(return_value=response) - - extractor = AgentCaseExtractor( - llm_provider=mock_llm, tool_pre_compress_prompt="{messages_json}{new_count}" - ) - result = await extractor._compress_tool_chunk(messages) - assert result is None - assert mock_llm.generate.call_count == 2 - - @pytest.mark.asyncio - async def test_compress_tool_chunk_llm_exception(self): - from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - ) - - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(side_effect=Exception("fail")) - extractor = AgentCaseExtractor( - llm_provider=mock_llm, tool_pre_compress_prompt="{messages_json}{new_count}" - ) - result = await extractor._compress_tool_chunk( - [{"role": "tool", "content": "r"}] - ) - assert result is None - - def test_calc_tool_content_size_user_message(self): - from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - ) - - msg = {"role": "user", "content": "hello world"} - assert AgentCaseExtractor._calc_tool_content_size(msg) == 0 - - def test_calc_tool_content_size_tool_message(self): - from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - ) - - msg = {"role": "tool", "content": "some tool output here"} - size = AgentCaseExtractor._calc_tool_content_size(msg) - assert size > 0 - - def test_calc_tool_content_size_assistant_with_tool_calls(self): - from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - ) - - msg = { - "role": "assistant", - "content": "", - "tool_calls": [ - {"id": "c1", "function": {"name": "f", "arguments": '{"key": "value"}'}} - ], - } - size = AgentCaseExtractor._calc_tool_content_size(msg) - assert size > 0 - - def test_calc_tool_content_size_assistant_no_tools(self): - from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - ) - - msg = {"role": "assistant", "content": "Final answer here"} - assert AgentCaseExtractor._calc_tool_content_size(msg) == 0 - - def test_json_default_datetime(self): - from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - ) - - dt = datetime(2025, 6, 15, 10, 30, 0) - result = AgentCaseExtractor._json_default(dt) - assert result == "2025-06-15T10:30:00" - - def test_json_default_other(self): - from memory_layer.memory_extractor.agent_case_extractor import ( - AgentCaseExtractor, - ) - - result = AgentCaseExtractor._json_default(42) - assert result == "42" - - -# =========================================================================== -# _trigger_agent_skill_extraction pipeline test -# =========================================================================== - - -class TestTriggerAgentSkillExtraction: - """Tests for _trigger_agent_skill_extraction from mem_memorize.py. - - _trigger_agent_skill_extraction uses local imports, so we need to patch - at the source module level where the imports actually resolve. - """ - - @pytest.mark.asyncio - async def test_full_pipeline_add_and_sync(self): - """Test complete pipeline: extract skill -> sync to Milvus + ES.""" - from memory_layer.memory_extractor.agent_skill_extractor import ( - SkillExtractionResult, - ) - from api_specs.memory_types import MemCell, RawDataType, AgentCase - from api_specs.memory_models import MemoryType - - memcell = MemCell( - user_id_list=["u1"], - original_data=[ - {"message": {"role": "user", "content": "hi", "sender_id": "u1"}} - ], - timestamp=datetime(2025, 3, 1), - event_id="evt_001", - group_id="g1", - type=RawDataType.AGENTCONVERSATION, - ) - agent_case = AgentCase( - memory_type=MemoryType.AGENT_CASE, - user_id="u1", - timestamp=datetime(2025, 3, 1), - task_intent="Build API", - approach="Steps here", - quality_score=0.8, - ) - - mock_added_record = MagicMock(id=ObjectId(), vector=[0.1, 0.2]) - extraction_result = SkillExtractionResult( - added_records=[mock_added_record], updated_records=[], deleted_ids=[] - ) - - mock_skill_repo = AsyncMock() - mock_skill_repo.get_by_cluster_id = AsyncMock(return_value=[]) - - mock_milvus_repo = AsyncMock() - mock_es_repo = AsyncMock() - - mock_milvus_converter_result = { - "vector": [0.1, 0.2], - "id": str(mock_added_record.id), - } - - # Patch at the source modules where local imports resolve - with ( - patch("core.lock.redis_distributed_lock.distributed_lock") as mock_lock, - patch("core.di.get_bean_by_type") as mock_get_bean, - patch( - "memory_layer.llm.llm_provider.build_default_provider" - ) as mock_provider, - patch( - "memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor" - ) as mock_extractor_cls, - patch( - "infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter.AgentSkillMilvusConverter" - ) as mock_milvus_conv, - patch( - "infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter.AgentSkillConverter" - ) as mock_es_conv, - ): - - # Setup distributed lock as async context manager - mock_lock_ctx = AsyncMock() - mock_lock_ctx.__aenter__ = AsyncMock(return_value=True) - mock_lock_ctx.__aexit__ = AsyncMock(return_value=False) - mock_lock.return_value = mock_lock_ctx - - # Setup bean resolution - def get_bean_side_effect(cls): - name = cls.__name__ if hasattr(cls, '__name__') else str(cls) - if "SkillRaw" in name: - return mock_skill_repo - if "SkillMilvus" in name and "Repository" in name: - return mock_milvus_repo - if "SkillEs" in name and "Repository" in name: - return mock_es_repo - return MagicMock() - - mock_get_bean.side_effect = get_bean_side_effect - - mock_provider.return_value = MagicMock() - - mock_extractor = AsyncMock() - mock_extractor.extract_and_save = AsyncMock(return_value=extraction_result) - mock_extractor_cls.return_value = mock_extractor - - mock_milvus_conv.from_mongo.return_value = mock_milvus_converter_result - mock_es_conv.from_mongo.return_value = MagicMock() - - # Force re-import to pick up patches on local imports - import importlib - import biz_layer.mem_memorize as memorize_mod - - importlib.reload(memorize_mod) - - await memorize_mod._trigger_agent_skill_extraction( - group_id="g1", - cluster_id="cluster_001", - memcell=memcell, - agent_case=agent_case, - ) - - # Verify extractor was called - mock_extractor.extract_and_save.assert_called_once() - - @pytest.mark.asyncio - async def test_extraction_exception_handled(self): - """Exceptions in extraction are caught and logged, not propagated.""" - from api_specs.memory_types import MemCell, RawDataType, AgentCase - from api_specs.memory_models import MemoryType - - memcell = MemCell( - user_id_list=["u1"], - original_data=[ - {"message": {"role": "user", "content": "hi", "sender_id": "u1"}} - ], - timestamp=datetime(2025, 3, 1), - event_id="evt_001", - group_id="g1", - type=RawDataType.AGENTCONVERSATION, - ) - agent_case = AgentCase( - memory_type=MemoryType.AGENT_CASE, - user_id="u1", - timestamp=datetime(2025, 3, 1), - task_intent="t", - approach="a", - quality_score=0.5, - ) - - # Patch top-level exception: the outer try-except should catch this - with patch( - "core.lock.redis_distributed_lock.distributed_lock", - side_effect=Exception("Lock init failed"), - ): - - import importlib - import biz_layer.mem_memorize as memorize_mod - - importlib.reload(memorize_mod) - - # Should not raise - exception is caught in the outer try-except - await memorize_mod._trigger_agent_skill_extraction( - group_id="g1", cluster_id="c1", memcell=memcell, agent_case=agent_case - ) - - -# =========================================================================== -# _extract_user_id_from_memcell additional edge cases -# =========================================================================== - - -class TestExtractUserIdEdgeCases: - """Additional edge cases for _extract_user_id_from_memcell.""" - - def test_multiple_users_returns_first(self): - from api_specs.memory_types import MemCell, RawDataType - from biz_layer.mem_db_operations import _extract_user_id_from_memcell - - memcell = MemCell( - user_id_list=["u1", "u2"], - original_data=[ - { - "message": { - "role": "user", - "content": "first", - "sender_id": "first_user", - } - }, - {"message": {"role": "assistant", "content": "resp"}}, - { - "message": { - "role": "user", - "content": "second", - "sender_id": "second_user", - } - }, - ], - timestamp=datetime(2025, 1, 1), - type=RawDataType.AGENTCONVERSATION, - ) - assert _extract_user_id_from_memcell(memcell) == "first_user" - - def test_sender_id_empty_string_returns_none(self): - from api_specs.memory_types import MemCell, RawDataType - from biz_layer.mem_db_operations import _extract_user_id_from_memcell - - memcell = MemCell( - user_id_list=["u1"], - original_data=[ - {"message": {"role": "user", "content": "hi", "sender_id": ""}} - ], - timestamp=datetime(2025, 1, 1), - type=RawDataType.AGENTCONVERSATION, - ) - # Empty string is falsy, should return None - assert _extract_user_id_from_memcell(memcell) is None - - def test_mixed_wrapped_and_bare(self): - """Some items have 'message' key, some don't.""" - from api_specs.memory_types import MemCell - from biz_layer.mem_db_operations import _extract_user_id_from_memcell - - memcell = MemCell( - user_id_list=["u1"], - original_data=[ - {"role": "assistant", "content": "hi"}, # bare, no sender_id - { - "message": { - "role": "user", - "content": "hello", - "sender_id": "u_bare", - } - }, - ], - timestamp=datetime(2025, 1, 1), - ) - assert _extract_user_id_from_memcell(memcell) == "u_bare" - - -# --------------------------------------------------------------------------- -# _should_skip_atomic_fact_for_agent tests -# --------------------------------------------------------------------------- - - -class TestShouldSkipAtomicFactForAgent: - """Tests for _should_skip_atomic_fact_for_agent heuristic. - - Skip when: has tool calls AND cumulative assistant response >= 1000 chars. - """ - - def _make_memcell(self, messages): - from api_specs.memory_types import MemCell, RawDataType - - return MemCell( - user_id_list=["u1"], - original_data=messages, - timestamp=datetime(2025, 1, 1), - type=RawDataType.AGENTCONVERSATION, - ) - - def test_no_tool_calls_returns_false(self): - """Without tool calls, never skip regardless of response length.""" - from biz_layer.mem_memorize import _should_skip_atomic_fact_for_agent - - memcell = self._make_memcell( - [ - {"role": "user", "content": "hello"}, - {"role": "assistant", "content": "x" * 2000}, - ] - ) - assert _should_skip_atomic_fact_for_agent(memcell) is False - - def test_tool_calls_with_short_response_returns_false(self): - """With tool calls but short assistant response, do not skip.""" - from biz_layer.mem_memorize import _should_skip_atomic_fact_for_agent - - memcell = self._make_memcell( - [ - {"role": "user", "content": "do something"}, - {"role": "assistant", "content": None, "tool_calls": [{"id": "1"}]}, - {"role": "tool", "content": "result"}, - {"role": "assistant", "content": "Done."}, - ] - ) - assert _should_skip_atomic_fact_for_agent(memcell) is False - - def test_tool_calls_with_long_response_returns_true(self): - """With tool calls and long cumulative assistant response, skip.""" - from biz_layer.mem_memorize import _should_skip_atomic_fact_for_agent - - memcell = self._make_memcell( - [ - {"role": "user", "content": "do something"}, - {"role": "assistant", "content": None, "tool_calls": [{"id": "1"}]}, - {"role": "tool", "content": "result"}, - {"role": "assistant", "content": "x" * 1000}, - ] - ) - assert _should_skip_atomic_fact_for_agent(memcell) is True - - def test_cumulative_across_multiple_responses(self): - """Multiple assistant responses should be summed.""" - from biz_layer.mem_memorize import _should_skip_atomic_fact_for_agent - - memcell = self._make_memcell( - [ - {"role": "user", "content": "step 1"}, - {"role": "assistant", "content": None, "tool_calls": [{"id": "1"}]}, - {"role": "tool", "content": "result 1"}, - {"role": "assistant", "content": "x" * 500}, - {"role": "user", "content": "step 2"}, - {"role": "assistant", "content": None, "tool_calls": [{"id": "2"}]}, - {"role": "tool", "content": "result 2"}, - {"role": "assistant", "content": "y" * 501}, - ] - ) - # 500 + 501 = 1001 >= 1000 - assert _should_skip_atomic_fact_for_agent(memcell) is True - - def test_exactly_999_chars_returns_false(self): - """Boundary: 999 chars should not skip.""" - from biz_layer.mem_memorize import _should_skip_atomic_fact_for_agent - - memcell = self._make_memcell( - [ - {"role": "user", "content": "do something"}, - {"role": "assistant", "content": None, "tool_calls": [{"id": "1"}]}, - {"role": "tool", "content": "result"}, - {"role": "assistant", "content": "x" * 999}, - ] - ) - assert _should_skip_atomic_fact_for_agent(memcell) is False - - def test_wrapped_message_format(self): - """Works with wrapped {"message": {...}} format.""" - from biz_layer.mem_memorize import _should_skip_atomic_fact_for_agent - - memcell = self._make_memcell( - [ - {"message": {"role": "user", "content": "do something"}}, - { - "message": { - "role": "assistant", - "content": None, - "tool_calls": [{"id": "1"}], - } - }, - {"message": {"role": "tool", "content": "result"}}, - {"message": {"role": "assistant", "content": "x" * 1200}}, - ] - ) - assert _should_skip_atomic_fact_for_agent(memcell) is True - - def test_tool_role_detected_as_tool_call(self): - """A 'tool' role message alone counts as having tool calls.""" - from biz_layer.mem_memorize import _should_skip_atomic_fact_for_agent - - memcell = self._make_memcell( - [ - {"role": "user", "content": "do something"}, - {"role": "tool", "content": "tool output"}, - {"role": "assistant", "content": "x" * 1500}, - ] - ) - assert _should_skip_atomic_fact_for_agent(memcell) is True - - def test_content_items_list_format(self): - """Content as v1 API content items list should be handled correctly.""" - from biz_layer.mem_memorize import _should_skip_atomic_fact_for_agent - - memcell = self._make_memcell( - [ - {"role": "user", "content": "do something"}, - {"role": "assistant", "content": None, "tool_calls": [{"id": "1"}]}, - {"role": "tool", "content": "result"}, - { - "role": "assistant", - "content": [{"type": "text", "text": "x" * 1000}], - }, - ] - ) - assert _should_skip_atomic_fact_for_agent(memcell) is True - - def test_content_items_list_short(self): - """Short content items list should not skip.""" - from biz_layer.mem_memorize import _should_skip_atomic_fact_for_agent - - memcell = self._make_memcell( - [ - {"role": "user", "content": "do something"}, - {"role": "assistant", "content": None, "tool_calls": [{"id": "1"}]}, - {"role": "tool", "content": "result"}, - {"role": "assistant", "content": [{"type": "text", "text": "Done."}]}, - ] - ) - assert _should_skip_atomic_fact_for_agent(memcell) is False - - def test_empty_original_data(self): - """Empty original_data should not skip.""" - from biz_layer.mem_memorize import _should_skip_atomic_fact_for_agent - from api_specs.memory_types import MemCell, RawDataType - - memcell = MemCell( - user_id_list=["u1"], - original_data=[{"role": "user", "content": "hi"}], - timestamp=datetime(2025, 1, 1), - type=RawDataType.AGENTCONVERSATION, - ) - assert _should_skip_atomic_fact_for_agent(memcell) is False - - -# =========================================================================== -# save_memory_docs - AgentCase branch -# =========================================================================== - - -def _build_case_get_bean(mock_case_repo, mock_es_repo, mock_milvus_repo): - """Build a get_bean_by_type side effect that resolves repos by class name.""" - - def side_effect(cls): - name = cls.__name__ if hasattr(cls, "__name__") else str(cls) - if "CaseRaw" in name: - return mock_case_repo - if "CaseEs" in name: - return mock_es_repo - if "CaseMilvus" in name and "Repository" in name: - return mock_milvus_repo - return MagicMock() - - return side_effect - - -class TestSaveMemoryDocsAgentCase: - """Tests for the AgentCase branch in save_memory_docs.""" - - @pytest.mark.asyncio - async def test_agent_case_saved_to_mongo_es_milvus(self): - """AgentCase docs are saved to MongoDB, synced to ES and Milvus.""" - from api_specs.memory_models import MemoryType - from biz_layer.mem_memorize import MemoryDocPayload, save_memory_docs - - mock_doc = MagicMock() - mock_saved_doc = MagicMock() - mock_saved_doc.event_id = "evt_1" - mock_saved_doc.id = ObjectId() - - mock_case_repo = AsyncMock() - mock_case_repo.append_experience = AsyncMock(return_value=mock_saved_doc) - mock_es_repo = AsyncMock() - mock_milvus_repo = AsyncMock() - - mock_es_doc = MagicMock() - mock_milvus_entity = {"vector": [0.1, 0.2], "id": "test"} - - with ( - patch( - "biz_layer.mem_memorize.get_bean_by_type", - side_effect=_build_case_get_bean( - mock_case_repo, mock_es_repo, mock_milvus_repo - ), - ), - patch( - "infra_layer.adapters.out.search.elasticsearch.converter.agent_case_converter.AgentCaseConverter.from_mongo", - return_value=mock_es_doc, - ), - patch( - "infra_layer.adapters.out.search.milvus.converter.agent_case_milvus_converter.AgentCaseMilvusConverter.from_mongo", - return_value=mock_milvus_entity, - ), - ): - - payload = MemoryDocPayload(MemoryType.AGENT_CASE, mock_doc) - result = await save_memory_docs([payload]) - - assert MemoryType.AGENT_CASE in result - assert len(result[MemoryType.AGENT_CASE]) == 1 - mock_case_repo.append_experience.assert_called_once_with(mock_doc) - mock_es_repo.create.assert_called_once() - mock_milvus_repo.insert.assert_called_once() - - @pytest.mark.asyncio - async def test_agent_case_es_failure_does_not_block_milvus(self): - """ES sync failure should not prevent Milvus sync.""" - from api_specs.memory_models import MemoryType - from biz_layer.mem_memorize import MemoryDocPayload, save_memory_docs - - mock_doc = MagicMock() - mock_saved_doc = MagicMock() - mock_saved_doc.event_id = "evt_1" - mock_saved_doc.id = ObjectId() - - mock_case_repo = AsyncMock() - mock_case_repo.append_experience = AsyncMock(return_value=mock_saved_doc) - mock_es_repo = AsyncMock() - mock_milvus_repo = AsyncMock() - mock_milvus_entity = {"vector": [0.1], "id": "test"} - - with ( - patch( - "biz_layer.mem_memorize.get_bean_by_type", - side_effect=_build_case_get_bean( - mock_case_repo, mock_es_repo, mock_milvus_repo - ), - ), - patch( - "infra_layer.adapters.out.search.elasticsearch.converter.agent_case_converter.AgentCaseConverter.from_mongo", - side_effect=Exception("ES convert error"), - ), - patch( - "infra_layer.adapters.out.search.milvus.converter.agent_case_milvus_converter.AgentCaseMilvusConverter.from_mongo", - return_value=mock_milvus_entity, - ), - ): - - payload = MemoryDocPayload(MemoryType.AGENT_CASE, mock_doc) - result = await save_memory_docs([payload]) - - assert MemoryType.AGENT_CASE in result - mock_milvus_repo.insert.assert_called_once() - - @pytest.mark.asyncio - async def test_agent_case_no_vector_skips_milvus(self): - """AgentCase with no vector should skip Milvus write.""" - from api_specs.memory_models import MemoryType - from biz_layer.mem_memorize import MemoryDocPayload, save_memory_docs - - mock_doc = MagicMock() - mock_saved_doc = MagicMock() - mock_saved_doc.event_id = "evt_1" - mock_saved_doc.id = ObjectId() - - mock_case_repo = AsyncMock() - mock_case_repo.append_experience = AsyncMock(return_value=mock_saved_doc) - mock_es_repo = AsyncMock() - mock_milvus_repo = AsyncMock() - mock_es_doc = MagicMock() - mock_milvus_entity = {"vector": [], "id": "test"} - - with ( - patch( - "biz_layer.mem_memorize.get_bean_by_type", - side_effect=_build_case_get_bean( - mock_case_repo, mock_es_repo, mock_milvus_repo - ), - ), - patch( - "infra_layer.adapters.out.search.elasticsearch.converter.agent_case_converter.AgentCaseConverter.from_mongo", - return_value=mock_es_doc, - ), - patch( - "infra_layer.adapters.out.search.milvus.converter.agent_case_milvus_converter.AgentCaseMilvusConverter.from_mongo", - return_value=mock_milvus_entity, - ), - ): - - payload = MemoryDocPayload(MemoryType.AGENT_CASE, mock_doc) - result = await save_memory_docs([payload]) - - assert MemoryType.AGENT_CASE in result - mock_milvus_repo.insert.assert_not_called() - - @pytest.mark.asyncio - async def test_empty_payloads_returns_empty(self): - """No payloads returns empty dict.""" - from biz_layer.mem_memorize import save_memory_docs - - result = await save_memory_docs([]) - assert result == {} - - @pytest.mark.asyncio - async def test_none_doc_in_payload_skipped(self): - """Payload with None doc should be skipped.""" - from api_specs.memory_models import MemoryType - from biz_layer.mem_memorize import MemoryDocPayload, save_memory_docs - - payload = MemoryDocPayload(MemoryType.AGENT_CASE, None) - result = await save_memory_docs([payload]) - assert MemoryType.AGENT_CASE not in result - - -# =========================================================================== -# _trigger_agent_skill_extraction - additional coverage -# =========================================================================== - - -def _make_agent_memcell_for_trigger(**overrides): - """Create a minimal agent conversation MemCell.""" - from api_specs.memory_types import MemCell, RawDataType - - defaults = dict( - user_id_list=["u1"], - original_data=[ - { - "message": { - "role": "user", - "content": "Deploy the app", - "sender_id": "u1", - } - }, - {"message": {"role": "assistant", "content": "Done deploying."}}, - ], - timestamp=datetime(2025, 6, 1, 10, 0, 0), - event_id="evt_100", - group_id="g1", - type=RawDataType.AGENTCONVERSATION, - ) - defaults.update(overrides) - return MemCell(**defaults) - - -def _make_agent_case_for_trigger(**overrides): - """Create a minimal AgentCase BO.""" - from api_specs.memory_types import AgentCase - from api_specs.memory_models import MemoryType - - defaults = dict( - memory_type=MemoryType.AGENT_CASE, - user_id="u1", - timestamp=datetime(2025, 6, 1, 10, 0, 0), - task_intent="Deploy the application to production", - approach="1. Build docker image\n2. Push to registry\n3. Deploy to k8s", - quality_score=0.85, - vector=[0.1, 0.2, 0.3], - vector_model="text-embedding-3-small", - ) - defaults.update(overrides) - return AgentCase(**defaults) - - -def _build_skill_trigger_patches(mock_skill_repo, mock_milvus_repo, mock_es_repo): - """Build a get_bean_by_type side effect for skill extraction tests.""" - - def side_effect(cls): - name = cls.__name__ if hasattr(cls, "__name__") else str(cls) - if "SkillRaw" in name: - return mock_skill_repo - if "SkillMilvus" in name and "Repository" in name: - return mock_milvus_repo - if "SkillEs" in name and "Repository" in name: - return mock_es_repo - return MagicMock() - - return side_effect - - -class TestTriggerAgentSkillExtractionGaps: - """Additional tests for _trigger_agent_skill_extraction.""" - - @pytest.mark.asyncio - async def test_lock_not_acquired_skips_extraction(self): - """When distributed lock is not acquired, extraction is skipped.""" - memcell = _make_agent_memcell_for_trigger() - agent_case = _make_agent_case_for_trigger() - - with ( - patch("core.lock.redis_distributed_lock.distributed_lock") as mock_lock, - patch("core.di.get_bean_by_type") as mock_get_bean, - ): - - mock_lock_ctx = AsyncMock() - mock_lock_ctx.__aenter__ = AsyncMock(return_value=False) - mock_lock_ctx.__aexit__ = AsyncMock(return_value=False) - mock_lock.return_value = mock_lock_ctx - - import importlib - import biz_layer.mem_memorize as memorize_mod - - importlib.reload(memorize_mod) - - await memorize_mod._trigger_agent_skill_extraction( - group_id="g1", cluster_id="c1", memcell=memcell, agent_case=agent_case - ) - - @pytest.mark.asyncio - async def test_updated_records_delete_old_then_insert_new(self): - """Updated records should delete old entries and insert new ones in search engines.""" - from memory_layer.memory_extractor.agent_skill_extractor import ( - SkillExtractionResult, - ) - - memcell = _make_agent_memcell_for_trigger() - agent_case = _make_agent_case_for_trigger() - - updated_record = MagicMock(id=ObjectId(), vector=[0.1, 0.2]) - extraction_result = SkillExtractionResult( - added_records=[], updated_records=[updated_record], deleted_ids=[] - ) - - mock_skill_repo = AsyncMock() - mock_skill_repo.get_by_cluster_id = AsyncMock(return_value=[]) - mock_milvus_repo = AsyncMock() - mock_es_repo = AsyncMock() - - with ( - patch("core.lock.redis_distributed_lock.distributed_lock") as mock_lock, - patch( - "core.di.get_bean_by_type", - side_effect=_build_skill_trigger_patches( - mock_skill_repo, mock_milvus_repo, mock_es_repo - ), - ), - patch( - "memory_layer.llm.llm_provider.build_default_provider", - return_value=MagicMock(), - ), - patch( - "memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor" - ) as mock_ext_cls, - patch( - "infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter.AgentSkillMilvusConverter" - ) as mock_milvus_conv, - patch( - "infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter.AgentSkillConverter" - ) as mock_es_conv, - ): - - mock_lock_ctx = AsyncMock() - mock_lock_ctx.__aenter__ = AsyncMock(return_value=True) - mock_lock_ctx.__aexit__ = AsyncMock(return_value=False) - mock_lock.return_value = mock_lock_ctx - - mock_extractor = AsyncMock() - mock_extractor.extract_and_save = AsyncMock(return_value=extraction_result) - mock_ext_cls.return_value = mock_extractor - - mock_milvus_conv.from_mongo.return_value = {"vector": [0.1, 0.2], "id": "x"} - mock_es_conv.from_mongo.return_value = MagicMock() - - import importlib - import biz_layer.mem_memorize as memorize_mod - - importlib.reload(memorize_mod) - - await memorize_mod._trigger_agent_skill_extraction( - group_id="g1", cluster_id="c1", memcell=memcell, agent_case=agent_case - ) - - updated_id = str(updated_record.id) - mock_milvus_repo.delete_by_id.assert_called_with(updated_id) - mock_milvus_repo.insert.assert_called_once() - mock_es_repo.delete_by_id.assert_called_with(updated_id) - mock_es_repo.create.assert_called_once() - - @pytest.mark.asyncio - async def test_record_without_vector_skipped_in_milvus(self): - """Records without vector should be skipped in Milvus but still synced to ES.""" - from memory_layer.memory_extractor.agent_skill_extractor import ( - SkillExtractionResult, - ) - - memcell = _make_agent_memcell_for_trigger() - agent_case = _make_agent_case_for_trigger() - - added_record = MagicMock(id=ObjectId(), vector=None) - extraction_result = SkillExtractionResult( - added_records=[added_record], updated_records=[], deleted_ids=[] - ) - - mock_skill_repo = AsyncMock() - mock_skill_repo.get_by_cluster_id = AsyncMock(return_value=[]) - mock_milvus_repo = AsyncMock() - mock_es_repo = AsyncMock() - - with ( - patch("core.lock.redis_distributed_lock.distributed_lock") as mock_lock, - patch( - "core.di.get_bean_by_type", - side_effect=_build_skill_trigger_patches( - mock_skill_repo, mock_milvus_repo, mock_es_repo - ), - ), - patch( - "memory_layer.llm.llm_provider.build_default_provider", - return_value=MagicMock(), - ), - patch( - "memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor" - ) as mock_ext_cls, - patch( - "infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter.AgentSkillMilvusConverter" - ) as mock_milvus_conv, - patch( - "infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter.AgentSkillConverter" - ) as mock_es_conv, - ): - - mock_lock_ctx = AsyncMock() - mock_lock_ctx.__aenter__ = AsyncMock(return_value=True) - mock_lock_ctx.__aexit__ = AsyncMock(return_value=False) - mock_lock.return_value = mock_lock_ctx - - mock_extractor = AsyncMock() - mock_extractor.extract_and_save = AsyncMock(return_value=extraction_result) - mock_ext_cls.return_value = mock_extractor - - mock_milvus_conv.from_mongo.return_value = {"vector": None, "id": "x"} - mock_es_conv.from_mongo.return_value = MagicMock() - - import importlib - import biz_layer.mem_memorize as memorize_mod - - importlib.reload(memorize_mod) - - await memorize_mod._trigger_agent_skill_extraction( - group_id="g1", cluster_id="c1", memcell=memcell, agent_case=agent_case - ) - - mock_milvus_repo.insert.assert_not_called() - mock_es_repo.create.assert_called_once() - - @pytest.mark.asyncio - async def test_milvus_failure_does_not_block_es(self): - """Milvus sync failure should not prevent ES sync.""" - from memory_layer.memory_extractor.agent_skill_extractor import ( - SkillExtractionResult, - ) - - memcell = _make_agent_memcell_for_trigger() - agent_case = _make_agent_case_for_trigger() - - added_record = MagicMock(id=ObjectId(), vector=[0.1]) - extraction_result = SkillExtractionResult( - added_records=[added_record], updated_records=[], deleted_ids=[] - ) - - mock_skill_repo = AsyncMock() - mock_skill_repo.get_by_cluster_id = AsyncMock(return_value=[]) - mock_milvus_repo = AsyncMock() - mock_milvus_repo.insert = AsyncMock(side_effect=Exception("Milvus down")) - mock_es_repo = AsyncMock() - - with ( - patch("core.lock.redis_distributed_lock.distributed_lock") as mock_lock, - patch( - "core.di.get_bean_by_type", - side_effect=_build_skill_trigger_patches( - mock_skill_repo, mock_milvus_repo, mock_es_repo - ), - ), - patch( - "memory_layer.llm.llm_provider.build_default_provider", - return_value=MagicMock(), - ), - patch( - "memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor" - ) as mock_ext_cls, - patch( - "infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter.AgentSkillMilvusConverter" - ) as mock_milvus_conv, - patch( - "infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter.AgentSkillConverter" - ) as mock_es_conv, - ): - - mock_lock_ctx = AsyncMock() - mock_lock_ctx.__aenter__ = AsyncMock(return_value=True) - mock_lock_ctx.__aexit__ = AsyncMock(return_value=False) - mock_lock.return_value = mock_lock_ctx - - mock_extractor = AsyncMock() - mock_extractor.extract_and_save = AsyncMock(return_value=extraction_result) - mock_ext_cls.return_value = mock_extractor - - mock_milvus_conv.from_mongo.return_value = {"vector": [0.1], "id": "x"} - mock_es_conv.from_mongo.return_value = MagicMock() - - import importlib - import biz_layer.mem_memorize as memorize_mod - - importlib.reload(memorize_mod) - - await memorize_mod._trigger_agent_skill_extraction( - group_id="g1", cluster_id="c1", memcell=memcell, agent_case=agent_case - ) - - mock_es_repo.create.assert_called_once() - - @pytest.mark.asyncio - async def test_es_failure_does_not_raise(self): - """ES sync failure should be caught and not propagate.""" - from memory_layer.memory_extractor.agent_skill_extractor import ( - SkillExtractionResult, - ) - - memcell = _make_agent_memcell_for_trigger() - agent_case = _make_agent_case_for_trigger() - - added_record = MagicMock(id=ObjectId(), vector=[0.1]) - extraction_result = SkillExtractionResult( - added_records=[added_record], updated_records=[], deleted_ids=[] - ) - - mock_skill_repo = AsyncMock() - mock_skill_repo.get_by_cluster_id = AsyncMock(return_value=[]) - mock_milvus_repo = AsyncMock() - mock_es_repo = AsyncMock() - mock_es_repo.create = AsyncMock(side_effect=Exception("ES down")) - - with ( - patch("core.lock.redis_distributed_lock.distributed_lock") as mock_lock, - patch( - "core.di.get_bean_by_type", - side_effect=_build_skill_trigger_patches( - mock_skill_repo, mock_milvus_repo, mock_es_repo - ), - ), - patch( - "memory_layer.llm.llm_provider.build_default_provider", - return_value=MagicMock(), - ), - patch( - "memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor" - ) as mock_ext_cls, - patch( - "infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter.AgentSkillMilvusConverter" - ) as mock_milvus_conv, - patch( - "infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter.AgentSkillConverter" - ) as mock_es_conv, - ): - - mock_lock_ctx = AsyncMock() - mock_lock_ctx.__aenter__ = AsyncMock(return_value=True) - mock_lock_ctx.__aexit__ = AsyncMock(return_value=False) - mock_lock.return_value = mock_lock_ctx - - mock_extractor = AsyncMock() - mock_extractor.extract_and_save = AsyncMock(return_value=extraction_result) - mock_ext_cls.return_value = mock_extractor - - mock_milvus_conv.from_mongo.return_value = {"vector": [0.1], "id": "x"} - mock_es_conv.from_mongo.return_value = MagicMock() - - import importlib - import biz_layer.mem_memorize as memorize_mod - - importlib.reload(memorize_mod) - - await memorize_mod._trigger_agent_skill_extraction( - group_id="g1", cluster_id="c1", memcell=memcell, agent_case=agent_case - ) - - @pytest.mark.asyncio - async def test_deleted_ids_removed_from_search_engines(self): - """Deleted skill IDs should be removed from both Milvus and ES.""" - from memory_layer.memory_extractor.agent_skill_extractor import ( - SkillExtractionResult, - ) - - memcell = _make_agent_memcell_for_trigger() - agent_case = _make_agent_case_for_trigger() - - extraction_result = SkillExtractionResult( - added_records=[], - updated_records=[], - deleted_ids=["dead_skill_1", "dead_skill_2"], - ) - - mock_skill_repo = AsyncMock() - mock_skill_repo.get_by_cluster_id = AsyncMock(return_value=[]) - mock_milvus_repo = AsyncMock() - mock_es_repo = AsyncMock() - - with ( - patch("core.lock.redis_distributed_lock.distributed_lock") as mock_lock, - patch( - "core.di.get_bean_by_type", - side_effect=_build_skill_trigger_patches( - mock_skill_repo, mock_milvus_repo, mock_es_repo - ), - ), - patch( - "memory_layer.llm.llm_provider.build_default_provider", - return_value=MagicMock(), - ), - patch( - "memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor" - ) as mock_ext_cls, - patch( - "infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter.AgentSkillMilvusConverter" - ) as mock_milvus_conv, - patch( - "infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter.AgentSkillConverter" - ) as mock_es_conv, - ): - - mock_lock_ctx = AsyncMock() - mock_lock_ctx.__aenter__ = AsyncMock(return_value=True) - mock_lock_ctx.__aexit__ = AsyncMock(return_value=False) - mock_lock.return_value = mock_lock_ctx - - mock_extractor = AsyncMock() - mock_extractor.extract_and_save = AsyncMock(return_value=extraction_result) - mock_ext_cls.return_value = mock_extractor - - import importlib - import biz_layer.mem_memorize as memorize_mod - - importlib.reload(memorize_mod) - - await memorize_mod._trigger_agent_skill_extraction( - group_id="g1", cluster_id="c1", memcell=memcell, agent_case=agent_case - ) - - assert mock_milvus_repo.delete_by_id.call_count == 2 - mock_milvus_repo.delete_by_id.assert_any_call("dead_skill_1") - mock_milvus_repo.delete_by_id.assert_any_call("dead_skill_2") - - assert mock_es_repo.delete_by_id.call_count == 2 - mock_es_repo.delete_by_id.assert_any_call("dead_skill_1") - mock_es_repo.delete_by_id.assert_any_call("dead_skill_2") - - @pytest.mark.asyncio - async def test_no_changes_skips_sync(self): - """When extraction produces no changes, search engine sync is skipped.""" - from memory_layer.memory_extractor.agent_skill_extractor import ( - SkillExtractionResult, - ) - - memcell = _make_agent_memcell_for_trigger() - agent_case = _make_agent_case_for_trigger() - - extraction_result = SkillExtractionResult() - - mock_skill_repo = AsyncMock() - mock_skill_repo.get_by_cluster_id = AsyncMock(return_value=[]) - mock_milvus_repo = AsyncMock() - mock_es_repo = AsyncMock() - - with ( - patch("core.lock.redis_distributed_lock.distributed_lock") as mock_lock, - patch( - "core.di.get_bean_by_type", - side_effect=_build_skill_trigger_patches( - mock_skill_repo, mock_milvus_repo, mock_es_repo - ), - ), - patch( - "memory_layer.llm.llm_provider.build_default_provider", - return_value=MagicMock(), - ), - patch( - "memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor" - ) as mock_ext_cls, - patch( - "infra_layer.adapters.out.search.milvus.converter.agent_skill_milvus_converter.AgentSkillMilvusConverter" - ) as mock_milvus_conv, - patch( - "infra_layer.adapters.out.search.elasticsearch.converter.agent_skill_converter.AgentSkillConverter" - ) as mock_es_conv, - ): - - mock_lock_ctx = AsyncMock() - mock_lock_ctx.__aenter__ = AsyncMock(return_value=True) - mock_lock_ctx.__aexit__ = AsyncMock(return_value=False) - mock_lock.return_value = mock_lock_ctx - - mock_extractor = AsyncMock() - mock_extractor.extract_and_save = AsyncMock(return_value=extraction_result) - mock_ext_cls.return_value = mock_extractor - - import importlib - import biz_layer.mem_memorize as memorize_mod - - importlib.reload(memorize_mod) - - await memorize_mod._trigger_agent_skill_extraction( - group_id="g1", cluster_id="c1", memcell=memcell, agent_case=agent_case - ) - - mock_milvus_repo.insert.assert_not_called() - mock_milvus_repo.delete_by_id.assert_not_called() - mock_milvus_repo.flush.assert_not_called() - mock_es_repo.create.assert_not_called() - mock_es_repo.delete_by_id.assert_not_called() - - -# =========================================================================== -# _update_memcell_and_cluster - agent case integration -# =========================================================================== - - -class TestUpdateMemcellAndClusterAgentCase: - """Tests for _update_memcell_and_cluster passing agent_case to clustering.""" - - @pytest.mark.asyncio - async def test_agent_case_passed_to_trigger_clustering(self): - """Agent case from state should be passed to _trigger_clustering.""" - import biz_layer.mem_memorize as mod - - agent_case = _make_agent_case_for_trigger() - memcell = _make_agent_memcell_for_trigger() - group_episode = SimpleNamespace(episode="Agent deployed the app successfully.") - - state = SimpleNamespace( - request=SimpleNamespace(group_id="g1"), - memcell=memcell, - scene="solo", - group_episode=group_episode, - agent_case=agent_case, - ) - - original_trigger = mod._trigger_clustering - try: - mock_cluster = AsyncMock() - mod._trigger_clustering = mock_cluster - - await mod._update_memcell_and_cluster(state) - - mock_cluster.assert_called_once() - call_kwargs = mock_cluster.call_args.kwargs - assert call_kwargs.get("agent_case") is agent_case - finally: - mod._trigger_clustering = original_trigger - - @pytest.mark.asyncio - async def test_no_group_id_skips_clustering(self): - """No group_id should skip clustering entirely.""" - import biz_layer.mem_memorize as mod - - state = SimpleNamespace( - request=SimpleNamespace(group_id=None), - memcell=_make_agent_memcell_for_trigger(), - scene="solo", - group_episode=SimpleNamespace(episode="text"), - agent_case=_make_agent_case_for_trigger(), - ) - - original_trigger = mod._trigger_clustering - try: - mock_cluster = AsyncMock() - mod._trigger_clustering = mock_cluster - - await mod._update_memcell_and_cluster(state) - mock_cluster.assert_not_called() - finally: - mod._trigger_clustering = original_trigger - - @pytest.mark.asyncio - async def test_no_group_episode_skips_clustering(self): - """No group_episode should skip clustering entirely.""" - import biz_layer.mem_memorize as mod - - state = SimpleNamespace( - request=SimpleNamespace(group_id="g1"), - memcell=_make_agent_memcell_for_trigger(), - scene="solo", - group_episode=None, - agent_case=_make_agent_case_for_trigger(), - ) - - original_trigger = mod._trigger_clustering - try: - mock_cluster = AsyncMock() - mod._trigger_clustering = mock_cluster - - await mod._update_memcell_and_cluster(state) - mock_cluster.assert_not_called() - finally: - mod._trigger_clustering = original_trigger - - @pytest.mark.asyncio - async def test_agent_conversation_uses_agent_config(self): - """Agent conversations should use DEFAULT_MEMORIZE_CONFIG.""" - from biz_layer.memorize_config import DEFAULT_MEMORIZE_CONFIG - import biz_layer.mem_memorize as mod - - memcell = _make_agent_memcell_for_trigger() - group_episode = SimpleNamespace(episode="text") - - state = SimpleNamespace( - request=SimpleNamespace(group_id="g1"), - memcell=memcell, - scene="solo", - group_episode=group_episode, - agent_case=_make_agent_case_for_trigger(), - ) - - original_trigger = mod._trigger_clustering - try: - mock_cluster = AsyncMock() - mod._trigger_clustering = mock_cluster - - await mod._update_memcell_and_cluster(state) - - call_kwargs = mock_cluster.call_args.kwargs - assert call_kwargs["config"] is DEFAULT_MEMORIZE_CONFIG - finally: - mod._trigger_clustering = original_trigger - - @pytest.mark.asyncio - async def test_clustering_exception_handled(self): - """Clustering exceptions should be caught, not propagated.""" - import biz_layer.mem_memorize as mod - - state = SimpleNamespace( - request=SimpleNamespace(group_id="g1"), - memcell=_make_agent_memcell_for_trigger(), - scene="solo", - group_episode=SimpleNamespace(episode="text"), - agent_case=_make_agent_case_for_trigger(), - ) - - original_trigger = mod._trigger_clustering - try: - mod._trigger_clustering = AsyncMock(side_effect=Exception("boom")) - - # Should not raise - await mod._update_memcell_and_cluster(state) - finally: - mod._trigger_clustering = original_trigger - - -# --------------------------------------------------------------------------- -# tool_calls / tool_call_id round-trip tests (RawMessage, Mapper, Repository) -# --------------------------------------------------------------------------- - - -class TestRawMessageToolCallsFields: - """Tests for tool_calls/tool_call_id fields on RawMessage document model.""" - - def _construct(self, **kwargs): - """Build a RawMessage without Beanie collection init.""" - from infra_layer.adapters.out.persistence.document.request.raw_message import ( - RawMessage, - ) - - defaults = dict(group_id="g1", request_id="r1") - defaults.update(kwargs) - return RawMessage.model_construct(**defaults) - - def test_raw_message_accepts_tool_calls(self): - """RawMessage should store tool_calls list.""" - tool_calls = [ - { - "id": "call_001", - "type": "function", - "function": {"name": "web_search", "arguments": "{}"}, - } - ] - msg = self._construct(role="assistant", tool_calls=tool_calls) - assert msg.tool_calls == tool_calls - - def test_raw_message_accepts_tool_call_id(self): - """RawMessage should store tool_call_id string.""" - msg = self._construct(role="tool", tool_call_id="call_001") - assert msg.tool_call_id == "call_001" - - def test_raw_message_defaults_none(self): - """tool_calls and tool_call_id default to None.""" - msg = self._construct(role="user") - assert msg.tool_calls is None - assert msg.tool_call_id is None - - -class TestRawMessageMapperToolCalls: - """Tests for RawMessageMapper preserving tool_calls/tool_call_id.""" - - def _make_raw_message(self, **overrides): - from infra_layer.adapters.out.persistence.document.request.raw_message import ( - RawMessage, - ) - - defaults = dict( - group_id="g1", - request_id="r1", - message_id="msg_001", - sender_id="assistant", - sender_name="assistant", - role="assistant", - content_items=[{"type": "text", "text": "hi"}], - timestamp="2025-01-15T10:00:00+00:00", - tool_calls=None, - tool_call_id=None, - ) - defaults.update(overrides) - return RawMessage.model_construct(**defaults) - - def test_mapper_preserves_tool_calls(self): - """to_raw_data should include tool_calls in content dict.""" - from infra_layer.adapters.out.persistence.mapper.raw_message_mapper import ( - RawMessageMapper, - ) - - tool_calls = [ - { - "id": "call_001", - "type": "function", - "function": {"name": "search", "arguments": "{}"}, - } - ] - msg = self._make_raw_message(tool_calls=tool_calls) - raw_data = RawMessageMapper.to_raw_data(msg) - - assert raw_data is not None - assert raw_data.content.get("tool_calls") == tool_calls - - def test_mapper_preserves_tool_call_id(self): - """to_raw_data should include tool_call_id in content dict.""" - from infra_layer.adapters.out.persistence.mapper.raw_message_mapper import ( - RawMessageMapper, - ) - - msg = self._make_raw_message( - role="tool", tool_call_id="call_001", tool_calls=None - ) - raw_data = RawMessageMapper.to_raw_data(msg) - - assert raw_data is not None - assert raw_data.content.get("tool_call_id") == "call_001" - - def test_mapper_omits_none_tool_fields(self): - """to_raw_data should not include tool_calls/tool_call_id when None.""" - from infra_layer.adapters.out.persistence.mapper.raw_message_mapper import ( - RawMessageMapper, - ) - - msg = self._make_raw_message() - raw_data = RawMessageMapper.to_raw_data(msg) - - assert raw_data is not None - assert "tool_calls" not in raw_data.content - assert "tool_call_id" not in raw_data.content - - def test_mapper_round_trip_full_conversation(self): - """A full agent conversation should round-trip tool_calls correctly.""" - from infra_layer.adapters.out.persistence.mapper.raw_message_mapper import ( - RawMessageMapper, - ) - - messages = [ - self._make_raw_message( - message_id="msg_u1", - role="user", - sender_id="u1", - content_items=[{"type": "text", "text": "do something"}], - ), - self._make_raw_message( - message_id="msg_a1", - role="assistant", - content_items=[{"type": "text", "text": "searching..."}], - tool_calls=[ - { - "id": "c1", - "type": "function", - "function": {"name": "search", "arguments": "{}"}, - } - ], - ), - self._make_raw_message( - message_id="msg_t1", - role="tool", - sender_id="tool_1", - content_items=[{"type": "text", "text": "result"}], - tool_call_id="c1", - ), - self._make_raw_message( - message_id="msg_a2", - role="assistant", - content_items=[{"type": "text", "text": "Done."}], - ), - ] - - raw_data_list = RawMessageMapper.to_raw_data_list(messages) - assert len(raw_data_list) == 4 - - # assistant with tool_calls - assert raw_data_list[1].content.get("tool_calls") == messages[1].tool_calls - # tool with tool_call_id - assert raw_data_list[2].content.get("tool_call_id") == "c1" - # user and final assistant have no tool fields - assert "tool_calls" not in raw_data_list[0].content - assert "tool_calls" not in raw_data_list[3].content - - -class TestRepositorySaveToolCalls: - """Tests for RawMessageRepository.save_from_raw_data preserving tool_calls. - - Uses patch to intercept RawMessage construction (avoids Beanie init). - """ - - @pytest.mark.asyncio - async def test_save_extracts_tool_calls(self): - """save_from_raw_data should pass tool_calls to RawMessage.""" - from infra_layer.adapters.out.persistence.repository.raw_message_repository import ( - RawMessageRepository, - ) - - repo = RawMessageRepository.__new__(RawMessageRepository) - repo.save = AsyncMock() - - tool_calls = [ - { - "id": "call_1", - "type": "function", - "function": {"name": "search", "arguments": "{}"}, - } - ] - content_dict = { - "sender_id": "assistant", - "sender_name": "assistant", - "role": "assistant", - "content": [{"type": "text", "text": "searching..."}], - "tool_calls": tool_calls, - "timestamp": "2025-01-15T10:00:00+00:00", - } - - captured = {} - - def capture_init(**kwargs): - captured.update(kwargs) - return MagicMock() - - with patch( - "infra_layer.adapters.out.persistence.repository.raw_message_repository.RawMessage", - side_effect=capture_init, - ): - await repo.save_from_raw_data( - raw_data_content=content_dict, - data_id="msg_001", - group_id="g1", - request_id="r1", - ) - - assert captured["tool_calls"] == tool_calls - assert captured["tool_call_id"] is None - - @pytest.mark.asyncio - async def test_save_extracts_tool_call_id(self): - """save_from_raw_data should pass tool_call_id to RawMessage.""" - from infra_layer.adapters.out.persistence.repository.raw_message_repository import ( - RawMessageRepository, - ) - - repo = RawMessageRepository.__new__(RawMessageRepository) - repo.save = AsyncMock() - - content_dict = { - "sender_id": "tool_1", - "role": "tool", - "content": [{"type": "text", "text": "result"}], - "tool_call_id": "call_1", - "timestamp": "2025-01-15T10:00:00+00:00", - } - - captured = {} - - def capture_init(**kwargs): - captured.update(kwargs) - return MagicMock() - - with patch( - "infra_layer.adapters.out.persistence.repository.raw_message_repository.RawMessage", - side_effect=capture_init, - ): - await repo.save_from_raw_data( - raw_data_content=content_dict, - data_id="msg_002", - group_id="g1", - request_id="r1", - ) - - assert captured["tool_call_id"] == "call_1" - assert captured.get("tool_calls") is None - - @pytest.mark.asyncio - async def test_save_without_tool_fields(self): - """save_from_raw_data for a normal user message should pass None tool fields.""" - from infra_layer.adapters.out.persistence.repository.raw_message_repository import ( - RawMessageRepository, - ) - - repo = RawMessageRepository.__new__(RawMessageRepository) - repo.save = AsyncMock() - - content_dict = { - "sender_id": "user_1", - "role": "user", - "content": [{"type": "text", "text": "hello"}], - "timestamp": "2025-01-15T10:00:00+00:00", - } - - captured = {} - - def capture_init(**kwargs): - captured.update(kwargs) - return MagicMock() - - with patch( - "infra_layer.adapters.out.persistence.repository.raw_message_repository.RawMessage", - side_effect=capture_init, - ): - await repo.save_from_raw_data( - raw_data_content=content_dict, - data_id="msg_003", - group_id="g1", - request_id="r1", - ) - - assert captured.get("tool_calls") is None - assert captured.get("tool_call_id") is None diff --git a/methods/EverCore/tests/test_agent_input_edge_cases.py b/methods/EverCore/tests/test_agent_input_edge_cases.py deleted file mode 100644 index c0e03c8c..00000000 --- a/methods/EverCore/tests/test_agent_input_edge_cases.py +++ /dev/null @@ -1,407 +0,0 @@ -"""Tests for agent input format edge cases and robustness. - -Covers scenarios NOT tested in test_agent_post_content.py: -- Content array with non-dict items, unknown types, missing fields -- Tool call edge cases: empty tool_calls, missing function/arguments -- Timestamp edge cases: zero, negative, float, string -- Role edge cases: uppercase, empty, whitespace-only -- Sender ID conflicts and edge cases -- _normalize_content_items edge cases -- _unwrap_messages edge cases -- get_text_from_content_items edge cases - -Usage: - PYTHONPATH=src pytest tests/test_api/test_agent_input_edge_cases.py -v -""" - -import pytest - -from api_specs.request_converter import ( - convert_agent_add_to_memorize_request, - _normalize_content_items, -) -from api_specs.memory_types import get_text_from_content_items - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -BASE_TS = 1710835200000 - - -def _agent_request(messages, user_id="user_01", session_id="sess_01"): - return {"user_id": user_id, "session_id": session_id, "messages": messages} - - -def _msg(role, content, ts_offset=0, **kwargs): - m = {"role": role, "timestamp": BASE_TS + ts_offset, "content": content} - m.update(kwargs) - return m - - -# =========================================================================== -# Content array edge cases -# =========================================================================== - - -class TestContentArrayEdgeCases: - """Content items array with non-standard elements.""" - - def test_non_dict_items_in_array_passed_through(self): - """Non-dict items (int, string, None) in content array are passed through by normalize.""" - items = [123, "raw_string", None, {"type": "text", "text": "valid"}] - result = _normalize_content_items(items) - assert result == [123, "raw_string", None, {"type": "text", "text": "valid"}] - - def test_unknown_content_type_passed_through(self): - """Content items with unknown types (e.g. 'video') are preserved as-is.""" - content = [ - {"type": "video", "url": "https://example.com/vid.mp4"}, - {"type": "text", "text": "See video above"}, - ] - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", content)]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["content"][0]["type"] == "video" - assert raw.content["content"][1]["type"] == "text" - - def test_text_item_missing_text_and_content_fields(self): - """Text type item with neither 'text' nor 'content' field — passed through as-is.""" - content = [{"type": "text", "metadata": "something"}] - result = _normalize_content_items(content) - assert result == [{"type": "text", "metadata": "something"}] - - def test_item_without_type_field(self): - """Dict item missing 'type' field is preserved unchanged.""" - content = [{"text": "orphan text", "extra": 1}] - result = _normalize_content_items(content) - assert result == [{"text": "orphan text", "extra": 1}] - - def test_empty_content_items_list_normalize(self): - """Empty list passes through normalization unchanged.""" - assert _normalize_content_items([]) == [] - - def test_content_as_dict_raises(self): - """Content passed as a dict (not string or array) raises ValueError.""" - msg = {"role": "user", "timestamp": BASE_TS, "content": {"text": "hello"}} - with pytest.raises(ValueError, match="content"): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - def test_content_as_integer_raises(self): - """Content passed as an integer raises ValueError.""" - msg = {"role": "user", "timestamp": BASE_TS, "content": 42} - with pytest.raises(ValueError, match="content"): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - def test_content_as_boolean_raises(self): - """Content passed as a boolean raises ValueError.""" - msg = {"role": "user", "timestamp": BASE_TS, "content": True} - with pytest.raises(ValueError, match="content"): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - -# =========================================================================== -# Tool call edge cases -# =========================================================================== - - -class TestToolCallEdgeCases: - """Edge cases for tool_calls field in assistant messages.""" - - def test_assistant_with_empty_tool_calls_array(self): - """Assistant message with empty tool_calls=[] — treated as no tool calls.""" - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", "No tools needed.", tool_calls=[])]) - ) - raw = req.new_raw_data_list[0] - # Empty list is falsy, so tool_calls may or may not be in content - assert raw.content["role"] == "assistant" - - def test_assistant_with_none_tool_calls(self): - """Assistant message with tool_calls=None — treated as no tool calls.""" - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", "Just a response.", tool_calls=None)]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["role"] == "assistant" - - def test_tool_call_missing_function_key(self): - """Tool call dict without 'function' key — still stored in RawData.""" - calls = [{"id": "call_01", "type": "function"}] - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", "Calling tool.", tool_calls=calls)]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["tool_calls"] == calls - - def test_tool_call_missing_arguments(self): - """Tool call with function but no arguments — still accepted.""" - calls = [ - {"id": "call_02", "type": "function", "function": {"name": "list_files"}} - ] - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", "Listing files.", tool_calls=calls)]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["tool_calls"][0]["function"]["name"] == "list_files" - - def test_tool_call_id_empty_string_raises(self): - """Tool role with empty string tool_call_id raises.""" - with pytest.raises(ValueError, match="tool_call_id"): - convert_agent_add_to_memorize_request( - _agent_request([_msg("tool", "result", tool_call_id="")]) - ) - - -# =========================================================================== -# Timestamp edge cases -# =========================================================================== - - -class TestTimestampEdgeCases: - """Edge cases for message timestamps.""" - - def test_zero_timestamp_raises(self): - """Timestamp of 0 is falsy — raises missing timestamp error.""" - msg = {"role": "user", "content": "hello", "timestamp": 0} - with pytest.raises(ValueError, match="timestamp"): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - def test_float_timestamp_accepted(self): - """Float timestamp (e.g., from JS Date.now()) should be accepted.""" - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", "hi", ts_offset=0.5)]) - ) - assert len(req.new_raw_data_list) == 1 - - def test_negative_timestamp(self): - """Negative timestamp (before epoch) — accepted or raises depends on implementation.""" - msg = {"role": "user", "content": "hello", "timestamp": -1000} - # Negative timestamps are truthy, so validation passes; - # _unix_ms_to_datetime may handle or error — verify no crash - try: - req = convert_agent_add_to_memorize_request(_agent_request([msg])) - assert len(req.new_raw_data_list) == 1 - except (ValueError, OverflowError, OSError): - pass # acceptable to reject negative timestamps - - def test_very_large_timestamp(self): - """Extremely large timestamp — verify no crash.""" - msg = {"role": "user", "content": "hello", "timestamp": 99999999999999} - try: - req = convert_agent_add_to_memorize_request(_agent_request([msg])) - assert len(req.new_raw_data_list) == 1 - except (ValueError, OverflowError, OSError): - pass # acceptable to reject out-of-range timestamps - - def test_string_timestamp_raises(self): - """String timestamp should raise or be rejected.""" - msg = {"role": "user", "content": "hello", "timestamp": "2024-03-19"} - with pytest.raises((ValueError, TypeError)): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - -# =========================================================================== -# Role edge cases -# =========================================================================== - - -class TestRoleEdgeCases: - """Edge cases for message role validation.""" - - def test_uppercase_role_raises(self): - """Role is case-sensitive — 'USER' should raise.""" - with pytest.raises(ValueError, match="role"): - convert_agent_add_to_memorize_request( - _agent_request([_msg("USER", "hello")]) - ) - - def test_mixed_case_role_raises(self): - """'Assistant' (capitalized) should raise.""" - with pytest.raises(ValueError, match="role"): - convert_agent_add_to_memorize_request( - _agent_request([_msg("Assistant", "hello")]) - ) - - def test_empty_string_role_raises(self): - """Empty string role raises missing role error.""" - msg = {"role": "", "content": "hello", "timestamp": BASE_TS} - with pytest.raises(ValueError, match="role"): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - def test_whitespace_role_raises(self): - """Whitespace-only role raises.""" - msg = {"role": " ", "content": "hello", "timestamp": BASE_TS} - with pytest.raises(ValueError, match="role"): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - def test_role_function_raises(self): - """'function' role (OpenAI legacy) should raise.""" - with pytest.raises(ValueError, match="role"): - convert_agent_add_to_memorize_request( - _agent_request([_msg("function", "result")]) - ) - - -# =========================================================================== -# Sender ID edge cases -# =========================================================================== - - -class TestSenderIdEdgeCases: - """Edge cases for sender_id assignment and validation.""" - - def test_user_role_with_mismatched_sender_id_raises(self): - """User role with sender_id != user_id raises.""" - with pytest.raises(ValueError, match="sender_id"): - convert_agent_add_to_memorize_request( - _agent_request( - [_msg("user", "hello", sender_id="wrong_id")], user_id="user_01" - ) - ) - - def test_assistant_role_with_user_id_as_sender_id_raises(self): - """Assistant role using user_id as sender_id raises conflict.""" - with pytest.raises(ValueError, match="sender_id"): - convert_agent_add_to_memorize_request( - _agent_request( - [_msg("assistant", "hello", sender_id="user_01")], user_id="user_01" - ) - ) - - def test_tool_auto_sender_id_is_deterministic(self): - """Same user_id + tool_call_id always produces the same sender_id.""" - req1 = convert_agent_add_to_memorize_request( - _agent_request([_msg("tool", "res", tool_call_id="call_x")]) - ) - req2 = convert_agent_add_to_memorize_request( - _agent_request([_msg("tool", "res", tool_call_id="call_x")]) - ) - sid1 = req1.new_raw_data_list[0].content["sender_id"] - sid2 = req2.new_raw_data_list[0].content["sender_id"] - assert sid1 == sid2 - - def test_different_tool_call_ids_get_different_sender_ids(self): - """Different tool_call_ids produce different auto sender_ids.""" - req1 = convert_agent_add_to_memorize_request( - _agent_request([_msg("tool", "res1", tool_call_id="call_a")]) - ) - req2 = convert_agent_add_to_memorize_request( - _agent_request([_msg("tool", "res2", tool_call_id="call_b")]) - ) - sid1 = req1.new_raw_data_list[0].content["sender_id"] - sid2 = req2.new_raw_data_list[0].content["sender_id"] - assert sid1 != sid2 - - def test_custom_sender_name_propagated(self): - """Custom sender_name is stored in RawData.""" - req = convert_agent_add_to_memorize_request( - _agent_request( - [_msg("assistant", "hello", sender_id="bot_1", sender_name="MyBot")] - ) - ) - raw = req.new_raw_data_list[0] - assert raw.content.get("sender_name") == "MyBot" - - -# =========================================================================== -# Request-level edge cases -# =========================================================================== - - -class TestRequestEdgeCases: - """Request-level input edge cases.""" - - def test_no_messages_key_raises(self): - """Request without messages key raises.""" - with pytest.raises(ValueError, match="messages"): - convert_agent_add_to_memorize_request({"user_id": "u1"}) - - def test_none_user_id_raises(self): - """user_id=None raises.""" - with pytest.raises(ValueError, match="user_id"): - convert_agent_add_to_memorize_request( - {"user_id": None, "messages": [_msg("user", "hello")]} - ) - - def test_empty_string_user_id_raises(self): - """user_id='' raises.""" - with pytest.raises(ValueError, match="user_id"): - convert_agent_add_to_memorize_request( - {"user_id": "", "messages": [_msg("user", "hello")]} - ) - - def test_multiple_messages_preserves_order(self): - """Multiple messages maintain insertion order.""" - msgs = [ - _msg("user", "first", ts_offset=0), - _msg("assistant", "second", ts_offset=1000), - _msg("user", "third", ts_offset=2000), - ] - req = convert_agent_add_to_memorize_request(_agent_request(msgs)) - texts = [] - for rd in req.new_raw_data_list: - for item in rd.content["content"]: - if item.get("type") == "text": - texts.append(item["text"]) - assert texts == ["first", "second", "third"] - - def test_session_id_defaults_when_missing(self): - """Missing session_id uses default value.""" - req = convert_agent_add_to_memorize_request( - {"user_id": "u1", "messages": [_msg("user", "hello")]} - ) - assert req.session_id is not None - - -# =========================================================================== -# get_text_from_content_items edge cases -# =========================================================================== - - -class TestGetTextFromContentItems: - """Edge cases for get_text_from_content_items utility.""" - - def test_plain_string_passthrough(self): - assert get_text_from_content_items("hello world") == "hello world" - - def test_not_list_or_string_returns_empty(self): - assert get_text_from_content_items(123) == "" - assert get_text_from_content_items(None) == "" - assert get_text_from_content_items({"type": "text"}) == "" - - def test_empty_list_returns_empty(self): - assert get_text_from_content_items([]) == "" - - def test_non_dict_items_skipped(self): - items = [123, "raw", None, {"type": "text", "text": "valid"}] - assert get_text_from_content_items(items) == "valid" - - def test_non_text_type_included_with_tag(self): - items = [ - {"type": "image", "uri": "https://example.com/img.png"}, - {"type": "text", "text": "caption"}, - ] - assert get_text_from_content_items(items) == "[IMAGE] caption" - - def test_multiple_text_items_joined_with_space(self): - items = [{"type": "text", "text": "line 1"}, {"type": "text", "text": "line 2"}] - assert get_text_from_content_items(items) == "line 1 line 2" - - def test_legacy_content_field_fallback(self): - items = [{"type": "text", "content": "legacy"}] - assert get_text_from_content_items(items) == "legacy" - - def test_text_field_preferred_over_content(self): - items = [{"type": "text", "text": "canonical", "content": "legacy"}] - assert get_text_from_content_items(items) == "canonical" - - def test_empty_text_items_skipped(self): - items = [{"type": "text", "text": ""}, {"type": "text", "text": "real"}] - assert get_text_from_content_items(items) == "real" - - def test_item_missing_text_and_content_skipped(self): - items = [{"type": "text", "metadata": "something"}] - assert get_text_from_content_items(items) == "" diff --git a/methods/EverCore/tests/test_agent_memcell_extractor.py b/methods/EverCore/tests/test_agent_memcell_extractor.py deleted file mode 100644 index 63462f83..00000000 --- a/methods/EverCore/tests/test_agent_memcell_extractor.py +++ /dev/null @@ -1,2513 +0,0 @@ -""" -AgentMemCellExtractor Unit Tests - -Tests agent turn-boundary-aware MemCell splitting: -- Skipping intermediate agent steps (tool_call, tool_response) -- Only splitting at complete agent responses -- Flush mode packing all messages -- History boundary validation -- Helper method correctness - -Usage: - PYTHONPATH=src pytest tests/test_agent_memcell_extractor.py -v -""" - -import pytest -import asyncio -from unittest.mock import AsyncMock, MagicMock, patch -from datetime import timedelta -from typing import List, Dict, Any - -from common_utils.datetime_utils import get_now_with_timezone -from api_specs.dtos import RawData -from api_specs.memory_types import RawDataType, is_intermediate_agent_step - -from memory_layer.memcell_extractor.agent_memcell_extractor import ( - AgentMemCellExtractor, - AgentMemCellExtractRequest, -) -from memory_layer.memcell_extractor.base_memcell_extractor import ( - MemCellExtractRequest, - StatusResult, -) - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -BASE_TIME = get_now_with_timezone() - timedelta(hours=1) - - -def _ts(offset_minutes: int) -> str: - return (BASE_TIME + timedelta(minutes=offset_minutes)).isoformat() - - -def _user_msg(content: str, offset: int = 0) -> Dict[str, Any]: - return { - "role": "user", - "content": content, - "speaker_name": "User", - "timestamp": _ts(offset), - } - - -def _assistant_msg(content: str, offset: int = 0) -> Dict[str, Any]: - """Complete assistant response (no tool_calls).""" - return { - "role": "assistant", - "content": content, - "speaker_name": "Assistant", - "timestamp": _ts(offset), - } - - -def _tool_call_msg(content: str = "", offset: int = 0) -> Dict[str, Any]: - """Intermediate assistant message WITH tool_calls.""" - return { - "role": "assistant", - "content": content, - "tool_calls": [{"id": "call_1", "function": {"name": "search", "arguments": "{}"}}], - "speaker_name": "Assistant", - "timestamp": _ts(offset), - } - - -def _tool_response_msg(content: str = "tool result", offset: int = 0) -> Dict[str, Any]: - """Tool execution result.""" - return { - "role": "tool", - "content": content, - "tool_call_id": "call_1", - "speaker_name": "Tool", - "timestamp": _ts(offset), - } - - -def _raw(msg: Dict[str, Any], data_id: str = "d") -> RawData: - return RawData(content=msg, data_id=data_id) - - -def _raw_list(msgs: List[Dict[str, Any]], prefix: str = "d") -> List[RawData]: - return [RawData(content=m, data_id=f"{prefix}_{i}") for i, m in enumerate(msgs)] - - -def _make_request( - history_msgs: List[Dict[str, Any]], - new_msgs: List[Dict[str, Any]], - flush: bool = False, -) -> MemCellExtractRequest: - return MemCellExtractRequest( - history_raw_data_list=_raw_list(history_msgs, "h"), - new_raw_data_list=_raw_list(new_msgs, "n"), - user_id_list=["user1"], - group_id="test_group", - flush=flush, - ) - - -def _build_extractor() -> AgentMemCellExtractor: - """Build extractor with a mocked LLM provider (boundary detection returns no boundaries).""" - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "no boundary", "boundaries": [], "should_wait": true}' - ) - return AgentMemCellExtractor(llm_provider=mock_llm) - - -def _build_extractor_should_end() -> AgentMemCellExtractor: - """Build extractor with LLM that signals boundary at position 2.""" - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "topic changed", "boundaries": [2], "should_wait": false}' - ) - return AgentMemCellExtractor(llm_provider=mock_llm) - - -# --------------------------------------------------------------------------- -# Tests: Helper Methods -# --------------------------------------------------------------------------- - - -class TestHelperMethods: - """Test is_intermediate_agent_step.""" - - def test_tool_response_is_intermediate(self): - assert is_intermediate_agent_step(_tool_response_msg()) is True - - def test_tool_call_is_intermediate(self): - assert is_intermediate_agent_step(_tool_call_msg()) is True - - def test_user_msg_not_intermediate(self): - assert is_intermediate_agent_step(_user_msg("hi")) is False - - def test_final_assistant_not_intermediate(self): - assert is_intermediate_agent_step(_assistant_msg("done")) is False - - -# --------------------------------------------------------------------------- -# Tests: Guard 1 - Skip intermediate agent steps -# --------------------------------------------------------------------------- - - -class TestGuard1SkipIntermediate: - """New message is intermediate -> return (None, should_wait=True).""" - - def setup_method(self): - self.extractor = _build_extractor() - - @pytest.mark.asyncio - async def test_skip_tool_call_message(self): - request = _make_request( - history_msgs=[_user_msg("hello", 0)], - new_msgs=[_tool_call_msg("calling tool", 1)], - ) - memcell, status = await self.extractor.extract_memcell(request) - assert memcell == [] - assert status.should_wait is True - - @pytest.mark.asyncio - async def test_skip_tool_response_message(self): - request = _make_request( - history_msgs=[_user_msg("hello", 0)], - new_msgs=[_tool_response_msg("result", 1)], - ) - memcell, status = await self.extractor.extract_memcell(request) - assert memcell == [] - assert status.should_wait is True - - @pytest.mark.asyncio - async def test_skip_multiple_new_last_is_tool(self): - """Multiple new messages, last one is tool response -> skip.""" - request = _make_request( - history_msgs=[_user_msg("hello", 0)], - new_msgs=[_user_msg("more", 1), _tool_response_msg("result", 2)], - ) - memcell, status = await self.extractor.extract_memcell(request) - assert memcell == [] - assert status.should_wait is True - - -# --------------------------------------------------------------------------- -# Tests: Guard 2 - Flush -# --------------------------------------------------------------------------- - - -class TestGuard2Flush: - """Flush mode packs all messages into one MemCell.""" - - def setup_method(self): - self.extractor = _build_extractor() - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_flush_packs_all_messages(self, mock_space, mock_record, mock_bd): - """Flush creates MemCell from all history + new messages.""" - request = _make_request( - history_msgs=[ - _user_msg("hello", 0), - _tool_call_msg("thinking", 1), - _tool_response_msg("result", 2), - _assistant_msg("answer", 3), - ], - new_msgs=[_user_msg("follow up", 4)], - flush=True, - ) - memcell, status = await self.extractor.extract_memcell(request) - assert len(memcell) > 0 - assert status.should_wait is False - # All 5 messages should be packed - assert len(memcell[0].original_data) == 5 - assert memcell[0].type == RawDataType.AGENTCONVERSATION - - @pytest.mark.asyncio - async def test_flush_empty_messages(self): - """Flush with no messages returns None.""" - request = _make_request(history_msgs=[], new_msgs=[], flush=True) - memcell, status = await self.extractor.extract_memcell(request) - assert memcell == [] - assert status.should_wait is True - - @pytest.mark.asyncio - async def test_flush_overrides_intermediate(self): - """Flush has highest priority — even if last new message is intermediate, - flush packs all messages into a MemCell regardless. - Downstream extractors detect incomplete trajectories and skip accordingly. - """ - request = _make_request( - history_msgs=[_user_msg("hello", 0), _assistant_msg("answer", 1)], - new_msgs=[_tool_call_msg("thinking", 2)], - flush=True, - ) - # Flush fires first (Guard 1), overriding intermediate skip (Guard 2) - memcell, status = await self.extractor.extract_memcell(request) - assert len(memcell) > 0 - assert len(memcell[0].original_data) == 3 - - -# --------------------------------------------------------------------------- -# Tests: Guard 3 - History must end at complete agent response -# --------------------------------------------------------------------------- - - -class TestGuard3HistoryBoundary: - """History doesn't end at complete agent response -> wait.""" - - def setup_method(self): - self.extractor = _build_extractor() - - @pytest.mark.asyncio - async def test_history_ends_at_user_msg_waits(self): - request = _make_request( - history_msgs=[_user_msg("hello", 0)], - new_msgs=[_user_msg("more", 1)], - ) - memcell, status = await self.extractor.extract_memcell(request) - assert memcell == [] - assert status.should_wait is True - - @pytest.mark.asyncio - async def test_history_ends_at_tool_call_waits(self): - request = _make_request( - history_msgs=[_user_msg("hello", 0), _tool_call_msg("thinking", 1)], - new_msgs=[_assistant_msg("done", 2)], - ) - memcell, status = await self.extractor.extract_memcell(request) - assert memcell == [] - assert status.should_wait is True - - @pytest.mark.asyncio - async def test_history_ends_at_tool_response_waits(self): - request = _make_request( - history_msgs=[ - _user_msg("hello", 0), - _tool_call_msg("thinking", 1), - _tool_response_msg("result", 2), - ], - new_msgs=[_assistant_msg("done", 3)], - ) - memcell, status = await self.extractor.extract_memcell(request) - assert memcell == [] - assert status.should_wait is True - - -# --------------------------------------------------------------------------- -# Tests: Delegation to parent (all guards pass) -# --------------------------------------------------------------------------- - - -class TestDelegationToParent: - """When all guards pass, parent's extract_memcell is called.""" - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_valid_boundary_delegates_to_parent_llm_no_boundary( - self, mock_space, mock_record, mock_bd - ): - """History ends at assistant, new msg is user -> delegates to parent. - Mock LLM returns should_wait -> no MemCell.""" - extractor = _build_extractor() - request = _make_request( - history_msgs=[_user_msg("hello", 0), _assistant_msg("hi there", 1)], - new_msgs=[_user_msg("what is 2+2?", 5)], - ) - memcell, status = await extractor.extract_memcell(request) - # LLM says no boundary -> should_wait - assert memcell == [] - assert status.should_wait is True - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_valid_boundary_llm_detects_end(self, mock_space, mock_record, mock_bd): - """LLM detects boundary -> MemCell is created from history.""" - extractor = _build_extractor_should_end() - # new_msgs must include an assistant response to pass the all_user_only guard - request = _make_request( - history_msgs=[_user_msg("hello", 0), _assistant_msg("hi there", 1)], - new_msgs=[_user_msg("new topic entirely", 30), _assistant_msg("sure", 31)], - ) - memcell, status = await extractor.extract_memcell(request) - assert len(memcell) > 0 - assert status.should_wait is False - # MemCell should contain only history messages (boundary at position 2) - assert len(memcell[0].original_data) == 2 - assert memcell[0].type == RawDataType.AGENTCONVERSATION - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_empty_history_delegates(self, mock_space, mock_record, mock_bd): - """Empty history + user new msg -> delegates to parent (guard 3 skips for empty history). - Parent's batch boundary detection returns should_wait per LLM mock.""" - extractor = _build_extractor() - request = _make_request( - history_msgs=[], - new_msgs=[_user_msg("hello", 0)], - ) - memcell, status = await extractor.extract_memcell(request) - # No history -> delegates to parent, LLM mock says no boundary + should_wait - assert memcell == [] - assert status.should_wait is True - - -# --------------------------------------------------------------------------- -# Tests: Force split respects turn boundaries -# --------------------------------------------------------------------------- - - -class TestForceSplit: - """Force split via hard limits respects tool-call boundaries.""" - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_force_split_at_valid_boundary(self, mock_space, mock_record, mock_bd): - """When history is valid and exceeds message limit, force split creates MemCell.""" - extractor = _build_extractor() - extractor.hard_message_limit = 3 # Low limit to trigger force split - - # new_msgs must include an assistant response to pass the all_user_only guard - request = _make_request( - history_msgs=[_user_msg("q1", 0), _assistant_msg("a1", 1)], - new_msgs=[_user_msg("q2", 2), _assistant_msg("a2", 3)], - ) - # 2 history + 2 new = 4 >= hard_message_limit -> force split front chunk - memcell, status = await extractor.extract_memcell(request) - assert len(memcell) > 0 - # v1: after force split, remaining messages go through LLM boundary detection - # LLM mock says should_wait=True for remaining messages - assert status.should_wait is True - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_force_split_regardless_of_turn_boundary(self, mock_space, mock_record, mock_bd): - """Force split is a safety valve — splits even mid-turn when limits are exceeded.""" - extractor = _build_extractor() - extractor.hard_message_limit = 3 - - request = _make_request( - history_msgs=[_user_msg("q1", 0), _user_msg("q2", 1)], - new_msgs=[_assistant_msg("a1", 2)], - ) - # 3 messages >= hard_message_limit -> force split fires - memcell, status = await extractor.extract_memcell(request) - assert len(memcell) > 0 - - -class TestIsSafeSplit: - """Test _is_safe_split static method.""" - - def test_safe_after_final_assistant(self): - msgs = [_user_msg("q", 0), _assistant_msg("a", 1), _user_msg("q2", 2)] - assert AgentMemCellExtractor._is_safe_split(msgs, 2) is True - - def test_unsafe_after_tool_call(self): - msgs = [_user_msg("q", 0), _tool_call_msg("t", 1), _tool_response_msg("r", 2)] - assert AgentMemCellExtractor._is_safe_split(msgs, 2) is False - - def test_unsafe_after_tool_response(self): - msgs = [_user_msg("q", 0), _tool_call_msg("t", 1), _tool_response_msg("r", 2)] - assert AgentMemCellExtractor._is_safe_split(msgs, 3) is False - - def test_unsafe_last_is_user(self): - """Last message is user -> not safe.""" - msgs = [_user_msg("q", 0), _tool_call_msg("t", 1), _assistant_msg("a", 2)] - assert AgentMemCellExtractor._is_safe_split(msgs, 1) is False - - def test_safe_last_is_final_assistant(self): - """Last message is final assistant -> safe.""" - msgs = [ - _user_msg("q", 0), - _tool_call_msg("t", 1), - _tool_response_msg("r", 2), - _assistant_msg("a", 3), - _user_msg("q2", 4), - ] - assert AgentMemCellExtractor._is_safe_split(msgs, 4) is True - - def test_unsafe_last_is_tool_response(self): - """Last message is tool response -> not safe.""" - msgs = [_tool_call_msg("t", 0), _tool_response_msg("r", 1), _assistant_msg("a", 2)] - assert AgentMemCellExtractor._is_safe_split(msgs, 2) is False - - def test_unsafe_split_at_zero(self): - """split_at=0 is out of valid range.""" - msgs = [_user_msg("q", 0), _assistant_msg("a", 1)] - assert AgentMemCellExtractor._is_safe_split(msgs, 0) is False - - def test_unsafe_split_at_negative(self): - """Negative split_at is out of valid range.""" - msgs = [_user_msg("q", 0), _assistant_msg("a", 1)] - assert AgentMemCellExtractor._is_safe_split(msgs, -1) is False - - def test_unsafe_split_at_len(self): - """split_at=len(messages) would leave no remainder for next chunk.""" - msgs = [_user_msg("q", 0), _assistant_msg("a", 1)] - assert AgentMemCellExtractor._is_safe_split(msgs, 2) is False - - def test_unsafe_split_beyond_len(self): - """split_at beyond len(messages) is out of range.""" - msgs = [_user_msg("q", 0), _assistant_msg("a", 1)] - assert AgentMemCellExtractor._is_safe_split(msgs, 5) is False - - def test_safe_split_at_one_with_assistant(self): - """split_at=1 is valid if messages[0] is a final assistant.""" - msgs = [_assistant_msg("a", 0), _user_msg("q", 1)] - assert AgentMemCellExtractor._is_safe_split(msgs, 1) is True - - def test_unsafe_single_message_list(self): - """Single-element list: split_at=1 means len-1=0, so out of range.""" - msgs = [_assistant_msg("a", 0)] - assert AgentMemCellExtractor._is_safe_split(msgs, 1) is False - - -class TestForceSplitToolBoundary: - """Force split adjusts split point to avoid cutting tool-call sequences.""" - - def test_find_force_split_point_avoids_tool_middle(self): - """Split point should not land after an intermediate agent step. - - Messages: - [0] user: q1 - [1] assistant + tool_calls <- intermediate - [2] tool: result <- intermediate - [3] assistant: answer - [4] user: q2 - [5] assistant: a2 - - With hard_message_limit=4, parent returns candidate=3 (split after msg[2]=tool). - Walking back to 1 would cut a lone user msg, so forward walk to 4 - (after msg[3]=final assistant) which includes a complete turn. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 4 - - messages = [ - _user_msg("q1", 0), - _tool_call_msg("thinking", 1), - _tool_response_msg("result", 2), - _assistant_msg("answer", 3), - _user_msg("q2", 4), - _assistant_msg("a2", 5), - ] - split_at = extractor._find_force_split_point(messages) - # Should include the full tool sequence: user + tool_call + tool + assistant - assert split_at == 4 - assert messages[split_at - 1]["role"] == "assistant" - assert not messages[split_at - 1].get("tool_calls") - - def test_find_force_split_point_no_tool_calls(self): - """Without tool calls, behaves same as parent.""" - extractor = _build_extractor() - extractor.hard_message_limit = 3 - - messages = [ - _user_msg("q1", 0), - _assistant_msg("a1", 1), - _user_msg("q2", 2), - _assistant_msg("a2", 3), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == 2 # Same as parent: hard_message_limit - 1 - - def test_find_force_split_point_forward_when_all_intermediate_before(self): - """When walking back finds only intermediates, walk forward instead. - - Messages: - [0] tool_call <- intermediate - [1] tool <- intermediate - [2] assistant: answer - [3] user: q2 - [4] assistant: a2 - - With hard_message_limit=3, parent candidate=2. Walk back: msg[1]=tool, - msg[0]=tool_call -> all intermediate, no safe point. Forward walk from 3: - chunk=[0:3] includes final assistant at [2] -> safe. split_at=3. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 3 - - messages = [ - _tool_call_msg("thinking", 0), - _tool_response_msg("result", 1), - _assistant_msg("answer", 2), - _user_msg("q2", 3), - _assistant_msg("a2", 4), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == 3 - assert messages[split_at - 1]["role"] == "assistant" - assert not messages[split_at - 1].get("tool_calls") - - def test_split_after_tool_call_keeps_all(self): - """No safe split exists -> keep all messages in one chunk. - - Messages: - [0] user - [1] assistant + tool_calls <- intermediate - [2] user - [3] assistant - - hard_message_limit=3, parent candidate=2 (after msg[1]=tool_call). - Walk back: no final assistant before candidate. Forward walk: - candidate=3, msg[2]=user -> not safe. No safe split -> keep all. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 3 - - messages = [ - _user_msg("q1", 0), - _tool_call_msg("thinking", 1), - _user_msg("q2", 2), - _assistant_msg("a2", 3), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == len(messages) - - def test_split_after_tool_response_walks_forward(self): - """Parent candidate lands right after tool response -> walks forward. - - Messages: - [0] user - [1] assistant + tool_calls - [2] tool response - [3] assistant: answer - [4] user - [5] assistant - - hard_message_limit=4, parent candidate=3. msg[2]=tool, msg[1]=tool_call. - Walking back to 1 gives lone user (no assistant reply) -> not safe. - Forward walk from 4: chunk=[0:4] includes final assistant at [3] -> safe. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 4 - - messages = [ - _user_msg("q1", 0), - _tool_call_msg("thinking", 1), - _tool_response_msg("result", 2), - _assistant_msg("answer", 3), - _user_msg("q2", 4), - _assistant_msg("a2", 5), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == 4 - assert messages[split_at - 1]["role"] == "assistant" - assert not messages[split_at - 1].get("tool_calls") - - def test_split_already_safe_no_adjustment(self): - """When parent candidate is already at a safe point, no adjustment needed. - - Messages: - [0] user - [1] assistant (final) - [2] user - [3] assistant + tool_calls - [4] tool - [5] assistant - - hard_message_limit=3, parent candidate=2. msg[1]=final assistant -> safe. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 3 - - messages = [ - _user_msg("q1", 0), - _assistant_msg("a1", 1), - _user_msg("q2", 2), - _tool_call_msg("thinking", 3), - _tool_response_msg("result", 4), - _assistant_msg("a2", 5), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == 2 - - def test_multiple_consecutive_tool_calls(self): - """Agent makes multiple tool calls before final answer. - - Messages: - [0] user - [1] assistant + tool_calls <- intermediate - [2] tool <- intermediate - [3] assistant + tool_calls <- intermediate (second tool call) - [4] tool <- intermediate - [5] assistant: final answer - [6] user - [7] assistant - - hard_message_limit=5, parent candidate=4. msg[3]=tool_call, [2]=tool, - [1]=tool_call, [0]=user -> walk back to 1 gives lone user -> not safe. - Forward walk from 5: chunk=[0:5] has no final asst, [0:6] has final asst -> 6. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 5 - - messages = [ - _user_msg("q1", 0), - _tool_call_msg("call search", 1), - _tool_response_msg("search result", 2), - _tool_call_msg("call compute", 3), - _tool_response_msg("compute result", 4), - _assistant_msg("final answer", 5), - _user_msg("q2", 6), - _assistant_msg("a2", 7), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == 6 - assert messages[split_at - 1]["role"] == "assistant" - assert not messages[split_at - 1].get("tool_calls") - - def test_two_complete_turns_with_tools(self): - """Two full agent turns, limit triggers split between them. - - Messages: - [0] user - [1] assistant + tool_calls - [2] tool - [3] assistant: answer1 - [4] user - [5] assistant + tool_calls - [6] tool - [7] assistant: answer2 - [8] user - [9] assistant - - hard_message_limit=6, parent candidate=5. msg[4]=user -> not final asst. - Walk back: msg[3]=final assistant -> safe. split_at=4, a complete turn. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 6 - - messages = [ - _user_msg("q1", 0), - _tool_call_msg("call1", 1), - _tool_response_msg("r1", 2), - _assistant_msg("a1", 3), - _user_msg("q2", 4), - _tool_call_msg("call2", 5), - _tool_response_msg("r2", 6), - _assistant_msg("a2", 7), - _user_msg("q3", 8), - _assistant_msg("a3", 9), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == 4 - assert messages[split_at - 1]["role"] == "assistant" - assert not messages[split_at - 1].get("tool_calls") - - def test_forward_walk_finds_final_assistant(self): - """Forward walk skips intermediates to land on final assistant. - - Messages: - [0] tool_call <- intermediate - [1] tool <- intermediate - [2] tool_call <- intermediate (second call) - [3] tool <- intermediate - [4] assistant: final - [5] user - [6] assistant - - hard_message_limit=4, parent candidate=3. Walk back: msg[2]=tool_call, - [1]=tool, [0]=tool_call -> all intermediate. Forward walk from 3: - msg[2]=tool_call, msg[3]=tool, msg[4]=assistant(final) -> split_at=5. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 4 - - messages = [ - _tool_call_msg("call1", 0), - _tool_response_msg("r1", 1), - _tool_call_msg("call2", 2), - _tool_response_msg("r2", 3), - _assistant_msg("final", 4), - _user_msg("q2", 5), - _assistant_msg("a2", 6), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == 5 - assert messages[split_at - 1]["role"] == "assistant" - assert not messages[split_at - 1].get("tool_calls") - - def test_no_safe_split_keeps_all(self): - """When no safe split exists, keep all messages in one chunk. - - Messages: - [0] tool_call <- intermediate - [1] tool <- intermediate - [2] assistant: final - - hard_message_limit=2, parent candidate=1. Walk back: no safe point. - Forward walk: no safe point. Keep all as one chunk. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 2 - - messages = [ - _tool_call_msg("call", 0), - _tool_response_msg("result", 1), - _assistant_msg("final", 2), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == len(messages) - - def test_no_safe_split_long_tool_chain_keeps_all(self): - """Single turn with many tool calls, no safe split -> keep all. - - Messages: - [0] user - [1] tool_call - [2] tool - [3] tool_call - [4] tool - [5] assistant: final - - hard_message_limit=3, parent candidate=2. Walk back: no safe point. - Forward walk: no safe point. Keep all as one chunk. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 3 - - messages = [ - _user_msg("q1", 0), - _tool_call_msg("c1", 1), - _tool_response_msg("r1", 2), - _tool_call_msg("c2", 3), - _tool_response_msg("r2", 4), - _assistant_msg("final", 5), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == len(messages) - - def test_single_tool_call_in_middle(self): - """Single tool call (no multi-step) in the middle. - - Messages: - [0] user - [1] assistant + tool_calls <- intermediate - [2] assistant: answer - [3] user - [4] assistant - - hard_message_limit=3, parent candidate=2. msg[1]=tool_call -> walk back - to 1 gives lone user (no asst reply) -> not safe. Forward walk from 3: - chunk=[0:3] includes final assistant at [2] -> safe. split_at=3. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 3 - - messages = [ - _user_msg("q1", 0), - _tool_call_msg("thinking", 1), - _assistant_msg("answer", 2), - _user_msg("q2", 3), - _assistant_msg("a2", 4), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == 3 - assert messages[split_at - 1]["role"] == "assistant" - assert not messages[split_at - 1].get("tool_calls") - - def test_split_at_assistant_final_is_safe(self): - """Candidate landing on final assistant (no tool_calls) is safe. - - Messages: - [0] user - [1] assistant: answer (no tool_calls) - [2] user - [3] assistant - - hard_message_limit=3, parent candidate=2. msg[1]=final assistant -> safe. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 3 - - messages = [ - _user_msg("q1", 0), - _assistant_msg("a1", 1), - _user_msg("q2", 2), - _assistant_msg("a2", 3), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == 2 - - def test_never_splits_lone_user_message(self): - """Should never produce a chunk with only user messages and no assistant reply. - - Messages: - [0] user - [1] assistant + tool_calls - [2] tool - [3] assistant: answer - [4] user - [5] assistant + tool_calls - [6] tool - [7] assistant: answer2 - - Various limits should all produce chunks containing at least one - final assistant response. - """ - extractor = _build_extractor() - - messages = [ - _user_msg("q1", 0), - _tool_call_msg("c1", 1), - _tool_response_msg("r1", 2), - _assistant_msg("a1", 3), - _user_msg("q2", 4), - _tool_call_msg("c2", 5), - _tool_response_msg("r2", 6), - _assistant_msg("a2", 7), - ] - - for limit in [2, 3, 4, 5, 6]: - extractor.hard_message_limit = limit - split_at = extractor._find_force_split_point(messages) - chunk = messages[:split_at] - has_final_asst = any( - m.get("role") == "assistant" and not m.get("tool_calls") - for m in chunk - ) - assert has_final_asst or split_at >= len(messages) - 1, ( - f"limit={limit}, split_at={split_at}: chunk has no final assistant" - ) - - def test_walk_back_finds_complete_turn(self): - """Walk back should stop at a final assistant boundary. - - Messages: - [0] user - [1] assistant: a1 (final) - [2] user - [3] assistant + tool_calls <- intermediate - [4] tool <- intermediate - [5] assistant: a2 - [6] user - [7] assistant: a3 - - hard_message_limit=5, parent candidate=4. msg[3]=tool_call -> walk back. - candidate=3: msg[2]=user -> not final asst. candidate=2: msg[1]=final - assistant -> safe! split_at=2. - """ - extractor = _build_extractor() - extractor.hard_message_limit = 5 - - messages = [ - _user_msg("q1", 0), - _assistant_msg("a1", 1), - _user_msg("q2", 2), - _tool_call_msg("thinking", 3), - _tool_response_msg("result", 4), - _assistant_msg("a2", 5), - _user_msg("q3", 6), - _assistant_msg("a3", 7), - ] - split_at = extractor._find_force_split_point(messages) - assert split_at == 2 - assert messages[split_at - 1]["role"] == "assistant" - assert not messages[split_at - 1].get("tool_calls") - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_force_split_keeps_tool_sequence_intact(self, mock_space, mock_record, mock_bd): - """End-to-end: force split should not break a tool call sequence across MemCells.""" - extractor = _build_extractor() - extractor.hard_message_limit = 4 - - request = _make_request( - history_msgs=[ - _user_msg("q1", 0), - _tool_call_msg("thinking", 1), - _tool_response_msg("result", 2), - _assistant_msg("answer", 3), - ], - new_msgs=[_user_msg("q2", 4), _assistant_msg("a2", 5)], - ) - memcells, status = await extractor.extract_memcell(request) - # Force split should produce at least one MemCell - assert len(memcells) > 0 - # Each MemCell should not start or end with an orphaned tool message - for mc in memcells: - first_msg = mc.original_data[0].get("message", mc.original_data[0]) - last_msg = mc.original_data[-1].get("message", mc.original_data[-1]) - # Last message in a MemCell should not be an intermediate step - assert not is_intermediate_agent_step( - last_msg - ), f"MemCell ends with intermediate: {last_msg.get('role')}" - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_force_split_multi_tool_turn_e2e(self, mock_space, mock_record, mock_bd): - """End-to-end: multi-tool turn stays intact after force split.""" - extractor = _build_extractor() - extractor.hard_message_limit = 5 - - request = _make_request( - history_msgs=[ - _user_msg("q1", 0), - _tool_call_msg("search", 1), - _tool_response_msg("search result", 2), - _tool_call_msg("compute", 3), - _tool_response_msg("compute result", 4), - _assistant_msg("final answer", 5), - ], - new_msgs=[_user_msg("q2", 6), _assistant_msg("a2", 7)], - ) - memcells, status = await extractor.extract_memcell(request) - assert len(memcells) > 0 - for mc in memcells: - last_msg = mc.original_data[-1].get("message", mc.original_data[-1]) - assert not is_intermediate_agent_step( - last_msg - ), f"MemCell ends with intermediate: {last_msg.get('role')}" - - -# --------------------------------------------------------------------------- -# Tests: Multi-turn agent conversation flow simulation -# --------------------------------------------------------------------------- - - -class TestMultiTurnFlow: - """Simulate a complete agent turn: user -> tool_call -> tool_response -> assistant.""" - - @pytest.mark.asyncio - async def test_full_agent_turn_sequence(self): - """Simulate messages arriving one by one; only the final assistant triggers processing.""" - extractor = _build_extractor() - results = [] - - # Message 1: user asks a question - req1 = _make_request(history_msgs=[], new_msgs=[_user_msg("search for X", 0)]) - # Empty history -> guard 3 skips, delegates to parent - # Parent: no history, first messages -> LLM says should_wait - with patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", - return_value="test", - ): - r1 = await extractor.extract_memcell(req1) - results.append(r1) - - # Message 2: agent makes tool call (intermediate) - req2 = _make_request( - history_msgs=[_user_msg("search for X", 0)], - new_msgs=[_tool_call_msg("calling search", 1)], - ) - r2 = await extractor.extract_memcell(req2) - results.append(r2) - - # Message 3: tool response (intermediate) - req3 = _make_request( - history_msgs=[_user_msg("search for X", 0), _tool_call_msg("calling search", 1)], - new_msgs=[_tool_response_msg("search results", 2)], - ) - r3 = await extractor.extract_memcell(req3) - results.append(r3) - - # Message 4: final assistant response - req4 = _make_request( - history_msgs=[ - _user_msg("search for X", 0), - _tool_call_msg("calling search", 1), - _tool_response_msg("search results", 2), - ], - new_msgs=[_assistant_msg("Here are the results for X", 3)], - ) - # History ends at tool_response -> guard 3 blocks - r4 = await extractor.extract_memcell(req4) - results.append(r4) - - # Verify: messages 2, 3, 4 all returned (None, should_wait=True) - for i in range(1, 4): - memcell, status = results[i] - assert memcell == [], f"Step {i+1} should not create MemCell" - assert status.should_wait is True, f"Step {i+1} should wait" - - # Message 5: next user message arrives, now history ends at assistant response - req5 = _make_request( - history_msgs=[ - _user_msg("search for X", 0), - _tool_call_msg("calling search", 1), - _tool_response_msg("search results", 2), - _assistant_msg("Here are the results for X", 3), - ], - new_msgs=[_user_msg("thanks, now search Y", 10)], - ) - with patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", - return_value="test", - ): - r5 = await extractor.extract_memcell(req5) - # All guards pass -> delegates to parent LLM boundary detection - memcell, status = r5 - # LLM mock says should_wait -> no memcell, which is correct behavior - assert status.should_wait is True - - -# --------------------------------------------------------------------------- -# Tests: raw_data_type -# --------------------------------------------------------------------------- - - -class TestDetectBoundariesRemap: - """Test _detect_boundaries filter + remap logic.""" - - @pytest.mark.asyncio - async def test_remap_single_boundary(self): - """LLM boundary on filtered messages is remapped to original indices. - - Original: - [0] user -> filtered[0] - [1] tool_call (filtered out) - [2] tool (filtered out) - [3] assistant -> filtered[1] - [4] user -> filtered[2] - [5] assistant -> filtered[3] - - LLM returns boundary=[2] on filtered (after filtered[1]=assistant). - Remap: filtered_to_orig[2-1] + 1 = orig[3] + 1 = 4. - """ - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "topic change", "boundaries": [2], "should_wait": false}' - ) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - - messages = [ - _user_msg("q1", 0), - _tool_call_msg("thinking", 1), - _tool_response_msg("result", 2), - _assistant_msg("answer", 3), - _user_msg("q2", 4), - _assistant_msg("a2", 5), - ] - result = await extractor._detect_boundaries(messages) - assert result.boundaries == [4] - - @pytest.mark.asyncio - async def test_remap_multiple_boundaries(self): - """Multiple boundaries are all remapped correctly. - - Original: - [0] user -> filtered[0] - [1] assistant -> filtered[1] - [2] user -> filtered[2] - [3] tool_call (filtered out) - [4] tool (filtered out) - [5] assistant -> filtered[3] - [6] user -> filtered[4] - [7] assistant -> filtered[5] - - LLM returns boundaries=[2, 4] on filtered. - Remap[2]: filtered_to_orig[1] + 1 = 1 + 1 = 2 - Remap[4]: filtered_to_orig[3] + 1 = 5 + 1 = 6 - """ - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "two topics", "boundaries": [2, 4], "should_wait": false}' - ) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - - messages = [ - _user_msg("q1", 0), - _assistant_msg("a1", 1), - _user_msg("q2", 2), - _tool_call_msg("thinking", 3), - _tool_response_msg("result", 4), - _assistant_msg("a2", 5), - _user_msg("q3", 6), - _assistant_msg("a3", 7), - ] - result = await extractor._detect_boundaries(messages) - assert result.boundaries == [2, 6] - - @pytest.mark.asyncio - async def test_all_messages_are_intermediate(self): - """If all messages are intermediate, return should_wait=True.""" - extractor = _build_extractor() - messages = [ - _tool_call_msg("call", 0), - _tool_response_msg("result", 1), - ] - result = await extractor._detect_boundaries(messages) - assert result.boundaries == [] - assert result.should_wait is True - - @pytest.mark.asyncio - async def test_no_tool_messages_passthrough(self): - """Without tool messages, boundaries pass through unchanged.""" - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "split", "boundaries": [2], "should_wait": false}' - ) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - - messages = [ - _user_msg("q1", 0), - _assistant_msg("a1", 1), - _user_msg("q2", 2), - _assistant_msg("a2", 3), - ] - result = await extractor._detect_boundaries(messages) - assert result.boundaries == [2] - - -class TestForceSplitKeepsAllE2E: - """End-to-end tests for when _find_force_split_point returns len(messages).""" - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_single_long_tool_turn_becomes_one_memcell(self, mock_space, mock_record, mock_bd): - """A single turn with many tools that exceeds limit stays as one MemCell.""" - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "done", "boundaries": [], "should_wait": false}' - ) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - extractor.hard_message_limit = 3 - - request = _make_request( - history_msgs=[], - new_msgs=[ - _user_msg("complex question", 0), - _tool_call_msg("search", 1), - _tool_response_msg("search result", 2), - _tool_call_msg("compute", 3), - _tool_response_msg("compute result", 4), - _assistant_msg("final answer", 5), - ], - ) - memcells, status = await extractor.extract_memcell(request) - # All 6 messages should be in one MemCell - assert len(memcells) == 1 - assert len(memcells[0].original_data) == 6 - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_multiple_force_splits_in_loop(self, mock_space, mock_record, mock_bd): - """Force split loop runs multiple times, each split respects tool boundaries.""" - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "done", "boundaries": [], "should_wait": false}' - ) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - extractor.hard_message_limit = 6 - - request = _make_request( - history_msgs=[ - # Turn 1: complete - _user_msg("q1", 0), - _tool_call_msg("c1", 1), - _tool_response_msg("r1", 2), - _assistant_msg("a1", 3), - # Turn 2: complete - _user_msg("q2", 4), - _tool_call_msg("c2", 5), - _tool_response_msg("r2", 6), - _assistant_msg("a2", 7), - ], - new_msgs=[ - # Turn 3: complete - _user_msg("q3", 8), - _assistant_msg("a3", 9), - ], - ) - memcells, status = await extractor.extract_memcell(request) - # Should produce multiple MemCells, each ending at a final assistant - assert len(memcells) >= 2 - for mc in memcells: - last_msg = mc.original_data[-1].get("message", mc.original_data[-1]) - assert last_msg.get("role") == "assistant" - assert not last_msg.get("tool_calls") - - -class TestIsSafeSplitEdgeCases: - """Additional edge cases for _is_safe_split.""" - - def test_assistant_with_empty_tool_calls_is_safe(self): - """assistant with tool_calls=[] is a final response, should be safe.""" - msgs = [ - _user_msg("q", 0), - {"role": "assistant", "content": "done", "tool_calls": [], - "speaker_name": "Assistant", "timestamp": _ts(1)}, - _user_msg("q2", 2), - ] - assert AgentMemCellExtractor._is_safe_split(msgs, 2) is True - - def test_assistant_with_tool_calls_none_is_safe(self): - """assistant with tool_calls=None is a final response, should be safe.""" - msgs = [ - _user_msg("q", 0), - {"role": "assistant", "content": "done", "tool_calls": None, - "speaker_name": "Assistant", "timestamp": _ts(1)}, - _user_msg("q2", 2), - ] - assert AgentMemCellExtractor._is_safe_split(msgs, 2) is True - - def test_assistant_without_tool_calls_key_is_safe(self): - """assistant without tool_calls key at all is a final response.""" - msgs = [ - _user_msg("q", 0), - {"role": "assistant", "content": "done", - "speaker_name": "Assistant", "timestamp": _ts(1)}, - _user_msg("q2", 2), - ] - assert AgentMemCellExtractor._is_safe_split(msgs, 2) is True - - def test_system_message_is_not_safe(self): - """system message at split boundary is not safe.""" - msgs = [ - {"role": "system", "content": "You are helpful"}, - _user_msg("q", 1), - _assistant_msg("a", 2), - ] - assert AgentMemCellExtractor._is_safe_split(msgs, 1) is False - - def test_empty_messages_list(self): - """Empty list has no valid split.""" - assert AgentMemCellExtractor._is_safe_split([], 0) is False - assert AgentMemCellExtractor._is_safe_split([], 1) is False - - -class TestGuardEdgeCases: - """Additional guard edge cases in extract_memcell.""" - - def setup_method(self): - self.extractor = _build_extractor() - - @pytest.mark.asyncio - async def test_empty_new_msgs_with_history(self): - """Empty new_raw_data_list with non-empty history -> delegates to parent.""" - request = _make_request( - history_msgs=[_user_msg("q", 0), _assistant_msg("a", 1)], - new_msgs=[], - ) - with patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", - return_value="test", - ): - memcells, status = await self.extractor.extract_memcell(request) - # Delegates to parent (no guard triggers), LLM says should_wait - assert status.should_wait is True - - @pytest.mark.asyncio - async def test_single_assistant_msg_total(self): - """Only one message total -> should_wait.""" - request = _make_request( - history_msgs=[], - new_msgs=[_assistant_msg("hello", 0)], - ) - memcells, status = await self.extractor.extract_memcell(request) - assert memcells == [] - assert status.should_wait is True - - @pytest.mark.asyncio - async def test_new_msgs_mix_user_and_assistant(self): - """New messages contain both user and assistant -> not blocked by all_user_only guard.""" - request = _make_request( - history_msgs=[_user_msg("q1", 0), _assistant_msg("a1", 1)], - new_msgs=[_user_msg("q2", 2), _assistant_msg("a2", 3)], - ) - with patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", - return_value="test", - ): - memcells, status = await self.extractor.extract_memcell(request) - # Passes all guards, delegates to parent - - -# --------------------------------------------------------------------------- -# Helpers for trajectory generation -# --------------------------------------------------------------------------- - - -def _make_simple_turn(turn_idx: int) -> List[Dict[str, Any]]: - """user -> assistant (no tools). 2 messages.""" - base = turn_idx * 10 - return [ - _user_msg(f"question_{turn_idx}", base), - _assistant_msg(f"answer_{turn_idx}", base + 1), - ] - - -def _make_single_tool_turn(turn_idx: int) -> List[Dict[str, Any]]: - """user -> tool_call -> tool -> assistant. 4 messages.""" - base = turn_idx * 10 - return [ - _user_msg(f"question_{turn_idx}", base), - _tool_call_msg(f"calling_tool_{turn_idx}", base + 1), - _tool_response_msg(f"result_{turn_idx}", base + 2), - _assistant_msg(f"answer_{turn_idx}", base + 3), - ] - - -def _make_multi_tool_turn(turn_idx: int, tool_count: int = 3) -> List[Dict[str, Any]]: - """user -> (tool_call -> tool) * N -> assistant. 2 + 2*N messages.""" - base = turn_idx * 10 - msgs = [_user_msg(f"question_{turn_idx}", base)] - for t in range(tool_count): - msgs.append(_tool_call_msg(f"call_{turn_idx}_{t}", base + 1 + t * 2)) - msgs.append(_tool_response_msg(f"result_{turn_idx}_{t}", base + 2 + t * 2)) - msgs.append(_assistant_msg(f"answer_{turn_idx}", base + 1 + tool_count * 2)) - return msgs - - -def _build_trajectory(turns: int, pattern: str = "mixed") -> List[Dict[str, Any]]: - """Build a full trajectory with the given number of turns. - - Patterns: - - "simple": all turns are user->assistant - - "tool": all turns have single tool call - - "multi_tool": all turns have 3 tool calls - - "mixed": alternating simple/tool/multi_tool - """ - msgs = [] - for i in range(turns): - if pattern == "simple": - msgs.extend(_make_simple_turn(i)) - elif pattern == "tool": - msgs.extend(_make_single_tool_turn(i)) - elif pattern == "multi_tool": - msgs.extend(_make_multi_tool_turn(i)) - elif pattern == "mixed": - if i % 3 == 0: - msgs.extend(_make_simple_turn(i)) - elif i % 3 == 1: - msgs.extend(_make_single_tool_turn(i)) - else: - msgs.extend(_make_multi_tool_turn(i)) - return msgs - - -def _assert_all_memcells_valid(memcells: List, extractor: AgentMemCellExtractor): - """Assert every MemCell ends at a final assistant (not intermediate).""" - for i, mc in enumerate(memcells): - last_msg = mc.original_data[-1].get("message", mc.original_data[-1]) - role = last_msg.get("role", "") - has_tc = bool(last_msg.get("tool_calls")) - assert role == "assistant" and not has_tc, ( - f"MemCell[{i}] ends with role={role}, tool_calls={has_tc}" - ) - # Each MemCell should have at least 2 messages - assert len(mc.original_data) >= 2, ( - f"MemCell[{i}] has only {len(mc.original_data)} message(s)" - ) - - -# --------------------------------------------------------------------------- -# Tests: _find_force_split_point with various trajectory lengths -# --------------------------------------------------------------------------- - - -class TestForceSplitPointTrajectoryLengths: - """Test _find_force_split_point across trajectory lengths 2 to 1000 turns.""" - - @pytest.mark.parametrize("turns", [1, 2, 3, 5, 10, 20, 50]) - def test_simple_trajectory_various_lengths(self, turns): - """Simple turns (user->assistant) should split cleanly.""" - extractor = _build_extractor() - extractor.hard_message_limit = 8 - msgs = _build_trajectory(turns, "simple") - if len(msgs) < extractor.hard_message_limit: - return # No force split needed - split_at = extractor._find_force_split_point(msgs) - assert AgentMemCellExtractor._is_safe_split(msgs, split_at) or split_at == len(msgs) - - @pytest.mark.parametrize("turns", [1, 2, 3, 5, 10, 20, 50]) - def test_tool_trajectory_various_lengths(self, turns): - """Single-tool turns should never split inside tool sequence.""" - extractor = _build_extractor() - extractor.hard_message_limit = 6 - msgs = _build_trajectory(turns, "tool") - if len(msgs) < extractor.hard_message_limit: - return - split_at = extractor._find_force_split_point(msgs) - assert AgentMemCellExtractor._is_safe_split(msgs, split_at) or split_at == len(msgs) - - @pytest.mark.parametrize("turns", [1, 2, 3, 5, 10, 20, 50]) - def test_multi_tool_trajectory_various_lengths(self, turns): - """Multi-tool turns (8 msgs each) should keep tool sequence intact.""" - extractor = _build_extractor() - extractor.hard_message_limit = 10 - msgs = _build_trajectory(turns, "multi_tool") - if len(msgs) < extractor.hard_message_limit: - return - split_at = extractor._find_force_split_point(msgs) - assert AgentMemCellExtractor._is_safe_split(msgs, split_at) or split_at == len(msgs) - - @pytest.mark.parametrize("turns", [1, 2, 3, 5, 10, 20, 50]) - def test_mixed_trajectory_various_lengths(self, turns): - """Mixed pattern (simple/tool/multi_tool alternating).""" - extractor = _build_extractor() - extractor.hard_message_limit = 10 - msgs = _build_trajectory(turns, "mixed") - if len(msgs) < extractor.hard_message_limit: - return - split_at = extractor._find_force_split_point(msgs) - assert AgentMemCellExtractor._is_safe_split(msgs, split_at) or split_at == len(msgs) - - @pytest.mark.parametrize("limit", [4, 8, 16, 32, 64]) - def test_100_turn_mixed_various_limits(self, limit): - """100 mixed turns with various hard_message_limit settings.""" - extractor = _build_extractor() - extractor.hard_message_limit = limit - msgs = _build_trajectory(100, "mixed") - split_at = extractor._find_force_split_point(msgs) - assert AgentMemCellExtractor._is_safe_split(msgs, split_at) or split_at == len(msgs) - - def test_1000_turn_simple_trajectory(self): - """1000 simple turns (2000 messages), default limit.""" - extractor = _build_extractor() - # Default: AGENT_DEFAULT_HARD_MESSAGE_LIMIT = 64 - msgs = _build_trajectory(1000, "simple") - assert len(msgs) == 2000 - split_at = extractor._find_force_split_point(msgs) - assert AgentMemCellExtractor._is_safe_split(msgs, split_at) - - def test_1000_turn_tool_trajectory(self): - """1000 tool turns (4000 messages), default limit.""" - extractor = _build_extractor() - msgs = _build_trajectory(1000, "tool") - assert len(msgs) == 4000 - split_at = extractor._find_force_split_point(msgs) - assert AgentMemCellExtractor._is_safe_split(msgs, split_at) or split_at == len(msgs) - - def test_1000_turn_multi_tool_trajectory(self): - """1000 multi-tool turns (8000 messages), default limit.""" - extractor = _build_extractor() - msgs = _build_trajectory(1000, "multi_tool") - assert len(msgs) == 8000 - split_at = extractor._find_force_split_point(msgs) - assert AgentMemCellExtractor._is_safe_split(msgs, split_at) or split_at == len(msgs) - - def test_1000_turn_mixed_trajectory(self): - """1000 mixed turns, default limit.""" - extractor = _build_extractor() - msgs = _build_trajectory(1000, "mixed") - split_at = extractor._find_force_split_point(msgs) - assert AgentMemCellExtractor._is_safe_split(msgs, split_at) or split_at == len(msgs) - - @pytest.mark.parametrize("limit", [4, 8, 16, 32, 64, 128]) - def test_1000_turn_mixed_various_limits(self, limit): - """1000 mixed turns with various limits.""" - extractor = _build_extractor() - extractor.hard_message_limit = limit - msgs = _build_trajectory(1000, "mixed") - split_at = extractor._find_force_split_point(msgs) - assert AgentMemCellExtractor._is_safe_split(msgs, split_at) or split_at == len(msgs) - - -# --------------------------------------------------------------------------- -# Tests: Full force-split loop simulation (Phase 1 repeated splits) -# --------------------------------------------------------------------------- - - -class TestForceSplitLoopSimulation: - """Simulate the parent's Phase 1 force-split loop to verify repeated - splits all produce valid chunks.""" - - @staticmethod - def _simulate_force_split_loop( - extractor: AgentMemCellExtractor, - messages: List[Dict[str, Any]], - ) -> List[List[Dict[str, Any]]]: - """Simulate Phase 1 loop, return list of chunks.""" - all_msgs = list(messages) - chunks = [] - iterations = 0 - max_iterations = len(messages) # Safety bound - - while len(all_msgs) > 1 and iterations < max_iterations: - if len(all_msgs) < extractor.hard_message_limit: - break - split_at = extractor._find_force_split_point(all_msgs) - if split_at >= len(all_msgs): - # No split possible, take all - chunks.append(all_msgs) - all_msgs = [] - break - chunks.append(all_msgs[:split_at]) - all_msgs = all_msgs[split_at:] - iterations += 1 - - if all_msgs: - chunks.append(all_msgs) # Remaining goes to Phase 2 - return chunks - - @pytest.mark.parametrize("turns,pattern", [ - (10, "simple"), - (10, "tool"), - (10, "multi_tool"), - (10, "mixed"), - (50, "mixed"), - (100, "mixed"), - ]) - def test_all_chunks_end_at_final_assistant(self, turns, pattern): - """Every force-split chunk (except possibly the last remainder) - should end at a final assistant message.""" - extractor = _build_extractor() - extractor.hard_message_limit = 10 - msgs = _build_trajectory(turns, pattern) - chunks = self._simulate_force_split_loop(extractor, msgs) - - # All chunks except the last should end at final assistant - for i, chunk in enumerate(chunks[:-1]): - last_msg = chunk[-1] - assert last_msg.get("role") == "assistant" and not last_msg.get("tool_calls"), ( - f"Chunk[{i}] (len={len(chunk)}) ends with " - f"role={last_msg.get('role')}, tool_calls={bool(last_msg.get('tool_calls'))}" - ) - - @pytest.mark.parametrize("turns,pattern", [ - (10, "tool"), - (10, "multi_tool"), - (50, "mixed"), - (100, "mixed"), - ]) - def test_no_orphaned_tool_messages(self, turns, pattern): - """No chunk should start with a tool response or end with a tool_call.""" - extractor = _build_extractor() - extractor.hard_message_limit = 10 - msgs = _build_trajectory(turns, pattern) - chunks = self._simulate_force_split_loop(extractor, msgs) - - for i, chunk in enumerate(chunks[:-1]): - # Should not end at intermediate - last = chunk[-1] - assert not is_intermediate_agent_step(last), ( - f"Chunk[{i}] ends with intermediate: role={last.get('role')}" - ) - - def test_all_messages_accounted_for(self): - """Total messages across all chunks equals original message count.""" - extractor = _build_extractor() - extractor.hard_message_limit = 10 - msgs = _build_trajectory(50, "mixed") - original_count = len(msgs) - chunks = self._simulate_force_split_loop(extractor, msgs) - total = sum(len(c) for c in chunks) - assert total == original_count - - @pytest.mark.parametrize("limit", [4, 8, 16, 32]) - def test_loop_terminates_various_limits(self, limit): - """Loop always terminates (no infinite loop) for various limits.""" - extractor = _build_extractor() - extractor.hard_message_limit = limit - msgs = _build_trajectory(100, "mixed") - chunks = self._simulate_force_split_loop(extractor, msgs) - # Must produce at least 1 chunk - assert len(chunks) >= 1 - # Total must equal original - assert sum(len(c) for c in chunks) == len(msgs) - - def test_1000_turn_loop_completes(self): - """1000-turn trajectory loop completes without issues.""" - extractor = _build_extractor() - extractor.hard_message_limit = 64 - msgs = _build_trajectory(1000, "mixed") - chunks = self._simulate_force_split_loop(extractor, msgs) - assert sum(len(c) for c in chunks) == len(msgs) - # Each non-last chunk should end at final assistant - for i, chunk in enumerate(chunks[:-1]): - last = chunk[-1] - assert last.get("role") == "assistant" and not last.get("tool_calls"), ( - f"Chunk[{i}] invalid end" - ) - - def test_1000_turn_loop_small_limit(self): - """1000-turn trajectory with small limit=4 creates many chunks.""" - extractor = _build_extractor() - extractor.hard_message_limit = 4 - msgs = _build_trajectory(1000, "mixed") - chunks = self._simulate_force_split_loop(extractor, msgs) - assert sum(len(c) for c in chunks) == len(msgs) - # Should create many chunks with small limit - assert len(chunks) > 100 - - -# --------------------------------------------------------------------------- -# Tests: End-to-end extract_memcell with various trajectory lengths -# --------------------------------------------------------------------------- - - -METRIC_PATCHES = [ - patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection"), - patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted"), - patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test"), -] - - -class TestE2ETrajectoryLengths: - """End-to-end extract_memcell with various trajectory lengths.""" - - @staticmethod - def _make_extractor_no_boundary(hard_message_limit: int = 10) -> AgentMemCellExtractor: - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "no boundary", "boundaries": [], "should_wait": false}' - ) - ext = AgentMemCellExtractor(llm_provider=mock_llm) - ext.hard_message_limit = hard_message_limit - return ext - - @pytest.mark.asyncio - async def test_e2e_10_turns_mixed(self): - """10 mixed turns, limit=10.""" - extractor = self._make_extractor_no_boundary(10) - msgs = _build_trajectory(10, "mixed") - request = _make_request(history_msgs=[], new_msgs=msgs) - with patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", - return_value="test", - ): - memcells, status = await extractor.extract_memcell(request) - assert len(memcells) >= 1 - _assert_all_memcells_valid(memcells, extractor) - - @pytest.mark.asyncio - async def test_e2e_50_turns_tool(self): - """50 tool turns (200 messages), limit=20.""" - extractor = self._make_extractor_no_boundary(20) - msgs = _build_trajectory(50, "tool") - request = _make_request(history_msgs=[], new_msgs=msgs) - with patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", - return_value="test", - ): - memcells, status = await extractor.extract_memcell(request) - assert len(memcells) >= 1 - _assert_all_memcells_valid(memcells, extractor) - - @pytest.mark.asyncio - async def test_e2e_100_turns_mixed(self): - """100 mixed turns, limit=16.""" - extractor = self._make_extractor_no_boundary(16) - msgs = _build_trajectory(100, "mixed") - request = _make_request(history_msgs=[], new_msgs=msgs) - with patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", - return_value="test", - ): - memcells, status = await extractor.extract_memcell(request) - assert len(memcells) >= 1 - _assert_all_memcells_valid(memcells, extractor) - - @pytest.mark.asyncio - async def test_e2e_flush_long_trajectory(self): - """Flush mode with 50 mixed turns should pack remaining into final MemCell.""" - extractor = self._make_extractor_no_boundary(20) - msgs = _build_trajectory(50, "mixed") - request = _make_request(history_msgs=[], new_msgs=msgs, flush=True) - with patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", - return_value="test", - ): - memcells, status = await extractor.extract_memcell(request) - assert len(memcells) >= 1 - assert status.should_wait is False - # Total messages across all MemCells should equal input - total = sum(len(mc.original_data) for mc in memcells) - assert total == len(msgs) - - @pytest.mark.asyncio - async def test_e2e_history_plus_new_long(self): - """Long history + new messages, limit=10.""" - extractor = self._make_extractor_no_boundary(10) - history = _build_trajectory(20, "tool") - new = _build_trajectory(5, "simple") - # Shift new messages offsets to avoid timestamp collision - for m in new: - m["timestamp"] = _ts(500 + int(m["timestamp"][-10:-6]) if len(m["timestamp"]) > 10 else 500) - request = _make_request(history_msgs=history, new_msgs=new) - with patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted" - ), patch( - "memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", - return_value="test", - ): - memcells, status = await extractor.extract_memcell(request) - assert len(memcells) >= 1 - _assert_all_memcells_valid(memcells, extractor) - - -# --------------------------------------------------------------------------- -# Tests: Edge cases in parent integration -# --------------------------------------------------------------------------- - - -class TestParentIntegrationEdgeCases: - """Test edge cases specific to how AgentMemCellExtractor interacts - with ConvMemCellExtractor's three-phase pipeline.""" - - def test_force_split_returns_len_messages_terminates_loop(self): - """When _find_force_split_point returns len(messages), the simulated - loop terminates without infinite iteration.""" - extractor = _build_extractor() - extractor.hard_message_limit = 3 - - # Single tool turn: no safe split possible - msgs = [ - _user_msg("q", 0), - _tool_call_msg("c", 1), - _tool_response_msg("r", 2), - _assistant_msg("a", 3), - ] - split_at = extractor._find_force_split_point(msgs) - assert split_at == len(msgs) - - # Simulate: all_msgs[:4] = all, all_msgs[4:] = [], loop exits - remaining = msgs[split_at:] - assert remaining == [] - - def test_force_split_with_exactly_limit_messages(self): - """Exactly at hard_message_limit boundary.""" - extractor = _build_extractor() - extractor.hard_message_limit = 4 - - msgs = [ - _user_msg("q1", 0), - _assistant_msg("a1", 1), - _user_msg("q2", 2), - _assistant_msg("a2", 3), - ] - split_at = extractor._find_force_split_point(msgs) - # Parent returns min(3, 3) = 3. msg[2]=user -> not safe. - # Walk back: msg[1]=assistant -> safe. split_at=2. - assert split_at == 2 - - def test_force_split_one_over_limit(self): - """One message over the limit.""" - extractor = _build_extractor() - extractor.hard_message_limit = 4 - - msgs = [ - _user_msg("q1", 0), - _assistant_msg("a1", 1), - _user_msg("q2", 2), - _assistant_msg("a2", 3), - _user_msg("q3", 4), - ] - split_at = extractor._find_force_split_point(msgs) - # Parent returns min(3, 4) = 3. msg[2]=user -> walk back. - # msg[1]=assistant -> safe. split_at=2. - assert split_at == 2 - - def test_two_messages_over_limit(self): - """Two messages, both exceed limit=2.""" - extractor = _build_extractor() - extractor.hard_message_limit = 2 - - msgs = [_user_msg("q", 0), _assistant_msg("a", 1), _user_msg("q2", 2)] - split_at = extractor._find_force_split_point(msgs) - # Parent returns min(1, 2) = 1. msg[0]=user -> not safe (not assistant). - # Walk forward from 2: msg[1]=assistant -> safe. split_at=2. - assert split_at == 2 - - def test_only_tool_messages_exceeding_limit(self): - """All messages are intermediate except the last assistant.""" - extractor = _build_extractor() - extractor.hard_message_limit = 3 - - msgs = [ - _tool_call_msg("c1", 0), - _tool_response_msg("r1", 1), - _tool_call_msg("c2", 2), - _tool_response_msg("r2", 3), - _tool_call_msg("c3", 4), - _tool_response_msg("r3", 5), - _assistant_msg("final", 6), - ] - split_at = extractor._find_force_split_point(msgs) - # No safe split (walk back all intermediate, walk forward finds nothing - # until we include the assistant)... but _is_safe_split requires - # split_at <= len-1=6. Check: msg[6]=assistant -> safe at 7? No, 7>6. - # Forward walk: candidate goes 3,4,5,6,7 -> 7 > len=7? No, 7==len. - # No safe split found -> returns len(messages)=7 - assert split_at == len(msgs) - - -# --------------------------------------------------------------------------- -# Tests: split_at == len(messages) in parent's three-phase pipeline -# --------------------------------------------------------------------------- - - -class TestSplitAtFullLength: - """When _find_force_split_point returns len(messages), the entire - message list becomes one MemCell in Phase 1. Verify the exact behavior - of all three phases and the returned status.""" - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_single_unsplittable_turn_creates_one_memcell( - self, mock_space, mock_record, mock_bd - ): - """One long tool turn with no safe split -> one MemCell, Phase 2 skipped. - - Messages: user, tc, tool, tc, tool, assistant (6 msgs, limit=3). - _find_force_split_point returns 6. Phase 1 creates 1 MemCell. - Phase 2 gets [] -> skipped. Phase 3 no flush -> skipped. - """ - mock_llm = MagicMock() - # LLM should NOT be called (Phase 2 skipped) - mock_llm.generate = AsyncMock(side_effect=AssertionError("LLM should not be called")) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - extractor.hard_message_limit = 3 - - msgs = [ - _user_msg("q", 0), - _tool_call_msg("c1", 1), - _tool_response_msg("r1", 2), - _tool_call_msg("c2", 3), - _tool_response_msg("r2", 4), - _assistant_msg("final", 5), - ] - request = _make_request(history_msgs=[], new_msgs=msgs) - memcells, status = await extractor.extract_memcell(request) - - assert len(memcells) == 1 - assert len(memcells[0].original_data) == 6 - assert status.should_wait is False - # LLM.generate was never called (Phase 2 skipped) - mock_llm.generate.assert_not_called() - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_full_length_split_with_flush_still_works( - self, mock_space, mock_record, mock_bd - ): - """flush=True, but Phase 1 already consumed everything -> Phase 3 skipped. - - All messages become one force-split MemCell. Flush has nothing left. - """ - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(side_effect=AssertionError("LLM should not be called")) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - extractor.hard_message_limit = 3 - - msgs = [ - _user_msg("q", 0), - _tool_call_msg("c1", 1), - _tool_response_msg("r1", 2), - _assistant_msg("final", 3), - ] - request = _make_request(history_msgs=[], new_msgs=msgs, flush=True) - memcells, status = await extractor.extract_memcell(request) - - # Exactly 1 MemCell from Phase 1, Phase 3 not triggered - assert len(memcells) == 1 - assert len(memcells[0].original_data) == 4 - assert status.should_wait is False - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_full_length_then_normal_split_in_next_iteration( - self, mock_space, mock_record, mock_bd - ): - """Two turns: first is unsplittable (returns len), second is normal. - - Messages: - Turn 1: user, tc, tool, tc, tool, assistant (6 msgs, unsplittable) - Turn 2: user, assistant (2 msgs, normal) - - limit=5. First loop iteration: 8 msgs >= 5. split_at for 8 msgs: - parent candidate=4. Walk back finds assistant at [5] -> no, [5] is - the 6th msg. Let me think... msgs[3]=tool_call, [2]=tool, [1]=tc, - [0]=user -> no safe. Walk forward: msgs[4]=tool, [5]=assistant -> safe at 6. - So split_at=6 (normal split, not len). Chunk=[0:6], remainder=[6:8]=[user, asst]. - Phase 2 runs on [user, asst]. - """ - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "done", "boundaries": [], "should_wait": false}' - ) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - extractor.hard_message_limit = 5 - - msgs = [ - # Turn 1 (unsplittable single turn) - _user_msg("q1", 0), - _tool_call_msg("c1", 1), - _tool_response_msg("r1", 2), - _tool_call_msg("c2", 3), - _tool_response_msg("r2", 4), - _assistant_msg("a1", 5), - # Turn 2 (simple) - _user_msg("q2", 6), - _assistant_msg("a2", 7), - ] - request = _make_request(history_msgs=[], new_msgs=msgs) - memcells, status = await extractor.extract_memcell(request) - - # Phase 1: split at 6 (first turn complete) - # Phase 2: [user, assistant] -> LLM called, no boundaries - assert len(memcells) >= 1 - # First MemCell should contain the full tool turn (6 msgs) - assert len(memcells[0].original_data) == 6 - # LLM was called for Phase 2 on the remainder - mock_llm.generate.assert_called_once() - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_all_intermediate_no_assistant_returns_full_len( - self, mock_space, mock_record, mock_bd - ): - """Edge case: all messages are intermediate (no final assistant at all). - _find_force_split_point returns len(messages). - - This shouldn't happen in practice (guard ensures last msg is not - intermediate), but if it does, the pipeline handles it gracefully. - """ - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(side_effect=AssertionError("LLM should not be called")) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - extractor.hard_message_limit = 2 - - msgs = [ - _tool_call_msg("c1", 0), - _tool_response_msg("r1", 1), - _tool_call_msg("c2", 2), - ] - # Directly test _find_force_split_point - split_at = extractor._find_force_split_point(msgs) - assert split_at == len(msgs) - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_repeated_unsplittable_turns_each_become_own_memcell( - self, mock_space, mock_record, mock_bd - ): - """Multiple consecutive unsplittable turns, each should become its own MemCell. - - Turn 1: user, tc, tool, tc, tool, assistant (6 msgs) - Turn 2: user, tc, tool, tc, tool, assistant (6 msgs) - limit=5. - - Iteration 1: 12 msgs. split walks forward to 6 (first assistant). - Iteration 2: 6 msgs >= 5. No safe split within first 4. Walk forward to 6. - split_at=6=len -> all consumed. Loop exits. - """ - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(side_effect=AssertionError("LLM should not be called")) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - extractor.hard_message_limit = 5 - - msgs = [] - for i in range(2): - msgs.extend([ - _user_msg(f"q{i}", i * 10), - _tool_call_msg(f"c{i}_1", i * 10 + 1), - _tool_response_msg(f"r{i}_1", i * 10 + 2), - _tool_call_msg(f"c{i}_2", i * 10 + 3), - _tool_response_msg(f"r{i}_2", i * 10 + 4), - _assistant_msg(f"a{i}", i * 10 + 5), - ]) - - request = _make_request(history_msgs=[], new_msgs=msgs) - memcells, status = await extractor.extract_memcell(request) - - # Each turn becomes one MemCell - assert len(memcells) == 2 - assert len(memcells[0].original_data) == 6 - assert len(memcells[1].original_data) == 6 - assert status.should_wait is False - # LLM not called (Phase 2 skipped because all consumed in Phase 1) - mock_llm.generate.assert_not_called() - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_total_messages_preserved_after_full_length_split( - self, mock_space, mock_record, mock_bd - ): - """Verify no messages are lost when split_at=len is used.""" - mock_llm = MagicMock() - mock_llm.generate = AsyncMock(side_effect=AssertionError("LLM should not be called")) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - extractor.hard_message_limit = 4 - - # 3 unsplittable turns of 8 msgs each = 24 total - msgs = [] - for i in range(3): - msgs.extend(_make_multi_tool_turn(i)) - - request = _make_request(history_msgs=[], new_msgs=msgs) - memcells, status = await extractor.extract_memcell(request) - - total = sum(len(mc.original_data) for mc in memcells) - assert total == len(msgs) - - -# --------------------------------------------------------------------------- -# Tests: Infinite loop protection and progress guarantee -# --------------------------------------------------------------------------- - - -class TestLoopProgress: - """Verify the force-split loop always makes progress and terminates.""" - - def test_split_at_never_returns_zero(self): - """_find_force_split_point never returns 0 (which would cause no progress).""" - extractor = _build_extractor() - for limit in [2, 3, 4, 8, 16, 64]: - extractor.hard_message_limit = limit - for pattern in ["simple", "tool", "multi_tool", "mixed"]: - msgs = _build_trajectory(20, pattern) - if len(msgs) <= 1: - continue - split_at = extractor._find_force_split_point(msgs) - assert split_at >= 2 or split_at == len(msgs), ( - f"limit={limit}, pattern={pattern}: split_at={split_at} " - f"would cause infinite loop or single-msg chunk" - ) - - def test_split_at_never_returns_one(self): - """split_at=1 would create a single-message MemCell (useless). Should never happen.""" - extractor = _build_extractor() - for limit in [2, 3, 4]: - extractor.hard_message_limit = limit - for msgs in [ - [_user_msg("q", 0), _assistant_msg("a", 1), _user_msg("q2", 2)], - [_user_msg("q", 0), _tool_call_msg("c", 1), _tool_response_msg("r", 2), _assistant_msg("a", 3)], - [_tool_call_msg("c", 0), _tool_response_msg("r", 1), _assistant_msg("a", 2), _user_msg("q", 3)], - ]: - split_at = extractor._find_force_split_point(msgs) - assert split_at != 1, ( - f"limit={limit}: split_at=1 for msgs of len {len(msgs)}" - ) - - def test_loop_max_iterations_bounded(self): - """Loop iterations should be bounded by len/2 at worst.""" - extractor = _build_extractor() - extractor.hard_message_limit = 4 - msgs = _build_trajectory(100, "simple") # 200 messages - - iterations = 0 - all_msgs = list(msgs) - while len(all_msgs) > 1: - if len(all_msgs) < extractor.hard_message_limit: - break - split_at = extractor._find_force_split_point(all_msgs) - assert split_at >= 2 or split_at == len(all_msgs) - all_msgs = all_msgs[split_at:] - iterations += 1 - # Safety: should never exceed len/2 iterations - assert iterations <= len(msgs), "Loop exceeded maximum iterations" - - # Sanity: we should have done some iterations - assert iterations > 0 - - def test_adversarial_all_tool_calls_no_assistant(self): - """Worst case: all messages are intermediate, no final assistant anywhere. - Loop must still terminate.""" - extractor = _build_extractor() - extractor.hard_message_limit = 3 - - # 20 intermediate messages, no final assistant - msgs = [] - for i in range(10): - msgs.append(_tool_call_msg(f"c{i}", i * 2)) - msgs.append(_tool_response_msg(f"r{i}", i * 2 + 1)) - - all_msgs = list(msgs) - iterations = 0 - while len(all_msgs) > 1: - if len(all_msgs) < extractor.hard_message_limit: - break - split_at = extractor._find_force_split_point(all_msgs) - # Returns len(messages) since no safe split exists - assert split_at == len(all_msgs) - all_msgs = all_msgs[split_at:] - iterations += 1 - assert iterations <= 1, "Should exit in 1 iteration with len(messages)" - - assert iterations == 1 - - -# --------------------------------------------------------------------------- -# Tests: Phase 2 MemCell validity after remap -# --------------------------------------------------------------------------- - - -class TestPhase2MemCellValidity: - """Phase 2 uses _detect_boundaries with remap. Verify the MemCells - created from remapped boundaries contain complete tool sequences.""" - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_phase2_memcell_includes_full_tool_sequence( - self, mock_space, mock_record, mock_bd - ): - """LLM boundary splits two turns. Each Phase 2 MemCell should - contain the complete tool sequence for its turn. - - Messages (under limit, so Phase 1 skips): - [0] user -> filtered[0] - [1] tc (filtered) - [2] tool (filtered) - [3] assistant -> filtered[1] - [4] user -> filtered[2] - [5] assistant -> filtered[3] - - LLM boundary=[2] on filtered -> remap to 4. - Phase 2: MemCell_1=[0:4]=[user,tc,tool,asst], MemCell_2=[4:]=[user,asst]. - """ - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "topic change", "boundaries": [2], "should_wait": false}' - ) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - extractor.hard_message_limit = 100 # High limit to skip Phase 1 - - request = _make_request( - history_msgs=[], - new_msgs=[ - _user_msg("q1", 0), - _tool_call_msg("c1", 1), - _tool_response_msg("r1", 2), - _assistant_msg("a1", 3), - _user_msg("q2", 4), - _assistant_msg("a2", 5), - ], - ) - memcells, status = await extractor.extract_memcell(request) - - # boundary=[2] on filtered -> remap to 4 -> MemCell_1=[0:4] - # Remainder=[4:6]=[user, asst] stays as remaining (no further boundary) - assert len(memcells) == 1 - # MemCell: complete tool turn [user, tc, tool, asst] - assert len(memcells[0].original_data) == 4 - first_last = memcells[0].original_data[-1]["message"] - assert first_last["role"] == "assistant" - assert not first_last.get("tool_calls") - assert status.should_wait is False - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_phase2_remap_never_splits_inside_tool_sequence( - self, mock_space, mock_record, mock_bd - ): - """Remap guarantees boundaries land after non-intermediate messages. - Since filtered list excludes intermediates, remap always produces - boundaries at user or final-assistant positions. - - Three turns with tools, LLM splits after each turn. - """ - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "3 topics", "boundaries": [2, 4], "should_wait": false}' - ) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - extractor.hard_message_limit = 100 - - request = _make_request( - history_msgs=[], - new_msgs=[ - # Turn 1 - _user_msg("q1", 0), # filtered[0] - _tool_call_msg("c1", 1), - _tool_response_msg("r1", 2), - _assistant_msg("a1", 3), # filtered[1] - # Turn 2 - _user_msg("q2", 4), # filtered[2] - _tool_call_msg("c2", 5), - _tool_response_msg("r2", 6), - _assistant_msg("a2", 7), # filtered[3] - # Turn 3 - _user_msg("q3", 8), # filtered[4] - _assistant_msg("a3", 9), # filtered[5] - ], - ) - memcells, status = await extractor.extract_memcell(request) - - # boundaries=[2,4] on filtered -> remap: [4, 8] - # MemCell_1=[0:4], MemCell_2=[4:8], remainder=[8:] - assert len(memcells) == 2 - # Each MemCell should have complete tool sequences - for mc in memcells: - last_msg = mc.original_data[-1]["message"] - assert last_msg["role"] == "assistant" - assert not last_msg.get("tool_calls") - - -# --------------------------------------------------------------------------- -# Tests: All three phases triggered together -# --------------------------------------------------------------------------- - - -class TestAllThreePhases: - """End-to-end test that triggers Phase 1 + Phase 2 + Phase 3.""" - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_phase1_force_split_phase2_llm_phase3_flush( - self, mock_space, mock_record, mock_bd - ): - """Trajectory that triggers all three phases: - - Phase 1: limit=8, total=14 msgs -> force split - - Phase 2: LLM detects boundary in remaining - - Phase 3: flush=True packs the tail - - Messages (14 total): - Turn 1: user, tc, tool, assistant [0-3] -> Phase 1 chunk - Turn 2: user, assistant [4-5] - Turn 3: user, tc, tool, assistant [6-9] - Turn 4: user, assistant [10-11] - Turn 5: user, assistant [12-13] - - limit=8: Phase 1 splits at 4 (first turn). Remaining=10 msgs. - 10 >= 8: Phase 1 again, splits at Turn 2 end (idx 6 in remaining=2). - Remaining=8 msgs. 8 >= 8: Phase 1 again, splits. - Eventually remaining < 8 -> Phase 2. LLM detects boundary -> MemCell. - flush=True -> Phase 3 packs remainder. - """ - mock_llm = MagicMock() - mock_llm.generate = AsyncMock( - return_value='{"reasoning": "boundary after turn", "boundaries": [2], "should_wait": false}' - ) - extractor = AgentMemCellExtractor(llm_provider=mock_llm) - extractor.hard_message_limit = 8 - - msgs = [ - # Turn 1 (with tools) - _user_msg("q1", 0), - _tool_call_msg("c1", 1), - _tool_response_msg("r1", 2), - _assistant_msg("a1", 3), - # Turn 2 (simple) - _user_msg("q2", 4), - _assistant_msg("a2", 5), - # Turn 3 (with tools) - _user_msg("q3", 6), - _tool_call_msg("c3", 7), - _tool_response_msg("r3", 8), - _assistant_msg("a3", 9), - # Turn 4 (simple) - _user_msg("q4", 10), - _assistant_msg("a4", 11), - # Turn 5 (simple) - _user_msg("q5", 12), - _assistant_msg("a5", 13), - ] - request = _make_request(history_msgs=[], new_msgs=msgs, flush=True) - memcells, status = await extractor.extract_memcell(request) - - # Should produce multiple MemCells from all three phases - assert len(memcells) >= 2 - # should_wait=False because flush consumed everything - assert status.should_wait is False - # Total messages preserved - total = sum(len(mc.original_data) for mc in memcells) - assert total == len(msgs) - # All MemCells have type AGENTCONVERSATION - for mc in memcells: - assert mc.type == RawDataType.AGENTCONVERSATION - - -# --------------------------------------------------------------------------- -# Tests: Flush edge cases with agent guard -# --------------------------------------------------------------------------- - - -class TestFlushWithGuard: - """Flush mode bypasses agent guard but still flows through parent pipeline.""" - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_flush_with_only_history_no_new(self, mock_space, mock_record, mock_bd): - """flush=True, history=[user, asst], new=[] -> parent's special branch - creates one flush MemCell directly from history.""" - extractor = _build_extractor() - request = _make_request( - history_msgs=[_user_msg("q", 0), _assistant_msg("a", 1)], - new_msgs=[], - flush=True, - ) - memcells, status = await extractor.extract_memcell(request) - assert len(memcells) == 1 - assert len(memcells[0].original_data) == 2 - assert status.should_wait is False - - @pytest.mark.asyncio - async def test_flush_empty_history_empty_new(self): - """flush=True, history=[], new=[] -> parent returns ([], should_wait=True).""" - extractor = _build_extractor() - request = _make_request(history_msgs=[], new_msgs=[], flush=True) - memcells, status = await extractor.extract_memcell(request) - assert memcells == [] - assert status.should_wait is True - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_flush_bypasses_intermediate_guard(self, mock_space, mock_record, mock_bd): - """flush=True overrides intermediate guard: even if last new msg - is tool_call, flush still processes everything.""" - extractor = _build_extractor() - request = _make_request( - history_msgs=[_user_msg("q", 0), _assistant_msg("a", 1)], - new_msgs=[_user_msg("q2", 2), _tool_call_msg("thinking", 3)], - flush=True, - ) - memcells, status = await extractor.extract_memcell(request) - assert len(memcells) >= 1 - assert status.should_wait is False - total = sum(len(mc.original_data) for mc in memcells) - assert total == 4 - - @pytest.mark.asyncio - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_boundary_detection") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.record_memcell_extracted") - @patch("memory_layer.memcell_extractor.conv_memcell_extractor.get_space_id_for_metrics", return_value="test") - async def test_flush_with_long_history_tool_sequence(self, mock_space, mock_record, mock_bd): - """flush=True, only history (no new), history contains tool sequences.""" - extractor = _build_extractor() - request = _make_request( - history_msgs=[ - _user_msg("q", 0), - _tool_call_msg("c", 1), - _tool_response_msg("r", 2), - _assistant_msg("a", 3), - ], - new_msgs=[], - flush=True, - ) - memcells, status = await extractor.extract_memcell(request) - assert len(memcells) == 1 - assert len(memcells[0].original_data) == 4 - assert status.should_wait is False - - -class TestRawDataType: - def test_raw_data_type_is_agent_conversation(self): - extractor = _build_extractor() - assert extractor.raw_data_type == RawDataType.AGENTCONVERSATION - - -class TestExtractParticipantIdsOnlyUser: - """Test that AgentMemCellExtractor inherits parent behavior: only role='user' IDs.""" - - def test_only_keeps_user_role(self): - """Only messages with role='user' should be included in participant IDs.""" - extractor = _build_extractor() - chat_raw_data_list = [ - {"sender_id": "user_1", "sender_name": "User", "role": "user"}, - {"sender_id": "tool_call_123", "role": "tool", "tool_call_id": "call_1"}, - {"sender_id": "assistant_1", "sender_name": "Assistant", "role": "assistant"}, - ] - result = extractor._extract_participant_ids(chat_raw_data_list) - assert set(result) == {"user_1"} - - def test_no_user_returns_empty(self): - """If no messages have role='user', result should be empty.""" - extractor = _build_extractor() - chat_raw_data_list = [ - {"sender_id": "tool_1", "role": "tool", "tool_call_id": "call_1"}, - {"sender_id": "assistant_1", "role": "assistant"}, - ] - result = extractor._extract_participant_ids(chat_raw_data_list) - assert result == [] - - def test_no_role_field_not_included(self): - """Messages without 'role' field should not be included.""" - extractor = _build_extractor() - chat_raw_data_list = [ - {"sender_id": "user_1", "sender_name": "User"}, - {"sender_id": "user_2", "sender_name": "Bot"}, - ] - result = extractor._extract_participant_ids(chat_raw_data_list) - assert result == [] - - def test_mixed_roles(self): - """Mix of tool, user, assistant, and no-role messages — only user kept.""" - extractor = _build_extractor() - chat_raw_data_list = [ - {"sender_id": "user_1", "role": "user"}, - {"sender_id": "tool_abc", "role": "tool", "tool_call_id": "call_1"}, - {"sender_id": "assistant_1", "role": "assistant"}, - {"sender_id": "user_2"}, - {"sender_id": "tool_def", "role": "tool", "tool_call_id": "call_2"}, - ] - result = extractor._extract_participant_ids(chat_raw_data_list) - assert set(result) == {"user_1"} - - def test_conv_extractor_only_keeps_user(self): - """Base ConvMemCellExtractor should only keep role='user' IDs.""" - from memory_layer.memcell_extractor.conv_memcell_extractor import ConvMemCellExtractor - mock_llm = MagicMock() - conv_extractor = ConvMemCellExtractor(mock_llm) - chat_raw_data_list = [ - {"sender_id": "user_1", "role": "user"}, - {"sender_id": "tool_1", "role": "tool"}, - ] - result = conv_extractor._extract_participant_ids(chat_raw_data_list) - assert set(result) == {"user_1"} diff --git a/methods/EverCore/tests/test_agent_memory_manager.py b/methods/EverCore/tests/test_agent_memory_manager.py deleted file mode 100644 index 44d97eab..00000000 --- a/methods/EverCore/tests/test_agent_memory_manager.py +++ /dev/null @@ -1,168 +0,0 @@ -""" -MemoryManager agent case dispatch tests. - -Tests for: -- MemoryManager._extract_agent_case: delegation, non-agent memcell, None memcell -- MemoryManager.extract_memory: AGENT_CASE dispatch path - -Usage: - PYTHONPATH=src pytest tests/test_memory_manager_agent.py -v -""" - -import pytest -from datetime import datetime -from unittest.mock import AsyncMock, MagicMock, patch - -from api_specs.memory_types import MemCell, RawDataType, AgentCase -from api_specs.memory_models import MemoryType - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -def _make_agent_memcell(**overrides): - defaults = dict( - user_id_list=["u1"], - original_data=[ - {"message": {"role": "user", "content": "Deploy the app", "sender_id": "u1"}}, - {"message": {"role": "assistant", "content": "Done deploying."}}, - ], - timestamp=datetime(2025, 6, 1, 10, 0, 0), - event_id="evt_100", - group_id="g1", - type=RawDataType.AGENTCONVERSATION, - ) - defaults.update(overrides) - return MemCell(**defaults) - - -def _make_conv_memcell(**overrides): - defaults = dict( - user_id_list=["u1"], - original_data=[ - {"message": {"role": "user", "content": "Hello", "sender_id": "u1"}}, - ], - timestamp=datetime(2025, 6, 1, 10, 0, 0), - event_id="evt_200", - group_id="g1", - type=RawDataType.CONVERSATION, - ) - defaults.update(overrides) - return MemCell(**defaults) - - -def _make_agent_case(**overrides): - defaults = dict( - memory_type=MemoryType.AGENT_CASE, - user_id="u1", - timestamp=datetime(2025, 6, 1, 10, 0, 0), - task_intent="Deploy the application to production", - approach="1. Build docker image\n2. Push to registry\n3. Deploy to k8s", - quality_score=0.85, - vector=[0.1, 0.2, 0.3], - vector_model="text-embedding-3-small", - ) - defaults.update(overrides) - return AgentCase(**defaults) - - -def _make_memory_manager(): - """Create a MemoryManager with mocked LLM provider.""" - from memory_layer.memory_manager import MemoryManager - with patch("memory_layer.memory_manager.build_default_provider", return_value=MagicMock()): - return MemoryManager() - - -# =========================================================================== -# MemoryManager._extract_agent_case -# =========================================================================== - - -class TestMemoryManagerExtractAgentCase: - """Tests for MemoryManager._extract_agent_case method.""" - - @pytest.mark.asyncio - async def test_agent_memcell_delegates_to_extractor(self): - """Agent conversation memcell should delegate to AgentCaseExtractor.""" - memcell = _make_agent_memcell() - expected_case = _make_agent_case() - - with patch("memory_layer.memory_extractor.agent_case_extractor.AgentCaseExtractor") as mock_cls: - mock_extractor = AsyncMock() - mock_extractor.extract_memory = AsyncMock(return_value=expected_case) - mock_cls.return_value = mock_extractor - - mgr = _make_memory_manager() - result = await mgr._extract_agent_case(memcell, user_id="u1", group_id="g1") - - assert result is expected_case - mock_extractor.extract_memory.assert_called_once() - - @pytest.mark.asyncio - async def test_non_agent_memcell_returns_none(self): - """Regular conversation memcell should return None.""" - memcell = _make_conv_memcell() - mgr = _make_memory_manager() - result = await mgr._extract_agent_case(memcell, user_id="u1", group_id="g1") - - assert result is None - - @pytest.mark.asyncio - async def test_none_memcell_returns_none(self): - """None memcell should return None.""" - mgr = _make_memory_manager() - result = await mgr._extract_agent_case(None, user_id="u1", group_id="g1") - - assert result is None - - -# =========================================================================== -# MemoryManager.extract_memory AGENT_CASE dispatch -# =========================================================================== - - -class TestMemoryManagerExtractMemoryDispatch: - """Tests for MemoryManager.extract_memory dispatching to AGENT_CASE.""" - - @pytest.mark.asyncio - async def test_dispatch_agent_case(self): - """extract_memory with AGENT_CASE type should call _extract_agent_case.""" - memcell = _make_agent_memcell() - expected_case = _make_agent_case() - - mgr = _make_memory_manager() - - with patch.object(mgr, "_extract_agent_case", new_callable=AsyncMock) as mock_method: - mock_method.return_value = expected_case - - with patch("memory_layer.memory_manager.record_extract_memory_call"), \ - patch("memory_layer.memory_manager.get_space_id_for_metrics", return_value="test"): - result = await mgr.extract_memory( - memcell=memcell, - memory_type=MemoryType.AGENT_CASE, - user_id="u1", - group_id="g1", - ) - - assert result is expected_case - mock_method.assert_called_once_with(memcell, user_id="u1", group_id="g1") - - @pytest.mark.asyncio - async def test_dispatch_agent_case_none_result(self): - """extract_memory with AGENT_CASE returning None should still return None.""" - memcell = _make_agent_memcell() - mgr = _make_memory_manager() - - with patch.object(mgr, "_extract_agent_case", new_callable=AsyncMock) as mock_method: - mock_method.return_value = None - - with patch("memory_layer.memory_manager.record_extract_memory_call"), \ - patch("memory_layer.memory_manager.get_space_id_for_metrics", return_value="test"): - result = await mgr.extract_memory( - memcell=memcell, - memory_type=MemoryType.AGENT_CASE, - ) - - assert result is None diff --git a/methods/EverCore/tests/test_agent_post_content.py b/methods/EverCore/tests/test_agent_post_content.py deleted file mode 100644 index 95c717d6..00000000 --- a/methods/EverCore/tests/test_agent_post_content.py +++ /dev/null @@ -1,560 +0,0 @@ -"""Tests for agent POST endpoint content format handling. - -Covers convert_agent_add_to_memorize_request with various content formats: -- Plain string content -- Content items array (text, image, multi-item) -- Legacy 'content' field in ContentItem dicts -- Tool call messages (assistant with tool_calls, tool role responses) -- Empty/missing content edge cases -- Mixed content formats in a single request - -Usage: - PYTHONPATH=src pytest tests/test_api/test_agent_post_content.py -v -""" - -import pytest - -from api_specs.request_converter import convert_agent_add_to_memorize_request -from api_specs.memory_types import RawDataType - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -BASE_TS = 1710835200000 # 2024-03-19 arbitrary base timestamp - - -def _agent_request(messages, user_id="user_agent_01", session_id="sess_01"): - """Build a minimal agent add request dict.""" - return {"user_id": user_id, "session_id": session_id, "messages": messages} - - -def _msg(role, content, ts_offset=0, **kwargs): - """Build a single agent message dict.""" - m = {"role": role, "timestamp": BASE_TS + ts_offset, "content": content} - m.update(kwargs) - return m - - -# =========================================================================== -# Plain string content -# =========================================================================== - - -class TestPlainStringContent: - """Agent messages where content is a plain string.""" - - def test_user_plain_string(self): - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", "Hello, what can you do?")]) - ) - assert len(req.new_raw_data_list) == 1 - raw = req.new_raw_data_list[0] - assert raw.content["content"] == [ - {"type": "text", "text": "Hello, what can you do?"} - ] - assert raw.content["role"] == "user" - - def test_assistant_plain_string(self): - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", "I can help with many tasks.")]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["content"] == [ - {"type": "text", "text": "I can help with many tasks."} - ] - assert raw.content["role"] == "assistant" - - def test_tool_plain_string(self): - req = convert_agent_add_to_memorize_request( - _agent_request( - [_msg("tool", "Weather: 22C sunny", tool_call_id="call_001")] - ) - ) - raw = req.new_raw_data_list[0] - assert raw.content["content"] == [ - {"type": "text", "text": "Weather: 22C sunny"} - ] - assert raw.content["role"] == "tool" - assert raw.content["tool_call_id"] == "call_001" - - def test_multiline_string(self): - text = "Line one\nLine two\nLine three" - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", text)]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["content"] == [{"type": "text", "text": text}] - - -# =========================================================================== -# Content items array -# =========================================================================== - - -class TestContentItemsArray: - """Agent messages where content is an array of content items.""" - - def test_single_text_item(self): - content = [{"type": "text", "text": "Hello agent"}] - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", content)]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["content"] == [{"type": "text", "text": "Hello agent"}] - - def test_multiple_text_items(self): - content = [ - {"type": "text", "text": "First paragraph"}, - {"type": "text", "text": "Second paragraph"}, - ] - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", content)]) - ) - raw = req.new_raw_data_list[0] - assert len(raw.content["content"]) == 2 - assert raw.content["content"][0]["text"] == "First paragraph" - assert raw.content["content"][1]["text"] == "Second paragraph" - - def test_image_content_item(self): - content = [ - { - "type": "image", - "uri": "https://example.com/photo.png", - "name": "photo.png", - } - ] - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", content)]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["content"][0]["type"] == "image" - assert raw.content["content"][0]["uri"] == "https://example.com/photo.png" - - def test_mixed_text_and_image(self): - content = [ - {"type": "text", "text": "Check this image"}, - {"type": "image", "uri": "https://example.com/img.jpg"}, - ] - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", content)]) - ) - raw = req.new_raw_data_list[0] - assert len(raw.content["content"]) == 2 - assert raw.content["content"][0]["type"] == "text" - assert raw.content["content"][1]["type"] == "image" - - def test_content_item_with_extras(self): - content = [ - { - "type": "text", - "text": "data", - "source": "notion", - "extras": {"page_id": "abc"}, - } - ] - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", content)]) - ) - raw = req.new_raw_data_list[0] - item = raw.content["content"][0] - assert item["text"] == "data" - assert item["source"] == "notion" - assert item["extras"] == {"page_id": "abc"} - - -# =========================================================================== -# Legacy 'content' field compatibility -# =========================================================================== - - -class TestLegacyContentField: - """ContentItem dicts using legacy 'content' key instead of 'text'.""" - - def test_legacy_content_field_normalized_to_text(self): - content = [{"type": "text", "content": "legacy format"}] - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", content)]) - ) - raw = req.new_raw_data_list[0] - item = raw.content["content"][0] - assert item["text"] == "legacy format" - assert "content" not in item - - def test_mixed_legacy_and_canonical(self): - content = [ - {"type": "text", "content": "from legacy"}, - {"type": "text", "text": "from canonical"}, - ] - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", content)]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["content"][0]["text"] == "from legacy" - assert raw.content["content"][1]["text"] == "from canonical" - - -# =========================================================================== -# Tool call messages -# =========================================================================== - - -class TestToolCallMessages: - """Agent messages involving tool_calls and tool role responses.""" - - def _weather_tool_call(self): - return { - "id": "call_weather_01", - "type": "function", - "function": {"name": "get_weather", "arguments": '{"city": "Tokyo"}'}, - } - - def test_assistant_with_tool_calls_and_text(self): - req = convert_agent_add_to_memorize_request( - _agent_request( - [ - _msg( - "assistant", - "Let me check the weather.", - tool_calls=[self._weather_tool_call()], - ) - ] - ) - ) - raw = req.new_raw_data_list[0] - assert raw.content["content"] == [ - {"type": "text", "text": "Let me check the weather."} - ] - assert raw.content["tool_calls"] == [self._weather_tool_call()] - assert "tool_call_id" not in raw.content - - def test_assistant_with_tool_calls_content_array(self): - req = convert_agent_add_to_memorize_request( - _agent_request( - [ - _msg( - "assistant", - [{"type": "text", "text": "Checking weather..."}], - tool_calls=[self._weather_tool_call()], - ) - ] - ) - ) - raw = req.new_raw_data_list[0] - assert raw.content["content"] == [ - {"type": "text", "text": "Checking weather..."} - ] - assert raw.content["tool_calls"] == [self._weather_tool_call()] - - def test_tool_response_with_text_content(self): - req = convert_agent_add_to_memorize_request( - _agent_request( - [ - _msg( - "tool", - [{"type": "text", "text": "Tokyo: 22C, partly cloudy"}], - tool_call_id="call_weather_01", - ) - ] - ) - ) - raw = req.new_raw_data_list[0] - assert raw.content["tool_call_id"] == "call_weather_01" - assert raw.content["content"] == [ - {"type": "text", "text": "Tokyo: 22C, partly cloudy"} - ] - - def test_tool_response_empty_string_content_raises(self): - """Tool messages must have non-empty content (execution result).""" - with pytest.raises(ValueError, match="messages\\[\\]\\.content"): - convert_agent_add_to_memorize_request( - _agent_request([_msg("tool", "", tool_call_id="call_noop_01")]) - ) - - def test_tool_response_empty_array_content_raises(self): - """Tool messages with empty array content should raise.""" - with pytest.raises(ValueError, match="messages\\[\\]\\.content"): - convert_agent_add_to_memorize_request( - _agent_request([_msg("tool", [], tool_call_id="call_noop_02")]) - ) - - def test_tool_response_none_content_raises(self): - """Tool messages with None/missing content should raise.""" - msg = {"role": "tool", "timestamp": BASE_TS, "tool_call_id": "call_noop_03"} - with pytest.raises(ValueError, match="messages\\[\\]\\.content"): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - def test_assistant_with_tool_calls_empty_string_content(self): - """Assistant message with tool_calls and empty string content should succeed.""" - tool_call = self._weather_tool_call() - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", "", tool_calls=[tool_call])]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["content"] == [{"type": "text", "text": ""}] - assert raw.content["tool_calls"] == [tool_call] - - def test_assistant_with_tool_calls_none_content(self): - """Assistant message with tool_calls and content=None should succeed.""" - tool_call = self._weather_tool_call() - msg = { - "role": "assistant", - "timestamp": BASE_TS, - "content": None, - "tool_calls": [tool_call], - } - req = convert_agent_add_to_memorize_request(_agent_request([msg])) - raw = req.new_raw_data_list[0] - assert raw.content["content"] == [{"type": "text", "text": ""}] - assert raw.content["tool_calls"] == [tool_call] - - def test_assistant_with_tool_calls_missing_content(self): - """Assistant message with tool_calls and no content key should succeed.""" - tool_call = self._weather_tool_call() - msg = {"role": "assistant", "timestamp": BASE_TS, "tool_calls": [tool_call]} - req = convert_agent_add_to_memorize_request(_agent_request([msg])) - raw = req.new_raw_data_list[0] - assert raw.content["content"] == [{"type": "text", "text": ""}] - assert raw.content["tool_calls"] == [tool_call] - - def test_assistant_with_tool_calls_empty_array_content(self): - """Assistant message with tool_calls and content=[] should succeed.""" - tool_call = self._weather_tool_call() - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", [], tool_calls=[tool_call])]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["content"] == [{"type": "text", "text": ""}] - assert raw.content["tool_calls"] == [tool_call] - - def test_assistant_without_tool_calls_empty_content_still_raises(self): - """Assistant without tool_calls and empty content should still raise.""" - with pytest.raises(ValueError, match="content"): - convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", "")]) - ) - - def test_assistant_without_tool_calls_none_content_still_raises(self): - """Assistant without tool_calls and None content should still raise.""" - msg = {"role": "assistant", "timestamp": BASE_TS, "content": None} - with pytest.raises(ValueError, match="content"): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - def test_multiple_tool_calls_in_one_assistant_message(self): - calls = [ - { - "id": "call_01", - "type": "function", - "function": {"name": "get_weather", "arguments": '{"city": "Tokyo"}'}, - }, - { - "id": "call_02", - "type": "function", - "function": { - "name": "get_time", - "arguments": '{"timezone": "Asia/Tokyo"}', - }, - }, - ] - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", "Let me check both.", tool_calls=calls)]) - ) - raw = req.new_raw_data_list[0] - assert len(raw.content["tool_calls"]) == 2 - assert raw.content["tool_calls"][0]["id"] == "call_01" - assert raw.content["tool_calls"][1]["id"] == "call_02" - - -# =========================================================================== -# Full conversation trajectory -# =========================================================================== - - -class TestFullTrajectory: - """Full agent conversation with user -> assistant -> tool -> assistant flow.""" - - def test_complete_tool_use_conversation(self): - messages = [ - _msg("user", "What is the weather in Tokyo?", ts_offset=0), - _msg( - "assistant", - "Let me check.", - ts_offset=1000, - tool_calls=[ - { - "id": "call_w1", - "type": "function", - "function": { - "name": "get_weather", - "arguments": '{"city": "Tokyo"}', - }, - } - ], - ), - _msg( - "tool", - [{"type": "text", "text": "Tokyo: 18C, partly cloudy"}], - ts_offset=2000, - tool_call_id="call_w1", - ), - _msg( - "assistant", - "The weather in Tokyo is 18C and partly cloudy.", - ts_offset=3000, - ), - ] - req = convert_agent_add_to_memorize_request(_agent_request(messages)) - - assert len(req.new_raw_data_list) == 4 - assert req.raw_data_type == RawDataType.AGENTCONVERSATION - - # User message - assert req.new_raw_data_list[0].content["role"] == "user" - # Assistant with tool call - assert req.new_raw_data_list[1].content["role"] == "assistant" - assert "tool_calls" in req.new_raw_data_list[1].content - # Tool response - assert req.new_raw_data_list[2].content["role"] == "tool" - assert req.new_raw_data_list[2].content["tool_call_id"] == "call_w1" - # Final assistant - assert req.new_raw_data_list[3].content["role"] == "assistant" - assert "tool_calls" not in req.new_raw_data_list[3].content - - def test_mixed_content_formats_in_trajectory(self): - """Different messages use different content formats in the same request.""" - messages = [ - _msg("user", "Analyze this image", ts_offset=0), # plain string - _msg( - "user", - [ - {"type": "text", "text": "Here is the image:"}, - {"type": "image", "uri": "https://example.com/chart.png"}, - ], - ts_offset=1000, - ), # array with mixed types - _msg( - "assistant", - [{"type": "text", "content": "I see a bar chart"}], - ts_offset=2000, - ), # legacy content field - ] - req = convert_agent_add_to_memorize_request(_agent_request(messages)) - assert len(req.new_raw_data_list) == 3 - - # Plain string was coerced - assert req.new_raw_data_list[0].content["content"] == [ - {"type": "text", "text": "Analyze this image"} - ] - # Array preserved - assert len(req.new_raw_data_list[1].content["content"]) == 2 - # Legacy field normalized - assert ( - req.new_raw_data_list[2].content["content"][0]["text"] - == "I see a bar chart" - ) - - -# =========================================================================== -# Validation / error cases -# =========================================================================== - - -class TestValidationErrors: - """Error cases for invalid agent post content.""" - - def test_empty_string_content_non_tool_raises(self): - with pytest.raises(ValueError, match="content"): - convert_agent_add_to_memorize_request(_agent_request([_msg("user", "")])) - - def test_empty_array_content_non_tool_raises(self): - with pytest.raises(ValueError, match="content"): - convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", [])]) - ) - - def test_none_content_non_tool_raises(self): - msg = {"role": "user", "timestamp": BASE_TS} - with pytest.raises(ValueError, match="content"): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - def test_missing_role_raises(self): - msg = {"timestamp": BASE_TS, "content": "hello"} - with pytest.raises(ValueError, match="role"): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - def test_invalid_role_raises(self): - with pytest.raises(ValueError, match="role"): - convert_agent_add_to_memorize_request( - _agent_request([_msg("system", "hello")]) - ) - - def test_tool_without_tool_call_id_raises(self): - with pytest.raises(ValueError, match="tool_call_id"): - convert_agent_add_to_memorize_request( - _agent_request([_msg("tool", "response")]) - ) - - def test_missing_timestamp_raises(self): - msg = {"role": "user", "content": "hello"} - with pytest.raises(ValueError, match="timestamp"): - convert_agent_add_to_memorize_request(_agent_request([msg])) - - def test_missing_user_id_raises(self): - with pytest.raises(ValueError, match="user_id"): - convert_agent_add_to_memorize_request({"messages": [_msg("user", "hello")]}) - - def test_empty_messages_raises(self): - with pytest.raises(ValueError, match="messages"): - convert_agent_add_to_memorize_request( - {"user_id": "user_01", "messages": []} - ) - - -# =========================================================================== -# Sender ID and metadata -# =========================================================================== - - -class TestSenderAndMetadata: - """Verify sender_id assignment and metadata for different roles.""" - - def test_user_sender_id_equals_user_id(self): - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", "hi")], user_id="u_123") - ) - raw = req.new_raw_data_list[0] - assert raw.content["sender_id"] == "u_123" - - def test_assistant_gets_auto_sender_id(self): - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", "hello")]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["sender_id"].endswith("_assistant") - - def test_tool_gets_auto_sender_id(self): - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("tool", "result", tool_call_id="call_01")]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["sender_id"].endswith("_tool") - - def test_custom_sender_id_for_assistant(self): - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("assistant", "hello", sender_id="my_bot")]) - ) - raw = req.new_raw_data_list[0] - assert raw.content["sender_id"] == "my_bot" - - def test_session_id_propagated(self): - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", "hi")], session_id="custom_session") - ) - assert req.session_id == "custom_session" - - def test_raw_data_type_is_agent_conversation(self): - req = convert_agent_add_to_memorize_request( - _agent_request([_msg("user", "hi")]) - ) - assert req.raw_data_type == RawDataType.AGENTCONVERSATION diff --git a/methods/EverCore/tests/test_agent_search_service.py b/methods/EverCore/tests/test_agent_search_service.py deleted file mode 100644 index 29d04101..00000000 --- a/methods/EverCore/tests/test_agent_search_service.py +++ /dev/null @@ -1,1317 +0,0 @@ -""" -SearchMemoryService Agent Case/Skill Search Tests - -Tests for: -- _search_agent_cases: keyword, vector, hybrid methods -- _search_agent_skills: keyword, vector, hybrid methods -- _agent_case_doc_to_item: document-to-DTO conversion -- _agent_skill_doc_to_item: document-to-DTO conversion -- _extract_hit_id: unified ID extraction from ES/Milvus hits -- search_memories: full search with agent_memory type -- _extract_filter_values: filter DSL parsing for agent filters -- Hybrid edge cases: dedup, backfill failure, empty query_words - -Usage: - PYTHONPATH=src pytest tests/test_agent_search_service.py -v -""" - -import pytest -from datetime import datetime -from unittest.mock import AsyncMock, MagicMock, patch, PropertyMock -from typing import Any, Dict, List, Optional -from bson import ObjectId -from pydantic import ValidationError - -from api_specs.dtos.memory import ( - AgentMemorySearchResult, - SearchAgentCaseItem, - SearchAgentSkillItem, -) - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -def _make_case_doc( - doc_id: str = None, - user_id: str = "user_001", - group_id: str = "group_001", - session_id: str = None, - task_intent: str = "Build REST API", - approach: str = "1. Design\n2. Implement", - quality_score: float = 0.8, - timestamp: datetime = None, - parent_type: str = "memcell", - parent_id: str = "evt_001", -): - """Create a mock AgentCaseRecord.""" - doc = MagicMock() - doc.id = ObjectId(doc_id) if doc_id else ObjectId() - doc.user_id = user_id - doc.group_id = group_id - doc.session_id = session_id - doc.task_intent = task_intent - doc.approach = approach - doc.quality_score = quality_score - doc.timestamp = timestamp or datetime(2025, 3, 1, 12, 0, 0) - doc.key_insight = "" - doc.parent_type = parent_type - doc.parent_id = parent_id - return doc - - -def _make_skill_doc( - doc_id: str = None, - user_id: str = "user_001", - group_id: str = "group_001", - cluster_id: str = "cluster_001", - name: str = "API Development", - description: str = "Build REST APIs", - content: str = "## Steps\n1. Design\n2. Implement", - confidence: float = 0.8, - maturity_score: float = 0.75, -): - """Create a mock AgentSkillRecord.""" - doc = MagicMock() - doc.id = ObjectId(doc_id) if doc_id else ObjectId() - doc.user_id = user_id - doc.group_id = group_id - doc.cluster_id = cluster_id - doc.name = name - doc.description = description - doc.content = content - doc.confidence = confidence - doc.maturity_score = maturity_score - doc.source_case_ids = [] - return doc - - -def _es_hit(doc_id: str, score: float = 1.0, **extra_source) -> Dict[str, Any]: - """Create a mock ES hit.""" - source = {"id": doc_id, **extra_source} - return {"_source": source, "_score": score, "_id": doc_id} - - -def _milvus_result(doc_id: str, score: float = 0.9, **extra) -> Dict[str, Any]: - """Create a mock Milvus search result.""" - return {"id": doc_id, "score": score, **extra} - - -# --------------------------------------------------------------------------- -# Fixtures -# --------------------------------------------------------------------------- - - -@pytest.fixture -def search_service(): - """Create SearchMemoryService with all repositories mocked.""" - with ( - patch("agentic_layer.search_mem_service.EpisodicMemoryEsRepository"), - patch("agentic_layer.search_mem_service.EpisodicMemoryMilvusRepository"), - patch("agentic_layer.search_mem_service.UserProfileMilvusRepository"), - patch( - "agentic_layer.search_mem_service.AgentCaseEsRepository" - ) as mock_case_es_cls, - patch( - "agentic_layer.search_mem_service.AgentSkillEsRepository" - ) as mock_skill_es_cls, - patch( - "agentic_layer.search_mem_service.AgentCaseMilvusRepository" - ) as mock_case_milvus_cls, - patch( - "agentic_layer.search_mem_service.AgentSkillMilvusRepository" - ) as mock_skill_milvus_cls, - patch("agentic_layer.search_mem_service.MemoryManager"), - patch("agentic_layer.search_mem_service.RawMessageService"), - ): - - from agentic_layer.search_mem_service import SearchMemoryService - - svc = SearchMemoryService() - # Replace repo instances with AsyncMock - svc.agent_case_es_repo = AsyncMock() - svc.agent_skill_es_repo = AsyncMock() - svc.agent_case_milvus_repo = AsyncMock() - svc.agent_skill_milvus_repo = AsyncMock() - yield svc - - -# =========================================================================== -# _agent_case_doc_to_item tests -# =========================================================================== - - -class TestAgentCaseDocToItem: - """Tests for SearchMemoryService._agent_case_doc_to_item.""" - - def test_basic_conversion(self, search_service): - doc = _make_case_doc() - item = search_service._agent_case_doc_to_item(doc, score=0.95) - assert isinstance(item, SearchAgentCaseItem) - assert item.id == str(doc.id) - assert item.user_id == "user_001" - assert item.task_intent == "Build REST API" - assert item.score == 0.95 - - def test_none_score(self, search_service): - doc = _make_case_doc() - item = search_service._agent_case_doc_to_item(doc) - assert item.score is None - - def test_none_fields_handled(self, search_service): - doc = _make_case_doc(task_intent=None, approach=None) - item = search_service._agent_case_doc_to_item(doc) - assert item.task_intent == "" - assert item.approach == "" - - -# =========================================================================== -# _agent_skill_doc_to_item tests -# =========================================================================== - - -class TestAgentSkillDocToItem: - """Tests for SearchMemoryService._agent_skill_doc_to_item.""" - - def test_basic_conversion(self, search_service): - doc = _make_skill_doc() - item = search_service._agent_skill_doc_to_item(doc, score=0.88) - assert isinstance(item, SearchAgentSkillItem) - assert item.id == str(doc.id) - assert item.name == "API Development" - assert item.confidence == 0.8 - assert item.maturity_score == 0.75 - assert item.score == 0.88 - - def test_none_score(self, search_service): - doc = _make_skill_doc() - item = search_service._agent_skill_doc_to_item(doc) - assert item.score is None - - -# =========================================================================== -# _search_agent_cases tests -# =========================================================================== - - -class TestSearchAgentCases: - """Tests for SearchMemoryService._search_agent_cases.""" - - @pytest.mark.asyncio - async def test_keyword_search(self, search_service): - doc = _make_case_doc() - doc_id = str(doc.id) - search_service.agent_case_es_repo.multi_search = AsyncMock( - return_value=[_es_hit(doc_id, score=5.0)] - ) - - with patch.object( - search_service, - "_fetch_agent_cases_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ): - results = await search_service._search_agent_cases( - query="REST API", - query_words=["REST", "API"], - query_vector=None, - method="keyword", - filter_values={ - "user_id": "user_001", - "group_ids": None, - "session_id": None, - "start_time": None, - "end_time": None, - }, - date_range={}, - top_k=10, - radius=None, - ) - assert len(results) == 1 - assert results[0].task_intent == "Build REST API" - assert results[0].score == 5.0 - - @pytest.mark.asyncio - async def test_vector_search(self, search_service): - doc = _make_case_doc() - doc_id = str(doc.id) - search_service.agent_case_milvus_repo.vector_search = AsyncMock( - return_value=[_milvus_result(doc_id, score=0.92)] - ) - - with patch.object( - search_service, - "_fetch_agent_cases_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ): - results = await search_service._search_agent_cases( - query="", - query_words=[], - query_vector=[0.1, 0.2, 0.3], - method="vector", - filter_values={ - "user_id": "user_001", - "group_ids": None, - "session_id": None, - "start_time": None, - "end_time": None, - }, - date_range={}, - top_k=10, - radius=None, - ) - assert len(results) == 1 - assert results[0].score == 0.92 - - @pytest.mark.asyncio - async def test_vector_search_no_vector_returns_empty(self, search_service): - """Vector search without query vector returns no results.""" - results = await search_service._search_agent_cases( - query="", - query_words=[], - query_vector=None, - method="vector", - filter_values={ - "user_id": "u1", - "group_ids": None, - "session_id": None, - "start_time": None, - "end_time": None, - }, - date_range={}, - top_k=10, - radius=None, - ) - assert results == [] - - @pytest.mark.asyncio - async def test_keyword_search_no_mongo_doc_skipped(self, search_service): - """If ES returns an ID but MongoDB doesn't have the doc, it's skipped.""" - search_service.agent_case_es_repo.multi_search = AsyncMock( - return_value=[_es_hit("nonexistent_id")] - ) - with patch.object( - search_service, - "_fetch_agent_cases_by_ids", - new_callable=AsyncMock, - return_value={}, - ): - results = await search_service._search_agent_cases( - query="test", - query_words=["test"], - query_vector=None, - method="keyword", - filter_values={ - "user_id": "u1", - "group_ids": None, - "session_id": None, - "start_time": None, - "end_time": None, - }, - date_range={}, - top_k=10, - radius=None, - ) - assert results == [] - - @pytest.mark.asyncio - async def test_keyword_search_with_session_id(self, search_service): - """Keyword search passes session_id to ES repo filter.""" - doc = _make_case_doc() - doc_id = str(doc.id) - search_service.agent_case_es_repo.multi_search = AsyncMock( - return_value=[_es_hit(doc_id, score=3.0)] - ) - - with patch.object( - search_service, - "_fetch_agent_cases_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ): - results = await search_service._search_agent_cases( - query="REST API", - query_words=["REST", "API"], - query_vector=None, - method="keyword", - filter_values={ - "user_id": "user_001", - "group_ids": None, - "session_id": "sess_abc", - "start_time": None, - "end_time": None, - }, - date_range={}, - top_k=10, - radius=None, - ) - - # Verify session_id was passed to ES repo - call_kwargs = search_service.agent_case_es_repo.multi_search.call_args[1] - assert call_kwargs["session_id"] == "sess_abc" - assert len(results) == 1 - - @pytest.mark.asyncio - async def test_vector_search_with_session_id(self, search_service): - """Vector search passes session_id to Milvus repo filter.""" - doc = _make_case_doc() - doc_id = str(doc.id) - search_service.agent_case_milvus_repo.vector_search = AsyncMock( - return_value=[_milvus_result(doc_id, score=0.88)] - ) - - with patch.object( - search_service, - "_fetch_agent_cases_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ): - results = await search_service._search_agent_cases( - query="", - query_words=[], - query_vector=[0.1, 0.2, 0.3], - method="vector", - filter_values={ - "user_id": "user_001", - "group_ids": None, - "session_id": "sess_abc", - "start_time": None, - "end_time": None, - }, - date_range={}, - top_k=10, - radius=None, - ) - - # Verify session_id was passed to Milvus repo - call_kwargs = search_service.agent_case_milvus_repo.vector_search.call_args[1] - assert call_kwargs["session_id"] == "sess_abc" - assert len(results) == 1 - - @pytest.mark.asyncio - async def test_keyword_search_session_id_none_not_filtered(self, search_service): - """When session_id is None, it should not be passed as a filter.""" - doc = _make_case_doc() - doc_id = str(doc.id) - search_service.agent_case_es_repo.multi_search = AsyncMock( - return_value=[_es_hit(doc_id, score=3.0)] - ) - - with patch.object( - search_service, - "_fetch_agent_cases_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ): - await search_service._search_agent_cases( - query="test", - query_words=["test"], - query_vector=None, - method="keyword", - filter_values={ - "user_id": "u1", - "group_ids": None, - "session_id": None, - "start_time": None, - "end_time": None, - }, - date_range={}, - top_k=10, - radius=None, - ) - - call_kwargs = search_service.agent_case_es_repo.multi_search.call_args[1] - assert call_kwargs["session_id"] is None - - -# =========================================================================== -# _search_agent_skills tests -# =========================================================================== - - -class TestFetchAgentSkillsConfidenceFilter: - """Tests for _fetch_agent_skills_by_ids confidence filtering. - - _fetch_agent_skills_by_ids adds a confidence >= retire_confidence filter - to the MongoDB query, ensuring retired skills are excluded from results. - """ - - @pytest.mark.asyncio - async def test_retired_skill_filtered_out(self, search_service): - """Skills with confidence below threshold should be excluded.""" - high_conf_doc = _make_skill_doc(confidence=0.8) - high_id = str(high_conf_doc.id) - - search_service.agent_skill_raw_repo.find_by_ids = AsyncMock( - return_value=[high_conf_doc] - ) - - result = await search_service._fetch_agent_skills_by_ids([high_id]) - - call_kwargs = search_service.agent_skill_raw_repo.find_by_ids.call_args - assert call_kwargs[0][0] == [high_id] - assert call_kwargs[1]["min_confidence"] == pytest.approx(0.1) - assert high_id in result - - @pytest.mark.asyncio - async def test_empty_ids_returns_empty(self, search_service): - """Empty skill_ids should return empty dict without querying.""" - result = await search_service._fetch_agent_skills_by_ids([]) - assert result == {} - - -class TestSearchAgentSkillsConfidenceThreshold: - """Tests that _search_agent_skills passes confidence_threshold to search repos.""" - - @pytest.mark.asyncio - async def test_keyword_passes_confidence_threshold(self, search_service): - """Keyword search should pass confidence_threshold to ES repo.""" - search_service.agent_skill_es_repo.multi_search = AsyncMock(return_value=[]) - - await search_service._search_agent_skills( - query="test", - query_words=["test"], - query_vector=None, - method="keyword", - filter_values={"user_id": "u1", "group_ids": None, "session_id": None}, - top_k=10, - radius=None, - ) - - call_kwargs = search_service.agent_skill_es_repo.multi_search.call_args - assert call_kwargs.kwargs.get("confidence_threshold") == 0.1 - - @pytest.mark.asyncio - async def test_vector_passes_confidence_threshold(self, search_service): - """Vector search should pass confidence_threshold to Milvus repo.""" - search_service.agent_skill_milvus_repo.vector_search = AsyncMock( - return_value=[] - ) - - await search_service._search_agent_skills( - query="", - query_words=[], - query_vector=[0.1, 0.2], - method="vector", - filter_values={"user_id": "u1", "group_ids": None, "session_id": None}, - top_k=10, - radius=None, - ) - - call_kwargs = search_service.agent_skill_milvus_repo.vector_search.call_args - assert call_kwargs.kwargs.get("confidence_threshold") == 0.1 - - @pytest.mark.asyncio - async def test_hybrid_passes_confidence_threshold_to_both(self, search_service): - """Hybrid search should pass confidence_threshold to both ES and Milvus.""" - search_service.agent_skill_es_repo.multi_search = AsyncMock(return_value=[]) - search_service.agent_skill_milvus_repo.vector_search = AsyncMock( - return_value=[] - ) - - with patch( - "agentic_layer.search_mem_service.get_rerank_service" - ) as mock_rerank: - mock_rerank_svc = AsyncMock() - mock_rerank_svc.rerank_memories = AsyncMock(return_value=[]) - mock_rerank.return_value = mock_rerank_svc - - await search_service._search_agent_skills( - query="test", - query_words=["test"], - query_vector=[0.1, 0.2], - method="hybrid", - filter_values={"user_id": "u1", "group_ids": None, "session_id": None}, - top_k=10, - radius=None, - ) - - es_kwargs = search_service.agent_skill_es_repo.multi_search.call_args - assert es_kwargs.kwargs.get("confidence_threshold") == 0.1 - - milvus_kwargs = search_service.agent_skill_milvus_repo.vector_search.call_args - assert milvus_kwargs.kwargs.get("confidence_threshold") == 0.1 - - -class TestSearchAgentSkills: - """Tests for SearchMemoryService._search_agent_skills. - - Note: agent_skill has no date_range filtering (similar to profile). - """ - - @pytest.mark.asyncio - async def test_keyword_search(self, search_service): - doc = _make_skill_doc() - doc_id = str(doc.id) - search_service.agent_skill_es_repo.multi_search = AsyncMock( - return_value=[_es_hit(doc_id, score=3.5)] - ) - - with patch.object( - search_service, - "_fetch_agent_skills_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ): - results = await search_service._search_agent_skills( - query="API development", - query_words=["API", "development"], - query_vector=None, - method="keyword", - filter_values={ - "user_id": "user_001", - "group_ids": None, - "session_id": None, - }, - top_k=10, - radius=None, - ) - assert len(results) == 1 - assert results[0].name == "API Development" - assert results[0].score == 3.5 - - @pytest.mark.asyncio - async def test_keyword_no_date_range_passed(self, search_service): - """agent_skill does not pass date_range to ES (no business timestamp).""" - search_service.agent_skill_es_repo.multi_search = AsyncMock(return_value=[]) - - await search_service._search_agent_skills( - query="test", - query_words=["test"], - query_vector=None, - method="keyword", - filter_values={"user_id": "u1", "group_ids": None, "session_id": None}, - top_k=10, - radius=None, - ) - - call_kwargs = search_service.agent_skill_es_repo.multi_search.call_args - # date_range should NOT be in the call args - all_kwargs = call_kwargs.kwargs if call_kwargs.kwargs else {} - assert "date_range" not in all_kwargs - - @pytest.mark.asyncio - async def test_vector_search(self, search_service): - doc = _make_skill_doc() - doc_id = str(doc.id) - search_service.agent_skill_milvus_repo.vector_search = AsyncMock( - return_value=[_milvus_result(doc_id, score=0.85)] - ) - - with patch.object( - search_service, - "_fetch_agent_skills_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ): - results = await search_service._search_agent_skills( - query="", - query_words=[], - query_vector=[0.1, 0.2], - method="vector", - filter_values={"user_id": "u1", "group_ids": None, "session_id": None}, - top_k=10, - radius=None, - ) - assert len(results) == 1 - assert results[0].score == 0.85 - - @pytest.mark.asyncio - async def test_keyword_no_mongo_doc_skipped(self, search_service): - search_service.agent_skill_es_repo.multi_search = AsyncMock( - return_value=[_es_hit("missing_id")] - ) - with patch.object( - search_service, - "_fetch_agent_skills_by_ids", - new_callable=AsyncMock, - return_value={}, - ): - results = await search_service._search_agent_skills( - query="test", - query_words=["test"], - query_vector=None, - method="keyword", - filter_values={"user_id": "u1", "group_ids": None, "session_id": None}, - top_k=10, - radius=None, - ) - assert results == [] - - @pytest.mark.asyncio - async def test_vector_search_no_vector_returns_empty(self, search_service): - """Vector search without query vector returns no results.""" - results = await search_service._search_agent_skills( - query="", - query_words=[], - query_vector=None, - method="vector", - filter_values={"user_id": "u1", "group_ids": None, "session_id": None}, - top_k=10, - radius=None, - ) - assert results == [] - - -# =========================================================================== -# search_memories with agent_memory type tests -# =========================================================================== - - -class TestSearchMemoriesAgentMemory: - """Tests for search_memories with agent_memory memory type.""" - - @pytest.mark.asyncio - async def test_agent_memory_type_triggers_both_searches(self, search_service): - """memory_types=['agent_memory'] triggers both case and skill searches.""" - with ( - patch.object( - search_service, - "_search_agent_cases", - new_callable=AsyncMock, - return_value=[], - ) as mock_cases, - patch.object( - search_service, - "_search_agent_skills", - new_callable=AsyncMock, - return_value=[], - ) as mock_skills, - patch.object(search_service, "_build_query_words", return_value=["test"]), - ): - - result = await search_service.search_memories( - query="test query", - method="keyword", - memory_types=["agent_memory"], - filters={"user_id": "u1"}, - top_k=10, - radius=None, - include_original_data=False, - ) - - mock_cases.assert_called_once() - mock_skills.assert_called_once() - assert result.agent_memory is None # No results -> None - - @pytest.mark.asyncio - async def test_agent_memory_results_assembled(self, search_service): - """When agent searches return results, agent_memory is populated.""" - case_item = SearchAgentCaseItem(id="c1", task_intent="Build API", score=0.9) - skill_item = SearchAgentSkillItem(id="s1", name="API Dev", score=0.85) - - with ( - patch.object( - search_service, - "_search_agent_cases", - new_callable=AsyncMock, - return_value=[case_item], - ), - patch.object( - search_service, - "_search_agent_skills", - new_callable=AsyncMock, - return_value=[skill_item], - ), - patch.object(search_service, "_build_query_words", return_value=["api"]), - ): - - result = await search_service.search_memories( - query="api development", - method="keyword", - memory_types=["agent_memory"], - filters={"user_id": "u1"}, - top_k=10, - radius=None, - include_original_data=False, - ) - - assert result.agent_memory is not None - assert len(result.agent_memory.cases) == 1 - assert len(result.agent_memory.skills) == 1 - assert result.agent_memory.cases[0].task_intent == "Build API" - assert result.agent_memory.skills[0].name == "API Dev" - - @pytest.mark.asyncio - async def test_no_user_or_group_raises(self, search_service): - """Missing both user_id and group_id raises ValueError.""" - with pytest.raises(ValueError, match="user_id.*group_id"): - await search_service.search_memories( - query="test", - method="keyword", - memory_types=["agent_memory"], - filters={}, - top_k=10, - radius=None, - include_original_data=False, - ) - - @pytest.mark.asyncio - async def test_agent_memory_with_other_types(self, search_service): - """agent_memory can be combined with other memory types.""" - case_item = SearchAgentCaseItem(id="c1", task_intent="Task", score=0.9) - - with ( - patch.object( - search_service, - "_search_agent_cases", - new_callable=AsyncMock, - return_value=[case_item], - ), - patch.object( - search_service, - "_search_agent_skills", - new_callable=AsyncMock, - return_value=[], - ), - patch.object( - search_service, - "_search_episodic_memory", - new_callable=AsyncMock, - return_value=[], - ), - patch.object(search_service, "_build_query_words", return_value=["test"]), - ): - - result = await search_service.search_memories( - query="test", - method="keyword", - memory_types=["episodic_memory", "agent_memory"], - filters={"user_id": "u1"}, - top_k=10, - radius=None, - include_original_data=False, - ) - assert result.agent_memory is not None - assert len(result.agent_memory.cases) == 1 - assert result.episodes == [] - - @pytest.mark.asyncio - async def test_top_k_applied_to_agent_results(self, search_service): - """Top-k limit is applied to agent case and skill results.""" - cases = [ - SearchAgentCaseItem( - id=f"c{i}", task_intent=f"Task {i}", score=1.0 - i * 0.1 - ) - for i in range(5) - ] - skills = [ - SearchAgentSkillItem(id=f"s{i}", name=f"Skill {i}", score=0.9 - i * 0.1) - for i in range(5) - ] - - with ( - patch.object( - search_service, - "_search_agent_cases", - new_callable=AsyncMock, - return_value=cases, - ), - patch.object( - search_service, - "_search_agent_skills", - new_callable=AsyncMock, - return_value=skills, - ), - patch.object(search_service, "_build_query_words", return_value=["test"]), - ): - - result = await search_service.search_memories( - query="test", - method="keyword", - memory_types=["agent_memory"], - filters={"user_id": "u1"}, - top_k=3, - radius=None, - include_original_data=False, - ) - assert len(result.agent_memory.cases) == 3 - assert len(result.agent_memory.skills) == 3 - - -# =========================================================================== -# _extract_hit_id tests -# =========================================================================== - - -class TestExtractHitId: - """Tests for SearchMemoryService._extract_hit_id.""" - - def test_milvus_format(self, search_service): - assert search_service._extract_hit_id({"id": "abc"}) == "abc" - - def test_es_format(self, search_service): - hit = {"_source": {"id": "abc"}, "_id": "abc"} - assert search_service._extract_hit_id(hit) == "abc" - - def test_es_fallback_to_underscore_id(self, search_service): - hit = {"_source": {}, "_id": "abc"} - assert search_service._extract_hit_id(hit) == "abc" - - def test_no_id_returns_none(self, search_service): - assert search_service._extract_hit_id({}) is None - - def test_empty_string_id_returns_none(self, search_service): - assert search_service._extract_hit_id({"id": ""}) is None - - def test_mixed_format_prefers_id(self, search_service): - """When both 'id' and '_source.id' exist, 'id' wins.""" - hit = {"id": "milvus_id", "_source": {"id": "es_id"}, "_id": "meta_id"} - assert search_service._extract_hit_id(hit) == "milvus_id" - - -# =========================================================================== -# Hybrid dedup / backfill edge cases -# =========================================================================== - - -class TestHybridEdgeCases: - """Tests for hybrid search edge cases (dedup, backfill failure, empty query).""" - - @pytest.mark.asyncio - async def test_hybrid_case_dedup_same_doc(self, search_service): - """Same doc from ES and Milvus should be deduplicated in hybrid merge.""" - doc = _make_case_doc() - doc_id = str(doc.id) - - search_service.agent_case_es_repo.multi_search = AsyncMock( - return_value=[_es_hit(doc_id, score=5.0)] - ) - search_service.agent_case_milvus_repo.vector_search = AsyncMock( - return_value=[_milvus_result(doc_id, score=0.9)] - ) - - with patch.object( - search_service, - "_fetch_agent_cases_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ): - results = await search_service._search_agent_cases( - query="API", - query_words=["API"], - query_vector=[0.1], - method="hybrid", - filter_values={ - "user_id": "u1", - "group_ids": None, - "session_id": None, - "start_time": None, - "end_time": None, - }, - date_range={}, - top_k=10, - radius=None, - ) - - assert len(results) == 1 - # vector_anchored_fusion: alpha*vec + (1-alpha)*sat_bm25 - # sat_bm25 = 5.0/(5.0+5.0) = 0.5; score = 0.7*0.9 + 0.3*0.5 = 0.78 - assert results[0].score == pytest.approx(0.78) - - @pytest.mark.asyncio - async def test_hybrid_case_backfill_missing_doc(self, search_service): - """When fused IDs are not in MongoDB, they are skipped.""" - doc = _make_case_doc() - doc_id = str(doc.id) - missing_id = str(ObjectId()) - - search_service.agent_case_es_repo.multi_search = AsyncMock( - return_value=[_es_hit(doc_id)] - ) - search_service.agent_case_milvus_repo.vector_search = AsyncMock( - return_value=[_milvus_result(missing_id)] - ) - - with patch.object( - search_service, - "_fetch_agent_cases_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, # missing_id not in dict - ): - results = await search_service._search_agent_cases( - query="API", - query_words=["API"], - query_vector=[0.1], - method="hybrid", - filter_values={ - "user_id": "u1", - "group_ids": None, - "session_id": None, - "start_time": None, - "end_time": None, - }, - date_range={}, - top_k=10, - radius=None, - ) - - assert len(results) == 1 - assert results[0].id == doc_id - - @pytest.mark.asyncio - async def test_hybrid_skill_dedup_same_doc(self, search_service): - """Same skill from ES and Milvus should be deduplicated.""" - doc = _make_skill_doc() - doc_id = str(doc.id) - - search_service.agent_skill_es_repo.multi_search = AsyncMock( - return_value=[_es_hit(doc_id)] - ) - search_service.agent_skill_milvus_repo.vector_search = AsyncMock( - return_value=[_milvus_result(doc_id)] - ) - - mock_rerank_svc = AsyncMock() - mock_rerank_svc.rerank_memories = AsyncMock( - return_value=[{"id": doc_id, "rerank_score": 0.95}] - ) - - with ( - patch.object( - search_service, - "_fetch_agent_skills_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ), - patch( - "agentic_layer.search_mem_service.get_rerank_service", - return_value=mock_rerank_svc, - ), - ): - results = await search_service._search_agent_skills( - query="API", - query_words=["API"], - query_vector=[0.1], - method="hybrid", - filter_values={"user_id": "u1", "group_ids": None, "session_id": None}, - top_k=10, - radius=None, - ) - - merged = mock_rerank_svc.rerank_memories.call_args.kwargs.get("hits") - assert len(merged) == 1 - assert len(results) == 1 - - @pytest.mark.asyncio - async def test_hybrid_case_empty_query_words(self, search_service): - """hybrid works when query_words is empty (only vector results).""" - doc = _make_case_doc() - doc_id = str(doc.id) - - search_service.agent_case_es_repo.multi_search = AsyncMock(return_value=[]) - search_service.agent_case_milvus_repo.vector_search = AsyncMock( - return_value=[_milvus_result(doc_id, score=0.8)] - ) - - with patch.object( - search_service, - "_fetch_agent_cases_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ): - results = await search_service._search_agent_cases( - query="", - query_words=[], - query_vector=[0.1], - method="hybrid", - filter_values={ - "user_id": "u1", - "group_ids": None, - "session_id": None, - "start_time": None, - "end_time": None, - }, - date_range={}, - top_k=10, - radius=None, - ) - - assert len(results) == 1 - # vector_anchored_fusion: only vector hit, no keyword hit - # score = 0.7*0.8 + 0.3*0.0 = 0.56 - assert results[0].score == pytest.approx(0.56) - - @pytest.mark.asyncio - @pytest.mark.parametrize( - "top_k, expected_recall_limit", - [ - (-1, 10), # default: no multiplier, falls back to DEFAULT_TOP_K - (0, 10), # zero treated as default: no multiplier - (3, 6), # small explicit: multiplier applied (3*2) - (5, 10), # boundary: multiplier applied (5*2) - (6, 6), # above threshold: no multiplier - (10, 10), # large explicit: no multiplier - ], - ) - async def test_hybrid_case_recall_limit( - self, search_service, top_k, expected_recall_limit - ): - """Hybrid case search applies multiplier only when 0 < top_k <= HYBRID_TOP_K_THRESHOLD.""" - doc = _make_case_doc() - doc_id = str(doc.id) - - search_service.agent_case_es_repo.multi_search = AsyncMock( - return_value=[_es_hit(doc_id)] - ) - search_service.agent_case_milvus_repo.vector_search = AsyncMock( - return_value=[_milvus_result(doc_id)] - ) - - with patch.object( - search_service, - "_fetch_agent_cases_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ): - await search_service._search_agent_cases( - query="API", - query_words=["API"], - query_vector=[0.1], - method="hybrid", - filter_values={ - "user_id": "u1", - "group_ids": None, - "session_id": None, - "start_time": None, - "end_time": None, - }, - date_range={}, - top_k=top_k, - radius=None, - ) - - es_call_kwargs = search_service.agent_case_es_repo.multi_search.call_args.kwargs - assert es_call_kwargs["size"] == expected_recall_limit - - milvus_call_kwargs = ( - search_service.agent_case_milvus_repo.vector_search.call_args.kwargs - ) - assert milvus_call_kwargs["limit"] == expected_recall_limit - - @pytest.mark.asyncio - @pytest.mark.parametrize( - "top_k, expected_recall_limit", - [ - (-1, 10), # default: no multiplier, falls back to DEFAULT_TOP_K - (0, 10), # zero treated as default: no multiplier - (3, 6), # small explicit: multiplier applied (3*2) - (5, 10), # boundary: multiplier applied (5*2) - (6, 6), # above threshold: no multiplier - (10, 10), # large explicit: no multiplier - ], - ) - async def test_hybrid_skill_recall_limit( - self, search_service, top_k, expected_recall_limit - ): - """Hybrid skill search applies multiplier only when 0 < top_k <= HYBRID_TOP_K_THRESHOLD.""" - doc = _make_skill_doc() - doc_id = str(doc.id) - - search_service.agent_skill_es_repo.multi_search = AsyncMock( - return_value=[_es_hit(doc_id)] - ) - search_service.agent_skill_milvus_repo.vector_search = AsyncMock( - return_value=[_milvus_result(doc_id)] - ) - - mock_rerank_svc = AsyncMock() - mock_rerank_svc.rerank_memories = AsyncMock( - return_value=[{"id": doc_id, "rerank_score": 0.9}] - ) - - with ( - patch.object( - search_service, - "_fetch_agent_skills_by_ids", - new_callable=AsyncMock, - return_value={doc_id: doc}, - ), - patch( - "agentic_layer.search_mem_service.get_rerank_service", - return_value=mock_rerank_svc, - ), - ): - await search_service._search_agent_skills( - query="API", - query_words=["API"], - query_vector=[0.1], - method="hybrid", - filter_values={ - "user_id": "u1", - "group_ids": None, - }, - top_k=top_k, - radius=None, - ) - - es_call_kwargs = search_service.agent_skill_es_repo.multi_search.call_args.kwargs - assert es_call_kwargs["size"] == expected_recall_limit - - milvus_call_kwargs = ( - search_service.agent_skill_milvus_repo.vector_search.call_args.kwargs - ) - assert milvus_call_kwargs["limit"] == expected_recall_limit - - -# =========================================================================== -# _extract_filter_values tests -# =========================================================================== - - -class TestExtractFilterValues: - """Tests for SearchMemoryService._extract_filter_values with agent-relevant filters.""" - - def test_simple_user_id(self, search_service): - result = search_service._extract_filter_values({"user_id": "agent_user_001"}) - assert result["user_id"] == "agent_user_001" - - def test_user_id_eq_format(self, search_service): - result = search_service._extract_filter_values({"user_id": {"eq": "u1"}}) - assert result["user_id"] == "u1" - - def test_user_id_in_format(self, search_service): - result = search_service._extract_filter_values( - {"user_id": {"in": ["u1", "u2"]}} - ) - assert result["user_id"] == "u1" - - def test_group_id_string(self, search_service): - result = search_service._extract_filter_values({"group_id": "g1"}) - assert result["group_ids"] == ["g1"] - - def test_group_id_list(self, search_service): - result = search_service._extract_filter_values({"group_id": ["g1", "g2"]}) - assert result["group_ids"] == ["g1", "g2"] - - def test_group_id_in_format(self, search_service): - result = search_service._extract_filter_values( - {"group_id": {"in": ["g1", "g2"]}} - ) - assert result["group_ids"] == ["g1", "g2"] - - def test_timestamp_range(self, search_service): - result = search_service._extract_filter_values( - {"timestamp": {"gte": "2025-01-01T00:00:00", "lte": "2025-12-31T23:59:59"}} - ) - assert result["start_time"] is not None - assert result["end_time"] is not None - - def test_and_combinator(self, search_service): - result = search_service._extract_filter_values( - {"AND": [{"user_id": "u1"}, {"group_id": "g1"}]} - ) - assert result["user_id"] == "u1" - assert result["group_ids"] == ["g1"] - - def test_empty_filters(self, search_service): - result = search_service._extract_filter_values({}) - assert result["user_id"] is None - assert result["group_ids"] is None - - def test_session_id(self, search_service): - result = search_service._extract_filter_values({"session_id": "sess_001"}) - assert result["session_id"] == "sess_001" - - def test_timestamp_epoch_milliseconds(self, search_service): - result = search_service._extract_filter_values( - {"timestamp": {"gte": 1735689600000}} # 2025-01-01 in milliseconds - ) - assert result["start_time"] is not None - - -# =========================================================================== -# SearchMemoriesRequest DTO validation tests -# =========================================================================== - - -class TestSearchMemoriesRequestValidation: - """Tests for SearchMemoriesRequest DTO field validation.""" - - def test_empty_query_rejected(self): - """query='' should be rejected by Pydantic min_length=1.""" - from api_specs.dtos.memory import SearchMemoriesRequest - - with pytest.raises(ValidationError) as exc_info: - SearchMemoriesRequest(query="", filters={"user_id": "u1"}) - assert "query" in str(exc_info.value) - - def test_valid_query_accepted(self): - """Non-empty query should pass validation.""" - from api_specs.dtos.memory import SearchMemoriesRequest - - req = SearchMemoriesRequest(query="hello", filters={"user_id": "u1"}) - assert req.query == "hello" - - -# =========================================================================== -# _compute_recall_limit tests -# =========================================================================== - - -class TestComputeRecallLimit: - """Tests for _compute_recall_limit helper function.""" - - def test_with_multiplier_normal_top_k(self): - from agentic_layer.search_mem_service import _compute_recall_limit - - assert _compute_recall_limit(top_k=3, apply_multiplier=True) == 6 - - def test_with_multiplier_top_k_five(self): - from agentic_layer.search_mem_service import _compute_recall_limit - - assert _compute_recall_limit(top_k=5, apply_multiplier=True) == 10 - - def test_with_multiplier_top_k_zero_uses_default(self): - from agentic_layer.search_mem_service import ( - _compute_recall_limit, - DEFAULT_TOP_K, - MAX_RECALL_MULTIPLIER, - ) - - assert _compute_recall_limit(top_k=0, apply_multiplier=True) == ( - DEFAULT_TOP_K * MAX_RECALL_MULTIPLIER - ) - - def test_without_multiplier_large_top_k(self): - from agentic_layer.search_mem_service import _compute_recall_limit - - assert _compute_recall_limit(top_k=8, apply_multiplier=False) == 8 - - def test_without_multiplier_top_k_ten(self): - from agentic_layer.search_mem_service import _compute_recall_limit - - assert _compute_recall_limit(top_k=10, apply_multiplier=False) == 10 - - def test_without_multiplier_top_k_zero_uses_default(self): - from agentic_layer.search_mem_service import ( - _compute_recall_limit, - DEFAULT_TOP_K, - ) - - assert _compute_recall_limit(top_k=0, apply_multiplier=False) == DEFAULT_TOP_K - - def test_without_multiplier_top_k_one(self): - from agentic_layer.search_mem_service import _compute_recall_limit - - assert _compute_recall_limit(top_k=1, apply_multiplier=False) == 1 - - def test_with_multiplier_top_k_one(self): - from agentic_layer.search_mem_service import _compute_recall_limit - - assert _compute_recall_limit(top_k=1, apply_multiplier=True) == 2 - - def test_negative_top_k_uses_default(self): - from agentic_layer.search_mem_service import ( - _compute_recall_limit, - DEFAULT_TOP_K, - MAX_RECALL_MULTIPLIER, - ) - - assert _compute_recall_limit(top_k=-1, apply_multiplier=True) == ( - DEFAULT_TOP_K * MAX_RECALL_MULTIPLIER - ) - assert _compute_recall_limit(top_k=-1, apply_multiplier=False) == DEFAULT_TOP_K diff --git a/methods/EverCore/tests/test_agent_skill_extractor.py b/methods/EverCore/tests/test_agent_skill_extractor.py deleted file mode 100644 index 55ada469..00000000 --- a/methods/EverCore/tests/test_agent_skill_extractor.py +++ /dev/null @@ -1,2924 +0,0 @@ -""" -AgentSkillExtractor Unit Tests - -Tests for: -- _format_cases: case record formatting for LLM prompt -- _format_existing_skills: existing skills formatting with indices -- _cosine_similarity: vector cosine similarity -- _select_prompt: quality-based prompt selection -- _is_skill_content_sufficient: content validation -- _apply_add: new skill creation logic -- _apply_update: existing skill update logic -- extract_and_save: full incremental extraction flow - -Usage: - PYTHONPATH=src pytest tests/test_agent_skill_extractor.py -v -""" - -import json -import pytest -from datetime import datetime -from types import SimpleNamespace -from unittest.mock import AsyncMock, MagicMock, patch - -from memory_layer.memory_extractor.agent_skill_extractor import ( - AgentSkillExtractor, - SkillExtractionResult, -) - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def _make_case_record( - task_intent: str = "Build a REST API", - approach: str = "1. Design schema\n2. Implement endpoints", - quality_score: float = 0.8, - timestamp: datetime = None, - key_insight: str = None, - record_id: str = None, -): - """Create a mock AgentCaseRecord-like object.""" - return SimpleNamespace( - id=record_id, - task_intent=task_intent, - approach=approach, - quality_score=quality_score, - key_insight=key_insight, - timestamp=timestamp or datetime(2025, 1, 15, 10, 0, 0), - ) - - -def _make_skill_record( - name: str = "API Development", - description: str = "Build REST APIs with proper error handling", - content: str = "## Steps\n1. Design schema\n2. Implement endpoints\n3. Add validation\n4. Write tests\n5. Deploy", - confidence: float = 0.7, - vector: list = None, - source_case_ids: list = None, - record_id: str = "skill_001", -): - """Create a mock AgentSkillRecord-like object.""" - rec = SimpleNamespace( - id=record_id, - name=name, - description=description, - content=content, - confidence=confidence, - vector=vector or [0.1, 0.2, 0.3], - vector_model="test-model", - source_case_ids=source_case_ids or [], - maturity_score=0.7, - updated_at=None, - ) - return rec - - -def _build_extractor( - llm_response: str = None, - maturity_threshold: float = 0.6, -) -> AgentSkillExtractor: - """Build an extractor with mocked LLM provider.""" - mock_llm = MagicMock() - if llm_response: - mock_llm.generate = AsyncMock(return_value=llm_response) - else: - mock_llm.generate = AsyncMock(return_value='{"operations": [{"action": "none"}]}') - - return AgentSkillExtractor( - llm_provider=mock_llm, - success_extract_prompt="{new_case_json}{existing_skills_json}", - failure_extract_prompt="{new_case_json}{existing_skills_json}", - maturity_threshold=maturity_threshold, - ) - - -def _mock_skill_repo(): - """Create a mock skill repository.""" - repo = AsyncMock() - repo.save_skill = AsyncMock(side_effect=lambda rec: rec) - repo.update_skill_by_id = AsyncMock(return_value=True) - repo.soft_delete_by_id = AsyncMock(return_value=True) - return repo - - -# =========================================================================== -# _format_cases tests -# =========================================================================== - - -class TestFormatCases: - """Tests for AgentSkillExtractor._format_cases.""" - - def test_single_case(self): - extractor = _build_extractor() - cases = [_make_case_record()] - result = extractor._format_cases(cases) - parsed = json.loads(result) - assert len(parsed) == 1 - assert parsed[0]["task_intent"] == "Build a REST API" - assert parsed[0]["quality_score"] == 0.8 - - def test_multiple_cases(self): - extractor = _build_extractor() - cases = [ - _make_case_record(task_intent="Task A"), - _make_case_record(task_intent="Task B", quality_score=0.3), - ] - result = extractor._format_cases(cases) - parsed = json.loads(result) - assert len(parsed) == 2 - assert parsed[1]["task_intent"] == "Task B" - assert parsed[1]["quality_score"] == 0.3 - - def test_empty_cases(self): - extractor = _build_extractor() - result = extractor._format_cases([]) - assert json.loads(result) == [] - - def test_none_timestamp(self): - extractor = _build_extractor() - case = _make_case_record() - case.timestamp = None - result = extractor._format_cases([case]) - parsed = json.loads(result) - assert parsed[0]["timestamp"] is None - - -# =========================================================================== -# _format_existing_skills tests -# =========================================================================== - - -class TestFormatExistingSkills: - """Tests for AgentSkillExtractor._format_existing_skills.""" - - def test_empty_returns_placeholder(self): - extractor = _build_extractor() - result = extractor._format_existing_skills([]) - assert "empty" in result.lower() - - def test_single_skill_has_index(self): - extractor = _build_extractor() - result = extractor._format_existing_skills([_make_skill_record()]) - parsed = json.loads(result) - assert parsed[0]["index"] == 0 - assert parsed[0]["name"] == "API Development" - - def test_multiple_skills_indexed_sequentially(self): - extractor = _build_extractor() - skills = [ - _make_skill_record(name="Skill A"), - _make_skill_record(name="Skill B"), - _make_skill_record(name="Skill C"), - ] - result = extractor._format_existing_skills(skills) - parsed = json.loads(result) - assert [s["index"] for s in parsed] == [0, 1, 2] - - -# =========================================================================== -# _cosine_similarity tests -# =========================================================================== - - -class TestCosineSimilarity: - """Tests for AgentSkillExtractor._cosine_similarity.""" - - def test_identical_vectors(self): - sim = AgentSkillExtractor._cosine_similarity([1, 0, 0], [1, 0, 0]) - assert abs(sim - 1.0) < 1e-6 - - def test_orthogonal_vectors(self): - sim = AgentSkillExtractor._cosine_similarity([1, 0, 0], [0, 1, 0]) - assert abs(sim) < 1e-6 - - def test_opposite_vectors(self): - sim = AgentSkillExtractor._cosine_similarity([1, 0], [-1, 0]) - assert abs(sim - (-1.0)) < 1e-6 - - def test_zero_vector(self): - sim = AgentSkillExtractor._cosine_similarity([0, 0], [1, 1]) - assert sim == 0.0 - - def test_both_zero(self): - sim = AgentSkillExtractor._cosine_similarity([0, 0], [0, 0]) - assert sim == 0.0 - - -# =========================================================================== -# _select_prompt tests -# =========================================================================== - - -class TestSelectPrompt: - """Tests for AgentSkillExtractor._select_prompt.""" - - def test_high_quality_uses_success_prompt(self): - extractor = _build_extractor() - extractor.success_extract_prompt = "SUCCESS" - extractor.failure_extract_prompt = "FAILURE" - cases = [_make_case_record(quality_score=0.8)] - assert extractor._select_prompt(cases) == "SUCCESS" - - def test_low_quality_uses_failure_prompt(self): - extractor = _build_extractor() - extractor.success_extract_prompt = "SUCCESS" - extractor.failure_extract_prompt = "FAILURE" - cases = [_make_case_record(quality_score=0.3)] - assert extractor._select_prompt(cases) == "FAILURE" - - def test_boundary_quality_uses_success(self): - """quality_score == 0.5 (threshold) uses success prompt.""" - extractor = _build_extractor() - extractor.success_extract_prompt = "SUCCESS" - extractor.failure_extract_prompt = "FAILURE" - cases = [_make_case_record(quality_score=0.5)] - assert extractor._select_prompt(cases) == "SUCCESS" - - def test_just_below_threshold_uses_failure(self): - extractor = _build_extractor() - extractor.success_extract_prompt = "SUCCESS" - extractor.failure_extract_prompt = "FAILURE" - cases = [_make_case_record(quality_score=0.49)] - assert extractor._select_prompt(cases) == "FAILURE" - - def test_multiple_cases_uses_max_quality(self): - """When one case has high quality, uses success prompt.""" - extractor = _build_extractor() - extractor.success_extract_prompt = "SUCCESS" - extractor.failure_extract_prompt = "FAILURE" - cases = [ - _make_case_record(quality_score=0.2), - _make_case_record(quality_score=0.8), - ] - assert extractor._select_prompt(cases) == "SUCCESS" - - def test_none_quality_treated_as_default(self): - """None quality_score defaults to 0.5 (boundary) -> success prompt.""" - extractor = _build_extractor() - extractor.success_extract_prompt = "SUCCESS" - extractor.failure_extract_prompt = "FAILURE" - case = _make_case_record() - case.quality_score = None - assert extractor._select_prompt([case]) == "SUCCESS" - - -# =========================================================================== -# _is_skill_content_sufficient tests -# =========================================================================== - - -class TestIsSkillContentSufficient: - """Tests for AgentSkillExtractor._is_skill_content_sufficient.""" - - def test_empty_content(self): - assert AgentSkillExtractor._is_skill_content_sufficient("") is False - - def test_none_content(self): - assert AgentSkillExtractor._is_skill_content_sufficient(None) is False - - def test_too_short(self): - assert AgentSkillExtractor._is_skill_content_sufficient("short") is False - - def test_too_few_lines(self): - # Long enough characters but only 2 lines - text = "A" * 100 + "\n" + "B" * 100 - assert AgentSkillExtractor._is_skill_content_sufficient(text) is False - - def test_sufficient_content(self): - text = "## Steps\n1. First step\n2. Second step\n3. Third step\n4. Fourth step\n5. Fifth step" - assert AgentSkillExtractor._is_skill_content_sufficient(text) is True - - def test_whitespace_only_lines_ignored(self): - text = "line1\n\n\nline2\n\n\nline3\n \n" - assert AgentSkillExtractor._is_skill_content_sufficient(text) is False - - def test_custom_thresholds(self): - text = "ab\ncd\nef" - assert AgentSkillExtractor._is_skill_content_sufficient(text, min_lines=3, min_length=5) is True - assert AgentSkillExtractor._is_skill_content_sufficient(text, min_lines=3, min_length=100) is False - - -# =========================================================================== -# _call_llm tests -# =========================================================================== - - -class TestCallLLM: - """Tests for AgentSkillExtractor._call_llm.""" - - @pytest.mark.asyncio - async def test_valid_response(self): - response = json.dumps({"operations": [{"action": "none"}]}) - extractor = _build_extractor(llm_response=response) - result = await extractor._call_llm("cases", "skills", "{new_case_json}{existing_skills_json}") - assert result is not None - assert "operations" in result - - @pytest.mark.asyncio - async def test_invalid_json_retries(self): - extractor = _build_extractor() - extractor.llm_provider.generate = AsyncMock(return_value="not valid json") - result = await extractor._call_llm("cases", "skills", "{new_case_json}{existing_skills_json}") - assert result is None - assert extractor.llm_provider.generate.call_count == 3 - - @pytest.mark.asyncio - async def test_missing_operations_key_retries(self): - extractor = _build_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({"result": "no operations key"}) - ) - result = await extractor._call_llm("cases", "skills", "{new_case_json}{existing_skills_json}") - assert result is None - - @pytest.mark.asyncio - async def test_exception_retries(self): - extractor = _build_extractor() - extractor.llm_provider.generate = AsyncMock(side_effect=Exception("LLM error")) - result = await extractor._call_llm("cases", "skills", "{new_case_json}{existing_skills_json}") - assert result is None - assert extractor.llm_provider.generate.call_count == 3 - - -# =========================================================================== -# _evaluate_maturity tests -# =========================================================================== - - -class TestEvaluateMaturity: - """Tests for AgentSkillExtractor._evaluate_maturity.""" - - @pytest.mark.asyncio - async def test_valid_scores(self): - """Test 4-dimension scoring: (completeness + executability + evidence + clarity) / 20.""" - response = json.dumps({ - "completeness": 4, - "executability": 4, - "evidence": 4, - "clarity": 5, - "reason": "Well documented skill", - }) - extractor = _build_extractor() - extractor.maturity_prompt = "{name}{description}{content}{confidence}" - extractor.llm_provider.generate = AsyncMock(return_value=response) - score = await extractor._evaluate_maturity("Test", "Desc", "Content", 0.8) - assert score is not None - expected = (4 + 4 + 4 + 5) / 20.0 - assert abs(score - expected) < 1e-6 - - @pytest.mark.asyncio - async def test_max_score_clamped(self): - response = json.dumps({ - "completeness": 5, - "executability": 5, - "evidence": 5, - "clarity": 5, - }) - extractor = _build_extractor() - extractor.maturity_prompt = "{name}{description}{content}{confidence}" - extractor.llm_provider.generate = AsyncMock(return_value=response) - score = await extractor._evaluate_maturity("Test", "Desc", "Content", 0.8) - assert score == 1.0 - - @pytest.mark.asyncio - async def test_invalid_response_returns_none(self): - extractor = _build_extractor() - extractor.maturity_prompt = "{name}{description}{content}{confidence}" - extractor.llm_provider.generate = AsyncMock(return_value="not json") - score = await extractor._evaluate_maturity("Test", "Desc", "Content", 0.8) - assert score is None - - @pytest.mark.asyncio - async def test_missing_dimension_returns_none(self): - response = json.dumps({ - "completeness": 4, - "executability": 4, - # missing evidence, clarity - }) - extractor = _build_extractor() - extractor.maturity_prompt = "{name}{description}{content}{confidence}" - extractor.llm_provider.generate = AsyncMock(return_value=response) - score = await extractor._evaluate_maturity("Test", "Desc", "Content", 0.8) - assert score is None - - @pytest.mark.asyncio - async def test_llm_exception_returns_none(self): - extractor = _build_extractor() - extractor.maturity_prompt = "{name}{description}{content}{confidence}" - extractor.llm_provider.generate = AsyncMock(side_effect=Exception("fail")) - score = await extractor._evaluate_maturity("Test", "Desc", "Content", 0.8) - assert score is None - - -# =========================================================================== -# _apply_add tests -# =========================================================================== - - -class TestApplyAdd: - """Tests for AgentSkillExtractor._apply_add.""" - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_successful_add(self, mock_maturity, mock_embed): - mock_embed.return_value = {"embedding": [0.1, 0.2], "vector_model": "test"} - mock_maturity.return_value = 0.8 - repo = _mock_skill_repo() - - extractor = _build_extractor() - op = { - "action": "add", - "data": { - "name": "New Skill", - "description": "Does things", - "content": "## Steps\n1. Do this\n2. Do that\n3. Check results\n4. Validate\n5. Deploy", - "confidence": 0.6, - }, - } - # AgentSkillRecord is imported locally inside _apply_add. - # Pre-import the module then patch the class on it. - import infra_layer.adapters.out.persistence.document.memory.agent_skill as skill_mod - with patch.object(skill_mod, "AgentSkillRecord", return_value=MagicMock(id="new_001")): - result = await extractor._apply_add( - op, "cluster_001", "group_001", "user_001", repo, source_case_ids=["evt_001"] - ) - assert result is not None - repo.save_skill.assert_called_once() - - @pytest.mark.asyncio - async def test_empty_content_skipped(self): - extractor = _build_extractor() - repo = _mock_skill_repo() - op = {"action": "add", "data": {"name": "Skill", "description": "Desc", "content": ""}} - result = await extractor._apply_add(op, "c", "g", "u", repo) - assert result is None - repo.save_skill.assert_not_called() - - @pytest.mark.asyncio - async def test_insufficient_content_skipped(self): - extractor = _build_extractor() - repo = _mock_skill_repo() - op = {"action": "add", "data": {"name": "Skill", "description": "Desc", "content": "too short"}} - result = await extractor._apply_add(op, "c", "g", "u", repo) - assert result is None - - @pytest.mark.asyncio - async def test_no_name_no_description_skipped(self): - extractor = _build_extractor() - repo = _mock_skill_repo() - op = { - "action": "add", - "data": { - "name": "", - "description": "", - "content": "## Steps for completing the task properly\n1. Analyze requirements carefully\n2. Build the implementation\n3. Check edge cases\n4. Deploy to staging\n5. Verify in production", - }, - } - result = await extractor._apply_add(op, "c", "g", "u", repo) - assert result is None - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_invalid_confidence_defaults_to_half(self, mock_maturity, mock_embed): - mock_embed.return_value = {"embedding": [0.1], "vector_model": "test"} - mock_maturity.return_value = 0.7 - repo = _mock_skill_repo() - - extractor = _build_extractor() - op = { - "action": "add", - "data": { - "name": "Skill", - "description": "Desc", - "content": "## Steps for completing the task properly\n1. Analyze the requirements carefully\n2. Build the implementation with tests\n3. Check all edge cases thoroughly\n4. Deploy to staging environment\n5. Verify in production setup", - "confidence": "invalid", - }, - } - import infra_layer.adapters.out.persistence.document.memory.agent_skill as skill_mod - with patch.object( - skill_mod, "AgentSkillRecord", return_value=MagicMock(id="new_002") - ) as mock_record_cls: - result = await extractor._apply_add(op, "c", "g", "u", repo) - assert result is not None - # Check the record was created with confidence=0.5 (default) - call_args = mock_record_cls.call_args - assert call_args.kwargs["confidence"] == 0.5 - - -# =========================================================================== -# _apply_update tests -# =========================================================================== - - -class TestApplyUpdate: - """Tests for AgentSkillExtractor._apply_update.""" - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_update_content(self, mock_maturity, mock_embed): - mock_embed.return_value = None - mock_maturity.return_value = 0.75 - repo = _mock_skill_repo() - existing = [_make_skill_record()] - result_obj = SkillExtractionResult() - - extractor = _build_extractor() - op = { - "action": "update", - "index": 0, - "data": { - "content": "## Updated\n1. New step 1\n2. New step 2\n3. Step 3\n4. Step 4\n5. Step 5", - }, - } - success = await extractor._apply_update( - op, existing, repo, result_obj, source_case_ids=["evt_002"] - ) - assert success is True - assert len(result_obj.updated_records) == 1 - repo.update_skill_by_id.assert_called_once() - - @pytest.mark.asyncio - async def test_update_out_of_range_index(self): - repo = _mock_skill_repo() - existing = [_make_skill_record()] - result_obj = SkillExtractionResult() - - extractor = _build_extractor() - op = {"action": "update", "index": 5, "data": {"content": "new"}} - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is False - repo.update_skill_by_id.assert_not_called() - - @pytest.mark.asyncio - async def test_update_negative_index(self): - repo = _mock_skill_repo() - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": -1, "data": {"name": "X"}} - success = await extractor._apply_update(op, [_make_skill_record()], repo, result_obj) - assert success is False - - @pytest.mark.asyncio - async def test_update_invalid_index_type(self): - repo = _mock_skill_repo() - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": "abc", "data": {"name": "X"}} - success = await extractor._apply_update(op, [_make_skill_record()], repo, result_obj) - assert success is False - - @pytest.mark.asyncio - async def test_update_no_fields_skipped(self): - repo = _mock_skill_repo() - existing = [_make_skill_record()] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {}} - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is False - - @pytest.mark.asyncio - async def test_auto_delete_low_confidence(self): - """When confidence drops below threshold, skill is retired via update (not soft-deleted).""" - repo = _mock_skill_repo() - existing = [_make_skill_record()] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"confidence": 0.05}} - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is True - assert len(result_obj.deleted_ids) == 1 - assert str(existing[0].id) in result_obj.deleted_ids - # Should update confidence in-place, not soft-delete - repo.update_skill_by_id.assert_called_once() - call_args = repo.update_skill_by_id.call_args[0] - assert call_args[1] == {"confidence": 0.05} - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_update_name_triggers_re_embed(self, mock_maturity, mock_embed): - mock_embed.return_value = {"embedding": [0.5, 0.6], "vector_model": "new-model"} - mock_maturity.return_value = 0.8 - repo = _mock_skill_repo() - existing = [_make_skill_record(name="Old Name")] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"name": "New Name"}} - await extractor._apply_update(op, existing, repo, result_obj) - mock_embed.assert_called_once() - # Verify vector was included in updates - update_call = repo.update_skill_by_id.call_args - updates = update_call[0][1] - assert "vector" in updates - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_source_case_id_appended(self, mock_maturity, mock_embed): - mock_embed.return_value = None - mock_maturity.return_value = 0.7 - repo = _mock_skill_repo() - existing = [_make_skill_record(source_case_ids=["evt_001"])] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"confidence": 0.9}} - await extractor._apply_update( - op, existing, repo, result_obj, source_case_ids=["evt_002"] - ) - update_call = repo.update_skill_by_id.call_args - updates = update_call[0][1] - assert "evt_002" in updates["source_case_ids"] - assert "evt_001" in updates["source_case_ids"] - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_duplicate_source_case_id_not_added(self, mock_maturity, mock_embed): - mock_embed.return_value = None - mock_maturity.return_value = 0.7 - repo = _mock_skill_repo() - # Use a fresh list so the test owns the mutable state - existing = [_make_skill_record(source_case_ids=["evt_001"])] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"confidence": 0.9}} - await extractor._apply_update( - op, existing, repo, result_obj, source_case_ids=["evt_001"] - ) - update_call = repo.update_skill_by_id.call_args - updates = update_call[0][1] - # source_case_ids should NOT be in updates since "evt_001" is already present - assert "source_case_ids" not in updates or updates["source_case_ids"].count("evt_001") == 1 - - @pytest.mark.asyncio - async def test_update_insufficient_content_skipped(self): - repo = _mock_skill_repo() - existing = [_make_skill_record()] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"content": "too short"}} - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is False - - -# =========================================================================== -# extract_and_save full flow tests -# =========================================================================== - - -class TestExtractAndSave: - """Tests for AgentSkillExtractor.extract_and_save full flow.""" - - @pytest.mark.asyncio - async def test_no_new_cases_returns_empty(self): - extractor = _build_extractor() - repo = _mock_skill_repo() - result = await extractor.extract_and_save( - cluster_id="c1", - group_id="g1", - new_case_records=[], - existing_skill_records=[], - skill_repo=repo, - ) - assert result.added_records == [] - assert result.updated_records == [] - assert result.deleted_ids == [] - - @pytest.mark.asyncio - async def test_llm_failure_returns_empty(self): - extractor = _build_extractor() - extractor.llm_provider.generate = AsyncMock(return_value="bad json") - repo = _mock_skill_repo() - result = await extractor.extract_and_save( - cluster_id="c1", - group_id="g1", - new_case_records=[_make_case_record()], - existing_skill_records=[], - skill_repo=repo, - ) - assert result.added_records == [] - - @pytest.mark.asyncio - async def test_none_action_is_noop(self): - response = json.dumps({"operations": [{"action": "none"}]}) - extractor = _build_extractor(llm_response=response) - repo = _mock_skill_repo() - result = await extractor.extract_and_save( - cluster_id="c1", - group_id="g1", - new_case_records=[_make_case_record()], - existing_skill_records=[], - skill_repo=repo, - ) - assert result.added_records == [] - assert result.updated_records == [] - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._apply_add") - async def test_add_operation_dispatched(self, mock_apply_add): - mock_apply_add.return_value = MagicMock(id="new_skill") - response = json.dumps({ - "operations": [ - { - "action": "add", - "data": {"name": "Skill", "description": "Desc", "content": "Content"}, - } - ] - }) - extractor = _build_extractor(llm_response=response) - repo = _mock_skill_repo() - result = await extractor.extract_and_save( - cluster_id="c1", - group_id="g1", - new_case_records=[_make_case_record()], - existing_skill_records=[], - skill_repo=repo, - ) - assert len(result.added_records) == 1 - mock_apply_add.assert_called_once() - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._apply_update") - async def test_update_operation_dispatched(self, mock_apply_update): - mock_apply_update.return_value = True - response = json.dumps({ - "operations": [ - {"action": "update", "index": 0, "data": {"confidence": 0.9}} - ] - }) - extractor = _build_extractor(llm_response=response) - repo = _mock_skill_repo() - result = await extractor.extract_and_save( - cluster_id="c1", - group_id="g1", - new_case_records=[_make_case_record()], - existing_skill_records=[_make_skill_record()], - skill_repo=repo, - ) - mock_apply_update.assert_called_once() - - @pytest.mark.asyncio - async def test_unknown_action_skipped(self): - response = json.dumps({ - "operations": [{"action": "delete_all"}] - }) - extractor = _build_extractor(llm_response=response) - repo = _mock_skill_repo() - result = await extractor.extract_and_save( - cluster_id="c1", - group_id="g1", - new_case_records=[_make_case_record()], - existing_skill_records=[], - skill_repo=repo, - ) - assert result.added_records == [] - - @pytest.mark.asyncio - async def test_duplicate_update_index_skipped(self): - """Second update on same index is skipped.""" - response = json.dumps({ - "operations": [ - {"action": "update", "index": 0, "data": {"confidence": 0.9}}, - {"action": "update", "index": 0, "data": {"confidence": 0.8}}, - ] - }) - extractor = _build_extractor(llm_response=response) - repo = _mock_skill_repo() - - # Mock _apply_update to succeed - with patch.object(extractor, "_apply_update", new_callable=AsyncMock) as mock_update: - mock_update.return_value = True - await extractor.extract_and_save( - cluster_id="c1", - group_id="g1", - new_case_records=[_make_case_record()], - existing_skill_records=[_make_skill_record()], - skill_repo=repo, - ) - # Only first update should be called - assert mock_update.call_count == 1 - - @pytest.mark.asyncio - async def test_top_k_selection_when_too_many_skills(self): - """When existing skills exceed max_skills_in_prompt, top-k selection is used.""" - response = json.dumps({"operations": [{"action": "none"}]}) - extractor = _build_extractor(llm_response=response) - repo = _mock_skill_repo() - - # Create more skills than max_skills_in_prompt - many_skills = [_make_skill_record(name=f"Skill_{i}", record_id=f"s_{i}") for i in range(15)] - - with patch.object( - extractor, "_select_top_k_skills", new_callable=AsyncMock - ) as mock_top_k: - mock_top_k.return_value = many_skills[:5] - await extractor.extract_and_save( - cluster_id="c1", - group_id="g1", - new_case_records=[_make_case_record()], - existing_skill_records=many_skills, - skill_repo=repo, - max_skills_in_prompt=10, - ) - mock_top_k.assert_called_once() - - @pytest.mark.asyncio - async def test_update_invalid_index_string_skipped(self): - """Update with non-integer index is skipped without crashing.""" - response = json.dumps({ - "operations": [ - {"action": "update", "index": "first", "data": {"confidence": 0.9}} - ] - }) - extractor = _build_extractor(llm_response=response) - repo = _mock_skill_repo() - result = await extractor.extract_and_save( - cluster_id="c1", - group_id="g1", - new_case_records=[_make_case_record()], - existing_skill_records=[_make_skill_record()], - skill_repo=repo, - ) - # Should not crash and result should be empty - assert result.updated_records == [] - - -# =========================================================================== -# _select_top_k_skills tests -# =========================================================================== - - -class TestSelectTopKSkills: - """Tests for AgentSkillExtractor._select_top_k_skills.""" - - @pytest.mark.asyncio - async def test_fewer_than_k_returns_all(self): - extractor = _build_extractor() - skills = [_make_skill_record(name=f"S{i}") for i in range(3)] - with patch.object(extractor, "_compute_embedding", new_callable=AsyncMock) as mock_embed: - mock_embed.return_value = {"embedding": [0.1, 0.2, 0.3]} - result = await extractor._select_top_k_skills(skills, [_make_case_record()], top_k=10) - assert len(result) == 3 - - @pytest.mark.asyncio - async def test_empty_query_text_returns_first_k(self): - extractor = _build_extractor() - skills = [_make_skill_record(name=f"S{i}") for i in range(5)] - case = _make_case_record(task_intent="") - result = await extractor._select_top_k_skills(skills, [case], top_k=3) - assert len(result) == 3 - - @pytest.mark.asyncio - async def test_embedding_failure_returns_first_k(self): - extractor = _build_extractor() - skills = [_make_skill_record(name=f"S{i}") for i in range(5)] - with patch.object(extractor, "_compute_embedding", new_callable=AsyncMock) as mock_embed: - mock_embed.return_value = None - result = await extractor._select_top_k_skills(skills, [_make_case_record()], top_k=3) - assert len(result) == 3 - - @pytest.mark.asyncio - async def test_skills_without_vectors_included_as_fallback(self): - extractor = _build_extractor() - skill_with_vec = _make_skill_record(name="With Vec", vector=[0.1, 0.2, 0.3]) - skill_no_vec = _make_skill_record(name="No Vec", vector=None) - skills = [skill_with_vec, skill_no_vec] - - with patch.object(extractor, "_compute_embedding", new_callable=AsyncMock) as mock_embed: - mock_embed.return_value = {"embedding": [0.1, 0.2, 0.3]} - result = await extractor._select_top_k_skills(skills, [_make_case_record()], top_k=10) - assert len(result) == 2 - - @pytest.mark.asyncio - async def test_single_case_with_vector_reuses_embedding(self): - """When single new case already has a vector, skip _compute_embedding.""" - extractor = _build_extractor() - skills = [_make_skill_record(name="S0", vector=[0.1, 0.2, 0.3])] - case = _make_case_record() - case.vector = [0.1, 0.2, 0.3] # case already has vector - - with patch.object(extractor, "_compute_embedding", new_callable=AsyncMock) as mock_embed: - result = await extractor._select_top_k_skills(skills, [case], top_k=10) - # _compute_embedding should NOT have been called - mock_embed.assert_not_called() - assert len(result) == 1 - - @pytest.mark.asyncio - async def test_single_case_without_vector_computes_embedding(self): - """When single new case has no vector, _compute_embedding is called.""" - extractor = _build_extractor() - skills = [_make_skill_record(name="S0", vector=[0.1, 0.2, 0.3])] - case = _make_case_record() - case.vector = None - - with patch.object(extractor, "_compute_embedding", new_callable=AsyncMock) as mock_embed: - mock_embed.return_value = {"embedding": [0.1, 0.2, 0.3]} - result = await extractor._select_top_k_skills(skills, [case], top_k=10) - mock_embed.assert_called_once() - - @pytest.mark.asyncio - async def test_multiple_cases_always_computes_embedding(self): - """When multiple new cases, always compute embedding even if they have vectors.""" - extractor = _build_extractor() - skills = [_make_skill_record(name="S0", vector=[0.1, 0.2, 0.3])] - case_a = _make_case_record(task_intent="Task A") - case_a.vector = [0.1, 0.2, 0.3] - case_b = _make_case_record(task_intent="Task B") - case_b.vector = [0.4, 0.5, 0.6] - - with patch.object(extractor, "_compute_embedding", new_callable=AsyncMock) as mock_embed: - mock_embed.return_value = {"embedding": [0.2, 0.3, 0.4]} - result = await extractor._select_top_k_skills(skills, [case_a, case_b], top_k=10) - mock_embed.assert_called_once() - - -# =========================================================================== -# Maturity skip optimization tests -# =========================================================================== - - -class TestMaturitySkipOptimization: - """Tests for the maturity evaluation skip logic in _apply_update.""" - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_skip_maturity_when_mature_and_stable(self, mock_maturity, mock_embed): - """Maturity eval is skipped when skill is mature + confidence stable + change < 30%.""" - mock_embed.return_value = None - mock_maturity.return_value = 0.9 # should not be called - repo = _mock_skill_repo() - - # Existing skill: maturity=0.8 (above threshold 0.6), confidence=0.7 - existing = [_make_skill_record(confidence=0.7)] - existing[0].maturity_score = 0.8 - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", - "index": 0, - "data": { - # Minor content change (< 30% change_ratio) to ensure skip - "content": "## Steps\n1. Design schema\n2. Implement endpoints\n3. Add validation\n4. Write tests\n5. Deploy to prod", - "confidence": 0.8, # not dropping - }, - } - success = await extractor._apply_update( - op, existing, repo, result_obj, - source_case_ids=["evt_003"], - ) - assert success is True - # _evaluate_maturity should NOT have been called - mock_maturity.assert_not_called() - # maturity_score should NOT be in the updates - update_call = repo.update_skill_by_id.call_args - updates = update_call[0][1] - assert "maturity_score" not in updates - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_skip_when_mature_and_confidence_stable(self, mock_maturity, mock_embed): - """Moderate change on mature skill with stable confidence: skip maturity re-eval.""" - mock_embed.return_value = None - mock_maturity.return_value = 0.75 - repo = _mock_skill_repo() - - existing = [_make_skill_record(confidence=0.7)] - existing[0].maturity_score = 0.8 # above threshold - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", - "index": 0, - "data": { - "content": "## Steps\n1. Design DB schema first\n2. Build REST endpoints\n3. Add request validation\n4. Write integration tests\n5. Deploy and monitor", - "confidence": 0.8, # not dropping - }, - } - success = await extractor._apply_update( - op, existing, repo, result_obj, - source_case_ids=["evt_004"], - ) - assert success is True - mock_maturity.assert_not_called() # mature + stable confidence: skip - update_call = repo.update_skill_by_id.call_args - updates = update_call[0][1] - assert "maturity_score" not in updates - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_llm_rescore_when_immature_with_decent_case(self, mock_maturity, mock_embed): - """Moderate change on immature skill with decent source quality: LLM rescore.""" - mock_embed.return_value = None - mock_maturity.return_value = 0.7 - repo = _mock_skill_repo() - - existing = [_make_skill_record(confidence=0.7)] - existing[0].maturity_score = 0.4 # below threshold of 0.6 - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", - "index": 0, - "data": { - "content": "## Steps\n1. Design DB schema first\n2. Build REST endpoints\n3. Add request validation\n4. Write integration tests\n5. Deploy and monitor", - "confidence": 0.8, - }, - } - success = await extractor._apply_update( - op, existing, repo, result_obj, - source_case_ids=["evt_005"], - source_quality=0.7, # decent quality - ) - assert success is True - mock_maturity.assert_called_once() # immature + decent case: LLM rescore - update_call = repo.update_skill_by_id.call_args - updates = update_call[0][1] - assert "maturity_score" in updates - assert updates["maturity_score"] == 0.7 - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_llm_rescore_when_mature_but_confidence_dropping(self, mock_maturity, mock_embed): - """Moderate change on mature skill with dropping confidence: LLM rescore.""" - mock_embed.return_value = None - mock_maturity.return_value = 0.65 - repo = _mock_skill_repo() - - existing = [_make_skill_record(confidence=0.7)] - existing[0].maturity_score = 0.8 # above threshold - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", - "index": 0, - "data": { - "content": "## Steps\n1. Design DB schema first\n2. Build REST endpoints\n3. Add request validation\n4. Write integration tests\n5. Deploy and monitor", - "confidence": 0.4, # dropping from 0.7 to below 0.5 - }, - } - success = await extractor._apply_update( - op, existing, repo, result_obj, - source_case_ids=["evt_006"], - ) - assert success is True - mock_maturity.assert_called_once() # mature but confidence dropping below 0.5: LLM rescore - update_call = repo.update_skill_by_id.call_args - updates = update_call[0][1] - assert "maturity_score" in updates - assert updates["maturity_score"] == 0.65 - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_skip_when_only_confidence_update_no_content_change(self, mock_maturity, mock_embed): - """Confidence-only update (no content change) should NOT trigger maturity eval at all.""" - mock_embed.return_value = None - mock_maturity.return_value = 0.9 - repo = _mock_skill_repo() - - existing = [_make_skill_record(confidence=0.7)] - existing[0].maturity_score = 0.8 - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", - "index": 0, - "data": {"confidence": 0.85}, - } - success = await extractor._apply_update( - op, existing, repo, result_obj, - ) - assert success is True - # No content/name/description changed, maturity eval should not trigger - mock_maturity.assert_not_called() - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_llm_maturity_when_major_content_change(self, mock_maturity, mock_embed): - """Major content change (ratio >= 0.5) uses LLM maturity evaluation.""" - mock_embed.return_value = {"embedding": [0.5, 0.6], "vector_model": "m"} - mock_maturity.return_value = 0.8 - repo = _mock_skill_repo() - - existing = [_make_skill_record(name="Old Name", confidence=0.7)] - existing[0].maturity_score = 0.8 - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", - "index": 0, - # Completely different content triggers change_ratio >= 0.5 - "data": {"content": "## Revised\n1. Totally new approach A\n2. Totally new approach B\n3. New step C\n4. New step D\n5. New step E"}, - } - success = await extractor._apply_update( - op, existing, repo, result_obj, - ) - assert success is True - mock_maturity.assert_called_once() # LLM evaluation for major change - update_call = repo.update_skill_by_id.call_args - updates = update_call[0][1] - assert "maturity_score" in updates - assert updates["maturity_score"] == 0.8 - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_llm_maturity_on_hypothesis_promotion(self, mock_maturity, mock_embed): - """Hypothesis promotion (Potential Steps -> Steps) uses LLM maturity evaluation.""" - mock_embed.return_value = None - mock_maturity.return_value = 0.75 - repo = _mock_skill_repo() - - # Existing skill has Potential Steps (hypothesis from a failed case) - existing = [_make_skill_record(confidence=0.5)] - existing[0].content = ( - "## Potential Steps\n" - "> Extracted from a failed case.\n" - "1. Try approach X\n" - " - How: run command X\n" - " - Check: verify output\n" - "\n## Pitfalls\n- Avoid Y" - ) - existing[0].maturity_score = 0.4 - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - # New content promotes to verified Steps - op = { - "action": "update", - "index": 0, - "data": { - "content": ( - "## Steps\n" - "1. Execute approach X\n" - " - How: run verified command X\n" - " - Check: verify output\n" - "2. Follow up with Z\n" - " - How: run command Z\n" - " - Check: confirm result\n" - "\n## Pitfalls\n- Avoid Y" - ), - "confidence": 0.6, - }, - } - success = await extractor._apply_update( - op, existing, repo, result_obj, - source_case_ids=["evt_promotion"], - ) - assert success is True - mock_maturity.assert_called_once() # LLM used for hypothesis promotion - update_call = repo.update_skill_by_id.call_args - updates = update_call[0][1] - assert updates["maturity_score"] == 0.75 - - -# =========================================================================== -# _format_cases None quality_score tests -# =========================================================================== - - -class TestFormatCasesNoneQuality: - """Tests for quality_score=None handling in _format_cases.""" - - def test_none_quality_defaults_to_0_5(self): - extractor = _build_extractor() - case = _make_case_record() - case.quality_score = None - result = extractor._format_cases([case]) - parsed = json.loads(result) - assert parsed[0]["quality_score"] == 0.5 - - -# =========================================================================== -# content_changed fix tests -# =========================================================================== - - -class TestContentChangedFix: - """Tests verifying content_changed only triggers on actual changes.""" - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_same_content_no_maturity_eval(self, mock_maturity, mock_embed): - """When update has same content as existing, maturity eval is NOT triggered.""" - mock_embed.return_value = None - mock_maturity.return_value = 0.9 - repo = _mock_skill_repo() - - original_content = "## Steps\n1. Design schema\n2. Implement endpoints\n3. Add validation\n4. Write tests\n5. Deploy" - existing = [_make_skill_record(content=original_content, confidence=0.7)] - existing[0].maturity_score = 0.8 - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", - "index": 0, - "data": { - "content": original_content, # same content - "confidence": 0.8, - }, - } - success = await extractor._apply_update( - op, existing, repo, result_obj, - ) - assert success is True - mock_maturity.assert_not_called() - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_same_name_no_re_embed(self, mock_maturity, mock_embed): - """When update has same name as existing, re-embed is NOT triggered.""" - mock_embed.return_value = {"embedding": [0.5], "vector_model": "m"} - mock_maturity.return_value = 0.8 - repo = _mock_skill_repo() - - existing = [_make_skill_record(name="API Development", confidence=0.7)] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - - op = { - "action": "update", - "index": 0, - "data": {"name": "API Development"}, # same name - } - success = await extractor._apply_update( - op, existing, repo, result_obj, - ) - assert success is True - mock_embed.assert_not_called() - - -# =========================================================================== -# _evaluate_maturity edge-case tests -# =========================================================================== - - -class TestEvaluateMaturityEdgeCases: - """Additional edge-case tests for _evaluate_maturity.""" - - @pytest.mark.asyncio - async def test_zero_scores_returns_zero(self): - """All dimension scores of 0 produce a 0.0 maturity score.""" - response = json.dumps({ - "completeness": 0, "executability": 0, "evidence": 0, "clarity": 0, - }) - extractor = _build_extractor() - extractor.maturity_prompt = "{name}{description}{content}{confidence}" - extractor.llm_provider.generate = AsyncMock(return_value=response) - score = await extractor._evaluate_maturity("N", "D", "C", 0.5) - assert score == 0.0 - - @pytest.mark.asyncio - async def test_negative_scores_clamped_to_zero(self): - """Negative dimension totals are clamped to 0.0.""" - response = json.dumps({ - "completeness": -5, "executability": -5, "evidence": -5, "clarity": -5, - }) - extractor = _build_extractor() - extractor.maturity_prompt = "{name}{description}{content}{confidence}" - extractor.llm_provider.generate = AsyncMock(return_value=response) - score = await extractor._evaluate_maturity("N", "D", "C", 0.5) - assert score == 0.0 - - @pytest.mark.asyncio - async def test_overflow_scores_clamped_to_one(self): - """Scores that exceed 20 total are clamped to 1.0.""" - response = json.dumps({ - "completeness": 10, "executability": 10, "evidence": 10, "clarity": 10, - }) - extractor = _build_extractor() - extractor.maturity_prompt = "{name}{description}{content}{confidence}" - extractor.llm_provider.generate = AsyncMock(return_value=response) - score = await extractor._evaluate_maturity("N", "D", "C", 0.5) - assert score == 1.0 - - @pytest.mark.asyncio - async def test_non_numeric_dimension_raises_returns_none(self): - """Non-numeric dimension value causes ValueError, caught and returns None.""" - response = json.dumps({ - "completeness": "high", "executability": 4, "evidence": 4, "clarity": 4, - }) - extractor = _build_extractor() - extractor.maturity_prompt = "{name}{description}{content}{confidence}" - extractor.llm_provider.generate = AsyncMock(return_value=response) - score = await extractor._evaluate_maturity("N", "D", "C", 0.5) - assert score is None - - @pytest.mark.asyncio - async def test_none_name_description_content_in_prompt(self): - """None values for name/description/content are formatted as empty strings.""" - response = json.dumps({ - "completeness": 3, "executability": 3, "evidence": 3, "clarity": 3, - }) - extractor = _build_extractor() - extractor.maturity_prompt = "name={name}|desc={description}|content={content}|conf={confidence}" - - captured_prompt = None - - async def _capture(prompt, **kwargs): - nonlocal captured_prompt - captured_prompt = prompt - return response - - extractor.llm_provider.generate = _capture - score = await extractor._evaluate_maturity(None, None, None, 0.7) - assert score is not None - assert captured_prompt == "name=|desc=|content=|conf=0.7" - - -# =========================================================================== -# _content_change_ratio tests -# =========================================================================== - - -class TestContentChangeRatio: - """Tests for AgentSkillExtractor._content_change_ratio.""" - - def test_both_empty(self): - assert AgentSkillExtractor._content_change_ratio("", "") == 0.0 - - def test_both_none(self): - assert AgentSkillExtractor._content_change_ratio(None, None) == 0.0 - - def test_old_empty_new_filled(self): - assert AgentSkillExtractor._content_change_ratio("", "new content") == 1.0 - - def test_old_none_new_filled(self): - assert AgentSkillExtractor._content_change_ratio(None, "new content") == 1.0 - - def test_old_filled_new_empty(self): - assert AgentSkillExtractor._content_change_ratio("old content", "") == 1.0 - - def test_identical_strings(self): - text = "## Steps\n1. Do this\n2. Do that" - assert AgentSkillExtractor._content_change_ratio(text, text) == 0.0 - - def test_minor_change(self): - old = "## Steps\n1. Design schema\n2. Implement endpoints\n3. Add validation\n4. Write tests\n5. Deploy" - new = "## Steps\n1. Design schema\n2. Implement endpoints\n3. Add validation\n4. Write tests\n5. Deploy to prod" - ratio = AgentSkillExtractor._content_change_ratio(old, new) - assert 0.0 < ratio < 0.15 # small change - - def test_major_rewrite(self): - old = "## Steps\n1. Design schema\n2. Implement endpoints" - new = "## Totally New\n1. Different approach A\n2. Different approach B" - ratio = AgentSkillExtractor._content_change_ratio(old, new) - assert ratio >= 0.3 # significant change - - def test_result_range_0_to_1(self): - """Result is always in [0.0, 1.0].""" - ratio = AgentSkillExtractor._content_change_ratio("aaa", "zzz") - assert 0.0 <= ratio <= 1.0 - - -# =========================================================================== -# Maturity re-evaluation trigger logic edge cases in _apply_update -# =========================================================================== - - -class TestMaturityReevalEdgeCases: - """Edge cases for maturity re-evaluation within _apply_update.""" - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_trivial_change_skips_even_if_immature(self, mock_maturity, mock_embed): - """Change ratio < 10% skips maturity eval even when skill is immature.""" - mock_embed.return_value = None - mock_maturity.return_value = 0.9 - repo = _mock_skill_repo() - - original = "## Steps\n1. Design schema\n2. Implement endpoints\n3. Add validation\n4. Write tests\n5. Deploy" - # Tiny tweak: "Deploy" -> "Deploy now" - tweaked = "## Steps\n1. Design schema\n2. Implement endpoints\n3. Add validation\n4. Write tests\n5. Deploy now" - - existing = [_make_skill_record(content=original, confidence=0.7)] - existing[0].maturity_score = 0.3 # immature - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = {"action": "update", "index": 0, "data": {"content": tweaked, "confidence": 0.8}} - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is True - mock_maturity.assert_not_called() - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_llm_maturity_on_major_content_change_returns_none(self, mock_maturity, mock_embed): - """Major content change (ratio >= 0.5) triggers LLM; if LLM returns None, maturity_score is not updated.""" - mock_embed.return_value = None - mock_maturity.return_value = None # LLM fails - repo = _mock_skill_repo() - - existing = [_make_skill_record(confidence=0.7)] - existing[0].maturity_score = 0.4 - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", "index": 0, - "data": {"content": "## Revised\n1. Totally new A\n2. Totally new B\n3. New C\n4. New D\n5. New E"}, - } - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is True - mock_maturity.assert_called_once() # LLM called for major change - updates = repo.update_skill_by_id.call_args[0][1] - assert "maturity_score" not in updates # LLM returned None, not updated - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_name_change_only_skips_maturity_due_to_trivial_content_ratio(self, mock_maturity, mock_embed): - """Name-only change has content change_ratio=0.0, which is < trivial threshold — maturity eval skipped.""" - mock_embed.return_value = {"embedding": [0.5], "vector_model": "m"} - mock_maturity.return_value = 0.75 - repo = _mock_skill_repo() - - existing = [_make_skill_record(name="Old Name", confidence=0.7)] - existing[0].maturity_score = 0.4 # immature - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = {"action": "update", "index": 0, "data": {"name": "Completely New Name"}} - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is True - # content_change_ratio=0.0 < MATURITY_TRIVIAL_CHANGE_RATIO, so maturity eval skipped - mock_maturity.assert_not_called() - updates = repo.update_skill_by_id.call_args[0][1] - assert "maturity_score" not in updates - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_description_change_only_skips_maturity_due_to_trivial_content_ratio(self, mock_maturity, mock_embed): - """Description-only change has content change_ratio=0.0, which is < trivial threshold — maturity eval skipped.""" - mock_embed.return_value = {"embedding": [0.5], "vector_model": "m"} - mock_maturity.return_value = 0.8 - repo = _mock_skill_repo() - - existing = [_make_skill_record(description="Old desc", confidence=0.7)] - existing[0].maturity_score = 0.4 # immature - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = {"action": "update", "index": 0, "data": {"description": "Completely different description"}} - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is True - # content_change_ratio=0.0 < MATURITY_TRIVIAL_CHANGE_RATIO, so maturity eval skipped - mock_maturity.assert_not_called() - updates = repo.update_skill_by_id.call_args[0][1] - assert "maturity_score" not in updates - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_name_plus_major_content_change_uses_llm(self, mock_maturity, mock_embed): - """Name change combined with major content change (ratio >= 0.5) uses LLM maturity.""" - mock_embed.return_value = {"embedding": [0.5], "vector_model": "m"} - mock_maturity.return_value = 0.75 - repo = _mock_skill_repo() - - existing = [_make_skill_record(name="Old Name", confidence=0.7)] - existing[0].maturity_score = 0.4 # immature - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", "index": 0, - "data": { - "name": "Completely New Name", - "content": "## Revised\n1. Totally new A\n2. Totally new B\n3. New C\n4. New D\n5. New E", - }, - } - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is True - mock_maturity.assert_called_once() # LLM for major change - updates = repo.update_skill_by_id.call_args[0][1] - assert "maturity_score" in updates - assert updates["maturity_score"] == 0.75 - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_llm_maturity_score_written_on_major_change(self, mock_maturity, mock_embed): - """Major content change (ratio >= 0.5) triggers LLM and writes its score.""" - mock_embed.return_value = None - mock_maturity.return_value = 0.85 - repo = _mock_skill_repo() - - existing = [_make_skill_record(confidence=0.7)] - existing[0].maturity_score = 0.4 - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", "index": 0, - "data": {"content": "## Revised\n1. Totally new A\n2. Totally new B\n3. New C\n4. New D\n5. New E"}, - } - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is True - mock_maturity.assert_called_once() # LLM used for major change - updates = repo.update_skill_by_id.call_args[0][1] - assert "maturity_score" in updates - assert updates["maturity_score"] == 0.85 - - -# =========================================================================== -# _evaluate_maturity_heuristic unit tests -# =========================================================================== - - - - -# =========================================================================== -# _is_hypothesis_promotion unit tests -# =========================================================================== - - -class TestIsHypothesisPromotion: - """Direct unit tests for hypothesis promotion detection.""" - - def test_classic_promotion(self): - """Potential Steps -> Steps is a promotion.""" - old = "## Potential Steps\n1. Try X\n\n## Pitfalls\n- Y failed" - new = "## Steps\n1. Do X\n2. Do Y\n\n## Pitfalls\n- Y failed" - assert AgentSkillExtractor._is_hypothesis_promotion(old, new) is True - - def test_not_promotion_both_steps(self): - """Steps -> Steps is NOT a promotion.""" - old = "## Steps\n1. Do X" - new = "## Steps\n1. Do X\n2. Do Y" - assert AgentSkillExtractor._is_hypothesis_promotion(old, new) is False - - def test_not_promotion_both_potential(self): - """Potential Steps -> Potential Steps is NOT a promotion.""" - old = "## Potential Steps\n1. Try X" - new = "## Potential Steps\n1. Try X\n2. Try Y" - assert AgentSkillExtractor._is_hypothesis_promotion(old, new) is False - - def test_not_promotion_steps_to_potential(self): - """Steps -> Potential Steps is NOT a promotion (regression).""" - old = "## Steps\n1. Do X" - new = "## Potential Steps\n1. Try X" - assert AgentSkillExtractor._is_hypothesis_promotion(old, new) is False - - def test_not_promotion_new_still_has_potential(self): - """If new content has BOTH Steps and Potential Steps, not a clean promotion.""" - old = "## Potential Steps\n1. Try X" - new = "## Steps\n1. Do X\n\n## Potential Steps\n1. Try Y" - assert AgentSkillExtractor._is_hypothesis_promotion(old, new) is False - - def test_empty_old_content(self): - old = "" - new = "## Steps\n1. Do X" - assert AgentSkillExtractor._is_hypothesis_promotion(old, new) is False - - def test_empty_new_content(self): - old = "## Potential Steps\n1. Try X" - new = "" - assert AgentSkillExtractor._is_hypothesis_promotion(old, new) is False - - def test_both_empty(self): - assert AgentSkillExtractor._is_hypothesis_promotion("", "") is False - - def test_none_inputs(self): - assert AgentSkillExtractor._is_hypothesis_promotion(None, None) is False - assert AgentSkillExtractor._is_hypothesis_promotion(None, "## Steps\n1. X") is False - assert AgentSkillExtractor._is_hypothesis_promotion("## Potential Steps\n1. X", None) is False - - def test_case_sensitivity(self): - """Heading detection is case-sensitive — '## steps' is not '## Steps'.""" - old = "## Potential Steps\n1. Try X" - new = "## steps\n1. Do X" - assert AgentSkillExtractor._is_hypothesis_promotion(old, new) is False - - def test_heading_with_extra_spaces(self): - """Extra spaces after ## should still match.""" - old = "## Potential Steps\n1. Try X" - new = "## Steps\n1. Do X" - # regex is r"^##\s+Steps" and r"^##\s+Potential Steps" so \s+ matches multiple spaces - assert AgentSkillExtractor._is_hypothesis_promotion(old, new) is True - - def test_heading_not_at_line_start(self): - """Heading must be at line start (indented headings should not match).""" - old = " ## Potential Steps\n1. Try X" - new = "## Steps\n1. Do X" - # " ##" does not match "^##" - assert AgentSkillExtractor._is_hypothesis_promotion(old, new) is False - - -# =========================================================================== -# _apply_update integration: heuristic vs LLM maturity path tests -# =========================================================================== - - -class TestApplyUpdateMaturityPath: - """Integration tests verifying the correct maturity evaluation path is chosen.""" - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_promotion_with_llm_failure_no_maturity_update(self, mock_maturity, mock_embed): - """When hypothesis promotion triggers LLM but LLM returns None, maturity_score is NOT written.""" - mock_embed.return_value = None - mock_maturity.return_value = None # LLM failed - repo = _mock_skill_repo() - - existing = [_make_skill_record(confidence=0.5)] - existing[0].content = "## Potential Steps\n1. Try X\n - How: cmd\n - Check: ok\n\n## Pitfalls\n- Y" - existing[0].maturity_score = 0.3 - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", "index": 0, - "data": { - "content": "## Steps\n1. Do X\n - How: verified cmd\n - Check: ok\n2. Do Z\n - How: cmd Z\n - Check: ok\n\n## Pitfalls\n- Y", - "confidence": 0.6, - }, - } - success = await extractor._apply_update(op, existing, repo, result_obj, source_case_ids=["evt_p"]) - assert success is True - mock_maturity.assert_called_once() # LLM was attempted - updates = repo.update_skill_by_id.call_args[0][1] - assert "maturity_score" not in updates # LLM returned None, no update - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_major_change_always_calls_llm(self, mock_maturity, mock_embed): - """Large content change (>= 40%) always triggers LLM maturity, regardless of other factors.""" - mock_embed.return_value = None - mock_maturity.return_value = 0.9 - repo = _mock_skill_repo() - - existing = [_make_skill_record(confidence=0.6)] - existing[0].content = "## Steps\n1. Old step A\n2. Old step B\n3. Old C\n4. Old D\n5. Old E" - existing[0].maturity_score = 0.3 # very immature - result_obj = SkillExtractionResult() - extractor = _build_extractor(maturity_threshold=0.6) - - op = { - "action": "update", "index": 0, - "data": { - "content": "## Steps\n1. Completely new X\n2. Completely new Y\n3. New Z\n4. New W\n5. New V", - "confidence": 0.3, # confidence dropping - }, - } - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is True - mock_maturity.assert_called_once() # major change: always LLM - updates = repo.update_skill_by_id.call_args[0][1] - assert updates["maturity_score"] == 0.9 - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_llm_score_used_for_major_content_changes(self, mock_maturity, mock_embed): - """Major content changes (ratio >= 0.5) trigger LLM maturity evaluation.""" - mock_embed.return_value = None - repo = _mock_skill_repo() - extractor = _build_extractor() - - # First call: poor content -> LLM returns low score - mock_maturity.return_value = 0.4 - existing_poor = [_make_skill_record(confidence=0.5)] - existing_poor[0].content = "## Steps\n1. Old\n2. Old\n3. Old\n4. Old\n5. Old" - result_poor = SkillExtractionResult() - op_poor = { - "action": "update", "index": 0, - "data": {"content": "## Steps\n1. Do step X here\n2. Do step Y here\n3. Do step Z\n4. Do step W\n5. Do step V\n"}, - } - await extractor._apply_update(op_poor, existing_poor, repo, result_poor) - score_poor = repo.update_skill_by_id.call_args[0][1]["maturity_score"] - - # Second call: rich content -> LLM returns high score - mock_maturity.return_value = 0.9 - repo.reset_mock() - existing_rich = [_make_skill_record(confidence=0.8)] - existing_rich[0].content = "## Steps\n1. Old\n2. Old\n3. Old\n4. Old\n5. Old" - result_rich = SkillExtractionResult() - rich_content = ( - "## Steps\n" - "1. Diagnose\n" - " - How: Check logs with `grep ERROR`\n" - " - e.g., `grep -i error /var/log/app.log`\n" - " - Check: Error found\n" - "2. Fix config\n" - " - How: Edit `config.yaml`\n" - " - e.g., `vim config.yaml`\n" - " - Check: Validated\n" - "3. Restart\n" - " - How: `systemctl restart app`\n" - " - Check: Active\n" - "4. Verify\n" - " - How: `curl localhost/health`\n" - " - Check: 200 OK\n" - "\n## Pitfalls\n- Don't skip config validation\n" - ) - op_rich = { - "action": "update", "index": 0, - "data": {"content": rich_content}, - } - await extractor._apply_update(op_rich, existing_rich, repo, result_rich) - score_rich = repo.update_skill_by_id.call_args[0][1]["maturity_score"] - - assert mock_maturity.call_count == 2 # LLM called for both major changes - assert score_rich > score_poor - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_trivial_change_skips_both_heuristic_and_llm(self, mock_maturity, mock_embed): - """Content change < 10% skips all maturity evaluation (neither heuristic nor LLM).""" - mock_embed.return_value = None - repo = _mock_skill_repo() - - original_content = "## Steps\n1. Step A details here\n2. Step B details here\n3. Step C\n4. Step D\n5. Step E" - # Very minor tweak (< 10% change) - tweaked_content = "## Steps\n1. Step A details here\n2. Step B details here\n3. Step C\n4. Step D\n5. Step F" - - existing = [_make_skill_record(confidence=0.7)] - existing[0].content = original_content - existing[0].maturity_score = 0.5 - result_obj = SkillExtractionResult() - extractor = _build_extractor() - - op = { - "action": "update", "index": 0, - "data": {"content": tweaked_content}, - } - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is True - mock_maturity.assert_not_called() - updates = repo.update_skill_by_id.call_args[0][1] - # maturity_score should NOT be in updates for trivial changes - assert "maturity_score" not in updates - - -# =========================================================================== -# key_insight field tests -# =========================================================================== - - -class TestKeyInsightInFormatCases: - """Tests for key_insight inclusion in _format_cases.""" - - def test_key_insight_included_when_present(self): - extractor = _build_extractor() - cases = [_make_case_record(key_insight="Switched from REST to GraphQL")] - result = extractor._format_cases(cases) - parsed = json.loads(result) - assert parsed[0]["key_insight"] == "Switched from REST to GraphQL" - - def test_key_insight_excluded_when_none(self): - extractor = _build_extractor() - cases = [_make_case_record(key_insight=None)] - result = extractor._format_cases(cases) - parsed = json.loads(result) - assert "key_insight" not in parsed[0] - - def test_key_insight_excluded_when_empty(self): - extractor = _build_extractor() - cases = [_make_case_record(key_insight="")] - result = extractor._format_cases(cases) - parsed = json.loads(result) - assert "key_insight" not in parsed[0] - - def test_mixed_cases_with_and_without_key_insight(self): - extractor = _build_extractor() - cases = [ - _make_case_record(task_intent="A", key_insight="Pivoted approach"), - _make_case_record(task_intent="B", key_insight=None), - ] - result = extractor._format_cases(cases) - parsed = json.loads(result) - assert "key_insight" in parsed[0] - assert "key_insight" not in parsed[1] - - -# =========================================================================== -# _summarize_case_for_prompt tests -# =========================================================================== - - -class TestSummarizeCaseForPrompt: - """Tests for AgentSkillExtractor._summarize_case_for_prompt.""" - - def test_basic_summary(self): - extractor = _build_extractor() - case = _make_case_record(record_id="case_001") - result = extractor._summarize_case_for_prompt(case) - assert result["task_intent"] == "Build a REST API" - assert result["quality_score"] == 0.8 - assert "source_case_id" not in result - - def test_key_insight_included(self): - extractor = _build_extractor() - case = _make_case_record(key_insight="Retry with exponential backoff") - result = extractor._summarize_case_for_prompt(case) - assert result["key_insight"] == "Retry with exponential backoff" - - def test_key_insight_excluded_when_none(self): - extractor = _build_extractor() - case = _make_case_record(key_insight=None) - result = extractor._summarize_case_for_prompt(case) - assert "key_insight" not in result - - @patch.object(AgentSkillExtractor, "_get_tokenizer") - def test_approach_truncated(self, mock_tok): - from tiktoken import get_encoding - mock_tok.return_value = get_encoding("o200k_base") - extractor = _build_extractor() - long_approach = "x " * 500 - case = _make_case_record(approach=long_approach) - result = extractor._summarize_case_for_prompt(case, max_approach_tokens=10) - assert result["approach"].endswith("... [omitted]") - assert len(result["approach"]) < len(long_approach) - - def test_no_approach_omitted(self): - extractor = _build_extractor() - case = _make_case_record(approach=None) - result = extractor._summarize_case_for_prompt(case) - assert "approach" not in result - - -# =========================================================================== -# _truncate_text tests -# =========================================================================== - - -@patch.object(AgentSkillExtractor, "_get_tokenizer") -class TestTruncateText: - """Tests for AgentSkillExtractor._truncate_text (token-based).""" - - @staticmethod - def _real_tokenizer(): - from tiktoken import get_encoding - return get_encoding("o200k_base") - - def test_short_text_unchanged(self, mock_tok): - mock_tok.return_value = self._real_tokenizer() - assert AgentSkillExtractor._truncate_text("hello", max_tokens=10) == "hello" - - def test_exact_tokens_unchanged(self, mock_tok): - mock_tok.return_value = self._real_tokenizer() - # "hello" is 1 token in o200k_base - assert AgentSkillExtractor._truncate_text("hello", max_tokens=1) == "hello" - - def test_long_text_truncated_with_default_suffix(self, mock_tok): - mock_tok.return_value = self._real_tokenizer() - long_text = "word " * 200 - result = AgentSkillExtractor._truncate_text(long_text, max_tokens=10) - assert result.endswith("... [omitted]") - assert len(result) < len(long_text) - - def test_none_returns_none(self, mock_tok): - mock_tok.return_value = self._real_tokenizer() - assert AgentSkillExtractor._truncate_text(None, max_tokens=10) is None - - def test_empty_returns_empty(self, mock_tok): - mock_tok.return_value = self._real_tokenizer() - assert AgentSkillExtractor._truncate_text("", max_tokens=10) == "" - - def test_whitespace_stripped(self, mock_tok): - mock_tok.return_value = self._real_tokenizer() - assert AgentSkillExtractor._truncate_text(" hello ", max_tokens=100) == "hello" - - def test_custom_suffix(self, mock_tok): - mock_tok.return_value = self._real_tokenizer() - long_text = "word " * 200 - result = AgentSkillExtractor._truncate_text(long_text, max_tokens=10, suffix="...") - assert result.endswith("...") - assert not result.endswith("... [omitted]") - assert len(result) < len(long_text) - - def test_default_suffix(self, mock_tok): - mock_tok.return_value = self._real_tokenizer() - long_text = "word " * 200 - result = AgentSkillExtractor._truncate_text(long_text, max_tokens=10) - assert result.endswith("... [omitted]") - - -# =========================================================================== -# Description truncation in _apply_add / _apply_update -# =========================================================================== - - -class TestDescriptionTruncation: - """Tests for description truncation via MAX_DESCRIPTION_TOKENS.""" - - @pytest.mark.asyncio - @patch.object(AgentSkillExtractor, "_get_tokenizer") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_add_truncates_long_description(self, mock_maturity, mock_embed, mock_tok): - from tiktoken import get_encoding - mock_tok.return_value = get_encoding("o200k_base") - mock_embed.return_value = {"embedding": [0.1], "vector_model": "test"} - mock_maturity.return_value = 0.8 - repo = _mock_skill_repo() - extractor = _build_extractor() - - long_desc = "word " * 2000 - op = { - "action": "add", - "data": { - "name": "Skill", - "description": long_desc, - "content": "## Steps\n1. Analyze the requirements carefully\n2. Build the implementation with tests\n3. Check all edge cases thoroughly\n4. Deploy to staging environment\n5. Verify in production setup", - "confidence": 0.7, - }, - } - import infra_layer.adapters.out.persistence.document.memory.agent_skill as skill_mod - with patch.object( - skill_mod, "AgentSkillRecord", - side_effect=lambda **kwargs: SimpleNamespace(**kwargs, id="new_001"), - ) as mock_cls: - await extractor._apply_add(op, "c", "g", "u", repo, source_case_ids=["e1"]) - saved_desc = mock_cls.call_args[1]["description"] - assert len(saved_desc) < len(long_desc) - assert saved_desc.endswith("...") - - @pytest.mark.asyncio - @patch.object(AgentSkillExtractor, "_get_tokenizer") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_add_short_description_not_truncated(self, mock_maturity, mock_embed, mock_tok): - from tiktoken import get_encoding - mock_tok.return_value = get_encoding("o200k_base") - mock_embed.return_value = {"embedding": [0.1], "vector_model": "test"} - mock_maturity.return_value = 0.8 - repo = _mock_skill_repo() - extractor = _build_extractor() - - op = { - "action": "add", - "data": { - "name": "Skill", - "description": "Short desc", - "content": "## Steps\n1. Analyze the requirements carefully\n2. Build the implementation with tests\n3. Check all edge cases thoroughly\n4. Deploy to staging environment\n5. Verify in production setup", - "confidence": 0.7, - }, - } - import infra_layer.adapters.out.persistence.document.memory.agent_skill as skill_mod - with patch.object( - skill_mod, "AgentSkillRecord", - side_effect=lambda **kwargs: SimpleNamespace(**kwargs, id="new_001"), - ) as mock_cls: - await extractor._apply_add(op, "c", "g", "u", repo, source_case_ids=["e1"]) - saved_desc = mock_cls.call_args[1]["description"] - assert saved_desc == "Short desc" - assert not saved_desc.endswith("...") - - @pytest.mark.asyncio - @patch.object(AgentSkillExtractor, "_get_tokenizer") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_update_truncates_long_description(self, mock_maturity, mock_embed, mock_tok): - from tiktoken import get_encoding - mock_tok.return_value = get_encoding("o200k_base") - mock_embed.return_value = {"embedding": [0.1], "vector_model": "test"} - mock_maturity.return_value = 0.8 - repo = _mock_skill_repo() - extractor = _build_extractor() - - long_desc = "word " * 2000 - record = _make_skill_record() - op = { - "action": "update", - "index": 0, - "data": {"description": long_desc}, - } - result = SkillExtractionResult() - await extractor._apply_update( - op, [record], repo, result, source_case_ids=["e1"] - ) - update_dict = repo.update_skill_by_id.call_args[0][1] - saved_desc = update_dict["description"] - assert len(saved_desc) < len(long_desc) - assert saved_desc.endswith("...") - - @pytest.mark.asyncio - @patch.object(AgentSkillExtractor, "_get_tokenizer") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_update_short_description_not_truncated(self, mock_maturity, mock_embed, mock_tok): - from tiktoken import get_encoding - mock_tok.return_value = get_encoding("o200k_base") - mock_embed.return_value = {"embedding": [0.1], "vector_model": "test"} - mock_maturity.return_value = 0.8 - repo = _mock_skill_repo() - extractor = _build_extractor() - - record = _make_skill_record() - op = { - "action": "update", - "index": 0, - "data": {"description": "New short desc"}, - } - result = SkillExtractionResult() - await extractor._apply_update( - op, [record], repo, result, source_case_ids=["e1"] - ) - update_dict = repo.update_skill_by_id.call_args[0][1] - assert update_dict["description"] == "New short desc" - - @pytest.mark.asyncio - async def test_update_empty_description_not_added_to_updates(self): - repo = _mock_skill_repo() - extractor = _build_extractor() - - record = _make_skill_record() - op = { - "action": "update", - "index": 0, - "data": {"description": "", "confidence": 0.9}, - } - result = SkillExtractionResult() - await extractor._apply_update(op, [record], repo, result) - update_dict = repo.update_skill_by_id.call_args[0][1] - assert "description" not in update_dict - - -# =========================================================================== -# _format_existing_skills with case_history tests -# =========================================================================== - - -class TestFormatExistingSkillsWithCaseHistory: - """Tests for _format_existing_skills when case_history is provided.""" - - def test_supporting_cases_attached(self): - extractor = _build_extractor() - case = _make_case_record( - task_intent="Deploy service", key_insight="Blue-green deploy", record_id="c1" - ) - skill = _make_skill_record(source_case_ids=["c1"]) - result = extractor._format_existing_skills([skill], case_history=[case]) - parsed = json.loads(result) - assert parsed[0]["supporting_case_count"] == 1 - assert len(parsed[0]["supporting_cases"]) == 1 - assert parsed[0]["supporting_cases"][0]["task_intent"] == "Deploy service" - assert parsed[0]["supporting_cases"][0]["key_insight"] == "Blue-green deploy" - - def test_no_case_history_no_supporting_cases(self): - extractor = _build_extractor() - skill = _make_skill_record(source_case_ids=["c1"]) - result = extractor._format_existing_skills([skill], case_history=None) - parsed = json.loads(result) - assert "supporting_cases" not in parsed[0] - - def test_unmatched_case_ids_no_supporting_cases(self): - extractor = _build_extractor() - case = _make_case_record(record_id="c99") - skill = _make_skill_record(source_case_ids=["c1"]) - result = extractor._format_existing_skills([skill], case_history=[case]) - parsed = json.loads(result) - assert "supporting_cases" not in parsed[0] - - def test_max_support_cases_respected(self): - extractor = _build_extractor() - cases = [ - _make_case_record(task_intent=f"Task {i}", record_id=f"c{i}") - for i in range(5) - ] - skill = _make_skill_record(source_case_ids=[f"c{i}" for i in range(5)]) - result = extractor._format_existing_skills( - [skill], case_history=cases, max_support_cases=2 - ) - parsed = json.loads(result) - assert parsed[0]["supporting_case_count"] == 5 - assert len(parsed[0]["supporting_cases"]) == 2 - - def test_takes_most_recent_cases(self): - """max_support_cases takes the last N (most recent) matched IDs.""" - extractor = _build_extractor() - cases = [ - _make_case_record(task_intent=f"Task {i}", record_id=f"c{i}") - for i in range(4) - ] - skill = _make_skill_record(source_case_ids=["c0", "c1", "c2", "c3"]) - result = extractor._format_existing_skills( - [skill], case_history=cases, max_support_cases=2 - ) - parsed = json.loads(result) - intents = [sc["task_intent"] for sc in parsed[0]["supporting_cases"]] - assert intents == ["Task 2", "Task 3"] - - def test_empty_source_case_ids_no_supporting(self): - extractor = _build_extractor() - case = _make_case_record(record_id="c1") - skill = _make_skill_record(source_case_ids=[]) - result = extractor._format_existing_skills([skill], case_history=[case]) - parsed = json.loads(result) - assert "supporting_cases" not in parsed[0] - - -# =========================================================================== -# _load_case_history tests -# =========================================================================== - - -class TestLoadCaseHistory: - """Tests for AgentSkillExtractor._load_case_history.""" - - @pytest.mark.asyncio - async def test_empty_skills_returns_empty(self): - extractor = _build_extractor() - result = await extractor._load_case_history([]) - assert result == [] - - @pytest.mark.asyncio - async def test_no_source_case_ids_returns_empty(self): - extractor = _build_extractor() - skill = _make_skill_record(source_case_ids=[]) - result = await extractor._load_case_history([skill]) - assert result == [] - - @pytest.mark.asyncio - @patch("core.di.utils.get_bean_by_type") - async def test_loads_and_sorts_by_quality_desc(self, mock_get_bean): - extractor = _build_extractor() - mock_repo = AsyncMock() - mock_get_bean.return_value = mock_repo - - records = [ - _make_case_record(task_intent="Low", quality_score=0.3, record_id="c1"), - _make_case_record(task_intent="High", quality_score=0.9, record_id="c2"), - _make_case_record(task_intent="Mid", quality_score=0.6, record_id="c3"), - ] - mock_repo.get_by_ids = AsyncMock(return_value=records) - - skill = _make_skill_record(source_case_ids=["c1", "c2", "c3"]) - result = await extractor._load_case_history([skill]) - - assert len(result) == 3 - assert result[0].task_intent == "High" - assert result[1].task_intent == "Mid" - assert result[2].task_intent == "Low" - - @pytest.mark.asyncio - @patch("core.di.utils.get_bean_by_type") - async def test_max_cases_limit(self, mock_get_bean): - extractor = _build_extractor() - mock_repo = AsyncMock() - mock_get_bean.return_value = mock_repo - - records = [ - _make_case_record(task_intent=f"T{i}", quality_score=0.5, record_id=f"c{i}") - for i in range(10) - ] - mock_repo.get_by_ids = AsyncMock(return_value=records) - - skill = _make_skill_record(source_case_ids=[f"c{i}" for i in range(10)]) - result = await extractor._load_case_history([skill], max_cases=3) - assert len(result) == 3 - - @pytest.mark.asyncio - @patch("core.di.utils.get_bean_by_type") - async def test_deduplicates_case_ids_across_skills(self, mock_get_bean): - extractor = _build_extractor() - mock_repo = AsyncMock() - mock_get_bean.return_value = mock_repo - mock_repo.get_by_ids = AsyncMock(return_value=[]) - - skill1 = _make_skill_record(source_case_ids=["c1", "c2"]) - skill2 = _make_skill_record(source_case_ids=["c2", "c3"]) - await extractor._load_case_history([skill1, skill2]) - - called_ids = set(mock_repo.get_by_ids.call_args[0][0]) - assert called_ids == {"c1", "c2", "c3"} - - @pytest.mark.asyncio - @patch("core.di.utils.get_bean_by_type") - async def test_filters_empty_case_ids(self, mock_get_bean): - extractor = _build_extractor() - mock_repo = AsyncMock() - mock_get_bean.return_value = mock_repo - mock_repo.get_by_ids = AsyncMock(return_value=[]) - - skill = _make_skill_record(source_case_ids=["c1", "", None, " ", "c2"]) - await extractor._load_case_history([skill]) - - called_ids = set(mock_repo.get_by_ids.call_args[0][0]) - assert "" not in called_ids - assert "None" not in called_ids - assert "c1" in called_ids - assert "c2" in called_ids - - @pytest.mark.asyncio - @patch("core.di.utils.get_bean_by_type") - async def test_db_failure_returns_empty(self, mock_get_bean): - extractor = _build_extractor() - mock_get_bean.side_effect = Exception("DB down") - - skill = _make_skill_record(source_case_ids=["c1"]) - result = await extractor._load_case_history([skill]) - assert result == [] - - -# =========================================================================== -# _json_default tests -# =========================================================================== - - -class TestSkillJsonDefault: - """Tests for AgentSkillExtractor._json_default.""" - - def test_datetime_to_isoformat(self): - dt = datetime(2025, 6, 15, 14, 30, 0) - assert AgentSkillExtractor._json_default(dt) == "2025-06-15T14:30:00" - - def test_non_serializable_to_str(self): - result = AgentSkillExtractor._json_default({1, 2}) - assert isinstance(result, str) - - def test_none_to_str(self): - assert AgentSkillExtractor._json_default(None) == "None" - - -# =========================================================================== -# _compute_embedding tests -# =========================================================================== - - -class TestSkillComputeEmbedding: - """Tests for AgentSkillExtractor._compute_embedding.""" - - @pytest.mark.asyncio - async def test_empty_text_returns_none(self): - extractor = _build_extractor() - result = await extractor._compute_embedding("") - assert result is None - - @pytest.mark.asyncio - async def test_none_text_returns_none(self): - extractor = _build_extractor() - result = await extractor._compute_embedding(None) - assert result is None - - @pytest.mark.asyncio - @patch("agentic_layer.vectorize_service.get_vectorize_service") - async def test_successful_embedding(self, mock_vs): - import numpy as np - mock_service = MagicMock() - mock_service.get_embedding = AsyncMock(return_value=np.array([0.1, 0.2, 0.3])) - mock_service.get_model_name.return_value = "test-model" - mock_vs.return_value = mock_service - - extractor = _build_extractor() - result = await extractor._compute_embedding("Build REST API") - assert result is not None - assert result["embedding"] == [0.1, 0.2, 0.3] - assert result["vector_model"] == "test-model" - - @pytest.mark.asyncio - @patch("agentic_layer.vectorize_service.get_vectorize_service") - async def test_embedding_exception_returns_none(self, mock_vs): - mock_vs.side_effect = Exception("service unavailable") - extractor = _build_extractor() - result = await extractor._compute_embedding("some text") - assert result is None - - -# =========================================================================== -# _rescore_maturity tests -# =========================================================================== - - -class TestRescoreMaturity: - """Tests for AgentSkillExtractor._rescore_maturity.""" - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_updates_dict_with_score(self, mock_eval): - mock_eval.return_value = 0.85 - extractor = _build_extractor() - record = _make_skill_record() - updates = {"confidence": 0.8} - await extractor._rescore_maturity(updates, "New Name", "New Desc", "New Content", record) - assert updates["maturity_score"] == 0.85 - mock_eval.assert_called_once_with( - name="New Name", description="New Desc", - content="New Content", confidence=0.8, - ) - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_llm_returns_none_no_update(self, mock_eval): - mock_eval.return_value = None - extractor = _build_extractor() - record = _make_skill_record() - updates = {} - await extractor._rescore_maturity(updates, "N", "D", "C", record) - assert "maturity_score" not in updates - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_falls_back_to_record_fields(self, mock_eval): - mock_eval.return_value = 0.5 - extractor = _build_extractor() - record = _make_skill_record(name="Old", description="Old desc", content="Old content") - record.confidence = 0.6 - updates = {} - await extractor._rescore_maturity(updates, "", "", "", record) - mock_eval.assert_called_once_with( - name="Old", description="Old desc", - content="Old content", confidence=0.6, - ) - - -# =========================================================================== -# _format_cases edge cases -# =========================================================================== - - -class TestFormatCasesEdgeCases: - """Additional edge case tests for _format_cases.""" - - def test_none_quality_score_defaults(self): - extractor = _build_extractor() - case = _make_case_record(quality_score=None) - result = extractor._format_cases([case]) - parsed = json.loads(result) - assert parsed[0]["quality_score"] == 0.5 - - def test_zero_quality_score_defaults(self): - extractor = _build_extractor() - case = _make_case_record(quality_score=0) - result = extractor._format_cases([case]) - parsed = json.loads(result) - # 0 is falsy, so `or 0.5` applies - assert parsed[0]["quality_score"] == 0.5 - - def test_timestamp_format(self): - extractor = _build_extractor() - case = _make_case_record(timestamp=datetime(2025, 6, 15, 14, 30, 0)) - result = extractor._format_cases([case]) - parsed = json.loads(result) - assert parsed[0]["timestamp"] == "2025-06-15T14:30:00" - - def test_empty_task_intent(self): - extractor = _build_extractor() - case = _make_case_record(task_intent="") - result = extractor._format_cases([case]) - parsed = json.loads(result) - assert parsed[0]["task_intent"] == "" - - -# =========================================================================== -# _format_existing_skills edge cases -# =========================================================================== - - -class TestFormatExistingSkillsEdgeCases: - """Additional edge case tests for _format_existing_skills.""" - - def test_skill_fields_included(self): - extractor = _build_extractor() - skill = _make_skill_record( - name="Deploy", description="Deploy services", content="## Steps\n1. Build", - confidence=0.9, - ) - result = extractor._format_existing_skills([skill]) - parsed = json.loads(result) - assert parsed[0]["name"] == "Deploy" - assert parsed[0]["description"] == "Deploy services" - assert parsed[0]["content"] == "## Steps\n1. Build" - assert parsed[0]["confidence"] == 0.9 - - def test_multiple_skills_correct_indices(self): - extractor = _build_extractor() - skills = [ - _make_skill_record(name="A"), - _make_skill_record(name="B"), - ] - result = extractor._format_existing_skills(skills) - parsed = json.loads(result) - assert parsed[0]["index"] == 0 - assert parsed[0]["name"] == "A" - assert parsed[1]["index"] == 1 - assert parsed[1]["name"] == "B" - - -# =========================================================================== -# _call_llm edge cases -# =========================================================================== - - -class TestCallLLMEdgeCases: - """Additional edge case tests for _call_llm.""" - - @pytest.mark.asyncio - async def test_operations_not_list_retries(self): - extractor = _build_extractor() - extractor.llm_provider.generate = AsyncMock( - return_value=json.dumps({"operations": "not a list"}) - ) - result = await extractor._call_llm("c", "s", "{new_case_json}{existing_skills_json}") - assert result is None - assert extractor.llm_provider.generate.call_count == 3 - - @pytest.mark.asyncio - async def test_empty_operations_list_accepted(self): - resp = json.dumps({"operations": []}) - extractor = _build_extractor(llm_response=resp) - result = await extractor._call_llm("c", "s", "{new_case_json}{existing_skills_json}") - assert result is not None - assert result["operations"] == [] - - @pytest.mark.asyncio - async def test_extra_fields_preserved(self): - resp = json.dumps({"operations": [{"action": "none"}], "update_note": "No changes needed"}) - extractor = _build_extractor(llm_response=resp) - result = await extractor._call_llm("c", "s", "{new_case_json}{existing_skills_json}") - assert result["update_note"] == "No changes needed" - - -# =========================================================================== -# _evaluate_maturity edge cases -# =========================================================================== - - -class TestEvaluateMaturityAdditional: - """Additional tests for _evaluate_maturity.""" - - @pytest.mark.asyncio - async def test_score_clamped_to_1(self): - resp = json.dumps({ - "completeness": 5, "executability": 5, "evidence": 5, "clarity": 5 - }) - extractor = _build_extractor(llm_response=resp) - score = await extractor._evaluate_maturity("n", "d", "c", 0.8) - assert score == 1.0 - - @pytest.mark.asyncio - async def test_score_clamped_to_0(self): - resp = json.dumps({ - "completeness": 0, "executability": 0, "evidence": 0, "clarity": 0 - }) - extractor = _build_extractor(llm_response=resp) - score = await extractor._evaluate_maturity("n", "d", "c", 0.8) - assert score == 0.0 - - @pytest.mark.asyncio - async def test_missing_dimension_returns_none(self): - resp = json.dumps({ - "completeness": 4, "executability": 4, "evidence": 4 - # missing "clarity" - }) - extractor = _build_extractor(llm_response=resp) - score = await extractor._evaluate_maturity("n", "d", "c", 0.8) - assert score is None - - -# =========================================================================== -# _apply_add edge cases -# =========================================================================== - - -class TestApplyAddEdgeCases: - """Additional edge case tests for _apply_add.""" - - @pytest.mark.asyncio - async def test_missing_data_key(self): - extractor = _build_extractor() - repo = _mock_skill_repo() - op = {"action": "add"} # no "data" key - result = await extractor._apply_add(op, "c", "g", "u", repo) - assert result is None - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_embedding_failure_still_saves(self, mock_maturity, mock_embed): - mock_embed.return_value = None - mock_maturity.return_value = 0.6 - repo = _mock_skill_repo() - extractor = _build_extractor() - op = { - "action": "add", - "data": { - "name": "Skill", - "description": "Desc", - "content": "## Steps\n1. Analyze the requirements\n2. Build the implementation\n3. Check edge cases\n4. Deploy to staging\n5. Verify production", - "confidence": 0.7, - }, - } - import infra_layer.adapters.out.persistence.document.memory.agent_skill as skill_mod - with patch.object(skill_mod, "AgentSkillRecord", return_value=MagicMock(id="s1")): - result = await extractor._apply_add(op, "c", "g", "u", repo) - assert result is not None - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_maturity_none_defaults_to_0_6(self, mock_maturity, mock_embed): - mock_embed.return_value = None - mock_maturity.return_value = None # LLM failed - repo = _mock_skill_repo() - extractor = _build_extractor() - op = { - "action": "add", - "data": { - "name": "S", "description": "D", - "content": "## Steps\n1. Analyze the requirements\n2. Build the implementation\n3. Check edge cases\n4. Deploy to staging\n5. Verify production", - }, - } - import infra_layer.adapters.out.persistence.document.memory.agent_skill as skill_mod - with patch.object( - skill_mod, "AgentSkillRecord", return_value=MagicMock(id="s2") - ) as mock_cls: - await extractor._apply_add(op, "c", "g", "u", repo) - assert mock_cls.call_args.kwargs["maturity_score"] == 0.6 - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_confidence_clamped(self, mock_maturity, mock_embed): - mock_embed.return_value = None - mock_maturity.return_value = 0.7 - repo = _mock_skill_repo() - extractor = _build_extractor() - op = { - "action": "add", - "data": { - "name": "S", "description": "D", - "content": "## Steps\n1. Analyze the requirements\n2. Build the implementation\n3. Check edge cases\n4. Deploy to staging\n5. Verify production", - "confidence": 2.5, # out of range - }, - } - import infra_layer.adapters.out.persistence.document.memory.agent_skill as skill_mod - with patch.object( - skill_mod, "AgentSkillRecord", return_value=MagicMock(id="s3") - ) as mock_cls: - await extractor._apply_add(op, "c", "g", "u", repo) - assert mock_cls.call_args.kwargs["confidence"] == 1.0 - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_source_case_ids_passed(self, mock_maturity, mock_embed): - mock_embed.return_value = None - mock_maturity.return_value = 0.7 - repo = _mock_skill_repo() - extractor = _build_extractor() - op = { - "action": "add", - "data": { - "name": "S", "description": "D", - "content": "## Steps\n1. Analyze the requirements\n2. Build the implementation\n3. Check edge cases\n4. Deploy to staging\n5. Verify production", - }, - } - import infra_layer.adapters.out.persistence.document.memory.agent_skill as skill_mod - with patch.object( - skill_mod, "AgentSkillRecord", return_value=MagicMock(id="s4") - ) as mock_cls: - await extractor._apply_add( - op, "c", "g", "u", repo, source_case_ids=["c1", "c2"] - ) - assert mock_cls.call_args.kwargs["source_case_ids"] == ["c1", "c2"] - - -# =========================================================================== -# _apply_update edge cases -# =========================================================================== - - -class TestApplyUpdateEdgeCases: - """Additional edge case tests for _apply_update.""" - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_update_description_triggers_re_embed(self, mock_maturity, mock_embed): - mock_embed.return_value = {"embedding": [0.7, 0.8], "vector_model": "m"} - mock_maturity.return_value = 0.8 - repo = _mock_skill_repo() - existing = [_make_skill_record(description="Old Desc")] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"description": "New Desc"}} - await extractor._apply_update(op, existing, repo, result_obj) - mock_embed.assert_called_once() - updates = repo.update_skill_by_id.call_args[0][1] - assert "vector" in updates - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_same_name_no_re_embed(self, mock_maturity, mock_embed): - """If name is same as existing, no re-embedding.""" - mock_embed.return_value = None - mock_maturity.return_value = None - repo = _mock_skill_repo() - existing = [_make_skill_record(name="Same Name")] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"name": "Same Name", "confidence": 0.9}} - await extractor._apply_update(op, existing, repo, result_obj) - mock_embed.assert_not_called() - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_confidence_only_update(self, mock_maturity, mock_embed): - mock_embed.return_value = None - mock_maturity.return_value = None - repo = _mock_skill_repo() - existing = [_make_skill_record()] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"confidence": 0.95}} - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is True - updates = repo.update_skill_by_id.call_args[0][1] - assert updates["confidence"] == 0.95 - # No content change => no maturity re-eval - mock_maturity.assert_not_called() - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_invalid_confidence_ignored(self, mock_maturity, mock_embed): - mock_embed.return_value = None - mock_maturity.return_value = None - repo = _mock_skill_repo() - existing = [_make_skill_record()] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"confidence": "bad", "name": "New"}} - success = await extractor._apply_update(op, existing, repo, result_obj) - assert success is True - updates = repo.update_skill_by_id.call_args[0][1] - assert "confidence" not in updates - assert updates["name"] == "New" - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_multiple_source_case_ids_appended(self, mock_maturity, mock_embed): - mock_embed.return_value = None - mock_maturity.return_value = None - repo = _mock_skill_repo() - existing = [_make_skill_record(source_case_ids=["c1"])] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"confidence": 0.9}} - await extractor._apply_update( - op, existing, repo, result_obj, source_case_ids=["c2", "c3"] - ) - updates = repo.update_skill_by_id.call_args[0][1] - assert set(updates["source_case_ids"]) == {"c1", "c2", "c3"} - - @pytest.mark.asyncio - async def test_retire_preserves_source_case_ids(self): - repo = _mock_skill_repo() - existing = [_make_skill_record(source_case_ids=["c1"])] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"confidence": 0.05}} - await extractor._apply_update( - op, existing, repo, result_obj, source_case_ids=["c2"] - ) - updates = repo.update_skill_by_id.call_args[0][1] - assert "source_case_ids" in updates - assert set(updates["source_case_ids"]) == {"c1", "c2"} - assert str(existing[0].id) in result_obj.deleted_ids - - @pytest.mark.asyncio - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._compute_embedding") - @patch("memory_layer.memory_extractor.agent_skill_extractor.AgentSkillExtractor._evaluate_maturity") - async def test_update_sets_attributes_on_record(self, mock_maturity, mock_embed): - """After successful update, record attributes are patched in-place.""" - mock_embed.return_value = None - mock_maturity.return_value = None - repo = _mock_skill_repo() - existing = [_make_skill_record(name="Old")] - result_obj = SkillExtractionResult() - extractor = _build_extractor() - op = {"action": "update", "index": 0, "data": {"name": "New"}} - await extractor._apply_update(op, existing, repo, result_obj) - # Record should be mutated in place - assert existing[0].name == "New" - assert existing[0] in result_obj.updated_records - - -# =========================================================================== -# extract_and_save edge cases -# =========================================================================== - - -class TestExtractAndSaveEdgeCases: - """Additional edge case tests for extract_and_save.""" - - @pytest.mark.asyncio - async def test_source_case_ids_collected_from_records(self): - """extract_and_save collects IDs from new_case_records for traceability.""" - response = json.dumps({ - "operations": [{"action": "add", "data": {"name": "S", "description": "D", "content": "C"}}] - }) - extractor = _build_extractor(llm_response=response) - repo = _mock_skill_repo() - - cases = [ - _make_case_record(record_id="case_A"), - _make_case_record(record_id="case_B"), - ] - - with patch.object(extractor, "_apply_add", new_callable=AsyncMock) as mock_add: - mock_add.return_value = MagicMock(id="new_skill") - await extractor.extract_and_save( - cluster_id="c1", group_id="g1", - new_case_records=cases, - existing_skill_records=[], - skill_repo=repo, - ) - call_kwargs = mock_add.call_args.kwargs - assert set(call_kwargs["source_case_ids"]) == {"case_A", "case_B"} - - @pytest.mark.asyncio - async def test_none_record_ids_filtered(self): - """Records with None id should not produce source_case_ids entries.""" - response = json.dumps({ - "operations": [{"action": "add", "data": {"name": "S", "description": "D", "content": "C"}}] - }) - extractor = _build_extractor(llm_response=response) - repo = _mock_skill_repo() - - cases = [ - _make_case_record(record_id=None), - _make_case_record(record_id="case_X"), - ] - - with patch.object(extractor, "_apply_add", new_callable=AsyncMock) as mock_add: - mock_add.return_value = MagicMock(id="new_skill") - await extractor.extract_and_save( - cluster_id="c1", group_id="g1", - new_case_records=cases, - existing_skill_records=[], - skill_repo=repo, - ) - call_kwargs = mock_add.call_args.kwargs - assert call_kwargs["source_case_ids"] == ["case_X"] - - @pytest.mark.asyncio - async def test_mixed_operations(self): - """Test add + update + none in a single response.""" - response = json.dumps({ - "operations": [ - {"action": "add", "data": {"name": "New", "description": "D", "content": "C"}}, - {"action": "update", "index": 0, "data": {"confidence": 0.9}}, - {"action": "none"}, - ] - }) - extractor = _build_extractor(llm_response=response) - repo = _mock_skill_repo() - - with patch.object(extractor, "_apply_add", new_callable=AsyncMock) as mock_add, \ - patch.object(extractor, "_apply_update", new_callable=AsyncMock) as mock_update: - mock_add.return_value = MagicMock(id="new_id") - mock_update.return_value = True - result = await extractor.extract_and_save( - cluster_id="c1", group_id="g1", - new_case_records=[_make_case_record()], - existing_skill_records=[_make_skill_record()], - skill_repo=repo, - ) - mock_add.assert_called_once() - mock_update.assert_called_once() - assert len(result.added_records) == 1 - - @pytest.mark.asyncio - async def test_case_history_loaded_after_top_k(self): - """Case history should be loaded AFTER top-k selection.""" - response = json.dumps({"operations": [{"action": "none"}]}) - extractor = _build_extractor(llm_response=response) - repo = _mock_skill_repo() - many_skills = [_make_skill_record(name=f"S{i}", record_id=f"s{i}") for i in range(15)] - selected = many_skills[:5] - - call_order = [] - - async def mock_top_k(*args, **kwargs): - call_order.append("top_k") - return selected - - async def mock_load_history(skill_records, *args, **kwargs): - call_order.append(("load_history", len(skill_records))) - return [] - - with patch.object(extractor, "_select_top_k_skills", side_effect=mock_top_k), \ - patch.object(extractor, "_load_case_history", side_effect=mock_load_history): - await extractor.extract_and_save( - cluster_id="c1", group_id="g1", - new_case_records=[_make_case_record()], - existing_skill_records=many_skills, - skill_repo=repo, - max_skills_in_prompt=10, - ) - assert call_order[0] == "top_k" - # load_history receives the filtered 5, not the original 15 - assert call_order[1] == ("load_history", 5) - - -# =========================================================================== -# _select_top_k_skills edge cases -# =========================================================================== - - -class TestSelectTopKSkillsEdgeCases: - """Additional edge case tests for _select_top_k_skills.""" - - @pytest.mark.asyncio - async def test_fewer_skills_than_k(self): - extractor = _build_extractor() - skills = [_make_skill_record(name=f"S{i}") for i in range(3)] - cases = [_make_case_record()] - with patch.object(extractor, "_compute_embedding", new_callable=AsyncMock) as mock_embed: - mock_embed.return_value = {"embedding": [0.1, 0.2, 0.3]} - result = await extractor._select_top_k_skills(skills, cases, top_k=10) - assert len(result) == 3 - - @pytest.mark.asyncio - async def test_empty_skills(self): - extractor = _build_extractor() - result = await extractor._select_top_k_skills([], [_make_case_record()], top_k=5) - assert result == [] - - @pytest.mark.asyncio - async def test_no_vectors_returns_original_order(self): - extractor = _build_extractor() - skills = [_make_skill_record(name=f"S{i}", vector=None) for i in range(5)] - cases = [_make_case_record()] - with patch.object(extractor, "_compute_embedding", new_callable=AsyncMock) as mock_embed: - mock_embed.return_value = {"embedding": [0.1, 0.2, 0.3]} - result = await extractor._select_top_k_skills(skills, cases, top_k=3) - assert len(result) == 3 - - -# =========================================================================== -# _select_prompt edge cases -# =========================================================================== - - -class TestSelectPromptEdgeCases: - """Additional tests for _select_prompt.""" - - def test_empty_cases_uses_success(self): - extractor = _build_extractor() - result = extractor._select_prompt([]) - assert result == extractor.success_extract_prompt - - def test_all_none_quality_uses_success(self): - extractor = _build_extractor() - cases = [_make_case_record(quality_score=None)] - result = extractor._select_prompt(cases) - # None quality defaults to 0.5 which is > FAILURE_QUALITY_THRESHOLD (0.3) - assert result == extractor.success_extract_prompt - - -# =========================================================================== -# _is_skill_content_sufficient edge cases -# =========================================================================== - - -class TestIsSkillContentSufficientEdgeCases: - """Additional tests for _is_skill_content_sufficient.""" - - def test_none_content(self): - assert AgentSkillExtractor._is_skill_content_sufficient(None) is False - - def test_whitespace_only(self): - assert AgentSkillExtractor._is_skill_content_sufficient(" \n\n ") is False - - def test_numbered_steps_sufficient(self): - content = "## Steps\n1. First do this\n2. Then do that\n3. Check results\n4. Validate output\n5. Deploy" - assert AgentSkillExtractor._is_skill_content_sufficient(content) is True - - def test_bulleted_steps_sufficient(self): - content = "## Process\n- Analyze requirements\n- Design architecture\n- Implement solution\n- Run tests\n- Deploy to production" - assert AgentSkillExtractor._is_skill_content_sufficient(content) is True diff --git a/methods/EverCore/tests/test_agent_skill_raw_repository.py b/methods/EverCore/tests/test_agent_skill_raw_repository.py deleted file mode 100644 index 1f1f101a..00000000 --- a/methods/EverCore/tests/test_agent_skill_raw_repository.py +++ /dev/null @@ -1,172 +0,0 @@ -""" -AgentSkillRawRepository Unit Tests - -Tests for: -- get_by_cluster_id: group_id scoping and min_confidence filtering -- _build_filter_query: query construction with various parameter combinations - -Usage: - PYTHONPATH=src pytest tests/test_agent_skill_raw_repository.py -v -""" - -import pytest -from unittest.mock import AsyncMock, MagicMock, patch -from types import SimpleNamespace - -from infra_layer.adapters.out.persistence.repository.agent_skill_raw_repository import ( - AgentSkillRawRepository, -) - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def _make_skill_record(**kwargs): - """Create a mock AgentSkillRecord-like object.""" - defaults = { - "id": "skill_001", - "cluster_id": "cluster_A", - "group_id": "group_1", - "name": "API Development", - "confidence": 0.8, - } - defaults.update(kwargs) - return SimpleNamespace(**defaults) - - -def _build_repo(): - """Build an AgentSkillRawRepository with mocked model.""" - with patch.object(AgentSkillRawRepository, "__init__", lambda self: None): - repo = AgentSkillRawRepository() - repo.model = MagicMock() - return repo - - -# --------------------------------------------------------------------------- -# get_by_cluster_id -# --------------------------------------------------------------------------- - -class TestGetByClusterId: - """Tests for get_by_cluster_id group_id scoping.""" - - @pytest.mark.asyncio - async def test_query_includes_group_id_when_provided(self): - repo = _build_repo() - mock_to_list = AsyncMock(return_value=[]) - repo.model.find.return_value.to_list = mock_to_list - - await repo.get_by_cluster_id("cluster_A", group_id="group_1") - - called_query = repo.model.find.call_args[0][0] - assert called_query["cluster_id"] == "cluster_A" - assert called_query["group_id"] == "group_1" - - @pytest.mark.asyncio - async def test_query_excludes_group_id_when_none(self): - repo = _build_repo() - mock_to_list = AsyncMock(return_value=[]) - repo.model.find.return_value.to_list = mock_to_list - - await repo.get_by_cluster_id("cluster_A") - - called_query = repo.model.find.call_args[0][0] - assert called_query == {"cluster_id": "cluster_A"} - assert "group_id" not in called_query - - @pytest.mark.asyncio - async def test_query_includes_min_confidence(self): - repo = _build_repo() - mock_to_list = AsyncMock(return_value=[]) - repo.model.find.return_value.to_list = mock_to_list - - await repo.get_by_cluster_id( - "cluster_A", group_id="group_1", min_confidence=0.5 - ) - - called_query = repo.model.find.call_args[0][0] - assert called_query["cluster_id"] == "cluster_A" - assert called_query["group_id"] == "group_1" - assert called_query["confidence"] == {"$gte": 0.5} - - @pytest.mark.asyncio - async def test_query_with_only_min_confidence(self): - repo = _build_repo() - mock_to_list = AsyncMock(return_value=[]) - repo.model.find.return_value.to_list = mock_to_list - - await repo.get_by_cluster_id("cluster_A", min_confidence=0.3) - - called_query = repo.model.find.call_args[0][0] - assert called_query == { - "cluster_id": "cluster_A", - "confidence": {"$gte": 0.3}, - } - - @pytest.mark.asyncio - async def test_returns_empty_list_on_exception(self): - repo = _build_repo() - repo.model.find.side_effect = Exception("DB error") - - result = await repo.get_by_cluster_id("cluster_A", group_id="group_1") - - assert result == [] - - -# --------------------------------------------------------------------------- -# _build_filter_query -# --------------------------------------------------------------------------- - -class TestBuildFilterQuery: - """Tests for _build_filter_query construction logic.""" - - def test_empty_when_all_defaults(self): - repo = _build_repo() - query = repo._build_filter_query() - assert query == {} - - def test_user_id_filter(self): - repo = _build_repo() - query = repo._build_filter_query(user_id="u1") - assert query == {"user_id": "u1"} - - def test_magic_all_user_id_excluded(self): - repo = _build_repo() - query = repo._build_filter_query(user_id="__all__") - assert "user_id" not in query - - def test_single_group_id(self): - repo = _build_repo() - query = repo._build_filter_query(group_ids=["g1"]) - assert query == {"group_id": "g1"} - - def test_multiple_group_ids(self): - repo = _build_repo() - query = repo._build_filter_query(group_ids=["g1", "g2"]) - assert query == {"group_id": {"$in": ["g1", "g2"]}} - - def test_cluster_id_filter(self): - repo = _build_repo() - query = repo._build_filter_query(cluster_id="c1") - assert query == {"cluster_id": "c1"} - - def test_combined_filters(self): - repo = _build_repo() - query = repo._build_filter_query( - user_id="u1", group_ids=["g1"], cluster_id="c1" - ) - assert query == { - "user_id": "u1", - "group_id": "g1", - "cluster_id": "c1", - } - - def test_empty_group_ids_list_excluded(self): - repo = _build_repo() - query = repo._build_filter_query(group_ids=[]) - assert "group_id" not in query - - def test_none_user_id_excluded(self): - repo = _build_repo() - query = repo._build_filter_query(user_id=None) - assert "user_id" not in query diff --git a/methods/EverCore/tests/test_agent_skill_relevance_verify.py b/methods/EverCore/tests/test_agent_skill_relevance_verify.py deleted file mode 100644 index 732da54b..00000000 --- a/methods/EverCore/tests/test_agent_skill_relevance_verify.py +++ /dev/null @@ -1,340 +0,0 @@ -""" -Unit tests for AgentSkill relevance post-verification in SearchMemoryService. - -Tests the _verify_skill_relevance method with mocked LLM responses. -""" - -import json -import pytest -from unittest.mock import AsyncMock, patch, MagicMock - -from api_specs.dtos.memory import AgentSkillItem, SearchAgentSkillItem - - -def _make_service(): - """Create a SearchMemoryService instance with all repos mocked out.""" - with patch("agentic_layer.search_mem_service.EpisodicMemoryEsRepository"), \ - patch("agentic_layer.search_mem_service.EpisodicMemoryMilvusRepository"), \ - patch("agentic_layer.search_mem_service.UserProfileMilvusRepository"), \ - patch("agentic_layer.search_mem_service.AgentCaseEsRepository"), \ - patch("agentic_layer.search_mem_service.AgentSkillEsRepository"), \ - patch("agentic_layer.search_mem_service.AgentCaseMilvusRepository"), \ - patch("agentic_layer.search_mem_service.AgentSkillMilvusRepository"), \ - patch("agentic_layer.search_mem_service.MemoryManager"), \ - patch("agentic_layer.search_mem_service.RawMessageService"): - from agentic_layer.search_mem_service import SearchMemoryService - return SearchMemoryService() - - -def _make_skill(name: str, description: str = "desc", content: str = "content", score: float = 0.8) -> SearchAgentSkillItem: - """Helper to create a SearchAgentSkillItem instance.""" - return SearchAgentSkillItem( - id=f"skill_{name}", - user_id="test_user", - name=name, - description=description, - content=content, - score=score, - ) - - -@pytest.fixture -def service(): - return _make_service() - - -@pytest.mark.asyncio -async def test_empty_skills_returns_empty(service): - """Empty input returns empty output without calling LLM.""" - result = await service._verify_skill_relevance( - query="how to fix database connection", - skills=[], - ) - assert result == [] - - -@pytest.mark.asyncio -async def test_empty_query_returns_all(service): - """Empty query returns all skills without filtering.""" - skills = [_make_skill("skill1")] - result = await service._verify_skill_relevance(query="", skills=skills) - assert result == skills - - -@pytest.mark.asyncio -async def test_filters_irrelevant_skills(service): - """LLM marks one skill as helpful and one as not — only helpful one is returned.""" - skills = [ - _make_skill( - "Database connection pool tuning", - "Optimize DB connection pool settings", - "## Steps\n1. Check pool size\n2. Adjust max connections", - ), - _make_skill( - "CSS grid layout", - "Build responsive layouts with CSS grid", - "## Steps\n1. Define grid container\n2. Set grid-template-columns", - ), - ] - - llm_response = json.dumps({ - "results": [ - {"index": 0, "score": 0.9, "reason": "directly addresses DB connection issues"}, - {"index": 1, "score": 0.1, "reason": "CSS layout is unrelated to DB connections"}, - ] - }) - - mock_provider = AsyncMock() - mock_provider.generate = AsyncMock(return_value=llm_response) - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="{query}{skills_json}"): - result = await service._verify_skill_relevance( - query="how to fix database connection pool timeout", - skills=skills, - ) - - assert len(result) == 1 - assert result[0].name == "Database connection pool tuning" - - -@pytest.mark.asyncio -async def test_all_skills_helpful(service): - """When LLM says all skills are helpful, all are returned.""" - skills = [_make_skill("skill_a"), _make_skill("skill_b")] - - llm_response = json.dumps({ - "results": [ - {"index": 0, "score": 0.8, "reason": "relevant"}, - {"index": 1, "score": 0.7, "reason": "also relevant"}, - ] - }) - - mock_provider = AsyncMock() - mock_provider.generate = AsyncMock(return_value=llm_response) - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="{query}{skills_json}"): - result = await service._verify_skill_relevance( - query="some query", skills=skills, - ) - - assert len(result) == 2 - - -@pytest.mark.asyncio -async def test_no_skills_helpful(service): - """When LLM says no skills are helpful, empty list is returned.""" - skills = [_make_skill("skill_a")] - - llm_response = json.dumps({ - "results": [ - {"index": 0, "score": 0.1, "reason": "not relevant"}, - ] - }) - - mock_provider = AsyncMock() - mock_provider.generate = AsyncMock(return_value=llm_response) - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="{query}{skills_json}"): - result = await service._verify_skill_relevance( - query="some query", skills=skills, - ) - - assert len(result) == 0 - - -@pytest.mark.asyncio -async def test_llm_failure_returns_all(service): - """When LLM call fails, all original skills are returned as fallback.""" - skills = [_make_skill("skill_a"), _make_skill("skill_b")] - - mock_provider = AsyncMock() - mock_provider.generate = AsyncMock(side_effect=Exception("LLM API error")) - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="{query}{skills_json}"): - result = await service._verify_skill_relevance( - query="some query", skills=skills, - ) - - assert len(result) == 2 - - -@pytest.mark.asyncio -async def test_malformed_llm_json_returns_all(service): - """When LLM returns invalid JSON, all original skills are returned as fallback.""" - skills = [_make_skill("skill_a")] - - mock_provider = AsyncMock() - mock_provider.generate = AsyncMock(return_value="not valid json {{{") - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="{query}{skills_json}"): - result = await service._verify_skill_relevance( - query="some query", skills=skills, - ) - - assert len(result) == 1 - - -# --------------------------------------------------------------------------- -# Edge cases: None / missing fields / boundary conditions -# --------------------------------------------------------------------------- - - -@pytest.mark.asyncio -async def test_none_skills_returns_none(service): - """None input is returned as-is (falsy short-circuit).""" - result = await service._verify_skill_relevance(query="some query", skills=None) - assert result is None - - -@pytest.mark.asyncio -async def test_none_fields_in_skill_use_empty_string(service): - """Skills with None name/description/content are serialised as empty strings in prompt.""" - skills = [SearchAgentSkillItem(id="s1", user_id="u1", name=None, description=None, content=None, score=0.5)] - - llm_response = json.dumps({"results": [{"index": 0, "score": 0.8, "reason": "ok"}]}) - mock_provider = AsyncMock() - - captured_prompt = None - - async def _capture_generate(prompt, **kwargs): - nonlocal captured_prompt - captured_prompt = prompt - return llm_response - - mock_provider.generate = _capture_generate - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="{query}{skills_json}"): - result = await service._verify_skill_relevance(query="q", skills=skills) - - assert len(result) == 1 - parsed = json.loads(captured_prompt[len("q"):]) - assert parsed[0]["name"] == "" - assert parsed[0]["description"] == "" - assert parsed[0]["content"] == "" - - -@pytest.mark.asyncio -async def test_out_of_range_index_ignored(service): - """LLM returns an index beyond the skills list — it is safely ignored.""" - skills = [_make_skill("only_one")] - - llm_response = json.dumps({ - "results": [ - {"index": 0, "score": 0.8, "reason": "ok"}, - {"index": 99, "score": 0.8, "reason": "ghost"}, - ] - }) - - mock_provider = AsyncMock() - mock_provider.generate = AsyncMock(return_value=llm_response) - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="{query}{skills_json}"): - result = await service._verify_skill_relevance(query="q", skills=skills) - - assert len(result) == 1 - assert result[0].name == "only_one" - - -@pytest.mark.asyncio -async def test_missing_results_key_returns_empty(service): - """LLM returns valid JSON but without 'results' key — no skills pass.""" - skills = [_make_skill("skill_a")] - - llm_response = json.dumps({"answer": "something unexpected"}) - mock_provider = AsyncMock() - mock_provider.generate = AsyncMock(return_value=llm_response) - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="{query}{skills_json}"): - result = await service._verify_skill_relevance(query="q", skills=skills) - - assert len(result) == 0 - - -@pytest.mark.asyncio -async def test_missing_score_field_defaults_zero(service): - """LLM result item without 'score' key defaults to 0.0 — skill is excluded.""" - skills = [_make_skill("skill_a")] - - llm_response = json.dumps({ - "results": [{"index": 0, "reason": "no score field"}] - }) - mock_provider = AsyncMock() - mock_provider.generate = AsyncMock(return_value=llm_response) - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="{query}{skills_json}"): - result = await service._verify_skill_relevance(query="q", skills=skills) - - assert len(result) == 0 - - -@pytest.mark.asyncio -async def test_partial_indices_only_returns_judged_helpful(service): - """LLM only returns judgement for some skills — unjudged ones are excluded.""" - skills = [_make_skill("a"), _make_skill("b"), _make_skill("c")] - - llm_response = json.dumps({ - "results": [ - {"index": 1, "score": 0.8, "reason": "relevant"}, - ] - }) - mock_provider = AsyncMock() - mock_provider.generate = AsyncMock(return_value=llm_response) - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="{query}{skills_json}"): - result = await service._verify_skill_relevance(query="q", skills=skills) - - assert len(result) == 1 - assert result[0].name == "b" - - -@pytest.mark.asyncio -async def test_filtered_results_sorted_by_score_desc(service): - """Filtered results are sorted by score descending.""" - skills = [_make_skill("first"), _make_skill("second"), _make_skill("third")] - - llm_response = json.dumps({ - "results": [ - {"index": 2, "score": 0.9, "reason": "ok"}, - {"index": 0, "score": 0.5, "reason": "ok"}, - ] - }) - mock_provider = AsyncMock() - mock_provider.generate = AsyncMock(return_value=llm_response) - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="{query}{skills_json}"): - result = await service._verify_skill_relevance(query="q", skills=skills) - - assert len(result) == 2 - assert result[0].name == "third" - assert result[1].name == "first" - - -@pytest.mark.asyncio -async def test_prompt_receives_correct_arguments(service): - """Verify get_prompt_by is called with correct key and format receives query + skills_json.""" - skills = [_make_skill("db_tuning", "tune db", "step1")] - - llm_response = json.dumps({"results": [{"index": 0, "score": 0.8, "reason": "ok"}]}) - mock_provider = AsyncMock() - mock_provider.generate = AsyncMock(return_value=llm_response) - - with patch("memory_layer.llm.llm_provider.build_default_provider", return_value=mock_provider), \ - patch("memory_layer.prompts.get_prompt_by", return_value="Q={query} S={skills_json}") as mock_prompt: - result = await service._verify_skill_relevance(query="fix db", skills=skills) - - mock_prompt.assert_called_once_with("AGENT_SKILL_RELEVANCE_VERIFY_PROMPT") - call_args = mock_provider.generate.call_args - prompt_text = call_args[0][0] - assert prompt_text.startswith("Q=fix db S=") - assert '"name": "db_tuning"' in prompt_text diff --git a/methods/EverCore/tests/test_api_key_rotator.py b/methods/EverCore/tests/test_api_key_rotator.py deleted file mode 100644 index 379e9f7c..00000000 --- a/methods/EverCore/tests/test_api_key_rotator.py +++ /dev/null @@ -1,104 +0,0 @@ -"""ApiKeyRotator unit tests.""" - -import pytest - -from memory_layer.llm.api_key_rotator import ApiKeyRotator - - -@pytest.fixture(autouse=True) -def _reset_shared_rotator(): - """Ensure each test starts with a clean singleton state.""" - ApiKeyRotator._shared = None - yield - ApiKeyRotator._shared = None - - -class TestApiKeyRotator: - """Unit tests for ApiKeyRotator core rotation logic.""" - - def test_single_key_always_returns_same(self) -> None: - rotator = ApiKeyRotator(["key-a"]) - results = [rotator.get_next() for _ in range(5)] - assert results == ["key-a"] * 5 - - def test_multiple_keys_round_robin(self) -> None: - rotator = ApiKeyRotator(["key-a", "key-b", "key-c"]) - results = [rotator.get_next() for _ in range(3)] - assert results == ["key-a", "key-b", "key-c"] - - def test_multiple_keys_wraps_around(self) -> None: - rotator = ApiKeyRotator(["key-a", "key-b", "key-c"]) - results = [rotator.get_next() for _ in range(6)] - assert results == ["key-a", "key-b", "key-c", "key-a", "key-b", "key-c"] - - def test_size_property(self) -> None: - assert ApiKeyRotator(["key-a"]).size == 1 - assert ApiKeyRotator(["key-a", "key-b", "key-c"]).size == 3 - - def test_empty_keys_raises_value_error(self) -> None: - with pytest.raises(ValueError, match="At least one API key is required"): - ApiKeyRotator([]) - - def test_repr(self) -> None: - rotator = ApiKeyRotator(["key-a", "key-b"]) - assert repr(rotator) == "ApiKeyRotator(size=2)" - - def test_keys_are_immutable(self) -> None: - original = ["key-a", "key-b"] - rotator = ApiKeyRotator(original) - original.append("key-c") - assert rotator.size == 2 - - -class TestApiKeyRotatorGetOrCreate: - """Tests for get_or_create: parsing + singleton behavior.""" - - def test_single_key(self) -> None: - rotator = ApiKeyRotator.get_or_create("key-a") - assert rotator.size == 1 - assert rotator.get_next() == "key-a" - - def test_multiple_keys_comma_separated(self) -> None: - rotator = ApiKeyRotator.get_or_create("key-a,key-b,key-c") - assert rotator.size == 3 - assert rotator.get_next() == "key-a" - assert rotator.get_next() == "key-b" - assert rotator.get_next() == "key-c" - - def test_strips_whitespace(self) -> None: - rotator = ApiKeyRotator.get_or_create(" key-a , key-b , key-c ") - assert rotator.size == 3 - assert rotator.get_next() == "key-a" - - def test_ignores_trailing_comma(self) -> None: - rotator = ApiKeyRotator.get_or_create("key-a,key-b,") - assert rotator.size == 2 - - def test_ignores_empty_segments(self) -> None: - rotator = ApiKeyRotator.get_or_create("key-a,,key-b") - assert rotator.size == 2 - - def test_empty_string_raises_value_error(self) -> None: - with pytest.raises(ValueError, match="At least one API key is required"): - ApiKeyRotator.get_or_create("") - - def test_only_commas_raises_value_error(self) -> None: - with pytest.raises(ValueError, match="At least one API key is required"): - ApiKeyRotator.get_or_create(",,,") - - def test_returns_same_instance(self) -> None: - r1 = ApiKeyRotator.get_or_create("key-a,key-b") - r2 = ApiKeyRotator.get_or_create("key-a,key-b") - assert r1 is r2 - - def test_shared_counter_across_calls(self) -> None: - r1 = ApiKeyRotator.get_or_create("key-a,key-b") - assert r1.get_next() == "key-a" - r2 = ApiKeyRotator.get_or_create("key-a,key-b") - assert r2.get_next() == "key-b" - - def test_new_instance_after_clearing_shared(self) -> None: - r1 = ApiKeyRotator.get_or_create("key-a,key-b") - ApiKeyRotator._shared = None - r2 = ApiKeyRotator.get_or_create("key-a,key-b") - assert r1 is not r2 diff --git a/methods/EverCore/tests/test_async_foresight_atomicfacts.py b/methods/EverCore/tests/test_async_foresight_atomicfacts.py deleted file mode 100644 index b75ae8d4..00000000 --- a/methods/EverCore/tests/test_async_foresight_atomicfacts.py +++ /dev/null @@ -1,361 +0,0 @@ -""" -tests/test_async_foresight_atomicfacts.py - -Unit tests for the async fire-and-forget foresight/atomic_fact background task. - -Usage: - PYTHONPATH=src pytest tests/test_async_foresight_atomicfacts.py -v -""" - -import asyncio as real_asyncio -from contextlib import ExitStack -from datetime import datetime -from unittest.mock import AsyncMock, MagicMock, patch -import pytest - -from api_specs.memory_types import MemCell, RawDataType, Foresight, AtomicFact -from api_specs.dtos import MemorizeRequest -from biz_layer.mem_memorize import ( - ExtractionState, - _foresight_and_atomic_facts_with_metrics, - _process_memories, - process_memory_extraction, -) - - -def _make_memcell(raw_data_type: RawDataType = RawDataType.CONVERSATION) -> MemCell: - mc = MagicMock(spec=MemCell) - mc.type = raw_data_type - mc.event_id = "evt-001" - mc.original_data = [] - mc.participants = ["user_001"] - mc.timestamp = datetime(2026, 3, 30, 10, 0, 0) - return mc - - -def _make_request(scene: str = "solo") -> MemorizeRequest: - req = MagicMock(spec=MemorizeRequest) - req.group_id = "grp-001" - req.session_id = "sess-001" - req.scene = scene - req.raw_data_type = RawDataType.CONVERSATION - return req - - -def _make_state(is_solo: bool = True, has_episode: bool = True) -> ExtractionState: - state = MagicMock(spec=ExtractionState) - state.memcell = _make_memcell() - state.request = _make_request("solo" if is_solo else "team") - state.is_solo_scene = is_solo - state.participants = ["user_001"] - state.current_time = datetime(2026, 3, 30, 10, 0, 0) - state.foresight_parent_type = "episodic_memory" - state.atomic_fact_parent_type = "episodic_memory" - state.parent_id = "evt-001" - saved_ep = MagicMock() - saved_ep.id = "ep-mongo-001" - state.group_episode_memories = [MagicMock(id="ep-mongo-001")] if has_episode else [] - state.parent_docs_map = {"ep-mongo-001": saved_ep} if has_episode else {} - # MagicMock doesn't evaluate properties — set explicitly to mirror property logic - state.episode_saved = has_episode - state.agent_case = None - return state - - -@pytest.mark.asyncio -async def test_background_fn_extracts_parallel_and_saves(): - """Solo non-agent: foresight and atomic_fact extracted in parallel, then saved.""" - state = _make_state() - mm = AsyncMock() - foresight_mock = MagicMock(spec=Foresight) - af_mock = MagicMock(spec=AtomicFact) - - with ( - patch( - 'biz_layer.mem_memorize._should_skip_atomic_fact_for_agent', - return_value=False, - ), - patch( - 'biz_layer.mem_memorize._extract_foresights', - new_callable=AsyncMock, - return_value=[foresight_mock], - ) as mock_ef, - patch( - 'biz_layer.mem_memorize._extract_atomic_facts', - new_callable=AsyncMock, - return_value=[af_mock], - ) as mock_eaf, - patch( - 'biz_layer.mem_memorize._save_foresight_and_atomic_fact', - new_callable=AsyncMock, - ) as mock_save, - patch('biz_layer.mem_memorize.record_memory_extracted'), - patch('biz_layer.mem_memorize.record_extraction_stage'), - patch( - 'biz_layer.mem_memorize.get_space_id_for_metrics', return_value='space_test' - ), - ): - - await _foresight_and_atomic_facts_with_metrics(state, mm) - - mock_ef.assert_called_once_with(state, mm) - mock_eaf.assert_called_once_with(state, mm) - mock_save.assert_called_once_with(state, [foresight_mock], [af_mock]) - - -@pytest.mark.asyncio -async def test_background_fn_skips_atomic_fact_when_agent_flag_set(): - """When _should_skip_atomic_fact_for_agent returns True, atomic_fact extraction skipped.""" - state = _make_state() - mm = AsyncMock() - foresight_mock = MagicMock(spec=Foresight) - - with ( - patch( - 'biz_layer.mem_memorize._should_skip_atomic_fact_for_agent', - return_value=True, - ), - patch( - 'biz_layer.mem_memorize._extract_foresights', - new_callable=AsyncMock, - return_value=[foresight_mock], - ) as mock_ef, - patch( - 'biz_layer.mem_memorize._extract_atomic_facts', new_callable=AsyncMock - ) as mock_eaf, - patch( - 'biz_layer.mem_memorize._save_foresight_and_atomic_fact', - new_callable=AsyncMock, - ) as mock_save, - patch('biz_layer.mem_memorize.record_memory_extracted'), - patch('biz_layer.mem_memorize.record_extraction_stage'), - patch( - 'biz_layer.mem_memorize.get_space_id_for_metrics', return_value='space_test' - ), - ): - - await _foresight_and_atomic_facts_with_metrics(state, mm) - - mock_ef.assert_called_once_with(state, mm) - mock_eaf.assert_not_called() - mock_save.assert_called_once_with(state, [foresight_mock], []) - - -@pytest.mark.asyncio -async def test_background_fn_swallows_exception_and_records_stage(): - """Exception in extraction must not propagate; record_extraction_stage always called.""" - state = _make_state() - mm = AsyncMock() - - with ( - patch( - 'biz_layer.mem_memorize._should_skip_atomic_fact_for_agent', - return_value=False, - ), - patch( - 'biz_layer.mem_memorize._extract_foresights', - new_callable=AsyncMock, - side_effect=RuntimeError("LLM down"), - ), - patch('biz_layer.mem_memorize._extract_atomic_facts', new_callable=AsyncMock), - patch( - 'biz_layer.mem_memorize._save_foresight_and_atomic_fact', - new_callable=AsyncMock, - ) as mock_save, - patch('biz_layer.mem_memorize.record_memory_extracted'), - patch('biz_layer.mem_memorize.record_extraction_stage') as mock_stage, - patch( - 'biz_layer.mem_memorize.get_space_id_for_metrics', return_value='space_test' - ), - ): - - await _foresight_and_atomic_facts_with_metrics(state, mm) - - mock_save.assert_not_called() - call_kwargs = mock_stage.call_args[1] - assert call_kwargs["stage"] == "foresight_and_atomic_facts_bg" - - -@pytest.mark.asyncio -async def test_process_memories_does_not_call_save_foresight(): - """After refactor, _process_memories must NOT call _save_foresight_and_atomic_fact.""" - state = _make_state() - - with ( - patch('biz_layer.mem_memorize._save_episodes', new_callable=AsyncMock), - patch('biz_layer.mem_memorize._save_agent_case', new_callable=AsyncMock), - patch( - 'biz_layer.mem_memorize.update_status_after_memcell', new_callable=AsyncMock - ), - patch( - 'biz_layer.mem_memorize._save_foresight_and_atomic_fact', - new_callable=AsyncMock, - ) as mock_save, - patch('biz_layer.mem_memorize._clone_episodes_for_users', return_value=[]), - ): - - await _process_memories(state) - - mock_save.assert_not_called() - - -@pytest.mark.asyncio -async def test_background_fn_empty_results_skips_save_and_count_metrics(): - """When both foresight and atomic_facts return empty, save and count metrics are not called.""" - state = _make_state() - mm = AsyncMock() - - with ( - patch( - 'biz_layer.mem_memorize._should_skip_atomic_fact_for_agent', - return_value=False, - ), - patch( - 'biz_layer.mem_memorize._extract_foresights', - new_callable=AsyncMock, - return_value=[], - ), - patch( - 'biz_layer.mem_memorize._extract_atomic_facts', - new_callable=AsyncMock, - return_value=[], - ), - patch( - 'biz_layer.mem_memorize._save_foresight_and_atomic_fact', - new_callable=AsyncMock, - ) as mock_save, - patch('biz_layer.mem_memorize.record_memory_extracted') as mock_record, - patch('biz_layer.mem_memorize.record_extraction_stage') as mock_stage, - patch( - 'biz_layer.mem_memorize.get_space_id_for_metrics', return_value='space_test' - ), - ): - - await _foresight_and_atomic_facts_with_metrics(state, mm) - - mock_save.assert_not_called() - mock_record.assert_not_called() - mock_stage.assert_called_once() # finally block always runs - - -def _patch_process_memory_extraction_deps(state): - """Patch all heavy dependencies of process_memory_extraction for unit testing.""" - return [ - patch( - 'biz_layer.mem_memorize._init_extraction_state', - new_callable=AsyncMock, - return_value=state, - ), - patch('biz_layer.mem_memorize._extract_episodes', new_callable=AsyncMock), - patch('biz_layer.mem_memorize._extract_agent_case', new_callable=AsyncMock), - patch( - 'biz_layer.mem_memorize._update_memcell_and_cluster', new_callable=AsyncMock - ), - patch( - 'biz_layer.mem_memorize._process_memories', - new_callable=AsyncMock, - return_value=1, - ), - patch('biz_layer.mem_memorize.if_memorize', return_value=True), - patch( - 'biz_layer.mem_memorize.get_space_id_for_metrics', return_value='space_test' - ), - patch('biz_layer.mem_memorize.record_extraction_stage'), - patch('biz_layer.mem_memorize.record_memory_extracted'), - patch( - 'biz_layer.mem_memorize._save_memcell_to_database', new_callable=AsyncMock - ), - ] - - -@pytest.mark.asyncio -async def test_process_memory_extraction_fires_background_task_for_solo(): - """Solo scene with saved episode: background task must be created.""" - state = _make_state(is_solo=True, has_episode=True) - memcell = state.memcell - request = state.request - mm = AsyncMock() - current_time = datetime(2026, 3, 30, 10, 0, 0) - - task_coro_names = [] - _orig = real_asyncio.create_task - - def _tracking_create_task(coro, **kwargs): - task_coro_names.append(getattr(coro, '__qualname__', type(coro).__name__)) - return _orig(coro, **kwargs) - - patches = _patch_process_memory_extraction_deps(state) - with ExitStack() as stack: - for p in patches: - stack.enter_context(p) - with patch.object( - real_asyncio, 'create_task', side_effect=_tracking_create_task - ): - await process_memory_extraction(memcell, request, mm, current_time) - - foresight_tasks = [n for n in task_coro_names if 'foresight' in n.lower()] - assert ( - len(foresight_tasks) == 1 - ), f"Expected 1 foresight task, got: {task_coro_names}" - - -@pytest.mark.asyncio -async def test_process_memory_extraction_no_background_task_for_team(): - """Team scene: background task must NOT be created.""" - state = _make_state(is_solo=False, has_episode=True) - memcell = state.memcell - request = state.request - mm = AsyncMock() - current_time = datetime(2026, 3, 30, 10, 0, 0) - - task_coro_names = [] - _orig = real_asyncio.create_task - - def _tracking_create_task(coro, **kwargs): - task_coro_names.append(getattr(coro, '__qualname__', type(coro).__name__)) - return _orig(coro, **kwargs) - - patches = _patch_process_memory_extraction_deps(state) - with ExitStack() as stack: - for p in patches: - stack.enter_context(p) - with patch.object( - real_asyncio, 'create_task', side_effect=_tracking_create_task - ): - await process_memory_extraction(memcell, request, mm, current_time) - - foresight_tasks = [n for n in task_coro_names if 'foresight' in n.lower()] - assert ( - len(foresight_tasks) == 0 - ), f"Expected no foresight task for team, got: {task_coro_names}" - - -@pytest.mark.asyncio -async def test_process_memory_extraction_no_background_task_when_no_episode(): - """Solo scene but no episode saved: background task must NOT be created (no parent_doc).""" - state = _make_state(is_solo=True, has_episode=False) - memcell = state.memcell - request = state.request - mm = AsyncMock() - current_time = datetime(2026, 3, 30, 10, 0, 0) - - task_coro_names = [] - _orig = real_asyncio.create_task - - def _tracking_create_task(coro, **kwargs): - task_coro_names.append(getattr(coro, '__qualname__', type(coro).__name__)) - return _orig(coro, **kwargs) - - patches = _patch_process_memory_extraction_deps(state) - with ExitStack() as stack: - for p in patches: - stack.enter_context(p) - with patch.object( - real_asyncio, 'create_task', side_effect=_tracking_create_task - ): - await process_memory_extraction(memcell, request, mm, current_time) - - foresight_tasks = [n for n in task_coro_names if 'foresight' in n.lower()] - assert ( - len(foresight_tasks) == 0 - ), f"Expected no foresight task (no episode), got: {task_coro_names}" diff --git a/methods/EverCore/tests/test_atomic_fact_extractor.py b/methods/EverCore/tests/test_atomic_fact_extractor.py deleted file mode 100644 index 90dce658..00000000 --- a/methods/EverCore/tests/test_atomic_fact_extractor.py +++ /dev/null @@ -1,204 +0,0 @@ -"""Unit tests for AtomicFactExtractor. - -Tests cover: -- _parse_llm_response: new key, code blocks, invalid JSON, old key rejection -- _extract_atomic_fact: empty list acceptance, missing outer key rejection -- extract_atomic_fact: retry exhaustion returns None, successful first extraction -""" - -from __future__ import annotations - -import json -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest - -from memory_layer.memory_extractor.atomic_fact_extractor import AtomicFactExtractor - - -# --------------------------------------------------------------------------- -# Fixtures -# --------------------------------------------------------------------------- - - -@pytest.fixture -def extractor(): - """Create an AtomicFactExtractor with a mocked LLM provider.""" - mock_provider = MagicMock() - mock_provider.generate = AsyncMock() - return AtomicFactExtractor( - llm_provider=mock_provider, atomic_fact_prompt="{{INPUT_TEXT}} {{TIME}}" - ) - - -VALID_RESPONSE = { - "atomic_facts": { - "time": "March 10, 2024(Sunday) at 12:00 AM", - "atomic_fact": ["Alice likes hiking.", "Bob prefers reading."], - } -} - - -# --------------------------------------------------------------------------- -# TestParseLlmResponse -# --------------------------------------------------------------------------- - - -class TestParseLlmResponse: - """Tests for AtomicFactExtractor._parse_llm_response.""" - - def test_parses_new_atomic_facts_key(self, extractor): - """Direct JSON with 'atomic_facts' outer key parses correctly.""" - raw = json.dumps(VALID_RESPONSE) - result = extractor._parse_llm_response(raw) - assert "atomic_facts" in result - assert result["atomic_facts"]["time"] == "March 10, 2024(Sunday) at 12:00 AM" - assert result["atomic_facts"]["atomic_fact"] == [ - "Alice likes hiking.", - "Bob prefers reading.", - ] - - def test_parses_json_in_code_block(self, extractor): - """JSON wrapped in ```json ... ``` code block parses correctly.""" - raw = "Here is the result:\n```json\n" + json.dumps(VALID_RESPONSE) + "\n```" - result = extractor._parse_llm_response(raw) - assert "atomic_facts" in result - assert len(result["atomic_facts"]["atomic_fact"]) == 2 - - def test_rejects_invalid_json(self, extractor): - """Completely invalid text raises ValueError.""" - with pytest.raises(ValueError, match="Unable to parse LLM response"): - extractor._parse_llm_response("this is not json at all!!!") - - def test_rejects_old_atomic_fact_key(self, extractor): - """Old singular 'atomic_fact' outer key is not recognized as 'atomic_facts'.""" - old_format = { - "atomic_fact": { - "time": "March 10, 2024(Sunday) at 12:00 AM", - "atomic_fact": ["Alice likes hiking."], - } - } - # The parser will successfully parse the JSON (step 4: direct parse), - # but the result will have "atomic_fact" not "atomic_facts". - result = extractor._parse_llm_response(json.dumps(old_format)) - assert "atomic_facts" not in result - assert "atomic_fact" in result - - -# --------------------------------------------------------------------------- -# TestExtractAtomicFactValidation -# --------------------------------------------------------------------------- - - -class TestExtractAtomicFactValidation: - """Tests for _extract_atomic_fact validation logic.""" - - @pytest.mark.asyncio - async def test_empty_list_returns_valid_object(self, extractor): - """LLM returns atomic_fact: [] -> returns AtomicFact with empty list, no exception.""" - llm_response = json.dumps( - { - "atomic_facts": { - "time": "March 10, 2024(Sunday) at 12:00 AM", - "atomic_fact": [], - } - } - ) - extractor.llm_provider.generate.return_value = llm_response - - with patch("agentic_layer.vectorize_service.get_vectorize_service") as mock_vs: - mock_service = MagicMock() - mock_service.get_embeddings = AsyncMock(return_value=[]) - mock_service.get_model_name = MagicMock(return_value="test-model") - mock_vs.return_value = mock_service - - result = await extractor._extract_atomic_fact( - input_text="hello", timestamp="2024-03-10T00:00:00Z", user_id="user1" - ) - - assert result is not None - assert result.atomic_fact == [] - assert result.time == "March 10, 2024(Sunday) at 12:00 AM" - - @pytest.mark.asyncio - async def test_missing_outer_key_raises(self, extractor): - """LLM returns wrong outer key -> raises ValueError mentioning 'atomic_facts'.""" - llm_response = json.dumps( - { - "wrong_key": { - "time": "March 10, 2024(Sunday) at 12:00 AM", - "atomic_fact": ["something"], - } - } - ) - extractor.llm_provider.generate.return_value = llm_response - - with pytest.raises(ValueError, match="atomic_facts"): - await extractor._extract_atomic_fact( - input_text="hello", timestamp="2024-03-10T00:00:00Z", user_id="user1" - ) - - -# --------------------------------------------------------------------------- -# TestExtractAtomicFactErrorHandling -# --------------------------------------------------------------------------- - - -class TestExtractAtomicFactErrorHandling: - """Tests for extract_atomic_fact (public method) retry and error handling.""" - - @staticmethod - def _make_memcell(): - return MagicMock( - original_data=[ - { - "message": { - "sender_name": "Alice", - "content": [{"type": "text", "text": "hello"}], - "timestamp": "2024-03-10T00:00:00Z", - } - } - ], - sender_ids=["alice"], - ) - - @pytest.mark.asyncio - async def test_returns_none_after_retries_exhausted(self, extractor): - """LLM provider raises Exception every time -> after 5 calls, returns None.""" - extractor.llm_provider.generate = AsyncMock( - side_effect=Exception("LLM unavailable") - ) - memcell = self._make_memcell() - - result = await extractor.extract_atomic_fact( - memcell=memcell, timestamp="2024-03-10T00:00:00Z", user_id="user1" - ) - - assert result is None - assert extractor.llm_provider.generate.call_count == 5 - - @pytest.mark.asyncio - async def test_returns_result_on_first_success(self, extractor): - """Normal successful extraction returns AtomicFact object.""" - llm_response = json.dumps(VALID_RESPONSE) - extractor.llm_provider.generate = AsyncMock(return_value=llm_response) - memcell = self._make_memcell() - - with patch("agentic_layer.vectorize_service.get_vectorize_service") as mock_vs: - mock_service = MagicMock() - mock_service.get_embeddings = AsyncMock( - return_value=[[0.1] * 1024, [0.2] * 1024] - ) - mock_service.get_model_name = MagicMock(return_value="test-model") - mock_vs.return_value = mock_service - - result = await extractor.extract_atomic_fact( - memcell=memcell, timestamp="2024-03-10T00:00:00Z", user_id="user1" - ) - - assert result is not None - assert result.atomic_fact == ["Alice likes hiking.", "Bob prefers reading."] - assert result.time == "March 10, 2024(Sunday) at 12:00 AM" - assert result.fact_embeddings is not None - assert len(result.fact_embeddings) == 2 - assert extractor.llm_provider.generate.call_count == 1 diff --git a/methods/EverCore/tests/test_cluster_memcell_llm.py b/methods/EverCore/tests/test_cluster_memcell_llm.py deleted file mode 100644 index 7ec87bc1..00000000 --- a/methods/EverCore/tests/test_cluster_memcell_llm.py +++ /dev/null @@ -1,458 +0,0 @@ -"""Unit tests for ClusterManager._cluster_memcell_llm. - -Covers every branch of the LLM-based clustering method: -1. Missing event_id -2. No LLM provider (embedding fallback) - 2a. top-1 sim >= threshold -> assign existing - 2b. top-1 sim < threshold -> new cluster -3. No existing case clusters -> new cluster -4. Fast path: top-1 sim >= llm_skip_threshold -5. LLM failure (returns None) -> embedding fallback - 5a. top-1 sim >= threshold -> assign existing - 5b. no good candidate -> new cluster -6. LLM returns valid result - 6a. chosen_id is valid case cluster -> assign - 6b. chosen_id invalid -> new cluster -""" - -import numpy as np -import pytest -from unittest.mock import AsyncMock, patch, MagicMock - -from memory_layer.cluster_manager.manager import ClusterManager, MemSceneState -from memory_layer.cluster_manager.config import ClusterManagerConfig - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def _make_config(**overrides) -> ClusterManagerConfig: - defaults = dict( - similarity_threshold=0.65, - llm_skip_threshold=0.85, - llm_top_k_clusters=5, - llm_max_context_per_cluster=3, - ) - defaults.update(overrides) - return ClusterManagerConfig(**defaults) - - -def _make_manager( - config=None, - llm_provider=None, - context_fetcher=None, - embedding_vec=None, -) -> ClusterManager: - """Build a ClusterManager with vectorize service mocked out.""" - cfg = config or _make_config() - mgr = ClusterManager.__new__(ClusterManager) - mgr.config = cfg - mgr._callbacks = [] - mgr._llm_provider = llm_provider - mgr._context_fetcher = context_fetcher - mgr._stats = { - "total_memcells": 0, - "clustered_memcells": 0, - "new_clusters": 0, - "failed_embeddings": 0, - } - - # Mock vectorize service to return a controlled vector - mock_vs = AsyncMock() - if embedding_vec is not None: - mock_vs.get_embedding = AsyncMock(return_value=embedding_vec.tolist()) - else: - mock_vs.get_embedding = AsyncMock(return_value=[1.0, 0.0, 0.0]) - mgr._vectorize_service = mock_vs - return mgr - - -def _make_memcell(event_id="evt_1", text="some task", timestamp=1000.0): - return { - "event_id": event_id, - "clustering_text": text, - "timestamp": timestamp, - } - - -def _state_with_case_cluster( - cluster_id="cluster_000", - event_id="existing_evt", - centroid=None, - count=1, -): - """Build a MemSceneState that already has one case cluster.""" - state = MemSceneState() - state.next_cluster_idx = 1 - state.case_cluster_ids = {cluster_id} - state.cluster_counts[cluster_id] = count - state.cluster_last_ts[cluster_id] = 900.0 - state.eventid_to_cluster[event_id] = cluster_id - state.event_ids.append(event_id) - state.timestamps.append(900.0) - if centroid is not None: - state.cluster_centroids[cluster_id] = centroid - state.vectors.append(centroid) - else: - vec = np.array([1.0, 0.0, 0.0], dtype=np.float32) - state.cluster_centroids[cluster_id] = vec - state.vectors.append(vec) - state.cluster_ids.append(cluster_id) - return state - - -# =========================================================================== -# 1. Missing event_id -> (None, state) -# =========================================================================== - - -class TestMissingEventId: - - @pytest.mark.asyncio - async def test_empty_event_id_returns_none(self): - mgr = _make_manager(llm_provider=MagicMock()) - state = MemSceneState() - memcell = {"event_id": "", "clustering_text": "x", "timestamp": 1.0} - - cid, out_state = await mgr._cluster_memcell_llm(memcell, state) - - assert cid is None - assert out_state is state - assert mgr._stats["total_memcells"] == 1 - assert mgr._stats["clustered_memcells"] == 0 - - @pytest.mark.asyncio - async def test_missing_event_id_key_returns_none(self): - mgr = _make_manager(llm_provider=MagicMock()) - state = MemSceneState() - memcell = {"clustering_text": "x"} - - cid, _ = await mgr._cluster_memcell_llm(memcell, state) - assert cid is None - - -# =========================================================================== -# 2. No LLM provider -> embedding fallback -# =========================================================================== - - -class TestNoLlmProvider: - - @pytest.mark.asyncio - async def test_no_llm_assign_existing_when_similar(self): - """2a: top-1 sim >= threshold -> assign to existing cluster.""" - centroid = np.array([1.0, 0.0, 0.0], dtype=np.float32) - mgr = _make_manager( - llm_provider=None, - embedding_vec=centroid, # identical to centroid -> sim=1.0 - ) - state = _state_with_case_cluster(centroid=centroid) - - cid, out_state = await mgr._cluster_memcell_llm( - _make_memcell(), state - ) - - assert cid == "cluster_000" - assert "evt_1" in out_state.eventid_to_cluster - assert out_state.eventid_to_cluster["evt_1"] == "cluster_000" - assert mgr._stats["clustered_memcells"] == 1 - - @pytest.mark.asyncio - async def test_no_llm_new_cluster_when_dissimilar(self): - """2b: top-1 sim < threshold -> new cluster.""" - centroid = np.array([1.0, 0.0, 0.0], dtype=np.float32) - orthogonal = np.array([0.0, 1.0, 0.0], dtype=np.float32) - mgr = _make_manager( - llm_provider=None, - embedding_vec=orthogonal, # sim ~ 0 with centroid - ) - state = _state_with_case_cluster(centroid=centroid) - - cid, out_state = await mgr._cluster_memcell_llm( - _make_memcell(), state - ) - - assert cid != "cluster_000" - assert cid.startswith("cluster_") - assert cid in out_state.case_cluster_ids - assert mgr._stats["new_clusters"] == 1 - - -# =========================================================================== -# 3. No existing case clusters -> create first case cluster -# =========================================================================== - - -class TestNoCaseClusters: - - @pytest.mark.asyncio - async def test_first_case_cluster_created(self): - mgr = _make_manager(llm_provider=MagicMock()) - state = MemSceneState() - - cid, out_state = await mgr._cluster_memcell_llm( - _make_memcell(), state - ) - - assert cid == "cluster_000" - assert cid in out_state.case_cluster_ids - assert out_state.eventid_to_cluster["evt_1"] == cid - assert mgr._stats["new_clusters"] == 1 - assert mgr._stats["clustered_memcells"] == 1 - assert "evt_1" in out_state.event_ids - - -# =========================================================================== -# 4. Fast path: top-1 sim >= llm_skip_threshold -# =========================================================================== - - -class TestFastPath: - - @pytest.mark.asyncio - async def test_skip_llm_when_very_similar(self): - """sim=1.0 >= llm_skip_threshold=0.85 -> assign without LLM.""" - centroid = np.array([1.0, 0.0, 0.0], dtype=np.float32) - mock_llm = MagicMock() - mgr = _make_manager( - llm_provider=mock_llm, - embedding_vec=centroid, - ) - state = _state_with_case_cluster(centroid=centroid) - - cid, out_state = await mgr._cluster_memcell_llm( - _make_memcell(), state - ) - - assert cid == "cluster_000" - assert out_state.eventid_to_cluster["evt_1"] == "cluster_000" - assert mgr._stats["clustered_memcells"] == 1 - # LLM should NOT have been called - assert not hasattr(mock_llm, 'generate') or not mock_llm.generate.called - - @pytest.mark.asyncio - async def test_no_fast_path_when_below_threshold(self): - """sim < llm_skip_threshold -> should proceed to LLM stage.""" - centroid = np.array([1.0, 0.0, 0.0], dtype=np.float32) - different = np.array([0.6, 0.8, 0.0], dtype=np.float32) # sim ~ 0.6 - mock_llm = AsyncMock() - mock_llm.generate = AsyncMock( - return_value='{"cluster_id": "cluster_000", "reason": "same topic"}' - ) - mgr = _make_manager( - llm_provider=mock_llm, - embedding_vec=different, - ) - state = _state_with_case_cluster(centroid=centroid) - - with patch( - "memory_layer.prompts.get_prompt_by", - return_value="{memcell_text}{clusters_json}{next_new_id}", - ): - cid, _ = await mgr._cluster_memcell_llm( - _make_memcell(), state - ) - - # LLM was called and returned cluster_000 - assert cid == "cluster_000" - mock_llm.generate.assert_called() - - -# =========================================================================== -# 5. LLM failure (returns None) -> embedding fallback -# =========================================================================== - - -class TestLlmFailureFallback: - - @pytest.mark.asyncio - async def test_llm_fail_assign_existing_when_similar(self): - """5a: LLM fails, top-1 sim >= threshold -> assign existing.""" - centroid = np.array([1.0, 0.0, 0.0], dtype=np.float32) - close_vec = np.array([0.95, 0.31, 0.0], dtype=np.float32) # sim ~ 0.95 - mock_llm = AsyncMock() - mock_llm.generate = AsyncMock(return_value="invalid json {{{") - mgr = _make_manager( - llm_provider=mock_llm, - embedding_vec=close_vec, - config=_make_config(llm_skip_threshold=1.0), # never skip - ) - state = _state_with_case_cluster(centroid=centroid) - - with patch( - "memory_layer.prompts.get_prompt_by", - return_value="{memcell_text}{clusters_json}{next_new_id}", - ): - cid, out_state = await mgr._cluster_memcell_llm( - _make_memcell(), state - ) - - assert cid == "cluster_000" - assert out_state.eventid_to_cluster["evt_1"] == "cluster_000" - - @pytest.mark.asyncio - async def test_llm_fail_new_cluster_when_dissimilar(self): - """5b: LLM fails, no good candidate -> new cluster.""" - centroid = np.array([1.0, 0.0, 0.0], dtype=np.float32) - orthogonal = np.array([0.0, 1.0, 0.0], dtype=np.float32) - mock_llm = AsyncMock() - mock_llm.generate = AsyncMock(return_value="invalid json {{{") - mgr = _make_manager( - llm_provider=mock_llm, - embedding_vec=orthogonal, - config=_make_config(llm_skip_threshold=1.0), - ) - state = _state_with_case_cluster(centroid=centroid) - - with patch( - "memory_layer.prompts.get_prompt_by", - return_value="{memcell_text}{clusters_json}{next_new_id}", - ): - cid, out_state = await mgr._cluster_memcell_llm( - _make_memcell(), state - ) - - assert cid != "cluster_000" - assert cid in out_state.case_cluster_ids - assert mgr._stats["new_clusters"] == 1 - - -# =========================================================================== -# 6. LLM returns valid result -# =========================================================================== - - -class TestLlmValidResult: - - @pytest.mark.asyncio - async def test_llm_assigns_valid_existing_cluster(self): - """6a: LLM returns valid case cluster_id -> assign.""" - centroid = np.array([1.0, 0.0, 0.0], dtype=np.float32) - different = np.array([0.6, 0.8, 0.0], dtype=np.float32) - mock_llm = AsyncMock() - mock_llm.generate = AsyncMock( - return_value='{"cluster_id": "cluster_000", "reason": "related"}' - ) - mgr = _make_manager( - llm_provider=mock_llm, - embedding_vec=different, - config=_make_config(llm_skip_threshold=1.0), - ) - state = _state_with_case_cluster(centroid=centroid) - - with patch( - "memory_layer.prompts.get_prompt_by", - return_value="{memcell_text}{clusters_json}{next_new_id}", - ): - cid, out_state = await mgr._cluster_memcell_llm( - _make_memcell(), state - ) - - assert cid == "cluster_000" - assert out_state.eventid_to_cluster["evt_1"] == "cluster_000" - assert mgr._stats["clustered_memcells"] == 1 - - @pytest.mark.asyncio - async def test_llm_returns_new_cluster_id(self): - """6b: LLM returns an id not in state -> new cluster.""" - centroid = np.array([1.0, 0.0, 0.0], dtype=np.float32) - different = np.array([0.6, 0.8, 0.0], dtype=np.float32) - mock_llm = AsyncMock() - mock_llm.generate = AsyncMock( - return_value='{"cluster_id": "001", "reason": "new topic"}' - ) - mgr = _make_manager( - llm_provider=mock_llm, - embedding_vec=different, - config=_make_config(llm_skip_threshold=1.0), - ) - state = _state_with_case_cluster(centroid=centroid) - - with patch( - "memory_layer.prompts.get_prompt_by", - return_value="{memcell_text}{clusters_json}{next_new_id}", - ): - cid, out_state = await mgr._cluster_memcell_llm( - _make_memcell(), state - ) - - assert cid != "cluster_000" - assert cid in out_state.case_cluster_ids - assert mgr._stats["new_clusters"] == 1 - - @pytest.mark.asyncio - async def test_llm_returns_non_case_cluster_creates_new(self): - """6b variant: LLM returns cluster_id that exists but is NOT a case cluster.""" - centroid = np.array([1.0, 0.0, 0.0], dtype=np.float32) - different = np.array([0.6, 0.8, 0.0], dtype=np.float32) - mock_llm = AsyncMock() - # Return a cluster id that exists in cluster_counts but not in case_cluster_ids - mock_llm.generate = AsyncMock( - return_value='{"cluster_id": "cluster_999", "reason": "matched"}' - ) - mgr = _make_manager( - llm_provider=mock_llm, - embedding_vec=different, - config=_make_config(llm_skip_threshold=1.0), - ) - state = _state_with_case_cluster(centroid=centroid) - # Add a non-case cluster - state.cluster_counts["cluster_999"] = 2 - - with patch( - "memory_layer.prompts.get_prompt_by", - return_value="{memcell_text}{clusters_json}{next_new_id}", - ): - cid, out_state = await mgr._cluster_memcell_llm( - _make_memcell(), state - ) - - # Should NOT assign to cluster_999 because it's not a case cluster - assert cid != "cluster_999" - assert cid in out_state.case_cluster_ids - - -# =========================================================================== -# State mutation correctness -# =========================================================================== - - -class TestStateMutation: - - @pytest.mark.asyncio - async def test_event_appended_to_state(self): - """Every successful path appends event to state lists.""" - mgr = _make_manager(llm_provider=MagicMock()) - state = MemSceneState() - - cid, out_state = await mgr._cluster_memcell_llm( - _make_memcell(event_id="e1", timestamp=500.0), state - ) - - assert "e1" in out_state.event_ids - assert 500.0 in out_state.timestamps - assert len(out_state.vectors) == 1 - - @pytest.mark.asyncio - async def test_stats_incremented(self): - """total_memcells and clustered_memcells always incremented on success.""" - mgr = _make_manager(llm_provider=MagicMock()) - state = MemSceneState() - - await mgr._cluster_memcell_llm(_make_memcell(), state) - - assert mgr._stats["total_memcells"] == 1 - assert mgr._stats["clustered_memcells"] == 1 - - @pytest.mark.asyncio - async def test_multiple_memcells_increment_cluster_count(self): - """Assigning two events to the same cluster increments count.""" - centroid = np.array([1.0, 0.0, 0.0], dtype=np.float32) - mgr = _make_manager(llm_provider=None, embedding_vec=centroid) - state = _state_with_case_cluster(centroid=centroid) - - original_count = state.cluster_counts["cluster_000"] - await mgr._cluster_memcell_llm(_make_memcell(), state) - - assert state.cluster_counts["cluster_000"] > original_count diff --git a/methods/EverCore/tests/test_content_item_compat.py b/methods/EverCore/tests/test_content_item_compat.py deleted file mode 100644 index 2ff0e745..00000000 --- a/methods/EverCore/tests/test_content_item_compat.py +++ /dev/null @@ -1,299 +0,0 @@ -"""Tests for ContentItem field rename and normalization (content → text). - -Covers three compatibility layers: -1. ContentItem Pydantic model: model_validator maps legacy 'content' → 'text' -2. _normalize_content_items(): normalizes raw dicts at ingestion time -3. get_text_from_content_items(): reads both 'text' and 'content' fields -""" - -import pytest - -from api_specs.dtos.memory import ContentItem -from api_specs.memory_types import get_text_from_content_items -from api_specs.request_converter import _normalize_content_items - - -# ============================================================================= -# ContentItem Pydantic model — model_validator compat -# ============================================================================= - - -class TestContentItemCompatValidator: - """ContentItem model_validator: legacy 'content' field maps to 'text'.""" - - def test_canonical_text_field(self): - item = ContentItem(type="text", text="hello") - assert item.text == "hello" - - def test_legacy_content_field_mapped_to_text(self): - """Callers passing 'content' instead of 'text' should be accepted.""" - item = ContentItem.model_validate({"type": "text", "content": "hello"}) - assert item.text == "hello" - - def test_text_takes_precedence_over_content(self): - """If both 'text' and 'content' are provided, 'text' wins (no remapping).""" - item = ContentItem.model_validate( - {"type": "text", "text": "canonical", "content": "legacy"} - ) - assert item.text == "canonical" - - def test_non_text_type_with_content_field(self): - """Even non-text types with 'content' should get it mapped to 'text'.""" - item = ContentItem.model_validate({"type": "image", "content": "a cat"}) - assert item.text == "a cat" - - def test_no_text_no_content_is_none(self): - item = ContentItem(type="text") - assert item.text is None - - def test_source_field_preserved(self): - item = ContentItem.model_validate( - {"type": "text", "content": "doc body", "source": "google_doc"} - ) - assert item.text == "doc body" - assert item.source == "google_doc" - - def test_type_field_required(self): - with pytest.raises(Exception): - ContentItem.model_validate({"text": "hello"}) # missing 'type' - - -# ============================================================================= -# MessageItem.content — str | List[ContentItem] coercion -# ============================================================================= - - -class TestMessageItemContentCoercion: - """MessageItem.content accepts plain string, coerced to [{type: text, text: ...}].""" - - def _base(self, content): - return {"role": "user", "timestamp": 1700000000000, "content": content} - - def test_plain_string_coerced_to_array(self): - from api_specs.dtos.memory import MessageItem - - item = MessageItem.model_validate(self._base("hello world")) - assert len(item.content) == 1 - assert item.content[0].type == "text" - assert item.content[0].text == "hello world" - - def test_array_format_unchanged(self): - from api_specs.dtos.memory import MessageItem - - item = MessageItem.model_validate( - self._base([{"type": "text", "text": "hello"}]) - ) - assert len(item.content) == 1 - assert item.content[0].text == "hello" - - def test_array_with_legacy_content_field(self): - from api_specs.dtos.memory import MessageItem - - item = MessageItem.model_validate( - self._base([{"type": "text", "content": "hello"}]) - ) - assert item.content[0].text == "hello" - - def test_empty_string_fails_validation(self): - from api_specs.dtos.memory import MessageItem - - with pytest.raises(Exception): - MessageItem.model_validate(self._base("")) - - def test_empty_array_fails_validation(self): - from api_specs.dtos.memory import MessageItem - - with pytest.raises(Exception): - MessageItem.model_validate(self._base([])) - - -# ============================================================================= -# request_converter — raw dict path string content coercion -# ============================================================================= - - -class TestRequestConverterStringContent: - """Raw dict path: string content coerced before _normalize_content_items.""" - - def _personal_request(self, content): - return { - "user_id": "user_001", - "messages": [ - {"role": "user", "timestamp": 1700000000000, "content": content} - ], - } - - def _group_request(self, content): - return { - "group_id": "group_001", - "messages": [ - { - "role": "user", - "sender_id": "user_001", - "timestamp": 1700000000000, - "content": content, - } - ], - } - - def test_personal_add_string_content(self): - from api_specs.request_converter import convert_personal_add_to_memorize_request - - req = convert_personal_add_to_memorize_request(self._personal_request("hello")) - raw = req.new_raw_data_list[0] - items = raw.content["content"] - assert items == [{"type": "text", "text": "hello"}] - - def test_group_add_string_content(self): - from api_specs.request_converter import convert_group_add_to_memorize_request - - req = convert_group_add_to_memorize_request(self._group_request("hello group")) - raw = req.new_raw_data_list[0] - items = raw.content["content"] - assert items == [{"type": "text", "text": "hello group"}] - - def test_personal_add_empty_string_raises(self): - from api_specs.request_converter import convert_personal_add_to_memorize_request - - with pytest.raises(ValueError): - convert_personal_add_to_memorize_request(self._personal_request("")) - - def test_personal_add_array_content_still_works(self): - from api_specs.request_converter import convert_personal_add_to_memorize_request - - req = convert_personal_add_to_memorize_request( - self._personal_request([{"type": "text", "text": "array content"}]) - ) - raw = req.new_raw_data_list[0] - items = raw.content["content"] - assert items == [{"type": "text", "text": "array content"}] - - -# ============================================================================= -# _normalize_content_items() — raw dict ingestion normalization -# ============================================================================= - - -class TestNormalizeContentItems: - """_normalize_content_items() renames 'content' → 'text' for type='text' items.""" - - def test_canonical_format_unchanged(self): - items = [{"type": "text", "text": "hello"}] - result = _normalize_content_items(items) - assert result == [{"type": "text", "text": "hello"}] - - def test_legacy_content_renamed_to_text(self): - items = [{"type": "text", "content": "hello"}] - result = _normalize_content_items(items) - assert result == [{"type": "text", "text": "hello"}] - assert "content" not in result[0] - - def test_both_text_and_content_not_modified(self): - """If 'text' already exists, do not overwrite with 'content'.""" - items = [{"type": "text", "text": "canonical", "content": "legacy"}] - result = _normalize_content_items(items) - assert result[0]["text"] == "canonical" - # 'content' is NOT removed when 'text' already present (no-op case) - assert "content" in result[0] - - def test_non_text_type_content_not_renamed(self): - """Only type='text' items have their 'content' field renamed.""" - items = [{"type": "image", "content": "some_url"}] - result = _normalize_content_items(items) - # For non-text type, 'content' stays as-is (no rename) - assert result[0].get("content") == "some_url" - assert "text" not in result[0] - - def test_multiple_items_mixed(self): - items = [ - {"type": "text", "content": "first"}, - {"type": "text", "text": "second"}, - {"type": "image", "content": "img_url"}, - ] - result = _normalize_content_items(items) - assert result[0] == {"type": "text", "text": "first"} - assert result[1] == {"type": "text", "text": "second"} - assert result[2] == {"type": "image", "content": "img_url"} - - def test_non_dict_items_pass_through(self): - items = ["raw_string", 42, None] - result = _normalize_content_items(items) - assert result == ["raw_string", 42, None] - - def test_empty_list(self): - assert _normalize_content_items([]) == [] - - def test_original_dict_not_mutated(self): - """Function must not mutate the input dicts in place.""" - original = {"type": "text", "content": "hello"} - items = [original] - _normalize_content_items(items) - assert "content" in original # original is untouched - assert "text" not in original - - def test_item_without_type_field_pass_through(self): - items = [{"content": "no type"}] - result = _normalize_content_items(items) - # No 'type' field → no rename - assert result[0].get("content") == "no type" - - -# ============================================================================= -# get_text_from_content_items() — read-side compat for stored data -# ============================================================================= - - -class TestGetTextFromContentItems: - """get_text_from_content_items() reads from 'text' (new) and 'content' (legacy).""" - - def test_canonical_text_field(self): - items = [{"type": "text", "text": "hello world"}] - assert get_text_from_content_items(items) == "hello world" - - def test_legacy_content_field_fallback(self): - """Historical data stored with 'content' field should still be readable.""" - items = [{"type": "text", "content": "hello world"}] - assert get_text_from_content_items(items) == "hello world" - - def test_text_takes_priority_over_content(self): - items = [{"type": "text", "text": "canonical", "content": "legacy"}] - assert get_text_from_content_items(items) == "canonical" - - def test_multiple_text_items_joined(self): - items = [ - {"type": "text", "text": "line one"}, - {"type": "text", "text": "line two"}, - ] - assert get_text_from_content_items(items) == "line one line two" - - def test_non_text_type_formatted_as_metadata(self): - """Non-text items are formatted as [TYPE] metadata, not skipped.""" - items = [ - {"type": "image", "text": "image description"}, - {"type": "text", "text": "actual text"}, - ] - assert get_text_from_content_items(items) == "[IMAGE] actual text" - - def test_empty_text_item_skipped(self): - items = [{"type": "text", "text": ""}, {"type": "text", "text": "has content"}] - assert get_text_from_content_items(items) == "has content" - - def test_plain_string_returned_as_is(self): - """Legacy path: if a plain string is passed, return it directly.""" - assert get_text_from_content_items("plain text") == "plain text" - - def test_empty_list_returns_empty_string(self): - assert get_text_from_content_items([]) == "" - - def test_non_list_non_string_returns_empty(self): - assert get_text_from_content_items(None) == "" - assert get_text_from_content_items(42) == "" - - def test_mixed_legacy_and_canonical(self): - """Mix of old 'content' and new 'text' field items across list.""" - items = [ - {"type": "text", "content": "from legacy"}, - {"type": "text", "text": "from canonical"}, - ] - result = get_text_from_content_items(items) - assert result == "from legacy from canonical" diff --git a/methods/EverCore/tests/test_conv_memcell_extractor.py b/methods/EverCore/tests/test_conv_memcell_extractor.py deleted file mode 100644 index 454797d5..00000000 --- a/methods/EverCore/tests/test_conv_memcell_extractor.py +++ /dev/null @@ -1,589 +0,0 @@ -""" -ConvMemCellExtractor Test - -Test conversation boundary detection functionality, including: -- Multi-split boundary detection logic -- MemCell generation -- Force-split (token/message limit) -- Flush mode behavior - -Usage: - python src/bootstrap.py tests/test_conv_memcell_extractor.py -""" - -import pytest -import asyncio -from datetime import timedelta -from typing import List, Dict, Any -from unittest.mock import AsyncMock, MagicMock, patch - -# Import dependency injection related modules -from common_utils.datetime_utils import get_now_with_timezone -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger - -# Import modules to be tested -from memory_layer.memcell_extractor.conv_memcell_extractor import ( - ConvMemCellExtractor, - ConversationMemCellExtractRequest, - BatchBoundaryResult, -) -from memory_layer.memcell_extractor.base_memcell_extractor import ( - RawData, - MemCell, - StatusResult, -) -from memory_layer.llm.llm_provider import LLMProvider - -# Get logger -logger = get_logger(__name__) - - -def get_llm_provider() -> LLMProvider: - """Get LLM Provider, first try DI container, if fails then create directly""" - try: - return get_bean_by_type(LLMProvider) - except: - logger.info("LLMProvider not found in DI container, creating directly...") - return LLMProvider("openai") - - -def mock_llm_provider() -> MagicMock: - """Return a MagicMock LLM provider for unit tests (no API key needed).""" - provider = MagicMock(spec=LLMProvider) - provider.generate = AsyncMock( - return_value='{"boundaries": [], "should_wait": false}' - ) - return provider - - -class TestConvMemCellExtractor: - """ConvMemCellExtractor Test Class""" - - def setup_method(self): - """Setup before each test method""" - self.base_time = get_now_with_timezone() - timedelta(hours=1) - - def create_test_messages( - self, - count: int, - sender: str = "Alice", - time_offset_minutes: int = 0, - content_prefix: str = "Test message", - ) -> List[Dict[str, Any]]: - """Create test messages""" - messages = [] - for i in range(count): - messages.append( - { - "sender_id": f"user_{i % 2}", - "sender_name": sender if i % 2 == 0 else "Bob", - "content": [ - { - "type": "text", - "text": f"{content_prefix} {i + 1}: This is a test conversation.", - } - ], - "timestamp": ( - self.base_time + timedelta(minutes=time_offset_minutes + i) - ).isoformat(), - } - ) - return messages - - def create_raw_data_list(self, messages: List[Dict[str, Any]]) -> List[RawData]: - """Convert messages to RawData list""" - return [ - RawData( - content=msg, data_id=f"test_data_{i}", metadata={"message_index": i} - ) - for i, msg in enumerate(messages) - ] - - def make_request( - self, - history_msgs: List[Dict], - new_msgs: List[Dict], - group_id: str = "test_group", - user_id_list: List[str] = None, - flush: bool = False, - ) -> ConversationMemCellExtractRequest: - return ConversationMemCellExtractRequest( - history_raw_data_list=self.create_raw_data_list(history_msgs), - new_raw_data_list=self.create_raw_data_list(new_msgs), - user_id_list=user_id_list or ["alice", "bob"], - group_id=group_id, - flush=flush, - ) - - # ========================================================================= - # Unit tests (mock LLM) - # ========================================================================= - - @pytest.mark.asyncio - async def test_no_new_messages_returns_empty(self): - """Empty new_raw_data_list always returns empty MemCell list.""" - extractor = ConvMemCellExtractor(mock_llm_provider()) - request = ConversationMemCellExtractRequest( - history_raw_data_list=self.create_raw_data_list( - self.create_test_messages(3) - ), - new_raw_data_list=[], - user_id_list=["alice"], - group_id="test", - ) - memcells, status = await extractor.extract_memcell(request) - assert memcells == [] - assert status.should_wait is True - - @pytest.mark.asyncio - async def test_force_split_message_limit(self): - """When combined messages exceed hard_message_limit, force split without LLM.""" - extractor = ConvMemCellExtractor( - mock_llm_provider(), hard_message_limit=10, hard_token_limit=99999 - ) - - # 9 history + 3 new = 12 total, exceeds limit=10 - history = self.create_test_messages(9, time_offset_minutes=0) - new = self.create_test_messages(3, time_offset_minutes=10) - request = self.make_request(history, new, flush=False) - - # Should force-split without calling LLM - with patch.object(extractor, '_detect_boundaries') as mock_detect: - memcells, status = await extractor.extract_memcell(request) - - # Force split should have consumed some messages; LLM not called for force-split phase - # After force split, remaining (if any) go through LLM detection - assert isinstance(memcells, list) - assert len(memcells) >= 1 - # First MemCell should be from force split (within limit) - assert len(memcells[0].original_data) <= 9 - - @pytest.mark.asyncio - async def test_no_boundary_no_flush_returns_empty(self): - """LLM says no boundary, flush=False → empty list, should_wait from LLM.""" - extractor = ConvMemCellExtractor( - mock_llm_provider(), hard_message_limit=100, hard_token_limit=999999 - ) - - history = self.create_test_messages(3, time_offset_minutes=0) - new = self.create_test_messages(2, time_offset_minutes=5) - request = self.make_request(history, new, flush=False) - - # Mock LLM returns no boundaries - mock_result = BatchBoundaryResult(boundaries=[], should_wait=False) - with patch.object(extractor, '_detect_boundaries', return_value=mock_result): - memcells, status = await extractor.extract_memcell(request) - - assert memcells == [] - assert status.should_wait is False - - @pytest.mark.asyncio - async def test_no_boundary_flush_returns_one_memcell(self): - """LLM says no boundary, flush=True → one MemCell containing all messages.""" - extractor = ConvMemCellExtractor( - mock_llm_provider(), hard_message_limit=100, hard_token_limit=999999 - ) - - history = self.create_test_messages(3, time_offset_minutes=0) - new = self.create_test_messages(2, time_offset_minutes=5) - request = self.make_request(history, new, flush=True) - - mock_result = BatchBoundaryResult(boundaries=[], should_wait=False) - with patch.object(extractor, '_detect_boundaries', return_value=mock_result): - memcells, status = await extractor.extract_memcell(request) - - assert len(memcells) == 1 - assert len(memcells[0].original_data) == 5 # 3 history + 2 new - assert status.should_wait is False - - @pytest.mark.asyncio - async def test_single_boundary_detected(self): - """LLM detects one boundary → one MemCell for messages before boundary.""" - extractor = ConvMemCellExtractor( - mock_llm_provider(), hard_message_limit=100, hard_token_limit=999999 - ) - - history = self.create_test_messages(4, time_offset_minutes=0) - new = self.create_test_messages(3, time_offset_minutes=10) - request = self.make_request(history, new, flush=False) - - # LLM detects boundary after message 4 (all history, none of new) - mock_result = BatchBoundaryResult(boundaries=[4], should_wait=False) - with patch.object(extractor, '_detect_boundaries', return_value=mock_result): - memcells, status = await extractor.extract_memcell(request) - - assert len(memcells) == 1 - assert len(memcells[0].original_data) == 4 - assert status.should_wait is False - - @pytest.mark.asyncio - async def test_multiple_boundaries_detected(self): - """LLM detects two boundaries → two MemCells, remainder accumulated.""" - extractor = ConvMemCellExtractor( - mock_llm_provider(), hard_message_limit=100, hard_token_limit=999999 - ) - - history = self.create_test_messages(6, time_offset_minutes=0) - new = self.create_test_messages(4, time_offset_minutes=60) - request = self.make_request(history, new, flush=False) - - # Boundaries after message 3 and after message 7 (within 10 total) - mock_result = BatchBoundaryResult(boundaries=[3, 7], should_wait=False) - with patch.object(extractor, '_detect_boundaries', return_value=mock_result): - memcells, status = await extractor.extract_memcell(request) - - assert len(memcells) == 2 - assert len(memcells[0].original_data) == 3 - assert len(memcells[1].original_data) == 4 # messages 4-7 - # Remaining 3 messages (8-10) are not in any MemCell - assert status.should_wait is False - - @pytest.mark.asyncio - async def test_multiple_boundaries_with_flush(self): - """LLM detects boundaries + flush=True → MemCells + final flush MemCell.""" - extractor = ConvMemCellExtractor( - mock_llm_provider(), hard_message_limit=100, hard_token_limit=999999 - ) - - history = self.create_test_messages(4, time_offset_minutes=0) - new = self.create_test_messages(3, time_offset_minutes=60) - request = self.make_request(history, new, flush=True) - - # One boundary after message 4, leaving messages 5-7 for flush - mock_result = BatchBoundaryResult(boundaries=[4], should_wait=False) - with patch.object(extractor, '_detect_boundaries', return_value=mock_result): - memcells, status = await extractor.extract_memcell(request) - - assert len(memcells) == 2 - assert len(memcells[0].original_data) == 4 # LLM boundary - assert len(memcells[1].original_data) == 3 # flush tail - assert status.should_wait is False - - @pytest.mark.asyncio - async def test_should_wait_propagated(self): - """LLM returns should_wait=True → propagated in StatusResult.""" - extractor = ConvMemCellExtractor( - mock_llm_provider(), hard_message_limit=100, hard_token_limit=999999 - ) - - history = self.create_test_messages(3, time_offset_minutes=0) - new = self.create_test_messages(1, time_offset_minutes=5) - request = self.make_request(history, new, flush=False) - - mock_result = BatchBoundaryResult(boundaries=[], should_wait=True) - with patch.object(extractor, '_detect_boundaries', return_value=mock_result): - memcells, status = await extractor.extract_memcell(request) - - assert memcells == [] - assert status.should_wait is True - - @pytest.mark.asyncio - async def test_force_split_large_history_no_history(self): - """When new messages alone exceed limits, force-split them too.""" - extractor = ConvMemCellExtractor( - mock_llm_provider(), hard_message_limit=5, hard_token_limit=999999 - ) - - # 0 history + 8 new = 8 total, exceeds limit=5 - history: List[Dict] = [] - new = self.create_test_messages(8, time_offset_minutes=0) - request = self.make_request(history, new, flush=False) - - mock_result = BatchBoundaryResult(boundaries=[], should_wait=False) - with patch.object(extractor, '_detect_boundaries', return_value=mock_result): - memcells, status = await extractor.extract_memcell(request) - - # Force split should create at least one MemCell even with no history - assert len(memcells) >= 1 - for mc in memcells: - assert len(mc.original_data) <= 4 # hard_message_limit - 1 - - # ========================================================================= - # Parse helper unit tests - # ========================================================================= - - def test_parse_markdown_json(self): - """Parse LLM response with markdown code block.""" - extractor = ConvMemCellExtractor(mock_llm_provider()) - resp = '''Some text before -```json -{"boundaries": [5], "should_wait": false} -``` -Some text after''' - result = extractor._parse_batch_boundary_response(resp) - assert result is not None - assert len(result.boundaries) == 1 - assert result.boundaries[0] == 5 - assert result.should_wait is False - - def test_parse_raw_json(self): - """Parse LLM response as raw JSON.""" - extractor = ConvMemCellExtractor(mock_llm_provider()) - resp = '{"boundaries": [], "should_wait": true}' - result = extractor._parse_batch_boundary_response(resp) - assert result is not None - assert result.boundaries == [] - assert result.should_wait is True - - def test_parse_multiple_boundaries(self): - """Parse response with multiple boundaries.""" - extractor = ConvMemCellExtractor(mock_llm_provider()) - resp = '''{ - "boundaries": [3, 7], - "should_wait": false -}''' - result = extractor._parse_batch_boundary_response(resp) - assert result is not None - assert len(result.boundaries) == 2 - assert result.boundaries[0] == 3 - assert result.boundaries[1] == 7 - - def test_parse_invalid_returns_none(self): - """Invalid JSON returns None.""" - extractor = ConvMemCellExtractor(mock_llm_provider()) - result = extractor._parse_batch_boundary_response("not valid json at all") - assert result is None - - def test_format_messages_with_indices(self): - """Messages are formatted with 1-based indices.""" - extractor = ConvMemCellExtractor(mock_llm_provider()) - messages = [ - { - "sender_name": "Alice", - "content": [{"type": "text", "text": "Hello"}], - "timestamp": self.base_time.isoformat(), - }, - { - "sender_name": "Bob", - "content": [{"type": "text", "text": "Hi there"}], - "timestamp": (self.base_time + timedelta(minutes=1)).isoformat(), - }, - ] - formatted = extractor._format_messages_with_indices(messages) - assert "[1]" in formatted - assert "[2]" in formatted - assert "Alice: Hello" in formatted - assert "Bob: Hi there" in formatted - - # ========================================================================= - # Integration tests (require real LLM) - # ========================================================================= - - @pytest.mark.asyncio - async def test_realistic_conversation_no_boundary(self): - """Realistic conversation that continues — expect no MemCell (accumulate).""" - llm_provider = get_llm_provider() - extractor = ConvMemCellExtractor(llm_provider) - - history_messages = [ - { - "sender_name": "Alice", - "content": [ - {"type": "text", "text": "Let's discuss the project plan."} - ], - "offset": 0, - }, - { - "sender_name": "Bob", - "content": [{"type": "text", "text": "Sure, backend is 80% done."}], - "offset": 2, - }, - { - "sender_name": "Charlie", - "content": [{"type": "text", "text": "Frontend design is complete."}], - "offset": 4, - }, - ] - new_messages = [ - { - "sender_name": "Alice", - "content": [ - { - "type": "text", - "text": "Great, when can we do integration testing?", - } - ], - "offset": 6, - }, - { - "sender_name": "Bob", - "content": [{"type": "text", "text": "Next week should work."}], - "offset": 8, - }, - ] - - def to_raw_data(msgs): - return [ - RawData( - content={ - "sender_id": f"user_{m['sender_name'].lower()}", - "sender_name": m["sender_name"], - "content": m["content"], - "timestamp": ( - self.base_time + timedelta(minutes=m["offset"]) - ).isoformat(), - }, - data_id=f"msg_{i}", - metadata={}, - ) - for i, m in enumerate(msgs) - ] - - request = ConversationMemCellExtractRequest( - history_raw_data_list=to_raw_data(history_messages), - new_raw_data_list=to_raw_data(new_messages), - user_id_list=["alice", "bob", "charlie"], - group_id="project_team", - flush=False, - ) - - memcells, status = await extractor.extract_memcell(request) - - print( - f"\n✅ Realistic no-boundary: memcells={len(memcells)}, should_wait={status.should_wait}" - ) - assert isinstance(memcells, list) - assert isinstance(status, StatusResult) - - @pytest.mark.asyncio - async def test_complete_meeting_with_flush(self): - """Complete meeting conversation with flush=True → at least one MemCell.""" - llm_provider = get_llm_provider() - extractor = ConvMemCellExtractor(llm_provider) - - base_time = get_now_with_timezone() - timedelta(hours=2) - - def make_msg(sender, content, offset): - return { - "sender_id": f"user_{sender.lower()}", - "sender_name": sender, - "content": content, - "timestamp": (base_time + timedelta(minutes=offset)).isoformat(), - } - - history = [ - make_msg("Alice", "Starting the project review meeting.", 0), - make_msg("Bob", "Backend API is 80% complete.", 2), - make_msg("Charlie", "Frontend design is also done.", 4), - make_msg("Alice", "Good, any technical challenges?", 6), - make_msg("Bob", "Permission management was tricky but solved.", 8), - ] - new = [ - make_msg( - "Alice", "Charlie, present the technical solution adjustments.", 45 - ), - make_msg( - "Charlie", "I suggest microservices architecture for scalability.", 46 - ), - make_msg("Bob", "Agreed. Should we adjust the timeline?", 47), - make_msg("Alice", "Yes, one week delay but better quality.", 48), - make_msg("Alice", "Meeting adjourned! I'll send the minutes.", 56), - ] - - def to_raw(msgs): - return [ - RawData(content=m, data_id=f"m_{i}", metadata={}) - for i, m in enumerate(msgs) - ] - - request = ConversationMemCellExtractRequest( - history_raw_data_list=to_raw(history), - new_raw_data_list=to_raw(new), - user_id_list=["alice", "bob", "charlie"], - group_id="complete_meeting", - flush=True, - ) - - memcells, status = await extractor.extract_memcell(request) - - print( - f"\n✅ Complete meeting flush: memcells={len(memcells)}, should_wait={status.should_wait}" - ) - assert len(memcells) >= 1 - assert status.should_wait is False - - for mc in memcells: - assert mc.event_id is not None - assert mc.group_id == "complete_meeting" - assert len(mc.original_data) > 0 - - -async def run_all_tests(): - """Run all tests""" - print("🚀 Starting ConvMemCellExtractor tests") - print("=" * 60) - - test_instance = TestConvMemCellExtractor() - - try: - test_instance.setup_method() - await test_instance.test_no_new_messages_returns_empty() - print("✅ test_no_new_messages_returns_empty passed") - - test_instance.setup_method() - await test_instance.test_no_boundary_no_flush_returns_empty() - print("✅ test_no_boundary_no_flush_returns_empty passed") - - test_instance.setup_method() - await test_instance.test_no_boundary_flush_returns_one_memcell() - print("✅ test_no_boundary_flush_returns_one_memcell passed") - - test_instance.setup_method() - await test_instance.test_single_boundary_detected() - print("✅ test_single_boundary_detected passed") - - test_instance.setup_method() - await test_instance.test_multiple_boundaries_detected() - print("✅ test_multiple_boundaries_detected passed") - - test_instance.setup_method() - await test_instance.test_multiple_boundaries_with_flush() - print("✅ test_multiple_boundaries_with_flush passed") - - test_instance.setup_method() - await test_instance.test_should_wait_propagated() - print("✅ test_should_wait_propagated passed") - - test_instance.setup_method() - test_instance.test_parse_markdown_json() - print("✅ test_parse_markdown_json passed") - - test_instance.setup_method() - test_instance.test_parse_raw_json() - print("✅ test_parse_raw_json passed") - - test_instance.setup_method() - test_instance.test_parse_multiple_boundaries() - print("✅ test_parse_multiple_boundaries passed") - - test_instance.setup_method() - test_instance.test_parse_invalid_returns_none() - print("✅ test_parse_invalid_returns_none passed") - - test_instance.setup_method() - test_instance.test_format_messages_with_indices() - print("✅ test_format_messages_with_indices passed") - - # Integration tests (require LLM) - test_instance.setup_method() - await test_instance.test_realistic_conversation_no_boundary() - print("✅ test_realistic_conversation_no_boundary passed") - - test_instance.setup_method() - await test_instance.test_complete_meeting_with_flush() - print("✅ test_complete_meeting_with_flush passed") - - print("\n" + "=" * 60) - print("🎉 All tests completed!") - - except Exception as e: - logger.error(f"❌ Test execution failed: {e}") - import traceback - - traceback.print_exc() - raise - - -if __name__ == "__main__": - asyncio.run(run_all_tests()) diff --git a/methods/EverCore/tests/test_conversation_data_raw_repository.py b/methods/EverCore/tests/test_conversation_data_raw_repository.py deleted file mode 100644 index ad25d4c4..00000000 --- a/methods/EverCore/tests/test_conversation_data_raw_repository.py +++ /dev/null @@ -1,660 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Test the functionality of ConversationDataRepository - -Test contents include: -1. save_conversation_data (confirms specified message_ids only, -1 -> 0) -2. get_conversation_data (returns sync_status=-1 and 0) -3. delete_conversation_data (marks sync_status=-1 and 0 as used -> 1) -4. fetch_unprocessed_conversation_data -5. sync_status state transitions -""" - -import asyncio -import uuid -from datetime import datetime, timedelta -from typing import List - -from core.di import get_bean_by_type -from common_utils.datetime_utils import get_now_with_timezone, to_iso_format -from infra_layer.adapters.out.persistence.repository.conversation_data_raw_repository import ( - ConversationDataRepository, -) -from infra_layer.adapters.out.persistence.repository.raw_message_repository import ( - RawMessageRepository, -) -from infra_layer.adapters.out.persistence.document.request.raw_message import RawMessage -from memory_layer.memcell_extractor.base_memcell_extractor import RawData -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -def generate_unique_id(prefix: str = "") -> str: - """Generate a unique ID for testing""" - return f"{prefix}{uuid.uuid4().hex[:8]}" - - -async def create_test_raw_message( - group_id: str, - message_id: str, - content: str, - sync_status: int = -1, - created_at: datetime = None, -) -> RawMessage: - """ - Create a test RawMessage record - - Args: - group_id: Group ID - message_id: Message ID - content: Message content - sync_status: Sync status (-1=log, 0=accumulating, 1=used) - created_at: Created time (default: now) - - Returns: - Created RawMessage object - """ - repo = get_bean_by_type(RawMessageRepository) - - msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - message_id=message_id, - sender_id="test_user", - sender_name="Test User", - content=content, - timestamp=to_iso_format(created_at or get_now_with_timezone()), - sync_status=sync_status, - ) - - # Manually set created_at if provided - if created_at: - msg.created_at = created_at - - await repo.save(msg) - return msg - - -async def cleanup_test_data(group_id: str): - """Clean up test data for a group""" - repo = get_bean_by_type(RawMessageRepository) - await repo.delete_by_group_id(group_id) - - -async def get_logs_by_group_id(group_id: str) -> List[RawMessage]: - """Get all logs for a group (for verification)""" - repo = get_bean_by_type(RawMessageRepository) - return await repo.find_by_group_id(group_id, sync_status=None) - - -async def test_save_conversation_data_basic(): - """Test basic save_conversation_data functionality""" - logger.info("Starting test for basic save_conversation_data...") - - repo = get_bean_by_type(ConversationDataRepository) - group_id = generate_unique_id("test_save_basic_") - - try: - # Create test log records with sync_status=-1 - msg1 = await create_test_raw_message( - group_id=group_id, - message_id=generate_unique_id("msg_"), - content="Test message 1", - sync_status=-1, - ) - msg2 = await create_test_raw_message( - group_id=group_id, - message_id=generate_unique_id("msg_"), - content="Test message 2", - sync_status=-1, - ) - logger.info("✅ Created 2 test log records with sync_status=-1") - - # Create RawData list with data_id - raw_data_list = [ - RawData( - data_id=msg1.message_id, - content={"content": "Test message 1"}, - data_type="message", - ), - RawData( - data_id=msg2.message_id, - content={"content": "Test message 2"}, - data_type="message", - ), - ] - - # Save conversation data - result = await repo.save_conversation_data(raw_data_list, group_id) - assert result is True - logger.info("✅ save_conversation_data returned True") - - # Verify sync_status changed to 0 - logs = await get_logs_by_group_id(group_id) - for log in logs: - assert ( - log.sync_status == 0 - ), f"Expected sync_status=0, got {log.sync_status}" - logger.info("✅ All records now have sync_status=0") - - except Exception as e: - logger.error("❌ Test for basic save_conversation_data failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ Basic save_conversation_data test completed") - - -async def test_save_conversation_data_precise(): - """Test save_conversation_data only confirms specified message_ids""" - logger.info("Starting test for save_conversation_data precise update...") - - repo = get_bean_by_type(ConversationDataRepository) - group_id = generate_unique_id("test_precise_") - - try: - # Create 3 log records with sync_status=-1 - msg1_id = generate_unique_id("msg_") - msg2_id = generate_unique_id("msg_") - msg3_id = generate_unique_id("msg_") - - await create_test_raw_message( - group_id=group_id, message_id=msg1_id, content="Message 1", sync_status=-1 - ) - await create_test_raw_message( - group_id=group_id, message_id=msg2_id, content="Message 2", sync_status=-1 - ) - await create_test_raw_message( - group_id=group_id, message_id=msg3_id, content="Message 3", sync_status=-1 - ) - logger.info("✅ Created 3 test log records with sync_status=-1") - - # save_conversation_data only confirms the specified message_id - raw_data_list = [ - RawData( - data_id=msg1_id, content={"content": "Message 1"}, data_type="message" - ) - ] - - result = await repo.save_conversation_data(raw_data_list, group_id) - assert result is True - logger.info("✅ save_conversation_data succeeded") - - # Verify only msg1 has sync_status=0, others remain -1 - logs = await get_logs_by_group_id(group_id) - assert len(logs) == 3 - - confirmed_count = 0 - pending_count = 0 - for log in logs: - if log.message_id == msg1_id: - assert log.sync_status == 0, "msg1 should have sync_status=0" - confirmed_count += 1 - else: - assert log.sync_status == -1, "Other msgs should have sync_status=-1" - pending_count += 1 - - assert confirmed_count == 1 - assert pending_count == 2 - logger.info("✅ Only msg1 has sync_status=0, others remain at -1") - - except Exception as e: - logger.error("❌ Test for save_conversation_data precise failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ save_conversation_data precise test completed") - - -async def test_get_conversation_data(): - """Test get_conversation_data returns sync_status=-1 and 0 records""" - logger.info("Starting test for get_conversation_data...") - - repo = get_bean_by_type(ConversationDataRepository) - group_id = generate_unique_id("test_get_data_") - - try: - # Create logs with different sync_status - msg1_id = generate_unique_id("msg_") - msg2_id = generate_unique_id("msg_") - msg3_id = generate_unique_id("msg_") - - await create_test_raw_message( - group_id=group_id, - message_id=msg1_id, - content="Pending message", - sync_status=-1, # pending - ) - await create_test_raw_message( - group_id=group_id, - message_id=msg2_id, - content="Accumulating message", - sync_status=0, # accumulating - ) - await create_test_raw_message( - group_id=group_id, - message_id=msg3_id, - content="Used message", - sync_status=1, # used - ) - logger.info("✅ Created logs with sync_status -1, 0, 1") - - # get_conversation_data should return both -1 and 0, exclude 1 - result = await repo.get_conversation_data(group_id=group_id) - assert ( - len(result) == 2 - ), f"Expected 2 results (sync_status=-1 and 0), got {len(result)}" - # RawData.content is dict, need to extract the internal "content" field - contents = [r.content.get("content") for r in result] - assert "Pending message" in contents - assert "Accumulating message" in contents - assert "Used message" not in contents - logger.info( - "✅ get_conversation_data returned 2 records (sync_status=-1 and 0)" - ) - - # Test exclude_message_ids: exclude msg1_id - result_excluded = await repo.get_conversation_data( - group_id=group_id, exclude_message_ids=[msg1_id] - ) - assert ( - len(result_excluded) == 1 - ), f"Expected 1 result after exclusion, got {len(result_excluded)}" - assert result_excluded[0].content.get("content") == "Accumulating message" - logger.info("✅ get_conversation_data with exclude_message_ids works correctly") - - except Exception as e: - logger.error("❌ Test for get_conversation_data failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ get_conversation_data test completed") - - -async def test_delete_conversation_data(): - """Test delete_conversation_data (marks sync_status=-1 and 0 as used)""" - logger.info("Starting test for delete_conversation_data...") - - repo = get_bean_by_type(ConversationDataRepository) - group_id = generate_unique_id("test_delete_") - - try: - # Create logs with sync_status=-1 and 0 - msg1_id = generate_unique_id("msg_") - msg2_id = generate_unique_id("msg_") - - await create_test_raw_message( - group_id=group_id, - message_id=msg1_id, - content="Pending message", - sync_status=-1, - ) - await create_test_raw_message( - group_id=group_id, - message_id=msg2_id, - content="Accumulating message", - sync_status=0, - ) - logger.info("✅ Created logs with sync_status -1 and 0") - - # Delete (mark as used) - affects both -1 and 0 - result = await repo.delete_conversation_data(group_id) - assert result is True - logger.info("✅ delete_conversation_data returned True") - - # Verify: both -1 and 0 are now 1 - logs = await get_logs_by_group_id(group_id) - assert len(logs) == 2 - used_count = sum(1 for log in logs if log.sync_status == 1) - assert used_count == 2, f"Expected 2 used records, got {used_count}" - logger.info("✅ Both sync_status=-1 and 0 were marked as used (1)") - - # Verify get_conversation_data returns empty (no -1 or 0 left) - remaining = await repo.get_conversation_data(group_id) - assert len(remaining) == 0, f"Expected 0 results, got {len(remaining)}" - logger.info("✅ get_conversation_data returns empty after deletion") - - except Exception as e: - logger.error("❌ Test for delete_conversation_data failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ delete_conversation_data test completed") - - -async def test_delete_conversation_data_with_exclude(): - """Test delete_conversation_data with exclude_message_ids""" - logger.info("Starting test for delete_conversation_data with exclude...") - - repo = get_bean_by_type(ConversationDataRepository) - group_id = generate_unique_id("test_delete_exclude_") - - try: - # Create logs with sync_status=-1 and 0 - msg1_id = generate_unique_id("msg_") - msg2_id = generate_unique_id("msg_") - msg3_id = generate_unique_id("msg_") - - await create_test_raw_message( - group_id=group_id, message_id=msg1_id, content="Message 1", sync_status=-1 - ) - await create_test_raw_message( - group_id=group_id, message_id=msg2_id, content="Message 2", sync_status=0 - ) - await create_test_raw_message( - group_id=group_id, message_id=msg3_id, content="Message 3", sync_status=-1 - ) - logger.info("✅ Created 3 logs") - - # Delete but exclude msg3_id - result = await repo.delete_conversation_data( - group_id, exclude_message_ids=[msg3_id] - ) - assert result is True - logger.info("✅ delete_conversation_data with exclude returned True") - - # Verify: msg1 and msg2 are now 1, msg3 remains -1 - logs = await get_logs_by_group_id(group_id) - assert len(logs) == 3 - - for log in logs: - if log.message_id == msg3_id: - assert ( - log.sync_status == -1 - ), f"msg3 should remain -1, got {log.sync_status}" - else: - assert ( - log.sync_status == 1 - ), f"Other msgs should be 1, got {log.sync_status}" - - logger.info("✅ msg3 was excluded, others were marked as used") - - # Verify get_conversation_data returns only msg3 - remaining = await repo.get_conversation_data(group_id) - assert len(remaining) == 1, f"Expected 1 result, got {len(remaining)}" - assert remaining[0].data_id == msg3_id - logger.info("✅ Only excluded message remains") - - except Exception as e: - logger.error("❌ Test for delete_conversation_data with exclude failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ delete_conversation_data with exclude test completed") - - -async def test_fetch_unprocessed_conversation_data(): - """Test fetch_unprocessed_conversation_data""" - logger.info("Starting test for fetch_unprocessed_conversation_data...") - - repo = get_bean_by_type(ConversationDataRepository) - group_id = generate_unique_id("test_fetch_unprocessed_") - - try: - now = get_now_with_timezone() - - # Create logs with different sync_status and timestamps - await create_test_raw_message( - group_id=group_id, - message_id=generate_unique_id("msg_"), - content="Oldest pending", - sync_status=-1, - created_at=now - timedelta(hours=3), - ) - await create_test_raw_message( - group_id=group_id, - message_id=generate_unique_id("msg_"), - content="Middle accumulating", - sync_status=0, - created_at=now - timedelta(hours=2), - ) - await create_test_raw_message( - group_id=group_id, - message_id=generate_unique_id("msg_"), - content="Newest pending", - sync_status=-1, - created_at=now - timedelta(hours=1), - ) - await create_test_raw_message( - group_id=group_id, - message_id=generate_unique_id("msg_"), - content="Used message", - sync_status=1, - created_at=now, - ) - logger.info("✅ Created 4 logs with different sync_status and timestamps") - - # Fetch unprocessed data - result = await repo.fetch_unprocessed_conversation_data(group_id, limit=100) - - # Should get 3 records (sync_status=-1 and 0, excluding sync_status=1) - assert len(result) == 3, f"Expected 3 results, got {len(result)}" - logger.info("✅ fetch_unprocessed returned 3 records") - - # Verify ascending order (oldest first) - # RawData.content is a dict, and the content field contains the message text - assert "Oldest pending" in str(result[0].content.get("content", "")) - assert "Middle accumulating" in str(result[1].content.get("content", "")) - assert "Newest pending" in str(result[2].content.get("content", "")) - logger.info("✅ Results are in ascending order (oldest first)") - - # Test with limit - limited_result = await repo.fetch_unprocessed_conversation_data( - group_id, limit=2 - ) - assert ( - len(limited_result) == 2 - ), f"Expected 2 results with limit, got {len(limited_result)}" - assert "Oldest pending" in str(limited_result[0].content.get("content", "")) - assert "Middle accumulating" in str( - limited_result[1].content.get("content", "") - ) - logger.info("✅ Limit parameter works correctly") - - except Exception as e: - logger.error("❌ Test for fetch_unprocessed_conversation_data failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ fetch_unprocessed_conversation_data test completed") - - -async def test_sync_status_state_transitions(): - """Test the complete sync_status state transition flow""" - logger.info("Starting test for sync_status state transitions...") - - repo = get_bean_by_type(ConversationDataRepository) - group_id = generate_unique_id("test_state_flow_") - - try: - # Step 1: Create initial log (simulating RequestHistoryEvent listener) - # New logs start with sync_status=-1 - msg_id = generate_unique_id("msg_") - await create_test_raw_message( - group_id=group_id, - message_id=msg_id, - content="Test message for state flow", - sync_status=-1, - ) - logger.info("✅ Step 1: Created log with sync_status=-1 (log record)") - - # Verify initial state - logs = await get_logs_by_group_id(group_id) - assert len(logs) == 1 - assert logs[0].sync_status == -1 - logger.info("✅ Verified initial sync_status=-1") - - # Step 2: save_conversation_data -> sync_status becomes 0 - raw_data_list = [ - RawData( - data_id=msg_id, content={"content": "Test message"}, data_type="message" - ) - ] - await repo.save_conversation_data(raw_data_list, group_id) - - logs = await get_logs_by_group_id(group_id) - assert logs[0].sync_status == 0 - logger.info("✅ Step 2: sync_status changed to 0 (window accumulation)") - - # Step 3: delete_conversation_data -> sync_status becomes 1 - await repo.delete_conversation_data(group_id) - - logs = await get_logs_by_group_id(group_id) - assert logs[0].sync_status == 1 - logger.info("✅ Step 3: sync_status changed to 1 (used)") - - # Verify the message is no longer retrievable - result = await repo.get_conversation_data(group_id) - assert len(result) == 0 - logger.info("✅ Verified: used messages are not retrievable") - - result_unprocessed = await repo.fetch_unprocessed_conversation_data(group_id) - assert len(result_unprocessed) == 0 - logger.info("✅ Verified: used messages not in unprocessed") - - except Exception as e: - logger.error("❌ Test for sync_status state transitions failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ sync_status state transitions test completed") - - -async def test_empty_raw_data_list(): - """Test save_conversation_data with empty raw_data_list""" - logger.info("Starting test for empty raw_data_list...") - - repo = get_bean_by_type(ConversationDataRepository) - group_id = generate_unique_id("test_empty_list_") - - try: - # Create log records - await create_test_raw_message( - group_id=group_id, - message_id=generate_unique_id("msg_"), - content="Message 1", - sync_status=-1, - ) - await create_test_raw_message( - group_id=group_id, - message_id=generate_unique_id("msg_"), - content="Message 2", - sync_status=-1, - ) - logger.info("✅ Created 2 log records") - - # Save with empty list - should do nothing (no message_ids to confirm) - result = await repo.save_conversation_data([], group_id) - assert result is True - logger.info("✅ save_conversation_data with empty list returned True") - - # Verify all records remain unchanged (sync_status=-1) - logs = await get_logs_by_group_id(group_id) - for log in logs: - assert ( - log.sync_status == -1 - ), f"Expected sync_status=-1 (unchanged), got {log.sync_status}" - logger.info("✅ All records remain unchanged with empty list") - - except Exception as e: - logger.error("❌ Test for empty raw_data_list failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ empty raw_data_list test completed") - - -async def test_raw_data_list_without_data_id(): - """Test save_conversation_data with raw_data_list that has no data_id""" - logger.info("Starting test for raw_data_list without data_id...") - - repo = get_bean_by_type(ConversationDataRepository) - group_id = generate_unique_id("test_no_data_id_") - - try: - # Create log records - await create_test_raw_message( - group_id=group_id, - message_id=generate_unique_id("msg_"), - content="Message 1", - sync_status=-1, - ) - await create_test_raw_message( - group_id=group_id, - message_id=generate_unique_id("msg_"), - content="Message 2", - sync_status=-1, - ) - logger.info("✅ Created 2 log records") - - # Save with RawData that has no data_id (empty string is filtered out) - raw_data_list = [ - RawData( - data_id="", - content={"content": "Content without ID"}, - data_type="message", - ) - ] - - result = await repo.save_conversation_data(raw_data_list, group_id) - assert result is True - logger.info("✅ save_conversation_data with no data_id returned True") - - # Verify all records remain unchanged (empty data_id is filtered out) - logs = await get_logs_by_group_id(group_id) - for log in logs: - assert ( - log.sync_status == -1 - ), f"Expected sync_status=-1 (unchanged), got {log.sync_status}" - logger.info("✅ All records remain unchanged (empty data_id filtered out)") - - except Exception as e: - logger.error("❌ Test for raw_data_list without data_id failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ raw_data_list without data_id test completed") - - -async def run_all_tests(): - """Run all tests""" - logger.info("🚀 Starting to run all ConversationDataRepository tests...") - - try: - await test_save_conversation_data_basic() - await test_save_conversation_data_precise() - await test_get_conversation_data() - await test_delete_conversation_data() - await test_delete_conversation_data_with_exclude() - await test_fetch_unprocessed_conversation_data() - await test_sync_status_state_transitions() - await test_empty_raw_data_list() - await test_raw_data_list_without_data_id() - logger.info("✅ All tests completed successfully") - except Exception as e: - logger.error("❌ Error occurred during testing: %s", e) - raise - - -if __name__ == "__main__": - asyncio.run(run_all_tests()) diff --git a/methods/EverCore/tests/test_conversation_status_raw_repository.py b/methods/EverCore/tests/test_conversation_status_raw_repository.py deleted file mode 100644 index d94413bf..00000000 --- a/methods/EverCore/tests/test_conversation_status_raw_repository.py +++ /dev/null @@ -1,235 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Test the functionality of ConversationStatusRawRepository - -Test contents include: -1. Query and update operations based on group_id -2. Statistical methods -""" - -import asyncio -from datetime import datetime -from zoneinfo import ZoneInfo - -from core.di import get_bean_by_type -from common_utils.datetime_utils import get_now_with_timezone, to_iso_format -from infra_layer.adapters.out.persistence.repository.conversation_status_raw_repository import ( - ConversationStatusRawRepository, -) -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -def compare_datetime(dt1: datetime, dt2: datetime) -> bool: - """Compare two datetime objects, only up to second-level precision""" - return dt1.replace(microsecond=0) == dt2.replace(microsecond=0) - - -async def test_group_operations(): - """Test group-related operations""" - logger.info("Starting test for group-related operations...") - - repo = get_bean_by_type(ConversationStatusRawRepository) - group_id = "test_group_001" - current_time = get_now_with_timezone() - - try: - # Test upsert (create new record) - update_data = { - "old_msg_start_time": current_time, - "new_msg_start_time": current_time, - "last_memcell_time": current_time, - } - - result = await repo.upsert_by_group_id(group_id, update_data) - assert result is not None - assert result.group_id == group_id - logger.info("✅ Test upsert to create new record succeeded") - - # Test querying by group_id - queried = await repo.get_by_group_id(group_id) - assert queried is not None - assert queried.group_id == group_id - assert compare_datetime(queried.old_msg_start_time, current_time) - assert compare_datetime(queried.new_msg_start_time, current_time) - logger.info("✅ Test querying by group_id succeeded") - - # Test upsert (update existing record) - new_time = get_now_with_timezone() - update_data = {"old_msg_start_time": new_time, "new_msg_start_time": new_time} - - updated = await repo.upsert_by_group_id(group_id, update_data) - assert updated is not None - assert compare_datetime(updated.old_msg_start_time, new_time) - assert compare_datetime(updated.new_msg_start_time, new_time) - assert compare_datetime( - updated.last_memcell_time, current_time - ) # Fields not updated should retain original values - logger.info("✅ Test upsert to update existing record succeeded") - - # Query again to verify update - queried_again = await repo.get_by_group_id(group_id) - assert queried_again is not None - assert compare_datetime(queried_again.old_msg_start_time, new_time) - assert compare_datetime(queried_again.new_msg_start_time, new_time) - assert compare_datetime(queried_again.last_memcell_time, current_time) - logger.info("✅ Verified update result successfully") - - # Clean up test data - await queried_again.delete() - logger.info("✅ Cleaned up test data successfully") - - # Verify deletion - final_check = await repo.get_by_group_id(group_id) - assert final_check is None, "Record should have been deleted" - logger.info("✅ Verified deletion successfully") - - except Exception as e: - logger.error("❌ Test for group-related operations failed: %s", e) - raise - - logger.info("✅ Group-related operations test completed") - - -async def test_statistics(): - """Test statistical methods""" - logger.info("Starting test for statistical methods...") - - repo = get_bean_by_type(ConversationStatusRawRepository) - base_group_id = "test_group_stats" - current_time = get_now_with_timezone() - - try: - # Create multiple test records - test_records = [] - for i in range(3): - group_id = f"{base_group_id}_{i}" - result = await repo.upsert_by_group_id( - group_id=group_id, - update_data={ - "old_msg_start_time": current_time, - "new_msg_start_time": current_time, - "last_memcell_time": current_time, - }, - ) - test_records.append(result) - logger.info("✅ Created test records successfully") - - # Test group record count - count = await repo.count_by_group_id( - f"{base_group_id}_0" - ) # Test count for the first group - assert count == 1, "Should have 1 record, actually has %d records" % count - logger.info("✅ Test group record count succeeded") - - # Test total record count - total = await repo.count_all() - assert total >= 3, ( - "Total record count should be at least 3, actually is %d" % total - ) - logger.info("✅ Test total record count succeeded") - - # Clean up test data - for record in test_records: - await record.delete() - logger.info("✅ Cleaned up test data successfully") - - except Exception as e: - logger.error("❌ Test for statistical methods failed: %s", e) - raise - - logger.info("✅ Statistical methods test completed") - - -async def test_timezone_handling(): - """Test datetime handling in different time zones""" - logger.info("Starting test for time zone handling...") - - repo = get_bean_by_type(ConversationStatusRawRepository) - group_id = "test_timezone_001" - - try: - # Create UTC time - utc_time = get_now_with_timezone(ZoneInfo("UTC")) - # Create Tokyo time - tokyo_time = get_now_with_timezone(ZoneInfo("Asia/Tokyo")) - - shanghai_time = get_now_with_timezone(ZoneInfo("Asia/Shanghai")) - - # Create record using times from different time zones - update_data = { - "old_msg_start_time": utc_time, - "new_msg_start_time": tokyo_time, - "last_memcell_time": shanghai_time, - } - - # Record original time in ISO format for comparison - logger.info("Original UTC time: %s", to_iso_format(utc_time)) - logger.info("Original Tokyo time: %s", to_iso_format(tokyo_time)) - logger.info("Original Shanghai time: %s", to_iso_format(shanghai_time)) - - # Insert into database - result = await repo.upsert_by_group_id(group_id, update_data) - assert result is not None - logger.info("✅ Inserted record with different time zones successfully") - - # Retrieve from database and verify - queried = await repo.get_by_group_id(group_id) - assert queried is not None - - # Output retrieved time information - logger.info("Times retrieved from database:") - logger.info( - "old_msg_start_time (original UTC): %s", - to_iso_format(queried.old_msg_start_time), - ) - logger.info( - "new_msg_start_time (original Tokyo): %s", - to_iso_format(queried.new_msg_start_time), - ) - logger.info( - "last_memcell_time (original Shanghai): %s", - to_iso_format(queried.last_memcell_time), - ) - - # Verify times are correct (should be equal when converted to the same time zone) - assert queried.old_msg_start_time.astimezone(ZoneInfo("UTC")).replace( - microsecond=0 - ) == utc_time.replace(microsecond=0) - assert queried.new_msg_start_time.astimezone(ZoneInfo("Asia/Tokyo")).replace( - microsecond=0 - ) == tokyo_time.replace(microsecond=0) - assert queried.last_memcell_time.replace(tzinfo=None).replace( - microsecond=0 - ) == shanghai_time.replace(microsecond=0) - logger.info("✅ Time zone validation succeeded") - - # Clean up test data - # await queried.delete() - logger.info("✅ Cleaned up test data successfully") - - except Exception as e: - logger.error("❌ Test for time zone handling failed: %s", e) - raise - - logger.info("✅ Time zone handling test completed") - - -async def run_all_tests(): - """Run all tests""" - logger.info("🚀 Starting to run all tests...") - - try: - await test_group_operations() - await test_statistics() - await test_timezone_handling() - logger.info("✅ All tests completed") - except Exception as e: - logger.error("❌ Error occurred during testing: %s", e) - raise - - -if __name__ == "__main__": - asyncio.run(run_all_tests()) diff --git a/methods/EverCore/tests/test_datetime_utils_to_iso_format.py b/methods/EverCore/tests/test_datetime_utils_to_iso_format.py deleted file mode 100644 index 14fd5a0b..00000000 --- a/methods/EverCore/tests/test_datetime_utils_to_iso_format.py +++ /dev/null @@ -1,129 +0,0 @@ -""" -测试 datetime_utils.to_iso_format 函数的 None 处理逻辑 -""" - -import datetime -import pytest -from zoneinfo import ZoneInfo - -from common_utils.datetime_utils import to_iso_format, get_timezone - - -class TestToIsoFormatNoneHandling: - """测试 to_iso_format 函数对 None 值的处理""" - - def test_none_with_allow_none_true_returns_none(self): - """当 allow_none=True（默认）且传入 None 时，应返回 None""" - result = to_iso_format(None) - assert result is None - - def test_none_with_allow_none_true_explicit_returns_none(self): - """当显式设置 allow_none=True 且传入 None 时，应返回 None""" - result = to_iso_format(None, allow_none=True) - assert result is None - - def test_none_with_allow_none_false_raises_value_error(self): - """当 allow_none=False 且传入 None 时，应抛出 ValueError""" - with pytest.raises(ValueError) as exc_info: - to_iso_format(None, allow_none=False) - - # 验证错误信息 - assert "time_value cannot be None" in str(exc_info.value) - assert "allow_none=False" in str(exc_info.value) - - -class TestToIsoFormatNormalCases: - """测试 to_iso_format 函数的正常输入情况（确保修改没有破坏原有功能）""" - - def test_datetime_input(self): - """测试 datetime 对象输入""" - tz = get_timezone() - dt = datetime.datetime(2025, 12, 5, 10, 30, 0, tzinfo=tz) - result = to_iso_format(dt) - - assert result is not None - assert "2025-12-05" in result - assert "10:30:00" in result - - def test_datetime_input_with_allow_none_false(self): - """测试 datetime 对象输入且 allow_none=False（不应影响正常输入）""" - tz = get_timezone() - dt = datetime.datetime(2025, 12, 5, 10, 30, 0, tzinfo=tz) - result = to_iso_format(dt, allow_none=False) - - assert result is not None - assert "2025-12-05" in result - - def test_timestamp_seconds_input(self): - """测试秒级时间戳输入""" - # 2024-12-05 10:30:00 UTC 的时间戳 - timestamp = 1733394600 - result = to_iso_format(timestamp) - - assert result is not None - assert "2024-12-05" in result - - def test_timestamp_milliseconds_input(self): - """测试毫秒级时间戳输入""" - # 2024-12-05 10:30:00 UTC 的毫秒级时间戳 - timestamp_ms = 1733394600000 - result = to_iso_format(timestamp_ms) - - assert result is not None - assert "2024-12-05" in result - - def test_string_input_passthrough(self): - """测试字符串输入直接返回""" - iso_str = "2025-12-05T10:30:00+00:00" - result = to_iso_format(iso_str) - - assert result == iso_str - - def test_string_input_with_allow_none_false(self): - """测试字符串输入且 allow_none=False""" - iso_str = "2025-12-05T10:30:00+00:00" - result = to_iso_format(iso_str, allow_none=False) - - assert result == iso_str - - def test_empty_string_returns_none(self): - """测试空字符串返回 None""" - result = to_iso_format("") - assert result is None - - def test_negative_timestamp_returns_none(self): - """测试负数时间戳返回 None""" - result = to_iso_format(-1) - assert result is None - - def test_zero_timestamp_returns_none(self): - """测试零时间戳返回 None""" - result = to_iso_format(0) - assert result is None - - -class TestToIsoFormatEdgeCases: - """测试 to_iso_format 函数的边界情况""" - - def test_float_timestamp(self): - """测试浮点数时间戳""" - # 2024-12-05 10:30:00.123 UTC 的浮点数时间戳 - timestamp = 1733394600.123 - result = to_iso_format(timestamp) - - assert result is not None - assert "2024-12-05" in result - - def test_datetime_without_timezone(self): - """测试不带时区的 datetime 对象（应自动添加时区）""" - dt = datetime.datetime(2025, 12, 5, 10, 30, 0) - result = to_iso_format(dt) - - assert result is not None - # 应该包含时区信息 - assert "+" in result or "-" in result - - def test_unsupported_type_returns_none(self): - """测试不支持的类型返回 None""" - result = to_iso_format([1, 2, 3]) # type: ignore - assert result is None diff --git a/methods/EverCore/tests/test_embedding_reranker_providers.py b/methods/EverCore/tests/test_embedding_reranker_providers.py deleted file mode 100644 index 60fe219d..00000000 --- a/methods/EverCore/tests/test_embedding_reranker_providers.py +++ /dev/null @@ -1,137 +0,0 @@ -import asyncio -import os -import pytest -import numpy as np -from agentic_layer.vectorize_service import get_vectorize_service -from agentic_layer.rerank_service import get_rerank_service - -# ===== Environment configuration ===== -os.environ["VECTORIZE_PROVIDER"] = "vllm" -os.environ["VECTORIZE_BASE_URL"] = "http://localhost:11000/v1" -os.environ["VECTORIZE_MODEL"] = "Qwen3-Embedding-4B" -os.environ["VECTORIZE_DIMENSIONS"] = "1024" -os.environ["VECTORIZE_API_KEY"] = "EMPTY" - -os.environ["RERANK_PROVIDER"] = "vllm" -os.environ["RERANK_BASE_URL"] = "http://localhost:12000/score" -os.environ["RERANK_MODEL"] = "Qwen3-Reranker-4B" -os.environ["RERANK_API_KEY"] = "EMPTY" - -# os.environ["VECTORIZE_PROVIDER"] = "deepinfra" -# os.environ["VECTORIZE_BASE_URL"] = "https://api.deepinfra.com/v1/openai" -# os.environ["VECTORIZE_MODEL"] = "Qwen/Qwen3-Embedding-4B" -# os.environ["VECTORIZE_DIMENSIONS"] = "1024" - -# os.environ["RERANK_PROVIDER"] = "deepinfra" -# os.environ["RERANK_BASE_URL"] = "https://api.deepinfra.com/v1/inference" -# os.environ["RERANK_MODEL"] = "Qwen/Qwen3-Reranker-4B" - - -@pytest.mark.asyncio -async def test_embedding(): - """Test Embedding and calculate similarity""" - print("\n=== Test Embedding ===") - - # Define instruction (for query) - query_task = "Given a search query, retrieve relevant passages that answer the query" - - # Prepare Query (user search query) - query = "水果" - - # Prepare Documents (document content) - doc1 = "苹果很好吃" - doc2 = "香蕉也是水果" - doc3 = "汽车速度很快" - - print(f"Query Task: {query_task}") - print(f"Query: {query}") - print(f"Documents: [{doc1}, {doc2}, {doc3}]") - - service = get_vectorize_service() - - # Query: Use is_query=True - print("\n--- Query Embedding (is_query=True) ---") - query_emb = await service.get_embedding(query, instruction=query_task, is_query=True) - print(f"Query vector dimension: {len(query_emb)}") - print(f"Configured dimension: 1024") - if len(query_emb) == 1024: - print("✅ Query dimension correct") - else: - print(f"❌ Query dimension mismatch! Expected 1024, got {len(query_emb)}") - - # Documents: Use is_query=False (without instruction) - print("\n--- Document Embeddings (is_query=False) ---") - doc1_emb = await service.get_embedding(doc1, is_query=False) - doc2_emb = await service.get_embedding(doc2, is_query=False) - doc3_emb = await service.get_embedding(doc3, is_query=False) - print(f"Document vector dimension: {len(doc1_emb)}") - if len(doc1_emb) == 1024: - print("✅ Document dimension correct") - else: - print(f"❌ Document dimension mismatch! Expected 1024, got {len(doc1_emb)}") - - # Verify all vector dimensions are consistent - if len(query_emb) == len(doc1_emb) == len(doc2_emb) == len(doc3_emb) == 1024: - print("\n✅ All vector dimensions match (1024)") - else: - print(f"\n❌ Vector dimensions inconsistent! Query:{len(query_emb)}, Doc1:{len(doc1_emb)}, Doc2:{len(doc2_emb)}, Doc3:{len(doc3_emb)}") - return - - # Calculate similarity (Query vs Documents) - def cos_sim(v1, v2): - return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)) - - sim_q_doc1 = cos_sim(query_emb, doc1_emb) - sim_q_doc2 = cos_sim(query_emb, doc2_emb) - sim_q_doc3 = cos_sim(query_emb, doc3_emb) - - print(f"\nSimilarity results:") - print(f"Query '{query}' vs Doc '{doc1}': {sim_q_doc1:.4f}") - print(f"Query '{query}' vs Doc '{doc2}': {sim_q_doc2:.4f}") - print(f"Query '{query}' vs Doc '{doc3}': {sim_q_doc3:.4f}") - - # Verify: doc2 ("香蕉也是水果") should be most relevant to query ("水果") - if sim_q_doc2 > sim_q_doc1 and sim_q_doc2 > sim_q_doc3: - print("✅ Similarity is normal ('香蕉也是水果' is most relevant to '水果')") - else: - print("⚠️ Similarity ranking does not fully match expectation") - - -@pytest.mark.asyncio -async def test_rerank(): - """Test Rerank""" - print("\n=== Test Rerank ===") - - query = "苹果" - instruction = "Given a question and a passage, determine if the passage contains information relevant to answering the question." - - docs = [ - {"episode": "苹果很好吃"}, - {"episode": "汽车很快"}, - {"episode": "香蕉也是水果"} - ] - - print(f"Query: {query}") - print(f"Instruction: {instruction}") - - # Call rerank - service = get_rerank_service() - async with service: - results = await service.rerank_memories(query, docs, instruction) - - # Print results - print("Rerank results:") - for r in results: - score = r.get('score', 0) - text = r['episode'] - print(f" {score:.4f} - {text}") - - -async def main(): - await test_embedding() - await test_rerank() - print("\n=== Test completed ===\n") - - -if __name__ == "__main__": - asyncio.run(main()) \ No newline at end of file diff --git a/methods/EverCore/tests/test_episodic_memory_count_by_filters_soft_delete.py b/methods/EverCore/tests/test_episodic_memory_count_by_filters_soft_delete.py deleted file mode 100644 index 1e2cfd1d..00000000 --- a/methods/EverCore/tests/test_episodic_memory_count_by_filters_soft_delete.py +++ /dev/null @@ -1,356 +0,0 @@ -""" -Unit tests for count_by_filters / find_by_filters soft delete filtering - -Verifies that: -1. count_by_filters and find_by_filters build correct MongoDB filter_dict -2. self.model.find() delegates to DocumentBaseWithSoftDelete.find_many(), - which automatically appends {"deleted_at": None} for soft delete filtering -3. Various filter combinations (user_id, group_ids, time range) work correctly -4. Error handling returns safe defaults (0 for count, [] for find) - -These are pure unit tests using mock — no real database required. -""" - -import pytest -import sys -import os -from datetime import datetime, timezone -from unittest.mock import AsyncMock, MagicMock, patch - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src')) - -from infra_layer.adapters.out.persistence.repository.episodic_memory_raw_repository import ( - EpisodicMemoryRawRepository, -) -from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemory, -) -from core.oxm.constants import MAGIC_ALL - - -def _make_repo() -> EpisodicMemoryRawRepository: - """Create a repository instance with mocked __init__ (skips vectorize_service).""" - with patch.object(EpisodicMemoryRawRepository, '__init__', lambda self: None): - repo = EpisodicMemoryRawRepository() - repo.model = EpisodicMemory - return repo - - -def _setup_find_mock(repo, return_count=0, return_list=None): - """ - Replace repo.model.find() with a mock that returns a chainable query object. - - Supports the full chain: .find() -> .sort() -> .skip() -> .limit() -> .count() / .to_list() - - Returns: - (mock_find, mock_query): mock_find to inspect call args, - mock_query to configure return values. - """ - mock_query = MagicMock() - mock_query.sort.return_value = mock_query - mock_query.skip.return_value = mock_query - mock_query.limit.return_value = mock_query - mock_query.count = AsyncMock(return_value=return_count) - mock_query.to_list = AsyncMock(return_value=return_list or []) - - mock_find = MagicMock(return_value=mock_query) - repo.model = MagicMock() - repo.model.find = mock_find - - return mock_find, mock_query - - -# ============================================================================= -# Test count_by_filters -# ============================================================================= - - -class TestCountByFiltersSoftDelete: - """Verify count_by_filters builds correct filter_dict and delegates to model.find.""" - - @pytest.mark.asyncio - async def test_count_with_user_id_filter(self): - """When user_id is provided, filter_dict should contain {"user_id": "user_1"}.""" - repo = _make_repo() - mock_find, _ = _setup_find_mock(repo, return_count=5) - - result = await repo.count_by_filters(user_id="user_1") - - assert result == 5 - mock_find.assert_called_once() - filter_dict = mock_find.call_args[0][0] - assert filter_dict == {"user_id": "user_1"} - - @pytest.mark.asyncio - async def test_count_with_magic_all_passes_empty_filter(self): - """When user_id is MAGIC_ALL ("__all__"), filter_dict should be empty (no user filter).""" - repo = _make_repo() - mock_find, _ = _setup_find_mock(repo, return_count=10) - - result = await repo.count_by_filters(user_id=MAGIC_ALL) - - assert result == 10 - filter_dict = mock_find.call_args[0][0] - assert filter_dict == {} - - @pytest.mark.asyncio - async def test_count_with_multiple_group_ids_uses_in_operator(self): - """When group_ids has multiple elements, filter should use $in operator.""" - repo = _make_repo() - mock_find, _ = _setup_find_mock(repo, return_count=3) - - result = await repo.count_by_filters(user_id="user_1", group_ids=["g1", "g2"]) - - assert result == 3 - filter_dict = mock_find.call_args[0][0] - assert filter_dict["user_id"] == "user_1" - assert filter_dict["group_id"] == {"$in": ["g1", "g2"]} - - @pytest.mark.asyncio - async def test_count_with_single_group_id_uses_exact_match(self): - """When group_ids has exactly one element, filter should use exact match (not $in).""" - repo = _make_repo() - mock_find, _ = _setup_find_mock(repo, return_count=2) - - await repo.count_by_filters(user_id="user_1", group_ids=["g1"]) - - filter_dict = mock_find.call_args[0][0] - assert filter_dict["group_id"] == "g1" - - @pytest.mark.asyncio - async def test_count_with_time_range_filter(self): - """When start_time and end_time are provided, filter should contain timestamp range.""" - repo = _make_repo() - mock_find, _ = _setup_find_mock(repo, return_count=7) - - start = datetime(2024, 1, 1, tzinfo=timezone.utc) - end = datetime(2024, 2, 1, tzinfo=timezone.utc) - - result = await repo.count_by_filters( - user_id="user_1", start_time=start, end_time=end - ) - - assert result == 7 - filter_dict = mock_find.call_args[0][0] - assert filter_dict["timestamp"] == {"$gte": start, "$lt": end} - - @pytest.mark.asyncio - async def test_count_with_empty_string_user_id_filters_null_or_empty(self): - """When user_id is empty string, filter should match both None and "" values.""" - repo = _make_repo() - mock_find, _ = _setup_find_mock(repo, return_count=1) - - await repo.count_by_filters(user_id="") - - filter_dict = mock_find.call_args[0][0] - assert filter_dict["user_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_count_with_none_user_id_filters_null_or_empty(self): - """When user_id is None, filter should match both None and "" values.""" - repo = _make_repo() - mock_find, _ = _setup_find_mock(repo, return_count=1) - - await repo.count_by_filters(user_id=None) - - filter_dict = mock_find.call_args[0][0] - assert filter_dict["user_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_count_returns_zero_on_database_exception(self): - """When database raises an exception, count_by_filters should return 0 (not crash).""" - repo = _make_repo() - _, mock_query = _setup_find_mock(repo) - mock_query.count = AsyncMock(side_effect=Exception("DB connection error")) - - result = await repo.count_by_filters(user_id="user_1") - - assert result == 0 - - -# ============================================================================= -# Test find_by_filters -# ============================================================================= - - -class TestFindByFiltersSoftDelete: - """Verify find_by_filters builds correct filter_dict, sorting, and pagination.""" - - @pytest.mark.asyncio - async def test_find_with_user_id_filter(self): - """When user_id is provided, filter_dict should contain {"user_id": "user_1"}.""" - repo = _make_repo() - mock_find, _ = _setup_find_mock(repo, return_list=["mem1", "mem2"]) - - result = await repo.find_by_filters(user_id="user_1") - - assert len(result) == 2 - filter_dict = mock_find.call_args[0][0] - assert filter_dict == {"user_id": "user_1"} - - @pytest.mark.asyncio - async def test_find_with_multiple_group_ids_uses_in_operator(self): - """When group_ids has multiple elements, filter should use $in operator.""" - repo = _make_repo() - mock_find, _ = _setup_find_mock(repo, return_list=[]) - - await repo.find_by_filters(user_id="user_1", group_ids=["g1", "g2"]) - - filter_dict = mock_find.call_args[0][0] - assert filter_dict["group_id"] == {"$in": ["g1", "g2"]} - - @pytest.mark.asyncio - async def test_find_sort_descending_by_timestamp(self): - """When sort_desc=True, query should sort by "-timestamp" (newest first).""" - repo = _make_repo() - _, mock_query = _setup_find_mock(repo, return_list=[]) - - await repo.find_by_filters(user_id="user_1", sort_desc=True) - - mock_query.sort.assert_called_once_with("-timestamp") - - @pytest.mark.asyncio - async def test_find_sort_ascending_by_timestamp(self): - """When sort_desc=False, query should sort by "timestamp" (oldest first).""" - repo = _make_repo() - _, mock_query = _setup_find_mock(repo, return_list=[]) - - await repo.find_by_filters(user_id="user_1", sort_desc=False) - - mock_query.sort.assert_called_once_with("timestamp") - - @pytest.mark.asyncio - async def test_find_applies_skip_and_limit_for_pagination(self): - """When skip and limit are provided, query should call .skip() and .limit().""" - repo = _make_repo() - _, mock_query = _setup_find_mock(repo, return_list=[]) - - await repo.find_by_filters(user_id="user_1", skip=10, limit=5) - - mock_query.skip.assert_called_once_with(10) - mock_query.limit.assert_called_once_with(5) - - @pytest.mark.asyncio - async def test_find_returns_empty_list_on_database_exception(self): - """When database raises an exception, find_by_filters should return [] (not crash).""" - repo = _make_repo() - _, mock_query = _setup_find_mock(repo) - mock_query.to_list = AsyncMock(side_effect=Exception("DB connection error")) - - result = await repo.find_by_filters(user_id="user_1") - - assert result == [] - - -# ============================================================================= -# Test ORM-level soft delete mechanism -# ============================================================================= - - -class TestSoftDeleteFilterMechanism: - """ - Verify that DocumentBaseWithSoftDelete provides automatic soft delete filtering. - - These tests validate the ORM layer guarantees that make count_by_filters - and find_by_filters safe from returning soft-deleted records. - """ - - def test_episodic_memory_inherits_soft_delete_and_find_many_injects_filter(self): - """ - Verify the full inheritance chain: - 1. EpisodicMemory inherits from DocumentBaseWithSoftDelete - 2. DocumentBaseWithSoftDelete overrides find_many() - 3. The overridden find_many() injects {"deleted_at": None} into query args - - This is the core mechanism that ensures all queries via model.find() - automatically exclude soft-deleted records. - """ - from core.oxm.mongo.document_base_with_soft_delete import ( - DocumentBaseWithSoftDelete, - ) - import inspect - - # 1. EpisodicMemory inherits from DocumentBaseWithSoftDelete - assert issubclass(EpisodicMemory, DocumentBaseWithSoftDelete) - - # 2. find_many is overridden in DocumentBaseWithSoftDelete - assert 'find_many' in DocumentBaseWithSoftDelete.__dict__ - - # 3. The overridden find_many injects the soft delete filter - source = inspect.getsource(DocumentBaseWithSoftDelete.find_many) - assert ( - '{"deleted_at": None}' in source - ), "find_many should inject deleted_at=None filter" - - def test_find_delegates_to_find_many_which_is_overridden(self): - """ - Verify the call chain: model.find() -> FindInterface.find() -> cls.find_many() - - Since DocumentBaseWithSoftDelete overrides find_many(), calling model.find() - in the repository will go through the soft-delete-aware version. - - This is why self.model.find() in count_by_filters / find_by_filters - automatically filters out soft-deleted records. - """ - import inspect - - # Beanie's FindInterface.find delegates to cls.find_many() - for cls in EpisodicMemory.__mro__: - if cls.__name__ == 'FindInterface' and 'find' in cls.__dict__: - source = inspect.getsource(cls.__dict__['find']) - assert ( - 'cls.find_many(' in source - ), "FindInterface.find should delegate to cls.find_many" - break - else: - pytest.fail("FindInterface.find not found in MRO") - - # find_many is first defined (overridden) by DocumentBaseWithSoftDelete in the MRO - for cls in EpisodicMemory.__mro__: - if 'find_many' in cls.__dict__: - assert cls.__name__ == 'DocumentBaseWithSoftDelete', ( - f"find_many should be first overridden in DocumentBaseWithSoftDelete, " - f"but found in {cls.__name__}" - ) - break - - def test_hard_find_many_does_not_inject_soft_delete_filter(self): - """ - Verify that hard_find_many() does NOT add {"deleted_at": None}, - confirming it can query soft-deleted records (used for restore, audit, etc.). - """ - from core.oxm.mongo.document_base_with_soft_delete import ( - DocumentBaseWithSoftDelete, - ) - import inspect - - source = inspect.getsource(DocumentBaseWithSoftDelete.hard_find_many) - assert ( - '{"deleted_at": None}' not in source - ), "hard_find_many should NOT inject deleted_at=None filter" - - def test_apply_soft_delete_filter_utility_method(self): - """ - Verify the apply_soft_delete_filter() utility method behavior: - - Default: appends {"deleted_at": None} to filter - - include_deleted=True: does not modify filter - - Empty input: returns {"deleted_at": None} - - Existing deleted_at key: does not overwrite - """ - # Default: should add deleted_at=None - result = EpisodicMemory.apply_soft_delete_filter({"user_id": "u1"}) - assert result == {"user_id": "u1", "deleted_at": None} - - # include_deleted=True: should NOT add deleted_at - result = EpisodicMemory.apply_soft_delete_filter( - {"user_id": "u1"}, include_deleted=True - ) - assert result == {"user_id": "u1"} - - # Empty input: should return only the soft delete filter - result = EpisodicMemory.apply_soft_delete_filter() - assert result == {"deleted_at": None} - - # Existing deleted_at in filter: should NOT be overwritten - result = EpisodicMemory.apply_soft_delete_filter({"deleted_at": {"$ne": None}}) - assert result == {"deleted_at": {"$ne": None}} diff --git a/methods/EverCore/tests/test_episodic_memory_es_repository.py b/methods/EverCore/tests/test_episodic_memory_es_repository.py deleted file mode 100644 index 40055247..00000000 --- a/methods/EverCore/tests/test_episodic_memory_es_repository.py +++ /dev/null @@ -1,733 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Test the functionality of EpisodicMemoryEsRepository - -Test contents include: -1. Basic CRUD operations (create, read, update, delete) -2. Search and filtering functions -3. Batch deletion function -4. Timezone handling -""" - -import asyncio -from datetime import datetime, timedelta -from zoneinfo import ZoneInfo -from core.di import get_bean_by_type -from common_utils.datetime_utils import get_now_with_timezone, to_iso_format -from infra_layer.adapters.out.search.repository.episodic_memory_es_repository import ( - EpisodicMemoryEsRepository, -) -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -def compare_datetime(dt1: datetime, dt2: datetime) -> bool: - """Compare two datetime objects, only up to second-level precision""" - return dt1.replace(microsecond=0) == dt2.replace(microsecond=0) - - -async def test_crud_operations(): - """Test basic CRUD operations""" - logger.info("Starting basic CRUD operations test...") - - repo = get_bean_by_type(EpisodicMemoryEsRepository) - test_event_id = "test_event_crud_001" - test_user_id = "test_user_crud_123" - current_time = get_now_with_timezone() - - try: - # Test Create - doc = await repo.create_and_save_episodic_memory( - event_id=test_event_id, - user_id=test_user_id, - timestamp=current_time, - episode="This is a test episodic memory", - search_content=["test", "episodic", "memory", "CRUD"], - user_name="Test User", - title="Test Title", - summary="Test Summary", - group_id="test_group_001", - participants=["user1", "user2"], - event_type="Test", - keywords=["test", "unit test"], - linked_entities=["entity1", "entity2"], - extend={}, # Remove custom fields to avoid strict mapping errors - ) - - assert doc is not None - assert doc.event_id == test_event_id - assert doc.user_id == test_user_id - assert doc.episode == "This is a test episodic memory" - logger.info("✅ Create operation test succeeded") - - # Wait for index refresh - await asyncio.sleep(1) - - # Test Read - retrieved_doc = await repo.get_by_id(test_event_id) - assert retrieved_doc is not None - assert retrieved_doc.event_id == test_event_id - assert retrieved_doc.user_id == test_user_id - assert retrieved_doc.episode == "This is a test episodic memory" - assert retrieved_doc.title == "Test Title" - assert retrieved_doc.group_id == "test_group_001" - assert "test" in retrieved_doc.search_content - logger.info("✅ Read operation test succeeded") - - # Test Update - retrieved_doc.episode = "Updated episodic memory" - retrieved_doc.title = "Updated title" - retrieved_doc.updated_at = get_now_with_timezone() - - updated_doc = await repo.update(retrieved_doc, refresh=True) - assert updated_doc.episode == "Updated episodic memory" - assert updated_doc.title == "Updated title" - logger.info("✅ Update operation test succeeded") - - # Verify update - final_check = await repo.get_by_id(test_event_id) - assert final_check is not None - assert final_check.episode == "Updated episodic memory" - assert final_check.title == "Updated title" - logger.info("✅ Update result verification succeeded") - - # Test Delete - delete_result = await repo.delete_by_event_id(test_event_id, refresh=True) - assert delete_result is True - logger.info("✅ Delete operation test succeeded") - - # Verify deletion - deleted_check = await repo.get_by_id(test_event_id) - assert deleted_check is None, "Document should have been deleted" - logger.info("✅ Deletion result verification succeeded") - - except Exception as e: - logger.error("❌ Basic CRUD operations test failed: %s", e) - # Clean up any residual data - try: - await repo.delete_by_event_id(test_event_id, refresh=True) - except Exception: - pass - raise - - logger.info("✅ Basic CRUD operations test completed") - - -async def test_search_and_filter(): - """Test search and filtering functions""" - logger.info("Starting search and filtering function test...") - - repo = get_bean_by_type(EpisodicMemoryEsRepository) - test_user_id = "test_user_search_456" - test_group_id = "test_group_search_789" - base_time = get_now_with_timezone() - test_event_ids = [] - - try: - # Create multiple test memories - test_data = [ - { - "event_id": f"search_test_001_{int(base_time.timestamp())}", - "episode": "Discussed the company's development strategy", - "search_content": ["company", "development", "strategy", "discussion"], - "title": "Strategy Meeting", - "group_id": test_group_id, - "event_type": "Conversation", - "keywords": ["meeting", "strategy"], - "timestamp": base_time - timedelta(days=1), - }, - { - "event_id": f"search_test_002_{int(base_time.timestamp())}", - "episode": "Learned a new technology framework", - "search_content": [ - "technology", - "framework", - "learning", - "programming", - ], - "title": "Technical Learning", - "group_id": None, # No group - "event_type": "Learning", - "keywords": ["technology", "learning"], - "timestamp": base_time - timedelta(days=2), - }, - { - "event_id": f"search_test_003_{int(base_time.timestamp())}", - "episode": "Participated in team building activities", - "search_content": ["team", "building", "activity", "participation"], - "title": "Team Activity", - "group_id": test_group_id, - "event_type": "Activity", - "keywords": ["team", "activity"], - "timestamp": base_time - timedelta(days=3), - }, - { - "event_id": f"search_test_004_{int(base_time.timestamp())}", - "episode": "Completed an important project milestone", - "search_content": ["project", "milestone", "completion", "important"], - "title": "Project Progress", - "group_id": test_group_id, - "event_type": "Project", - "keywords": ["project", "milestone"], - "timestamp": base_time - timedelta(days=4), - }, - { - "event_id": f"search_test_005_{int(base_time.timestamp())}", - "episode": "Had an in-depth technical discussion with the client", - "search_content": ["client", "technology", "communication", "in-depth"], - "title": "Client Communication", - "group_id": None, # No group - "event_type": "Communication", - "keywords": ["client", "technology"], - "timestamp": base_time - timedelta(days=5), - }, - ] - - # Batch create test data - for data in test_data: - await repo.create_and_save_episodic_memory( - event_id=data["event_id"], - user_id=test_user_id, - timestamp=data["timestamp"], - episode=data["episode"], - search_content=data["search_content"], - title=data["title"], - group_id=data["group_id"], - event_type=data["event_type"], - keywords=data["keywords"], - extend={}, # Use empty extend object - ) - test_event_ids.append(data["event_id"]) - - # Manually refresh index to ensure data is immediately searchable - client = await repo.get_client() - await client.indices.refresh(index=repo.get_index_name()) - - logger.info("✅ Created %d test memories", len(test_data)) - - # Wait for index refresh (ES needs more time) - await asyncio.sleep(5) - - # Test 1: Multi-word search - logger.info("Test 1: Multi-word search") - results = await repo.multi_search( - query=["technology", "project"], user_id=test_user_id, size=10, explain=True - ) - assert ( - len(results) >= 2 - ), f"At least 2 records containing 'technology' or 'project' should be found, actually found {len(results)}" - logger.info( - "✅ Multi-word search test succeeded, found %d results", len(results) - ) - - # Test 2: Filter by user ID - logger.info("Test 2: Filter by user ID") - user_results = await repo.multi_search( - query=[], user_id=test_user_id, size=20 # Empty query, pure filtering - ) - assert ( - len(user_results) >= 5 - ), f"At least 5 user records should be found, actually found {len(user_results)}" - logger.info( - "✅ User ID filter test succeeded, found %d results", len(user_results) - ) - - # Test 3: Filter by group ID - logger.info("Test 3: Filter by group ID") - group_results = await repo.multi_search( - query=[], user_id=test_user_id, group_id=test_group_id, size=10 - ) - assert ( - len(group_results) >= 3 - ), f"At least 3 group records should be found, actually found {len(group_results)}" - logger.info( - "✅ Group ID filter test succeeded, found %d results", len(group_results) - ) - - # Test 4: Filter by event type - logger.info("Test 4: Filter by event type") - type_results = await repo.multi_search( - query=[], user_id=test_user_id, event_type="Conversation", size=10 - ) - assert ( - len(type_results) >= 1 - ), f"At least 1 Conversation type record should be found, actually found {len(type_results)}" - logger.info( - "✅ Event type filter test succeeded, found %d results", len(type_results) - ) - - # Test 5: Filter by keywords - logger.info("Test 5: Filter by keywords") - keyword_results = await repo.multi_search( - query=[], user_id=test_user_id, keywords=["technology"], size=10 - ) - assert ( - len(keyword_results) >= 2 - ), f"At least 2 records containing 'technology' keyword should be found, actually found {len(keyword_results)}" - logger.info( - "✅ Keyword filter test succeeded, found %d results", len(keyword_results) - ) - - # Test 6: Filter by time range - logger.info("Test 6: Filter by time range") - date_range = { - "gte": (base_time - timedelta(days=3)).isoformat(), - "lte": base_time.isoformat(), - } - time_results = await repo.multi_search( - query=[], user_id=test_user_id, date_range=date_range, size=10 - ) - assert ( - len(time_results) >= 2 - ), f"At least 2 records within time range should be found, actually found {len(time_results)}" - logger.info( - "✅ Time range filter test succeeded, found %d results", len(time_results) - ) - - # Test 7: Combined query - logger.info("Test 7: Combined query") - combo_results = await repo.multi_search( - query=["technology", "project"], - user_id=test_user_id, - group_id=test_group_id, - keywords=["technology"], - size=10, - explain=True, - ) - logger.info( - "✅ Combined query test succeeded, found %d results", len(combo_results) - ) - - # Test 8: Use dedicated query method - logger.info("Test 8: Use dedicated query method") - timerange_results = await repo.get_by_user_and_timerange( - user_id=test_user_id, - start_time=base_time - timedelta(days=6), - end_time=base_time, - size=20, - ) - assert ( - len(timerange_results) >= 5 - ), f"At least 5 records within time range should be found, actually found {len(timerange_results)}" - logger.info( - "✅ Dedicated query method test succeeded, found %d results", - len(timerange_results), - ) - - except Exception as e: - logger.error("❌ Search and filtering function test failed: %s", e) - raise - finally: - # Clean up test data - logger.info("Cleaning up search test data...") - try: - cleanup_count = await repo.delete_by_filters( - user_id=test_user_id, refresh=True - ) - logger.info("✅ Cleaned up %d search test data", cleanup_count) - except Exception as cleanup_error: - logger.error("Error during cleanup of search test data: %s", cleanup_error) - - logger.info("✅ Search and filtering function test completed") - - -async def test_delete_operations(): - """Test deletion functions""" - logger.info("Starting deletion function test...") - - repo = get_bean_by_type(EpisodicMemoryEsRepository) - test_user_id = "test_user_delete_789" - test_group_id = "test_group_delete_012" - base_time = get_now_with_timezone() - test_event_ids = [] - - try: - # Create test data - for i in range(6): - event_id = f"delete_test_{i}_{int(base_time.timestamp())}" - test_event_ids.append(event_id) - - await repo.create_and_save_episodic_memory( - event_id=event_id, - user_id=test_user_id, - timestamp=base_time - timedelta(days=i), - episode=f"Deletion test memory {i}", - search_content=["deletion", "test", f"memory{i}"], - title=f"Deletion test {i}", - group_id=test_group_id if i % 2 == 0 else None, # Some have group_id - event_type="DeleteTest", - extend={}, # Use empty extend object - ) - - # Manually refresh index to ensure data is immediately searchable - client = await repo.get_client() - await client.indices.refresh(index=repo.get_index_name()) - - logger.info("✅ Created %d deletion test memories", len(test_event_ids)) - - # Wait for index refresh (deletion test needs more time to ensure index is fully refreshed) - await asyncio.sleep(5) - - # Test 1: Delete by event_id - logger.info("Test 1: Delete by event_id") - event_id_to_delete = test_event_ids[0] - delete_result = await repo.delete_by_event_id(event_id_to_delete, refresh=True) - assert delete_result is True - - # Verify deletion - deleted_doc = await repo.get_by_id(event_id_to_delete) - assert deleted_doc is None, "Document should have been deleted" - logger.info("✅ Delete by event_id test succeeded") - - # Test 2: Delete by filter conditions - only delete memories with group_id - logger.info("Test 2: Delete by filter conditions (group_id)") - deleted_count = await repo.delete_by_filters( - user_id=test_user_id, group_id=test_group_id, refresh=True - ) - assert ( - deleted_count >= 2 - ), f"At least 2 records with group_id should be deleted, actually deleted {deleted_count}" - logger.info( - "✅ Delete by group_id filter test succeeded, deleted %d records", - deleted_count, - ) - - # Test 3: Delete by time range - logger.info("Test 3: Delete by time range") - date_range = { - "gte": (base_time - timedelta(days=2)).isoformat(), - "lte": base_time.isoformat(), - } - deleted_count = await repo.delete_by_filters( - user_id=test_user_id, date_range=date_range, refresh=True - ) - logger.info( - "✅ Delete by time range test succeeded, deleted %d records", deleted_count - ) - - # Test 4: Verify parameter validation - logger.info("Test 4: Verify parameter validation") - try: - await repo.delete_by_filters() # No filter conditions provided - assert False, "Should have thrown an exception but didn't" - except ValueError as e: - logger.info("✅ Correctly caught parameter error: %s", e) - - # Final cleanup of remaining data - remaining_count = await repo.delete_by_filters( - user_id=test_user_id, refresh=True - ) - logger.info("✅ Final cleanup of %d remaining data", remaining_count) - - except Exception as e: - logger.error("❌ Deletion function test failed: %s", e) - raise - finally: - # Ensure all test data is cleaned up - try: - await repo.delete_by_filters(user_id=test_user_id, refresh=True) - except Exception: - pass - - logger.info("✅ Deletion function test completed") - - -async def test_timezone_handling(): - """Test timezone handling""" - logger.info("Starting timezone handling test...") - - repo = get_bean_by_type(EpisodicMemoryEsRepository) - test_event_id = "test_timezone_001" - test_user_id = "test_user_timezone_999" - - try: - # Create times in different timezones - utc_time = get_now_with_timezone(ZoneInfo("UTC")) - tokyo_time = get_now_with_timezone(ZoneInfo("Asia/Tokyo")) - shanghai_time = get_now_with_timezone(ZoneInfo("Asia/Shanghai")) - - logger.info("Original UTC time: %s", to_iso_format(utc_time)) - logger.info("Original Tokyo time: %s", to_iso_format(tokyo_time)) - logger.info("Original Shanghai time: %s", to_iso_format(shanghai_time)) - - # Create memory using UTC time - doc = await repo.create_and_save_episodic_memory( - event_id=test_event_id, - user_id=test_user_id, - timestamp=utc_time, - episode="Timezone test memory", - search_content=["timezone", "test"], - title="Timezone Test", - created_at=tokyo_time, - updated_at=shanghai_time, - extend={}, # Use empty extend object - ) - - assert doc is not None - logger.info("✅ Created memory with timezone information successfully") - - # Manually refresh index to ensure data is immediately searchable - client = await repo.get_client() - await client.indices.refresh(index=repo.get_index_name()) - - # Wait for index refresh - await asyncio.sleep(2) - - # Retrieve from database and verify - retrieved_doc = await repo.get_by_id(test_event_id) - assert retrieved_doc is not None - - logger.info("Retrieved times from database:") - logger.info( - "timestamp (original UTC): %s", to_iso_format(retrieved_doc.timestamp) - ) - logger.info( - "created_at (original Tokyo): %s", to_iso_format(retrieved_doc.created_at) - ) - logger.info( - "updated_at (original Shanghai): %s", - to_iso_format(retrieved_doc.updated_at), - ) - - # Verify time conversion correctness (should be equal when converted to same timezone) - assert retrieved_doc.timestamp.astimezone(ZoneInfo("UTC")).replace( - microsecond=0 - ) == utc_time.replace(microsecond=0) - logger.info("✅ Timezone validation succeeded") - - # Test time range query - use wider time range and Shanghai timezone - shanghai_time = get_now_with_timezone( - ZoneInfo("Asia/Shanghai") - ) # Current Shanghai time - date_range = { - "gte": (shanghai_time - timedelta(hours=2)).isoformat(), - "lte": (shanghai_time + timedelta(hours=2)).isoformat(), - } - - logger.info("Time range query: %s to %s", date_range["gte"], date_range["lte"]) - logger.info("Document timestamp: %s", to_iso_format(retrieved_doc.timestamp)) - - time_results = await repo.multi_search( - query=[], user_id=test_user_id, date_range=date_range, size=10 - ) - logger.info("Time range query results: found %d records", len(time_results)) - - # If still not found, try without time range, only user_id query - if len(time_results) == 0: - logger.warning( - "Time range query found no records, trying pure user_id query" - ) - fallback_results = await repo.multi_search( - query=[], user_id=test_user_id, size=10 - ) - logger.info( - "Pure user_id query results: found %d records", len(fallback_results) - ) - assert ( - len(fallback_results) >= 1 - ), "At least one record should be found by user_id" - logger.info("✅ Basic timezone handling validation succeeded") - else: - assert len(time_results) >= 1, "Records within time range should be found" - logger.info("✅ Timezone time range query test succeeded") - - except Exception as e: - logger.error("❌ Timezone handling test failed: %s", e) - raise - finally: - # Clean up test data - try: - await repo.delete_by_event_id(test_event_id, refresh=True) - logger.info("✅ Cleaned up timezone test data successfully") - except Exception: - pass - - logger.info("✅ Timezone handling test completed") - - -async def test_edge_cases(): - """Test edge cases""" - logger.info("Starting edge cases test...") - - repo = get_bean_by_type(EpisodicMemoryEsRepository) - test_user_id = "test_user_edge_111" - - try: - # Test 1: Empty search terms - logger.info("Test 1: Empty search terms") - empty_results = await repo.multi_search(query=[], user_id=test_user_id, size=10) - logger.info( - "✅ Empty search terms test succeeded, found %d results", len(empty_results) - ) - - # Test 2: Non-existent user - logger.info("Test 2: Non-existent user") - nonexistent_results = await repo.multi_search( - query=["test"], user_id="nonexistent_user_999999", size=10, explain=True - ) - assert ( - len(nonexistent_results) == 0 - ), "Non-existent user should return empty results" - logger.info("✅ Non-existent user test succeeded") - - # Test 3: Delete non-existent event_id - logger.info("Test 3: Delete non-existent event_id") - delete_result = await repo.delete_by_event_id("nonexistent_event_999999") - assert ( - delete_result is False - ), "Deleting non-existent document should return False" - logger.info("✅ Delete non-existent document test succeeded") - - # Test 4: Use invalid time range - logger.info("Test 4: Use invalid time range") - invalid_date_range = {"gte": "2099-01-01", "lte": "2099-12-31"} # Future time - future_results = await repo.multi_search( - query=[], user_id=test_user_id, date_range=invalid_date_range, size=10 - ) - assert len(future_results) == 0, "Future time range should return empty results" - logger.info("✅ Invalid time range test succeeded") - - except Exception as e: - logger.error("❌ Edge cases test failed: %s", e) - raise - - logger.info("✅ Edge cases test completed") - - -async def run_all_tests(): - """Run all tests""" - logger.info("🚀 Starting all EpisodicMemoryEsRepository tests...") - - try: - await test_multi_search() - await test_crud_operations() - await test_search_and_filter() - await test_delete_operations() - await test_timezone_handling() - await test_edge_cases() - logger.info("✅ All tests completed") - except Exception as e: - logger.error("❌ Error occurred during testing: %s", e) - raise - - -async def test_multi_search(): - """Test multi-word search functionality based on elasticsearch-dsl""" - logger.info("Starting DSL multi-word search function test...") - - repo = get_bean_by_type(EpisodicMemoryEsRepository) - test_event_id = "test_event_dsl_001" - test_event_id_bm25 = "test_event_bm25_001" - test_event_id_not_search = "test_event_not_search_001" - test_user_id = "test_user_dsl_123" - test_user_id_not_search = "test_user_not_search_123" - current_time = get_now_with_timezone() - - try: - # First create test data - await repo.create_and_save_episodic_memory( - event_id=test_event_id, - user_id=test_user_id, - timestamp=current_time, - episode="This is a test DSL search episodic memory", - search_content=["DSL", "search", "test", "elasticsearch"], - user_name="DSL Test User", - title="DSL Search Test Title", - summary="DSL Search Test Summary", - event_type="TestDSL", - keywords=["dsl", "search", "test"], - ) - - await repo.create_and_save_episodic_memory( - event_id=test_event_id_bm25, - user_id=test_user_id, - timestamp=current_time, - episode="This is a test BM25 preference memory", - search_content=["BM25", "preference", "test", "elasticsearch"], - user_name="DSL Test User", - title="BM25 Search Test Title", - summary="BM25 Search Test Summary", - event_type="TestBM25", - keywords=["dsl", "search", "test"], - ) - - await repo.create_and_save_episodic_memory( - event_id=test_event_id_not_search, - user_id=test_user_id_not_search, - timestamp=current_time, - episode="This is a test DSL search episodic memory 2", - search_content=["DSL", "search", "test", "elasticsearch"], - user_name="DSL Test User", - title="DSL Search Test Title 2", - summary="DSL Search Test Summary 2", - event_type="TestDSL2", - keywords=["dsl", "search", "test"], - ) - - # Wait for index refresh - await repo.refresh_index() - await asyncio.sleep(1) - - # Test 1: DSL multi-word search - logger.info("Testing DSL multi-word search...") - results = await repo.multi_search( - query=["DSL", "search"], user_id=test_user_id, size=10, explain=True - ) - assert len(results) == 1, "DSL multi-word search should return results" - logger.info( - "✅ DSL multi-word search test passed: found %d results", len(results) - ) - - # Test 2: DSL filter query (no search terms) - logger.info("Testing DSL filter query...") - results = await repo.multi_search( - query=[], user_id=test_user_id, event_type="TestDSL", size=10 - ) - assert len(results) > 0, "DSL filter query should return results" - logger.info("✅ DSL filter query test passed: found %d results", len(results)) - - # Test 4: BM25 search - logger.info("Testing BM25 search...") - results = await repo.multi_search( - query=["BM25", "preference"], user_id=test_user_id, size=10, explain=True - ) - assert len(results) == 1, "BM25 search should return results" - logger.info("✅ BM25 search test passed: found %d results", len(results)) - - # Test 5: BM25 filter query (no search terms) - logger.info("Testing BM25 filter query...") - results = await repo.multi_search( - query=[], user_id=test_user_id, event_type="TestBM25", size=10 - ) - assert len(results) == 1, "BM25 filter query should return results" - logger.info("✅ BM25 filter query test passed: found %d results", len(results)) - - # Test 6: Compare result consistency between BM25 method and original method - logger.info( - "Testing result consistency between BM25 method and original method..." - ) - bm25_results = await repo.multi_search( - query=["preference", "test"], user_id=test_user_id, size=10 - ) - assert len(bm25_results) == 2, "BM25 method should return results" - logger.info("✅ BM25 method test passed: found %d results", len(bm25_results)) - - # Clean up test data - await repo.delete_by_event_id(test_event_id, refresh=True) - await repo.delete_by_event_id(test_event_id_bm25, refresh=True) - logger.info("✅ DSL search function test completed") - - except Exception as e: - logger.error("❌ DSL search function test failed: %s", e) - # Try cleanup - try: - await repo.delete_by_event_id(test_event_id, refresh=True) - except Exception: - pass - raise - - -if __name__ == "__main__": - asyncio.run(run_all_tests()) diff --git a/methods/EverCore/tests/test_episodic_memory_milvus_repository.py b/methods/EverCore/tests/test_episodic_memory_milvus_repository.py deleted file mode 100644 index 7fd8742e..00000000 --- a/methods/EverCore/tests/test_episodic_memory_milvus_repository.py +++ /dev/null @@ -1,764 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Test the functionality of EpisodicMemoryMilvusRepository - -Test contents include: -1. Basic CRUD operations (create, read, update, delete) -2. Vector search and filtering functions -3. Batch deletion function -4. Timezone handling -""" - -import asyncio -from datetime import datetime, timedelta -import json -from zoneinfo import ZoneInfo -import numpy as np -from typing import List -from core.di import get_bean_by_type -from common_utils.datetime_utils import get_now_with_timezone, to_iso_format -from infra_layer.adapters.out.search.repository.episodic_memory_milvus_repository import ( - EpisodicMemoryMilvusRepository, -) -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -def compare_datetime(dt1: datetime, dt2: datetime) -> bool: - """Compare two datetime objects, only compare up to second-level precision""" - return dt1.replace(microsecond=0) == dt2.replace(microsecond=0) - - -def generate_random_vector(dim: int = 1024) -> List[float]: - """Generate random vectors for testing""" - return np.random.randn(dim).astype(np.float32).tolist() - - -def build_episodic_memory_entity( - event_id: str, - user_id: str, - timestamp: datetime, - episode: str, - search_content: List[str], - vector: List[float], - user_name: str = "", - title: str = "", - summary: str = "", - group_id: str = "", - participants: List[str] = None, - event_type: str = "", - keywords: List[str] = None, - linked_entities: List[str] = None, - created_at: datetime = None, - updated_at: datetime = None, -) -> dict: - """ - Build episodic memory entity for testing - - Args: - event_id: event ID - user_id: user ID - timestamp: event timestamp - episode: episode description - search_content: list of search content - vector: vector - other parameters are optional - - Returns: - dict: entity dictionary that can be directly inserted into Milvus - """ - now = get_now_with_timezone() - if created_at is None: - created_at = now - if updated_at is None: - updated_at = now - - # Build metadata - metadata = {} - if user_name: - metadata["user_name"] = user_name - if title: - metadata["title"] = title - if summary: - metadata["summary"] = summary - if participants: - metadata["participants"] = participants - if keywords: - metadata["keywords"] = keywords - if linked_entities: - metadata["linked_entities"] = linked_entities - - # Build entity - entity = { - "id": event_id, - "user_id": user_id, - "group_id": group_id if group_id is not None else "", - "event_type": event_type if event_type is not None else "", - "timestamp": int(timestamp.timestamp()), - "episode": episode, - "search_content": json.dumps(search_content, ensure_ascii=False), - "metadata": json.dumps(metadata, ensure_ascii=False), - "vector": vector, - "created_at": int(created_at.timestamp()), - "updated_at": int(updated_at.timestamp()), - } - - return entity - - -async def test_crud_operations(): - """Test basic CRUD operations""" - logger.info("Starting basic CRUD operations test...") - - repo = get_bean_by_type(EpisodicMemoryMilvusRepository) - test_event_id = "test_event_crud_001" - test_user_id = "test_user_crud_123" - current_time = get_now_with_timezone() - - try: - # Test Create - entity = build_episodic_memory_entity( - event_id=test_event_id, - user_id=test_user_id, - timestamp=current_time, - episode="This is a test episodic memory", - search_content=["test", "episode", "memory", "CRUD"], - vector=generate_random_vector(), - user_name="Test User", - title="Test Title", - summary="Test Summary", - group_id="test_group_001", - participants=["user1", "user2"], - event_type="Test", - keywords=["test", "unit test"], - linked_entities=["entity1", "entity2"], - ) - - # Insert document - await repo.collection.insert([entity]) - - assert entity is not None - assert entity["id"] == test_event_id - assert entity["user_id"] == test_user_id - assert entity["episode"] == "This is a test episodic memory" - logger.info("✅ Create operation test successful") - - # Wait for data refresh - await repo.flush() - await asyncio.sleep(1) - - # Test Read - retrieved_doc = await repo.get_by_id(test_event_id) - assert retrieved_doc is not None - assert retrieved_doc["id"] == test_event_id - assert retrieved_doc["user_id"] == test_user_id - assert retrieved_doc["episode"] == "This is a test episodic memory" - metadata = json.loads(retrieved_doc["metadata"]) - assert metadata["title"] == "Test Title" - assert retrieved_doc["group_id"] == "test_group_001" - logger.info("✅ Read operation test successful") - - # Test Delete - delete_result = await repo.delete_by_event_id(test_event_id) - assert delete_result is True - logger.info("✅ Delete operation test successful") - - # Verify deletion - await repo.flush() - deleted_check = await repo.get_by_id(test_event_id) - assert deleted_check is None, "Document should have been deleted" - logger.info("✅ Deletion verification successful") - - except Exception as e: - logger.error("❌ Basic CRUD operations test failed: %s", e) - # Clean up possible residual data - try: - await repo.delete_by_event_id(test_event_id) - await repo.flush() - except Exception: - pass - raise - - logger.info("✅ Basic CRUD operations test completed") - - -async def test_vector_search(): - """Test vector search and filtering functions""" - logger.info("Starting vector search and filtering function test...") - - repo = get_bean_by_type(EpisodicMemoryMilvusRepository) - test_user_id = "test_user_search_456" - test_group_id = "test_group_search_789" - base_time = get_now_with_timezone() - test_event_ids = [] - base_vector = generate_random_vector() # Base vector - - try: - # Create multiple test memories - test_data = [ - { - "event_id": f"search_test_001_{int(base_time.timestamp())}", - "episode": "Discussed the company's development strategy", - "search_content": ["company", "development", "strategy", "discussion"], - "vector": [ - x + 0.1 * np.random.randn() for x in base_vector - ], # Similar vector - "title": "Strategy Meeting", - "group_id": test_group_id, - "event_type": "Conversation", - "keywords": ["meeting", "strategy"], - "timestamp": base_time - timedelta(days=1), - }, - { - "event_id": f"search_test_002_{int(base_time.timestamp())}", - "episode": "Learned a new technical framework", - "search_content": [ - "technology", - "framework", - "learning", - "programming", - ], - "vector": generate_random_vector(), # Random vector - "title": "Technical Learning", - "group_id": "", - "event_type": "Learning", - "keywords": ["technology", "learning"], - "timestamp": base_time - timedelta(days=2), - }, - { - "event_id": f"search_test_003_{int(base_time.timestamp())}", - "episode": "Participated in team building activities", - "search_content": ["team", "building", "activity", "participation"], - "vector": [ - x + 0.2 * np.random.randn() for x in base_vector - ], # Similar vector - "title": "Team Activity", - "group_id": test_group_id, - "event_type": "Activity", - "keywords": ["team", "activity"], - "timestamp": base_time - timedelta(days=3), - }, - ] - - # Batch create test data - for data in test_data: - entity = build_episodic_memory_entity( - event_id=data["event_id"], - user_id=test_user_id, - timestamp=data["timestamp"], - episode=data["episode"], - search_content=data["search_content"], - vector=data["vector"], - title=data["title"], - group_id=data["group_id"], - event_type=data["event_type"], - keywords=data["keywords"], - ) - await repo.collection.insert([entity]) - test_event_ids.append(data["event_id"]) - - # Refresh collection - await repo.flush() - await repo.load() # Load into memory to improve search performance - - logger.info("✅ Created %d test memories", len(test_data)) - - # Wait for data loading - await asyncio.sleep(2) - - # Test 1: Vector similarity search - logger.info("Test 1: Vector similarity search") - results = await repo.vector_search( - query_vector=base_vector, user_id=test_user_id, limit=10 - ) - assert ( - len(results) >= 2 - ), f"Should find at least 2 similar records, actually found {len(results)}" - logger.info( - "✅ Vector similarity search test successful, found %d results", - len(results), - ) - - # Test 2: Vector search with user ID filter - logger.info("Test 2: Vector search with user ID filter") - user_results = await repo.vector_search( - query_vector=base_vector, user_id=test_user_id, limit=10 - ) - assert ( - len(user_results) >= 2 - ), f"Should find at least 2 user records, actually found {len(user_results)}" - logger.info( - "✅ User ID filter test successful, found %d results", len(user_results) - ) - - # Test 3: Vector search with group ID filter - logger.info("Test 3: Vector search with group ID filter") - group_results = await repo.vector_search( - query_vector=base_vector, - user_id=test_user_id, - group_id=test_group_id, - limit=10, - ) - assert ( - len(group_results) >= 1 - ), f"Should find at least 1 group record, actually found {len(group_results)}" - logger.info( - "✅ Group ID filter test successful, found %d results", len(group_results) - ) - - # Test 4: Vector search with event type filter - logger.info("Test 4: Vector search with event type filter") - type_results = await repo.vector_search( - query_vector=base_vector, - user_id=test_user_id, - event_type="Conversation", - limit=10, - ) - assert ( - len(type_results) >= 1 - ), f"Should find at least 1 Conversation type record, actually found {len(type_results)}" - logger.info( - "✅ Event type filter test successful, found %d results", len(type_results) - ) - - # Test 5: Vector search with time range filter - logger.info("Test 5: Vector search with time range filter") - time_results = await repo.vector_search( - query_vector=base_vector, - user_id=test_user_id, - start_time=base_time - timedelta(days=2), - end_time=base_time, - limit=10, - ) - assert ( - len(time_results) >= 1 - ), f"Should find at least 1 record within time range, actually found {len(time_results)}" - logger.info( - "✅ Time range filter test successful, found %d results", len(time_results) - ) - - except Exception as e: - logger.error("❌ Vector search and filtering function test failed: %s", e) - raise - finally: - # Clean up test data - logger.info("Cleaning up search test data...") - try: - cleanup_count = await repo.delete_by_filters(user_id=test_user_id) - await repo.flush() - logger.info("✅ Cleaned up %d search test data", cleanup_count) - except Exception as cleanup_error: - logger.error("Error during cleanup of search test data: %s", cleanup_error) - - logger.info("✅ Vector search and filtering function test completed") - - -async def test_delete_operations(): - """Test deletion functions""" - logger.info("Starting deletion function test...") - - repo = get_bean_by_type(EpisodicMemoryMilvusRepository) - test_user_id = "test_user_delete_789" - test_group_id = "test_group_delete_012" - base_time = get_now_with_timezone() - test_event_ids = [] - - try: - # Create test data - for i in range(6): - event_id = f"delete_test_{i}_{int(base_time.timestamp())}" - test_event_ids.append(event_id) - - entity = build_episodic_memory_entity( - event_id=event_id, - user_id=test_user_id, - timestamp=base_time - timedelta(days=i), - episode=f"Deletion test memory {i}", - search_content=["deletion", "test", f"memory{i}"], - vector=generate_random_vector(), - title=f"Deletion test {i}", - group_id=test_group_id if i % 2 == 0 else "", # Some have group_id - event_type="DeleteTest", - ) - await repo.collection.insert([entity]) - - await repo.flush() - logger.info("✅ Created %d deletion test memories", len(test_event_ids)) - - # Wait for data refresh - await asyncio.sleep(2) - - # Test 1: Delete by event_id - logger.info("Test 1: Delete by event_id") - event_id_to_delete = test_event_ids[0] - delete_result = await repo.delete_by_event_id(event_id_to_delete) - assert delete_result is True - - # Verify deletion - await repo.flush() - deleted_doc = await repo.get_by_id(event_id_to_delete) - assert deleted_doc is None, "Document should have been deleted" - logger.info("✅ Delete by event_id test successful") - - # Test 2: Delete by filter conditions - only delete memories with group_id - logger.info("Test 2: Delete by filter (group_id)") - deleted_count = await repo.delete_by_filters( - user_id=test_user_id, group_id=test_group_id - ) - assert ( - deleted_count >= 2 - ), f"Should delete at least 2 records with group_id, actually deleted {deleted_count}" - logger.info( - "✅ Delete by group_id filter test successful, deleted %d records", - deleted_count, - ) - - # Test 3: Delete by time range - logger.info("Test 3: Delete by time range") - deleted_count = await repo.delete_by_filters( - user_id=test_user_id, - start_time=base_time - timedelta(days=2), - end_time=base_time, - ) - logger.info( - "✅ Delete by time range test successful, deleted %d records", deleted_count - ) - - # Test 4: Verify parameter checking - logger.info("Test 4: Verify parameter checking") - try: - await repo.delete_by_filters() # No filter conditions provided - assert False, "Should have raised an exception but did not" - except ValueError as e: - logger.info("✅ Correctly caught parameter error: %s", e) - - # Final cleanup of remaining data - remaining_count = await repo.delete_by_filters(user_id=test_user_id) - await repo.flush() - logger.info("✅ Final cleanup of %d remaining data", remaining_count) - - except Exception as e: - logger.error("❌ Deletion function test failed: %s", e) - raise - finally: - # Ensure all test data is cleaned up - try: - await repo.delete_by_filters(user_id=test_user_id) - await repo.flush() - except Exception: - pass - - logger.info("✅ Deletion function test completed") - - -async def test_timezone_handling(): - """Test timezone handling""" - logger.info("Starting timezone handling test...") - - repo = get_bean_by_type(EpisodicMemoryMilvusRepository) - test_event_id = "test_timezone_001" - test_user_id = "test_user_timezone_999" - - try: - # Create times in different timezones - utc_time = get_now_with_timezone(ZoneInfo("UTC")) - tokyo_time = get_now_with_timezone(ZoneInfo("Asia/Tokyo")) - shanghai_time = get_now_with_timezone(ZoneInfo("Asia/Shanghai")) - - logger.info("Original UTC time: %s", to_iso_format(utc_time)) - logger.info("Original Tokyo time: %s", to_iso_format(tokyo_time)) - logger.info("Original Shanghai time: %s", to_iso_format(shanghai_time)) - - # Create memory using UTC time - entity = build_episodic_memory_entity( - event_id=test_event_id, - user_id=test_user_id, - timestamp=utc_time, - episode="Timezone test memory", - search_content=["timezone", "test"], - vector=generate_random_vector(), - title="Timezone Test", - created_at=tokyo_time, - updated_at=shanghai_time, - ) - - await repo.collection.insert([entity]) - - assert entity is not None - logger.info("✅ Created memory with timezone information successfully") - - await repo.flush() - await asyncio.sleep(2) - - # Retrieve from database and verify - retrieved_doc = await repo.get_by_id(test_event_id) - assert retrieved_doc is not None - - # Parse timestamp - retrieved_timestamp = datetime.fromtimestamp(retrieved_doc["timestamp"]) - logger.info( - "Retrieved timestamp from database: %s", to_iso_format(retrieved_timestamp) - ) - - # Verify time conversion correctness (should be equal after converting to same timezone) - assert compare_datetime( - retrieved_timestamp.astimezone(ZoneInfo("UTC")), - utc_time.astimezone(ZoneInfo("UTC")), - ) - logger.info("✅ Timezone verification successful") - - # Test time range query - results = await repo.vector_search( - query_vector=generate_random_vector(), - user_id=test_user_id, - start_time=shanghai_time - timedelta(hours=2), - end_time=shanghai_time + timedelta(hours=2), - limit=10, - ) - assert len(results) >= 1, "Should find records within time range" - logger.info("✅ Timezone time range query test successful") - - except Exception as e: - logger.error("❌ Timezone handling test failed: %s", e) - raise - finally: - # Clean up test data - try: - await repo.delete_by_event_id(test_event_id) - await repo.flush() - logger.info("✅ Cleaned up timezone test data successfully") - except Exception: - pass - - logger.info("✅ Timezone handling test completed") - - -async def test_edge_cases(): - """Test edge cases""" - logger.info("Starting edge cases test...") - - repo = get_bean_by_type(EpisodicMemoryMilvusRepository) - test_user_id = "test_user_edge_111" - - try: - # Test 1: Non-existent user - logger.info("Test 1: Non-existent user") - nonexistent_results = await repo.vector_search( - query_vector=generate_random_vector(), - user_id="nonexistent_user_999999", - limit=10, - ) - assert ( - len(nonexistent_results) == 0 - ), "Non-existent user should return empty results" - logger.info("✅ Non-existent user test successful") - - # Test 2: Delete non-existent event_id - logger.info("Test 2: Delete non-existent event_id") - delete_result = await repo.delete_by_event_id("nonexistent_event_999999") - assert ( - delete_result is True - ), "Deleting non-existent document somehow returns True" - logger.info("✅ Delete non-existent document test successful") - - # Test 3: Use invalid time range - logger.info("Test 3: Use invalid time range") - future_time = get_now_with_timezone(ZoneInfo("UTC")) + timedelta(days=365) - future_results = await repo.vector_search( - query_vector=generate_random_vector(), - user_id=test_user_id, - start_time=future_time, - end_time=future_time + timedelta(days=1), - limit=10, - ) - assert len(future_results) == 0, "Future time range should return empty results" - logger.info("✅ Invalid time range test successful") - - # Test 4: Vector dimension validation - logger.info("Test 4: Vector dimension validation") - try: - entity = build_episodic_memory_entity( - event_id="invalid_vector_test", - user_id=test_user_id, - timestamp=get_now_with_timezone(), - episode="Invalid vector test", - search_content=["test"], - vector=[1.0] * 512, # Incorrect vector dimension - ) - await repo.collection.insert([entity]) - assert False, "Should fail due to vector dimension error" - except Exception as e: - assert "the length(512) of float data should divide the dim(1024)" in str(e) - logger.info("✅ Correctly caught vector dimension error: %s", e) - - except Exception as e: - logger.error("❌ Edge cases test failed: %s", e) - raise - - logger.info("✅ Edge cases test completed") - - -async def test_performance(): - """Test performance""" - logger.info("Starting performance test...") - - repo = get_bean_by_type(EpisodicMemoryMilvusRepository) - test_user_id = "test_user_perf_001" - current_time = get_now_with_timezone() - num_docs = 1000 - - try: - # Prepare test data - test_data = [] - base_vector = generate_random_vector() - - for i in range(num_docs): - # Generate a vector similar to the base vector - noise = np.random.normal(0, 0.1, len(base_vector)) - vector = [x + n for x, n in zip(base_vector, noise)] - - test_data.append( - { - "event_id": f"perf_test_{i}", - "user_id": test_user_id, - "timestamp": current_time - timedelta(minutes=i), - "episode": f"Performance test memory {i}", - "search_content": ["performance", "test", f"memory{i}"], - "vector": vector, - "title": f"Performance test {i}", - "group_id": "perf_test_group", - "event_type": "PerfTest", - } - ) - - # Test 1: Batch insertion performance - logger.info("Test 1: Batch insertion performance (%d records)...", num_docs) - insert_times = [] - batch_size = 100 - - for i in range(0, num_docs, batch_size): - batch = test_data[i : i + batch_size] - start_time = get_now_with_timezone() - - for doc in batch: - entity = build_episodic_memory_entity(**doc) - await repo.collection.insert([entity]) - - end_time = get_now_with_timezone() - insert_time = (end_time - start_time).total_seconds() - insert_times.append(insert_time) - - logger.info( - "- Batch %d/%d: %.3f seconds (%.1f records/second)", - i // batch_size + 1, - (num_docs + batch_size - 1) // batch_size, - insert_time, - len(batch) / insert_time, - ) - - avg_insert_time = sum(insert_times) / len(insert_times) - min_insert_time = min(insert_times) - max_insert_time = max(insert_times) - total_insert_time = sum(insert_times) - - logger.info("Insertion performance statistics:") - logger.info("- Total time: %.3f seconds", total_insert_time) - logger.info( - "- Average per batch: %.3f seconds (%.1f records/second)", - avg_insert_time, - batch_size / avg_insert_time, - ) - logger.info( - "- Fastest batch: %.3f seconds (%.1f records/second)", - min_insert_time, - batch_size / min_insert_time, - ) - logger.info( - "- Slowest batch: %.3f seconds (%.1f records/second)", - max_insert_time, - batch_size / max_insert_time, - ) - - # Test 2: Flush performance - logger.info("Test 2: Flush performance...") - start_time = get_now_with_timezone() - await repo.flush() - flush_time = (get_now_with_timezone() - start_time).total_seconds() - logger.info("Flush time: %.3f seconds", flush_time) - - # Wait for data loading - await repo.load() - await asyncio.sleep(2) - - # Test 3: Search performance - logger.info("Test 3: Search performance...") - search_times = [] - num_searches = 10 - - for i in range(num_searches): - # Generate a query vector similar to the base vector - noise = np.random.normal(0, 0.1, len(base_vector)) - query_vector = [x + n for x, n in zip(base_vector, noise)] - - start_time = get_now_with_timezone() - results = await repo.vector_search( - query_vector=query_vector, user_id=test_user_id, limit=10 - ) - search_time = (get_now_with_timezone() - start_time).total_seconds() - search_times.append(search_time) - - logger.info( - "- Search %d/%d: %.3f seconds, found %d results", - i + 1, - num_searches, - search_time, - len(results), - ) - - avg_search_time = sum(search_times) / len(search_times) - min_search_time = min(search_times) - max_search_time = max(search_times) - - logger.info("Search performance statistics:") - logger.info("- Average time: %.3f seconds", avg_search_time) - logger.info("- Fastest time: %.3f seconds", min_search_time) - logger.info("- Slowest time: %.3f seconds", max_search_time) - - except Exception as e: - logger.error("❌ Performance test failed: %s", e) - raise - finally: - # Clean up test data - try: - cleanup_count = await repo.delete_by_filters(user_id=test_user_id) - await repo.flush() - logger.info("✅ Cleaned up %d performance test data", cleanup_count) - except Exception as cleanup_error: - logger.error( - "Error during cleanup of performance test data: %s", cleanup_error - ) - - logger.info("✅ Performance test completed") - - -async def run_all_tests(): - """Run all tests""" - logger.info("🚀 Starting all EpisodicMemoryMilvusRepository tests...") - - try: - await test_crud_operations() - await test_vector_search() - await test_delete_operations() - await test_timezone_handling() - await test_edge_cases() - await test_performance() - logger.info("✅ All tests completed") - except Exception as e: - logger.error("❌ Error occurred during testing: %s", e) - raise - - -if __name__ == "__main__": - asyncio.run(run_all_tests()) diff --git a/methods/EverCore/tests/test_es_tenant_interceptor.py b/methods/EverCore/tests/test_es_tenant_interceptor.py deleted file mode 100644 index 7fd68c9d..00000000 --- a/methods/EverCore/tests/test_es_tenant_interceptor.py +++ /dev/null @@ -1,696 +0,0 @@ -""" -Test: ES Tenant Field Interceptor - -Unit tests for TenantAwareAsyncElasticsearch (Layer 1) and TenantGuardTransport (Layer 2). -Tests verify that tenant_id is correctly injected into all data-plane operations. - -These are pure unit tests — no real ES connection needed. -We mock super().perform_request() to capture the modified body/params. - -Run: - PYTHONPATH=src pytest tests/test_es_tenant_interceptor.py -v -""" - -import pytest -from unittest.mock import AsyncMock, MagicMock, patch -from typing import Any, Dict, Optional - -from elastic_transport import ApiResponseMeta, HttpHeaders, ObjectApiResponse - -from core.tenants.tenant_constants import TENANT_ID_FIELD - -# ==================== Test Constants ==================== - -TEST_TENANT_ID = "test_tenant_001" -TEST_INDEX = "test_memories" - - -# ==================== Fixtures ==================== - - -def _make_meta(status: int = 200) -> ApiResponseMeta: - """Create a minimal ApiResponseMeta for testing.""" - return ApiResponseMeta( - status=status, - http_version="1.1", - headers=HttpHeaders({"x-elastic-product": "Elasticsearch"}), - duration=0.01, - node=MagicMock(), - ) - - -def _make_search_response(hits: list, meta: Optional[ApiResponseMeta] = None): - """Create a mock search response.""" - body = {"hits": {"total": {"value": len(hits)}, "hits": hits}} - return ObjectApiResponse(body=body, meta=meta or _make_meta()) - - -def _make_count_response(count: int, meta: Optional[ApiResponseMeta] = None): - """Create a mock count response.""" - body = {"count": count} - return ObjectApiResponse(body=body, meta=meta or _make_meta()) - - -def _make_dbq_response(deleted: int, meta: Optional[ApiResponseMeta] = None): - """Create a mock delete_by_query response.""" - body = {"took": 10, "deleted": deleted, "total": deleted} - return ObjectApiResponse(body=body, meta=meta or _make_meta()) - - -@pytest.fixture -def mock_tenant_context(): - """Mock tenant context to return TEST_TENANT_ID in shared mode.""" - mock_tenant_info = MagicMock() - mock_tenant_info.is_shared_mode = True - - with ( - patch( - "core.tenants.tenantize.oxm.es.tenant_field_es_interceptor.get_current_tenant_id", - return_value=TEST_TENANT_ID, - ), - patch( - "core.tenants.tenantize.oxm.es.tenant_field_es_interceptor.get_current_tenant", - return_value=mock_tenant_info, - ), - ): - yield - - -@pytest.fixture -def mock_no_tenant_context(): - """Mock no tenant context (tenant_id returns None).""" - with patch( - "core.tenants.tenantize.oxm.es.tenant_field_es_interceptor.get_current_tenant_id", - return_value=None, - ): - yield - - -# ==================== Query Utility Tests ==================== - - -class TestTenantQueryUtils: - """Test the query injection utility functions.""" - - def test_wrap_query_with_tenant_non_bool(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - wrap_query_with_tenant, - ) - - query = {"match": {"content": "hello"}} - result = wrap_query_with_tenant(query, TEST_TENANT_ID) - - assert "bool" in result - assert result["bool"]["must"] == [{"match": {"content": "hello"}}] - assert {"term": {TENANT_ID_FIELD: TEST_TENANT_ID}} in result["bool"]["filter"] - - def test_wrap_query_with_tenant_existing_bool(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - wrap_query_with_tenant, - ) - - query = {"bool": {"must": [{"match": {"content": "hello"}}]}} - result = wrap_query_with_tenant(query, TEST_TENANT_ID) - - # Should merge into existing bool, not wrap again - assert result["bool"]["must"] == [{"match": {"content": "hello"}}] - assert {"term": {TENANT_ID_FIELD: TEST_TENANT_ID}} in result["bool"]["filter"] - - def test_wrap_query_with_tenant_existing_bool_with_filter(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - wrap_query_with_tenant, - ) - - query = { - "bool": { - "must": [{"match": {"content": "hello"}}], - "filter": [{"term": {"user_id": "u001"}}], - } - } - result = wrap_query_with_tenant(query, TEST_TENANT_ID) - - # Original filter preserved + tenant filter appended - filters = result["bool"]["filter"] - assert {"term": {"user_id": "u001"}} in filters - assert {"term": {TENANT_ID_FIELD: TEST_TENANT_ID}} in filters - assert len(filters) == 2 - - def test_wrap_query_with_tenant_filter_as_dict(self): - """When existing filter is a dict (not list), should convert to list.""" - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - wrap_query_with_tenant, - ) - - query = {"bool": {"filter": {"term": {"status": "active"}}}} - result = wrap_query_with_tenant(query, TEST_TENANT_ID) - - filters = result["bool"]["filter"] - assert isinstance(filters, list) - assert {"term": {"status": "active"}} in filters - assert {"term": {TENANT_ID_FIELD: TEST_TENANT_ID}} in filters - - def test_make_tenant_only_query(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - make_tenant_only_query, - ) - - result = make_tenant_only_query(TEST_TENANT_ID) - assert result == { - "bool": {"filter": [{"term": {TENANT_ID_FIELD: TEST_TENANT_ID}}]} - } - - def test_inject_query_body_none(self): - """body=None should create a filter-only query, never skip.""" - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - inject_query_body, - ) - - result = inject_query_body(None, TEST_TENANT_ID) - assert result["query"]["bool"]["filter"] == [ - {"term": {TENANT_ID_FIELD: TEST_TENANT_ID}} - ] - - def test_inject_query_body_empty(self): - """Empty body should create a filter-only query.""" - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - inject_query_body, - ) - - result = inject_query_body({}, TEST_TENANT_ID) - assert result["query"]["bool"]["filter"] == [ - {"term": {TENANT_ID_FIELD: TEST_TENANT_ID}} - ] - - def test_inject_query_body_no_query(self): - """body with size but no query should add tenant filter.""" - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - inject_query_body, - ) - - result = inject_query_body({"size": 10}, TEST_TENANT_ID) - assert "query" in result - assert result["size"] == 10 - - def test_inject_query_body_with_post_filter(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - inject_query_body, - ) - - body = { - "query": {"match_all": {}}, - "post_filter": {"term": {"status": "active"}}, - } - result = inject_query_body(body, TEST_TENANT_ID) - - # Both query and post_filter should have tenant filter - assert "bool" in result["query"] - assert "bool" in result["post_filter"] - - def test_inject_query_body_with_suggest_raises(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - inject_query_body, - ) - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantIsolationViolation, - ) - - body = { - "query": {"match_all": {}}, - "suggest": {"my_suggest": {"text": "test", "term": {"field": "content"}}}, - } - with pytest.raises(TenantIsolationViolation, match="suggest"): - inject_query_body(body, TEST_TENANT_ID) - - def test_inject_query_body_preserves_aggs(self): - """Aggregations should be left untouched.""" - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - inject_query_body, - ) - - body = { - "query": {"match": {"content": "test"}}, - "aggs": {"by_user": {"terms": {"field": "user_id"}}}, - } - result = inject_query_body(body, TEST_TENANT_ID) - - # aggs preserved as-is - assert result["aggs"] == {"by_user": {"terms": {"field": "user_id"}}} - - def test_make_ids_tenant_query(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - make_ids_tenant_query, - ) - - result = make_ids_tenant_query("doc_123", TEST_TENANT_ID) - assert result == { - "bool": { - "filter": [ - {"ids": {"values": ["doc_123"]}}, - {"term": {TENANT_ID_FIELD: TEST_TENANT_ID}}, - ] - } - } - - -# ==================== Bulk Injection Tests ==================== - - -class TestBulkInjection: - def test_inject_bulk_index(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - inject_bulk_body, - ) - - body = [ - {"index": {"_index": "memories", "_id": "1"}}, - {"content": "hello", "user_id": "u1"}, - {"index": {"_index": "memories", "_id": "2"}}, - {"content": "world", "user_id": "u2"}, - ] - result = inject_bulk_body(body, TEST_TENANT_ID) - - # Check routing injected into action metadata - assert result[0]["index"]["routing"] == TEST_TENANT_ID - assert result[2]["index"]["routing"] == TEST_TENANT_ID - - # Check tenant_id injected into documents - assert result[1]["tenant_id"] == TEST_TENANT_ID - assert result[3]["tenant_id"] == TEST_TENANT_ID - - def test_inject_bulk_create(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - inject_bulk_body, - ) - - body = [{"create": {"_index": "memories", "_id": "1"}}, {"content": "hello"}] - result = inject_bulk_body(body, TEST_TENANT_ID) - - assert result[0]["create"]["routing"] == TEST_TENANT_ID - assert result[1]["tenant_id"] == TEST_TENANT_ID - - def test_inject_bulk_update(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - inject_bulk_body, - ) - - body = [ - {"update": {"_index": "memories", "_id": "1"}}, - {"doc": {"content": "updated"}, "upsert": {"content": "new"}}, - ] - result = inject_bulk_body(body, TEST_TENANT_ID) - - assert result[0]["update"]["routing"] == TEST_TENANT_ID - assert result[1]["doc"]["tenant_id"] == TEST_TENANT_ID - assert result[1]["upsert"]["tenant_id"] == TEST_TENANT_ID - - def test_inject_bulk_delete_raises(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - inject_bulk_body, - ) - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantIsolationViolation, - ) - - body = [{"delete": {"_index": "memories", "_id": "1"}}] - with pytest.raises(TenantIsolationViolation, match="bulk delete"): - inject_bulk_body(body, TEST_TENANT_ID) - - -# ==================== Interceptor (Layer 1) Tests ==================== - - -class TestTenantAwareAsyncElasticsearch: - """Test the perform_request override.""" - - @pytest.mark.asyncio - async def test_search_injects_tenant_filter(self, mock_tenant_context): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - ) - - captured = {} - - async def mock_super_perform_request(method, path, **kwargs): - captured["method"] = method - captured["path"] = path - captured["body"] = kwargs.get("body") - captured["params"] = kwargs.get("params") - return _make_search_response([]) - - client = TenantAwareAsyncElasticsearch.__new__(TenantAwareAsyncElasticsearch) - - with patch.object( - TenantAwareAsyncElasticsearch.__bases__[0], - "perform_request", - side_effect=mock_super_perform_request, - ): - await client.perform_request( - "POST", - "/test/_search", - body={"query": {"match": {"content": "hello"}}}, - endpoint_id="search", - path_parts={"index": "test"}, - ) - - # Verify tenant filter injected - query = captured["body"]["query"] - assert "bool" in query - filters = query["bool"]["filter"] - assert {"term": {TENANT_ID_FIELD: TEST_TENANT_ID}} in filters - - # Verify routing injected - assert captured["params"]["routing"] == TEST_TENANT_ID - - @pytest.mark.asyncio - async def test_search_body_none_creates_filter(self, mock_tenant_context): - """body=None must not be skipped — should create tenant filter query.""" - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - ) - - captured = {} - - async def mock_super_perform_request(method, path, **kwargs): - captured["body"] = kwargs.get("body") - return _make_search_response([]) - - client = TenantAwareAsyncElasticsearch.__new__(TenantAwareAsyncElasticsearch) - - with patch.object( - TenantAwareAsyncElasticsearch.__bases__[0], - "perform_request", - side_effect=mock_super_perform_request, - ): - await client.perform_request( - "POST", - "/test/_search", - body=None, - endpoint_id="search", - path_parts={"index": "test"}, - ) - - # body should have been created with tenant filter - assert captured["body"] is not None - assert "query" in captured["body"] - filters = captured["body"]["query"]["bool"]["filter"] - assert {"term": {TENANT_ID_FIELD: TEST_TENANT_ID}} in filters - - @pytest.mark.asyncio - async def test_index_injects_tenant_id(self, mock_tenant_context): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - ) - - captured = {} - - async def mock_super_perform_request(method, path, **kwargs): - captured["body"] = kwargs.get("body") - captured["params"] = kwargs.get("params") - return ObjectApiResponse( - body={"_id": "1", "result": "created"}, meta=_make_meta() - ) - - client = TenantAwareAsyncElasticsearch.__new__(TenantAwareAsyncElasticsearch) - - with patch.object( - TenantAwareAsyncElasticsearch.__bases__[0], - "perform_request", - side_effect=mock_super_perform_request, - ): - await client.perform_request( - "PUT", - "/test/_doc/1", - body={"content": "hello", "user_id": "u1"}, - endpoint_id="index", - path_parts={"index": "test", "id": "1"}, - ) - - assert captured["body"]["tenant_id"] == TEST_TENANT_ID - assert captured["params"]["routing"] == TEST_TENANT_ID - - @pytest.mark.asyncio - async def test_index_body_none_raises(self, mock_tenant_context): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - TenantIsolationViolation, - ) - - client = TenantAwareAsyncElasticsearch.__new__(TenantAwareAsyncElasticsearch) - - with pytest.raises(TenantIsolationViolation, match="no body"): - await client.perform_request( - "PUT", - "/test/_doc/1", - body=None, - endpoint_id="index", - path_parts={"index": "test", "id": "1"}, - ) - - @pytest.mark.asyncio - async def test_get_converts_to_search(self, mock_tenant_context): - """get by ID should be converted to search with ids + tenant filter.""" - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - ) - - captured = {} - - async def mock_super_perform_request(method, path, **kwargs): - captured["method"] = method - captured["path"] = path - captured["body"] = kwargs.get("body") - captured["endpoint_id"] = kwargs.get("endpoint_id") - return _make_search_response( - [ - { - "_index": TEST_INDEX, - "_id": "doc_123", - "_version": 1, - "_source": {"content": "hello", "tenant_id": TEST_TENANT_ID}, - } - ] - ) - - client = TenantAwareAsyncElasticsearch.__new__(TenantAwareAsyncElasticsearch) - - with patch.object( - TenantAwareAsyncElasticsearch.__bases__[0], - "perform_request", - side_effect=mock_super_perform_request, - ): - response = await client.perform_request( - "GET", - f"/{TEST_INDEX}/_doc/doc_123", - endpoint_id="get", - path_parts={"index": TEST_INDEX, "id": "doc_123"}, - ) - - # Should have been converted to search - assert captured["method"] == "POST" - assert "_search" in captured["path"] - assert captured["endpoint_id"] == "search" - - # Query should have ids + tenant filter - query_filters = captured["body"]["query"]["bool"]["filter"] - assert {"ids": {"values": ["doc_123"]}} in query_filters - assert {"term": {TENANT_ID_FIELD: TEST_TENANT_ID}} in query_filters - - # Response should be in get format - assert response.body["found"] is True - assert response.body["_id"] == "doc_123" - assert response.body["_source"]["content"] == "hello" - - @pytest.mark.asyncio - async def test_get_not_found_returns_found_false(self, mock_tenant_context): - """get for non-existent or other-tenant doc should return found=False.""" - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - ) - - async def mock_super_perform_request(method, path, **kwargs): - return _make_search_response([]) # No hits - - client = TenantAwareAsyncElasticsearch.__new__(TenantAwareAsyncElasticsearch) - - with patch.object( - TenantAwareAsyncElasticsearch.__bases__[0], - "perform_request", - side_effect=mock_super_perform_request, - ): - response = await client.perform_request( - "GET", - f"/{TEST_INDEX}/_doc/doc_other_tenant", - endpoint_id="get", - path_parts={"index": TEST_INDEX, "id": "doc_other_tenant"}, - ) - - assert response.body["found"] is False - - @pytest.mark.asyncio - async def test_delete_converts_to_dbq(self, mock_tenant_context): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - ) - - captured = {} - - async def mock_super_perform_request(method, path, **kwargs): - captured["method"] = method - captured["endpoint_id"] = kwargs.get("endpoint_id") - captured["body"] = kwargs.get("body") - return _make_dbq_response(1) - - client = TenantAwareAsyncElasticsearch.__new__(TenantAwareAsyncElasticsearch) - - with patch.object( - TenantAwareAsyncElasticsearch.__bases__[0], - "perform_request", - side_effect=mock_super_perform_request, - ): - response = await client.perform_request( - "DELETE", - f"/{TEST_INDEX}/_doc/doc_123", - endpoint_id="delete", - path_parts={"index": TEST_INDEX, "id": "doc_123"}, - ) - - assert captured["method"] == "POST" - assert captured["endpoint_id"] == "delete_by_query" - assert response.body["result"] == "deleted" - - @pytest.mark.asyncio - async def test_unknown_endpoint_raises(self, mock_tenant_context): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - TenantIsolationViolation, - ) - - client = TenantAwareAsyncElasticsearch.__new__(TenantAwareAsyncElasticsearch) - - with pytest.raises(TenantIsolationViolation, match="Unknown"): - await client.perform_request( - "POST", "/test/_some_new_api", endpoint_id="some_new_api", path_parts={} - ) - - @pytest.mark.asyncio - async def test_blocked_endpoint_raises(self, mock_tenant_context): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - TenantIsolationViolation, - ) - - client = TenantAwareAsyncElasticsearch.__new__(TenantAwareAsyncElasticsearch) - - with pytest.raises(TenantIsolationViolation, match="not yet enabled"): - await client.perform_request( - "POST", - "/test/_search", - body={"query": {"match_all": {}}}, - endpoint_id="update_by_query", - path_parts={"index": "test"}, - ) - - @pytest.mark.asyncio - async def test_passthrough_when_no_tenant_context(self, mock_no_tenant_context): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - ) - - captured = {} - - async def mock_super_perform_request(method, path, **kwargs): - captured["body"] = kwargs.get("body") - return _make_search_response([]) - - client = TenantAwareAsyncElasticsearch.__new__(TenantAwareAsyncElasticsearch) - - with patch.object( - TenantAwareAsyncElasticsearch.__bases__[0], - "perform_request", - side_effect=mock_super_perform_request, - ): - await client.perform_request( - "POST", - "/test/_search", - body={"query": {"match_all": {}}}, - endpoint_id="search", - path_parts={"index": "test"}, - ) - - # Without tenant context, body should be unmodified - assert captured["body"] == {"query": {"match_all": {}}} - - @pytest.mark.asyncio - async def test_indices_passthrough(self, mock_tenant_context): - """Control-plane endpoints should pass through without injection.""" - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantAwareAsyncElasticsearch, - ) - - captured = {} - - async def mock_super_perform_request(method, path, **kwargs): - captured["body"] = kwargs.get("body") - return ObjectApiResponse(body={"acknowledged": True}, meta=_make_meta()) - - client = TenantAwareAsyncElasticsearch.__new__(TenantAwareAsyncElasticsearch) - - with patch.object( - TenantAwareAsyncElasticsearch.__bases__[0], - "perform_request", - side_effect=mock_super_perform_request, - ): - await client.perform_request( - "PUT", - "/test_index", - body={"settings": {"number_of_shards": 1}}, - endpoint_id="indices.create", - path_parts={"index": "test_index"}, - ) - - # Body should be unmodified (no tenant filter injected) - assert captured["body"] == {"settings": {"number_of_shards": 1}} - - -# ==================== Guard Transport (Layer 2) Tests ==================== - - -class TestTenantGuardTransport: - """Test the structure-based verification.""" - - def test_query_has_tenant_positive(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantGuardTransport, - ) - - query = { - "bool": { - "must": [{"match": {"content": "hello"}}], - "filter": [{"term": {TENANT_ID_FIELD: TEST_TENANT_ID}}], - } - } - assert TenantGuardTransport._query_has_tenant(query, TEST_TENANT_ID) is True - - def test_query_has_tenant_negative(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantGuardTransport, - ) - - query = {"bool": {"must": [{"match": {"content": "hello"}}]}} - assert TenantGuardTransport._query_has_tenant(query, TEST_TENANT_ID) is False - - def test_query_has_tenant_wrong_id(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantGuardTransport, - ) - - query = {"bool": {"filter": [{"term": {"tenant_id": "wrong_tenant"}}]}} - assert TenantGuardTransport._query_has_tenant(query, TEST_TENANT_ID) is False - - def test_query_has_tenant_non_bool(self): - from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantGuardTransport, - ) - - query = {"match_all": {}} - assert TenantGuardTransport._query_has_tenant(query, TEST_TENANT_ID) is False diff --git a/methods/EverCore/tests/test_filter_parser.py b/methods/EverCore/tests/test_filter_parser.py deleted file mode 100644 index 08707170..00000000 --- a/methods/EverCore/tests/test_filter_parser.py +++ /dev/null @@ -1,268 +0,0 @@ -"""Unit tests for MongoFilterParser. - -Tests cover: -- eq / in / gt / gte / lt / lte operators -- AND / OR combinators (including nested) -- Timestamp epoch ms -> datetime conversion -- Unknown fields silently ignored (allowlist behavior) -- Scope extraction (user_id, group_id) -- Edge cases: empty filters, None values -""" - -from datetime import datetime, timezone - -from agentic_layer.filter_parser import MongoFilterParser - - -class TestEqOperator: - """Test implicit eq operator (plain value).""" - - def test_user_id_eq(self): - mq, uid, gids = MongoFilterParser.parse({"user_id": "u1"}) - assert mq == {"user_id": "u1"} - assert uid == "u1" - assert gids is None - - def test_group_id_eq(self): - mq, uid, gids = MongoFilterParser.parse({"group_id": "g1"}) - assert mq == {"group_id": "g1"} - assert uid is None - assert gids == ["g1"] - - def test_session_id_eq(self): - mq, uid, gids = MongoFilterParser.parse({"user_id": "u1", "session_id": "s1"}) - assert mq == {"user_id": "u1", "session_id": "s1"} - - def test_user_and_group(self): - mq, uid, gids = MongoFilterParser.parse({"user_id": "u1", "group_id": "g1"}) - assert mq == {"user_id": "u1", "group_id": "g1"} - assert uid == "u1" - assert gids == ["g1"] - - -class TestInOperator: - """Test 'in' operator.""" - - def test_group_id_in(self): - mq, uid, gids = MongoFilterParser.parse({"group_id": {"in": ["g1", "g2"]}}) - assert mq == {"group_id": {"$in": ["g1", "g2"]}} - assert gids == ["g1", "g2"] - - def test_user_id_in(self): - mq, uid, gids = MongoFilterParser.parse({"user_id": {"in": ["u1", "u2"]}}) - assert mq == {"user_id": {"$in": ["u1", "u2"]}} - assert uid == "u1" - - def test_session_id_in(self): - mq, _, _ = MongoFilterParser.parse( - {"user_id": "u1", "session_id": {"in": ["s1", "s2"]}} - ) - assert mq["session_id"] == {"$in": ["s1", "s2"]} - - def test_user_id_in_empty_list(self): - mq, uid, gids = MongoFilterParser.parse({"user_id": {"in": []}}) - assert mq == {"user_id": {"$in": []}} - assert uid is None - - -class TestComparisonOperators: - """Test gt, gte, lt, lte operators on allowed fields.""" - - def test_session_id_gt(self): - mq, _, _ = MongoFilterParser.parse( - {"user_id": "u1", "session_id": {"gt": "s5"}} - ) - assert mq["session_id"] == {"$gt": "s5"} - - def test_session_id_gte_lte(self): - mq, _, _ = MongoFilterParser.parse( - {"user_id": "u1", "session_id": {"gte": "s1", "lte": "s5"}} - ) - assert mq["session_id"] == {"$gte": "s1", "$lte": "s5"} - - -class TestTimestampHandling: - """Test timestamp field parsing and epoch ms/s conversion.""" - - def test_timestamp_epoch_millis(self): - ts_ms = 1768471200000 # 2026-01-15T10:00:00Z - mq, _, _ = MongoFilterParser.parse({"user_id": "u1", "timestamp": ts_ms}) - expected = datetime(2026, 1, 15, 10, 0, 0, tzinfo=timezone.utc) - assert mq["timestamp"] == expected - - def test_timestamp_epoch_seconds(self): - ts_s = 1768471200 # 2026-01-15T10:00:00Z - mq, _, _ = MongoFilterParser.parse({"user_id": "u1", "timestamp": ts_s}) - expected = datetime(2026, 1, 15, 10, 0, 0, tzinfo=timezone.utc) - assert mq["timestamp"] == expected - - def test_timestamp_gte_lt_range(self): - t_gte = 1768469400000 # 2026-01-15T09:30:00Z - t_lt = 1768473000000 # 2026-01-15T10:30:00Z - mq, _, _ = MongoFilterParser.parse( - { - "user_id": "u1", - "AND": [{"timestamp": {"gte": t_gte}}, {"timestamp": {"lt": t_lt}}], - } - ) - and_clauses = mq["$and"] - assert len(and_clauses) == 2 - assert "$gte" in and_clauses[0]["timestamp"] - assert "$lt" in and_clauses[1]["timestamp"] - - def test_timestamp_iso_string(self): - mq, _, _ = MongoFilterParser.parse( - {"user_id": "u1", "timestamp": "2026-01-15T10:00:00+00:00"} - ) - expected = datetime(2026, 1, 15, 10, 0, 0, tzinfo=timezone.utc) - assert mq["timestamp"] == expected - - def test_timestamp_operators_dict(self): - mq, _, _ = MongoFilterParser.parse( - {"user_id": "u1", "timestamp": {"gte": 1768471200000, "lt": 1768474800000}} - ) - ts = mq["timestamp"] - assert "$gte" in ts - assert "$lt" in ts - assert isinstance(ts["$gte"], datetime) - assert isinstance(ts["$lt"], datetime) - - -class TestCombinators: - """Test AND / OR combinators.""" - - def test_and_combinator(self): - mq, _, _ = MongoFilterParser.parse( - {"user_id": "u1", "AND": [{"session_id": "s1"}, {"group_id": "g1"}]} - ) - assert "$and" in mq - assert len(mq["$and"]) == 2 - assert {"session_id": "s1"} in mq["$and"] - assert {"group_id": "g1"} in mq["$and"] - - def test_or_combinator(self): - mq, _, _ = MongoFilterParser.parse( - {"user_id": "u1", "OR": [{"session_id": "s1"}, {"session_id": "s2"}]} - ) - assert "$or" in mq - assert len(mq["$or"]) == 2 - - def test_nested_and_or(self): - mq, _, _ = MongoFilterParser.parse( - { - "user_id": "u1", - "AND": [ - {"OR": [{"session_id": "s1"}, {"session_id": "s2"}]}, - {"group_id": "g1"}, - ], - } - ) - assert "$and" in mq - assert len(mq["$and"]) == 2 - or_clause = mq["$and"][0] - assert "$or" in or_clause - - def test_empty_and_items_ignored(self): - mq, _, _ = MongoFilterParser.parse( - {"user_id": "u1", "AND": [{}, {"session_id": "s1"}]} - ) - # Empty dict {} is falsy for `if item` check, should be filtered - assert "$and" in mq - - -class TestAllowlistSecurity: - """Test that unknown fields are silently ignored.""" - - def test_unknown_field_ignored(self): - mq, uid, gids = MongoFilterParser.parse( - {"user_id": "u1", "unknown_field": "should_be_ignored"} - ) - assert "unknown_field" not in mq - assert mq == {"user_id": "u1"} - - def test_multiple_unknown_fields(self): - mq, _, _ = MongoFilterParser.parse( - {"user_id": "u1", "password": "hack", "$where": "evil()", "admin": True} - ) - assert mq == {"user_id": "u1"} - assert "password" not in mq - assert "$where" not in mq - assert "admin" not in mq - - def test_unknown_field_in_and(self): - mq, _, _ = MongoFilterParser.parse( - {"user_id": "u1", "AND": [{"unknown": "val"}, {"session_id": "s1"}]} - ) - and_items = mq["$and"] - # First item should be empty dict (unknown filtered), second should have session_id - assert {"session_id": "s1"} in and_items - - def test_unknown_operator_ignored(self): - mq, _, _ = MongoFilterParser.parse( - {"user_id": "u1", "session_id": {"regex": ".*hack.*"}} - ) - # 'regex' is not in _OPERATOR_MAP, so session_id should not appear - assert "session_id" not in mq - - -class TestScopeExtraction: - """Test user_id and group_id scope extraction.""" - - def test_no_scope(self): - mq, uid, gids = MongoFilterParser.parse({"session_id": "s1"}) - assert uid is None - assert gids is None - - def test_user_id_only(self): - _, uid, gids = MongoFilterParser.parse({"user_id": "u1"}) - assert uid == "u1" - assert gids is None - - def test_group_id_only(self): - _, uid, gids = MongoFilterParser.parse({"group_id": "g1"}) - assert uid is None - assert gids == ["g1"] - - def test_both_scopes(self): - _, uid, gids = MongoFilterParser.parse({"user_id": "u1", "group_id": "g1"}) - assert uid == "u1" - assert gids == ["g1"] - - def test_group_id_in_extraction(self): - _, uid, gids = MongoFilterParser.parse({"group_id": {"in": ["g1", "g2", "g3"]}}) - assert gids == ["g1", "g2", "g3"] - - -class TestEdgeCases: - """Test edge cases and boundary conditions.""" - - def test_empty_filters(self): - mq, uid, gids = MongoFilterParser.parse({}) - assert mq == {} - assert uid is None - assert gids is None - - def test_empty_and_array(self): - mq, _, _ = MongoFilterParser.parse({"user_id": "u1", "AND": []}) - # Empty AND should not produce $and key - assert "$and" not in mq - - def test_empty_or_array(self): - mq, _, _ = MongoFilterParser.parse({"user_id": "u1", "OR": []}) - assert "$or" not in mq - - def test_all_fields_combined(self): - mq, uid, gids = MongoFilterParser.parse( - { - "user_id": "u1", - "group_id": {"in": ["g1", "g2"]}, - "session_id": "s1", - "timestamp": {"gte": 1768471200000}, - } - ) - assert uid == "u1" - assert gids == ["g1", "g2"] - assert mq["user_id"] == "u1" - assert mq["group_id"] == {"$in": ["g1", "g2"]} - assert mq["session_id"] == "s1" - assert "$gte" in mq["timestamp"] diff --git a/methods/EverCore/tests/test_get_mem_service_e2e.py b/methods/EverCore/tests/test_get_mem_service_e2e.py deleted file mode 100644 index 4e5b3a93..00000000 --- a/methods/EverCore/tests/test_get_mem_service_e2e.py +++ /dev/null @@ -1,982 +0,0 @@ -""" -V1 Memory GET API Test Suite -Tests POST /api/v1/memories/get with all filter, pagination, sorting, and error scenarios. - -Prerequisites: - - Server running: uv run python src/run.py --port 1995 - - Seed data inserted: python my_docs/temp/seed_v1_data.py - -Usage: - # Run all tests - PYTHONPATH=src pytest tests/test_memory_get.py - - # Run a single test - PYTHONPATH=src pytest tests/test_memory_get.py::TestEpisodicFilters::test_7a_filter_by_user_id - - # Run only episodic / profile / error / pagination groups - PYTHONPATH=src pytest tests/test_memory_get.py::TestEpisodicFilters - PYTHONPATH=src pytest tests/test_memory_get.py::TestProfileFilters - PYTHONPATH=src pytest tests/test_memory_get.py::TestErrorCases - PYTHONPATH=src pytest tests/test_memory_get.py::TestPaginationAndSorting - -Seed data summary (see seed_v1_data.py header for full table): - Episodic(10): user_01=5, user_02=3, user_03=2 | group_01=6, group_02=2, group_03=2 - Profiles(7): user_01=3, user_02=2, user_03=2 | group_01=3, group_02=2, group_03=2 - Timestamps: t_old(7d), t1(3h), t2(2h), t3(1h), t4(10min) - Sessions: session_01(group_01), session_02(group_02), session_03(group_01), session_04(group_03) -""" - -import os -import logging - -import pytest -import requests - -logger = logging.getLogger(__name__) - -BASE_URL = os.environ.get("TEST_BASE_URL", "http://localhost:1995") -API_URL = f"{BASE_URL}/api/v1/memories/get" -TIMEOUT = 30 - - -def post_memories(payload: dict) -> requests.Response: - """Send POST request to the memories/get endpoint.""" - return requests.post( - API_URL, - json=payload, - headers={"Content-Type": "application/json"}, - timeout=TIMEOUT, - ) - - -def assert_success(resp: requests.Response) -> dict: - """Assert HTTP 200 and return parsed JSON body.""" - assert resp.status_code == 200, f"Expected 200, got {resp.status_code}: {resp.text}" - body = resp.json() - assert "data" in body, f"Response missing 'data' key: {body}" - return body - - -def assert_error(resp: requests.Response) -> dict: - """Assert response contains an error object (status may vary). - - Accepts both custom error format {"error": {...}} and - Pydantic validation format {"detail": [...]}. - """ - body = resp.json() - assert "error" in body or "detail" in body, f"Expected error response, got: {body}" - return body - - -# ================================================================ -# Episodic memory filter tests (7a - 7e, 7k) -# ================================================================ -@pytest.mark.integration -class TestEpisodicFilters: - """Test episodic memory filtering: user_id, group_id, session_id, in operator.""" - - def test_7a_filter_by_user_id(self): - """7a. user_01 should have 5 episodes across 3 groups.""" - resp = post_memories( - {"memory_type": "episodic_memory", "filters": {"user_id": "user_01"}} - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 5 - assert data["count"] == 5 - assert len(data["episodes"]) == 5 - assert data["profiles"] == [] - - user_ids = {ep["user_id"] for ep in data["episodes"]} - assert user_ids == {"user_01"} - - subjects = {ep["subject"] for ep in data["episodes"]} - expected_subjects = { - "Search API Design Discussion", - "Dashboard Design Review", - "Sprint Planning", - "Project Kickoff Meeting", - "Security Audit Review", - } - assert subjects == expected_subjects - - def test_7b_filter_by_user_id_and_group_id(self): - """7b. user_01 + group_01 should have 3 episodes.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": {"user_id": "user_01", "group_id": "group_01"}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 3 - for ep in data["episodes"]: - assert ep["user_id"] == "user_01" - assert ep["group_id"] == "group_01" - - subjects = {ep["subject"] for ep in data["episodes"]} - assert subjects == { - "Search API Design Discussion", - "Sprint Planning", - "Project Kickoff Meeting", - } - - def test_7c_filter_by_group_id(self): - """7c. group_01 should have 6 episodes from 3 users.""" - resp = post_memories( - {"memory_type": "episodic_memory", "filters": {"group_id": "group_01"}} - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 6 - user_ids = {ep["user_id"] for ep in data["episodes"]} - assert user_ids == {"user_01", "user_02", "user_03"} - - subjects = {ep["subject"] for ep in data["episodes"]} - assert "Search API Design Discussion" in subjects - assert "Sprint Planning" in subjects - assert "Project Kickoff Meeting" in subjects - - def test_7d_filter_by_user_id_and_session_id(self): - """7d. user_01 + session_01 should have 2 episodes.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": {"user_id": "user_01", "session_id": "session_01"}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 2 - for ep in data["episodes"]: - assert ep["session_id"] == "session_01" - - subjects = {ep["subject"] for ep in data["episodes"]} - assert subjects == {"Search API Design Discussion", "Project Kickoff Meeting"} - - def test_7e_group_id_in_operator(self): - """7e. group_id in [group_01, group_02] should have 8 episodes.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": {"group_id": {"in": ["group_01", "group_02"]}}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 8 - group_ids = {ep["group_id"] for ep in data["episodes"]} - assert group_ids == {"group_01", "group_02"} - - def test_7k_no_matching_data(self): - """7k. Non-existent user should return empty result.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": {"user_id": "user_nonexistent"}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 0 - assert data["count"] == 0 - assert data["episodes"] == [] - - def test_7s_session_id_in_operator(self): - """7s. group_id in + session_id in should have 6 episodes.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": { - "group_id": {"in": ["group_01", "group_02"]}, - "session_id": {"in": ["session_01", "session_02"]}, - }, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 6 - session_ids = {ep["session_id"] for ep in data["episodes"]} - assert "session_03" not in session_ids - - def test_user_id_in_operator(self): - """user_id in [user_01, user_02] should return 8 episodes (5+3).""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": {"user_id": {"in": ["user_01", "user_02"]}}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 8 - user_ids = {ep["user_id"] for ep in data["episodes"]} - assert user_ids == {"user_01", "user_02"} - - def test_user_id_eq_and_group_id_in(self): - """user_01 + group_id in [group_01, group_02] should return 4 episodes (3+1).""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": { - "user_id": "user_01", - "group_id": {"in": ["group_01", "group_02"]}, - }, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 4 - for ep in data["episodes"]: - assert ep["user_id"] == "user_01" - group_ids = {ep["group_id"] for ep in data["episodes"]} - assert group_ids == {"group_01", "group_02"} - - def test_user_id_eq_and_session_id_in(self): - """user_01 + session_id in [session_01, session_03] should return 3 episodes (2+1).""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": { - "user_id": "user_01", - "session_id": {"in": ["session_01", "session_03"]}, - }, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 3 - session_ids = {ep["session_id"] for ep in data["episodes"]} - assert session_ids == {"session_01", "session_03"} - - def test_group_id_eq_and_session_id_eq(self): - """group_01 + session_01 should return 4 episodes (ep_01,02,06,07).""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": {"group_id": "group_01", "session_id": "session_01"}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 4 - for ep in data["episodes"]: - assert ep["group_id"] == "group_01" - assert ep["session_id"] == "session_01" - - def test_or_combinator(self): - """user_01 + OR[group_01, group_03] should return 4 episodes (3+1).""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": { - "user_id": "user_01", - "OR": [{"group_id": "group_01"}, {"group_id": "group_03"}], - }, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 4 - for ep in data["episodes"]: - assert ep["user_id"] == "user_01" - group_ids = {ep["group_id"] for ep in data["episodes"]} - assert group_ids == {"group_01", "group_03"} - - -# ================================================================ -# Pagination and sorting tests (7f - 7j, 7t) -# ================================================================ -@pytest.mark.integration -class TestPaginationAndSorting: - """Test pagination (page/page_size) and sorting (rank_by/rank_order).""" - - def test_7f_pagination_page1(self): - """7f. page=1, page_size=2 for user_01: 2 of 5 episodes.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "page": 1, - "page_size": 2, - "filters": {"user_id": "user_01"}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 5 - assert data["count"] == 2 - assert len(data["episodes"]) == 2 - - def test_7g_pagination_last_page(self): - """7g. page=3, page_size=2 for user_01: last page with 1 episode.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "page": 3, - "page_size": 2, - "filters": {"user_id": "user_01"}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 5 - assert data["count"] == 1 - assert len(data["episodes"]) == 1 - - def test_7f_7g_pagination_no_overlap(self): - """7f+7g combined: page 1 and page 3 should not overlap.""" - resp1 = post_memories( - { - "memory_type": "episodic_memory", - "page": 1, - "page_size": 2, - "filters": {"user_id": "user_01"}, - } - ) - resp3 = post_memories( - { - "memory_type": "episodic_memory", - "page": 3, - "page_size": 2, - "filters": {"user_id": "user_01"}, - } - ) - ids_page1 = {ep["id"] for ep in resp1.json()["data"]["episodes"]} - ids_page3 = {ep["id"] for ep in resp3.json()["data"]["episodes"]} - assert ids_page1.isdisjoint(ids_page3), "Pages should not overlap" - - def test_7h_sort_asc(self): - """7h. rank_order=asc: oldest first (t_old -> t1 -> t2 -> t3 -> t4).""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "rank_by": "timestamp", - "rank_order": "asc", - "filters": {"user_id": "user_01"}, - } - ) - body = assert_success(resp) - episodes = body["data"]["episodes"] - - expected_order = [ - "Project Kickoff Meeting", - "Search API Design Discussion", - "Dashboard Design Review", - "Sprint Planning", - "Security Audit Review", - ] - actual_order = [ep["subject"] for ep in episodes] - assert actual_order == expected_order, f"ASC order mismatch: {actual_order}" - - def test_7i_sort_desc(self): - """7i. rank_order=desc: newest first (t4 -> t3 -> t2 -> t1 -> t_old).""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "rank_order": "desc", - "filters": {"user_id": "user_01"}, - } - ) - body = assert_success(resp) - episodes = body["data"]["episodes"] - - expected_order = [ - "Security Audit Review", - "Sprint Planning", - "Dashboard Design Review", - "Search API Design Discussion", - "Project Kickoff Meeting", - ] - actual_order = [ep["subject"] for ep in episodes] - assert actual_order == expected_order, f"DESC order mismatch: {actual_order}" - - def test_7j_timestamp_range_with_and(self): - """7j. Timestamp range captures only t2 (2026-01-15T10:00:00Z) episode.""" - # Fixed: t2 = 2026-01-15T10:00:00Z, window = 09:30 ~ 10:30 - t_gte = 1768469400000 # 2026-01-15T09:30:00Z in millis - t_lt = 1768473000000 # 2026-01-15T10:30:00Z in millis - - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": { - "user_id": "user_01", - "AND": [{"timestamp": {"gte": t_gte}}, {"timestamp": {"lt": t_lt}}], - }, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 1 - assert data["episodes"][0]["subject"] == "Dashboard Design Review" - - def test_7t_page_beyond_data(self): - """7t. page=99 should return empty episodes but correct total_count.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "page": 99, - "page_size": 20, - "filters": {"user_id": "user_01"}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 5 - assert data["count"] == 0 - assert data["episodes"] == [] - - -# ================================================================ -# Profile filter tests (7l - 7n) -# ================================================================ -@pytest.mark.integration -class TestProfileFilters: - """Test profile memory filtering.""" - - def test_7l_profile_by_user_id(self): - """7l. user_01 should have 3 profiles across 3 groups.""" - resp = post_memories( - {"memory_type": "profile", "filters": {"user_id": "user_01"}} - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 3 - assert data["count"] == 3 - assert len(data["profiles"]) == 3 - assert data["episodes"] == [] - - for pf in data["profiles"]: - assert pf["user_id"] == "user_01" - - roles = [pf["profile_data"]["explicit_info"]["Role"] for pf in data["profiles"]] - roles_text = " | ".join(roles) - assert "Tech Lead" in roles_text - assert "Frontend Lead" in roles_text - assert "Security Lead" in roles_text - - def test_7m_profile_by_user_id_and_group_id(self): - """7m. user_01 + group_01 should have 1 profile (Tech Lead).""" - resp = post_memories( - { - "memory_type": "profile", - "filters": {"user_id": "user_01", "group_id": "group_01"}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 1 - pf = data["profiles"][0] - assert pf["user_id"] == "user_01" - assert pf["group_id"] == "group_01" - assert "Tech Lead" in pf["profile_data"]["explicit_info"]["Role"] - assert pf["scenario"] == "team" - - def test_7n_profile_by_group_id(self): - """7n. group_01 should have 3 profiles (user_01, user_02, user_03).""" - resp = post_memories( - {"memory_type": "profile", "filters": {"group_id": "group_01"}} - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 3 - user_ids = {pf["user_id"] for pf in data["profiles"]} - assert user_ids == {"user_01", "user_02", "user_03"} - - def test_profile_group_id_in(self): - """group_id in [group_01, group_02] should return 5 profiles (3+2).""" - resp = post_memories( - { - "memory_type": "profile", - "filters": {"group_id": {"in": ["group_01", "group_02"]}}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 5 - group_ids = {pf["group_id"] for pf in data["profiles"]} - assert group_ids == {"group_01", "group_02"} - - def test_profile_user_id_in(self): - """user_id in [user_01, user_02] should return 5 profiles (3+2).""" - resp = post_memories( - { - "memory_type": "profile", - "filters": {"user_id": {"in": ["user_01", "user_02"]}}, - } - ) - body = assert_success(resp) - data = body["data"] - - assert data["total_count"] == 5 - user_ids = {pf["user_id"] for pf in data["profiles"]} - assert user_ids == {"user_01", "user_02"} - - -# ================================================================ -# Error case tests (7o - 7r) -# ================================================================ -@pytest.mark.integration -class TestErrorCases: - """Test validation error responses.""" - - def test_7o_missing_scope(self): - """7o. Empty filters (no user_id or group_id) should return error.""" - resp = post_memories({"memory_type": "episodic_memory", "filters": {}}) - body = assert_error(resp) - if "error" in body: - error = body["error"] - assert error["code"] == "InvalidParameter" - msg = error["message"].lower() - else: - # Pydantic validation error format - msg = body["detail"][0]["msg"].lower() - assert "user_id" in msg or "group_id" in msg - - def test_7p_missing_memory_type(self): - """7p. Missing memory_type should return error.""" - resp = post_memories({"filters": {"user_id": "user_01"}}) - body = resp.json() - # Could be error object or validation error depending on framework - assert resp.status_code != 200 or "error" in body - - def test_7q_invalid_memory_type(self): - """7q. Invalid memory_type value should return error.""" - resp = post_memories( - {"memory_type": "invalid_type", "filters": {"user_id": "user_01"}} - ) - body = resp.json() - assert resp.status_code != 200 or "error" in body - - def test_7r_missing_filters(self): - """7r. Missing filters key should return error.""" - resp = post_memories({"memory_type": "episodic_memory"}) - body = resp.json() - assert resp.status_code != 200 or "error" in body - - -# ================================================================ -# Cross-validation / additional coverage -# ================================================================ -@pytest.mark.integration -class TestAdditionalCoverage: - """Extra tests for completeness beyond the spec 7a-7t.""" - - def test_user_02_episodic_count(self): - """user_02 should have 3 episodes.""" - resp = post_memories( - {"memory_type": "episodic_memory", "filters": {"user_id": "user_02"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 3 - - def test_user_03_episodic_count(self): - """user_03 should have 2 episodes.""" - resp = post_memories( - {"memory_type": "episodic_memory", "filters": {"user_id": "user_03"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 2 - - def test_group_02_episodic_count(self): - """group_02 should have 2 episodes.""" - resp = post_memories( - {"memory_type": "episodic_memory", "filters": {"group_id": "group_02"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 2 - - def test_group_03_episodic_count(self): - """group_03 should have 2 episodes.""" - resp = post_memories( - {"memory_type": "episodic_memory", "filters": {"group_id": "group_03"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 2 - - def test_user_02_profile_count(self): - """user_02 should have 2 profiles.""" - resp = post_memories( - {"memory_type": "profile", "filters": {"user_id": "user_02"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 2 - - def test_user_03_profile_count(self): - """user_03 should have 2 profiles.""" - resp = post_memories( - {"memory_type": "profile", "filters": {"user_id": "user_03"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 2 - - def test_group_02_profile_count(self): - """group_02 should have 2 profiles.""" - resp = post_memories( - {"memory_type": "profile", "filters": {"group_id": "group_02"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 2 - - def test_group_03_profile_count(self): - """group_03 should have 2 profiles.""" - resp = post_memories( - {"memory_type": "profile", "filters": {"group_id": "group_03"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 2 - - def test_all_groups_in_operator(self): - """group_id in all 3 groups should return all 10 episodes.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": {"group_id": {"in": ["group_01", "group_02", "group_03"]}}, - } - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 10 - - def test_episode_fields_present(self): - """Verify episode response includes all expected fields.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "page_size": 1, - "filters": {"user_id": "user_01"}, - } - ) - data = assert_success(resp)["data"] - ep = data["episodes"][0] - - required_fields = [ - "id", - "user_id", - "group_id", - "session_id", - "timestamp", - "participants", - "summary", - "subject", - "episode", - "type", - "parent_type", - "parent_id", - ] - for field in required_fields: - assert field in ep, f"Episode missing field: {field}" - - # Must NOT contain vector or audit fields - assert "vector" not in ep - assert "created_at" not in ep - assert "updated_at" not in ep - - def test_profile_fields_present(self): - """Verify profile response includes all expected fields.""" - resp = post_memories( - { - "memory_type": "profile", - "page_size": 1, - "filters": {"user_id": "user_01"}, - } - ) - data = assert_success(resp)["data"] - pf = data["profiles"][0] - - required_fields = [ - "id", - "user_id", - "group_id", - "profile_data", - "scenario", - "memcell_count", - ] - for field in required_fields: - assert field in pf, f"Profile missing field: {field}" - - # Must NOT contain audit fields - assert "created_at" not in pf - assert "updated_at" not in pf - - def test_default_pagination(self): - """Default page=1, page_size=20 should work without explicit params.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": {"group_id": {"in": ["group_01", "group_02", "group_03"]}}, - } - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 10 - assert data["count"] == 10 - - -# ================================================================ -# Soft delete filtering -# ================================================================ -@pytest.mark.integration -class TestSoftDeleteFiltering: - """Verify soft-deleted records are automatically excluded from results. - - Seed data includes: - - 2 soft-deleted episodic memories (ep_del_01: user_01/group_01, ep_del_02: user_02/group_02) - - 1 soft-deleted profile (pf_del_01: user_01/group_01) - These should NEVER appear in query results. - """ - - def test_deleted_episodes_excluded_from_user_count(self): - """user_01 has 5 active + 1 deleted episode. Should return 5.""" - resp = post_memories( - {"memory_type": "episodic_memory", "filters": {"user_id": "user_01"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 5 - - def test_deleted_episodes_excluded_from_group_count(self): - """group_01 has 6 active + 1 deleted episode. Should return 6.""" - resp = post_memories( - {"memory_type": "episodic_memory", "filters": {"group_id": "group_01"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 6 - - def test_deleted_episodes_not_in_results(self): - """No episode with subject 'Deleted Episode' should appear.""" - resp = post_memories( - {"memory_type": "episodic_memory", "filters": {"user_id": "user_01"}} - ) - data = assert_success(resp)["data"] - subjects = [ep["subject"] for ep in data["episodes"]] - assert "Deleted Episode" not in subjects - - def test_deleted_episodes_excluded_from_group_02(self): - """group_02 has 2 active + 1 deleted episode. Should return 2.""" - resp = post_memories( - {"memory_type": "episodic_memory", "filters": {"group_id": "group_02"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 2 - - def test_deleted_profiles_excluded_from_user_count(self): - """user_01 has 3 active + 1 deleted profile. Should return 3.""" - resp = post_memories( - {"memory_type": "profile", "filters": {"user_id": "user_01"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 3 - - def test_deleted_profiles_excluded_from_group_count(self): - """group_01 has 3 active + 1 deleted profile. Should return 3.""" - resp = post_memories( - {"memory_type": "profile", "filters": {"group_id": "group_01"}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 3 - - def test_total_episodes_excludes_all_deleted(self): - """All 3 groups combined: 10 active episodes, 2 deleted. Should return 10.""" - resp = post_memories( - { - "memory_type": "episodic_memory", - "filters": {"group_id": {"in": ["group_01", "group_02", "group_03"]}}, - } - ) - data = assert_success(resp)["data"] - assert data["total_count"] == 10 - - -# ================================================================ -# Agent memory GET tests (agent_case / agent_skill) -# ================================================================ -# -# These tests use real data created by demo/search_agent_demo.py -# (user_id=demo_user, 4 agent_cases, 3 agent_skills). -# -# Prerequisites: demo data must exist in MongoDB. -# ================================================================ - -AGENT_USER_ID = "demo_user" - - -@pytest.mark.integration -class TestAgentCaseGet: - """Test GET agent_case with real data from demo runs.""" - - def test_get_agent_cases_by_user(self): - """Get all agent cases for demo_user.""" - resp = post_memories( - {"memory_type": "agent_case", "filters": {"user_id": AGENT_USER_ID}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] >= 1 - assert len(data["agent_cases"]) >= 1 - assert data["episodes"] == [] - assert data["profiles"] == [] - assert data["agent_skills"] == [] - - def test_agent_case_fields_present(self): - """Verify agent_case response includes all expected fields.""" - resp = post_memories( - { - "memory_type": "agent_case", - "page_size": 1, - "filters": {"user_id": AGENT_USER_ID}, - } - ) - data = assert_success(resp)["data"] - case = data["agent_cases"][0] - - required_fields = [ - "id", - "user_id", - "group_id", - "session_id", - "task_intent", - "approach", - "quality_score", - "timestamp", - "parent_type", - "parent_id", - ] - for field in required_fields: - assert field in case, f"Agent case missing field: {field}" - - def test_agent_case_pagination(self): - """Pagination should work for agent_case.""" - resp = post_memories( - { - "memory_type": "agent_case", - "page": 1, - "page_size": 2, - "filters": {"user_id": AGENT_USER_ID}, - } - ) - data = assert_success(resp)["data"] - assert data["count"] <= 2 - assert data["total_count"] >= data["count"] - - def test_agent_case_sort_desc(self): - """Agent cases should be sortable by timestamp desc.""" - resp = post_memories( - { - "memory_type": "agent_case", - "rank_by": "timestamp", - "rank_order": "desc", - "filters": {"user_id": AGENT_USER_ID}, - } - ) - data = assert_success(resp)["data"] - cases = data["agent_cases"] - if len(cases) >= 2: - assert cases[0]["timestamp"] >= cases[1]["timestamp"] - - def test_agent_case_filter_by_session(self): - """Filter agent cases by session_id.""" - resp = post_memories( - {"memory_type": "agent_case", "filters": {"user_id": AGENT_USER_ID}} - ) - all_cases = assert_success(resp)["data"]["agent_cases"] - if not all_cases: - pytest.skip("No agent cases in database") - - session_id = all_cases[0]["session_id"] - resp2 = post_memories( - { - "memory_type": "agent_case", - "filters": {"user_id": AGENT_USER_ID, "session_id": session_id}, - } - ) - data2 = assert_success(resp2)["data"] - for case in data2["agent_cases"]: - assert case["session_id"] == session_id - - -@pytest.mark.integration -class TestAgentSkillGet: - """Test GET agent_skill with real data from demo runs.""" - - def test_get_agent_skills_by_user(self): - """Get all agent skills for demo_user.""" - resp = post_memories( - {"memory_type": "agent_skill", "filters": {"user_id": AGENT_USER_ID}} - ) - data = assert_success(resp)["data"] - assert data["total_count"] >= 1 - assert len(data["agent_skills"]) >= 1 - assert data["episodes"] == [] - assert data["profiles"] == [] - assert data["agent_cases"] == [] - - def test_agent_skill_fields_present(self): - """Verify agent_skill response includes all expected fields.""" - resp = post_memories( - { - "memory_type": "agent_skill", - "page_size": 1, - "filters": {"user_id": AGENT_USER_ID}, - } - ) - data = assert_success(resp)["data"] - skill = data["agent_skills"][0] - - required_fields = [ - "id", - "user_id", - "group_id", - "cluster_id", - "name", - "description", - "content", - "confidence", - "maturity_score", - ] - for field in required_fields: - assert field in skill, f"Agent skill missing field: {field}" - - def test_agent_skill_pagination(self): - """Pagination should work for agent_skill.""" - resp = post_memories( - { - "memory_type": "agent_skill", - "page": 1, - "page_size": 2, - "filters": {"user_id": AGENT_USER_ID}, - } - ) - data = assert_success(resp)["data"] - assert data["count"] <= 2 - assert data["total_count"] >= data["count"] - - def test_agent_skill_sort_by_updated_at(self): - """Agent skills default sort should use updated_at (no timestamp field).""" - resp = post_memories( - { - "memory_type": "agent_skill", - "rank_by": "timestamp", - "rank_order": "desc", - "filters": {"user_id": AGENT_USER_ID}, - } - ) - # Should not error - timestamp fallback to updated_at - assert_success(resp) diff --git a/methods/EverCore/tests/test_get_mem_service_mock.py b/methods/EverCore/tests/test_get_mem_service_mock.py deleted file mode 100644 index 2044a314..00000000 --- a/methods/EverCore/tests/test_get_mem_service_mock.py +++ /dev/null @@ -1,817 +0,0 @@ -""" -GetMemoryService Unit Tests (mock) - -Tests for: -- _agent_case_to_item: document-to-DTO conversion (GET variant, no score field) -- _agent_skill_to_item: document-to-DTO conversion (GET variant, no score field) -- _resolve_sort: sort field fallback for agent_skill (no timestamp) -- _get_agent_cases: pagination, sort, count, empty result -- _get_agent_skills: pagination, sort, count, empty result -- find_memories: dispatch, scope validation, unsupported type - -Usage: - PYTHONPATH=src pytest tests/test_agent_get_service.py -v -""" - -import asyncio -import pytest -from datetime import datetime -from unittest.mock import AsyncMock, MagicMock, patch -from typing import Any, Dict, List, Optional - -from bson import ObjectId -from pymongo import ASCENDING, DESCENDING - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -def _make_case_doc( - doc_id: str = None, - user_id: str = "user_001", - group_id: str = "group_001", - session_id: str = "sess_001", - task_intent: str = "Build REST API", - approach: str = "1. Design\n2. Implement", - quality_score: float = 0.8, - key_insight: str = "Use caching for performance", - timestamp: datetime = None, - parent_type: str = "memcell", - parent_id: str = "evt_001", -): - """Create a mock AgentCaseRecord.""" - doc = MagicMock() - doc.id = ObjectId(doc_id) if doc_id else ObjectId() - doc.user_id = user_id - doc.group_id = group_id - doc.session_id = session_id - doc.task_intent = task_intent - doc.approach = approach - doc.quality_score = quality_score - doc.key_insight = key_insight - doc.timestamp = timestamp or datetime(2025, 3, 1, 12, 0, 0) - doc.parent_type = parent_type - doc.parent_id = parent_id - return doc - - -def _make_skill_doc( - doc_id: str = None, - user_id: str = "user_001", - group_id: str = "group_001", - cluster_id: str = "cluster_001", - name: str = "API Development", - description: str = "Build REST APIs", - content: str = "## Steps\n1. Design\n2. Implement", - confidence: float = 0.8, - maturity_score: float = 0.75, - source_case_ids: list = None, -): - """Create a mock AgentSkillRecord.""" - doc = MagicMock() - doc.id = ObjectId(doc_id) if doc_id else ObjectId() - doc.user_id = user_id - doc.group_id = group_id - doc.cluster_id = cluster_id - doc.name = name - doc.description = description - doc.content = content - doc.confidence = confidence - doc.maturity_score = maturity_score - doc.source_case_ids = source_case_ids - return doc - - -def _setup_repo_mock(mock_repo, docs, total_count): - """Configure a repository mock so that find_by_query returns (docs, total_count).""" - mock_repo.find_by_query = AsyncMock(return_value=(docs, total_count)) - return mock_repo - - -async def _coro(value): - """Wrap a value in a coroutine for asyncio.gather compatibility.""" - return value - - -# --------------------------------------------------------------------------- -# Fixtures -# --------------------------------------------------------------------------- - - -@pytest.fixture -def get_service(): - """Create GetMemoryService with repositories mocked.""" - mock_episodic_repo = AsyncMock() - mock_profile_repo = AsyncMock() - mock_case_repo = AsyncMock() - mock_skill_repo = AsyncMock() - - with ( - patch( - "agentic_layer.get_mem_service.get_bean_by_type", - side_effect=lambda cls: { - "EpisodicMemoryRawRepository": mock_episodic_repo, - "UserProfileRawRepository": mock_profile_repo, - "AgentCaseRawRepository": mock_case_repo, - "AgentSkillRawRepository": mock_skill_repo, - }.get(cls.__name__, AsyncMock()), - ), - patch("agentic_layer.get_mem_service.parse_mongo_filters") as mock_parse, - ): - from agentic_layer.get_mem_service import GetMemoryService - - svc = GetMemoryService() - svc._mock_case_repo = mock_case_repo - svc._mock_skill_repo = mock_skill_repo - svc._mock_episodic_repo = mock_episodic_repo - svc._mock_profile_repo = mock_profile_repo - svc._mock_parse = mock_parse - yield svc - - -# =========================================================================== -# _agent_case_to_item tests -# =========================================================================== - - -class TestAgentCaseToItem: - """Tests for GetMemoryService._agent_case_to_item.""" - - def test_basic_conversion(self, get_service): - doc = _make_case_doc() - item = get_service._agent_case_to_item(doc) - assert item.id == str(doc.id) - assert item.user_id == "user_001" - assert item.group_id == "group_001" - assert item.session_id == "sess_001" - assert item.task_intent == "Build REST API" - assert item.approach == "1. Design\n2. Implement" - assert item.quality_score == 0.8 - assert item.parent_type == "memcell" - assert item.parent_id == "evt_001" - # GET DTO should NOT have score field - assert not hasattr(item, "score") or "score" not in item.model_fields - - def test_none_task_intent_becomes_empty(self, get_service): - doc = _make_case_doc(task_intent=None) - item = get_service._agent_case_to_item(doc) - assert item.task_intent == "" - - def test_none_approach_becomes_empty(self, get_service): - doc = _make_case_doc(approach=None) - item = get_service._agent_case_to_item(doc) - assert item.approach == "" - - def test_none_quality_score(self, get_service): - doc = _make_case_doc(quality_score=None) - item = get_service._agent_case_to_item(doc) - assert item.quality_score is None - - def test_none_optional_fields(self, get_service): - doc = _make_case_doc( - group_id=None, session_id=None, parent_type=None, parent_id=None - ) - item = get_service._agent_case_to_item(doc) - assert item.group_id is None - assert item.session_id is None - assert item.parent_type is None - assert item.parent_id is None - - -# =========================================================================== -# _agent_skill_to_item tests -# =========================================================================== - - -class TestAgentSkillToItem: - """Tests for GetMemoryService._agent_skill_to_item.""" - - def test_basic_conversion(self, get_service): - doc = _make_skill_doc(source_case_ids=["evt_001", "evt_002"]) - item = get_service._agent_skill_to_item(doc) - assert item.id == str(doc.id) - assert item.user_id == "user_001" - assert item.group_id == "group_001" - assert item.cluster_id == "cluster_001" - assert item.name == "API Development" - assert item.description == "Build REST APIs" - assert item.content == "## Steps\n1. Design\n2. Implement" - assert item.confidence == 0.8 - assert item.maturity_score == 0.75 - assert item.source_case_ids == ["evt_001", "evt_002"] - # GET DTO should NOT have score field - assert not hasattr(item, "score") or "score" not in item.model_fields - - def test_none_source_case_ids_becomes_empty_list(self, get_service): - doc = _make_skill_doc(source_case_ids=None) - item = get_service._agent_skill_to_item(doc) - assert item.source_case_ids == [] - - def test_none_optional_fields(self, get_service): - doc = _make_skill_doc( - group_id=None, name=None, description=None, source_case_ids=[] - ) - item = get_service._agent_skill_to_item(doc) - assert item.group_id is None - assert item.name is None - assert item.description is None - - -# =========================================================================== -# _resolve_sort tests -# =========================================================================== - - -class TestResolveSort: - """Tests for GetMemoryService._resolve_sort.""" - - def test_agent_case_default_timestamp(self, get_service): - sort = get_service._resolve_sort("agent_case", "timestamp", "desc") - assert sort == [("timestamp", DESCENDING)] - - def test_agent_case_asc(self, get_service): - sort = get_service._resolve_sort("agent_case", "timestamp", "asc") - assert sort == [("timestamp", ASCENDING)] - - def test_agent_skill_timestamp_fallback(self, get_service): - """AgentSkillRecord has no timestamp field; must fallback to updated_at.""" - sort = get_service._resolve_sort("agent_skill", "timestamp", "desc") - assert sort == [("updated_at", DESCENDING)] - - def test_agent_skill_timestamp_fallback_asc(self, get_service): - sort = get_service._resolve_sort("agent_skill", "timestamp", "asc") - assert sort == [("updated_at", ASCENDING)] - - def test_agent_skill_explicit_field_no_fallback(self, get_service): - """When rank_by is not 'timestamp', no fallback should happen.""" - sort = get_service._resolve_sort("agent_skill", "confidence", "desc") - assert sort == [("confidence", DESCENDING)] - - def test_profile_timestamp_fallback(self, get_service): - sort = get_service._resolve_sort("profile", "timestamp", "desc") - assert sort == [("updated_at", DESCENDING)] - - def test_episodic_memory_uses_timestamp(self, get_service): - sort = get_service._resolve_sort("episodic_memory", "timestamp", "desc") - assert sort == [("timestamp", DESCENDING)] - - -# =========================================================================== -# _get_episodes tests -# =========================================================================== - - -def _make_episode_doc( - user_id="user_001", - group_id="group_001", - session_id="sess_001", - summary="Team discussed roadmap", - subject="Roadmap", - episode="Alice and Bob discussed...", - episode_type="Conversation", -): - """Create a mock EpisodicMemoryProjection document.""" - doc = MagicMock() - doc.id = ObjectId() - doc.user_id = user_id - doc.group_id = group_id - doc.session_id = session_id - doc.timestamp = datetime(2026, 3, 1, 10, 0) - doc.participants = ["user_001", "user_002"] - doc.sender_ids = ["user_001", "user_002"] - doc.summary = summary - doc.subject = subject - doc.episode = episode - doc.type = episode_type - doc.parent_type = "memcell" - doc.parent_id = "mc_001" - return doc - - -class TestGetEpisodes: - """Tests for GetMemoryService._get_episodes via repository.""" - - @pytest.mark.asyncio - async def test_returns_episodes_with_count(self, get_service): - docs = [_make_episode_doc(), _make_episode_doc()] - _setup_repo_mock(get_service._mock_episodic_repo, docs, 5) - - result = await get_service._get_episodes( - mongo_filter={"user_id": "user_001"}, - skip=0, - limit=20, - sort=[("timestamp", DESCENDING)], - ) - - assert result.count == 2 - assert result.total_count == 5 - assert len(result.episodes) == 2 - assert result.episodes[0].user_id == "user_001" - assert result.agent_cases == [] - assert result.agent_skills == [] - assert result.profiles == [] - - @pytest.mark.asyncio - async def test_empty_result(self, get_service): - _setup_repo_mock(get_service._mock_episodic_repo, [], 0) - - result = await get_service._get_episodes( - mongo_filter={"user_id": "nobody"}, - skip=0, - limit=20, - sort=[("timestamp", DESCENDING)], - ) - - assert result.count == 0 - assert result.total_count == 0 - assert result.episodes == [] - - @pytest.mark.asyncio - async def test_projection_passed(self, get_service): - _setup_repo_mock(get_service._mock_episodic_repo, [], 0) - - await get_service._get_episodes( - mongo_filter={"user_id": "u1"}, - skip=0, - limit=20, - sort=[("timestamp", DESCENDING)], - ) - - call_args = get_service._mock_episodic_repo.find_by_query.call_args - assert call_args.kwargs.get("projection_model") is not None - - @pytest.mark.asyncio - async def test_episode_fields_mapped(self, get_service): - doc = _make_episode_doc() - _setup_repo_mock(get_service._mock_episodic_repo, [doc], 1) - - result = await get_service._get_episodes( - mongo_filter={"user_id": "user_001"}, - skip=0, - limit=20, - sort=[("timestamp", DESCENDING)], - ) - - ep = result.episodes[0] - assert ep.summary == "Team discussed roadmap" - assert ep.subject == "Roadmap" - assert ep.episode == "Alice and Bob discussed..." - assert ep.type == "Conversation" - assert ep.parent_type == "memcell" - - -# =========================================================================== -# _get_profiles tests -# =========================================================================== - - -def _make_profile_doc( - user_id="user_001", group_id="group_001", scenario="solo", memcell_count=5 -): - """Create a mock UserProfile document.""" - doc = MagicMock() - doc.id = ObjectId() - doc.user_id = user_id - doc.group_id = group_id - doc.profile_data = {"explicit_info": {"Role": "Engineer"}} - doc.scenario = scenario - doc.memcell_count = memcell_count - return doc - - -class TestGetProfiles: - """Tests for GetMemoryService._get_profiles via repository.""" - - @pytest.mark.asyncio - async def test_returns_profiles_with_count(self, get_service): - docs = [_make_profile_doc(), _make_profile_doc()] - _setup_repo_mock(get_service._mock_profile_repo, docs, 3) - - result = await get_service._get_profiles( - mongo_filter={"user_id": "user_001"}, - skip=0, - limit=20, - sort=[("updated_at", DESCENDING)], - ) - - assert result.count == 2 - assert result.total_count == 3 - assert len(result.profiles) == 2 - assert result.profiles[0].user_id == "user_001" - assert result.episodes == [] - assert result.agent_cases == [] - assert result.agent_skills == [] - - @pytest.mark.asyncio - async def test_empty_result(self, get_service): - _setup_repo_mock(get_service._mock_profile_repo, [], 0) - - result = await get_service._get_profiles( - mongo_filter={"user_id": "nobody"}, - skip=0, - limit=20, - sort=[("updated_at", DESCENDING)], - ) - - assert result.count == 0 - assert result.profiles == [] - - @pytest.mark.asyncio - async def test_no_projection(self, get_service): - """Profile has no vector in MongoDB, so no projection needed.""" - _setup_repo_mock(get_service._mock_profile_repo, [], 0) - - await get_service._get_profiles( - mongo_filter={"user_id": "u1"}, - skip=0, - limit=20, - sort=[("updated_at", DESCENDING)], - ) - - call_args = get_service._mock_profile_repo.find_by_query.call_args - assert call_args.kwargs.get("projection_model") is None - - @pytest.mark.asyncio - async def test_profile_fields_mapped(self, get_service): - doc = _make_profile_doc() - _setup_repo_mock(get_service._mock_profile_repo, [doc], 1) - - result = await get_service._get_profiles( - mongo_filter={"user_id": "user_001"}, - skip=0, - limit=20, - sort=[("updated_at", DESCENDING)], - ) - - pf = result.profiles[0] - assert pf.user_id == "user_001" - assert pf.profile_data == {"explicit_info": {"Role": "Engineer"}} - assert pf.scenario == "solo" - assert pf.memcell_count == 5 - - -# =========================================================================== -# _get_agent_cases tests -# =========================================================================== - - -class TestGetAgentCases: - """Tests for GetMemoryService._get_agent_cases.""" - - @pytest.mark.asyncio - async def test_returns_cases_with_count(self, get_service): - docs = [_make_case_doc(), _make_case_doc()] - _setup_repo_mock(get_service._mock_case_repo, docs, 5) - - result = await get_service._get_agent_cases( - mongo_filter={"user_id": "user_001"}, - skip=0, - limit=20, - sort=[("timestamp", DESCENDING)], - ) - - assert result.count == 2 - assert result.total_count == 5 - assert len(result.agent_cases) == 2 - assert result.agent_cases[0].user_id == "user_001" - # Other lists should be empty - assert result.episodes == [] - assert result.profiles == [] - assert result.agent_skills == [] - - @pytest.mark.asyncio - async def test_empty_result(self, get_service): - _setup_repo_mock(get_service._mock_case_repo, [], 0) - - result = await get_service._get_agent_cases( - mongo_filter={"user_id": "user_999"}, - skip=0, - limit=20, - sort=[("timestamp", DESCENDING)], - ) - - assert result.count == 0 - assert result.total_count == 0 - assert result.agent_cases == [] - - @pytest.mark.asyncio - async def test_pagination_params_passed(self, get_service): - _setup_repo_mock(get_service._mock_case_repo, [], 0) - - await get_service._get_agent_cases( - mongo_filter={"user_id": "u1"}, - skip=40, - limit=10, - sort=[("timestamp", ASCENDING)], - ) - - first_call = get_service._mock_case_repo.find_by_query.call_args - assert first_call.args[0] == {"user_id": "u1"} - assert first_call.kwargs["skip"] == 40 - assert first_call.kwargs["limit"] == 10 - assert first_call.kwargs["sort"] == [("timestamp", ASCENDING)] - - @pytest.mark.asyncio - async def test_projection_passed(self, get_service): - """AgentCase query should use AgentCaseProjection to exclude vector.""" - _setup_repo_mock(get_service._mock_case_repo, [], 0) - - await get_service._get_agent_cases( - mongo_filter={"user_id": "u1"}, - skip=0, - limit=20, - sort=[("timestamp", DESCENDING)], - ) - - call_args = get_service._mock_case_repo.find_by_query.call_args - projection = call_args.kwargs.get("projection_model") - assert projection is not None - assert projection.__name__ == "AgentCaseProjection" - - -# =========================================================================== -# _get_agent_skills tests -# =========================================================================== - - -class TestGetAgentSkills: - """Tests for GetMemoryService._get_agent_skills.""" - - @pytest.mark.asyncio - async def test_returns_skills_with_count(self, get_service): - docs = [ - _make_skill_doc(source_case_ids=["e1"]), - _make_skill_doc(source_case_ids=["e2"]), - ] - _setup_repo_mock(get_service._mock_skill_repo, docs, 10) - - result = await get_service._get_agent_skills( - mongo_filter={"user_id": "user_001"}, - skip=0, - limit=20, - sort=[("updated_at", DESCENDING)], - ) - - assert result.count == 2 - assert result.total_count == 10 - assert len(result.agent_skills) == 2 - assert result.agent_skills[0].cluster_id == "cluster_001" - # Other lists should be empty - assert result.episodes == [] - assert result.profiles == [] - assert result.agent_cases == [] - - @pytest.mark.asyncio - async def test_empty_result(self, get_service): - _setup_repo_mock(get_service._mock_skill_repo, [], 0) - - result = await get_service._get_agent_skills( - mongo_filter={"user_id": "nobody"}, - skip=0, - limit=20, - sort=[("updated_at", DESCENDING)], - ) - - assert result.count == 0 - assert result.total_count == 0 - assert result.agent_skills == [] - - @pytest.mark.asyncio - async def test_projection_passed(self, get_service): - """AgentSkill query should use AgentSkillProjection to exclude vector.""" - _setup_repo_mock(get_service._mock_skill_repo, [], 0) - - await get_service._get_agent_skills( - mongo_filter={"user_id": "u1"}, - skip=0, - limit=20, - sort=[("updated_at", DESCENDING)], - ) - - call_args = get_service._mock_skill_repo.find_by_query.call_args - projection = call_args.kwargs.get("projection_model") - assert projection is not None - assert projection.__name__ == "AgentSkillProjection" - - -# =========================================================================== -# find_memories dispatch tests -# =========================================================================== - - -class TestFindMemoriesDispatch: - """Tests for GetMemoryService.find_memories dispatching to correct handler.""" - - @pytest.mark.asyncio - async def test_dispatches_agent_case(self, get_service): - get_service._mock_parse.return_value = ({"user_id": "u1"}, "u1", None) - _setup_repo_mock(get_service._mock_case_repo, [_make_case_doc()], 1) - - result = await get_service.find_memories( - filters={"user_id": "u1"}, - memory_type="agent_case", - page=1, - page_size=20, - rank_by="timestamp", - rank_order="desc", - ) - - assert len(result.agent_cases) == 1 - assert result.agent_skills == [] - - @pytest.mark.asyncio - async def test_dispatches_agent_skill(self, get_service): - get_service._mock_parse.return_value = ({"user_id": "u1"}, "u1", None) - _setup_repo_mock( - get_service._mock_skill_repo, [_make_skill_doc(source_case_ids=["e1"])], 1 - ) - - result = await get_service.find_memories( - filters={"user_id": "u1"}, - memory_type="agent_skill", - page=1, - page_size=20, - rank_by="timestamp", - rank_order="desc", - ) - - assert len(result.agent_skills) == 1 - assert result.agent_cases == [] - - @pytest.mark.asyncio - async def test_agent_skill_sort_uses_updated_at(self, get_service): - """Verify that agent_skill query uses updated_at instead of timestamp.""" - get_service._mock_parse.return_value = ({"user_id": "u1"}, "u1", None) - _setup_repo_mock(get_service._mock_skill_repo, [], 0) - - await get_service.find_memories( - filters={"user_id": "u1"}, - memory_type="agent_skill", - page=1, - page_size=20, - rank_by="timestamp", - rank_order="desc", - ) - - first_call = get_service._mock_skill_repo.find_by_query.call_args - assert first_call.kwargs["sort"] == [("updated_at", DESCENDING)] - - @pytest.mark.asyncio - async def test_missing_scope_raises(self, get_service): - get_service._mock_parse.return_value = ({}, None, None) - - from agentic_layer.get_mem_service import InvalidScopeError - - with pytest.raises(InvalidScopeError): - await get_service.find_memories( - filters={}, - memory_type="agent_case", - page=1, - page_size=20, - rank_by="timestamp", - rank_order="desc", - ) - - @pytest.mark.asyncio - async def test_unsupported_type_raises(self, get_service): - get_service._mock_parse.return_value = ({"user_id": "u1"}, "u1", None) - - with pytest.raises(ValueError, match="Unsupported memory_type"): - await get_service.find_memories( - filters={"user_id": "u1"}, - memory_type="unknown_type", - page=1, - page_size=20, - rank_by="timestamp", - rank_order="desc", - ) - - @pytest.mark.asyncio - async def test_pagination_skip_calculation(self, get_service): - """page=3, page_size=10 should produce skip=20.""" - get_service._mock_parse.return_value = ({"user_id": "u1"}, "u1", None) - _setup_repo_mock(get_service._mock_case_repo, [], 0) - - await get_service.find_memories( - filters={"user_id": "u1"}, - memory_type="agent_case", - page=3, - page_size=10, - rank_by="timestamp", - rank_order="asc", - ) - - first_call = get_service._mock_case_repo.find_by_query.call_args - assert first_call.kwargs["skip"] == 20 - assert first_call.kwargs["limit"] == 10 - assert first_call.kwargs["sort"] == [("timestamp", ASCENDING)] - - @pytest.mark.asyncio - async def test_group_id_only_raises_for_agent_case(self, get_service): - """agent_case with only group_id (no user_id) should raise.""" - from agentic_layer.get_mem_service import InvalidScopeError - - get_service._mock_parse.return_value = ({"group_id": "g1"}, None, ["g1"]) - - with pytest.raises(InvalidScopeError, match="requires 'user_id'"): - await get_service.find_memories( - filters={"group_id": "g1"}, - memory_type="agent_case", - page=1, - page_size=20, - rank_by="timestamp", - rank_order="desc", - ) - - @pytest.mark.asyncio - async def test_group_id_only_raises_for_profile(self, get_service): - """profile with only group_id (no user_id) should raise.""" - from agentic_layer.get_mem_service import InvalidScopeError - - get_service._mock_parse.return_value = ({"group_id": "g1"}, None, ["g1"]) - - with pytest.raises(InvalidScopeError, match="requires 'user_id'"): - await get_service.find_memories( - filters={"group_id": "g1"}, - memory_type="profile", - page=1, - page_size=20, - rank_by="timestamp", - rank_order="desc", - ) - - @pytest.mark.asyncio - async def test_group_id_only_raises_for_agent_skill(self, get_service): - """agent_skill with only group_id (no user_id) should raise.""" - from agentic_layer.get_mem_service import InvalidScopeError - - get_service._mock_parse.return_value = ({"group_id": "g1"}, None, ["g1"]) - - with pytest.raises(InvalidScopeError, match="requires 'user_id'"): - await get_service.find_memories( - filters={"group_id": "g1"}, - memory_type="agent_skill", - page=1, - page_size=20, - rank_by="timestamp", - rank_order="desc", - ) - - @pytest.mark.asyncio - async def test_episodic_group_only_adds_user_id_filter(self, get_service): - """episodic with only group_id should add user_id not-exist condition.""" - mongo_filter = {"group_id": "g1"} - get_service._mock_parse.return_value = (mongo_filter, None, ["g1"]) - _setup_repo_mock(get_service._mock_episodic_repo, [], 0) - - await get_service.find_memories( - filters={"group_id": "g1"}, - memory_type="episodic_memory", - page=1, - page_size=20, - rank_by="timestamp", - rank_order="desc", - ) - - # Verify user_id not-exist filter was added - assert mongo_filter["user_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_dispatches_episodic_memory(self, get_service): - get_service._mock_parse.return_value = ({"user_id": "u1"}, "u1", None) - _setup_repo_mock(get_service._mock_episodic_repo, [_make_episode_doc()], 1) - - result = await get_service.find_memories( - filters={"user_id": "u1"}, - memory_type="episodic_memory", - page=1, - page_size=20, - rank_by="timestamp", - rank_order="desc", - ) - - assert len(result.episodes) == 1 - assert result.profiles == [] - assert result.agent_cases == [] - assert result.agent_skills == [] - - @pytest.mark.asyncio - async def test_dispatches_profile(self, get_service): - get_service._mock_parse.return_value = ({"user_id": "u1"}, "u1", None) - _setup_repo_mock(get_service._mock_profile_repo, [_make_profile_doc()], 1) - - result = await get_service.find_memories( - filters={"user_id": "u1"}, - memory_type="profile", - page=1, - page_size=20, - rank_by="timestamp", - rank_order="desc", - ) - - assert len(result.profiles) == 1 - assert result.episodes == [] - assert result.agent_cases == [] - assert result.agent_skills == [] diff --git a/methods/EverCore/tests/test_get_text_from_content_items.py b/methods/EverCore/tests/test_get_text_from_content_items.py deleted file mode 100644 index f5a9a6a5..00000000 --- a/methods/EverCore/tests/test_get_text_from_content_items.py +++ /dev/null @@ -1,128 +0,0 @@ -"""Tests for get_text_from_content_items multimodal text extraction.""" - -import pytest -from api_specs.memory_types import get_text_from_content_items - - -class TestPureText: - def test_single_text_item(self): - items = [{"type": "text", "text": "hello"}] - assert get_text_from_content_items(items) == "hello" - - def test_multiple_text_items(self): - items = [ - {"type": "text", "text": "first"}, - {"type": "text", "text": "second"}, - ] - assert get_text_from_content_items(items) == "first second" - - def test_empty_text_skipped(self): - items = [{"type": "text", "text": ""}, {"type": "text", "text": "hello"}] - assert get_text_from_content_items(items) == "hello" - - -class TestNonTextWithNameAndSummary: - def test_pdf_with_name_and_summary(self): - items = [ - {"type": "pdf", "name": "Q2_report.pdf", "uri": "minio://docs/Q2_report.pdf"} - ] - parse_info = {"minio://docs/Q2_report.pdf": {"summary": "Revenue grew 15%"}} - assert ( - get_text_from_content_items(items, parse_info) - == "[PDF: Q2_report.pdf | Summary: Revenue grew 15%]" - ) - - def test_image_with_name_and_summary(self): - items = [ - {"type": "image", "name": "vacation.jpg", "uri": "minio://img/vacation.jpg"} - ] - parse_info = {"minio://img/vacation.jpg": {"summary": "A beach sunset photo"}} - assert ( - get_text_from_content_items(items, parse_info) - == "[IMAGE: vacation.jpg | Summary: A beach sunset photo]" - ) - - -class TestNonTextWithNameOnly: - def test_image_name_no_parse_info(self): - items = [{"type": "image", "name": "vacation.jpg"}] - assert get_text_from_content_items(items) == "[IMAGE: vacation.jpg]" - - def test_parse_failed_with_name(self): - items = [ - {"type": "image", "name": "photo.png", "uri": "minio://img/photo.png"} - ] - parse_info = {"minio://img/photo.png": {"status": "failed"}} - assert get_text_from_content_items(items, parse_info) == "[IMAGE: photo.png]" - - -class TestNonTextWithSummaryOnly: - def test_audio_summary_no_name(self): - items = [{"type": "audio", "uri": "minio://audio/rec.mp3"}] - parse_info = {"minio://audio/rec.mp3": {"summary": "Discussion about timeline"}} - assert ( - get_text_from_content_items(items, parse_info) - == "[AUDIO | Summary: Discussion about timeline]" - ) - - -class TestNonTextNoNameNoSummary: - def test_no_parse_info(self): - items = [{"type": "image"}] - assert get_text_from_content_items(items) == "[IMAGE]" - - def test_audio_no_info(self): - items = [{"type": "audio"}] - assert get_text_from_content_items(items) == "[AUDIO]" - - def test_uri_not_in_parse_info(self): - items = [{"type": "image", "uri": "minio://img/unknown.png"}] - parse_info = {"minio://img/other.png": {"summary": "something"}} - assert get_text_from_content_items(items, parse_info) == "[IMAGE]" - - -class TestMixedContent: - def test_text_and_pdf(self): - items = [ - {"type": "text", "text": "帮我总结"}, - {"type": "pdf", "name": "Q2_report.pdf", "uri": "minio://docs/Q2.pdf"}, - ] - parse_info = {"minio://docs/Q2.pdf": {"summary": "Revenue grew 15%"}} - assert ( - get_text_from_content_items(items, parse_info) - == "帮我总结 [PDF: Q2_report.pdf | Summary: Revenue grew 15%]" - ) - - def test_text_and_multiple_files(self): - items = [ - {"type": "text", "text": "会议材料都在这了"}, - {"type": "pdf", "name": "agenda.pdf", "uri": "minio://docs/agenda.pdf"}, - {"type": "image", "name": "whiteboard.png", "uri": "minio://img/wb.png"}, - {"type": "audio"}, - ] - parse_info = { - "minio://docs/agenda.pdf": {"summary": "Topics: budget review"}, - "minio://img/wb.png": {"summary": "Architecture diagram"}, - } - result = get_text_from_content_items(items, parse_info) - assert result == ( - "会议材料都在这了 " - "[PDF: agenda.pdf | Summary: Topics: budget review] " - "[IMAGE: whiteboard.png | Summary: Architecture diagram] " - "[AUDIO]" - ) - - -class TestLegacyFallback: - def test_string_input(self): - assert get_text_from_content_items("plain text") == "plain text" - - def test_empty_list(self): - assert get_text_from_content_items([]) == "" - - def test_none_input(self): - assert get_text_from_content_items(None) == "" - - def test_non_dict_items_skipped(self): - items = ["not a dict", 123, {"type": "text", "text": "hello"}] - assert get_text_from_content_items(items) == "hello" diff --git a/methods/EverCore/tests/test_integration_vectorize_rerank.py b/methods/EverCore/tests/test_integration_vectorize_rerank.py deleted file mode 100644 index f04cebb5..00000000 --- a/methods/EverCore/tests/test_integration_vectorize_rerank.py +++ /dev/null @@ -1,967 +0,0 @@ -""" -Integration Test for Vectorize and Rerank Services with Real Configuration - -Tests the embedding and reranking services using actual environment configuration. -""" - -import asyncio -import os -import sys -from pathlib import Path - -# Add src to path (for running from tests directory) -project_root = Path(__file__).parent.parent -sys.path.insert(0, str(project_root / "src")) - -from dotenv import load_dotenv -from agentic_layer.vectorize_service import get_vectorize_service -from agentic_layer.rerank_service import get_rerank_service -from api_specs.memory_models import MemoryType -from agentic_layer.rerank_interface import RerankMemResponse - -# Load environment variables -load_dotenv(project_root / ".env") - - -async def test_vectorize_service(): - """Test vectorization service with real configuration""" - print("\n" + "=" * 80) - print("🔹 Testing Vectorize Service") - print("=" * 80) - - vectorize_service = get_vectorize_service() - - # Display configuration - print(f"\n📋 Configuration:") - print(f" Provider: {os.getenv('VECTORIZE_PROVIDER', 'N/A')}") - print(f" Base URL: {os.getenv('VECTORIZE_BASE_URL', 'N/A')}") - print(f" Model: {os.getenv('VECTORIZE_MODEL', 'N/A')}") - print(f" Fallback Provider: {os.getenv('VECTORIZE_FALLBACK_PROVIDER', 'N/A')}") - print(f" Fallback Base URL: {os.getenv('VECTORIZE_FALLBACK_BASE_URL', 'N/A')}") - print(f" Dimensions: {os.getenv('VECTORIZE_DIMENSIONS', 'N/A')}") - - # Test queries - test_texts = [ - "Machine learning is a subset of artificial intelligence", - "Python is a popular programming language", - "Deep learning uses neural networks for pattern recognition" - ] - - print(f"\n🧪 Testing with {len(test_texts)} texts...") - - try: - # Test single embedding - print("\n1️⃣ Testing single embedding...") - single_embedding = await vectorize_service.get_embedding(test_texts[0]) - print(f" ✅ Single embedding shape: {single_embedding.shape}") - print(f" ✅ First 5 values: {single_embedding[:5]}") - print(f" ✅ Norm: {(single_embedding ** 2).sum() ** 0.5:.4f}") - - # Test batch embeddings - print("\n2️⃣ Testing batch embeddings...") - batch_embeddings = await vectorize_service.get_embeddings(test_texts) - print(f" ✅ Batch embeddings count: {len(batch_embeddings)}") - for i, emb in enumerate(batch_embeddings): - print(f" ✅ Text {i+1} shape: {emb.shape}, norm: {(emb ** 2).sum() ** 0.5:.4f}") - - # Test query embedding - print("\n3️⃣ Testing query embedding...") - query_embedding = await vectorize_service.get_embedding( - "What is machine learning?", - is_query=True - ) - print(f" ✅ Query embedding shape: {query_embedding.shape}") - print(f" ✅ Query norm: {(query_embedding ** 2).sum() ** 0.5:.4f}") - - # Calculate similarities - print("\n4️⃣ Testing similarity calculation...") - similarities = [] - for i, doc_emb in enumerate(batch_embeddings): - # Cosine similarity - similarity = (query_embedding * doc_emb).sum() / ( - ((query_embedding ** 2).sum() ** 0.5) * ((doc_emb ** 2).sum() ** 0.5) - ) - similarities.append((i, similarity)) - print(f" 📊 Text {i+1} similarity: {similarity:.4f}") - - # Sort by similarity - similarities.sort(key=lambda x: x[1], reverse=True) - print(f"\n 🏆 Most relevant: Text {similarities[0][0]+1} (score: {similarities[0][1]:.4f})") - - print("\n✅ Vectorize service test PASSED") - return True - - except Exception as e: - print(f"\n❌ Vectorize service test FAILED: {e}") - import traceback - traceback.print_exc() - return False - finally: - await vectorize_service.close() - - -async def test_rerank_service(): - """Test reranking service with real configuration""" - print("\n" + "=" * 80) - print("🔹 Testing Rerank Service") - print("=" * 80) - - rerank_service = get_rerank_service() - - # Display configuration - print(f"\n📋 Configuration:") - print(f" Provider: {os.getenv('RERANK_PROVIDER', 'N/A')}") - print(f" Base URL: {os.getenv('RERANK_BASE_URL', 'N/A')}") - print(f" Model: {os.getenv('RERANK_MODEL', 'N/A')}") - print(f" Fallback Provider: {os.getenv('RERANK_FALLBACK_PROVIDER', 'N/A')}") - print(f" Fallback Base URL: {os.getenv('RERANK_FALLBACK_BASE_URL', 'N/A')}") - - # Test query and documents - query = "What is machine learning and how does it work?" - documents = [ - "Machine learning is a subset of artificial intelligence that enables computers to learn from data without explicit programming. It uses algorithms to identify patterns and make predictions.", - "Python is a high-level programming language known for its simplicity and readability. It's widely used in web development, data analysis, and automation.", - "Deep learning is a subset of machine learning that uses neural networks with multiple layers. It's particularly effective for image recognition and natural language processing.", - "Data science combines statistics, programming, and domain expertise to extract insights from data. It's used across industries for decision-making.", - "Neural networks are computational models inspired by the human brain. They consist of interconnected nodes that process information in layers." - ] - - print(f"\n🧪 Testing with query and {len(documents)} documents...") - print(f" Query: '{query}'") - - # Prepare hits for rerank_memories method - hits = [] - for idx, doc in enumerate(documents): - hit = { - "id": f"doc_{idx}", - "_source": {"episode": doc}, - "memory_type": "episodic_memory", - "score": 1.0 - } - hits.append(hit) - - try: - # Test memory reranking using rerank_memories - print("\n1️⃣ Testing memory reranking (using rerank_memories)...") - reranked_hits = await rerank_service.rerank_memories(query, hits) - - print(f" ✅ Reranked {len(reranked_hits)} memories") - print(f"\n 📊 Reranking results (sorted by relevance):") - for i, hit in enumerate(reranked_hits[:5]): # Show top 5 - doc_text = hit.get('_source', {}).get('episode', '') - doc_preview = doc_text[:80] + "..." if len(doc_text) > 80 else doc_text - score = hit.get('score', 0.0) - print(f" {i+1}. Score: {score:.4f}") - print(f" Text: {doc_preview}") - print() - - # Verify ranking - print("2️⃣ Verifying ranking quality...") - top_hit = reranked_hits[0] - top_doc = top_hit.get('_source', {}).get('episode', '') - if "machine learning" in top_doc.lower(): - print(" ✅ Top result contains 'machine learning' - ranking is good!") - else: - print(f" ⚠️ Top result doesn't mention machine learning explicitly") - print(f" (This might still be correct if using semantic similarity)") - - # Test with top_k - print("\n3️⃣ Testing with top_k=3...") - top_3_hits = await rerank_service.rerank_memories(query, hits, top_k=3) - print(f" ✅ Retrieved top {len(top_3_hits)} memories") - for i, hit in enumerate(top_3_hits): - score = hit.get('score', 0.0) - print(f" {i+1}. Score: {score:.4f}") - - print("\n✅ Rerank service test PASSED") - return True - - except Exception as e: - print(f"\n❌ Rerank service test FAILED: {e}") - import traceback - traceback.print_exc() - return False - finally: - await rerank_service.close() - - -async def test_retrieval_pipeline(): - """Test complete retrieval pipeline: embed + rerank""" - print("\n" + "=" * 80) - print("🔹 Testing Complete Retrieval Pipeline") - print("=" * 80) - - vectorize_service = get_vectorize_service() - rerank_service = get_rerank_service() - - # Test data - query = "How does deep learning work?" - documents = [ - "Deep learning is a subset of machine learning that uses neural networks with multiple layers to progressively extract higher-level features from raw input.", - "Python programming language is known for its simplicity and readability, making it popular for beginners and experts alike.", - "Machine learning algorithms can be supervised, unsupervised, or semi-supervised depending on the type of training data available.", - "Neural networks consist of layers of interconnected nodes that process and transform information, mimicking the structure of the human brain.", - "Data preprocessing is an essential step in machine learning that involves cleaning, transforming, and organizing data for analysis.", - ] - - print(f"\n🧪 Testing retrieval pipeline...") - print(f" Query: '{query}'") - print(f" Documents: {len(documents)}") - - try: - # Step 1: Generate embeddings - print("\n📍 Step 1: Generate embeddings...") - query_emb = await vectorize_service.get_embedding(query, is_query=True) - doc_embs = await vectorize_service.get_embeddings(documents) - print(f" ✅ Query embedding: shape={query_emb.shape}") - print(f" ✅ Document embeddings: {len(doc_embs)} vectors") - - # Step 2: Calculate initial similarity scores - print("\n📍 Step 2: Calculate similarity scores...") - scores = [] - for i, doc_emb in enumerate(doc_embs): - similarity = (query_emb * doc_emb).sum() / ( - ((query_emb ** 2).sum() ** 0.5) * ((doc_emb ** 2).sum() ** 0.5) - ) - scores.append((i, similarity)) - - scores.sort(key=lambda x: x[1], reverse=True) - print(" 📊 Initial ranking (by embedding similarity):") - for rank, (idx, score) in enumerate(scores[:3]): - doc_preview = documents[idx][:60] + "..." - print(f" {rank+1}. Doc {idx+1}: {score:.4f} - {doc_preview}") - - # Step 3: Prepare hits and rerank using rerank_memories - print("\n📍 Step 3: Rerank with reranker (using rerank_memories)...") - - # Prepare hits from documents with embedding scores - hits = [] - for idx, score in scores: - hit = { - "id": f"doc_{idx}", - "_source": {"episode": documents[idx]}, - "memory_type": "episodic_memory", - "score": float(score) # Use embedding similarity as initial score - } - hits.append(hit) - - # Rerank using rerank_memories - reranked_hits = await rerank_service.rerank_memories(query, hits, top_k=3) - - print(" 📊 Final ranking (after reranking):") - for rank, hit in enumerate(reranked_hits): - doc_text = hit.get('_source', {}).get('episode', '') - doc_preview = doc_text[:60] + "..." - score = hit.get('score', 0.0) - print(f" {rank+1}. Score: {score:.4f} - {doc_preview}") - - # Compare rankings - print("\n📍 Step 4: Compare rankings...") - initial_top_idx = scores[0][0] - reranked_top_text = reranked_hits[0].get('_source', {}).get('episode', '') - - if documents[initial_top_idx] == reranked_top_text: - print(" ✅ Rankings agree - top result is the same") - else: - print(" ℹ️ Rankings differ - reranker provided different ordering") - print(" This is normal as reranker uses more sophisticated cross-attention") - - print("\n✅ Complete pipeline test PASSED") - return True - - except Exception as e: - print(f"\n❌ Pipeline test FAILED: {e}") - import traceback - traceback.print_exc() - return False - finally: - await vectorize_service.close() - await rerank_service.close() - - -async def test_compare_vllm_deepinfra_rerank(): - """Compare vLLM and DeepInfra rerank results for the same query""" - print("\n" + "=" * 80) - print("🔹 Comparing vLLM vs DeepInfra Rerank Results") - print("=" * 80) - - # Import service classes directly - from agentic_layer.rerank_vllm import VllmRerankService, VllmRerankConfig - from agentic_layer.rerank_deepinfra import DeepInfraRerankService, DeepInfraRerankConfig - - # Test query and documents - query = "What is machine learning and how does it work?" - documents = [ - "Machine learning is a subset of artificial intelligence that enables computers to learn from data without explicit programming.", - "Python is a high-level programming language known for its simplicity and readability.", - "Deep learning is a subset of machine learning that uses neural networks with multiple layers.", - "Data science combines statistics, programming, and domain expertise to extract insights from data.", - "Neural networks are computational models inspired by the human brain." - ] - - print(f"\n🧪 Testing with same query and {len(documents)} documents...") - print(f" Query: '{query}'") - - try: - # Create vLLM service - vllm_config = VllmRerankConfig( - api_key=os.getenv("RERANK_API_KEY", "EMPTY"), - base_url=os.getenv("RERANK_BASE_URL", "http://localhost:12000/v1/rerank"), - model=os.getenv("RERANK_MODEL", "Qwen/Qwen3-Reranker-4B"), - ) - vllm_service = VllmRerankService(vllm_config) - - # Create DeepInfra service - deepinfra_config = DeepInfraRerankConfig( - api_key=os.getenv("RERANK_FALLBACK_API_KEY", ""), - base_url=os.getenv("RERANK_FALLBACK_BASE_URL", "https://api.deepinfra.com/v1/inference"), - model=os.getenv("RERANK_MODEL", "Qwen/Qwen3-Reranker-4B"), - ) - deepinfra_service = DeepInfraRerankService(deepinfra_config) - - print(f"\n📋 Service Configuration:") - print(f" vLLM: {vllm_config.base_url}") - print(f" DeepInfra: {deepinfra_config.base_url}") - print(f" Model: {vllm_config.model}") - - # Prepare hits for rerank_memories method - # Format: List[Dict] with _source and memory_type - hits = [] - for idx, doc in enumerate(documents): - hit = { - "id": f"test_doc_{idx}", - "_source": {"episode": doc}, - "memory_type": "episodic_memory", - "score": 1.0 # Initial score - } - hits.append(hit) - - # Test vLLM using rerank_memories - print("\n1️⃣ Testing vLLM reranking (using rerank_memories)...") - vllm_results = await vllm_service.rerank_memories(query, hits) - print(f" ✅ vLLM returned {len(vllm_results)} results") - - # Test DeepInfra using rerank_memories - print("\n2️⃣ Testing DeepInfra reranking (using rerank_memories)...") - deepinfra_results = await deepinfra_service.rerank_memories(query, hits) - print(f" ✅ DeepInfra returned {len(deepinfra_results)} results") - - # Compare results - print("\n3️⃣ Comparing results...") - print("\n 📊 vLLM Rankings:") - for i, hit in enumerate(vllm_results[:5]): - doc_text = hit.get('_source', {}).get('episode', '')[:60] + "..." - score = hit.get('score', 0.0) - print(f" {i+1}. Score: {score:.4f} | {doc_text}") - - print("\n 📊 DeepInfra Rankings:") - for i, hit in enumerate(deepinfra_results[:5]): - doc_text = hit.get('_source', {}).get('episode', '')[:60] + "..." - score = hit.get('score', 0.0) - print(f" {i+1}. Score: {score:.4f} | {doc_text}") - - # Calculate ranking correlation - print("\n4️⃣ Analyzing ranking consistency...") - - # Get top document from each service - vllm_top_doc = vllm_results[0].get('_source', {}).get('episode', '') - deepinfra_top_doc = deepinfra_results[0].get('_source', {}).get('episode', '') - - if vllm_top_doc == deepinfra_top_doc: - print(" ✅ Top ranked document is the SAME across both services") - else: - print(" ℹ️ Top ranked documents DIFFER between services") - print(f" This is expected as different implementations may have slight variations") - - # Calculate score correlation for top 3 - print("\n 📈 Score comparison (Top 3):") - for i in range(min(3, len(vllm_results), len(deepinfra_results))): - vllm_score = vllm_results[i].get('score', 0.0) - deepinfra_score = deepinfra_results[i].get('score', 0.0) - diff = abs(vllm_score - deepinfra_score) - print(f" Doc {i+1}: vLLM={vllm_score:.4f}, DeepInfra={deepinfra_score:.4f}, diff={diff:.4f}") - - # Check if rankings are similar (allowing for small variations) - print("\n5️⃣ Ranking similarity analysis...") - - # Extract document texts from results - vllm_docs = [hit.get('_source', {}).get('episode', '') for hit in vllm_results] - deepinfra_docs = [hit.get('_source', {}).get('episode', '') for hit in deepinfra_results] - - # Find indices in original documents list - vllm_indices = [] - for doc_text in vllm_docs: - if doc_text in documents: - vllm_indices.append(documents.index(doc_text)) - - deepinfra_indices = [] - for doc_text in deepinfra_docs: - if doc_text in documents: - deepinfra_indices.append(documents.index(doc_text)) - - # Compare top 3 rankings - vllm_top3 = set(vllm_indices[:3]) - deepinfra_top3 = set(deepinfra_indices[:3]) - overlap = vllm_top3.intersection(deepinfra_top3) - overlap_rate = len(overlap) / 3 if len(vllm_top3) >= 3 and len(deepinfra_top3) >= 3 else 0 - - print(f" Top-3 overlap: {len(overlap)}/3 documents ({overlap_rate*100:.0f}%)") - - if overlap_rate >= 0.67: # At least 2 out of 3 match - print(" ✅ Rankings are highly consistent between services") - else: - print(" ⚠️ Rankings show significant differences") - print(" This may be due to model version differences or API variations") - - print("\n✅ Comparison test PASSED") - return True - - except Exception as e: - print(f"\n❌ Comparison test FAILED: {e}") - import traceback - traceback.print_exc() - return False - finally: - await vllm_service.close() - await deepinfra_service.close() - - -async def test_compare_vllm_deepinfra_embedding(): - """Compare vLLM and DeepInfra embedding results for multiple queries""" - print("\n" + "=" * 80) - print("🔹 Comparing vLLM vs DeepInfra Embedding Results") - print("=" * 80) - - # Import service classes directly - from agentic_layer.vectorize_vllm import VllmVectorizeService, VllmVectorizeConfig - from agentic_layer.vectorize_deepinfra import DeepInfraVectorizeService, DeepInfraVectorizeConfig - import numpy as np - - # Test queries (at least 5 diverse queries) - test_queries = [ - "What is machine learning and how does it work?", - "Explain the concept of deep learning neural networks", - "How to build a web application using Python", - "What are the benefits of cloud computing?", - "Describe the process of natural language processing", - "How does blockchain technology ensure security?", - "What is the difference between AI and machine learning?", - ] - - print(f"\n🧪 Testing with {len(test_queries)} diverse queries...") - - try: - # Create vLLM service - vllm_config = VllmVectorizeConfig( - api_key=os.getenv("VECTORIZE_API_KEY", "EMPTY"), - base_url=os.getenv("VECTORIZE_BASE_URL", "http://localhost:8000/v1"), - model=os.getenv("VECTORIZE_MODEL", "Qwen/Qwen3-Embedding-4B"), - dimensions=int(os.getenv("VECTORIZE_DIMENSIONS", "1024")), - ) - vllm_service = VllmVectorizeService(vllm_config) - - # Create DeepInfra service - deepinfra_config = DeepInfraVectorizeConfig( - api_key=os.getenv("VECTORIZE_FALLBACK_API_KEY", ""), - base_url=os.getenv("VECTORIZE_FALLBACK_BASE_URL", "https://api.deepinfra.com/v1/openai"), - model=os.getenv("VECTORIZE_MODEL", "Qwen/Qwen3-Embedding-4B"), - dimensions=int(os.getenv("VECTORIZE_DIMENSIONS", "1024")), - ) - deepinfra_service = DeepInfraVectorizeService(deepinfra_config) - - print(f"\n📋 Service Configuration:") - print(f" vLLM: {vllm_config.base_url}") - print(f" DeepInfra: {deepinfra_config.base_url}") - print(f" Model: {vllm_config.model}") - print(f" Dimensions: {vllm_config.dimensions}") - - # Test each query - print("\n1️⃣ Generating embeddings for all queries...") - - vllm_embeddings = [] - deepinfra_embeddings = [] - - for i, query in enumerate(test_queries): - # Get vLLM embedding - vllm_emb = await vllm_service.get_embedding(query, is_query=True) - vllm_embeddings.append(vllm_emb) - - # Get DeepInfra embedding - deepinfra_emb = await deepinfra_service.get_embedding(query, is_query=True) - deepinfra_embeddings.append(deepinfra_emb) - - print(f" Query {i+1}: ✅ vLLM dim={vllm_emb.shape[0]}, DeepInfra dim={deepinfra_emb.shape[0]}") - - # Compare embeddings - print("\n2️⃣ Comparing embedding properties...") - - # Check dimensions - print("\n 📏 Dimension Check:") - all_same_dim = all( - vllm_emb.shape[0] == deepinfra_emb.shape[0] - for vllm_emb, deepinfra_emb in zip(vllm_embeddings, deepinfra_embeddings) - ) - if all_same_dim: - print(f" ✅ All embeddings have the same dimension: {vllm_embeddings[0].shape[0]}") - else: - print(f" ⚠️ Dimension mismatch detected!") - - # Compare norms - print("\n 📊 Norm Comparison:") - for i, (vllm_emb, deepinfra_emb) in enumerate(zip(vllm_embeddings, deepinfra_embeddings)): - vllm_norm = np.linalg.norm(vllm_emb) - deepinfra_norm = np.linalg.norm(deepinfra_emb) - print(f" Query {i+1}: vLLM={vllm_norm:.4f}, DeepInfra={deepinfra_norm:.4f}") - - # Calculate cosine similarity between same queries from different services - print("\n3️⃣ Cross-service similarity analysis...") - print("\n 🔄 Cosine Similarity (vLLM vs DeepInfra for same query):") - - similarities = [] - for i, (vllm_emb, deepinfra_emb) in enumerate(zip(vllm_embeddings, deepinfra_embeddings)): - # Cosine similarity - similarity = np.dot(vllm_emb, deepinfra_emb) / ( - np.linalg.norm(vllm_emb) * np.linalg.norm(deepinfra_emb) - ) - similarities.append(similarity) - query_preview = test_queries[i][:50] + "..." if len(test_queries[i]) > 50 else test_queries[i] - print(f" Query {i+1}: {similarity:.4f} | {query_preview}") - - avg_similarity = np.mean(similarities) - min_similarity = np.min(similarities) - max_similarity = np.max(similarities) - - print(f"\n 📈 Statistics:") - print(f" Average similarity: {avg_similarity:.4f}") - print(f" Min similarity: {min_similarity:.4f}") - print(f" Max similarity: {max_similarity:.4f}") - - # Analyze element-wise differences - print("\n4️⃣ Element-wise difference analysis...") - - total_diffs = [] - for i, (vllm_emb, deepinfra_emb) in enumerate(zip(vllm_embeddings, deepinfra_embeddings)): - diff = np.abs(vllm_emb - deepinfra_emb) - mean_diff = np.mean(diff) - max_diff = np.max(diff) - total_diffs.append(mean_diff) - - if i < 3: # Show details for first 3 queries - print(f" Query {i+1}: mean_diff={mean_diff:.6f}, max_diff={max_diff:.6f}") - - avg_element_diff = np.mean(total_diffs) - print(f"\n Average element-wise difference: {avg_element_diff:.6f}") - - # Test embedding consistency within each service - print("\n5️⃣ Consistency check (same query, multiple calls)...") - - test_query = test_queries[0] - print(f" Testing query: '{test_query[:60]}...'") - - # Get 3 embeddings from vLLM for the same query - vllm_consistency = [] - for _ in range(3): - emb = await vllm_service.get_embedding(test_query, is_query=True) - vllm_consistency.append(emb) - - # Calculate pairwise similarities - vllm_sim_1_2 = np.dot(vllm_consistency[0], vllm_consistency[1]) / ( - np.linalg.norm(vllm_consistency[0]) * np.linalg.norm(vllm_consistency[1]) - ) - vllm_sim_1_3 = np.dot(vllm_consistency[0], vllm_consistency[2]) / ( - np.linalg.norm(vllm_consistency[0]) * np.linalg.norm(vllm_consistency[2]) - ) - - print(f" vLLM consistency (call 1 vs 2): {vllm_sim_1_2:.6f}") - print(f" vLLM consistency (call 1 vs 3): {vllm_sim_1_3:.6f}") - - if vllm_sim_1_2 > 0.9999 and vllm_sim_1_3 > 0.9999: - print(f" ✅ vLLM produces highly consistent embeddings") - else: - print(f" ⚠️ vLLM shows some variability") - - # Summary and conclusion - print("\n6️⃣ Summary and Conclusion...") - - if avg_similarity > 0.99: - print(f" ✅ Embeddings are HIGHLY SIMILAR (avg={avg_similarity:.4f})") - print(f" Services are likely using the same model checkpoint") - elif avg_similarity > 0.95: - print(f" ✅ Embeddings are VERY SIMILAR (avg={avg_similarity:.4f})") - print(f" Small differences likely due to implementation details") - elif avg_similarity > 0.90: - print(f" ⚠️ Embeddings are MODERATELY SIMILAR (avg={avg_similarity:.4f})") - print(f" May indicate different model versions or post-processing") - else: - print(f" ⚠️ Embeddings show SIGNIFICANT DIFFERENCES (avg={avg_similarity:.4f})") - print(f" Likely different model versions or configurations") - - print(f"\n 💡 Recommendation:") - if avg_similarity > 0.95: - print(f" Safe to use either service for retrieval") - print(f" Fallback between services should work well") - else: - print(f" Consider using a single service for consistency") - print(f" Fallback may produce different retrieval results") - - print("\n✅ Embedding comparison test PASSED") - return True - - except Exception as e: - print(f"\n❌ Embedding comparison test FAILED: {e}") - import traceback - traceback.print_exc() - return False - finally: - await vllm_service.close() - await deepinfra_service.close() - - -async def test_detailed_rerank_comparison(): - """Detailed comparison of vLLM vs DeepInfra rerank across multiple test cases""" - print("\n" + "=" * 80) - print("🔹 Detailed Rerank Comparison: Multiple Test Cases") - print("=" * 80) - - # Import service classes - from agentic_layer.rerank_vllm import VllmRerankService, VllmRerankConfig - from agentic_layer.rerank_deepinfra import DeepInfraRerankService, DeepInfraRerankConfig - import numpy as np - - # Define multiple test cases across different domains - test_cases = [ - { - "name": "Technology - Machine Learning", - "query": "What is machine learning and how does it work?", - "documents": [ - "Machine learning is a subset of AI that enables computers to learn from data without explicit programming. It uses algorithms to identify patterns.", - "Python is a programming language widely used in web development and data analysis.", - "Deep learning is a subset of machine learning using neural networks with multiple layers.", - "Cloud computing provides on-demand access to computing resources over the internet.", - "Neural networks are computational models inspired by the human brain structure.", - ] - }, - { - "name": "Healthcare - Diabetes", - "query": "What are the symptoms and treatment of type 2 diabetes?", - "documents": [ - "Type 2 diabetes symptoms include increased thirst, frequent urination, and unexplained weight loss. Treatment involves medication, diet, and exercise.", - "Regular exercise helps maintain cardiovascular health and reduces stress levels.", - "Type 1 diabetes is an autoimmune condition where the pancreas produces little or no insulin.", - "A balanced diet rich in vegetables and whole grains promotes overall health.", - "Diabetes complications can include heart disease, kidney damage, and vision problems if left untreated.", - ] - }, - { - "name": "Finance - Stock Market", - "query": "How does the stock market work and what factors affect stock prices?", - "documents": [ - "Stock markets facilitate buying and selling of company shares. Prices are affected by supply, demand, company performance, and economic indicators.", - "Cryptocurrency trading has grown popular with blockchain technology enabling decentralized transactions.", - "Interest rates set by central banks influence borrowing costs and investment decisions.", - "Real estate investment can provide steady income through rental properties.", - "Stock market volatility increases during economic uncertainty, causing price fluctuations.", - ] - }, - { - "name": "Climate - Global Warming", - "query": "What causes global warming and what are its effects?", - "documents": [ - "Global warming is caused by greenhouse gas emissions from burning fossil fuels. Effects include rising temperatures, melting ice caps, and extreme weather.", - "Renewable energy sources like solar and wind power reduce carbon emissions.", - "Ocean acidification occurs when seawater absorbs excess carbon dioxide from the atmosphere.", - "Deforestation reduces the planet's capacity to absorb CO2, contributing to climate change.", - "Electric vehicles produce zero direct emissions and help reduce air pollution.", - ] - }, - { - "name": "Programming - Web Development", - "query": "What are the best practices for building modern web applications?", - "documents": [ - "Modern web development uses frameworks like React, Vue, or Angular. Best practices include responsive design, performance optimization, and security.", - "Mobile app development requires knowledge of iOS and Android platforms.", - "Database optimization improves query performance and reduces server load.", - "Web applications should implement proper authentication, input validation, and secure data transmission.", - "Version control systems like Git help teams collaborate on code efficiently.", - ] - }, - { - "name": "History - World War II", - "query": "What were the main causes and consequences of World War II?", - "documents": [ - "World War II was caused by factors including the Treaty of Versailles, economic depression, and rise of totalitarian regimes. Consequences included millions of deaths and geopolitical changes.", - "The Cold War was a period of geopolitical tension between the Soviet Union and the United States.", - "The Industrial Revolution transformed economies from agriculture-based to manufacturing-focused.", - "World War II ended with the atomic bombings of Hiroshima and Nagasaki, leading to Japan's surrender.", - "The United Nations was established after WWII to promote international cooperation and prevent future conflicts.", - ] - }, - { - "name": "Nutrition - Healthy Diet", - "query": "What constitutes a healthy balanced diet?", - "documents": [ - "A balanced diet includes fruits, vegetables, whole grains, lean proteins, and healthy fats. It provides essential nutrients for optimal health.", - "Regular physical activity combined with proper nutrition helps maintain healthy body weight.", - "Processed foods high in sugar and saturated fats should be limited in a healthy diet.", - "Hydration is important; adults should drink 6-8 glasses of water daily.", - "Vitamins and minerals from diverse food sources support immune function and overall wellbeing.", - ] - }, - ] - - print(f"\n🧪 Testing {len(test_cases)} different scenarios...") - - try: - # Create services - vllm_config = VllmRerankConfig( - api_key=os.getenv("RERANK_API_KEY", "EMPTY"), - base_url=os.getenv("RERANK_BASE_URL", "http://localhost:12000/v1/rerank"), - model=os.getenv("RERANK_MODEL", "Qwen/Qwen3-Reranker-4B"), - ) - vllm_service = VllmRerankService(vllm_config) - - deepinfra_config = DeepInfraRerankConfig( - api_key=os.getenv("RERANK_FALLBACK_API_KEY", ""), - base_url=os.getenv("RERANK_FALLBACK_BASE_URL", "https://api.deepinfra.com/v1/inference"), - model=os.getenv("RERANK_MODEL", "Qwen/Qwen3-Reranker-4B"), - ) - deepinfra_service = DeepInfraRerankService(deepinfra_config) - - print(f"\n📋 Services:") - print(f" vLLM: {vllm_config.base_url}") - print(f" DeepInfra: {deepinfra_config.base_url}") - - # Store results for analysis - all_results = [] - - # Test each case - for case_idx, test_case in enumerate(test_cases): - print(f"\n{'='*80}") - print(f"Test Case {case_idx + 1}: {test_case['name']}") - print(f"{'='*80}") - print(f"Query: {test_case['query']}") - print(f"Documents: {len(test_case['documents'])}") - - # Prepare hits - hits = [] - for doc_idx, doc in enumerate(test_case['documents']): - hit = { - "id": f"case{case_idx}_doc{doc_idx}", - "_source": {"episode": doc}, - "memory_type": "episodic_memory", - "score": 1.0 - } - hits.append(hit) - - # Get rerank results from both services - print(f"\n⏳ Reranking with vLLM...") - vllm_results = await vllm_service.rerank_memories(test_case['query'], hits.copy()) - - print(f"⏳ Reranking with DeepInfra...") - deepinfra_results = await deepinfra_service.rerank_memories(test_case['query'], hits.copy()) - - # Extract rankings and scores - vllm_docs = [hit.get('_source', {}).get('episode', '') for hit in vllm_results] - vllm_scores = [hit.get('score', 0.0) for hit in vllm_results] - - deepinfra_docs = [hit.get('_source', {}).get('episode', '') for hit in deepinfra_results] - deepinfra_scores = [hit.get('score', 0.0) for hit in deepinfra_results] - - # Map back to original indices - vllm_order = [test_case['documents'].index(doc) for doc in vllm_docs if doc in test_case['documents']] - deepinfra_order = [test_case['documents'].index(doc) for doc in deepinfra_docs if doc in test_case['documents']] - - # Print results - print(f"\n📊 vLLM Rankings:") - for i, (doc, score) in enumerate(zip(vllm_docs, vllm_scores)): - doc_preview = doc[:70] + "..." if len(doc) > 70 else doc - orig_idx = test_case['documents'].index(doc) if doc in test_case['documents'] else -1 - print(f" {i+1}. [Doc {orig_idx}] Score: {score:.4f} | {doc_preview}") - - print(f"\n📊 DeepInfra Rankings:") - for i, (doc, score) in enumerate(zip(deepinfra_docs, deepinfra_scores)): - doc_preview = doc[:70] + "..." if len(doc) > 70 else doc - orig_idx = test_case['documents'].index(doc) if doc in test_case['documents'] else -1 - print(f" {i+1}. [Doc {orig_idx}] Score: {score:.4f} | {doc_preview}") - - # Calculate metrics - print(f"\n📈 Analysis:") - - # 1. Score comparison - print(f"\n Score Comparison:") - score_diffs = [] - for i in range(min(len(vllm_scores), len(deepinfra_scores))): - diff = abs(vllm_scores[i] - deepinfra_scores[i]) - score_diffs.append(diff) - print(f" Position {i+1}: vLLM={vllm_scores[i]:.4f}, DeepInfra={deepinfra_scores[i]:.4f}, diff={diff:.4f}") - - avg_score_diff = np.mean(score_diffs) if score_diffs else 0 - max_score_diff = np.max(score_diffs) if score_diffs else 0 - print(f" Avg score difference: {avg_score_diff:.4f}") - print(f" Max score difference: {max_score_diff:.4f}") - - # 2. Ranking consistency - print(f"\n Ranking Consistency:") - print(f" vLLM order: {vllm_order}") - print(f" DeepInfra order: {deepinfra_order}") - - # Top-1 match - top1_match = vllm_order[0] == deepinfra_order[0] if vllm_order and deepinfra_order else False - print(f" Top-1 match: {'✅ YES' if top1_match else '❌ NO'}") - - # Top-3 overlap - vllm_top3 = set(vllm_order[:3]) - deepinfra_top3 = set(deepinfra_order[:3]) - top3_overlap = len(vllm_top3.intersection(deepinfra_top3)) - print(f" Top-3 overlap: {top3_overlap}/3 documents") - - # Kendall's Tau (rank correlation) - from scipy.stats import kendalltau - if len(vllm_order) == len(deepinfra_order): - tau, p_value = kendalltau(vllm_order, deepinfra_order) - print(f" Kendall's Tau: {tau:.4f} (p={p_value:.4f})") - - # Spearman's correlation on scores - from scipy.stats import spearmanr - if len(vllm_scores) >= 2 and len(deepinfra_scores) >= 2: - # Create score mapping for original documents - vllm_score_map = {test_case['documents'].index(doc): score - for doc, score in zip(vllm_docs, vllm_scores) - if doc in test_case['documents']} - deepinfra_score_map = {test_case['documents'].index(doc): score - for doc, score in zip(deepinfra_docs, deepinfra_scores) - if doc in test_case['documents']} - - # Get scores in original document order - vllm_scores_ordered = [vllm_score_map.get(i, 0) for i in range(len(test_case['documents']))] - deepinfra_scores_ordered = [deepinfra_score_map.get(i, 0) for i in range(len(test_case['documents']))] - - rho, p_value = spearmanr(vllm_scores_ordered, deepinfra_scores_ordered) - print(f" Spearman's ρ: {rho:.4f} (p={p_value:.4f})") - - # Store results - all_results.append({ - "name": test_case['name'], - "top1_match": top1_match, - "top3_overlap": top3_overlap, - "avg_score_diff": avg_score_diff, - "max_score_diff": max_score_diff, - "vllm_order": vllm_order, - "deepinfra_order": deepinfra_order, - "vllm_scores": vllm_scores, - "deepinfra_scores": deepinfra_scores, - }) - - # Overall summary - print(f"\n{'='*80}") - print(f"📊 Overall Summary Across All Test Cases") - print(f"{'='*80}") - - top1_matches = sum(1 for r in all_results if r['top1_match']) - avg_top3_overlap = np.mean([r['top3_overlap'] for r in all_results]) - avg_score_diff_all = np.mean([r['avg_score_diff'] for r in all_results]) - max_score_diff_all = np.max([r['max_score_diff'] for r in all_results]) - - print(f"\n🎯 Ranking Consistency:") - print(f" Top-1 matches: {top1_matches}/{len(test_cases)} ({top1_matches/len(test_cases)*100:.1f}%)") - print(f" Avg Top-3 overlap: {avg_top3_overlap:.2f}/3 ({avg_top3_overlap/3*100:.1f}%)") - - print(f"\n📊 Score Differences:") - print(f" Average score diff: {avg_score_diff_all:.4f}") - print(f" Maximum score diff: {max_score_diff_all:.4f}") - - print(f"\n💡 Interpretation:") - if top1_matches >= len(test_cases) * 0.8: - print(f" ✅ EXCELLENT: Services agree on top result in most cases") - elif top1_matches >= len(test_cases) * 0.6: - print(f" ✅ GOOD: Services show reasonable consistency") - else: - print(f" ⚠️ MODERATE: Significant ranking differences between services") - - if avg_score_diff_all < 0.1: - print(f" ✅ Score differences are minimal") - elif avg_score_diff_all < 0.2: - print(f" ⚠️ Score differences are moderate") - else: - print(f" ⚠️ Score differences are significant") - - # Case-by-case summary table - print(f"\n📋 Summary Table:") - print(f" {'Case':<35} {'Top-1':<8} {'Top-3':<8} {'Avg Diff':<10}") - print(f" {'-'*35} {'-'*8} {'-'*8} {'-'*10}") - for r in all_results: - top1_sym = "✅" if r['top1_match'] else "❌" - top3_str = f"{r['top3_overlap']}/3" - print(f" {r['name']:<35} {top1_sym:<8} {top3_str:<8} {r['avg_score_diff']:<10.4f}") - - print("\n✅ Detailed rerank comparison PASSED") - return True - - except Exception as e: - print(f"\n❌ Detailed comparison FAILED: {e}") - import traceback - traceback.print_exc() - return False - finally: - await vllm_service.close() - await deepinfra_service.close() - - -async def main(): - """Run all integration tests""" - print("\n" + "🚀 " * 20) - print(" Integration Tests for Vectorize & Rerank Services") - print("🚀 " * 20) - - results = [] - - # Test vectorize service - result_vectorize = await test_vectorize_service() - results.append(("Vectorize Service", result_vectorize)) - - # Test rerank service - result_rerank = await test_rerank_service() - results.append(("Rerank Service", result_rerank)) - - # Test complete pipeline - result_pipeline = await test_retrieval_pipeline() - results.append(("Complete Pipeline", result_pipeline)) - - # Test comparison between vLLM and DeepInfra rerank - result_rerank_comparison = await test_compare_vllm_deepinfra_rerank() - results.append(("vLLM vs DeepInfra Rerank", result_rerank_comparison)) - - # Test comparison between vLLM and DeepInfra embedding - result_embedding_comparison = await test_compare_vllm_deepinfra_embedding() - results.append(("vLLM vs DeepInfra Embedding", result_embedding_comparison)) - - # Detailed rerank comparison with multiple test cases - result_detailed_rerank = await test_detailed_rerank_comparison() - results.append(("Detailed Rerank Comparison", result_detailed_rerank)) - - # Summary - print("\n" + "=" * 80) - print("📊 Test Summary") - print("=" * 80) - - for test_name, result in results: - status = "✅ PASSED" if result else "❌ FAILED" - print(f" {test_name:40s} {status}") - - total = len(results) - passed = sum(1 for _, r in results if r) - - print(f"\n Total: {passed}/{total} tests passed") - - if passed == total: - print("\n 🎉 All tests PASSED!") - return 0 - else: - print(f"\n ⚠️ {total - passed} test(s) FAILED") - return 1 - - -if __name__ == "__main__": - exit_code = asyncio.run(main()) - sys.exit(exit_code) - diff --git a/methods/EverCore/tests/test_interceptor_episodic_memory.py b/methods/EverCore/tests/test_interceptor_episodic_memory.py deleted file mode 100644 index c1145afa..00000000 --- a/methods/EverCore/tests/test_interceptor_episodic_memory.py +++ /dev/null @@ -1,427 +0,0 @@ -#!/usr/bin/env python3 -""" -Test: TenantCommandInterceptor with real EpisodicMemory collection - -Verifies the interceptor works against the actual project document model -and real data in MongoDB. - -Run: - uv run python src/bootstrap.py tests/test_interceptor_episodic_memory.py -""" - -import asyncio -import os - -from pymongo import monitoring -from typing import Any - -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import set_current_tenant, clear_current_tenant -from core.tenants.tenant_models import TenantInfo, TenantDetail -from core.tenants.tenantize.oxm.mongo.tenant_field_command_interceptor import ( - install_tenant_interceptor, -) -from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemory, -) - -logger = get_logger(__name__) - -TEST_TENANT_ID = "test_tenant_ep_001" - - -class CommandCapture(monitoring.CommandListener): - """Capture commands after interceptor processing.""" - - def __init__(self): - self.commands: list[dict[str, Any]] = [] - self._capture = False - - def start(self): - self.commands.clear() - self._capture = True - - def stop(self) -> list[dict[str, Any]]: - self._capture = False - result = list(self.commands) - self.commands.clear() - return result - - def started(self, event: monitoring.CommandStartedEvent): - if self._capture: - self.commands.append( - { - "name": event.command_name, - "cmd": dict(event.command), - "db": event.database_name, - } - ) - - def succeeded(self, event): - pass - - def failed(self, event): - pass - - -def print_cmd(label: str, cmds: list[dict], target_cmd: str): - """Print captured command details for inspection.""" - matched = [c for c in cmds if c["name"] == target_cmd] - if not matched: - print( - f" [{label}] No '{target_cmd}' command found. Got: {[c['name'] for c in cmds]}" - ) - return - - cmd = matched[-1]["cmd"] - print(f" [{label}] command={target_cmd}, db={matched[-1]['db']}") - - if target_cmd == "find": - print(f" filter = {cmd.get('filter', {})}") - elif target_cmd == "insert": - docs = cmd.get("documents", []) - for i, d in enumerate(docs): - print(f" documents[{i}].tenant_id = {d.get('tenant_id', 'MISSING')}") - elif target_cmd == "update": - for i, u in enumerate(cmd.get("updates", [])): - print(f" updates[{i}].q = {u.get('q', {})}") - elif target_cmd == "delete": - for i, d in enumerate(cmd.get("deletes", [])): - print(f" deletes[{i}].q = {d.get('q', {})}") - elif target_cmd == "aggregate": - pipeline = cmd.get("pipeline", []) - print(f" pipeline[0] = {pipeline[0] if pipeline else 'EMPTY'}") - elif target_cmd == "count": - print(f" query = {cmd.get('query', {})}") - elif target_cmd == "findAndModify": - print(f" query = {cmd.get('query', {})}") - - has_tenant = _has_tenant_id(target_cmd, cmd) - status = "PASS" if has_tenant else "FAIL" - print(f" tenant_id present: {has_tenant} [{status}]") - - -def _has_tenant_id(cmd_name: str, cmd: dict) -> bool: - if cmd_name == "find": - return cmd.get("filter", {}).get("tenant_id") == TEST_TENANT_ID - elif cmd_name == "insert": - return all( - d.get("tenant_id") == TEST_TENANT_ID for d in cmd.get("documents", [{}]) - ) - elif cmd_name == "update": - return all( - u.get("q", {}).get("tenant_id") == TEST_TENANT_ID - for u in cmd.get("updates", [{}]) - ) - elif cmd_name == "delete": - return all( - d.get("q", {}).get("tenant_id") == TEST_TENANT_ID - for d in cmd.get("deletes", [{}]) - ) - elif cmd_name == "findAndModify": - return cmd.get("query", {}).get("tenant_id") == TEST_TENANT_ID - elif cmd_name == "aggregate": - pipeline = cmd.get("pipeline", []) - return ( - bool(pipeline) - and pipeline[0].get("$match", {}).get("tenant_id") == TEST_TENANT_ID - ) - elif cmd_name == "count": - return cmd.get("query", {}).get("tenant_id") == TEST_TENANT_ID - elif cmd_name == "distinct": - return cmd.get("query", {}).get("tenant_id") == TEST_TENANT_ID - return False - - -async def main(): - print("\n" + "=" * 70) - print(" TenantCommandInterceptor — EpisodicMemory Live Test") - print("=" * 70) - - # 1. Get the real PyMongo client that Beanie uses (via collection → database → client) - client = EpisodicMemory.get_pymongo_collection().database.client - print(f"\n Client type: {type(client).__name__}") - - # 2. Install interceptor - capture = CommandCapture() - - # Register command listener (need to check if we can add dynamically) - # PyMongo doesn't support adding listeners after client creation, - # so we use a workaround: access the internal listeners - if hasattr(client, "_event_listeners") and client._event_listeners is not None: - # Python name mangling: __command_listeners → _EventListeners__command_listeners - listeners = client._event_listeners - listeners._EventListeners__command_listeners.append(capture) - listeners._EventListeners__enabled_for_commands = True - print(" CommandListener: injected into existing client") - else: - print( - " WARNING: Cannot inject CommandListener. Will still test interceptor but cannot verify commands." - ) - - install_tenant_interceptor(client) - print(f" Interceptor: installed") - - # 3. Set tenant context - tenant_info = TenantInfo( - tenant_id=TEST_TENANT_ID, - tenant_detail=TenantDetail(tenant_info={}, storage_info={}), - ) - set_current_tenant(tenant_info) - print(f" Tenant context: {TEST_TENANT_ID}") - - collection_name = EpisodicMemory.get_collection_name() - print(f" Collection: {collection_name}") - - pass_count = 0 - fail_count = 0 - - def check(label, cmds, cmd_name): - nonlocal pass_count, fail_count - print_cmd(label, cmds, cmd_name) - matched = [c for c in cmds if c["name"] == cmd_name] - if matched and _has_tenant_id(cmd_name, matched[-1]["cmd"]): - pass_count += 1 - else: - fail_count += 1 - - # ==================== Tests ==================== - - print(f"\n--- Beanie Document Operations on {collection_name} ---\n") - - # Test 1: find_many (with soft delete filter) - print(" [1] EpisodicMemory.find_many() — soft delete aware find") - capture.start() - results = await EpisodicMemory.find_many({"user_id": "test_user"}).to_list() - cmds = capture.stop() - check("find_many", cmds, "find") - print(f" results: {len(results)} docs\n") - - # Test 2: find_one - print(" [2] EpisodicMemory.find_one()") - capture.start() - result = await EpisodicMemory.find_one({"user_id": "test_user"}) - cmds = capture.stop() - check("find_one", cmds, "find") - print(f" result: {result}\n") - - # Test 3: find with sort/skip/limit chain - print(" [3] EpisodicMemory.find().sort().skip().limit()") - capture.start() - results = await ( - EpisodicMemory.find({"user_id": {"$exists": True}}) - .sort("-timestamp") - .skip(0) - .limit(5) - .to_list() - ) - cmds = capture.stop() - check("find_chained", cmds, "find") - print(f" results: {len(results)} docs\n") - - # Test 4: find().count() - print(" [4] EpisodicMemory.find().count()") - capture.start() - count = await EpisodicMemory.find({"user_id": {"$exists": True}}).count() - cmds = capture.stop() - agg_cmds = [c for c in cmds if c["name"] == "aggregate"] - if agg_cmds: - check("find_count", cmds, "aggregate") - else: - check("find_count", cmds, "count") - print(f" count: {count}\n") - - # Test 5: Direct PyMongo collection.find() - print(" [5] get_pymongo_collection().find()") - collection = EpisodicMemory.get_pymongo_collection() - capture.start() - results = await collection.find({"user_id": "test_user"}).to_list(length=5) - cmds = capture.stop() - check("pymongo_find", cmds, "find") - print(f" results: {len(results)} docs\n") - - # Test 6: PyMongo aggregate - print(" [6] get_pymongo_collection().aggregate()") - capture.start() - pipeline = [ - {"$match": {"user_id": {"$exists": True}}}, - {"$group": {"_id": "$user_id", "count": {"$sum": 1}}}, - {"$sort": {"count": -1}}, - {"$limit": 5}, - ] - cursor = await collection.aggregate(pipeline) - agg_results = await cursor.to_list(length=100) - cmds = capture.stop() - check("pymongo_aggregate", cmds, "aggregate") - print(f" results: {len(agg_results)} groups\n") - - # Test 7: PyMongo count_documents - print(" [7] get_pymongo_collection().count_documents()") - capture.start() - cnt = await collection.count_documents({"user_id": {"$exists": True}}) - cmds = capture.stop() - agg_cmds = [c for c in cmds if c["name"] == "aggregate"] - if agg_cmds: - check("pymongo_count_documents", cmds, "aggregate") - else: - check("pymongo_count_documents", cmds, "count") - print(f" count: {cnt}\n") - - # Test 8: PyMongo estimated_document_count - print(" [8] get_pymongo_collection().estimated_document_count()") - capture.start() - est = await collection.estimated_document_count() - cmds = capture.stop() - count_cmds = [c for c in cmds if c["name"] == "count"] - if count_cmds: - cmd = count_cmds[-1]["cmd"] - q = cmd.get("query", {}) - has = q.get("tenant_id") == TEST_TENANT_ID - print(f" [estimated_count] query = {q}") - print( - f" tenant_id present: {has} [{'PASS' if has else 'WARN — count cmd has no query param by design'}]" - ) - pass_count += 1 # This is a known limitation - print(f" estimate: {est}\n") - - # Test 9: Beanie insert single - print(" [9] EpisodicMemory().insert() — single document insert") - from common_utils.datetime_utils import get_now_with_timezone - - test_doc_ids = [] # Track for cleanup - now = get_now_with_timezone() - doc = EpisodicMemory( - user_id="__interceptor_test__", - group_id="__interceptor_test_group__", - timestamp=now, - summary="Interceptor test single insert", - episode="Test episode for interceptor verification", - ) - capture.start() - await doc.insert() - cmds = capture.stop() - check("beanie_insert_single", cmds, "insert") - test_doc_ids.append(doc.id) - print() - - # Test 10: Beanie insert_many - print(" [10] EpisodicMemory.insert_many() — bulk insert") - docs = [ - EpisodicMemory( - user_id="__interceptor_test__", - group_id="__interceptor_test_group__", - timestamp=now, - summary=f"Interceptor test bulk {i}", - episode=f"Test episode bulk {i}", - ) - for i in range(3) - ] - capture.start() - await EpisodicMemory.insert_many(docs) - cmds = capture.stop() - check("beanie_insert_many", cmds, "insert") - test_doc_ids.extend([d.id for d in docs]) - print() - - # Test 11: Beanie save (update via replace) - print(" [11] document.save() — update existing document") - doc.summary = "Interceptor test UPDATED" - capture.start() - await doc.save() - cmds = capture.stop() - update_cmds = [c for c in cmds if c["name"] in ("update", "findAndModify")] - if update_cmds: - cmd_name = update_cmds[-1]["name"] - check("beanie_save", cmds, cmd_name) - else: - print( - f" [beanie_save] No update/findAndModify captured. Got: {[c['name'] for c in cmds]}" - ) - fail_count += 1 - print() - - # Test 12: PyMongo update_one (used by soft delete internally) - print(" [12] collection.update_one() — direct PyMongo update") - capture.start() - await collection.update_one( - {"user_id": "__interceptor_test__"}, {"$set": {"summary": "PyMongo updated"}} - ) - cmds = capture.stop() - check("pymongo_update_one", cmds, "update") - print() - - # Test 13: Soft delete single - print(" [13] document.delete(deleted_by=...) — soft delete") - capture.start() - await doc.delete(deleted_by="interceptor_test") - cmds = capture.stop() - check("soft_delete_single", cmds, "update") - print() - - # Test 14: PyMongo delete_many (hard delete for cleanup) - print(" [14] collection.delete_many() — hard delete") - capture.start() - await collection.delete_many({"user_id": "__interceptor_test__"}) - cmds = capture.stop() - check("pymongo_delete_many", cmds, "delete") - print() - - # Test 15: Cursor with async for - print(" [15] async for doc in EpisodicMemory.find():") - capture.start() - fetched = [] - async for doc in EpisodicMemory.find({"user_id": {"$exists": True}}).limit(3): - fetched.append(doc) - cmds = capture.stop() - check("cursor_async_for", cmds, "find") - print(f" fetched: {len(fetched)} docs\n") - - # Test 16: hard_find_many (include soft-deleted) - print(" [16] EpisodicMemory.hard_find_many() — include deleted") - capture.start() - results = await EpisodicMemory.hard_find_many({"user_id": "test_user"}).to_list() - cmds = capture.stop() - check("hard_find_many", cmds, "find") - print(f" results: {len(results)} docs\n") - - # Test 17: No tenant context — check behavior after clearing - print(" [17] clear_current_tenant() — verify tenant_id changes or absent") - clear_current_tenant() - capture.start() - await collection.find_one({"user_id": "test_user"}) - cmds = capture.stop() - find_cmds = [c for c in cmds if c["name"] == "find"] - if find_cmds: - f = find_cmds[-1]["cmd"].get("filter", {}) - tid_in_filter = f.get("tenant_id") - # After clear, tenant context may fall back to system default (e.g. TENANT_SINGLE_TENANT_ID) - # The key assertion: it should NOT be our test tenant - not_test_tenant = tid_in_filter != TEST_TENANT_ID - print(f" [clear_tenant] filter = {f}") - print(f" tenant_id in filter: {tid_in_filter}") - print( - f" not our test tenant: {not_test_tenant} [{'PASS' if not_test_tenant else 'FAIL'}]" - ) - if not_test_tenant: - pass_count += 1 - else: - fail_count += 1 - print() - - # ==================== Summary ==================== - print("=" * 70) - total = pass_count + fail_count - print(f" Results: {pass_count}/{total} PASS, {fail_count}/{total} FAIL") - if fail_count == 0: - print(" ALL OPERATIONS ON EpisodicMemory INTERCEPTED SUCCESSFULLY") - else: - print(" SOME OPERATIONS NOT INTERCEPTED — CHECK ABOVE") - print("=" * 70 + "\n") - - # Cleanup: remove interceptor - client._encrypter = None - clear_current_tenant() - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/tests/test_llm_metrics.py b/methods/EverCore/tests/test_llm_metrics.py deleted file mode 100644 index 06d748f3..00000000 --- a/methods/EverCore/tests/test_llm_metrics.py +++ /dev/null @@ -1,200 +0,0 @@ -"""LLM Prometheus metrics integration tests (mock HTTP).""" - -import pytest -import aiohttp -from unittest.mock import AsyncMock, patch - -from memory_layer.llm.openai_provider import OpenAIProvider -from memory_layer.llm.protocol import LLMError -from memory_layer.llm.api_key_rotator import ApiKeyRotator - - -@pytest.fixture(autouse=True) -def _reset_shared_rotator(): - """Ensure each test starts with a clean singleton state.""" - ApiKeyRotator._shared = None - yield - ApiKeyRotator._shared = None - - -def _success_body(content: str = "hello") -> dict: - return { - "choices": [{"message": {"content": content}, "finish_reason": "stop"}], - "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}, - } - - -def _error_body(message: str = "error") -> dict: - return {"error": {"message": message}} - - -METRICS_PATCH = "memory_layer.llm.openai_provider.record_llm_request" - - -class TestMetricsOnSuccess: - """HTTP 200: record status=success.""" - - @pytest.mark.asyncio - async def test_success_records_metric(self) -> None: - provider = OpenAIProvider( - api_key="key-a", base_url="https://fake.api", model="test-model" - ) - - async def mock_request(data: dict, api_key: str) -> tuple[int, dict]: - return 200, _success_body("ok") - - provider._do_request = mock_request - - with patch(METRICS_PATCH) as mock_record: - await provider.generate("test") - mock_record.assert_called_once_with("test-model", "success") - - -class TestMetricsOnRateLimit: - """HTTP 429 (all keys exhausted): record status=rate_limit.""" - - @pytest.mark.asyncio - async def test_429_all_keys_exhausted_records_rate_limit(self) -> None: - provider = OpenAIProvider( - api_key="key-a,key-b", base_url="https://fake.api", model="test-model" - ) - - async def always_429(data: dict, api_key: str) -> tuple[int, dict]: - return 429, _error_body("rate limited") - - provider._do_request = always_429 - - with patch(METRICS_PATCH) as mock_record: - with pytest.raises(LLMError, match="keys exhausted"): - await provider.generate("test") - mock_record.assert_called_once_with("test-model", "rate_limit") - - -class TestMetricsOnKeyError: - """HTTP 401/402/403 (all keys exhausted): record status=key_error.""" - - @pytest.mark.asyncio - async def test_401_all_keys_exhausted_records_key_error(self) -> None: - provider = OpenAIProvider( - api_key="key-a", base_url="https://fake.api", model="test-model" - ) - - async def always_401(data: dict, api_key: str) -> tuple[int, dict]: - return 401, _error_body("unauthorized") - - provider._do_request = always_401 - - with patch(METRICS_PATCH) as mock_record: - with pytest.raises(LLMError, match="keys exhausted"): - await provider.generate("test") - mock_record.assert_called_once_with("test-model", "key_error") - - -class TestMetricsOnServerError: - """HTTP 5xx (after max retries): record status=server_error.""" - - @pytest.mark.asyncio - async def test_5xx_exhausted_records_server_error(self) -> None: - provider = OpenAIProvider( - api_key="key-a", base_url="https://fake.api", model="test-model" - ) - - async def always_502(data: dict, api_key: str) -> tuple[int, dict]: - return 502, _error_body("bad gateway") - - provider._do_request = always_502 - - with ( - patch(METRICS_PATCH) as mock_record, - patch( - "memory_layer.llm.openai_provider.asyncio.sleep", new_callable=AsyncMock - ), - ): - with pytest.raises(LLMError, match="after 5 retries"): - await provider.generate("test") - mock_record.assert_called_once_with("test-model", "server_error") - - -class TestMetricsOnClientError: - """Network errors (after max retries): record status=client_error.""" - - @pytest.mark.asyncio - async def test_network_error_records_client_error(self) -> None: - provider = OpenAIProvider( - api_key="key-a", base_url="https://fake.api", model="test-model" - ) - - async def always_fail(data: dict, api_key: str) -> tuple[int, dict]: - raise aiohttp.ClientError("connection reset") - - provider._do_request = always_fail - - with patch(METRICS_PATCH) as mock_record: - with pytest.raises(LLMError, match="Request failed"): - await provider.generate("test") - mock_record.assert_called_once_with("test-model", "client_error") - - -class TestMetricsOnRequestError: - """HTTP 400/404/422: record status=request_error.""" - - @pytest.mark.asyncio - async def test_400_records_request_error(self) -> None: - provider = OpenAIProvider( - api_key="key-a", base_url="https://fake.api", model="test-model" - ) - - async def always_400(data: dict, api_key: str) -> tuple[int, dict]: - return 400, _error_body("bad request") - - provider._do_request = always_400 - - with patch(METRICS_PATCH) as mock_record: - with pytest.raises(LLMError, match="HTTP Error 400"): - await provider.generate("test") - mock_record.assert_called_once_with("test-model", "request_error") - - -class TestMetricsNotRecordedOnRetry: - """Metrics only recorded on final outcome, not intermediate retries.""" - - @pytest.mark.asyncio - async def test_429_then_success_records_only_success(self) -> None: - """429 followed by 200: only 'success' is recorded.""" - provider = OpenAIProvider( - api_key="key-a,key-b", base_url="https://fake.api", model="test-model" - ) - - responses = [(429, _error_body()), (200, _success_body("ok"))] - - async def mock_request(data: dict, api_key: str) -> tuple[int, dict]: - return responses.pop(0) - - provider._do_request = mock_request - - with patch(METRICS_PATCH) as mock_record: - await provider.generate("test") - mock_record.assert_called_once_with("test-model", "success") - - @pytest.mark.asyncio - async def test_5xx_then_success_records_only_success(self) -> None: - """502 followed by 200: only 'success' is recorded.""" - provider = OpenAIProvider( - api_key="key-a", base_url="https://fake.api", model="test-model" - ) - - responses = [(502, _error_body()), (200, _success_body("ok"))] - - async def mock_request(data: dict, api_key: str) -> tuple[int, dict]: - return responses.pop(0) - - provider._do_request = mock_request - - with ( - patch(METRICS_PATCH) as mock_record, - patch( - "memory_layer.llm.openai_provider.asyncio.sleep", new_callable=AsyncMock - ), - ): - await provider.generate("test") - mock_record.assert_called_once_with("test-model", "success") diff --git a/methods/EverCore/tests/test_llm_switching_e2e.py b/methods/EverCore/tests/test_llm_switching_e2e.py deleted file mode 100644 index c92483f8..00000000 --- a/methods/EverCore/tests/test_llm_switching_e2e.py +++ /dev/null @@ -1,686 +0,0 @@ -# -*- coding: utf-8 -*- -""" -LLM Dynamic Switching End-to-End Test - -Tests: -1. Switch provider between messages (openrouter <-> openai) -2. Switch model within same provider -3. Verify LLM calls work correctly - -Usage: - # Requires running server on port 1995 - cd /Users/admin/Applications/cursor_project/evermemos/evermemos-opensource - source .venv/bin/activate - - # Run all E2E tests - PYTHONPATH=src python tests/test_llm_switching_e2e.py --base-url http://localhost:1995 - - # Run specific test - PYTHONPATH=src python tests/test_llm_switching_e2e.py --test switch_provider - PYTHONPATH=src python tests/test_llm_switching_e2e.py --test switch_model - PYTHONPATH=src python tests/test_llm_switching_e2e.py --test all -""" - -import argparse -import json -import time -import requests -import uuid -from datetime import datetime, timezone -from typing import Dict, Optional - - -class LLMSwitchingE2ETest: - """End-to-end test for LLM dynamic switching feature""" - - def __init__(self, base_url: str, timeout: int = 120): - self.base_url = base_url.rstrip("/") - self.timeout = timeout - self.test_id = uuid.uuid4().hex[:8] - - def log(self, message: str, level: str = "INFO"): - timestamp = datetime.now().strftime("%H:%M:%S") - symbols = {"INFO": "ℹ️", "SUCCESS": "✅", "ERROR": "❌", "WARN": "⚠️"} - print(f"[{timestamp}] {symbols.get(level, '•')} {message}") - - def api_request( - self, method: str, endpoint: str, data: Optional[Dict] = None - ) -> requests.Response: - url = f"{self.base_url}{endpoint}" - headers = {"Content-Type": "application/json"} - if method.upper() == "GET": - return requests.get(url, headers=headers, timeout=self.timeout) - elif method.upper() == "POST": - return requests.post(url, headers=headers, json=data, timeout=self.timeout) - elif method.upper() == "PATCH": - return requests.patch(url, headers=headers, json=data, timeout=self.timeout) - raise ValueError(f"Unsupported method: {method}") - - def update_llm_config(self, config: Dict) -> bool: - """Update global llm_custom_setting""" - response = self.api_request( - "PUT", "/api/v1/settings", {"llm_custom_setting": config} - ) - return response.status_code == 200 - - def send_message(self, group_id: str, content: str, msg_id: str) -> bool: - """Send a message to trigger LLM processing""" - data = { - "message_id": msg_id, - "create_time": datetime.now(timezone.utc).isoformat(), - "sender": "user_001", - "sender_name": "Test User", - "content": content, - "group_id": group_id, - } - response = self.api_request("POST", "/api/v0/memories", data) - return response.status_code in [200, 201, 202] - - def setup_global_config(self, llm_config: Dict) -> bool: - """Ensure global settings exists with llm_custom_setting""" - data = { - "scene": "solo", - "scene_desc": {"description": "LLM switching test"}, - "llm_custom_setting": llm_config, - } - response = self.api_request("PUT", "/api/v1/settings", data) - return response.status_code in [200, 201] - - # ========== Test Cases ========== - - def test_switch_provider(self) -> bool: - """Test 1: Switch model within OpenRouter (only OpenRouter, no OpenAI)""" - self.log("=" * 70) - self.log("TEST: Switch Model Within OpenRouter") - self.log("=" * 70) - - group_id = f"test_provider_switch_{self.test_id}" - - # Step 1: Setup with OpenRouter gpt-4.1-mini - config_a = { - "boundary": {"provider": "openrouter", "model": "openai/gpt-4.1-mini"}, - "extraction": {"provider": "openrouter", "model": "openai/gpt-4.1-mini"}, - } - self.log("Step 1: Setting up with OpenRouter (gpt-4.1-mini)...") - if not self.setup_global_config(config_a): - self.log("Failed to setup global config", "ERROR") - return False - self.log("Global config set to OpenRouter", "SUCCESS") - - # Create group config - if not self.create_group_config(group_id): - self.log("Failed to create group config", "ERROR") - return False - - # Step 2: Send message - self.log("Step 2: Sending message with gpt-4.1-mini...") - if not self.send_message( - group_id, "Hello, testing OpenRouter gpt-4.1-mini.", f"{group_id}_msg_1" - ): - self.log("Failed to send message", "ERROR") - return False - self.log("Message sent with gpt-4.1-mini", "SUCCESS") - time.sleep(1) - - # Step 3: Switch to qwen model - config_b = { - "boundary": { - "provider": "openrouter", - "model": "qwen/qwen3-235b-a22b-2507", - }, - "extraction": { - "provider": "openrouter", - "model": "qwen/qwen3-235b-a22b-2507", - }, - } - self.log("Step 3: Switching to qwen model...") - if not self.update_llm_config(config_b): - self.log("Failed to update config", "ERROR") - return False - self.log("Config switched to qwen", "SUCCESS") - - # Step 4: Send message with qwen - self.log("Step 4: Sending message with qwen...") - if not self.send_message( - group_id, "Now testing qwen model.", f"{group_id}_msg_2" - ): - self.log("Failed to send message", "ERROR") - return False - self.log("Message sent with qwen", "SUCCESS") - time.sleep(1) - - # Step 5: Switch back to gpt-4.1-mini - self.log("Step 5: Switching back to gpt-4.1-mini...") - if not self.update_llm_config(config_a): - self.log("Failed to update config", "ERROR") - return False - self.log("Config switched back to gpt-4.1-mini", "SUCCESS") - - # Step 6: Send another message - self.log("Step 6: Sending message with gpt-4.1-mini again...") - if not self.send_message( - group_id, "Back to gpt-4.1-mini.", f"{group_id}_msg_3" - ): - self.log("Failed to send message", "ERROR") - return False - self.log("Message sent", "SUCCESS") - - self.log("-" * 70) - self.log("Model switching test PASSED!", "SUCCESS") - return True - - def test_switch_model(self) -> bool: - """Test 2: Switch model within same provider""" - self.log("=" * 70) - self.log("TEST: Switch Model Within Same Provider") - self.log("=" * 70) - - group_id = f"test_model_switch_{self.test_id}" - - # Step 1: Setup with model A (allowed model) - config_model_a = { - "boundary": {"provider": "openrouter", "model": "openai/gpt-4.1-mini"}, - "extraction": {"provider": "openrouter", "model": "openai/gpt-4.1-mini"}, - } - self.log("Step 1: Setting up with model: openai/gpt-4.1-mini...") - if not self.setup_global_config(config_model_a): - self.log("Failed to setup global config", "ERROR") - return False - self.log("Model A configured", "SUCCESS") - - # Create group config - if not self.create_group_config(group_id): - self.log("Failed to create group config", "ERROR") - return False - - # Step 2: Send message with model A - self.log("Step 2: Sending message with gpt-4.1-mini...") - if not self.send_message( - group_id, "Testing with gpt-4.1-mini model.", f"{group_id}_msg_1" - ): - self.log("Failed to send message", "ERROR") - return False - self.log("Message sent with gpt-4.1-mini", "SUCCESS") - self.log(" -> Check logs: model=openai/gpt-4.1-mini") - time.sleep(1) - - # Step 3: Switch to model B (allowed qwen model) - config_model_b = { - "boundary": { - "provider": "openrouter", - "model": "qwen/qwen3-235b-a22b-2507", - }, - "extraction": { - "provider": "openrouter", - "model": "qwen/qwen3-235b-a22b-2507", - }, - } - self.log("Step 3: Switching to model: qwen/qwen3-235b-a22b-2507...") - if not self.update_llm_config(config_model_b): - self.log("Failed to update config", "ERROR") - return False - self.log("Model B configured", "SUCCESS") - - # Step 4: Send message with model B - self.log("Step 4: Sending message with qwen...") - if not self.send_message( - group_id, "Now using qwen model.", f"{group_id}_msg_2" - ): - self.log("Failed to send message", "ERROR") - return False - self.log("Message sent with qwen", "SUCCESS") - self.log(" -> Check logs: model=qwen/qwen3-235b-a22b-2507") - - self.log("-" * 70) - self.log("Model switching test PASSED!", "SUCCESS") - return True - - def test_openrouter_model_restriction(self) -> bool: - """Test: OpenRouter only allows specific models (qwen, gpt-4.1-mini) - - Note: Validation happens at LLM call time (bottom layer), not at config save time. - So config save always succeeds, but message sending fails with disallowed models. - """ - self.log("=" * 70) - self.log("TEST: OpenRouter Model Restriction (Bottom Layer Validation)") - self.log("=" * 70) - - group_id = f"test_restriction_{self.test_id}" - - # Step 1: Setup with allowed model and send message - should succeed - allowed_config = { - "boundary": {"provider": "openrouter", "model": "openai/gpt-4.1-mini"}, - "extraction": {"provider": "openrouter", "model": "openai/gpt-4.1-mini"}, - } - self.log("Step 1: Setup with allowed model (gpt-4.1-mini)...") - if not self.setup_global_config(allowed_config): - self.log("Failed to setup global config", "ERROR") - return False - if not self.create_group_config(group_id): - self.log("Failed to create group config", "ERROR") - return False - - self.log("Step 2: Sending message with allowed model...") - if not self.send_message( - group_id, "Test with allowed model", f"{group_id}_msg_1" - ): - self.log("Failed - allowed model should work", "ERROR") - return False - self.log("Allowed model works correctly", "SUCCESS") - - self.log("-" * 70) - self.log("OpenRouter model restriction test PASSED!", "SUCCESS") - return True - - def test_whitelist_e2e(self) -> bool: - """Test: Whitelist enforcement in real server (requires PROVIDER_WHITE_LIST in .env) - - Verifies: - 1. Allowed model → message sent successfully (202/200) - 2. Disallowed model → config saves OK, but message processing fails at LLM layer - """ - self.log("=" * 70) - self.log("TEST: Whitelist E2E (requires server with WHITE_LIST in .env)") - self.log("=" * 70) - - group_id_ok = f"test_wl_ok_{self.test_id}" - group_id_bad = f"test_wl_bad_{self.test_id}" - - # --- Part A: Allowed model should work --- - self.log("Part A: Allowed model (openrouter / openai/gpt-4.1-mini)") - allowed_config = { - "boundary": {"provider": "openrouter", "model": "openai/gpt-4.1-mini"}, - "extraction": {"provider": "openrouter", "model": "openai/gpt-4.1-mini"}, - } - if not self.setup_global_config(allowed_config): - self.log("Failed to setup global config", "ERROR") - return False - if not self.create_group_config(group_id_ok): - self.log("Failed to create group config", "ERROR") - return False - - self.log(" Sending message with allowed model...") - if not self.send_message( - group_id_ok, "Whitelist allowed model test", f"{group_id_ok}_msg_1" - ): - self.log(" FAILED: allowed model should succeed", "ERROR") - return False - self.log(" Allowed model accepted by server", "SUCCESS") - time.sleep(2) - - # --- Part B: Disallowed model - config saves OK but LLM call should fail --- - self.log("Part B: Disallowed model (openrouter / x-ai/grok-4-fast)") - disallowed_config = { - "boundary": {"provider": "openrouter", "model": "x-ai/grok-4-fast"}, - "extraction": {"provider": "openrouter", "model": "x-ai/grok-4-fast"}, - } - self.log(" Saving config with disallowed model...") - if not self.update_llm_config(disallowed_config): - # Config save itself might succeed (validation is at LLM call time) - self.log(" Config save failed (unexpected but acceptable)", "WARN") - - if not self.create_group_config(group_id_bad): - self.log(" Failed to create group config", "ERROR") - return False - - self.log(" Sending message with disallowed model...") - sent = self.send_message( - group_id_bad, "Whitelist disallowed model test", f"{group_id_bad}_msg_1" - ) - if sent: - self.log( - " Message accepted (202) - check server logs for ValueError", "WARN" - ) - self.log(" -> Expected: server logs should contain:", "INFO") - self.log( - " 'Provider 'openrouter' only supports: ... Got: x-ai/grok-4-fast'", - "INFO", - ) - else: - self.log( - " Message rejected by server (whitelist enforced at API level)", - "SUCCESS", - ) - time.sleep(2) - - # --- Part C: Restore allowed config & verify recovery --- - self.log("Part C: Restore allowed model and verify recovery") - if not self.update_llm_config(allowed_config): - self.log(" Failed to restore config", "ERROR") - return False - if not self.send_message( - group_id_ok, "Recovery after disallowed model", f"{group_id_ok}_msg_2" - ): - self.log(" FAILED: recovery message should succeed", "ERROR") - return False - self.log(" Recovery successful", "SUCCESS") - - self.log("-" * 70) - self.log("Whitelist E2E test PASSED!", "SUCCESS") - self.log( - " IMPORTANT: Also check server logs to confirm ValueError was raised for Part B", - "WARN", - ) - return True - - def test_mixed_config(self) -> bool: - """Test 3: Mixed model config (boundary=gpt-4.1-mini, extraction=qwen)""" - self.log("=" * 70) - self.log("TEST: Mixed Model Config (Different models per stage)") - self.log("=" * 70) - - group_id = f"test_mixed_{self.test_id}" - - # Setup mixed config (both openrouter with different allowed models) - config_mixed = { - "boundary": {"provider": "openrouter", "model": "openai/gpt-4.1-mini"}, - "extraction": { - "provider": "openrouter", - "model": "qwen/qwen3-235b-a22b-2507", - }, - } - self.log("Setting up mixed config:") - self.log(" boundary: openrouter / openai/gpt-4.1-mini") - self.log(" extraction: openrouter / qwen/qwen3-235b-a22b-2507") - - if not self.setup_global_config(config_mixed): - self.log("Failed to setup global config", "ERROR") - return False - self.log("Mixed config set", "SUCCESS") - - # Create group config - if not self.create_group_config(group_id): - self.log("Failed to create group config", "ERROR") - return False - - # Send message - self.log("Sending message with mixed config...") - if not self.send_message( - group_id, "Testing mixed provider configuration.", f"{group_id}_msg_1" - ): - self.log("Failed to send message", "ERROR") - return False - self.log("Message sent", "SUCCESS") - self.log(" -> Check logs:") - self.log(" boundary: model=openai/gpt-4.1-mini") - self.log(" extraction: model=qwen/qwen3-235b-a22b-2507") - - self.log("-" * 70) - self.log("Mixed config test PASSED!", "SUCCESS") - return True - - def run_all_tests(self) -> bool: - """Run all tests""" - self.log("=" * 70) - self.log("LLM DYNAMIC SWITCHING E2E TEST SUITE") - self.log(f"Base URL: {self.base_url}") - self.log(f"Test ID: {self.test_id}") - self.log("=" * 70) - - results = {} - - # Test 1: OpenRouter model restriction - results["openrouter_restriction"] = self.test_openrouter_model_restriction() - time.sleep(2) - - # Test 2: Switch provider - results["switch_provider"] = self.test_switch_provider() - time.sleep(2) - - # Test 3: Switch model - results["switch_model"] = self.test_switch_model() - time.sleep(2) - - # Test 4: Mixed config - results["mixed_config"] = self.test_mixed_config() - time.sleep(2) - - # Test 5: Whitelist E2E - results["whitelist_e2e"] = self.test_whitelist_e2e() - - # Summary - self.log("\n" + "=" * 70) - self.log("TEST RESULTS SUMMARY") - self.log("=" * 70) - - all_passed = True - for test_name, passed in results.items(): - status = "PASSED" if passed else "FAILED" - level = "SUCCESS" if passed else "ERROR" - self.log(f" {test_name}: {status}", level) - if not passed: - all_passed = False - - self.log("=" * 70) - if all_passed: - self.log("ALL TESTS PASSED!", "SUCCESS") - else: - self.log("SOME TESTS FAILED!", "ERROR") - - return all_passed - - -class WhitelistUnitTest: - """Unit tests for provider model whitelist (no server required). - - These tests validate that _validate_model_whitelist reads - {PROVIDER}_WHITE_LIST from env and enforces restrictions correctly. - """ - - def __init__(self): - self.passed = 0 - self.failed = 0 - - def log(self, message: str, level: str = "INFO"): - timestamp = datetime.now().strftime("%H:%M:%S") - symbols = {"INFO": "ℹ️", "SUCCESS": "✅", "ERROR": "❌", "WARN": "⚠️"} - print(f"[{timestamp}] {symbols.get(level, '•')} {message}") - - def assert_raises(self, exc_type, fn, *args, **kwargs) -> bool: - try: - fn(*args, **kwargs) - return False - except exc_type: - return True - except Exception: - return False - - def run_all(self) -> bool: - import os - from memory_layer.llm.openai_provider import OpenAIProvider - - self.log("=" * 70) - self.log("WHITELIST UNIT TESTS (no server required)") - self.log("=" * 70) - - saved_env = {} - env_keys = [ - "OPENROUTER_WHITE_LIST", - "OPENAI_WHITE_LIST", - "TESTPROVIDER_WHITE_LIST", - ] - for k in env_keys: - saved_env[k] = os.environ.pop(k, None) - - try: - # --- Test 1: No whitelist env → no restriction --- - self.log("Test 1: No whitelist set → all models allowed") - for k in env_keys: - os.environ.pop(k, None) - try: - OpenAIProvider._validate_model_whitelist( - "testprovider", "any-model-name" - ) - self.log(" PASSED: no restriction when env not set", "SUCCESS") - self.passed += 1 - except Exception as e: - self.log(f" FAILED: unexpected error: {e}", "ERROR") - self.failed += 1 - - # --- Test 2: Empty whitelist → no restriction --- - self.log("Test 2: Empty whitelist → all models allowed") - os.environ["TESTPROVIDER_WHITE_LIST"] = "" - try: - OpenAIProvider._validate_model_whitelist( - "testprovider", "any-model-name" - ) - self.log(" PASSED: no restriction when env is empty", "SUCCESS") - self.passed += 1 - except Exception as e: - self.log(f" FAILED: unexpected error: {e}", "ERROR") - self.failed += 1 - - # --- Test 3: Whitelist set, model in list → allowed --- - self.log("Test 3: Model in whitelist → allowed") - os.environ["OPENROUTER_WHITE_LIST"] = ( - "qwen/qwen3-235b-a22b-2507,openai/gpt-4.1-mini" - ) - try: - OpenAIProvider._validate_model_whitelist( - "openrouter", "openai/gpt-4.1-mini" - ) - OpenAIProvider._validate_model_whitelist( - "openrouter", "qwen/qwen3-235b-a22b-2507" - ) - self.log(" PASSED: allowed models pass validation", "SUCCESS") - self.passed += 1 - except Exception as e: - self.log(f" FAILED: unexpected error: {e}", "ERROR") - self.failed += 1 - - # --- Test 4: Whitelist set, model NOT in list → rejected --- - self.log("Test 4: Model not in whitelist → ValueError raised") - os.environ["OPENROUTER_WHITE_LIST"] = ( - "qwen/qwen3-235b-a22b-2507,openai/gpt-4.1-mini" - ) - if self.assert_raises( - ValueError, - OpenAIProvider._validate_model_whitelist, - "openrouter", - "openai/gpt-4o", - ): - self.log(" PASSED: disallowed model rejected", "SUCCESS") - self.passed += 1 - else: - self.log(" FAILED: should have raised ValueError", "ERROR") - self.failed += 1 - - # --- Test 5: OpenAI whitelist --- - self.log("Test 5: OpenAI whitelist enforcement") - os.environ["OPENAI_WHITE_LIST"] = "gpt-5-mini,gpt-4.1-mini" - try: - OpenAIProvider._validate_model_whitelist("openai", "gpt-5-mini") - OpenAIProvider._validate_model_whitelist("openai", "gpt-4.1-mini") - self.log(" PASSED: allowed OpenAI models pass", "SUCCESS") - self.passed += 1 - except Exception as e: - self.log(f" FAILED: unexpected error: {e}", "ERROR") - self.failed += 1 - - self.log("Test 6: OpenAI whitelist rejects unlisted model") - if self.assert_raises( - ValueError, OpenAIProvider._validate_model_whitelist, "openai", "gpt-4o" - ): - self.log(" PASSED: disallowed OpenAI model rejected", "SUCCESS") - self.passed += 1 - else: - self.log(" FAILED: should have raised ValueError", "ERROR") - self.failed += 1 - - # --- Test 7: Whitespace handling --- - self.log("Test 7: Whitespace in whitelist is trimmed") - os.environ["OPENAI_WHITE_LIST"] = " gpt-5-mini , gpt-4.1-mini " - try: - OpenAIProvider._validate_model_whitelist("openai", "gpt-5-mini") - self.log(" PASSED: whitespace trimmed correctly", "SUCCESS") - self.passed += 1 - except Exception as e: - self.log(f" FAILED: unexpected error: {e}", "ERROR") - self.failed += 1 - - # --- Test 8: Provider without whitelist has no restriction --- - self.log("Test 8: Provider without whitelist → unrestricted") - os.environ.pop("TESTPROVIDER_WHITE_LIST", None) - os.environ["OPENAI_WHITE_LIST"] = "gpt-5-mini" - try: - # testprovider has no WHITE_LIST set, should not be restricted - OpenAIProvider._validate_model_whitelist( - "testprovider", "anything-goes" - ) - self.log(" PASSED: unrelated provider is unrestricted", "SUCCESS") - self.passed += 1 - except Exception as e: - self.log(f" FAILED: unexpected error: {e}", "ERROR") - self.failed += 1 - - finally: - # Restore original env - for k in env_keys: - os.environ.pop(k, None) - if saved_env[k] is not None: - os.environ[k] = saved_env[k] - - # Summary - self.log("=" * 70) - self.log(f"WHITELIST UNIT TESTS: {self.passed} passed, {self.failed} failed") - if self.failed == 0: - self.log("ALL WHITELIST TESTS PASSED!", "SUCCESS") - else: - self.log("SOME WHITELIST TESTS FAILED!", "ERROR") - self.log("=" * 70) - return self.failed == 0 - - -def main(): - parser = argparse.ArgumentParser(description="LLM Switching E2E Test") - parser.add_argument( - "--base-url", default="http://localhost:1995", help="Base URL of the API server" - ) - parser.add_argument( - "--test", - choices=[ - "openrouter_restriction", - "switch_provider", - "switch_model", - "mixed_config", - "whitelist", - "whitelist_e2e", - "all", - ], - default="all", - help="Which test to run", - ) - parser.add_argument( - "--timeout", type=int, default=120, help="Request timeout in seconds" - ) - - args = parser.parse_args() - - # Whitelist unit test can run standalone (no server needed) - if args.test == "whitelist": - tester = WhitelistUnitTest() - success = tester.run_all() - exit(0 if success else 1) - - tester = LLMSwitchingE2ETest(base_url=args.base_url, timeout=args.timeout) - - if args.test == "all": - success = tester.run_all_tests() - elif args.test == "openrouter_restriction": - success = tester.test_openrouter_model_restriction() - elif args.test == "switch_provider": - success = tester.test_switch_provider() - elif args.test == "switch_model": - success = tester.test_switch_model() - elif args.test == "mixed_config": - success = tester.test_mixed_config() - elif args.test == "whitelist_e2e": - success = tester.test_whitelist_e2e() - else: - success = False - - exit(0 if success else 1) - - -if __name__ == "__main__": - main() diff --git a/methods/EverCore/tests/test_memcell_raw_repository.py b/methods/EverCore/tests/test_memcell_raw_repository.py deleted file mode 100644 index 411cb027..00000000 --- a/methods/EverCore/tests/test_memcell_raw_repository.py +++ /dev/null @@ -1,1213 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Test the functionality of MemCellRawRepository - -Test contents include: -1. CRUD operations based on event_id -2. Queries based on user_id -3. Queries based on time range (including segmented queries) -4. Queries based on group_id -5. Queries based on participants -6. Queries based on keywords -7. Batch deletion operations -8. Statistical and aggregation queries -""" - -import asyncio -from common_utils.datetime_utils import get_now_with_timezone -from datetime import timedelta, datetime -from bson import ObjectId -from pydantic import BaseModel, Field - -from core.di import get_bean_by_type -from infra_layer.adapters.out.persistence.repository.memcell_raw_repository import ( - MemCellRawRepository, -) -from infra_layer.adapters.out.persistence.document.memory.memcell import ( - MemCell, - DataTypeEnum, -) -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -# ==================== Projection Model Definition ==================== -class MemCellProjection(BaseModel): - """ - MemCell projection model - used to test field projection functionality - Includes only partial fields, excluding large fields such as original_data - """ - - id: ObjectId = Field(alias="_id") - user_id: str - timestamp: datetime - summary: str - type: DataTypeEnum - - class Config: - populate_by_name = True - arbitrary_types_allowed = True - - -async def test_basic_crud_operations(): - """Test basic CRUD operations based on event_id""" - logger.info("Starting test of basic CRUD operations based on event_id...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_001" - - try: - # First clean up any existing test data (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # Test creating a new MemCell - now = get_now_with_timezone() - memcell = MemCell( - user_id=user_id, - timestamp=now, - summary="This is a test memory: discussed the project's technical solution", - type=DataTypeEnum.CONVERSATION, - keywords=["technical solution", "project discussion"], - participants=["Zhang San", "Li Si"], - ) - - created = await repo.append_memcell(memcell) - assert created is not None - assert created.user_id == user_id - assert created.summary == "This is a test memory: discussed the project's technical solution" - assert created.event_id is not None - logger.info("✅ Test creating new MemCell succeeded, event_id=%s", created.event_id) - - event_id = str(created.event_id) - - # Test querying by event_id - queried = await repo.get_by_event_id(event_id) - assert queried is not None - assert queried.user_id == user_id - assert str(queried.event_id) == event_id - logger.info("✅ Test querying by event_id succeeded") - - # Test updating MemCell - update_data = { - "summary": "Updated summary: project technical solution has been confirmed", - "keywords": ["technical solution", "project discussion", "confirmed"], - } - - updated = await repo.update_by_event_id(event_id, update_data) - assert updated is not None - assert updated.summary == "Updated summary: project technical solution has been confirmed" - assert len(updated.keywords) == 3 - logger.info("✅ Test updating MemCell succeeded") - - # Test deleting MemCell - deleted = await repo.delete_by_event_id(event_id) - assert deleted is True - logger.info("✅ Test deleting MemCell succeeded") - - # Verify deletion - final_check = await repo.get_by_event_id(event_id) - assert final_check is None, "Record should have been deleted" - logger.info("✅ Verified deletion succeeded") - - except Exception as e: - logger.error("❌ Basic CRUD operations test failed: %s", e) - raise - - logger.info("✅ Basic CRUD operations test completed") - - -async def test_find_by_user_id(): - """Test queries based on user_id""" - logger.info("Starting test of queries based on user_id...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_002" - - try: - # First clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # Create multiple records - now = get_now_with_timezone() - for i in range(5): - memcell = MemCell( - user_id=user_id, - timestamp=now - timedelta(hours=i), - summary=f"Test memory {i+1}", - type=DataTypeEnum.CONVERSATION, - ) - await repo.append_memcell(memcell) - - logger.info("✅ Created 5 test records") - - # Test querying all records (descending) - results = await repo.find_by_user_id(user_id, sort_desc=True) - assert len(results) == 5 - assert results[0].summary == "Test memory 1" # Latest - logger.info("✅ Test querying all records (descending) succeeded") - - # Test querying all records (ascending) - results_asc = await repo.find_by_user_id(user_id, sort_desc=False) - assert len(results_asc) == 5 - assert results_asc[0].summary == "Test memory 5" # Earliest - logger.info("✅ Test querying all records (ascending) succeeded") - - # Test limiting number - limited_results = await repo.find_by_user_id(user_id, limit=2) - assert len(limited_results) == 2 - logger.info("✅ Test limiting number succeeded") - - # Test skip and limit - skip_results = await repo.find_by_user_id(user_id, skip=2, limit=2) - assert len(skip_results) == 2 - logger.info("✅ Test skip and limit succeeded") - - # Clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up test data successfully") - - except Exception as e: - logger.error("❌ Test based on user_id query failed: %s", e) - raise - - logger.info("✅ Queries based on user_id test completed") - - -async def test_find_by_time_range(): - """Test queries based on time range (including segmented queries)""" - logger.info("Starting test of queries based on time range...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_003" - - try: - # First clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # Create test data with a large span (10 days) - # Use time from 1990 to avoid conflicts with existing data - # Note: Must use timezone-aware time, otherwise it will not match the timezone stored in MongoDB - from common_utils.datetime_utils import get_timezone - - tz = get_timezone() - start_time = datetime(1990, 1, 1, 0, 0, 0, tzinfo=tz) - - # Create one record per day - created_timestamps = [] - for i in range(10): - ts = start_time + timedelta(days=i) - created_timestamps.append(ts) - memcell = MemCell( - user_id=user_id, - timestamp=ts, - summary=f"Day {i+1} memory", - type=DataTypeEnum.CONVERSATION, - ) - await repo.append_memcell(memcell) - - logger.info("✅ Created 10 days of test data") - logger.info( - " Timestamp range: %s to %s", created_timestamps[0], created_timestamps[-1] - ) - - # Test small range query (3 days, does not trigger segmentation) - # Query day 0, 1, 2 (total 3 records) - small_start = start_time # 1990-01-01 00:00:00 - small_end = start_time + timedelta(days=3) # 1990-01-04 00:00:00 (exclusive) - small_results = await repo.find_by_time_range(small_start, small_end) - logger.info(" Small range query returned %d records (expected 3)", len(small_results)) - assert ( - len(small_results) == 3 - ), f"Expected 3 records, got {len(small_results)}" - logger.info("✅ Test small range query (3 days) succeeded, found %d records", len(small_results)) - - # Test large range query (10 days, triggers segmented query) - # Query day 0-9 (total 10 records) - # The last record is 1990-01-10 00:00:00, query uses $lt, so end time must be > 1990-01-10 - large_start = start_time # 1990-01-01 00:00:00 - large_end = start_time + timedelta( - days=10, seconds=1 - ) # 1990-01-11 00:00:01 (ensure day 9 is included) - logger.info(" Query time range: %s to %s", large_start, large_end) - large_results = await repo.find_by_time_range(large_start, large_end) - logger.info(" Large range query returned %d records (expected 10)", len(large_results)) - - # Print returned record timestamps for debugging - logger.info(" Returned record details:") - for idx, mc in enumerate(large_results): - logger.info(" [%d] %s - %s", idx, mc.timestamp, mc.summary) - - if len(large_results) != 10: - logger.warning(" ⚠️ Record count mismatch!") - logger.warning(" Expected timestamps:") - for idx, ts in enumerate(created_timestamps): - logger.warning(" [%d] %s", idx, ts) - - # Find missing records - returned_timestamps = {mc.timestamp for mc in large_results} - missing = [ts for ts in created_timestamps if ts not in returned_timestamps] - if missing: - logger.error(" ❌ Missing timestamps:") - for ts in missing: - logger.error(" - %s", ts) - - assert ( - len(large_results) == 10 - ), f"Expected 10 records, got {len(large_results)}" - logger.info("✅ Test large range query (10 days) succeeded, found %d records", len(large_results)) - - # Test descending query - desc_results = await repo.find_by_time_range( - large_start, large_end, sort_desc=True - ) - assert len(desc_results) == 10 - assert "Day 10" in desc_results[0].summary # Latest first - logger.info("✅ Test descending query succeeded") - - # Test ascending query - asc_results = await repo.find_by_time_range( - large_start, large_end, sort_desc=False - ) - assert len(asc_results) == 10 - assert "Day 1" in asc_results[0].summary # Earliest first - logger.info("✅ Test ascending query succeeded") - - # Test pagination - page_results = await repo.find_by_time_range(large_start, large_end, limit=5) - assert len(page_results) == 5 - logger.info("✅ Test pagination succeeded") - - # Clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up test data successfully") - - except Exception as e: - logger.error("❌ Test time range query failed: %s", e) - raise - - logger.info("✅ Time range query test completed") - - -async def test_find_by_user_and_time_range(): - """Test queries based on user and time range""" - logger.info("Starting test of queries based on user and time range...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_004" - - try: - # First clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # Create test data - now = get_now_with_timezone() - start_time = now - timedelta(days=5) - - for i in range(5): - memcell = MemCell( - user_id=user_id, - timestamp=start_time + timedelta(days=i), - summary=f"User memory {i+1}", - type=DataTypeEnum.CONVERSATION, - ) - await repo.append_memcell(memcell) - - logger.info("✅ Created 5 test records") - - # Test querying data for middle 3 days - query_start = start_time + timedelta(days=1) - query_end = start_time + timedelta(days=4) - results = await repo.find_by_user_and_time_range( - user_id, query_start, query_end - ) - - assert len(results) == 3 - logger.info("✅ Test user and time range query succeeded, found %d records", len(results)) - - # Clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up test data successfully") - - except Exception as e: - logger.error("❌ Test user and time range query failed: %s", e) - raise - - logger.info("✅ User and time range query test completed") - - -async def test_find_by_group_id(): - """Test queries based on group_id""" - logger.info("Starting test of queries based on group_id...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_005" - group_id = "test_group_001" - - try: - # First clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # Create group records - now = get_now_with_timezone() - for i in range(3): - memcell = MemCell( - user_id=user_id, - group_id=group_id, - timestamp=now - timedelta(hours=i), - summary=f"Group memory {i+1}", - type=DataTypeEnum.CONVERSATION, - ) - await repo.append_memcell(memcell) - - logger.info("✅ Created 3 group records") - - # Test query - results = await repo.find_by_group_id(group_id) - assert len(results) == 3 - logger.info("✅ Test querying by group_id succeeded, found %d records", len(results)) - - # Clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up test data successfully") - - except Exception as e: - logger.error("❌ Test group_id query failed: %s", e) - raise - - logger.info("✅ group_id query test completed") - - -async def test_find_by_participants(): - """Test queries based on participants""" - logger.info("Starting test of queries based on participants...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_006" - - try: - # First clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # Create test data - now = get_now_with_timezone() - - # Record 1: Zhang San, Li Si - memcell1 = MemCell( - user_id=user_id, - timestamp=now - timedelta(hours=1), - summary="Record 1: Conversation between Zhang San and Li Si", - participants=["Zhang San", "Li Si"], - ) - await repo.append_memcell(memcell1) - - # Record 2: Zhang San, Wang Wu - memcell2 = MemCell( - user_id=user_id, - timestamp=now - timedelta(hours=2), - summary="Record 2: Conversation between Zhang San and Wang Wu", - participants=["Zhang San", "Wang Wu"], - ) - await repo.append_memcell(memcell2) - - # Record 3: Li Si, Wang Wu - memcell3 = MemCell( - user_id=user_id, - timestamp=now - timedelta(hours=3), - summary="Record 3: Conversation between Li Si and Wang Wu", - participants=["Li Si", "Wang Wu"], - ) - await repo.append_memcell(memcell3) - - logger.info("✅ Created 3 test records") - - # Test matching any participant (containing "Zhang San") - results_any = await repo.find_by_participants(["Zhang San"], match_all=False) - assert len(results_any) == 2 - logger.info("✅ Test matching any participant succeeded, found %d records", len(results_any)) - - # Test matching all participants (containing both "Zhang San" and "Li Si") - results_all = await repo.find_by_participants(["Zhang San", "Li Si"], match_all=True) - assert len(results_all) == 1 - logger.info("✅ Test matching all participants succeeded, found %d records", len(results_all)) - - # Clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up test data successfully") - - except Exception as e: - logger.error("❌ Test participant query failed: %s", e) - raise - - logger.info("✅ Participant query test completed") - - -async def test_search_by_keywords(): - """Test queries based on keywords""" - logger.info("Starting test of queries based on keywords...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_007" - - try: - # First clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # Create test data - now = get_now_with_timezone() - - # Record 1: technology, Python - memcell1 = MemCell( - user_id=user_id, - timestamp=now - timedelta(hours=1), - summary="Record 1: Python technology discussion", - keywords=["technology", "Python"], - ) - await repo.append_memcell(memcell1) - - # Record 2: technology, Java - memcell2 = MemCell( - user_id=user_id, - timestamp=now - timedelta(hours=2), - summary="Record 2: Java technology discussion", - keywords=["technology", "Java"], - ) - await repo.append_memcell(memcell2) - - # Record 3: design, architecture - memcell3 = MemCell( - user_id=user_id, - timestamp=now - timedelta(hours=3), - summary="Record 3: Architecture design discussion", - keywords=["design", "architecture"], - ) - await repo.append_memcell(memcell3) - - logger.info("✅ Created 3 test records") - - # Test matching any keyword (containing "technology") - results_any = await repo.search_by_keywords(["technology"], match_all=False) - assert len(results_any) == 2 - logger.info("✅ Test matching any keyword succeeded, found %d records", len(results_any)) - - # Test matching all keywords (containing both "technology" and "Python") - results_all = await repo.search_by_keywords(["technology", "Python"], match_all=True) - assert len(results_all) == 1 - logger.info("✅ Test matching all keywords succeeded, found %d records", len(results_all)) - - # Clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up test data successfully") - - except Exception as e: - logger.error("❌ Test keyword query failed: %s", e) - raise - - logger.info("✅ Keyword query test completed") - - -async def test_batch_delete_operations(): - """Test batch deletion operations (now soft delete by default)""" - logger.info("Starting test of batch deletion operations...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_008" - - try: - # First clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # Create test data - now = get_now_with_timezone() - for i in range(10): - memcell = MemCell( - user_id=user_id, - timestamp=now - timedelta(days=i), - summary=f"Test memory {i+1}", - type=DataTypeEnum.CONVERSATION, - ) - await repo.append_memcell(memcell) - - logger.info("✅ Created 10 test records") - - # Test soft deleting records within a time range (first 5 days) - delete_start = now - timedelta(days=5) - delete_end = now - deleted_count = await repo.delete_by_time_range( - delete_start, delete_end, user_id=user_id, deleted_by="test" - ) - assert deleted_count == 5 - logger.info("✅ Test soft deleting records within time range succeeded, deleted %d records", deleted_count) - - # Verify remaining records (常规查询只返回未删除的) - remaining = await repo.find_by_user_id(user_id) - assert len(remaining) == 5 - logger.info("✅ Verified remaining records successfully, %d records left", len(remaining)) - - # Test soft deleting all remaining user records - total_deleted = await repo.delete_by_user_id(user_id, deleted_by="test") - assert total_deleted == 5 - logger.info("✅ Test soft deleting all user records succeeded, deleted %d records", total_deleted) - - # Verify all soft deleted (常规查询找不到) - final_check = await repo.find_by_user_id(user_id) - assert len(final_check) == 0 - logger.info("✅ Verified all soft deleted successfully (not visible in regular queries)") - - # Verify using hard_find_many can still find them - hard_check = await MemCell.hard_find_many({"user_id": user_id}).to_list() - assert len(hard_check) == 10 - logger.info("✅ Verified all 10 records still exist (soft deleted)") - - # Final cleanup with hard delete - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Hard deleted all records for cleanup") - - except Exception as e: - logger.error("❌ Test batch deletion operations failed: %s", e) - raise - - logger.info("✅ Batch deletion operations test completed") - - -async def test_statistics_and_aggregation(): - """Test statistical and aggregation queries""" - logger.info("Starting test of statistical and aggregation queries...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_009" - - try: - # First clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # Create test data of different types - now = get_now_with_timezone() - start_time = now - timedelta(days=7) - - # Create 6 conversation memories (Note: Originally 3 conversations, 2 emails, 1 document, but now only CONVERSATION type) - for i in range(3): - memcell = MemCell( - user_id=user_id, - timestamp=start_time + timedelta(days=i), - summary=f"Conversation memory {i+1}", - type=DataTypeEnum.CONVERSATION, - ) - await repo.append_memcell(memcell) - - for i in range(2): - memcell = MemCell( - user_id=user_id, - timestamp=start_time + timedelta(days=i + 3), - summary=f"Email memory {i+1}", - type=DataTypeEnum.CONVERSATION, - ) - await repo.append_memcell(memcell) - - memcell = MemCell( - user_id=user_id, - timestamp=start_time + timedelta(days=5), - summary="Document memory", - type=DataTypeEnum.CONVERSATION, - ) - await repo.append_memcell(memcell) - - logger.info("✅ Created 6 test records (all CONVERSATION type)") - - # Test counting total user records - total_count = await repo.count_by_user_id(user_id) - assert total_count == 6 - logger.info("✅ Test counting total user records succeeded, total %d records", total_count) - - # Test counting records within a time range - range_start = start_time - range_end = start_time + timedelta(days=4) - range_count = await repo.count_by_time_range( - range_start, range_end, user_id=user_id - ) - assert range_count == 4 # Records from first 4 days (3 conversation memories + 1 email memory) - logger.info("✅ Test counting records within time range succeeded, total %d records", range_count) - - # Test getting user's latest records - latest = await repo.get_latest_by_user(user_id, limit=3) - assert len(latest) == 3 - assert latest[0].summary == "Document memory" # Latest - logger.info("✅ Test getting user's latest records succeeded") - - # Clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up test data successfully") - - except Exception as e: - logger.error("❌ Test statistical and aggregation queries failed: %s", e) - raise - - logger.info("✅ Statistical and aggregation queries test completed") - - -async def test_get_by_event_ids(): - """Test batch query by event_ids""" - logger.info("Starting test of batch query by event_ids...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_010" - - try: - # First clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # Create test data - now = get_now_with_timezone() - created_memcells = [] - - for i in range(5): - memcell = MemCell( - user_id=user_id, - timestamp=now - timedelta(hours=i), - summary=f"Test memory {i+1}", - episode=f"This is the detailed content of test memory {i+1}", - type=DataTypeEnum.CONVERSATION, - keywords=[f"keyword{i+1}", "test"], - ) - created = await repo.append_memcell(memcell) - created_memcells.append(created) - - logger.info("✅ Created 5 test records") - - # Prepare event_ids - event_ids = [str(mc.event_id) for mc in created_memcells[:3]] - logger.info(" Preparing to query event_ids: %s", event_ids) - - # Test 1: Batch query (without projection) - results = await repo.get_by_event_ids(event_ids) - assert isinstance(results, dict), "Return result should be a dictionary" - assert len(results) == 3, f"Should return 3 records, got {len(results)}" - - # Verify returned is a dictionary, key is event_id - for event_id in event_ids: - assert event_id in results, f"event_id {event_id} should be in results" - memcell = results[event_id] - assert memcell.user_id == user_id - assert memcell.episode is not None - - logger.info("✅ Test batch query (without projection) succeeded, returned %d records", len(results)) - - # Test 2: Batch query (with field projection) - # Use Pydantic projection model to return only specified fields, excluding large fields like original_data - results_with_projection = await repo.get_by_event_ids( - event_ids, projection_model=MemCellProjection - ) - - assert isinstance(results_with_projection, dict), "Return result should be a dictionary" - assert ( - len(results_with_projection) == 3 - ), f"Should return 3 records, got {len(results_with_projection)}" - - # Verify projection effect: returned should be MemCellProjection instances - for event_id, memcell_projection in results_with_projection.items(): - assert isinstance( - memcell_projection, MemCellProjection - ), "Returned should be MemCellProjection instance" - assert memcell_projection.summary is not None, "summary field should exist" - assert memcell_projection.timestamp is not None, "timestamp field should exist" - assert memcell_projection.type is not None, "type field should exist" - assert memcell_projection.user_id == user_id, "user_id should match" - # Verify fields not defined in projection model are not included - assert not hasattr( - memcell_projection, 'original_data' - ), "original_data field should not exist" - assert not hasattr(memcell_projection, 'episode'), "episode field should not exist" - - logger.info( - "✅ Test batch query (with field projection) succeeded, returned %d records", - len(results_with_projection), - ) - - # Test 3: Query partially valid event_ids (including an invalid one) - mixed_event_ids = event_ids[:2] + ["invalid_id_123", "507f1f77bcf86cd799439011"] - results_mixed = await repo.get_by_event_ids(mixed_event_ids) - - # Should only return 2 valid records - assert ( - len(results_mixed) == 2 - ), f"Should return 2 records, got {len(results_mixed)}" - assert event_ids[0] in results_mixed - assert event_ids[1] in results_mixed - assert "invalid_id_123" not in results_mixed - assert "507f1f77bcf86cd799439011" not in results_mixed - - logger.info( - "✅ Test querying partially valid event_ids succeeded, returned %d records", len(results_mixed) - ) - - # Test 4: Empty list input - results_empty = await repo.get_by_event_ids([]) - assert isinstance(results_empty, dict), "Return result should be a dictionary" - assert len(results_empty) == 0, "Empty list should return empty dictionary" - logger.info("✅ Test empty list input succeeded") - - # Test 5: Query non-existent event_ids - non_existent_ids = ["507f1f77bcf86cd799439011", "507f1f77bcf86cd799439012"] - results_non_existent = await repo.get_by_event_ids(non_existent_ids) - assert isinstance(results_non_existent, dict), "Return result should be a dictionary" - assert len(results_non_existent) == 0, "Non-existent event_ids should return empty dictionary" - logger.info("✅ Test querying non-existent event_ids succeeded") - - # Test 6: Verify returned data integrity - first_event_id = event_ids[0] - first_memcell = results[first_event_id] - original_memcell = created_memcells[0] - - assert str(first_memcell.event_id) == str(original_memcell.event_id) - assert first_memcell.summary == original_memcell.summary - assert first_memcell.user_id == original_memcell.user_id - logger.info("✅ Verified returned data integrity succeeded") - - # Clean up (使用硬删除清理测试数据) - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up test data successfully") - - except Exception as e: - logger.error("❌ Test batch query by event_ids failed: %s", e) - import traceback - - logger.error("Detailed error: %s", traceback.format_exc()) - raise - - logger.info("✅ Batch query by event_ids test completed") - - -async def test_soft_delete_single(): - """测试单个软删除功能""" - logger.info("Starting test of soft delete single record...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_soft_delete_001" - - try: - # 清理旧数据 - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # 创建测试数据 - now = get_now_with_timezone() - memcell = MemCell( - user_id=user_id, - timestamp=now, - summary="测试软删除的记录", - type=DataTypeEnum.CONVERSATION, - ) - created = await repo.append_memcell(memcell) - event_id = str(created.event_id) - logger.info("✅ Created test record, event_id=%s", event_id) - - # 验证可以查到 - found = await repo.get_by_event_id(event_id) - assert found is not None, "Should be able to find the record" - assert not found.is_deleted(), "Record should not be marked as deleted" - logger.info("✅ Verified: Can query the record before deletion") - - # 执行软删除 - deleted = await repo.delete_by_event_id(event_id, deleted_by="test_admin") - assert deleted is True, "Soft delete should succeed" - logger.info("✅ Soft delete succeeded") - - # 验证常规查询找不到 - not_found = await repo.get_by_event_id(event_id) - assert not_found is None, "Regular query should not find soft-deleted record" - logger.info("✅ Verified: Regular query cannot find soft-deleted record") - - # 使用 hard_find_one 可以找到 - hard_found = await MemCell.hard_find_one({"_id": created.id}) - assert hard_found is not None, "hard_find_one should find deleted record" - assert hard_found.is_deleted(), "Record should be marked as deleted" - assert hard_found.deleted_by == "test_admin", "Should record who deleted it" - assert hard_found.deleted_at is not None, "Should have deletion timestamp" - assert hard_found.deleted_id != 0, "deleted_id should be set" - logger.info("✅ Verified: hard_find_one can find deleted record") - logger.info(" - deleted_by: %s", hard_found.deleted_by) - logger.info(" - deleted_at: %s", hard_found.deleted_at) - logger.info(" - deleted_id: %s", hard_found.deleted_id) - - # 恢复记录 - restored = await repo.restore_by_event_id(event_id) - assert restored is True, "Restore should succeed" - logger.info("✅ Restore succeeded") - - # 验证恢复后可以查到 - restored_memcell = await repo.get_by_event_id(event_id) - assert restored_memcell is not None, "Should find record after restore" - assert not restored_memcell.is_deleted(), "Should not be marked as deleted after restore" - assert restored_memcell.deleted_at is None, "deleted_at should be cleared" - assert restored_memcell.deleted_by is None, "deleted_by should be cleared" - assert restored_memcell.deleted_id == 0, "deleted_id should be reset to 0" - logger.info("✅ Verified: Record is normal after restore") - - # 清理 - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Test completed, cleaned up test data") - - except Exception as e: - logger.error("❌ Soft delete single test failed: %s", e) - import traceback - logger.error(traceback.format_exc()) - raise - - logger.info("✅ Soft delete single test completed") - - -async def test_soft_delete_batch(): - """测试批量软删除功能""" - logger.info("Starting test of soft delete batch...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_soft_delete_002" - - try: - # 清理旧数据 - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # 创建5条测试数据 - now = get_now_with_timezone() - for i in range(5): - memcell = MemCell( - user_id=user_id, - timestamp=now - timedelta(hours=i), - summary=f"测试记录 {i+1}", - type=DataTypeEnum.CONVERSATION, - ) - await repo.append_memcell(memcell) - logger.info("✅ Created 5 test records") - - # 验证可以查到5条 - results = await repo.find_by_user_id(user_id) - assert len(results) == 5, f"Should have 5 records, got {len(results)}" - logger.info("✅ Verified: Can query 5 records") - - # 批量软删除 - deleted_count = await repo.delete_by_user_id(user_id, deleted_by="batch_admin") - assert deleted_count == 5, f"Should soft delete 5 records, got {deleted_count}" - logger.info("✅ Batch soft delete succeeded, deleted %d records", deleted_count) - - # 验证常规查询找不到 - results_after_delete = await repo.find_by_user_id(user_id) - assert len(results_after_delete) == 0, "Regular query should not find soft-deleted records" - logger.info("✅ Verified: Regular query cannot find soft-deleted records") - - # 使用 hard_find_many 可以找到 - hard_results = await MemCell.hard_find_many({"user_id": user_id}).to_list() - assert len(hard_results) == 5, f"hard_find_many should find 5 records, got {len(hard_results)}" - logger.info("✅ Verified: hard_find_many can find 5 deleted records") - - # 验证所有记录都被标记为已删除 - for mc in hard_results: - assert mc.is_deleted(), "All records should be marked as deleted" - assert mc.deleted_at is not None, "Should have deletion timestamp" - logger.info("✅ Verified: All records are correctly marked as deleted") - - # 批量恢复 - restored_count = await repo.restore_by_user_id(user_id) - assert restored_count == 5, f"Should restore 5 records, got {restored_count}" - logger.info("✅ Batch restore succeeded, restored %d records", restored_count) - - # 验证恢复后可以查到 - results_after_restore = await repo.find_by_user_id(user_id) - assert len(results_after_restore) == 5, f"Should have 5 records after restore, got {len(results_after_restore)}" - logger.info("✅ Verified: Can query 5 records after restore") - - # 清理 - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Test completed, cleaned up test data") - - except Exception as e: - logger.error("❌ Soft delete batch test failed: %s", e) - import traceback - logger.error(traceback.format_exc()) - raise - - logger.info("✅ Soft delete batch test completed") - - -async def test_hard_delete(): - """测试硬删除功能""" - logger.info("Starting test of hard delete...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_hard_delete_001" - - try: - # 清理旧数据 - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # 创建测试数据 - now = get_now_with_timezone() - memcell = MemCell( - user_id=user_id, - timestamp=now, - summary="测试硬删除的记录", - type=DataTypeEnum.CONVERSATION, - ) - created = await repo.append_memcell(memcell) - event_id = str(created.event_id) - logger.info("✅ Created test record, event_id=%s", event_id) - - # 执行硬删除 - deleted = await repo.hard_delete_by_event_id(event_id) - assert deleted is True, "Hard delete should succeed" - logger.info("✅ Hard delete succeeded") - - # 验证 hard_find_one 也找不到（记录被物理删除） - hard_found = await MemCell.hard_find_one({"_id": created.id}) - assert hard_found is None, "hard_find_one should not find hard-deleted record" - logger.info("✅ Verified: Record is completely removed after hard delete") - - logger.info("✅ Test completed") - - except Exception as e: - logger.error("❌ Hard delete test failed: %s", e) - import traceback - logger.error(traceback.format_exc()) - raise - - logger.info("✅ Hard delete test completed") - - -async def test_query_with_soft_delete_filtering(): - """测试查询自动过滤软删除记录""" - logger.info("Starting test of query filtering with soft delete...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_query_filter_001" - - try: - # 清理旧数据 - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # 创建10条记录 - now = get_now_with_timezone() - event_ids = [] - for i in range(10): - memcell = MemCell( - user_id=user_id, - timestamp=now - timedelta(hours=i), - summary=f"测试记录 {i+1}", - type=DataTypeEnum.CONVERSATION, - ) - created = await repo.append_memcell(memcell) - event_ids.append(str(created.event_id)) - logger.info("✅ Created 10 test records") - - # 软删除前5条 - for i in range(5): - await repo.delete_by_event_id(event_ids[i], deleted_by="filter_test") - logger.info("✅ Soft deleted first 5 records") - - # 测试 find_by_user_id（应该只返回5条未删除的） - results = await repo.find_by_user_id(user_id) - assert len(results) == 5, f"find_by_user_id should return 5, got {len(results)}" - logger.info("✅ Verified: find_by_user_id returns only 5 non-deleted records") - - # 测试 count_by_user_id（应该只计数未删除的） - count = await repo.count_by_user_id(user_id) - assert count == 5, f"count_by_user_id should return 5, got {count}" - logger.info("✅ Verified: count_by_user_id counts only non-deleted records") - - # 使用 hard_find_many 应该能找到所有10条 - all_results = await MemCell.hard_find_many({"user_id": user_id}).to_list() - assert len(all_results) == 10, f"hard_find_many should return 10, got {len(all_results)}" - logger.info("✅ Verified: hard_find_many returns all 10 records including deleted") - - # 统计已删除和未删除的数量 - deleted_count = sum(1 for mc in all_results if mc.is_deleted()) - active_count = sum(1 for mc in all_results if not mc.is_deleted()) - assert deleted_count == 5, f"Should have 5 deleted, got {deleted_count}" - assert active_count == 5, f"Should have 5 active, got {active_count}" - logger.info("✅ Verified: %d deleted, %d active", deleted_count, active_count) - - # 清理 - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Test completed, cleaned up test data") - - except Exception as e: - logger.error("❌ Query filtering test failed: %s", e) - import traceback - logger.error(traceback.format_exc()) - raise - - logger.info("✅ Query filtering test completed") - - -async def test_prevent_duplicate_soft_delete(): - """测试防止重复软删除（保护审计记录）""" - logger.info("Starting test of preventing duplicate soft delete...") - - repo = get_bean_by_type(MemCellRawRepository) - user_id = "test_user_duplicate_delete_001" - - try: - # 清理旧数据 - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Cleaned up existing test data") - - # 创建5条测试记录 - now = get_now_with_timezone() - event_ids = [] - for i in range(5): - memcell = MemCell( - user_id=user_id, - timestamp=now - timedelta(hours=i), - summary=f"测试记录 {i+1}", - type=DataTypeEnum.CONVERSATION, - ) - created = await repo.append_memcell(memcell) - event_ids.append(str(created.event_id)) - logger.info("✅ Created 5 test records") - - # 第一次软删除前3条记录 - first_delete_time = get_now_with_timezone() - for i in range(3): - await repo.delete_by_event_id(event_ids[i], deleted_by="admin_1") - logger.info("✅ First soft delete of 3 records by admin_1") - - # 获取已删除记录的审计信息 - deleted_records = [] - for i in range(3): - mc = await MemCell.hard_find_one({"_id": ObjectId(event_ids[i])}) - assert mc is not None - assert mc.is_deleted() - deleted_records.append({ - "event_id": event_ids[i], - "deleted_at": mc.deleted_at, - "deleted_by": mc.deleted_by, - "deleted_id": mc.deleted_id, - }) - logger.info("✅ Captured audit info from first delete") - - # 等待一小段时间，确保时间戳会不同 - from asyncio import sleep - await sleep(0.01) - - # 尝试再次软删除同样的记录（应该被忽略） - result = await repo.delete_by_user_id(user_id, deleted_by="admin_2") - # 注意：delete_by_user_id 会尝试删除所有记录，但只有未删除的会被修改 - # 前3条已删除，后2条未删除，所以应该只修改2条 - assert result == 2, f"Should only soft delete 2 un-deleted records, got {result}" - logger.info("✅ Second delete only affected 2 un-deleted records") - - # 验证前3条记录的审计信息没有被修改 - for i, original in enumerate(deleted_records): - mc = await MemCell.hard_find_one({"_id": ObjectId(original["event_id"])}) - assert mc is not None - assert mc.is_deleted() - assert mc.deleted_at == original["deleted_at"], \ - f"deleted_at should not change for record {i}" - assert mc.deleted_by == original["deleted_by"], \ - f"deleted_by should not change for record {i}, expected {original['deleted_by']}, got {mc.deleted_by}" - assert mc.deleted_id == original["deleted_id"], \ - f"deleted_id should not change for record {i}" - logger.info("✅ Verified: First 3 records' audit info was NOT modified") - - # 验证后2条记录被新的删除操作标记 - for i in range(3, 5): - mc = await MemCell.hard_find_one({"_id": ObjectId(event_ids[i])}) - assert mc is not None - assert mc.is_deleted() - # 这两条应该被新删除操作标记 - assert mc.deleted_at > first_delete_time, "Should have newer deletion time" - # 注意：由于是批量删除，deleted_by 可能是 admin_2 - logger.info("✅ Verified: Last 2 records were soft deleted by second operation") - - # 测试单个文档的重复删除保护 - test_record = await MemCell.hard_find_one({"_id": ObjectId(event_ids[0])}) - original_deleted_at = test_record.deleted_at - original_deleted_by = test_record.deleted_by - original_deleted_id = test_record.deleted_id - - # 再次尝试删除（应该被忽略） - await test_record.delete(deleted_by="admin_3") - - # 重新获取记录 - test_record_after = await MemCell.hard_find_one({"_id": ObjectId(event_ids[0])}) - assert test_record_after.deleted_at == original_deleted_at, \ - "deleted_at should not change on duplicate delete" - assert test_record_after.deleted_by == original_deleted_by, \ - "deleted_by should not change on duplicate delete" - assert test_record_after.deleted_id == original_deleted_id, \ - "deleted_id should not change on duplicate delete" - logger.info("✅ Verified: Instance method delete() also prevents duplicate deletion") - - # 清理 - await repo.hard_delete_by_user_id(user_id) - logger.info("✅ Test completed, cleaned up test data") - - except Exception as e: - logger.error("❌ Prevent duplicate soft delete test failed: %s", e) - import traceback - logger.error(traceback.format_exc()) - raise - - logger.info("✅ Prevent duplicate soft delete test completed") - - -async def run_all_tests(): - """Run all tests""" - logger.info("🚀 Starting to run all MemCellRawRepository tests...") - - try: - await test_basic_crud_operations() - await test_find_by_user_id() - await test_find_by_time_range() - await test_find_by_user_and_time_range() - await test_find_by_group_id() - await test_find_by_participants() - await test_search_by_keywords() - await test_batch_delete_operations() - await test_statistics_and_aggregation() - await test_get_by_event_ids() - - # 软删除功能测试 - logger.info("") - logger.info("=" * 60) - logger.info("Starting Soft Delete Feature Tests...") - logger.info("=" * 60) - await test_soft_delete_single() - await test_soft_delete_batch() - await test_hard_delete() - await test_query_with_soft_delete_filtering() - await test_prevent_duplicate_soft_delete() - - logger.info("") - logger.info("=" * 60) - logger.info("✅✅✅ All tests completed!") - logger.info("=" * 60) - logger.info("") - logger.info("软删除功能验证总结：") - logger.info("1. ✅ 单个软删除正常工作") - logger.info("2. ✅ 批量软删除正常工作") - logger.info("3. ✅ 恢复功能正常工作") - logger.info("4. ✅ 查询自动过滤已删除记录") - logger.info("5. ✅ hard_find 可以查询已删除记录") - logger.info("6. ✅ 硬删除（物理删除）正常工作") - logger.info("7. ✅ deleted_by、deleted_at、deleted_id 字段正确设置") - logger.info("8. ✅ 防止重复软删除，保护审计记录") - except Exception as e: - logger.error("❌ Error occurred during testing: %s", e) - raise - - -if __name__ == "__main__": - asyncio.run(run_all_tests()) \ No newline at end of file diff --git a/methods/EverCore/tests/test_memory_controller.py b/methods/EverCore/tests/test_memory_controller.py deleted file mode 100644 index f9d7bbe1..00000000 --- a/methods/EverCore/tests/test_memory_controller.py +++ /dev/null @@ -1,2704 +0,0 @@ -""" -Memory Controller API Test Script -Verify input and output structures of all endpoints under /api/v0/memories - -Usage: - # Run all tests - python tests/test_memory_controller.py - - # Specify API address - python tests/test_memory_controller.py --base-url http://localhost:1995 - - # Specify test user - python tests/test_memory_controller.py --base-url http://dev-server:1995 --user-id test_user_123 - - # Test by category (batch execution) - python tests/test_memory_controller.py --test-method fetch # Run all fetch tests - python tests/test_memory_controller.py --test-method retrieve # Run all retrieve/search tests - python tests/test_memory_controller.py --test-method search # Same as retrieve - python tests/test_memory_controller.py --test-method memorize # Run memorization tests - python tests/test_memory_controller.py --test-method meta # Run metadata tests - - # Test a specific method - python tests/test_memory_controller.py --test-method fetch_episodic - python tests/test_memory_controller.py --test-method fetch_atomic_fact - python tests/test_memory_controller.py --test-method fetch_group_filter - python tests/test_memory_controller.py --test-method fetch_time_range - python tests/test_memory_controller.py --test-method fetch_combined_filters - python tests/test_memory_controller.py --test-method fetch_all_types - python tests/test_memory_controller.py --test-method search_keyword - - # Test all methods except certain ones (parameters separated by commas) - python tests/test_memory_controller.py --except-test-method memorize - python tests/test_memory_controller.py --except-test-method memorize,fetch_episodic - python tests/test_memory_controller.py --except-test-method save_meta,patch_meta - - # Disable sync mode (use background mode) - python tests/test_memory_controller.py --sync-mode false -""" - -import argparse -import json -from zoneinfo import ZoneInfo -import uuid -from datetime import datetime, timedelta -import requests - - -class MemoryControllerTester: - """Memory Controller API Test Class""" - - # Default tenant information - DEFAULT_ORGANIZATION_ID = "test_memory_api_organization" - DEFAULT_SPACE_ID = "test_memory_api_space" - DEFAULT_HASH_KEY = "test_memory_api_hash_key" - - def __init__( - self, - base_url: str, - user_id: str, - group_id: str, - organization_id: str = None, - space_id: str = None, - hash_key: str = None, - timeout: int = 180, - sync_mode: bool = True, - ): - """ - Initialize tester - - Args: - base_url: API base URL - user_id: Test user ID - group_id: Test group ID - organization_id: Organization ID (default: test_memory_api_organization) - space_id: Space ID (default: test_memory_api_space) - hash_key: Hash key (default: test_memory_api_hash_key) - timeout: Request timeout in seconds, default 180 seconds (3 minutes) - sync_mode: Whether to enable sync mode (default: True, server default is also True so param is only sent when False) - """ - self.base_url = base_url - self.api_prefix = "/api/v0/memories" - self.user_id = user_id - self.group_id = group_id - self.organization_id = organization_id or self.DEFAULT_ORGANIZATION_ID - self.space_id = space_id or self.DEFAULT_SPACE_ID - self.hash_key = hash_key or self.DEFAULT_HASH_KEY - self.timeout = timeout - self.sync_mode = sync_mode - - def get_tenant_headers(self) -> dict: - """ - Get tenant-related request headers - - Returns: - dict: Dictionary containing X-Organization-Id, X-Space-Id, and optional X-Hash-Key - """ - headers = { - "X-Organization-Id": self.organization_id, - "X-Space-Id": self.space_id, - } - if self.hash_key: - headers["X-Hash-Key"] = self.hash_key - return headers - - def init_database(self) -> bool: - """ - Initialize tenant database - - Call /internal/tenant/init-db endpoint to initialize database. - - Returns: - bool: Whether initialization was successful - """ - url = f"{self.base_url}/internal/tenant/init-db" - headers = self.get_tenant_headers() - - print("\n" + "=" * 80) - print(" Initialize Tenant Database") - print("=" * 80) - print(f"📍 URL: POST {url}") - print( - f"📤 Tenant Info: organization_id={self.organization_id}, space_id={self.space_id}" - ) - print( - f"📤 Request Headers: {json.dumps(headers, indent=2, ensure_ascii=False)}" - ) - - try: - response = requests.post(url, headers=headers, timeout=self.timeout) - print(f"\n📥 Response Status Code: {response.status_code}") - response_json = response.json() - print("📥 Response Data:") - print(json.dumps(response_json, indent=2, ensure_ascii=False)) - - if response.status_code == 200 and response_json.get("success"): - print( - f"\n✅ Database initialization successful: tenant_id={response_json.get('tenant_id')}" - ) - return True - else: - print( - f"\n⚠️ Database initialization returned: {response_json.get('message', 'Unknown')}" - ) - # Continue even if failed, possibly database already exists - return True - except Exception as e: # noqa: BLE001 - print(f"\n❌ Database initialization failed: {e}") - return False - - def print_section(self, title: str): - """Print section separator""" - print("\n" + "=" * 80) - print(f" {title}") - print("=" * 80) - - def _get_sync_mode_params(self) -> dict: - """ - Get query parameters for sync mode - - Returns: - dict: Dictionary containing sync_mode parameter only when sync_mode is False - (sync_mode=true is the server default, no need to send explicitly) - """ - if not self.sync_mode: - return {"sync_mode": "false"} - return {} - - def call_post_api(self, endpoint: str, data: dict): - """ - Call POST API and print results - - Args: - endpoint: API endpoint - data: Request data - - Returns: - (status_code, response_json) - """ - # If it's the memorize endpoint and sender is not provided, generate one randomly - if endpoint == "" and "sender" not in data: - data["sender"] = f"user_{uuid.uuid4().hex[:12]}" - print(f"⚠️ Sender not provided, auto-generated: {data['sender']}") - - url = f"{self.base_url}{self.api_prefix}{endpoint}" - headers = self.get_tenant_headers() - params = self._get_sync_mode_params() - - print(f"\n📍 URL: POST {url}") - if params: - print(f"📤 Query Parameters: {params}") - print("📤 Request Data:") - print(json.dumps(data, indent=2, ensure_ascii=False)) - - try: - response = requests.post( - url, json=data, headers=headers, params=params, timeout=self.timeout - ) - print(f"\n📥 Response Status Code: {response.status_code}") - print("📥 Response Data:") - response_json = response.json() - print(json.dumps(response_json, indent=2, ensure_ascii=False)) - return response.status_code, response_json - except ( - Exception - ) as e: # noqa: BLE001 Need to catch all exceptions to ensure script continues - print(f"\n❌ Request failed: {e}") - return None, None - - def call_get_api(self, endpoint: str, params: dict = None): - """ - Call GET API and print results - - Args: - endpoint: API endpoint - params: Query parameters - - Returns: - (status_code, response_json) - """ - url = f"{self.base_url}{self.api_prefix}{endpoint}" - headers = self.get_tenant_headers() - - # Merge sync mode parameters - merged_params = self._get_sync_mode_params() - if params: - merged_params.update(params) - - print(f"\n📍 URL: GET {url}") - if merged_params: - print("📤 Query Parameters:") - print(json.dumps(merged_params, indent=2, ensure_ascii=False)) - - try: - response = requests.get( - url, params=merged_params, headers=headers, timeout=self.timeout - ) - print(f"\n📥 Response Status Code: {response.status_code}") - print("📥 Response Data:") - response_json = response.json() - print(json.dumps(response_json, indent=2, ensure_ascii=False)) - return response.status_code, response_json - except ( - Exception - ) as e: # noqa: BLE001 Need to catch all exceptions to ensure script continues - print(f"\n❌ Request failed: {e}") - return None, None - - def call_get_with_body_api(self, endpoint: str, data: dict): - """ - Call GET API (with body) and print results - - Although uncommon, some search interfaces (e.g., Elasticsearch) use GET + body to pass complex parameters - - Args: - endpoint: API endpoint - data: Request data (placed in body) - - Returns: - (status_code, response_json) - """ - url = f"{self.base_url}{self.api_prefix}{endpoint}" - headers = self.get_tenant_headers() - params = self._get_sync_mode_params() - - print(f"\n📍 URL: GET {url} (with body)") - if params: - print(f"📤 Query Parameters: {params}") - print("📤 Request Data:") - print(json.dumps(data, indent=2, ensure_ascii=False)) - - try: - # GET request with body (requests library supports this, though not common) - response = requests.request( - "GET", - url, - json=data, - headers=headers, - params=params, - timeout=self.timeout, - ) - print(f"\n📥 Response Status Code: {response.status_code}") - print("📥 Response Data:") - response_json = response.json() - print(json.dumps(response_json, indent=2, ensure_ascii=False)) - return response.status_code, response_json - except ( - Exception - ) as e: # noqa: BLE001 Need to catch all exceptions to ensure script continues - print(f"\n❌ Request failed: {e}") - return None, None - - def call_patch_api(self, endpoint: str, data: dict): - """ - Call PATCH API and print results - - Args: - endpoint: API endpoint - data: Request data - - Returns: - (status_code, response_json) - """ - url = f"{self.base_url}{self.api_prefix}{endpoint}" - headers = self.get_tenant_headers() - params = self._get_sync_mode_params() - - print(f"\n📍 URL: PATCH {url}") - if params: - print(f"📤 Query Parameters: {params}") - print("📤 Request Data:") - print(json.dumps(data, indent=2, ensure_ascii=False)) - - try: - response = requests.patch( - url, json=data, headers=headers, params=params, timeout=self.timeout - ) - print(f"\n📥 Response Status Code: {response.status_code}") - print("📥 Response Data:") - response_json = response.json() - print(json.dumps(response_json, indent=2, ensure_ascii=False)) - return response.status_code, response_json - except ( - Exception - ) as e: # noqa: BLE001 Need to catch all exceptions to ensure script continues - print(f"\n❌ Request failed: {e}") - return None, None - - def test_memorize_single_message(self): - """Test 1: POST /api/v0/memories - Store conversation memory (send multiple messages to trigger boundary detection)""" - self.print_section("Test 1: POST /api/v0/memories - Store Conversation Memory") - - # Prepare a simple conversation to simulate user and assistant interaction - # Sending multiple messages can trigger boundary detection and extract memories - base_time = datetime.now(ZoneInfo("UTC")) - - # Generate unique message ID prefix for this test run to avoid duplicate detection - msg_prefix = uuid.uuid4().hex[:8] - - # Build conversation sequence, triggering boundary detection through: - # 1. First scenario: Discussion about coffee preferences (4 messages) - # 2. Second scenario: Start new topic (trigger boundary via time gap + topic switch) - messages = [ - # Scenario 1: Discuss coffee preferences (complete conversation episode) - { - "group_id": self.group_id, - "message_id": f"msg_{msg_prefix}_001", - "create_time": base_time.isoformat(), - "sender": self.user_id, - "sender_name": "Test User", - "content": "I recently want to develop a habit of drinking coffee, do you have any suggestions?", - "refer_list": [], - }, - { - "group_id": self.group_id, - "message_id": f"msg_{msg_prefix}_002", - "create_time": (base_time + timedelta(seconds=30)).isoformat(), - "sender": "assistant_001", - "sender_name": "AI Assistant", - "content": "Of course! Coffee comes in many varieties, from strong espresso to mild Americano. You can choose based on your taste. I suggest starting with Americano.", - "role": "assistant", - "refer_list": [], - }, - { - "group_id": self.group_id, - "message_id": f"msg_{msg_prefix}_003", - "create_time": (base_time + timedelta(minutes=1)).isoformat(), - "sender": self.user_id, - "sender_name": "Test User", - "content": "I like drinking Americano, no sugar, no milk, the stronger the better.", - "refer_list": [], - }, - { - "group_id": self.group_id, - "message_id": f"msg_{msg_prefix}_004", - "create_time": ( - base_time + timedelta(minutes=1, seconds=30) - ).isoformat(), - "sender": "assistant_001", - "sender_name": "AI Assistant", - "content": "I understand your preference! Black Americano can fully experience the flavor of coffee beans. I suggest choosing dark roasted beans for a stronger taste.", - "role": "assistant", - "refer_list": [], - }, - # Scenario 2: Start new topic (trigger boundary via longer time gap + topic switch) - # According to boundary detection rules: time gap over 4 hours and content unrelated will trigger boundary - { - "group_id": self.group_id, - "message_id": f"msg_{msg_prefix}_005", - "create_time": (base_time + timedelta(hours=24)).isoformat(), - "sender": self.user_id, - "sender_name": "Test User", - "content": "By the way, how is the weekend project progressing?", - "role": "user", - "refer_list": [], - }, - { - "group_id": self.group_id, - "message_id": f"msg_{msg_prefix}_006", - "create_time": ( - base_time + timedelta(hours=24, seconds=30) - ).isoformat(), - "sender": "assistant_001", - "sender_name": "AI Assistant", - "content": "The project is progressing smoothly, main features are 80% complete, expected to submit for testing next week.", - "refer_list": [], - }, - ] - - # Send messages one by one - print("\n📨 Starting to send conversation sequence...") - print( - "💡 Strategy Explanation: First 4 messages form complete scenario 1 (coffee preference discussion)" - ) - print( - "💡 5th message triggers boundary detection via 5-hour time gap + new topic" - ) - print("💡 This ensures memory from scenario 1 is successfully extracted") - - last_response = None - for i, msg in enumerate(messages, 1): - if i == 5: - print( - f"\n🔄 --- Scenario Switch: Sending message {i}/{len(messages)} (triggering boundary) ---" - ) - else: - print(f"\n--- Sending message {i}/{len(messages)} ---") - - status_code, response = self.call_post_api("", msg) - - # Verify each message is successfully processed - assert ( - status_code == 200 - ), f"Message {i} status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Message {i} status should be ok" - - last_response = response - - # Use the response from the last message for validation - status_code = 200 - response = last_response - - # Assert: Validate result structure - print("\n📊 Validating conversation memory extraction results...") - assert "result" in response, "Successful response should contain result field" - result = response["result"] - assert "saved_memories" in result, "result should contain saved_memories field" - assert "count" in result, "result should contain count field" - assert "status_info" in result, "result should contain status_info field" - - # Validate saved_memories is a list - assert isinstance( - result["saved_memories"], list - ), "saved_memories should be a list" - assert result["count"] >= 0, "count should be >= 0" - assert result["status_info"] in [ - "accumulated", - "extracted", - ], "status_info should be accumulated or extracted" - - # If there are extracted memories, validate each memory's structure - if result["count"] > 0: - print(f"\n✅ Successfully extracted {result['count']} memories!") - print( - f"✅ Boundary detection successful: triggered by time gap (5 hours) + topic switch" - ) - for idx, memory in enumerate(result["saved_memories"], 1): - assert isinstance(memory, dict), f"Memory {idx} should be a dictionary" - # Note: Different memory types may have different field structures - # Here only basic field existence is validated - memory_type = memory.get('memory_type', 'unknown') - summary = memory.get('summary', memory.get('content', 'no summary'))[ - :50 - ] - print(f" Memory {idx}: {memory_type} - {summary}...") - else: - print( - f"\n⚠️ Messages accumulated, waiting for boundary detection (status_info: {result['status_info']})" - ) - print( - f" Sent {len(messages)} messages, but boundary detection conditions may not be met" - ) - print( - f" 💡 Tip: Boundary detection requires one of the following conditions:" - ) - print( - f" 1. Cross-day (new message date differs from previous message)" - ) - print(f" 2. Long interruption (over 4 hours) + topic switch") - print(f" 3. Clear scene/topic switch signal") - - print(f"\n✅ Memorize Test Completed") - return status_code, response - - def test_fetch_episodic(self): - """Test 2: GET /api/v0/memories - Fetch user episodic memory (episodic_memory type, pass parameters via body) - - Tests multiple scenarios: - 1. Only user_id (group_id NOT provided in request) - 2. user_id + group_id=None (explicitly null) - 3. user_id + group_id="" (explicitly empty string) - 4. user_id + group_id both have valid values - 5. user_id="__all__" + valid group_id - """ - self.print_section("Test 2: GET /api/v0/memories - Fetch User Episodic Memory") - - # Scenario 1: Only user_id, group_id NOT provided (parameter doesn't exist) - print("\n--- Scenario 1: Only user_id (group_id NOT provided) ---") - data = { - "user_id": self.user_id, - "memory_type": "episodic_memory", - "limit": 10, - "offset": 0, - # group_id is NOT in the request at all - } - - status_code, response = self.call_get_with_body_api("", data) - - # Assert: Precisely validate response structure - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert ( - response.get("status") == "ok" - ), f"Status should be ok, actual: {response.get('status')}" - assert "result" in response, "Response should contain result field" - - result = response["result"] - assert "memories" in result, "result should contain memories field" - assert "total_count" in result, "result should contain total_count field" - assert "has_more" in result, "result should contain has_more field" - assert "metadata" in result, "result should contain metadata field" - - # Validate data types - assert isinstance(result["memories"], list), "memories should be a list" - assert result["total_count"] >= 0, "total_count should be >= 0" - assert isinstance(result["has_more"], bool), "has_more should be boolean" - - # Validate metadata structure - metadata = result["metadata"] - assert isinstance(metadata, dict), "metadata should be a dictionary" - assert "source" in metadata, "metadata should contain source field" - assert "user_id" in metadata, "metadata should contain user_id field" - assert "memory_type" in metadata, "metadata should contain memory_type field" - assert metadata.get("user_id") == self.user_id, "metadata user_id should match" - - # If there are memories, deeply validate structure - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - assert isinstance(memory, dict), f"Memory {idx} should be a dictionary" - assert "user_id" in memory, f"Memory {idx} should contain user_id" - assert "timestamp" in memory, f"Memory {idx} should contain timestamp" - assert ( - memory.get("user_id") == self.user_id - ), f"Memory {idx} user_id should match" - - print( - f"✅ Scenario 1 successful, returned {result['total_count']} episodic memories" - ) - else: - print( - f"✅ Scenario 1 successful, returned {result['total_count']} episodic memories" - ) - - # Scenario 2: user_id + group_id=None (explicitly null) - print("\n--- Scenario 2: user_id + group_id=None (explicitly null) ---") - data = { - "user_id": self.user_id, - "group_id": None, # Explicitly set to None - "memory_type": "episodic_memory", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate that returned memories have null or empty group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - group_id_value = memory.get("group_id") - assert group_id_value in ( - None, - "", - ), f"Memory {idx} group_id should be None or empty string, actual: {group_id_value}" - print( - f"✅ Scenario 2 successful, returned {result['total_count']} episodic memories with null/empty group_id" - ) - else: - print( - f"✅ Scenario 2 successful, returned {result['total_count']} episodic memories" - ) - - # Scenario 3: user_id + group_id="" (explicitly empty string) - print("\n--- Scenario 3: user_id + group_id='' (explicitly empty string) ---") - data = { - "user_id": self.user_id, - "group_id": "", # Explicitly set to empty string - "memory_type": "episodic_memory", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate that returned memories have null or empty group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - group_id_value = memory.get("group_id") - assert group_id_value in ( - None, - "", - ), f"Memory {idx} group_id should be None or empty string, actual: {group_id_value}" - print( - f"✅ Scenario 3 successful, returned {result['total_count']} episodic memories with null/empty group_id" - ) - else: - print( - f"✅ Scenario 3 successful, returned {result['total_count']} episodic memories" - ) - - # Scenario 4: user_id + group_id both have valid values - print("\n--- Scenario 4: user_id + group_id both have valid values ---") - data = { - "user_id": self.user_id, - "group_id": self.group_id, - "memory_type": "episodic_memory", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate metadata includes both user_id and group_id - metadata = result["metadata"] - assert metadata.get("user_id") == self.user_id, "metadata user_id should match" - assert ( - metadata.get("group_id") == self.group_id - ), "metadata group_id should match" - - # Validate that returned memories have the requested group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - assert ( - memory.get("group_id") == self.group_id - ), f"Memory {idx} group_id should be {self.group_id}, actual: {memory.get('group_id')}" - assert ( - memory.get("user_id") == self.user_id - ), f"Memory {idx} user_id should be {self.user_id}, actual: {memory.get('user_id')}" - print( - f"✅ Scenario 4 successful, returned {result['total_count']} episodic memories with matching filters" - ) - else: - print( - f"✅ Scenario 4 successful, returned {result['total_count']} episodic memories" - ) - - # Scenario 5: user_id="__all__" + valid group_id - print("\n--- Scenario 5: user_id='__all__' + valid group_id ---") - data = { - "user_id": "__all__", - "group_id": self.group_id, - "memory_type": "episodic_memory", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate metadata includes group_id - metadata = result["metadata"] - assert ( - metadata.get("group_id") == self.group_id - ), "metadata group_id should match" - - # Validate that returned memories have the requested group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - assert ( - memory.get("group_id") == self.group_id - ), f"Memory {idx} group_id should be {self.group_id}, actual: {memory.get('group_id')}" - print( - f"✅ Scenario 5 successful, returned {result['total_count']} episodic memories with group_id={self.group_id}" - ) - else: - print( - f"✅ Scenario 5 successful, returned {result['total_count']} episodic memories" - ) - - return status_code, response - - def test_fetch_foresight(self): - """Test 3: GET /api/v0/memories - Fetch foresight (foresight type, pass parameters via body) - - Tests multiple scenarios: - 1. Only user_id (group_id NOT provided in request) - 2. user_id + group_id=None (explicitly null) - 3. user_id + group_id="" (explicitly empty string) - 4. user_id + group_id both have valid values - 5. user_id="__all__" + valid group_id - """ - self.print_section("Test 3: GET /api/v0/memories - Fetch Foresight") - - # Scenario 1: Only user_id, group_id NOT provided (parameter doesn't exist) - print("\n--- Scenario 1: Only user_id (group_id NOT provided) ---") - data = { - "user_id": self.user_id, - "memory_type": "foresight", - "limit": 10, - "offset": 0, - # group_id is NOT in the request at all - } - - status_code, response = self.call_get_with_body_api("", data) - - # Assert: Precisely validate response structure - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert ( - response.get("status") == "ok" - ), f"Status should be ok, actual: {response.get('status')}" - assert "result" in response, "Response should contain result field" - - result = response["result"] - assert "memories" in result, "result should contain memories field" - assert "total_count" in result, "result should contain total_count field" - assert "has_more" in result, "result should contain has_more field" - assert "metadata" in result, "result should contain metadata field" - - # Validate data types - assert isinstance(result["memories"], list), "memories should be a list" - assert result["total_count"] >= 0, "total_count should be >= 0" - assert isinstance(result["has_more"], bool), "has_more should be boolean" - - # Validate metadata structure - metadata = result["metadata"] - assert isinstance(metadata, dict), "metadata should be a dictionary" - assert "source" in metadata, "metadata should contain source field" - assert "user_id" in metadata, "metadata should contain user_id field" - assert "memory_type" in metadata, "metadata should contain memory_type field" - assert metadata.get("user_id") == self.user_id, "metadata user_id should match" - - # If there are memories, deeply validate structure - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - assert isinstance(memory, dict), f"Memory {idx} should be a dictionary" - assert "content" in memory, f"Memory {idx} should contain content" - assert ( - "parent_type" in memory - ), f"Memory {idx} should contain parent_type" - assert "parent_id" in memory, f"Memory {idx} should contain parent_id" - # Foresight user_id may be None (group scenario), so not enforced - - print( - f"✅ Scenario 1 successful, returned {result['total_count']} foresights" - ) - else: - print( - f"✅ Scenario 1 successful, returned {result['total_count']} foresights" - ) - - # Scenario 2: user_id + group_id=None (explicitly null) - print("\n--- Scenario 2: user_id + group_id=None (explicitly null) ---") - data = { - "user_id": self.user_id, - "group_id": None, # Explicitly set to None - "memory_type": "foresight", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate that returned memories have null or empty group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - group_id_value = memory.get("group_id") - assert group_id_value in ( - None, - "", - ), f"Memory {idx} group_id should be None or empty string, actual: {group_id_value}" - print( - f"✅ Scenario 2 successful, returned {result['total_count']} foresights with null/empty group_id" - ) - else: - print( - f"✅ Scenario 2 successful, returned {result['total_count']} foresights" - ) - - # Scenario 3: user_id + group_id="" (explicitly empty string) - print("\n--- Scenario 3: user_id + group_id='' (explicitly empty string) ---") - data = { - "user_id": self.user_id, - "group_id": "", # Explicitly set to empty string - "memory_type": "foresight", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate that returned memories have null or empty group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - group_id_value = memory.get("group_id") - assert group_id_value in ( - None, - "", - ), f"Memory {idx} group_id should be None or empty string, actual: {group_id_value}" - print( - f"✅ Scenario 3 successful, returned {result['total_count']} foresights with null/empty group_id" - ) - else: - print( - f"✅ Scenario 3 successful, returned {result['total_count']} foresights" - ) - - # Scenario 4: user_id + group_id both have valid values - print("\n--- Scenario 4: user_id + group_id both have valid values ---") - data = { - "user_id": self.user_id, - "group_id": self.group_id, - "memory_type": "foresight", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate metadata includes both user_id and group_id - metadata = result["metadata"] - assert metadata.get("user_id") == self.user_id, "metadata user_id should match" - assert ( - metadata.get("group_id") == self.group_id - ), "metadata group_id should match" - - # Validate that returned memories have the requested group_id - # Note: foresight user_id may be None in some cases, so only validate group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - assert ( - memory.get("group_id") == self.group_id - ), f"Memory {idx} group_id should be {self.group_id}, actual: {memory.get('group_id')}" - print( - f"✅ Scenario 4 successful, returned {result['total_count']} foresights with group_id={self.group_id}" - ) - else: - print( - f"✅ Scenario 4 successful, returned {result['total_count']} foresights" - ) - - # Scenario 5: user_id="__all__" + valid group_id - print("\n--- Scenario 5: user_id='__all__' + valid group_id ---") - data = { - "user_id": "__all__", - "group_id": self.group_id, - "memory_type": "foresight", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate metadata includes group_id - metadata = result["metadata"] - assert ( - metadata.get("group_id") == self.group_id - ), "metadata group_id should match" - - # Validate that returned memories have the requested group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - assert ( - memory.get("group_id") == self.group_id - ), f"Memory {idx} group_id should be {self.group_id}, actual: {memory.get('group_id')}" - print( - f"✅ Scenario 5 successful, returned {result['total_count']} foresights with group_id={self.group_id}" - ) - else: - print( - f"✅ Scenario 5 successful, returned {result['total_count']} foresights" - ) - - return status_code, response - - def test_fetch_atomic_fact(self): - """Test 4: GET /api/v0/memories - Fetch user atomic fact (atomic_fact type, pass parameters via body) - - Tests multiple scenarios: - 1. Only user_id (group_id NOT provided in request) - 2. user_id + group_id=None (explicitly null) - 3. user_id + group_id="" (explicitly empty string) - 4. user_id + group_id both have valid values - 5. user_id="__all__" + valid group_id - """ - self.print_section("Test 4: GET /api/v0/memories - Fetch User Atomic Fact") - - # Scenario 1: Only user_id, group_id NOT provided (parameter doesn't exist) - print("\n--- Scenario 1: Only user_id (group_id NOT provided) ---") - data = { - "user_id": self.user_id, - "memory_type": "atomic_fact", - "limit": 10, - "offset": 0, - # group_id is NOT in the request at all - } - - status_code, response = self.call_get_with_body_api("", data) - - # Assert: Precisely validate response structure - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert ( - response.get("status") == "ok" - ), f"Status should be ok, actual: {response.get('status')}" - assert "result" in response, "Response should contain result field" - - result = response["result"] - assert "memories" in result, "result should contain memories field" - assert "total_count" in result, "result should contain total_count field" - assert "has_more" in result, "result should contain has_more field" - assert "metadata" in result, "result should contain metadata field" - - # Validate data types - assert isinstance(result["memories"], list), "memories should be a list" - assert result["total_count"] >= 0, "total_count should be >= 0" - assert isinstance(result["has_more"], bool), "has_more should be boolean" - - # Validate metadata structure - metadata = result["metadata"] - assert isinstance(metadata, dict), "metadata should be a dictionary" - assert "source" in metadata, "metadata should contain source field" - assert "user_id" in metadata, "metadata should contain user_id field" - assert "memory_type" in metadata, "metadata should contain memory_type field" - assert metadata.get("user_id") == self.user_id, "metadata user_id should match" - - # If there are atomic facts, deeply validate structure - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - assert isinstance(memory, dict), f"Memory {idx} should be a dictionary" - assert ( - "atomic_fact" in memory - ), f"Memory {idx} should contain atomic_fact" - assert "timestamp" in memory, f"Memory {idx} should contain timestamp" - assert "user_id" in memory, f"Memory {idx} should contain user_id" - assert ( - memory.get("user_id") == self.user_id - ), f"Memory {idx} user_id should match" - - print( - f"✅ Scenario 1 successful, returned {result['total_count']} atomic facts" - ) - else: - print( - f"✅ Scenario 1 successful, returned {result['total_count']} atomic facts" - ) - - # Scenario 2: user_id + group_id=None (explicitly null) - print("\n--- Scenario 2: user_id + group_id=None (explicitly null) ---") - data = { - "user_id": self.user_id, - "group_id": None, # Explicitly set to None - "memory_type": "atomic_fact", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate that returned memories have null or empty group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - group_id_value = memory.get("group_id") - assert group_id_value in ( - None, - "", - ), f"Memory {idx} group_id should be None or empty string, actual: {group_id_value}" - print( - f"✅ Scenario 2 successful, returned {result['total_count']} atomic facts with null/empty group_id" - ) - else: - print( - f"✅ Scenario 2 successful, returned {result['total_count']} atomic facts" - ) - - # Scenario 3: user_id + group_id="" (explicitly empty string) - print("\n--- Scenario 3: user_id + group_id='' (explicitly empty string) ---") - data = { - "user_id": self.user_id, - "group_id": "", # Explicitly set to empty string - "memory_type": "atomic_fact", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate that returned memories have null or empty group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - group_id_value = memory.get("group_id") - assert group_id_value in ( - None, - "", - ), f"Memory {idx} group_id should be None or empty string, actual: {group_id_value}" - print( - f"✅ Scenario 3 successful, returned {result['total_count']} atomic facts with null/empty group_id" - ) - else: - print( - f"✅ Scenario 3 successful, returned {result['total_count']} atomic facts" - ) - - # Scenario 4: user_id + group_id both have valid values - print("\n--- Scenario 4: user_id + group_id both have valid values ---") - data = { - "user_id": self.user_id, - "group_id": self.group_id, - "memory_type": "atomic_fact", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate metadata includes both user_id and group_id - metadata = result["metadata"] - assert metadata.get("user_id") == self.user_id, "metadata user_id should match" - assert ( - metadata.get("group_id") == self.group_id - ), "metadata group_id should match" - - # Validate that returned memories have the requested group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - assert ( - memory.get("group_id") == self.group_id - ), f"Memory {idx} group_id should be {self.group_id}, actual: {memory.get('group_id')}" - assert ( - memory.get("user_id") == self.user_id - ), f"Memory {idx} user_id should be {self.user_id}, actual: {memory.get('user_id')}" - print( - f"✅ Scenario 4 successful, returned {result['total_count']} atomic facts with matching filters" - ) - else: - print( - f"✅ Scenario 4 successful, returned {result['total_count']} atomic facts" - ) - - # Scenario 5: user_id="__all__" + valid group_id - print("\n--- Scenario 5: user_id='__all__' + valid group_id ---") - data = { - "user_id": "__all__", - "group_id": self.group_id, - "memory_type": "atomic_fact", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - result = response["result"] - - # Validate metadata includes group_id - metadata = result["metadata"] - assert ( - metadata.get("group_id") == self.group_id - ), "metadata group_id should match" - - # Validate that returned memories have the requested group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - assert ( - memory.get("group_id") == self.group_id - ), f"Memory {idx} group_id should be {self.group_id}, actual: {memory.get('group_id')}" - print( - f"✅ Scenario 5 successful, returned {result['total_count']} atomic facts with group_id={self.group_id}" - ) - else: - print( - f"✅ Scenario 5 successful, returned {result['total_count']} atomic facts" - ) - - return status_code, response - - def test_fetch_with_group_filter(self): - """Test: GET /api/v0/memories - Fetch memories with group_id filter""" - self.print_section("Test: GET /api/v0/memories - Fetch with group_id Filter") - - # Test different memory types with group_id filter - memory_types = ["episodic_memory", "atomic_fact", "foresight"] - - for memory_type in memory_types: - print(f"\n--- Testing memory_type: {memory_type} with group_id ---") - - data = { - "user_id": "__all__", # Query all users - "group_id": self.group_id, # Filter by group - "memory_type": memory_type, - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - # Assert: Validate response structure - assert ( - status_code == 200 - ), f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - assert "result" in response, "Response should contain result field" - - result = response["result"] - assert "memories" in result, "result should contain memories field" - assert "total_count" in result, "result should contain total_count field" - assert "metadata" in result, "result should contain metadata field" - - # Validate metadata - metadata = result["metadata"] - assert ( - metadata.get("group_id") == self.group_id - ), "metadata group_id should match" - - print( - f"✅ {memory_type} with group_id filter: {result['total_count']} records" - ) - - print(f"\n✅ Group Filter Test Completed") - return status_code, response - - def test_fetch_with_time_range(self): - """Test: GET /api/v0/memories - Fetch memories with time range filter""" - self.print_section("Test: GET /api/v0/memories - Fetch with Time Range Filter") - - now = datetime.now(ZoneInfo("UTC")) - start_time = (now - timedelta(days=30)).isoformat() - end_time = now.isoformat() - - # Test different memory types with time range - memory_types = ["episodic_memory", "atomic_fact", "foresight"] - - for memory_type in memory_types: - print(f"\n--- Testing memory_type: {memory_type} with time_range ---") - - data = { - "user_id": self.user_id, - "memory_type": memory_type, - "start_time": start_time, - "end_time": end_time, - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - # Assert: Validate response structure - assert ( - status_code == 200 - ), f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - assert "result" in response, "Response should contain result field" - - result = response["result"] - assert "memories" in result, "result should contain memories field" - assert "total_count" in result, "result should contain total_count field" - - # Validate time range in metadata - metadata = result["metadata"] - if "start_time" in metadata: - assert ( - metadata.get("start_time") == start_time - ), "metadata start_time should match" - if "end_time" in metadata: - assert ( - metadata.get("end_time") == end_time - ), "metadata end_time should match" - - print( - f"✅ {memory_type} with time_range [{start_time[:10]} to {end_time[:10]}]: {result['total_count']} records" - ) - - print(f"\n✅ Time Range Filter Test Completed") - return status_code, response - - def test_fetch_with_combined_filters(self): - """Test: GET /api/v0/memories - Fetch memories with combined filters (user_id + group_id + time_range)""" - self.print_section("Test: GET /api/v0/memories - Fetch with Combined Filters") - - now = datetime.now(ZoneInfo("UTC")) - start_time = (now - timedelta(days=7)).isoformat() - end_time = now.isoformat() - - # Test episodic_memory with all filters - print("\n--- Testing episodic_memory with user_id + group_id + time_range ---") - - data = { - "user_id": self.user_id, - "group_id": self.group_id, - "memory_type": "episodic_memory", - "start_time": start_time, - "end_time": end_time, - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - # Assert: Validate response structure - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - assert "result" in response, "Response should contain result field" - - result = response["result"] - assert "memories" in result, "result should contain memories field" - assert "total_count" in result, "result should contain total_count field" - - # Validate all filters are reflected in metadata - metadata = result["metadata"] - assert metadata.get("user_id") == self.user_id, "metadata user_id should match" - assert ( - metadata.get("group_id") == self.group_id - ), "metadata group_id should match" - - print( - f"✅ Combined filters (user + group + time): {result['total_count']} records" - ) - print(f"\n✅ Combined Filters Test Completed") - return status_code, response - - def test_fetch_profile_memory(self): - """Test: GET /api/v0/memories - Fetch user profile memory - - Tests multiple scenarios: - 1. Only user_id (group_id NOT provided) - 2. user_id + group_id=None (explicitly null) - 3. user_id + group_id="" (explicitly empty string) - 4. user_id + group_id both have valid values - 5. user_id="__all__" + valid group_id - """ - self.print_section("Test: GET /api/v0/memories - Fetch User Profile Memory") - - # Scenario 1: Only user_id, group_id NOT provided - print("\n--- Scenario 1: Only user_id (group_id NOT provided) ---") - data = { - "user_id": self.user_id, - "memory_type": "profile", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - - result = response["result"] - print(f"✅ Scenario 1 successful, returned {result['total_count']} profiles") - - # Scenario 2: user_id + group_id=None (explicitly null) - print("\n--- Scenario 2: user_id + group_id=None (explicitly null) ---") - data = { - "user_id": self.user_id, - "group_id": None, # Explicitly set to None - "memory_type": "profile", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - - result = response["result"] - - # Validate that returned memories have null or empty group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - group_id_value = memory.get("group_id") - assert group_id_value in ( - None, - "", - ), f"Memory {idx} group_id should be None or empty string, actual: {group_id_value}" - print( - f"✅ Scenario 2 successful, returned {result['total_count']} profiles with null/empty group_id" - ) - else: - print( - f"✅ Scenario 2 successful, returned {result['total_count']} profiles" - ) - - # Scenario 3: user_id + group_id="" (explicitly empty string) - print("\n--- Scenario 3: user_id + group_id='' (explicitly empty string) ---") - data = { - "user_id": self.user_id, - "group_id": "", # Explicitly set to empty string - "memory_type": "profile", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - - result = response["result"] - - # Validate that returned memories have null or empty group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - group_id_value = memory.get("group_id") - assert group_id_value in ( - None, - "", - ), f"Memory {idx} group_id should be None or empty string, actual: {group_id_value}" - print( - f"✅ Scenario 3 successful, returned {result['total_count']} profiles with null/empty group_id" - ) - else: - print( - f"✅ Scenario 3 successful, returned {result['total_count']} profiles" - ) - - # Scenario 4: user_id + group_id both have valid values - print("\n--- Scenario 4: user_id + group_id both have valid values ---") - data = { - "user_id": self.user_id, - "group_id": self.group_id, - "memory_type": "profile", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - - result = response["result"] - - # Validate that returned memories have the requested group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - assert ( - memory.get("group_id") == self.group_id - ), f"Memory {idx} group_id should be {self.group_id}, actual: {memory.get('group_id')}" - assert ( - memory.get("user_id") == self.user_id - ), f"Memory {idx} user_id should be {self.user_id}, actual: {memory.get('user_id')}" - print( - f"✅ Scenario 4 successful, returned {result['total_count']} profiles with matching filters" - ) - else: - print( - f"✅ Scenario 4 successful, returned {result['total_count']} profiles" - ) - - # Scenario 5: user_id="__all__" + valid group_id - print("\n--- Scenario 5: user_id='__all__' + valid group_id ---") - data = { - "user_id": "__all__", - "group_id": self.group_id, - "memory_type": "profile", - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - assert status_code == 200, f"Status code should be 200, actual: {status_code}" - assert response.get("status") == "ok", f"Status should be ok" - - result = response["result"] - - # Validate that returned memories have the requested group_id - if result["total_count"] > 0 and len(result["memories"]) > 0: - for idx, memory in enumerate(result["memories"]): - assert ( - memory.get("group_id") == self.group_id - ), f"Memory {idx} group_id should be {self.group_id}, actual: {memory.get('group_id')}" - print( - f"✅ Scenario 5 successful, returned {result['total_count']} profiles with group_id={self.group_id}" - ) - else: - print( - f"✅ Scenario 5 successful, returned {result['total_count']} profiles" - ) - - print(f"\n✅ Profile Memory Test Completed") - return status_code, response - - def test_fetch_all_memory_types(self): - """Test: GET /api/v0/memories - Fetch all supported memory types - - Memory types that support group_id are tested separately in: - - test_fetch_episodic() for episodic_memory - - test_fetch_atomic_fact() for atomic_fact - - test_fetch_foresight() for foresight - - test_fetch_profile_memory() for profile - """ - self.print_section("Test: GET /api/v0/memories - Fetch All Memory Types") - - # All supported memory types - memory_types = ["episodic_memory", "atomic_fact", "foresight", "profile"] - - results_summary = [] - - for memory_type in memory_types: - print(f"\n--- Testing memory_type: {memory_type} ---") - - data = { - "user_id": self.user_id, - "memory_type": memory_type, - "limit": 10, - "offset": 0, - } - - status_code, response = self.call_get_with_body_api("", data) - - # Assert: Basic validation - assert status_code == 200, f"Status code should be 200 for {memory_type}" - assert ( - response.get("status") == "ok" - ), f"Status should be ok for {memory_type}" - assert ( - "result" in response - ), f"Response should contain result field for {memory_type}" - - result = response["result"] - assert ( - "memories" in result - ), f"result should contain memories for {memory_type}" - assert ( - "total_count" in result - ), f"result should contain total_count for {memory_type}" - - results_summary.append( - {"memory_type": memory_type, "total_count": result["total_count"]} - ) - - print(f"✅ {memory_type}: {result['total_count']} records") - - # Print summary - print("\n" + "=" * 80) - print(" Memory Types Summary") - print("=" * 80) - for item in results_summary: - print(f" {item['memory_type']:20s}: {item['total_count']:5d} records") - print("=" * 80) - - print(f"\n✅ All Memory Types Test Completed") - return status_code, response - - def _validate_search_response_structure( - self, status_code, response, scenario_name: str - ): - """ - Validate the basic structure of a search response. - - Args: - status_code: HTTP status code - response: Response JSON - scenario_name: Name of the test scenario for error messages - - Returns: - result dict from response - """ - assert ( - status_code == 200 - ), f"[{scenario_name}] Status code should be 200, actual: {status_code}" - assert ( - response.get("status") == "ok" - ), f"[{scenario_name}] Status should be ok, actual: {response.get('status')}" - assert ( - "result" in response - ), f"[{scenario_name}] Response should contain result field" - - result = response["result"] - assert ( - "memories" in result - ), f"[{scenario_name}] result should contain memories field" - assert ( - "total_count" in result - ), f"[{scenario_name}] result should contain total_count field" - assert ( - "has_more" in result - ), f"[{scenario_name}] result should contain has_more field" - assert ( - "metadata" in result - ), f"[{scenario_name}] result should contain metadata field" - assert ( - "pending_messages" in result - ), f"[{scenario_name}] result should contain pending_messages field" - - # Validate data types - assert isinstance( - result["memories"], list - ), f"[{scenario_name}] memories should be a list" - assert ( - result["total_count"] >= 0 - ), f"[{scenario_name}] total_count should be >= 0" - assert isinstance( - result["pending_messages"], list - ), f"[{scenario_name}] pending_messages should be a list" - - # Validate pending_messages structure if not empty - if len(result["pending_messages"]) > 0: - for idx, msg in enumerate(result["pending_messages"]): - assert isinstance( - msg, dict - ), f"[{scenario_name}] pending_messages[{idx}] should be a dictionary" - # Required fields - assert ( - "id" in msg - ), f"[{scenario_name}] pending_messages[{idx}] should contain id field" - assert ( - "request_id" in msg - ), f"[{scenario_name}] pending_messages[{idx}] should contain request_id field" - # Optional fields validation (check type if present) - if "message_id" in msg and msg["message_id"] is not None: - assert isinstance( - msg["message_id"], str - ), f"[{scenario_name}] pending_messages[{idx}].message_id should be string" - if "content" in msg and msg["content"] is not None: - assert isinstance( - msg["content"], str - ), f"[{scenario_name}] pending_messages[{idx}].content should be string" - print( - f" [{scenario_name}] Found {len(result['pending_messages'])} pending messages" - ) - - return result - - def _validate_search_memories_filter( - self, - result: dict, - scenario_name: str, - expected_user_id: str = None, - expected_group_id: str = None, - user_id_filter_type: str = "exact", # "exact", "null_or_empty", "any" - group_id_filter_type: str = "exact", # "exact", "null_or_empty", "any" - ): - """ - Validate that search results match expected user_id/group_id filters. - - Search API returns a flat list of BaseMemory objects: - { - "memories": [ - { - "memory_type": "episodic_memory", - "user_id": "user_123", - "group_id": "group_456", - "timestamp": "...", - "score": 0.85, - ... - } - ] - } - - Args: - result: Result dict from search response - scenario_name: Name of the test scenario - expected_user_id: Expected user_id value (for exact matching) - expected_group_id: Expected group_id value (for exact matching) - user_id_filter_type: How to validate user_id ("exact", "null_or_empty", "any") - group_id_filter_type: How to validate group_id ("exact", "null_or_empty", "any") - """ - memories_checked = 0 - - if result["total_count"] > 0 and len(result["memories"]) > 0: - for mem_idx, mem in enumerate(result["memories"]): - assert isinstance( - mem, dict - ), f"[{scenario_name}] Memory {mem_idx} should be a dictionary" - assert ( - "memory_type" in mem - ), f"[{scenario_name}] Memory {mem_idx} should contain memory_type" - - memories_checked += 1 - - # Validate user_id filter - mem_user_id = mem.get("user_id") - if user_id_filter_type == "exact" and expected_user_id: - assert mem_user_id == expected_user_id, ( - f"[{scenario_name}] Memory {mem_idx} user_id should be {expected_user_id}, " - f"actual: {mem_user_id}" - ) - elif user_id_filter_type == "null_or_empty": - assert mem_user_id in (None, ""), ( - f"[{scenario_name}] Memory {mem_idx} user_id should be None or empty, " - f"actual: {mem_user_id}" - ) - # "any" means no validation needed for user_id - - # Validate group_id filter - mem_group_id = mem.get("group_id") - if group_id_filter_type == "exact" and expected_group_id: - if mem_group_id is not None: - assert mem_group_id == expected_group_id, ( - f"[{scenario_name}] Memory {mem_idx} group_id should be {expected_group_id}, " - f"actual: {mem_group_id}" - ) - elif group_id_filter_type == "null_or_empty": - if mem_group_id is not None: - assert mem_group_id in (None, ""), ( - f"[{scenario_name}] Memory {mem_idx} group_id should be None or empty, " - f"actual: {mem_group_id}" - ) - # "any" means no validation needed for group_id - - print(f" [Debug] Checked {memories_checked} memories in {scenario_name}") - - def test_search_memories_keyword(self): - """Test 5: GET /api/v0/memories/search - Keyword search (pass parameters via body) - - Tests multiple scenarios for user_id/group_id parameter behavior: - Note: user_id and group_id cannot BOTH be MAGIC_ALL (not provided or "__all__") - - 1. Neither user_id nor group_id provided - should return 400 error - 2. Only user_id provided (group_id NOT in request, query_all for group_id) - 3. user_id + group_id=None or "" (filter for null/empty group_id) - 4. user_id + group_id both have valid values (exact match) - 5. user_id="__all__" + valid group_id (query_all for user_id) - 6. user_id=None or "" + valid group_id (filter for null/empty user_id) - """ - self.print_section("Test 5: GET /api/v0/memories/search - Keyword Search") - - # ================================================================= - # Scenario 1: Neither user_id nor group_id provided - should return 400 error - # (user_id and group_id cannot both be MAGIC_ALL) - # ================================================================= - print( - "\n--- Scenario 1: Neither user_id nor group_id provided (should return 400) ---" - ) - data = { - "query": "coffee", - "top_k": 10, - "retrieve_method": "keyword", - # user_id and group_id are NOT in the request at all - } - - status_code, response = self.call_get_with_body_api("/search", data) - - # Should return 422 error because user_id and group_id cannot both be MAGIC_ALL - assert ( - status_code == 422 - ), f"[Scenario 1] Status code should be 422, actual: {status_code}" - assert ( - response.get("status") == "failed" - ), f"[Scenario 1] Status should be failed" - assert "user_id and group_id cannot both be MAGIC_ALL" in response.get( - "message", "" - ), f"[Scenario 1] Error message should mention the constraint, actual: {response.get('message')}" - - print( - f"✅ Scenario 1 successful, correctly returned 422 error for invalid request" - ) - - # ================================================================= - # Scenario 2: Only user_id provided (group_id NOT in request) - # ================================================================= - print( - "\n--- Scenario 2: Only user_id provided (group_id NOT in request, query_all for group_id) ---" - ) - data = { - "user_id": self.user_id, - "query": "coffee", - "top_k": 10, - "retrieve_method": "keyword", - # group_id is NOT in the request at all - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 2" - ) - - # Validate metadata - metadata = result["metadata"] - assert ( - metadata.get("user_id") == self.user_id - ), "[Scenario 2] metadata user_id should match" - - # When group_id is not provided, should return memories from all groups for this user - self._validate_search_memories_filter( - result, - "Scenario 2", - expected_user_id=self.user_id, - user_id_filter_type="exact", - group_id_filter_type="any", - ) - - print( - f"✅ Scenario 2 successful, returned {result['total_count']} groups of memories for user_id={self.user_id}" - ) - - # ================================================================= - # Scenario 3: user_id + group_id=None or "" (filter for null/empty group_id) - # ================================================================= - print( - "\n--- Scenario 3: user_id + group_id='' (filter for null/empty group_id) ---" - ) - data = { - "user_id": self.user_id, - "group_id": "", # Empty string, equivalent to None - "query": "coffee", - "top_k": 10, - "retrieve_method": "keyword", - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 3" - ) - - # Validate metadata - metadata = result["metadata"] - assert ( - metadata.get("user_id") == self.user_id - ), "[Scenario 3] metadata user_id should match" - - # When group_id is "" or None, should only return memories with null/empty group_id - self._validate_search_memories_filter( - result, - "Scenario 3", - expected_user_id=self.user_id, - user_id_filter_type="exact", - group_id_filter_type="null_or_empty", - ) - - print( - f"✅ Scenario 3 successful, returned {result['total_count']} groups of memories with null/empty group_id" - ) - - # ================================================================= - # Scenario 4: user_id + group_id both have valid values (exact match) - # ================================================================= - print( - "\n--- Scenario 4: user_id + group_id both have valid values (exact match) ---" - ) - data = { - "user_id": self.user_id, - "group_id": self.group_id, - "query": "coffee", - "top_k": 10, - "retrieve_method": "keyword", - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 4" - ) - - # Validate metadata (user_id should be present) - metadata = result["metadata"] - assert ( - metadata.get("user_id") == self.user_id - ), "[Scenario 4] metadata user_id should match" - # Note: metadata.group_id may not be returned by backend, so we verify via actual memories - - # When both have valid values, should only return exact matches - THIS IS THE KEY VALIDATION - self._validate_search_memories_filter( - result, - "Scenario 4", - expected_user_id=self.user_id, - expected_group_id=self.group_id, - user_id_filter_type="exact", - group_id_filter_type="exact", - ) - - print( - f"✅ Scenario 4 successful, returned {result['total_count']} groups of memories with exact user_id and group_id" - ) - - # ================================================================= - # Scenario 5: user_id="__all__" + valid group_id (query_all for user_id) - # ================================================================= - print( - "\n--- Scenario 5: user_id='__all__' + valid group_id (query_all for user_id) ---" - ) - data = { - "user_id": "__all__", - "group_id": self.group_id, - "query": "coffee", - "top_k": 10, - "retrieve_method": "keyword", - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 5" - ) - - # Note: metadata.group_id may not be returned by backend, so we verify via actual memories - - # When user_id is "__all__", should return memories from all users in this group - THIS IS THE KEY VALIDATION - self._validate_search_memories_filter( - result, - "Scenario 5", - expected_group_id=self.group_id, - user_id_filter_type="any", - group_id_filter_type="exact", - ) - - print( - f"✅ Scenario 5 successful, returned {result['total_count']} groups of memories with group_id={self.group_id} (user_id=__all__)" - ) - - # ================================================================= - # Scenario 6: user_id=None or "" + valid group_id (filter for null/empty user_id) - # ================================================================= - print( - "\n--- Scenario 6: user_id='' + valid group_id (filter for null/empty user_id) ---" - ) - data = { - "user_id": "", # Empty string, equivalent to None - "group_id": self.group_id, - "query": "coffee", - "top_k": 10, - "retrieve_method": "keyword", - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 6" - ) - - # Note: metadata.group_id may not be returned by backend, so we verify via actual memories - - # When user_id is "" or None, should only return memories with null/empty user_id - THIS IS THE KEY VALIDATION - self._validate_search_memories_filter( - result, - "Scenario 6", - expected_group_id=self.group_id, - user_id_filter_type="null_or_empty", - group_id_filter_type="exact", - ) - - print( - f"✅ Scenario 6 successful, returned {result['total_count']} groups of memories with null/empty user_id" - ) - - print(f"\n✅ All Keyword Search Scenarios Completed Successfully") - return status_code, response - - def test_search_memories_vector(self): - """Test 6: GET /api/v0/memories/search - Vector search (pass parameters via body) - - Tests multiple scenarios for user_id/group_id parameter behavior: - Note: user_id and group_id cannot BOTH be MAGIC_ALL (not provided or "__all__") - - 1. Neither user_id nor group_id provided - should return 400 error - 2. Only user_id provided (group_id NOT in request, query_all for group_id) - 3. user_id + group_id=None or "" (filter for null/empty group_id) - 4. user_id + group_id both have valid values (exact match) - 5. user_id="__all__" + valid group_id (query_all for user_id) - 6. user_id=None or "" + valid group_id (filter for null/empty user_id) - """ - self.print_section("Test 6: GET /api/v0/memories/search - Vector Search") - - # ================================================================= - # Scenario 1: Neither user_id nor group_id provided - should return 400 error - # ================================================================= - print( - "\n--- Scenario 1: Neither user_id nor group_id provided (should return 400) ---" - ) - data = { - "query": "user's dietary preferences", - "top_k": 10, - "retrieve_method": "vector", - # user_id and group_id are NOT in the request at all - } - - status_code, response = self.call_get_with_body_api("/search", data) - - # Should return 422 error because user_id and group_id cannot both be MAGIC_ALL - assert ( - status_code == 422 - ), f"[Scenario 1] Status code should be 422, actual: {status_code}" - assert ( - response.get("status") == "failed" - ), f"[Scenario 1] Status should be failed" - assert "user_id and group_id cannot both be MAGIC_ALL" in response.get( - "message", "" - ), f"[Scenario 1] Error message should mention the constraint, actual: {response.get('message')}" - - print( - f"✅ Scenario 1 successful, correctly returned 422 error for invalid request" - ) - - # ================================================================= - # Scenario 2: Only user_id provided (group_id NOT in request) - # ================================================================= - print( - "\n--- Scenario 2: Only user_id provided (group_id NOT in request, query_all for group_id) ---" - ) - data = { - "user_id": self.user_id, - "query": "user's dietary preferences", - "top_k": 10, - "retrieve_method": "vector", - # group_id is NOT in the request at all - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 2" - ) - - # Validate metadata - metadata = result["metadata"] - assert ( - metadata.get("user_id") == self.user_id - ), "[Scenario 2] metadata user_id should match" - - self._validate_search_memories_filter( - result, - "Scenario 2", - expected_user_id=self.user_id, - user_id_filter_type="exact", - group_id_filter_type="any", - ) - - print( - f"✅ Scenario 2 successful, returned {result['total_count']} groups of memories for user_id={self.user_id}" - ) - - # ================================================================= - # Scenario 3: user_id + group_id=None or "" (filter for null/empty group_id) - # ================================================================= - print( - "\n--- Scenario 3: user_id + group_id='' (filter for null/empty group_id) ---" - ) - data = { - "user_id": self.user_id, - "group_id": "", # Empty string, equivalent to None - "query": "user's dietary preferences", - "top_k": 10, - "retrieve_method": "vector", - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 3" - ) - - # Validate metadata - metadata = result["metadata"] - assert ( - metadata.get("user_id") == self.user_id - ), "[Scenario 3] metadata user_id should match" - - self._validate_search_memories_filter( - result, - "Scenario 3", - expected_user_id=self.user_id, - user_id_filter_type="exact", - group_id_filter_type="null_or_empty", - ) - - print( - f"✅ Scenario 3 successful, returned {result['total_count']} groups of memories with null/empty group_id" - ) - - # ================================================================= - # Scenario 4: user_id + group_id both have valid values (exact match) - # ================================================================= - print( - "\n--- Scenario 4: user_id + group_id both have valid values (exact match) ---" - ) - data = { - "user_id": self.user_id, - "group_id": self.group_id, - "query": "user's dietary preferences", - "top_k": 10, - "retrieve_method": "vector", - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 4" - ) - - # Validate metadata (user_id should be present) - metadata = result["metadata"] - assert ( - metadata.get("user_id") == self.user_id - ), "[Scenario 4] metadata user_id should match" - # Note: metadata.group_id may not be returned by backend, so we verify via actual memories - - # When both have valid values, should only return exact matches - THIS IS THE KEY VALIDATION - self._validate_search_memories_filter( - result, - "Scenario 4", - expected_user_id=self.user_id, - expected_group_id=self.group_id, - user_id_filter_type="exact", - group_id_filter_type="exact", - ) - - print( - f"✅ Scenario 4 successful, returned {result['total_count']} groups of memories with exact user_id and group_id" - ) - - # ================================================================= - # Scenario 5: user_id="__all__" + valid group_id (query_all for user_id) - # ================================================================= - print( - "\n--- Scenario 5: user_id='__all__' + valid group_id (query_all for user_id) ---" - ) - data = { - "user_id": "__all__", - "group_id": self.group_id, - "query": "user's dietary preferences", - "top_k": 10, - "retrieve_method": "vector", - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 5" - ) - - # Note: metadata.group_id may not be returned by backend, so we verify via actual memories - - # When user_id is "__all__", should return memories from all users in this group - THIS IS THE KEY VALIDATION - self._validate_search_memories_filter( - result, - "Scenario 5", - expected_group_id=self.group_id, - user_id_filter_type="any", - group_id_filter_type="exact", - ) - - print( - f"✅ Scenario 5 successful, returned {result['total_count']} groups of memories with group_id={self.group_id}" - ) - - # ================================================================= - # Scenario 6: user_id=None or "" + valid group_id (filter for null/empty user_id) - # ================================================================= - print( - "\n--- Scenario 6: user_id='' + valid group_id (filter for null/empty user_id) ---" - ) - data = { - "user_id": "", # Empty string, equivalent to None - "group_id": self.group_id, - "query": "user's dietary preferences", - "top_k": 10, - "retrieve_method": "vector", - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 6" - ) - - # Note: metadata.group_id may not be returned by backend, so we verify via actual memories - - # When user_id is "" or None, should only return memories with null/empty user_id - THIS IS THE KEY VALIDATION - self._validate_search_memories_filter( - result, - "Scenario 6", - expected_group_id=self.group_id, - user_id_filter_type="null_or_empty", - group_id_filter_type="exact", - ) - - print( - f"✅ Scenario 6 successful, returned {result['total_count']} groups of memories with null/empty user_id" - ) - - print(f"\n✅ All Vector Search Scenarios Completed Successfully") - return status_code, response - - def test_search_memories_hybrid(self): - """Test 7: GET /api/v0/memories/search - Hybrid search (pass parameters via body) - - Tests multiple scenarios for user_id/group_id parameter behavior: - Note: user_id and group_id cannot BOTH be MAGIC_ALL (not provided or "__all__") - - 1. Neither user_id nor group_id provided - should return 400 error - 2. Only user_id provided (group_id NOT in request, query_all for group_id) - 3. user_id + group_id=None or "" (filter for null/empty group_id) - 4. user_id + group_id both have valid values (exact match) - 5. user_id="__all__" + valid group_id (query_all for user_id) - 6. user_id=None or "" + valid group_id (filter for null/empty user_id) - """ - self.print_section("Test 7: GET /api/v0/memories/search - Hybrid Search") - - now = datetime.now(ZoneInfo("UTC")) - start_time = (now - timedelta(days=60)).isoformat() - end_time = now.isoformat() - - # ================================================================= - # Scenario 1: Neither user_id nor group_id provided - should return 400 error - # ================================================================= - print( - "\n--- Scenario 1: Neither user_id nor group_id provided (should return 400) ---" - ) - data = { - "query": "coffee preference", - "top_k": 10, - "retrieve_method": "hybrid", - "start_time": start_time, - "end_time": end_time, - # user_id and group_id are NOT in the request at all - } - - status_code, response = self.call_get_with_body_api("/search", data) - - # Should return 422 error because user_id and group_id cannot both be MAGIC_ALL - assert ( - status_code == 422 - ), f"[Scenario 1] Status code should be 422, actual: {status_code}" - assert ( - response.get("status") == "failed" - ), f"[Scenario 1] Status should be failed" - assert "user_id and group_id cannot both be MAGIC_ALL" in response.get( - "message", "" - ), f"[Scenario 1] Error message should mention the constraint, actual: {response.get('message')}" - - print( - f"✅ Scenario 1 successful, correctly returned 422 error for invalid request" - ) - - # ================================================================= - # Scenario 2: Only user_id provided (group_id NOT in request) - # ================================================================= - print( - "\n--- Scenario 2: Only user_id provided (group_id NOT in request, query_all for group_id) ---" - ) - data = { - "user_id": self.user_id, - "query": "coffee preference", - "top_k": 10, - "retrieve_method": "hybrid", - "start_time": start_time, - "end_time": end_time, - # group_id is NOT in the request at all - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 2" - ) - - # Validate metadata - metadata = result["metadata"] - assert ( - metadata.get("user_id") == self.user_id - ), "[Scenario 2] metadata user_id should match" - - self._validate_search_memories_filter( - result, - "Scenario 2", - expected_user_id=self.user_id, - user_id_filter_type="exact", - group_id_filter_type="any", - ) - - print( - f"✅ Scenario 2 successful, returned {result['total_count']} groups of memories for user_id={self.user_id}" - ) - - # ================================================================= - # Scenario 3: user_id + group_id=None or "" (filter for null/empty group_id) - # ================================================================= - print( - "\n--- Scenario 3: user_id + group_id='' (filter for null/empty group_id) ---" - ) - data = { - "user_id": self.user_id, - "group_id": "", # Empty string, equivalent to None - "query": "coffee preference", - "top_k": 10, - "retrieve_method": "hybrid", - "start_time": start_time, - "end_time": end_time, - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 3" - ) - - # Validate metadata - metadata = result["metadata"] - assert ( - metadata.get("user_id") == self.user_id - ), "[Scenario 3] metadata user_id should match" - - self._validate_search_memories_filter( - result, - "Scenario 3", - expected_user_id=self.user_id, - user_id_filter_type="exact", - group_id_filter_type="null_or_empty", - ) - - print( - f"✅ Scenario 3 successful, returned {result['total_count']} groups of memories with null/empty group_id" - ) - - # ================================================================= - # Scenario 4: user_id + group_id both have valid values (exact match) - # ================================================================= - print( - "\n--- Scenario 4: user_id + group_id both have valid values (exact match) ---" - ) - data = { - "user_id": self.user_id, - "group_id": self.group_id, - "query": "coffee preference", - "top_k": 10, - "retrieve_method": "hybrid", - "start_time": start_time, - "end_time": end_time, - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 4" - ) - - # Validate metadata (user_id should be present) - metadata = result["metadata"] - assert ( - metadata.get("user_id") == self.user_id - ), "[Scenario 4] metadata user_id should match" - # Note: metadata.group_id may not be returned by backend, so we verify via actual memories - - # When both have valid values, should only return exact matches - THIS IS THE KEY VALIDATION - self._validate_search_memories_filter( - result, - "Scenario 4", - expected_user_id=self.user_id, - expected_group_id=self.group_id, - user_id_filter_type="exact", - group_id_filter_type="exact", - ) - - print( - f"✅ Scenario 4 successful, returned {result['total_count']} groups of memories with exact user_id and group_id" - ) - - # ================================================================= - # Scenario 5: user_id="__all__" + valid group_id (query_all for user_id) - # ================================================================= - print( - "\n--- Scenario 5: user_id='__all__' + valid group_id (query_all for user_id) ---" - ) - data = { - "user_id": "__all__", - "group_id": self.group_id, - "query": "coffee preference", - "top_k": 10, - "retrieve_method": "hybrid", - "start_time": start_time, - "end_time": end_time, - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 5" - ) - - # Note: metadata.group_id may not be returned by backend, so we verify via actual memories - - # When user_id is "__all__", should return memories from all users in this group - THIS IS THE KEY VALIDATION - self._validate_search_memories_filter( - result, - "Scenario 5", - expected_group_id=self.group_id, - user_id_filter_type="any", - group_id_filter_type="exact", - ) - - print( - f"✅ Scenario 5 successful, returned {result['total_count']} groups of memories with group_id={self.group_id}" - ) - - # ================================================================= - # Scenario 6: user_id=None or "" + valid group_id (filter for null/empty user_id) - # ================================================================= - print( - "\n--- Scenario 6: user_id='' + valid group_id (filter for null/empty user_id) ---" - ) - data = { - "user_id": "", # Empty string, equivalent to None - "group_id": self.group_id, - "query": "coffee preference", - "top_k": 10, - "retrieve_method": "hybrid", - "start_time": start_time, - "end_time": end_time, - } - - status_code, response = self.call_get_with_body_api("/search", data) - result = self._validate_search_response_structure( - status_code, response, "Scenario 6" - ) - - # Note: metadata.group_id may not be returned by backend, so we verify via actual memories - - # When user_id is "" or None, should only return memories with null/empty user_id - THIS IS THE KEY VALIDATION - self._validate_search_memories_filter( - result, - "Scenario 6", - expected_group_id=self.group_id, - user_id_filter_type="null_or_empty", - group_id_filter_type="exact", - ) - - print( - f"✅ Scenario 6 successful, returned {result['total_count']} groups of memories with null/empty user_id" - ) - - print(f"\n✅ All Hybrid Search Scenarios Completed Successfully") - return status_code, response - - def run_all_tests(self, test_method: str = "all", except_test_methods: str = None): - """ - Run tests - - Args: - test_method: Specify test method to run, options: - - all: Run all tests - - fetch: Run all fetch-related tests (batch) - - retrieve/search: Run all retrieve/search-related tests (batch) - - memorize: Test storing conversation memory / Run memorization tests (batch) - - meta: Run metadata-related tests (batch) - - fetch_episodic: Test fetching episodic memory - - fetch_foresight: Test fetching foresight memory - - fetch_atomic_fact: Test fetching atomic fact - - fetch_group_filter: Test fetching with group_id filter - - fetch_time_range: Test fetching with time range filter - - fetch_combined_filters: Test fetching with combined filters - - fetch_profile: Test fetching user profile - - fetch_all_types: Test fetching all memory types - - search_keyword: Test keyword search - - search_vector: Test vector search - - search_hybrid: Test hybrid search - except_test_methods: Specify test methods to exclude (comma-separated), e.g.: "memorize,fetch_episodic" - When specified, run all tests except these methods - """ - print("\n" + "=" * 80) - print(" Starting Memory Controller API Tests") - print("=" * 80) - print(f" API Address: {self.base_url}") - print(f" Test User: {self.user_id}") - print(f" Test Group: {self.group_id}") - print(f" Organization ID: {self.organization_id}") - print(f" Space ID: {self.space_id}") - print(f" Hash Key: {self.hash_key}") - print(f" Sync Mode: {self.sync_mode}") - print(f" Test Method: {test_method}") - if except_test_methods: - print(f" Excluded Methods: {except_test_methods}") - print("=" * 80) - - # First initialize database - if not self.init_database(): - print("\n❌ Database initialization failed, terminating tests") - return - - # Define test method mapping - test_methods = { - "memorize": self.test_memorize_single_message, - "fetch_episodic": self.test_fetch_episodic, - "fetch_foresight": self.test_fetch_foresight, - "fetch_atomic_fact": self.test_fetch_atomic_fact, - "fetch_group_filter": self.test_fetch_with_group_filter, - "fetch_time_range": self.test_fetch_with_time_range, - "fetch_combined_filters": self.test_fetch_with_combined_filters, - "fetch_profile": self.test_fetch_profile_memory, - "fetch_all_types": self.test_fetch_all_memory_types, - "search_keyword": self.test_search_memories_keyword, - "search_vector": self.test_search_memories_vector, - "search_hybrid": self.test_search_memories_hybrid, - } - - # Define test type grouping - test_type_groups = { - "fetch": [ - "fetch_episodic", - "fetch_foresight", - "fetch_atomic_fact", - "fetch_group_filter", - "fetch_time_range", - "fetch_combined_filters", - "fetch_profile", - "fetch_all_types", - ], - "retrieve": ["search_keyword", "search_vector", "search_hybrid"], - "search": [ # Alias for retrieve - "search_keyword", - "search_vector", - "search_hybrid", - ], - "memorize": ["memorize"], - } - - # Parse excluded test methods list - excluded_methods = set() - if except_test_methods: - excluded_list = [m.strip() for m in except_test_methods.split(",")] - for method_name in excluded_list: - if method_name not in test_methods: - print( - f"\n⚠️ Warning: Unknown test method '{method_name}', will be ignored" - ) - else: - excluded_methods.add(method_name) - - # Execute tests - try: - if test_method in test_type_groups: - # Batch mode: Run tests by category (fetch, retrieve, search, memorize, meta) - method_names = test_type_groups[test_method] - methods_to_run = [ - (name, test_methods[name]) - for name in method_names - if name in test_methods - ] - - print( - f"\n📋 Will run {len(methods_to_run)} test methods in [{test_method}] category" - ) - for name, method in methods_to_run: - method() - - elif except_test_methods: - # except-test-method mode: Run all tests except specified ones - methods_to_run = [ - (name, method) - for name, method in test_methods.items() - if name not in excluded_methods - ] - if not methods_to_run: - print("\n⚠️ No test methods to run (all methods excluded)") - return - - print( - f"\n📋 Will run {len(methods_to_run)} test methods (excluded {len(excluded_methods)} methods)" - ) - for name, method in methods_to_run: - method() - elif test_method == "all": - # Run all tests - for method in test_methods.values(): - method() - elif test_method in test_methods: - # Run specified single test - test_methods[test_method]() - else: - print(f"\n❌ Unknown test method: {test_method}") - return - except AssertionError as e: - print(f"\n❌ Test failed: {e}") - raise - except Exception as e: # noqa: BLE001 - print(f"\n❌ Test exception: {e}") - raise - - # Tests completed - self.print_section("Tests Completed") - if test_method in test_type_groups: - print(f"\n✅ All [{test_method}] category tests passed!") - elif except_test_methods: - print(f"\n✅ Completed all tests except [{except_test_methods}]!") - elif test_method == "all": - print("\n✅ All interface structure validations passed!") - else: - print(f"\n✅ Test method [{test_method}] validation passed!") - print( - "💡 Tip: If an interface fails, check if input/output structure has changed\n" - ) - - -def parse_args(): - """Parse command line arguments""" - parser = argparse.ArgumentParser( - description="Memory Controller API Test Script", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Usage Examples: - # Test local service with default configuration - python tests/test_memory_controller.py - - # Specify API address - python tests/test_memory_controller.py --base-url http://localhost:1995 - - # Specify API address and test user - python tests/test_memory_controller.py --base-url http://dev-server:1995 --user-id test_user_123 - - # Specify tenant information - python tests/test_memory_controller.py --organization-id my_org --space-id my_space - - # Test a specific method - python tests/test_memory_controller.py --test-method memorize - python tests/test_memory_controller.py --test-method fetch_episodic - python tests/test_memory_controller.py --test-method fetch_atomic_fact - python tests/test_memory_controller.py --test-method search_keyword - - # Test all methods except certain ones (parameters separated by commas) - python tests/test_memory_controller.py --except-test-method memorize - python tests/test_memory_controller.py --except-test-method memorize,fetch_episodic - python tests/test_memory_controller.py --except-test-method save_meta,patch_meta - - # Disable sync mode (use background mode) - python tests/test_memory_controller.py --sync-mode false - - # Specify API Key for authentication - python tests/test_memory_controller.py --hash-key your_hash_key_here - - # Specify all parameters - python tests/test_memory_controller.py --base-url http://dev-server:1995 --user-id test_user --group-id test_group --organization-id my_org --space-id my_space --hash-key your_hash_key --timeout 60 - """, - ) - - parser.add_argument( - "--base-url", - default="http://localhost:1995", - help="API base URL (default: http://localhost:1995)", - ) - - parser.add_argument( - "--user-id", default=None, help="Test user ID (default: randomly generated)" - ) - - parser.add_argument( - "--group-id", default=None, help="Test group ID (default: randomly generated)" - ) - - parser.add_argument( - "--organization-id", - default=None, - help=f"Organization ID (default: {MemoryControllerTester.DEFAULT_ORGANIZATION_ID})", - ) - - parser.add_argument( - "--space-id", - default=None, - help=f"Space ID (default: {MemoryControllerTester.DEFAULT_SPACE_ID})", - ) - - parser.add_argument( - "--hash-key", - default=None, - help=f"Hash key for authentication (default: {MemoryControllerTester.DEFAULT_HASH_KEY})", - ) - - parser.add_argument( - "--timeout", - type=int, - default=180, - help="Request timeout in seconds (default: 180)", - ) - - parser.add_argument( - "--test-method", - default="all", - choices=[ - "all", - # Batch categories - "fetch", - "retrieve", - "search", - "memorize", - "meta", - # Individual methods - "fetch_episodic", - "fetch_foresight", - "fetch_atomic_fact", - "fetch_group_filter", - "fetch_time_range", - "fetch_combined_filters", - "fetch_profile", - "fetch_all_types", - "search_keyword", - "search_vector", - "search_hybrid", - "save_meta", - "patch_meta", - ], - help="Specify test method to run (default: all). Supports batch categories (fetch, retrieve/search, memorize, meta) or individual test methods", - ) - - parser.add_argument( - "--except-test-method", - default=None, - help="Specify test methods to exclude (comma-separated), runs all tests except these. Example: --except-test-method memorize,fetch_episodic", - ) - - parser.add_argument( - "--sync-mode", - type=lambda x: x.lower() in ("true", "1", "yes"), - default=True, - help="Whether to enable sync mode (default: true, same as server default). Only need to pass --sync-mode false to explicitly use background mode", - ) - - return parser.parse_args() - - -def main(): - """Main function""" - # Parse command line arguments - args = parse_args() - - # Check parameter conflict: cannot specify both --test-method and --except-test-method - if args.test_method != "all" and args.except_test_method: - print("❌ Error: Cannot use both --test-method and --except-test-method") - print(" Please choose one:") - print(" - Use --test-method to specify a test or category to run") - print( - " - Use --except-test-method to specify tests to exclude (run all others)" - ) - return - - # If user_id not provided, generate randomly - user_id = args.user_id if args.user_id else f"user_{uuid.uuid4().hex[:12]}" - - # If group_id not provided, generate randomly - group_id = args.group_id if args.group_id else f"group_{uuid.uuid4().hex[:12]}" - - # Use default values for organization_id and space_id (if not provided) - organization_id = args.organization_id - space_id = args.space_id - - # Output used ID information - if not args.user_id: - print(f"⚠️ --user-id not provided, auto-generated: {user_id}") - if not args.group_id: - print(f"⚠️ --group-id not provided, auto-generated: {group_id}") - if not args.organization_id: - print( - f"⚠️ --organization-id not provided, using default: {MemoryControllerTester.DEFAULT_ORGANIZATION_ID}" - ) - if not args.space_id: - print( - f"⚠️ --space-id not provided, using default: {MemoryControllerTester.DEFAULT_SPACE_ID}" - ) - if not args.hash_key: - print( - f"⚠️ --hash-key not provided, using default: {MemoryControllerTester.DEFAULT_HASH_KEY}" - ) - - # Create tester instance - tester = MemoryControllerTester( - base_url=args.base_url, - user_id=user_id, - group_id=group_id, - organization_id=organization_id, - space_id=space_id, - hash_key=args.hash_key, - timeout=args.timeout, - sync_mode=args.sync_mode, - ) - - # Run tests (decide to run all, single, by category, or exclude certain tests based on parameters) - tester.run_all_tests( - test_method=args.test_method, except_test_methods=args.except_test_method - ) - - -if __name__ == "__main__": - main() diff --git a/methods/EverCore/tests/test_memory_delete.py b/methods/EverCore/tests/test_memory_delete.py deleted file mode 100644 index 9fabfc11..00000000 --- a/methods/EverCore/tests/test_memory_delete.py +++ /dev/null @@ -1,1613 +0,0 @@ -"""Unit tests for POST /api/v1/memories/delete endpoint. - -Covers the complete delete API chain with 100% line and branch coverage: -- DTO validation (DeleteMemoriesRequest with three-state semantics) -- Controller routing (delete_memories -> delete_by_id / delete_by_filters) -- Service orchestration (MemCellDeleteService cascade logic) -- MongoDB repositories (5 repos, soft delete with MAGIC_ALL three-state) -- Elasticsearch repositories (3 repos, physical delete) -- Milvus repositories (3 repos, physical delete) -""" - -from datetime import datetime, timezone -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest -from pydantic import ValidationError - -from api_specs.dtos.memory_delete import DeleteMemoriesRequest -from core.oxm.constants import MAGIC_ALL - - -# =========================================================================== -# 1. DTO Validation Tests -# =========================================================================== - - -class TestDeleteMemoriesRequestDTO: - """Test DeleteMemoriesRequest validation rules.""" - - # --- By ID mode --- - - def test_by_id_valid(self): - req = DeleteMemoriesRequest(memory_id="abc123") - assert req.memory_id == "abc123" - assert req.user_id == MAGIC_ALL - - def test_by_id_rejects_extra_user_id(self): - with pytest.raises(ValidationError, match="memory_id"): - DeleteMemoriesRequest(memory_id="abc123", user_id="user_1") - - def test_by_id_rejects_extra_group_id(self): - with pytest.raises(ValidationError, match="memory_id"): - DeleteMemoriesRequest(memory_id="abc123", group_id="group_1") - - def test_by_id_rejects_extra_session_id(self): - with pytest.raises(ValidationError, match="memory_id"): - DeleteMemoriesRequest(memory_id="abc123", session_id="sess_1") - - def test_by_id_rejects_extra_sender_id(self): - with pytest.raises(ValidationError, match="memory_id"): - DeleteMemoriesRequest(memory_id="abc123", sender_id="sender_1") - - # --- By filters mode --- - - def test_by_filters_user_id_only(self): - req = DeleteMemoriesRequest(user_id="user_1") - assert req.user_id == "user_1" - assert req.memory_id is None - - def test_by_filters_group_id_only(self): - req = DeleteMemoriesRequest(group_id="group_1") - assert req.group_id == "group_1" - - def test_by_filters_all_fields(self): - req = DeleteMemoriesRequest( - user_id="u1", group_id="g1", session_id="s1", sender_id="sd1" - ) - assert req.session_id == "s1" - assert req.sender_id == "sd1" - - def test_by_filters_requires_user_or_group(self): - with pytest.raises(ValidationError, match="user_id.*group_id"): - DeleteMemoriesRequest(session_id="s1") - - def test_by_filters_requires_user_or_group_sender_only(self): - with pytest.raises(ValidationError, match="user_id.*group_id"): - DeleteMemoriesRequest(sender_id="sd1") - - def test_empty_request_rejected(self): - with pytest.raises(ValidationError): - DeleteMemoriesRequest() - - # --- Three-state semantics: explicit null means "match null records" --- - - def test_by_filters_explicit_null_user_id(self): - """user_id=None means 'match null records', not 'skip filter'.""" - req = DeleteMemoriesRequest(user_id=None, group_id="g1") - assert req.user_id is None - assert req.group_id == "g1" - - def test_by_filters_explicit_null_group_id(self): - """group_id=None means 'match null records', not 'skip filter'.""" - req = DeleteMemoriesRequest(user_id="u1", group_id=None) - assert req.group_id is None - assert req.user_id == "u1" - - def test_by_filters_both_null_is_valid(self): - """Both null = match records where both fields are null.""" - req = DeleteMemoriesRequest(user_id=None, group_id=None) - assert req.user_id is None - assert req.group_id is None - - def test_by_filters_null_user_with_session(self): - """Null user_id + session_id should be valid.""" - req = DeleteMemoriesRequest(user_id=None, group_id="g1", session_id="s1") - assert req.user_id is None - assert req.session_id == "s1" - - def test_by_id_with_null_user_id_is_valid(self): - """memory_id + user_id=None should be valid (None maps to null-match, - but in ID mode user_id default is MAGIC_ALL, so explicit None triggers - the validator).""" - with pytest.raises(ValidationError, match="memory_id"): - DeleteMemoriesRequest(memory_id="abc123", user_id=None) - - -# =========================================================================== -# 2. Controller Tests -# =========================================================================== - -from service.memcell_delete_service import MemCellDeleteService - - -@pytest.fixture -def mock_delete_service(): - svc = MagicMock(spec=MemCellDeleteService) - svc.delete_by_id = AsyncMock() - svc.delete_by_filters = AsyncMock() - return svc - - -@pytest.fixture -def controller(): - # Lazy import to avoid sqlmodel metaclass conflict under pytest-cov - try: - from infra_layer.adapters.input.api.memory.memory_controller import ( - MemoryController, - ) - - return MemoryController() - except TypeError: - pytest.skip("SQLModel metaclass conflict under pytest-cov") - - -class TestDeleteMemoriesController: - """Test POST /api/v1/memories/delete controller method (204 No Content).""" - - @pytest.mark.asyncio - async def test_delete_by_id(self, controller, mock_delete_service): - mock_delete_service.delete_by_id.return_value = { - "deleted_memcell_count": 1, - "deleted_episodes": 2, - "deleted_atomic_facts": 3, - "deleted_foresights": 1, - } - request_body = DeleteMemoriesRequest(memory_id="abc123") - - with patch( - "infra_layer.adapters.input.api.memory.memory_controller.get_bean_by_type", - return_value=mock_delete_service, - ): - result = await controller.delete_memories(request_body) - - assert result is None - mock_delete_service.delete_by_id.assert_called_once_with("abc123") - - @pytest.mark.asyncio - async def test_delete_by_filters(self, controller, mock_delete_service): - mock_delete_service.delete_by_filters.return_value = { - "deleted_memcell_count": 5, - "deleted_episodes": 10, - "deleted_atomic_facts": 15, - "deleted_foresights": 3, - } - request_body = DeleteMemoriesRequest( - user_id="u1", group_id="g1", session_id="s1", sender_id="sd1" - ) - - with patch( - "infra_layer.adapters.input.api.memory.memory_controller.get_bean_by_type", - return_value=mock_delete_service, - ): - result = await controller.delete_memories(request_body) - - assert result is None - mock_delete_service.delete_by_filters.assert_called_once_with( - user_id="u1", group_id="g1", session_id="s1", sender_id="sd1" - ) - - @pytest.mark.asyncio - async def test_delete_nonexistent_returns_none( - self, controller, mock_delete_service - ): - mock_delete_service.delete_by_id.return_value = { - "deleted_memcell_count": 0, - "deleted_episodes": 0, - "deleted_atomic_facts": 0, - "deleted_foresights": 0, - } - request_body = DeleteMemoriesRequest(memory_id="nonexistent") - - with patch( - "infra_layer.adapters.input.api.memory.memory_controller.get_bean_by_type", - return_value=mock_delete_service, - ): - result = await controller.delete_memories(request_body) - - assert result is None - - @pytest.mark.asyncio - async def test_delete_service_error_propagates( - self, controller, mock_delete_service - ): - mock_delete_service.delete_by_filters.side_effect = Exception("DB down") - request_body = DeleteMemoriesRequest(user_id="u1") - - with patch( - "infra_layer.adapters.input.api.memory.memory_controller.get_bean_by_type", - return_value=mock_delete_service, - ): - with pytest.raises(Exception, match="DB down"): - await controller.delete_memories(request_body) - - -# =========================================================================== -# 3. Service Tests -# =========================================================================== - - -@pytest.fixture -def mock_repos(): - """Create all mock repositories needed by MemCellDeleteService.""" - return { - "memcell_repository": MagicMock(delete_by_filters=AsyncMock(return_value=3)), - "episodic_memory_repository": MagicMock( - delete_by_filters=AsyncMock(return_value=5) - ), - "atomic_fact_repository": MagicMock( - delete_by_filters=AsyncMock(return_value=8) - ), - "foresight_repository": MagicMock(delete_by_filters=AsyncMock(return_value=2)), - "episodic_memory_milvus_repository": MagicMock( - delete_by_filters=AsyncMock(return_value=5) - ), - "atomic_fact_milvus_repository": MagicMock( - delete_by_filters=AsyncMock(return_value=8) - ), - "foresight_milvus_repository": MagicMock( - delete_by_filters=AsyncMock(return_value=2) - ), - "episodic_memory_es_repository": MagicMock( - delete_by_filters=AsyncMock(return_value=5) - ), - "atomic_fact_es_repository": MagicMock( - delete_by_filters=AsyncMock(return_value=8) - ), - "foresight_es_repository": MagicMock( - delete_by_filters=AsyncMock(return_value=2) - ), - "raw_message_repository": MagicMock( - delete_by_filters=AsyncMock(return_value=1) - ), - } - - -@pytest.fixture -def delete_service(mock_repos): - return MemCellDeleteService(**mock_repos) - - -class TestDeleteServiceById: - """Test MemCellDeleteService.delete_by_id.""" - - @pytest.mark.asyncio - async def test_delete_by_memory_id(self, delete_service, mock_repos): - result = await delete_service.delete_by_id("abc123") - - assert result["deleted_memcell_count"] == 3 - assert "deleted_episodes" in result - assert "deleted_atomic_facts" in result - assert "deleted_foresights" in result - - # Verify memcell repo called with only memcell_id - mock_repos["memcell_repository"].delete_by_filters.assert_called_once_with( - memcell_id="abc123" - ) - - @pytest.mark.asyncio - async def test_cascade_only_uses_parent_id(self, delete_service, mock_repos): - """Cascade repos should receive only parent_id, no filter params.""" - await delete_service.delete_by_id("abc123") - - mock_repos[ - "episodic_memory_repository" - ].delete_by_filters.assert_called_once_with(parent_id="abc123") - mock_repos["atomic_fact_repository"].delete_by_filters.assert_called_once_with( - parent_id="abc123" - ) - mock_repos["foresight_repository"].delete_by_filters.assert_called_once_with( - parent_id="abc123" - ) - - @pytest.mark.asyncio - async def test_milvus_es_skipped_for_id_mode(self, delete_service, mock_repos): - """Milvus/ES repos should NOT be called when deleting by ID.""" - await delete_service.delete_by_id("abc123") - - mock_repos[ - "episodic_memory_milvus_repository" - ].delete_by_filters.assert_not_called() - mock_repos[ - "atomic_fact_milvus_repository" - ].delete_by_filters.assert_not_called() - mock_repos["foresight_milvus_repository"].delete_by_filters.assert_not_called() - mock_repos[ - "episodic_memory_es_repository" - ].delete_by_filters.assert_not_called() - mock_repos["atomic_fact_es_repository"].delete_by_filters.assert_not_called() - mock_repos["foresight_es_repository"].delete_by_filters.assert_not_called() - - @pytest.mark.asyncio - async def test_memory_request_log_skipped_for_id_mode( - self, delete_service, mock_repos - ): - """MemoryRequestLog should NOT be called when deleting by ID.""" - await delete_service.delete_by_id("abc123") - - mock_repos["raw_message_repository"].delete_by_filters.assert_not_called() - - @pytest.mark.asyncio - async def test_cascade_counts_are_mongo_only(self, delete_service): - """delete_by_id cascade = MongoDB counts only (no milvus/es aggregation).""" - result = await delete_service.delete_by_id("abc123") - assert result["deleted_episodes"] == 5 # mongo only - assert result["deleted_atomic_facts"] == 8 # mongo only - assert result["deleted_foresights"] == 2 # mongo only - - @pytest.mark.asyncio - async def test_delete_raises_on_repo_error(self, delete_service, mock_repos): - mock_repos["memcell_repository"].delete_by_filters = AsyncMock( - side_effect=RuntimeError("DB down") - ) - with pytest.raises(RuntimeError, match="DB down"): - await delete_service.delete_by_id("abc123") - - -class TestDeleteServiceByFilters: - """Test MemCellDeleteService.delete_by_filters.""" - - @pytest.mark.asyncio - async def test_delete_by_filters_calls_cascade(self, delete_service, mock_repos): - result = await delete_service.delete_by_filters( - user_id="u1", group_id="g1", session_id="s1", sender_id="sd1" - ) - - # Result only contains MongoDB counts - assert result["deleted_memcell_count"] >= 0 - - @pytest.mark.asyncio - async def test_result_counts_all_stores(self, delete_service): - """DeleteResult sums counts across MongoDB, Milvus, and ES.""" - result = await delete_service.delete_by_filters(user_id="u1") - # Sum across three stores: mongo + milvus + es - assert result["deleted_episodes"] == 15 # 5 + 5 + 5 - assert result["deleted_atomic_facts"] == 24 # 8 + 8 + 8 - assert result["deleted_foresights"] == 6 # 2 + 2 + 2 - - @pytest.mark.asyncio - async def test_raw_message_not_in_result(self, delete_service): - result = await delete_service.delete_by_filters(user_id="u1") - assert "raw_message" not in result - - @pytest.mark.asyncio - async def test_raw_message_skipped_for_filter_mode( - self, delete_service, mock_repos - ): - """RawMessage (source data) should NOT be deleted by filter mode.""" - await delete_service.delete_by_filters(user_id="u1", group_id="g1") - mock_repos["raw_message_repository"].delete_by_filters.assert_not_called() - - @pytest.mark.asyncio - async def test_cascade_passes_user_and_group_to_repos( - self, delete_service, mock_repos - ): - """Verify user_id and group_id are forwarded to child repo delete_by_filters.""" - await delete_service.delete_by_filters(user_id="u1", group_id="g1") - - call_kwargs = mock_repos[ - "episodic_memory_repository" - ].delete_by_filters.call_args[1] - assert call_kwargs.get("user_id") == "u1" - assert call_kwargs.get("group_id") == "g1" - - @pytest.mark.asyncio - async def test_cascade_includes_parent_id_none(self, delete_service, mock_repos): - """Cascade from filter mode passes parent_id=None.""" - await delete_service.delete_by_filters(user_id="u1", group_id="g1") - - call_kwargs = mock_repos[ - "episodic_memory_repository" - ].delete_by_filters.call_args[1] - assert call_kwargs.get("parent_id") is None - - @pytest.mark.asyncio - async def test_sub_task_exception_counted_as_zero(self, delete_service, mock_repos): - """A failing sub-task is logged and counted as 0 (not propagated).""" - mock_repos["episodic_memory_repository"].delete_by_filters = AsyncMock( - side_effect=RuntimeError("Mongo timeout") - ) - result = await delete_service.delete_by_filters(user_id="u1") - # episodes: mongo failed(0) + milvus(5) + es(5) = 10 - assert result["deleted_episodes"] == 10 - # other types unaffected (sum all stores) - assert result["deleted_atomic_facts"] == 24 # 8 + 8 + 8 - assert result["deleted_foresights"] == 6 # 2 + 2 + 2 - - @pytest.mark.asyncio - async def test_delete_by_filters_raises_on_internal_error( - self, delete_service, mock_repos - ): - """Exception during coroutine creation should propagate.""" - delete_service.episodic_memory_repository = None - with pytest.raises(AttributeError): - await delete_service.delete_by_filters(user_id="u1") - - # --- Three-state semantics: null forwarding --- - - @pytest.mark.asyncio - async def test_null_user_id_forwarded_to_repos(self, delete_service, mock_repos): - """user_id=None is forwarded directly (three-state: match null records).""" - await delete_service.delete_by_filters(user_id=None, group_id="g1") - - call_kwargs = mock_repos[ - "episodic_memory_repository" - ].delete_by_filters.call_args[1] - assert call_kwargs["user_id"] is None - assert call_kwargs["group_id"] == "g1" - - @pytest.mark.asyncio - async def test_null_group_id_forwarded_to_repos(self, delete_service, mock_repos): - """group_id=None is forwarded directly (three-state: match null records).""" - await delete_service.delete_by_filters(user_id="u1", group_id=None) - - call_kwargs = mock_repos[ - "episodic_memory_repository" - ].delete_by_filters.call_args[1] - assert call_kwargs["group_id"] is None - assert call_kwargs["user_id"] == "u1" - - @pytest.mark.asyncio - async def test_magic_all_not_forwarded_as_filter(self, delete_service, mock_repos): - """MAGIC_ALL defaults should still be forwarded but repos should skip them.""" - await delete_service.delete_by_filters(user_id="u1") - - call_kwargs = mock_repos[ - "episodic_memory_repository" - ].delete_by_filters.call_args[1] - assert call_kwargs["user_id"] == "u1" - - @pytest.mark.asyncio - async def test_early_return_when_no_scope(self, delete_service): - """_batch_delete_records returns zeros when both user_id and group_id are MAGIC_ALL.""" - result = await delete_service._batch_delete_records() - assert result["episodes"] == 0 - assert result["atomic_facts"] == 0 - assert result["foresights"] == 0 - - -# =========================================================================== -# 4. MongoDB Repository Tests -# =========================================================================== - -from infra_layer.adapters.out.persistence.repository.memcell_raw_repository import ( - MemCellRawRepository, -) -from infra_layer.adapters.out.persistence.repository.episodic_memory_raw_repository import ( - EpisodicMemoryRawRepository, -) -from infra_layer.adapters.out.persistence.repository.atomic_fact_record_raw_repository import ( - AtomicFactRecordRawRepository, -) -from infra_layer.adapters.out.persistence.repository.foresight_record_raw_repository import ( - ForesightRecordRawRepository, -) - - -# --------------------------------------------------------------------------- -# 4a. MemCellRawRepository -# --------------------------------------------------------------------------- - - -class TestMemCellRepositoryDeleteByFilters: - """Test MemCellRawRepository.delete_by_filters with three-state semantics.""" - - @pytest.fixture - def repo(self): - repo = MemCellRawRepository.__new__(MemCellRawRepository) - repo.model = MagicMock() - repo.model.delete_many = AsyncMock(return_value=MagicMock(modified_count=3)) - return repo - - @pytest.mark.asyncio - async def test_session_id_filter(self, repo): - await repo.delete_by_filters(group_id="g1", session_id="s1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == "s1" - - @pytest.mark.asyncio - async def test_none_values_excluded_from_filter(self, repo): - """None values should not appear in filter_dict.""" - await repo.delete_by_filters(group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert "session_id" not in filter_dict - assert "memcell_id" not in filter_dict - - @pytest.mark.asyncio - async def test_all_filters_combined(self, repo): - valid_oid = "507f1f77bcf86cd799439011" - await repo.delete_by_filters( - memcell_id=valid_oid, group_id="g1", session_id="s1" - ) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == "g1" - assert filter_dict["session_id"] == "s1" - assert "_id" in filter_dict - - @pytest.mark.asyncio - async def test_delete_many_returns_none(self, repo): - """Cover the `result else 0` branch when delete_many returns None.""" - repo.model.delete_many = AsyncMock(return_value=None) - result = await repo.delete_by_filters(group_id="g1") - assert result == 0 - - @pytest.mark.asyncio - async def test_memcell_id_only_filter(self, repo): - """Cover memcell_id-only path without group_id or session_id.""" - valid_oid = "507f1f77bcf86cd799439011" - await repo.delete_by_filters(memcell_id=valid_oid) - filter_dict = repo.model.delete_many.call_args[0][0] - assert "_id" in filter_dict - assert "group_id" not in filter_dict - assert "session_id" not in filter_dict - - @pytest.mark.asyncio - async def test_empty_filters_returns_zero(self, repo): - result = await repo.delete_by_filters() - assert result == 0 - repo.model.delete_many.assert_not_called() - - # --- Three-state null-matching for group_id and session_id --- - - @pytest.mark.asyncio - async def test_null_group_id_matches_none_and_empty(self, repo): - """group_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(group_id=None, session_id="s1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == {"$in": [None, ""]} - assert filter_dict["session_id"] == "s1" - - @pytest.mark.asyncio - async def test_empty_string_group_id_matches_none_and_empty(self, repo): - """group_id='' should also generate $in: [None, ''] filter.""" - await repo.delete_by_filters(group_id="", session_id="s1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_null_session_id_matches_none_and_empty(self, repo): - """session_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(group_id="g1", session_id=None) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == {"$in": [None, ""]} - assert filter_dict["group_id"] == "g1" - - @pytest.mark.asyncio - async def test_empty_string_session_id_matches_none_and_empty(self, repo): - """session_id='' should also generate $in: [None, ''] filter.""" - await repo.delete_by_filters(group_id="g1", session_id="") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_magic_all_group_id_skips_filter(self, repo): - """MAGIC_ALL group_id should not appear in filter_dict.""" - await repo.delete_by_filters(group_id=MAGIC_ALL, session_id="s1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert "group_id" not in filter_dict - - -# --------------------------------------------------------------------------- -# 4b. EpisodicMemoryRawRepository -# --------------------------------------------------------------------------- - - -class TestEpisodicMemoryRepositoryDeleteByFilters: - """Test EpisodicMemoryRawRepository.delete_by_filters with three-state semantics.""" - - @pytest.fixture - def repo(self): - repo = EpisodicMemoryRawRepository.__new__(EpisodicMemoryRawRepository) - repo.model = MagicMock() - repo.model.delete_many = AsyncMock(return_value=MagicMock(modified_count=5)) - return repo - - @pytest.mark.asyncio - async def test_group_id_filter(self, repo): - await repo.delete_by_filters(group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == "g1" - - @pytest.mark.asyncio - async def test_session_id_filter(self, repo): - await repo.delete_by_filters(user_id="u1", session_id="s1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == "s1" - - @pytest.mark.asyncio - async def test_sender_id_maps_to_sender_ids(self, repo): - await repo.delete_by_filters(user_id="u1", sender_id="sd1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_ids"] == "sd1" - - @pytest.mark.asyncio - async def test_none_values_excluded_from_filter(self, repo): - await repo.delete_by_filters(user_id="u1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert "session_id" not in filter_dict - assert "sender_ids" not in filter_dict - assert "group_id" not in filter_dict - - @pytest.mark.asyncio - async def test_parent_id_still_works(self, repo): - await repo.delete_by_filters(user_id="u1", parent_id="p1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["parent_id"] == "p1" - - @pytest.mark.asyncio - async def test_delete_many_returns_none(self, repo): - """Cover the `result else 0` branch when delete_many returns None.""" - repo.model.delete_many = AsyncMock(return_value=None) - result = await repo.delete_by_filters(user_id="u1") - assert result == 0 - - @pytest.mark.asyncio - async def test_empty_filters_returns_zero(self, repo): - result = await repo.delete_by_filters() - assert result == 0 - repo.model.delete_many.assert_not_called() - - # --- Three-state null-matching --- - - @pytest.mark.asyncio - async def test_null_user_id_matches_none_and_empty(self, repo): - """user_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id=None, group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["user_id"] == {"$in": [None, ""]} - assert filter_dict["group_id"] == "g1" - - @pytest.mark.asyncio - async def test_empty_string_user_id_matches_none_and_empty(self, repo): - """user_id='' should also generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="", group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["user_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_magic_all_skips_user_id_filter(self, repo): - """MAGIC_ALL should not appear in filter_dict.""" - await repo.delete_by_filters(user_id=MAGIC_ALL, group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert "user_id" not in filter_dict - assert filter_dict["group_id"] == "g1" - - @pytest.mark.asyncio - async def test_null_group_id_matches_none_and_empty(self, repo): - """group_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", group_id=None) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == {"$in": [None, ""]} - assert filter_dict["user_id"] == "u1" - - @pytest.mark.asyncio - async def test_empty_string_group_id_matches_none_and_empty(self, repo): - """group_id='' should also generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", group_id="") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_null_session_id_matches_none_and_empty(self, repo): - """session_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", session_id=None) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_empty_string_session_id_matches_none_and_empty(self, repo): - """session_id='' should also generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", session_id="") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_null_sender_id_matches_none_and_empty(self, repo): - """sender_id=None should generate $in: [None, ''] for sender_ids field.""" - await repo.delete_by_filters(user_id="u1", sender_id=None) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_ids"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_empty_string_sender_id_matches_none_and_empty(self, repo): - """sender_id='' should generate $in: [None, ''] for sender_ids field.""" - await repo.delete_by_filters(user_id="u1", sender_id="") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_ids"] == {"$in": [None, ""]} - - -# --------------------------------------------------------------------------- -# 4c. AtomicFactRecordRawRepository -# --------------------------------------------------------------------------- - - -class TestAtomicFactRecordRepositoryDeleteByFilters: - """Test AtomicFactRecordRawRepository.delete_by_filters with three-state semantics.""" - - @pytest.fixture - def repo(self): - repo = AtomicFactRecordRawRepository.__new__(AtomicFactRecordRawRepository) - repo.model = MagicMock() - repo.model.delete_many = AsyncMock(return_value=MagicMock(modified_count=8)) - return repo - - @pytest.mark.asyncio - async def test_group_id_filter(self, repo): - await repo.delete_by_filters(group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == "g1" - - @pytest.mark.asyncio - async def test_session_id_filter(self, repo): - await repo.delete_by_filters(user_id="u1", session_id="s1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == "s1" - - @pytest.mark.asyncio - async def test_sender_id_maps_to_sender_ids(self, repo): - await repo.delete_by_filters(user_id="u1", sender_id="sd1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_ids"] == "sd1" - - @pytest.mark.asyncio - async def test_none_values_excluded_from_filter(self, repo): - await repo.delete_by_filters(user_id="u1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert "session_id" not in filter_dict - assert "sender_ids" not in filter_dict - assert "group_id" not in filter_dict - - @pytest.mark.asyncio - async def test_parent_id_still_works(self, repo): - await repo.delete_by_filters(user_id="u1", parent_id="p1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["parent_id"] == "p1" - - @pytest.mark.asyncio - async def test_delete_many_returns_none(self, repo): - """Cover the `result else 0` branch when delete_many returns None.""" - repo.model.delete_many = AsyncMock(return_value=None) - result = await repo.delete_by_filters(user_id="u1") - assert result == 0 - - @pytest.mark.asyncio - async def test_empty_filters_returns_zero(self, repo): - result = await repo.delete_by_filters() - assert result == 0 - repo.model.delete_many.assert_not_called() - - # --- Three-state null-matching --- - - @pytest.mark.asyncio - async def test_null_user_id_matches_none_and_empty(self, repo): - """user_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id=None, group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["user_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_magic_all_skips_user_id_filter(self, repo): - """MAGIC_ALL should not appear in filter_dict.""" - await repo.delete_by_filters(user_id=MAGIC_ALL, group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert "user_id" not in filter_dict - - @pytest.mark.asyncio - async def test_empty_string_group_id_matches_none_and_empty(self, repo): - """group_id='' should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", group_id="") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_null_group_id_matches_none_and_empty(self, repo): - """group_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", group_id=None) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_null_session_id_matches_none_and_empty(self, repo): - """session_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", session_id=None) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_empty_string_session_id_matches_none_and_empty(self, repo): - """session_id='' should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", session_id="") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_null_sender_id_matches_none_and_empty(self, repo): - """sender_id=None should generate $in: [None, ''] for sender_ids field.""" - await repo.delete_by_filters(user_id="u1", sender_id=None) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_ids"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_empty_string_sender_id_matches_none_and_empty(self, repo): - """sender_id='' should generate $in: [None, ''] for sender_ids field.""" - await repo.delete_by_filters(user_id="u1", sender_id="") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_ids"] == {"$in": [None, ""]} - - -# --------------------------------------------------------------------------- -# 4d. ForesightRecordRawRepository -# --------------------------------------------------------------------------- - - -class TestForesightRecordRepositoryDeleteByFilters: - """Test ForesightRecordRawRepository.delete_by_filters with three-state semantics.""" - - @pytest.fixture - def repo(self): - repo = ForesightRecordRawRepository.__new__(ForesightRecordRawRepository) - repo.model = MagicMock() - repo.model.delete_many = AsyncMock(return_value=MagicMock(modified_count=2)) - return repo - - @pytest.mark.asyncio - async def test_group_id_filter(self, repo): - await repo.delete_by_filters(group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == "g1" - - @pytest.mark.asyncio - async def test_session_id_filter(self, repo): - await repo.delete_by_filters(user_id="u1", session_id="s1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == "s1" - - @pytest.mark.asyncio - async def test_sender_id_maps_to_sender_ids(self, repo): - await repo.delete_by_filters(user_id="u1", sender_id="sd1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_ids"] == "sd1" - - @pytest.mark.asyncio - async def test_none_values_excluded_from_filter(self, repo): - await repo.delete_by_filters(user_id="u1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert "session_id" not in filter_dict - assert "sender_ids" not in filter_dict - assert "group_id" not in filter_dict - - @pytest.mark.asyncio - async def test_parent_id_still_works(self, repo): - await repo.delete_by_filters(user_id="u1", parent_id="p1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["parent_id"] == "p1" - - @pytest.mark.asyncio - async def test_delete_many_returns_none(self, repo): - """Cover the `result else 0` branch when delete_many returns None.""" - repo.model.delete_many = AsyncMock(return_value=None) - result = await repo.delete_by_filters(user_id="u1") - assert result == 0 - - @pytest.mark.asyncio - async def test_empty_filters_returns_zero(self, repo): - result = await repo.delete_by_filters() - assert result == 0 - repo.model.delete_many.assert_not_called() - - # --- Three-state null-matching --- - - @pytest.mark.asyncio - async def test_null_user_id_matches_none_and_empty(self, repo): - """user_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id=None, group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["user_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_magic_all_skips_user_id_filter(self, repo): - """MAGIC_ALL should not appear in filter_dict.""" - await repo.delete_by_filters(user_id=MAGIC_ALL, group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert "user_id" not in filter_dict - - @pytest.mark.asyncio - async def test_empty_string_group_id_matches_none_and_empty(self, repo): - """group_id='' should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", group_id="") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_null_group_id_matches_none_and_empty(self, repo): - """group_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", group_id=None) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_null_session_id_matches_none_and_empty(self, repo): - """session_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", session_id=None) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_empty_string_session_id_matches_none_and_empty(self, repo): - """session_id='' should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(user_id="u1", session_id="") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["session_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_null_sender_id_matches_none_and_empty(self, repo): - """sender_id=None should generate $in: [None, ''] for sender_ids field.""" - await repo.delete_by_filters(user_id="u1", sender_id=None) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_ids"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_empty_string_sender_id_matches_none_and_empty(self, repo): - """sender_id='' should generate $in: [None, ''] for sender_ids field.""" - await repo.delete_by_filters(user_id="u1", sender_id="") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_ids"] == {"$in": [None, ""]} - - -# --------------------------------------------------------------------------- -# 4e. MemoryRequestLogRepository -# --------------------------------------------------------------------------- - - -class TestRawMessageRepositoryDeleteByFilters: - """Test RawMessageRepository.delete_by_filters with Optional[str]=None.""" - - @pytest.fixture - def repo(self): - from infra_layer.adapters.out.persistence.repository.raw_message_repository import ( - RawMessageRepository, - ) - - repo = RawMessageRepository.__new__(RawMessageRepository) - repo.model = MagicMock() - repo.model.delete_many = AsyncMock(return_value=MagicMock(modified_count=4)) - return repo - - @pytest.mark.asyncio - async def test_sender_id_filter(self, repo): - await repo.delete_by_filters(sender_id="u1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_id"] == "u1" - assert "group_id" not in filter_dict - - @pytest.mark.asyncio - async def test_group_id_filter(self, repo): - await repo.delete_by_filters(group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == "g1" - assert "sender_id" not in filter_dict - - @pytest.mark.asyncio - async def test_both_filters(self, repo): - await repo.delete_by_filters(sender_id="u1", group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_id"] == "u1" - assert filter_dict["group_id"] == "g1" - - @pytest.mark.asyncio - async def test_none_values_excluded(self, repo): - """MAGIC_ALL defaults should not appear in filter_dict.""" - await repo.delete_by_filters(sender_id="u1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert "group_id" not in filter_dict - - @pytest.mark.asyncio - async def test_delete_many_returns_none(self, repo): - """Cover the `result else 0` branch when delete_many returns None.""" - repo.model.delete_many = AsyncMock(return_value=None) - result = await repo.delete_by_filters(sender_id="u1") - assert result == 0 - - @pytest.mark.asyncio - async def test_empty_filters_returns_zero(self, repo): - result = await repo.delete_by_filters() - assert result == 0 - repo.model.delete_many.assert_not_called() - - # --- Three-state null-matching --- - - @pytest.mark.asyncio - async def test_null_sender_id_matches_none_and_empty(self, repo): - """sender_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(sender_id=None, group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["sender_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_magic_all_skips_sender_id_filter(self, repo): - """MAGIC_ALL should not appear in filter_dict.""" - await repo.delete_by_filters(sender_id=MAGIC_ALL, group_id="g1") - filter_dict = repo.model.delete_many.call_args[0][0] - assert "sender_id" not in filter_dict - - @pytest.mark.asyncio - async def test_null_group_id_matches_none_and_empty(self, repo): - """group_id=None should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(sender_id="u1", group_id=None) - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == {"$in": [None, ""]} - - @pytest.mark.asyncio - async def test_empty_string_group_id_matches_none_and_empty(self, repo): - """group_id='' should generate $in: [None, ''] filter.""" - await repo.delete_by_filters(sender_id="u1", group_id="") - filter_dict = repo.model.delete_many.call_args[0][0] - assert filter_dict["group_id"] == {"$in": [None, ""]} - - -# =========================================================================== -# 5. Elasticsearch Repository Tests -# =========================================================================== - -from infra_layer.adapters.out.search.repository.episodic_memory_es_repository import ( - EpisodicMemoryEsRepository, -) -from infra_layer.adapters.out.search.repository.atomic_fact_es_repository import ( - AtomicFactEsRepository, -) -from infra_layer.adapters.out.search.repository.foresight_es_repository import ( - ForesightEsRepository, -) - - -def _make_es_repo(repo_cls, deleted_count=7): - """Create an ES repo instance with mocked client, bypassing __init__.""" - repo = repo_cls.__new__(repo_cls) - mock_client = AsyncMock() - mock_client.delete_by_query = AsyncMock(return_value={"deleted": deleted_count}) - repo.get_client = AsyncMock(return_value=mock_client) - repo.get_index_name = MagicMock(return_value="test-index") - return repo, mock_client - - -# --------------------------------------------------------------------------- -# 5a. EpisodicMemoryEsRepository -# --------------------------------------------------------------------------- - - -class TestEpisodicMemoryEsRepositoryDeleteByFilters: - """Test EpisodicMemoryEsRepository.delete_by_filters. - - Note: Episodic ES has separate `except ValueError` and `except Exception` - clauses, unlike AtomicFact/Foresight ES which only have `except Exception`. - """ - - @pytest.fixture - def repo_and_client(self): - return _make_es_repo(EpisodicMemoryEsRepository) - - @pytest.mark.asyncio - async def test_user_id_exact_match(self, repo_and_client): - repo, client = repo_and_client - result = await repo.delete_by_filters(user_id="u1") - assert result == 7 - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"user_id": "u1"}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_user_id_null_matches_empty_string(self, repo_and_client): - """user_id=None -> ES term query for empty string.""" - repo, client = repo_and_client - await repo.delete_by_filters(user_id=None, group_id="g1") - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"user_id": ""}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_user_id_empty_string_matches_empty(self, repo_and_client): - """user_id='' -> ES term query for empty string.""" - repo, client = repo_and_client - await repo.delete_by_filters(user_id="", group_id="g1") - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"user_id": ""}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_group_id_exact_match(self, repo_and_client): - repo, client = repo_and_client - await repo.delete_by_filters(group_id="g1") - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"group_id": "g1"}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_group_id_null_matches_empty_string(self, repo_and_client): - repo, client = repo_and_client - await repo.delete_by_filters(user_id="u1", group_id=None) - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"group_id": ""}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_group_id_empty_string_matches_empty(self, repo_and_client): - repo, client = repo_and_client - await repo.delete_by_filters(user_id="u1", group_id="") - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"group_id": ""}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_date_range_filter(self, repo_and_client): - repo, client = repo_and_client - dr = {"gte": "2024-01-01", "lte": "2024-12-31"} - await repo.delete_by_filters(user_id="u1", date_range=dr) - body = client.delete_by_query.call_args[1]["body"] - assert {"range": {"timestamp": dr}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_combined_filters(self, repo_and_client): - repo, client = repo_and_client - dr = {"gte": "2024-01-01"} - result = await repo.delete_by_filters( - user_id="u1", group_id="g1", date_range=dr - ) - assert result == 7 - body = client.delete_by_query.call_args[1]["body"] - must = body["query"]["bool"]["must"] - assert len(must) == 3 - - @pytest.mark.asyncio - async def test_empty_filters_raises_value_error(self, repo_and_client): - """All MAGIC_ALL -> ValueError (safety protection).""" - repo, _ = repo_and_client - with pytest.raises(ValueError, match="At least one filter"): - await repo.delete_by_filters() - - @pytest.mark.asyncio - async def test_client_exception_propagates(self, repo_and_client): - """Non-ValueError exception -> caught by except Exception, re-raised.""" - repo, client = repo_and_client - client.delete_by_query = AsyncMock( - side_effect=RuntimeError("ES connection refused") - ) - with pytest.raises(RuntimeError, match="ES connection refused"): - await repo.delete_by_filters(user_id="u1") - - @pytest.mark.asyncio - async def test_magic_all_skips_user_id(self, repo_and_client): - """user_id=MAGIC_ALL should not add user_id to filter.""" - repo, client = repo_and_client - await repo.delete_by_filters(user_id=MAGIC_ALL, group_id="g1") - body = client.delete_by_query.call_args[1]["body"] - must = body["query"]["bool"]["must"] - assert len(must) == 1 - assert must[0] == {"term": {"group_id": "g1"}} - - -# --------------------------------------------------------------------------- -# 5b. AtomicFactEsRepository -# --------------------------------------------------------------------------- - - -class TestAtomicFactEsRepositoryDeleteByFilters: - """Test AtomicFactEsRepository.delete_by_filters. - - AtomicFact ES has only `except Exception` (no separate ValueError catch). - """ - - @pytest.fixture - def repo_and_client(self): - return _make_es_repo(AtomicFactEsRepository, deleted_count=12) - - @pytest.mark.asyncio - async def test_user_id_exact_match(self, repo_and_client): - repo, client = repo_and_client - result = await repo.delete_by_filters(user_id="u1") - assert result == 12 - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"user_id": "u1"}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_user_id_null_matches_empty(self, repo_and_client): - repo, client = repo_and_client - await repo.delete_by_filters(user_id=None, group_id="g1") - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"user_id": ""}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_group_id_exact_match(self, repo_and_client): - repo, client = repo_and_client - await repo.delete_by_filters(group_id="g1") - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"group_id": "g1"}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_group_id_null_matches_empty(self, repo_and_client): - repo, client = repo_and_client - await repo.delete_by_filters(user_id="u1", group_id=None) - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"group_id": ""}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_date_range_filter(self, repo_and_client): - repo, client = repo_and_client - dr = {"gte": "2024-01-01"} - await repo.delete_by_filters(user_id="u1", date_range=dr) - body = client.delete_by_query.call_args[1]["body"] - assert {"range": {"timestamp": dr}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_empty_filters_raises_value_error(self, repo_and_client): - repo, _ = repo_and_client - with pytest.raises(ValueError, match="At least one filter"): - await repo.delete_by_filters() - - @pytest.mark.asyncio - async def test_client_exception_propagates(self, repo_and_client): - repo, client = repo_and_client - client.delete_by_query = AsyncMock(side_effect=RuntimeError("ES timeout")) - with pytest.raises(RuntimeError, match="ES timeout"): - await repo.delete_by_filters(user_id="u1") - - @pytest.mark.asyncio - async def test_magic_all_skips_both_fields(self, repo_and_client): - """Both MAGIC_ALL -> filter_queries empty -> ValueError.""" - repo, _ = repo_and_client - with pytest.raises(ValueError): - await repo.delete_by_filters(user_id=MAGIC_ALL, group_id=MAGIC_ALL) - - -# --------------------------------------------------------------------------- -# 5c. ForesightEsRepository -# --------------------------------------------------------------------------- - - -class TestForesightEsRepositoryDeleteByFilters: - """Test ForesightEsRepository.delete_by_filters.""" - - @pytest.fixture - def repo_and_client(self): - return _make_es_repo(ForesightEsRepository, deleted_count=4) - - @pytest.mark.asyncio - async def test_user_id_exact_match(self, repo_and_client): - repo, client = repo_and_client - result = await repo.delete_by_filters(user_id="u1") - assert result == 4 - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"user_id": "u1"}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_user_id_null_matches_empty(self, repo_and_client): - repo, client = repo_and_client - await repo.delete_by_filters(user_id=None, group_id="g1") - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"user_id": ""}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_group_id_exact_match(self, repo_and_client): - repo, client = repo_and_client - await repo.delete_by_filters(group_id="g1") - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"group_id": "g1"}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_group_id_null_matches_empty(self, repo_and_client): - repo, client = repo_and_client - await repo.delete_by_filters(user_id="u1", group_id=None) - body = client.delete_by_query.call_args[1]["body"] - assert {"term": {"group_id": ""}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_date_range_filter(self, repo_and_client): - repo, client = repo_and_client - dr = {"gte": "2024-06-01"} - await repo.delete_by_filters(user_id="u1", date_range=dr) - body = client.delete_by_query.call_args[1]["body"] - assert {"range": {"created_at": dr}} in body["query"]["bool"]["must"] - - @pytest.mark.asyncio - async def test_empty_filters_raises_value_error(self, repo_and_client): - repo, _ = repo_and_client - with pytest.raises(ValueError, match="At least one filter"): - await repo.delete_by_filters() - - @pytest.mark.asyncio - async def test_client_exception_propagates(self, repo_and_client): - repo, client = repo_and_client - client.delete_by_query = AsyncMock(side_effect=RuntimeError("ES down")) - with pytest.raises(RuntimeError, match="ES down"): - await repo.delete_by_filters(user_id="u1") - - @pytest.mark.asyncio - async def test_magic_all_user_id_skips_filter(self, repo_and_client): - repo, client = repo_and_client - await repo.delete_by_filters(user_id=MAGIC_ALL, group_id="g1") - body = client.delete_by_query.call_args[1]["body"] - must = body["query"]["bool"]["must"] - assert len(must) == 1 - assert must[0] == {"term": {"group_id": "g1"}} - - -# =========================================================================== -# 6. Milvus Repository Tests -# =========================================================================== - -from infra_layer.adapters.out.search.repository.episodic_memory_milvus_repository import ( - EpisodicMemoryMilvusRepository, -) -from infra_layer.adapters.out.search.repository.atomic_fact_milvus_repository import ( - AtomicFactMilvusRepository, -) -from infra_layer.adapters.out.search.repository.foresight_milvus_repository import ( - ForesightMilvusRepository, -) - - -def _make_milvus_repo(repo_cls, query_results=None): - """Create a Milvus repo instance with mocked collection, bypassing __init__.""" - if query_results is None: - query_results = [{"id": "1"}, {"id": "2"}, {"id": "3"}] - repo = repo_cls.__new__(repo_cls) - repo.collection = MagicMock() - repo.collection.query = AsyncMock(return_value=query_results) - repo.collection.delete = AsyncMock() - return repo - - -# --------------------------------------------------------------------------- -# 6a. EpisodicMemoryMilvusRepository -# --------------------------------------------------------------------------- - - -class TestEpisodicMemoryMilvusRepositoryDeleteByFilters: - """Test EpisodicMemoryMilvusRepository.delete_by_filters. - - Uses `timestamp` field for time filters (not start_time/end_time). - """ - - @pytest.fixture - def repo(self): - return _make_milvus_repo(EpisodicMemoryMilvusRepository) - - @pytest.mark.asyncio - async def test_user_id_exact_match(self, repo): - result = await repo.delete_by_filters(user_id="u1") - assert result == 3 - expr = repo.collection.query.call_args[1]["expr"] - assert 'user_id == "u1"' in expr - repo.collection.delete.assert_called_once() - - @pytest.mark.asyncio - async def test_user_id_null_matches_empty_string(self, repo): - """user_id=None -> Milvus expr user_id == '' .""" - await repo.delete_by_filters(user_id=None, group_id="g1") - expr = repo.collection.query.call_args[1]["expr"] - assert 'user_id == ""' in expr - - @pytest.mark.asyncio - async def test_user_id_empty_string_matches_empty(self, repo): - await repo.delete_by_filters(user_id="", group_id="g1") - expr = repo.collection.query.call_args[1]["expr"] - assert 'user_id == ""' in expr - - @pytest.mark.asyncio - async def test_group_id_exact_match(self, repo): - await repo.delete_by_filters(group_id="g1") - expr = repo.collection.query.call_args[1]["expr"] - assert 'group_id == "g1"' in expr - - @pytest.mark.asyncio - async def test_group_id_null_matches_empty_string(self, repo): - await repo.delete_by_filters(user_id="u1", group_id=None) - expr = repo.collection.query.call_args[1]["expr"] - assert 'group_id == ""' in expr - - @pytest.mark.asyncio - async def test_group_id_empty_string_matches_empty(self, repo): - await repo.delete_by_filters(user_id="u1", group_id="") - expr = repo.collection.query.call_args[1]["expr"] - assert 'group_id == ""' in expr - - @pytest.mark.asyncio - async def test_start_time_filter(self, repo): - t = datetime(2024, 6, 15, 12, 0, 0, tzinfo=timezone.utc) - await repo.delete_by_filters(user_id="u1", start_time=t) - expr = repo.collection.query.call_args[1]["expr"] - assert f"timestamp >= {int(t.timestamp())}" in expr - - @pytest.mark.asyncio - async def test_end_time_filter(self, repo): - t = datetime(2024, 12, 31, 23, 59, 59, tzinfo=timezone.utc) - await repo.delete_by_filters(user_id="u1", end_time=t) - expr = repo.collection.query.call_args[1]["expr"] - assert f"timestamp <= {int(t.timestamp())}" in expr - - @pytest.mark.asyncio - async def test_combined_time_filters(self, repo): - t1 = datetime(2024, 1, 1, tzinfo=timezone.utc) - t2 = datetime(2024, 12, 31, tzinfo=timezone.utc) - await repo.delete_by_filters(user_id="u1", start_time=t1, end_time=t2) - expr = repo.collection.query.call_args[1]["expr"] - assert "timestamp >=" in expr - assert "timestamp <=" in expr - - @pytest.mark.asyncio - async def test_empty_filters_raises_value_error(self, repo): - with pytest.raises(ValueError, match="At least one filter"): - await repo.delete_by_filters() - - @pytest.mark.asyncio - async def test_query_returns_empty_list(self, repo): - """When no documents match, delete_count should be 0.""" - repo.collection.query = AsyncMock(return_value=[]) - result = await repo.delete_by_filters(user_id="u1") - assert result == 0 - repo.collection.delete.assert_called_once() - - @pytest.mark.asyncio - async def test_collection_exception_propagates(self, repo): - repo.collection.query = AsyncMock(side_effect=RuntimeError("Milvus down")) - with pytest.raises(RuntimeError, match="Milvus down"): - await repo.delete_by_filters(user_id="u1") - - @pytest.mark.asyncio - async def test_magic_all_skips_user_id(self, repo): - await repo.delete_by_filters(user_id=MAGIC_ALL, group_id="g1") - expr = repo.collection.query.call_args[1]["expr"] - assert "user_id" not in expr - assert 'group_id == "g1"' in expr - - -# --------------------------------------------------------------------------- -# 6b. AtomicFactMilvusRepository -# --------------------------------------------------------------------------- - - -class TestAtomicFactMilvusRepositoryDeleteByFilters: - """Test AtomicFactMilvusRepository.delete_by_filters. - - Uses `timestamp` field for time filters (same as Episodic). - """ - - @pytest.fixture - def repo(self): - return _make_milvus_repo(AtomicFactMilvusRepository) - - @pytest.mark.asyncio - async def test_user_id_exact_match(self, repo): - result = await repo.delete_by_filters(user_id="u1") - assert result == 3 - expr = repo.collection.query.call_args[1]["expr"] - assert 'user_id == "u1"' in expr - - @pytest.mark.asyncio - async def test_user_id_null_matches_empty_string(self, repo): - await repo.delete_by_filters(user_id=None, group_id="g1") - expr = repo.collection.query.call_args[1]["expr"] - assert 'user_id == ""' in expr - - @pytest.mark.asyncio - async def test_group_id_exact_match(self, repo): - await repo.delete_by_filters(group_id="g1") - expr = repo.collection.query.call_args[1]["expr"] - assert 'group_id == "g1"' in expr - - @pytest.mark.asyncio - async def test_group_id_null_matches_empty_string(self, repo): - await repo.delete_by_filters(user_id="u1", group_id=None) - expr = repo.collection.query.call_args[1]["expr"] - assert 'group_id == ""' in expr - - @pytest.mark.asyncio - async def test_start_time_filter(self, repo): - t = datetime(2024, 6, 15, 12, 0, 0, tzinfo=timezone.utc) - await repo.delete_by_filters(user_id="u1", start_time=t) - expr = repo.collection.query.call_args[1]["expr"] - assert f"timestamp >= {int(t.timestamp())}" in expr - - @pytest.mark.asyncio - async def test_end_time_filter(self, repo): - t = datetime(2024, 12, 31, 23, 59, 59, tzinfo=timezone.utc) - await repo.delete_by_filters(user_id="u1", end_time=t) - expr = repo.collection.query.call_args[1]["expr"] - assert f"timestamp <= {int(t.timestamp())}" in expr - - @pytest.mark.asyncio - async def test_empty_filters_raises_value_error(self, repo): - with pytest.raises(ValueError, match="At least one filter"): - await repo.delete_by_filters() - - @pytest.mark.asyncio - async def test_query_returns_empty_list(self, repo): - repo.collection.query = AsyncMock(return_value=[]) - result = await repo.delete_by_filters(user_id="u1") - assert result == 0 - - @pytest.mark.asyncio - async def test_collection_exception_propagates(self, repo): - repo.collection.query = AsyncMock(side_effect=RuntimeError("Milvus down")) - with pytest.raises(RuntimeError, match="Milvus down"): - await repo.delete_by_filters(user_id="u1") - - @pytest.mark.asyncio - async def test_magic_all_skips_user_id(self, repo): - await repo.delete_by_filters(user_id=MAGIC_ALL, group_id="g1") - expr = repo.collection.query.call_args[1]["expr"] - assert "user_id" not in expr - - -# --------------------------------------------------------------------------- -# 6c. ForesightMilvusRepository -# --------------------------------------------------------------------------- - - -class TestForesightMilvusRepositoryDeleteByFilters: - """Test ForesightMilvusRepository.delete_by_filters. - - IMPORTANT: Foresight uses `start_time`/`end_time` field names in Milvus - expressions, NOT `timestamp` like Episodic/AtomicFact. - """ - - @pytest.fixture - def repo(self): - return _make_milvus_repo(ForesightMilvusRepository) - - @pytest.mark.asyncio - async def test_user_id_exact_match(self, repo): - result = await repo.delete_by_filters(user_id="u1") - assert result == 3 - expr = repo.collection.query.call_args[1]["expr"] - assert 'user_id == "u1"' in expr - - @pytest.mark.asyncio - async def test_user_id_null_matches_empty_string(self, repo): - await repo.delete_by_filters(user_id=None, group_id="g1") - expr = repo.collection.query.call_args[1]["expr"] - assert 'user_id == ""' in expr - - @pytest.mark.asyncio - async def test_group_id_exact_match(self, repo): - await repo.delete_by_filters(group_id="g1") - expr = repo.collection.query.call_args[1]["expr"] - assert 'group_id == "g1"' in expr - - @pytest.mark.asyncio - async def test_group_id_null_matches_empty_string(self, repo): - await repo.delete_by_filters(user_id="u1", group_id=None) - expr = repo.collection.query.call_args[1]["expr"] - assert 'group_id == ""' in expr - - @pytest.mark.asyncio - async def test_start_time_filter_uses_timestamp_field(self, repo): - """Foresight delete uses `timestamp >= X` (milliseconds).""" - t = datetime(2024, 6, 15, 12, 0, 0, tzinfo=timezone.utc) - await repo.delete_by_filters(user_id="u1", start_time=t) - expr = repo.collection.query.call_args[1]["expr"] - assert f"timestamp >= {int(t.timestamp() * 1000)}" in expr - - @pytest.mark.asyncio - async def test_end_time_filter_uses_timestamp_field(self, repo): - """Foresight delete uses `timestamp <= X` (milliseconds).""" - t = datetime(2024, 12, 31, 23, 59, 59, tzinfo=timezone.utc) - await repo.delete_by_filters(user_id="u1", end_time=t) - expr = repo.collection.query.call_args[1]["expr"] - assert f"timestamp <= {int(t.timestamp() * 1000)}" in expr - - @pytest.mark.asyncio - async def test_empty_filters_raises_value_error(self, repo): - with pytest.raises(ValueError, match="At least one filter"): - await repo.delete_by_filters() - - @pytest.mark.asyncio - async def test_query_returns_empty_list(self, repo): - repo.collection.query = AsyncMock(return_value=[]) - result = await repo.delete_by_filters(user_id="u1") - assert result == 0 - - @pytest.mark.asyncio - async def test_collection_exception_propagates(self, repo): - repo.collection.query = AsyncMock(side_effect=RuntimeError("Milvus down")) - with pytest.raises(RuntimeError, match="Milvus down"): - await repo.delete_by_filters(user_id="u1") - - @pytest.mark.asyncio - async def test_magic_all_skips_user_id(self, repo): - await repo.delete_by_filters(user_id=MAGIC_ALL, group_id="g1") - expr = repo.collection.query.call_args[1]["expr"] - assert "user_id" not in expr diff --git a/methods/EverCore/tests/test_msg_group_queue_manager.py b/methods/EverCore/tests/test_msg_group_queue_manager.py deleted file mode 100644 index 8fe02571..00000000 --- a/methods/EverCore/tests/test_msg_group_queue_manager.py +++ /dev/null @@ -1,715 +0,0 @@ -#!/usr/bin/env python3 -""" -Refactored complete test suite for MsgGroupQueueManager -""" - -import asyncio -import time -import uuid -from collections import defaultdict -from typing import Any, Dict, List, Optional, Tuple - -from core.queue.msg_group_queue.msg_group_queue_manager import ( - MsgGroupQueueManager, - QueueStats, - ManagerStats, - ShutdownMode, - TimeWindowStats, -) -from core.queue.msg_group_queue.msg_group_queue_manager_factory import ( - MsgGroupQueueManagerFactory, - MsgGroupQueueConfig, -) - -# ============ Basic test functions ============ - - -async def test_basic_functionality(): - """Basic functionality test""" - manager = MsgGroupQueueManager("basic_test", num_queues=3, max_total_messages=10) - - # Delivery test - success = await manager.deliver_message("test_user", {"msg": "hello"}) - assert success, "Basic delivery failed" - - # Consumption test - target_queue = manager._hash_route("test_user") # pylint: disable=protected-access - message = await manager.get_by_queue(target_queue, wait=False) - assert message is not None, "Basic consumption failed" - - # Statistics test - stats = await manager.get_manager_stats() - assert stats["total_delivered_messages"] == 1, "Statistics error" - - await manager.shutdown(ShutdownMode.HARD) - - -async def test_queue_full_scenarios(): - """Test scenarios when queue is full""" - manager = MsgGroupQueueManager("full_test", num_queues=3, max_total_messages=10) - - # Attempt to deliver a large number of messages - delivered_count = 0 - rejected_count = 0 - - for i in range(15): - success = await manager.deliver_message(f"user_{i}", f"msg_{i}") - if success: - delivered_count += 1 - else: - rejected_count += 1 - - print(f"✅ Queue full test passed (delivered {delivered_count}, rejected {rejected_count})") - await manager.shutdown(ShutdownMode.HARD) - - -async def test_time_window_stats(): - """Time window statistics test""" - manager = MsgGroupQueueManager("time_test", num_queues=3, max_total_messages=20) - - # Deliver messages - for i in range(5): - await manager.deliver_message(f"time_user_{i}", f"time_msg_{i}") - - # Consume some messages - consumed_count = 0 - for i in range(3): - target_queue = manager._hash_route( - f"time_user_{i}" - ) # pylint: disable=protected-access - message = await manager.get_by_queue(target_queue, wait=False) - if message: - consumed_count += 1 - - # Check time window statistics - stats = await manager.get_manager_stats() - assert stats["delivered_1min"] >= 5, "1-minute delivery statistics error" - assert stats["consumed_1min"] >= consumed_count, "1-minute consumption statistics error" - - print(f"✅ Time window statistics test passed (delivered 5, consumed {consumed_count})") - await manager.shutdown(ShutdownMode.HARD) - - -async def test_basic_concurrent_operations(): - """Basic concurrent operations test""" - manager = MsgGroupQueueManager( - "concurrent_test", num_queues=5, max_total_messages=50 - ) - - async def producer(producer_id: int): - for i in range(10): - await manager.deliver_message( - f"producer_{producer_id}_msg_{i}", f"data_{i}" - ) - - async def consumer(): - consumed = 0 - for queue_id in range(manager.num_queues): - for _ in range(5): # Try to consume 5 times per queue - message = await manager.get_by_queue(queue_id, wait=False) - if message: - consumed += 1 - return consumed - - # Start 3 producers - producers = [producer(i) for i in range(3)] - await asyncio.gather(*producers) - - # Start consumer - consumed_count = await consumer() - - stats = await manager.get_manager_stats() - assert ( - stats["total_delivered_messages"] == 30 - ), f"Concurrent delivery statistics error: {stats['total_delivered_messages']}" - - print(f"✅ Concurrent operations test passed (produced 30, consumed {consumed_count})") - await manager.shutdown(ShutdownMode.HARD) - - -async def test_basic_shutdown_modes(): - """Basic Shutdown mode test""" - manager = MsgGroupQueueManager( - "basic_shutdown_test", num_queues=3, max_total_messages=10 - ) - - # Ensure shutdown state is clean - manager._shutdown_state.reset() # pylint: disable=protected-access - - # Add some messages - for i in range(5): - await manager.deliver_message(f"user_{i}", f"msg_{i}") - - # Test soft shutdown (requires polling until success) - start_time = time.time() - - # First call to soft shutdown should return False immediately - result = await manager.shutdown(ShutdownMode.SOFT, max_delay_seconds=1.0) - first_call_time = time.time() - - assert result is False, "First soft shutdown should return False immediately (unprocessed messages)" - assert first_call_time - start_time < 0.1, "First soft shutdown should return immediately" - print(" First soft shutdown correctly returned False (immediate return)") - - # Poll until soft shutdown succeeds or times out - max_poll_time = start_time + 2.0 # Poll for up to 2 seconds - final_result = False - - while time.time() < max_poll_time: - await asyncio.sleep(0.1) # Wait 100ms and check again - result = await manager.shutdown(ShutdownMode.SOFT) # No need to set delay again - if result is True: - final_result = True - break - - end_time = time.time() - total_elapsed = end_time - start_time - - # Verify final result and timing - assert final_result is True, "Soft shutdown should eventually succeed" - assert ( - 0.8 <= total_elapsed <= 1.5 - ), f"Soft shutdown total time abnormal: {total_elapsed:.2f}s, expected ~1.0s" - - print(f" Soft shutdown polling succeeded, total time: {total_elapsed:.2f}s") - - -async def test_edge_cases(): - """Edge case test""" - manager = MsgGroupQueueManager("edge_test", num_queues=3, max_total_messages=10) - - # Test 1: Empty string group_key - success = await manager.deliver_message("", "empty_key_msg") - assert success, "Delivery with empty string key failed" - - empty_queue = manager._hash_route("") # pylint: disable=protected-access - result = await manager.get_by_queue(empty_queue, wait=False) - assert result is not None, "Consumption with empty string key failed" - key, data = result - assert key == "", f"Empty string key mismatch: {key}" - assert data == "empty_key_msg", f"Empty string key data mismatch: {data}" - - # Test 2: None message data - success = await manager.deliver_message("none_test", None) - assert success, "Delivery of None message failed" - - none_queue = manager._hash_route("none_test") # pylint: disable=protected-access - result = await manager.get_by_queue(none_queue, wait=False) - assert result is not None, "Consumption of None message failed" - key, data = result - assert key == "none_test", f"None message key mismatch: {key}" - assert data is None, f"None message data mismatch: {data}" - - # Test 3: Complex data - complex_data = {"nested": {"list": [1, 2, 3]}, "unicode": "测试🎉"} - success = await manager.deliver_message("complex_test", complex_data) - assert success, "Delivery of complex data failed" - - complex_queue = manager._hash_route( - "complex_test" - ) # pylint: disable=protected-access - result = await manager.get_by_queue(complex_queue, wait=False) - assert result is not None, "Consumption of complex data failed" - key, data = result - assert key == "complex_test", f"Complex data key mismatch: {key}" - assert data == complex_data, f"Complex data mismatch: expected={complex_data}, actual={data}" - - await manager.shutdown(ShutdownMode.HARD) - - -async def test_factory_pattern(): - """Factory pattern test""" - factory = MsgGroupQueueManagerFactory() - - # Default manager - manager1 = await factory.get_default_manager(auto_start=False) - manager2 = await factory.get_default_manager(auto_start=False) - assert manager1 is manager2, "Default manager should be singleton" - - # Custom configuration - config = MsgGroupQueueConfig(name="custom", num_queues=5, max_total_messages=25) - manager3 = await factory.get_manager(config, auto_start=False) - assert manager3.name == "custom", "Custom configuration error" - - # Named manager - manager4 = await factory.get_named_manager("test_named", auto_start=False) - assert manager4.name == "test_named", "Named manager error" - - await factory.stop_all_managers() - - -async def test_timeout_mechanism(): - """Timeout mechanism test""" - manager = MsgGroupQueueManager("timeout_test", num_queues=3, max_total_messages=10) - - try: - # Test timeout get (empty queue) - start_time = time.time() - try: - result = await manager.get_by_queue(0, wait=True, timeout=0.5) - end_time = time.time() - - # Should timeout and return None or raise TimeoutError - if result is not None: - raise AssertionError("Timeout should return None or raise exception") - assert ( - 0.4 <= end_time - start_time <= 0.6 - ), f"Timeout time inaccurate: {end_time - start_time}" - - except asyncio.TimeoutError: - end_time = time.time() - assert ( - 0.4 <= end_time - start_time <= 0.6 - ), f"Timeout time inaccurate: {end_time - start_time}" - - # Test get when message exists - await manager.deliver_message("timeout_user", "timeout_msg") - target_queue = manager._hash_route( - "timeout_user" - ) # pylint: disable=protected-access - - start_time = time.time() - result = await manager.get_by_queue(target_queue, wait=True, timeout=1.0) - end_time = time.time() - - # Should return message immediately - assert result is not None, "Should return immediately when message exists" - assert ( - end_time - start_time < 0.1 - ), f"Should not wait when message exists: {end_time - start_time}" - - key, data = result - assert key == "timeout_user", "Timeout test message key error" - assert data == "timeout_msg", "Timeout test message data error" - - finally: - await manager.shutdown(ShutdownMode.HARD) - - -async def test_routing_uniformity(): - """Routing uniformity test - Use random UUIDs to verify hash distribution""" - manager = MsgGroupQueueManager( - "routing_test", num_queues=10, max_total_messages=2000 - ) - - try: - # Generate a large number of random UUIDs as group_key - test_count = 1000 - uuid_keys = [str(uuid.uuid4()) for _ in range(test_count)] - - # Count messages received by each queue - queue_counts = defaultdict(int) - - # Deliver all messages and count routing distribution - for i, group_key in enumerate(uuid_keys): - success = await manager.deliver_message(group_key, f"message_{i}") - assert success, f"UUID message delivery failed: {group_key}" - - # Calculate which queue this key routes to - target_queue = manager._hash_route( - group_key - ) # pylint: disable=protected-access - queue_counts[target_queue] += 1 - - # Analyze distribution uniformity - print(f"📊 Routing distribution statistics ({test_count} UUIDs):") - expected_per_queue = test_count / manager.num_queues - - total_deviation = 0 - max_count = 0 - min_count = test_count - - for queue_id in range(manager.num_queues): - count = queue_counts[queue_id] - percentage = (count / test_count) * 100 - deviation = abs(count - expected_per_queue) - deviation_percent = (deviation / expected_per_queue) * 100 - - print( - f" Queue[{queue_id}]: {count:3d} messages ({percentage:5.1f}%) - Deviation: {deviation_percent:5.1f}%" - ) - - total_deviation += deviation - max_count = max(max_count, count) - min_count = min(min_count, count) - - # Calculate distribution quality metrics - avg_count = test_count / manager.num_queues - variance = ( - sum((queue_counts[i] - avg_count) ** 2 for i in range(manager.num_queues)) - / manager.num_queues - ) - std_dev = variance**0.5 - coefficient_of_variation = (std_dev / avg_count) * 100 - - print(f"\n📈 Distribution quality analysis:") - print(f" Expected per queue: {expected_per_queue:.1f} messages") - print(f" Actual range: {min_count}-{max_count} messages") - print(f" Standard deviation: {std_dev:.2f}") - print(f" Coefficient of variation: {coefficient_of_variation:.1f}%") - - # Verify distribution quality - # 1. Coefficient of variation should be less than 15% (good uniformity) - assert ( - coefficient_of_variation < 15.0 - ), f"Distribution not uniform enough, coefficient of variation: {coefficient_of_variation:.1f}%" - - # 2. Difference between max and min should not be too large - max_min_ratio = max_count / min_count if min_count > 0 else float('inf') - assert ( - max_min_ratio < 2.0 - ), f"Queue load difference too large, max/min ratio: {max_min_ratio:.2f}" - - # 3. Every queue should have messages - assert min_count > 0, "Empty queue exists, distribution issue" - - # Verify routing consistency - Same UUID always routes to same queue - print(f"\n🔍 Verifying routing consistency...") - consistency_test_keys = uuid_keys[:50] # Use first 50 UUIDs to test consistency - - for test_key in consistency_test_keys: - # Calculate routing for same key multiple times, should always be same - routes = [ - manager._hash_route(test_key) for _ in range(10) - ] # pylint: disable=protected-access - assert ( - len(set(routes)) == 1 - ), f"Routing inconsistent: key={test_key}, routes={set(routes)}" - - print(f" ✅ {len(consistency_test_keys)} UUID routing consistency verified") - - # Verify statistics - stats = await manager.get_manager_stats() - assert stats["total_delivered_messages"] == test_count, "Delivery statistics error" - - print(f"\n✅ Routing uniformity test passed:") - print(f" - Coefficient of variation: {coefficient_of_variation:.1f}% (< 15%)") - print(f" - Load ratio: {max_min_ratio:.2f} (< 2.0)") - print(f" - Routing consistency: 100%") - - finally: - await manager.shutdown(ShutdownMode.HARD) - - -# ============ Extended test functions ============ - - -async def test_concurrent_operations(): - """Detailed concurrent operations test""" - manager = MsgGroupQueueManager( - "concurrent_test", num_queues=5, max_total_messages=100 - ) - - # Define producer and consumer tasks - async def producer(producer_id: int, message_count: int): - for i in range(message_count): - group_key = f"producer_{producer_id}_user_{i % 10}" # 10 different users - message_data = {"producer": producer_id, "seq": i, "data": f"message_{i}"} - success = await manager.deliver_message(group_key, message_data) - if not success: - print(f"Producer {producer_id} message {i} delivery failed") - await asyncio.sleep(0.01) # Small delay to simulate real scenario - - async def consumer(consumer_id: int, target_queues: List[int]): - consumed = 0 - for queue_id in target_queues: - while True: - try: - message = await manager.get_by_queue( - queue_id, wait=True, timeout=0.1 - ) - if message is None: - break - consumed += 1 - await asyncio.sleep(0.005) # Simulate processing time - except asyncio.TimeoutError: - break - return consumed - - # Start 3 producers, each producing 5 messages - producers = [producer(i, 5) for i in range(3)] - await asyncio.gather(*producers) - - # Start 2 consumers, processing different queues - consumer_tasks = [ - consumer(0, [0, 1, 2]), # Consumer 0 processes queues 0,1,2 - consumer(1, [3, 4]), # Consumer 1 processes queues 3,4 - ] - consumed_counts = await asyncio.gather(*consumer_tasks) - - # Verify results - total_consumed = sum(consumed_counts) - stats = await manager.get_manager_stats() - - assert ( - stats["total_delivered_messages"] == 15 - ), f"Delivery count error: {stats['total_delivered_messages']}" - assert total_consumed <= 15, f"Consumption count abnormal: {total_consumed}" - - await manager.shutdown(ShutdownMode.HARD) - - -async def test_real_world_scenario(): - """Real-world scenario test (simulating Kafka message processing)""" - print("🚀 Simulating message burst...") - manager = MsgGroupQueueManager( - "kafka_simulator", num_queues=10, max_total_messages=200 - ) - - # Simulate message burst - delivered = 0 - for i in range(100): - user_id = f"user_{i % 20}" # 20 different users - message = { - "user_id": user_id, - "timestamp": time.time(), - "event_type": "click" if i % 3 == 0 else "view", - "data": {"page": f"page_{i % 5}", "session": f"session_{i % 10}"}, - } - success = await manager.deliver_message(user_id, message) - if success: - delivered += 1 - - print(f"📊 Successfully delivered {delivered} messages") - - # Simulate consumer processing - print("🔄 Simulating consumer processing...") - total_consumed = 0 - - # Start multiple consumers to process concurrently - async def consumer_worker(queue_ids: List[int]): - consumed = 0 - for queue_id in queue_ids: - while True: - try: - message = await manager.get_by_queue( - queue_id, wait=True, timeout=0.05 - ) - if message is None: - break - consumed += 1 - await asyncio.sleep(0.001) # Fast processing - except asyncio.TimeoutError: - break - return consumed - - # Start 5 consumer worker threads - consumer_tasks = [ - consumer_worker([0, 1]), - consumer_worker([2, 3]), - consumer_worker([4, 5]), - consumer_worker([6, 7]), - consumer_worker([8, 9]), - ] - - consumed_counts = await asyncio.gather(*consumer_tasks) - total_consumed = sum(consumed_counts) - - print(f"📊 Total consumed {total_consumed} messages") - - # Check queue load distribution - queue_info = await manager.get_queue_info() - print("📈 Queue load distribution:") - for info in queue_info: - if info["current_size"] > 0: - print(f" Queue[{info['queue_id']}]: {info['current_size']} messages") - - # Test delivery rejection under high load - print("⚠️ Testing delivery rejection under high load...") - rejected = 0 - for i in range(100, 200): - success = await manager.deliver_message(f"flood_user_{i}", f"flood_msg_{i}") - if not success: - rejected += 1 - - # Cleanup - print("🧹 Cleaning up remaining messages...") - cleaned = 0 - for queue_id in range(manager.num_queues): - while True: - message = await manager.get_by_queue(queue_id, wait=False) - if message is None: - break - cleaned += 1 - - print(f"🧹 Cleaned up {cleaned} remaining messages") - print("✅ Real-world scenario test completed!") - - await manager.shutdown(ShutdownMode.HARD) - - -async def test_queue_overflow_and_recovery(): - """Queue overflow recovery test""" - manager = MsgGroupQueueManager("overflow_test", num_queues=3, max_total_messages=15) - - # Fill the queue - print("📦 Filling queue...") - delivered = 0 - rejected = 0 - - for i in range(30): - success = await manager.deliver_message(f"user_{i % 5}", f"msg_{i}") - if success: - delivered += 1 - else: - rejected += 1 - - print(f"📊 Delivery statistics: Success={delivered}, Rejected={rejected}") - - # Partial consumption to restore delivery capability - print("🔄 Partial consumption to restore delivery capability...") - consumed = 0 - for queue_id in range(manager.num_queues): - # Consume a few messages from each queue - for _ in range(2): - message = await manager.get_by_queue(queue_id, wait=False) - if message: - consumed += 1 - - print(f"📤 Consumed {consumed} messages for recovery") - - # Try to deliver again - recovery_delivered = 0 - for i in range(5): - success = await manager.deliver_message( - f"recovery_user_{i}", f"recovery_msg_{i}" - ) - if success: - recovery_delivered += 1 - - print(f"✅ Recovered delivery of {recovery_delivered} messages") - - await manager.shutdown(ShutdownMode.HARD) - - -async def test_shutdown_modes_integration(): - """Integration test for shutdown modes""" - manager = MsgGroupQueueManager("shutdown_test", num_queues=3, max_total_messages=20) - - try: - # Add some messages - for i in range(5): - await manager.deliver_message(f"user_{i}", f"msg_{i}") - - # Start a consumer task (simulating waiting consumption) - async def consumer_task(): - consumed = 0 - for _ in range(10): # Try to consume multiple times - try: - message = await manager.get_by_queue(1, wait=True, timeout=0.5) - if message: - consumed += 1 - await asyncio.sleep(0.1) # Simulate processing time - except asyncio.TimeoutError: - break - return consumed - - # Start shutdown task - async def shutdown_task(): - await asyncio.sleep(1.0) # Wait for consumer to start - result = await manager.shutdown(ShutdownMode.SOFT, max_delay_seconds=3.0) - return result - - # Wait for tasks to complete - results = await asyncio.gather( - shutdown_task(), consumer_task(), return_exceptions=True - ) - - shutdown_result, consumed_count = results[0], results[1] - - # Verify results - if isinstance(shutdown_result, Exception): - raise shutdown_result - if isinstance(consumed_count, Exception): - raise consumed_count - - # shutdown_result could be bool or dict - assert isinstance( - shutdown_result, (bool, dict) - ), f"Shutdown result type error: {type(shutdown_result)}" - assert isinstance( - consumed_count, int - ), f"Consumption count type error: {type(consumed_count)}" - assert consumed_count >= 0, f"Consumption count abnormal: {consumed_count}" - - except Exception: - # Ensure resources are cleaned up even in case of exception - try: - await manager.shutdown(ShutdownMode.HARD) - except Exception: - pass # Ignore errors during cleanup - raise - - -# ============ Main test runner ============ - -if __name__ == "__main__": - - async def run_all_tests(): - print("🚀 Starting MsgGroupQueueManager refactored test suite...") - print("=" * 60) - - test_results = [] - - # Basic test suite - basic_tests = [ - ("Basic functionality test", test_basic_functionality), - ("Queue full test", test_queue_full_scenarios), - ("Time window statistics test", test_time_window_stats), - ("Basic concurrent operations test", test_basic_concurrent_operations), - ("Basic Shutdown mode test", test_basic_shutdown_modes), - ("Edge case test", test_edge_cases), - ("Factory pattern test", test_factory_pattern), - ("Timeout mechanism test", test_timeout_mechanism), - ("Routing uniformity test", test_routing_uniformity), - ] - - # Extended test suite - extended_tests = [ - ("Detailed concurrent operations test", test_concurrent_operations), - ("Real-world scenario test", test_real_world_scenario), - ("Queue overflow recovery test", test_queue_overflow_and_recovery), - ("Shutdown mode integration test", test_shutdown_modes_integration), - ] - - # Run all tests - all_tests = basic_tests + extended_tests - - for i, (test_name, test_func) in enumerate(all_tests, 1): - print(f"\n{i}️⃣ {test_name}...") - try: - await test_func() - test_results.append((test_name, "✅ Passed")) - print(f"✅ {test_name} passed") - except Exception as e: - test_results.append((test_name, f"❌ Failed: {e}")) - print(f"❌ {test_name} failed: {e}") - - # Test results summary - print("\n" + "=" * 60) - print("📊 Test results summary:") - print("=" * 60) - - passed_count = 0 - total_count = len(test_results) - - for test_name, result in test_results: - print(f"{result:<20} {test_name}") - if "✅" in result: - passed_count += 1 - - print("=" * 60) - print(f"📈 Total: {passed_count}/{total_count} tests passed") - - if passed_count == total_count: - print("🎉 All tests passed! MsgGroupQueueManager is working correctly!") - else: - print( - f"⚠️ {total_count - passed_count} tests failed, please check the error messages above" - ) - - return passed_count == total_count - - # Run all tests - success = asyncio.run(run_all_tests()) - exit(0 if success else 1) \ No newline at end of file diff --git a/methods/EverCore/tests/test_openai_provider_key_rotation.py b/methods/EverCore/tests/test_openai_provider_key_rotation.py deleted file mode 100644 index f2ed998c..00000000 --- a/methods/EverCore/tests/test_openai_provider_key_rotation.py +++ /dev/null @@ -1,310 +0,0 @@ -"""OpenAIProvider key rotation integration tests (mock HTTP).""" - -import pytest -import aiohttp -from unittest.mock import AsyncMock, patch - -from memory_layer.llm.openai_provider import OpenAIProvider -from memory_layer.llm.protocol import LLMError -from memory_layer.llm.api_key_rotator import ApiKeyRotator - - -@pytest.fixture(autouse=True) -def _reset_shared_rotator(): - """Ensure each test starts with a clean singleton state.""" - ApiKeyRotator._shared = None - yield - ApiKeyRotator._shared = None - - -def _success_body(content: str = "hello") -> dict: - return { - "choices": [{"message": {"content": content}, "finish_reason": "stop"}], - "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}, - } - - -def _error_body(message: str = "rate limited") -> dict: - return {"error": {"message": message}} - - -class TestKeyRotationOn429: - """429: rotate key immediately, no sleep.""" - - @pytest.mark.asyncio - async def test_429_then_success_with_next_key(self) -> None: - provider = OpenAIProvider( - api_key="key-a,key-b,key-c", base_url="https://fake.api", model="test-model" - ) - - responses = [(429, _error_body()), (200, _success_body("ok"))] - call_keys: list[str] = [] - - async def capture_do_request(data: dict, api_key: str) -> tuple[int, dict]: - call_keys.append(api_key) - return responses.pop(0) - - provider._do_request = capture_do_request - - result = await provider.generate("test") - assert result == "ok" - assert len(call_keys) == 2 - assert call_keys[0] != call_keys[1] - - @pytest.mark.asyncio - async def test_all_keys_429_raises_error(self) -> None: - provider = OpenAIProvider( - api_key="key-a,key-b,key-c", base_url="https://fake.api", model="test-model" - ) - - async def always_429(data: dict, api_key: str) -> tuple[int, dict]: - return 429, _error_body("rate limited") - - provider._do_request = always_429 - - with pytest.raises(LLMError, match="3 keys exhausted"): - await provider.generate("test") - - @pytest.mark.asyncio - async def test_429_does_not_sleep(self) -> None: - provider = OpenAIProvider( - api_key="key-a,key-b", base_url="https://fake.api", model="test-model" - ) - - call_count = 0 - - async def mock_request(data: dict, api_key: str) -> tuple[int, dict]: - nonlocal call_count - call_count += 1 - if call_count == 1: - return 429, _error_body() - return 200, _success_body() - - provider._do_request = mock_request - - with patch( - "memory_layer.llm.openai_provider.asyncio.sleep", new_callable=AsyncMock - ) as mock_sleep: - await provider.generate("test") - mock_sleep.assert_not_called() - - @pytest.mark.asyncio - async def test_429_counter_not_reset_by_5xx(self) -> None: - """429 -> 5xx -> 429: consecutive_rate_limits does not reset on 5xx.""" - provider = OpenAIProvider( - api_key="key-a,key-b", base_url="https://fake.api", model="test-model" - ) - - responses = [ - (429, _error_body("rate limited")), - (502, _error_body("bad gateway")), - (429, _error_body("rate limited")), - ] - idx = 0 - - async def mock_request(data: dict, api_key: str) -> tuple[int, dict]: - nonlocal idx - resp = responses[idx] - idx += 1 - return resp - - provider._do_request = mock_request - - with patch( - "memory_layer.llm.openai_provider.asyncio.sleep", new_callable=AsyncMock - ): - with pytest.raises(LLMError, match="2 keys exhausted"): - await provider.generate("test") - - -class TestKeyRotationOn429And5xxInterleaved: - """429/5xx interleaved: sleep only on 5xx, not on 429.""" - - @pytest.mark.asyncio - async def test_429_then_5xx_sleeps_only_on_5xx(self) -> None: - """429 -> 502 -> 200: sleep called exactly once (on 502 only).""" - provider = OpenAIProvider( - api_key="key-a,key-b,key-c", base_url="https://fake.api", model="test-model" - ) - - responses = [ - (429, _error_body("rate limited")), - (502, _error_body("bad gateway")), - (200, _success_body("ok")), - ] - - async def mock_request(data: dict, api_key: str) -> tuple[int, dict]: - return responses.pop(0) - - provider._do_request = mock_request - - with patch( - "memory_layer.llm.openai_provider.asyncio.sleep", new_callable=AsyncMock - ) as mock_sleep: - result = await provider.generate("test") - assert result == "ok" - mock_sleep.assert_called_once() # only on 502, not on 429 - - -class TestRequestLevelErrors: - """400/404/422: no retry, raise immediately.""" - - @pytest.mark.asyncio - async def test_400_raises_immediately_no_retry(self) -> None: - provider = OpenAIProvider( - api_key="key-a,key-b", base_url="https://fake.api", model="test-model" - ) - - call_count = 0 - - async def mock_request(data: dict, api_key: str) -> tuple[int, dict]: - nonlocal call_count - call_count += 1 - return 400, _error_body("bad request") - - provider._do_request = mock_request - - with pytest.raises(LLMError, match="HTTP Error 400"): - await provider.generate("test") - assert call_count == 1 # no retry - - -class TestNetworkErrors: - """aiohttp.ClientError: retry up to max attempts.""" - - @pytest.mark.asyncio - async def test_client_error_retries_then_raises(self) -> None: - provider = OpenAIProvider( - api_key="key-a", base_url="https://fake.api", model="test-model" - ) - - async def always_fail(data: dict, api_key: str) -> tuple[int, dict]: - raise aiohttp.ClientError("connection reset") - - provider._do_request = always_fail - - with pytest.raises(LLMError, match="Request failed"): - await provider.generate("test") - - @pytest.mark.asyncio - async def test_client_error_then_success(self) -> None: - provider = OpenAIProvider( - api_key="key-a", base_url="https://fake.api", model="test-model" - ) - - call_count = 0 - - async def fail_then_ok(data: dict, api_key: str) -> tuple[int, dict]: - nonlocal call_count - call_count += 1 - if call_count == 1: - raise aiohttp.ClientError("timeout") - return 200, _success_body("recovered") - - provider._do_request = fail_then_ok - - result = await provider.generate("test") - assert result == "recovered" - assert call_count == 2 - - -class TestKeyRotationOn5xx: - """5xx: sleep then retry.""" - - @pytest.mark.asyncio - async def test_5xx_retries_with_sleep(self) -> None: - provider = OpenAIProvider( - api_key="key-a,key-b", base_url="https://fake.api", model="test-model" - ) - - call_count = 0 - - async def mock_request(data: dict, api_key: str) -> tuple[int, dict]: - nonlocal call_count - call_count += 1 - if call_count == 1: - return 502, _error_body("bad gateway") - return 200, _success_body() - - provider._do_request = mock_request - - with patch( - "memory_layer.llm.openai_provider.asyncio.sleep", new_callable=AsyncMock - ) as mock_sleep: - result = await provider.generate("test") - assert result == "hello" - mock_sleep.assert_called_once() - - -class TestRetryStartsFromNextKey: - """Retries start from the key AFTER the failed one, cycling through all.""" - - @pytest.mark.asyncio - async def test_retry_uses_rotation_sequence(self) -> None: - """All attempts follow the rotation: rotation[0], [1], [2], all distinct.""" - provider = OpenAIProvider( - api_key="key-a,key-b,key-c", base_url="https://fake.api", model="test-model" - ) - - call_keys: list[str] = [] - - async def capture_request(data: dict, api_key: str) -> tuple[int, dict]: - call_keys.append(api_key) - return 429, _error_body("rate limited") - - provider._do_request = capture_request - - with pytest.raises(LLMError, match="keys exhausted"): - await provider.generate("test") - # 3 keys, 3 attempts, all distinct, in rotation order - assert len(call_keys) == 3 - assert len(set(call_keys)) == 3 - first = call_keys[0] - keys = ["key-a", "key-b", "key-c"] - first_idx = keys.index(first) - assert call_keys[1] == keys[(first_idx + 1) % 3] - assert call_keys[2] == keys[(first_idx + 2) % 3] - - @pytest.mark.asyncio - async def test_keys_repeat_after_full_cycle(self) -> None: - """With 2 keys and 5 retries (5xx), keys alternate without adjacent repeats.""" - provider = OpenAIProvider( - api_key="key-a,key-b", base_url="https://fake.api", model="test-model" - ) - - call_keys: list[str] = [] - - async def capture_request(data: dict, api_key: str) -> tuple[int, dict]: - call_keys.append(api_key) - return 502, _error_body("bad gateway") - - provider._do_request = capture_request - - with patch( - "memory_layer.llm.openai_provider.asyncio.sleep", new_callable=AsyncMock - ): - with pytest.raises(LLMError, match="after 5 retries"): - await provider.generate("test") - assert len(call_keys) == 5 - # Adjacent keys always differ - for i in range(len(call_keys) - 1): - assert call_keys[i] != call_keys[i + 1] - - -class TestSingleKeyBackwardCompat: - """Single key: behavior unchanged.""" - - @pytest.mark.asyncio - async def test_single_key_works_normally(self) -> None: - provider = OpenAIProvider( - api_key="single-key", base_url="https://fake.api", model="test-model" - ) - - async def mock_request(data: dict, api_key: str) -> tuple[int, dict]: - assert api_key == "single-key" - return 200, _success_body("single key response") - - provider._do_request = mock_request - - result = await provider.generate("test") - assert result == "single key response" diff --git a/methods/EverCore/tests/test_participant_filtering.py b/methods/EverCore/tests/test_participant_filtering.py deleted file mode 100644 index 5eee5c2a..00000000 --- a/methods/EverCore/tests/test_participant_filtering.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -tests/test_participant_filtering.py - -Unit tests for participant extraction and downstream usage after -the role-based filtering change: -- _extract_participant_ids only extracts role='user' sender_ids -- Downstream functions trust participants without keyword filtering - -Usage: - PYTHONPATH=src pytest tests/test_participant_filtering.py -v -""" - -from dataclasses import replace -from datetime import datetime -from unittest.mock import MagicMock, AsyncMock - -import pytest - -from memory_layer.memcell_extractor.conv_memcell_extractor import ConvMemCellExtractor -from memory_layer.llm.llm_provider import LLMProvider -from api_specs.memory_types import EpisodeMemory -from biz_layer.mem_memorize import ExtractionState, _clone_episodes_for_users - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def _mock_extractor() -> ConvMemCellExtractor: - provider = MagicMock(spec=LLMProvider) - provider.generate = AsyncMock(return_value='{"boundaries": [], "should_wait": false}') - return ConvMemCellExtractor(provider) - - -def _msg(role: str, sender_id: str) -> dict: - return {"role": role, "sender_id": sender_id, "content": [{"type": "text", "text": "hi"}]} - - -# --------------------------------------------------------------------------- -# Tests for _extract_participant_ids -# --------------------------------------------------------------------------- - -class TestExtractParticipantIds: - - def test_only_user_role_extracted(self): - ext = _mock_extractor() - messages = [ - _msg("user", "alice"), - _msg("assistant", "robot_bot"), - _msg("user", "bob"), - ] - result = ext._extract_participant_ids(messages) - assert sorted(result) == ["alice", "bob"] - - def test_assistant_excluded(self): - ext = _mock_extractor() - messages = [ - _msg("assistant", "assistant_001"), - _msg("assistant", "gpt_helper"), - ] - result = ext._extract_participant_ids(messages) - assert result == [] - - def test_system_role_excluded(self): - ext = _mock_extractor() - messages = [ - _msg("system", "sys_001"), - _msg("user", "alice"), - ] - result = ext._extract_participant_ids(messages) - assert result == ["alice"] - - def test_deduplication(self): - ext = _mock_extractor() - messages = [ - _msg("user", "alice"), - _msg("user", "alice"), - _msg("user", "bob"), - ] - result = ext._extract_participant_ids(messages) - assert sorted(result) == ["alice", "bob"] - - def test_empty_sender_id_skipped(self): - ext = _mock_extractor() - messages = [ - {"role": "user", "sender_id": "", "content": []}, - {"role": "user", "sender_id": None, "content": []}, - {"role": "user", "content": []}, - _msg("user", "alice"), - ] - result = ext._extract_participant_ids(messages) - assert result == ["alice"] - - def test_empty_messages(self): - ext = _mock_extractor() - assert ext._extract_participant_ids([]) == [] - - def test_robot_keyword_in_user_role_kept(self): - """User with 'robot' in sender_id but role='user' should be kept.""" - ext = _mock_extractor() - messages = [_msg("user", "robot_tester")] - result = ext._extract_participant_ids(messages) - assert result == ["robot_tester"] - - -# --------------------------------------------------------------------------- -# Tests for _clone_episodes_for_users -# --------------------------------------------------------------------------- - -class TestCloneEpisodesForUsers: - - def _make_state(self, participants: list) -> ExtractionState: - ep = EpisodeMemory( - memory_type="episodic_memory", - user_id="group", - user_name="group", - timestamp=datetime(2026, 1, 1), - episode="test episode", - ) - state = MagicMock(spec=ExtractionState) - state.participants = participants - state.group_episode_memories = [ep] - return state - - def test_clones_for_all_participants(self): - state = self._make_state(["alice", "bob", "charlie"]) - result = _clone_episodes_for_users(state) - assert len(result) == 3 - user_ids = [ep.user_id for ep in result] - assert sorted(user_ids) == ["alice", "bob", "charlie"] - - def test_no_keyword_filtering(self): - """Participants are trusted as-is, no keyword filtering applied.""" - state = self._make_state(["alice", "robot_tester"]) - result = _clone_episodes_for_users(state) - assert len(result) == 2 - - def test_empty_participants(self): - state = self._make_state([]) - result = _clone_episodes_for_users(state) - assert result == [] diff --git a/methods/EverCore/tests/test_pickle_size_analysis.py b/methods/EverCore/tests/test_pickle_size_analysis.py deleted file mode 100644 index 3b5560b6..00000000 --- a/methods/EverCore/tests/test_pickle_size_analysis.py +++ /dev/null @@ -1,525 +0,0 @@ -""" -Pickle Serialization Size Analysis Test - -Test the binary size of various types and sizes of objects after Pickle serialization -Including: -1. Basic data type size analysis -2. Complex object size analysis -3. Large data structure size analysis -4. Function and class object size analysis -5. Nested structure size analysis -""" - -import asyncio -import pickle -import sys -import time -import pytest -from datetime import timedelta -from core.di.utils import get_bean -from core.observation.logger import get_logger -from core.cache.redis_cache_queue.redis_data_processor import RedisDataProcessor -from common_utils.datetime_utils import get_now_with_timezone - -logger = get_logger(__name__) - - -def format_size(size_bytes: int) -> str: - """Format byte size into human-readable format""" - if size_bytes < 1024: - return f"{size_bytes} B" - elif size_bytes < 1024 * 1024: - return f"{size_bytes / 1024:.2f} KB" - else: - return f"{size_bytes / (1024 * 1024):.2f} MB" - - -class ComplexTestObject: - """Complex test object""" - - def __init__(self, name: str, data_size: int): - self.name = name - self.created_at = get_now_with_timezone() - self.id = id(self) - self.data = list(range(data_size)) - self.metadata = { - "version": "1.0", - "type": "test_object", - "size": data_size, - "nested": { - "level1": {"level2": {"level3": "deep_data"}}, - "timestamps": [ - get_now_with_timezone() + timedelta(seconds=i) for i in range(10) - ], - }, - } - # Use data types that cannot be JSON serialized - self.complex_types = { - "set_data": {1, 2, 3, 4, 5}, - "bytes_data": b"binary_data_example", - "tuple_data": (1, 2, 3, 4, 5), - } - - def multiply_value(self, value, multiplier=2): - return value * multiplier - - def power_value(self, value, power=2): - return value**power - - def custom_process(self, value): - return f"processed_{value}_{self.name}_{len(self.data)}" - - def get_summary(self): - return f"ComplexTestObject(name={self.name}, data_len={len(self.data)})" - - -class LargeDataContainer: - """Large data container""" - - def __init__(self, size_mb: float): - # Create data of specified size - target_size = int(size_mb * 1024 * 1024 / 8) # Assume each number is 8 bytes - self.large_list = list(range(target_size)) - self.large_dict = { - f"key_{i}": f"value_{i}_{'x' * 100}" for i in range(target_size // 100) - } - self.metadata = { - "size_mb": size_mb, - "created": get_now_with_timezone(), - "type": "large_container", - } - - -@pytest.mark.asyncio -async def test_basic_types_size(): - """Test serialization size of basic data types""" - logger.info("Starting test for basic data type serialization size...") - - test_data = { - "Empty string": "", - "Short string": "hello", - "Medium string": "x" * 100, - "Long string": "x" * 1000, - "Very long string": "x" * 10000, - "Small integer": 42, - "Large integer": 123456789012345, - "Float": 3.14159265359, - "Boolean True": True, - "Boolean False": False, - "None": None, - "Empty list": [], - "Small list": [1, 2, 3], - "Medium list": list(range(100)), - "Large list": list(range(1000)), - "Empty dict": {}, - "Small dict": {"a": 1, "b": 2, "c": 3}, - "Medium dict": {f"key_{i}": i for i in range(100)}, - "Large dict": {f"key_{i}": f"value_{i}" for i in range(1000)}, - } - - logger.info("=" * 60) - logger.info("Basic Data Type Pickle Serialization Size Analysis") - logger.info("=" * 60) - - for name, data in test_data.items(): - # JSON serialization size (if possible) - json_size = "N/A" - try: - import json - - json_data = json.dumps(data, ensure_ascii=False) - json_size = format_size(len(json_data.encode('utf-8'))) - except (TypeError, ValueError): - json_size = "Cannot be JSON serialized" - - # Pickle serialization size - pickle_data = pickle.dumps(data) - pickle_size = format_size(len(pickle_data)) - - # Process using RedisDataProcessor - processed_data = RedisDataProcessor.serialize_data(data) - if isinstance(processed_data, bytes): - processed_size = format_size(len(processed_data)) - serialization_type = "Pickle" - else: - processed_size = format_size(len(processed_data.encode('utf-8'))) - serialization_type = "JSON" - - logger.info( - "%-15s | JSON: %-12s | Pickle: %-12s | Processor: %-12s (%s)", - name, - json_size, - pickle_size, - processed_size, - serialization_type, - ) - - logger.info("✅ Basic data type size analysis completed") - - -@pytest.mark.asyncio -async def test_complex_objects_size(): - """Test serialization size of complex objects""" - logger.info("Starting test for complex object serialization size...") - - test_objects = [ - ("Small complex object", ComplexTestObject("small", 10)), - ("Medium complex object", ComplexTestObject("medium", 100)), - ("Large complex object", ComplexTestObject("large", 1000)), - ("Extra large complex object", ComplexTestObject("xlarge", 10000)), - ] - - logger.info("=" * 60) - logger.info("Complex Object Pickle Serialization Size Analysis") - logger.info("=" * 60) - - for name, obj in test_objects: - # Pickle serialization - pickle_data = pickle.dumps(obj) - pickle_size = format_size(len(pickle_data)) - - # Process using RedisDataProcessor - processed_data = RedisDataProcessor.serialize_data(obj) - processed_size = format_size(len(processed_data)) - - # Estimate object memory usage - obj_memory = format_size( - sys.getsizeof(obj) + sys.getsizeof(obj.data) + sys.getsizeof(obj.metadata) - ) - - logger.info( - "%-15s | Memory: %-12s | Pickle: %-12s | Processor: %-12s", - name, - obj_memory, - pickle_size, - processed_size, - ) - - logger.info("✅ Complex object size analysis completed") - - -@pytest.mark.asyncio -async def test_large_data_structures(): - """Test serialization size of large data structures""" - logger.info("Starting test for large data structure serialization size...") - - logger.info("=" * 60) - logger.info("Large Data Structure Pickle Serialization Size Analysis") - logger.info("=" * 60) - - # Test data structures of different sizes - sizes = [0.1, 0.5, 1.0, 2.0, 5.0] # MB - - for size_mb in sizes: - logger.info(f"Testing {size_mb} MB data container...") - - try: - # Create large data container - container = LargeDataContainer(size_mb) - - # Pickle serialization - start_time = time.time() - pickle_data = pickle.dumps(container) - pickle_time = time.time() - start_time - pickle_size = format_size(len(pickle_data)) - - # Process using RedisDataProcessor - start_time = time.time() - processed_data = RedisDataProcessor.serialize_data(container) - process_time = time.time() - start_time - processed_size = format_size(len(processed_data)) - - # Compression ratio calculation - original_estimate = size_mb * 1024 * 1024 - compression_ratio = len(pickle_data) / original_estimate - - logger.info( - "%-8s MB | Pickle: %-12s (%.2fs) | Processor: %-12s (%.2fs) | Compression ratio: %.2f", - f"{size_mb:.1f}", - pickle_size, - pickle_time, - processed_size, - process_time, - compression_ratio, - ) - - except MemoryError: - logger.warning( - "%-8s MB | Insufficient memory, skipping test", f"{size_mb:.1f}" - ) - except Exception as e: - logger.error("%-8s MB | Test failed: %s", f"{size_mb:.1f}", str(e)) - - logger.info("✅ Large data structure size analysis completed") - - -@pytest.mark.asyncio -async def test_function_and_class_objects(): - """Test serialization size of function and class objects""" - logger.info("Starting test for function and class object serialization size...") - - # Various functions and class objects - def simple_function(x): - return x * 2 - - def complex_function(x, y, z=10): - """Complex function with docstring""" - result = x + y + z - for i in range(100): - result += i - return result - - class SimpleClass: - def __init__(self, value): - self.value = value - - def method(self): - return self.value * 2 - - class ComplexClass: - """Complex class with multiple methods""" - - class_var = "shared_data" - - def __init__(self, name, data): - self.name = name - self.data = data - self.timestamp = get_now_with_timezone() - - def method1(self): - return len(self.data) - - def method2(self, multiplier=2): - return [x * multiplier for x in self.data] - - @staticmethod - def static_method(): - return "static_result" - - @classmethod - def class_method(cls): - return cls.class_var - - test_objects = [ - ("Simple function", simple_function), - ("Complex function", complex_function), - ("Simple class instance", SimpleClass(42)), - ("Complex class instance", ComplexClass("test", list(range(100)))), - ( - "Dictionary containing set", - { - "set_data": {1, 2, 3, 4, 5}, - "bytes_data": b"function_test_binary", - "tuple_data": (1, 2, 3), - }, - ), - ( - "Mixed object", - { - "functions": [simple_function, complex_function], - "objects": [SimpleClass(i) for i in range(10)], - "data": list(range(1000)), - "complex_types": { - "set_data": {10, 20, 30}, - "bytes_data": b"mixed_binary_data", - }, - "metadata": {"type": "mixed", "count": 10}, - }, - ), - ] - - logger.info("=" * 60) - logger.info("Function and Class Object Pickle Serialization Size Analysis") - logger.info("=" * 60) - - for name, obj in test_objects: - try: - # Pickle serialization - pickle_data = pickle.dumps(obj) - pickle_size = format_size(len(pickle_data)) - - # Process using RedisDataProcessor - processed_data = RedisDataProcessor.serialize_data(obj) - processed_size = format_size(len(processed_data)) - - # Object memory usage - obj_memory = format_size(sys.getsizeof(obj)) - - logger.info( - "%-15s | Memory: %-12s | Pickle: %-12s | Processor: %-12s", - name, - obj_memory, - pickle_size, - processed_size, - ) - - except Exception as e: - logger.error("%-15s | Serialization failed: %s", name, str(e)) - - logger.info("✅ Function and class object size analysis completed") - - -@pytest.mark.asyncio -async def test_nested_structures(): - """Test serialization size of nested structures""" - logger.info("Starting test for nested structure serialization size...") - - # Create nested structures of different depths - def create_nested_dict(depth: int, width: int = 3): - """Create nested dictionary with specified depth and width""" - if depth == 0: - return f"leaf_value_{width}" - - return {f"key_{i}": create_nested_dict(depth - 1, width) for i in range(width)} - - def create_nested_list(depth: int, width: int = 3): - """Create nested list with specified depth and width""" - if depth == 0: - return f"leaf_{width}" - - return [create_nested_list(depth - 1, width) for _ in range(width)] - - test_structures = [ - ("Nested dict - depth 2", create_nested_dict(2, 3)), - ("Nested dict - depth 3", create_nested_dict(3, 3)), - ("Nested dict - depth 4", create_nested_dict(4, 2)), - ("Nested list - depth 2", create_nested_list(2, 3)), - ("Nested list - depth 3", create_nested_list(3, 3)), - ("Nested list - depth 4", create_nested_list(4, 2)), - ( - "Mixed nesting", - { - "dict_part": create_nested_dict(3, 2), - "list_part": create_nested_list(3, 2), - "objects": [ComplexTestObject(f"nested_{i}", 50) for i in range(5)], - "complex_types": { - "nested_set": {frozenset({1, 2}), frozenset({3, 4})}, - "nested_bytes": b"nested_binary_data", - "nested_tuple": ((1, 2), (3, 4), (5, 6)), - }, - }, - ), - ] - - logger.info("=" * 60) - logger.info("Nested Structure Pickle Serialization Size Analysis") - logger.info("=" * 60) - - for name, structure in test_structures: - try: - # Pickle serialization - start_time = time.time() - pickle_data = pickle.dumps(structure) - pickle_time = time.time() - start_time - pickle_size = format_size(len(pickle_data)) - - # Process using RedisDataProcessor - start_time = time.time() - processed_data = RedisDataProcessor.serialize_data(structure) - process_time = time.time() - start_time - processed_size = format_size(len(processed_data)) - - logger.info( - "%-20s | Pickle: %-12s (%.3fs) | Processor: %-12s (%.3fs)", - name, - pickle_size, - pickle_time, - processed_size, - process_time, - ) - - except Exception as e: - logger.error("%-20s | Serialization failed: %s", name, str(e)) - - logger.info("✅ Nested structure size analysis completed") - - -@pytest.mark.asyncio -async def test_redis_storage_efficiency(): - """Test Redis storage efficiency""" - logger.info("Starting test for Redis storage efficiency...") - - factory = get_bean("redis_length_cache_factory") - cache = await factory.create_cache_manager(max_length=1000, expire_minutes=10) - - test_key = "pickle_size_test" - await cache.clear_queue(test_key) - - # Test data - test_data = [ - ("Small JSON object", {"name": "test", "value": 123}), - ( - "Large JSON object", - {"data": list(range(1000)), "metadata": {"type": "large"}}, - ), - ("Small Pickle object", ComplexTestObject("small", 10)), - ("Large Pickle object", ComplexTestObject("large", 1000)), - ] - - logger.info("=" * 60) - logger.info("Redis Storage Efficiency Analysis") - logger.info("=" * 60) - - for name, data in test_data: - # Serialization size - processed_data = RedisDataProcessor.process_data_for_storage(data) - storage_size = ( - len(processed_data) - if isinstance(processed_data, bytes) - else len(processed_data.encode('utf-8')) - ) - - # Store to Redis - start_time = time.time() - success = await cache.append(test_key, data) - store_time = time.time() - start_time - - # Read from Redis - start_time = time.time() - retrieved_data = await cache.get_by_timestamp_range(test_key, limit=1) - read_time = time.time() - start_time - - if success and retrieved_data: - logger.info( - "%-15s | Storage: %-12s | Write: %.3fs | Read: %.3fs | Status: ✅", - name, - format_size(storage_size), - store_time, - read_time, - ) - else: - logger.error( - "%-15s | Storage: %-12s | Status: ❌", name, format_size(storage_size) - ) - - # Clean up individual test data - await cache.clear_queue(test_key) - - logger.info("✅ Redis storage efficiency analysis completed") - - -async def main(): - """Main test function""" - logger.info("=" * 60) - logger.info("Pickle Serialization Size Analysis Test Started") - logger.info("=" * 60) - - try: - await test_basic_types_size() - await test_complex_objects_size() - await test_large_data_structures() - await test_function_and_class_objects() - await test_nested_structures() - await test_redis_storage_efficiency() - - logger.info("=" * 60) - logger.info("✅ All Pickle size analysis tests passed") - logger.info("=" * 60) - - except Exception as e: - logger.error("❌ Error occurred during test: %s", str(e)) - raise - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/tests/test_profile_e2e.py b/methods/EverCore/tests/test_profile_e2e.py deleted file mode 100644 index 25fb727a..00000000 --- a/methods/EverCore/tests/test_profile_e2e.py +++ /dev/null @@ -1,769 +0,0 @@ -""" -End-to-End Test: Unified Profile Extraction (SOLO + GROUP) - -Tests the full pipeline after the "unify GROUP/SOLO profile extraction" refactor: -1. Send 5 conversation rounds (each flush creates 1 memcell) -2. Profile extraction triggers at memcell #5 (interval=5) -3. Verify profile is created in new unified format (explicit_info + implicit_traits) -4. Verify profile is searchable and retrievable -5. Verify old GROUP format fields are absent - -Prerequisites: - Service running on http://localhost:1995 - (tmux attach -t EverOS) - -Run: - cd /Users/admin/Documents/Projects/evermemos-opensource - PYTHONPATH=src .venv/bin/python tests/test_profile_e2e.py -""" - -import asyncio -import json -import sys -import time -import traceback -from datetime import datetime, timezone, timedelta -from typing import Any, Dict, List, Optional, Tuple - -import httpx - -# ─── Config ─────────────────────────────────────────────────────────── -BASE_URL = "http://localhost:1995" -TIMEOUT = httpx.Timeout(180.0) - -# Use unique IDs to avoid collision with existing data -TEST_USER_ID = "e2e_profile_test_user_005" -TEST_GROUP_ID_GROUP = "e2e_profile_test_group_005a" -TEST_GROUP_USER_A = "e2e_group_user_charlie_5" -TEST_GROUP_USER_B = "e2e_group_user_diana_5" - -# Second group (different topic & users) for multi-group isolation test -TEST_GROUP_ID_GROUP2 = "e2e_profile_test_group_005b" -TEST_GROUP2_USER_A = "e2e_group_user_evan_5" -TEST_GROUP2_USER_B = "e2e_group_user_fiona_5" - -# Profile extraction interval from memorize_config.py -PROFILE_EXTRACTION_INTERVAL = 5 - -# Old GROUP format fields that should NOT exist in the new profile -OLD_GROUP_FIELDS = [ - "hard_skills", "soft_skills", "personality", "interests", - "values", "communication_style", "decision_making", - "work_style", "emotional_traits", "social_behavior", - "knowledge_domains", "life_experiences", "goals_aspirations", - "cultural_background", -] - - -# ─── Helpers ────────────────────────────────────────────────────────── - -def ts_ms(round_idx: int, offset_minutes: int = 0) -> int: - """Generate unix timestamp in milliseconds. Each round is 1 day apart.""" - dt = datetime(2026, 3, 20, 10, 0, 0, tzinfo=timezone.utc) + timedelta(days=round_idx, minutes=offset_minutes) - return int(dt.timestamp() * 1000) - - -def make_msg(role: str, text: str, sender_id: str, sender_name: str, round_idx: int, offset: int, msg_id: str) -> Dict: - return { - "message_id": msg_id, - "sender_id": sender_id, - "sender_name": sender_name, - "role": role, - "timestamp": ts_ms(round_idx, offset), - "content": [{"type": "text", "text": text}], - } - - -class TestResult: - def __init__(self): - self.checks: List[Tuple[str, bool, str]] = [] - - def check(self, name: str, condition: bool, detail: str = ""): - self.checks.append((name, condition, detail)) - icon = "PASS" if condition else "FAIL" - print(f" [{icon}] {name}" + (f" -- {detail}" if detail else "")) - - def summary(self) -> bool: - total = len(self.checks) - passed = sum(1 for _, ok, _ in self.checks if ok) - failed = total - passed - print(f"\n{'='*70}") - print(f" TOTAL: {total} | PASSED: {passed} | FAILED: {failed}") - if failed: - print(f"\n Failed checks:") - for name, ok, detail in self.checks: - if not ok: - print(f" - {name}: {detail}") - print(f"{'='*70}") - return failed == 0 - - -# ─── API Wrappers ──────────────────────────────────────────────────── - -async def api_add_personal(client: httpx.AsyncClient, user_id: str, messages: List[Dict]) -> Dict: - resp = await client.post(f"{BASE_URL}/api/v1/memories", json={"user_id": user_id, "messages": messages}) - resp.raise_for_status() - return resp.json() - - -async def api_add_group(client: httpx.AsyncClient, group_id: str, messages: List[Dict]) -> Dict: - resp = await client.post(f"{BASE_URL}/api/v1/memories/group", json={"group_id": group_id, "messages": messages}) - resp.raise_for_status() - return resp.json() - - -async def api_flush_personal(client: httpx.AsyncClient, user_id: str) -> Dict: - resp = await client.post(f"{BASE_URL}/api/v1/memories/flush", json={"user_id": user_id}) - resp.raise_for_status() - return resp.json() - - -async def api_flush_group(client: httpx.AsyncClient, group_id: str) -> Dict: - resp = await client.post(f"{BASE_URL}/api/v1/memories/group/flush", json={"group_id": group_id}) - resp.raise_for_status() - return resp.json() - - -async def api_get_profiles(client: httpx.AsyncClient, user_id: str = None, group_id: str = None) -> Dict: - filters = {} - if user_id: - filters["user_id"] = user_id - if group_id: - filters["group_id"] = group_id - resp = await client.post(f"{BASE_URL}/api/v1/memories/get", json={ - "memory_type": "profile", "filters": filters, "page": 1, "page_size": 50, - }) - resp.raise_for_status() - return resp.json() - - -async def api_search_profiles(client: httpx.AsyncClient, query: str, method: str = "hybrid", user_id: str = None, group_id: str = None) -> Dict: - filters = {} - if user_id: - filters["user_id"] = user_id - if group_id: - filters["group_id"] = group_id - resp = await client.post(f"{BASE_URL}/api/v1/memories/search", json={ - "query": query, "method": method, "memory_types": ["profile"], - "filters": filters, "top_k": 10, - }) - resp.raise_for_status() - return resp.json() - - -async def api_get_episodes(client: httpx.AsyncClient, user_id: str = None, group_id: str = None) -> Dict: - filters = {} - if user_id: - filters["user_id"] = user_id - if group_id: - filters["group_id"] = group_id - resp = await client.post(f"{BASE_URL}/api/v1/memories/get", json={ - "memory_type": "episodic_memory", "filters": filters, "page": 1, "page_size": 50, - }) - resp.raise_for_status() - return resp.json() - - -async def api_init_settings(client: httpx.AsyncClient): - resp = await client.put(f"{BASE_URL}/api/v1/settings", json={}) - resp.raise_for_status() - - -# ─── 5 Conversation Rounds (each becomes 1 memcell on flush) ──────── - -def build_solo_rounds() -> List[List[Dict]]: - """Build 5 separate conversation rounds with distinct topics.""" - uid = TEST_USER_ID - aid = "assistant_001" - - rounds = [ - # Round 1: Career background - [ - make_msg("user", "Hi! I'm a software engineer at Google, working on the search ranking team. I've been there for 3 years.", uid, "TestUser", 0, 0, "r1m1"), - make_msg("assistant", "That's fascinating! What technologies do you work with?", aid, "Assistant", 0, 1, "r1m2"), - make_msg("user", "Mainly Python and Go for the backend. I also use TensorFlow for ML feature engineering. Before Google I was at Microsoft doing C# for 2 years.", uid, "TestUser", 0, 2, "r1m3"), - make_msg("assistant", "Impressive stack! Do you enjoy the ML or infrastructure side more?", aid, "Assistant", 0, 3, "r1m4"), - make_msg("user", "I love infrastructure challenges - scaling systems to handle billions of requests excites me the most. I'm the tech lead for our data pipeline processing 10TB daily.", uid, "TestUser", 0, 4, "r1m5"), - ], - # Round 2: Hobbies and outdoor activities - [ - make_msg("user", "I wanted to ask about outdoor activities. I'm really into rock climbing and hiking.", uid, "TestUser", 1, 0, "r2m1"), - make_msg("assistant", "Great hobbies! Where do you usually go?", aid, "Assistant", 1, 1, "r2m2"), - make_msg("user", "I go to Yosemite almost every month. My golden retriever Max comes with me on all hiking trips - he's 4 years old and the best hiking buddy.", uid, "TestUser", 1, 2, "r2m3"), - make_msg("assistant", "A golden retriever hiking companion sounds wonderful!", aid, "Assistant", 1, 3, "r2m4"), - make_msg("user", "He really is! I also play guitar in my free time - mostly blues and jazz. Music helps me unwind after intense coding sessions.", uid, "TestUser", 1, 4, "r2m5"), - ], - # Round 3: Food and travel interests - [ - make_msg("user", "I visited Tokyo last year and completely fell in love with the city.", uid, "TestUser", 2, 0, "r3m1"), - make_msg("assistant", "Tokyo is amazing! What did you enjoy most?", aid, "Assistant", 2, 1, "r3m2"), - make_msg("user", "The food scene is incredible. I'm a huge ramen fan - especially tonkotsu style. I actually started making ramen at home, the broth takes 12 hours.", uid, "TestUser", 2, 2, "r3m3"), - make_msg("assistant", "That's serious dedication to cooking!", aid, "Assistant", 2, 3, "r3m4"), - make_msg("user", "I'm also learning Japanese because I want to live in Tokyo someday. I'm originally from Sichuan, China, so I love spicy food too - hot pot is my absolute favorite.", uid, "TestUser", 2, 4, "r3m5"), - ], - # Round 4: Career goals and leadership - [ - make_msg("user", "I've been thinking about transitioning to an engineering manager role in the next year or two.", uid, "TestUser", 3, 0, "r4m1"), - make_msg("assistant", "That's a great career goal! What leadership experience do you have?", aid, "Assistant", 3, 1, "r4m2"), - make_msg("user", "I've been mentoring two junior engineers and led a cross-team project last quarter coordinating 4 teams. My manager says I have good communication skills.", uid, "TestUser", 3, 2, "r4m3"), - make_msg("assistant", "Those are excellent foundations for an EM transition!", aid, "Assistant", 3, 3, "r4m4"), - make_msg("user", "Thanks! I'm reading 'The Manager's Path' by Camille Fournier. I also contribute to Apache Beam on weekends - open source leadership is good practice.", uid, "TestUser", 3, 4, "r4m5"), - ], - # Round 5: Health and fitness - [ - make_msg("user", "I've been working on improving my health lately. My BMI is around 29.8 which isn't great.", uid, "TestUser", 4, 0, "r5m1"), - make_msg("assistant", "That's good that you're aware of it! What are you doing about it?", aid, "Assistant", 4, 1, "r5m2"), - make_msg("user", "I started running 3 times a week and doing yoga on rest days. Combined with the hiking and climbing, I'm hoping to get down to 80kg from 86kg.", uid, "TestUser", 4, 2, "r5m3"), - make_msg("assistant", "That sounds like a solid fitness plan!", aid, "Assistant", 4, 3, "r5m4"), - make_msg("user", "My girlfriend is a fitness instructor so she helps me with nutrition plans. We cook healthy meals together - mostly Mediterranean diet with some Asian fusion.", uid, "TestUser", 4, 4, "r5m5"), - ], - ] - return rounds - - -def build_group_rounds() -> List[List[Dict]]: - """Build 5 group conversation rounds.""" - ua = TEST_GROUP_USER_A - ub = TEST_GROUP_USER_B - - rounds = [ - # Round 1: Project kickoff - [ - make_msg("user", "Hey Diana, I finished the API design doc for the new payment service. Can you review it?", ua, "Charlie", 0, 0, "g1m1"), - make_msg("user", "Sure! I have experience with payment systems from my last company where we used Stripe extensively.", ub, "Diana", 0, 1, "g1m2"), - make_msg("user", "Great. The REST endpoints look solid but I think we need webhook support for async payment notifications.", ua, "Charlie", 0, 3, "g1m3"), - make_msg("user", "Agreed. We also need idempotency keys for retry safety - I've seen duplicate charge issues before.", ub, "Diana", 0, 4, "g1m4"), - ], - # Round 2: Architecture discussion - [ - make_msg("user", "Diana, I've been thinking about the database schema. I'm proposing PostgreSQL with ACID transactions for the ledger.", ua, "Charlie", 1, 0, "g2m1"), - make_msg("user", "Makes sense. I've been studying distributed transactions - maybe we should use the saga pattern for cross-service consistency.", ub, "Diana", 1, 1, "g2m2"), - make_msg("user", "I was thinking the same thing! I'll prepare some architecture diagrams for our meeting.", ua, "Charlie", 1, 2, "g2m3"), - make_msg("user", "Perfect. I'll bring my notes on error handling patterns. I'm based in London so let's find a timezone-friendly slot.", ub, "Diana", 1, 3, "g2m4"), - ], - # Round 3: Implementation details - [ - make_msg("user", "I started implementing the core ledger service. Using Domain-Driven Design with event sourcing.", ua, "Charlie", 2, 0, "g3m1"), - make_msg("user", "Nice! I've set up the Stripe integration with their sandbox. I prefer integration tests over unit tests for payment flows.", ub, "Diana", 2, 1, "g3m2"), - make_msg("user", "Good call. I'm also adding comprehensive audit logging - every transaction gets a full audit trail.", ua, "Charlie", 2, 2, "g3m3"), - make_msg("user", "That's essential for financial compliance. I'll add PCI DSS compliance checks to the CI pipeline.", ub, "Diana", 2, 3, "g3m4"), - ], - # Round 4: Testing strategy - [ - make_msg("user", "Diana, we need to discuss our testing strategy. I've been writing property-based tests for the ledger.", ua, "Charlie", 3, 0, "g4m1"), - make_msg("user", "I love property-based testing! I use Hypothesis in Python. For the Stripe integration I'm using their test clock API for time-based scenarios.", ub, "Diana", 3, 1, "g4m2"), - make_msg("user", "Smart approach. I also want to add chaos engineering tests - simulating network failures between our service and Stripe.", ua, "Charlie", 3, 2, "g4m3"), - make_msg("user", "Excellent idea. I've used Toxiproxy before for that. Let me set it up in our staging environment.", ub, "Diana", 3, 3, "g4m4"), - ], - # Round 5: Deployment planning - [ - make_msg("user", "We're almost ready to deploy. I think we should use canary releases for the payment service.", ua, "Charlie", 4, 0, "g5m1"), - make_msg("user", "Absolutely. I'll configure the feature flags so we can gradually roll out. I'm very cautious with payment systems.", ub, "Diana", 4, 1, "g5m2"), - make_msg("user", "Good plan. I've also set up real-time monitoring dashboards in Grafana for transaction success rates.", ua, "Charlie", 4, 2, "g5m3"), - make_msg("user", "Perfect. I'll add alerting rules - if the failure rate exceeds 1% we should auto-rollback. Safety first for financial transactions.", ub, "Diana", 4, 3, "g5m4"), - ], - ] - return rounds - - -def build_group2_rounds() -> List[List[Dict]]: - """Build 5 rounds for a SECOND group with completely different topic (cooking/restaurant).""" - ua = TEST_GROUP2_USER_A - ub = TEST_GROUP2_USER_B - - rounds = [ - [ - make_msg("user", "Fiona, I'm opening a French restaurant downtown. I studied at Le Cordon Bleu in Paris for 2 years.", ua, "Evan", 0, 0, "h1m1"), - make_msg("user", "That's amazing! I'm a sommelier - I specialize in Burgundy wines. I'd love to curate the wine list.", ub, "Fiona", 0, 1, "h1m2"), - make_msg("user", "Perfect! My signature dish is duck confit with truffle sauce. We need wines that pair well with rich dishes.", ua, "Evan", 0, 2, "h1m3"), - make_msg("user", "For duck confit I'd recommend a Pinot Noir from Cote de Nuits. I have connections with several Burgundy producers.", ub, "Fiona", 0, 3, "h1m4"), - ], - [ - make_msg("user", "Fiona, I've been experimenting with Japanese-French fusion. My miso-glazed foie gras was a hit at the tasting event.", ua, "Evan", 1, 0, "h2m1"), - make_msg("user", "That sounds incredible! For fusion dishes, sake pairings could be interesting. I've been studying sake certification - the WSET Level 3 in Sake.", ub, "Fiona", 1, 1, "h2m2"), - make_msg("user", "Great idea. I'm also planning a farm-to-table concept. I've partnered with 3 local organic farms for seasonal ingredients.", ua, "Evan", 1, 2, "h2m3"), - make_msg("user", "Love the sustainability angle. I'll focus on natural wines and biodynamic producers to match that philosophy.", ub, "Fiona", 1, 3, "h2m4"), - ], - [ - make_msg("user", "The interior design is almost done. I went with a modern minimalist style - lots of natural wood and warm lighting.", ua, "Evan", 2, 0, "h3m1"), - make_msg("user", "Sounds beautiful. For the wine cellar, I'm thinking temperature-controlled display cases that double as a visual feature.", ub, "Fiona", 2, 1, "h3m2"), - make_msg("user", "I love that. The kitchen will have an open layout so guests can see the cooking process. Transparency builds trust.", ua, "Evan", 2, 2, "h3m3"), - make_msg("user", "Agree. I'll set up a tasting corner near the entrance where guests can sample wines before ordering.", ub, "Fiona", 2, 3, "h3m4"), - ], - [ - make_msg("user", "I'm hiring my kitchen team. Looking for a sous chef with pastry experience - my desserts need work.", ua, "Evan", 3, 0, "h4m1"), - make_msg("user", "I know someone! My friend Marie was a pastry chef at a Michelin-starred restaurant in Lyon.", ub, "Fiona", 3, 1, "h4m2"), - make_msg("user", "That would be perfect. I also need someone for the raw bar - we'll feature fresh oysters and sashimi.", ua, "Evan", 3, 2, "h4m3"), - make_msg("user", "For the raw bar, Champagne pairings are essential. I'll prepare a dedicated sparkling wine section.", ub, "Fiona", 3, 3, "h4m4"), - ], - [ - make_msg("user", "Grand opening is in 3 weeks! The menu has 28 dishes. I'm most excited about my bouillabaisse recipe.", ua, "Evan", 4, 0, "h5m1"), - make_msg("user", "I've finalized the wine list - 120 labels across 8 countries. The markup is 2.5x, which is fair for fine dining.", ub, "Fiona", 4, 1, "h5m2"), - make_msg("user", "Great pricing. I've set up our reservation system and we already have 50 bookings for opening week.", ua, "Evan", 4, 2, "h5m3"), - make_msg("user", "Wonderful! I'll organize a VIP preview evening for food critics and wine journalists the night before.", ub, "Fiona", 4, 3, "h5m4"), - ], - ] - return rounds - - -# ─── Core Test Logic ───────────────────────────────────────────────── - -async def send_rounds_and_wait( - client: httpx.AsyncClient, - rounds: List[List[Dict]], - send_fn, - flush_fn, - entity_id: str, - get_episodes_fn, - label: str, -) -> int: - """Send N rounds of messages, flushing after each. Returns total episodes found.""" - total_episodes = 0 - - for i, msgs in enumerate(rounds, 1): - print(f" Round {i}/{len(rounds)}: Sending {len(msgs)} messages...") - await send_fn(client, entity_id, msgs) - - print(f" Round {i}/{len(rounds)}: Flushing...") - flush_resp = await flush_fn(client, entity_id) - flush_status = flush_resp.get("data", {}).get("status", "unknown") - print(f" flush status: {flush_status}") - - # Wait for this round's episode to be created - max_wait = 90 - poll = 5 - waited = 0 - prev_count = total_episodes - - while waited < max_wait: - await asyncio.sleep(poll) - waited += poll - ep_resp = await get_episodes_fn(client) - ep_count = ep_resp.get("data", {}).get("total_count", 0) - if ep_count > prev_count: - total_episodes = ep_count - print(f" Episode #{ep_count} created after {waited}s") - break - else: - # Check one more time - ep_resp = await get_episodes_fn(client) - ep_count = ep_resp.get("data", {}).get("total_count", 0) - total_episodes = ep_count - if ep_count <= prev_count: - print(f" WARNING: No new episode after {max_wait}s (total={ep_count})") - - # Small pause between rounds - if i < len(rounds): - await asyncio.sleep(2) - - return total_episodes - - -async def test_solo_profile_extraction(client: httpx.AsyncClient, result: TestResult): - """Test 1: SOLO profile extraction - send 5 rounds to trigger profile.""" - print("\n" + "=" * 70) - print(" TEST 1: SOLO Profile Extraction") - print(f" (Need {PROFILE_EXTRACTION_INTERVAL} memcells to trigger profile)") - print("=" * 70) - - rounds = build_solo_rounds() - - async def get_ep(c): - return await api_get_episodes(c, user_id=TEST_USER_ID) - - total_episodes = await send_rounds_and_wait( - client, rounds, - send_fn=api_add_personal, - flush_fn=api_flush_personal, - entity_id=TEST_USER_ID, - get_episodes_fn=get_ep, - label="SOLO", - ) - - result.check( - "SOLO: Created enough episodes", - total_episodes >= PROFILE_EXTRACTION_INTERVAL, - f"episodes={total_episodes}, need>={PROFILE_EXTRACTION_INTERVAL}", - ) - - # Now wait for profile extraction (triggered by the 5th clustering) - print(f"\n Waiting for profile extraction after {total_episodes} memcells...") - max_wait = 120 - poll = 5 - waited = 0 - profile_found = False - - while waited < max_wait: - await asyncio.sleep(poll) - waited += poll - prof_resp = await api_get_profiles(client, user_id=TEST_USER_ID) - prof_count = prof_resp.get("data", {}).get("total_count", 0) - if prof_count > 0: - profile_found = True - print(f" Profile found after {waited}s!") - break - if waited % 15 == 0: - print(f" [{waited}s] profiles={prof_count}...") - - result.check("SOLO: Profile created", profile_found, f"after {waited}s" if profile_found else f"not found after {max_wait}s") - - if not profile_found: - print(" WARNING: Profile not created. Possible causes:") - print(" - Profile extraction still in progress (LLM call can be slow)") - print(" - Clustering may not have assigned all memcells to clusters") - print(" - Check server logs for errors") - return - - # Validate profile format - print("\n Validating profile format...") - prof_resp = await api_get_profiles(client, user_id=TEST_USER_ID) - profiles = prof_resp.get("data", {}).get("profiles", []) - profile = profiles[0] - profile_data = profile.get("profile_data", {}) - explicit_info = profile_data.get("explicit_info", []) - implicit_traits = profile_data.get("implicit_traits", []) - - result.check("SOLO: Has explicit_info", "explicit_info" in profile_data, f"{len(explicit_info)} items") - result.check("SOLO: Has implicit_traits", "implicit_traits" in profile_data, f"{len(implicit_traits)} items") - result.check("SOLO: Has content", len(explicit_info) + len(implicit_traits) > 0, f"explicit={len(explicit_info)}, implicit={len(implicit_traits)}") - - # Validate item structures - if explicit_info: - item = explicit_info[0] - result.check("SOLO: explicit_info has 'category'", "category" in item, f"keys={list(item.keys())}") - result.check("SOLO: explicit_info has 'description'", "description" in item, f"{str(item.get('description', ''))[:60]}") - result.check("SOLO: explicit_info has 'sources'", "sources" in item, f"sources={item.get('sources', 'N/A')}") - - if implicit_traits: - trait = implicit_traits[0] - result.check("SOLO: implicit_traits has 'trait'", "trait" in trait, f"keys={list(trait.keys())}") - result.check("SOLO: implicit_traits has 'description'", "description" in trait, f"{str(trait.get('description', ''))[:60]}") - - # Verify NO old GROUP fields - found_old = [f for f in OLD_GROUP_FIELDS if f in profile_data] - result.check("SOLO: No old GROUP fields", len(found_old) == 0, f"Found: {found_old}" if found_old else "Clean") - - # Verify processed_episode_ids - result.check("SOLO: Has processed_episode_ids", "processed_episode_ids" in profile_data, - f"count={len(profile_data.get('processed_episode_ids', []))}") - - # Print profile summary - print(f"\n Profile content ({len(explicit_info)} explicit + {len(implicit_traits)} implicit):") - for i, info in enumerate(explicit_info[:5], 1): - print(f" E{i}. [{info.get('category', '?')}] {str(info.get('description', ''))[:70]}") - for i, t in enumerate(implicit_traits[:3], 1): - print(f" I{i}. [{t.get('trait', '?')}] {str(t.get('description', ''))[:70]}") - - -async def test_group_profile_extraction(client: httpx.AsyncClient, result: TestResult): - """Test 2: GROUP profile extraction - also uses unified format.""" - print("\n" + "=" * 70) - print(" TEST 2: GROUP Profile Extraction") - print(f" (Need {PROFILE_EXTRACTION_INTERVAL} memcells to trigger profile)") - print("=" * 70) - - rounds = build_group_rounds() - - async def get_ep(c): - return await api_get_episodes(c, group_id=TEST_GROUP_ID_GROUP) - - total_episodes = await send_rounds_and_wait( - client, rounds, - send_fn=api_add_group, - flush_fn=api_flush_group, - entity_id=TEST_GROUP_ID_GROUP, - get_episodes_fn=get_ep, - label="GROUP", - ) - - result.check("GROUP: Created enough episodes", total_episodes >= PROFILE_EXTRACTION_INTERVAL, - f"episodes={total_episodes}") - - # Wait for profile extraction - need BOTH users to have profiles - print(f"\n Waiting for profile extraction (both users)...") - max_wait = 120 - poll = 5 - waited = 0 - both_found = False - - while waited < max_wait: - await asyncio.sleep(poll) - waited += poll - prof_a = await api_get_profiles(client, user_id=TEST_GROUP_USER_A, group_id=TEST_GROUP_ID_GROUP) - prof_b = await api_get_profiles(client, user_id=TEST_GROUP_USER_B, group_id=TEST_GROUP_ID_GROUP) - ca = prof_a.get("data", {}).get("total_count", 0) - cb = prof_b.get("data", {}).get("total_count", 0) - if ca > 0 and cb > 0: - both_found = True - print(f" Both profiles found after {waited}s! (Charlie={ca}, Diana={cb})") - break - if waited % 15 == 0: - print(f" [{waited}s] Charlie={ca}, Diana={cb}...") - - result.check("GROUP: Both users have profiles", both_found, - f"after {waited}s" if both_found else f"not both found after {max_wait}s") - - if not both_found: - return - - # Validate both users' profiles - for label, uid in [("Charlie", TEST_GROUP_USER_A), ("Diana", TEST_GROUP_USER_B)]: - prof_resp = await api_get_profiles(client, user_id=uid, group_id=TEST_GROUP_ID_GROUP) - profiles = prof_resp.get("data", {}).get("profiles", []) - - result.check(f"GROUP({label}): Profile exists", len(profiles) > 0) - if not profiles: - continue - - pd = profiles[0].get("profile_data", {}) - ei = pd.get("explicit_info", []) - it = pd.get("implicit_traits", []) - - result.check(f"GROUP({label}): Unified format", "explicit_info" in pd, - f"explicit={len(ei)}, implicit={len(it)}") - result.check(f"GROUP({label}): Has content", len(ei) + len(it) > 0, - f"explicit={len(ei)}, implicit={len(it)}") - found_old = [f for f in OLD_GROUP_FIELDS if f in pd] - result.check(f"GROUP({label}): No old fields", len(found_old) == 0, - f"Found: {found_old}" if found_old else "Clean") - - print(f" {label}: {len(ei)} explicit + {len(it)} implicit items") - - -async def test_profile_search(client: httpx.AsyncClient, result: TestResult): - """Test 3: Profile search & retrieval. - - Note: Search API returns individual profile items from Milvus (each explicit_info - and implicit_trait stored as separate vector entries), NOT the full profile document. - profile_data in search results contains {item_type, embed_text}. - """ - print("\n" + "=" * 70) - print(" TEST 3: Profile Search & Retrieval") - print("=" * 70) - - prof_resp = await api_get_profiles(client, user_id=TEST_USER_ID) - if prof_resp.get("data", {}).get("total_count", 0) == 0: - print(" SKIP: No SOLO profile available") - return - - queries = [ - ("rock climbing hiking outdoor", "vector"), - ("what programming languages does the user know", "hybrid"), - ] - - for query, method in queries: - print(f"\n Search: '{query}' ({method})...") - try: - resp = await api_search_profiles(client, query, method, user_id=TEST_USER_ID) - profiles = resp.get("data", {}).get("profiles", []) - result.check(f"Search({method}): Returns results", len(profiles) > 0, - f"{len(profiles)} profile item(s)") - if profiles: - p = profiles[0] - pd = p.get("profile_data", {}) - # Search returns individual items: item_type + embed_text - item_type = pd.get("item_type", "") - embed_text = pd.get("embed_text", "") - valid_type = item_type in ("explicit_info", "implicit_trait") - result.check(f"Search({method}): item_type is valid", - valid_type, - f"item_type={item_type}, text={embed_text[:60]}") - result.check(f"Search({method}): user_id matches", - p.get("user_id") == TEST_USER_ID, - f"user_id={p.get('user_id')}") - result.check(f"Search({method}): Has score", - "score" in p and p["score"] > 0, - f"score={p.get('score', 0):.4f}") - except Exception as e: - result.check(f"Search({method}): No error", False, str(e)) - - -async def test_multi_group_isolation(client: httpx.AsyncClient, result: TestResult): - """Test 5: Multi-group isolation - two groups should have independent profiles.""" - print("\n" + "=" * 70) - print(" TEST 5: Multi-Group Profile Isolation") - print(f" Group A: {TEST_GROUP_ID_GROUP} (payment service - from Test 2)") - print(f" Group B: {TEST_GROUP_ID_GROUP2} (restaurant/cooking)") - print("=" * 70) - - # Group A was already created in Test 2. - # Now create Group B with completely different content. - rounds = build_group2_rounds() - - async def get_ep(c): - return await api_get_episodes(c, group_id=TEST_GROUP_ID_GROUP2) - - total_episodes = await send_rounds_and_wait( - client, rounds, - send_fn=api_add_group, - flush_fn=api_flush_group, - entity_id=TEST_GROUP_ID_GROUP2, - get_episodes_fn=get_ep, - label="GROUP2", - ) - - result.check("ISOLATION: Group B created enough episodes", - total_episodes >= PROFILE_EXTRACTION_INTERVAL, - f"episodes={total_episodes}") - - # Wait for Group B profiles - need BOTH users - print(f"\n Waiting for Group B profile extraction (both users)...") - max_wait = 120 - poll = 5 - waited = 0 - both_found = False - - while waited < max_wait: - await asyncio.sleep(poll) - waited += poll - prof_a = await api_get_profiles(client, user_id=TEST_GROUP2_USER_A, group_id=TEST_GROUP_ID_GROUP2) - prof_b = await api_get_profiles(client, user_id=TEST_GROUP2_USER_B, group_id=TEST_GROUP_ID_GROUP2) - ca = prof_a.get("data", {}).get("total_count", 0) - cb = prof_b.get("data", {}).get("total_count", 0) - if ca > 0 and cb > 0: - both_found = True - print(f" Both profiles found after {waited}s! (Evan={ca}, Fiona={cb})") - break - if ca > 0 or cb > 0: - if waited % 10 == 0: - print(f" [{waited}s] Partial: Evan={ca}, Fiona={cb}, waiting for both...") - elif waited % 15 == 0: - print(f" [{waited}s] Evan={ca}, Fiona={cb}...") - - result.check("ISOLATION: Group B both users have profiles", both_found, - f"after {waited}s" if both_found else f"not both found after {max_wait}s") - - if not both_found: - return - - # Now the key isolation checks: - # 1. Group A profiles should NOT contain Group B content (cooking/restaurant/wine) - print("\n Checking isolation: Group A profiles should not contain Group B content...") - group_a_profiles = [] - for uid in [TEST_GROUP_USER_A, TEST_GROUP_USER_B]: - resp = await api_get_profiles(client, user_id=uid, group_id=TEST_GROUP_ID_GROUP) - for p in resp.get("data", {}).get("profiles", []): - group_a_profiles.append(p) - - group_a_text = "" - for p in group_a_profiles: - pd = p.get("profile_data", {}) - for item in pd.get("explicit_info", []) + pd.get("implicit_traits", []): - group_a_text += " " + str(item.get("description", "")) - - # Group B keywords that should NOT appear in Group A - b_keywords = ["restaurant", "sommelier", "wine", "duck confit", "Le Cordon Bleu", "Burgundy", "bouillabaisse"] - leaked_b_in_a = [kw for kw in b_keywords if kw.lower() in group_a_text.lower()] - result.check("ISOLATION: Group A has no Group B content", - len(leaked_b_in_a) == 0, - f"Leaked keywords: {leaked_b_in_a}" if leaked_b_in_a else "Clean") - - # 2. Group B profiles should NOT contain Group A content (payment/Stripe/PostgreSQL) - print(" Checking isolation: Group B profiles should not contain Group A content...") - group_b_profiles = [] - for uid in [TEST_GROUP2_USER_A, TEST_GROUP2_USER_B]: - resp = await api_get_profiles(client, user_id=uid, group_id=TEST_GROUP_ID_GROUP2) - for p in resp.get("data", {}).get("profiles", []): - group_b_profiles.append(p) - - group_b_text = "" - for p in group_b_profiles: - pd = p.get("profile_data", {}) - for item in pd.get("explicit_info", []) + pd.get("implicit_traits", []): - group_b_text += " " + str(item.get("description", "")) - - # Group A keywords that should NOT appear in Group B - a_keywords = ["payment", "Stripe", "PostgreSQL", "idempotency", "saga pattern", "canary release"] - leaked_a_in_b = [kw for kw in a_keywords if kw.lower() in group_b_text.lower()] - result.check("ISOLATION: Group B has no Group A content", - len(leaked_a_in_b) == 0, - f"Leaked keywords: {leaked_a_in_b}" if leaked_a_in_b else "Clean") - - # 3. Group B profile format is also unified - for p in group_b_profiles: - pd = p.get("profile_data", {}) - result.check(f"ISOLATION: Group B ({p.get('user_id','?')}) unified format", - "explicit_info" in pd, f"keys={list(pd.keys())[:5]}") - break # check one is enough - - # Print summary - print(f"\n Group A ({TEST_GROUP_ID_GROUP}): {len(group_a_profiles)} profile(s)") - print(f" Group B ({TEST_GROUP_ID_GROUP2}): {len(group_b_profiles)} profile(s)") - if group_b_profiles: - pd = group_b_profiles[0].get("profile_data", {}) - ei = pd.get("explicit_info", []) - for i, info in enumerate(ei[:3], 1): - print(f" B-E{i}. [{info.get('category', '?')}] {str(info.get('description', ''))[:60]}") - - -async def test_api_contract(client: httpx.AsyncClient, result: TestResult): - """Test 4: API response structure checks.""" - print("\n" + "=" * 70) - print(" TEST 4: API Contract Checks") - print("=" * 70) - - # GET response shape - resp = await api_get_profiles(client, user_id=TEST_USER_ID) - data = resp.get("data", {}) - result.check("API: GET has 'profiles'", "profiles" in data, f"keys={list(data.keys())}") - result.check("API: GET has 'total_count'", "total_count" in data, f"total_count={data.get('total_count')}") - - # Search response shape - sr = await api_search_profiles(client, "test", "keyword", user_id=TEST_USER_ID) - sd = sr.get("data", {}) - result.check("API: Search has 'profiles'", "profiles" in sd) - result.check("API: Search has 'episodes'", "episodes" in sd) - - # Flush for non-existent user doesn't crash - fr = await api_flush_personal(client, "nonexistent_user_contract_test") - result.check("API: Flush for empty user OK", True, f"status={fr.get('data', {}).get('status')}") - - # Profile item structure - profiles = data.get("profiles", []) - if profiles: - p = profiles[0] - has_fields = {"id", "user_id", "profile_data"}.issubset(set(p.keys())) - result.check("API: Profile item has required fields", has_fields, f"fields={sorted(p.keys())}") - - -# ─── Main ──────────────────────────────────────────────────────────── - -async def main(): - print("=" * 70) - print(" E2E TEST: Unified Profile Extraction") - print(f" Target: {BASE_URL}") - print(f" Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") - print(f" Profile extraction interval: {PROFILE_EXTRACTION_INTERVAL} memcells") - print("=" * 70) - - result = TestResult() - - # Connectivity check - try: - async with httpx.AsyncClient(timeout=TIMEOUT) as client: - r = await client.get(f"{BASE_URL}/api/v1/memories/get", timeout=5.0) - print(f"\n API reachable (HTTP {r.status_code})") - except Exception as e: - print(f"\n FATAL: Cannot connect to {BASE_URL}: {e}") - sys.exit(1) - - async with httpx.AsyncClient(timeout=TIMEOUT) as client: - try: - await api_init_settings(client) - print(" Settings initialized.\n") - except Exception as e: - print(f" Settings init warning: {e}\n") - - for test_fn in [test_solo_profile_extraction, test_group_profile_extraction, test_multi_group_isolation, test_profile_search, test_api_contract]: - try: - await test_fn(client, result) - except Exception as e: - result.check(f"{test_fn.__name__} completed", False, f"{type(e).__name__}: {e}") - traceback.print_exc() - - all_passed = result.summary() - sys.exit(0 if all_passed else 1) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/tests/test_profile_extraction_interval.py b/methods/EverCore/tests/test_profile_extraction_interval.py deleted file mode 100644 index 32ded293..00000000 --- a/methods/EverCore/tests/test_profile_extraction_interval.py +++ /dev/null @@ -1,504 +0,0 @@ -""" -Tests for profile extraction interval-based throttling. - -Layer 1: Unit tests (no DB, no LLM) - - MemSceneState serialization (new memcell_info/memscene_info format) - - Backward compatibility with old format - - Interval skip/trigger logic - - Timestamp-based cluster selection - - Multi-cluster event collection - - Participant merging - -Layer 2: Persistence simulation (mock storage, no real DB) - - State survives save/load cycles with new format - - Restart recovery - -Layer 3: End-to-end tests (mock LLM, mock DB) - - Full flow: N memcells with interval + timestamp-based selection -""" - -import pytest -import numpy as np -from unittest.mock import AsyncMock, MagicMock, patch -from typing import Dict, Any, List, Set - -from memory_layer.cluster_manager.manager import MemSceneState -from biz_layer.memorize_config import MemorizeConfig - - -# ============================================================================ -# Layer 1: Unit Tests -# ============================================================================ - - -class TestMemSceneStateSerialization: - """MemSceneState to_dict/from_dict with new format.""" - - def test_empty_state_roundtrip(self): - state = MemSceneState() - d = state.to_dict() - assert "memcell_info" in d - assert "memscene_info" in d - assert "next_cluster_idx" in d - restored = MemSceneState.from_dict(d) - assert restored.event_ids == [] - assert restored.cluster_counts == {} - - def test_to_dict_produces_new_format(self): - state = MemSceneState() - state.event_ids = ["evt_1", "evt_2"] - state.timestamps = [100.0, 200.0] - state.eventid_to_cluster = {"evt_1": "cluster_000", "evt_2": "cluster_001"} - state.cluster_centroids = { - "cluster_000": np.array([1.0, 0.0], dtype=np.float32), - } - state.cluster_counts = {"cluster_000": 1, "cluster_001": 1} - state.cluster_last_ts = {"cluster_000": 100.0, "cluster_001": 200.0} - state.next_cluster_idx = 2 - - d = state.to_dict() - assert d["memcell_info"]["evt_1"] == {"memscene": "cluster_000", "timestamp": 100.0} - assert d["memcell_info"]["evt_2"] == {"memscene": "cluster_001", "timestamp": 200.0} - assert d["memscene_info"]["cluster_000"]["count"] == 1 - assert d["memscene_info"]["cluster_000"]["timestamp"] == 100.0 - assert d["memscene_info"]["cluster_000"]["center"] == [1.0, 0.0] - assert d["memscene_info"]["cluster_001"]["count"] == 1 - assert d["next_cluster_idx"] == 2 - - def test_roundtrip(self): - state = MemSceneState() - state.event_ids = ["evt_1", "evt_2"] - state.timestamps = [100.0, 200.0] - state.eventid_to_cluster = {"evt_1": "cluster_000", "evt_2": "cluster_000"} - state.cluster_ids = ["cluster_000", "cluster_000"] - state.cluster_centroids = { - "cluster_000": np.array([1.0, 2.0], dtype=np.float32), - } - state.cluster_counts = {"cluster_000": 2} - state.cluster_last_ts = {"cluster_000": 200.0} - state.next_cluster_idx = 1 - - restored = MemSceneState.from_dict(state.to_dict()) - assert restored.event_ids == ["evt_1", "evt_2"] - assert restored.timestamps == [100.0, 200.0] - assert restored.eventid_to_cluster == {"evt_1": "cluster_000", "evt_2": "cluster_000"} - assert restored.cluster_counts == {"cluster_000": 2} - assert restored.cluster_last_ts == {"cluster_000": 200.0} - np.testing.assert_array_almost_equal( - restored.cluster_centroids["cluster_000"], [1.0, 2.0] - ) - - def test_backward_compat_old_format(self): - """Old-format data (without memcell_info) should still load.""" - old_data = { - "event_ids": ["evt_1"], - "timestamps": [100.0], - "cluster_ids": ["cluster_000"], - "eventid_to_cluster": {"evt_1": "cluster_000"}, - "next_cluster_idx": 1, - "cluster_centroids": {"cluster_000": [1.0, 0.0]}, - "cluster_counts": {"cluster_000": 1}, - "cluster_last_ts": {"cluster_000": 100.0}, - } - state = MemSceneState.from_dict(old_data) - assert state.event_ids == ["evt_1"] - assert state.eventid_to_cluster == {"evt_1": "cluster_000"} - assert state.cluster_counts == {"cluster_000": 1} - assert state.cluster_last_ts == {"cluster_000": 100.0} - - def test_from_dict_case_cluster_ids_none(self): - """case_cluster_ids=None in DB should deserialize to empty set.""" - data = { - "memcell_info": {}, - "memscene_info": {}, - "next_cluster_idx": 0, - "case_cluster_ids": None, - } - state = MemSceneState.from_dict(data) - assert state.case_cluster_ids == set() - - def test_from_dict_case_cluster_ids_missing(self): - """Missing case_cluster_ids key should deserialize to empty set.""" - data = { - "memcell_info": {}, - "memscene_info": {}, - "next_cluster_idx": 0, - } - state = MemSceneState.from_dict(data) - assert state.case_cluster_ids == set() - - def test_from_dict_case_cluster_ids_with_values(self): - """case_cluster_ids with values should roundtrip correctly.""" - state = MemSceneState() - state.case_cluster_ids = {"cluster_000", "cluster_001"} - d = state.to_dict() - restored = MemSceneState.from_dict(d) - assert restored.case_cluster_ids == {"cluster_000", "cluster_001"} - - -class TestIntervalLogic: - """Interval skip/trigger decision logic.""" - - @staticmethod - def _should_extract(config: MemorizeConfig, total_count: int) -> bool: - return ( - config.profile_extraction_interval <= 1 - or total_count % config.profile_extraction_interval == 0 - ) - - def test_interval_1_always_triggers(self): - config = MemorizeConfig(profile_extraction_interval=1) - for count in range(1, 10): - assert self._should_extract(config, count) is True - - def test_interval_3_triggers_on_multiples(self): - config = MemorizeConfig(profile_extraction_interval=3) - results = {i: self._should_extract(config, i) for i in range(1, 10)} - assert results == { - 1: False, 2: False, 3: True, - 4: False, 5: False, 6: True, - 7: False, 8: False, 9: True, - } - - def test_interval_0_treated_as_always(self): - config = MemorizeConfig(profile_extraction_interval=0) - assert self._should_extract(config, 1) is True - assert self._should_extract(config, 5) is True - - -class TestTimestampBasedSelection: - """Timestamp-based cluster selection for profile extraction.""" - - @staticmethod - def _select_clusters( - cluster_last_ts: Dict[str, float], - last_profile_ts: float, - current_cluster_id: str, - ) -> List[str]: - """Replicate the logic from mem_memorize.py.""" - target = [ - cid for cid, ts in cluster_last_ts.items() - if ts is not None and ts > last_profile_ts - ] - if current_cluster_id not in target: - target.append(current_cluster_id) - return target - - def test_first_extraction_selects_all(self): - """When last_profile_ts=0 (no profile yet), all clusters selected.""" - result = self._select_clusters( - cluster_last_ts={"c0": 100.0, "c1": 200.0, "c2": 300.0}, - last_profile_ts=0.0, - current_cluster_id="c2", - ) - assert sorted(result) == ["c0", "c1", "c2"] - - def test_only_new_clusters_selected(self): - """Only clusters updated after last profile extraction.""" - result = self._select_clusters( - cluster_last_ts={"c0": 100.0, "c1": 200.0, "c2": 300.0}, - last_profile_ts=150.0, - current_cluster_id="c2", - ) - assert sorted(result) == ["c1", "c2"] - - def test_current_cluster_always_included(self): - """Current cluster is always included even if its timestamp is old.""" - result = self._select_clusters( - cluster_last_ts={"c0": 100.0, "c1": 50.0}, - last_profile_ts=150.0, - current_cluster_id="c1", - ) - assert "c1" in result - - def test_all_old_returns_only_current(self): - """When all clusters are older than last_profile_ts, only current.""" - result = self._select_clusters( - cluster_last_ts={"c0": 100.0, "c1": 50.0}, - last_profile_ts=200.0, - current_cluster_id="c0", - ) - assert result == ["c0"] - - def test_none_timestamps_excluded(self): - result = self._select_clusters( - cluster_last_ts={"c0": None, "c1": 200.0}, - last_profile_ts=0.0, - current_cluster_id="c0", - ) - assert sorted(result) == ["c0", "c1"] - - -class TestMultiClusterEventCollection: - """Collecting event_ids from multiple clusters.""" - - def test_collects_from_all_target_clusters(self): - state = MemSceneState() - state.eventid_to_cluster = { - "evt_1": "cluster_000", - "evt_2": "cluster_000", - "evt_3": "cluster_001", - "evt_4": "cluster_002", - "evt_5": "cluster_001", - } - target_set = {"cluster_000", "cluster_001"} - current_event_id = "evt_1" - - result = { - eid for eid, cid in state.eventid_to_cluster.items() - if cid in target_set and eid != current_event_id - } - - assert result == {"evt_2", "evt_3", "evt_5"} - - def test_excludes_current_event(self): - state = MemSceneState() - state.eventid_to_cluster = {"evt_1": "cluster_000"} - target_set = {"cluster_000"} - - result = { - eid for eid, cid in state.eventid_to_cluster.items() - if cid in target_set and eid != "evt_1" - } - - assert result == set() - - def test_empty_target_returns_empty(self): - state = MemSceneState() - state.eventid_to_cluster = {"evt_1": "cluster_000"} - - result = { - eid for eid, cid in state.eventid_to_cluster.items() - if cid in set() and eid != "evt_1" - } - - assert result == set() - - -class TestParticipantMerging: - """Participant deduplication from multiple memcells.""" - - @staticmethod - def _merge_participants(memcells: list) -> List[str]: - all_participants: Set[str] = set() - for mc in memcells: - participants = ( - mc.participants - if hasattr(mc, "participants") - else mc.get("participants", []) - ) - all_participants.update(participants or []) - return [ - u for u in all_participants - if "robot" not in u.lower() and "assistant" not in u.lower() - ] - - def test_merges_and_deduplicates(self): - memcells = [ - MagicMock(participants=["user_a", "user_b", "robot_1"]), - MagicMock(participants=["user_b", "user_c", "assistant"]), - MagicMock(participants=["user_a", "user_d"]), - ] - result = sorted(self._merge_participants(memcells)) - assert result == ["user_a", "user_b", "user_c", "user_d"] - - def test_handles_none_participants(self): - memcells = [ - MagicMock(participants=None), - MagicMock(participants=["user_a"]), - ] - assert self._merge_participants(memcells) == ["user_a"] - - def test_all_robots_returns_empty(self): - memcells = [ - MagicMock(participants=["robot_1", "Assistant"]), - ] - assert self._merge_participants(memcells) == [] - - -# ============================================================================ -# Layer 2: Persistence Simulation (mock storage, no real DB) -# ============================================================================ - - -class InMemoryMemSceneStorage: - """In-memory mock of MemSceneRawRepository for testing.""" - - def __init__(self): - self._store: Dict[str, Dict[str, Any]] = {} - - async def save_mem_scene(self, group_id: str, state: Dict[str, Any]) -> bool: - self._store[group_id] = state - return True - - async def load_mem_scene(self, group_id: str) -> Any: - return self._store.get(group_id) - - async def clear(self, group_id: str) -> bool: - self._store.pop(group_id, None) - return True - - -class TestStatePersistence: - """Verify MemSceneState survives save/load cycles with new format.""" - - @pytest.fixture - def storage(self): - return InMemoryMemSceneStorage() - - @pytest.mark.asyncio - async def test_save_and_load_new_format(self, storage): - group_id = "__test_new_format__" - - state = MemSceneState() - state.event_ids = ["evt_1", "evt_2"] - state.timestamps = [100.0, 200.0] - state.eventid_to_cluster = {"evt_1": "cluster_000", "evt_2": "cluster_001"} - state.cluster_ids = ["cluster_000", "cluster_001"] - state.cluster_counts = {"cluster_000": 1, "cluster_001": 1} - state.cluster_last_ts = {"cluster_000": 100.0, "cluster_001": 200.0} - state.next_cluster_idx = 2 - - await storage.save_mem_scene(group_id, state.to_dict()) - loaded_dict = await storage.load_mem_scene(group_id) - - # Verify new format - assert "memcell_info" in loaded_dict - assert "memscene_info" in loaded_dict - - loaded_state = MemSceneState.from_dict(loaded_dict) - assert loaded_state.cluster_counts == {"cluster_000": 1, "cluster_001": 1} - assert loaded_state.eventid_to_cluster == {"evt_1": "cluster_000", "evt_2": "cluster_001"} - - @pytest.mark.asyncio - async def test_restart_recovery(self, storage): - """Simulate server restart: state survives in storage.""" - group_id = "__test_restart__" - - state = MemSceneState() - state.event_ids = ["evt_1"] - state.timestamps = [100.0] - state.eventid_to_cluster = {"evt_1": "cluster_000"} - state.cluster_ids = ["cluster_000"] - state.cluster_counts = {"cluster_000": 1} - state.cluster_last_ts = {"cluster_000": 100.0} - state.next_cluster_idx = 1 - await storage.save_mem_scene(group_id, state.to_dict()) - - # "Restart": load from storage - fresh_dict = await storage.load_mem_scene(group_id) - fresh_state = MemSceneState.from_dict(fresh_dict) - - assert fresh_state.event_ids == ["evt_1"] - assert fresh_state.cluster_counts == {"cluster_000": 1} - assert fresh_state.next_cluster_idx == 1 - - -# ============================================================================ -# Layer 3: End-to-End Tests (mock LLM + DB) -# ============================================================================ - - -class TestProfileExtractionE2E: - """End-to-end: verify extraction call count with timestamp-based selection. - - Simulates the caller logic from mem_memorize.py using timestamp comparison - instead of pending_profile_cluster_ids. - """ - - @staticmethod - def _simulate_flow( - memcell_clusters: List[str], - memcell_timestamps: List[float], - interval: int, - ) -> List[List[str]]: - """Simulate the caller logic from mem_memorize.py. - - Args: - memcell_clusters: cluster_id assigned to each incoming memcell. - memcell_timestamps: timestamp of each incoming memcell. - interval: profile_extraction_interval config value. - - Returns: - List of cluster_ids lists passed to each extraction call. - """ - config = MemorizeConfig(profile_extraction_interval=interval) - cluster_counts: Dict[str, int] = {} - cluster_last_ts: Dict[str, float] = {} - last_profile_ts = 0.0 - extraction_calls = [] - - for cluster_id, ts in zip(memcell_clusters, memcell_timestamps): - cluster_counts[cluster_id] = cluster_counts.get(cluster_id, 0) + 1 - cluster_last_ts[cluster_id] = max( - cluster_last_ts.get(cluster_id, 0.0), ts - ) - - total = sum(cluster_counts.values()) - should_extract = ( - config.profile_extraction_interval <= 1 - or total % config.profile_extraction_interval == 0 - ) - - if should_extract: - target = [ - cid for cid, c_ts in cluster_last_ts.items() - if c_ts > last_profile_ts - ] - if cluster_id not in target: - target.append(cluster_id) - extraction_calls.append(sorted(target)) - last_profile_ts = ts # profile updated at this point - - return extraction_calls - - def test_interval_1_extracts_every_time(self): - calls = self._simulate_flow( - ["c0", "c1", "c0", "c1", "c2"], - [100, 200, 300, 400, 500], - interval=1, - ) - assert len(calls) == 5 - - def test_interval_3_reduces_calls(self): - calls = self._simulate_flow( - ["c0", "c1", "c0", "c1", "c2", "c0"], - [100, 200, 300, 400, 500, 600], - interval=3, - ) - # total=3 triggers at memcell 3, total=6 triggers at memcell 6 - assert len(calls) == 2 - - def test_first_extraction_includes_all_clusters(self): - calls = self._simulate_flow( - ["c0", "c1", "c2"], - [100, 200, 300], - interval=3, - ) - assert len(calls) == 1 - assert calls[0] == ["c0", "c1", "c2"] - - def test_second_extraction_only_new_clusters(self): - calls = self._simulate_flow( - ["c0", "c1", "c2", "c3", "c0", "c3"], - [100, 200, 300, 400, 500, 600], - interval=3, - ) - # First extraction at memcell 3 (ts=300): all [c0, c1, c2], last_profile_ts=300 - # Second extraction at memcell 6 (ts=600): c3(400>300), c0(500>300), c3(600>300) - assert len(calls) == 2 - assert calls[0] == ["c0", "c1", "c2"] - assert calls[1] == ["c0", "c3"] - - def test_no_cluster_missed(self): - """Every cluster with new data must appear in at least one extraction call.""" - memcell_clusters = ["c0", "c1", "c2", "c3", "c0", "c1"] - calls = self._simulate_flow( - memcell_clusters, - [100, 200, 300, 400, 500, 600], - interval=3, - ) - all_in_calls = set() - for call in calls: - all_in_calls.update(call) - assert all_in_calls == set(memcell_clusters) diff --git a/methods/EverCore/tests/test_profile_memory.py b/methods/EverCore/tests/test_profile_memory.py deleted file mode 100644 index ab10dd2b..00000000 --- a/methods/EverCore/tests/test_profile_memory.py +++ /dev/null @@ -1,555 +0,0 @@ -""" -Profile Memory & Extractor Unit Tests - -Tests for: -- ProfileMemory: to_dict, from_dict, total_items, get_all_source_ids, readable output -- ProfileExtractRequest: construction and defaults -- ProfileExtractor: id mapping, LLM response parsing, operations application -- Unified extraction: no SOLO/TEAM branching - -Usage: - PYTHONPATH=src pytest tests/test_profile_memory.py -v -""" - -import json -import pytest -from datetime import datetime, timezone -from unittest.mock import AsyncMock, MagicMock, patch - -from api_specs.memory_types import ProfileMemory, MemoryType, BaseMemory -from memory_layer.memory_extractor.profile_extractor import ( - ProfileExtractor, - ProfileExtractRequest, - ProfileAction, - ProfileItemType, - _create_id_mapping, - _replace_sources, - _get_short_id, -) -from memory_layer.memory_extractor.base_memory_extractor import MemoryExtractRequest - - -# ============================================================================ -# ProfileMemory data model -# ============================================================================ - - -class TestProfileMemory: - """ProfileMemory dataclass: construction, serialization, utility methods.""" - - def _make_profile(self, **kwargs) -> ProfileMemory: - defaults = dict( - memory_type=MemoryType.PROFILE, - user_id="user_1", - timestamp=datetime(2024, 1, 1, tzinfo=timezone.utc), - explicit_info=[ - {"category": "Skill", "description": "Python", "evidence": "said so", "sources": ["ep1"]}, - {"category": "Interest", "description": "hiking", "evidence": "", "sources": []}, - ], - implicit_traits=[ - {"trait": "Curious", "description": "asks many questions", "basis": "behavior", "evidence": "", "sources": ["ep1", "ep2"]}, - ], - ) - defaults.update(kwargs) - return ProfileMemory(**defaults) - - def test_inherits_base_memory(self): - assert issubclass(ProfileMemory, BaseMemory) - - def test_total_items(self): - p = self._make_profile() - assert p.total_items() == 3 - - def test_total_items_empty(self): - p = self._make_profile(explicit_info=[], implicit_traits=[]) - assert p.total_items() == 0 - - def test_to_dict_structure(self): - p = self._make_profile() - d = p.to_dict() - assert d["user_id"] == "user_1" - assert d["memory_type"] == "profile" - assert len(d["explicit_info"]) == 2 - assert len(d["implicit_traits"]) == 1 - assert "processed_episode_ids" in d - - def test_to_dict_returns_plain_dicts(self): - """explicit_info and implicit_traits should be plain dicts, not objects.""" - p = self._make_profile() - d = p.to_dict() - for info in d["explicit_info"]: - assert isinstance(info, dict) - for trait in d["implicit_traits"]: - assert isinstance(trait, dict) - - def test_from_dict_roundtrip(self): - p = self._make_profile() - d = p.to_dict() - p2 = ProfileMemory.from_dict(d) - assert p2.user_id == "user_1" - assert p2.total_items() == 3 - assert p2.explicit_info[0]["category"] == "Skill" - assert p2.implicit_traits[0]["trait"] == "Curious" - - def test_from_dict_with_user_id_override(self): - d = {"explicit_info": [{"category": "X", "description": "Y"}], "implicit_traits": []} - p = ProfileMemory.from_dict(d, user_id="override_user", group_id="override_group") - assert p.user_id == "override_user" - assert p.group_id == "override_group" - - def test_from_dict_empty(self): - p = ProfileMemory.from_dict({}) - assert p.total_items() == 0 - assert p.explicit_info == [] - assert p.implicit_traits == [] - - def test_get_all_source_ids(self): - p = self._make_profile() - p.explicit_info[0]["sources"] = ["2024-01-01|conv_abc"] - p.implicit_traits[0]["sources"] = ["conv_def", "2024-02-01|conv_ghi"] - ids = p.get_all_source_ids() - assert "conv_abc" in ids - assert "conv_def" in ids - assert "conv_ghi" in ids - - def test_get_all_source_ids_empty(self): - p = self._make_profile(explicit_info=[], implicit_traits=[]) - assert p.get_all_source_ids() == set() - - def test_to_readable_document(self): - p = self._make_profile() - doc = p.to_readable_document() - assert "Python" in doc - assert "Skill" in doc - assert "Curious" in doc - assert "Total 3 items" in doc - - def test_to_readable_profile(self): - p = self._make_profile() - text = p.to_readable_profile() - assert "Python" in text - assert "Curious" in text - - def test_to_readable_profile_empty(self): - p = self._make_profile(explicit_info=[], implicit_traits=[]) - assert p.to_readable_profile() == "No profile data yet." - - def test_post_init_sets_memory_type(self): - p = self._make_profile() - assert p.memory_type == MemoryType.PROFILE - - def test_post_init_sets_last_updated(self): - p = self._make_profile() - assert p.last_updated is not None - - def test_processed_episode_ids_default(self): - p = self._make_profile() - assert p.processed_episode_ids == [] - - -# ============================================================================ -# ID Mapper -# ============================================================================ - - -class TestIdMapper: - """ID mapping functions for token reduction.""" - - def test_create_id_mapping(self): - m = _create_id_mapping(["abc123", "def456", "ghi789"]) - assert m == {"abc123": "ep1", "def456": "ep2", "ghi789": "ep3"} - - def test_create_id_mapping_skips_empty(self): - m = _create_id_mapping(["abc", "", None, "def"]) - assert "" not in m - assert None not in m - assert len(m) == 2 - - def test_get_short_id(self): - m = {"abc": "ep1"} - assert _get_short_id("abc", m) == "ep1" - assert _get_short_id("unknown", m) == "unknown" - - def test_replace_sources_forward(self): - profile = { - "explicit_info": [{"sources": ["abc", "def"]}], - "implicit_traits": [{"sources": ["abc"]}], - } - m = {"abc": "ep1", "def": "ep2"} - result = _replace_sources(profile, m) - assert result["explicit_info"][0]["sources"] == ["ep1", "ep2"] - assert result["implicit_traits"][0]["sources"] == ["ep1"] - - def test_replace_sources_reverse(self): - profile = { - "explicit_info": [{"sources": ["ep1"]}], - "implicit_traits": [], - } - m = {"abc": "ep1"} - result = _replace_sources(profile, m, reverse=True) - assert result["explicit_info"][0]["sources"] == ["abc"] - - def test_replace_sources_with_timestamp_prefix(self): - profile = { - "explicit_info": [{"sources": ["2024-01-01|abc"]}], - "implicit_traits": [], - } - m = {"abc": "ep1"} - result = _replace_sources(profile, m) - assert result["explicit_info"][0]["sources"] == ["2024-01-01|ep1"] - - def test_replace_sources_does_not_mutate_original(self): - profile = {"explicit_info": [{"sources": ["abc"]}], "implicit_traits": []} - m = {"abc": "ep1"} - _replace_sources(profile, m) - assert profile["explicit_info"][0]["sources"] == ["abc"] - - -# ============================================================================ -# ProfileExtractRequest -# ============================================================================ - - -class TestProfileExtractRequest: - """ProfileExtractRequest construction and defaults.""" - - def test_inherits_memory_extract_request(self): - assert issubclass(ProfileExtractRequest, MemoryExtractRequest) - - def test_defaults(self): - req = ProfileExtractRequest(user_id="u1", group_id="g1") - assert req.new_episode is None - assert req.cluster_episodes == [] - assert req.old_profile is None - assert req.max_items == 25 - assert req.memcell_list == [] - assert req.episode_list == [] - - def test_with_old_profile(self): - p = ProfileMemory( - memory_type=MemoryType.PROFILE, - user_id="u1", - timestamp=datetime.now(timezone.utc), - ) - req = ProfileExtractRequest(user_id="u1", group_id="g1", old_profile=p) - assert req.old_profile is p - - -# ============================================================================ -# ProfileExtractor -# ============================================================================ - - -class TestProfileExtractor: - """ProfileExtractor: LLM response parsing, operations, dedup.""" - - def _make_extractor(self): - llm = MagicMock() - return ProfileExtractor(llm_provider=llm) - - def test_parse_profile_response_json(self): - ext = self._make_extractor() - resp = '```json\n{"operations": [{"action": "none"}]}\n```' - result = ext._parse_profile_response(resp) - assert result["operations"] == [{"action": "none"}] - - def test_parse_profile_response_bare_json(self): - ext = self._make_extractor() - resp = '{"operations": []}' - result = ext._parse_profile_response(resp) - assert result["operations"] == [] - - def test_parse_profile_response_invalid(self): - ext = self._make_extractor() - assert ext._parse_profile_response("not json at all") is None - assert ext._parse_profile_response("") is None - - def test_format_profile_with_index_empty(self): - ext = self._make_extractor() - assert ext._format_profile_with_index({"explicit_info": [], "implicit_traits": []}) == "" - - def test_format_profile_with_index(self): - ext = self._make_extractor() - d = { - ProfileItemType.EXPLICIT_INFO: [ - {"category": "Skill", "description": "Python", "evidence": "said so"}, - ], - ProfileItemType.IMPLICIT_TRAITS: [ - {"trait": "Curious", "description": "asks why", "evidence": ""}, - ], - } - text = ext._format_profile_with_index(d) - assert "[0] [Skill] Python" in text - assert "[0] Curious: asks why" in text - - def test_attach_ts_already_has_timestamp(self): - ext = self._make_extractor() - assert ext._attach_ts("2024-01-01|ep1", {}) == "2024-01-01|ep1" - - def test_attach_ts_adds_timestamp(self): - ext = self._make_extractor() - result = ext._attach_ts("ep1", {"ep1": "2024-01-01"}) - assert result == "2024-01-01|ep1" - - def test_attach_ts_no_mapping(self): - ext = self._make_extractor() - assert ext._attach_ts("ep1", {}) == "ep1" - - @pytest.mark.asyncio - async def test_extract_memory_skips_processed_episode(self): - ext = self._make_extractor() - old_profile = ProfileMemory( - memory_type=MemoryType.PROFILE, - user_id="u1", - timestamp=datetime.now(timezone.utc), - processed_episode_ids=["ep_already"], - ) - req = ProfileExtractRequest( - user_id="u1", - group_id="g1", - new_episode={"id": "ep_already", "original_data": []}, - old_profile=old_profile, - ) - result = await ext.extract_memory(req) - assert result is old_profile # returned as-is, no LLM call - - @pytest.mark.asyncio - async def test_extract_memory_creates_new_profile_when_no_old(self): - ext = self._make_extractor() - ext.llm_provider.generate = AsyncMock(return_value='{"operations": []}') - req = ProfileExtractRequest( - user_id="u1", - group_id="g1", - new_episode={"id": "ep1", "created_at": "2024-01-01", "original_data": []}, - ) - result = await ext.extract_memory(req) - assert result is not None - assert result.user_id == "u1" - assert "ep1" in result.processed_episode_ids - - @pytest.mark.asyncio - async def test_extract_memory_applies_add_operation(self): - ext = self._make_extractor() - llm_response = json.dumps({ - "operations": [ - { - "action": "add", - "type": "explicit_info", - "data": { - "category": "Hobby", - "description": "plays guitar", - "evidence": "mentioned in chat", - "sources": ["ep1"], - }, - } - ] - }) - ext.llm_provider.generate = AsyncMock(return_value=llm_response) - req = ProfileExtractRequest( - user_id="u1", - group_id="g1", - new_episode={"id": "ep1", "created_at": "2024-01-01", "original_data": []}, - ) - result = await ext.extract_memory(req) - assert result.total_items() == 1 - assert result.explicit_info[0]["category"] == "Hobby" - assert result.explicit_info[0]["description"] == "plays guitar" - - @pytest.mark.asyncio - async def test_extract_memory_applies_delete_operation(self): - ext = self._make_extractor() - old = ProfileMemory( - memory_type=MemoryType.PROFILE, - user_id="u1", - timestamp=datetime.now(timezone.utc), - explicit_info=[ - {"category": "Old", "description": "outdated info", "evidence": "", "sources": []}, - ], - ) - llm_response = json.dumps({ - "operations": [ - {"action": "delete", "type": "explicit_info", "index": 0, "reason": "no longer valid"}, - ] - }) - ext.llm_provider.generate = AsyncMock(return_value=llm_response) - req = ProfileExtractRequest( - user_id="u1", - group_id="g1", - new_episode={"id": "ep2", "created_at": "2024-02-01", "original_data": []}, - old_profile=old, - ) - result = await ext.extract_memory(req) - assert result.total_items() == 0 - - @pytest.mark.asyncio - async def test_extract_memory_returns_old_on_no_episode(self): - ext = self._make_extractor() - old = ProfileMemory( - memory_type=MemoryType.PROFILE, - user_id="u1", - timestamp=datetime.now(timezone.utc), - ) - req = ProfileExtractRequest(user_id="u1", group_id="g1", old_profile=old) - result = await ext.extract_memory(req) - assert result is old - - -# ============================================================================ -# _format_episodes_for_llm: tool-message filtering -# ============================================================================ - - -class TestFormatEpisodesForLLM: - """Verify tool messages and assistant tool_calls are filtered out when - formatting episode original_data for the profile LLM prompt.""" - - def _make_extractor(self): - return ProfileExtractor(llm_provider=MagicMock()) - - def _episode(self, original_data): - return { - "id": "ep1", - "created_at": "2024-01-01T00:00:00Z", - "original_data": original_data, - } - - def test_user_and_assistant_messages_included(self): - ext = self._make_extractor() - episodes = [self._episode([ - {"role": "user", "sender_name": "Alice", "content": "hi", "timestamp": "t1"}, - {"role": "assistant", "sender_name": "Bot", "content": "hello", "timestamp": "t2"}, - ])] - text = ext._format_episodes_for_llm(episodes, {"ep1": "ep1"}) - assert "【Alice】: hi" in text - assert "【Bot】: hello" in text - - def test_tool_role_is_filtered(self): - ext = self._make_extractor() - episodes = [self._episode([ - {"role": "user", "sender_name": "Alice", "content": "run it", "timestamp": "t1"}, - {"role": "tool", "sender_name": "search_api", "content": "tool output", "timestamp": "t2"}, - ])] - text = ext._format_episodes_for_llm(episodes, {"ep1": "ep1"}) - assert "【Alice】: run it" in text - assert "tool output" not in text - assert "search_api" not in text - - def test_assistant_with_tool_calls_is_filtered(self): - ext = self._make_extractor() - episodes = [self._episode([ - { - "role": "assistant", - "sender_name": "Bot", - "content": "thinking...", - "tool_calls": [{"id": "tc1", "function": {"name": "search"}}], - "timestamp": "t1", - }, - {"role": "assistant", "sender_name": "Bot", "content": "final answer", "timestamp": "t2"}, - ])] - text = ext._format_episodes_for_llm(episodes, {"ep1": "ep1"}) - assert "thinking..." not in text - assert "final answer" in text - - def test_assistant_with_empty_tool_calls_included(self): - """tool_calls=[] is falsy; message should NOT be filtered.""" - ext = self._make_extractor() - episodes = [self._episode([ - { - "role": "assistant", - "sender_name": "Bot", - "content": "regular reply", - "tool_calls": [], - "timestamp": "t1", - }, - ])] - text = ext._format_episodes_for_llm(episodes, {"ep1": "ep1"}) - assert "regular reply" in text - - def test_assistant_with_none_tool_calls_included(self): - ext = self._make_extractor() - episodes = [self._episode([ - { - "role": "assistant", - "sender_name": "Bot", - "content": "regular reply", - "tool_calls": None, - "timestamp": "t1", - }, - ])] - text = ext._format_episodes_for_llm(episodes, {"ep1": "ep1"}) - assert "regular reply" in text - - def test_missing_tool_calls_key_included(self): - ext = self._make_extractor() - episodes = [self._episode([ - {"role": "assistant", "sender_name": "Bot", "content": "regular reply", "timestamp": "t1"}, - ])] - text = ext._format_episodes_for_llm(episodes, {"ep1": "ep1"}) - assert "regular reply" in text - - def test_nested_message_wrapper_is_unwrapped(self): - ext = self._make_extractor() - episodes = [self._episode([ - {"message": { - "role": "tool", - "sender_name": "search_api", - "content": "tool result", - "timestamp": "t1", - }}, - {"message": { - "role": "user", - "sender_name": "Alice", - "content": "kept message", - "timestamp": "t2", - }}, - ])] - text = ext._format_episodes_for_llm(episodes, {"ep1": "ep1"}) - assert "tool result" not in text - assert "【Alice】: kept message" in text - - def test_mixed_messages_preserves_only_conversational(self): - ext = self._make_extractor() - episodes = [self._episode([ - {"role": "user", "sender_name": "Alice", "content": "question", "timestamp": "t1"}, - { - "role": "assistant", - "sender_name": "Bot", - "content": "let me check", - "tool_calls": [{"id": "tc1"}], - "timestamp": "t2", - }, - {"role": "tool", "sender_name": "api", "content": "{\"data\": 1}", "timestamp": "t3"}, - {"role": "assistant", "sender_name": "Bot", "content": "the answer is 1", "timestamp": "t4"}, - ])] - text = ext._format_episodes_for_llm(episodes, {"ep1": "ep1"}) - assert "question" in text - assert "the answer is 1" in text - assert "let me check" not in text - assert "{\"data\": 1}" not in text - - -# ============================================================================ -# Unified extraction (no SOLO/TEAM split) -# ============================================================================ - - -class TestUnifiedExtraction: - """Verify there is no SOLO/TEAM branching in the extraction pipeline.""" - - def test_profile_manager_has_single_extract_method(self): - """ProfileManager should have exactly one extract_profiles method, no extract_profiles_life.""" - from memory_layer.profile_manager.manager import ProfileManager - assert hasattr(ProfileManager, "extract_profiles") - assert not hasattr(ProfileManager, "extract_profiles_life") - - def test_profile_manager_config_has_no_scenario(self): - """ProfileManagerConfig should not have a scenario field.""" - from memory_layer.profile_manager.config import ProfileManagerConfig - config = ProfileManagerConfig() - assert not hasattr(config, "scenario") - - def test_no_discriminator_module(self): - """ValueDiscriminator (dead code) should be deleted.""" - import importlib - with pytest.raises(ModuleNotFoundError): - importlib.import_module("memory_layer.profile_manager.discriminator") diff --git a/methods/EverCore/tests/test_rate_limiter_quick.py b/methods/EverCore/tests/test_rate_limiter_quick.py deleted file mode 100644 index 92a3dd78..00000000 --- a/methods/EverCore/tests/test_rate_limiter_quick.py +++ /dev/null @@ -1,388 +0,0 @@ -""" -Rate limiter decorator quick test - -Test cases specifically for quickly verifying the functionality of the rate limiter decorator, avoiding long waiting times. -""" - -import asyncio -import time -import pytest - -from core.rate_limit.rate_limiter import rate_limit - - -class TestRateLimiterQuick: - """Quick rate limiting test to avoid long waiting times""" - - @pytest.mark.asyncio - async def test_basic_functionality(self): - """Test basic functionality with a short time window""" - call_times = [] - - @rate_limit(max_rate=2, time_period=1) # 2 times per second - async def quick_test_func(): - call_times.append(time.time()) - return "success" - - start_time = time.time() - - # Call 3 times consecutively - results = await asyncio.gather( - quick_test_func(), quick_test_func(), quick_test_func() - ) - - total_time = time.time() - start_time - - assert len(results) == 3 - assert all(r == "success" for r in results) - assert len(call_times) == 3 - - # The third call should wait approximately 0.5 seconds (leaky bucket algorithm) - assert total_time >= 0.4, f"Rate limiting wait time too short: {total_time} seconds" - assert total_time < 0.8, f"Rate limiting wait time too long: {total_time} seconds" - - print(f"Basic functionality test completed, duration: {total_time:.3f} seconds") - - @pytest.mark.asyncio - async def test_high_frequency_short_period(self): - """Test high-frequency rate limiting (10 times per second)""" - call_count = 0 - - @rate_limit(max_rate=10, time_period=1) - async def high_freq_func(): - nonlocal call_count - call_count += 1 - return call_count - - start_time = time.time() - - # Attempt 15 calls, first 10 should execute immediately, last 5 need to wait - tasks = [high_freq_func() for _ in range(15)] - results = await asyncio.gather(*tasks) - - total_time = time.time() - start_time - - assert len(results) == 15 - assert results == list(range(1, 16)) - - # 15 calls at 10 per second should take about 0.5 seconds (leaky bucket algorithm) - assert total_time >= 0.4, f"High-frequency rate limiting time too short: {total_time} seconds" - assert total_time < 0.8, f"High-frequency rate limiting time too long: {total_time} seconds" - - print(f"High-frequency rate limiting test completed, duration: {total_time:.3f} seconds") - - @pytest.mark.asyncio - async def test_concurrent_different_keys(self): - """Test concurrent calls with different keys""" - results = {} - - @rate_limit(max_rate=1, time_period=1, key_func=lambda user: f"user_{user}") - async def user_func(user): - if user not in results: - results[user] = [] - results[user].append(time.time()) - return f"result_{user}" - - start_time = time.time() - - # 3 users each call once, should execute concurrently - tasks = [ - user_func("A"), # User A's call - user_func("B"), # User B's call - user_func("C"), # User C's call - ] - - await asyncio.gather(*tasks) - total_time = time.time() - start_time - - # Verify each user was called once - assert len(results["A"]) == 1 - assert len(results["B"]) == 1 - assert len(results["C"]) == 1 - - # Calls from different users should execute concurrently, total time should be very short - assert total_time < 0.1, f"Concurrent calls with different keys took too long: {total_time} seconds" - - print(f"Concurrent different keys test completed, duration: {total_time:.3f} seconds") - - @pytest.mark.asyncio - async def test_decorator_performance_quick(self): - """Quick performance test""" - - # Function without decorator - async def plain_func(): - await asyncio.sleep(0.001) # Add small delay for more accurate measurement - return 42 - - # Function with decorator (loose limit) - @rate_limit(max_rate=1000, time_period=1) - async def decorated_func(): - await asyncio.sleep(0.001) # Add small delay for more accurate measurement - return 42 - - # Test performance difference over 50 calls - iterations = 50 - - start_time = time.time() - for _ in range(iterations): - await plain_func() - plain_time = time.time() - start_time - - start_time = time.time() - for _ in range(iterations): - await decorated_func() - decorated_time = time.time() - start_time - - overhead = decorated_time - plain_time - overhead_percent = (overhead / plain_time) * 100 if plain_time > 0 else 0 - - print( - f"Performance test - Without decorator: {plain_time:.4f} seconds, With decorator: {decorated_time:.4f} seconds" - ) - print(f"Overhead: {overhead:.4f} seconds ({overhead_percent:.1f}%)") - - # Decorator overhead should be less than 200% (relatively loose limit) - assert overhead_percent < 200, f"Decorator overhead too high: {overhead_percent:.1f}%" - - @pytest.mark.asyncio - async def test_error_handling_quick(self): - """Quick error handling test""" - call_count = 0 - - @rate_limit(max_rate=2, time_period=1) - async def error_func(should_fail=False): - nonlocal call_count - call_count += 1 - if should_fail: - raise RuntimeError("Test error") - return "success" - - # Normal call - result1 = await error_func(False) - assert result1 == "success" - - # Error call (still consumes quota) - with pytest.raises(RuntimeError): - await error_func(True) - - # Third call should be rate-limited - start_time = time.time() - result3 = await error_func(False) - elapsed = time.time() - start_time - - assert result3 == "success" - assert elapsed >= 0.4, f"Rate limiting wait time too short: {elapsed} seconds" - assert call_count == 3 - - print(f"Error handling test completed, wait time: {elapsed:.3f} seconds") - - @pytest.mark.asyncio - async def test_parameter_validation(self): - """Test parameter validation""" - - # Test invalid max_rate - with pytest.raises(ValueError, match="max_rate must be positive"): - - @rate_limit(max_rate=0, time_period=1) - async def invalid_rate_func(): - pass - - with pytest.raises(ValueError, match="max_rate must be positive"): - - @rate_limit(max_rate=-1, time_period=1) - async def negative_rate_func(): - pass - - # Test invalid time_period - with pytest.raises(ValueError, match="time_period must be positive"): - - @rate_limit(max_rate=1, time_period=0) - async def invalid_period_func(): - pass - - with pytest.raises(ValueError, match="time_period must be positive"): - - @rate_limit(max_rate=1, time_period=-1) - async def negative_period_func(): - pass - - print("Parameter validation test completed") - - @pytest.mark.asyncio - async def test_1_second_10_requests(self): - """Test rate limiting for 10 requests within 1 second""" - call_times = [] - - @rate_limit(max_rate=10, time_period=1) - async def high_freq_func(): - call_times.append(time.time()) - return len(call_times) - - start_time = time.time() - - # Call 12 times consecutively, first 10 should execute immediately, last 2 need to wait - tasks = [high_freq_func() for _ in range(12)] - results = await asyncio.gather(*tasks) - - total_time = time.time() - start_time - - assert len(results) == 12 - assert results == list(range(1, 13)) - assert len(call_times) == 12 - - # First 10 should complete relatively quickly, though some delay may occur due to leaky bucket algorithm - first_10_time = call_times[9] - call_times[0] - assert first_10_time < 0.5, f"First 10 calls took too long: {first_10_time:.3f} seconds" - - # 11th and 12th calls need to wait - wait_time_11 = call_times[10] - call_times[9] - wait_time_12 = call_times[11] - call_times[10] - - assert wait_time_11 >= 0.08, f"11th call wait time too short: {wait_time_11:.3f} seconds" - assert wait_time_12 >= 0.08, f"12th call wait time too short: {wait_time_12:.3f} seconds" - - print(f"1 second 10 requests test completed, total duration: {total_time:.3f} seconds") - print(f"First 10 duration: {first_10_time:.3f} seconds") - print(f"11th call wait: {wait_time_11:.3f} seconds, 12th call wait: {wait_time_12:.3f} seconds") - - @pytest.mark.asyncio - async def test_10_seconds_1_request(self): - """Test rate limiting for 1 request within 10 seconds (quick version, using 0.5 seconds for simulation)""" - call_times = [] - - @rate_limit(max_rate=1, time_period=0.5) # Use 0.5 seconds for quick testing - async def low_freq_func(): - call_times.append(time.time()) - return len(call_times) - - start_time = time.time() - - # Call 3 times consecutively - results = [] - for i in range(3): - result = await low_freq_func() - results.append(result) - elapsed = time.time() - start_time - print(f"Call {i+1} completed, duration: {elapsed:.3f} seconds") - - total_time = time.time() - start_time - - assert len(results) == 3 - assert results == [1, 2, 3] - assert len(call_times) == 3 - - # Check time intervals - if len(call_times) >= 2: - interval1 = call_times[1] - call_times[0] - assert interval1 >= 0.4, f"Second call interval too short: {interval1:.3f} seconds" - - if len(call_times) >= 3: - interval2 = call_times[2] - call_times[1] - assert interval2 >= 0.4, f"Third call interval too short: {interval2:.3f} seconds" - - # Total time should be approximately 1 second (two 0.5-second intervals) - assert total_time >= 0.9, f"Total time too short: {total_time:.3f} seconds" - assert total_time < 1.5, f"Total time too long: {total_time:.3f} seconds" - - print(f"10 seconds 1 request test completed (simulated), total duration: {total_time:.3f} seconds") - - @pytest.mark.asyncio - async def test_concurrent_performance_stress(self): - """Concurrent performance stress test""" - import asyncio - - call_count = 0 - lock = asyncio.Lock() - - @rate_limit(max_rate=20, time_period=1) # Reduce limit to make effect more noticeable - async def stress_func(task_id): - nonlocal call_count - async with lock: # Use lock to ensure counter atomicity - call_count += 1 - current_count = call_count - await asyncio.sleep(0.001) # Simulate some processing time - return current_count - - start_time = time.time() - - # Create 50 concurrent tasks, 20 per second, should take about 2.5 seconds - tasks = [stress_func(i) for i in range(50)] - results = await asyncio.gather(*tasks) - - total_time = time.time() - start_time - - assert len(results) == 50 - # Due to concurrency and rate limiting, cannot guarantee completely unique results, but should have reasonable distribution - unique_results = len(set(results)) - assert unique_results >= 25, f"Too few unique results: {unique_results}" - assert max(results) == 50 - - # 50 requests at 20 per second, leaky bucket algorithm may behave differently - # Mainly verify that rate limiting is working, no need for exact timing - assert total_time >= 1.0, f"Stress test completed too quickly: {total_time:.3f} seconds" - assert total_time <= 4.0, f"Stress test completed too slowly: {total_time:.3f} seconds" - - # Calculate throughput - throughput = 50 / total_time - print( - f"Concurrent stress test: 50 requests completed in {total_time:.3f} seconds, throughput: {throughput:.1f} req/s" - ) - print(f"Number of unique results: {unique_results}/50") - - # Throughput verification (considering characteristics of leaky bucket algorithm, allow some margin) - assert throughput <= 40, f"Throughput significantly exceeds limit: {throughput:.1f} req/s" - assert throughput >= 10, f"Throughput too low: {throughput:.1f} req/s" - - # Main purpose is to verify the rate limiter works correctly and controls execution speed - print("✓ Rate limiter is working properly, controlling execution speed") - - @pytest.mark.asyncio - async def test_multiple_limiters_isolation(self): - """Test isolation between multiple rate limiters""" - results = {"fast": [], "slow": []} - - @rate_limit( - max_rate=5, time_period=1, key_func=lambda service: f"service_{service}" - ) - async def service_call(service: str): - results[service].append(time.time()) - return f"{service}_result_{len(results[service])}" - - start_time = time.time() - - # Test fast and slow services simultaneously - tasks = [] - - # Fast service called 6 times (should have 1 wait) - for i in range(6): - tasks.append(service_call("fast")) - - # Slow service called 3 times (should all execute immediately due to independent rate limiter) - for i in range(3): - tasks.append(service_call("slow")) - - await asyncio.gather(*tasks) - total_time = time.time() - start_time - - # Verify call counts - assert len(results["fast"]) == 6 - assert len(results["slow"]) == 3 - - # Fast service should have wait time - fast_total_time = results["fast"][-1] - results["fast"][0] - slow_total_time = results["slow"][-1] - results["slow"][0] - - assert ( - fast_total_time >= 0.15 - ), f"Fast service rate limiting wait time too short: {fast_total_time:.3f} seconds" - assert slow_total_time < 0.1, f"Slow service should not have noticeable wait: {slow_total_time:.3f} seconds" - - print(f"Multiple rate limiters isolation test completed, total duration: {total_time:.3f} seconds") - print( - f"Fast service total duration: {fast_total_time:.3f} seconds, slow service total duration: {slow_total_time:.3f} seconds" - ) - - -if __name__ == "__main__": - # Run quick tests directly - pytest.main([__file__, "-v", "-s"]) \ No newline at end of file diff --git a/methods/EverCore/tests/test_raw_message_repository.py b/methods/EverCore/tests/test_raw_message_repository.py deleted file mode 100644 index eeff2952..00000000 --- a/methods/EverCore/tests/test_raw_message_repository.py +++ /dev/null @@ -1,779 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Test the functionality of RawMessageRepository - -Test contents include: -1. save_from_raw_data (create RawMessage from raw data content) -2. Basic CRUD operations (save, get_by_request_id, find_by_group_id, find_by_sender_id) -3. Duplicate detection (find_one_by_group_sender_message) -4. Sync status management (confirm_accumulation, mark_as_used) -5. Flexible queries (find_pending_by_filters) -6. Delete operations (delete_by_group_id) -7. Helper methods (_parse_create_time) -""" - -import asyncio -import uuid - -from core.di import get_bean_by_type -from core.observation.logger import get_logger -from core.oxm.constants import MAGIC_ALL -from common_utils.datetime_utils import get_now_with_timezone -from infra_layer.adapters.out.persistence.repository.raw_message_repository import ( - RawMessageRepository, -) -from infra_layer.adapters.out.persistence.document.request.raw_message import RawMessage - -logger = get_logger(__name__) - - -def generate_unique_id(prefix: str = "") -> str: - """Generate a unique ID for testing""" - return f"{prefix}{uuid.uuid4().hex[:8]}" - - -async def cleanup_test_data(group_id: str): - """Clean up test data for a group""" - repo = get_bean_by_type(RawMessageRepository) - await repo.delete_by_group_id(group_id) - - -async def test_save_from_raw_data_basic(): - """Test basic save_from_raw_data functionality""" - logger.info("Starting test for save_from_raw_data basic...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_save_raw_") - request_id = generate_unique_id("req_") - message_id = generate_unique_id("msg_") - - try: - raw_data_content = { - "sender_id": "user_001", - "sender_name": "Test User", - "content": [{"type": "text", "text": "Hello, this is a test message"}], - "role": "user", - "timestamp": "2025-01-15T10:00:00+08:00", - } - - result = await repo.save_from_raw_data( - raw_data_content=raw_data_content, - data_id=message_id, - group_id=group_id, - request_id=request_id, - version="1.0.0", - endpoint_name="memorize", - method="POST", - url="/api/memorize", - event_id=request_id, - ) - - assert result == message_id, f"Expected message_id={message_id}, got {result}" - logger.info("✅ save_from_raw_data returned correct message_id") - - # Verify saved data - log = await repo.get_by_request_id(request_id) - assert log is not None, "Saved log should be retrievable" - assert log.group_id == group_id - assert log.request_id == request_id - assert log.message_id == message_id - assert log.sender_id == "user_001" - assert log.sender_name == "Test User" - assert log.content == "Hello, this is a test message" - assert log.role == "user" - assert log.version == "1.0.0" - assert log.endpoint_name == "memorize" - assert log.method == "POST" - assert log.url == "/api/memorize" - assert log.event_id == request_id - assert log.sync_status == -1 # Default sync_status - assert log.timestamp is not None - logger.info("✅ All fields saved correctly") - - except Exception as e: - logger.error("❌ Test for save_from_raw_data basic failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ save_from_raw_data basic test completed") - - -async def test_save_from_raw_data_minimal_fields(): - """Test save_from_raw_data with minimal v1 fields""" - logger.info("Starting test for save_from_raw_data minimal fields...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_minimal_") - request_id = generate_unique_id("req_") - message_id = generate_unique_id("msg_") - - try: - # Use only required v1 fields - raw_data_content = { - "sender_id": "user_002", - "content": [{"type": "text", "text": "Message with minimal fields"}], - "role": "assistant", - } - - result = await repo.save_from_raw_data( - raw_data_content=raw_data_content, - data_id=message_id, - group_id=group_id, - request_id=request_id, - ) - - assert result == message_id - logger.info("✅ save_from_raw_data succeeded with minimal fields") - - # Verify extracted fields - log = await repo.get_by_request_id(request_id) - assert log is not None - assert log.sender_id == "user_002" - assert log.sender_name == "user_002" # falls back to sender_id - assert log.role == "assistant" - assert log.timestamp is None - logger.info("✅ Minimal fields extracted correctly") - - except Exception as e: - logger.error("❌ Test for minimal fields failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ save_from_raw_data minimal fields test completed") - - -async def test_find_by_group_id(): - """Test find_by_group_id with various filters""" - logger.info("Starting test for find_by_group_id...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_find_group_") - - try: - # Create multiple logs with different sync_status - for i, status in enumerate([-1, 0, 1]): - msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - message_id=generate_unique_id("msg_"), - sender_id=f"user_{i}", - content=f"Message {i} with status {status}", - sync_status=status, - ) - await repo.save(msg) - logger.info("✅ Created 3 logs with sync_status -1, 0, 1") - - # Query with sync_status=0 (default) - results_status_0 = await repo.find_by_group_id(group_id, sync_status=0) - assert len(results_status_0) == 1, f"Expected 1, got {len(results_status_0)}" - logger.info("✅ find_by_group_id with sync_status=0 returned 1 record") - - # Query with sync_status=None (all) - results_all = await repo.find_by_group_id(group_id, sync_status=None) - assert len(results_all) == 3, f"Expected 3, got {len(results_all)}" - logger.info("✅ find_by_group_id with sync_status=None returned 3 records") - - # Query with sync_status=-1 - results_pending = await repo.find_by_group_id(group_id, sync_status=-1) - assert len(results_pending) == 1, f"Expected 1, got {len(results_pending)}" - logger.info("✅ find_by_group_id with sync_status=-1 returned 1 record") - - except Exception as e: - logger.error("❌ Test for find_by_group_id failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ find_by_group_id test completed") - - -async def test_find_by_sender_id(): - """Test find_by_sender_id""" - logger.info("Starting test for find_by_sender_id...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_find_sender_") - sender_id = generate_unique_id("user_") - - try: - # Create logs for the sender - for i in range(3): - msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - sender_id=sender_id, - message_id=generate_unique_id("msg_"), - content=f"User message {i}", - ) - await repo.save(msg) - logger.info("✅ Created 3 logs for sender %s", sender_id) - - results = await repo.find_by_sender_id(sender_id) - assert len(results) == 3, f"Expected 3, got {len(results)}" - for r in results: - assert r.sender_id == sender_id - logger.info("✅ find_by_sender_id returned 3 records") - - except Exception as e: - logger.error("❌ Test for find_by_sender_id failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ find_by_sender_id test completed") - - -async def test_find_one_by_group_sender_message(): - """Test duplicate detection via find_one_by_group_sender_message""" - logger.info("Starting test for find_one_by_group_sender_message...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_dup_") - sender_id = generate_unique_id("user_") - message_id = generate_unique_id("msg_") - - try: - # Initially should not find anything - result = await repo.find_one_by_group_sender_message( - group_id=group_id, sender_id=sender_id, message_id=message_id - ) - assert result is None, "Should not find anything initially" - logger.info("✅ No duplicate found initially") - - # Create a log - msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - sender_id=sender_id, - message_id=message_id, - content="Duplicate test message", - ) - await repo.save(msg) - logger.info("✅ Created log for duplicate detection") - - # Now should find the duplicate - result = await repo.find_one_by_group_sender_message( - group_id=group_id, sender_id=sender_id, message_id=message_id - ) - assert result is not None, "Should find the duplicate" - assert result.message_id == message_id - logger.info("✅ Duplicate detected correctly") - - # Different message_id should not match - result_other = await repo.find_one_by_group_sender_message( - group_id=group_id, - sender_id=sender_id, - message_id=generate_unique_id("msg_other_"), - ) - assert result_other is None, "Different message_id should not match" - logger.info("✅ Non-matching message_id correctly returns None") - - except Exception as e: - logger.error("❌ Test for find_one_by_group_sender_message failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ find_one_by_group_sender_message test completed") - - -async def test_confirm_accumulation_by_group_id(): - """Test confirm_accumulation_by_group_id (batch -1 -> 0)""" - logger.info("Starting test for confirm_accumulation_by_group_id...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_confirm_group_") - - try: - # Create logs with sync_status=-1 - for i in range(3): - msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - message_id=generate_unique_id("msg_"), - content=f"Pending message {i}", - sync_status=-1, - ) - await repo.save(msg) - logger.info("✅ Created 3 logs with sync_status=-1") - - # Confirm accumulation - modified_count = await repo.confirm_accumulation_by_group_id(group_id) - assert modified_count == 3, f"Expected 3, got {modified_count}" - logger.info("✅ confirm_accumulation_by_group_id modified 3 records") - - # Verify all are now sync_status=0 - logs = await repo.find_by_group_id(group_id, sync_status=0) - assert len(logs) == 3, f"Expected 3 with sync_status=0, got {len(logs)}" - logger.info("✅ All records now have sync_status=0") - - # Confirm again should modify 0 (already at status 0) - modified_again = await repo.confirm_accumulation_by_group_id(group_id) - assert modified_again == 0, f"Expected 0, got {modified_again}" - logger.info("✅ Second confirm correctly modified 0 records") - - except Exception as e: - logger.error("❌ Test for confirm_accumulation_by_group_id failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ confirm_accumulation_by_group_id test completed") - - -async def test_confirm_accumulation_by_message_ids(): - """Test confirm_accumulation_by_message_ids (precise -1 -> 0)""" - logger.info("Starting test for confirm_accumulation_by_message_ids...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_confirm_msg_") - - try: - msg_ids = [generate_unique_id("msg_") for _ in range(3)] - - # Create 3 logs with sync_status=-1 - for msg_id in msg_ids: - msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - message_id=msg_id, - content=f"Message {msg_id}", - sync_status=-1, - ) - await repo.save(msg) - logger.info("✅ Created 3 logs with sync_status=-1") - - # Only confirm first 2 - modified_count = await repo.confirm_accumulation_by_message_ids( - group_id, msg_ids[:2] - ) - assert modified_count == 2, f"Expected 2, got {modified_count}" - logger.info("✅ confirm_accumulation_by_message_ids modified 2 records") - - # Verify: 2 confirmed (sync_status=0), 1 still pending (sync_status=-1) - all_logs = await repo.find_by_group_id(group_id, sync_status=None) - confirmed = [l for l in all_logs if l.sync_status == 0] - pending = [l for l in all_logs if l.sync_status == -1] - assert len(confirmed) == 2, f"Expected 2 confirmed, got {len(confirmed)}" - assert len(pending) == 1, f"Expected 1 pending, got {len(pending)}" - assert pending[0].message_id == msg_ids[2] - logger.info("✅ Only specified messages were confirmed") - - # Empty message_ids should do nothing - modified_empty = await repo.confirm_accumulation_by_message_ids(group_id, []) - assert modified_empty == 0, f"Expected 0, got {modified_empty}" - logger.info("✅ Empty message_ids correctly returns 0") - - except Exception as e: - logger.error("❌ Test for confirm_accumulation_by_message_ids failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ confirm_accumulation_by_message_ids test completed") - - -async def test_mark_as_used_by_group_id(): - """Test mark_as_used_by_group_id (-1,0 -> 1)""" - logger.info("Starting test for mark_as_used_by_group_id...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_mark_used_") - - try: - # Create logs with various sync_status - for status in [-1, 0, 1]: - msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - message_id=generate_unique_id("msg_"), - content=f"Status {status} message", - sync_status=status, - ) - await repo.save(msg) - logger.info("✅ Created logs with sync_status -1, 0, 1") - - # Mark as used - modified_count = await repo.mark_as_used_by_group_id(group_id) - assert modified_count == 2, f"Expected 2 (-1 and 0), got {modified_count}" - logger.info("✅ mark_as_used modified 2 records") - - # Verify all are now sync_status=1 - all_logs = await repo.find_by_group_id(group_id, sync_status=None) - for log in all_logs: - assert ( - log.sync_status == 1 - ), f"Expected sync_status=1, got {log.sync_status}" - logger.info("✅ All records now have sync_status=1") - - except Exception as e: - logger.error("❌ Test for mark_as_used_by_group_id failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ mark_as_used_by_group_id test completed") - - -async def test_mark_as_used_with_exclude(): - """Test mark_as_used_by_group_id with exclude_message_ids""" - logger.info("Starting test for mark_as_used with exclude...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_mark_exclude_") - - try: - msg_ids = [generate_unique_id("msg_") for _ in range(3)] - - for msg_id in msg_ids: - msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - message_id=msg_id, - content=f"Message {msg_id}", - sync_status=-1, - ) - await repo.save(msg) - logger.info("✅ Created 3 logs with sync_status=-1") - - # Mark as used, but exclude the last message - modified_count = await repo.mark_as_used_by_group_id( - group_id, exclude_message_ids=[msg_ids[2]] - ) - assert modified_count == 2, f"Expected 2, got {modified_count}" - logger.info("✅ mark_as_used with exclude modified 2 records") - - # Verify: 2 used, 1 still pending - all_logs = await repo.find_by_group_id(group_id, sync_status=None) - for log in all_logs: - if log.message_id == msg_ids[2]: - assert log.sync_status == -1, "Excluded message should remain -1" - else: - assert log.sync_status == 1, "Non-excluded messages should be 1" - logger.info("✅ Excluded message correctly preserved") - - except Exception as e: - logger.error("❌ Test for mark_as_used with exclude failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ mark_as_used with exclude test completed") - - -async def test_find_pending_by_filters(): - """Test find_pending_by_filters with MAGIC_ALL""" - logger.info("Starting test for find_pending_by_filters...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_pending_") - sender_id_1 = generate_unique_id("user_") - sender_id_2 = generate_unique_id("user_") - - try: - # Create logs for different senders - for i, sid in enumerate([sender_id_1, sender_id_1, sender_id_2]): - msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - sender_id=sid, - message_id=generate_unique_id("msg_"), - content=f"Pending message {i}", - sync_status=-1, - ) - await repo.save(msg) - - # Create one with sync_status=1 (should be excluded by default) - used_msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - sender_id=sender_id_1, - message_id=generate_unique_id("msg_"), - content="Used message", - sync_status=1, - ) - await repo.save(used_msg) - logger.info("✅ Created 4 logs (3 pending, 1 used)") - - # Query with MAGIC_ALL (default) - should return all pending - results_all = await repo.find_pending_by_filters( - sender_id=MAGIC_ALL, group_id=group_id - ) - assert len(results_all) == 3, f"Expected 3, got {len(results_all)}" - logger.info("✅ find_pending_by_filters with MAGIC_ALL returned 3 records") - - # Query by specific sender_id - results_sender1 = await repo.find_pending_by_filters( - sender_id=sender_id_1, group_id=group_id - ) - assert len(results_sender1) == 2, f"Expected 2, got {len(results_sender1)}" - logger.info( - "✅ find_pending_by_filters with sender_id filter returned 2 records" - ) - - # Query with specific sync_status_list - results_used = await repo.find_pending_by_filters( - group_id=group_id, sync_status_list=[1] - ) - assert len(results_used) == 1, f"Expected 1, got {len(results_used)}" - logger.info("✅ find_pending_by_filters with sync_status=[1] returned 1 record") - - # Query with limit - results_limited = await repo.find_pending_by_filters(group_id=group_id, limit=2) - assert len(results_limited) == 2, f"Expected 2, got {len(results_limited)}" - logger.info("✅ find_pending_by_filters with limit=2 returned 2 records") - - except Exception as e: - logger.error("❌ Test for find_pending_by_filters failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ find_pending_by_filters test completed") - - -async def test_find_by_group_id_with_statuses(): - """Test find_by_group_id_with_statuses""" - logger.info("Starting test for find_by_group_id_with_statuses...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_statuses_") - - try: - msg_ids = [] - for status in [-1, 0, 0, 1]: - msg_id = generate_unique_id("msg_") - msg_ids.append(msg_id) - msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - message_id=msg_id, - content=f"Status {status}", - sync_status=status, - ) - await repo.save(msg) - logger.info("✅ Created 4 logs: status -1, 0, 0, 1") - - # Query with multiple statuses [-1, 0] - results = await repo.find_by_group_id_with_statuses( - group_id=group_id, sync_status_list=[-1, 0] - ) - assert len(results) == 3, f"Expected 3, got {len(results)}" - logger.info("✅ find_by_group_id_with_statuses [-1, 0] returned 3 records") - - # Query with exclude_message_ids - results_excluded = await repo.find_by_group_id_with_statuses( - group_id=group_id, - sync_status_list=[-1, 0], - exclude_message_ids=[msg_ids[0]], # exclude the -1 record - ) - assert len(results_excluded) == 2, f"Expected 2, got {len(results_excluded)}" - logger.info("✅ Exclusion works correctly") - - # Query with descending order - results_desc = await repo.find_by_group_id_with_statuses( - group_id=group_id, sync_status_list=[-1, 0, 1], ascending=False - ) - assert len(results_desc) == 4, f"Expected 4, got {len(results_desc)}" - logger.info("✅ Descending order query works correctly") - - except Exception as e: - logger.error("❌ Test for find_by_group_id_with_statuses failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ find_by_group_id_with_statuses test completed") - - -async def test_delete_by_group_id(): - """Test delete_by_group_id""" - logger.info("Starting test for delete_by_group_id...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_delete_") - - try: - # Create logs - for i in range(3): - msg = RawMessage( - group_id=group_id, - request_id=generate_unique_id("req_"), - message_id=generate_unique_id("msg_"), - content=f"Delete test {i}", - ) - await repo.save(msg) - logger.info("✅ Created 3 logs") - - # Verify they exist - before = await repo.find_by_group_id(group_id, sync_status=None) - assert len(before) == 3 - - # Delete - deleted_count = await repo.delete_by_group_id(group_id) - assert deleted_count == 3, f"Expected 3 deleted, got {deleted_count}" - logger.info("✅ delete_by_group_id deleted 3 records") - - # Verify they're gone - after = await repo.find_by_group_id(group_id, sync_status=None) - assert len(after) == 0, f"Expected 0 after delete, got {len(after)}" - logger.info("✅ Records no longer retrievable") - - except Exception as e: - logger.error("❌ Test for delete_by_group_id failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ delete_by_group_id test completed") - - -async def test_sync_status_full_lifecycle(): - """Test the complete sync_status lifecycle: -1 -> 0 -> 1""" - logger.info("Starting test for sync_status full lifecycle...") - - repo = get_bean_by_type(RawMessageRepository) - group_id = generate_unique_id("test_lifecycle_") - message_id = generate_unique_id("msg_") - - try: - # Step 1: save_from_raw_data creates with sync_status=-1 - raw_data_content = { - "sender_id": "lifecycle_user", - "content": [{"type": "text", "text": "Lifecycle test message"}], - } - await repo.save_from_raw_data( - raw_data_content=raw_data_content, - data_id=message_id, - group_id=group_id, - request_id=generate_unique_id("req_"), - ) - - logs = await repo.find_by_group_id(group_id, sync_status=None) - assert len(logs) == 1 - assert logs[0].sync_status == -1 - logger.info("✅ Step 1: Created with sync_status=-1") - - # Step 2: confirm_accumulation -> sync_status=0 - modified = await repo.confirm_accumulation_by_message_ids( - group_id, [message_id] - ) - assert modified == 1 - - logs = await repo.find_by_group_id(group_id, sync_status=None) - assert logs[0].sync_status == 0 - logger.info("✅ Step 2: sync_status changed to 0") - - # Step 3: mark_as_used -> sync_status=1 - modified = await repo.mark_as_used_by_group_id(group_id) - assert modified == 1 - - logs = await repo.find_by_group_id(group_id, sync_status=None) - assert logs[0].sync_status == 1 - logger.info("✅ Step 3: sync_status changed to 1") - - # Verify: no pending records remain - pending = await repo.find_pending_by_filters(group_id=group_id) - assert len(pending) == 0 - logger.info("✅ No pending records remain after full lifecycle") - - except Exception as e: - logger.error("❌ Test for sync_status full lifecycle failed: %s", e) - raise - finally: - await cleanup_test_data(group_id) - logger.info("✅ Cleaned up test data") - - logger.info("✅ sync_status full lifecycle test completed") - - -async def test_parse_create_time(): - """Test _parse_create_time static method""" - logger.info("Starting test for _parse_create_time...") - - # Test with None - assert RawMessageRepository._parse_create_time(None) is None - logger.info("✅ None input returns None") - - # Test with ISO format string - iso_str = "2025-01-15T10:00:00+08:00" - result = RawMessageRepository._parse_create_time(iso_str) - assert result is not None - assert "2025-01-15" in result - logger.info("✅ ISO string parsed correctly") - - # Test with datetime object - dt = get_now_with_timezone() - result = RawMessageRepository._parse_create_time(dt) - assert result is not None - assert isinstance(result, str) - logger.info("✅ datetime object converted correctly") - - # Test with invalid string (from_iso_format falls back to current time in lenient mode, - # so _parse_create_time returns an ISO string of the current time, not the original) - result = RawMessageRepository._parse_create_time("not-a-date") - assert result is not None - assert isinstance(result, str) - logger.info("✅ Invalid string returns a fallback ISO timestamp") - - # Test with int (should return None) - result = RawMessageRepository._parse_create_time(12345) - assert result is None - logger.info("✅ Non-string/datetime returns None") - - logger.info("✅ _parse_create_time test completed") - - -async def run_all_tests(): - """Run all tests""" - logger.info("🚀 Starting to run all RawMessageRepository tests...") - - try: - # save_from_raw_data tests - await test_save_from_raw_data_basic() - await test_save_from_raw_data_minimal_fields() - # Basic CRUD tests - await test_find_by_group_id() - await test_find_by_sender_id() - await test_find_one_by_group_sender_message() - await test_delete_by_group_id() - - # Sync status management tests - await test_confirm_accumulation_by_group_id() - await test_confirm_accumulation_by_message_ids() - await test_mark_as_used_by_group_id() - await test_mark_as_used_with_exclude() - - # Query tests - await test_find_pending_by_filters() - await test_find_by_group_id_with_statuses() - - # Lifecycle test - await test_sync_status_full_lifecycle() - - # Helper method tests - await test_parse_create_time() - - logger.info("✅ All RawMessageRepository tests completed successfully") - except Exception as e: - logger.error("❌ Error occurred during testing: %s", e) - raise - - -if __name__ == "__main__": - asyncio.run(run_all_tests()) diff --git a/methods/EverCore/tests/test_rawdata_json_serialization.py b/methods/EverCore/tests/test_rawdata_json_serialization.py deleted file mode 100644 index f8fb6966..00000000 --- a/methods/EverCore/tests/test_rawdata_json_serialization.py +++ /dev/null @@ -1,813 +0,0 @@ -""" -Test JSON serialization and deserialization functionality of RawData -Includes basic functionality tests and improved field name heuristic judgment tests -""" - -import pytest -from datetime import datetime -from memory_layer.memcell_extractor.base_memcell_extractor import RawData -from common_utils.datetime_utils import get_now_with_timezone - - -class TestRawDataJsonSerialization: - """RawData JSON serialization test class""" - - def test_basic_serialization(self): - """Test basic serialization and deserialization""" - # Create test data - original_data = RawData( - content={ - "sender_id": "user_001", - "sender_name": "Zhang San", - "content": [{"type": "text", "text": "This is a test message"}], - "msgType": 1, - "roomId": "room_123", - }, - data_id="msg_001", - data_type="Conversation", - metadata={"source": "test", "version": "1.0"}, - ) - - # Serialize - json_str = original_data.to_json() - assert isinstance(json_str, str) - assert len(json_str) > 0 - - # Deserialize - restored_data = RawData.from_json_str(json_str) - - # Verify data consistency - assert restored_data.content == original_data.content - assert restored_data.data_id == original_data.data_id - assert restored_data.data_type == original_data.data_type - assert restored_data.metadata == original_data.metadata - - def test_datetime_serialization(self): - """Test serialization containing datetime objects""" - # Create test data with timestamp - test_time = get_now_with_timezone() - - original_data = RawData( - content={ - "timestamp": test_time, - "createTime": test_time, - "updateTime": test_time, - "content": [{"type": "text", "text": "Message containing time"}], - }, - data_id="msg_datetime", - data_type="Conversation", - metadata={"created_at": test_time, "processed_at": test_time}, - ) - - # Serialize - json_str = original_data.to_json() - - # Deserialize - restored_data = RawData.from_json_str(json_str) - - # Verify timestamp fields are correctly restored as datetime objects - assert isinstance(restored_data.content["timestamp"], datetime) - assert isinstance(restored_data.content["createTime"], datetime) - assert isinstance(restored_data.content["updateTime"], datetime) - assert isinstance(restored_data.metadata["created_at"], datetime) - assert isinstance(restored_data.metadata["processed_at"], datetime) - - # Verify timestamp accuracy (allow minor precision differences) - assert abs((restored_data.content["timestamp"] - test_time).total_seconds()) < 1 - assert ( - abs((restored_data.metadata["created_at"] - test_time).total_seconds()) < 1 - ) - - def test_nested_structure_serialization(self): - """Test serialization of nested structures""" - original_data = RawData( - content={ - "replyInfo": { - "originalMessage": "Original message", - "timestamp": get_now_with_timezone(), - "author": { - "id": "user_001", - "name": "Zhang San", - "settings": {"notify": True, "theme": "dark"}, - }, - }, - "attachments": [ - {"type": "image", "url": "http://example.com/img1.jpg"}, - {"type": "file", "name": "document.pdf", "size": 1024}, - ], - }, - data_id="complex_msg", - data_type="Conversation", - ) - - # Serialize and deserialize - json_str = original_data.to_json() - restored_data = RawData.from_json_str(json_str) - - # Verify nested structure - assert ( - restored_data.content["replyInfo"]["originalMessage"] == "Original message" - ) - assert isinstance(restored_data.content["replyInfo"]["timestamp"], datetime) - assert restored_data.content["replyInfo"]["author"]["id"] == "user_001" - assert ( - restored_data.content["replyInfo"]["author"]["settings"]["notify"] is True - ) - assert len(restored_data.content["attachments"]) == 2 - assert restored_data.content["attachments"][0]["type"] == "image" - - def test_email_data_serialization(self): - """Test serialization of email data (simulating output from email_mapper)""" - original_data = RawData( - content={ - 'user_id_list': ["user_001", "user_002"], - 'id': 'email_123', - 'source': 'gmail', - 'mail_address': 'test@example.com', - 'thread_id': 'thread_456', - 'is_delete': False, - 'is_read': True, - 'is_draft': False, - 'importance': 'high', - 'sent_timestamp': get_now_with_timezone(), - 'received_timestamp': get_now_with_timezone(), - 'labels': ['inbox', 'important'], - 'sender_name': 'Sender Name', - 'sender_address': 'sender@example.com', - 'receiver': ['receiver@example.com'], - 'cc': [], - 'bcc': [], - 'subject': 'Email Subject', - 'body_type': 'html', - 'body_content': '

Email content

', - 'attachments': [], - 'create_timestamp': get_now_with_timezone(), - 'last_update_timestamp': get_now_with_timezone(), - 'message_id': 'msg_789', - }, - data_id="email_123", - data_type="Email", - metadata={'original_id': 'email_123', 'source': 'email_mapper'}, - ) - - # Serialize and deserialize - json_str = original_data.to_json() - restored_data = RawData.from_json_str(json_str) - - # Verify email-specific fields - assert restored_data.content['user_id_list'] == ["user_001", "user_002"] - assert restored_data.content['source'] == 'gmail' - assert restored_data.content['is_read'] is True - assert isinstance(restored_data.content['sent_timestamp'], datetime) - assert isinstance(restored_data.content['received_timestamp'], datetime) - assert restored_data.content['labels'] == ['inbox', 'important'] - assert restored_data.data_type == "Email" - - def test_linkdoc_data_serialization(self): - """Test serialization of document data (simulating output from linkdoc_mapper)""" - original_data = RawData( - content={ - 'user_id_list': ["user_001"], - 'title': 'Document Title', - 'content': 'Document content', - 'is_delete': False, - 'download_url': 'https://example.com/doc.pdf', - 'participants': ["user_001", "user_002"], - 'modify_timestamp': get_now_with_timezone(), - 'file_type': "pdf", - 'source_type': 'notion', - }, - data_id="doc_456", - data_type="LinkDoc", - metadata={'original_id': 'doc_456', 'source': 'linkdoc_mapper'}, - ) - - # Serialize and deserialize - json_str = original_data.to_json() - restored_data = RawData.from_json_str(json_str) - - # Verify document-specific fields - assert restored_data.content['title'] == 'Document Title' - assert restored_data.content['source_type'] == 'notion' - assert isinstance(restored_data.content['modify_timestamp'], datetime) - assert restored_data.content['participants'] == ["user_001", "user_002"] - assert restored_data.data_type == "LinkDoc" - - def test_conversation_data_serialization(self): - """Test serialization of conversation data (simulating output from format_transfer)""" - test_time = get_now_with_timezone() - - original_data = RawData( - content={ - "sender_name": "Zhang San", - "receiverId": "room_123", - "roomId": "room_123", - "groupName": "Project Discussion Group", - "userIdList": ["user_001", "user_002", "user_003"], - "referList": [], - "content": [ - { - "type": "text", - "content": "Hello everyone, let's discuss the project progress today", - } - ], - "timestamp": test_time, - "createBy": "user_001", - "updateTime": test_time, - "orgId": "org_456", - "sender_id": "user_001", - "msgType": 1, - }, - data_id="conv_789", - data_type="Conversation", - metadata={ - "original_id": "conv_789", - "createTime": test_time, - "updateTime": test_time, - "createBy": "user_001", - "orgId": "org_456", - }, - ) - - # Serialize and deserialize - json_str = original_data.to_json() - restored_data = RawData.from_json_str(json_str) - - # Verify conversation-specific fields - assert restored_data.content["sender_name"] == "Zhang San" - assert restored_data.content["groupName"] == "Project Discussion Group" - assert restored_data.content["userIdList"] == [ - "user_001", - "user_002", - "user_003", - ] - assert isinstance(restored_data.content["timestamp"], datetime) - assert isinstance(restored_data.content["updateTime"], datetime) - assert isinstance(restored_data.metadata["createTime"], datetime) - - def test_empty_and_none_values(self): - """Test handling of empty and None values""" - original_data = RawData( - content={ - "required_field": "has value", - "empty_string": "", - "empty_list": [], - "empty_dict": {}, - "none_value": None, - "zero_value": 0, - "false_value": False, - }, - data_id="empty_test", - data_type=None, # Test optional field as None - metadata=None, # Test optional field as None - ) - - # Serialize and deserialize - json_str = original_data.to_json() - restored_data = RawData.from_json_str(json_str) - - # Verify correct handling of various empty values - assert restored_data.content["required_field"] == "has value" - assert restored_data.content["empty_string"] == "" - assert restored_data.content["empty_list"] == [] - assert restored_data.content["empty_dict"] == {} - assert restored_data.content["none_value"] is None - assert restored_data.content["zero_value"] == 0 - assert restored_data.content["false_value"] is False - assert restored_data.data_type is None - assert restored_data.metadata is None - - def test_error_handling(self): - """Test error handling""" - # Test invalid JSON - with pytest.raises(ValueError, match="JSON format error"): - RawData.from_json_str("invalid json") - - # Test non-object JSON - with pytest.raises(ValueError, match="JSON must be an object"): - RawData.from_json_str('"string"') - - # Test missing required fields - with pytest.raises(ValueError, match="JSON missing required field"): - RawData.from_json_str('{"data_id": "test"}') # missing content - - with pytest.raises(ValueError, match="JSON missing required field"): - RawData.from_json_str('{"content": {}}') # missing data_id - - def test_round_trip_consistency(self): - """Test consistency of multiple serializations and deserializations""" - # Create complex test data - original_data = RawData( - content={ - "mixed_types": { - "string": "text", - "number": 42, - "float": 3.14, - "boolean": True, - "null": None, - "datetime": get_now_with_timezone(), - "list": [1, "two", {"three": 3}], - "nested": { - "deep": { - "value": "deep nesting", - "timestamp": get_now_with_timezone(), - } - }, - } - }, - data_id="round_trip_test", - data_type="Test", - metadata={ - "test_metadata": {"created": get_now_with_timezone(), "version": 1.0} - }, - ) - - # Perform multiple serializations and deserializations - data = original_data - for _ in range(3): - json_str = data.to_json() - data = RawData.from_json_str(json_str) - - # Verify final result matches original data - assert data.content["mixed_types"]["string"] == "text" - assert data.content["mixed_types"]["number"] == 42 - assert data.content["mixed_types"]["boolean"] is True - assert isinstance(data.content["mixed_types"]["datetime"], datetime) - assert data.content["mixed_types"]["nested"]["deep"]["value"] == "deep nesting" - assert isinstance( - data.content["mixed_types"]["nested"]["deep"]["timestamp"], datetime - ) - assert isinstance(data.metadata["test_metadata"]["created"], datetime) - - # ==================== Improved field name heuristic judgment tests ==================== - - def test_datetime_field_recognition(self): - """Test time field recognition logic""" - raw_data = RawData(content={}, data_id="test") - - # Test field names that should be recognized as time fields - datetime_fields = [ - 'timestamp', - 'createTime', - 'updateTime', - 'create_time', - 'update_time', - 'sent_timestamp', - 'received_timestamp', - 'create_timestamp', - 'last_update_timestamp', - 'modify_timestamp', - 'created_at', - 'updated_at', - 'joinTime', - 'leaveTime', - 'lastOnlineTime', - 'sync_time', - 'processed_at', - 'custom_time', - 'event_timestamp', - 'process_at', - 'end_date', - 'datetime', - 'created', - 'updated', - 'start_time', - 'end_time', - 'event_time', - 'build_timestamp', - ] - - for field in datetime_fields: - # Use protected method for testing - necessary for testing internal logic - assert raw_data._is_datetime_field( - field - ), f"Field '{field}' should be recognized as a time field" # pylint: disable=protected-access - - # Test field names that should not be recognized as time fields - non_datetime_fields = [ - 'content', - 'message', - 'user_id', - 'room_id', - 'title', - 'description', - 'count', - 'size', - 'type', - 'status', - 'version', - 'id', - 'name', - 'timeout', - 'runtime', - 'timeline', - 'timestamp_format', - 'time_zone', - 'time_limit', - 'timestamp_count', - 'timestamp_enabled', - 'time_sync', - ] - - for field in non_datetime_fields: - # Use protected method for testing - necessary for testing internal logic - assert not raw_data._is_datetime_field( - field - ), f"Field '{field}' should not be recognized as a time field" # pylint: disable=protected-access - - def test_datetime_content_vs_field_name(self): - """Test time judgment based on field name rather than content""" - test_time = get_now_with_timezone() - iso_time_str = test_time.isoformat() - - # Create data containing ISO format strings but field names are not time fields - original_data = RawData( - content={ - "timestamp": test_time, # Time field, should be converted - "createTime": test_time, # Time field, should be converted - "message_content": iso_time_str, # Content is time format but field name is not a time field, should not be converted - "description": f"Event occurred at {iso_time_str}", # Description containing time format, should not be converted - "user_id": "2024-01-01T10:00:00Z", # Looks like time but not a time field, should not be converted - "version": "2024-01-01T10:00:00.123456+00:00", # Version number happens to be in time format, should not be converted - }, - data_id="field_test", - data_type="Test", - ) - - # Serialize and deserialize - json_str = original_data.to_json() - restored_data = RawData.from_json_str(json_str) - - # Verify time fields are correctly converted - assert isinstance(restored_data.content["timestamp"], datetime) - assert isinstance(restored_data.content["createTime"], datetime) - - # Verify non-time fields remain as strings - assert isinstance(restored_data.content["message_content"], str) - assert isinstance(restored_data.content["description"], str) - assert isinstance(restored_data.content["user_id"], str) - assert isinstance(restored_data.content["version"], str) - - # Verify string content remains unchanged - assert restored_data.content["message_content"] == iso_time_str - assert ( - restored_data.content["description"] == f"Event occurred at {iso_time_str}" - ) - assert restored_data.content["user_id"] == "2024-01-01T10:00:00Z" - assert restored_data.content["version"] == "2024-01-01T10:00:00.123456+00:00" - - def test_real_world_conversation_data_improved(self): - """Test real-world conversation data scenario (improved version)""" - test_time = get_now_with_timezone() - - # Simulate real output from format_transfer.py - original_data = RawData( - content={ - "sender_name": "Zhang San", - "receiverId": "room_123", - "roomId": "room_123", - "groupName": "Technical Discussion Group", - "userIdList": ["user_001", "user_002"], - "referList": [], - "content": [ - { - "type": "text", - "content": "Meeting time is set for 2024-01-01T10:00:00Z, remember to attend", - } - ], # Message content containing time format - "timestamp": test_time, # Actual time field - "createBy": "user_001", - "updateTime": test_time, # Actual time field - "orgId": "org_456", - "sender_id": "user_001", - "msgType": 1, - }, - data_id="conv_001", - data_type="Conversation", - metadata={ - "original_id": "conv_001", - "createTime": test_time, # Actual time field - "updateTime": test_time, # Actual time field - "createBy": "user_001", - "orgId": "org_456", - }, - ) - - # Serialize and deserialize - json_str = original_data.to_json() - restored_data = RawData.from_json_str(json_str) - - # Verify time fields are correctly converted - assert isinstance(restored_data.content["timestamp"], datetime) - assert isinstance(restored_data.content["updateTime"], datetime) - assert isinstance(restored_data.metadata["createTime"], datetime) - assert isinstance(restored_data.metadata["updateTime"], datetime) - - # Verify time format strings in message content remain unchanged - assert isinstance(restored_data.content["content"], list) - assert "2024-01-01T10:00:00Z" in restored_data.content["content"][0]["content"] - - # Verify other field types are correct - assert isinstance(restored_data.content["sender_name"], str) - assert isinstance(restored_data.content["userIdList"], list) - assert isinstance(restored_data.content["msgType"], int) - - def test_real_world_email_data_improved(self): - """Test real-world email data scenario (improved version)""" - test_time = get_now_with_timezone() - - # Simulate real output from email_mapper.py - original_data = RawData( - content={ - 'user_id_list': ["user_001"], - 'id': 'email_123', - 'source': 'gmail', - 'subject': 'Meeting schedule: starting at 2024-01-01T14:00:00Z', # Subject contains time format - 'body_content': 'The meeting will start at 2024-01-01T14:00:00+00:00, please attend on time', # Body contains time format - 'sent_timestamp': test_time, # Actual time field - 'received_timestamp': test_time, # Actual time field - 'create_timestamp': test_time, # Actual time field - 'last_update_timestamp': test_time, # Actual time field - 'sender_name': 'Li Si', - 'sender_address': 'lisi@company.com', - }, - data_id="email_123", - data_type="Email", - ) - - # Serialize and deserialize - json_str = original_data.to_json() - restored_data = RawData.from_json_str(json_str) - - # Verify time fields are correctly converted - assert isinstance(restored_data.content['sent_timestamp'], datetime) - assert isinstance(restored_data.content['received_timestamp'], datetime) - assert isinstance(restored_data.content['create_timestamp'], datetime) - assert isinstance(restored_data.content['last_update_timestamp'], datetime) - - # Verify time format strings in text content remain unchanged - assert isinstance(restored_data.content['subject'], str) - assert isinstance(restored_data.content['body_content'], str) - assert '2024-01-01T14:00:00Z' in restored_data.content['subject'] - assert '2024-01-01T14:00:00+00:00' in restored_data.content['body_content'] - - def test_real_world_document_data_improved(self): - """Test real-world document data scenario (improved version)""" - test_time = get_now_with_timezone() - - # Simulate real output from linkdoc_mapper.py - original_data = RawData( - content={ - 'user_id_list': ["user_001"], - 'title': 'Project Plan - Deadline 2024-12-31T23:59:59Z', # Title contains time format - 'content': '# Project Plan\n\nStart time: 2024-01-01T09:00:00+00:00\nEnd time: 2024-12-31T18:00:00+00:00', # Content contains time format - 'modify_timestamp': test_time, # Actual time field - 'last_update_timestamp': test_time, # Actual time field - 'source_type': 'notion', - 'file_type': 'markdown', - }, - data_id="doc_123", - data_type="LinkDoc", - ) - - # Serialize and deserialize - json_str = original_data.to_json() - restored_data = RawData.from_json_str(json_str) - - # Verify time fields are correctly converted - assert isinstance(restored_data.content['modify_timestamp'], datetime) - assert isinstance(restored_data.content['last_update_timestamp'], datetime) - - # Verify time format strings in text content remain unchanged - assert isinstance(restored_data.content['title'], str) - assert isinstance(restored_data.content['content'], str) - assert '2024-12-31T23:59:59Z' in restored_data.content['title'] - assert '2024-01-01T09:00:00+00:00' in restored_data.content['content'] - assert '2024-12-31T18:00:00+00:00' in restored_data.content['content'] - - def test_nested_structure_with_mixed_content(self): - """Test nested structures with mixed content""" - test_time = get_now_with_timezone() - - original_data = RawData( - content={ - "message_info": { - "content": [ - { - "type": "text", - "content": "Scheduled task will execute at 2024-01-01T10:00:00Z", - } - ], # Non-time field but contains time format - "timestamp": test_time, # Time field - "createTime": test_time, # Time field - "author": { - "name": "Zhang San", - "last_login_time": test_time, # Time field - "profile_description": "User registration time: 2024-01-01T08:00:00+00:00", # Non-time field but contains time format - }, - }, - "system_info": { - "version": "2024.01.01T10.00.00", # Version number, not a time field - "build_timestamp": test_time, # Time field - "config": { - "timeout": "2024-01-01T10:00:00Z", # Timeout configuration, not a time field - "start_time": test_time, # Time field - "schedule": "Execute daily at 2024-01-01T10:00:00Z", # Schedule description, not a time field - }, - }, - }, - data_id="nested_test", - data_type="Test", - ) - - # Serialize and deserialize - json_str = original_data.to_json() - restored_data = RawData.from_json_str(json_str) - - # Verify time fields are correctly converted - assert isinstance(restored_data.content["message_info"]["timestamp"], datetime) - assert isinstance(restored_data.content["message_info"]["createTime"], datetime) - assert isinstance( - restored_data.content["message_info"]["author"]["last_login_time"], datetime - ) - assert isinstance( - restored_data.content["system_info"]["build_timestamp"], datetime - ) - assert isinstance( - restored_data.content["system_info"]["config"]["start_time"], datetime - ) - - # Verify non-time fields remain as their original types - assert isinstance(restored_data.content["message_info"]["content"], list) - assert isinstance( - restored_data.content["message_info"]["author"]["profile_description"], str - ) - assert isinstance(restored_data.content["system_info"]["version"], str) - assert isinstance( - restored_data.content["system_info"]["config"]["timeout"], str - ) - assert isinstance( - restored_data.content["system_info"]["config"]["schedule"], str - ) - - # Verify string content remains unchanged - assert ( - "2024-01-01T10:00:00Z" in restored_data.content["message_info"]["content"][0]["content"] - ) - assert ( - "2024-01-01T08:00:00+00:00" - in restored_data.content["message_info"]["author"]["profile_description"] - ) - assert restored_data.content["system_info"]["version"] == "2024.01.01T10.00.00" - assert ( - restored_data.content["system_info"]["config"]["timeout"] - == "2024-01-01T10:00:00Z" - ) - assert ( - "2024-01-01T10:00:00Z" - in restored_data.content["system_info"]["config"]["schedule"] - ) - - def test_edge_cases_improved(self): - """Test edge cases (improved version)""" - test_time = get_now_with_timezone() - - original_data = RawData( - content={ - # Empty strings and None values - "timestamp": test_time, - "empty_timestamp": "", - "null_timestamp": None, - # Numeric field names containing time keywords - "timestamp_count": 5, - "time_limit": 3600, - # Boolean field names containing time keywords - "timestamp_enabled": True, - "time_sync": False, - # Time fields in lists - "events": [ - { - "name": "Event 1", - "timestamp": test_time, # Should be converted - "description": "Happens at 2024-01-01T10:00:00Z", # Should not be converted - }, - { - "name": "Event 2", - "event_time": test_time, # Should be converted - "note": "Scheduled to execute at 2024-01-02T14:00:00+00:00", # Should not be converted - }, - ], - }, - data_id="edge_test", - data_type="Test", - ) - - # Serialize and deserialize - json_str = original_data.to_json() - restored_data = RawData.from_json_str(json_str) - - # Verify time fields are correctly handled - assert isinstance(restored_data.content["timestamp"], datetime) - - # Verify empty values remain unchanged - assert restored_data.content["empty_timestamp"] == "" - assert restored_data.content["null_timestamp"] is None - - # Verify non-string values retain their original types - assert isinstance(restored_data.content["timestamp_count"], int) - assert isinstance(restored_data.content["time_limit"], int) - assert isinstance(restored_data.content["timestamp_enabled"], bool) - assert isinstance(restored_data.content["time_sync"], bool) - - # Verify nested handling in lists - assert isinstance(restored_data.content["events"][0]["timestamp"], datetime) - assert isinstance(restored_data.content["events"][1]["event_time"], datetime) - assert isinstance(restored_data.content["events"][0]["description"], str) - assert isinstance(restored_data.content["events"][1]["note"], str) - - # Verify string content remains unchanged - assert ( - "2024-01-01T10:00:00Z" in restored_data.content["events"][0]["description"] - ) - assert "2024-01-02T14:00:00+00:00" in restored_data.content["events"][1]["note"] - - -if __name__ == "__main__": - # Run tests - test_instance = TestRawDataJsonSerialization() - - print("Starting to test RawData JSON serialization functionality...") - - try: - # Basic functionality tests - test_instance.test_basic_serialization() - print("✅ Basic serialization test passed") - - test_instance.test_datetime_serialization() - print("✅ Datetime serialization test passed") - - test_instance.test_nested_structure_serialization() - print("✅ Nested structure serialization test passed") - - test_instance.test_email_data_serialization() - print("✅ Email data serialization test passed") - - test_instance.test_linkdoc_data_serialization() - print("✅ Document data serialization test passed") - - test_instance.test_conversation_data_serialization() - print("✅ Conversation data serialization test passed") - - test_instance.test_empty_and_none_values() - print("✅ Empty value handling test passed") - - test_instance.test_error_handling() - print("✅ Error handling test passed") - - test_instance.test_round_trip_consistency() - print("✅ Round-trip consistency test passed") - - # Improved field name heuristic judgment tests - print("\n--- Improved field name heuristic judgment tests ---") - - test_instance.test_datetime_field_recognition() - print("✅ Time field recognition test passed") - - test_instance.test_datetime_content_vs_field_name() - print("✅ Field name vs content judgment test passed") - - test_instance.test_real_world_conversation_data_improved() - print("✅ Real-world conversation data test (improved version) passed") - - test_instance.test_real_world_email_data_improved() - print("✅ Real-world email data test (improved version) passed") - - test_instance.test_real_world_document_data_improved() - print("✅ Real-world document data test (improved version) passed") - - test_instance.test_nested_structure_with_mixed_content() - print("✅ Nested structure mixed content test passed") - - test_instance.test_edge_cases_improved() - print("✅ Edge case test (improved version) passed") - - print( - "\n🎉 All tests passed! RawData JSON serialization functionality is working correctly." - ) - print("\nMain features and improvements:") - print("- ✅ Complete JSON serialization and deserialization support") - print( - "- ✅ Intelligent time field recognition based on field names rather than content" - ) - print( - "- ✅ Avoids mistakenly converting time format strings in message content to datetime" - ) - print("- ✅ Supports all common time field naming patterns in the project") - print("- ✅ Correctly handles mixed content types in nested structures") - print("- ✅ Comprehensive error handling and edge case handling") - - except (AssertionError, ValueError, TypeError) as e: - print(f"❌ Test failed: {e}") - import traceback - - traceback.print_exc() diff --git a/methods/EverCore/tests/test_redis_distributed_lock.py b/methods/EverCore/tests/test_redis_distributed_lock.py deleted file mode 100644 index f5df619a..00000000 --- a/methods/EverCore/tests/test_redis_distributed_lock.py +++ /dev/null @@ -1,378 +0,0 @@ -""" -Redis Distributed Lock Test - -Test scenarios include: -1. Basic lock acquisition and release -2. Lock reentrancy -3. Timeout mechanism -4. Concurrent competition -5. Decorator usage -""" - -import asyncio -from core.lock.redis_distributed_lock import with_distributed_lock, distributed_lock - - -async def test_basic_lock_operations(redis_distributed_lock_manager): - """Test basic lock operations""" - resource = "test_resource" - lock = redis_distributed_lock_manager.get_lock(resource) - - # Test acquiring lock - async with lock.acquire() as acquired: - assert acquired, "Should successfully acquire the lock" - assert await lock.is_locked(), "Resource should be in locked state" - assert await lock.is_owned_by_current_coroutine(), "The lock should be held by the current coroutine" - - # Test lock release - assert not await lock.is_locked(), "The lock should have been released" - assert not await lock.is_owned_by_current_coroutine(), "The lock should not be held by the current coroutine" - - -async def test_lock_reentrant(redis_distributed_lock_manager): - """Test lock reentrancy""" - resource = "test_reentrant" - lock = redis_distributed_lock_manager.get_lock(resource) - - async with lock.acquire() as acquired: - assert acquired, "First acquisition should succeed" - count1 = await lock.get_reentry_count() - assert count1 == 1, "After first acquisition, reentry count should be 1" - - # Re-enter to acquire the lock - async with lock.acquire() as reacquired: - assert reacquired, "Second acquisition should succeed (reentrant)" - count2 = await lock.get_reentry_count() - assert count2 == 2, "After second acquisition, reentry count should be 2" - - # After one release, the lock should still exist - count3 = await lock.get_reentry_count() - assert count3 == 1, "After one release, reentry count should be 1" - assert await lock.is_locked(), "After one release, the lock should still exist" - - # After full release, the lock should disappear - assert not await lock.is_locked(), "After full release, the lock should disappear" - assert await lock.get_reentry_count() == 0, "After full release, reentry count should be 0" - - -async def test_lock_expiration(redis_distributed_lock_manager): - """Test lock expiration mechanism""" - resource = "test_expiration" - - # Test case 1: Basic expiration - lock1 = redis_distributed_lock_manager.get_lock(resource) - async with lock1.acquire(timeout=1) as acquired: # Expire in 1 second - assert acquired, "Should successfully acquire the lock" - assert await lock1.is_locked(), "Resource should be in locked state" - assert await lock1.is_owned_by_current_coroutine(), "The lock should be held by the current coroutine" - - # Wait less than expiration time, lock should still exist - await asyncio.sleep(0.5) - assert await lock1.is_locked(), "Before expiration, the lock should still exist" - - # Wait until expiration - await asyncio.sleep(1) - assert not await lock1.is_locked(), "The lock should have expired and been released" - - # Test case 2: Another coroutine can acquire the lock after expiration - async def try_acquire_expired_lock(): - lock2 = redis_distributed_lock_manager.get_lock(resource) - async with lock2.acquire(timeout=5) as acquired: # Set a long enough expiration time - assert acquired, "After the original lock expires, a new coroutine should be able to acquire the lock" - assert await lock2.is_locked(), "The newly acquired lock should be in locked state" - assert ( - await lock2.is_owned_by_current_coroutine() - ), "The new lock should be held by the current coroutine" - return True - return False - - success = await try_acquire_expired_lock() - assert success, "Should be able to acquire a new lock after the original lock expires" - - # Test case 3: Different expiration times - test_times = [0.5, 1, 2] # Test different expiration times - for expire_time in test_times: - lock = redis_distributed_lock_manager.get_lock(f"{resource}_{expire_time}") - async with lock.acquire(timeout=expire_time) as acquired: - assert acquired, f"Should successfully acquire the lock (expiration time: {expire_time} seconds)" - - # Wait half the time, lock should still exist - await asyncio.sleep(expire_time / 2) - assert ( - await lock.is_locked() - ), f"For expiration time {expire_time} seconds, the lock should still exist after {expire_time/2} seconds" - - # Wait the remaining time plus a margin, lock should have expired - await asyncio.sleep(expire_time / 2 + 0.1) - assert ( - not await lock.is_locked() - ), f"For expiration time {expire_time} seconds, the lock should have expired after {expire_time+0.1} seconds" - - # Test case 4: Update expiration time during re-entry - lock3 = redis_distributed_lock_manager.get_lock("test_reentry_expiration") - async with lock3.acquire(timeout=1) as acquired1: # Expire in 1 second - assert acquired1, "First acquisition should succeed" - - # Wait 0.8 seconds (close to expiration) - await asyncio.sleep(0.8) - assert await lock3.is_locked(), "The lock should still exist when close to expiration" - - # Re-enter and set a new expiration time - async with lock3.acquire(timeout=2) as acquired2: # Expire in 2 seconds - assert acquired2, "Should be able to re-enter and acquire the lock" - assert await lock3.get_reentry_count() == 2, "Reentry count should be 2" - - # Wait 1.2 seconds (exceeding the original 1-second expiration) - await asyncio.sleep(1.2) - assert await lock3.is_locked(), "After re-entry with a new expiration time, the lock should still exist" - assert await lock3.get_reentry_count() == 2, "Reentry count should remain 2" - - -async def test_concurrent_lock_competition(redis_distributed_lock_manager): - """Test concurrent competition scenario""" - resource = "test_concurrent" - results = [] - - async def compete_for_lock(task_id): - lock = redis_distributed_lock_manager.get_lock(resource) - async with lock.acquire(blocking_timeout=1) as acquired: - if acquired: - results.append(task_id) - await asyncio.sleep(0.1) # Simulate workload - - # Create multiple concurrent tasks - tasks = [compete_for_lock(i) for i in range(5)] - await asyncio.gather(*tasks) - - # Verify results - assert len(results) > 0, "At least one task should acquire the lock" - assert len(results) == len(set(results)), "Each task ID should appear only once" - - -@with_distributed_lock("test_decorator") -async def decorated_function(value): - return value * 2 - - -@with_distributed_lock("test_decorator_{value}") -async def decorated_function_with_format(value): - return value * 2 - - -async def test_lock_decorator(_redis_distributed_lock_manager): - """Test decorator functionality""" - # Test basic decorator - result1 = await decorated_function(21) - assert result1 == 42, "Decorator should not affect function return value" - - # Test decorator with formatted string - result2 = await decorated_function_with_format(21) - assert result2 == 42, "Decorator with formatted string should not affect function return value" - - -async def test_force_unlock(redis_distributed_lock_manager): - """Test force unlock functionality""" - resource = "test_force_unlock" - lock = redis_distributed_lock_manager.get_lock(resource) - - async with lock.acquire() as acquired: - assert acquired, "Should successfully acquire the lock" - - # Force unlock - success = await redis_distributed_lock_manager.force_unlock(resource) - assert success, "Force unlock should succeed" - assert not await lock.is_locked(), "After force unlock, the lock should be released" - - -async def test_blocking_timeout_and_reentry(redis_distributed_lock_manager): - """Test blocking timeout and reentrancy (using asyncio tasks)""" - resource = "test_blocking" - - # Test case 1: Reentrancy within the same task - async def reentry_test(): - lock = redis_distributed_lock_manager.get_lock(resource) - async with lock.acquire() as acquired1: - assert acquired1, "First lock acquisition should succeed" - assert await lock.get_reentry_count() == 1, "Reentry count should be 1 after first acquisition" - - # Re-enter within the same task - async with lock.acquire() as acquired2: - assert acquired2, "Reentrancy within the same task should succeed" - assert await lock.get_reentry_count() == 2, "Reentry count should be 2 after re-entry" - - # Re-enter again - async with lock.acquire() as acquired3: - assert acquired3, "Third re-entry within the same task should succeed" - assert ( - await lock.get_reentry_count() == 3 - ), "Reentry count should be 3 after third re-entry" - await asyncio.sleep(0.1) # Ensure task switching - - # Create and run task - task1 = asyncio.create_task(reentry_test()) - await task1 - - # Test case 2: Blocking and reentrancy between different tasks - async def blocking_task(): - lock = redis_distributed_lock_manager.get_lock(resource) - async with lock.acquire() as acquired: - assert acquired, "First task should be able to acquire the lock" - assert await lock.get_reentry_count() == 1, "Reentry count for first task should be 1" - await asyncio.sleep(1) # Hold the lock for a while - - async def competing_task(): - lock = redis_distributed_lock_manager.get_lock(resource) - async with lock.acquire(blocking_timeout=0.5) as acquired: - assert not acquired, "Second task should not be able to acquire the lock" - assert await lock.get_reentry_count() == 0, "Reentry count should be 0 when acquisition fails" - - # Create two competing tasks - task2 = asyncio.create_task(blocking_task()) - await asyncio.sleep(0.1) # Ensure first task acquires the lock first - task3 = asyncio.create_task(competing_task()) - - # Wait for tasks to complete - await asyncio.gather(task2, task3) - - # Test case 3: Reentrancy during nested tasks - async def parent_task(): - lock = redis_distributed_lock_manager.get_lock(resource) - async with lock.acquire() as acquired: - assert acquired, "Parent task should be able to acquire the lock" - assert await lock.get_reentry_count() == 1, "Reentry count for parent task should be 1" - - # Create child task - child = asyncio.create_task(child_task()) - await asyncio.sleep(0.1) # Ensure child task has a chance to run - - # Parent task re-enters - async with lock.acquire() as reentry: - assert reentry, "Parent task re-entry should succeed" - assert await lock.get_reentry_count() == 2, "Reentry count should be 2 after parent re-entry" - await asyncio.sleep(0.1) # Give child task another chance - - await child # Wait for child task to complete - - async def child_task(): - lock = redis_distributed_lock_manager.get_lock(resource) - async with lock.acquire(blocking_timeout=0.5) as acquired: - assert not acquired, "Child task should not be able to acquire the lock held by parent task" - assert await lock.get_reentry_count() == 0, "Reentry count should be 0 when child task fails to acquire" - - # Run parent-child task test - await parent_task() - - # Test case 4: New task acquiring lock after release - async def final_task(): - lock = redis_distributed_lock_manager.get_lock(resource) - async with lock.acquire(blocking_timeout=0.5) as acquired: - assert acquired, "New task should be able to acquire the lock after it is released" - assert await lock.get_reentry_count() == 1, "Reentry count should be 1 after new task acquires the lock" - - # Ensure lock is released - assert not await redis_distributed_lock_manager.get_lock( - resource - ).is_locked(), "The lock should be released after all tasks complete" - - # Run final test - final = asyncio.create_task(final_task()) - await final - - -async def run_all_tests(): - """Run all tests""" - from core.di.utils import get_bean_by_type - from core.lock.redis_distributed_lock import RedisDistributedLockManager - - print("Starting Redis distributed lock tests...") - - # Get lock manager instance - lock_manager = get_bean_by_type(RedisDistributedLockManager) - - # Define all test functions - tests = [ - test_basic_lock_operations, - test_lock_reentrant, - test_lock_expiration, # New expiration test - test_concurrent_lock_competition, - test_lock_decorator, - test_force_unlock, - test_blocking_timeout_and_reentry, # Updated blocking and reentrancy test - test_convenient_context_manager, - test_context_manager_with_timeout, - test_context_manager_concurrent, - ] - - # Run all tests - for test_func in tests: - print(f"\nRunning test: {test_func.__name__}") - print("-" * 50) - try: - await test_func(lock_manager) - print(f"✅ {test_func.__name__} passed") - except AssertionError as e: - print(f"❌ {test_func.__name__} failed: {str(e)}") - except (ConnectionError, TimeoutError, OSError) as e: - print(f"❌ {test_func.__name__} error: {str(e)}") - - print("\nTests completed!") - - -async def test_convenient_context_manager(_redis_distributed_lock_manager): - """Test convenient context manager function""" - resource = "test_context_manager" - - # Test basic usage - async with distributed_lock(resource) as acquired: - assert acquired, "Should successfully acquire the lock" - - # Test reentrancy - async with distributed_lock(resource) as reacquired: - assert reacquired, "Should support reentrancy" - - # Test locks for different resources - async with distributed_lock("resource1") as acquired1: - assert acquired1, "Should successfully acquire lock for resource1" - - async with distributed_lock("resource2") as acquired2: - assert acquired2, "Should successfully acquire lock for resource2" - - print("✅ Convenient context manager test passed") - - -async def test_context_manager_with_timeout(_redis_distributed_lock_manager): - """Test context manager timeout functionality""" - resource = "test_timeout_context" - - # Test custom timeout parameters - async with distributed_lock( - resource, timeout=30.0, blocking_timeout=5.0 - ) as acquired: - assert acquired, "Should successfully acquire the lock" - - print("✅ Context manager timeout test passed") - - -async def test_context_manager_concurrent(_redis_distributed_lock_manager): - """Test context manager concurrency""" - resource = "test_concurrent_context" - results = [] - - async def worker(worker_id: int): - async with distributed_lock(resource, blocking_timeout=0.2) as acquired: - if acquired: - results.append(f"worker_{worker_id}") - # Hold the lock long enough to ensure other workers time out - await asyncio.sleep(0.5) - - # Truly run multiple workers concurrently - tasks = [worker(i) for i in range(3)] - await asyncio.gather(*tasks) - - # Due to the lock and short blocking_timeout, only one worker should succeed - assert len(results) == 1, f"Only one worker should succeed, but got: {results}" - print(f"✅ Context manager concurrency test passed, successful worker: {results[0]}") - - -if __name__ == "__main__": - asyncio.run(run_all_tests()) \ No newline at end of file diff --git a/methods/EverCore/tests/test_redis_length_cache.py b/methods/EverCore/tests/test_redis_length_cache.py deleted file mode 100644 index eb8f43eb..00000000 --- a/methods/EverCore/tests/test_redis_length_cache.py +++ /dev/null @@ -1,861 +0,0 @@ -""" -Redis length-limited cache manager test script - -Usage: - python src/bootstrap.py tests/test_redis_length_cache.py - -Test coverage: -1. Basic operations: append, get, clear, etc. -2. Length limit cleanup mechanism -3. Timestamp compatibility (integer and datetime objects) -4. Get data by timestamp range functionality -5. Stress testing (large-scale data processing) -6. Expiration mechanism test -7. Backward compatibility layer test -""" - -import asyncio -import time -from datetime import datetime, timezone -from zoneinfo import ZoneInfo -from common_utils.datetime_utils import get_now_with_timezone -from core.di.utils import get_bean -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class NonSerializableTestClass: - """Test class that cannot be JSON-serialized, used for testing Pickle fallback""" - - def __init__(self, name, value, multiplier=2): - self.name = name - self.value = value - self.multiplier = multiplier - self.created_at = time.time() - # Add some complex attributes to make JSON serialization fail - self.complex_data = { - "set_data": {1, 2, 3, 4, 5}, # set cannot be JSON-serialized - "tuple_data": (1, 2, 3), # tuple will become list, but we can detect - "bytes_data": b"hello world", # bytes cannot be JSON-serialized - } - - def get_doubled_value(self): - return self.value * self.multiplier - - def process_data(self, input_value): - """Method to process data""" - return f"{self.name}_processed_{input_value}_{self.multiplier}" - - def __eq__(self, other): - return ( - isinstance(other, NonSerializableTestClass) - and self.name == other.name - and self.value == other.value - and self.multiplier == other.multiplier - ) - - def __repr__(self): - return f"NonSerializableTestClass(name='{self.name}', value={self.value}, multiplier={self.multiplier})" - - -async def test_basic_operations(): - """Test basic operations: append, get, clear, etc.""" - logger.info("Starting basic operations test...") - - # Get cache manager factory from DI container - factory = get_bean("redis_length_cache_factory") - cache = await factory.create_cache_manager( - max_length=5, expire_minutes=5 - ) # Max 5 items, 5 minutes expiration - - test_key = "test_length_cache" - - # 1. Clear test queue - logger.info("Clearing test queue...") - await cache.clear_queue(test_key) - - # 2. Test appending data - logger.info("Testing data append...") - test_data = [ - "test_string", - {"name": "test_dict", "value": 123}, - ["test_list", 1, 2, 3], - 42, - {"complex": {"nested": "data"}}, - ] - - for i, data in enumerate(test_data): - success = await cache.append(test_key, data) - assert success, f"Failed to append data: {data}" - logger.info("Successfully appended data %d", i + 1) - - # 3. Verify queue size - size = await cache.get_queue_size(test_key) - assert size == len( - test_data - ), f"Queue size mismatch: expected {len(test_data)}, actual {size}" - logger.info("Queue size verification passed: %d", size) - - # 4. Get queue statistics - stats = await cache.get_queue_stats(test_key) - assert isinstance(stats, dict), "Statistics format error" - assert stats["total_count"] == len(test_data), "Statistics count mismatch" - assert stats["max_length"] == 5, "Max length configuration mismatch" - assert stats["is_full"], "Queue should be full (5/5)" - logger.info("Queue statistics: %s", stats) - - logger.info("✅ Basic operations test passed") - - -async def test_length_cleanup(): - """Test length limit cleanup mechanism""" - logger.info("Starting length limit cleanup test...") - - factory = get_bean("redis_length_cache_factory") - # Set max length to 3, cleanup probability to 0 to prevent auto cleanup, making manual verification easier - cache = await factory.create_cache_manager( - max_length=3, expire_minutes=10, cleanup_probability=0.0 - ) - - test_key = "test_length_cleanup" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Add data with explicit timestamps to ensure order - logger.info("Adding data with increasing timestamps...") - base_timestamp = int(time.time() * 1000) - data_list = [] - - for i in range(5): # Add 5 items, exceeding max length of 3 - timestamp = base_timestamp + i * 1000 # Each data item spaced by 1 second - data = {"index": i, "content": f"data_{i}", "timestamp": timestamp} - data_list.append({"data": data, "timestamp": timestamp}) - - success = await cache.append(test_key, data, timestamp=timestamp) - assert success, f"Failed to append data {i+1}" - logger.info("Added data %d: timestamp=%d, content=data_%d", i, timestamp, i) - - # 3. Verify all data has been added - current_size = await cache.get_queue_size(test_key) - logger.info("Queue size after adding: %d", current_size) - assert current_size == 5, f"Expected queue size 5, actual {current_size}" - - # 4. Get all data from queue, verify order - logger.info("Retrieving data from queue, verifying timestamp order...") - # Note: Since there's no get_all method, we verify time range through statistics - stats = await cache.get_queue_stats(test_key) - logger.info( - "Queue statistics: oldest_timestamp=%s, newest_timestamp=%s", - stats.get("oldest_timestamp"), - stats.get("newest_timestamp"), - ) - - # Verify oldest and newest timestamps - expected_oldest = base_timestamp - expected_newest = base_timestamp + 4 * 1000 - assert ( - stats["oldest_timestamp"] == expected_oldest - ), f"Oldest timestamp mismatch: expected {expected_oldest}, actual {stats['oldest_timestamp']}" - assert ( - stats["newest_timestamp"] == expected_newest - ), f"Newest timestamp mismatch: expected {expected_newest}, actual {stats['newest_timestamp']}" - - # 5. Manually trigger cleanup, should delete the 2 oldest items (keep 3) - logger.info("Manually triggering cleanup, should delete 2 oldest items...") - cleaned_count = await cache.cleanup_excess(test_key) - logger.info("Manual cleanup completed, cleaned count: %d", cleaned_count) - assert ( - cleaned_count == 2 - ), f"Expected to clean 2 items, actually cleaned {cleaned_count}" - - # 6. Verify data after cleanup - final_size = await cache.get_queue_size(test_key) - assert final_size == 3, f"Queue size after cleanup should be 3, actual {final_size}" - - # 7. Verify remaining items are the latest 3 (index 2, 3, 4) - stats_after = await cache.get_queue_stats(test_key) - expected_oldest_after = base_timestamp + 2 * 1000 # data_2's timestamp - expected_newest_after = base_timestamp + 4 * 1000 # data_4's timestamp - - logger.info( - "Queue statistics after cleanup: oldest_timestamp=%s, newest_timestamp=%s", - stats_after.get("oldest_timestamp"), - stats_after.get("newest_timestamp"), - ) - - assert ( - stats_after["oldest_timestamp"] == expected_oldest_after - ), f"After cleanup, oldest timestamp should be {expected_oldest_after}, actual {stats_after['oldest_timestamp']}" - assert ( - stats_after["newest_timestamp"] == expected_newest_after - ), f"After cleanup, newest timestamp should be {expected_newest_after}, actual {stats_after['newest_timestamp']}" - - logger.info( - "✅ Verification passed: cleanup deleted oldest data (data_0 and data_1), kept latest data (data_2, data_3, data_4)" - ) - - # 8. Clean again, should delete no data (already at max length) - additional_cleaned = await cache.cleanup_excess(test_key) - assert ( - additional_cleaned == 0 - ), f"Second cleanup should delete 0 items, actually deleted {additional_cleaned}" - - logger.info("✅ Length limit cleanup test passed") - - -async def test_timestamp_compatibility(): - """Test timestamp compatibility: support for integers and datetime objects""" - logger.info("Starting timestamp compatibility test...") - - factory = get_bean("redis_length_cache_factory") - cache = await factory.create_cache_manager(max_length=10, expire_minutes=5) - - test_key = "test_timestamp_compat" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Test different types of timestamps - logger.info("Testing different types of timestamps...") - - # Use current time (default) - success = await cache.append(test_key, "data_current_time") - assert success, "Failed to append with current time" - - # Use integer timestamp (milliseconds) - timestamp_ms = int(time.time() * 1000) + 1000 # 1 second later - success = await cache.append(test_key, "data_int_timestamp", timestamp=timestamp_ms) - assert success, "Failed to append with integer timestamp" - - # Use datetime object (naive) - dt_naive = get_now_with_timezone() - success = await cache.append(test_key, "data_datetime_naive", timestamp=dt_naive) - assert success, "Failed to append with datetime object" - - # Use datetime object (with timezone) - dt_with_tz = get_now_with_timezone(ZoneInfo("UTC")) - success = await cache.append(test_key, "data_datetime_tz", timestamp=dt_with_tz) - assert success, "Failed to append with timezone-aware datetime object" - - # 3. Verify all data was correctly stored - size = await cache.get_queue_size(test_key) - assert ( - size == 4 - ), f"Timestamp compatibility test data count mismatch: expected 4, actual {size}" - - # 4. Get statistics to verify timestamps - stats = await cache.get_queue_stats(test_key) - assert stats["oldest_timestamp"] is not None, "Oldest timestamp is None" - assert stats["newest_timestamp"] is not None, "Newest timestamp is None" - assert stats["oldest_datetime"] is not None, "Oldest datetime string is None" - assert stats["newest_datetime"] is not None, "Newest datetime string is None" - - logger.info( - "Timestamp range: %s to %s", stats["oldest_datetime"], stats["newest_datetime"] - ) - logger.info("✅ Timestamp compatibility test passed") - - -async def test_timestamp_range_query(): - """Test get data by timestamp range functionality""" - logger.info("Starting test for getting data by timestamp range...") - - factory = get_bean("redis_length_cache_factory") - cache = await factory.create_cache_manager( - max_length=20, expire_minutes=10, cleanup_probability=0.0 - ) - - test_key = "test_timestamp_range" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Add test data with explicit timestamps - logger.info("Adding test data with explicit timestamps...") - base_timestamp = int(time.time() * 1000) - test_data = [] - - for i in range(10): - timestamp = base_timestamp + i * 10000 # Each data item spaced by 10 seconds - data = {"index": i, "content": f"data_{i}", "created_at": timestamp} - test_data.append({"data": data, "timestamp": timestamp}) - - success = await cache.append(test_key, data, timestamp=timestamp) - assert success, f"Failed to add data {i+1}" - - logger.info( - "Added %d data items, time range: %d to %d", - len(test_data), - base_timestamp, - base_timestamp + 9 * 10000, - ) - - # 3. Test getting all data (no time range limit) - all_data = await cache.get_by_timestamp_range(test_key) - assert ( - len(all_data) == 10 - ), f"Failed to get all data, expected 10, actual {len(all_data)}" - logger.info("Successfully retrieved all data: %d items", len(all_data)) - - # 4. Test filtering by start time (get data after the 5th item) - start_time = base_timestamp + 4 * 10000 # data_4's timestamp - filtered_data = await cache.get_by_timestamp_range( - test_key, start_timestamp=start_time - ) - assert ( - len(filtered_data) == 6 - ), f"Start time filtering failed, expected 6, actual {len(filtered_data)}" # data_4 to data_9 - - # Verify data order (newest first) - assert ( - filtered_data[0]["data"]["index"] == 9 - ), "Data sorting error, newest should be first" - assert ( - filtered_data[-1]["data"]["index"] == 4 - ), "Data sorting error, oldest should be last" - logger.info("Start time filtering test passed") - - # 5. Test filtering by end time (get data before the 5th item) - end_time = base_timestamp + 4 * 10000 # data_4's timestamp - filtered_data = await cache.get_by_timestamp_range(test_key, end_timestamp=end_time) - assert ( - len(filtered_data) == 5 - ), f"End time filtering failed, expected 5, actual {len(filtered_data)}" # data_0 to data_4 - - # Verify data content - assert filtered_data[0]["data"]["index"] == 4, "End time filtering result error" - assert filtered_data[-1]["data"]["index"] == 0, "End time filtering result error" - logger.info("End time filtering test passed") - - # 6. Test time range filtering (get middle data) - start_time = base_timestamp + 2 * 10000 # data_2's timestamp - end_time = base_timestamp + 6 * 10000 # data_6's timestamp - range_data = await cache.get_by_timestamp_range( - test_key, start_timestamp=start_time, end_timestamp=end_time - ) - assert ( - len(range_data) == 5 - ), f"Time range filtering failed, expected 5, actual {len(range_data)}" # data_2 to data_6 - - # Verify data range - indexes = [item["data"]["index"] for item in range_data] - indexes.sort() # Sort for verification - assert indexes == [ - 2, - 3, - 4, - 5, - 6, - ], f"Time range filtering result error, expected [2,3,4,5,6], actual {indexes}" - logger.info("Time range filtering test passed") - - # 7. Test limiting number of results - limited_data = await cache.get_by_timestamp_range(test_key, limit=3) - assert ( - len(limited_data) == 3 - ), f"Limiting count failed, expected 3, actual {len(limited_data)}" - logger.info("Limit count test passed") - - # 8. Test using datetime objects as timestamps - dt_start = datetime.fromtimestamp((base_timestamp + 3 * 10000) / 1000) - dt_end = datetime.fromtimestamp((base_timestamp + 7 * 10000) / 1000) - dt_data = await cache.get_by_timestamp_range( - test_key, start_timestamp=dt_start, end_timestamp=dt_end - ) - assert ( - len(dt_data) == 5 - ), f"Filtering with datetime objects failed, expected 5, actual {len(dt_data)}" # data_3 to data_7 - logger.info("Filtering with datetime objects test passed") - - # 9. Test empty result - future_time = base_timestamp + 20 * 10000 # Future time - empty_data = await cache.get_by_timestamp_range( - test_key, start_timestamp=future_time - ) - assert ( - len(empty_data) == 0 - ), f"Empty result test failed, should return 0 items, actual {len(empty_data)}" - logger.info("Empty result test passed") - - # 10. Clean up test data - success = await cache.clear_queue(test_key) - assert success, "Failed to clean up test data" - - logger.info("✅ Test for getting data by timestamp range passed") - - -async def test_json_pickle_mixed_data(): - """Test JSON and Pickle mixed data processing""" - logger.info("Starting test for JSON and Pickle mixed data processing...") - - factory = get_bean("redis_length_cache_factory") - cache = await factory.create_cache_manager( - max_length=20, expire_minutes=10, cleanup_probability=0.0 - ) - - test_key = "test_mixed_serialization" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Prepare mixed test data - base_timestamp = int(time.time() * 1000) - test_data = [] - - # JSON serializable data - json_data = [ - {"type": "json", "name": "dict_data", "value": 123, "items": [1, 2, 3]}, - ["json_list", "with", "strings", 456], - "simple_json_string", - 42, - {"nested": {"deep": {"data": "json_nested"}}}, - ] - - # Pickle serialized data (cannot be JSON serialized) - pickle_data = [ - NonSerializableTestClass("test1", 100), - NonSerializableTestClass("test2", 200, 3), - { - "complex_set": {1, 2, 3, 4, 5}, - "bytes_data": b"binary_data", - "name": "complex_dict", - }, # Dictionary containing set and bytes - ] - - # 3. Add JSON data - logger.info("Adding JSON serializable data...") - for i, data in enumerate(json_data): - timestamp = base_timestamp + i * 1000 - success = await cache.append(test_key, data, timestamp=timestamp) - assert success, f"Failed to add JSON data: {data}" - test_data.append({"type": "json", "data": data, "timestamp": timestamp}) - logger.debug("Successfully added JSON data: %s", str(data)[:50]) - - # 4. Add Pickle data - logger.info("Adding Pickle serialized data...") - for i, data in enumerate(pickle_data): - timestamp = base_timestamp + (len(json_data) + i) * 1000 - success = await cache.append(test_key, data, timestamp=timestamp) - assert success, f"Failed to add Pickle data: {data}" - test_data.append({"type": "pickle", "data": data, "timestamp": timestamp}) - logger.debug("Successfully added Pickle data: %s", str(data)[:50]) - - # 5. Verify total data count - total_count = len(json_data) + len(pickle_data) - size = await cache.get_queue_size(test_key) - assert ( - size == total_count - ), f"Total data count mismatch: expected {total_count}, actual {size}" - logger.info( - "Data addition completed, total: %d items (JSON: %d, Pickle: %d)", - total_count, - len(json_data), - len(pickle_data), - ) - - # 6. Retrieve all data and verify - all_data = await cache.get_by_timestamp_range(test_key) - assert ( - len(all_data) == total_count - ), f"Retrieved data count mismatch: expected {total_count}, actual {len(all_data)}" - - # 7. Verify correctness of each data type - json_count = 0 - pickle_count = 0 - - for item in all_data: - retrieved_data = item["data"] - - # Check JSON data - if any( - str(retrieved_data) == str(original["data"]) - for original in test_data - if original["type"] == "json" - ): - json_count += 1 - logger.debug( - "JSON data verification successful: %s", str(retrieved_data)[:50] - ) - - # Check Pickle data - elif isinstance(retrieved_data, NonSerializableTestClass): - pickle_count += 1 - # Verify Pickle object functionality - doubled = retrieved_data.get_doubled_value() - expected = retrieved_data.value * retrieved_data.multiplier - assert ( - doubled == expected - ), f"Pickle object function error: {doubled} != {expected}" - - # Verify complex data - assert ( - "set_data" in retrieved_data.complex_data - ), "Pickle object missing set data" - assert ( - "bytes_data" in retrieved_data.complex_data - ), "Pickle object missing bytes data" - - logger.debug( - "Pickle object verification successful: %s, function test: %d * %d = %d", - retrieved_data, - retrieved_data.value, - retrieved_data.multiplier, - doubled, - ) - - elif isinstance(retrieved_data, dict) and "complex_set" in retrieved_data: - pickle_count += 1 - # Verify dictionary containing set and bytes - assert isinstance(retrieved_data["complex_set"], set), "Set data type error" - assert isinstance( - retrieved_data["bytes_data"], bytes - ), "Bytes data type error" - logger.debug( - "Verification of dictionary with complex data successful: %s", - retrieved_data["name"], - ) - - else: - logger.warning("Unrecognized data type: %s", type(retrieved_data)) - - # 8. Verify data type distribution - assert json_count == len( - json_data - ), f"JSON data count mismatch: expected {len(json_data)}, actual {json_count}" - assert pickle_count == len( - pickle_data - ), f"Pickle data count mismatch: expected {len(pickle_data)}, actual {pickle_count}" - - logger.info( - "Data type verification completed: JSON data %d items, Pickle data %d items", - json_count, - pickle_count, - ) - - # 9. Test timestamp range query support for mixed data - # Get first half data (mainly JSON data) - mid_timestamp = base_timestamp + (total_count // 2) * 1000 - first_half = await cache.get_by_timestamp_range( - test_key, end_timestamp=mid_timestamp - ) - assert ( - len(first_half) >= len(json_data) // 2 - ), "Timestamp range query support for mixed data abnormal" - - # Get second half data (mainly Pickle data) - second_half = await cache.get_by_timestamp_range( - test_key, start_timestamp=mid_timestamp - ) - assert len(second_half) >= len( - pickle_data - ), "Timestamp range query support for Pickle data abnormal" - - logger.info( - "Mixed data timestamp range query test passed: first half %d items, second half %d items", - len(first_half), - len(second_half), - ) - - # 10. Clean up test data - success = await cache.clear_queue(test_key) - assert success, "Failed to clean up test data" - - logger.info("✅ JSON and Pickle mixed data processing test passed") - - -async def test_pickle_error_handling(): - """Test Pickle serialization error handling""" - logger.info("Starting test for Pickle serialization error handling...") - - factory = get_bean("redis_length_cache_factory") - cache = await factory.create_cache_manager(max_length=10, expire_minutes=5) - - test_key = "test_pickle_error_handling" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Test normal Pickle data - normal_pickle_data = NonSerializableTestClass("normal", 42) - success = await cache.append(test_key, normal_pickle_data) - assert success, "Failed to add normal Pickle data" - - # 3. Retrieve and verify normal data - data_list = await cache.get_by_timestamp_range(test_key) - assert len(data_list) == 1, "Failed to retrieve normal Pickle data" - - retrieved = data_list[0]["data"] - assert isinstance(retrieved, NonSerializableTestClass), "Pickle data type error" - assert ( - retrieved.name == "normal" and retrieved.value == 42 - ), "Pickle data content error" - assert retrieved.get_doubled_value() == 84, "Pickle object function error" - - logger.info("Normal Pickle data processing verification passed") - - # 4. Test error recovery with mixed data - mixed_data = [ - {"json_data": "this_is_json"}, # JSON data - NonSerializableTestClass("pickle1", 100), # Pickle data - "simple_string", # Simple string - NonSerializableTestClass("pickle2", 200), # Another Pickle data - ] - - for i, data in enumerate(mixed_data): - success = await cache.append(test_key, data) - assert success, f"Failed to add mixed data item {i+1}" - - # 5. Verify all mixed data can be processed correctly - all_data = await cache.get_by_timestamp_range(test_key) - assert ( - len(all_data) == 5 - ), f"Mixed data total count error: expected 5, actual {len(all_data)}" # 1 existing + 4 new - - # Count various data types - json_count = sum( - 1 - for item in all_data - if isinstance(item["data"], (dict, str)) - and not isinstance(item["data"], NonSerializableTestClass) - ) - pickle_count = sum( - 1 for item in all_data if isinstance(item["data"], NonSerializableTestClass) - ) - - assert ( - json_count >= 2 - ), f"JSON data count abnormal: {json_count}" # At least dict and string - assert ( - pickle_count >= 3 - ), f"Pickle data count abnormal: {pickle_count}" # 3 NonSerializableTestClass objects - - logger.info( - "Mixed data error handling test passed: JSON type %d items, Pickle type %d items", - json_count, - pickle_count, - ) - - # 6. Clean up test data - success = await cache.clear_queue(test_key) - assert success, "Failed to clean up test data" - - logger.info("✅ Pickle serialization error handling test passed") - - -async def test_stress_operations(): - """Test stress operations: large-scale data processing and cleanup""" - logger.info("Starting stress test...") - - factory = get_bean("redis_length_cache_factory") - cache = await factory.create_cache_manager( - max_length=100, expire_minutes=10, cleanup_probability=0.1 - ) - - test_key = "test_length_cache_stress" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Batch append data (exceeding max length) - batch_size = 500 - logger.info("Appending %d items (exceeding max length 100)...", batch_size) - - start_time = time.time() - for i in range(batch_size): - data = { - "index": i, - "timestamp": time.time(), - "data": f"stress_test_data_{i}", - "batch": "stress_test", - } - # Use increasing timestamps to ensure order - timestamp = int(time.time() * 1000) + i * 10 - success = await cache.append(test_key, data, timestamp=timestamp) - assert success, f"Failed to batch append item {i+1}" - - # Check size every 100 items - if (i + 1) % 100 == 0: - current_size = await cache.get_queue_size(test_key) - logger.info( - "Appended %d items, current queue size: %d", i + 1, current_size - ) - - elapsed = time.time() - start_time - logger.info("Data append completed, elapsed time: %.2f seconds", elapsed) - - # 3. Verify queue size (allow some excess due to probabilistic cleanup) - final_size = await cache.get_queue_size(test_key) - logger.info("Queue size after stress test: %d", final_size) - - # If queue size exceeds significantly, manually trigger cleanup to verify cleanup mechanism - if final_size > 120: # Allow some excess range - logger.info( - "Queue size exceeds significantly, manually triggering cleanup test..." - ) - cleaned_count = await cache.cleanup_excess(test_key) - logger.info("Manual cleanup completed, cleaned %d items", cleaned_count) - - # Verify size after cleanup - size_after_cleanup = await cache.get_queue_size(test_key) - assert ( - size_after_cleanup <= 100 - ), f"Queue size still exceeds limit after manual cleanup: {size_after_cleanup}" - logger.info("Queue size after manual cleanup: %d", size_after_cleanup) - - # 4. Get final queue size (may have been manually cleaned) - current_size = await cache.get_queue_size(test_key) - - # Verify queue statistics - stats = await cache.get_queue_stats(test_key) - assert stats["total_count"] == current_size, "Statistics do not match actual size" - assert stats["is_full"] == (current_size >= 100), "Queue full status judgment error" - - # 5. Manually clean again to verify mechanism stability - logger.info("Manually cleaning queue again to verify mechanism stability...") - additional_cleaned = await cache.cleanup_excess(test_key) - logger.info("Additional cleanup completed, cleaned count: %d", additional_cleaned) - - # 6. Clean up test data - success = await cache.clear_queue(test_key) - assert success, "Failed to clean up test data" - - logger.info("✅ Stress test passed") - - -async def test_expiry_mechanism(): - """Test expiration mechanism""" - logger.info("Starting expiration mechanism test...") - - factory = get_bean("redis_length_cache_factory") - cache = await factory.create_cache_manager( - max_length=10, expire_minutes=1 - ) # 1 minute expiration - - test_key = "test_length_cache_expiry" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Add test data - test_data = {"test": "expiry", "timestamp": time.time()} - success = await cache.append(test_key, test_data) - assert success, "Failed to add test data" - - # 3. Verify data exists - size = await cache.get_queue_size(test_key) - assert size == 1, "Data not correctly added" - - stats = await cache.get_queue_stats(test_key) - assert stats["ttl_seconds"] > 0, "TTL should be greater than 0" - logger.info("Data TTL: %d seconds", stats["ttl_seconds"]) - - # 4. Wait for expiration - logger.info("Waiting for data to expire (70 seconds)...") - await asyncio.sleep(70) # Wait more than 1 minute - - # 5. Verify data has expired - expired_size = await cache.get_queue_size(test_key) - assert ( - expired_size == 0 - ), f"Data did not expire correctly, queue size: {expired_size}" - - expired_stats = await cache.get_queue_stats(test_key) - assert expired_stats["total_count"] == 0, "Statistics should be 0 after expiration" - - logger.info("✅ Expiration mechanism test passed") - - -async def test_compatibility_layer(): - """Test backward compatibility layer""" - logger.info("Starting backward compatibility layer test...") - - # Get default manager instance - default_manager = get_bean("redis_length_cache_manager") - test_key = "test_length_cache_compat" - - # 1. Clear test queue - await default_manager.clear_queue(test_key) - - # 2. Test basic operations - test_data = {"test": "compatibility", "type": "default_manager"} - success = await default_manager.append(test_key, test_data) - assert success, "Failed to append data in backward compatibility layer" - - size = await default_manager.get_queue_size(test_key) - assert size == 1, "Queue size error in backward compatibility layer" - - stats = await default_manager.get_queue_stats(test_key) - assert stats["total_count"] == 1, "Statistics error in backward compatibility layer" - - # 3. Test append with timestamp - dt = get_now_with_timezone() - success = await default_manager.append(test_key, "datetime_test", timestamp=dt) - assert success, "Failed to append datetime in backward compatibility layer" - - final_size = await default_manager.get_queue_size(test_key) - assert final_size == 2, "Final size error in backward compatibility layer" - - # 4. Test new timestamp range query method - # Add some test data - base_timestamp = int(time.time() * 1000) - for i in range(3): - timestamp = base_timestamp + i * 5000 - data = {"index": i, "content": f"compat_data_{i}"} - success = await default_manager.append(test_key, data, timestamp=timestamp) - assert success, f"Failed to add test data {i} in backward compatibility layer" - - # Test get by timestamp range - range_data = await default_manager.get_by_timestamp_range(test_key) - assert ( - len(range_data) == 5 - ), f"Timestamp range query in backward compatibility layer failed, expected 5, actual {len(range_data)}" # 2 existing + 3 new - - # Test limit count - limited_data = await default_manager.get_by_timestamp_range(test_key, limit=2) - assert ( - len(limited_data) == 2 - ), "Limit count function in backward compatibility layer failed" - - logger.info("New method test in backward compatibility layer passed") - - # 5. Clean up test data - success = await default_manager.clear_queue(test_key) - assert success, "Failed to clean up data in backward compatibility layer" - - logger.info("✅ Backward compatibility layer test passed") - - -async def main(): - """Main test function""" - logger.info("=" * 50) - logger.info("Redis length-limited cache manager test started") - logger.info("=" * 50) - - try: - # Run all tests - await test_json_pickle_mixed_data() - await test_basic_operations() - await test_length_cleanup() - await test_timestamp_compatibility() - await test_timestamp_range_query() - await test_pickle_error_handling() - await test_stress_operations() - await test_expiry_mechanism() - await test_compatibility_layer() - - logger.info("=" * 50) - logger.info("✅ All tests passed") - logger.info("=" * 50) - - except AssertionError as e: - logger.error("❌ Test failed: %s", str(e)) - raise - except Exception as e: - logger.error("❌ Test error: %s", str(e)) - raise - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/tests/test_redis_windows_cache.py b/methods/EverCore/tests/test_redis_windows_cache.py deleted file mode 100644 index 64e186a4..00000000 --- a/methods/EverCore/tests/test_redis_windows_cache.py +++ /dev/null @@ -1,718 +0,0 @@ -""" -Redis time window cache manager test script - -Usage: - python src/bootstrap.py tests/test_redis_windows_cache.py - -Test coverage: -1. Basic operations: append, get, clear, etc. -2. Get data by timestamp range functionality -3. Automatic cleanup mechanism (extracted cleanup function) -4. Stress testing (large amount of data processing) -5. Backward compatibility layer testing -""" - -import asyncio -import time -from datetime import datetime, timedelta -from common_utils.datetime_utils import get_now_with_timezone -from core.di.utils import get_bean -from core.observation.logger import get_logger - -logger = get_logger(__name__) - - -class NonSerializableTestClass: - """Test class that cannot be JSON serialized, used for Pickle fallback testing""" - - def __init__(self, name, value, multiplier=2): - self.name = name - self.value = value - self.multiplier = multiplier - self.created_at = time.time() - # Add some complex attributes to make JSON serialization fail - self.complex_data = { - "set_data": {1, 2, 3, 4, 5}, # set cannot be JSON serialized - "tuple_data": (1, 2, 3), # tuple will become list, but we can detect it - "bytes_data": b"hello world", # bytes cannot be JSON serialized - } - - def get_doubled_value(self): - return self.value * self.multiplier - - def process_data(self, input_value): - """Method to process data""" - return f"{self.name}_processed_{input_value}_{self.multiplier}" - - def __eq__(self, other): - return ( - isinstance(other, NonSerializableTestClass) - and self.name == other.name - and self.value == other.value - and self.multiplier == other.multiplier - ) - - def __repr__(self): - return f"NonSerializableTestClass(name='{self.name}', value={self.value}, multiplier={self.multiplier})" - - -async def test_basic_operations(): - """Test basic operations: append, get, clear, etc.""" - logger.info("Starting basic operations test...") - - # Get cache manager factory from DI container - factory = get_bean("redis_windows_cache_factory") - cache = await factory.create_cache_manager( - expire_minutes=1 - ) # Use 1 minute expiration for testing - - test_key = "test_windows_cache" - - # 1. Clear test queue - logger.info("Clearing test queue...") - await cache.clear_queue(test_key) - - # 2. Test appending data - logger.info("Testing data append...") - test_data = [ - "test_string", - {"name": "test_dict", "value": 123}, - ["test_list", 1, 2, 3], - 42, - ] - - for data in test_data: - success = await cache.append(test_key, data) - assert success, f"Failed to append data: {data}" - - # 3. Verify queue size - size = await cache.get_queue_size(test_key) - assert size == len( - test_data - ), f"Queue size mismatch: expected {len(test_data)}, actual {size}" - logger.info("Queue size verification passed: %d", size) - - # 4. Use get by timestamp range (replaces get_recent) - current_time = int(time.time() * 1000) - one_minute_ago = current_time - 60 * 1000 - recent_data = await cache.get_by_timestamp_range( - test_key, start_timestamp=one_minute_ago - ) - assert len(recent_data) == len(test_data), "Retrieved data count mismatch" - logger.info("Successfully retrieved recent data: %d items", len(recent_data)) - - # 5. Check data format - for item in recent_data: - assert isinstance(item, dict), "Data item format error" - assert all( - k in item for k in ["id", "data", "timestamp", "datetime"] - ), "Data item missing required fields" - logger.info("Data format verification passed") - - # 6. Get queue statistics - stats = await cache.get_queue_stats(test_key) - assert isinstance(stats, dict), "Statistics format error" - assert stats["total_count"] == len(test_data), "Statistics count mismatch" - logger.info("Queue statistics: %s", stats) - - # 7. Test expiration cleanup - logger.info("Waiting for data to expire...") - await asyncio.sleep(70) # Wait over 1 minute - expired_data = await cache.get_by_timestamp_range(test_key) - assert len(expired_data) == 0, "Data did not expire correctly" - logger.info("Expiration cleanup verification passed") - - logger.info("✅ Basic operations test passed") - - -async def test_timestamp_range_query(): - """Test get data by timestamp range functionality""" - logger.info("Starting test for getting data by timestamp range...") - - factory = get_bean("redis_windows_cache_factory") - cache = await factory.create_cache_manager( - expire_minutes=10, cleanup_probability=0.0 - ) # Disable random cleanup - - test_key = "test_windows_timestamp_range" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Add test data with explicit timestamps - logger.info("Adding test data with explicit timestamps...") - base_timestamp = int(time.time() * 1000) - test_data = [] - - for i in range(8): - timestamp = base_timestamp + i * 15000 # Each data item spaced 15 seconds apart - data = {"index": i, "content": f"windows_data_{i}", "created_at": timestamp} - test_data.append({"data": data, "timestamp": timestamp}) - - success = await cache.append( - test_key, data - ) # Let system assign timestamp automatically - assert success, f"Failed to add data {i+1}" - - # Slight delay to ensure timestamp differences - await asyncio.sleep(0.1) - - logger.info("Added %d data items", len(test_data)) - - # 3. Test getting all data (no time range limit) - all_data = await cache.get_by_timestamp_range(test_key) - assert ( - len(all_data) == 8 - ), f"Failed to get all data, expected 8, actual {len(all_data)}" - logger.info("Successfully retrieved all data: %d items", len(all_data)) - - # 4. Test filtering by start time - # Get data from last 5 minutes - five_minutes_ago = int(time.time() * 1000) - 5 * 60 * 1000 - recent_data = await cache.get_by_timestamp_range( - test_key, start_timestamp=five_minutes_ago - ) - assert ( - len(recent_data) == 8 - ), f"Failed to get last 5 minutes data, expected 8, actual {len(recent_data)}" - logger.info("Start time filtering test passed") - - # 5. Test filtering by end time - # Get data from 1 minute ago - one_minute_ago = int(time.time() * 1000) - 60 * 1000 - old_data = await cache.get_by_timestamp_range( - test_key, end_timestamp=one_minute_ago - ) - logger.info("Retrieved %d items by end time filtering", len(old_data)) - - # 6. Test time range filtering - # Get data between 3 minutes ago and 1 minute ago - three_minutes_ago = int(time.time() * 1000) - 3 * 60 * 1000 - range_data = await cache.get_by_timestamp_range( - test_key, start_timestamp=three_minutes_ago, end_timestamp=one_minute_ago - ) - logger.info("Retrieved %d items by time range filtering", len(range_data)) - - # 7. Test limiting number of results - limited_data = await cache.get_by_timestamp_range(test_key, limit=3) - assert ( - len(limited_data) == 3 - ), f"Limiting failed, expected 3, actual {len(limited_data)}" - logger.info("Limit count test passed") - - # 8. Test using datetime objects as timestamps - dt_start = get_now_with_timezone() - timedelta(minutes=10) - dt_end = get_now_with_timezone() - dt_data = await cache.get_by_timestamp_range( - test_key, start_timestamp=dt_start, end_timestamp=dt_end - ) - assert len(dt_data) >= 0, "Filtering with datetime objects failed" - logger.info( - "Datetime object filtering test passed, retrieved %d items", len(dt_data) - ) - - # 9. Verify data format - if len(all_data) > 0: - sample_item = all_data[0] - assert isinstance(sample_item["data"], dict), "Data format error" - assert "timestamp" in sample_item, "Missing timestamp field" - assert "datetime" in sample_item, "Missing formatted time field" - logger.info("Data format verification passed") - - # 10. Clean up test data - success = await cache.clear_queue(test_key) - assert success, "Failed to clean up test data" - - logger.info("✅ Get data by timestamp range test passed") - - -async def test_json_pickle_mixed_data(): - """Test JSON and Pickle mixed data processing""" - logger.info("Starting test for JSON and Pickle mixed data processing...") - - factory = get_bean("redis_windows_cache_factory") - cache = await factory.create_cache_manager( - expire_minutes=10, cleanup_probability=0.0 - ) - - test_key = "test_windows_mixed_serialization" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Prepare mixed test data - test_data = [] - - # JSON serializable data - json_data = [ - {"type": "json", "name": "windows_dict", "timestamp": time.time()}, - ["windows_list", "json", "data", 789], - "windows_json_string", - {"nested": {"windows": {"data": True}}}, - ] - - # Pickle serialized data (cannot be JSON serialized) - pickle_data = [ - NonSerializableTestClass("windows1", 150), - NonSerializableTestClass("windows2", 250, 4), - { - "windows_set": {10, 20, 30}, - "windows_bytes": b"windows_binary", - "type": "windows_complex", - }, - ] - - # 3. Alternately add JSON and Pickle data - logger.info("Alternately adding JSON and Pickle data...") - all_test_data = [] - - # Alternate data arrangement - max_len = max(len(json_data), len(pickle_data)) - for i in range(max_len): - if i < len(json_data): - all_test_data.append(("json", json_data[i])) - if i < len(pickle_data): - all_test_data.append(("pickle", pickle_data[i])) - - # Add all data - for data_type, data in all_test_data: - success = await cache.append(test_key, data) - assert success, f"Failed to add {data_type} data: {data}" - test_data.append({"type": data_type, "data": data}) - logger.debug("Successfully added %s data: %s", data_type, str(data)[:50]) - - # Slight delay to ensure different timestamps - await asyncio.sleep(0.01) - - # 4. Verify total data count - total_count = len(all_test_data) - size = await cache.get_queue_size(test_key) - assert ( - size == total_count - ), f"Data count mismatch: expected {total_count}, actual {size}" - logger.info( - "Data addition completed, total: %d items (JSON: %d, Pickle: %d)", - total_count, - len(json_data), - len(pickle_data), - ) - - # 5. Retrieve and verify all data - all_data = await cache.get_by_timestamp_range(test_key) - assert ( - len(all_data) == total_count - ), f"Retrieved data count mismatch: expected {total_count}, actual {len(all_data)}" - - # 6. Verify correctness of each data type - json_count = 0 - pickle_count = 0 - - logger.info("Starting to verify %d items for type and content", len(all_data)) - for i, item in enumerate(all_data): - retrieved_data = item["data"] - logger.debug( - "Processing item %d: %s (%s)", - i + 1, - type(retrieved_data), - str(retrieved_data)[:100], - ) - - # First check special Pickle data (dictionary containing set and bytes) - if isinstance(retrieved_data, dict) and "windows_set" in retrieved_data: - pickle_count += 1 - # Verify dictionary with set and bytes - assert isinstance(retrieved_data["windows_set"], set), "Set data type error" - assert isinstance( - retrieved_data["windows_bytes"], bytes - ), "Bytes data type error" - logger.debug( - "Successfully verified dictionary with complex data: %s", - retrieved_data.get("type", "unknown"), - ) - - # Check Pickle data - elif isinstance(retrieved_data, NonSerializableTestClass): - pickle_count += 1 - # Verify Pickle object functionality - doubled = retrieved_data.get_doubled_value() - expected = retrieved_data.value * retrieved_data.multiplier - assert ( - doubled == expected - ), f"Pickle object function error: {doubled} != {expected}" - - # Verify complex data - assert ( - "set_data" in retrieved_data.complex_data - ), "Pickle object missing set data" - assert ( - "bytes_data" in retrieved_data.complex_data - ), "Pickle object missing bytes data" - - logger.debug( - "Successfully verified Pickle object: %s, function test: %d * %d = %d", - retrieved_data, - retrieved_data.value, - retrieved_data.multiplier, - doubled, - ) - - # Check JSON data - elif isinstance( - retrieved_data, (dict, list, str, int, float, bool) - ) and not isinstance(retrieved_data, NonSerializableTestClass): - # Verify if it's one of our added JSON data - found = False - for original in json_data: - if str(retrieved_data) == str(original): - found = True - break - - if found or isinstance(retrieved_data, (str, int, float, bool)): - json_count += 1 - logger.debug( - "Successfully verified JSON data: %s", str(retrieved_data)[:50] - ) - - else: - logger.warning( - "Unrecognized data type: %s - %s", - type(retrieved_data), - str(retrieved_data)[:100], - ) - # Specifically check if it's a dictionary with complex data - if isinstance(retrieved_data, dict): - logger.warning("Dictionary keys: %s", retrieved_data.keys()) - for key, value in retrieved_data.items(): - logger.warning(" %s: %s (%s)", key, value, type(value)) - - # 7. Verify data type distribution - assert json_count == len( - json_data - ), f"JSON data count mismatch: expected {len(json_data)}, actual {json_count}" - assert pickle_count == len( - pickle_data - ), f"Pickle data count mismatch: expected {len(pickle_data)}, actual {pickle_count}" - - logger.info( - "Data type verification completed: JSON data %d items, Pickle data %d items", - json_count, - pickle_count, - ) - - # 8. Test timestamp range query support for mixed data - # Get data from last 5 minutes - five_minutes_ago = int(time.time() * 1000) - 5 * 60 * 1000 - recent_data = await cache.get_by_timestamp_range( - test_key, start_timestamp=five_minutes_ago - ) - assert ( - len(recent_data) == total_count - ), "Timestamp range query support for mixed data failed" - - # Get first half of data - if len(all_data) > 2: - mid_timestamp = all_data[len(all_data) // 2]["timestamp"] - first_half = await cache.get_by_timestamp_range( - test_key, end_timestamp=mid_timestamp - ) - assert len(first_half) > 0, "Timestamp range query first half data failed" - - second_half = await cache.get_by_timestamp_range( - test_key, start_timestamp=mid_timestamp - ) - assert len(second_half) > 0, "Timestamp range query second half data failed" - - logger.info( - "Timestamp range query mixed data test passed: first half %d items, second half %d items", - len(first_half), - len(second_half), - ) - - # 9. Clean up test data - success = await cache.clear_queue(test_key) - assert success, "Failed to clean up test data" - - logger.info("✅ JSON and Pickle mixed data processing test passed") - - -async def test_pickle_performance(): - """Test Pickle serialization performance""" - logger.info("Starting Pickle serialization performance test...") - - factory = get_bean("redis_windows_cache_factory") - cache = await factory.create_cache_manager(expire_minutes=5) - - test_key = "test_windows_pickle_performance" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Prepare performance test data - batch_size = 50 - json_data = [ - {"index": i, "type": "json", "data": f"json_item_{i}"} - for i in range(batch_size) - ] - pickle_data = [ - NonSerializableTestClass(f"perf_{i}", i * 10) for i in range(batch_size) - ] - - # 3. Test JSON data performance - logger.info("Testing JSON data write performance...") - json_start = time.time() - for data in json_data: - success = await cache.append(test_key, data) - assert success, f"JSON data write failed: {data}" - json_write_time = time.time() - json_start - - # 4. Test Pickle data performance - logger.info("Testing Pickle data write performance...") - pickle_start = time.time() - for data in pickle_data: - success = await cache.append(test_key, data) - assert success, f"Pickle data write failed: {data}" - pickle_write_time = time.time() - pickle_start - - # 5. Test read performance - logger.info("Testing mixed data read performance...") - read_start = time.time() - all_data = await cache.get_by_timestamp_range(test_key) - read_time = time.time() - read_start - - # 6. Verify data correctness - assert ( - len(all_data) == batch_size * 2 - ), f"Data count error: expected {batch_size * 2}, actual {len(all_data)}" - - # Count data types - json_retrieved = sum( - 1 - for item in all_data - if isinstance(item["data"], dict) - and "type" in item["data"] - and item["data"]["type"] == "json" - ) - pickle_retrieved = sum( - 1 for item in all_data if isinstance(item["data"], NonSerializableTestClass) - ) - - assert ( - json_retrieved == batch_size - ), f"JSON data read count error: expected {batch_size}, actual {json_retrieved}" - assert ( - pickle_retrieved == batch_size - ), f"Pickle data read count error: expected {batch_size}, actual {pickle_retrieved}" - - # 7. Output performance results - logger.info("Performance test results:") - logger.info( - " JSON write %d items: %.3f seconds (average %.3f ms/item)", - batch_size, - json_write_time, - json_write_time * 1000 / batch_size, - ) - logger.info( - " Pickle write %d items: %.3f seconds (average %.3f ms/item)", - batch_size, - pickle_write_time, - pickle_write_time * 1000 / batch_size, - ) - logger.info( - " Mixed read %d items: %.3f seconds (average %.3f ms/item)", - len(all_data), - read_time, - read_time * 1000 / len(all_data), - ) - - # 8. Performance reasonableness check - assert json_write_time < 10.0, "JSON write performance too slow" - assert pickle_write_time < 15.0, "Pickle write performance too slow" - assert read_time < 5.0, "Read performance too slow" - - # 9. Clean up test data - success = await cache.clear_queue(test_key) - assert success, "Failed to clean up test data" - - logger.info("✅ Pickle serialization performance test passed") - - -async def test_stress_operations(): - """Test stress operations: large amount of data processing""" - logger.info("Starting stress test...") - - factory = get_bean("redis_windows_cache_factory") - cache = await factory.create_cache_manager(expire_minutes=5) - - test_key = "test_windows_cache_stress" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Batch append data - batch_size = 1000 - logger.info("Appending %d items...", batch_size) - - start_time = time.time() - for i in range(batch_size): - data = {"index": i, "timestamp": time.time(), "data": f"test_data_{i}"} - await cache.append(test_key, data) - - elapsed = time.time() - start_time - logger.info("Data append completed, elapsed time: %.2f seconds", elapsed) - - # 3. Verify data count - size = await cache.get_queue_size(test_key) - assert ( - size == batch_size - ), f"Data count mismatch: expected {batch_size}, actual {size}" - - # 4. Test performance of getting large amount of data - start_time = time.time() - recent_data = await cache.get_by_timestamp_range(test_key) - elapsed = time.time() - start_time - - assert len(recent_data) == batch_size, "Retrieved data count mismatch" - logger.info( - "Retrieved %d items, elapsed time: %.2f seconds", len(recent_data), elapsed - ) - - # 5. Clean up test data - success = await cache.clear_queue(test_key) - assert success, "Failed to clean up test data" - - logger.info("✅ Stress test passed") - - -async def test_auto_cleanup(): - """Test automatic cleanup mechanism""" - logger.info("Starting automatic cleanup test...") - - # Get cache manager factory from DI container - factory = get_bean("redis_windows_cache_factory") - cache = await factory.create_cache_manager(expire_minutes=1) # 1 minute expiration - - test_key = "test_windows_cache_auto_cleanup" - - # 1. Clear test queue - await cache.clear_queue(test_key) - - # 2. Add first message - first_msg = {"id": 1, "content": "first message"} - success = await cache.append(test_key, first_msg) - assert success, "Failed to add first message" - logger.info("Successfully added first message") - - # 3. Wait 40 seconds - logger.info("Waiting 40 seconds...") - await asyncio.sleep(40) - - # 4. Add second message - second_msg = {"id": 2, "content": "second message"} - success = await cache.append(test_key, second_msg) - assert success, "Failed to add second message" - logger.info("Successfully added second message") - - # 5. Wait 25 seconds before cleanup (first message now 55 seconds old, second message 15 seconds old) - logger.info("Waiting 25 seconds before cleanup...") - await asyncio.sleep(25) - - # 6. Manually trigger cleanup and verify result - cleaned_count = await cache.cleanup_expired(test_key) - logger.info("Cleanup completed, cleaned count: %d", cleaned_count) - - # 7. Get current data - current_data = await cache.get_by_timestamp_range(test_key) - logger.info("Remaining data count after cleanup: %d", len(current_data)) - - # Note: Due to changes in cleanup logic, these assertions may need adjustment - # We mainly verify that cleanup function works properly - if len(current_data) > 0: - logger.info("Remaining data: %s", [item["data"] for item in current_data]) - - # Verify cleanup function executed at least - assert cleaned_count >= 0, "Cleanup function execution error" - - logger.info("✅ Automatic cleanup test passed") - - -async def test_compatibility_layer(): - """Test backward compatibility layer""" - logger.info("Starting backward compatibility layer test...") - - # Get default manager instance - default_manager = get_bean("redis_windows_cache_manager") - test_key = "test_windows_cache_compat" - - # 1. Clear test queue - await default_manager.clear_queue(test_key) - - # 2. Test basic operations - test_data = {"test": "compatibility"} - success = await default_manager.append(test_key, test_data) - assert success, "Backward compatibility layer append data failed" - - # Use new get by timestamp range method - recent_data = await default_manager.get_by_timestamp_range(test_key) - assert len(recent_data) == 1, "Backward compatibility layer get data failed" - - stats = await default_manager.get_queue_stats(test_key) - assert stats["total_count"] == 1, "Backward compatibility layer statistics error" - - # 3. Test new timestamp range query method - # Add some test data - for i in range(2): - data = {"index": i, "content": f"compat_data_{i}"} - success = await default_manager.append(test_key, data) - assert success, f"Backward compatibility layer add test data {i} failed" - - # Test get by timestamp range - range_data = await default_manager.get_by_timestamp_range(test_key) - assert ( - len(range_data) == 3 - ), f"Backward compatibility layer timestamp range query failed, expected 3, actual {len(range_data)}" # 1 original data + 2 new data - - # Test limit count - limited_data = await default_manager.get_by_timestamp_range(test_key, limit=2) - assert ( - len(limited_data) == 2 - ), "Backward compatibility layer limit count function failed" - - logger.info("Backward compatibility layer new method test passed") - - # 4. Clean up test data - success = await default_manager.clear_queue(test_key) - assert success, "Backward compatibility layer clean up data failed" - - logger.info("✅ Backward compatibility layer test passed") - - -async def main(): - """Main test function""" - logger.info("=" * 50) - logger.info("Redis Time Window Cache Manager Test Started") - logger.info("=" * 50) - - try: - # Run all tests - await test_json_pickle_mixed_data() - await test_basic_operations() - await test_timestamp_range_query() - await test_pickle_performance() - await test_stress_operations() - await test_auto_cleanup() - await test_compatibility_layer() - - logger.info("=" * 50) - logger.info("✅ All tests passed") - logger.info("=" * 50) - - except AssertionError as e: - logger.error("❌ Test failed: %s", str(e)) - raise - except Exception as e: - logger.error("❌ Test error: %s", str(e)) - raise - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/tests/test_request_id_e2e.py b/methods/EverCore/tests/test_request_id_e2e.py deleted file mode 100644 index af2ac1ce..00000000 --- a/methods/EverCore/tests/test_request_id_e2e.py +++ /dev/null @@ -1,142 +0,0 @@ -"""End-to-end integration test for request_id auto-injection in logs. - -Verifies the complete chain: - Request (X-Request-Id header) - -> AppLogicMiddleware (extracts request_id, sets ContextVar) - -> RequestIdFilter (reads ContextVar, injects into LogRecord) - -> Log output contains [request_id] - -Does NOT require external services (MongoDB, ES, Milvus, Redis). -""" - -import io -import logging -import re -from unittest.mock import patch - -from fastapi import FastAPI -from starlette.testclient import TestClient - -from core.observation.logger import RequestIdFilter, get_logger -from core.middleware.app_logic_middleware import AppLogicMiddleware -from core.request.app_logic_provider import AppLogicProviderImpl - - -def _create_test_app() -> FastAPI: - """Create a minimal FastAPI app with AppLogicMiddleware. - - Patches DI resolution so no container setup is needed. - """ - app = FastAPI() - - with patch( - "core.middleware.app_logic_middleware.get_bean_by_type", - return_value=AppLogicProviderImpl(), - ): - app.add_middleware(AppLogicMiddleware) - - test_logger = get_logger("test.e2e.request_id") - - @app.get("/test-log") - async def test_log_endpoint(): - test_logger.info("Integration test log message") - return {"status": "ok"} - - return app - - -class TestRequestIdE2EIntegration: - """End-to-end: request -> middleware -> ContextVar -> Filter -> log output.""" - - def setup_method(self): - """Attach a capturing handler to root logger.""" - self.stream = io.StringIO() - self.handler = logging.StreamHandler(self.stream) - self.handler.setFormatter( - logging.Formatter( - "%(levelname)s - [%(request_id)s] - %(name)s - %(message)s" - ) - ) - self.handler.addFilter(RequestIdFilter()) - logging.root.addHandler(self.handler) - - def teardown_method(self): - """Remove the capturing handler.""" - logging.root.removeHandler(self.handler) - - def test_custom_request_id_from_header_appears_in_log(self): - """X-Request-Id header value should appear in log output.""" - app = _create_test_app() - client = TestClient(app, raise_server_exceptions=False) - - response = client.get("/test-log", headers={"X-Request-Id": "e2e-test-12345"}) - - assert response.status_code == 200 - log_output = self.stream.getvalue() - assert ( - "[e2e-test-12345]" in log_output - ), f"Expected [e2e-test-12345] in log output, got:\n{log_output}" - assert "Integration test log message" in log_output - - def test_auto_generated_uuid_when_no_header(self): - """Without X-Request-Id header, a UUID should be auto-generated (not '-').""" - app = _create_test_app() - client = TestClient(app, raise_server_exceptions=False) - - response = client.get("/test-log") - - assert response.status_code == 200 - log_output = self.stream.getvalue() - # Should NOT be "-" (that's for no-context scenarios like startup) - assert ( - "[-]" not in log_output - or "Integration test log message" not in log_output.split("[-]")[-1] - ), f"Expected auto-generated UUID, not '-', in log output:\n{log_output}" - # Should contain a UUID pattern - match = re.search( - r"\[([a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{12})\]", - log_output, - ) - assert match is not None, f"Expected UUID in log output, got:\n{log_output}" - - def test_lowercase_x_request_id_header(self): - """x-request-id (lowercase) should also be extracted correctly.""" - app = _create_test_app() - client = TestClient(app, raise_server_exceptions=False) - - response = client.get( - "/test-log", headers={"x-request-id": "lowercase-header-test"} - ) - - assert response.status_code == 200 - log_output = self.stream.getvalue() - assert ( - "[lowercase-header-test]" in log_output - ), f"Expected [lowercase-header-test] in log output, got:\n{log_output}" - - def test_multiple_requests_have_isolated_request_ids(self): - """Consecutive requests should each get their own request_id in logs.""" - app = _create_test_app() - client = TestClient(app, raise_server_exceptions=False) - - client.get("/test-log", headers={"X-Request-Id": "req-AAA"}) - client.get("/test-log", headers={"X-Request-Id": "req-BBB"}) - - log_output = self.stream.getvalue() - lines = log_output.strip().split("\n") - - # Find lines containing our test messages - test_lines = [l for l in lines if "Integration test log message" in l] - assert ( - len(test_lines) >= 2 - ), f"Expected >= 2 test log lines, got {len(test_lines)}" - - assert ( - "[req-AAA]" in test_lines[0] - ), f"First request should have req-AAA: {test_lines[0]}" - assert ( - "[req-BBB]" in test_lines[1] - ), f"Second request should have req-BBB: {test_lines[1]}" - # Verify no cross-contamination - assert "[req-BBB]" not in test_lines[0] - assert "[req-AAA]" not in test_lines[1] diff --git a/methods/EverCore/tests/test_request_id_filter.py b/methods/EverCore/tests/test_request_id_filter.py deleted file mode 100644 index 2b671b02..00000000 --- a/methods/EverCore/tests/test_request_id_filter.py +++ /dev/null @@ -1,140 +0,0 @@ -"""Unit tests for RequestIdFilter. - -Tests cover: -- request_id injected from app_info_context -- Fallback to "-" when no context set -- Fallback to "-" when app_info has no request_id key -- Filter always returns True (never suppresses records) -- Log format includes [request_id] section -- End-to-end log output verification -""" - -import io -import logging -from unittest.mock import patch - -from core.observation.logger import RequestIdFilter - - -class TestRequestIdFilter: - """Test RequestIdFilter injects request_id into LogRecord.""" - - def _make_record(self) -> logging.LogRecord: - return logging.LogRecord( - name="test", - level=logging.INFO, - pathname="test.py", - lineno=1, - msg="test message", - args=(), - exc_info=None, - ) - - def test_injects_request_id_from_context(self): - """When app_info has request_id, it should be injected.""" - f = RequestIdFilter() - record = self._make_record() - with patch( - "core.observation.logger.get_current_app_info", - return_value={"request_id": "abc-123"}, - ): - result = f.filter(record) - assert result is True - assert record.request_id == "abc-123" - - def test_fallback_when_no_context(self): - """When app_info is None, request_id should be '-'.""" - f = RequestIdFilter() - record = self._make_record() - with patch("core.observation.logger.get_current_app_info", return_value=None): - f.filter(record) - assert record.request_id == "-" - - def test_fallback_when_no_request_id_key(self): - """When app_info exists but has no request_id, should be '-'.""" - f = RequestIdFilter() - record = self._make_record() - with patch( - "core.observation.logger.get_current_app_info", - return_value={"other_key": "value"}, - ): - f.filter(record) - assert record.request_id == "-" - - def test_always_returns_true(self): - """Filter should never suppress log records.""" - f = RequestIdFilter() - record = self._make_record() - with patch("core.observation.logger.get_current_app_info", return_value=None): - assert f.filter(record) is True - - -class TestLogFormatIntegration: - """Test that log format and filter are properly registered.""" - - def test_root_handler_has_request_id_in_format(self): - """Root handler format should contain %(request_id)s.""" - has_format = any( - h.formatter and "%(request_id)s" in h.formatter._fmt - for h in logging.root.handlers - ) - assert has_format, "No root handler has %(request_id)s in format" - - def test_root_handler_has_request_id_filter(self): - """At least one root handler should have RequestIdFilter attached.""" - has_filter = any( - RequestIdFilter in [type(f) for f in h.filters] - for h in logging.root.handlers - ) - assert has_filter, "No root handler has RequestIdFilter attached" - - def test_end_to_end_log_output_with_request_id(self): - """Log output should contain [request_id] when context is set.""" - stream = io.StringIO() - handler = logging.StreamHandler(stream) - handler.setFormatter( - logging.Formatter("%(levelname)s - [%(request_id)s] - %(message)s") - ) - handler.addFilter(RequestIdFilter()) - - test_logger = logging.getLogger("test.e2e") - test_logger.addHandler(handler) - test_logger.setLevel(logging.INFO) - - try: - with patch( - "core.observation.logger.get_current_app_info", - return_value={"request_id": "e2e-test-id"}, - ): - test_logger.info("hello") - - output = stream.getvalue() - assert "[e2e-test-id]" in output - assert "hello" in output - finally: - test_logger.removeHandler(handler) - - def test_end_to_end_log_output_without_context(self): - """Log output should contain [-] when no context is set.""" - stream = io.StringIO() - handler = logging.StreamHandler(stream) - handler.setFormatter( - logging.Formatter("%(levelname)s - [%(request_id)s] - %(message)s") - ) - handler.addFilter(RequestIdFilter()) - - test_logger = logging.getLogger("test.e2e.no_ctx") - test_logger.addHandler(handler) - test_logger.setLevel(logging.INFO) - - try: - with patch( - "core.observation.logger.get_current_app_info", return_value=None - ): - test_logger.info("startup") - - output = stream.getvalue() - assert "[-]" in output - assert "startup" in output - finally: - test_logger.removeHandler(handler) diff --git a/methods/EverCore/tests/test_rerank_extract_text.py b/methods/EverCore/tests/test_rerank_extract_text.py deleted file mode 100644 index e299d4dd..00000000 --- a/methods/EverCore/tests/test_rerank_extract_text.py +++ /dev/null @@ -1,672 +0,0 @@ -"""Unit tests for extract_text_from_hit - -Usage: - PYTHONPATH=src pytest tests/test_rerank_extract_text.py -v -""" - -import time -import pytest -from unittest.mock import patch, MagicMock, AsyncMock -from agentic_layer.rerank_interface import extract_text_from_hit - - -class TestExtractTextFromHit: - """Test extract_text_from_hit with various memory types""" - - def test_episodic_memory(self): - hit = { - "memory_type": "episodic_memory", - "_source": {"episode": "User likes coffee"}, - } - assert extract_text_from_hit(hit) == "Episode Memory: User likes coffee" - - def test_foresight_with_evidence(self): - hit = { - "memory_type": "foresight", - "_source": { - "foresight": "Will need more storage", - "evidence": "Usage growing 10% monthly", - }, - } - result = extract_text_from_hit(hit) - assert ( - result - == "Foresight: Will need more storage (Evidence: Usage growing 10% monthly)" - ) - - def test_foresight_without_evidence(self): - hit = { - "memory_type": "foresight", - "_source": {"foresight": "Will need more storage"}, - } - assert extract_text_from_hit(hit) == "Foresight: Will need more storage" - - def test_atomic_fact(self): - hit = { - "memory_type": "atomic_fact", - "_source": {"atomic_fact": "User is 30 years old"}, - } - assert extract_text_from_hit(hit) == "Atomic Fact: User is 30 years old" - - def test_fallback_episode_no_type(self): - hit = {"memory_type": "", "_source": {"episode": "Some episode"}} - assert extract_text_from_hit(hit) == "Some episode" - - def test_fallback_content(self): - hit = {"memory_type": "", "_source": {"content": "Some content"}} - assert extract_text_from_hit(hit) == "Some content" - - def test_fallback_to_str(self): - hit = {"memory_type": "", "_source": {}} - result = extract_text_from_hit(hit) - assert isinstance(result, str) - assert len(result) > 0 - - def test_no_source_key_uses_hit_directly(self): - hit = {"memory_type": "", "episode": "Direct episode"} - assert extract_text_from_hit(hit) == "Direct episode" - - def test_foresight_content_fallback(self): - hit = { - "memory_type": "foresight", - "_source": {"content": "Foresight via content field"}, - } - assert extract_text_from_hit(hit) == "Foresight: Foresight via content field" - - def test_episodic_memory_empty_episode_falls_to_fallback(self): - hit = { - "memory_type": "episodic_memory", - "_source": {"episode": "", "content": "fallback content"}, - } - assert extract_text_from_hit(hit) == "fallback content" - - def test_foresight_both_empty_falls_to_generic_summary(self): - """foresight type with both foresight and content empty falls through to generic fallback (summary).""" - hit = { - "memory_type": "foresight", - "_source": {"foresight": "", "content": "", "summary": "via summary"}, - } - assert extract_text_from_hit(hit) == "via summary" - - def test_atomic_fact_empty_falls_to_generic_subject(self): - """atomic_fact type with empty atomic_fact falls through to generic fallback (subject).""" - hit = { - "memory_type": "atomic_fact", - "_source": {"atomic_fact": "", "subject": "via subject"}, - } - assert extract_text_from_hit(hit) == "via subject" - - def test_no_type_has_atomic_fact(self): - """No type with atomic_fact field uses the generic atomic_fact fallback.""" - hit = {"memory_type": "", "_source": {"atomic_fact": "Some fact"}} - assert extract_text_from_hit(hit) == "Some fact" - - def test_no_type_has_foresight(self): - """No type with foresight field uses the generic foresight fallback.""" - hit = {"memory_type": "", "_source": {"foresight": "Some foresight"}} - assert extract_text_from_hit(hit) == "Some foresight" - - def test_no_type_has_summary(self): - """No type with summary field uses the generic summary fallback.""" - hit = {"memory_type": "", "_source": {"summary": "Some summary"}} - assert extract_text_from_hit(hit) == "Some summary" - - def test_no_type_has_subject(self): - """No type with subject field uses the generic subject fallback.""" - hit = {"memory_type": "", "_source": {"subject": "Some subject"}} - assert extract_text_from_hit(hit) == "Some subject" - - -class TestRerankInputLogging: - """Test rerank input logging in HybridRerankService""" - - @pytest.fixture - def mock_tokenizer_factory(self): - factory = MagicMock() - tokenizer = MagicMock() - tokenizer.encode.return_value = [1] * 100 # 100 tokens - factory.get_tokenizer_from_tiktoken.return_value = tokenizer - return factory - - @pytest.fixture - def hybrid_service(self): - with patch( - "agentic_layer.rerank_service._create_service_from_config" - ) as mock_create: - mock_primary = AsyncMock() - mock_primary.rerank_documents = AsyncMock( - return_value={ - "results": [ - {"index": 0, "score": 0.9, "rank": 0}, - {"index": 1, "score": 0.5, "rank": 1}, - ] - } - ) - mock_create.return_value = mock_primary - - from agentic_layer.rerank_service import ( - HybridRerankService, - HybridRerankConfig, - ) - - config = HybridRerankConfig() - config.enable_fallback = False - service = HybridRerankService(config) - yield service - - @pytest.mark.asyncio - async def test_logs_doc_count_and_tokens( - self, hybrid_service, mock_tokenizer_factory - ): - hits = [ - { - "memory_type": "episodic_memory", - "_source": {"episode": "User likes coffee"}, - }, - {"memory_type": "atomic_fact", "_source": {"atomic_fact": "User is 30"}}, - ] - - with ( - patch( - "agentic_layer.rerank_service.get_bean_by_type", - return_value=mock_tokenizer_factory, - ), - patch("agentic_layer.rerank_service.logger") as mock_logger, - ): - await hybrid_service.rerank_memories("coffee", hits) - - mock_logger.info.assert_any_call("Rerank input: %d docs, %d tokens", 2, 100) - - @pytest.mark.asyncio - async def test_logs_fallback_on_tokenizer_error(self, hybrid_service): - hits = [ - { - "memory_type": "episodic_memory", - "_source": {"episode": "User likes coffee"}, - } - ] - - with ( - patch( - "agentic_layer.rerank_service.get_bean_by_type", - side_effect=Exception("no tokenizer"), - ), - patch("agentic_layer.rerank_service.logger") as mock_logger, - ): - await hybrid_service.rerank_memories("coffee", hits) - - mock_logger.info.assert_any_call( - "Rerank input: %d docs (token count unavailable)", 1 - ) - mock_logger.debug.assert_any_call("Token count failed", exc_info=True) - - @pytest.mark.asyncio - async def test_error_path_records_error_metric_and_reraises(self, hybrid_service): - """When execute_with_fallback raises, record_rerank_request is called with status='error'.""" - hits = [{"memory_type": "episodic_memory", "_source": {"episode": "A hit"}}] - - with ( - patch( - "agentic_layer.rerank_service.get_bean_by_type", - side_effect=Exception("no tokenizer"), - ), - patch.object( - hybrid_service.primary_service, - "rerank_memories", - new_callable=AsyncMock, - side_effect=RuntimeError("rerank failed"), - ), - patch("agentic_layer.rerank_service.record_rerank_request") as mock_record, - ): - with pytest.raises(Exception): - await hybrid_service.rerank_memories("query", hits) - - # Verify error metric recorded - mock_record.assert_called_once() - call_kwargs = mock_record.call_args.kwargs - assert call_kwargs["status"] == "error" - - -class TestCreateServiceFromConfig: - """Tests for _create_service_from_config factory function (L154-177)""" - - def test_creates_vllm_service(self): - from agentic_layer.rerank_service import _create_service_from_config - from agentic_layer.rerank_vllm import VllmRerankService - - service = _create_service_from_config( - "vllm", "key", "http://localhost", "model", 3, 2, 10, 5 - ) - assert isinstance(service, VllmRerankService) - - def test_creates_deepinfra_service(self): - from agentic_layer.rerank_service import _create_service_from_config - from agentic_layer.rerank_deepinfra import DeepInfraRerankService - - service = _create_service_from_config( - "deepinfra", "key", "http://localhost", "model", 3, 2, 10, 5 - ) - assert isinstance(service, DeepInfraRerankService) - - def test_raises_for_unsupported_provider(self): - from agentic_layer.rerank_service import _create_service_from_config - from agentic_layer.rerank_interface import RerankError - - with pytest.raises(RerankError, match="Unsupported provider"): - _create_service_from_config( - "unknown", "key", "http://localhost", "model", 3, 2, 10, 5 - ) - - -class TestHybridRerankServiceInit: - """Tests for HybridRerankService.__init__ (L203, L222)""" - - def test_init_with_default_config(self): - """L203: config=None causes HybridRerankConfig() to be created internally""" - with patch( - "agentic_layer.rerank_service._create_service_from_config" - ) as mock_create: - mock_create.return_value = MagicMock() - from agentic_layer.rerank_service import HybridRerankService - - service = HybridRerankService(config=None) - assert service.config is not None - - def test_init_with_fallback_enabled(self): - """L222: fallback service is created when enable_fallback is True""" - with patch( - "agentic_layer.rerank_service._create_service_from_config" - ) as mock_create: - mock_primary = MagicMock() - mock_fallback = MagicMock() - mock_create.side_effect = [mock_primary, mock_fallback] - - from agentic_layer.rerank_service import ( - HybridRerankService, - HybridRerankConfig, - ) - - config = HybridRerankConfig.__new__(HybridRerankConfig) - # Manually set all fields, bypassing __post_init__ env reads - config.primary_provider = "vllm" - config.fallback_provider = "deepinfra" - config.primary_api_key = "" - config.primary_base_url = "http://primary" - config.fallback_api_key = "key" - config.fallback_base_url = "http://fallback" - config.model = "test-model" - config.timeout = 3 - config.max_retries = 2 - config.batch_size = 10 - config.max_concurrent_requests = 5 - config.enable_fallback = True - config.max_primary_failures = 3 - config.failure_reset_interval = 300 - config._primary_failure_count = 0 - config._last_failure_time = 0.0 - - service = HybridRerankService(config) - assert service.fallback_service is not None - assert mock_create.call_count == 2 - - -class TestHybridRerankServiceMethods: - """Tests for get_service (L250), get_model_name (L351), rerank_documents (L367-368)""" - - @pytest.fixture - def service_with_mock(self): - with patch( - "agentic_layer.rerank_service._create_service_from_config" - ) as mock_create: - mock_primary = AsyncMock() - mock_primary.get_model_name = MagicMock(return_value="test-model") - mock_primary.close = AsyncMock() - mock_create.return_value = mock_primary - from agentic_layer.rerank_service import ( - HybridRerankService, - HybridRerankConfig, - ) - - config = HybridRerankConfig.__new__(HybridRerankConfig) - config.primary_provider = "vllm" - config.fallback_provider = "deepinfra" - config.primary_api_key = "" - config.primary_base_url = "" - config.fallback_api_key = "" - config.fallback_base_url = "" - config.model = "test-model" - config.timeout = 3 - config.max_retries = 2 - config.batch_size = 10 - config.max_concurrent_requests = 5 - config.enable_fallback = False - config.max_primary_failures = 3 - config.failure_reset_interval = 300 - config._primary_failure_count = 0 - config._last_failure_time = 0.0 - yield HybridRerankService(config) - - def test_get_service(self, service_with_mock): - result = service_with_mock.get_service() - assert result == service_with_mock.primary_service - - def test_get_model_name(self, service_with_mock): - assert service_with_mock.get_model_name() == "test-model" - - @pytest.mark.asyncio - async def test_rerank_documents(self, service_with_mock): - """L367-368: rerank_documents delegates via execute_with_fallback""" - service_with_mock.primary_service.rerank_documents = AsyncMock( - return_value={"results": [{"index": 0, "score": 0.9, "rank": 0}]} - ) - result = await service_with_mock.rerank_documents("query", ["doc1"]) - assert "results" in result - - -class TestExecuteWithFallback: - """Tests for execute_with_fallback (L406-512)""" - - @pytest.fixture - def service_with_fallback(self): - with patch( - "agentic_layer.rerank_service._create_service_from_config" - ) as mock_create: - mock_primary = AsyncMock() - mock_fallback = AsyncMock() - mock_create.side_effect = [mock_primary, mock_fallback] - from agentic_layer.rerank_service import ( - HybridRerankService, - HybridRerankConfig, - ) - - config = HybridRerankConfig.__new__(HybridRerankConfig) - config.primary_provider = "vllm" - config.fallback_provider = "deepinfra" - config.primary_api_key = "" - config.primary_base_url = "http://primary" - config.fallback_api_key = "key" - config.fallback_base_url = "http://fallback" - config.model = "test-model" - config.timeout = 3 - config.max_retries = 2 - config.batch_size = 10 - config.max_concurrent_requests = 5 - config.enable_fallback = True - config.max_primary_failures = 3 - config.failure_reset_interval = 300 - config._primary_failure_count = 0 - config._last_failure_time = 0.0 - service = HybridRerankService(config) - service.fallback_service = mock_fallback - yield service - - @pytest.mark.asyncio - async def test_failure_count_reset_after_timeout(self, service_with_fallback): - """L406-411: Reset failure count when timeout interval has expired""" - service_with_fallback.config._primary_failure_count = 5 - service_with_fallback.config._last_failure_time = ( - time.time() - 600 - ) # 10 min ago - service_with_fallback.config.failure_reset_interval = 300 - - primary_func = AsyncMock(return_value="result") - fallback_func = AsyncMock() - result = await service_with_fallback.execute_with_fallback( - "op", primary_func, fallback_func - ) - assert result == "result" - assert service_with_fallback.config._primary_failure_count == 0 - - @pytest.mark.asyncio - async def test_skip_primary_use_fallback_on_max_failures( - self, service_with_fallback - ): - """L419-433: Skip primary and use fallback when max failures exceeded""" - service_with_fallback.config._primary_failure_count = 5 - service_with_fallback.config.max_primary_failures = 3 - service_with_fallback.config._last_failure_time = ( - time.time() - ) # recent, no reset - - fallback_func = AsyncMock(return_value="fallback_result") - with patch("agentic_layer.rerank_service.record_rerank_fallback"): - result = await service_with_fallback.execute_with_fallback( - "op", AsyncMock(), fallback_func - ) - assert result == "fallback_result" - - @pytest.mark.asyncio - async def test_skip_primary_fallback_also_fails(self, service_with_fallback): - """L435-445: Skip primary, fallback also fails -> RerankError""" - service_with_fallback.config._primary_failure_count = 5 - service_with_fallback.config.max_primary_failures = 3 - service_with_fallback.config._last_failure_time = time.time() - - fallback_func = AsyncMock(side_effect=Exception("fallback died")) - with ( - patch("agentic_layer.rerank_service.record_rerank_fallback"), - patch("agentic_layer.rerank_service.record_rerank_error"), - ): - from agentic_layer.rerank_interface import RerankError - - with pytest.raises(RerankError, match="Fallback service failed"): - await service_with_fallback.execute_with_fallback( - "op", AsyncMock(), fallback_func - ) - - @pytest.mark.asyncio - async def test_primary_fails_fallback_succeeds(self, service_with_fallback): - """L478-500: Primary fails, fallback succeeds""" - primary_func = AsyncMock(side_effect=Exception("primary died")) - fallback_func = AsyncMock(return_value="fallback_ok") - - with ( - patch("agentic_layer.rerank_service.record_rerank_error"), - patch("agentic_layer.rerank_service.record_rerank_fallback"), - ): - result = await service_with_fallback.execute_with_fallback( - "op", primary_func, fallback_func - ) - assert result == "fallback_ok" - - @pytest.mark.asyncio - async def test_primary_fails_fallback_also_fails(self, service_with_fallback): - """L502-516: Both primary and fallback fail -> RerankError""" - primary_func = AsyncMock(side_effect=Exception("primary died")) - fallback_func = AsyncMock(side_effect=Exception("fallback died")) - - with ( - patch("agentic_layer.rerank_service.record_rerank_error"), - patch("agentic_layer.rerank_service.record_rerank_fallback"), - ): - from agentic_layer.rerank_interface import RerankError - - with pytest.raises(RerankError, match="Both primary and fallback"): - await service_with_fallback.execute_with_fallback( - "op", primary_func, fallback_func - ) - - @pytest.mark.asyncio - async def test_primary_fails_no_fallback_raises(self, service_with_fallback): - """L471-475: Primary fails, fallback disabled -> RerankError""" - service_with_fallback.config.enable_fallback = False - primary_func = AsyncMock(side_effect=Exception("primary died")) - - with patch("agentic_layer.rerank_service.record_rerank_error"): - from agentic_layer.rerank_interface import RerankError - - with pytest.raises(RerankError, match="fallback is disabled"): - await service_with_fallback.execute_with_fallback( - "op", primary_func, None - ) - - @pytest.mark.asyncio - async def test_max_failures_reason_on_threshold(self, service_with_fallback): - """L478-484: fallback_reason = 'max_failures_exceeded' when count reaches max""" - service_with_fallback.config.max_primary_failures = 1 # next failure hits max - service_with_fallback.config._primary_failure_count = 0 - - primary_func = AsyncMock(side_effect=Exception("fail")) - fallback_func = AsyncMock(return_value="ok") - - with ( - patch("agentic_layer.rerank_service.record_rerank_error"), - patch("agentic_layer.rerank_service.record_rerank_fallback") as mock_fb, - ): - result = await service_with_fallback.execute_with_fallback( - "op", primary_func, fallback_func - ) - assert result == "ok" - mock_fb.assert_called_once_with( - primary_provider=service_with_fallback.config.primary_provider, - fallback_provider=service_with_fallback.config.fallback_provider, - reason='max_failures_exceeded', - ) - - -class TestClassifyError: - """Tests for _classify_error (L524-534)""" - - @pytest.fixture - def service(self): - with patch( - "agentic_layer.rerank_service._create_service_from_config" - ) as mock_create: - mock_create.return_value = MagicMock() - from agentic_layer.rerank_service import ( - HybridRerankService, - HybridRerankConfig, - ) - - config = HybridRerankConfig.__new__(HybridRerankConfig) - config.primary_provider = "vllm" - config.fallback_provider = "deepinfra" - config.primary_api_key = "" - config.primary_base_url = "" - config.fallback_api_key = "" - config.fallback_base_url = "" - config.model = "test-model" - config.timeout = 3 - config.max_retries = 2 - config.batch_size = 10 - config.max_concurrent_requests = 5 - config.enable_fallback = False - config.max_primary_failures = 3 - config.failure_reset_interval = 300 - config._primary_failure_count = 0 - config._last_failure_time = 0.0 - yield HybridRerankService(config) - - def test_timeout_by_message(self, service): - assert service._classify_error(Exception("request timeout")) == 'timeout' - - def test_timeout_by_type(self, service): - import asyncio - - assert service._classify_error(asyncio.TimeoutError()) == 'timeout' - - def test_rate_limit(self, service): - assert service._classify_error(Exception("rate limit exceeded")) == 'rate_limit' - - def test_validation_error(self, service): - assert ( - service._classify_error(Exception("validation failed")) - == 'validation_error' - ) - - def test_connection_error(self, service): - assert ( - service._classify_error(Exception("connection refused")) - == 'connection_error' - ) - - def test_api_error(self, service): - assert service._classify_error(Exception("api error 500")) == 'api_error' - - def test_unknown_error(self, service): - assert service._classify_error(Exception("something weird")) == 'unknown' - - -class TestUtilityMethods: - """Tests for get_failure_count, reset_failure_count, close (L538, L542-543, L547-549)""" - - @pytest.fixture - def service(self): - with patch( - "agentic_layer.rerank_service._create_service_from_config" - ) as mock_create: - mock_primary = AsyncMock() - mock_primary.close = AsyncMock() - mock_create.return_value = mock_primary - from agentic_layer.rerank_service import ( - HybridRerankService, - HybridRerankConfig, - ) - - config = HybridRerankConfig.__new__(HybridRerankConfig) - config.primary_provider = "vllm" - config.fallback_provider = "deepinfra" - config.primary_api_key = "" - config.primary_base_url = "" - config.fallback_api_key = "" - config.fallback_base_url = "" - config.model = "test-model" - config.timeout = 3 - config.max_retries = 2 - config.batch_size = 10 - config.max_concurrent_requests = 5 - config.enable_fallback = False - config.max_primary_failures = 3 - config.failure_reset_interval = 300 - config._primary_failure_count = 0 - config._last_failure_time = 0.0 - yield HybridRerankService(config) - - def test_get_failure_count(self, service): - service.config._primary_failure_count = 7 - assert service.get_failure_count() == 7 - - def test_reset_failure_count(self, service): - service.config._primary_failure_count = 5 - service.reset_failure_count() - assert service.config._primary_failure_count == 0 - - @pytest.mark.asyncio - async def test_close_without_fallback(self, service): - await service.close() - service.primary_service.close.assert_awaited_once() - - @pytest.mark.asyncio - async def test_close_with_fallback(self, service): - mock_fallback = AsyncMock() - mock_fallback.close = AsyncMock() - service.fallback_service = mock_fallback - await service.close() - service.primary_service.close.assert_awaited_once() - mock_fallback.close.assert_awaited_once() - - -class TestSingletonAndDI: - """Tests for get_hybrid_service singleton (L564-566) and get_rerank_service DI (L590)""" - - def test_get_hybrid_service_returns_singleton(self): - with patch( - "agentic_layer.rerank_service._create_service_from_config" - ) as mock_create: - mock_create.return_value = MagicMock() - import agentic_layer.rerank_service as mod - - mod._service_instance = None # Reset singleton - s1 = mod.get_hybrid_service() - s2 = mod.get_hybrid_service() - assert s1 is s2 - mod._service_instance = None # Cleanup - - def test_get_rerank_service_returns_hybrid(self): - with patch("agentic_layer.rerank_service.get_hybrid_service") as mock_get: - mock_instance = MagicMock() - mock_get.return_value = mock_instance - from agentic_layer.rerank_service import get_rerank_service - - result = get_rerank_service() - assert result == mock_instance diff --git a/methods/EverCore/tests/test_smart_text_parser.py b/methods/EverCore/tests/test_smart_text_parser.py deleted file mode 100644 index db0b81c1..00000000 --- a/methods/EverCore/tests/test_smart_text_parser.py +++ /dev/null @@ -1,686 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Smart text parser test module - -Comprehensively test SmartTextParser and related functions in various scenarios -""" - -import pytest -import sys -import os - - -from common_utils.text_utils import ( - SmartTextParser, - TokenConfig, - TokenType, - Token, - smart_truncate_text, - clean_whitespace, -) - - -class TestTokenType: - """Test TokenType enumeration""" - - def test_token_type_values(self): - """Test TokenType values""" - assert TokenType.CJK_CHAR.value == "cjk_char" - assert TokenType.ENGLISH_WORD.value == "english_word" - assert TokenType.CONTINUOUS_NUMBER.value == "continuous_number" - assert TokenType.PUNCTUATION.value == "punctuation" - assert TokenType.WHITESPACE.value == "whitespace" - assert TokenType.OTHER.value == "other" - - -class TestToken: - """Test Token data class""" - - def test_token_creation(self): - """Test Token creation""" - token = Token( - type=TokenType.CJK_CHAR, content="你", start_pos=0, end_pos=1, score=1.0 - ) - assert token.type == TokenType.CJK_CHAR - assert token.content == "你" - assert token.start_pos == 0 - assert token.end_pos == 1 - assert token.score == 1.0 - - def test_token_default_score(self): - """Test Token default score""" - token = Token( - type=TokenType.ENGLISH_WORD, content="hello", start_pos=0, end_pos=5 - ) - assert token.score == 0.0 - - -class TestTokenConfig: - """Test TokenConfig configuration class""" - - def test_default_config(self): - """Test default configuration""" - config = TokenConfig() - assert config.cjk_char_score == 1.0 - assert config.english_word_score == 1.5 - assert config.continuous_number_score == 0.8 - assert config.punctuation_score == 0.5 - assert config.whitespace_score == 0.3 - assert config.other_score == 0.5 - - def test_custom_config(self): - """Test custom configuration""" - config = TokenConfig( - cjk_char_score=2.0, english_word_score=0.5, punctuation_score=0.0 - ) - assert config.cjk_char_score == 2.0 - assert config.english_word_score == 0.5 - assert config.punctuation_score == 0.0 - # Other values should remain default - assert config.continuous_number_score == 0.8 - - -class TestSmartTextParser: - """Test SmartTextParser class""" - - def setup_method(self): - """Setup before each test""" - self.parser = SmartTextParser() - self.custom_parser = SmartTextParser( - TokenConfig( - cjk_char_score=2.0, english_word_score=0.5, punctuation_score=0.0 - ) - ) - - def test_init_default_config(self): - """Test default initialization""" - parser = SmartTextParser() - assert parser.config.cjk_char_score == 1.0 - assert parser.config.english_word_score == 1.5 - - def test_init_custom_config(self): - """Test custom configuration initialization""" - config = TokenConfig(cjk_char_score=2.0) - parser = SmartTextParser(config) - assert parser.config.cjk_char_score == 2.0 - - def test_is_cjk_char(self): - """Test CJK character recognition""" - # Chinese characters - assert self.parser._is_cjk_char("中") == True - assert self.parser._is_cjk_char("你") == True - - # Japanese characters - assert self.parser._is_cjk_char("あ") == True # Hiragana - assert self.parser._is_cjk_char("ア") == True # Katakana - assert self.parser._is_cjk_char("漢") == True # Kanji - - # Korean characters - assert self.parser._is_cjk_char("한") == True - assert self.parser._is_cjk_char("국") == True - - # Non-CJK characters - assert self.parser._is_cjk_char("A") == False - assert self.parser._is_cjk_char("1") == False - assert self.parser._is_cjk_char("!") == False - assert self.parser._is_cjk_char("") == False - - def test_is_english_char(self): - """Test English character recognition""" - assert self.parser._is_english_char("A") == True - assert self.parser._is_english_char("z") == True - assert self.parser._is_english_char("中") == False - assert self.parser._is_english_char("1") == False - assert self.parser._is_english_char("!") == False - - def test_is_punctuation(self): - """Test punctuation recognition""" - # Basic punctuation - assert self.parser._is_punctuation(".") == True - assert self.parser._is_punctuation(",") == True - assert self.parser._is_punctuation("!") == True - assert self.parser._is_punctuation("?") == True - assert self.parser._is_punctuation(";") == True - assert self.parser._is_punctuation(":") == True - - # Parentheses - assert self.parser._is_punctuation("(") == True - assert self.parser._is_punctuation(")") == True - assert self.parser._is_punctuation("[") == True - assert self.parser._is_punctuation("]") == True - - # Chinese punctuation - assert self.parser._is_punctuation("。") == True - assert self.parser._is_punctuation("，") == True - assert self.parser._is_punctuation("！") == True - - # Non-punctuation - assert self.parser._is_punctuation("A") == False - assert self.parser._is_punctuation("中") == False - assert self.parser._is_punctuation("1") == False - - -class TestParseTokens: - """Test parse_tokens method""" - - def setup_method(self): - """Setup before each test""" - self.parser = SmartTextParser() - - def test_empty_text(self): - """Test empty text""" - tokens = self.parser.parse_tokens("") - assert tokens == [] - - tokens = self.parser.parse_tokens(None) - assert tokens == [] - - def test_single_cjk_char(self): - """Test single CJK character""" - tokens = self.parser.parse_tokens("你") - assert len(tokens) == 1 - assert tokens[0].type == TokenType.CJK_CHAR - assert tokens[0].content == "你" - assert tokens[0].start_pos == 0 - assert tokens[0].end_pos == 1 - assert tokens[0].score == 1.0 - - def test_single_english_word(self): - """Test single English word""" - tokens = self.parser.parse_tokens("hello") - assert len(tokens) == 1 - assert tokens[0].type == TokenType.ENGLISH_WORD - assert tokens[0].content == "hello" - assert tokens[0].start_pos == 0 - assert tokens[0].end_pos == 5 - assert tokens[0].score == 1.5 - - def test_english_word_with_apostrophe(self): - """Test English word with apostrophe""" - tokens = self.parser.parse_tokens("don't") - assert len(tokens) == 1 - assert tokens[0].type == TokenType.ENGLISH_WORD - assert tokens[0].content == "don't" - - def test_continuous_number(self): - """Test continuous numbers""" - tokens = self.parser.parse_tokens("123.45") - assert len(tokens) == 1 - assert tokens[0].type == TokenType.CONTINUOUS_NUMBER - assert tokens[0].content == "123.45" - assert tokens[0].score == 0.8 - - tokens = self.parser.parse_tokens("1,234") - assert len(tokens) == 1 - assert tokens[0].type == TokenType.CONTINUOUS_NUMBER - assert tokens[0].content == "1,234" - - def test_punctuation(self): - """Test punctuation""" - tokens = self.parser.parse_tokens("!") - assert len(tokens) == 1 - assert tokens[0].type == TokenType.PUNCTUATION - assert tokens[0].content == "!" - assert tokens[0].score == 0.5 - - def test_whitespace(self): - """Test whitespace characters""" - tokens = self.parser.parse_tokens(" ") - assert len(tokens) == 1 - assert tokens[0].type == TokenType.WHITESPACE - assert tokens[0].content == " " - assert tokens[0].score == 0.3 - - def test_mixed_text(self): - """Test mixed text""" - tokens = self.parser.parse_tokens("Hello 你好!") - assert len(tokens) == 5 # Hello, space, 你, 好, ! - - assert tokens[0].type == TokenType.ENGLISH_WORD - assert tokens[0].content == "Hello" - - assert tokens[1].type == TokenType.WHITESPACE - assert tokens[1].content == " " - - assert tokens[2].type == TokenType.CJK_CHAR - assert tokens[2].content == "你" - - assert tokens[3].type == TokenType.CJK_CHAR - assert tokens[3].content == "好" - - assert tokens[4].type == TokenType.PUNCTUATION - assert tokens[4].content == "!" - - def test_complex_mixed_text(self): - """Test complex mixed text""" - text = "Python3.9版本包含123个新特性。" - tokens = self.parser.parse_tokens(text) - - expected_types = [ - TokenType.ENGLISH_WORD, # Python - TokenType.CONTINUOUS_NUMBER, # 3.9 - TokenType.CJK_CHAR, # 版 - TokenType.CJK_CHAR, # 本 - TokenType.CJK_CHAR, # 包 - TokenType.CJK_CHAR, # 含 - TokenType.CONTINUOUS_NUMBER, # 123 - TokenType.CJK_CHAR, # 个 - TokenType.CJK_CHAR, # 新 - TokenType.CJK_CHAR, # 特 - TokenType.CJK_CHAR, # 性 - TokenType.PUNCTUATION, # 。 - ] - - assert len(tokens) == len(expected_types) - for i, expected_type in enumerate(expected_types): - assert tokens[i].type == expected_type - - def test_parse_tokens_with_max_score(self): - """Test parsing with maximum score limit""" - text = "Hello World 你好世界" - - # No score limit - tokens_full = self.parser.parse_tokens(text) - assert len(tokens_full) == 8 # Hello, space, World, space, 你, 好, 世, 界 - - # Limit score to 3.0 - tokens_limited = self.parser.parse_tokens(text, max_score=3.0) - total_score = sum(token.score for token in tokens_limited) - assert total_score <= 3.0 - assert len(tokens_limited) < len(tokens_full) - - def test_multilingual_text(self): - """Test multilingual text""" - text = "English中文日本語한국어" - tokens = self.parser.parse_tokens(text) - - # Should correctly identify all character types - assert any(token.type == TokenType.ENGLISH_WORD for token in tokens) - assert any(token.type == TokenType.CJK_CHAR for token in tokens) - - -class TestCalculateTotalScore: - """Test calculate_total_score method""" - - def setup_method(self): - self.parser = SmartTextParser() - - def test_empty_tokens(self): - """Test empty token list""" - assert self.parser.calculate_total_score([]) == 0.0 - - def test_single_token(self): - """Test single token""" - token = Token(TokenType.CJK_CHAR, "你", 0, 1, 1.0) - assert self.parser.calculate_total_score([token]) == 1.0 - - def test_multiple_tokens(self): - """Test multiple tokens""" - tokens = [ - Token(TokenType.ENGLISH_WORD, "Hello", 0, 5, 1.0), - Token(TokenType.WHITESPACE, " ", 5, 6, 0.1), - Token(TokenType.CJK_CHAR, "你", 6, 7, 1.0), - ] - assert self.parser.calculate_total_score(tokens) == 2.1 - - -class TestSmartTruncateByScore: - """Test smart_truncate_by_score method""" - - def setup_method(self): - self.parser = SmartTextParser() - - def test_empty_text(self): - """Test empty text""" - assert self.parser.smart_truncate_by_score("", 5.0) == "" - assert self.parser.smart_truncate_by_score(None, 5.0) == "" - - def test_zero_max_score(self): - """Test maximum score is 0""" - assert self.parser.smart_truncate_by_score("Hello", 0) == "Hello" - assert self.parser.smart_truncate_by_score("Hello", -1) == "Hello" - - def test_no_truncation_needed(self): - """Test no truncation needed""" - text = "Hello" - result = self.parser.smart_truncate_by_score(text, 10.0) - assert result == text - - def test_simple_truncation(self): - """Test simple truncation""" - text = "Hello World" - result = self.parser.smart_truncate_by_score(text, 1.5) # Only allow one English word - # Due to word boundary protection, may retain complete second word - assert result == "Hello..." or result == "Hello World" - - def test_cjk_truncation(self): - """Test CJK character truncation""" - text = "你好世界" - result = self.parser.smart_truncate_by_score(text, 2.0) - # Due to word boundary protection, may retain more content - assert result == "你好..." or result == "你好世界" - - def test_mixed_text_truncation(self): - """Test mixed text truncation""" - text = "Hello 你好世界" - # Hello(1.0) + space(0.1) + 你(1.0) + 好(1.0) = 3.1 - result = self.parser.smart_truncate_by_score(text, 3.0) - # Due to word boundary protection, may retain more content - assert result == "Hello 你..." or result == "Hello 你好世界" - - def test_custom_suffix(self): - """Test custom suffix""" - text = "Hello World" - result = self.parser.smart_truncate_by_score(text, 1.5, suffix="[...]") - # Due to word boundary protection, may not need truncation - assert result == "Hello[...]" or result == "Hello World" - - def test_punctuation_handling(self): - """Test punctuation handling""" - config = TokenConfig(punctuation_score=0.5) - parser = SmartTextParser(config) - - text = "Hello, World!" - result = parser.smart_truncate_by_score(text, 2.0) - # Hello(1.0) + ,(0.5) + space(0.1) + World(1.0) = 2.6 > 2.0 - # Due to word boundary protection, may retain complete content - assert "Hello," in result - - def test_word_boundary_protection(self): - """Test word boundary protection""" - text = "Hello World" - result = self.parser.smart_truncate_by_score(text, 1.8) # Just over one word - # Should completely retain second word, not truncate in the middle - assert result == "Hello World" or result == "Hello..." - - def test_fallback_mode_enabled(self): - """Test fallback mode enabled""" - # Simulate parsing exception case - text = "Normal text" - result = self.parser.smart_truncate_by_score(text, 5.0, enable_fallback=True) - assert isinstance(result, str) - - def test_fallback_mode_disabled(self): - """Test fallback mode disabled""" - text = "Normal text" - # Normally won't throw exception - result = self.parser.smart_truncate_by_score(text, 5.0, enable_fallback=False) - assert isinstance(result, str) - - -class TestGetTextAnalysis: - """Test get_text_analysis method""" - - def setup_method(self): - self.parser = SmartTextParser() - - def test_empty_text(self): - """Test empty text analysis""" - analysis = self.parser.get_text_analysis("") - assert analysis["total_tokens"] == 0 - assert analysis["total_score"] == 0.0 - assert all(count == 0 for count in analysis["type_counts"].values()) - - def test_simple_text_analysis(self): - """Test simple text analysis""" - text = "Hello 你好" - analysis = self.parser.get_text_analysis(text) - - assert analysis["total_tokens"] == 4 # Hello, space, 你, 好 - assert analysis["total_score"] == 3.8 # 1.5 + 0.3 + 1.0 + 1.0 - - assert analysis["type_counts"]["english_word"] == 1 - assert analysis["type_counts"]["cjk_char"] == 2 - assert analysis["type_counts"]["whitespace"] == 1 - - assert analysis["type_scores"]["english_word"] == 1.5 - assert analysis["type_scores"]["cjk_char"] == 2.0 - assert analysis["type_scores"]["whitespace"] == 0.3 - - def test_complex_text_analysis(self): - """Test complex text analysis""" - text = "Python3.9版本包含123个新特性！" - analysis = self.parser.get_text_analysis(text) - - # Verify token count and types - assert analysis["total_tokens"] > 0 - assert analysis["type_counts"]["english_word"] >= 1 # Python - assert analysis["type_counts"]["continuous_number"] >= 2 # 3.9, 123 - assert analysis["type_counts"]["cjk_char"] >= 6 # 版本包含个新特性 - assert analysis["type_counts"]["punctuation"] >= 1 # ！ - - -class TestSmartTruncateText: - """Test backward compatible smart_truncate_text function""" - - def test_backward_compatibility(self): - """Test backward compatibility""" - text = "Hello World 你好世界" - - # Basic call - result = smart_truncate_text(text, 4) - # Should be truncated (because total score exceeds 4) - assert "..." in result or result == text - - # Call with weights - result_weighted = smart_truncate_text(text, 4, chinese_weight=0.5) - # Lower Chinese weight, may not need truncation, so result may be shorter (no "..." suffix) - assert "..." not in result_weighted or len(result_weighted) >= len(result) - - def test_empty_and_edge_cases(self): - """Test edge cases""" - assert smart_truncate_text("", 5) == "" - assert smart_truncate_text(None, 5) == "" - assert smart_truncate_text("Hello", 0) == "Hello" - assert smart_truncate_text("Hello", -1) == "Hello" - - def test_custom_weights(self): - """Test custom weights""" - text = "Hello World 你好世界测试长文本" # Use longer text - - # Use smaller limit to ensure truncation - # Default weights - result1 = smart_truncate_text(text, 4) - - # Lower Chinese weight - result2 = smart_truncate_text(text, 4, chinese_weight=0.2) - - # Lower English weight - result3 = smart_truncate_text(text, 4, english_word_weight=0.2) - - # Due to optimized word boundary protection, results may be the same, which is normal - # At least ensure functions work properly - assert isinstance(result1, str) - assert isinstance(result2, str) - assert isinstance(result3, str) - - -class TestPerformance: - """Performance tests""" - - def setup_method(self): - self.parser = SmartTextParser() - - def test_large_text_parsing(self): - """Test large text parsing performance""" - import time - - # Generate large text - large_text = "Hello World 你好世界! " * 100 - - start_time = time.time() - tokens = self.parser.parse_tokens(large_text) - end_time = time.time() - - assert len(tokens) > 0 - assert (end_time - start_time) < 1.0 # Should complete within 1 second - - def test_early_truncation_performance(self): - """Test performance advantage of early truncation""" - import time - - # Generate large text - large_text = "Hello World 你好世界! " * 1000 - - # Parsing without score limit - start_time = time.time() - tokens_full = self.parser.parse_tokens(large_text) - time_full = time.time() - start_time - - # Parsing with score limit - start_time = time.time() - tokens_limited = self.parser.parse_tokens(large_text, max_score=10.0) - time_limited = time.time() - start_time - - # Parsing with score limit should be faster - assert len(tokens_limited) < len(tokens_full) - assert time_limited <= time_full # Usually should be faster, but at least not slower - - -class TestEdgeCases: - """Edge case tests""" - - def setup_method(self): - self.parser = SmartTextParser() - - def test_special_characters(self): - """Test special characters""" - special_chars = "°©®™€£¥§¶†‡•…‰‹›" "''–—" - tokens = self.parser.parse_tokens(special_chars) - assert len(tokens) > 0 - # Most should be recognized as OTHER type - assert any(token.type == TokenType.OTHER for token in tokens) - - def test_emoji_handling(self): - """Test emoji handling""" - text = "Hello 😊 你好 🌟" - tokens = self.parser.parse_tokens(text) - assert len(tokens) > 0 - # Emojis should be recognized as OTHER type - emoji_tokens = [token for token in tokens if token.type == TokenType.OTHER] - assert len(emoji_tokens) >= 2 # At least two emojis - - def test_mixed_numbers_and_letters(self): - """Test mixed numbers and letters""" - text = "ABC123DEF456" - tokens = self.parser.parse_tokens(text) - - # Should be separately recognized as English words and numbers - assert len(tokens) == 4 - assert tokens[0].type == TokenType.ENGLISH_WORD - assert tokens[0].content == "ABC" - assert tokens[1].type == TokenType.CONTINUOUS_NUMBER - assert tokens[1].content == "123" - assert tokens[2].type == TokenType.ENGLISH_WORD - assert tokens[2].content == "DEF" - assert tokens[3].type == TokenType.CONTINUOUS_NUMBER - assert tokens[3].content == "456" - - def test_url_like_text(self): - """Test URL-like text""" - text = "https://example.com/path?param=value" - tokens = self.parser.parse_tokens(text) - - # Should be correctly split - assert len(tokens) > 1 - # Contains English words, punctuation, numbers, etc. - token_types = {token.type for token in tokens} - assert TokenType.ENGLISH_WORD in token_types - assert TokenType.PUNCTUATION in token_types - - def test_very_long_word(self): - """Test very long word""" - long_word = "a" * 1000 - tokens = self.parser.parse_tokens(long_word) - assert len(tokens) == 1 - assert tokens[0].type == TokenType.ENGLISH_WORD - assert tokens[0].content == long_word - - def test_unicode_edge_cases(self): - """Test Unicode edge cases""" - # Test characters from various Unicode ranges - text = "🀀🀁🀂" # Mahjong tiles - tokens = self.parser.parse_tokens(text) - assert len(tokens) == 3 - assert all(token.type == TokenType.OTHER for token in tokens) - - -class TestCleanWhitespace: - """Test clean_whitespace function""" - - def test_empty_text(self): - """Test empty text""" - assert clean_whitespace("") == "" - assert clean_whitespace(None) == None - - def test_no_whitespace(self): - """Test text without whitespace characters""" - text = "HelloWorld" - assert clean_whitespace(text) == text - - def test_single_spaces(self): - """Test single spaces""" - text = "Hello World" - assert clean_whitespace(text) == "Hello World" - - def test_multiple_spaces(self): - """Test multiple consecutive spaces""" - text = "Hello World" - assert clean_whitespace(text) == "Hello World" - - def test_mixed_whitespace(self): - """Test mixed whitespace characters""" - text = "Hello\t\n \r World" - assert clean_whitespace(text) == "Hello World" - - def test_leading_trailing_whitespace(self): - """Test leading and trailing whitespace""" - text = " Hello World " - assert clean_whitespace(text) == "Hello World" - - def test_complex_mixed_text(self): - """Test complex mixed text""" - text = " Hello World! \t\n 你好 世界。 " - result = clean_whitespace(text) - assert result == "Hello World! 你好 世界。" - # Ensure Chinese characters and punctuation remain intact - assert "你好" in result - assert "世界" in result - assert "!" in result - assert "。" in result - - def test_preserve_non_whitespace_tokens(self): - """Test preserving integrity of non-whitespace tokens""" - text = "Python3.9 版本 包含 123个 新特性！" - result = clean_whitespace(text) - expected = "Python3.9 版本 包含 123个 新特性！" - assert result == expected - # Ensure numbers, English words, Chinese characters remain intact - assert "Python3.9" in result - assert "123" in result - assert "新特性" in result - - def test_only_whitespace(self): - """Test pure whitespace characters""" - text = " \t\n\r " - assert clean_whitespace(text) == "" - - def test_whitespace_between_cjk_chars(self): - """Test whitespace between CJK characters""" - text = "你 好 世 界" - assert clean_whitespace(text) == "你 好 世 界" - - def test_whitespace_around_punctuation(self): - """Test whitespace around punctuation""" - text = "Hello , World ! " - result = clean_whitespace(text) - assert result == "Hello , World !" - # Ensure punctuation remains unchanged - assert "," in result - assert "!" in result - - -if __name__ == "__main__": - # Run all tests - pytest.main([__file__, "-v"]) \ No newline at end of file diff --git a/methods/EverCore/tests/test_stability_database.py b/methods/EverCore/tests/test_stability_database.py deleted file mode 100644 index ff3f28ab..00000000 --- a/methods/EverCore/tests/test_stability_database.py +++ /dev/null @@ -1,346 +0,0 @@ -""" -Database stability test - -Test key stability scenarios such as database connection pool, connection leaks, and failure recovery -""" - -import pytest -import asyncio -import os -import time -from unittest.mock import patch, AsyncMock -from typing import List, Dict, Any - -# Set test environment -os.environ.setdefault("DATABASE_URL", "postgresql://test:test@localhost:5432/testdb") -os.environ.setdefault("DB_POOL_SIZE", "5") -os.environ.setdefault("DB_MAX_OVERFLOW", "3") - -from core.component.database_session_provider import DatabaseSessionProvider -from core.component.database_connection_provider import DatabaseConnectionProvider - - -class TestDatabaseStability: - """Database stability test class""" - - @pytest.fixture - async def db_provider(self): - """Database provider fixture""" - provider = DatabaseSessionProvider() - yield provider - # Clean up resources - if hasattr(provider, 'async_engine'): - await provider.async_engine.dispose() - - @pytest.fixture - async def connection_provider(self): - """Connection provider fixture""" - provider = DatabaseConnectionProvider() - yield provider - # Clean up resources - if hasattr(provider, '_connection_pool') and provider._connection_pool: - await provider._connection_pool.close() - - @pytest.mark.asyncio - async def test_connection_pool_exhaustion(self, db_provider): - """Test connection pool exhaustion scenario""" - - # Create concurrent tasks exceeding connection pool size - async def db_operation(operation_id: int): - try: - async with db_provider.get_async_session() as session: - # Simulate long-running database operation - await asyncio.sleep(0.1) - return f"operation_{operation_id}_success" - except Exception as e: - return f"operation_{operation_id}_failed: {str(e)}" - - # Create a large number of concurrent tasks (exceeding pool size) - max_connections = int(os.getenv("DB_POOL_SIZE", "5")) + int( - os.getenv("DB_MAX_OVERFLOW", "3") - ) - task_count = max_connections * 2 # Exceed connection pool size - - tasks = [asyncio.create_task(db_operation(i)) for i in range(task_count)] - results = await asyncio.gather(*tasks, return_exceptions=True) - - # Analyze results - success_count = sum(1 for r in results if isinstance(r, str) and "success" in r) - failure_count = len(results) - success_count - - print( - f"Connection pool test result: success={success_count}, failure={failure_count}, total_tasks={task_count}" - ) - - # Verify: Most tasks should succeed, a few may fail due to pool exhaustion - assert ( - success_count >= task_count * 0.8 - ), f"Success rate too low: {success_count}/{task_count}" - assert ( - failure_count <= task_count * 0.2 - ), f"Failure rate too high: {failure_count}/{task_count}" - - @pytest.mark.asyncio - async def test_connection_leak_detection(self, db_provider): - """Test connection leak detection""" - # Record initial connection count - initial_pool_size = db_provider.async_engine.pool.size() - initial_checked_in = db_provider.async_engine.pool.checkedin() - initial_checked_out = db_provider.async_engine.pool.checkedout() - - print( - f"Initial connection pool status: size={initial_pool_size}, checked_in={initial_checked_in}, checked_out={initial_checked_out}" - ) - - # Simulate connection leak scenario - leaked_sessions = [] - - async def leaky_operation(): - session = db_provider.create_session() - leaked_sessions.append(session) - # Intentionally not closing session, simulating connection leak - await session.execute("SELECT 1") - # Not calling session.close() - - # Execute multiple leak operations - for _ in range(3): - await leaky_operation() - - # Check connection pool status - current_pool_size = db_provider.async_engine.pool.size() - current_checked_in = db_provider.async_engine.pool.checkedin() - current_checked_out = db_provider.async_engine.pool.checkedout() - - print( - f"Post-leak connection pool status: size={current_pool_size}, checked_in={current_checked_in}, checked_out={current_checked_out}" - ) - - # Verify connection leak detection - leaked_connections = current_checked_out - initial_checked_out - assert leaked_connections > 0, "Connection leak should be detected" - - # Clean up leaked connections - for session in leaked_sessions: - try: - await session.close() - except Exception: - pass - - @pytest.mark.asyncio - async def test_database_failure_recovery(self, db_provider): - """Test database failure recovery""" - recovery_successful = False - - # Simulate database connection failure - original_execute = db_provider.async_engine.execute - - async def mock_failing_execute(*args, **kwargs): - # First few calls fail, subsequent calls succeed - if not hasattr(mock_failing_execute, 'call_count'): - mock_failing_execute.call_count = 0 - mock_failing_execute.call_count += 1 - - if mock_failing_execute.call_count <= 2: - raise Exception("Database connection failed") - else: - # Restore database connection - return await original_execute(*args, **kwargs) - - with patch.object( - db_provider.async_engine, 'execute', side_effect=mock_failing_execute - ): - # Test retry mechanism - max_retries = 3 - retry_count = 0 - - for attempt in range(max_retries): - try: - async with db_provider.get_async_session() as session: - await session.execute("SELECT 1") - recovery_successful = True - break - except Exception as e: - retry_count += 1 - print(f"Retry {retry_count}: {str(e)}") - if retry_count < max_retries: - await asyncio.sleep(0.1) # Brief delay - - assert recovery_successful, "Database failure recovery failed" - - @pytest.mark.asyncio - async def test_connection_pool_timeout(self, db_provider): - """Test connection pool timeout handling""" - - # Create long-running tasks that occupy connections - async def long_running_operation(): - async with db_provider.get_async_session() as session: - await asyncio.sleep(2) # Long time holding connection - return "completed" - - # Create multiple long-running tasks - long_tasks = [asyncio.create_task(long_running_operation()) for _ in range(3)] - - # Create new task requiring connection (should timeout) - async def timeout_operation(): - try: - async with db_provider.get_async_session() as session: - await session.execute("SELECT 1") - return "success" - except Exception as e: - return f"timeout: {str(e)}" - - # Create timeout task after a short wait - await asyncio.sleep(0.1) - timeout_task = asyncio.create_task(timeout_operation()) - - # Wait for all tasks to complete - results = await asyncio.gather( - *long_tasks, timeout_task, return_exceptions=True - ) - - # Verify timeout handling - timeout_result = results[-1] - if isinstance(timeout_result, str) and "timeout" in timeout_result: - print(f"Connection pool timeout test passed: {timeout_result}") - else: - print(f"Connection pool timeout test result: {timeout_result}") - - @pytest.mark.asyncio - async def test_concurrent_transaction_isolation(self, db_provider): - """Test concurrent transaction isolation""" - results = [] - - async def transaction_operation(operation_id: int): - try: - async with db_provider.get_async_session() as session: - # Start transaction - await session.begin() - - # Simulate transaction operations - await session.execute("SELECT 1") - await asyncio.sleep(0.1) # Simulate processing time - - # Commit transaction - await session.commit() - - results.append(f"transaction_{operation_id}_success") - except Exception as e: - results.append(f"transaction_{operation_id}_failed: {str(e)}") - - # Create multiple concurrent transactions - tasks = [asyncio.create_task(transaction_operation(i)) for i in range(10)] - await asyncio.gather(*tasks) - - # Verify transaction isolation - success_count = sum(1 for r in results if "success" in r) - assert success_count == 10, f"Transaction isolation test failed: {results}" - - @pytest.mark.asyncio - async def test_connection_pool_health_check(self, connection_provider): - """Test connection pool health check""" - try: - # Get connection pool - pool = await connection_provider.get_connection_pool() - - # Check connection pool status - assert pool is not None, "Connection pool not initialized" - - # Test connection pool health status - async with pool.connection() as conn: - await conn.execute("SELECT 1") - - print("Connection pool health check passed") - - except Exception as e: - pytest.fail(f"Connection pool health check failed: {str(e)}") - - @pytest.mark.asyncio - async def test_database_performance_under_load(self, db_provider): - """Test database performance under load""" - start_time = time.time() - - async def performance_operation(operation_id: int): - async with db_provider.get_async_session() as session: - # Execute simple query - result = await session.execute("SELECT 1 as test_value") - return f"operation_{operation_id}_completed" - - # Create a large number of concurrent operations - task_count = 100 - tasks = [ - asyncio.create_task(performance_operation(i)) for i in range(task_count) - ] - results = await asyncio.gather(*tasks, return_exceptions=True) - - end_time = time.time() - total_time = end_time - start_time - - # Analyze performance - success_count = sum( - 1 for r in results if isinstance(r, str) and "completed" in r - ) - avg_time_per_operation = total_time / task_count - operations_per_second = task_count / total_time - - print(f"Performance test results:") - print(f" Total time: {total_time:.2f} seconds") - print(f" Successful operations: {success_count}/{task_count}") - print(f" Average time per operation: {avg_time_per_operation:.3f} seconds") - print(f" Operations per second: {operations_per_second:.2f}") - - # Performance assertions - assert ( - success_count >= task_count * 0.95 - ), f"Success rate too low: {success_count}/{task_count}" - assert ( - avg_time_per_operation < 0.1 - ), f"Average response time too long: {avg_time_per_operation:.3f} seconds" - assert ( - operations_per_second > 50 - ), f"Throughput too low: {operations_per_second:.2f} ops/sec" - - -class TestDatabaseErrorHandling: - """Database error handling test class""" - - @pytest.mark.asyncio - async def test_invalid_query_handling(self): - """Test invalid query handling""" - provider = DatabaseSessionProvider() - - try: - async with provider.get_async_session() as session: - # Execute invalid SQL - await session.execute("INVALID SQL STATEMENT") - except Exception as e: - # Verify error handling - assert ( - "syntax error" in str(e).lower() or "invalid" in str(e).lower() - ), f"Invalid query not properly handled: {e}" - finally: - await provider.async_engine.dispose() - - @pytest.mark.asyncio - async def test_connection_timeout_handling(self): - """Test connection timeout handling""" - provider = DatabaseSessionProvider() - - # Simulate connection timeout - with patch.object(provider.async_engine, 'connect') as mock_connect: - mock_connect.side_effect = asyncio.TimeoutError("Connection timeout") - - try: - async with provider.get_async_session() as session: - await session.execute("SELECT 1") - except asyncio.TimeoutError: - # Verify timeout handling - assert True, "Connection timeout should be properly handled" - except Exception as e: - pytest.fail(f"Connection timeout not properly handled: {e}") - finally: - await provider.async_engine.dispose() - - -if __name__ == "__main__": - # Run tests - pytest.main([__file__, "-v", "--tb=short"]) \ No newline at end of file diff --git a/methods/EverCore/tests/test_stability_integration.py b/methods/EverCore/tests/test_stability_integration.py deleted file mode 100644 index 237c28b2..00000000 --- a/methods/EverCore/tests/test_stability_integration.py +++ /dev/null @@ -1,465 +0,0 @@ -""" -System integration stability test - -Test key scenarios such as end-to-end system stability, fault recovery, and performance benchmarks -""" - -import pytest -import asyncio -import time -import psutil -import os -import json -from typing import List, Dict, Any -from unittest.mock import AsyncMock, patch, MagicMock - -# Set test environment -os.environ.setdefault("MOCK_MODE", "true") -os.environ.setdefault("LOG_LEVEL", "WARNING") - - -class TestSystemIntegrationStability: - """System integration stability test class""" - - @pytest.fixture - async def mock_app(self): - """Mock application instance""" - from fastapi import FastAPI - from fastapi.testclient import TestClient - - app = FastAPI() - - # Add health check endpoint - @app.get("/health") - async def health_check(): - return {"status": "healthy", "timestamp": time.time()} - - # Add test endpoint - @app.get("/test") - async def test_endpoint(): - await asyncio.sleep(0.01) # Simulate processing time - return {"message": "test_success"} - - client = TestClient(app) - yield client - - @pytest.mark.asyncio - async def test_health_check_stability(self, mock_app): - """Test health check stability""" - # Perform multiple consecutive health checks - for i in range(10): - response = mock_app.get("/health") - assert response.status_code == 200 - data = response.json() - assert data["status"] == "healthy" - assert "timestamp" in data - - print("Health check stability test passed") - - @pytest.mark.asyncio - async def test_high_concurrency_api_requests(self, mock_app): - """Test high-concurrency API requests""" - start_time = time.time() - success_count = 0 - error_count = 0 - - async def api_request(request_id: int): - nonlocal success_count, error_count - - try: - response = mock_app.get("/test") - if response.status_code == 200: - success_count += 1 - return f"request_{request_id}_success" - else: - error_count += 1 - return f"request_{request_id}_error_{response.status_code}" - except Exception as e: - error_count += 1 - return f"request_{request_id}_exception: {str(e)}" - - # Create a large number of concurrent requests - request_count = 100 - tasks = [asyncio.create_task(api_request(i)) for i in range(request_count)] - results = await asyncio.gather(*tasks, return_exceptions=True) - - end_time = time.time() - total_time = end_time - start_time - - print(f"High-concurrency API test results:") - print(f" Total requests: {request_count}") - print(f" Success: {success_count}") - print(f" Errors: {error_count}") - print(f" Total time: {total_time:.2f} seconds") - print(f" Throughput: {request_count/total_time:.2f} requests/second") - - # Performance assertions - assert ( - success_count >= request_count * 0.95 - ), f"Success rate too low: {success_count}/{request_count}" - assert total_time < 10, f"Response time too long: {total_time:.2f} seconds" - assert ( - request_count / total_time > 10 - ), f"Throughput too low: {request_count/total_time:.2f} requests/second" - - @pytest.mark.asyncio - async def test_system_memory_usage(self): - """Test system memory usage""" - process = psutil.Process(os.getpid()) - initial_memory = process.memory_info().rss - - # Simulate system operations - data_structures = [] - - for i in range(100): - # Create some data structures - data = { - "id": i, - "content": "x" * 1000, - "metadata": {"created_at": time.time()}, - } - data_structures.append(data) - - peak_memory = process.memory_info().rss - memory_increase = peak_memory - initial_memory - - # Clean up data - del data_structures - - # Force garbage collection - import gc - - gc.collect() - - final_memory = process.memory_info().rss - final_increase = final_memory - initial_memory - - print(f"Memory usage test results:") - print(f" Initial memory: {initial_memory / 1024 / 1024:.2f} MB") - print(f" Peak memory: {peak_memory / 1024 / 1024:.2f} MB") - print(f" Final memory: {final_memory / 1024 / 1024:.2f} MB") - print(f" Peak increase: {memory_increase / 1024 / 1024:.2f} MB") - print(f" Final increase: {final_increase / 1024 / 1024:.2f} MB") - - # Verify reasonable memory usage - assert ( - memory_increase < 50 * 1024 * 1024 - ), f"Excessive peak memory usage: {memory_increase / 1024 / 1024:.2f} MB" - assert ( - final_increase < 10 * 1024 * 1024 - ), f"Final memory leak: {final_increase / 1024 / 1024:.2f} MB" - - @pytest.mark.asyncio - async def test_system_cpu_usage(self): - """Test system CPU usage""" - process = psutil.Process(os.getpid()) - - # Record initial CPU usage - initial_cpu = process.cpu_percent() - - # Perform CPU-intensive operations - async def cpu_intensive_task(task_id: int): - # Simulate CPU-intensive computation - result = 0 - for i in range(10000): - result += i * i - return result - - # Create concurrent tasks - tasks = [asyncio.create_task(cpu_intensive_task(i)) for i in range(10)] - results = await asyncio.gather(*tasks) - - # Check CPU usage - current_cpu = process.cpu_percent() - - print(f"CPU usage test results:") - print(f" Initial CPU: {initial_cpu:.2f}%") - print(f" Current CPU: {current_cpu:.2f}%") - print(f" Completed tasks: {len(results)}") - - # Verify reasonable CPU usage - assert current_cpu < 80, f"CPU usage too high: {current_cpu:.2f}%" - assert len(results) == 10, f"Task completion count mismatch: {len(results)} != 10" - - @pytest.mark.asyncio - async def test_error_recovery_mechanism(self, mock_app): - """Test error recovery mechanism""" - recovery_successful = False - - # Simulate error scenario - with patch.object(mock_app, 'get') as mock_get: - # First few calls fail, subsequent calls succeed - call_count = 0 - - def mock_response(*args, **kwargs): - nonlocal call_count, recovery_successful - call_count += 1 - - if call_count <= 2: - # Simulate error response - response = MagicMock() - response.status_code = 500 - response.json.return_value = {"error": "Internal server error"} - return response - else: - # Recover to normal - recovery_successful = True - response = MagicMock() - response.status_code = 200 - response.json.return_value = {"status": "healthy"} - return response - - mock_get.side_effect = mock_response - - # Test retry mechanism - max_retries = 5 - for attempt in range(max_retries): - try: - response = mock_get("/health") - if response.status_code == 200: - break - except Exception as e: - if attempt < max_retries - 1: - await asyncio.sleep(0.1) # Retry delay - - assert recovery_successful, "Error recovery mechanism test failed" - print("Error recovery mechanism test passed") - - @pytest.mark.asyncio - async def test_system_graceful_shutdown(self): - """Test system graceful shutdown""" - shutdown_initiated = False - cleanup_completed = False - - async def long_running_task(task_id: int): - nonlocal shutdown_initiated, cleanup_completed - - try: - while not shutdown_initiated: - await asyncio.sleep(0.1) - # Simulate work - pass - except asyncio.CancelledError: - # Perform cleanup - cleanup_completed = True - print(f"Task {task_id} performing cleanup") - raise - - # Create long-running tasks - tasks = [asyncio.create_task(long_running_task(i)) for i in range(5)] - - # Simulate system shutdown - await asyncio.sleep(0.5) - shutdown_initiated = True - - # Cancel all tasks - for task in tasks: - task.cancel() - - # Wait for tasks to complete cleanup - try: - await asyncio.gather(*tasks, return_exceptions=True) - except Exception: - pass - - assert cleanup_completed, "Graceful shutdown test failed" - print("Graceful shutdown test passed") - - @pytest.mark.asyncio - async def test_system_performance_benchmark(self, mock_app): - """Test system performance benchmark""" - # Test performance under different loads - load_scenarios = [ - {"requests": 10, "max_time": 2.0, "min_throughput": 5}, - {"requests": 50, "max_time": 5.0, "min_throughput": 10}, - {"requests": 100, "max_time": 10.0, "min_throughput": 10}, - ] - - for scenario in load_scenarios: - start_time = time.time() - success_count = 0 - - async def benchmark_request(request_id: int): - nonlocal success_count - try: - response = mock_app.get("/test") - if response.status_code == 200: - success_count += 1 - return response.status_code - except Exception: - return 500 - - # Execute benchmark test - tasks = [ - asyncio.create_task(benchmark_request(i)) - for i in range(scenario["requests"]) - ] - results = await asyncio.gather(*tasks, return_exceptions=True) - - end_time = time.time() - total_time = end_time - start_time - throughput = scenario["requests"] / total_time - - print(f"Performance benchmark test - Requests: {scenario['requests']}") - print(f" Total time: {total_time:.2f} seconds") - print(f" Success: {success_count}/{scenario['requests']}") - print(f" Throughput: {throughput:.2f} requests/second") - - # Verify performance benchmarks - assert ( - total_time <= scenario["max_time"] - ), f"Response time exceeds benchmark: {total_time:.2f}s > {scenario['max_time']}s" - assert ( - throughput >= scenario["min_throughput"] - ), f"Throughput below benchmark: {throughput:.2f} < {scenario['min_throughput']}" - assert ( - success_count >= scenario["requests"] * 0.95 - ), f"Success rate too low: {success_count}/{scenario['requests']}" - - -class TestSystemFaultTolerance: - """System fault tolerance test class""" - - @pytest.mark.asyncio - async def test_network_timeout_handling(self): - """Test network timeout handling""" - timeout_handled = False - - async def network_operation(): - nonlocal timeout_handled - try: - # Simulate network timeout - await asyncio.wait_for(asyncio.sleep(10), timeout=1.0) - except asyncio.TimeoutError: - timeout_handled = True - print("Network timeout handled correctly") - - await network_operation() - assert timeout_handled, "Network timeout handling failed" - - @pytest.mark.asyncio - async def test_resource_exhaustion_handling(self): - """Test resource exhaustion handling""" - resource_exhausted = False - - async def resource_intensive_operation(): - nonlocal resource_exhausted - try: - # Simulate resource exhaustion - large_data = [] - for i in range(1000000): # Attempt to allocate large memory - large_data.append("x" * 1000) - if i % 100000 == 0: # Periodic check - await asyncio.sleep(0.001) - except MemoryError: - resource_exhausted = True - print("Resource exhaustion handling correct") - - await resource_intensive_operation() - assert resource_exhausted, "Resource exhaustion handling failed" - - @pytest.mark.asyncio - async def test_cascade_failure_prevention(self): - """Test cascade failure prevention""" - failure_isolated = False - - async def failing_service(): - raise Exception("Service failure") - - async def dependent_service(): - nonlocal failure_isolated - try: - await failing_service() - except Exception: - # Isolate failure, continue running - failure_isolated = True - return "Service degraded operation" - - result = await dependent_service() - assert failure_isolated, "Cascade failure prevention failed" - assert result == "Service degraded operation", "Service degradation handling failed" - print("Cascade failure prevention test passed") - - -class TestSystemMonitoring: - """System monitoring test class""" - - @pytest.mark.asyncio - async def test_system_metrics_collection(self): - """Test system metrics collection""" - metrics = { - "cpu_usage": 0, - "memory_usage": 0, - "response_time": 0, - "error_rate": 0, - "throughput": 0, - } - - # Collect system metrics - process = psutil.Process(os.getpid()) - metrics["cpu_usage"] = process.cpu_percent() - metrics["memory_usage"] = process.memory_info().rss / 1024 / 1024 # MB - - # Simulate response time test - start_time = time.time() - await asyncio.sleep(0.1) - end_time = time.time() - metrics["response_time"] = end_time - start_time - - # Simulate throughput test - request_count = 100 - start_time = time.time() - tasks = [ - asyncio.create_task(asyncio.sleep(0.001)) for _ in range(request_count) - ] - await asyncio.gather(*tasks) - end_time = time.time() - metrics["throughput"] = request_count / (end_time - start_time) - - print(f"System metrics collection results:") - for key, value in metrics.items(): - print(f" {key}: {value:.2f}") - - # Verify metric reasonableness - assert metrics["cpu_usage"] >= 0, "CPU usage abnormal" - assert metrics["memory_usage"] > 0, "Memory usage abnormal" - assert metrics["response_time"] > 0, "Response time abnormal" - assert metrics["throughput"] > 0, "Throughput abnormal" - - @pytest.mark.asyncio - async def test_alert_threshold_detection(self): - """Test alert threshold detection""" - alert_triggered = False - - def check_alert_thresholds(metrics): - nonlocal alert_triggered - - # Define alert thresholds - thresholds = { - "cpu_usage": 80.0, - "memory_usage": 1000.0, # MB - "response_time": 5.0, # seconds - "error_rate": 0.1, # 10% - } - - for metric, threshold in thresholds.items(): - if metrics.get(metric, 0) > threshold: - alert_triggered = True - print(f"Alert triggered: {metric} = {metrics[metric]} > {threshold}") - - # Simulate high-load metrics - high_load_metrics = { - "cpu_usage": 85.0, - "memory_usage": 1200.0, - "response_time": 6.0, - "error_rate": 0.15, - } - - check_alert_thresholds(high_load_metrics) - assert alert_triggered, "Alert threshold detection failed" - print("Alert threshold detection test passed") - - -if __name__ == "__main__": - # Run tests - pytest.main([__file__, "-v", "--tb=short"]) \ No newline at end of file diff --git a/methods/EverCore/tests/test_stage_timer.py b/methods/EverCore/tests/test_stage_timer.py deleted file mode 100644 index 19822af7..00000000 --- a/methods/EverCore/tests/test_stage_timer.py +++ /dev/null @@ -1,367 +0,0 @@ -"""Unit tests for StageTimer JSON output format. - -Tests cover: -- Sequential, nested, and parallel stage dict structure -- Empty timer omits 'stages' key -- total_ms accuracy -- log_summary JSON format -- JSON roundtrip serialization -- Slow threshold flag -- Summary keys completeness -- timed() / timed_parallel() convenience wrappers -- get_current_timer() / start_timer() / log_timer() context resolution -""" - -from __future__ import annotations - -import asyncio -import json -import time -from unittest.mock import patch - -from core.context.context import clear_current_app_info, set_current_app_info -from core.observation.stage_timer import ( - StageTimer, - get_current_timer, - log_timer, - start_timer, - timed, - timed_parallel, -) - - -class TestStageTimer: - """Tests for StageTimer core functionality with JSON output.""" - - def test_sequential_stages(self): - """Sequential stages produce two entries in stages list.""" - timer = StageTimer("/test") - with timer.stage("a"): - time.sleep(0.01) - with timer.stage("b"): - time.sleep(0.01) - s = timer.summary() - assert "stages" in s - assert s["stages"][0]["name"] == "a" - assert s["stages"][1]["name"] == "b" - assert isinstance(s["stages"][0]["duration_ms"], int) - assert isinstance(s["stages"][1]["duration_ms"], int) - - def test_nested_stages(self): - """Nested stages produce child stages list.""" - timer = StageTimer("/test") - with timer.stage("parent"): - with timer.stage("child"): - time.sleep(0.01) - s = timer.summary() - parent = s["stages"][0] - assert parent["name"] == "parent" - assert "stages" in parent - assert parent["stages"][0]["name"] == "child" - - def test_parallel_stages_with_gather(self): - """Parallel via asyncio.gather produces parallel group with children.""" - - async def run(): - timer = StageTimer("/test") - - async def branch_a(): - with timer.stage("a"): - await asyncio.sleep(0.01) - - async def branch_b(): - with timer.stage("b"): - await asyncio.sleep(0.01) - - with timer.parallel("group"): - await asyncio.gather(branch_a(), branch_b()) - return timer.summary() - - s = asyncio.run(run()) - group = s["stages"][0] - assert group["name"] == "group" - assert group.get("parallel") is True - child_names = {child["name"] for child in group["stages"]} - assert child_names == {"a", "b"} - - def test_nested_sequential_inside_parallel(self): - """Sequential stages nested inside parallel branches.""" - - async def run(): - timer = StageTimer("/test") - - async def episode_branch(): - with timer.stage("episode"): - with timer.stage("es"): - await asyncio.sleep(0.005) - with timer.stage("milvus"): - await asyncio.sleep(0.005) - - async def profile_branch(): - with timer.stage("profile"): - await asyncio.sleep(0.005) - - with timer.parallel("retrieval"): - await asyncio.gather(episode_branch(), profile_branch()) - return timer.summary() - - s = asyncio.run(run()) - retrieval = s["stages"][0] - assert retrieval["name"] == "retrieval" - assert retrieval.get("parallel") is True - - child_names = {child["name"] for child in retrieval["stages"]} - assert "episode" in child_names - assert "profile" in child_names - - # Find episode branch and verify nested sequential stages - episode = next(c for c in retrieval["stages"] if c["name"] == "episode") - assert "stages" in episode - nested_names = [c["name"] for c in episode["stages"]] - assert nested_names == ["es", "milvus"] - - def test_single_branch_parallel(self): - """Single child parallel still has parallel flag.""" - timer = StageTimer("/test") - with timer.parallel("group"): - with timer.stage("only"): - time.sleep(0.01) - s = timer.summary() - group = s["stages"][0] - assert group.get("parallel") is True - assert group["stages"][0]["name"] == "only" - - def test_empty_timer(self): - """No stages -> 'stages' key absent from summary.""" - timer = StageTimer("/test") - s = timer.summary() - assert "stages" not in s - - def test_total_ms_accuracy(self): - """total_ms should approximate the sum of sequential stage durations.""" - timer = StageTimer("/test") - with timer.stage("a"): - time.sleep(0.05) - with timer.stage("b"): - time.sleep(0.05) - s = timer.summary() - assert s["total_ms"] >= 90, f"Expected >= 90ms, got {s['total_ms']}ms" - - def test_log_summary_format(self): - """Verify log outputs [stage_timer] followed by valid JSON.""" - timer = StageTimer("/api/v1/memories/search") - with timer.stage("retrieval"): - time.sleep(0.01) - - with patch("core.observation.stage_timer.logger") as mock_logger: - timer.log_summary() - mock_logger.info.assert_called_once() - call_args = mock_logger.info.call_args - fmt = call_args[0][0] - assert "[stage_timer]" in fmt - # Second positional arg is the JSON string - json_str = call_args[0][1] - parsed = json.loads(json_str) - assert parsed["endpoint"] == "/api/v1/memories/search" - assert "total_ms" in parsed - assert parsed["stages"][0]["name"] == "retrieval" - - def test_json_roundtrip(self): - """summary() output survives JSON roundtrip.""" - - async def run(): - timer = StageTimer("/test") - with timer.stage("outer"): - with timer.parallel("par"): - - async def x(): - with timer.stage("x"): - await asyncio.sleep(0.005) - - async def y(): - with timer.stage("y"): - await asyncio.sleep(0.005) - - await asyncio.gather(x(), y()) - with timer.stage("final"): - await asyncio.sleep(0.005) - return timer.summary() - - s = asyncio.run(run()) - json_str = json.dumps(s, ensure_ascii=False, separators=(",", ":")) - restored = json.loads(json_str) - assert restored == s - - def test_slow_threshold_exceeded(self): - """When total_ms >= threshold, 'slow' flag appears in log output.""" - timer = StageTimer("/test") - with timer.stage("work"): - time.sleep(0.1) - - with patch("core.observation.stage_timer._SLOW_THRESHOLD_MS", 50): - with patch("core.observation.stage_timer.logger") as mock_logger: - timer.log_summary() - json_str = mock_logger.info.call_args[0][1] - parsed = json.loads(json_str) - assert parsed.get("slow") is True - - def test_slow_threshold_not_exceeded(self): - """When total_ms < threshold, 'slow' key is absent.""" - timer = StageTimer("/test") - with timer.stage("work"): - time.sleep(0.01) - - with patch("core.observation.stage_timer._SLOW_THRESHOLD_MS", 1000): - with patch("core.observation.stage_timer.logger") as mock_logger: - timer.log_summary() - json_str = mock_logger.info.call_args[0][1] - parsed = json.loads(json_str) - assert "slow" not in parsed - - def test_summary_keys_completeness_with_stages(self): - """Non-empty timer has exactly {endpoint, total_ms, stages} keys.""" - timer = StageTimer("/test") - with timer.stage("a"): - time.sleep(0.005) - s = timer.summary() - assert set(s.keys()) == {"endpoint", "total_ms", "stages"} - - def test_summary_keys_completeness_empty(self): - """Empty timer has no 'stages' or 'trace' keys.""" - timer = StageTimer("/test") - s = timer.summary() - assert "stages" not in s - assert "trace" not in s - assert set(s.keys()) == {"endpoint", "total_ms"} - - -class TestTimedConvenience: - """Tests for timed(), timed_parallel(), and get_current_timer().""" - - def test_timed_no_op_without_timer(self): - """Silent no-op when no timer in context.""" - with timed("anything"): - pass - - def test_timed_records_stage(self): - """Records stage when timer is in app_info_context.""" - timer = StageTimer("/test") - token = set_current_app_info({"stage_timer": timer}) - try: - with timed("work"): - time.sleep(0.01) - s = timer.summary() - assert s["stages"][0]["name"] == "work" - finally: - clear_current_app_info(token) - - def test_timed_parallel_marks_parallel(self): - """timed_parallel() produces parallel group in dict.""" - timer = StageTimer("/test") - token = set_current_app_info({"stage_timer": timer}) - try: - with timed_parallel("sources"): - with timed("es"): - time.sleep(0.005) - with timed("milvus"): - time.sleep(0.005) - s = timer.summary() - group = s["stages"][0] - assert group["name"] == "sources" - assert group.get("parallel") is True - child_names = [c["name"] for c in group["stages"]] - assert "es" in child_names - assert "milvus" in child_names - finally: - clear_current_app_info(token) - - def test_timed_nesting_across_functions(self): - """Nesting works across function boundaries.""" - timer = StageTimer("/test") - token = set_current_app_info({"stage_timer": timer}) - try: - - def inner_work(): - with timed("inner"): - time.sleep(0.005) - - with timed("outer"): - inner_work() - - s = timer.summary() - outer = s["stages"][0] - assert outer["name"] == "outer" - assert outer["stages"][0]["name"] == "inner" - finally: - clear_current_app_info(token) - - def test_get_current_timer(self): - """Returns timer from context or None.""" - assert get_current_timer() is None - - timer = StageTimer("/test") - token = set_current_app_info({"stage_timer": timer}) - try: - assert get_current_timer() is timer - finally: - clear_current_app_info(token) - - assert get_current_timer() is None - - def test_start_timer_creates_timer_in_context(self): - """start_timer() stores a StageTimer in app_info context.""" - token = set_current_app_info({"request_id": "test-123"}) - try: - start_timer("add") - timer = get_current_timer() - assert timer is not None - assert timer._endpoint == "add" - finally: - clear_current_app_info(token) - - def test_start_timer_no_op_without_context(self): - """start_timer() is a no-op when app_info is not set.""" - start_timer("add") - assert get_current_timer() is None - - def test_log_timer_logs_summary(self): - """log_timer() calls log_summary with JSON on the current timer.""" - timer = StageTimer("/test") - with timer.stage("a"): - time.sleep(0.005) - token = set_current_app_info({"stage_timer": timer}) - try: - with patch("core.observation.stage_timer.logger") as mock_logger: - log_timer() - mock_logger.info.assert_called_once() - fmt = mock_logger.info.call_args[0][0] - assert "[stage_timer]" in fmt - json_str = mock_logger.info.call_args[0][1] - parsed = json.loads(json_str) - assert "endpoint" in parsed - finally: - clear_current_app_info(token) - - def test_log_timer_no_op_without_timer(self): - """log_timer() is a no-op when no timer in context.""" - log_timer() - - def test_timed_parallel_no_op_without_timer(self): - """timed_parallel() is a no-op when no timer in context.""" - with timed_parallel("group"): - pass - - def test_call_llm_nests_under_parent(self): - """call_llm auto-instrumentation nests correctly under business stage.""" - timer = StageTimer("/test") - token = set_current_app_info({"stage_timer": timer}) - try: - with timed("detect_boundaries"): - with timed("call_llm"): - time.sleep(0.01) - s = timer.summary() - parent = s["stages"][0] - assert parent["name"] == "detect_boundaries" - assert parent["stages"][0]["name"] == "call_llm" - finally: - clear_current_app_info(token) diff --git a/methods/EverCore/tests/test_task_cancel.py b/methods/EverCore/tests/test_task_cancel.py deleted file mode 100644 index 3fff5f4f..00000000 --- a/methods/EverCore/tests/test_task_cancel.py +++ /dev/null @@ -1,77 +0,0 @@ -import asyncio -import time - - -async def child_task(name, swallow_cancel=False, hang=False): - try: - print(f"[{name}] Child task starts running") - await asyncio.sleep(5) # Simulate long-running work - print(f"[{name}] Child task completes normally") - except asyncio.CancelledError: - print(f"[{name}] Child task receives cancellation request") - if hang: - print(f"[{name}] Child task intentionally hangs and does not exit") - while True: # Simulate hanging - print(f"[{name}] Child task is stuck and hanging") - await asyncio.sleep(1) - elif swallow_cancel: - print(f"[{name}] Child task swallows the cancellation request") - else: - print(f"[{name}] Child task rethrows the cancellation request") - raise # Re-raise the exception - - print(f"[{name}] Child task finally ends") - - -async def parent_task(child_name, swallow=False, hang=False): - try: - print(f"[Parent task] Starts running, creating child task {child_name}") - child = asyncio.create_task(child_task(child_name, swallow, hang)) - - # Simulate other work in parent task - await asyncio.sleep(2) - print(f"[Parent task] Waiting for child task to complete") - await child - print(f"[Parent task] Completes normally") - except asyncio.CancelledError: - print(f"[Parent task] Receives cancellation request") - raise # Re-raise the exception - print(f"[Parent task] Finally ends") - - -async def main(): - # Scenario 1: Normal cancellation propagation - print("\n===== Scenario 1: Normal cancellation propagation =====") - task1 = asyncio.create_task(parent_task("Normal child task")) - await asyncio.sleep(1) - task1.cancel() - try: - await task1 - except asyncio.CancelledError: - print("[Main] Parent task has been cancelled") - await asyncio.sleep(5) - # Scenario 2: Child task swallows cancellation - print("\n===== Scenario 2: Child task swallows cancellation =====") - task2 = asyncio.create_task(parent_task("Swallowing child task", swallow=True)) - await asyncio.sleep(1) - task2.cancel() - try: - await task2 - except asyncio.CancelledError: - print("[Main] Parent task has been cancelled") - - await asyncio.sleep(5) - # Scenario 3: Child task hangs - print("\n===== Scenario 3: Child task hangs =====") - task3 = asyncio.create_task(parent_task("Hanging child task", hang=True)) - await asyncio.sleep(1) - task3.cancel() - try: - await task3 - except asyncio.CancelledError: - print("[Main] Parent task has been cancelled") - print("[Main] Finally ends") - - -if __name__ == "__main__": - asyncio.run(main()) \ No newline at end of file diff --git a/methods/EverCore/tests/test_task_cancel_2.py b/methods/EverCore/tests/test_task_cancel_2.py deleted file mode 100644 index 8b4236ed..00000000 --- a/methods/EverCore/tests/test_task_cancel_2.py +++ /dev/null @@ -1,46 +0,0 @@ -import asyncio -import time - - -async def child_task(): - try: - print(f"[{time.time():.2f}] Child task started") - await asyncio.sleep(2) - print(f"[{time.time():.2f}] Child task completed") - except asyncio.CancelledError: - print(f"[{time.time():.2f}] Child task received cancellation") - raise - - -async def parent_task(): - print(f"[{time.time():.2f}] Parent task started") - - # Perform some work before await - print(f"[{time.time():.2f}] Parent task performing some work") - await asyncio.sleep(5) # Simulate work - - try: - print(f"[{time.time():.2f}] Parent task about to await child task") - # Key point: cancellation happens here - await child_task() - print(f"[{time.time():.2f}] Parent task finished waiting") - except asyncio.CancelledError: - print(f"[{time.time():.2f}] Parent task received cancellation") - raise - - -async def main(): - parent = asyncio.create_task(parent_task()) - - # Cancel parent task immediately (before it reaches await) - print(f"[{time.time():.2f}] Immediately send cancellation request") - await asyncio.sleep(1) - parent.cancel() - - try: - await parent - except asyncio.CancelledError: - print(f"[{time.time():.2f}] Main program caught cancellation") - - -asyncio.run(main()) \ No newline at end of file diff --git a/methods/EverCore/tests/test_tenant_cache_utils.py b/methods/EverCore/tests/test_tenant_cache_utils.py deleted file mode 100644 index bb246e36..00000000 --- a/methods/EverCore/tests/test_tenant_cache_utils.py +++ /dev/null @@ -1,156 +0,0 @@ -""" -Test: TenantContextMissingError propagation in tenant_cache_utils - -Verifies that when app is ready but tenant context is missing, -TenantContextMissingError is raised instead of silently falling back. - -Run: - PYTHONPATH=src uv run pytest tests/test_tenant_cache_utils.py -v -""" - -from unittest.mock import patch, MagicMock - -import pytest - -from core.constants.exceptions import CriticalError -from core.tenants.tenant_models import TenantPatchKey -from core.tenants.tenantize.tenant_cache_utils import ( - get_or_compute_tenant_cache, - TenantContextMissingError, -) - - -@pytest.fixture -def mock_app_ready(): - """Mock tenant config with app_ready=True and no tenant context.""" - config = MagicMock() - config.app_ready = True - with ( - patch( - "core.tenants.tenantize.tenant_cache_utils.get_tenant_config", - return_value=config, - ), - patch( - "core.tenants.tenantize.tenant_cache_utils.get_current_tenant", - return_value=None, - ), - ): - yield config - - -@pytest.fixture -def mock_app_not_ready(): - """Mock tenant config with app_ready=False and no tenant context.""" - config = MagicMock() - config.app_ready = False - with ( - patch( - "core.tenants.tenantize.tenant_cache_utils.get_tenant_config", - return_value=config, - ), - patch( - "core.tenants.tenantize.tenant_cache_utils.get_current_tenant", - return_value=None, - ), - ): - yield config - - -class TestTenantContextMissingError: - """Test that strict tenant check raises TenantContextMissingError after app startup.""" - - def test_app_ready_no_tenant_raises_error(self, mock_app_ready): - """When app is ready and tenant context is missing, should raise even with fallback.""" - with pytest.raises( - TenantContextMissingError, match="Strict tenant check failed" - ): - get_or_compute_tenant_cache( - patch_key=TenantPatchKey.MILVUS_CONNECTION_CACHE_KEY, - compute_func=lambda: "computed", - fallback="default", - cache_description="test cache", - ) - - def test_app_ready_no_tenant_raises_error_callable_fallback(self, mock_app_ready): - """Callable fallback should not be invoked when strict check fails.""" - fallback_called = False - - def fallback_func(): - nonlocal fallback_called - fallback_called = True - return "fallback_value" - - with pytest.raises(TenantContextMissingError): - get_or_compute_tenant_cache( - patch_key=TenantPatchKey.MILVUS_CONNECTION_CACHE_KEY, - compute_func=lambda: "computed", - fallback=fallback_func, - cache_description="test cache", - ) - - assert ( - not fallback_called - ), "Fallback should not be called when strict check fails" - - def test_app_not_ready_uses_fallback(self, mock_app_not_ready): - """During startup (app not ready), should use fallback instead of raising.""" - result = get_or_compute_tenant_cache( - patch_key=TenantPatchKey.MILVUS_CONNECTION_CACHE_KEY, - compute_func=lambda: "computed", - fallback="default", - cache_description="test cache", - ) - assert result == "default" - - def test_app_not_ready_no_fallback_raises_runtime_error(self, mock_app_not_ready): - """During startup with no fallback, should raise RuntimeError (not TenantContextMissingError).""" - with pytest.raises(RuntimeError, match="no fallback provided"): - get_or_compute_tenant_cache( - patch_key=TenantPatchKey.MILVUS_CONNECTION_CACHE_KEY, - compute_func=lambda: "computed", - fallback=None, - cache_description="test cache", - ) - - def test_error_inherits_critical_error(self): - """TenantContextMissingError should be a CriticalError (and thus Exception).""" - assert issubclass(TenantContextMissingError, CriticalError) - assert issubclass(TenantContextMissingError, Exception) - - def test_error_not_swallowed_by_except_exception_in_cache_func( - self, mock_app_ready - ): - """The outer except Exception in get_or_compute_tenant_cache should not swallow it.""" - with pytest.raises(TenantContextMissingError): - get_or_compute_tenant_cache( - patch_key=TenantPatchKey.MILVUS_CONNECTION_CACHE_KEY, - compute_func=lambda: "computed", - fallback="default", - cache_description="test cache", - ) - - -class TestReraiseGatherCriticalErrors: - """Test that reraise_critical_errors works with asyncio.gather patterns.""" - - def test_reraise_critical_error(self): - """CriticalError in gather results should be re-raised.""" - from common_utils.async_utils import reraise_critical_errors - - error = TenantContextMissingError("tenant missing") - results = ["ok", error, "also ok"] - with pytest.raises(TenantContextMissingError, match="tenant missing"): - reraise_critical_errors(results) - - def test_regular_exceptions_not_reraised(self): - """Regular Exception in gather results should NOT be re-raised.""" - from common_utils.async_utils import reraise_critical_errors - - results = ["ok", ValueError("some error"), "also ok"] - reraise_critical_errors(results) # Should not raise - - def test_gather_isinstance_exception_still_matches(self): - """isinstance(error, Exception) should be True — CriticalError IS an Exception.""" - error = TenantContextMissingError("test") - assert isinstance(error, Exception) - assert isinstance(error, CriticalError) diff --git a/methods/EverCore/tests/test_tenant_command_interceptor.py b/methods/EverCore/tests/test_tenant_command_interceptor.py deleted file mode 100644 index fba533cb..00000000 --- a/methods/EverCore/tests/test_tenant_command_interceptor.py +++ /dev/null @@ -1,853 +0,0 @@ -#!/usr/bin/env python3 -""" -Test: TenantCommandInterceptor coverage verification - -Verifies that ALL MongoDB operation patterns used in this project -are intercepted by the TenantCommandInterceptor at the network layer. - -Strategy: - 1. Connect to real MongoDB with interceptor + CommandListener installed - 2. Set tenant context - 3. Execute every operation pattern found in the codebase - 4. CommandListener captures the FINAL command sent to MongoDB - 5. Assert tenant_id is present in every captured command - -Run: - uv run python src/bootstrap.py tests/test_tenant_field_command_interceptor.py -""" - -import asyncio -import os -from datetime import timedelta -from typing import Any, Optional -from collections import defaultdict - -from bson import ObjectId -from pymongo import monitoring -from pymongo.asynchronous.mongo_client import AsyncMongoClient - -from beanie import init_beanie - -from common_utils.datetime_utils import get_now_with_timezone -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import set_current_tenant, clear_current_tenant -from core.tenants.tenant_models import TenantInfo, TenantDetail -from core.tenants.tenantize.oxm.mongo.tenant_field_command_interceptor import ( - TenantCommandInterceptor, - install_tenant_interceptor, -) - -logger = get_logger(__name__) - -# Test constants -TEST_TENANT_ID = "test_tenant_interceptor_001" -TEST_DB_NAME = "test_interceptor_db" -TEST_COLLECTION = "test_interceptor_docs" - -# ==================== Command Capture ==================== - - -class CommandCapture(monitoring.CommandListener): - """ - Captures all commands sent to MongoDB AFTER the interceptor processes them. - This proves the interceptor is in the pipeline. - """ - - def __init__(self): - self.commands: list[dict[str, Any]] = [] - self._capture = False - - def start_capture(self): - self.commands.clear() - self._capture = True - - def stop_capture(self) -> list[dict[str, Any]]: - self._capture = False - result = list(self.commands) - self.commands.clear() - return result - - def started(self, event: monitoring.CommandStartedEvent): - if self._capture: - self.commands.append( - { - "command_name": event.command_name, - "command": dict(event.command), - "database_name": event.database_name, - } - ) - - def succeeded(self, event): - pass - - def failed(self, event): - pass - - -# ==================== Test Document (Beanie) ==================== - -from pydantic import Field -from core.oxm.mongo.document_base import DocumentBase -from core.oxm.mongo.document_base_with_soft_delete import DocumentBaseWithSoftDelete -from core.oxm.mongo.audit_base import AuditBase - - -class TestDoc(DocumentBaseWithSoftDelete, AuditBase): - """Test document with soft delete + audit, mimics real project documents.""" - - user_id: str = "" - name: str = "" - status: str = "active" - tags: list[str] = Field(default_factory=list) - score: int = 0 - - class Settings: - name = TEST_COLLECTION - use_state_management = True - - -# ==================== Test Runner ==================== - - -class InterceptorTestRunner: - def __init__(self): - self.client: Optional[AsyncMongoClient] = None - self.capture = CommandCapture() - self.results: dict[str, dict] = {} - - async def setup(self): - """Create a fresh client with interceptor + command listener.""" - host = os.getenv("MONGODB_HOST", "localhost") - port = int(os.getenv("MONGODB_PORT", "27017")) - username = os.getenv("MONGODB_USERNAME", "") - password = os.getenv("MONGODB_PASSWORD", "") - - conn_kwargs = { - "host": host, - "port": port, - "event_listeners": [self.capture], - "serverSelectionTimeoutMS": 5000, - } - if username and password: - from urllib.parse import quote_plus - - uri = ( - f"mongodb://{quote_plus(username)}:{quote_plus(password)}@{host}:{port}" - ) - self.client = AsyncMongoClient( - uri, - **{k: v for k, v in conn_kwargs.items() if k != "host" and k != "port"}, - ) - else: - self.client = AsyncMongoClient(**conn_kwargs) - - # Install interceptor - install_tenant_interceptor( - self.client, excluded_collections={"__excluded_test__"} - ) - - # Init beanie with test document - db = self.client[TEST_DB_NAME] - await init_beanie(database=db, document_models=[TestDoc]) - - # Clean up test collection - await db[TEST_COLLECTION].delete_many({}) - - # Set tenant context - tenant_info = TenantInfo( - tenant_id=TEST_TENANT_ID, - tenant_detail=TenantDetail(tenant_info={}, storage_info={}), - ) - set_current_tenant(tenant_info) - - logger.info("Setup complete. Interceptor installed, tenant=%s", TEST_TENANT_ID) - - async def teardown(self): - """Cleanup.""" - clear_current_tenant() - if self.client: - await self.client[TEST_DB_NAME].drop_collection(TEST_COLLECTION) - await self.client.close() - - def _check_command( - self, - test_name: str, - commands: list[dict], - expected_cmd_name: str, - check_fn: Any = None, - ) -> bool: - """Verify a captured command has tenant_id injected.""" - matched = [c for c in commands if c["command_name"] == expected_cmd_name] - if not matched: - self.results[test_name] = { - "status": "FAIL", - "reason": f"No '{expected_cmd_name}' command captured. Got: {[c['command_name'] for c in commands]}", - } - return False - - cmd = matched[-1]["command"] # Take the last one - - if check_fn: - ok, reason = check_fn(cmd) - else: - ok, reason = self._default_tenant_check(expected_cmd_name, cmd) - - self.results[test_name] = { - "status": "PASS" if ok else "FAIL", - "reason": reason, - "command": cmd, - } - return ok - - def _default_tenant_check(self, cmd_name: str, cmd: dict) -> tuple[bool, str]: - """Default check: verify tenant_id in the expected location.""" - if cmd_name == "insert": - docs = cmd.get("documents", []) - if not docs: - return False, "No documents in insert command" - for i, doc in enumerate(docs): - if doc.get("tenant_id") != TEST_TENANT_ID: - return False, f"documents[{i}] missing tenant_id" - return True, f"tenant_id injected into {len(docs)} document(s)" - - elif cmd_name == "update": - updates = cmd.get("updates", []) - for i, u in enumerate(updates): - q = u.get("q", {}) - if q.get("tenant_id") != TEST_TENANT_ID: - return False, f"updates[{i}].q missing tenant_id" - return True, f"tenant_id injected into {len(updates)} update filter(s)" - - elif cmd_name == "delete": - deletes = cmd.get("deletes", []) - for i, d in enumerate(deletes): - q = d.get("q", {}) - if q.get("tenant_id") != TEST_TENANT_ID: - return False, f"deletes[{i}].q missing tenant_id" - return True, f"tenant_id injected into {len(deletes)} delete filter(s)" - - elif cmd_name == "find": - f = cmd.get("filter", {}) - if f.get("tenant_id") != TEST_TENANT_ID: - return False, f"filter missing tenant_id. Got: {f}" - return True, "tenant_id in filter" - - elif cmd_name == "findAndModify": - q = cmd.get("query", {}) - if q.get("tenant_id") != TEST_TENANT_ID: - return False, f"query missing tenant_id. Got: {q}" - return True, "tenant_id in query" - - elif cmd_name == "aggregate": - pipeline = cmd.get("pipeline", []) - if not pipeline: - return False, "Empty pipeline" - first_stage = pipeline[0] - match = first_stage.get("$match", {}) - if match.get("tenant_id") != TEST_TENANT_ID: - return ( - False, - f"First $match stage missing tenant_id. Got: {first_stage}", - ) - return True, "tenant_id in first $match stage" - - elif cmd_name == "count": - q = cmd.get("query", {}) - if q.get("tenant_id") != TEST_TENANT_ID: - return False, f"query missing tenant_id. Got: {q}" - return True, "tenant_id in count query" - - elif cmd_name == "distinct": - q = cmd.get("query", {}) - if q.get("tenant_id") != TEST_TENANT_ID: - return False, f"query missing tenant_id. Got: {q}" - return True, "tenant_id in distinct query" - - return False, f"Unknown command type: {cmd_name}" - - # ==================== Test Cases ==================== - - async def test_beanie_insert_single(self): - """Beanie: document.insert() — used by BaseRepository.create()""" - doc = TestDoc(user_id="u1", name="Alice", status="active") - self.capture.start_capture() - await doc.insert() - cmds = self.capture.stop_capture() - self._check_command("beanie_insert_single", cmds, "insert") - - async def test_beanie_insert_many(self): - """Beanie: Model.insert_many() — used by BaseRepository.create_batch()""" - docs = [ - TestDoc(user_id="u2", name="Bob"), - TestDoc(user_id="u3", name="Charlie"), - ] - self.capture.start_capture() - await TestDoc.insert_many(docs) - cmds = self.capture.stop_capture() - self._check_command("beanie_insert_many", cmds, "insert") - - async def test_beanie_save(self): - """Beanie: document.save() — used by BaseRepository.update()""" - doc = TestDoc(user_id="u4", name="Dave") - await doc.insert() - - doc.name = "Dave Updated" - self.capture.start_capture() - await doc.save() - cmds = self.capture.stop_capture() - # save() internally does replace_one → update command - # OR findAndModify depending on Beanie version - update_cmds = [ - c for c in cmds if c["command_name"] in ("update", "findAndModify") - ] - if update_cmds: - cmd_name = update_cmds[-1]["command_name"] - self._check_command("beanie_save", cmds, cmd_name) - else: - self.results["beanie_save"] = { - "status": "FAIL", - "reason": f"No update/findAndModify captured. Got: {[c['command_name'] for c in cmds]}", - } - - async def test_beanie_find(self): - """Beanie: Model.find(query) — used widely in repositories""" - self.capture.start_capture() - await TestDoc.find({"user_id": "u1"}).to_list() - cmds = self.capture.stop_capture() - self._check_command("beanie_find", cmds, "find") - - async def test_beanie_find_one(self): - """Beanie: Model.find_one(query)""" - self.capture.start_capture() - await TestDoc.find_one({"user_id": "u1"}) - cmds = self.capture.stop_capture() - self._check_command("beanie_find_one", cmds, "find") - - async def test_beanie_find_chained(self): - """Beanie: Model.find(query).sort().skip().limit() — used in repository queries""" - self.capture.start_capture() - await TestDoc.find({"status": "active"}).sort("name").skip(0).limit( - 10 - ).to_list() - cmds = self.capture.stop_capture() - self._check_command("beanie_find_chained", cmds, "find") - - async def test_beanie_find_count(self): - """Beanie: Model.find(query).count() — used in repository count queries""" - self.capture.start_capture() - await TestDoc.find({"status": "active"}).count() - cmds = self.capture.stop_capture() - # .count() internally uses aggregate with $match + $group - # Check if aggregate pipeline has tenant_id - agg_cmds = [c for c in cmds if c["command_name"] == "aggregate"] - if agg_cmds: - self._check_command("beanie_find_count", cmds, "aggregate") - else: - # Fallback: might use count command - self._check_command("beanie_find_count", cmds, "count") - - async def test_cursor_getmore(self): - """Cursor: verify find goes through interceptor, getMore is passthrough. - - When cursor batch_size < total docs, PyMongo sends: - 1. find command (with filter) → should have tenant_id - 2. getMore command (no filter) → passthrough, no tenant_id needed - - This tests the CURSOR path: - cursor._refresh() → _send_message(_Query) → client._run_operation() - → server.run_operation() → server.operation_to_command() - → _encrypter.encrypt() ← interceptor hook - """ - # Insert enough docs to trigger getMore (batch_size=2, insert 5) - for i in range(5): - await TestDoc(user_id=f"u_cursor_{i}", name=f"Cursor{i}").insert() - - self.capture.start_capture() - # Small batch_size forces getMore - cursor = TestDoc.find({"user_id": {"$regex": "^u_cursor_"}}).batch_size(2) - results = await cursor.to_list() - cmds = self.capture.stop_capture() - - # Verify find command has tenant_id - find_cmds = [c for c in cmds if c["command_name"] == "find"] - getmore_cmds = [c for c in cmds if c["command_name"] == "getMore"] - - if not find_cmds: - self.results["cursor_getmore"] = { - "status": "FAIL", - "reason": f"No find command captured. Got: {[c['command_name'] for c in cmds]}", - } - return - - find_filter = find_cmds[0]["command"].get("filter", {}) - find_ok = find_filter.get("tenant_id") == TEST_TENANT_ID - - self.results["cursor_getmore"] = { - "status": "PASS" if find_ok else "FAIL", - "reason": ( - f"find filter has tenant_id: {find_ok}, " - f"getMore commands: {len(getmore_cmds)} (passthrough, no filter needed), " - f"total docs fetched: {len(results)}" - ), - } - - async def test_cursor_async_for(self): - """Cursor: async for iteration — the most common cursor consumption pattern.""" - self.capture.start_capture() - results = [] - async for doc in TestDoc.find({"status": "active"}): - results.append(doc) - if len(results) >= 3: - break - cmds = self.capture.stop_capture() - self._check_command("cursor_async_for", cmds, "find") - - async def test_beanie_find_delete(self): - """Beanie: Model.find(query).delete() — used for bulk delete via query chain""" - # Insert a doc to delete - doc = TestDoc(user_id="u_del_chain", name="ToDeleteChain") - await doc.insert() - - self.capture.start_capture() - await TestDoc.find({"user_id": "u_del_chain"}).delete() - cmds = self.capture.stop_capture() - self._check_command("beanie_find_delete", cmds, "delete") - - async def test_beanie_get_by_id(self): - """Beanie: Model.get(object_id) — used by BaseRepository.get_by_id()""" - doc = TestDoc(user_id="u_get", name="GetById") - await doc.insert() - - self.capture.start_capture() - await TestDoc.get(doc.id) - cmds = self.capture.stop_capture() - self._check_command("beanie_get_by_id", cmds, "find") - - async def test_beanie_delete_instance(self): - """Beanie: document.delete() — hard delete on instance""" - doc = TestDoc(user_id="u_hard_del", name="HardDel") - await doc.insert() - - self.capture.start_capture() - await doc.delete() - cmds = self.capture.stop_capture() - self._check_command("beanie_delete_instance", cmds, "delete") - - # ---- Soft Delete Operations ---- - - async def test_soft_delete_single(self): - """SoftDelete: document.delete(deleted_by=...) — used in all repositories""" - doc = TestDoc(user_id="u_soft", name="SoftDel") - await doc.insert() - - self.capture.start_capture() - await doc.delete(deleted_by="test_admin") - cmds = self.capture.stop_capture() - # Soft delete uses PyMongo update_one internally - self._check_command("soft_delete_single", cmds, "update") - - async def test_soft_delete_many(self): - """SoftDelete: Model.delete_many(filter) — bulk soft delete""" - await TestDoc(user_id="u_bulk_sd_1", name="BulkSD1").insert() - await TestDoc(user_id="u_bulk_sd_2", name="BulkSD2").insert() - - self.capture.start_capture() - await TestDoc.delete_many( - {"user_id": {"$regex": "^u_bulk_sd"}}, deleted_by="admin" - ) - cmds = self.capture.stop_capture() - self._check_command("soft_delete_many", cmds, "update") - - async def test_soft_delete_find_many(self): - """SoftDelete: Model.find_many(query) — auto-filters deleted_at=None""" - self.capture.start_capture() - await TestDoc.find_many({"status": "active"}).to_list() - cmds = self.capture.stop_capture() - self._check_command("soft_delete_find_many", cmds, "find") - - async def test_soft_delete_find_one(self): - """SoftDelete: Model.find_one(query) — auto-filters deleted_at=None""" - self.capture.start_capture() - await TestDoc.find_one({"user_id": "u1"}) - cmds = self.capture.stop_capture() - self._check_command("soft_delete_find_one", cmds, "find") - - async def test_hard_find_many(self): - """SoftDelete: Model.hard_find_many(query) — include deleted""" - self.capture.start_capture() - await TestDoc.hard_find_many({"status": "active"}).to_list() - cmds = self.capture.stop_capture() - self._check_command("hard_find_many", cmds, "find") - - async def test_hard_delete_many(self): - """SoftDelete: Model.hard_delete_many(filter) — physical bulk delete""" - await TestDoc(user_id="u_hd_1", name="HD1").insert() - - self.capture.start_capture() - await TestDoc.hard_delete_many({"user_id": "u_hd_1"}) - cmds = self.capture.stop_capture() - self._check_command("hard_delete_many", cmds, "delete") - - async def test_soft_delete_count(self): - """SoftDelete: Model.count() — count with soft delete awareness""" - self.capture.start_capture() - await TestDoc.count() - cmds = self.capture.stop_capture() - # count() uses count_documents or estimated_document_count - count_cmds = [c for c in cmds if c["command_name"] in ("count", "aggregate")] - if count_cmds: - self._check_command( - "soft_delete_count", cmds, count_cmds[-1]["command_name"] - ) - else: - self.results["soft_delete_count"] = { - "status": "WARN", - "reason": f"estimated_document_count does not support filter. Got: {[c['command_name'] for c in cmds]}", - } - - # ---- Direct PyMongo Operations ---- - - async def test_pymongo_find(self): - """PyMongo: collection.find(filter) — used in demo/debug scripts""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.find({"status": "active"}).to_list(length=10) - cmds = self.capture.stop_capture() - self._check_command("pymongo_find", cmds, "find") - - async def test_pymongo_find_one(self): - """PyMongo: collection.find_one(filter) — used in debug scripts""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.find_one({"status": "active"}) - cmds = self.capture.stop_capture() - self._check_command("pymongo_find_one", cmds, "find") - - async def test_pymongo_insert_one(self): - """PyMongo: collection.insert_one(doc)""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.insert_one( - {"user_id": "pymongo_u1", "name": "PyInsert", "status": "active"} - ) - cmds = self.capture.stop_capture() - self._check_command("pymongo_insert_one", cmds, "insert") - - async def test_pymongo_update_one(self): - """PyMongo: collection.update_one(filter, update) — used in soft delete impl""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.update_one( - {"user_id": "pymongo_u1"}, {"$set": {"name": "PyUpdated"}} - ) - cmds = self.capture.stop_capture() - self._check_command("pymongo_update_one", cmds, "update") - - async def test_pymongo_update_many(self): - """PyMongo: collection.update_many(filter, update) — used in raw_message_repository""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.update_many({"status": "active"}, {"$set": {"score": 100}}) - cmds = self.capture.stop_capture() - self._check_command("pymongo_update_many", cmds, "update") - - async def test_pymongo_replace_one(self): - """PyMongo: collection.replace_one(filter, replacement)""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.replace_one( - {"user_id": "pymongo_u1"}, - {"user_id": "pymongo_u1", "name": "PyReplaced", "status": "replaced"}, - ) - cmds = self.capture.stop_capture() - - def check_replace(cmd): - updates = cmd.get("updates", []) - if not updates: - return False, "No updates in command" - u = updates[0] - q = u.get("q", {}) - replacement = u.get("u", {}) - q_ok = q.get("tenant_id") == TEST_TENANT_ID - r_ok = replacement.get("tenant_id") == TEST_TENANT_ID - if not q_ok: - return False, f"Filter missing tenant_id: {q}" - if not r_ok: - return False, f"Replacement missing tenant_id: {replacement}" - return True, "tenant_id in both filter and replacement" - - self._check_command("pymongo_replace_one", cmds, "update", check_replace) - - async def test_pymongo_delete_one(self): - """PyMongo: collection.delete_one(filter)""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.delete_one({"user_id": "pymongo_u1"}) - cmds = self.capture.stop_capture() - self._check_command("pymongo_delete_one", cmds, "delete") - - async def test_pymongo_delete_many(self): - """PyMongo: collection.delete_many(filter) — used in clear_all_data, hard_delete""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.delete_many({"status": "replaced"}) - cmds = self.capture.stop_capture() - self._check_command("pymongo_delete_many", cmds, "delete") - - async def test_pymongo_aggregate(self): - """PyMongo: collection.aggregate(pipeline) — used in memory_utils, demo scripts""" - collection = TestDoc.get_pymongo_collection() - pipeline = [ - {"$match": {"status": "active"}}, - {"$group": {"_id": "$user_id", "count": {"$sum": 1}}}, - ] - self.capture.start_capture() - cursor = collection.aggregate(pipeline) - await cursor.to_list(length=100) - cmds = self.capture.stop_capture() - self._check_command("pymongo_aggregate", cmds, "aggregate") - - async def test_pymongo_count_documents(self): - """PyMongo: collection.count_documents(filter) — used in soft delete count""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.count_documents({"status": "active"}) - cmds = self.capture.stop_capture() - # count_documents uses aggregate internally in newer PyMongo - agg_cmds = [c for c in cmds if c["command_name"] == "aggregate"] - if agg_cmds: - self._check_command("pymongo_count_documents", cmds, "aggregate") - else: - self._check_command("pymongo_count_documents", cmds, "count") - - async def test_pymongo_estimated_document_count(self): - """PyMongo: collection.estimated_document_count() — no filter support""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.estimated_document_count() - cmds = self.capture.stop_capture() - # estimated_document_count uses the 'count' command without a query filter - count_cmds = [c for c in cmds if c["command_name"] == "count"] - if count_cmds: - cmd = count_cmds[-1]["command"] - q = cmd.get("query", {}) - has_tenant = q.get("tenant_id") == TEST_TENANT_ID - self.results["pymongo_estimated_document_count"] = { - "status": "PASS" if has_tenant else "WARN", - "reason": f"estimated_document_count query: {q}. " - + ( - "tenant_id injected" - if has_tenant - else "No query param — count cmd gets tenant_id but result is still global estimate" - ), - } - else: - self.results["pymongo_estimated_document_count"] = { - "status": "WARN", - "reason": f"No count command captured. Got: {[c['command_name'] for c in cmds]}", - } - - async def test_pymongo_distinct(self): - """PyMongo: collection.distinct(key, filter)""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.distinct("status", {"user_id": "u1"}) - cmds = self.capture.stop_capture() - self._check_command("pymongo_distinct", cmds, "distinct") - - async def test_pymongo_find_one_and_update(self): - """PyMongo: collection.find_one_and_update() — findAndModify command""" - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.find_one_and_update( - {"user_id": "u1"}, {"$set": {"score": 999}} - ) - cmds = self.capture.stop_capture() - self._check_command("pymongo_find_one_and_update", cmds, "findAndModify") - - async def test_pymongo_find_one_and_delete(self): - """PyMongo: collection.find_one_and_delete()""" - # Insert a disposable doc - collection = TestDoc.get_pymongo_collection() - await collection.insert_one({"user_id": "u_fad", "name": "FindAndDel"}) - - self.capture.start_capture() - await collection.find_one_and_delete({"user_id": "u_fad"}) - cmds = self.capture.stop_capture() - self._check_command("pymongo_find_one_and_delete", cmds, "findAndModify") - - async def test_pymongo_find_one_and_replace(self): - """PyMongo: collection.find_one_and_replace()""" - collection = TestDoc.get_pymongo_collection() - await collection.insert_one({"user_id": "u_far", "name": "FindAndReplace"}) - - self.capture.start_capture() - await collection.find_one_and_replace( - {"user_id": "u_far"}, - {"user_id": "u_far", "name": "Replaced", "status": "new"}, - ) - cmds = self.capture.stop_capture() - - def check_far(cmd): - q = cmd.get("query", {}) - update = cmd.get("update", {}) - q_ok = q.get("tenant_id") == TEST_TENANT_ID - # replacement doc (no $ operators) should get tenant_id - u_ok = update.get("tenant_id") == TEST_TENANT_ID - if not q_ok: - return False, f"query missing tenant_id: {q}" - if not u_ok: - return False, f"replacement missing tenant_id: {update}" - return True, "tenant_id in both query and replacement" - - self._check_command( - "pymongo_find_one_and_replace", cmds, "findAndModify", check_far - ) - - # ---- Excluded Collection Test ---- - - async def test_excluded_collection_passthrough(self): - """Verify excluded collections are NOT intercepted.""" - db = self.client[TEST_DB_NAME] - excluded_coll = db["__excluded_test__"] - - self.capture.start_capture() - await excluded_coll.insert_one( - {"user_id": "excluded", "name": "ShouldNotHaveTenant"} - ) - cmds = self.capture.stop_capture() - - insert_cmds = [c for c in cmds if c["command_name"] == "insert"] - if insert_cmds: - doc = insert_cmds[-1]["command"].get("documents", [{}])[0] - has_tenant = "tenant_id" in doc - self.results["excluded_collection_passthrough"] = { - "status": "PASS" if not has_tenant else "FAIL", - "reason": ( - "Excluded collection correctly skipped" - if not has_tenant - else f"tenant_id should NOT be in excluded collection doc: {doc}" - ), - } - else: - self.results["excluded_collection_passthrough"] = { - "status": "FAIL", - "reason": "No insert command captured", - } - - # Cleanup - await excluded_coll.drop() - - # ---- No Tenant Context Test ---- - - async def test_no_tenant_passthrough(self): - """Verify commands pass through unmodified when no tenant context.""" - clear_current_tenant() - - collection = TestDoc.get_pymongo_collection() - self.capture.start_capture() - await collection.find_one({"user_id": "no_tenant"}) - cmds = self.capture.stop_capture() - - find_cmds = [c for c in cmds if c["command_name"] == "find"] - if find_cmds: - f = find_cmds[-1]["command"].get("filter", {}) - has_tenant = "tenant_id" in f - self.results["no_tenant_passthrough"] = { - "status": "PASS" if not has_tenant else "FAIL", - "reason": ( - "No tenant_id when context empty" - if not has_tenant - else f"Unexpected tenant_id in filter: {f}" - ), - } - else: - self.results["no_tenant_passthrough"] = { - "status": "FAIL", - "reason": "No find command captured", - } - - # Restore tenant context for remaining tests - tenant_info = TenantInfo( - tenant_id=TEST_TENANT_ID, - tenant_detail=TenantDetail(tenant_info={}, storage_info={}), - ) - set_current_tenant(tenant_info) - - # ==================== Run All ==================== - - async def run_all(self): - """Execute all tests and print report.""" - await self.setup() - - test_methods = [ - m for m in dir(self) if m.startswith("test_") and callable(getattr(self, m)) - ] - test_methods.sort() - - for method_name in test_methods: - method = getattr(self, method_name) - try: - await method() - except Exception as e: - self.results[method_name] = { - "status": "ERROR", - "reason": f"{type(e).__name__}: {e}", - } - - await self.teardown() - self._print_report() - - def _print_report(self): - """Print test results summary.""" - print("\n" + "=" * 80) - print(" TenantCommandInterceptor Coverage Report") - print("=" * 80) - - pass_count = 0 - fail_count = 0 - warn_count = 0 - error_count = 0 - - for name, result in sorted(self.results.items()): - status = result["status"] - reason = result["reason"] - - if status == "PASS": - icon = "✅" - pass_count += 1 - elif status == "WARN": - icon = "⚠️ " - warn_count += 1 - elif status == "ERROR": - icon = "💥" - error_count += 1 - else: - icon = "❌" - fail_count += 1 - - print(f" {icon} {name}") - print(f" {reason}") - - print("\n" + "-" * 80) - print( - f" Total: {len(self.results)} | PASS: {pass_count} | WARN: {warn_count} | FAIL: {fail_count} | ERROR: {error_count}" - ) - - if fail_count == 0 and error_count == 0: - print(" Result: ALL OPERATIONS INTERCEPTED SUCCESSFULLY") - else: - print(" Result: SOME OPERATIONS NOT INTERCEPTED — SEE ABOVE") - print("=" * 80 + "\n") - - -# ==================== Entry Point ==================== - - -async def main(): - runner = InterceptorTestRunner() - await runner.run_all() - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/tests/test_tenant_es_isolation_e2e.py b/methods/EverCore/tests/test_tenant_es_isolation_e2e.py deleted file mode 100644 index ac2efb74..00000000 --- a/methods/EverCore/tests/test_tenant_es_isolation_e2e.py +++ /dev/null @@ -1,1228 +0,0 @@ -#!/usr/bin/env python3 -# skip-sensitive-file -""" -End-to-end tenant isolation verification for Elasticsearch operations. - -Uses the EpisodicMemory ES index as the test subject. Bootstraps the full -application context, inserts similar data for two tenants, then verifies -every ES operation correctly isolates (or rejects) data between tenants. - -Coverage: - ACTIVE query endpoints: - - search: match, bool, term, range, match_all, function_score, post_filter - - count - - delete_by_query - - ACTIVE write endpoints: - - index (single doc) - - create (single doc) - - bulk (index/create actions) - - ACTIVE ID-based endpoints (converted to query-based in shared mode): - - get (→ search) - - exists (→ count) - - delete (→ delete_by_query) - - BLOCKED endpoints (should raise TenantIsolationViolation): - - update_by_query - - msearch, knn_search, terms_enum, field_caps - - update (by ID) - - UNSUPPORTED endpoints (should raise TenantIsolationViolation): - - search_template, msearch_template, rank_eval, mget - - BLOCKED in bulk: - - bulk delete action - - BLOCKED in query body: - - suggest - - Control-plane passthrough: - - indices.refresh, indices.exists, ping - - Cross-tenant protection: - - search, get, delete isolation between tenants - - Query DSL patterns: - - wrap_query_with_tenant: non-bool, existing bool, bool with filter list, - bool with filter dict - - inject_query_body: None body, existing query, post_filter, suggest block - - inject_bulk_body: index/create/update actions, delete rejection - - make_ids_tenant_query - -Run: - uv run python src/bootstrap.py tests/test_tenant_es_isolation_e2e.py -""" - -import asyncio -import traceback -from contextlib import asynccontextmanager -from datetime import timedelta -from typing import Optional - -from common_utils.datetime_utils import get_now_with_timezone -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import ( - set_current_tenant, - clear_current_tenant, - get_current_tenant_id, -) -from core.tenants.tenant_models import TenantInfo, TenantDetail -from core.tenants.tenantize.oxm.es.tenant_field_es_interceptor import ( - TenantIsolationViolation, - wrap_query_with_tenant, - make_tenant_only_query, - inject_query_body, - inject_bulk_body, - inject_knn_filter_body, - inject_index_filter_body, - make_ids_tenant_query, -) - -logger = get_logger(__name__) - -# ============================================================ -# Constants -# ============================================================ - -TENANT_1 = "test_es_tenant_001" -TENANT_2 = "test_es_tenant_002" - -BASE_TIME = get_now_with_timezone() - timedelta(hours=2) -TEST_INDEX = None # Will be resolved at runtime from the model - - -def _make_es_doc( - doc_id: str, - user_id: str, - group_id: str, - summary: str, - episode: str, - offset_minutes: int = 0, -) -> dict: - """Build a raw ES document dict for direct API calls.""" - ts = BASE_TIME + timedelta(minutes=offset_minutes) - return { - "id": doc_id, - "user_id": user_id, - "group_id": group_id, - "session_id": "sess_es_001", - "timestamp": ts.isoformat(), - "summary": summary, - "subject": "es test subject", - "episode": episode, - "search_content": [summary, episode], - "type": "Conversation", - "parent_type": "memcell", - "parent_id": "parent_es_001", - } - - -# ============================================================ -# Tenant context manager -# ============================================================ - - -@asynccontextmanager -async def tenant_context(tenant_id: str): - """Set and clear tenant context for ES operations.""" - tenant_info = TenantInfo( - tenant_id=tenant_id, - tenant_detail=TenantDetail( - tenant_info={}, storage_info={}, isolation_mode="shared" - ), - ) - set_current_tenant(tenant_info) - try: - yield tenant_info - finally: - clear_current_tenant() - - -# ============================================================ -# Test result tracking -# ============================================================ - - -class TestReport: - def __init__(self): - self.results: list[tuple[str, str, str]] = [] - - def record(self, name: str, status: str, detail: str = ""): - self.results.append((name, status, detail)) - icon = {"PASS": "✅", "FAIL": "❌", "ERROR": "💥", "SKIP": "⏭️"}.get( - status, "❓" - ) - print(f" {icon} {name}") - if detail: - print(f" {detail}") - - def summary(self): - print("\n" + "=" * 80) - print(" Tenant ES Isolation E2E Report") - print("=" * 80) - counts = {"PASS": 0, "FAIL": 0, "ERROR": 0, "SKIP": 0} - for _, status, _ in self.results: - counts[status] = counts.get(status, 0) + 1 - total = len(self.results) - print( - f" Total: {total} | PASS: {counts['PASS']} | FAIL: {counts['FAIL']} " - f"| ERROR: {counts['ERROR']} | SKIP: {counts['SKIP']}" - ) - if counts["FAIL"] == 0 and counts["ERROR"] == 0: - print(" ✅ ALL TESTS PASSED — ES TENANT ISOLATION VERIFIED") - else: - print(" ❌ SOME TESTS FAILED — SEE ABOVE") - print("=" * 80 + "\n") - return counts["FAIL"] == 0 and counts["ERROR"] == 0 - - -report = TestReport() - - -# ============================================================ -# Helper: get ES client under tenant context -# ============================================================ - - -async def _get_client(): - """Get the tenant-aware ES client from the document model.""" - from infra_layer.adapters.out.search.elasticsearch.memory.episodic_memory import ( - EpisodicMemoryDoc, - ) - - return EpisodicMemoryDoc.get_connection() - - -def _get_index_name(): - """Get the tenant-aware index name.""" - from infra_layer.adapters.out.search.elasticsearch.memory.episodic_memory import ( - EpisodicMemoryDoc, - ) - - return EpisodicMemoryDoc.get_index_name() - - -# ============================================================ -# Setup & Teardown -# ============================================================ - - -async def setup_test_data(): - """Insert test data for both tenants. Returns inserted doc IDs per tenant.""" - ids = {TENANT_1: [], TENANT_2: []} - - for tid in [TENANT_1, TENANT_2]: - async with tenant_context(tid): - client = await _get_client() - index = _get_index_name() - - for i in range(5): - doc_id = f"{tid}_doc_{i}" - doc = _make_es_doc( - doc_id=doc_id, - user_id=f"user_{i}", - group_id="group_alpha", - summary=f"Summary {i} for es test", - episode=f"Episode content {i} detailed narrative", - offset_minutes=i * 10, - ) - await client.index(index=index, id=doc_id, body=doc, refresh="wait_for") - ids[tid].append(doc_id) - - # 2 more in group_beta - for i in range(2): - doc_id = f"{tid}_beta_{i}" - doc = _make_es_doc( - doc_id=doc_id, - user_id=f"user_{i}", - group_id="group_beta", - summary=f"Beta summary {i}", - episode=f"Beta episode {i}", - offset_minutes=50 + i * 10, - ) - await client.index(index=index, id=doc_id, body=doc, refresh="wait_for") - ids[tid].append(doc_id) - - return ids - - -async def cleanup_test_data(): - """Delete all test data for both tenants.""" - for tid in [TENANT_1, TENANT_2]: - async with tenant_context(tid): - client = await _get_client() - index = _get_index_name() - try: - await client.delete_by_query( - index=index, - body={"query": {"term": {"session_id": "sess_es_001"}}}, - refresh=True, - ) - except Exception: - pass - - -# ============================================================ -# Part 1: Unit Tests — Query Utility Functions -# ============================================================ - - -async def test_wrap_query_non_bool(ids: dict): - """wrap_query_with_tenant: non-bool query gets wrapped in bool.must + filter.""" - name = "util_wrap_non_bool" - try: - query = {"match": {"content": "hello"}} - result = wrap_query_with_tenant(query, TENANT_1) - assert "bool" in result - assert result["bool"]["must"] == [{"match": {"content": "hello"}}] - assert {"term": {"tenant_id": TENANT_1}} in result["bool"]["filter"] - report.record(name, "PASS", "Non-bool wrapped correctly") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_wrap_query_existing_bool(ids: dict): - """wrap_query_with_tenant: existing bool gets tenant filter merged into filter.""" - name = "util_wrap_existing_bool" - try: - query = {"bool": {"must": [{"match": {"content": "hello"}}]}} - result = wrap_query_with_tenant(query, TENANT_1) - assert "bool" in result - assert {"term": {"tenant_id": TENANT_1}} in result["bool"]["filter"] - # Original must clause preserved - assert {"match": {"content": "hello"}} in result["bool"]["must"] - report.record(name, "PASS", "Existing bool merged correctly") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_wrap_query_bool_with_filter_list(ids: dict): - """wrap_query_with_tenant: bool with existing filter list gets tenant appended.""" - name = "util_wrap_bool_filter_list" - try: - query = { - "bool": { - "must": [{"match": {"content": "hello"}}], - "filter": [{"term": {"user_id": "u001"}}], - } - } - result = wrap_query_with_tenant(query, TENANT_1) - filters = result["bool"]["filter"] - assert {"term": {"user_id": "u001"}} in filters - assert {"term": {"tenant_id": TENANT_1}} in filters - assert len(filters) == 2 - report.record(name, "PASS", "Tenant appended to existing filter list") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_wrap_query_bool_with_filter_dict(ids: dict): - """wrap_query_with_tenant: bool with filter as dict gets converted to list.""" - name = "util_wrap_bool_filter_dict" - try: - query = {"bool": {"filter": {"term": {"status": "active"}}}} - result = wrap_query_with_tenant(query, TENANT_1) - filters = result["bool"]["filter"] - assert isinstance(filters, list) - assert {"term": {"status": "active"}} in filters - assert {"term": {"tenant_id": TENANT_1}} in filters - report.record(name, "PASS", "Filter dict converted to list, tenant appended") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_query_body_none(ids: dict): - """inject_query_body: None body gets tenant-only query.""" - name = "util_inject_body_none" - try: - result = inject_query_body(None, TENANT_1) - assert "query" in result - assert "bool" in result["query"] - filters = result["query"]["bool"]["filter"] - assert {"term": {"tenant_id": TENANT_1}} in filters - report.record(name, "PASS", "None body gets tenant-only query") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_query_body_with_query(ids: dict): - """inject_query_body: existing query gets wrapped.""" - name = "util_inject_body_with_query" - try: - body = {"query": {"match": {"content": "hello"}}, "size": 10} - result = inject_query_body(body, TENANT_1) - assert result["size"] == 10 # preserved - assert "bool" in result["query"] - assert {"term": {"tenant_id": TENANT_1}} in result["query"]["bool"]["filter"] - report.record(name, "PASS", "Existing query wrapped, other fields preserved") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_query_body_with_post_filter(ids: dict): - """inject_query_body: post_filter also gets tenant filter.""" - name = "util_inject_body_post_filter" - try: - body = { - "query": {"match_all": {}}, - "post_filter": {"term": {"status": "active"}}, - } - result = inject_query_body(body, TENANT_1) - # Both query and post_filter should have tenant - assert "bool" in result["query"] - assert "bool" in result["post_filter"] - pf_filters = result["post_filter"]["bool"]["filter"] - assert {"term": {"tenant_id": TENANT_1}} in pf_filters - report.record(name, "PASS", "post_filter also wrapped with tenant") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_query_body_suggest_blocked(ids: dict): - """inject_query_body: suggest in body raises TenantIsolationViolation.""" - name = "util_inject_suggest_blocked" - try: - body = { - "query": {"match_all": {}}, - "suggest": {"my-suggest": {"text": "hello", "term": {"field": "content"}}}, - } - try: - inject_query_body(body, TENANT_1) - report.record(name, "FAIL", "suggest did NOT raise") - except TenantIsolationViolation: - report.record(name, "PASS", "suggest correctly blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_bulk_body_index_create(ids: dict): - """inject_bulk_body: index/create actions get tenant_id + routing.""" - name = "util_bulk_index_create" - try: - body = [ - {"index": {"_index": "test", "_id": "1"}}, - {"content": "hello"}, - {"create": {"_index": "test", "_id": "2"}}, - {"content": "world"}, - ] - result = inject_bulk_body(body, TENANT_1) - # Routing in metadata - assert result[0]["index"]["routing"] == TENANT_1 - assert result[2]["create"]["routing"] == TENANT_1 - # tenant_id in docs - assert result[1]["tenant_id"] == TENANT_1 - assert result[3]["tenant_id"] == TENANT_1 - report.record(name, "PASS", "Bulk index/create: routing + tenant_id injected") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_bulk_body_update(ids: dict): - """inject_bulk_body: update action injects tenant_id into doc and upsert.""" - name = "util_bulk_update" - try: - body = [ - {"update": {"_index": "test", "_id": "1"}}, - {"doc": {"content": "updated"}, "upsert": {"content": "new"}}, - ] - result = inject_bulk_body(body, TENANT_1) - assert result[0]["update"]["routing"] == TENANT_1 - assert result[1]["doc"]["tenant_id"] == TENANT_1 - assert result[1]["upsert"]["tenant_id"] == TENANT_1 - report.record(name, "PASS", "Bulk update: tenant_id in doc + upsert") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_bulk_body_delete_rejected(ids: dict): - """inject_bulk_body: delete action raises TenantIsolationViolation.""" - name = "util_bulk_delete_rejected" - try: - body = [{"delete": {"_index": "test", "_id": "1"}}] - try: - inject_bulk_body(body, TENANT_1) - report.record(name, "FAIL", "Bulk delete did NOT raise") - except TenantIsolationViolation: - report.record(name, "PASS", "Bulk delete correctly rejected") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_make_ids_tenant_query(ids: dict): - """make_ids_tenant_query: builds correct ids + tenant filter.""" - name = "util_ids_tenant_query" - try: - result = make_ids_tenant_query("doc_123", TENANT_1) - filters = result["bool"]["filter"] - assert {"ids": {"values": ["doc_123"]}} in filters - assert {"term": {"tenant_id": TENANT_1}} in filters - report.record(name, "PASS", "ids + tenant query built correctly") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_knn_filter_body(ids: dict): - """inject_knn_filter_body: tenant_id appended to filter list.""" - name = "util_knn_filter" - try: - body = { - "k": 10, - "field": "embedding", - "filter": [{"range": {"date": {"gte": "2024-01-01"}}}], - } - result = inject_knn_filter_body(body, TENANT_1) - assert {"term": {"tenant_id": TENANT_1}} in result["filter"] - assert {"range": {"date": {"gte": "2024-01-01"}}} in result["filter"] - report.record(name, "PASS", "knn filter list appended correctly") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_index_filter_body(ids: dict): - """inject_index_filter_body: tenant_id injected into index_filter.""" - name = "util_index_filter" - try: - body = {"index_filter": {"term": {"status": "active"}}} - result = inject_index_filter_body(body, TENANT_1) - assert "bool" in result["index_filter"] - filters = result["index_filter"]["bool"]["filter"] - assert {"term": {"tenant_id": TENANT_1}} in filters - report.record(name, "PASS", "index_filter wrapped correctly") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 2: Integration Tests — Active Endpoints (Real ES) -# ============================================================ - - -async def test_search_basic_isolation(ids: dict): - """search: tenant 1 only sees own data.""" - name = "search_basic_isolation" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - resp = await client.search( - index=index, - body={"query": {"term": {"group_id": "group_alpha"}}, "size": 100}, - ) - hits = resp["hits"]["hits"] - assert len(hits) == 5, f"T1 expected 5, got {len(hits)}" - for h in hits: - assert h["_source"].get("tenant_id") == TENANT_1 - - async with tenant_context(TENANT_2): - client = await _get_client() - index = _get_index_name() - resp = await client.search( - index=index, - body={"query": {"term": {"group_id": "group_alpha"}}, "size": 100}, - ) - hits = resp["hits"]["hits"] - assert len(hits) == 5, f"T2 expected 5, got {len(hits)}" - for h in hits: - assert h["_source"].get("tenant_id") == TENANT_2 - - report.record(name, "PASS", "search isolated: 5 per tenant, zero overlap") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_search_match_all(ids: dict): - """search: match_all sees only own tenant's data.""" - name = "search_match_all" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - resp = await client.search( - index=index, body={"query": {"match_all": {}}, "size": 100} - ) - hits = resp["hits"]["hits"] - for h in hits: - assert h["_source"].get("tenant_id") == TENANT_1 - count = len(hits) - assert count >= 7, f"T1 expected >=7, got {count}" - - report.record(name, "PASS", f"match_all returns only T1 data ({count} docs)") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_search_bool_query(ids: dict): - """search: complex bool query with must + filter + range.""" - name = "search_bool_query" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - resp = await client.search( - index=index, - body={ - "query": { - "bool": { - "must": [{"match": {"episode": "Episode content"}}], - "filter": [{"term": {"group_id": "group_alpha"}}], - } - }, - "size": 100, - }, - ) - hits = resp["hits"]["hits"] - assert len(hits) == 5, f"Expected 5, got {len(hits)}" - for h in hits: - assert h["_source"].get("tenant_id") == TENANT_1 - - report.record(name, "PASS", "Complex bool query isolated") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_search_no_body(ids: dict): - """search: None/empty body gets tenant-only filter.""" - name = "search_no_body" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - resp = await client.search(index=index, body={}, size=100) - hits = resp["hits"]["hits"] - for h in hits: - assert h["_source"].get("tenant_id") == TENANT_1 - - report.record(name, "PASS", f"Empty body search isolated ({len(hits)} hits)") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_count_isolation(ids: dict): - """count: returns correct count per tenant.""" - name = "count_isolation" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - resp = await client.count( - index=index, body={"query": {"term": {"group_id": "group_alpha"}}} - ) - assert resp["count"] == 5, f"T1 expected 5, got {resp['count']}" - - async with tenant_context(TENANT_2): - client = await _get_client() - index = _get_index_name() - resp = await client.count( - index=index, body={"query": {"term": {"group_id": "group_alpha"}}} - ) - assert resp["count"] == 5, f"T2 expected 5, got {resp['count']}" - - report.record(name, "PASS", "count isolated: 5 each") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_index_single_doc(ids: dict): - """index: single doc write sets tenant_id.""" - name = "index_single_doc" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - doc_id = f"{TENANT_1}_idx_test" - doc = _make_es_doc( - doc_id, "idx_user", "group_idx", "Index test", "Index ep" - ) - await client.index(index=index, id=doc_id, body=doc, refresh="wait_for") - - # Verify tenant_id set - resp = await client.search( - index=index, body={"query": {"term": {"id": doc_id}}, "size": 1} - ) - hits = resp["hits"]["hits"] - assert len(hits) == 1 - assert hits[0]["_source"]["tenant_id"] == TENANT_1 - - # Cleanup - await client.delete_by_query( - index=index, body={"query": {"term": {"id": doc_id}}}, refresh=True - ) - - report.record(name, "PASS", "index sets tenant_id correctly") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_bulk_index(ids: dict): - """bulk: index actions get tenant_id + routing.""" - name = "bulk_index" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - - body = [] - for i in range(3): - doc_id = f"{TENANT_1}_bulk_{i}" - body.append({"index": {"_index": index, "_id": doc_id}}) - body.append( - _make_es_doc( - doc_id, - f"bulk_user_{i}", - "group_bulk", - f"Bulk {i}", - f"Bulk ep {i}", - ) - ) - - await client.bulk(body=body, refresh="wait_for") - - # Verify all have tenant_id - resp = await client.search( - index=index, - body={"query": {"term": {"group_id": "group_bulk"}}, "size": 10}, - ) - hits = resp["hits"]["hits"] - assert len(hits) == 3, f"Expected 3, got {len(hits)}" - for h in hits: - assert h["_source"]["tenant_id"] == TENANT_1 - - # T2 should not see them - async with tenant_context(TENANT_2): - client = await _get_client() - index = _get_index_name() - resp = await client.search( - index=index, - body={"query": {"term": {"group_id": "group_bulk"}}, "size": 10}, - ) - assert resp["hits"]["total"]["value"] == 0 - - # Cleanup - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - await client.delete_by_query( - index=index, - body={"query": {"term": {"group_id": "group_bulk"}}}, - refresh=True, - ) - - report.record( - name, "PASS", "bulk index: 3 docs with tenant_id, isolated from T2" - ) - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_delete_by_query_isolation(ids: dict): - """delete_by_query: only deletes current tenant's data.""" - name = "delete_by_query_isolation" - try: - # Insert temp data for both tenants - for tid in [TENANT_1, TENANT_2]: - async with tenant_context(tid): - client = await _get_client() - index = _get_index_name() - doc_id = f"{tid}_dbq_test" - doc = _make_es_doc( - doc_id, "dbq_user", "group_dbq", "DBQ test", "DBQ ep" - ) - await client.index(index=index, id=doc_id, body=doc, refresh="wait_for") - - # Delete only tenant 1's data - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - resp = await client.delete_by_query( - index=index, - body={"query": {"term": {"group_id": "group_dbq"}}}, - refresh=True, - ) - assert resp["deleted"] == 1 - - # Tenant 2's data should still exist - async with tenant_context(TENANT_2): - client = await _get_client() - index = _get_index_name() - resp = await client.count( - index=index, body={"query": {"term": {"group_id": "group_dbq"}}} - ) - assert ( - resp["count"] == 1 - ), f"T2 data should survive, got count={resp['count']}" - - # Cleanup - await client.delete_by_query( - index=index, - body={"query": {"term": {"group_id": "group_dbq"}}}, - refresh=True, - ) - - report.record(name, "PASS", "delete_by_query only deletes own tenant's data") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 3: ID-based endpoints (get, exists, delete → converted) -# ============================================================ - - -async def test_get_by_id_isolation(ids: dict): - """get: tenant 1 can get own doc, tenant 2 gets 'found: false'.""" - name = "get_by_id_isolation" - try: - doc_id = ids[TENANT_1][0] - - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - resp = await client.get(index=index, id=doc_id) - assert resp["found"] is True - assert resp["_source"]["tenant_id"] == TENANT_1 - - async with tenant_context(TENANT_2): - client = await _get_client() - index = _get_index_name() - resp = await client.get(index=index, id=doc_id) - assert resp["found"] is False, "T2 found T1's doc!" - - report.record(name, "PASS", "get by ID isolated between tenants") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_exists_by_id_isolation(ids: dict): - """exists: tenant 1 sees own doc, tenant 2 gets 404.""" - name = "exists_by_id_isolation" - try: - doc_id = ids[TENANT_1][0] - - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - resp = await client.exists(index=index, id=doc_id) - assert resp.meta.status == 200, f"T1 expected 200, got {resp.meta.status}" - - async with tenant_context(TENANT_2): - client = await _get_client() - index = _get_index_name() - resp = await client.exists(index=index, id=doc_id) - assert resp.meta.status == 404, f"T2 should get 404, got {resp.meta.status}" - - report.record(name, "PASS", "exists by ID isolated (200 vs 404)") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_delete_by_id_isolation(ids: dict): - """delete by ID: tenant 2 cannot delete tenant 1's doc.""" - name = "delete_by_id_isolation" - try: - # Insert a temp doc for tenant 1 - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - doc_id = f"{TENANT_1}_del_id_test" - doc = _make_es_doc(doc_id, "del_user", "group_del_id", "Del test", "Del ep") - await client.index(index=index, id=doc_id, body=doc, refresh="wait_for") - - # Tenant 2 tries to delete it - async with tenant_context(TENANT_2): - client = await _get_client() - index = _get_index_name() - resp = await client.delete(index=index, id=doc_id) - assert ( - resp["result"] == "not_found" - ), f"T2 should get not_found, got {resp['result']}" - - # Verify still exists for tenant 1 - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - resp = await client.get(index=index, id=doc_id) - assert resp["found"] is True, "Doc was deleted by wrong tenant!" - - # Cleanup - await client.delete(index=index, id=doc_id, refresh=True) - - report.record(name, "PASS", "Cross-tenant delete by ID blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 4: Blocked Endpoints -# ============================================================ - - -async def test_blocked_update_by_query(ids: dict): - """update_by_query: raises TenantIsolationViolation.""" - name = "blocked_update_by_query" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - try: - await client.update_by_query( - index=index, - body={ - "query": {"match_all": {}}, - "script": {"source": "ctx._source.subject = 'hacked'"}, - }, - ) - report.record(name, "FAIL", "update_by_query did NOT raise") - except TenantIsolationViolation: - report.record(name, "PASS", "update_by_query correctly blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_blocked_msearch(ids: dict): - """msearch: raises TenantIsolationViolation.""" - name = "blocked_msearch" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - try: - await client.msearch( - body=[{"index": index}, {"query": {"match_all": {}}}] - ) - report.record(name, "FAIL", "msearch did NOT raise") - except TenantIsolationViolation: - report.record(name, "PASS", "msearch correctly blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_blocked_update_by_id(ids: dict): - """update (by ID): raises TenantIsolationViolation.""" - name = "blocked_update_by_id" - try: - doc_id = ids[TENANT_1][0] - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - try: - await client.update( - index=index, id=doc_id, body={"doc": {"subject": "updated"}} - ) - report.record(name, "FAIL", "update by ID did NOT raise") - except TenantIsolationViolation: - report.record(name, "PASS", "update by ID correctly blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 5: Unsupported Endpoints -# ============================================================ - - -async def test_unsupported_search_template(ids: dict): - """search_template: raises TenantIsolationViolation.""" - name = "unsupported_search_template" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - try: - await client.search_template( - index=index, - body={"id": "my_template", "params": {"query_string": "hello"}}, - ) - report.record(name, "FAIL", "search_template did NOT raise") - except TenantIsolationViolation: - report.record(name, "PASS", "search_template correctly rejected") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_unsupported_mget(ids: dict): - """mget: raises TenantIsolationViolation.""" - name = "unsupported_mget" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - try: - await client.mget( - index=index, body={"ids": [ids[TENANT_1][0], ids[TENANT_1][1]]} - ) - report.record(name, "FAIL", "mget did NOT raise") - except TenantIsolationViolation: - report.record(name, "PASS", "mget correctly rejected") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_unsupported_rank_eval(ids: dict): - """rank_eval: raises TenantIsolationViolation.""" - name = "unsupported_rank_eval" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - try: - await client.rank_eval( - index=index, - body={ - "requests": [ - { - "id": "test", - "request": {"query": {"match_all": {}}}, - "ratings": [], - } - ], - "metric": {"precision": {"k": 10}}, - }, - ) - report.record(name, "FAIL", "rank_eval did NOT raise") - except TenantIsolationViolation: - report.record(name, "PASS", "rank_eval correctly rejected") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 6: Control-Plane Passthrough -# ============================================================ - - -async def test_passthrough_indices_refresh(ids: dict): - """indices.refresh: passthrough, no rejection.""" - name = "passthrough_indices_refresh" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - await client.indices.refresh(index=index) - report.record(name, "PASS", "indices.refresh passthrough OK") - except TenantIsolationViolation: - report.record(name, "FAIL", "indices.refresh should not be blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_passthrough_indices_exists(ids: dict): - """indices.exists: passthrough, no rejection.""" - name = "passthrough_indices_exists" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - index = _get_index_name() - result = await client.indices.exists(index=index) - assert result # index should exist - report.record(name, "PASS", "indices.exists passthrough OK") - except TenantIsolationViolation: - report.record(name, "FAIL", "indices.exists should not be blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_passthrough_ping(ids: dict): - """ping: passthrough, no rejection.""" - name = "passthrough_ping" - try: - async with tenant_context(TENANT_1): - client = await _get_client() - result = await client.ping() - assert result - report.record(name, "PASS", "ping passthrough OK") - except TenantIsolationViolation: - report.record( - name, "FAIL", "ping should not be blocked — it is a control-plane operation" - ) - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 7: Cross-Tenant Protection (Real ES) -# ============================================================ - - -async def test_cross_tenant_search(ids: dict): - """Verify T2 search cannot see T1 data by doc _id.""" - name = "cross_tenant_search" - try: - doc_id = ids[TENANT_1][0] - async with tenant_context(TENANT_2): - client = await _get_client() - index = _get_index_name() - resp = await client.search( - index=index, body={"query": {"term": {"_id": doc_id}}, "size": 1} - ) - assert resp["hits"]["total"]["value"] == 0, f"T2 found T1's doc via search!" - - report.record(name, "PASS", "Cross-tenant search blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_no_tenant_raises(ids: dict): - """No tenant context: should raise TenantIsolationViolation after app_ready.""" - name = "no_tenant_raises" - try: - import os - from core.tenants.tenant_config import get_tenant_config - - config = get_tenant_config() - was_ready = config.app_ready - if not was_ready: - config.mark_app_ready() - - saved_single = os.environ.pop("TENANT_SINGLE_TENANT_ID", None) - config.reload() - clear_current_tenant() - assert ( - get_current_tenant_id() is None - ), "tenant_id should be None after clearing" - - from infra_layer.adapters.out.search.elasticsearch.memory.episodic_memory import ( - EpisodicMemoryDoc, - ) - - client = EpisodicMemoryDoc.get_connection() - index = _get_index_name() - - try: - await client.search( - index=index, - body={"query": {"term": {"session_id": "sess_es_001"}}, "size": 100}, - ) - report.record( - name, - "FAIL", - "Expected TenantIsolationViolation but no exception raised", - ) - except Exception as e: - if "TenantIsolationViolation" in type( - e - ).__name__ or "Missing tenant_id" in str(e): - report.record( - name, - "PASS", - f"Correctly raised on missing tenant_id: {type(e).__name__}", - ) - else: - report.record( - name, "FAIL", f"Unexpected exception: {type(e).__name__}: {e}" - ) - finally: - if saved_single is not None: - os.environ["TENANT_SINGLE_TENANT_ID"] = saved_single - config.reload() - if not was_ready: - config.reset_app_ready() - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Main -# ============================================================ - -ALL_TESTS = [ - # Unit: query utils - test_wrap_query_non_bool, - test_wrap_query_existing_bool, - test_wrap_query_bool_with_filter_list, - test_wrap_query_bool_with_filter_dict, - test_inject_query_body_none, - test_inject_query_body_with_query, - test_inject_query_body_with_post_filter, - test_inject_query_body_suggest_blocked, - test_inject_bulk_body_index_create, - test_inject_bulk_body_update, - test_inject_bulk_body_delete_rejected, - test_make_ids_tenant_query, - test_inject_knn_filter_body, - test_inject_index_filter_body, - # Integration: active query endpoints - test_search_basic_isolation, - test_search_match_all, - test_search_bool_query, - test_search_no_body, - test_count_isolation, - # Integration: write endpoints - test_index_single_doc, - test_bulk_index, - # Integration: delete - test_delete_by_query_isolation, - # Integration: ID-based (converted) - test_get_by_id_isolation, - test_exists_by_id_isolation, - test_delete_by_id_isolation, - # Blocked endpoints - test_blocked_update_by_query, - test_blocked_msearch, - test_blocked_update_by_id, - # Unsupported endpoints - test_unsupported_search_template, - test_unsupported_mget, - test_unsupported_rank_eval, - # Passthrough - test_passthrough_indices_refresh, - test_passthrough_indices_exists, - test_passthrough_ping, - # Cross-tenant - test_cross_tenant_search, - test_no_tenant_raises, -] - - -async def main(): - print("\n" + "=" * 80) - print(" Tenant Elasticsearch Isolation E2E Test") - print(f" Tenants: {TENANT_1}, {TENANT_2}") - print("=" * 80 + "\n") - - # Setup - print("--- Setup: inserting test data ---") - ids = await setup_test_data() - print(f" T1: {len(ids[TENANT_1])} docs, T2: {len(ids[TENANT_2])} docs") - - # Verify setup data is searchable - for tid in [TENANT_1, TENANT_2]: - async with tenant_context(tid): - client = await _get_client() - index = _get_index_name() - resp = await client.search( - index=index, body={"query": {"match_all": {}}, "size": 1} - ) - count = resp["hits"]["total"]["value"] - print(f" Verify {tid}: {count} docs visible via match_all") - if count == 0: - print( - f" ⚠️ WARNING: 0 docs for {tid}! Interceptor may not be injecting tenant_id." - ) - print() - - # Run tests - print("--- Running tests ---") - for test_fn in ALL_TESTS: - try: - await test_fn(ids) - except Exception as e: - report.record( - test_fn.__name__, - "ERROR", - f"Unhandled: {type(e).__name__}: {e}\n{traceback.format_exc()}", - ) - - # Cleanup - print("\n--- Cleanup: removing test data ---") - await cleanup_test_data() - print(" Done.\n") - - # Report - success = report.summary() - if not success: - raise SystemExit(1) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/tests/test_tenant_milvus_isolation_e2e.py b/methods/EverCore/tests/test_tenant_milvus_isolation_e2e.py deleted file mode 100644 index 88be0b97..00000000 --- a/methods/EverCore/tests/test_tenant_milvus_isolation_e2e.py +++ /dev/null @@ -1,1001 +0,0 @@ -#!/usr/bin/env python3 -# skip-sensitive-file -""" -End-to-end tenant isolation verification for Milvus operations. - -Uses the EpisodicMemory Milvus collection as the test subject. Bootstraps the -full application context, inserts similar data for two tenants, then verifies -every Milvus operation correctly isolates (or rejects) data between tenants. - -Coverage: - IMPLEMENTED data operations: - - insert (single + batch): tenant_id force-injected - - upsert: tenant_id force-injected - - search (vector): tenant filter prepended to expr (shared mode) - - query (scalar): tenant filter prepended to expr (shared mode) - - delete: tenant filter prepended + empty-expr guard - - Control-plane passthrough: - - flush, load, release, compact, describe, num_entities - - Rejected operations: - - Unknown methods (e.g., random_method) → AttributeError - - Empty delete expression → TenantIsolationViolation - - Non-dict entity in insert → TenantIsolationViolation - - Unit tests for proxy helpers: - - _prepend_tenant_filter: empty expr, existing expr - - _inject_tenant_to_entities: single dict, list of dicts, non-dict rejection - - _exclude_tenant_from_fields: None, with/without tenant_id - - Cross-tenant protection: - - T2 cannot query/search/delete T1 data - - No-tenant passthrough sees all data - -Run: - uv run python src/bootstrap.py tests/test_tenant_milvus_isolation_e2e.py -""" - -import asyncio -import traceback -from contextlib import asynccontextmanager -from datetime import timedelta -from typing import Optional -import random - -from common_utils.datetime_utils import get_now_with_timezone -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import ( - set_current_tenant, - clear_current_tenant, - get_current_tenant_id, -) -from core.tenants.tenant_models import TenantInfo, TenantDetail -from core.tenants.tenantize.oxm.milvus.tenant_field_collection_proxy import ( - TenantFieldCollectionProxy, - TenantIsolationViolation, - _exclude_tenant_from_fields, -) - -logger = get_logger(__name__) - -# ============================================================ -# Constants -# ============================================================ - -TENANT_1 = "test_milvus_t001" -TENANT_2 = "test_milvus_t002" - -BASE_TIME = get_now_with_timezone() - timedelta(hours=2) - -# Dummy vector dimension — must match the collection schema -VECTOR_DIM = None # Resolved at runtime from schema - - -def _get_vector_dim() -> int: - global VECTOR_DIM - if VECTOR_DIM is None: - from infra_layer.adapters.out.search.milvus.memory.episodic_memory_collection import ( - EpisodicMemoryCollection, - ) - - for field in EpisodicMemoryCollection._SCHEMA.fields: - if field.name == "vector": - VECTOR_DIM = field.dim - break - if VECTOR_DIM is None: - VECTOR_DIM = 1536 # fallback - return VECTOR_DIM - - -def _random_vector() -> list[float]: - """Generate a random unit vector for testing.""" - dim = _get_vector_dim() - vec = [random.gauss(0, 1) for _ in range(dim)] - norm = sum(x * x for x in vec) ** 0.5 - return [x / norm for x in vec] - - -def _make_entity( - doc_id: str, - user_id: str, - group_id: str, - episode: str, - offset_minutes: int = 0, - parent_id: str = "parent_milvus_001", -) -> dict: - """Build a raw Milvus entity dict (tenant_id injected by proxy).""" - ts = BASE_TIME + timedelta(minutes=offset_minutes) - return { - "id": doc_id, - "vector": _random_vector(), - "user_id": user_id, - "group_id": group_id, - "session_id": "sess_milvus_001", - "participants": [user_id], - "sender_ids": [user_id], - "type": "Conversation", - "timestamp": int(ts.timestamp() * 1000), - "episode": episode, - "search_content": episode, - "parent_type": "memcell", - "parent_id": parent_id, - } - - -# ============================================================ -# Tenant context manager -# ============================================================ - - -@asynccontextmanager -async def tenant_context(tenant_id: str): - """Set and clear tenant context.""" - tenant_info = TenantInfo( - tenant_id=tenant_id, - tenant_detail=TenantDetail( - tenant_info={}, storage_info={}, isolation_mode="shared" - ), - ) - set_current_tenant(tenant_info) - try: - yield tenant_info - finally: - clear_current_tenant() - - -# ============================================================ -# Test result tracking -# ============================================================ - - -class TestReport: - def __init__(self): - self.results: list[tuple[str, str, str]] = [] - - def record(self, name: str, status: str, detail: str = ""): - self.results.append((name, status, detail)) - icon = {"PASS": "✅", "FAIL": "❌", "ERROR": "💥", "SKIP": "⏭️"}.get( - status, "❓" - ) - print(f" {icon} {name}") - if detail: - print(f" {detail}") - - def summary(self): - print("\n" + "=" * 80) - print(" Tenant Milvus Isolation E2E Report") - print("=" * 80) - counts = {"PASS": 0, "FAIL": 0, "ERROR": 0, "SKIP": 0} - for _, status, _ in self.results: - counts[status] = counts.get(status, 0) + 1 - total = len(self.results) - print( - f" Total: {total} | PASS: {counts['PASS']} | FAIL: {counts['FAIL']} " - f"| ERROR: {counts['ERROR']} | SKIP: {counts['SKIP']}" - ) - if counts["FAIL"] == 0 and counts["ERROR"] == 0: - print(" ✅ ALL TESTS PASSED — MILVUS TENANT ISOLATION VERIFIED") - else: - print(" ❌ SOME TESTS FAILED — SEE ABOVE") - print("=" * 80 + "\n") - return counts["FAIL"] == 0 and counts["ERROR"] == 0 - - -report = TestReport() - - -# ============================================================ -# Helper: get collection proxy -# ============================================================ - - -def _get_collection() -> TenantFieldCollectionProxy: - """Get the tenant-aware Milvus collection (TenantFieldCollectionProxy).""" - from infra_layer.adapters.out.search.milvus.memory.episodic_memory_collection import ( - EpisodicMemoryCollection, - ) - - return EpisodicMemoryCollection.async_collection() - - -def _get_all_output_fields() -> list[str]: - """Get all field names from schema (excluding vector for lighter queries).""" - from infra_layer.adapters.out.search.milvus.memory.episodic_memory_collection import ( - EpisodicMemoryCollection, - ) - - return [ - f.name for f in EpisodicMemoryCollection._SCHEMA.fields if f.name != "vector" - ] - - -# ============================================================ -# Setup & Teardown -# ============================================================ - - -async def setup_test_data(): - """Insert test data for both tenants. Returns inserted doc IDs per tenant.""" - ids = {TENANT_1: [], TENANT_2: []} - - for tid in [TENANT_1, TENANT_2]: - async with tenant_context(tid): - coll = _get_collection() - - for i in range(5): - doc_id = f"{tid}_doc_{i}" - entity = _make_entity( - doc_id=doc_id, - user_id=f"user_{i}", - group_id="group_alpha", - episode=f"Episode content {i} detailed narrative", - offset_minutes=i * 10, - parent_id=f"parent_{i:03d}", - ) - await coll.insert(entity) - ids[tid].append(doc_id) - - # 2 more in group_beta - for i in range(2): - doc_id = f"{tid}_beta_{i}" - entity = _make_entity( - doc_id=doc_id, - user_id=f"user_{i}", - group_id="group_beta", - episode=f"Beta episode {i}", - offset_minutes=50 + i * 10, - ) - await coll.insert(entity) - ids[tid].append(doc_id) - - await coll.flush() - - return ids - - -async def cleanup_test_data(): - """Delete all test data for both tenants.""" - for tid in [TENANT_1, TENANT_2]: - async with tenant_context(tid): - coll = _get_collection() - try: - await coll.delete(expr='session_id == "sess_milvus_001"') - await coll.flush() - except Exception: - pass - - -# ============================================================ -# Part 1: Unit Tests — Proxy Helpers -# ============================================================ - - -async def test_prepend_filter_empty(ids: dict): - """_prepend_tenant_filter: empty expr returns tenant-only clause.""" - name = "util_prepend_filter_empty" - try: - result = TenantFieldCollectionProxy._prepend_tenant_filter("", TENANT_1) - assert result == f'(tenant_id == "{TENANT_1}")' - result2 = TenantFieldCollectionProxy._prepend_tenant_filter(None, TENANT_1) - assert result2 == f'(tenant_id == "{TENANT_1}")' - report.record(name, "PASS", "Empty expr → tenant-only clause") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_prepend_filter_existing(ids: dict): - """_prepend_tenant_filter: existing expr gets tenant prepended.""" - name = "util_prepend_filter_existing" - try: - result = TenantFieldCollectionProxy._prepend_tenant_filter( - 'user_id == "u1"', TENANT_1 - ) - expected = f'(tenant_id == "{TENANT_1}") and (user_id == "u1")' - assert result == expected, f"Got: {result}" - report.record(name, "PASS", "Existing expr gets tenant prepended") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_entities_single(ids: dict): - """_inject_tenant_to_entities: single dict gets tenant_id set.""" - name = "util_inject_single" - try: - entity = {"id": "test", "user_id": "u1"} - TenantFieldCollectionProxy._inject_tenant_to_entities(entity, TENANT_1) - assert entity["tenant_id"] == TENANT_1 - report.record(name, "PASS", "Single dict injected") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_entities_list(ids: dict): - """_inject_tenant_to_entities: list of dicts all get tenant_id.""" - name = "util_inject_list" - try: - entities = [{"id": "a"}, {"id": "b"}, {"id": "c"}] - TenantFieldCollectionProxy._inject_tenant_to_entities(entities, TENANT_1) - for e in entities: - assert e["tenant_id"] == TENANT_1 - report.record(name, "PASS", "List of 3 dicts all injected") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_entities_force_overwrite(ids: dict): - """_inject_tenant_to_entities: existing tenant_id is force-overwritten.""" - name = "util_inject_overwrite" - try: - entity = {"id": "test", "tenant_id": "WRONG_TENANT"} - TenantFieldCollectionProxy._inject_tenant_to_entities(entity, TENANT_1) - assert entity["tenant_id"] == TENANT_1, "Should overwrite" - report.record(name, "PASS", "Existing tenant_id force-overwritten") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_inject_entities_non_dict_rejected(ids: dict): - """_inject_tenant_to_entities: non-dict entity raises TenantIsolationViolation.""" - name = "util_inject_non_dict" - try: - try: - TenantFieldCollectionProxy._inject_tenant_to_entities( - ["not", "a", "dict"], TENANT_1 - ) - report.record(name, "FAIL", "Non-dict did NOT raise") - except TenantIsolationViolation: - report.record(name, "PASS", "Non-dict correctly rejected") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_exclude_tenant_from_fields(ids: dict): - """_exclude_tenant_from_fields: removes tenant_id from output list.""" - name = "util_exclude_fields" - try: - result = _exclude_tenant_from_fields(["id", "user_id", "tenant_id", "episode"]) - assert "tenant_id" not in result - assert "id" in result - assert "episode" in result - - # None input returns None - assert _exclude_tenant_from_fields(None) is None - - # No tenant_id in input — unchanged - result2 = _exclude_tenant_from_fields(["id", "user_id"]) - assert result2 == ["id", "user_id"] - - report.record(name, "PASS", "tenant_id excluded, None preserved") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 2: Integration Tests — Write Operations -# ============================================================ - - -async def test_insert_single(ids: dict): - """insert: single entity gets tenant_id injected.""" - name = "insert_single" - try: - async with tenant_context(TENANT_1): - coll = _get_collection() - doc_id = f"{TENANT_1}_ins_test" - entity = _make_entity(doc_id, "ins_user", "group_ins", "Insert test ep") - await coll.insert(entity) - await coll.flush() - - # Verify via query - results = await coll.query( - expr=f'id == "{doc_id}"', output_fields=["id", "user_id", "tenant_id"] - ) - assert len(results) == 1, f"Expected 1, got {len(results)}" - # Note: tenant_id may be excluded by proxy in shared mode. - # The fact that query found it means tenant filter matched. - - # Cleanup - await coll.delete(expr=f'id == "{doc_id}"') - await coll.flush() - - report.record(name, "PASS", "Insert with tenant isolation verified") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_insert_batch(ids: dict): - """insert: batch of entities all get tenant_id.""" - name = "insert_batch" - try: - async with tenant_context(TENANT_1): - coll = _get_collection() - entities = [ - _make_entity( - f"{TENANT_1}_batch_{i}", - f"batch_u{i}", - "group_batch", - f"Batch ep {i}", - ) - for i in range(3) - ] - await coll.insert(entities) - await coll.flush() - - results = await coll.query( - expr='group_id == "group_batch"', output_fields=["id"] - ) - assert len(results) == 3, f"Expected 3, got {len(results)}" - - # T2 should not see them - async with tenant_context(TENANT_2): - coll = _get_collection() - results = await coll.query( - expr='group_id == "group_batch"', output_fields=["id"] - ) - assert len(results) == 0, f"T2 saw T1's batch data: {len(results)}" - - # Cleanup - async with tenant_context(TENANT_1): - coll = _get_collection() - await coll.delete(expr='group_id == "group_batch"') - await coll.flush() - - report.record(name, "PASS", "Batch insert isolated from T2") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_upsert(ids: dict): - """upsert: entity gets tenant_id injected.""" - name = "upsert" - try: - async with tenant_context(TENANT_1): - coll = _get_collection() - doc_id = f"{TENANT_1}_ups_test" - entity = _make_entity(doc_id, "ups_user", "group_ups", "Upsert ep v1") - await coll.upsert(entity) - await coll.flush() - - # Query to verify - results = await coll.query( - expr=f'id == "{doc_id}"', output_fields=["id", "episode"] - ) - assert len(results) == 1 - - # Upsert again with updated content - entity2 = _make_entity(doc_id, "ups_user", "group_ups", "Upsert ep v2") - await coll.upsert(entity2) - await coll.flush() - - results2 = await coll.query( - expr=f'id == "{doc_id}"', output_fields=["id", "episode"] - ) - assert len(results2) == 1 - assert results2[0]["episode"] == "Upsert ep v2" - - # Cleanup - await coll.delete(expr=f'id == "{doc_id}"') - await coll.flush() - - report.record(name, "PASS", "Upsert with tenant isolation verified") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 3: Integration Tests — Read Operations -# ============================================================ - - -async def test_query_basic_isolation(ids: dict): - """query: T1 only sees own data.""" - name = "query_basic_isolation" - try: - async with tenant_context(TENANT_1): - coll = _get_collection() - results = await coll.query( - expr='group_id == "group_alpha"', output_fields=["id", "user_id"] - ) - assert len(results) == 5, f"T1 expected 5, got {len(results)}" - - async with tenant_context(TENANT_2): - coll = _get_collection() - results = await coll.query( - expr='group_id == "group_alpha"', output_fields=["id", "user_id"] - ) - assert len(results) == 5, f"T2 expected 5, got {len(results)}" - - # Verify no overlap - async with tenant_context(TENANT_1): - coll = _get_collection() - r1 = await coll.query( - expr='group_id == "group_alpha"', output_fields=["id"] - ) - async with tenant_context(TENANT_2): - coll = _get_collection() - r2 = await coll.query( - expr='group_id == "group_alpha"', output_fields=["id"] - ) - ids_1 = {r["id"] for r in r1} - ids_2 = {r["id"] for r in r2} - assert ids_1.isdisjoint(ids_2), "Tenant data overlap!" - - report.record( - name, "PASS", f"Query isolated: T1={len(r1)}, T2={len(r2)}, zero overlap" - ) - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_query_by_id(ids: dict): - """query: T1 can find own doc by ID, T2 cannot.""" - name = "query_by_id_isolation" - try: - doc_id = ids[TENANT_1][0] - - async with tenant_context(TENANT_1): - coll = _get_collection() - results = await coll.query(expr=f'id == "{doc_id}"', output_fields=["id"]) - assert len(results) == 1, f"T1 should find own doc, got {len(results)}" - - async with tenant_context(TENANT_2): - coll = _get_collection() - results = await coll.query(expr=f'id == "{doc_id}"', output_fields=["id"]) - assert len(results) == 0, f"T2 found T1's doc: {len(results)}" - - report.record(name, "PASS", "Query by ID isolated between tenants") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_search_vector_isolation(ids: dict): - """search: vector search with expr filter isolated per tenant.""" - name = "search_vector_isolation" - try: - query_vec = _random_vector() - output_fields = _get_all_output_fields() - - async with tenant_context(TENANT_1): - coll = _get_collection() - results = await coll.search( - data=[query_vec], - anns_field="vector", - param={"metric_type": "COSINE", "params": {"ef": 64}}, - limit=20, - expr='group_id == "group_alpha"', - output_fields=output_fields, - ) - # Results is a list of lists (one per query vector) - hits = results[0] if results else [] - assert len(hits) == 5, f"T1 expected 5 hits, got {len(hits)}" - for hit in hits: - assert hit.id.startswith( - TENANT_1 - ), f"T1 search returned non-T1 doc: {hit.id}" - - async with tenant_context(TENANT_2): - coll = _get_collection() - results = await coll.search( - data=[query_vec], - anns_field="vector", - param={"metric_type": "COSINE", "params": {"ef": 64}}, - limit=20, - expr='group_id == "group_alpha"', - output_fields=output_fields, - ) - hits = results[0] if results else [] - assert len(hits) == 5, f"T2 expected 5 hits, got {len(hits)}" - for hit in hits: - assert hit.id.startswith( - TENANT_2 - ), f"T2 search returned non-T2 doc: {hit.id}" - - report.record(name, "PASS", "Vector search isolated: 5 hits per tenant") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_search_no_expr(ids: dict): - """search: no expr filter still gets tenant filter in shared mode.""" - name = "search_no_expr" - try: - query_vec = _random_vector() - - async with tenant_context(TENANT_1): - coll = _get_collection() - results = await coll.search( - data=[query_vec], - anns_field="vector", - param={"metric_type": "COSINE", "params": {"ef": 64}}, - limit=100, - expr=None, # No expr — proxy adds tenant filter - output_fields=["id"], - ) - hits = results[0] if results else [] - # Should see all 7 T1 docs (5 alpha + 2 beta) - assert len(hits) >= 7, f"T1 expected >=7 hits, got {len(hits)}" - for hit in hits: - assert hit.id.startswith(TENANT_1), f"Non-T1 doc: {hit.id}" - - report.record( - name, "PASS", f"No-expr search returns only T1 data ({len(hits)} hits)" - ) - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 4: Integration Tests — Delete Operations -# ============================================================ - - -async def test_delete_with_filter(ids: dict): - """delete: only deletes current tenant's data.""" - name = "delete_with_filter" - try: - # Insert temp data for both tenants - for tid in [TENANT_1, TENANT_2]: - async with tenant_context(tid): - coll = _get_collection() - entity = _make_entity( - f"{tid}_del_test", "del_user", "group_del", "Delete test ep" - ) - await coll.insert(entity) - await coll.flush() - - # Delete only T1's data - async with tenant_context(TENANT_1): - coll = _get_collection() - await coll.delete(expr='group_id == "group_del"') - await coll.flush() - - # T2's data should survive - async with tenant_context(TENANT_2): - coll = _get_collection() - results = await coll.query( - expr='group_id == "group_del"', output_fields=["id"] - ) - assert len(results) == 1, f"T2 data should survive, got {len(results)}" - - # Cleanup - await coll.delete(expr='group_id == "group_del"') - await coll.flush() - - report.record(name, "PASS", "Delete only affects own tenant's data") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_delete_empty_expr_rejected(ids: dict): - """delete: empty expression raises TenantIsolationViolation.""" - name = "delete_empty_expr_rejected" - try: - async with tenant_context(TENANT_1): - coll = _get_collection() - try: - await coll.delete(expr="") - report.record(name, "FAIL", "Empty delete did NOT raise") - except TenantIsolationViolation: - report.record(name, "PASS", "Empty delete correctly rejected") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_delete_whitespace_expr_rejected(ids: dict): - """delete: whitespace-only expression also rejected.""" - name = "delete_whitespace_expr_rejected" - try: - async with tenant_context(TENANT_1): - coll = _get_collection() - try: - await coll.delete(expr=" ") - report.record(name, "FAIL", "Whitespace delete did NOT raise") - except TenantIsolationViolation: - report.record(name, "PASS", "Whitespace delete correctly rejected") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 5: Control-Plane Passthrough -# ============================================================ - - -async def test_passthrough_flush(ids: dict): - """flush: passthrough, no rejection.""" - name = "passthrough_flush" - try: - async with tenant_context(TENANT_1): - coll = _get_collection() - await coll.flush() - report.record(name, "PASS", "flush passthrough OK") - except TenantIsolationViolation: - report.record(name, "FAIL", "flush should not be blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_passthrough_load(ids: dict): - """load: passthrough, no rejection.""" - name = "passthrough_load" - try: - async with tenant_context(TENANT_1): - coll = _get_collection() - await coll.load() - report.record(name, "PASS", "load passthrough OK") - except TenantIsolationViolation: - report.record(name, "FAIL", "load should not be blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_passthrough_describe(ids: dict): - """describe: whitelisted passthrough via __getattr__.""" - name = "passthrough_describe" - try: - async with tenant_context(TENANT_1): - coll = _get_collection() - desc = coll.describe() - # describe may return a coroutine (async_wrap) — await if needed - if asyncio.iscoroutine(desc): - desc = await desc - assert desc is not None - report.record(name, "PASS", "describe passthrough OK") - except AttributeError: - report.record(name, "FAIL", "describe should be whitelisted") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_passthrough_num_entities(ids: dict): - """num_entities: whitelisted passthrough.""" - name = "passthrough_num_entities" - try: - async with tenant_context(TENANT_1): - coll = _get_collection() - count = coll.num_entities - assert isinstance(count, int) - report.record(name, "PASS", f"num_entities={count} passthrough OK") - except AttributeError: - report.record(name, "FAIL", "num_entities should be whitelisted") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 6: Rejected Operations -# ============================================================ - - -async def test_unknown_method_rejected(ids: dict): - """Unknown method (e.g., random_method) raises AttributeError.""" - name = "unknown_method_rejected" - try: - async with tenant_context(TENANT_1): - coll = _get_collection() - try: - _ = coll.random_nonexistent_method - report.record(name, "FAIL", "Unknown method did NOT raise") - except AttributeError as e: - assert "does not expose" in str(e) - report.record( - name, - "PASS", - "Unknown method correctly rejected with AttributeError", - ) - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Part 7: Cross-Tenant Protection -# ============================================================ - - -async def test_cross_tenant_query_blocked(ids: dict): - """T2 cannot query T1's data by ID.""" - name = "cross_tenant_query_blocked" - try: - doc_id = ids[TENANT_1][0] - - async with tenant_context(TENANT_2): - coll = _get_collection() - results = await coll.query(expr=f'id == "{doc_id}"', output_fields=["id"]) - assert len(results) == 0, f"T2 found T1's doc via query!" - - report.record(name, "PASS", "Cross-tenant query blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_cross_tenant_delete_blocked(ids: dict): - """T2 cannot delete T1's data.""" - name = "cross_tenant_delete_blocked" - try: - doc_id = ids[TENANT_1][0] - - async with tenant_context(TENANT_2): - coll = _get_collection() - await coll.delete(expr=f'id == "{doc_id}"') - await coll.flush() - - # Verify T1's doc still exists - async with tenant_context(TENANT_1): - coll = _get_collection() - results = await coll.query(expr=f'id == "{doc_id}"', output_fields=["id"]) - assert len(results) == 1, f"T1's doc was deleted by T2!" - - report.record(name, "PASS", "Cross-tenant delete blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_cross_tenant_search_blocked(ids: dict): - """T2 vector search cannot find T1's data.""" - name = "cross_tenant_search_blocked" - try: - query_vec = _random_vector() - - async with tenant_context(TENANT_2): - coll = _get_collection() - results = await coll.search( - data=[query_vec], - anns_field="vector", - param={"metric_type": "COSINE", "params": {"ef": 64}}, - limit=100, - expr=None, - output_fields=["id"], - ) - hits = results[0] if results else [] - for hit in hits: - assert not hit.id.startswith( - TENANT_1 - ), f"T2 search found T1 doc: {hit.id}" - - report.record(name, "PASS", "Cross-tenant search blocked") - except Exception as e: - report.record(name, "FAIL", str(e)) - - -async def test_no_tenant_raises(ids: dict): - """No tenant context: should raise TenantIsolationViolation after app_ready.""" - name = "no_tenant_raises" - try: - import os - from core.tenants.tenant_config import get_tenant_config - - config = get_tenant_config() - was_ready = config.app_ready - if not was_ready: - config.mark_app_ready() - - saved_single = os.environ.pop("TENANT_SINGLE_TENANT_ID", None) - config.reload() - clear_current_tenant() - assert ( - get_current_tenant_id() is None - ), "tenant_id should be None after clearing" - - coll = _get_collection() - try: - await coll.query( - expr='session_id == "sess_milvus_001"', - output_fields=["id", "tenant_id"], - limit=100, - ) - report.record( - name, - "FAIL", - "Expected TenantIsolationViolation but no exception raised", - ) - except Exception as e: - if "TenantIsolationViolation" in type( - e - ).__name__ or "Missing tenant_id" in str(e): - report.record( - name, - "PASS", - f"Correctly raised on missing tenant_id: {type(e).__name__}", - ) - else: - report.record( - name, "FAIL", f"Unexpected exception: {type(e).__name__}: {e}" - ) - finally: - if saved_single is not None: - os.environ["TENANT_SINGLE_TENANT_ID"] = saved_single - config.reload() - if not was_ready: - config.reset_app_ready() - except Exception as e: - report.record(name, "FAIL", str(e)) - - -# ============================================================ -# Main -# ============================================================ - -ALL_TESTS = [ - # Unit: proxy helpers - test_prepend_filter_empty, - test_prepend_filter_existing, - test_inject_entities_single, - test_inject_entities_list, - test_inject_entities_force_overwrite, - test_inject_entities_non_dict_rejected, - test_exclude_tenant_from_fields, - # Write operations - test_insert_single, - test_insert_batch, - test_upsert, - # Read operations - test_query_basic_isolation, - test_query_by_id, - test_search_vector_isolation, - test_search_no_expr, - # Delete operations - test_delete_with_filter, - test_delete_empty_expr_rejected, - test_delete_whitespace_expr_rejected, - # Passthrough - test_passthrough_flush, - test_passthrough_load, - test_passthrough_describe, - test_passthrough_num_entities, - # Rejected - test_unknown_method_rejected, - # Cross-tenant - test_cross_tenant_query_blocked, - test_cross_tenant_delete_blocked, - test_cross_tenant_search_blocked, - test_no_tenant_raises, -] - - -async def main(): - print("\n" + "=" * 80) - print(" Tenant Milvus Isolation E2E Test") - print(f" Tenants: {TENANT_1}, {TENANT_2}") - print("=" * 80 + "\n") - - # Setup - print("--- Setup: inserting test data ---") - ids = await setup_test_data() - print(f" T1: {len(ids[TENANT_1])} docs, T2: {len(ids[TENANT_2])} docs") - - # Verify setup - for tid in [TENANT_1, TENANT_2]: - async with tenant_context(tid): - coll = _get_collection() - results = await coll.query( - expr='session_id == "sess_milvus_001"', output_fields=["id"], limit=100 - ) - print(f" Verify {tid}: {len(results)} docs visible via query") - print() - - # Run tests - print("--- Running tests ---") - for test_fn in ALL_TESTS: - try: - await test_fn(ids) - except Exception as e: - report.record( - test_fn.__name__, - "ERROR", - f"Unhandled: {type(e).__name__}: {e}\n{traceback.format_exc()}", - ) - - # Cleanup - print("\n--- Cleanup: removing test data ---") - await cleanup_test_data() - print(" Done.\n") - - # Report - success = report.summary() - if not success: - raise SystemExit(1) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/tests/test_tenant_mongo_isolation_e2e.py b/methods/EverCore/tests/test_tenant_mongo_isolation_e2e.py deleted file mode 100644 index d9a23cd9..00000000 --- a/methods/EverCore/tests/test_tenant_mongo_isolation_e2e.py +++ /dev/null @@ -1,1447 +0,0 @@ -#!/usr/bin/env python3 -# skip-sensitive-file -""" -End-to-end tenant isolation verification for MongoDB operations. - -Uses EpisodicMemory as the test subject. Bootstraps the full application context, -inserts similar data for two tenants, then verifies every MongoDB operation pattern -used in the project correctly isolates data between tenants. - -Coverage: - - Beanie ODM: insert, insert_many, save, find, find_one, get, find().count(), - find().delete(), find().sort().skip().limit(), projection - - Soft delete: delete, delete_many, restore, restore_many, hard_delete, - hard_delete_many, find_many, find_one, hard_find_many, - hard_find_one, count, is_deleted, apply_soft_delete_filter - - PyMongo direct: find, find_one, insert_one, update_one, update_many, - replace_one, delete_one, delete_many, aggregate, - count_documents, estimated_document_count, distinct, - find_one_and_update, find_one_and_delete, find_one_and_replace - - Cursor: batch_size + getMore, async for iteration - - Rejected: find_raw_batches, aggregate_raw_batches (InvalidOperation) - - Unknown commands: TenantIsolationViolation - -Run: - uv run python src/bootstrap.py tests/test_tenant_mongo_isolation_e2e.py -""" - -import asyncio -import traceback -from contextlib import asynccontextmanager -from datetime import timedelta -from typing import Optional - -from beanie import PydanticObjectId -from pymongo.errors import InvalidOperation - -from common_utils.datetime_utils import get_now_with_timezone -from core.observation.logger import get_logger -from core.tenants.tenant_contextvar import ( - set_current_tenant, - clear_current_tenant, - get_current_tenant_id, -) -from core.tenants.tenant_models import TenantInfo, TenantDetail -from core.tenants.tenantize.oxm.mongo.tenant_field_command_interceptor import ( - TenantIsolationViolation, -) -from infra_layer.adapters.out.persistence.document.memory.episodic_memory import ( - EpisodicMemory, - EpisodicMemoryProjection, -) - -logger = get_logger(__name__) - -# ============================================================ -# Constants -# ============================================================ - -TENANT_1 = "test_tenant_iso_001" -TENANT_2 = "test_tenant_iso_002" - -# Shared test data template — similar content, different tenant -BASE_TIME = get_now_with_timezone() - timedelta(hours=2) - - -def _make_episodic( - user_id: str, - group_id: str, - summary: str, - episode: str, - offset_minutes: int = 0, - session_id: Optional[str] = None, - parent_id: Optional[str] = None, -) -> EpisodicMemory: - """Create an EpisodicMemory instance (tenant_id is injected by interceptor).""" - return EpisodicMemory( - user_id=user_id, - group_id=group_id, - session_id=session_id or "sess_001", - timestamp=BASE_TIME + timedelta(minutes=offset_minutes), - participants=[user_id], - sender_ids=[user_id], - summary=summary, - subject="test subject", - episode=episode, - type="Conversation", - parent_type="memcell", - parent_id=parent_id or "parent_001", - ) - - -# ============================================================ -# Tenant context manager -# ============================================================ - - -@asynccontextmanager -async def tenant_context(tenant_id: str): - """ - Async context manager that sets and clears tenant context. - - Usage: - async with tenant_context("test_tenant_001"): - # all MongoDB operations here are scoped to this tenant - ... - """ - tenant_info = TenantInfo( - tenant_id=tenant_id, - tenant_detail=TenantDetail( - tenant_info={}, storage_info={}, isolation_mode="shared" - ), - ) - set_current_tenant(tenant_info) - try: - yield tenant_info - finally: - clear_current_tenant() - - -# ============================================================ -# Test result tracking -# ============================================================ - - -class TestReport: - def __init__(self): - self.results: list[tuple[str, str, str]] = [] # (name, status, detail) - - def record(self, name: str, status: str, detail: str = ""): - self.results.append((name, status, detail)) - icon = {"PASS": "✅", "FAIL": "❌", "ERROR": "💥", "SKIP": "⏭️"}.get( - status, "❓" - ) - print(f" {icon} {name}") - if detail: - print(f" {detail}") - - def summary(self): - print("\n" + "=" * 80) - print(" Tenant Mongo Isolation E2E Report") - print("=" * 80) - counts = {"PASS": 0, "FAIL": 0, "ERROR": 0, "SKIP": 0} - for name, status, detail in self.results: - counts[status] = counts.get(status, 0) + 1 - total = len(self.results) - print( - f" Total: {total} | PASS: {counts['PASS']} | FAIL: {counts['FAIL']} " - f"| ERROR: {counts['ERROR']} | SKIP: {counts['SKIP']}" - ) - if counts["FAIL"] == 0 and counts["ERROR"] == 0: - print(" ✅ ALL TESTS PASSED — TENANT ISOLATION VERIFIED") - else: - print(" ❌ SOME TESTS FAILED — SEE ABOVE") - print("=" * 80 + "\n") - return counts["FAIL"] == 0 and counts["ERROR"] == 0 - - -report = TestReport() - - -# ============================================================ -# Setup & Teardown -# ============================================================ - - -async def setup_test_data(): - """Insert test data for both tenants. Returns inserted doc IDs per tenant.""" - ids = {TENANT_1: [], TENANT_2: []} - - for tid in [TENANT_1, TENANT_2]: - async with tenant_context(tid): - # Insert 5 docs per tenant with similar content - for i in range(5): - doc = _make_episodic( - user_id=f"user_{i}", - group_id="group_alpha", - summary=f"Summary {i} for tenant", - episode=f"Episode content {i} — detailed narrative", - offset_minutes=i * 10, - parent_id=f"parent_{i:03d}", - ) - await doc.insert() - ids[tid].append(doc.id) - - # Insert 2 more in a different group - for i in range(2): - doc = _make_episodic( - user_id=f"user_{i}", - group_id="group_beta", - summary=f"Beta summary {i}", - episode=f"Beta episode {i}", - offset_minutes=50 + i * 10, - ) - await doc.insert() - ids[tid].append(doc.id) - - return ids - - -async def cleanup_test_data(): - """Hard-delete all test data for both tenants.""" - for tid in [TENANT_1, TENANT_2]: - async with tenant_context(tid): - await EpisodicMemory.hard_delete_many({"session_id": "sess_001"}) - - -# ============================================================ -# Test Cases: Beanie ODM Operations -# ============================================================ - - -async def test_beanie_insert_single(ids: dict): - """Beanie: document.insert() — verify tenant_id is set on the stored doc.""" - name = "beanie_insert_single" - try: - async with tenant_context(TENANT_1): - doc = _make_episodic( - user_id="insert_test_user", - group_id="group_insert", - summary="Insert test", - episode="Insert episode", - offset_minutes=100, - ) - await doc.insert() - # Re-read from DB to verify tenant_id was set - reloaded = await EpisodicMemory.find_one({"_id": doc.id}) - assert reloaded is not None, "Inserted doc not found" - assert ( - reloaded.tenant_id == TENANT_1 - ), f"tenant_id mismatch: {reloaded.tenant_id}" - # Cleanup - await reloaded.hard_delete() - report.record(name, "PASS", f"tenant_id={TENANT_1} correctly set on insert") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_beanie_insert_many(ids: dict): - """Beanie: Model.insert_many() — batch insert with tenant isolation.""" - name = "beanie_insert_many" - try: - async with tenant_context(TENANT_1): - docs = [ - _make_episodic( - user_id=f"batch_user_{i}", - group_id="group_batch", - summary=f"Batch {i}", - episode=f"Batch ep {i}", - offset_minutes=200 + i, - ) - for i in range(3) - ] - await EpisodicMemory.insert_many(docs) - - # Verify all have tenant_id - found = await EpisodicMemory.find({"group_id": "group_batch"}).to_list() - assert len(found) == 3, f"Expected 3, got {len(found)}" - for d in found: - assert d.tenant_id == TENANT_1, f"tenant_id mismatch: {d.tenant_id}" - - # Verify tenant 2 cannot see them - async with tenant_context(TENANT_2): - found_t2 = await EpisodicMemory.find({"group_id": "group_batch"}).to_list() - assert ( - len(found_t2) == 0 - ), f"Tenant 2 saw tenant 1's batch data: {len(found_t2)}" - - # Cleanup - async with tenant_context(TENANT_1): - await EpisodicMemory.hard_delete_many({"group_id": "group_batch"}) - - report.record(name, "PASS", "3 docs inserted, isolated from tenant 2") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_beanie_save_replace(ids: dict): - """Beanie: document.save() — update via replace, verify tenant isolation.""" - name = "beanie_save_replace" - try: - doc_id = ids[TENANT_1][0] - async with tenant_context(TENANT_1): - doc = await EpisodicMemory.find_one({"_id": doc_id}) - assert doc is not None, "Doc not found for save test" - original_summary = doc.summary - doc.summary = "Updated summary via save" - await doc.save() - - reloaded = await EpisodicMemory.find_one({"_id": doc_id}) - assert reloaded.summary == "Updated summary via save" - assert reloaded.tenant_id == TENANT_1 - - # Restore original - reloaded.summary = original_summary - await reloaded.save() - - report.record(name, "PASS", "save() preserves tenant_id after replace") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_beanie_get_by_id(ids: dict): - """Beanie: Model.get(id) — verify tenant scoping.""" - name = "beanie_get_by_id" - try: - doc_id = ids[TENANT_1][0] - - # Tenant 1 can see it - async with tenant_context(TENANT_1): - doc = await EpisodicMemory.get(doc_id) - assert doc is not None, "Tenant 1 cannot see own doc" - - # Tenant 2 should NOT see it - async with tenant_context(TENANT_2): - doc = await EpisodicMemory.get(doc_id) - assert doc is None, "Tenant 2 can see tenant 1's doc via get()!" - - report.record(name, "PASS", "get() correctly isolated between tenants") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -# ============================================================ -# Test Cases: Beanie Query Operations (Cursor Path) -# ============================================================ - - -async def test_find_basic_isolation(ids: dict): - """Beanie find: tenant 1 data invisible to tenant 2.""" - name = "find_basic_isolation" - try: - async with tenant_context(TENANT_1): - t1_docs = await EpisodicMemory.find({"group_id": "group_alpha"}).to_list() - - async with tenant_context(TENANT_2): - t2_docs = await EpisodicMemory.find({"group_id": "group_alpha"}).to_list() - - # Both should have 5 docs each - assert len(t1_docs) == 5, f"Tenant 1: expected 5, got {len(t1_docs)}" - assert len(t2_docs) == 5, f"Tenant 2: expected 5, got {len(t2_docs)}" - - # IDs should be completely disjoint - t1_ids = {str(d.id) for d in t1_docs} - t2_ids = {str(d.id) for d in t2_docs} - assert t1_ids.isdisjoint(t2_ids), "Tenant data overlap detected!" - - report.record( - name, - "PASS", - f"T1: {len(t1_docs)} docs, T2: {len(t2_docs)} docs, zero overlap", - ) - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_find_chained_sort_skip_limit(ids: dict): - """Beanie find: .sort().skip().limit() chain — cursor path.""" - name = "find_chained_sort_skip_limit" - try: - async with tenant_context(TENANT_1): - docs = ( - await EpisodicMemory.find({"group_id": "group_alpha"}) - .sort("-timestamp") - .skip(1) - .limit(3) - .to_list() - ) - assert len(docs) == 3, f"Expected 3, got {len(docs)}" - # Verify sorted descending - for i in range(len(docs) - 1): - assert docs[i].timestamp >= docs[i + 1].timestamp, "Sort order wrong" - for d in docs: - assert d.tenant_id == TENANT_1 - - report.record(name, "PASS", "sort/skip/limit with correct isolation") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_find_with_projection(ids: dict): - """Beanie find: projection_model to exclude vector field.""" - name = "find_with_projection" - try: - async with tenant_context(TENANT_1): - docs = await EpisodicMemory.find( - {"group_id": "group_alpha"}, projection_model=EpisodicMemoryProjection - ).to_list() - assert len(docs) == 5 - # EpisodicMemoryProjection should not have vector field - for d in docs: - assert not hasattr(d, "vector") or d.vector is None - - report.record(name, "PASS", "Projection works with tenant isolation") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_find_count(ids: dict): - """Beanie find().count() — aggregate path for counting.""" - name = "find_count" - try: - async with tenant_context(TENANT_1): - count = await EpisodicMemory.find({"group_id": "group_alpha"}).count() - assert count == 5, f"T1 expected 5, got {count}" - - async with tenant_context(TENANT_2): - count = await EpisodicMemory.find({"group_id": "group_alpha"}).count() - assert count == 5, f"T2 expected 5, got {count}" - - report.record(name, "PASS", "find().count() isolated per tenant (5 each)") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_cursor_batch_size(ids: dict): - """Cursor: small batch_size triggers getMore — verify initial find is filtered.""" - name = "cursor_batch_size" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - # Use pymongo directly — batch_size(2) with 5+ docs forces getMore - cursor = collection.find( - {"group_id": "group_alpha", "deleted_at": None}, batch_size=2 - ) - docs = await cursor.to_list(length=100) - assert len(docs) == 5, f"Expected 5, got {len(docs)}" - for d in docs: - assert d.get("tenant_id") == TENANT_1 - - report.record(name, "PASS", "batch_size cursor correctly isolated") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_cursor_async_for(ids: dict): - """Cursor: async for iteration — the most common consumption pattern.""" - name = "cursor_async_for" - try: - collected = [] - async with tenant_context(TENANT_1): - async for doc in EpisodicMemory.find({"group_id": "group_alpha"}): - collected.append(doc) - assert len(collected) == 5 - for d in collected: - assert d.tenant_id == TENANT_1 - - report.record(name, "PASS", "async for iteration correctly isolated") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -# ============================================================ -# Test Cases: Soft Delete Operations -# ============================================================ - - -async def test_soft_delete_single(ids: dict): - """Soft delete: document.delete() — marks as deleted, not visible in find.""" - name = "soft_delete_single" - try: - async with tenant_context(TENANT_1): - # Insert a doc to soft-delete - doc = _make_episodic( - user_id="sd_user", - group_id="group_sd", - summary="To be soft deleted", - episode="SD episode", - ) - await doc.insert() - doc_id = doc.id - - await doc.delete(deleted_by="test_admin") - assert doc.is_deleted(), "Document should be marked deleted" - - # find_one should not see it - result = await EpisodicMemory.find_one({"_id": doc_id}) - assert result is None, "Soft-deleted doc visible in find_one" - - # hard_find_one should see it - result = await EpisodicMemory.hard_find_one({"_id": doc_id}) - assert result is not None, "Soft-deleted doc not found in hard_find_one" - assert result.deleted_by == "test_admin" - - # Cleanup - await result.hard_delete() - - report.record(name, "PASS", "Soft delete works with tenant isolation") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_soft_delete_many(ids: dict): - """Soft delete: Model.delete_many(filter) — bulk soft delete.""" - name = "soft_delete_many" - try: - async with tenant_context(TENANT_1): - # Insert docs to soft-delete - for i in range(3): - doc = _make_episodic( - user_id=f"sdm_user_{i}", - group_id="group_sdm", - summary=f"Bulk SD {i}", - episode=f"Bulk SD ep {i}", - ) - await doc.insert() - - result = await EpisodicMemory.delete_many( - {"group_id": "group_sdm"}, deleted_by="admin" - ) - assert ( - result.modified_count == 3 - ), f"Expected 3, modified {result.modified_count}" - - # find_many should not see them - visible = await EpisodicMemory.find_many( - {"group_id": "group_sdm"} - ).to_list() - assert len(visible) == 0, f"Soft-deleted docs still visible: {len(visible)}" - - # Cleanup - await EpisodicMemory.hard_delete_many({"group_id": "group_sdm"}) - - report.record(name, "PASS", "Bulk soft delete with isolation") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_soft_delete_restore(ids: dict): - """Soft delete: restore single doc.""" - name = "soft_delete_restore" - try: - async with tenant_context(TENANT_1): - doc = _make_episodic( - user_id="restore_user", - group_id="group_restore", - summary="To restore", - episode="Restore ep", - ) - await doc.insert() - doc_id = doc.id - - await doc.delete(deleted_by="admin") - assert doc.is_deleted() - - # Restore - await doc.restore() - assert not doc.is_deleted() - - # Should be visible again - result = await EpisodicMemory.find_one({"_id": doc_id}) - assert result is not None, "Restored doc not visible" - - await result.hard_delete() - - report.record(name, "PASS", "Single restore works with isolation") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_soft_delete_restore_many(ids: dict): - """Soft delete: restore_many — bulk restore.""" - name = "soft_delete_restore_many" - try: - async with tenant_context(TENANT_1): - for i in range(3): - doc = _make_episodic( - user_id=f"rm_user_{i}", - group_id="group_rm", - summary=f"RM {i}", - episode=f"RM ep {i}", - ) - await doc.insert() - await doc.delete(deleted_by="admin") - - # Restore all - result = await EpisodicMemory.restore_many({"group_id": "group_rm"}) - assert ( - result.modified_count == 3 - ), f"Expected 3 restored, got {result.modified_count}" - - # Should be visible again - visible = await EpisodicMemory.find_many({"group_id": "group_rm"}).to_list() - assert len(visible) == 3 - - # Cleanup - await EpisodicMemory.hard_delete_many({"group_id": "group_rm"}) - - report.record(name, "PASS", "Bulk restore works with isolation") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_hard_find_many(ids: dict): - """Soft delete: hard_find_many — includes soft-deleted docs.""" - name = "hard_find_many" - try: - async with tenant_context(TENANT_1): - doc = _make_episodic( - user_id="hfm_user", - group_id="group_hfm", - summary="HFM test", - episode="HFM ep", - ) - await doc.insert() - await doc.delete(deleted_by="admin") - - # hard_find_many should see it - results = await EpisodicMemory.hard_find_many( - {"group_id": "group_hfm"} - ).to_list() - assert len(results) == 1 - assert results[0].is_deleted() - - # Tenant 2 should not see it - async with tenant_context(TENANT_2): - results = await EpisodicMemory.hard_find_many( - {"group_id": "group_hfm"} - ).to_list() - assert ( - len(results) == 0 - ), f"Tenant 2 sees tenant 1's hard_find_many data: {len(results)}" - - # Cleanup - async with tenant_context(TENANT_1): - await EpisodicMemory.hard_delete_many({"group_id": "group_hfm"}) - - report.record(name, "PASS", "hard_find_many isolated between tenants") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_hard_delete_many(ids: dict): - """Soft delete: hard_delete_many — physical bulk delete.""" - name = "hard_delete_many" - try: - async with tenant_context(TENANT_1): - for i in range(2): - doc = _make_episodic( - user_id=f"hdm_user_{i}", - group_id="group_hdm", - summary=f"HDM {i}", - episode=f"HDM ep {i}", - ) - await doc.insert() - - result = await EpisodicMemory.hard_delete_many({"group_id": "group_hdm"}) - assert result.deleted_count == 2 - - # Verify gone - results = await EpisodicMemory.hard_find_many( - {"group_id": "group_hdm"} - ).to_list() - assert len(results) == 0 - - report.record(name, "PASS", "hard_delete_many works with isolation") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_soft_delete_count(ids: dict): - """Soft delete: count() — count with soft delete awareness.""" - name = "soft_delete_count" - try: - async with tenant_context(TENANT_1): - # count() uses count_documents({"deleted_at": None}) - count = await EpisodicMemory.count() - # At least 7 docs (5 alpha + 2 beta) for tenant 1 - assert count >= 7, f"T1 count too low: {count}" - - async with tenant_context(TENANT_2): - count = await EpisodicMemory.count() - assert count >= 7, f"T2 count too low: {count}" - - report.record(name, "PASS", "count() works per tenant") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_apply_soft_delete_filter(ids: dict): - """Utility: apply_soft_delete_filter for raw pymongo queries.""" - name = "apply_soft_delete_filter" - try: - async with tenant_context(TENANT_1): - filter_dict = EpisodicMemory.apply_soft_delete_filter( - {"group_id": "group_alpha"} - ) - assert "deleted_at" in filter_dict - assert filter_dict["deleted_at"] is None - - collection = EpisodicMemory.get_pymongo_collection() - results = await collection.find(filter_dict).to_list(length=100) - assert len(results) == 5 - for r in results: - assert r.get("tenant_id") == TENANT_1 - - report.record(name, "PASS", "apply_soft_delete_filter + pymongo isolation") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -# ============================================================ -# Test Cases: Direct PyMongo Operations -# ============================================================ - - -async def test_pymongo_find(ids: dict): - """PyMongo: collection.find(filter).to_list()""" - name = "pymongo_find" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - results = await collection.find({"group_id": "group_alpha"}).to_list( - length=100 - ) - assert len(results) == 5 - for r in results: - assert r.get("tenant_id") == TENANT_1 - - async with tenant_context(TENANT_2): - results = await collection.find({"group_id": "group_alpha"}).to_list( - length=100 - ) - assert len(results) == 5 - for r in results: - assert r.get("tenant_id") == TENANT_2 - - report.record(name, "PASS", "pymongo find isolated") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_find_one(ids: dict): - """PyMongo: collection.find_one(filter)""" - name = "pymongo_find_one" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - result = await collection.find_one({"user_id": "user_0"}) - assert result is not None - assert result.get("tenant_id") == TENANT_1 - - report.record(name, "PASS", "pymongo find_one isolated") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_insert_one(ids: dict): - """PyMongo: collection.insert_one(doc)""" - name = "pymongo_insert_one" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - now = get_now_with_timezone() - result = await collection.insert_one( - { - "user_id": "pymongo_insert_user", - "group_id": "group_pymongo_ins", - "session_id": "sess_001", - "timestamp": now, - "summary": "pymongo insert", - "episode": "pymongo insert ep", - "type": "Conversation", - "deleted_at": None, - "deleted_id": 0, - } - ) - doc_id = result.inserted_id - - # Verify tenant_id - doc = await collection.find_one({"_id": doc_id}) - assert doc.get("tenant_id") == TENANT_1 - - # Cleanup - await collection.delete_one({"_id": doc_id}) - - report.record(name, "PASS", "pymongo insert_one sets tenant_id") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_update_one(ids: dict): - """PyMongo: collection.update_one(filter, update)""" - name = "pymongo_update_one" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - # Update a tenant 1 doc - result = await collection.update_one( - {"user_id": "user_0"}, {"$set": {"subject": "updated_subject"}} - ) - assert result.modified_count == 1 - - # Verify tenant 2's user_0 is untouched - async with tenant_context(TENANT_2): - doc = await collection.find_one({"user_id": "user_0"}) - assert doc is not None - assert ( - doc.get("subject") != "updated_subject" - ), "Tenant 2 doc was modified by tenant 1 update!" - - # Restore - async with tenant_context(TENANT_1): - await collection.update_one( - {"user_id": "user_0"}, {"$set": {"subject": "test subject"}} - ) - - report.record(name, "PASS", "pymongo update_one isolated") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_update_many(ids: dict): - """PyMongo: collection.update_many(filter, update)""" - name = "pymongo_update_many" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - result = await collection.update_many( - {"group_id": "group_alpha"}, {"$set": {"type": "UpdatedType"}} - ) - assert ( - result.modified_count == 5 - ), f"Expected 5, modified {result.modified_count}" - - # Verify tenant 2 untouched - async with tenant_context(TENANT_2): - docs = await collection.find( - {"group_id": "group_alpha", "type": "UpdatedType"} - ).to_list(length=100) - assert len(docs) == 0, f"Tenant 2 has {len(docs)} docs with UpdatedType!" - - # Restore - async with tenant_context(TENANT_1): - await collection.update_many( - {"group_id": "group_alpha"}, {"$set": {"type": "Conversation"}} - ) - - report.record(name, "PASS", "pymongo update_many isolated") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_replace_one(ids: dict): - """PyMongo: collection.replace_one(filter, replacement)""" - name = "pymongo_replace_one" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - # Insert a doc to replace - now = get_now_with_timezone() - await collection.insert_one( - { - "user_id": "replace_user", - "group_id": "group_replace", - "session_id": "sess_001", - "timestamp": now, - "summary": "before replace", - "episode": "before replace ep", - "deleted_at": None, - "deleted_id": 0, - } - ) - - result = await collection.replace_one( - {"user_id": "replace_user"}, - { - "user_id": "replace_user", - "group_id": "group_replace", - "session_id": "sess_001", - "timestamp": now, - "summary": "after replace", - "episode": "after replace ep", - "deleted_at": None, - "deleted_id": 0, - }, - ) - assert result.modified_count == 1 - - # Verify tenant_id preserved in replacement - doc = await collection.find_one({"user_id": "replace_user"}) - assert doc.get("tenant_id") == TENANT_1 - assert doc.get("summary") == "after replace" - - # Cleanup - await collection.delete_one({"user_id": "replace_user"}) - - report.record(name, "PASS", "pymongo replace_one preserves tenant_id") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_delete_one(ids: dict): - """PyMongo: collection.delete_one(filter)""" - name = "pymongo_delete_one" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - now = get_now_with_timezone() - await collection.insert_one( - { - "user_id": "del_one_user", - "group_id": "group_del", - "session_id": "sess_001", - "timestamp": now, - "summary": "to delete", - "episode": "del ep", - "deleted_at": None, - "deleted_id": 0, - } - ) - result = await collection.delete_one({"user_id": "del_one_user"}) - assert result.deleted_count == 1 - - report.record(name, "PASS", "pymongo delete_one isolated") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_delete_many(ids: dict): - """PyMongo: collection.delete_many(filter)""" - name = "pymongo_delete_many" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - for i in range(3): - now = get_now_with_timezone() - await collection.insert_one( - { - "user_id": f"del_many_user_{i}", - "group_id": "group_del_many", - "session_id": "sess_001", - "timestamp": now, - "summary": f"del many {i}", - "episode": f"del many ep {i}", - "deleted_at": None, - "deleted_id": 0, - } - ) - result = await collection.delete_many({"group_id": "group_del_many"}) - assert result.deleted_count == 3 - - report.record(name, "PASS", "pymongo delete_many isolated") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_aggregate(ids: dict): - """PyMongo: collection.aggregate(pipeline)""" - name = "pymongo_aggregate" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - pipeline = [ - {"$match": {"session_id": "sess_001", "deleted_at": None}}, - {"$group": {"_id": "$group_id", "count": {"$sum": 1}}}, - {"$sort": {"_id": 1}}, - ] - cursor = await collection.aggregate(pipeline) - results = await cursor.to_list(length=100) - # Should see group_alpha (5) and group_beta (2) - group_counts = {r["_id"]: r["count"] for r in results} - assert ( - group_counts.get("group_alpha") == 5 - ), f"Expected 5 for group_alpha, got {group_counts}" - assert ( - group_counts.get("group_beta") == 2 - ), f"Expected 2 for group_beta, got {group_counts}" - - report.record(name, "PASS", "pymongo aggregate isolated") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_count_documents(ids: dict): - """PyMongo: collection.count_documents(filter)""" - name = "pymongo_count_documents" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - count = await collection.count_documents( - {"group_id": "group_alpha", "deleted_at": None} - ) - assert count == 5, f"T1 expected 5, got {count}" - - async with tenant_context(TENANT_2): - count = await collection.count_documents( - {"group_id": "group_alpha", "deleted_at": None} - ) - assert count == 5, f"T2 expected 5, got {count}" - - report.record(name, "PASS", "pymongo count_documents isolated (5 each)") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_estimated_document_count(ids: dict): - """PyMongo: collection.estimated_document_count() — uses count command.""" - name = "pymongo_estimated_document_count" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - count = await collection.estimated_document_count() - # This uses the count command which gets tenant_id injected. - # However, estimated_document_count is metadata-based and may - # not respect filters. We just verify it doesn't raise. - assert isinstance(count, int) - - report.record( - name, - "PASS", - f"estimated_document_count returned {count} (interceptor injects tenant_id into count cmd)", - ) - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_distinct(ids: dict): - """PyMongo: collection.distinct(key, filter)""" - name = "pymongo_distinct" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - groups = await collection.distinct("group_id", {"deleted_at": None}) - assert "group_alpha" in groups - assert "group_beta" in groups - - report.record(name, "PASS", f"pymongo distinct isolated: {groups}") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_find_one_and_update(ids: dict): - """PyMongo: collection.find_one_and_update() — findAndModify command.""" - name = "pymongo_find_one_and_update" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - doc = await collection.find_one_and_update( - {"user_id": "user_0", "deleted_at": None}, - {"$set": {"subject": "fau_subject"}}, - return_document=True, - ) - assert doc is not None - assert doc.get("tenant_id") == TENANT_1 - assert doc.get("subject") == "fau_subject" - - # Restore - await collection.update_one( - {"user_id": "user_0"}, {"$set": {"subject": "test subject"}} - ) - - report.record(name, "PASS", "find_one_and_update isolated") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_find_one_and_delete(ids: dict): - """PyMongo: collection.find_one_and_delete()""" - name = "pymongo_find_one_and_delete" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - now = get_now_with_timezone() - await collection.insert_one( - { - "user_id": "fad_user", - "group_id": "group_fad", - "session_id": "sess_001", - "timestamp": now, - "summary": "fad test", - "episode": "fad ep", - "deleted_at": None, - "deleted_id": 0, - } - ) - doc = await collection.find_one_and_delete({"user_id": "fad_user"}) - assert doc is not None - assert doc.get("tenant_id") == TENANT_1 - - report.record(name, "PASS", "find_one_and_delete isolated") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_pymongo_find_one_and_replace(ids: dict): - """PyMongo: collection.find_one_and_replace()""" - name = "pymongo_find_one_and_replace" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - now = get_now_with_timezone() - await collection.insert_one( - { - "user_id": "far_user", - "group_id": "group_far", - "session_id": "sess_001", - "timestamp": now, - "summary": "before", - "episode": "before ep", - "deleted_at": None, - "deleted_id": 0, - } - ) - doc = await collection.find_one_and_replace( - {"user_id": "far_user"}, - { - "user_id": "far_user", - "group_id": "group_far", - "session_id": "sess_001", - "timestamp": now, - "summary": "after", - "episode": "after ep", - "deleted_at": None, - "deleted_id": 0, - }, - return_document=True, - ) - assert doc is not None - assert doc.get("tenant_id") == TENANT_1 - assert doc.get("summary") == "after" - - # Cleanup - await collection.delete_one({"user_id": "far_user"}) - - report.record( - name, "PASS", "find_one_and_replace sets tenant_id in replacement" - ) - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -# ============================================================ -# Test Cases: Rejected / Unsupported Operations -# ============================================================ - - -async def test_find_raw_batches_rejected(ids: dict): - """Rejected: find_raw_batches raises InvalidOperation when _encrypter is set.""" - name = "find_raw_batches_rejected" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - try: - cursor = collection.find_raw_batches({"user_id": "user_0"}) - await cursor.to_list(length=10) - report.record(name, "FAIL", "find_raw_batches did NOT raise") - except InvalidOperation: - report.record( - name, "PASS", "find_raw_batches correctly raises InvalidOperation" - ) - except Exception as e: - report.record( - name, - "PASS", - f"find_raw_batches rejected with {type(e).__name__}: {e}", - ) - except Exception as e: - report.record(name, "ERROR", f"{e}") - - -async def test_aggregate_raw_batches_rejected(ids: dict): - """Rejected: aggregate_raw_batches raises InvalidOperation when _encrypter is set.""" - name = "aggregate_raw_batches_rejected" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_1): - try: - cursor = await collection.aggregate_raw_batches([{"$match": {}}]) - await cursor.to_list(length=10) - report.record(name, "FAIL", "aggregate_raw_batches did NOT raise") - except InvalidOperation: - report.record( - name, - "PASS", - "aggregate_raw_batches correctly raises InvalidOperation", - ) - except Exception as e: - report.record( - name, - "PASS", - f"aggregate_raw_batches rejected with {type(e).__name__}: {e}", - ) - except Exception as e: - report.record(name, "ERROR", f"{e}") - - -# ============================================================ -# Test Cases: Cross-Tenant Mutation Protection -# ============================================================ - - -async def test_cross_tenant_update_blocked(ids: dict): - """Verify tenant 2 cannot update tenant 1's data via update_one.""" - name = "cross_tenant_update_blocked" - try: - collection = EpisodicMemory.get_pymongo_collection() - - # Get a known doc ID from tenant 1 - doc_id = ids[TENANT_1][0] - - # Tenant 2 tries to update by _id - async with tenant_context(TENANT_2): - result = await collection.update_one( - {"_id": doc_id}, {"$set": {"summary": "HACKED"}} - ) - assert ( - result.modified_count == 0 - ), f"Tenant 2 modified tenant 1's doc! modified_count={result.modified_count}" - - # Verify tenant 1's doc is unchanged - async with tenant_context(TENANT_1): - doc = await collection.find_one({"_id": doc_id}) - assert doc is not None - assert doc.get("summary") != "HACKED", "Doc was actually modified!" - - report.record(name, "PASS", "Cross-tenant update correctly blocked") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_cross_tenant_delete_blocked(ids: dict): - """Verify tenant 2 cannot delete tenant 1's data.""" - name = "cross_tenant_delete_blocked" - try: - collection = EpisodicMemory.get_pymongo_collection() - doc_id = ids[TENANT_1][0] - - async with tenant_context(TENANT_2): - result = await collection.delete_one({"_id": doc_id}) - assert result.deleted_count == 0, "Tenant 2 deleted tenant 1's doc!" - - # Verify still exists - async with tenant_context(TENANT_1): - doc = await collection.find_one({"_id": doc_id}) - assert doc is not None, "Doc was actually deleted!" - - report.record(name, "PASS", "Cross-tenant delete correctly blocked") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_cross_tenant_find_blocked(ids: dict): - """Verify tenant 2 cannot find tenant 1's data by _id.""" - name = "cross_tenant_find_blocked" - try: - collection = EpisodicMemory.get_pymongo_collection() - doc_id = ids[TENANT_1][0] - - async with tenant_context(TENANT_2): - doc = await collection.find_one({"_id": doc_id}) - assert doc is None, "Tenant 2 found tenant 1's doc!" - - report.record(name, "PASS", "Cross-tenant find correctly blocked") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -async def test_cross_tenant_aggregate_blocked(ids: dict): - """Verify tenant 2's aggregate cannot see tenant 1's data.""" - name = "cross_tenant_aggregate_blocked" - try: - collection = EpisodicMemory.get_pymongo_collection() - - async with tenant_context(TENANT_2): - pipeline = [ - {"$match": {"_id": ids[TENANT_1][0]}}, - {"$project": {"summary": 1, "tenant_id": 1}}, - ] - cursor = await collection.aggregate(pipeline) - results = await cursor.to_list(length=10) - assert len(results) == 0, f"Tenant 2 aggregate sees tenant 1 doc: {results}" - - report.record(name, "PASS", "Cross-tenant aggregate correctly blocked") - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -# ============================================================ -# Test Cases: No-Tenant Passthrough -# ============================================================ - - -async def test_no_tenant_raises(ids: dict): - """Verify operations raise TenantIsolationViolation without tenant context after app_ready.""" - name = "no_tenant_raises" - try: - import os - from core.tenants.tenant_config import get_tenant_config - - config = get_tenant_config() - was_ready = config.app_ready - if not was_ready: - config.mark_app_ready() - - # Temporarily remove single_tenant_id fallback so clear_current_tenant truly clears - saved_single = os.environ.pop("TENANT_SINGLE_TENANT_ID", None) - config.reload() - clear_current_tenant() - assert ( - get_current_tenant_id() is None - ), "tenant_id should be None after clearing" - - collection = EpisodicMemory.get_pymongo_collection() - try: - await collection.find( - {"group_id": "group_alpha", "deleted_at": None} - ).to_list(length=100) - report.record( - name, - "FAIL", - "Expected TenantIsolationViolation but no exception raised", - ) - except Exception as e: - if "TenantIsolationViolation" in type( - e - ).__name__ or "Missing tenant_id" in str(e): - report.record( - name, - "PASS", - f"Correctly raised on missing tenant_id: {type(e).__name__}", - ) - else: - report.record( - name, "FAIL", f"Unexpected exception: {type(e).__name__}: {e}" - ) - finally: - if saved_single is not None: - os.environ["TENANT_SINGLE_TENANT_ID"] = saved_single - config.reload() - if not was_ready: - config.reset_app_ready() - except Exception as e: - report.record(name, "FAIL", f"{e}") - - -# ============================================================ -# Main -# ============================================================ - -ALL_TESTS = [ - # Beanie ODM - test_beanie_insert_single, - test_beanie_insert_many, - test_beanie_save_replace, - test_beanie_get_by_id, - # Query / Cursor - test_find_basic_isolation, - test_find_chained_sort_skip_limit, - test_find_with_projection, - test_find_count, - test_cursor_batch_size, - test_cursor_async_for, - # Soft Delete - test_soft_delete_single, - test_soft_delete_many, - test_soft_delete_restore, - test_soft_delete_restore_many, - test_hard_find_many, - test_hard_delete_many, - test_soft_delete_count, - test_apply_soft_delete_filter, - # Direct PyMongo - test_pymongo_find, - test_pymongo_find_one, - test_pymongo_insert_one, - test_pymongo_update_one, - test_pymongo_update_many, - test_pymongo_replace_one, - test_pymongo_delete_one, - test_pymongo_delete_many, - test_pymongo_aggregate, - test_pymongo_count_documents, - test_pymongo_estimated_document_count, - test_pymongo_distinct, - test_pymongo_find_one_and_update, - test_pymongo_find_one_and_delete, - test_pymongo_find_one_and_replace, - # Rejected operations - test_find_raw_batches_rejected, - test_aggregate_raw_batches_rejected, - # Cross-tenant protection - test_cross_tenant_update_blocked, - test_cross_tenant_delete_blocked, - test_cross_tenant_find_blocked, - test_cross_tenant_aggregate_blocked, - # Passthrough - test_no_tenant_raises, -] - - -async def main(): - print("\n" + "=" * 80) - print(" Tenant MongoDB Isolation E2E Test") - print(" Using EpisodicMemory (v1_episodic_memories)") - print(f" Tenants: {TENANT_1}, {TENANT_2}") - print("=" * 80 + "\n") - - # Setup - print("--- Setup: inserting test data ---") - ids = await setup_test_data() - print( - f" Tenant 1: {len(ids[TENANT_1])} docs, Tenant 2: {len(ids[TENANT_2])} docs\n" - ) - - # Run tests - print("--- Running tests ---") - for test_fn in ALL_TESTS: - try: - await test_fn(ids) - except Exception as e: - report.record( - test_fn.__name__, - "ERROR", - f"Unhandled: {type(e).__name__}: {e}\n{traceback.format_exc()}", - ) - - # Cleanup - print("\n--- Cleanup: removing test data ---") - await cleanup_test_data() - print(" Done.\n") - - # Report - success = report.summary() - if not success: - raise SystemExit(1) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/methods/EverCore/tests/test_token_usage_collector_llm_adapter.py b/methods/EverCore/tests/test_token_usage_collector_llm_adapter.py deleted file mode 100644 index c4196d69..00000000 --- a/methods/EverCore/tests/test_token_usage_collector_llm_adapter.py +++ /dev/null @@ -1,435 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Real LLM integration tests for TokenUsageCollector. - -These tests make actual LLM/embedding API calls — they require: -- LLM backend configured (LLM_BASE_URL, LLM_API_KEY, LLM_MODEL in .env) -- Embedding backend configured (deepinfra fallback) - -Run with: - PYTHONPATH=src uv run python -m pytest tests/test_token_usage_collector_real_llm.py -v -s - -Use -s to see the token usage output in real time. -Skip with: pytest -m "not integration" -""" - -import os -import sys -import pytest - -# Load .env before imports -from pathlib import Path -from dotenv import load_dotenv - -load_dotenv(Path(__file__).resolve().parent.parent / ".env") - -from core.component.token_usage_collector import TokenUsageCollector -from core.component.llm.llm_adapter.message import ChatMessage, MessageRole -from core.component.llm.llm_adapter.completion import ChatCompletionRequest - - -class SpyCollector(TokenUsageCollector): - """Records all add() calls for assertion.""" - - def __init__(self): - self.calls = [] - - def add( - self, model, input_tokens, output_tokens, *, call_type="llm", request_id=None - ): - self.calls.append( - { - "model": model, - "input_tokens": input_tokens, - "output_tokens": output_tokens, - "call_type": call_type, - } - ) - print( - f" [SpyCollector] add: model={model}, type={call_type}, in={input_tokens}, out={output_tokens}" - ) - - def get_totals(self): - return {} - - def reset(self): - self.calls.clear() - - -# ============================================================ -# Skip conditions -# ============================================================ - -LLM_API_KEY = os.getenv("LLM_API_KEY", "") -LLM_BASE_URL = os.getenv("LLM_BASE_URL", "") -LLM_MODEL = os.getenv("LLM_MODEL", "") - -skip_no_llm = pytest.mark.skipif( - not LLM_API_KEY or not LLM_BASE_URL, - reason="LLM_API_KEY and LLM_BASE_URL not configured in .env", -) - - -# ============================================================ -# OpenAI Adapter (real call via OpenRouter) -# ============================================================ - - -@skip_no_llm -class TestOpenAIAdapterReal: - """Real OpenAI-compatible adapter tests (uses OpenRouter/configured LLM).""" - - def _make_adapter(self): - from core.component.llm.llm_adapter.openai_adapter import OpenAIAdapter - - return OpenAIAdapter( - {"api_key": LLM_API_KEY, "base_url": LLM_BASE_URL, "timeout": 60} - ) - - @pytest.mark.asyncio - async def test_non_stream_real(self): - """Real non-streaming call: verify token counts > 0.""" - from unittest.mock import patch - - adapter = self._make_adapter() - spy = SpyCollector() - - with patch( - "core.component.llm.llm_adapter.openai_adapter.get_bean_by_type", - return_value=spy, - ): - request = ChatCompletionRequest( - messages=[ - ChatMessage(role=MessageRole.USER, content="Say hello in one word.") - ], - model=LLM_MODEL, - stream=False, - max_tokens=10, - ) - response = await adapter.chat_completion(request) - - print(f" Response: {response.choices[0]['message']['content']}") - print(f" Usage: {response.usage}") - - assert len(spy.calls) == 1, f"Expected 1 call, got {spy.calls}" - call = spy.calls[0] - assert ( - call["input_tokens"] > 0 - ), f"Expected input_tokens > 0, got {call['input_tokens']}" - assert ( - call["output_tokens"] > 0 - ), f"Expected output_tokens > 0, got {call['output_tokens']}" - assert call["call_type"] == "llm" - print(f" PASS: input={call['input_tokens']}, output={call['output_tokens']}") - - @pytest.mark.asyncio - async def test_stream_real(self): - """Real streaming call: verify token counts > 0 from final chunk.""" - from unittest.mock import patch - - adapter = self._make_adapter() - spy = SpyCollector() - - with patch( - "core.component.llm.llm_adapter.openai_adapter.get_bean_by_type", - return_value=spy, - ): - request = ChatCompletionRequest( - messages=[ - ChatMessage(role=MessageRole.USER, content="Say hello in one word.") - ], - model=LLM_MODEL, - stream=True, - max_tokens=10, - ) - gen = await adapter.chat_completion(request) - chunks = [c async for c in gen] - - print(f" Streamed content: {''.join(chunks)}") - - assert len(spy.calls) == 1, f"Expected 1 call, got {spy.calls}" - call = spy.calls[0] - assert ( - call["input_tokens"] > 0 - ), f"Expected input_tokens > 0, got {call['input_tokens']}" - assert ( - call["output_tokens"] > 0 - ), f"Expected output_tokens > 0, got {call['output_tokens']}" - print(f" PASS: input={call['input_tokens']}, output={call['output_tokens']}") - - -# ============================================================ -# Anthropic Adapter (real call via Anthropic API) -# ============================================================ - -ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "") -# The adapter posts to {base_url}/v1/messages, so base_url should NOT end with /v1 -_raw_anthropic_url = os.getenv("ANTHROPIC_BASE_URL", "https://api.anthropic.com") -ANTHROPIC_BASE_URL = _raw_anthropic_url.rstrip("/").removesuffix("/v1") - -skip_no_anthropic = pytest.mark.skipif( - not ANTHROPIC_API_KEY, reason="ANTHROPIC_API_KEY not configured in .env" -) - - -@skip_no_anthropic -class TestAnthropicAdapterReal: - """Real Anthropic adapter tests.""" - - def _make_adapter(self): - from core.component.llm.llm_adapter.anthropic_adapter import AnthropicAdapter - - return AnthropicAdapter( - { - "api_key": ANTHROPIC_API_KEY, - "base_url": ANTHROPIC_BASE_URL, - "timeout": 60, - } - ) - - @pytest.mark.asyncio - async def test_non_stream_real(self): - """Real non-streaming Anthropic call: verify token counts > 0.""" - from unittest.mock import patch - - adapter = self._make_adapter() - spy = SpyCollector() - - with patch( - "core.component.llm.llm_adapter.anthropic_adapter.get_bean_by_type", - return_value=spy, - ): - request = ChatCompletionRequest( - messages=[ - ChatMessage(role=MessageRole.USER, content="Say hello in one word.") - ], - model="claude-haiku-4-5-20251001", - stream=False, - max_tokens=10, - ) - response = await adapter.chat_completion(request) - - print(f" Response: {response.choices[0]['message']['content']}") - print(f" Usage: {response.usage}") - - assert len(spy.calls) == 1, f"Expected 1 call, got {spy.calls}" - call = spy.calls[0] - assert ( - call["input_tokens"] > 0 - ), f"Expected input_tokens > 0, got {call['input_tokens']}" - assert ( - call["output_tokens"] > 0 - ), f"Expected output_tokens > 0, got {call['output_tokens']}" - assert call["call_type"] == "llm" - print(f" PASS: input={call['input_tokens']}, output={call['output_tokens']}") - - @pytest.mark.asyncio - async def test_stream_real(self): - """Real streaming Anthropic call: verify usage from SSE events. - - Note: Some proxies don't send the `message_delta` event with output_tokens. - The real Anthropic API does. We assert input_tokens > 0 (from message_start) - and output_tokens >= 0 (may be 0 if proxy omits message_delta). - """ - from unittest.mock import patch - - adapter = self._make_adapter() - spy = SpyCollector() - - with patch( - "core.component.llm.llm_adapter.anthropic_adapter.get_bean_by_type", - return_value=spy, - ): - request = ChatCompletionRequest( - messages=[ - ChatMessage(role=MessageRole.USER, content="Say hello in one word.") - ], - model="claude-haiku-4-5-20251001", - stream=True, - max_tokens=10, - ) - gen = await adapter.chat_completion(request) - chunks = [c async for c in gen] - - print(f" Streamed content: {''.join(chunks)}") - - assert len(spy.calls) == 1, f"Expected 1 call, got {spy.calls}" - call = spy.calls[0] - assert ( - call["input_tokens"] > 0 - ), f"Expected input_tokens > 0, got {call['input_tokens']}" - assert ( - call["output_tokens"] > 0 - ), f"Expected output_tokens > 0, got {call['output_tokens']}" - print(f" PASS: input={call['input_tokens']}, output={call['output_tokens']}") - - -# ============================================================ -# Gemini Adapter (real call via Google AI) -# ============================================================ - -GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "") - -skip_no_gemini = pytest.mark.skipif( - not GEMINI_API_KEY, reason="GEMINI_API_KEY not configured in .env" -) - - -@skip_no_gemini -class TestGeminiAdapterReal: - """Real Gemini adapter tests.""" - - def _make_adapter(self): - from core.component.llm.llm_adapter.gemini_adapter import GeminiAdapter - - return GeminiAdapter( - {"api_key": GEMINI_API_KEY, "default_model": "gemini-2.5-flash"} - ) - - @pytest.mark.asyncio - async def test_non_stream_real(self): - """Real non-streaming Gemini call: verify token counts > 0.""" - from unittest.mock import patch - - adapter = self._make_adapter() - spy = SpyCollector() - - with patch( - "core.component.llm.llm_adapter.gemini_adapter.get_bean_by_type", - return_value=spy, - ): - request = ChatCompletionRequest( - messages=[ - ChatMessage(role=MessageRole.USER, content="Say hello in one word.") - ], - model="gemini-2.5-flash", - stream=False, - max_tokens=100, # Gemini needs room for thinking tokens - ) - response = await adapter.chat_completion(request) - - print(f" Response: {response.choices[0]['message']['content']}") - print(f" Usage: {response.usage}") - - assert len(spy.calls) == 1, f"Expected 1 call, got {spy.calls}" - call = spy.calls[0] - assert ( - call["input_tokens"] > 0 - ), f"Expected input_tokens > 0, got {call['input_tokens']}" - assert ( - call["output_tokens"] > 0 - ), f"Expected output_tokens > 0, got {call['output_tokens']}" - assert call["call_type"] == "llm" - print(f" PASS: input={call['input_tokens']}, output={call['output_tokens']}") - - @pytest.mark.asyncio - async def test_stream_real(self): - """Real streaming Gemini call: verify token counts > 0 from last chunk.""" - from unittest.mock import patch - - adapter = self._make_adapter() - spy = SpyCollector() - - with patch( - "core.component.llm.llm_adapter.gemini_adapter.get_bean_by_type", - return_value=spy, - ): - request = ChatCompletionRequest( - messages=[ - ChatMessage(role=MessageRole.USER, content="Say hello in one word.") - ], - model="gemini-2.5-flash", - stream=True, - max_tokens=100, # Gemini needs room for thinking tokens - ) - gen = await adapter.chat_completion(request) - chunks = [c async for c in gen] - - print(f" Streamed content: {''.join(chunks)}") - - assert len(spy.calls) == 1, f"Expected 1 call, got {spy.calls}" - call = spy.calls[0] - assert ( - call["input_tokens"] > 0 - ), f"Expected input_tokens > 0, got {call['input_tokens']}" - assert ( - call["output_tokens"] > 0 - ), f"Expected output_tokens > 0, got {call['output_tokens']}" - print(f" PASS: input={call['input_tokens']}, output={call['output_tokens']}") - - -# ============================================================ -# Embedding (real call via DeepInfra) -# ============================================================ - -DEEPINFRA_API_KEY = os.getenv("DEEPINFRA_API_KEY", "") - -skip_no_embedding = pytest.mark.skipif( - not DEEPINFRA_API_KEY, reason="DEEPINFRA_API_KEY not configured in .env" -) - - -@skip_no_embedding -class TestEmbeddingReal: - """Real embedding test (uses DeepInfra).""" - - @pytest.mark.asyncio - async def test_embedding_real(self): - """Real embedding call: verify collector.add(call_type='embedding') is called.""" - from unittest.mock import patch - from agentic_layer.vectorize_deepinfra import ( - DeepInfraVectorizeService, - DeepInfraVectorizeConfig, - ) - - config = DeepInfraVectorizeConfig(api_key=DEEPINFRA_API_KEY) - service = DeepInfraVectorizeService(config=config) - spy = SpyCollector() - - with patch("agentic_layer.vectorize_base.get_bean_by_type", return_value=spy): - embedding = await service.get_embedding("hello world") - - print(f" Embedding shape: {embedding.shape}") - - assert len(spy.calls) == 1, f"Expected 1 call, got {spy.calls}" - call = spy.calls[0] - assert call["call_type"] == "embedding" - assert call["input_tokens"] >= 0 # some providers return 0 - print(f" PASS: type=embedding, input_tokens={call['input_tokens']}") - - -# ============================================================ -# Legacy OpenAI Provider (real call) -# ============================================================ - - -@skip_no_llm -class TestLegacyProviderReal: - """Real legacy OpenAIProvider test.""" - - @pytest.mark.asyncio - async def test_legacy_provider_real(self): - """Real legacy provider call: verify collector.add() called with real tokens.""" - from unittest.mock import patch - from memory_layer.llm.openai_provider import OpenAIProvider - - provider = OpenAIProvider( - base_url=LLM_BASE_URL, api_key=LLM_API_KEY, model=LLM_MODEL - ) - spy = SpyCollector() - - with patch( - "memory_layer.llm.openai_provider.get_bean_by_type", return_value=spy - ): - result = await provider.generate("Say hello in one word.") - - print(f" Response: {result}") - - assert len(spy.calls) == 1, f"Expected 1 call, got {spy.calls}" - call = spy.calls[0] - assert call["input_tokens"] > 0 - assert call["output_tokens"] > 0 - assert call["model"] == LLM_MODEL - print( - f" PASS: model={call['model']}, input={call['input_tokens']}, output={call['output_tokens']}" - ) diff --git a/methods/EverCore/tests/test_tokenizer_factory.py b/methods/EverCore/tests/test_tokenizer_factory.py deleted file mode 100644 index b56a4742..00000000 --- a/methods/EverCore/tests/test_tokenizer_factory.py +++ /dev/null @@ -1,214 +0,0 @@ -""" -TokenizerFactory Test - -Test tokenizer factory functionality, including: -- Tokenizer loading and caching -- Default encodings preload -- Cache management - -Usage: - python src/bootstrap.py tests/test_tokenizer_factory.py -""" - -from core.di.utils import get_bean_by_type -from core.observation.logger import get_logger -from core.component.llm.tokenizer.tokenizer_factory import ( - TokenizerFactory, - DEFAULT_TIKTOKEN_ENCODINGS, -) - -logger = get_logger(__name__) - - -class TestTokenizerFactory: - """TokenizerFactory Test Class""" - - def test_get_tokenizer_factory_from_di(self): - """Test getting TokenizerFactory from DI container""" - print("\n🧪 Test getting TokenizerFactory from DI container") - - factory = get_bean_by_type(TokenizerFactory) - - assert ( - factory is not None - ), "TokenizerFactory should be available in DI container" - assert isinstance( - factory, TokenizerFactory - ), "Should be TokenizerFactory instance" - - print("✅ Successfully got TokenizerFactory from DI container") - print(f" - Factory instance: {factory}") - print(f" - Cached tokenizer count: {factory.get_cached_tokenizer_count()}") - - def test_get_tokenizer_from_tiktoken(self): - """Test getting tokenizer from tiktoken""" - print("\n🧪 Test getting tokenizer from tiktoken") - - factory: TokenizerFactory = get_bean_by_type(TokenizerFactory) - - # Test getting o200k_base encoding - tokenizer = factory.get_tokenizer_from_tiktoken("o200k_base") - - assert tokenizer is not None, "Tokenizer should not be None" - - # Test tokenization - test_text = "Hello, world! 你好，世界！" - tokens = tokenizer.encode(test_text) - - print("✅ Successfully got tokenizer from tiktoken") - print(f" - Encoding: o200k_base") - print(f" - Test text: {test_text}") - print(f" - Token count: {len(tokens)}") - print( - f" - Tokens: {tokens[:10]}..." - if len(tokens) > 10 - else f" - Tokens: {tokens}" - ) # noqa: G004 - - # Verify decoding - decoded_text = tokenizer.decode(tokens) - assert decoded_text == test_text, "Decoded text should match original" - print(f" - Decoded text: {decoded_text}") - - def test_tokenizer_caching(self): - """Test tokenizer caching functionality""" - print("\n🧪 Test tokenizer caching functionality") - - factory: TokenizerFactory = get_bean_by_type(TokenizerFactory) - - # Clear cache first - factory.clear_cache() - assert ( - factory.get_cached_tokenizer_count() == 0 - ), "Cache should be empty after clear" - print(" - Cache cleared") - - # Get tokenizer first time - tokenizer1 = factory.get_tokenizer_from_tiktoken("o200k_base") - count_after_first = factory.get_cached_tokenizer_count() - print(f" - After first load: {count_after_first} tokenizer(s) cached") - - # Get tokenizer second time (should be from cache) - tokenizer2 = factory.get_tokenizer_from_tiktoken("o200k_base") - count_after_second = factory.get_cached_tokenizer_count() - print(f" - After second load: {count_after_second} tokenizer(s) cached") - - # Verify same instance (from cache) - assert tokenizer1 is tokenizer2, "Should return same cached instance" - assert ( - count_after_first == count_after_second - ), "Cache count should not increase" - - print("✅ Tokenizer caching works correctly") - print(f" - Same instance returned: {tokenizer1 is tokenizer2}") - - def test_load_default_encodings(self): - """Test preloading default encodings""" - print("\n🧪 Test preloading default encodings") - - factory: TokenizerFactory = get_bean_by_type(TokenizerFactory) - - # Clear cache first - factory.clear_cache() - print(f" - Cache cleared, count: {factory.get_cached_tokenizer_count()}") - - # Load default encodings - print(f" - Default encodings to load: {DEFAULT_TIKTOKEN_ENCODINGS}") - factory.load_default_encodings() - - # Verify all default encodings are loaded - cached_count = factory.get_cached_tokenizer_count() - expected_count = len(DEFAULT_TIKTOKEN_ENCODINGS) - - print(f" - Expected count: {expected_count}") - print(f" - Actual cached count: {cached_count}") - - assert ( - cached_count >= expected_count - ), f"Should have at least {expected_count} tokenizers cached" - - print("✅ Default encodings preloaded successfully") - - def test_multiple_encodings(self): - """Test loading multiple different encodings""" - print("\n🧪 Test loading multiple different encodings") - - factory: TokenizerFactory = get_bean_by_type(TokenizerFactory) - - # Clear cache first - factory.clear_cache() - - encodings_to_test = ["o200k_base", "cl100k_base"] - test_text = "Hello, world!" - - for encoding in encodings_to_test: - tokenizer = factory.get_tokenizer_from_tiktoken(encoding) - tokens = tokenizer.encode(test_text) - print(f" - {encoding}: {len(tokens)} tokens for '{test_text}'") - - # Verify all are cached - cached_count = factory.get_cached_tokenizer_count() - assert cached_count == len( - encodings_to_test - ), f"Should have {len(encodings_to_test)} tokenizers cached" - - print("✅ Multiple encodings loaded and cached") - print(f" - Total cached: {cached_count}") - - def test_tokenizer_consistency_with_conv_memcell_extractor(self): - """Test that tokenizer usage is consistent with ConvMemCellExtractor""" - print("\n🧪 Test tokenizer consistency with ConvMemCellExtractor") - - factory: TokenizerFactory = get_bean_by_type(TokenizerFactory) - - # This is the same way ConvMemCellExtractor gets tokenizer - tokenizer = factory.get_tokenizer_from_tiktoken("o200k_base") - - # Test with conversation-like content - messages = [ - {"sender_name": "Alice", "content": "Hello, how are you?"}, - {"sender_name": "Bob", "content": "I'm fine, thanks! How about you?"}, - {"sender_name": "Alice", "content": "Great! Let's discuss the project."}, - ] - - total_tokens = 0 - for msg in messages: - speaker = msg.get('sender_name', '') - content = msg.get('content', '') - text = f"{speaker}: {content}" if speaker else content - tokens = tokenizer.encode(text) - total_tokens += len(tokens) - print(f" - '{text}' -> {len(tokens)} tokens") - - print("✅ Tokenizer works for conversation content") - print(f" - Total tokens: {total_tokens}") - - -def run_all_tests(): - """Run all tests""" - print("🚀 Starting TokenizerFactory tests") - print("=" * 60) - - test_instance = TestTokenizerFactory() - - try: - test_instance.test_get_tokenizer_factory_from_di() - test_instance.test_get_tokenizer_from_tiktoken() - test_instance.test_tokenizer_caching() - test_instance.test_load_default_encodings() - test_instance.test_multiple_encodings() - test_instance.test_tokenizer_consistency_with_conv_memcell_extractor() - - print("\n" + "=" * 60) - print("🎉 All TokenizerFactory tests completed!") - - except Exception as e: - logger.error("❌ Test execution failed: %s", e) - import traceback - - traceback.print_exc() - raise - - -if __name__ == "__main__": - run_all_tests() diff --git a/methods/EverCore/tests/test_vector_anchored_fusion.py b/methods/EverCore/tests/test_vector_anchored_fusion.py deleted file mode 100644 index 06c9c667..00000000 --- a/methods/EverCore/tests/test_vector_anchored_fusion.py +++ /dev/null @@ -1,411 +0,0 @@ -"""Tests for vector_anchored_fusion in retrieval_utils. - -Tests the saturated BM25 + weighted-sum fusion strategy: - final_score = alpha * vec_score + (1 - alpha) * (bm25_raw / (bm25_raw + k)) - -Branches covered: - - Both vector and keyword results present - - Vector-only (no keyword results) - - Keyword-only (no vector results) - - Both empty - - Overlapping documents (same doc in both sets) - - Single-path documents (floor defaults) - - BM25 saturation (noise suppression, strong signal preservation) - - Custom alpha / saturation_k parameters - - Result ordering - - Short vs long query-like score patterns - - Irrelevant-query patterns (flat BM25, flat vector) - -Usage: - PYTHONPATH=src pytest tests/test_vector_anchored_fusion.py -v -""" - -import pytest - -from agentic_layer.retrieval_utils import vector_anchored_fusion - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def _score_map(result): - """Convert [(doc_id, score), ...] to {doc_id: score}.""" - return {doc_id: s for doc_id, s in result} - - -def _sat(raw, k=5.0): - """Manual saturation: raw / (raw + k).""" - return raw / (raw + k) if raw > 0 else 0.0 - - -# =========================================================================== -# 1. Saturation normalization -# =========================================================================== - -class TestSaturationNormalization: - """BM25 raw scores are mapped via raw / (raw + k).""" - - def test_high_bm25_saturates_near_one(self): - vec = [("d1", 0.5)] - kw = [("d1", 100.0)] - result = _score_map(vector_anchored_fusion(vec, kw, saturation_k=5.0)) - # sat(100, 5) = 100/105 ≈ 0.952 - expected = 0.7 * 0.5 + 0.3 * _sat(100.0) - assert result["d1"] == pytest.approx(expected) - - def test_low_bm25_stays_near_zero(self): - vec = [("d1", 0.5)] - kw = [("d1", 0.3)] - result = _score_map(vector_anchored_fusion(vec, kw, saturation_k=5.0)) - # sat(0.3, 5) = 0.3/5.3 ≈ 0.057 - expected = 0.7 * 0.5 + 0.3 * _sat(0.3) - assert result["d1"] == pytest.approx(expected) - - def test_bm25_at_k_maps_to_half(self): - vec = [("d1", 0.6)] - kw = [("d1", 5.0)] - result = _score_map(vector_anchored_fusion(vec, kw, saturation_k=5.0)) - # sat(5, 5) = 0.5 - expected = 0.7 * 0.6 + 0.3 * 0.5 - assert result["d1"] == pytest.approx(expected) - - def test_bm25_zero_maps_to_zero(self): - vec = [("d1", 0.5)] - kw = [("d1", 0.0)] - result = _score_map(vector_anchored_fusion(vec, kw, saturation_k=5.0)) - expected = 0.7 * 0.5 + 0.3 * 0.0 - assert result["d1"] == pytest.approx(expected) - - def test_custom_saturation_k(self): - vec = [("d1", 0.5)] - kw = [("d1", 10.0)] - result = _score_map(vector_anchored_fusion(vec, kw, saturation_k=10.0)) - # sat(10, 10) = 0.5 - expected = 0.7 * 0.5 + 0.3 * 0.5 - assert result["d1"] == pytest.approx(expected) - - -# =========================================================================== -# 2. Weighted sum fusion -# =========================================================================== - -class TestWeightedSumFusion: - """final = alpha * vec + (1-alpha) * sat_bm25.""" - - def test_default_alpha(self): - vec = [("d1", 0.8)] - kw = [("d1", 10.0)] # sat = 10/15 = 0.667 - result = _score_map(vector_anchored_fusion(vec, kw)) - expected = 0.7 * 0.8 + 0.3 * _sat(10.0) - assert result["d1"] == pytest.approx(expected) - - def test_custom_alpha_vector_dominant(self): - vec = [("d1", 0.9)] - kw = [("d1", 5.0)] # sat = 0.5 - result = _score_map(vector_anchored_fusion(vec, kw, alpha=0.9)) - expected = 0.9 * 0.9 + 0.1 * 0.5 - assert result["d1"] == pytest.approx(expected) - - def test_custom_alpha_keyword_dominant(self): - vec = [("d1", 0.4)] - kw = [("d1", 15.0)] # sat = 15/20 = 0.75 - result = _score_map(vector_anchored_fusion(vec, kw, alpha=0.3)) - expected = 0.3 * 0.4 + 0.7 * _sat(15.0) - assert result["d1"] == pytest.approx(expected) - - def test_both_high_scores_combine(self): - vec = [("d1", 0.9)] - kw = [("d1", 20.0)] # sat = 20/25 = 0.8 - result = _score_map(vector_anchored_fusion(vec, kw)) - expected = 0.7 * 0.9 + 0.3 * _sat(20.0) - assert result["d1"] == pytest.approx(expected) - assert result["d1"] > 0.8 # strong agreement -> high score - - def test_vec_high_kw_low_moderate_score(self): - vec = [("d1", 0.8)] - kw = [("d1", 0.5)] # sat ≈ 0.09 - result = _score_map(vector_anchored_fusion(vec, kw)) - expected = 0.7 * 0.8 + 0.3 * _sat(0.5) - assert result["d1"] == pytest.approx(expected) - assert result["d1"] < 0.8 # kw drags it down slightly - - -# =========================================================================== -# 3. Floor defaults for single-path documents -# =========================================================================== - -class TestFloorDefaults: - """Missing side uses min score of that path, not 0.""" - - def test_vector_only_doc_uses_kw_floor(self): - vec = [("d1", 0.8), ("d2", 0.6)] - kw = [("d1", 10.0), ("d3", 2.0)] - result = _score_map(vector_anchored_fusion(vec, kw)) - # d2 only in vector; kw_floor = sat(2.0) = 2/7 ≈ 0.286 - kw_floor = _sat(2.0) - expected_d2 = 0.7 * 0.6 + 0.3 * kw_floor - assert result["d2"] == pytest.approx(expected_d2) - - def test_keyword_only_doc_uses_vec_floor(self): - vec = [("d1", 0.8), ("d2", 0.4)] - kw = [("d3", 15.0)] - result = _score_map(vector_anchored_fusion(vec, kw)) - # d3 only in kw; vec_floor = min(0.8, 0.4) = 0.4 - expected_d3 = 0.7 * 0.4 + 0.3 * _sat(15.0) - assert result["d3"] == pytest.approx(expected_d3) - - def test_floor_not_zero(self): - """Floor should be min of the path, not 0.""" - vec = [("d1", 0.9), ("d2", 0.5)] - kw = [("d1", 10.0)] - result = _score_map(vector_anchored_fusion(vec, kw)) - # d2 is vector-only; kw_floor = sat(10) (only one kw doc, floor = its sat) - kw_floor = _sat(10.0) - expected_d2 = 0.7 * 0.5 + 0.3 * kw_floor - assert result["d2"] == pytest.approx(expected_d2) - # Should be higher than if floor were 0 - zero_floor = 0.7 * 0.5 + 0.3 * 0.0 - assert result["d2"] > zero_floor - - def test_keyword_only_doc_not_buried(self): - """A high-BM25 keyword-only doc should still rank reasonably.""" - vec = [("d1", 0.6), ("d2", 0.4)] - kw = [("d3", 20.0)] # sat = 0.8, strong keyword match - result = _score_map(vector_anchored_fusion(vec, kw)) - # d3: vec_floor=0.4, sat=0.8 -> 0.7*0.4 + 0.3*0.8 = 0.52 - vec_floor = 0.4 - expected_d3 = 0.7 * vec_floor + 0.3 * _sat(20.0) - assert result["d3"] == pytest.approx(expected_d3) - assert result["d3"] > 0.5 # not buried - - -# =========================================================================== -# 4. Edge cases: empty inputs -# =========================================================================== - -class TestEmptyInputs: - """Handle empty result sets gracefully.""" - - def test_both_empty(self): - assert vector_anchored_fusion([], []) == [] - - def test_vector_only_no_keyword(self): - vec = [("d1", 0.9), ("d2", 0.5)] - result = vector_anchored_fusion(vec, []) - score_map = _score_map(result) - # kw_floor = 0.0 (no kw results) - assert score_map["d1"] == pytest.approx(0.7 * 0.9 + 0.3 * 0.0) - assert score_map["d2"] == pytest.approx(0.7 * 0.5 + 0.3 * 0.0) - - def test_keyword_only_no_vector(self): - kw = [("d1", 10.0), ("d2", 3.0)] - result = vector_anchored_fusion([], kw) - score_map = _score_map(result) - # vec_floor = 0.0 (no vec results) - assert score_map["d1"] == pytest.approx(0.7 * 0.0 + 0.3 * _sat(10.0)) - assert score_map["d2"] == pytest.approx(0.7 * 0.0 + 0.3 * _sat(3.0)) - - -# =========================================================================== -# 5. Overlapping documents -# =========================================================================== - -class TestOverlappingDocuments: - """Documents appearing in both result sets get combined scores.""" - - def test_overlap_both_strong(self): - vec = [("d1", 0.85)] - kw = [("d1", 12.0)] - result = _score_map(vector_anchored_fusion(vec, kw)) - expected = 0.7 * 0.85 + 0.3 * _sat(12.0) - assert result["d1"] == pytest.approx(expected) - - def test_overlap_beats_single_path(self): - """Doc in both paths should score higher than similar single-path doc.""" - vec = [("d1", 0.7), ("d2", 0.7)] - kw = [("d1", 10.0)] # d1 in both, d2 vector-only - result = _score_map(vector_anchored_fusion(vec, kw)) - # d1: 0.7*0.7 + 0.3*sat(10) = 0.49 + 0.3*0.667 = 0.69 - # d2: 0.7*0.7 + 0.3*sat(10) = same floor, same result when only 1 kw doc - # But with multiple kw docs, d2 would use kw_floor which is lower - assert result["d1"] >= result["d2"] - - def test_mixed_overlap_and_unique(self): - vec = [("d1", 0.8), ("d2", 0.5)] - kw = [("d1", 10.0), ("d3", 8.0)] - result = _score_map(vector_anchored_fusion(vec, kw)) - # All 3 docs should be present - assert len(result) == 3 - assert "d1" in result - assert "d2" in result - assert "d3" in result - - -# =========================================================================== -# 6. Result ordering -# =========================================================================== - -class TestResultOrdering: - """Results are always sorted by score descending.""" - - def test_sorted_descending(self): - vec = [("d1", 0.3), ("d2", 0.9)] - kw = [("d3", 20.0), ("d4", 1.0)] - result = vector_anchored_fusion(vec, kw) - scores = [s for _, s in result] - assert scores == sorted(scores, reverse=True) - - def test_many_docs_sorted(self): - vec = [("d1", 0.2), ("d2", 0.5), ("d3", 0.8), ("d4", 0.1)] - kw = [("d1", 1.0), ("d2", 10.0), ("d5", 20.0)] - result = vector_anchored_fusion(vec, kw) - scores = [s for _, s in result] - assert scores == sorted(scores, reverse=True) - - -# =========================================================================== -# 7. Discrimination: relevant vs irrelevant patterns -# =========================================================================== - -class TestDiscrimination: - """Verify the fusion produces good separation between relevant and noise.""" - - def test_relevant_scores_above_irrelevant(self): - """Simulate: one relevant doc (high vec + high BM25) vs noise.""" - vec = [ - ("relevant", 0.8), - ("noise1", 0.35), - ("noise2", 0.33), - ] - kw = [ - ("relevant", 10.0), - ("noise1", 0.4), - ("noise2", 0.3), - ] - result = _score_map(vector_anchored_fusion(vec, kw)) - assert result["relevant"] > result["noise1"] - assert result["relevant"] > result["noise2"] - # Gap should be meaningful - gap = result["relevant"] - result["noise1"] - assert gap > 0.2 - - def test_flat_bm25_noise_suppressed(self): - """When BM25 scores are all similarly low (irrelevant query), - fusion score should be lower than pure vector.""" - vec = [("d1", 0.35), ("d2", 0.34), ("d3", 0.33)] - kw = [("d1", 0.4), ("d2", 0.38), ("d3", 0.36)] - result = _score_map(vector_anchored_fusion(vec, kw)) - # All scores should be below the pure vector scores because - # sat(0.4) ≈ 0.07 drags them down - for doc_id in ["d1", "d2", "d3"]: - pure_vec = dict(vec)[doc_id] - assert result[doc_id] < pure_vec - - def test_strong_bm25_boosts_relevant(self): - """A doc with strong BM25 match should score higher than - a doc with same vector score but weak BM25.""" - vec = [("strong_kw", 0.6), ("weak_kw", 0.6)] - kw = [("strong_kw", 12.0), ("weak_kw", 0.5)] - result = _score_map(vector_anchored_fusion(vec, kw)) - assert result["strong_kw"] > result["weak_kw"] - - def test_multiple_relevant_ranked_correctly(self): - """Multiple relevant docs with varying match quality.""" - vec = [ - ("exact", 0.85), - ("related", 0.55), - ("noise", 0.35), - ] - kw = [ - ("exact", 15.0), - ("related", 5.0), - ("noise", 0.3), - ] - result = vector_anchored_fusion(vec, kw) - ids = [doc_id for doc_id, _ in result] - assert ids[0] == "exact" - assert ids[1] == "related" - assert ids[2] == "noise" - - -# =========================================================================== -# 8. Realistic scenario: simulating actual search data -# =========================================================================== - -class TestRealisticScenarios: - """Test with score distributions resembling real retrieval results.""" - - def test_exact_keyword_match(self): - """Query like 'implement rate limiting middleware' -- strong BM25 + vec.""" - vec = [ - ("rate_limit", 0.74), - ("500err", 0.40), - ("403err", 0.38), - ] - kw = [ - ("rate_limit", 9.88), - ("input_val", 2.61), - ("500err", 1.50), - ] - result = vector_anchored_fusion(vec, kw) - # rate_limit should be Top1 by a clear margin - assert result[0][0] == "rate_limit" - assert result[0][1] > 0.6 - - def test_irrelevant_query(self): - """Query like 'how to train a puppy' -- low BM25, low vector.""" - vec = [ - ("d1", 0.35), - ("d2", 0.34), - ("d3", 0.33), - ] - kw = [ - ("d1", 0.36), - ("d2", 0.35), - ("d3", 0.34), - ] - result = vector_anchored_fusion(vec, kw) - # All scores should be low (noise suppressed) - for _, score in result: - assert score < 0.30 - # Very small spread -- no meaningful ranking - spread = result[0][1] - result[-1][1] - assert spread < 0.05 - - def test_semantic_match_no_keyword_overlap(self): - """Query semantically matches but shares few keywords. - Vector should dominate via alpha=0.7.""" - vec = [ - ("target", 0.72), - ("other1", 0.40), - ("other2", 0.38), - ] - kw = [ - ("other1", 3.0), - ("other2", 2.0), - ("target", 1.0), - ] - result = vector_anchored_fusion(vec, kw) - # target should still be Top1 thanks to high vector score - assert result[0][0] == "target" - - def test_long_query_strong_signal(self): - """Long detailed query -- both BM25 and vector agree strongly.""" - vec = [ - ("match", 0.85), - ("partial", 0.50), - ("noise", 0.35), - ] - kw = [ - ("match", 12.0), - ("partial", 3.0), - ("noise", 0.5), - ] - result = vector_anchored_fusion(vec, kw) - scores = _score_map(result) - # Clear ranking: match >> partial >> noise - assert scores["match"] > scores["partial"] - assert scores["partial"] > scores["noise"] - # Large gap between match and noise - assert scores["match"] - scores["noise"] > 0.3 diff --git a/methods/EverCore/tests/test_wait_for.py b/methods/EverCore/tests/test_wait_for.py deleted file mode 100644 index 09aa52c7..00000000 --- a/methods/EverCore/tests/test_wait_for.py +++ /dev/null @@ -1,31 +0,0 @@ -import asyncio -import time - - -async def short_task(): - await asyncio.sleep(0.1) - return "I am done!" - - -async def main(): - print(f"[{time.time():.2f}] Main started.") - - # 1. Create and wait for task1 to complete - task1 = asyncio.create_task(short_task()) - result1 = await task1 - print(f"[{time.time():.2f}] Task 1 has already finished. Result: {result1}") - print(f"[{time.time():.2f}] Is Task 1 done? {task1.done()}") - - # 2. 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Use these projects as runnable systems, research references, or starting points for building an agent memory layer. - -## Included Methods - -| Method | What it is | Start here | -| :--- | :--- | :--- | -| **EverCore** | A long-term memory operating system for agents, with ingestion, structuring, retrieval, APIs, demos, and evaluation tooling. | [EverCore/](EverCore/) | -| **HyperMem** | A hypergraph-based hierarchical memory architecture for long-term conversations, organized around topic, episode, and fact layers. | [HyperMem/](HyperMem/) | - -## How to Use This Folder - -- Start with [EverCore/](EverCore/) if you want to run a memory system locally or integrate memory into an agent application. -- Start with [HyperMem/](HyperMem/) if you want to study or reproduce the hypergraph memory architecture. -- Use the top-level [Architecture Methods](../README.md#architecture-methods) section for a quick comparison of the methods. diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..924c4356 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,221 @@ +[project] +name = "everos" +version = "1.0.0" +description = "EverOS — local-first markdown memory framework for AI agents and user chats; lightweight, dev-friendly, small-team" +license = {text = "Apache-2.0"} +readme = "README.md" +requires-python = ">=3.12" +authors = [ + {name = "EverMind AI"}, +] +keywords = ["memory", "ai-agent", "markdown", "lancedb", "rag", "everos"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] +dependencies = [ + # Core data validation & config + "pydantic>=2.7.1", + "pydantic-settings>=2.0.0", + "python-dotenv>=1.0.1", + + # Storage stack (md-first three-piece set) + "lancedb>=0.13.0", # Vector + BM25 + scalar filter (Arrow-based) + "aiosqlite>=0.20.0", # Async SQLite driver (used by SA async engine) + "sqlmodel>=0.0.22", # ORM (Pydantic + SQLAlchemy 2.0 async) + "alembic>=1.13.0", # SQLite schema migrations + "greenlet>=3.0", # Required by SQLAlchemy async + + # LLM & embedding (one provider per file pattern) + "openai>=1.0.0", + + # Markdown / file system + "PyYAML>=6.0", # YAML frontmatter parsing + "watchdog>=4.0.0", # Cross-platform file system events (cascade daemon) + + # Web framework (entrypoints/api) + "fastapi>=0.104.0", + "uvicorn[standard]>=0.24.0", + + # Observability (core/observability) + "structlog>=24.0.0", + "prometheus-client>=0.20.0", + + # CLI + "typer>=0.12.0", + + # Tokenization (BM25 Chinese support) + "jieba==0.42.1", + + # OME (Offline Memory Evolution) async scheduler & file I/O + "apscheduler>=3.10.4,<4.0", # async strategy scheduler chassis + "portalocker>=2.8.2", # cross-platform file lock for single-engine guard + "watchfiles>=0.21.0", # native fs watcher for config hot reload + "anyio>=4.0", # Async file I/O (anyio.Path, to_thread.run_sync) for the markdown layer + + # Algorithm library (everalgo monorepo, published on PyPI). + "everalgo-core==0.2.0", + "everalgo-boundary==0.2.0", + "everalgo-user-memory==0.2.0", + "everalgo-agent-memory==0.2.0", + "everalgo-rank==0.3.0", +] + +[project.optional-dependencies] +multimodal = ["everalgo-parser[svg]>=0.1.0"] # [svg] bundles cairosvg → SVG works by default + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/everos"] + +# ``env.template`` lives at src/everos/templates/env.template and is +# auto-included via ``packages``. ``everos init`` reads it through +# ``importlib.resources``. No force-include needed. + +# sdist uses an include-whitelist (not exclude) so we don't accidentally ship +# things like CLAUDE.md, .claude/, CI configs, or IDE settings. New top-level +# files default to NOT-shipped — you have to opt them in here. +# +# exclude is kept as belt-and-suspenders for build artefacts that CI generates +# inside the project tree (UV_CACHE_DIR=.uv-cache in .gitlab-ci.yml). +[tool.hatch.build.targets.sdist] +include = [ + "/src", + "/tests", + "/data", + "/docs", + "/pyproject.toml", + "/uv.lock", + "/README.md", + "/QUICKSTART.md", + "/CHANGELOG.md", + "/CONTRIBUTING.md", + "/CODE_OF_CONDUCT.md", + "/SECURITY.md", + "/ACKNOWLEDGMENTS.md", + "/CITATION.md", + "/LICENSE", + "/NOTICE", + "/config.example.toml", +] +exclude = [".uv-cache", ".venv"] + +[project.scripts] +everos = "everos.entrypoints.cli.main:app" + +# ─────────────────────────────────────────────────────────────────────────────── +# Tooling +# ─────────────────────────────────────────────────────────────────────────────── + +[tool.ruff] +line-length = 88 +target-version = "py312" +extend-exclude = ["src_old"] + +[tool.ruff.lint] +select = ["E", "F", "I", "N", "UP", "B", "SIM", "ASYNC"] + +[tool.ruff.lint.per-file-ignores] +# LoCoMo benchmark embeds LLM prompts (ANSWER_PROMPT / JUDGE_*_PROMPT) and +# verbose argparse help strings as multi-line string literals. Line breaks +# would change what the LLM actually sees or noise the --help output, so +# E501 is silenced for this file only. +"tests/test_locomo.py" = ["E501"] + +[tool.pytest.ini_options] +testpaths = ["tests"] +python_files = ["test_*.py"] +python_functions = ["test_*"] +asyncio_mode = "auto" +addopts = "-v --tb=short -m 'not slow and not live_llm'" +markers = [ + "slow: runs that take >=10s (regardless of dependency); CI default excludes these. Run with `pytest -m slow`.", + "live_llm: requires real LLM / embedder / reranker credentials from .env; burns tokens. CI default excludes. Run with `pytest -m live_llm`.", +] + +[tool.coverage.run] +source = ["src/everos"] +branch = true +omit = ["**/__init__.py"] + +[tool.importlinter] +root_packages = ["everos"] + +[[tool.importlinter.contracts]] +name = "Layered architecture" +type = "layers" +layers = [ + "everos.entrypoints", + "everos.service", + "everos.memory", + "everos.infra", +] + +[[tool.importlinter.contracts]] +name = "Subpackage internals are private" +type = "forbidden" +source_modules = ["everos.service", "everos.memory", "everos.entrypoints"] +# `**` matches any descendant (writer.py, reader.py, sub.foo.bar, ...). +# Public API must be reached via the subpackage `__init__.py`. +forbidden_modules = [ + "everos.infra.persistence.markdown.**", + "everos.infra.persistence.lancedb.**", + "everos.infra.persistence.sqlite.**", +] +# `forbidden` contracts run a *transitive closure* — any path from a +# source module to a forbidden module fails the check, even when the +# source only imports the subpackage top-level. The whitelist below +# carves out **intra-subpackage wiring**: each storage subpackage's +# own ``__init__.py`` imports its own ``tables`` / ``repos`` / manager +# children to assemble the public API surface that outer layers reach. +# These edges are normal Python package wiring, *not* a privacy +# breach — without these ignores, even a legitimate +# ``from everos.infra.persistence.sqlite import X`` would be punished +# because the transitive scan walks one hop further into the package. +# +# Direct imports from outer layers into a subpackage internal module +# (e.g. ``from everos.infra.persistence.sqlite.tables import X`` written +# in ``service`` or ``entrypoints``) remain blocked — that edge is not +# in this whitelist. +ignore_imports = [ + "everos.infra.persistence.sqlite -> everos.infra.persistence.sqlite.tables", + "everos.infra.persistence.sqlite -> everos.infra.persistence.sqlite.repos", + "everos.infra.persistence.sqlite -> everos.infra.persistence.sqlite.sqlite_manager", + "everos.infra.persistence.lancedb -> everos.infra.persistence.lancedb.tables", + "everos.infra.persistence.lancedb -> everos.infra.persistence.lancedb.repos", + "everos.infra.persistence.lancedb -> everos.infra.persistence.lancedb.lancedb_manager", + "everos.infra.persistence.markdown -> everos.infra.persistence.markdown.mds", + "everos.infra.persistence.markdown -> everos.infra.persistence.markdown.writers", + "everos.infra.persistence.markdown -> everos.infra.persistence.markdown.readers", +] + +[[tool.importlinter.contracts]] +name = "OME does not depend on memory/service/entrypoints or sibling infra subpackages" +type = "forbidden" +source_modules = ["everos.infra.ome"] +forbidden_modules = [ + "everos.infra.persistence", + "everos.memory", + "everos.service", + "everos.entrypoints", +] + +[dependency-groups] +dev = [ + "ruff>=0.8.0", + "pytest>=8.4.0", + "pytest-asyncio>=1.1.0", + "pytest-cov>=6.0.0", + "pytest-rerunfailures>=15.0", + "import-linter>=2.0", + "pre-commit>=4.0.0", + "ipdb>=0.13.13", + "pyinstrument>=5.0.0", +] diff --git a/scripts/check_cjk.py b/scripts/check_cjk.py new file mode 100644 index 00000000..669ac9e7 --- /dev/null +++ b/scripts/check_cjk.py @@ -0,0 +1,93 @@ +#!/usr/bin/env python3 +"""Scan tracked text files for CJK characters outside the language-policy allowlist. + +Replaces the grep-based reference command that used to live in +``.claude/rules/language-policy.md``. That command silently produced false +negatives on this repo: the ``grep -zZv`` + ``xargs -0`` NUL pipeline +mis-parsed the path list and reported "clean" even when violations existed. + +Exit code 0 = clean, 1 = violations found (paths + line numbers printed). + +Usage: + python scripts/check_cjk.py # scan all tracked files + python scripts/check_cjk.py a.py b.md # scan specific files (pre-commit) + python scripts/check_cjk.py --quiet # per-file counts only +""" + +from __future__ import annotations + +import argparse +import os +import re +import subprocess + +# CJK / fullwidth code points: CJK symbols & ideographs, Hangul syllables, +# and halfwidth/fullwidth forms. Kept as escapes so this file stays ASCII. +_CJK = re.compile("[\\u3000-\\u9fff\\uac00-\\ud7af\\uff00-\\uffef]") + + +def _is_allowlisted(path: str) -> bool: + """Return True if CJK is permitted in this path (see language-policy.md).""" + name = os.path.basename(path) + # 1. Tests: fixtures, sample inputs, and CJK-behavior assertions. + if path.startswith("tests/"): + return True + # 2. Tokenizer NLP resources (stopword lists, segmentation examples). + if path.startswith("src/everos/component/tokenizer/"): + return True + # 3. Locale-suffixed sample data, e.g. data/solo_chat_zh.json. + if re.match(r"data/.*_(zh|ja|ko)\.", path): + return True + # 4. Translated doc mirrors, e.g. README.zh.md. + if re.search(r"\.(zh|ja|ko)\.md$", path): + return True + # 5. Filenames explicitly marked with a CJK/locale token. + return bool(re.search(r"(^|[._-])(cjk|zh|ja|ko)([._-]|$)", name)) + + +def _tracked_files() -> list[str]: + out = subprocess.check_output(["git", "ls-files"], text=True) + return out.splitlines() + + +def main() -> int: + parser = argparse.ArgumentParser(description="CJK language-policy scanner.") + parser.add_argument("files", nargs="*", help="files to scan (default: all tracked)") + parser.add_argument("--quiet", action="store_true", help="per-file counts only") + args = parser.parse_args() + + paths = args.files or _tracked_files() + violations: list[tuple[str, int, str]] = [] + for path in paths: + if _is_allowlisted(path): + continue + try: + with open(path, encoding="utf-8") as fh: + lines = fh.readlines() + except (UnicodeDecodeError, FileNotFoundError, IsADirectoryError): + continue # binary / missing / directory: nothing to scan + for i, line in enumerate(lines, start=1): + if _CJK.search(line): + violations.append((path, i, line.strip())) + + if not violations: + print("CJK language-policy: clean") + return 0 + + by_file: dict[str, int] = {} + for path, _lineno, _text in violations: + by_file[path] = by_file.get(path, 0) + 1 + + print(f"CJK language-policy: {len(violations)} hit(s) in {len(by_file)} file(s)\n") + if args.quiet: + for path, count in sorted(by_file.items(), key=lambda kv: -kv[1]): + print(f" {count:4d} {path}") + else: + for path, lineno, text in violations: + print(f" {path}:{lineno}: {text[:100]}") + print("\nAllowed CJK locations are defined in .claude/rules/language-policy.md") + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/check_consistency.py b/scripts/check_consistency.py new file mode 100755 index 00000000..3b8bdaec --- /dev/null +++ b/scripts/check_consistency.py @@ -0,0 +1,350 @@ +#!/usr/bin/env python +"""Check md ↔ LanceDB consistency for an everos corpus. + +Three checks per kind: + 1. id set equality — md entry ids == LanceDB row entry_ids + 2. content_sha256 equality — every shared id matches on both sides + 3. id monotonicity (md-only) — within each daily-log md, the numeric + counter at the end of entry.id ascends + from 1 with no gap and no dupe + +Two modes: + --mode lifespan (default) Full strict check through the everos app + lifespan stack (sqlite + lance + cascade + + ome). Safe ONLY on an idle corpus (no live + server writing). Covers every kind in + KIND_REGISTRY. + --mode readonly Bypass the lifespan stack, open LanceDB with + a fresh read connection, read md directly. + Safe even on an active corpus, but only + covers the three daily-log kinds (episode / + atomic_fact / foresight). + +Examples: + scripts/check_consistency.py ~/.everos-locomo-all-kv-fast + scripts/check_consistency.py ~/.everos-locomo-all-kv-fast --mode readonly + scripts/check_consistency.py ~/.everos-locomo-all-kv-fast --owners joanna,nate +""" +# This script must mutate sys.path before importing everos/tests, and +# uses synchronous pathlib because it's a one-shot CLI, not server code. +# ruff: noqa: E402, ASYNC240 + +from __future__ import annotations + +import argparse +import asyncio +import dataclasses +import os +import re +import sys +from collections import Counter +from pathlib import Path + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "src")) + +from dotenv import load_dotenv + +load_dotenv(ROOT / ".env") + + +# ── shared: id counter parsing ────────────────────────────────────────── + +_ID_NUM_RE = re.compile(r"_(\d+)$") + + +def _entry_counter(entry_id: str) -> int | None: + m = _ID_NUM_RE.search(entry_id) + return int(m.group(1)) if m else None + + +@dataclasses.dataclass +class MonotonicityReport: + path: str + total: int + not_sorted: bool + starts_at_1: bool + gaps: list[int] + dupes: list[int] + bad_format: list[str] + + @property + def ok(self) -> bool: + return self.total == 0 or ( + not self.not_sorted + and self.starts_at_1 + and not self.gaps + and not self.dupes + and not self.bad_format + ) + + +async def _scan_monotonicity(corpus: Path) -> list[MonotonicityReport]: + """Walk all daily-log md files; report id-counter monotonicity per file.""" + from everos.core.persistence import MarkdownReader + + daily_dirs = ("/episodes/", "/.atomic_facts/", "/.foresights/", "/.agent_cases/") + reports: list[MonotonicityReport] = [] + for md in sorted(corpus.rglob("*.md")): + rel = md.relative_to(corpus).as_posix() + if not (rel.startswith("users/") or rel.startswith("agents/")): + continue + if not any(d in "/" + rel for d in daily_dirs): + continue + parsed = await MarkdownReader.read(md) + counters: list[int] = [] + bad_format: list[str] = [] + for entry in parsed.entries: + c = _entry_counter(entry.id) + if c is None: + bad_format.append(entry.id) + else: + counters.append(c) + not_sorted = counters != sorted(counters) + starts_at_1 = bool(counters) and min(counters) == 1 + gaps: list[int] = [] + dupes: list[int] = [] + if counters: + seen = set(counters) + for i in range(1, max(counters) + 1): + if i not in seen: + gaps.append(i) + cc = Counter(counters) + dupes = sorted(v for v, n in cc.items() if n > 1) + reports.append( + MonotonicityReport( + path=rel, + total=len(parsed.entries), + not_sorted=not_sorted, + starts_at_1=starts_at_1 if parsed.entries else True, + gaps=gaps, + dupes=dupes, + bad_format=bad_format, + ) + ) + return reports + + +def _print_monotonicity(reports: list[MonotonicityReport]) -> int: + issues = sum(1 for r in reports if not r.ok) + if issues == 0: + print( + f" all {len(reports)} daily-log md files have strictly ascending" + " ids from 1" + ) + return 0 + print(f" ⚠ {issues}/{len(reports)} md files have id-counter issues:") + for r in reports: + if r.ok: + continue + problems = [] + if r.not_sorted: + problems.append("not-sorted") + if not r.starts_at_1 and r.total: + problems.append("not-from-1") + if r.gaps: + preview = r.gaps[:5] + problems.append(f"gaps={preview}{'...' if len(r.gaps) > 5 else ''}") + if r.dupes: + problems.append(f"dupes={r.dupes}") + if r.bad_format: + problems.append(f"bad-format×{len(r.bad_format)}") + print(f" {r.path}: total={r.total} {' '.join(problems)}") + return issues + + +# ── mode: lifespan ────────────────────────────────────────────────────── + + +async def run_lifespan_mode(corpus: Path) -> int: + """Full strict check via app lifespan; covers every kind in KIND_REGISTRY.""" + os.environ["EVEROS_MEMORY__ROOT"] = str(corpus) + from everos.config import load_settings + + load_settings.cache_clear() + + from everos.entrypoints.api.app import create_app + from tests._consistency_assertions import assert_md_lance_strict_consistent + + app = create_app() + rc = 0 + async with app.router.lifespan_context(app): + # 1+2. id set + sha + print("─── md ↔ LanceDB strict consistency ───") + try: + stats = await assert_md_lance_strict_consistent(corpus) + print(" PASS") + except AssertionError as e: + print(f" DRIFT:\n{e}") + rc = 1 + stats = None + + if stats is not None: + print() + print( + f" {'kind':<15s} {'md_files':>10s}" + f" {'md_entries':>12s} {'lance_rows':>12s}" + ) + print(" " + "─" * 53) + for kind, s in stats.items(): + print( + f" {kind:<15s} {s.md_file_count:>10d}" + f" {s.md_entry_count:>12d} {s.lance_row_count:>12d}" + ) + + # 3. id monotonicity + print() + print("─── id monotonicity ───") + reports = await _scan_monotonicity(corpus) + if _print_monotonicity(reports) > 0: + rc = max(rc, 2) + return rc + + +# ── mode: readonly ────────────────────────────────────────────────────── + + +async def run_readonly_mode(corpus: Path, owners_filter: list[str] | None) -> int: + """Direct LanceDB read + md read; no lifespan / cascade / ome started. + + Covers the three daily-log kinds; agent_case + user_profile + agent_skill + are NOT checked in this mode (use --mode lifespan on an idle corpus + snapshot for full coverage). + """ + import lancedb + + from everos.core.persistence import MarkdownReader + from everos.memory.cascade.handlers.atomic_fact import AtomicFactHandler + from everos.memory.cascade.handlers.episode import EpisodeHandler + from everos.memory.cascade.handlers.foresight import ForesightHandler + from tests._consistency_assertions import _daily_log_sha_for_entry + + db = lancedb.connect(str(corpus / ".index" / "lancedb")) + + kinds = [ + ("episode", "episodes", "episode-", EpisodeHandler), + ("atomic_fact", ".atomic_facts", "atomic_fact-", AtomicFactHandler), + ("foresight", ".foresights", "foresight-", ForesightHandler), + ] + + # Pick owners + if owners_filter: + owners = owners_filter + else: + owners = ( + sorted(p.name for p in (corpus / "users").iterdir() if p.is_dir()) + if (corpus / "users").exists() + else [] + ) + + print("─── md ↔ LanceDB consistency (readonly) ───") + rc = 0 + for table_name, dir_name, prefix, handler_cls in kinds: + try: + table = db.open_table(table_name) + except FileNotFoundError: + print(f" {table_name}: table not in lancedb (skip)") + continue + for owner in owners: + md_dir = corpus / "users" / owner / dir_name + if not md_dir.exists(): + continue + md_files = sorted(md_dir.glob(f"{prefix}*.md")) + md_sha_total: dict[str, str] = {} + for md in md_files: + parsed = await MarkdownReader.read(md) + for entry in parsed.entries: + md_sha_total[entry.id] = _daily_log_sha_for_entry( + handler_cls, entry.as_structured() + ) + arr = ( + table.search().where(f"owner_id = '{owner}'").limit(100_000).to_arrow() + ) + lance_sha = dict( + zip( + arr["entry_id"].to_pylist(), + arr["content_sha256"].to_pylist(), + strict=True, + ) + ) + only_md = sorted(set(md_sha_total) - set(lance_sha)) + only_lance = sorted(set(lance_sha) - set(md_sha_total)) + sha_mismatch = sorted( + k + for k in set(md_sha_total) & set(lance_sha) + if md_sha_total[k] != lance_sha[k] + ) + ok = not (only_md or only_lance or sha_mismatch) + status = "OK" if ok else "DRIFT" + if not ok: + rc = 1 + print( + f" {table_name:<12s} owner={owner:<12s}" + f" md={len(md_sha_total):5d} lance={len(lance_sha):5d}" + f" {status}" + ) + if only_md: + print(f" only_in_md (first 5): {only_md[:5]}") + if only_lance: + print(f" only_in_lance (first 5): {only_lance[:5]}") + if sha_mismatch: + print(f" sha_mismatch (first 5): {sha_mismatch[:5]}") + + # id monotonicity (md-only, owner-filtered if provided) + print() + print("─── id monotonicity ───") + reports = await _scan_monotonicity(corpus) + if owners_filter: + owner_paths = tuple(f"users/{o}/" for o in owners_filter) + reports = [r for r in reports if any(r.path.startswith(p) for p in owner_paths)] + if _print_monotonicity(reports) > 0: + rc = max(rc, 2) + return rc + + +# ── main ──────────────────────────────────────────────────────────────── + + +def _parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + p.add_argument("corpus", help="memory root (e.g. ~/.everos-locomo-all-kv-fast)") + p.add_argument( + "--mode", + choices=("lifespan", "readonly"), + default="lifespan", + help="lifespan = full strict check (idle corpus only); " + "readonly = direct lance read (safe on active corpus)", + ) + p.add_argument( + "--owners", + help="comma-separated owner filter (readonly mode only)", + ) + return p.parse_args() + + +async def main() -> int: + args = _parse_args() + corpus = Path(args.corpus).expanduser().resolve() + if not corpus.exists(): + print(f"ERROR: corpus does not exist: {corpus}") + return 1 + owners = ( + [o.strip() for o in args.owners.split(",") if o.strip()] + if args.owners + else None + ) + print(f"corpus: {corpus}") + print(f"mode: {args.mode}") + if owners: + print(f"owners: {owners}") + print() + if args.mode == "lifespan": + return await run_lifespan_mode(corpus) + return await run_readonly_mode(corpus, owners) + + +if __name__ == "__main__": + sys.exit(asyncio.run(main())) diff --git a/scripts/check_datetime_discipline.py b/scripts/check_datetime_discipline.py new file mode 100644 index 00000000..b57b2d80 --- /dev/null +++ b/scripts/check_datetime_discipline.py @@ -0,0 +1,138 @@ +"""CI gate: enforce the two-zone discipline at the source-code level. + +Scans `src/` for code patterns that bypass +:mod:`everos.component.utils.datetime` and would silently introduce +naive or local-tz datetimes. Exits non-zero on any hit. + +Forbidden patterns (with a small allowlist): + +1. ``datetime.now()`` / ``datetime.utcnow()`` / ``datetime.today()`` — + naive constructors / deprecated. Use ``get_utc_now()`` (storage) or + ``get_now_with_timezone()`` (display). +2. ``time.time()`` / ``time.time_ns()`` — bypasses the helper module. + Use ``to_timestamp_ms(get_utc_now())`` if you really need ms epoch. +3. Direct ``datetime(YYYY, ...)`` constructor without ``tzinfo=`` — + produces naive datetimes; use ``ensure_utc(datetime(...))`` instead. +4. ``.astimezone(`` / ``.replace(tzinfo=`` outside the helper module — + should go through ``to_display_tz`` / ``ensure_utc``. + +Allowlist (legitimate uses): + +* ``src/everos/component/utils/datetime.py`` — the helper module itself. +* ``src/everos/core/persistence/sqlite/base.py`` — the SQLAlchemy ``load`` + event listener that re-attaches UTC on hydrate. + +Run:: + + python scripts/check_datetime_discipline.py + +Wired into ``make ci``; any violation fails the build. +""" + +from __future__ import annotations + +import re +import sys +from pathlib import Path + +_ROOT = Path(__file__).resolve().parent.parent +_SRC = _ROOT / "src" + +_ALLOWLIST: set[Path] = { + _ROOT / "src/everos/component/utils/datetime.py", + _ROOT / "src/everos/core/persistence/sqlite/base.py", +} + +# (regex, message) pairs. Each regex must match on a single line. +_HELPER_HINT = "use get_utc_now() / get_now_with_timezone()" +_PATTERNS: list[tuple[re.Pattern[str], str]] = [ + ( + re.compile(r"\bdatetime\.now\s*\(\s*\)"), + f"datetime.now() returns naive — {_HELPER_HINT}", + ), + ( + re.compile(r"\bdatetime\.utcnow\s*\("), + "datetime.utcnow() is deprecated and naive — use get_utc_now()", + ), + ( + re.compile(r"\bdatetime\.today\s*\("), + "datetime.today() returns naive — use today_with_timezone()", + ), + ( + re.compile(r"\bdt\.datetime\.now\s*\(\s*\)"), + f"dt.datetime.now() returns naive — {_HELPER_HINT}", + ), + ( + re.compile(r"\bdt\.datetime\.utcnow\s*\("), + "dt.datetime.utcnow() is deprecated and naive — use get_utc_now()", + ), + ( + re.compile(r"\b_dt\.datetime\.now\s*\(\s*\)"), + f"_dt.datetime.now() returns naive — {_HELPER_HINT}", + ), + ( + re.compile(r"\btime\.time(?:_ns)?\s*\("), + "time.time() bypasses the helper — use to_timestamp_ms(get_utc_now())", + ), + ( + re.compile(r"\.astimezone\s*\("), + ".astimezone(...) outside helper — use to_display_tz() / ensure_utc()", + ), + ( + re.compile(r"\.replace\s*\(\s*tzinfo\s*="), + ".replace(tzinfo=...) outside helper — use ensure_utc() / to_display_tz()", + ), +] + +# Skip lines that match these (comments, docstrings, `# tz-noqa`). +_COMMENT_RE = re.compile(r"^\s*#") +_DOCSTRING_TRIPLE = '"""' + + +def _scan_file(path: Path) -> list[tuple[int, str, str]]: + """Return list of (line_no, line, message) violations in *path*.""" + if path in _ALLOWLIST: + return [] + hits: list[tuple[int, str, str]] = [] + try: + text = path.read_text(encoding="utf-8") + except (OSError, UnicodeDecodeError): + return [] + + # Strip out triple-quoted blocks (docstrings + multi-line literals). + text_no_docstrings = re.sub(r'""".*?"""', "", text, flags=re.DOTALL) + text_no_docstrings = re.sub(r"'''.*?'''", "", text_no_docstrings, flags=re.DOTALL) + + for lineno, line in enumerate(text_no_docstrings.splitlines(), start=1): + if _COMMENT_RE.match(line): + continue + if "# tz-noqa" in line: + continue + # Strip inline trailing comment to avoid false positives in + # comment text like ``# replace(tzinfo=...) — explanation``. + code_part = line.split("#", 1)[0] + for pat, msg in _PATTERNS: + if pat.search(code_part): + hits.append((lineno, line.rstrip(), msg)) + break + return hits + + +def main() -> int: + rc = 0 + for py in sorted(_SRC.rglob("*.py")): + violations = _scan_file(py) + if not violations: + continue + rel = py.relative_to(_ROOT) + for lineno, line, msg in violations: + print(f"{rel}:{lineno}: {msg}") + print(f" {line}") + rc = 1 + if rc == 0: + print("OK — datetime discipline clean.") + return rc + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/dump_openapi.py b/scripts/dump_openapi.py new file mode 100644 index 00000000..2cd5d4fd --- /dev/null +++ b/scripts/dump_openapi.py @@ -0,0 +1,128 @@ +"""Dump the FastAPI OpenAPI schema to ``docs/openapi.json``. + +Static export — does **not** start the server. Calls ``app.openapi()`` +directly on the FastAPI instance returned by ``create_app()``, which +the runtime ``GET /openapi.json`` handler returns verbatim. No lifespan +is run, so this is fast and side-effect-free. + +Modes: + +* default — write ``docs/openapi.json``. +* ``--check`` — write to a temp file and ``diff`` against the on-disk + copy. Exits non-zero on drift, so it can be wired into ``make lint`` + to fail PRs that touch the API surface without regenerating the + committed schema. Same shape as ``check_datetime_discipline.py``. + +Run:: + + python scripts/dump_openapi.py # write docs/openapi.json + python scripts/dump_openapi.py --check # CI gate +""" + +from __future__ import annotations + +import argparse +import json +import sys +import tempfile +from pathlib import Path + +_ROOT = Path(__file__).resolve().parent.parent +_TARGET = _ROOT / "docs" / "openapi.json" + + +def _build_schema() -> dict: + """Return the FastAPI app's full OpenAPI schema. + + Force ``ENV=DEV`` so the ``openapi_url`` route is enabled — without + it the dev-mode endpoint check (see ``app.py``) shadows the route. + The schema content itself is identical in dev vs prod; the flag only + controls whether the runtime ``GET /openapi.json`` is exposed. We + flip it here so the static export matches the dev-mode endpoint + output the e2e test compares against. + """ + import os + + os.environ["ENV"] = "DEV" + # Local import so an import-time evaluation of ``ENV`` (read inside + # ``create_app``) sees the override above. + from everos.entrypoints.api.app import create_app + + # Pass an empty lifespan list so we don't pull up SQLite / LanceDB / + # OME — the schema is computed from static route declarations alone. + app = create_app(lifespan_providers=[]) + return app.openapi() + + +def _render(schema: dict) -> str: + """Pretty-print the schema as JSON with stable key order + trailing newline.""" + return json.dumps(schema, indent=2, ensure_ascii=False, sort_keys=False) + "\n" + + +def _write_target(content: str) -> None: + _TARGET.parent.mkdir(parents=True, exist_ok=True) + _TARGET.write_text(content, encoding="utf-8") + + +def _check_against_target(content: str) -> int: + if not _TARGET.is_file(): + print( + f"error: {_TARGET.relative_to(_ROOT)} does not exist; " + f"run `make openapi` to generate it.", + file=sys.stderr, + ) + return 1 + existing = _TARGET.read_text(encoding="utf-8") + if existing == content: + print(f"OK — {_TARGET.relative_to(_ROOT)} matches app.openapi() output.") + return 0 + # Drift: print a unified diff to stderr so CI / reviewer can see what changed. + import difflib + + diff = "".join( + difflib.unified_diff( + existing.splitlines(keepends=True), + content.splitlines(keepends=True), + fromfile=f"{_TARGET.relative_to(_ROOT)} (committed)", + tofile="app.openapi() (current)", + ) + ) + # Limit to first ~200 lines so a giant schema rewrite stays scannable. + capped = "".join(diff.splitlines(keepends=True)[:200]) + print( + f"error: {_TARGET.relative_to(_ROOT)} is out of date.\n" + "Run `make openapi` and commit the result.\n\n" + capped, + file=sys.stderr, + ) + if len(diff.splitlines()) > 200: + print( + f"... (truncated; full diff is {len(diff.splitlines())} lines)", + file=sys.stderr, + ) + return 1 + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument( + "--check", + action="store_true", + help="Compare against docs/openapi.json without writing; exit 1 on drift.", + ) + args = parser.parse_args(argv) + + schema = _build_schema() + content = _render(schema) + + if args.check: + return _check_against_target(content) + + _write_target(content) + print(f"wrote {_TARGET.relative_to(_ROOT)} ({len(content)} bytes)") + return 0 + + +if __name__ == "__main__": + # Silence the unused-import warning on tempfile (kept for future use). + _ = tempfile + sys.exit(main()) diff --git a/scripts/e2e_memorize/README.md b/scripts/e2e_memorize/README.md new file mode 100644 index 00000000..97ba45e1 --- /dev/null +++ b/scripts/e2e_memorize/README.md @@ -0,0 +1,123 @@ +# End-to-end memorize test + +In-process driver that pushes a realistic fixture through `service.memorize`, +batching by 6 messages per `/add` call and then `/flush` at the end. + +## What's here + +| File | Purpose | +|---|---| +| `fixtures/chat_session.json` | 22 messages · 3 topic shifts · multi-user (Alice → Bob) — chat-mode fixture | +| `fixtures/agent_session.json` | 21 items · 2 task threads · interleaved `tool_calls` / `tool` results — agent-mode fixture | +| `run.py` | In-process runner (no HTTP) | + +## Prereqs + +1. **LLM client configured** in `.env`: + - `EVEROS_LLM__API_KEY=...` + - `EVEROS_LLM__BASE_URL=...` (OpenAI-compatible) + - `EVEROS_LLM__MODEL=...` (defaults to `gpt-4o-mini`) + - Without these, the boundary stage logs `memorize_no_llm_client` and skips the run. +2. **Memory root**: defaults to `~/.everos`; override with `EVEROS_MEMORY__ROOT=...`. +3. **Mode** is read from `settings.memorize.mode` (toml/env) before the first `memorize()` call. + +## Run + +```bash +# Chat mode — boundary uses everalgo.boundary.detect_boundaries +EVEROS_MEMORIZE__MODE=chat uv run python scripts/e2e_memorize/run.py \ + --fixture scripts/e2e_memorize/fixtures/chat_session.json \ + --expected-mode chat + +# Agent mode — boundary uses everalgo.agent_memory.AgentBoundaryDetector +# (filter→detect→remap; tool items preserved in cells) +EVEROS_MEMORIZE__MODE=agent uv run python scripts/e2e_memorize/run.py \ + --fixture scripts/e2e_memorize/fixtures/agent_session.json \ + --expected-mode agent + +# Dry run (print batch plan, no LLM calls) +uv run python scripts/e2e_memorize/run.py \ + --fixture scripts/e2e_memorize/fixtures/chat_session.json --dry-run +``` + +## What to verify after a run + +### 1. Console output + +Each batch prints `status=` (`accumulated` while buffering, `extracted` when +cells got cut). Final `flush` should be `extracted` if any cell remained +in the tail. The trailing file walker lists md / sqlite files modified +in the last 10 minutes. + +### 2. Episode md (sync — 4A) + +``` +~/.everos/users//episodes/episode-YYYY-MM-DD.md +``` + +- Chat fixture: 2 owners (`u_alice`, `u_bob`) — expect Episodes split into + ~3-4 cells aligned with topic shifts (Python bug → weekend ramen → Q3 + review → SRE handoff/ramen wrap). +- Agent fixture: 1 user (`u_alice`) — expect ~2 Episodes aligned with the + two task threads (latency rollback → DB index fix). + +### 3. SQLite memcell rows + +```bash +sqlite3 ~/.everos/.index/sqlite/system.db \ + "select memcell_id, track, owner_id, owner_type, json_array_length(sender_ids_json) as senders + from memcell order by timestamp" +``` + +- Chat run: rows with `track=user_memory`, `owner_type=user`. +- Agent run: parallel rows for both tracks (`user_memory` **and** + `agent_memory`) since agent mode dispatches both pipelines. + +### 4. Unprocessed buffer + +```bash +sqlite3 ~/.everos/.index/sqlite/system.db \ + "select session_id, count(*) from unprocessed_buffer + where track='memorize' group by session_id" +``` + +After `flush` the buffer should be empty for the test session. + +### 5. OME async output (only if subscribers exist) + +- `users//atomic_facts/atomic_fact-YYYY-MM-DD.md` (always; `extract_atomic_facts` is registered) +- `users//foresights/foresight-YYYY-MM-DD.md` (always; `extract_foresight` is registered) +- `agents//agent_cases/agent_case-YYYY-MM-DD.md` (**only after `extract_agent_cases` strategy is written + registered** — currently absent, the emit is a no-op) + +### 6. Reset between runs + +The fixture's session_id is randomised per invocation, so previous runs +don't pollute the new one. To wipe everything: + +```bash +rm -rf ~/.everos/users ~/.everos/agents ~/.everos/.index/sqlite/system.db +``` + +## Boundary expectations cheat sheet + +### Chat fixture topic shifts (timestamps ms) + +| Range | Topic | +|---|---| +| msgs 1-6 (`1747396800–1747397010`) | Python KeyError debugging | +| msgs 7-12 (`1747400400–1747400610`) | Weekend ramen plans | +| msgs 13-16 (`1747407600–1747407720`) | Q3 revenue review meeting prep | +| msgs 17-22 (`1747411200–1747411410`) | Bob joins, SRE handoff + ramen + Q3 deck deadline | + +Boundary detector should cut on topic gaps; 3 cuts → 4 cells is the most likely outcome. + +### Agent fixture task threads + +| Range | Task | +|---|---| +| items 1-13 (`1747396800–1747397140`) | API latency spike → identify keepalive pool regression → rollback | +| items 14-21 (`1747400400–1747400720`) | DB connection pool exhaustion → find unindexed query → CREATE INDEX CONCURRENTLY | + +Boundary detector should cut between item 13 and item 14 (timestamp jump +~55 minutes, topic flip). Tool items inside each cell stay attached to +their initiating chat turn. diff --git a/scripts/e2e_memorize/fixtures/agent_session.json b/scripts/e2e_memorize/fixtures/agent_session.json new file mode 100644 index 00000000..7a23992c --- /dev/null +++ b/scripts/e2e_memorize/fixtures/agent_session.json @@ -0,0 +1,33 @@ +{ + "_doc": "Agent-mode end-to-end fixture. 23 items across 2 task threads: (1) production latency diagnosis with rollback, (2) DB connection-pool exhaustion investigation. Includes ToolCallRequest + ToolCallResult interleaved with user/assistant text turns, parallel tool calls, one assistant turn with empty content (only tool_calls), one extended chain-of-thought reasoning, and one tool failure + retry in task 2. Run with EVEROS_MEMORIZE__MODE=agent. Expected boundary detector cuts ~2 cells aligned with the two task threads; tool items fold into the cell containing the preceding chat turn.", + "session_id_hint": "e2e_agent", + "messages": [ + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747396800000, "content": "Production API p99 latency spiked from 280ms to 2.5s starting around 21:00 UTC. Customer reports rolling in. Can you investigate?"}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747396820000, "content": "I'll pull the recent deploys and check for correlation with the spike timestamp.", "tool_calls": [{"id": "call_001", "type": "function", "function": {"name": "list_recent_deploys", "arguments": "{\"hours_back\": 4, \"service\": \"api-gateway\"}"}}]}, + {"sender_id": "tool_runner", "sender_name": null, "role": "tool", "timestamp": 1747396825000, "tool_call_id": "call_001", "content": "[{\"deploy_id\": \"d-9f3a\", \"service\": \"api-gateway\", \"deployed_at\": \"2026-05-16T20:47:12Z\", \"author\": \"bob\", \"commit_sha\": \"a1b2c3d\", \"diff_summary\": \"refactor: switch upstream client to keepalive pool\"}, {\"deploy_id\": \"d-9f29\", \"service\": \"api-gateway\", \"deployed_at\": \"2026-05-16T15:12:03Z\", \"author\": \"carol\", \"commit_sha\": \"e4f5g6h\", \"diff_summary\": \"chore: bump otel-sdk to 1.31\"}]"}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747396870000, "content": "", "tool_calls": [{"id": "call_002", "type": "function", "function": {"name": "get_deploy_diff", "arguments": "{\"deploy_id\": \"d-9f3a\", \"files\": [\"upstream/client.go\", \"config/pool.yaml\"]}"}}]}, + {"sender_id": "tool_runner", "sender_name": null, "role": "tool", "timestamp": 1747396880000, "tool_call_id": "call_002", "content": "config/pool.yaml: max_idle_conns_per_host changed from 32 → 4. upstream/client.go: new transport with IdleConnTimeout=10s (was 90s). The default Go transport now reuses far fewer connections."}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747396930000, "content": "Two changes in d-9f3a stand out, and the interaction between them likely explains the cliff. First, max_idle_conns_per_host went from 32 down to 4 — that alone caps how many warm sockets the upstream client can pin per backend. Second, IdleConnTimeout dropped from 90s to 10s, so even the few warm sockets we do keep get torn down between requests during normal traffic gaps. Combined effect: under sustained load p99 of ~250rps × ~6 backends, the pool is permanently saturated and every spillover request pays a TCP+TLS handshake (~800ms-1.5s on the affected region) instead of reusing a connection. That maps directly to the 280ms → 2.5s latency jump. Before I file a rollback recommendation I want to confirm the prediction against live pool metrics — if reuse_ratio collapsed and new-connection rate is up >10x, that's enough signal.", "tool_calls": [{"id": "call_003", "type": "function", "function": {"name": "query_pool_metrics", "arguments": "{\"service\": \"api-gateway\", \"window_min\": 15}"}}]}, + {"sender_id": "tool_runner", "sender_name": null, "role": "tool", "timestamp": 1747396938000, "tool_call_id": "call_003", "content": "{\"idle_conns_avg\": 1.2, \"new_conn_rate_per_s\": 184, \"new_conn_rate_baseline\": 6, \"reuse_ratio\": 0.04, \"reuse_ratio_baseline\": 0.91}"}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747396980000, "content": "Confirmed: reuse_ratio collapsed from 0.91 to 0.04, new-connection rate is 30x baseline. This is the root cause. Recommend rollback of d-9f3a — the keepalive refactor needs a config review before re-deploying. Want me to initiate rollback?"}, + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747397040000, "content": "Yes, roll it back. Page Bob async so he sees the rollback when he comes online tomorrow — no need to wake him up."}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747397070000, "content": "Initiating rollback to d-9f29 and queuing a non-paging Slack DM for Bob.", "tool_calls": [{"id": "call_004", "type": "function", "function": {"name": "rollback_deploy", "arguments": "{\"service\": \"api-gateway\", \"to_deploy_id\": \"d-9f29\", \"reason\": \"p99 latency regression from keepalive pool refactor (max_idle_conns 32->4)\"}"}}, {"id": "call_005", "type": "function", "function": {"name": "slack_dm", "arguments": "{\"user\": \"bob\", \"channel\": \"async\", \"message\": \"Rolled back api-gateway d-9f3a at 21:04 UTC — keepalive pool change caused p99 spike. Logs in #incident-2026-05-16.\"}"}}]}, + {"sender_id": "tool_runner", "sender_name": null, "role": "tool", "timestamp": 1747397115000, "tool_call_id": "call_004", "content": "{\"status\": \"ok\", \"rolled_back_to\": \"d-9f29\", \"completed_at\": \"2026-05-16T21:05:01Z\", \"verification\": {\"p99_5min_after\": \"312ms\", \"reuse_ratio_after\": 0.88}}"}, + {"sender_id": "tool_runner", "sender_name": null, "role": "tool", "timestamp": 1747397116000, "tool_call_id": "call_005", "content": "{\"status\": \"queued\", \"message_id\": \"slack_msg_xyz\"}"}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747397140000, "content": "Rollback complete. p99 dropped back to 312ms within 5 minutes, reuse ratio recovered to 0.88. Slack DM queued for Bob. Incident-2026-05-16 is the channel I logged it to."}, + + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747400400000, "content": "Different issue — we're seeing connection pool exhaustion warnings on the auth-service Postgres pool the last hour. Can you check whether it's a leak or just a sustained traffic spike?"}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747400420000, "content": "Pulling pool stats and recent traffic shape.", "tool_calls": [{"id": "call_006", "type": "function", "function": {"name": "query_db_pool", "arguments": "{\"service\": \"auth-service\", \"window_min\": 60}"}}, {"id": "call_007", "type": "function", "function": {"name": "query_request_rate", "arguments": "{\"service\": \"auth-service\", \"window_min\": 60}"}}]}, + {"sender_id": "tool_runner", "sender_name": null, "role": "tool", "timestamp": 1747400430000, "tool_call_id": "call_006", "content": "{\"max_conns\": 50, \"in_use_now\": 49, \"in_use_p99_1h\": 50, \"idle_p99_1h\": 1, \"wait_queue_depth_p99\": 18, \"long_running_queries_above_30s\": 12}"}, + {"sender_id": "tool_runner", "sender_name": null, "role": "tool", "timestamp": 1747400431000, "tool_call_id": "call_007", "content": "{\"rps_now\": 142, \"rps_baseline\": 138, \"rps_p99_1h\": 151}"}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747400470000, "content": "Traffic is flat (rps 142 vs baseline 138), but 12 long-running queries above 30s and wait queue depth p99 of 18. This is a leak / unbounded query, not a traffic surge. Let me find the offending queries.", "tool_calls": [{"id": "call_008", "type": "function", "function": {"name": "list_long_running_queries", "arguments": "{\"service\": \"auth-service\", \"min_duration_s\": 30, \"window_min\": 60}"}}]}, + {"sender_id": "tool_runner", "sender_name": null, "role": "tool", "timestamp": 1747400478000, "tool_call_id": "call_008", "content": "{\"error\": \"timeout\", \"detail\": \"query against pg_stat_activity exceeded 15s while pool was saturated; retry with a narrower window\"}"}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747400490000, "content": "Tool itself is starved by the same pool exhaustion — fair enough. Retrying against the last 10 minutes only so pg_stat_activity has a tighter slice to scan.", "tool_calls": [{"id": "call_008b", "type": "function", "function": {"name": "list_long_running_queries", "arguments": "{\"service\": \"auth-service\", \"min_duration_s\": 30, \"window_min\": 10}"}}]}, + {"sender_id": "tool_runner", "sender_name": null, "role": "tool", "timestamp": 1747400499000, "tool_call_id": "call_008b", "content": "[{\"query_sha\": \"q-71f\", \"sql_truncated\": \"SELECT u.* FROM users u JOIN sessions s ON s.user_id = u.id WHERE s.expired_at > now() - interval '30 day'\", \"avg_duration_s\": 84, \"calls_last_hour\": 11, \"missing_index\": \"sessions.expired_at\"}]"}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747400510000, "content": "Single offender — query q-71f is doing a full sessions-table scan because sessions.expired_at is unindexed. Each call takes ~84s and holds a connection. 11 calls in the last hour saturate the pool. Recommend: (1) add the index now (concurrent), (2) add a query timeout cap on this endpoint long-term. Want me to add the index?"}, + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747400560000, "content": "Yes please add the index. Use CREATE INDEX CONCURRENTLY so we don't block writes. Then post in #incident-2026-05-16 with the runbook entry."}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747400590000, "content": "On it. Creating the concurrent index and posting the runbook update.", "tool_calls": [{"id": "call_009", "type": "function", "function": {"name": "execute_sql", "arguments": "{\"db\": \"auth-service\", \"sql\": \"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_sessions_expired_at ON sessions(expired_at)\", \"timeout_s\": 600}"}}]}, + {"sender_id": "tool_runner", "sender_name": null, "role": "tool", "timestamp": 1747400650000, "tool_call_id": "call_009", "content": "{\"status\": \"ok\", \"duration_s\": 47, \"rows_indexed\": 1841229}"}, + {"sender_id": "ops_agent", "sender_name": "OpsAgent", "role": "assistant", "timestamp": 1747400720000, "content": "Index created in 47s over 1.84M rows. Query q-71f should drop from ~84s to sub-second. Pool will drain in the next minute as long-running queries finish. Posted runbook to #incident-2026-05-16."} + ] +} diff --git a/scripts/e2e_memorize/fixtures/chat_session.json b/scripts/e2e_memorize/fixtures/chat_session.json new file mode 100644 index 00000000..4bc23729 --- /dev/null +++ b/scripts/e2e_memorize/fixtures/chat_session.json @@ -0,0 +1,31 @@ +{ + "_doc": "Chat-mode end-to-end fixture. 22 messages across 3 topics + a multi-user handoff. Run with EVEROS_MEMORIZE__MODE=chat. Expected boundary detector cuts ~3-4 cells (Python bug → weekend plans → cross-team meeting).", + "session_id_hint": "e2e_chat", + "messages": [ + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747396800000, "content": "I'm getting a weird KeyError on a dict comprehension when the source list has None entries. The traceback points at the dict literal, not the comprehension itself."}, + {"sender_id": "assistant", "sender_name": "Bot", "role": "assistant", "timestamp": 1747396830000, "content": "Can you share a minimal repro? Something like the comprehension expression plus 2-3 sample input items. I want to see whether the None is on the key side or the value side."}, + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747396890000, "content": "Sure: `{r['id']: r['name'] for r in rows}` where rows comes from a left-join and sometimes the right side returns None. So r is None for those rows."}, + {"sender_id": "assistant", "sender_name": "Bot", "role": "assistant", "timestamp": 1747396920000, "content": "Got it. The None is the row itself, not a field. `None['id']` raises TypeError actually, but if you preprocess the rows to dicts upstream, the missing key on a partial dict would be KeyError. Either way, filter before the comprehension: `{r['id']: r['name'] for r in rows if r is not None and 'id' in r}`."}, + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747396980000, "content": "That worked, thanks. Also occasionally the SQL itself wraps the row in a tuple instead of dict — pyodbc behaviour. Got bitten by it last quarter too."}, + {"sender_id": "assistant", "sender_name": "Bot", "role": "assistant", "timestamp": 1747397010000, "content": "pyodbc returns Row objects by default; you'd need `cursor.execute(...).fetchall()` then iterate. Or set `cursor.row_factory = lambda c, r: dict(zip([col[0] for col in c.description], r))` to normalise upstream."}, + + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747400400000, "content": "Switching topics — any weekend plans? I'm thinking of trying the new ramen place on Carmine Street, supposedly the tonkotsu is decent."}, + {"sender_id": "assistant", "sender_name": "Bot", "role": "assistant", "timestamp": 1747400430000, "content": "Tonkotsu sounds good. If you go Saturday before 1pm you'll skip the queue. There's also a coffee place two doors down that does decent cold brew if you want a pre-meal stop."}, + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747400490000, "content": "Cold brew yes. Bob mentioned wanting to come too, do you think the ramen place takes reservations for three?"}, + {"sender_id": "assistant", "sender_name": "Bot", "role": "assistant", "timestamp": 1747400520000, "content": "Their site says walk-in only on weekends. For three people the wait is usually 25-40 minutes around noon — manageable but worth knowing."}, + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747400580000, "content": "OK we'll show up at 11:30 then. Will send Bob the address."}, + {"sender_id": "assistant", "sender_name": "Bot", "role": "assistant", "timestamp": 1747400610000, "content": "Sensible. Reminder it's cash-only for the first 6 months apparently — saw that on a Reddit thread last week."}, + + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747407600000, "content": "One more thing — the Q3 revenue review is next Thursday at 2pm, who from engineering needs to attend? I want to make sure platform team is represented since we're presenting the infra cost reductions."}, + {"sender_id": "assistant", "sender_name": "Bot", "role": "assistant", "timestamp": 1747407630000, "content": "Standard attendees are eng director + relevant TL. For platform cost reductions, your eng lead Carol should be there since she owns the runbook. Add Dan as a backup in case Carol's on PTO."}, + {"sender_id": "u_alice", "sender_name": "Alice", "role": "user", "timestamp": 1747407690000, "content": "Good call. Carol mentioned PTO that week actually. Let me ping Dan to confirm he can do the slides handoff."}, + {"sender_id": "assistant", "sender_name": "Bot", "role": "assistant", "timestamp": 1747407720000, "content": "If Dan has bandwidth, also ask him to pre-share the deck Monday so finance has time to review the cost numbers before the meeting. Saves a lot of meeting time clarifying figures."}, + + {"sender_id": "u_bob", "sender_name": "Bob", "role": "user", "timestamp": 1747411200000, "content": "Hey, Alice forwarded me about the ramen plan and the Thursday meeting. I can do both but the Thursday slot conflicts with the SRE oncall handoff at 1:45 — anyone we can swap with?"}, + {"sender_id": "assistant", "sender_name": "Bot", "role": "assistant", "timestamp": 1747411230000, "content": "Erin usually does the Wednesday → Thursday handoff for SRE. She owes you one from last cycle. Ask her to take the 1:45 slot and you can ack the comms async at 1:30."}, + {"sender_id": "u_bob", "sender_name": "Bob", "role": "user", "timestamp": 1747411290000, "content": "Erin's good. I'll ping her on Slack today. About the ramen — Alice said 11:30 Saturday, that works for me, walking distance from my place."}, + {"sender_id": "assistant", "sender_name": "Bot", "role": "assistant", "timestamp": 1747411320000, "content": "Great. Just remind Alice it's cash-only for now — she'll forget otherwise based on past experience."}, + {"sender_id": "u_bob", "sender_name": "Bob", "role": "user", "timestamp": 1747411380000, "content": "Ha, true. I'll grab some cash on the way. Also can you remind me about the Q3 revenue deck — when does it need to be done by, EOD Tuesday?"}, + {"sender_id": "assistant", "sender_name": "Bot", "role": "assistant", "timestamp": 1747411410000, "content": "Yes — Dan agreed to pre-share Monday, so practical deadline is EOD Tuesday for finance to review Wednesday. After Wednesday review they may ask for last-minute number tweaks; build slack into Thursday morning for that."} + ] +} diff --git a/scripts/e2e_memorize/run.py b/scripts/e2e_memorize/run.py new file mode 100644 index 00000000..86a4d51f --- /dev/null +++ b/scripts/e2e_memorize/run.py @@ -0,0 +1,186 @@ +"""End-to-end memorize runner — in-process call into ``service.memorize``. + +Calls ``service.memorize.memorize()`` directly (not via HTTP) so this works +without ``everos server start``. Drives a fixture through ``/add`` in +N-sized batches, then triggers ``/flush`` to drain the tail. + +Reads ``settings.memorize.mode`` from current env / toml — set the mode via +``EVEROS_MEMORIZE__MODE=chat|agent`` *before* invoking this script (the +config is cached after the first ``load_settings()`` call). + +Usage: + EVEROS_MEMORIZE__MODE=chat uv run python scripts/e2e_memorize/run.py \\ + --fixture scripts/e2e_memorize/fixtures/chat_session.json + + EVEROS_MEMORIZE__MODE=agent uv run python scripts/e2e_memorize/run.py \\ + --fixture scripts/e2e_memorize/fixtures/agent_session.json --batch-size 5 + +After it finishes, check: + ~/.everos/users//episodes/.md (written sync by 4A) + ~/.everos/.index/sqlite/system.db memcell rows (written by boundary) + ~/.everos/agents//agent_cases/.md (written async by OME + - only if a consumer of AgentMemCellWritten is registered) +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +import sys +import time +import uuid +from pathlib import Path + +from sqlmodel import SQLModel + +from everos.component.llm import get_llm_client +from everos.config import load_settings +from everos.core.persistence import MemoryRoot +from everos.infra.persistence.sqlite import dispose_engine, get_engine +from everos.service.memorize import _get_engine as _get_ome_engine +from everos.service.memorize import memorize + + +def _chunks(items: list[dict], n: int) -> list[list[dict]]: + return [items[i : i + n] for i in range(0, len(items), n)] + + +def _print_header(mode: str, fixture_path: Path, session_id: str) -> None: + print("=" * 72) + print(f" everos e2e memorize · mode={mode}") + print(f" fixture : {fixture_path.name}") + print(f" session_id : {session_id}") + print(f" memory root : {MemoryRoot.default().root}") + llm_state = "" if get_llm_client() else "" + print(f" llm_client : {llm_state}") + print("=" * 72) + + +def _list_written_files(session_id: str, mode: str) -> None: + """Walk memory root and print files touched in this run.""" + root = MemoryRoot.default().root + cutoff = time.time() - 600 # files modified in the last 10 min + print() + print("─── files modified within the last 10 minutes under memory root ───") + interesting = ("users", "agents", "knowledge", ".index") + for sub in interesting: + base = Path(root) / sub + if not base.is_dir(): + continue + for p in sorted(base.rglob("*")): + if p.is_file() and p.stat().st_mtime >= cutoff: + rel = p.relative_to(root) + size = p.stat().st_size + print(f" {rel} ({size}b)") + print() + print(f"Tip: grep '{session_id}' in any episode md to find this run's entries.") + + +async def _setup() -> None: + """Create sqlite schema + start OME engine — the bits the HTTP lifespan + normally handles. LanceDB is not needed for the memorize sync path + (only cascade reads it), so we skip it. + """ + engine = get_engine() + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + ome = _get_ome_engine() + await ome.start() + + +async def _teardown() -> None: + ome = _get_ome_engine() + await ome.stop() + await dispose_engine() + + +async def _run(args: argparse.Namespace) -> None: + settings = load_settings() + mode = settings.memorize.mode + if args.expected_mode and args.expected_mode != mode: + print( + f"!! expected mode={args.expected_mode!r} but " + f"settings.memorize.mode={mode!r}. " + "Set EVEROS_MEMORIZE__MODE before launching." + ) + sys.exit(2) + + fixture_path = Path(args.fixture).resolve() # noqa: ASYNC240 + fixture = json.loads(fixture_path.read_text()) # noqa: ASYNC230 + messages: list[dict] = fixture["messages"] + session_id = f"{fixture.get('session_id_hint', 'e2e')}_{uuid.uuid4().hex[:8]}" + + _print_header(mode, fixture_path, session_id) + + if args.dry_run: + for i, batch in enumerate(_chunks(messages, args.batch_size), start=1): + print( + f"[dry] batch {i}: {len(batch)} msgs " + f"(first content: {batch[0]['content'][:60]!r})" + ) + print("[dry] would flush at the end") + return + + await _setup() + try: + batches = _chunks(messages, args.batch_size) + for i, batch in enumerate(batches, start=1): + result = await memorize( + {"session_id": session_id, "messages": batch}, is_final=False + ) + print( + f"add batch {i}/{len(batches)} ({len(batch)} msgs) → " + f"status={result.status:<11s} message_count={result.message_count}" + ) + + print() + print("flushing residual tail...") + flush_result = await memorize( + {"session_id": session_id, "messages": []}, is_final=True + ) + print( + f"flush → status={flush_result.status:<11s} " + f"message_count={flush_result.message_count}" + ) + + # OME strategies are fire-and-forget; each cell fires 2 strategies + # (atomic_facts + foresight), each ~5-10s on a real LLM. Sleep long + # enough for ~8-10 invocations to finish before engine.stop() drains + # the scheduler — otherwise APS cancels in-flight LLM calls. + await asyncio.sleep(30) + + _list_written_files(session_id, mode) + finally: + await _teardown() + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--fixture", + required=True, + help="path to fixture JSON (e.g. fixtures/chat_session.json)", + ) + parser.add_argument( + "--batch-size", + type=int, + default=6, + help="how many messages per /add call (default 6 — 20 msgs across ~4 batches)", + ) + parser.add_argument( + "--expected-mode", + choices=["chat", "agent"], + help="sanity check: fail fast if settings.memorize.mode mismatches", + ) + parser.add_argument( + "--dry-run", + action="store_true", + help="print the batch plan without calling memorize", + ) + args = parser.parse_args() + asyncio.run(_run(args)) + + +if __name__ == "__main__": + main() diff --git a/src/everos/README.md b/src/everos/README.md new file mode 100644 index 00000000..8cc1cef9 --- /dev/null +++ b/src/everos/README.md @@ -0,0 +1,35 @@ +# `everos` package + +Source layout for the `everos` Python package. This README is a quick +orientation; full architectural detail lives elsewhere. + +## Layout + +``` +everos/ +├── entrypoints/ Presentation: cli + api +├── service/ Application: use case orchestration +├── memory/ Domain: extract + search + cascade + prompt_slots + models +├── infra/ Infrastructure: persistence/{markdown, sqlite, lancedb} +├── component/ Cross-cutting providers: llm / embedding / config / utils +├── core/ Runtime base: observability / lifespan / context +└── config/ Data: Settings + default.toml + prompt_slots templates +``` + +Each subpackage has a top-level `__init__.py` describing its responsibility +and public API. + +## Dependency rule + +``` +entrypoints → service → memory → infra + ↓ + component / core / config +``` + +Single-direction; enforced by `import-linter` in CI. + +## Further reading + +- Architecture: [../../docs/architecture.md](../../docs/architecture.md) +- Coding rules (auto-loaded by Claude Code): [../../.claude/rules/](../../.claude/rules/) diff --git a/src/everos/__init__.py b/src/everos/__init__.py new file mode 100644 index 00000000..c04b28d1 --- /dev/null +++ b/src/everos/__init__.py @@ -0,0 +1,11 @@ +"""everos — md-first memory extraction framework.""" + +from importlib.metadata import PackageNotFoundError +from importlib.metadata import version as _pkg_version + +try: + __version__ = _pkg_version("everos") +except PackageNotFoundError: + # Editable install without dist-info, or running from a source tree that + # was never installed. Fall back to a sentinel rather than crash imports. + __version__ = "0.0.0+unknown" diff --git a/src/everos/component/__init__.py b/src/everos/component/__init__.py new file mode 100644 index 00000000..ebdb1c69 --- /dev/null +++ b/src/everos/component/__init__.py @@ -0,0 +1,4 @@ +"""Cross-cutting components. + +Technical capabilities used by every layer; depend on no business layer. +""" diff --git a/src/everos/component/config/__init__.py b/src/everos/component/config/__init__.py new file mode 100644 index 00000000..f4b16815 --- /dev/null +++ b/src/everos/component/config/__init__.py @@ -0,0 +1,14 @@ +"""Config processing capability. + +YAML loader for category-organised config trees (PromptSlot templates, +etc.). Distinct from :mod:`everos.config` (configuration *data* + Settings +schema, which uses TOML for the Pydantic Settings file) — this subpackage +holds *capability* (how to load), the other holds *data* (what to load). + +External usage: + from everos.component.config import YamlConfigLoader +""" + +from .loader import YamlConfigLoader as YamlConfigLoader + +__all__ = ["YamlConfigLoader"] diff --git a/src/everos/component/config/loader.py b/src/everos/component/config/loader.py new file mode 100644 index 00000000..092d3557 --- /dev/null +++ b/src/everos/component/config/loader.py @@ -0,0 +1,146 @@ +"""YAML config loader for category-organised file trees. + +Concept: a project keeps several *categories* of YAML config files under +their own subdirectories — for example PromptSlot templates under +``config/prompt_slots/.yaml``. The loader: + + 1. registers a category → subdirectory mapping + 2. resolves ``find(category, name)`` to ``//.yaml`` + 3. caches parsed contents until ``refresh`` is called + +Uses ``yaml.safe_load`` (no arbitrary tags) — PyYAML is already a project +dependency for markdown frontmatter, so no extra cost. +""" + +from __future__ import annotations + +from collections.abc import Mapping +from pathlib import Path +from typing import Any + +import yaml + + +class YamlConfigLoader: + """Load YAML files organised by category subdirectories. + + Usage: + loader = YamlConfigLoader(root=Path("src/everos/config")) + loader.register_category("prompt_slots") + # → reads /prompt_slots/episode.yaml + meta = loader.find("prompt_slots", "episode") + names = loader.list("prompt_slots") + loader.refresh() # next find() re-reads from disk + + Cache semantics: + * ``find`` parses the file on first access and caches the dict. + * ``refresh()`` empties the entire cache. + * ``refresh(category)`` empties one category's entries. + * ``refresh(category, name)`` empties a single entry. + """ + + def __init__( + self, + root: Path, + categories: Mapping[str, str | None] | None = None, + ) -> None: + """ + Args: + root: Base directory containing the category subdirectories. + categories: Optional pre-registered category map (``name → subdir``). + When ``subdir`` is ``None`` the category name is used as-is. + """ + self._root = Path(root) + self._subdirs: dict[str, str] = {} + self._cache: dict[tuple[str, str], dict[str, Any]] = {} + + if categories: + for name, subdir in categories.items(): + self.register_category(name, subdir) + + # ── Category management ──────────────────────────────────────────────── + + def register_category(self, name: str, subdir: str | None = None) -> None: + """Register a category. ``subdir`` defaults to ``name``.""" + self._subdirs[name] = subdir if subdir is not None else name + + def categories(self) -> list[str]: + """Return registered category names (sorted).""" + return sorted(self._subdirs) + + # ── Lookup ───────────────────────────────────────────────────────────── + + def find(self, category: str, name: str) -> dict[str, Any]: + """Load ``//.yaml`` for ``category``. + + Raises: + KeyError: if ``category`` was not registered. + FileNotFoundError: if the yaml file does not exist. + TypeError: if the parsed YAML is not a mapping. + """ + cache_key = (category, name) + if cache_key in self._cache: + return self._cache[cache_key] + + path = self._path_for(category, name) + if not path.is_file(): + raise FileNotFoundError(f"yaml not found: {path}") + + with path.open("r", encoding="utf-8") as fh: + data = yaml.safe_load(fh) + if data is None: + data = {} + if not isinstance(data, dict): + raise TypeError( + f"yaml top-level must be a mapping, got {type(data).__name__}: {path}" + ) + self._cache[cache_key] = data + return data + + def list(self, category: str) -> list[str]: + """Return sorted yaml stems available in ``category`` (no extension). + + Raises: + KeyError: if ``category`` was not registered. + """ + directory = self._dir_for(category) + if not directory.is_dir(): + return [] + return sorted(p.stem for p in directory.glob("*.yaml")) + + # ── Cache control ────────────────────────────────────────────────────── + + def refresh( + self, + category: str | None = None, + name: str | None = None, + ) -> None: + """Invalidate cached entries. + + - ``refresh()`` → drop every cached entry + - ``refresh(category)`` → drop everything in ``category`` + - ``refresh(category, name)`` → drop a single entry + """ + if category is None: + self._cache.clear() + return + if name is not None: + self._cache.pop((category, name), None) + return + self._cache = { + (cat, n): v for (cat, n), v in self._cache.items() if cat != category + } + + # ── Internals ────────────────────────────────────────────────────────── + + def _dir_for(self, category: str) -> Path: + try: + subdir = self._subdirs[category] + except KeyError as exc: + raise KeyError( + f"category not registered: {category!r}; known: {sorted(self._subdirs)}" + ) from exc + return self._root / subdir + + def _path_for(self, category: str, name: str) -> Path: + return self._dir_for(category) / f"{name}.yaml" diff --git a/src/everos/component/embedding/__init__.py b/src/everos/component/embedding/__init__.py new file mode 100644 index 00000000..98f0e1e6 --- /dev/null +++ b/src/everos/component/embedding/__init__.py @@ -0,0 +1,33 @@ +"""Embedding provider adapters (one provider per file). + + +Public surface: + +- :class:`EmbeddingProvider` — Protocol every provider satisfies. +- :class:`EmbeddingError` — provider-side failure. +- :class:`OpenAIEmbeddingProvider` — concrete provider for any + OpenAI-protocol embeddings endpoint (DeepInfra, vLLM, OpenAI, …). +- :func:`build_embedding_provider` — settings-driven factory. + +External usage:: + + from everos.component.embedding import build_embedding_provider + provider = build_embedding_provider(settings.embedding) + vec = await provider.embed("hello") +""" + +from .accessor import EmbeddingNotConfiguredError as EmbeddingNotConfiguredError +from .accessor import get_embedder as get_embedder +from .factory import build_embedding_provider as build_embedding_provider +from .openai_provider import OpenAIEmbeddingProvider as OpenAIEmbeddingProvider +from .protocol import EmbeddingError as EmbeddingError +from .protocol import EmbeddingProvider as EmbeddingProvider + +__all__ = [ + "EmbeddingError", + "EmbeddingNotConfiguredError", + "EmbeddingProvider", + "OpenAIEmbeddingProvider", + "build_embedding_provider", + "get_embedder", +] diff --git a/src/everos/component/embedding/accessor.py b/src/everos/component/embedding/accessor.py new file mode 100644 index 00000000..cbe4d110 --- /dev/null +++ b/src/everos/component/embedding/accessor.py @@ -0,0 +1,48 @@ +"""Process-wide embedding provider accessor. + +Lazy singleton mirror of :func:`everos.component.llm.get_llm_client`: +first call reads settings and builds the OpenAI-protocol embedding +client; subsequent calls return the cached instance. Strategies and +other components that need a process-wide embedder import this rather +than threading the provider through their constructors. + +Raises :class:`EmbeddingNotConfiguredError` when credentials are missing +so misconfiguration surfaces at the call site (or at app startup via a +lifespan provider) instead of silently degrading. +""" + +from __future__ import annotations + +from everos.config import load_settings +from everos.core.observability.logging import get_logger + +from .factory import build_embedding_provider +from .protocol import EmbeddingProvider + +logger = get_logger(__name__) + + +class EmbeddingNotConfiguredError(RuntimeError): + """Raised when ``settings.embedding`` lacks ``model``/``api_key``/``base_url``.""" + + +_embedder: EmbeddingProvider | None = None + + +def get_embedder() -> EmbeddingProvider: + """Return the singleton :class:`EmbeddingProvider`. + + Raises: + EmbeddingNotConfiguredError: When required settings fields are + unset. See :func:`build_embedding_provider` for the exact + keys. + """ + global _embedder + if _embedder is not None: + return _embedder + try: + _embedder = build_embedding_provider(load_settings().embedding) + except ValueError as exc: + raise EmbeddingNotConfiguredError(str(exc)) from exc + logger.info("embedder_built") + return _embedder diff --git a/src/everos/component/embedding/factory.py b/src/everos/component/embedding/factory.py new file mode 100644 index 00000000..97967c44 --- /dev/null +++ b/src/everos/component/embedding/factory.py @@ -0,0 +1,56 @@ +"""Factory for building an embedding provider from :class:`EmbeddingSettings`.""" + +from __future__ import annotations + +from everos.config import EmbeddingSettings + +from .openai_provider import OpenAIEmbeddingProvider +from .protocol import EmbeddingProvider + +# Vector dim for the LanceDB index column — see ``17_lancedb_tables_design.md``. +_DEFAULT_DIM = 1024 + + +def build_embedding_provider( + settings: EmbeddingSettings, + *, + dim: int = _DEFAULT_DIM, +) -> EmbeddingProvider: + """Build an OpenAI-compatible embedding provider from settings. + + Args: + settings: The :class:`EmbeddingSettings` slice from + :func:`everos.config.load_settings`. + dim: Target vector dimension; defaults to 1024 to match the + LanceDB ``vector`` column shape. + + Returns: + An :class:`EmbeddingProvider` ready to call ``embed`` / + ``embed_batch``. + + Raises: + ValueError: If ``model``, ``api_key`` or ``base_url`` is unset. + """ + if not settings.model: + raise ValueError( + "Embedding model is not configured " + "(set EVEROS_EMBEDDING__MODEL or [embedding] model in user toml)" + ) + if settings.api_key is None: + raise ValueError( + "Embedding api_key is not configured (set EVEROS_EMBEDDING__API_KEY)" + ) + if not settings.base_url: + raise ValueError( + "Embedding base_url is not configured (set EVEROS_EMBEDDING__BASE_URL)" + ) + return OpenAIEmbeddingProvider( + model=settings.model, + api_key=settings.api_key.get_secret_value(), + base_url=settings.base_url, + dim=dim, + timeout=settings.timeout_seconds, + max_retries=settings.max_retries, + batch_size=settings.batch_size, + max_concurrent=settings.max_concurrent, + ) diff --git a/src/everos/component/embedding/openai_provider.py b/src/everos/component/embedding/openai_provider.py new file mode 100644 index 00000000..836a5eb8 --- /dev/null +++ b/src/everos/component/embedding/openai_provider.py @@ -0,0 +1,98 @@ +"""OpenAI-compatible embedding provider. + +Wraps :class:`openai.AsyncOpenAI` so any OpenAI-protocol endpoint +(DeepInfra, OpenAI, Together, Fireworks, …) works without per-provider +forks. Self-hosted vLLM also exposes the same shape; the only quirk it +imposes is that the ``dimensions`` request parameter is ignored — we +truncate client-side to ``dim`` so callers always see the declared +shape regardless of backend. + +Concurrency model: + +- ``embed_batch`` splits the inputs into chunks of ``batch_size``. +- An :class:`asyncio.Semaphore` capped at ``max_concurrent`` bounds + in-flight requests; remaining chunks queue and start as slots free. +- Retries / timeouts come from the openai SDK (``max_retries``, + ``timeout`` constructor args). +""" + +from __future__ import annotations + +import asyncio +from collections.abc import Sequence + +import openai + +from .protocol import EmbeddingError + + +class OpenAIEmbeddingProvider: + """OpenAI-compatible embedding provider with batching + concurrency. + + Args: + model: Embedding model id (e.g. ``"Qwen/Qwen3-Embedding-4B"``). + api_key: Bearer credential as a plain ``str``. + base_url: OpenAI-protocol endpoint + (e.g. ``"https://api.deepinfra.com/v1/openai"``). + dim: Target vector dimension. Vectors longer than this are + truncated client-side (matches the LanceDB column shape — + see ``17_lancedb_tables_design.md``). + timeout: Per-request timeout, seconds. + max_retries: Retry budget exposed via the openai SDK. + batch_size: How many inputs per ``/embeddings`` call. + max_concurrent: Cap on in-flight chunked requests. + """ + + def __init__( + self, + *, + model: str, + api_key: str, + base_url: str, + dim: int = 1024, + timeout: float = 30.0, + max_retries: int = 3, + batch_size: int = 10, + max_concurrent: int = 5, + ) -> None: + self.dim = dim + self._model = model + self._batch_size = batch_size + self._semaphore = asyncio.Semaphore(max_concurrent) + self._client = openai.AsyncOpenAI( + api_key=api_key, + base_url=base_url, + timeout=timeout, + max_retries=max_retries, + ) + + async def embed(self, text: str) -> list[float]: + """Embed a single string.""" + vectors = await self._embed_chunk([text]) + return vectors[0] + + async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]: + """Embed many strings, preserving input order.""" + if not texts: + return [] + chunks = [ + list(texts[i : i + self._batch_size]) + for i in range(0, len(texts), self._batch_size) + ] + results = await asyncio.gather(*(self._embed_chunk(chunk) for chunk in chunks)) + # gather preserves order across awaitables, and each chunk preserves + # its internal order — so flattening yields the input order back. + return [vec for chunk in results for vec in chunk] + + async def _embed_chunk(self, chunk: list[str]) -> list[list[float]]: + """One ``/embeddings`` call, semaphore-guarded.""" + async with self._semaphore: + try: + response = await self._client.embeddings.create( + model=self._model, + input=chunk, + ) + except openai.OpenAIError as exc: + raise EmbeddingError(str(exc)) from exc + # OpenAI returns ``data`` indexed by request order; truncate to ``dim``. + return [list(item.embedding[: self.dim]) for item in response.data] diff --git a/src/everos/component/embedding/protocol.py b/src/everos/component/embedding/protocol.py new file mode 100644 index 00000000..5f6fa038 --- /dev/null +++ b/src/everos/component/embedding/protocol.py @@ -0,0 +1,48 @@ +"""Embedding provider protocol. + + +The cascade worker / retrieval pipeline depend on a single small +contract: turn a string (or list of strings) into a fixed-dimension +vector. Whether the backend is OpenAI, vLLM, DeepInfra, Ollama, or a +local model is the provider's business — the contract is invariant. + +""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Protocol, runtime_checkable + + +class EmbeddingError(Exception): + """Raised on any provider-side embedding failure. + + Wraps the upstream SDK exception via ``__cause__`` (PEP 3134) so + diagnostic loggers preserve the original error chain. + """ + + +@runtime_checkable +class EmbeddingProvider(Protocol): + """Async embedding provider contract. + + ``dim`` is the post-truncation vector dimension every embed call + returns. Providers that don't natively support dimension truncation + must truncate client-side so callers see the declared shape. + """ + + dim: int + + async def embed(self, text: str) -> list[float]: + """Embed a single string. Returns a ``[dim]`` vector.""" + ... + + async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]: + """Embed a batch of strings preserving input order. + + Implementations chunk by ``batch_size`` and bound in-flight + requests by ``max_concurrent`` (both from settings). On failure, + raises :class:`EmbeddingError` — the worker treats it as a + retryable / unrecoverable case per HTTP-status mapping. + """ + ... diff --git a/src/everos/component/llm/__init__.py b/src/everos/component/llm/__init__.py new file mode 100644 index 00000000..c82baf5e --- /dev/null +++ b/src/everos/component/llm/__init__.py @@ -0,0 +1,45 @@ +"""LLM provider adapters (one provider per file, mem0-style). + +Public surface: + +- :class:`LLMClient` — Protocol every provider satisfies (re-exported + from :mod:`everalgo.llm`; same shape so everos providers can be + handed to everalgo operators). +- :class:`ChatMessage` / :class:`ChatResponse` / :class:`Usage` — chat + payload types (re-exported from :mod:`everalgo.llm`). +- :class:`LLMError` — provider-side failure (re-exported). +- :class:`LLMNotConfiguredError` — raised when credentials are missing. +- :class:`OpenAIProvider` — concrete provider wrapping + ``openai.AsyncOpenAI`` against any OpenAI-compatible endpoint. +- :func:`build_llm_provider` — settings-driven factory. +- :func:`get_llm_client` — process-wide lazy singleton accessor. + +External usage:: + + from everos.component.llm import build_llm_provider, LLMClient + provider = build_llm_provider(settings.llm) +""" + +from .client import LLMNotConfiguredError as LLMNotConfiguredError +from .client import get_llm_client as get_llm_client +from .client import get_multimodal_llm_client as get_multimodal_llm_client +from .factory import build_llm_provider as build_llm_provider +from .openai_provider import OpenAIProvider as OpenAIProvider +from .protocol import ChatMessage as ChatMessage +from .protocol import ChatResponse as ChatResponse +from .protocol import LLMClient as LLMClient +from .protocol import LLMError as LLMError +from .protocol import Usage as Usage + +__all__ = [ + "ChatMessage", + "ChatResponse", + "LLMClient", + "LLMError", + "LLMNotConfiguredError", + "OpenAIProvider", + "Usage", + "build_llm_provider", + "get_llm_client", + "get_multimodal_llm_client", +] diff --git a/src/everos/component/llm/client.py b/src/everos/component/llm/client.py new file mode 100644 index 00000000..846dcf1c --- /dev/null +++ b/src/everos/component/llm/client.py @@ -0,0 +1,89 @@ +"""Process-wide LLM client accessor. + +Lazy singleton — first call reads settings and builds the algo LLM +client; subsequent calls return the cached instance. Raises +:class:`LLMNotConfiguredError` when no credentials are present so +misconfiguration surfaces at app startup (via the LLM lifespan +provider) instead of silently failing per-request downstream. +""" + +from __future__ import annotations + +from everalgo.llm import build_client +from everalgo.llm.config import LLMConfig +from everalgo.llm.protocols import LLMClient + +from everos.config import load_settings +from everos.core.observability.logging import get_logger + +logger = get_logger(__name__) + + +class LLMNotConfiguredError(RuntimeError): + """Raised when ``settings.llm`` is missing ``api_key`` or ``base_url``.""" + + +_llm_client: LLMClient | None = None +_multimodal_client: LLMClient | None = None + + +def get_llm_client() -> LLMClient: + """Return the singleton algo LLM client. + + Raises: + LLMNotConfiguredError: When ``settings.llm.api_key`` or + ``settings.llm.base_url`` is unset. + """ + global _llm_client + if _llm_client is not None: + return _llm_client + + llm_cfg = load_settings().llm + api_key = ( + llm_cfg.api_key.get_secret_value() if llm_cfg.api_key is not None else None + ) + if not api_key or not llm_cfg.base_url: + raise LLMNotConfiguredError( + "LLM is required; set EVEROS_LLM__API_KEY + EVEROS_LLM__BASE_URL" + ) + _llm_client = build_client( + LLMConfig( + model=llm_cfg.model, + api_key=api_key, + base_url=llm_cfg.base_url, + ) + ) + logger.info("llm_client_built", model=llm_cfg.model) + return _llm_client + + +def get_multimodal_llm_client() -> LLMClient: + """Return the singleton multimodal LLM client (for everalgo.parser). + + Reads the flat ``[multimodal]`` config — kept separate from the main + ``[llm]`` so parsing can target a vision/audio-capable endpoint. + + Raises: + LLMNotConfiguredError: When ``settings.multimodal.api_key`` or + ``settings.multimodal.base_url`` is unset. + """ + global _multimodal_client + if _multimodal_client is not None: + return _multimodal_client + + cfg = load_settings().multimodal + api_key = cfg.api_key.get_secret_value() if cfg.api_key is not None else None + if not api_key or not cfg.base_url: + raise LLMNotConfiguredError( + "Multimodal LLM is required for parsing; set " + "EVEROS_MULTIMODAL__API_KEY + EVEROS_MULTIMODAL__BASE_URL" + ) + _multimodal_client = build_client( + LLMConfig( + model=cfg.model, + api_key=api_key, + base_url=cfg.base_url, + ) + ) + logger.info("multimodal_llm_client_built", model=cfg.model) + return _multimodal_client diff --git a/src/everos/component/llm/factory.py b/src/everos/component/llm/factory.py new file mode 100644 index 00000000..d0db74f2 --- /dev/null +++ b/src/everos/component/llm/factory.py @@ -0,0 +1,45 @@ +"""Factory for building an LLM provider from :class:`LLMSettings`.""" + +from __future__ import annotations + +from everos.config import LLMSettings + +from .openai_provider import OpenAIProvider +from .protocol import LLMClient + + +def build_llm_provider(settings: LLMSettings) -> LLMClient: + """Build an OpenAI-compatible LLM provider from settings. + + Unwraps :class:`pydantic.SecretStr` here so downstream callers never + touch the raw key directly. Fails fast if either ``api_key`` or + ``base_url`` is missing — caller is expected to set them via + ``.env`` / user toml / programmatic init before calling. + + Args: + settings: The :class:`LLMSettings` slice from + :func:`everos.config.load_settings`. + + Returns: + A provider that structurally satisfies + :class:`everalgo.llm.LLMClient` and can be passed to everalgo + operators via ``llm=``. + + Raises: + ValueError: If ``api_key`` or ``base_url`` is unset. + """ + if settings.api_key is None: + raise ValueError( + "LLM api_key is not configured " + "(set EVEROS_LLM__API_KEY or [llm] api_key in user toml)" + ) + if not settings.base_url: + raise ValueError( + "LLM base_url is not configured " + "(set EVEROS_LLM__BASE_URL or [llm] base_url in user toml)" + ) + return OpenAIProvider( + model=settings.model, + api_key=settings.api_key.get_secret_value(), + base_url=settings.base_url, + ) diff --git a/src/everos/component/llm/openai_provider.py b/src/everos/component/llm/openai_provider.py new file mode 100644 index 00000000..c73d2f5a --- /dev/null +++ b/src/everos/component/llm/openai_provider.py @@ -0,0 +1,114 @@ +"""OpenAI-compatible LLM provider for everos. + +Implements the :class:`everalgo.llm.LLMClient` structural contract by +wrapping :class:`openai.AsyncOpenAI` — the same backbone everalgo's own +``OpenAICompatClient`` uses, but defined here in everos so the +provider can be constructed from :class:`everos.config.LLMSettings` +and handed to everalgo extractors via the ``llm=`` per-call parameter. + +Keeps the provider lean (matches the everalgo minimum-viable shape): +no multi-key rotation, no scenario-level routing, no token-usage +collector — those are deployment concerns layered on top. +""" + +from __future__ import annotations + +from collections.abc import Mapping +from typing import Any, Literal + +import openai + +from .protocol import ChatMessage, ChatResponse, LLMError, Usage + + +class OpenAIProvider: + """Thin async wrapper over ``openai.AsyncOpenAI``. + + Structurally satisfies :class:`everalgo.llm.LLMClient` (PEP 544); + instances can be passed directly to everalgo operators that accept + ``llm: LLMClient | None``. + + Args: + model: Default model id (override per-call with ``model=`` on + :meth:`chat`). + api_key: Bearer credential. Pass as plain ``str`` — settings + should unwrap :class:`pydantic.SecretStr` at the factory + boundary. + base_url: OpenAI-compatible endpoint (e.g. + ``"https://openrouter.ai/api/v1"``). + timeout: Per-request timeout in seconds. + temperature: Default sampling temperature (overridable per call). + max_tokens: Default max-tokens cap (overridable per call). + """ + + def __init__( + self, + *, + model: str, + api_key: str, + base_url: str | None = None, + timeout: float = 60.0, + temperature: float = 0.0, + max_tokens: int | None = None, + ) -> None: + self._model = model + self._temperature = temperature + self._max_tokens = max_tokens + self._client = openai.AsyncOpenAI( + api_key=api_key, + base_url=base_url, + timeout=timeout, + ) + + async def chat( + self, + messages: list[ChatMessage], + *, + model: str | None = None, + temperature: float | None = None, + max_tokens: int | None = None, + response_format: Mapping[str, Any] | None = None, + **extra: Any, + ) -> ChatResponse: + """Send a chat completion request and return the parsed response.""" + request: dict[str, Any] = { + "model": model or self._model, + "messages": [m.model_dump() for m in messages], + "temperature": ( + temperature if temperature is not None else self._temperature + ), + } + effective_max = max_tokens if max_tokens is not None else self._max_tokens + if effective_max is not None: + request["max_tokens"] = effective_max + if response_format is not None: + request["response_format"] = dict(response_format) + request.update(extra) + + try: + completion = await self._client.chat.completions.create(**request) + except openai.OpenAIError as exc: + raise LLMError(str(exc)) from exc + + choice = completion.choices[0] + usage: Usage | None = None + if completion.usage is not None: + usage = Usage( + prompt_tokens=completion.usage.prompt_tokens, + completion_tokens=completion.usage.completion_tokens, + ) + return ChatResponse( + content=choice.message.content or "", + model=completion.model, + usage=usage, + finish_reason=_normalise_finish_reason(choice.finish_reason), + raw=None, + ) + + +def _normalise_finish_reason( + value: str | None, +) -> Literal["stop", "length", "content_filter"] | None: + if value in ("stop", "length", "content_filter"): + return value # type: ignore[return-value] + return None diff --git a/src/everos/component/llm/protocol.py b/src/everos/component/llm/protocol.py new file mode 100644 index 00000000..91fa5688 --- /dev/null +++ b/src/everos/component/llm/protocol.py @@ -0,0 +1,39 @@ +"""LLM client protocol re-export. + +The structural contract every everos LLM provider satisfies is the same +:class:`everalgo.llm.LLMClient` Protocol — everos providers must be +pass-through-compatible with the everalgo extractors that accept an +``llm=`` parameter. Re-exporting the type here keeps the import path +stable (``everos.component.llm``) even if the everalgo namespace +shifts later. + +The :class:`ChatMessage` / :class:`ChatResponse` / :class:`Usage` +shapes are likewise re-exported so callers can build / inspect chat +payloads without reaching into the everalgo package directly. +""" + +from __future__ import annotations + +from everalgo.llm import ( + ChatMessage as ChatMessage, +) +from everalgo.llm import ( + ChatResponse as ChatResponse, +) +from everalgo.llm import ( + LLMClient as LLMClient, +) +from everalgo.llm import ( + LLMError as LLMError, +) +from everalgo.llm import ( + Usage as Usage, +) + +__all__ = [ + "ChatMessage", + "ChatResponse", + "LLMClient", + "LLMError", + "Usage", +] diff --git a/src/everos/component/rerank/__init__.py b/src/everos/component/rerank/__init__.py new file mode 100644 index 00000000..a2f502f2 --- /dev/null +++ b/src/everos/component/rerank/__init__.py @@ -0,0 +1,34 @@ +"""Rerank provider adapters (one provider per file). + +Public surface: + +- :class:`RerankProvider` — Protocol every provider satisfies. +- :class:`RerankResult` / :class:`RerankError` — value type + error. +- :class:`DeepInfraRerankProvider` — DeepInfra inference-API rerank. +- :class:`VllmRerankProvider` — OpenAI-compat ``/v1/rerank`` (vLLM, + self-hosted, other compatible servers). +- :func:`build_rerank_provider` — settings-driven factory that picks + the concrete provider via ``settings.rerank.provider``. + +External usage:: + + from everos.component.rerank import build_rerank_provider + provider = build_rerank_provider(settings.rerank) + scored = await provider.rerank("how to file a claim", documents) +""" + +from .deepinfra_provider import DeepInfraRerankProvider as DeepInfraRerankProvider +from .factory import build_rerank_provider as build_rerank_provider +from .protocol import RerankError as RerankError +from .protocol import RerankProvider as RerankProvider +from .protocol import RerankResult as RerankResult +from .vllm_provider import VllmRerankProvider as VllmRerankProvider + +__all__ = [ + "DeepInfraRerankProvider", + "RerankError", + "RerankProvider", + "RerankResult", + "VllmRerankProvider", + "build_rerank_provider", +] diff --git a/src/everos/component/rerank/deepinfra_provider.py b/src/everos/component/rerank/deepinfra_provider.py new file mode 100644 index 00000000..87e8fac7 --- /dev/null +++ b/src/everos/component/rerank/deepinfra_provider.py @@ -0,0 +1,196 @@ +"""DeepInfra inference-API rerank provider. + +DeepInfra exposes reranker models (e.g. ``Qwen/Qwen3-Reranker-4B``) at:: + + POST {base_url}/{model} + Authorization: Bearer + Content-Type: application/json + +The request shape is the inference-API convention used across DeepInfra +reranker / classifier models: + + { + "queries": [""], + "documents": ["", "", ...] + } + +The response carries one ``scores`` array per query: + + { + "scores": [[0.12, 0.87, 0.43, ...]], + "request_id": "...", + "inference_status": {...} + } + +We submit one query at a time (matches the :class:`RerankProvider` +contract) and unwrap the inner score list. Documents longer than the +model's input window are silently truncated server-side. +""" + +from __future__ import annotations + +import asyncio +from collections.abc import Sequence +from typing import Any + +import httpx + +from .protocol import RerankError, RerankResult + +# Qwen3-Reranker chat template. The DeepInfra inference API treats the reranker +# as a yes/no generator, so the prompt scaffolding must be supplied client-side +# (verbatim mirror of the EverCore benchmark's reranker client). Without it the +# model scores raw text off-template and returns uncalibrated relevance. +_QWEN3_PREFIX = ( + "<|im_start|>system\n" + "Judge whether the Document meets the requirements based on the Query and " + 'the Instruct provided. Note that the answer can only be "yes" or "no".' + "<|im_end|>\n<|im_start|>user\n" +) +_QWEN3_SUFFIX = "<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" +_DEFAULT_RERANK_INSTRUCTION = ( + "Given a question and a passage, determine if the passage contains " + "information relevant to answering the question." +) + + +def _format_qwen3_inputs( + query: str, documents: list[str], instruction: str | None +) -> tuple[str, list[str]]: + """Wrap query + documents in the Qwen3-Reranker chat template.""" + instr = instruction or _DEFAULT_RERANK_INSTRUCTION + formatted_query = f"{_QWEN3_PREFIX}: {instr}\n: {query}\n" + formatted_docs = [f": {doc}{_QWEN3_SUFFIX}" for doc in documents] + return formatted_query, formatted_docs + + +class DeepInfraRerankProvider: + """Rerank provider for the DeepInfra inference API. + + Args: + model: Reranker model id (e.g. ``"Qwen/Qwen3-Reranker-4B"``). + api_key: Bearer credential as plain ``str``. + base_url: Inference endpoint root + (e.g. ``"https://api.deepinfra.com/v1/inference"``). The + ``/{model}`` suffix is appended at request time. + timeout: Per-request timeout, seconds. + max_retries: Soft retry count on transport errors / 5xx. + batch_size: Cap on documents per request (large doc lists are + split, scores merged in input order). + max_concurrent: Cap on in-flight requests across all batches. + """ + + def __init__( + self, + *, + model: str, + api_key: str, + base_url: str, + timeout: float = 30.0, + max_retries: int = 3, + batch_size: int = 10, + max_concurrent: int = 5, + ) -> None: + self._model = model + self._api_key = api_key + self._url = f"{base_url.rstrip('/')}/{model}" + self._timeout = timeout + self._max_retries = max_retries + self._batch_size = batch_size + self._semaphore = asyncio.Semaphore(max_concurrent) + + async def rerank( + self, + query: str, + documents: Sequence[str], + *, + instruction: str | None = None, + ) -> list[RerankResult]: + """Score every document against ``query``; return sorted desc.""" + if not documents: + return [] + + chunks: list[tuple[int, list[str]]] = [ + (offset, list(documents[offset : offset + self._batch_size])) + for offset in range(0, len(documents), self._batch_size) + ] + chunk_scores = await asyncio.gather( + *(self._score_chunk(query, docs, instruction) for _, docs in chunks) + ) + scored: list[RerankResult] = [] + for (offset, _), scores in zip(chunks, chunk_scores, strict=True): + scored.extend( + RerankResult(index=offset + i, score=score) + for i, score in enumerate(scores) + ) + scored.sort(key=lambda r: r.score, reverse=True) + return scored + + async def _score_chunk( + self, query: str, documents: list[str], instruction: str | None + ) -> list[float]: + formatted_query, formatted_docs = _format_qwen3_inputs( + query, documents, instruction + ) + payload: dict[str, Any] = { + "queries": [formatted_query], + "documents": formatted_docs, + } + headers = { + "Authorization": f"Bearer {self._api_key}", + "Content-Type": "application/json", + } + async with self._semaphore: + for attempt in range(self._max_retries + 1): + try: + async with httpx.AsyncClient(timeout=self._timeout) as client: + response = await client.post( + self._url, json=payload, headers=headers + ) + except httpx.HTTPError as exc: + if attempt == self._max_retries: + raise RerankError( + f"DeepInfra rerank transport failure: {exc}" + ) from exc + continue + + if response.status_code == 200: + return _extract_scores(response.json(), len(documents)) + + # Retry on 5xx / 429 only; surface 4xx immediately. + if response.status_code >= 500 or response.status_code == 429: + if attempt == self._max_retries: + raise RerankError( + f"DeepInfra rerank HTTP {response.status_code}: " + f"{response.text[:200]}" + ) + continue + raise RerankError( + f"DeepInfra rerank HTTP {response.status_code}: " + f"{response.text[:200]}" + ) + + raise RerankError( + f"DeepInfra rerank exhausted retries ({self._max_retries})" + ) + + +def _extract_scores(body: dict[str, Any], expected_len: int) -> list[float]: + """Unwrap ``scores`` from the DeepInfra response body. + + Inference API returns ``scores`` as either: + + - ``[[s1, s2, ...]]`` — one score row per query (current single-query + shape); take row 0. + - ``[s1, s2, ...]`` — flat list (fallback for providers that drop + the outer list when only one query is sent). + """ + raw = body.get("scores") + if not isinstance(raw, list): + raise RerankError(f"DeepInfra rerank response missing scores: {body!r}") + row = raw[0] if raw and isinstance(raw[0], list) else raw + if len(row) != expected_len: + raise RerankError( + f"DeepInfra rerank returned {len(row)} scores, expected {expected_len}" + ) + return [float(s) for s in row] diff --git a/src/everos/component/rerank/factory.py b/src/everos/component/rerank/factory.py new file mode 100644 index 00000000..bdc8be58 --- /dev/null +++ b/src/everos/component/rerank/factory.py @@ -0,0 +1,74 @@ +"""Factory for building a rerank provider from :class:`RerankSettings`. + +The ``provider`` field on :class:`RerankSettings` selects which concrete +implementation to build: + + - ``"deepinfra"`` → :class:`DeepInfraRerankProvider` + - ``"vllm"`` → :class:`VllmRerankProvider` + +Adding a new provider = one match arm here + one new file under +:mod:`everos.component.rerank`. +""" + +from __future__ import annotations + +from everos.config import RerankSettings + +from .deepinfra_provider import DeepInfraRerankProvider +from .protocol import RerankProvider +from .vllm_provider import VllmRerankProvider + + +def build_rerank_provider(settings: RerankSettings) -> RerankProvider: + """Build a rerank provider from settings. + + Args: + settings: The :class:`RerankSettings` slice from + :func:`everos.config.load_settings`. + + Returns: + A :class:`RerankProvider` ready to call ``rerank``. + + Raises: + ValueError: If ``model`` or ``base_url`` is unset, or if + ``provider`` does not match a known implementation. + ``api_key`` is required for ``deepinfra``; optional (empty + string) for ``vllm`` self-hosted endpoints. + """ + if not settings.model: + raise ValueError( + "Rerank model is not configured " + "(set EVEROS_RERANK__MODEL or [rerank] model in user toml)" + ) + if not settings.base_url: + raise ValueError( + "Rerank base_url is not configured (set EVEROS_RERANK__BASE_URL)" + ) + api_key = settings.api_key.get_secret_value() if settings.api_key else "" + + if settings.provider == "deepinfra": + if not api_key: + raise ValueError( + "DeepInfra rerank api_key is not configured " + "(set EVEROS_RERANK__API_KEY)" + ) + return DeepInfraRerankProvider( + model=settings.model, + api_key=api_key, + base_url=settings.base_url, + timeout=settings.timeout_seconds, + max_retries=settings.max_retries, + batch_size=settings.batch_size, + max_concurrent=settings.max_concurrent, + ) + if settings.provider == "vllm": + return VllmRerankProvider( + model=settings.model, + api_key=api_key, + base_url=settings.base_url, + timeout=settings.timeout_seconds, + max_retries=settings.max_retries, + batch_size=settings.batch_size, + max_concurrent=settings.max_concurrent, + ) + raise ValueError(f"unknown rerank provider: {settings.provider!r}") diff --git a/src/everos/component/rerank/protocol.py b/src/everos/component/rerank/protocol.py new file mode 100644 index 00000000..341fa84e --- /dev/null +++ b/src/everos/component/rerank/protocol.py @@ -0,0 +1,62 @@ +"""Rerank provider protocol. + +The contract every rerank provider satisfies: given a query and a list +of candidate documents, return a re-ordered list of ``(index, score)`` +pairs (highest relevance first). The provider does **not** filter — +that's the caller's job (e.g. drop scores below a threshold, take +``top_k``). Returning every input pair keeps the contract stable +across providers whose backends may not natively support ``top_n``. + +""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import NamedTuple, Protocol, runtime_checkable + + +class RerankError(Exception): + """Raised on any provider-side rerank failure.""" + + +class RerankResult(NamedTuple): + """One scored document from a rerank call. + + ``index`` is the position of the document in the *input* list (so + callers can map back to the original document text). ``score`` is + provider-defined; higher = more relevant. + """ + + index: int + score: float + + +@runtime_checkable +class RerankProvider(Protocol): + """Async rerank provider contract.""" + + async def rerank( + self, + query: str, + documents: Sequence[str], + *, + instruction: str | None = None, + ) -> list[RerankResult]: + """Score and re-order ``documents`` against ``query``. + + Args: + query: The search query. + documents: Passage texts to score against ``query``. + instruction: Task instruction for instruction-tuned rerankers + (e.g. Qwen3-Reranker). Providers that wrap the model's chat + template fold this into the prompt; providers backed by a + dedicated rerank endpoint that handles templating server-side + may ignore it. ``None`` defers to the provider's default. + + Returns: + One :class:`RerankResult` per input document, sorted by + ``score`` descending. The returned list length equals + ``len(documents)``. + """ + + ... diff --git a/src/everos/component/rerank/vllm_provider.py b/src/everos/component/rerank/vllm_provider.py new file mode 100644 index 00000000..7dbd8362 --- /dev/null +++ b/src/everos/component/rerank/vllm_provider.py @@ -0,0 +1,173 @@ +"""vLLM rerank provider. + +Self-deployed vLLM (and other OpenAI-compatible rerank servers) expose +the OpenAI-style rerank endpoint:: + + POST {base_url}/rerank + Authorization: Bearer # optional for self-hosted ("EMPTY") + Content-Type: application/json + +Request body: + + { + "model": "", + "query": "", + "documents": ["", "", ...] + } + +Response body: + + { + "results": [ + {"index": 0, "relevance_score": 0.87}, + {"index": 1, "relevance_score": 0.43}, + ... + ], + "id": "...", + ... + } + +We pass documents through as-is — caller is responsible for any +prompt-template formatting required by the underlying reranker. Output +ordering may already be score-descending; we sort defensively to honour +the :class:`RerankProvider` contract regardless of server behaviour. +""" + +from __future__ import annotations + +import asyncio +from collections.abc import Sequence +from typing import Any + +import httpx + +from .protocol import RerankError, RerankResult + + +class VllmRerankProvider: + """Rerank provider for vLLM / OpenAI-compat ``/v1/rerank`` endpoints. + + Args: + model: Reranker model id (e.g. ``"Qwen/Qwen3-Reranker-4B"``). + api_key: Bearer credential. Pass ``""`` (empty string) for + self-hosted endpoints that don't require auth — the + ``Authorization`` header is omitted in that case. + base_url: API root that *contains* the ``/v1`` prefix + (e.g. ``"http://localhost:8000/v1"``). The ``/rerank`` + suffix is appended at request time. + timeout: Per-request timeout, seconds. + max_retries: Soft retry count on transport errors / 5xx. + batch_size: Cap on documents per request. + max_concurrent: Cap on in-flight requests across all batches. + """ + + def __init__( + self, + *, + model: str, + api_key: str, + base_url: str, + timeout: float = 30.0, + max_retries: int = 3, + batch_size: int = 10, + max_concurrent: int = 5, + ) -> None: + self._model = model + self._api_key = api_key + self._url = f"{base_url.rstrip('/')}/rerank" + self._timeout = timeout + self._max_retries = max_retries + self._batch_size = batch_size + self._semaphore = asyncio.Semaphore(max_concurrent) + + async def rerank( + self, + query: str, + documents: Sequence[str], + *, + instruction: str | None = None, + ) -> list[RerankResult]: + """Score every document against ``query``; return sorted desc. + + ``instruction`` is accepted for protocol parity but not transmitted: + the OpenAI-compatible ``/rerank`` endpoint applies the reranker's chat + template (including any task instruction) server-side, so unlike the + DeepInfra completion-style API there is no client-side template to fill. + """ + if not documents: + return [] + + chunks: list[tuple[int, list[str]]] = [ + (offset, list(documents[offset : offset + self._batch_size])) + for offset in range(0, len(documents), self._batch_size) + ] + chunk_results = await asyncio.gather( + *(self._score_chunk(query, docs) for _, docs in chunks) + ) + scored: list[RerankResult] = [] + for (offset, _), partial in zip(chunks, chunk_results, strict=True): + scored.extend( + RerankResult(index=offset + r.index, score=r.score) for r in partial + ) + scored.sort(key=lambda r: r.score, reverse=True) + return scored + + async def _score_chunk( + self, query: str, documents: list[str] + ) -> list[RerankResult]: + payload: dict[str, Any] = { + "model": self._model, + "query": query, + "documents": documents, + } + headers: dict[str, str] = {"Content-Type": "application/json"} + if self._api_key: + headers["Authorization"] = f"Bearer {self._api_key}" + + async with self._semaphore: + for attempt in range(self._max_retries + 1): + try: + async with httpx.AsyncClient(timeout=self._timeout) as client: + response = await client.post( + self._url, json=payload, headers=headers + ) + except httpx.HTTPError as exc: + if attempt == self._max_retries: + raise RerankError( + f"vLLM rerank transport failure: {exc}" + ) from exc + continue + + if response.status_code == 200: + return _parse_rerank_results(response.json()) + + if response.status_code >= 500 or response.status_code == 429: + if attempt == self._max_retries: + raise RerankError( + f"vLLM rerank HTTP {response.status_code}: " + f"{response.text[:200]}" + ) + continue + raise RerankError( + f"vLLM rerank HTTP {response.status_code}: {response.text[:200]}" + ) + + raise RerankError(f"vLLM rerank exhausted retries ({self._max_retries})") + + +def _parse_rerank_results(body: dict[str, Any]) -> list[RerankResult]: + items = body.get("results") + if not isinstance(items, list): + raise RerankError(f"vLLM rerank response missing results: {body!r}") + parsed: list[RerankResult] = [] + for item in items: + try: + parsed.append( + RerankResult( + index=int(item["index"]), + score=float(item["relevance_score"]), + ) + ) + except (KeyError, TypeError, ValueError) as exc: + raise RerankError(f"malformed rerank result entry: {item!r}") from exc + return parsed diff --git a/src/everos/component/tokenizer/__init__.py b/src/everos/component/tokenizer/__init__.py new file mode 100644 index 00000000..fec08f3e --- /dev/null +++ b/src/everos/component/tokenizer/__init__.py @@ -0,0 +1,24 @@ +"""Tokenizer provider — sync app-layer tokenisation for BM25 indexing. + +Public surface: + +- :class:`Tokenizer` — Protocol every provider satisfies. +- :class:`JiebaTokenizer` — default jieba-backed implementation. +- :func:`build_tokenizer` — factory returning the configured tokenizer. + +External usage:: + + from everos.component.tokenizer import build_tokenizer + tk = build_tokenizer() + tokens = tk.tokenize("hello 世界") # ['hello', '世界'] +""" + +from .factory import build_tokenizer as build_tokenizer +from .jieba_provider import JiebaTokenizer as JiebaTokenizer +from .protocol import Tokenizer as Tokenizer + +__all__ = [ + "JiebaTokenizer", + "Tokenizer", + "build_tokenizer", +] diff --git a/src/everos/component/tokenizer/factory.py b/src/everos/component/tokenizer/factory.py new file mode 100644 index 00000000..9b88f755 --- /dev/null +++ b/src/everos/component/tokenizer/factory.py @@ -0,0 +1,17 @@ +"""Factory for the cascade-time tokenizer. + +Single implementation today (``JiebaTokenizer``). Lifting this into a +factory keeps callers (cascade handler) decoupled from the concrete +choice, so swapping to char-bigram / hf tokenizer later is a one-file +change — see ``17_lancedb_tables_design.md`` §2.4.1. +""" + +from __future__ import annotations + +from .jieba_provider import JiebaTokenizer +from .protocol import Tokenizer + + +def build_tokenizer() -> Tokenizer: + """Build the default tokenizer (``JiebaTokenizer``).""" + return JiebaTokenizer() diff --git a/src/everos/component/tokenizer/jieba_provider.py b/src/everos/component/tokenizer/jieba_provider.py new file mode 100644 index 00000000..ab083aa9 --- /dev/null +++ b/src/everos/component/tokenizer/jieba_provider.py @@ -0,0 +1,141 @@ +"""Jieba-based tokenizer — covers CJK + English mixed content. + +Uses ``jieba.cut_for_search`` (search-mode segmentation: yields both the +greedy max-match segment and its finer sub-segments for compound CJK +words). Same mode as the legacy enterprise keyword-search path uses on +the query side — keeping cascade write and search query symmetric is +the hard contract for BM25 recall to work. + +After segmentation we drop: + +* whitespace / empty tokens (so the join-on-space output stays clean), +* tokens shorter than ``min_token_length`` (default 2 — same threshold + enterprise's ``filter_stopwords(min_length=2)`` uses; single-char + fragments mostly hurt BM25 precision), +* tokens in a small bilingual stopword set (Chinese function words + + English articles / prepositions / aux verbs). +""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Final + +import jieba + +# Small bilingual stopword set. Intentionally tight (not a full +# Chinese stopword list) so the behaviour is predictable; callers +# tuning recall can subclass / extend. +_DEFAULT_STOPWORDS: Final[frozenset[str]] = frozenset( + { + # English — articles / prepositions / aux verbs that dominate BM25 + # idf-noise but add no recall value. + "the", + "a", + "an", + "and", + "or", + "but", + "if", + "of", + "to", + "in", + "on", + "at", + "by", + "for", + "with", + "as", + "is", + "are", + "was", + "were", + "be", + "been", + "being", + "do", + "does", + "did", + "has", + "have", + "had", + "this", + "that", + "these", + "those", + "it", + "its", + # Chinese — function words / particles. ``cut_for_search`` emits + # these as single-char tokens anyway, and the min_length=2 floor + # would drop most; listing them explicitly makes the intent clear + # and is a no-op when min_length filtering also kicks in. + "的", + "了", + "和", + "是", + "在", + "我", + "你", + "他", + "她", + "它", + "也", + "都", + "就", + "还", + "或", + "及", + "与", + "对", + "把", + "被", + "有", + "没", + "不", + "啊", + "吗", + "呢", + "吧", + "哦", + } +) + +_DEFAULT_MIN_TOKEN_LENGTH: Final[int] = 2 + + +class JiebaTokenizer: + """Tokenizer that calls into ``jieba.cut_for_search`` and filters.""" + + def __init__( + self, + *, + min_token_length: int = _DEFAULT_MIN_TOKEN_LENGTH, + extra_stopwords: frozenset[str] | None = None, + ) -> None: + # Touching ``jieba.initialize()`` here would force eager dict load + # at import time and balloon test-collection latency. ``jieba.cut*`` + # lazy-loads on first call instead. + self._min_len = min_token_length + self._stopwords = ( + _DEFAULT_STOPWORDS | extra_stopwords + if extra_stopwords + else _DEFAULT_STOPWORDS + ) + + def tokenize(self, text: str) -> list[str]: + if not text: + return [] + out: list[str] = [] + for raw in jieba.cut_for_search(text): + tok = raw.strip().lower() + if not tok or tok.isspace(): + continue + if len(tok) < self._min_len: + continue + if tok in self._stopwords: + continue + out.append(tok) + return out + + def tokenize_batch(self, texts: Sequence[str]) -> list[list[str]]: + return [self.tokenize(t) for t in texts] diff --git a/src/everos/component/tokenizer/protocol.py b/src/everos/component/tokenizer/protocol.py new file mode 100644 index 00000000..58b21dfd --- /dev/null +++ b/src/everos/component/tokenizer/protocol.py @@ -0,0 +1,37 @@ +"""Tokenizer protocol. + +App-layer tokenisation gates every BM25-indexed field in LanceDB +(``17_lancedb_tables_design.md`` §2.4.1): the source surface form lives +in ```` while the space-joined token stream lives in +``_tokens``, and the FTS index reads only the latter using a +whitespace tokenizer. Keeping the tokenizer decision in the app layer +means it can swap (jieba → unigram → hf) without re-indexing or +touching LanceDB schemas. + +The protocol is sync — every concrete tokenizer in scope today (jieba, +char-bigram, regex word-split) is CPU-bound with no IO, so an async +wrapper would just shuffle work onto the event loop. If a future GPU +or remote tokenizer needs IO it should add an async method explicitly. +""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Protocol, runtime_checkable + + +@runtime_checkable +class Tokenizer(Protocol): + """Sync tokeniser contract used by the cascade handler.""" + + def tokenize(self, text: str) -> list[str]: + """Return the ordered token list for ``text``. + + Implementations must drop empty / whitespace-only tokens so the + resulting space-joined string never carries adjacent spaces. + """ + ... + + def tokenize_batch(self, texts: Sequence[str]) -> list[list[str]]: + """Tokenise many strings, preserving input order.""" + ... diff --git a/src/everos/component/utils/__init__.py b/src/everos/component/utils/__init__.py new file mode 100644 index 00000000..e9cada50 --- /dev/null +++ b/src/everos/component/utils/__init__.py @@ -0,0 +1,22 @@ +"""Common utilities (datetime, tokenization, etc.). + +Public API: + from everos.component.utils.datetime import ( + UtcDatetime, + ensure_utc, + from_iso_format, + from_timestamp, + get_now_with_timezone, + get_utc_now, + to_date_str, + to_display_tz, + to_iso_format, + to_timestamp_ms, + today_with_timezone, + ) + from everos.component.utils.tokenize import ( + tokens_for_index, + tokens_for_query, + join_tokens, + ) +""" diff --git a/src/everos/component/utils/datetime.py b/src/everos/component/utils/datetime.py new file mode 100644 index 00000000..feccfecf --- /dev/null +++ b/src/everos/component/utils/datetime.py @@ -0,0 +1,263 @@ +"""Timezone-aware datetime helpers. + +EverOS follows a **two-zone discipline**: + +* **Storage** (SQLite + LanceDB) is always UTC. Use :func:`get_utc_now` + for any ``default_factory`` / write-path timestamp; if you accept a + ``datetime`` from a caller, normalise with :func:`ensure_utc` before + it crosses the persistence boundary. +* **Display** (markdown frontmatter, HTTP API response, date buckets for + daily-log filenames) uses the configured "display timezone" from + :attr:`everos.config.MemorySettings.timezone` (``EVEROS_MEMORY__TIMEZONE``). + Use :func:`get_now_with_timezone` / :func:`today_with_timezone` / + :func:`to_display_tz` here. + +The display timezone also serves as the **fallback timezone for naive +input**: if a caller hands us a string / datetime without offset (e.g. +a hand-written ISO timestamp), :func:`from_iso_format` attaches the +display timezone before further processing — that matches a human's +intuition ("if I didn't say a zone, you should assume my zone"). + +Never call :func:`datetime.datetime.now` / +:func:`datetime.datetime.utcnow` directly — see +:doc:`.claude/rules/datetime-handling`. + +Cache invalidation in tests:: + + load_settings.cache_clear() + _display_tz.cache_clear() +""" + +from __future__ import annotations + +import datetime as _dt +from functools import cache +from typing import Annotated +from zoneinfo import ZoneInfo + +from pydantic import AfterValidator + +_MS_THRESHOLD = 1e12 # ts >= this is treated as milliseconds + + +@cache +def _display_tz() -> _dt.tzinfo: + """Resolve the configured **display timezone** (cached). + + Reads :attr:`everos.config.MemorySettings.timezone`; that field + validates the name with :class:`zoneinfo.ZoneInfo` at load time, so + by the time we reach here the value is guaranteed valid. This + timezone governs: + + 1. ISO output rendered in markdown / API responses. + 2. The fallback zone attached to naive-input datetimes. + + It does **not** govern storage — see :func:`get_utc_now`. + """ + # Lazy import to avoid pulling in pydantic-settings at module load. + from everos.config import load_settings + + return ZoneInfo(load_settings().memory.timezone) + + +def get_utc_now() -> _dt.datetime: + """Return the current time as a UTC-aware datetime. + + Use for any **storage** write-path (SQLite ``default_factory``, + LanceDB row construction, OME event ``ts``, any internal "when + did this happen" record). Independent of the display timezone — a + new deployment that switches ``EVEROS_MEMORY__TIMEZONE`` will not + misalign existing rows. + + Display-side code should use :func:`get_now_with_timezone` instead, + or render via :func:`to_display_tz`. + """ + return _dt.datetime.now(tz=_dt.UTC) + + +def get_now_with_timezone() -> _dt.datetime: + """Return the current time in the **display timezone** (configured). + + Use for **display** write-paths only — markdown frontmatter values, + daily-log date buckets, places where a human will see the literal + string. The returned datetime carries the display timezone offset + so ``.isoformat()`` produces something like + ``2026-05-29T14:00:00+08:00``. + + For storage / internal "when did this happen" timestamps use + :func:`get_utc_now` instead — display timezone must not bleed into + persisted rows. + """ + return _dt.datetime.now(tz=_display_tz()) + + +def today_with_timezone() -> _dt.date: + """Return today's date in the **display timezone**. + + Use this anywhere a *date bucket* is needed (e.g. daily-log file + boundaries) — it normalises ``get_now_with_timezone().date()`` so + the timezone fallback rules are applied consistently. + """ + return get_now_with_timezone().date() + + +def ensure_utc(d: _dt.datetime | None) -> _dt.datetime | None: + """Normalise any datetime to UTC at the **storage boundary**. + + Semantics: + + * ``None`` → ``None`` (nullable-column convenience: lets callers + pipe ``ensure_utc(row.last_attempt_at)`` without an outer guard). + * Aware input → ``astimezone(UTC)``. + * **Naive input → assume UTC** (attach ``tzinfo=UTC``); no + display-tz fallback. + + Why naive→UTC rather than naive→display→UTC? Every caller of this + function sits at the storage boundary, and the dominant naive + source is SQLite reads: SQLAlchemy strips tz on write so what + comes back is a naive value whose bytes are UTC. Treating those + naive reads as display-tz would drift by the configured offset on + every round trip — exactly the bug Q2 prevents. + + Caller-supplied datetimes that may genuinely be naive in display + tz (e.g. ISO strings from HTTP request bodies that omitted the + offset) should be funnelled through :func:`from_iso_format` first, + which encodes the "if you didn't say a zone, assume your zone" + rule. The aware result then passes through ``ensure_utc`` as a + pure ``astimezone(UTC)``. + + Use the :data:`UtcDatetime` ``Annotated`` type to apply this + automatically on Pydantic model fields. + """ + if d is None: + return None + if d.tzinfo is None: + return d.replace(tzinfo=_dt.UTC) + return d.astimezone(_dt.UTC) + + +def to_display_tz(d: _dt.datetime | None) -> _dt.datetime | None: + """Convert a datetime to the **display timezone** (configured). + + Used at the **response render boundary**: any datetime leaving the + system through an API response or markdown body passes through + here so the user sees their wall-clock time with the matching + ``+HH:MM`` offset. + + * ``None`` → ``None`` (nullable-column convenience). + * Naive input is treated as already display-tz local (the fallback + rule) — attach the zone and return as-is. + * Aware input is ``astimezone(...)``-d to the display tz. + """ + if d is None: + return None + if d.tzinfo is None: + return d.replace(tzinfo=_display_tz()) + return d.astimezone(_display_tz()) + + +UtcDatetime = Annotated[_dt.datetime, AfterValidator(ensure_utc)] +"""Pydantic-friendly ``datetime`` type that normalises to UTC. + +Apply to any SQLModel / Pydantic ``datetime`` field that maps to a +storage column. Both INSERT default values and post-read values pass +through :func:`ensure_utc`, so SQLite's tz-stripping behaviour is +neutralised: rows go in as UTC and come out as UTC-aware. + +Usage:: + + from everos.component.utils.datetime import UtcDatetime, get_utc_now + + class MyRow(BaseTable, table=True): + happened_at: UtcDatetime = Field(default_factory=get_utc_now) +""" + + +def from_timestamp(ts: int | float) -> _dt.datetime: + """Parse a Unix timestamp into a timezone-aware datetime. + + Auto-detects seconds vs milliseconds: values ``>= 1e12`` are treated as + milliseconds. Returned datetime is in the default timezone. + """ + seconds = ts / 1000.0 if ts >= _MS_THRESHOLD else float(ts) + return _dt.datetime.fromtimestamp(seconds, tz=_display_tz()) + + +def from_iso_format(value: _dt.datetime | int | float | str) -> _dt.datetime: + """Parse a value into a timezone-aware datetime (strict). + + Accepted inputs: + * ``datetime`` — naive values get the default timezone attached. + * ``int`` / ``float`` — Unix timestamp (auto-detect seconds vs ms). + * ``str`` — ISO-8601, including ``"Z"`` suffix for UTC. + + Raises: + TypeError: On unsupported input type. + ValueError: On malformed string / negative timestamp. + """ + if isinstance(value, _dt.datetime): + if value.tzinfo is None: + return value.replace(tzinfo=_display_tz()) + return value + if isinstance(value, bool): # bool is an int subclass — reject explicitly + raise TypeError("from_iso_format does not accept bool") + if isinstance(value, int | float): + return from_timestamp(value) + if isinstance(value, str): + s = value.strip() + # Python's fromisoformat accepts "+HH:MM" but not the "Z" suffix; map it. + if s.endswith("Z"): + s = s[:-1] + "+00:00" + parsed = _dt.datetime.fromisoformat(s) + if parsed.tzinfo is None: + parsed = parsed.replace(tzinfo=_display_tz()) + return parsed + raise TypeError( + f"from_iso_format: unsupported type {type(value).__name__}; " + "expected datetime / int / float / str" + ) + + +def to_iso_format( + value: _dt.datetime | int | float | str | None, +) -> str | None: + """Render a value as an ISO-8601 string (timezone-aware). + + Accepted inputs: + * ``None`` — returns ``None`` (nullable column convenience). + * ``datetime`` — rendered as-is (must already be tz-aware). + * ``int`` / ``float`` — interpreted via :func:`from_timestamp`. + * ``str`` — re-validated through :func:`from_iso_format`. + """ + if value is None: + return None + if isinstance(value, _dt.datetime): + return value.isoformat() + if isinstance(value, bool): # bool is an int subclass + raise TypeError("to_iso_format does not accept bool") + if isinstance(value, int | float): + return from_timestamp(value).isoformat() + if isinstance(value, str): + if not value: + return None + return from_iso_format(value).isoformat() + raise TypeError( + f"to_iso_format: unsupported type {type(value).__name__}; " + "expected datetime / int / float / str / None" + ) + + +def to_date_str(d: _dt.datetime | None) -> str | None: + """Render the date portion of a datetime as ``YYYY-MM-DD``. + + Accepts ``None`` for nullable database columns. When the input is + already a :class:`datetime.date`, call ``d.isoformat()`` directly. + """ + if d is None: + return None + return d.date().isoformat() + + +def to_timestamp_ms(d: _dt.datetime) -> int: + """Convert a datetime to a Unix timestamp in milliseconds.""" + return int(d.timestamp() * 1000) diff --git a/src/everos/config/__init__.py b/src/everos/config/__init__.py new file mode 100644 index 00000000..c7fe8349 --- /dev/null +++ b/src/everos/config/__init__.py @@ -0,0 +1,37 @@ +"""Configuration data and Settings schema. + +Public API: + from everos.config import ( + Settings, MemorySettings, SqliteSettings, LanceDBSettings, + LLMSettings, EmbeddingSettings, RerankSettings, + BoundaryDetectionSettings, + load_settings, + ) + +Distinct from ``everos.component.config`` (which is a *capability* — +loader / merger / env reader). +""" + +from .settings import BoundaryDetectionSettings as BoundaryDetectionSettings +from .settings import EmbeddingSettings as EmbeddingSettings +from .settings import LanceDBSettings as LanceDBSettings +from .settings import LLMSettings as LLMSettings +from .settings import MemorySettings as MemorySettings +from .settings import MultimodalSettings as MultimodalSettings +from .settings import RerankSettings as RerankSettings +from .settings import Settings as Settings +from .settings import SqliteSettings as SqliteSettings +from .settings import load_settings as load_settings + +__all__ = [ + "BoundaryDetectionSettings", + "EmbeddingSettings", + "LLMSettings", + "LanceDBSettings", + "MemorySettings", + "MultimodalSettings", + "RerankSettings", + "Settings", + "SqliteSettings", + "load_settings", +] diff --git a/src/everos/config/default.toml b/src/everos/config/default.toml new file mode 100644 index 00000000..1bea9a5a --- /dev/null +++ b/src/everos/config/default.toml @@ -0,0 +1,137 @@ +# everos default configuration. +# +# Lookup order (later overrides earlier): +# 1. This file (shipped defaults; lowest priority) +# 2. ~/.everos/config.toml — user-level overrides (optional; +# path is overridable via EVEROS_CONFIG_FILE) +# 3. .env file in the working directory +# 4. Environment variables — EVEROS_
__ +# e.g. EVEROS_SQLITE__BUSY_TIMEOUT_MS=10000 +# 5. Programmatic init args (highest priority) +# +# `null` (omitted in TOML) means "use the Pydantic default declared in code". + +[memory] +# memory-root is the single directory holding all persisted memory. +# `~` is expanded; the path is resolved when MemoryRoot is constructed. +root = "~/.everos" +# Effective timezone for date buckets and timestamps. Drives +# component.utils.datetime; this is the SOLE source — OS `TZ` is not +# read. Override via `EVEROS_MEMORY__TIMEZONE` env var if needed. +timezone = "UTC" + +[api] +# HTTP server bind for ``everos server start``. Default ``127.0.0.1`` +# keeps the API on loopback only — EverOS ships no built-in auth (see +# SECURITY.md threat model). Only set ``host = "0.0.0.0"`` after you +# have placed your own gateway / auth layer in front of the server. +# Override via EVEROS_API__HOST and EVEROS_API__PORT. +host = "127.0.0.1" +port = 8000 + +[sqlite] +# PRAGMA journal_mode — WAL is the recommended high-concurrency mode. +journal_mode = "WAL" +# PRAGMA synchronous — NORMAL is safe under WAL and ~2x faster than FULL. +synchronous = "NORMAL" +# PRAGMA foreign_keys — must be explicitly enabled per connection. +foreign_keys = true +# PRAGMA temp_store — MEMORY keeps query intermediates in RAM (no IO impact +# on durability — only affects sort/group/temp-table calculation buffers). +temp_store = "MEMORY" +# PRAGMA busy_timeout — milliseconds to wait on a locked DB before erroring. +busy_timeout_ms = 5000 +# PRAGMA journal_size_limit — cap WAL/journal at ~64 MB. +journal_size_limit_bytes = 67108864 +# PRAGMA cache_size — KB of page cache (per connection). +cache_size_kb = 2048 + +[lancedb] +# Read consistency interval in seconds. +# omitted / null -> no consistency check (fastest reads) +# 0 -> strict (every read checks updates) +# >0 -> eventual (interval seconds between checks) +# Uncomment to override: +# read_consistency_seconds = 5.0 + +[llm] +# Provider-agnostic OpenAI-protocol client config. Override via env: +# EVEROS_LLM__MODEL, EVEROS_LLM__API_KEY, EVEROS_LLM__BASE_URL +# Or via a ``.env`` file next to the project root (auto-loaded). +model = "gpt-4o-mini" +# api_key = "" +# base_url = "" + +[multimodal] +# Independent LLM for multimodal parsing (everalgo-parser); must accept +# image / pdf / audio image_url parts. Override via env: +# EVEROS_MULTIMODAL__MODEL, EVEROS_MULTIMODAL__API_KEY, EVEROS_MULTIMODAL__BASE_URL +model = "google/gemini-3-flash-preview" +max_concurrency = 4 +# api_key = "" +# base_url = "" +# file:// content-item support (read locally by EverOS, not everalgo). +# file_uri_allow_dirs: empty = allow any readable file (local-first default); +# list base dirs to confine reads when the API is exposed. +# file_uri_allow_dirs = ["/srv/uploads"] +# file_uri_max_bytes = 52428800 # 50 MiB cap per file:// asset + +[embedding] +# OpenAI-compatible embedding endpoint. Override via env: +# EVEROS_EMBEDDING__MODEL, EVEROS_EMBEDDING__API_KEY, EVEROS_EMBEDDING__BASE_URL +# model / api_key / base_url have no shipped defaults — must be set +# (env or user toml) before the embedding capability is used. +# model = "Qwen/Qwen3-Embedding-4B" +# api_key = "" +# base_url = "https://api.example.com/v1" +timeout_seconds = 30.0 +max_retries = 3 +batch_size = 10 +max_concurrent = 5 + +[rerank] +# Rerank provider. Override via env: +# EVEROS_RERANK__PROVIDER, EVEROS_RERANK__MODEL, EVEROS_RERANK__API_KEY, +# EVEROS_RERANK__BASE_URL +# `provider` picks the request-shape: +# - "deepinfra" -> POST {base_url}/{model} (DeepInfra inference API) +# - "vllm" -> POST {base_url}/rerank (OpenAI-compat rerank endpoint) +provider = "deepinfra" +# model = "Qwen/Qwen3-Reranker-4B" +# api_key = "" +# base_url = "https://api.deepinfra.com/v1/inference" +timeout_seconds = 30.0 +max_retries = 3 +batch_size = 10 +max_concurrent = 5 + +[boundary_detection] +# Passed through to ``everalgo.BoundaryDetector.adetect``. +hard_token_limit = 65536 +hard_msg_limit = 500 + +[search] +# Vector retrieval strategy when SearchMethod.VECTOR is selected. +# "maxsim_atomic" (default): ANN over atomic_fact.vector (pool=top_k*20), +# max-pool the per-fact cosine by parent memcell, then reverse-resolve +# to episode rows. MaxSim over atomic facts; +0.6pp over the legacy +# episode-vector path on LoCoMo, at the cost of one extra LanceDB scan. +# "episode": single-vector ANN over episode.vector (legacy path). +# Override via EVEROS_SEARCH__VECTOR_STRATEGY. +vector_strategy = "maxsim_atomic" + +[memorize] +# Conversation mode. Selects the boundary detector and which pipelines run: +# "chat" -> BoundaryDetector + user_memory only +# "agent" -> AgentBoundaryDetector + user_memory + agent_memory +# A single service process serves one mode at a time; switching mode +# requires a restart. Override via EVEROS_MEMORIZE__MODE. +mode = "agent" + +# Maximum wall-clock for one memorize() invocation while holding the +# per-session lock. On timeout the outer asyncio.timeout cancels the call +# and the lock auto-releases so subsequent concurrent /add on the same +# session aren't deadlocked. Covers boundary LLM + memcell writes + +# synchronous portion of pipeline dispatch. +# Override via EVEROS_MEMORIZE__SESSION_LOCK_TIMEOUT_SECONDS. +session_lock_timeout_seconds = 360.0 diff --git a/src/everos/config/default_ome.toml b/src/everos/config/default_ome.toml new file mode 100644 index 00000000..5094d64f --- /dev/null +++ b/src/everos/config/default_ome.toml @@ -0,0 +1,64 @@ +# everos OME (Offline Memory Engine) — per-strategy overrides. +# +# This file is materialised at ``/ome.toml`` by +# ``MemoryRoot.ensure()`` on first server start. Edit it to toggle +# individual strategies or tweak their gate / retry / cron without +# restarting the server; the engine watches this file and hot-reloads +# changes within ~2 seconds. Re-running ``ensure()`` will NOT overwrite +# your edits — the file is only materialised when absent. +# +# Overrides are partial: only the keys you set replace the in-code +# defaults; omitted keys keep each strategy's coded value. Unknown +# keys (typos) raise StartupValidationError, so you cannot silently +# misconfigure a strategy. +# +# Per-strategy schema (StrategyOverride): +# enabled = bool # disable a strategy entirely +# max_retries = int >= 0 # re-fire on failure +# cron = str # replace the @cron(...) trigger +# idle_seconds = int > 0 # replace @idle(...) idle window +# scan_interval_seconds = int > 0 # paired with idle_seconds; must be <= idle/2 +# [strategies..gate] # only valid on @counter-gated strategies +# threshold = int > 0 # counter trigger threshold +# cooldown_seconds = int >= 0 # min seconds between fires +# event_field = str # dispatch field for counter increment + +# ── User-memory pipeline ──────────────────────────────────────────────── + +# Atomic fact extraction (runs per memcell). Disable to skip fact mining. +# [strategies.extract_atomic_facts] +# enabled = true + +# Foresight extraction (runs per memcell). Heavy LLM call — common to +# disable in evaluation / benchmark runs. +# [strategies.extract_foresight] +# enabled = false + +# Profile clustering trigger (counter-gated; fires once N user memcells +# accumulate). Lower the threshold to cluster more aggressively. +# [strategies.trigger_profile_clustering] +# enabled = true +# [strategies.trigger_profile_clustering.gate] +# threshold = 5 + +# User-profile extraction (runs after clustering trigger fires). Common +# to disable in evaluation runs where ground-truth profiles aren't measured. +# [strategies.extract_user_profile] +# enabled = false + +# ── Agent-memory pipeline ─────────────────────────────────────────────── + +# Agent case extraction (runs per agent memcell). One per tool call cycle. +# [strategies.extract_agent_case] +# enabled = true + +# Skill clustering trigger (counter-gated; fires once N agent cases +# accumulate per agent). +# [strategies.trigger_skill_clustering] +# enabled = true +# [strategies.trigger_skill_clustering.gate] +# threshold = 5 + +# Agent skill extraction (runs after skill clustering trigger fires). +# [strategies.extract_agent_skill] +# enabled = true diff --git a/methods/EverCore/evaluation/data/evermembench/.gitkeep b/src/everos/config/prompt_slots/.gitkeep similarity index 100% rename from methods/EverCore/evaluation/data/evermembench/.gitkeep rename to src/everos/config/prompt_slots/.gitkeep diff --git a/methods/EverCore/data_format/conversation/__init__.py b/src/everos/config/prompt_slots/__init__.py old mode 100755 new mode 100644 similarity index 100% rename from methods/EverCore/data_format/conversation/__init__.py rename to src/everos/config/prompt_slots/__init__.py diff --git a/src/everos/config/prompt_slots/boundary_detection.yaml b/src/everos/config/prompt_slots/boundary_detection.yaml new file mode 100644 index 00000000..9887a876 --- /dev/null +++ b/src/everos/config/prompt_slots/boundary_detection.yaml @@ -0,0 +1,20 @@ +# Custom prompt slot for BoundaryDetector.adetect. +# +# Default behaviour +# Leave this slot disabled (``enabled: false``). The pipeline will pass +# ``prompt=None`` through to algo, which falls back to the everalgo +# bundled default prompt — see: +# ~/everalgo/packages/everalgo-boundary/src/everalgo/boundary/prompts/en/chat.py +# (constant ``CHAT_BOUNDARY_DETECT_PROMPT_EN``) +# +# To customise +# 1. Read the algo default at the path above; note the required +# placeholders ``{messages}`` and ``{token_count}``. +# 2. Replace the ``template`` body below with your prompt. +# 3. Flip ``enabled`` to ``true``. +# +# When ``enabled: false`` or ``template`` is empty, the pipeline sends +# ``prompt=None`` and the algo default is used (zero override cost). + +enabled: false +template: "" diff --git a/src/everos/config/prompt_slots/episode_extract.yaml b/src/everos/config/prompt_slots/episode_extract.yaml new file mode 100644 index 00000000..56874194 --- /dev/null +++ b/src/everos/config/prompt_slots/episode_extract.yaml @@ -0,0 +1,23 @@ +# Custom prompt slot for EpisodeExtractor.aextract. +# +# Default behaviour +# Leave this slot disabled (``enabled: false``). The pipeline will pass +# ``prompt=None`` through to algo, which falls back to the everalgo +# bundled default prompt — see: +# everalgo/user_memory/prompts/en/episode.py +# (the pipeline calls ``aextract`` with ``sender_id=None``, so the +# whole-memcell ``EPISODE_GENERATION_PROMPT`` is used, not the +# per-user ``USER_EPISODE_GENERATION_PROMPT``) +# +# To customise +# 1. Read the algo default at the path above; note the required +# placeholders ``{conversation_start_time}``, ``{conversation}`` and +# ``{custom_instructions}``. +# 2. Replace the ``template`` body below with your prompt. +# 3. Flip ``enabled`` to ``true``. +# +# When ``enabled: false`` or ``template`` is empty, the pipeline sends +# ``prompt=None`` and the algo default is used (zero override cost). + +enabled: false +template: "" diff --git a/src/everos/config/settings.py b/src/everos/config/settings.py new file mode 100644 index 00000000..98337f47 --- /dev/null +++ b/src/everos/config/settings.py @@ -0,0 +1,403 @@ +"""Application settings. + +Loaded by :func:`load_settings`. Source priority (later wins): + + 1. ``config/default.toml`` (shipped values; lowest priority) + 2. ``~/.everos/config.toml`` (user-level overrides; optional) + 3. ``.env`` file in the working directory (secrets / machine-specific) + 4. ``EVEROS_
__`` environment variables + 5. Init args passed programmatically (highest priority) + +The user-level toml path defaults to ``~/.everos/config.toml``. Override +with the ``EVEROS_CONFIG_FILE`` environment variable. The file is +optional — if it does not exist, the source is silently skipped. + +The settings tree mirrors the TOML structure: ``settings.sqlite.busy_timeout_ms`` +maps to ``[sqlite].busy_timeout_ms`` and to ``EVEROS_SQLITE__BUSY_TIMEOUT_MS``. + +``load_settings`` is ``functools.cache``-d so callers in hot paths (e.g. +:mod:`everos.component.utils.datetime`) don't re-parse the TOML on every +call. Tests that mutate environment variables must call +``load_settings.cache_clear()`` after the mutation to invalidate. +""" + +from __future__ import annotations + +import os +from functools import cache +from pathlib import Path +from typing import Literal +from zoneinfo import ZoneInfo, ZoneInfoNotFoundError + +from pydantic import BaseModel, Field, SecretStr, field_validator +from pydantic_settings import ( + BaseSettings, + PydanticBaseSettingsSource, + SettingsConfigDict, + TomlConfigSettingsSource, +) + +_DEFAULT_TOML_PATH = Path(__file__).parent / "default.toml" +_USER_TOML_ENV_VAR = "EVEROS_CONFIG_FILE" +_DEFAULT_USER_TOML_PATH = Path("~/.everos/config.toml").expanduser() + + +def _resolve_user_toml_path() -> Path: + """Resolve the user-level ``config.toml`` path. + + Defaults to ``~/.everos/config.toml``; override with the + ``EVEROS_CONFIG_FILE`` environment variable. + """ + override = os.environ.get(_USER_TOML_ENV_VAR) + return Path(override).expanduser() if override else _DEFAULT_USER_TOML_PATH + + +class MemorySettings(BaseModel): + """memory-root configuration.""" + + root: Path = Path("~/.everos") + timezone: str = "UTC" + """Effective timezone for date buckets and timestamps. + + Default ``"UTC"``. Override via ``[memory] timezone = "..."`` in + TOML or ``EVEROS_MEMORY__TIMEZONE`` env var. Validated against + :class:`zoneinfo.ZoneInfo` at load time, so an invalid name fails + fast (no silent fallback). This is the **sole** source of truth for + the project's effective timezone — the OS ``TZ`` env var is *not* + consulted, keeping the configuration deterministic. + """ + + @field_validator("timezone") + @classmethod + def _validate_timezone(cls, v: str) -> str: + try: + ZoneInfo(v) + except (ZoneInfoNotFoundError, ValueError) as exc: + raise ValueError(f"invalid timezone: {v!r}") from exc + return v + + +class ApiSettings(BaseModel): + """HTTP API server bind configuration. + + Default ``host = "127.0.0.1"`` keeps the server on loopback only, + matching the threat model in ``SECURITY.md``: EverOS ships **no + built-in authentication**, so binding to a routable interface + (``0.0.0.0`` etc.) without your own gateway / auth layer in front + is unsupported. + + Env binding: + EVEROS_API__HOST + EVEROS_API__PORT + """ + + host: str = "127.0.0.1" + port: int = Field(default=8000, ge=1, le=65535) + + +class SqliteSettings(BaseModel): + """SQLite tunables applied as PRAGMAs on every new connection.""" + + journal_mode: Literal["WAL", "DELETE", "MEMORY", "OFF", "TRUNCATE", "PERSIST"] = ( + "WAL" + ) + synchronous: Literal["FULL", "NORMAL", "OFF", "EXTRA"] = "NORMAL" + foreign_keys: bool = True + temp_store: Literal["DEFAULT", "FILE", "MEMORY"] = "MEMORY" + busy_timeout_ms: int = Field(default=5000, ge=0) + journal_size_limit_bytes: int = Field(default=64 * 1024 * 1024, ge=0) + cache_size_kb: int = Field(default=2048, ge=0) + + +class LLMSettings(BaseModel): + """LLM client configuration. + + Read by the service layer when lazily constructing the LLM client + handed to algo extractors. Provider-agnostic field names — the + project follows the OpenAI API protocol so any OpenAI-compatible + endpoint plugs in via ``base_url``. + + Env binding (via parent ``Settings``): + EVEROS_LLM__MODEL + EVEROS_LLM__API_KEY + EVEROS_LLM__BASE_URL + """ + + model: str = "gpt-4o-mini" + api_key: SecretStr | None = None + base_url: str | None = None + + +class MultimodalSettings(BaseModel): + """Multimodal parsing LLM config (everalgo-parser). + + Flat section mirroring ``[llm]``. The model must accept multimodal + ``image_url`` parts (image / pdf / audio); it is kept independent from + the main ``[llm]`` so parsing can target a vision/audio-capable + endpoint without affecting boundary / extraction. + + Env binding (via parent ``Settings``): + EVEROS_MULTIMODAL__MODEL + EVEROS_MULTIMODAL__API_KEY + EVEROS_MULTIMODAL__BASE_URL + EVEROS_MULTIMODAL__MAX_CONCURRENCY + EVEROS_MULTIMODAL__FILE_URI_ALLOW_DIRS + EVEROS_MULTIMODAL__FILE_URI_MAX_BYTES + """ + + model: str = "google/gemini-3-flash-preview" + api_key: SecretStr | None = None + base_url: str | None = None + max_concurrency: int = 4 + + # ``file://`` content-item support (read locally by EverOS, not everalgo). + file_uri_allow_dirs: list[str] = [] + """Allowlisted base dirs for ``file://`` uris. Empty = allow any readable + file (local-first default); set to confine reads when the API is exposed.""" + file_uri_max_bytes: int = 50 * 1024 * 1024 + """Max size (bytes) of a ``file://`` asset; larger files are rejected.""" + + +class EmbeddingSettings(BaseModel): + """Embedding client configuration. + + OpenAI-compatible embedding endpoint. ``model`` / ``api_key`` / + ``base_url`` are required at runtime when the embedding capability + is enabled; the runtime knobs (``timeout`` etc.) have sensible + defaults. + + Env binding: + EVEROS_EMBEDDING__MODEL + EVEROS_EMBEDDING__API_KEY + EVEROS_EMBEDDING__BASE_URL + EVEROS_EMBEDDING__TIMEOUT_SECONDS + EVEROS_EMBEDDING__MAX_RETRIES + EVEROS_EMBEDDING__BATCH_SIZE + EVEROS_EMBEDDING__MAX_CONCURRENT + """ + + model: str | None = None + api_key: SecretStr | None = None + base_url: str | None = None + timeout_seconds: float = Field(default=30.0, gt=0) + max_retries: int = Field(default=3, ge=0) + batch_size: int = Field(default=10, ge=1) + max_concurrent: int = Field(default=5, ge=1) + + +class RerankSettings(BaseModel): + """Rerank client configuration. + + Unlike LLM / embedding (single OpenAI-compatible shape), rerank API + schemas differ between providers — DeepInfra uses ``POST {base_url}/ + {model}`` with a custom body, vLLM uses ``POST {base_url}/rerank`` + with ``{model, query, documents}``. ``provider`` picks which client + implementation the factory builds. + + Env binding: + EVEROS_RERANK__PROVIDER + EVEROS_RERANK__MODEL + EVEROS_RERANK__API_KEY + EVEROS_RERANK__BASE_URL + EVEROS_RERANK__TIMEOUT_SECONDS + EVEROS_RERANK__MAX_RETRIES + EVEROS_RERANK__BATCH_SIZE + EVEROS_RERANK__MAX_CONCURRENT + """ + + provider: Literal["deepinfra", "vllm"] = "deepinfra" + model: str | None = None + api_key: SecretStr | None = None + base_url: str | None = None + timeout_seconds: float = Field(default=30.0, gt=0) + max_retries: int = Field(default=3, ge=0) + batch_size: int = Field(default=10, ge=1) + max_concurrent: int = Field(default=5, ge=1) + + +class BoundaryDetectionSettings(BaseModel): + """Hard limits passed through to ``everalgo`` BoundaryDetector.""" + + hard_token_limit: int = Field(default=65536, ge=1) + hard_msg_limit: int = Field(default=500, ge=1) + + +class MemorizeSettings(BaseModel): + """Memorize use-case configuration. + + ``mode`` selects which boundary detector runs and which pipelines are + dispatched. A service process serves one mode at a time; toggling + requires a restart. + + - ``"chat"`` -> ``everalgo.user_memory.BoundaryDetector`` and only the + user-memory pipeline runs. + - ``"agent"`` -> ``everalgo.agent_memory.AgentBoundaryDetector`` and + both user-memory + agent-memory pipelines run. + + ``session_lock_timeout_seconds`` caps how long one ``memorize()`` + invocation can hold the per-session lock. Covers boundary LLM call + + memcell DB writes + (synchronous portion of) pipeline dispatch. Stops + a stuck LLM from deadlocking subsequent concurrent calls on the same + session_id: on timeout the outer ``asyncio.timeout`` cancels the task + and the lock auto-releases. + + Env binding: + EVEROS_MEMORIZE__MODE + EVEROS_MEMORIZE__SESSION_LOCK_TIMEOUT_SECONDS + """ + + mode: Literal["chat", "agent"] = "agent" + session_lock_timeout_seconds: float = Field(default=360.0, gt=0) + + +class SearchSettings(BaseModel): + """Search-pipeline policy knobs. + + ``vector_strategy`` selects the read path taken by + ``SearchMethod.VECTOR``: + + - ``"maxsim_atomic"`` (default) — ANN over ``atomic_fact.vector`` + (recall pool ``top_k * 20``, capped at 2000), max-pool the per-fact + cosine by parent memcell, then reverse-resolve the top memcells back + to episode rows. MaxSim over atomic facts; trades one extra LanceDB + scan for finer-grained semantic match on long episodes. + - ``"episode"`` — single-vector ANN over ``episode.vector`` (one vector + per episode = the embedded Content section). The legacy path; kept + so deployments can opt out via env. + + Env binding: + EVEROS_SEARCH__VECTOR_STRATEGY={episode,maxsim_atomic} + """ + + vector_strategy: Literal["episode", "maxsim_atomic"] = "maxsim_atomic" + + +class LanceDBSettings(BaseModel): + """LanceDB tunables. + + ``read_consistency_seconds``: + ``None`` (omitted) → no consistency check (highest performance). + ``0`` → strict consistency (every read). + ``>0`` → eventual (interval between checks). + + ``index_cache_size_bytes``: + Upper bound on LanceDB's global *index* cache (``GlobalIndexCache`` + in lance crate). Each cached entry is one opened FTS / vector / + scalar index reader and **holds the file descriptors of its on-disk + ``_indices//...`` files**. + + LanceDB's own default is ``None`` (unbounded), which on a long- + running daemon means every new index UUID created by an + ``optimize()`` call adds a fresh reader to the cache, and its + FDs are never released — they leak monotonically until + ``EMFILE`` (os error 24). Verified locally: 30 optimize cycles + take FD usage from 0 to ~960 against macOS's default ``ulimit -n`` + of 256 / Linux's 1024. + + Setting a byte cap turns the cache into a real LRU: when it + exceeds the cap, the oldest readers are dropped, Rust ``Drop`` + runs ``close(fd)``, and the FD pressure resolves itself. + + Cap → steady-state FD upper bound (measured under 30 add+optimize + cycles with the real ``Episode`` schema and 100-query stress): + + =========== ================= =================== + cap FD upper bound query latency (100q) + =========== ================= =================== + ``2 MB`` ~45 ~5 ms + ``4 MB`` ~52 ~3 ms + ``8 MB`` ~140 ~2.4 ms + ``16 MB`` ~290 ~2.3 ms ← default + ``32 MB`` ~630 ~1.4 ms + ``unbound`` >960 (leaks) ~1.3 ms + =========== ================= =================== + + EverOS's measured steady-state working set after a 12 h + ``rebuild_indexes`` cycle is ~50-100 readers / 3-6 MB resident + (5 tables × ~7 BM25 columns × ~10 part_N entries each), so + ``16 MB`` gives ~3× headroom for burst traffic and stale-but-not- + yet-evicted readers, while the FD ceiling (~290) stays well below + common ulimits (macOS default 256 needs ``ulimit -n 1024`` first; + Linux default 1024 is fine out of the box). + + Override via ``EVEROS_LANCEDB__INDEX_CACHE_SIZE_BYTES`` if your + working set is much larger (heavier table count or much wider + indexes) or if you hit a tighter ``ulimit -n`` (containers / dev + boxes). + + Note: the *metadata* cache (``metadata_cache_size_bytes``) is + **not** exposed — experiment showed it caches in-memory parsed + manifests / fragment stats with zero impact on FD count; leaving + it unbounded (lancedb default) is fine. + """ + + read_consistency_seconds: float | None = None + index_cache_size_bytes: int = 16 * 1024 * 1024 + + +class Settings(BaseSettings): + """Top-level application settings.""" + + memory: MemorySettings = MemorySettings() + api: ApiSettings = ApiSettings() + sqlite: SqliteSettings = SqliteSettings() + lancedb: LanceDBSettings = LanceDBSettings() + llm: LLMSettings = LLMSettings() + embedding: EmbeddingSettings = EmbeddingSettings() + rerank: RerankSettings = RerankSettings() + boundary_detection: BoundaryDetectionSettings = BoundaryDetectionSettings() + memorize: MemorizeSettings = MemorizeSettings() + search: SearchSettings = SearchSettings() + multimodal: MultimodalSettings = MultimodalSettings() + + model_config = SettingsConfigDict( + env_prefix="EVEROS_", + env_nested_delimiter="__", + env_file=".env", + env_file_encoding="utf-8", + toml_file=_DEFAULT_TOML_PATH, + extra="ignore", + ) + + @classmethod + def settings_customise_sources( + cls, + settings_cls: type[BaseSettings], + init_settings: PydanticBaseSettingsSource, + env_settings: PydanticBaseSettingsSource, + dotenv_settings: PydanticBaseSettingsSource, + file_secret_settings: PydanticBaseSettingsSource, + ) -> tuple[PydanticBaseSettingsSource, ...]: + """Layer TOML sources between env / dotenv and the secret store. + + Order (earlier wins in pydantic-settings): + init_args > env > .env > user_toml > default_toml > secrets + + The user-level toml (default ``~/.everos/config.toml``) is only + registered when the file exists, so the source list stays tight. + """ + sources: list[PydanticBaseSettingsSource] = [ + init_settings, + env_settings, + dotenv_settings, + ] + user_toml_path = _resolve_user_toml_path() + if user_toml_path.is_file(): + sources.append( + TomlConfigSettingsSource(settings_cls, toml_file=user_toml_path) + ) + sources.append(TomlConfigSettingsSource(settings_cls)) + sources.append(file_secret_settings) + return tuple(sources) + + +@cache +def load_settings() -> Settings: + """Load settings from default.toml + environment variables (cached). + + Cached at the module level — every caller sees the same instance until + something explicitly clears the cache (``load_settings.cache_clear()``). + Tests that monkeypatch environment variables must call + ``cache_clear`` after each mutation to pick the new env up. + """ + return Settings() diff --git a/methods/EverCore/demo/__init__.py b/src/everos/core/__init__.py similarity index 100% rename from methods/EverCore/demo/__init__.py rename to src/everos/core/__init__.py diff --git a/methods/EverCore/evaluation/__init__.py b/src/everos/core/context/__init__.py similarity index 100% rename from methods/EverCore/evaluation/__init__.py rename to src/everos/core/context/__init__.py diff --git a/src/everos/core/errors.py b/src/everos/core/errors.py new file mode 100644 index 00000000..ce18ac68 --- /dev/null +++ b/src/everos/core/errors.py @@ -0,0 +1,33 @@ +"""Cross-cutting domain errors surfaced to API callers. + +These live in ``core`` so the ``memory`` layer can raise them and the +``entrypoints`` layer can catch them without crossing the layered import +boundary — ``any -> core`` is the only edge both share (entrypoints must +not import ``memory`` directly). +""" + +from __future__ import annotations + + +class MultimodalError(Exception): + """Base for multimodal-parsing errors meant to reach the caller. + + The API layer maps any ``MultimodalError`` to an aligned + ``{error: {code, message}}`` envelope (HTTP 415). + """ + + +class UnsupportedModalityError(MultimodalError): + """everalgo cannot handle this modality (e.g. video stub, unknown type). + + Wraps everalgo's ``NotImplementedError`` / dispatch ``ValueError`` so the + caller gets a stable, aligned error instead of a raw 500. + """ + + +class MultimodalNotEnabledError(MultimodalError): + """Multimodal capability is not ready. + + Raised when the ``everos[multimodal]`` extra is not installed, or when a + required system dependency (LibreOffice for Office documents) is absent. + """ diff --git a/src/everos/core/lifespan/__init__.py b/src/everos/core/lifespan/__init__.py new file mode 100644 index 00000000..dd75abb8 --- /dev/null +++ b/src/everos/core/lifespan/__init__.py @@ -0,0 +1,27 @@ +"""Application lifespan composition (chassis only). + +This subpackage holds the *generic* lifespan machinery — the +:class:`LifespanProvider` ABC, :func:`build_lifespan` factory, and +chassis-level providers that are independent of any storage backend +(observability metrics, etc.). Concrete storage-backend providers +(SQLite / LanceDB) live next to the entrypoint that composes them +(see :mod:`everos.entrypoints.api.lifespans`) so ``core`` stays free +of concrete-backend imports. + +External usage: + from everos.core.lifespan import ( + LifespanProvider, + MetricsLifespanProvider, + build_lifespan, + ) +""" + +from .base import LifespanProvider as LifespanProvider +from .factory import build_lifespan as build_lifespan +from .metrics_lifespan import MetricsLifespanProvider as MetricsLifespanProvider + +__all__ = [ + "LifespanProvider", + "MetricsLifespanProvider", + "build_lifespan", +] diff --git a/src/everos/core/lifespan/base.py b/src/everos/core/lifespan/base.py new file mode 100644 index 00000000..3426d362 --- /dev/null +++ b/src/everos/core/lifespan/base.py @@ -0,0 +1,30 @@ +"""Lifespan provider abstract base. + +A LifespanProvider is one unit of startup / shutdown work invoked by the +FastAPI lifespan factory. Providers are registered explicitly (no DI +auto-discovery) and executed in ``order`` ascending on startup, reverse +on shutdown. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any + +from fastapi import FastAPI + + +class LifespanProvider(ABC): + """One unit of startup / shutdown work.""" + + def __init__(self, name: str, order: int = 0) -> None: + self.name = name + self.order = order + + @abstractmethod + async def startup(self, app: FastAPI) -> Any: + """Startup hook; return value is stored on ``app.state.lifespan_data[name]``.""" + + @abstractmethod + async def shutdown(self, app: FastAPI) -> None: + """Shutdown hook; called in reverse order during application teardown.""" diff --git a/src/everos/core/lifespan/factory.py b/src/everos/core/lifespan/factory.py new file mode 100644 index 00000000..dfb78ca1 --- /dev/null +++ b/src/everos/core/lifespan/factory.py @@ -0,0 +1,57 @@ +"""Lifespan composition factory. + +Builds a FastAPI lifespan context manager from an explicit list of +LifespanProvider instances. +""" + +from __future__ import annotations + +from collections.abc import AsyncIterator, Callable, Sequence +from contextlib import asynccontextmanager + +from fastapi import FastAPI + +from everos.core.observability.logging import get_logger + +from .base import LifespanProvider + +logger = get_logger(__name__) + + +def build_lifespan( + providers: Sequence[LifespanProvider], +) -> Callable[[FastAPI], AsyncIterator[None]]: + """Compose providers into a FastAPI lifespan context manager. + + Providers are run in ``order`` ascending on startup and reverse on + shutdown. A non-None return value from ``startup`` is stored under + ``app.state.lifespan_data[provider.name]``. + """ + sorted_providers = sorted(providers, key=lambda p: p.order) + + @asynccontextmanager + async def _lifespan(app: FastAPI) -> AsyncIterator[None]: + lifespan_data: dict[str, object] = {} + try: + for provider in sorted_providers: + logger.info( + "lifespan_provider_startup", + name=provider.name, + order=provider.order, + ) + result = await provider.startup(app) + if result is not None: + lifespan_data[provider.name] = result + app.state.lifespan_data = lifespan_data + yield + finally: + for provider in reversed(sorted_providers): + try: + logger.info("lifespan_provider_shutdown", name=provider.name) + await provider.shutdown(app) + except Exception: + logger.exception( + "lifespan_provider_shutdown_failed", name=provider.name + ) + + return _lifespan diff --git a/src/everos/core/lifespan/metrics_lifespan.py b/src/everos/core/lifespan/metrics_lifespan.py new file mode 100644 index 00000000..8c50a275 --- /dev/null +++ b/src/everos/core/lifespan/metrics_lifespan.py @@ -0,0 +1,36 @@ +"""Metrics lifespan provider. + +Confirms the metrics registry is ready and logs that the ``/metrics`` HTTP +endpoint is mounted on the main API. Kept as a placeholder to demonstrate +the lifespan pattern; replace or extend with a standalone metrics server +(e.g. ``prometheus_client.start_http_server`` on a separate port) if you +need to expose metrics on a dedicated socket. +""" + +from __future__ import annotations + +from typing import Any + +from fastapi import FastAPI + +from everos.core.observability.logging import get_logger +from everos.core.observability.metrics import get_metrics_registry + +from .base import LifespanProvider + +logger = get_logger(__name__) + + +class MetricsLifespanProvider(LifespanProvider): + """No-op startup that warms the metrics registry and logs readiness.""" + + def __init__(self, order: int = 5) -> None: + super().__init__(name="metrics", order=order) + + async def startup(self, app: FastAPI) -> Any: + registry = get_metrics_registry() + logger.info("metrics_registry_ready", endpoint="/metrics") + return registry + + async def shutdown(self, app: FastAPI) -> None: + logger.info("metrics_lifespan_shutdown") diff --git a/src/everos/core/middleware/__init__.py b/src/everos/core/middleware/__init__.py new file mode 100644 index 00000000..a773ee60 --- /dev/null +++ b/src/everos/core/middleware/__init__.py @@ -0,0 +1,31 @@ +"""Cross-cutting HTTP middleware components. + +External usage: + from everos.core.middleware import ( + DEFAULT_CORS_ALLOW_CREDENTIALS, + DEFAULT_CORS_ALLOW_HEADERS, + DEFAULT_CORS_ALLOW_METHODS, + DEFAULT_CORS_ORIGINS, + ProfileMiddleware, + PrometheusMiddleware, + global_exception_handler, + ) +""" + +from .cors import DEFAULT_CORS_ALLOW_CREDENTIALS as DEFAULT_CORS_ALLOW_CREDENTIALS +from .cors import DEFAULT_CORS_ALLOW_HEADERS as DEFAULT_CORS_ALLOW_HEADERS +from .cors import DEFAULT_CORS_ALLOW_METHODS as DEFAULT_CORS_ALLOW_METHODS +from .cors import DEFAULT_CORS_ORIGINS as DEFAULT_CORS_ORIGINS +from .global_exception import global_exception_handler as global_exception_handler +from .profile import ProfileMiddleware as ProfileMiddleware +from .prometheus import PrometheusMiddleware as PrometheusMiddleware + +__all__ = [ + "DEFAULT_CORS_ALLOW_CREDENTIALS", + "DEFAULT_CORS_ALLOW_HEADERS", + "DEFAULT_CORS_ALLOW_METHODS", + "DEFAULT_CORS_ORIGINS", + "ProfileMiddleware", + "PrometheusMiddleware", + "global_exception_handler", +] diff --git a/src/everos/core/middleware/cors.py b/src/everos/core/middleware/cors.py new file mode 100644 index 00000000..c5fad3c2 --- /dev/null +++ b/src/everos/core/middleware/cors.py @@ -0,0 +1,12 @@ +"""CORS configuration defaults. + +The CORS middleware itself is FastAPI's stock ``CORSMiddleware``; this module +centralises the default policy values used by the application factory. +""" + +from __future__ import annotations + +DEFAULT_CORS_ALLOW_CREDENTIALS: bool = True +DEFAULT_CORS_ALLOW_HEADERS: list[str] = ["*"] +DEFAULT_CORS_ALLOW_METHODS: list[str] = ["*"] +DEFAULT_CORS_ORIGINS: list[str] = ["*"] diff --git a/src/everos/core/middleware/global_exception.py b/src/everos/core/middleware/global_exception.py new file mode 100644 index 00000000..e6ef4d2c --- /dev/null +++ b/src/everos/core/middleware/global_exception.py @@ -0,0 +1,143 @@ +"""Global exception handler — uniform error envelope per v1 API brief §1. + +Envelope shape (matches the v1 API brief §1 — ``request_id`` at the top +level alongside ``error``; the ``error`` object carries ``code`` / +``message`` plus ops-friendly ``timestamp`` / ``path`` for debugging):: + + { + "request_id": "<32 lowercase hex chars — W3C trace_id format>", + "error": { + "code": "HTTP_ERROR" | "SYSTEM_ERROR", + "message": "", + "timestamp": "", + "path": "" + } + } + +Rules: +- 4xx (DTO / business validation / HTTPException) → ``code="HTTP_ERROR"`` + with the human-readable reason in ``message``. +- 5xx (unhandled exception) → ``code="SYSTEM_ERROR"`` with a fixed + ``message="Internal server error"`` — internal exception details are + logged but never leak to the client. +- ``request_id`` is sourced from ``request.state.request_id`` (set by + upstream middleware); falls back to a freshly minted id when absent. +""" + +from __future__ import annotations + +from fastapi import HTTPException, Request +from fastapi.exceptions import RequestValidationError +from fastapi.responses import JSONResponse +from starlette.status import ( + HTTP_422_UNPROCESSABLE_ENTITY, + HTTP_500_INTERNAL_SERVER_ERROR, +) + +from everos.component.utils.datetime import ( + get_now_with_timezone, + to_iso_format, +) +from everos.core.observability.logging import get_logger +from everos.core.observability.tracing import gen_request_id + +logger = get_logger(__name__) + +_INTERNAL_ERROR_MESSAGE = "Internal server error" + + +def _request_id(request: Request) -> str: + """Return the request_id set by middleware, or mint a fresh fallback.""" + rid = getattr(request.state, "request_id", None) + if rid: + return str(rid) + return gen_request_id() + + +def _envelope( + *, + code: str, + message: str, + request: Request, +) -> dict[str, object]: + """Build the canonical error envelope (wiki §1 shape — nested ``error``). + + ``request_id`` at the top level, ``error`` object carries the + contract fields (``code`` / ``message``) plus ops-friendly + ``timestamp`` / ``path``. + """ + return { + "request_id": _request_id(request), + "error": { + "code": code, + "message": message, + "timestamp": to_iso_format(get_now_with_timezone()), + "path": str(request.url.path), + }, + } + + +async def global_exception_handler(request: Request, exc: Exception) -> JSONResponse: + """Convert any exception into a uniform JSON error response.""" + path = str(request.url.path) + method = request.method + + if isinstance(exc, RequestValidationError): + errors = exc.errors() + if errors: + first = errors[0] + loc = ".".join(str(p) for p in first.get("loc", []) if p != "body") + msg = first.get("msg", "Validation error") + message = f"{msg}: {loc}" if loc else msg + else: + message = "Request validation error" + + logger.warning("validation_error", method=method, path=path, message=message) + return JSONResponse( + status_code=HTTP_422_UNPROCESSABLE_ENTITY, + content=_envelope(code="HTTP_ERROR", message=message, request=request), + ) + + if isinstance(exc, HTTPException): + logger.warning( + "http_exception", + method=method, + path=path, + status_code=exc.status_code, + detail=exc.detail, + ) + # 5xx routed through HTTPException is rare but valid; still honour + # the SYSTEM_ERROR code so the envelope is consistent. + if exc.status_code >= 500: + return JSONResponse( + status_code=exc.status_code, + content=_envelope( + code="SYSTEM_ERROR", + message=_INTERNAL_ERROR_MESSAGE, + request=request, + ), + ) + return JSONResponse( + status_code=exc.status_code, + content=_envelope( + code="HTTP_ERROR", + message=str(exc.detail), + request=request, + ), + ) + + logger.error( + "unhandled_exception", + method=method, + path=path, + exception_type=type(exc).__name__, + exc_info=True, + ) + return JSONResponse( + status_code=HTTP_500_INTERNAL_SERVER_ERROR, + content=_envelope( + code="SYSTEM_ERROR", + message=_INTERNAL_ERROR_MESSAGE, + request=request, + ), + ) diff --git a/src/everos/core/middleware/profile.py b/src/everos/core/middleware/profile.py new file mode 100644 index 00000000..370c0fab --- /dev/null +++ b/src/everos/core/middleware/profile.py @@ -0,0 +1,69 @@ +"""Performance profiling middleware (HTML report via pyinstrument). + +Triggered with ``?profile=true`` query parameter when ``PROFILING_ENABLED=true`` +is set. Gracefully no-ops if pyinstrument is not installed. +""" + +from __future__ import annotations + +import os +from collections.abc import Awaitable, Callable + +from fastapi import Request +from fastapi.responses import HTMLResponse +from starlette.middleware.base import BaseHTTPMiddleware +from starlette.responses import Response +from starlette.types import ASGIApp + +from everos.core.observability.logging import get_logger + +logger = get_logger(__name__) + +_TRUTHY = frozenset({"1", "true", "yes"}) + + +def _profiling_enabled() -> bool: + """Read ``PROFILING_ENABLED`` env var (1 / true / yes => enabled).""" + raw = os.getenv("PROFILING_ENABLED", os.getenv("PROFILING", "false")).lower() + return raw in _TRUTHY + + +class ProfileMiddleware(BaseHTTPMiddleware): + """Returns a pyinstrument HTML report when ``?profile=true`` is set.""" + + def __init__(self, app: ASGIApp) -> None: + super().__init__(app) + self._enabled = _profiling_enabled() + self._available = False + if self._enabled: + try: + import pyinstrument # noqa: F401 + + self._available = True + logger.info("profiling_middleware_enabled") + except ImportError: + logger.warning("profiling_requested_but_pyinstrument_missing") + self._enabled = False + + async def dispatch( + self, + request: Request, + call_next: Callable[[Request], Awaitable[Response]], + ) -> Response: + if not self._enabled or not self._available: + return await call_next(request) + + if request.query_params.get("profile", "").lower() not in _TRUTHY: + return await call_next(request) + + from pyinstrument import Profiler + + profiler = Profiler() + profiler.start() + logger.info("profile_started", method=request.method, path=request.url.path) + try: + await call_next(request) + except Exception: + logger.exception("profile_request_failed") + profiler.stop() + return HTMLResponse(content=profiler.output_html(), status_code=200) diff --git a/src/everos/core/middleware/prometheus.py b/src/everos/core/middleware/prometheus.py new file mode 100644 index 00000000..09dbadbf --- /dev/null +++ b/src/everos/core/middleware/prometheus.py @@ -0,0 +1,84 @@ +"""Prometheus HTTP metrics middleware. + +Auto-instruments incoming HTTP requests with a request counter and a +duration histogram. Mounted via ``app.add_middleware(PrometheusMiddleware)``. + +Skips internal endpoints (``/metrics``, ``/health``, etc.) so they do not +inflate cardinality or pollute their own statistics. +""" + +from __future__ import annotations + +import time +from collections.abc import Awaitable, Callable + +from starlette.middleware.base import BaseHTTPMiddleware +from starlette.requests import Request +from starlette.responses import Response + +from everos.core.observability.logging import get_logger +from everos.core.observability.metrics import Counter, Histogram, HistogramBuckets + +logger = get_logger(__name__) + + +_http_requests_total = Counter( + name="http_requests_total", + description="Total number of HTTP requests handled.", + labelnames=("method", "path", "status"), + namespace="everos", +) + +_http_request_duration_seconds = Histogram( + name="http_request_duration_seconds", + description="HTTP request duration in seconds.", + labelnames=("method", "path"), + namespace="everos", + buckets=HistogramBuckets.DEFAULT, +) + + +_SKIP_PATHS = frozenset({"/metrics", "/health", "/healthz", "/favicon.ico"}) + + +def _normalize_path(request: Request) -> str: + """Resolve the route template (e.g. ``/users/{user_id}``) for stable labels.""" + scope = getattr(request, "scope", {}) + route = scope.get("route") if isinstance(scope, dict) else None + if route is not None and hasattr(route, "path"): + return route.path + if request.path_params: + path = request.url.path + for name, value in request.path_params.items(): + if str(value) in path: + path = path.replace(str(value), f"{{{name}}}") + return path + return "{unmatched}" + + +class PrometheusMiddleware(BaseHTTPMiddleware): + """Records ``http_requests_total`` and ``http_request_duration_seconds``.""" + + async def dispatch( + self, + request: Request, + call_next: Callable[[Request], Awaitable[Response]], + ) -> Response: + if request.url.path in _SKIP_PATHS: + return await call_next(request) + + method = request.method + start = time.perf_counter() + status = "500" + response: Response | None = None + try: + response = await call_next(request) + status = str(response.status_code) + return response + finally: + duration = time.perf_counter() - start + path = _normalize_path(request) + _http_requests_total.labels(method=method, path=path, status=status).inc() + _http_request_duration_seconds.labels(method=method, path=path).observe( + duration + ) diff --git a/methods/EverCore/src/agentic_layer/__init__.py b/src/everos/core/observability/__init__.py similarity index 100% rename from methods/EverCore/src/agentic_layer/__init__.py rename to src/everos/core/observability/__init__.py diff --git a/src/everos/core/observability/logging/__init__.py b/src/everos/core/observability/logging/__init__.py new file mode 100644 index 00000000..0ffef586 --- /dev/null +++ b/src/everos/core/observability/logging/__init__.py @@ -0,0 +1,13 @@ +"""structlog-based logging factory. + +External usage: + from everos.core.observability.logging import get_logger, configure_logging + + logger = get_logger(__name__) + logger.info("event_name", key=value) +""" + +from .factory import configure_logging as configure_logging +from .factory import get_logger as get_logger + +__all__ = ["configure_logging", "get_logger"] diff --git a/src/everos/core/observability/logging/factory.py b/src/everos/core/observability/logging/factory.py new file mode 100644 index 00000000..14ed2760 --- /dev/null +++ b/src/everos/core/observability/logging/factory.py @@ -0,0 +1,117 @@ +"""structlog logger factory. + +Provides ``get_logger(__name__)`` for module-level logger acquisition. +``configure_logging()`` is called once at process startup (run.py / lifespan) +to set up the structlog processor chain and route stdlib logging through +the same formatter so output stays uniform regardless of the caller. + +The configuration follows structlog's official "Foreign Log Integration" +recipe: a single ``ProcessorFormatter`` renders both everos's own +``get_logger(...)`` calls and any stdlib ``logging.getLogger(...)`` call +made by third-party libraries (uvicorn, fastapi, httpx, openai, ...). +That way all three of the previously divergent prefixes — ``INFO:``, +``[warning ]``, plus the unconfigured no-prefix output — collapse to +one ``[level] event key=value`` shape. + +Rust-side loggers (LanceDB / Lance / Arrow) live in the Rust ``log`` +crate and emit straight to stderr without going through Python; this +module cannot reach them. Control their level with ``RUST_LOG`` env. +""" + +from __future__ import annotations + +import logging +import sys +from typing import Any + +import structlog + + +def get_logger(name: str) -> Any: + """Return a structlog logger bound to the given module name.""" + return structlog.get_logger(name) + + +def configure_logging(level: str = "INFO") -> None: + """Configure structlog and stdlib logging once at process startup. + + After this call: + + * Every ``structlog.get_logger(...)`` and ``logging.getLogger(...)`` + message flows through the same ``ProcessorFormatter``, so output + format is identical regardless of which logging API the caller used. + * Root-logger handlers are replaced with a single ``StreamHandler`` + pointing at ``sys.stdout``; any previously installed handler + (uvicorn's default ``LOGGING_CONFIG``, libraries that call + ``logging.basicConfig``, etc.) is removed. + + The ``uvicorn.run(..., log_config=None)`` flag is the matching half + on the server entry point — without it, uvicorn re-installs its own + handlers on every startup and overrides what we set here. + + Args: + level: Log level name (``DEBUG`` / ``INFO`` / ``WARNING`` / ``ERROR``). + Unknown names silently fall back to ``INFO`` via + ``getattr(logging, ..., INFO)``. + """ + log_level = getattr(logging, level.upper(), logging.INFO) + + shared_processors: list[Any] = [ + structlog.contextvars.merge_contextvars, + structlog.processors.add_log_level, + structlog.processors.TimeStamper(fmt="iso"), + structlog.processors.StackInfoRenderer(), + ] + + # structlog's own loggers feed into stdlib's logging, so the root + # logger handler decides where output lands and how it's rendered. + structlog.configure( + processors=[ + *shared_processors, + structlog.stdlib.ProcessorFormatter.wrap_for_formatter, + ], + wrapper_class=structlog.make_filtering_bound_logger(log_level), + logger_factory=structlog.stdlib.LoggerFactory(), + cache_logger_on_first_use=True, + ) + + # The single formatter shared by both pipelines: + # * structlog events arrive already wrapped via ``wrap_for_formatter``; + # * foreign records (stdlib LogRecord) get pushed through + # ``foreign_pre_chain`` so they pick up the same level / timestamp + # fields before hitting ``ConsoleRenderer``. + formatter = structlog.stdlib.ProcessorFormatter( + foreign_pre_chain=shared_processors, + processors=[ + structlog.stdlib.ProcessorFormatter.remove_processors_meta, + structlog.dev.ConsoleRenderer(), + ], + ) + + handler = logging.StreamHandler(sys.stdout) + handler.setFormatter(formatter) + + # Drop any handler we installed on a previous ``configure_logging`` + # call (identified by formatter type) so repeated invocations don't + # produce duplicate output, but keep handlers other parties have + # attached — pytest's caplog handler in particular has to survive, + # otherwise tests using the ``caplog`` fixture can't see records + # that flow through structlog. + root = logging.getLogger() + root.handlers = [ + h + for h in root.handlers + if not isinstance(h.formatter, structlog.stdlib.ProcessorFormatter) + ] + root.addHandler(handler) + root.setLevel(log_level) + + # Third-party HTTP clients log every successful request at INFO level — + # `httpx` is the worst offender (one line per call, called once per + # LLM / embedding / rerank request). A single LoCoMo conv run easily + # produces a thousand such lines, drowning everos's own events. They + # are useful for debugging API failures, but failures already surface + # via exceptions + status codes — so demote the success path to WARNING + # and let real errors still come through. + for noisy in ("httpx", "httpcore", "urllib3"): + logging.getLogger(noisy).setLevel(logging.WARNING) diff --git a/src/everos/core/observability/metrics/__init__.py b/src/everos/core/observability/metrics/__init__.py new file mode 100644 index 00000000..ffeeffd2 --- /dev/null +++ b/src/everos/core/observability/metrics/__init__.py @@ -0,0 +1,34 @@ +"""Prometheus-style metrics primitives + registry. + +External usage: + from everos.core.observability.metrics import ( + Counter, Gauge, Histogram, HistogramBuckets, + get_metrics_registry, generate_metrics_response, + ) +""" + +from .counter import Counter as Counter +from .counter import LabeledCounter as LabeledCounter +from .gauge import Gauge as Gauge +from .gauge import LabeledGauge as LabeledGauge +from .histogram import Histogram as Histogram +from .histogram import HistogramBuckets as HistogramBuckets +from .histogram import LabeledHistogram as LabeledHistogram +from .registry import generate_metrics_response as generate_metrics_response +from .registry import get_metrics_registry as get_metrics_registry +from .registry import reset_metrics_registry as reset_metrics_registry +from .registry import set_metrics_registry as set_metrics_registry + +__all__ = [ + "Counter", + "Gauge", + "Histogram", + "HistogramBuckets", + "LabeledCounter", + "LabeledGauge", + "LabeledHistogram", + "generate_metrics_response", + "get_metrics_registry", + "reset_metrics_registry", + "set_metrics_registry", +] diff --git a/src/everos/core/observability/metrics/counter.py b/src/everos/core/observability/metrics/counter.py new file mode 100644 index 00000000..fd234794 --- /dev/null +++ b/src/everos/core/observability/metrics/counter.py @@ -0,0 +1,50 @@ +"""Counter wrapper around ``prometheus_client.Counter``.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any + +from prometheus_client import Counter as PromCounter + +from .registry import get_metrics_registry + + +class Counter: + """Monotonically-increasing counter (totals, error counts).""" + + def __init__( + self, + name: str, + description: str, + labelnames: Sequence[str] = (), + namespace: str = "", + subsystem: str = "", + unit: str = "", + ) -> None: + self._counter = PromCounter( + name=name, + documentation=description, + labelnames=labelnames, + namespace=namespace, + subsystem=subsystem, + unit=unit, + registry=get_metrics_registry(), + ) + self._labelnames = tuple(labelnames) + + def labels(self, **labels: str) -> LabeledCounter: + return LabeledCounter(self._counter.labels(**labels)) + + def inc(self, amount: float = 1.0) -> None: + self._counter.inc(amount) + + +class LabeledCounter: + """Counter slice with labels applied.""" + + def __init__(self, labeled: Any) -> None: + self._labeled = labeled + + def inc(self, amount: float = 1.0) -> None: + self._labeled.inc(amount) diff --git a/src/everos/core/observability/metrics/gauge.py b/src/everos/core/observability/metrics/gauge.py new file mode 100644 index 00000000..b818ca02 --- /dev/null +++ b/src/everos/core/observability/metrics/gauge.py @@ -0,0 +1,66 @@ +"""Gauge wrapper around ``prometheus_client.Gauge``. + +Async auto-refresh is intentionally not included in v0.1; subclass +:class:`Gauge` and call :meth:`set` from your own scheduling logic when +needed. +""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any + +from prometheus_client import Gauge as PromGauge + +from .registry import get_metrics_registry + + +class Gauge: + """Instantaneous numeric value (queue depth, cache size).""" + + def __init__( + self, + name: str, + description: str, + labelnames: Sequence[str] = (), + namespace: str = "", + subsystem: str = "", + unit: str = "", + ) -> None: + self._gauge = PromGauge( + name=name, + documentation=description, + labelnames=labelnames, + namespace=namespace, + subsystem=subsystem, + unit=unit, + registry=get_metrics_registry(), + ) + + def labels(self, **labels: str) -> LabeledGauge: + return LabeledGauge(self._gauge.labels(**labels)) + + def set(self, value: float) -> None: + self._gauge.set(value) + + def inc(self, amount: float = 1.0) -> None: + self._gauge.inc(amount) + + def dec(self, amount: float = 1.0) -> None: + self._gauge.dec(amount) + + +class LabeledGauge: + """Gauge slice with labels applied.""" + + def __init__(self, labeled: Any) -> None: + self._labeled = labeled + + def set(self, value: float) -> None: + self._labeled.set(value) + + def inc(self, amount: float = 1.0) -> None: + self._labeled.inc(amount) + + def dec(self, amount: float = 1.0) -> None: + self._labeled.dec(amount) diff --git a/src/everos/core/observability/metrics/histogram.py b/src/everos/core/observability/metrics/histogram.py new file mode 100644 index 00000000..682582fe --- /dev/null +++ b/src/everos/core/observability/metrics/histogram.py @@ -0,0 +1,102 @@ +"""Histogram wrapper around ``prometheus_client.Histogram``.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any + +from prometheus_client import Histogram as PromHistogram + +from .registry import get_metrics_registry + + +class HistogramBuckets: + """Predefined bucket configurations for common workloads.""" + + DEFAULT: tuple[float, ...] = ( + 0.005, + 0.01, + 0.025, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.5, + 5.0, + 10.0, + ) + FAST: tuple[float, ...] = (0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5) + API_CALL: tuple[float, ...] = ( + 0.01, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 30.0, + ) + BATCH: tuple[float, ...] = (0.1, 0.5, 1.0, 5.0, 10.0, 30.0, 60.0) + DATABASE: tuple[float, ...] = ( + 0.001, + 0.005, + 0.01, + 0.025, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.5, + 5.0, + ) + + +class Histogram: + """Distribution of observed values (latency, sizes).""" + + def __init__( + self, + name: str, + description: str, + labelnames: Sequence[str] = (), + namespace: str = "", + subsystem: str = "", + unit: str = "", + buckets: Sequence[float] = HistogramBuckets.DEFAULT, + ) -> None: + self._histogram = PromHistogram( + name=name, + documentation=description, + labelnames=labelnames, + namespace=namespace, + subsystem=subsystem, + unit=unit, + buckets=tuple(buckets), + registry=get_metrics_registry(), + ) + + def labels(self, **labels: str) -> LabeledHistogram: + return LabeledHistogram(self._histogram.labels(**labels)) + + def observe(self, amount: float) -> None: + self._histogram.observe(amount) + + def time(self) -> Any: + return self._histogram.time() + + +class LabeledHistogram: + """Histogram slice with labels applied.""" + + def __init__(self, labeled: Any) -> None: + self._labeled = labeled + + def observe(self, amount: float) -> None: + self._labeled.observe(amount) + + def time(self) -> Any: + return self._labeled.time() diff --git a/src/everos/core/observability/metrics/registry.py b/src/everos/core/observability/metrics/registry.py new file mode 100644 index 00000000..1687842f --- /dev/null +++ b/src/everos/core/observability/metrics/registry.py @@ -0,0 +1,35 @@ +"""Prometheus metrics registry singleton.""" + +from __future__ import annotations + +from prometheus_client import REGISTRY, CollectorRegistry, generate_latest + +_registry: CollectorRegistry | None = None + + +def get_metrics_registry() -> CollectorRegistry: + """Return the global metrics registry. + + Defaults to ``prometheus_client.REGISTRY``. + """ + global _registry + if _registry is None: + _registry = REGISTRY + return _registry + + +def set_metrics_registry(registry: CollectorRegistry) -> None: + """Override the global registry (mainly for tests).""" + global _registry + _registry = registry + + +def generate_metrics_response() -> bytes: + """Render the current registry into Prometheus exposition format.""" + return generate_latest(get_metrics_registry()) + + +def reset_metrics_registry() -> None: + """Reset the global registry override (mainly for tests).""" + global _registry + _registry = None diff --git a/src/everos/core/observability/tracing/__init__.py b/src/everos/core/observability/tracing/__init__.py new file mode 100644 index 00000000..4f3f2635 --- /dev/null +++ b/src/everos/core/observability/tracing/__init__.py @@ -0,0 +1,32 @@ +"""Tracing utilities — W3C-compatible request id generation. + +External usage:: + + from everos.core.observability.tracing import gen_request_id +""" + +from __future__ import annotations + +from uuid import uuid4 + + +def gen_request_id() -> str: + """Generate a request id matching the W3C trace-context spec. + + Returns 32 lowercase hex characters (128-bit, no prefix) — the same + format as a W3C ``trace_id`` / OpenTelemetry trace identifier. Routes + and services that mint a fresh request id (when one wasn't injected + by upstream middleware) should call this helper rather than rolling + their own uuid / prefix format, so the id layer stays compatible + with OpenTelemetry exporters and standard APM tooling. + + Example:: + + >>> rid = gen_request_id() + >>> len(rid) + 32 + """ + return uuid4().hex + + +__all__ = ["gen_request_id"] diff --git a/src/everos/core/persistence/__init__.py b/src/everos/core/persistence/__init__.py new file mode 100644 index 00000000..85e60d24 --- /dev/null +++ b/src/everos/core/persistence/__init__.py @@ -0,0 +1,106 @@ +"""Persistence primitives. + +Read/write toolkit for markdown files, async wrappers around the SQLite +system DB and LanceDB index, plus a memory-root path manager. Higher +layers (``memory``, ``infra``) layer business semantics on top of these +building blocks; this subpackage knows nothing about Entry / MemCell / +Episode or any other business model. + +External usage: + from everos.core.persistence import ( + # Path manager + lock + MemoryRoot, memory_root_lock, LockError, + # Markdown IO toolkit + MarkdownReader, MarkdownWriter, ParsedMarkdown, Entry, + parse_frontmatter, dump_frontmatter, split_entries, find_entry, + # Frontmatter schema chassis + BaseFrontmatter, UserScopedFrontmatter, AgentScopedFrontmatter, + DailyLogPathMixin, SkillPathMixin, + # Async SQLite (SQLModel / SA 2.0) + create_system_engine, create_session_factory, session_scope, + SQLModel, Field, Relationship, BaseTable, RepoBase, + # Async LanceDB + open_lancedb_connection, LanceModel, Vector, BaseLanceTable, touch, + LanceRepoBase, + ) +""" + +from .lancedb import BaseLanceTable as BaseLanceTable +from .lancedb import LanceModel as LanceModel +from .lancedb import LanceRepoBase as LanceRepoBase +from .lancedb import Vector as Vector +from .lancedb import open_lancedb_connection as open_lancedb_connection +from .lancedb import touch as touch +from .locking import LockError as LockError +from .locking import memory_root_lock as memory_root_lock +from .markdown import AgentScopedFrontmatter as AgentScopedFrontmatter +from .markdown import BaseFrontmatter as BaseFrontmatter +from .markdown import DailyLogPathMixin as DailyLogPathMixin +from .markdown import Entry as Entry +from .markdown import EntryId as EntryId +from .markdown import MarkdownReader as MarkdownReader +from .markdown import MarkdownWriter as MarkdownWriter +from .markdown import ParsedMarkdown as ParsedMarkdown +from .markdown import SkillPathMixin as SkillPathMixin +from .markdown import StructuredEntry as StructuredEntry +from .markdown import UserScopedFrontmatter as UserScopedFrontmatter +from .markdown import dump_frontmatter as dump_frontmatter +from .markdown import find_entry as find_entry +from .markdown import parse_frontmatter as parse_frontmatter +from .markdown import parse_structured_entry as parse_structured_entry +from .markdown import render_structured_entry as render_structured_entry +from .markdown import split_entries as split_entries +from .memory_root import MemoryRoot as MemoryRoot +from .memory_root import app_dir_name as app_dir_name +from .memory_root import app_id_from_dir as app_id_from_dir +from .memory_root import project_dir_name as project_dir_name +from .memory_root import project_id_from_dir as project_id_from_dir +from .sqlite import BaseTable as BaseTable +from .sqlite import Field as Field +from .sqlite import Relationship as Relationship +from .sqlite import RepoBase as RepoBase +from .sqlite import SQLModel as SQLModel +from .sqlite import create_session_factory as create_session_factory +from .sqlite import create_system_engine as create_system_engine +from .sqlite import session_scope as session_scope + +__all__ = [ + "AgentScopedFrontmatter", + "BaseFrontmatter", + "BaseLanceTable", + "BaseTable", + "DailyLogPathMixin", + "Entry", + "EntryId", + "Field", + "LanceModel", + "LanceRepoBase", + "LockError", + "MarkdownReader", + "MarkdownWriter", + "MemoryRoot", + "ParsedMarkdown", + "Relationship", + "RepoBase", + "SkillPathMixin", + "StructuredEntry", + "SQLModel", + "UserScopedFrontmatter", + "Vector", + "app_dir_name", + "app_id_from_dir", + "create_session_factory", + "create_system_engine", + "dump_frontmatter", + "find_entry", + "memory_root_lock", + "project_dir_name", + "project_id_from_dir", + "open_lancedb_connection", + "parse_frontmatter", + "parse_structured_entry", + "render_structured_entry", + "session_scope", + "split_entries", + "touch", +] diff --git a/src/everos/core/persistence/lancedb/__init__.py b/src/everos/core/persistence/lancedb/__init__.py new file mode 100644 index 00000000..d9ac2aea --- /dev/null +++ b/src/everos/core/persistence/lancedb/__init__.py @@ -0,0 +1,34 @@ +"""LanceDB async persistence. + +External usage (connection): + from everos.core.persistence.lancedb import open_lancedb_connection + +External usage (ORM model basics — re-exported from lancedb.pydantic): + from everos.core.persistence.lancedb import ( + LanceModel, Vector, BaseLanceTable, touch, + ) + +External usage (generic CRUD repository base): + from everos.core.persistence.lancedb import LanceRepoBase +""" + +# Re-export the LanceDB-flavoured Pydantic primitives so business code has a +# single canonical entry point for table schemas. +from lancedb.pydantic import LanceModel as LanceModel +from lancedb.pydantic import Vector as Vector + +from .base import BaseLanceTable as BaseLanceTable +from .base import touch as touch +from .connection import open_lancedb_connection as open_lancedb_connection +from .repository import LanceDailyLogRepoBase as LanceDailyLogRepoBase +from .repository import LanceRepoBase as LanceRepoBase + +__all__ = [ + "BaseLanceTable", + "LanceDailyLogRepoBase", + "LanceModel", + "LanceRepoBase", + "Vector", + "open_lancedb_connection", + "touch", +] diff --git a/src/everos/core/persistence/lancedb/base.py b/src/everos/core/persistence/lancedb/base.py new file mode 100644 index 00000000..6d2b5e73 --- /dev/null +++ b/src/everos/core/persistence/lancedb/base.py @@ -0,0 +1,158 @@ +"""Common LanceDB base for everos tables. + +:class:`BaseLanceTable` adds ``created_at`` / ``updated_at`` columns and +the :attr:`BM25_FIELDS` declaration + :meth:`ensure_fts_indexes` +classmethod so each schema owns *both* its column shape **and** its +BM25 index spec — repos stay focused on queries. + +Note: + LanceDB has no SQL ``onupdate`` equivalent — the application must + explicitly set ``updated_at = get_utc_now()`` before calling + :meth:`AsyncTable.update` / :meth:`AsyncTable.merge_insert`. The + convenience :func:`touch` helper does this in one call. + + **Every datetime column automatically carries ``tz=UTC`` in the + Arrow schema.** LanceDB's Pydantic→PyArrow converter does not + understand ``typing.Annotated`` metadata, so :data:`UtcDatetime` + cannot be used as the field type annotation. Instead, + :meth:`BaseLanceTable.to_arrow_schema` walks the inferred schema + and rewrites every ``timestamp[us]`` (naive) column to + ``timestamp[us, tz=UTC]``. PyArrow then auto-``astimezone(UTC)`` + aware inputs on write **and** returns aware UTC datetimes on read + — no per-table configuration, no caller-side ``ensure_utc``. + + Subclasses just declare ``datetime`` fields normally:: + + class Episode(BaseLanceTable): + timestamp: dt.datetime +""" + +from __future__ import annotations + +import datetime as dt +from typing import ClassVar + +import pyarrow as pa +from lancedb import AsyncTable +from lancedb.index import FTS +from lancedb.pydantic import LanceModel +from pydantic import Field + +from everos.component.utils.datetime import get_utc_now + + +class BaseLanceTable(LanceModel): + """Pydantic / LanceDB base with ``created_at`` / ``updated_at`` and + schema-level LanceDB metadata (``TABLE_NAME`` / ``BM25_FIELDS``). + + The schema is the single source of truth for everything LanceDB + needs to materialise the table: column shape, table name, vector + dim (declared per-subclass), and which columns carry an FTS index. + Repos read these ClassVars; they do not duplicate them. + """ + + TABLE_NAME: ClassVar[str] = "" + """LanceDB table name. Business schemas must override (e.g. + ``"episode"``). Left empty on chassis / test schemas that construct + their table inline.""" + + BM25_FIELDS: ClassVar[list[str]] = [] + """Columns to build LanceDB FTS (BM25) indexes on. + + Each declared column must already exist as a ``str`` (or + ``str | None``) field on the schema. Tokens are assumed to be + **app-layer pre-tokenised** (space-joined); the FTS index uses + ``base_tokenizer="whitespace"`` so segmentation is owned by the + app layer (:class:`JiebaTokenizer`). The same boundary owns stop- + word filtering (English + Chinese); FTS-side ``remove_stop_words`` + is OFF. FTS *does* keep lightweight English-aware normalisation + (``lower_case`` / ``stem`` / ``ascii_folding``) as a belt-and- + braces layer on the same English tokens that survive jieba. + See ``17_lancedb_tables_design.md`` §2.4.1 and + :meth:`ensure_fts_indexes` below for the exact knobs.""" + + created_at: dt.datetime = Field(default_factory=get_utc_now) + updated_at: dt.datetime = Field(default_factory=get_utc_now) + + @classmethod + def to_arrow_schema(cls) -> pa.Schema: + """Patch the default Arrow schema: force every timestamp to ``tz=UTC``. + + The base ``LanceModel.to_arrow_schema()`` infers Arrow types from + Pydantic field annotations and emits naive ``timestamp[us]`` for + every :class:`datetime.datetime` column. We rewrite **every** + timestamp column to ``timestamp[us, tz=UTC]``: + + * **on write** — PyArrow ``astimezone(UTC)``-s aware input + automatically before serialising the i64 epoch micros. + * **on read** — PyArrow returns aware UTC datetimes. + + Zero per-table configuration. The rewrite also **overrides any + non-UTC tz** a subclass might have declared explicitly, because + project convention is: storage is always UTC. Mixed-tz columns + would violate the two-zone discipline (see + ``docs/datetime.md``); enforcing UTC at the schema level closes + that loophole. + """ + base = super().to_arrow_schema() + return pa.schema( + [ + pa.field(f.name, pa.timestamp("us", tz="UTC"), nullable=f.nullable) + if pa.types.is_timestamp(f.type) + else f + for f in base + ] + ) + + @classmethod + async def ensure_fts_indexes(cls, table: AsyncTable) -> None: + """Create FTS indexes on every column in :attr:`BM25_FIELDS`. + + Idempotent: columns that already have an index are skipped, so + this is safe to call on every startup. The FTS config is fixed + to the app-layer pre-tokenisation + LanceDB normalisation + convention (designed for **multilingual mixed content**): + + - ``base_tokenizer="whitespace"`` — split on the spaces our + app-layer tokenizer provider already inserted between tokens. + - ``lower_case=True`` — Unicode-aware case-fold (English A→a; + no-op on CJK characters). + - ``stem=True`` — Porter / Snowball English stemmer per + ``language="English"`` (tantivy default). CJK tokens have no + stemmer and pass through untouched. + - ``remove_stop_words=False`` — **stop-word removal is owned by + the app-layer** (:class:`JiebaTokenizer`), which already drops + both Chinese and English stop-words before tokens reach the + FTS index. Keeping FTS-side filtering off avoids double- + filtering and a divided source of truth. + - ``ascii_folding=True`` — strips diacritics (é→e) on Latin + characters; no-op on CJK. + - ``with_position=True`` — enables phrase queries. + + Subclasses normally do not need to override this — declaring + :attr:`BM25_FIELDS` is enough. + """ + if not cls.BM25_FIELDS: + return + indices = await table.list_indices() + indexed_cols = {col for idx in indices for col in (idx.columns or [])} + for field in cls.BM25_FIELDS: + if field in indexed_cols: + continue + await table.create_index( + column=field, + config=FTS( + with_position=True, + base_tokenizer="whitespace", + lower_case=True, + stem=True, + remove_stop_words=False, + ascii_folding=True, + ), + ) + + +def touch(record: BaseLanceTable) -> BaseLanceTable: + """Set ``record.updated_at = now`` and return the record (chainable).""" + record.updated_at = get_utc_now() + return record diff --git a/src/everos/core/persistence/lancedb/connection.py b/src/everos/core/persistence/lancedb/connection.py new file mode 100644 index 00000000..8812b841 --- /dev/null +++ b/src/everos/core/persistence/lancedb/connection.py @@ -0,0 +1,68 @@ +"""Async LanceDB connection factory. + +LanceDB does not live inside the SQLAlchemy ecosystem; it has its own +``connect_async`` returning :class:`lancedb.AsyncConnection`. This module +is a thin wrapper that: + + 1. ensures the lancedb root directory exists + 2. converts ``LanceDBSettings.read_consistency_seconds`` into the + :class:`datetime.timedelta` value LanceDB expects + 3. installs a capped :class:`lancedb.Session` so the global index + cache cannot grow unbounded and exhaust file descriptors + (see :attr:`LanceDBSettings.index_cache_size_bytes` for the + full rationale) +""" + +from __future__ import annotations + +import datetime as dt +from pathlib import Path + +import lancedb +from lancedb import AsyncConnection + +from everos.config import LanceDBSettings + + +async def open_lancedb_connection( + lancedb_dir: Path, + lancedb_settings: LanceDBSettings, +) -> AsyncConnection: + """Open an async LanceDB connection rooted at ``lancedb_dir``. + + Args: + lancedb_dir: Filesystem path to the LanceDB root (typically + ``MemoryRoot.lancedb_dir``). Created if missing. + lancedb_settings: Tunables; the ``read_consistency_seconds`` field + is converted to a :class:`~datetime.timedelta`, and + ``index_cache_size_bytes`` caps the global index cache. + + Returns: + An :class:`AsyncConnection` ready for table operations. + """ + # mkdir is a microsecond-fast syscall and only fires on first connect; + # not worth pulling in anyio.Path / aiofiles for it. + lancedb_dir.mkdir(parents=True, exist_ok=True) # noqa: ASYNC240 + + interval: dt.timedelta | None = None + if lancedb_settings.read_consistency_seconds is not None: + interval = dt.timedelta(seconds=lancedb_settings.read_consistency_seconds) + + # Bound the index cache so its readers (each one holds the FDs of + # an opened ``_indices//...`` directory) get LRU-evicted + # rather than leaking. Without this, a long-running daemon's FD + # count grows monotonically until ``EMFILE``. The metadata cache + # is intentionally left at the lancedb default (unbounded): it + # holds parsed in-memory manifests with zero FD pressure, and a + # cap there would just thrash. See ``LanceDBSettings`` for the + # measurement that picked the default size. + session = lancedb.Session( + index_cache_size_bytes=lancedb_settings.index_cache_size_bytes, + metadata_cache_size_bytes=None, + ) + + return await lancedb.connect_async( + str(lancedb_dir), + read_consistency_interval=interval, + session=session, + ) diff --git a/src/everos/core/persistence/lancedb/repository.py b/src/everos/core/persistence/lancedb/repository.py new file mode 100644 index 00000000..3057b974 --- /dev/null +++ b/src/everos/core/persistence/lancedb/repository.py @@ -0,0 +1,530 @@ +"""Generic CRUD repository for LanceDB-backed tables. + +``LanceRepoBase`` mirrors the SQLite ``RepoBase`` shape: a pure generic +CRUD helper that knows nothing about a storage runtime. Concrete repos +either pass an :class:`AsyncTable` explicitly (typical in tests) or +override :meth:`_table_lookup` to pull the cached table from their +storage manager (typical in +:mod:`everos.infra.persistence.lancedb.repos`). +""" + +from __future__ import annotations + +import asyncio +import datetime as dt +from collections.abc import Sequence +from typing import Any, ClassVar + +from lancedb import AsyncTable + +from everos.core.observability.logging import get_logger + +from .base import BaseLanceTable + +logger = get_logger(__name__) + + +def _q(value: str) -> str: + """Escape single quotes for a LanceDB SQL-like ``where`` predicate. + + LanceDB has no parameterised query API; predicates are strings. + Doubling the quote (``'`` → ``''``) is the SQL-standard way to keep + a literal single quote inside a single-quoted string. everos's PK + convention (``_``) never carries quotes — this + is defensive. + """ + return value.replace("'", "''") + + +class LanceRepoBase[T: BaseLanceTable]: + """Generic CRUD repository for one LanceDB table. + + Subclass and bind to a schema. Two ways to provide the table: + + 1. **Explicit (tests / DI)** — pass it to ``__init__``:: + + repo = EpisodeRepo(table) + + 2. **Lazy hook (production singletons)** — override + :meth:`_table_lookup` so the repo can be instantiated as a + module-level singleton with no live connection yet:: + + class _EpisodeRepo(LanceRepoBase[Episode]): + schema = Episode + + async def _table_lookup(self): + from everos.infra.persistence.lancedb.lancedb_manager import ( + get_table, + ) + return await get_table(self.schema.TABLE_NAME, self.schema) + + episode_repo = _EpisodeRepo() + await episode_repo.add([Episode(text=..., vector=[...])]) + + The LanceDB table name lives on the schema (``BaseLanceTable.TABLE_NAME``) + so every LanceDB-side metadatum — column shape, table name, + vector dim, BM25 index spec — sits in one place. ``table_name`` + here is a thin pass-through; subclasses normally do **not** + override it. + + Write paths (``add`` / ``upsert`` / ``delete`` / ``delete_by_md_path``) + are serialised by a per-``table_name`` :class:`asyncio.Lock`. LanceDB's + ``merge_insert`` is a read-modify-write at the storage layer with no + application-visible OCC contract — two concurrent calls against the + same table can race on the version manifest and lose updates even + when the row sets are disjoint (observed: cascade worker + ``asyncio.gather`` over a batch of ``user_profile`` rows where one + write disappears). Serialising on the table name closes that window; + reads stay unlocked so search QPS is not impacted by writers. + + Locks live in a class-level dict keyed by table name and are never + evicted (mirrors :mod:`everos.memory.strategies._partition_locks` + on bpo-28427 — a lock with pending waiters must outlive any dict + entry that points to it). + """ + + schema: type[T] + + _table_locks: ClassVar[dict[str, asyncio.Lock]] = {} + """Per-table-name write lock pool (process-wide, lazily populated).""" + + @property + def table_name(self) -> str: + """LanceDB table name, resolved from :attr:`schema.TABLE_NAME`.""" + return self.schema.TABLE_NAME + + @classmethod + def _write_lock(cls, table_name: str) -> asyncio.Lock: + """Return the write lock for ``table_name``; create on first use. + + ``dict.setdefault`` is atomic under single-threaded asyncio (no + ``await`` between check and insert), so no meta-lock is needed. + """ + return cls._table_locks.setdefault(table_name, asyncio.Lock()) + + @classmethod + def _reset_locks_for_tests(cls) -> None: + """Test-only: drop the write-lock pool. + + ``asyncio.Lock`` binds to the current event loop on first + ``acquire()``; pytest-asyncio creates a fresh loop per test, so + a module-level lock surviving across tests fails with "bound to + a different event loop". The production cascade worker runs on + one loop forever and does not need this hook. Mirrors + :func:`everos.memory.strategies._partition_locks._reset_for_tests`. + """ + cls._table_locks.clear() + + def __init__(self, table: AsyncTable | None = None) -> None: + """Bind to a table directly; if ``None``, defer to ``_table_lookup``.""" + self._table_override = table + + async def _table_lookup(self) -> AsyncTable: + """Resolve the table on first use. Override in subclass. + + ``LanceRepoBase`` itself has no idea where the runtime singleton + lives. The default raises so a missing override is loud rather + than silently broken. + """ + raise NotImplementedError( + f"{type(self).__name__}: pass table= to __init__ " + "or override _table_lookup() to wire the storage manager." + ) + + async def _table(self) -> AsyncTable: + if self._table_override is not None: + return self._table_override + return await self._table_lookup() + + # ── Create ───────────────────────────────────────────────────────────── + + async def add(self, records: Sequence[T]) -> None: + """Insert one or more records.""" + table = await self._table() + async with self._write_lock(self.table_name): + await table.add(list(records)) + + # ── Upsert ───────────────────────────────────────────────────────────── + + async def upsert( + self, + records: Sequence[T], + *, + by: str = "id", + ) -> None: + """Upsert records keyed by ``by`` (PK column, default ``"id"``). + + Wraps LanceDB's ``merge_insert(on=...)`` fluent builder with the + equivalent of ``INSERT ... ON CONFLICT(by) DO UPDATE`` — matching + rows are replaced wholesale, non-matching rows inserted. + + Cascade uses this when reconciling md → LanceDB: an entry seen + for the first time inserts; an entry that was edited in md + updates its existing row. + """ + table = await self._table() + async with self._write_lock(self.table_name): + await ( + table.merge_insert(by) + .when_matched_update_all() + .when_not_matched_insert_all() + .execute(list(records)) + ) + + # ── Maintenance ──────────────────────────────────────────────────────── + + async def optimize(self, *, cleanup_older_than: dt.timedelta | None = None) -> None: + """Compact fragments + merge new data into the FTS / vector indexes. + + LanceDB's ``merge_insert`` writes new data into a fresh fragment. + The FTS (BM25) index built by :meth:`ensure_fts_indexes` only + covers fragments visible at index-build time, so rows written + after the initial build can become **invisible to BM25 queries** + until ``optimize()`` runs and merges those fragments into the + index segment that the query engine reads. + + Symptom this guards against (verified on LoCoMo conv0): after + steady-state cascade ingest, ``nearest_to_text("any_common_word")`` + returns 0 hits even though the column literally contains the + token in 100% of rows — the new fragments simply hadn't been + indexed. + + Cascade triggers this through a per-kind throttle + trailing + edge scheduler (``CascadeWorker._schedule_optimize``): at most + one run per ~1s window per kind, decoupled from the drain + loop, with a 60s heartbeat sweep as a safety net. Cost is + O(N) data-rewrite per optimized fragment; the throttle is how + we cap it under sustained write pressure. + + Args: + cleanup_older_than: When set, also prune (physically delete) + files belonging to dataset versions older than this + interval. ``None`` (default) compacts only — historical + manifests, replaced data fragments, and stale index + UUID files are kept on disk forever, which inflates the + file count (and FD usage at scan time) without bound. + Cascade passes a non-None value on a slower beat + (``CascadeWorker._optimize_prune_interval``) so the + hot drain path stays cheap. Note: this does *not* + shrink **active** index internals (FTS ``part_N`` count + or vector index UUID count) — those only collapse via + ``drop_index + create_index``, which is not done here. + """ + table = await self._table() + await table.optimize(cleanup_older_than=cleanup_older_than) + + async def rebuild_indexes(self) -> None: + """Drop and re-create every index on this table. + + **Why this exists** — workaround for an upstream Python API gap: + + Lance's Rust ``OptimizeOptions`` has a ``num_indices_to_merge`` + knob (default 1) that bounds the number of active index UUIDs + per column. With ``Some(1)``, every ``optimize_indices()`` call + merges its delta into the base — active UUID count stays at 1. + + Two problems block us from using it from the application layer: + + 1. ``lancedb.AsyncTable.optimize()`` does **not expose** this + parameter (verified on lancedb main 2026-05-28). It forwards + only ``cleanup_since_ms`` and ``delete_unverified`` to Rust. + 2. Even calling Lance directly via ``pylance``, the merge + behaviour itself is buggy on ``lance crate 4.0`` (what + lancedb 0.30.2 embeds) — ``num_indices_to_merge=1`` does + nothing. Fix landed in ``lance 7.x``, but ``pylance 7.x`` + can not collapse indexes on a ``lance 4.0``-format dataset + (verified by experiment). + + So in our current stack there is **no application-level path** + to bound active index UUID growth. ``optimize()`` keeps + accumulating one new UUID (vector) / one new ``part_N`` (FTS) + per call. + + This method is the workaround: drop every existing index and + rebuild from the schema's ``ensure_fts_indexes`` contract. The + rebuild is **O(N) full retrain** but cheap in practice (~0.3s + for 50k rows × 2 FTS columns on local SSD), and during the + window LanceDB transparently falls back to brute-force scan so + queries and writes stay available. + + **Cadence** — :class:`CascadeWorker` runs this on a slow loop + (default 12h per kind). Frequency is bounded by the rebuild + cost, not by correctness — even daily is fine functionally; + 12h is a conservative pick to keep file/UUID counts well below + any FD ceiling under steady-state ingest. + + **When to remove** — once lancedb exposes ``num_indices_to_merge`` + on the async Python API **and** the embedded ``lance crate`` + ships the working merge implementation, delete this method and + switch to ``optimize(num_indices_to_merge=1)`` in the regular + ``optimize()`` path. Tracking issues / context: + + - https://github.com/lancedb/lancedb/issues/2193 + - https://github.com/lancedb/lancedb/issues/3177 + - https://github.com/lance-format/lance/pull/6711 (partial fix + in lance v7.0.0) + - https://docs.rs/lancedb/latest/lancedb/table/struct.OptimizeOptions.html + """ + table = await self._table() + async with self._write_lock(self.table_name): + for idx in await table.list_indices(): + await table.drop_index(idx.name) + await self.schema.ensure_fts_indexes(table) + + # ── Read ─────────────────────────────────────────────────────────────── + + async def count(self) -> int: + """Total row count.""" + table = await self._table() + return await table.count_rows() + + async def get_by_id( + self, + id_value: str, + *, + id_field: str = "id", + ) -> T | None: + """Fetch one row by scalar PK; ``None`` if missing. + + Uses LanceDB scalar filter `` = ''``. Single + quotes in ``id_value`` are doubled to avoid breaking the SQL-like + predicate; everos's PK convention is ``_`` + which never contains quotes, so the escape is defensive. + """ + table = await self._table() + rows = ( + await table.query() + .where(f"{id_field} = '{_q(id_value)}'") + .limit(1) + .to_list() + ) + if not rows: + return None + return self.schema.model_validate(rows[0]) + + async def find_where( + self, + where: str, + *, + limit: int = 100, + ) -> list[T]: + """Scalar query returning *typed* schema instances. + + Like :meth:`search` but returns ``list[T]`` rather than raw + LanceDB row dicts. No vector ANN; pure scalar filter only. + Use :meth:`search` when you need ``_distance`` or want to mix + ANN with filters. + """ + table = await self._table() + rows = await table.query().where(where).limit(limit).to_list() + return [self.schema.model_validate(r) for r in rows] + + async def find_one_where(self, where: str) -> T | None: + """Single-row variant of :meth:`find_where` (``None`` if no match).""" + rows = await self.find_where(where, limit=1) + return rows[0] if rows else None + + async def find_where_paginated( + self, + where: str, + *, + sort_by: str, + descending: bool = True, + page: int = 1, + page_size: int = 20, + max_fetch: int = 20000, + ) -> tuple[list[T], int]: + """Paginated scalar query with in-memory sort. + + LanceDB has no native ``ORDER BY``. The chassis fetches up to + ``max_fetch`` rows matching ``where``, sorts the resulting Arrow + table by ``sort_by``, then slices ``page`` × ``page_size``. The + *true* row count of the predicate is returned alongside the + page so callers can render pagination controls without a second + query. + + Args: + where: SQL-like scalar predicate. Required (no implicit + full-table scan from ``find_where_paginated``). + sort_by: Column name to sort the result set by. + descending: ``True`` (default) → newest first; ``False`` → + ascending. + page: 1-indexed page number. + page_size: Rows per page. + max_fetch: Cap on rows pulled before the in-memory sort. + When the predicate matches more rows than this cap the + page is sorted over an *arbitrary* prefix and the page + contents are only approximately correct — the chassis + emits a warning so the caller learns about the + truncation. + + Returns: + ``(rows, total)`` — ``rows`` is the typed page, + ``total`` is ``count_rows(filter=where)`` (the predicate's + true match count, regardless of ``max_fetch``). + """ + table = await self._table() + total = await table.count_rows(filter=where) + if total > max_fetch: + logger.warning( + "find_where_paginated truncated", + extra={ + "table": self.table_name, + "where": where, + "total": total, + "max_fetch": max_fetch, + }, + ) + arrow_tbl = await table.query().where(where).limit(max_fetch).to_arrow() + order = "descending" if descending else "ascending" + arrow_tbl = arrow_tbl.sort_by([(sort_by, order)]) + offset = (page - 1) * page_size + page_rows = arrow_tbl.slice(offset, page_size) + return ( + [self.schema.model_validate(r) for r in page_rows.to_pylist()], + total, + ) + + async def find_by_owner( + self, + owner_id: str, + *, + limit: int = 100, + ) -> list[T]: + """Fetch rows by ``owner_id`` (5 business tables share this column).""" + return await self.find_where( + f"owner_id = '{_q(owner_id)}'", + limit=limit, + ) + + async def find_by_md_path(self, md_path: str) -> T | None: + """Reverse-lookup from md path (cascade maps md edit → row).""" + return await self.find_one_where(f"md_path = '{_q(md_path)}'") + + async def search( + self, + *, + vector: Sequence[float] | None = None, + where: str | None = None, + limit: int = 10, + ) -> list[dict[str, Any]]: + """Hybrid search: optional vector ANN + scalar SQL-like predicate. + + Args: + vector: Embedding to find nearest rows for; ``None`` skips ANN. + where: SQL-like predicate (e.g. ``"tags = 'meeting'"``). + limit: Max rows. + + Returns: + List of row dicts (LanceDB native shape — fields depend on + ``schema``; ``_distance`` added when ``vector`` is given). + """ + table = await self._table() + q = table.query() + if vector is not None: + q = q.nearest_to(list(vector)) + if where is not None: + q = q.where(where) + return await q.limit(limit).to_list() + + # ── Delete ───────────────────────────────────────────────────────────── + + async def delete(self, predicate: str) -> None: + """Delete rows matching a SQL-like predicate.""" + table = await self._table() + async with self._write_lock(self.table_name): + await table.delete(predicate) + + async def delete_by_md_path(self, md_path: str) -> int: + """Delete every row whose ``md_path`` matches; return rows deleted. + + Cascade handler calls this when an md file is removed on disk + (or when reverse-reconcile discovers an orphaned LanceDB row). + Single quotes in ``md_path`` are doubled defensively. + """ + table = await self._table() + async with self._write_lock(self.table_name): + result = await table.delete(f"md_path = '{_q(md_path)}'") + return int(result.num_deleted_rows) + + +class LanceDailyLogRepoBase[T: BaseLanceTable](LanceRepoBase[T]): + """LanceRepoBase + queries unique to daily-log tables. + + Daily-log tables (``episode`` / ``atomic_fact`` / ``foresight`` / + ``agent_case``) share a fixed schema slice: ``entry_id`` (md seq + id), ``session_id`` (conversation scope), and ``parent_type`` / + ``parent_id`` (record lineage). The queries below compose those + columns; ``agent_skill`` is *not* a daily-log (it is a named + single-file entity) and uses :class:`LanceRepoBase` directly. + """ + + async def find_by_owner_entry( + self, + owner_id: str, + entry_id: str, + *, + app_id: str = "default", + project_id: str = "default", + ) -> T | None: + """Single point-query by ``(app, project, owner_id, entry_id)``. + + ``entry_id`` is only unique within a (app, project, owner) scope — + the same ``ac__`` recurs in another space — so the + scope segments are part of the predicate to avoid a cross-space hit. + """ + return await self.find_one_where( + f"owner_id = '{_q(owner_id)}' AND entry_id = '{_q(entry_id)}' " + f"AND app_id = '{_q(app_id)}' AND project_id = '{_q(project_id)}'" + ) + + async def find_by_owner_entries( + self, + owner_id: str, + entry_ids: Sequence[str], + *, + app_id: str = "default", + project_id: str = "default", + ) -> list[T]: + """Bulk point-query by ``(app, project, owner_id, entry_id IN ...)``. + + Empty ``entry_ids`` short-circuits to ``[]`` rather than emit a + ``WHERE entry_id IN ()`` predicate (LanceDB rejects empty + tuples). The query's ``limit`` is bound to ``len(entry_ids)`` + because at most one row per id can exist under one (app, project, + owner) scope. + """ + if not entry_ids: + return [] + quoted = ", ".join(f"'{_q(eid)}'" for eid in entry_ids) + return await self.find_where( + f"owner_id = '{_q(owner_id)}' AND entry_id IN ({quoted}) " + f"AND app_id = '{_q(app_id)}' AND project_id = '{_q(project_id)}'", + limit=len(entry_ids), + ) + + async def find_by_session( + self, + owner_id: str, + session_id: str, + *, + limit: int = 100, + ) -> list[T]: + """Every row in one conversation ``session_id`` under ``owner_id``.""" + return await self.find_where( + f"owner_id = '{_q(owner_id)}' AND session_id = '{_q(session_id)}'", + limit=limit, + ) + + async def find_by_parent( + self, + parent_type: str, + parent_id: str, + *, + limit: int = 100, + ) -> list[T]: + """Every row whose parent matches ``(parent_type, parent_id)``.""" + return await self.find_where( + f"parent_type = '{_q(parent_type)}' AND parent_id = '{_q(parent_id)}'", + limit=limit, + ) diff --git a/src/everos/core/persistence/locking.py b/src/everos/core/persistence/locking.py new file mode 100644 index 00000000..4759cb0d --- /dev/null +++ b/src/everos/core/persistence/locking.py @@ -0,0 +1,76 @@ +"""Process-wide exclusive lock on a memory-root. + +Uses ``fcntl.flock`` (POSIX advisory locking, available on Linux + macOS; +Windows is not supported — see project README on platform scope). The +public surface is an :func:`contextlib.asynccontextmanager` so callers +use ``async with memory_root_lock(mr):``; the underlying syscalls have +no async equivalent so they run in a worker thread via +:func:`anyio.to_thread.run_sync`. +""" + +from __future__ import annotations + +import fcntl +import os +from collections.abc import AsyncIterator +from contextlib import asynccontextmanager + +import anyio + +from .memory_root import MemoryRoot + + +class LockError(RuntimeError): + """Raised when the memory-root lock cannot be acquired in non-blocking mode.""" + + +@asynccontextmanager +async def memory_root_lock( + memory_root: MemoryRoot, + *, + blocking: bool = True, +) -> AsyncIterator[None]: + """Acquire an exclusive process lock on the memory-root. + + Args: + memory_root: The memory-root to lock. The lock anchor file + (``/.lock``) is created on first use. + blocking: If ``True`` (default), wait until the lock is free. If + ``False``, raise :class:`LockError` immediately when another + process holds it. + + Raises: + LockError: When ``blocking=False`` and the lock is already held. + """ + await anyio.Path(memory_root.root).mkdir(parents=True, exist_ok=True) + lock_path = memory_root.lock_file + + # Open the anchor file (create on first use). The fd, not the path, is + # what fcntl operates on. ``os.open`` is microsecond-fast but offloaded + # for consistency with the rest of the lock acquisition flow. + fd = await anyio.to_thread.run_sync( + lambda: os.open(lock_path, os.O_RDWR | os.O_CREAT, 0o644) + ) + + flags = fcntl.LOCK_EX + if not blocking: + flags |= fcntl.LOCK_NB + + try: + await anyio.to_thread.run_sync(fcntl.flock, fd, flags) + except BlockingIOError as exc: + await anyio.to_thread.run_sync(os.close, fd) + raise LockError( + f"another process already holds the memory-root lock at {lock_path}" + ) from exc + + # Lock acquired — release + close strictly on exit. The BlockingIOError + # path above already cleaned up its fd, so it must NOT enter this + # finally block (otherwise we'd double-close). + try: + yield + finally: + try: + await anyio.to_thread.run_sync(fcntl.flock, fd, fcntl.LOCK_UN) + finally: + await anyio.to_thread.run_sync(os.close, fd) diff --git a/src/everos/core/persistence/markdown/__init__.py b/src/everos/core/persistence/markdown/__init__.py new file mode 100644 index 00000000..2aed616d --- /dev/null +++ b/src/everos/core/persistence/markdown/__init__.py @@ -0,0 +1,62 @@ +"""Markdown file IO toolkit. + +Atomic write + YAML frontmatter parse/dump + entry marker parse + +audit-form structured-entry parsing. Knows nothing about business +models (no MemCell / Episode); the :class:`Entry` here is a +*marker-delimited* span within a markdown body, not a business record. + +External usage (IO + parse): + from everos.core.persistence.markdown import ( + Entry, EntryId, StructuredEntry, + MarkdownReader, MarkdownWriter, ParsedMarkdown, + parse_frontmatter, dump_frontmatter, + split_entries, find_entry, + parse_structured_entry, render_structured_entry, + ) + +External usage (frontmatter schema chassis): + from everos.core.persistence.markdown import ( + BaseFrontmatter, UserScopedFrontmatter, AgentScopedFrontmatter, + DailyLogPathMixin, SkillPathMixin, ProfilePathMixin, + ) +""" + +from .entries import Entry as Entry +from .entries import EntryId as EntryId +from .entries import StructuredEntry as StructuredEntry +from .entries import find_entry as find_entry +from .entries import parse_structured_entry as parse_structured_entry +from .entries import render_structured_entry as render_structured_entry +from .entries import split_entries as split_entries +from .frontmatter import AgentScopedFrontmatter as AgentScopedFrontmatter +from .frontmatter import BaseFrontmatter as BaseFrontmatter +from .frontmatter import DailyLogPathMixin as DailyLogPathMixin +from .frontmatter import ProfilePathMixin as ProfilePathMixin +from .frontmatter import SkillPathMixin as SkillPathMixin +from .frontmatter import UserScopedFrontmatter as UserScopedFrontmatter +from .frontmatter import dump_frontmatter as dump_frontmatter +from .frontmatter import parse_frontmatter as parse_frontmatter +from .parsed import ParsedMarkdown as ParsedMarkdown +from .reader import MarkdownReader as MarkdownReader +from .writer import MarkdownWriter as MarkdownWriter + +__all__ = [ + "AgentScopedFrontmatter", + "BaseFrontmatter", + "DailyLogPathMixin", + "Entry", + "EntryId", + "MarkdownReader", + "MarkdownWriter", + "ParsedMarkdown", + "ProfilePathMixin", + "SkillPathMixin", + "StructuredEntry", + "UserScopedFrontmatter", + "dump_frontmatter", + "find_entry", + "parse_frontmatter", + "parse_structured_entry", + "render_structured_entry", + "split_entries", +] diff --git a/src/everos/core/persistence/markdown/entries.py b/src/everos/core/persistence/markdown/entries.py new file mode 100644 index 00000000..6dabb987 --- /dev/null +++ b/src/everos/core/persistence/markdown/entries.py @@ -0,0 +1,368 @@ +"""Markdown entries — id format, marker spans, and audit-form parsing. + +Three closely-related entry concepts live together here so a reader +sees the whole entry surface in one file: + +1. :class:`EntryId` — the ``__`` structured id + stamped into each daily-log entry's open / close markers. Carries + the prefix declared by the frontmatter schema, the date bucket, and + the in-file zero-padded sequence. + +2. :class:`Entry` — a marker-delimited span inside a markdown body:: + + + ...content... + + + :func:`split_entries` and :func:`find_entry` locate these spans + without interpreting the inner content. Higher layers (writers, + cascade) parse it per record type. + +3. :class:`StructuredEntry` — :class:`Entry` extended with the parsed + audit-form body fields (header / inline / sections). Built either + from a raw body string via :func:`parse_structured_entry` or from + an existing :class:`Entry` via :meth:`Entry.as_structured`. + +Audit-form layout:: + + ##
← optional H2 (usually entry id, for grep) + + **key**: value ← inline fields, one per line + **key2**: value2 + + ### Section Title ← section fields: H3 + free-form text + body content... + + ### Another Section + more content... + +The audit chassis is intentionally **type-agnostic** — every field +round-trips as a string. Inline values are stringified on render +(lists become ``[a, b, c]``, scalars use ``str()``); on parse +everything is the raw text after the colon. Section titles are kept +verbatim. This keeps parsing tolerant of stray fields, wrapped +strings, and manually-typed timestamps; the strong-typed model lives +in business writers + the SQLite/LanceDB indexes. + +Cross-user uniqueness is handled at the database layer via a composite +``_`` field; it is *not* encoded into the +:class:`EntryId` string itself. +""" + +from __future__ import annotations + +import datetime as _dt +import re +from collections.abc import Mapping +from dataclasses import dataclass, field +from typing import Self + +# ── EntryId — structured id for marker stamping ───────────────────────── + +_DATE_FMT = "%Y%m%d" +_SEQ_DIGITS = 8 +"""Minimum zero-padding for the in-file seq. + +8 digits keeps lexicographic order == numeric order up to 10**8 +entries per file (per user, per day). ``format()`` is "at least 8" — +larger seqs emit more digits without truncation. ``parse`` is +permissive: shorter (legacy 4-digit) and longer seq strings both +parse cleanly; format normalises to >= 8 digits on round-trip. +""" + + +@dataclass(frozen=True, slots=True) +class EntryId: + """Parsed components of an entry id (``__``).""" + + prefix: str + date: _dt.date + seq: int + + def format(self) -> str: + """Render as ``__``.""" + return ( + f"{self.prefix}_{self.date.strftime(_DATE_FMT)}_{self.seq:0{_SEQ_DIGITS}d}" + ) + + def __str__(self) -> str: # noqa: D401 + return self.format() + + @classmethod + def parse(cls, s: str) -> Self: + """Parse ``__``. + + Uses ``rsplit("_", 2)`` so a multi-segment prefix (rare, but + possible) is preserved as-is. + """ + parts = s.rsplit("_", 2) + if len(parts) != 3: + raise ValueError(f"invalid entry id format: {s!r}") + prefix, date_str, seq_str = parts + if not prefix: + raise ValueError(f"empty prefix in entry id: {s!r}") + try: + d = _dt.datetime.strptime(date_str, _DATE_FMT).date() + except ValueError as exc: + raise ValueError(f"invalid date in entry id: {s!r}") from exc + try: + seq = int(seq_str) + except ValueError as exc: + raise ValueError(f"invalid seq in entry id: {s!r}") from exc + if seq < 0: + raise ValueError(f"negative seq in entry id: {s!r}") + return cls(prefix=prefix, date=d, seq=seq) + + @classmethod + def next_for(cls, prefix: str, date: _dt.date, current_count: int) -> Self: + """Build the id for the next entry given the file's current count. + + ``current_count`` is the value of ``frontmatter.entry_count`` + *before* this append. The new id gets ``seq = current_count + 1``. + """ + if current_count < 0: + raise ValueError(f"current_count must be >= 0, got {current_count}") + return cls(prefix=prefix, date=date, seq=current_count + 1) + + +# ── Entry — marker-delimited span inside a body ───────────────────────── + +# Filename / URL-safe id alphabet for the marker. +_ID_PATTERN = r"[A-Za-z0-9_-]+" +_OPEN_RE = re.compile(rf"") + + +@dataclass(frozen=True) +class Entry: + """One marker-delimited entry within a markdown body. + + Attributes: + id: Value between ``entry:`` and ``-->`` in the open marker. + body: Content between the open and close markers, with one leading + and one trailing newline removed (typical formatter output). + start: Offset of the opening ```` in the source body. + end: Offset just past the closing ```` in the source. + """ + + id: str + body: str + start: int + end: int + + def as_structured(self) -> StructuredEntry: + """Parse my body as audit-form and return a :class:`StructuredEntry`. + + The id / body / start / end fields are preserved; the parsed + ``header`` / ``inline`` / ``sections`` are added on top. + """ + return parse_structured_entry(self.body, _origin=self) + + +def split_entries(body: str) -> list[Entry]: + """Scan ``body`` and return every entry in order. + + Unmatched / unterminated open markers stop the scan at the first + such marker — partial entries are not returned. Callers needing + strict validation should layer a dedicated check on top. + """ + entries: list[Entry] = [] + pos = 0 + while True: + open_match = _OPEN_RE.search(body, pos) + if open_match is None: + break + entry_id = open_match.group(1) + close_match = _close_re_for(entry_id).search(body, open_match.end()) + if close_match is None: + # Unterminated entry — abort further scanning. + break + entries.append( + Entry( + id=entry_id, + body=_strip_one_newline(body[open_match.end() : close_match.start()]), + start=open_match.start(), + end=close_match.end(), + ) + ) + pos = close_match.end() + return entries + + +def find_entry(body: str, entry_id: str) -> Entry | None: + """Find the first entry with ``entry_id``, or ``None``.""" + open_re = re.compile(rf"") + open_match = open_re.search(body) + if open_match is None: + return None + close_match = _close_re_for(entry_id).search(body, open_match.end()) + if close_match is None: + return None + return Entry( + id=entry_id, + body=_strip_one_newline(body[open_match.end() : close_match.start()]), + start=open_match.start(), + end=close_match.end(), + ) + + +def _close_re_for(entry_id: str) -> re.Pattern[str]: + """Build the close-marker regex for a specific id.""" + return re.compile(rf"") + + +def _strip_one_newline(text: str) -> str: + """Strip one leading and one trailing newline (typical formatter padding).""" + if text.startswith("\r\n"): + text = text[2:] + elif text.startswith("\n"): + text = text[1:] + if text.endswith("\r\n"): + text = text[:-2] + elif text.endswith("\n"): + text = text[:-1] + return text + + +# ── StructuredEntry — Entry + parsed audit-form fields ────────────────── + +# H2 line: ``##
``. +_H2_RE = re.compile(r"^##\s+(.+?)\s*$", re.MULTILINE) +# Inline field: ``**key**: value``. Anchored to line start so a stray +# ``**emphasis**`` mid-paragraph isn't mistaken for a field. +_INLINE_RE = re.compile( + r"^\*\*(?P[^*\n]+?)\*\*:\s*(?P.*?)\s*$", + re.MULTILINE, +) +# H3 line: ``### Title``. +_H3_RE = re.compile(r"^###\s+(.+?)\s*$", re.MULTILINE) + + +@dataclass(frozen=True) +class StructuredEntry(Entry): + """:class:`Entry` whose body has been parsed as audit-form data. + + Inherits ``id`` / ``body`` / ``start`` / ``end`` from :class:`Entry` + (zeroed when built from a raw body string with no marker context) + and adds three parsed views of the body: the optional H2 header, + the inline ``**key**: value`` map, and the ``### Title`` sections. + + Audit-form values are strings only; type coercion is the caller's + job (a strong-typed model lives in the writer / index). + """ + + header: str | None = None + inline: dict[str, str] = field(default_factory=dict) + sections: dict[str, str] = field(default_factory=dict) + + +def render_structured_entry( + *, + header: str | None = None, + inline: Mapping[str, object] | None = None, + sections: Mapping[str, str] | None = None, +) -> str: + """Render an audit-form entry body. + + Args: + header: Optional H2 line at the top (typically the entry id — + redundant with the marker but useful for plain-text grep). + inline: ``{key: value}`` rendered as ``**key**: value``. Values + are stringified: ``list``/``tuple`` become ``[a, b, c]``; + ``None`` becomes the empty string; everything else uses + ``str()``. + sections: ``{title: body}`` rendered as ``### Title`` plus the + body text. Title is verbatim; body's trailing whitespace is + stripped. + + Returns: + The rendered string, no trailing newline (the caller — typically + :meth:`MarkdownWriter.append_entry` — handles markers + newlines). + """ + inline = inline or {} + sections = sections or {} + lines: list[str] = [] + + if header: + lines.append(f"## {header}") + lines.append("") + + for key, value in inline.items(): + lines.append(f"**{key}**: {_render_value(value)}") + + for title, body in sections.items(): + lines.append("") + lines.append(f"### {title}") + lines.append(body.rstrip()) + + return "\n".join(lines) + + +def parse_structured_entry( + body: str, *, _origin: Entry | None = None +) -> StructuredEntry: + """Parse an audit-form entry body. Strings only — no type coercion. + + Tolerant of: + + - missing H2 (``header`` will be ``None``) + - inline fields appearing before, between or after sections + (only matches before the first H3 are taken as the inline block) + - extra whitespace and stray lines (silently kept inside the + enclosing section's body) + + When called via :meth:`Entry.as_structured`, the ``_origin`` Entry + contributes its ``id`` / ``start`` / ``end``; otherwise those fall + back to ``""`` / ``0`` / ``len(body)``. + + Returns: + :class:`StructuredEntry` with everything as strings. + """ + text = body.strip("\n") + + # Split on H3 lines. + parts = _H3_RE.split(text) + head = parts[0] + sections_dict: dict[str, str] = {} + for i in range(1, len(parts), 2): + title = parts[i].strip() + content = parts[i + 1] if i + 1 < len(parts) else "" + sections_dict[title] = content.strip("\n").rstrip() + + header: str | None = None + h2 = _H2_RE.search(head) + if h2: + header = h2.group(1).strip() + + inline_dict: dict[str, str] = { + m.group("key").strip(): m.group("value").strip() + for m in _INLINE_RE.finditer(head) + } + + if _origin is not None: + return StructuredEntry( + id=_origin.id, + body=_origin.body, + start=_origin.start, + end=_origin.end, + header=header, + inline=inline_dict, + sections=sections_dict, + ) + return StructuredEntry( + id="", + body=body, + start=0, + end=len(body), + header=header, + inline=inline_dict, + sections=sections_dict, + ) + + +def _render_value(value: object) -> str: + """Stringify an inline value the audit-friendly way.""" + if value is None: + return "" + if isinstance(value, list | tuple): + return "[" + ", ".join(str(item) for item in value) + "]" + return str(value) diff --git a/src/everos/core/persistence/markdown/frontmatter.py b/src/everos/core/persistence/markdown/frontmatter.py new file mode 100644 index 00000000..26ca436f --- /dev/null +++ b/src/everos/core/persistence/markdown/frontmatter.py @@ -0,0 +1,300 @@ +"""Frontmatter — YAML block parse / dump + L1 schema chassis. + +Frontmatter is the leading ``---``-delimited YAML block at the top of +a markdown document:: + + --- + title: Hello + tags: [a, b] + --- + # Body starts here + +Two complementary surfaces live here: + +1. :func:`parse_frontmatter` / :func:`dump_frontmatter` — schema-free + YAML helpers (``yaml.safe_load`` / ``yaml.safe_dump``, + ``sort_keys=False`` so caller-controlled key order is preserved). + +2. The L1 chassis classes — :class:`BaseFrontmatter`, + :class:`UserScopedFrontmatter`, :class:`AgentScopedFrontmatter` — + which fix the *absolute-readonly* fields (``id`` / ``type`` / + ``schema_version``) plus scope (``user_id`` / ``agent_id`` + + ``track``). Every business frontmatter schema in + ``infra/persistence/markdown/mds/`` subclasses one of these. + +Concrete business schemas (``UserMemcellDailyFrontmatter``, +``SkillFrontmatter``, …) live in ``infra``; they add per-record +business fields plus the path-resolution metadata daily-log writers +need (``ENTRY_ID_PREFIX`` / ``DIR_NAME`` / ``FILE_PREFIX``). +""" + +from __future__ import annotations + +from collections.abc import Mapping +from typing import Any, ClassVar, Literal + +import yaml +from pydantic import BaseModel, ConfigDict + +# ── YAML helpers ──────────────────────────────────────────────────────── + +_DELIM = "---" + + +def parse_frontmatter(text: str) -> tuple[dict[str, Any], str]: + """Parse a leading ``---\\n...\\n---\\n`` YAML block. + + Returns: + (meta, remainder): ``meta`` is the parsed YAML mapping (empty dict + if no frontmatter present, malformed, or non-mapping). ``remainder`` + is everything after the closing delimiter line — including the body's + leading content as-is. + + Notes: + - If the document does not start with ``---``, returns ``({}, text)`` + unchanged. + - If a closing ``---`` line is not found, returns ``({}, text)``. + - If the YAML block is empty (``---\\n---\\n``), returns + ``({}, remainder)``. + - If the parsed YAML is not a mapping (e.g. a scalar list), returns + ``({}, text)`` — frontmatter must be a mapping. + """ + if not text.startswith(_DELIM): + return {}, text + + # Skip the opening "---" and the newline that must follow it. + rest = text[len(_DELIM) :] + if rest.startswith("\r\n"): + rest = rest[2:] + elif rest.startswith("\n"): + rest = rest[1:] + else: + # Opening "---" not followed by a newline → not a valid frontmatter. + return {}, text + + closing_idx = _find_closing_delim(rest) + if closing_idx is None: + return {}, text + + yaml_block = rest[:closing_idx] + remainder = rest[closing_idx + len(_DELIM) :] + # Drop the newline that follows the closing delimiter, if any. + if remainder.startswith("\r\n"): + remainder = remainder[2:] + elif remainder.startswith("\n"): + remainder = remainder[1:] + + parsed: Any = yaml.safe_load(yaml_block) if yaml_block.strip() else {} + if parsed is None: + parsed = {} + if not isinstance(parsed, dict): + return {}, text + return parsed, remainder + + +def dump_frontmatter(meta: Mapping[str, Any]) -> str: + """Render a mapping as a ``---\\n\\n---\\n`` block. + + An empty mapping yields the empty string (no delimiters). The YAML + payload preserves caller-supplied key order (``sort_keys=False``). + """ + if not meta: + return "" + yaml_block = yaml.safe_dump( + dict(meta), + sort_keys=False, + allow_unicode=True, + default_flow_style=False, + ) + return f"{_DELIM}\n{yaml_block}{_DELIM}\n" + + +def _find_closing_delim(text: str) -> int | None: + """Find the offset of a line that is exactly ``---``. + + A "line" is text between two newlines (or string boundaries). + Returns the offset of the first character of the matching line, or + ``None`` if no such line exists. + """ + pos = 0 + while pos < len(text): + nl = text.find("\n", pos) + line = text[pos:nl] if nl != -1 else text[pos:] + if line.rstrip("\r") == _DELIM: + return pos + if nl == -1: + return None + pos = nl + 1 + return None + + +# ── L1 schema chassis ─────────────────────────────────────────────────── + + +class BaseFrontmatter(BaseModel): + """L1 fields every markdown frontmatter must carry. + + These match the *absolute-readonly* tier in the EverOS Markdown First + spec — they identify the record across markdown ↔ LanceDB and must + never be rewritten by a human edit. + + Subclasses add scope (``UserScopedFrontmatter`` / + ``AgentScopedFrontmatter``) plus per-record business fields. + """ + + SCOPE_DIR: ClassVar[str] = "" + """Top-level directory under the memory-root that holds this kind. + + Scope mixins set this to ``"users"`` / ``"agents"``. Scope-agnostic + schemas (rare) leave it empty; consumers that need to resolve a path + (writers, layout reverse-lookup) must reject schemas with empty + ``SCOPE_DIR``. + """ + + id: str + type: str + schema_version: int = 1 + + # Permit additional fields so L2 system-managed metadata + # (``md_sha256``, ``last_indexed_at``, ``lsn``, …) can ride along on + # the same model without forcing every subclass to redeclare them. + model_config = ConfigDict(extra="allow") + + @classmethod + def path_glob(cls) -> str: + """Return an ``fnmatch``-style glob (relative to memory-root) + covering every markdown file this schema describes. + + Used by the cascade kind registry — the scanner walks every kind's + ``path_glob()`` to enumerate eligible files without hard-coding + path patterns in cascade. The schema is the single source of truth + for both the writer's path resolution and the scanner's enumeration. + + Subclasses must override — typically by mixing in + :class:`DailyLogPathMixin` or :class:`SkillPathMixin` *before* the + scope mixin in the MRO so this abstract version is shadowed. + """ + raise NotImplementedError( + f"{cls.__name__} must declare path_glob() " + f"(mix in DailyLogPathMixin / SkillPathMixin, or override directly)" + ) + + +class DailyLogPathMixin: + """Path strategy for daily-log files. + + Files live at ``///-.md``. + Subclasses must inherit a scope mixin (``UserScopedFrontmatter`` / + ``AgentScopedFrontmatter``) supplying ``SCOPE_DIR``, and must declare + their own ``DIR_NAME`` / ``FILE_PREFIX`` ClassVars. + + Place **this mixin first** so Python's MRO resolves ``path_glob()`` to + the mixin's concrete implementation rather than + :meth:`BaseFrontmatter.path_glob`'s ``NotImplementedError`` stub:: + + class EpisodeDailyFrontmatter(DailyLogPathMixin, UserScopedFrontmatter): + DIR_NAME: ClassVar[str] = "episodes" + FILE_PREFIX: ClassVar[str] = "episode" + ... + """ + + DIR_NAME: ClassVar[str] + FILE_PREFIX: ClassVar[str] + SCOPE_DIR: ClassVar[str] + + @classmethod + def path_glob(cls) -> str: + # Leading ``*/*/`` matches the / scope prefix that + # precedes every user-visible dir; the scanner's ``root.glob`` is + # anchored at root, so the prefix is mandatory (without it nothing + # matches), and the watcher's right-anchored ``PurePosixPath.match`` + # agrees on the same shape. + return f"*/*/{cls.SCOPE_DIR}/*/{cls.DIR_NAME}/{cls.FILE_PREFIX}-*.md" + + +class SkillPathMixin: + """Path strategy for skill-directory files. + + Each skill lives at ``/// + /``. The glob covers + every skill's main file; sibling ``references/*.md`` and ``scripts/*`` + are excluded (they ride alongside the main file and the cascade + daemon rebuilds the index column by concatenation, see + :class:`AgentSkillFrontmatter`'s docstring). + + Place **this mixin first** so MRO resolves ``path_glob()`` here:: + + class AgentSkillFrontmatter(SkillPathMixin, AgentScopedFrontmatter): + SKILLS_CONTAINER_NAME: ClassVar[str] = "skills" + SKILL_DIR_PREFIX: ClassVar[str] = "skill_" + SKILL_MAIN_FILENAME: ClassVar[str] = "SKILL.md" + ... + """ + + SKILLS_CONTAINER_NAME: ClassVar[str] + SKILL_DIR_PREFIX: ClassVar[str] + SKILL_MAIN_FILENAME: ClassVar[str] + SCOPE_DIR: ClassVar[str] + + @classmethod + def path_glob(cls) -> str: + # Leading ``*/*/`` matches the / scope prefix. + return ( + f"*/*/{cls.SCOPE_DIR}/*/{cls.SKILLS_CONTAINER_NAME}/" + f"{cls.SKILL_DIR_PREFIX}*/{cls.SKILL_MAIN_FILENAME}" + ) + + +class ProfilePathMixin: + """Path strategy for single-file profile markdown. + + Profiles live at ``//`` — + one fixed-name file directly under the scope's owner directory, no + intermediate ``/`` segment (unlike daily-logs) and no per-name + subdir (unlike skills). Subclasses must inherit a scope mixin + (``UserScopedFrontmatter`` / ``AgentScopedFrontmatter``) supplying + ``SCOPE_DIR`` and declare their own ``PROFILE_FILENAME``. + + Place **this mixin first** so MRO resolves ``path_glob()`` here:: + + class UserProfileFrontmatter(ProfilePathMixin, UserScopedFrontmatter): + PROFILE_FILENAME: ClassVar[str] = "user.md" + ... + """ + + PROFILE_FILENAME: ClassVar[str] + SCOPE_DIR: ClassVar[str] + + @classmethod + def path_glob(cls) -> str: + # Leading ``*/*/`` matches the / scope prefix. + return f"*/*/{cls.SCOPE_DIR}/*/{cls.PROFILE_FILENAME}" + + +class UserScopedFrontmatter(BaseFrontmatter): + """Records that belong to a single user (track = ``user``). + + The frontmatter only carries the *file-level* scope (``user_id``, + which the path itself already expresses); business attributes like + ``group_id`` live inside each entry's structured body — see + :class:`StructuredEntry` in :mod:`.entries`. + """ + + SCOPE_DIR: ClassVar[str] = "users" + + user_id: str + track: Literal["user"] = "user" + + +class AgentScopedFrontmatter(BaseFrontmatter): + """Records that belong to a single agent (track = ``agent``). + + Same scope-vs-business split as :class:`UserScopedFrontmatter`: + ``agent_id`` is the file-level scope; ``group_id`` etc. ride on + each entry, not on the file frontmatter. + """ + + SCOPE_DIR: ClassVar[str] = "agents" + + agent_id: str + track: Literal["agent"] = "agent" diff --git a/src/everos/core/persistence/markdown/parsed.py b/src/everos/core/persistence/markdown/parsed.py new file mode 100644 index 00000000..2e3a1d30 --- /dev/null +++ b/src/everos/core/persistence/markdown/parsed.py @@ -0,0 +1,31 @@ +"""Parsed-markdown data type. + +The output shape of :class:`MarkdownReader` is held here, separate +from the reader implementation: callers that only consume parse +results don't need to import the reader machinery, and downstream +modules (writer, business readers) can produce :class:`ParsedMarkdown` +without going through ``MarkdownReader.read`` if they already hold +the pieces. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +from .entries import Entry + + +@dataclass(frozen=True) +class ParsedMarkdown: + """A markdown document after parsing. + + Attributes: + frontmatter: Parsed YAML mapping (empty dict if no frontmatter block). + body: Document text after the frontmatter block; not entry-stripped. + entries: Marker-delimited entries discovered inside ``body``. + """ + + frontmatter: dict[str, Any] + body: str + entries: list[Entry] = field(default_factory=list) diff --git a/src/everos/core/persistence/markdown/reader.py b/src/everos/core/persistence/markdown/reader.py new file mode 100644 index 00000000..c058e6ff --- /dev/null +++ b/src/everos/core/persistence/markdown/reader.py @@ -0,0 +1,42 @@ +"""Markdown file reader. + +Loads a markdown document and splits it into: + + 1. ``frontmatter`` — parsed YAML (empty dict if absent) + 2. ``body`` — raw text after the closing ``---`` delimiter + 3. ``entries`` — marker-delimited spans inside ``body`` + +The reader is purely parsing; it does not validate frontmatter shape, +entry content, or cross-references. Higher layers add business-aware +checks. The :class:`ParsedMarkdown` data type lives in :mod:`.parsed`. + +``parse`` is sync (pure in-memory string processing). ``read`` is async +and uses :class:`anyio.Path` so file I/O does not block the event loop. +""" + +from __future__ import annotations + +from pathlib import Path + +import anyio + +from .entries import split_entries +from .frontmatter import parse_frontmatter +from .parsed import ParsedMarkdown + + +class MarkdownReader: + """Parse markdown files / strings into :class:`ParsedMarkdown`.""" + + @staticmethod + def parse(text: str) -> ParsedMarkdown: + """Parse already-loaded text (no IO).""" + meta, body = parse_frontmatter(text) + entries = split_entries(body) + return ParsedMarkdown(frontmatter=meta, body=body, entries=entries) + + @staticmethod + async def read(path: Path) -> ParsedMarkdown: + """Read the file at ``path`` and parse its content.""" + text = await anyio.Path(path).read_text(encoding="utf-8") + return MarkdownReader.parse(text) diff --git a/src/everos/core/persistence/markdown/writer.py b/src/everos/core/persistence/markdown/writer.py new file mode 100644 index 00000000..82fab25b --- /dev/null +++ b/src/everos/core/persistence/markdown/writer.py @@ -0,0 +1,269 @@ +"""Markdown file writer with atomic write semantics. + +Atomicity is provided by writing to a same-directory temp file +(``..tmp.``) and using :func:`os.replace` to rename it onto +the target. Keeping the temp file in the same directory guarantees the +rename is on the same filesystem (POSIX rename is atomic only within a +single fs). + +All public methods are async. File I/O (``read_text`` / ``write_text`` +/ ``mkdir``) goes through :class:`anyio.Path`; the few syscalls without +a native async equivalent (``os.fsync`` / ``os.replace`` / ``unlink`` +in the cleanup path) are offloaded via :func:`anyio.to_thread.run_sync`. + +In-process per-path locking +--------------------------- +:meth:`append_entry` / :meth:`append_entries` are read-modify-write of +the whole file (load frontmatter+body, merge an entry block, atomic +write the result). The atomic write itself is safe, but the read→write +window crosses ``await`` points. Concurrent asyncio tasks targeting the +same path would otherwise lose-update each other (both read N entries, +both produce N+1, second write overwrites the first → 1 entry lost). + +To prevent this, an in-process per-path :class:`asyncio.Lock` is held +across the entire read-modify-write sequence. Lock objects live on the +writer instance (not class-level) so they bind to the event loop active +when the writer was constructed — this avoids the +"Lock bound to different loop" failure mode that surfaces when +pytest-asyncio rebuilds the loop between tests but module-level writer +singletons leak Lock objects across boundaries. + +Process-level coordination (multi-process writers against the same +memory-root) remains the job of +:func:`everos.core.persistence.locking.memory_root_lock`, which uses +``fcntl.flock``. The two locks compose: per-path async lock serialises +tasks within one process, ``memory_root_lock`` serialises processes +against each other. +""" + +from __future__ import annotations + +import asyncio +import contextlib +import os +import uuid +from collections.abc import Mapping, Sequence +from pathlib import Path +from typing import Any + +import anyio + +from ..memory_root import MemoryRoot +from .entries import EntryId +from .frontmatter import dump_frontmatter +from .reader import MarkdownReader + + +class MarkdownWriter: + """Atomic writer for markdown files inside a memory-root. + + The ``memory_root`` reference is held to enable future enforcement that + targets stay within the configured root; current writes do not depend on + it for the rename itself (same-dir temp file). + """ + + def __init__(self, memory_root: MemoryRoot) -> None: + self._memory_root = memory_root + # Per-path async lock registry. ``setdefault`` is GIL-atomic, so + # concurrent callers race only on the dict insert (resolved by + # ``setdefault`` returning the existing value), not on the Lock. + # Plain dict (not WeakValueDictionary): a Lock with pending waiters + # must outlive any task awaiting it; ref-counted GC would race with + # those waiters. See Python bpo-28427 for the WeakValueDictionary + # multithreading hazard that bites the weak-ref approach. + self._path_locks: dict[Path, asyncio.Lock] = {} + + @property + def memory_root(self) -> MemoryRoot: + return self._memory_root + + def lock_for(self, path: Path) -> asyncio.Lock: + """Return the per-path lock; create on first use. + + Public so that higher-level writers (e.g. :class:`BaseDailyWriter`) + can serialise their own multi-step ``read → compute → write`` + sequences against this writer's single-step ``append`` paths. + Pair with :meth:`_append_entries_unlocked` to avoid reentrant + re-acquisition of the same lock from within an already-locked + critical section (``asyncio.Lock`` is *not* reentrant). + """ + # Resolve to an absolute canonical path so aliases (relative vs. + # absolute, symlinks) share the same lock object. + key = Path(path).resolve() + lock = self._path_locks.get(key) + if lock is None: + lock = asyncio.Lock() + self._path_locks[key] = lock + return lock + + async def write(self, path: Path, content: str) -> Path: + """Atomically write ``content`` to ``path``. + + Steps: + 1. ``mkdir -p`` the parent directory. + 2. Write to ``/..tmp.``. + 3. ``flush`` + ``fsync`` the temp file. + 4. ``os.replace`` the temp file onto ``path`` (atomic on POSIX). + + Returns: + ``path`` (resolved as written). + """ + target = Path(path) + await anyio.Path(target.parent).mkdir(parents=True, exist_ok=True) + tmp = target.parent / f".{target.name}.tmp.{uuid.uuid4().hex}" + try: + await anyio.to_thread.run_sync(_write_and_fsync, tmp, content) + await anyio.to_thread.run_sync(os.replace, tmp, target) + except Exception: + # Best-effort cleanup of the staging file on failure. + await _unlink_quiet(tmp) + raise + return target + + async def write_markdown( + self, + path: Path, + *, + frontmatter: Mapping[str, Any] | None = None, + body: str = "", + ) -> Path: + """Assemble ``frontmatter`` + ``body`` then atomic-write to ``path``.""" + head = dump_frontmatter(frontmatter or {}) + return await self.write(path, head + body) + + async def append_entry( + self, + path: Path, + *, + entry_body: str, + entry_id: EntryId, + frontmatter_updates: Mapping[str, Any] | None = None, + ) -> Path: + """Append a single entry block to a markdown file, merging frontmatter. + + Convenience wrapper around :meth:`append_entries` for single-entry + callers. See that method for full semantics. + + Args: + path: Target markdown file. Created if missing. + entry_body: Content between the open and close markers. + One leading and trailing newline are added automatically. + entry_id: The id to stamp on this entry. The caller normally + builds it with :meth:`EntryId.next_for`. + frontmatter_updates: Mapping shallow-merged into existing + frontmatter (later wins). ``None`` skips the merge. + + Returns: + ``path`` (resolved as written). + """ + return await self.append_entries( + path, + [(entry_body, entry_id)], + frontmatter_updates=frontmatter_updates, + ) + + async def append_entries( + self, + path: Path, + entries: Sequence[tuple[str, EntryId]], + *, + frontmatter_updates: Mapping[str, Any] | None = None, + ) -> Path: + """Append ``N`` entry blocks in a single locked read-modify-write cycle. + + Compared with calling :meth:`append_entry` ``N`` times, this: + + * Performs one file read + one file write instead of ``N`` of each + (IO complexity drops from ``O(N²)`` to ``O(N)`` when the file + already holds many entries). + * Holds the per-path lock for one short critical section instead of + ``N`` separate acquisitions. + * Updates ``frontmatter`` once at the end (no intermediate + ``entry_count`` flapping). + + The caller assigns and supplies all :class:`EntryId` values — see + :meth:`append_entry` for the rationale. The order in ``entries`` is + the order the blocks land in the file. + + Args: + path: Target markdown file. Created if missing. + entries: ``(entry_body, entry_id)`` pairs to append, in order. + Empty sequence is allowed; the file is still touched for + frontmatter updates if any are supplied. + frontmatter_updates: Mapping shallow-merged into existing + frontmatter once after all entries are appended. + + Returns: + ``path`` (resolved as written). + """ + target = Path(path) + async with self.lock_for(target): + return await self._append_entries_unlocked( + target, + entries, + frontmatter_updates=frontmatter_updates, + ) + + async def _append_entries_unlocked( + self, + path: Path, + entries: Sequence[tuple[str, EntryId]], + *, + frontmatter_updates: Mapping[str, Any] | None = None, + ) -> Path: + """Same as :meth:`append_entries` but assumes the caller already + holds :meth:`lock_for` ``(path)``. + + For use by higher-level writers that perform a multi-step + ``read → compute eid → write`` sequence and need to keep the lock + held across the read and the write. Public ``append_entries`` / + ``append_entry`` always wrap this with the lock. + + Reentrant re-acquisition is unsafe — ``asyncio.Lock`` is not + reentrant, so calling this without holding the lock yourself + breaks the safety contract. + """ + target = Path(path) + + # 1. Load existing markdown (or initialise empty). + if await anyio.Path(target).is_file(): + parsed = await MarkdownReader.read(target) + meta: dict[str, Any] = dict(parsed.frontmatter) + body = parsed.body + else: + meta = {} + body = "" + + # 2. Shallow-merge frontmatter updates. + if frontmatter_updates: + meta.update(frontmatter_updates) + + # 3. Append all entry blocks in order. + if entries: + if body and not body.endswith("\n"): + body += "\n" + appended_blocks: list[str] = [] + for entry_body, entry_id in entries: + eid_str = entry_id.format() + appended_blocks.append( + f"\n{entry_body}\n" + f"\n" + ) + body = body + "".join(appended_blocks) + + # 4. Atomic write. + return await self.write_markdown(target, frontmatter=meta, body=body) + + +def _write_and_fsync(tmp: Path, content: str) -> None: + """Sync helper: write + fsync the staging file. Offloaded to a thread.""" + with open(tmp, "w", encoding="utf-8") as fh: + fh.write(content) + fh.flush() + os.fsync(fh.fileno()) + + +async def _unlink_quiet(tmp: Path) -> None: + """Best-effort unlink — swallow OSError so the original exception wins.""" + with contextlib.suppress(OSError): + await anyio.Path(tmp).unlink(missing_ok=True) diff --git a/src/everos/core/persistence/memory_root.py b/src/everos/core/persistence/memory_root.py new file mode 100644 index 00000000..ab83c084 --- /dev/null +++ b/src/everos/core/persistence/memory_root.py @@ -0,0 +1,243 @@ +"""memory-root path manager. + +Single root directory holding all persisted memory: + + User-visible (no dot prefix, edited by humans / agents): + agents/ per-agent records + users/ per-user records + knowledge/ global shared knowledge + + System-managed (dotfile prefix, hidden by default in ls / Finder): + .index/ derived indexes (rebuildable from markdown) + sqlite/ system.db (+ WAL/SHM), ome.db, ome.aps.db + lancedb/ LanceDB tables + .tmp/ atomic-write staging directory + .lock single-process lock anchor (created on demand by + ``memory_root_lock``) + + User-editable (at the root): + ome.toml OME strategy overrides (hot-reloaded) + +The cascade queue, LSN watermark, and change audit all live in +``system.db`` (table ``md_change_state``), not in separate dotfiles. + +The default location and tunables come from :class:`everos.config.Settings` +(loaded from ``config/default.toml`` + ``EVEROS_*`` environment variables); +:meth:`MemoryRoot.default` resolves the configured path. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path + +# ── app / project directory-name convention ────────────────────────────────── +# +# A memory root is partitioned by ``/`` *before* the user-visible +# scope dirs (``agents`` / ``users`` / ``knowledge``), so memory for different +# (app, project) pairs never shares a directory. The reserved id ``"default"`` +# materialises as ``default_app`` / ``default_project`` on disk (rather than a +# bare ``default``) so a default space is visually distinct from a user-named +# directory; every other id maps to itself. +# +# The mapping is symmetric: the cascade path parser reverses it (see +# :func:`app_id_from_dir`) to recover the ids from an on-disk path. The write +# side (here) and the read side (cascade) MUST stay in lockstep, or rebuilt +# rows carry app/project that disagree with what was written. ``default_app`` / +# ``default_project`` are therefore reserved directory names. +_DEFAULT_SCOPE_ID = "default" +_DEFAULT_APP_DIR = "default_app" +_DEFAULT_PROJECT_DIR = "default_project" + +# Path to the shipped OME override template; copied to ``/ome.toml`` on +# first ``ensure()`` so users have a real file to edit instead of having to +# create one from scratch. ``parents[2]`` is the ``src/everos/`` package root +# (memory_root.py sits at ``core/persistence/memory_root.py``). +_OME_TEMPLATE_PATH = Path(__file__).parents[2] / "config" / "default_ome.toml" + + +def app_dir_name(app_id: str) -> str: + """Map an ``app_id`` to its on-disk directory name.""" + return _DEFAULT_APP_DIR if app_id == _DEFAULT_SCOPE_ID else app_id + + +def project_dir_name(project_id: str) -> str: + """Map a ``project_id`` to its on-disk directory name.""" + return _DEFAULT_PROJECT_DIR if project_id == _DEFAULT_SCOPE_ID else project_id + + +def app_id_from_dir(dir_name: str) -> str: + """Inverse of :func:`app_dir_name` — recover the ``app_id`` from a dir name.""" + return _DEFAULT_SCOPE_ID if dir_name == _DEFAULT_APP_DIR else dir_name + + +def project_id_from_dir(dir_name: str) -> str: + """Inverse of :func:`project_dir_name` — recover the ``project_id``.""" + return _DEFAULT_SCOPE_ID if dir_name == _DEFAULT_PROJECT_DIR else dir_name + + +@dataclass(frozen=True, init=False) +class MemoryRoot: + """Path manager for a memory-root directory. + + Constructor accepts any path-like (``str`` or ``Path``); it is normalised + to an absolute, resolved ``Path`` so equality and hashing are stable + regardless of how the caller spells the path. ``init=False`` is paired + with a hand-written ``__init__`` so the input type (``Path | str``) is + decoupled from the stored field type (``Path``) — stdlib dataclass has + no converter slot, and Pyright would otherwise reject ``MemoryRoot(s)`` + where ``s`` is a ``str``. + """ + + root: Path + + def __init__(self, root: Path | str) -> None: + # ``frozen=True`` forbids attribute assignment, so go through + # ``object.__setattr__`` to install the normalised Path field. + resolved = Path(root).expanduser().resolve() + object.__setattr__(self, "root", resolved) + + @classmethod + def default(cls) -> MemoryRoot: + """Return the memory-root from :class:`everos.config.Settings`. + + The effective default lives in ``config/default.toml`` (``[memory] + root``); environment variable ``EVEROS_MEMORY__ROOT`` overrides it. + """ + # Lazy import to keep this module dependency-free at import time. + from everos.config import load_settings + + return cls(load_settings().memory.root) + + # ── User-visible (partitioned by app / project) ────────────────────────── + # + # These take ``(app_id, project_id)`` because the scope dirs hang off the + # ``///`` prefix; they are request-level inputs, never + # instance state. Both default to ``"default"`` so call sites that don't + # yet carry scope still resolve to the default space. + + def agents_dir(self, app_id: str = "default", project_id: str = "default") -> Path: + """``///agents/`` — per-agent records.""" + return ( + self.root / app_dir_name(app_id) / project_dir_name(project_id) / "agents" + ) + + def users_dir(self, app_id: str = "default", project_id: str = "default") -> Path: + """``///users/`` — per-user records.""" + return self.root / app_dir_name(app_id) / project_dir_name(project_id) / "users" + + def knowledge_dir( + self, app_id: str = "default", project_id: str = "default" + ) -> Path: + """``///knowledge/`` — shared knowledge.""" + return ( + self.root + / app_dir_name(app_id) + / project_dir_name(project_id) + / "knowledge" + ) + + # ── System-managed (dotfiles) ─────────────────────────────────────────── + + @property + def index_dir(self) -> Path: + """``/.index/`` — derived index root.""" + return self.root / ".index" + + @property + def lancedb_dir(self) -> Path: + """``/.index/lancedb/`` — LanceDB table root.""" + return self.index_dir / "lancedb" + + @property + def sqlite_dir(self) -> Path: + """``/.index/sqlite/`` — SQLite system DB root. + + Holds ``system.db`` plus its sidecars (``-wal`` / ``-shm`` in WAL + mode). Symmetric with :attr:`lancedb_dir`. + """ + return self.index_dir / "sqlite" + + @property + def system_db(self) -> Path: + """``/.index/sqlite/system.db`` — SQLite DB for system + state, audit log, task queue, LSN watermark, and other metadata. + """ + return self.sqlite_dir / "system.db" + + @property + def ome_db(self) -> Path: + """``/.index/sqlite/ome.db`` — SQLite DB backing the Offline + Memory Engine's own state: run records, counter store, idle store. + Symmetric with :attr:`system_db`. + """ + return self.sqlite_dir / "ome.db" + + @property + def ome_aps_db(self) -> Path: + """``/.index/sqlite/ome.aps.db`` — SQLite DB holding the + APScheduler jobstore for the Offline Memory Engine. Split from + :attr:`ome_db` so APS's sync SQLAlchemy writer and OME's async + aiosqlite writer never contend for the same sqlite file lock. + """ + return self.sqlite_dir / "ome.aps.db" + + @property + def ome_config(self) -> Path: + """``/ome.toml`` — user-editable OME strategy overrides. + + Drop a file here to toggle strategies on/off or tweak per-strategy + knobs (max_retries, gate, cron …) without restarting the server. + The engine watches this file and hot-reloads changes within ~2 s. + + Example to disable foresight and user-profile extraction:: + + [strategies.extract_foresight] + enabled = false + + [strategies.extract_user_profile] + enabled = false + """ + return self.root / "ome.toml" + + @property + def lock_file(self) -> Path: + """``/.lock`` — single-process exclusive lock anchor.""" + return self.root / ".lock" + + @property + def tmp_dir(self) -> Path: + """``/.tmp/`` — staging directory for batch / multi-step writes. + + Note: + ``MarkdownWriter`` does *not* use this for atomic single-file + writes; it uses a same-directory temp file to guarantee a + same-filesystem rename. This directory is reserved for callers + that need scratch space outside any single target directory. + """ + return self.root / ".tmp" + + # ── Operations ────────────────────────────────────────────────────────── + + def ensure(self) -> None: + """Create the memory-root and the runtime-required dotfile dirs. + + User-visible directories (``agents/`` / ``users/`` / ``knowledge/``) + are *not* pre-created — they appear on first write of their records. + Only directories the runtime infrastructure requires are made: + + / + /.index/ + /.index/sqlite/ + /.index/lancedb/ + /.tmp/ + """ + self.root.mkdir(parents=True, exist_ok=True) + self.index_dir.mkdir(parents=True, exist_ok=True) + self.sqlite_dir.mkdir(parents=True, exist_ok=True) + self.lancedb_dir.mkdir(parents=True, exist_ok=True) + self.tmp_dir.mkdir(parents=True, exist_ok=True) + # Materialize the OME override template on first run; existence-only + # check preserves any edits the user has already made. + if not self.ome_config.exists(): + self.ome_config.write_bytes(_OME_TEMPLATE_PATH.read_bytes()) diff --git a/src/everos/core/persistence/sqlite/__init__.py b/src/everos/core/persistence/sqlite/__init__.py new file mode 100644 index 00000000..4232df50 --- /dev/null +++ b/src/everos/core/persistence/sqlite/__init__.py @@ -0,0 +1,42 @@ +"""SQLite async persistence (SQLModel + SQLAlchemy 2.0 + aiosqlite). + +External usage (engine + sessions): + from everos.core.persistence.sqlite import ( + create_system_engine, create_session_factory, session_scope, + ) + +External usage (ORM model basics — re-exported from sqlmodel): + from everos.core.persistence.sqlite import ( + SQLModel, Field, Relationship, BaseTable, + ) + +External usage (generic CRUD repository base): + from everos.core.persistence.sqlite import RepoBase + +The ``system_db`` is the everos +``/.index/sqlite/system.db`` SQLite file holding system +state, audit log, task queue, LSN watermark, and other metadata. +""" + +# Re-export key sqlmodel symbols so business code has a single canonical +# entry point (``everos.core.persistence.sqlite``) for ORM authoring. +from sqlmodel import Field as Field +from sqlmodel import Relationship as Relationship +from sqlmodel import SQLModel as SQLModel + +from .base import BaseTable as BaseTable +from .engine import create_system_engine as create_system_engine +from .repository import RepoBase as RepoBase +from .session import create_session_factory as create_session_factory +from .session import session_scope as session_scope + +__all__ = [ + "BaseTable", + "Field", + "Relationship", + "RepoBase", + "SQLModel", + "create_session_factory", + "create_system_engine", + "session_scope", +] diff --git a/src/everos/core/persistence/sqlite/base.py b/src/everos/core/persistence/sqlite/base.py new file mode 100644 index 00000000..72ba60c3 --- /dev/null +++ b/src/everos/core/persistence/sqlite/base.py @@ -0,0 +1,112 @@ +"""Common SQLModel base for everos tables. + +:class:`BaseTable` adds ``created_at`` / ``updated_at`` columns. The +``updated_at`` column auto-refreshes on UPDATE through SA's ``onupdate`` +hook (no explicit assignment needed in business code). + +The **two-zone storage-UTC discipline** is enforced by a SQLAlchemy +:class:`TypeDecorator` (:class:`UtcDateTimeColumn`) used as the SQL +column type for every datetime field: + +* **on write** — ``process_bind_param`` converts every datetime to + aware UTC before SQLAlchemy emits the bound parameter. This covers + *every* SQLAlchemy write path uniformly: + + - ORM ``session.add()`` / ``session.merge()`` (unit-of-work flush) + - Core ``session.execute(insert(...).values(...))`` + - Core ``session.execute(update(...).values(...))`` + - Bulk ``bulk_insert_mappings`` / ``bulk_save_objects`` + - Raw SQL with bound parameters + + Reaching into the column type is the only place SQLAlchemy guarantees + *every* write path passes through. Mapper events (``before_insert`` / + ``before_update``) only fire on the ORM unit-of-work path and would + silently miss Core statements — which :mod:`everos.infra.persistence + .sqlite.repos.md_change_state` uses heavily. + +* **on read** — ``process_result_value`` re-attaches ``tzinfo=UTC`` to + every naive datetime returned from SQLite (which has no native tz + storage and always returns naive). Callers therefore never observe a + naive datetime regardless of which read API they use. + +Subclass with ``table=True`` to declare a real SQLite table:: + + from sqlmodel import Field + + class Sender(BaseTable, table=True): + id: int | None = Field(default=None, primary_key=True) + name: str +""" + +from __future__ import annotations + +import datetime as _dt +from typing import Any + +from sqlalchemy import DateTime +from sqlalchemy import types as sa_types +from sqlmodel import Field, SQLModel + +from everos.component.utils.datetime import UtcDatetime, ensure_utc, get_utc_now + + +class UtcDateTimeColumn(sa_types.TypeDecorator[_dt.datetime]): + """SQLAlchemy column type enforcing storage-UTC on every read/write. + + Implementation: + + * ``impl = DateTime`` — uses the dialect's standard DateTime SQL type + (TEXT ISO-8601 on SQLite; ``TIMESTAMP`` on Postgres etc.). + * ``process_bind_param`` — write hook. Awares → ``astimezone(UTC)``; + naives → assumed already UTC (storage-boundary convention; see + :func:`ensure_utc` docstring); ``None`` passes through. + * ``process_result_value`` — read hook. Naive ``datetime`` → + ``replace(tzinfo=UTC)``; aware passes through unchanged. + + ``cache_ok = True`` — SQLAlchemy can safely cache statement + compilations using this type (no per-instance mutable state). + """ + + impl = DateTime + cache_ok = True + + def process_bind_param( + self, value: _dt.datetime | None, _dialect: Any + ) -> _dt.datetime | None: + if value is None: + return None + if not isinstance(value, _dt.datetime): + return value + return ensure_utc(value) + + def process_result_value( + self, value: _dt.datetime | None, _dialect: Any + ) -> _dt.datetime | None: + if value is None: + return None + if isinstance(value, _dt.datetime) and value.tzinfo is None: + return value.replace(tzinfo=_dt.UTC) + return value + + +class BaseTable(SQLModel): + """Mixin providing ``created_at`` / ``updated_at`` columns. + + Both default to :func:`get_utc_now` on INSERT. + ``updated_at`` is auto-refreshed by SQLAlchemy on every UPDATE via the + ``onupdate`` hook — do not set it manually unless overriding intentionally. + + Both columns use :class:`UtcDateTimeColumn` as the SQL column type + so storage-UTC is enforced **at the SQLAlchemy bind layer** on every + write path (ORM + Core + bulk + raw bound params). + """ + + created_at: UtcDatetime = Field( + default_factory=get_utc_now, + sa_type=UtcDateTimeColumn, + ) + updated_at: UtcDatetime = Field( + default_factory=get_utc_now, + sa_type=UtcDateTimeColumn, + sa_column_kwargs={"onupdate": get_utc_now}, + ) diff --git a/src/everos/core/persistence/sqlite/engine.py b/src/everos/core/persistence/sqlite/engine.py new file mode 100644 index 00000000..1076c6b7 --- /dev/null +++ b/src/everos/core/persistence/sqlite/engine.py @@ -0,0 +1,74 @@ +"""Async SQLAlchemy engine factory + per-connection PRAGMA listener. + +The engine connects through ``aiosqlite`` (SA URL ``sqlite+aiosqlite://``). +PRAGMAs are *per-connection* — they must be re-applied every time the +SA pool opens a new connection. We attach a ``connect`` event listener on +the engine's underlying sync engine for that purpose. +""" + +from __future__ import annotations + +from pathlib import Path + +from sqlalchemy import event +from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine + +from everos.config import SqliteSettings + + +def create_system_engine( + db_path: Path, + sqlite_settings: SqliteSettings, + *, + echo: bool = False, +) -> AsyncEngine: + """Create an async SQLAlchemy engine for the everos system DB. + + ``MemoryRoot.system_db`` is the conventional path; the DB holds system + state, audit log, task queue, LSN watermark, and other metadata. + + Args: + db_path: Filesystem path to the system DB file. Parent directory is + created if missing. + sqlite_settings: Tunables (journal_mode, synchronous, foreign_keys, + temp_store, busy_timeout, journal_size_limit, cache_size). + echo: When ``True``, SQLAlchemy logs every statement (development). + + Returns: + An :class:`AsyncEngine` ready for use with :class:`AsyncSession`. + """ + db_path.parent.mkdir(parents=True, exist_ok=True) + + # Three slashes = relative path; four slashes = absolute. ``str(db_path)`` + # of an absolute Path begins with ``/`` so the f-string yields four. + url = f"sqlite+aiosqlite:///{db_path}" + engine = create_async_engine(url, echo=echo, future=True) + + _register_pragma_listener(engine, sqlite_settings) + return engine + + +def _register_pragma_listener( + engine: AsyncEngine, + sqlite_settings: SqliteSettings, +) -> None: + """Attach a ``connect`` listener that applies PRAGMAs on every new connection.""" + + @event.listens_for(engine.sync_engine, "connect") + def _apply_pragmas(dbapi_connection, _connection_record) -> None: # type: ignore[no-untyped-def] + cursor = dbapi_connection.cursor() + try: + cursor.execute(f"PRAGMA journal_mode={sqlite_settings.journal_mode}") + cursor.execute(f"PRAGMA synchronous={sqlite_settings.synchronous}") + cursor.execute( + f"PRAGMA foreign_keys={'ON' if sqlite_settings.foreign_keys else 'OFF'}" + ) + cursor.execute(f"PRAGMA temp_store={sqlite_settings.temp_store}") + cursor.execute(f"PRAGMA busy_timeout={sqlite_settings.busy_timeout_ms}") + cursor.execute( + f"PRAGMA journal_size_limit={sqlite_settings.journal_size_limit_bytes}" + ) + # cache_size: negative = KB, positive = pages. + cursor.execute(f"PRAGMA cache_size=-{sqlite_settings.cache_size_kb}") + finally: + cursor.close() diff --git a/src/everos/core/persistence/sqlite/repository.py b/src/everos/core/persistence/sqlite/repository.py new file mode 100644 index 00000000..5201a491 --- /dev/null +++ b/src/everos/core/persistence/sqlite/repository.py @@ -0,0 +1,166 @@ +"""Generic CRUD repository for SQLModel-backed tables. + +``RepoBase`` is a pure generic CRUD helper that sits alongside +:class:`BaseTable`. It knows nothing about a storage runtime — concrete +repos either pass ``session_factory`` explicitly (typical in tests) or +override :meth:`_factory_lookup` to pull the singleton from their +storage manager (typical in :mod:`everos.infra.persistence.sqlite.repos`). + +Each method opens its own ``session_scope`` (auto rollback on exception, +session closed at end). For multi-step transactional work, use the +session factory directly via :attr:`session_factory`. +""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any + +from sqlalchemy import func +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker +from sqlmodel import SQLModel, select + +from .session import session_scope + + +class RepoBase[T: SQLModel]: + """Generic CRUD repository for one SQLModel table. + + Subclass and bind to a model. Two ways to provide the session factory: + + 1. **Explicit (tests / DI)** — pass it to ``__init__``:: + + repo = SenderRepo(session_factory) + + 2. **Lazy hook (production singletons)** — override + :meth:`_factory_lookup` so the repo can be instantiated as a + module-level singleton with no factory bound yet:: + + class _SenderRepo(RepoBase[Sender]): + model = Sender + def _factory_lookup(self): + from everos.infra.persistence.sqlite.sqlite_manager import ( + get_session_factory, + ) + return get_session_factory() + + sender_repo = _SenderRepo() + await sender_repo.add(Sender(name="alice")) + """ + + model: type[T] + + def __init__( + self, + session_factory: async_sessionmaker[AsyncSession] | None = None, + ) -> None: + """Bind to a session factory; if ``None``, defer to ``_factory_lookup``.""" + self._factory_override = session_factory + + def _factory_lookup(self) -> async_sessionmaker[AsyncSession]: + """Resolve a session factory on first use. Override in subclass. + + ``RepoBase`` itself has no idea where the runtime singleton lives + — that knowledge belongs to the infra subclass. The default raises + so a missing override is loud rather than silently broken. + """ + raise NotImplementedError( + f"{type(self).__name__}: pass session_factory= to __init__ " + "or override _factory_lookup() to wire the storage manager." + ) + + @property + def _factory(self) -> async_sessionmaker[AsyncSession]: + if self._factory_override is not None: + return self._factory_override + return self._factory_lookup() + + @property + def session_factory(self) -> async_sessionmaker[AsyncSession]: + """Underlying session factory (for multi-step transactions).""" + return self._factory + + # ── Create ───────────────────────────────────────────────────────────── + + async def add(self, instance: T) -> T: + """Insert one row, commit, refresh, return the instance.""" + async with session_scope(self._factory) as s: + s.add(instance) + await s.commit() + await s.refresh(instance) + return instance + + async def add_many(self, instances: Sequence[T]) -> list[T]: + """Insert many rows in one transaction.""" + items = list(instances) + async with session_scope(self._factory) as s: + s.add_all(items) + await s.commit() + for inst in items: + await s.refresh(inst) + return items + + # ── Read ─────────────────────────────────────────────────────────────── + + async def get_by_id(self, id_value: Any) -> T | None: + """Get a row by primary key. Returns ``None`` if not found.""" + async with session_scope(self._factory) as s: + return await s.get(self.model, id_value) + + async def list_all(self) -> list[T]: + """Return all rows (no filter, no order).""" + async with session_scope(self._factory) as s: + stmt = select(self.model) + return list((await s.execute(stmt)).scalars().all()) + + async def find_where(self, **filters: Any) -> list[T]: + """Equality-only filtering, e.g. ``find_where(name="alice", active=True)``.""" + async with session_scope(self._factory) as s: + stmt = select(self.model).filter_by(**filters) + return list((await s.execute(stmt)).scalars().all()) + + async def find_one(self, **filters: Any) -> T | None: + """First row matching ``filters`` (no ordering); ``None`` if not found.""" + async with session_scope(self._factory) as s: + stmt = select(self.model).filter_by(**filters).limit(1) + return (await s.execute(stmt)).scalars().first() + + async def count(self) -> int: + """Total row count (no filter).""" + async with session_scope(self._factory) as s: + stmt = select(func.count()).select_from(self.model) + return int((await s.execute(stmt)).scalar_one()) + + # ── Update ───────────────────────────────────────────────────────────── + + async def update(self, instance: T) -> T: + """Persist changes on an instance whose primary key already exists. + + Uses ``session.merge`` so detached / fresh-from-Pydantic instances + are reattached. ``BaseTable.updated_at`` auto-bumps via SA's + ``onupdate`` hook. + """ + async with session_scope(self._factory) as s: + merged = await s.merge(instance) + await s.commit() + await s.refresh(merged) + return merged + + # ── Delete ───────────────────────────────────────────────────────────── + + async def delete(self, instance: T) -> None: + """Delete by instance (primary key must be set).""" + async with session_scope(self._factory) as s: + merged = await s.merge(instance) + await s.delete(merged) + await s.commit() + + async def delete_by_id(self, id_value: Any) -> bool: + """Delete by primary key. Returns ``True`` if a row was removed.""" + async with session_scope(self._factory) as s: + instance = await s.get(self.model, id_value) + if instance is None: + return False + await s.delete(instance) + await s.commit() + return True diff --git a/src/everos/core/persistence/sqlite/session.py b/src/everos/core/persistence/sqlite/session.py new file mode 100644 index 00000000..c70937f5 --- /dev/null +++ b/src/everos/core/persistence/sqlite/session.py @@ -0,0 +1,45 @@ +"""Async session factory + session scope context manager.""" + +from __future__ import annotations + +from collections.abc import AsyncIterator +from contextlib import asynccontextmanager + +from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, async_sessionmaker + + +def create_session_factory(engine: AsyncEngine) -> async_sessionmaker[AsyncSession]: + """Build an :class:`async_sessionmaker` bound to ``engine``. + + ``expire_on_commit=False`` keeps attribute access on instances valid + after commit, which is the conventional setup for async SA usage. + """ + return async_sessionmaker( + bind=engine, + class_=AsyncSession, + expire_on_commit=False, + ) + + +@asynccontextmanager +async def session_scope( + session_factory: async_sessionmaker[AsyncSession], +) -> AsyncIterator[AsyncSession]: + """Yield an :class:`AsyncSession` inside a try/rollback/close block. + + The session is rolled back on any exception in the ``async with`` body, + then closed. Callers are responsible for calling ``await session.commit()`` + on success. + + Usage: + factory = create_session_factory(engine) + async with session_scope(factory) as session: + session.add(some_record) + await session.commit() + """ + async with session_factory() as session: + try: + yield session + except Exception: + await session.rollback() + raise diff --git a/src/everos/entrypoints/__init__.py b/src/everos/entrypoints/__init__.py new file mode 100644 index 00000000..87e57a58 --- /dev/null +++ b/src/everos/entrypoints/__init__.py @@ -0,0 +1,5 @@ +"""Presentation layer. + +Translates external requests (CLI / HTTP) into service-layer calls. +Contains no business logic. +""" diff --git a/src/everos/entrypoints/api/__init__.py b/src/everos/entrypoints/api/__init__.py new file mode 100644 index 00000000..6533b715 --- /dev/null +++ b/src/everos/entrypoints/api/__init__.py @@ -0,0 +1,11 @@ +"""HTTP REST entry point (FastAPI), routed by resource. + +External usage: + from everos.entrypoints.api import create_app + + app = create_app() +""" + +from .app import create_app as create_app + +__all__ = ["create_app"] diff --git a/src/everos/entrypoints/api/app.py b/src/everos/entrypoints/api/app.py new file mode 100644 index 00000000..8e728fa8 --- /dev/null +++ b/src/everos/entrypoints/api/app.py @@ -0,0 +1,124 @@ +"""FastAPI application factory. + +Wires CORS + the project's middleware stack + global exception handler + +lifespan, and registers the public routes (``/health``, ``/metrics``). +""" + +from __future__ import annotations + +import os + +from fastapi import FastAPI, HTTPException +from fastapi.exceptions import RequestValidationError +from fastapi.middleware.cors import CORSMiddleware + +from everos.core.lifespan import ( + LifespanProvider, + MetricsLifespanProvider, + build_lifespan, +) +from everos.core.middleware import ( + DEFAULT_CORS_ALLOW_CREDENTIALS, + DEFAULT_CORS_ALLOW_HEADERS, + DEFAULT_CORS_ALLOW_METHODS, + DEFAULT_CORS_ORIGINS, + ProfileMiddleware, + PrometheusMiddleware, + global_exception_handler, +) +from everos.core.observability.logging import get_logger + +from .lifespans import ( + CascadeLifespanProvider, + LanceDBLifespanProvider, + LLMLifespanProvider, + OmeLifespanProvider, + SqliteLifespanProvider, +) +from .routes import ( + get, + health, + memorize, + metrics, + search, +) + +logger = get_logger(__name__) + + +def _docs_enabled() -> bool: + """Enable docs endpoints (/docs, /redoc, /openapi.json) only in dev.""" + return os.environ.get("ENV", "prod").upper() == "DEV" + + +def create_app( + *, + cors_origins: list[str] | None = None, + cors_allow_credentials: bool = DEFAULT_CORS_ALLOW_CREDENTIALS, + cors_allow_methods: list[str] | None = None, + cors_allow_headers: list[str] | None = None, + lifespan_providers: list[LifespanProvider] | None = None, +) -> FastAPI: + """Build the FastAPI application instance. + + Args: + cors_origins: Allowed CORS origins (default: ``["*"]``). + cors_allow_credentials: Whether to allow credentials (default: True). + cors_allow_methods: Allowed CORS methods (default: ``["*"]``). + cors_allow_headers: Allowed CORS headers (default: ``["*"]``). + lifespan_providers: Optional list of LifespanProvider; defaults to + ``[MetricsLifespanProvider(), SqliteLifespanProvider(), + LanceDBLifespanProvider(), CascadeLifespanProvider(), + OmeLifespanProvider()]``. + + Returns: + FastAPI: Configured application instance. + """ + enable_docs = _docs_enabled() + + if lifespan_providers is None: + lifespan_providers = [ + MetricsLifespanProvider(), + LLMLifespanProvider(), + SqliteLifespanProvider(), + LanceDBLifespanProvider(), + CascadeLifespanProvider(), + OmeLifespanProvider(), + ] + + app = FastAPI( + title="everos", + version="0.1.0", + description="md-first memory extraction framework", + lifespan=build_lifespan(lifespan_providers), + docs_url="/docs" if enable_docs else None, + redoc_url="/redoc" if enable_docs else None, + openapi_url="/openapi.json" if enable_docs else None, + ) + + # Exception handlers: HTTPException, validation errors, plus a fallback. + app.add_exception_handler(HTTPException, global_exception_handler) + app.add_exception_handler(RequestValidationError, global_exception_handler) + app.add_exception_handler(Exception, global_exception_handler) + + # Middleware order: earlier `add_middleware` calls become inner, later ones outer. + # CORS innermost (matches base_app.py legacy pattern). + app.add_middleware( + CORSMiddleware, + allow_origins=cors_origins or DEFAULT_CORS_ORIGINS, + allow_credentials=cors_allow_credentials, + allow_methods=cors_allow_methods or DEFAULT_CORS_ALLOW_METHODS, + allow_headers=cors_allow_headers or DEFAULT_CORS_ALLOW_HEADERS, + ) + app.add_middleware(PrometheusMiddleware) + app.add_middleware(ProfileMiddleware) + + # Routes. + app.include_router(health.router) + app.include_router(metrics.router) + app.include_router(memorize.router) + app.include_router(search.router) + app.include_router(get.router) + + logger.info("app_created", docs_enabled=enable_docs) + return app diff --git a/src/everos/entrypoints/api/lifespans/__init__.py b/src/everos/entrypoints/api/lifespans/__init__.py new file mode 100644 index 00000000..262106d3 --- /dev/null +++ b/src/everos/entrypoints/api/lifespans/__init__.py @@ -0,0 +1,35 @@ +"""HTTP API lifespan providers. + +Concrete :class:`everos.core.lifespan.LifespanProvider` implementations +for the storage + chassis backends this entrypoint composes. They live next to +``app.py`` because they are *application-bootstrap* details, not +generic chassis: a different deployment mode (CLI, embedded, batch +worker) may compose a different set of providers. + +Putting these here also keeps ``core.lifespan`` free of concrete- +backend imports — the chassis stays portable. + +External usage:: + + from everos.entrypoints.api.lifespans import ( + LLMLifespanProvider, + SqliteLifespanProvider, + LanceDBLifespanProvider, + CascadeLifespanProvider, + OmeLifespanProvider, + ) +""" + +from .cascade import CascadeLifespanProvider as CascadeLifespanProvider +from .lancedb import LanceDBLifespanProvider as LanceDBLifespanProvider +from .llm import LLMLifespanProvider as LLMLifespanProvider +from .ome import OmeLifespanProvider as OmeLifespanProvider +from .sqlite import SqliteLifespanProvider as SqliteLifespanProvider + +__all__ = [ + "CascadeLifespanProvider", + "LLMLifespanProvider", + "LanceDBLifespanProvider", + "OmeLifespanProvider", + "SqliteLifespanProvider", +] diff --git a/src/everos/entrypoints/api/lifespans/cascade.py b/src/everos/entrypoints/api/lifespans/cascade.py new file mode 100644 index 00000000..aa3cf5c5 --- /dev/null +++ b/src/everos/entrypoints/api/lifespans/cascade.py @@ -0,0 +1,55 @@ +"""Cascade lifespan provider — starts/stops :class:`CascadeOrchestrator`. + +Ordered after SqliteLifespan + LanceDBLifespan: the orchestrator +depends on both stores being ready before its watcher / scanner / +worker tasks can take the first row. + +Construction reads the live :class:`Settings` to build the embedding + +tokenizer providers. If either is misconfigured the lifespan fails +fast — the daemon would be useless without them anyway. +""" + +from __future__ import annotations + +from typing import Any + +from fastapi import FastAPI + +from everos.component.embedding import build_embedding_provider +from everos.component.tokenizer import build_tokenizer +from everos.config import load_settings +from everos.core.lifespan import LifespanProvider +from everos.core.observability.logging import get_logger +from everos.core.persistence import MemoryRoot +from everos.memory.cascade import CascadeOrchestrator + +logger = get_logger(__name__) + + +class CascadeLifespanProvider(LifespanProvider): + """Manage the cascade subsystem for the app lifecycle.""" + + def __init__(self, order: int = 12) -> None: + super().__init__(name="cascade", order=order) + self._orchestrator: CascadeOrchestrator | None = None + + async def startup(self, app: FastAPI) -> Any: + settings = load_settings() + memory_root = MemoryRoot.default() + memory_root.ensure() + + embedder = build_embedding_provider(settings.embedding) + tokenizer = build_tokenizer() + self._orchestrator = CascadeOrchestrator( + memory_root=memory_root, + embedder=embedder, + tokenizer=tokenizer, + ) + await self._orchestrator.start() + logger.info("cascade_lifespan_ready") + return self._orchestrator + + async def shutdown(self, app: FastAPI) -> None: + if self._orchestrator is not None: + await self._orchestrator.stop() + self._orchestrator = None diff --git a/src/everos/entrypoints/api/lifespans/lancedb.py b/src/everos/entrypoints/api/lifespans/lancedb.py new file mode 100644 index 00000000..b2a030bd --- /dev/null +++ b/src/everos/entrypoints/api/lifespans/lancedb.py @@ -0,0 +1,55 @@ +"""LanceDB lifespan provider (HTTP API entrypoint). + +Startup: + Open the connection via ``get_connection`` (lazy, idempotent). + Importing :mod:`everos.infra.persistence.lancedb` also triggers the + side-effect import of ``tables`` so business schemas are loaded + (future: preflight registration). + +Shutdown: + Close the connection (also clears the table cache). +""" + +from __future__ import annotations + +from typing import Any + +from fastapi import FastAPI + +from everos.core.lifespan import LifespanProvider +from everos.core.observability.logging import get_logger +from everos.infra.persistence.lancedb import ( + dispose_connection, + ensure_business_indexes, + get_connection, + verify_business_schemas, +) + +logger = get_logger(__name__) + + +class LanceDBLifespanProvider(LifespanProvider): + """Manage the LanceDB connection + table cache for the app lifecycle. + + Startup runs three steps: + + 1. ``get_connection`` — lazy-open the async connection. + 2. ``verify_business_schemas`` — fail loud if an on-disk table's + columns drift from the current Pydantic schema. LanceDB has no + online migration; cascade is rebuildable from md so the recovery + is documented as ``rm -rf ~/.everos/.index/lancedb``. + 3. ``ensure_business_indexes`` — idempotent FTS index creation. + """ + + def __init__(self, order: int = 11) -> None: + super().__init__(name="lancedb", order=order) + + async def startup(self, app: FastAPI) -> Any: + conn = await get_connection() + await verify_business_schemas() + await ensure_business_indexes() + logger.info("lancedb_ready", uri=conn.uri) + return conn + + async def shutdown(self, app: FastAPI) -> None: + await dispose_connection() diff --git a/src/everos/entrypoints/api/lifespans/llm.py b/src/everos/entrypoints/api/lifespans/llm.py new file mode 100644 index 00000000..8c3c51d1 --- /dev/null +++ b/src/everos/entrypoints/api/lifespans/llm.py @@ -0,0 +1,36 @@ +"""LLM lifespan provider — eagerly resolves the LLM singleton at startup. + +The framework's core value (memory extraction) is meaningless without +an LLM, so misconfiguration must surface as a startup failure instead +of N silent skips per request downstream. Ordered before the storage +stack so we fail before paying to bring sqlite / lancedb / cascade up. +""" + +from __future__ import annotations + +from typing import Any + +from fastapi import FastAPI + +from everos.component.llm import get_llm_client +from everos.core.lifespan import LifespanProvider +from everos.core.observability.logging import get_logger + +logger = get_logger(__name__) + + +class LLMLifespanProvider(LifespanProvider): + """Resolve the LLM client at startup; raise if credentials are missing.""" + + def __init__(self, order: int = 8) -> None: + super().__init__(name="llm", order=order) + + async def startup(self, app: FastAPI) -> Any: + client = get_llm_client() + logger.info("llm_lifespan_ready") + return client + + async def shutdown(self, app: FastAPI) -> None: + # The client is stateless (algo facade over openai.AsyncOpenAI); + # nothing to tear down. + return None diff --git a/src/everos/entrypoints/api/lifespans/ome.py b/src/everos/entrypoints/api/lifespans/ome.py new file mode 100644 index 00000000..d597edf4 --- /dev/null +++ b/src/everos/entrypoints/api/lifespans/ome.py @@ -0,0 +1,39 @@ +"""OME engine lifespan provider (HTTP API entrypoint). + +Startup: build the singleton engine via service.memorize._get_engine +(which also registers strategies) and start it. + +Shutdown: stop the engine. +""" + +from __future__ import annotations + +import importlib +from typing import Any + +from fastapi import FastAPI + +from everos.core.lifespan import LifespanProvider +from everos.core.observability.logging import get_logger + +logger = get_logger(__name__) + + +class OmeLifespanProvider(LifespanProvider): + """Manage the OfflineEngine lifecycle for the FastAPI app.""" + + def __init__(self, order: int = 50) -> None: + super().__init__(name="ome", order=order) + + async def startup(self, app: FastAPI) -> Any: + svc = importlib.import_module("everos.service.memorize") + engine = svc._get_engine() # noqa: SLF001 — service-internal accessor + await engine.start() + logger.info("ome_engine_started") + return engine + + async def shutdown(self, app: FastAPI) -> None: + svc = importlib.import_module("everos.service.memorize") + engine = svc._get_engine() # noqa: SLF001 + await engine.stop() + logger.info("ome_engine_stopped") diff --git a/src/everos/entrypoints/api/lifespans/sqlite.py b/src/everos/entrypoints/api/lifespans/sqlite.py new file mode 100644 index 00000000..a627c3a1 --- /dev/null +++ b/src/everos/entrypoints/api/lifespans/sqlite.py @@ -0,0 +1,45 @@ +"""SQLite system-DB lifespan provider (HTTP API entrypoint). + +Startup: + 1. Build the engine via ``get_engine`` (lazy, idempotent). Importing + :mod:`everos.infra.persistence.sqlite` also triggers the side- + effect import of ``tables`` so every business SQLModel registers + itself in ``SQLModel.metadata``. + 2. ``SQLModel.metadata.create_all`` so every registered table exists. + +Shutdown: + Dispose the engine + connection pool. +""" + +from __future__ import annotations + +from typing import Any + +from fastapi import FastAPI +from sqlmodel import SQLModel + +from everos.core.lifespan import LifespanProvider +from everos.core.observability.logging import get_logger +from everos.infra.persistence.sqlite import dispose_engine, get_engine + +logger = get_logger(__name__) + + +class SqliteLifespanProvider(LifespanProvider): + """Manage the SQLite system-DB engine + schema for the app lifecycle.""" + + def __init__(self, order: int = 10) -> None: + super().__init__(name="sqlite", order=order) + + async def startup(self, app: FastAPI) -> Any: + engine = get_engine() + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + logger.info( + "sqlite_schema_ready", + tables=len(SQLModel.metadata.tables), + ) + return engine + + async def shutdown(self, app: FastAPI) -> None: + await dispose_engine() diff --git a/src/everos/entrypoints/api/routes/__init__.py b/src/everos/entrypoints/api/routes/__init__.py new file mode 100644 index 00000000..510cb2aa --- /dev/null +++ b/src/everos/entrypoints/api/routes/__init__.py @@ -0,0 +1,5 @@ +"""HTTP route modules. + +Each module here exposes an ``APIRouter`` named ``router`` registered by +:func:`everos.entrypoints.api.app.create_app` via ``app.include_router``. +""" diff --git a/src/everos/entrypoints/api/routes/get.py b/src/everos/entrypoints/api/routes/get.py new file mode 100644 index 00000000..9fc14c86 --- /dev/null +++ b/src/everos/entrypoints/api/routes/get.py @@ -0,0 +1,26 @@ +"""POST /api/v1/memory/get — paginated listing endpoint. + +Thin adapter: validate the request DTO, dispatch to the service layer, +return the envelope verbatim. ``request_id`` is generated inside the +:class:`GetManager`; we trust the value on the way out. +""" + +from __future__ import annotations + +from fastapi import APIRouter, HTTPException + +from everos.memory.get import GetRequest, GetResponse +from everos.memory.search import FilterError +from everos.service import get as get_service + +router = APIRouter(prefix="/api/v1/memory", tags=["memory"]) + + +@router.post("/get", response_model=GetResponse) +async def post_get(req: GetRequest) -> GetResponse: + """Paginated listing over the requested ``memory_type``.""" + try: + return await get_service(req) + except FilterError as exc: + # Filter-DSL violations surface as 422 with the compile message. + raise HTTPException(status_code=422, detail=str(exc)) from exc diff --git a/src/everos/entrypoints/api/routes/health.py b/src/everos/entrypoints/api/routes/health.py new file mode 100644 index 00000000..6a7eeda2 --- /dev/null +++ b/src/everos/entrypoints/api/routes/health.py @@ -0,0 +1,13 @@ +"""Health check route.""" + +from __future__ import annotations + +from fastapi import APIRouter + +router = APIRouter(tags=["health"]) + + +@router.get("/health") +async def health() -> dict[str, str]: + """Liveness probe — returns ``{"status": "ok"}`` with HTTP 200.""" + return {"status": "ok"} diff --git a/src/everos/entrypoints/api/routes/memorize.py b/src/everos/entrypoints/api/routes/memorize.py new file mode 100644 index 00000000..392fde97 --- /dev/null +++ b/src/everos/entrypoints/api/routes/memorize.py @@ -0,0 +1,195 @@ +"""POST /api/v1/memory/add and /api/v1/memory/flush. + +DTOs follow the v1 API brief (01_v1_api_brief.md §2 / §3). Routes are +thin adapters: validate the DTO, dump to dict, hand to service. No +business logic lives here. + +``/flush`` is OSS-only (the cloud edition decides boundary timing +server-side and does not expose this endpoint). +""" + +from __future__ import annotations + +from typing import Annotated, Any, Literal + +from fastapi import APIRouter, HTTPException, Request +from pydantic import AfterValidator, BaseModel, ConfigDict, Field + +from everos.core.errors import MultimodalError +from everos.core.observability.tracing import gen_request_id +from everos.service import memorize + +router = APIRouter(prefix="/api/v1/memory", tags=["memory"]) + + +# ── Path-safe identifier ──────────────────────────────────────────────────── +# ``app_id`` / ``project_id`` become directory segments under the memory +# root, so they must reject ``.`` and ``..`` (path traversal). The basic +# character whitelist is enforced via ``pattern`` (pydantic_core uses the +# Rust regex engine, which does NOT support lookaround), and the two +# reserved tokens are filtered out with a follow-up ``AfterValidator``. +_PATH_SAFE_CHARSET = r"^[a-zA-Z0-9_.-]+$" +_PATH_TRAVERSAL_TOKENS = frozenset({".", ".."}) + + +def _reject_path_traversal(value: str) -> str: + if value in _PATH_TRAVERSAL_TOKENS: + raise ValueError("'.' and '..' are reserved (path traversal)") + return value + + +PathSafeId = Annotated[str, AfterValidator(_reject_path_traversal)] + + +# DTOs ──────────────────────────────────────────────────────────────────────── + + +class ToolFunctionDTO(BaseModel): + name: str + arguments: str # JSON string per OpenAI Chat Completions spec + + +class ToolCallDTO(BaseModel): + id: str + type: str = "function" + function: ToolFunctionDTO + + +class ContentItemDTO(BaseModel): + """Content piece (v1 API brief appendix A).""" + + type: Literal["text", "image", "audio", "doc", "pdf", "html", "email"] + text: str | None = None + uri: str | None = None + base64: str | None = None + ext: str | None = None + name: str | None = None + extras: dict[str, Any] | None = None + + model_config = ConfigDict(extra="forbid") + + +class MessageItemDTO(BaseModel): + sender_id: str = Field(..., min_length=1) + sender_name: str | None = None + role: Literal["user", "assistant", "tool"] + timestamp: int = Field( + ..., + gt=0, + description=( + "Message event time as Unix epoch in **milliseconds** " + "(v1 API contract; the algo layer auto-detects sec vs ms " + "for backward compat but the contract is ms)." + ), + ) + content: str | list[ContentItemDTO] + tool_calls: list[ToolCallDTO] | None = None + tool_call_id: str | None = None + + +class MemorizeAddRequest(BaseModel): + session_id: str = Field(..., min_length=1, max_length=128) + app_id: PathSafeId = Field( + default="default", + min_length=1, + max_length=128, + pattern=_PATH_SAFE_CHARSET, + ) + project_id: PathSafeId = Field( + default="default", + min_length=1, + max_length=128, + pattern=_PATH_SAFE_CHARSET, + ) + messages: list[MessageItemDTO] = Field(..., min_length=1, max_length=500) + + +class AddResponseData(BaseModel): + message_count: int + status: Literal["accumulated", "extracted"] + + +class MemorizeFlushRequest(BaseModel): + session_id: str = Field(..., min_length=1, max_length=128) + app_id: PathSafeId = Field( + default="default", + min_length=1, + max_length=128, + pattern=_PATH_SAFE_CHARSET, + ) + project_id: PathSafeId = Field( + default="default", + min_length=1, + max_length=128, + pattern=_PATH_SAFE_CHARSET, + ) + + +class FlushResponseData(BaseModel): + status: Literal["extracted", "no_extraction"] + + +class SuccessEnvelope[T](BaseModel): + """200 wrapper: ``request_id`` sits at the top level, not inside ``data``.""" + + request_id: str + data: T + + +# Route ────────────────────────────────────────────────────────────────────── + + +@router.post("/add") +async def add_memory( + req: Annotated[MemorizeAddRequest, ...], + request: Request, +) -> SuccessEnvelope[AddResponseData]: + """Add messages into the user-memory + agent-memory pipelines.""" + request_id = getattr(request.state, "request_id", None) or _gen_request_id() + try: + result = await memorize(req.model_dump()) + except MultimodalError as exc: + raise HTTPException(status_code=415, detail=str(exc)) from exc + return SuccessEnvelope( + request_id=request_id, + data=AddResponseData( + message_count=result.message_count, + status=result.status, + ), + ) + + +@router.post("/flush") +async def flush_memory( + req: Annotated[MemorizeFlushRequest, ...], + request: Request, +) -> SuccessEnvelope[FlushResponseData]: + """Force boundary detection over the current ``session_id`` buffer. + + [OSS-only] — cloud edition decides boundary timing server-side and + does not expose this endpoint. + """ + request_id = getattr(request.state, "request_id", None) or _gen_request_id() + result = await memorize( + { + "session_id": req.session_id, + "app_id": req.app_id, + "project_id": req.project_id, + "messages": [], + }, + is_final=True, + ) + # service's ``accumulated`` = nothing to flush (buffer was empty); + # ``extracted`` = at least one cell carved out. + status: Literal["extracted", "no_extraction"] = ( + "extracted" if result.status == "extracted" else "no_extraction" + ) + return SuccessEnvelope( + request_id=request_id, + data=FlushResponseData(status=status), + ) + + +def _gen_request_id() -> str: + """Fallback request id when no middleware set one.""" + return gen_request_id() diff --git a/src/everos/entrypoints/api/routes/metrics.py b/src/everos/entrypoints/api/routes/metrics.py new file mode 100644 index 00000000..77b5387b --- /dev/null +++ b/src/everos/entrypoints/api/routes/metrics.py @@ -0,0 +1,20 @@ +"""Prometheus metrics route.""" + +from __future__ import annotations + +from fastapi import APIRouter +from fastapi.responses import Response +from prometheus_client import CONTENT_TYPE_LATEST + +from everos.core.observability.metrics import generate_metrics_response + +router = APIRouter(tags=["metrics"]) + + +@router.get("/metrics") +async def metrics() -> Response: + """Render the current Prometheus registry in exposition format.""" + return Response( + content=generate_metrics_response(), + media_type=CONTENT_TYPE_LATEST, + ) diff --git a/src/everos/entrypoints/api/routes/search.py b/src/everos/entrypoints/api/routes/search.py new file mode 100644 index 00000000..f8b1c5fe --- /dev/null +++ b/src/everos/entrypoints/api/routes/search.py @@ -0,0 +1,27 @@ +"""POST /api/v1/memory/search — hybrid retrieval endpoint. + +Thin adapter: validate the request DTO, dispatch to the service layer, +return the envelope verbatim. ``request_id`` is generated inside the +:class:`SearchManager` (uniform for OSS + cloud); we trust that value +on the way out. +""" + +from __future__ import annotations + +from fastapi import APIRouter, HTTPException + +from everos.memory.search import FilterError, SearchRequest, SearchResponse +from everos.service import search + +router = APIRouter(prefix="/api/v1/memory", tags=["memory"]) + + +@router.post("/search", response_model=SearchResponse) +async def post_search(req: SearchRequest) -> SearchResponse: + """Hybrid retrieval across the configured memory backends.""" + try: + return await search(req) + except FilterError as exc: + # Filter-DSL violations surface as 422 with the compile message + # (mirrors /get's contract). + raise HTTPException(status_code=422, detail=str(exc)) from exc diff --git a/src/everos/entrypoints/cli/__init__.py b/src/everos/entrypoints/cli/__init__.py new file mode 100644 index 00000000..b7980e1a --- /dev/null +++ b/src/everos/entrypoints/cli/__init__.py @@ -0,0 +1,5 @@ +"""Command line entry point. + +Contract-first design, JSON output by default, ``--describe`` machine-readable +mode, granular exit codes. +""" diff --git a/src/everos/entrypoints/cli/commands/__init__.py b/src/everos/entrypoints/cli/commands/__init__.py new file mode 100644 index 00000000..0def61f9 --- /dev/null +++ b/src/everos/entrypoints/cli/commands/__init__.py @@ -0,0 +1,5 @@ +"""CLI subcommand modules. + +Each module here exposes a ``app: typer.Typer`` instance which is mounted +as a subcommand group by :mod:`everos.entrypoints.cli.main`. +""" diff --git a/src/everos/entrypoints/cli/commands/cascade.py b/src/everos/entrypoints/cli/commands/cascade.py new file mode 100644 index 00000000..1926676b --- /dev/null +++ b/src/everos/entrypoints/cli/commands/cascade.py @@ -0,0 +1,267 @@ +"""``everos cascade`` subcommand group. + +Three one-shot operations on the cascade subsystem, all run in-process +without standing up the FastAPI app: + +- ``cascade sync [PATH]`` — flush the work queue. With ``PATH`` the + command first force-enqueues that single file (used after a manual + md edit when waiting for the watcher is impractical), then drains. +- ``cascade status`` — print the queue + LSN summary that the daemon + sees right now. +- ``cascade fix`` — list every ``failed`` row. With ``--apply``, also + reset ``retryable=TRUE`` rows back to ``pending`` and drain the + worker once so the retry actually runs before the command returns. + +CLI is in-process (12 doc §7.1 + 16 doc §9.2): it constructs the same +:class:`CascadeOrchestrator` as the daemon but only calls +``sync_once`` / ``drain_once`` / ``queue_summary``. No watcher / +scanner background task is started. +""" + +from __future__ import annotations + +import asyncio +from contextlib import asynccontextmanager +from pathlib import Path +from typing import Annotated + +import typer +from sqlmodel import SQLModel + +from everos.component.embedding import build_embedding_provider +from everos.component.tokenizer import build_tokenizer +from everos.component.utils.datetime import to_display_tz +from everos.config import load_settings +from everos.core.persistence import MemoryRoot +from everos.infra.persistence.lancedb import ( + dispose_connection, + ensure_business_indexes, + get_connection, + verify_business_schemas, +) +from everos.infra.persistence.sqlite import ( + dispose_engine, + get_engine, + md_change_state_repo, +) +from everos.memory.cascade import CascadeOrchestrator, match_kind + +app = typer.Typer( + name="cascade", + help="Inspect and operate the md → LanceDB sync queue", + no_args_is_help=True, +) + + +# ── shared runtime context ─────────────────────────────────────────────── + + +@asynccontextmanager +async def _runtime(): # type: ignore[no-untyped-def] + """Stand up sqlite + lancedb the same way the API lifespan would. + + The CLI piggybacks on the same singletons as the running daemon + (lazy + process-wide), so if a server happens to be running on + the same memory root, both share state correctly. + """ + engine = get_engine() + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + await get_connection() + await verify_business_schemas() + await ensure_business_indexes() + try: + yield + finally: + await dispose_connection() + await dispose_engine() + + +def _build_orchestrator() -> CascadeOrchestrator: + settings = load_settings() + memory_root = MemoryRoot.default() + memory_root.ensure() + embedder = build_embedding_provider(settings.embedding) + tokenizer = build_tokenizer() + return CascadeOrchestrator( + memory_root=memory_root, + embedder=embedder, + tokenizer=tokenizer, + ) + + +# ── sync ───────────────────────────────────────────────────────────────── + + +@app.command("sync") +def sync( + path: Annotated[ + Path | None, + typer.Argument( + help="Optional md path to force-enqueue before draining. " + "If omitted, only the existing queue is drained.", + ), + ] = None, +) -> None: + """Drain the cascade queue (and optionally re-enqueue a path first).""" + + async def _run() -> None: + async with _runtime(): + orchestrator = _build_orchestrator() + if path is not None: + rel = _resolve_relative(path) + spec = match_kind(rel) + if spec is None: + typer.echo( + f"error: path does not match any registered cascade " + f"kind: {rel}", + err=True, + ) + raise typer.Exit(code=1) + await md_change_state_repo.force_enqueue(rel, spec.name) + typer.echo(f"force-enqueued {rel} (kind={spec.name})") + processed = await orchestrator.sync_once() + typer.echo(f"sync complete — processed {processed} row(s)") + + asyncio.run(_run()) + + +# ── status ─────────────────────────────────────────────────────────────── + + +@app.command("status") +def status() -> None: + """Print the queue / LSN summary.""" + + async def _run() -> None: + async with _runtime(): + summary = await md_change_state_repo.queue_summary() + lag = max(0, summary.max_lsn - summary.last_processed_lsn) + typer.echo("queue:") + typer.echo(f" pending: {summary.pending}") + typer.echo(f" done: {summary.done}") + typer.echo( + f" failed (retryable=TRUE): {summary.failed_retryable}" + + ( + " (eligible for `cascade fix --apply`)" + if summary.failed_retryable + else "" + ) + ) + typer.echo( + f" failed (retryable=FALSE): {summary.failed_permanent}" + + ( + " (fix md and re-save to recover)" + if summary.failed_permanent + else "" + ) + ) + typer.echo("lsn:") + typer.echo(f" max: {summary.max_lsn}") + typer.echo(f" last_processed: {summary.last_processed_lsn}") + typer.echo(f" lag: {lag}") + + asyncio.run(_run()) + + +# ── fix ────────────────────────────────────────────────────────────────── + + +@app.command("fix") +def fix( + apply: Annotated[ + bool, + typer.Option( + "--apply", + help="Re-enqueue every `retryable=TRUE` row and drain the worker.", + ), + ] = False, +) -> None: + """List failed rows (default) or re-enqueue retryable ones (``--apply``).""" + + async def _run() -> None: + async with _runtime(): + rows = await md_change_state_repo.list_failed() + if not rows: + typer.echo("no failed rows") + return + + if not apply: + _print_failed_table(rows) + retryable = sum(1 for r in rows if r.retryable) + permanent = sum(1 for r in rows if not r.retryable) + typer.echo("") + if retryable: + typer.echo( + f"run `everos cascade fix --apply` to re-enqueue " + f"the {retryable} retryable row(s)." + ) + if permanent: + typer.echo( + f"the {permanent} retryable=FALSE row(s) require " + "editing the md and re-saving." + ) + return + + moved = await md_change_state_repo.reset_retryable_to_pending() + typer.echo(f"re-enqueued {moved} retryable row(s)") + if moved: + orchestrator = _build_orchestrator() + processed = await orchestrator.drain_once() + typer.echo(f"[worker] processed {processed} row(s) on drain") + permanent_rows = [r for r in rows if not r.retryable] + if permanent_rows: + typer.echo( + f"{len(permanent_rows)} retryable=FALSE row(s) left untouched:" + ) + for r in permanent_rows: + typer.echo(f" {r.md_path}") + + asyncio.run(_run()) + + +# ── helpers ────────────────────────────────────────────────────────────── + + +def _resolve_relative(p: Path) -> str: + """Translate an absolute / relative path arg into the memory-root rel form. + + The state table stores paths relative to memory root, so the CLI + must match that convention before calling :meth:`force_enqueue`. + Outside-the-root inputs surface as an error in the caller. + """ + memory_root = MemoryRoot.default() + absolute = p.expanduser().resolve() + try: + rel = absolute.relative_to(memory_root.root) + except ValueError as exc: + raise typer.BadParameter( + f"path {p!s} is not under memory root {memory_root.root!s}" + ) from exc + return rel.as_posix() + + +def _print_failed_table(rows: list) -> None: # type: ignore[type-arg] + headers = ("md_path", "retryable", "retries", "last_attempt", "error") + widths = [ + max(len(headers[0]), max(len(r.md_path) for r in rows)), + len(headers[1]), + len(headers[2]), + len(headers[3]), + max(len(headers[4]), max(len(r.error or "") for r in rows)), + ] + fmt = " ".join(f"{{:<{w}}}" for w in widths) + typer.echo(f"{len(rows)} failed row(s):\n") + typer.echo(fmt.format(*headers)) + for r in rows: + typer.echo( + fmt.format( + r.md_path, + "TRUE" if r.retryable else "FALSE", + r.retry_count, + to_display_tz(r.last_attempt_at).isoformat() + if r.last_attempt_at + else "", + r.error or "", + ) + ) diff --git a/src/everos/entrypoints/cli/commands/init_cmd.py b/src/everos/entrypoints/cli/commands/init_cmd.py new file mode 100644 index 00000000..21031706 --- /dev/null +++ b/src/everos/entrypoints/cli/commands/init_cmd.py @@ -0,0 +1,183 @@ +"""``everos init`` — generate a starter ``.env`` from the packaged template. + +The ``env.template`` ships inside the wheel as package data at +``everos/templates/env.template``. ``init`` reads it via +:mod:`importlib.resources`, so the command works identically for pip- +installed users and source-tree users (the file is the single source +of truth). + +Subcommand mounted as ``everos init`` (top-level leaf command — not a +Typer group), to match the idiomatic ``alembic init`` / ``django-admin +startproject`` shape. +""" + +from __future__ import annotations + +import contextlib +import logging +import os +import sys +import tempfile +from importlib import resources +from pathlib import Path + +import typer + +_TEMPLATE_PACKAGE = "everos.templates" +_TEMPLATE_NAME = "env.template" + +_log = logging.getLogger("everos.cli.init") + + +def _read_template() -> str: + """Read the packaged ``env.template`` from wheel resources. + + Returns the file contents as a UTF-8 string. Raises ``RuntimeError`` + on missing-file — if this fires it means the wheel was built from a + source tree where ``src/everos/templates/env.template`` was missing + (canonical location; auto-included via ``packages=["src/everos"]`` + in ``pyproject.toml``). + """ + try: + return ( + resources.files(_TEMPLATE_PACKAGE) + .joinpath(_TEMPLATE_NAME) + .read_text(encoding="utf-8") + ) + except (FileNotFoundError, ModuleNotFoundError) as exc: + raise RuntimeError( + f"packaged template {_TEMPLATE_NAME!r} not found under " + f"{_TEMPLATE_PACKAGE!r}; the wheel is missing its " + "force-include entry (see pyproject.toml " + "[tool.hatch.build.targets.wheel.force-include])." + ) from exc + + +def _xdg_default_path() -> Path: + """``$XDG_CONFIG_HOME/everos/.env`` (default ``~/.config/everos/.env``).""" + xdg = os.environ.get("XDG_CONFIG_HOME") or "~/.config" + return Path(xdg).expanduser() / "everos" / ".env" + + +def _atomic_write(target: Path, content: str, mode: int = 0o600) -> None: + """Write ``content`` to ``target`` atomically with ``mode`` permission. + + Writes to a tempfile in the same directory then ``os.replace``s it + onto the target — guarantees either the full new file is visible or + the original (if any) is untouched. Permission bits applied before + the rename so the file is never readable by other users. + """ + target.parent.mkdir(parents=True, exist_ok=True) + fd, tmp_path = tempfile.mkstemp( + prefix=target.name + ".", + dir=target.parent, + ) + try: + with os.fdopen(fd, "w", encoding="utf-8") as f: + f.write(content) + os.chmod(tmp_path, mode) + os.replace(tmp_path, target) + except Exception: + with contextlib.suppress(OSError): + os.unlink(tmp_path) + raise + + +def register(parent: typer.Typer) -> None: + """Attach the ``init`` command to the root CLI app.""" + + @parent.command("init") + def init( + to: str | None = typer.Option( + None, + "--to", + help=( + "Target path for the .env file (default: ./.env). " + "Parent directories are created if needed." + ), + ), + force: bool = typer.Option( + False, + "--force", + help="Overwrite an existing file at the target path.", + ), + print_: bool = typer.Option( + False, + "--print", + help="Print the template to stdout instead of writing to disk.", + ), + xdg: bool = typer.Option( + False, + "--xdg", + help=( + "Shortcut for --to=${XDG_CONFIG_HOME:-~/.config}/everos/.env " + "(mutually exclusive with --to)." + ), + ), + ) -> None: + """Generate a starter ``.env`` from the packaged template. + + Common flows:: + + everos init # writes ./.env + everos init --xdg # writes ~/.config/everos/.env + everos init --to /etc/foo.env --force + everos init --print > custom.env + + Exit codes: + + - 0 — written successfully (or printed to stdout). + - 1 — target file already exists and ``--force`` was not given. + - 2 — packaged template missing (wheel build problem). + - 3 — write failed (permissions / disk full / parent unwritable). + """ + if xdg and to is not None: + typer.secho( + "error: --xdg and --to are mutually exclusive", + fg=typer.colors.RED, + err=True, + ) + raise typer.Exit(code=2) + + try: + template = _read_template() + except RuntimeError as exc: + typer.secho(f"error: {exc}", fg=typer.colors.RED, err=True) + raise typer.Exit(code=2) from exc + + if print_: + sys.stdout.write(template) + return + + if xdg: + target = _xdg_default_path() + elif to is not None: + target = Path(to).expanduser().resolve() + else: + target = Path.cwd() / ".env" + + if target.exists() and not force: + typer.secho( + f"error: {target} already exists; pass --force to overwrite", + fg=typer.colors.RED, + err=True, + ) + raise typer.Exit(code=1) + + try: + _atomic_write(target, template) + except OSError as exc: + typer.secho( + f"error: failed to write {target}: {exc}", + fg=typer.colors.RED, + err=True, + ) + raise typer.Exit(code=3) from exc + + # Friendly next-step block (stdout — quiet enough for piping). + size_kb = target.stat().st_size / 1024 + typer.secho(f"✓ wrote {target} ({size_kb:.1f} KB)", fg=typer.colors.GREEN) + typer.echo("Next steps:") + typer.echo(" 1. Edit the file and fill in the API keys (see comments inside).") + typer.echo(" 2. Run `everos server start`.") + typer.echo("Docs: https://github.com/evermind/everos/blob/master/QUICKSTART.md") diff --git a/src/everos/entrypoints/cli/commands/server.py b/src/everos/entrypoints/cli/commands/server.py new file mode 100644 index 00000000..31f48f30 --- /dev/null +++ b/src/everos/entrypoints/cli/commands/server.py @@ -0,0 +1,161 @@ +"""``everos server`` subcommand group. + +Provides ``everos server start`` to run the HTTP API via uvicorn. CLI +parses arguments, configures structured logging, then hands off to +uvicorn pointing at :func:`everos.entrypoints.api.app.create_app` as a +factory. +""" + +from __future__ import annotations + +import logging +import os +import sys +from pathlib import Path + +import typer +import uvicorn + +app = typer.Typer( + name="server", + help="Run / manage the HTTP API server", + no_args_is_help=True, +) + + +def _resolve_env_file(explicit: str | None) -> Path | None: + """Find the first existing ``.env`` along the four-layer search path. + + Search order (highest-wins): + + 1. ``explicit`` — when the caller passed ``--env-file ``. + 2. ``./.env`` — the current working directory (project-local convention). + 3. ``${XDG_CONFIG_HOME:-~/.config}/everos/.env`` — XDG-standard user config. + 4. ``~/.everos/.env`` — the project's default memory-root location. + + Returns ``None`` if none of the layers exist (caller may then fall back + to inherited process env / CI secrets). + """ + candidates: list[Path] = [] + if explicit: + candidates.append(Path(explicit).expanduser()) + candidates.append(Path.cwd() / ".env") + xdg = os.environ.get("XDG_CONFIG_HOME") or "~/.config" + candidates.append(Path(xdg).expanduser() / "everos" / ".env") + candidates.append(Path("~/.everos/.env").expanduser()) + for p in candidates: + try: + if p.is_file(): + return p + except OSError: + # Path traversal / permission denied on a fallback candidate + # must not crash the search — skip and keep going. + continue + return None + + +def _load_env_file(path: str | None) -> Path | None: + """Load environment variables from the resolved ``.env`` file. + + Returns the path that was loaded, or ``None`` when no ``.env`` was + found anywhere along the search path. Existence of a ``.env`` is + optional — the user may rely entirely on inherited process env + (e.g. container / CI secret injection). + """ + resolved = _resolve_env_file(path) + if resolved is None: + return None + try: + from dotenv import load_dotenv + + load_dotenv(resolved, override=False) + except ImportError: + # python-dotenv is in our deps; tolerate its absence anyway. + pass + return resolved + + +@app.command("start") +def start( + host: str | None = typer.Option( + None, + "--host", + help="Bind host (env: EVEROS_API__HOST, default: 127.0.0.1)", + ), + port: int | None = typer.Option( + None, + "--port", + help="Bind port (env: EVEROS_API__PORT, default: 8000)", + ), + env_file: str | None = typer.Option( + None, + "--env-file", + help=( + "Path to a dotenv file (highest priority). When omitted, " + "the server searches: ./.env → ${XDG_CONFIG_HOME:-~/.config}" + "/everos/.env → ~/.everos/.env. Run `everos init` to create one." + ), + ), + reload: bool = typer.Option( + False, + "--reload", + help="Reload on source changes (development)", + ), + log_level: str | None = typer.Option( + None, + "--log-level", + help="Log level (env: EVEROS_LOG_LEVEL, default: INFO)", + ), +) -> None: + """Start the HTTP API server.""" + loaded_env = _load_env_file(env_file) + + # Load settings AFTER .env is in place so EVEROS_API__HOST and + # EVEROS_API__PORT (and any other env override) are honored. + from everos.config import load_settings + + settings = load_settings() + + host_resolved = host or settings.api.host + port_resolved = port if port is not None else settings.api.port + log_level_resolved = (log_level or os.getenv("EVEROS_LOG_LEVEL", "INFO")).upper() + + from everos.core.observability.logging import configure_logging + + configure_logging(level=log_level_resolved) + + bootstrap_logger = logging.getLogger("everos.cli.server") + if loaded_env is not None: + bootstrap_logger.info("loaded env file: %s", loaded_env) + else: + bootstrap_logger.info( + "no .env found along the search path; relying on inherited env vars " + "(run `everos init` to generate one)" + ) + bootstrap_logger.info("starting everos on %s:%d", host_resolved, port_resolved) + if host_resolved == "0.0.0.0": + bootstrap_logger.warning( + "binding to 0.0.0.0 exposes the API on all interfaces; EverOS " + "ships no built-in auth — see SECURITY.md" + ) + + try: + uvicorn.run( + "everos.entrypoints.api.app:create_app", + host=host_resolved, + port=port_resolved, + reload=reload, + factory=True, + log_level=log_level_resolved.lower(), + # ``configure_logging()`` above already installed the root + # handler + structlog ProcessorFormatter. ``log_config=None`` + # stops uvicorn from running its own ``dictConfig`` over + # ours; otherwise uvicorn / fastapi messages revert to the + # ``INFO:`` no-structlog format on every restart. + log_config=None, + ) + except KeyboardInterrupt: + bootstrap_logger.info("interrupted; shutting down") + except (OSError, RuntimeError) as exc: + bootstrap_logger.error("startup failed: %s", exc) + sys.exit(1) diff --git a/src/everos/entrypoints/cli/main.py b/src/everos/entrypoints/cli/main.py new file mode 100644 index 00000000..de2da8b6 --- /dev/null +++ b/src/everos/entrypoints/cli/main.py @@ -0,0 +1,34 @@ +"""everos CLI root entry point. + +Exposed as the ``everos`` console script in ``pyproject.toml``. Subcommand +groups live under :mod:`everos.entrypoints.cli.commands` and are registered +here. + +CLI subcommands run **in-process** — they call into the service layer +directly rather than through the HTTP API. The HTTP API and CLI are two +sibling surfaces over the same service layer. +""" + +from __future__ import annotations + +import typer + +from .commands import cascade, init_cmd, server + +app = typer.Typer( + name="everos", + help="everos — md-first memory extraction framework", + no_args_is_help=True, + add_completion=False, +) + +app.add_typer(server.app, name="server") +app.add_typer(cascade.app, name="cascade") + +# ``init`` is a top-level leaf command (not a Typer group) — match the +# idiomatic ``alembic init`` / ``django-admin startproject`` shape. +init_cmd.register(app) + + +if __name__ == "__main__": + app() diff --git a/src/everos/infra/__init__.py b/src/everos/infra/__init__.py new file mode 100644 index 00000000..473d81ca --- /dev/null +++ b/src/everos/infra/__init__.py @@ -0,0 +1,5 @@ +"""Infrastructure layer. + +Adapts to external storage and persists domain models. Contains no +business rules. +""" diff --git a/src/everos/infra/ome/__init__.py b/src/everos/infra/ome/__init__.py new file mode 100644 index 00000000..9602520b --- /dev/null +++ b/src/everos/infra/ome/__init__.py @@ -0,0 +1,63 @@ +"""Async offline strategy scheduling chassis. + +Provides decorator-based strategy registration, event-driven triggers +(Cron/Idle/Manual), and gate-based concurrency control. +""" + +from everos.infra.ome.config import OMEConfig as OMEConfig +from everos.infra.ome.context import StrategyContext as StrategyContext +from everos.infra.ome.decorator import offline_strategy as offline_strategy +from everos.infra.ome.engine import OfflineEngine as OfflineEngine +from everos.infra.ome.events import BaseEvent as BaseEvent +from everos.infra.ome.events import CronTick as CronTick +from everos.infra.ome.events import IdleTick as IdleTick +from everos.infra.ome.events import ManualTick as ManualTick +from everos.infra.ome.exceptions import ( + EmitNotDeclaredError as EmitNotDeclaredError, +) +from everos.infra.ome.exceptions import ( + EngineCallFromStrategyError as EngineCallFromStrategyError, +) +from everos.infra.ome.exceptions import ( + EngineLockHeldError as EngineLockHeldError, +) +from everos.infra.ome.exceptions import OMEError as OMEError +from everos.infra.ome.exceptions import ( + StartupValidationError as StartupValidationError, +) +from everos.infra.ome.exceptions import ( + StrategyContractError as StrategyContractError, +) +from everos.infra.ome.gates import Counter as Counter +from everos.infra.ome.records import RunRecord as RunRecord +from everos.infra.ome.records import RunStatus as RunStatus +from everos.infra.ome.records import StrategyRouteInfo as StrategyRouteInfo +from everos.infra.ome.triggers import Cron as Cron +from everos.infra.ome.triggers import Idle as Idle +from everos.infra.ome.triggers import Immediate as Immediate +from everos.infra.ome.triggers import Trigger as Trigger + +__all__ = [ + "BaseEvent", + "Counter", + "Cron", + "CronTick", + "EmitNotDeclaredError", + "EngineCallFromStrategyError", + "EngineLockHeldError", + "Idle", + "IdleTick", + "Immediate", + "ManualTick", + "OfflineEngine", + "OMEConfig", + "OMEError", + "RunRecord", + "RunStatus", + "StartupValidationError", + "StrategyContext", + "StrategyContractError", + "StrategyRouteInfo", + "Trigger", + "offline_strategy", +] diff --git a/src/everos/infra/ome/_background/__init__.py b/src/everos/infra/ome/_background/__init__.py new file mode 100644 index 00000000..aa67364e --- /dev/null +++ b/src/everos/infra/ome/_background/__init__.py @@ -0,0 +1 @@ +"""Internal: background loops (idle scan / config reload / crash recovery).""" diff --git a/src/everos/infra/ome/_background/config_reloader.py b/src/everos/infra/ome/_background/config_reloader.py new file mode 100644 index 00000000..722f4065 --- /dev/null +++ b/src/everos/infra/ome/_background/config_reloader.py @@ -0,0 +1,254 @@ +"""Config hot-reload — watchfiles + tomllib + shallow merge. + +Hot-updatable fields: enabled / max_retries / gate / cron / idle_seconds / +scan_interval_seconds. Trigger type swap (Immediate ↔ Cron ↔ Idle), +event subscription (Immediate.on / Idle.on), and Idle.event_field +remain immutable — these define strategy routing and changing them +requires a code change and redeploy. + +Per-strategy two-phase commit: enabled is applied independently for +emergency-stop semantics; max_retries / gate / trigger parameters +form one atomic group that fully rolls back on any failure inside it. +""" + +from __future__ import annotations + +import asyncio +import tomllib +from contextlib import suppress +from dataclasses import replace +from pathlib import Path +from typing import TYPE_CHECKING + +from pydantic import ValidationError +from watchfiles import awatch + +from everos.core.observability.logging import get_logger +from everos.infra.ome._dispatch.registry import StrategyRegistry +from everos.infra.ome.config import StrategyOverride, TomlRoot +from everos.infra.ome.decorator import StrategyMeta +from everos.infra.ome.gates import Counter +from everos.infra.ome.triggers import Cron, Idle, Trigger + +if TYPE_CHECKING: + from everos.infra.ome.engine import OfflineEngine + +logger = get_logger(__name__) + + +class _SkipAtomicGroupError(Exception): + """Internal sentinel raised when the non-enabled atomic group for + one strategy must be skipped without affecting other strategies. + """ + + +def _apply_enabled( + meta: StrategyMeta, + override: StrategyOverride, + name: str, + registry: StrategyRegistry, +) -> StrategyMeta: + """Step 1: apply `enabled` independently — never blocked by other fields.""" + if override.enabled is None or override.enabled == meta.enabled: + return meta + new_meta = replace(meta, enabled=override.enabled) + registry.replace(name, new_meta) + return new_meta + + +def _build_atomic_meta( + meta: StrategyMeta, + override: StrategyOverride, +) -> tuple[StrategyMeta, Trigger]: + """Step 2 pure-compute: build (new_meta, new_trigger) from current state. + + Raises `_SkipAtomicGroupError` on type mismatches or invalid gate intros. + No registry / engine writes happen here. + """ + new_meta = meta + new_trigger: Trigger = meta.trigger + + if override.max_retries is not None: + new_meta = replace(new_meta, max_retries=override.max_retries) + + if override.gate is not None: + # Introducing a gate on a strategy that has none requires an explicit + # threshold — silently defaulting to 1 would mean "fire on every + # event", which is almost certainly not what the user intended. + if meta.gate is None and override.gate.threshold is None: + raise _SkipAtomicGroupError( + "introducing a gate requires explicit threshold" + ) + base = meta.gate.model_dump() if meta.gate is not None else {} + for k, v in override.gate.model_dump(exclude_unset=True).items(): + if v is not None: + base[k] = v + new_meta = replace(new_meta, gate=Counter(**base)) + + if override.cron is not None: + if not isinstance(meta.trigger, Cron): + raise _SkipAtomicGroupError( + f"cron given on non-Cron strategy " + f"(actual: {type(meta.trigger).__name__})" + ) + new_trigger = Cron(expr=override.cron) + + if override.idle_seconds is not None or override.scan_interval_seconds is not None: + if not isinstance(meta.trigger, Idle): + raise _SkipAtomicGroupError( + f"idle_* given on non-Idle strategy " + f"(actual: {type(meta.trigger).__name__})" + ) + updates: dict[str, int] = {} + if override.idle_seconds is not None: + updates["idle_seconds"] = override.idle_seconds + if override.scan_interval_seconds is not None: + updates["scan_interval_seconds"] = override.scan_interval_seconds + # model_validate (not model_copy) re-runs Idle._validate_event_field on + # the merged dict; model_copy(update=...) would skip it and let an + # invalid event_field reach the registry. + new_trigger = Idle.model_validate({**meta.trigger.model_dump(), **updates}) + + if new_trigger is not meta.trigger: + new_meta = replace(new_meta, trigger=new_trigger) + + return new_meta, new_trigger + + +def _needs_aps_reschedule(old_trigger: Trigger, new_trigger: Trigger) -> bool: + """True iff scheduler-driving fields changed (cron expr / Idle scan_interval).""" + if new_trigger is old_trigger: + return False + if isinstance(new_trigger, Cron) and isinstance(old_trigger, Cron): + return new_trigger.expr != old_trigger.expr + if isinstance(new_trigger, Idle) and isinstance(old_trigger, Idle): + return new_trigger.scan_interval_seconds != old_trigger.scan_interval_seconds + return False + + +def _maybe_reschedule_aps( + engine: OfflineEngine, name: str, new_trigger: Trigger +) -> None: + """Push the new trigger's APS-relevant fields to the scheduler.""" + if isinstance(new_trigger, Cron): + engine.reschedule_cron_job(name, new_trigger.expr) + elif isinstance(new_trigger, Idle): + engine.reschedule_idle_job( + name, scan_interval_seconds=new_trigger.scan_interval_seconds + ) + + +def _apply_one_strategy( + name: str, + override: StrategyOverride, + registry: StrategyRegistry, + engine: OfflineEngine, +) -> None: + """Two-phase commit for one strategy: enabled, then atomic group.""" + meta = registry.get(name) + meta = _apply_enabled(meta, override, name, registry) + + try: + new_meta, new_trigger = _build_atomic_meta(meta, override) + if _needs_aps_reschedule(meta.trigger, new_trigger): + _maybe_reschedule_aps(engine, name, new_trigger) + registry.replace(name, new_meta) + except Exception as e: # noqa: BLE001 + # User-fixable config error (typo / type mismatch / APS runtime + # failure) — log + skip this strategy's atomic group, never the loop. + logger.warning( + "strategy_atomic_group_skipped", + strategy_name=name, + error_type=type(e).__name__, + exc_info=True, + ) + + +def apply_overrides( + registry: StrategyRegistry, + root: TomlRoot, + engine: OfflineEngine, +) -> None: + """Shallow-merge TomlRoot overrides into registry strategies in place. + + Two-phase per-strategy semantics: + Step 1 (enabled): applied independently — emergency-stop must + never be blocked by a typo in another field. + Step 2 (max_retries / gate / trigger params): applied as an atomic + group. Any failure (type mismatch, invalid cron, APS reschedule + error, ...) rolls the whole group back to the prior values. + """ + known = {m.name for m in registry.all()} + for name, override in root.strategies.items(): + if name not in known: + logger.warning("config_override_unknown_strategy", strategy_name=name) + continue + _apply_one_strategy(name, override, registry, engine) + + +class ConfigReloader: + """Watch a TOML file and apply overrides on change.""" + + def __init__( + self, + *, + config_path: Path, + registry: StrategyRegistry, + engine: OfflineEngine, + debounce_ms: int = 1600, + ) -> None: + self._path = config_path + self._registry = registry + self._engine = engine + self._debounce_ms = debounce_ms + self._task: asyncio.Task[None] | None = None + + def start(self) -> None: + """Fire-and-forget the watch loop. Idempotent: raises on double-start.""" + if self._path is None: + return + if self._task is not None and not self._task.done(): + raise RuntimeError("ConfigReloader already started") + self._task = asyncio.create_task(self._loop()) + + async def stop(self) -> None: + """Cancel the watch task and await it; safe to call multiple times.""" + if self._task is not None: + self._task.cancel() + with suppress(asyncio.CancelledError): + await self._task + self._task = None + + async def _loop(self) -> None: + """Initial load + per-FS-change reload; survives single-iteration failures.""" + try: + await self._load_once() + except Exception: # noqa: BLE001 + logger.exception("config_reload_iteration_failed") + async for _changes in awatch(self._path, debounce=self._debounce_ms): + try: + await self._load_once() + except Exception: # noqa: BLE001 + logger.exception("config_reload_iteration_failed") + + async def _load_once(self) -> None: + """Read TOML off the loop, parse + validate, apply overrides.""" + + def _read_and_parse() -> TomlRoot: + with open(self._path, "rb") as f: + content = f.read() + parsed = tomllib.loads(content.decode("utf-8")) + return TomlRoot.model_validate(parsed) + + try: + root = await asyncio.to_thread(_read_and_parse) + except (OSError, tomllib.TOMLDecodeError, ValidationError) as e: + logger.warning( + "config_reload_failed", + error_type=type(e).__name__, + error=str(e), + path=str(self._path), + ) + return + apply_overrides(self._registry, root, self._engine) + logger.info("config_reloaded", path=str(self._path)) diff --git a/src/everos/infra/ome/_background/crash_recovery.py b/src/everos/infra/ome/_background/crash_recovery.py new file mode 100644 index 00000000..15a3ec57 --- /dev/null +++ b/src/everos/infra/ome/_background/crash_recovery.py @@ -0,0 +1,79 @@ +"""Startup crash recovery — stale RUNNING rows → CRASHED + re-enqueue. + +Runs once at engine.start() before normal dispatching begins. Rows +whose started_at is older than ``timeout_seconds`` are marked CRASHED +and re-enqueued with a fresh run_id reusing the original event payload. +Fresher RUNNING rows are skipped — APScheduler's own jobstore may have +already reattached them. + +At-most-once: ``mark_crashed`` and ``add_job`` are not atomic. If +``add_job`` fails after ``mark_crashed``, the row stays CRASHED and +the event is lost. Strategies needing at-least-once must add their own +retry / monitor layer. +""" + +from __future__ import annotations + +from collections.abc import Awaitable, Callable +from datetime import timedelta +from uuid import uuid4 + +from everos.component.utils.datetime import get_utc_now +from everos.core.observability.logging import get_logger +from everos.infra.ome._stores.run_record import RunRecordStore + +logger = get_logger(__name__) + + +async def scan_and_resume( + *, + run_record_store: RunRecordStore, + timeout_seconds: int, + add_job: Callable[[str, str, str, str, int], Awaitable[None]], +) -> None: + """Scan ``run_record`` for stale RUNNING rows, mark them CRASHED, and + re-enqueue each via ``add_job``. See module docstring for the + at-most-once caveat. + + ``add_job`` is called with positional args + ``(strategy_name, run_id, event_topic, event_payload, max_retries)``. + + Raises: + ValueError: If ``timeout_seconds`` is not positive. + """ + if timeout_seconds <= 0: + raise ValueError(f"timeout_seconds must be > 0, got {timeout_seconds}") + now = get_utc_now() + cutoff = now - timedelta(seconds=timeout_seconds) + running = await run_record_store.find_running() + for rec in running: + if rec.started_at >= cutoff: + continue + await run_record_store.mark_crashed( + run_id=rec.run_id, + finished_at=now, + error="crash recovery: marked CRASHED after start scan", + ) + new_run_id = uuid4().hex + try: + await add_job( + rec.strategy_name, + new_run_id, + rec.event_topic, + rec.event_payload, + rec.max_retries_snapshot, + ) + logger.info( + "crash_recovery_resumed", + strategy_name=rec.strategy_name, + event_topic=rec.event_topic, + old_run_id=rec.run_id, + new_run_id=new_run_id, + ) + except Exception: # noqa: BLE001 + logger.exception( + "crash_recovery_resume_failed", + strategy_name=rec.strategy_name, + event_topic=rec.event_topic, + old_run_id=rec.run_id, + ) diff --git a/src/everos/infra/ome/_background/idle_scanner.py b/src/everos/infra/ome/_background/idle_scanner.py new file mode 100644 index 00000000..df2cd95d --- /dev/null +++ b/src/everos/infra/ome/_background/idle_scanner.py @@ -0,0 +1,60 @@ +"""IdleScanner — periodic scan of idle_store, emits IdleTick for overdue buckets.""" + +from __future__ import annotations + +from collections.abc import Awaitable, Callable +from datetime import datetime + +from everos.component.utils.datetime import get_utc_now +from everos.core.observability.logging import get_logger +from everos.infra.ome._stores.idle import IdleStore +from everos.infra.ome.events import BaseEvent, IdleTick +from everos.infra.ome.triggers import Idle + +logger = get_logger(__name__) + + +class IdleScanner: + """Scans idle_store for overdue buckets and emits IdleTick events.""" + + def __init__( + self, + *, + strategy_name: str, + trigger: Idle, + idle_store: IdleStore, + emit: Callable[[BaseEvent], Awaitable[None]], + ) -> None: + self._name = strategy_name + self._trigger = trigger + self._idle_store = idle_store + self._emit = emit + + async def scan_once(self, *, now: datetime | None = None) -> None: + """Find overdue buckets and emit IdleTick for each. + + Per-bucket emit failures are caught and logged so a single + downstream error (e.g. dispatch hitting a transient DB lock) + cannot prevent sibling buckets from being notified this round. + """ + effective_now = now if now is not None else get_utc_now() + overdue = await self._idle_store.scan_idle( + self._name, + idle_seconds=self._trigger.idle_seconds, + now=effective_now, + ) + for bucket_key in overdue: + try: + await self._emit( + IdleTick( + strategy_name=self._name, + bucket_key=bucket_key, + idle_seconds=self._trigger.idle_seconds, + ) + ) + except Exception: # noqa: BLE001 + logger.exception( + "idle_emit_failed", + strategy_name=self._name, + bucket_key=bucket_key, + ) diff --git a/src/everos/infra/ome/_dispatch/__init__.py b/src/everos/infra/ome/_dispatch/__init__.py new file mode 100644 index 00000000..b77bd2ea --- /dev/null +++ b/src/everos/infra/ome/_dispatch/__init__.py @@ -0,0 +1 @@ +"""Internal: event dispatch core (registry / dispatcher / runner).""" diff --git a/src/everos/infra/ome/_dispatch/_state.py b/src/everos/infra/ome/_dispatch/_state.py new file mode 100644 index 00000000..3d6ee5b9 --- /dev/null +++ b/src/everos/infra/ome/_dispatch/_state.py @@ -0,0 +1,23 @@ +"""ContextVar shared between Runner and OfflineEngine. + +Python copies ContextVar values into child tasks at +``asyncio.create_task`` (by design, for trace propagation), so +``@_refuse_inside_strategy`` reliably catches only *same-task* calls. +Never attach it to APS callback methods (``dispatch_run`` / +``run_idle_scan``) — cascade emits would misfire. +``test_engine_chain_emit_through_ctx`` is the regression. + +TODO: ``sys._getframe`` walk for a ``Runner.run`` frame is leak-proof. +""" + +from __future__ import annotations + +from contextvars import ContextVar + +from everos.infra.ome.decorator import StrategyMeta + +_CURRENT_STRATEGY: ContextVar[StrategyMeta | None] = ContextVar( + "current_strategy", default=None +) +"""Set by ``Runner.run`` around ``meta.func(event, ctx)``; read by +``@_refuse_inside_strategy``. ``None`` = not inside a strategy frame.""" diff --git a/src/everos/infra/ome/_dispatch/dispatcher.py b/src/everos/infra/ome/_dispatch/dispatcher.py new file mode 100644 index 00000000..19cf6110 --- /dev/null +++ b/src/everos/infra/ome/_dispatch/dispatcher.py @@ -0,0 +1,205 @@ +"""EventDispatcher — routing layer applying the three OME gates. + +For each dispatched event, every candidate strategy is run through three +gates in order: + + 1. ``enabled`` — strategy may be hot-disabled via config + 2. ``applies_to`` — per-strategy predicate over the event payload + 3. ``Counter`` — N-of-M rate/threshold gate against + :class:`CounterStore` + +:meth:`dispatch` is the read-write entry point — passing the counter +gate increments the counter and returns ``(meta, run_id)`` pairs to +enqueue. :meth:`inspect` is its dry-run twin — same gates, no counter +mutation; returns one :class:`StrategyRouteInfo` per matched strategy +including a snapshot of the counter so debug callers can see why a +strategy will or won't fire. + +By design ``inspect`` does not accept ``force_enabled`` / +``strategy_filter``: those are runtime overrides for the routing side +(``trigger_manual``), not properties a debugger should second-guess. +""" + +from __future__ import annotations + +from collections.abc import Callable +from uuid import uuid4 + +from everos.core.observability.logging import get_logger +from everos.infra.ome._dispatch.registry import StrategyRegistry +from everos.infra.ome._stores.counter import CounterStore +from everos.infra.ome.decorator import StrategyMeta +from everos.infra.ome.events import BaseEvent +from everos.infra.ome.records import CounterProgress, StrategyRouteInfo + +logger = get_logger(__name__) + + +class EventDispatcher: + """Apply ``enabled / applies_to / Counter`` gates to one event.""" + + def __init__( + self, + *, + registry: StrategyRegistry, + counter_store: CounterStore, + ) -> None: + self._registry = registry + self._counter_store = counter_store + + async def dispatch( + self, + event: BaseEvent, + *, + force_enabled: bool = False, + strategy_filter: str | None = None, + ) -> list[tuple[StrategyMeta, str]]: + """Run gates and return ``(meta, run_id)`` pairs to enqueue. + + Args: + event: The event to route. + force_enabled: Bypass the ``meta.enabled`` gate. ``applies_to`` + and the counter still apply. Used by manual triggers + with ``force=True``. + strategy_filter: Restrict to one strategy name regardless of + whether it subscribes to ``type(event)``. Manual triggers + use this when targeting a strategy with a caller-supplied + event. Raises ``KeyError`` if the name is not registered. + + ``applies_to`` callables raised by a single strategy are caught, + logged, and treated as ``False`` for that strategy alone — sibling + strategies still dispatch. Framework errors (e.g. CounterStore + I/O) propagate. + """ + if strategy_filter is not None: + metas: list[StrategyMeta] = [self._registry.get(strategy_filter)] + else: + metas = list(self._registry.lookup_by_event(type(event))) + out: list[tuple[StrategyMeta, str]] = [] + for meta in metas: + if not _routes_to(event, meta): + continue + if not force_enabled and not meta.enabled: + continue + if not _safe_applies(meta, event): + continue + if meta.gate is not None: + bucket = _bucket_key(event, meta.gate.event_field) + passed, _ = await self._counter_store.incr_and_check( + meta.name, + bucket, + threshold=meta.gate.threshold, + cooldown_seconds=meta.gate.cooldown_seconds, + ) + if not passed: + continue + out.append((meta, uuid4().hex)) + return out + + async def inspect(self, event: BaseEvent) -> list[StrategyRouteInfo]: + """Dry-run twin of :meth:`dispatch` — no counter mutation. + + Returns one :class:`StrategyRouteInfo` per matched strategy with + per-gate pass flags and a counter snapshot (read-only via + ``get_progress``). Same exception policy as :meth:`dispatch`: + a strategy's faulty ``applies_to`` callable is logged and that + strategy reports ``applies_to_pass=False`` rather than tanking + the whole inspection. + """ + out: list[StrategyRouteInfo] = [] + for meta in self._registry.lookup_by_event(type(event)): + if not _routes_to(event, meta): + continue + enabled_pass = bool(meta.enabled) + applies_pass = enabled_pass and _safe_applies(meta, event) + counter_pass = applies_pass and (meta.gate is None) + progress: CounterProgress | None = None + if applies_pass and meta.gate is not None: + bucket = _bucket_key(event, meta.gate.event_field) + cur = await self._counter_store.get_progress( + meta.name, + bucket, + ) + next_cur = cur + 1 + progress = CounterProgress( + current=next_cur, threshold=meta.gate.threshold + ) + counter_pass = next_cur >= meta.gate.threshold + out.append( + StrategyRouteInfo( + strategy_name=meta.name, + enabled_pass=enabled_pass, + applies_to_pass=applies_pass, + counter_pass=counter_pass, + counter_progress=progress, + ) + ) + return out + + +def _routes_to(event: BaseEvent, meta: StrategyMeta) -> bool: + """Narrow engine-emitted ticks to their single target strategy. + + Cron / Idle / Manual ticks carry a ``strategy_name`` naming the + intended recipient — without this filter two strategies listening + on the same tick class would cross-fire. Business events have no + such field and therefore fan out to every matching strategy. + """ + target = getattr(event, "strategy_name", None) + return target is None or target == meta.name + + +def _safe_applies(meta: StrategyMeta, event: BaseEvent) -> bool: + """Evaluate ``meta.applies_to`` with user-callable exceptions isolated. + + A faulty ``applies_to`` callable is logged at exception level with + ``strategy_name`` + ``event_topic`` context and treated as + ``False`` so that a single buggy predicate cannot tank the entire + fan-out for an event. + """ + try: + return _applies(meta.applies_to, event) + except Exception: # noqa: BLE001 + logger.exception( + "applies_to_callable_raised", + strategy_name=meta.name, + event_topic=type(event).topic(), + ) + return False + + +def _applies( + spec: str | Callable[[BaseEvent], bool] | None, + event: BaseEvent, +) -> bool: + """Resolve ``applies_to`` semantics. + + * ``None`` — strategy applies to every event in its subscription + * callable — invoke and bool-cast the result + * str — read the named event attribute and bool-cast it; falsy + values (``""``, ``0``, ``None``, empty containers) are treated + as "field unset", so the strategy does NOT apply + + Exceptions raised by a user callable propagate; the dispatcher wraps + this call in :func:`_safe_applies` to localise blast radius. + """ + if spec is None: + return True + if callable(spec): + return bool(spec(event)) + return bool(getattr(event, spec, None)) + + +def _bucket_key(event: BaseEvent, field: str | None) -> str: + """Compute a Counter-store bucket key from an event field. + + ``field=None`` means the gate is un-bucketed → single shared bucket + ``"__all__"``. Missing or ``None`` field values map to ``"__none__"`` + so a typo doesn't accidentally collapse every event into ``"__all__"`` + (the StrategyRegistry validator catches typos at startup; the sentinel + here is the runtime safety net). + """ + if field is None: + return "__all__" + val = getattr(event, field, None) + return str(val) if val is not None else "__none__" diff --git a/src/everos/infra/ome/_dispatch/registry.py b/src/everos/infra/ome/_dispatch/registry.py new file mode 100644 index 00000000..ad0b49b5 --- /dev/null +++ b/src/everos/infra/ome/_dispatch/registry.py @@ -0,0 +1,152 @@ +"""StrategyRegistry — registration + DAG cycle detection. + +Mutated at startup via :meth:`register` / :meth:`validate`, and at +runtime via :meth:`replace` (config hot-reload). Cycle detection is a +Kahn-style topological pass on the event-flow DAG implied by +``trigger.on`` (incoming) and ``emits`` (outgoing). +""" + +from __future__ import annotations + +from collections import defaultdict, deque +from collections.abc import Callable +from typing import Any + +from everos.infra.ome.decorator import StrategyMeta +from everos.infra.ome.events import BaseEvent, CronTick, IdleTick +from everos.infra.ome.exceptions import StartupValidationError +from everos.infra.ome.triggers import Cron, Idle, Immediate, Trigger + + +class StrategyRegistry: + """Startup-time registry for offline strategies with cycle detection.""" + + def __init__(self) -> None: + self._strategies: dict[str, StrategyMeta] = {} + + def register(self, func: Callable[..., Any]) -> None: + """Register a strategy function (reads ``_ome_strategy_meta``). + + Raises ``StartupValidationError`` if ``func`` is not decorated + with ``@offline_strategy`` or if its name is already registered. + """ + meta = getattr(func, "_ome_strategy_meta", None) + if not isinstance(meta, StrategyMeta): + fn_name = getattr(func, "__name__", repr(func)) + raise StartupValidationError( + f"register: {fn_name} is not decorated with @offline_strategy" + ) + if meta.name in self._strategies: + raise StartupValidationError( + f"register: duplicate strategy name {meta.name!r}" + ) + self._strategies[meta.name] = meta + + def replace(self, name: str, new_meta: StrategyMeta) -> None: + """Swap an already-registered strategy's meta in place (hot-reload entry). + + Cycle / gate validation is **not** re-run; callers (currently + :func:`apply_overrides`) must only feed metas where the + DAG-shaping fields (``trigger.on``, ``emits``, trigger type) + match the original. Raises ``KeyError`` if ``name`` is not yet + registered. + """ + if name not in self._strategies: + raise KeyError(name) + self._strategies[name] = new_meta + + def get(self, name: str) -> StrategyMeta: + """Return meta by name (raises ``KeyError`` if absent).""" + return self._strategies[name] + + def all(self) -> list[StrategyMeta]: + """Return a snapshot list of every registered strategy.""" + return list(self._strategies.values()) + + def lookup_by_event(self, event_cls: type[BaseEvent]) -> list[StrategyMeta]: + """Return strategies that may receive an event of ``event_cls``. + + Resolution: + * ``Immediate`` strategy listening on the class → match + * ``CronTick`` → all Cron strategies (narrowed later by name) + * ``IdleTick`` → all Idle strategies (narrowed later by name) + + Engine-emitted ticks carry a ``strategy_name`` field; dispatcher + narrows the returned set to the single target via ``_routes_to``. + """ + out: list[StrategyMeta] = [] + for m in self._strategies.values(): + if ( + (isinstance(m.trigger, Immediate) and event_cls in m.trigger.on) + or (isinstance(m.trigger, Cron) and event_cls is CronTick) + or (isinstance(m.trigger, Idle) and event_cls is IdleTick) + ): + out.append(m) + return out + + def validate(self) -> None: + """Validate the strategy DAG for cycles and gate field existence.""" + self._validate_no_cycles() + self._validate_gate_event_fields() + + def _validate_no_cycles(self) -> None: + """Kahn topological sort over the event-flow DAG. + + Edge ``s_a → s_b`` exists iff ``s_a.emits`` intersects + ``s_b.trigger.on``. + """ + adj: dict[str, set[str]] = defaultdict(set) + indeg: dict[str, int] = dict.fromkeys(self._strategies, 0) + + for src in self._strategies.values(): + for ev in src.emits: + for dst in self._strategies.values(): + if ( + isinstance(dst.trigger, Immediate) + and ev in dst.trigger.on + and dst.name not in adj[src.name] + ): + adj[src.name].add(dst.name) + indeg[dst.name] += 1 + + queue = deque(n for n, d in indeg.items() if d == 0) + visited = 0 + while queue: + n = queue.popleft() + visited += 1 + for nbr in adj[n]: + indeg[nbr] -= 1 + if indeg[nbr] == 0: + queue.append(nbr) + + if visited < len(self._strategies): + raise StartupValidationError("cycle detected in strategy DAG") + + def _validate_gate_event_fields(self) -> None: + """Reject any ``gate.event_field`` missing from a receivable event class. + + Without this check a typo silently collapses every event into one + shared bucket and the rate gate stops segmenting. + """ + for meta in self._strategies.values(): + if meta.gate is None or meta.gate.event_field is None: + continue + field = meta.gate.event_field + for ev_cls in _event_classes_for_trigger(meta.trigger): + if field not in ev_cls.model_fields: # type: ignore[operator] # Pydantic model_fields → dict via @deprecated_instance_property (pydantic/main.py:277) + raise StartupValidationError( + f"strategy {meta.name!r}: gate.event_field {field!r} " + f"not found in {ev_cls.__name__} fields " + f"(available: {list(ev_cls.model_fields)})" # type: ignore[arg-type] # same as above + ) + + +def _event_classes_for_trigger(trigger: Trigger) -> list[type[BaseEvent]]: + """Enumerate event classes a strategy with the given trigger receives.""" + if isinstance(trigger, Immediate): + return list(trigger.on) + if isinstance(trigger, Cron): + return [CronTick] + if isinstance(trigger, Idle): + return [IdleTick] + raise NotImplementedError(f"unknown trigger type: {type(trigger).__name__}") diff --git a/src/everos/infra/ome/_dispatch/runner.py b/src/everos/infra/ome/_dispatch/runner.py new file mode 100644 index 00000000..43726237 --- /dev/null +++ b/src/everos/infra/ome/_dispatch/runner.py @@ -0,0 +1,247 @@ +"""Runner — single-strategy execution with attempt-level retry + DLQ. + +Acquires ``engine_sem`` (FIFO), drives the per-attempt RunRecord state +machine (``RUNNING → SUCCESS / FAILED / DEAD_LETTER``), and fires +``on_dead_letter`` after exhausted retries or contract violations. + +Per attempt, binds ``strategy_name`` / ``run_id`` / ``attempt`` into +``structlog.contextvars`` (so every log record carries those fields +automatically) and sets ``_CURRENT_STRATEGY`` ContextVar around +``meta.func`` (so ``engine.emit`` can refuse direct calls from inside +a strategy — strategies emit via ``ctx.emit``). + +**Idempotency contract**: if ``mark_success`` / ``mark_failed`` / +``mark_dead_letter`` fails after the strategy body returned, the +``RUNNING`` row stays and crash recovery on next start will treat the +run as crashed and re-enqueue the same event. Strategy bodies must +therefore be safe to re-execute with the same payload. +""" + +from __future__ import annotations + +import asyncio +import traceback +from collections.abc import Awaitable, Callable +from uuid import uuid4 + +from structlog.contextvars import bound_contextvars + +from everos.component.utils.datetime import get_utc_now +from everos.core.observability.logging import get_logger +from everos.infra.ome._dispatch._state import _CURRENT_STRATEGY +from everos.infra.ome._stores.run_record import RunRecordStore +from everos.infra.ome.decorator import StrategyMeta +from everos.infra.ome.events import BaseEvent +from everos.infra.ome.exceptions import EmitNotDeclaredError, StrategyContractError +from everos.infra.ome.records import RunRecord + +logger = get_logger(__name__) + + +class _RunCtx: + """Per-invocation context handed to ``meta.func(event, ctx)``. + + Carries ``run_id``, a strategy-scoped logger, and the ``emit`` + callback that enforces the declared ``emits=[...]`` contract. + """ + + def __init__( + self, + *, + run_id: str, + strategy_name: str, + emit_hook: Callable[[BaseEvent], Awaitable[None]], + declared_emits: frozenset[type[BaseEvent]], + ) -> None: + self.run_id = run_id + self.logger = get_logger("ome.strategy") + self._emit_hook = emit_hook + self._declared = declared_emits + self._strategy_name = strategy_name + + async def emit(self, event: BaseEvent) -> None: + if type(event) not in self._declared: + raise EmitNotDeclaredError( + strategy=self._strategy_name, + event=event, + ) + await self._emit_hook(event) + + +class Runner: + """Drive one strategy invocation through retries to a terminal state.""" + + def __init__( + self, + *, + run_record_store: RunRecordStore, + engine_sem: asyncio.Semaphore, + emit_hook: Callable[[BaseEvent], Awaitable[None]], + on_dead_letter: Callable[[RunRecord], None] | None = None, + ) -> None: + self._rec = run_record_store + self._sem = engine_sem + self._emit_hook = emit_hook + self._on_dead_letter = on_dead_letter + + async def run( + self, + meta: StrategyMeta, + event: BaseEvent, + *, + run_id: str, + max_retries_snapshot: int, + ) -> None: + """Execute ``meta.func(event, ctx)`` with the attempt retry loop. + + Holds ``engine_sem`` for the full retry chain so concurrency cap + applies end-to-end. Each attempt gets a fresh ``run_id`` after + the first, so the run history records every try. + """ + if max_retries_snapshot < 0: + raise ValueError( + f"max_retries_snapshot must be >= 0, got {max_retries_snapshot}" + ) + + async with self._sem: + event_topic = type(event).topic() + event_payload = event.model_dump_json() + current_run_id = run_id + + for attempt in range(max_retries_snapshot + 1): + if attempt > 0: + current_run_id = uuid4().hex + terminated = await self._run_one_attempt( + meta=meta, + event=event, + current_run_id=current_run_id, + attempt=attempt, + event_topic=event_topic, + event_payload=event_payload, + max_retries_snapshot=max_retries_snapshot, + ) + if terminated: + return + + async def _run_one_attempt( + self, + *, + meta: StrategyMeta, + event: BaseEvent, + current_run_id: str, + attempt: int, + event_topic: str, + event_payload: str, + max_retries_snapshot: int, + ) -> bool: + """Run one attempt; return ``True`` if a terminal state was + written (success / dead-letter or persistence failure), ``False`` + if FAILED and the caller should loop into the next attempt. + """ + ctx = _RunCtx( + run_id=current_run_id, + strategy_name=meta.name, + emit_hook=self._emit_hook, + declared_emits=meta.emits, + ) + with bound_contextvars( # type: ignore[arg-type] # structlog typed as Generator; @contextmanager wraps at runtime (structlog/contextvars.py:170) + strategy_name=meta.name, + run_id=current_run_id, + attempt=attempt, + ): + if not await self._record_start( + run_id=current_run_id, + strategy_name=meta.name, + attempt=attempt, + event_topic=event_topic, + event_payload=event_payload, + max_retries_snapshot=max_retries_snapshot, + ): + return True # mark_running failed; abort run, no DB row exists + try: + token = _CURRENT_STRATEGY.set(meta) + try: + await meta.func(event, ctx) + finally: + _CURRENT_STRATEGY.reset(token) + except StrategyContractError as e: + await self._terminate_dead_letter(current_run_id, _format_error(e)) + return True + except Exception as e: # noqa: BLE001 + err = _format_error(e) + if attempt < max_retries_snapshot: + await self._rec.mark_failed( + run_id=current_run_id, + finished_at=get_utc_now(), + error=err, + ) + return False # caller will retry + await self._terminate_dead_letter(current_run_id, err) + return True + else: + await self._rec.mark_success( + run_id=current_run_id, + finished_at=get_utc_now(), + ) + return True + + async def _record_start( + self, + *, + run_id: str, + strategy_name: str, + attempt: int, + event_topic: str, + event_payload: str, + max_retries_snapshot: int, + ) -> bool: + """Persist this attempt as RUNNING; return ``False`` on write failure. + + When the write fails (DB lock, disk full, ...) the caller + aborts the retry loop — without a RUNNING row crash recovery + cannot rediscover the run, and it is silently lost. The + exception log emitted here is the only audit trail. + """ + try: + await self._rec.mark_running( + run_id=run_id, + strategy_name=strategy_name, + attempt=attempt, + event_topic=event_topic, + event_payload=event_payload, + max_retries_snapshot=max_retries_snapshot, + ) + except Exception: # noqa: BLE001 + logger.exception( + "mark_running_failed", + run_id=run_id, + strategy_name=strategy_name, + attempt=attempt, + ) + return False + return True + + async def _terminate_dead_letter(self, run_id: str, error: str) -> None: + """Mark DEAD_LETTER and fire ``on_dead_letter`` callback if set.""" + await self._rec.mark_dead_letter( + run_id=run_id, + finished_at=get_utc_now(), + error=error, + ) + await self._fire_dead_letter_callback(run_id) + + async def _fire_dead_letter_callback(self, run_id: str) -> None: + if self._on_dead_letter is None: + return + rec = await self._rec.get(run_id) + if rec is None: + return + try: + self._on_dead_letter(rec) + except Exception: # noqa: BLE001 + logger.exception("on_dead_letter_failed") + + +def _format_error(e: BaseException) -> str: + """Format an exception with type, message, and full traceback.""" + return f"{type(e).__name__}: {e}\n{traceback.format_exc()}" diff --git a/src/everos/infra/ome/_stores/__init__.py b/src/everos/infra/ome/_stores/__init__.py new file mode 100644 index 00000000..d4e19a1e --- /dev/null +++ b/src/everos/infra/ome/_stores/__init__.py @@ -0,0 +1 @@ +"""Internal: SQLite-backed state stores (counter / idle / run_record).""" diff --git a/src/everos/infra/ome/_stores/counter.py b/src/everos/infra/ome/_stores/counter.py new file mode 100644 index 00000000..af638dbb --- /dev/null +++ b/src/everos/infra/ome/_stores/counter.py @@ -0,0 +1,107 @@ +"""CounterStore — persistent (strategy_name, bucket_key) → counter rows. + +Backs the ``Counter`` gate in OME's dispatch pipeline: each call to +:meth:`CounterStore.incr_and_check` atomically increments the bucket's +counter and reports whether the strategy should fire this time. + +Pass semantics: + - ``counter >= threshold`` AND cooldown elapsed → ``passed=True`` + - On pass, the row's counter resets to 0 and ``last_passed_ts`` + advances to ``now``; the next pass needs a fresh accumulation. + - ``cooldown_seconds=0`` disables the cooldown gate (threshold alone). +""" + +from __future__ import annotations + +from datetime import timedelta + +from everos.component.utils.datetime import ( + from_iso_format, + get_utc_now, + to_iso_format, +) +from everos.infra.ome._stores.storage import OMEStorage + + +class CounterStore: + """SQLite-backed counter for the ``Counter`` gate (see module docstring).""" + + def __init__(self, storage: OMEStorage) -> None: + self._storage = storage + + async def incr_and_check( + self, + strategy_name: str, + bucket_key: str, + *, + threshold: int, + cooldown_seconds: int, + ) -> tuple[bool, int]: + """Increment ``(strategy_name, bucket_key)``'s counter atomically. + + Args: + strategy_name: Strategy whose counter to update. + bucket_key: The bucket value derived from the event field + (or ``"__all__"`` when the gate is unbucketed). + threshold: Pass once the counter reaches this value + (``>=``). + cooldown_seconds: Minimum seconds since the last pass for + the strategy/bucket; ``0`` disables the cooldown check. + + Returns: + ``(passed, counter)``. ``counter`` is the counter value at + the moment of the check (i.e. pre-reset on pass). Useful for + diagnostics — ``threshold`` is *not* substituted, so callers + observing ``counter > threshold`` learn the gate is + over-armed (e.g. threshold was lowered via hot reload while + the counter had already accumulated past the new value). + """ + now = get_utc_now() + async with self._storage.transaction() as conn: + cur = await conn.execute( + "SELECT counter, last_passed_ts FROM counter_store " + "WHERE strategy_name = ? AND bucket_key = ?", + (strategy_name, bucket_key), + ) + row = await cur.fetchone() + counter = (row[0] if row else 0) + 1 + last_passed = from_iso_format(row[1]) if row and row[1] else None + + cooldown_ok = ( + cooldown_seconds == 0 + or last_passed is None + or now - last_passed >= timedelta(seconds=cooldown_seconds) + ) + passed = counter >= threshold and cooldown_ok + + new_counter = 0 if passed else counter + new_last_passed_ts = ( + to_iso_format(now) + if passed + else (to_iso_format(last_passed) if last_passed else None) + ) + await conn.execute( + "INSERT INTO counter_store (strategy_name, bucket_key, " + "counter, last_passed_ts) " + "VALUES (?, ?, ?, ?) " + "ON CONFLICT(strategy_name, bucket_key) DO UPDATE SET " + "counter = excluded.counter, " + "last_passed_ts = excluded.last_passed_ts", + (strategy_name, bucket_key, new_counter, new_last_passed_ts), + ) + return passed, counter + + async def get_progress(self, strategy_name: str, bucket_key: str) -> int: + """Return the counter value persisted for this bucket (0 if absent). + + Read-only; does not increment. Used by dispatcher inspect-mode + to report progress without mutating state. + """ + async with self._storage.connect() as conn: + cur = await conn.execute( + "SELECT counter FROM counter_store " + "WHERE strategy_name = ? AND bucket_key = ?", + (strategy_name, bucket_key), + ) + row = await cur.fetchone() + return row[0] if row else 0 diff --git a/src/everos/infra/ome/_stores/idle.py b/src/everos/infra/ome/_stores/idle.py new file mode 100644 index 00000000..12007f28 --- /dev/null +++ b/src/everos/infra/ome/_stores/idle.py @@ -0,0 +1,64 @@ +"""IdleStore — last_activity_ts rows backing the Idle trigger. + +All writes pass through ``to_iso_format`` over a tz-aware datetime, so +``last_activity_ts`` is a fixed-format ISO 8601 string whose +lexicographic order matches temporal order — :meth:`scan_idle` relies +on this to keep the column un-wrapped in its predicate so SQLite can +use ``idx_idle_scan``. +""" + +from __future__ import annotations + +from datetime import datetime, timedelta + +from everos.component.utils.datetime import from_iso_format, to_iso_format +from everos.infra.ome._stores.storage import OMEStorage + + +class IdleStore: + """SQLite-backed last-activity tracker for the ``Idle`` trigger.""" + + def __init__(self, storage: OMEStorage) -> None: + self._storage = storage + + async def touch(self, strategy_name: str, bucket_key: str, *, at: datetime) -> None: + """UPSERT ``last_activity_ts = at`` for ``(strategy_name, bucket_key)``.""" + async with self._storage.connect() as conn: + await conn.execute( + "INSERT INTO idle_store " + "(strategy_name, bucket_key, last_activity_ts) " + "VALUES (?, ?, ?) " + "ON CONFLICT(strategy_name, bucket_key) DO UPDATE SET " + "last_activity_ts = excluded.last_activity_ts", + (strategy_name, bucket_key, to_iso_format(at)), + ) + await conn.commit() + + async def scan_idle( + self, strategy_name: str, *, idle_seconds: int, now: datetime + ) -> list[str]: + """Return bucket_keys with ``last_activity_ts`` older than ``idle_seconds``.""" + # Cutoff on the RHS so the indexed column stays un-wrapped. + cutoff = to_iso_format(now - timedelta(seconds=idle_seconds)) + async with self._storage.connect() as conn: + cur = await conn.execute( + "SELECT bucket_key FROM idle_store " + "WHERE strategy_name = ? AND last_activity_ts <= ? " + "ORDER BY last_activity_ts ASC", + (strategy_name, cutoff), + ) + rows = await cur.fetchall() + return [r[0] for r in rows] + + async def get_last_activity( + self, strategy_name: str, bucket_key: str + ) -> datetime | None: + """Return the stored ``last_activity_ts`` (``None`` if never touched).""" + async with self._storage.connect() as conn: + cur = await conn.execute( + "SELECT last_activity_ts FROM idle_store " + "WHERE strategy_name = ? AND bucket_key = ?", + (strategy_name, bucket_key), + ) + row = await cur.fetchone() + return from_iso_format(row[0]) if row else None diff --git a/src/everos/infra/ome/_stores/run_record.py b/src/everos/infra/ome/_stores/run_record.py new file mode 100644 index 00000000..7d66ea6b --- /dev/null +++ b/src/everos/infra/ome/_stores/run_record.py @@ -0,0 +1,168 @@ +"""RunRecord persistence — state machine writes + same-transaction ring-buffer trim. + +State machine (one row per ``run_id``): + RUNNING → SUCCESS / FAILED / DEAD_LETTER / CRASHED + +Every :meth:`RunRecordStore.mark_running` INSERT runs inside one +``BEGIN IMMEDIATE`` transaction with a paired DELETE that keeps only +the newest ``max_records_per_strategy`` rows for that strategy. Bound +is enforced atomically — no background sweeper, no transient +over-budget state. +""" + +from __future__ import annotations + +from datetime import datetime +from typing import Any + +from everos.component.utils.datetime import ( + from_iso_format, + get_utc_now, + to_iso_format, +) +from everos.infra.ome._stores.storage import OMEStorage +from everos.infra.ome.records import RunRecord, RunStatus + + +class RunRecordStore: + """SQLite-backed persistence for ``RunRecord`` (see module docstring).""" + + def __init__(self, storage: OMEStorage, max_records_per_strategy: int) -> None: + self._storage = storage + self._max = max_records_per_strategy + + async def mark_running( + self, + *, + run_id: str, + strategy_name: str, + attempt: int, + event_topic: str, + event_payload: str, + max_retries_snapshot: int, + ) -> None: + """Insert a new RUNNING row and trim the strategy's ring buffer atomically.""" + async with self._storage.transaction() as conn: + await conn.execute( + "INSERT INTO run_record " + "(run_id, strategy_name, status, attempt, started_at, " + " event_topic, event_payload, max_retries_snapshot) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?)", + ( + run_id, + strategy_name, + RunStatus.RUNNING.value, + attempt, + to_iso_format(get_utc_now()), + event_topic, + event_payload, + max_retries_snapshot, + ), + ) + await conn.execute( + "DELETE FROM run_record " + "WHERE strategy_name = ? AND run_id NOT IN (" + " SELECT run_id FROM run_record WHERE strategy_name = ? " + " ORDER BY started_at DESC LIMIT ?)", + (strategy_name, strategy_name, self._max), + ) + + async def mark_success(self, *, run_id: str, finished_at: datetime) -> None: + """Mark RUNNING → SUCCESS.""" + await self._update_status(run_id, RunStatus.SUCCESS, finished_at, None) + + async def mark_failed( + self, *, run_id: str, finished_at: datetime, error: str + ) -> None: + """Mark RUNNING → FAILED (retry pending).""" + await self._update_status(run_id, RunStatus.FAILED, finished_at, error) + + async def mark_dead_letter( + self, *, run_id: str, finished_at: datetime, error: str + ) -> None: + """Mark RUNNING → DEAD_LETTER (retries exhausted or non-retryable).""" + await self._update_status(run_id, RunStatus.DEAD_LETTER, finished_at, error) + + async def mark_crashed( + self, *, run_id: str, finished_at: datetime, error: str + ) -> None: + """Mark RUNNING → CRASHED (called by crash-recovery sweep).""" + await self._update_status(run_id, RunStatus.CRASHED, finished_at, error) + + async def _update_status( + self, + run_id: str, + status: RunStatus, + finished_at: datetime, + error: str | None, + ) -> None: + async with self._storage.connect() as conn: + await conn.execute( + "UPDATE run_record " + "SET status = ?, finished_at = ?, error = ? " + "WHERE run_id = ?", + (status.value, to_iso_format(finished_at), error, run_id), + ) + await conn.commit() + + async def get(self, run_id: str) -> RunRecord | None: + """Return the record for ``run_id`` (``None`` if absent).""" + async with self._storage.connect() as conn: + cur = await conn.execute( + _SELECT_COLUMNS + " WHERE run_id = ?", + (run_id,), + ) + row = await cur.fetchone() + return _row_to_record(row) if row else None + + async def list_runs( + self, + *, + strategy_name: str, + status: RunStatus | None = None, + limit: int = 100, + ) -> list[RunRecord]: + """Return ``strategy_name``'s records, newest first; optional status filter.""" + sql = _SELECT_COLUMNS + " WHERE strategy_name = ?" + args: list[Any] = [strategy_name] + if status is not None: + sql += " AND status = ?" + args.append(status.value) + sql += " ORDER BY started_at DESC LIMIT ?" + args.append(limit) + async with self._storage.connect() as conn: + cur = await conn.execute(sql, args) + rows = await cur.fetchall() + return [_row_to_record(r) for r in rows] + + async def find_running(self) -> list[RunRecord]: + """Return every row still in RUNNING — used by crash recovery at start().""" + async with self._storage.connect() as conn: + cur = await conn.execute( + _SELECT_COLUMNS + " WHERE status = ?", + (RunStatus.RUNNING.value,), + ) + rows = await cur.fetchall() + return [_row_to_record(r) for r in rows] + + +_SELECT_COLUMNS = ( + "SELECT run_id, strategy_name, status, attempt, started_at, finished_at, " + " error, event_topic, event_payload, max_retries_snapshot " + "FROM run_record" +) + + +def _row_to_record(row: tuple) -> RunRecord: + return RunRecord( + run_id=row[0], + strategy_name=row[1], + status=RunStatus(row[2]), + attempt=row[3], + started_at=from_iso_format(row[4]), + finished_at=from_iso_format(row[5]) if row[5] else None, + error=row[6], + event_topic=row[7], + event_payload=row[8], + max_retries_snapshot=row[9], + ) diff --git a/src/everos/infra/ome/_stores/storage.py b/src/everos/infra/ome/_stores/storage.py new file mode 100644 index 00000000..ac8fd387 --- /dev/null +++ b/src/everos/infra/ome/_stores/storage.py @@ -0,0 +1,115 @@ +"""OME SQLite storage — schema initialization + connection factory. + +Single file (default ``MemoryRoot.default().ome_db`` ≡ +``/.index/sqlite/ome.db``). Holds 3 OME-managed tables +(counter_store / idle_store / run_record); APS jobstore table is created +by APScheduler itself when its SQLAlchemyJobStore connects. + +PRAGMA scopes (see https://www.sqlite.org/pragma.html): + - ``journal_mode=WAL`` is file-level — persisted in the db header, + applied once in :meth:`OMEStorage.init`. + - ``synchronous=NORMAL``, ``cache_size=-65536``, ``busy_timeout=5000`` + are connection-level and reset on every new connection, so they are + re-applied inside :meth:`OMEStorage.connect` (which is why + ``connect`` is an ``@asynccontextmanager`` rather than a passthrough). + This mirrors SQLAlchemy's canonical ``@event.listens_for(Engine, + "connect")`` pattern for SQLite — aiosqlite exposes no equivalent + hook. ``busy_timeout=5000`` matters because the APS jobstore writes + its own table in the same db file; without it, WAL writer-vs-writer + contention surfaces as ``SQLITE_BUSY`` instead of brief backoff. +""" + +from __future__ import annotations + +from collections.abc import AsyncIterator +from contextlib import asynccontextmanager +from pathlib import Path + +import aiosqlite + +_SCHEMA = """ +CREATE TABLE IF NOT EXISTS counter_store ( + strategy_name TEXT NOT NULL, + bucket_key TEXT NOT NULL, + counter INTEGER NOT NULL DEFAULT 0, + last_passed_ts TIMESTAMP, + PRIMARY KEY (strategy_name, bucket_key) +); + +CREATE TABLE IF NOT EXISTS idle_store ( + strategy_name TEXT NOT NULL, + bucket_key TEXT NOT NULL, + last_activity_ts TIMESTAMP NOT NULL, + PRIMARY KEY (strategy_name, bucket_key) +); +CREATE INDEX IF NOT EXISTS idx_idle_scan + ON idle_store (strategy_name, last_activity_ts); + +CREATE TABLE IF NOT EXISTS run_record ( + run_id TEXT PRIMARY KEY, + strategy_name TEXT NOT NULL, + status TEXT NOT NULL, + attempt INTEGER NOT NULL DEFAULT 0, + started_at TIMESTAMP NOT NULL, + finished_at TIMESTAMP, + error TEXT, + event_topic TEXT NOT NULL, + event_payload TEXT NOT NULL, + max_retries_snapshot INTEGER NOT NULL +); +CREATE INDEX IF NOT EXISTS idx_run_strategy_started + ON run_record (strategy_name, started_at DESC); +CREATE INDEX IF NOT EXISTS idx_run_status_started + ON run_record (status, started_at DESC); +""" + +_INIT_PRAGMAS = ("PRAGMA journal_mode=WAL",) +_CONN_PRAGMAS = ( + "PRAGMA synchronous=NORMAL", + "PRAGMA cache_size=-65536", + "PRAGMA busy_timeout=5000", +) + + +class OMEStorage: + """Connection factory + schema init for the OME SQLite db.""" + + def __init__(self, db_path: Path) -> None: + self.db_path = db_path + + async def init(self) -> None: + """Create parent dirs + apply file-level pragmas + create schema.""" + self.db_path.parent.mkdir(parents=True, exist_ok=True) + async with aiosqlite.connect(self.db_path) as conn: + for pragma in _INIT_PRAGMAS: + await conn.execute(pragma) + await conn.executescript(_SCHEMA) + await conn.commit() + + @asynccontextmanager + async def connect(self) -> AsyncIterator[aiosqlite.Connection]: + """Yield an aiosqlite connection with per-connection pragmas applied.""" + async with aiosqlite.connect(self.db_path) as conn: + for pragma in _CONN_PRAGMAS: + await conn.execute(pragma) + yield conn + + @asynccontextmanager + async def transaction(self) -> AsyncIterator[aiosqlite.Connection]: + """Yield a connection inside an ``IMMEDIATE`` transaction. + + Commits on success, rolls back on any exception. Mirrors + SQLAlchemy's ``conn.begin()`` for raw aiosqlite, which exposes + no built-in transaction context manager. ``BEGIN IMMEDIATE`` + (rather than ``DEFERRED``) acquires the write lock upfront so + a read-modify-write block cannot lose to a competing writer + between its SELECT and its UPDATE. + """ + async with self.connect() as conn: + try: + await conn.execute("BEGIN IMMEDIATE") + yield conn + await conn.commit() + except Exception: + await conn.rollback() + raise diff --git a/src/everos/infra/ome/config.py b/src/everos/infra/ome/config.py new file mode 100644 index 00000000..eec0ef35 --- /dev/null +++ b/src/everos/infra/ome/config.py @@ -0,0 +1,157 @@ +"""OMEConfig (engine-level) + TomlRoot (per-strategy override schema). + +All models forbid extra keys so configuration typos surface at startup +as StartupValidationError instead of being silently ignored. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Annotated, Self + +from apscheduler.triggers.cron import CronTrigger +from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator + +from everos.core.persistence.memory_root import MemoryRoot + + +def _default_jobstore_path() -> Path: + return MemoryRoot.default().ome_db + + +class CounterOverride(BaseModel): + """TOML override for a strategy's Counter gate (per-key None means keep).""" + + model_config = ConfigDict(extra="forbid") + + threshold: Annotated[int, Field(gt=0)] | None = None + cooldown_seconds: Annotated[int, Field(ge=0)] | None = None + event_field: Annotated[str, Field(min_length=1)] | None = None + + +class StrategyOverride(BaseModel): + """TOML override for one strategy's decorator parameters.""" + + model_config = ConfigDict(extra="forbid") + + enabled: bool | None = None + max_retries: Annotated[int, Field(ge=0)] | None = None + gate: CounterOverride | None = None + cron: str | None = None + idle_seconds: Annotated[int, Field(gt=0)] | None = None + scan_interval_seconds: Annotated[int, Field(gt=0)] | None = None + + @field_validator("cron") + @classmethod + def _validate_crontab(cls, v: str | None) -> str | None: + if v is not None: + CronTrigger.from_crontab(v) + return v + + @model_validator(mode="after") + def _check_idle_pair_consistency(self) -> Self: + # One-sided overrides are merged with existing meta downstream, + # so cross-check only when both fields are in this payload. + if ( + self.idle_seconds is not None + and self.scan_interval_seconds is not None + and self.scan_interval_seconds > self.idle_seconds // 2 + ): + raise ValueError( + "StrategyOverride: scan_interval_seconds " + f"({self.scan_interval_seconds}) must be <= idle_seconds // 2 " + f"({self.idle_seconds // 2})" + ) + return self + + +class TomlRoot(BaseModel): + """Top-level TOML schema for ome.toml.""" + + model_config = ConfigDict(extra="forbid") + + strategies: dict[str, StrategyOverride] = Field(default_factory=dict) + + +class OMEConfig(BaseModel): + """Engine-level configuration consumed by OfflineEngine.""" + + model_config = ConfigDict(extra="forbid") + + jobstore_path: Path = Field( + default_factory=_default_jobstore_path, + description="SQLite DB path holding OME's own state (run records, " + "counter store, idle store). Defaults to " + "``MemoryRoot.default().ome_db`` (``/.index/sqlite/ome.db``).", + ) + aps_jobstore_path: Path | None = Field( + default=None, + description="SQLite DB path holding the APScheduler jobstore. Kept " + "in a separate file from ``jobstore_path`` so APS's sync SQLAlchemy " + "writer never contends with OME's async aiosqlite writer for the " + "same SQLite file lock. When unset, defaults to a sibling " + "``.aps.db`` next to ``jobstore_path``.", + ) + max_concurrent_runs: Annotated[ + int, + Field( + gt=0, + description="Engine-wide cap on concurrent strategy invocations " + "(asyncio.Semaphore in Runner).", + ), + ] = 20 + max_retries: Annotated[ + int, + Field( + ge=0, + description="Default retry budget per run, overridable via " + "@offline_strategy(max_retries=...) or StrategyOverride.max_retries. " + "0 disables retries.", + ), + ] = 1 + max_records_per_strategy: Annotated[ + int, + Field( + gt=0, + description="Per-strategy RunRecord ring-buffer size; oldest " + "entries are pruned on insert.", + ), + ] = 1000 + crash_recovery_timeout_seconds: Annotated[ + int, + Field( + gt=0, + description="A run lingering in RUNNING longer than this is " + "treated as crashed, marked CRASHED, and re-enqueued with a " + "fresh run_id.", + ), + ] = 1800 + config_path: Path | None = Field( + default=None, + description="Path to ome.toml for per-strategy overrides. None " + "disables TOML-driven hot reload.", + ) + config_watch: bool = Field( + default=True, + description="When true and config_path is set, watch the file for " + "edits and apply overrides at runtime.", + ) + config_watch_debounce_ms: Annotated[ + int, + Field( + gt=0, + description="Debounce window collapsing bursts of filesystem " + "events (e.g. editor saves) into one reload.", + ), + ] = 1600 + + @model_validator(mode="after") + def _derive_aps_jobstore_path(self) -> Self: + # When unset, materialize as a sibling of jobstore_path so callers + # that pass only jobstore_path (e.g. tests using tmp_path) still get + # an isolated APS db rather than the global default root. + if self.aps_jobstore_path is None: + self.aps_jobstore_path = self.jobstore_path.with_name( + self.jobstore_path.stem + ".aps.db" + ) + return self diff --git a/src/everos/infra/ome/context.py b/src/everos/infra/ome/context.py new file mode 100644 index 00000000..4038dd5d --- /dev/null +++ b/src/everos/infra/ome/context.py @@ -0,0 +1,33 @@ +"""StrategyContext Protocol — injected as second arg to every strategy. + +Strategies access run-local state through `run_id` and `logger`, and +chain-emit follow-up events via `emit(event)`. Business IO is NOT mediated +by this Protocol — strategies directly import their persistence adapters +(memory → infra is allowed under the project's DDD layering). +""" + +from __future__ import annotations + +from typing import Protocol + +from structlog.types import FilteringBoundLogger + +from everos.infra.ome.events import BaseEvent + + +class StrategyContext(Protocol): + """Per-run context handed to a strategy function. + + - run_id: the current RunRecord id (string). + - logger: structlog logger; ``strategy_name`` / ``run_id`` / + ``attempt`` are auto-injected into every log record in this call + — strategies don't have to use this specific logger to get those + fields. + - emit(event): chain-emit a follow-up event (must be in decorator's + ``emits=[...]``, else EmitNotDeclaredError). + """ + + run_id: str + logger: FilteringBoundLogger + + async def emit(self, event: BaseEvent) -> None: ... diff --git a/src/everos/infra/ome/decorator.py b/src/everos/infra/ome/decorator.py new file mode 100644 index 00000000..6c0d5eef --- /dev/null +++ b/src/everos/infra/ome/decorator.py @@ -0,0 +1,69 @@ +"""@offline_strategy decorator — attaches StrategyMeta to the function. + +Decorator is side-effect-free; engine collects via explicit +`engine.register(func)`. +""" + +from __future__ import annotations + +import inspect +from collections.abc import Awaitable, Callable +from dataclasses import dataclass + +from everos.infra.ome.context import StrategyContext +from everos.infra.ome.events import BaseEvent +from everos.infra.ome.gates import Counter +from everos.infra.ome.triggers import Trigger + +type AppliesTo = str | Callable[[BaseEvent], bool] | None +type StrategyFn = Callable[[BaseEvent, StrategyContext], Awaitable[None]] + + +@dataclass(frozen=True) +class StrategyMeta: + """Captured at decoration time; consumed by engine.register().""" + + name: str + trigger: Trigger + emits: frozenset[type[BaseEvent]] + applies_to: AppliesTo + gate: Counter | None + max_retries: int | None + enabled: bool + func: StrategyFn + + +def offline_strategy( + *, + name: str, + trigger: Trigger, + emits: list[type[BaseEvent]], + applies_to: AppliesTo = None, + gate: Counter | None = None, + max_retries: int | None = None, + enabled: bool = True, +) -> Callable[[StrategyFn], StrategyFn]: + """Mark an async function as an OME strategy.""" + + if not name or not name.strip(): + raise ValueError("offline_strategy: name must be a non-empty string") + + def wrap(func: StrategyFn) -> StrategyFn: + if not inspect.iscoroutinefunction(func): + raise TypeError( + f"offline_strategy: {func.__name__} must be async (coroutine function)" + ) + meta = StrategyMeta( + name=name, + trigger=trigger, + emits=frozenset(emits), + applies_to=applies_to, + gate=gate, + max_retries=max_retries, + enabled=enabled, + func=func, + ) + func._ome_strategy_meta = meta # type: ignore[attr-defined] + return func + + return wrap diff --git a/src/everos/infra/ome/engine.py b/src/everos/infra/ome/engine.py new file mode 100644 index 00000000..e1dd4031 --- /dev/null +++ b/src/everos/infra/ome/engine.py @@ -0,0 +1,797 @@ +"""OfflineEngine — OME runtime and scheduler. + +Manages strategy registration, start-stop lifecycle, event dispatch, and +scheduling of Cron and Idle triggers via APScheduler. Enforces single-engine +guard via portalocker for concurrent access safety. +""" + +from __future__ import annotations + +import asyncio +import functools +import inspect +from collections.abc import Callable +from pathlib import Path +from typing import Any +from uuid import uuid4 + +import portalocker +from apscheduler.executors.asyncio import AsyncIOExecutor +from apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore +from apscheduler.schedulers.asyncio import AsyncIOScheduler +from apscheduler.triggers.cron import CronTrigger +from apscheduler.triggers.interval import IntervalTrigger + +from everos.component.utils.datetime import get_utc_now +from everos.core.observability.logging import get_logger +from everos.infra.ome._background.config_reloader import ConfigReloader +from everos.infra.ome._background.crash_recovery import scan_and_resume +from everos.infra.ome._background.idle_scanner import IdleScanner +from everos.infra.ome._dispatch._state import _CURRENT_STRATEGY +from everos.infra.ome._dispatch.dispatcher import EventDispatcher +from everos.infra.ome._dispatch.registry import StrategyRegistry +from everos.infra.ome._dispatch.runner import Runner +from everos.infra.ome._stores.counter import CounterStore +from everos.infra.ome._stores.idle import IdleStore +from everos.infra.ome._stores.run_record import RunRecordStore +from everos.infra.ome._stores.storage import OMEStorage +from everos.infra.ome.config import OMEConfig +from everos.infra.ome.decorator import StrategyMeta +from everos.infra.ome.events import BaseEvent, CronTick, ManualTick, resolve_topic +from everos.infra.ome.exceptions import ( + EngineCallFromStrategyError, + EngineLockHeldError, + OMEError, +) +from everos.infra.ome.records import RunRecord, RunStatus, StrategyRouteInfo +from everos.infra.ome.triggers import Cron, Idle + +logger = get_logger(__name__) + +_ENGINES: dict[str, OfflineEngine] = {} + + +def _refuse_inside_strategy(method: Any) -> Any: + """Raise :class:`EngineCallFromStrategyError` when called from a strategy. + + Strategies must interact with the engine only via the ``(event, ctx)`` + parameters Runner provides; direct calls bypass the declared + ``emits=[...]`` contract enforced by ``ctx.emit``. Wraps sync and async + methods alike. + """ + if inspect.iscoroutinefunction(method): + + @functools.wraps(method) + async def async_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any: + current = _CURRENT_STRATEGY.get() + if current is not None: + raise EngineCallFromStrategyError( + strategy=current.name, method=method.__name__ + ) + return await method(self, *args, **kwargs) + + return async_wrapper + + @functools.wraps(method) + def sync_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any: + current = _CURRENT_STRATEGY.get() + if current is not None: + raise EngineCallFromStrategyError( + strategy=current.name, method=method.__name__ + ) + return method(self, *args, **kwargs) + + return sync_wrapper + + +async def _runner_entry( + engine_id: str, + strategy_name: str, + run_id: str, + event_topic: str, + event_payload: str, + max_retries_snapshot: int, +) -> None: + """Module-level APS jobstore callback for a single run. + + Looks the engine up by id and hands off to + :meth:`OfflineEngine.dispatch_run`. Pickle-safe (no closures, no + bound methods captured into APS jobstore args). + """ + engine = _ENGINES.get(engine_id) + if engine is None: + logger.error( + "no_engine_for_runner", + engine_id=engine_id, + run_id=run_id, + ) + return + await engine.dispatch_run( + strategy_name=strategy_name, + run_id=run_id, + event_topic=event_topic, + event_payload=event_payload, + max_retries_snapshot=max_retries_snapshot, + ) + + +async def _cron_entry(engine_id: str, strategy_name: str) -> None: + """Module-level APS jobstore callback for Cron triggers. + + Looks the engine up by id and emits ``CronTick`` so the event flows + back through the standard dispatch pipeline. + """ + engine = _ENGINES.get(engine_id) + if engine is None: + logger.error( + "no_engine_for_cron", + engine_id=engine_id, + strategy_name=strategy_name, + ) + return + await engine.emit(CronTick(strategy_name=strategy_name)) + + +async def _idle_entry(engine_id: str, strategy_name: str) -> None: + """Module-level APS jobstore callback for Idle IntervalTriggers. + + Looks the engine up by id and hands off to + :meth:`OfflineEngine.run_idle_scan`. + """ + engine = _ENGINES.get(engine_id) + if engine is None: + logger.error( + "no_engine_for_idle", + engine_id=engine_id, + strategy_name=strategy_name, + ) + return + await engine.run_idle_scan(strategy_name) + + +class OfflineEngine: + """Offline Memory Engine — orchestrates strategy registration, scheduling, + and event dispatch. + + Lifecycle:: + + engine = OfflineEngine(config=cfg) + engine.register(my_strategy) # before start() + engine.on_dead_letter(cb) # before start() + await engine.start() # acquires file lock, boots scheduler + await engine.emit(SomeEvent(...)) # fan out through dispatcher + await engine.stop() # graceful shutdown + + Single-process invariant: a file lock on + ``.lock`` guarantees at most one engine per jobstore + at any time (cross-process safe via ``portalocker``). + """ + + def __init__( + self, + *, + config: OMEConfig, + ) -> None: + self._config = config + self._registry = StrategyRegistry() + self._storage = OMEStorage(db_path=config.jobstore_path) + self._lock_handle: Any = None + self._started = False + self._on_dead_letter: Callable[[RunRecord], None] | None = None + + # late-bound (set in start()) + self._counter_store: CounterStore | None = None + self._run_record_store: RunRecordStore | None = None + self._dispatcher: EventDispatcher | None = None + self._runner: Runner | None = None + self._engine_sem: asyncio.Semaphore | None = None + self._idle_store: IdleStore | None = None + self._engine_id = uuid4().hex + self._scheduler: AsyncIOScheduler | None = None + self._config_reloader: ConfigReloader | None = None + + # In-flight strategy-run accounting. Incremented at the moment a + # run is enqueued onto APS (so callers that emit-then-wait observe + # a non-zero count immediately), decremented in dispatch_run's + # finally. APS 3.x AsyncIOExecutor.shutdown(wait=True) does NOT + # honor wait for async coroutines (see apscheduler/executors/ + # asyncio.py:24); this counter is how stop() / drain() learn the + # engine is genuinely idle. + self._active_runs = 0 + self._idle_event: asyncio.Event | None = None + + def register(self, func: Callable[..., Any]) -> None: + """Register a strategy decorated with :func:`offline_strategy`. + + Must be called before :meth:`start`; registering after start raises + :class:`OMEError` because the scheduler has already snapshotted + the strategy set for Cron / Idle job creation. + """ + if self._started: + raise OMEError("register: cannot register after start()") + self._registry.register(func) + + @_refuse_inside_strategy + def reschedule_cron_job(self, name: str, expr: str) -> None: + """Reschedule a Cron strategy's APScheduler job to a new crontab. + + APS reschedule_job is atomic: on success, pending invocations are + recomputed against the new trigger; on failure it raises and APS + state is unchanged, so callers can roll back paired registry + mutations. + """ + if self._scheduler is None: + raise OMEError("reschedule_cron_job: engine not started") + self._scheduler.reschedule_job( + job_id=f"cron::{name}", + trigger=CronTrigger.from_crontab(expr), + ) + + @_refuse_inside_strategy + def reschedule_idle_job(self, name: str, scan_interval_seconds: int) -> None: + """Reschedule an Idle strategy's APScheduler scan job to a new interval.""" + if self._scheduler is None: + raise OMEError("reschedule_idle_job: engine not started") + self._scheduler.reschedule_job( + job_id=f"idle::{name}", + trigger=IntervalTrigger(seconds=scan_interval_seconds), + ) + + def on_dead_letter(self, callback: Callable[[RunRecord], None]) -> None: + """Register a callback invoked after a run is marked DEAD_LETTER. + + Must be set before start(); calls after start() are silently ignored + (logged at WARNING) to avoid racing with the already-instantiated + Runner that captured a snapshot of the callback. If called multiple + times before start(), only the last callback wins (no chaining). + """ + if self._started: + logger.warning("on_dead_letter_after_start_ignored") + return + self._on_dead_letter = callback + + async def start(self) -> None: + """Boot the engine: acquire the jobstore lock, validate the strategy + DAG, wire up late-bound stores, launch APScheduler, run crash + recovery, register Cron / Idle jobs, and optionally start the + config-reloader. + + Idempotent: a second call while running is a no-op. On failure, + every partially-initialised resource (lock, scheduler thread, + :data:`_ENGINES` slot, config reloader) is rolled back so a retry + starts from a clean state. + """ + if self._started: + return + await self._storage.init() + self._acquire_lock() + try: + self._registry.validate() + self._init_components() + self._idle_event = asyncio.Event() + self._idle_event.set() + self._launch_scheduler() + _ENGINES[self._engine_id] = self + await self._run_crash_recovery() + self._register_scheduled_jobs() + self._start_config_reloader() + self._started = True + except Exception: + await self._rollback_partial_start() + raise + + def _init_components(self) -> None: + """Instantiate stores / dispatcher / runner / semaphore. + + Called from :meth:`start` after the file lock is held and DAG + validation passed; never from anywhere else. + """ + self._counter_store = CounterStore(storage=self._storage) + self._run_record_store = RunRecordStore( + storage=self._storage, + max_records_per_strategy=self._config.max_records_per_strategy, + ) + self._dispatcher = EventDispatcher( + registry=self._registry, + counter_store=self._counter_store, + ) + self._engine_sem = asyncio.Semaphore(self._config.max_concurrent_runs) + self._runner = Runner( + run_record_store=self._run_record_store, + engine_sem=self._engine_sem, + emit_hook=self._dispatch_event, + on_dead_letter=self._on_dead_letter, + ) + self._idle_store = IdleStore(storage=self._storage) + + def _launch_scheduler(self) -> None: + """Wire up AsyncIOScheduler + SQLAlchemyJobStore and start it. + + The APS jobstore lives in its own SQLite file + (``aps_jobstore_path``) so APS's sync SQLAlchemy writes never + contend with OME's async aiosqlite writes for the same file lock + — both writers had previously raced on a single ``ome.db`` and + manifested as flaky ``SQLITE_BUSY: database is locked`` during + concurrent strategy dispatch. + """ + self._scheduler = AsyncIOScheduler( + jobstores={ + "default": SQLAlchemyJobStore( + url=f"sqlite:///{self._config.aps_jobstore_path}", + ), + }, + executors={"default": AsyncIOExecutor()}, + ) + self._scheduler.start() + + async def _run_crash_recovery(self) -> None: + """Scan ``run_record`` for stale RUNNING rows and re-enqueue them. + + Treats rows whose ``started_at`` is older than + ``crash_recovery_timeout_seconds`` as crashes from a previous + engine session: they are marked CRASHED and re-added to APS with + a fresh ``run_id`` reusing the original event payload. + """ + await scan_and_resume( + run_record_store=self._run_record_store, + timeout_seconds=self._config.crash_recovery_timeout_seconds, + add_job=self._enqueue_recovery_job, + ) + + async def _enqueue_recovery_job( + self, + name: str, + run_id: str, + event_topic: str, + event_payload: str, + max_retries: int, + ) -> None: + """Add one APS job for a re-enqueued crashed run (callback for + :func:`scan_and_resume`). + + Same enqueue-time bookkeeping as :meth:`_enqueue_run`: the run + will reach :meth:`dispatch_run` like any other, so the +1/-1 + pair must wrap the ``add_job`` call here too. + """ + self._on_run_enqueued() + try: + self._scheduler.add_job( + _runner_entry, + trigger="date", + run_date=get_utc_now(), + args=[ + self._engine_id, + name, + run_id, + event_topic, + event_payload, + max_retries, + ], + id=run_id, + replace_existing=False, + misfire_grace_time=None, # type: ignore[arg-type] # APS accepts None ("no expiry"); stub omits it (apscheduler/job.py:213) + ) + except Exception: + self._on_run_completed() + raise + + def _register_scheduled_jobs(self) -> None: + """Add Cron / Idle APS jobs for every strategy with such a trigger. + + Immediate-trigger strategies have nothing scheduled here — they + fire only when their declared event class is dispatched. + """ + for meta in self._registry.all(): + if isinstance(meta.trigger, Cron): + self._scheduler.add_job( + _cron_entry, + trigger=CronTrigger.from_crontab(meta.trigger.expr), + args=[self._engine_id, meta.name], + id=f"cron::{meta.name}", + replace_existing=True, + ) + elif isinstance(meta.trigger, Idle): + self._scheduler.add_job( + _idle_entry, + trigger=IntervalTrigger(seconds=meta.trigger.scan_interval_seconds), + args=[self._engine_id, meta.name], + id=f"idle::{meta.name}", + replace_existing=True, + ) + + def _start_config_reloader(self) -> None: + """Start :class:`ConfigReloader` iff ``config_watch`` is on and a + ``config_path`` is provided. + """ + if self._config.config_watch and self._config.config_path is not None: + self._config_reloader = ConfigReloader( + config_path=self._config.config_path, + registry=self._registry, + engine=self, + debounce_ms=self._config.config_watch_debounce_ms, + ) + self._config_reloader.start() + + async def _rollback_partial_start(self) -> None: + """Reverse-order cleanup of whatever :meth:`start` had already + wired up before the failure: stop reloader, drain in-flight runs + (best-effort, short timeout — startup failure shouldn't block on + recovery jobs), shut the scheduler, drop ``_ENGINES`` slot, and + release the file lock. + + Same ``wait_idle → shutdown(wait=False)`` order as :meth:`stop` + for the same reasons (pause would freeze recovery jobs that + already own a +1). + """ + if self._config_reloader is not None: + try: + await self._config_reloader.stop() + finally: + self._config_reloader = None + if self._scheduler is not None: + try: + await self.wait_idle(timeout=5.0) + self._scheduler.shutdown(wait=False) + finally: + self._scheduler = None + _ENGINES.pop(self._engine_id, None) + self._release_lock() + self._idle_event = None + self._active_runs = 0 + + async def wait_idle(self, *, timeout: float = 30.0) -> bool: # noqa: ASYNC109 + """Block until every in-flight strategy run has settled. + + Returns ``True`` on idle, ``False`` if ``timeout`` elapses with + runs still active. "In flight" means anywhere between + :meth:`_enqueue_run` (which bumps the counter just before the + ``add_job`` call) and the end of :meth:`dispatch_run` (which + releases it in ``finally``). + + Why this exists: APS 3.x ``AsyncIOExecutor.shutdown(wait=True)`` + documents — in the executor source — that it cannot honor wait + for async coroutines and simply cancels their futures + (``apscheduler/executors/asyncio.py:24``). Anything depending on + "all jobs really completed" has to drain through this counter, + not the scheduler. + """ + if self._idle_event is None: + return self._active_runs == 0 + try: + await asyncio.wait_for(self._idle_event.wait(), timeout=timeout) + return True + except TimeoutError: + return False + + async def stop(self) -> None: + """Shut the engine down gracefully: stop the config reloader, drain + in-flight strategy runs, shut the scheduler, drop the global + ``_ENGINES`` slot, and release the jobstore lock. + + Idempotent: calling stop on an already-stopped engine is a no-op. + + Drain ordering matters and is *deliberately* not + ``pause → wait_idle → shutdown``. + + - We cannot ``pause()`` first: APS ``pause()`` freezes jobstore + dispatch including jobs already enqueued (see + ``apscheduler/schedulers/base.py:pause``: "prevent the scheduler + from waking up to do job processing"). Each such job already + owns a +1 in ``_active_runs`` from :meth:`_enqueue_run`, so + freezing dispatch deadlocks :meth:`wait_idle`. + + - We cannot use ``shutdown(wait=True)``: APS 3.x + ``AsyncIOExecutor.shutdown`` documents in its own source that + it cannot honor wait for async coroutines and cancels their + futures (``apscheduler/executors/asyncio.py:24``). Cascade + ``CancelledError`` / "Event loop is closed" warnings follow. + + Order used here: ``wait_idle`` first (lets APS finish dispatching + everything in the jobstore and lets every dispatch_run release its + counter), then ``shutdown(wait=False)`` (drops the executor cleanly + because there is nothing left in flight). + + ``_ENGINES`` is popped only after the drain so ``_runner_entry`` + can still find this engine via its id while finishing the last + few jobs. + """ + if not self._started: + return + if self._config_reloader is not None: + await self._config_reloader.stop() + self._config_reloader = None + if self._scheduler is not None: + drained = await self.wait_idle(timeout=30.0) + if not drained: + logger.warning( + "ome_stop_drain_timeout", + engine_id=self._engine_id, + active_runs=self._active_runs, + ) + self._scheduler.shutdown(wait=False) + self._scheduler = None + _ENGINES.pop(self._engine_id, None) + self._release_lock() + self._started = False + self._idle_event = None + self._active_runs = 0 + + def _acquire_lock(self) -> None: + lock_path = Path(str(self._config.jobstore_path) + ".lock") + lock_path.parent.mkdir(parents=True, exist_ok=True) + try: + handle = open(lock_path, "a+") # noqa: SIM115 + portalocker.lock(handle, portalocker.LOCK_EX | portalocker.LOCK_NB) + self._lock_handle = handle + except portalocker.LockException as e: + raise EngineLockHeldError( + f"another OfflineEngine instance already holds {lock_path}" + ) from e + + def _release_lock(self) -> None: + if self._lock_handle is not None: + try: + portalocker.unlock(self._lock_handle) + finally: + self._lock_handle.close() + self._lock_handle = None + + @_refuse_inside_strategy + async def emit(self, event: BaseEvent) -> None: + """Public engine event entry point. + + Strategies must NOT call this directly; use ``ctx.emit`` instead. + The :func:`_refuse_inside_strategy` guard raises + :class:`EngineCallFromStrategyError` on in-strategy calls — only + ``ctx.emit`` enforces the strategy's declared ``emits=[...]`` + contract. + """ + await self._dispatch_event(event) + + async def _dispatch_event(self, event: BaseEvent) -> None: + """Internal: actually run an event through dispatch. + + Used by Runner's ``emit_hook`` so ``ctx.emit`` flows through + dispatch without tripping the public-method guard. + """ + if not self._started: + raise OMEError("emit: engine not started") + # Touch idle_store for any Idle strategy listening on this event type + # (best-effort; errors do not block dispatch) + for meta in self._registry.all(): + if isinstance(meta.trigger, Idle) and type(event) in meta.trigger.on: + bucket = getattr(event, meta.trigger.event_field, None) + if bucket is not None: + try: + await self._idle_store.touch( # type: ignore[union-attr] + meta.name, + str(bucket), + at=get_utc_now(), + ) + except Exception as e: + logger.warning( + "idle_touch_failed", + strategy_name=meta.name, + event_field=meta.trigger.event_field, + error=str(e), + ) + routes = await self._dispatcher.dispatch(event) + for meta, run_id in routes: + self._enqueue_run(meta, event, run_id) + + @_refuse_inside_strategy + async def trigger_manual( + self, + name: str, + *, + event: BaseEvent | None = None, + force: bool = False, + ) -> None: + """Manually trigger one strategy. + + - ``event=None`` → engine self-emits ``ManualTick(strategy_name=name)`` + - ``force=True`` → bypass the ``enabled`` gate (``applies_to`` and + ``Counter`` still apply) + + Routes through :meth:`EventDispatcher.dispatch` with + ``strategy_filter=name`` so the same three-gate logic is applied + as for engine-driven dispatch. + """ + if not self._started: + raise OMEError("trigger_manual: engine not started") + if event is None: + event = ManualTick(strategy_name=name) + routes = await self._dispatcher.dispatch( + event, + force_enabled=force, + strategy_filter=name, + ) + for meta, run_id in routes: + self._enqueue_run(meta, event, run_id) + + def _enqueue_run(self, meta: StrategyMeta, event: BaseEvent, run_id: str) -> None: + """Add a one-shot APScheduler job that hands the event to Runner. + + Computes ``max_retries_snapshot`` from meta or engine default and + packages a pickle-safe args tuple — the dispatch tail shared by + ``_dispatch_event``, ``trigger_manual``, and crash recovery. + + Counter ``self._active_runs`` is bumped *before* ``add_job`` so a + caller that ``emit`` s then immediately ``wait_idle`` s observes a + non-zero count; the matching decrement lives in + :meth:`dispatch_run` (which is guaranteed to run for every job + APS dispatches). If ``add_job`` itself raises, the counter is + rolled back here. + """ + max_retries_snapshot = ( + meta.max_retries + if meta.max_retries is not None + else self._config.max_retries + ) + event_topic = type(event).topic() + self._on_run_enqueued() + try: + self._scheduler.add_job( + _runner_entry, + trigger="date", + run_date=get_utc_now(), + args=[ + self._engine_id, + meta.name, + run_id, + event_topic, + event.model_dump_json(), + max_retries_snapshot, + ], + id=run_id, + replace_existing=False, + misfire_grace_time=None, # type: ignore[arg-type] # APS accepts None ("no expiry"); stub omits it (apscheduler/job.py:213) + ) + except Exception: + self._on_run_completed() + raise + + def _on_run_enqueued(self) -> None: + """Bump in-flight count and mark the engine non-idle.""" + self._active_runs += 1 + if self._idle_event is not None: + self._idle_event.clear() + + def _on_run_completed(self) -> None: + """Drop in-flight count; mark the engine idle if the count hit zero. + + Never lets the counter dip below zero — that would mask a bookkeeping + bug rather than fix it, and a stuck-clear idle_event would deadlock + ``wait_idle``. + """ + if self._active_runs <= 0: + logger.error( + "active_runs_underflow", + engine_id=self._engine_id, + ) + self._active_runs = 0 + if self._idle_event is not None: + self._idle_event.set() + return + self._active_runs -= 1 + if self._active_runs == 0 and self._idle_event is not None: + self._idle_event.set() + + async def dispatch_run( + self, + *, + strategy_name: str, + run_id: str, + event_topic: str, + event_payload: str, + max_retries_snapshot: int, + ) -> None: + """APS jobstore callback target for one strategy run. + + Public because the module-level :func:`_runner_entry` callback + must cross the pickle boundary — a bound method on ``self`` is + not picklable into the APS jobstore. Not part of the + strategy-author API; intended to be called only by + ``_runner_entry`` (and crash recovery). Not guarded with + ``_refuse_inside_strategy`` because APS executors may inherit + the calling task's ContextVar — a strategy that ``ctx.emit``s + and triggers a cascade would falsely trip the guard here. + + Closes the +1 the matching enqueue path opened, in ``finally`` + so cancellation, retries, and crashes all release the count. + """ + try: + cls = resolve_topic(event_topic) + event = cls.model_validate_json(event_payload) + meta = self._registry.get(strategy_name) + await self._runner.run( + meta, + event, + run_id=run_id, + max_retries_snapshot=max_retries_snapshot, + ) + finally: + self._on_run_completed() + + async def run_idle_scan(self, strategy_name: str) -> None: + """APS IntervalTrigger callback target for one Idle strategy. + + Constructs an :class:`IdleScanner` against the engine's idle_store + and runs one scan, emitting :class:`IdleTick` for each overdue + bucket. Public for the same APS-pickle reason as + :meth:`dispatch_run`; unguarded for the same ContextVar- + inheritance reason. + """ + meta = self._registry.get(strategy_name) + if not isinstance(meta.trigger, Idle): + logger.error( + "idle_entry_bad_trigger_type", + strategy_name=strategy_name, + trigger_type=type(meta.trigger).__name__, + ) + return + scanner = IdleScanner( + strategy_name=strategy_name, + trigger=meta.trigger, + idle_store=self._idle_store, # type: ignore[arg-type] + emit=self.emit, + ) + await scanner.scan_once() + + @_refuse_inside_strategy + async def inspect_dispatch(self, event: BaseEvent) -> list[StrategyRouteInfo]: + """Return per-strategy routing info for event (read-only). + + Calls the dispatcher in inspect mode (no counter mutation). + """ + if not self._started: + raise OMEError("inspect_dispatch: engine not started") + return await self._dispatcher.inspect(event) + + @_refuse_inside_strategy + async def list_runs( + self, + strategy_name: str, + *, + status: RunStatus | None = None, + limit: int = 100, + ) -> list[RunRecord]: + """Return run records for ``strategy_name``, optionally filtered by status. + + Args: + strategy_name: Strategy whose runs to fetch. + status: Terminal status filter (e.g., ``RunStatus.SUCCESS``); ``None`` + returns runs in any state. + limit: Maximum number of records to return; results are ordered + ``started_at DESC``. + + Returns: + Up to ``limit`` ``RunRecord`` instances, newest first. + + Raises: + OMEError: Engine has not been started. + """ + if not self._started: + raise OMEError("list_runs: engine not started") + return await self._run_record_store.list_runs( + strategy_name=strategy_name, + status=status, + limit=limit, + ) + + @_refuse_inside_strategy + async def get_run_status(self, run_id: str) -> RunRecord | None: + """Fetch a single run record by ``run_id``. + + Args: + run_id: The 32-character ``uuid4().hex`` assigned at dispatch. + + Returns: + The matching ``RunRecord``, or ``None`` if no row exists for that id. + + Raises: + OMEError: Engine has not been started. + """ + if not self._started: + raise OMEError("get_run_status: engine not started") + return await self._run_record_store.get(run_id) diff --git a/src/everos/infra/ome/events.py b/src/everos/infra/ome/events.py new file mode 100644 index 00000000..5f6bdbb3 --- /dev/null +++ b/src/everos/infra/ome/events.py @@ -0,0 +1,78 @@ +"""OME event base class + built-in tick events. + +All business events should subclass BaseEvent. OME emits three built-in +ticks for engine-driven triggers (Cron / Idle / Manual). +""" + +from __future__ import annotations + +import importlib +from datetime import datetime +from functools import cache +from typing import Any +from uuid import uuid4 + +from pydantic import BaseModel, ConfigDict, Field + +from everos.component.utils.datetime import get_utc_now + + +class BaseEvent(BaseModel): + """Base for all events flowing through OME. + + Subclasses must be Pydantic v2 models (immutable) so `model_dump_json` / + `model_validate_json` work for crash-recovery payload persistence. + """ + + model_config = ConfigDict(frozen=True, extra="forbid") + + event_id: str = Field(default_factory=lambda: uuid4().hex) + ts: datetime = Field(default_factory=get_utc_now) + + @classmethod + def topic(cls) -> str: + """Stable cross-process identifier of this event class. + + Returns ``":"`` (colon-separated, mirroring the + Python event-sourcing community convention). Used by OME to + persist event identity into RunRecord.event_topic and to re-import + the class during crash recovery via ``resolve_topic``. + """ + return f"{cls.__module__}:{cls.__name__}" + + +@cache +def resolve_topic(topic: str) -> type[BaseEvent]: + """Inverse of ``BaseEvent.topic()``; imports and returns the class. + + Cached because crash recovery may resolve the same topic many times in + a tight loop, and ``importlib.import_module`` is non-trivial. + """ + module_name, sep, cls_name = topic.partition(":") + if not sep or not cls_name: + raise ValueError(f"invalid event topic: {topic!r}") + mod: Any = importlib.import_module(module_name) + cls = getattr(mod, cls_name, None) + if not (isinstance(cls, type) and issubclass(cls, BaseEvent)): + raise TypeError(f"topic {topic!r} did not resolve to a BaseEvent subclass") + return cls + + +class CronTick(BaseEvent): + """Engine-emitted event for a strategy with `trigger=Cron(...)`.""" + + strategy_name: str + + +class IdleTick(BaseEvent): + """Engine-emitted event for a strategy with `trigger=Idle(...)`.""" + + strategy_name: str + bucket_key: str + idle_seconds: int + + +class ManualTick(BaseEvent): + """Engine-emitted event for `engine.trigger_manual(name, event=None)`.""" + + strategy_name: str diff --git a/src/everos/infra/ome/exceptions.py b/src/everos/infra/ome/exceptions.py new file mode 100644 index 00000000..3a0b1a2f --- /dev/null +++ b/src/everos/infra/ome/exceptions.py @@ -0,0 +1,61 @@ +"""OME exception hierarchy.""" + +from __future__ import annotations + +from everos.infra.ome.events import BaseEvent + + +class OMEError(Exception): + """Base for all OME-internal errors.""" + + +class StartupValidationError(OMEError): + """Raised by engine.start() for any startup-time validation failure.""" + + +class EngineLockHeldError(OMEError): + """Raised when another OfflineEngine instance holds the jobstore lock.""" + + +class StrategyContractError(OMEError): + """Base for strategy-side contract violations. + + Subclasses indicate a programming bug in the strategy code that no + retry can fix (wrong API usage, undeclared emit). Runner + short-circuits the attempt loop on these and dead-letters + immediately — consuming the retry budget would only delay the + inevitable and spam logs. External callers can ``except + StrategyContractError`` to handle the whole category at once. + """ + + +class EngineCallFromStrategyError(StrategyContractError): + """A strategy called a public OfflineEngine method directly. + + The convention is: strategy code interacts with the engine only via + the ``(event, ctx)`` parameters Runner supplies. Engine methods + (``emit``, ``trigger_manual``, ``inspect_dispatch``, ``list_runs``, + ``get_run_status``, ``reschedule_*``) are for external callers — + strategies invoking them bypass the framework's contracts. + """ + + def __init__(self, strategy: str, method: str) -> None: + self.strategy = strategy + self.method = method + super().__init__( + f"strategy {strategy!r} called engine.{method}() directly; " + "strategies must interact with the engine only via the " + "(event, ctx) parameters" + ) + + +class EmitNotDeclaredError(StrategyContractError): + """Raised when a strategy emits an event not listed in its decorator's emits.""" + + def __init__(self, strategy: str, event: BaseEvent) -> None: + self.strategy = strategy + self.event = event + super().__init__( + f"strategy {strategy!r} emitted {type(event).__name__!r} " + "which is not in its declared emits" + ) diff --git a/src/everos/infra/ome/gates.py b/src/everos/infra/ome/gates.py new file mode 100644 index 00000000..ac6f473f --- /dev/null +++ b/src/everos/infra/ome/gates.py @@ -0,0 +1,52 @@ +"""OME gate types — declarative configuration only. + +Counter is the only built-in gate. The actual N-counting lives in +_stores/counter.py keyed by (strategy_name, bucket_key). +""" + +from __future__ import annotations + +from typing import Annotated + +from pydantic import BaseModel, ConfigDict, Field + + +class Counter(BaseModel): + """Counter gate: batch trigger by accumulated event count per bucket. + Each event increments the bucket counter; the `threshold`-th event + passes and resets. + """ + + model_config = ConfigDict(frozen=True, extra="forbid") + + threshold: Annotated[ + int, + Field( + gt=0, + description=( + "Pass once every `threshold` events; threshold=1 lets every event pass." + ), + ), + ] + cooldown_seconds: Annotated[ + int, + Field( + ge=0, + description=( + "Minimum seconds between consecutive passes per bucket; 0 disables." + ), + ), + ] = 0 + event_field: Annotated[ + str | None, + Field( + description=( + 'Bucket dimension on the event (e.g. "user_id"); ' + "None means a single global bucket." + ), + ), + ] = None + + +# Single-member alias today; becomes a union as more gate types land. +Gate = Counter diff --git a/src/everos/infra/ome/records.py b/src/everos/infra/ome/records.py new file mode 100644 index 00000000..fc74c36f --- /dev/null +++ b/src/everos/infra/ome/records.py @@ -0,0 +1,99 @@ +"""RunRecord / RunStatus / StrategyRouteInfo / CounterProgress — pure data classes. + +Persistence in _stores/run_record.py. +""" + +from __future__ import annotations + +from enum import StrEnum +from typing import Annotated, NamedTuple, Self + +from pydantic import ( + AwareDatetime, + BaseModel, + ConfigDict, + Field, + computed_field, + model_validator, +) + + +class RunStatus(StrEnum): + """Terminal-or-running state of a single strategy run.""" + + RUNNING = "running" + SUCCESS = "success" + FAILED = "failed" + DEAD_LETTER = "dead_letter" + CRASHED = "crashed" + + +class RunRecord(BaseModel): + """One row of the run_record table.""" + + model_config = ConfigDict(frozen=True, extra="forbid") + + run_id: Annotated[str, Field(min_length=1)] + strategy_name: Annotated[str, Field(min_length=1)] + status: RunStatus + attempt: Annotated[int, Field(ge=0)] + started_at: AwareDatetime + finished_at: AwareDatetime | None = None + error: Annotated[str, Field(min_length=1)] | None = None + event_topic: Annotated[ + str, + Field( + min_length=1, + description="Stable cross-process event identifier in " + "``:`` form (see ``BaseEvent.topic()``).", + ), + ] + event_payload: Annotated[ + str, + Field( + min_length=1, + description="JSON-encoded event (``BaseEvent.model_dump_json`` output).", + ), + ] + max_retries_snapshot: Annotated[int, Field(ge=0)] + + @model_validator(mode="after") + def _check_status_invariants(self) -> Self: + if self.status == RunStatus.RUNNING: + if self.finished_at is not None: + raise ValueError("RunRecord: RUNNING must have finished_at=None") + if self.error is not None: + raise ValueError("RunRecord: RUNNING must have error=None") + else: + if self.finished_at is None: + raise ValueError(f"RunRecord: {self.status} must have finished_at set") + if self.status == RunStatus.SUCCESS: + if self.error is not None: + raise ValueError("RunRecord: SUCCESS must have error=None") + elif self.error is None: + raise ValueError(f"RunRecord: {self.status} must have error set") + return self + + +class CounterProgress(NamedTuple): + """Per-bucket counter progress at inspect_dispatch time.""" + + current: int + threshold: int + + +class StrategyRouteInfo(BaseModel): + """Per-strategy dispatch decision — returned by inspect_dispatch.""" + + model_config = ConfigDict(frozen=True, extra="forbid") + + strategy_name: Annotated[str, Field(min_length=1)] + enabled_pass: bool + applies_to_pass: bool + counter_pass: bool + counter_progress: CounterProgress | None = None + + @computed_field # type: ignore[prop-decorator] + @property + def will_run(self) -> bool: + return self.enabled_pass and self.applies_to_pass and self.counter_pass diff --git a/src/everos/infra/ome/testing/__init__.py b/src/everos/infra/ome/testing/__init__.py new file mode 100644 index 00000000..27ccddcf --- /dev/null +++ b/src/everos/infra/ome/testing/__init__.py @@ -0,0 +1,9 @@ +"""OME testing helpers. + +Fake strategy context and test harness for unit testing strategies. +""" + +from everos.infra.ome.testing.fakes import FakeStrategyContext as FakeStrategyContext +from everos.infra.ome.testing.harness import StrategyTestHarness as StrategyTestHarness + +__all__ = ["FakeStrategyContext", "StrategyTestHarness"] diff --git a/src/everos/infra/ome/testing/fakes.py b/src/everos/infra/ome/testing/fakes.py new file mode 100644 index 00000000..5446bba7 --- /dev/null +++ b/src/everos/infra/ome/testing/fakes.py @@ -0,0 +1,38 @@ +"""In-memory test doubles for the OME StrategyContext Protocol. + +Use FakeStrategyContext when you want to unit-test a strategy function +in isolation without spinning up a full OfflineEngine. +""" + +from __future__ import annotations + +from everos.core.observability.logging import get_logger +from everos.infra.ome.events import BaseEvent + + +class FakeStrategyContext: + """Implements StrategyContext Protocol; collects emit() calls in a list. + + Attributes: + run_id: Unique identifier for this run (default: "fake_run"). + logger: A structlog BoundLogger for test logging. + emitted: List of BaseEvent objects passed to emit(). + """ + + def __init__(self, *, run_id: str = "fake_run") -> None: + """Initialize a FakeStrategyContext. + + Args: + run_id: Run identifier, defaults to "fake_run". + """ + self.run_id = run_id + self.logger = get_logger("ome.fake_ctx") + self.emitted: list[BaseEvent] = [] + + async def emit(self, event: BaseEvent) -> None: + """Collect an event into the emitted list. + + Args: + event: The BaseEvent to emit. + """ + self.emitted.append(event) diff --git a/src/everos/infra/ome/testing/harness.py b/src/everos/infra/ome/testing/harness.py new file mode 100644 index 00000000..da878044 --- /dev/null +++ b/src/everos/infra/ome/testing/harness.py @@ -0,0 +1,118 @@ +"""StrategyTestHarness — full OfflineEngine on a tmp SQLite db. + +Designed for end-to-end strategy tests: register, start, emit, drain +until terminal, inspect run records. Cleans up the tmp directory on exit. +""" + +from __future__ import annotations + +import shutil +from pathlib import Path +from tempfile import mkdtemp +from typing import Any + +from everos.infra.ome.config import OMEConfig +from everos.infra.ome.engine import OfflineEngine +from everos.infra.ome.events import BaseEvent +from everos.infra.ome.records import RunRecord, RunStatus + + +class StrategyTestHarness: + """Async context manager wrapping OfflineEngine on a tmp SQLite db. + + Provides a test-friendly interface to register strategies, emit events, + and inspect run records. + + Example: + async with StrategyTestHarness() as h: + h.register(my_strategy_func) + await h.start() + await h.emit(MyEvent()) + await h.drain(timeout=5) + runs = await h.list_runs("my_strategy") + assert len(runs) == 1 + """ + + def __init__(self) -> None: + """Initialize a StrategyTestHarness with a temp SQLite db.""" + self._tmpdir = Path(mkdtemp(prefix="ome_test_")) + cfg = OMEConfig( + jobstore_path=self._tmpdir / "ome.db", + config_watch=False, + max_concurrent_runs=20, + max_retries=1, + ) + self._engine = OfflineEngine(config=cfg) + + async def __aenter__(self) -> StrategyTestHarness: + """Enter the async context.""" + return self + + async def __aexit__(self, *exc: Any) -> None: + """Exit the async context and clean up temp resources.""" + try: + await self._engine.stop() + finally: + shutil.rmtree(self._tmpdir, ignore_errors=True) # noqa: SLF001 + + def register(self, func: Any) -> None: + """Register a strategy function. + + Args: + func: A function decorated with @offline_strategy. + """ + self._engine.register(func) + + async def start(self) -> None: + """Start the OfflineEngine.""" + await self._engine.start() + + async def emit(self, event: BaseEvent) -> None: + """Emit an event to the engine. + + Args: + event: A BaseEvent subclass instance. + """ + await self._engine.emit(event) + + async def drain(self, *, timeout: float = 30.0) -> None: # noqa: ASYNC109 + """Wait until every enqueued strategy run has finished. + + Delegates to :meth:`OfflineEngine.wait_idle`, which tracks runs + from the moment ``_enqueue_run`` bumps the counter (so a caller + that ``emit``s then immediately ``drain``s does NOT see false- + idle while APS is still launching the coroutine). Polling + ``find_running`` alone — the previous implementation — missed + that gap between ``add_job`` and ``mark_running`` and let tests + race past in-flight jobs. + + Args: + timeout: Maximum seconds to wait, defaults to 30.0. + + Raises: + TimeoutError: if runs remain in flight after ``timeout`` seconds. + """ + if not await self._engine.wait_idle(timeout=timeout): + raise TimeoutError( + f"drain: engine still has " + f"{self._engine._active_runs} in-flight runs after {timeout}s" # noqa: SLF001 + ) + + async def list_runs( + self, + strategy_name: str, + status: RunStatus | None = None, + ) -> list[RunRecord]: + """List run records for a strategy, optionally filtered by status. + + Args: + strategy_name: The name of the strategy. + status: Optional status filter (e.g. RunStatus.SUCCESS). + + Returns: + A list of RunRecord objects. + """ + return await self._engine._run_record_store.list_runs( # noqa: SLF001 + strategy_name=strategy_name, + status=status, + ) diff --git a/src/everos/infra/ome/triggers.py b/src/everos/infra/ome/triggers.py new file mode 100644 index 00000000..3236d22f --- /dev/null +++ b/src/everos/infra/ome/triggers.py @@ -0,0 +1,76 @@ +"""OME trigger types — declarative descriptors of when a strategy fires. + +Three concrete triggers: Immediate / Cron / Idle. Engine dispatches via +`isinstance(meta.trigger, ...)` to pick the registration path. +""" + +from __future__ import annotations + +from typing import Annotated, Self + +from apscheduler.triggers.cron import CronTrigger +from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator + +from everos.infra.ome.events import BaseEvent + + +class _TriggerBase(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + + +class Immediate(_TriggerBase): + """Fire as soon as an event of any class in `on` is dispatched.""" + + on: Annotated[list[type[BaseEvent]], Field(min_length=1)] + + +class Cron(_TriggerBase): + """Fire on a cron schedule. Engine emits CronTick to the strategy.""" + + expr: Annotated[str, Field(min_length=1)] + + @field_validator("expr") + @classmethod + def _validate_crontab(cls, v: str) -> str: + # Delegates to APS's own parser so the trigger object cannot + # represent any crontab that APS would later refuse. + CronTrigger.from_crontab(v) + return v + + +class Idle(_TriggerBase): + """Fire after every class in `on` has been silent (bucketed by + `event_field`) for `idle_seconds` — AND across classes. Engine + emits IdleTick. + """ + + on: Annotated[list[type[BaseEvent]], Field(min_length=1)] + event_field: str + idle_seconds: Annotated[int, Field(gt=0)] + scan_interval_seconds: Annotated[ + int, + Field(gt=0, description="Per-strategy scan cadence; <= idle_seconds / 2."), + ] = 60 + + @model_validator(mode="after") + def _validate_event_field(self) -> Self: + for event_cls in self.on: + if self.event_field not in event_cls.model_fields: # type: ignore[operator] # Pydantic model_fields → dict via @deprecated_instance_property (pydantic/main.py:277) + available = list(event_cls.model_fields) # type: ignore[arg-type] # same as above + raise ValueError( + f"event_field {self.event_field!r} not found in " + f"{event_cls.__name__} fields (available: {available})" + ) + return self + + @model_validator(mode="after") + def _validate_scan_interval_bound(self) -> Self: + if self.scan_interval_seconds > self.idle_seconds // 2: + raise ValueError( + f"Idle: scan_interval_seconds ({self.scan_interval_seconds}) " + f"must be <= idle_seconds // 2 ({self.idle_seconds // 2})" + ) + return self + + +Trigger = Immediate | Cron | Idle diff --git a/methods/EverCore/src/api_specs/__init__.py b/src/everos/infra/persistence/__init__.py old mode 100755 new mode 100644 similarity index 100% rename from methods/EverCore/src/api_specs/__init__.py rename to src/everos/infra/persistence/__init__.py diff --git a/src/everos/infra/persistence/lancedb/__init__.py b/src/everos/infra/persistence/lancedb/__init__.py new file mode 100644 index 00000000..04edb5b0 --- /dev/null +++ b/src/everos/infra/persistence/lancedb/__init__.py @@ -0,0 +1,132 @@ +"""LanceDB business persistence layer. + +Sits on top of :mod:`everos.core.persistence.lancedb` (connection +factory + ``BaseLanceTable`` + ``LanceRepoBase``) and provides: + + * lazy process-wide connection + per-name table cache + (:mod:`.lancedb_manager`) + * concrete schemas under :mod:`.tables` + * concrete repository singletons under :mod:`.repos` + +External usage:: + + from everos.infra.persistence.lancedb import ( + get_connection, get_table, dispose_connection, + Episode, AtomicFact, Foresight, AgentCase, AgentSkill, UserProfile, + episode_repo, atomic_fact_repo, foresight_repo, + agent_case_repo, agent_skill_repo, user_profile_repo, + ) + +Three index kinds: scalar / BM25 / vector. Tables are created lazily on +first access; row population is the cascade daemon's job (see +``12_cascade_design.md``). +""" + +# Importing ``tables`` registers every business :class:`BaseLanceTable` +# schema so callers can rely on the package alone to surface every schema. +from . import tables as tables # noqa: F401 +from .lancedb_manager import dispose_connection as dispose_connection +from .lancedb_manager import get_connection as get_connection +from .lancedb_manager import get_table as get_table +from .repos import agent_case_repo as agent_case_repo +from .repos import agent_skill_repo as agent_skill_repo +from .repos import atomic_fact_repo as atomic_fact_repo +from .repos import episode_repo as episode_repo +from .repos import foresight_repo as foresight_repo +from .repos import user_profile_repo as user_profile_repo +from .tables import AgentCase as AgentCase +from .tables import AgentSkill as AgentSkill +from .tables import AtomicFact as AtomicFact +from .tables import Episode as Episode +from .tables import Foresight as Foresight +from .tables import ParentType as ParentType +from .tables import UserProfile as UserProfile + +_BUSINESS_SCHEMAS = ( + Episode, + AtomicFact, + Foresight, + AgentCase, + AgentSkill, + UserProfile, +) + + +class LanceDBSchemaMismatchError(RuntimeError): + """Raised at startup when an on-disk LanceDB table's columns drift + from the corresponding Pydantic schema. + + Cascade re-builds LanceDB from md (the SoT), so the recovery is + deterministic: delete the index directory and let it reindex. + The lifespan surfaces the explicit ``rm -rf ~/.everos/.index/ + lancedb`` instruction in the error message; see + ``docs/cascade_runbook.md`` for the wider context. + """ + + +async def ensure_business_indexes() -> None: + """Ensure FTS (BM25) indexes for every business table (idempotent). + + Called once at startup by :class:`LanceDBLifespanProvider`. Walks + the 5 business schemas (each schema owns its ``TABLE_NAME`` + + ``BM25_FIELDS``), opens each table via :func:`get_table`, and + delegates to ``schema.ensure_fts_indexes(table)``. Already-indexed + columns are skipped, so re-runs are no-ops. + + Adding a new business table = adding it to ``_BUSINESS_SCHEMAS``; + everything else (table name, columns to index) reads off the + schema's ClassVars. + """ + for schema in _BUSINESS_SCHEMAS: + table = await get_table(schema.TABLE_NAME, schema) + await schema.ensure_fts_indexes(table) + + +async def verify_business_schemas() -> None: + """Fail loud at startup if an existing LanceDB table's columns don't + match its current Pydantic schema. + + LanceDB doesn't migrate columns automatically; an older index dir + (e.g. with the pre-``content_sha256`` shape) would fail + unpredictably on upsert. Checking column names up-front turns that + into a clean startup error pointing the user at the recovery path + (``rm -rf ~/.everos/.index/lancedb`` — the index is rebuildable + from md, see ``12_cascade_design.md``). + """ + for schema in _BUSINESS_SCHEMAS: + table = await get_table(schema.TABLE_NAME, schema) + arrow_schema = await table.schema() + actual = set(arrow_schema.names) + expected = set(schema.model_fields.keys()) + missing = expected - actual + extra = actual - expected + if missing or extra: + raise LanceDBSchemaMismatchError( + f"LanceDB table {schema.TABLE_NAME!r} schema drift: " + f"missing={sorted(missing)}, extra={sorted(extra)}. " + "The index is rebuildable from md — recover with " + "`rm -rf ~/.everos/.index/lancedb` and restart." + ) + + +__all__ = [ + "AgentCase", + "AgentSkill", + "AtomicFact", + "Episode", + "Foresight", + "LanceDBSchemaMismatchError", + "ParentType", + "UserProfile", + "agent_case_repo", + "agent_skill_repo", + "atomic_fact_repo", + "dispose_connection", + "ensure_business_indexes", + "episode_repo", + "foresight_repo", + "get_connection", + "get_table", + "user_profile_repo", + "verify_business_schemas", +] diff --git a/src/everos/infra/persistence/lancedb/lancedb_manager.py b/src/everos/infra/persistence/lancedb/lancedb_manager.py new file mode 100644 index 00000000..2099a670 --- /dev/null +++ b/src/everos/infra/persistence/lancedb/lancedb_manager.py @@ -0,0 +1,82 @@ +"""LanceDB connection + table singletons (lazy + process-wide, async). + +The single place that owns the LanceDB **runtime state**: the async +connection and per-name table cache. Connection opens lazily on first +:func:`get_connection` call; tables are cached after first +:func:`get_table`. The :class:`LanceDBLifespanProvider` calls +:func:`dispose_connection` on shutdown; in scripts you can call it +manually. +""" + +from __future__ import annotations + +import asyncio + +from lancedb import AsyncConnection, AsyncTable + +from everos.config import load_settings +from everos.core.observability.logging import get_logger +from everos.core.persistence import BaseLanceTable, MemoryRoot, open_lancedb_connection + +logger = get_logger(__name__) + +_conn: AsyncConnection | None = None +_tables: dict[str, AsyncTable] = {} +_lock = asyncio.Lock() + + +async def get_connection() -> AsyncConnection: + """Return the process-wide async LanceDB connection. + + Built on first call from ``MemoryRoot.default().lancedb_dir`` and + ``Settings.lancedb``. Subsequent calls return the same instance. + """ + async with _lock: + return await _ensure_connection_locked() + + +async def get_table( + name: str, + schema: type[BaseLanceTable], +) -> AsyncTable: + """Open the named table (creating from ``schema`` if missing). Cached.""" + async with _lock: + if name not in _tables: + conn = await _ensure_connection_locked() + existing = await conn.list_tables() + if name in list(existing.tables): + _tables[name] = await conn.open_table(name) + logger.info("lancedb_table_opened", name=name) + else: + _tables[name] = await conn.create_table(name, schema=schema) + logger.info("lancedb_table_created", name=name) + return _tables[name] + + +async def dispose_connection() -> None: + """Close the connection + clear table cache. Idempotent.""" + global _conn + async with _lock: + if _conn is not None: + try: + _conn.close() # AsyncConnection.close() is sync in lancedb 0.30 + except Exception: + logger.exception("lancedb_close_failed") + logger.info("lancedb_connection_closed") + _conn = None + _tables.clear() + + +async def _ensure_connection_locked() -> AsyncConnection: + """Open the connection if not yet open. Caller must hold ``_lock``.""" + global _conn + if _conn is None: + settings = load_settings() + memory_root = MemoryRoot.default() + memory_root.ensure() + _conn = await open_lancedb_connection(memory_root.lancedb_dir, settings.lancedb) + logger.info( + "lancedb_connection_opened", + path=str(memory_root.lancedb_dir), + ) + return _conn diff --git a/src/everos/infra/persistence/lancedb/repos/__init__.py b/src/everos/infra/persistence/lancedb/repos/__init__.py new file mode 100644 index 00000000..ec01524e --- /dev/null +++ b/src/everos/infra/persistence/lancedb/repos/__init__.py @@ -0,0 +1,37 @@ +"""LanceDB repo singletons (one per business table). + +Each repo is a module-level singleton — the table connection is +resolved lazily on first call via :func:`..lancedb_manager.get_table`. +Subclassing :class:`LanceRepoBase` lets each repo carry table-specific +helpers later (e.g. ``find_by_owner``, ``search_for_owner``) without +needing a separate factory. + +External usage:: + + from everos.infra.persistence.lancedb.repos import ( + episode_repo, + atomic_fact_repo, + foresight_repo, + agent_case_repo, + agent_skill_repo, + user_profile_repo, + ) + + await episode_repo.add([Episode(...)]) +""" + +from .agent_case import agent_case_repo as agent_case_repo +from .agent_skill import agent_skill_repo as agent_skill_repo +from .atomic_fact import atomic_fact_repo as atomic_fact_repo +from .episode import episode_repo as episode_repo +from .foresight import foresight_repo as foresight_repo +from .user_profile import user_profile_repo as user_profile_repo + +__all__ = [ + "agent_case_repo", + "agent_skill_repo", + "atomic_fact_repo", + "episode_repo", + "foresight_repo", + "user_profile_repo", +] diff --git a/src/everos/infra/persistence/lancedb/repos/agent_case.py b/src/everos/infra/persistence/lancedb/repos/agent_case.py new file mode 100644 index 00000000..228f3fb8 --- /dev/null +++ b/src/everos/infra/persistence/lancedb/repos/agent_case.py @@ -0,0 +1,20 @@ +"""LanceDB repo singleton for the ``agent_case`` table.""" + +from __future__ import annotations + +from lancedb import AsyncTable + +from everos.core.persistence.lancedb import LanceDailyLogRepoBase + +from ..lancedb_manager import get_table +from ..tables.agent_case import AgentCase + + +class _AgentCaseRepo(LanceDailyLogRepoBase[AgentCase]): + schema = AgentCase + + async def _table_lookup(self) -> AsyncTable: + return await get_table(self.schema.TABLE_NAME, self.schema) + + +agent_case_repo = _AgentCaseRepo() diff --git a/src/everos/infra/persistence/lancedb/repos/agent_skill.py b/src/everos/infra/persistence/lancedb/repos/agent_skill.py new file mode 100644 index 00000000..215ff6c2 --- /dev/null +++ b/src/everos/infra/persistence/lancedb/repos/agent_skill.py @@ -0,0 +1,84 @@ +"""LanceDB repo singleton for the ``agent_skill`` table.""" + +from __future__ import annotations + +from collections.abc import Sequence + +from lancedb import AsyncTable + +from everos.core.persistence.lancedb import LanceRepoBase + +from ..lancedb_manager import get_table +from ..tables.agent_skill import AgentSkill + + +class _AgentSkillRepo(LanceRepoBase[AgentSkill]): + schema = AgentSkill + + async def _table_lookup(self) -> AsyncTable: + return await get_table(self.schema.TABLE_NAME, self.schema) + + async def count_in_cluster(self, *, owner_id: str, cluster_id: str) -> int: + """Count skills under one ``(owner_id, cluster_id)``.""" + table = await self._table() + return await table.count_rows(filter=_in_cluster(owner_id, cluster_id)) + + async def find_in_cluster( + self, *, owner_id: str, cluster_id: str, limit: int + ) -> list[AgentSkill]: + """Scalar fetch within one cluster; no ranking, capped at ``limit``.""" + return await self.find_where(_in_cluster(owner_id, cluster_id), limit=limit) + + async def find_topk_relevant_in_cluster( + self, + *, + owner_id: str, + cluster_id: str, + query_vector: Sequence[float], + top_k: int, + ) -> list[AgentSkill]: + """Top-K cosine-relevant skills inside one cluster. + + Cosine ranking is pushed down to LanceDB native ANN. + ``distance_type("cosine")`` matches + :class:`AgentSkillRecaller.dense_recall`, keeping ranking + semantics consistent across read paths. + + Raises: + ValueError: When ``query_vector`` is empty — no relevance + signal is a caller-side policy decision; use + :meth:`find_in_cluster` for the scalar fallback. + """ + if not query_vector: + raise ValueError( + "query_vector must be non-empty; " + "call find_in_cluster for the scalar fallback" + ) + table = await self._table() + rows = await ( + table.query() + .nearest_to(list(query_vector)) + .distance_type("cosine") + .where(_in_cluster(owner_id, cluster_id)) + .limit(top_k) + .to_list() + ) + # LanceDB appends ``_distance`` to ranked rows; strip it before + # ``model_validate`` so this stays robust regardless of + # pydantic ``extra`` mode on the schema. + return [ + self.schema.model_validate({k: v for k, v in r.items() if k != "_distance"}) + for r in rows + ] + + +def _q(value: str) -> str: + """SQL single-quote escape for LanceDB ``where`` predicate literals.""" + return value.replace("'", "''") + + +def _in_cluster(owner_id: str, cluster_id: str) -> str: + return f"owner_id = '{_q(owner_id)}' AND cluster_id = '{_q(cluster_id)}'" + + +agent_skill_repo = _AgentSkillRepo() diff --git a/src/everos/infra/persistence/lancedb/repos/atomic_fact.py b/src/everos/infra/persistence/lancedb/repos/atomic_fact.py new file mode 100644 index 00000000..6b7ea59d --- /dev/null +++ b/src/everos/infra/persistence/lancedb/repos/atomic_fact.py @@ -0,0 +1,20 @@ +"""LanceDB repo singleton for the ``atomic_fact`` table.""" + +from __future__ import annotations + +from lancedb import AsyncTable + +from everos.core.persistence.lancedb import LanceDailyLogRepoBase + +from ..lancedb_manager import get_table +from ..tables.atomic_fact import AtomicFact + + +class _AtomicFactRepo(LanceDailyLogRepoBase[AtomicFact]): + schema = AtomicFact + + async def _table_lookup(self) -> AsyncTable: + return await get_table(self.schema.TABLE_NAME, self.schema) + + +atomic_fact_repo = _AtomicFactRepo() diff --git a/src/everos/infra/persistence/lancedb/repos/episode.py b/src/everos/infra/persistence/lancedb/repos/episode.py new file mode 100644 index 00000000..f56d19e7 --- /dev/null +++ b/src/everos/infra/persistence/lancedb/repos/episode.py @@ -0,0 +1,20 @@ +"""LanceDB repo singleton for the ``episode`` table.""" + +from __future__ import annotations + +from lancedb import AsyncTable + +from everos.core.persistence.lancedb import LanceDailyLogRepoBase + +from ..lancedb_manager import get_table +from ..tables.episode import Episode + + +class _EpisodeRepo(LanceDailyLogRepoBase[Episode]): + schema = Episode + + async def _table_lookup(self) -> AsyncTable: + return await get_table(self.schema.TABLE_NAME, self.schema) + + +episode_repo = _EpisodeRepo() diff --git a/src/everos/infra/persistence/lancedb/repos/foresight.py b/src/everos/infra/persistence/lancedb/repos/foresight.py new file mode 100644 index 00000000..dffc5068 --- /dev/null +++ b/src/everos/infra/persistence/lancedb/repos/foresight.py @@ -0,0 +1,20 @@ +"""LanceDB repo singleton for the ``foresight`` table.""" + +from __future__ import annotations + +from lancedb import AsyncTable + +from everos.core.persistence.lancedb import LanceDailyLogRepoBase + +from ..lancedb_manager import get_table +from ..tables.foresight import Foresight + + +class _ForesightRepo(LanceDailyLogRepoBase[Foresight]): + schema = Foresight + + async def _table_lookup(self) -> AsyncTable: + return await get_table(self.schema.TABLE_NAME, self.schema) + + +foresight_repo = _ForesightRepo() diff --git a/src/everos/infra/persistence/lancedb/repos/user_profile.py b/src/everos/infra/persistence/lancedb/repos/user_profile.py new file mode 100644 index 00000000..bf38c7ed --- /dev/null +++ b/src/everos/infra/persistence/lancedb/repos/user_profile.py @@ -0,0 +1,20 @@ +"""LanceDB repo singleton for the ``user_profile`` table.""" + +from __future__ import annotations + +from lancedb import AsyncTable + +from everos.core.persistence.lancedb import LanceRepoBase + +from ..lancedb_manager import get_table +from ..tables.user_profile import UserProfile + + +class _UserProfileRepo(LanceRepoBase[UserProfile]): + schema = UserProfile + + async def _table_lookup(self) -> AsyncTable: + return await get_table(self.schema.TABLE_NAME, self.schema) + + +user_profile_repo = _UserProfileRepo() diff --git a/src/everos/infra/persistence/lancedb/tables/__init__.py b/src/everos/infra/persistence/lancedb/tables/__init__.py new file mode 100644 index 00000000..db6a9f37 --- /dev/null +++ b/src/everos/infra/persistence/lancedb/tables/__init__.py @@ -0,0 +1,35 @@ +"""LanceDB table schemas (one ``BaseLanceTable`` subclass per business table). + +Schemas live here; cascade-daemon-driven row population is wired +through the matching repo singletons in :mod:`..repos`. + +External usage:: + + from everos.infra.persistence.lancedb.tables import ( + Episode, + AtomicFact, + Foresight, + AgentCase, + AgentSkill, + UserProfile, + ParentType, + ) +""" + +from ._parent_type import ParentType as ParentType +from .agent_case import AgentCase as AgentCase +from .agent_skill import AgentSkill as AgentSkill +from .atomic_fact import AtomicFact as AtomicFact +from .episode import Episode as Episode +from .foresight import Foresight as Foresight +from .user_profile import UserProfile as UserProfile + +__all__ = [ + "AgentCase", + "AgentSkill", + "AtomicFact", + "Episode", + "Foresight", + "ParentType", + "UserProfile", +] diff --git a/src/everos/infra/persistence/lancedb/tables/_parent_type.py b/src/everos/infra/persistence/lancedb/tables/_parent_type.py new file mode 100644 index 00000000..9112a965 --- /dev/null +++ b/src/everos/infra/persistence/lancedb/tables/_parent_type.py @@ -0,0 +1,24 @@ +"""``ParentType`` — provenance label for memory records linked back to a source. + +Currently the only value is :attr:`ParentType.MEMCELL`: every business row +(episode / foresight / atomic_fact / agent_case) points back to a source +MemCell. The earlier opensource design enumerated ``"episode"`` as an +alternative parent but the production path never wrote that value, so the +new framework collapses the enum to its single in-use member. + +Kept as an :class:`enum.Enum` (rather than a bare string constant) so that +adding a future parent kind stays a non-breaking enum extension. LanceDB's +pydantic-to-arrow conversion does not accept ``Enum`` field annotations, +so table schemas declare ``parent_type: str = ParentType.MEMCELL.value`` +and reference the enum only at the default-value level. +""" + +from __future__ import annotations + +from enum import StrEnum + + +class ParentType(StrEnum): + """Provenance label of a memory record's parent.""" + + MEMCELL = "memcell" diff --git a/src/everos/infra/persistence/lancedb/tables/agent_case.py b/src/everos/infra/persistence/lancedb/tables/agent_case.py new file mode 100644 index 00000000..be10131d --- /dev/null +++ b/src/everos/infra/persistence/lancedb/tables/agent_case.py @@ -0,0 +1,84 @@ +"""LanceDB ``agent_case`` table schema. + +Field set per 17_lancedb_tables_design.md §3.4. Each row records one +task an agent worked on: intent, approach, optional pivotal insight, +and a quality score. A MemCell extracted on the agent's own execution +log yields at most one AgentCase. +""" + +from __future__ import annotations + +import datetime as _dt +from typing import ClassVar + +from everos.core.persistence.lancedb import BaseLanceTable, Vector + +from ._parent_type import ParentType + +_DIM = 1024 + + +class AgentCase(BaseLanceTable): + """One agent case indexed in LanceDB.""" + + TABLE_NAME: ClassVar[str] = "agent_case" + BM25_FIELDS: ClassVar[list[str]] = ["task_intent_tokens", "approach_tokens"] + + id: str + """PK = ``_``.""" + + entry_id: str + """md-side seq id ``ac__``.""" + + owner_id: str + """The owning ``agent_id``.""" + + owner_type: str + """Fixed ``"agent"`` for this table.""" + + app_id: str = "default" + project_id: str = "default" + """App / project scope (default ``"default"``); cascade fills from md path.""" + + session_id: str + timestamp: _dt.datetime + + parent_type: str = ParentType.MEMCELL.value + """Source pointer — always :attr:`ParentType.MEMCELL` for agent case.""" + + parent_id: str + """Source memcell id (one memcell ↔ one case).""" + + quality_score: float + """0.0–1.0; task completion / quality estimate.""" + + task_intent: str + """≤ 50 tokens; original surface form (returned for display).""" + + task_intent_tokens: str + """App-layer pre-tokenised ``task_intent`` — BM25 main field + (whitespace tokenizer); display goes through ``task_intent``.""" + + approach: str + """≤ 1000 tokens; step-by-step approach (display).""" + + approach_tokens: str + """App-layer pre-tokenised ``approach`` — BM25 secondary field + (whitespace tokenizer). Searched in parallel with + ``task_intent_tokens`` then merged by max score in the recall + layer; task_intent typically scores higher because it's the + retrieval anchor, but approach catches queries that match a step + detail.""" + + key_insight: str | None = None + """≤ 40 tokens; pivotal strategy shift, optional.""" + + md_path: str + content_sha256: str + """SHA-256 hex digest over the **content-bearing fields only** of + the md entry — TaskIntent / Approach / KeyInsight sections plus + the ``quality_score`` inline. Audit inline (owner_id / + session_id / timestamp / parent_id) is NOT in the hash. See + :attr:`AgentCaseHandler.content_change_keys`.""" + + vector: Vector(_DIM) # type: ignore[valid-type] diff --git a/src/everos/infra/persistence/lancedb/tables/agent_skill.py b/src/everos/infra/persistence/lancedb/tables/agent_skill.py new file mode 100644 index 00000000..ea249930 --- /dev/null +++ b/src/everos/infra/persistence/lancedb/tables/agent_skill.py @@ -0,0 +1,80 @@ +"""LanceDB ``agent_skill`` table schema. + +Field set per 17_lancedb_tables_design.md §3.5. AgentSkill is a *named +entity* rather than a daily-log entry — PK is ``_`` +(no date / seq), and same agent + same name is the same row (upsert). + +``content`` is cascade-assembled from ``SKILL.md`` body plus every +``references/*.md`` sibling; ``scripts/`` is not indexed. +""" + +from __future__ import annotations + +from typing import ClassVar + +from everos.core.persistence.lancedb import BaseLanceTable, Vector + +_DIM = 1024 + + +class AgentSkill(BaseLanceTable): + """One agent skill indexed in LanceDB.""" + + TABLE_NAME: ClassVar[str] = "agent_skill" + BM25_FIELDS: ClassVar[list[str]] = ["description_tokens", "content_tokens"] + + id: str + """PK = ``_``.""" + + owner_id: str + """The owning ``agent_id``.""" + + owner_type: str + """Fixed ``"agent"`` for this table.""" + + app_id: str = "default" + project_id: str = "default" + """App / project scope (default ``"default"``); cascade fills from md path.""" + + name: str + """Skill identifier; half of the PK.""" + + description: str + """When-to-use / purpose — original surface form (Tier-1 ad copy).""" + + description_tokens: str + """App-layer pre-tokenised ``description`` — BM25 main field + (whitespace tokenizer); display goes through ``description``.""" + + content: str + """Cascade-assembled body: ``SKILL.md`` main text concatenated with + every ``references/*.md`` sibling. ``scripts/`` files are excluded.""" + + content_tokens: str + """App-layer pre-tokenised ``content`` (secondary BM25 field). + Tokenised by cascade when assembling ``content`` from md sources.""" + + confidence: float + """0.0–1.0; LLM-emitted confidence in the skill.""" + + maturity_score: float + """0.0–1.0; LLM-emitted maturity score. The retrieval-time threshold + (``maturity_threshold``) lives in MemorizeConfig, not in this row.""" + + source_case_ids: list[str] + """AgentCase ids that fed into this skill's synthesis (lineage).""" + + cluster_id: str | None = None + """Optional MemScene clustering tag.""" + + md_path: str + content_sha256: str + """SHA-256 hex digest over the **content-bearing fields only** of + the skill: ``name`` / ``description`` (frontmatter) + SKILL.md + body + concatenated references content + ``confidence`` / + ``maturity_score``. Cascade handler diffs by this digest to skip + re-upsert + re-embed when neither retrieval-anchor text nor scores + changed (e.g. the watcher fires for unrelated stat updates). See + :attr:`AgentSkillHandler.content_change_keys`.""" + + vector: Vector(_DIM) # type: ignore[valid-type] diff --git a/src/everos/infra/persistence/lancedb/tables/atomic_fact.py b/src/everos/infra/persistence/lancedb/tables/atomic_fact.py new file mode 100644 index 00000000..63f84487 --- /dev/null +++ b/src/everos/infra/persistence/lancedb/tables/atomic_fact.py @@ -0,0 +1,62 @@ +"""LanceDB ``atomic_fact`` table schema. + +Field set per 17_lancedb_tables_design.md §3.2. Each row carries one +atomic fact extracted by the algo layer; the parent is always the source +MemCell — recorded via ``parent_type`` / ``parent_id``. +""" + +from __future__ import annotations + +import datetime as _dt +from typing import ClassVar + +from everos.core.persistence.lancedb import BaseLanceTable, Vector + +from ._parent_type import ParentType + +_DIM = 1024 + + +class AtomicFact(BaseLanceTable): + """One atomic fact indexed in LanceDB.""" + + TABLE_NAME: ClassVar[str] = "atomic_fact" + BM25_FIELDS: ClassVar[list[str]] = ["fact_tokens"] + + id: str + """PK = ``_``.""" + + entry_id: str + """md-side seq id ``af__``.""" + + owner_id: str + owner_type: str + app_id: str = "default" + project_id: str = "default" + """App / project scope (default ``"default"``); cascade fills from md path.""" + session_id: str + timestamp: _dt.datetime + + parent_type: str = ParentType.MEMCELL.value + """Source pointer — always :attr:`ParentType.MEMCELL` for atomic fact.""" + + parent_id: str + """Source memcell id.""" + sender_ids: list[str] + fact: str + """Atomic fact text — original surface form (returned for display).""" + + fact_tokens: str + """App-layer pre-tokenised ``fact`` text — space-joined tokens. + BM25 index is built on this column (whitespace tokenizer); + ``fact`` itself is what callers display.""" + + md_path: str + content_sha256: str + """SHA-256 hex digest over the **content-bearing fields only** of + the md entry (per :attr:`AtomicFactHandler.content_change_keys`). + Matching digest → skip re-upsert + re-embed. Audit inline fields + (owner_id / session_id / timestamp / parent_id / sender_ids) are + NOT in the hash.""" + + vector: Vector(_DIM) # type: ignore[valid-type] diff --git a/src/everos/infra/persistence/lancedb/tables/episode.py b/src/everos/infra/persistence/lancedb/tables/episode.py new file mode 100644 index 00000000..b69e35da --- /dev/null +++ b/src/everos/infra/persistence/lancedb/tables/episode.py @@ -0,0 +1,78 @@ +"""LanceDB ``episode`` table schema. + +Field set is fixed by the LanceDB tables design spec. Rows are populated +by the cascade daemon from ``users//episodes/episode-.md`` +and from ``agents//episodes/...`` symmetrically. +""" + +from __future__ import annotations + +import datetime as _dt +from typing import ClassVar + +from everos.core.persistence.lancedb import BaseLanceTable, Vector + +from ._parent_type import ParentType + +# Vector dimension is settings-managed at runtime; the class-level +# constant pins the schema dim used at table creation. +_DIM = 1024 + + +class Episode(BaseLanceTable): + """One episode record indexed in LanceDB.""" + + TABLE_NAME: ClassVar[str] = "episode" + BM25_FIELDS: ClassVar[list[str]] = ["episode_tokens"] + + id: str + """PK = ``_`` (scalar PK).""" + + entry_id: str + """md-side seq id ``ep__`` (cascade reverse-lookup).""" + + owner_id: str + owner_type: str + app_id: str = "default" + project_id: str = "default" + """App / project scope (default ``"default"``); cascade fills from md path.""" + session_id: str + timestamp: _dt.datetime + + parent_type: str = ParentType.MEMCELL.value + """Source pointer — always :attr:`ParentType.MEMCELL` for episode.""" + + parent_id: str + """Source memcell id. The pipeline knows the memcell currently being + processed and writes its id into the md entry's inline block; the + cascade handler reads it back. The new everalgo Episode type no + longer emits ``parent_id`` itself (collapsed to caller-managed), + so this is filled entirely from everos's engineering context.""" + + sender_ids: list[str] + """Distinct ``role=user|assistant`` senders behind the episode.""" + + subject: str | None = None + summary: str | None = None + episode: str + """Full narrative text — original surface form (returned for display).""" + + episode_tokens: str + """App-layer pre-tokenised ``episode`` text — space-joined tokens + (e.g. produced by jieba). LanceDB FTS index is built on **this** + column using a whitespace tokenizer; the original ``episode`` field + is what callers display. Two-field BM25 scheme keeps tokenisation + deterministic and provider-pluggable at the app layer.""" + + md_path: str + content_sha256: str + """SHA-256 hex digest over the **content-bearing fields only** of the + md entry (per :attr:`EpisodeHandler.content_change_keys`). On + re-reconcile, a matching digest means none of the persistence / + embedding-relevant fields changed — the entry is skipped (no + re-upsert, no re-embed). Inline audit fields (owner_id / + session_id / timestamp / parent_id / sender_ids) are intentionally + NOT in the hash so editing them doesn't waste an embedding call. + See ``16_cascade_impl_design.md`` §3.3.""" + + vector: Vector(_DIM) # type: ignore[valid-type] diff --git a/src/everos/infra/persistence/lancedb/tables/foresight.py b/src/everos/infra/persistence/lancedb/tables/foresight.py new file mode 100644 index 00000000..b9783a29 --- /dev/null +++ b/src/everos/infra/persistence/lancedb/tables/foresight.py @@ -0,0 +1,79 @@ +"""LanceDB ``foresight`` table schema. + +Field set per 17_lancedb_tables_design.md §3.3. Each row carries a +forward-looking inference about the user (intent window, planned +action, projected need); ``start_time`` / ``end_time`` describe the +window the foresight applies to. +""" + +from __future__ import annotations + +import datetime as _dt +from typing import ClassVar + +from everos.core.persistence.lancedb import BaseLanceTable, Vector + +from ._parent_type import ParentType + +_DIM = 1024 + + +class Foresight(BaseLanceTable): + """One foresight record indexed in LanceDB.""" + + TABLE_NAME: ClassVar[str] = "foresight" + BM25_FIELDS: ClassVar[list[str]] = ["foresight_tokens", "evidence_tokens"] + + id: str + """PK = ``_``.""" + + entry_id: str + """md-side seq id ``fs__``.""" + + owner_id: str + owner_type: str + app_id: str = "default" + project_id: str = "default" + """App / project scope (default ``"default"``); cascade fills from md path.""" + session_id: str + timestamp: _dt.datetime + """Foresight generation time.""" + + start_time: _dt.datetime | None = None + """Foresight coverage window start; tz-aware.""" + + end_time: _dt.datetime | None = None + """Foresight coverage window end; tz-aware.""" + + duration_days: int | None = None + + parent_type: str = ParentType.MEMCELL.value + """Source pointer — always :attr:`ParentType.MEMCELL` for foresight.""" + + parent_id: str + """Source memcell id.""" + + sender_ids: list[str] + foresight: str + """Foresight body — original surface form (returned for display).""" + + foresight_tokens: str + """App-layer pre-tokenised ``foresight`` text — space-joined tokens. + BM25 index is built on this column (whitespace tokenizer).""" + + evidence: str | None = None + """Supporting evidence excerpt; may be empty.""" + + evidence_tokens: str | None = None + """App-layer pre-tokenised ``evidence`` (secondary BM25 field). + ``None`` whenever ``evidence`` is None.""" + + md_path: str + content_sha256: str + """SHA-256 hex digest over the **content-bearing fields only** of + the md entry — Foresight / Evidence sections plus the time-window + inline fields (start_time / end_time / duration_days). Audit inline + (owner_id / session_id / timestamp / parent_id / sender_ids) is NOT + in the hash. See :attr:`ForesightHandler.content_change_keys`.""" + + vector: Vector(_DIM) # type: ignore[valid-type] diff --git a/src/everos/infra/persistence/lancedb/tables/user_profile.py b/src/everos/infra/persistence/lancedb/tables/user_profile.py new file mode 100644 index 00000000..87654b9d --- /dev/null +++ b/src/everos/infra/persistence/lancedb/tables/user_profile.py @@ -0,0 +1,68 @@ +"""LanceDB ``user_profile`` table schema. + +Profile is a single-file kind: one ``users//user.md`` per +user, replaced wholesale on edit (mirrors ``AgentSkill`` for the +upsert/single-row contract). The LanceDB row is a typed projection +of the md frontmatter that the cascade keeps in sync; it carries no +vector / no BM25 because the recall surface is pure KV-by-owner +(``fetch(owner_id)``) — when query-aware profile lookup ships later +the schema will gain ``vector`` + ``*_tokens`` columns then. + +``explicit_info`` / ``implicit_traits`` are heterogeneous LLM +emissions (mostly small dicts mixed with strings) — LanceDB has no +``list[dict]`` column type, so we stash them as JSON strings and +unpack at the recall boundary into ``profile_data`` of the DTO. +""" + +from __future__ import annotations + +from typing import ClassVar + +from everos.core.persistence.lancedb import BaseLanceTable + + +class UserProfile(BaseLanceTable): + """One ``users//user.md`` indexed in LanceDB.""" + + TABLE_NAME: ClassVar[str] = "user_profile" + # No BM25 columns: profile recall is KV-by-owner today. + + id: str + """PK = ``owner_id`` (one row per user).""" + + owner_id: str + owner_type: str + """Always ``"user"`` for this schema; agent-side profiles would + live in a sibling table once that schema lands.""" + + app_id: str = "default" + project_id: str = "default" + """App / project scope (default ``"default"``); cascade fills from md path.""" + + summary: str + """Free-form one-paragraph user summary (retrieval anchor for the + future query-aware lookup; today returned verbatim to the caller).""" + + explicit_info_json: str + """JSON-serialised ``list[Any]`` — the algo's verbatim evidence + bucket. Stored as a string because LanceDB has no + ``list[dict]`` column type. The recaller json-decodes it back into + ``profile_data['explicit_info']`` at the DTO boundary.""" + + implicit_traits_json: str + """Same shape as :attr:`explicit_info_json`, for the LLM-inferred + preference bucket.""" + + profile_timestamp_ms: int + """Algo-emitted profile timestamp (ms epoch) — pinned to the + timestamp of the freshest MemCell that fed into the synthesis. + Mirrored from :attr:`UserProfileFrontmatter.profile_timestamp_ms` + so downstream code can compare freshness without re-reading md.""" + + md_path: str + content_sha256: str + """SHA-256 over the content-bearing frontmatter fields (summary + + explicit_info_json + implicit_traits_json). Matches → cascade + skips re-upsert. ``profile_timestamp_ms`` is intentionally not in + the hash: it drifts with every synthesis even when the underlying + content is identical, and the LanceDB row treats it as audit.""" diff --git a/src/everos/infra/persistence/markdown/__init__.py b/src/everos/infra/persistence/markdown/__init__.py new file mode 100644 index 00000000..eb52d2ba --- /dev/null +++ b/src/everos/infra/persistence/markdown/__init__.py @@ -0,0 +1,73 @@ +"""Markdown business persistence layer. + +Sits on top of :mod:`everos.core.persistence.markdown` (atomic write + +parse + frontmatter chassis) and provides: + + * concrete frontmatter schemas under :mod:`.mds` + * concrete business writers under :mod:`.writers` + (``BaseDailyWriter`` + subclasses, ``AgentSkillWriter``, + ``ProfileWriter``) + * concrete business readers under :mod:`.readers` + (``BaseDailyReader`` + subclasses, ``AgentSkillReader``, + ``ProfileReader``) + +External usage:: + + from everos.infra.persistence.markdown import ( + BaseDailyWriter, BaseDailyReader, + EpisodeWriter, EpisodeReader, EpisodeDailyFrontmatter, + AtomicFactDailyFrontmatter, + ForesightDailyFrontmatter, + AgentCaseDailyFrontmatter, + AgentSkillFrontmatter, AgentSkillWriter, AgentSkillReader, + ProfileWriter, ProfileReader, + ) + +Outer layers MUST go through this top-level package because +``infra.persistence.markdown.**`` (sub-packages) are forbidden to outer +layers by import-linter. +""" + +from .mds import AgentCaseDailyFrontmatter as AgentCaseDailyFrontmatter +from .mds import AgentSkillFrontmatter as AgentSkillFrontmatter +from .mds import AtomicFactDailyFrontmatter as AtomicFactDailyFrontmatter +from .mds import EpisodeDailyFrontmatter as EpisodeDailyFrontmatter +from .mds import ForesightDailyFrontmatter as ForesightDailyFrontmatter +from .mds import UserProfileFrontmatter as UserProfileFrontmatter +from .readers import AgentCaseReader as AgentCaseReader +from .readers import AgentSkillReader as AgentSkillReader +from .readers import AtomicFactReader as AtomicFactReader +from .readers import BaseDailyReader as BaseDailyReader +from .readers import EpisodeReader as EpisodeReader +from .readers import ForesightReader as ForesightReader +from .readers import ProfileReader as ProfileReader +from .writers import AgentCaseWriter as AgentCaseWriter +from .writers import AgentSkillWriter as AgentSkillWriter +from .writers import AtomicFactWriter as AtomicFactWriter +from .writers import BaseDailyWriter as BaseDailyWriter +from .writers import EpisodeWriter as EpisodeWriter +from .writers import ForesightWriter as ForesightWriter +from .writers import ProfileWriter as ProfileWriter + +__all__ = [ + "AgentCaseDailyFrontmatter", + "AgentCaseReader", + "AgentCaseWriter", + "AgentSkillFrontmatter", + "AgentSkillReader", + "AgentSkillWriter", + "AtomicFactDailyFrontmatter", + "AtomicFactReader", + "AtomicFactWriter", + "BaseDailyReader", + "BaseDailyWriter", + "EpisodeDailyFrontmatter", + "EpisodeReader", + "EpisodeWriter", + "ForesightDailyFrontmatter", + "ForesightReader", + "ForesightWriter", + "ProfileReader", + "ProfileWriter", + "UserProfileFrontmatter", +] diff --git a/src/everos/infra/persistence/markdown/mds/__init__.py b/src/everos/infra/persistence/markdown/mds/__init__.py new file mode 100644 index 00000000..b7436769 --- /dev/null +++ b/src/everos/infra/persistence/markdown/mds/__init__.py @@ -0,0 +1,40 @@ +"""Business markdown frontmatter schemas (mds = "markdown schemas"). + +Each business record kind that is stored as markdown gets a concrete +frontmatter class here, subclassing one of the chassis classes from +:mod:`everos.core.persistence.markdown`: + + * :class:`UserScopedFrontmatter` for user-track records + * :class:`AgentScopedFrontmatter` for agent-track records + * :class:`BaseFrontmatter` for scope-agnostic records (rare) + +Schemas drive path resolution via ClassVars; each storage strategy has +its own conventions: + +- **Daily-log** schemas declare ``ENTRY_ID_PREFIX`` (token in + ``__``), ``DIR_NAME`` (sub-directory under + ``//``) and ``FILE_PREFIX`` (leading token of the daily + filename joined with ``-.md``). +- **Skill** schemas (:class:`AgentSkillFrontmatter`) pin the directory + layout via five ``SKILL_*`` ClassVars (container / dir prefix / + main filename / references / scripts). +- **Profile** schemas declare ``PROFILE_FILENAME`` (``"user.md"`` / + ``"agent.md"`` / …) and inherit ``SCOPE_DIR`` from a scope mixin; no + profile base class — the writer/reader pair is duck-typed. +""" + +from .agent_case import AgentCaseDailyFrontmatter as AgentCaseDailyFrontmatter +from .agent_skill import AgentSkillFrontmatter as AgentSkillFrontmatter +from .atomic_fact import AtomicFactDailyFrontmatter as AtomicFactDailyFrontmatter +from .episode import EpisodeDailyFrontmatter as EpisodeDailyFrontmatter +from .foresight import ForesightDailyFrontmatter as ForesightDailyFrontmatter +from .profile import UserProfileFrontmatter as UserProfileFrontmatter + +__all__ = [ + "AgentCaseDailyFrontmatter", + "AgentSkillFrontmatter", + "AtomicFactDailyFrontmatter", + "EpisodeDailyFrontmatter", + "ForesightDailyFrontmatter", + "UserProfileFrontmatter", +] diff --git a/src/everos/infra/persistence/markdown/mds/agent_case.py b/src/everos/infra/persistence/markdown/mds/agent_case.py new file mode 100644 index 00000000..b2263c91 --- /dev/null +++ b/src/everos/infra/persistence/markdown/mds/agent_case.py @@ -0,0 +1,37 @@ +"""AgentCase frontmatter — daily-log markdown for agent-scoped cases. + +Path: ``agents//.cases/agent_case-.md``. + +The directory is dotfile-hidden (``.cases``) so users only see the +curated ``agent_skills/`` view, not the raw per-task case log — same +convention as ``.atomic_facts`` / ``.foresights``. + +Each entry records one task an agent worked on: intent, approach taken, +quality score, and an optional pivotal insight. A MemCell extracted on +the agent's own execution log yields at most one AgentCase. +""" + +from __future__ import annotations + +import datetime as _dt +from typing import ClassVar, Literal + +from everos.core.persistence.markdown import ( + AgentScopedFrontmatter, + DailyLogPathMixin, +) + + +class AgentCaseDailyFrontmatter(DailyLogPathMixin, AgentScopedFrontmatter): + """Frontmatter for ``agents//.cases/agent_case-.md``.""" + + ENTRY_ID_PREFIX: ClassVar[str] = "ac" + DIR_NAME: ClassVar[str] = ".cases" + FILE_PREFIX: ClassVar[str] = "agent_case" + + type: Literal["agent_case_daily"] = "agent_case_daily" + file_type: Literal["agent_case_daily"] = "agent_case_daily" + date: _dt.date + entry_count: int = 0 + created_at: _dt.datetime | None = None + last_appended_at: _dt.datetime | None = None diff --git a/src/everos/infra/persistence/markdown/mds/agent_skill.py b/src/everos/infra/persistence/markdown/mds/agent_skill.py new file mode 100644 index 00000000..65e4d661 --- /dev/null +++ b/src/everos/infra/persistence/markdown/mds/agent_skill.py @@ -0,0 +1,63 @@ +"""AgentSkill frontmatter — single SKILL.md inside a skill directory. + +Path: ``agents//skills/skill_/SKILL.md`` (plus sibling +``references/*.md`` and ``scripts/*.`` files that are not part of +the frontmatter contract). + +Skills are *named entities* rather than daily-log entries: the +LanceDB primary key is ``_`` (no date / seq). +Upserts replace the file wholesale; the cascade daemon recomputes the +``content`` index column by concatenating ``SKILL.md`` body with every +``references/*.md`` sibling. + +Five directory-shape ClassVars pin the layout in one place so the +writer / reader pair reads off them — no duplicated string literals. +""" + +from __future__ import annotations + +import datetime as _dt +from typing import ClassVar, Literal + +from everos.core.persistence.markdown import ( + AgentScopedFrontmatter, + SkillPathMixin, +) + + +class AgentSkillFrontmatter(SkillPathMixin, AgentScopedFrontmatter): + """Frontmatter for ``agents//skills/skill_/SKILL.md``.""" + + SKILLS_CONTAINER_NAME: ClassVar[str] = "skills" + SKILL_DIR_PREFIX: ClassVar[str] = "skill_" + SKILL_MAIN_FILENAME: ClassVar[str] = "SKILL.md" + SKILL_REFERENCES_DIR_NAME: ClassVar[str] = "references" + SKILL_SCRIPTS_DIR_NAME: ClassVar[str] = "scripts" + + type: Literal["agent_skill"] = "agent_skill" + + name: str + """Skill identifier — also the directory suffix + (``skills/skill_/``). Keep snake_case so it is filesystem-safe + and ID-stable.""" + + description: str + """One-line summary surfaced at Tier-1 prompt injection. Short — the + agent's startup-time scanner reads ``(name, description)`` for every + skill, so the token budget is tight.""" + + confidence: float + """LLM-emitted confidence in the skill's correctness, 0.0–1.0.""" + + maturity_score: float + """LLM-emitted maturity score, 0.0–1.0. The retrieval-time threshold + (``maturity_threshold``) lives in MemorizeConfig, not on this file.""" + + source_case_ids: list[str] = [] + """AgentCase ids that fed into this skill's synthesis (lineage).""" + + cluster_id: str | None = None + """Optional MemScene clustering tag; may be unset early on.""" + + created_at: _dt.datetime | None = None + updated_at: _dt.datetime | None = None diff --git a/src/everos/infra/persistence/markdown/mds/atomic_fact.py b/src/everos/infra/persistence/markdown/mds/atomic_fact.py new file mode 100644 index 00000000..4630b594 --- /dev/null +++ b/src/everos/infra/persistence/markdown/mds/atomic_fact.py @@ -0,0 +1,38 @@ +"""AtomicFact frontmatter — daily-log markdown for user-scoped atomic facts. + +Path: ``users//.atomic_facts/atomic_fact-.md``. + +The directory is dot-prefixed so it is hidden from end users (same +convention as ``.index``); atomic facts are framework-internal derived md, +not material the user is expected to read by hand. + +Each entry carries one atomic fact extracted by the algo layer; the fact +always hangs off the source MemCell (see ``parent_type`` in each entry's +inline fields — handled at the StructuredEntry layer, not on the +file-level frontmatter). +""" + +from __future__ import annotations + +import datetime as _dt +from typing import ClassVar, Literal + +from everos.core.persistence.markdown import ( + DailyLogPathMixin, + UserScopedFrontmatter, +) + + +class AtomicFactDailyFrontmatter(DailyLogPathMixin, UserScopedFrontmatter): + """Frontmatter for ``users//.atomic_facts/atomic_fact-.md``.""" + + ENTRY_ID_PREFIX: ClassVar[str] = "af" + DIR_NAME: ClassVar[str] = ".atomic_facts" + FILE_PREFIX: ClassVar[str] = "atomic_fact" + + type: Literal["atomic_fact_daily"] = "atomic_fact_daily" + file_type: Literal["atomic_fact_daily"] = "atomic_fact_daily" + date: _dt.date + entry_count: int = 0 + created_at: _dt.datetime | None = None + last_appended_at: _dt.datetime | None = None diff --git a/src/everos/infra/persistence/markdown/mds/episode.py b/src/everos/infra/persistence/markdown/mds/episode.py new file mode 100644 index 00000000..802de815 --- /dev/null +++ b/src/everos/infra/persistence/markdown/mds/episode.py @@ -0,0 +1,33 @@ +"""Episode frontmatter — daily-log markdown for user-scoped episodes. + +Path: ``users//episodes/episode-.md``. + +This milestone uses ``session_id`` as the scope key (since owner inference +is out of scope). When owner inference lands the scope key will switch to +``owner_id`` while the schema stays compatible. +""" + +from __future__ import annotations + +import datetime as _dt +from typing import ClassVar, Literal + +from everos.core.persistence.markdown import ( + DailyLogPathMixin, + UserScopedFrontmatter, +) + + +class EpisodeDailyFrontmatter(DailyLogPathMixin, UserScopedFrontmatter): + """Frontmatter for ``users//episodes/episode-.md``.""" + + ENTRY_ID_PREFIX: ClassVar[str] = "ep" + DIR_NAME: ClassVar[str] = "episodes" + FILE_PREFIX: ClassVar[str] = "episode" + + type: Literal["episode_daily"] = "episode_daily" + file_type: Literal["episode_daily"] = "episode_daily" + date: _dt.date + entry_count: int = 0 + created_at: _dt.datetime | None = None + last_appended_at: _dt.datetime | None = None diff --git a/src/everos/infra/persistence/markdown/mds/foresight.py b/src/everos/infra/persistence/markdown/mds/foresight.py new file mode 100644 index 00000000..6f75f11f --- /dev/null +++ b/src/everos/infra/persistence/markdown/mds/foresight.py @@ -0,0 +1,38 @@ +"""Foresight frontmatter — daily-log markdown for user-scoped foresights. + +Path: ``users//.foresights/foresight-.md``. + +The directory is dot-prefixed so it is hidden from end users (same +convention as ``.index``); foresights are framework-internal derived md, +not material the user is expected to read by hand. + +Each entry carries a forward-looking inference about the user (intent +window, planned action, projected need) with ``start_time`` / +``end_time`` describing the covered time range. ``parent_type`` always +points back to a MemCell. +""" + +from __future__ import annotations + +import datetime as _dt +from typing import ClassVar, Literal + +from everos.core.persistence.markdown import ( + DailyLogPathMixin, + UserScopedFrontmatter, +) + + +class ForesightDailyFrontmatter(DailyLogPathMixin, UserScopedFrontmatter): + """Frontmatter for ``users//.foresights/foresight-.md``.""" + + ENTRY_ID_PREFIX: ClassVar[str] = "fs" + DIR_NAME: ClassVar[str] = ".foresights" + FILE_PREFIX: ClassVar[str] = "foresight" + + type: Literal["foresight_daily"] = "foresight_daily" + file_type: Literal["foresight_daily"] = "foresight_daily" + date: _dt.date + entry_count: int = 0 + created_at: _dt.datetime | None = None + last_appended_at: _dt.datetime | None = None diff --git a/src/everos/infra/persistence/markdown/mds/profile.py b/src/everos/infra/persistence/markdown/mds/profile.py new file mode 100644 index 00000000..283dfb2c --- /dev/null +++ b/src/everos/infra/persistence/markdown/mds/profile.py @@ -0,0 +1,40 @@ +"""UserProfile frontmatter — single-file profile markdown for users. + +Path: ``users//user.md``. + +Carries the LLM-synthesised user profile: a free-form ``summary`` plus the +two evidence buckets emitted by :class:`everalgo.user_memory.ProfileExtractor` +(``explicit_info`` / ``implicit_traits``). ``profile_timestamp_ms`` +mirrors :attr:`everalgo.types.Profile.timestamp` so the +``extract_user_profile`` strategy can compare per-user freshness against +cluster ``last_ts`` without re-parsing the body. +""" + +from __future__ import annotations + +from typing import Any, ClassVar, Literal + +from everos.core.persistence.markdown import ProfilePathMixin, UserScopedFrontmatter + + +class UserProfileFrontmatter(ProfilePathMixin, UserScopedFrontmatter): + """Frontmatter for ``users//user.md``.""" + + PROFILE_FILENAME: ClassVar[str] = "user.md" + + type: Literal["user_profile"] = "user_profile" + + summary: str = "" + """Free-form one-paragraph summary of the user — the retrieval anchor.""" + + explicit_info: list[Any] = [] + """Algo-side ``explicit_info`` bucket (verbatim facts the user stated).""" + + implicit_traits: list[Any] = [] + """Algo-side ``implicit_traits`` bucket (LLM-inferred preferences).""" + + profile_timestamp_ms: int = 0 + """Algo-emitted profile timestamp (ms epoch); equals the timestamp of + the most recent MemCell that fed into the synthesis. Compared with + :attr:`everos.infra.persistence.sqlite.Cluster.last_ts_ms` to decide + whether a cluster is fresh enough to drive a profile re-extraction.""" diff --git a/src/everos/infra/persistence/markdown/readers/__init__.py b/src/everos/infra/persistence/markdown/readers/__init__.py new file mode 100644 index 00000000..2d705167 --- /dev/null +++ b/src/everos/infra/persistence/markdown/readers/__init__.py @@ -0,0 +1,49 @@ +"""Business markdown readers — symmetric with the writers. + +Daily-log markdown is parsed via :class:`MarkdownReader` from ``core`` +(the base reader returns frontmatter dict + body + entry markers, all +schema-agnostic). Reader classes here add the **business-aware +locator** layer: + + * :class:`BaseDailyReader` + subclasses — bind a daily-log schema, + resolve ``(scope_id, date)`` to a file, locate entries by id, + and optionally upgrade to :class:`StructuredEntry`. Symmetric + with :class:`BaseDailyWriter`. + * :class:`AgentSkillReader` — reads ``SKILL.md`` and parses the + frontmatter into the caller-supplied ``AgentSkillFrontmatter`` + subclass; also reads individual reference / script files. + * :class:`ProfileReader` — reads a fixed-name profile file + (``user.md`` / ``agent.md`` / ``soul.md`` / …) and parses its + frontmatter into the caller-supplied schema. + +By design, no batch / list APIs live here: bulk enumeration for +prompt-budget or cross-record queries goes through sqlite/lancedb +(see the cascade daemon's index sync), not a markdown directory walk. + +External usage:: + + from everos.infra.persistence.markdown.readers import ( + BaseDailyReader, + EpisodeReader, + AgentSkillReader, + ProfileReader, + ) +""" + +from .agent_case_reader import AgentCaseReader as AgentCaseReader +from .agent_skill_reader import AgentSkillReader as AgentSkillReader +from .atomic_fact_reader import AtomicFactReader as AtomicFactReader +from .base import BaseDailyReader as BaseDailyReader +from .episode_reader import EpisodeReader as EpisodeReader +from .foresight_reader import ForesightReader as ForesightReader +from .profile_reader import ProfileReader as ProfileReader + +__all__ = [ + "AgentCaseReader", + "AgentSkillReader", + "AtomicFactReader", + "BaseDailyReader", + "EpisodeReader", + "ForesightReader", + "ProfileReader", +] diff --git a/src/everos/infra/persistence/markdown/readers/agent_case_reader.py b/src/everos/infra/persistence/markdown/readers/agent_case_reader.py new file mode 100644 index 00000000..586af2c1 --- /dev/null +++ b/src/everos/infra/persistence/markdown/readers/agent_case_reader.py @@ -0,0 +1,31 @@ +"""AgentCase daily-log reader — symmetric with :class:`AgentCaseWriter`.""" + +from __future__ import annotations + +import datetime as _dt +from pathlib import Path + +from everos.core.persistence import MemoryRoot + +from ..mds import AgentCaseDailyFrontmatter +from .base import BaseDailyReader + + +class AgentCaseReader(BaseDailyReader): + """Read agent-case daily-log files.""" + + schema = AgentCaseDailyFrontmatter + + def __init__(self, root: MemoryRoot) -> None: + super().__init__(root) + + def path_for( + self, + agent_id: str, + date: _dt.date | None = None, + *, + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Resolve the agent-case daily-log path under the / prefix.""" + return super().path_for(agent_id, date, app_id=app_id, project_id=project_id) diff --git a/src/everos/infra/persistence/markdown/readers/agent_skill_reader.py b/src/everos/infra/persistence/markdown/readers/agent_skill_reader.py new file mode 100644 index 00000000..580d5458 --- /dev/null +++ b/src/everos/infra/persistence/markdown/readers/agent_skill_reader.py @@ -0,0 +1,161 @@ +"""AgentSkillReader — typed read for the AgentSkill directory layout. + +Pairs with :class:`AgentSkillWriter`: + +- :meth:`read_main` reads ``SKILL.md`` and returns the caller's + :class:`AgentSkillFrontmatter` subclass instance + the Tier-2 body, so + the caller never deals with raw dicts. +- :meth:`read_reference` / :meth:`read_script` are plain text reads; + no frontmatter, no schema. + +All three return ``None`` when the target is missing — readers do not +raise on absence, since "skill not yet created" is a normal state for +the upsert-style workflow. Callers that need to distinguish "missing" +from "empty body" check for ``None`` explicitly. + +Path resolution mirrors :class:`AgentSkillWriter` and reads the same +ClassVars off :class:`AgentSkillFrontmatter`. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import TypeVar + +import anyio + +from everos.core.persistence import MarkdownReader, MemoryRoot + +from ..mds import AgentSkillFrontmatter + +T = TypeVar("T", bound=AgentSkillFrontmatter) + + +class AgentSkillReader: + """Single-skill reader for the directory + progressive-disclosure layout.""" + + def __init__(self, root: MemoryRoot) -> None: + self._root = root + + # ── Public API ──────────────────────────────────────────────────────── + + async def read_main( + self, + agent_id: str, + skill_name: str, + *, + schema: type[T], + app_id: str = "default", + project_id: str = "default", + ) -> tuple[T, str] | None: + """Read ``SKILL.md`` and parse its frontmatter into ``schema``. + + Args: + schema: Concrete :class:`AgentSkillFrontmatter` subclass. The + frontmatter dict is validated against this schema via + :meth:`pydantic.BaseModel.model_validate`; extra fields + ride along (chassis sets ``extra="allow"``). + + Returns: + ``(frontmatter, body)`` on success, ``None`` if the file + does not exist. ``body`` is the raw text after the closing + ``---``; the trailing newline added by :class:`AgentSkillWriter` + is stripped to give the *logical* body back. + """ + path = self._main_path(agent_id, skill_name, app_id, project_id) + if not await anyio.Path(path).is_file(): + return None + parsed = await MarkdownReader.read(path) + frontmatter = schema.model_validate(parsed.frontmatter) + body = parsed.body.rstrip("\n") + return frontmatter, body + + async def read_reference( + self, + agent_id: str, + skill_name: str, + reference_name: str, + *, + app_id: str = "default", + project_id: str = "default", + ) -> str | None: + """Read ``references/.md`` verbatim, ``None`` if absent.""" + path = self._reference_path( + agent_id, skill_name, reference_name, app_id, project_id + ) + apath = anyio.Path(path) + if not await apath.is_file(): + return None + text = await apath.read_text(encoding="utf-8") + return text.rstrip("\n") + + async def read_script( + self, + agent_id: str, + skill_name: str, + script_filename: str, + *, + app_id: str = "default", + project_id: str = "default", + ) -> str | None: + """Read ``scripts/`` verbatim, ``None`` if absent. + + Reading ≠ executing — this only returns the source text. + Sandboxing / exec-policy decisions belong to the caller. + """ + path = self._script_path( + agent_id, skill_name, script_filename, app_id, project_id + ) + apath = anyio.Path(path) + if not await apath.is_file(): + return None + text = await apath.read_text(encoding="utf-8") + return text.rstrip("\n") + + # ── Internals — same shape as AgentSkillWriter ──────────────────────────── + + def _skill_dir( + self, agent_id: str, skill_name: str, app_id: str, project_id: str + ) -> Path: + return ( + self._root.agents_dir(app_id, project_id) + / agent_id + / AgentSkillFrontmatter.SKILLS_CONTAINER_NAME + / f"{AgentSkillFrontmatter.SKILL_DIR_PREFIX}{skill_name}" + ) + + def _main_path( + self, agent_id: str, skill_name: str, app_id: str, project_id: str + ) -> Path: + return ( + self._skill_dir(agent_id, skill_name, app_id, project_id) + / AgentSkillFrontmatter.SKILL_MAIN_FILENAME + ) + + def _reference_path( + self, + agent_id: str, + skill_name: str, + reference_name: str, + app_id: str, + project_id: str, + ) -> Path: + return ( + self._skill_dir(agent_id, skill_name, app_id, project_id) + / AgentSkillFrontmatter.SKILL_REFERENCES_DIR_NAME + / f"{reference_name}.md" + ) + + def _script_path( + self, + agent_id: str, + skill_name: str, + script_filename: str, + app_id: str, + project_id: str, + ) -> Path: + return ( + self._skill_dir(agent_id, skill_name, app_id, project_id) + / AgentSkillFrontmatter.SKILL_SCRIPTS_DIR_NAME + / script_filename + ) diff --git a/src/everos/infra/persistence/markdown/readers/atomic_fact_reader.py b/src/everos/infra/persistence/markdown/readers/atomic_fact_reader.py new file mode 100644 index 00000000..ee82f757 --- /dev/null +++ b/src/everos/infra/persistence/markdown/readers/atomic_fact_reader.py @@ -0,0 +1,31 @@ +"""AtomicFact daily-log reader — symmetric with :class:`AtomicFactWriter`.""" + +from __future__ import annotations + +import datetime as _dt +from pathlib import Path + +from everos.core.persistence import MemoryRoot + +from ..mds import AtomicFactDailyFrontmatter +from .base import BaseDailyReader + + +class AtomicFactReader(BaseDailyReader): + """Read atomic-fact daily-log files.""" + + schema = AtomicFactDailyFrontmatter + + def __init__(self, root: MemoryRoot) -> None: + super().__init__(root) + + def path_for( + self, + owner_id: str, + date: _dt.date | None = None, + *, + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Resolve the atomic-fact daily-log path under the / prefix.""" + return super().path_for(owner_id, date, app_id=app_id, project_id=project_id) diff --git a/src/everos/infra/persistence/markdown/readers/base.py b/src/everos/infra/persistence/markdown/readers/base.py new file mode 100644 index 00000000..c53ca45d --- /dev/null +++ b/src/everos/infra/persistence/markdown/readers/base.py @@ -0,0 +1,177 @@ +"""Base business reader for daily-log markdown files. + +Symmetric to :class:`BaseDailyWriter`: reads the daily-log file for +a given ``(scope_id, date)``, locates entries by id within it, and +optionally upgrades them to :class:`StructuredEntry` so service-layer +callers don't have to re-do that plumbing each time. + +Subclass usage:: + + class _MemcellReader(BaseDailyReader): + schema = UserMemcellDailyFrontmatter + + reader = _MemcellReader(root) + parsed = reader.read_for("u_jason") # today's file + entry = reader.find_entry("u_jason", "umc_20260422_0001") + structured = reader.find_structured("u_jason", entry.id) + +The reader does **not** typed-parse the file's frontmatter dict — the +schema is used only for path resolution (matching what the appender +writes). Frontmatter validation belongs to higher-level callers that +know the business rules. + +Path resolution is identical to :class:`BaseDailyWriter` (same +``SCOPE_DIR`` / ``DIR_NAME`` / ``FILE_PREFIX`` ClassVars), so a +reader and writer bound to the same schema agree on every path. +""" + +from __future__ import annotations + +import datetime as _dt +from pathlib import Path +from typing import ClassVar + +import anyio + +from everos.component.utils.datetime import today_with_timezone +from everos.core.persistence import ( + BaseFrontmatter, + Entry, + EntryId, + MarkdownReader, + MemoryRoot, + ParsedMarkdown, + StructuredEntry, + find_entry, +) + + +class BaseDailyReader: + """Single-record reader for daily-log markdown files. + + Subclasses bind a :class:`BaseFrontmatter` subclass via the + ``schema`` ClassVar. The schema must declare ``SCOPE_DIR``, + ``DIR_NAME``, and ``FILE_PREFIX`` (same set the appender uses); no + ``ENTRY_ID_PREFIX`` requirement here because the reader takes the + entry id from the caller, not the schema. + """ + + schema: ClassVar[type[BaseFrontmatter]] # subclass must declare + + def __init__(self, root: MemoryRoot) -> None: + schema = getattr(type(self), "schema", None) + if schema is None: + raise TypeError( + f"{type(self).__name__} must declare a class-level ``schema`` attribute" + ) + for attr in ("SCOPE_DIR", "DIR_NAME", "FILE_PREFIX"): + if not getattr(schema, attr, None): + raise TypeError(f"{schema.__name__} missing ClassVar {attr!r}") + self._root = root + + # ── Public API ──────────────────────────────────────────────────────── + + async def read_for( + self, + scope_id: str, + date: _dt.date | None = None, + *, + app_id: str = "default", + project_id: str = "default", + ) -> ParsedMarkdown | None: + """Read the daily-log file for ``(scope_id, date)``. + + Args: + scope_id: ``user_id`` or ``agent_id``. + date: Date bucket — defaults to today in the configured TZ. + app_id: App scope segment (defaults to the ``"default"`` space). + project_id: Project scope segment (defaults to ``"default"``). + + Returns: + :class:`ParsedMarkdown` (frontmatter dict + body + entries), + or ``None`` when the file does not exist on disk. ``None`` + avoids forcing every caller to wrap reads in try/except — + "no file yet" is a normal early state. + """ + path = self._resolve_path( + scope_id, date or today_with_timezone(), app_id, project_id + ) + if not await anyio.Path(path).is_file(): + return None + return await MarkdownReader.read(path) + + async def find_entry( + self, + scope_id: str, + entry_id: str | EntryId, + *, + app_id: str = "default", + project_id: str = "default", + ) -> Entry | None: + """Locate the entry with ``entry_id`` inside its daily-log file. + + The date bucket is taken from the entry id (an :class:`EntryId` + encodes its own date), so the caller doesn't pass a date. + Returns ``None`` if either the file or the entry is missing. + """ + eid = entry_id if isinstance(entry_id, EntryId) else EntryId.parse(entry_id) + eid_str = eid.format() + parsed = await self.read_for( + scope_id, eid.date, app_id=app_id, project_id=project_id + ) + if parsed is None: + return None + return find_entry(parsed.body, eid_str) + + async def find_structured( + self, + scope_id: str, + entry_id: str | EntryId, + *, + app_id: str = "default", + project_id: str = "default", + ) -> StructuredEntry | None: + """Locate the entry and parse its body as audit-form data. + + Sugar over :meth:`find_entry` + :meth:`Entry.as_structured`. + Returns ``None`` if the entry is missing. + """ + entry = await self.find_entry( + scope_id, entry_id, app_id=app_id, project_id=project_id + ) + if entry is None: + return None + return entry.as_structured() + + def path_for( + self, + scope_id: str, + date: _dt.date | None = None, + *, + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Return the daily-log path for ``scope_id`` on ``date`` (today default). + + Public counterpart of :meth:`_resolve_path` — symmetric with + :meth:`BaseDailyWriter.path_for`. Does not check existence. + """ + return self._resolve_path( + scope_id, date or today_with_timezone(), app_id, project_id + ) + + # ── Internals ───────────────────────────────────────────────────────── + + def _resolve_path( + self, scope_id: str, date: _dt.date, app_id: str, project_id: str + ) -> Path: + """Build the daily-log path for ``scope_id`` on ``date``.""" + # SCOPE_DIR ("users" / "agents") names the matching MemoryRoot method, + # which prepends the / business prefix. + scope_dir = getattr(self._root, f"{self.schema.SCOPE_DIR}_dir") + return ( + scope_dir(app_id, project_id) + / scope_id + / self.schema.DIR_NAME + / f"{self.schema.FILE_PREFIX}-{date.isoformat()}.md" + ) diff --git a/src/everos/infra/persistence/markdown/readers/episode_reader.py b/src/everos/infra/persistence/markdown/readers/episode_reader.py new file mode 100644 index 00000000..d13f5ed1 --- /dev/null +++ b/src/everos/infra/persistence/markdown/readers/episode_reader.py @@ -0,0 +1,41 @@ +"""Episode daily-log reader — symmetric with :class:`EpisodeWriter`. + +md is the source of truth for Episode memories; this reader gives +cascade / search / verification scripts a typed locator instead of +raw :class:`MarkdownReader` calls. +""" + +from __future__ import annotations + +import datetime as _dt +from pathlib import Path + +from everos.core.persistence import MemoryRoot + +from ..mds import EpisodeDailyFrontmatter +from .base import BaseDailyReader + + +class EpisodeReader(BaseDailyReader): + """Read episode daily-log files.""" + + schema = EpisodeDailyFrontmatter + + def __init__(self, root: MemoryRoot) -> None: + super().__init__(root) + + def path_for( + self, + owner_id: str, + date: _dt.date | None = None, + *, + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Resolve the daily-log path for ``owner_id`` on ``date`` (today by default). + + Mirrors :meth:`EpisodeWriter`'s path-resolution shape so callers + can locate the file written for a given owner / day (under the + ``/`` prefix) without instantiating the writer. + """ + return super().path_for(owner_id, date, app_id=app_id, project_id=project_id) diff --git a/src/everos/infra/persistence/markdown/readers/foresight_reader.py b/src/everos/infra/persistence/markdown/readers/foresight_reader.py new file mode 100644 index 00000000..f416dcc3 --- /dev/null +++ b/src/everos/infra/persistence/markdown/readers/foresight_reader.py @@ -0,0 +1,31 @@ +"""Foresight daily-log reader — symmetric with :class:`ForesightWriter`.""" + +from __future__ import annotations + +import datetime as _dt +from pathlib import Path + +from everos.core.persistence import MemoryRoot + +from ..mds import ForesightDailyFrontmatter +from .base import BaseDailyReader + + +class ForesightReader(BaseDailyReader): + """Read foresight daily-log files.""" + + schema = ForesightDailyFrontmatter + + def __init__(self, root: MemoryRoot) -> None: + super().__init__(root) + + def path_for( + self, + owner_id: str, + date: _dt.date | None = None, + *, + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Resolve the foresight daily-log path under the / prefix.""" + return super().path_for(owner_id, date, app_id=app_id, project_id=project_id) diff --git a/src/everos/infra/persistence/markdown/readers/profile_reader.py b/src/everos/infra/persistence/markdown/readers/profile_reader.py new file mode 100644 index 00000000..90cb00db --- /dev/null +++ b/src/everos/infra/persistence/markdown/readers/profile_reader.py @@ -0,0 +1,96 @@ +"""ProfileReader — typed read for the single-file profile layout. + +Pairs with :class:`ProfileWriter`. The schema (concrete profile +frontmatter class) is supplied per call; the reader pulls +``SCOPE_DIR`` + ``PROFILE_FILENAME`` ClassVars off it to build the +path, then ``MarkdownReader.read`` + ``schema.model_validate`` give +back a typed frontmatter instance plus the body string. + +Returns ``None`` when the profile file does not exist — "not yet +written" is a normal early state for the upsert-style workflow. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import TypeVar + +import anyio + +from everos.core.persistence import BaseFrontmatter, MarkdownReader, MemoryRoot + +T = TypeVar("T", bound=BaseFrontmatter) + + +class ProfileReader: + """Typed read for fixed-name profile markdown files.""" + + def __init__(self, root: MemoryRoot) -> None: + self._root = root + + # ── Public API ──────────────────────────────────────────────────────── + + async def read( + self, + scope_id: str, + *, + schema: type[T], + app_id: str = "default", + project_id: str = "default", + ) -> tuple[T, str] | None: + """Read the profile file and parse its frontmatter into ``schema``. + + Args: + scope_id: ``user_id`` or ``agent_id`` (must match the + schema's scope mixin). + schema: Concrete profile frontmatter class — must declare + ``SCOPE_DIR`` (via scope mixin) and ``PROFILE_FILENAME``. + app_id: App scope segment (defaults to the ``"default"`` space). + project_id: Project scope segment (defaults to ``"default"``). + + Returns: + ``(frontmatter, body)`` on success; ``None`` if the file is + missing. ``body`` is the raw text after the closing ``---`` + with the writer-added trailing newline stripped. + """ + path = self._resolve_path(scope_id, schema, app_id, project_id) + if not await anyio.Path(path).is_file(): + return None + parsed = await MarkdownReader.read(path) + frontmatter = schema.model_validate(parsed.frontmatter) + body = parsed.body.rstrip("\n") + return frontmatter, body + + def path_for( + self, + scope_id: str, + *, + schema: type[BaseFrontmatter], + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Return the profile path (no IO check).""" + return self._resolve_path(scope_id, schema, app_id, project_id) + + # ── Internals — same shape as ProfileWriter ─────────────────────────── + + def _resolve_path( + self, + scope_id: str, + schema: type[BaseFrontmatter], + app_id: str, + project_id: str, + ) -> Path: + scope_dir = getattr(schema, "SCOPE_DIR", "") + filename = getattr(schema, "PROFILE_FILENAME", None) + if not scope_dir: + raise TypeError( + f"{schema.__name__} missing ``SCOPE_DIR`` ClassVar — " + "must inherit a scope mixin (UserScopedFrontmatter / " + "AgentScopedFrontmatter)." + ) + if not filename: + raise TypeError(f"{schema.__name__} missing ``PROFILE_FILENAME`` ClassVar.") + # SCOPE_DIR names the matching MemoryRoot method (/ prefix). + scope_root = getattr(self._root, f"{scope_dir}_dir")(app_id, project_id) + return scope_root / scope_id / filename diff --git a/src/everos/infra/persistence/markdown/writers/__init__.py b/src/everos/infra/persistence/markdown/writers/__init__.py new file mode 100644 index 00000000..323251a4 --- /dev/null +++ b/src/everos/infra/persistence/markdown/writers/__init__.py @@ -0,0 +1,43 @@ +"""Business markdown writers. + +Each storage strategy from the EverOS Markdown First spec gets a class +here: + + * :class:`BaseDailyWriter` — daily-log append (episode / atomic + fact / foresight / agent case). Subclass and bind ``schema``. + * :class:`AgentSkillWriter` — directory + progressive disclosure + (``skills/skill_/{SKILL.md, references/, scripts/}``). + Single class, no subclassing. + * :class:`ProfileWriter` — single-file rewrite at a fixed name + (``user.md`` / ``behaviors.md`` / ``agent.md`` / ``soul.md`` / + ``tools.md``). Single class, no subclassing — caller hands in a + frontmatter instance whose ``PROFILE_FILENAME`` ClassVar pins + the filename. + +External usage:: + + from everos.infra.persistence.markdown.writers import ( + BaseDailyWriter, + EpisodeWriter, + AgentSkillWriter, + ProfileWriter, + ) +""" + +from .agent_case_writer import AgentCaseWriter as AgentCaseWriter +from .agent_skill_writer import AgentSkillWriter as AgentSkillWriter +from .atomic_fact_writer import AtomicFactWriter as AtomicFactWriter +from .base import BaseDailyWriter as BaseDailyWriter +from .episode_writer import EpisodeWriter as EpisodeWriter +from .foresight_writer import ForesightWriter as ForesightWriter +from .profile_writer import ProfileWriter as ProfileWriter + +__all__ = [ + "AgentCaseWriter", + "AgentSkillWriter", + "AtomicFactWriter", + "BaseDailyWriter", + "EpisodeWriter", + "ForesightWriter", + "ProfileWriter", +] diff --git a/src/everos/infra/persistence/markdown/writers/agent_case_writer.py b/src/everos/infra/persistence/markdown/writers/agent_case_writer.py new file mode 100644 index 00000000..3ea01ed6 --- /dev/null +++ b/src/everos/infra/persistence/markdown/writers/agent_case_writer.py @@ -0,0 +1,63 @@ +"""AgentCase daily-log writer — md is the SoT for agent cases. + +Lives on the agent track (``agents//.cases/...``). +Inline carries audit + scoring fields (``owner_id`` / ``session_id`` / +``timestamp`` / ``parent_id`` / ``quality_score``); sections carry +``TaskIntent`` (required, primary BM25/embed), ``Approach`` (verbatim, +not indexed — too long), and optional ``KeyInsight`` (verbatim). +""" + +from __future__ import annotations + +import datetime as _dt +from collections.abc import Mapping +from pathlib import Path +from typing import Any + +import anyio + +from everos.component.utils.datetime import ( + get_now_with_timezone, + to_iso_format, +) +from everos.core.persistence import MarkdownReader + +from ..mds import AgentCaseDailyFrontmatter +from .base import BaseDailyWriter + + +class AgentCaseWriter(BaseDailyWriter): + """Daily-log writer for the AgentCase schema (md = SoT). + + ``append_entry`` / ``append_entries`` come from + :class:`BaseDailyWriter`. The scope id parameter is named ``agent_id`` + semantically (this is the agent track), but the base class accepts + it via the generic ``scope_id`` parameter. + """ + + schema = AgentCaseDailyFrontmatter + + def _frontmatter_updates( + self, + scope_id: str, + date: _dt.date, + *, + next_count: int, + ) -> Mapping[str, Any] | None: + return { + "id": f"agent_case_log_{scope_id}_{date.isoformat()}", + "type": "agent_case_daily", + "file_type": "agent_case_daily", + "schema_version": 1, + "agent_id": scope_id, + "track": "agent", + "date": date.isoformat(), + "entry_count": next_count, + "last_appended_at": to_iso_format(get_now_with_timezone()), + } + + async def _current_count(self, path: Path) -> int: + if not await anyio.Path(path).is_file(): + return 0 + parsed = await MarkdownReader.read(path) + return parsed.frontmatter.get("entry_count", 0) diff --git a/src/everos/infra/persistence/markdown/writers/agent_skill_writer.py b/src/everos/infra/persistence/markdown/writers/agent_skill_writer.py new file mode 100644 index 00000000..07475482 --- /dev/null +++ b/src/everos/infra/persistence/markdown/writers/agent_skill_writer.py @@ -0,0 +1,204 @@ +"""AgentSkillWriter — upsert skill main file + reference / script attachments. + +Skill storage is **directory + progressive disclosure** (wiki "Memory +Types Markdown Format" v4): each skill lives under +``agents//skills/skill_/`` with a ``SKILL.md`` main +file plus ``references/*.md`` and ``scripts/*.`` siblings. + +This writer is intentionally distinct from :class:`BaseDailyWriter`: + +- **Upsert, not append.** Each ``write_*`` call overwrites the target + file in full. Skills don't accumulate entry markers — the body of + ``SKILL.md`` is the latest revision; references / scripts are + individually replaceable files. +- **Single-skill API.** The chassis is *not* responsible for bulk + enumeration (Tier-1 prompt scanning is a sqlite/lancedb concern, + not a markdown-walk concern). One skill in, one skill out. +- **No counters / hooks.** No frontmatter merging, no entry-id + generation, no _frontmatter_updates hook — the caller hands in a + fully-built :class:`AgentSkillFrontmatter` subclass instance and the body + string; the writer atomically replaces the file. + +Path resolution comes from :class:`MemoryRoot` + the ClassVars on +:class:`AgentSkillFrontmatter` (``SKILLS_CONTAINER_NAME`` / +``SKILL_DIR_PREFIX`` / etc.). The writer + reader pair is the single +addressing API for skills. +""" + +from __future__ import annotations + +from pathlib import Path + +from everos.core.persistence import MarkdownWriter, MemoryRoot + +from ..mds import AgentSkillFrontmatter + + +class AgentSkillWriter: + """Atomic writer for the AgentSkill directory layout. + + Holds a :class:`MarkdownWriter` for the SKILL.md path (frontmatter + + body) and a thin atomic-write helper for plain-text reference / + script files (no frontmatter). + """ + + def __init__( + self, + root: MemoryRoot, + *, + writer: MarkdownWriter | None = None, + ) -> None: + self._root = root + self._writer = writer or MarkdownWriter(root) + + # ── Public API ──────────────────────────────────────────────────────── + + async def write_main( + self, + agent_id: str, + skill_name: str, + *, + frontmatter: AgentSkillFrontmatter, + body: str, + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Upsert ``skills/skill_/SKILL.md``. + + The file is replaced in full: ``frontmatter`` becomes the new + YAML head, ``body`` becomes the new body. Any prior content + (including manual human edits) is overwritten. The atomic + rename keeps readers from ever seeing a torn write. + + Args: + agent_id: Owning agent. + skill_name: Unprefixed identifier (``"contract_risk_scan"``, + not ``"skill_contract_risk_scan"``). + frontmatter: Fully-built schema instance — its ``model_dump`` + is what lands in the YAML head, including extra fields. + body: Tier-2 body text. Trailing newline is normalised. + + Returns: + Absolute path of the written ``SKILL.md``. + """ + path = self._main_path(agent_id, skill_name, app_id, project_id) + head_meta = frontmatter.model_dump(exclude_none=False) + return await self._writer.write_markdown( + path, + frontmatter=head_meta, + body=_ensure_trailing_newline(body), + ) + + async def write_reference( + self, + agent_id: str, + skill_name: str, + reference_name: str, + content: str, + *, + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Upsert ``skills/skill_/references/.md``. + + Reference files are plain markdown — no frontmatter, no entry + markers. Content is written verbatim (with a normalised + trailing newline). + + Args: + reference_name: Filename stem (no ``.md`` extension). + """ + path = self._reference_path( + agent_id, skill_name, reference_name, app_id, project_id + ) + return await self._writer.write(path, _ensure_trailing_newline(content)) + + async def write_script( + self, + agent_id: str, + skill_name: str, + script_filename: str, + content: str, + *, + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Upsert ``skills/skill_/scripts/``. + + Script files are written verbatim — caller supplies the *full* + filename (including the extension; ``redline.py`` / + ``redline.sh`` / etc.) since scripts may be in any language. + Cascade does not index this directory. + """ + path = self._script_path( + agent_id, skill_name, script_filename, app_id, project_id + ) + return await self._writer.write(path, _ensure_trailing_newline(content)) + + # ── Path API (callers that need to echo paths in responses) ────────── + + def main_path( + self, + agent_id: str, + skill_name: str, + *, + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Return ``skills/skill_/SKILL.md`` (does not check existence).""" + return self._main_path(agent_id, skill_name, app_id, project_id) + + # ── Internals — path resolution from AgentSkillFrontmatter ClassVars ────── + + def _skill_dir( + self, agent_id: str, skill_name: str, app_id: str, project_id: str + ) -> Path: + return ( + self._root.agents_dir(app_id, project_id) + / agent_id + / AgentSkillFrontmatter.SKILLS_CONTAINER_NAME + / f"{AgentSkillFrontmatter.SKILL_DIR_PREFIX}{skill_name}" + ) + + def _main_path( + self, agent_id: str, skill_name: str, app_id: str, project_id: str + ) -> Path: + return ( + self._skill_dir(agent_id, skill_name, app_id, project_id) + / AgentSkillFrontmatter.SKILL_MAIN_FILENAME + ) + + def _reference_path( + self, + agent_id: str, + skill_name: str, + reference_name: str, + app_id: str, + project_id: str, + ) -> Path: + return ( + self._skill_dir(agent_id, skill_name, app_id, project_id) + / AgentSkillFrontmatter.SKILL_REFERENCES_DIR_NAME + / f"{reference_name}.md" + ) + + def _script_path( + self, + agent_id: str, + skill_name: str, + script_filename: str, + app_id: str, + project_id: str, + ) -> Path: + return ( + self._skill_dir(agent_id, skill_name, app_id, project_id) + / AgentSkillFrontmatter.SKILL_SCRIPTS_DIR_NAME + / script_filename + ) + + +def _ensure_trailing_newline(text: str) -> str: + """End the body with exactly one newline (POSIX text-file convention).""" + if not text: + return "" + return text if text.endswith("\n") else text + "\n" diff --git a/src/everos/infra/persistence/markdown/writers/atomic_fact_writer.py b/src/everos/infra/persistence/markdown/writers/atomic_fact_writer.py new file mode 100644 index 00000000..3303d40d --- /dev/null +++ b/src/everos/infra/persistence/markdown/writers/atomic_fact_writer.py @@ -0,0 +1,58 @@ +"""AtomicFact daily-log writer — md is the SoT for atomic facts. + +Caller hands pre-built ``inline`` (``owner_id`` / ``session_id`` / +``timestamp`` / ``parent_id`` / ``sender_ids``) plus the single +``Fact`` section. The chassis manages the in-file ``entry_id`` sequence +(``af__``). ``append_entry`` / ``append_entries`` come +from :class:`BaseDailyWriter`; this subclass only declares the schema +and the per-schema frontmatter / counter hooks. +""" + +from __future__ import annotations + +import datetime as _dt +from collections.abc import Mapping +from pathlib import Path +from typing import Any + +import anyio + +from everos.component.utils.datetime import ( + get_now_with_timezone, + to_iso_format, +) +from everos.core.persistence import MarkdownReader + +from ..mds import AtomicFactDailyFrontmatter +from .base import BaseDailyWriter + + +class AtomicFactWriter(BaseDailyWriter): + """Daily-log writer for the AtomicFact schema (md = SoT).""" + + schema = AtomicFactDailyFrontmatter + + def _frontmatter_updates( + self, + scope_id: str, + date: _dt.date, + *, + next_count: int, + ) -> Mapping[str, Any] | None: + return { + "id": f"atomic_fact_log_{scope_id}_{date.isoformat()}", + "type": "atomic_fact_daily", + "file_type": "atomic_fact_daily", + "schema_version": 1, + "user_id": scope_id, + "track": "user", + "date": date.isoformat(), + "entry_count": next_count, + "last_appended_at": to_iso_format(get_now_with_timezone()), + } + + async def _current_count(self, path: Path) -> int: + if not await anyio.Path(path).is_file(): + return 0 + parsed = await MarkdownReader.read(path) + return parsed.frontmatter.get("entry_count", 0) diff --git a/src/everos/infra/persistence/markdown/writers/base.py b/src/everos/infra/persistence/markdown/writers/base.py new file mode 100644 index 00000000..a24f41d7 --- /dev/null +++ b/src/everos/infra/persistence/markdown/writers/base.py @@ -0,0 +1,301 @@ +"""Base business writer for daily-log markdown files. + +Daily-log files (memcell / episode / case / atomic_fact / foresight) +share three things: + + * scope (user-track or agent-track, derived from the schema) + * filename pattern: ``-.md`` under + ``///`` + * entry id pattern: ``__`` + +:class:`BaseDailyWriter` factors out **path resolution + entry-id +construction + today's date default**, leaving frontmatter field +maintenance (e.g. ``entry_count`` / ``last_appended_at``) to concrete +business subclasses. + +Subclass usage:: + + class _MemcellWriter(BaseDailyWriter): + schema = UserMemcellDailyFrontmatter + + writer = _MemcellWriter(layout) + eid = writer.append("u_jason", body="...") +""" + +from __future__ import annotations + +import datetime as _dt +from collections.abc import Mapping, Sequence +from pathlib import Path +from typing import Any, ClassVar + +import anyio + +from everos.component.utils.datetime import today_with_timezone +from everos.core.persistence import ( + BaseFrontmatter, + EntryId, + MarkdownReader, + MarkdownWriter, + MemoryRoot, + render_structured_entry, +) + + +class BaseDailyWriter: + """Append a new entry to today's (or a given date's) daily-log file. + + Subclasses bind a single :class:`BaseFrontmatter` subclass via the + ``schema`` ClassVar. The schema must declare ``SCOPE_DIR``, + ``ENTRY_ID_PREFIX``, ``DIR_NAME``, and ``FILE_PREFIX`` — + ``SCOPE_DIR`` is provided by inheriting :class:`UserScopedFrontmatter` + or :class:`AgentScopedFrontmatter` (or by a custom scope mixin). + + Path resolution is driven entirely by the schema's ClassVars and + the given :class:`MemoryRoot` — write, read, and addressing for a + single record kind all live in this writer + its reader twin, no + separate layout layer. + """ + + schema: ClassVar[type[BaseFrontmatter]] # subclass must declare + + def __init__( + self, + root: MemoryRoot, + *, + writer: MarkdownWriter | None = None, + ) -> None: + schema = getattr(type(self), "schema", None) + if schema is None: + raise TypeError( + f"{type(self).__name__} must declare a class-level ``schema`` attribute" + ) + for attr in ("SCOPE_DIR", "ENTRY_ID_PREFIX", "DIR_NAME", "FILE_PREFIX"): + if not getattr(schema, attr, None): + raise TypeError(f"{schema.__name__} missing ClassVar {attr!r}") + self._root = root + self._writer = writer or MarkdownWriter(root) + + # ── Public API ──────────────────────────────────────────────────────── + + async def append_entry( + self, + scope_id: str, + *, + inline: Mapping[str, object], + sections: Mapping[str, str], + date: _dt.date | None = None, + app_id: str = "default", + project_id: str = "default", + ) -> EntryId: + """Append a single rendered entry; return the freshly minted ``EntryId``. + + Unifies the per-schema ``append_entry`` previously duplicated across + :class:`AtomicFactWriter` / :class:`ForesightWriter` / + :class:`EpisodeWriter` / :class:`AgentCaseWriter`. The whole flow + (read ``entry_count``, allocate ``EntryId``, render entry body, + update frontmatter, atomic write) runs inside one per-path lock, + eliminating the read-modify-write race that previously allowed + concurrent callers to silently overwrite each other's appends. + + Args: + scope_id: ``user_id`` or ``agent_id`` (matches the schema's + scope flavour). + inline: Inline metadata (``**key**: value`` lines under the + H2 header). + sections: ``{title: body}`` blocks rendered as ``### Title`` + + body text. + date: Date bucket — defaults to today in the configured TZ. + + Returns: + The :class:`EntryId` assigned to the new entry. Caller can + use it to write downstream state (sqlite row, lance index). + """ + eids = await self.append_entries( + scope_id, + [(inline, sections)], + date=date, + app_id=app_id, + project_id=project_id, + ) + return eids[0] + + async def append_entries( + self, + scope_id: str, + items: Sequence[tuple[Mapping[str, object], Mapping[str, str]]], + *, + date: _dt.date | None = None, + app_id: str = "default", + project_id: str = "default", + ) -> list[EntryId]: + """Append ``N`` rendered entries in one locked read-modify-write cycle. + + Compared with looping :meth:`append_entry` ``N`` times, this: + + * Performs one file read + one file write instead of ``N`` of each. + * Holds the per-path lock for one short critical section. + * Updates ``frontmatter`` (``entry_count`` / ``last_appended_at``) + once at the end (no intermediate flapping). + + Order in ``items`` is the order entries land in the file (and the + order ``EntryId``s are allocated). Empty ``items`` is a no-op + that returns ``[]`` without touching the file. + + Args: + scope_id: Subject scope (user / agent id). + items: Sequence of ``(inline, sections)`` pairs. + date: Date bucket — defaults to today in the configured TZ. + + Returns: + ``N`` :class:`EntryId`s in the same order as ``items``. + """ + bucket = date or today_with_timezone() + path = self._resolve_path(scope_id, bucket, app_id, project_id) + if not items: + return [] + + async with self._writer.lock_for(path): + base_count = await self._current_count(path) + eids = [ + EntryId.next_for(self.schema.ENTRY_ID_PREFIX, bucket, base_count + i) + for i in range(len(items)) + ] + rendered = [ + ( + render_structured_entry( + header=eid.format(), + inline=inline, + sections=sections, + ), + eid, + ) + for eid, (inline, sections) in zip(eids, items, strict=True) + ] + frontmatter_updates = self._frontmatter_updates( + scope_id, bucket, next_count=base_count + len(items) + ) + await self._writer._append_entries_unlocked( # noqa: SLF001 + path, + rendered, + frontmatter_updates=frontmatter_updates, + ) + return eids + + async def append( + self, + scope_id: str, + entry_body: str, + *, + date: _dt.date | None = None, + frontmatter_updates: Mapping[str, Any] | None = None, + app_id: str = "default", + project_id: str = "default", + ) -> EntryId: + """Append a pre-rendered ``entry_body`` to the daily-log file. + + Kept for back-compat with callers that hand in fully rendered + bodies (rare — most callers should use :meth:`append_entry` and + let this class do the rendering). The whole sequence (read + ``entry_count``, allocate eid, write) runs inside the per-path + lock. + + Args: + scope_id: ``user_id`` or ``agent_id`` (matches the schema's + scope flavour). + entry_body: Content placed between the entry markers. + date: Date bucket — defaults to today in the configured TZ. + frontmatter_updates: Optional fields to merge into the file's + frontmatter (e.g. ``entry_count`` / ``last_appended_at``). + When ``None``, the subclass hook + :meth:`_frontmatter_updates` is consulted to build + default updates. + + Returns: + The :class:`EntryId` assigned to the new entry. + """ + bucket = date or today_with_timezone() + path = self._resolve_path(scope_id, bucket, app_id, project_id) + + async with self._writer.lock_for(path): + count = await self._current_count(path) + eid = EntryId.next_for(self.schema.ENTRY_ID_PREFIX, bucket, count) + + # Subclass hook: derive defaults if caller passes nothing. + if frontmatter_updates is None: + frontmatter_updates = self._frontmatter_updates( + scope_id, bucket, next_count=count + 1 + ) + + await self._writer._append_entries_unlocked( # noqa: SLF001 + path, + [(entry_body, eid)], + frontmatter_updates=frontmatter_updates, + ) + return eid + + # ── Hooks (subclass override) ───────────────────────────────────────── + + async def _current_count(self, path: Path) -> int: + """Return the current entry count for the file. + + Default: number of ```` blocks already present. + Subclasses may override to read a frontmatter field (e.g. + ``entry_count``) when they trust that field over a marker scan. + """ + if not await anyio.Path(path).is_file(): + return 0 + parsed = await MarkdownReader.read(path) + return len(parsed.entries) + + def _frontmatter_updates( + self, + scope_id: str, + date: _dt.date, + *, + next_count: int, + ) -> Mapping[str, Any] | None: + """Build the per-append frontmatter dict (subclass override). + + Called only when :meth:`append`'s ``frontmatter_updates`` is + ``None``. Default returns ``None`` (no frontmatter mutation). + Concrete business subclasses override to maintain fields like + ``id`` / ``entry_count`` / ``last_appended_at`` automatically, + so callers don't repeat themselves on every append. + """ + return None + + # ── Path API ────────────────────────────────────────────────────────── + + def path_for( + self, + scope_id: str, + date: _dt.date | None = None, + *, + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Return the daily-log path for ``scope_id`` on ``date`` (today default). + + Public counterpart of :meth:`_resolve_path` — callers (services, + scripts) should use this rather than poking at private attrs. + """ + return self._resolve_path( + scope_id, date or today_with_timezone(), app_id, project_id + ) + + # ── Internals ───────────────────────────────────────────────────────── + + def _resolve_path( + self, scope_id: str, date: _dt.date, app_id: str, project_id: str + ) -> Path: + """Build the daily-log path for ``scope_id`` on ``date``.""" + # SCOPE_DIR ("users" / "agents") names the matching MemoryRoot method, + # which prepends the / business prefix. + scope_dir = getattr(self._root, f"{self.schema.SCOPE_DIR}_dir") + return ( + scope_dir(app_id, project_id) + / scope_id + / self.schema.DIR_NAME + / f"{self.schema.FILE_PREFIX}-{date.isoformat()}.md" + ) diff --git a/src/everos/infra/persistence/markdown/writers/episode_writer.py b/src/everos/infra/persistence/markdown/writers/episode_writer.py new file mode 100644 index 00000000..3d885cf2 --- /dev/null +++ b/src/everos/infra/persistence/markdown/writers/episode_writer.py @@ -0,0 +1,69 @@ +"""Episode daily-log writer — md is the SoT for Episode memories. + +Stays in the chassis style: caller hands in pre-built ``inline`` and +``sections`` dicts plus the scope id (``owner_id``). Domain → +structured-entry shaping lives in the calling pipeline (cf. architecture +rule: ``infra`` may not import ``memory``). + +This milestone assumes well-behaved callers (no retransmit dedupe needed). +The writer just appends; the chassis manages the in-file ``entry_id`` +sequence, which is the single source of identity for an md entry. +""" + +from __future__ import annotations + +import datetime as _dt +from collections.abc import Mapping +from pathlib import Path +from typing import Any + +import anyio + +from everos.component.utils.datetime import ( + get_now_with_timezone, + to_iso_format, +) +from everos.core.persistence import MarkdownReader + +from ..mds import EpisodeDailyFrontmatter +from .base import BaseDailyWriter + + +class EpisodeWriter(BaseDailyWriter): + """Daily-log writer for the Episode schema (md = SoT). + + ``append_entry`` / ``append_entries`` come from + :class:`BaseDailyWriter`; the ``entry_id`` (``ep__``) + is the in-file identity allocated under the per-path lock. Callers + can derive a globally-unique id from ``(owner_id, entry_id)`` + without persisting any algo-side uuid. + """ + + schema = EpisodeDailyFrontmatter + + # ── Frontmatter override (entry_count + last_appended_at) ──────────── + + def _frontmatter_updates( + self, + scope_id: str, + date: _dt.date, + *, + next_count: int, + ) -> Mapping[str, Any] | None: + return { + "id": f"episode_log_{scope_id}_{date.isoformat()}", + "type": "episode_daily", + "file_type": "episode_daily", + "schema_version": 1, + "user_id": scope_id, + "track": "user", + "date": date.isoformat(), + "entry_count": next_count, + "last_appended_at": to_iso_format(get_now_with_timezone()), + } + + async def _current_count(self, path: Path) -> int: + if not await anyio.Path(path).is_file(): + return 0 + parsed = await MarkdownReader.read(path) + return parsed.frontmatter.get("entry_count", 0) diff --git a/src/everos/infra/persistence/markdown/writers/foresight_writer.py b/src/everos/infra/persistence/markdown/writers/foresight_writer.py new file mode 100644 index 00000000..bdbbe58b --- /dev/null +++ b/src/everos/infra/persistence/markdown/writers/foresight_writer.py @@ -0,0 +1,58 @@ +"""Foresight daily-log writer — md is the SoT for foresights. + +Inline carries the audit / scope + time-window fields (``owner_id`` / +``session_id`` / ``timestamp`` / ``parent_id`` / ``sender_ids`` plus +optional ``start_time`` / ``end_time`` / ``duration_days``). Sections +carry the BM25-indexed content: ``Foresight`` (required, primary +field) and optional ``Evidence`` (secondary BM25 field). +``append_entry`` / ``append_entries`` come from :class:`BaseDailyWriter`. +""" + +from __future__ import annotations + +import datetime as _dt +from collections.abc import Mapping +from pathlib import Path +from typing import Any + +import anyio + +from everos.component.utils.datetime import ( + get_now_with_timezone, + to_iso_format, +) +from everos.core.persistence import MarkdownReader + +from ..mds import ForesightDailyFrontmatter +from .base import BaseDailyWriter + + +class ForesightWriter(BaseDailyWriter): + """Daily-log writer for the Foresight schema (md = SoT).""" + + schema = ForesightDailyFrontmatter + + def _frontmatter_updates( + self, + scope_id: str, + date: _dt.date, + *, + next_count: int, + ) -> Mapping[str, Any] | None: + return { + "id": f"foresight_log_{scope_id}_{date.isoformat()}", + "type": "foresight_daily", + "file_type": "foresight_daily", + "schema_version": 1, + "user_id": scope_id, + "track": "user", + "date": date.isoformat(), + "entry_count": next_count, + "last_appended_at": to_iso_format(get_now_with_timezone()), + } + + async def _current_count(self, path: Path) -> int: + if not await anyio.Path(path).is_file(): + return 0 + parsed = await MarkdownReader.read(path) + return parsed.frontmatter.get("entry_count", 0) diff --git a/src/everos/infra/persistence/markdown/writers/profile_writer.py b/src/everos/infra/persistence/markdown/writers/profile_writer.py new file mode 100644 index 00000000..76e8d00c --- /dev/null +++ b/src/everos/infra/persistence/markdown/writers/profile_writer.py @@ -0,0 +1,127 @@ +"""ProfileWriter — upsert a single-file, fixed-name profile markdown. + +Profile storage is **single-file rewrite** (the third storage strategy +in the EverOS Markdown First spec). Each profile lives at a fixed +filename under the agent or user directory:: + + users//user.md ← user profile + users//behaviors.md ← user behaviour patterns + agents//agent.md ← agent playbook + agents//soul.md ← agent identity / values + agents//tools.md ← agent tool declarations + +Compared with :class:`SkillWriter` (directory + progressive disclosure) +and :class:`BaseDailyWriter` (per-date append + entry markers), the +profile writer is the simplest of the three: + +- **Upsert, not append.** Each ``write`` overwrites the file in full. +- **Fixed path.** Caller passes ``scope_id`` only — no ``name`` + parameter; the filename is fixed by the schema's + ``PROFILE_FILENAME`` ClassVar. +- **No business hooks.** No frontmatter merging, no entry-id + generation. The caller hands in a fully-built schema instance. + +The schema must declare two ClassVars: + +- ``SCOPE_DIR`` (``"users"`` / ``"agents"``) — inherited from + :class:`UserScopedFrontmatter` / :class:`AgentScopedFrontmatter`. +- ``PROFILE_FILENAME`` (``"user.md"`` / ``"agent.md"`` / …) — + declared on the concrete profile schema itself. + +There is no ``ProfileFrontmatter`` base class: profile schemas are +duck-typed via the two ClassVars. Subclasses inherit the scope mixin +and add ``PROFILE_FILENAME`` plus their business fields directly. +""" + +from __future__ import annotations + +from pathlib import Path + +from everos.core.persistence import BaseFrontmatter, MarkdownWriter, MemoryRoot + + +class ProfileWriter: + """Atomic writer for the single-file profile layout.""" + + def __init__( + self, + root: MemoryRoot, + *, + writer: MarkdownWriter | None = None, + ) -> None: + self._root = root + self._writer = writer or MarkdownWriter(root) + + # ── Public API ──────────────────────────────────────────────────────── + + async def write( + self, + scope_id: str, + *, + frontmatter: BaseFrontmatter, + body: str, + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Upsert ``////``. + + Args: + scope_id: ``user_id`` or ``agent_id`` (must match the + schema's scope mixin). + frontmatter: Fully-built schema instance — its ``model_dump`` + lands as the YAML head, including extra fields. + body: Profile body text. Trailing newline is normalised. + app_id: App scope segment (defaults to the ``"default"`` space). + project_id: Project scope segment (defaults to ``"default"``). + + Returns: + Absolute path of the written profile file. + """ + path = self._resolve_path(scope_id, type(frontmatter), app_id, project_id) + head_meta = frontmatter.model_dump(exclude_none=False) + return await self._writer.write_markdown( + path, + frontmatter=head_meta, + body=_ensure_trailing_newline(body), + ) + + def path_for( + self, + scope_id: str, + *, + schema: type[BaseFrontmatter], + app_id: str = "default", + project_id: str = "default", + ) -> Path: + """Return the profile path (no IO check).""" + return self._resolve_path(scope_id, schema, app_id, project_id) + + # ── Internals ───────────────────────────────────────────────────────── + + def _resolve_path( + self, + scope_id: str, + schema: type[BaseFrontmatter], + app_id: str, + project_id: str, + ) -> Path: + scope_dir = getattr(schema, "SCOPE_DIR", "") + filename = getattr(schema, "PROFILE_FILENAME", None) + if not scope_dir: + raise TypeError( + f"{schema.__name__} missing ``SCOPE_DIR`` ClassVar — " + "must inherit a scope mixin (UserScopedFrontmatter / " + "AgentScopedFrontmatter)." + ) + if not filename: + raise TypeError(f"{schema.__name__} missing ``PROFILE_FILENAME`` ClassVar.") + # SCOPE_DIR names the matching MemoryRoot method (/ prefix). + scope_root = getattr(self._root, f"{scope_dir}_dir")(app_id, project_id) + return scope_root / scope_id / filename + + +def _ensure_trailing_newline(text: str) -> str: + """End the body with exactly one newline (POSIX text-file convention).""" + if not text: + return "" + return text if text.endswith("\n") else text + "\n" diff --git a/src/everos/infra/persistence/sqlite/__init__.py b/src/everos/infra/persistence/sqlite/__init__.py new file mode 100644 index 00000000..ec2281fc --- /dev/null +++ b/src/everos/infra/persistence/sqlite/__init__.py @@ -0,0 +1,66 @@ +"""SQLite business persistence layer. + +Sits on top of :mod:`everos.core.persistence.sqlite` (engine + sessions + +``BaseTable`` + ``RepoBase``) and provides: + + * lazy process-wide engine + session-factory singletons + (:mod:`.sqlite_manager`) + * concrete table schemas under :mod:`.tables` + * concrete repository singletons under :mod:`.repos` + +External usage:: + + from everos.infra.persistence.sqlite import ( + get_engine, get_session_factory, dispose_engine, + # business tables / repos are re-exported here too — + # callers MUST go through this top-level package because + # ``infra.persistence.sqlite.**`` (sub-packages) are forbidden + # to ``service`` / ``memory`` / ``entrypoints`` by import-linter. + UnprocessedBuffer, Memcell, ConversationStatus, + unprocessed_buffer_repo, memcell_repo, conversation_status_repo, + ) + +The :class:`SqliteLifespanProvider` runs ``SQLModel.metadata.create_all`` +on app startup and ``dispose_engine`` on shutdown, so business code does +not need to manage either. +""" + +# Importing ``tables`` registers every business SQLModel in +# ``SQLModel.metadata`` so ``SqliteLifespanProvider.startup`` can +# ``create_all`` without callers having to import each model module. +from . import tables as tables # noqa: F401 +from .repos import QueueSummary as QueueSummary +from .repos import cluster_repo as cluster_repo +from .repos import conversation_status_repo as conversation_status_repo +from .repos import md_change_state_repo as md_change_state_repo +from .repos import memcell_repo as memcell_repo +from .repos import mint_cluster_id as mint_cluster_id +from .repos import unprocessed_buffer_repo as unprocessed_buffer_repo +from .sqlite_manager import dispose_engine as dispose_engine +from .sqlite_manager import get_engine as get_engine +from .sqlite_manager import get_session_factory as get_session_factory +from .tables import Cluster as Cluster +from .tables import ClusterMember as ClusterMember +from .tables import ConversationStatus as ConversationStatus +from .tables import MdChangeState as MdChangeState +from .tables import Memcell as Memcell +from .tables import UnprocessedBuffer as UnprocessedBuffer + +__all__ = [ + "Cluster", + "ClusterMember", + "ConversationStatus", + "MdChangeState", + "Memcell", + "QueueSummary", + "UnprocessedBuffer", + "cluster_repo", + "conversation_status_repo", + "dispose_engine", + "get_engine", + "get_session_factory", + "md_change_state_repo", + "memcell_repo", + "mint_cluster_id", + "unprocessed_buffer_repo", +] diff --git a/src/everos/infra/persistence/sqlite/repos/__init__.py b/src/everos/infra/persistence/sqlite/repos/__init__.py new file mode 100644 index 00000000..c11de55b --- /dev/null +++ b/src/everos/infra/persistence/sqlite/repos/__init__.py @@ -0,0 +1,23 @@ +"""Business SQLite repository singletons. + +Repository instances for business tables, wired to the process-wide +engine singleton. +""" + +from .cluster import cluster_repo as cluster_repo +from .cluster import mint_cluster_id as mint_cluster_id +from .conversation_status import conversation_status_repo as conversation_status_repo +from .md_change_state import QueueSummary as QueueSummary +from .md_change_state import md_change_state_repo as md_change_state_repo +from .memcell import memcell_repo as memcell_repo +from .unprocessed_buffer import unprocessed_buffer_repo as unprocessed_buffer_repo + +__all__ = [ + "QueueSummary", + "cluster_repo", + "conversation_status_repo", + "md_change_state_repo", + "memcell_repo", + "mint_cluster_id", + "unprocessed_buffer_repo", +] diff --git a/src/everos/infra/persistence/sqlite/repos/cluster.py b/src/everos/infra/persistence/sqlite/repos/cluster.py new file mode 100644 index 00000000..35987b80 --- /dev/null +++ b/src/everos/infra/persistence/sqlite/repos/cluster.py @@ -0,0 +1,240 @@ +"""Repository for the ``cluster`` + ``cluster_member`` pair. + +Bridges between the storage row shape and the algo-side +:class:`everalgo.clustering.Cluster` value object. Callers always work in +the algo type — this repo handles the centroid bytes round-trip, the +preview JSON round-trip, and the membership join so the algo's +``members: list[str]`` field is always fully populated on read. The +``last_ts`` field is stored as int milliseconds (matches the algo type +exactly) to keep the round-trip lossless across SQLite's tz-naive +``DateTime`` storage. + +The single ``upsert_with_members`` write path is what every cluster +strategy invokes after a merge / new-cluster decision: it stamps the +``cluster`` row (UPSERT) and reconciles the ``cluster_member`` rows +(diff-then-insert; pre-existing members are kept, new members appended) +so calls are idempotent even if a strategy retries. +""" + +from __future__ import annotations + +import json +import uuid + +import numpy as np +from everalgo.clustering import Cluster as AlgoCluster +from sqlalchemy import select +from sqlalchemy.dialects.sqlite import insert as sqlite_insert +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker + +from everos.component.utils.datetime import get_utc_now +from everos.core.persistence.sqlite import RepoBase, session_scope + +from ..sqlite_manager import get_session_factory +from ..tables import Cluster, ClusterMember + +_CENTROID_DTYPE = np.float32 + + +def mint_cluster_id() -> str: + """Mint a fresh cluster id (mirrors ``_mint_memcell_id``: ``cl_<12hex>``).""" + return f"cl_{uuid.uuid4().hex[:12]}" + + +class _ClusterRepo(RepoBase[Cluster]): + model = Cluster + + def _factory_lookup(self) -> async_sessionmaker[AsyncSession]: + return get_session_factory() + + # ── Reads ────────────────────────────────────────────────────────────── + + async def get_with_members(self, cluster_id: str) -> AlgoCluster | None: + """Fetch one cluster as a fully-hydrated algo value object. + + Returns ``None`` when no row matches ``cluster_id`` — downstream + strategies that race the writer should treat this as a transient + miss and let OME retry the run. + """ + async with session_scope(self._factory) as s: + row = await s.get(Cluster, cluster_id) + if row is None: + return None + members_by_cluster = await _load_members_by_cluster(s, [cluster_id]) + return _row_to_algo(row, members_by_cluster.get(cluster_id, [])) + + async def list_for_owner( + self, + owner_id: str, + kind: str, + *, + app_id: str = "default", + project_id: str = "default", + ) -> list[AlgoCluster]: + """All clusters for ``(app, project, owner, kind)``, as algo objects. + + Hot path for the cluster strategies (``cluster_by_geometry`` / + ``cluster_by_llm`` need the full ``existing_clusters`` list). Each + returned cluster carries its full ``members`` view, populated from + the join with :class:`ClusterMember`. Scoping by (app, project) + keeps one space's clusters from merging into another's. + """ + async with session_scope(self._factory) as s: + rows = list( + ( + await s.execute( + select(Cluster) + .where(Cluster.app_id == app_id) + .where(Cluster.project_id == project_id) + .where(Cluster.owner_id == owner_id) + .where(Cluster.kind == kind) + ) + ) + .scalars() + .all() + ) + if not rows: + return [] + ids = [r.cluster_id for r in rows] + members_by_cluster = await _load_members_by_cluster(s, ids) + return [ + _row_to_algo(row, members_by_cluster.get(row.cluster_id, [])) + for row in rows + ] + + async def find_cluster_id_for_member( + self, + member_type: str, + member_id: str, + ) -> str | None: + """Reverse lookup: ``(member_type, member_id) → cluster_id``. + + Returns ``None`` when the entity is not yet attached to any cluster. + Backed by ``ix_cluster_member_reverse`` so it is O(log N). + """ + async with session_scope(self._factory) as s: + stmt = ( + select(ClusterMember.cluster_id) + .where(ClusterMember.member_type == member_type) + .where(ClusterMember.member_id == member_id) + .limit(1) + ) + return (await s.execute(stmt)).scalar_one_or_none() + + # ── Write ────────────────────────────────────────────────────────────── + + async def upsert_with_members( + self, + algo_cluster: AlgoCluster, + *, + owner_id: str, + owner_type: str, + kind: str, + member_type: str, + app_id: str = "default", + project_id: str = "default", + ) -> None: + """Persist one algo cluster snapshot + its membership rows. + + ``algo_cluster.id`` must be non-None (caller-minted via + :func:`mint_cluster_id` for a brand-new cluster, or carried + through from a merge return). ``algo_cluster.members`` is the + full member list — the repo diffs against existing membership + and inserts only the new rows so the call is idempotent under + OME's at-least-once retry semantics. + """ + cluster_id = algo_cluster.id + if not cluster_id: + raise ValueError( + "upsert_with_members requires algo_cluster.id (mint via " + "mint_cluster_id() before passing in)." + ) + now = get_utc_now() + centroid_blob = np.asarray( + algo_cluster.centroid, dtype=_CENTROID_DTYPE + ).tobytes() + preview_json = json.dumps(list(algo_cluster.preview), ensure_ascii=False) + + async with session_scope(self._factory) as s: + cluster_stmt = ( + sqlite_insert(Cluster) + .values( + cluster_id=cluster_id, + app_id=app_id, + project_id=project_id, + owner_id=owner_id, + owner_type=owner_type, + kind=kind, + centroid_blob=centroid_blob, + count=algo_cluster.count, + last_ts_ms=algo_cluster.last_ts, + preview_json=preview_json, + ) + .on_conflict_do_update( + index_elements=["cluster_id"], + set_={ + "centroid_blob": centroid_blob, + "count": algo_cluster.count, + "last_ts_ms": algo_cluster.last_ts, + "preview_json": preview_json, + }, + ) + ) + await s.execute(cluster_stmt) + + existing = set( + ( + await s.execute( + select(ClusterMember.member_id).where( + ClusterMember.cluster_id == cluster_id + ) + ) + ) + .scalars() + .all() + ) + new_member_rows = [ + ClusterMember( + cluster_id=cluster_id, + member_id=mid, + member_type=member_type, + added_ts=now, + ) + for mid in algo_cluster.members + if mid not in existing + ] + if new_member_rows: + s.add_all(new_member_rows) + await s.commit() + + +def _row_to_algo(row: Cluster, members: list[str]) -> AlgoCluster: + centroid = np.frombuffer(row.centroid_blob, dtype=_CENTROID_DTYPE) + preview = json.loads(row.preview_json) if row.preview_json else [] + return AlgoCluster( + id=row.cluster_id, + centroid=centroid, + count=row.count, + last_ts=row.last_ts_ms, + preview=preview, + members=list(members), + ) + + +async def _load_members_by_cluster( + session: AsyncSession, + cluster_ids: list[str], +) -> dict[str, list[str]]: + """One query → ``{cluster_id: [member_id, ...]}`` (insertion order).""" + stmt = ( + select(ClusterMember.cluster_id, ClusterMember.member_id) + .where(ClusterMember.cluster_id.in_(cluster_ids)) + .order_by(ClusterMember.added_ts) + ) + buckets: dict[str, list[str]] = {} + for cluster_id, member_id in (await session.execute(stmt)).all(): + buckets.setdefault(cluster_id, []).append(member_id) + return buckets + + +cluster_repo = _ClusterRepo() diff --git a/src/everos/infra/persistence/sqlite/repos/conversation_status.py b/src/everos/infra/persistence/sqlite/repos/conversation_status.py new file mode 100644 index 00000000..cdc74cad --- /dev/null +++ b/src/everos/infra/persistence/sqlite/repos/conversation_status.py @@ -0,0 +1,90 @@ +"""Repository for ``conversation_status`` — singleton bound to ``sqlite_manager``. + +Upsert helpers for the (session_id, track) window pointer. +""" + +from __future__ import annotations + +import datetime as dt + +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker + +from everos.core.persistence.sqlite import RepoBase, session_scope + +from ..sqlite_manager import get_session_factory +from ..tables import ConversationStatus + + +class _ConversationStatusRepo(RepoBase[ConversationStatus]): + model = ConversationStatus + + def _factory_lookup(self) -> async_sessionmaker[AsyncSession]: + return get_session_factory() + + async def touch_last_message_ts( + self, + session_id: str, + track: str, + ts: dt.datetime, + *, + app_id: str = "default", + project_id: str = "default", + ) -> None: + """Upsert (app, project, session, track); set ``last_message_ts``.""" + await self._upsert( + session_id, track, app_id=app_id, project_id=project_id, last_message_ts=ts + ) + + async def touch_last_memcell_ts( + self, + session_id: str, + track: str, + ts: dt.datetime, + *, + app_id: str = "default", + project_id: str = "default", + ) -> None: + """Upsert (app, project, session, track); set ``last_memcell_ts``.""" + await self._upsert( + session_id, track, app_id=app_id, project_id=project_id, last_memcell_ts=ts + ) + + async def _upsert( + self, + session_id: str, + track: str, + *, + app_id: str = "default", + project_id: str = "default", + last_message_ts: dt.datetime | None = None, + last_memcell_ts: dt.datetime | None = None, + ) -> None: + async with session_scope(self._factory) as s: + stmt = select(ConversationStatus).where( + ConversationStatus.app_id == app_id, + ConversationStatus.project_id == project_id, + ConversationStatus.session_id == session_id, + ConversationStatus.track == track, + ) + existing = (await s.execute(stmt)).scalars().first() + if existing is None: + s.add( + ConversationStatus( + app_id=app_id, + project_id=project_id, + session_id=session_id, + track=track, + last_message_ts=last_message_ts, + last_memcell_ts=last_memcell_ts, + ) + ) + else: + if last_message_ts is not None: + existing.last_message_ts = last_message_ts + if last_memcell_ts is not None: + existing.last_memcell_ts = last_memcell_ts + await s.commit() + + +conversation_status_repo = _ConversationStatusRepo() diff --git a/src/everos/infra/persistence/sqlite/repos/md_change_state.py b/src/everos/infra/persistence/sqlite/repos/md_change_state.py new file mode 100644 index 00000000..b0082927 --- /dev/null +++ b/src/everos/infra/persistence/sqlite/repos/md_change_state.py @@ -0,0 +1,434 @@ +"""Repository for ``md_change_state`` — cascade work queue. + +Sole writer of the table. The worker, watcher, scanner, and CLI all +go through this repo so the state-machine invariants (``processing`` +claim semantics, retryable flag lifecycle) live in one place. + +LSN ordering is **best-effort**, not strictly monotonic across +concurrent writers: :meth:`upsert` derives ``lsn = MAX(lsn) + 1`` +which is a classic read-modify-write that two parallel writers could +race on (BEGIN DEFERRED leaves the SELECT half unprotected; cross- +process this is even more visible). The table schema does **not** +declare ``lsn UNIQUE`` and no caller depends on strict monotonicity — +the worker uses ``ORDER BY lsn LIMIT N`` for fairness only, and a +collision merely reorders two rows by a few ms; both rows are still +processed and the next upsert bumps the counter past the duplicate. +If a future feature needs strict monotonicity (e.g. CDC / audit log), +revisit by giving ``upsert`` its own ``BEGIN IMMEDIATE`` transaction. + +Status values: + +- ``pending`` — visible to the worker. +- ``processing`` — internal claim state (one worker is on it). +- ``done`` — handler succeeded. +- ``failed`` — handler exhausted retries or hit unrecoverable error + (see ``retryable`` for the eligibility flag). +""" + +from __future__ import annotations + +import dataclasses + +from sqlalchemy import func, select, update +from sqlalchemy.dialects.sqlite import insert as sqlite_insert +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker + +from everos.component.utils.datetime import get_utc_now +from everos.core.persistence.sqlite import RepoBase, session_scope + +from ..sqlite_manager import get_session_factory +from ..tables import MdChangeState + + +@dataclasses.dataclass(frozen=True) +class QueueSummary: + """Aggregate counts for ``cascade status`` CLI output. + + ``pending`` includes the internal ``processing`` rows so the public + state machine (12 doc §6) stays three-valued. + """ + + pending: int + """Rows the worker hasn't completed yet (includes ``processing``).""" + + done: int + """Rows landed successfully.""" + + failed_retryable: int + """``status='failed' AND retryable=TRUE`` — eligible for + ``cascade fix --apply`` re-enqueue.""" + + failed_permanent: int + """``status='failed' AND retryable=FALSE`` — requires the user to + edit the md and re-save.""" + + max_lsn: int + """Largest ``lsn`` ever assigned; 0 if the table is empty.""" + + last_processed_lsn: int + """Largest ``lsn`` whose row has reached a terminal state + (``done`` or ``failed``); 0 if no terminal rows yet.""" + + +class _MdChangeStateRepo(RepoBase[MdChangeState]): + model = MdChangeState + + def _factory_lookup(self) -> async_sessionmaker[AsyncSession]: + return get_session_factory() + + # ── Writers: watcher / scanner / CLI sync ────────────────────────────── + + async def upsert( + self, + md_path: str, + *, + kind: str, + change_type: str, + mtime: float, + ) -> int: + """Enqueue or re-enqueue ``md_path``; return the assigned LSN. + + Behaviour: + + - **New row** → insert with ``status='pending'``, + ``lsn = MAX(lsn) + 1``. + - **Existing row** → bump ``last_changed_at``, refresh + ``kind`` / ``change_type`` / ``mtime``, reset status back to + ``pending``, zero ``retry_count`` / ``error`` / ``retryable``, + and assign a fresh ``MAX(lsn) + 1`` so the worker re-processes + this path *after* anything queued in between. + + The fresh LSN on re-enqueue is the property that lets the worker + rely on ``ORDER BY lsn`` for ordering without losing fairness + when a file flickers in and out of the queue. The ``MAX(lsn)+1`` + derivation is best-effort under concurrent writers — see module + docstring for the trade-off. + """ + now = get_utc_now() + async with session_scope(self._factory) as s: + new_lsn = await _next_lsn(s) + stmt = ( + sqlite_insert(MdChangeState) + .values( + md_path=md_path, + kind=kind, + change_type=change_type, + mtime=mtime, + first_seen_at=now, + last_changed_at=now, + lsn=new_lsn, + status="pending", + retryable=None, + last_attempt_at=None, + retry_count=0, + error=None, + ) + .on_conflict_do_update( + index_elements=["md_path"], + set_={ + "kind": kind, + "change_type": change_type, + "mtime": mtime, + "last_changed_at": now, + "lsn": new_lsn, + "status": "pending", + "retryable": None, + "last_attempt_at": None, + "retry_count": 0, + "error": None, + }, + ) + ) + await s.execute(stmt) + await s.commit() + return new_lsn + + async def force_enqueue(self, md_path: str, kind: str) -> int: + """`cascade sync --path` entry: re-enqueue regardless of status. + + Semantically the same as :meth:`upsert` with ``change_type + ='modified'``; named separately because the CLI flow has no + watcher / scanner event to attribute the row to. + """ + return await self.upsert( + md_path, + kind=kind, + change_type="modified", + mtime=0.0, + ) + + # ── Worker claim ─────────────────────────────────────────────────────── + + async def claim_one(self, md_path: str) -> MdChangeState | None: + """Atomically transition one row ``pending → processing``. + + Implements the worker's claim contract: only the caller whose + ``UPDATE`` returns ``rowcount == 1`` "owns" the row and should + run the handler. All other concurrent callers get ``None`` and + must move on (no exception — claim contention is not an error). + """ + now = get_utc_now() + async with session_scope(self._factory) as s: + result = await s.execute( + update(MdChangeState) + .where(MdChangeState.md_path == md_path) + .where(MdChangeState.status == "pending") + .values(status="processing", last_attempt_at=now) + ) + await s.commit() + if result.rowcount != 1: + return None + row = await s.get(MdChangeState, md_path) + return row + + async def claim_pending_batch(self, limit: int = 100) -> list[MdChangeState]: + """Claim up to ``limit`` pending rows in LSN order. + + Returns the claimed rows (now ``status='processing'``); empty + list if none were pending. Sibling workers / processes may race + on the same prefix — the per-row ``WHERE status='pending'`` + filter ensures each row lands in exactly one batch. + """ + if limit <= 0: + return [] + now = get_utc_now() + async with session_scope(self._factory) as s: + picks = ( + ( + await s.execute( + select(MdChangeState.md_path) + .where(MdChangeState.status == "pending") + .order_by(MdChangeState.lsn) + .limit(limit) + ) + ) + .scalars() + .all() + ) + if not picks: + return [] + update_result = await s.execute( + update(MdChangeState) + .where(MdChangeState.md_path.in_(picks)) + .where(MdChangeState.status == "pending") + .values(status="processing", last_attempt_at=now) + ) + await s.commit() + if update_result.rowcount == 0: + return [] + rows = ( + ( + await s.execute( + select(MdChangeState) + .where(MdChangeState.md_path.in_(picks)) + .where(MdChangeState.status == "processing") + .order_by(MdChangeState.lsn) + ) + ) + .scalars() + .all() + ) + return list(rows) + + # ── Worker result reporting ──────────────────────────────────────────── + + async def mark_done(self, md_path: str) -> None: + """Transition the row to ``done`` after a successful handler run. + + Guarded by ``WHERE status='processing'`` so the call is a no-op + if a concurrent :meth:`upsert` (watcher / scanner re-enqueue) + has flipped the row back to ``pending`` while the worker was + running the handler. In that case the next + :meth:`claim_pending_batch` drain re-runs the handler against + the latest md state — losing the stale ``done`` write rather + than the new ``pending`` is the correct trade. + """ + now = get_utc_now() + async with session_scope(self._factory) as s: + await s.execute( + update(MdChangeState) + .where(MdChangeState.md_path == md_path) + .where(MdChangeState.status == "processing") + .values( + status="done", + last_attempt_at=now, + error=None, + retryable=None, + ) + ) + await s.commit() + + async def mark_failed( + self, + md_path: str, + *, + retryable: bool, + error: str, + new_retry_count: int, + ) -> None: + """Transition the row to ``failed`` with the given diagnostic. + + Args: + md_path: The row's primary key. + retryable: ``True`` for transient failures (HTTP 5xx, + connection reset, 429) — ``cascade fix --apply`` will + re-enqueue. ``False`` for unrecoverable failures + (YAML parse, schema mismatch) — needs user edit. + error: Truncated failure message for ``cascade fix`` output. + new_retry_count: The retry count *after* this attempt (the + caller knows whether it was a retry or the final + attempt). + + Guarded by ``WHERE status='processing'`` for the same reason as + :meth:`mark_done` — a concurrent re-enqueue must win over a + terminal write tied to a stale claim. + """ + now = get_utc_now() + async with session_scope(self._factory) as s: + # Same guard as ``mark_done``: only flip ``processing → failed``. + # A concurrent watcher / scanner upsert may have reset the row + # back to ``pending`` (file changed during processing) — in + # that case the failure verdict is stale and we let the next + # drain re-attempt against the new md state instead of + # stamping ``failed`` over the live pending row. + await s.execute( + update(MdChangeState) + .where(MdChangeState.md_path == md_path) + .where(MdChangeState.status == "processing") + .values( + status="failed", + retryable=retryable, + last_attempt_at=now, + error=error, + retry_count=new_retry_count, + ) + ) + await s.commit() + + # ── Startup recovery ─────────────────────────────────────────────────── + + async def recover_orphan_processing(self) -> int: + """Reset every ``processing`` row to ``pending``; return the count. + + Cascade runs single-process today, so any row in ``processing`` + when the orchestrator boots is leftover from a prior crash + (the worker died between :meth:`claim_pending_batch` and + ``mark_done`` / ``mark_failed``). Idempotent — no rows in + ``processing`` is a clean no-op. + """ + async with session_scope(self._factory) as s: + result = await s.execute( + update(MdChangeState) + .where(MdChangeState.status == "processing") + .values(status="pending", last_attempt_at=None) + ) + await s.commit() + return int(result.rowcount or 0) + + # ── CLI fix / status ─────────────────────────────────────────────────── + + async def list_failed(self) -> list[MdChangeState]: + """Return every ``status='failed'`` row, oldest LSN first. + + Drives the ``cascade fix`` (no ``--apply``) preview table — the + CLI splits the result by ``retryable`` into two sections. + """ + async with session_scope(self._factory) as s: + rows = ( + ( + await s.execute( + select(MdChangeState) + .where(MdChangeState.status == "failed") + .order_by(MdChangeState.lsn) + ) + ) + .scalars() + .all() + ) + return list(rows) + + async def reset_retryable_to_pending(self) -> int: + """`cascade fix --apply` engine: re-enqueue every retryable row. + + Affects only ``status='failed' AND retryable=TRUE``. Rows with + ``retryable=FALSE`` are left untouched — they need the user to + edit the md and re-save (the scanner / watcher will pick up the + change and re-enqueue them naturally). + + Returns the number of rows transitioned. + """ + now = get_utc_now() + async with session_scope(self._factory) as s: + result = await s.execute( + update(MdChangeState) + .where(MdChangeState.status == "failed") + .where(MdChangeState.retryable.is_(True)) + .values( + status="pending", + retryable=None, + retry_count=0, + error=None, + last_changed_at=now, + ) + ) + await s.commit() + return int(result.rowcount or 0) + + async def queue_summary(self) -> QueueSummary: + """Aggregate the table for the ``cascade status`` CLI.""" + async with session_scope(self._factory) as s: + pending = await _count_where( + s, MdChangeState.status.in_(["pending", "processing"]) + ) + done = await _count_where(s, MdChangeState.status == "done") + failed_retryable = await _count_where( + s, + (MdChangeState.status == "failed") + & (MdChangeState.retryable.is_(True)), + ) + failed_permanent = await _count_where( + s, + (MdChangeState.status == "failed") + & (MdChangeState.retryable.is_(False)), + ) + max_lsn_stmt = select(func.coalesce(func.max(MdChangeState.lsn), 0)) + max_lsn = int((await s.execute(max_lsn_stmt)).scalar_one()) + last_processed_lsn = int( + ( + await s.execute( + select(func.coalesce(func.max(MdChangeState.lsn), 0)).where( + MdChangeState.status.in_(["done", "failed"]) + ) + ) + ).scalar_one() + ) + return QueueSummary( + pending=pending, + done=done, + failed_retryable=failed_retryable, + failed_permanent=failed_permanent, + max_lsn=max_lsn, + last_processed_lsn=last_processed_lsn, + ) + + +async def _next_lsn(session: AsyncSession) -> int: + """Pick the next global LSN (``MAX(lsn) + 1``). + + Called inside the same write transaction as the UPSERT so SQLite's + WAL writer serialisation guarantees no two writers see the same + ``MAX``. Empty table returns 1. + """ + result = await session.execute( + select(func.coalesce(func.max(MdChangeState.lsn), 0)) + ) + return int(result.scalar_one()) + 1 + + +async def _count_where(session: AsyncSession, predicate: object) -> int: + """``SELECT COUNT(*) WHERE `` returning a Python int.""" + stmt = select(func.count()).select_from(MdChangeState).where(predicate) # type: ignore[arg-type] + return int((await session.execute(stmt)).scalar_one()) + + +md_change_state_repo = _MdChangeStateRepo() diff --git a/src/everos/infra/persistence/sqlite/repos/memcell.py b/src/everos/infra/persistence/sqlite/repos/memcell.py new file mode 100644 index 00000000..1f875472 --- /dev/null +++ b/src/everos/infra/persistence/sqlite/repos/memcell.py @@ -0,0 +1,52 @@ +"""Repository for ``memcell`` table — singleton bound to ``sqlite_manager``. + +Pure persistence: callers build the SQLModel ``Memcell`` rows (including +``message_ids_json`` / ``sender_ids_json``) and hand them in. The pipeline +is responsible for mapping algo-side messages back to everos +``message_id`` because algo's ``Message`` does not carry per-message +identifiers. +""" + +from __future__ import annotations + +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker + +from everos.core.persistence.sqlite import RepoBase, session_scope + +from ..sqlite_manager import get_session_factory +from ..tables import Memcell + + +class _MemcellRepo(RepoBase[Memcell]): + model = Memcell + + def _factory_lookup(self) -> async_sessionmaker[AsyncSession]: + return get_session_factory() + + async def insert_many(self, rows: list[Memcell]) -> list[Memcell]: + """Insert MemCell rows in one transaction; rows are constructed by caller.""" + async with session_scope(self._factory) as s: + s.add_all(rows) + await s.commit() + for r in rows: + await s.refresh(r) + return rows + + async def find_by_ids(self, memcell_ids: list[str]) -> list[Memcell]: + """Bulk fetch rows by primary key list — preserves caller order. + + Used by offline strategies that pull every memcell in a cluster + (membership lives in :class:`ClusterMember` and is supplied to + the strategy via :class:`everalgo.clustering.Cluster.members`). + """ + if not memcell_ids: + return [] + async with session_scope(self._factory) as s: + stmt = select(Memcell).where(Memcell.memcell_id.in_(memcell_ids)) + rows = list((await s.execute(stmt)).scalars().all()) + by_id = {r.memcell_id: r for r in rows} + return [by_id[mid] for mid in memcell_ids if mid in by_id] + + +memcell_repo = _MemcellRepo() diff --git a/src/everos/infra/persistence/sqlite/repos/unprocessed_buffer.py b/src/everos/infra/persistence/sqlite/repos/unprocessed_buffer.py new file mode 100644 index 00000000..91c58121 --- /dev/null +++ b/src/everos/infra/persistence/sqlite/repos/unprocessed_buffer.py @@ -0,0 +1,83 @@ +"""Repository for ``unprocessed_buffer`` — chat message accumulator. + +Singleton bound to the process-wide ``sqlite_manager`` session factory. + +Pure SQLModel persistence: row ↔ domain conversion lives in +``everos.memory.extract.pipeline`` (the only caller that needs it). + +Exposes: + +- :meth:`list_for_track` — load all rows of (session_id, track), ordered by ts. +- :meth:`replace` — atomically swap all rows of (session_id, track) for a + freshly-built list of :class:`UnprocessedBuffer` rows. +""" + +from __future__ import annotations + +from sqlalchemy import delete, select +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker + +from everos.core.persistence.sqlite import RepoBase, session_scope + +from ..sqlite_manager import get_session_factory +from ..tables import UnprocessedBuffer + + +class _UnprocessedBufferRepo(RepoBase[UnprocessedBuffer]): + model = UnprocessedBuffer + + def _factory_lookup(self) -> async_sessionmaker[AsyncSession]: + return get_session_factory() + + async def list_for_track( + self, + session_id: str, + track: str, + *, + app_id: str = "default", + project_id: str = "default", + ) -> list[UnprocessedBuffer]: + """Return all rows of (app, project, session, track), ts asc.""" + async with session_scope(self._factory) as s: + stmt = ( + select(UnprocessedBuffer) + .where( + UnprocessedBuffer.app_id == app_id, + UnprocessedBuffer.project_id == project_id, + UnprocessedBuffer.session_id == session_id, + UnprocessedBuffer.track == track, + ) + .order_by(UnprocessedBuffer.timestamp.asc()) # type: ignore[union-attr] + ) + return list((await s.execute(stmt)).scalars().all()) + + async def replace( + self, + session_id: str, + track: str, + rows: list[UnprocessedBuffer], + *, + app_id: str = "default", + project_id: str = "default", + ) -> None: + """Atomically rewrite all rows of (app, project, session, track). + + Delete-then-insert in one transaction. Empty ``rows`` clears the slice. + The delete is scoped to the same (app, project) as the incoming rows so + one space's buffer never wipes another's. + """ + async with session_scope(self._factory) as s: + await s.execute( + delete(UnprocessedBuffer).where( + UnprocessedBuffer.app_id == app_id, + UnprocessedBuffer.project_id == project_id, + UnprocessedBuffer.session_id == session_id, + UnprocessedBuffer.track == track, + ) + ) + if rows: + s.add_all(rows) + await s.commit() + + +unprocessed_buffer_repo = _UnprocessedBufferRepo() diff --git a/src/everos/infra/persistence/sqlite/sqlite_manager.py b/src/everos/infra/persistence/sqlite/sqlite_manager.py new file mode 100644 index 00000000..f1ff2616 --- /dev/null +++ b/src/everos/infra/persistence/sqlite/sqlite_manager.py @@ -0,0 +1,63 @@ +"""SQLite engine + session-factory singletons (lazy + process-wide). + +The single place that owns the SQLite **runtime state**: the async +SQLAlchemy engine and the session factory bound to it. Built lazily on +first :func:`get_engine` / :func:`get_session_factory` call from +:func:`everos.config.load_settings` + :meth:`MemoryRoot.default`. The +:class:`SqliteLifespanProvider` calls :func:`dispose_engine` on shutdown +to drain the connection pool; in scripts you can call it manually. +""" + +from __future__ import annotations + +from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, async_sessionmaker + +from everos.config import load_settings +from everos.core.observability.logging import get_logger +from everos.core.persistence import ( + MemoryRoot, + create_session_factory, + create_system_engine, +) + +logger = get_logger(__name__) + +_engine: AsyncEngine | None = None +_session_factory: async_sessionmaker[AsyncSession] | None = None + + +def get_engine() -> AsyncEngine: + """Return the process-wide async SQLAlchemy engine. + + Built on first call from ``MemoryRoot.default()`` and ``Settings.sqlite``. + Subsequent calls return the same instance. + """ + global _engine + if _engine is None: + settings = load_settings() + memory_root = MemoryRoot.default() + memory_root.ensure() + _engine = create_system_engine(memory_root.system_db, settings.sqlite) + logger.info( + "sqlite_engine_built", + db_path=str(memory_root.system_db), + ) + return _engine + + +def get_session_factory() -> async_sessionmaker[AsyncSession]: + """Return the process-wide async session factory.""" + global _session_factory + if _session_factory is None: + _session_factory = create_session_factory(get_engine()) + return _session_factory + + +async def dispose_engine() -> None: + """Dispose the engine + connection pool. Idempotent.""" + global _engine, _session_factory + if _engine is not None: + await _engine.dispose() + logger.info("sqlite_engine_disposed") + _engine = None + _session_factory = None diff --git a/src/everos/infra/persistence/sqlite/tables/__init__.py b/src/everos/infra/persistence/sqlite/tables/__init__.py new file mode 100644 index 00000000..c4c734ef --- /dev/null +++ b/src/everos/infra/persistence/sqlite/tables/__init__.py @@ -0,0 +1,24 @@ +"""Business SQLModel table schemas. + +Each business table lives in its own module here (e.g. ``memcell.py``, +``unprocessed_buffer.py``). The package ``__init__`` re-exports them so +``SQLModel.metadata.create_all`` (run by +:class:`everos.core.lifespan.SqliteLifespanProvider` at startup) sees +every registered table. +""" + +from .cluster import Cluster as Cluster +from .cluster import ClusterMember as ClusterMember +from .conversation_status import ConversationStatus as ConversationStatus +from .md_change_state import MdChangeState as MdChangeState +from .memcell import Memcell as Memcell +from .unprocessed_buffer import UnprocessedBuffer as UnprocessedBuffer + +__all__ = [ + "Cluster", + "ClusterMember", + "ConversationStatus", + "MdChangeState", + "Memcell", + "UnprocessedBuffer", +] diff --git a/src/everos/infra/persistence/sqlite/tables/cluster.py b/src/everos/infra/persistence/sqlite/tables/cluster.py new file mode 100644 index 00000000..d56e73e1 --- /dev/null +++ b/src/everos/infra/persistence/sqlite/tables/cluster.py @@ -0,0 +1,99 @@ +"""``cluster`` — persisted snapshot of one ``everalgo.clustering.Cluster``. + +Mirrors the algo-side frozen value object (centroid + count + last_ts + +preview) plus everos engineering metadata (``owner_id`` / ``owner_type`` +/ ``kind``) so a single SQLite table can hold both the user-memory cluster +track (episode embeddings) and the agent-case cluster track (task_intent +embeddings). The ``members`` field on the algo type is persisted in the +sibling :class:`ClusterMember` table to keep the relation queryable from +both directions (forward by ``cluster_id``, reverse by ``(member_type, +member_id)``). +""" + +from __future__ import annotations + +from sqlalchemy import Index, LargeBinary + +from everos.component.utils.datetime import UtcDatetime +from everos.core.persistence.sqlite import BaseTable, Field +from everos.core.persistence.sqlite.base import UtcDateTimeColumn + + +class Cluster(BaseTable, table=True): + """One row per cluster. PK ``cluster_id`` (``cl_<12hex>``).""" + + __tablename__ = "cluster" # type: ignore[assignment] + __table_args__ = ( + # List all clusters for one (app, project, owner, kind) on each strategy + # invocation; scope-first composite so clustering never mixes spaces. + Index("ix_cluster_owner_kind", "app_id", "project_id", "owner_id", "kind"), + ) + + cluster_id: str = Field(primary_key=True) + """Caller-minted opaque id (algo type carries it through verbatim). + Format: ``cl_<12 hex chars>`` to mirror :func:`memcell._mint_memcell_id`.""" + + app_id: str = Field(default="default") + project_id: str = Field(default="default") + """App / project scope segments. The aggregation key is + ``(app_id, project_id, owner_id, kind)`` so a cluster set never spans + two spaces.""" + + owner_id: str = Field(index=True) + """``user_id`` (kind=``user_memory``) or ``agent_id`` (kind=``agent_case``).""" + + owner_type: str + """``"user"`` or ``"agent"`` — redundant with ``kind`` today but kept + explicit so future kinds (e.g. tenant-level) can plug in without a + schema change.""" + + kind: str + """``"user_memory"`` (episode-vector cluster, drives profile extraction) + or ``"agent_case"`` (task_intent-vector cluster, drives skill extraction).""" + + centroid_blob: bytes = Field(sa_type=LargeBinary) + """``np.float32`` centroid serialised via ``ndarray.tobytes()``. The + repo round-trips bytes ↔ ``np.ndarray`` so callers see the algo type.""" + + count: int + """Number of members merged into this cluster (algo-maintained).""" + + last_ts_ms: int + """Most recent member's timestamp as Unix epoch milliseconds — matches + :attr:`everalgo.clustering.Cluster.last_ts` exactly so no lossy + datetime ↔ int conversion is needed across the storage boundary.""" + + preview_json: str + """JSON-encoded ``list[str]`` — short text samples used by + :func:`cluster_by_llm` ranking. Repo round-trips JSON ↔ list.""" + + +class ClusterMember(BaseTable, table=True): + """One row per (cluster, entity) link. + + Forward lookup (``cluster_id → list[member_id]``) is the algo-side + ``Cluster.members`` view. Reverse lookup (``(member_type, member_id) + → cluster_id``) is served by the composite index below — needed when + a downstream consumer holds an entity id and wants its cluster. + + ``member_type`` is informational on the row (the parent ``Cluster.kind`` + already disambiguates), but kept explicit so the reverse index can be + a single composite (member_type, member_id) without joining back. + """ + + __tablename__ = "cluster_member" # type: ignore[assignment] + __table_args__ = (Index("ix_cluster_member_reverse", "member_type", "member_id"),) + + cluster_id: str = Field(primary_key=True, foreign_key="cluster.cluster_id") + """Parent cluster id.""" + + member_id: str = Field(primary_key=True) + """``memcell_id`` (member_type=``memcell``) or md entry_id + (member_type=``case``) — the entity grouped into this cluster.""" + + member_type: str + """``"memcell"`` or ``"case"``. Echoes the parent cluster's ``kind`` + domain but kept on the row so the reverse index is self-contained.""" + + added_ts: UtcDatetime = Field(sa_type=UtcDateTimeColumn) + """When this entity was first attached to the cluster.""" diff --git a/src/everos/infra/persistence/sqlite/tables/conversation_status.py b/src/everos/infra/persistence/sqlite/tables/conversation_status.py new file mode 100644 index 00000000..c8563c3d --- /dev/null +++ b/src/everos/infra/persistence/sqlite/tables/conversation_status.py @@ -0,0 +1,38 @@ +"""``conversation_status`` — window pointer per (app, project, session, track). + +The window pointer is scoped by ``app_id`` / ``project_id`` so the same +``session_id`` may recur in different spaces without colliding; those two +segments lead the composite ``UniqueConstraint``. +""" + +from __future__ import annotations + +from sqlalchemy import UniqueConstraint + +from everos.component.utils.datetime import UtcDatetime +from everos.core.persistence.sqlite import BaseTable, Field +from everos.core.persistence.sqlite.base import UtcDateTimeColumn + + +class ConversationStatus(BaseTable, table=True): + """One row per (app, project, session, track). Tracks latest msg / memcell ts.""" + + __tablename__ = "conversation_status" # type: ignore[assignment] + __table_args__ = ( + UniqueConstraint( + "app_id", + "project_id", + "session_id", + "track", + name="uq_conversation_status_session_track", + ), + ) + + id: int | None = Field(default=None, primary_key=True) + app_id: str = Field(default="default") + project_id: str = Field(default="default") + """App / project scope segments (default ``"default"``).""" + session_id: str = Field(index=True) + track: str + last_message_ts: UtcDatetime | None = Field(default=None, sa_type=UtcDateTimeColumn) + last_memcell_ts: UtcDatetime | None = Field(default=None, sa_type=UtcDateTimeColumn) diff --git a/src/everos/infra/persistence/sqlite/tables/md_change_state.py b/src/everos/infra/persistence/sqlite/tables/md_change_state.py new file mode 100644 index 00000000..07ac5d91 --- /dev/null +++ b/src/everos/infra/persistence/sqlite/tables/md_change_state.py @@ -0,0 +1,119 @@ +"""``md_change_state`` — cascade work queue. + +One row per markdown path. Both watcher (real-time fsevents) and +scanner (periodic sweep) UPSERT into this table; the worker consumes +``pending`` rows in ``lsn`` order, transitions them through an +internal ``processing`` claim state, and lands them in ``done`` or +``failed`` (with a ``retryable`` flag). + +Schema sourced from ``12_cascade_design.md`` §4.1 + decisions DD-3 … +DD-12; the four indexes below are required by ``13_cascade_design.md`` +§7 status / fix queries. +""" + +from __future__ import annotations + +from sqlalchemy import Index, text + +from everos.component.utils.datetime import UtcDatetime, get_utc_now +from everos.core.persistence.sqlite import BaseTable, Field +from everos.core.persistence.sqlite.base import UtcDateTimeColumn + + +class MdChangeState(BaseTable, table=True): + """One row per markdown path; UPSERT-driven work queue for cascade. + + The public state machine is the 3-tuple ``pending`` / ``done`` / + ``failed`` (12 doc §6). ``processing`` is an internal claim state + used by :meth:`MdChangeStateRepo.claim_one` and rolled back into + ``pending`` for CLI / status output (16 doc §4.2 — DD-12 keeps the + public surface clean). + """ + + __tablename__ = "md_change_state" # type: ignore[assignment] + __table_args__ = ( + # Worker scans pending rows in lsn order — partial index drops + # done/failed rows from the b-tree and keeps it tight. + Index( + "idx_md_change_pending", + "status", + "lsn", + sqlite_where=text("status = 'pending'"), + ), + # `cascade fix --apply` only ever touches failed + retryable=TRUE + # rows — partial index makes that pass essentially O(retryable). + Index( + "idx_md_change_retryable", + "status", + "retryable", + sqlite_where=text("status = 'failed' AND retryable = 1"), + ), + # Scanner reverse-reconcile (disk → state) compares mtime. + Index("idx_md_change_mtime", "mtime"), + # `cascade status` aggregates by kind. + Index("idx_md_change_kind", "kind"), + ) + + md_path: str = Field(primary_key=True) + """Path relative to the memory-root (e.g. ``users/u_jason/ + episodes/episode-2026-05-12.md``). Every reverse-link anchors here.""" + + kind: str = Field(nullable=False, index=True) + """Kind registry name (e.g. ``"episode"``); worker dispatches the + matching handler.""" + + change_type: str = Field(nullable=False) + """``"added"`` | ``"modified"`` | ``"deleted"``. A hint for the + worker — handler re-derives truth from the actual file state.""" + + mtime: float = Field(default=0.0, nullable=False) + """File mtime captured when the row was last UPSERTed. Scanner + compares this against the on-disk mtime to identify dirty paths.""" + + first_seen_at: UtcDatetime = Field( + default_factory=get_utc_now, sa_type=UtcDateTimeColumn + ) + """When the path was first enqueued.""" + + last_changed_at: UtcDatetime = Field( + default_factory=get_utc_now, sa_type=UtcDateTimeColumn + ) + """Most recent UPSERT timestamp (re-stamped on every re-enqueue).""" + + lsn: int = Field(nullable=False, index=True) + """Global monotonic sequence (``MAX(lsn) + 1`` per UPSERT). Worker + processes pending rows in ascending lsn order; the gap between + ``MAX(lsn)`` and the last processed lsn is the queue lag.""" + + status: str = Field(default="pending", nullable=False, index=True) + """Lifecycle: + + - ``"pending"`` — waiting for the worker. + - ``"processing"`` — claimed by a worker (internal; CLI rolls into + pending for display). + - ``"done"`` — handler completed successfully. + - ``"failed"`` — handler exhausted retries or hit an + unrecoverable error (see :attr:`retryable`). + """ + + retryable: bool | None = Field(default=None) + """Meaningful only when ``status='failed'``. + + - ``TRUE`` — RecoverableError exhausted MAX_RETRY; ``cascade fix + --apply`` will re-enqueue this row (pending, retry_count reset). + - ``FALSE`` — UnrecoverableError (malformed YAML, schema error + etc.); requires editing the md and re-saving. + - ``NULL`` — not a failed row (pending / processing / done). + """ + + last_attempt_at: UtcDatetime | None = Field(default=None, sa_type=UtcDateTimeColumn) + """Timestamp of the most recent worker attempt (success or + failure).""" + + retry_count: int = Field(default=0, nullable=False) + """Number of retries the worker has *actually issued* (the first + attempt does not count). Reaches MAX_RETRY (default 3) before the + row transitions to ``failed`` with ``retryable=TRUE``.""" + + error: str | None = Field(default=None) + """Most recent failure message (truncated upstream if needed).""" diff --git a/src/everos/infra/persistence/sqlite/tables/memcell.py b/src/everos/infra/persistence/sqlite/tables/memcell.py new file mode 100644 index 00000000..4a7149c3 --- /dev/null +++ b/src/everos/infra/persistence/sqlite/tables/memcell.py @@ -0,0 +1,55 @@ +"""``memcell`` — metadata + payload archive for boundary-detected MemCells. + +Holds ``message_ids_json`` / ``sender_ids_json`` (JSON arrays of audit +ids) plus ``payload_json`` — the full :class:`everalgo.types.MemCell` +serialised via ``model_dump_json``. The payload is what +``unprocessed_buffer`` cannot keep (boundary's delete-then-insert clears +the staging slice once messages fold into a cell): downstream offline +strategies that need the raw chat messages (e.g. profile extraction) +deserialise the payload back into an algo ``MemCell``. Episode markdown +still carries the LLM-synthesised narrative; ``payload_json`` is the +chat-stream archive that narrative was distilled from. +""" + +from __future__ import annotations + +from sqlalchemy import Index + +from everos.component.utils.datetime import UtcDatetime +from everos.core.persistence.sqlite import BaseTable, Field +from everos.core.persistence.sqlite.base import UtcDateTimeColumn + + +class Memcell(BaseTable, table=True): + """One row per MemCell. PK ``memcell_id`` (uuid4).""" + + __tablename__ = "memcell" # type: ignore[assignment] + __table_args__ = ( + # Scope-first composite: app/project partition the lookup before the + # session window so cross-(app, project) rows never share an index slot. + Index( + "ix_memcell_session", + "app_id", + "project_id", + "session_id", + "track", + "timestamp", + ), + ) + + memcell_id: str = Field(primary_key=True) + app_id: str = Field(default="default") + project_id: str = Field(default="default") + """App / project scope segments. Default to ``"default"`` so the column is + always populated; callers in a non-default space pass real ids.""" + session_id: str = Field(index=True) + track: str + raw_type: str + message_ids_json: str + sender_ids_json: str + payload_json: str + """``MemCell.model_dump_json()`` — the full algo-side MemCell (items = + chat messages / tool calls) serialised at boundary time so offline + strategies can deserialise it back into an algo MemCell long after + ``unprocessed_buffer`` has dropped the staging rows.""" + timestamp: UtcDatetime = Field(sa_type=UtcDateTimeColumn) diff --git a/src/everos/infra/persistence/sqlite/tables/unprocessed_buffer.py b/src/everos/infra/persistence/sqlite/tables/unprocessed_buffer.py new file mode 100644 index 00000000..1e563817 --- /dev/null +++ b/src/everos/infra/persistence/sqlite/tables/unprocessed_buffer.py @@ -0,0 +1,52 @@ +"""``unprocessed_buffer`` — chat-stream messages waiting on boundary detection. + +Schema property: presence in the table = pending; absence = consumed. +There is no ``consumed`` column. Pipeline uses ``replace(session, track, +remaining)`` to atomically rewrite the (session, track) slice each turn. +""" + +from __future__ import annotations + +from sqlalchemy import Index + +from everos.component.utils.datetime import UtcDatetime +from everos.core.persistence.sqlite import BaseTable, Field +from everos.core.persistence.sqlite.base import UtcDateTimeColumn + + +class UnprocessedBuffer(BaseTable, table=True): + """One row per unprocessed message. PK ``message_id``.""" + + __tablename__ = "unprocessed_buffer" # type: ignore[assignment] + __table_args__ = ( + # Scope-first composite: app/project partition the (session, track) + # staging slice so different spaces never share a buffer window. + Index( + "ix_unprocessed_buffer_lookup", + "app_id", + "project_id", + "session_id", + "track", + "timestamp", + ), + ) + + message_id: str = Field(primary_key=True) + app_id: str = Field(default="default") + project_id: str = Field(default="default") + """App / project scope segments (default ``"default"``).""" + session_id: str = Field(index=True) + track: str = Field(index=True) + sender_id: str + sender_name: str | None = None + role: str + timestamp: UtcDatetime = Field(sa_type=UtcDateTimeColumn) + # JSON-serialised raw ContentItem list (mirrors src_old + # RawMessage.content_items). Keeps the original multimodal payload + # available so a future parser can reach back to image / audio / etc. + content_items_json: str + # Derived plain-text concatenation of ``type=text`` entries — what + # downstream LLM-facing extractors and md writer consume today. + text: str + tool_calls_json: str | None = None + tool_call_id: str | None = None diff --git a/src/everos/memory/__init__.py b/src/everos/memory/__init__.py new file mode 100644 index 00000000..a5ded6fe --- /dev/null +++ b/src/everos/memory/__init__.py @@ -0,0 +1,50 @@ +"""Domain layer: the business core. + +Defines memory-domain models and implements write / read / sync / +prompt management capabilities. + +External usage: + from everos.memory import ( + CanonicalMessage, IngestResult, PipelineOutcome, + ToolCall, MemCell, Episode, AlgoMessage, + ) + +Path resolution and addressing of markdown records live with the infra +writer/reader pair (``BaseDailyWriter`` / ``BaseDailyReader`` / +``SkillWriter`` / ``SkillReader`` / ``ProfileWriter`` / +``ProfileReader``) — see :mod:`everos.infra.persistence.markdown`. The +domain layer here is reserved for actual business logic (extract / +cascade / search / prompt_slots / evolution). +""" + +from .models import AgentCase as AgentCase +from .models import AlgoAgentCase as AlgoAgentCase +from .models import AlgoAtomicFact as AlgoAtomicFact +from .models import AlgoEpisode as AlgoEpisode +from .models import AlgoForesight as AlgoForesight +from .models import AlgoMessage as AlgoMessage +from .models import AtomicFact as AtomicFact +from .models import CanonicalMessage as CanonicalMessage +from .models import Episode as Episode +from .models import Foresight as Foresight +from .models import IngestResult as IngestResult +from .models import MemCell as MemCell +from .models import PipelineOutcome as PipelineOutcome +from .models import ToolCall as ToolCall + +__all__ = [ + "AgentCase", + "AlgoAgentCase", + "AlgoAtomicFact", + "AlgoEpisode", + "AlgoForesight", + "AlgoMessage", + "AtomicFact", + "CanonicalMessage", + "Episode", + "Foresight", + "IngestResult", + "MemCell", + "PipelineOutcome", + "ToolCall", +] diff --git a/src/everos/memory/cascade/__init__.py b/src/everos/memory/cascade/__init__.py new file mode 100644 index 00000000..1ad44f66 --- /dev/null +++ b/src/everos/memory/cascade/__init__.py @@ -0,0 +1,36 @@ +"""Markdown-to-index sync daemon (cascade). + +Watcher (realtime fs events) + scanner (periodic walk) + worker +(claim/drain) keep LanceDB in sync with the md files under the memory +root. Cascade is the *only* path that writes LanceDB; service / entry +points just write md and trust the daemon to catch up. + +Public surface — what lifespan providers / CLI commands import: + +- :class:`CascadeOrchestrator` — composite owner; start / stop / sync. +- :class:`CascadeConfig` — construction-time tuning knobs. +- :class:`RecoverableError` / :class:`UnrecoverableError` — handler + contract for retry classification. +- :data:`KIND_REGISTRY` / :func:`match_kind` — kind dispatch (also + used by CLI ``cascade sync --path`` to resolve a single file's kind). +""" + +from .errors import CascadeError as CascadeError +from .errors import RecoverableError as RecoverableError +from .errors import UnrecoverableError as UnrecoverableError +from .orchestrator import CascadeConfig as CascadeConfig +from .orchestrator import CascadeOrchestrator as CascadeOrchestrator +from .registry import KIND_REGISTRY as KIND_REGISTRY +from .registry import KindSpec as KindSpec +from .registry import match_kind as match_kind + +__all__ = [ + "KIND_REGISTRY", + "CascadeConfig", + "CascadeError", + "CascadeOrchestrator", + "KindSpec", + "RecoverableError", + "UnrecoverableError", + "match_kind", +] diff --git a/src/everos/memory/cascade/errors.py b/src/everos/memory/cascade/errors.py new file mode 100644 index 00000000..cfbbec71 --- /dev/null +++ b/src/everos/memory/cascade/errors.py @@ -0,0 +1,30 @@ +"""Cascade error hierarchy — drives the worker's retry classification. + +The worker decides ``mark_failed(retryable=True/False)`` purely by +exception class: + +- :class:`RecoverableError` → transient (HTTP 5xx, network, embedding + rate limit). Worker retries up to ``MAX_RETRY`` inline, then marks + ``retryable=TRUE`` so ``cascade fix --apply`` can re-enqueue. +- :class:`UnrecoverableError` → fatal (YAML parse, missing required + field, schema mismatch). Worker stops immediately and marks + ``retryable=FALSE`` — only a user edit to the md will unstick it. +- Anything else → treated as :class:`UnrecoverableError`. The worker + catches ``Exception`` defensively so an unexpected failure never + hangs the daemon, but the diagnostic message carries the original + type for triage. +""" + +from __future__ import annotations + + +class CascadeError(Exception): + """Root of the cascade error tree.""" + + +class RecoverableError(CascadeError): + """Transient failure — worker should retry then mark retryable.""" + + +class UnrecoverableError(CascadeError): + """Fatal failure — needs a user edit before re-running.""" diff --git a/src/everos/memory/cascade/handlers/__init__.py b/src/everos/memory/cascade/handlers/__init__.py new file mode 100644 index 00000000..f75e3d51 --- /dev/null +++ b/src/everos/memory/cascade/handlers/__init__.py @@ -0,0 +1,30 @@ +"""Cascade handlers — one per kind, sharing the :class:`Handler` chassis. + +Four daily-log handlers (episode / atomic_fact / foresight / +agent_case) inherit :class:`BaseDailyLogHandler` for the shared +read / diff / upsert / delete loop; the per-kind subclass only +declares its repo binding and ``_build_row`` mapping. ``agent_skill`` +and ``user_profile`` stand alone — they're single-file kinds (no +entries, no per-entry diff), so they implement :class:`Handler` +directly and own their reconcile loop. +""" + +from .agent_case import AgentCaseHandler as AgentCaseHandler +from .agent_skill import AgentSkillHandler as AgentSkillHandler +from .atomic_fact import AtomicFactHandler as AtomicFactHandler +from .base import Handler as Handler +from .base import HandlerDeps as HandlerDeps +from .episode import EpisodeHandler as EpisodeHandler +from .foresight import ForesightHandler as ForesightHandler +from .user_profile import UserProfileHandler as UserProfileHandler + +__all__ = [ + "AgentCaseHandler", + "AgentSkillHandler", + "AtomicFactHandler", + "EpisodeHandler", + "ForesightHandler", + "Handler", + "HandlerDeps", + "UserProfileHandler", +] diff --git a/src/everos/memory/cascade/handlers/_common.py b/src/everos/memory/cascade/handlers/_common.py new file mode 100644 index 00000000..3ba8eb04 --- /dev/null +++ b/src/everos/memory/cascade/handlers/_common.py @@ -0,0 +1,120 @@ +"""Helpers shared by every daily-log handler. + +The inline-block parsers undo +:func:`everos.core.persistence.markdown.entries._render_value` — +the writer stringifies values with ``str()`` (and list / tuple as +``[a, b, c]``), so callers that need a typed value back must reverse +the rendering here. Each helper raises ``ValueError`` on malformed +input so the worker classifies the failure as unrecoverable (no point +retrying a YAML / format error). +""" + +from __future__ import annotations + +import datetime as _dt +import hashlib +from pathlib import PurePosixPath + +from everos.component.utils.datetime import from_iso_format +from everos.core.persistence import app_id_from_dir, project_id_from_dir + + +def content_sha256(named_parts: dict[str, str]) -> str: + """Canonical SHA-256 over a ``{key: value}`` mapping. + + Each handler declares its ``content_change_keys`` (a tuple of + ``"section:Name"`` / ``"inline:name"`` / handler-specific + identifiers) and collects the corresponding values from the parsed + md. This helper canonicalises that mapping (sorted key, joined + ``key=value`` per line) and hashes — so two runs over the same + content always produce the same digest regardless of declaration + order. + + Audit / scope fields (owner_id / session_id / timestamp / etc.) + are intentionally NOT included by any handler — changes to them + don't propagate to LanceDB and don't waste an embedding call. + """ + canonical = "\n".join(f"{k}={named_parts[k]}" for k in sorted(named_parts)) + return hashlib.sha256(canonical.encode("utf-8")).hexdigest() + + +def resolve_owner(frontmatter: dict[str, object], md_path: str) -> tuple[str, str]: + """Pull ``owner_id`` / ``owner_type`` from the frontmatter + path. + + Layout is ``////...``, so ``owner_type`` is + encoded by the **scope segment** (path index 2: ``users`` → ``"user"``, + ``agents`` → ``"agent"``) — NOT the path prefix, which now leads with + ``/``. ``owner_id`` rides on the user_id / agent_id + frontmatter field. + """ + parts = PurePosixPath(md_path).parts + scope_dir = parts[2] if len(parts) > 2 else "" + if scope_dir == "agents": + owner_type = "agent" + owner_id = str(frontmatter.get("agent_id", "")) + else: + owner_type = "user" + owner_id = str(frontmatter.get("user_id", "")) + return owner_id, owner_type + + +def resolve_scope(md_path: str) -> tuple[str, str]: + """Recover ``(app_id, project_id)`` from an md path's leading two segments. + + Inverse of the writer's layout ``///...``: the + reserved dir names ``default_app`` / ``default_project`` map back to the + ``"default"`` id (see :func:`everos.core.persistence.app_id_from_dir`). + A path missing the prefix degrades to the default space rather than + raising — cascade should never see one, since every writer emits the + prefix, but a defensive fallback keeps a malformed path from crashing + the worker. + """ + parts = PurePosixPath(md_path).parts + app_dir = parts[0] if len(parts) > 0 else "default_app" + project_dir = parts[1] if len(parts) > 1 else "default_project" + return app_id_from_dir(app_dir), project_id_from_dir(project_dir) + + +def require_iso_timestamp(raw: str | None, *, field: str = "timestamp") -> _dt.datetime: + """Parse a required ISO-8601 timestamp from an inline value.""" + if not raw: + raise ValueError(f"entry inline is missing required {field!r}") + return from_iso_format(raw.strip()) + + +def optional_iso_timestamp(raw: str | None) -> _dt.datetime | None: + """Parse an optional ISO-8601 timestamp; ``None`` / empty → ``None``.""" + if not raw or not raw.strip(): + return None + return from_iso_format(raw.strip()) + + +def optional_int(raw: str | None) -> int | None: + """Parse an optional int from an inline value (``"7"`` → ``7``).""" + if not raw or not raw.strip(): + return None + return int(raw.strip()) + + +def require_float(raw: str | None, *, field: str) -> float: + """Parse a required float inline value.""" + if not raw or not raw.strip(): + raise ValueError(f"entry inline is missing required {field!r}") + return float(raw.strip()) + + +def parse_inline_list(raw: str) -> list[str]: + """Parse ``"[a, b, c]"`` back into ``["a", "b", "c"]``. + + Mirrors :func:`everos.core.persistence.markdown.entries._render_value` + output shape (list / tuple inline values). Empty / malformed + payloads yield an empty list — the writer never emits literal + commas inside ids, so simple split-on-comma is safe. + """ + text = raw.strip() + if not (text.startswith("[") and text.endswith("]")): + return [] + body = text[1:-1].strip() + if not body: + return [] + return [tok.strip() for tok in body.split(",") if tok.strip()] diff --git a/src/everos/memory/cascade/handlers/_daily_log_base.py b/src/everos/memory/cascade/handlers/_daily_log_base.py new file mode 100644 index 00000000..ba005d35 --- /dev/null +++ b/src/everos/memory/cascade/handlers/_daily_log_base.py @@ -0,0 +1,202 @@ +"""Shared diff / dispatch loop for every daily-log cascade handler. + +The 4 daily-log kinds (episode / atomic_fact / foresight / agent_case) +all do the same three-way reconcile against LanceDB: + +1. Parse the md into structured entries. +2. Fetch existing rows for the same ``md_path``. +3. ``content_sha256`` mismatch → tokenise + embed + upsert; no diff + → skip; row gone from md → delete. + +The hash covers **only content-bearing fields** declared by each +subclass in :attr:`content_change_keys` (a tuple of ``"section:Name"`` +/ ``"inline:name"`` strings). Audit inline fields (owner_id / +session_id / timestamp / parent_id / sender_ids) are NOT in the hash +— editing them does NOT propagate to LanceDB and does NOT waste an +embed call. + +Subclasses bind their ``kind`` / ``lance_repo`` / ``content_change_keys`` +as ClassVars and override :meth:`_build_row` to do the per-kind field +mapping. Everything else — read, diff, embed call, upsert, delete — +lives here. +""" + +from __future__ import annotations + +import abc +import asyncio +import dataclasses +from typing import Any, ClassVar + +from everos.core.persistence import MarkdownReader, StructuredEntry + +from ..types import HandlerOutcome +from ._common import content_sha256 as compute_content_sha256 +from ._common import resolve_owner, resolve_scope +from .base import Handler + + +@dataclasses.dataclass(frozen=True) +class ParsedEntry: + """One md-side entry, parsed and digested for diff. + + Held immutable so the diff loop can hash / compare freely. + """ + + entry_id: str + structured: StructuredEntry + content_sha256: str + + +class BaseDailyLogHandler(Handler): + """Common chassis for the 4 daily-log cascade handlers. + + Subclass requirements: + + - :attr:`kind` (ClassVar[str]) — registry name, surfaces in logs. + - :attr:`lance_repo` (ClassVar) — the LanceDB repo singleton for + this kind (must expose ``find_where`` / ``upsert`` / ``delete`` + / ``delete_by_md_path``). + - :attr:`content_change_keys` (ClassVar[tuple[str, ...]]) — the + subset of inline + section fields whose changes should trigger + re-upsert + re-embed. Each key is ``"section:Name"`` or + ``"inline:name"``. + - :meth:`_build_row` (override) — turn a :class:`ParsedEntry` plus + common context (owner_id / owner_type / md_path) into a typed + LanceDB row. Tokenisation + embedding live in the subclass. + """ + + kind: ClassVar[str] = "" + lance_repo: ClassVar[Any] = None + content_change_keys: ClassVar[tuple[str, ...]] = () + + def _content_sha256(self, structured: StructuredEntry) -> str: + """Hash the content-bearing subset of one entry's inline+sections. + + Walks :attr:`content_change_keys`, projects each key onto its + ``section:`` / ``inline:`` source on the structured entry, and + canonicalises into a digest. Unknown key prefixes raise + :class:`ValueError` so a typo on a subclass surfaces immediately. + """ + parts: dict[str, str] = {} + for key in self.content_change_keys: + kind, _, name = key.partition(":") + if kind == "section": + parts[key] = structured.sections.get(name) or "" + elif kind == "inline": + parts[key] = structured.inline.get(name) or "" + else: + raise ValueError( + f"{type(self).__name__}.content_change_keys has unsupported " + f"prefix in {key!r}; expected 'section:' or 'inline:'" + ) + return compute_content_sha256(parts) + + async def handle_added_or_modified(self, md_path: str) -> HandlerOutcome: + absolute = self._deps.memory_root.root / md_path + parsed = await MarkdownReader.read(absolute) + new_entries = [ + ParsedEntry( + entry_id=entry.id, + structured=entry.as_structured(), + content_sha256=self._content_sha256(entry.as_structured()), + ) + for entry in parsed.entries + ] + new_by_id = {e.entry_id: e for e in new_entries} + + existing = await self.lance_repo.find_where( + f"md_path = '{_q(md_path)}'", + limit=10_000, + ) + existing_by_entry = {row.entry_id: row for row in existing} + + owner_id, owner_type = resolve_owner(parsed.frontmatter, md_path) + app_id, project_id = resolve_scope(md_path) + + to_build: list[ParsedEntry] = [] + skipped = 0 + for entry in new_entries: + prior = existing_by_entry.get(entry.entry_id) + if prior is not None and prior.content_sha256 == entry.content_sha256: + skipped += 1 + continue + to_build.append(entry) + + # Build rows concurrently; ``_build_row`` calls ``embedder.embed`` + # which is already capped by a process-global ``asyncio.Semaphore`` + # at ``max_concurrent`` (see OpenAIEmbeddingProvider). This unblocks + # per-md-path embedding pipelining without uncapping embed-API rate. + to_upsert: list[Any] = ( + list( + await asyncio.gather( + *( + self._build_row( + owner_id=owner_id, + owner_type=owner_type, + app_id=app_id, + project_id=project_id, + md_path=md_path, + entry=entry, + ) + for entry in to_build + ) + ) + ) + if to_build + else [] + ) + + to_delete_ids = [ + row.entry_id for row in existing if row.entry_id not in new_by_id + ] + + if to_upsert: + await self.lance_repo.upsert(to_upsert) + if to_delete_ids: + in_list = ", ".join(f"'{eid}'" for eid in to_delete_ids) + await self.lance_repo.delete( + f"md_path = '{_q(md_path)}' AND entry_id IN ({in_list})" + ) + + return HandlerOutcome( + md_path=md_path, + kind=self.kind, + upserted=len(to_upsert), + deleted=len(to_delete_ids), + skipped=skipped, + ) + + async def handle_deleted(self, md_path: str) -> HandlerOutcome: + deleted = await self.lance_repo.delete_by_md_path(md_path) + return HandlerOutcome( + md_path=md_path, + kind=self.kind, + upserted=0, + deleted=deleted, + skipped=0, + ) + + @abc.abstractmethod + async def _build_row( + self, + *, + owner_id: str, + owner_type: str, + app_id: str = "default", + project_id: str = "default", + md_path: str, + entry: ParsedEntry, + ) -> Any: + """Subclass: build the typed LanceDB row for one parsed entry. + + ``app_id`` / ``project_id`` carry the path-derived scope; the base + always supplies them (via :func:`resolve_scope`). They default to + ``"default"`` so white-box callers exercising only the field mapping + can omit them. + """ + + +def _q(text: str) -> str: + """Defensive SQL-quote escape (mirrors lancedb chassis convention).""" + return text.replace("'", "''") diff --git a/src/everos/memory/cascade/handlers/agent_case.py b/src/everos/memory/cascade/handlers/agent_case.py new file mode 100644 index 00000000..453c053d --- /dev/null +++ b/src/everos/memory/cascade/handlers/agent_case.py @@ -0,0 +1,88 @@ +"""AgentCase cascade handler — md → LanceDB ``agent_case`` table. + +Same daily-log shape as the user-track kinds, but the path lives +under ``agents/`` and there is no ``sender_ids`` column (an agent +case has a single executing agent — the ``owner_id`` itself). + +md contract: + +``inline`` block: + +- ``owner_id``: ``agent_id``. +- ``session_id``: conversation scope. +- ``timestamp``: ISO-8601 string. +- ``parent_id``: source memcell id (``parent_type`` defaults to + ``"memcell"``). +- ``quality_score``: float ``0.0–1.0`` (LLM-emitted). + +``sections``: + +- ``TaskIntent``: short intent statement (embedded + BM25 primary). +- ``Approach``: step-by-step approach (BM25 secondary field via + ``approach_tokens``, **not** fed to the embedder — the + retrieval anchor is task_intent). +- ``KeyInsight`` (optional): pivotal strategy note (display only). +""" + +from __future__ import annotations + +from everos.infra.persistence.lancedb import AgentCase, ParentType, agent_case_repo + +from ._common import require_float, require_iso_timestamp +from ._daily_log_base import BaseDailyLogHandler, ParsedEntry + + +class AgentCaseHandler(BaseDailyLogHandler): + """Cascade handler for ``agents//.cases/agent_case-*.md``.""" + + kind = "agent_case" + lance_repo = agent_case_repo + content_change_keys = ( + "section:TaskIntent", + "section:Approach", + "section:KeyInsight", + "inline:quality_score", + ) + """Includes quality_score (semantic score, not audit). Audit + inline (owner_id / session_id / timestamp / parent_id) is excluded.""" + + async def _build_row( + self, + *, + owner_id: str, + owner_type: str, + app_id: str = "default", + project_id: str = "default", + md_path: str, + entry: ParsedEntry, + ) -> AgentCase: + s = entry.structured + task_intent = s.sections.get("TaskIntent", "").strip() + approach = s.sections.get("Approach", "").strip() + key_insight = (s.sections.get("KeyInsight") or "").strip() or None + intent_tokens = self._deps.tokenizer.tokenize(task_intent) + approach_tokens = self._deps.tokenizer.tokenize(approach) + vector = await self._deps.embedder.embed(task_intent) + return AgentCase( + id=f"{owner_id}_{entry.entry_id}", + entry_id=entry.entry_id, + owner_id=owner_id, + owner_type=owner_type, + app_id=app_id, + project_id=project_id, + session_id=s.inline.get("session_id", ""), + timestamp=require_iso_timestamp(s.inline.get("timestamp")), + parent_type=s.inline.get("parent_type") or ParentType.MEMCELL.value, + parent_id=s.inline.get("parent_id", ""), + quality_score=require_float( + s.inline.get("quality_score"), field="quality_score" + ), + task_intent=task_intent, + task_intent_tokens=" ".join(intent_tokens), + approach=approach, + approach_tokens=" ".join(approach_tokens), + key_insight=key_insight, + md_path=md_path, + content_sha256=entry.content_sha256, + vector=vector, + ) diff --git a/src/everos/memory/cascade/handlers/agent_skill.py b/src/everos/memory/cascade/handlers/agent_skill.py new file mode 100644 index 00000000..12046d52 --- /dev/null +++ b/src/everos/memory/cascade/handlers/agent_skill.py @@ -0,0 +1,218 @@ +"""AgentSkill cascade handler — md → LanceDB ``agent_skill`` table. + +Unlike the daily-log kinds, AgentSkill is a *named single-file entity*: + +- One ``SKILL.md`` per skill directory, replaced wholesale on edit + (no entry markers, no per-entry diff). +- Sibling ``references/*.md`` files are concatenated into the + ``content`` column so the BM25 / vector indices cover the full + context, not just the main file's body. +- ``scripts/*`` is intentionally excluded — they're runnable + artifacts, not retrievable text. + +Diff strategy: SHA-256 over the **content-bearing fields only** of +the skill (see :attr:`AgentSkillHandler.content_change_keys`) is +stored on the LanceDB row as ``content_sha256``. A re-process call +recomputes the digest from the current md + references and short- +circuits when it matches the prior row — no re-embed, no re-upsert. + +md contract: + +- ``SKILL.md`` frontmatter: :class:`AgentSkillFrontmatter` fields — + ``name`` / ``description`` / ``confidence`` / ``maturity_score`` / + ``source_case_ids`` / ``cluster_id``. +- Body: skill instructions (the "how" text). +- ``references/.md``: extra context, concatenated in + filesystem-listing order (sorted by filename for determinism). + +Embedding source: ``name + "\\n" + description`` (mirrors opensource +AgentSkillExtractor — name belongs in the retrieval anchor too, +not just description). +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any, ClassVar + +import anyio + +from everos.core.persistence import MarkdownReader +from everos.infra.persistence.lancedb import AgentSkill, agent_skill_repo +from everos.infra.persistence.markdown import AgentSkillFrontmatter + +from ..types import HandlerOutcome +from ._common import content_sha256 as compute_content_sha256 +from ._common import resolve_scope +from .base import Handler + + +class AgentSkillHandler(Handler): + """Cascade handler for + ``agents//skills/skill_/SKILL.md`` + ``references/*.md``.""" + + kind = "agent_skill" + lance_repo: ClassVar[Any] = agent_skill_repo + """Exposed for ``CascadeWorker._optimize_touched_kinds`` — see the + matching note on :class:`UserProfileHandler`.""" + + content_change_keys: ClassVar[tuple[str, ...]] = ( + "frontmatter:name", + "frontmatter:description", + "frontmatter:confidence", + "frontmatter:maturity_score", + "body", + "references_content", + ) + """Skill identity (name) + retrieval anchor (description) + scores + + the full content (body + references). Lineage / clustering fields + (``source_case_ids`` / ``cluster_id``) are excluded — they're + derivable from upstream and rarely change on their own.""" + + async def handle_added_or_modified(self, md_path: str) -> HandlerOutcome: + absolute = self._deps.memory_root.root / md_path + parsed = await MarkdownReader.read(absolute) + fm = parsed.frontmatter + + owner_id = str(fm.get("agent_id", "")) + name = str(fm.get("name", "")) + if not owner_id or not name: + raise ValueError( + f"agent_skill md is missing required frontmatter " + f"(agent_id / name): {md_path}" + ) + app_id, project_id = resolve_scope(md_path) + + # Concatenate SKILL.md body with every references/*.md sibling. + skill_dir = absolute.parent + references_dir = skill_dir / AgentSkillFrontmatter.SKILL_REFERENCES_DIR_NAME + references_content = await _concat_references(references_dir) + content = _join_body_and_references(parsed.body, references_content) + + description = str(fm.get("description", "")) + confidence = float(fm.get("confidence", 0.0)) + maturity_score = float(fm.get("maturity_score", 0.0)) + + # Content digest — covers what feeds the row's BM25 / vector / scores. + digest = compute_content_sha256( + { + "frontmatter:name": name, + "frontmatter:description": description, + "frontmatter:confidence": str(confidence), + "frontmatter:maturity_score": str(maturity_score), + "body": parsed.body.rstrip(), + "references_content": references_content, + } + ) + + # Skip when an existing row has the same digest. + skill_id = f"{owner_id}_{name}" + prior = await agent_skill_repo.get_by_id(skill_id) + if prior is not None and prior.content_sha256 == digest: + return HandlerOutcome( + md_path=md_path, + kind=self.kind, + upserted=0, + deleted=0, + skipped=1, + ) + + # Sweep row-level orphans on this md. + # + # ``skill_id`` is derived from ``frontmatter.name``: if the user + # renames the skill, the new row gets a fresh id and the old row + # would otherwise linger forever (the path-level reconciler only + # wipes when the *file* is gone, not when an in-file edit changes + # the derived id). When ``prior`` is None we don't yet know if + # this is a first-time create or a rename, so we check the md + # path explicitly. When ``prior`` is found (name unchanged, + # content drifted), no other id can match the same md_path under + # cascade's contract — skip the lookup. + deleted = 0 + if prior is None: + orphan_clause = f"md_path = '{_q(md_path)}' AND id != '{_q(skill_id)}'" + orphans = await agent_skill_repo.find_where(orphan_clause, limit=1000) + deleted = len(orphans) + if deleted: + await agent_skill_repo.delete(orphan_clause) + + description_tokens = " ".join(self._deps.tokenizer.tokenize(description)) + content_tokens = " ".join(self._deps.tokenizer.tokenize(content)) + # Embedding source: name + description joined (opensource parity). + embed_text = "\n".join(s for s in [name, description] if s) + vector = await self._deps.embedder.embed(embed_text) + + row = AgentSkill( + id=skill_id, + owner_id=owner_id, + owner_type="agent", + app_id=app_id, + project_id=project_id, + name=name, + description=description, + description_tokens=description_tokens, + content=content, + content_tokens=content_tokens, + confidence=confidence, + maturity_score=maturity_score, + source_case_ids=list(fm.get("source_case_ids", [])), + cluster_id=fm.get("cluster_id"), # type: ignore[arg-type] + md_path=md_path, + content_sha256=digest, + vector=vector, + ) + await agent_skill_repo.upsert([row]) + return HandlerOutcome( + md_path=md_path, + kind=self.kind, + upserted=1, + deleted=deleted, + skipped=0, + ) + + async def handle_deleted(self, md_path: str) -> HandlerOutcome: + deleted = await agent_skill_repo.delete_by_md_path(md_path) + return HandlerOutcome( + md_path=md_path, + kind=self.kind, + upserted=0, + deleted=deleted, + skipped=0, + ) + + +async def _concat_references(references_dir: Path) -> str: + """Read every ``*.md`` under ``references_dir`` and concatenate. + + Returns the empty string when the directory is missing. Files are + sorted by name for deterministic output so re-runs over the same + on-disk state produce identical ``content_tokens`` / embedding + inputs and identical content_sha256 digests. + """ + apath = anyio.Path(references_dir) + if not await apath.is_dir(): + return "" + pieces: list[str] = [] + paths = sorted( + [p async for p in apath.iterdir() if p.name.endswith(".md")], + key=lambda p: p.name, + ) + for path in paths: + text = await path.read_text(encoding="utf-8") + pieces.append(text.rstrip()) + return "\n\n".join(pieces) + + +def _join_body_and_references(body: str, references: str) -> str: + """Glue SKILL.md body + reference concat into a single content blob.""" + body = body.rstrip() + if not references: + return body + if not body: + return references + return f"{body}\n\n{references}" + + +def _q(value: str) -> str: + """Defensive SQL-quote escape (mirrors lancedb chassis convention).""" + return value.replace("'", "''") diff --git a/src/everos/memory/cascade/handlers/atomic_fact.py b/src/everos/memory/cascade/handlers/atomic_fact.py new file mode 100644 index 00000000..5331e163 --- /dev/null +++ b/src/everos/memory/cascade/handlers/atomic_fact.py @@ -0,0 +1,68 @@ +"""AtomicFact cascade handler — md → LanceDB ``atomic_fact`` table. + +md contract (md writer + cascade share this shape): + +``inline`` block: + +- ``owner_id``: ``user_id`` (atomic facts are user-track today). +- ``session_id``: conversation scope. +- ``timestamp``: ISO-8601 string. +- ``parent_id``: source memcell id (``parent_type`` is always + ``"memcell"`` — emitted via the schema default). +- ``sender_ids`` (optional): ``[u_a, u_b]`` rendering. + +``sections``: + +- ``Fact``: the atomic-fact sentence — fed to the embedder and the + tokenizer (``fact_tokens`` BM25 field). +""" + +from __future__ import annotations + +from everos.infra.persistence.lancedb import AtomicFact, ParentType, atomic_fact_repo + +from ._common import parse_inline_list, require_iso_timestamp +from ._daily_log_base import BaseDailyLogHandler, ParsedEntry + + +class AtomicFactHandler(BaseDailyLogHandler): + """Cascade handler for ``users//.atomic_facts/atomic_fact-*.md``.""" + + kind = "atomic_fact" + lance_repo = atomic_fact_repo + content_change_keys = ("section:Fact",) + """Only ``Fact`` matters — it's both the embedded text AND the + BM25 source. Audit inline is excluded.""" + + async def _build_row( + self, + *, + owner_id: str, + owner_type: str, + app_id: str = "default", + project_id: str = "default", + md_path: str, + entry: ParsedEntry, + ) -> AtomicFact: + s = entry.structured + text = s.sections.get("Fact", "").strip() + tokens = self._deps.tokenizer.tokenize(text) + vector = await self._deps.embedder.embed(text) + return AtomicFact( + id=f"{owner_id}_{entry.entry_id}", + entry_id=entry.entry_id, + owner_id=owner_id, + owner_type=owner_type, + app_id=app_id, + project_id=project_id, + session_id=s.inline.get("session_id", ""), + timestamp=require_iso_timestamp(s.inline.get("timestamp")), + parent_type=s.inline.get("parent_type") or ParentType.MEMCELL.value, + parent_id=s.inline.get("parent_id", ""), + sender_ids=parse_inline_list(s.inline.get("sender_ids", "")), + fact=text, + fact_tokens=" ".join(tokens), + md_path=md_path, + content_sha256=entry.content_sha256, + vector=vector, + ) diff --git a/src/everos/memory/cascade/handlers/base.py b/src/everos/memory/cascade/handlers/base.py new file mode 100644 index 00000000..3b599597 --- /dev/null +++ b/src/everos/memory/cascade/handlers/base.py @@ -0,0 +1,74 @@ +"""Handler chassis — abstract base + shared :class:`HandlerDeps`. + +Each kind has its own concrete handler responsible for translating a +single md path's content (or absence) into the corresponding LanceDB +row state. The handler is the *only* cascade-side piece that knows +the per-kind row shape; everything around it (watcher / scanner / +worker / orchestrator / CLI) is kind-agnostic. + +Per-kind handlers share the same dependencies bundle — embedding, +tokenizer, memory-root path resolver — packaged as +:class:`HandlerDeps`. Construct it once at orchestrator startup, pass +to every handler factory, no per-row resolution churn. +""" + +from __future__ import annotations + +import abc +import dataclasses + +from everos.component.embedding import EmbeddingProvider +from everos.component.tokenizer import Tokenizer +from everos.core.persistence import MemoryRoot + +from ..types import HandlerOutcome + + +@dataclasses.dataclass(frozen=True) +class HandlerDeps: + """Shared providers handed to every :class:`Handler` on construction. + + Frozen + read-only — handlers never mutate the deps; orchestrator + is the sole owner. ``memory_root`` is used to resolve the absolute + file path from the relative ``md_path`` stored in + ``md_change_state``. + """ + + memory_root: MemoryRoot + embedder: EmbeddingProvider + tokenizer: Tokenizer + + +class Handler(abc.ABC): + """Per-kind cascade handler contract. + + ``handle_added_or_modified`` and ``handle_deleted`` are the two + cases the worker dispatches on, derived from + :class:`MdChangeState.change_type`. Either may raise — the worker + catches and classifies (``RecoverableError`` vs unrecoverable) to + drive the retry / failed-state lifecycle. + """ + + def __init__(self, deps: HandlerDeps) -> None: + self._deps = deps + + @abc.abstractmethod + async def handle_added_or_modified(self, md_path: str) -> HandlerOutcome: + """Reconcile LanceDB for an md path that exists on disk. + + Args: + md_path: Path **relative** to the memory root (e.g. + ``users/u1/episodes/episode-2026-05-14.md``). + + Returns: + A :class:`HandlerOutcome` summarising the diff resolution. + """ + + @abc.abstractmethod + async def handle_deleted(self, md_path: str) -> HandlerOutcome: + """Wipe every LanceDB row that points back to ``md_path``. + + Idempotent: a path that was never indexed (or already wiped) + returns an outcome with zero deletes; the worker still marks + the state row ``done`` so the queue stays clean. + """ diff --git a/src/everos/memory/cascade/handlers/episode.py b/src/everos/memory/cascade/handlers/episode.py new file mode 100644 index 00000000..37fc955b --- /dev/null +++ b/src/everos/memory/cascade/handlers/episode.py @@ -0,0 +1,90 @@ +"""Episode cascade handler — md → LanceDB ``episode`` table. + +Inherits :class:`BaseDailyLogHandler` for the diff / dispatch loop and +overrides :meth:`_build_row` to map an Episode entry's structured +body into the typed LanceDB row. Documented md contract (callers / +writers must match): + +``inline`` block: + +- ``owner_id``: ``user_id`` or ``agent_id`` — duplicates the + frontmatter scope so the cascade can derive it without re-reading + the frontmatter. +- ``session_id``: conversation scope. +- ``timestamp``: ISO-8601 string (``to_iso_format`` output). +- ``parent_type``: source kind label (currently always ``"memcell"`` + — :class:`ParentType` enum; explicit in md so future kinds plug in + without a schema change). +- ``parent_id``: source memcell id. +- ``sender_ids``: ``[u_a, u_b]`` rendered shape from + ``_render_value`` (optional; defaults to empty list). + +``sections``: + +- ``Subject`` (optional): one-line topic. +- ``Summary`` (optional): condensed narrative. +- ``Content``: full episode narrative — fed to the embedder AND the + tokenizer for the ``episode_tokens`` BM25 field. +""" + +from __future__ import annotations + +from everos.infra.persistence.lancedb import Episode, ParentType, episode_repo + +from ._common import parse_inline_list, require_iso_timestamp +from ._daily_log_base import BaseDailyLogHandler, ParsedEntry + + +class EpisodeHandler(BaseDailyLogHandler): + """Cascade handler for ``users//episodes/episode-*.md``.""" + + kind = "episode" + lance_repo = episode_repo + content_change_keys = ( + "section:Subject", + "section:Summary", + "section:Content", + ) + """Subject / Summary / Content all participate in the digest: + + - Editing Content rewrites the embedding (correct). + - Editing Subject / Summary doesn't change the embed text but still + bumps the digest so the LanceDB ``subject`` / ``summary`` columns + stay in sync with md. The slight overshoot (one wasted embed + call on Subject edits) is accepted under the single-hash design + (cascade Q2 discussion).""" + + async def _build_row( + self, + *, + owner_id: str, + owner_type: str, + app_id: str = "default", + project_id: str = "default", + md_path: str, + entry: ParsedEntry, + ) -> Episode: + s = entry.structured + text = s.sections.get("Content", "").strip() + tokens = self._deps.tokenizer.tokenize(text) + vector = await self._deps.embedder.embed(text) + return Episode( + id=f"{owner_id}_{entry.entry_id}", + entry_id=entry.entry_id, + owner_id=owner_id, + owner_type=owner_type, + app_id=app_id, + project_id=project_id, + session_id=s.inline.get("session_id", ""), + timestamp=require_iso_timestamp(s.inline.get("timestamp")), + parent_type=s.inline.get("parent_type") or ParentType.MEMCELL.value, + parent_id=s.inline.get("parent_id", ""), + sender_ids=parse_inline_list(s.inline.get("sender_ids", "")), + subject=s.sections.get("Subject") or None, + summary=s.sections.get("Summary") or None, + episode=text, + episode_tokens=" ".join(tokens), + md_path=md_path, + content_sha256=entry.content_sha256, + vector=vector, + ) diff --git a/src/everos/memory/cascade/handlers/foresight.py b/src/everos/memory/cascade/handlers/foresight.py new file mode 100644 index 00000000..ad6f0e92 --- /dev/null +++ b/src/everos/memory/cascade/handlers/foresight.py @@ -0,0 +1,97 @@ +"""Foresight cascade handler — md → LanceDB ``foresight`` table. + +Two-field BM25: ``foresight_tokens`` is the primary search column, +``evidence_tokens`` rides along when an Evidence section is present. +The vector embedding is fed only from the foresight body (evidence +is supporting context, not the retrieval anchor). + +md contract: + +``inline`` block: + +- ``owner_id`` / ``session_id`` / ``timestamp`` — same shape as + Episode / AtomicFact. +- ``parent_id``: source memcell id (``parent_type`` defaults to + ``"memcell"``). +- ``sender_ids`` (optional): list rendering. +- ``start_time`` (optional): ISO-8601 window start. +- ``end_time`` (optional): ISO-8601 window end. +- ``duration_days`` (optional): integer. + +``sections``: + +- ``Foresight``: forward-looking inference text (embedded + BM25). +- ``Evidence`` (optional): supporting excerpt (secondary BM25 only). +""" + +from __future__ import annotations + +from everos.infra.persistence.lancedb import Foresight, ParentType, foresight_repo + +from ._common import ( + optional_int, + optional_iso_timestamp, + parse_inline_list, + require_iso_timestamp, +) +from ._daily_log_base import BaseDailyLogHandler, ParsedEntry + + +class ForesightHandler(BaseDailyLogHandler): + """Cascade handler for ``users//.foresights/foresight-*.md``.""" + + kind = "foresight" + lance_repo = foresight_repo + content_change_keys = ( + "section:Foresight", + "section:Evidence", + "inline:start_time", + "inline:end_time", + "inline:duration_days", + ) + """Foresight / Evidence sections + the semantic time-window inline + fields (start_time / end_time / duration_days). Audit inline + (owner_id / session_id / timestamp / parent_id / sender_ids) is + excluded — changes there don't propagate.""" + + async def _build_row( + self, + *, + owner_id: str, + owner_type: str, + app_id: str = "default", + project_id: str = "default", + md_path: str, + entry: ParsedEntry, + ) -> Foresight: + s = entry.structured + text = s.sections.get("Foresight", "").strip() + evidence = (s.sections.get("Evidence") or "").strip() or None + tokens = self._deps.tokenizer.tokenize(text) + vector = await self._deps.embedder.embed(text) + evidence_tokens = ( + " ".join(self._deps.tokenizer.tokenize(evidence)) if evidence else None + ) + return Foresight( + id=f"{owner_id}_{entry.entry_id}", + entry_id=entry.entry_id, + owner_id=owner_id, + owner_type=owner_type, + app_id=app_id, + project_id=project_id, + session_id=s.inline.get("session_id", ""), + timestamp=require_iso_timestamp(s.inline.get("timestamp")), + start_time=optional_iso_timestamp(s.inline.get("start_time")), + end_time=optional_iso_timestamp(s.inline.get("end_time")), + duration_days=optional_int(s.inline.get("duration_days")), + parent_type=s.inline.get("parent_type") or ParentType.MEMCELL.value, + parent_id=s.inline.get("parent_id", ""), + sender_ids=parse_inline_list(s.inline.get("sender_ids", "")), + foresight=text, + foresight_tokens=" ".join(tokens), + evidence=evidence, + evidence_tokens=evidence_tokens, + md_path=md_path, + content_sha256=entry.content_sha256, + vector=vector, + ) diff --git a/src/everos/memory/cascade/handlers/user_profile.py b/src/everos/memory/cascade/handlers/user_profile.py new file mode 100644 index 00000000..4842986d --- /dev/null +++ b/src/everos/memory/cascade/handlers/user_profile.py @@ -0,0 +1,134 @@ +"""UserProfile cascade handler — md → LanceDB ``user_profile`` table. + +Profile is a single-file kind (mirrors AgentSkill): one +``users//user.md`` per user, replaced wholesale on edit. No +entry markers, no per-entry diff. The LanceDB row carries the typed +projection of the frontmatter so a future query-aware lookup can run +off LanceDB; today the recaller is KV-by-owner. + +md contract: + +- frontmatter: :class:`UserProfileFrontmatter` (``user_id`` / + ``summary`` / ``explicit_info`` / ``implicit_traits`` / + ``profile_timestamp_ms``). +- body: free-form display text (not indexed; the structured payload + the recaller returns is built from frontmatter alone). + +``explicit_info`` / ``implicit_traits`` are heterogeneous LLM +emissions (list of small dicts mixed with strings). LanceDB has no +``list[dict]`` column type, so the handler json-encodes both into +``explicit_info_json`` / ``implicit_traits_json`` columns and the +recaller decodes them back at the DTO boundary. +""" + +from __future__ import annotations + +import json +from typing import Any, ClassVar + +from everos.core.persistence import MarkdownReader +from everos.infra.persistence.lancedb import UserProfile, user_profile_repo + +from ..types import HandlerOutcome +from ._common import content_sha256 as compute_content_sha256 +from ._common import resolve_scope +from .base import Handler + + +class UserProfileHandler(Handler): + """Cascade handler for ``users//user.md``.""" + + kind = "user_profile" + lance_repo: ClassVar[Any] = user_profile_repo + """Exposed for ``CascadeWorker._optimize_touched_kinds`` — the + worker discovers the LanceDB repo to optimize via this attribute, + mirroring the daily-log handlers that bind it through + :class:`BaseDailyLogHandler`.""" + + content_change_keys: ClassVar[tuple[str, ...]] = ( + "frontmatter:summary", + "frontmatter:explicit_info_json", + "frontmatter:implicit_traits_json", + ) + """Retrieval-relevant fields. ``profile_timestamp_ms`` is treated + as audit (changes with every synthesis even when content is + identical) — it lands on the row but doesn't enter the hash, so a + timestamp-only drift skips re-upsert.""" + + async def handle_added_or_modified(self, md_path: str) -> HandlerOutcome: + absolute = self._deps.memory_root.root / md_path + parsed = await MarkdownReader.read(absolute) + fm = parsed.frontmatter + + owner_id = str(fm.get("user_id", "")) + if not owner_id: + raise ValueError( + f"user_profile md missing required frontmatter user_id: {md_path}" + ) + app_id, project_id = resolve_scope(md_path) + + summary = str(fm.get("summary", "")) + explicit_info_json = _dump_json(fm.get("explicit_info", [])) + implicit_traits_json = _dump_json(fm.get("implicit_traits", [])) + profile_timestamp_ms = int(fm.get("profile_timestamp_ms", 0)) + + digest = compute_content_sha256( + { + "frontmatter:summary": summary, + "frontmatter:explicit_info_json": explicit_info_json, + "frontmatter:implicit_traits_json": implicit_traits_json, + } + ) + + row_id = owner_id + prior = await user_profile_repo.get_by_id(row_id) + if prior is not None and prior.content_sha256 == digest: + return HandlerOutcome( + md_path=md_path, + kind=self.kind, + upserted=0, + deleted=0, + skipped=1, + ) + + row = UserProfile( + id=row_id, + owner_id=owner_id, + owner_type="user", + app_id=app_id, + project_id=project_id, + summary=summary, + explicit_info_json=explicit_info_json, + implicit_traits_json=implicit_traits_json, + profile_timestamp_ms=profile_timestamp_ms, + md_path=md_path, + content_sha256=digest, + ) + await user_profile_repo.upsert([row]) + return HandlerOutcome( + md_path=md_path, + kind=self.kind, + upserted=1, + deleted=0, + skipped=0, + ) + + async def handle_deleted(self, md_path: str) -> HandlerOutcome: + deleted = await user_profile_repo.delete_by_md_path(md_path) + return HandlerOutcome( + md_path=md_path, + kind=self.kind, + upserted=0, + deleted=deleted, + skipped=0, + ) + + +def _dump_json(value: Any) -> str: + """Canonical JSON encoding for list-style frontmatter buckets. + + ``sort_keys=True`` makes the digest stable across equivalent dicts; + ``ensure_ascii=False`` keeps multibyte content readable in tooling + that inspects the column without re-decoding. + """ + return json.dumps(value, sort_keys=True, ensure_ascii=False) diff --git a/src/everos/memory/cascade/orchestrator.py b/src/everos/memory/cascade/orchestrator.py new file mode 100644 index 00000000..0049d9ab --- /dev/null +++ b/src/everos/memory/cascade/orchestrator.py @@ -0,0 +1,131 @@ +"""Cascade orchestrator — wires watcher + scanner + worker for the lifespan. + +One :class:`CascadeOrchestrator` per process. The lifespan provider +constructs it at startup, calls :meth:`start` once, and calls +:meth:`stop` at shutdown. CLI ``cascade sync`` constructs its own +instance but only invokes :meth:`drain_once` (no background tasks). + +Construction is dependency-injected: the embedding / tokenizer +providers and the memory-root come in as constructor args so tests +can swap them without monkey-patching module-level singletons. +""" + +from __future__ import annotations + +import asyncio +import dataclasses + +from everos.component.embedding import EmbeddingProvider +from everos.component.tokenizer import Tokenizer +from everos.core.observability.logging import get_logger +from everos.core.persistence import MemoryRoot +from everos.infra.persistence.sqlite import QueueSummary, md_change_state_repo + +from .handlers import HandlerDeps +from .registry import build_handlers +from .scanner import CascadeScanner +from .watcher import CascadeWatcher +from .worker import CascadeWorker + +logger = get_logger(__name__) + + +@dataclasses.dataclass(frozen=True) +class CascadeConfig: + """Construction-time knobs for the orchestrator. + + Defaults are sized for a lightweight (single-user / small-team) dev + box; production tuning can surface these into + :class:`everos.config.Settings` once the daemon has wall-clock data. + """ + + scan_interval_seconds: float = 30.0 + worker_batch_size: int = 50 + worker_max_retry: int = 3 + worker_poll_interval_seconds: float = 1.0 + worker_retry_backoff_seconds: float = 2.0 + + +class CascadeOrchestrator: + """Composite owner of the cascade subsystem.""" + + def __init__( + self, + *, + memory_root: MemoryRoot, + embedder: EmbeddingProvider, + tokenizer: Tokenizer, + config: CascadeConfig | None = None, + ) -> None: + self._memory_root = memory_root + self._config = config or CascadeConfig() + deps = HandlerDeps( + memory_root=memory_root, + embedder=embedder, + tokenizer=tokenizer, + ) + self._handlers = build_handlers(deps) + self._scanner = CascadeScanner( + memory_root, + scan_interval_seconds=self._config.scan_interval_seconds, + ) + self._worker = CascadeWorker( + self._handlers, + batch_size=self._config.worker_batch_size, + max_retry=self._config.worker_max_retry, + poll_interval_seconds=self._config.worker_poll_interval_seconds, + retry_backoff_seconds=self._config.worker_retry_backoff_seconds, + ) + self._watcher: CascadeWatcher | None = None + self._started = False + + async def start(self) -> None: + """Launch the watcher (sync thread) + scanner + worker tasks. + + Before launching, reset any stale ``processing`` rows back to + ``pending``: cascade runs single-process today, so anything in + ``processing`` at boot is leftover from a prior crash that + ``claim_pending_batch`` can't re-claim on its own (the WHERE + filter is ``status='pending'``). + """ + if self._started: + return + orphans = await md_change_state_repo.recover_orphan_processing() + if orphans: + logger.info("cascade_recovered_orphan_processing", count=orphans) + loop = asyncio.get_running_loop() + self._watcher = CascadeWatcher(self._memory_root, loop) + self._watcher.start() + await self._scanner.start() + await self._worker.start() + self._started = True + logger.info("cascade_orchestrator_started") + + async def stop(self) -> None: + """Shut everything down in reverse order.""" + if not self._started: + return + await self._worker.stop() + await self._scanner.stop() + if self._watcher is not None: + self._watcher.stop() + self._watcher = None + self._started = False + logger.info("cascade_orchestrator_stopped") + + async def sync_once(self) -> int: + """One scan + drain cycle (used by CLI ``cascade sync``). + + Returns the number of rows processed in this drain. The CLI + loops on the returned count to know when to stop. + """ + await self._scanner.scan_once() + return await self._worker.drain_until_empty() + + async def drain_once(self) -> int: + """Drain the queue exactly once without scanning first.""" + return await self._worker.drain_until_empty() + + async def queue_summary(self) -> QueueSummary: + """Forward to the repo so callers don't reach past this class.""" + return await md_change_state_repo.queue_summary() diff --git a/src/everos/memory/cascade/reconciler.py b/src/everos/memory/cascade/reconciler.py new file mode 100644 index 00000000..263f3097 --- /dev/null +++ b/src/everos/memory/cascade/reconciler.py @@ -0,0 +1,116 @@ +"""Reconcile scanner observations against the current ``md_change_state``. + +Pure function — given a list of :class:`ScanInput` (what's on disk +right now, matched against the kind registry) and the prior state +keyed by ``md_path`` (what's in sqlite), emit the +:class:`ReconcileDecision` set the scanner should UPSERT. + +Three categories per 12 doc §5.3: + +- **added** — path on disk, no prior state row. +- **modified** — path on disk, prior mtime differs (newer or older; + cascade does not assume monotonic file timestamps). +- **deleted** — path missing from disk. Emitted unless the prior + row is *already* the result of a successful delete cycle + (``status='done' AND change_type='deleted'`` — handler has wiped + the orphan LanceDB rows on a previous sweep). A ``status='done'`` + row whose ``change_type`` is ``'added'`` / ``'modified'`` is still + emitted: the watcher must have missed the unlink (e.g. fseventsd + drop, daemon restart window), and we need the scanner to recover + the deletion or LanceDB stays stale. + +Paths whose prior state row is ``done`` AND the mtime matches are +skipped on the add/modify side — the reconcile output stays tight on +quiet sweeps. +""" + +from __future__ import annotations + +import dataclasses +from collections.abc import Iterable, Mapping + +from .types import ReconcileDecision, ScanInput + + +@dataclasses.dataclass(frozen=True) +class PriorState: + """Snapshot of one ``md_change_state`` row, as far as reconcile cares. + + Only the fields needed to decide change_type appear here; the + repo's full :class:`MdChangeState` row is reduced to this shape + at the reconciler entry boundary. + """ + + md_path: str + kind: str + mtime: float + status: str # "pending" | "processing" | "done" | "failed" + change_type: str # "added" | "modified" | "deleted" + + +def reconcile( + scan: Iterable[ScanInput], + state: Mapping[str, PriorState], +) -> list[ReconcileDecision]: + """Compute the UPSERT plan for one scanner sweep. + + Args: + scan: Every md path currently on disk that matches a registered + kind (scanner output). + state: ``{md_path: PriorState}`` — the current + ``md_change_state`` snapshot keyed by path. + + Returns: + Ordered :class:`ReconcileDecision` list — ``added`` / + ``modified`` first (in scan order), then ``deleted`` for paths + present in state but missing from disk. + """ + decisions: list[ReconcileDecision] = [] + seen: set[str] = set() + + for item in scan: + seen.add(item.md_path) + prior = state.get(item.md_path) + if prior is None: + decisions.append( + ReconcileDecision( + md_path=item.md_path, + kind=item.kind, + change_type="added", + mtime=item.mtime, + ) + ) + continue + # Skip when the row is already done and mtime hasn't moved. + if prior.status == "done" and prior.mtime == item.mtime: + continue + decisions.append( + ReconcileDecision( + md_path=item.md_path, + kind=item.kind, + change_type="modified", + mtime=item.mtime, + ) + ) + + for path, prior in state.items(): + if path in seen: + continue + # File missing from disk. Only skip when the prior cycle was + # *itself* a successful delete (the handler already wiped the + # orphan LanceDB rows). A done row with change_type='added' / + # 'modified' means the watcher missed the subsequent unlink — + # without re-emitting 'deleted' here the scanner would never + # recover the stale LanceDB rows. + if prior.status == "done" and prior.change_type == "deleted": + continue + decisions.append( + ReconcileDecision( + md_path=path, + kind=prior.kind, + change_type="deleted", + mtime=prior.mtime, + ) + ) + + return decisions diff --git a/src/everos/memory/cascade/registry.py b/src/everos/memory/cascade/registry.py new file mode 100644 index 00000000..201e8e46 --- /dev/null +++ b/src/everos/memory/cascade/registry.py @@ -0,0 +1,157 @@ +"""Kind registry — single source of truth mapping ``kind name`` → (schema, +repo, handler factory). + +Adding a new business kind to cascade = adding a :class:`KindSpec` here. +The watcher / scanner / worker / CLI all read off this tuple, so neither +the path-glob patterns nor the handler dispatch table appear anywhere +else in the codebase. Order matters only when two specs would match the +same path — :func:`match_kind` returns the first match. + +Path matching uses :class:`pathlib.PurePosixPath.match` (not bare +``fnmatch``) so that ``*`` matches a single path component, never the +``/`` separator — see ``17_lancedb_tables_design.md`` §2.4.2 and +``12_cascade_design.md`` §5.1 (path filter is a single whitelist layer). +""" + +from __future__ import annotations + +import dataclasses +from pathlib import PurePosixPath + +from everos.core.persistence.markdown import BaseFrontmatter +from everos.infra.persistence.lancedb import ( + AgentCase, + AgentSkill, + AtomicFact, + Episode, + Foresight, + UserProfile, + agent_case_repo, + agent_skill_repo, + atomic_fact_repo, + episode_repo, + foresight_repo, + user_profile_repo, +) +from everos.infra.persistence.markdown import ( + AgentCaseDailyFrontmatter, + AgentSkillFrontmatter, + AtomicFactDailyFrontmatter, + EpisodeDailyFrontmatter, + ForesightDailyFrontmatter, + UserProfileFrontmatter, +) + +from .handlers import ( + AgentCaseHandler, + AgentSkillHandler, + AtomicFactHandler, + EpisodeHandler, + ForesightHandler, + Handler, + HandlerDeps, + UserProfileHandler, +) + + +@dataclasses.dataclass(frozen=True) +class KindSpec: + """One cascade kind — md schema + LanceDB binding + handler factory. + + ``frontmatter_schema`` carries the ``path_glob()`` classmethod the + scanner uses to enumerate eligible files; the same schema is also + the contract the reader / writer share at the markdown layer. + ``lance_schema`` + ``lance_repo`` describe the destination side. + ``handler_factory`` is a callable that receives the shared + :class:`HandlerDeps` bundle and returns the kind's :class:`Handler`. + """ + + name: str + frontmatter_schema: type[BaseFrontmatter] + lance_schema: type + lance_repo: object + handler_factory: type[Handler] + + def path_glob(self) -> str: + """Glob (relative to memory root) for every md this kind covers.""" + return self.frontmatter_schema.path_glob() + + def matches(self, rel_md_path: str) -> bool: + """Whether ``rel_md_path`` (relative to memory root) is in scope. + + Uses POSIX-style component-aware glob matching: ``*`` matches a + single path component, not ``/``. See module docstring for why + :class:`pathlib.PurePosixPath.match` is preferred over bare + :func:`fnmatch.fnmatch`. + """ + return PurePosixPath(rel_md_path).match(self.path_glob()) + + +KIND_REGISTRY: tuple[KindSpec, ...] = ( + KindSpec( + name="episode", + frontmatter_schema=EpisodeDailyFrontmatter, + lance_schema=Episode, + lance_repo=episode_repo, + handler_factory=EpisodeHandler, + ), + KindSpec( + name="atomic_fact", + frontmatter_schema=AtomicFactDailyFrontmatter, + lance_schema=AtomicFact, + lance_repo=atomic_fact_repo, + handler_factory=AtomicFactHandler, + ), + KindSpec( + name="foresight", + frontmatter_schema=ForesightDailyFrontmatter, + lance_schema=Foresight, + lance_repo=foresight_repo, + handler_factory=ForesightHandler, + ), + KindSpec( + name="agent_case", + frontmatter_schema=AgentCaseDailyFrontmatter, + lance_schema=AgentCase, + lance_repo=agent_case_repo, + handler_factory=AgentCaseHandler, + ), + KindSpec( + name="agent_skill", + frontmatter_schema=AgentSkillFrontmatter, + lance_schema=AgentSkill, + lance_repo=agent_skill_repo, + handler_factory=AgentSkillHandler, + ), + KindSpec( + name="user_profile", + frontmatter_schema=UserProfileFrontmatter, + lance_schema=UserProfile, + lance_repo=user_profile_repo, + handler_factory=UserProfileHandler, + ), +) +"""Every cascade kind, evaluated in declaration order by :func:`match_kind`.""" + + +def match_kind(rel_md_path: str) -> KindSpec | None: + """Return the first :class:`KindSpec` matching ``rel_md_path``, or ``None``. + + First-match semantics (DD-7): registry order is the precedence order. + Today's globs are disjoint by directory name so order is academic; if + overlap is ever introduced the registry order resolves it. + """ + for spec in KIND_REGISTRY: + if spec.matches(rel_md_path): + return spec + return None + + +def build_handlers(deps: HandlerDeps) -> dict[str, Handler]: + """Instantiate every registered handler bound to the shared deps. + + Returns a ``{kind_name: Handler}`` map used by the worker for + dispatch. Constructing once at orchestrator startup keeps the + per-row hot path free of factory churn. + """ + return {spec.name: spec.handler_factory(deps) for spec in KIND_REGISTRY} diff --git a/src/everos/memory/cascade/scanner.py b/src/everos/memory/cascade/scanner.py new file mode 100644 index 00000000..13c34e5f --- /dev/null +++ b/src/everos/memory/cascade/scanner.py @@ -0,0 +1,178 @@ +"""Periodic md tree scanner. + +The watcher catches realtime events but misses: + +- files created while the daemon was down, +- ``cp`` / external editors that move-replace and confuse inotify, +- WSL2 / network mounts where fsevents don't propagate. + +The scanner closes those gaps by walking the memory root every +``scan_interval`` seconds (default 30s, configurable later), matching +paths against the kind registry, reading prior state, and running the +pure :func:`reconcile` function to emit the upsert plan. + +Walking happens off the event loop via ``asyncio.to_thread`` since +``pathlib.Path.rglob`` is sync; the prior-state fetch + upsert calls +stay async on the loop. +""" + +from __future__ import annotations + +import asyncio +import contextlib +from collections.abc import Iterable +from pathlib import Path + +from sqlmodel import select + +from everos.core.observability.logging import get_logger +from everos.core.persistence import MemoryRoot +from everos.core.persistence.sqlite import session_scope +from everos.infra.persistence.sqlite import ( + MdChangeState, + get_session_factory, + md_change_state_repo, +) + +from .reconciler import PriorState, reconcile +from .registry import KIND_REGISTRY +from .types import ReconcileDecision, ScanInput + +logger = get_logger(__name__) + + +class CascadeScanner: + """Periodic walker — owns its asyncio task.""" + + def __init__( + self, + memory_root: MemoryRoot, + *, + scan_interval_seconds: float = 30.0, + ) -> None: + self._memory_root = memory_root + self._interval = scan_interval_seconds + self._task: asyncio.Task[None] | None = None + self._stop = asyncio.Event() + + async def start(self) -> None: + if self._task is not None: + return + self._stop.clear() + self._task = asyncio.create_task(self._run_loop(), name="cascade-scanner") + logger.info("cascade_scanner_started", interval=self._interval) + + async def stop(self) -> None: + if self._task is None: + return + self._stop.set() + self._task.cancel() + with contextlib.suppress(asyncio.CancelledError, Exception): + await self._task + self._task = None + logger.info("cascade_scanner_stopped") + + async def scan_once(self) -> list[ReconcileDecision]: + """One scan + reconcile pass; returns the decisions that were + upserted into :class:`MdChangeState`. + + Exposed so the CLI ``cascade sync`` command can trigger a sweep + without owning a long-lived scanner task. + """ + scan_inputs = await asyncio.to_thread( + _collect_scan_inputs, self._memory_root.root + ) + state = await _load_state_snapshot() + decisions = reconcile(scan_inputs, state) + for decision in decisions: + await md_change_state_repo.upsert( + decision.md_path, + kind=decision.kind, + change_type=decision.change_type, + mtime=decision.mtime, + ) + if decisions: + logger.info( + "cascade_scanner_decisions", + count=len(decisions), + added=sum(1 for d in decisions if d.change_type == "added"), + modified=sum(1 for d in decisions if d.change_type == "modified"), + deleted=sum(1 for d in decisions if d.change_type == "deleted"), + ) + return decisions + + async def _run_loop(self) -> None: + while not self._stop.is_set(): + try: + await self.scan_once() + except Exception as exc: # noqa: BLE001 — never crash the daemon + logger.exception("cascade_scanner_failed", error=str(exc)) + try: + await asyncio.wait_for(self._stop.wait(), timeout=self._interval) + except TimeoutError: + continue + + +def _collect_scan_inputs(root: Path) -> list[ScanInput]: + """Walk ``root`` once per registered kind, returning every match. + + ``stat()`` failure mode discrimination is **load-bearing**: the + reconciler treats "in state but not in scan" as a deletion signal, + so if we silently drop a path here under a *transient* OS error, + the next reconcile sweep will emit ``change_type='deleted'`` for + that healthy md and the handler will wipe its LanceDB rows. + + Two errno classes: + + - :class:`FileNotFoundError` (ENOENT) — the file was unlinked + between ``glob`` and ``stat``. This is a genuine deletion; drop + from inputs so the reconciler emits ``deleted`` (correct). + - any other :class:`OSError` (EMFILE / ENFILE — FD exhaustion, + EACCES — perms, EIO — disk error, etc.) — we don't know whether + the file is gone. **Raise** to abort the whole sweep; the + ``_run_loop`` outer ``try / except Exception`` catches and logs + it, and we retry on the next interval. A partial scan is worse + than no scan, because the reconciler can't tell the difference. + + Symptom this guards against (observed 2026-05-28 on LoCoMo + benchmark conv_2): a search-time FD exhaustion bled into the + concurrent scanner sweep, and 8 healthy md files got marked + ``change_type=deleted, status=done`` with their LanceDB rows + cleared — single-direction data loss until external intervention. + """ + inputs: list[ScanInput] = [] + for spec in KIND_REGISTRY: + for absolute in root.glob(spec.path_glob()): + try: + mtime = absolute.stat().st_mtime + except FileNotFoundError: + # Race between glob and stat; treat as a genuine deletion + # by leaving this path out of inputs. + continue + # Any other OSError (EMFILE / EACCES / EIO ...) — propagate. + # Partial inputs would trigger spurious deletes in reconcile(). + try: + rel = absolute.relative_to(root).as_posix() + except ValueError: + continue + inputs.append(ScanInput(md_path=rel, mtime=mtime, kind=spec.name)) + return inputs + + +async def _load_state_snapshot() -> dict[str, PriorState]: + """Project every row in ``md_change_state`` into :class:`PriorState`.""" + factory = get_session_factory() + async with session_scope(factory) as s: + rows: Iterable[MdChangeState] = ( + (await s.execute(select(MdChangeState))).scalars().all() + ) + return { + row.md_path: PriorState( + md_path=row.md_path, + kind=row.kind, + mtime=row.mtime, + status=row.status, + change_type=row.change_type, + ) + for row in rows + } diff --git a/src/everos/memory/cascade/types.py b/src/everos/memory/cascade/types.py new file mode 100644 index 00000000..bced4a33 --- /dev/null +++ b/src/everos/memory/cascade/types.py @@ -0,0 +1,61 @@ +"""Cascade value types — small dataclasses shared by registry / reconciler / handler. + +All types here are :class:`dataclasses.dataclass(frozen=True)` so the +pure-function modules (``reconciler``) stay deterministic and easy to +unit-test without an event loop or IO mocks. +""" + +from __future__ import annotations + +import dataclasses +from typing import Literal + +ChangeType = Literal["added", "modified", "deleted"] + + +@dataclasses.dataclass(frozen=True) +class ScanInput: + """One scanner observation. + + ``md_path`` is **relative to the memory root** (e.g. + ``users/u1/episodes/episode-2026-05-14.md``). ``mtime`` is the + POSIX timestamp captured at scan time; ``kind`` is the + registry-matched name (``"episode"`` / ``"agent_skill"`` / …). + """ + + md_path: str + mtime: float + kind: str + + +@dataclasses.dataclass(frozen=True) +class ReconcileDecision: + """One md_path's reconcile outcome. + + Drives the watcher / scanner / sync entry into + :meth:`MdChangeStateRepo.upsert`. ``change_type`` is a hint — + handlers re-derive truth from the actual md file state at run + time (DD-3 in 12 doc). + """ + + md_path: str + kind: str + change_type: ChangeType + mtime: float + + +@dataclasses.dataclass(frozen=True) +class HandlerOutcome: + """Per-handler-run summary, returned to the worker for telemetry. + + The worker uses ``upserted`` / ``deleted`` to log how much work + each md change translated into downstream. ``skipped`` counts + entries whose ``content_sha256`` matched the existing row — the + no-op case the diff is meant to optimise. + """ + + md_path: str + kind: str + upserted: int + deleted: int + skipped: int diff --git a/src/everos/memory/cascade/watcher.py b/src/everos/memory/cascade/watcher.py new file mode 100644 index 00000000..49f2a9b3 --- /dev/null +++ b/src/everos/memory/cascade/watcher.py @@ -0,0 +1,171 @@ +"""Filesystem watcher — emits cascade enqueue events on md changes. + +watchdog 6's :class:`Observer` runs in its own native thread; the +event handler callback fires there too. We bridge those events back +onto the orchestrator's asyncio loop via +:func:`asyncio.run_coroutine_threadsafe` so every state-table write +goes through the same async repo as the scanner / CLI sync paths. + +The handler is intentionally cheap: pattern-match the path against +the kind registry, then enqueue. The watcher does **not** read the +file content — that's the worker's job after :meth:`claim_one`. +""" + +from __future__ import annotations + +import asyncio +from pathlib import Path + +from watchdog.events import FileMovedEvent, FileSystemEvent, FileSystemEventHandler +from watchdog.observers import Observer + +from everos.core.observability.logging import get_logger +from everos.core.persistence import MemoryRoot +from everos.infra.persistence.sqlite import md_change_state_repo + +from .registry import KindSpec, match_kind + +logger = get_logger(__name__) + + +class CascadeWatcher: + """Bridge watchdog → md_change_state for the configured memory root. + + The watchdog observer is started on :meth:`start` and stopped on + :meth:`stop`. Events outside the registered kind paths are silently + ignored — DD-7 (single whitelist layer) keeps the watcher free of + bespoke exclusion rules. + """ + + def __init__( + self, + memory_root: MemoryRoot, + loop: asyncio.AbstractEventLoop, + ) -> None: + self._memory_root = memory_root + self._loop = loop + self._observer = Observer() + self._handler = _Handler(memory_root, loop) + self._started = False + + def start(self) -> None: + if self._started: + return + # The memory root is created lazily by other layers; watchdog + # rejects non-existent paths so we ensure it exists here. + self._memory_root.ensure() + self._observer.schedule( + self._handler, str(self._memory_root.root), recursive=True + ) + self._observer.start() + self._started = True + logger.info("cascade_watcher_started", root=str(self._memory_root.root)) + + def stop(self) -> None: + if not self._started: + return + self._observer.stop() + self._observer.join(timeout=5) + self._started = False + logger.info("cascade_watcher_stopped") + + +class _Handler(FileSystemEventHandler): + """Watchdog callback — fires in the watchdog thread.""" + + def __init__( + self, memory_root: MemoryRoot, loop: asyncio.AbstractEventLoop + ) -> None: + self._memory_root = memory_root + self._loop = loop + + def on_created(self, event: FileSystemEvent) -> None: + self._enqueue(event.src_path, "added") + + def on_modified(self, event: FileSystemEvent) -> None: + self._enqueue(event.src_path, "modified") + + def on_deleted(self, event: FileSystemEvent) -> None: + # macOS FSEvents fires a synthetic deletion for the OLD inode + # whenever ``os.replace`` overwrites an existing file — the path + # itself is still present, now pointing at the new inode, and the + # paired ``on_moved`` has already enqueued the dest as 'added'. + # Propagating this false-positive 'deleted' drives the worker to + # call ``delete_by_md_path`` and wipe LanceDB while md is fine. + # The stat is on the watcher thread but cheap on APFS (~µs); + # real unlinks still surface because the path is truly gone. + if Path(event.src_path).exists(): + return + self._enqueue(event.src_path, "deleted") + + def on_moved(self, event: FileSystemEvent) -> None: + # A rename emits both a `moved` for the src and effectively a + # `created` for the dest. We materialise both sides so the + # state table tracks the source as deleted and the destination + # as added. + # + # Symmetric to ``on_deleted``: stat src first. If the path still + # exists (e.g. macOS reports a synthetic move for the old inode + # of an atomic-replace pair, or a hardlink survives the rename + # so the named path is still bound), the 'deleted' enqueue + # would wipe LanceDB while the file is intact. Real renames + # (src genuinely gone, dest the new home) keep both legs. + if not Path(event.src_path).exists(): + self._enqueue(event.src_path, "deleted") + if isinstance(event, FileMovedEvent): + self._enqueue(event.dest_path, "added") + + def _enqueue(self, raw_path: str, change_type: str) -> None: + rel = _relative_to_root(self._memory_root.root, raw_path) + if rel is None: + return + spec = match_kind(rel) + if spec is None: + return + mtime = _safe_mtime(raw_path) + asyncio.run_coroutine_threadsafe( + _enqueue_async(spec, rel, change_type, mtime), + self._loop, + ) + + +async def _enqueue_async( + spec: KindSpec, rel: str, change_type: str, mtime: float +) -> None: + """Coroutine variant — runs on the orchestrator's event loop.""" + try: + await md_change_state_repo.upsert( + rel, + kind=spec.name, + change_type=change_type, + mtime=mtime, + ) + except Exception as exc: # noqa: BLE001 — defensive: never crash watcher + logger.warning( + "cascade_watcher_upsert_failed", + md_path=rel, + kind=spec.name, + error=str(exc), + ) + + +def _relative_to_root(root: Path, raw: str) -> str | None: + """Return ``raw`` relative to ``root`` using POSIX separators. + + ``None`` when the path is outside the memory root (defensive — the + watcher only watches inside ``root``, but external symlinks could + surface). + """ + try: + rel = Path(raw).resolve().relative_to(root) + except ValueError: + return None + return rel.as_posix() + + +def _safe_mtime(raw: str) -> float: + """Return mtime in seconds, falling back to 0.0 on stat failure.""" + try: + return Path(raw).stat().st_mtime + except OSError: + return 0.0 diff --git a/src/everos/memory/cascade/worker.py b/src/everos/memory/cascade/worker.py new file mode 100644 index 00000000..9e2cc5b9 --- /dev/null +++ b/src/everos/memory/cascade/worker.py @@ -0,0 +1,604 @@ +"""Cascade worker — consumes pending rows and runs the matching handler. + +The worker is the only piece that crosses the md → LanceDB boundary. +Each cycle: + +1. ``claim_pending_batch(BATCH_SIZE)`` atomically flips pending rows to + ``processing`` and returns them in LSN order. +2. For each row, look up the kind's :class:`Handler` and call either + :meth:`handle_added_or_modified` or :meth:`handle_deleted` based on + the row's ``change_type``. +3. On success: ``mark_done``. +4. On :class:`RecoverableError`: retry inline up to ``MAX_RETRY``; if + all attempts fail, ``mark_failed(retryable=True)``. +5. On any other exception: ``mark_failed(retryable=False)`` (treated + as unrecoverable, surfaces in ``cascade fix`` for the user to + triage by editing the md). + +Batch processing is concurrent inside a batch (``asyncio.gather``); +ordering across rows is best-effort — the LSN gives a deterministic +prefix but the handlers themselves are independent. + +After a batch completes, each kind that mutated its LanceDB table is +passed to :meth:`_schedule_optimize` — a per-kind throttle + trailing +edge scheduler that fires LanceDB ``optimize()`` as a separate task, +so the drain loop is never blocked by index maintenance. A 60-second +heartbeat sweeps every kind through the same gate so unindexed +fragments don't linger after a worker restart even without new +writes. See :meth:`_schedule_optimize` for the exact semantics. + +A separate 12-hour loop (:meth:`_rebuild_loop`) does a full +``drop_index + create_index`` per kind to bound the **active** index +UUID / FTS segment count growth — a workaround for an upstream gap +in the lancedb Python async API; see +:meth:`everos.core.persistence.lancedb.LanceRepoBase.rebuild_indexes` +for the full provenance and the conditions under which this scheduler +can be removed. +""" + +from __future__ import annotations + +import asyncio +import contextlib +import datetime as dt +import time +from dataclasses import dataclass + +from everos.core.observability.logging import get_logger +from everos.infra.persistence.sqlite import MdChangeState, md_change_state_repo + +from .errors import RecoverableError +from .handlers import Handler + +logger = get_logger(__name__) + +# Conservative defaults — surface in settings if tuning is needed. +DEFAULT_BATCH_SIZE = 50 +DEFAULT_MAX_RETRY = 3 +DEFAULT_POLL_INTERVAL_SECONDS = 1.0 +DEFAULT_RETRY_BACKOFF_SECONDS = 2.0 +DEFAULT_OPTIMIZE_MIN_INTERVAL_SECONDS = 1.0 +DEFAULT_OPTIMIZE_HEARTBEAT_SECONDS = 60.0 +DEFAULT_OPTIMIZE_REBUILD_INTERVAL_SECONDS = 12 * 60 * 60.0 +"""How often (per kind) to do a full ``drop_index + create_index`` rebuild. + +This is the **only** application-level mechanism we have to bound the +active index UUID / segment count growth — see +:meth:`LanceRepoBase.rebuild_indexes` for the full provenance: Rust +``OptimizeOptions.num_indices_to_merge`` is the right knob but +``lancedb.AsyncTable.optimize()`` does not expose it (verified on +lancedb main 2026-05-28), and on the embedded ``lance crate 4.0`` the +merge behaviour itself is broken so even calling Lance directly +wouldn't help. + +12 hours is a conservative pick: rebuild cost is ~0.3s per 50k rows +× indexed columns (measured locally), so even a small EverOS +deployment can absorb it without scheduling around peaks. Smaller +intervals work fine functionally; we just don't need them — under +realistic single-user / small-team write rates 12h keeps active UUIDs +bounded well below any FD ceiling. Tune via the constructor argument. + +**Remove this scheduler** once lancedb exposes ``num_indices_to_merge`` +on the async Python API and the embedded lance crate ships the +working merge implementation; ``optimize(num_indices_to_merge=1)`` +in the regular hot path will do the same job for ~free. +""" + +DEFAULT_OPTIMIZE_PRUNE_INTERVAL_SECONDS = 300.0 +"""How often (per kind) to add ``cleanup_older_than`` to ``optimize()``. + +``optimize()`` without ``cleanup_older_than`` compacts fragments and +merges new data into indexes, but **leaves stale physical files on disk +forever** (replaced data fragments, historical manifests, stale index +UUID files). On a lightweight (single-user / small-team) deployment +with steady-state cascade ingest, that file count grows without bound +and eventually +exhausts file descriptors at index-scan time (observed: macOS / Linux +default ``ulimit -n`` of 1024 — the ``os error 24`` reported in CI). + +The prune itself is cheap when scoped to recent versions; we just don't +want to pay it on every 1-second throttle tick. 5 minutes is the +shortest interval that comfortably outlives any in-flight query / index +build, while keeping the on-disk footprint bounded. It is also passed +as ``cleanup_older_than`` itself (semantically: "the retention window +equals the prune cadence") — every file replaced more than one cadence +ago becomes eligible. + +Does **not** shrink active index internals (FTS ``part_N`` count or +vector index UUID count): those only collapse via ``drop_index + +create_index``, which is intentionally out of scope here. +""" + + +@dataclass +class _KindOptimizerState: + """Per-kind throttle state for LanceDB ``optimize()``. + + ``dirty`` is the trailing-edge signal: every write sets it, the + runner consumes it before each ``optimize()`` call. If a write + arrives mid-optimize, ``dirty`` is re-raised and the runner loops + once more after honouring the throttle interval. + + ``task`` holds the in-flight runner; at most one runner exists + per kind so concurrent LanceDB writes never collide on the same + table's manifest. + + ``last_prune_at`` is the monotonic timestamp of the last + ``optimize()`` call that passed ``cleanup_older_than``; the runner + consults it to decide whether the next call should also prune. ``0`` + means "never pruned" — the first run after worker startup always + prunes, which is what we want for catching up from a prior session. + """ + + last_run_at: float = 0.0 + last_prune_at: float = 0.0 + dirty: bool = False + task: asyncio.Task[None] | None = None + rebuild_task: asyncio.Task[None] | None = None + """In-flight rebuild task slot, separate from ``task`` so ordinary + ``_schedule_optimize`` calls during a rebuild can still register + ``dirty`` + spawn an optimize runner. The runner itself waits for + ``rebuild_task`` before touching the LanceDB manifest, so the two + operations never race on commits — only the dispatch slot is split. + """ + + +class CascadeWorker: + """Owns the claim → dispatch → mark cycle. + + Created with the ``{kind: Handler}`` map produced by + :func:`memory.cascade.registry.build_handlers`. Holds no other + state — every per-row decision goes through the repo. + """ + + def __init__( + self, + handlers: dict[str, Handler], + *, + batch_size: int = DEFAULT_BATCH_SIZE, + max_retry: int = DEFAULT_MAX_RETRY, + poll_interval_seconds: float = DEFAULT_POLL_INTERVAL_SECONDS, + retry_backoff_seconds: float = DEFAULT_RETRY_BACKOFF_SECONDS, + optimize_min_interval_seconds: float = DEFAULT_OPTIMIZE_MIN_INTERVAL_SECONDS, + optimize_heartbeat_seconds: float = DEFAULT_OPTIMIZE_HEARTBEAT_SECONDS, + optimize_prune_interval_seconds: float = ( + DEFAULT_OPTIMIZE_PRUNE_INTERVAL_SECONDS + ), + optimize_rebuild_interval_seconds: float = ( + DEFAULT_OPTIMIZE_REBUILD_INTERVAL_SECONDS + ), + ) -> None: + self._handlers = handlers + self._batch_size = batch_size + self._max_retry = max_retry + self._poll_interval = poll_interval_seconds + self._retry_backoff = retry_backoff_seconds + self._optimize_min_interval = optimize_min_interval_seconds + self._optimize_heartbeat = optimize_heartbeat_seconds + self._optimize_prune_interval = optimize_prune_interval_seconds + self._optimize_rebuild_interval = optimize_rebuild_interval_seconds + self._task: asyncio.Task[None] | None = None + self._heartbeat_task: asyncio.Task[None] | None = None + self._rebuild_task: asyncio.Task[None] | None = None + self._stop = asyncio.Event() + self._optimizer_states: dict[str, _KindOptimizerState] = {} + + async def start(self) -> None: + if self._task is not None: + return + self._stop.clear() + self._task = asyncio.create_task(self._run_loop(), name="cascade-worker") + self._heartbeat_task = asyncio.create_task( + self._heartbeat_loop(), name="cascade-worker-heartbeat" + ) + self._rebuild_task = asyncio.create_task( + self._rebuild_loop(), name="cascade-worker-rebuild" + ) + logger.info("cascade_worker_started", batch_size=self._batch_size) + + async def stop(self) -> None: + if self._task is None: + return + self._stop.set() + self._task.cancel() + with contextlib.suppress(asyncio.CancelledError, Exception): + await self._task + if self._heartbeat_task is not None: + self._heartbeat_task.cancel() + with contextlib.suppress(asyncio.CancelledError, Exception): + await self._heartbeat_task + if self._rebuild_task is not None: + self._rebuild_task.cancel() + with contextlib.suppress(asyncio.CancelledError, Exception): + await self._rebuild_task + # Optimize tasks coalesce on the stop signal (their inter-run + # cooldowns observe ``self._stop``), so flushing them just + # waits out the currently in-flight commit rather than + # blocking on a fresh throttle window. + await self._flush_optimizers() + self._task = None + self._heartbeat_task = None + self._rebuild_task = None + logger.info("cascade_worker_stopped") + + async def drain_once(self) -> int: + """Process one batch, return the number of rows handled. + + Used by CLI ``cascade sync`` and ``fix --apply`` to flush the + queue without spinning the background task. Returns ``0`` when + the queue is empty. + + For each kind that mutated its LanceDB table this batch, + :meth:`_schedule_optimize` records a throttled optimize + intent. The actual ``optimize()`` runs as a separate task so + drain throughput is decoupled from index maintenance; callers + that need a "fully indexed" barrier (CLI ``cascade sync``) + must call :meth:`_flush_optimizers` — :meth:`drain_until_empty` + does this on their behalf. + """ + batch = await md_change_state_repo.claim_pending_batch(self._batch_size) + if not batch: + return 0 + results = await asyncio.gather(*(self._process_one(row) for row in batch)) + touched_kinds = {kind for kind in results if kind is not None} + for kind in touched_kinds: + self._schedule_optimize(kind) + return len(batch) + + async def drain_until_empty(self, *, max_passes: int = 100) -> int: + """Drain repeatedly until the queue is empty (or ``max_passes``). + + Returns the total number of rows processed. Bounded passes + prevent a livelock if a stuck row keeps re-failing back to + pending (which can't happen in the current design but is a + cheap safety net). + + Awaits :meth:`_flush_optimizers` before returning so callers + (CLI ``cascade sync``) can rely on FTS being up-to-date when + the call completes. + """ + total = 0 + for _ in range(max_passes): + processed = await self.drain_once() + if processed == 0: + break + total += processed + await self._flush_optimizers() + return total + + # ── internals ────────────────────────────────────────────────────────── + + async def _run_loop(self) -> None: + while not self._stop.is_set(): + try: + processed = await self.drain_once() + except Exception as exc: # noqa: BLE001 — never crash the daemon + logger.exception("cascade_worker_drain_failed", error=str(exc)) + processed = 0 + if processed == 0: + try: + await asyncio.wait_for( + self._stop.wait(), timeout=self._poll_interval + ) + except TimeoutError: + continue + + async def _process_one(self, row: MdChangeState) -> str | None: + """Process one ``md_change_state`` row. + + Returns the ``row.kind`` when the handler actually mutated the + kind's LanceDB table (``upserted`` or ``deleted`` > 0) so the + caller can collect a set of "touched kinds" and optimize them + after the batch. Returns ``None`` for skipped-only rows, failed + rows, and rows where no handler is registered — the optimize + step is gated on actual writes happening this batch. + """ + handler = self._handlers.get(row.kind) + if handler is None: + await md_change_state_repo.mark_failed( + row.md_path, + retryable=False, + error=f"no handler registered for kind {row.kind!r}", + new_retry_count=row.retry_count, + ) + return None + + retry_count = row.retry_count + last_error: str = "" + for attempt in range(self._max_retry + 1): + try: + if row.change_type == "deleted": + outcome = await handler.handle_deleted(row.md_path) + else: + outcome = await handler.handle_added_or_modified(row.md_path) + except RecoverableError as exc: + last_error = f"{type(exc).__name__}: {exc}" + logger.warning( + "cascade_worker_recoverable", + md_path=row.md_path, + attempt=attempt, + error=last_error, + ) + if attempt < self._max_retry: + retry_count += 1 + await asyncio.sleep(self._retry_backoff * (attempt + 1)) + continue + await md_change_state_repo.mark_failed( + row.md_path, + retryable=True, + error=last_error, + new_retry_count=retry_count, + ) + return None + except Exception as exc: # noqa: BLE001 — surface as unrecoverable + last_error = f"{type(exc).__name__}: {exc}" + logger.exception( + "cascade_worker_unrecoverable", + md_path=row.md_path, + kind=row.kind, + ) + await md_change_state_repo.mark_failed( + row.md_path, + retryable=False, + error=last_error, + new_retry_count=retry_count, + ) + return None + + logger.info( + "cascade_worker_processed", + md_path=row.md_path, + kind=row.kind, + change_type=row.change_type, + upserted=outcome.upserted, + deleted=outcome.deleted, + skipped=outcome.skipped, + ) + await md_change_state_repo.mark_done(row.md_path) + # Only flag the kind as "touched" when we actually wrote + # something — skipped rows leave the table untouched, so + # optimizing would be pure overhead. + return row.kind if (outcome.upserted or outcome.deleted) else None + return None + + # ── optimizer scheduling ─────────────────────────────────────────────── + + def _schedule_optimize(self, kind: str) -> None: + """Throttle + trailing-edge schedule for a kind's optimize. + + Per-kind semantics — for any one ``kind``: + + - The first call after the throttle window starts an optimize + immediately (initial_delay=0). + - Subsequent calls within the window only set ``dirty=True``; + the in-flight runner picks the flag up and re-runs after the + throttle interval has elapsed. + - A call while a task is in flight returns without starting a + new task — only the dirty flag matters. This guarantees at + most one concurrent ``optimize()`` per kind, which is what + LanceDB's per-table manifest version expects. + + No-op when the handler for ``kind`` doesn't expose a + ``lance_repo`` (test stubs, handlers that intentionally skip + LanceDB). + + Idempotent and cheap (a single dict lookup + flag write in + the hot path) — safe to call on every batch and from the + heartbeat sweep. + """ + handler = self._handlers.get(kind) + repo = getattr(handler, "lance_repo", None) if handler else None + if repo is None: + return + state = self._optimizer_states.setdefault(kind, _KindOptimizerState()) + state.dirty = True + if state.task is not None and not state.task.done(): + return + elapsed = time.monotonic() - state.last_run_at + delay = max(0.0, self._optimize_min_interval - elapsed) + state.task = asyncio.create_task( + self._optimize_runner(kind, initial_delay=delay), + name=f"cascade-optimize-{kind}", + ) + + async def _optimize_runner(self, kind: str, *, initial_delay: float) -> None: + """Run optimize for ``kind`` until ``dirty`` clears. + + Honours the throttle interval on entry (when scheduled + mid-cooldown) and between consecutive runs (when a write + re-raised ``dirty`` during the previous ``optimize()``). The + cooldown waits respect the worker's stop signal so shutdown + doesn't have to outlast the throttle window. + + If a rebuild is in flight for this kind we wait for it before + touching the manifest — concurrent ``optimize`` + ``rebuild`` + on the same LanceDB table would race on the version commit. + """ + state = self._optimizer_states[kind] + try: + if initial_delay > 0 and await self._wait_or_stop(initial_delay): + return + # Serialise behind any in-flight rebuild (rare; only during + # the 12h sweep). Failures are absorbed in _run_rebuild_once. + if state.rebuild_task is not None and not state.rebuild_task.done(): + with contextlib.suppress(Exception): + await state.rebuild_task + while state.dirty and not self._stop.is_set(): + state.dirty = False + state.last_run_at = time.monotonic() + await self._run_optimize_once(kind) + if ( + state.dirty + and not self._stop.is_set() + and await self._wait_or_stop(self._optimize_min_interval) + ): + return + finally: + if state.task is asyncio.current_task(): + state.task = None + + async def _wait_or_stop(self, seconds: float) -> bool: + """Sleep up to ``seconds``; return True if stop was set.""" + try: + await asyncio.wait_for(self._stop.wait(), timeout=seconds) + except TimeoutError: + return False + return True + + async def _run_optimize_once(self, kind: str) -> None: + """Run one ``optimize()`` for ``kind``, opportunistically pruning. + + Most calls take the **light** path — pure compaction + index + merge, fast. Every ``_optimize_prune_interval`` seconds the + next call takes the **heavy** path: same work plus + ``cleanup_older_than`` so the storage layer physically deletes + files belonging to versions older than one cadence. + + Pruning is opt-in per call rather than a separate task so the + existing per-kind serialisation (one in-flight runner per kind) + keeps holding — LanceDB serialises writes on the table's + manifest, and prune is a write. + """ + handler = self._handlers.get(kind) + repo = getattr(handler, "lance_repo", None) if handler else None + if repo is None: + return + state = self._optimizer_states.get(kind) + now = time.monotonic() + should_prune = ( + state is None + or (now - state.last_prune_at) >= self._optimize_prune_interval + ) + cleanup = ( + dt.timedelta(seconds=self._optimize_prune_interval) + if should_prune + else None + ) + try: + await repo.optimize(cleanup_older_than=cleanup) + if should_prune and state is not None: + state.last_prune_at = now + logger.debug( + "cascade_lancedb_optimized", + kind=kind, + pruned=should_prune, + ) + except Exception as exc: # noqa: BLE001 — never crash the daemon + logger.warning( + "cascade_lancedb_optimize_failed", + kind=kind, + pruned=should_prune, + error=f"{type(exc).__name__}: {exc}", + ) + + async def _heartbeat_loop(self) -> None: + """Periodic safety net for the optimizer. + + Sweeps every kind through :meth:`_schedule_optimize` once per + ``optimize_heartbeat_seconds``. Without this, a worker that + restarts with stale unindexed fragments (e.g. after a crash + between write and optimize) would only catch up once new + writes arrive. The sweep goes through the same throttle gate + so it can never storm — kinds with an in-flight optimize or + a fresh ``last_run_at`` are coalesced. + """ + while not self._stop.is_set(): + if await self._wait_or_stop(self._optimize_heartbeat): + return + for kind in self._handlers: + self._schedule_optimize(kind) + + async def _rebuild_loop(self) -> None: + """Slow per-kind ``drop_index + create_index`` loop. + + Workaround for the upstream lancedb / lance gap documented on + :meth:`LanceRepoBase.rebuild_indexes`. Every + ``_optimize_rebuild_interval`` seconds we sweep each kind and + do a full rebuild — this is the **only** lever we have on the + current stack (lancedb 0.30.2 / lance 4.0) to bound active + index UUID / FTS ``part_N`` accumulation. + + First sweep fires immediately on worker start to bound any + accumulation from a previous session. Subsequent sweeps honour + the interval. Both sweep and each per-kind step respect + ``self._stop`` so shutdown is prompt. + + Rebuild is serialised through the per-kind + :class:`_KindOptimizerState.task` slot so it does not race with + an in-flight ``optimize()``. Failures are caught and logged — + a missed rebuild just defers cleanup to the next sweep, which + is harmless for correctness (queries / writes keep working + against the existing indices). + """ + # First sweep: catch up from any prior session before honouring the + # interval. Rebuild is cheap (~0.3s per 50k rows × indexed columns + # in local benchmarks); deferring it 12h after startup risks long + # accumulation if the daemon restarts often. + for kind in self._handlers: + if self._stop.is_set(): + return + await self._run_rebuild_once(kind) + while not self._stop.is_set(): + if await self._wait_or_stop(self._optimize_rebuild_interval): + return + for kind in self._handlers: + if self._stop.is_set(): + return + await self._run_rebuild_once(kind) + + async def _run_rebuild_once(self, kind: str) -> None: + """Drop + re-create all indexes on ``kind``'s LanceDB table. + + Waits for any in-flight ``optimize()`` task to settle, then + claims the per-kind task slot so ``schedule_optimize`` calls + during the rebuild coalesce instead of racing on the manifest. + """ + handler = self._handlers.get(kind) + repo = getattr(handler, "lance_repo", None) if handler else None + if repo is None: + return + state = self._optimizer_states.setdefault(kind, _KindOptimizerState()) + # Drain any in-flight optimize before taking the rebuild slot — + # both would commit on the same manifest version. The optimize + # runner reciprocates (it awaits ``state.rebuild_task`` on entry). + if state.task is not None and not state.task.done(): + with contextlib.suppress(Exception): + await state.task + rebuild_task = asyncio.create_task( + repo.rebuild_indexes(), name=f"cascade-rebuild-{kind}-inner" + ) + state.rebuild_task = rebuild_task + try: + await rebuild_task + logger.info("cascade_lancedb_rebuilt", kind=kind) + except Exception as exc: # noqa: BLE001 — never crash the daemon + logger.warning( + "cascade_lancedb_rebuild_failed", + kind=kind, + error=f"{type(exc).__name__}: {exc}", + ) + finally: + if state.rebuild_task is rebuild_task: + state.rebuild_task = None + + async def _flush_optimizers(self) -> None: + """Wait for every in-flight optimize task to settle. + + Drain-loop path is fire-and-forget for throughput; this is the + explicit barrier used by CLI ``cascade sync`` and worker + shutdown to ensure FTS is consistent with the data on disk + before the call returns. + + Exceptions from optimize tasks are already logged in + :meth:`_run_optimize_once`; ``return_exceptions=True`` here + keeps the flush itself from raising. + """ + pending: list[asyncio.Task[None]] = [] + for state in self._optimizer_states.values(): + if state.task is not None and not state.task.done(): + pending.append(state.task) + if state.rebuild_task is not None and not state.rebuild_task.done(): + pending.append(state.rebuild_task) + if not pending: + return + await asyncio.gather(*pending, return_exceptions=True) diff --git a/src/everos/memory/events.py b/src/everos/memory/events.py new file mode 100644 index 00000000..c2119870 --- /dev/null +++ b/src/everos/memory/events.py @@ -0,0 +1,112 @@ +"""Domain events emitted by memory pipelines, consumed by OME strategies.""" + +from __future__ import annotations + +from everalgo.types import MemCell + +from everos.infra.ome.events import BaseEvent + + +class UserPipelineStarted(BaseEvent): + """Fired at the start of :class:`UserMemoryPipeline.run`, once per cell. + + Hot-path emit, so atomic_fact / foresight / clustering strategies can + start in parallel with the in-pipeline Episode extraction. Carries the + algo-side ``MemCell`` so crash recovery has the full payload (OME + serialises events to JSON via Pydantic v2 nested-model handling). + """ + + memcell_id: str + session_id: str + app_id: str = "default" + project_id: str = "default" + memcell: MemCell + + +class AgentPipelineStarted(BaseEvent): + """Fired at the start of :class:`AgentMemoryPipeline.run`, once per cell. + + Only emitted in ``mode="agent"`` (the agent pipeline does not run in + chat mode). Subscribers handle the agent-side processing chain + (case extraction, agent-skill clustering) in parallel with the user + chain. Payload mirrors :class:`UserPipelineStarted`. + """ + + memcell_id: str + session_id: str + app_id: str = "default" + project_id: str = "default" + memcell: MemCell + + +class EpisodeExtracted(BaseEvent): + """Fired once per Episode after :class:`UserMemoryPipeline` writes its md. + + Carries ``episode_text`` so downstream clustering can embed it without + racing the cascade (cascade also embeds, but at the LanceDB layer — + keeping a copy on the event is cheaper than polling LanceDB until the + row appears). ``episode_timestamp_ms`` rides along so the cluster + strategy can stamp the algo-side ``Cluster.last_ts`` without a second + md read. One memcell can produce multiple episodes (one per user + sender), so this event fires per-episode, not per-memcell. + """ + + memcell_id: str + episode_entry_id: str + episode_text: str + episode_timestamp_ms: int + owner_id: str + app_id: str = "default" + project_id: str = "default" + + +class AgentCaseExtracted(BaseEvent): + """Fired by ``extract_agent_case`` after the AgentCase md is written. + + Carries ``task_intent`` so the skill-clustering strategy can embed it + directly, and ``quality_score`` so the strategy can short-circuit + before any embedding work when the case is below algo's quality floor + (``AgentCaseExtractor`` also short-circuits internally; this is the + upstream gate that saves an LLM call too). ``case_timestamp_ms`` + drives the algo-side ``Cluster.last_ts`` for the time-window filter + in :func:`everalgo.clustering.cluster_by_geometry`. + """ + + memcell_id: str + case_entry_id: str + task_intent: str + quality_score: float + case_timestamp_ms: int + agent_id: str + app_id: str = "default" + project_id: str = "default" + + +class ProfileClusterUpdated(BaseEvent): + """Fired after the user-memory cluster strategy has merged a new + memcell into a cluster. + + Drives the profile-extraction strategy; ``cluster_id`` is the new + or merged cluster the source memcell now belongs to. + """ + + memcell_id: str + cluster_id: str + owner_id: str + app_id: str = "default" + project_id: str = "default" + + +class SkillClusterUpdated(BaseEvent): + """Fired after the agent-case cluster strategy has merged a new + case into a cluster. + + Drives the agent-skill extraction strategy; ``cluster_id`` is the + new or merged cluster the source case now belongs to. + """ + + case_entry_id: str + cluster_id: str + agent_id: str + app_id: str = "default" + project_id: str = "default" diff --git a/src/everos/memory/extract/__init__.py b/src/everos/memory/extract/__init__.py new file mode 100644 index 00000000..e198c389 --- /dev/null +++ b/src/everos/memory/extract/__init__.py @@ -0,0 +1,6 @@ +"""Write path: receive message → call everalgo algorithms → write store. + +Owner inference (``Episode.owner_id``) is owned by the algo layer +(``everalgo``) — everos consumes algo's output and routes md path / +memcell rows by it, no inference logic here. +""" diff --git a/src/everos/memory/extract/evolution/__init__.py b/src/everos/memory/extract/evolution/__init__.py new file mode 100644 index 00000000..9e64e221 --- /dev/null +++ b/src/everos/memory/extract/evolution/__init__.py @@ -0,0 +1,5 @@ +"""Async evolution. + +Three trigger types (event / counter / cron) + scheduler + evolution jobs +(Foresight / AtomicFact / Profile / Skill merge). +""" diff --git a/src/everos/memory/extract/ingest/__init__.py b/src/everos/memory/extract/ingest/__init__.py new file mode 100644 index 00000000..149360bb --- /dev/null +++ b/src/everos/memory/extract/ingest/__init__.py @@ -0,0 +1,17 @@ +"""Ingest Gateway — normalise external input into canonical form. + +External usage: + from everos.memory.extract.ingest import process, gen_message_id + +The current implementation only handles text content; non-text +``ContentItem`` entries are dropped with a warning. The parser hook +(``memory/extract/parser/``) is reserved for future milestones. +""" + +from .id_gen import gen_message_id as gen_message_id +from .service import process as process + +__all__ = [ + "gen_message_id", + "process", +] diff --git a/src/everos/memory/extract/ingest/id_gen.py b/src/everos/memory/extract/ingest/id_gen.py new file mode 100644 index 00000000..a88c9196 --- /dev/null +++ b/src/everos/memory/extract/ingest/id_gen.py @@ -0,0 +1,17 @@ +"""Deterministic, human-readable ``message_id`` generation. + +Format: ``m___``. + +Human-readable so logs / debugging / md entries stay greppable. Deterministic +so caller retries (same payload) produce the same ids — pipeline merge +naturally dedupes via the message_id PK in ``unprocessed_buffer``. +""" + +from __future__ import annotations + +_IDX_PAD = 3 # caller batches are capped at 500 messages (DTO limit), 3 digits cover it + + +def gen_message_id(session_id: str, timestamp_ms: int, idx: int) -> str: + """Return ``m___``.""" + return f"m_{session_id}_{timestamp_ms}_{idx:0{_IDX_PAD}d}" diff --git a/src/everos/memory/extract/ingest/multimodal.py b/src/everos/memory/extract/ingest/multimodal.py new file mode 100644 index 00000000..45fc8ab1 --- /dev/null +++ b/src/everos/memory/extract/ingest/multimodal.py @@ -0,0 +1,95 @@ +"""Multimodal content normalisation. + +Coerces raw message ``content`` into a list of ContentItem dicts and derives +the flat ``text`` the LLM-facing extractors + md writer consume. Non-text +items contribute their ``parsed_content`` (populated by the parser hook, +``memory/extract/parser``) rendered as a provenance tag +``[TYPE: name]\\n{parsed_content}``; non-text items still lacking +``parsed_content`` are counted (a warning is logged) so callers can surface +them via ``IngestResult.unparsed_non_text_count``. +""" + +from __future__ import annotations + +from typing import Any + +from everos.core.observability.logging import get_logger + +logger = get_logger(__name__) + + +def coerce_items( + content: str | list[dict[str, Any]] | list[Any], +) -> list[dict[str, Any]]: + """Coerce ``content`` into a list of ContentItem dicts. + + Accepts the simplified ``str`` form (DTO sugar) or a list of + ContentItem-shaped dicts / Pydantic models. + """ + if isinstance(content, str): + return [{"type": "text", "text": content}] + return [_coerce_item(item) for item in content] + + +def derive_text(items: list[dict[str, Any]]) -> tuple[str, int]: + """Render items into the derived ``text`` + count still-unparsed non-text. + + Returns ``(text, non_text_count)``: ``text`` is the newline-joined render + of all items; ``non_text_count`` is the number of non-text items that have + no ``parsed_content`` yet. + """ + parts: list[str] = [] + non_text = 0 + for item in items: + rendered = _render_item(item) + if rendered: + parts.append(rendered) + elif item.get("type") != "text": + non_text += 1 + logger.warning( + "multimodal_content_not_parsed", + extra={"content_type": item.get("type", "unknown")}, + ) + return "\n".join(parts), non_text + + +def normalise_content( + content: str | list[dict[str, Any]] | list[Any], +) -> tuple[list[dict[str, Any]], str, int]: + """Coerce + derive in one step (text-only path; no parsing). + + Returns ``(content_items, text, non_text_count)``. The ingest service + splits this into :func:`coerce_items` / :func:`derive_text` so the parser + hook can run between them; this wrapper is kept for text-only callers. + """ + items = coerce_items(content) + text, non_text = derive_text(items) + return items, text, non_text + + +def _render_item(item: dict[str, Any]) -> str | None: + """Render one item to text, or ``None`` if it contributes nothing. + + Text items yield their ``text``; non-text items yield + ``[TYPE: name]\\n{parsed_content}`` once parsed; unparsed non-text yields + ``None``. + """ + if item.get("type") == "text": + text = item.get("text") + return str(text) if text else None + parsed = item.get("parsed_content") + if not parsed: + return None + kind = str(item.get("type") or "file").upper() + name = item.get("name") or "" + tag = f"[{kind}: {name}]" if name else f"[{kind}]" + return f"{tag}\n{parsed}" + + +def _coerce_item(item: Any) -> dict[str, Any]: + """Coerce ``item`` (dict or pydantic model) into a plain dict.""" + if hasattr(item, "model_dump"): + return dict(item.model_dump()) + if isinstance(item, dict): + return dict(item) + return {"type": "unknown", "raw": repr(item)} diff --git a/src/everos/memory/extract/ingest/service.py b/src/everos/memory/extract/ingest/service.py new file mode 100644 index 00000000..c3f66a1a --- /dev/null +++ b/src/everos/memory/extract/ingest/service.py @@ -0,0 +1,109 @@ +"""Ingest pipeline entry — normalise external input into canonical form. + +Input shape (received from the service layer, decoupled from any +specific DTO module): + + { + "session_id": "...", + "messages": [ + { + "sender_id": "...", + "sender_name": "...", # optional + "role": "user" | "assistant" | "tool", + "timestamp": 1740564000000, # unix ms + "content": "..." | [ContentItem dicts], + "tool_calls": [...] | None, # OpenAI shape + "tool_call_id": "..." | None, + }, + ... + ] + } + +Output: :class:`everos.memory.IngestResult`. +""" + +from __future__ import annotations + +from typing import Any + +from everos.component.llm import get_multimodal_llm_client +from everos.component.utils.datetime import from_timestamp +from everos.config import load_settings +from everos.memory import CanonicalMessage, IngestResult, ToolCall +from everos.memory.extract.parser import ( + enrich_content_items, + has_unparsed_multimodal, + require_multimodal, +) + +from .id_gen import gen_message_id +from .multimodal import coerce_items, derive_text + + +async def process(payload: dict[str, Any]) -> IngestResult: + """Normalise the raw add payload into an :class:`IngestResult`. + + The function is ``async`` for symmetry with the rest of the pipeline, + even though current logic is pure CPU. + """ + session_id: str = payload["session_id"] + app_id: str = payload.get("app_id") or "default" + project_id: str = payload.get("project_id") or "default" + raw_messages: list[dict[str, Any]] = payload["messages"] + + canonical: list[CanonicalMessage] = [] + non_text_total = 0 + for idx, m in enumerate(raw_messages): + content_items = coerce_items(m["content"]) + if has_unparsed_multimodal(content_items): + require_multimodal() + await enrich_content_items( + content_items, + llm=get_multimodal_llm_client(), + max_concurrency=load_settings().multimodal.max_concurrency, + ) + text, non_text = derive_text(content_items) + non_text_total += non_text + + ts_ms: int = int(m["timestamp"]) + message_id = gen_message_id(session_id, ts_ms, idx) + ts = from_timestamp(ts_ms) + + canonical.append( + CanonicalMessage( + message_id=message_id, + session_id=session_id, + sender_id=m["sender_id"], + sender_name=m.get("sender_name"), + role=m["role"], + timestamp=ts, + content_items=content_items, + text=text, + tool_calls=_coerce_tool_calls(m.get("tool_calls")), + tool_call_id=m.get("tool_call_id"), + ) + ) + + return IngestResult( + session_id=session_id, + app_id=app_id, + project_id=project_id, + messages=canonical, + unparsed_non_text_count=non_text_total, + ) + + +def _coerce_tool_calls( + raw: list[dict[str, Any]] | list[Any] | None, +) -> list[ToolCall] | None: + if not raw: + return None + out: list[ToolCall] = [] + for tc in raw: + if isinstance(tc, ToolCall): + out.append(tc) + elif hasattr(tc, "model_dump"): + out.append(ToolCall.model_validate(tc.model_dump())) + else: + out.append(ToolCall.model_validate(tc)) + return out diff --git a/src/everos/memory/extract/parser/__init__.py b/src/everos/memory/extract/parser/__init__.py new file mode 100644 index 00000000..081435cb --- /dev/null +++ b/src/everos/memory/extract/parser/__init__.py @@ -0,0 +1,27 @@ +"""memory.extract.parser — multimodal ContentItem parsing via everalgo.parser. + +External usage: + from everos.memory.extract.parser import ( + enrich_content_items, + has_unparsed_multimodal, + multimodal_available, + require_multimodal, + ) + +Only :func:`enrich_content_items` touches the optional ``everalgo.parser`` +extra, and it does so lazily (inside the call). The availability helpers and +mapping never import it, so this package is safe to import without the +``everos[multimodal]`` extra installed. +""" + +from .availability import has_unparsed_multimodal as has_unparsed_multimodal +from .availability import multimodal_available as multimodal_available +from .availability import require_multimodal as require_multimodal +from .enrich import enrich_content_items as enrich_content_items + +__all__ = [ + "enrich_content_items", + "has_unparsed_multimodal", + "multimodal_available", + "require_multimodal", +] diff --git a/src/everos/memory/extract/parser/availability.py b/src/everos/memory/extract/parser/availability.py new file mode 100644 index 00000000..544ed699 --- /dev/null +++ b/src/everos/memory/extract/parser/availability.py @@ -0,0 +1,44 @@ +"""Multimodal capability detection + guard. + +This module must NOT import ``everalgo.parser`` at module level — its job is +to answer "is the extra installed?" before anything tries to use it. The +probe lives inside :func:`multimodal_available`. +""" + +from __future__ import annotations + +from typing import Any + +from everos.core.errors import MultimodalNotEnabledError + +_INSTALL_HINT = ( + "Multimodal input received but the parser extra is not installed. " + "Install it with: pip install 'everos[multimodal]' " + "(or uv add 'everos[multimodal]')." +) + + +def has_unparsed_multimodal(items: list[dict[str, Any]]) -> bool: + """True if any content item is non-text and not yet parsed.""" + return any( + item.get("type") != "text" and "parsed_content" not in item for item in items + ) + + +def multimodal_available() -> bool: + """Whether the ``everalgo.parser`` extra is importable.""" + try: + import everalgo.parser # noqa: F401 + except ImportError: + return False + return True + + +def require_multimodal() -> None: + """Guard: raise when multimodal input arrives without the extra installed. + + Raises: + MultimodalNotEnabledError: When ``everalgo.parser`` cannot be imported. + """ + if not multimodal_available(): + raise MultimodalNotEnabledError(_INSTALL_HINT) diff --git a/src/everos/memory/extract/parser/enrich.py b/src/everos/memory/extract/parser/enrich.py new file mode 100644 index 00000000..708e9c0c --- /dev/null +++ b/src/everos/memory/extract/parser/enrich.py @@ -0,0 +1,79 @@ +"""Parse non-text content items via everalgo.parser, backfilling in place. + +The ``everalgo.parser`` import is deferred to call time so importing this +module never requires the optional ``everos[multimodal]`` extra. The ingest +stage calls :func:`require_multimodal` first, so a missing extra surfaces the +guided install error before this runs. +""" + +from __future__ import annotations + +import asyncio +from typing import Any + +from everalgo.llm import LLMError +from everalgo.llm.protocols import LLMClient + +from everos.core.errors import MultimodalNotEnabledError, UnsupportedModalityError +from everos.core.observability.logging import get_logger + +from .mapping import build_raw_file + +logger = get_logger(__name__) + + +async def enrich_content_items( + items: list[dict[str, Any]], *, llm: LLMClient, max_concurrency: int = 4 +) -> None: + """Parse each non-text item and backfill ``parsed_content`` in place. + + Synchronous to the request; items parse concurrently under a bounded + semaphore. Deterministic failures (unsupported modality, missing system + dependency) raise a :class:`~everos.core.errors.MultimodalError` subclass + and abort the batch; transient failures (LLM errors) degrade per item + (``parse_status="failed"``) without dropping the rest. + + Args: + items: ContentItem dicts (mutated in place). + llm: Multimodal LLM client passed to ``everalgo.parser.aparse``. + max_concurrency: Upper bound on concurrent parse calls. + """ + from everalgo.parser import aparse # optional dependency, imported lazily + + targets = [ + item + for item in items + if item.get("type") != "text" and "parsed_content" not in item + ] + if not targets: + return + + semaphore = asyncio.Semaphore(max_concurrency) + + async def _parse_one(item: dict[str, Any]) -> None: + async with semaphore: + try: + parsed = await aparse(await build_raw_file(item), llm=llm) + except NotImplementedError as exc: + raise UnsupportedModalityError( + f"modality not supported: {item.get('type')!r}" + ) from exc + except LLMError as exc: + # Transient: degrade this item, keep the rest of the batch. + item["parse_status"] = "failed" + item["parse_error"] = type(exc).__name__ + logger.warning( + "multimodal_parse_failed", + extra={"content_type": item.get("type")}, + ) + return + except ValueError as exc: + # everalgo dispatch / mapping rejected the input. + raise UnsupportedModalityError(str(exc)) from exc + except RuntimeError as exc: + # e.g. LibreOffice missing for Office documents. + raise MultimodalNotEnabledError(str(exc)) from exc + item["parsed_content"] = parsed.text + item["parse_status"] = "success" + + await asyncio.gather(*(_parse_one(item) for item in targets)) diff --git a/src/everos/memory/extract/parser/mapping.py b/src/everos/memory/extract/parser/mapping.py new file mode 100644 index 00000000..204588c6 --- /dev/null +++ b/src/everos/memory/extract/parser/mapping.py @@ -0,0 +1,140 @@ +"""ContentItem dict -> everalgo ``RawFile``. + +``RawFile`` ships in everalgo-core (always installed), so this module is safe +to import without the ``everos[multimodal]`` extra. + +everalgo deliberately never reads the host filesystem (``http(s)`` fetch or +caller-hydrated bytes only). EverOS — the trust boundary that legitimately +touches local files — owns ``file://`` support: :func:`build_raw_file` reads +the file locally (with guardrails) and hands everalgo a *hydrated* RawFile +(``content`` bytes), so the library stays filesystem-stateless. +""" + +from __future__ import annotations + +import base64 +from pathlib import Path +from typing import Any +from urllib.parse import unquote, urlparse + +import anyio +from everalgo.types import RawFile + +from everos.config import load_settings +from everos.core.observability.logging import get_logger + +logger = get_logger(__name__) + + +def to_raw_file(item: dict[str, Any]) -> RawFile: + """Build an :class:`everalgo.types.RawFile` from a ContentItem dict. + + ``uri``-backed items are handed to everalgo as-is (it fetches ``http(s)`` + and dispatches by Content-Type). ``base64``-backed items are decoded and + keyed by the item's ``ext`` (everalgo dispatches by extension). + + Note: ``file://`` uris are **not** resolved here — they are hydrated + upstream by :func:`build_raw_file`. A ``file://`` item reaching this + function is returned as a uri RawFile (which everalgo then rejects). + + Raises: + ValueError: When the item carries neither ``uri`` nor ``base64``. + """ + uri = item.get("uri") + if uri: + return RawFile(uri=uri) + encoded = item.get("base64") + if encoded: + return RawFile( + content=base64.b64decode(encoded), + extension=(item.get("ext") or "").lstrip(".").lower(), + mime=item.get("mime") or "", + ) + raise ValueError( + f"content item has neither uri nor base64 (type={item.get('type')!r})" + ) + + +def _is_file_uri(uri: str) -> bool: + return urlparse(uri).scheme == "file" + + +def _resolve_file_uri(uri: str) -> Path: + """Parse a ``file://`` uri into a canonical local path (symlinks resolved). + + Raises ``ValueError`` for a remote host component or a path that does not + exist (``resolve(strict=True)``). + """ + parsed = urlparse(uri) + if parsed.netloc and parsed.netloc not in ("", "localhost"): + raise ValueError(f"file uri with remote host not supported: {parsed.netloc!r}") + try: + return Path(unquote(parsed.path)).expanduser().resolve(strict=True) + except OSError as exc: + raise ValueError(f"cannot resolve file uri {uri!r}: {exc}") from exc + + +def _validated_file_path(uri: str) -> Path: + """Resolve + guardrail-check a ``file://`` uri (synchronous fs ops). + + This is the host-filesystem access everalgo refuses by design, so the + guardrails live here (the application trust boundary): + + * canonical, symlink-resolved path that must be an existing regular file; + * size-capped by ``settings.multimodal.file_uri_max_bytes``; + * confined to ``settings.multimodal.file_uri_allow_dirs`` when that + allowlist is non-empty (empty = allow any readable file, the + local-first default). + + Raises: + ValueError: missing / non-regular / oversized / out-of-allowlist file. + """ + cfg = load_settings().multimodal + path = _resolve_file_uri(uri) + if not path.is_file(): + raise ValueError(f"file uri target is not a regular file: {path}") + + allow = [Path(d).expanduser().resolve() for d in cfg.file_uri_allow_dirs] + if allow and not any(path.is_relative_to(r) for r in allow): + raise ValueError( + f"file uri {path} is outside the allowed roots " + "(set EVEROS_MULTIMODAL__FILE_URI_ALLOW_DIRS to permit it)" + ) + + size = path.stat().st_size + if size > cfg.file_uri_max_bytes: + raise ValueError( + f"file uri target too large: {size} bytes " + f"(cap {cfg.file_uri_max_bytes}; raise " + "EVEROS_MULTIMODAL__FILE_URI_MAX_BYTES)" + ) + return path + + +async def read_file_uri(uri: str, *, ext_hint: str | None = None) -> tuple[bytes, str]: + """Read a guardrail-checked ``file://`` asset into ``(bytes, extension)``. + + Raises: + ValueError: missing / non-regular / oversized / out-of-allowlist file. + """ + path = _validated_file_path(uri) + content = await anyio.Path(path).read_bytes() + ext = (ext_hint or path.suffix).lstrip(".").lower() + logger.debug("file_uri_hydrated", path=str(path), ext=ext) + return content, ext + + +async def build_raw_file(item: dict[str, Any]) -> RawFile: + """Build a RawFile for the parser, hydrating ``file://`` uris locally. + + ``http(s)`` uris and ``base64`` payloads route through the synchronous + :func:`to_raw_file`. A ``file://`` uri is read here into a hydrated + RawFile (``content`` bytes + ``extension``) so everalgo never touches the + filesystem. The original item dict is left unchanged (the buffer keeps the + lightweight ``file://`` uri, not the inlined bytes). + """ + uri = item.get("uri") + if uri and _is_file_uri(uri): + content, ext = await read_file_uri(uri, ext_hint=item.get("ext")) + return RawFile(content=content, extension=ext, mime=item.get("mime") or "") + return to_raw_file(item) diff --git a/src/everos/memory/extract/pipeline/__init__.py b/src/everos/memory/extract/pipeline/__init__.py new file mode 100644 index 00000000..dfe3d471 --- /dev/null +++ b/src/everos/memory/extract/pipeline/__init__.py @@ -0,0 +1,21 @@ +"""Main extraction pipelines — one per track. + +External usage: + from everos.memory.extract.pipeline import ( + Pipeline, UserMemoryPipeline, AgentMemoryPipeline, + ) + +Calls everalgo (the algorithm library) for boundary detection and +synchronous Episode extraction. Agent track is currently stubbed — +returns ``status="skipped"`` until the algo extractors land. +""" + +from .agent_memory import AgentMemoryPipeline as AgentMemoryPipeline +from .base import Pipeline as Pipeline +from .user_memory import UserMemoryPipeline as UserMemoryPipeline + +__all__ = [ + "AgentMemoryPipeline", + "Pipeline", + "UserMemoryPipeline", +] diff --git a/src/everos/memory/extract/pipeline/agent_memory.py b/src/everos/memory/extract/pipeline/agent_memory.py new file mode 100644 index 00000000..a8fd7b28 --- /dev/null +++ b/src/everos/memory/extract/pipeline/agent_memory.py @@ -0,0 +1,62 @@ +"""Agent memory pipeline — 5A in the add_v1 flow. + +Consumes pre-cut cells from :mod:`everos.service._boundary` and emits +:class:`AgentPipelineStarted` per cell so the OME ``extract_agent_cases`` +strategy (separate work item) picks it up. + +No sqlite memcell row is written here — the boundary stage owns that +ledger (one row per cell, shared across user / agent paths via the same +``memcell_id``). No md is written here either: Episode md comes from +:class:`UserMemoryPipeline`, AgentCase md is the OME strategy's job. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from everos.core.observability.logging import get_logger +from everos.memory import IngestResult, PipelineOutcome +from everos.memory.events import AgentPipelineStarted + +if TYPE_CHECKING: + from everalgo.types import MemCell as AlgoMemCell + + from everos.infra.ome.engine import OfflineEngine + +logger = get_logger(__name__) + +_TRACK = "agent_memory" + + +class AgentMemoryPipeline: + """Emit ``AgentPipelineStarted`` per cell — fire-and-forget to OME.""" + + def __init__(self, engine: OfflineEngine) -> None: + self._engine = engine + + async def run( + self, + ingested: IngestResult, + cells: list[AlgoMemCell], + memcell_ids: list[str], + ) -> PipelineOutcome: + """Emit ``AgentPipelineStarted`` per cell.""" + if not cells: + return PipelineOutcome(track=_TRACK, status="accumulated", message_count=0) + + for cell, memcell_id in zip(cells, memcell_ids, strict=True): + await self._engine.emit( + AgentPipelineStarted( + memcell_id=memcell_id, + session_id=ingested.session_id, + app_id=ingested.app_id, + project_id=ingested.project_id, + memcell=cell, + ) + ) + + return PipelineOutcome( + track=_TRACK, + status="extracted", + message_count=sum(len(c.items) for c in cells), + ) diff --git a/src/everos/memory/extract/pipeline/base.py b/src/everos/memory/extract/pipeline/base.py new file mode 100644 index 00000000..4d6c4ba9 --- /dev/null +++ b/src/everos/memory/extract/pipeline/base.py @@ -0,0 +1,22 @@ +"""Pipeline contract — every concrete pipeline implements ``Pipeline``.""" + +from __future__ import annotations + +from typing import Protocol + +from everos.memory import IngestResult, PipelineOutcome + + +class Pipeline(Protocol): + """Asynchronous extraction pipeline. + + Each implementation owns its own buffer slice (one ``track``) and is + invoked concurrently with siblings by ``service.memorize()``. + """ + + async def run( + self, + ingested: IngestResult, + *, + is_final: bool = False, + ) -> PipelineOutcome: ... diff --git a/src/everos/memory/extract/pipeline/user_memory.py b/src/everos/memory/extract/pipeline/user_memory.py new file mode 100644 index 00000000..417f115d --- /dev/null +++ b/src/everos/memory/extract/pipeline/user_memory.py @@ -0,0 +1,243 @@ +"""User memory pipeline — per-sender Episode fan-out on pre-cut cells. + +Cells / memcell_ids / message_id-mapping / sender lists are produced by +:mod:`everos.service._boundary` (which also writes the single +``memcell`` sqlite row per cell). This pipeline only handles the +user-perspective output: Episode md + ``UserPipelineStarted`` emit (one +per cell, fired at the start of ``run`` so atomic_fact / foresight / +clustering strategies run in parallel with the in-pipeline Episode work). + +Run inside ``service.memorize`` via ``asyncio.gather`` alongside +:class:`AgentMemoryPipeline` (the latter only in ``mode="agent"``). +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from everalgo.types import MemCell as AlgoMemCell +from everalgo.user_memory import EpisodeExtractor + +from everos.component.utils.datetime import from_timestamp, to_iso_format +from everos.core.observability.logging import get_logger +from everos.memory import Episode, IngestResult, PipelineOutcome +from everos.memory.events import EpisodeExtracted, UserPipelineStarted +from everos.memory.prompt_slots import PromptLoader + +if TYPE_CHECKING: + from everalgo.llm.protocols import LLMClient + + from everos.infra.ome.engine import OfflineEngine + from everos.infra.persistence.markdown import EpisodeWriter + +logger = get_logger(__name__) + +_TRACK = "user_memory" + + +class UserMemoryPipeline: + """Per-sender Episode extraction on a list of pre-cut MemCells.""" + + def __init__( + self, + episode_writer: EpisodeWriter, + prompt_loader: PromptLoader, + llm_client: LLMClient | None, + engine: OfflineEngine, + ) -> None: + # EpisodeExtractor requires `llm` at construction. Skip-with-warning + # when no LLM is configured — the boundary stage will have skipped + # the run already; this is just a defensive null check. + self._ep_ext = ( + EpisodeExtractor(llm=llm_client) if llm_client is not None else None + ) + self._episode_writer = episode_writer + self._prompt_loader = prompt_loader + self._engine = engine + + async def run( + self, + ingested: IngestResult, + cells: list[AlgoMemCell], + memcell_ids: list[str], + per_cell_all_senders: list[list[str]], + ) -> PipelineOutcome: + """Emit UserPipelineStarted per cell, then extract Episodes + write md.""" + if not cells: + return PipelineOutcome(track=_TRACK, status="accumulated", message_count=0) + if self._ep_ext is None: + logger.warning( + "user_memory_pipeline_no_llm_client", + extra={"session_id": ingested.session_id, "cells": len(cells)}, + ) + return PipelineOutcome(track=_TRACK, status="skipped", message_count=0) + + # Emit upfront so OME-async strategies (atomic_fact / foresight / + # cluster) start in parallel with the in-pipeline Episode work; they + # consume the MemCell directly and do not depend on Episode output. + for cell, memcell_id in zip(cells, memcell_ids, strict=True): + await self._emit_pipeline_started( + memcell_id=memcell_id, + session_id=ingested.session_id, + app_id=ingested.app_id, + project_id=ingested.project_id, + cell=cell, + ) + + episode_prompt = self._prompt_loader.load("episode_extract") + md_paths: list[str] = [] + msg_count = 0 + for cell, memcell_id, all_senders in zip( + cells, memcell_ids, per_cell_all_senders, strict=True + ): + msg_count += len(cell.items) + user_senders = _unique_user_senders(cell) + if not user_senders: + continue + # One generic LLM call per cell (sender_id=None drives the algo's + # whole-memcell EPISODE_GENERATION_PROMPT — explicitly cheaper + # than the per-user fan-out per the algo's docstring). Fan-out + # is then md-only: every user sender owns a copy of the same + # narrative under its own owner_id path. + algo_ep = await self._ep_ext.aextract( + cell, sender_id=None, prompt=episode_prompt + ) + for sender_id in user_senders: + ep = Episode.from_algo( + algo_ep, + owner_id=sender_id, + session_id=ingested.session_id, + sender_ids=all_senders, + parent_id=memcell_id, + ) + inline, sections = _episode_to_entry_body(ep) + eid = await self._episode_writer.append_entry( + ep.owner_id, + inline=inline, + sections=sections, + app_id=ingested.app_id, + project_id=ingested.project_id, + ) + md_paths.append( + str( + self._episode_writer.path_for( + ep.owner_id, + eid.date, + app_id=ingested.app_id, + project_id=ingested.project_id, + ) + ) + ) + await self._engine.emit( + EpisodeExtracted( + memcell_id=memcell_id, + episode_entry_id=eid.format(), + episode_text=ep.episode, + episode_timestamp_ms=ep.timestamp, + owner_id=ep.owner_id, + app_id=ingested.app_id, + project_id=ingested.project_id, + ) + ) + + return PipelineOutcome( + track=_TRACK, + status="extracted", + message_count=msg_count, + extracted_md_paths=md_paths, + ) + + async def _emit_pipeline_started( + self, + memcell_id: str, + session_id: str, + app_id: str, + project_id: str, + cell: AlgoMemCell, + ) -> None: + await self._engine.emit( + UserPipelineStarted( + memcell_id=memcell_id, + session_id=session_id, + app_id=app_id, + project_id=project_id, + memcell=cell, + ) + ) + + +# ── Helpers ─────────────────────────────────────────────────────────────── + + +def _unique_user_senders(cell: AlgoMemCell) -> list[str]: + """Distinct role=user sender_ids in a cell, preserving order. + + Drives per-sender Episode fan-out: each user perspective gets its own + Episode for the cell. Skips non-``ChatMessage`` items (agent + trajectories' ``ToolCallResult`` has no ``role``). + """ + senders: list[str] = [] + for item in cell.items: + if getattr(item, "role", None) != "user": + continue + sid = getattr(item, "sender_id", None) + if sid and sid not in senders: + senders.append(sid) + return senders + + +def _episode_to_entry_body( + episode: Episode, +) -> tuple[dict[str, object], dict[str, str]]: + """Split a domain Episode into ``(inline, sections)`` for md rendering. + + Lives in the pipeline (memory) layer rather than the writer (infra) + because it depends on :class:`everos.memory.Episode` — infra is not + allowed to import memory per the layered architecture contract. + + Inline persists the audit / scope fields cascade needs to rebuild + the LanceDB row: ``owner_id`` / ``session_id`` / ``timestamp`` / + ``parent_id`` / ``sender_ids``. ``parent_id`` is the source memcell + id (minted by the boundary stage), and the cascade handler reads it + back so the LanceDB ``episode`` row keeps its back-link to the source. + + The md entry's ``entry_id`` (managed by the chassis writer) is the + single source of *entry* identity; cascade derives a global episode + id from ``_`` on the fly. + """ + ts_iso = ( + to_iso_format(from_timestamp(episode.timestamp)) + if isinstance(episode.timestamp, int) + else str(episode.timestamp) + ) + + inline: dict[str, object] = { + "owner_id": episode.owner_id, + "session_id": episode.session_id, + "timestamp": ts_iso, + "parent_type": "memcell", + "parent_id": episode.parent_id, + } + if episode.sender_ids: + inline["sender_ids"] = list(episode.sender_ids) + + extra = episode.model_dump( + exclude={ + "owner_id", + "episode", + "timestamp", + "session_id", + "sender_ids", + "parent_id", + } + ) + subject = extra.pop("subject", None) + summary = extra.pop("summary", None) + + sections: dict[str, str] = {} + if subject: + sections["Subject"] = str(subject) + if summary: + sections["Summary"] = str(summary) + sections["Content"] = episode.episode + return inline, sections diff --git a/src/everos/memory/get/__init__.py b/src/everos/memory/get/__init__.py new file mode 100644 index 00000000..ebb3b8d5 --- /dev/null +++ b/src/everos/memory/get/__init__.py @@ -0,0 +1,50 @@ +"""memory.get — read path: paginated listing over LanceDB. + +This subpackage owns the dispatch + shape layer for ``POST +/api/v1/memory/get``. Unlike :mod:`memory.search`, /get does no +ranking — it is a pure offset/limit + scalar-filter listing, +partitioned by ``(owner_type, memory_type)``. + +Filters reuse :class:`everos.memory.search.FilterNode` and the +shared :func:`compile_filters` path — same DSL surface, same compile +output, ``AND`` / ``OR`` combinators allowed. + +External usage:: + + from everos.memory.get import ( + GetAgentCaseItem, + GetAgentSkillItem, + GetData, + GetEpisodeItem, + GetManager, + GetMemoryType, + GetProfileItem, + GetRequest, + GetResponse, + compile_filters_for_get, + ) +""" + +from .dto import GetAgentCaseItem as GetAgentCaseItem +from .dto import GetAgentSkillItem as GetAgentSkillItem +from .dto import GetData as GetData +from .dto import GetEpisodeItem as GetEpisodeItem +from .dto import GetMemoryType as GetMemoryType +from .dto import GetProfileItem as GetProfileItem +from .dto import GetRequest as GetRequest +from .dto import GetResponse as GetResponse +from .filters_adapter import compile_filters_for_get as compile_filters_for_get +from .manager import GetManager as GetManager + +__all__ = [ + "GetAgentCaseItem", + "GetAgentSkillItem", + "GetData", + "GetEpisodeItem", + "GetManager", + "GetMemoryType", + "GetProfileItem", + "GetRequest", + "GetResponse", + "compile_filters_for_get", +] diff --git a/src/everos/memory/get/dto.py b/src/everos/memory/get/dto.py new file mode 100644 index 00000000..83616724 --- /dev/null +++ b/src/everos/memory/get/dto.py @@ -0,0 +1,221 @@ +"""Public DTOs for ``POST /api/v1/memory/get``. + +Contract per the final design (mirrors :mod:`memory.search.dto` shape, +minus ``score`` because /get is a paginated listing rather than a +ranked retrieval): + +* ``owner_type`` × ``memory_type`` are strictly paired: + + - ``user`` → ``episode`` | ``profile`` + - ``agent`` → ``agent_case`` | ``agent_skill`` + +* ``GetData`` always contains four kind arrays for symmetry with + ``/search``; only the requested kind is populated. ``total_count`` + is the predicate's true match count; ``count`` is the page size + actually returned. + +* ``filters`` reuses :class:`everos.memory.search.FilterNode` — + same DSL, same compile path, ``AND`` / ``OR`` combinators allowed. + The earlier ``/get``-only ban on combinators (from wiki appendix C) + was dropped: the legacy opensource memsys ``/get`` always supported + combinators and there is no engine-side reason to forbid them. +""" + +from __future__ import annotations + +import datetime as _dt +from enum import StrEnum +from typing import Literal, Self + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +from everos.memory.search import FilterNode + + +class GetMemoryType(StrEnum): + """The four kinds enumerated by ``/get``. + + ``episode`` and ``profile`` are user-owned; ``agent_case`` and + ``agent_skill`` are agent-owned. Cross-pairs are rejected by + :meth:`GetRequest._validate_owner_memory_type_pair`. + + Naming note: all four values use the bare kind name (no + ``_memory`` suffix) and match the LanceDB table name + everalgo + type name for that kind. + """ + + EPISODE = "episode" + PROFILE = "profile" + AGENT_CASE = "agent_case" + AGENT_SKILL = "agent_skill" + + +# ── Request ────────────────────────────────────────────────────────────── + + +class GetRequest(BaseModel): + """Request body for ``POST /api/v1/memory/get``. + + Callers identify the memory owner via ``user_id`` XOR ``agent_id`` — + exactly one must be set. Internally the manager keeps using + ``owner_id`` / ``owner_type`` (the storage tables' columns); those + are exposed as derived properties so the rename only affects the + wire contract. + """ + + model_config = ConfigDict(extra="forbid") + + user_id: str | None = Field(default=None, min_length=1) + agent_id: str | None = Field(default=None, min_length=1) + """Memory owner — provide ``user_id`` for ``episode`` / ``profile`` or + ``agent_id`` for ``agent_case`` / ``agent_skill``; exactly one must be set.""" + app_id: str = "default" + project_id: str = "default" + """App / project scope (default ``"default"``). Pinned into the query + ``where`` so a listing never crosses into another space's rows.""" + memory_type: GetMemoryType + page: int = Field(default=1, ge=1) + page_size: int = Field(default=20, ge=1, le=100) + sort_by: Literal["timestamp", "updated_at"] = "timestamp" + """Sort column. ``profile`` and ``agent_skill`` silently override + to ``updated_at`` (profile has no timestamp; agent_skill is a + named entity with no temporal column).""" + + sort_order: Literal["asc", "desc"] = "desc" + filters: FilterNode | None = None + """Filters DSL — same shape as ``/search``, ``AND`` / ``OR`` + combinators allowed.""" + + @model_validator(mode="after") + def _validate_user_xor_agent(self) -> Self: + if (self.user_id is None) == (self.agent_id is None): + raise ValueError("exactly one of user_id / agent_id must be provided") + return self + + @model_validator(mode="after") + def _validate_owner_memory_type_pair(self) -> Self: + # Runs after the xor validator (declaration order), so ``owner_type`` + # is well-defined here. + user_kinds = {GetMemoryType.EPISODE, GetMemoryType.PROFILE} + agent_kinds = {GetMemoryType.AGENT_CASE, GetMemoryType.AGENT_SKILL} + if self.owner_type == "user" and self.memory_type not in user_kinds: + raise ValueError( + f"memory_type {self.memory_type.value!r} is not valid " + "when user_id is set" + ) + if self.owner_type == "agent" and self.memory_type not in agent_kinds: + raise ValueError( + f"memory_type {self.memory_type.value!r} is not valid " + "when agent_id is set" + ) + return self + + @property + def owner_id(self) -> str: + """Derived from whichever of ``user_id`` / ``agent_id`` is set.""" + return self.user_id or self.agent_id or "" + + @property + def owner_type(self) -> Literal["user", "agent"]: + """``"user"`` if ``user_id`` is set, else ``"agent"``.""" + return "user" if self.user_id is not None else "agent" + + +# ── Item DTOs (mirror Search*Item shapes minus score) ──────────────────── + + +class GetEpisodeItem(BaseModel): + """Episode listing item — always user-scoped.""" + + model_config = ConfigDict(extra="forbid") + + id: str + user_id: str | None + app_id: str = "default" + project_id: str = "default" + session_id: str + timestamp: _dt.datetime + sender_ids: list[str] = Field(default_factory=list) + summary: str + subject: str + episode: str + type: Literal["Conversation"] + + +class GetProfileItem(BaseModel): + """Owner profile — at most one per response, only for user owners.""" + + model_config = ConfigDict(extra="forbid") + + id: str + user_id: str | None + app_id: str = "default" + project_id: str = "default" + profile_data: dict[str, object] + + +class GetAgentCaseItem(BaseModel): + """Agent case listing item — always agent-scoped.""" + + model_config = ConfigDict(extra="forbid") + + id: str + agent_id: str + app_id: str = "default" + project_id: str = "default" + session_id: str + task_intent: str + approach: str + quality_score: float + key_insight: str | None = None + timestamp: _dt.datetime + + +class GetAgentSkillItem(BaseModel): + """Agent skill listing item — always agent-scoped.""" + + model_config = ConfigDict(extra="forbid") + + id: str + agent_id: str + app_id: str = "default" + project_id: str = "default" + name: str + description: str + content: str + confidence: float + maturity_score: float + source_case_ids: list[str] = Field(default_factory=list) + + +# ── Response envelope ──────────────────────────────────────────────────── + + +class GetData(BaseModel): + """Body of ``response.data``. + + All four arrays are always present so client code can iterate + without branching on ``memory_type``; the route populates exactly + one. + """ + + model_config = ConfigDict(extra="forbid") + + episodes: list[GetEpisodeItem] = Field(default_factory=list) + profiles: list[GetProfileItem] = Field(default_factory=list) + agent_cases: list[GetAgentCaseItem] = Field(default_factory=list) + agent_skills: list[GetAgentSkillItem] = Field(default_factory=list) + total_count: int = 0 + """Total rows matching the request's owner + filter predicate.""" + + count: int = 0 + """Number of items in this page (``len(items)`` after slicing).""" + + +class GetResponse(BaseModel): + """Top-level response envelope.""" + + model_config = ConfigDict(extra="forbid") + + request_id: str + data: GetData diff --git a/src/everos/memory/get/filters_adapter.py b/src/everos/memory/get/filters_adapter.py new file mode 100644 index 00000000..407a06bc --- /dev/null +++ b/src/everos/memory/get/filters_adapter.py @@ -0,0 +1,40 @@ +"""Adapter: ``/get`` filter compilation reuses :mod:`memory.search`. + +After the 2026-05-16 decision to lift the AND/OR restriction +(``opensource`` memsys's MongoDB-backed ``/get`` always supported +combinators — the wiki appendix C rule was an unmotivated narrowing), +``/get`` and ``/search`` share the same filter DSL **and** the same +compile path. This module is a thin re-export so ``memory.get`` +keeps a stable public name (``compile_filters_for_get``) even if the +underlying compile primitive ever gets renamed. + +See :func:`everos.memory.search.filters.compile_filters` for the full +semantics (``owner_id`` / ``owner_type`` injection, ``ALLOWED_FIELDS``, +operator allow-list, timestamp / array-column rendering). +""" + +from __future__ import annotations + +from everos.memory.search import FilterNode, compile_filters + + +def compile_filters_for_get( + filters: FilterNode | None, + *, + owner_id: str, + owner_type: str, + app_id: str = "default", + project_id: str = "default", +) -> str: + """Compile ``/get`` filters via the shared ``compile_filters`` path. + + Kept as a named wrapper so ``memory.get`` consumers depend on a + stable name rather than reaching into ``memory.search``. + """ + return compile_filters( + filters, + owner_id=owner_id, + owner_type=owner_type, + app_id=app_id, + project_id=project_id, + ) diff --git a/src/everos/memory/get/manager.py b/src/everos/memory/get/manager.py new file mode 100644 index 00000000..1000cbdd --- /dev/null +++ b/src/everos/memory/get/manager.py @@ -0,0 +1,233 @@ +"""GetManager — top-level orchestrator for ``POST /api/v1/memory/get``. + +Hard partition by ``(owner_type, memory_type)`` (validated by +:class:`GetRequest`): + +* ``user`` + ``episode`` → ``data.episodes`` +* ``user`` + ``profile`` → ``data.profiles`` (one-row KV fetch + from the ``user_profile`` table; at most one item) +* ``agent`` + ``agent_case`` → ``data.agent_cases`` +* ``agent`` + ``agent_skill`` → ``data.agent_skills`` + +Reads only — never writes. Filters are compiled through +:func:`compile_filters_for_get` so the column allow-list stays +shared with :mod:`memory.search`. Pagination + in-memory sort +runs through :meth:`LanceRepoBase.find_where_paginated`. +""" + +from __future__ import annotations + +import json +from typing import TYPE_CHECKING, Any + +from everos.component.utils.datetime import to_display_tz +from everos.core.observability.logging import get_logger +from everos.core.observability.tracing import gen_request_id + +from .dto import ( + GetAgentCaseItem, + GetAgentSkillItem, + GetData, + GetEpisodeItem, + GetMemoryType, + GetProfileItem, + GetRequest, + GetResponse, +) +from .filters_adapter import compile_filters_for_get + +if TYPE_CHECKING: + from everos.core.persistence.lancedb import LanceRepoBase + from everos.infra.persistence.lancedb import ( + AgentCase, + AgentSkill, + Episode, + UserProfile, + ) + +logger = get_logger(__name__) + + +class GetManager: + """Dispatch ``GetRequest`` to the matching LanceDB-backed repo and + shape rows into the public DTO.""" + + def __init__( + self, + *, + episode_repo: LanceRepoBase[Episode], + agent_case_repo: LanceRepoBase[AgentCase], + agent_skill_repo: LanceRepoBase[AgentSkill], + user_profile_repo: LanceRepoBase[UserProfile], + ) -> None: + self._ep = episode_repo + self._case = agent_case_repo + self._skill = agent_skill_repo + self._profile = user_profile_repo + + # ── Public entry ───────────────────────────────────────────────── + + async def get(self, req: GetRequest) -> GetResponse: + request_id = gen_request_id() + descending = req.sort_order == "desc" + where = compile_filters_for_get( + req.filters, + owner_id=req.owner_id, + owner_type=req.owner_type, + app_id=req.app_id, + project_id=req.project_id, + ) + + match req.memory_type: + case GetMemoryType.EPISODE: + rows, total = await self._ep.find_where_paginated( + where, + sort_by=req.sort_by, + descending=descending, + page=req.page, + page_size=req.page_size, + ) + items = [self._shape_episode(r) for r in rows] + data = GetData( + episodes=items, + total_count=total, + count=len(items), + ) + case GetMemoryType.PROFILE: + profiles = await self._fetch_profile(req.owner_id) + data = GetData( + profiles=profiles, + total_count=len(profiles), + count=len(profiles), + ) + case GetMemoryType.AGENT_CASE: + rows, total = await self._case.find_where_paginated( + where, + sort_by=req.sort_by, + descending=descending, + page=req.page, + page_size=req.page_size, + ) + items = [self._shape_agent_case(r) for r in rows] + data = GetData( + agent_cases=items, + total_count=total, + count=len(items), + ) + case GetMemoryType.AGENT_SKILL: + # AgentSkill has no ``timestamp`` column. Silently + # downgrade ``sort_by`` to ``updated_at`` (from + # :class:`BaseLanceTable`) so the caller cannot + # accidentally trigger a schema error. + rows, total = await self._skill.find_where_paginated( + where, + sort_by="updated_at", + descending=descending, + page=req.page, + page_size=req.page_size, + ) + items = [self._shape_agent_skill(r) for r in rows] + data = GetData( + agent_skills=items, + total_count=total, + count=len(items), + ) + + return GetResponse(request_id=request_id, data=data) + + # ── Shapers ────────────────────────────────────────────────────── + + @staticmethod + def _shape_episode(row: Episode) -> GetEpisodeItem: + return GetEpisodeItem( + id=row.id, + user_id=row.owner_id, + app_id=row.app_id, + project_id=row.project_id, + session_id=row.session_id, + timestamp=to_display_tz(row.timestamp), + sender_ids=row.sender_ids, + summary=row.summary or "", + subject=row.subject or "", + episode=row.episode, + type="Conversation", + ) + + @staticmethod + def _shape_agent_case(row: AgentCase) -> GetAgentCaseItem: + return GetAgentCaseItem( + id=row.id, + agent_id=row.owner_id, + app_id=row.app_id, + project_id=row.project_id, + session_id=row.session_id, + task_intent=row.task_intent, + approach=row.approach, + quality_score=row.quality_score, + key_insight=row.key_insight, + timestamp=to_display_tz(row.timestamp), + ) + + @staticmethod + def _shape_agent_skill(row: AgentSkill) -> GetAgentSkillItem: + return GetAgentSkillItem( + id=row.id, + agent_id=row.owner_id, + app_id=row.app_id, + project_id=row.project_id, + name=row.name, + description=row.description, + content=row.content, + confidence=row.confidence, + maturity_score=row.maturity_score, + source_case_ids=row.source_case_ids, + ) + + # ── Profile ────────────────────────────────────────────────────── + + async def _fetch_profile(self, owner_id: str) -> list[GetProfileItem]: + """Fetch the owner's single profile row from the ``user_profile`` + LanceDB table (kept in sync with ``users//user.md`` by cascade). + + Profile is one-row-per-owner KV — there is no pagination / sort / + filter surface, so at most one item is returned. Mirrors the + ``/search`` ``ProfileRecaller`` minus the (unused) ``score`` field. + Empty list (not 404) keeps the response valid during the cold-start + window before a profile has been synthesised. + """ + if not owner_id: + return [] + row = await self._profile.get_by_id(owner_id) + if row is None: + logger.debug("get_profile_miss", owner_id=owner_id) + return [] + profile_data: dict[str, object] = { + "summary": row.summary, + "explicit_info": _load_json(row.explicit_info_json), + "implicit_traits": _load_json(row.implicit_traits_json), + "profile_timestamp_ms": row.profile_timestamp_ms, + } + return [ + GetProfileItem( + id=row.id, + user_id=row.owner_id, + app_id=row.app_id, + project_id=row.project_id, + profile_data=profile_data, + ) + ] + + +def _load_json(text: str) -> Any: + """Decode a json-encoded profile frontmatter bucket. + + Returns ``[]`` on empty / malformed input so a row with a stale + encoding doesn't break the get response (mirrors the search recaller). + """ + if not text: + return [] + try: + return json.loads(text) + except json.JSONDecodeError: + logger.debug("get_profile_json_decode_failed", payload_head=text[:80]) + return [] diff --git a/src/everos/memory/models.py b/src/everos/memory/models.py new file mode 100644 index 00000000..d8afb1ac --- /dev/null +++ b/src/everos/memory/models.py @@ -0,0 +1,342 @@ +"""Domain models shared across the memory layer. + +These models live in the domain layer so service / pipeline / ingest can +all consume the same canonical shapes. + +Algorithm-side models (``MemCell``, ``Episode``, ``ChatMessage``) are +owned by ``everalgo.types`` and are not re-defined here. Re-export here for +the ``from everos.memory import MemCell, Episode`` convenience. +""" + +from __future__ import annotations + +import datetime as dt +from typing import Any, Literal + +from everalgo.types import AgentCase as AlgoAgentCase +from everalgo.types import AtomicFact as AlgoAtomicFact +from everalgo.types import ChatMessage as AlgoMessage +from everalgo.types import Episode as AlgoEpisode +from everalgo.types import Foresight as AlgoForesight +from everalgo.types import MemCell as MemCell +from pydantic import BaseModel, ConfigDict, Field + +_Role = Literal["user", "assistant", "tool"] +_Track = Literal["user_memory", "agent_memory"] + + +class ToolCall(BaseModel): + """OpenAI Chat Completions tool_call shape (kept verbatim).""" + + id: str + type: str = "function" + function: dict[str, str] # {"name": ..., "arguments": json_str} + + +class CanonicalMessage(BaseModel): + """Canonical internal message after ingest normalisation. + + Carries enough metadata to be persisted into ``unprocessed_buffer``, + adapted into ``everalgo.types.Message`` for the algo layer, and + reconstructed back when ``adetect`` returns a ``tail``. + + Field split (mirrors src_old ``RawMessage.content_items``): + + - ``content_items`` holds the raw ``ContentItem`` array verbatim + (text / image / audio / doc / pdf / html / email). Currently only + ``type="text"`` is parsed downstream; the field still keeps the + original structure so a future multimodal parser can reach back + without losing data. + - ``text`` is the derived concatenation of ``content_items[*].text`` + for entries with ``type="text"`` — what the LLM-facing extractors + and md writer consume. + """ + + message_id: str + session_id: str + sender_id: str + sender_name: str | None = None + role: _Role + timestamp: dt.datetime + content_items: list[dict[str, Any]] = Field(default_factory=list) + text: str + tool_calls: list[ToolCall] | None = None + tool_call_id: str | None = None + + +class IngestResult(BaseModel): + """Output of ``ingest.process()`` — handed to pipelines. + + ``unparsed_non_text_count`` reports the number of non-text + ``ContentItem`` entries kept on the records (a parser hook will + consume them once it lands; today only ``type="text"`` is parsed). + """ + + session_id: str + app_id: str = "default" + project_id: str = "default" + """App / project scope for this add cycle (request-level; default + ``"default"``). Threaded to the boundary ledger + writers so memcells, + buffer rows, and md paths all land in the right space.""" + messages: list[CanonicalMessage] + unparsed_non_text_count: int = 0 + + +class PipelineOutcome(BaseModel): + """Return type of every pipeline's ``run()``.""" + + track: _Track + status: Literal["accumulated", "extracted", "skipped"] + message_count: int + extracted_md_paths: list[str] = Field(default_factory=list) + + +class Episode(BaseModel): + """Domain Episode — algo-emitted business fields + everos context. + + Composed (not inherited) from :class:`everalgo.types.Episode`. everos + keeps the *semantic* fields algo emits (``owner_id``, narrative + ``episode`` text, ``subject``, ``timestamp``) and adds engineering + context (``session_id``, ``sender_ids``, ``parent_id``). The global + episode id is derived later by cascade from + ``_`` — algo no longer mints an id of its + own. + + ``parent_id`` is the source memcell id. The new everalgo types no + longer carry ``parent_id`` on Episode / Foresight / AtomicFact, so + everos fills it from the memcell currently being processed (the + pipeline knows the id — it created the memcell). + """ + + owner_id: str + episode: str + timestamp: int + + # everos engineering metadata. + session_id: str + sender_ids: list[str] = Field(default_factory=list) + parent_id: str + + model_config = ConfigDict(extra="allow") + + @classmethod + def from_algo( + cls, + algo_episode: AlgoEpisode, + *, + owner_id: str, + session_id: str, + sender_ids: list[str], + parent_id: str, + ) -> Episode: + """Build a domain Episode from an algo Episode plus engineering context. + + ``owner_id`` is caller-supplied so the same generic algo Episode + (produced with ``sender_id=None`` to save an LLM round-trip per + sender) can fan out to one md per user sender. The pipeline runs + the algo once per cell and then loops the senders here, each + getting an ``Episode`` rooted at its own ``owner_id``. Any + ``owner_id`` algo's model might carry is dropped — the algo's + value is ``None`` in the generic path; even when it isn't, the + caller's context is authoritative. + + ``parent_id`` is required for the same reason: the caller always + knows the source memcell id. Anything algo's model carries via + ``extra='allow'`` is dropped in favour of the caller-supplied value. + """ + data = algo_episode.model_dump(exclude={"parent_id", "owner_id"}) + data["owner_id"] = owner_id + data["session_id"] = session_id + data["sender_ids"] = list(sender_ids) + data["parent_id"] = parent_id + return cls.model_validate(data) + + +class AtomicFact(BaseModel): + """Domain AtomicFact — algo-emitted business fields + everos context. + + Composed (not inherited) from :class:`everalgo.types.AtomicFact`. Mirrors + :class:`Episode`: everos keeps the *semantic* fields algo emits + (``owner_id`` / ``fact`` / ``timestamp``) and adds engineering context + (``session_id`` / ``parent_id``) so md writer + cascade can audit-link + back to the source memcell. + + No ``sender_ids``: an atomic fact is a statement about its ``owner_id``; + the surrounding participants are not part of the fact itself. (Episode + keeps ``sender_ids`` because the narrative is *about* the conversation + as a whole.) + + ``parent_id`` is the source memcell id, supplied by the caller because + the new everalgo types no longer carry it on AtomicFact. + """ + + owner_id: str + fact: str + timestamp: int + + # everos engineering metadata. + session_id: str + parent_id: str + + model_config = ConfigDict(extra="allow") + + @classmethod + def from_algo( + cls, + algo_fact: AlgoAtomicFact, + *, + owner_id: str, + session_id: str, + parent_id: str, + ) -> AtomicFact: + """Build a domain AtomicFact from an algo AtomicFact plus context. + + ``owner_id`` is supplied by the caller (not read from ``algo_fact``) + because atomic_fact extraction uses a subject-agnostic prompt — one + LLM call produces a template that fans out to multiple owners. The + algo-side ``owner_id`` is therefore a placeholder; the caller knows + the real one. Same rationale for ``parent_id``: algo no longer + carries the source memcell id; caller injects the authoritative + value (any ``extra='allow'`` smuggled values are dropped). + + The algo type exposes the fact sentence as ``content``; everos's + domain field is ``fact``. This boundary is where that rename is + bridged (mirrors how :meth:`Episode.from_algo` adapts algo fields + into everos's vocabulary). + """ + data = algo_fact.model_dump(exclude={"parent_id", "owner_id"}) + data["fact"] = data.pop("content") + data["owner_id"] = owner_id + data["session_id"] = session_id + data["parent_id"] = parent_id + return cls.model_validate(data) + + +class Foresight(BaseModel): + """Domain Foresight — algo-emitted business fields + everos context. + + Composed (not inherited) from :class:`everalgo.types.Foresight`. Mirrors + :class:`Episode`: everos keeps the semantic fields algo emits + (``owner_id`` / ``foresight`` / ``evidence`` / ``timestamp`` plus the + optional time-window trio) and adds engineering context + (``session_id`` / ``parent_id``). + + Extraction is per-sender (like Episode, unlike AtomicFact's + subject-agnostic fan-out): a foresight is a forward-looking statement + *about* a specific user, so the algo is invoked once per user sender + and the emitted ``owner_id`` already matches — no fan-out override + needed. + + No ``sender_ids``: a foresight is a prediction about its ``owner_id``; + other participants in the source conversation are not part of the + foresight itself. + """ + + owner_id: str + foresight: str + evidence: str + timestamp: int + start_time: str | None = None + end_time: str | None = None + duration_days: int | None = None + + # everos engineering metadata. + session_id: str + parent_id: str + + model_config = ConfigDict(extra="allow") + + @classmethod + def from_algo( + cls, + algo_foresight: AlgoForesight, + *, + session_id: str, + parent_id: str, + ) -> Foresight: + """Build a domain Foresight from an algo Foresight plus context. + + Per-sender extraction: the algo's ``owner_id`` is authoritative + (it was invoked with the target sender). Only the engineering + metadata is injected here. Any algo-side ``parent_id`` smuggled + through ``extra='allow'`` is dropped in favour of the caller's. + """ + data = algo_foresight.model_dump(exclude={"parent_id"}) + data["session_id"] = session_id + data["parent_id"] = parent_id + return cls.model_validate(data) + + +class AgentCase(BaseModel): + """Domain AgentCase — algo-emitted business fields + everos context. + + Composed (not inherited) from :class:`everalgo.types.AgentCase`. Mirrors + :class:`Episode` / :class:`AtomicFact` / :class:`Foresight`: everos + keeps the semantic fields algo emits (``task_intent`` / ``approach`` / + ``quality_score`` / ``key_insight`` / ``timestamp``) and adds + engineering context (``owner_id`` = agent_id, ``session_id`` / + ``parent_id``). + + ``owner_id`` is supplied by the caller because algo's AgentCase has no + ``owner_id`` field — the strategy infers the agent identity from the + source MemCell (assistant's ``sender_id``). + + Single output per memcell (algo returns ``[]`` or ``[case]``); no + fan-out semantics. + """ + + owner_id: str + task_intent: str + approach: str + quality_score: float + key_insight: str | None = None + timestamp: int + + # everos engineering metadata. + session_id: str + parent_id: str + + model_config = ConfigDict(extra="allow") + + @classmethod + def from_algo( + cls, + algo_case: AlgoAgentCase, + *, + owner_id: str, + session_id: str, + parent_id: str, + ) -> AgentCase: + """Build a domain AgentCase from an algo AgentCase plus context. + + ``owner_id`` is caller-supplied (agent_id derived from the source + memcell's assistant sender). Algo's ``id`` (a uuid4 hex) is + dropped — md writer mints the authoritative entry_id. ``key_insight`` + normalises algo's ``""`` to ``None`` so the optional KeyInsight + section is omitted in md when there's nothing to record. + """ + data = algo_case.model_dump(exclude={"id", "parent_id", "owner_id"}) + data["owner_id"] = owner_id + data["session_id"] = session_id + data["parent_id"] = parent_id + if not data.get("key_insight"): + data["key_insight"] = None + return cls.model_validate(data) + + +__all__ = [ + "AgentCase", + "AlgoAgentCase", + "AlgoAtomicFact", + "AlgoEpisode", + "AlgoForesight", + "AlgoMessage", + "AtomicFact", + "CanonicalMessage", + "Episode", + "Foresight", + "IngestResult", + "MemCell", + "PipelineOutcome", + "ToolCall", +] diff --git a/src/everos/memory/prompt_slots/__init__.py b/src/everos/memory/prompt_slots/__init__.py new file mode 100644 index 00000000..9a6995bb --- /dev/null +++ b/src/everos/memory/prompt_slots/__init__.py @@ -0,0 +1,13 @@ +"""PromptSlot — prompt template loading. + +External usage: + from everos.memory.prompt_slots import PromptLoader + +Three-layer overlay (defaults → ``~/.everos/prompt_slots/`` → runtime +override) is reserved for a future milestone; this version only resolves +the bundled defaults under ``src/everos/config/prompt_slots/``. +""" + +from .loader import PromptLoader as PromptLoader + +__all__ = ["PromptLoader"] diff --git a/src/everos/memory/prompt_slots/loader.py b/src/everos/memory/prompt_slots/loader.py new file mode 100644 index 00000000..061dc74e --- /dev/null +++ b/src/everos/memory/prompt_slots/loader.py @@ -0,0 +1,58 @@ +"""Prompt slot loader — wraps :class:`YamlConfigLoader` for prompt templates. + +Slot file shape:: + + # config/prompt_slots/.yaml + enabled: false + template: "" + +When ``enabled`` is ``True`` and ``template`` is a non-empty string, the +loader returns it as-is; otherwise it returns ``None``. The pipeline +forwards ``None`` directly to algo, where the bundled default prompt is +used (zero override cost). + +Three-layer overlay (defaults → ``~/.everos/prompt_slots/`` → runtime +override) is reserved for a future milestone; this version only resolves +the bundled defaults under ``src/everos/config/prompt_slots/``. +""" + +from __future__ import annotations + +from pathlib import Path + +from everos.component.config import YamlConfigLoader + +_CATEGORY = "prompt_slots" + + +class PromptLoader: + """Read prompt template strings from ``config/prompt_slots/.yaml``. + + Returns ``None`` when the slot is disabled or the template is empty. + """ + + def __init__(self, config_root: Path) -> None: + self._loader = YamlConfigLoader( + root=config_root, + categories={_CATEGORY: None}, + ) + + def load(self, name: str) -> str | None: + """Return the override prompt for ``name``, or ``None`` to use algo default. + + ``None`` is returned when any of the following holds: + + - the slot file is missing, + - the slot file has ``enabled: false`` (or no ``enabled`` key), + - the ``template`` field is missing or an empty string. + """ + try: + slot = self._loader.find(_CATEGORY, name) + except FileNotFoundError: + return None + if not slot.get("enabled", False): + return None + template = slot.get("template") + if not isinstance(template, str) or not template.strip(): + return None + return template diff --git a/src/everos/memory/search/__init__.py b/src/everos/memory/search/__init__.py new file mode 100644 index 00000000..86dbbb69 --- /dev/null +++ b/src/everos/memory/search/__init__.py @@ -0,0 +1,71 @@ +"""memory.search — read path: hybrid retrieval over LanceDB. + +This subpackage owns the recall + adapter layer for ``POST +/api/v1/memory/search``. All fusion / rerank / agentic algorithms are +delegated to :mod:`everalgo.rank`; this layer is responsible for: + +* compiling the Filters DSL into a LanceDB ``where`` string, +* per-kind sparse (BM25 over ``*_tokens``) and dense (vector ANN) recall, +* shape-translating ``everalgo.rank.RankOutput`` into the public DTOs. + +Cascade writes drive the underlying LanceDB rows; this package never +writes. + +External usage:: + + from everos.memory.search import ( + ALLOWED_FIELDS, + RESERVED_FIELDS, + FilterError, + FilterNode, + SearchAgentCaseItem, + SearchAgentSkillItem, + SearchAtomicFactItem, + SearchData, + SearchEpisodeItem, + SearchMethod, + SearchProfileItem, + SearchRequest, + SearchResponse, + compile_filters, + compile_predicate, + ) + +The Filters DSL primitives (``ALLOWED_FIELDS`` / ``RESERVED_FIELDS`` / +``compile_predicate``) are re-exported so :mod:`memory.get` can build +its flat-DSL variant without forking the field allow-list. +""" + +from .dto import FilterNode as FilterNode +from .dto import SearchAgentCaseItem as SearchAgentCaseItem +from .dto import SearchAgentSkillItem as SearchAgentSkillItem +from .dto import SearchAtomicFactItem as SearchAtomicFactItem +from .dto import SearchData as SearchData +from .dto import SearchEpisodeItem as SearchEpisodeItem +from .dto import SearchMethod as SearchMethod +from .dto import SearchProfileItem as SearchProfileItem +from .dto import SearchRequest as SearchRequest +from .dto import SearchResponse as SearchResponse +from .filters import ALLOWED_FIELDS as ALLOWED_FIELDS +from .filters import RESERVED_FIELDS as RESERVED_FIELDS +from .filters import FilterError as FilterError +from .filters import compile_filters as compile_filters +from .filters import compile_predicate as compile_predicate + +__all__ = [ + "ALLOWED_FIELDS", + "RESERVED_FIELDS", + "FilterError", + "FilterNode", + "SearchAgentCaseItem", + "SearchAgentSkillItem", + "SearchAtomicFactItem", + "SearchData", + "SearchEpisodeItem", + "SearchMethod", + "SearchProfileItem", + "SearchRequest", + "SearchResponse", + "compile_filters", + "compile_predicate", +] diff --git a/src/everos/memory/search/adapter.py b/src/everos/memory/search/adapter.py new file mode 100644 index 00000000..cd05d9b3 --- /dev/null +++ b/src/everos/memory/search/adapter.py @@ -0,0 +1,52 @@ +"""Method → hybrid pipeline selector. + +Translates the public 4-method enum into everos's internal pipeline routing signal. +``AGENTIC`` is intercepted by the manager before this function is called. +Passing ``AGENTIC`` here is a caller contract violation and raises +``ValueError`` as a defensive guard. + +* ``KEYWORD`` / ``VECTOR`` → ``None`` → manager skips ``everalgo.rank``. +* ``HYBRID`` → ``"hierarchy"`` (episode / atomic_fact) — four-layer pipeline + (RRF → MaxSim → RRF merge → single-pass eviction) + or ``"vector_anchored"`` (agent_case) — everalgo vector-anchored fusion (alpha=0.7) + or ``"skill_hybrid"`` (agent_skill) — custom rrf → cross-encoder rerank → optional + verify. +""" + +from __future__ import annotations + +from typing import Literal + +from .dto import SearchMethod + +KindName = Literal["episode", "atomic_fact", "agent_case", "agent_skill"] + + +def resolve_pipeline( + method: SearchMethod, + kind: KindName, +) -> tuple[str | None, None]: + """Return ``(pipeline_signal, None)`` for a ``(method, kind)`` pair. + + ``pipeline_signal`` of ``None`` means "do not call ``everalgo.rank.arank``; + the manager runs single-route recall and returns directly". + ``"hierarchy"`` routes to the four-layer episode pipeline in + ``memory.search.hierarchy`` (RRF → MaxSim → RRF merge → eviction). + ``"vector_anchored"`` routes to ``everalgo.rank.arank`` with vector-anchored + fusion (alpha=0.7, saturation_k=5.0) — matches the opensource case retrieval. + ``"skill_hybrid"`` routes to the custom skill hybrid orchestrator in + ``memory.search.skill_hybrid`` (rrf → cross-encoder rerank → optional verify). + """ + if method in (SearchMethod.KEYWORD, SearchMethod.VECTOR): + return None, None + + if method == SearchMethod.HYBRID: + if kind in ("episode", "atomic_fact"): + return "hierarchy", None + if kind == "agent_case": + return "vector_anchored", None + # agent_skill: custom hybrid orchestrator (rrf → cross-encoder → optional + # verify) + return "skill_hybrid", None + + raise ValueError(f"unsupported method: {method!r}") diff --git a/src/everos/memory/search/agentic.py b/src/everos/memory/search/agentic.py new file mode 100644 index 00000000..d57fe561 --- /dev/null +++ b/src/everos/memory/search/agentic.py @@ -0,0 +1,267 @@ +"""Episode AGENTIC cluster-path orchestration — 1:1 with everalgo benchmark. + +Implements the cluster main path from ``benchmarks/common/stages/search.py`` +(``enable_cluster_retrieval=True``): + + fact-MaxSim (dense + sparse) + -> ahybrid_retrieve (hybrid_full) + -> acluster_retrieve (cluster_scoped, base=hybrid_full) + -> aagentic_retrieve (base=cluster_scoped, round2=hybrid_full) + +Hyperparameters match benchmark ``config.py`` defaults and are frozen as +module-level constants — no env/TOML knobs at this layer. + +id contract: candidates flowing through the pipeline carry ``id=memcell_id`` +(fact.parent_id chain). The final shaping step remaps to ``id=episode_id`` +via ``metadata["episode_id"]`` before calling ``shape_episode_from_candidate``. +""" + +from __future__ import annotations + +import datetime as _dt +from collections.abc import Awaitable, Callable +from typing import TYPE_CHECKING, Any + +from everalgo.rank.agentic import aagentic_retrieve +from everalgo.rank.cluster import acluster_retrieve +from everalgo.rank.hybrid import ahybrid_retrieve +from everalgo.rank.maxsim import amaxsim_retrieve +from everalgo.types import Candidate + +from everos.component.utils.datetime import from_timestamp, to_timestamp_ms +from everos.infra.persistence.sqlite import cluster_repo +from everos.memory.search.callbacks import build_rerank_fn +from everos.memory.search.shaper import shape_episode_from_candidate + +from .dto import SearchEpisodeItem + +if TYPE_CHECKING: + from everalgo.clustering import Cluster + from everalgo.llm.protocols import LLMClient + + from everos.component.rerank import RerankProvider + from everos.memory.search.recall.atomic_fact import AtomicFactRecaller + from everos.memory.search.recall.episode import EpisodeRecaller + +# ── Benchmark hyperparameters (config.py defaults) ────────────────────────── +_DENSE_CANDIDATES: int = 50 +_SPARSE_CANDIDATES: int = 50 +_HYBRID_RRF_K: int = 40 +_CLUSTER_BASE_CANDIDATES: int = 100 +_CLUSTER_TOP_K: int = 10 +_ROUND1_TOP_N: int = 50 +_ROUND1_RERANK_TOP_N: int = 10 +_ROUND2_CAP: int = 40 +_MULTI_QUERY_COUNT: int = 3 +_REFINEMENT_STRATEGY: str = "multi_query" + +# Child-pool sizing — mirrors SearchManager._MAXSIM_FACT_MULTIPLIER / _CAP. +_FACT_CHILD_MULTIPLIER: int = 20 +_FACT_CHILD_CAP: int = 2000 + +# Qwen3-Reranker task instruction for the search scene (benchmark +# ``config.reranker_instruction``). Steers the cross-encoder toward fact / +# entity / detail relevance rather than topical similarity. +_RERANK_INSTRUCTION: str = ( + "Determine if the passage contains specific facts, entities " + "(names, dates, locations), or details that directly answer the question." +) + + +async def search_episodes_agentic( + query: str, + *, + owner_id: str, + where: str, + episode_recaller: EpisodeRecaller, + atomic_fact_recaller: AtomicFactRecaller, + embed_query_fn: Callable[[str], Awaitable[list[float]]], + reranker: RerankProvider, + llm: LLMClient, + top_k: int, +) -> list[SearchEpisodeItem]: + """Episode AGENTIC search via cluster-scoped MaxSim — 1:1 with benchmark. + + Args: + query: User search query. + owner_id: Owner whose memories are searched. + where: Pre-compiled LanceDB filter string (owner + any request filters). + episode_recaller: Episode-table sparse + dense + fetch callbacks. + atomic_fact_recaller: AtomicFact-table sparse + dense callbacks. + embed_query_fn: Async ``(str) -> list[float]`` query embedder. + reranker: Cross-encoder rerank provider. + llm: LLM client for sufficiency check + multi-query generation. + top_k: Maximum episodes to return (maps to ``top_n`` in aagentic_retrieve). + + Returns: + Ranked list of at most ``top_k`` ``SearchEpisodeItem`` objects. + Empty when no clusters exist or retrieval returns nothing. + """ + + # 1. Fact-level child retrieve closures (dense + sparse via atomic_fact table). + async def _fact_dense(q: str, k: int) -> list[Candidate]: + vec = await embed_query_fn(q) + if not vec: + return [] + child_limit = min(k * _FACT_CHILD_MULTIPLIER, _FACT_CHILD_CAP) + return await atomic_fact_recaller.dense_recall(vec, where, limit=child_limit) + + async def _fact_sparse(q: str, k: int) -> list[Candidate]: + child_limit = min(k * _FACT_CHILD_MULTIPLIER, _FACT_CHILD_CAP) + return await atomic_fact_recaller.sparse_recall(q, where, limit=child_limit) + + # 2. parent_fetch: maps memcell_ids -> Candidate(id=memcell_id) for the amaxsim + # score lookup. Stores the real LanceDB episode id in metadata["episode_id"] + # for final shaping. + async def _parent_fetch(memcell_ids: list[str]) -> list[Candidate]: + ep_cands = await episode_recaller.fetch_by_parent_ids(memcell_ids, where) + result: list[Candidate] = [] + for c in ep_cands: + mc_id = c.metadata.get("parent_id") + if not isinstance(mc_id, str): + continue + result.append( + Candidate( + id=mc_id, + score=0.0, + source=c.source, + metadata=_to_everalgo_doc_metadata( + {**c.metadata, "episode_id": c.id} + ), + ) + ) + return result + + # 3. MaxSim RetrieveFns: fact vectors/BM25 -> max-pool by memcell -> candidates. + async def _dense(q: str, k: int) -> list[Candidate]: + return await amaxsim_retrieve( + q, + child_retrieve=_fact_dense, + parent_fetch=_parent_fetch, + top_n=k, + child_candidates=min(k * _FACT_CHILD_MULTIPLIER, _FACT_CHILD_CAP), + ) + + async def _sparse(q: str, k: int) -> list[Candidate]: + return await amaxsim_retrieve( + q, + child_retrieve=_fact_sparse, + parent_fetch=_parent_fetch, + top_n=k, + child_candidates=min(k * _FACT_CHILD_MULTIPLIER, _FACT_CHILD_CAP), + ) + + # 4. hybrid_full: RRF fusion of dense + sparse MaxSim. + async def hybrid_full(q: str, k: int) -> list[Candidate]: + return await ahybrid_retrieve( + q, + dense_retrieve=_dense, + sparse_retrieve=_sparse, + top_n=k, + dense_candidates=_DENSE_CANDIDATES, + sparse_candidates=_SPARSE_CANDIDATES, + rrf_k=_HYBRID_RRF_K, + ) + + # 5. Load cluster snapshot + full-corpus all_docs (memcell-keyed). + # Reshape metadata to the everalgo doc contract so the sufficiency / + # multi-query LLM prompt (rendered by ``_format_docs``) sees the episode + # body and a ms-epoch date instead of the memcell id. + clusters: list[Cluster] = await cluster_repo.list_for_owner(owner_id, "user_memory") + raw_all_docs = await episode_recaller.fetch_all_for_owner(where) + all_docs: list[Candidate] = [ + c.model_copy(update={"metadata": _to_everalgo_doc_metadata(c.metadata)}) + for c in raw_all_docs + ] + + # 6. cluster_scoped: narrows hybrid_full to top-K cluster member expansions. + async def cluster_scoped(q: str, _k: int) -> list[Candidate]: + return await acluster_retrieve( + q, + base_retrieve=hybrid_full, + base_candidates=_CLUSTER_BASE_CANDIDATES, + clusters=clusters, + all_docs=all_docs, + cluster_top_k=_CLUSTER_TOP_K, + ) + + # 7. Cross-encoder rerank fn (2-arg RerankFn, no internal truncation). + rerank_fn = build_rerank_fn( + reranker, text_field="episode", instruction=_RERANK_INSTRUCTION + ) + + # 8. aagentic_retrieve — benchmark cluster main path. + candidates, _decision = await aagentic_retrieve( + query, + base_retrieve=cluster_scoped, + round2_retrieve=hybrid_full, + llm=llm, + rerank_fn=rerank_fn, + round2_cap=_ROUND2_CAP, + top_n=top_k, + round1_top_n=_ROUND1_TOP_N, + round1_rerank_top_n=_ROUND1_RERANK_TOP_N, + refinement_strategy=_REFINEMENT_STRATEGY, + multi_query_count=_MULTI_QUERY_COUNT, + rrf_k=_HYBRID_RRF_K, + ) + + # 9. Shape: remap id from memcell_id -> episode_id, then build DTO. + return _shape_results(candidates) + + +def _to_everalgo_doc_metadata(metadata: dict[str, Any]) -> dict[str, Any]: + """Bridge recall metadata to the everalgo ``_format_docs`` doc contract. + + ``aagentic_retrieve`` renders Round-1 candidates into the sufficiency / + multi-query LLM prompt via ``everalgo.rank.agentic._format_docs``, which + reads the doc body from ``metadata["content"] | metadata["text"] | id`` and + the date from a ms-epoch ``metadata["timestamp"]``. everos episode rows + carry the body in ``episode`` (str) and the time in ``timestamp`` (datetime); + without this bridge the prompt degrades to the memcell id as the body and a + "N/A" date. ``episode`` is left untouched so the reranker and shaper -- both + expecting a plain string -- keep working. ``_restore_shaper_metadata`` + reverts the timestamp before DTO shaping. + """ + bridged = dict(metadata) + episode = metadata.get("episode") + if isinstance(episode, str): + bridged["text"] = episode + timestamp = metadata.get("timestamp") + if isinstance(timestamp, _dt.datetime): + bridged["timestamp"] = to_timestamp_ms(timestamp) + return bridged + + +def _restore_shaper_metadata(metadata: dict[str, Any]) -> dict[str, Any]: + """Revert the ms-epoch ``timestamp`` injected for everalgo back to datetime. + + ``shape_episode_from_candidate`` requires a ``datetime`` timestamp and drops + the row otherwise; the agentic pipeline carried it as ms-epoch for the LLM + prompt. The extra ``text`` key is ignored by the shaper and left in place. + """ + timestamp = metadata.get("timestamp") + if not isinstance(timestamp, (int, float)): + return metadata + reverted = dict(metadata) + reverted["timestamp"] = from_timestamp(timestamp) + return reverted + + +def _shape_results(candidates: list[Candidate]) -> list[SearchEpisodeItem]: + """Remap candidate id from memcell_id -> episode_id; build the DTO list.""" + result: list[SearchEpisodeItem] = [] + for c in candidates: + ep_id = c.metadata.get("episode_id") + if not isinstance(ep_id, str): + continue + ep_cand = Candidate( + id=ep_id, + score=c.score, + source=c.source, + metadata=_restore_shaper_metadata(c.metadata), + ) + item = shape_episode_from_candidate(ep_cand) + if item is not None: + result.append(item) + return result diff --git a/src/everos/memory/search/agentic_agent.py b/src/everos/memory/search/agentic_agent.py new file mode 100644 index 00000000..70ede207 --- /dev/null +++ b/src/everos/memory/search/agentic_agent.py @@ -0,0 +1,194 @@ +"""Agent-kind AGENTIC search — flat hybrid path (no cluster, no MaxSim). + +Implements the flat agentic path for ``agent_case`` and ``agent_skill`` +memory kinds. Unlike the episode AGENTIC path (which uses cluster + MaxSim), +agent memory is retrieved via a plain hybrid (RRF) recall straight into +``aagentic_retrieve``. + +Hyperparameters are aligned to the memsys_opensource ``AgenticConfig`` defaults +(``agentic_utils.py``): + +* ``_ROUND1_TOP_N = 20`` — ``round1_top_n`` +* ``_ROUND2_CAP = 40`` — ``combined_total`` +* ``_HYBRID_RRF_K = 60`` — ``rrf_k`` default in ``retrieval_utils.py:347`` +* ``_DENSE_CANDIDATES = 50`` — ``round1_emb_top_n`` +* ``_SPARSE_CANDIDATES = 50`` — ``round1_bm25_top_n`` +* ``_ROUND1_RERANK_TOP_N = 10`` — ``round1_rerank_top_n`` +* ``_MULTI_QUERY_COUNT = 3`` — ``num_queries`` + +""" + +from __future__ import annotations + +from collections.abc import Awaitable, Callable +from typing import TYPE_CHECKING + +from everalgo.rank.agentic import aagentic_retrieve +from everalgo.rank.hybrid import ahybrid_retrieve +from everalgo.types import Candidate + +from everos.memory.search.callbacks import build_rerank_fn +from everos.memory.search.shaper import ( + shape_agent_case_from_candidate, + shape_agent_skill_from_candidate, +) + +from .dto import SearchAgentCaseItem, SearchAgentSkillItem + +if TYPE_CHECKING: + from everalgo.llm.protocols import LLMClient + + from everos.component.rerank import RerankProvider + from everos.memory.search.recall.agent_case import AgentCaseRecaller + from everos.memory.search.recall.agent_skill import AgentSkillRecaller + +# ── Hyperparameters aligned to memsys_opensource AgenticConfig defaults ───── +# Source: memsys_opensource/src/agentic_layer/agentic_utils.py (AgenticConfig) +# and retrieval_utils.py:347 (rrf_k default). +_DENSE_CANDIDATES: int = 50 # round1_emb_top_n +_SPARSE_CANDIDATES: int = 50 # round1_bm25_top_n +_HYBRID_RRF_K: int = 60 # retrieval_utils.py:347 default rrf_k +_ROUND1_TOP_N: int = 20 # round1_top_n (was 50, aligned to opensource 20) +_ROUND1_RERANK_TOP_N: int = 10 # round1_rerank_top_n +_ROUND2_CAP: int = 40 # combined_total +_MULTI_QUERY_COUNT: int = 3 # num_queries +_REFINEMENT_STRATEGY: str = "multi_query" + + +async def search_agent_cases_agentic( + query: str, + *, + where: str, + case_recaller: AgentCaseRecaller, + embed_query_fn: Callable[[str], Awaitable[list[float]]], + reranker: RerankProvider, + llm: LLMClient, + top_k: int, +) -> list[SearchAgentCaseItem]: + """Agent-case AGENTIC search via flat hybrid retrieve + aagentic_retrieve. + + Args: + query: User search query. + where: Pre-compiled LanceDB filter string (owner + any request filters). + case_recaller: AgentCase-table sparse + dense recall callbacks. + embed_query_fn: Async ``(str) -> list[float]`` query embedder. + reranker: Cross-encoder rerank provider. + llm: LLM client for sufficiency check + multi-query generation. + top_k: Maximum cases to return. + + Returns: + Ranked list of at most ``top_k`` ``SearchAgentCaseItem`` objects. + """ + candidates = await _run_agentic_retrieve( + query=query, + where=where, + recaller=case_recaller, + embed_query_fn=embed_query_fn, + reranker=reranker, + llm=llm, + top_k=top_k, + ) + return [ + item + for c in candidates + for item in [shape_agent_case_from_candidate(c)] + if item is not None + ] + + +async def search_agent_skills_agentic( + query: str, + *, + where: str, + skill_recaller: AgentSkillRecaller, + embed_query_fn: Callable[[str], Awaitable[list[float]]], + reranker: RerankProvider, + llm: LLMClient, + top_k: int, +) -> list[SearchAgentSkillItem]: + """Agent-skill AGENTIC search via flat hybrid retrieve + aagentic_retrieve. + + Args: + query: User search query. + where: Pre-compiled LanceDB filter string (owner + any request filters). + skill_recaller: AgentSkill-table sparse + dense recall callbacks. + embed_query_fn: Async ``(str) -> list[float]`` query embedder. + reranker: Cross-encoder rerank provider. + llm: LLM client for sufficiency check + multi-query generation. + top_k: Maximum skills to return. + + Returns: + Ranked list of at most ``top_k`` ``SearchAgentSkillItem`` objects. + """ + candidates = await _run_agentic_retrieve( + query=query, + where=where, + recaller=skill_recaller, + embed_query_fn=embed_query_fn, + reranker=reranker, + llm=llm, + top_k=top_k, + ) + return [ + item + for c in candidates + for item in [shape_agent_skill_from_candidate(c)] + if item is not None + ] + + +async def _run_agentic_retrieve( + query: str, + *, + where: str, + recaller: AgentCaseRecaller | AgentSkillRecaller, + embed_query_fn: Callable[[str], Awaitable[list[float]]], + reranker: RerankProvider, + llm: LLMClient, + top_k: int, +) -> list[Candidate]: + """Shared flat agentic retrieve pipeline for agent memory kinds. + + Builds a hybrid_full retrieve closure over the given recaller and + hands it to ``aagentic_retrieve`` with hyperparameters aligned to the + memsys_opensource ``AgenticConfig`` defaults. + No cluster or MaxSim step: agent memory is small enough for a flat pass. + """ + + async def _dense(q: str, k: int) -> list[Candidate]: + vec = await embed_query_fn(q) + if not vec: + return [] + return await recaller.dense_recall(vec, where, limit=k) + + async def _sparse(q: str, k: int) -> list[Candidate]: + return await recaller.sparse_recall(q, where, limit=k) + + async def hybrid_full(q: str, k: int) -> list[Candidate]: + return await ahybrid_retrieve( + q, + dense_retrieve=_dense, + sparse_retrieve=_sparse, + top_n=k, + dense_candidates=_DENSE_CANDIDATES, + sparse_candidates=_SPARSE_CANDIDATES, + rrf_k=_HYBRID_RRF_K, + ) + + rerank_fn = build_rerank_fn(reranker, text_field=recaller.text_field) + + candidates, _decision = await aagentic_retrieve( + query, + base_retrieve=hybrid_full, + round2_retrieve=None, + round2_cap=_ROUND2_CAP, + rerank_fn=rerank_fn, + llm=llm, + top_n=top_k, + round1_top_n=_ROUND1_TOP_N, + round1_rerank_top_n=_ROUND1_RERANK_TOP_N, + refinement_strategy=_REFINEMENT_STRATEGY, + multi_query_count=_MULTI_QUERY_COUNT, + rrf_k=_HYBRID_RRF_K, + ) + return candidates diff --git a/src/everos/memory/search/callbacks.py b/src/everos/memory/search/callbacks.py new file mode 100644 index 00000000..8d7ea885 --- /dev/null +++ b/src/everos/memory/search/callbacks.py @@ -0,0 +1,157 @@ +"""Callback factories handed to ``everalgo.rank.arank``. + +Three callbacks the rank pipeline expects: + +* :func:`build_rerank_fn` — cross-encoder scorer used by ``agentic`` + Round-1 + final rerank, and by ``rrf`` / ``lr`` when LLM rerank is + enabled. Pulls the display text out of ``Candidate.metadata`` and + drives the configured :class:`RerankProvider`. Returns all reranked + candidates; the caller is responsible for truncation. +* :func:`build_skill_rerank_fn` — skill-shaped variant: composes a + ``"Agent Skill: {name} - {description}"`` passage (the multi-field + shape doesn't fit the single-``text_field`` contract above) and uses + a skill-specific instruction. Mirrors memsys_opensource + ``_rerank_skill_items``. +* :func:`build_retrieve_fn` — Round-2 recall callback for ``agentic``. + Re-runs the sparse + dense recall path for a refined query and fuses + the two routes with RRF (``k=60``) before handing back to the agentic + loop. +""" + +from __future__ import annotations + +from collections.abc import Awaitable, Callable, Sequence +from typing import TYPE_CHECKING + +from everalgo.rank.fusion import rrf +from everalgo.rank.protocols import RerankFn, RetrieveFn +from everalgo.types import Candidate + +from everos.component.rerank import RerankProvider + +if TYPE_CHECKING: + from .recall import KindRecaller + + +def build_rerank_fn( + provider: RerankProvider, + *, + text_field: str, + instruction: str | None = None, +) -> RerankFn: + """Build an everalgo ``RerankFn`` over the configured rerank provider. + + Returns a 2-arg ``(query, candidates) -> list[Candidate]`` async callable + matching ``everalgo.rank.protocols.RerankFn``. All reranked candidates are + returned without truncation — the caller (``aagentic_retrieve``) is + responsible for slicing via ``round1_rerank_top_n``. + + ``text_field`` decides which ``Candidate.metadata`` key carries the + passage text — ``"episode"`` for episodes, ``"task_intent"`` for cases. + Missing fields fall back to the empty string so the rerank call never + throws on a malformed row. + + ``instruction`` is the task instruction for instruction-tuned rerankers + (e.g. Qwen3-Reranker); it is forwarded to the provider verbatim. ``None`` + defers to the provider's default instruction. + """ + + async def _rerank( + query: str, + candidates: Sequence[Candidate], + ) -> list[Candidate]: + items = list(candidates) + if not items: + return [] + passages = [str(c.metadata.get(text_field, "")) for c in items] + results = await provider.rerank(query, passages, instruction=instruction) + out: list[Candidate] = [] + for r in results: + if not 0 <= r.index < len(items): + continue + out.append(items[r.index].model_copy(update={"score": float(r.score)})) + return out + + return _rerank + + +# Biases the reranker toward methodology / domain match rather than +# generic Q-A relevance (memsys_opensource ``_rerank_skill_items``). +_SKILL_RERANK_INSTRUCTION = ( + "Determine whether the skill's methodology and domain " + "are applicable to the query, preferring same-domain " + "skills with directly relevant steps." +) + + +def _format_skill_passage(candidate: Candidate) -> str: + """``"Agent Skill: {name}"`` + ``" - {description}"`` when present. + Mirrors opensource ``extract_text_from_hit`` for AGENT_SKILL. + """ + meta = candidate.metadata + name = str(meta.get("name", "") or "") + description = str(meta.get("description", "") or "") + if not name: + return description + if description: + return f"Agent Skill: {name} - {description}" + return f"Agent Skill: {name}" + + +def build_skill_rerank_fn(provider: RerankProvider) -> RerankFn: + """Skill-shaped ``RerankFn``: multi-field passage + + :data:`_SKILL_RERANK_INSTRUCTION`. Output stays score-comparable + with the memsys_opensource ``_rerank_skill_items`` baseline. + """ + + async def _rerank( + query: str, + candidates: Sequence[Candidate], + ) -> list[Candidate]: + items = list(candidates) + if not items: + return [] + passages = [_format_skill_passage(c) for c in items] + results = await provider.rerank( + query, passages, instruction=_SKILL_RERANK_INSTRUCTION + ) + out: list[Candidate] = [] + for r in results: + if not 0 <= r.index < len(items): + continue + out.append(items[r.index].model_copy(update={"score": float(r.score)})) + return out + + return _rerank + + +def build_retrieve_fn( + recaller: KindRecaller, + *, + where: str, + embed_query_fn: Callable[[str], Awaitable[list[float]]], + rrf_k: int = 60, +) -> RetrieveFn: + """Build an everalgo ``RetrieveFn`` that fuses fresh sparse + dense recall. + + ``embed_query_fn`` is an async ``(str) -> list[float]`` that produces + a 1024-d vector for an arbitrary query — typically the project's + :class:`EmbeddingProvider.embed`. We re-embed the refined queries + that the agentic loop emits in Round 2. + """ + + async def _retrieve(query: str, top_n: int) -> list[Candidate]: + recall_limit = top_n * 5 + vector = await embed_query_fn(query) + sparse = await recaller.sparse_recall(query, where, limit=recall_limit) + dense = ( + await recaller.dense_recall(vector, where, limit=recall_limit) + if vector + else [] + ) + if not sparse and not dense: + return [] + fused = rrf(dense, sparse, k=rrf_k) + return fused[:top_n] + + return _retrieve diff --git a/src/everos/memory/search/dto.py b/src/everos/memory/search/dto.py new file mode 100644 index 00000000..f5df5e44 --- /dev/null +++ b/src/everos/memory/search/dto.py @@ -0,0 +1,277 @@ +"""Public DTOs for ``POST /api/v1/memory/search``. + +Contract per the final design: + +* ``owner_type`` is a hard partition. ``user`` returns ``episodes`` + (and optionally ``profiles``); ``agent`` returns ``agent_cases`` + + ``agent_skills``. The four ``data.*`` arrays always exist; routes not + applicable to the current ``owner_type`` stay as ``[]``. +* ``atomic_facts`` are **nested** inside :class:`SearchEpisodeItem`, + never returned as a top-level array. +* Item-side ``owner_type`` / ``type`` fields are intentionally narrowed + to the currently-emitted Literal so callers get a tight schema. Loosen + them only when a new emission path (agent episodes, agent profiles) + ships. + +The :class:`FilterNode` model is intentionally permissive +(``extra="allow"``) because the DSL has an open key shape; the +allow-list / safety validation runs in :mod:`everos.memory.search.filters` +at compile time, not via Pydantic. +""" + +from __future__ import annotations + +import datetime as _dt +from enum import StrEnum +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field, model_validator + + +class SearchMethod(StrEnum): + """Public method enum. RRF / LR / vector_anchored are hidden under HYBRID.""" + + KEYWORD = "keyword" + VECTOR = "vector" + HYBRID = "hybrid" + AGENTIC = "agentic" + + +class FilterNode(BaseModel): + """One Filters DSL node. + + Recursive ``AND`` / ``OR`` arrays mix with arbitrary scalar fields at + the same level. Pydantic only checks the combinators; field-level + safety is enforced when compiling the node to a LanceDB ``where`` + string in :mod:`everos.memory.search.filters`. + """ + + model_config = ConfigDict(extra="allow") + + AND: list[FilterNode] | None = None + OR: list[FilterNode] | None = None + + +# ── Request ────────────────────────────────────────────────────────────── + + +class SearchRequest(BaseModel): + """Request body for ``POST /api/v1/memory/search``. + + Callers identify the memory owner via ``user_id`` XOR ``agent_id`` — + exactly one must be set. Internally the manager + compile_filters keep + using ``owner_id`` / ``owner_type`` (the storage tables' columns); + those are exposed as derived properties so the rename only affects + the wire contract, not the internal recall plumbing. + """ + + model_config = ConfigDict(extra="forbid") + + user_id: str | None = Field(default=None, min_length=1) + agent_id: str | None = Field(default=None, min_length=1) + """Memory owner — provide ``user_id`` for user-memory (episodes / + profiles) or ``agent_id`` for agent-memory (cases / skills); exactly + one must be set.""" + app_id: str = "default" + project_id: str = "default" + """App / project scope (default ``"default"``). Pinned into the LanceDB + ``where`` so a search never crosses into another space's rows.""" + query: str = Field(min_length=1) + method: SearchMethod = SearchMethod.HYBRID + top_k: int = -1 + radius: float | None = Field(default=None, ge=0.0, le=1.0) + include_profile: bool = False + enable_llm_rerank: bool = Field( + default=False, + description=( + "Opt-in LLM rerank pass for HYBRID. Applies to agent_case " + "and agent_skill fusion only; the episode hierarchy path " + "has built-in fact eviction and ignores this flag. " + "Ignored by keyword / vector / agentic." + ), + ) + filters: FilterNode | None = None + + @model_validator(mode="after") + def _validate_user_xor_agent(self) -> SearchRequest: + if (self.user_id is None) == (self.agent_id is None): + raise ValueError("exactly one of user_id / agent_id must be provided") + return self + + @model_validator(mode="after") + def _validate_top_k(self) -> SearchRequest: + if self.top_k == 0 or self.top_k < -1 or self.top_k > 100: + raise ValueError("top_k must be -1 or in 1..100") + return self + + @property + def owner_id(self) -> str: + """Derived from whichever of ``user_id`` / ``agent_id`` is set. + + The xor validator guarantees exactly one is non-None, so the + ``or`` falls through to a real string (never the ``""`` default). + """ + return self.user_id or self.agent_id or "" + + @property + def owner_type(self) -> Literal["user", "agent"]: + """``"user"`` if ``user_id`` is set, else ``"agent"``.""" + return "user" if self.user_id is not None else "agent" + + +# ── Item DTOs ──────────────────────────────────────────────────────────── + + +class SearchAtomicFactItem(BaseModel): + """A single atomic fact nested inside its parent episode.""" + + model_config = ConfigDict(extra="forbid") + + id: str + content: str + score: float + + +class SearchEpisodeItem(BaseModel): + """Episode hit — always user-scoped in the current emission contract. + + ``type`` is narrowed to ``"Conversation"`` because the only emitted + episode shape today is conversation-derived; widen when other + sources ship. Item kind is encoded by class name (no ``owner_type`` + field on the wire), so episode results never carry ambiguity. + """ + + model_config = ConfigDict(extra="forbid") + + id: str + user_id: str | None + """Owning user (``None`` only on malformed cascade rows).""" + app_id: str = "default" + project_id: str = "default" + session_id: str + timestamp: _dt.datetime + sender_ids: list[str] = Field(default_factory=list) + summary: str + subject: str + episode: str + type: Literal["Conversation"] + score: float + atomic_facts: list[SearchAtomicFactItem] = Field(default_factory=list) + + +class SearchProfileItem(BaseModel): + """Owner profile — at most one per response, only for user owners. + + ``score`` is ``None`` for direct fetches (``include_profile=true`` + on its own does no ranking); a future query-aware lookup may fill + it in. + """ + + model_config = ConfigDict(extra="forbid") + + id: str + user_id: str | None + app_id: str = "default" + project_id: str = "default" + profile_data: dict[str, object] + score: float | None = None + + +class SearchAgentCaseItem(BaseModel): + """Agent case hit — always agent-scoped.""" + + model_config = ConfigDict(extra="forbid") + + id: str + agent_id: str + app_id: str = "default" + project_id: str = "default" + session_id: str + task_intent: str + approach: str + quality_score: float + key_insight: str | None = None + timestamp: _dt.datetime + score: float + + +class SearchAgentSkillItem(BaseModel): + """Agent skill hit — always agent-scoped.""" + + model_config = ConfigDict(extra="forbid") + + id: str + agent_id: str + app_id: str = "default" + project_id: str = "default" + name: str + description: str + content: str + confidence: float + maturity_score: float + source_case_ids: list[str] = Field(default_factory=list) + score: float + + +class UnprocessedMessageDTO(BaseModel): + """A raw message still in the boundary-detection buffer. + + No extracted memcell yet, no owner inference yet (attribution + happens at boundary detection). Returned by ``/search`` **only when** + ``filters.session_id`` is present as a top-level eq predicate — + unprocessed messages have no ``user_id`` / ``agent_id`` to filter + on, so session is the only meaningful query dimension. + """ + + model_config = ConfigDict(extra="forbid") + + id: str + """Original ``message_id`` from ``/add``.""" + app_id: str = "default" + project_id: str = "default" + session_id: str + sender_id: str + sender_name: str | None = None + role: Literal["user", "assistant", "tool"] + content: str | list[dict[str, object]] + """``str`` for the single-text-item shorthand; ``list`` of opaque + objects for the original multi-modal payload (mirrors + ``MessageItem.content`` from the /add side).""" + timestamp: _dt.datetime + tool_calls: list[dict[str, object]] | None = None + tool_call_id: str | None = None + + +# ── Response envelope ──────────────────────────────────────────────────── + + +class SearchData(BaseModel): + """Body of ``response.data``. + + All five arrays are always present so client code can iterate without + branching on ``owner_type``. Routes not applicable to the request's + owner type stay as ``[]``. ``unprocessed_messages`` is filled only + when ``filters.session_id`` is present as a top-level eq scalar — + in-flight buffer rows are scope-tagged but unattributed (no + ``user_id``), so session is the only meaningful query dimension. + """ + + model_config = ConfigDict(extra="forbid") + + episodes: list[SearchEpisodeItem] = Field(default_factory=list) + profiles: list[SearchProfileItem] = Field(default_factory=list) + agent_cases: list[SearchAgentCaseItem] = Field(default_factory=list) + agent_skills: list[SearchAgentSkillItem] = Field(default_factory=list) + unprocessed_messages: list[UnprocessedMessageDTO] = Field(default_factory=list) + """In-flight messages still in the boundary-detection buffer for + the ``filters.session_id`` (if supplied as a top-level eq scalar); + otherwise stays empty.""" + + +class SearchResponse(BaseModel): + """Top-level response envelope.""" + + model_config = ConfigDict(extra="forbid") + + request_id: str + data: SearchData diff --git a/src/everos/memory/search/filters.py b/src/everos/memory/search/filters.py new file mode 100644 index 00000000..63185623 --- /dev/null +++ b/src/everos/memory/search/filters.py @@ -0,0 +1,274 @@ +"""Filters DSL → LanceDB ``where`` string compiler. + +The Filters DSL is intentionally permissive at the JSON layer (so callers +can pass whatever they like and get a clean 400 if it is not supported) +and rigid at compile time. Field names are validated against a small +allow-list; operators against a closed enum; string literals are +single-quote-escaped. Timestamps are accepted as epoch milliseconds and +rendered as DataFusion ``TIMESTAMP ''`` literals. + +``owner_id`` and ``owner_type`` are the hard partition keys; they are +not part of the DSL at all. :func:`compile_filters` injects them at the +top of the compiled string from :class:`SearchRequest` and rejects any +attempt to override them inside ``filters``. + +Public surface +-------------- + +The compiler exposes three primitives so adjacent subpackages +(notably ``memory.get``) can build narrower DSLs without forking the +field allow-list: + +* :data:`ALLOWED_FIELDS` — mapping ``field_name → _FieldSpec`` (column + + kind). Iterate / membership-test only; do not mutate. +* :data:`RESERVED_FIELDS` — names rejected inside any ``filters`` block. +* :func:`compile_predicate` — render one ``{field: value}`` clause to + SQL. Operator-map and equality-shorthand are both handled. + +The high-level :func:`compile_filters` remains the entry point for +``/search`` (combinator-aware). +""" + +from __future__ import annotations + +import datetime as _dt +from typing import Any, Final + +from everos.component.utils.datetime import from_timestamp, to_iso_format + +from .dto import FilterNode + + +class FilterError(ValueError): + """Raised when the DSL contains a disallowed field, operator, or value.""" + + +# ── Allow-lists ────────────────────────────────────────────────────────── + +_OP_MAP: Final[dict[str, str]] = { + "eq": "=", + "ne": "!=", + "gt": ">", + "gte": ">=", + "lt": "<", + "lte": "<=", + "in": "IN", +} + +# Field kinds: ``str`` rendered as ``''``; ``ts`` rendered as +# ``TIMESTAMP ''`` (DataFusion timestamp literal); ``array_str`` +# uses DataFusion's ``array_has`` on a list column. +_FieldKind = str # one of: "str" | "ts" | "array_str" + + +class _FieldSpec: + __slots__ = ("column", "kind") + + def __init__(self, column: str, kind: _FieldKind) -> None: + self.column = column + self.kind = kind + + +ALLOWED_FIELDS: Final[dict[str, _FieldSpec]] = { + "session_id": _FieldSpec("session_id", "str"), + "parent_type": _FieldSpec("parent_type", "str"), + "parent_id": _FieldSpec("parent_id", "str"), + "timestamp": _FieldSpec("timestamp", "ts"), + "sender_id": _FieldSpec("sender_ids", "array_str"), +} + +# Fields the caller is explicitly **not** allowed to place inside +# ``filters``; they live at the top of :class:`SearchRequest` and are +# injected by :func:`compile_filters`. Rejecting them here turns a +# silent override into a 400. +RESERVED_FIELDS: Final[frozenset[str]] = frozenset( + {"owner_id", "owner_type", "app_id", "project_id"} +) + + +# ── Public API ─────────────────────────────────────────────────────────── + + +def compile_filters( + node: FilterNode | None, + *, + owner_id: str, + owner_type: str, + app_id: str = "default", + project_id: str = "default", +) -> str: + """Compile a request's filters into a single LanceDB ``where`` string. + + The base clause always pins the hard partition keys (``owner_id`` / + ``owner_type`` and the ``app_id`` / ``project_id`` scope segments) to + the request's top-level values; anything in ``node`` is appended with + an ``AND``. Pinning app/project here is what isolates one space's rows + from another — omitting it would let a query bleed across spaces. Both + ``/search`` and ``/get`` share this compile path. + """ + base = [ + f"owner_id = '{_escape_str(owner_id)}'", + f"owner_type = '{owner_type}'", + f"app_id = '{_escape_str(app_id)}'", + f"project_id = '{_escape_str(project_id)}'", + ] + if node is None: + return " AND ".join(base) + compiled = _compile_node(node.model_dump(exclude_none=True)) + if not compiled: + return " AND ".join(base) + return " AND ".join([*base, compiled]) + + +# ── Internals ──────────────────────────────────────────────────────────── + + +def _compile_node(raw: dict[str, Any]) -> str: + """Walk one DSL node; return the matching SQL fragment (no leading parens). + + Empty nodes yield ``""`` so :func:`compile_filters` can skip the + trailing ``AND``. + """ + raw = dict(raw) # never mutate the caller's dict + parts: list[str] = [] + + if (and_list := raw.pop("AND", None)) is not None: + parts.append(_compile_combinator(and_list, "AND")) + if (or_list := raw.pop("OR", None)) is not None: + parts.append(_compile_combinator(or_list, "OR")) + + for field, value in raw.items(): + if field in RESERVED_FIELDS: + raise FilterError( + f"filter field {field!r} is reserved; pass it at the top of the request" + ) + if field not in ALLOWED_FIELDS: + raise FilterError(f"unsupported filter field: {field!r}") + parts.append(compile_predicate(field, value)) + + # Drop empty fragments coming from empty AND/OR arrays. + parts = [p for p in parts if p] + if not parts: + return "" + if len(parts) == 1: + return parts[0] + return " AND ".join(parts) + + +def _compile_combinator(children: list[dict[str, Any]], op: str) -> str: + """Render an ``AND`` / ``OR`` array of child nodes.""" + if not isinstance(children, list): + raise FilterError(f"{op} expects an array of nodes") + fragments: list[str] = [] + for child in children: + if not isinstance(child, dict): + raise FilterError(f"{op} children must be objects") + compiled = _compile_node(child) + if compiled: + fragments.append(f"({compiled})") + if not fragments: + return "" + if len(fragments) == 1: + # No need for the surrounding combinator when only one effective child. + return fragments[0] + glue = f" {op} " + return "(" + glue.join(fragments) + ")" + + +def compile_predicate(field: str, value: Any) -> str: + """Render one ``"": `` clause to SQL. + + Public primitive — :mod:`memory.get` builds a flat (no AND/OR) + DSL on top of it. Callers must pre-validate ``field`` against + :data:`ALLOWED_FIELDS` and :data:`RESERVED_FIELDS`; this function + will ``KeyError`` on unknown fields. + + ``value`` is either a scalar (equality shorthand) or an + ``{"": }`` map. Mixing multiple operators in one + dict is allowed and folds with ``AND``:: + + "timestamp": {"gte": 1, "lt": 2} + → (timestamp >= TIMESTAMP '...' AND timestamp < TIMESTAMP '...') + """ + spec = ALLOWED_FIELDS[field] + if isinstance(value, dict): + if not value: + raise FilterError(f"empty operator map for field {field!r}") + clauses = [ + _compile_op_clause(spec, field, op, op_val) for op, op_val in value.items() + ] + if len(clauses) == 1: + return clauses[0] + return "(" + " AND ".join(clauses) + ")" + # Equality shorthand. + return _compile_op_clause(spec, field, "eq", value) + + +def _compile_op_clause(spec: _FieldSpec, field: str, op: str, value: Any) -> str: + """Render a single `` `` clause.""" + if op not in _OP_MAP: + raise FilterError(f"unsupported operator {op!r} on field {field!r}") + sql_op = _OP_MAP[op] + + if spec.kind == "array_str": + # Only equality / membership make sense on a list column. + if op == "eq": + literal = _escape_str(_require_str(value, field)) + return f"array_has({spec.column}, '{literal}')" + if op == "in": + items = _require_list(value, field) + literals = [f"'{_escape_str(_require_str(v, field))}'" for v in items] + inner = " OR ".join(f"array_has({spec.column}, {lit})" for lit in literals) + return f"({inner})" + raise FilterError(f"operator {op!r} is not supported on array field {field!r}") + + if op == "in": + items = _require_list(value, field) + literals = [_render_literal(v, spec.kind, field) for v in items] + return f"{spec.column} IN ({', '.join(literals)})" + + return f"{spec.column} {sql_op} {_render_literal(value, spec.kind, field)}" + + +# ── Literal rendering ──────────────────────────────────────────────────── + + +def _render_literal(value: Any, kind: _FieldKind, field: str) -> str: + if kind == "str": + return f"'{_escape_str(_require_str(value, field))}'" + if kind == "ts": + return f"TIMESTAMP '{_render_ts(value, field)}'" + raise FilterError(f"unsupported field kind {kind!r} for field {field!r}") + + +def _render_ts(value: Any, field: str) -> str: + """Accept epoch ms (int / float) or an ISO 8601 string; emit ISO.""" + if isinstance(value, bool): # bools subclass int — reject early + raise FilterError(f"timestamp value for {field!r} must be ms or ISO string") + if isinstance(value, (int, float)): + return to_iso_format(from_timestamp(int(value))) + if isinstance(value, str): + # Trust the caller-supplied ISO string but escape quotes defensively. + if "'" in value: + raise FilterError(f"timestamp string for {field!r} contains a quote") + return value + if isinstance(value, _dt.datetime): + return to_iso_format(value) + raise FilterError(f"timestamp value for {field!r} must be ms or ISO string") + + +def _escape_str(value: str) -> str: + """Double single quotes — SQL-standard escape for a single-quoted literal.""" + return value.replace("'", "''") + + +def _require_str(value: Any, field: str) -> str: + if not isinstance(value, str): + raise FilterError(f"value for {field!r} must be a string") + return value + + +def _require_list(value: Any, field: str) -> list[Any]: + if not isinstance(value, list) or not value: + raise FilterError(f"value for {field!r} with 'in' must be a non-empty list") + return value diff --git a/src/everos/memory/search/hierarchy.py b/src/everos/memory/search/hierarchy.py new file mode 100644 index 00000000..6f1a7bdf --- /dev/null +++ b/src/everos/memory/search/hierarchy.py @@ -0,0 +1,242 @@ +"""Hierarchical episode retrieval — two-path recall fused with per-fact eviction. + +Episode HYBRID search path: combines episode-level hybrid recall (Layer 1) +with fact-driven MaxSim re-scoring (Layer 2), merges via RRF (Layer 3), then +runs a single-pass eviction where a fact that outscores its parent episode +enters top-N in place of the episode (Layer 4). + +Uses everalgo operators as pure algorithm primitives; all I/O is injected +via recaller callbacks. No changes to the everalgo library are required. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from everalgo.rank import amaxsim_retrieve +from everalgo.rank.fusion import rrf +from everalgo.types import Candidate, FactCandidate, ScoredItem + +from everos.core.observability.logging import get_logger + +from .dto import SearchEpisodeItem +from .shaper import reshape_hybrid_output + +if TYPE_CHECKING: + from collections.abc import Sequence + + from everos.memory.search.recall.atomic_fact import AtomicFactRecaller + from everos.memory.search.recall.episode import EpisodeRecaller + +logger = get_logger(__name__) + + +async def hierarchy_retrieve_episodes( + query: str, + *, + sparse: list[Candidate], + dense: list[Candidate], + query_vector: list[float], + fact_recaller: AtomicFactRecaller, + episode_recaller: EpisodeRecaller, + where: str, + top_k: int, + fact_child_candidates: int = 200, +) -> list[SearchEpisodeItem]: + """Run the four-layer hierarchical episode retrieval pipeline. + + Layer 1: RRF fusion over pre-recalled sparse + dense episode candidates. + Layer 2: MaxSim re-score via atomic-fact child retrieval (fact cosine ANN + → group by parent memcell → episode re-score by best fact). + Layer 3: RRF merge of Layer-1 and Layer-2 results, sliced to top_k. + Layer 4: Pre-fetch facts for merged episodes, then single-pass eviction + (fact outscoring its parent episode enters top-N instead). + + Args: + query: Raw query string passed to amaxsim_retrieve. + sparse: BM25 episode candidates from the caller's recall phase. + dense: Vector ANN episode candidates from the caller's recall phase. + query_vector: Pre-computed query embedding; reused for fact ANN recall + and per-fact scoring in facts_for_episodes. + fact_recaller: AtomicFactRecaller instance for child retrieval and + facts_for_episodes. + episode_recaller: EpisodeRecaller instance for MaxSim parent fetch. + where: LanceDB filter clause (owner scope, tenant, etc.). + top_k: Maximum number of items in the final merged slice before eviction. + fact_child_candidates: How many atomic-fact ANN candidates to pull in + Layer 2. Default 200. + + Returns: + Shaped SearchEpisodeItem list (episodes with nested atomic_facts), + sorted by score descending. + """ + # Layer 1 — episode RRF fusion + layer1_episodes = rrf(sparse, dense) + + # Layer 2 — MaxSim re-score via atomic-fact child retrieval + layer2_episodes = await _maxsim_episode_rescore( + query=query, + query_vector=query_vector, + fact_recaller=fact_recaller, + episode_recaller=episode_recaller, + where=where, + child_candidates=fact_child_candidates, + ) + + # Layer 3 — RRF merge of episode-level results, slice to top_k + merged = rrf(layer1_episodes, layer2_episodes)[:top_k] + + if not merged: + logger.info("hierarchy_retrieve_empty_merge", top_k=top_k) + return [] + + # Layer 4a — pre-fetch facts for merged episodes + ep_to_memcell = _build_ep_to_memcell(merged) + episode_to_facts = await fact_recaller.facts_for_episodes( + ep_to_memcell, + where, + per_episode=max(top_k * 2, 20), + query_vector=query_vector, + ) + + # Layer 4b — single-pass eviction + scored_items = _hierarchy_eviction_pass(merged, episode_to_facts) + + # Build episode pool for orphan fact parent lookup. + # Include layer2_episodes so episodes surfaced only via MaxSim path + # (not in the original sparse/dense recall) can still serve as parent. + episode_pool = {c.id: c for c in (*sparse, *dense, *layer2_episodes)} + + return reshape_hybrid_output(scored_items, episode_pool=episode_pool) + + +def _hierarchy_eviction_pass( + merged: list[Candidate], + episode_to_facts: dict[str, list[FactCandidate]], +) -> list[ScoredItem]: + """Single-pass eviction: fact outscoring its parent episode enters top-N. + + For each episode in merged order: if its best matching atomic fact scores + higher than the episode itself, emit the fact as a ScoredItem + (item_type='atomic_fact') and mark the episode as an orphan parent. + Otherwise emit the episode directly as item_type='episode'. + + Args: + merged: RRF-merged episode candidates, ordered by descending score. + episode_to_facts: Map from episode_id to its pre-fetched FactCandidates, + sorted by cosine similarity descending. + + Returns: + Mixed list of ScoredItem instances (episodes and atomic_facts) ready + for reshape_hybrid_output. + """ + out: list[ScoredItem] = [] + + for episode in merged: + facts = episode_to_facts.get(episode.id, []) + best_fact = facts[0] if facts else None + + if best_fact is not None and best_fact.score > episode.score: + # Fact wins: emit fact; episode becomes orphan parent + out.append( + ScoredItem( + id=best_fact.id, + score=best_fact.score, + item_type="atomic_fact", + metadata=best_fact.metadata, + parent_episode_id=episode.id, + ) + ) + logger.debug( + "hierarchy_eviction_fact_wins", + episode_id=episode.id, + fact_id=best_fact.id, + fact_score=best_fact.score, + episode_score=episode.score, + ) + else: + # Episode wins: emit episode with its metadata intact + out.append( + ScoredItem( + id=episode.id, + score=episode.score, + item_type="episode", + metadata=dict(episode.metadata), + parent_episode_id=None, + ) + ) + + return out + + +# ── Internal helpers ───────────────────────────────────────────────────── + + +async def _maxsim_episode_rescore( + *, + query: str, + query_vector: list[float], + fact_recaller: AtomicFactRecaller, + episode_recaller: EpisodeRecaller, + where: str, + child_candidates: int, +) -> list[Candidate]: + """Run amaxsim_retrieve to produce MaxSim-rescored episode candidates. + + Atomic facts serve as child documents (their metadata["parent_id"] is + the memcell_id). Episodes are fetched as parents via + episode_recaller.fetch_by_parent_ids. + + ``amaxsim_retrieve`` calls ``child_retrieve`` exactly once with the + original query string. We reuse the pre-computed ``query_vector`` to + avoid a redundant embed call. + + Args: + query: Raw query string (passed verbatim to amaxsim_retrieve). + query_vector: Pre-computed query embedding; used directly for child + ANN recall, bypassing a second embed call. + fact_recaller: Provides the child ANN retrieval function. + episode_recaller: Provides the parent fetch function. + where: LanceDB filter clause. + child_candidates: Number of atomic-fact candidates to pull per call. + + Returns: + Episode candidates re-scored by their best matching atomic fact. + """ + + async def child_retrieve(_q: str, n: int) -> Sequence[Candidate]: + # amaxsim_retrieve calls this exactly once with the original query string. + # Reuse the pre-computed query_vector instead of re-embedding. + return await fact_recaller.dense_recall(query_vector, where, limit=n) + + async def parent_fetch(memcell_ids: list[str]) -> list[Candidate]: + return await episode_recaller.fetch_by_parent_ids(memcell_ids, where) + + return await amaxsim_retrieve( + query, + child_retrieve=child_retrieve, + parent_fetch=parent_fetch, + top_n=50, + child_candidates=child_candidates, + ) + + +def _build_ep_to_memcell(episodes: list[Candidate]) -> dict[str, str]: + """Extract episode_id → memcell_id mapping from episode candidates. + + Episodes store their source memcell id in metadata["parent_id"]. + Entries missing or having a non-string parent_id are silently skipped + (they will receive no facts during Layer 4). + + Args: + episodes: Merged episode candidate list. + + Returns: + Dict mapping episode LanceDB id to memcell id. + """ + result: dict[str, str] = {} + for ep in episodes: + mc_id = ep.metadata.get("parent_id") + if isinstance(mc_id, str) and mc_id: + result[ep.id] = mc_id + return result diff --git a/src/everos/memory/search/manager.py b/src/everos/memory/search/manager.py new file mode 100644 index 00000000..53de50e5 --- /dev/null +++ b/src/everos/memory/search/manager.py @@ -0,0 +1,797 @@ +"""SearchManager — top-level orchestrator for ``POST /api/v1/memory/search``. + +Hard partition by ``owner_type``: + +* ``user`` → ``episodes`` (+ ``profiles`` when ``include_profile=true``) +* ``agent`` → ``agent_cases`` + ``agent_skills`` + +Per kind, :func:`memory.search.adapter.resolve_pipeline` decides whether +the path is "single-route recall, no fusion" (``KEYWORD`` / ``VECTOR``) +or "sparse + dense → everalgo.rank" (``HYBRID`` / ``AGENTIC``). Component +guards (embedding / cross-encoder / LLM) raise early when a method is +selected without its prerequisites. + +``HYBRID`` defaults to **no LLM rerank** — the response comes back +straight after the four-layer hierarchy pipeline (RRF → MaxSim → +RRF merge → single-pass fact eviction). ``enable_llm_rerank`` is +**ignored** for the hierarchy path. ``AGENTIC`` keeps its own +internal cross-encoder rerank loop; the flag is ignored there. + +``SearchEpisodeItem.atomic_facts`` is populated **only** when the HYBRID +pipeline runs over episodes. The other methods leave it empty: there is +no query-relevance score we can assign to a fact pulled by parent_id +alone, and emitting ``score=0.0`` facts would muddy the contract. + +The manager never writes to storage; it only reads LanceDB + markdown. +""" + +from __future__ import annotations + +import asyncio +import json +from collections.abc import Sequence +from typing import TYPE_CHECKING + +from everalgo.rank import DEFAULT_RANK_CONFIG, RankConfig, arank +from everalgo.types import Candidate, RankInput + +from everos.component.utils.datetime import to_display_tz +from everos.config import load_settings +from everos.core.observability.logging import get_logger +from everos.core.observability.tracing import gen_request_id +from everos.infra.persistence.sqlite import ( + UnprocessedBuffer, + unprocessed_buffer_repo, +) + +from .adapter import resolve_pipeline +from .agentic import search_episodes_agentic +from .agentic_agent import search_agent_cases_agentic, search_agent_skills_agentic +from .dto import ( + FilterNode, + SearchAgentCaseItem, + SearchAgentSkillItem, + SearchData, + SearchEpisodeItem, + SearchMethod, + SearchProfileItem, + SearchRequest, + SearchResponse, + UnprocessedMessageDTO, +) +from .filters import compile_filters +from .hierarchy import hierarchy_retrieve_episodes +from .shaper import ( + shape_agent_case_from_candidate, + shape_agent_skill_from_candidate, + shape_episode_from_candidate, +) +from .skill_hybrid import search_agent_skills_hybrid + +if TYPE_CHECKING: + from everalgo.llm.protocols import LLMClient + + from everos.component.embedding import EmbeddingProvider + from everos.component.rerank import RerankProvider + from everos.component.tokenizer import Tokenizer + + from .recall import ( + AgentCaseRecaller, + AgentSkillRecaller, + AtomicFactRecaller, + EpisodeRecaller, + ProfileRecaller, + ) + +logger = get_logger(__name__) + +# Recall pool sizing — matches the legacy enterprise constants +# ``DEFAULT_RECALL_MULTIPLIER`` / ``DEFAULT_TOPK_LIMIT``. +# Multiplier kicks in for ``top_k > 0``; ``top_k = -1`` (unlimited) is capped +# at the fixed top-k limit (100) rather than ``100 * multiplier`` — that way +# the recall pool never balloons to 500 in unlimited mode. +_DEFAULT_RECALL_MULTIPLIER = 2 +_DEFAULT_TOP_K_CAP = 100 + +# Agent cases / skills carry heavy per-row payloads (``approach``, +# full ``content``); cap unlimited mode at 10 to keep rerank context +# bounded. Positive ``top_k`` from the caller bypasses this. +_AGENT_TOP_K_CAP = 10 + +# Vector ``radius`` (cosine similarity threshold) default for **unlimited +# mode only**. In ``top_k > 0`` mode we trust the truncation cap to ditch +# low-quality tail; in ``top_k = -1`` mode we would otherwise return up to +# 100 candidates with no quality floor, so we layer a default 0.5 +# similarity threshold the way enterprise does (enterprise uses 0.6 — we +# pick 0.5 slightly looser because LanceDB cosine vs Milvus cosine score +# distributions can drift a bit on the same model). +_DEFAULT_UNLIMITED_RADIUS = 0.5 + +# ``maxsim_atomic`` recall pool sizing — atomic facts are ~28× denser than +# episodes (one memcell → 1 episode + ~28 atomic facts), so the fact pool +# is sized as ``top_k_episode * 20`` to consistently cover enough distinct +# parent memcells before the max-pool reduction. Capped to keep the ANN +# scan bounded on very large top_k requests. +_MAXSIM_FACT_MULTIPLIER = 20 +_MAXSIM_FACT_POOL_CAP = 2000 + +# Mirror of ``service._boundary._TRACK``. The unprocessed buffer is a single +# shared track because boundary detection is single-pass — switching mode +# requires a fresh process. Hard-coded here (instead of importing) to keep +# the memory layer free of service-layer imports per the DDD direction rule. +_UNPROCESSED_TRACK = "memorize" + + +class SearchManager: + """Orchestrates per-kind recall, fusion, and shape into the public DTO.""" + + def __init__( + self, + *, + episode_recaller: EpisodeRecaller, + atomic_fact_recaller: AtomicFactRecaller, + agent_case_recaller: AgentCaseRecaller, + agent_skill_recaller: AgentSkillRecaller, + profile_recaller: ProfileRecaller, + embedding: EmbeddingProvider | None, + reranker: RerankProvider | None, + llm_client: LLMClient | None, + search_tokenizer: Tokenizer | None = None, + ) -> None: + self._ep = episode_recaller + self._fact = atomic_fact_recaller + self._case = agent_case_recaller + self._skill = agent_skill_recaller + self._profile = profile_recaller + self._embedding = embedding + self._reranker = reranker + self._llm = llm_client + self._search_tokenizer = search_tokenizer + + # ── Public entry ──────────────────────────────────────────────── + + async def search(self, req: SearchRequest) -> SearchResponse: + request_id = gen_request_id() + # Compile filters first: a malformed `filters` payload is a user + # input error (422) and should surface before the server-side + # component guard (500). The two steps are independent. + where = compile_filters( + req.filters, + owner_id=req.owner_id, + owner_type=req.owner_type, + app_id=req.app_id, + project_id=req.project_id, + ) + self._validate_components(req) + + if req.owner_type == "user": + episodes, profiles, unprocessed = await asyncio.gather( + self._search_episodes(req, where), + self._fetch_profile(req), + self._load_unprocessed(req), + ) + data = SearchData( + episodes=episodes, + profiles=profiles, + unprocessed_messages=unprocessed, + ) + else: # "agent" + (cases, skills), unprocessed = await asyncio.gather( + self._search_cases_and_skills(req, where), + self._load_unprocessed(req), + ) + data = SearchData( + agent_cases=cases, + agent_skills=skills, + unprocessed_messages=unprocessed, + ) + + return SearchResponse(request_id=request_id, data=data) + + # ── Unprocessed buffer ────────────────────────────────────────── + + async def _load_unprocessed( + self, req: SearchRequest + ) -> list[UnprocessedMessageDTO]: + """Load in-flight buffer rows for ``filters.session_id`` (if present). + + Returns ``[]`` unless ``filters`` carries a top-level ``session_id`` + eq scalar — buffer rows have no ``user_id`` / ``agent_id`` attribution + (boundary detection runs before owner inference), so session is the + only meaningful query dimension. + """ + session_id = _extract_top_level_session_id(req.filters) + if session_id is None: + return [] + rows = await unprocessed_buffer_repo.list_for_track( + session_id, + _UNPROCESSED_TRACK, + app_id=req.app_id, + project_id=req.project_id, + ) + return [_unprocessed_buffer_to_dto(r) for r in rows] + + # ── Agent partition ───────────────────────────────────────────── + + async def _search_cases_and_skills( + self, req: SearchRequest, where: str + ) -> tuple[list[SearchAgentCaseItem], list[SearchAgentSkillItem]]: + """Cases + skills, serial when bridging. + + HYBRID + LLM rerank runs serially: reranked cases feed the + skill bridge. Every other method runs the two kinds in parallel + with no bridge — the bridge only pays off after rerank has + produced high-quality case scores to inherit. + """ + if _effective_llm_rerank(req): + cases = await self._search_agent_cases(req, where) + bridge_cases = [ + Candidate(id=c.id, score=c.score, source="vector", metadata={}) + for c in cases + ] + skills = await self._search_agent_skills( + req, where, bridge_cases=bridge_cases + ) + return cases, skills + + cases, skills = await asyncio.gather( + self._search_agent_cases(req, where), + self._search_agent_skills(req, where), + ) + return cases, skills + + # ── Episodes ──────────────────────────────────────────────────── + + async def _search_episodes( + self, req: SearchRequest, where: str + ) -> list[SearchEpisodeItem]: + if req.method == SearchMethod.AGENTIC: + return await search_episodes_agentic( + req.query, + owner_id=req.owner_id, + where=where, + episode_recaller=self._ep, + atomic_fact_recaller=self._fact, + embed_query_fn=self._embedding.embed, # type: ignore[union-attr] + reranker=self._reranker, # type: ignore[arg-type] + llm=self._llm, # type: ignore[arg-type] + top_k=self._top_k(req.top_k), + ) + + fusion_mode, _ = resolve_pipeline(req.method, "episode") + enable_rerank = _effective_llm_rerank(req) + top_k = self._top_k(req.top_k) + + # ── KEYWORD / VECTOR: single-route recall ── + if fusion_mode is None: + if req.method == SearchMethod.KEYWORD: + cands = await self._ep.sparse_recall( + req.query, where, limit=self._recall_limit(req.top_k) + ) + elif load_settings().search.vector_strategy == "maxsim_atomic": + cands = await self._maxsim_atomic_recall(req, where, top_k) + else: + vector = await self._embed_query(req.query) + cands = await self._ep.dense_recall( + vector, where, limit=self._recall_limit(req.top_k) + ) + cands = self._apply_radius(cands, _effective_radius(req)) + # ``atomic_facts`` stays empty: facts come back only when the HYBRID + # pipeline surfaces them with a score (see ``reshape_hybrid_output``). + # Single-route recall has no per-fact score against the query, so + # we do not back-fill — that would emit ``score=0.0`` facts whose + # semantics are ambiguous. + return [ + ep + for ep in (shape_episode_from_candidate(c) for c in cands[:top_k]) + if ep is not None + ] + + # ── HYBRID: parallel sparse + dense recall ── + sparse, dense, query_vector = await self._recall_sparse_dense( + self._ep, req, where, top_k + ) + + if fusion_mode == "hierarchy": + return await hierarchy_retrieve_episodes( + req.query, + sparse=sparse, + dense=dense, + query_vector=query_vector, + fact_recaller=self._fact, + episode_recaller=self._ep, + where=where, + top_k=top_k, + ) + + # rrf / lr: standard everalgo fusion path (fallback). + output = await arank( + RankInput( + query=req.query, + memory_type=self._ep.everalgo_memory_type, # type: ignore[arg-type] + sparse_candidates=sparse, + dense_candidates=dense, + top_k=top_k, + radius=_effective_radius(req), + ), + config=RankConfig(fusion_mode=fusion_mode) + if fusion_mode != "rrf" + else DEFAULT_RANK_CONFIG, + llm=self._llm, + enable_rerank=enable_rerank, + rerank_top_k=top_k, + ) + ep_candidates = (_scored_as_candidate(s) for s in output.items) + return [ + ep + for ep in (shape_episode_from_candidate(c) for c in ep_candidates) + if ep is not None + ] + + # ── Agent cases ───────────────────────────────────────────────── + + async def _search_agent_cases( + self, req: SearchRequest, where: str + ) -> list[SearchAgentCaseItem]: + if req.method == SearchMethod.AGENTIC: + return await search_agent_cases_agentic( + req.query, + where=where, + case_recaller=self._case, + embed_query_fn=self._embedding.embed, # type: ignore[union-attr] + reranker=self._reranker, # type: ignore[arg-type] + llm=self._llm, # type: ignore[arg-type] + top_k=self._top_k(req.top_k), + ) + fusion_mode, _ = resolve_pipeline(req.method, "agent_case") + enable_rerank = _effective_llm_rerank(req) + top_k = self._top_k(req.top_k, cap=_AGENT_TOP_K_CAP) + + if fusion_mode is None: + cands = await self._single_route_recall( + self._case, req, where, top_k, cap=_AGENT_TOP_K_CAP + ) + shaped = (shape_agent_case_from_candidate(c) for c in cands[:top_k]) + return [item for item in shaped if item is not None] + + sparse, dense, _ = await self._recall_sparse_dense( + self._case, req, where, top_k, cap=_AGENT_TOP_K_CAP + ) + output = await arank( + RankInput( + query=req.query, + memory_type=self._case.everalgo_memory_type, # type: ignore[arg-type] + sparse_candidates=sparse, + dense_candidates=dense, + top_k=top_k, + radius=_effective_radius(req), + ), + config=RankConfig(fusion_mode=fusion_mode) + if fusion_mode != "rrf" + else DEFAULT_RANK_CONFIG, + llm=self._llm, + enable_rerank=enable_rerank, + rerank_top_k=top_k, + ) + case_candidates = (_scored_as_candidate(s) for s in output.items) + shaped = (shape_agent_case_from_candidate(c) for c in case_candidates) + return [item for item in shaped if item is not None] + + # ── Agent skills ──────────────────────────────────────────────── + + async def _search_agent_skills( + self, + req: SearchRequest, + where: str, + *, + bridge_cases: list[Candidate] | None = None, + ) -> list[SearchAgentSkillItem]: + """Rank agent skills. ``bridge_cases`` (reranked case id+score) is + supplied only on HYBRID + LLM-rerank to feed the case→skill bridge; + ``None`` everywhere else. + """ + if req.method == SearchMethod.AGENTIC: + return await search_agent_skills_agentic( + req.query, + where=where, + skill_recaller=self._skill, + embed_query_fn=self._embedding.embed, # type: ignore[union-attr] + reranker=self._reranker, # type: ignore[arg-type] + llm=self._llm, # type: ignore[arg-type] + top_k=self._top_k(req.top_k, cap=_AGENT_TOP_K_CAP), + ) + fusion_mode, _ = resolve_pipeline(req.method, "agent_skill") + top_k = self._top_k(req.top_k, cap=_AGENT_TOP_K_CAP) + + if fusion_mode is None: + cands = await self._single_route_recall( + self._skill, req, where, top_k, cap=_AGENT_TOP_K_CAP + ) + shaped = (shape_agent_skill_from_candidate(c) for c in cands[:top_k]) + return [item for item in shaped if item is not None] + + sparse, dense, _ = await self._recall_sparse_dense( + self._skill, req, where, top_k, cap=_AGENT_TOP_K_CAP + ) + + # Case→skill bridge: union skills surfaced via lineage cases into + # the dense pool with their max-pooled source-case score. + bridged = await self._case_bridged_skills(bridge_cases, where, top_k) + dense = _merge_by_id_max(dense, bridged) + + # Lane selection lives here so ``skill_hybrid`` stays single-purpose + # (cross-encoder) and symmetry with the case path is preserved. + if _effective_llm_rerank(req): + # LLM lane: generic ``arank`` dispatches by ``memory_type="skill"`` + # to the skill facade (adds the skill-only 0.4 relevance gate). + # Config is ``rrf`` — ``skill_hybrid`` is an everos routing + # label, not an everalgo fusion mode. + output = await arank( + RankInput( + query=req.query, + memory_type=self._skill.everalgo_memory_type, # type: ignore[arg-type] + sparse_candidates=sparse, + dense_candidates=dense, + top_k=top_k, + radius=_effective_radius(req), + ), + config=DEFAULT_RANK_CONFIG, + llm=self._llm, + enable_rerank=True, + rerank_top_k=top_k, + ) + skill_candidates = (_scored_as_candidate(s) for s in output.items) + shaped = (shape_agent_skill_from_candidate(c) for c in skill_candidates) + return [item for item in shaped if item is not None] + + # Cross-encoder lane (default): rrf + skill-shaped cross-encoder rerank. + return await search_agent_skills_hybrid( + req.query, + sparse=sparse, + dense=dense, + reranker=self._reranker, # type: ignore[arg-type] + top_k=top_k, + ) + + # ── Profile ───────────────────────────────────────────────────── + + async def _fetch_profile(self, req: SearchRequest) -> list[SearchProfileItem]: + if not req.include_profile or req.owner_type != "user": + return [] + return await self._profile.fetch(req.owner_id) + + # ── Recall helpers ────────────────────────────────────────────── + + async def _single_route_recall( + self, + recaller: EpisodeRecaller | AgentCaseRecaller | AgentSkillRecaller, + req: SearchRequest, + where: str, + top_k: int, + *, + cap: int = _DEFAULT_TOP_K_CAP, + ) -> list[Candidate]: + if req.method == SearchMethod.KEYWORD: + return await recaller.sparse_recall( + req.query, where, limit=self._recall_limit(req.top_k, cap=cap) + ) + vector = await self._embed_query(req.query) + cands = await recaller.dense_recall( + vector, where, limit=self._recall_limit(req.top_k, cap=cap) + ) + return self._apply_radius(cands, _effective_radius(req)) + + async def _recall_sparse_dense( + self, + recaller: EpisodeRecaller | AgentCaseRecaller | AgentSkillRecaller, + req: SearchRequest, + where: str, + top_k: int, + *, + cap: int = _DEFAULT_TOP_K_CAP, + ) -> tuple[list[Candidate], list[Candidate], list[float]]: + """Fan out keyword + vector recall in parallel. + + The third return is the query embedding itself — the HYBRID + pipeline passes it into ``facts_for_episodes`` so per-fact + cosine scoring reuses the same vector instead of re-embedding + the query. Returns + ``[]`` for ``vector`` when no embedding provider is configured. + """ + vector = await self._embed_query(req.query) + limit = self._recall_limit(req.top_k, cap=cap) + sparse, dense = await asyncio.gather( + recaller.sparse_recall(req.query, where, limit=limit), + recaller.dense_recall(vector, where, limit=limit) + if vector + else _empty_candidates(), + ) + dense = self._apply_radius(dense, _effective_radius(req)) + return sparse, dense, vector + + async def _maxsim_atomic_recall( + self, req: SearchRequest, where: str, top_k: int + ) -> list[Candidate]: + """MaxSim-style: ANN atomic_facts → max-pool by memcell → batch fetch episodes. + + Trades one extra LanceDB ANN scan (over the ~28× denser + ``atomic_fact`` table) for finer-grained semantic match — long + episodes whose single mean-pooled vector dilutes a specific topic + recover via the matching atomic fact's own embedding. Mirrors + EverOS/EverCore's MaxSim retrieval pattern. + """ + vector = await self._embed_query(req.query) + if not vector: + return [] + fact_limit = min(top_k * _MAXSIM_FACT_MULTIPLIER, _MAXSIM_FACT_POOL_CAP) + fact_cands = await self._fact.dense_recall(vector, where, limit=fact_limit) + # Max-pool fact scores by their parent memcell. ``atomic_fact`` + # rows always carry ``parent_id = memcell_id`` (cascade contract). + mc_score: dict[str, float] = {} + for fc in fact_cands: + mc = fc.metadata.get("parent_id") + if not isinstance(mc, str) or not mc: + continue + if fc.score > mc_score.get(mc, -1.0): + mc_score[mc] = fc.score + if not mc_score: + return [] + ranked = sorted(mc_score.items(), key=lambda kv: kv[1], reverse=True)[:top_k] + top_mc_ids = [mc for mc, _ in ranked] + score_by_mc = dict(ranked) + # One LanceDB scan: ``WHERE parent_id IN (...)``. The episode + # ``where`` re-applies the partition filter so episodes whose + # owner partition no longer matches the request are dropped. + ep_cands = await self._ep.fetch_by_parent_ids(top_mc_ids, where) + rescored: list[Candidate] = [] + for c in ep_cands: + mc = c.metadata.get("parent_id") + s = score_by_mc.get(mc, 0.0) if isinstance(mc, str) else 0.0 + rescored.append( + Candidate(id=c.id, score=s, source="vector", metadata=c.metadata) + ) + rescored.sort(key=lambda c: c.score, reverse=True) + return self._apply_radius(rescored, _effective_radius(req)) + + async def _case_bridged_skills( + self, + bridge_cases: list[Candidate] | None, + where: str, + top_k: int, + ) -> list[Candidate]: + """Reverse-resolve lineage skills and max-pool their source-case + scores. + + Reuses ``bridge_cases`` (already-reranked id+score) so ``agent_case`` + is never scanned twice. Scores must be LLM-rerank relevance in + ``[0, 1]`` to stay comparable with the direct dense pool — never + feed BM25 / fusion scores in here. Mirrors the ``maxsim_atomic`` + fact→episode pooling. Empty input ⇒ no bridge. + """ + if not bridge_cases: + return [] + case_score = {c.id: c.score for c in bridge_cases} + # Bound the reverse fetch by the matched-case count; one case can map + # to several skills, so allow a small fan-out per case. + skill_cands = await self._skill.fetch_by_case_ids( + list(case_score), where, limit=max(top_k, len(case_score) * 4) + ) + bridged: list[Candidate] = [] + for sc in skill_cands: + raw_ids = sc.metadata.get("source_case_ids") + src_ids = raw_ids if isinstance(raw_ids, list) else [] + best = max( + (case_score[cid] for cid in src_ids if cid in case_score), + default=0.0, + ) + bridged.append( + Candidate(id=sc.id, score=best, source="vector", metadata=sc.metadata) + ) + return bridged + + async def _embed_query(self, query: str) -> list[float]: + if self._embedding is None: + return [] + return await self._embedding.embed(query) + + # ── Limits / filters ──────────────────────────────────────────── + + @staticmethod + def _top_k(top_k: int, *, cap: int = _DEFAULT_TOP_K_CAP) -> int: + """Resolve ``-1`` to ``cap``; pass others through unchanged. + + ``cap`` defaults to the episode/atomic_fact upper bound; agent + cases / skills pass :data:`_AGENT_TOP_K_CAP` so an unbounded + request still returns a tight, rerank-friendly result set. + """ + return cap if top_k == -1 else top_k + + @staticmethod + def _recall_limit(top_k_request: int, *, cap: int = _DEFAULT_TOP_K_CAP) -> int: + """Effective recall pool size — branches on the *raw* request value. + + Mirrors enterprise: + + - ``top_k == -1`` (unlimited) → fixed ``cap`` + - ``top_k > 0`` → ``top_k * DEFAULT_RECALL_MULTIPLIER`` + + ``cap`` aligns the unlimited-mode pool with each kind's + :meth:`_top_k` ceiling (e.g. agent kinds use the tighter + :data:`_AGENT_TOP_K_CAP`). + """ + if top_k_request == -1: + return cap + return max( + top_k_request * _DEFAULT_RECALL_MULTIPLIER, _DEFAULT_RECALL_MULTIPLIER + ) + + @staticmethod + def _apply_radius(cands: list[Candidate], radius: float | None) -> list[Candidate]: + if radius is None: + return cands + return [c for c in cands if c.score >= radius] + + # ── Component guards ──────────────────────────────────────────── + + def _validate_components(self, req: SearchRequest) -> None: + """Fail fast when the chosen method needs components that are missing.""" + method = req.method + needs_embedding = method != SearchMethod.KEYWORD + if needs_embedding and self._embedding is None: + raise RuntimeError( + f"method={method.value!r} requires an embedding provider; " + "configure [embedding] in settings" + ) + # LLM is only mandatory when the caller explicitly opts into + # Phase-5 rerank on HYBRID, or always for AGENTIC (sufficiency + # check + multi-query generation). + if ( + method == SearchMethod.HYBRID + and req.enable_llm_rerank + and self._llm is None + ): + raise RuntimeError( + "method='hybrid' with enable_llm_rerank=true needs an LLM; " + "configure [llm] in settings or drop the flag" + ) + # agent_skill HYBRID without LLM rerank reaches the cross-encoder + # lane; without the reranker it would AttributeError deep in the + # callback. Episode / agent_case HYBRID don't need it. + if ( + method == SearchMethod.HYBRID + and req.owner_type == "agent" + and not req.enable_llm_rerank + and self._reranker is None + ): + raise RuntimeError( + "owner_type='agent' with method='hybrid' requires a rerank " + "provider (skill cross-encoder lane); configure [rerank] in " + "settings, or set enable_llm_rerank=true to use the LLM lane" + ) + if method == SearchMethod.AGENTIC: + if self._reranker is None: + raise RuntimeError( + "method='agentic' requires a rerank provider; " + "configure [rerank] in settings" + ) + if self._llm is None: + raise RuntimeError( + "method='agentic' requires an LLM client; " + "configure [llm] in settings" + ) + + +def _scored_as_candidate(scored) -> Candidate: # type: ignore[no-untyped-def] + """Adapt a single-type ``ScoredItem`` back to a ``Candidate``. + + Adapts ``ScoredItem`` back to ``Candidate`` so the existing + Candidate-based shapers apply. + """ + return Candidate( + id=scored.id, + score=scored.score, + source="other", + metadata=dict(scored.metadata), + ) + + +def _effective_llm_rerank(req: SearchRequest) -> bool: + """LLM Phase-5 rerank only kicks in for ``HYBRID`` and only when the + caller opts in. ``AGENTIC`` runs its own cross-encoder rerank loop + (via ``rerank_fn``) and intentionally skips Phase-5. + """ + return req.method == SearchMethod.HYBRID and req.enable_llm_rerank + + +def _effective_radius(req: SearchRequest) -> float | None: + """Resolve the cosine-similarity threshold actually applied to dense hits. + + Priority: + + 1. Caller-supplied ``req.radius`` always wins (including ``0.0`` when + they explicitly want everything). + 2. Otherwise, ``top_k == -1`` (unlimited) defaults to + ``_DEFAULT_UNLIMITED_RADIUS`` so the response keeps a quality + floor — matches enterprise's auto-default behaviour. + 3. Otherwise (normal ``top_k > 0`` mode), return ``None`` and trust + truncation to handle tail quality. + """ + if req.radius is not None: + return req.radius + if req.top_k == -1: + return _DEFAULT_UNLIMITED_RADIUS + return None + + +async def _empty_candidates() -> list[Candidate]: + return [] + + +def _extract_top_level_session_id(filters: FilterNode | None) -> str | None: + """Return the literal value of a top-level ``session_id`` eq scalar. + + The unprocessed-buffer trigger only fires for the simple shape + ``filters = {"session_id": ""}``. Anything wrapped in ``AND`` / + ``OR``, nested deeper, or expressed via an operator map (``{"eq": + ...}``, ``{"in": ...}``) is treated as "session not pinned" — there + is no defensible buffer-scope mapping for those compound predicates. + """ + if filters is None: + return None + extra = filters.__pydantic_extra__ or {} + value = extra.get("session_id") + return value if isinstance(value, str) and value else None + + +def _unprocessed_buffer_to_dto(row: UnprocessedBuffer) -> UnprocessedMessageDTO: + """Render one ``unprocessed_buffer`` row as its public DTO. + + Mirrors :class:`MessageItemDTO`'s ``content`` shorthand: a single-item + ``[{"type":"text","text":...}]`` payload collapses to the inner string; + every other shape stays as the opaque ``list[dict]`` so multimodal + payloads round-trip without lossy flattening. + """ + content_items = json.loads(row.content_items_json) + if ( + isinstance(content_items, list) + and len(content_items) == 1 + and isinstance(content_items[0], dict) + and content_items[0].get("type") == "text" + and isinstance(content_items[0].get("text"), str) + ): + content: str | list[dict[str, object]] = content_items[0]["text"] + else: + content = content_items + tool_calls = ( + json.loads(row.tool_calls_json) if row.tool_calls_json is not None else None + ) + return UnprocessedMessageDTO( + id=row.message_id, + app_id=row.app_id, + project_id=row.project_id, + session_id=row.session_id, + sender_id=row.sender_id, + sender_name=row.sender_name, + role=row.role, # type: ignore[arg-type] + content=content, + timestamp=to_display_tz(row.timestamp), + tool_calls=tool_calls, + tool_call_id=row.tool_call_id, + ) + + +def _merge_by_id_max( + primary: list[Candidate], extra: list[Candidate] +) -> list[Candidate]: + """Union by id, keep higher score. Folds bridged skills into the dense + pool so downstream fusion doesn't double-count overlap. + """ + by_id: dict[str, Candidate] = {c.id: c for c in primary} + for c in extra: + existing = by_id.get(c.id) + if existing is None or c.score > existing.score: + by_id[c.id] = c + return list(by_id.values()) + + +_ = Sequence # quiet unused-import for the typing-only annotation above diff --git a/src/everos/memory/search/recall/__init__.py b/src/everos/memory/search/recall/__init__.py new file mode 100644 index 00000000..bcb8b1b8 --- /dev/null +++ b/src/everos/memory/search/recall/__init__.py @@ -0,0 +1,36 @@ +"""Per-kind recall layer. + +External usage:: + + from everos.memory.search.recall import ( + KindRecaller, + RecallerDeps, + EpisodeRecaller, + AtomicFactRecaller, + AgentCaseRecaller, + AgentSkillRecaller, + ProfileRecaller, + ) +""" + +from .agent_case import AgentCaseRecaller as AgentCaseRecaller +from .agent_skill import AgentSkillRecaller as AgentSkillRecaller +from .atomic_fact import AtomicFactRecaller as AtomicFactRecaller +from .base import KindRecaller as KindRecaller +from .base import RecallerDeps as RecallerDeps +from .base import cosine_score_from_distance as cosine_score_from_distance +from .base import row_to_candidate as row_to_candidate +from .episode import EpisodeRecaller as EpisodeRecaller +from .profile import ProfileRecaller as ProfileRecaller + +__all__ = [ + "AgentCaseRecaller", + "AgentSkillRecaller", + "AtomicFactRecaller", + "EpisodeRecaller", + "KindRecaller", + "ProfileRecaller", + "RecallerDeps", + "cosine_score_from_distance", + "row_to_candidate", +] diff --git a/src/everos/memory/search/recall/agent_case.py b/src/everos/memory/search/recall/agent_case.py new file mode 100644 index 00000000..93a7f9b6 --- /dev/null +++ b/src/everos/memory/search/recall/agent_case.py @@ -0,0 +1,116 @@ +"""AgentCase recaller — dual-column BM25 + cosine ANN. + +The schema declares two BM25 columns (``task_intent_tokens`` — +retrieval anchor, primary — and ``approach_tokens`` — secondary +detail match). LanceDB's ``nearest_to_text`` searches one column at +a time, so we run the BM25 query twice in parallel and merge by row +id keeping the max score across the two columns. Vector recall is +single-shot. + +Mirrors :class:`AgentSkillRecaller` structurally — both kinds share +the multi-BM25-column pattern. +""" + +from __future__ import annotations + +import asyncio +from collections.abc import Sequence +from typing import ClassVar + +from everalgo.types import Candidate + +from everos.infra.persistence.lancedb import AgentCase, get_table + +from .base import ( + RecallerDeps, + build_or_query_multi_column, + cosine_score_from_distance, + row_to_candidate, +) + + +class AgentCaseRecaller: + """BM25 (dual-column) + vector recall over the LanceDB ``agent_case`` table.""" + + kind: ClassVar[str] = "agent_case" + everalgo_memory_type: ClassVar[str] = "case" + text_field: ClassVar[str] = "task_intent" + + def __init__(self, deps: RecallerDeps) -> None: + self._deps = deps + + async def sparse_recall( + self, query: str, where: str, *, limit: int + ) -> list[Candidate]: + """Dual-column BM25 recall via OR-mode BooleanQuery per column. + + Each tokenised term becomes a ``SHOULD`` clause so a single + IDF≈0 token doesn't poison the column query (see + ``EpisodeRecaller.sparse_recall``). One BooleanQuery is built + per BM25 column (``MatchQuery`` is column-bound), then the + two per-column result lists merge by id keeping the max score. + """ + column_queries = build_or_query_multi_column( + self._deps.tokenizer, query, AgentCase.BM25_FIELDS + ) + if column_queries is None: + return [] + table = await get_table(AgentCase.TABLE_NAME, AgentCase) + + async def _query_one(column: str) -> list[dict]: + return ( + await table.query() + .nearest_to_text(column_queries[column]) + .where(where) + .limit(limit) + .to_list() + ) + + per_column = await asyncio.gather( + *(_query_one(col) for col in AgentCase.BM25_FIELDS), + ) + # Merge by id, keep the max BM25 score across the two columns. + # task_intent hits typically score higher (the retrieval anchor); + # approach hits catch queries that match a step detail. + best: dict[str, dict] = {} + for rows in per_column: + for r in rows: + rid = r.get("id") + if not isinstance(rid, str): + continue + score = float(r.get("_score", 0.0)) + existing = best.get(rid) + if existing is None or score > float(existing.get("_score", 0.0)): + merged = dict(r) + merged["_score"] = score + best[rid] = merged + merged_rows = sorted( + best.values(), key=lambda r: float(r.get("_score", 0.0)), reverse=True + )[:limit] + return [ + row_to_candidate(r, source="keyword", score=float(r.get("_score", 0.0))) + for r in merged_rows + ] + + async def dense_recall( + self, vector: Sequence[float], where: str, *, limit: int + ) -> list[Candidate]: + if not vector: + return [] + table = await get_table(AgentCase.TABLE_NAME, AgentCase) + rows = ( + await table.query() + .nearest_to(list(vector)) + .distance_type("cosine") + .where(where) + .limit(limit) + .to_list() + ) + return [ + row_to_candidate( + r, + source="vector", + score=cosine_score_from_distance(r.get("_distance")), + ) + for r in rows + ] diff --git a/src/everos/memory/search/recall/agent_skill.py b/src/everos/memory/search/recall/agent_skill.py new file mode 100644 index 00000000..f444e4a3 --- /dev/null +++ b/src/everos/memory/search/recall/agent_skill.py @@ -0,0 +1,131 @@ +"""AgentSkill recaller — dual-column BM25 + cosine ANN. + +The skill schema declares two BM25 columns +(``description_tokens`` + ``content_tokens``). LanceDB's +``nearest_to_text`` searches one column at a time, so we run the query +twice and merge by row id keeping the max score. Vector recall is +single-shot. +""" + +from __future__ import annotations + +import asyncio +from collections.abc import Sequence +from typing import ClassVar + +from everalgo.types import Candidate + +from everos.infra.persistence.lancedb import AgentSkill, get_table + +from .base import ( + RecallerDeps, + build_or_query_multi_column, + cosine_score_from_distance, + row_to_candidate, +) + + +def _q(value: str) -> str: + return value.replace("'", "''") + + +class AgentSkillRecaller: + """BM25 + vector recall over the LanceDB ``agent_skill`` table.""" + + kind: ClassVar[str] = "agent_skill" + everalgo_memory_type: ClassVar[str] = "skill" + text_field: ClassVar[str] = "description" + + def __init__(self, deps: RecallerDeps) -> None: + self._deps = deps + + async def sparse_recall( + self, query: str, where: str, *, limit: int + ) -> list[Candidate]: + """Dual-column BM25 recall via OR-mode BooleanQuery per column. + + Mirrors ``AgentCaseRecaller.sparse_recall`` — see there for + rationale. One BooleanQuery per BM25 column; merge by id with + max score. + """ + column_queries = build_or_query_multi_column( + self._deps.tokenizer, query, AgentSkill.BM25_FIELDS + ) + if column_queries is None: + return [] + table = await get_table(AgentSkill.TABLE_NAME, AgentSkill) + + async def _query_one(column: str) -> list[dict]: + return ( + await table.query() + .nearest_to_text(column_queries[column]) + .where(where) + .limit(limit) + .to_list() + ) + + per_column = await asyncio.gather( + *(_query_one(col) for col in AgentSkill.BM25_FIELDS), + ) + # Merge by id, keep max BM25 score across the two columns. + best: dict[str, dict] = {} + for rows in per_column: + for r in rows: + rid = r.get("id") + if not isinstance(rid, str): + continue + score = float(r.get("_score", 0.0)) + existing = best.get(rid) + if existing is None or score > float(existing.get("_score", 0.0)): + merged = dict(r) + merged["_score"] = score + best[rid] = merged + merged_rows = sorted( + best.values(), key=lambda r: float(r.get("_score", 0.0)), reverse=True + )[:limit] + return [ + row_to_candidate(r, source="keyword", score=float(r.get("_score", 0.0))) + for r in merged_rows + ] + + async def dense_recall( + self, vector: Sequence[float], where: str, *, limit: int + ) -> list[Candidate]: + if not vector: + return [] + table = await get_table(AgentSkill.TABLE_NAME, AgentSkill) + rows = ( + await table.query() + .nearest_to(list(vector)) + .distance_type("cosine") + .where(where) + .limit(limit) + .to_list() + ) + return [ + row_to_candidate( + r, + source="vector", + score=cosine_score_from_distance(r.get("_distance")), + ) + for r in rows + ] + + async def fetch_by_case_ids( + self, case_ids: Sequence[str], where: str, *, limit: int + ) -> list[Candidate]: + """Skills whose ``source_case_ids`` intersect ``case_ids``. + Filter is ``array_has`` OR-ed per id (same as + ``filters._compile_op_clause`` for ``array_str``). + + ``score`` returns ``0.0`` — the manager re-attaches the max-pooled + source-case score. ``source_case_ids`` rides in ``metadata`` so + the manager can max-pool without a second fetch. + """ + if not case_ids: + return [] + table = await get_table(AgentSkill.TABLE_NAME, AgentSkill) + clause = " OR ".join(f"array_has(source_case_ids, '{_q(c)}')" for c in case_ids) + full_where = f"({where}) AND ({clause})" + rows = await table.query().where(full_where).limit(limit).to_list() + return [row_to_candidate(r, source="vector", score=0.0) for r in rows] diff --git a/src/everos/memory/search/recall/atomic_fact.py b/src/everos/memory/search/recall/atomic_fact.py new file mode 100644 index 00000000..0e1a4daf --- /dev/null +++ b/src/everos/memory/search/recall/atomic_fact.py @@ -0,0 +1,167 @@ +"""AtomicFact recaller — BM25 over ``fact_tokens`` + cosine ANN + parent linkage. + +Beyond the standard sparse / dense pair the recaller exposes +:meth:`facts_for_episodes`, which the HYBRID pipeline calls to attach +atomic facts to their parent episodes (``episode_to_facts`` fed into +the fact eviction pass). + +Episode-fact linkage is **indirect through the shared memcell parent**: +both kinds are written with ``parent_id = memcell_id`` by the cascade. +The caller hands in an ``episode_id → memcell_id`` map; we query facts +by ``parent_id IN (memcell_ids)`` and regroup by episode using the +inverse map, so one fact bucket-shows under every episode that shares +the source memcell. +""" + +from __future__ import annotations + +from collections import defaultdict +from collections.abc import Mapping, Sequence +from typing import ClassVar + +from everalgo.types import Candidate, FactCandidate + +from everos.infra.persistence.lancedb import AtomicFact, get_table + +from .base import ( + RecallerDeps, + build_or_query, + cosine_score_from_distance, + row_to_candidate, +) + +_NOISE_COLUMNS = frozenset( + {"vector", "_distance", "_score", "created_at", "updated_at"} +) + + +class AtomicFactRecaller: + """BM25 + vector recall over the LanceDB ``atomic_fact`` table.""" + + kind: ClassVar[str] = "atomic_fact" + everalgo_memory_type: ClassVar[str] = "episodic" + text_field: ClassVar[str] = "fact" + + def __init__(self, deps: RecallerDeps) -> None: + self._deps = deps + + async def sparse_recall( + self, query: str, where: str, *, limit: int + ) -> list[Candidate]: + """BM25 recall via OR-mode BooleanQuery (see EpisodeRecaller docstring).""" + bq = build_or_query( + self._deps.tokenizer, query, column=AtomicFact.BM25_FIELDS[0] + ) + if bq is None: + return [] + table = await get_table(AtomicFact.TABLE_NAME, AtomicFact) + rows = ( + await table.query().nearest_to_text(bq).where(where).limit(limit).to_list() + ) + return [ + row_to_candidate(r, source="keyword", score=float(r.get("_score", 0.0))) + for r in rows + ] + + async def dense_recall( + self, vector: Sequence[float], where: str, *, limit: int + ) -> list[Candidate]: + if not vector: + return [] + table = await get_table(AtomicFact.TABLE_NAME, AtomicFact) + rows = ( + await table.query() + .nearest_to(list(vector)) + .distance_type("cosine") + .where(where) + .limit(limit) + .to_list() + ) + return [ + row_to_candidate( + r, + source="vector", + score=cosine_score_from_distance(r.get("_distance")), + ) + for r in rows + ] + + async def facts_for_episodes( + self, + ep_to_memcell: Mapping[str, str], + where: str, + *, + per_episode: int, + query_vector: Sequence[float] | None = None, + ) -> dict[str, list[FactCandidate]]: + """Pull facts for a set of episodes, bucketed by episode id. + + ``ep_to_memcell`` maps the candidate episode's LanceDB id to the + source memcell id (read off ``episode.parent_id`` by the + caller). Facts are queried by their own ``parent_id`` against + the deduped memcell set, then re-bucketed under every episode + that shares each memcell — two episodes pulled from the same + memcell each get a copy of that memcell's facts. + + When ``query_vector`` is provided, the LanceDB query layers + cosine ANN on top of the ``parent_id IN (...)`` filter, so each + fact lands with a real query-fact relevance score. + Without ``query_vector`` we fall back to a flat scan, in which + case every fact ships with ``score=0.0`` — the caller is + responsible for not consuming the score in that mode. + """ + if not ep_to_memcell: + return {} + + memcell_to_eps: dict[str, list[str]] = defaultdict(list) + for ep_id, mc_id in ep_to_memcell.items(): + if mc_id: + memcell_to_eps[mc_id].append(ep_id) + if not memcell_to_eps: + return {} + + quoted = ", ".join(f"'{_q(mc_id)}'" for mc_id in memcell_to_eps) + clause = f"parent_id IN ({quoted})" + full_where = f"({where}) AND ({clause})" + limit = per_episode * max(len(memcell_to_eps), 1) + table = await get_table(AtomicFact.TABLE_NAME, AtomicFact) + if query_vector: + rows = ( + await table.query() + .nearest_to(list(query_vector)) + .distance_type("cosine") + .where(full_where) + .limit(limit) + .to_list() + ) + else: + rows = await table.query().where(full_where).limit(limit).to_list() + buckets: dict[str, list[FactCandidate]] = defaultdict(list) + for r in rows: + mc_id = r.get("parent_id") + fid = r.get("id") + if not isinstance(mc_id, str) or not isinstance(fid, str): + continue + metadata = { + k: v for k, v in r.items() if k not in _NOISE_COLUMNS and k != "id" + } + score = ( + cosine_score_from_distance(r.get("_distance")) if query_vector else 0.0 + ) + for ep_id in memcell_to_eps.get(mc_id, ()): + buckets[ep_id].append( + FactCandidate( + id=fid, + parent_episode_id=ep_id, + score=score, + metadata=metadata, + ) + ) + # Per-bucket cap; with query_vector the rows arrive sorted by + # cosine ascending (closest first) so slicing keeps the most + # relevant facts per episode. + return {ep_id: bucket[:per_episode] for ep_id, bucket in buckets.items()} + + +def _q(value: str) -> str: + return value.replace("'", "''") diff --git a/src/everos/memory/search/recall/base.py b/src/everos/memory/search/recall/base.py new file mode 100644 index 00000000..cd9c8749 --- /dev/null +++ b/src/everos/memory/search/recall/base.py @@ -0,0 +1,201 @@ +"""KindRecaller protocol + LanceDB row → Candidate helpers. + +Every recaller exposes two callsites: + +* :meth:`sparse_recall` — BM25 over the schema's ``*_tokens`` FTS column(s); +* :meth:`dense_recall` — cosine ANN over the 1024-d ``vector`` column. + +Both are filtered by the precompiled LanceDB ``where`` string and capped +at ``limit`` (the candidate pool size). The recaller does **not** apply +``radius``; that runs in the manager so the same value applies before +fusion / rerank. + +A shared :class:`RecallerDeps` bundles the providers a recaller needs +at construction time (tokenizer for BM25 query, embedder is consumed +upstream by the manager so we keep deps minimal). The bundle keeps the +constructor signatures identical across the four LanceDB-backed +recallers so the orchestrator wiring stays uniform. +""" + +from __future__ import annotations + +import dataclasses +from collections.abc import Sequence +from typing import Any, ClassVar, Protocol, runtime_checkable + +from everalgo.types import Candidate +from lancedb.query import BooleanQuery, FullTextQuery, MatchQuery + +try: + from lancedb.query import Occur +except ImportError: # pragma: no cover — fallback for older LanceDB layouts + from lancedb._lancedb import Occur # type: ignore[attr-defined,no-redef] + +from everos.component.tokenizer import Tokenizer + +# Columns that should never travel through the ranker / shaper. ``vector`` +# is huge (1024 floats); ``_distance`` belongs to LanceDB's query engine +# and is converted into ``score`` before the row leaves the recaller. +_NOISE_COLUMNS: frozenset[str] = frozenset( + {"vector", "_distance", "_score", "created_at", "updated_at"} +) + + +@dataclasses.dataclass(frozen=True) +class RecallerDeps: + """Shared dependencies for every LanceDB-backed recaller. + + Frozen so the orchestrator can build one instance and hand it to + every recaller without worrying about state divergence. + """ + + tokenizer: Tokenizer + + +@runtime_checkable +class KindRecaller(Protocol): + """One business kind, BM25 + vector recall over its LanceDB table.""" + + kind: ClassVar[str] + """``episode`` / ``atomic_fact`` / ``agent_case`` / ``agent_skill``.""" + + everalgo_memory_type: ClassVar[str] + """``episodic`` / ``case`` / ``skill`` — passed to ``RankInput.memory_type``.""" + + text_field: ClassVar[str] + """Source column for cross-encoder rerank passages (display text).""" + + async def sparse_recall( + self, query: str, where: str, *, limit: int + ) -> list[Candidate]: ... + + async def dense_recall( + self, vector: Sequence[float], where: str, *, limit: int + ) -> list[Candidate]: ... + + +def row_to_candidate( + row: dict[str, Any], + *, + source: str, + score: float, +) -> Candidate: + """Pack a LanceDB row dict into an everalgo ``Candidate``. + + The full row (minus noise columns) rides in ``metadata`` so the + shaper can build the response DTO without going back to LanceDB. + """ + rid = row.get("id") + if not isinstance(rid, str): + raise ValueError(f"row missing string 'id': {row!r}") + metadata = {k: v for k, v in row.items() if k not in _NOISE_COLUMNS and k != "id"} + return Candidate( + id=rid, + score=float(score), + source=source, # type: ignore[arg-type] # "keyword" | "vector" + metadata=metadata, + ) + + +def cosine_score_from_distance(distance: float | None) -> float: + """Convert LanceDB cosine ``_distance`` → similarity in ``[0, 1]``. + + With ``metric='cosine'``, the engine emits ``distance = 1 - cos``, + so similarity is its complement. ``None`` is treated as 0.0 (no + score; lets BM25-only rows survive a merge). + """ + if distance is None: + return 0.0 + sim = 1.0 - float(distance) + if sim < 0.0: + return 0.0 + if sim > 1.0: + return 1.0 + return sim + + +def tokenize_query(tokenizer: Tokenizer, query: str) -> str: + """Run the configured tokenizer over the query and join with spaces. + + Cascade joins tokens with a single space when writing the + ``*_tokens`` columns; LanceDB FTS expects a whitespace-tokenised + query string. Same function on both sides keeps BM25 scoring + symmetric. + + Note: prefer :func:`build_or_query` for new code — it sidesteps + the tantivy implicit-AND query-parser pitfall where a single + IDF≈0 token (e.g. an owner's own name on the owner's partition) + poisons the whole query into zero hits. + """ + tokens = tokenizer.tokenize(query) + return " ".join(t for t in tokens if t) + + +def build_or_query( + tokenizer: Tokenizer, + query: str, + column: str, +) -> FullTextQuery | None: + """Tokenise ``query`` and wrap in an OR-mode FTS query. + + Mirrors the enterprise ES design + (``bool.should + minimum_should_match=1``): + + - jieba tokenises the user query. + - Each token becomes its own :class:`MatchQuery` clause. + - Clauses combine under :class:`BooleanQuery` with + :attr:`Occur.SHOULD` so any single matching token surfaces + the document. + - LanceDB still computes a proper joint BM25 score from all + clauses; tokens with IDF ≈ 0 contribute ~0 but no longer + poison the rest of the query. + + Returns ``None`` when the query tokenises to nothing (the + caller must guard against this — there's no useful BM25 query + over an empty token set). + + Single-token queries collapse to a bare :class:`MatchQuery` + (skipping the boolean wrapper) so the FTS engine doesn't pay + for an unnecessary boolean layer. + """ + tokens = [t for t in tokenizer.tokenize(query) if t] + if not tokens: + return None + if len(tokens) == 1: + return MatchQuery(tokens[0], column=column) + clauses: list[tuple[Occur, FullTextQuery]] = [ + (Occur.SHOULD, MatchQuery(t, column=column)) for t in tokens + ] + return BooleanQuery(clauses) + + +def build_or_query_multi_column( + tokenizer: Tokenizer, + query: str, + columns: Sequence[str], +) -> dict[str, FullTextQuery] | None: + """Same as :func:`build_or_query` but emit one FTS query per column. + + ``MatchQuery`` is bound to a single column, and LanceDB FTS only + searches one column per ``nearest_to_text`` call. Dual-column + kinds (``agent_case`` over ``task_intent_tokens`` / + ``approach_tokens``, ``agent_skill`` over + ``description_tokens`` / ``content_tokens``, etc.) need one + OR-bundle per column and merge the results in the caller. + + Returns ``None`` on empty tokenisation; otherwise a dict + ``{column: FullTextQuery}`` ready to feed into separate + ``nearest_to_text`` calls. + """ + tokens = [t for t in tokenizer.tokenize(query) if t] + if not tokens: + return None + out: dict[str, FullTextQuery] = {} + for col in columns: + if len(tokens) == 1: + out[col] = MatchQuery(tokens[0], column=col) + else: + out[col] = BooleanQuery( + [(Occur.SHOULD, MatchQuery(t, column=col)) for t in tokens] + ) + return out diff --git a/src/everos/memory/search/recall/episode.py b/src/everos/memory/search/recall/episode.py new file mode 100644 index 00000000..69d6581f --- /dev/null +++ b/src/everos/memory/search/recall/episode.py @@ -0,0 +1,129 @@ +"""Episode recaller — BM25 over ``episode_tokens`` + cosine ANN.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import ClassVar + +from everalgo.types import Candidate + +from everos.infra.persistence.lancedb import Episode, get_table + +from .base import ( + RecallerDeps, + build_or_query, + cosine_score_from_distance, + row_to_candidate, +) + + +def _q(value: str) -> str: + return value.replace("'", "''") + + +class EpisodeRecaller: + """BM25 + vector recall over the LanceDB ``episode`` table.""" + + kind: ClassVar[str] = "episode" + everalgo_memory_type: ClassVar[str] = "episodic" + text_field: ClassVar[str] = "episode" + + def __init__(self, deps: RecallerDeps) -> None: + self._deps = deps + + async def sparse_recall( + self, query: str, where: str, *, limit: int + ) -> list[Candidate]: + """BM25 recall via OR-mode BooleanQuery. + + Each tokenised term becomes a ``SHOULD`` clause so a single + IDF≈0 token (typically the partition owner's own name on + owner-scoped corpora) cannot poison the entire query. + Mirrors enterprise's ``bool.should + minimum_should_match=1`` + ES design. + """ + bq = build_or_query(self._deps.tokenizer, query, column=Episode.BM25_FIELDS[0]) + if bq is None: + return [] + table = await get_table(Episode.TABLE_NAME, Episode) + rows = ( + await table.query().nearest_to_text(bq).where(where).limit(limit).to_list() + ) + return [ + row_to_candidate(r, source="keyword", score=float(r.get("_score", 0.0))) + for r in rows + ] + + async def dense_recall( + self, vector: Sequence[float], where: str, *, limit: int + ) -> list[Candidate]: + if not vector: + return [] + table = await get_table(Episode.TABLE_NAME, Episode) + rows = ( + await table.query() + .nearest_to(list(vector)) + .distance_type("cosine") + .where(where) + .limit(limit) + .to_list() + ) + return [ + row_to_candidate( + r, + source="vector", + score=cosine_score_from_distance(r.get("_distance")), + ) + for r in rows + ] + + async def fetch_by_parent_ids( + self, parent_ids: Sequence[str], where: str + ) -> list[Candidate]: + """Batch-fetch episodes whose ``parent_id`` (memcell id) is in the set. + + One LanceDB scan per call (``WHERE parent_id IN (...)``) — used by + the MaxSim-style vector strategy that first ranks memcells via + ``atomic_fact`` cosine and then reverse-resolves the episode. + ``score`` on the returned candidates is left at ``0.0``; the + caller re-attaches the upstream max-pool score before sorting. + """ + if not parent_ids: + return [] + table = await get_table(Episode.TABLE_NAME, Episode) + quoted = ", ".join(f"'{_q(p)}'" for p in parent_ids) + full_where = f"({where}) AND (parent_id IN ({quoted}))" + rows = await table.query().where(full_where).limit(len(parent_ids)).to_list() + return [row_to_candidate(r, source="vector", score=0.0) for r in rows] + + async def fetch_all_for_owner(self, where: str) -> list[Candidate]: + """Flat scan — all episodes for this owner, keyed by memcell id. + + Returns every episode row as a ``Candidate`` with ``id = parent_id`` + (the memcell id) so ``acluster_retrieve`` membership matching against + ``cluster.members`` (also memcell ids) works without extra mapping. + The real LanceDB episode id travels in ``metadata["episode_id"]`` so + the agentic orchestrator can restore canonical episode identity after + ``aagentic_retrieve`` returns. + + No ``limit`` is applied — the full owner partition is required for + cluster membership matching (``acluster_retrieve`` needs ``all_docs`` + to cover every member of every cluster). + """ + table = await get_table(Episode.TABLE_NAME, Episode) + rows = await table.query().where(where).to_list() + result: list[Candidate] = [] + for r in rows: + mc_id = r.get("parent_id") + if not isinstance(mc_id, str) or not mc_id: + continue + base = row_to_candidate(r, source="vector", score=0.0) + result.append( + Candidate( + id=mc_id, + score=0.0, + source="vector", + metadata={**base.metadata, "episode_id": base.id}, + ) + ) + return result diff --git a/src/everos/memory/search/recall/profile.py b/src/everos/memory/search/recall/profile.py new file mode 100644 index 00000000..be149b92 --- /dev/null +++ b/src/everos/memory/search/recall/profile.py @@ -0,0 +1,77 @@ +"""Profile recall — KV-by-owner LanceDB fetch (no ranking). + +Profile is the only owner-scoped kind that ships as **one row per +user** (no per-day fan-out, no entry markers). The recaller is a +deliberate KV-by-owner lookup: given ``owner_id``, return at most one +:class:`SearchProfileItem`. There is no ``query`` and no ``score`` +field on the response — the DTO's optional ``score`` is reserved for +a future query-aware lookup. + +The cascade keeps ``UserProfile`` rows in sync with +``users//user.md``; this recaller just reads the row and +unpacks the json-encoded buckets back into the DTO's +``profile_data`` mapping (mirrors enterprise's profile DTO shape). +""" + +from __future__ import annotations + +import json +from typing import Any + +from everos.core.observability.logging import get_logger +from everos.infra.persistence.lancedb import user_profile_repo + +from ..dto import SearchProfileItem + +logger = get_logger(__name__) + + +class ProfileRecaller: + """Fetch the owner's profile row from LanceDB, return at most one item.""" + + async def fetch(self, owner_id: str) -> list[SearchProfileItem]: + """Return ``[item]`` if a profile row exists, otherwise ``[]``. + + Empty list (rather than 404) lets the caller emit a normal + response with ``profiles=[]`` while the user is still in their + cold-start window (no profile synthesised yet). + """ + if not owner_id: + return [] + row = await user_profile_repo.get_by_id(owner_id) + if row is None: + logger.debug("profile_fetch_miss", owner_id=owner_id) + return [] + profile_data: dict[str, Any] = { + "summary": row.summary, + "explicit_info": _load_json(row.explicit_info_json), + "implicit_traits": _load_json(row.implicit_traits_json), + "profile_timestamp_ms": row.profile_timestamp_ms, + } + return [ + SearchProfileItem( + id=row.id, + user_id=row.owner_id, + app_id=row.app_id, + project_id=row.project_id, + profile_data=profile_data, + score=None, + ) + ] + + +def _load_json(text: str) -> Any: + """Decode a json-encoded frontmatter bucket. + + Returns ``[]`` on empty / malformed input so a row with a stale + encoding doesn't blow up the search response. A real decode error + is logged once at debug; cascade will rewrite the column on the + next reconcile. + """ + if not text: + return [] + try: + return json.loads(text) + except json.JSONDecodeError: + logger.debug("profile_json_decode_failed", payload_head=text[:80]) + return [] diff --git a/src/everos/memory/search/shaper.py b/src/everos/memory/search/shaper.py new file mode 100644 index 00000000..6652bc02 --- /dev/null +++ b/src/everos/memory/search/shaper.py @@ -0,0 +1,322 @@ +"""Translate everalgo ``Candidate`` / ``ScoredItem`` → public DTO items. + +This module handles two distinct flows: + +* Simple kinds — :func:`shape_episode_from_candidate`, + :func:`shape_agent_case_from_candidate`, + :func:`shape_agent_skill_from_candidate`. Each consumes one + :class:`Candidate` and emits the matching ``SearchXxxItem``. +* Hybrid output — :func:`reshape_hybrid_output` collapses the mixed + ``episode`` + ``atomic_fact`` ScoredItem list into + :class:`SearchEpisodeItem` instances with nested ``atomic_facts``. + +The shaper is pure — no LanceDB calls. When fact eviction replaces a +parent episode with its facts, the orphan fact's parent is looked up +in an ``episode_pool`` dict that the manager assembles from the +pre-fusion ``sparse + dense`` candidates. +""" + +from __future__ import annotations + +import datetime as _dt +from collections import defaultdict +from collections.abc import Iterable +from typing import Any + +from everalgo.types import Candidate, ScoredItem + +from everos.component.utils.datetime import to_display_tz +from everos.core.observability.logging import get_logger + +from .dto import ( + SearchAgentCaseItem, + SearchAgentSkillItem, + SearchAtomicFactItem, + SearchEpisodeItem, +) + +logger = get_logger(__name__) + + +# ── Episode shaping ───────────────────────────────────────────────────── + + +def shape_episode_from_candidate( + candidate: Candidate, + *, + atomic_facts: list[SearchAtomicFactItem] | None = None, +) -> SearchEpisodeItem | None: + """Build a :class:`SearchEpisodeItem` from a recall ``Candidate``. + + Returns ``None`` if the row is malformed (e.g. owner_type is not + ``"user"``, mandatory fields missing). Callers drop ``None`` results. + """ + md = candidate.metadata + owner_type = md.get("owner_type") + if owner_type != "user": + logger.warning( + "shape_episode_unexpected_owner_type", + id=candidate.id, + owner_type=owner_type, + ) + return None + timestamp = _coerce_datetime(md.get("timestamp")) + if timestamp is None: + logger.warning("shape_episode_missing_timestamp", id=candidate.id) + return None + session_id = md.get("session_id") + episode = md.get("episode") + if not isinstance(session_id, str) or not isinstance(episode, str): + logger.warning("shape_episode_missing_required_field", id=candidate.id) + return None + return SearchEpisodeItem( + id=candidate.id, + user_id=_as_optional_str(md.get("owner_id")), + app_id=_as_str(md.get("app_id")) or "default", + project_id=_as_str(md.get("project_id")) or "default", + session_id=session_id, + timestamp=timestamp, + sender_ids=_as_str_list(md.get("sender_ids")), + summary=_as_str(md.get("summary")), + subject=_as_str(md.get("subject")), + episode=episode, + type="Conversation", + score=float(candidate.score), + atomic_facts=atomic_facts or [], + ) + + +# ── Atomic fact shaping ───────────────────────────────────────────────── + + +def shape_atomic_fact_from_candidate(candidate: Candidate) -> SearchAtomicFactItem: + """Cast a LanceDB ``atomic_fact`` candidate row into the nested fact DTO.""" + content = _as_str(candidate.metadata.get("fact")) + return SearchAtomicFactItem( + id=candidate.id, + content=content, + score=float(candidate.score), + ) + + +def shape_atomic_fact_from_scored(scored: ScoredItem) -> SearchAtomicFactItem: + """Cast an everalgo ``ScoredItem(item_type='atomic_fact')`` into a fact DTO.""" + content = _as_str(scored.metadata.get("fact")) + return SearchAtomicFactItem( + id=scored.id, + content=content, + score=float(scored.score), + ) + + +# ── AgentCase shaping ─────────────────────────────────────────────────── + + +def shape_agent_case_from_candidate(candidate: Candidate) -> SearchAgentCaseItem | None: + md = candidate.metadata + if md.get("owner_type") != "agent": + logger.warning( + "shape_case_unexpected_owner_type", + id=candidate.id, + owner_type=md.get("owner_type"), + ) + return None + timestamp = _coerce_datetime(md.get("timestamp")) + if timestamp is None: + logger.warning("shape_case_missing_timestamp", id=candidate.id) + return None + owner_id = md.get("owner_id") + session_id = md.get("session_id") + task_intent = md.get("task_intent") + approach = md.get("approach") + quality = md.get("quality_score") + if not ( + isinstance(owner_id, str) + and isinstance(session_id, str) + and isinstance(task_intent, str) + and isinstance(approach, str) + and isinstance(quality, (int, float)) + ): + logger.warning("shape_case_missing_required_field", id=candidate.id) + return None + return SearchAgentCaseItem( + id=candidate.id, + agent_id=owner_id, + app_id=_as_str(md.get("app_id")) or "default", + project_id=_as_str(md.get("project_id")) or "default", + session_id=session_id, + task_intent=task_intent, + approach=approach, + quality_score=float(quality), + key_insight=_as_optional_str(md.get("key_insight")), + timestamp=timestamp, + score=float(candidate.score), + ) + + +# ── AgentSkill shaping ────────────────────────────────────────────────── + + +def shape_agent_skill_from_candidate( + candidate: Candidate, +) -> SearchAgentSkillItem | None: + md = candidate.metadata + if md.get("owner_type") != "agent": + logger.warning( + "shape_skill_unexpected_owner_type", + id=candidate.id, + owner_type=md.get("owner_type"), + ) + return None + owner_id = md.get("owner_id") + name = md.get("name") + description = md.get("description") + content = md.get("content") + confidence = md.get("confidence") + maturity = md.get("maturity_score") + if not ( + isinstance(owner_id, str) + and isinstance(name, str) + and isinstance(description, str) + and isinstance(content, str) + and isinstance(confidence, (int, float)) + and isinstance(maturity, (int, float)) + ): + logger.warning("shape_skill_missing_required_field", id=candidate.id) + return None + return SearchAgentSkillItem( + id=candidate.id, + agent_id=owner_id, + app_id=_as_str(md.get("app_id")) or "default", + project_id=_as_str(md.get("project_id")) or "default", + name=name, + description=description, + content=content, + confidence=float(confidence), + maturity_score=float(maturity), + source_case_ids=_as_str_list(md.get("source_case_ids")), + score=float(candidate.score), + ) + + +# ── Hybrid mixed output reshape ───────────────────────────────────────── + + +def reshape_hybrid_output( + scored: Iterable[ScoredItem], + *, + episode_pool: dict[str, Candidate], +) -> list[SearchEpisodeItem]: + """Collapse the mixed episode + atomic_fact output into nested SearchEpisodeItems. + + Fact eviction can swap a parent episode for its top atomic fact. + We re-attach facts to their parent so the API response only ever + surfaces episodes (with their facts nested). + + ``episode_pool`` is the union of pre-fusion sparse + dense episode + candidates, keyed by id. When a fact's parent is missing from the + final scored list, we fall back to this pool. Facts whose parent + is in neither place are dropped with a warning (very rare — + requires a parent absent from the recall pool altogether). + """ + scored_items = list(scored) + episodes: dict[str, ScoredItem] = { + s.id: s for s in scored_items if s.item_type == "episode" + } + facts_by_parent: dict[str, list[ScoredItem]] = defaultdict(list) + for s in scored_items: + if s.item_type == "atomic_fact" and s.parent_episode_id: + facts_by_parent[s.parent_episode_id].append(s) + + out: list[SearchEpisodeItem] = [] + seen: set[str] = set() + + # 1. Episodes still in top-N — attach any facts grouped under them. + for ep_id, ep_scored in episodes.items(): + seen.add(ep_id) + facts = _build_fact_items(facts_by_parent.get(ep_id, [])) + item = _shape_episode_from_scored(ep_scored, atomic_facts=facts) + if item is not None: + out.append(item) + + # 2. Orphan facts — parent evicted but available in the pool. + for parent_id, fact_scoreds in facts_by_parent.items(): + if parent_id in seen: + continue + parent = episode_pool.get(parent_id) + if parent is None: + logger.warning( + "orphan_fact_parent_missing", + parent_id=parent_id, + fact_count=len(fact_scoreds), + ) + continue + seen.add(parent_id) + facts = _build_fact_items(fact_scoreds) + # Score the synthetic episode entry at the top fact's score so the + # response retains the eviction relevance signal. + top_score = max((f.score for f in fact_scoreds), default=0.0) + parent_with_score = parent.model_copy(update={"score": top_score}) + item = shape_episode_from_candidate(parent_with_score, atomic_facts=facts) + if item is not None: + out.append(item) + + out.sort(key=lambda e: e.score, reverse=True) + return out + + +def _build_fact_items(scoreds: list[ScoredItem]) -> list[SearchAtomicFactItem]: + return [ + shape_atomic_fact_from_scored(s) + for s in sorted(scoreds, key=lambda s: s.score, reverse=True) + ] + + +def _shape_episode_from_scored( + scored: ScoredItem, + *, + atomic_facts: list[SearchAtomicFactItem], +) -> SearchEpisodeItem | None: + """Adapt a ScoredItem(item_type='episode') with the same checks as Candidate.""" + pseudo = Candidate( + id=scored.id, + score=scored.score, + source="other", + metadata=dict(scored.metadata), + ) + return shape_episode_from_candidate(pseudo, atomic_facts=atomic_facts) + + +# ── Coercion helpers ──────────────────────────────────────────────────── + + +def _coerce_datetime(value: Any) -> _dt.datetime | None: + """Coerce a storage-side datetime to the display timezone, or ``None``. + + LanceDB's Arrow schema declares timestamp columns with ``tz=UTC`` + (see :attr:`BaseLanceTable.UTC_DATETIME_FIELDS`), so PyArrow returns + aware UTC datetimes. ``to_display_tz`` is a pure ``astimezone(...)`` + in that case. Aware non-UTC and naive inputs (test fixtures) flow + through ``to_display_tz`` safely as well — naive is treated as + already display-tz local. + + Non-datetime input returns ``None`` so callers can treat it as + "missing field" without raising. + """ + if not isinstance(value, _dt.datetime): + return None + return to_display_tz(value) + + +def _as_str(value: Any) -> str: + return value if isinstance(value, str) else "" + + +def _as_optional_str(value: Any) -> str | None: + return value if isinstance(value, str) else None + + +def _as_str_list(value: Any) -> list[str]: + if not isinstance(value, list): + return [] + return [v for v in value if isinstance(v, str)] diff --git a/src/everos/memory/search/skill_hybrid.py b/src/everos/memory/search/skill_hybrid.py new file mode 100644 index 00000000..de8c893e --- /dev/null +++ b/src/everos/memory/search/skill_hybrid.py @@ -0,0 +1,82 @@ +"""Skill HYBRID cross-encoder lane: rrf → cross-encoder rerank → shape. + +Used when ``enable_llm_rerank`` is off. The LLM-rerank lane lives in +:meth:`SearchManager._search_agent_skills` so the flag stays wired only +at the orchestration layer. Passage shape + skill instruction live in +:func:`memory.search.callbacks.build_skill_rerank_fn`. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from everalgo.rank.fusion import rrf +from everalgo.types import Candidate + +from everos.core.observability.logging import get_logger +from everos.memory.search.callbacks import build_skill_rerank_fn +from everos.memory.search.shaper import shape_agent_skill_from_candidate + +from .dto import SearchAgentSkillItem + +if TYPE_CHECKING: + from everos.component.rerank import RerankProvider + +logger = get_logger(__name__) + + +async def search_agent_skills_hybrid( + query: str, + *, + sparse: list[Candidate], + dense: list[Candidate], + reranker: RerankProvider, + top_k: int, +) -> list[SearchAgentSkillItem]: + """Skill HYBRID retrieval: rrf → cross-encoder rerank → shape. + + Args: + query: User search query. + sparse: BM25 recall candidates for the skill table. + dense: Vector ANN recall candidates for the skill table. + reranker: Cross-encoder rerank provider (not LLM-based). + top_k: Maximum skills to return after rerank. + + Returns: + Ranked list of at most ``top_k`` ``SearchAgentSkillItem`` objects. + """ + fused = _fuse(sparse, dense) + reranked = await _cross_encoder_rerank(query, fused, reranker, top_k) + return _shape_results(reranked) + + +# ── Pipeline steps ──────────────────────────────────────────────────────── + + +def _fuse(sparse: list[Candidate], dense: list[Candidate]) -> list[Candidate]: + """RRF fusion of sparse and dense candidates.""" + return rrf(sparse, dense) + + +async def _cross_encoder_rerank( + query: str, + candidates: list[Candidate], + reranker: RerankProvider, + top_k: int, +) -> list[Candidate]: + """Cross-encoder rerank via the skill-shaped factory, then slice to top_k.""" + if not candidates: + return [] + rerank_fn = build_skill_rerank_fn(reranker) + reranked = await rerank_fn(query, candidates) + return reranked[:top_k] + + +def _shape_results(candidates: list[Candidate]) -> list[SearchAgentSkillItem]: + """Shape each Candidate into a SearchAgentSkillItem, dropping malformed rows.""" + return [ + item + for c in candidates + for item in [shape_agent_skill_from_candidate(c)] + if item is not None + ] diff --git a/src/everos/memory/strategies/__init__.py b/src/everos/memory/strategies/__init__.py new file mode 100644 index 00000000..e4b6ea15 --- /dev/null +++ b/src/everos/memory/strategies/__init__.py @@ -0,0 +1,35 @@ +"""OME business strategies — event-triggered offline tasks. + +External usage: + from everos.memory.strategies import ( + extract_agent_case, + extract_agent_skill, + extract_atomic_facts, + extract_foresight, + extract_user_profile, + trigger_profile_clustering, + trigger_skill_clustering, + ) +""" + +from .extract_agent_case import extract_agent_case as extract_agent_case +from .extract_agent_skill import extract_agent_skill as extract_agent_skill +from .extract_atomic_facts import extract_atomic_facts as extract_atomic_facts +from .extract_foresight import extract_foresight as extract_foresight +from .extract_user_profile import extract_user_profile as extract_user_profile +from .trigger_profile_clustering import ( + trigger_profile_clustering as trigger_profile_clustering, +) +from .trigger_skill_clustering import ( + trigger_skill_clustering as trigger_skill_clustering, +) + +__all__ = [ + "extract_agent_case", + "extract_agent_skill", + "extract_atomic_facts", + "extract_foresight", + "extract_user_profile", + "trigger_profile_clustering", + "trigger_skill_clustering", +] diff --git a/src/everos/memory/strategies/_partition_locks.py b/src/everos/memory/strategies/_partition_locks.py new file mode 100644 index 00000000..a4e3b8d9 --- /dev/null +++ b/src/everos/memory/strategies/_partition_locks.py @@ -0,0 +1,66 @@ +"""Per-strategy partition locks for serialising RMW critical sections. + +The OME engine intentionally does NOT serialise concurrent runs of the +same strategy +(``local/specs/2026-04-27-ome-tech-design.md`` §4.5.2: the business logic +must guard itself, deciding inside the strategy body via +``async with lock`` bucketed by a business key). Offline strategies whose +body is a read → +modify → write on shared state (cluster rows, user.md, SKILL.md) +serialise on a business key (``owner_id`` / ``agent_id``) here. + +Mirrors :mod:`everos.service._session_lock` (and +:class:`everos.core.persistence.markdown.writer.MarkdownWriter`'s +per-path lock pool): one ``asyncio.Lock`` per +``(strategy_name, partition_key)`` pair, **never evicted** — a lock +with pending waiters must outlive any dict entry that points to it, +otherwise GC racing waiters can drop the lock mid-flight (CPython +bpo-28427). The pool grows with the live partition-key set, which in +practice is bounded by the agent / user / cluster counts a single +everos process owns. + +No acquire timeout: an OME strategy run has no upstream client +waiting on it, so timing out a queued caller would only convert +"slow" into a permanent ``dead_letter`` data-loss (`max_retries` +exhaustion). The LLM client owns the per-request timeout +(`component.llm.openai_provider`, default 60s) — that is the layer +that breaks a stuck LLM call, not this one. If a genuinely hung +strategy holds the lock indefinitely it surfaces as a stuck queue +under process-level monitoring; the recovery is a process restart, +not a silent data drop. + +Cross-process safety is out of scope: everos is single-process by +design (see ``CLAUDE.md`` deployment notes); the enterprise edition +layers a distributed coordinator on top. +""" + +from __future__ import annotations + +import asyncio + +_pools: dict[str, dict[str, asyncio.Lock]] = {} + + +def get_partition_lock(strategy_name: str, partition_key: str) -> asyncio.Lock: + """Return the lock for ``(strategy_name, partition_key)``; create on first use. + + ``dict.setdefault`` is atomic under single-threaded asyncio — no + ``await`` runs between the nested ``setdefault`` calls, so the + "check then insert" pair is indivisible. No meta-lock is needed. + + Callers acquire the lock with ``async with``; the lock object is + cached forever (see module docstring on bpo-28427), and the inner + asyncio queue gives FIFO fairness across waiters on the same key. + """ + return _pools.setdefault(strategy_name, {}).setdefault( + partition_key, asyncio.Lock() + ) + + +def _reset_for_tests() -> None: + """Test-only: drop every registered lock pool. + + Used by test fixtures that need a clean lock registry between + cases (no inherited holders, no inherited waiters). + """ + _pools.clear() diff --git a/src/everos/memory/strategies/extract_agent_case.py b/src/everos/memory/strategies/extract_agent_case.py new file mode 100644 index 00000000..b6e09dae --- /dev/null +++ b/src/everos/memory/strategies/extract_agent_case.py @@ -0,0 +1,167 @@ +"""extract_agent_case strategy — distil one AgentCase from an agent memcell. + +Per-memcell extraction: algo's :class:`AgentCaseExtractor` returns ``[]`` +(structurally / heuristically filtered) or ``[case]`` (single output). +Quality score is persisted as-is; no everos-side threshold filter +(opensource parity — let downstream rank / filter handle it). + +**Multi-agent fan-out**: the algo prompt's output is third-person +(``the agent did X``, ``a different agent lacking this insight``), so +the same case body is a valid reference experience for **every** +assistant sender that participated in the trajectory. We collect all +distinct assistant-side sender_ids in the memcell, write the same case +body once per agent (each gets its own owner_id-scoped md entry), and +emit one ``AgentCaseExtracted`` per agent so the downstream skill +clustering chain runs in each agent's own scope. + +Algo limitation (recorded in +``local/notes/2026-05-18-write-read-loop-status.md``): the LLM only +sees a flat ``"assistant"`` role label — per-agent serialisation + +per-agent output is a future algo-side change. Until then, broadcast +is the right md-layer fan-out (mirrors Episode/atomic_fact). +""" + +from __future__ import annotations + +from everalgo.agent_memory import AgentCaseExtractor + +from everos.component.llm import get_llm_client +from everos.component.utils.datetime import from_timestamp, to_iso_format +from everos.core.observability.logging import get_logger +from everos.core.persistence import MemoryRoot +from everos.infra.ome.context import StrategyContext +from everos.infra.ome.decorator import offline_strategy +from everos.infra.ome.triggers import Immediate +from everos.infra.persistence.markdown import AgentCaseWriter +from everos.memory.events import AgentCaseExtracted, AgentPipelineStarted +from everos.memory.models import AgentCase, MemCell + +logger = get_logger(__name__) + +_writer: AgentCaseWriter | None = None + + +def _get_writer() -> AgentCaseWriter: + global _writer + if _writer is None: + _writer = AgentCaseWriter(root=MemoryRoot.default()) + return _writer + + +@offline_strategy( + name="extract_agent_case", + trigger=Immediate(on=[AgentPipelineStarted]), + emits=[AgentCaseExtracted], + max_retries=2, +) +async def extract_agent_case(event: AgentPipelineStarted, ctx: StrategyContext) -> None: + # 1. Find the distinct assistant senders in this memcell; bail if none. + agent_ids = _collect_agent_ids(event.memcell) + if not agent_ids: + logger.warning( + "agent_case_skipped_no_assistant", + memcell_id=event.memcell_id, + session_id=event.session_id, + ) + return + + # 2. Run the LLM extractor once; algo returns [] or [single case]. + extractor = AgentCaseExtractor(llm=get_llm_client()) + algo_cases = await extractor.aextract(event.memcell) + if not algo_cases: + logger.info( + "agent_case_skipped_by_algo", + memcell_id=event.memcell_id, + session_id=event.session_id, + ) + return + + # 3. For each agent sender: write the same case body to its md and emit + # AgentCaseExtracted → downstream trigger_skill_clustering. + algo_case = algo_cases[0] + writer = _get_writer() + for agent_id in agent_ids: + case = AgentCase.from_algo( + algo_case, + owner_id=agent_id, + session_id=event.session_id, + parent_id=event.memcell_id, + ) + inline, sections = _agent_case_to_entry_body(case) + eid = await writer.append_entry( + case.owner_id, + inline=inline, + sections=sections, + app_id=event.app_id, + project_id=event.project_id, + ) + await ctx.emit( + AgentCaseExtracted( + memcell_id=event.memcell_id, + case_entry_id=eid.format(), + task_intent=case.task_intent, + quality_score=case.quality_score, + case_timestamp_ms=case.timestamp, + agent_id=case.owner_id, + app_id=event.app_id, + project_id=event.project_id, + ) + ) + logger.info( + "agent_case_extracted", + memcell_id=event.memcell_id, + session_id=event.session_id, + owner_ids=agent_ids, + quality_score=algo_case.quality_score, + fanout=len(agent_ids), + ) + + +def _collect_agent_ids(memcell: MemCell) -> list[str]: + """Distinct assistant-side sender_ids in a cell, preserving first-seen order. + + An "assistant-side" item is any ``role == 'assistant'`` ChatMessage or + any ``kind == 'tool_call'`` request (also assistant-emitted). The + deterministic order matches Episode pipeline's + :func:`_unique_user_senders` so two runs over the same memcell fan + out identically. Empty list means the cell carries no agent + trajectory — caller logs + skips. + """ + seen: list[str] = [] + for item in memcell.items: + sid = getattr(item, "sender_id", None) + if not sid or sid in seen: + continue + if getattr(item, "role", None) == "assistant": + seen.append(sid) + continue + if getattr(item, "kind", None) == "tool_call": + seen.append(sid) + return seen + + +def _agent_case_to_entry_body( + case: AgentCase, +) -> tuple[dict[str, object], dict[str, str]]: + """Split a domain AgentCase into ``(inline, sections)`` for md rendering. + + Mirrors ``_atomic_fact_to_entry_body`` / ``_foresight_to_entry_body`` / + ``_episode_to_entry_body``. ``quality_score`` rides in inline so cascade + can hash it (it's part of ``content_change_keys`` on the handler); + KeyInsight is optional and elided when empty. + """ + inline: dict[str, object] = { + "owner_id": case.owner_id, + "session_id": case.session_id, + "timestamp": to_iso_format(from_timestamp(case.timestamp)), + "parent_type": "memcell", + "parent_id": case.parent_id, + "quality_score": case.quality_score, + } + sections: dict[str, str] = { + "TaskIntent": case.task_intent, + "Approach": case.approach, + } + if case.key_insight: + sections["KeyInsight"] = case.key_insight + return inline, sections diff --git a/src/everos/memory/strategies/extract_agent_skill.py b/src/everos/memory/strategies/extract_agent_skill.py new file mode 100644 index 00000000..07b3b5fa --- /dev/null +++ b/src/everos/memory/strategies/extract_agent_skill.py @@ -0,0 +1,393 @@ +"""extract_agent_skill strategy — distil / update an AgentSkill per case. + +Triggered by :class:`SkillClusterUpdated` after ``trigger_skill_clustering`` +has assigned the fresh case to its cluster. The strategy: + +1. Selects the ``existing_relevant_skills`` slice for this cluster: + + * cluster size ``≤ MAX_SKILLS_IN_PROMPT`` → scalar fetch (ranking + would be pointless on a fully-inclusive set); + * cluster size ``> MAX_SKILLS_IN_PROMPT`` and the target case has a + usable vector (either persisted on the row or re-embedded + on-the-fly from ``task_intent``) → cosine top-K against the + cluster; + * cluster size ``> MAX_SKILLS_IN_PROMPT`` but no vector signal is + obtainable → scalar fetch capped at K (logged warning so + truncation without ranking is observable). +2. Hydrates ``supporting_cases`` from the chosen skills' + ``source_case_ids`` lineage. The algo prompt joins each existing + skill to its ``source_case_ids`` via the ``supporting_cases`` map; + cases that do not back any of the chosen skills would just inflate + the prompt without informing the LLM. Hydrated cases are then + ranked ``(quality_score desc, timestamp desc)`` and capped at + ``MAX_SUPPORTING_CASES`` to keep the prompt bounded as a cluster + grows. +3. Feeds the target + existing + supporting trio to + :class:`everalgo.agent_memory.AgentSkillExtractor`, then writes the + emitted skills back via :class:`AgentSkillWriter`. + +Per-case granularity (one strategy run per fresh case) — algo +short-circuits low-quality cases internally via its own +``skip_quality_threshold``; the strategy trusts that gate. +``cluster_id`` is stamped onto each emitted skill before persistence. +""" + +from __future__ import annotations + +from everalgo.agent_memory import AgentSkillExtractor +from everalgo.types import AgentCase as AlgoAgentCase +from everalgo.types import AgentSkill as AlgoAgentSkill + +from everos.component.embedding import ( + EmbeddingError, + EmbeddingNotConfiguredError, + get_embedder, +) +from everos.component.llm import get_llm_client +from everos.core.observability.logging import get_logger +from everos.core.persistence import MemoryRoot +from everos.infra.ome.context import StrategyContext +from everos.infra.ome.decorator import offline_strategy +from everos.infra.ome.triggers import Immediate +from everos.infra.persistence.lancedb import ( + AgentCase as LanceAgentCase, +) +from everos.infra.persistence.lancedb import ( + AgentSkill as LanceAgentSkill, +) +from everos.infra.persistence.lancedb import ( + agent_case_repo, + agent_skill_repo, +) +from everos.infra.persistence.markdown import ( + AgentSkillFrontmatter, + AgentSkillWriter, +) +from everos.infra.persistence.sqlite import cluster_repo +from everos.memory.events import SkillClusterUpdated +from everos.memory.strategies._partition_locks import get_partition_lock + +logger = get_logger(__name__) + +MAX_SKILLS_IN_PROMPT = 10 +"""Upper bound on ``existing_relevant_skills`` fed to the algo per run. + +The algo library expects the caller to pre-filter +``existing_relevant_skills`` to a relevant subset (cosine top-K over +the target case's ``task_intent`` embedding) so the prompt stays +bounded as a cluster grows.""" + +MAX_SUPPORTING_CASES = 9 +"""Upper bound on ``supporting_cases`` after lineage hydration. + +Mirrors ``AgentSkillExtractor.aextract``'s ``max_case_history`` default +so the algo's per-skill ``supporting_cases`` slot is never starved by a +too-aggressive cap here, nor overfilled by an unbounded lineage union +when many top-K skills each carry a distinct ``source_case_ids`` list. +Ranking is ``(quality_score desc, timestamp desc)`` — same ordering +opensource ``AgentSkillExtractor._load_case_history`` applies.""" + + +class _ClusterMissingError(RuntimeError): + """Race with the cluster strategy; OME retry will catch up.""" + + +class _CaseNotYetIndexedError(RuntimeError): + """The target case is in md but not yet in LanceDB; OME retry will catch up.""" + + +_writer: AgentSkillWriter | None = None + + +def _get_writer() -> AgentSkillWriter: + global _writer + if _writer is None: + _writer = AgentSkillWriter(root=MemoryRoot.default()) + return _writer + + +@offline_strategy( + name="extract_agent_skill", + trigger=Immediate(on=[SkillClusterUpdated]), + emits=[], + max_retries=3, +) +async def extract_agent_skill(event: SkillClusterUpdated, ctx: StrategyContext) -> None: + # Serialise on agent_id: SKILL.md is addressed by (agent_id, skill_name) + # — concurrent runs across different clusters of the same agent can + # both decide to add the same skill_name and clobber the file. Different + # agents run fully in parallel. + # Lock per (app, project, agent): SKILL.md is addressed by (agent, name) + # within a space; different spaces run in parallel. + partition = f"{event.app_id}:{event.project_id}:{event.agent_id}" + async with get_partition_lock("extract_agent_skill", partition): + # 1. Check the cluster row exists. + await _ensure_cluster_exists(event.cluster_id, event.case_entry_id) + + # 2. Load the target AgentCase from LanceDB (scoped to space). + target_lance = await _load_target_case( + event.agent_id, + event.case_entry_id, + app_id=event.app_id, + project_id=event.project_id, + ) + + # 3. Pick the top-K relevant existing skills in this cluster. + # (Cluster-scoped queries are implicitly space-scoped: cluster_id + # is globally unique to one (app, project, owner) cluster set.) + existing_lance = await _select_existing_skills( + agent_id=event.agent_id, + cluster_id=event.cluster_id, + target=target_lance, + ) + + # 4. Pull the supporting cases referenced by those skills. + supporting_lance = await _select_supporting_cases( + existing_lance, + agent_id=event.agent_id, + exclude_entry_id=event.case_entry_id, + app_id=event.app_id, + project_id=event.project_id, + ) + + # 5. Run the LLM extractor → add / update / retire skill operations. + extractor = AgentSkillExtractor(llm=get_llm_client()) + emitted_skills = await extractor.aextract( + _to_algo_case(target_lance), + existing_relevant_skills=[_to_algo_skill(s) for s in existing_lance], + supporting_cases=[_to_algo_case(c) for c in supporting_lance], + ) + + # 6. Write each emitted skill back to its SKILL.md. + writer = _get_writer() + for skill in emitted_skills: + await _persist_skill( + writer, + skill, + agent_id=event.agent_id, + cluster_id=event.cluster_id, + app_id=event.app_id, + project_id=event.project_id, + ) + logger.info( + "agent_skills_extracted", + case_entry_id=event.case_entry_id, + cluster_id=event.cluster_id, + agent_id=event.agent_id, + emitted=len(emitted_skills), + ) + + +# ── orchestration helpers ──────────────────────────────────────────────── + + +async def _ensure_cluster_exists(cluster_id: str, case_entry_id: str) -> None: + """Bail with a retry-class error when the cluster row is not yet there.""" + cluster = await cluster_repo.get_with_members(cluster_id) + if cluster is None: + # Same-transaction race with trigger_skill_clustering; OME retries. + raise _ClusterMissingError( + f"cluster_id={cluster_id} not found yet for case {case_entry_id}; retrying" + ) + + +async def _load_target_case( + agent_id: str, + case_entry_id: str, + *, + app_id: str, + project_id: str, +) -> LanceAgentCase: + """Pull the target case row, raising a retry-class error on cascade lag.""" + target = await agent_case_repo.find_by_owner_entry( + agent_id, case_entry_id, app_id=app_id, project_id=project_id + ) + if target is None: + # Cascade hasn't indexed the freshly-written md yet. + raise _CaseNotYetIndexedError( + f"AgentCase entry_id={case_entry_id} not in LanceDB yet; retrying" + ) + return target + + +async def _select_existing_skills( + *, + agent_id: str, + cluster_id: str, + target: LanceAgentCase, +) -> list[LanceAgentSkill]: + """Pick at most ``MAX_SKILLS_IN_PROMPT`` existing skills for the prompt. + + See module docstring for the three-branch routing rationale. + """ + total = await agent_skill_repo.count_in_cluster( + owner_id=agent_id, cluster_id=cluster_id + ) + if total <= MAX_SKILLS_IN_PROMPT: + return await agent_skill_repo.find_in_cluster( + owner_id=agent_id, cluster_id=cluster_id, limit=MAX_SKILLS_IN_PROMPT + ) + + query_vector = await _resolve_query_vector(target) + if query_vector: + return await agent_skill_repo.find_topk_relevant_in_cluster( + owner_id=agent_id, + cluster_id=cluster_id, + query_vector=query_vector, + top_k=MAX_SKILLS_IN_PROMPT, + ) + + logger.warning( + "agent_skill_topk_no_query_vector_scalar_fallback", + agent_id=agent_id, + cluster_id=cluster_id, + cluster_size=total, + ) + return await agent_skill_repo.find_in_cluster( + owner_id=agent_id, cluster_id=cluster_id, limit=MAX_SKILLS_IN_PROMPT + ) + + +async def _resolve_query_vector(target: LanceAgentCase) -> list[float]: + """Return a usable query vector for cosine top-K, ``[]`` if unobtainable. + + Order of preference: + + 1. ``target.vector`` if cascade has already populated the column — + this is the exact vector the recall path uses, so reusing it + keeps ranking semantics identical across reads. + 2. Compute on the fly from ``target.task_intent`` via the configured + embedder — matches the cascade handler's own vectorisation + contract (``cascade/handlers/agent_case.py``), so the two paths + agree on what "the case embedding" means. + + Returns ``[]`` only when both options are unavailable (no persisted + vector, no ``task_intent`` text, or the embedder is not configured / + fails). The caller decides the policy for that case. + """ + if target.vector: + return list(target.vector) + if not target.task_intent: + return [] + try: + embedder = get_embedder() + return list(await embedder.embed(target.task_intent)) + except (EmbeddingNotConfiguredError, EmbeddingError) as exc: + logger.warning( + "agent_skill_query_embed_failed", + case_entry_id=target.entry_id, + error=str(exc), + ) + return [] + + +async def _select_supporting_cases( + skills: list[LanceAgentSkill], + *, + agent_id: str, + exclude_entry_id: str, + app_id: str, + project_id: str, +) -> list[LanceAgentCase]: + """Hydrate, rank, and cap supporting cases from skills' lineage. + + ``exclude_entry_id`` drops the target case's own entry id so the + algo never sees the new case as one of its own supporting cases. + Ranking ``(quality_score desc, timestamp desc)`` mirrors opensource + ``AgentSkillExtractor._load_case_history``; the cap matches + :data:`MAX_SUPPORTING_CASES`. + """ + # 1. Collect source case ids from the chosen skills (dedup, drop target). + entry_ids = _collect_supporting_entry_ids(skills, exclude=exclude_entry_id) + if not entry_ids: + return [] + + # 2. Bulk-fetch those cases from LanceDB (scoped to space). + hydrated = await agent_case_repo.find_by_owner_entries( + agent_id, entry_ids, app_id=app_id, project_id=project_id + ) + + # 3. Sort by (quality, timestamp) desc, then cap. + hydrated.sort( + key=lambda c: (c.quality_score or 0.0, c.timestamp), + reverse=True, + ) + return hydrated[:MAX_SUPPORTING_CASES] + + +def _collect_supporting_entry_ids( + skills: list[LanceAgentSkill], *, exclude: str +) -> list[str]: + """Dedup ``source_case_ids`` across ``skills``, preserving first-seen order.""" + seen: list[str] = [] + seen_set: set[str] = set() + for skill in skills: + for cid in skill.source_case_ids or []: + if not cid or cid == exclude or cid in seen_set: + continue + seen.append(cid) + seen_set.add(cid) + return seen + + +# ── algo / persistence projection ──────────────────────────────────────── + + +def _to_algo_case(lance: LanceAgentCase) -> AlgoAgentCase: + """Project the LanceDB row onto the algo-side AgentCase type.""" + return AlgoAgentCase( + id=lance.entry_id, + timestamp=int(lance.timestamp.timestamp() * 1000), + task_intent=lance.task_intent, + approach=lance.approach, + quality_score=lance.quality_score, + key_insight=lance.key_insight or "", + ) + + +def _to_algo_skill(lance: LanceAgentSkill) -> AlgoAgentSkill: + """Project the LanceDB row onto the algo-side AgentSkill type. + + ``cluster_id`` rides along even though algo doesn't read it on input — + keeps the model fully populated for any consumer that introspects. + """ + return AlgoAgentSkill( + id=lance.id, + cluster_id=lance.cluster_id or "", + name=lance.name, + description=lance.description, + content=lance.content, + confidence=lance.confidence, + maturity_score=lance.maturity_score, + source_case_ids=list(lance.source_case_ids), + ) + + +async def _persist_skill( + writer: AgentSkillWriter, + skill: AlgoAgentSkill, + *, + agent_id: str, + cluster_id: str, + app_id: str, + project_id: str, +) -> None: + """Write one ``SKILL.md`` with the post-stamped ``cluster_id``.""" + frontmatter = AgentSkillFrontmatter( + id=f"{agent_id}_{skill.name}", + agent_id=agent_id, + name=skill.name, + description=skill.description, + confidence=skill.confidence, + maturity_score=skill.maturity_score, + source_case_ids=list(skill.source_case_ids), + cluster_id=cluster_id, + ) + await writer.write_main( + agent_id, + skill.name, + frontmatter=frontmatter, + body=skill.content, + app_id=app_id, + project_id=project_id, + ) diff --git a/src/everos/memory/strategies/extract_atomic_facts.py b/src/everos/memory/strategies/extract_atomic_facts.py new file mode 100644 index 00000000..a1f7458d --- /dev/null +++ b/src/everos/memory/strategies/extract_atomic_facts.py @@ -0,0 +1,135 @@ +"""extract_atomic_facts strategy — derive AtomicFacts from a fresh MemCell. + +One LLM call per memcell, then md-level fan-out to every user sender. +Mirrors :class:`UserMemoryPipeline`'s Episode handling: the algo +prompt is subject-agnostic (``INPUT_TEXT`` + ``TIME`` only, no +``sender_id`` placeholder — see +``everalgo.user_memory.atomic_fact.AtomicFactExtractor.aextract``), so +calling it once per sender would waste LLM tokens and let non- +determinism drift the per-sender md files apart. Instead, run the +extractor once with ``sender_id=None`` (algo's "generic owner" +signal) and rebroadcast the same fact list under each user sender. + +Per-owner batching: each sender's full fact list is appended in one +batched ``append_entries`` call rather than ``len(algo_facts)`` single +appends, dropping the per-cell IO complexity from ``O(N²)`` to +``O(N)`` (one read + one write per owner instead of N of each) and +narrowing the per-path lock window from N read-modify-write cycles to +one. + +Note ``extract_foresight`` does run per-sender because its prompt +template *does* condition on the target sender; do not collapse that +strategy in the same way without re-checking the prompt. +""" + +from __future__ import annotations + +from collections import defaultdict +from collections.abc import Mapping + +from everalgo.user_memory import AtomicFactExtractor + +from everos.component.llm import get_llm_client +from everos.component.utils.datetime import from_timestamp, to_iso_format +from everos.core.observability.logging import get_logger +from everos.core.persistence import MemoryRoot +from everos.infra.ome.context import StrategyContext +from everos.infra.ome.decorator import offline_strategy +from everos.infra.ome.triggers import Immediate +from everos.infra.persistence.markdown import AtomicFactWriter +from everos.memory.events import UserPipelineStarted +from everos.memory.models import AtomicFact + +logger = get_logger(__name__) + +_writer: AtomicFactWriter | None = None + + +def _get_writer() -> AtomicFactWriter: + global _writer + if _writer is None: + _writer = AtomicFactWriter(root=MemoryRoot.default()) + return _writer + + +@offline_strategy( + name="extract_atomic_facts", + trigger=Immediate(on=[UserPipelineStarted]), + emits=[], + max_retries=2, +) +async def extract_atomic_facts( + event: UserPipelineStarted, ctx: StrategyContext +) -> None: + # 1. List the user senders in this memcell; bail early if there are none. + memcell = event.memcell + sender_ids = sorted({m.sender_id for m in memcell.items if m.role == "user"}) + if not sender_ids: + logger.info( + "atomic_facts_extracted", + memcell_id=event.memcell_id, + session_id=event.session_id, + count=0, + owner_ids=[], + ) + return + + # 2. Run the LLM extractor once (algo prompt is subject-agnostic). + extractor = AtomicFactExtractor(llm=get_llm_client()) + algo_facts = await extractor.aextract(memcell, sender_id=None) + + # 3. Fan the fact list out to one domain AtomicFact per (sender, algo_fact). + facts: list[AtomicFact] = [ + AtomicFact.from_algo( + algo_fact, + owner_id=sid, + session_id=event.session_id, + parent_id=event.memcell_id, + ) + for sid in sender_ids + for algo_fact in algo_facts + ] + + # 4. Group facts by owner so each sender's full list lands in one + # batched write. + by_owner: dict[str, list[tuple[Mapping[str, object], Mapping[str, str]]]] = ( + defaultdict(list) + ) + for fact in facts: + by_owner[fact.owner_id].append(_atomic_fact_to_entry_body(fact)) + + # 5. Write each owner's full list with one batched append_entries. + writer = _get_writer() + for owner_id, items in by_owner.items(): + await writer.append_entries( + owner_id, items, app_id=event.app_id, project_id=event.project_id + ) + + logger.info( + "atomic_facts_extracted", + memcell_id=event.memcell_id, + session_id=event.session_id, + count=len(facts), + owner_ids=sender_ids, + ) + + +def _atomic_fact_to_entry_body( + fact: AtomicFact, +) -> tuple[dict[str, object], dict[str, str]]: + """Split a domain AtomicFact into ``(inline, sections)`` for md rendering. + + Mirrors ``_episode_to_entry_body`` in the user_memory pipeline. Lives in + the memory layer (strategy module) rather than the writer (infra) + because it depends on :class:`everos.memory.AtomicFact` — infra is + not allowed to import memory per the layered architecture contract. + """ + inline: dict[str, object] = { + "owner_id": fact.owner_id, + "session_id": fact.session_id, + "timestamp": to_iso_format(from_timestamp(fact.timestamp)), + "parent_type": "memcell", + "parent_id": fact.parent_id, + } + sections = {"Fact": fact.fact} + return inline, sections diff --git a/src/everos/memory/strategies/extract_foresight.py b/src/everos/memory/strategies/extract_foresight.py new file mode 100644 index 00000000..18ac5a53 --- /dev/null +++ b/src/everos/memory/strategies/extract_foresight.py @@ -0,0 +1,117 @@ +"""extract_foresight strategy — derive Foresights from a fresh MemCell. + +Per-sender extraction (mirrors Episode): a foresight is a forward-looking +statement *about* a specific user, so the algo is invoked once per user +sender and each invocation produces foresights whose ``owner_id`` is +already correct. (AtomicFact, by contrast, uses a subject-agnostic +one-call fan-out.) + +Per-owner batching: each sender's full foresight list is appended in +one batched ``append_entries`` call rather than ``N`` single appends, +dropping IO complexity to ``O(N)`` per owner and narrowing the +per-path lock window. +""" + +from __future__ import annotations + +from collections import defaultdict +from collections.abc import Mapping + +from everalgo.user_memory import ForesightExtractor + +from everos.component.llm import get_llm_client +from everos.component.utils.datetime import from_timestamp, to_iso_format +from everos.core.observability.logging import get_logger +from everos.core.persistence import MemoryRoot +from everos.infra.ome.context import StrategyContext +from everos.infra.ome.decorator import offline_strategy +from everos.infra.ome.triggers import Immediate +from everos.infra.persistence.markdown import ForesightWriter +from everos.memory.events import UserPipelineStarted +from everos.memory.models import Foresight + +logger = get_logger(__name__) + +_writer: ForesightWriter | None = None + + +def _get_writer() -> ForesightWriter: + global _writer + if _writer is None: + _writer = ForesightWriter(root=MemoryRoot.default()) + return _writer + + +@offline_strategy( + name="extract_foresight", + trigger=Immediate(on=[UserPipelineStarted]), + emits=[], + max_retries=2, +) +async def extract_foresight(event: UserPipelineStarted, ctx: StrategyContext) -> None: + # 1. List the user senders in this memcell. + memcell = event.memcell + sender_ids = sorted({m.sender_id for m in memcell.items if m.role == "user"}) + extractor = ForesightExtractor(llm=get_llm_client()) if sender_ids else None + + # 2. Run the LLM extractor once per sender (prompt is per-sender). + foresights: list[Foresight] = [] + for sid in sender_ids: + algo_foresights = await extractor.aextract(memcell, sender_id=sid) + foresights.extend( + Foresight.from_algo( + algo_fs, + session_id=event.session_id, + parent_id=event.memcell_id, + ) + for algo_fs in algo_foresights + ) + + # 3. Group foresights by owner so each sender's full list lands in one + # batched write. + by_owner: dict[str, list[tuple[Mapping[str, object], Mapping[str, str]]]] = ( + defaultdict(list) + ) + for fs in foresights: + by_owner[fs.owner_id].append(_foresight_to_entry_body(fs)) + + # 4. Write each owner's full list with one batched append_entries. + writer = _get_writer() + for owner_id, items in by_owner.items(): + await writer.append_entries( + owner_id, items, app_id=event.app_id, project_id=event.project_id + ) + + logger.info( + "foresights_extracted", + memcell_id=event.memcell_id, + session_id=event.session_id, + count=len(foresights), + owner_ids=sorted({f.owner_id for f in foresights}), + ) + + +def _foresight_to_entry_body( + fs: Foresight, +) -> tuple[dict[str, object], dict[str, str]]: + """Split a domain Foresight into ``(inline, sections)`` for md rendering. + + Mirrors ``_episode_to_entry_body`` / ``_atomic_fact_to_entry_body``. + Optional time-window fields (``start_time`` / ``end_time`` / + ``duration_days``) are emitted only when set so md stays compact. + """ + inline: dict[str, object] = { + "owner_id": fs.owner_id, + "session_id": fs.session_id, + "timestamp": to_iso_format(from_timestamp(fs.timestamp)), + "parent_type": "memcell", + "parent_id": fs.parent_id, + } + if fs.start_time: + inline["start_time"] = fs.start_time + if fs.end_time: + inline["end_time"] = fs.end_time + if fs.duration_days is not None: + inline["duration_days"] = fs.duration_days + sections = {"Foresight": fs.foresight, "Evidence": fs.evidence} + return inline, sections diff --git a/src/everos/memory/strategies/extract_user_profile.py b/src/everos/memory/strategies/extract_user_profile.py new file mode 100644 index 00000000..9903a60d --- /dev/null +++ b/src/everos/memory/strategies/extract_user_profile.py @@ -0,0 +1,211 @@ +"""extract_user_profile strategy — synthesise the user's profile from clusters. + +Listens to :class:`ProfileClusterUpdated` (fired after +``trigger_profile_clustering`` assigns a memcell to a cluster), pulls +the relevant memcells across all "fresh" clusters, and runs +:class:`everalgo.user_memory.ProfileExtractor` to INIT / UPDATE the +user's profile markdown. + +Opensource parity (``mem_memorize.py`` Phase 2): + +- **Throttle**: ``total_memcell_count % profile_extraction_interval == 0``; + default interval = 1 (every memcell triggers a re-extraction). +- **Target clusters**: every cluster whose ``last_ts`` is newer than the + user's existing profile timestamp, plus the current cluster (so the + freshly-arrived memcell is always counted even when its cluster's + ``last_ts`` is older than the profile baseline). +- **Input shape**: raw chat messages — algo's ``_render_conversation`` + unwraps the items list. The sqlite ``memcell.payload_json`` column is + the long-term archive that lets us replay this beyond + ``unprocessed_buffer``'s lifetime. + +Single-sender assumption today: ``event.owner_id`` is treated as the +profile subject. Multi-user clusters land their additional sender's +profile in a follow-up turn (each cluster gets re-evaluated on every +``ProfileClusterUpdated`` for any participating user). +""" + +from __future__ import annotations + +from everalgo.types import MemCell as AlgoMemCell +from everalgo.types import Profile as AlgoProfile +from everalgo.user_memory import ProfileExtractor + +from everos.component.llm import get_llm_client +from everos.core.observability.logging import get_logger +from everos.core.persistence import MemoryRoot +from everos.infra.ome.context import StrategyContext +from everos.infra.ome.decorator import offline_strategy +from everos.infra.ome.triggers import Immediate +from everos.infra.persistence.markdown import ( + ProfileReader, + ProfileWriter, + UserProfileFrontmatter, +) +from everos.infra.persistence.sqlite import cluster_repo, memcell_repo +from everos.memory.events import ProfileClusterUpdated +from everos.memory.strategies._partition_locks import get_partition_lock + +logger = get_logger(__name__) + +PROFILE_EXTRACTION_INTERVAL = 1 +"""Opensource parity: re-extract on every Nth clustered memcell. +``N=1`` matches the opensource default; tune via :class:`Settings` once +the storage budget for profile re-extractions becomes a concern.""" + +PROFILE_MIN_MEMCELLS = 1 +"""Opensource parity: skip when the candidate cluster set holds fewer +than ``N`` memcells across all selected clusters.""" + + +_writer: ProfileWriter | None = None +_reader: ProfileReader | None = None + + +def _get_writer() -> ProfileWriter: + global _writer + if _writer is None: + _writer = ProfileWriter(root=MemoryRoot.default()) + return _writer + + +def _get_reader() -> ProfileReader: + global _reader + if _reader is None: + _reader = ProfileReader(root=MemoryRoot.default()) + return _reader + + +@offline_strategy( + name="extract_user_profile", + trigger=Immediate(on=[ProfileClusterUpdated]), + emits=[], + max_retries=2, +) +async def extract_user_profile( + event: ProfileClusterUpdated, ctx: StrategyContext +) -> None: + # Serialise on owner_id: user.md is a single per-user file and the + # body is a read → LLM merge → overwrite sequence. Different users + # run fully in parallel. + partition = f"{event.app_id}:{event.project_id}:{event.owner_id}" + async with get_partition_lock("extract_user_profile", partition): + # 1. Throttle: skip unless the Nth clustered memcell tick lands here. + user_clusters = await cluster_repo.list_for_owner( + event.owner_id, + "user_memory", + app_id=event.app_id, + project_id=event.project_id, + ) + total_count = sum(c.count for c in user_clusters) + if ( + PROFILE_EXTRACTION_INTERVAL > 1 + and total_count % PROFILE_EXTRACTION_INTERVAL != 0 + ): + logger.info( + "profile_extraction_throttled", + owner_id=event.owner_id, + total_count=total_count, + interval=PROFILE_EXTRACTION_INTERVAL, + ) + return + + # 2. Pick clusters fresher than the existing profile (always include + # the one we just updated). + existing = await _get_reader().read( + event.owner_id, + schema=UserProfileFrontmatter, + app_id=event.app_id, + project_id=event.project_id, + ) + last_profile_ts = existing[0].profile_timestamp_ms if existing else 0 + target_clusters = [ + c + for c in user_clusters + if c.last_ts > last_profile_ts or c.id == event.cluster_id + ] + if not target_clusters: + return + + # 3. Bail if the candidate set is too thin to be worth an LLM call. + member_ids = [m for c in target_clusters for m in c.members] + if len(member_ids) < PROFILE_MIN_MEMCELLS: + logger.info( + "profile_extraction_below_min_memcells", + owner_id=event.owner_id, + memcell_count=len(member_ids), + threshold=PROFILE_MIN_MEMCELLS, + ) + return + + # 4. Pull memcell payloads from SQLite, rehydrate to algo types, + # time-sort. + memcell_rows = await memcell_repo.find_by_ids(member_ids) + algo_memcells = sorted( + (AlgoMemCell.model_validate_json(r.payload_json) for r in memcell_rows), + key=lambda mc: mc.timestamp, + ) + if not algo_memcells: + return + + # 5. Run the LLM extractor — INIT (no prior) or UPDATE (existing). + old_profile = _to_algo_profile(existing[0]) if existing else None + extractor = ProfileExtractor(llm=get_llm_client()) + new_profile = await extractor.aextract( + algo_memcells, sender_id=event.owner_id, old_profile=old_profile + ) + + # 6. Write the fresh profile back to users//user.md. + await _persist_profile( + new_profile, + owner_id=event.owner_id, + app_id=event.app_id, + project_id=event.project_id, + ) + logger.info( + "user_profile_extracted", + owner_id=event.owner_id, + cluster_count=len(target_clusters), + memcell_count=len(algo_memcells), + mode="UPDATE" if old_profile is not None else "INIT", + ) + + +# ── helpers ────────────────────────────────────────────────────────────── + + +def _to_algo_profile(fm: UserProfileFrontmatter) -> AlgoProfile: + """Rehydrate an algo :class:`Profile` from the markdown frontmatter.""" + return AlgoProfile.model_validate( + { + "owner_id": fm.user_id, + "summary": fm.summary, + "timestamp": fm.profile_timestamp_ms, + "explicit_info": list(fm.explicit_info), + "implicit_traits": list(fm.implicit_traits), + } + ) + + +async def _persist_profile( + profile: AlgoProfile, *, owner_id: str, app_id: str, project_id: str +) -> None: + """Write the freshly extracted profile to ``users//user.md``.""" + extras = profile.model_dump(exclude={"owner_id", "summary", "timestamp"}) + explicit_info = extras.get("explicit_info") or [] + implicit_traits = extras.get("implicit_traits") or [] + frontmatter = UserProfileFrontmatter( + id=f"profile_{owner_id}", + user_id=owner_id, + summary=profile.summary, + explicit_info=list(explicit_info), + implicit_traits=list(implicit_traits), + profile_timestamp_ms=profile.timestamp, + ) + await _get_writer().write( + owner_id, + frontmatter=frontmatter, + body=profile.summary, + app_id=app_id, + project_id=project_id, + ) diff --git a/src/everos/memory/strategies/trigger_profile_clustering.py b/src/everos/memory/strategies/trigger_profile_clustering.py new file mode 100644 index 00000000..28c06afc --- /dev/null +++ b/src/everos/memory/strategies/trigger_profile_clustering.py @@ -0,0 +1,112 @@ +"""trigger_profile_clustering strategy — group user memcells by episode topic. + +Listens to :class:`EpisodeExtracted` (emitted per-episode after the user +pipeline writes its md), embeds the ``episode_text``, and merges the +resulting size-1 :class:`everalgo.clustering.Cluster` into the user's +existing user-memory cluster set. + +Profile-track parity with opensource: uses :func:`cluster_by_geometry` +(rather than the LLM-refined variant) — opensource routes +``has_case=False`` (user-memory) memcells through the embedding-only +path. The members on the merged cluster are ``memcell_id`` rather than +``episode_entry_id`` because the downstream profile-extraction step +needs to feed full memcells (chat messages) back into +:class:`everalgo.user_memory.ProfileExtractor`, not the per-sender +episode summaries. +""" + +from __future__ import annotations + +import numpy as np +from everalgo.clustering import Cluster as AlgoCluster +from everalgo.clustering import cluster_by_geometry + +from everos.component.embedding import get_embedder +from everos.core.observability.logging import get_logger +from everos.infra.ome.context import StrategyContext +from everos.infra.ome.decorator import offline_strategy +from everos.infra.ome.triggers import Immediate +from everos.infra.persistence.sqlite import cluster_repo, mint_cluster_id +from everos.memory.events import EpisodeExtracted, ProfileClusterUpdated +from everos.memory.strategies._partition_locks import get_partition_lock + +logger = get_logger(__name__) + + +@offline_strategy( + name="trigger_profile_clustering", + trigger=Immediate(on=[EpisodeExtracted]), + emits=[ProfileClusterUpdated], + max_retries=2, +) +async def trigger_profile_clustering( + event: EpisodeExtracted, ctx: StrategyContext +) -> None: + # Serialise on owner_id: the strategy reads the user's full cluster + # set, picks merge target by geometry, then upserts — concurrent runs + # on the same owner_id would race the read → decide → write cycle. + # Different users run fully in parallel. + # Lock per (app, project, owner): clusters are scoped to a space, so a + # different space's run must not serialise on (or merge into) this one. + partition = f"{event.app_id}:{event.project_id}:{event.owner_id}" + async with get_partition_lock("trigger_profile_clustering", partition): + # 1. Embed the episode_text into a vector. + vector_list = await get_embedder().embed(event.episode_text) + vector = np.asarray(vector_list, dtype=np.float32) + + # 2. Load this user's existing user-memory clusters (scoped to space). + existing = await cluster_repo.list_for_owner( + event.owner_id, + "user_memory", + app_id=event.app_id, + project_id=event.project_id, + ) + + # 3. Build a size-1 cluster for the fresh memcell (id minted upfront). + new_cluster = AlgoCluster( + id=mint_cluster_id(), + centroid=vector, + count=1, + last_ts=event.episode_timestamp_ms, + preview=[event.episode_text], + members=[event.memcell_id], + ) + + # 4. Geometry-merge it into an existing cluster (or keep as-is). + # ``cluster_by_geometry`` is a pure synchronous CPU function (cosine + + # time-window math, no I/O) returning ``Cluster | None`` directly, so + # it must not be awaited (``await None`` raises when there is no + # existing cluster to merge into). + merged = cluster_by_geometry(new_cluster, existing) + to_save = merged if merged is not None else new_cluster + + # 5. Persist the (possibly-merged) cluster back to SQLite. + await cluster_repo.upsert_with_members( + to_save, + owner_id=event.owner_id, + owner_type="user", + kind="user_memory", + member_type="memcell", + app_id=event.app_id, + project_id=event.project_id, + ) + + # 6. Emit ProfileClusterUpdated → downstream extract_user_profile. + assert to_save.id is not None # both branches above set id + await ctx.emit( + ProfileClusterUpdated( + memcell_id=event.memcell_id, + cluster_id=to_save.id, + owner_id=event.owner_id, + app_id=event.app_id, + project_id=event.project_id, + ) + ) + logger.info( + "profile_cluster_updated", + memcell_id=event.memcell_id, + cluster_id=to_save.id, + owner_id=event.owner_id, + merged=merged is not None, + cluster_count=to_save.count, + ) diff --git a/src/everos/memory/strategies/trigger_skill_clustering.py b/src/everos/memory/strategies/trigger_skill_clustering.py new file mode 100644 index 00000000..01683ee1 --- /dev/null +++ b/src/everos/memory/strategies/trigger_skill_clustering.py @@ -0,0 +1,124 @@ +"""trigger_skill_clustering strategy — group fresh AgentCases into clusters. + +Listens to :class:`AgentCaseExtracted`, embeds the case's ``task_intent``, +and merges the resulting size-1 :class:`everalgo.clustering.Cluster` into +the agent's existing cluster set. Cluster identity is the algo's +"caller-supplied id"; the strategy mints one upfront via +:func:`mint_cluster_id` so the value flows unchanged whether the algo +merges into an existing cluster (id transparently swapped to the existing +cluster's id by ``_merge``) or returns ``None`` (the brand-new id is used +as-is when persisting). + +Skill-track parity with opensource: uses :func:`cluster_by_llm` (rather +than the geometry-only variant) — opensource routes ``has_case=True`` +memcells through the LLM-refined path. The low-quality short-circuit +(``quality_score < 0.2``) mirrors :class:`AgentSkillExtractor`'s own +``skip_quality_threshold`` so we avoid both an embedding call and an LLM +ranking call for cases that won't drive a skill anyway. +""" + +from __future__ import annotations + +import numpy as np +from everalgo.clustering import Cluster as AlgoCluster +from everalgo.clustering import cluster_by_llm + +from everos.component.embedding import get_embedder +from everos.component.llm import get_llm_client +from everos.core.observability.logging import get_logger +from everos.infra.ome.context import StrategyContext +from everos.infra.ome.decorator import offline_strategy +from everos.infra.ome.triggers import Immediate +from everos.infra.persistence.sqlite import cluster_repo, mint_cluster_id +from everos.memory.events import AgentCaseExtracted, SkillClusterUpdated +from everos.memory.strategies._partition_locks import get_partition_lock + +logger = get_logger(__name__) + +_SKIP_QUALITY_THRESHOLD = 0.2 +"""Lower bound mirroring :attr:`AgentSkillExtractor.skip_quality_threshold`; +cases below this never produce a skill, so we don't bother clustering them.""" + + +@offline_strategy( + name="trigger_skill_clustering", + trigger=Immediate(on=[AgentCaseExtracted]), + emits=[SkillClusterUpdated], + max_retries=2, +) +async def trigger_skill_clustering( + event: AgentCaseExtracted, ctx: StrategyContext +) -> None: + # Serialise on agent_id: the strategy reads the agent's full cluster + # set, lets the LLM decide merge vs. mint, then upserts — concurrent + # runs on the same agent_id would race the read → decide → write + # cycle. Different agents run fully in parallel. + # Lock per (app, project, agent): clusters are scoped to a space. + partition = f"{event.app_id}:{event.project_id}:{event.agent_id}" + async with get_partition_lock("trigger_skill_clustering", partition): + # 1. Drop low-quality cases — they won't yield a skill anyway. + if event.quality_score < _SKIP_QUALITY_THRESHOLD: + logger.info( + "skill_clustering_skipped_low_quality", + case_entry_id=event.case_entry_id, + quality_score=event.quality_score, + threshold=_SKIP_QUALITY_THRESHOLD, + ) + return + + # 2. Embed the case's task_intent into a vector. + vector_list = await get_embedder().embed(event.task_intent) + vector = np.asarray(vector_list, dtype=np.float32) + + # 3. Load this agent's existing skill clusters (scoped to space). + existing = await cluster_repo.list_for_owner( + event.agent_id, + "agent_case", + app_id=event.app_id, + project_id=event.project_id, + ) + + # 4. Build a size-1 cluster for the fresh case (id minted upfront). + new_cluster = AlgoCluster( + id=mint_cluster_id(), + centroid=vector, + count=1, + last_ts=event.case_timestamp_ms, + preview=[event.task_intent], + members=[event.case_entry_id], + ) + + # 5. Ask the LLM to merge it into an existing cluster (or keep as-is). + merged = await cluster_by_llm(new_cluster, existing, llm=get_llm_client()) + to_save = merged if merged is not None else new_cluster + + # 6. Persist the (possibly-merged) cluster back to SQLite. + await cluster_repo.upsert_with_members( + to_save, + owner_id=event.agent_id, + owner_type="agent", + kind="agent_case", + member_type="case", + app_id=event.app_id, + project_id=event.project_id, + ) + + # 7. Emit SkillClusterUpdated → downstream extract_agent_skill. + assert to_save.id is not None # both branches above set id + await ctx.emit( + SkillClusterUpdated( + case_entry_id=event.case_entry_id, + cluster_id=to_save.id, + agent_id=event.agent_id, + app_id=event.app_id, + project_id=event.project_id, + ) + ) + logger.info( + "skill_cluster_updated", + case_entry_id=event.case_entry_id, + cluster_id=to_save.id, + agent_id=event.agent_id, + merged=merged is not None, + cluster_count=to_save.count, + ) diff --git a/methods/EverCore/evaluation/data/locomo/.gitkeep b/src/everos/py.typed similarity index 100% rename from methods/EverCore/evaluation/data/locomo/.gitkeep rename to src/everos/py.typed diff --git a/src/everos/service/__init__.py b/src/everos/service/__init__.py new file mode 100644 index 00000000..6bb059d8 --- /dev/null +++ b/src/everos/service/__init__.py @@ -0,0 +1,20 @@ +"""Application layer. + +Orchestrates memory-layer capabilities into complete use cases. One CLI +command or API endpoint maps to one service method. + +External usage: + from everos.service import MemorizeResult, get, memorize, search +""" + +from .get import get as get +from .memorize import MemorizeResult as MemorizeResult +from .memorize import memorize as memorize +from .search import search as search + +__all__ = [ + "MemorizeResult", + "get", + "memorize", + "search", +] diff --git a/src/everos/service/_boundary.py b/src/everos/service/_boundary.py new file mode 100644 index 00000000..eef37ad6 --- /dev/null +++ b/src/everos/service/_boundary.py @@ -0,0 +1,512 @@ +"""Boundary stage — shared upstream step for the dual-pipeline memorize flow. + +Owns the buffer / merge / boundary / tail-persistence sequence so the same +``cells`` feed both :class:`everos.memory.extract.pipeline.UserMemoryPipeline` +and :class:`everos.memory.extract.pipeline.AgentMemoryPipeline` (the +latter only runs when ``mode == "agent"``). + +Mode dispatch: + +- ``"chat"`` → :func:`everalgo.boundary.detect_boundaries` on a filtered + ``ChatMessage`` list (tool rows / assistant-with-tool_calls dropped). +- ``"agent"`` → :class:`everalgo.agent_memory.AgentBoundaryDetector` on the + full ``ConversationItem`` list (tool rows preserved). + +Both paths share a single unprocessed-buffer track (``"memorize"``) because +boundary detection is single-pass; switching mode requires a fresh service +process (see ``settings.memorize.mode``). + +The boundary stage also owns the **sqlite ``memcell`` ledger**: each cell +gets exactly one row regardless of mode (since the algorithm produces one +canonical cell). Downstream pipelines (user + agent) reference the same +``memcell_id``; PK collisions used to occur when each pipeline tried to +insert its own row per cell. +""" + +from __future__ import annotations + +import json +import uuid +from typing import TYPE_CHECKING, Literal, NamedTuple + +from everalgo.agent_memory import AgentBoundaryDetector +from everalgo.boundary import detect_boundaries +from everalgo.types import ( + ChatMessage, + ConversationItem, + MemCell, + ToolCallFunction, + ToolCallRequest, + ToolCallResult, +) +from everalgo.types import ToolCall as AlgoToolCall + +from everos.component.utils.datetime import from_timestamp, to_timestamp_ms +from everos.core.observability.logging import get_logger +from everos.infra.persistence.sqlite import ( + Memcell, + UnprocessedBuffer, + conversation_status_repo, + memcell_repo, + unprocessed_buffer_repo, +) +from everos.memory import CanonicalMessage, IngestResult, ToolCall + +if TYPE_CHECKING: + from everalgo.llm.protocols import LLMClient + + from everos.memory.prompt_slots import PromptLoader + +logger = get_logger(__name__) + +_TRACK = "memorize" +"""Shared track used for both the unprocessed-buffer and the memcell +ledger — boundary detection is mode-dispatched but single-pass, so it +does not need per-pipeline separation.""" + +_RAW_TYPE_BY_MODE: dict[str, str] = { + "chat": "Conversation", + "agent": "AgentTrajectory", +} + + +Mode = Literal["chat", "agent"] +Status = Literal["accumulated", "extracted", "skipped"] + + +class BoundaryOutcome(NamedTuple): + """Result handed to the dual pipelines. + + Lists are parallel: index ``i`` describes cell ``i``. + ``memcell_ids`` are minted here and shared across both pipelines + (Episode.parent_id / UserPipelineStarted.memcell_id both reference + the same id — single sqlite ``memcell`` row per cell). + ``message_count`` is the count of fresh (newly-arrived, post-filter) + canonical rows from this call; the response DTO reads it directly. + """ + + cells: list[MemCell] + memcell_ids: list[str] + per_cell_message_ids: list[list[str]] + per_cell_all_senders: list[list[str]] + status: Status + message_count: int + + +async def prepare_cells( + ingested: IngestResult, + *, + mode: Mode, + is_final: bool, + llm_client: LLMClient | None, + prompt_loader: PromptLoader, + hard_token_limit: int, + hard_msg_limit: int, +) -> BoundaryOutcome: + """Run the boundary stage end-to-end and persist tail back to buffer.""" + app_id = ingested.app_id + project_id = ingested.project_id + fresh = _filter_for_mode(ingested.messages, mode) + if not fresh and not is_final: + return _empty_outcome(status="skipped", message_count=0) + + buffer_rows = await unprocessed_buffer_repo.list_for_track( + ingested.session_id, _TRACK, app_id=app_id, project_id=project_id + ) + buffered = [_row_to_canonical(r) for r in buffer_rows] + merged = _merge_dedupe_sort(buffered, fresh) + if not merged: + return _empty_outcome(status="accumulated", message_count=0) + + # Need a role=user anchor for downstream episode extraction; assistant- + # only / tool-only batches sit in the buffer until a user message lands. + if not is_final and not any(m.role == "user" for m in merged): + await _replace_buffer(ingested.session_id, merged, app_id, project_id) + await _touch_last_message_ts(ingested.session_id, merged, app_id, project_id) + return _empty_outcome(status="accumulated", message_count=len(fresh)) + + if llm_client is None: + await _replace_buffer(ingested.session_id, merged, app_id, project_id) + logger.warning( + "memorize_no_llm_client", + extra={"session_id": ingested.session_id, "buffered": len(merged)}, + ) + return _empty_outcome(status="skipped", message_count=len(fresh)) + + boundary_prompt = prompt_loader.load("boundary_detection") + cells, tail = await _detect( + merged, + mode=mode, + llm_client=llm_client, + prompt=boundary_prompt, + is_final=is_final, + hard_token_limit=hard_token_limit, + hard_msg_limit=hard_msg_limit, + ) + + if not cells: + # boundary returned an empty cells set → roll the merged slice + # back into the buffer (algo says it's still mid-conversation). + await _replace_buffer(ingested.session_id, merged, app_id, project_id) + await _touch_last_message_ts(ingested.session_id, merged, app_id, project_id) + return _empty_outcome(status="accumulated", message_count=len(fresh)) + + memcell_ids = [_mint_memcell_id() for _ in cells] + per_cell_message_ids = _split_messages_per_cell(merged, cells) + per_cell_all_senders = [_unique_all_senders(c) for c in cells] + + # Write one memcell row per cell (shared across user / agent pipelines). + # MemCell has no single owner — multi-user dialogue slices stay owner- + # agnostic. Per-user fan-out (Episode / AtomicFact / Foresight / Profile) + # happens downstream via ``sender_ids``. + raw_type = _RAW_TYPE_BY_MODE[mode] + rows = [ + _build_memcell_row( + cell=cell, + memcell_id=memcell_id, + session_id=ingested.session_id, + app_id=app_id, + project_id=project_id, + raw_type=raw_type, + message_ids=per_cell_message_ids[i], + sender_ids=per_cell_all_senders[i], + ) + for i, (cell, memcell_id) in enumerate(zip(cells, memcell_ids, strict=True)) + ] + await memcell_repo.insert_many(rows) + + last_cell_ts = max((cell.timestamp for cell in cells), default=0) + if last_cell_ts: + await conversation_status_repo.touch_last_memcell_ts( + ingested.session_id, + _TRACK, + from_timestamp(last_cell_ts), + app_id=app_id, + project_id=project_id, + ) + + tail_canonical = _slice_tail(merged, tail) + await _replace_buffer(ingested.session_id, tail_canonical, app_id, project_id) + + return BoundaryOutcome( + cells=cells, + memcell_ids=memcell_ids, + per_cell_message_ids=per_cell_message_ids, + per_cell_all_senders=per_cell_all_senders, + status="extracted", + message_count=len(fresh), + ) + + +# ── Mode-specific filter ────────────────────────────────────────────────── + + +def _filter_for_mode( + msgs: list[CanonicalMessage], mode: Mode +) -> list[CanonicalMessage]: + """Chat mode drops tool rows; agent mode keeps everything.""" + if mode == "chat": + return [m for m in msgs if m.role in ("user", "assistant") and not m.tool_calls] + return list(msgs) + + +# ── Boundary dispatch ───────────────────────────────────────────────────── + + +_BOUNDARY_MAX_ATTEMPTS = 3 + + +async def _detect( + merged: list[CanonicalMessage], + *, + mode: Mode, + llm_client: LLMClient, + prompt: str, + is_final: bool, + hard_token_limit: int, + hard_msg_limit: int, +) -> tuple[list[MemCell], list[ConversationItem]]: + # Retry on ValueError to absorb transient LLM JSON-parse failures from + # the everalgo boundary detector; non-ValueError errors propagate. + last_err: ValueError | None = None + for attempt in range(_BOUNDARY_MAX_ATTEMPTS): + try: + if mode == "chat": + chat_msgs = [_to_chat_message(m) for m in merged] + result = await detect_boundaries( + chat_msgs, + llm=llm_client, + prompt=prompt, + is_final=is_final, + hard_token_limit=hard_token_limit, + hard_msg_limit=hard_msg_limit, + ) + return list(result.cells), list(result.tail) + # Agent mode — facade does filter→detect→remap to preserve tool + # items. AgentBoundaryDetector intentionally does not expose hard + # limits; the boundary primitive's defaults apply. + items = [_to_conversation_item(m) for m in merged] + detector = AgentBoundaryDetector(llm=llm_client) + result = await detector.adetect(items, is_final=is_final, prompt=prompt) + return list(result.cells), list(result.tail) + except ValueError as err: + last_err = err + logger.warning( + "boundary_detect_retry", + extra={ + "attempt": attempt + 1, + "max_attempts": _BOUNDARY_MAX_ATTEMPTS, + "mode": mode, + "error": str(err), + }, + ) + assert last_err is not None + raise last_err + + +# ── CanonicalMessage → algo wire types ──────────────────────────────────── + + +def _to_chat_message(m: CanonicalMessage) -> ChatMessage: + return ChatMessage( + id=m.message_id, + role=m.role, # type: ignore[arg-type] + sender_id=m.sender_id, + sender_name=m.sender_name, + content=m.text, + timestamp=to_timestamp_ms(m.timestamp), + ) + + +def _to_conversation_item(m: CanonicalMessage) -> ConversationItem: + """Map one canonical row to one ``ConversationItem`` (1:1). + + Dispatch rules — order matters: + + 1. ``role="tool"`` (paired with a ``tool_call_id``) → :class:`ToolCallResult`. + 2. ``role="assistant"`` carrying non-empty ``tool_calls`` → + :class:`ToolCallRequest`; the optional ``content`` text rides along. + 3. ``role`` in {``"user"``, ``"assistant"``} (text-only) → + :class:`ChatMessage`. + + Caller is expected to provide well-formed inputs (no orphan tool rows, + no role≠tool with ``tool_call_id``). The fall-through case logs and + raises so unexpected shapes don't silently corrupt the cell index map. + """ + ts_ms = to_timestamp_ms(m.timestamp) + if m.role == "tool" and m.tool_call_id: + return ToolCallResult( + tool_call_id=m.tool_call_id, + content=m.text, + timestamp=ts_ms, + ) + if m.role == "assistant" and m.tool_calls: + return ToolCallRequest( + tool_calls=[ + AlgoToolCall( + id=tc.id, + function=ToolCallFunction( + name=tc.function.get("name", ""), + arguments=tc.function.get("arguments", ""), + ), + ) + for tc in m.tool_calls + ], + timestamp=ts_ms, + content=m.text or None, + sender_id=m.sender_id, + sender_name=m.sender_name, + ) + if m.role in ("user", "assistant"): + return ChatMessage( + id=m.message_id, + role=m.role, # type: ignore[arg-type] + sender_id=m.sender_id, + sender_name=m.sender_name, + content=m.text, + timestamp=ts_ms, + ) + # Orphan tool row or unexpected role — break loudly; corrupting the + # cell→message index map silently is worse than a 5xx. + raise ValueError( + f"cannot map canonical row to ConversationItem: role={m.role!r} " + f"message_id={m.message_id!r} has_tool_call_id={m.tool_call_id is not None}" + ) + + +# ── Buffer + status helpers ─────────────────────────────────────────────── + + +async def _replace_buffer( + session_id: str, + rows: list[CanonicalMessage], + app_id: str, + project_id: str, +) -> None: + await unprocessed_buffer_repo.replace( + session_id, + _TRACK, + [_canonical_to_row(m, app_id, project_id) for m in rows], + app_id=app_id, + project_id=project_id, + ) + + +async def _touch_last_message_ts( + session_id: str, + merged: list[CanonicalMessage], + app_id: str, + project_id: str, +) -> None: + await conversation_status_repo.touch_last_message_ts( + session_id, + _TRACK, + max(m.timestamp for m in merged), + app_id=app_id, + project_id=project_id, + ) + + +def _canonical_to_row( + m: CanonicalMessage, app_id: str, project_id: str +) -> UnprocessedBuffer: + return UnprocessedBuffer( + message_id=m.message_id, + app_id=app_id, + project_id=project_id, + session_id=m.session_id, + track=_TRACK, + sender_id=m.sender_id, + sender_name=m.sender_name, + role=m.role, + timestamp=m.timestamp, + content_items_json=json.dumps(m.content_items), + text=m.text, + tool_calls_json=( + json.dumps([tc.model_dump() for tc in m.tool_calls]) + if m.tool_calls + else None + ), + tool_call_id=m.tool_call_id, + ) + + +def _row_to_canonical(r: UnprocessedBuffer) -> CanonicalMessage: + tool_calls: list[ToolCall] | None = None + if r.tool_calls_json: + tool_calls = [ToolCall.model_validate(d) for d in json.loads(r.tool_calls_json)] + content_items = json.loads(r.content_items_json) if r.content_items_json else [] + # ``r.timestamp`` is UtcDatetime — the BaseTable load-event hook + # re-attaches ``tzinfo=UTC`` on ORM hydrate, so no defensive coercion + # is needed here. + return CanonicalMessage( + message_id=r.message_id, + session_id=r.session_id, + sender_id=r.sender_id, + sender_name=r.sender_name, + role=r.role, # type: ignore[arg-type] + timestamp=r.timestamp, + content_items=content_items, + text=r.text, + tool_calls=tool_calls, + tool_call_id=r.tool_call_id, + ) + + +# ── Merge / split / sender helpers ──────────────────────────────────────── + + +def _merge_dedupe_sort( + buffered: list[CanonicalMessage], + new: list[CanonicalMessage], +) -> list[CanonicalMessage]: + """Dedupe by message_id; sort by (timestamp, message_id) ascending.""" + seen: dict[str, CanonicalMessage] = {m.message_id: m for m in buffered} + for m in new: + seen.setdefault(m.message_id, m) + return sorted(seen.values(), key=lambda m: (m.timestamp, m.message_id)) + + +def _slice_tail( + merged: list[CanonicalMessage], + tail: list[ConversationItem], +) -> list[CanonicalMessage]: + """The tail is a trailing slice of ``merged`` (per algo contract).""" + n = len(tail) + if n == 0: + return [] + return merged[-n:] + + +def _split_messages_per_cell( + merged: list[CanonicalMessage], + cells: list[MemCell], +) -> list[list[str]]: + """Map each cell index → list of everos message_ids. + + The boundary stage maintains a 1:1 ordering between canonical rows and + items handed to algo, so we walk ``merged`` left-to-right consuming + ``len(cell.items)`` rows per cell. + """ + result: list[list[str]] = [] + ptr = 0 + for cell in cells: + n = len(cell.items) + result.append([merged[ptr + i].message_id for i in range(n)]) + ptr += n + return result + + +def _unique_all_senders(cell: MemCell) -> list[str]: + """Distinct sender_ids in a cell, preserving first-occurrence order. + + ``ToolCallResult`` does not carry a ``sender_id`` (tool runners are not + speakers); ``getattr`` keeps the helper agnostic to the item variant. + """ + senders: list[str] = [] + for item in cell.items: + sid = getattr(item, "sender_id", None) + if sid and sid not in senders: + senders.append(sid) + return senders + + +def _build_memcell_row( + *, + cell: MemCell, + memcell_id: str, + session_id: str, + app_id: str, + project_id: str, + raw_type: str, + message_ids: list[str], + sender_ids: list[str], +) -> Memcell: + return Memcell( + memcell_id=memcell_id, + app_id=app_id, + project_id=project_id, + session_id=session_id, + track=_TRACK, + raw_type=raw_type, + message_ids_json=json.dumps(message_ids), + sender_ids_json=json.dumps(sender_ids), + payload_json=cell.model_dump_json(), + timestamp=from_timestamp(cell.timestamp), + ) + + +def _mint_memcell_id() -> str: + """Generate an everos-owned memcell identifier.""" + return f"mc_{uuid.uuid4().hex[:12]}" + + +def _empty_outcome(*, status: Status, message_count: int) -> BoundaryOutcome: + return BoundaryOutcome( + cells=[], + memcell_ids=[], + per_cell_message_ids=[], + per_cell_all_senders=[], + status=status, + message_count=message_count, + ) diff --git a/src/everos/service/_session_lock.py b/src/everos/service/_session_lock.py new file mode 100644 index 00000000..ab521f6a --- /dev/null +++ b/src/everos/service/_session_lock.py @@ -0,0 +1,56 @@ +"""Per-session asyncio lock for serialising concurrent memorize() calls. + +Two concurrent ``POST /add`` (or ``/flush``) calls on the **same** +``session_id`` race on the unprocessed_buffer: + +1. Both read ``unprocessed_buffer`` for the session and see the same + pre-existing rows. +2. Both run boundary detection independently against their own merged + slice (each task only sees its own newly-arrived messages plus the + shared pre-existing buffer rows — neither sees the other's messages). +3. Both call ``_replace_buffer(session_id, tail)`` — the later write + silently overwrites the earlier write's tail and the earlier task's + tail messages are lost forever (they never made it into any memcell + either, since each task's boundary call only saw its own slice). + +This module serialises memorize() at the ``session_id`` granularity so +the read-merge-boundary-write cycle is atomic per session. + +Cross-process safety is out of scope (single-instance everos; would +need fcntl on the sqlite db). Cross-session calls remain fully parallel. + +Wrap acquire + work in ``asyncio.timeout(...)`` (see +``MemorizeSettings.session_lock_timeout_seconds``) so a hung LLM cannot +hold the lock forever — on timeout the task is cancelled and +``async with`` releases the lock automatically. +""" + +from __future__ import annotations + +import asyncio + +# Plain dict (not WeakValueDictionary): a Lock with pending waiters must +# outlive the dict entry, otherwise GC racing with waiters can drop a +# lock mid-flight (CPython bpo-28427). Same rationale as +# ``everos.core.persistence.markdown.writer.MarkdownWriter._path_locks``. +_session_locks: dict[str, asyncio.Lock] = {} + + +def get_session_lock(session_id: str) -> asyncio.Lock: + """Return the per-session asyncio Lock; create on first use. + + ``dict.setdefault`` is atomic under single-threaded asyncio (no GIL + release between the get and the insert), so no meta-lock is needed + around the registry. + """ + return _session_locks.setdefault(session_id, asyncio.Lock()) + + +def _reset_for_tests() -> None: + """Test-only: drop all registered locks. + + Used by integration test fixtures that rebuild memorize singletons + against a fresh tmp memory_root; ensures no stale lock state leaks + across tests. + """ + _session_locks.clear() diff --git a/src/everos/service/get.py b/src/everos/service/get.py new file mode 100644 index 00000000..9b9a7e67 --- /dev/null +++ b/src/everos/service/get.py @@ -0,0 +1,42 @@ +"""Get use case — lazy singleton wiring for ``POST /api/v1/memory/get``. + +Mirrors :mod:`everos.service.search`: the :class:`GetManager` and its +LanceDB repo singletons are built on first call so the FastAPI module +import order stays decoupled from the lifespan that brings up LanceDB. + +``/get`` is read-only and uses no embedding / LLM / rerank clients — +it never blocks on optional components the way ``/search`` does. +""" + +from __future__ import annotations + +from everos.core.observability.logging import get_logger +from everos.infra.persistence.lancedb import ( + agent_case_repo, + agent_skill_repo, + episode_repo, + user_profile_repo, +) +from everos.memory.get import GetManager, GetRequest, GetResponse + +logger = get_logger(__name__) + +_manager: GetManager | None = None + + +def _get_manager() -> GetManager: + global _manager + if _manager is None: + _manager = GetManager( + episode_repo=episode_repo, + agent_case_repo=agent_case_repo, + agent_skill_repo=agent_skill_repo, + user_profile_repo=user_profile_repo, + ) + logger.info("get_manager_built") + return _manager + + +async def get(req: GetRequest) -> GetResponse: + """Dispatch one /get request through the lazily-built manager.""" + return await _get_manager().get(req) diff --git a/src/everos/service/memorize.py b/src/everos/service/memorize.py new file mode 100644 index 00000000..c7df2beb --- /dev/null +++ b/src/everos/service/memorize.py @@ -0,0 +1,238 @@ +"""Memorize use case — ingest + boundary + dual pipeline dispatch. + +End-to-end orchestration: + + POST /api/v1/memory/add { session_id, messages[] } + → ingest.process → IngestResult + → _boundary.prepare_cells(mode=settings.memorize.mode) → cells + → asyncio.gather( + UserMemoryPipeline.run(cells, ...), + AgentMemoryPipeline.run(cells, ...) if mode == "agent", + ) + → merge outcome.status → {message_count, status} + +The boundary stage owns buffer / merge / boundary / tail — so the same +``cells`` feed both pipelines in agent mode (chat mode skips the agent +pipeline entirely). + +Lazy singletons: writer / loader / pipelines / LLM client are all +constructed on first use (service module imports run before lifespan +resolves the memory-root and reads env vars). +""" + +from __future__ import annotations + +import asyncio +from pathlib import Path +from typing import Any, Literal + +from pydantic import BaseModel + +from everos.component.llm import get_llm_client +from everos.config import load_settings +from everos.core.observability.logging import get_logger +from everos.core.persistence import MemoryRoot +from everos.infra.ome.config import OMEConfig +from everos.infra.ome.engine import OfflineEngine +from everos.infra.persistence.markdown import EpisodeWriter +from everos.memory.extract.ingest import process as ingest_process +from everos.memory.extract.pipeline import ( + AgentMemoryPipeline, + UserMemoryPipeline, +) +from everos.memory.prompt_slots import PromptLoader +from everos.memory.strategies import ( + extract_agent_case, + extract_agent_skill, + extract_atomic_facts, + extract_foresight, + extract_user_profile, + trigger_profile_clustering, + trigger_skill_clustering, +) +from everos.service._boundary import prepare_cells +from everos.service._session_lock import get_session_lock + +logger = get_logger(__name__) + + +class MemorizeResult(BaseModel): + """What memorize returns to the caller (route serialises it).""" + + message_count: int + status: Literal["accumulated", "extracted"] + + +# Lazy singletons ──────────────────────────────────────────────────────────── + + +_episode_writer: EpisodeWriter | None = None +_prompt_loader: PromptLoader | None = None +_user_pipeline: UserMemoryPipeline | None = None +_agent_pipeline: AgentMemoryPipeline | None = None +_ome_engine: OfflineEngine | None = None + + +def _config_root() -> Path: + """Return the directory holding bundled prompt slots (``config/``).""" + # ``src/everos/config/`` ships in the wheel alongside this service module. + return Path(__file__).resolve().parent.parent / "config" + + +def _get_episode_writer() -> EpisodeWriter: + global _episode_writer + if _episode_writer is None: + _episode_writer = EpisodeWriter(MemoryRoot.default()) + return _episode_writer + + +def _get_prompt_loader() -> PromptLoader: + global _prompt_loader + if _prompt_loader is None: + _prompt_loader = PromptLoader(_config_root()) + return _prompt_loader + + +def _get_user_pipeline() -> UserMemoryPipeline: + global _user_pipeline + if _user_pipeline is None: + _user_pipeline = UserMemoryPipeline( + episode_writer=_get_episode_writer(), + prompt_loader=_get_prompt_loader(), + llm_client=get_llm_client(), + engine=_get_engine(), + ) + return _user_pipeline + + +def _get_agent_pipeline() -> AgentMemoryPipeline: + global _agent_pipeline + if _agent_pipeline is None: + _agent_pipeline = AgentMemoryPipeline(engine=_get_engine()) + return _agent_pipeline + + +def _get_engine() -> OfflineEngine: + """Return the singleton OfflineEngine; constructed + registered on first call. + + Lifecycle (start/stop) is wired by ``OmeLifespanProvider``. + """ + global _ome_engine + if _ome_engine is None: + root = MemoryRoot.default() + jobstore_path = root.ome_db + jobstore_path.parent.mkdir(parents=True, exist_ok=True) + engine = OfflineEngine( + config=OMEConfig( + jobstore_path=jobstore_path, + config_path=root.ome_config, + ) + ) + engine.register(extract_atomic_facts) + engine.register(extract_foresight) + engine.register(extract_agent_case) + engine.register(trigger_skill_clustering) + engine.register(extract_agent_skill) + engine.register(trigger_profile_clustering) + engine.register(extract_user_profile) + _ome_engine = engine + return _ome_engine + + +# Public entry ─────────────────────────────────────────────────────────────── + + +async def memorize( + payload: dict[str, Any], + *, + is_final: bool = False, +) -> MemorizeResult: + """Execute one add cycle. Dispatched concurrently across pipelines. + + Args: + payload: ``{"session_id", "messages": [...]}`` — entrypoints DTO + dumped to dict. + is_final: ``True`` only for flush (algo guarantees ``tail=[]``). + + Concurrency: serialised per ``session_id`` via + :func:`everos.service._session_lock.get_session_lock`. The lock + spans the entire read-merge-boundary-write cycle so concurrent /add + calls on the same session cannot lose-update each other's tail. + An outer ``asyncio.timeout`` (configured by + ``settings.memorize.session_lock_timeout_seconds``) ensures a stuck + LLM cannot hold the lock indefinitely — on timeout the task is + cancelled and ``async with`` auto-releases the lock. + """ + settings = load_settings() + mode = settings.memorize.mode + boundary_cfg = settings.boundary_detection + session_id = payload["session_id"] + + async with asyncio.timeout(settings.memorize.session_lock_timeout_seconds): + async with get_session_lock(session_id): + return await _memorize_locked( + payload, + mode=mode, + boundary_cfg=boundary_cfg, + is_final=is_final, + ) + + +async def _memorize_locked( + payload: dict[str, Any], + *, + mode: Literal["chat", "agent"], + boundary_cfg: Any, + is_final: bool, +) -> MemorizeResult: + """Inner critical section — runs under the per-session lock.""" + ingested = await ingest_process(payload) + boundary = await prepare_cells( + ingested, + mode=mode, + is_final=is_final, + llm_client=get_llm_client(), + prompt_loader=_get_prompt_loader(), + hard_token_limit=boundary_cfg.hard_token_limit, + hard_msg_limit=boundary_cfg.hard_msg_limit, + ) + + if not boundary.cells: + # Nothing went past the boundary stage — no pipelines to dispatch. + return MemorizeResult( + message_count=len(payload.get("messages", [])), + status=_merge_status(boundary.status, "skipped"), + ) + + user_task = _get_user_pipeline().run( + ingested, + cells=boundary.cells, + memcell_ids=boundary.memcell_ids, + per_cell_all_senders=boundary.per_cell_all_senders, + ) + if mode == "agent": + agent_task = _get_agent_pipeline().run( + ingested, + cells=boundary.cells, + memcell_ids=boundary.memcell_ids, + ) + user_outcome, agent_outcome = await asyncio.gather(user_task, agent_task) + merged_status = _merge_status(user_outcome.status, agent_outcome.status) + else: + user_outcome = await user_task + merged_status = _merge_status(user_outcome.status, "skipped") + + return MemorizeResult( + message_count=len(payload.get("messages", [])), + status=merged_status, + ) + + +def _merge_status( + user: Literal["accumulated", "extracted", "skipped"], + agent: Literal["accumulated", "extracted", "skipped"], +) -> Literal["accumulated", "extracted"]: + """Either ``extracted`` wins; otherwise ``accumulated``.""" + if user == "extracted" or agent == "extracted": + return "extracted" + return "accumulated" diff --git a/src/everos/service/search.py b/src/everos/service/search.py new file mode 100644 index 00000000..234e6b00 --- /dev/null +++ b/src/everos/service/search.py @@ -0,0 +1,140 @@ +"""Search use case — lazy singleton wiring for the public search endpoint. + +Mirrors the lazy-build pattern in :mod:`everos.service.memorize`: the +manager and all its dependencies are constructed on first call so that +the FastAPI module-level import order doesn't conflict with the +lifespan that brings up LanceDB / settings. + +Component policy (matches :class:`SearchManager` guards): + +* Embedding / rerank / LLM clients are **optional at boot**; they are + built lazily, and only the methods that need them fail (with a clear + message) when the corresponding section of settings is empty. +* ``KEYWORD`` searches therefore work without any of the three clients, + which makes the endpoint usable in a freshly-installed dev setup. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from everos.component.tokenizer import build_tokenizer +from everos.core.observability.logging import get_logger +from everos.memory.search import SearchRequest, SearchResponse +from everos.memory.search.manager import SearchManager +from everos.memory.search.recall import ( + AgentCaseRecaller, + AgentSkillRecaller, + AtomicFactRecaller, + EpisodeRecaller, + ProfileRecaller, + RecallerDeps, +) + +if TYPE_CHECKING: + from everos.component.embedding import EmbeddingProvider + from everos.component.llm import LLMClient + from everos.component.rerank import RerankProvider + +logger = get_logger(__name__) + +# Lazy singletons ──────────────────────────────────────────────────────── + +_manager: SearchManager | None = None +_embedding: EmbeddingProvider | None = None +_reranker: RerankProvider | None = None +_llm_client: LLMClient | None = None +_embedding_resolved = False +_rerank_resolved = False +_llm_resolved = False + + +def _get_embedding() -> EmbeddingProvider | None: + """Build the embedding client on first call. ``None`` when not configured.""" + global _embedding, _embedding_resolved + if _embedding_resolved: + return _embedding + + from everos.component.embedding import build_embedding_provider + from everos.config import load_settings + + cfg = load_settings().embedding + if not cfg.model or cfg.api_key is None: + logger.warning( + "embedding_not_configured", + hint="set [embedding] model / api_key to enable vector / hybrid search", + ) + _embedding = None + else: + _embedding = build_embedding_provider(cfg) + logger.info("search_embedding_built", model=cfg.model) + _embedding_resolved = True + return _embedding + + +def _get_reranker() -> RerankProvider | None: + """Build the rerank client on first call. ``None`` when not configured.""" + global _reranker, _rerank_resolved + if _rerank_resolved: + return _reranker + + from everos.component.rerank import build_rerank_provider + from everos.config import load_settings + + cfg = load_settings().rerank + if not cfg.model or not cfg.base_url: + logger.warning( + "rerank_not_configured", + hint="set [rerank] model / base_url to enable agentic search", + ) + _reranker = None + else: + _reranker = build_rerank_provider(cfg) + logger.info("search_rerank_built", model=cfg.model, provider=cfg.provider) + _rerank_resolved = True + return _reranker + + +def _get_llm_client() -> LLMClient | None: + """Lazily build the LLM client from settings (shared with memorize).""" + global _llm_client, _llm_resolved + if _llm_resolved: + return _llm_client + + from everos.component.llm import build_llm_provider + from everos.config import load_settings + + cfg = load_settings().llm + if cfg.api_key is None or not cfg.base_url: + logger.warning( + "llm_not_configured", + hint="set [llm] api_key / base_url to enable hybrid / agentic search", + ) + _llm_client = None + else: + _llm_client = build_llm_provider(cfg) + logger.info("search_llm_built", model=cfg.model) + _llm_resolved = True + return _llm_client + + +def _get_manager() -> SearchManager: + global _manager + if _manager is None: + deps = RecallerDeps(tokenizer=build_tokenizer()) + _manager = SearchManager( + episode_recaller=EpisodeRecaller(deps), + atomic_fact_recaller=AtomicFactRecaller(deps), + agent_case_recaller=AgentCaseRecaller(deps), + agent_skill_recaller=AgentSkillRecaller(deps), + profile_recaller=ProfileRecaller(), + embedding=_get_embedding(), + reranker=_get_reranker(), + llm_client=_get_llm_client(), + ) + return _manager + + +async def search(req: SearchRequest) -> SearchResponse: + """Dispatch one search request through the lazily-built manager.""" + return await _get_manager().search(req) diff --git a/methods/EverCore/src/config/__init__.py b/src/everos/templates/__init__.py similarity index 100% rename from methods/EverCore/src/config/__init__.py rename to src/everos/templates/__init__.py diff --git a/src/everos/templates/env.template b/src/everos/templates/env.template new file mode 100755 index 00000000..b287b183 --- /dev/null +++ b/src/everos/templates/env.template @@ -0,0 +1,114 @@ +# ===================================================== +# EverOS — md-first Memory Extraction Framework +# Configuration Template +# ===================================================== +# +# Setup: +# 1. cp env.template .env +# 2. Edit .env with your values +# 3. .env is gitignored (never commit) +# +# Override priority (low → high): +# src/everos/config/default.toml (shipped baseline) +# ↓ +# ~/.everos/config.toml (user-level overrides; optional) +# ↓ +# .env (this file; gitignored) +# ↓ +# EVEROS_
__ process envs +# ↓ +# programmatic init args / CLI flags +# +# The user-level toml path defaults to ~/.everos/config.toml; override +# with EVEROS_CONFIG_FILE=/path/to/your.toml. Missing file is skipped. +# ===================================================== + + +# ─── LLM (OpenAI-protocol compatible) ──────────────── +# Any OpenAI-API-compatible endpoint plugs in via base_url. Defaults +# below target OpenRouter (one key, broad model catalogue); switch to +# OpenAI, vLLM, Ollama (OpenAI bridge), or any other compatible endpoint +# by changing model + base_url + api_key. + +EVEROS_LLM__MODEL=openai/gpt-4.1-mini +EVEROS_LLM__API_KEY= +EVEROS_LLM__BASE_URL=https://openrouter.ai/api/v1 + + +# ─── Multimodal LLM (independent from [llm]; vision/audio capable) ──── +# Separate model for parsing multimodal content items (image / pdf / +# audio / ...); must support OpenAI image_url parts. Defaults target +# Gemini via OpenRouter so the same key covers chat + multimodal. + +EVEROS_MULTIMODAL__MODEL=google/gemini-3-flash-preview +EVEROS_MULTIMODAL__API_KEY= +EVEROS_MULTIMODAL__BASE_URL=https://openrouter.ai/api/v1 +# Concurrency cap for parallel multimodal calls (default 4): +# EVEROS_MULTIMODAL__MAX_CONCURRENCY=4 +# +# file:// content-item support (read locally by EverOS, not everalgo). +# Size cap per file:// asset (bytes; default 50 MiB): +# EVEROS_MULTIMODAL__FILE_URI_MAX_BYTES=52428800 +# Allowlisted base dirs for file:// uris (JSON list). Empty/unset = allow any +# readable file (local-first default); set to confine reads when the API is +# exposed beyond loopback: +# EVEROS_MULTIMODAL__FILE_URI_ALLOW_DIRS=["/srv/uploads"] + + +# ─── Embedding (OpenAI-protocol /embeddings) ───────── +# Any OpenAI-compatible embedding endpoint plugs in via base_url. +# model / api_key / base_url have no shipped default — set them here +# or in ~/.everos/config.toml before the embedding capability is used. + +EVEROS_EMBEDDING__MODEL=Qwen/Qwen3-Embedding-4B +EVEROS_EMBEDDING__API_KEY= +EVEROS_EMBEDDING__BASE_URL=https://api.deepinfra.com/v1/openai +# Runtime knobs — uncomment to override defaults (30s / 3 / 10 / 5): +# EVEROS_EMBEDDING__TIMEOUT_SECONDS=30 +# EVEROS_EMBEDDING__MAX_RETRIES=3 +# EVEROS_EMBEDDING__BATCH_SIZE=10 +# EVEROS_EMBEDDING__MAX_CONCURRENT=5 + + +# ─── Rerank (OpenAI-protocol /rerank) ──────────────── +# base_url should point at the rerank endpoint (e.g. .../v1/rerank). + +EVEROS_RERANK__MODEL=Qwen/Qwen3-Reranker-4B +EVEROS_RERANK__API_KEY= +EVEROS_RERANK__BASE_URL=https://api.deepinfra.com/v1/inference +# Runtime knobs — uncomment to override defaults (30s / 3 / 10 / 5): +# EVEROS_RERANK__TIMEOUT_SECONDS=30 +# EVEROS_RERANK__MAX_RETRIES=3 +# EVEROS_RERANK__BATCH_SIZE=10 +# EVEROS_RERANK__MAX_CONCURRENT=5 + + +# ─── Storage paths ─────────────────────────────────── +# memory-root holds md files + .index/ (LanceDB) + .system.db (SQLite) + ... +# Override the default with EVEROS_MEMORY__ROOT (note the double-underscore +# for nested config keys); see config/default.toml for all tunables. + +# EVEROS_MEMORY__ROOT=~/.everos + + +# ─── HTTP API ──────────────────────────────────────── +# Bind for ``everos server start``. Default ``127.0.0.1`` keeps the +# server on loopback only; EverOS ships no built-in authentication (see +# SECURITY.md). Set HOST=0.0.0.0 only after you have your own gateway / +# auth layer in front — the CLI logs a warning if you bind to 0.0.0.0. + +# EVEROS_API__HOST=127.0.0.1 +# EVEROS_API__PORT=8000 + + +# ─── Observability ─────────────────────────────────── + +EVEROS_LOG_LEVEL=INFO # DEBUG | INFO | WARNING | ERROR +EVEROS_LOG_FORMAT=json # json | text +# EVEROS_OTEL_ENDPOINT=http://localhost:4317 # OTel exporter (optional) + + +# ─── Runtime ───────────────────────────────────────── + +# TZ used by component.utils.datetime when input has no timezone +TZ=UTC diff --git a/methods/EverCore/src/core/addons/__init__.py b/tests/__init__.py similarity index 100% rename from methods/EverCore/src/core/addons/__init__.py rename to tests/__init__.py diff --git a/tests/_consistency_assertions.py b/tests/_consistency_assertions.py new file mode 100644 index 00000000..fc3693f3 --- /dev/null +++ b/tests/_consistency_assertions.py @@ -0,0 +1,362 @@ +"""Strict md ↔ LanceDB consistency check for all cascade kinds. + +Walks ``memory_root`` for every kind in :data:`cascade.KIND_REGISTRY`, +parses each md, and asserts byte-exact parity with the corresponding +LanceDB row(s): + +- **id set** — md entry id (or single-row PK) == LanceDB row id. +- **content** — md-recomputed ``content_sha256`` == + LanceDB row ``content_sha256``. + +This is the e2e tail check meant to follow ``add+flush+cascade-drain`` +pipelines (see ``tests/e2e/test_add_flush_*_pipeline_e2e.py``). It +exercises every kind that writes md and indexes into LanceDB, not just +the 4 daily-log kinds covered by the white-box integration test. + +Daily-log kinds (atomic_fact / episode / foresight / agent_case) hold +many entries per md and use a per-entry digest; user_profile + +agent_skill are single-md-per-row and digest the file as a whole +(agent_skill additionally folds in concatenated ``references/*.md``). + +Mirroring vs. importing handler digests +--------------------------------------- + +The per-kind digest construction here mirrors the handler implementation +**by hand** rather than calling into the handler instance: + +- Daily-log digests use the handler's ``content_change_keys`` ClassVar, + so the mirror is one short loop that's robust against handler + refactors (re-ordering, renaming keys) as long as the ClassVar drives + truth. +- ``UserProfileHandler`` / ``AgentSkillHandler`` build their digest + inline in ``handle_added_or_modified``; the field set is duplicated + here with a comment pointing at the source location. If a handler + changes its digest formula, the consistency check will fail loudly — + intentional friction so the test stays a real consumer of the + contract, not a moving target. +""" + +from __future__ import annotations + +import dataclasses +import json +import logging +from pathlib import Path +from typing import Any + +import anyio + +from everos.core.persistence import MarkdownReader +from everos.core.persistence.markdown.entries import StructuredEntry +from everos.infra.persistence.markdown import AgentSkillFrontmatter +from everos.memory.cascade.handlers._common import content_sha256 +from everos.memory.cascade.handlers._daily_log_base import BaseDailyLogHandler +from everos.memory.cascade.handlers.agent_skill import AgentSkillHandler +from everos.memory.cascade.handlers.user_profile import UserProfileHandler +from everos.memory.cascade.registry import KIND_REGISTRY, KindSpec + +# stdlib logging (not structlog) so pytest --log-cli-level=INFO picks +# this up live without -s. Project rule 18 (use get_logger) covers src/ +# code; tests are infrastructure and may use stdlib logging directly +# when it integrates with the test harness. +logger = logging.getLogger("everos.tests.consistency") + + +@dataclasses.dataclass(frozen=True) +class KindConsistencyStats: + """Per-kind counts after a consistency sweep. + + ``md_file_count`` is the number of md files matched by the kind's + path glob; ``md_entry_count`` is the total rows that *should* exist + in LanceDB (= sum of entries per daily-log md, = number of md files + for single-file kinds); ``lance_row_count`` is the number of rows + that *do* exist (cross-md count via :meth:`find_where` / + ``count_rows``, before any filter). + """ + + kind: str + md_file_count: int + md_entry_count: int + lance_row_count: int + + +async def assert_md_lance_strict_consistent( + memory_root: Path, + *, + expect_at_least: dict[str, int] | None = None, +) -> dict[str, KindConsistencyStats]: + """For every kind in :data:`KIND_REGISTRY`, assert md ↔ LanceDB parity. + + For each kind: + + 1. Walks every md matching the kind's path glob. + 2. Computes the expected ``content_sha256`` for each entry / row + using the same digest formula as the handler. + 3. Asserts id set + per-id ``content_sha256`` parity vs. LanceDB. + 4. Logs a per-kind summary (file / entry / lance counts). + + Args: + memory_root: Absolute path to the memory root directory + (e.g. the value of ``EVEROS_MEMORY__ROOT`` / + ``MemoryRoot.root``). + expect_at_least: Optional ``{kind_name: min_md_files}`` map. + Raises ``AssertionError`` if a listed kind has fewer md + files than the threshold — the caller's hook to assert + "this pipeline must produce at least N of this kind", which + an empty glob would otherwise silently pass. + + Returns: + ``{kind_name: KindConsistencyStats}`` for every kind in the + registry, so the caller can attach further assertions or log + the totals. + """ + root = memory_root + stats: dict[str, KindConsistencyStats] = {} + for spec in KIND_REGISTRY: + md_paths = sorted( + p.relative_to(root).as_posix() for p in root.glob(spec.path_glob()) + ) + if spec.handler_factory is UserProfileHandler: + entry_total, lance_total = await _check_user_profile(spec, root, md_paths) + elif spec.handler_factory is AgentSkillHandler: + entry_total, lance_total = await _check_agent_skill(spec, root, md_paths) + else: + entry_total, lance_total = await _check_daily_log(spec, root, md_paths) + + report = KindConsistencyStats( + kind=spec.name, + md_file_count=len(md_paths), + md_entry_count=entry_total, + lance_row_count=lance_total, + ) + stats[spec.name] = report + logger.info( + "md_lance_consistent kind=%s md_files=%d md_entries=%d lance_rows=%d", + report.kind, + report.md_file_count, + report.md_entry_count, + report.lance_row_count, + ) + + if expect_at_least: + violations = { + kind: (expect_at_least[kind], stats[kind].md_file_count) + for kind in expect_at_least + if kind in stats and stats[kind].md_file_count < expect_at_least[kind] + } + unknown = sorted(set(expect_at_least) - set(stats)) + if unknown: + raise AssertionError( + f"expect_at_least references unknown kinds: {unknown!r}; " + f"known kinds are {sorted(stats)!r}" + ) + if violations: + details = ", ".join( + f"{kind}: got {got} md files, expected at least {want}" + for kind, (want, got) in sorted(violations.items()) + ) + raise AssertionError(f"md file count below threshold — {details}") + + return stats + + +# ── Daily-log kinds (atomic_fact / episode / foresight / agent_case) ── + + +def _daily_log_sha_for_entry( + handler_cls: type[BaseDailyLogHandler], structured: StructuredEntry +) -> str: + """Mirror :meth:`BaseDailyLogHandler._content_sha256` without an instance. + + Walks the handler's ``content_change_keys`` ClassVar (the same data + the handler instance uses), so the mirror tracks any handler-side + change to the key set automatically. + """ + parts: dict[str, str] = {} + for key in handler_cls.content_change_keys: + kind, _, name = key.partition(":") + if kind == "section": + parts[key] = structured.sections.get(name) or "" + elif kind == "inline": + parts[key] = structured.inline.get(name) or "" + else: + raise AssertionError( + f"{handler_cls.__name__}.content_change_keys has unsupported " + f"prefix in {key!r}; expected 'section:' or 'inline:'" + ) + return content_sha256(parts) + + +async def _check_daily_log( + spec: KindSpec, root: Path, md_paths: list[str] +) -> tuple[int, int]: + md_entry_total = 0 + lance_row_total = 0 + for md_path in md_paths: + absolute = root / md_path + parsed = await MarkdownReader.read(absolute) + md_sha_by_id = { + entry.id: _daily_log_sha_for_entry( + spec.handler_factory, entry.as_structured() + ) + for entry in parsed.entries + } + lance_rows = await spec.lance_repo.find_where( + f"md_path = '{_q(md_path)}'", limit=10_000 + ) + lance_sha_by_id = {r.entry_id: r.content_sha256 for r in lance_rows} + if md_sha_by_id != lance_sha_by_id: + raise AssertionError( + f"{spec.name} mismatch @ {md_path}:\n" + f" md entries: {len(md_sha_by_id)}\n" + f" lance rows: {len(lance_sha_by_id)}\n" + f" {_diff_dicts(md_sha_by_id, lance_sha_by_id)}" + ) + md_entry_total += len(md_sha_by_id) + lance_row_total += len(lance_sha_by_id) + return md_entry_total, lance_row_total + + +# ── user_profile (single-md = single-row, PK = owner_id) ─────────────── + + +async def _check_user_profile( + spec: KindSpec, root: Path, md_paths: list[str] +) -> tuple[int, int]: + """Mirror :meth:`UserProfileHandler.handle_added_or_modified` digest.""" + seen_ids: set[str] = set() + for md_path in md_paths: + absolute = root / md_path + parsed = await MarkdownReader.read(absolute) + fm = parsed.frontmatter + owner_id = str(fm.get("user_id", "")) + if not owner_id: + raise AssertionError( + f"user_profile md missing required frontmatter user_id: {md_path}" + ) + # Mirror of UserProfileHandler.handle_added_or_modified. + md_sha = content_sha256( + { + "frontmatter:summary": str(fm.get("summary", "")), + "frontmatter:explicit_info_json": _dump_json( + fm.get("explicit_info", []) + ), + "frontmatter:implicit_traits_json": _dump_json( + fm.get("implicit_traits", []) + ), + } + ) + lance_row = await spec.lance_repo.get_by_id(owner_id) + if lance_row is None: + raise AssertionError( + f"user_profile row missing for owner {owner_id!r} @ {md_path}" + ) + if lance_row.content_sha256 != md_sha: + raise AssertionError( + f"user_profile sha mismatch @ {md_path}:\n" + f" md sha: {md_sha}\n" + f" lance sha: {lance_row.content_sha256}" + ) + if lance_row.md_path != md_path: + raise AssertionError( + f"user_profile md_path drift @ {md_path}: " + f"lance row has md_path={lance_row.md_path!r}" + ) + seen_ids.add(owner_id) + # Reverse direction: lance row whose md is gone. + _ = seen_ids # orphan check is per-md_path inside the daily-log check; + # user_profile orphans are out-of-scope for the add+flush pipeline + # (no path-level scanner sweep runs in the test). + n = len(md_paths) + return n, n + + +# ── agent_skill (SKILL.md + references/*.md, PK = _) ────── + + +async def _check_agent_skill( + spec: KindSpec, root: Path, md_paths: list[str] +) -> tuple[int, int]: + """Mirror :meth:`AgentSkillHandler.handle_added_or_modified` digest.""" + for md_path in md_paths: + absolute = root / md_path + parsed = await MarkdownReader.read(absolute) + fm = parsed.frontmatter + owner_id = str(fm.get("agent_id", "")) + name = str(fm.get("name", "")) + if not owner_id or not name: + raise AssertionError( + f"agent_skill md missing required frontmatter " + f"(agent_id / name): {md_path}" + ) + skill_id = f"{owner_id}_{name}" + + skill_dir = absolute.parent + references_dir = skill_dir / AgentSkillFrontmatter.SKILL_REFERENCES_DIR_NAME + references_content = await _concat_references(references_dir) + + # Mirror of AgentSkillHandler.handle_added_or_modified. + md_sha = content_sha256( + { + "frontmatter:name": name, + "frontmatter:description": str(fm.get("description", "")), + "frontmatter:confidence": str(float(fm.get("confidence", 0.0))), + "frontmatter:maturity_score": str(float(fm.get("maturity_score", 0.0))), + "body": parsed.body.rstrip(), + "references_content": references_content, + } + ) + lance_row = await spec.lance_repo.get_by_id(skill_id) + if lance_row is None: + raise AssertionError( + f"agent_skill row missing for skill {skill_id!r} @ {md_path}" + ) + if lance_row.content_sha256 != md_sha: + raise AssertionError( + f"agent_skill sha mismatch @ {md_path}:\n" + f" md sha: {md_sha}\n" + f" lance sha: {lance_row.content_sha256}" + ) + if lance_row.md_path != md_path: + raise AssertionError( + f"agent_skill md_path drift @ {md_path}: " + f"lance row has md_path={lance_row.md_path!r}" + ) + n = len(md_paths) + return n, n + + +async def _concat_references(references_dir: Path) -> str: + """Mirror :func:`agent_skill._concat_references` for the test side.""" + apath = anyio.Path(references_dir) + if not await apath.is_dir(): + return "" + paths = sorted( + [p async for p in apath.iterdir() if p.name.endswith(".md")], + key=lambda p: p.name, + ) + pieces: list[str] = [] + for path in paths: + text = await path.read_text(encoding="utf-8") + pieces.append(text.rstrip()) + return "\n\n".join(pieces) + + +# ── small utilities ──────────────────────────────────────────────────── + + +def _dump_json(value: Any) -> str: + """Canonical JSON shape used by UserProfileHandler's digest input.""" + return json.dumps(value, sort_keys=True, ensure_ascii=False) + + +def _diff_dicts(a: dict[str, str], b: dict[str, str]) -> str: + only_a = sorted(set(a) - set(b)) + only_b = sorted(set(b) - set(a)) + mismatched = sorted(k for k in set(a) & set(b) if a[k] != b[k]) + return f"only_in_md={only_a}, only_in_lance={only_b}, sha_mismatch_ids={mismatched}" + + +def _q(text: str) -> str: + """SQL-quote escape; mirrors lancedb chassis convention.""" + return text.replace("'", "''") diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 00000000..c069c8ee --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,54 @@ +"""Shared pytest fixtures. + +Cache invalidation: + ``load_settings`` (and the timezone helper that reads it) are + ``functools.cache``-d for hot paths in production. Tests that + monkeypatch ``EVEROS_*`` env vars must see fresh settings on each + function — clear both caches around every test to keep results + deterministic regardless of declaration order. + +Cross-suite fixtures: + ``long_conversation`` lives here (not under ``tests/e2e/conftest.py``) + because both ``tests/e2e/`` and ``tests/integration/search/`` depend + on it — pytest conftest cascades down the directory tree, so a + fixture defined under ``tests/e2e/`` is invisible to siblings. +""" + +from __future__ import annotations + +import json +from collections.abc import Iterator +from pathlib import Path + +import pytest + +_FIXTURE_DIR = Path(__file__).resolve().parent / "fixtures" +_LONG_CONV_PATH = _FIXTURE_DIR / "long_conversation_locomo_caroline_melanie.json" + + +@pytest.fixture(autouse=True) +def _reset_settings_cache() -> Iterator[None]: + import structlog + + from everos.component.utils import datetime as dt_module + from everos.config import load_settings + + # ``configure_logging`` (called by some e2e fixtures / the CLI entry) + # sets ``cache_logger_on_first_use=True``; once a logger is cached, + # ``structlog.testing.capture_logs`` can no longer intercept events, + # which silently breaks log-assertion tests that run *after* it in the + # same process. Reset structlog to defaults around every test so that + # global config never leaks across the suite. + structlog.reset_defaults() + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + yield + structlog.reset_defaults() + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + + +@pytest.fixture(scope="session") +def long_conversation() -> dict: + """LoCoMo conv_0 fixture (419 messages, 19 batches, one session).""" + return json.loads(_LONG_CONV_PATH.read_text()) diff --git a/methods/EverCore/src/core/cache/redis_cache_queue/__init__.py b/tests/e2e/__init__.py similarity index 100% rename from methods/EverCore/src/core/cache/redis_cache_queue/__init__.py rename to tests/e2e/__init__.py diff --git a/tests/e2e/conftest.py b/tests/e2e/conftest.py new file mode 100644 index 00000000..3adf2287 --- /dev/null +++ b/tests/e2e/conftest.py @@ -0,0 +1,286 @@ +"""Shared fixtures for ``tests/e2e/``. + +Provides: + +- ``core_pipeline_runtime``: tmp memory root + reset memorize singletons. + Uses the **real** LLM / embedding / rerank creds from ``.env`` per the + project test policy. +- ``async_client``: ``httpx.AsyncClient`` wired into ``create_app()`` with + the full lifespan stack (SQLite + LanceDB + Cascade + OME). +- ``cascade_done_poll``: wait until ``md_change_state`` queue is fully + drained (``pending`` rows == 0; includes the internal ``processing``). +- ``pipeline_done_poll``: composite drain — waits until OME strategy runs AND + ``md_change_state`` queue both drain (use for tests that exercise the full + OME → md → cascade pipeline). +- ``buffer_count`` / ``memcell_count``: raw counts for buffer-delta and + memcell-growth assertions. + +The ``long_conversation`` fixture (LoCoMo conv_0) lives in +:mod:`tests.conftest` so both ``tests/e2e/`` and +``tests/integration/search/`` can depend on it. + +Conventions: + +- ``.env`` is loaded at import time (before any everos module reads + settings) — overrides for ``EVEROS_MEMORY__ROOT`` happen per-test. +- This file does **not** define ``cascade_runtime`` — that name belongs + to ``tests/integration/test_cascade_integration.py``'s local fixture. + The pipeline test uses ``core_pipeline_runtime`` to avoid name + collision. +""" + +from __future__ import annotations + +import asyncio +import importlib +import json +from collections.abc import AsyncIterator, Awaitable, Callable +from pathlib import Path + +import httpx +import pytest +import pytest_asyncio +from dotenv import load_dotenv +from sqlalchemy import text + +# Load real .env creds before any everos import touches load_settings(). +_PROJECT_ROOT = Path(__file__).resolve().parents[2] +load_dotenv(_PROJECT_ROOT / ".env", override=False) + +_FIXTURE_DIR = _PROJECT_ROOT / "tests" / "fixtures" +_SEARCH_SEED_DIR = _FIXTURE_DIR / "search_seed" + +# Memorize service module-level singletons that survive across tests; we +# null them out so each test rebuilds against its own ``tmp_path``. +_MEMORIZE_SINGLETONS: tuple[str, ...] = ( + "_episode_writer", + "_prompt_loader", + "_user_pipeline", + "_agent_pipeline", + "_ome_engine", +) + +# OME strategy modules carry module-level lazy singletons (``_writer`` / +# ``_reader``) that capture ``MemoryRoot.default()`` at first call. They +# survive across tests, so the second test writes its output to the +# **first test's** tmp_path. Reset all of them per-test. +_STRATEGY_SINGLETONS: tuple[tuple[str, tuple[str, ...]], ...] = ( + ("everos.memory.strategies.extract_atomic_facts", ("_writer",)), + ("everos.memory.strategies.extract_foresight", ("_writer",)), + ("everos.memory.strategies.extract_user_profile", ("_writer", "_reader")), + ("everos.memory.strategies.extract_agent_case", ("_writer",)), + ("everos.memory.strategies.extract_agent_skill", ("_writer",)), +) + + +def _reset_strategy_singletons(monkeypatch: pytest.MonkeyPatch) -> None: + """Null every strategy ``_writer`` / ``_reader`` so the next test + rebuilds against its own ``MemoryRoot.default()`` (driven by the + fresh ``EVEROS_MEMORY__ROOT`` env var set by the calling fixture). + """ + for mod_name, attrs in _STRATEGY_SINGLETONS: + mod = importlib.import_module(mod_name) + for attr in attrs: + monkeypatch.setattr(mod, attr, None, raising=False) + + +# --------------------------------------------------------------------------- +# Data fixture +# --------------------------------------------------------------------------- + + +@pytest.fixture(scope="session") +def search_seed() -> dict[str, list[dict]]: + """Load the search seed slice produced by ``_dump_search_seed.py``. + + Returns a dict with four keys (``episode`` / ``atomic_fact`` / + ``foresight`` / ``user_profile``); each value is a list of raw row + dicts ready to be fed into ``Model.model_validate`` for LanceDB. + + Tests pick the subset they need and may mutate per-row fields + (e.g. set distinct ``session_id`` values to exercise filter DSL) + before instantiating the pydantic model. + """ + return { + name: json.loads((_SEARCH_SEED_DIR / f"{name}.json").read_text()) + for name in ("episode", "atomic_fact", "foresight", "user_profile") + } + + +# --------------------------------------------------------------------------- +# Runtime fixture: tmp memory root + singleton reset (no app lifespan) +# --------------------------------------------------------------------------- + + +@pytest_asyncio.fixture +async def core_pipeline_runtime( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> AsyncIterator[Path]: + """Prepare clean memory root + reset memorize singletons. + + Keeps real LLM / embedding settings from ``.env`` (do NOT overwrite + ``EVEROS_LLM__*`` or ``EVEROS_EMBEDDING__*``). + """ + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + + from everos.config import load_settings + + load_settings.cache_clear() + + svc = importlib.import_module("everos.service.memorize") + client_mod = importlib.import_module("everos.component.llm.client") + + for attr in _MEMORIZE_SINGLETONS: + monkeypatch.setattr(svc, attr, None, raising=False) + monkeypatch.setattr(client_mod, "_llm_client", None, raising=False) + _reset_strategy_singletons(monkeypatch) + + yield tmp_path + + +# --------------------------------------------------------------------------- +# Async client fixture (full app lifespan) +# --------------------------------------------------------------------------- + + +@pytest_asyncio.fixture +async def async_client( + core_pipeline_runtime: Path, +) -> AsyncIterator[httpx.AsyncClient]: + """Bring up the full everos app with lifespan, return an httpx client. + + The lifespan starts: SQLite engine, LanceDB connection + business + indexes, Cascade orchestrator (watcher + scanner + worker), OME + engine. Teardown stops everything in reverse. + """ + from everos.entrypoints.api.app import create_app + + app = create_app() + transport = httpx.ASGITransport(app=app) + + # Drive starlette's lifespan_context explicitly — httpx.ASGITransport + # does not run startup / shutdown on its own. + async with ( + app.router.lifespan_context(app), + httpx.AsyncClient(transport=transport, base_url="http://test") as client, + ): + yield client + + +# --------------------------------------------------------------------------- +# Poll helpers +# --------------------------------------------------------------------------- + + +async def _poll( + condition: Callable[[], Awaitable[bool]], + *, + deadline_seconds: float, + interval: float = 0.5, +) -> None: + """Poll an async predicate until truthy; ``TimeoutError`` on deadline.""" + async with asyncio.timeout(deadline_seconds): + while True: + if await condition(): + return + await asyncio.sleep(interval) + + +@pytest.fixture +def cascade_done_poll() -> Callable[..., Awaitable[None]]: + """Wait until ``md_change_state`` queue is drained (no pending/processing).""" + + async def _wait(*, deadline_seconds: float = 180.0) -> None: + from everos.infra.persistence.sqlite import md_change_state_repo + + async def _drained() -> bool: + summary = await md_change_state_repo.queue_summary() + # `pending` includes the internal `processing` rows (see QueueSummary). + return summary.pending == 0 + + await _poll(_drained, deadline_seconds=deadline_seconds) + + return _wait + + +@pytest.fixture +def pipeline_done_poll() -> Callable[..., Awaitable[None]]: + """Wait until OME strategy runs AND ``md_change_state`` queue both drain. + + Composite drain — fixes the trap where :func:`cascade_done_poll` + alone returns immediately while a slow LLM-driven strategy is still + in flight (the strategy has not written md yet, so the cascade queue + is momentarily empty). Pipeline tests that touch the full async + chain (OME -> md -> cascade -> LanceDB) must use this instead of + ``cascade_done_poll``. + """ + + async def _wait(*, deadline_seconds: float = 180.0) -> None: + from everos.infra.persistence.sqlite import md_change_state_repo + from everos.service.memorize import _get_engine + + engine = _get_engine() + + async def _drained() -> bool: + # OME side first: cascade can only fire after a strategy + # writes md, so an in-flight run means the queue check below + # is premature. + if not await engine.wait_idle(timeout=0.5): + return False + # `pending` includes the internal `processing` rows (see + # QueueSummary). + summary = await md_change_state_repo.queue_summary() + return summary.pending == 0 + + await _poll(_drained, deadline_seconds=deadline_seconds) + + return _wait + + +# --------------------------------------------------------------------------- +# Count helpers (used directly by tests for buffer-delta assertions) +# --------------------------------------------------------------------------- + + +@pytest.fixture +def buffer_count() -> Callable[[str], Awaitable[int]]: + """Return an async callable: ``await buffer_count(session_id) -> int``.""" + + async def _count(session_id: str) -> int: + from everos.infra.persistence.sqlite import get_engine + + engine = get_engine() + async with engine.connect() as conn: + result = await conn.execute( + text("SELECT COUNT(*) FROM unprocessed_buffer WHERE session_id = :sid"), + {"sid": session_id}, + ) + return int(result.scalar() or 0) + + return _count + + +@pytest.fixture +def memcell_count() -> Callable[[str], Awaitable[int]]: + """Return an async callable: ``await memcell_count(user_id_or_session) -> int``. + + Counts memcell rows; pass session_id to count by session, or omit to + count all. + """ + + async def _count(session_id: str | None = None) -> int: + from everos.infra.persistence.sqlite import get_engine + + engine = get_engine() + async with engine.connect() as conn: + if session_id is None: + result = await conn.execute(text("SELECT COUNT(*) FROM memcell")) + else: + result = await conn.execute( + text("SELECT COUNT(*) FROM memcell WHERE session_id = :sid"), + {"sid": session_id}, + ) + return int(result.scalar() or 0) + + return _count diff --git a/tests/e2e/test_add_flush_agent_pipeline_e2e.py b/tests/e2e/test_add_flush_agent_pipeline_e2e.py new file mode 100644 index 00000000..404b6f33 --- /dev/null +++ b/tests/e2e/test_add_flush_agent_pipeline_e2e.py @@ -0,0 +1,206 @@ +"""Agent pipeline e2e: 5 SWE-bench trajectories drive /add + /flush. + +Drives the full HTTP route through to storage, exercising the agent-track +pipeline (boundary → memcell → extract_agent_case → trigger_skill_clustering +→ extract_agent_skill) with real LLM and real embedder credentials. + +Mixed tenancy by design (sender_id alignment from fixture): + + agent_pytest (1 session, pytest-dev/pytest-7236) ┐ independent + agent_sympy (1 session, sympy/sympy-18763) ┘ owners + agent_django (3 sessions, django/django-{14311,16255,16263}) shared + +Concurrency strategy (workaround for the known +``trigger_skill_clustering`` read-modify-write race on a shared owner_id): + + Phase 1: pytest + sympy concurrent via asyncio.gather (disjoint owners) + Phase 2: 3 django sessions sequential (same owner, would race) + +Once the cluster race is fixed in production, Phase 2 can collapse into +the same gather and the test will still pass — the assertions are +race-free, only the driver is conservative. + +White-box assertions (audit trail of internal surfaces touched): + - sqlite ``memcell`` rows per session_id + - filesystem ``/agents//.cases/*.md`` presence + - LanceDB ``agent_case`` rows by ``owner_id`` (count + session_id set) + - LanceDB ``agent_skill`` rows by ``owner_id`` (soft — LLM-dependent) +""" + +from __future__ import annotations + +import asyncio +import json +from collections.abc import Awaitable, Callable +from pathlib import Path + +import httpx +import pytest + +from everos.infra.persistence.lancedb import agent_case_repo, agent_skill_repo +from everos.infra.persistence.markdown import AgentCaseDailyFrontmatter + +_FIXTURE_DIR = Path(__file__).resolve().parents[1] / "fixtures" / "agent_trajectories" + +# Hand-picked trajectories (kept in-tree as fixtures; this selection is +# the source of truth — the original converter is not in the repo). +_PYTEST_SESSION = "session_pytest_7236" +_SYMPY_SESSION = "session_sympy_18763" +_DJANGO_SESSIONS = ( + "session_django_14311", + "session_django_16255", + "session_django_16263", +) + +_AGENT_PYTEST = "agent_pytest" +_AGENT_SYMPY = "agent_sympy" +_AGENT_DJANGO = "agent_django" + +# Phase 3 drain budget: OME chain (case → cluster → skill) writes md in +# stages, each picked up by cascade. Multiple drain rounds with brief +# sleeps let the chain quiesce without false-positive completion. +_DRAIN_ROUNDS = 4 +_DRAIN_TIMEOUT_SECONDS = 300.0 +_DRAIN_INTER_ROUND_SLEEP_SECONDS = 5.0 + + +def _load_fixture(session_id: str) -> dict: + return json.loads((_FIXTURE_DIR / f"{session_id}.json").read_text()) + + +async def _drive_session( + client: httpx.AsyncClient, session_data: dict +) -> tuple[str, str]: + """Run /add followed by /flush for one trajectory; return status.""" + sid = session_data["everos_session_id"] + msgs = session_data["messages"] + # MessageItemDTO.max_length=500; our largest fixture has 324 messages. + r = await client.post( + "/api/v1/memory/add", + json={"session_id": sid, "messages": msgs}, + timeout=600.0, + ) + assert r.status_code == 200, ( + f"{sid}: /add returned {r.status_code} — {r.text[:300]}" + ) + r = await client.post( + "/api/v1/memory/flush", + json={"session_id": sid}, + timeout=600.0, + ) + assert r.status_code == 200, ( + f"{sid}: /flush returned {r.status_code} — {r.text[:300]}" + ) + return sid, r.json()["data"]["status"] + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_agent_pipeline_e2e_mixed_tenancy( + async_client: httpx.AsyncClient, + core_pipeline_runtime: Path, + pipeline_done_poll: Callable[..., Awaitable[None]], + memcell_count: Callable[..., Awaitable[int]], +) -> None: + """5 SWE-bench trajectories → agent_case + agent_skill on three agents.""" + memory_root = core_pipeline_runtime + + pytest_fx = _load_fixture(_PYTEST_SESSION) + sympy_fx = _load_fixture(_SYMPY_SESSION) + django_fxs = [_load_fixture(s) for s in _DJANGO_SESSIONS] + + # ── Phase 1: independent owners concurrent ──────────────────────────── + await asyncio.gather( + _drive_session(async_client, pytest_fx), + _drive_session(async_client, sympy_fx), + ) + + # ── Phase 2: shared owner_id, sequential to dodge cluster race ──────── + for fx in django_fxs: + await _drive_session(async_client, fx) + + # ── Phase 3: drain OME chain + cascade ──────────────────────────────── + for _ in range(_DRAIN_ROUNDS): + await pipeline_done_poll(deadline_seconds=_DRAIN_TIMEOUT_SECONDS) + await asyncio.sleep(_DRAIN_INTER_ROUND_SLEEP_SECONDS) + + # ── Phase 4: assertions ─────────────────────────────────────────────── + + # 4.1 every session produced ≥1 memcell + all_sessions = (_PYTEST_SESSION, _SYMPY_SESSION, *_DJANGO_SESSIONS) + for sid in all_sessions: + n = await memcell_count(sid) + assert n >= 1, f"no memcell for session {sid!r} (got {n})" + + # 4.2 each agent has a .cases dir with ≥1 .md file + agents_dir = memory_root / "default_app" / "default_project" / "agents" + case_dir_name = AgentCaseDailyFrontmatter.DIR_NAME + for agent_id in (_AGENT_PYTEST, _AGENT_SYMPY, _AGENT_DJANGO): + case_dir = agents_dir / agent_id / case_dir_name + assert case_dir.is_dir(), f"missing {case_dir!s} for agent={agent_id!r}" + md_files = list(case_dir.glob("*.md")) + assert md_files, f"no agent_case md under {case_dir!s}" + + # 4.3 LanceDB agent_case rows per owner + pytest_cases = await agent_case_repo.find_where(f"owner_id = '{_AGENT_PYTEST}'") + sympy_cases = await agent_case_repo.find_where(f"owner_id = '{_AGENT_SYMPY}'") + django_cases = await agent_case_repo.find_where(f"owner_id = '{_AGENT_DJANGO}'") + + assert len(pytest_cases) >= 1, ( + f"no agent_pytest rows in LanceDB (got {len(pytest_cases)})" + ) + assert len(sympy_cases) >= 1, ( + f"no agent_sympy rows in LanceDB (got {len(sympy_cases)})" + ) + # Each django session writes at least one cell → at least one case per + # session. Lower bound 3 covers the minimum; LLM may produce more. + assert len(django_cases) >= 3, ( + f"agent_django expected ≥3 LanceDB cases (3 sessions), got {len(django_cases)}" + ) + + # 4.4 cross-owner isolation — each agent's cases trace back only to + # its own sessions + pytest_session_ids = {c.session_id for c in pytest_cases} + assert pytest_session_ids == {_PYTEST_SESSION}, ( + f"agent_pytest cases leaked across sessions: {pytest_session_ids}" + ) + sympy_session_ids = {c.session_id for c in sympy_cases} + assert sympy_session_ids == {_SYMPY_SESSION}, ( + f"agent_sympy cases leaked across sessions: {sympy_session_ids}" + ) + django_session_ids = {c.session_id for c in django_cases} + assert django_session_ids == set(_DJANGO_SESSIONS), ( + f"agent_django session set mismatch — got {django_session_ids}, " + f"want {set(_DJANGO_SESSIONS)}" + ) + + # 4.5 agent_skill — soft: emission depends on LLM clustering quality + # gate (skip_quality_threshold + cluster size). pytest/sympy are + # single-case clusters and may legitimately yield 0 skills. django + # has 3 cases and should aggregate into ≥1 cluster of size ≥2, + # producing ≥1 skill — but we keep this informational (LLM-dependent) + # rather than a hard floor to avoid flaky CI signal. + pytest_skills = await agent_skill_repo.find_where(f"owner_id = '{_AGENT_PYTEST}'") + sympy_skills = await agent_skill_repo.find_where(f"owner_id = '{_AGENT_SYMPY}'") + django_skills = await agent_skill_repo.find_where(f"owner_id = '{_AGENT_DJANGO}'") + # Hard sanity: counts non-negative (the repo isn't broken). + assert len(pytest_skills) >= 0 + assert len(sympy_skills) >= 0 + assert len(django_skills) >= 0 + + # 4.6 strict md ↔ LanceDB parity across every cascade kind + # + # The per-owner counts above are loose (LLM-emission-dependent); this + # check enforces byte-exact id-set + content_sha256 parity across + # every md the agent pipeline wrote. + # + # ``expect_at_least`` pins agent_case (every session writes ≥1 case) + # so an empty glob would fail loudly. agent_skill is NOT pinned — + # emission depends on the LLM clustering quality gate per 4.5; a + # legitimately empty agent_skill md set is still a passing run. + from tests._consistency_assertions import assert_md_lance_strict_consistent + + await assert_md_lance_strict_consistent( + memory_root, + expect_at_least={"agent_case": 1}, + ) diff --git a/tests/e2e/test_add_flush_user_pipeline_e2e.py b/tests/e2e/test_add_flush_user_pipeline_e2e.py new file mode 100644 index 00000000..61e66e5b --- /dev/null +++ b/tests/e2e/test_add_flush_user_pipeline_e2e.py @@ -0,0 +1,337 @@ +"""Add + Flush core pipeline smoke — long real-conversation drive. + +Goal: prove the user-side add/flush chain is end-to-end live. Feeds +**419 real LoCoMo messages** through ``POST /api/v1/memory/add`` (in 19 +batches sharing one session_id) then a final ``POST /flush``, and +verifies: + +1. Each /add returns a sane status and the unprocessed_buffer delta + matches what the service claims (accumulated → grew by batch size; + extracted → shrank or stayed flat). +2. After /flush the buffer is empty and the memcell table has rows. +3. After cascade drains, episode md files exist and LanceDB rows + reflect them with valid content_sha256 + vector. +4. OME-driven async strategies have produced atomic_fact / foresight / + profile md files. + +Real LLM + real embedder (creds via ``.env``). Marked ``slow`` — +``pytest -m slow tests/integration/test_add_flush_core_pipeline_smoke.py``. +""" + +from __future__ import annotations + +import os +import shutil +from collections.abc import Awaitable, Callable +from pathlib import Path + +import httpx +import pytest + +from everos.infra.persistence.markdown import ( + AtomicFactDailyFrontmatter, + EpisodeDailyFrontmatter, + ForesightDailyFrontmatter, +) + +# Directory names live on the frontmatter schemas (single source of truth); +# atomic_facts / foresights are dotfile-hidden so users only see episodes. +_EPISODE_DIR = EpisodeDailyFrontmatter.DIR_NAME +_ATOMIC_FACT_DIR = AtomicFactDailyFrontmatter.DIR_NAME +_FORESIGHT_DIR = ForesightDailyFrontmatter.DIR_NAME + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _to_add_messages(batch: dict) -> list[dict]: + """Strip ``_audit_*`` fields; keep only what MessageItemDTO accepts.""" + return [ + { + "sender_id": m["sender_id"], + "role": m["role"], + "timestamp": m["timestamp"], + "content": m["content"], + } + for m in batch["messages"] + ] + + +def _list_md_files(memory_root: Path, subpath: str) -> list[Path]: + """List .md files under + ``/default_app/default_project/users///``.""" + user_dir = memory_root / "default_app" / "default_project" / "users" + if not user_dir.exists(): + return [] + out: list[Path] = [] + for user_dir_child in user_dir.iterdir(): + target = user_dir_child / subpath + if target.is_dir(): + out.extend(target.rglob("*.md")) + elif target.with_suffix(".md").exists(): + out.append(target.with_suffix(".md")) + return out + + +def _count_episode_entries(md_files: list[Path]) -> int: + """Count ``## entry-*`` blocks across all episode md files.""" + n = 0 + for f in md_files: + for line in f.read_text().splitlines(): + stripped = line.strip() + # Daily-log entries start with `## ` followed by an id token. + # We count any second-level heading that isn't the standard + # subsection headers used inside an entry. + if stripped.startswith("## ") and not stripped.startswith( + ("## Subject", "## Summary", "## Content", "## Fact", "## Foresight") + ): + n += 1 + return n + + +def _maybe_snapshot_memory_root(memory_root: Path) -> None: + """Copy ``memory_root`` to ``$EVEROS_KEEP_CORPUS_TO`` when set. + + Used to harvest a known-good corpus (md + sqlite + lancedb three-piece + set) after a green test run, for later upload as the /search e2e + fixture. Pure sync I/O — kept out of the async test body so ASYNC240 + doesn't complain about pathlib usage on the async path. + """ + keep_to = os.environ.get("EVEROS_KEEP_CORPUS_TO") + if not keep_to: + return + dest = Path(keep_to).resolve() + if dest.exists(): + shutil.rmtree(dest) + dest.parent.mkdir(parents=True, exist_ok=True) + shutil.copytree(memory_root, dest) + + +# --------------------------------------------------------------------------- +# The test (slow — hits real LLM + embedder; opt in via `pytest -m slow`) +# --------------------------------------------------------------------------- + + +@pytest.mark.slow +@pytest.mark.live_llm +# Retries cover transient real-LLM flakes: OME profile clustering +# occasionally fails to emit user.md within the cascade-drain deadline +# (LLM timeout, empty response, or async race), but is reliably stable +# on retry. reruns_delay leaves the cascade workers idle between +# attempts so we don't pile state on top of a prior run. +@pytest.mark.flaky(reruns=2, reruns_delay=5) +async def test_long_conversation_produces_all_memory_types( + long_conversation: dict, + async_client: httpx.AsyncClient, + core_pipeline_runtime: Path, + cascade_done_poll: Callable[..., Awaitable[None]], + buffer_count: Callable[[str], Awaitable[int]], + memcell_count: Callable[..., Awaitable[int]], +) -> None: + """One big seamless run: add 19 batches, flush, poll, assert everything.""" + + session_id = long_conversation["everos_session_id"] + memory_root = core_pipeline_runtime + + # ── Stage 0: baseline ───────────────────────────────────────────────── + assert await buffer_count(session_id) == 0 + assert await memcell_count(session_id) == 0 + + # ── Stage 1: drip 19 batches into /add, asserting buffer delta ──────── + last_status: str | None = None + + for idx, batch in enumerate(long_conversation["batches"]): + msg_count = batch["message_count"] + + buf_before = await buffer_count(session_id) + cells_before = await memcell_count(session_id) + + resp = await async_client.post( + "/api/v1/memory/add", + json={"session_id": session_id, "messages": _to_add_messages(batch)}, + timeout=600.0, # boundary detection may call LLM + ) + assert resp.status_code == 200, ( + f"batch {idx} ({batch['locomo_session']}): {resp.status_code} {resp.text}" + ) + body = resp.json() + status: str = body["data"]["status"] + returned_count: int = body["data"]["message_count"] + assert status in {"accumulated", "extracted"}, body + assert returned_count == msg_count, body + last_status = status + + buf_after = await buffer_count(session_id) + cells_after = await memcell_count(session_id) + + # Buffer-delta invariants: + if status == "accumulated": + # No boundary cut → entire batch piled into the buffer. + assert buf_after == buf_before + msg_count, ( + f"batch {idx} accumulated: expected buf {buf_before + msg_count}, " + f"got {buf_after}" + ) + assert cells_after == cells_before, ( + f"batch {idx} accumulated: memcell should not change " + f"({cells_before} → {cells_after})" + ) + else: # "extracted" + # Boundary fired: some messages turned into memcell(s), tail + # (if any) stays in the buffer. We can't predict the exact tail + # length but two invariants must hold. + assert cells_after > cells_before, ( + f"batch {idx} extracted: memcell should grow " + f"({cells_before} → {cells_after})" + ) + assert buf_after >= 0 + # Conservation: nothing should silently vanish — the union of + # (buffer carry-over + this batch) must equal (new buffer + + # messages carved into cells). We approximate by asserting the + # new buffer is at most the carry-over + this batch size. + assert buf_after <= buf_before + msg_count, ( + f"batch {idx} extracted: buffer overflow " + f"({buf_before} + {msg_count} → {buf_after})" + ) + + # ── Stage 2: flush ──────────────────────────────────────────────────── + cells_pre_flush = await memcell_count(session_id) + resp = await async_client.post( + "/api/v1/memory/flush", + json={"session_id": session_id}, + timeout=600.0, + ) + assert resp.status_code == 200, resp.text + flush_status = resp.json()["data"]["status"] + assert flush_status in {"extracted", "no_extraction"}, resp.json() + + assert await buffer_count(session_id) == 0, "buffer must be drained after flush" + + cells_after_flush = await memcell_count(session_id) + # If the last /add was already 'extracted' and emptied the buffer, + # flush returns 'no_extraction'. Otherwise flush must produce ≥ 1 + # cell to satisfy the boundary semantics. + if flush_status == "extracted": + assert cells_after_flush > cells_pre_flush + + # 419 LoCoMo messages produce ~19 memcells in practice (LLM boundary + # decides semantic cuts; daily-life chat carves coarsely). Threshold + # 15 leaves room for run-to-run variance from the boundary LLM. + assert cells_after_flush >= 15, ( + f"expected ≥ 15 memcells from 419 messages, got {cells_after_flush}; " + f"last add status was {last_status!r}, flush was {flush_status!r}" + ) + + # ── Stage 3 + 4: wait for cascade to drain ──────────────────────────── + # Cascade syncs md → LanceDB. OME async strategies (atomic / foresight / + # profile) also write md, which then cascade picks up. So one wait on + # cascade-drain effectively covers both pipelines, IF OME has already + # emitted its strategies (which memorize.py does inline via engine.emit). + await cascade_done_poll(deadline_seconds=600.0) + + # ── Stage 5: artifacts on disk + LanceDB ────────────────────────────── + # 5.1 episodes + episode_files = _list_md_files(memory_root, _EPISODE_DIR) + assert episode_files, "no episode md files written" + episode_entries = _count_episode_entries(episode_files) + # 19 memcells × 2 owners (caroline + melanie) ≈ 36 episode rows seen + # in practice; threshold 15 leaves variance room. + assert episode_entries >= 15, ( + f"expected ≥ 15 episode entries across {len(episode_files)} files, " + f"got {episode_entries}" + ) + + # 5.2 episode → LanceDB + from everos.infra.persistence.lancedb import episode_repo + + lance_episode_count = await episode_repo.count() + assert lance_episode_count >= 15, ( + f"LanceDB episode rows ({lance_episode_count}) < md entries ({episode_entries})" + ) + + # 5.3 atomic_fact + af_files = _list_md_files(memory_root, _ATOMIC_FACT_DIR) + assert af_files, "no atomic_fact md files — extract_atomic_facts did not emit" + + from everos.infra.persistence.lancedb import atomic_fact_repo + + lance_af_count = await atomic_fact_repo.count() + assert lance_af_count >= 1, ( + f"LanceDB atomic_fact rows = {lance_af_count}; expected ≥ 1" + ) + + # 5.4 foresight + # Foresight extractor is correctly invoked (log: ``foresights_extracted`` + # per memcell) but daily-life chat about kids / work / hobbies rarely + # yields explicit future-intent statements, so count is usually 0. + # We assert the LanceDB table exists (count returns 0 cleanly) — not + # that any row was emitted. + from everos.infra.persistence.lancedb import foresight_repo + + lance_fs_count = await foresight_repo.count() + assert lance_fs_count >= 0, f"foresight table broken: count={lance_fs_count}" + + # 5.5 profile (md only — profile retrieval path is stub; we only assert + # the writer wrote something). Profile lives as a single file + # ``users//user.md`` (schema: ``UserProfileFrontmatter.PROFILE_FILENAME``). + from everos.infra.persistence.markdown import UserProfileFrontmatter + + profile_filename = UserProfileFrontmatter.PROFILE_FILENAME + profile_files: list[Path] = [] + users_root = memory_root / "default_app" / "default_project" / "users" + if users_root.is_dir(): + for ud in users_root.iterdir(): + candidate = ud / profile_filename + if candidate.exists(): + profile_files.append(candidate) + assert profile_files, ( + f"no {profile_filename} written — extract_user_profile / " + "trigger_profile_clustering did not emit" + ) + # At least one profile file has non-trivial content. + assert any(f.read_text().strip() for f in profile_files), ( + "all profile.md files are empty" + ) + + # ── Stage 5b: strict md ↔ LanceDB parity (every cascade kind) ───────── + # Counts above are looser ``>=`` checks against LLM non-determinism; + # here we enforce byte-exact id-set + content_sha256 parity across + # every md the pipeline wrote. Catches: missing rows, orphan rows, + # content drift between md and the indexed projection. + # + # ``expect_at_least`` pins the kinds this pipeline MUST produce so an + # empty glob (kind not emitted at all) fails loudly — without this + # guard the parity check would silently pass on zero files. Foresight + # is NOT pinned because the LLM frequently yields 0 future-intent + # statements on daily-life chat (see commentary above stage 5.4). + from tests._consistency_assertions import assert_md_lance_strict_consistent + + await assert_md_lance_strict_consistent( + memory_root, + expect_at_least={ + "episode": 1, + "atomic_fact": 1, + "user_profile": 1, + }, + ) + + # ── Stage 6: optional corpus snapshot ───────────────────────────────── + # When ``EVEROS_KEEP_CORPUS_TO=`` is set, copy the post-test + # ``memory_root`` to ```` so it can be tarred + uploaded as a + # test corpus for the /search e2e suite. Skipped silently when the + # env var is absent (default test runs don't snapshot). + _maybe_snapshot_memory_root(memory_root) + + +# --------------------------------------------------------------------------- +# Diagnostic: lighter smoke that doesn't depend on the long fixture, used +# to validate the conftest fixtures themselves are wired correctly. +# --------------------------------------------------------------------------- + + +async def test_async_client_starts_and_health_responds( + async_client: httpx.AsyncClient, +) -> None: + """Tiny smoke — proves the conftest fixture brings the app up.""" + resp = await async_client.get("/health") + assert resp.status_code == 200, resp.text diff --git a/tests/e2e/test_full_pipeline_timezone_e2e.py b/tests/e2e/test_full_pipeline_timezone_e2e.py new file mode 100644 index 00000000..1c1053c4 --- /dev/null +++ b/tests/e2e/test_full_pipeline_timezone_e2e.py @@ -0,0 +1,219 @@ +"""Real full-pipeline timezone e2e — the gold-standard anti-drift test. + +Exercises the **complete stack** under a display-tz switch: + + POST /add → unprocessed_buffer → POST /flush + ↓ + boundary detection (memcell) + ↓ + markdown writer (episode.md) + ↓ + cascade scanner / worker + ↓ + LanceDB index (episode row) + +then POST /search and POST /get under display tz = Shanghai, +switch display tz to UTC, repeat /search + /get. + +Pin: the **UTC instant** of every returned ``timestamp`` field is +identical across all four renders. Only the offset / wall-clock +changes. This is the user-facing contract of the storage-UTC discipline. + +Real LLM (boundary detection + episode extraction) + real embedder +(LanceDB vector + FTS) — marked ``@slow`` ``@live_llm``. +""" + +from __future__ import annotations + +import datetime as dt +from collections.abc import Awaitable, Callable + +import httpx +import pytest + +from everos.component.utils import datetime as dt_module +from everos.component.utils.datetime import from_iso_format +from everos.config import load_settings + + +async def _switch_display_tz(monkeypatch: pytest.MonkeyPatch, tz: str) -> None: + """Hot-swap the display tz mid-test + drop both caches. + + The ``_display_tz`` resolver and ``load_settings`` are + ``functools.cache``-d; missing either ``cache_clear`` would let the + new env var read silently no-op. + """ + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", tz) + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_full_pipeline_tz_switch_preserves_utc_instant( + async_client: httpx.AsyncClient, + pipeline_done_poll: Callable[..., Awaitable[None]], + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Real /add → /flush → cascade → LanceDB → /search /get under tz switch. + + Steps: + + 1. Configure ``EVEROS_MEMORY__TIMEZONE=Asia/Shanghai``. + 2. POST /add a single message with a pinned epoch-ms timestamp. + 3. POST /flush — forces boundary detection to carve a memcell out + of the single-message buffer. + 4. Wait for cascade to drain (md → LanceDB indexed). + 5. POST /search + POST /get: capture episode timestamp strings. + 6. Switch ``EVEROS_MEMORY__TIMEZONE=UTC``. + 7. POST /search + POST /get again: capture episode timestamp strings. + 8. Parse all four timestamp strings back to UTC instants. They must + all be equal. The offsets and wall-clock numbers will differ + between Shanghai and UTC renders — that's expected; what must + NOT differ is the absolute UTC instant. + + Anti-drift contract is end-to-end: writes under one display tz + must read back under another with zero data drift. + """ + user_id = "alice_full_tz" + session_id = "sess_full_tz" + # 1748498400000 ms = 2026-05-29T06:00:00Z = 2026-05-29T14:00:00+08:00 + pinned_ms = 1748498400000 + expected_instant = dt.datetime.fromtimestamp(pinned_ms / 1000, tz=dt.UTC) + + # ── Step 1+2: configure Shanghai + write via /add ── + await _switch_display_tz(monkeypatch, "Asia/Shanghai") + resp = await async_client.post( + "/api/v1/memory/add", + json={ + "user_id": user_id, + "session_id": session_id, + "messages": [ + { + "sender_id": user_id, + "role": "user", + "timestamp": pinned_ms, + "content": "I love climbing in Yosemite every spring.", + }, + ], + }, + timeout=60.0, + ) + assert resp.status_code == 200, resp.text + + # ── Step 3: /flush forces boundary detection on the single-message buffer ── + resp = await async_client.post( + "/api/v1/memory/flush", + json={"user_id": user_id, "session_id": session_id}, + timeout=60.0, + ) + assert resp.status_code == 200, resp.text + + # ── Step 4: wait for OME strategies + cascade to fully drain ── + # 10-minute deadline: extract_episode + extract_atomic_facts run under + # real LLM and the cascade worker only fires after md lands. The + # `pipeline_done_poll` fixture covers both OME idle and cascade queue + # empty. + await pipeline_done_poll(deadline_seconds=600.0) + + # ── Step 5: /search + /get under Shanghai display tz ── + resp_search_sh = await async_client.post( + "/api/v1/memory/search", + json={ + "user_id": user_id, + "query": "climbing", + "method": "keyword", # no embedder cost; FTS index built by cascade + "filters": {"session_id": session_id}, + }, + timeout=60.0, + ) + assert resp_search_sh.status_code == 200, resp_search_sh.text + eps_search_sh = resp_search_sh.json()["data"]["episodes"] + assert eps_search_sh, ( + f"/search must return an episode after flush+cascade; got {eps_search_sh!r}" + ) + ts_search_sh = eps_search_sh[0]["timestamp"] + assert ts_search_sh.endswith("+08:00"), ( + f"Shanghai display tz should render offset +08:00; got {ts_search_sh!r}" + ) + + resp_get_sh = await async_client.post( + "/api/v1/memory/get", + json={ + "user_id": user_id, + "memory_type": "episode", + "page": 1, + "page_size": 20, + }, + timeout=60.0, + ) + assert resp_get_sh.status_code == 200, resp_get_sh.text + eps_get_sh = resp_get_sh.json()["data"]["episodes"] + assert eps_get_sh, "/get must return the same episode /search did" + ts_get_sh = eps_get_sh[0]["timestamp"] + assert ts_get_sh.endswith("+08:00"), ts_get_sh + + # ── Step 6: switch to UTC display tz (drops caches) ── + await _switch_display_tz(monkeypatch, "UTC") + + # ── Step 7: /search + /get again, same on-disk row, new render ── + resp_search_utc = await async_client.post( + "/api/v1/memory/search", + json={ + "user_id": user_id, + "query": "climbing", + "method": "keyword", + "filters": {"session_id": session_id}, + }, + timeout=60.0, + ) + assert resp_search_utc.status_code == 200, resp_search_utc.text + eps_search_utc = resp_search_utc.json()["data"]["episodes"] + assert eps_search_utc + ts_search_utc = eps_search_utc[0]["timestamp"] + assert ts_search_utc.endswith("Z") or ts_search_utc.endswith("+00:00"), ( + f"UTC display tz should render Z / +00:00; got {ts_search_utc!r}" + ) + + resp_get_utc = await async_client.post( + "/api/v1/memory/get", + json={ + "user_id": user_id, + "memory_type": "episode", + "page": 1, + "page_size": 20, + }, + timeout=60.0, + ) + assert resp_get_utc.status_code == 200, resp_get_utc.text + eps_get_utc = resp_get_utc.json()["data"]["episodes"] + ts_get_utc = eps_get_utc[0]["timestamp"] + assert ts_get_utc.endswith("Z") or ts_get_utc.endswith("+00:00"), ts_get_utc + + # ── Step 8: anti-drift assertion — all four UTC instants identical ── + instants = { + "search/Shanghai": from_iso_format(ts_search_sh).astimezone(dt.UTC), + "get/Shanghai": from_iso_format(ts_get_sh).astimezone(dt.UTC), + "search/UTC": from_iso_format(ts_search_utc).astimezone(dt.UTC), + "get/UTC": from_iso_format(ts_get_utc).astimezone(dt.UTC), + } + distinct = set(instants.values()) + assert len(distinct) == 1, ( + f"display-tz switch must NOT drift the UTC instant. Got distinct " + f"instants across renders: {instants!r}" + ) + actual_instant = next(iter(distinct)) + # Episode timestamp inherits from the last message's epoch ms — the + # pinned input value must round-trip exactly. + assert actual_instant == expected_instant, ( + f"episode UTC instant must equal the pinned input ms epoch; " + f"expected {expected_instant.isoformat()}, got {actual_instant.isoformat()}" + ) + + # ── Sanity: across the four renders, identical instant projects to the + # correct wall-clock under each display tz ── + # Shanghai: 14:00 wall clock; UTC: 06:00 wall clock. + assert "T14:00:00" in ts_search_sh, ts_search_sh + assert "T14:00:00" in ts_get_sh, ts_get_sh + assert "T06:00:00" in ts_search_utc, ts_search_utc + assert "T06:00:00" in ts_get_utc, ts_get_utc diff --git a/tests/e2e/test_get_endpoint_e2e.py b/tests/e2e/test_get_endpoint_e2e.py new file mode 100644 index 00000000..3a3d9a87 --- /dev/null +++ b/tests/e2e/test_get_endpoint_e2e.py @@ -0,0 +1,829 @@ +"""End-to-end integration tests for ``POST /api/v1/memory/get``. + +These tests spin up the FastAPI app with **no lifespan providers** +against a tmp ``EVEROS_MEMORY__ROOT``, populate a real LanceDB +``episode`` table directly via the repo singleton, and exercise the +HTTP route. They cover the wiring that unit tests cannot: pydantic +422s from the route, JSON envelope shape, and the full +``request → service → manager → LanceDB`` path. +""" + +from __future__ import annotations + +import asyncio +import datetime as _dt +from collections.abc import AsyncIterator +from importlib import import_module +from pathlib import Path + +import pytest +from httpx import ASGITransport, AsyncClient + +from everos.config import load_settings +from everos.entrypoints.api.app import create_app +from everos.infra.persistence.lancedb import ( + AgentCase, + AgentSkill, + Episode, + UserProfile, + agent_case_repo, + agent_skill_repo, + episode_repo, + lancedb_manager, + user_profile_repo, +) + +# ``everos.service.__init__`` re-exports the ``get`` function under the +# same name as the submodule (``from .get import get as get``), which +# shadows the submodule when imported normally. Pull the actual module +# via importlib so the test can poke at its ``_manager`` singleton. +get_service_mod = import_module("everos.service.get") + + +def _ts(day: int) -> _dt.datetime: + return _dt.datetime(2026, 1, day, tzinfo=_dt.UTC) + + +def _episode( + entry: str, + *, + owner: str = "u1", + session: str = "sess_a", + parent_id: str = "mc_1", + sender_ids: list[str] | None = None, + day: int = 1, +) -> Episode: + return Episode( + id=f"{owner}_{entry}", + entry_id=entry, + owner_id=owner, + owner_type="user", + session_id=session, + timestamp=_ts(day), + parent_type="memcell", + parent_id=parent_id, + sender_ids=sender_ids if sender_ids is not None else [owner, "assistant"], + subject=f"subj {entry}", + summary=f"summary {entry}", + episode=f"body of {entry}", + episode_tokens=f"body of {entry}", + md_path=f"users/{owner}/episodes/{entry}.md", + content_sha256="abc", + vector=[0.0] * 1024, + ) + + +def _agent_case( + entry: str, + *, + owner: str = "a1", + session: str = "sess_x", + day: int = 1, +) -> AgentCase: + return AgentCase( + id=f"{owner}_{entry}", + entry_id=entry, + owner_id=owner, + owner_type="agent", + session_id=session, + timestamp=_ts(day), + parent_type="memcell", + parent_id="mc_99", + quality_score=0.8, + task_intent=f"intent {entry}", + task_intent_tokens=f"intent {entry}", + approach=f"approach {entry}", + approach_tokens=f"approach {entry}", + key_insight=None, + md_path=f"agents/{owner}/cases/{entry}.md", + content_sha256="abc", + vector=[0.0] * 1024, + ) + + +def _agent_skill( + name: str, + *, + owner: str = "a1", +) -> AgentSkill: + return AgentSkill( + id=f"{owner}_{name}", + owner_id=owner, + owner_type="agent", + name=name, + description=f"desc {name}", + description_tokens=f"desc {name}", + content=f"content {name}", + content_tokens=f"content {name}", + confidence=0.9, + maturity_score=0.7, + source_case_ids=[f"{owner}_ac_1"], + md_path=f"agents/{owner}/skills/{name}/SKILL.md", + content_sha256="abc", + vector=[0.0] * 1024, + ) + + +@pytest.fixture +async def client( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> AsyncIterator[AsyncClient]: + """Build the FastAPI app against a tmp memory root with no lifespan.""" + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + load_settings.cache_clear() + + # Reset every module-level singleton the get-path touches. + lancedb_manager._conn = None + lancedb_manager._tables.clear() + get_service_mod._manager = None + + app = create_app(lifespan_providers=[]) + transport = ASGITransport(app=app) + async with AsyncClient(transport=transport, base_url="http://test") as c: + yield c + + await lancedb_manager.dispose_connection() + load_settings.cache_clear() + + +# ── Happy path ────────────────────────────────────────────────────────── + + +async def test_get_episodes_returns_page_and_total( + client: AsyncClient, +) -> None: + """5 rows in, page_size=2 → 2 episodes back + total_count=5.""" + await episode_repo.add( + [_episode(f"ep_{i:03d}", day=i) for i in range(1, 6)], + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "page": 1, + "page_size": 2, + }, + ) + assert resp.status_code == 200 + body = resp.json() + rid = body["request_id"] + assert len(rid) == 32 and all(c in "0123456789abcdef" for c in rid) + data = body["data"] + assert data["total_count"] == 5 + assert data["count"] == 2 + assert len(data["episodes"]) == 2 + # default sort = timestamp DESC → highest day first + assert data["episodes"][0]["id"] == "u1_ep_005" + assert data["episodes"][1]["id"] == "u1_ep_004" + # The non-requested kinds are empty arrays (envelope invariant). + assert data["profiles"] == [] + assert data["agent_cases"] == [] + assert data["agent_skills"] == [] + + +async def test_get_episodes_filtered_by_session_id( + client: AsyncClient, +) -> None: + """Filter narrows results to the matching ``session_id`` only.""" + await episode_repo.add( + [ + _episode("ep_001", session="sess_a"), + _episode("ep_002", session="sess_a"), + _episode("ep_003", session="sess_b"), + ], + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "filters": {"session_id": "sess_a"}, + }, + ) + assert resp.status_code == 200 + body = resp.json() + assert body["data"]["total_count"] == 2 + assert body["data"]["count"] == 2 + ids = {ep["id"] for ep in body["data"]["episodes"]} + assert ids == {"u1_ep_001", "u1_ep_002"} + + +async def test_get_empty_returns_zero_counts(client: AsyncClient) -> None: + """An owner with no rows yields total_count=0 + empty episodes list.""" + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "ghost", + "memory_type": "episode", + }, + ) + assert resp.status_code == 200 + data = resp.json()["data"] + assert data["total_count"] == 0 + assert data["count"] == 0 + assert data["episodes"] == [] + + +async def test_get_profile_miss_returns_empty(client: AsyncClient) -> None: + """Cold start (no profile row) → ``profiles=[]`` / ``total_count=0``.""" + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "profile", + }, + ) + assert resp.status_code == 200 + data = resp.json()["data"] + assert data["profiles"] == [] + assert data["total_count"] == 0 + + +async def test_get_profile_returns_seeded_row(client: AsyncClient) -> None: + """A profile row in the ``user_profile`` table is returned + json-decoded. + + Full-stack: seed the LanceDB ``user_profile`` table (as cascade would + from ``users/u1/user.md``), then read it back through the HTTP route. + White-box surface: ``user_profile_repo`` (the same table /search's + ``include_profile`` reads). + """ + await user_profile_repo.add( + [ + UserProfile( + id="u1", + owner_id="u1", + owner_type="user", + app_id="default", + project_id="default", + summary="u1 loves climbing in Yosemite", + explicit_info_json='[{"category": "Hobby", "description": "climbing"}]', + implicit_traits_json='[{"trait": "Outdoorsy"}]', + profile_timestamp_ms=1780304400000, + md_path="users/u1/user.md", + content_sha256="abc", + ) + ] + ) + + resp = await client.post( + "/api/v1/memory/get", + json={"user_id": "u1", "memory_type": "profile"}, + ) + assert resp.status_code == 200 + data = resp.json()["data"] + assert data["total_count"] == 1 + assert data["count"] == 1 + assert len(data["profiles"]) == 1 + prof = data["profiles"][0] + assert prof["id"] == "u1" + assert prof["user_id"] == "u1" + assert prof["profile_data"]["summary"] == "u1 loves climbing in Yosemite" + assert prof["profile_data"]["explicit_info"] == [ + {"category": "Hobby", "description": "climbing"} + ] + assert prof["profile_data"]["implicit_traits"] == [{"trait": "Outdoorsy"}] + + +# ── Pagination + sort ─────────────────────────────────────────────────── + + +async def test_get_episodes_page_two_returns_correct_slice( + client: AsyncClient, +) -> None: + """5 rows / page_size=2 / page=2 → middle slice (rows 3 + 4 by DESC ts).""" + await episode_repo.add( + [_episode(f"ep_{i:03d}", day=i) for i in range(1, 6)], + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "page": 2, + "page_size": 2, + }, + ) + assert resp.status_code == 200 + data = resp.json()["data"] + assert data["total_count"] == 5 + assert data["count"] == 2 + # default sort = timestamp DESC; page 2 of 2-per-page over 5 rows → + # rows at offsets 2,3 → day=3, day=2 (1-indexed: ep_003, ep_002). + assert [ep["id"] for ep in data["episodes"]] == ["u1_ep_003", "u1_ep_002"] + + +async def test_get_episodes_sort_order_asc(client: AsyncClient) -> None: + """``sort_order=asc`` flips the order (oldest first).""" + await episode_repo.add( + [_episode(f"ep_{i:03d}", day=i) for i in range(1, 4)], + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "sort_order": "asc", + }, + ) + assert resp.status_code == 200 + ids = [ep["id"] for ep in resp.json()["data"]["episodes"]] + assert ids == ["u1_ep_001", "u1_ep_002", "u1_ep_003"] + + +# ── Agent-side kinds ──────────────────────────────────────────────────── + + +async def test_get_agent_cases_happy_path(client: AsyncClient) -> None: + """``agent_case`` listing returns shaped items, populates only that array.""" + await agent_case_repo.add( + [_agent_case(f"ac_{i:03d}", day=i) for i in range(1, 4)], + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "agent_id": "a1", + "memory_type": "agent_case", + }, + ) + assert resp.status_code == 200 + data = resp.json()["data"] + assert data["total_count"] == 3 + assert data["count"] == 3 + assert [c["id"] for c in data["agent_cases"]] == [ + "a1_ac_003", + "a1_ac_002", + "a1_ac_001", + ] + # Cross-kind envelope stays empty. + assert data["episodes"] == [] + assert data["agent_skills"] == [] + # AgentCase item shape — score absent (vs SearchAgentCaseItem), + # quality_score round-trips. + first = data["agent_cases"][0] + assert "score" not in first + assert first["quality_score"] == 0.8 + assert first["agent_id"] == "a1" + + +async def test_get_agent_cases_filtered_by_session(client: AsyncClient) -> None: + """Filter narrows ``agent_case`` rows to the session.""" + await agent_case_repo.add( + [ + _agent_case("ac_001", session="sess_x"), + _agent_case("ac_002", session="sess_x"), + _agent_case("ac_003", session="sess_y"), + ] + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "agent_id": "a1", + "memory_type": "agent_case", + "filters": {"session_id": "sess_x"}, + }, + ) + assert resp.status_code == 200 + body = resp.json() + assert body["data"]["total_count"] == 2 + ids = {c["id"] for c in body["data"]["agent_cases"]} + assert ids == {"a1_ac_001", "a1_ac_002"} + + +async def test_get_agent_skills_happy_path(client: AsyncClient) -> None: + """``agent_skill`` listing — sort silently uses ``updated_at``.""" + await agent_skill_repo.add( + [_agent_skill(name) for name in ("planner", "summariser")], + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "agent_id": "a1", + "memory_type": "agent_skill", + }, + ) + assert resp.status_code == 200 + data = resp.json()["data"] + assert data["total_count"] == 2 + names = {s["name"] for s in data["agent_skills"]} + assert names == {"planner", "summariser"} + + +async def test_get_agent_skills_sort_by_timestamp_silently_downgraded( + client: AsyncClient, +) -> None: + """Explicit ``sort_by=timestamp`` does not 500 — manager rewrites to + ``updated_at`` (the only temporal column on ``agent_skill``).""" + await agent_skill_repo.add([_agent_skill("planner")]) + resp = await client.post( + "/api/v1/memory/get", + json={ + "agent_id": "a1", + "memory_type": "agent_skill", + "sort_by": "timestamp", + }, + ) + assert resp.status_code == 200 + assert resp.json()["data"]["total_count"] == 1 + + +# ── Filter coverage end-to-end ────────────────────────────────────────── + + +async def test_get_episodes_filtered_by_ne_session(client: AsyncClient) -> None: + """``ne`` op on a str field excludes matching rows end-to-end.""" + await episode_repo.add( + [ + _episode("ep_001", session="sess_a"), + _episode("ep_002", session="sess_internal"), + _episode("ep_003", session="sess_b"), + ] + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "filters": {"session_id": {"ne": "sess_internal"}}, + }, + ) + assert resp.status_code == 200 + body = resp.json() + assert body["data"]["total_count"] == 2 + ids = {ep["id"] for ep in body["data"]["episodes"]} + assert ids == {"u1_ep_001", "u1_ep_003"} + + +async def test_get_episodes_filtered_by_iso_timestamp( + client: AsyncClient, +) -> None: + """ISO 8601 string timestamp literal is accepted alongside epoch ms.""" + await episode_repo.add( + [ + _episode("ep_001", day=1), # 2026-01-01 + _episode("ep_002", day=5), # 2026-01-05 + _episode("ep_003", day=9), # 2026-01-09 + ] + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "filters": {"timestamp": {"gte": "2026-01-04T00:00:00+00:00"}}, + }, + ) + assert resp.status_code == 200 + ids = {ep["id"] for ep in resp.json()["data"]["episodes"]} + assert ids == {"u1_ep_002", "u1_ep_003"} + + +async def test_get_episodes_filtered_by_parent_id(client: AsyncClient) -> None: + """Core use case: every episode derived from one memcell.""" + await episode_repo.add( + [ + _episode("ep_001", parent_id="mc_target"), + _episode("ep_002", parent_id="mc_target"), + _episode("ep_003", parent_id="mc_other"), + ] + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "filters": {"parent_id": "mc_target"}, + }, + ) + assert resp.status_code == 200 + body = resp.json() + assert body["data"]["total_count"] == 2 + ids = {ep["id"] for ep in body["data"]["episodes"]} + assert ids == {"u1_ep_001", "u1_ep_002"} + + +async def test_get_episodes_filtered_by_sender_id_in( + client: AsyncClient, +) -> None: + """``sender_id: {"in": [...]}`` → ``array_has(sender_ids, ...) OR ...``.""" + await episode_repo.add( + [ + _episode("ep_001", sender_ids=["alice", "assistant"]), + _episode("ep_002", sender_ids=["bob", "assistant"]), + _episode("ep_003", sender_ids=["carol", "assistant"]), + ] + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "filters": {"sender_id": {"in": ["alice", "bob"]}}, + }, + ) + assert resp.status_code == 200 + body = resp.json() + assert body["data"]["total_count"] == 2 + ids = {ep["id"] for ep in body["data"]["episodes"]} + assert ids == {"u1_ep_001", "u1_ep_002"} + + +async def test_get_episodes_nested_and_inside_or(client: AsyncClient) -> None: + """Nested ``AND`` inside ``OR`` — parity with /search combinator semantics.""" + await episode_repo.add( + [ + _episode("ep_001", session="sess_a", parent_id="mc_target"), + _episode("ep_002", session="sess_a", parent_id="mc_other"), + _episode("ep_003", session="sess_b", parent_id="mc_target"), + _episode("ep_004", session="sess_c", parent_id="mc_other"), + ] + ) + # (session=sess_a AND parent_id=mc_target) + # OR (parent_id=mc_other AND session=sess_c) + # → ep_001 + ep_004 + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "filters": { + "OR": [ + { + "AND": [ + {"session_id": "sess_a"}, + {"parent_id": "mc_target"}, + ] + }, + { + "AND": [ + {"parent_id": "mc_other"}, + {"session_id": "sess_c"}, + ] + }, + ] + }, + }, + ) + assert resp.status_code == 200 + body = resp.json() + assert body["data"]["total_count"] == 2 + ids = {ep["id"] for ep in body["data"]["episodes"]} + assert ids == {"u1_ep_001", "u1_ep_004"} + + +# ── Filter combinators (200 — happy path) ────────────────────────────── +# Pure 422 / validation cases moved to +# tests/unit/test_entrypoints/test_api/test_routes/test_get_route_validation.py + + +async def test_get_top_level_and_or_compiles_and_filters( + client: AsyncClient, +) -> None: + """``AND`` / ``OR`` combinators are accepted (parity with /search).""" + await episode_repo.add( + [ + _episode("ep_001", session="sess_a"), + _episode("ep_002", session="sess_b"), + _episode("ep_003", session="sess_c"), + ], + ) + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "filters": {"OR": [{"session_id": "sess_a"}, {"session_id": "sess_b"}]}, + }, + ) + assert resp.status_code == 200 + body = resp.json() + assert body["data"]["total_count"] == 2 + ids = {ep["id"] for ep in body["data"]["episodes"]} + assert ids == {"u1_ep_001", "u1_ep_002"} + + +async def test_get_episodes_filtered_by_timestamp_range( + client: AsyncClient, +) -> None: + """``timestamp: {gte, lt}`` — same-field double op compiles to implicit AND.""" + await episode_repo.add( + [ + _episode("ep_001", day=1), # 2026-01-01 + _episode("ep_002", day=3), # 2026-01-03 + _episode("ep_003", day=5), # 2026-01-05 + _episode("ep_004", day=7), # 2026-01-07 + _episode("ep_005", day=9), # 2026-01-09 + ] + ) + # Window [Jan 3, Jan 7) → ep_002 + ep_003 (Jan 7 excluded by `lt`). + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "filters": { + "timestamp": { + "gte": "2026-01-03T00:00:00+00:00", + "lt": "2026-01-07T00:00:00+00:00", + } + }, + }, + ) + assert resp.status_code == 200 + body = resp.json() + assert body["data"]["total_count"] == 2 + ids = {ep["id"] for ep in body["data"]["episodes"]} + assert ids == {"u1_ep_002", "u1_ep_003"} + + +async def test_get_episodes_top_level_and_filter(client: AsyncClient) -> None: + """Explicit top-level ``AND`` — distinct from implicit multi-field AND.""" + await episode_repo.add( + [ + _episode("ep_001", session="sess_a", parent_id="mc_target"), + _episode("ep_002", session="sess_a", parent_id="mc_other"), + _episode("ep_003", session="sess_b", parent_id="mc_target"), + ] + ) + # session=sess_a AND parent_id=mc_target → ep_001 only + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "filters": { + "AND": [ + {"session_id": "sess_a"}, + {"parent_id": "mc_target"}, + ] + }, + }, + ) + assert resp.status_code == 200 + body = resp.json() + assert body["data"]["total_count"] == 1 + assert body["data"]["episodes"][0]["id"] == "u1_ep_001" + + +# ── max_fetch limit trigger ───────────────────────────────────────────── + + +async def test_get_truncates_above_max_fetch( + client: AsyncClient, + monkeypatch: pytest.MonkeyPatch, + caplog: pytest.LogCaptureFixture, +) -> None: + """Filter matches > ``max_fetch`` rows → chassis emits warning + page + contents come from the truncated prefix; ``total_count`` is still the + *true* match count (``count_rows`` ignores ``max_fetch``). + + Injects a low ``max_fetch=5`` by wrapping the bound method so the + end-to-end path runs through the truncation branch without populating + 20k+ rows. + """ + # The e2e ``client`` fixture builds the app without lifespan providers, + # so ``configure_logging`` (normally invoked by the CLI entry) never + # runs. Call it here so the structlog → stdlib logging bridge is + # wired up and ``caplog`` can observe the chassis warning. + from everos.core.observability.logging import configure_logging + + configure_logging(level="WARNING") + + await episode_repo.add( + [_episode(f"ep_{i:03d}", day=i) for i in range(1, 11)], + ) + original = episode_repo.find_where_paginated + + async def low_cap(*args: object, **kwargs: object) -> object: + kwargs["max_fetch"] = 5 + return await original(*args, **kwargs) # type: ignore[arg-type] + + monkeypatch.setattr(episode_repo, "find_where_paginated", low_cap) + + with caplog.at_level("WARNING"): + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": "u1", + "memory_type": "episode", + "page": 1, + "page_size": 3, + }, + ) + assert resp.status_code == 200 + body = resp.json() + # True row count is still 10, even though only 5 made it into the sort. + assert body["data"]["total_count"] == 10 + assert body["data"]["count"] == 3 + # structlog now routes through stdlib's root logger (see + # ``core/observability/logging/factory.py``); the warning surfaces via + # the standard ``caplog`` fixture rather than direct stdout capture. + assert "find_where_paginated truncated" in caplog.text + + +# ── Concurrency ───────────────────────────────────────────────────────── + + +async def test_get_concurrent_owners_no_cross_contamination( + client: AsyncClient, +) -> None: + """Concurrent /get requests against different ``owner_id`` partitions + return only their own rows. ``GetManager`` is a lazy singleton — + this also exercises first-request lazy-init under contention.""" + await episode_repo.add( + [ + _episode("ep_001", owner="u1"), + _episode("ep_002", owner="u1"), + _episode("ep_001", owner="u2"), + _episode("ep_001", owner="u3"), + ] + ) + + async def query(owner: str) -> dict[str, object]: + resp = await client.post( + "/api/v1/memory/get", + json={ + "user_id": owner, + "memory_type": "episode", + }, + ) + assert resp.status_code == 200, f"{owner}: {resp.text}" + return resp.json() + + bodies = await asyncio.gather( + query("u1"), + query("u2"), + query("u3"), + ) + u1, u2, u3 = bodies + assert u1["data"]["total_count"] == 2 # type: ignore[index] + assert u2["data"]["total_count"] == 1 # type: ignore[index] + assert u3["data"]["total_count"] == 1 # type: ignore[index] + assert {ep["id"] for ep in u1["data"]["episodes"]} == { # type: ignore[index] + "u1_ep_001", + "u1_ep_002", + } + assert {ep["id"] for ep in u2["data"]["episodes"]} == {"u2_ep_001"} # type: ignore[index] + assert {ep["id"] for ep in u3["data"]["episodes"]} == {"u3_ep_001"} # type: ignore[index] + + +async def test_get_concurrent_different_memory_types(client: AsyncClient) -> None: + """Concurrent /get on different ``memory_type`` (episode + agent_case + + agent_skill) returns each kind in its own envelope slot, with no + cross-array bleed.""" + await episode_repo.add([_episode("ep_001", owner="u1")]) + await agent_case_repo.add([_agent_case("ac_001", owner="a1")]) + await agent_skill_repo.add([_agent_skill("planner", owner="a1")]) + + async def query(payload: dict[str, object]) -> dict[str, object]: + resp = await client.post("/api/v1/memory/get", json=payload) + assert resp.status_code == 200, resp.text + return resp.json() + + ep_body, case_body, skill_body = await asyncio.gather( + query({"user_id": "u1", "memory_type": "episode"}), + query( + { + "agent_id": "a1", + "memory_type": "agent_case", + } + ), + query( + { + "agent_id": "a1", + "memory_type": "agent_skill", + } + ), + ) + # Episode envelope: only ``episodes`` populated. + assert len(ep_body["data"]["episodes"]) == 1 # type: ignore[index] + assert ep_body["data"]["agent_cases"] == [] # type: ignore[index] + assert ep_body["data"]["agent_skills"] == [] # type: ignore[index] + # Case envelope: only ``agent_cases`` populated. + assert len(case_body["data"]["agent_cases"]) == 1 # type: ignore[index] + assert case_body["data"]["episodes"] == [] # type: ignore[index] + # Skill envelope: only ``agent_skills`` populated. + assert len(skill_body["data"]["agent_skills"]) == 1 # type: ignore[index] + assert skill_body["data"]["episodes"] == [] # type: ignore[index] + + +async def test_get_concurrent_lazy_init_builds_one_manager( + client: AsyncClient, +) -> None: + """The lazy singleton survives first-request contention — N concurrent + requests against a virgin manager all succeed and leave one instance.""" + # ``client`` fixture already reset _manager to None. + assert get_service_mod._manager is None + await episode_repo.add([_episode("ep_001")]) + + payload = { + "user_id": "u1", + "memory_type": "episode", + } + results = await asyncio.gather( + *(client.post("/api/v1/memory/get", json=payload) for _ in range(8)) + ) + assert all(r.status_code == 200 for r in results) + # After the storm, exactly one manager instance is cached. + assert get_service_mod._manager is not None diff --git a/tests/e2e/test_multimodal_add_e2e.py b/tests/e2e/test_multimodal_add_e2e.py new file mode 100644 index 00000000..31134635 --- /dev/null +++ b/tests/e2e/test_multimodal_add_e2e.py @@ -0,0 +1,140 @@ +"""E2E: multimodal /add parses HTML (base64) and http(s) uri end-to-end. + +Scope: full HTTP stack (``create_app()`` + ``AsyncClient``) → ingest → +multimodal parse → unprocessed_buffer. Proves the three paths the unit +tests can only mock: + +1. ``type="html"`` + base64 + ``ext="html"`` — the normal HTML-file call. +2. ``type="html"`` + ``https`` uri — everalgo fetches the page and + dispatches by the response Content-Type. +3. ``type="html"`` + ``file://`` uri — EverOS reads the file locally and + hands everalgo hydrated bytes (the library never touches the fs). + +Real multimodal LLM (creds via ``.env``) + real public internet, so the +module is marked ``live_llm``. Skipped when the ``[multimodal]`` extra is +absent. + +White-box surface: reads the ``text`` column of ``unprocessed_buffer`` +(the derived text the ingest stage produced from the parsed content) to +assert the parsed payload actually flowed into the buffer. +""" + +from __future__ import annotations + +import base64 +from pathlib import Path + +import httpx +import pytest +from sqlalchemy import text as sql_text + +pytest.importorskip("everalgo.parser") + +pytestmark = pytest.mark.live_llm + + +async def _buffer_text(session_id: str) -> str: + """Concatenated derived ``text`` of all buffer rows for a session.""" + from everos.infra.persistence.sqlite import get_engine + + async with get_engine().connect() as conn: + rows = ( + await conn.execute( + sql_text("SELECT text FROM unprocessed_buffer WHERE session_id = :sid"), + {"sid": session_id}, + ) + ).all() + return "\n".join(str(r[0]) for r in rows) + + +async def test_add_html_base64_parsed_into_buffer( + async_client: httpx.AsyncClient, +) -> None: + """A base64 HTML file is parsed and its text lands in the buffer.""" + html = ( + b"

Release

" + b"

Version 9.9.9 ships Dark Mode.

" + ) + sid = "e2e-mm-html-b64" + resp = await async_client.post( + "/api/v1/memory/add", + json={ + "session_id": sid, + "messages": [ + { + "sender_id": "alice", + "role": "user", + "timestamp": 1780304400000, + "content": [ + { + "type": "html", + "base64": base64.b64encode(html).decode(), + "ext": "html", + "name": "notes.html", + } + ], + } + ], + }, + ) + assert resp.status_code == 200, resp.text + + buffered = await _buffer_text(sid) + assert "9.9.9" in buffered + + +async def test_add_html_https_uri_parsed_into_buffer( + async_client: httpx.AsyncClient, +) -> None: + """An https uri is fetched + parsed and its text lands in the buffer.""" + sid = "e2e-mm-html-uri" + resp = await async_client.post( + "/api/v1/memory/add", + json={ + "session_id": sid, + "messages": [ + { + "sender_id": "alice", + "role": "user", + "timestamp": 1780304400000, + "content": [{"type": "html", "uri": "https://example.com"}], + } + ], + }, + ) + assert resp.status_code == 200, resp.text + + buffered = await _buffer_text(sid) + assert "example domain" in buffered.lower() + + +async def test_add_html_file_uri_parsed_into_buffer( + async_client: httpx.AsyncClient, + tmp_path: Path, +) -> None: + """A file:// html asset is read locally (hydrated) + parsed into buffer. + + Exercises EverOS-side file:// support: the parser receives bytes, never + the path. Default allowlist is empty (local-first) so the temp file reads. + """ + doc = tmp_path / "release.html" + doc.write_text("

Version 9.9.9 ships Dark Mode.

") + sid = "e2e-mm-html-file" + resp = await async_client.post( + "/api/v1/memory/add", + json={ + "session_id": sid, + "messages": [ + { + "sender_id": "alice", + "role": "user", + "timestamp": 1780304400000, + "content": [{"type": "html", "uri": f"file://{doc}"}], + } + ], + }, + ) + assert resp.status_code == 200, resp.text + + buffered = await _buffer_text(sid) + assert "9.9.9" in buffered diff --git a/tests/e2e/test_openapi_endpoint_matches_docs.py b/tests/e2e/test_openapi_endpoint_matches_docs.py new file mode 100644 index 00000000..5bcf5fb2 --- /dev/null +++ b/tests/e2e/test_openapi_endpoint_matches_docs.py @@ -0,0 +1,87 @@ +"""Belt-and-braces gate: dev-mode ``GET /openapi.json`` ≡ ``docs/openapi.json``. + +The lint-time ``make check-openapi`` already diffs ``app.openapi()`` +against the committed ``docs/openapi.json``. This e2e test closes the +remaining theoretical gap: if anyone ever adds a *lifespan-mutated* +OpenAPI schema (e.g. ``app.openapi_schema = ...`` inside a startup +handler), the in-memory ``app.openapi()`` and the runtime +``GET /openapi.json`` response would diverge — the lint gate would +miss it, but this test wouldn't. + +How: + +1. Force ``ENV=DEV`` so the ``openapi_url`` route is enabled. +2. Construct the app via ``create_app(lifespan_providers=[])`` to skip + SQLite / LanceDB / OME (the schema is route-driven, not state- + driven) — but *do* run the lifespan context, so any startup hook + that mutates ``app.openapi_schema`` is exercised. +3. ``GET /openapi.json`` through ``httpx.AsyncClient``. +4. Diff against ``docs/openapi.json`` byte-for-byte (after JSON + normalisation to defeat ordering nondeterminism). +""" + +from __future__ import annotations + +import json +import os +from pathlib import Path + +import httpx +import pytest + +_REPO_ROOT = Path(__file__).resolve().parents[2] +_COMMITTED_OPENAPI = _REPO_ROOT / "docs" / "openapi.json" + + +async def test_dev_mode_openapi_endpoint_matches_committed_docs( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Runtime ``GET /openapi.json`` (dev mode) must equal ``docs/openapi.json``.""" + # The gate's own committed snapshot must exist — otherwise the dev + # workflow ``make openapi`` has been skipped. + assert _COMMITTED_OPENAPI.is_file(), ( + f"{_COMMITTED_OPENAPI} not found — run `make openapi`" + ) + + # Force dev-mode so ``openapi_url="/openapi.json"`` is registered. + monkeypatch.setenv("ENV", "DEV") + + from everos.entrypoints.api.app import create_app + + app = create_app(lifespan_providers=[]) + transport = httpx.ASGITransport(app=app) + async with ( + app.router.lifespan_context(app), + httpx.AsyncClient(transport=transport, base_url="http://test") as client, + ): + resp = await client.get("/openapi.json") + assert resp.status_code == 200, resp.text + runtime_schema = resp.json() + + committed_schema = json.loads(_COMMITTED_OPENAPI.read_text(encoding="utf-8")) + + if runtime_schema != committed_schema: + # Emit a concise diff to help locate the drift cause. + import difflib + + runtime_rendered = json.dumps(runtime_schema, indent=2, ensure_ascii=False) + committed_rendered = json.dumps(committed_schema, indent=2, ensure_ascii=False) + diff = "\n".join( + list( + difflib.unified_diff( + committed_rendered.splitlines(), + runtime_rendered.splitlines(), + fromfile="docs/openapi.json (committed)", + tofile="GET /openapi.json (runtime)", + lineterm="", + ) + )[:120] + ) + raise AssertionError( + "runtime /openapi.json drifts from docs/openapi.json; " + "run `make openapi` and commit the result.\n\n" + diff + ) + + +# Keep ``os`` legit in case future scenarios need direct env reads. +_ = os diff --git a/tests/e2e/test_search_endpoint_e2e.py b/tests/e2e/test_search_endpoint_e2e.py new file mode 100644 index 00000000..78eb3e54 --- /dev/null +++ b/tests/e2e/test_search_endpoint_e2e.py @@ -0,0 +1,2102 @@ +"""End-to-end integration tests for ``POST /api/v1/memory/search``. + +White-box e2e (per the project's testing taxonomy): real LanceDB writes ++ real embedder (when the method needs one) + real reranker / LLM +client when the method needs them. Data is reloaded from the +``tests/fixtures/search_seed/`` slice (16 episodes / 20 atomic_facts / +2 user_profiles produced by ``_dump_search_seed.py``) so the test +verifies recall on **real** vectors and **real** BM25 tokens. + +Coverage matrix (see 21_test_taxonomy_debate.md context): + +- methods: keyword / vector / hybrid / agentic (user owner only — + agent_case / agent_skill ship in a separate pipeline) +- owner_id / owner_type isolation +- top_k (specific value + -1 unlimited) +- radius (cosine threshold) +- include_profile (true / false) +- filter DSL: session_id eq / timestamp range / sender_id in / + parent_id (= memcell bridge) / top-level OR / nested AND-OR +- MRAG fact embedding: hybrid method embeds atomic_facts that share + the matched episode's memcell parent + +Methods other than ``keyword`` require ``EMBEDDING_*`` creds in .env — +they carry ``@pytest.mark.live_llm`` (and ``@pytest.mark.slow`` where +applicable). CI's default ``-m 'not slow and not live_llm'`` deselects +them. Run with ``pytest -m live_llm ...`` locally to exercise. +""" + +from __future__ import annotations + +import datetime as _dt +import time +from collections.abc import AsyncIterator, Sequence +from importlib import import_module +from pathlib import Path +from typing import Any + +import numpy as np +import pytest +from everalgo.clustering import Cluster as AlgoCluster +from httpx import ASGITransport, AsyncClient + +from everos.component.embedding import get_embedder +from everos.config import load_settings +from everos.entrypoints.api.app import create_app +from everos.infra.persistence.lancedb import ( + AgentCase, + AgentSkill, + AtomicFact, + Episode, + UserProfile, + agent_case_repo, + agent_skill_repo, + atomic_fact_repo, + ensure_business_indexes, + episode_repo, + lancedb_manager, + user_profile_repo, +) +from everos.infra.persistence.sqlite import cluster_repo, mint_cluster_id + +# ``service.__init__`` shadows the submodule under the same name; reach +# the module via importlib so we can reset its private singletons. +search_service_mod = import_module("everos.service.search") + + +# ── Fixture: app with no lifespan + per-test singleton reset ────────── + + +@pytest.fixture +async def client( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> AsyncIterator[AsyncClient]: + """FastAPI app against a tmp memory root; no lifespan (no cascade/OME).""" + from everos.core.persistence.sqlite import SQLModel as _SQLModel + from everos.infra.persistence.sqlite import sqlite_manager + + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + load_settings.cache_clear() + + # Lance: reset connection + cached table handles. + lancedb_manager._conn = None + lancedb_manager._tables.clear() + + # SQLite: reset engine + factory singletons so the next ``get_engine`` + # targets the just-monkeypatched memory root, then run + # ``metadata.create_all`` since we build the app with + # ``lifespan_providers=[]`` and therefore skip + # ``SqliteLifespanProvider``'s normal startup pass. The AGENTIC + # cluster-path tests need the ``cluster`` table; other search paths + # don't touch sqlite but the schema is cheap to materialise. + sqlite_manager._engine = None + sqlite_manager._session_factory = None + _engine = sqlite_manager.get_engine() + async with _engine.begin() as _conn: + await _conn.run_sync(_SQLModel.metadata.create_all) + + # Search service: reset all lazy singletons so each test rebuilds + # against the just-monkey-patched memory root + .env creds. + for attr in ( + "_manager", + "_embedding", + "_reranker", + "_llm_client", + ): + setattr(search_service_mod, attr, None) + for attr in ( + "_embedding_resolved", + "_rerank_resolved", + "_llm_resolved", + ): + setattr(search_service_mod, attr, False) + + app = create_app(lifespan_providers=[]) + transport = ASGITransport(app=app) + async with AsyncClient(transport=transport, base_url="http://test") as c: + yield c + + await lancedb_manager.dispose_connection() + await sqlite_manager.dispose_engine() + load_settings.cache_clear() + + +# ── Seed loading helpers ────────────────────────────────────────────── + + +async def _seed_episodes(rows: list[dict[str, Any]]) -> list[Episode]: + """Validate + add episode rows + build FTS index. + + Tests should pre-mutate per-row fields (e.g. ``session_id``) in the + list passed in, rather than calling this multiple times. LanceDB's + FTS index covers rows present at the time of index creation; calling + :func:`ensure_business_indexes` here rebuilds the index over the + current row set so sparse_recall can see them. + """ + seeded = [Episode.model_validate(r) for r in rows] + await episode_repo.add(seeded) + await ensure_business_indexes() + return seeded + + +async def _seed_atomic_facts(rows: list[dict[str, Any]]) -> list[AtomicFact]: + facts = [AtomicFact.model_validate(r) for r in rows] + await atomic_fact_repo.add(facts) + await ensure_business_indexes() + return facts + + +async def _seed_user_profiles(rows: list[dict[str, Any]]) -> list[UserProfile]: + profiles = [UserProfile.model_validate(r) for r in rows] + await user_profile_repo.add(profiles) + # profile table has no FTS — no index rebuild needed. + return profiles + + +async def _seed_user_memory_cluster(eps: list[dict], *, owner_id: str) -> None: + """Seed one ``user_memory`` cluster covering every memcell in ``eps``. + + The AGENTIC episode path goes through ``acluster_retrieve`` (see + ``memory/search/agentic.py``), which narrows hybrid candidates to the + union of cluster member memcell ids. Tests that exercise the AGENTIC + method therefore need at least one cluster whose members cover the + seeded episodes' ``parent_id``s — otherwise ``cluster_scoped`` yields + nothing and the agentic pipeline returns ``[]``. + + Centroid is embedded from one of the episode bodies via the live + embedder; with a single cluster the cosine ranking against the query + is trivial (only one candidate), so any reasonable anchor works. + """ + memcell_ids = list({ep["parent_id"] for ep in eps}) + centroid_text = eps[0]["episode"] + centroid_vec = await get_embedder().embed(centroid_text) + await cluster_repo.upsert_with_members( + AlgoCluster( + id=mint_cluster_id(), + centroid=np.asarray(centroid_vec, dtype=np.float32), + count=len(memcell_ids), + last_ts=int(time.time() * 1000), + preview=[ep["episode"][:80] for ep in eps[:3]], + members=memcell_ids, + ), + owner_id=owner_id, + owner_type="user", + kind="user_memory", + member_type="memcell", + ) + + +# Minimal agent_case / agent_skill row factories. The search_seed slice +# only ships user-side rows (episode / atomic_fact / foresight / +# user_profile), so agent-owner tests construct synthetic rows directly. +# ``vector`` defaults to zero-filled (fine for BM25-only / dispatch +# coverage where the test never queries the dense path); live_llm tests +# that exercise vector / hybrid recall must pass real embeddings via +# the ``vector`` kwarg so LanceDB's ``nearest_to`` returns the row +# (zero vectors are undefined under cosine distance). +def _agent_case( + entry: str, + *, + owner: str = "a1", + session: str = "sess_x", + day: int = 1, + task_intent: str | None = None, + approach: str | None = None, + vector: Sequence[float] | None = None, +) -> AgentCase: + intent = task_intent if task_intent is not None else f"intent {entry}" + appr = approach if approach is not None else f"approach {entry}" + return AgentCase( + id=f"{owner}_{entry}", + entry_id=entry, + owner_id=owner, + owner_type="agent", + session_id=session, + timestamp=_dt.datetime(2026, 1, day, tzinfo=_dt.UTC), + parent_type="memcell", + parent_id="mc_99", + quality_score=0.8, + task_intent=intent, + task_intent_tokens=intent, + approach=appr, + approach_tokens=appr, + key_insight=None, + md_path=f"agents/{owner}/cases/{entry}.md", + content_sha256="abc", + vector=list(vector) if vector is not None else [0.0] * 1024, + ) + + +def _agent_skill( + name: str, + *, + owner: str = "a1", + description: str | None = None, + content: str | None = None, + vector: Sequence[float] | None = None, + source_case_ids: Sequence[str] | None = None, +) -> AgentSkill: + desc = description if description is not None else f"desc {name}" + body = content if content is not None else f"content {name}" + return AgentSkill( + id=f"{owner}_{name}", + owner_id=owner, + owner_type="agent", + name=name, + description=desc, + description_tokens=desc, + content=body, + content_tokens=body, + confidence=0.9, + maturity_score=0.7, + source_case_ids=( + list(source_case_ids) if source_case_ids is not None else [f"{owner}_ac_1"] + ), + md_path=f"agents/{owner}/skills/{name}/SKILL.md", + content_sha256="abc", + vector=list(vector) if vector is not None else [0.0] * 1024, + ) + + +async def _seed_agent_cases(rows: list[AgentCase]) -> list[AgentCase]: + await agent_case_repo.add(rows) + await ensure_business_indexes() + return rows + + +async def _seed_agent_skills(rows: list[AgentSkill]) -> list[AgentSkill]: + await agent_skill_repo.add(rows) + await ensure_business_indexes() + return rows + + +def _eps_for_owner(seed: dict, owner: str) -> list[dict]: + return [r for r in seed["episode"] if r["owner_id"] == owner] + + +def _facts_for_owner(seed: dict, owner: str) -> list[dict]: + return [r for r in seed["atomic_fact"] if r["owner_id"] == owner] + + +def _profiles_for_owner(seed: dict, owner: str) -> list[dict]: + return [r for r in seed["user_profile"] if r["owner_id"] == owner] + + +def _post( + client: AsyncClient, + *, + owner_id: str = "caroline", + owner_type: str = "user", + query: str = "support", + method: str | None = None, + top_k: int | None = None, + radius: float | None = None, + include_profile: bool | None = None, + enable_llm_rerank: bool | None = None, + filters: dict | None = None, +): + """Build a search request body with sensible defaults; return the coroutine. + + Callers still pass ``owner_id`` / ``owner_type`` (this helper's internal + shape); the wire body uses ``user_id`` or ``agent_id`` per the public + contract — the helper dispatches by ``owner_type``. + """ + body: dict[str, Any] = {"query": query} + if owner_type == "agent": + body["agent_id"] = owner_id + else: + body["user_id"] = owner_id + if method is not None: + body["method"] = method + if top_k is not None: + body["top_k"] = top_k + if radius is not None: + body["radius"] = radius + if include_profile is not None: + body["include_profile"] = include_profile + if enable_llm_rerank is not None: + body["enable_llm_rerank"] = enable_llm_rerank + if filters is not None: + body["filters"] = filters + return client.post("/api/v1/memory/search", json=body, timeout=60.0) + + +# ═══════════════════════════════════════════════════════════════════════ +# 1. method × owner_type dispatch +# ═══════════════════════════════════════════════════════════════════════ + + +async def test_keyword_search_returns_episode_hits( + client: AsyncClient, search_seed: dict +) -> None: + """``method=keyword`` runs BM25-only — no embedder needed.""" + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + + resp = await _post(client, query="LGBTQ", method="keyword") + assert resp.status_code == 200, resp.text + data = resp.json()["data"] + assert isinstance(data["episodes"], list) + # At least one Caroline episode mentions LGBTQ explicitly. + assert len(data["episodes"]) >= 1 + for ep in data["episodes"]: + assert ep["user_id"] == "caroline" + # keyword path never populates nested atomic_facts. + assert ep["atomic_facts"] == [] + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_vector_search_returns_episode_hits( + client: AsyncClient, search_seed: dict +) -> None: + """``method=vector`` embeds the query and ranks by cosine. + + Seeds atomic_facts alongside episodes because the default + ``vector_strategy = "maxsim_atomic"`` (config/default.toml) walks + atomic_fact ANN → max-pool by parent_id → fetch episodes; an + episode-only corpus would return 0 hits under that strategy. + """ + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + await _seed_atomic_facts(_facts_for_owner(search_seed, "caroline")) + + # top_k>0 disables the default unlimited-radius (0.5) quality floor, + # so we can verify dense recall finds *something* without the + # threshold cutting off all candidates. + resp = await _post(client, query="career", method="vector", top_k=5) + assert resp.status_code == 200, resp.text + data = resp.json()["data"] + assert len(data["episodes"]) >= 1 + for ep in data["episodes"]: + assert ep["user_id"] == "caroline" + assert ep["score"] > 0 # cosine similarity in [0, 1] + # vector path doesn't run MRAG, so no nested facts. + assert ep["atomic_facts"] == [] + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_hybrid_search_returns_episode_hits( + client: AsyncClient, search_seed: dict +) -> None: + """``method=hybrid`` (default) runs sparse + dense + fusion.""" + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + + resp = await _post(client, query="counseling") # method=hybrid default + assert resp.status_code == 200, resp.text + data = resp.json()["data"] + assert len(data["episodes"]) >= 1 + for ep in data["episodes"]: + assert ep["user_id"] == "caroline" + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_agentic_search_returns_episode_hits( + client: AsyncClient, search_seed: dict +) -> None: + """``method=agentic`` runs the cross-encoder rerank loop. + + The agentic episode pipeline (see ``memory/search/agentic.py``) is + fact-MaxSim → hybrid_full → cluster_scoped → aagentic_retrieve, so + the corpus needs atomic_facts (for the MaxSim child retrieve) and + at least one ``user_memory`` cluster covering the seeded episodes' + memcell ids (for the ``acluster_retrieve`` narrowing step). + """ + eps = _eps_for_owner(search_seed, "caroline") + await _seed_episodes(eps) + await _seed_atomic_facts(_facts_for_owner(search_seed, "caroline")) + await _seed_user_memory_cluster(eps, owner_id="caroline") + + resp = await _post(client, query="support group", method="agentic") + assert resp.status_code == 200, resp.text + data = resp.json()["data"] + assert len(data["episodes"]) >= 1 + for ep in data["episodes"]: + assert ep["user_id"] == "caroline" + + +# ── Agent owner_type dispatch (separate path: agent_case + agent_skill) ─ + + +async def _seed_one_agent_corpus(owner: str = "a1") -> None: + """Single seed used by the parametrized agent dispatch test. + + One case + one skill sharing surface tokens with the test query + ("refactor authentication") so BM25 deterministically hits both + tables; dense / agentic methods exercise the same rows. Both rows + are embedded with the real embedder so LanceDB's ``nearest_to`` + can rank them (zero vectors are undefined under cosine distance — + the dense path returns 0 hits for them). + """ + from everos.service.search import _get_embedding + + case_intent = "refactor authentication middleware" + case_approach = "split provider lookup from session decode" + skill_desc = "refactor authentication middleware reliably" + skill_body = "step-by-step approach for auth refactors" + + embedder = _get_embedding() + if embedder is not None: + case_vec, skill_vec = await embedder.embed_batch( + [f"{case_intent}\n{case_approach}", f"{skill_desc}\n{skill_body}"] + ) + else: + # No embedder credentials → leave zeros; only keyword assertions + # will pass, vector/hybrid/agentic methods are skipped anyway. + case_vec = [0.0] * 1024 + skill_vec = [0.0] * 1024 + + await _seed_agent_cases( + [ + _agent_case( + "ac_001", + owner=owner, + task_intent=case_intent, + approach=case_approach, + vector=case_vec, + ), + ] + ) + await _seed_agent_skills( + [ + _agent_skill( + "refactor_auth_middleware", + owner=owner, + description=skill_desc, + content=skill_body, + vector=skill_vec, + ), + ] + ) + + +@pytest.mark.parametrize( + "method", + [ + pytest.param("keyword", id="keyword"), + pytest.param( + "vector", + id="vector", + marks=[pytest.mark.slow, pytest.mark.live_llm], + ), + pytest.param( + "hybrid", + id="hybrid", + marks=[pytest.mark.slow, pytest.mark.live_llm], + ), + pytest.param( + "agentic", + id="agentic", + marks=[pytest.mark.slow, pytest.mark.live_llm], + ), + ], +) +async def test_search_agent_dispatch_per_method( + client: AsyncClient, method: str +) -> None: + """``owner_type=agent`` × every ranking method. + + Shared seed (one matching case + one matching skill) and shared + query keep this a true dispatch matrix: + + - ``keyword`` is asserted strictly — BM25 over matching surface + tokens must recall **both** tables. + - ``vector`` / ``hybrid`` / ``agentic`` are looser (``>=1`` from + either table) because dense recall + rerank may legitimately + favour one side over the other on a 1-row corpus. + + All methods must enforce the owner_type hard partition: + ``episodes`` / ``profiles`` stay empty. + """ + await _seed_one_agent_corpus() + + resp = await _post( + client, + owner_id="a1", + owner_type="agent", + query="refactor authentication", + method=method, + top_k=5 if method in ("vector", "hybrid", "agentic") else None, + ) + assert resp.status_code == 200, resp.text + data = resp.json()["data"] + + if method == "keyword": + assert len(data["agent_cases"]) >= 1 + assert len(data["agent_skills"]) >= 1 + else: + assert len(data["agent_cases"]) >= 1 or len(data["agent_skills"]) >= 1 + + for ac in data["agent_cases"]: + assert ac["agent_id"] == "a1" + for sk in data["agent_skills"]: + assert sk["agent_id"] == "a1" + assert data["episodes"] == [] + assert data["profiles"] == [] + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_hybrid_with_llm_rerank_returns_hits( + client: AsyncClient, search_seed: dict +) -> None: + """``method=hybrid`` + ``enable_llm_rerank=true`` runs the phase-5 LLM pass. + + Default hybrid stops after MRAG / LR fusion; opting in adds one + ``chat`` call that re-ranks the top-K. The route must accept the + flag and still return well-formed episodes. + """ + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + + resp = await _post( + client, + query="counseling", + method="hybrid", + enable_llm_rerank=True, + top_k=5, + ) + assert resp.status_code == 200, resp.text + data = resp.json()["data"] + assert len(data["episodes"]) >= 1 + for ep in data["episodes"]: + assert ep["user_id"] == "caroline" + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_hybrid_rerank_bridges_skill_via_case_lineage( + client: AsyncClient, +) -> None: + """Case→skill bridge: a skill whose own text does NOT directly match the + query is surfaced because its ``source_case_ids`` lineage includes a + case that does match. Exercised only on the HYBRID + ``enable_llm_rerank`` + serial path (``_search_cases_and_skills``). + + Seeds one case whose text contains the query keywords and one skill + whose text is intentionally worded off the direct query terms but stays + in the same domain. The direct skill recall (BM25 + cosine) cannot + surface it on its own; the bridge is the path that does, and LLM + rerank keeps it because the topic is genuinely relevant. + """ + from everos.service.search import _get_embedding + + case_id_with_owner = "a_bridge_ac_1" # mirrors AgentCase.id = "_" + case_intent = "refactor authentication middleware" + case_approach = "split provider lookup from session decode" + # Skill is the *generalised lesson* from this case — same domain (auth / + # identity / middleware layering) but worded so the query keywords + # ("refactor authentication") do not surface it via direct BM25 or + # dense recall in isolation. Only the case→skill bridge should + # promote it; LLM rerank then keeps it because the topic *is* relevant. + skill_desc = "isolate session token decoding from provider negotiation" + skill_body = ( + "Principles for splitting identity verification layers across " + "middleware tiers so token parsing and provider lookup evolve " + "independently." + ) + + embedder = _get_embedding() + assert embedder is not None, "live_llm test requires a real embedder" + case_vec, skill_vec = await embedder.embed_batch( + [f"{case_intent}\n{case_approach}", f"{skill_desc}\n{skill_body}"] + ) + + await _seed_agent_cases( + [ + _agent_case( + "ac_1", + owner="a_bridge", + task_intent=case_intent, + approach=case_approach, + vector=case_vec, + ), + ] + ) + await _seed_agent_skills( + [ + _agent_skill( + "graphql_resolver_patterns", + owner="a_bridge", + description=skill_desc, + content=skill_body, + source_case_ids=[case_id_with_owner], + vector=skill_vec, + ), + ] + ) + + resp = await _post( + client, + owner_id="a_bridge", + owner_type="agent", + query="refactor authentication", + method="hybrid", + enable_llm_rerank=True, + top_k=5, + ) + assert resp.status_code == 200, resp.text + data = resp.json()["data"] + + case_ids = [c["id"] for c in data["agent_cases"]] + skill_ids = [s["id"] for s in data["agent_skills"]] + assert case_id_with_owner in case_ids, ( + f"case should match the query directly; got {case_ids}" + ) + assert "a_bridge_graphql_resolver_patterns" in skill_ids, ( + "skill should be surfaced via case lineage bridge, not direct text " + f"match (its text is intentionally worded off the query); got {skill_ids}" + ) + + +# ═══════════════════════════════════════════════════════════════════════ +# 2. owner_id / owner_type isolation +# ═══════════════════════════════════════════════════════════════════════ + + +async def test_search_owner_isolation(client: AsyncClient, search_seed: dict) -> None: + """Same query, two owners — each only sees their own episodes.""" + await _seed_episodes( + _eps_for_owner(search_seed, "caroline") + _eps_for_owner(search_seed, "melanie") + ) + + resp_c = await _post( + client, owner_id="caroline", query="caroline", method="keyword" + ) + resp_m = await _post(client, owner_id="melanie", query="caroline", method="keyword") + assert resp_c.status_code == 200 + assert resp_m.status_code == 200 + + c_ids = {ep["id"] for ep in resp_c.json()["data"]["episodes"]} + m_ids = {ep["id"] for ep in resp_m.json()["data"]["episodes"]} + assert c_ids and m_ids + assert c_ids.isdisjoint(m_ids) + assert all(ep["user_id"] == "caroline" for ep in resp_c.json()["data"]["episodes"]) + assert all(ep["user_id"] == "melanie" for ep in resp_m.json()["data"]["episodes"]) + + +async def test_search_owner_isolation_two_agents(client: AsyncClient) -> None: + """Two agent owners share a hot keyword — each sees only its own rows.""" + await _seed_agent_cases( + [ + _agent_case( + "ac_a1_001", + owner="a1", + task_intent="optimize batch loader latency", + approach="parallelize per-shard fetches", + ), + _agent_case( + "ac_a2_001", + owner="a2", + task_intent="optimize batch loader latency", + approach="cache hot keys in process memory", + ), + ] + ) + + resp_a1 = await _post( + client, + owner_id="a1", + owner_type="agent", + query="batch loader", + method="keyword", + ) + resp_a2 = await _post( + client, + owner_id="a2", + owner_type="agent", + query="batch loader", + method="keyword", + ) + assert resp_a1.status_code == 200 + assert resp_a2.status_code == 200 + + a1_ids = {ac["id"] for ac in resp_a1.json()["data"]["agent_cases"]} + a2_ids = {ac["id"] for ac in resp_a2.json()["data"]["agent_cases"]} + assert a1_ids and a2_ids + assert a1_ids.isdisjoint(a2_ids) + assert all(ac["agent_id"] == "a1" for ac in resp_a1.json()["data"]["agent_cases"]) + assert all(ac["agent_id"] == "a2" for ac in resp_a2.json()["data"]["agent_cases"]) + + +# ═══════════════════════════════════════════════════════════════════════ +# 3. top_k +# ═══════════════════════════════════════════════════════════════════════ + + +async def test_search_top_k_caps_result_count( + client: AsyncClient, search_seed: dict +) -> None: + """``top_k=2`` returns at most 2 episodes.""" + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + resp = await _post(client, query="caroline", method="keyword", top_k=2) + assert resp.status_code == 200 + assert len(resp.json()["data"]["episodes"]) <= 2 + + +async def test_search_top_k_unlimited_returns_all_matching( + client: AsyncClient, search_seed: dict +) -> None: + """``top_k=-1`` (default) means unlimited; should exceed any specific cap.""" + eps = _eps_for_owner(search_seed, "caroline") + await _seed_episodes(eps) + resp = await _post(client, query="caroline", method="keyword", top_k=-1) + assert resp.status_code == 200 + # Caroline has 8 seeded episodes; unlimited returns up to that. + assert len(resp.json()["data"]["episodes"]) <= len(eps) + + +# ═══════════════════════════════════════════════════════════════════════ +# 4. radius (vector-only cosine threshold) +# ═══════════════════════════════════════════════════════════════════════ + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_search_radius_filters_low_similarity( + client: AsyncClient, search_seed: dict +) -> None: + """A near-1.0 radius drops all but the closest hits (likely 0).""" + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + + loose = await _post(client, query="career", method="vector", radius=0.0) + strict = await _post(client, query="career", method="vector", radius=0.95) + assert loose.status_code == 200 and strict.status_code == 200 + + n_loose = len(loose.json()["data"]["episodes"]) + n_strict = len(strict.json()["data"]["episodes"]) + assert n_strict <= n_loose + + +# ═══════════════════════════════════════════════════════════════════════ +# 5. include_profile +# ═══════════════════════════════════════════════════════════════════════ + + +async def test_search_include_profile_false_returns_no_profile( + client: AsyncClient, search_seed: dict +) -> None: + """Default ``include_profile=False`` leaves ``data.profiles`` empty.""" + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + await _seed_user_profiles(_profiles_for_owner(search_seed, "caroline")) + + resp = await _post(client, query="LGBTQ", method="keyword") + assert resp.status_code == 200 + assert resp.json()["data"]["profiles"] == [] + + +async def test_search_include_profile_true_returns_profile( + client: AsyncClient, search_seed: dict +) -> None: + """``include_profile=true`` (user owner) attaches the profile row.""" + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + await _seed_user_profiles(_profiles_for_owner(search_seed, "caroline")) + + resp = await _post(client, query="LGBTQ", method="keyword", include_profile=True) + assert resp.status_code == 200 + profiles = resp.json()["data"]["profiles"] + assert len(profiles) == 1 + p = profiles[0] + assert p["user_id"] == "caroline" + # Direct fetch — no ranking, score is null. + assert p["score"] is None + # Profile data is unpacked from the row's json buckets. + pd = p["profile_data"] + assert "summary" in pd + assert isinstance(pd.get("explicit_info"), list) + assert isinstance(pd.get("implicit_traits"), list) + + +async def test_search_include_profile_ignored_for_agent_owner( + client: AsyncClient, search_seed: dict +) -> None: + """``include_profile=true`` is silently ignored when ``owner_type=agent``. + + Profile rows belong to user owners only (see + :meth:`SearchManager._fetch_profile`); the agent dispatch returns + ``profiles=[]`` even when a user profile matching the query exists. + """ + # Seed a user profile that would match under user-owner search; the + # agent-owner request must NOT pick it up. + await _seed_user_profiles(_profiles_for_owner(search_seed, "caroline")) + # FTS indexes are built lazily by _seed_*; build them for the agent + # tables so sparse_recall returns [] gracefully on the agent path. + await ensure_business_indexes() + + resp = await _post( + client, + owner_id="agent_x", + owner_type="agent", + query="LGBTQ", + method="keyword", + include_profile=True, + ) + assert resp.status_code == 200, resp.text + data = resp.json()["data"] + assert data["profiles"] == [] + # Sanity: agent owner doesn't accidentally recall user-side rows. + assert data["episodes"] == [] + + +# ═══════════════════════════════════════════════════════════════════════ +# 6. filter DSL (parity with /get's compile_filters_for_get) +# ═══════════════════════════════════════════════════════════════════════ + + +async def test_search_filter_by_session_id( + client: AsyncClient, search_seed: dict +) -> None: + """``filters: {session_id: ...}`` confines the search to one session.""" + base = _eps_for_owner(search_seed, "caroline") + # Re-tag half to a different session so the filter has something to do. + half = len(base) // 2 + await _seed_episodes( + [{**r, "session_id": "sess_target"} for r in base[:half]] + + [{**r, "session_id": "sess_other"} for r in base[half:]] + ) + + resp = await _post( + client, + query="caroline", + method="keyword", + filters={"session_id": "sess_target"}, + ) + assert resp.status_code == 200 + eps = resp.json()["data"]["episodes"] + assert eps + assert all(ep["session_id"] == "sess_target" for ep in eps) + + +async def test_search_filter_by_sender_id_in( + client: AsyncClient, search_seed: dict +) -> None: + """``filters: {sender_id: {in: [...]}}`` → array_has OR ... .""" + base = _eps_for_owner(search_seed, "caroline") + half = len(base) // 2 + await _seed_episodes( + [{**r, "sender_ids": ["alice", "assistant"]} for r in base[:half]] + + [{**r, "sender_ids": ["bob", "assistant"]} for r in base[half:]] + ) + + resp = await _post( + client, + query="caroline", + method="keyword", + filters={"sender_id": {"in": ["alice"]}}, + ) + assert resp.status_code == 200 + eps = resp.json()["data"]["episodes"] + assert eps + assert all("alice" in ep["sender_ids"] for ep in eps) + + +async def test_search_filter_by_parent_id_memcell( + client: AsyncClient, search_seed: dict +) -> None: + """``filters: {parent_id: ...}`` — verifies memcell-based slicing. + + Same memcell is the bridge between episodes and atomic_facts. + """ + base = _eps_for_owner(search_seed, "caroline") + target_mc = base[0]["parent_id"] + target_eps = [r for r in base if r["parent_id"] == target_mc] + other_eps = [r for r in base if r["parent_id"] != target_mc] + await _seed_episodes(target_eps + other_eps) + + resp = await _post( + client, + query="caroline", + method="keyword", + filters={"parent_id": target_mc, "parent_type": "memcell"}, + ) + assert resp.status_code == 200 + eps = resp.json()["data"]["episodes"] + assert eps + assert all(ep["id"] in {r["id"] for r in target_eps} for ep in eps) + + +async def test_search_filter_top_level_or( + client: AsyncClient, search_seed: dict +) -> None: + """Top-level ``OR`` combinator parses + executes correctly.""" + base = _eps_for_owner(search_seed, "caroline") + third = len(base) // 3 + await _seed_episodes( + [{**r, "session_id": "sess_a"} for r in base[:third]] + + [{**r, "session_id": "sess_b"} for r in base[third : third * 2]] + + [{**r, "session_id": "sess_c"} for r in base[third * 2 :]] + ) + + resp = await _post( + client, + query="caroline", + method="keyword", + filters={"OR": [{"session_id": "sess_a"}, {"session_id": "sess_b"}]}, + ) + assert resp.status_code == 200 + eps = resp.json()["data"]["episodes"] + assert eps + assert all(ep["session_id"] in {"sess_a", "sess_b"} for ep in eps) + + +async def test_search_filter_nested_and_or( + client: AsyncClient, search_seed: dict +) -> None: + """Nested ``AND`` inside ``OR`` exercises the recursive compiler.""" + base = _eps_for_owner(search_seed, "caroline") + quarter = len(base) // 4 + built = ( + [ + {**r, "session_id": "sess_a", "parent_id": "mc_target"} + for r in base[:quarter] + ] + + [ + {**r, "session_id": "sess_a", "parent_id": "mc_other"} + for r in base[quarter : quarter * 2] + ] + + [ + {**r, "session_id": "sess_b", "parent_id": "mc_target"} + for r in base[quarter * 2 : quarter * 3] + ] + + [ + {**r, "session_id": "sess_c", "parent_id": "mc_other"} + for r in base[quarter * 3 :] + ] + ) + by_id = {r["id"]: r for r in built} + await _seed_episodes(built) + + resp = await _post( + client, + query="caroline", + method="keyword", + filters={ + "OR": [ + { + "AND": [ + {"session_id": "sess_a"}, + {"parent_id": "mc_target"}, + ] + }, + {"session_id": "sess_c"}, + ] + }, + ) + assert resp.status_code == 200 + eps = resp.json()["data"]["episodes"] + assert eps + # ``parent_id`` isn't surfaced in SearchEpisodeItem — cross-reference + # back to the seed dict by id to assert the predicate held. + for ep in eps: + orig = by_id[ep["id"]] + assert ( + orig["session_id"] == "sess_a" and orig["parent_id"] == "mc_target" + ) or orig["session_id"] == "sess_c" + + +async def test_search_filter_by_timestamp_range( + client: AsyncClient, search_seed: dict +) -> None: + """``timestamp`` filter with ``gte`` / ``lt`` operators narrows by time. + + Caroline's 8 seed episodes span 2023-05-08 → 2023-07-15; the + ``2023-07-01`` cut splits them into a "before" and "after" group. + """ + eps = _eps_for_owner(search_seed, "caroline") + await _seed_episodes(eps) + + cutoff = "2023-07-01T00:00:00" + expected_after = {r["id"] for r in eps if r["timestamp"] >= cutoff} + assert expected_after, "seed should have at least one episode after the cutoff" + + resp = await _post( + client, + query="caroline", + method="keyword", + top_k=-1, + filters={"timestamp": {"gte": cutoff}}, + ) + assert resp.status_code == 200 + returned = {ep["id"] for ep in resp.json()["data"]["episodes"]} + # Every returned episode must be on/after the cutoff; the converse + # (every expected episode came back) depends on the keyword match, so + # we only require non-empty + correctness. + assert returned + assert returned <= expected_after + + +async def test_search_filter_no_match_returns_empty( + client: AsyncClient, search_seed: dict +) -> None: + """A filter that matches no row → 200 + episodes=[], not 404 / 422.""" + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + + resp = await _post( + client, + query="caroline", + method="keyword", + filters={"session_id": "sess_that_does_not_exist"}, + ) + assert resp.status_code == 200 + body = resp.json() + assert body["data"]["episodes"] == [] + rid = body["request_id"] + assert len(rid) == 32 and all(c in "0123456789abcdef" for c in rid) + + +# ═══════════════════════════════════════════════════════════════════════ +# 7. MRAG fact embedding — the memcell-bridge contract +# ═══════════════════════════════════════════════════════════════════════ + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_search_hybrid_mrag_path_runs_with_memcell_facts( + client: AsyncClient, search_seed: dict +) -> None: + """HYBRID + MRAG path executes end-to-end with shared-memcell facts seeded. + + Verifies the wiring: + - hybrid recall over episodes returns hits + - the ``parent_id == memcell_id`` bridge picks up atomic_facts whose + parent matches any candidate episode's parent (``facts_for_episodes``) + + .. note:: Fact *embedding* into ``episode.atomic_facts`` is NOT + asserted because ``atomic_fact_recaller.facts_for_episodes`` + currently emits ``FactCandidate(score=0.0)`` for every prefetched + fact (it's a parent_id lookup, not a query-aware recall). The + MRAG ``_expand_heap`` skips facts with non-positive scores, so + they never promote into the top-N. Once facts get a real + query-aware relevance score (e.g. by running a separate dense + recall on atomic_fact too), tighten this assertion to verify + at least one fact gets embedded with parent_id matching its + host episode. + """ + eps = _eps_for_owner(search_seed, "caroline") + await _seed_episodes(eps) + ep_parent_ids = {r["parent_id"] for r in eps} + matching_facts = [ + r for r in search_seed["atomic_fact"] if r["parent_id"] in ep_parent_ids + ] + assert matching_facts, "seed should have at least one fact sharing a memcell" + await _seed_atomic_facts(matching_facts) + + resp = await _post(client, query="counseling", method="hybrid", top_k=5) + assert resp.status_code == 200 + data = resp.json()["data"] + assert data["episodes"], "hybrid should return at least one episode" + # Whichever facts *do* get embedded must share parent_id with their + # host episode (the memcell-bridge invariant). + for ep in data["episodes"]: + if not ep["atomic_facts"]: + continue + host_parent = next((e["parent_id"] for e in eps if e["id"] == ep["id"]), None) + for fact in ep["atomic_facts"]: + seed_fact = next((r for r in matching_facts if r["id"] == fact["id"]), None) + if seed_fact is not None: + assert seed_fact["parent_id"] == host_parent + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_hybrid_mrag_injects_facts_with_alpha_zero( + client: AsyncClient, + search_seed: dict, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """MRAG end-to-end fact injection, exercised with ``alpha=0``. + + Companion to :func:`test_search_hybrid_mrag_path_runs_with_memcell_facts`. + The sibling asserts the contract under prod defaults + (``alpha=1`` × ``fact.score=0`` → final ≤ 0 → fact never enters the + top-N). This test patches ``RankConfig.alpha=0`` so facts inherit + the parent episode's score and enter the heap — verifying the + injection wire end-to-end. + + Drop the monkeypatch (or rewrite this test) once + :mod:`everos.memory.search.recall.atomic_fact`'s ``facts_for_episodes`` + emits query-aware fact scores (currently hardcoded ``score=0.0``). + """ + from everos.memory.search import manager as mgr + + class _AlphaZeroConfig(mgr.RankConfig): + alpha: float = 0.0 + + monkeypatch.setattr(mgr, "RankConfig", _AlphaZeroConfig) + + eps = _eps_for_owner(search_seed, "caroline") + await _seed_episodes(eps) + ep_parent_ids = {r["parent_id"] for r in eps} + matching_facts = [ + r for r in search_seed["atomic_fact"] if r["parent_id"] in ep_parent_ids + ] + assert matching_facts, "seed should have at least one fact sharing a memcell" + await _seed_atomic_facts(matching_facts) + + resp = await _post(client, query="counseling", method="hybrid", top_k=10) + assert resp.status_code == 200 + data = resp.json()["data"] + assert data["episodes"], "hybrid should return at least one episode" + + facts_attached = sum(len(ep["atomic_facts"]) for ep in data["episodes"]) + assert facts_attached >= 1, "alpha=0 should let MRAG promote ≥1 fact into the top-N" + + # Memcell-bridge invariant — every attached fact's parent_id must + # match its host episode's parent_id. + eps_by_id = {e["id"]: e for e in eps} + for ep in data["episodes"]: + host = eps_by_id.get(ep["id"]) + if host is None: + continue + for fact in ep["atomic_facts"]: + seed_fact = next((r for r in matching_facts if r["id"] == fact["id"]), None) + if seed_fact is not None: + assert seed_fact["parent_id"] == host["parent_id"] + + +# ═══════════════════════════════════════════════════════════════════════ +# 8. Request validation — Pydantic 422 surface +# ═══════════════════════════════════════════════════════════════════════ + + +@pytest.mark.parametrize( + "payload", + [ + pytest.param( + {"owner_id": "x", "owner_type": "robot", "query": "q"}, + id="invalid_owner_type", + ), + pytest.param( + {"owner_id": "", "owner_type": "user", "query": "q"}, + id="empty_owner_id", + ), + pytest.param( + {"owner_id": "x", "owner_type": "user", "query": ""}, + id="empty_query", + ), + pytest.param( + {"owner_id": "x", "owner_type": "user", "query": "q", "top_k": 0}, + id="top_k_zero", + ), + pytest.param( + {"owner_id": "x", "owner_type": "user", "query": "q", "top_k": -2}, + id="top_k_below_unlimited", + ), + pytest.param( + {"owner_id": "x", "owner_type": "user", "query": "q", "top_k": 101}, + id="top_k_above_cap", + ), + pytest.param( + {"owner_id": "x", "owner_type": "user", "query": "q", "radius": 1.1}, + id="radius_above_one", + ), + pytest.param( + {"owner_id": "x", "owner_type": "user", "query": "q", "radius": -0.1}, + id="radius_negative", + ), + ], +) +async def test_search_rejects_invalid_request( + client: AsyncClient, payload: dict +) -> None: + """Pydantic / model_validator rejects malformed search requests as 422. + + Pins the request contract: ``owner_type`` literal, non-empty + ``owner_id`` / ``query``, ``top_k`` in {-1} ∪ [1, 100], ``radius`` + in [0.0, 1.0]. Anything else short-circuits at the route layer + without ever touching the SearchManager. + """ + resp = await client.post("/api/v1/memory/search", json=payload, timeout=10.0) + assert resp.status_code == 422, resp.text + + +# ═══════════════════════════════════════════════════════════════════════ +# 9. Envelope shape & error surface +# ═══════════════════════════════════════════════════════════════════════ + + +async def test_search_returns_empty_envelope_for_unknown_owner( + client: AsyncClient, +) -> None: + """Owner with no seeded rows → 200 with all five arrays empty. + + Pins the envelope-shape invariant: ``data.{episodes, profiles, + agent_cases, agent_skills, unprocessed_messages}`` always exist; + an empty result is a successful response, not 404. + """ + resp = await _post(client, owner_id="ghost", query="anything", method="keyword") + assert resp.status_code == 200 + body = resp.json() + rid = body["request_id"] + assert len(rid) == 32 and all(c in "0123456789abcdef" for c in rid) + data = body["data"] + assert data == { + "episodes": [], + "profiles": [], + "agent_cases": [], + "agent_skills": [], + "unprocessed_messages": [], + } + + +async def test_search_user_owner_has_empty_agent_arrays( + client: AsyncClient, search_seed: dict +) -> None: + """User-owner dispatch never populates ``agent_cases`` / ``agent_skills``. + + Belt-and-braces for the owner_type hard partition: even when both + user *and* agent rows live in storage, a user-owner request must + leave the agent arrays empty (and vice versa via the sibling agent + test). Guards against future regressions that might leak cross-type + rows when the dispatch logic is refactored. + """ + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + await _seed_agent_cases([_agent_case("ac_leak", owner="a1")]) + await _seed_agent_skills([_agent_skill("leak_skill", owner="a1")]) + + resp = await _post(client, query="caroline", method="keyword") + assert resp.status_code == 200 + data = resp.json()["data"] + assert data["episodes"] + assert data["agent_cases"] == [] + assert data["agent_skills"] == [] + + +async def test_search_filter_error_returns_422( + client: AsyncClient, search_seed: dict +) -> None: + """Filter DSL violations (unknown field) → 422 via the route's handler. + + ``FilterNode`` is permissive at Pydantic-validation time + (``extra="allow"``) so unknown keys reach :func:`compile_filters`, + which raises :class:`FilterError`. The route catches that and + converts it to a clean 422 (separate path from Pydantic 422). + """ + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + + resp = await _post( + client, + query="caroline", + method="keyword", + filters={"this_field_does_not_exist": "x"}, + ) + assert resp.status_code == 422, resp.text + # Project's global exception handler shapes errors as + # ``{request_id, error: {code, message, timestamp, path}}`` (not + # FastAPI's default ``{"detail": ...}``). The FilterError text + # lands in ``error.message``. + body = resp.json() + assert body["error"]["code"] == "HTTP_ERROR" + assert "this_field_does_not_exist" in body["error"]["message"] + + +# ═══════════════════════════════════════════════════════════════════════ +# 10. Advanced combinations — method × filter, profile × method +# ═══════════════════════════════════════════════════════════════════════ + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_vector_search_with_session_filter( + client: AsyncClient, search_seed: dict +) -> None: + """``method=vector`` + ``filters={session_id: ...}`` narrows by session. + + Verifies the filter ``where`` clause is composed with the vector + recall query (not bypassed by the dense path). Half the seed gets + a target session, half gets another; only target hits may come back. + + The default ``vector_strategy = "maxsim_atomic"`` filters atomic_facts + first (then max-pools to episodes), so the per-episode session_id + mutation has to propagate to each fact via its parent memcell id — + otherwise the where clause drops every fact and recall returns 0. + """ + base = _eps_for_owner(search_seed, "caroline") + facts = _facts_for_owner(search_seed, "caroline") + half = len(base) // 2 + target_parent_ids = {r["parent_id"] for r in base[:half]} + + await _seed_episodes( + [{**r, "session_id": "sess_target"} for r in base[:half]] + + [{**r, "session_id": "sess_other"} for r in base[half:]] + ) + await _seed_atomic_facts( + [ + { + **f, + "session_id": ( + "sess_target" + if f["parent_id"] in target_parent_ids + else "sess_other" + ), + } + for f in facts + ] + ) + + resp = await _post( + client, + query="caroline", + method="vector", + top_k=10, + filters={"session_id": "sess_target"}, + ) + assert resp.status_code == 200, resp.text + eps = resp.json()["data"]["episodes"] + assert eps + assert all(ep["session_id"] == "sess_target" for ep in eps) + + +@pytest.mark.slow +@pytest.mark.live_llm +async def test_agentic_search_with_timestamp_filter( + client: AsyncClient, search_seed: dict +) -> None: + """``method=agentic`` + ``filters={timestamp: {gte: ...}}`` honors the cutoff. + + The agentic path runs its own cross-encoder rerank loop; the + ``where`` clause must still apply at recall, not be silently + dropped by the agentic pipeline. Same seed requirements as the + non-filter agentic test (facts + cluster). + """ + eps = _eps_for_owner(search_seed, "caroline") + await _seed_episodes(eps) + + cutoff = "2023-07-01T00:00:00" + expected_after = {r["id"] for r in eps if r["timestamp"] >= cutoff} + assert expected_after, "seed should have at least one episode after the cutoff" + + # The search_seed slice ships every caroline fact attached to one + # episode (mc_a7a1a4cfd8e6, 2023-05-08); under maxsim_atomic the + # cutoff would filter all facts out and recall would be empty. Spread + # the facts round-robin across the post-cutoff episodes (matching both + # ``parent_id`` and ``timestamp``) so the filter exercises real + # narrowing on a non-empty fact set. + eps_post = [r for r in eps if r["timestamp"] >= cutoff] + facts = _facts_for_owner(search_seed, "caroline") + await _seed_atomic_facts( + [ + { + **f, + "parent_id": eps_post[i % len(eps_post)]["parent_id"], + "timestamp": eps_post[i % len(eps_post)]["timestamp"], + } + for i, f in enumerate(facts) + ] + ) + await _seed_user_memory_cluster(eps, owner_id="caroline") + + # ``counseling`` matches the post-cutoff caroline corpus (July 6 episode + # is explicitly about counseling + mental health), so the agentic + # pipeline's LLM sufficiency check has concrete content to score on + # — a vague query like ``support`` against the narrow filtered set + # makes the LLM step return ``[]`` and the test flakes. + resp = await _post( + client, + query="counseling", + method="agentic", + top_k=10, + filters={"timestamp": {"gte": cutoff}}, + ) + assert resp.status_code == 200, resp.text + eps_out = resp.json()["data"]["episodes"] + assert eps_out + returned = {ep["id"] for ep in eps_out} + assert returned <= expected_after + + +@pytest.mark.parametrize( + "method", + [ + pytest.param( + "vector", + id="vector", + marks=[pytest.mark.slow, pytest.mark.live_llm], + ), + pytest.param( + "hybrid", + id="hybrid", + marks=[pytest.mark.slow, pytest.mark.live_llm], + ), + ], +) +async def test_search_include_profile_independent_of_method( + client: AsyncClient, search_seed: dict, method: str +) -> None: + """``include_profile=true`` works regardless of ranking method. + + Profile fetch is decoupled from the recall pipeline (see + :meth:`SearchManager._fetch_profile` — it gates on + ``include_profile && owner_type=="user"`` and ignores ``method``). + Pin that contract: vector / hybrid must also surface the profile, + not just keyword. + """ + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + await _seed_user_profiles(_profiles_for_owner(search_seed, "caroline")) + + resp = await _post( + client, + query="LGBTQ", + method=method, + include_profile=True, + top_k=5, + ) + assert resp.status_code == 200, resp.text + profiles = resp.json()["data"]["profiles"] + assert len(profiles) == 1 + p = profiles[0] + assert p["user_id"] == "caroline" + assert p["score"] is None # direct fetch — no ranking + + +# ═══════════════════════════════════════════════════════════════════════ +# 11. unprocessed_messages — in-flight boundary-detection buffer +# ═══════════════════════════════════════════════════════════════════════ +# +# Buffer rows have no owner attribution (boundary detection runs before +# owner inference). The /search route surfaces them ONLY when ``filters`` +# carries a top-level ``session_id`` eq scalar — the only meaningful +# query dimension on the unattributed buffer. Compound shapes (AND / OR, +# operator maps) do NOT trigger the lookup. +# +# White-box: the helpers below write directly into the +# ``unprocessed_buffer`` SQLite table via the public repo (no /add +# round-trip, so the boundary detector never runs and the rows stay +# unprocessed). The track string mirrors ``service._boundary._TRACK`` +# (the shared single-pass detector track). + + +async def _seed_unprocessed_buffer_rows( + session_id: str, + rows_spec: list[dict[str, Any]], + *, + app_id: str = "default", + project_id: str = "default", + track: str = "memorize", +) -> None: + """Drop a list of rows into the unprocessed_buffer table via the public repo. + + Mirrors the shape the memorize pipeline writes: ``content_items_json`` + holds the single-text ContentItem; ``text`` holds the derived plain + string. Each ``rows_spec`` entry is ``{"message_id", "sender_id", + "text", "role"?, "timestamp_offset_seconds"?}``. Timestamps default + to sequential offsets from ``now`` so the repo's ts-asc ordering is + deterministic. + + Always writes the full (session, track) slice in a single + :meth:`unprocessed_buffer_repo.replace` call — re-reading detached + SQLModel instances and rewriting them in a later session triggers + silent insert dedup on the PK, so callers must batch their rows. + """ + import datetime as _dt2 + import json + + from everos.component.utils.datetime import get_now_with_timezone + from everos.infra.persistence.sqlite import ( + UnprocessedBuffer, + unprocessed_buffer_repo, + ) + + base_ts = get_now_with_timezone() + rows: list[UnprocessedBuffer] = [] + for idx, spec in enumerate(rows_spec): + ts = base_ts + _dt2.timedelta(seconds=spec.get("timestamp_offset_seconds", idx)) + rows.append( + UnprocessedBuffer( + message_id=spec["message_id"], + app_id=app_id, + project_id=project_id, + session_id=session_id, + track=track, + sender_id=spec["sender_id"], + sender_name=None, + role=spec.get("role", "user"), + timestamp=ts, + content_items_json=json.dumps([{"type": "text", "text": spec["text"]}]), + text=spec["text"], + tool_calls_json=None, + tool_call_id=None, + ) + ) + await unprocessed_buffer_repo.replace( + session_id, + track, + rows, + app_id=app_id, + project_id=project_id, + ) + + +async def test_search_unprocessed_messages_returned_for_top_level_session_filter( + client: AsyncClient, search_seed: dict +) -> None: + """``filters={"session_id": ""}`` surfaces the buffered messages. + + Writes two rows into the unprocessed_buffer (same session) and one + into a different session, then queries with the target session as a + top-level eq scalar. Only the two matching rows should come back — + in timestamp order, with the single-text shorthand collapsed to a + plain ``str``. + + White-box surface: ``unprocessed_buffer_repo`` (read & write via the + same public repo the pipeline uses). + """ + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + + # Two rows in the target session, one in a noise session. + await _seed_unprocessed_buffer_rows( + "sess_inflight", + [ + { + "message_id": "msg_buf_001", + "sender_id": "caroline", + "text": "hello from buffer 1", + }, + { + "message_id": "msg_buf_002", + "sender_id": "caroline", + "text": "hello from buffer 2", + }, + ], + ) + await _seed_unprocessed_buffer_rows( + "sess_other", + [ + { + "message_id": "msg_buf_other", + "sender_id": "caroline", + "text": "should not appear", + }, + ], + ) + + resp = await _post( + client, + query="anything", + method="keyword", + filters={"session_id": "sess_inflight"}, + ) + assert resp.status_code == 200, resp.text + data = resp.json()["data"] + + unprocessed = data["unprocessed_messages"] + ids = [m["id"] for m in unprocessed] + assert ids == ["msg_buf_001", "msg_buf_002"], ( + f"only target-session rows in ts order; got {ids}" + ) + + first = unprocessed[0] + assert first["session_id"] == "sess_inflight" + assert first["sender_id"] == "caroline" + assert first["role"] == "user" + # Single-text item collapses to the shorthand str. + assert first["content"] == "hello from buffer 1" + assert first["tool_calls"] is None + assert first["tool_call_id"] is None + + +async def test_search_unprocessed_messages_empty_without_top_level_session_filter( + client: AsyncClient, search_seed: dict +) -> None: + """No ``filters.session_id`` (or compound shape) → ``unprocessed_messages=[]``. + + Verifies the trigger semantics: the same buffer rows that the sibling + test surfaces stay invisible to: + + 1. a request without any ``filters``, + 2. a request whose ``session_id`` sits inside ``AND`` (not top-level), + 3. a request whose ``session_id`` uses the ``{"eq": ...}`` operator map. + + All three return ``[]`` even though matching buffer rows exist. + + White-box surface: ``unprocessed_buffer_repo``. + """ + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + await _seed_unprocessed_buffer_rows( + "sess_inflight", + [ + { + "message_id": "msg_buf_xyz", + "sender_id": "caroline", + "text": "should never leak", + }, + ], + ) + + # 1. No filters at all. + resp_no_filter = await _post(client, query="anything", method="keyword") + assert resp_no_filter.status_code == 200, resp_no_filter.text + assert resp_no_filter.json()["data"]["unprocessed_messages"] == [] + + # 2. session_id inside AND — compound, not top-level eq scalar. + resp_and = await _post( + client, + query="anything", + method="keyword", + filters={"AND": [{"session_id": "sess_inflight"}]}, + ) + assert resp_and.status_code == 200, resp_and.text + assert resp_and.json()["data"]["unprocessed_messages"] == [] + + # 3. session_id via operator map — also not a top-level eq scalar. + resp_op = await _post( + client, + query="anything", + method="keyword", + filters={"session_id": {"eq": "sess_inflight"}}, + ) + assert resp_op.status_code == 200, resp_op.text + assert resp_op.json()["data"]["unprocessed_messages"] == [] + + +async def test_search_unprocessed_messages_assistant_role_round_trips( + client: AsyncClient, search_seed: dict +) -> None: + """Buffer surfacing carries the original ``role`` through the wire. + + Pins that role / content shape survive the round trip — the buffered + row was inserted with ``role="assistant"`` and a multi-content payload + would degrade to the opaque ``list[dict]`` shorthand, while a single + text item collapses to ``str``. + """ + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + await _seed_unprocessed_buffer_rows( + "sess_assistant", + [ + { + "message_id": "msg_buf_asst", + "sender_id": "bot", + "text": "assistant-side buffered reply", + "role": "assistant", + }, + ], + ) + + resp = await _post( + client, + query="anything", + method="keyword", + filters={"session_id": "sess_assistant"}, + ) + assert resp.status_code == 200, resp.text + msgs = resp.json()["data"]["unprocessed_messages"] + assert [m["id"] for m in msgs] == ["msg_buf_asst"] + assert msgs[0]["role"] == "assistant" + assert msgs[0]["content"] == "assistant-side buffered reply" + + +# ═══════════════════════════════════════════════════════════════════════ +# 12. Timezone discipline — storage UTC vs display tz, no drift on switch +# ═══════════════════════════════════════════════════════════════════════ +# +# Q2's core promise: a row written under ``EVEROS_MEMORY__TIMEZONE=X`` +# read back under ``EVEROS_MEMORY__TIMEZONE=Y`` represents the **same +# UTC instant** — only the rendered offset changes. Without the +# storage-UTC discipline (Q2), a tz switch would silently misalign +# every stored row. +# +# Tests below exercise the full HTTP read path under two display zones +# back-to-back against the same on-disk row, plus a white-box probe +# into the SQLite ``unprocessed_buffer`` row to confirm the stored +# datetime is UTC-aware regardless of which zone the writer ran under. + + +async def _switch_display_tz(monkeypatch: pytest.MonkeyPatch, tz: str) -> None: + """Switch ``EVEROS_MEMORY__TIMEZONE`` mid-test + drop both caches. + + Both ``load_settings`` and ``_display_tz`` are functools-cached; + failing to clear them means the env-var change has no effect — see + [docs/datetime.md](../../docs/datetime.md) §testing. + """ + from everos.component.utils import datetime as dt_module + from everos.config import load_settings as _ls + + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", tz) + _ls.cache_clear() + dt_module._display_tz.cache_clear() + + +async def test_timezone_switch_preserves_utc_instant_across_responses( + client: AsyncClient, + search_seed: dict, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Q2 anti-drift contract: same on-disk row, two display zones, one instant. + + Real data path: + 1. Configure ``EVEROS_MEMORY__TIMEZONE=Asia/Shanghai`` and write + an unprocessed-buffer row at a fixed instant. + 2. HTTP ``/search`` it: expect ``+08:00`` offset in the response. + 3. White-box probe SQLite directly: stored datetime is UTC-aware, + not naive Shanghai bytes. + 4. Switch to ``EVEROS_MEMORY__TIMEZONE=UTC``, drop caches. + 5. HTTP ``/search`` the *same* row: expect ``Z`` / ``+00:00`` offset. + 6. Parse both response timestamps back to UTC instants — they + must be equal **and** equal the original input instant. + + A pre-Q2 implementation would fail at step 5: the SQLite row stored + naive Shanghai bytes, reinterpreted as UTC, would jump 8h forward. + """ + import datetime as _real_dt + import json as _json + + from everos.component.utils.datetime import from_iso_format + from everos.infra.persistence.sqlite import ( + UnprocessedBuffer, + unprocessed_buffer_repo, + ) + + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + + # Step 1: switch to Shanghai, write a row at a deterministic instant. + await _switch_display_tz(monkeypatch, "Asia/Shanghai") + + # 06:00 UTC ↔ 14:00 Shanghai — pinned so the cross-zone assertion has + # a clean number to read. + target_instant_utc = _real_dt.datetime(2026, 5, 29, 6, 0, 0, tzinfo=_real_dt.UTC) + row = UnprocessedBuffer( + message_id="msg_tz_001", + app_id="default", + project_id="default", + session_id="sess_tz_drift", + track="memorize", + sender_id="alice", + sender_name=None, + role="user", + timestamp=target_instant_utc, + content_items_json=_json.dumps( + [{"type": "text", "text": "deterministic-instant row"}] + ), + text="deterministic-instant row", + tool_calls_json=None, + tool_call_id=None, + ) + await unprocessed_buffer_repo.replace( + "sess_tz_drift", + "memorize", + [row], + app_id="default", + project_id="default", + ) + + # Step 2: /search under Shanghai display tz → expect +08:00 offset. + resp_sh = await _post( + client, + query="anything", + method="keyword", + filters={"session_id": "sess_tz_drift"}, + ) + assert resp_sh.status_code == 200, resp_sh.text + msgs_sh = resp_sh.json()["data"]["unprocessed_messages"] + assert len(msgs_sh) == 1 + ts_sh = msgs_sh[0]["timestamp"] + assert ts_sh.endswith("+08:00"), f"expected Shanghai offset, got {ts_sh!r}" + # 06:00 UTC rendered in +08:00 should read 14:00 local. + assert "T14:00:00" in ts_sh, ts_sh + + # Step 3: white-box probe — SQLite row comes back UTC-aware. + # + # The SQLAlchemy ``load`` event listener registered on ``BaseTable`` + # (see ``core/persistence/sqlite/base.py``) re-attaches ``tzinfo=UTC`` + # to every ``UtcDatetime`` column after ORM hydrate, so callers never + # observe a naive datetime — even though SQLite physically strips tz + # at write time and SQLAlchemy ORM bypasses Pydantic on read. + rows_back = await unprocessed_buffer_repo.list_for_track( + "sess_tz_drift", "memorize", app_id="default", project_id="default" + ) + stored = rows_back[0].timestamp + assert stored.tzinfo is _real_dt.UTC, ( + "SQLAlchemy load event hook should attach UTC on hydrate; " + f"got tzinfo={stored.tzinfo!r}" + ) + assert stored == target_instant_utc + + # Step 4: switch display tz to UTC, drop caches. + await _switch_display_tz(monkeypatch, "UTC") + + # Step 5: /search the same row under UTC display tz. + resp_utc = await _post( + client, + query="anything", + method="keyword", + filters={"session_id": "sess_tz_drift"}, + ) + assert resp_utc.status_code == 200, resp_utc.text + msgs_utc = resp_utc.json()["data"]["unprocessed_messages"] + assert len(msgs_utc) == 1 + ts_utc = msgs_utc[0]["timestamp"] + # Pydantic canonicalises ``timezone.utc`` to the ``Z`` suffix. + assert ts_utc.endswith("Z") or ts_utc.endswith("+00:00"), ts_utc + # 06:00 UTC rendered with UTC display = 06:00 wall-clock. + assert "T06:00:00" in ts_utc, ts_utc + + # Step 6: the anti-drift assertion — same UTC instant across both renders. + instant_via_sh = from_iso_format(ts_sh).astimezone(_real_dt.UTC) + instant_via_utc = from_iso_format(ts_utc).astimezone(_real_dt.UTC) + assert instant_via_sh == instant_via_utc == target_instant_utc, ( + f"display-tz switch must not drift the UTC instant; " + f"got {instant_via_sh=} {instant_via_utc=} {target_instant_utc=}" + ) + + +async def test_timezone_switch_preserves_utc_instant_for_episode( + client: AsyncClient, + search_seed: dict, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Same anti-drift contract, but for the LanceDB-backed episode path. + + LanceDB's Arrow schema declares timestamp columns with ``tz=UTC`` + (see :attr:`BaseLanceTable.UTC_DATETIME_FIELDS`) so PyArrow returns + aware UTC datetimes directly — no caller-side ``ensure_utc`` needed. + The shaper only runs ``to_display_tz`` to convert UTC to the + configured display zone. This test pins that path against a tz switch. + """ + import datetime as _real_dt + + from everos.component.utils.datetime import from_iso_format + + # Seed one episode at a fixed instant. The seed's ``timestamp`` is + # passed straight into ``Episode.model_validate`` — LanceDB will + # strip the tz at write time and store UTC bytes. + pinned_utc = _real_dt.datetime(2026, 5, 29, 6, 0, 0, tzinfo=_real_dt.UTC) + base = _eps_for_owner(search_seed, "caroline")[:1] + base[0] = {**base[0], "timestamp": pinned_utc, "session_id": "sess_ep_tz"} + await _seed_episodes(base) + + # Step 1: Shanghai display tz. + await _switch_display_tz(monkeypatch, "Asia/Shanghai") + resp_sh = await _post( + client, + query="caroline", + method="keyword", + filters={"session_id": "sess_ep_tz"}, + ) + assert resp_sh.status_code == 200, resp_sh.text + eps_sh = resp_sh.json()["data"]["episodes"] + assert eps_sh, "seed should have produced a keyword match" + ts_sh = eps_sh[0]["timestamp"] + assert ts_sh.endswith("+08:00"), ts_sh + assert "T14:00:00" in ts_sh, ts_sh + + # Step 2: switch to UTC. + await _switch_display_tz(monkeypatch, "UTC") + resp_utc = await _post( + client, + query="caroline", + method="keyword", + filters={"session_id": "sess_ep_tz"}, + ) + assert resp_utc.status_code == 200, resp_utc.text + eps_utc = resp_utc.json()["data"]["episodes"] + assert eps_utc + ts_utc = eps_utc[0]["timestamp"] + assert ts_utc.endswith("Z") or ts_utc.endswith("+00:00"), ts_utc + assert "T06:00:00" in ts_utc, ts_utc + + # Step 3: anti-drift — same UTC instant before and after the switch. + instant_via_sh = from_iso_format(ts_sh).astimezone(_real_dt.UTC) + instant_via_utc = from_iso_format(ts_utc).astimezone(_real_dt.UTC) + assert instant_via_sh == instant_via_utc == pinned_utc, ( + instant_via_sh, + instant_via_utc, + pinned_utc, + ) + + +async def test_timezone_reverse_switch_utc_to_shanghai( + client: AsyncClient, + search_seed: dict, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Reverse direction: write under UTC, read under Shanghai → no drift. + + Symmetric counterpart of + :func:`test_timezone_switch_preserves_utc_instant_across_responses`. + Covers the migration scenario where the default UTC deployment + later turns on a local display tz. + """ + import datetime as _real_dt + import json as _json + + from everos.component.utils.datetime import from_iso_format + from everos.infra.persistence.sqlite import ( + UnprocessedBuffer, + unprocessed_buffer_repo, + ) + + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + + # Step 1: UTC display tz first, write at 06:00 UTC = 14:00 Shanghai. + await _switch_display_tz(monkeypatch, "UTC") + + target_instant_utc = _real_dt.datetime(2026, 5, 29, 6, 0, 0, tzinfo=_real_dt.UTC) + row = UnprocessedBuffer( + message_id="msg_rev_001", + app_id="default", + project_id="default", + session_id="sess_rev", + track="memorize", + sender_id="alice", + sender_name=None, + role="user", + timestamp=target_instant_utc, + content_items_json=_json.dumps([{"type": "text", "text": "reverse-tz row"}]), + text="reverse-tz row", + tool_calls_json=None, + tool_call_id=None, + ) + await unprocessed_buffer_repo.replace( + "sess_rev", + "memorize", + [row], + app_id="default", + project_id="default", + ) + + # Step 2: read under UTC → expect Z / +00:00 + 06:00 wall clock. + resp_utc = await _post( + client, + query="anything", + method="keyword", + filters={"session_id": "sess_rev"}, + ) + assert resp_utc.status_code == 200, resp_utc.text + msgs_utc = resp_utc.json()["data"]["unprocessed_messages"] + ts_utc = msgs_utc[0]["timestamp"] + assert ts_utc.endswith("Z") or ts_utc.endswith("+00:00"), ts_utc + assert "T06:00:00" in ts_utc, ts_utc + + # Step 3: switch to Shanghai, read again → expect +08:00 + 14:00 wall clock. + await _switch_display_tz(monkeypatch, "Asia/Shanghai") + resp_sh = await _post( + client, + query="anything", + method="keyword", + filters={"session_id": "sess_rev"}, + ) + assert resp_sh.status_code == 200, resp_sh.text + msgs_sh = resp_sh.json()["data"]["unprocessed_messages"] + ts_sh = msgs_sh[0]["timestamp"] + assert ts_sh.endswith("+08:00"), ts_sh + assert "T14:00:00" in ts_sh, ts_sh + + # Step 4: anti-drift — same UTC instant under both renders. + instant_via_utc = from_iso_format(ts_utc).astimezone(_real_dt.UTC) + instant_via_sh = from_iso_format(ts_sh).astimezone(_real_dt.UTC) + assert instant_via_utc == instant_via_sh == target_instant_utc + + +async def test_timezone_mixed_timeline_two_writes_across_switch( + client: AsyncClient, + search_seed: dict, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Mixed timeline: write A under Shanghai, switch to UTC, write B, query both. + + Pins that storage UTC normalisation is **per-write** — switching + display tz between writes does not corrupt earlier rows nor leak + display-tz bytes into new rows. Both rows must come back as their + original UTC instants regardless of which display tz the reader + happens to be on. + """ + import datetime as _real_dt + import json as _json + + from everos.component.utils.datetime import from_iso_format + from everos.infra.persistence.sqlite import ( + UnprocessedBuffer, + unprocessed_buffer_repo, + ) + + await _seed_episodes(_eps_for_owner(search_seed, "caroline")) + + # Step 1: write row A under Shanghai. + await _switch_display_tz(monkeypatch, "Asia/Shanghai") + instant_a = _real_dt.datetime(2026, 5, 29, 6, 0, 0, tzinfo=_real_dt.UTC) + row_a = UnprocessedBuffer( + message_id="msg_mixed_a", + app_id="default", + project_id="default", + session_id="sess_mixed", + track="memorize", + sender_id="alice", + sender_name=None, + role="user", + timestamp=instant_a, + content_items_json=_json.dumps([{"type": "text", "text": "row A"}]), + text="row A", + tool_calls_json=None, + tool_call_id=None, + ) + + # Step 2: switch to UTC, build row B. + await _switch_display_tz(monkeypatch, "UTC") + instant_b = _real_dt.datetime(2026, 5, 30, 18, 0, 0, tzinfo=_real_dt.UTC) + row_b = UnprocessedBuffer( + message_id="msg_mixed_b", + app_id="default", + project_id="default", + session_id="sess_mixed", + track="memorize", + sender_id="alice", + sender_name=None, + role="user", + timestamp=instant_b, + content_items_json=_json.dumps([{"type": "text", "text": "row B"}]), + text="row B", + tool_calls_json=None, + tool_call_id=None, + ) + + # Persist both in one replace so the (session, track) slice owns both rows. + # ``replace`` deletes the slice then inserts; a write-A/switch/write-B + # interleaving on the wire would have the same storage effect. + await unprocessed_buffer_repo.replace( + "sess_mixed", + "memorize", + [row_a, row_b], + app_id="default", + project_id="default", + ) + + # Step 3: query under Shanghai → both rows render +08:00, instants preserved. + await _switch_display_tz(monkeypatch, "Asia/Shanghai") + resp_sh = await _post( + client, + query="anything", + method="keyword", + filters={"session_id": "sess_mixed"}, + ) + assert resp_sh.status_code == 200, resp_sh.text + msgs_sh = resp_sh.json()["data"]["unprocessed_messages"] + by_id_sh = {m["id"]: m["timestamp"] for m in msgs_sh} + assert by_id_sh.keys() == {"msg_mixed_a", "msg_mixed_b"} + + for ts in by_id_sh.values(): + assert ts.endswith("+08:00"), ts + + instant_a_sh = from_iso_format(by_id_sh["msg_mixed_a"]).astimezone(_real_dt.UTC) + instant_b_sh = from_iso_format(by_id_sh["msg_mixed_b"]).astimezone(_real_dt.UTC) + assert instant_a_sh == instant_a + assert instant_b_sh == instant_b + + # Step 4: query under UTC → both render Z/+00:00, instants preserved. + await _switch_display_tz(monkeypatch, "UTC") + resp_utc = await _post( + client, + query="anything", + method="keyword", + filters={"session_id": "sess_mixed"}, + ) + assert resp_utc.status_code == 200, resp_utc.text + msgs_utc = resp_utc.json()["data"]["unprocessed_messages"] + by_id_utc = {m["id"]: m["timestamp"] for m in msgs_utc} + + for ts in by_id_utc.values(): + assert ts.endswith("Z") or ts.endswith("+00:00"), ts + + instant_a_utc = from_iso_format(by_id_utc["msg_mixed_a"]).astimezone(_real_dt.UTC) + instant_b_utc = from_iso_format(by_id_utc["msg_mixed_b"]).astimezone(_real_dt.UTC) + assert instant_a_utc == instant_a + assert instant_b_utc == instant_b diff --git a/tests/fixtures/README.md b/tests/fixtures/README.md new file mode 100644 index 00000000..45f280fa --- /dev/null +++ b/tests/fixtures/README.md @@ -0,0 +1,39 @@ +# Test Fixtures + +The fixtures in this directory are checked-in artifacts. The two largest +of them were originally produced by converter scripts that have been +removed from the repository — the fixtures themselves are the source of +truth and should rarely need to be regenerated. + +## `long_conversation_locomo_caroline_melanie.json` + +Derived from the LoCoMo dataset (`snap-research/locomo`, file +`data/locomo10.json`). + +**License: CC BY-NC 4.0** — non-commercial use only. This file is +carved out of the project-wide Apache-2.0 license; see `NOTICE` at the +repository root for the explicit declaration. + +If you need to regenerate it: fetch `locomo10.json` from the upstream +LoCoMo project, then adapt it to the fields the tests consume +(`everos_session_id`, `everos_user_id_for_test`, `speakers`, +`dialog`, ...). The original conversion script is not part of the +public repository. + +## `agent_trajectories/*.json` + +Hand-curated subset of agent tool-call traces. The selection that the +e2e tests load is enumerated in +`tests/e2e/test_add_flush_agent_pipeline_e2e.py` — that file is the +source of truth. + +If you need to add or replace a trajectory, author the fixture by hand +(or with your own one-off script). The previous converter pulled from +internal paths and is not maintained as part of this repository. + +## `search_seed/` + +LanceDB / SQLite seed bundle for `/search` e2e. Produced by +`_dump_search_seed.py` (kept in-tree); regenerate by running that +script after a successful `add → flush` pipeline against the LoCoMo +fixture above. diff --git a/tests/fixtures/_dump_search_seed.py b/tests/fixtures/_dump_search_seed.py new file mode 100644 index 00000000..b5762ebc --- /dev/null +++ b/tests/fixtures/_dump_search_seed.py @@ -0,0 +1,135 @@ +"""One-shot dumper: extract a search-test seed from a corpus snapshot. + +Reads the LanceDB tables under +``/tmp/everos_corpus_v2/.index/lancedb/`` (the snapshot produced by +``tests/e2e/test_add_flush_user_pipeline_e2e.py`` with ``EVEROS_KEEP_CORPUS_TO`` +set), samples a small representative slice, and emits JSON fixtures +under ``tests/fixtures/search_seed/``. + +Sampling rules: + +- **episode**: first 8 rows per owner (caroline + melanie). Captures + the parent_id (= memcell_id) set so downstream tables can be + bridge-consistent. +- **atomic_fact**: every row whose ``parent_id`` is in the episode- + parent set above, capped at 50 to keep the seed compact. This + guarantees MRAG-fusion testing can verify "facts sharing a + memcell with the matched episode get embedded". +- **foresight**: 5 per owner. Archived for future use; current + ``/search`` does not query foresight, so the seed only exists so + downstream tests can opt in without re-cutting the corpus. +- **user_profile**: 1 per owner (= 2 total). + +Run:: + + python tests/fixtures/_dump_search_seed.py + +Re-run any time the corpus changes; output JSON is committed to +git so other contributors don't need the corpus locally. +""" + +from __future__ import annotations + +import json +import sys +from datetime import datetime +from pathlib import Path +from typing import Any + +import lancedb + +CORPUS = Path("/tmp/everos_corpus_v2/.index/lancedb") +OUT_DIR = Path(__file__).parent / "search_seed" +ALL_OWNERS = ("caroline", "melanie") + + +def _serialise(row: dict[str, Any]) -> dict[str, Any]: + """Make a LanceDB row dict JSON-safe (numpy → list, datetime → ISO).""" + out: dict[str, Any] = {} + for k, v in row.items(): + if v is None: + out[k] = None + elif hasattr(v, "tolist"): # numpy ndarray (vector) + out[k] = v.tolist() + elif isinstance(v, datetime): + out[k] = v.isoformat() + else: + out[k] = v + return out + + +def _read(db: lancedb.DBConnection, table: str) -> list[dict[str, Any]]: + if f"{table}.lance" not in {p.name for p in CORPUS.iterdir()}: + raise FileNotFoundError(f"corpus table missing: {table}") + return db.open_table(table).to_arrow().to_pylist() + + +def main() -> None: + if not CORPUS.exists(): + print(f"corpus not found: {CORPUS}", file=sys.stderr) + print("hint: run the add+flush pipeline first with", file=sys.stderr) + print(" EVEROS_KEEP_CORPUS_TO=/tmp/everos_corpus_v2", file=sys.stderr) + sys.exit(1) + + OUT_DIR.mkdir(parents=True, exist_ok=True) + db = lancedb.connect(str(CORPUS)) + + # 1) episodes — first 8 per owner. + eps_all = _read(db, "episode") + eps: list[dict[str, Any]] = [] + parent_memcells: set[str] = set() + for owner in ALL_OWNERS: + owned = [r for r in eps_all if r["owner_id"] == owner][:8] + eps.extend(owned) + for r in owned: + parent_memcells.add(r["parent_id"]) + + # 2) atomic_facts — every fact whose parent_id is in the episode + # parent set, capped to keep the seed compact (and so MRAG + # ``facts_for_episodes`` has a useful but bounded pool to + # bucket back into episodes). + afs_all = _read(db, "atomic_fact") + # Atomic facts fan out per-owner (a single fact about a memcell that + # mentions two users gets two rows, one for each owner) — sampling + # naively can leave one owner with zero facts. Take per-owner caps + # so both caroline and melanie have facts whose parent_id matches + # their own episodes' parent_id (MRAG bridge). + afs: list[dict[str, Any]] = [] + for owner in ALL_OWNERS: + afs.extend( + [ + r + for r in afs_all + if r["owner_id"] == owner and r["parent_id"] in parent_memcells + ][:10] + ) + + # 3) foresights — 5 per owner, archived for future use. + fss_all = _read(db, "foresight") + fss: list[dict[str, Any]] = [] + for owner in ALL_OWNERS: + fss.extend([r for r in fss_all if r["owner_id"] == owner][:5]) + + # 4) user_profile — 1 per owner. + ups_all = _read(db, "user_profile") + ups = [r for r in ups_all if r["owner_id"] in ALL_OWNERS] + + written: list[tuple[str, int, int]] = [] + for name, rows in ( + ("episode", eps), + ("atomic_fact", afs), + ("foresight", fss), + ("user_profile", ups), + ): + serialised = [_serialise(r) for r in rows] + out = OUT_DIR / f"{name}.json" + out.write_text(json.dumps(serialised, indent=2, default=str)) + written.append((name, len(serialised), out.stat().st_size)) + + for name, count, size in written: + print(f" {name:14s}: {count:3d} rows ({size // 1024} KB)") + print(f" parent_memcells captured: {len(parent_memcells)}") + + +if __name__ == "__main__": + main() diff --git a/tests/fixtures/agent_trajectories/session_django_14311.json b/tests/fixtures/agent_trajectories/session_django_14311.json new file mode 100644 index 00000000..ceca37eb --- /dev/null +++ b/tests/fixtures/agent_trajectories/session_django_14311.json @@ -0,0 +1,1183 @@ +{ + "source_file": "0422.json", + "swe_bench_task": "django/django-14311", + "everos_session_id": "session_django_14311", + "everos_agent_sender_id": "agent_django", + "everos_user_sender_id": "user_001", + "everos_tool_sender_id": "tool_runner", + "base_timestamp_ms": 1700000000000, + "inter_message_gap_ms": 1000, + "message_count": 102, + "conversion_rules": { + "role": "verbatim from source (user / assistant / tool)", + "sender_id": "user→user_001, assistant→agent_, tool→tool_runner", + "timestamp": "BASE_TIMESTAMP_MS + idx * 1000", + "content": "verbatim; empty string for assistant tool-call-only turns", + "tool_calls": "kept on assistant rows (OpenAI Chat Completions shape)", + "tool_call_id": "kept on tool rows" + }, + "messages": [ + { + "sender_id": "user_001", + "role": "user", + "timestamp": 1700000000000, + "content": "WRAPPER_PATH: /tmp/sweb.eval.django__django-14311.nb397b-baseline-exec\n\n你需要修复一个开源项目的 bug。代码仓库是 django/django，已经 checkout 到了 bug 存在的版本。\n任务总时限 30 分钟，请合理安排时间。\n\n【严格规则】\n- 所有命令必须通过 WRAPPER_PATH 执行\n- 严禁使用 read_file、write_file、edit_file 工具（这些工具操作的是宿主机，不是任务环境）\n- 严禁直接使用 exec 运行命令（必须通过 WRAPPER_PATH）\n- 写文件必须用 WRAPPER_PATH write，不要用 tmux-run + cat/heredoc（会有转义问题）\n- 不要修改测试文件\n- 严禁执行 git commit、git add、git stash 等 git 操作，只需修改文件即可，评测系统会自动收集你的改动\n\n操作方式：\n 执行命令: exec(\"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"命令\\\" 等待秒数\")\n 轮询等待: exec(\"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"\\\" 10\")\n 写文件: exec(\"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec write /目标路径 << 'EOF'\\n文件内容\\nEOF\")\n 中断命令: exec(\"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"ctrl-c\\\" 3\")\n\n## Bug 描述\n\nAllow autoreloading of `python -m custom_module runserver`\nDescription\n\t \n\t\t(last modified by Mariusz Felisiak)\n\t \nThe original fix [1] only attempted to deal with -m foo.bar where bar is a package and __main__.py exists under foo/bar.\nWhen a dotted name for a module (for example, foo.bar.baz where baz.py resides under foo/bar) is specified like -m foo.bar.baz, the resulting arguments end up being -m foo.bar, which is uncalled for.\n[1] ​https://github.com/django/django/commit/ec6d2531c59466924b645f314ac33f54470d7ac3 \nFixed detection when started non-django modules with \"python -m\" in autoreloader.\n\n\n修复完成后回复 TASK_COMPLETE。\n" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000001000, + "content": "I'll help you fix this bug in the Django project. Let me start by exploring the codebase to understand the issue.\n\n", + "tool_calls": [ + { + "id": "avjKgFyfR", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /workspace && find . -type f -name '*.py' | grep -E '(autoreload|runserver)' | head -20\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000002000, + "content": "(testbed) root@b88eec434adf:/testbed# cd /workspace && find . -type f -name '*.py' | grep -E '(autoreload|runserver)' | head -20\nbash: cd: /workspace: No such file or directory\n(testbed) root@b88eec434adf:/testbed#\n\n\n... (42 lines omitted) ...\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", + "tool_call_id": "avjKgFyfR" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000003000, + "content": "", + "tool_calls": [ + { + "id": "jBqSEtQ15", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"find /testbed -type f -name '*.py' | grep -E '(autoreload|runserver)' | head -20\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000004000, + "content": "(testbed) root@b88eec434adf:/testbed# find /testbed -type f -name '*.py' | grep -E '(autoreload|runserver)' | head -20\n/testbed/django/utils/autoreload.py\n/testbed/django/contrib/staticfiles/management/commands/runserver.py\n/testbed/django/core/management/commands/runserver.py\n/testbed/django/template/autoreload.py\n... (38 lines omitted) ...\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", + "tool_call_id": "jBqSEtQ15" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000005000, + "content": "", + "tool_calls": [ + { + "id": "jctQUcMOq", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\\\\\"cat /testbed/django/utils/autoreload.py\\\\\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000006000, + "content": "[ERROR] Syntax error: unclosed quote, heredoc, or bracket.\n\n\nExit code: 1", + "tool_call_id": "jctQUcMOq" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000007000, + "content": "", + "tool_calls": [ + { + "id": "ZA4R8AW9W", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cat /testbed/django/utils/autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000008000, + "content": "(testbed) root@b88eec434adf:/testbed# cat /testbed/django/utils/autoreload.py\nimport functools\nimport itertools\nimport logging\nimport os\n... (586 lines omitted) ...\n raise WatchmanUnavailable('Cannot connect to the watchman service.')\n version = get_version_tuple(result['version'])\n # Watchman 4.9 includes multiple improvements to watching project\n # directories as well as case insensitive filesystems.\n logger.debug('Watchman version %s', version)\n i\n... (truncated)", + "tool_call_id": "ZA4R8AW9W" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000009000, + "content": "", + "tool_calls": [ + { + "id": "2HZxXTdUK", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"grep -n 'run_with_reloader\\\\\\\\|restart_with_reloader\\\\\\\\|-m' /testbed/django/utils/autoreload.py | head -30\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000010000, + "content": "(testbed) root@b88eec434adf:/testbed# grep -n 'run_with_reloader\\|restart_with_reloader\\|-m' /testbed/django/utils/autoreload.py | head -30\n223: # __spec__ is set when the server was started with the `-m` option,\n227: args += ['-m', __main__.__spec__.parent]\n251:def restart_with_reloader():\n613: django_main_thread = threading.Thread(target=main_func, args=args, kwargs=kwargs, name='django-main-thread')\n628:def run_with_reloader(main_func, *args, **kwargs):\n636: exit_code \n... (truncated)", + "tool_call_id": "2HZxXTdUK" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000011000, + "content": "", + "tool_calls": [ + { + "id": "Q0B0T2XFm", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"sed -n '210,260p' /testbed/django/utils/autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000012000, + "content": "(testbed) root@b88eec434adf:/testbed# sed -n '210,260p' /testbed/django/utils/autoreload.py\n yield resolved_path\n\n\ndef get_child_arguments():\n \"\"\"\n Return the executable. This contains a workaround for Windows if the\n executable is reported to not have the .exe extension which can cause bugs\n on reloading.\n \"\"\"\n import __main__\n py_script = Path(sys.argv[0])\n\n args = [sys.executable] + ['-W%s' % o for o in sys.warnoptions]\n # __spec__ is set when the server \n... (truncated)", + "tool_call_id": "Q0B0T2XFm" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000013000, + "content": "Let me check the git history to understand the original fix better:\n\n", + "tool_calls": [ + { + "id": "qA2Wy2lvz", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed && git log --oneline --all | head -20\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000014000, + "content": "(testbed) root@b88eec434adf:/testbed# cd /testbed && git log --oneline --all | head -20\nb246a41fd7 SWE-bench\n5a8e8f80bb Fixed #32772 -- Made database cache count size once per set.\n12b19a1d76 Fixed #32783 -- Fixed crash of autoreloader when __main__ module doesn't have __spec__ attribute.\n1143f3bb5e Fixed #32543 -- Added search_help_text to ModelAdmin.\nb46dbd4e3e Added stub release notes and date for Django 3.2.4, 3.1.12, and 2.2.24.\n68357b2ca9 Fixed #32744 -- Normalized to pathlib.Path in autor\n... (truncated)", + "tool_call_id": "qA2Wy2lvz" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000015000, + "content": "", + "tool_calls": [ + { + "id": "oK0VzRmPe", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed && git show ec6d2531c59466924b645f314ac33f54470d7ac3 --stat\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000016000, + "content": "(testbed) root@b88eec434adf:/testbed# cd /testbed && git show ec6d2531c59466924b645f314ac33f54470d7ac3 --stat\ncommit ec6d2531c59466924b645f314ac33f54470d7ac3\nAuthor: William Schwartz \nDate: Mon Jan 4 08:50:49 2021 -0600\n\n Fixed #32314 -- Fixed detection when started non-django modules with \"python -m\" in autoreloader.\n\n django.utils.autoreload.get_child_arguments() detected when Python was\n started with the `-m` option only for `django` module. This commit\n ch\n... (truncated)", + "tool_call_id": "oK0VzRmPe" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000017000, + "content": "", + "tool_calls": [ + { + "id": "0NdRY2jLB", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed && git show ec6d2531c59466924b645f314ac33f54470d7ac3\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000018000, + "content": "(testbed) root@b88eec434adf:/testbed# cd /testbed && git show ec6d2531c59466924b645f314ac33f54470d7ac3\ncommit ec6d2531c59466924b645f314ac33f54470d7ac3\nAuthor: William Schwartz \nDate: Mon Jan 4 08:50:49 2021 -0600\n\n... (34 lines omitted) ...\ndiff --git a/tests/utils_tests/test_autoreload.py b/tests/utils_tests/test_autoreload.py\nindex b9f2db7276..a43d7c2fde 100644\n--- a/tests/utils_tests/test_autoreload.py\n+++ b/tests/utils_tests/test_autoreload.py\n@@ -23,6 +23,7 @@ from d\n... (truncated)", + "tool_call_id": "0NdRY2jLB" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000019000, + "content": "Let me look at the test file to understand the expected behavior:\n\n", + "tool_calls": [ + { + "id": "QN4Wv6qih", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"grep -n 'test_run_as\\\\\\\\|__spec__\\\\\\\\|parent' /testbed/tests/utils_tests/test_autoreload.py | head -30\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000020000, + "content": "(testbed) root@b88eec434adf:/testbed# grep -n 'test_run_as\\|__spec__\\|parent' /testbed/tests/utils_tests/test_autoreload.py | head -30\n75: with extend_sys_path(str(filename.parent)):\n88: with extend_sys_path(str(filename.parent)):\n103: with extend_sys_path(str(filename.parent)):\n130: with extend_sys_path(str(compiled_file.parent)):\n143: del module.__spec__\n170: def test_run_as_module(self):\n179: def test_run_as_non_django_module(self):\n226: del mod\n... (truncated)", + "tool_call_id": "QN4Wv6qih" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000021000, + "content": "", + "tool_calls": [ + { + "id": "bqZnjCNNm", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"sed -n '165,200p' /testbed/tests/utils_tests/test_autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000022000, + "content": "(testbed) root@b88eec434adf:/testbed# sed -n '165,200p' /testbed/tests/utils_tests/test_autoreload.py\n\nclass TestChildArguments(SimpleTestCase):\n @mock.patch.dict(sys.modules, {'__main__': django.__main__})\n @mock.patch('sys.argv', [django.__main__.__file__, 'runserver'])\n @mock.patch('sys.warnoptions', [])\n def test_run_as_module(self):\n self.assertEqual(\n autoreload.get_child_arguments(),\n [sys.executable, '-m', 'django', 'runserver']\n )\n\n @mo\n... (truncated)", + "tool_call_id": "bqZnjCNNm" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000023000, + "content": "", + "tool_calls": [ + { + "id": "raB3MioyP", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"ls -la /testbed/tests/utils_tests/test_module/\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000024000, + "content": "(testbed) root@b88eec434adf:/testbed# ls -la /testbed/tests/utils_tests/test_module/\ntotal 32\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 .\ndrwxrwxrwx 7 root root 4096 Sep 10 2025 ..\n-rw-r--r-- 1 root root 55 Sep 10 2025 __init__.py\n-rw-r--r-- 1 root root 0 Sep 10 2025 __main__.py\n-rw-r--r-- 1 root root 140 Sep 10 2025 another_bad_module.py\n-rw-r--r-- 1 root root 102 Sep 10 2025 another_good_module.py\n-rw-r--r-- 1 root root 74 Sep 10 2025 bad_module.py\ndrwxrwxrwx 2 root root 4096 Sep\n... (truncated)", + "tool_call_id": "raB3MioyP" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000025000, + "content": "", + "tool_calls": [ + { + "id": "bOgQiCf4i", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"ls -la /testbed/tests/utils_tests/test_module/child_module/\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000026000, + "content": "(testbed) root@b88eec434adf:/testbed# ls -la /testbed/tests/utils_tests/test_module/child_module/\ntotal 12\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 .\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 ..\n-rw-r--r-- 1 root root 0 Sep 10 2025 __init__.py\n-rw-r--r-- 1 root root 30 Sep 10 2025 grandchild_module.py\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "bOgQiCf4i" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000027000, + "content": "", + "tool_calls": [ + { + "id": "dENhOOZA0", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cat /testbed/tests/utils_tests/test_module/__init__.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000028000, + "content": "(testbed) root@b88eec434adf:/testbed# cat /testbed/tests/utils_tests/test_module/__init__.py\nclass SiteMock:\n _registry = {}\n\n\nsite = SiteMock()\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "dENhOOZA0" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000029000, + "content": "", + "tool_calls": [ + { + "id": "TtRtZGjzL", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"sed -n '1,30p' /testbed/tests/utils_tests/test_autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000030000, + "content": "(testbed) root@b88eec434adf:/testbed# sed -n '1,30p' /testbed/tests/utils_tests/test_autoreload.py\nimport contextlib\nimport os\nimport py_compile\nimport shutil\nimport sys\nimport tempfile\nimport threading\nimport time\nimport types\nimport weakref\nimport zipfile\nfrom importlib import import_module\nfrom pathlib import Path\nfrom subprocess import CompletedProcess\nfrom unittest import mock, skip, skipIf\n\nimport pytz\n\nimport django.__main__\nfrom django.apps.registry import Apps\nfrom django.test import Si\n... (truncated)", + "tool_call_id": "TtRtZGjzL" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000031000, + "content": "", + "tool_calls": [ + { + "id": "Qq6pwiZWW", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 -c \\\\\\\"import sys; sys.path.insert(0, '/testbed/tests'); from utils_tests.test_module import __main__ as tm; print('name:', tm.__spec__.name); print('parent:', tm.__spec__.parent)\\\\\\\"\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000032000, + "content": "(testbed) root@b88eec434adf:/testbed# python3 -c \"import sys; sys.path.insert(0, '/testbed/tests'); from utils_tests.test_module import __main__ as tm; print('name:', tm.__spec__.name); print('parent:\n', tm.__spec__.parent)\"\nname: utils_tests.test_module.__main__\nparent: utils_tests.test_module\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "Qq6pwiZWW" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000033000, + "content": "", + "tool_calls": [ + { + "id": "5UAJJNxZI", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 -c \\\\\\\"import sys; sys.path.insert(0, '/testbed/tests'); from utils_tests.test_module import good_module as gm; print('name:', gm.__spec__.name); print('parent:', gm.__spec__.parent)\\\\\\\"\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000034000, + "content": "(testbed) root@b88eec434adf:/testbed# python3 -c \"import sys; sys.path.insert(0, '/testbed/tests'); from utils_tests.test_module import good_module as gm; print('name:', gm.__spec__.name); print('pare\nnt:', gm.__spec__.parent)\"\nname: utils_tests.test_module.good_module\nparent: utils_tests.test_module\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "5UAJJNxZI" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000035000, + "content": "", + "tool_calls": [ + { + "id": "gIzFSDnS0", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"sed -n '215,245p' /testbed/django/utils/autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000036000, + "content": "(testbed) root@b88eec434adf:/testbed# sed -n '215,245p' /testbed/django/utils/autoreload.py\n Return the executable. This contains a workaround for Windows if the\n executable is reported to not have the .exe extension which can cause bugs\n on reloading.\n \"\"\"\n import __main__\n py_script = Path(sys.argv[0])\n\n args = [sys.executable] + ['-W%s' % o for o in sys.warnoptions]\n # __spec__ is set when the server was started with the `-m` option,\n # see https://docs.python.org/3\n... (truncated)", + "tool_call_id": "gIzFSDnS0" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000037000, + "content": "", + "tool_calls": [ + { + "id": "heGf9ASpM", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 -c \\\\\\\"import django.__main__; print('name:', django.__main__.__spec__.name); print('parent:', repr(django.__main__.__spec__.parent))\\\\\\\"\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000038000, + "content": "(testbed) root@b88eec434adf:/testbed# python3 -c \"import django.__main__; print('name:', django.__main__.__spec__.name); print('parent:', repr(django.__main__.__spec__.parent))\"\nname: django.__main__\nparent: 'django'\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "heGf9ASpM" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000039000, + "content": "Now I understand the issue. The current code uses `__main__.__spec__.parent` which incorrectly strips the last component of the module name for non-`__main__` modules. The fix should:\n- Use `__spec__.parent` when the module name ends with `.__main__` (for packages)\n- Use `__spec__.name` otherwise (for regular modules)\n\nLet me create the fix:\n\n", + "tool_calls": [ + { + "id": "0YdCeQ45P", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec write /testbed/django/utils/autoreload.py << 'EOF'\\nimport functools\\nimport itertools\\nimport logging\\nimport os\\nimport re\\nimport signal\\nimport subprocess\\nimport sys\\nimport threading\\nimport time\\nimport traceback\\nimport webbrowser\\nfrom collections import defaultdict\\nfrom pathlib import Path\\nfrom types import ModuleType\\nfrom typing import Any, Callable, Iterator, Optional, Set, Tuple, Union\\n\\nimport django\\nfrom django.apps import apps\\nfrom django.conf import settings\\nfrom django.core.signals import request_finished\\nfrom django.dispatch import Signal\\nfrom django.utils.functional import cached_property\\n\\nmodule_changed = Signal()\\nfile_changed = Signal()\\n\\nlogger = logging.getLogger('django.utils.autoreload')\\n\\n\\ndef check_errors(main_func: Callable) -> Callable:\\n \\\"\\\"\\\"\\n Wrap a function to catch and log errors during autoreload.\\n \\\"\\\"\\\"\\n @functools.wraps(main_func)\\n def wrapper(*args, **kwargs):\\n try:\\n return main_func(*args, **kwargs)\\n except Exception as e:\\n logger.error('Error in main function: %s', e)\\n logger.debug('Traceback:', exc_info=True)\\n raise\\n return wrapper\\n\\n\\ndef is_django_module(module: ModuleType) -> bool:\\n \\\"\\\"\\\"Return True if module is part of Django.\\\"\\\"\\\"\\n name = module.__name__\\n return name == 'django' or name.startswith('django.')\\n\\n\\ndef is_protected_path(path: Path) -> bool:\\n \\\"\\\"\\\"Return True if path is in a protected directory that shouldn't trigger reload.\\\"\\\"\\\"\\n protected_paths = [\\n Path(django.__file__).parent.parent / 'site-packages',\\n Path(django.__file__).parent.parent / 'dist-packages',\\n Path(django.__file__).parent.parent / 'lib',\\n ]\\n try:\\n resolved = path.resolve()\\n except (OSError, RuntimeError):\\n return False\\n return any(str(resolved).startswith(str(p)) for p in protected_paths)\\n\\n\\ndef find_all_py_modules_in_directory(directory: Path) -> Iterator[ModuleType]:\\n \\\"\\\"\\\"Find all Python modules in a directory.\\\"\\\"\\\"\\n for path in directory.rglob('*.py'):\\n if path.is_file() and not path.name.startswith('_'):\\n yield path\\n\\n\\ndef iter_all_python_module_files() -> Iterator[Path]:\\n \\\"\\\"\\\"Iterate over all Python module files.\\\"\\\"\\\"\\n # This is a simplified version - the actual implementation may vary\\n for module in sys.modules.values():\\n if module is None:\\n continue\\n if not getattr(module, '__file__', None):\\n continue\\n try:\\n path = Path(module.__file__)\\n except (TypeError, ValueError):\\n continue\\n if path.suffix == '.py':\\n yield path\\n\\n\\ndef iter_module_and_files(module: ModuleType) -> Iterator[Tuple[ModuleType, Path]]:\\n \\\"\\\"\\\"Iterate over a module and its files.\\\"\\\"\\\"\\n if not getattr(module, '__file__', None):\\n return\\n try:\\n path = Path(module.__file__)\\n except (TypeError, ValueError):\\n return\\n if path.suffix in ('.py', '.pyc', '.pyo'):\\n yield module, path\\n\\n\\ndef iter_all_python_module_files_with_modules() -> Iterator[Tuple[ModuleType, Path]]:\\n \\\"\\\"\\\"Iterate over all Python modules and their files.\\\"\\\"\\\"\\n for module in sys.modules.values():\\n if module is None:\\n continue\\n for _, path in iter_module_and_files(module):\\n yield module, path\\n\\n\\ndef get_child_arguments():\\n \\\"\\\"\\\"\\n Return the executable. This contains a workaround for Windows if the\\n executable is reported to not have the .exe extension which can cause bugs\\n on reloading.\\n \\\"\\\"\\\"\\n import __main__\\n py_script = Path(sys.argv[0])\\n\\n args = [sys.executable] + ['-W%s' % o for o in sys.warnoptions]\\n # __spec__ is set when the server was started with the `-m` option,\\n # see https://docs.python.org/3/reference/import.html#main-spec\\n # __spec__ may not exist, e.g. when running in a Conda env.\\n if getattr(__main__, '__spec__', None) is not None:\\n spec_name = __main__.__spec__.name\\n # For packages with __main__.py, __spec__.name ends with '.__main__'\\n # and we should use __spec__.parent to get the package name.\\n # For regular modules, we should use __spec__.name to preserve the full path.\\n if spec_name.endswith('.__main__'):\\n args += ['-m', __main__.__spec__.parent]\\n else:\\n args += ['-m', spec_name]\\n args += sys.argv[1:]\\n elif not py_script.exists():\\n # sys.argv[0] may not exist for several reasons on Windows.\\n # It may exist with a .exe extension or have a -script.py suffix.\\n exe_entrypoint = py_script.with_suffix('.exe')\\n if exe_entrypoint.exists():\\n # Should be executed directly, ignoring sys.executable.\\n return [exe_entrypoint, *sys.argv[1:]]\\n script_entrypoint = py_script.with_name('%s-script.py' % py_script.name)\\n if script_entrypoint.exists():\\n # Should be executed as usual.\\n return [*args, script_entrypoint, *sys.argv[1:]]\\n raise RuntimeError('Script %s does not exist.' % py_script)\\n else:\\n args += sys.argv\\n return args\\n\\n\\ndef trigger_reload(filename):\\n logger.info('%s changed, reloading.', filename)\\n sys.exit(3)\\n\\n\\ndef restart_with_reloader():\\n new_environ = {**os.environ, DJANGO_AUTORELOAD_ENV: 'true'}\\n args = get_child_arguments()\\n while True:\\n p = subprocess.run(args, env=new_environ, close_fds=False)\\n if p.returncode != 3:\\n return p.returncode\\n\\n\\nclass BaseReloader:\\n def __init__(self):\\n self.extra_files = defaultdict(set)\\n self.directory_globs = defaultdict(set)\\n\\n def watch_dir(self, path, glob):\\n pass\\n\\n def watched_files(self):\\n return iter([])\\n\\n def run(self, main_func):\\n pass\\n\\n @property\\n def should_stop(self):\\n return False\\n\\n\\nclass StatReloader(BaseReloader):\\n pass\\n\\n\\nclass WatchmanReloader(BaseReloader):\\n pass\\n\\n\\ndef get_reloader():\\n \\\"\\\"\\\"Return the most suitable reloader for this environment.\\\"\\\"\\\"\\n try:\\n WatchmanReloader.check_availability()\\n except WatchmanUnavailable:\\n return StatReloader()\\n return WatchmanReloader()\\n\\n\\ndef start_django(reloader, main_func, *args, **kwargs):\\n ensure_echo_on()\\n\\n main_func = check_errors(main_func)\\n django_main_thread = threading.Thread(target=main_func, args=args, kwargs=kwargs, name='django-main-thread')\\n django_main_thread.daemon = True\\n django_main_thread.start()\\n\\n while not reloader.should_stop:\\n try:\\n reloader.run(django_main_thread)\\n except WatchmanUnavailable as ex:\\n # It's possible that the watchman service shuts down or otherwise\\n # becomes unavailable. In that case, use the StatReloader.\\n reloader = StatReloader()\\n logger.error('Error connecting to Watchman: %s', ex)\\n logger.info('Watching for file changes with %s', reloader.__class__.__name__)\\n\\n\\nDJANGO_AUTORELOAD_ENV = 'RUN_MAIN'\\n\\n\\ndef ensure_echo_on():\\n \\\"\\\"\\\"Ensure that the echo is on.\\\"\\\"\\\"\\n pass\\n\\n\\ndef get_version_tuple(version):\\n \\\"\\\"\\\"Parse version string to tuple.\\\"\\\"\\\"\\n pass\\n\\n\\nclass WatchmanUnavailable(Exception):\\n pass\\n\\n\\ndef run_with_reloader(main_func, *args, **kwargs):\\n signal.signal(signal.SIGTERM, lambda *args: sys.exit(0))\\n try:\\n if os.environ.get(DJANGO_AUTORELOAD_ENV) == 'true':\\n reloader = get_reloader()\\n logger.info('Watching for file changes with %s', reloader.__class__.__name__)\\n start_django(reloader, main_func, *args, **kwargs)\\n else:\\n exit_code = restart_with_reloader()\\n sys.exit(exit_code)\\n except KeyboardInterrupt:\\n pass\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000040000, + "content": "Written 7609 bytes to /testbed/django/utils/autoreload.py\n", + "tool_call_id": "0YdCeQ45P" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000041000, + "content": "I made a mistake by overwriting the entire file. Let me restore the original and make a targeted fix:\n\n", + "tool_calls": [ + { + "id": "EREZRK0ID", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed && git checkout django/utils/autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000042000, + "content": "(testbed) root@b88eec434adf:/testbed# cd /testbed && git checkout django/utils/autoreload.py\nUpdated 1 path from the index\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "EREZRK0ID" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000043000, + "content": "", + "tool_calls": [ + { + "id": "50Gm9nBfD", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"sed -n '220,235p' /testbed/django/utils/autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000044000, + "content": "(testbed) root@b88eec434adf:/testbed# sed -n '220,235p' /testbed/django/utils/autoreload.py\n py_script = Path(sys.argv[0])\n\n args = [sys.executable] + ['-W%s' % o for o in sys.warnoptions]\n # __spec__ is set when the server was started with the `-m` option,\n # see https://docs.python.org/3/reference/import.html#main-spec\n # __spec__ may not exist, e.g. when running in a Conda env.\n if getattr(__main__, '__spec__', None) is not None and __main__.__spec__.parent:\n args += \n... (truncated)", + "tool_call_id": "50Gm9nBfD" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000045000, + "content": "Now let me make a targeted fix. I'll read the full file first to understand its structure:\n\n", + "tool_calls": [ + { + "id": "y6CLK8ltK", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"wc -l /testbed/django/utils/autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000046000, + "content": "(testbed) root@b88eec434adf:/testbed# wc -l /testbed/django/utils/autoreload.py\n639 /testbed/django/utils/autoreload.py\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "y6CLK8ltK" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000047000, + "content": "Let me use sed to make the targeted change:\n\n", + "tool_calls": [ + { + "id": "EZIuKkk5u", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"sed -n '226,228p' /testbed/django/utils/autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000048000, + "content": "(testbed) root@b88eec434adf:/testbed# sed -n '226,228p' /testbed/django/utils/autoreload.py\n if getattr(__main__, '__spec__', None) is not None and __main__.__spec__.parent:\n args += ['-m', __main__.__spec__.parent]\n args += sys.argv[1:]\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "EZIuKkk5u" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000049000, + "content": "", + "tool_calls": [ + { + "id": "t6Il1F2fQ", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 << 'PYEOF'\\\\nimport re\\\\n\\\\nwith open('/testbed/django/utils/autoreload.py', 'r') as f:\\\\n content = f.read()\\\\n\\\\nold_code = ''' if getattr(__main__, '__spec__', None) is not None and __main__.__spec__.parent:\\\\n args += ['-m', __main__.__spec__.parent]\\\\n args += sys.argv[1:]'''\\\\n\\\\nnew_code = ''' if getattr(__main__, '__spec__', None) is not None:\\\\n spec_name = __main__.__spec__.name\\\\n # For packages with __main__.py, __spec__.name ends with '.__main__'\\\\n # and we should use __spec__.parent to get the package name.\\\\n # For regular modules, we should use __spec__.name to preserve the full path.\\\\n if spec_name.endswith('.__main__'):\\\\n args += ['-m', __main__.__spec__.parent]\\\\n else:\\\\n args += ['-m', spec_name]\\\\n args += sys.argv[1:]'''\\\\n\\\\nif old_code in content:\\\\n content = content.replace(old_code, new_code)\\\\n with open('/testbed/django/utils/autoreload.py', 'w') as f:\\\\n f.write(content)\\\\n print('Successfully modified the file')\\\\nelse:\\\\n print('Could not find the old code pattern')\\\\nPYEOF\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000050000, + "content": "[ERROR] Syntax error: unclosed quote, heredoc, or bracket.\n\n\nExit code: 1", + "tool_call_id": "t6Il1F2fQ" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000051000, + "content": "Let me write a Python script to a file first:\n\n", + "tool_calls": [ + { + "id": "NVpoxBf1E", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec write /tmp/fix_autoreload.py << 'EOF'\\nimport re\\n\\nwith open('/testbed/django/utils/autoreload.py', 'r') as f:\\n content = f.read()\\n\\nold_code = ''' if getattr(__main__, '__spec__', None) is not None and __main__.__spec__.parent:\\n args += ['-m', __main__.__spec__.parent]\\n args += sys.argv[1:]'''\\n\\nnew_code = ''' if getattr(__main__, '__spec__', None) is not None:\\n spec_name = __main__.__spec__.name\\n # For packages with __main__.py, __spec__.name ends with '.__main__'\\n # and we should use __spec__.parent to get the package name.\\n # For regular modules, we should use __spec__.name to preserve the full path.\\n if spec_name.endswith('.__main__'):\\n args += ['-m', __main__.__spec__.parent]\\n else:\\n args += ['-m', spec_name]\\n args += sys.argv[1:]'''\\n\\nif old_code in content:\\n content = content.replace(old_code, new_code)\\n with open('/testbed/django/utils/autoreload.py', 'w') as f:\\n f.write(content)\\n print('Successfully modified the file')\\nelse:\\n print('Could not find the old code pattern')\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000052000, + "content": "Written 1065 bytes to /tmp/fix_autoreload.py\n", + "tool_call_id": "NVpoxBf1E" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000053000, + "content": "", + "tool_calls": [ + { + "id": "XmqRo2UI2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 /tmp/fix_autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000054000, + "content": "(testbed) root@b88eec434adf:/testbed# python3 /tmp/fix_autoreload.py\nSuccessfully modified the file\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "XmqRo2UI2" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000055000, + "content": "Let me verify the change:\n\n", + "tool_calls": [ + { + "id": "cPgNijbr3", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"sed -n '220,240p' /testbed/django/utils/autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000056000, + "content": "(testbed) root@b88eec434adf:/testbed# sed -n '220,240p' /testbed/django/utils/autoreload.py\n py_script = Path(sys.argv[0])\n\n args = [sys.executable] + ['-W%s' % o for o in sys.warnoptions]\n # __spec__ is set when the server was started with the `-m` option,\n # see https://docs.python.org/3/reference/import.html#main-spec\n # __spec__ may not exist, e.g. when running in a Conda env.\n if getattr(__main__, '__spec__', None) is not None:\n spec_name = __main__.__spec__.name\n \n... (truncated)", + "tool_call_id": "cPgNijbr3" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000057000, + "content": "The fix looks correct. Now let me run the existing tests to verify the fix doesn't break anything:\n\n", + "tool_calls": [ + { + "id": "sEdCzhG11", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed && python -m pytest tests/utils_tests/test_autoreload.py::TestChildArguments -xvs\\\" 30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000058000, + "content": "(testbed) root@b88eec434adf:/testbed# cd /testbed && python -m pytest tests/utils_tests/test_autoreload.py::TestChildArguments -xvs\n/opt/miniconda3/envs/testbed/bin/python: No module named pytest\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "sEdCzhG11" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000059000, + "content": "", + "tool_calls": [ + { + "id": "C2eGBEus1", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed && python ./tests/runtests.py tests.utils_tests.test_autoreload.TestChildArguments\\\" 30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000060000, + "content": "(testbed) root@b88eec434adf:/testbed# cd /testbed && python ./tests/runtests.py tests.utils_tests.test_autoreload.TestChildArguments\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 1 tests.\nSystem check identified no issues (0 silenced).\nE\n======================================================================\nERROR: tests (unittest.loader._FailedTest)\n----------------------------------------------------------------------\nImportError: Failed to import test mod\n... (truncated)", + "tool_call_id": "C2eGBEus1" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000061000, + "content": "", + "tool_calls": [ + { + "id": "AdRKUnpjG", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed/tests && python -m django test utils_tests.test_autoreload.TestChildArguments --settings=test_sqlite\\\" 30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000062000, + "content": "(testbed) root@b88eec434adf:/testbed# cd /testbed/tests && python -m django test utils_tests.test_autoreload.TestChildArguments --settings=test_sqlite\nFound 7 tests.\nSystem check identified no issues (0 silenced).\nFF.F..F\n======================================================================\n... (37 lines omitted) ...\nFirst extra element 2:\n'django'\n\n- ['/opt/miniconda3/envs/testbed/bin/python', '-m', 'django', 'runserver']\n+ [PosixPath('/tmp/tmpzd0yqqxt/django-admin.exe'), 'runserver']\n\n=======\n... (truncated)", + "tool_call_id": "AdRKUnpjG" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000063000, + "content": "Let me look at the failing tests to understand what's expected:\n\n", + "tool_calls": [ + { + "id": "Po62TIHNt", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"sed -n '165,195p' /testbed/tests/utils_tests/test_autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000064000, + "content": "(testbed) root@b88eec434adf:/testbed/tests# sed -n '165,195p' /testbed/tests/utils_tests/test_autoreload.py\n\nclass TestChildArguments(SimpleTestCase):\n @mock.patch.dict(sys.modules, {'__main__': django.__main__})\n @mock.patch('sys.argv', [django.__main__.__file__, 'runserver'])\n @mock.patch('sys.warnoptions', [])\n def test_run_as_module(self):\n self.assertEqual(\n autoreload.get_child_arguments(),\n [sys.executable, '-m', 'django', 'runserver']\n )\n\n \n... (truncated)", + "tool_call_id": "Po62TIHNt" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000065000, + "content": "", + "tool_calls": [ + { + "id": "RpvpQlzzd", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 -c \\\\\\\"import django.__main__; print(django.__main__.__file__)\\\\\\\"\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000066000, + "content": "(testbed) root@b88eec434adf:/testbed/tests# python3 -c \"import django.__main__; print(django.__main__.__file__)\"\n/testbed/django/__main__.py\n(testbed) root@b88eec434adf:/testbed/tests#\n", + "tool_call_id": "RpvpQlzzd" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000067000, + "content": "Let me check if the tests were passing before my change:\n\n", + "tool_calls": [ + { + "id": "AQjsDAxGm", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed && git diff django/utils/autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000068000, + "content": "(testbed) root@b88eec434adf:/testbed/tests# cd /testbed && git diff django/utils/autoreload.py\ndiff --git a/django/utils/autoreload.py b/django/utils/autoreload.py\nindex b6af5f5491..0a875b944b 100644\n--- a/django/utils/autoreload.py\n+++ b/django/utils/autoreload.py\n@@ -223,8 +223,15 @@ def get_child_arguments():\n # __spec__ is set when the server was started with the `-m` option,\n # see https://docs.python.org/3/reference/import.html#main-spec\n # __spec__ may not exist, e.g. when run\n... (truncated)", + "tool_call_id": "AQjsDAxGm" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000069000, + "content": "", + "tool_calls": [ + { + "id": "lYzPY8i4u", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed && git checkout django/utils/autoreload.py && python ./tests/runtests.py utils_tests.test_autoreload.TestChildArguments 2>&1 | head -30\\\" 30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000070000, + "content": "(testbed) root@b88eec434adf:/testbed# cd /testbed && git checkout django/utils/autoreload.py && python ./tests/runtests.py utils_tests.test_autoreload.TestChildArguments 2>&1 | head -30\nUpdated 1 path from the index\n.......\n----------------------------------------------------------------------\nRan 7 tests in 0.002s\n\nOK\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 7 tests.\nSystem check identified no issues (0 silenced).\n(testbed) root@b88eec434adf:/testbed#\n... (truncated)", + "tool_call_id": "lYzPY8i4u" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000071000, + "content": "I see the issue. The original code checks `__main__.__spec__.parent` to ensure it's truthy before using `-m`. My change removed this check. Let me fix it:\n\n", + "tool_calls": [ + { + "id": "Ith8rVbYo", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec write /tmp/fix_autoreload2.py << 'EOF'\\nwith open('/testbed/django/utils/autoreload.py', 'r') as f:\\n content = f.read()\\n\\nold_code = ''' if getattr(__main__, '__spec__', None) is not None and __main__.__spec__.parent:\\n args += ['-m', __main__.__spec__.parent]\\n args += sys.argv[1:]'''\\n\\nnew_code = ''' if getattr(__main__, '__spec__', None) is not None:\\n spec_name = __main__.__spec__.name\\n # For packages with __main__.py, __spec__.name ends with '.__main__'\\n # and we should use __spec__.parent to get the package name.\\n # For regular modules, we should use __spec__.name to preserve the full path.\\n if spec_name.endswith('.__main__'):\\n module_name = __main__.__spec__.parent\\n else:\\n module_name = spec_name\\n if module_name:\\n args += ['-m', module_name]\\n args += sys.argv[1:]\\n else:\\n # Fallback for top-level modules without a parent\\n args += sys.argv'''\\n\\nif old_code in content:\\n content = content.replace(old_code, new_code)\\n with open('/testbed/django/utils/autoreload.py', 'w') as f:\\n f.write(content)\\n print('Successfully modified the file')\\nelse:\\n print('Could not find the old code pattern')\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000072000, + "content": "Written 1223 bytes to /tmp/fix_autoreload2.py\n", + "tool_call_id": "Ith8rVbYo" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000073000, + "content": "", + "tool_calls": [ + { + "id": "oeWKBllgK", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 /tmp/fix_autoreload2.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000074000, + "content": "(testbed) root@b88eec434adf:/testbed# python3 /tmp/fix_autoreload2.py\nSuccessfully modified the file\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "oeWKBllgK" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000075000, + "content": "", + "tool_calls": [ + { + "id": "br2Efe3OQ", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"sed -n '220,245p' /testbed/django/utils/autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000076000, + "content": "(testbed) root@b88eec434adf:/testbed# sed -n '220,245p' /testbed/django/utils/autoreload.py\n py_script = Path(sys.argv[0])\n\n args = [sys.executable] + ['-W%s' % o for o in sys.warnoptions]\n # __spec__ is set when the server was started with the `-m` option,\n # see https://docs.python.org/3/reference/import.html#main-spec\n # __spec__ may not exist, e.g. when running in a Conda env.\n if getattr(__main__, '__spec__', None) is not None:\n spec_name = __main__.__spec__.name\n \n... (truncated)", + "tool_call_id": "br2Efe3OQ" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000077000, + "content": "Let me check what `sys.argv[0]` is when running with `-m`:\n\n", + "tool_calls": [ + { + "id": "MYfReoiTR", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 -m django --version 2>&1 | head -5\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000078000, + "content": "(testbed) root@b88eec434adf:/testbed# python3 -m django --version 2>&1 | head -5\n4.0.dev20250910115335\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "MYfReoiTR" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000079000, + "content": "", + "tool_calls": [ + { + "id": "30l1OBmqq", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 -c \\\\\\\"import sys; print('argv[0]:', sys.argv[0])\\\\\\\" -m test\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000080000, + "content": "(testbed) root@b88eec434adf:/testbed# python3 -c \"import sys; print('argv[0]:', sys.argv[0])\" -m test\nargv[0]: -c\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "30l1OBmqq" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000081000, + "content": "", + "tool_calls": [ + { + "id": "rab1bDmnM", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"echo 'import sys; print(\\\\\\\"argv[0]:\\\\\\\", sys.argv[0]); print(\\\\\\\"__spec__:\\\\\\\", getattr(__import__(\\\\\\\"__main__\\\\\\\"), \\\\\\\"__spec__\\\\\\\", None))' > /tmp/test_mod.py && cd /tmp && python3 -m test_mod\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000082000, + "content": "(testbed) root@b88eec434adf:/testbed# echo 'import sys; print(\"argv[0]:\", sys.argv[0]); print(\"__spec__:\", getattr(__import__(\"__main__\"), \"__spec__\", None))' > /tmp/test_mod.py && cd /tmp && python3\n-m test_mod\nargv[0]: /tmp/test_mod.py\n__spec__: ModuleSpec(name='test_mod', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7fc5ff38fc70>, origin='/tmp/test_mod.py')\n(testbed) root@b88eec434adf:/tmp#\n", + "tool_call_id": "rab1bDmnM" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000083000, + "content": "", + "tool_calls": [ + { + "id": "GXkuFDsSJ", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed && python ./tests/runtests.py utils_tests.test_autoreload.TestChildArguments 2>&1\\\" 30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000084000, + "content": "(testbed) root@b88eec434adf:/tmp# cd /testbed && python ./tests/runtests.py utils_tests.test_autoreload.TestChildArguments 2>&1\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 7 tests.\nSystem check identified no issues (0 silenced).\n.......\n----------------------------------------------------------------------\nRan 7 tests in 0.002s\n\nOK\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "GXkuFDsSJ" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000085000, + "content": "All tests pass. Now let me create a test to verify the fix works for the bug case (dotted module name):\n\n", + "tool_calls": [ + { + "id": "tQYFsheDr", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 << 'EOF'\\\\nimport sys\\\\nsys.path.insert(0, '/testbed/tests')\\\\n\\\\n# Simulate running python -m utils_tests.test_module.child_module.grandchild_module\\\\nfrom utils_tests.test_module.child_module import grandchild_module as gcm\\\\n\\\\n# Mock __main__ to have the spec of grandchild_module\\\\nimport types\\\\nmock_main = types.ModuleType('__main__')\\\\nmock_main.__spec__ = gcm.__spec__\\\\n\\\\n# Save original __main__\\\\noriginal_main = sys.modules['__main__']\\\\n\\\\n# Replace __main__\\\\nsys.modules['__main__'] = mock_main\\\\n\\\\n# Also need to update sys.argv\\\\noriginal_argv = sys.argv[:]\\\\nsys.argv = [gcm.__file__, 'runserver']\\\\n\\\\n# Now test get_child_arguments\\\\nfrom django.utils import autoreload\\\\nargs = autoreload.get_child_arguments()\\\\n\\\\nprint('Result:', args)\\\\nprint('Expected: python -m utils_tests.test_module.child_module.grandchild_module runserver')\\\\nprint('Match:', '-m' in args and 'utils_tests.test_module.child_module.grandchild_module' in args)\\\\n\\\\n# Restore\\\\nsys.modules['__main__'] = original_main\\\\nsys.argv = original_argv\\\\nEOF\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000086000, + "content": "[ERROR] Syntax error: unclosed quote, heredoc, or bracket.\n\n\nExit code: 1", + "tool_call_id": "tQYFsheDr" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000087000, + "content": "", + "tool_calls": [ + { + "id": "qH9b6o76O", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec write /tmp/test_fix.py << 'EOF'\\nimport sys\\nsys.path.insert(0, '/testbed/tests')\\n\\n# Simulate running python -m utils_tests.test_module.child_module.grandchild_module\\nfrom utils_tests.test_module.child_module import grandchild_module as gcm\\n\\n# Mock __main__ to have the spec of grandchild_module\\nimport types\\nmock_main = types.ModuleType('__main__')\\nmock_main.__spec__ = gcm.__spec__\\n\\n# Save original __main__\\noriginal_main = sys.modules['__main__']\\n\\n# Replace __main__\\nsys.modules['__main__'] = mock_main\\n\\n# Also need to update sys.argv\\noriginal_argv = sys.argv[:]\\nsys.argv = [gcm.__file__, 'runserver']\\n\\n# Now test get_child_arguments\\nfrom django.utils import autoreload\\nargs = autoreload.get_child_arguments()\\n\\nprint('Result:', args)\\nprint('Expected: python -m utils_tests.test_module.child_module.grandchild_module runserver')\\nprint('Match:', '-m' in args and 'utils_tests.test_module.child_module.grandchild_module' in args)\\n\\n# Restore\\nsys.modules['__main__'] = original_main\\nsys.argv = original_argv\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000088000, + "content": "Written 972 bytes to /tmp/test_fix.py\n", + "tool_call_id": "qH9b6o76O" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000089000, + "content": "", + "tool_calls": [ + { + "id": "3210UrNeV", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 /tmp/test_fix.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000090000, + "content": "(testbed) root@b88eec434adf:/testbed# python3 /tmp/test_fix.py\nResult: ['/opt/miniconda3/envs/testbed/bin/python3', '-m', 'utils_tests.test_module.child_module.grandchild_module', 'runserver']\nExpected: python -m utils_tests.test_module.child_module.grandchild_module runserver\nMatch: True\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "3210UrNeV" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000091000, + "content": "The fix works for the dotted module case. Let me also verify the package with `__main__.py` case still works:\n\n", + "tool_calls": [ + { + "id": "VokZxCGDe", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec write /tmp/test_fix2.py << 'EOF'\\nimport sys\\nsys.path.insert(0, '/testbed/tests')\\n\\n# Simulate running python -m utils_tests.test_module (package with __main__.py)\\nfrom utils_tests.test_module import __main__ as test_main\\n\\n# Mock __main__ to have the spec of test_main\\nimport types\\nmock_main = types.ModuleType('__main__')\\nmock_main.__spec__ = test_main.__spec__\\n\\n# Save original __main__\\noriginal_main = sys.modules['__main__']\\n\\n# Replace __main__\\nsys.modules['__main__'] = mock_main\\n\\n# Also need to update sys.argv\\noriginal_argv = sys.argv[:]\\nsys.argv = [test_main.__file__, 'runserver']\\n\\n# Now test get_child_arguments\\nfrom django.utils import autoreload\\nargs = autoreload.get_child_arguments()\\n\\nprint('Result:', args)\\nprint('Expected: python -m utils_tests.test_module runserver')\\nprint('Match:', '-m' in args and 'utils_tests.test_module' in args and '.__main__' not in args)\\n\\n# Restore\\nsys.modules['__main__'] = original_main\\nsys.argv = original_argv\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000092000, + "content": "Written 922 bytes to /tmp/test_fix2.py\n", + "tool_call_id": "VokZxCGDe" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000093000, + "content": "", + "tool_calls": [ + { + "id": "D1HaT3LOE", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"python3 /tmp/test_fix2.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000094000, + "content": "(testbed) root@b88eec434adf:/testbed# python3 /tmp/test_fix2.py\nResult: ['/opt/miniconda3/envs/testbed/bin/python3', '-m', 'utils_tests.test_module', 'runserver']\nExpected: python -m utils_tests.test_module runserver\nMatch: True\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "D1HaT3LOE" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000095000, + "content": "Both cases work correctly. Let me run all the autoreload tests to ensure nothing is broken:\n\n", + "tool_calls": [ + { + "id": "2FD4ZNsm2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed && python ./tests/runtests.py utils_tests.test_autoreload 2>&1 | tail -20\\\" 60\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000096000, + "content": "(testbed) root@b88eec434adf:/testbed# cd /testbed && python ./tests/runtests.py utils_tests.test_autoreload 2>&1 | tail -20\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 79 tests.\nSystem check identified no issues (0 silenced).\n.ssssssssssssssssssss..........................................................\n----------------------------------------------------------------------\nRan 79 tests in 0.355s\n\nOK (skipped=20)\n(testbed) root@b88eec434adf:/testbed#\n", + "tool_call_id": "2FD4ZNsm2" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000097000, + "content": "All tests pass. Let me verify the final diff:\n\n", + "tool_calls": [ + { + "id": "n7v0VO8tF", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-14311.nb397b-baseline-exec tmux-run \\\"cd /testbed && git diff django/utils/autoreload.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000098000, + "content": "(testbed) root@b88eec434adf:/testbed# cd /testbed && git diff django/utils/autoreload.py\ndiff --git a/django/utils/autoreload.py b/django/utils/autoreload.py\nindex b6af5f5491..2191e350c1 100644\n--- a/django/utils/autoreload.py\n+++ b/django/utils/autoreload.py\n@@ -223,9 +223,21 @@ def get_child_arguments():\n # __spec__ is set when the server was started with the `-m` option,\n # see https://docs.python.org/3/reference/import.html#main-spec\n # __spec__ may not exist, e.g. when running i\n... (truncated)", + "tool_call_id": "n7v0VO8tF" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000099000, + "content": "The fix is complete. Let me provide a summary:\n\n## Summary\n\n**Bug:** When running `python -m foo.bar.baz runserver` where `baz.py` is a module file (not a package with `__main__.py`), the autoreloader incorrectly restarts with `python -m foo.bar runserver` instead of `python -m foo.bar.baz runserver`.\n\n**Root Cause:** The original code used `__main__.__spec__.parent` which only gives the parent package name, not the full module name.\n\n**Fix:** Modified `get_child_arguments()` in `/testbed/django/utils/autoreload.py` to:\n1. Check if `__spec__.name` ends with `.__main__` (indicating a package with `__main__.py`)\n2. If yes, use `__spec__.parent` to get the package name (strips the `.__main__` suffix)\n3. If no, use `__spec__.name` to preserve the full module path\n\n**Testing:**\n- All 79 existing autoreload tests pass\n- Verified the fix works for:\n - Packages with `__main__.py`: `python -m utils_tests.test_module` → correctly uses `utils_tests.test_module`\n - Dotted modules: `python -m utils_tests.test_module.child_module.grandchild_module` → correctly uses full path\n\n", + "tool_calls": [ + { + "id": "3M1VwGuM8", + "type": "function", + "function": { + "name": "message", + "arguments": "{\"content\": \"TASK_COMPLETE\\n\\nThe bug has been fixed in `/testbed/django/utils/autoreload.py`. The fix correctly handles autoreloading when running `python -m custom_module runserver` where the module is a dotted name (e.g., `foo.bar.baz`).\\n\\n**Changes made:**\\n- Modified `get_child_arguments()` function to use `__spec__.name` for regular modules instead of `__spec__.parent`\\n- For packages with `__main__.py`, it still uses `__spec__.parent` to strip the `.__main__` suffix\\n- All 79 existing autoreload tests pass\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000100000, + "content": "Message sent to cli:direct", + "tool_call_id": "3M1VwGuM8" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000101000, + "content": "TASK_COMPLETE" + } + ] +} diff --git a/tests/fixtures/agent_trajectories/session_django_16255.json b/tests/fixtures/agent_trajectories/session_django_16255.json new file mode 100644 index 00000000..f1fe67c7 --- /dev/null +++ b/tests/fixtures/agent_trajectories/session_django_16255.json @@ -0,0 +1,355 @@ +{ + "source_file": "0043.json", + "swe_bench_task": "django/django-16255", + "everos_session_id": "session_django_16255", + "everos_agent_sender_id": "agent_django", + "everos_user_sender_id": "user_001", + "everos_tool_sender_id": "tool_runner", + "base_timestamp_ms": 1700000000000, + "inter_message_gap_ms": 1000, + "message_count": 30, + "conversion_rules": { + "role": "verbatim from source (user / assistant / tool)", + "sender_id": "user→user_001, assistant→agent_, tool→tool_runner", + "timestamp": "BASE_TIMESTAMP_MS + idx * 1000", + "content": "verbatim; empty string for assistant tool-call-only turns", + "tool_calls": "kept on assistant rows (OpenAI Chat Completions shape)", + "tool_call_id": "kept on tool rows" + }, + "messages": [ + { + "sender_id": "user_001", + "role": "user", + "timestamp": 1700000000000, + "content": "WRAPPER_PATH: /tmp/sweb.eval.django__django-16255.full-run-exec\n\n你需要修复一个开源项目的 bug。代码仓库是 django/django，已经 checkout 到了 bug 存在的版本。\n任务总时限 30 分钟，请合理安排时间。\n\n【严格规则】\n- 所有命令必须通过 WRAPPER_PATH 执行\n- 严禁使用 read、write、edit、Read、Write、Edit 工具（这些工具操作的是宿主机，不是任务环境）\n- 严禁直接使用 exec 运行命令（必须通过 WRAPPER_PATH）\n- 写文件必须用 WRAPPER_PATH write，不要用 tmux-run + cat/heredoc（会有转义问题）\n- 不要修改测试文件\n- 严禁执行 git commit、git add、git stash 等 git 操作，只需修改文件即可，评测系统会自动收集你的改动\n\n操作方式：\n 执行命令: exec(\"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"命令\\\" 等待秒数\")\n 轮询等待: exec(\"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"\\\" 10\")\n 写文件: exec(\"/tmp/sweb.eval.django__django-16255.full-run-exec write /目标路径 << 'EOF'\\n文件内容\\nEOF\")\n 中断命令: exec(\"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"ctrl-c\\\" 3\")\n\n## Bug 描述\n\nSitemaps without items raise ValueError on callable lastmod.\nDescription\n\t\nWhen sitemap contains not items, but supports returning lastmod for an item, it fails with a ValueError:\nTraceback (most recent call last):\n File \"/usr/local/lib/python3.10/site-packages/django/core/handlers/exception.py\", line 55, in inner\n\tresponse = get_response(request)\n File \"/usr/local/lib/python3.10/site-packages/django/core/handlers/base.py\", line 197, in _get_response\n\tresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\n File \"/usr/local/lib/python3.10/site-packages/django/utils/decorators.py\", line 133, in _wrapped_view\n\tresponse = view_func(request, *args, **kwargs)\n File \"/usr/local/lib/python3.10/site-packages/django/contrib/sitemaps/views.py\", line 34, in inner\n\tresponse = func(request, *args, **kwargs)\n File \"/usr/local/lib/python3.10/site-packages/django/contrib/sitemaps/views.py\", line 76, in index\n\tsite_lastmod = site.get_latest_lastmod()\n File \"/usr/local/lib/python3.10/site-packages/django/contrib/sitemaps/__init__.py\", line 170, in get_latest_lastmod\n\treturn max([self.lastmod(item) for item in self.items()])\nException Type: ValueError at /sitemap.xml\nException Value: max() arg is an empty sequence\nSomething like this might be a solution:\n\t def get_latest_lastmod(self):\n\t\t if not hasattr(self, \"lastmod\"):\n\t\t\t return None\n\t\t if callable(self.lastmod):\n\t\t\t try:\n\t\t\t\t return max([self.lastmod(item) for item in self.items()])\n-\t\t\texcept TypeError:\n+\t\t\texcept (TypeError, ValueError):\n\t\t\t\t return None\n\t\t else:\n\t\t\t return self.lastmod\n\n\n修复完成后回复 TASK_COMPLETE。" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000001000, + "content": "", + "tool_calls": [ + { + "id": "call_2a516e487ab64c7093b0e8da", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"find /testbed -name '*.py' -path '*/sitemaps/*' | head -20\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000002000, + "content": "(testbed) root@55ab59df2a68:/testbed# find /testbed -name '*.py' -path '*/sitemaps/*' | head -20\n/testbed/django/contrib/gis/sitemaps/__init__.py\n/testbed/django/contrib/gis/sitemaps/views.py\n/testbed/django/contrib/gis/sitemaps/kml.py\n/testbed/django/contrib/sitemaps/management/commands/__init__.py\n... (42 lines omitted) ...", + "tool_call_id": "call_2a516e487ab64c7093b0e8da" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000003000, + "content": "", + "tool_calls": [ + { + "id": "call_6142f3a745ac480dbe29f2f1", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"cat /testbed/django/contrib/sitemaps/__init__.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000004000, + "content": "(testbed) root@55ab59df2a68:/testbed# cat /testbed/django/contrib/sitemaps/__init__.py\nimport warnings\nfrom urllib.parse import urlencode\nfrom urllib.request import urlopen\n\n... (205 lines omitted) ...\n }\n )\n if self.x_default:\n lang_code = settings.LANGUAGE_CODE\n loc = f\"{protocol}://{domain}{self._location(item, lang_code)}\"\n loc = loc.replace(f\"/{lang_code}/\", \"/\", 1)\n url_info[\"alternates\"].append(\n {\n \"location\": loc,\n \"lang_code\": \"x-default\",\n }\n )\n\n urls.append(url_info)\n\n if all_items_lastmod and latest_lastmod:\n self.latest_lastmod = latest_lastmod\n\n return urls\n\n\nclass GenericSitemap(Sitemap):\n priority = None\n changefreq = None\n\n def __init__(self, info_dict, priority=None, changefreq=None, protocol=None):\n self.queryset = info_dict[\"queryset\"]\n self.date_field = info_dict.get(\"date_field\")\n self.priority = self.priority or priority\n self.changefreq = self.changefreq or changefreq\n self.protocol = self.protocol or protocol\n\n def items(self):\n # Make sure to return a clone; we don't want premature evaluation.\n return self.queryset.filter()\n\n def lastmod(self, item):\n if self.date_field is not None:\n return getattr(item, self.date_field)\n return None\n\n def get_latest_lastmod(self):\n if self.date_field is not None:\n return (\n self.queryset.order_by(\"-\" + self.date_field)\n .values_list(self.date_field, flat=True)\n .first()\n )\n return None\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_6142f3a745ac480dbe29f2f1" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000005000, + "content": "", + "tool_calls": [ + { + "id": "call_c991e3fac2c24689989e7fe3", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"grep -n 'get_latest_lastmod' /testbed/django/contrib/sitemaps/__init__.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000006000, + "content": "(testbed) root@55ab59df2a68:/testbed# grep -n 'get_latest_lastmod' /testbed/django/contrib/sitemaps/__init__.py\n165: def get_latest_lastmod(self):\n251: def get_latest_lastmod(self):\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_c991e3fac2c24689989e7fe3" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000007000, + "content": "", + "tool_calls": [ + { + "id": "call_337dae1280a8457ea798ce4b", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"sed -n '160,180p' /testbed/django/contrib/sitemaps/__init__.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000008000, + "content": "(testbed) root@55ab59df2a68:/testbed# sed -n '160,180p' /testbed/django/contrib/sitemaps/__init__.py\n def get_urls(self, page=1, site=None, protocol=None):\n protocol = self.get_protocol(protocol)\n domain = self.get_domain(site)\n return self._urls(page, protocol, domain)\n\n def get_latest_lastmod(self):\n if not hasattr(self, \"lastmod\"):\n return None\n if callable(self.lastmod):\n try:\n return max([self.lastmod(item) for item in self.items()])\n except TypeError:\n return None\n else:\n return self.lastmod\n\n def _urls(self, page, protocol, domain):\n urls = []\n latest_lastmod = None\n all_items_lastmod = True # track if all items have a lastmod\n\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_337dae1280a8457ea798ce4b" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000009000, + "content": "Now I can see the bug. The `get_latest_lastmod` method at line 165-174 catches `TypeError` but not `ValueError`. When `self.items()` returns an empty list, `max()` raises `ValueError: max() arg is an empty sequence`.\n\nLet me fix this by adding `ValueError` to the exception handling:", + "tool_calls": [ + { + "id": "call_a763a7e71fc8408fb45e9ce0", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"sed -n '165,175p' /testbed/django/contrib/sitemaps/__init__.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000010000, + "content": "(testbed) root@55ab59df2a68:/testbed# sed -n '165,175p' /testbed/django/contrib/sitemaps/__init__.py\n def get_latest_lastmod(self):\n if not hasattr(self, \"lastmod\"):\n return None\n if callable(self.lastmod):\n try:\n return max([self.lastmod(item) for item in self.items()])\n except TypeError:\n return None\n else:\n return self.lastmod\n\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_a763a7e71fc8408fb45e9ce0" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000011000, + "content": "", + "tool_calls": [ + { + "id": "call_9602e2041f164372812d86e2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"sed -i 's/except TypeError:/except (TypeError, ValueError):/' /testbed/django/contrib/sitemaps/__init__.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000012000, + "content": "(testbed) root@55ab59df2a68:/testbed# sed -i 's/except TypeError:/except (TypeError, ValueError):/' /testbed/django/contrib/sitemaps/__init__.py\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_9602e2041f164372812d86e2" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000013000, + "content": "", + "tool_calls": [ + { + "id": "call_dfd83b1b3eee49fbb28a9654", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"sed -n '165,175p' /testbed/django/contrib/sitemaps/__init__.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000014000, + "content": "(testbed) root@55ab59df2a68:/testbed# sed -n '165,175p' /testbed/django/contrib/sitemaps/__init__.py\n def get_latest_lastmod(self):\n if not hasattr(self, \"lastmod\"):\n return None\n if callable(self.lastmod):\n try:\n return max([self.lastmod(item) for item in self.items()])\n except (TypeError, ValueError):\n return None\n else:\n return self.lastmod\n\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_dfd83b1b3eee49fbb28a9654" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000015000, + "content": "The fix has been applied. Let me verify it works by running the sitemap tests:", + "tool_calls": [ + { + "id": "call_fd3ac38f848f45d594861cf5", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"cd /testbed && python -m pytest tests/sitemaps_tests/test_sitemap.py -v -x 2>&1 | head -100\\\" 30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000016000, + "content": "(testbed) root@55ab59df2a68:/testbed# cd /testbed && python -m pytest tests/sitemaps_tests/test_sitemap.py -v -x 2>&1 | head -100\n/opt/miniconda3/envs/testbed/bin/python: No module named pytest\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_fd3ac38f848f45d594861cf5" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000017000, + "content": "", + "tool_calls": [ + { + "id": "call_749cde7219dd49d6a193fe05", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"cd /testbed && python runtests.py sitemaps_tests 2>&1 | tail -50\\\" 30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000018000, + "content": "(testbed) root@55ab59df2a68:/testbed# cd /testbed && python runtests.py sitemaps_tests 2>&1 | tail -50\npython: can't open file '/testbed/runtests.py': [Errno 2] No such file or directory\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_749cde7219dd49d6a193fe05" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000019000, + "content": "", + "tool_calls": [ + { + "id": "call_058e8950b1234318b8360376", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"find /testbed -name 'runtests.py' 2>/dev/null | head -5\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000020000, + "content": "(testbed) root@55ab59df2a68:/testbed# find /testbed -name 'runtests.py' 2>/dev/null | head -5\n/testbed/tests/runtests.py\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_058e8950b1234318b8360376" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000021000, + "content": "", + "tool_calls": [ + { + "id": "call_be1f9922d97f4387a78de883", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"cd /testbed && python tests/runtests.py sitemaps_tests 2>&1 | tail -50\\\" 30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000022000, + "content": "(testbed) root@55ab59df2a68:/testbed# cd /testbed && python tests/runtests.py sitemaps_tests 2>&1 | tail -50\nCreating test database for alias 'default'...\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 60 test(s).\nCloning test database for alias 'default'...\nCloning test database for alias 'default'...\nCloning test database for alias 'default'...\nCloning test database for alias 'default'...\nCloning test database for alias 'default'...\nCloning test database for alias 'default'...\nCloning test database for alias 'default'...\nSystem check identified no issues (0 silenced).\n............................................................\n----------------------------------------------------------------------\nRan 60 tests in 0.133s\n\nOK\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_be1f9922d97f4387a78de883" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000023000, + "content": "All 60 sitemap tests pass. Let me also create a quick test to verify the specific bug scenario is fixed:", + "tool_calls": [ + { + "id": "call_a9cced7b7ac140d3b46b54d0", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"cd /testbed && python -c \\\\\\\"\\\\nfrom django.contrib.sitemaps import Sitemap\\\\n\\\\nclass EmptySitemap(Sitemap):\\\\n def items(self):\\\\n return []\\\\n \\\\n def lastmod(self, item):\\\\n return None\\\\n\\\\nsitemap = EmptySitemap()\\\\nresult = sitemap.get_latest_lastmod()\\\\nprint(f'Result: {result}')\\\\nprint('Bug is fixed! No ValueError raised.')\\\\n\\\\\\\" 2>&1\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000024000, + "content": "(testbed) root@55ab59df2a68:/testbed# cd /testbed && python -c \"\\nfrom django.contrib.sitemaps import Sitemap\\n\\nclass EmptySitemap(Sitemap):\\n def items(self):\\n return []\\n \\n def la\nstmod(self, item):\\n return None\\n\\nsitemap = EmptySitemap()\\nresult = sitemap.get_latest_lastmod()\\nprint(f'Result: {result}')\\nprint('Bug is fixed! No ValueError raised.')\\n\" 2>&1\n File \"\", line 1\n \\nfrom django.contrib.sitemaps import Sitemap\\n\\nclass EmptySitemap(Sitemap):\\n def items(self):\\n return []\\n \\n def lastmod(self, item):\\n return None\\n\\nsitemap = EmptySi\ntemap()\\nresult = sitemap.get_latest_lastmod()\\nprint(f'Result: {result}')\\nprint('Bug is fixed! No ValueError raised.')\\n\n ^\nSyntaxError: unexpected character after line continuation character\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_a9cced7b7ac140d3b46b54d0" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000025000, + "content": "", + "tool_calls": [ + { + "id": "call_113222361ba64a98b38b2211", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec write /tmp/test_fix.py << 'EOF'\\nfrom django.contrib.sitemaps import Sitemap\\n\\nclass EmptySitemap(Sitemap):\\n def items(self):\\n return []\\n \\n def lastmod(self, item):\\n return None\\n\\nsitemap = EmptySitemap()\\nresult = sitemap.get_latest_lastmod()\\nprint(f'Result: {result}')\\nprint('Bug is fixed! No ValueError raised.')\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000026000, + "content": "Written 303 bytes to /tmp/test_fix.py", + "tool_call_id": "call_113222361ba64a98b38b2211" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000027000, + "content": "", + "tool_calls": [ + { + "id": "call_72954bffd06a4878ad2340d1", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16255.full-run-exec tmux-run \\\"cd /testbed && python /tmp/test_fix.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000028000, + "content": "(testbed) root@55ab59df2a68:/testbed# cd /testbed && python /tmp/test_fix.py\nResult: None\nBug is fixed! No ValueError raised.\n(testbed) root@55ab59df2a68:/testbed#", + "tool_call_id": "call_72954bffd06a4878ad2340d1" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000029000, + "content": "The bug is fixed. The fix:\n- Changed `except TypeError:` to `except (TypeError, ValueError):` in the `get_latest_lastmod` method\n- This handles the case when `self.items()` returns an empty list, which causes `max()` to raise `ValueError: max() arg is an empty sequence`\n- All 60 sitemap tests pass\n- The specific bug scenario (empty sitemap with callable lastmod) now returns `None` instead of raising an exception\n\nTASK_COMPLETE" + } + ] +} diff --git a/tests/fixtures/agent_trajectories/session_django_16263.json b/tests/fixtures/agent_trajectories/session_django_16263.json new file mode 100644 index 00000000..fb51fca9 --- /dev/null +++ b/tests/fixtures/agent_trajectories/session_django_16263.json @@ -0,0 +1,3736 @@ +{ + "source_file": "0022.json", + "swe_bench_task": "django/django-16263", + "everos_session_id": "session_django_16263", + "everos_agent_sender_id": "agent_django", + "everos_user_sender_id": "user_001", + "everos_tool_sender_id": "tool_runner", + "base_timestamp_ms": 1700000000000, + "inter_message_gap_ms": 1000, + "message_count": 324, + "conversion_rules": { + "role": "verbatim from source (user / assistant / tool)", + "sender_id": "user→user_001, assistant→agent_, tool→tool_runner", + "timestamp": "BASE_TIMESTAMP_MS + idx * 1000", + "content": "verbatim; empty string for assistant tool-call-only turns", + "tool_calls": "kept on assistant rows (OpenAI Chat Completions shape)", + "tool_call_id": "kept on tool rows" + }, + "messages": [ + { + "sender_id": "user_001", + "role": "user", + "timestamp": 1700000000000, + "content": "WRAPPER_PATH: /tmp/sweb.eval.django__django-16263.full-run-exec\n\n你需要修复一个开源项目的 bug。代码仓库是 django/django，已经 checkout 到了 bug 存在的版本。\n任务总时限 30 分钟，请合理安排时间。\n\n【严格规则】\n- 所有命令必须通过 WRAPPER_PATH 执行\n- 严禁使用 read、write、edit、Read、Write、Edit 工具（这些工具操作的是宿主机，不是任务环境）\n- 严禁直接使用 exec 运行命令（必须通过 WRAPPER_PATH）\n- 写文件必须用 WRAPPER_PATH write，不要用 tmux-run + cat/heredoc（会有转义问题）\n- 不要修改测试文件\n- 严禁执行 git commit、git add、git stash 等 git 操作，只需修改文件即可，评测系统会自动收集你的改动\n\n操作方式：\n 执行命令: exec(\"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"命令\\\" 等待秒数\")\n 轮询等待: exec(\"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"\\\" 10\")\n 写文件: exec(\"/tmp/sweb.eval.django__django-16263.full-run-exec write /目标路径 << 'EOF'\\n文件内容\\nEOF\")\n 中断命令: exec(\"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"ctrl-c\\\" 3\")\n\n## Bug 描述\n\nStrip unused annotations from count queries\nDescription\n\t\nThe query below produces a SQL statement that includes the Count('chapters'), despite not not being used in any filter operations.\nBook.objects.annotate(Count('chapters')).count()\nIt produces the same results as:\nBook.objects.count()\nDjango could be more intelligent about what annotations to include in the query produced by queryset.count(), stripping out any annotations that are not referenced by filters, other annotations or ordering. This should speed up calls to count() with complex annotations.\nThere seems to be precedent for this: select_related calls are ignored with count() queries.\n\n\n修复完成后回复 TASK_COMPLETE。" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000001000, + "content": "", + "tool_calls": [ + { + "id": "call_599ec6cc57c9444996a93a38", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && ls -la\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000002000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && ls -la\ntotal 188\ndrwxrwxrwx 1 root root 4096 Sep 10 2025 .\ndrwxr-xr-x 1 root root 4096 Mar 17 21:10 ..\n-rw-r--r-- 1 root root 806 Sep 10 2025 .editorconfig\n... (42 lines omitted) ...", + "tool_call_id": "call_599ec6cc57c9444996a93a38" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000003000, + "content": "", + "tool_calls": [ + { + "id": "call_5d529e2722ab4a0e8cc11a90", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && find . -name \\\\\\\"*.py\\\\\\\" -type f | xargs grep -l \\\\\\\"annotate.*count\\\\\\\" | head -20\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000004000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && find . -name \"*.py\" -type f | xargs grep -l \"annotate.*count\" | head -20\n./tests/annotations/tests.py\n./tests/model_fields/test_jsonfield.py\n./tests/expressions/tests.py\n./tests/gis_tests/distapp/tests.py\n./tests/gis_tests/geoapp/test_expressions.py\n./tests/get_earliest_or_latest/tests.py\n./tests/queries/tests.py\n./tests/ordering/tests.py\n./tests/update/tests.py\n./tests/aggregation_regress/tests.py\n./tests/aggregation/tests.py\n./tests/backends/postgresql/test_server_side_cursors.py\n./tests/queryset_pickle/tests.py\n./tests/expressions_window/tests.py\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_5d529e2722ab4a0e8cc11a90" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000005000, + "content": "", + "tool_calls": [ + { + "id": "call_db833a1b724743be958d1705", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"def count\\\\\\\" django/db/models/query.py | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000006000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"def count\" django/db/models/query.py | head -20\n614: def count(self):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_db833a1b724743be958d1705" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000007000, + "content": "", + "tool_calls": [ + { + "id": "call_9c9fc90dcf8941c286a5f5a6", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '614,680p' django/db/models/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000008000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '614,680p' django/db/models/query.py\n def count(self):\n \"\"\"\n Perform a SELECT COUNT() and return the number of records as an\n integer.\n... (14 lines omitted) ...\n Perform the query and return a single object matching the given\n keyword arguments.\n \"\"\"\n if self.query.combinator and (args or kwargs):\n raise NotSupportedError(\n \"Calling QuerySet.get(...) with filters after %s() is not \"\n \"supported.\" % self.query.combinator\n )\n clone = self._chain() if self.query.combinator else self.filter(*args, **kwargs)\n if self.query.can_filter() and not self.query.distinct_fields:\n clone = clone.order_by()\n limit = None\n if (\n not clone.query.select_for_update\n or connections[clone.db].features.supports_select_for_update_with_limit\n ):\n limit = MAX_GET_RESULTS\n clone.query.set_limits(high=limit)\n num = len(clone)\n if num == 1:\n return clone._result_cache[0]\n if not num:\n raise self.model.DoesNotExist(\n \"%s matching query does not exist.\" % self.model._meta.object_name\n )\n raise self.model.MultipleObjectsReturned(\n \"get() returned more than one %s -- it returned %s!\"\n % (\n self.model._meta.object_name,\n num if not limit or num < limit else \"more than %s\" % (limit - 1),\n )\n )\n\n async def aget(self, *args, **kwargs):\n return await sync_to_async(self.get)(*args, **kwargs)\n\n def create(self, **kwargs):\n \"\"\"\n Create a new object with the given kwargs, saving it to the database\n and returning the created object.\n \"\"\"\n obj = self.model(**kwargs)\n self._for_write = True\n obj.save(force_insert=True, using=self.db)\n return obj\n\n async def acreate(self, **kwargs):\n return await sync_to_async(self.create)(**kwargs)\n\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_9c9fc90dcf8941c286a5f5a6" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000009000, + "content": "", + "tool_calls": [ + { + "id": "call_912d3a69ec9f46f3b7987196", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '614,650p' django/db/models/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000010000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '614,650p' django/db/models/query.py\n def count(self):\n \"\"\"\n Perform a SELECT COUNT() and return the number of records as an\n integer.\n\n If the QuerySet is already fully cached, return the length of the\n cached results set to avoid multiple SELECT COUNT(*) calls.\n \"\"\"\n if self._result_cache is not None:\n return len(self._result_cache)\n\n return self.query.get_count(using=self.db)\n\n async def acount(self):\n return await sync_to_async(self.count)()\n\n def get(self, *args, **kwargs):\n \"\"\"\n Perform the query and return a single object matching the given\n keyword arguments.\n \"\"\"\n if self.query.combinator and (args or kwargs):\n raise NotSupportedError(\n \"Calling QuerySet.get(...) with filters after %s() is not \"\n \"supported.\" % self.query.combinator\n )\n clone = self._chain() if self.query.combinator else self.filter(*args, **kwargs)\n if self.query.can_filter() and not self.query.distinct_fields:\n clone = clone.order_by()\n limit = None\n if (\n not clone.query.select_for_update\n or connections[clone.db].features.supports_select_for_update_with_limit\n ):\n limit = MAX_GET_RESULTS\n clone.query.set_limits(high=limit)\n num = len(clone)\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_912d3a69ec9f46f3b7987196" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000011000, + "content": "", + "tool_calls": [ + { + "id": "call_c1411ff6a9994bde9d4be570", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"def get_count\\\\\\\" django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000012000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"def get_count\" django/db/models/sql/query.py\n548: def get_count(self, using):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_c1411ff6a9994bde9d4be570" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000013000, + "content": "", + "tool_calls": [ + { + "id": "call_a9de799f2d41499c80bfbf1a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '548,620p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000014000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '548,620p' django/db/models/sql/query.py\n def get_count(self, using):\n \"\"\"\n Perform a COUNT() query using the current filter constraints.\n \"\"\"\n... (20 lines omitted) ...\n combined_query.exists(limit=False)\n for combined_query in q.combined_queries\n )\n q.clear_ordering(force=True)\n if limit:\n q.set_limits(high=1)\n q.add_annotation(Value(1), \"a\")\n return q\n\n def has_results(self, using):\n q = self.exists(using)\n compiler = q.get_compiler(using=using)\n return compiler.has_results()\n\n def explain(self, using, format=None, **options):\n q = self.clone()\n for option_name in options:\n if (\n not EXPLAIN_OPTIONS_PATTERN.fullmatch(option_name)\n or \"--\" in option_name\n ):\n raise ValueError(f\"Invalid option name: {option_name!r}.\")\n q.explain_info = ExplainInfo(format, options)\n compiler = q.get_compiler(using=using)\n return \"\\n\".join(compiler.explain_query())\n\n def combine(self, rhs, connector):\n \"\"\"\n Merge the 'rhs' query into the current one (with any 'rhs' effects\n being applied *after* (that is, \"to the right of\") anything in the\n current query. 'rhs' is not modified during a call to this function.\n\n The 'connector' parameter describes how to connect filters from the\n 'rhs' query.\n \"\"\"\n if self.model != rhs.model:\n raise TypeError(\"Cannot combine queries on two different base models.\")\n if self.is_sliced:\n raise TypeError(\"Cannot combine queries once a slice has been taken.\")\n if self.distinct != rhs.distinct:\n raise TypeError(\"Cannot combine a unique query with a non-unique query.\")\n if self.distinct_fields != rhs.distinct_fields:\n raise TypeError(\"Cannot combine queries with different distinct fields.\")\n\n # If lhs and rhs shares the same alias prefix, it is possible to have\n # conflicting alias changes like T4 -> T5, T5 -> T6, which might end up\n # as T4 -> T6 while combining two querysets. To prevent this, change an\n # alias prefix of the rhs and update current aliases accordingly,\n # except if the alias is the base table since it must be present in the\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_a9de799f2d41499c80bfbf1a" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000015000, + "content": "", + "tool_calls": [ + { + "id": "call_854aff91792f4c5c8d45da73", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '548,590p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000016000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '548,590p' django/db/models/sql/query.py\n def get_count(self, using):\n \"\"\"\n Perform a COUNT() query using the current filter constraints.\n \"\"\"\n obj = self.clone()\n obj.add_annotation(Count(\"*\"), alias=\"__count\", is_summary=True)\n return obj.get_aggregation(using, [\"__count\"])[\"__count\"]\n\n def has_filters(self):\n return self.where\n\n def exists(self, limit=True):\n q = self.clone()\n if not (q.distinct and q.is_sliced):\n if q.group_by is True:\n q.add_fields(\n (f.attname for f in self.model._meta.concrete_fields), False\n )\n # Disable GROUP BY aliases to avoid orphaning references to the\n # SELECT clause which is about to be cleared.\n q.set_group_by(allow_aliases=False)\n q.clear_select_clause()\n if q.combined_queries and q.combinator == \"union\":\n q.combined_queries = tuple(\n combined_query.exists(limit=False)\n for combined_query in q.combined_queries\n )\n q.clear_ordering(force=True)\n if limit:\n q.set_limits(high=1)\n q.add_annotation(Value(1), \"a\")\n return q\n\n def has_results(self, using):\n q = self.exists(using)\n compiler = q.get_compiler(using=using)\n return compiler.has_results()\n\n def explain(self, using, format=None, **options):\n q = self.clone()\n for option_name in options:\n if (\n not EXPLAIN_OPTIONS_PATTERN.fullmatch(option_name)\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_854aff91792f4c5c8d45da73" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000017000, + "content": "", + "tool_calls": [ + { + "id": "call_bd674f8bd5a04d578a642c34", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"def get_aggregation\\\\\\\" django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000018000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"def get_aggregation\" django/db/models/sql/query.py\n438: def get_aggregation(self, using, added_aggregate_names):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_bd674f8bd5a04d578a642c34" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000019000, + "content": "", + "tool_calls": [ + { + "id": "call_13b231ce9bff491c89ac8e89", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '438,550p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000020000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '438,550p' django/db/models/sql/query.py\n def get_aggregation(self, using, added_aggregate_names):\n \"\"\"\n Return the dictionary with the values of the existing aggregations.\n \"\"\"\n... (60 lines omitted) ...\n annotation_select_mask = inner_query.annotation_select_mask\n if expression.is_summary:\n expression, col_cnt = inner_query.rewrite_cols(expression, col_cnt)\n outer_query.annotations[alias] = expression.relabeled_clone(\n relabels\n )\n del inner_query.annotations[alias]\n annotation_select_mask.remove(alias)\n # Make sure the annotation_select wont use cached results.\n inner_query.set_annotation_mask(inner_query.annotation_select_mask)\n if (\n inner_query.select == ()\n and not inner_query.default_cols\n and not inner_query.annotation_select_mask\n ):\n # In case of Model.objects[0:3].count(), there would be no\n # field selected in the inner query, yet we must use a subquery.\n # So, make sure at least one field is selected.\n inner_query.select = (\n self.model._meta.pk.get_col(inner_query.get_initial_alias()),\n )\n else:\n outer_query = self\n self.select = ()\n self.default_cols = False\n self.extra = {}\n\n empty_set_result = [\n expression.empty_result_set_value\n for expression in outer_query.annotation_select.values()\n ]\n elide_empty = not any(result is NotImplemented for result in empty_set_result)\n outer_query.clear_ordering(force=True)\n outer_query.clear_limits()\n outer_query.select_for_update = False\n outer_query.select_related = False\n compiler = outer_query.get_compiler(using, elide_empty=elide_empty)\n result = compiler.execute_sql(SINGLE)\n if result is None:\n result = empty_set_result\n\n converters = compiler.get_converters(outer_query.annotation_select.values())\n result = next(compiler.apply_converters((result,), converters))\n\n return dict(zip(outer_query.annotation_select, result))\n\n def get_count(self, using):\n \"\"\"\n Perform a COUNT() query using the current filter constraints.\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_13b231ce9bff491c89ac8e89" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000021000, + "content": "", + "tool_calls": [ + { + "id": "call_777d3a77b8e0409abaab3287", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '438,500p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000022000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '438,500p' django/db/models/sql/query.py\n def get_aggregation(self, using, added_aggregate_names):\n \"\"\"\n Return the dictionary with the values of the existing aggregations.\n \"\"\"\n... (10 lines omitted) ...\n # must produce just one result and thus must not use GROUP BY. But we\n # aren't smart enough to remove the existing annotations from the\n # query, so those would force us to use GROUP BY.\n #\n # If the query has limit or distinct, or uses set operations, then\n # those operations must be done in a subquery so that the query\n # aggregates on the limit and/or distinct results instead of applying\n # the distinct and limit after the aggregation.\n if (\n isinstance(self.group_by, tuple)\n or self.is_sliced\n or existing_annotations\n or self.distinct\n or self.combinator\n ):\n from django.db.models.sql.subqueries import AggregateQuery\n\n inner_query = self.clone()\n inner_query.subquery = True\n outer_query = AggregateQuery(self.model, inner_query)\n inner_query.select_for_update = False\n inner_query.select_related = False\n inner_query.set_annotation_mask(self.annotation_select)\n # Queries with distinct_fields need ordering and when a limit is\n # applied we must take the slice from the ordered query. Otherwise\n # no need for ordering.\n inner_query.clear_ordering(force=False)\n if not inner_query.distinct:\n # If the inner query uses default select and it has some\n # aggregate annotations, then we must make sure the inner\n # query is grouped by the main model's primary key. However,\n # clearing the select clause can alter results if distinct is\n # used.\n has_existing_aggregate_annotations = any(\n annotation\n for annotation in existing_annotations\n if getattr(annotation, \"contains_aggregate\", True)\n )\n if inner_query.default_cols and has_existing_aggregate_annotations:\n inner_query.group_by = (\n self.model._meta.pk.get_col(inner_query.get_initial_alias()),\n )\n inner_query.default_cols = False\n\n relabels = {t: \"subquery\" for t in inner_query.alias_map}\n relabels[None] = \"subquery\"\n # Remove any aggregates marked for reduction from the subquery\n # and move them to the outer AggregateQuery.\n col_cnt = 0\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_777d3a77b8e0409abaab3287" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000023000, + "content": "", + "tool_calls": [ + { + "id": "call_92fa166229864c2eb82d7f41", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '438,460p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000024000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '438,460p' django/db/models/sql/query.py\n def get_aggregation(self, using, added_aggregate_names):\n \"\"\"\n Return the dictionary with the values of the existing aggregations.\n \"\"\"\n if not self.annotation_select:\n return {}\n existing_annotations = [\n annotation\n for alias, annotation in self.annotations.items()\n if alias not in added_aggregate_names\n ]\n # Decide if we need to use a subquery.\n #\n # Existing annotations would cause incorrect results as get_aggregation()\n # must produce just one result and thus must not use GROUP BY. But we\n # aren't smart enough to remove the existing annotations from the\n # query, so those would force us to use GROUP BY.\n #\n # If the query has limit or distinct, or uses set operations, then\n # those operations must be done in a subquery so that the query\n # aggregates on the limit and/or distinct results instead of applying\n # the distinct and limit after the aggregation.\n if (\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_92fa166229864c2eb82d7f41" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000025000, + "content": "", + "tool_calls": [ + { + "id": "call_ee84b9630d064162a9177cba", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"count\\\\\\\" tests/annotations/tests.py | head -30\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000026000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"count\" tests/annotations/tests.py | head -30\n275: self.assertEqual(len(books), Book.objects.count())\n283: self.assertEqual(len(books), Book.objects.count())\n290: self.assertEqual(len(books), Book.objects.count())\n297: self.assertEqual(len(books), Book.objects.count())\n303: rating_count=Count(\"rating\"),\n305: self.assertEqual([book.rating_count for book in qs], [1])\n311: self.assertEqual(qs[\"selected__sum\"], Book.objects.count())\n318: books = Book.objects.annotate(is_book=Value(1), rating_count=Count(\"rating\"))\n321: self.assertEqual(book.rating_count, 1)\n328: rating_count=Count(\"rating\"),\n331: self.assertEqual(book.rating_count, 1)\n340: rating_count=Count(\"rating\"),\n345: self.assertEqual(book.rating_count, 1)\n356: rating_count=Count(\"rating\"),\n361: self.assertEqual(book.rating_count, 1)\n371: count_id=Count(\"id\"),\n373: .values(\"under_40\", \"count_id\")\n378: {\"under_40\": False, \"count_id\": 3},\n379: {\"under_40\": True, \"count_id\": 6},\n651: .annotate(age_count=Count(\"age\"))\n652: .order_by(\"age_count\", \"age\")\n666: lambda a: (a[\"age\"], a[\"age_count\"]),\n976: .annotate(count=Count(\"pk\"))\n977: .values(\"count\")\n1292: rating_count_alias=Count(\"rating\"),\n1295: rating_count=F(\"rating_count_alias\"),\n1299: self.assertIs(hasattr(book, \"rating_count_alias\"), False)\n1303: self.assertEqual(book.rating_count, 1)\n1317: self.assertEqual(qs.count(), Book.objects.count())\n1324: self.assertEqual(qs.count(), Book.objects.count())\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_ee84b9630d064162a9177cba" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000027000, + "content": "", + "tool_calls": [ + { + "id": "call_2c41692d42534bf187f14d85", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"annotate.*count()\\\\\\\" tests/annotations/tests.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000028000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"annotate.*count()\" tests/annotations/tests.py\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_2c41692d42534bf187f14d85" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000029000, + "content": "", + "tool_calls": [ + { + "id": "call_056cbd5917d04fd683000196", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"\\\\\\\\.count()\\\\\\\" tests/annotations/tests.py | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000030000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"\\.count()\" tests/annotations/tests.py | head -20\n275: self.assertEqual(len(books), Book.objects.count())\n283: self.assertEqual(len(books), Book.objects.count())\n290: self.assertEqual(len(books), Book.objects.count())\n297: self.assertEqual(len(books), Book.objects.count())\n311: self.assertEqual(qs[\"selected__sum\"], Book.objects.count())\n1317: self.assertEqual(qs.count(), Book.objects.count())\n1324: self.assertEqual(qs.count(), Book.objects.count())\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_056cbd5917d04fd683000196" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000031000, + "content": "", + "tool_calls": [ + { + "id": "call_7893fff795e048039bd9a06a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '1310,1330p' tests/annotations/tests.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000032000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '1310,1330p' tests/annotations/tests.py\n self.assertSequenceEqual(qs, [self.b1])\n\n def test_filter_alias_with_double_f(self):\n qs = Book.objects.alias(\n other_rating=F(\"rating\"),\n ).filter(other_rating=F(\"rating\"))\n self.assertIs(hasattr(qs.first(), \"other_rating\"), False)\n self.assertEqual(qs.count(), Book.objects.count())\n\n def test_filter_alias_agg_with_double_f(self):\n qs = Book.objects.alias(\n sum_rating=Sum(\"rating\"),\n ).filter(sum_rating=F(\"sum_rating\"))\n self.assertIs(hasattr(qs.first(), \"sum_rating\"), False)\n self.assertEqual(qs.count(), Book.objects.count())\n\n def test_update_with_alias(self):\n Book.objects.alias(\n other_rating=F(\"rating\") - 1,\n ).update(rating=F(\"other_rating\"))\n self.b1.refresh_from_db()\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_7893fff795e048039bd9a06a" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000033000, + "content": "", + "tool_calls": [ + { + "id": "call_f6437b379147434aacd2c8eb", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -rn \\\\\\\"16263\\\\\\\" tests/\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000034000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -rn \"16263\" tests/\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_f6437b379147434aacd2c8eb" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000035000, + "content": "", + "tool_calls": [ + { + "id": "call_dfee47d6c7fa4445a6c84e5a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -rn \\\\\\\"strip\\\\\\\" tests/annotations/ 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000036000, + "content": "/bin/bash: -c: line 1: unexpected EOF while looking for matching `\"'\n/bin/bash: -c: line 2: syntax error: unexpected end of file\n\n(Command exited with code 2)", + "tool_call_id": "call_dfee47d6c7fa4445a6c84e5a" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000037000, + "content": "", + "tool_calls": [ + { + "id": "call_639e19b02c4642149085e86d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -rn 'strip' tests/annotations/\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000038000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -rn 'strip' tests/annotations/\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_639e19b02c4642149085e86d" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000039000, + "content": "", + "tool_calls": [ + { + "id": "call_f27981b3e3884d52a4d87b18", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -rn 'annotate.*Count.*count' tests/\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000040000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -rn 'annotate.*Count.*count' tests/\ntests/aggregation_regress/tests.py:616: self.assertEqual(Book.objects.annotate(num_authors=Count(\"authors\")).count(), 6)\ntests/aggregation_regress/tests.py:1069: Book.objects.values(\"publisher\").annotate(Count(\"publisher\")).count(), 4\ntests/aggregation_regress/tests.py:1072: Book.objects.annotate(Count(\"publisher\")).values(\"publisher\").count(), 6\ntests/aggregation_regress/tests.py:1160: Author.objects.filter(id__in=[]).annotate(Count(\"friends\")).count(), 0\ntests/aggregation_regress/tests.py:1408: Author.objects.annotate(Count(\"book\")).aggregate(Max(\"book__count\")),\ntests/aggregation_regress/tests.py:1631: results = Book.objects.annotate(Count(\"tags\")).order_by(\"-tags__count\", \"name\")\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_f27981b3e3884d52a4d87b18" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000041000, + "content": "", + "tool_calls": [ + { + "id": "call_2da1bff3030543599a7033ec", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '610,630p' tests/aggregation_regress/tests.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000042000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '610,630p' tests/aggregation_regress/tests.py\n Book.objects.annotate(num_authors=Count(\"authors__id\")).aggregate(\n Max(\"foo\")\n )\n\n def test_more(self):\n # Old-style count aggregations can be mixed with new-style\n self.assertEqual(Book.objects.annotate(num_authors=Count(\"authors\")).count(), 6)\n\n # Non-ordinal, non-computed Aggregates over annotations correctly\n # inherit the annotation's internal type if the annotation is ordinal\n # or computed\n vals = Book.objects.annotate(num_authors=Count(\"authors\")).aggregate(\n Max(\"num_authors\")\n )\n self.assertEqual(vals, {\"num_authors__max\": 3})\n\n vals = Publisher.objects.annotate(avg_price=Avg(\"book__price\")).aggregate(\n Max(\"avg_price\")\n )\n self.assertEqual(vals, {\"avg_price__max\": 75.0})\n\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_2da1bff3030543599a7033ec" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000043000, + "content": "", + "tool_calls": [ + { + "id": "call_229a84e6cb294960801c5e06", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && python -c \\\\\\\"\\nfrom django.test.utils import setup_test_environment, teardown_test_environment\\nfrom django.db import connection\\nfrom django.conf import settings\\nimport os\\n\\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'tests.settings')\\n\\nimport django\\ndjango.setup()\\n\\nfrom django.test import TestCase\\nfrom django.db.models import Count\\nfrom tests.aggregation.models import Book\\n\\n# Enable SQL logging\\nfrom django.db import connection\\nconnection.force_debug_cursor = True\\n\\n# Test the query\\nqs = Book.objects.annotate(Count('pages')).count()\\nprint('Result:', qs)\\n\\\\\\\" 2>&1 | head -50\\\" 10\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000044000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && python -c \"\n> from django.test.utils import setup_test_environment, teardown_test_environment\n> from django.db import connection\n> from django.conf import settings\n> import os\n>\n> os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'tests.settings')\n>\n> import django\n> django.setup()\n>\n> from django.test import TestCase\n> from django.db.models import Count\n> from tests.aggregation.models import Book\n>\n> # Enable SQL logging\n> from django.db import connection\n> connection.force_debug_cursor = True\n>\n> # Test the query\n> qs = Book.objects.annotate(Count('pages')).count()\n> print('Result:', qs)\n> \" 2>&1 | head -50\nTraceback (most recent call last):\n File \"\", line 10, in \n File \"/testbed/django/__init__.py\", line 19, in setup\n configure_logging(settings.LOGGING_CONFIG, settings.LOGGING)\n File \"/testbed/django/conf/__init__.py\", line 92, in __getattr__\n self._setup(name)\n File \"/testbed/django/conf/__init__.py\", line 79, in _setup\n self._wrapped = Settings(settings_module)\n File \"/testbed/django/conf/__init__.py\", line 190, in __init__\n mod = importlib.import_module(self.SETTINGS_MODULE)\n File \"/opt/miniconda3/envs/testbed/lib/python3.9/importlib/__init__.py\", line 127, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n File \"\", line 1030, in _gcd_import\n File \"\", line 1007, in _find_and_load\n File \"\", line 984, in _find_and_load_unlocked\nModuleNotFoundError: No module named 'tests.settings'\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_229a84e6cb294960801c5e06" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000045000, + "content": "", + "tool_calls": [ + { + "id": "call_fb614a1d127246f6bebb4aa3", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && find . -name \\\\\\\"settings.py\\\\\\\" -path \\\\\\\"*/tests/*\\\\\\\" | head -10\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000046000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && find . -name \"settings.py\" -path \"*/tests/*\" | head -10\n./tests/i18n/sampleproject/sampleproject/settings.py\n./tests/staticfiles_tests/settings.py\n./tests/auth_tests/settings.py\n./tests/admin_scripts/custom_templates/project_template/project_name/settings.py\n./tests/flatpages_tests/settings.py\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_fb614a1d127246f6bebb4aa3" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000047000, + "content": "", + "tool_calls": [ + { + "id": "call_966a66c6b3234a31a26f0c22", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && ls tests/*.py | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000048000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && ls tests/*.py | head -20\ntests/runtests.py\ntests/test_sqlite.py\ntests/urls.py\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_966a66c6b3234a31a26f0c22" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000049000, + "content": "", + "tool_calls": [ + { + "id": "call_c0365dd7f41c45f39f6e2e67", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && head -50 tests/runtests.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000050000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && head -50 tests/runtests.py\n#!/usr/bin/env python\nimport argparse\nimport atexit\nimport copy\nimport gc\nimport multiprocessing\nimport os\nimport shutil\nimport socket\nimport subprocess\nimport sys\nimport tempfile\nimport warnings\nfrom pathlib import Path\n\ntry:\n import django\nexcept ImportError as e:\n raise RuntimeError(\n \"Django module not found, reference tests/README.rst for instructions.\"\n ) from e\nelse:\n from django.apps import apps\n from django.conf import settings\n from django.core.exceptions import ImproperlyConfigured\n from django.db import connection, connections\n from django.test import TestCase, TransactionTestCase\n from django.test.runner import get_max_test_processes, parallel_type\n from django.test.selenium import SeleniumTestCaseBase\n from django.test.utils import NullTimeKeeper, TimeKeeper, get_runner\n from django.utils.deprecation import (\n RemovedInDjango50Warning,\n RemovedInDjango51Warning,\n )\n from django.utils.log import DEFAULT_LOGGING\n\ntry:\n import MySQLdb\nexcept ImportError:\n pass\nelse:\n # Ignore informational warnings from QuerySet.explain().\n warnings.filterwarnings(\"ignore\", r\"\\(1003, *\", category=MySQLdb.Warning)\n\n# Make deprecation warnings errors to ensure no usage of deprecated features.\nwarnings.simplefilter(\"error\", RemovedInDjango50Warning)\nwarnings.simplefilter(\"error\", RemovedInDjango51Warning)\n# Make resource and runtime warning errors to ensure no usage of error prone\n# patterns.\nwarnings.simplefilter(\"error\", ResourceWarning)\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_c0365dd7f41c45f39f6e2e67" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000051000, + "content": "", + "tool_calls": [ + { + "id": "call_ca2e09506ad84d7cb7382471", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && python tests/runtests.py --debug-sql --verbosity=2 aggregation_regress.Tests.test_more 2>&1 | head -100\\\" 15\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000052000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && python tests/runtests.py --debug-sql --verbosity=2 aggregation_regress.Tests.test_more 2>&1 | head -100\nTests (unittest.loader._FailedTest) ... ERROR\n----------------------------------------------------------------------\n\n======================================================================\nERROR: Tests (unittest.loader._FailedTest)\n----------------------------------------------------------------------\nImportError: Failed to import test module: Tests\nTraceback (most recent call last):\n File \"/opt/miniconda3/envs/testbed/lib/python3.9/unittest/loader.py\", line 154, in loadTestsFromName\n module = __import__(module_name)\nModuleNotFoundError: No module named 'aggregation_regress.Tests'\n\n\n----------------------------------------------------------------------\n\n----------------------------------------------------------------------\nRan 1 test in 0.000s\n\nFAILED (errors=1)\nTesting against Django installed in '/testbed/django' with up to 128 processes\nImporting application aggregation_regress\nFound 1 test(s).\nSkipping setup of unused database(s): default, other.\nSystem check identified no issues (0 silenced).\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_ca2e09506ad84d7cb7382471" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000053000, + "content": "", + "tool_calls": [ + { + "id": "call_344a39ab7b3c42198912a76b", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && python tests/runtests.py --debug-sql --verbosity=2 aggregation_regress.AggregationTests.test_more 2>&1 | head -100\\\" 15\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000054000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && python tests/runtests.py --debug-sql --verbosity=2 aggregation_regress.AggregationTests.test_more 2>&1 | head -100\nAggregationTests (unittest.loader._FailedTest) ... ERROR\n----------------------------------------------------------------------\n\n======================================================================\nERROR: AggregationTests (unittest.loader._FailedTest)\n----------------------------------------------------------------------\nImportError: Failed to import test module: AggregationTests\nTraceback (most recent call last):\n File \"/opt/miniconda3/envs/testbed/lib/python3.9/unittest/loader.py\", line 154, in loadTestsFromName\n module = __import__(module_name)\nModuleNotFoundError: No module named 'aggregation_regress.AggregationTests'\n\n\n----------------------------------------------------------------------\n\n----------------------------------------------------------------------\nRan 1 test in 0.000s\n\nFAILED (errors=1)\nTesting against Django installed in '/testbed/django' with up to 128 processes\nImporting application aggregation_regress\nFound 1 test(s).\nSkipping setup of unused database(s): default, other.\nSystem check identified no issues (0 silenced).\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_344a39ab7b3c42198912a76b" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000055000, + "content": "", + "tool_calls": [ + { + "id": "call_7ee93bfa66ac4a30a9bdf83c", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"class.*Test\\\\\\\" tests/aggregation_regress/tests.py | head -10\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000056000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"class.*Test\" tests/aggregation_regress/tests.py | head -10\n46:class AggregationTests(TestCase):\n1831:class JoinPromotionTests(TestCase):\n1867:class SelfReferentialFKTests(TestCase):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_7ee93bfa66ac4a30a9bdf83c" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000057000, + "content": "", + "tool_calls": [ + { + "id": "call_f06d20f047c54fcfbb64345f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && python tests/runtests.py --debug-sql --verbosity=2 aggregation_regress.AggregationTests.test_more 2>&1 | head -100\\\" 15\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000058000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && python tests/runtests.py --debug-sql --verbosity=2 aggregation_regress.AggregationTests.test_more 2>&1 | head -100\nAggregationTests (unittest.loader._FailedTest) ... ERROR\n----------------------------------------------------------------------\n\n======================================================================\nERROR: AggregationTests (unittest.loader._FailedTest)\n----------------------------------------------------------------------\nImportError: Failed to import test module: AggregationTests\nTraceback (most recent call last):\n File \"/opt/miniconda3/envs/testbed/lib/python3.9/unittest/loader.py\", line 154, in loadTestsFromName\n module = __import__(module_name)\nModuleNotFoundError: No module named 'aggregation_regress.AggregationTests'\n\n\n----------------------------------------------------------------------\n\n----------------------------------------------------------------------\nRan 1 test in 0.000s\n\nFAILED (errors=1)\nTesting against Django installed in '/testbed/django' with up to 128 processes\nImporting application aggregation_regress\nFound 1 test(s).\nSkipping setup of unused database(s): default, other.\nSystem check identified no issues (0 silenced).\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_f06d20f047c54fcfbb64345f" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000059000, + "content": "", + "tool_calls": [ + { + "id": "call_57644778bf4443a3a8fdc3ce", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && python tests/runtests.py aggregation_regress 2>&1 | tail -30\\\" 30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000060000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && python tests/runtests.py aggregation_regress 2>&1 | tail -30\nCreating test database for alias 'default'...\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 69 test(s).\nCloning test database for alias 'default'...\nCloning test database for alias 'default'...\nCloning test database for alias 'default'...\nSystem check identified no issues (0 silenced).\n......sss..ss........................................................\n----------------------------------------------------------------------\nRan 69 tests in 0.156s\n\nOK (skipped=5)\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_57644778bf4443a3a8fdc3ce" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000061000, + "content": "", + "tool_calls": [ + { + "id": "call_9cd97518ce3c47afad54244f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '548,560p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000062000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '548,560p' django/db/models/sql/query.py\n def get_count(self, using):\n \"\"\"\n Perform a COUNT() query using the current filter constraints.\n \"\"\"\n obj = self.clone()\n obj.add_annotation(Count(\"*\"), alias=\"__count\", is_summary=True)\n return obj.get_aggregation(using, [\"__count\"])[\"__count\"]\n\n def has_filters(self):\n return self.where\n\n def exists(self, limit=True):\n q = self.clone()\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_9cd97518ce3c47afad54244f" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000063000, + "content": "", + "tool_calls": [ + { + "id": "call_02425a63a1b2453e8dacfb97", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"annotation_select\\\\\\\" django/db/models/sql/query.py | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000064000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"annotation_select\" django/db/models/sql/query.py | head -20\n95: self.annotation_select = {}\n205: # excluding annotation_select and extra_select.\n209: annotation_select_mask = None\n210: _annotation_select_cache = None\n264: elif len(self.annotation_select) == 1:\n265: return next(iter(self.annotation_select.values())).output_field\n330: if self.annotation_select_mask is not None:\n331: obj.annotation_select_mask = self.annotation_select_mask.copy()\n336: # _annotation_select_cache cannot be copied, as doing so breaks the\n338: # _annotation_select_cache point to the same underlying objects.\n341: obj._annotation_select_cache = None\n414: for col_alias, selected_annotation in self.annotation_select.items():\n442: if not self.annotation_select:\n474: inner_query.set_annotation_mask(self.annotation_select)\n501: for alias, expression in list(inner_query.annotation_select.items()):\n502: annotation_select_mask = inner_query.annotation_select_mask\n509: annotation_select_mask.remove(alias)\n510: # Make sure the annotation_select wont use cached results.\n511: inner_query.set_annotation_mask(inner_query.annotation_select_mask)\n515: and not inner_query.annotation_select_mask\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_02425a63a1b2453e8dacfb97" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000065000, + "content": "", + "tool_calls": [ + { + "id": "call_ca8c9685c60e40ff9059394b", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"def annotation_select\\\\\\\" django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000066000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"def annotation_select\" django/db/models/sql/query.py\n2439: def annotation_select(self):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_ca8c9685c60e40ff9059394b" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000067000, + "content": "", + "tool_calls": [ + { + "id": "call_d8453a62c52c41b6920172ff", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '2439,2460p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000068000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '2439,2460p' django/db/models/sql/query.py\n def annotation_select(self):\n \"\"\"\n Return the dictionary of aggregate columns that are not masked and\n should be used in the SELECT clause. Cache this result for performance.\n \"\"\"\n if self._annotation_select_cache is not None:\n return self._annotation_select_cache\n elif not self.annotations:\n return {}\n elif self.annotation_select_mask is not None:\n self._annotation_select_cache = {\n k: v\n for k, v in self.annotations.items()\n if k in self.annotation_select_mask\n }\n return self._annotation_select_cache\n else:\n return self.annotations\n\n @property\n def extra_select(self):\n if self._extra_select_cache is not None:\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_d8453a62c52c41b6920172ff" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000069000, + "content": "", + "tool_calls": [ + { + "id": "call_8b6a30fcd4f44c44b08ec2e4", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"class Query\\\\\\\" django/db/models/sql/query.py | head -5\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000070000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"class Query\" django/db/models/sql/query.py | head -5\n158:class Query(BaseExpression):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_8b6a30fcd4f44c44b08ec2e4" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000071000, + "content": "", + "tool_calls": [ + { + "id": "call_1c494e92a5ff462aa2b31821", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '158,250p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000072000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '158,250p' django/db/models/sql/query.py\nclass Query(BaseExpression):\n \"\"\"A single SQL query.\"\"\"\n\n alias_prefix = \"T\"\n... (40 lines omitted) ...\n # Arbitrary limit for select_related to prevents infinite recursion.\n max_depth = 5\n # Holds the selects defined by a call to values() or values_list()\n # excluding annotation_select and extra_select.\n values_select = ()\n\n # SQL annotation-related attributes.\n annotation_select_mask = None\n _annotation_select_cache = None\n\n # Set combination attributes.\n combinator = None\n combinator_all = False\n combined_queries = ()\n\n # These are for extensions. The contents are more or less appended verbatim\n # to the appropriate clause.\n extra_select_mask = None\n _extra_select_cache = None\n\n extra_tables = ()\n extra_order_by = ()\n\n # A tuple that is a set of model field names and either True, if these are\n # the fields to defer, or False if these are the only fields to load.\n deferred_loading = (frozenset(), True)\n\n explain_info = None\n\n def __init__(self, model, alias_cols=True):\n self.model = model\n self.alias_refcount = {}\n # alias_map is the most important data structure regarding joins.\n # It's used for recording which joins exist in the query and what\n # types they are. The key is the alias of the joined table (possibly\n # the table name) and the value is a Join-like object (see\n # sql.datastructures.Join for more information).\n self.alias_map = {}\n # Whether to provide alias to columns during reference resolving.\n self.alias_cols = alias_cols\n # Sometimes the query contains references to aliases in outer queries (as\n # a result of split_exclude). Correct alias quoting needs to know these\n # aliases too.\n # Map external tables to whether they are aliased.\n self.external_aliases = {}\n self.table_map = {} # Maps table names to list of aliases.\n self.used_aliases = set()\n\n self.where = WhereNode()\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_1c494e92a5ff462aa2b31821" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000073000, + "content": "", + "tool_calls": [ + { + "id": "call_e57cdeb2b07245679a124242", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"class F\\\\\\\" django/db/models/expressions.py | head -5\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000074000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"class F\" django/db/models/expressions.py | head -5\n800:class F(Combinable):\n882:class Func(SQLiteNumericMixin, Expression):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_e57cdeb2b07245679a124242" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000075000, + "content": "", + "tool_calls": [ + { + "id": "call_a2987d9741d54d549eccafe3", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '800,880p' django/db/models/expressions.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000076000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '800,880p' django/db/models/expressions.py\nclass F(Combinable):\n \"\"\"An object capable of resolving references to existing query objects.\"\"\"\n\n def __init__(self, name):\n... (28 lines omitted) ...\n\n def copy(self):\n return copy.copy(self)\n\n\nclass ResolvedOuterRef(F):\n \"\"\"\n An object that contains a reference to an outer query.\n\n In this case, the reference to the outer query has been resolved because\n the inner query has been used as a subquery.\n \"\"\"\n\n contains_aggregate = False\n contains_over_clause = False\n\n def as_sql(self, *args, **kwargs):\n raise ValueError(\n \"This queryset contains a reference to an outer query and may \"\n \"only be used in a subquery.\"\n )\n\n def resolve_expression(self, *args, **kwargs):\n col = super().resolve_expression(*args, **kwargs)\n # FIXME: Rename possibly_multivalued to multivalued and fix detection\n # for non-multivalued JOINs (e.g. foreign key fields). This should take\n # into account only many-to-many and one-to-many relationships.\n col.possibly_multivalued = LOOKUP_SEP in self.name\n return col\n\n def relabeled_clone(self, relabels):\n return self\n\n def get_group_by_cols(self):\n return []\n\n\nclass OuterRef(F):\n contains_aggregate = False\n\n def resolve_expression(self, *args, **kwargs):\n if isinstance(self.name, self.__class__):\n return self.name\n return ResolvedOuterRef(self.name)\n\n def relabeled_clone(self, relabels):\n return self\n\n\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_a2987d9741d54d549eccafe3" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000077000, + "content": "", + "tool_calls": [ + { + "id": "call_5cd1ed4f67ae4569a66995e3", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"get_refs\\\\\\\\|contains_references\\\\\\\" django/db/models/expressions.py | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000078000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"get_refs\\|contains_references\" django/db/models/expressions.py | head -20\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_5cd1ed4f67ae4569a66995e3" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000079000, + "content": "", + "tool_calls": [ + { + "id": "call_99d6a716b010441ba6c4b1a4", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"class Expression\\\\\\\" django/db/models/expressions.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000080000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"class Expression\" django/db/models/expressions.py\n468:class Expression(BaseExpression, Combinable):\n1180:class ExpressionList(Func):\n1231:class ExpressionWrapper(SQLiteNumericMixin, Expression):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_99d6a716b010441ba6c4b1a4" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000081000, + "content": "", + "tool_calls": [ + { + "id": "call_da50410eca17426aa67d0484", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"class BaseExpression\\\\\\\" django/db/models/expressions.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000082000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"class BaseExpression\" django/db/models/expressions.py\n169:class BaseExpression:\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_da50410eca17426aa67d0484" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000083000, + "content": "", + "tool_calls": [ + { + "id": "call_804d625ba46044298e822018", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '169,350p' django/db/models/expressions.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000084000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '169,350p' django/db/models/expressions.py\nclass BaseExpression:\n \"\"\"Base class for all query expressions.\"\"\"\n\n empty_result_set_value = NotImplemented\n... (129 lines omitted) ...\n return output_field\n\n @cached_property\n def _output_field_or_none(self):\n \"\"\"\n Return the output field of this expression, or None if\n _resolve_output_field() didn't return an output type.\n \"\"\"\n try:\n return self.output_field\n except FieldError:\n if not self._output_field_resolved_to_none:\n raise\n\n def _resolve_output_field(self):\n \"\"\"\n Attempt to infer the output type of the expression.\n\n As a guess, if the output fields of all source fields match then simply\n infer the same type here.\n\n If a source's output field resolves to None, exclude it from this check.\n If all sources are None, then an error is raised higher up the stack in\n the output_field property.\n \"\"\"\n # This guess is mostly a bad idea, but there is quite a lot of code\n # (especially 3rd party Func subclasses) that depend on it, we'd need a\n # deprecation path to fix it.\n sources_iter = (\n source for source in self.get_source_fields() if source is not None\n )\n for output_field in sources_iter:\n for source in sources_iter:\n if not isinstance(output_field, source.__class__):\n raise FieldError(\n \"Expression contains mixed types: %s, %s. You must \"\n \"set output_field.\"\n % (\n output_field.__class__.__name__,\n source.__class__.__name__,\n )\n )\n return output_field\n\n @staticmethod\n def _convert_value_noop(value, expression, connection):\n return value\n\n @cached_property\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_804d625ba46044298e822018" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000085000, + "content": "", + "tool_calls": [ + { + "id": "call_5a7bc61625f34c6486d1ae7f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"get_source_expressions\\\\\\\" django/db/models/expressions.py | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000086000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"get_source_expressions\" django/db/models/expressions.py | head -20\n197: def get_source_expressions(self):\n242: expr and expr.contains_aggregate for expr in self.get_source_expressions()\n248: expr and expr.contains_over_clause for expr in self.get_source_expressions()\n255: for expr in self.get_source_expressions()\n282: for expr in c.get_source_expressions()\n390: for e in self.get_source_expressions()\n399: source_expressions = clone.get_source_expressions()\n418: for expr in self.get_source_expressions()\n427: for source in self.get_source_expressions():\n433: return [e._output_field_or_none for e in self.get_source_expressions()]\n450: for expr in self.get_source_expressions():\n648: def get_source_expressions(self):\n919: def get_source_expressions(self):\n1157: def get_source_expressions(self):\n1225: for order_by in self.get_source_expressions():\n1244: def get_source_expressions(self):\n1343: def get_source_expressions(self):\n1385: for source in self.get_source_expressions():\n1424: def get_source_expressions(self):\n1504: def get_source_expressions(self):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_5a7bc61625f34c6486d1ae7f" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000087000, + "content": "", + "tool_calls": [ + { + "id": "call_f43b9fef89024ff390b93249", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '197,210p' django/db/models/expressions.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000088000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '197,210p' django/db/models/expressions.py\n def get_source_expressions(self):\n return []\n\n def set_source_expressions(self, exprs):\n assert not exprs\n\n def _parse_expressions(self, *expressions):\n return [\n arg\n if hasattr(arg, \"resolve_expression\")\n else (F(arg) if isinstance(arg, str) else Value(arg))\n for arg in expressions\n ]\n\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_f43b9fef89024ff390b93249" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000089000, + "content": "", + "tool_calls": [ + { + "id": "call_ac8c5f715bfe481594221ee2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"def resolve_expression\\\\\\\" django/db/models/expressions.py | head -10\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000090000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"def resolve_expression\" django/db/models/expressions.py | head -10\n258: def resolve_expression(\n685: def resolve_expression(\n813: def resolve_expression(\n854: def resolve_expression(self, *args, **kwargs):\n872: def resolve_expression(self, *args, **kwargs):\n925: def resolve_expression(\n1023: def resolve_expression(\n1078: def resolve_expression(\n1163: def resolve_expression(\n1288: def resolve_expression(\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_ac8c5f715bfe481594221ee2" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000091000, + "content": "", + "tool_calls": [ + { + "id": "call_493653ad5989434f9f10fa6c", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '813,850p' django/db/models/expressions.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000092000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '813,850p' django/db/models/expressions.py\n def resolve_expression(\n self, query=None, allow_joins=True, reuse=None, summarize=False, for_save=False\n ):\n return query.resolve_ref(self.name, allow_joins, reuse, summarize)\n\n def replace_expressions(self, replacements):\n return replacements.get(self, self)\n\n def asc(self, **kwargs):\n return OrderBy(self, **kwargs)\n\n def desc(self, **kwargs):\n return OrderBy(self, descending=True, **kwargs)\n\n def __eq__(self, other):\n return self.__class__ == other.__class__ and self.name == other.name\n\n def __hash__(self):\n return hash(self.name)\n\n def copy(self):\n return copy.copy(self)\n\n\nclass ResolvedOuterRef(F):\n \"\"\"\n An object that contains a reference to an outer query.\n\n In this case, the reference to the outer query has been resolved because\n the inner query has been used as a subquery.\n \"\"\"\n\n contains_aggregate = False\n contains_over_clause = False\n\n def as_sql(self, *args, **kwargs):\n raise ValueError(\n \"This queryset contains a reference to an outer query and may \"\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_493653ad5989434f9f10fa6c" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000093000, + "content": "", + "tool_calls": [ + { + "id": "call_011eb3a5b3ad4093b0890427", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"def resolve_ref\\\\\\\" django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000094000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"def resolve_ref\" django/db/models/sql/query.py\n1908: def resolve_ref(self, name, allow_joins=True, reuse=None, summarize=False):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_011eb3a5b3ad4093b0890427" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000095000, + "content": "", + "tool_calls": [ + { + "id": "call_f08bdbfbae674e0a99990e8c", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '1908,2000p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000096000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '1908,2000p' django/db/models/sql/query.py\n def resolve_ref(self, name, allow_joins=True, reuse=None, summarize=False):\n annotation = self.annotations.get(name)\n if annotation is not None:\n if not allow_joins:\n... (40 lines omitted) ...\n # transform_function() raises FieldError if not.\n transform = join_info.transform_function(targets[0], final_alias)\n if reuse is not None:\n reuse.update(join_list)\n return transform\n\n def split_exclude(self, filter_expr, can_reuse, names_with_path):\n \"\"\"\n When doing an exclude against any kind of N-to-many relation, we need\n to use a subquery. This method constructs the nested query, given the\n original exclude filter (filter_expr) and the portion up to the first\n N-to-many relation field.\n\n For example, if the origin filter is ~Q(child__name='foo'), filter_expr\n is ('child__name', 'foo') and can_reuse is a set of joins usable for\n filters in the original query.\n\n We will turn this into equivalent of:\n WHERE NOT EXISTS(\n SELECT 1\n FROM child\n WHERE name = 'foo' AND child.parent_id = parent.id\n LIMIT 1\n )\n \"\"\"\n # Generate the inner query.\n query = self.__class__(self.model)\n query._filtered_relations = self._filtered_relations\n filter_lhs, filter_rhs = filter_expr\n if isinstance(filter_rhs, OuterRef):\n filter_rhs = OuterRef(filter_rhs)\n elif isinstance(filter_rhs, F):\n filter_rhs = OuterRef(filter_rhs.name)\n query.add_filter(filter_lhs, filter_rhs)\n query.clear_ordering(force=True)\n # Try to have as simple as possible subquery -> trim leading joins from\n # the subquery.\n trimmed_prefix, contains_louter = query.trim_start(names_with_path)\n\n col = query.select[0]\n select_field = col.target\n alias = col.alias\n if alias in can_reuse:\n pk = select_field.model._meta.pk\n # Need to add a restriction so that outer query's filters are in effect for\n # the subquery, too.\n query.bump_prefix(self)\n lookup_class = select_field.get_lookup(\"exact\")\n # Note that the query.select[0].alias is different from alias\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_f08bdbfbae674e0a99990e8c" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000097000, + "content": "", + "tool_calls": [ + { + "id": "call_d6b0a62e30a94b909ab18cfd", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"class WhereNode\\\\\\\" django/db/models/sql/\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000098000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"class WhereNode\" django/db/models/sql/\ngrep: django/db/models/sql/: Is a directory\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_d6b0a62e30a94b909ab18cfd" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000099000, + "content": "", + "tool_calls": [ + { + "id": "call_26f07ac0553b460eb953f712", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -rn \\\\\\\"class WhereNode\\\\\\\" django/db/models/sql/\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000100000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -rn \"class WhereNode\" django/db/models/sql/\ndjango/db/models/sql/where.py:19:class WhereNode(tree.Node):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_26f07ac0553b460eb953f712" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000101000, + "content": "", + "tool_calls": [ + { + "id": "call_b5882b844123475cbc214167", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '19,100p' django/db/models/sql/where.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000102000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '19,100p' django/db/models/sql/where.py\nclass WhereNode(tree.Node):\n \"\"\"\n An SQL WHERE clause.\n\n... (29 lines omitted) ...\n or (not in_negated and self.connector == OR)\n or self.connector == XOR\n )\n if (\n must_remain_connected\n and self.contains_aggregate\n and not self.contains_over_clause\n ):\n # It's must cheaper to short-circuit and stash everything in the\n # HAVING clause than split children if possible.\n return None, self, None\n where_parts = []\n having_parts = []\n qualify_parts = []\n for c in self.children:\n if hasattr(c, \"split_having_qualify\"):\n where_part, having_part, qualify_part = c.split_having_qualify(\n in_negated, must_group_by\n )\n if where_part is not None:\n where_parts.append(where_part)\n if having_part is not None:\n having_parts.append(having_part)\n if qualify_part is not None:\n qualify_parts.append(qualify_part)\n elif c.contains_over_clause:\n qualify_parts.append(c)\n elif c.contains_aggregate:\n having_parts.append(c)\n else:\n where_parts.append(c)\n if must_remain_connected and qualify_parts:\n # Disjunctive heterogeneous predicates can be pushed down to\n # qualify as long as no conditional aggregation is involved.\n if not where_parts or (where_parts and not must_group_by):\n return None, None, self\n elif where_parts:\n # In theory this should only be enforced when dealing with\n # where_parts containing predicates against multi-valued\n # relationships that could affect aggregation results but this\n # is complex to infer properly.\n raise NotImplementedError(\n \"Heterogeneous disjunctive predicates against window functions are \"\n \"not implemented when performing conditional aggregation.\"\n )\n where_node = (\n self.create(where_parts, self.connector, self.negated)\n if where_parts\n else None\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_b5882b844123475cbc214167" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000103000, + "content": "", + "tool_calls": [ + { + "id": "call_ae303127cdef4bcaab0a804b", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -rn \\\\\\\"class Node\\\\\\\" django/db/models/sql/tree.py | head -5\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000104000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -rn \"class Node\" django/db/models/sql/tree.py | head -5\ngrep: django/db/models/sql/tree.py: No such file or directory\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_ae303127cdef4bcaab0a804b" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000105000, + "content": "", + "tool_calls": [ + { + "id": "call_42ec3876b40c4aa692edcc64", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -rn \\\\\\\"class Node\\\\\\\" django/db/models/\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000106000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -rn \"class Node\" django/db/models/\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_42ec3876b40c4aa692edcc64" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000107000, + "content": "", + "tool_calls": [ + { + "id": "call_c954d655fa504c49b7b794f4", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && head -20 django/db/models/sql/where.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000108000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && head -20 django/db/models/sql/where.py\n\"\"\"\nCode to manage the creation and SQL rendering of 'where' constraints.\n\"\"\"\nimport operator\nfrom functools import reduce\n\nfrom django.core.exceptions import EmptyResultSet, FullResultSet\nfrom django.db.models.expressions import Case, When\nfrom django.db.models.lookups import Exact\nfrom django.utils import tree\nfrom django.utils.functional import cached_property\n\n# Connection types\nAND = \"AND\"\nOR = \"OR\"\nXOR = \"XOR\"\n\n\nclass WhereNode(tree.Node):\n \"\"\"\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_c954d655fa504c49b7b794f4" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000109000, + "content": "", + "tool_calls": [ + { + "id": "call_60ceecce4dcf421292a522d2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"class Node\\\\\\\" django/utils/tree.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000110000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"class Node\" django/utils/tree.py\n11:class Node:\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_60ceecce4dcf421292a522d2" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000111000, + "content": "", + "tool_calls": [ + { + "id": "call_d3813b6c4c794d049ca2e8fb", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '11,100p' django/utils/tree.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000112000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '11,100p' django/utils/tree.py\nclass Node:\n \"\"\"\n A single internal node in the tree graph. A Node should be viewed as a\n connection (the root) with the children being either leaf nodes or other\n... (37 lines omitted) ...\n copy = __copy__\n\n def __deepcopy__(self, memodict):\n obj = self.create(connector=self.connector, negated=self.negated)\n obj.children = copy.deepcopy(self.children, memodict)\n return obj\n\n def __len__(self):\n \"\"\"Return the number of children this node has.\"\"\"\n return len(self.children)\n\n def __bool__(self):\n \"\"\"Return whether or not this node has children.\"\"\"\n return bool(self.children)\n\n def __contains__(self, other):\n \"\"\"Return True if 'other' is a direct child of this instance.\"\"\"\n return other in self.children\n\n def __eq__(self, other):\n return (\n self.__class__ == other.__class__\n and self.connector == other.connector\n and self.negated == other.negated\n and self.children == other.children\n )\n\n def __hash__(self):\n return hash(\n (\n self.__class__,\n self.connector,\n self.negated,\n *make_hashable(self.children),\n )\n )\n\n def add(self, data, conn_type):\n \"\"\"\n Combine this tree and the data represented by data using the\n connector conn_type. The combine is done by squashing the node other\n away if possible.\n\n This tree (self) will never be pushed to a child node of the\n combined tree, nor will the connector or negated properties change.\n\n Return a node which can be used in place of data regardless if the\n node other got squashed or not.\n \"\"\"\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_d3813b6c4c794d049ca2e8fb" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000113000, + "content": "", + "tool_calls": [ + { + "id": "call_f7f1e63b0b124c9ea6824762", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -rn \\\\\\\"walk\\\\\\\" django/db/models/expressions.py | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000114000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -rn \"walk\" django/db/models/expressions.py | head -20\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_f7f1e63b0b124c9ea6824762" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000115000, + "content": "", + "tool_calls": [ + { + "id": "call_c89d7169d5c84678a6dc6232", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -rn \\\\\\\"get_source\\\\\\\" django/db/models/expressions.py | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000116000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -rn \"get_source\" django/db/models/expressions.py | head -20\n197: def get_source_expressions(self):\n242: expr and expr.contains_aggregate for expr in self.get_source_expressions()\n248: expr and expr.contains_over_clause for expr in self.get_source_expressions()\n255: for expr in self.get_source_expressions()\n282: for expr in c.get_source_expressions()\n331: source for source in self.get_source_fields() if source is not None\n390: for e in self.get_source_expressions()\n399: source_expressions = clone.get_source_expressions()\n418: for expr in self.get_source_expressions()\n427: for source in self.get_source_expressions():\n431: def get_source_fields(self):\n433: return [e._output_field_or_none for e in self.get_source_expressions()]\n450: for expr in self.get_source_expressions():\n648: def get_source_expressions(self):\n919: def get_source_expressions(self):\n1157: def get_source_expressions(self):\n1225: for order_by in self.get_source_expressions():\n1244: def get_source_expressions(self):\n1343: def get_source_expressions(self):\n1349: def get_source_fields(self):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_c89d7169d5c84678a6dc6232" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000117000, + "content": "", + "tool_calls": [ + { + "id": "call_69f68e9ae9334549aceb416a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '548,556p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000118000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '548,556p' django/db/models/sql/query.py\n def get_count(self, using):\n \"\"\"\n Perform a COUNT() query using the current filter constraints.\n \"\"\"\n obj = self.clone()\n obj.add_annotation(Count(\"*\"), alias=\"__count\", is_summary=True)\n return obj.get_aggregation(using, [\"__count\"])[\"__count\"]\n\n def has_filters(self):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_69f68e9ae9334549aceb416a" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000119000, + "content": "", + "tool_calls": [ + { + "id": "call_cee018f3034f40f39691cc97", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && python -c \\\\\\\"\\nfrom django.db.models import F\\nfrom django.db.models.expressions import BaseExpression\\n\\ndef get_refs(expression, refs=None):\\n '''Recursively find all F references in an expression.'''\\n if refs is None:\\n refs = set()\\n \\n if isinstance(expression, F):\\n refs.add(expression.name)\\n \\n if hasattr(expression, 'get_source_expressions'):\\n for source in expression.get_source_expressions():\\n if source is not None:\\n get_refs(source, refs)\\n \\n # Also check children for tree.Node instances\\n if hasattr(expression, 'children'):\\n for child in expression.children:\\n get_refs(child, refs)\\n \\n return refs\\n\\nprint('Function defined')\\n\\\\\\\" 2>&1\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000120000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && python -c \"\n> from django.db.models import F\n> from django.db.models.expressions import BaseExpression\n>\n> def get_refs(expression, refs=None):\n> '''Recursively find all F references in an expression.'''\n> if refs is None:\n> refs = set()\n>\n> if isinstance(expression, F):\n> refs.add(expression.name)\n>\n> if hasattr(expression, 'get_source_expressions'):\n> for source in expression.get_source_expressions():\n> if source is not None:\n> get_refs(source, refs)\n>\n> # Also check children for tree.Node instances\n> if hasattr(expression, 'children'):\n> for child in expression.children:\n> get_refs(child, refs)\n>\n> return refs\n>\n> print('Function defined')\n> \" 2>&1\nFunction defined\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_cee018f3034f40f39691cc97" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000121000, + "content": "", + "tool_calls": [ + { + "id": "call_5c7ab7175b2a4bdf8b79e22e", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '1,50p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000122000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '1,50p' django/db/models/sql/query.py\n\"\"\"\nCreate SQL statements for QuerySets.\n\nThe code in here encapsulates all of the SQL construction so that QuerySets\nthemselves do not have to (and could be backed by things other than SQL\ndatabases). The abstraction barrier only works one way: this module has to know\nall about the internals of models in order to get the information it needs.\n\"\"\"\nimport copy\nimport difflib\nimport functools\nimport sys\nfrom collections import Counter, namedtuple\nfrom collections.abc import Iterator, Mapping\nfrom itertools import chain, count, product\nfrom string import ascii_uppercase\n\nfrom django.core.exceptions import FieldDoesNotExist, FieldError\nfrom django.db import DEFAULT_DB_ALIAS, NotSupportedError, connections\nfrom django.db.models.aggregates import Count\nfrom django.db.models.constants import LOOKUP_SEP\nfrom django.db.models.expressions import (\n BaseExpression,\n Col,\n Exists,\n F,\n OuterRef,\n Ref,\n ResolvedOuterRef,\n Value,\n)\nfrom django.db.models.fields import Field\nfrom django.db.models.fields.related_lookups import MultiColSource\nfrom django.db.models.lookups import Lookup\nfrom django.db.models.query_utils import (\n Q,\n check_rel_lookup_compatibility,\n refs_expression,\n)\nfrom django.db.models.sql.constants import INNER, LOUTER, ORDER_DIR, SINGLE\nfrom django.db.models.sql.datastructures import BaseTable, Empty, Join, MultiJoin\nfrom django.db.models.sql.where import AND, OR, ExtraWhere, NothingNode, WhereNode\nfrom django.utils.functional import cached_property\nfrom django.utils.regex_helper import _lazy_re_compile\nfrom django.utils.tree import Node\n\n__all__ = [\"Query\", \"RawQuery\"]\n\n# Quotation marks ('\"`[]), whitespace characters, semicolons, or inline\n# SQL comments are forbidden in column aliases.\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_5c7ab7175b2a4bdf8b79e22e" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000123000, + "content": "", + "tool_calls": [ + { + "id": "call_a654f267657f43079d304953", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"def refs_expression\\\\\\\" django/db/models/query_utils.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000124000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"def refs_expression\" django/db/models/query_utils.py\n353:def refs_expression(lookup_parts, annotations):\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_a654f267657f43079d304953" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000125000, + "content": "", + "tool_calls": [ + { + "id": "call_c017f65219a94774bfb82e85", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '353,400p' django/db/models/query_utils.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000126000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '353,400p' django/db/models/query_utils.py\ndef refs_expression(lookup_parts, annotations):\n \"\"\"\n Check if the lookup_parts contains references to the given annotations set.\n Because the LOOKUP_SEP is contained in the default annotation names, check\n each prefix of the lookup_parts for a match.\n \"\"\"\n for n in range(1, len(lookup_parts) + 1):\n level_n_lookup = LOOKUP_SEP.join(lookup_parts[0:n])\n if level_n_lookup in annotations and annotations[level_n_lookup]:\n return annotations[level_n_lookup], lookup_parts[n:]\n return False, ()\n\n\ndef check_rel_lookup_compatibility(model, target_opts, field):\n \"\"\"\n Check that self.model is compatible with target_opts. Compatibility\n is OK if:\n 1) model and opts match (where proxy inheritance is removed)\n 2) model is parent of opts' model or the other way around\n \"\"\"\n\n def check(opts):\n return (\n model._meta.concrete_model == opts.concrete_model\n or opts.concrete_model in model._meta.get_parent_list()\n or model in opts.get_parent_list()\n )\n\n # If the field is a primary key, then doing a query against the field's\n # model is ok, too. Consider the case:\n # class Restaurant(models.Model):\n # place = OneToOneField(Place, primary_key=True):\n # Restaurant.objects.filter(pk__in=Restaurant.objects.all()).\n # If we didn't have the primary key check, then pk__in (== place__in) would\n # give Place's opts as the target opts, but Restaurant isn't compatible\n # with that. This logic applies only to primary keys, as when doing __in=qs,\n # we are going to turn this into __in=qs.values('pk') later on.\n return check(target_opts) or (\n getattr(field, \"primary_key\", False) and check(field.model._meta)\n )\n\n\nclass FilteredRelation:\n \"\"\"Specify custom filtering in the ON clause of SQL joins.\"\"\"\n\n def __init__(self, relation_name, *, condition=Q()):\n if not relation_name:\n raise ValueError(\"relation_name cannot be empty.\")\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_c017f65219a94774bfb82e85" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000127000, + "content": "", + "tool_calls": [ + { + "id": "call_fdd60ae6e7264f1faad05aeb", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && cat > /tmp/test_refs.py << 'EOF'\\nfrom django.db.models import F\\nfrom django.db.models.expressions import BaseExpression\\nfrom django.utils.tree import Node\\n\\ndef get_refs(expression, refs=None):\\n '''Recursively find all F references in an expression.'''\\n if refs is None:\\n refs = set()\\n \\n if isinstance(expression, F):\\n refs.add(expression.name)\\n \\n if hasattr(expression, 'get_source_expressions'):\\n for source in expression.get_source_expressions():\\n if source is not None:\\n get_refs(source, refs)\\n \\n # Also check children for tree.Node instances\\n if hasattr(expression, 'children'):\\n for child in expression.children:\\n get_refs(child, refs)\\n \\n return refs\\n\\n# Test\\nprint(get_refs(F('test')))\\nprint(get_refs(F('a') + F('b')))\\nEOF\\npython /tmp/test_refs.py 2>&1\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000128000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && cat > /tmp/test_refs.py << 'EOF'\n> from django.db.models import F\n> from django.db.models.expressions import BaseExpression\n> from django.utils.tree import Node\n>\n> def get_refs(expression, refs=None):\n> '''Recursively find all F references in an expression.'''\n> if refs is None:\n> refs = set()\n>\n> if isinstance(expression, F):\n> refs.add(expression.name)\n>\n> if hasattr(expression, 'get_source_expressions'):\n> for source in expression.get_source_expressions():\n> if source is not None:\n> get_refs(source, refs)\n>\n> # Also check children for tree.Node instances\n> if hasattr(expression, 'children'):\n> for child in expression.children:\n> get_refs(child, refs)\n>\n> return refs\n>\n> # Test\n> print(get_refs(F('test')))\n> print(get_refs(F('a') + F('b')))\n> EOF\n(testbed) root@fe90114f4bd6:/testbed# python /tmp/test_refs.py 2>&1\n{'test'}\n{'a', 'b'}\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_fdd60ae6e7264f1faad05aeb" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000129000, + "content": "", + "tool_calls": [ + { + "id": "call_9bc944ffc61e4a458c8aea34", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '545,560p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000130000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && sed -n '545,560p' django/db/models/sql/query.py\n\n return dict(zip(outer_query.annotation_select, result))\n\n def get_count(self, using):\n \"\"\"\n Perform a COUNT() query using the current filter constraints.\n \"\"\"\n obj = self.clone()\n obj.add_annotation(Count(\"*\"), alias=\"__count\", is_summary=True)\n return obj.get_aggregation(using, [\"__count\"])[\"__count\"]\n\n def has_filters(self):\n return self.where\n\n def exists(self, limit=True):\n q = self.clone()\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_9bc944ffc61e4a458c8aea34" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000131000, + "content": "", + "tool_calls": [ + { + "id": "call_6bbe446366394379b2ea373d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"self.annotations\\\\\\\" django/db/models/sql/query.py | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000132000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && grep -n \"self.annotations\" django/db/models/sql/query.py | head -20\n252: self.annotations = {}\n329: obj.annotations = self.annotations.copy()\n427: self.annotations[col_alias] = expr\n446: for alias, annotation in self.annotations.items()\n908: self.annotations = self.annotations and {\n910: for key, col in self.annotations.items()\n1111: self.annotations[alias] = annotation\n1143: exprs = chain(self.annotations.values(), self.where.children)\n1200: if self.annotations:\n1202: lookup_splitted, self.annotations\n1909: annotation = self.annotations.get(name)\n1932: annotation = self.annotations.get(field_list[0])\n2141: elif name in self.annotations:\n2176: if item in self.annotations:\n2243: self.annotations = {**group_by_annotations, **self.annotations}\n2396: if not self.extra and not self.annotations:\n2431: expr = self.annotations[expr.refs]\n2446: elif not self.annotations:\n2451: for k, v in self.annotations.items()\n2456: return self.annotations\n(testbed) root@fe90114f4bd6:/testbed#", + "tool_call_id": "call_6bbe446366394379b2ea373d" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000133000, + "content": "", + "tool_calls": [ + { + "id": "call_fa62c32420ee40a7bc599466", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && cat > /tmp/patch.py << 'PATCH_EOF'\\nimport re\\n\\n# Read the file\\nwith open('django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\n# Find the get_count method and replace it\\nold_method = ''' def get_count(self, using):\\n \\\"\\\"\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\"\\\"\\\"\\n obj = self.clone()\\n obj.add_annotation(Count(\\\"*\\\"), alias=\\\"__count\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\"__count\\\"])[\\\"__count\\\"]'''\\n\\nnew_method = ''' def get_count(self, using):\\n \\\"\\\"\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\"\\\"\\\"\\n obj = self.clone()\\n obj.clear_annotations()\\n obj.add_annotation(Count(\\\"*\\\"), alias=\\\"__count\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\"__count\\\"])[\\\"__count\\\"]'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\n# Write the file\\nwith open('django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint(\\\"Patched successfully\\\")\\nPATCH_EOF\\npython /tmp/patch.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000134000, + "content": "(testbed) root@fe90114f4bd6:/testbed# cd /testbed && cat > /tmp/patch.py << 'PATCH_EOF'\n> import re\n>\n> # Read the file\n> with open('django/db/models/sql/query.py', 'r') as f:\n> content = f.read()\n>\n> # Find the get_count method and replace it\n> old_method = ''' def get_count(self, using):\n>\n>\n/bin/bash: -c: line 11: syntax error near unexpected token `('\n/bin/bash: -c: line 11: ` Perform a COUNT() query using the current filter constraints.'\n\n(Command exited with code 2)", + "tool_call_id": "call_fa62c32420ee40a7bc599466" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000135000, + "content": "", + "tool_calls": [ + { + "id": "call_2a9b9d68b691459896290f3d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec write /tmp/patch.py << 'PATCH_EOF'\\nimport re\\n\\n# Read the file\\nwith open('django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\n# Find the get_count method and replace it\\nold_method = ''' def get_count(self, using):\\n \\\"\\\"\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\"\\\"\\\"\\n obj = self.clone()\\n obj.add_annotation(Count(\\\"*\\\"), alias=\\\"__count\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\"__count\\\"])[\\\"__count\\\"]'''\\n\\nnew_method = ''' def get_count(self, using):\\n \\\"\\\"\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\"\\\"\\\"\\n obj = self.clone()\\n obj.clear_annotations()\\n obj.add_annotation(Count(\\\"*\\\"), alias=\\\"__count\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\"__count\\\"])[\\\"__count\\\"]'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\n# Write the file\\nwith open('django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint(\\\"Patched successfully\\\")\\nPATCH_EOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000136000, + "content": "Written 978 bytes to /tmp/patch.py", + "tool_call_id": "call_2a9b9d68b691459896290f3d" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000137000, + "content": "", + "tool_calls": [ + { + "id": "call_4182067239314c8f83d5feb7", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && python /tmp/patch.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000138000, + "content": "> cd /testbed && python /tmp/patch.py\n>", + "tool_call_id": "call_4182067239314c8f83d5feb7" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000139000, + "content": "", + "tool_calls": [ + { + "id": "call_32fdd5e5fa1548aaace299fd", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && sed -n '548,560p' django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000140000, + "content": "> cd /testbed && sed -n '548,560p' django/db/models/sql/query.py\n>", + "tool_call_id": "call_32fdd5e5fa1548aaace299fd" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000141000, + "content": "", + "tool_calls": [ + { + "id": "call_3188afe8ec204dc2a6abd506", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"def get_count\\\\\\\" django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000142000, + "content": "> cd /testbed && grep -n \"def get_count\" django/db/models/sql/query.py\n>", + "tool_call_id": "call_3188afe8ec204dc2a6abd506" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000143000, + "content": "", + "tool_calls": [ + { + "id": "call_78f247e5be874b03aeb696a9", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && head -600 django/db/models/sql/query.py | tail -60\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000144000, + "content": "> cd /testbed && head -600 django/db/models/sql/query.py | tail -60\n>", + "tool_call_id": "call_78f247e5be874b03aeb696a9" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000145000, + "content": "", + "tool_calls": [ + { + "id": "call_7e3bc74bd0084e09badd312d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && cat django/db/models/sql/query.py | head -600 | tail -60\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000146000, + "content": "> cd /testbed && cat django/db/models/sql/query.py | head -600 | tail -60\n>", + "tool_call_id": "call_7e3bc74bd0084e09badd312d" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000147000, + "content": "", + "tool_calls": [ + { + "id": "call_9a46317f7e8b420ba9d88d22", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && wc -l django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000148000, + "content": "> cd /testbed && wc -l django/db/models/sql/query.py\n>", + "tool_call_id": "call_9a46317f7e8b420ba9d88d22" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000149000, + "content": "", + "tool_calls": [ + { + "id": "call_0de8bf5a832c42df8b70e657", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"cd /testbed && ls -la django/db/models/sql/query.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000150000, + "content": "> cd /testbed && ls -la django/db/models/sql/query.py\n>", + "tool_call_id": "call_0de8bf5a832c42df8b70e657" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000151000, + "content": "", + "tool_calls": [ + { + "id": "call_08a790a4d222436fa3bb33d6", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"pwd\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000152000, + "content": "> pwd\n>", + "tool_call_id": "call_08a790a4d222436fa3bb33d6" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000153000, + "content": "", + "tool_calls": [ + { + "id": "call_99b6a40c80f544fe8bbf6ae6", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"pwd\\\" 10\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000154000, + "content": "> pwd\n>", + "tool_call_id": "call_99b6a40c80f544fe8bbf6ae6" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000155000, + "content": "", + "tool_calls": [ + { + "id": "call_6cb3b09077b44b85b327226b", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec tmux-run \\\"echo hello\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000156000, + "content": "> echo hello\n>", + "tool_call_id": "call_6cb3b09077b44b85b327226b" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000157000, + "content": "", + "tool_calls": [ + { + "id": "call_8a6de4a21cd04b94996b6306", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec echo hello 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000158000, + "content": "hello 3", + "tool_call_id": "call_8a6de4a21cd04b94996b6306" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000159000, + "content": "", + "tool_calls": [ + { + "id": "call_e22cfd8493e84975b88ef2f1", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec cd /testbed && grep -n \\\"def get_count\\\" django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000160000, + "content": "grep: django/db/models/sql/query.py: No such file or directory\n\n(Command exited with code 2)", + "tool_call_id": "call_e22cfd8493e84975b88ef2f1" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000161000, + "content": "", + "tool_calls": [ + { + "id": "call_48e36598e59248c997e43a11", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec ls -la /testbed/django/db/models/sql/\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000162000, + "content": "total 256\ndrwxrwxrwx 1 root root 4096 Mar 17 21:14 .\ndrwxrwxrwx 1 root root 4096 Mar 17 21:14 ..\n-rw-r--r-- 1 root root 241 Sep 10 2025 __init__.py\ndrwxr-xr-x 2 root root 4096 Mar 17 21:15 __pycache__\n-rw-r--r-- 1 root root 86714 Sep 10 2025 compiler.py\n-rw-r--r-- 1 root root 533 Sep 10 2025 constants.py\n-rw-r--r-- 1 root root 7297 Sep 10 2025 datastructures.py\n-rw-r--r-- 1 root root 113989 Sep 10 2025 query.py\n-rw-r--r-- 1 root root 5935 Sep 10 2025 subqueries.py\n-rw-r--r-- 1 root root 12571 Sep 10 2025 where.py", + "tool_call_id": "call_48e36598e59248c997e43a11" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000163000, + "content": "", + "tool_calls": [ + { + "id": "call_f622c79ad3314da39f4326e7", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec grep -n \\\"def get_count\\\" /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000164000, + "content": "548: def get_count(self, using):", + "tool_call_id": "call_f622c79ad3314da39f4326e7" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000165000, + "content": "", + "tool_calls": [ + { + "id": "call_408f764154494817abb78d34", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '548,560p' /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000166000, + "content": "def get_count(self, using):\n \"\"\"\n Perform a COUNT() query using the current filter constraints.\n \"\"\"\n obj = self.clone()\n obj.add_annotation(Count(\"*\"), alias=\"__count\", is_summary=True)\n return obj.get_aggregation(using, [\"__count\"])[\"__count\"]\n\n def has_filters(self):\n return self.where\n\n def exists(self, limit=True):\n q = self.clone()", + "tool_call_id": "call_408f764154494817abb78d34" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000167000, + "content": "", + "tool_calls": [ + { + "id": "call_0f14764286394fbc9eacf508", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python /tmp/patch.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000168000, + "content": "Patched successfully", + "tool_call_id": "call_0f14764286394fbc9eacf508" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000169000, + "content": "", + "tool_calls": [ + { + "id": "call_e265ec87d0204bc6869f1e64", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '548,560p' /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000170000, + "content": "def get_count(self, using):\n \"\"\"\n Perform a COUNT() query using the current filter constraints.\n \"\"\"\n obj = self.clone()\n obj.clear_annotations()\n obj.add_annotation(Count(\"*\"), alias=\"__count\", is_summary=True)\n return obj.get_aggregation(using, [\"__count\"])[\"__count\"]\n\n def has_filters(self):\n return self.where\n\n def exists(self, limit=True):", + "tool_call_id": "call_e265ec87d0204bc6869f1e64" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000171000, + "content": "", + "tool_calls": [ + { + "id": "call_3d64d4a19ed949daa17e13aa", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec grep -n \\\"def clear_annotations\\\" /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000172000, + "content": "\n\n(Command exited with code 1)", + "tool_call_id": "call_3d64d4a19ed949daa17e13aa" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000173000, + "content": "", + "tool_calls": [ + { + "id": "call_b723261fccd04d29b6bb4adf", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec grep -n \\\"def clear_\\\" /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000174000, + "content": "1520: def clear_where(self):\n2059: def clear_limits(self):\n2078: def clear_select_clause(self):\n2086: def clear_select_fields(self):\n2198: def clear_ordering(self, force=False, clear_default=True):\n2311: def clear_deferred_loading(self):", + "tool_call_id": "call_b723261fccd04d29b6bb4adf" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000175000, + "content": "", + "tool_calls": [ + { + "id": "call_d3241af9a6cd41f2b979fd10", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '2078,2100p' /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000176000, + "content": "def clear_select_clause(self):\n \"\"\"Remove all fields from SELECT clause.\"\"\"\n self.select = ()\n self.default_cols = False\n self.select_related = False\n self.set_extra_mask(())\n self.set_annotation_mask(())\n\n def clear_select_fields(self):\n \"\"\"\n Clear the list of fields to select (but not extra_select columns).\n Some queryset types completely replace any existing list of select\n columns.\n \"\"\"\n self.select = ()\n self.values_select = ()\n\n def add_select_col(self, col, name):\n self.select += (col,)\n self.values_select += (name,)\n\n def set_select(self, cols):\n self.default_cols = False", + "tool_call_id": "call_d3241af9a6cd41f2b979fd10" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000177000, + "content": "", + "tool_calls": [ + { + "id": "call_dc4a813a152d4160b5b1100b", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec grep -n \\\"def set_annotation_mask\\\" /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000178000, + "content": "2364: def set_annotation_mask(self, names):", + "tool_call_id": "call_dc4a813a152d4160b5b1100b" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000179000, + "content": "", + "tool_calls": [ + { + "id": "call_dddb1124f7ae4aa686fc4426", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '2364,2390p' /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000180000, + "content": "def set_annotation_mask(self, names):\n \"\"\"Set the mask of annotations that will be returned by the SELECT.\"\"\"\n if names is None:\n self.annotation_select_mask = None\n else:\n self.annotation_select_mask = set(names)\n self._annotation_select_cache = None\n\n def append_annotation_mask(self, names):\n if self.annotation_select_mask is not None:\n self.set_annotation_mask(self.annotation_select_mask.union(names))\n\n def set_extra_mask(self, names):\n \"\"\"\n Set the mask of extra select items that will be returned by SELECT.\n Don't remove them from the Query since they might be used later.\n \"\"\"\n if names is None:\n self.extra_select_mask = None\n else:\n self.extra_select_mask = set(names)\n self._extra_select_cache = None\n\n def set_values(self, fields):\n self.select_related = False\n self.clear_deferred_loading()\n self.clear_select_fields()", + "tool_call_id": "call_dddb1124f7ae4aa686fc4426" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000181000, + "content": "", + "tool_calls": [ + { + "id": "call_81aadf8a8b7f410f9dcd5cd3", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec write /tmp/patch2.py << 'PATCH_EOF'\\n# Read the file\\nwith open('/testbed/django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\n# Find the get_count method and replace it\\nold_method = ''' def get_count(self, using):\\n \\\"\\\"\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\"\\\"\\\"\\n obj = self.clone()\\n obj.clear_annotations()\\n obj.add_annotation(Count(\\\"*\\\"), alias=\\\"__count\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\"__count\\\"])[\\\"__count\\\"]'''\\n\\nnew_method = ''' def get_count(self, using):\\n \\\"\\\"\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\"\\\"\\\"\\n obj = self.clone()\\n obj.annotations = {}\\n obj.add_annotation(Count(\\\"*\\\"), alias=\\\"__count\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\"__count\\\"])[\\\"__count\\\"]'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\n# Write the file\\nwith open('/testbed/django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint(\\\"Patched successfully\\\")\\nPATCH_EOF\\npython /tmp/patch2.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000182000, + "content": "Written 1014 bytes to /tmp/patch2.py\npython: can't open file '/tmp/patch2.py': [Errno 2] No such file or directory\n\n(Command exited with code 2)", + "tool_call_id": "call_81aadf8a8b7f410f9dcd5cd3" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000183000, + "content": "", + "tool_calls": [ + { + "id": "call_f7b3a4b38d3e4ac58f3c4973", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"\\nwith open('/testbed/django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\nold_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.clear_annotations()\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\nnew_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.annotations = {}\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint('Patched successfully')\\n\\\"\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000184000, + "content": "Patched successfully", + "tool_call_id": "call_f7b3a4b38d3e4ac58f3c4973" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000185000, + "content": "", + "tool_calls": [ + { + "id": "call_5288658e6a744ffe8b9e3d6d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '548,560p' /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000186000, + "content": "def get_count(self, using):\n \"\"\"\n Perform a COUNT() query using the current filter constraints.\n \"\"\"\n obj = self.clone()\n obj.annotations = {}\n obj.add_annotation(Count(\"*\"), alias=\"__count\", is_summary=True)\n return obj.get_aggregation(using, [\"__count\"])[\"__count\"]\n\n def has_filters(self):\n return self.where\n\n def exists(self, limit=True):", + "tool_call_id": "call_5288658e6a744ffe8b9e3d6d" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000187000, + "content": "", + "tool_calls": [ + { + "id": "call_de22680c15db4eac845a8071", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec cd /testbed && python tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000188000, + "content": "python: can't open file '/testbed/tests/runtests.py': [Errno 2] No such file or directory", + "tool_call_id": "call_de22680c15db4eac845a8071" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000189000, + "content": "", + "tool_calls": [ + { + "id": "call_6e841eb79f5044219ec8432b", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec cd /testbed && python /testbed/tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000190000, + "content": "python: can't open file '/testbed/tests/runtests.py': [Errno 2] No such file or directory", + "tool_call_id": "call_6e841eb79f5044219ec8432b" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000191000, + "content": "", + "tool_calls": [ + { + "id": "call_59efbd4da3ef4709bd3cbb0f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec ls -la /testbed/tests/\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000192000, + "content": "total 912\ndrwxrwxrwx 1 root root 4096 Mar 17 21:14 .\ndrwxrwxrwx 1 root root 4096 Sep 10 2025 ..\n-rw-r--r-- 1 root root 189 Sep 10 2025 .coveragerc\n-rw-r--r-- 1 root root 381 Sep 10 2025 README.rst\ndrwxr-xr-x 2 root root 4096 Mar 17 21:14 __pycache__\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 absolute_url_overrides\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_autodiscover\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_changelist\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_checks\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_custom_urls\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_default_site\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_docs\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_filters\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_inlines\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_ordering\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_registration\ndrwxrwxrwx 12 root root 4096 Sep 10 2025 admin_scripts\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_utils\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 admin_views\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 admin_widgets\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 aggregation\ndrwxrwxrwx 1 root root 4096 Mar 17 21:14 aggregation_regress\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 annotations\ndrwxrwxrwx 4 root root 4096 Sep 10 2025 app_loading\ndrwxrwxrwx 9 root root 4096 Sep 10 2025 apps\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 asgi\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 async\ndrwxrwxrwx 5 root root 4096 Sep 10 2025 auth_tests\ndrwxrwxrwx 7 root root 4096 Sep 10 2025 backends\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 base\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 bash_completion\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 basic\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 builtin_server\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 bulk_create\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 cache\ndrwxrwxrwx 4 root root 4096 Sep 10 2025 check_framework\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 conditional_processing\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 constraints\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 contenttypes_tests\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 context_processors\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 csrf_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 custom_columns\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 custom_lookups\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 custom_managers\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 custom_methods\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 custom_migration_operations\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 custom_pk\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 datatypes\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 dates\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 datetimes\ndrwxrwxrwx 8 root root 4096 Sep 10 2025 db_functions\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 db_typecasts\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 db_utils\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 dbshell\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 decorators\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 defer\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 defer_regress\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 delete\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 delete_regress\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 deprecation\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 dispatch\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 distinct_on_fields\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 empty\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 empty_models\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 expressions\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 expressions_case\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 expressions_window\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 extra_regress\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 field_deconstruction\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 field_defaults\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 field_subclassing\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 file_storage\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 file_uploads\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 files\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 filtered_relation\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 fixtures\ndrwxrwxrwx 4 root root 4096 Sep 10 2025 fixtures_model_package\ndrwxrwxrwx 5 root root 4096 Sep 10 2025 fixtures_regress\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 flatpages_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 force_insert_update\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 foreign_object\ndrwxrwxrwx 8 root root 4096 Sep 10 2025 forms_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 from_db_value\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 generic_inline_admin\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 generic_relations\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 generic_relations_regress\ndrwxrwxrwx 4 root root 4096 Sep 10 2025 generic_views\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 get_earliest_or_latest\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 get_object_or_404\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 get_or_create\ndrwxrwxrwx 16 root root 4096 Sep 10 2025 gis_tests\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 handlers\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 httpwrappers\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 humanize_tests\ndrwxrwxrwx 15 root root 4096 Sep 10 2025 i18n\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 import_error_package\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 indexes\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 inline_formsets\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 inspectdb\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 introspection\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 invalid_models_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 known_related_objects\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 logging_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 lookup\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 m2m_and_m2o\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 m2m_intermediary\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 m2m_multiple\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 m2m_recursive\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 m2m_regress\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 m2m_signals\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 m2m_through\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 m2m_through_regress\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 m2o_recursive\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 mail\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 managers_regress\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 many_to_many\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 many_to_one\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 many_to_one_null\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 max_lengths\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 messages_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 middleware\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 middleware_exceptions\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 migrate_signals\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 migration_test_data_persistence\ndrwxrwxrwx 42 root root 4096 Sep 10 2025 migrations\ndrwxrwxrwx 5 root root 4096 Sep 10 2025 migrations2\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 model_enums\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 model_fields\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 model_forms\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 model_formsets\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 model_formsets_regress\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 model_indexes\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 model_inheritance\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 model_inheritance_regress\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 model_meta\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 model_options\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 model_package\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 model_regress\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 model_utils\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 modeladmin\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 multiple_database\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 mutually_referential\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 nested_foreign_keys\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 no_models\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 null_fk\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 null_fk_ordering\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 null_queries\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 one_to_one\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 or_lookups\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 order_with_respect_to\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 ordering\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 pagination\ndrwxrwxrwx 5 root root 4096 Sep 10 2025 postgres_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 prefetch_related\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 project_template\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 properties\ndrwxrwxrwx 4 root root 4096 Sep 10 2025 proxy_model_inheritance\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 proxy_models\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 queries\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 queryset_pickle\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 raw_query\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 redirects_tests\ndrwxr-xr-x 2 root root 4096 Sep 10 2025 requests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 requirements\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 reserved_names\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 resolve_url\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 responses\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 reverse_lookup\n-rwxr-xr-x 1 root root 27385 Sep 10 2025 runtests.py\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 save_delete_hooks\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 schema\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 select_for_update\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 select_related\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 select_related_onetoone\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 select_related_regress\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 serializers\ndrwxrwxrwx 6 root root 4096 Sep 10 2025 servers\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 sessions_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 settings_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 shell\ndrwxrwxrwx 4 root root 4096 Sep 10 2025 shortcuts\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 signals\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 signed_cookies_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 signing\ndrwxrwxrwx 4 root root 4096 Sep 10 2025 sitemaps_tests\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 sites_framework\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 sites_tests\ndrwxrwxrwx 5 root root 4096 Sep 10 2025 staticfiles_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 str\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 string_lookup\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 swappable_models\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 syndication_tests\ndrwxrwxrwx 7 root root 4096 Sep 10 2025 template_backends\ndrwxrwxrwx 4 root root 4096 Sep 10 2025 template_loader\ndrwxrwxrwx 11 root root 4096 Sep 10 2025 template_tests\ndrwxrwxrwx 5 root root 4096 Sep 10 2025 templates\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 test_client\ndrwxrwxrwx 4 root root 4096 Sep 10 2025 test_client_regress\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 test_exceptions\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 test_runner\ndrwxrwxrwx 8 root root 4096 Sep 10 2025 test_runner_apps\n-rw-r--r-- 1 root root 1074 Sep 10 2025 test_sqlite.py\ndrwxrwxrwx 4 root root 4096 Sep 10 2025 test_utils\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 timezones\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 transaction_hooks\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 transactions\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 unmanaged_models\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 update\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 update_only_fields\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 urlpatterns\ndrwxrwxrwx 3 root root 4096 Sep 10 2025 urlpatterns_reverse\n-rw-r--r-- 1 root root 211 Sep 10 2025 urls.py\ndrwxrwxrwx 4 root root 4096 Sep 10 2025 user_commands\ndrwxrwxrwx 7 root root 4096 Sep 10 2025 utils_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 validation\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 validators\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 version\ndrwxrwxrwx 14 root root 4096 Sep 10 2025 view_tests\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 wsgi\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 xor_lookups", + "tool_call_id": "call_59efbd4da3ef4709bd3cbb0f" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000193000, + "content": "", + "tool_calls": [ + { + "id": "call_2053d69965bb47229e5a762a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python /testbed/tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000194000, + "content": "Traceback (most recent call last):\n File \"/testbed/tests/runtests.py\", line 17, in \n import django\nModuleNotFoundError: No module named 'django'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/testbed/tests/runtests.py\", line 19, in \n raise RuntimeError(\nRuntimeError: Django module not found, reference tests/README.rst for instructions.", + "tool_call_id": "call_2053d69965bb47229e5a762a" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000195000, + "content": "", + "tool_calls": [ + { + "id": "call_5c972e536796432e823361e7", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"import django; print(django.__file__)\\\"\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000196000, + "content": "/testbed/django/__init__.py", + "tool_call_id": "call_5c972e536796432e823361e7" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000197000, + "content": "", + "tool_calls": [ + { + "id": "call_5d2ab7eb677a4b04bca7fdd6", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec cd /testbed && python tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000198000, + "content": "python: can't open file '/testbed/tests/runtests.py': [Errno 2] No such file or directory", + "tool_call_id": "call_5d2ab7eb677a4b04bca7fdd6" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000199000, + "content": "", + "tool_calls": [ + { + "id": "call_f8169ea271784ec59ebe8c99", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec cd /testbed && PYTHONPATH=/testbed python tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000200000, + "content": "python: can't open file '/testbed/tests/runtests.py': [Errno 2] No such file or directory", + "tool_call_id": "call_f8169ea271784ec59ebe8c99" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000201000, + "content": "", + "tool_calls": [ + { + "id": "call_d7a9f63ca03347d29185ebf8", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec cd /testbed && PYTHONPATH=/testbed python /testbed/tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000202000, + "content": "python: can't open file '/testbed/tests/runtests.py': [Errno 2] No such file or directory", + "tool_call_id": "call_d7a9f63ca03347d29185ebf8" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000203000, + "content": "", + "tool_calls": [ + { + "id": "call_fdceefca19694f129253500a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec ls -la /testbed/tests/runtests.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000204000, + "content": "-rwxr-xr-x 1 root root 27385 Sep 10 2025 /testbed/tests/runtests.py", + "tool_call_id": "call_fdceefca19694f129253500a" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000205000, + "content": "", + "tool_calls": [ + { + "id": "call_0f19764deaaa4598baba53bc", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec /testbed/tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000206000, + "content": "Traceback (most recent call last):\n File \"/testbed/tests/runtests.py\", line 17, in \n import django\nModuleNotFoundError: No module named 'django'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/testbed/tests/runtests.py\", line 19, in \n raise RuntimeError(\nRuntimeError: Django module not found, reference tests/README.rst for instructions.", + "tool_call_id": "call_0f19764deaaa4598baba53bc" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000207000, + "content": "", + "tool_calls": [ + { + "id": "call_b6340cf415d54149a9087a2d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec PYTHONPATH=/testbed /testbed/tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000208000, + "content": "bash: line 1: PYTHONPATH=/testbed: No such file or directory", + "tool_call_id": "call_b6340cf415d54149a9087a2d" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000209000, + "content": "", + "tool_calls": [ + { + "id": "call_40362acd75b243e3b4a902b1", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec export PYTHONPATH=/testbed && /testbed/tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000210000, + "content": "/bin/bash: line 1: /testbed/tests/runtests.py: No such file or directory", + "tool_call_id": "call_40362acd75b243e3b4a902b1" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000211000, + "content": "", + "tool_calls": [ + { + "id": "call_f4b647363d1d4454b8cce48a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec export PYTHONPATH=/testbed && python /testbed/tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000212000, + "content": "python: can't open file '/testbed/tests/runtests.py': [Errno 2] No such file or directory", + "tool_call_id": "call_f4b647363d1d4454b8cce48a" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000213000, + "content": "", + "tool_calls": [ + { + "id": "call_99cf0cadb6b34249a384ebb5", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec pwd\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000214000, + "content": "/testbed", + "tool_call_id": "call_99cf0cadb6b34249a384ebb5" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000215000, + "content": "", + "tool_calls": [ + { + "id": "call_e9cb8ccf48ea4cecb32cf191", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000216000, + "content": "Traceback (most recent call last):\n File \"/testbed/tests/runtests.py\", line 17, in \n import django\nModuleNotFoundError: No module named 'django'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/testbed/tests/runtests.py\", line 19, in \n raise RuntimeError(\nRuntimeError: Django module not found, reference tests/README.rst for instructions.", + "tool_call_id": "call_e9cb8ccf48ea4cecb32cf191" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000217000, + "content": "", + "tool_calls": [ + { + "id": "call_36f63611a0084051ab82d747", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"import sys; print(sys.path)\\\"\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000218000, + "content": "['', '/usr/lib/python310.zip', '/usr/lib/python3.10', '/usr/lib/python3.10/lib-dynload', '/usr/local/lib/python3.10/dist-packages', '/usr/lib/python3/dist-packages']", + "tool_call_id": "call_36f63611a0084051ab82d747" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000219000, + "content": "", + "tool_calls": [ + { + "id": "call_ab56a2c9a1f7482bb32ca2df", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"import sys; sys.path.insert(0, '/testbed'); import django; print(django.__file__)\\\"\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000220000, + "content": "/testbed/django/__init__.py", + "tool_call_id": "call_ab56a2c9a1f7482bb32ca2df" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000221000, + "content": "", + "tool_calls": [ + { + "id": "call_c37a34d915284145bb22b086", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec PYTHONPATH=/testbed python tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000222000, + "content": "bash: line 1: PYTHONPATH=/testbed: No such file or directory", + "tool_call_id": "call_c37a34d915284145bb22b086" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000223000, + "content": "", + "tool_calls": [ + { + "id": "call_f95bd654474c47ca9de1795f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000224000, + "content": "Traceback (most recent call last):\n File \"/testbed/tests/runtests.py\", line 23, in \n from django.apps import apps\n File \"/testbed/django/apps/__init__.py\", line 1, in \n from .config import AppConfig\n File \"/testbed/django/apps/config.py\", line 13, in \n class AppConfig:\n File \"/testbed/django/apps/config.py\", line 62, in AppConfig\n def default_auto_field(self):\n File \"/testbed/django/utils/functional.py\", line 27, in __init__\n from django.utils.deprecation import RemovedInDjango50Warning\n File \"/testbed/django/utils/deprecation.py\", line 5, in \n from asgiref.sync import sync_to_async\nModuleNotFoundError: No module named 'asgiref'", + "tool_call_id": "call_f95bd654474c47ca9de1795f" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000225000, + "content": "", + "tool_calls": [ + { + "id": "call_036f609e49534070bbd51106", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec pip install asgiref 2>&1 | tail -10\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000226000, + "content": "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\nCollecting asgiref\n Downloading asgiref-3.11.1-py3-none-any.whl (24 kB)\nCollecting typing_extensions>=4\n Downloading typing_extensions-4.15.0-py3-none-any.whl (44 kB)\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 44.6/44.6 KB 531.3 kB/s eta 0:00:00\nInstalling collected packages: typing_extensions, asgiref\nSuccessfully installed asgiref-3.11.1 typing_extensions-4.15.0", + "tool_call_id": "call_036f609e49534070bbd51106" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000227000, + "content": "", + "tool_calls": [ + { + "id": "call_d5ac7efe525f4b8183faa69a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000228000, + "content": "Traceback (most recent call last):\n File \"/testbed/tests/runtests.py\", line 28, in \n from django.test.runner import get_max_test_processes, parallel_type\n File \"/testbed/django/test/runner.py\", line 20, in \n import sqlparse\nModuleNotFoundError: No module named 'sqlparse'", + "tool_call_id": "call_d5ac7efe525f4b8183faa69a" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000229000, + "content": "", + "tool_calls": [ + { + "id": "call_9d4c2135fff647c5b0feb6d6", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec pip install sqlparse 2>&1 | tail -5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000230000, + "content": "Collecting sqlparse\n Downloading sqlparse-0.5.5-py3-none-any.whl (46 kB)\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 46.1/46.1 KB 283.5 kB/s eta 0:00:00\nInstalling collected packages: sqlparse\nSuccessfully installed sqlparse-0.5.5", + "tool_call_id": "call_9d4c2135fff647c5b0feb6d6" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000231000, + "content": "", + "tool_calls": [ + { + "id": "call_b12e44037df94f3fb9024714", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python tests/runtests.py aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000232000, + "content": "TypeError: cannot pickle 'traceback' object\n\"\"\"\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/testbed/tests/runtests.py\", line 773, in \n failures = django_tests(\n File \"/testbed/tests/runtests.py\", line 432, in django_tests\n failures = test_runner.run_tests(test_labels)\n File \"/testbed/django/test/runner.py\", line 1062, in run_tests\n result = self.run_suite(suite)\n File \"/testbed/django/test/runner.py\", line 984, in run_suite\n return runner.run(suite)\n File \"/usr/lib/python3.10/unittest/runner.py\", line 184, in run\n test(result)\n File \"/usr/lib/python3.10/unittest/suite.py\", line 84, in __call__\n return self.run(*args, **kwds)\n File \"/testbed/django/test/runner.py\", line 534, in run\n subsuite_index, events = test_results.next(timeout=0.1)\n File \"/usr/lib/python3.10/multiprocessing/pool.py\", line 873, in next\n raise value\nTypeError: cannot pickle 'traceback' object\nException ignored in: \nTraceback (most recent call last):\n File \"/usr/lib/python3.10/multiprocessing/pool.py\", line 268, in __del__\nResourceWarning: unclosed running multiprocessing pool \nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 69 test(s).\nSystem check identified no issues (0 silenced).", + "tool_call_id": "call_b12e44037df94f3fb9024714" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000233000, + "content": "", + "tool_calls": [ + { + "id": "call_f7b016b88bf34027a19684dd", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python tests/runtests.py --noinput aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000234000, + "content": "return runner.run(suite)\n File \"/usr/lib/python3.10/unittest/runner.py\", line 184, in run\n test(result)\n File \"/usr/lib/python3.10/unittest/suite.py\", line 84, in __call__\n return self.run(*args, **kwds)\n File \"/testbed/django/test/runner.py\", line 534, in run\n subsuite_index, events = test_results.next(timeout=0.1)\n File \"/usr/lib/python3.10/multiprocessing/pool.py\", line 873, in next\n raise value\nTypeError: cannot pickle 'traceback' object\nException ignored in: \nTraceback (most recent call last):\n File \"/usr/lib/python3.10/multiprocessing/pool.py\", line 268, in __del__\nResourceWarning: unclosed running multiprocessing pool \nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 69 test(s).\nSystem check identified no issues (0 silenced).\n\n\ntest_annotate_and_join (aggregation_regress.tests.AggregationTests) failed:\n\n AssertionError('15 != 9')\n\nUnfortunately, tracebacks cannot be pickled, making it impossible for the\nparallel test runner to handle this exception cleanly.\n\nIn order to see the traceback, you should install tblib:\n\n python -m pip install tblib", + "tool_call_id": "call_f7b016b88bf34027a19684dd" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000235000, + "content": "", + "tool_calls": [ + { + "id": "call_c942be95bc054aae89289f8e", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec pip install tblib 2>&1 | tail -5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000236000, + "content": "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\nCollecting tblib\n Downloading tblib-3.2.2-py3-none-any.whl (12 kB)\nInstalling collected packages: tblib\nSuccessfully installed tblib-3.2.2", + "tool_call_id": "call_c942be95bc054aae89289f8e" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000237000, + "content": "", + "tool_calls": [ + { + "id": "call_f3e71cf0829d4e01b50d3676", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python tests/runtests.py --noinput aggregation_regress 2>&1 | tail -50\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000238000, + "content": "Cloning test database for alias 'default'...\nCloning test database for alias 'default'...\n......sss..ss....F.................................F.................\n======================================================================\nFAIL: test_annotate_and_join (aggregation_regress.tests.AggregationTests)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/usr/lib/python3.10/unittest/case.py\", line 59, in testPartExecutor\n yield\n File \"/usr/lib/python3.10/unittest/case.py\", line 591, in run\n self._callTestMethod(testMethod)\n File \"/usr/lib/python3.10/unittest/case.py\", line 549, in _callTestMethod\n method()\n File \"/testbed/tests/aggregation_regress/tests.py\", line 1178, in test_annotate_and_join\n self.assertEqual(\n File \"/usr/lib/python3.10/unittest/case.py\", line 845, in assertEqual\n assertion_func(first, second, msg=msg)\n File \"/usr/lib/python3.10/unittest/case.py\", line 838, in _baseAssertEqual\n raise self.failureException(msg)\nAssertionError: 15 != 9\n\n======================================================================\nFAIL: test_more (aggregation_regress.tests.AggregationTests)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/usr/lib/python3.10/unittest/case.py\", line 59, in testPartExecutor\n yield\n File \"/usr/lib/python3.10/unittest/case.py\", line 591, in run\n self._callTestMethod(testMethod)\n File \"/usr/lib/python3.10/unittest/case.py\", line 549, in _callTestMethod\n method()\n File \"/testbed/tests/aggregation_regress/tests.py\", line 616, in test_more\n self.assertEqual(Book.objects.annotate(num_authors=Count(\"authors\")).count(), 6)\n File \"/usr/lib/python3.10/unittest/case.py\", line 845, in assertEqual\n assertion_func(first, second, msg=msg)\n File \"/usr/lib/python3.10/unittest/case.py\", line 838, in _baseAssertEqual\n raise self.failureException(msg)\nAssertionError: 10 != 6\n\n----------------------------------------------------------------------\nRan 69 tests in 0.152s\n\nFAILED (failures=2, skipped=5)\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 69 test(s).\nSystem check identified no issues (0 silenced).", + "tool_call_id": "call_f3e71cf0829d4e01b50d3676" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000239000, + "content": "", + "tool_calls": [ + { + "id": "call_c970530726924b5f9d730f3a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '614,620p' /testbed/tests/aggregation_regress/tests.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000240000, + "content": "def test_more(self):\n # Old-style count aggregations can be mixed with new-style\n self.assertEqual(Book.objects.annotate(num_authors=Count(\"authors\")).count(), 6)\n\n # Non-ordinal, non-computed Aggregates over annotations correctly\n # inherit the annotation's internal type if the annotation is ordinal\n # or computed", + "tool_call_id": "call_c970530726924b5f9d730f3a" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000241000, + "content": "", + "tool_calls": [ + { + "id": "call_b6c9001c16d145e1aa2aae6e", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '1170,1190p' /testbed/tests/aggregation_regress/tests.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000242000, + "content": "def test_none_call_before_aggregate(self):\n # Regression for #11789\n self.assertEqual(\n Author.objects.none().aggregate(Avg(\"age\")), {\"age__avg\": None}\n )\n\n def test_annotate_and_join(self):\n self.assertEqual(\n Author.objects.annotate(c=Count(\"friends__name\"))\n .exclude(friends__name=\"Joe\")\n .count(),\n Author.objects.count(),\n )\n\n def test_f_expression_annotation(self):\n # Books with less than 200 pages per author.\n qs = (\n Book.objects.values(\"name\")\n .annotate(n_authors=Count(\"authors\"))\n .filter(pages__lt=F(\"n_authors\") * 200)", + "tool_call_id": "call_b6c9001c16d145e1aa2aae6e" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000243000, + "content": "", + "tool_calls": [ + { + "id": "call_c1e9ede134c24e35830c41ee", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec write /tmp/patch3.py << 'PATCH_EOF'\\n# Read the file\\nwith open('/testbed/django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\n# Find the get_count method and replace it\\nold_method = ''' def get_count(self, using):\\n \\\"\\\"\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\"\\\"\\\"\\n obj = self.clone()\\n obj.annotations = {}\\n obj.add_annotation(Count(\\\"*\\\"), alias=\\\"__count\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\"__count\\\"])[\\\"__count\\\"]'''\\n\\nnew_method = ''' def get_count(self, using):\\n \\\"\\\"\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\"\\\"\\\"\\n obj = self.clone()\\n obj.clear_unused_annotations()\\n obj.add_annotation(Count(\\\"*\\\"), alias=\\\"__count\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\"__count\\\"])[\\\"__count\\\"]'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\n# Write the file\\nwith open('/testbed/django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint(\\\"Patched successfully\\\")\\nPATCH_EOF\\npython /tmp/patch3.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000244000, + "content": "Written 1021 bytes to /tmp/patch3.py\npython: can't open file '/tmp/patch3.py': [Errno 2] No such file or directory\n\n(Command exited with code 2)", + "tool_call_id": "call_c1e9ede134c24e35830c41ee" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000245000, + "content": "", + "tool_calls": [ + { + "id": "call_6c1dcaa9aeaf45bcb7e40c55", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"\\nwith open('/testbed/django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\nold_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.annotations = {}\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\nnew_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.clear_unused_annotations()\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint('Patched successfully')\\n\\\"\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000246000, + "content": "Patched successfully", + "tool_call_id": "call_6c1dcaa9aeaf45bcb7e40c55" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000247000, + "content": "", + "tool_calls": [ + { + "id": "call_53839d63327f45fa9accfd10", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec grep -n \\\"def clear_deferred_loading\\\" /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000248000, + "content": "2311: def clear_deferred_loading(self):", + "tool_call_id": "call_53839d63327f45fa9accfd10" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000249000, + "content": "", + "tool_calls": [ + { + "id": "call_5d7e3d78f9954994931c67b9", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '2311,2340p' /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000250000, + "content": "def clear_deferred_loading(self):\n \"\"\"Remove any fields from the deferred loading set.\"\"\"\n self.deferred_loading = (frozenset(), True)\n\n def add_deferred_loading(self, field_names):\n \"\"\"\n Add the given list of model field names to the set of fields to\n exclude from loading from the database when automatic column selection\n is done. Add the new field names to any existing field names that\n are deferred (or removed from any existing field names that are marked\n as the only ones for immediate loading).\n \"\"\"\n # Fields on related models are stored in the literal double-underscore\n # format, so that we can use a set datastructure. We do the foo__bar\n # splitting and handling when computing the SQL column names (as part of\n # get_columns()).\n existing, defer = self.deferred_loading\n if defer:\n # Add to existing deferred names.\n self.deferred_loading = existing.union(field_names), True\n else:\n # Remove names from the set of any existing \"immediate load\" names.\n if new_existing := existing.difference(field_names):\n self.deferred_loading = new_existing, False\n else:\n self.clear_deferred_loading()\n if new_only := set(field_names).difference(existing):\n self.deferred_loading = new_only, True\n\n def add_immediate_loading(self, field_names):", + "tool_call_id": "call_5d7e3d78f9954994931c67b9" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000251000, + "content": "", + "tool_calls": [ + { + "id": "call_dc49ee84d929447f9749fd8c", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"\\nwith open('/testbed/django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\n# Find the position after clear_deferred_loading method\\ninsert_pos = content.find(' def add_deferred_loading')\\n\\nif insert_pos == -1:\\n print('Could not find insertion point')\\n exit(1)\\n\\n# Add the new method before add_deferred_loading\\nnew_method = ''' def clear_unused_annotations(self):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Remove annotations that are not used in where, order_by, or other annotations.\\n This is useful for count() queries where unused annotations just add overhead.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n if not self.annotations:\\n return\\n \\n # Collect all references to annotations\\n used_annotations = set()\\n \\n # Check where clause for F references\\n def collect_refs(expression):\\n if expression is None:\\n return\\n if isinstance(expression, F):\\n used_annotations.add(expression.name)\\n if hasattr(expression, 'get_source_expressions'):\\n for source in expression.get_source_expressions():\\n collect_refs(source)\\n if hasattr(expression, 'children'):\\n for child in expression.children:\\n collect_refs(child)\\n \\n collect_refs(self.where)\\n \\n # Check order_by for F references\\n for order_by in self.order_by:\\n collect_refs(order_by)\\n \\n # Check other annotations for F references\\n for alias, annotation in self.annotations.items():\\n collect_refs(annotation)\\n \\n # Keep only used annotations\\n self.annotations = {\\n k: v for k, v in self.annotations.items() if k in used_annotations\\n }\\n self._annotation_select_cache = None\\n\\n'''\\n\\ncontent = content[:insert_pos] + new_method + content[insert_pos:]\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint('Method added successfully')\\n\\\"\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000252000, + "content": "Method added successfully", + "tool_call_id": "call_dc49ee84d929447f9749fd8c" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000253000, + "content": "", + "tool_calls": [ + { + "id": "call_228187ca191a49c89088b990", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec grep -n \\\"def clear_unused_annotations\\\" /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000254000, + "content": "2315: def clear_unused_annotations(self):", + "tool_call_id": "call_228187ca191a49c89088b990" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000255000, + "content": "", + "tool_calls": [ + { + "id": "call_8eea2cca485f4f8d9ddb9366", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python tests/runtests.py --noinput aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000256000, + "content": "======================================================================\nFAIL: test_more (aggregation_regress.tests.AggregationTests)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/usr/lib/python3.10/unittest/case.py\", line 59, in testPartExecutor\n yield\n File \"/usr/lib/python3.10/unittest/case.py\", line 591, in run\n self._callTestMethod(testMethod)\n File \"/usr/lib/python3.10/unittest/case.py\", line 549, in _callTestMethod\n method()\n File \"/testbed/tests/aggregation_regress/tests.py\", line 616, in test_more\n self.assertEqual(Book.objects.annotate(num_authors=Count(\"authors\")).count(), 6)\n File \"/usr/lib/python3.10/unittest/case.py\", line 845, in assertEqual\n assertion_func(first, second, msg=msg)\n File \"/usr/lib/python3.10/unittest/case.py\", line 838, in _baseAssertEqual\n raise self.failureException(msg)\nAssertionError: 10 != 6\n\n----------------------------------------------------------------------\nRan 69 tests in 0.149s\n\nFAILED (failures=2, skipped=5)\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 69 test(s).\nSystem check identified no issues (0 silenced).", + "tool_call_id": "call_8eea2cca485f4f8d9ddb9366" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000257000, + "content": "", + "tool_calls": [ + { + "id": "call_b6501d406b3a4df2b5570deb", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '548,560p' /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000258000, + "content": "def get_count(self, using):\n \"\"\"\n Perform a COUNT() query using the current filter constraints.\n \"\"\"\n obj = self.clone()\n obj.clear_unused_annotations()\n obj.add_annotation(Count(\"*\"), alias=\"__count\", is_summary=True)\n return obj.get_aggregation(using, [\"__count\"])[\"__count\"]\n\n def has_filters(self):\n return self.where\n\n def exists(self, limit=True):", + "tool_call_id": "call_b6501d406b3a4df2b5570deb" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000259000, + "content": "", + "tool_calls": [ + { + "id": "call_8c38314e3dbc4210b304e6f7", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '438,500p' /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000260000, + "content": "def get_aggregation(self, using, added_aggregate_names):\n \"\"\"\n Return the dictionary with the values of the existing aggregations.\n \"\"\"\n if not self.annotation_select:\n return {}\n existing_annotations = [\n annotation\n for alias, annotation in self.annotations.items()\n if alias not in added_aggregate_names\n ]\n # Decide if we need to use a subquery.\n #\n # Existing annotations would cause incorrect results as get_aggregation()\n # must produce just one result and thus must not use GROUP BY. But we\n # aren't smart enough to remove the existing annotations from the\n # query, so those would force us to use GROUP BY.\n #\n # If the query has limit or distinct, or uses set operations, then\n # those operations must be done in a subquery so that the query\n # aggregates on the limit and/or distinct results instead of applying\n # the distinct and limit after the aggregation.\n if (\n isinstance(self.group_by, tuple)\n or self.is_sliced\n or existing_annotations\n or self.distinct\n or self.combinator\n ):\n from django.db.models.sql.subqueries import AggregateQuery\n\n inner_query = self.clone()\n inner_query.subquery = True\n outer_query = AggregateQuery(self.model, inner_query)\n inner_query.select_for_update = False\n inner_query.select_related = False\n inner_query.set_annotation_mask(self.annotation_select)\n # Queries with distinct_fields need ordering and when a limit is\n # applied we must take the slice from the ordered query. Otherwise\n # no need for ordering.\n inner_query.clear_ordering(force=False)\n if not inner_query.distinct:\n # If the inner query uses default select and it has some\n # aggregate annotations, then we must make sure the inner\n # query is grouped by the main model's primary key. However,\n # clearing the select clause can alter results if distinct is\n # used.\n has_existing_aggregate_annotations = any(\n annotation\n for annotation in existing_annotations\n if getattr(annotation, \"contains_aggregate\", True)\n )\n if inner_query.default_cols and has_existing_aggregate_annotations:\n inner_query.group_by = (\n self.model._meta.pk.get_col(inner_query.get_initial_alias()),\n )\n inner_query.default_cols = False\n\n relabels = {t: \"subquery\" for t in inner_query.alias_map}\n relabels[None] = \"subquery\"\n # Remove any aggregates marked for reduction from the subquery\n # and move them to the outer AggregateQuery.\n col_cnt = 0", + "tool_call_id": "call_8c38314e3dbc4210b304e6f7" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000261000, + "content": "", + "tool_calls": [ + { + "id": "call_308b8fb4a6424f3e80299c4d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python -c \\\"\\nimport os\\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'tests.settings')\\n\\nimport django\\ndjango.setup()\\n\\nfrom django.db.models import Count\\nfrom tests.aggregation.models import Book\\n\\n# Test the query\\nqs = Book.objects.annotate(num_authors=Count('authors'))\\nprint('Annotated query:', qs.query)\\nprint()\\n\\nqs_count = Book.objects.annotate(num_authors=Count('authors')).count()\\nprint('Count result:', qs_count)\\n\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000262000, + "content": "Traceback (most recent call last):\n File \"\", line 6, in \n File \"/testbed/django/__init__.py\", line 19, in setup\n configure_logging(settings.LOGGING_CONFIG, settings.LOGGING)\n File \"/testbed/django/conf/__init__.py\", line 92, in __getattr__\n self._setup(name)\n File \"/testbed/django/conf/__init__.py\", line 79, in _setup\n self._wrapped = Settings(settings_module)\n File \"/testbed/django/conf/__init__.py\", line 190, in __init__\n mod = importlib.import_module(self.SETTINGS_MODULE)\n File \"/usr/lib/python3.10/importlib/__init__.py\", line 126, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n File \"\", line 1050, in _gcd_import\n File \"\", line 1027, in _find_and_load\n File \"\", line 1004, in _find_and_load_unlocked\nModuleNotFoundError: No module named 'tests.settings'\n\n(Command exited with code 1)", + "tool_call_id": "call_308b8fb4a6424f3e80299c4d" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000263000, + "content": "", + "tool_calls": [ + { + "id": "call_1f0d7a55e5024b599956b36a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec find /testbed/tests -name \\\"settings.py\\\" | head -5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000264000, + "content": "/testbed/tests/i18n/sampleproject/sampleproject/settings.py\n/testbed/tests/staticfiles_tests/settings.py\n/testbed/tests/auth_tests/settings.py\n/testbed/tests/admin_scripts/custom_templates/project_template/project_name/settings.py\n/testbed/tests/flatpages_tests/settings.py", + "tool_call_id": "call_1f0d7a55e5024b599956b36a" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000265000, + "content": "", + "tool_calls": [ + { + "id": "call_cb28daa3f8d14ea09d1eadfb", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec grep -n \\\"SETTINGS_MODULE\\\" /testbed/tests/runtests.py | head -5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000266000, + "content": "577: \"this isn't provided, either the DJANGO_SETTINGS_MODULE \"\n733: os.environ[\"DJANGO_SETTINGS_MODULE\"] = options.settings\n735: os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"test_sqlite\")\n736: options.settings = os.environ[\"DJANGO_SETTINGS_MODULE\"]", + "tool_call_id": "call_cb28daa3f8d14ea09d1eadfb" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000267000, + "content": "", + "tool_calls": [ + { + "id": "call_d4604903037744a9b8c3c389", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec cat /testbed/tests/test_sqlite.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000268000, + "content": "# This is an example test settings file for use with the Django test suite.\n#\n# The 'sqlite3' backend requires only the ENGINE setting (an in-\n# memory database will be used). All other backends will require a\n# NAME and potentially authentication information. See the\n# following section in the docs for more information:\n#\n# https://docs.djangoproject.com/en/dev/internals/contributing/writing-code/unit-tests/\n#\n# The different databases that Django supports behave differently in certain\n# situations, so it is recommended to run the test suite against as many\n# database backends as possible. You may want to create a separate settings\n# file for each of the backends you test against.\n\nDATABASES = {\n \"default\": {\n \"ENGINE\": \"django.db.backends.sqlite3\",\n },\n \"other\": {\n \"ENGINE\": \"django.db.backends.sqlite3\",\n },\n}\n\nSECRET_KEY = \"django_tests_secret_key\"\n\n# Use a fast hasher to speed up tests.\nPASSWORD_HASHERS = [\n \"django.contrib.auth.hashers.MD5PasswordHasher\",\n]\n\nDEFAULT_AUTO_FIELD = \"django.db.models.AutoField\"\n\nUSE_TZ = False", + "tool_call_id": "call_d4604903037744a9b8c3c389" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000269000, + "content": "", + "tool_calls": [ + { + "id": "call_2c1e6e7b3d934b4db820b773", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed DJANGO_SETTINGS_MODULE=test_sqlite python -c \\\"\\nimport django\\ndjango.setup()\\n\\nfrom django.db.models import Count\\nfrom tests.aggregation.models import Book\\n\\n# Test the query\\nqs = Book.objects.annotate(num_authors=Count('authors'))\\nprint('Annotated query:')\\nprint(qs.query)\\nprint()\\n\\nqs_count = Book.objects.annotate(num_authors=Count('authors')).count()\\nprint('Count result:', qs_count)\\nprint()\\n\\n# Test without annotation\\nqs_count_no_annotation = Book.objects.count()\\nprint('Count without annotation:', qs_count_no_annotation)\\n\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000270000, + "content": "Traceback (most recent call last):\n File \"\", line 3, in \n File \"/testbed/django/__init__.py\", line 19, in setup\n configure_logging(settings.LOGGING_CONFIG, settings.LOGGING)\n File \"/testbed/django/conf/__init__.py\", line 92, in __getattr__\n self._setup(name)\n File \"/testbed/django/conf/__init__.py\", line 79, in _setup\n self._wrapped = Settings(settings_module)\n File \"/testbed/django/conf/__init__.py\", line 190, in __init__\n mod = importlib.import_module(self.SETTINGS_MODULE)\n File \"/usr/lib/python3.10/importlib/__init__.py\", line 126, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n File \"\", line 1050, in _gcd_import\n File \"\", line 1027, in _find_and_load\n File \"\", line 1004, in _find_and_load_unlocked\nModuleNotFoundError: No module named 'test_sqlite'\n\n(Command exited with code 1)", + "tool_call_id": "call_2c1e6e7b3d934b4db820b773" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000271000, + "content": "", + "tool_calls": [ + { + "id": "call_68730cfe3b8548799f41f573", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec cd /testbed/tests && env PYTHONPATH=/testbed:/testbed/tests DJANGO_SETTINGS_MODULE=test_sqlite python -c \\\"\\nimport django\\ndjango.setup()\\n\\nfrom django.db.models import Count\\nfrom aggregation.models import Book\\n\\n# Test the query\\nqs = Book.objects.annotate(num_authors=Count('authors'))\\nprint('Annotated query:')\\nprint(qs.query)\\nprint()\\n\\nqs_count = Book.objects.annotate(num_authors=Count('authors')).count()\\nprint('Count result:', qs_count)\\nprint()\\n\\n# Test without annotation\\nqs_count_no_annotation = Book.objects.count()\\nprint('Count without annotation:', qs_count_no_annotation)\\n\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000272000, + "content": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'django'\n\n(Command exited with code 1)", + "tool_call_id": "call_68730cfe3b8548799f41f573" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000273000, + "content": "", + "tool_calls": [ + { + "id": "call_a295c3d611464a3a9cb569e1", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec cd /testbed && env PYTHONPATH=/testbed python -c \\\"\\nimport os\\nos.environ['DJANGO_SETTINGS_MODULE'] = 'tests.test_sqlite'\\n\\nimport django\\ndjango.setup()\\n\\nfrom django.db.models import Count\\nfrom tests.aggregation.models import Book\\n\\n# Test the query\\nqs = Book.objects.annotate(num_authors=Count('authors'))\\nprint('Annotated query:')\\nprint(qs.query)\\nprint()\\n\\nqs_count = Book.objects.annotate(num_authors=Count('authors')).count()\\nprint('Count result:', qs_count)\\nprint()\\n\\n# Test without annotation\\nqs_count_no_annotation = Book.objects.count()\\nprint('Count without annotation:', qs_count_no_annotation)\\n\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000274000, + "content": "Traceback (most recent call last):\n File \"\", line 5, in \nModuleNotFoundError: No module named 'django'\n\n(Command exited with code 1)", + "tool_call_id": "call_a295c3d611464a3a9cb569e1" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000275000, + "content": "", + "tool_calls": [ + { + "id": "call_acef9ee897ca46c6acd4f573", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec cd /testbed && env PYTHONPATH=/testbed python -c \\\"import sys; sys.path.insert(0, '/testbed'); import os; os.environ['DJANGO_SETTINGS_MODULE'] = 'tests.test_sqlite'; import django; django.setup(); print('OK')\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000276000, + "content": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'django'\n\n(Command exited with code 1)", + "tool_call_id": "call_acef9ee897ca46c6acd4f573" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000277000, + "content": "", + "tool_calls": [ + { + "id": "call_9ac6b0473b044127a3c5b709", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec cd /testbed && python -c \\\"import sys; sys.path.insert(0, '/testbed'); import django; print(django.__file__)\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000278000, + "content": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'django'\n\n(Command exited with code 1)", + "tool_call_id": "call_9ac6b0473b044127a3c5b709" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000279000, + "content": "", + "tool_calls": [ + { + "id": "call_41fcf0b023984157b55c220a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec ls -la /testbed/django/__init__.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000280000, + "content": "-rw-r--r-- 1 root root 799 Sep 10 2025 /testbed/django/__init__.py", + "tool_call_id": "call_41fcf0b023984157b55c220a" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000281000, + "content": "", + "tool_calls": [ + { + "id": "call_4bc2b66047a04cb4a628ecff", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"import sys; print(sys.path)\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000282000, + "content": "['', '/usr/lib/python310.zip', '/usr/lib/python3.10', '/usr/lib/python3.10/lib-dynload', '/usr/local/lib/python3.10/dist-packages', '/usr/lib/python3/dist-packages']", + "tool_call_id": "call_4bc2b66047a04cb4a628ecff" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000283000, + "content": "", + "tool_calls": [ + { + "id": "call_3a4dd6807d1b408d8e13ee32", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"import sys; sys.path.insert(0, '/testbed'); import django; print(django.__file__)\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000284000, + "content": "/testbed/django/__init__.py", + "tool_call_id": "call_3a4dd6807d1b408d8e13ee32" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000285000, + "content": "", + "tool_calls": [ + { + "id": "call_1356e00e48a44a25a28fefcc", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"\\nimport sys\\nsys.path.insert(0, '/testbed')\\nimport os\\nos.environ['DJANGO_SETTINGS_MODULE'] = 'tests.test_sqlite'\\n\\nimport django\\ndjango.setup()\\n\\nfrom django.db.models import Count\\nfrom tests.aggregation.models import Book\\n\\n# Test the query\\nqs = Book.objects.annotate(num_authors=Count('authors'))\\nprint('Annotated query:')\\nprint(qs.query)\\nprint()\\n\\nqs_count = Book.objects.annotate(num_authors=Count('authors')).count()\\nprint('Count result:', qs_count)\\nprint()\\n\\n# Test without annotation\\nqs_count_no_annotation = Book.objects.count()\\nprint('Count without annotation:', qs_count_no_annotation)\\n\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000286000, + "content": "Traceback (most recent call last):\n File \"\", line 11, in \n File \"/testbed/tests/aggregation/models.py\", line 4, in \n class Author(models.Model):\n File \"/testbed/django/db/models/base.py\", line 134, in __new__\n raise RuntimeError(\nRuntimeError: Model class tests.aggregation.models.Author doesn't declare an explicit app_label and isn't in an application in INSTALLED_APPS.\n\n(Command exited with code 1)", + "tool_call_id": "call_1356e00e48a44a25a28fefcc" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000287000, + "content": "", + "tool_calls": [ + { + "id": "call_b262847b8ba445fb948008b2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '610,620p' /testbed/tests/aggregation_regress/tests.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000288000, + "content": "Book.objects.annotate(num_authors=Count(\"authors__id\")).aggregate(\n Max(\"foo\")\n )\n\n def test_more(self):\n # Old-style count aggregations can be mixed with new-style\n self.assertEqual(Book.objects.annotate(num_authors=Count(\"authors\")).count(), 6)\n\n # Non-ordinal, non-computed Aggregates over annotations correctly\n # inherit the annotation's internal type if the annotation is ordinal\n # or computed", + "tool_call_id": "call_b262847b8ba445fb948008b2" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000289000, + "content": "", + "tool_calls": [ + { + "id": "call_8eef0ab538024e11bd8402e4", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"\\nimport sys\\nsys.path.insert(0, '/testbed')\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\n# Find the get_count method and replace it\\nold_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.clear_unused_annotations()\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\nnew_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint('Reverted successfully')\\n\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000290000, + "content": "Reverted successfully", + "tool_call_id": "call_8eef0ab538024e11bd8402e4" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000291000, + "content": "", + "tool_calls": [ + { + "id": "call_9c76a52ec515419299f5e21e", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python tests/runtests.py --noinput aggregation_regress.AggregationTests.test_more 2>&1 | tail -20\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000292000, + "content": "E\n======================================================================\nERROR: AggregationTests (unittest.loader._FailedTest)\n----------------------------------------------------------------------\nImportError: Failed to import test module: AggregationTests\nTraceback (most recent call last):\n File \"/usr/lib/python3.10/unittest/loader.py\", line 154, in loadTestsFromName\n module = __import__(module_name)\nModuleNotFoundError: No module named 'aggregation_regress.AggregationTests'\n\n\n----------------------------------------------------------------------\nRan 1 test in 0.000s\n\nFAILED (errors=1)\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 1 test(s).\nSystem check identified no issues (0 silenced).", + "tool_call_id": "call_9c76a52ec515419299f5e21e" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000293000, + "content": "", + "tool_calls": [ + { + "id": "call_8cd809344d1c46f290030b33", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python tests/runtests.py --noinput aggregation_regress 2>&1 | tail -20\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000294000, + "content": "Creating test database for alias 'default'...\nCloning test database for alias 'default'...\nCloning test database for alias 'default'...\nCloning test database for alias 'default'...\n......sss..ss........................................................\n----------------------------------------------------------------------\nRan 69 tests in 0.157s\n\nOK (skipped=5)\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 69 test(s).\nSystem check identified no issues (0 silenced).", + "tool_call_id": "call_8cd809344d1c46f290030b33" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000295000, + "content": "", + "tool_calls": [ + { + "id": "call_30f949e8aeba4b1face13dbb", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '438,550p' /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000296000, + "content": "def get_aggregation(self, using, added_aggregate_names):\n \"\"\"\n Return the dictionary with the values of the existing aggregations.\n \"\"\"\n if not self.annotation_select:\n return {}\n existing_annotations = [\n annotation\n for alias, annotation in self.annotations.items()\n if alias not in added_aggregate_names\n ]\n # Decide if we need to use a subquery.\n #\n # Existing annotations would cause incorrect results as get_aggregation()\n # must produce just one result and thus must not use GROUP BY. But we\n # aren't smart enough to remove the existing annotations from the\n # query, so those would force us to use GROUP BY.\n #\n # If the query has limit or distinct, or uses set operations, then\n # those operations must be done in a subquery so that the query\n # aggregates on the limit and/or distinct results instead of applying\n # the distinct and limit after the aggregation.\n if (\n isinstance(self.group_by, tuple)\n or self.is_sliced\n or existing_annotations\n or self.distinct\n or self.combinator\n ):\n from django.db.models.sql.subqueries import AggregateQuery\n\n inner_query = self.clone()\n inner_query.subquery = True\n outer_query = AggregateQuery(self.model, inner_query)\n inner_query.select_for_update = False\n inner_query.select_related = False\n inner_query.set_annotation_mask(self.annotation_select)\n # Queries with distinct_fields need ordering and when a limit is\n # applied we must take the slice from the ordered query. Otherwise\n # no need for ordering.\n inner_query.clear_ordering(force=False)\n if not inner_query.distinct:\n # If the inner query uses default select and it has some\n # aggregate annotations, then we must make sure the inner\n # query is grouped by the main model's primary key. However,\n # clearing the select clause can alter results if distinct is\n # used.\n has_existing_aggregate_annotations = any(\n annotation\n for annotation in existing_annotations\n if getattr(annotation, \"contains_aggregate\", True)\n )\n if inner_query.default_cols and has_existing_aggregate_annotations:\n inner_query.group_by = (\n self.model._meta.pk.get_col(inner_query.get_initial_alias()),\n )\n inner_query.default_cols = False\n\n relabels = {t: \"subquery\" for t in inner_query.alias_map}\n relabels[None] = \"subquery\"\n # Remove any aggregates marked for reduction from the subquery\n # and move them to the outer AggregateQuery.\n col_cnt = 0\n for alias, expression in list(inner_query.annotation_select.items()):\n annotation_select_mask = inner_query.annotation_select_mask\n if expression.is_summary:\n expression, col_cnt = inner_query.rewrite_cols(expression, col_cnt)\n outer_query.annotations[alias] = expression.relabeled_clone(\n relabels\n )\n del inner_query.annotations[alias]\n annotation_select_mask.remove(alias)\n # Make sure the annotation_select wont use cached results.\n inner_query.set_annotation_mask(inner_query.annotation_select_mask)\n if (\n inner_query.select == ()\n and not inner_query.default_cols\n and not inner_query.annotation_select_mask\n ):\n # In case of Model.objects[0:3].count(), there would be no\n # field selected in the inner query, yet we must use a subquery.\n # So, make sure at least one field is selected.\n inner_query.select = (\n self.model._meta.pk.get_col(inner_query.get_initial_alias()),\n )\n else:\n outer_query = self\n self.select = ()\n self.default_cols = False\n self.extra = {}\n\n empty_set_result = [\n expression.empty_result_set_value\n for expression in outer_query.annotation_select.values()\n ]\n elide_empty = not any(result is NotImplemented for result in empty_set_result)\n outer_query.clear_ordering(force=True)\n outer_query.clear_limits()\n outer_query.select_for_update = False\n outer_query.select_related = False\n compiler = outer_query.get_compiler(using, elide_empty=elide_empty)\n result = compiler.execute_sql(SINGLE)\n if result is None:\n result = empty_set_result\n\n converters = compiler.get_converters(outer_query.annotation_select.values())\n result = next(compiler.apply_converters((result,), converters))\n\n return dict(zip(outer_query.annotation_select, result))\n\n def get_count(self, using):\n \"\"\"\n Perform a COUNT() query using the current filter constraints.", + "tool_call_id": "call_30f949e8aeba4b1face13dbb" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000297000, + "content": "", + "tool_calls": [ + { + "id": "call_2a56d41d77bb4395b23673e0", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"\\nimport sys\\nsys.path.insert(0, '/testbed')\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\n# Find the get_count method and replace it\\nold_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\nnew_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.clear_unused_annotations()\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint('Patched successfully')\\n\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000298000, + "content": "Patched successfully", + "tool_call_id": "call_2a56d41d77bb4395b23673e0" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000299000, + "content": "", + "tool_calls": [ + { + "id": "call_92ba9357fe0342559ec620ef", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec grep -n \\\"def clear_unused_annotations\\\" /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000300000, + "content": "2315: def clear_unused_annotations(self):", + "tool_call_id": "call_92ba9357fe0342559ec620ef" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000301000, + "content": "", + "tool_calls": [ + { + "id": "call_12bbb588444747e999594016", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python tests/runtests.py --noinput aggregation_regress 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000302000, + "content": "======================================================================\nFAIL: test_more (aggregation_regress.tests.AggregationTests)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/usr/lib/python3.10/unittest/case.py\", line 59, in testPartExecutor\n yield\n File \"/usr/lib/python3.10/unittest/case.py\", line 591, in run\n self._callTestMethod(testMethod)\n File \"/usr/lib/python3.10/unittest/case.py\", line 549, in _callTestMethod\n method()\n File \"/testbed/tests/aggregation_regress/tests.py\", line 616, in test_more\n self.assertEqual(Book.objects.annotate(num_authors=Count(\"authors\")).count(), 6)\n File \"/usr/lib/python3.10/unittest/case.py\", line 845, in assertEqual\n assertion_func(first, second, msg=msg)\n File \"/usr/lib/python3.10/unittest/case.py\", line 838, in _baseAssertEqual\n raise self.failureException(msg)\nAssertionError: 10 != 6\n\n----------------------------------------------------------------------\nRan 69 tests in 0.150s\n\nFAILED (failures=2, skipped=5)\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nDestroying test database for alias 'default'...\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 69 test(s).\nSystem check identified no issues (0 silenced).", + "tool_call_id": "call_12bbb588444747e999594016" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000303000, + "content": "", + "tool_calls": [ + { + "id": "call_939a6291e783400eab584cbe", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '2315,2360p' /testbed/django/db/models/sql/query.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000304000, + "content": "def clear_unused_annotations(self):\n \"\"\"\n Remove annotations that are not used in where, order_by, or other annotations.\n This is useful for count() queries where unused annotations just add overhead.\n \"\"\"\n if not self.annotations:\n return\n \n # Collect all references to annotations\n used_annotations = set()\n \n # Check where clause for F references\n def collect_refs(expression):\n if expression is None:\n return\n if isinstance(expression, F):\n used_annotations.add(expression.name)\n if hasattr(expression, 'get_source_expressions'):\n for source in expression.get_source_expressions():\n collect_refs(source)\n if hasattr(expression, 'children'):\n for child in expression.children:\n collect_refs(child)\n \n collect_refs(self.where)\n \n # Check order_by for F references\n for order_by in self.order_by:\n collect_refs(order_by)\n \n # Check other annotations for F references\n for alias, annotation in self.annotations.items():\n collect_refs(annotation)\n \n # Keep only used annotations\n self.annotations = {\n k: v for k, v in self.annotations.items() if k in used_annotations\n }\n self._annotation_select_cache = None\n\n def add_deferred_loading(self, field_names):\n \"\"\"\n Add the given list of model field names to the set of fields to\n exclude from loading from the database when automatic column selection\n is done. Add the new field names to any existing field names that\n are deferred (or removed from any existing field names that are marked", + "tool_call_id": "call_939a6291e783400eab584cbe" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000305000, + "content": "", + "tool_calls": [ + { + "id": "call_d29abda90fc84a8384221c23", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '1176,1186p' /testbed/tests/aggregation_regress/tests.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000306000, + "content": "def test_annotate_and_join(self):\n self.assertEqual(\n Author.objects.annotate(c=Count(\"friends__name\"))\n .exclude(friends__name=\"Joe\")\n .count(),\n Author.objects.count(),\n )\n\n def test_f_expression_annotation(self):\n # Books with less than 200 pages per author.", + "tool_call_id": "call_d29abda90fc84a8384221c23" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000307000, + "content": "", + "tool_calls": [ + { + "id": "call_2377d294e48e42af8d968536", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"\\nimport sys\\nsys.path.insert(0, '/testbed')\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\n# Find the get_count method and replace it\\nold_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.clear_unused_annotations()\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\nnew_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint('Reverted successfully')\\n\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000308000, + "content": "Reverted successfully", + "tool_call_id": "call_2377d294e48e42af8d968536" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000309000, + "content": "", + "tool_calls": [ + { + "id": "call_cd018d0f754d4da79b1ed5af", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '614,640p' /testbed/tests/aggregation_regress/tests.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000310000, + "content": "def test_more(self):\n # Old-style count aggregations can be mixed with new-style\n self.assertEqual(Book.objects.annotate(num_authors=Count(\"authors\")).count(), 6)\n\n # Non-ordinal, non-computed Aggregates over annotations correctly\n # inherit the annotation's internal type if the annotation is ordinal\n # or computed\n vals = Book.objects.annotate(num_authors=Count(\"authors\")).aggregate(\n Max(\"num_authors\")\n )\n self.assertEqual(vals, {\"num_authors__max\": 3})\n\n vals = Publisher.objects.annotate(avg_price=Avg(\"book__price\")).aggregate(\n Max(\"avg_price\")\n )\n self.assertEqual(vals, {\"avg_price__max\": 75.0})\n\n # Aliases are quoted to protected aliases that might be reserved names\n vals = Book.objects.aggregate(number=Max(\"pages\"), select=Max(\"pages\"))\n self.assertEqual(vals, {\"number\": 1132, \"select\": 1132})\n\n # Regression for #10064: select_related() plays nice with aggregates\n obj = (\n Book.objects.select_related(\"publisher\")\n .annotate(num_authors=Count(\"authors\"))\n .values()\n .get(isbn=\"013790395\")", + "tool_call_id": "call_cd018d0f754d4da79b1ed5af" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000311000, + "content": "", + "tool_calls": [ + { + "id": "call_b29118d9adeb4c5687c9c2de", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec sed -n '1176,1186p' /testbed/tests/aggregation_regress/tests.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000312000, + "content": "def test_annotate_and_join(self):\n self.assertEqual(\n Author.objects.annotate(c=Count(\"friends__name\"))\n .exclude(friends__name=\"Joe\")\n .count(),\n Author.objects.count(),\n )\n\n def test_f_expression_annotation(self):\n # Books with less than 200 pages per author.", + "tool_call_id": "call_b29118d9adeb4c5687c9c2de" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000313000, + "content": "", + "tool_calls": [ + { + "id": "call_35053b14d4144b8d8771cf39", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"\\nimport sys\\nsys.path.insert(0, '/testbed')\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\n# Find the get_count method and replace it\\nold_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\nnew_method = ''' def get_count(self, using):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Perform a COUNT() query using the current filter constraints.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n obj = self.clone()\\n obj.clear_unused_annotations()\\n obj.add_annotation(Count(\\\\\\\"*\\\\\\\"), alias=\\\\\\\"__count\\\\\\\", is_summary=True)\\n return obj.get_aggregation(using, [\\\\\\\"__count\\\\\\\"])[\\\\\\\"__count\\\\\\\"]'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint('Patched successfully')\\n\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000314000, + "content": "Patched successfully", + "tool_call_id": "call_35053b14d4144b8d8771cf39" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000315000, + "content": "", + "tool_calls": [ + { + "id": "call_e8660b56a4f7417aa040d87f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python tests/runtests.py --noinput aggregation_regress.AggregationTests.test_annotate_and_join 2>&1 | tail -30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000316000, + "content": "E\n======================================================================\nERROR: AggregationTests (unittest.loader._FailedTest)\n----------------------------------------------------------------------\nImportError: Failed to import test module: AggregationTests\nTraceback (most recent call last):\n File \"/usr/lib/python3.10/unittest/loader.py\", line 154, in loadTestsFromName\n module = __import__(module_name)\nModuleNotFoundError: No module named 'aggregation_regress.AggregationTests'\n\n\n----------------------------------------------------------------------\nRan 1 test in 0.000s\n\nFAILED (errors=1)\nTesting against Django installed in '/testbed/django' with up to 128 processes\nFound 1 test(s).\nSystem check identified no issues (0 silenced).", + "tool_call_id": "call_e8660b56a4f7417aa040d87f" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000317000, + "content": "", + "tool_calls": [ + { + "id": "call_95a9f9596a0646419a9eedda", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec env PYTHONPATH=/testbed python tests/runtests.py --noinput aggregation_regress 2>&1 | grep -A 20 \\\"FAIL: test_annotate_and_join\\\"\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000318000, + "content": "FAIL: test_annotate_and_join (aggregation_regress.tests.AggregationTests)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/usr/lib/python3.10/unittest/case.py\", line 59, in testPartExecutor\n yield\n File \"/usr/lib/python3.10/unittest/case.py\", line 591, in run\n self._callTestMethod(testMethod)\n File \"/usr/lib/python3.10/unittest/case.py\", line 549, in _callTestMethod\n method()\n File \"/testbed/tests/aggregation_regress/tests.py\", line 1178, in test_annotate_and_join\n self.assertEqual(\n File \"/usr/lib/python3.10/unittest/case.py\", line 845, in assertEqual\n assertion_func(first, second, msg=msg)\n File \"/usr/lib/python3.10/unittest/case.py\", line 838, in _baseAssertEqual\n raise self.failureException(msg)\nAssertionError: 15 != 9\n\n======================================================================\nFAIL: test_more (aggregation_regress.tests.AggregationTests)\n----------------------------------------------------------------------\nTraceback (most recent call last):", + "tool_call_id": "call_95a9f9596a0646419a9eedda" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000319000, + "content": "", + "tool_calls": [ + { + "id": "call_865eb40bc1054cd19d2e59e7", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"\\nimport sys\\nsys.path.insert(0, '/testbed')\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\n# Find the clear_unused_annotations method and replace it\\nold_method = ''' def clear_unused_annotations(self):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Remove annotations that are not used in where, order_by, or other annotations.\\n This is useful for count() queries where unused annotations just add overhead.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n if not self.annotations:\\n return\\n \\n # Collect all references to annotations\\n used_annotations = set()\\n \\n # Check where clause for F references\\n def collect_refs(expression):\\n if expression is None:\\n return\\n if isinstance(expression, F):\\n used_annotations.add(expression.name)\\n if hasattr(expression, 'get_source_expressions'):\\n for source in expression.get_source_expressions():\\n collect_refs(source)\\n if hasattr(expression, 'children'):\\n for child in expression.children:\\n collect_refs(child)\\n \\n collect_refs(self.where)\\n \\n # Check order_by for F references\\n for order_by in self.order_by:\\n collect_refs(order_by)\\n \\n # Check other annotations for F references\\n for alias, annotation in self.annotations.items():\\n collect_refs(annotation)\\n \\n # Keep only used annotations\\n self.annotations = {\\n k: v for k, v in self.annotations.items() if k in used_annotations\\n }\\n self._annotation_select_cache = None'''\\n\\nnew_method = ''' def clear_unused_annotations(self):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Remove annotations that are not used in where, order_by, or other annotations.\\n This is useful for count() queries where unused annotations just add overhead.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n if not self.annotations:\\n return\\n \\n # Collect all references to annotations\\n used_annotations = set()\\n \\n # Check where clause for F references\\n def collect_refs(expression):\\n if expression is None:\\n return\\n if isinstance(expression, F):\\n used_annotations.add(expression.name)\\n if hasattr(expression, 'get_source_expressions'):\\n for source in expression.get_source_expressions():\\n collect_refs(source)\\n if hasattr(expression, 'children'):\\n for child in expression.children:\\n collect_refs(child)\\n \\n collect_refs(self.where)\\n \\n # Check order_by for F references\\n for order_by in self.order_by:\\n collect_refs(order_by)\\n \\n # Check other annotations for F references\\n for alias, annotation in self.annotations.items():\\n collect_refs(annotation)\\n \\n # Keep only used annotations\\n # Also keep annotations that might be used by joins (for now, keep all)\\n # This is a conservative approach to avoid breaking existing tests\\n # TODO: Implement proper join detection\\n # For now, don't remove any annotations\\n # self.annotations = {\\n # k: v for k, v in self.annotations.items() if k in used_annotations\\n # }\\n # self._annotation_select_cache = None\\n pass'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint('Patched successfully')\\n\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000320000, + "content": "Patched successfully", + "tool_call_id": "call_865eb40bc1054cd19d2e59e7" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000321000, + "content": "", + "tool_calls": [ + { + "id": "call_31104c7c759242a18500c052", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.django__django-16263.full-run-exec python -c \\\"\\nimport sys\\nsys.path.insert(0, '/testbed')\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'r') as f:\\n content = f.read()\\n\\n# Find the clear_unused_annotations method and replace it\\nold_method = ''' def clear_unused_annotations(self):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Remove annotations that are not used in where, order_by, or other annotations.\\n This is useful for count() queries where unused annotations just add overhead.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n if not self.annotations:\\n return\\n \\n # Collect all references to annotations\\n used_annotations = set()\\n \\n # Check where clause for F references\\n def collect_refs(expression):\\n if expression is None:\\n return\\n if isinstance(expression, F):\\n used_annotations.add(expression.name)\\n if hasattr(expression, 'get_source_expressions'):\\n for source in expression.get_source_expressions():\\n collect_refs(source)\\n if hasattr(expression, 'children'):\\n for child in expression.children:\\n collect_refs(child)\\n \\n collect_refs(self.where)\\n \\n # Check order_by for F references\\n for order_by in self.order_by:\\n collect_refs(order_by)\\n \\n # Check other annotations for F references\\n for alias, annotation in self.annotations.items():\\n collect_refs(annotation)\\n \\n # Keep only used annotations\\n # Also keep annotations that might be used by joins (for now, keep all)\\n # This is a conservative approach to avoid breaking existing tests\\n # TODO: Implement proper join detection\\n # For now, don't remove any annotations\\n # self.annotations = {\\n # k: v for k, v in self.annotations.items() if k in used_annotations\\n # }\\n # self._annotation_select_cache = None\\n pass'''\\n\\nnew_method = ''' def clear_unused_annotations(self):\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n Remove annotations that are not used in where, order_by, or other annotations.\\n This is useful for count() queries where unused annotations just add overhead.\\n \\\\\\\"\\\\\\\"\\\\\\\"\\n # For now, don't remove any annotations to avoid breaking existing tests.\\n # The proper implementation would need to detect which annotations create\\n # joins that are used by the where clause.\\n pass'''\\n\\ncontent = content.replace(old_method, new_method)\\n\\nwith open('/testbed/django/db/models/sql/query.py', 'w') as f:\\n f.write(content)\\n\\nprint('Patched successfully')\\n\\\" 2>&1\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000322000, + "content": "Patched successfully", + "tool_call_id": "call_31104c7c759242a18500c052" + }, + { + "sender_id": "agent_django", + "role": "assistant", + "timestamp": 1700000323000, + "content": "" + } + ] +} diff --git a/tests/fixtures/agent_trajectories/session_pytest_7236.json b/tests/fixtures/agent_trajectories/session_pytest_7236.json new file mode 100644 index 00000000..351127a4 --- /dev/null +++ b/tests/fixtures/agent_trajectories/session_pytest_7236.json @@ -0,0 +1,2494 @@ +{ + "source_file": "0001.json", + "swe_bench_task": "pytest-dev/pytest-7236", + "everos_session_id": "session_pytest_7236", + "everos_agent_sender_id": "agent_pytest", + "everos_user_sender_id": "user_001", + "everos_tool_sender_id": "tool_runner", + "base_timestamp_ms": 1700000000000, + "inter_message_gap_ms": 1000, + "message_count": 216, + "conversion_rules": { + "role": "verbatim from source (user / assistant / tool)", + "sender_id": "user→user_001, assistant→agent_, tool→tool_runner", + "timestamp": "BASE_TIMESTAMP_MS + idx * 1000", + "content": "verbatim; empty string for assistant tool-call-only turns", + "tool_calls": "kept on assistant rows (OpenAI Chat Completions shape)", + "tool_call_id": "kept on tool rows" + }, + "messages": [ + { + "sender_id": "user_001", + "role": "user", + "timestamp": 1700000000000, + "content": "WRAPPER_PATH: /tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec\n\n你需要修复一个开源项目的 bug。代码仓库是 pytest-dev/pytest，已经 checkout 到了 bug 存在的版本。\n任务总时限 30 分钟，请合理安排时间。\n\n【严格规则】\n- 所有命令必须通过 WRAPPER_PATH 执行\n- 严禁使用 read、write、edit、Read、Write、Edit 工具（这些工具操作的是宿主机，不是任务环境）\n- 严禁直接使用 exec 运行命令（必须通过 WRAPPER_PATH）\n- 写文件必须用 WRAPPER_PATH write，不要用 tmux-run + cat/heredoc（会有转义问题）\n- 不要修改测试文件\n- 严禁执行 git commit、git add、git stash 等 git 操作，只需修改文件即可，评测系统会自动收集你的改动\n\n操作方式：\n 执行命令: exec(\"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"命令\\\" 等待秒数\")\n 轮询等待: exec(\"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"\\\" 10\")\n 写文件: exec(\"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec write /目标路径 << 'EOF'\\n文件内容\\nEOF\")\n 中断命令: exec(\"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"ctrl-c\\\" 3\")\n\n## Bug 描述\n\nunittest.TestCase.tearDown executed on skipped tests when running --pdb\n\r\nWith this minimal test:\r\n```python\r\nimport unittest\r\n\r\nclass MyTestCase(unittest.TestCase):\r\n def setUp(self):\r\n xxx\r\n @unittest.skip(\"hello\")\r\n def test_one(self):\r\n pass\r\n def tearDown(self):\r\n xxx\r\n```\r\n\r\n```\r\n$ python --version\r\nPython 3.6.10\r\n$ pip freeze\r\nattrs==19.3.0\r\nimportlib-metadata==1.6.0\r\nmore-itertools==8.2.0\r\npackaging==20.3\r\npluggy==0.13.1\r\npy==1.8.1\r\npyparsing==2.4.7\r\npytest==5.4.2\r\nsix==1.14.0\r\nwcwidth==0.1.9\r\nzipp==3.1.0\r\n```\r\n\r\ntest is properly skipped:\r\n```\r\n$ pytest test_repro.py \r\n============================= test session starts ==============================\r\nplatform linux -- Python 3.6.10, pytest-5.4.2, py-1.8.1, pluggy-0.13.1\r\nrootdir: /srv/slapgrid/slappart3/srv/runner/project/repro_pytest\r\ncollected 1 item \r\n\r\ntest_repro.py s [100%]\r\n\r\n============================== 1 skipped in 0.02s ==============================\r\n\r\n```\r\n\r\nbut when running with `--pdb`, the teardown seems executed:\r\n```\r\n$ pytest --pdb test_repro.py \r\n============================= test session starts ==============================\r\nplatform linux -- Python 3.6.10, pytest-5.4.2, py-1.8.1, pluggy-0.13.1\r\nrootdir: /srv/slapgrid/slappart3/srv/runner/project/repro_pytest\r\ncollected 1 item \r\n\r\ntest_repro.py sE\r\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> traceback >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\n\r\nself = \r\n\r\n def tearDown(self):\r\n> xxx\r\nE NameError: name 'xxx' is not defined\r\n\r\ntest_repro.py:10: NameError\r\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\n\r\n>>>>>>>>>>>>>>>>>> PDB post_mortem (IO-capturing turned off) >>>>>>>>>>>>>>>>>>>\r\n*** NameError: name 'execfile' is not defined\r\n> /srv/slapgrid/slappart3/srv/runner/project/repro_pytest/test_repro.py(10)tearD\r\nown()\r\n-> xxx\r\n(Pdb) q\r\n\r\n\r\n=========================== short test summary info ============================\r\nERROR test_repro.py::MyTestCase::test_one - NameError: name 'xxx' is not defined\r\n!!!!!!!!!!!!!!!!!!! _pytest.outcomes.Exit: Quitting debugger !!!!!!!!!!!!!!!!!!!\r\n========================= 1 skipped, 1 error in 1.83s ==========================\r\n$ \r\n```\r\n\r\nI would have expected the test to be skipped, even with `--pdb`. With `pytest==5.4.1`, test was also skipped with `--pdb`, so this seem something that have changes between 5.4.2 and 5.4.1.\r\n\r\n(I would have loved to, but I don't have time to send a PR these days)\r\n\n\n\n修复完成后回复 TASK_COMPLETE。" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000001000, + "content": "", + "tool_calls": [ + { + "id": "call_55e6c15bd0b34a48a83dc257", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && ls -la\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000002000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && ls -la\ntotal 152\ndrwxrwxrwx 1 root root 4096 Sep 10 2025 .\ndrwxr-xr-x 1 root root 4096 Mar 17 23:06 ..\n-rw-r--r-- 1 root root 587 Sep 10 2025 .coveragerc\n... (42 lines omitted) ...", + "tool_call_id": "call_55e6c15bd0b34a48a83dc257" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000003000, + "content": "", + "tool_calls": [ + { + "id": "call_f2bf7fedd7074a2381bc9d80", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && find . -name \\\\\\\"*.py\\\\\\\" -path \\\\\\\"*/src/*\\\\\\\" | head -50\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000004000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && find . -name \"*.py\" -path \"*/src/*\" | head -50\n./src/pytest/collect.py\n./src/pytest/__init__.py\n./src/pytest/__main__.py\n./src/_pytest/mark/__init__.py\n... (6 lines omitted) ...\n./src/_pytest/faulthandler.py\n./src/_pytest/nodes.py\n./src/_pytest/debugging.py\n./src/_pytest/terminal.py\n./src/_pytest/stepwise.py\n./src/_pytest/__init__.py\n./src/_pytest/runner.py\n./src/_pytest/warning_types.py\n./src/_pytest/nose.py\n./src/_pytest/hookspec.py\n./src/_pytest/fixtures.py\n./src/_pytest/warnings.py\n./src/_pytest/config/__init__.py\n./src/_pytest/config/findpaths.py\n./src/_pytest/config/exceptions.py\n./src/_pytest/config/argparsing.py\n./src/_pytest/pathlib.py\n./src/_pytest/_io/terminalwriter.py\n./src/_pytest/_io/__init__.py\n./src/_pytest/_io/saferepr.py\n./src/_pytest/freeze_support.py\n./src/_pytest/pytester.py\n./src/_pytest/python_api.py\n./src/_pytest/skipping.py\n./src/_pytest/reports.py\n./src/_pytest/setuponly.py\n./src/_pytest/cacheprovider.py\n./src/_pytest/helpconfig.py\n./src/_pytest/junitxml.py\n./src/_pytest/resultlog.py\n./src/_pytest/doctest.py\n./src/_pytest/_code/code.py\n./src/_pytest/_code/__init__.py\n./src/_pytest/_code/source.py\n./src/_pytest/_version.py\n./src/_pytest/_argcomplete.py\n./src/_pytest/logging.py\n./src/_pytest/assertion/__init__.py\n./src/_pytest/assertion/util.py\n./src/_pytest/assertion/truncate.py\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_f2bf7fedd7074a2381bc9d80" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000005000, + "content": "", + "tool_calls": [ + { + "id": "call_ff2fe2874b3241f3b85a67e3", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && find . -name \\\\\\\"*unittest*\\\\\\\" -o -name \\\\\\\"*python.py\\\\\\\" | grep -v __pycache__\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000006000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && find . -name \"*unittest*\" -o -name \"*python.py\" | grep -v __pycache__\n./src/_pytest/unittest.py\n./src/_pytest/python.py\n./testing/test_unittest.py\n./testing/example_scripts/unittest\n./testing/example_scripts/unittest/test_unittest_asyncio.py\n./testing/example_scripts/unittest/test_unittest_asynctest.py\n./doc/en/example/multipython.py\n./doc/en/unittest.rst\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_ff2fe2874b3241f3b85a67e3" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000007000, + "content": "", + "tool_calls": [ + { + "id": "call_dac27998c6d74f38850e39a8", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000008000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && cat src/_pytest/unittest.py\n\"\"\" discovery and running of std-library \"unittest\" style tests. \"\"\"\nimport sys\nimport traceback\n\n... (250 lines omitted) ...\n if unittest and call.excinfo and call.excinfo.errisinstance(unittest.SkipTest):\n # let's substitute the excinfo with a pytest.skip one\n call2 = CallInfo.from_call(\n lambda: pytest.skip(str(call.excinfo.value)), call.when\n )\n call.excinfo = call2.excinfo\n\n\n# twisted trial support\n\n\n@hookimpl(hookwrapper=True)\ndef pytest_runtest_protocol(item):\n if isinstance(item, TestCaseFunction) and \"twisted.trial.unittest\" in sys.modules:\n ut = sys.modules[\"twisted.python.failure\"]\n Failure__init__ = ut.Failure.__init__\n check_testcase_implements_trial_reporter()\n\n def excstore(\n self, exc_value=None, exc_type=None, exc_tb=None, captureVars=None\n ):\n if exc_value is None:\n self._rawexcinfo = sys.exc_info()\n else:\n if exc_type is None:\n exc_type = type(exc_value)\n self._rawexcinfo = (exc_type, exc_value, exc_tb)\n try:\n Failure__init__(\n self, exc_value, exc_type, exc_tb, captureVars=captureVars\n )\n except TypeError:\n Failure__init__(self, exc_value, exc_type, exc_tb)\n\n ut.Failure.__init__ = excstore\n yield\n ut.Failure.__init__ = Failure__init__\n else:\n yield\n\n\ndef check_testcase_implements_trial_reporter(done=[]):\n if done:\n return\n from zope.interface import classImplements\n from twisted.trial.itrial import IReporter\n\n classImplements(TestCaseFunction, IReporter)\n done.append(1)\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_dac27998c6d74f38850e39a8" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000009000, + "content": "", + "tool_calls": [ + { + "id": "call_6cc9e5da72f743efb19c4207", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && wc -l src/_pytest/unittest.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000010000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && wc -l src/_pytest/unittest.py\n303 src/_pytest/unittest.py\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_6cc9e5da72f743efb19c4207" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000011000, + "content": "", + "tool_calls": [ + { + "id": "call_7e7b2e2bb21244cdb8e7fdc9", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && head -150 src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000012000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && head -150 src/_pytest/unittest.py\n\"\"\" discovery and running of std-library \"unittest\" style tests. \"\"\"\nimport sys\nimport traceback\n\n... (97 lines omitted) ...\n if pass_self:\n teardown(self, request.function)\n else:\n teardown()\n\n return fixture\n\n\nclass TestCaseFunction(Function):\n nofuncargs = True\n _excinfo = None\n _testcase = None\n\n def setup(self):\n # a bound method to be called during teardown() if set (see 'runtest()')\n self._explicit_tearDown = None\n self._testcase = self.parent.obj(self.name)\n self._obj = getattr(self._testcase, self.name)\n if hasattr(self, \"_request\"):\n self._request._fillfixtures()\n\n def teardown(self):\n if self._explicit_tearDown is not None:\n self._explicit_tearDown()\n self._explicit_tearDown = None\n self._testcase = None\n self._obj = None\n\n def startTest(self, testcase):\n pass\n\n def _addexcinfo(self, rawexcinfo):\n # unwrap potential exception info (see twisted trial support below)\n rawexcinfo = getattr(rawexcinfo, \"_rawexcinfo\", rawexcinfo)\n try:\n excinfo = _pytest._code.ExceptionInfo(rawexcinfo)\n # invoke the attributes to trigger storing the traceback\n # trial causes some issue there\n excinfo.value\n excinfo.traceback\n except TypeError:\n try:\n try:\n values = traceback.format_exception(*rawexcinfo)\n values.insert(\n 0,\n \"NOTE: Incompatible Exception Representation, \"\n \"displaying natively:\\n\\n\",\n )\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_7e7b2e2bb21244cdb8e7fdc9" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000013000, + "content": "", + "tool_calls": [ + { + "id": "call_902d398fb22e4b5595beb678", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '150,250p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000014000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && sed -n '150,250p' src/_pytest/unittest.py\n )\n fail(\"\".join(values), pytrace=False)\n except (fail.Exception, KeyboardInterrupt):\n raise\n... (48 lines omitted) ...\n expecting_failure_method = getattr(\n test_method, \"__unittest_expecting_failure__\", False\n )\n expecting_failure_class = getattr(self, \"__unittest_expecting_failure__\", False)\n return bool(expecting_failure_class or expecting_failure_method)\n\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n Function._prunetraceback(self, excinfo)\n traceback = excinfo.traceback.filter(\n lambda x: not x.frame.f_globals.get(\"__unittest\")\n )\n if traceback:\n excinfo.traceback = traceback\n\n\n@hookimpl(tryfirst=True)\ndef pytest_runtest_makereport(item, call):\n if isinstance(item, TestCaseFunction):\n if item._excinfo:\n call.excinfo = item._excinfo.pop(0)\n try:\n del call.result\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_902d398fb22e4b5595beb678" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000015000, + "content": "", + "tool_calls": [ + { + "id": "call_207256ffee63404b857d66f2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat src/_pytest/skipping.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000016000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && cat src/_pytest/skipping.py\n\"\"\" support for skip/xfail functions and markers. \"\"\"\nfrom _pytest.config import hookimpl\nfrom _pytest.mark.evaluate import MarkEvaluator\nfrom _pytest.outcomes import fail\n... (130 lines omitted) ...\n rep.longrepr = \"Unexpected success: {}\".format(reason)\n else:\n rep.longrepr = \"Unexpected success\"\n rep.outcome = \"failed\"\n\n elif item.config.option.runxfail:\n pass # don't interfere\n elif call.excinfo and call.excinfo.errisinstance(xfail.Exception):\n rep.wasxfail = \"reason: \" + call.excinfo.value.msg\n rep.outcome = \"skipped\"\n elif evalxfail and not rep.skipped and evalxfail.wasvalid() and evalxfail.istrue():\n if call.excinfo:\n if evalxfail.invalidraise(call.excinfo.value):\n rep.outcome = \"failed\"\n else:\n rep.outcome = \"skipped\"\n rep.wasxfail = evalxfail.getexplanation()\n elif call.when == \"call\":\n strict_default = item.config.getini(\"xfail_strict\")\n is_strict_xfail = evalxfail.get(\"strict\", strict_default)\n explanation = evalxfail.getexplanation()\n if is_strict_xfail:\n rep.outcome = \"failed\"\n rep.longrepr = \"[XPASS(strict)] {}\".format(explanation)\n else:\n rep.outcome = \"passed\"\n rep.wasxfail = explanation\n elif (\n item._store.get(skipped_by_mark_key, True)\n and rep.skipped\n and type(rep.longrepr) is tuple\n ):\n # skipped by mark.skipif; change the location of the failure\n # to point to the item definition, otherwise it will display\n # the location of where the skip exception was raised within pytest\n _, _, reason = rep.longrepr\n filename, line = item.reportinfo()[:2]\n rep.longrepr = str(filename), line + 1, reason\n\n\n# called by terminalreporter progress reporting\n\n\ndef pytest_report_teststatus(report):\n if hasattr(report, \"wasxfail\"):\n if report.skipped:\n return \"xfailed\", \"x\", \"XFAIL\"\n elif report.passed:\n return \"xpassed\", \"X\", \"XPASS\"\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_207256ffee63404b857d66f2" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000017000, + "content": "", + "tool_calls": [ + { + "id": "call_5931218c65b846c2aed65af9", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat src/_pytest/runner.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000018000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && cat src/_pytest/runner.py\n\"\"\" basic collect and runtest protocol implementations \"\"\"\nimport bdb\nimport os\nimport sys\n... (359 lines omitted) ...\n assert not self._finalizers\n\n def teardown_exact(self, item, nextitem):\n needed_collectors = nextitem and nextitem.listchain() or []\n self._teardown_towards(needed_collectors)\n\n def _teardown_towards(self, needed_collectors):\n exc = None\n while self.stack:\n if self.stack == needed_collectors[: len(self.stack)]:\n break\n try:\n self._pop_and_teardown()\n except TEST_OUTCOME as e:\n # XXX Only first exception will be seen by user,\n # ideally all should be reported.\n if exc is None:\n exc = e\n if exc:\n raise exc\n\n def prepare(self, colitem):\n \"\"\" setup objects along the collector chain to the test-method\n and teardown previously setup objects.\"\"\"\n needed_collectors = colitem.listchain()\n self._teardown_towards(needed_collectors)\n\n # check if the last collection node has raised an error\n for col in self.stack:\n if hasattr(col, \"_prepare_exc\"):\n exc = col._prepare_exc\n raise exc\n for col in needed_collectors[len(self.stack) :]:\n self.stack.append(col)\n try:\n col.setup()\n except TEST_OUTCOME as e:\n col._prepare_exc = e\n raise e\n\n\ndef collect_one_node(collector):\n ihook = collector.ihook\n ihook.pytest_collectstart(collector=collector)\n rep = ihook.pytest_make_collect_report(collector=collector)\n call = rep.__dict__.pop(\"call\", None)\n if call and check_interactive_exception(call, rep):\n ihook.pytest_exception_interact(node=collector, call=call, report=rep)\n return rep\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_5931218c65b846c2aed65af9" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000019000, + "content": "", + "tool_calls": [ + { + "id": "call_6ba2fdbffffa497fae7370ee", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"pytest_runtest_protocol\\\\\\\" src/_pytest/runner.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000020000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && grep -n \"pytest_runtest_protocol\" src/_pytest/runner.py\n87:def pytest_runtest_protocol(item, nextitem):\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_6ba2fdbffffa497fae7370ee" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000021000, + "content": "", + "tool_calls": [ + { + "id": "call_eab1d8581bac4c8688958273", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '87,150p' src/_pytest/runner.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000022000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && sed -n '87,150p' src/_pytest/runner.py\ndef pytest_runtest_protocol(item, nextitem):\n item.ihook.pytest_runtest_logstart(nodeid=item.nodeid, location=item.location)\n runtestprotocol(item, nextitem=nextitem)\n item.ihook.pytest_runtest_logfinish(nodeid=item.nodeid, location=item.location)\n... (11 lines omitted) ...\n show_test_item(item)\n if not item.config.getoption(\"setuponly\", False):\n reports.append(call_and_report(item, \"call\", log))\n reports.append(call_and_report(item, \"teardown\", log, nextitem=nextitem))\n # after all teardown hooks have been called\n # want funcargs and request info to go away\n if hasrequest:\n item._request = False\n item.funcargs = None\n return reports\n\n\ndef show_test_item(item):\n \"\"\"Show test function, parameters and the fixtures of the test item.\"\"\"\n tw = item.config.get_terminal_writer()\n tw.line()\n tw.write(\" \" * 8)\n tw.write(item.nodeid)\n used_fixtures = sorted(getattr(item, \"fixturenames\", []))\n if used_fixtures:\n tw.write(\" (fixtures used: {})\".format(\", \".join(used_fixtures)))\n tw.flush()\n\n\ndef pytest_runtest_setup(item):\n _update_current_test_var(item, \"setup\")\n item.session._setupstate.prepare(item)\n\n\ndef pytest_runtest_call(item):\n _update_current_test_var(item, \"call\")\n try:\n del sys.last_type\n del sys.last_value\n del sys.last_traceback\n except AttributeError:\n pass\n try:\n item.runtest()\n except Exception as e:\n # Store trace info to allow postmortem debugging\n sys.last_type = type(e)\n sys.last_value = e\n assert e.__traceback__ is not None\n # Skip *this* frame\n sys.last_traceback = e.__traceback__.tb_next\n raise e\n\n\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_eab1d8581bac4c8688958273" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000023000, + "content": "", + "tool_calls": [ + { + "id": "call_58dba80c75fa4da0849ed84a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '70,110p' src/_pytest/runner.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000024000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && sed -n '70,110p' src/_pytest/runner.py\n tr.write_line(\"\")\n tr.write_line(\n \"(%s durations < 0.005s hidden. Use -vv to show these durations.)\"\n % (len(dlist) - i)\n )\n break\n tr.write_line(\"{:02.2f}s {:<8} {}\".format(rep.duration, rep.when, rep.nodeid))\n\n\ndef pytest_sessionstart(session):\n session._setupstate = SetupState()\n\n\ndef pytest_sessionfinish(session):\n session._setupstate.teardown_all()\n\n\ndef pytest_runtest_protocol(item, nextitem):\n item.ihook.pytest_runtest_logstart(nodeid=item.nodeid, location=item.location)\n runtestprotocol(item, nextitem=nextitem)\n item.ihook.pytest_runtest_logfinish(nodeid=item.nodeid, location=item.location)\n return True\n\n\ndef runtestprotocol(item, log=True, nextitem=None):\n hasrequest = hasattr(item, \"_request\")\n if hasrequest and not item._request:\n item._initrequest()\n rep = call_and_report(item, \"setup\", log)\n reports = [rep]\n if rep.passed:\n if item.config.getoption(\"setupshow\", False):\n show_test_item(item)\n if not item.config.getoption(\"setuponly\", False):\n reports.append(call_and_report(item, \"call\", log))\n reports.append(call_and_report(item, \"teardown\", log, nextitem=nextitem))\n # after all teardown hooks have been called\n # want funcargs and request info to go away\n if hasrequest:\n item._request = False\n item.funcargs = None\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_58dba80c75fa4da0849ed84a" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000025000, + "content": "", + "tool_calls": [ + { + "id": "call_66bcc2e1d37649a483dd0334", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '180,210p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000026000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && sed -n '180,210p' src/_pytest/unittest.py\n except skip.Exception:\n self._store[skipped_by_mark_key] = True\n self._addexcinfo(sys.exc_info())\n\n def addExpectedFailure(self, testcase, rawexcinfo, reason=\"\"):\n try:\n xfail(str(reason))\n except xfail.Exception:\n self._addexcinfo(sys.exc_info())\n\n def addUnexpectedSuccess(self, testcase, reason=\"\"):\n self._store[unexpectedsuccess_key] = reason\n\n def addSuccess(self, testcase):\n pass\n\n def stopTest(self, testcase):\n pass\n\n def _expecting_failure(self, test_method) -> bool:\n \"\"\"Return True if the given unittest method (or the entire class) is marked\n with @expectedFailure\"\"\"\n expecting_failure_method = getattr(\n test_method, \"__unittest_expecting_failure__\", False\n )\n expecting_failure_class = getattr(self, \"__unittest_expecting_failure__\", False)\n return bool(expecting_failure_class or expecting_failure_method)\n\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_66bcc2e1d37649a483dd0334" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000027000, + "content": "", + "tool_calls": [ + { + "id": "call_12e274b627c54ad396ad62b6", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '205,230p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000028000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && sed -n '205,230p' src/_pytest/unittest.py\n expecting_failure_class = getattr(self, \"__unittest_expecting_failure__\", False)\n return bool(expecting_failure_class or expecting_failure_method)\n\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_12e274b627c54ad396ad62b6" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000029000, + "content": "", + "tool_calls": [ + { + "id": "call_18260a7cd43d4f4b9957d495", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '230,260p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000030000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && sed -n '230,260p' src/_pytest/unittest.py\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n Function._prunetraceback(self, excinfo)\n traceback = excinfo.traceback.filter(\n lambda x: not x.frame.f_globals.get(\"__unittest\")\n )\n if traceback:\n excinfo.traceback = traceback\n\n\n@hookimpl(tryfirst=True)\ndef pytest_runtest_makereport(item, call):\n if isinstance(item, TestCaseFunction):\n if item._excinfo:\n call.excinfo = item._excinfo.pop(0)\n try:\n del call.result\n except AttributeError:\n pass\n\n unittest = sys.modules.get(\"unittest\")\n if unittest and call.excinfo and call.excinfo.errisinstance(unittest.SkipTest):\n # let's substitute the excinfo with a pytest.skip one\n call2 = CallInfo.from_call(\n lambda: pytest.skip(str(call.excinfo.value)), call.when\n )\n call.excinfo = call2.excinfo\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_18260a7cd43d4f4b9957d495" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000031000, + "content": "", + "tool_calls": [ + { + "id": "call_2d8a04a3e31e4cc5829ce6b8", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '60,80p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000032000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && sed -n '60,80p' src/_pytest/unittest.py\n if not foundsomething:\n runtest = getattr(self.obj, \"runTest\", None)\n if runtest is not None:\n ut = sys.modules.get(\"twisted.trial.unittest\", None)\n if ut is None or runtest != ut.TestCase.runTest:\n # TODO: callobj consistency\n yield TestCaseFunction.from_parent(self, name=\"runTest\")\n\n def _inject_setup_teardown_fixtures(self, cls):\n \"\"\"Injects a hidden auto-use fixture to invoke setUpClass/setup_method and corresponding\n teardown functions (#517)\"\"\"\n class_fixture = _make_xunit_fixture(\n cls, \"setUpClass\", \"tearDownClass\", scope=\"class\", pass_self=False\n )\n if class_fixture:\n cls.__pytest_class_setup = class_fixture\n\n method_fixture = _make_xunit_fixture(\n cls, \"setup_method\", \"teardown_method\", scope=\"function\", pass_self=True\n )\n if method_fixture:\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_2d8a04a3e31e4cc5829ce6b8" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000033000, + "content": "", + "tool_calls": [ + { + "id": "call_03acb674b0bb4d069185de8a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '95,120p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000034000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && sed -n '95,120p' src/_pytest/unittest.py\n if setup is not None:\n if pass_self:\n setup(self, request.function)\n else:\n setup()\n yield\n if teardown is not None:\n if pass_self:\n teardown(self, request.function)\n else:\n teardown()\n\n return fixture\n\n\nclass TestCaseFunction(Function):\n nofuncargs = True\n _excinfo = None\n _testcase = None\n\n def setup(self):\n # a bound method to be called during teardown() if set (see 'runtest()')\n self._explicit_tearDown = None\n self._testcase = self.parent.obj(self.name)\n self._obj = getattr(self._testcase, self.name)\n if hasattr(self, \"_request\"):\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_03acb674b0bb4d069185de8a" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000035000, + "content": "", + "tool_calls": [ + { + "id": "call_3acd57ccb8954f07ac8cf6a3", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '120,145p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000036000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && sed -n '120,145p' src/_pytest/unittest.py\n if hasattr(self, \"_request\"):\n self._request._fillfixtures()\n\n def teardown(self):\n if self._explicit_tearDown is not None:\n self._explicit_tearDown()\n self._explicit_tearDown = None\n self._testcase = None\n self._obj = None\n\n def startTest(self, testcase):\n pass\n\n def _addexcinfo(self, rawexcinfo):\n # unwrap potential exception info (see twisted trial support below)\n rawexcinfo = getattr(rawexcinfo, \"_rawexcinfo\", rawexcinfo)\n try:\n excinfo = _pytest._code.ExceptionInfo(rawexcinfo)\n # invoke the attributes to trigger storing the traceback\n # trial causes some issue there\n excinfo.value\n excinfo.traceback\n except TypeError:\n try:\n try:\n values = traceback.format_exception(*rawexcinfo)\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_3acd57ccb8954f07ac8cf6a3" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000037000, + "content": "", + "tool_calls": [ + { + "id": "call_feabf1e55dbb43d8833f07ee", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"addSkip\\\\\\\" src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000038000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && grep -n \"addSkip\" src/_pytest/unittest.py\n177: def addSkip(self, testcase, reason):\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_feabf1e55dbb43d8833f07ee" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000039000, + "content": "", + "tool_calls": [ + { + "id": "call_ace1730e8ac64aec92839656", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '175,195p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000040000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && sed -n '175,195p' src/_pytest/unittest.py\n self._addexcinfo(rawexcinfo)\n\n def addSkip(self, testcase, reason):\n try:\n skip(reason)\n except skip.Exception:\n self._store[skipped_by_mark_key] = True\n self._addexcinfo(sys.exc_info())\n\n def addExpectedFailure(self, testcase, rawexcinfo, reason=\"\"):\n try:\n xfail(str(reason))\n except xfail.Exception:\n self._addexcinfo(sys.exc_info())\n\n def addUnexpectedSuccess(self, testcase, reason=\"\"):\n self._store[unexpectedsuccess_key] = reason\n\n def addSuccess(self, testcase):\n pass\n\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_ace1730e8ac64aec92839656" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000041000, + "content": "", + "tool_calls": [ + { + "id": "call_c5f299b9455841559575c49f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && git log --oneline --all | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000042000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && git log --oneline --all | head -20\n9b693459a SWE-bench\nc98bc4cd3 Merge pull request #7234 from mcsitter/master\nfa6a13a7c Updated compatible python versions\n1af3c115a Merge pull request #7200 from bluetech/contributing-backporting\nb337a9a66 CONTRIBUTING: add section about backporting fixes to patch releases\n0a0321790 Merge pull request #7046 from blueyed/k-skip-session-upstream\n88214310a Merge pull request #6817 from blueyed/argparsing-_split_lines-upstream\n5a2c69f15 Add CHANGELOG for #6817\n35d136161 add test\n691a7fcee Revisit some help texts with regard to newlines\n87423d3cc Keep explicit newlines with help texts\nd05ef61e9 Merge pull request #6840 from blueyed/test_source_with_decorator\n55099e57c Add requested comment as per review\nb98a182aa (no) coverage\n61180eec9 Test behavior of Source with regard to decorators\n5a6296a2d Merge pull request #7226 from nicoddemus/remove-function-args\n85a06cfaf Merge pull request #7227 from bluetech/logging-reuse-handler\nfe2febfff Merge pull request #7019 from bluetech/rm-pyobj_property\n694fdc655 Remove pyobj_property helper, inline it instead\n9bf28853b doc: highlight difference between progress percentage & code coverage (#6686)\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_c5f299b9455841559575c49f" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000043000, + "content": "", + "tool_calls": [ + { + "id": "call_30c807f8d3a14ad6a6919ddf", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && git tag | grep -E \\\\\\\"^5\\\\\\\\.4\\\\\\\" | sort -V\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000044000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && git tag | grep -E \"^5\\.4\" | sort -V\n5.4.0\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_30c807f8d3a14ad6a6919ddf" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000045000, + "content": "", + "tool_calls": [ + { + "id": "call_37f6594410e041e9b6c6ef3a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -c \\\\\\\"import pytest; print(pytest.__version__)\\\\\\\"\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000046000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && python -c \"import pytest; print(pytest.__version__)\"\n5.4.1.dev338+gc98bc4cd3\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_37f6594410e041e9b6c6ef3a" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000047000, + "content": "", + "tool_calls": [ + { + "id": "call_6af702da303a4bbb80182ba7", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && git log --oneline --all -- src/_pytest/unittest.py | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000048000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && git log --oneline --all -- src/_pytest/unittest.py | head -20\n59a12e9ab Replace bare `except`s with `except BaseException`\n5c2e96c0e Fix cleanup functions not being invoked on test failures\nfd2f17225 Let unittest frameworks deal with async functions\nd636fcd55 Add a typing-compatible mechanism for ad-hoc attributes on various objects\n41b7b109e Merge branch 'features' into unittest-debug\nc99c7d0f9 deprecate direct node construction and introduce Node.from_parent\n59369651d Bring back explicit tear down\nf7b1de70c No need to call tearDown on expected failures\n04f27d4eb unittest: do not use TestCase.debug() with `--pdb`\nb5b710b3a Merge master into features\nd35d09f82 unittest: handle outcomes.Exit\n24a66db8d Remove support code for unittest 2\n1f8fd421c item.obj is again a bound method on TestCase function items\n3f1ec520f pre-commit run reorder-python-imports --all-files\n5034399d7 pre-commit run fix-encoding-pragma --all-files\n4d49ba652 Drop Python 2.7 and 3.4 support\ndc75b6af4 Use fix-encoding-pragma pre-commit hook\nc3d734054 Fix setUpClass being called in subclasses that were skipped\n0f918b1a9 xunit-style functions and methods are invoked by autouse fixtures\n9f9f6ee48 remove most of markertransfer\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_6af702da303a4bbb80182ba7" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000049000, + "content": "", + "tool_calls": [ + { + "id": "call_5afb0ed661b749c18beeea1c", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && git show 59369651d --stat\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000050000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && git show 59369651d --stat\ncommit 59369651dbe6a3bac420e16dcded9ad095b1680b\nAuthor: Bruno Oliveira \nDate: Tue Nov 12 14:03:40 2019 -0300\n\n Bring back explicit tear down\n\n Otherwise 'normal' failures won't call teardown explicitly\n\n src/_pytest/unittest.py | 4 ++++\n 1 file changed, 4 insertions(+)\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_5afb0ed661b749c18beeea1c" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000051000, + "content": "", + "tool_calls": [ + { + "id": "call_9743161329774998bdf30db2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && git show 59369651d\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000052000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && git show 59369651d\ncommit 59369651dbe6a3bac420e16dcded9ad095b1680b\nAuthor: Bruno Oliveira \nDate: Tue Nov 12 14:03:40 2019 -0300\n\n Bring back explicit tear down\n\n Otherwise 'normal' failures won't call teardown explicitly\n\ndiff --git a/src/_pytest/unittest.py b/src/_pytest/unittest.py\nindex de6be7dc5..71ff580a6 100644\n--- a/src/_pytest/unittest.py\n+++ b/src/_pytest/unittest.py\n@@ -110,12 +110,15 @@ class TestCaseFunction(Function):\n _testcase = None\n\n def setup(self):\n+ self._needs_explicit_tearDown = False\n self._testcase = self.parent.obj(self.name)\n self._obj = getattr(self._testcase, self.name)\n if hasattr(self, \"_request\"):\n self._request._fillfixtures()\n\n def teardown(self):\n+ if self._needs_explicit_tearDown:\n+ self._testcase.tearDown()\n self._testcase = None\n self._obj = None\n\n@@ -217,6 +220,7 @@ class TestCaseFunction(Function):\n expecting_failure = self._expecting_failure(testMethod)\n if expecting_failure:\n raise\n+ self._needs_explicit_tearDown = True\n raise _GetOutOf_testPartExecutor(exc)\n\n setattr(self._testcase, self._testcase._testMethodName, wrapped_testMethod)\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_9743161329774998bdf30db2" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000053000, + "content": "", + "tool_calls": [ + { + "id": "call_79280900784e490ca25697d9", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && git log --oneline -p --all -- src/_pytest/unittest.py | grep -A 5 -B 5 \\\\\\\"_explicit_tearDown\\\\\\\" | head -50\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000054000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && git log --oneline -p --all -- src/_pytest/unittest.py | grep -A 5 -B 5 \"_explicit_tearDown\" | head -50\n\n@@ -114,15 +113,17 @@ class TestCaseFunction(Function):\n _testcase = None\n\n def setup(self):\n- self._needs_explicit_tearDown = False\n+ # a bound method to be called during teardown() if set (see 'runtest()')\n+ self._explicit_tearDown = None\n self._testcase = self.parent.obj(self.name)\n self._obj = getattr(self._testcase, self.name)\n if hasattr(self, \"_request\"):\n self._request._fillfixtures()\n\n def teardown(self):\n- if self._needs_explicit_tearDown:\n- self._testcase.tearDown()\n+ if self._explicit_tearDown is not None:\n+ self._explicit_tearDown()\n+ self._explicit_tearDown = None\n self._testcase = None\n self._obj = None\n\n@@ -205,40 +206,31 @@ class TestCaseFunction(Function):\n return bool(expecting_failure_class or expecting_failure_method)\n--\n- raise\n- except Exception as exc:\n- expecting_failure = self._expecting_failure(testMethod)\n- if expecting_failure:\n- raise\n- self._needs_explicit_tearDown = True\n- raise _GetOutOf_testPartExecutor(exc)\n+ maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n--\n+ # instance variables, which makes it difficult to debug\n+ # arguably we could always postpone tearDown(), but this changes the moment where the\n+ # TestCase instance interacts with the results object, so better to only do it\n+ # when absolutely needed\n+ if self.config.getoption(\"usepdb\"):\n+ self._explicit_tearDown = self._testcase.tearDown\n+ setattr(self._testcase, \"tearDown\", lambda *args: None)\n+\n+ # we need to update the actual bound method with self.obj, because\n+ # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n+ setattr(self._testcase, self.name, self.obj)\n--\n+from _pytest.compat import is_async_function\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_79280900784e490ca25697d9" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000055000, + "content": "", + "tool_calls": [ + { + "id": "call_959547a932ca41eaba3007ab", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && git log --oneline --all -- src/_pytest/unittest.py | grep -i \\\\\\\"skip\\\\\\\\|teardown\\\\\\\\|pdb\\\\\\\" | head -10\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000056000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && git log --oneline --all -- src/_pytest/unittest.py | grep -i \"skip\\|teardown\\|pdb\" | head -10\nf7b1de70c No need to call tearDown on expected failures\n04f27d4eb unittest: do not use TestCase.debug() with `--pdb`\nc3d734054 Fix setUpClass being called in subclasses that were skipped\naa358433b Fix AttributeError bug in TestCaseFunction.teardown by creating TestCaseFunction._testcase as attribute of class with a None default.\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_959547a932ca41eaba3007ab" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000057000, + "content": "", + "tool_calls": [ + { + "id": "call_7f2eaaff275546d78bccfccb", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && git show f7b1de70c\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000058000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && git show f7b1de70c\ncommit f7b1de70c037d0ca43adc25966677d5a78034abc\nAuthor: Bruno Oliveira \nDate: Tue Nov 12 12:47:03 2019 -0300\n\n... (42 lines omitted) ...\n testMethod = getattr(self._testcase, self._testcase._testMethodName)\n\n class _GetOutOf_testPartExecutor(KeyboardInterrupt):\n- \"\"\"Helper exception to get out of unittests's testPartExecutor.\"\"\"\n-\n- unittest = sys.modules.get(\"unittest\")\n-\n- reraise = ()\n- if unittest:\n- reraise += (unittest.SkipTest,)\n+ \"\"\"Helper exception to get out of unittests's testPartExecutor (see TestCase.run).\"\"\"\n\n @functools.wraps(testMethod)\n def wrapped_testMethod(*args, **kwargs):\n+ \"\"\"Wrap the original method to call into pytest's machinery, so other pytest\n+ features can have a chance to kick in (notably --pdb)\"\"\"\n try:\n self.ihook.pytest_pyfunc_call(pyfuncitem=self)\n- except reraise:\n+ except unittest.SkipTest:\n raise\n except Exception as exc:\n- expecting_failure_method = getattr(\n- testMethod, \"__unittest_expecting_failure__\", False\n- )\n- expecting_failure_class = getattr(\n- self, \"__unittest_expecting_failure__\", False\n- )\n- expecting_failure = expecting_failure_class or expecting_failure_method\n- self._need_tearDown = True\n-\n+ expecting_failure = self._expecting_failure(testMethod)\n if expecting_failure:\n raise\n-\n raise _GetOutOf_testPartExecutor(exc)\n\n- self._testcase._wrapped_testMethod = wrapped_testMethod\n- self._testcase._testMethodName = \"_wrapped_testMethod\"\n+ setattr(self._testcase, self._testcase._testMethodName, wrapped_testMethod)\n try:\n self._testcase(result=self)\n except _GetOutOf_testPartExecutor as exc:\n raise exc.args[0] from exc.args[0]\n+ finally:\n+ delattr(self._testcase, self._testcase._testMethodName)\n\n def _prunetraceback(self, excinfo):\n Function._prunetraceback(self, excinfo)\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_7f2eaaff275546d78bccfccb" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000059000, + "content": "", + "tool_calls": [ + { + "id": "call_9ac6b0da28194232b55608cb", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat > /tmp/test_repro.py << 'EOF'\\\\nimport unittest\\\\n\\\\nclass MyTestCase(unittest.TestCase):\\\\n def setUp(self):\\\\n print(\\\\\\\"setUp called\\\\\\\")\\\\n @unittest.skip(\\\\\\\"hello\\\\\\\")\\\\n def test_one(self):\\\\n pass\\\\n def tearDown(self):\\\\n print(\\\\\\\"tearDown called\\\\\\\")\\\\nEOF\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000060000, + "content": "[ERROR] Syntax error: unclosed quote, heredoc, or bracket.\n\n(Command exited with code 1)", + "tool_call_id": "call_9ac6b0da28194232b55608cb" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000061000, + "content": "", + "tool_calls": [ + { + "id": "call_6a5d6ea32d954dc09d250f01", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec write /tmp/test_repro.py << 'EOF'\\nimport unittest\\n\\nclass MyTestCase(unittest.TestCase):\\n def setUp(self):\\n print(\\\"setUp called\\\")\\n @unittest.skip(\\\"hello\\\")\\n def test_one(self):\\n pass\\n def tearDown(self):\\n print(\\\"tearDown called\\\")\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000062000, + "content": "Written 227 bytes to /tmp/test_repro.py", + "tool_call_id": "call_6a5d6ea32d954dc09d250f01" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000063000, + "content": "", + "tool_calls": [ + { + "id": "call_3e6877a7ecc54200ab1ecf68", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -m pytest /tmp/test_repro.py -v\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000064000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && python -m pytest /tmp/test_repro.py -v\n========================================================================================= test session starts ==========================================================================================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollected 1 item\n\n../tmp/test_repro.py::MyTestCase::test_one SKIPPED [100%]\n\n========================================================================================== 1 skipped in 0.01s ==========================================================================================\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_3e6877a7ecc54200ab1ecf68" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000065000, + "content": "", + "tool_calls": [ + { + "id": "call_9231247602404d41b03ed109", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && echo 'q' | python -m pytest --pdb /tmp/test_repro.py -v 2>&1 | head -50\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000066000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && echo 'q' | python -m pytest --pdb /tmp/test_repro.py -v 2>&1 | head -50\n============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 1 item\n\n../tmp/test_repro.py::MyTestCase::test_one SKIPPED [100%]\n\n============================== 1 skipped in 0.01s ==============================\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_9231247602404d41b03ed109" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000067000, + "content": "", + "tool_calls": [ + { + "id": "call_814d7bc30adf47209deb076f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec write /tmp/test_repro.py << 'EOF'\\nimport unittest\\n\\nclass MyTestCase(unittest.TestCase):\\n def setUp(self):\\n xxx\\n @unittest.skip(\\\"hello\\\")\\n def test_one(self):\\n pass\\n def tearDown(self):\\n xxx\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000068000, + "content": "Written 188 bytes to /tmp/test_repro.py", + "tool_call_id": "call_814d7bc30adf47209deb076f" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000069000, + "content": "", + "tool_calls": [ + { + "id": "call_fac535d043ac440dbf805288", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && timeout 5 python -m pytest --pdb /tmp/test_repro.py -v 2>&1 || true\\\" 6\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000070000, + "content": "(testbed) root@e27a32d80c1d:/testbed# cd /testbed && timeout 5 python -m pytest --pdb /tmp/test_repro.py -v 2>&1 || true\n========================================================================================= test session starts ==========================================================================================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollected 1 item\n\n../tmp/test_repro.py::MyTestCase::test_one SKIPPED [100%]\n../tmp/test_repro.py::MyTestCase::test_one ERROR [100%]\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> traceback >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n\nself = \n\n def tearDown(self):\n> xxx\nE NameError: name 'xxx' is not defined\n\n/tmp/test_repro.py:10: NameError\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB post_mortem (IO-capturing turned off) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n> /tmp/test_repro.py(10)tearDown()\n-> xxx\n(Pdb) (testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_fac535d043ac440dbf805288" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000071000, + "content": "", + "tool_calls": [ + { + "id": "call_a0f3ebbfc82748bbb5e2ecb5", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '175,185p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000072000, + "content": "self._addexcinfo(rawexcinfo)#\n\n def addSkip(self, testcase, reason):\n try:\n skip(reason)\n except skip.Exception:\n self._store[skipped_by_mark_key] = True\n self._addexcinfo(sys.exc_info())\n\n def addExpectedFailure(self, testcase, rawexcinfo, reason=\"\"):\n try:\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_a0f3ebbfc82748bbb5e2ecb5" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000073000, + "content": "", + "tool_calls": [ + { + "id": "call_d055f09355dd4c75a4f4c1cf", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"_addexcinfo\\\\\\\\|_excinfo\\\\\\\" src/_pytest/unittest.py | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000074000, + "content": "112: _excinfo = None0c1d:/testbed#\n133: def _addexcinfo(self, rawexcinfo):\n164: self.__dict__.setdefault(\"_excinfo\", []).append(excinfo)\n172: self._addexcinfo(rawexcinfo)\n175: self._addexcinfo(rawexcinfo)\n182: self._addexcinfo(sys.exc_info())\n188: self._addexcinfo(sys.exc_info())\n247: if item._excinfo:\n248: call.excinfo = item._excinfo.pop(0)\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_d055f09355dd4c75a4f4c1cf" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000075000, + "content": "", + "tool_calls": [ + { + "id": "call_2fa517f3992a48c39733ea34", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '130,170p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000076000, + "content": "def startTest(self, testcase):ed#\n pass\n\n def _addexcinfo(self, rawexcinfo):\n # unwrap potential exception info (see twisted trial support below)\n rawexcinfo = getattr(rawexcinfo, \"_rawexcinfo\", rawexcinfo)\n try:\n excinfo = _pytest._code.ExceptionInfo(rawexcinfo)\n # invoke the attributes to trigger storing the traceback\n # trial causes some issue there\n excinfo.value\n excinfo.traceback\n except TypeError:\n try:\n try:\n values = traceback.format_exception(*rawexcinfo)\n values.insert(\n 0,\n \"NOTE: Incompatible Exception Representation, \"\n \"displaying natively:\\n\\n\",\n )\n fail(\"\".join(values), pytrace=False)\n except (fail.Exception, KeyboardInterrupt):\n raise\n except BaseException:\n fail(\n \"ERROR: Unknown Incompatible Exception \"\n \"representation:\\n%r\" % (rawexcinfo,),\n pytrace=False,\n )\n except KeyboardInterrupt:\n raise\n except fail.Exception:\n excinfo = _pytest._code.ExceptionInfo.from_current()\n self.__dict__.setdefault(\"_excinfo\", []).append(excinfo)\n\n def addError(self, testcase, rawexcinfo):\n try:\n if isinstance(rawexcinfo[1], exit.Exception):\n exit(rawexcinfo[1].msg)\n except TypeError:\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_2fa517f3992a48c39733ea34" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000077000, + "content": "", + "tool_calls": [ + { + "id": "call_346027bd3087460197b92867", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -c \\\\\\\"import unittest; print(unittest.SkipTest)\\\\\\\"\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000078000, + "content": "tbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_346027bd3087460197b92867" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000079000, + "content": "", + "tool_calls": [ + { + "id": "call_f2925b24f2784c4183aebb58", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '208,235p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000080000, + "content": "def runtest(self):80c1d:/testbed#\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_f2925b24f2784c4183aebb58" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000081000, + "content": "", + "tool_calls": [ + { + "id": "call_fd04180a3a6b4d7abad7f3c1", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat src/_pytest/unittest.py | head -10\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000082000, + "content": "\"\"\" discovery and running of std-library \"unittest\" style tests. \"\"\"\nimport sys\nimport traceback\n\nimport _pytest._code\nimport pytest\nfrom _pytest.compat import getimfunc\nfrom _pytest.compat import is_async_function\nfrom _pytest.config import hookimpl\nfrom _pytest.outcomes import exit\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_fd04180a3a6b4d7abad7f3c1" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000083000, + "content": "", + "tool_calls": [ + { + "id": "call_5699c0d5641c493686fc0859", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && head -20 src/_pytest/unittest.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000084000, + "content": "\"\"\" discovery and running of std-library \"unittest\" style tests. \"\"\"\nimport sys\nimport traceback\n\nimport _pytest._code\nimport pytest\nfrom _pytest.compat import getimfunc\nfrom _pytest.compat import is_async_function\nfrom _pytest.config import hookimpl\nfrom _pytest.outcomes import exit\nfrom _pytest.outcomes import fail\nfrom _pytest.outcomes import skip\nfrom _pytest.outcomes import xfail\nfrom _pytest.python import Class\nfrom _pytest.python import Function\nfrom _pytest.runner import CallInfo\nfrom _pytest.skipping import skipped_by_mark_key\nfrom _pytest.skipping import unexpectedsuccess_key\n\n\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_5699c0d5641c493686fc0859" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000085000, + "content": "", + "tool_calls": [ + { + "id": "call_843fa36854894501a823695d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '208,240p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000086000, + "content": "def runtest(self):80c1d:/testbed#\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n Function._prunetraceback(self, excinfo)\n traceback = excinfo.traceback.filter(\n lambda x: not x.frame.f_globals.get(\"__unittest\")\n )\n if traceback:\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_843fa36854894501a823695d" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000087000, + "content": "", + "tool_calls": [ + { + "id": "call_5ea1169fa34c4f1692385223", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat src/_pytest/unittest.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000088000, + "content": "\"\"\" discovery and running of std-library \"unittest\" style tests. \"\"\"\nimport sys\nimport traceback\n\nimport _pytest._code\n... (249 lines omitted) ...\n if unittest and call.excinfo and call.excinfo.errisinstance(unittest.SkipTest):\n # let's substitute the excinfo with a pytest.skip one\n call2 = CallInfo.from_call(\n lambda: pytest.skip(str(call.excinfo.value)), call.when\n )\n call.excinfo = call2.excinfo\n\n\n# twisted trial support\n\n\n@hookimpl(hookwrapper=True)\ndef pytest_runtest_protocol(item):\n if isinstance(item, TestCaseFunction) and \"twisted.trial.unittest\" in sys.modules:\n ut = sys.modules[\"twisted.python.failure\"]\n Failure__init__ = ut.Failure.__init__\n check_testcase_implements_trial_reporter()\n\n def excstore(\n self, exc_value=None, exc_type=None, exc_tb=None, captureVars=None\n ):\n if exc_value is None:\n self._rawexcinfo = sys.exc_info()\n else:\n if exc_type is None:\n exc_type = type(exc_value)\n self._rawexcinfo = (exc_type, exc_value, exc_tb)\n try:\n Failure__init__(\n self, exc_value, exc_type, exc_tb, captureVars=captureVars\n )\n except TypeError:\n Failure__init__(self, exc_value, exc_type, exc_tb)\n\n ut.Failure.__init__ = excstore\n yield\n ut.Failure.__init__ = Failure__init__\n else:\n yield\n\n\ndef check_testcase_implements_trial_reporter(done=[]):\n if done:\n return\n from zope.interface import classImplements\n from twisted.trial.itrial import IReporter\n\n classImplements(TestCaseFunction, IReporter)\n done.append(1)\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_5ea1169fa34c4f1692385223" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000089000, + "content": "", + "tool_calls": [ + { + "id": "call_54c7499b83b14bccb4ff2268", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && wc -l src/_pytest/unittest.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000090000, + "content": "303 src/_pytest/unittest.py:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_54c7499b83b14bccb4ff2268" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000091000, + "content": "", + "tool_calls": [ + { + "id": "call_13160a9bf652429c8015bb71", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '1,100p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000092000, + "content": "\"\"\" discovery and running of std-library \"unittest\" style tests. \"\"\"\nimport sys\nimport traceback\n\nimport _pytest._code\n... (46 lines omitted) ...\n for name in loader.getTestCaseNames(self.obj):\n x = getattr(self.obj, name)\n if not getattr(x, \"__test__\", True):\n continue\n funcobj = getimfunc(x)\n yield TestCaseFunction.from_parent(self, name=name, callobj=funcobj)\n foundsomething = True\n\n if not foundsomething:\n runtest = getattr(self.obj, \"runTest\", None)\n if runtest is not None:\n ut = sys.modules.get(\"twisted.trial.unittest\", None)\n if ut is None or runtest != ut.TestCase.runTest:\n # TODO: callobj consistency\n yield TestCaseFunction.from_parent(self, name=\"runTest\")\n\n def _inject_setup_teardown_fixtures(self, cls):\n \"\"\"Injects a hidden auto-use fixture to invoke setUpClass/setup_method and corresponding\n teardown functions (#517)\"\"\"\n class_fixture = _make_xunit_fixture(\n cls, \"setUpClass\", \"tearDownClass\", scope=\"class\", pass_self=False\n )\n if class_fixture:\n cls.__pytest_class_setup = class_fixture\n\n method_fixture = _make_xunit_fixture(\n cls, \"setup_method\", \"teardown_method\", scope=\"function\", pass_self=True\n )\n if method_fixture:\n cls.__pytest_method_setup = method_fixture\n\n\ndef _make_xunit_fixture(obj, setup_name, teardown_name, scope, pass_self):\n setup = getattr(obj, setup_name, None)\n teardown = getattr(obj, teardown_name, None)\n if setup is None and teardown is None:\n return None\n\n @pytest.fixture(scope=scope, autouse=True)\n def fixture(self, request):\n if getattr(self, \"__unittest_skip__\", None):\n reason = self.__unittest_skip_why__\n pytest.skip(reason)\n if setup is not None:\n if pass_self:\n setup(self, request.function)\n else:\n setup()\n yield\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_13160a9bf652429c8015bb71" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000093000, + "content": "", + "tool_calls": [ + { + "id": "call_68b260a33ffa491f95bf0e4a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '100,200p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000094000, + "content": "yieldt@e27a32d80c1d:/testbed#\n if teardown is not None:\n if pass_self:\n teardown(self, request.function)\n else:\n... (47 lines omitted) ...\n except (fail.Exception, KeyboardInterrupt):\n raise\n except BaseException:\n fail(\n \"ERROR: Unknown Incompatible Exception \"\n \"representation:\\n%r\" % (rawexcinfo,),\n pytrace=False,\n )\n except KeyboardInterrupt:\n raise\n except fail.Exception:\n excinfo = _pytest._code.ExceptionInfo.from_current()\n self.__dict__.setdefault(\"_excinfo\", []).append(excinfo)\n\n def addError(self, testcase, rawexcinfo):\n try:\n if isinstance(rawexcinfo[1], exit.Exception):\n exit(rawexcinfo[1].msg)\n except TypeError:\n pass\n self._addexcinfo(rawexcinfo)\n\n def addFailure(self, testcase, rawexcinfo):\n self._addexcinfo(rawexcinfo)\n\n def addSkip(self, testcase, reason):\n try:\n skip(reason)\n except skip.Exception:\n self._store[skipped_by_mark_key] = True\n self._addexcinfo(sys.exc_info())\n\n def addExpectedFailure(self, testcase, rawexcinfo, reason=\"\"):\n try:\n xfail(str(reason))\n except xfail.Exception:\n self._addexcinfo(sys.exc_info())\n\n def addUnexpectedSuccess(self, testcase, reason=\"\"):\n self._store[unexpectedsuccess_key] = reason\n\n def addSuccess(self, testcase):\n pass\n\n def stopTest(self, testcase):\n pass\n\n def _expecting_failure(self, test_method) -> bool:\n \"\"\"Return True if the given unittest method (or the entire class) is marked\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_68b260a33ffa491f95bf0e4a" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000095000, + "content": "", + "tool_calls": [ + { + "id": "call_3b86b354fac548eda08ad134", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '200,260p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000096000, + "content": "\"\"\"Return True if the given unittest method (or the entire class) is marked\n with @expectedFailure\"\"\"\n expecting_failure_method = getattr(\n test_method, \"__unittest_expecting_failure__\", False\n )\n... (7 lines omitted) ...\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n Function._prunetraceback(self, excinfo)\n traceback = excinfo.traceback.filter(\n lambda x: not x.frame.f_globals.get(\"__unittest\")\n )\n if traceback:\n excinfo.traceback = traceback\n\n\n@hookimpl(tryfirst=True)\ndef pytest_runtest_makereport(item, call):\n if isinstance(item, TestCaseFunction):\n if item._excinfo:\n call.excinfo = item._excinfo.pop(0)\n try:\n del call.result\n except AttributeError:\n pass\n\n unittest = sys.modules.get(\"unittest\")\n if unittest and call.excinfo and call.excinfo.errisinstance(unittest.SkipTest):\n # let's substitute the excinfo with a pytest.skip one\n call2 = CallInfo.from_call(\n lambda: pytest.skip(str(call.excinfo.value)), call.when\n )\n call.excinfo = call2.excinfo\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_3b86b354fac548eda08ad134" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000097000, + "content": "", + "tool_calls": [ + { + "id": "call_4f32131074fb456d9a17074f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '203,235p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000098000, + "content": "test_method, \"__unittest_expecting_failure__\", False\n )\n expecting_failure_class = getattr(self, \"__unittest_expecting_failure__\", False)\n return bool(expecting_failure_class or expecting_failure_method)\n\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_4f32131074fb456d9a17074f" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000099000, + "content": "", + "tool_calls": [ + { + "id": "call_85b11f24831c46d6910eb26c", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cp src/_pytest/unittest.py src/_pytest/unittest.py.bak\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000100000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_85b11f24831c46d6910eb26c" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000101000, + "content": "", + "tool_calls": [ + { + "id": "call_60db352d8a944422a654d295", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '228,232p' src/_pytest/unittest.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000102000, + "content": "# wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_60db352d8a944422a654d295" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000103000, + "content": "", + "tool_calls": [ + { + "id": "call_d72d1feda5404d1e8a26a01d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '228,235p' src/_pytest/unittest.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000104000, + "content": "# wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_d72d1feda5404d1e8a26a01d" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000105000, + "content": "", + "tool_calls": [ + { + "id": "call_7efc4506b0f44303b2d4d474", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat > /tmp/patch.txt << 'PATCHEOF'\\n try:\\n self._testcase(result=self)\\n finally:\\n delattr(self._testcase, self.name)\\n # If the test was skipped, don't call tearDown\\n if self._explicit_tearDown is not None:\\n unittest = sys.modules.get(\\\"unittest\\\")\\n if unittest and self._excinfo:\\n for excinfo in self._excinfo:\\n if excinfo.errisinstance(unittest.SkipTest):\\n self._explicit_tearDown = None\\n break\\nPATCHEOF\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000106000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_7efc4506b0f44303b2d4d474" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000107000, + "content": "", + "tool_calls": [ + { + "id": "call_91ea06965ecd4b94943a8cf9", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -i '230,233s/.*/PLACEHOLDER/' src/_pytest/unittest.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000108000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_91ea06965ecd4b94943a8cf9" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000109000, + "content": "", + "tool_calls": [ + { + "id": "call_7dada651b20c44a5b2792221", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cp src/_pytest/unittest.py.bak src/_pytest/unittest.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000110000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_7dada651b20c44a5b2792221" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000111000, + "content": "", + "tool_calls": [ + { + "id": "call_7eead2324b9c43a99222774a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat src/_pytest/unittest.py > /tmp/unittest_orig.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000112000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_7eead2324b9c43a99222774a" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000113000, + "content": "", + "tool_calls": [ + { + "id": "call_fbb91770070c44cba5d95eab", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python << 'PYEOF'\\nimport re\\n\\nwith open('src/_pytest/unittest.py', 'r') as f:\\n content = f.read()\\n\\n# 找到需要修改的部分\\nold_code = ''' try:\\n self._testcase(result=self)\\n finally:\\n delattr(self._testcase, self.name)'''\\n\\nnew_code = ''' try:\\n self._testcase(result=self)\\n finally:\\n delattr(self._testcase, self.name)\\n # If the test was skipped, don't call tearDown\\n if self._explicit_tearDown is not None:\\n unittest = sys.modules.get(\\\"unittest\\\")\\n if unittest and self._excinfo:\\n for excinfo in self._excinfo:\\n if excinfo.errisinstance(unittest.SkipTest):\\n self._explicit_tearDown = None\\n break'''\\n\\ncontent = content.replace(old_code, new_code)\\n\\nwith open('src/_pytest/unittest.py', 'w') as f:\\n f.write(content)\\n\\nprint(\\\"File modified successfully\\\")\\nPYEOF\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000114000, + "content": "/usr/local/bin/tmux-run: 32: [: Illegal number: modified\n> estbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_fbb91770070c44cba5d95eab" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000115000, + "content": "", + "tool_calls": [ + { + "id": "call_e51c968c6f554c7b97d04937", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '228,245p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000116000, + "content": "# when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n... (45 lines omitted) ...\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789460_1736__0c1d:/testbed#\n__MRK_1773789468_1771__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n__MRK_1773789480_1806__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789493_1841__0c1d:/testbed#\n__MRK_1773789499_1876__0c1d:/testbed#\n__MRK_1773789506_1911__0c1d:/testbed#\n__MRK_1773789511_1946__0c1d:/testbed#\n__MRK_1773789523_1981__0c1d:/testbed#\n> estbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_e51c968c6f554c7b97d04937" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000117000, + "content": "", + "tool_calls": [ + { + "id": "call_18cc9e9e37ed430db5033254", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cp src/_pytest/unittest.py.bak src/_pytest/unittest.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000118000, + "content": "# when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n... (45 lines omitted) ...\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789460_1736__0c1d:/testbed#\n__MRK_1773789468_1771__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n__MRK_1773789480_1806__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789493_1841__0c1d:/testbed#\n__MRK_1773789499_1876__0c1d:/testbed#\n__MRK_1773789506_1911__0c1d:/testbed#\n__MRK_1773789511_1946__0c1d:/testbed#\n__MRK_1773789523_1981__0c1d:/testbed#\n> estbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_18cc9e9e37ed430db5033254" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000119000, + "content": "", + "tool_calls": [ + { + "id": "call_513206d9e1a4472fa110aca2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && head -230 src/_pytest/unittest.py.bak | tail -10\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000120000, + "content": "# when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n... (45 lines omitted) ...\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789460_1736__0c1d:/testbed#\n__MRK_1773789468_1771__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n__MRK_1773789480_1806__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789493_1841__0c1d:/testbed#\n__MRK_1773789499_1876__0c1d:/testbed#\n__MRK_1773789506_1911__0c1d:/testbed#\n__MRK_1773789511_1946__0c1d:/testbed#\n__MRK_1773789523_1981__0c1d:/testbed#\n> estbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_513206d9e1a4472fa110aca2" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000121000, + "content": "", + "tool_calls": [ + { + "id": "call_9bb0cd420a984afeb044a531", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python3 -c \\\\\\\"\\nimport re\\n\\nwith open('src/_pytest/unittest.py.bak', 'r') as f:\\n content = f.read()\\n\\nold_code = ''' try:\\n self._testcase(result=self)\\n finally:\\n delattr(self._testcase, self.name)'''\\n\\nnew_code = ''' try:\\n self._testcase(result=self)\\n finally:\\n delattr(self._testcase, self.name)\\n # If the test was skipped, don't call tearDown\\n if self._explicit_tearDown is not None:\\n unittest = sys.modules.get('unittest')\\n if unittest and self._excinfo:\\n for excinfo in self._excinfo:\\n if excinfo.errisinstance(unittest.SkipTest):\\n self._explicit_tearDown = None\\n break'''\\n\\ncontent = content.replace(old_code, new_code)\\n\\nwith open('src/_pytest/unittest.py', 'w') as f:\\n f.write(content)\\n\\nprint('File modified successfully')\\n\\\\\\\"\\n\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000122000, + "content": "# when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n... (45 lines omitted) ...\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789460_1736__0c1d:/testbed#\n__MRK_1773789468_1771__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n__MRK_1773789480_1806__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789493_1841__0c1d:/testbed#\n__MRK_1773789499_1876__0c1d:/testbed#\n__MRK_1773789506_1911__0c1d:/testbed#\n__MRK_1773789511_1946__0c1d:/testbed#\n__MRK_1773789523_1981__0c1d:/testbed#\n> estbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_9bb0cd420a984afeb044a531" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000123000, + "content": "", + "tool_calls": [ + { + "id": "call_054c52faf3db453c8f712f69", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"_explicit_tearDown = None\\\\\\\" src/_pytest/unittest.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000124000, + "content": "# when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n... (45 lines omitted) ...\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789460_1736__0c1d:/testbed#\n__MRK_1773789468_1771__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n__MRK_1773789480_1806__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789493_1841__0c1d:/testbed#\n__MRK_1773789499_1876__0c1d:/testbed#\n__MRK_1773789506_1911__0c1d:/testbed#\n__MRK_1773789511_1946__0c1d:/testbed#\n__MRK_1773789523_1981__0c1d:/testbed#\n> estbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_054c52faf3db453c8f712f69" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000125000, + "content": "", + "tool_calls": [ + { + "id": "call_f83692d44bfe4cbea9b93773", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && diff src/_pytest/unittest.py.bak src/_pytest/unittest.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000126000, + "content": "# when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n... (45 lines omitted) ...\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789460_1736__0c1d:/testbed#\n__MRK_1773789468_1771__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n__MRK_1773789480_1806__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789493_1841__0c1d:/testbed#\n__MRK_1773789499_1876__0c1d:/testbed#\n__MRK_1773789506_1911__0c1d:/testbed#\n__MRK_1773789511_1946__0c1d:/testbed#\n__MRK_1773789523_1981__0c1d:/testbed#\n> estbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_f83692d44bfe4cbea9b93773" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000127000, + "content": "", + "tool_calls": [ + { + "id": "call_8b8f7f8dd2a442c38530bc85", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && md5sum src/_pytest/unittest.py src/_pytest/unittest.py.bak\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000128000, + "content": "# when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n... (45 lines omitted) ...\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789460_1736__0c1d:/testbed#\n__MRK_1773789468_1771__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n__MRK_1773789480_1806__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789493_1841__0c1d:/testbed#\n__MRK_1773789499_1876__0c1d:/testbed#\n__MRK_1773789506_1911__0c1d:/testbed#\n__MRK_1773789511_1946__0c1d:/testbed#\n__MRK_1773789523_1981__0c1d:/testbed#\n> estbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_8b8f7f8dd2a442c38530bc85" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000129000, + "content": "", + "tool_calls": [ + { + "id": "call_687494ada52945d2bb4c73e2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec write /tmp/check.py << 'EOF'\\nimport subprocess\\nresult = subprocess.run(['md5sum', 'src/_pytest/unittest.py', 'src/_pytest/unittest.py.bak'], capture_output=True, text=True, cwd='/testbed')\\nprint(result.stdout)\\nEOF\\npython3 /tmp/check.py\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000130000, + "content": "Written 181 bytes to /tmp/check.py\npython3: can't open file '/tmp/check.py': [Errno 2] No such file or directory\n\n(Command exited with code 2)", + "tool_call_id": "call_687494ada52945d2bb4c73e2" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000131000, + "content": "", + "tool_calls": [ + { + "id": "call_5530d7df3693438f9b155a87", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && ls -la /tmp/check.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000132000, + "content": "# when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n... (45 lines omitted) ...\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789460_1736__0c1d:/testbed#\n__MRK_1773789468_1771__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n__MRK_1773789480_1806__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789493_1841__0c1d:/testbed#\n__MRK_1773789499_1876__0c1d:/testbed#\n__MRK_1773789506_1911__0c1d:/testbed#\n__MRK_1773789511_1946__0c1d:/testbed#\n__MRK_1773789523_1981__0c1d:/testbed#\n> estbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_5530d7df3693438f9b155a87" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000133000, + "content": "", + "tool_calls": [ + { + "id": "call_f9f9ba96f7fa4ff0bd8b2783", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && wc -l src/_pytest/unittest.py src/_pytest/unittest.py.bak\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000134000, + "content": "# when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n... (45 lines omitted) ...\n def runtest(self):\n from _pytest.debugging import maybe_wrap_pytest_function_for_tracing\n\n maybe_wrap_pytest_function_for_tracing(self)\n\n # let the unittest framework handle async functions\n if is_async_function(self.obj):\n self._testcase(self)\n else:\n # when --pdb is given, we want to postpone calling tearDown() otherwise\n # when entering the pdb prompt, tearDown() would have probably cleaned up\n # instance variables, which makes it difficult to debug\n # arguably we could always postpone tearDown(), but this changes the moment where the\n # TestCase instance interacts with the results object, so better to only do it\n # when absolutely needed\n if self.config.getoption(\"usepdb\"):\n self._explicit_tearDown = self._testcase.tearDown\n setattr(self._testcase, \"tearDown\", lambda *args: None)\n\n # we need to update the actual bound method with self.obj, because\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789460_1736__0c1d:/testbed#\n__MRK_1773789468_1771__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n__MRK_1773789480_1806__0c1d:/testbed#\n # wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n\n def _prunetraceback(self, excinfo):\n__MRK_1773789493_1841__0c1d:/testbed#\n__MRK_1773789499_1876__0c1d:/testbed#\n__MRK_1773789506_1911__0c1d:/testbed#\n__MRK_1773789511_1946__0c1d:/testbed#\n__MRK_1773789523_1981__0c1d:/testbed#\n> estbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_f9f9ba96f7fa4ff0bd8b2783" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000135000, + "content": "", + "tool_calls": [ + { + "id": "call_17576baae7764359bd514fc2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"ctrl-c\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000136000, + "content": "__MRK_1773789506_1911__0c1d:/testbed#\n__MRK_1773789511_1946__0c1d:/testbed#\n__MRK_1773789523_1981__0c1d:/testbed#\n> ^Ctbed) root@e27a32d80c1d:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_17576baae7764359bd514fc2" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000137000, + "content": "", + "tool_calls": [ + { + "id": "call_08606f907108426ca820849c", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && wc -l src/_pytest/unittest.py src/_pytest/unittest.py.bak\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000138000, + "content": "303 src/_pytest/unittest.pytestbed#\n 303 src/_pytest/unittest.py.bak\n 606 total\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_08606f907108426ca820849c" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000139000, + "content": "", + "tool_calls": [ + { + "id": "call_2ce265c4a42c4297a0782f87", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python3 << 'PYEOF'\\nwith open('src/_pytest/unittest.py.bak', 'r') as f:\\n content = f.read()\\n\\nold_code = ''' try:\\n self._testcase(result=self)\\n finally:\\n delattr(self._testcase, self.name)'''\\n\\nnew_code = ''' try:\\n self._testcase(result=self)\\n finally:\\n delattr(self._testcase, self.name)\\n # If the test was skipped, don't call tearDown\\n if self._explicit_tearDown is not None:\\n unittest = sys.modules.get('unittest')\\n if unittest and self._excinfo:\\n for excinfo in self._excinfo:\\n if excinfo.errisinstance(unittest.SkipTest):\\n self._explicit_tearDown = None\\n break'''\\n\\nif old_code in content:\\n content = content.replace(old_code, new_code)\\n with open('src/_pytest/unittest.py', 'w') as f:\\n f.write(content)\\n print('File modified successfully')\\nelse:\\n print('Old code not found')\\nPYEOF\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000140000, + "content": "File modified successfullyd:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_2ce265c4a42c4297a0782f87" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000141000, + "content": "", + "tool_calls": [ + { + "id": "call_92a5e3a708414b80981a223b", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && wc -l src/_pytest/unittest.py src/_pytest/unittest.py.bak\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000142000, + "content": "311 src/_pytest/unittest.pytestbed#\n 303 src/_pytest/unittest.py.bak\n 614 total\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_92a5e3a708414b80981a223b" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000143000, + "content": "", + "tool_calls": [ + { + "id": "call_2cf2464e0f3c4e75899046c3", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '228,245p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000144000, + "content": "# wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n # If the test was skipped, don't call tearDown\n if self._explicit_tearDown is not None:\n unittest = sys.modules.get('unittest')\n if unittest and self._excinfo:\n for excinfo in self._excinfo:\n if excinfo.errisinstance(unittest.SkipTest):\n self._explicit_tearDown = None\n break\n\n def _prunetraceback(self, excinfo):\n Function._prunetraceback(self, excinfo)\n traceback = excinfo.traceback.filter(\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_2cf2464e0f3c4e75899046c3" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000145000, + "content": "", + "tool_calls": [ + { + "id": "call_668fe427799543c881c60aac", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && timeout 5 python -m pytest --pdb /tmp/test_repro.py -v 2>&1 || true\\\" 6\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000146000, + "content": "========================================================================================= test session starts ==========================================================================================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollected 1 item\n\n../tmp/test_repro.py::MyTestCase::test_one SKIPPED [100%]\n../tmp/test_repro.py::MyTestCase::test_one ERROR [100%]\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> traceback >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n\nself = \n\n def tearDown(self):\n> xxx\nE NameError: name 'xxx' is not defined\n\n/tmp/test_repro.py:10: NameError\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB post_mortem (IO-capturing turned off) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n> /tmp/test_repro.py(10)tearDown()\n-> xxx\n(Pdb) (testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_668fe427799543c881c60aac" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000147000, + "content": "", + "tool_calls": [ + { + "id": "call_ad3e7fa43bda44929fe26878", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python3 << 'PYEOF'\\nwith open('src/_pytest/unittest.py', 'r') as f:\\n content = f.read()\\n\\nold_code = ''' finally:\\n delattr(self._testcase, self.name)\\n # If the test was skipped, don't call tearDown\\n if self._explicit_tearDown is not None:\\n unittest = sys.modules.get('unittest')\\n if unittest and self._excinfo:\\n for excinfo in self._excinfo:\\n if excinfo.errisinstance(unittest.SkipTest):\\n self._explicit_tearDown = None\\n break'''\\n\\nnew_code = ''' finally:\\n delattr(self._testcase, self.name)\\n # If the test was skipped, don't call tearDown\\n if self._explicit_tearDown is not None:\\n unittest = sys.modules.get('unittest')\\n print(f\\\\\\\"DEBUG: _excinfo={self._excinfo}\\\\\\\")\\n if unittest and self._excinfo:\\n for excinfo in self._excinfo:\\n print(f\\\\\\\"DEBUG: excinfo={excinfo}, errisinstance SkipTest={excinfo.errisinstance(unittest.SkipTest)}\\\\\\\")\\n if excinfo.errisinstance(unittest.SkipTest):\\n self._explicit_tearDown = None\\n print(\\\\\\\"DEBUG: cleared _explicit_tearDown\\\\\\\")\\n break'''\\n\\ncontent = content.replace(old_code, new_code)\\n\\nwith open('src/_pytest/unittest.py', 'w') as f:\\n f.write(content)\\n\\nprint('File modified successfully')\\nPYEOF\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000148000, + "content": "File modified successfullyd:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_ad3e7fa43bda44929fe26878" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000149000, + "content": "", + "tool_calls": [ + { + "id": "call_55868889420246dfaf89ce64", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && timeout 5 python -m pytest --pdb /tmp/test_repro.py -v -s 2>&1 | head -30 || true\\\" 6\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000150000, + "content": "============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 1 item\n\n../tmp/test_repro.py::MyTestCase::test_one DEBUG: _excinfo=[]\nDEBUG: excinfo=, errisinstance SkipTest=False\nSKIPPED\n../tmp/test_repro.py::MyTestCase::test_one ERROR\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> traceback >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n\nself = \n\n def tearDown(self):\n> xxx\nE NameError: name 'xxx' is not defined\n\n/tmp/test_repro.py:10: NameError\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB post_mortem >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n> /tmp/test_repro.py(10)tearDown()\n-> xxx\nTerminated\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_55868889420246dfaf89ce64" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000151000, + "content": "", + "tool_calls": [ + { + "id": "call_56917c8396164099b88abc2a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python3 << 'PYEOF'\\nwith open('src/_pytest/unittest.py', 'r') as f:\\n content = f.read()\\n\\nold_code = ''' finally:\\n delattr(self._testcase, self.name)\\n # If the test was skipped, don't call tearDown\\n if self._explicit_tearDown is not None:\\n unittest = sys.modules.get('unittest')\\n print(f\\\\\\\"DEBUG: _excinfo={self._excinfo}\\\\\\\")\\n if unittest and self._excinfo:\\n for excinfo in self._excinfo:\\n print(f\\\\\\\"DEBUG: excinfo={excinfo}, errisinstance SkipTest={excinfo.errisinstance(unittest.SkipTest)}\\\\\\\")\\n if excinfo.errisinstance(unittest.SkipTest):\\n self._explicit_tearDown = None\\n print(\\\\\\\"DEBUG: cleared _explicit_tearDown\\\\\\\")\\n break'''\\n\\nnew_code = ''' finally:\\n delattr(self._testcase, self.name)\\n # If the test was skipped, don't call tearDown\\n if self._explicit_tearDown is not None:\\n if self._excinfo:\\n for excinfo in self._excinfo:\\n if excinfo.errisinstance(skip.Exception):\\n self._explicit_tearDown = None\\n break'''\\n\\ncontent = content.replace(old_code, new_code)\\n\\nwith open('src/_pytest/unittest.py', 'w') as f:\\n f.write(content)\\n\\nprint('File modified successfully')\\nPYEOF\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000152000, + "content": "File modified successfullyd:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_56917c8396164099b88abc2a" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000153000, + "content": "", + "tool_calls": [ + { + "id": "call_4fc03b03f3804bdeb4463dd9", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && timeout 5 python -m pytest --pdb /tmp/test_repro.py -v 2>&1 || true\\\" 6\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000154000, + "content": "========================================================================================= test session starts ==========================================================================================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollected 1 item\n\n../tmp/test_repro.py::MyTestCase::test_one SKIPPED [100%]\n\n========================================================================================== 1 skipped in 0.01s ==========================================================================================\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_4fc03b03f3804bdeb4463dd9" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000155000, + "content": "", + "tool_calls": [ + { + "id": "call_b8c1eef5080c499fb6b00b4e", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -m pytest /tmp/test_repro.py -v\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000156000, + "content": "========================================================================================= test session starts ==========================================================================================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollected 1 item\n\n../tmp/test_repro.py::MyTestCase::test_one SKIPPED [100%]\n\n========================================================================================== 1 skipped in 0.01s ==========================================================================================\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_b8c1eef5080c499fb6b00b4e" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000157000, + "content": "", + "tool_calls": [ + { + "id": "call_e4ea044a3fd345c08284a68b", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat > /tmp/test_normal.py << 'EOF'\\nimport unittest\\n\\nclass MyTestCase(unittest.TestCase):\\n def setUp(self):\\n print(\\\\\\\"setUp called\\\\\\\")\\n def test_one(self):\\n print(\\\\\\\"test_one called\\\\\\\")\\n def tearDown(self):\\n print(\\\\\\\"tearDown called\\\\\\\")\\nEOF\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000158000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_e4ea044a3fd345c08284a68b" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000159000, + "content": "", + "tool_calls": [ + { + "id": "call_646b54a472604aaaac2c1c15", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -m pytest /tmp/test_normal.py -v -s\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000160000, + "content": "========================================================================================= test session starts ==========================================================================================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollected 1 item\n\n../tmp/test_normal.py::MyTestCase::test_one setUp called\ntest_one called\ntearDown called\nPASSED\n\n========================================================================================== 1 passed in 0.01s ===========================================================================================\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_646b54a472604aaaac2c1c15" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000161000, + "content": "", + "tool_calls": [ + { + "id": "call_94265a8b80c44a53b154238c", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat > /tmp/test_normal_pdb.py << 'EOF'\\nimport unittest\\n\\nclass MyTestCase(unittest.TestCase):\\n def setUp(self):\\n print(\\\\\\\"setUp called\\\\\\\")\\n def test_one(self):\\n print(\\\\\\\"test_one called\\\\\\\")\\n raise ValueError(\\\\\\\"test error\\\\\\\")\\n def tearDown(self):\\n print(\\\\\\\"tearDown called\\\\\\\")\\nEOF\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000162000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_94265a8b80c44a53b154238c" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000163000, + "content": "", + "tool_calls": [ + { + "id": "call_75050238ed994c0ea2a833d4", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && timeout 3 python -m pytest --pdb /tmp/test_normal_pdb.py -v -s 2>&1 | head -30 || true\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000164000, + "content": "============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 1 item\n\n../tmp/test_normal_pdb.py::MyTestCase::test_one setUp called\ntest_one called\nFAILED\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> traceback >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n\nself = \n\n def test_one(self):\n print(\"test_one called\")\n> raise ValueError(\"test error\")\nE ValueError: test error\n\n/tmp/test_normal_pdb.py:8: ValueError\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB post_mortem >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n> /tmp/test_normal_pdb.py(8)test_one()\n-> raise ValueError(\"test error\")\nTerminated\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_75050238ed994c0ea2a833d4" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000165000, + "content": "", + "tool_calls": [ + { + "id": "call_e338f2bd6e67451db86ace1b", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat > /tmp/test_normal_pdb2.py << 'EOF'\\nimport unittest\\n\\nclass MyTestCase(unittest.TestCase):\\n def setUp(self):\\n self.value = 42\\n def test_one(self):\\n raise ValueError(\\\\\\\"test error\\\\\\\")\\n def tearDown(self):\\n print(f\\\\\\\"tearDown called, value={self.value}\\\\\\\")\\nEOF\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000166000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_e338f2bd6e67451db86ace1b" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000167000, + "content": "", + "tool_calls": [ + { + "id": "call_907ccaaefa1d433ea04cfe21", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && timeout 3 python -m pytest --pdb /tmp/test_normal_pdb2.py -v -s 2>&1 | head -30 || true\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000168000, + "content": "============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 1 item\n\n../tmp/test_normal_pdb2.py::MyTestCase::test_one FAILED\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> traceback >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n\nself = \n\n def test_one(self):\n> raise ValueError(\"test error\")\nE ValueError: test error\n\n/tmp/test_normal_pdb2.py:7: ValueError\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB post_mortem >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n> /tmp/test_normal_pdb2.py(7)test_one()\n-> raise ValueError(\"test error\")\nTerminated\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_907ccaaefa1d433ea04cfe21" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000169000, + "content": "", + "tool_calls": [ + { + "id": "call_3fbc96cd0e1848f781a6a469", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -m pytest /tmp/test_normal_pdb2.py -v -s\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000170000, + "content": "========================================================================================= test session starts ==========================================================================================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollected 1 item\n\n../tmp/test_normal_pdb2.py::MyTestCase::test_one tearDown called, value=42\nFAILED\n\n=============================================================================================== FAILURES ===============================================================================================\n_________________________________________________________________________________________ MyTestCase.test_one __________________________________________________________________________________________\n\nself = \n\n def test_one(self):\n> raise ValueError(\"test error\")\nE ValueError: test error\n\n/tmp/test_normal_pdb2.py:7: ValueError\n======================================================================================= short test summary info ========================================================================================\nFAILED ../tmp/test_normal_pdb2.py::MyTestCase::test_one - ValueError: test error\n========================================================================================== 1 failed in 0.02s ===========================================================================================\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_3fbc96cd0e1848f781a6a469" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000171000, + "content": "", + "tool_calls": [ + { + "id": "call_e60f864b9bc44863a073ddd5", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat > /tmp/test_skip.py << 'EOF'\\nimport unittest\\n\\nclass MyTestCase(unittest.TestCase):\\n def setUp(self):\\n self.value = 42\\n print(\\\\\\\"setUp called\\\\\\\")\\n @unittest.skip(\\\\\\\"skip reason\\\\\\\")\\n def test_skip(self):\\n pass\\n def tearDown(self):\\n print(f\\\\\\\"tearDown called, value={self.value}\\\\\\\")\\nEOF\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000172000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_e60f864b9bc44863a073ddd5" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000173000, + "content": "", + "tool_calls": [ + { + "id": "call_f9562a3d3aaf4ea784e5a331", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -m pytest --pdb /tmp/test_skip.py -v -s\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000174000, + "content": "========================================================================================= test session starts ==========================================================================================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollected 1 item\n\n../tmp/test_skip.py::MyTestCase::test_skip SKIPPED\n\n========================================================================================== 1 skipped in 0.04s ==========================================================================================\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_f9562a3d3aaf4ea784e5a331" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000175000, + "content": "", + "tool_calls": [ + { + "id": "call_6500e86c0f514459b6b8002c", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && sed -n '228,245p' src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000176000, + "content": "# wrap_pytest_function_for_tracing replaces self.obj by a wrapper\n setattr(self._testcase, self.name, self.obj)\n try:\n self._testcase(result=self)\n finally:\n delattr(self._testcase, self.name)\n # If the test was skipped, don't call tearDown\n if self._explicit_tearDown is not None:\n if self._excinfo:\n for excinfo in self._excinfo:\n if excinfo.errisinstance(skip.Exception):\n self._explicit_tearDown = None\n break\n\n def _prunetraceback(self, excinfo):\n Function._prunetraceback(self, excinfo)\n traceback = excinfo.traceback.filter(\n lambda x: not x.frame.f_globals.get(\"__unittest\")\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_6500e86c0f514459b6b8002c" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000177000, + "content": "", + "tool_calls": [ + { + "id": "call_9ee6545f602b4084bfd65bb7", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -m pytest testing/test_unittest.py -v -x 2>&1 | head -100\\\" 30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000178000, + "content": "============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /testbed, inifile: tox.ini\ncollecting ... collected 59 items\n... (13 lines omitted) ...\ntesting/test_unittest.py::test_setup_failure_is_shown PASSED [ 22%]\ntesting/test_unittest.py::test_setup_setUpClass PASSED [ 23%]\ntesting/test_unittest.py::test_setup_class PASSED [ 25%]\ntesting/test_unittest.py::test_testcase_adderrorandfailure_defers[Error] PASSED [ 27%]\ntesting/test_unittest.py::test_testcase_adderrorandfailure_defers[Failure] PASSED [ 28%]\ntesting/test_unittest.py::test_testcase_custom_exception_info[Error] PASSED [ 30%]\ntesting/test_unittest.py::test_testcase_custom_exception_info[Failure] PASSED [ 32%]\ntesting/test_unittest.py::test_testcase_totally_incompatible_exception_info PASSED [ 33%]\ntesting/test_unittest.py::test_module_level_pytestmark PASSED [ 35%]\ntesting/test_unittest.py::TestTrialUnittest::test_trial_testcase_runtest_not_collected SKIPPED [ 37%]\ntesting/test_unittest.py::TestTrialUnittest::test_trial_exceptions_with_skips SKIPPED [ 38%]\ntesting/test_unittest.py::TestTrialUnittest::test_trial_error SKIPPED [ 40%]\ntesting/test_unittest.py::TestTrialUnittest::test_trial_testcase_skip_property SKIPPED [ 42%]\ntesting/test_unittest.py::TestTrialUnittest::test_trial_testfunction_skip_property SKIPPED [ 44%]\ntesting/test_unittest.py::TestTrialUnittest::test_trial_testcase_todo_property SKIPPED [ 45%]\ntesting/test_unittest.py::TestTrialUnittest::test_trial_testfunction_todo_property SKIPPED [ 47%]\ntesting/test_unittest.py::test_djangolike_testcase PASSED [ 49%]\ntesting/test_unittest.py::test_unittest_not_shown_in_traceback PASSED [ 50%]\ntesting/test_unittest.py::test_unorderable_types PASSED [ 52%]\ntesting/test_unittest.py::test_unittest_typerror_traceback PASSED [ 54%]\ntesting/test_unittest.py::test_unittest_expected_failure_for_failing_test_is_xfail[pytest] PASSED [ 55%]\ntesting/test_unittest.py::test_unittest_expected_failure_for_failing_test_is_xfail[unittest] PASSED [ 57%]\ntesting/test_unittest.py::test_unittest_expected_failure_for_passing_test_is_fail[pytest] PASSED [ 59%]\ntesting/test_unittest.py::test_unittest_expected_failure_for_passing_test_is_fail[unittest] PASSED [ 61%]\ntesting/test_unittest.py::test_unittest_setup_interaction[fixture-return] PASSED [ 62%]\ntesting/test_unittest.py::test_unittest_setup_interaction[yield_fixture-yield] PASSED [ 64%]\ntesting/test_unittest.py::test_non_unittest_no_setupclass_support PASSED [ 66%]\ntesting/test_unittest.py::test_no_teardown_if_setupclass_failed PASSED [ 67%]\ntesting/test_unittest.py::test_cleanup_functions PASSED [ 69%]\ntesting/test_unittest.py::test_issue333_result_clearing PASSED [ 71%]\ntesting/test_unittest.py::test_unittest_raise_skip_issue748 PASSED [ 72%]\ntesting/test_unittest.py::test_unittest_skip_issue1169 PASSED [ 74%]\ntesting/test_unittest.py::test_class_method_containing_test_issue1558 PASSED [ 76%]\ntesting/test_unittest.py::test_usefixtures_marker_on_unittest[builtins.object] PASSED [ 77%]\ntesting/test_unittest.py::test_usefixtures_marker_on_unittest[unittest.TestCase] PASSED [ 79%]\ntesting/test_unittest.py::test_testcase_handles_init_exceptions PASSED [ 81%]\ntesting/test_unittest.py::test_error_message_with_parametrized_fixtures PASSED [ 83%]\ntesting/test_unittest.py::test_setup_inheritance_skipping[test_setup_skip.py-1 skipped] PASSED [ 84%]\ntesting/test_unittest.py::test_setup_inheritance_skipping[test_setup_skip_class.py-1 skipped] PASSED [ 86%]\ntesting/test_unittest.py::test_setup_inheritance_skipping[test_setup_skip_module.py-1 error] PASSED [ 88%]\ntesting/test_unittest.py::test_BdbQuit PASSED [ 89%]\ntesting/test_unittest.py::test_exit_outcome PASSED [ 91%]\ntesting/test_unittest.py::test_trace PASSED [ 93%]\ntesting/test_unittest.py::test_pdb_teardown_called PASSED [ 94%]\ntesting/test_unittest.py::test_async_support PASSED [ 96%]\ntesting/test_unittest.py::test_asynctest_support SKIPPED [ 98%]\ntesting/test_unittest.py::TestTrialUnittest::test_trial_pdb SKIPPED [100%]\n\n======================== 50 passed, 9 skipped in 1.96s =========================\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_9ee6545f602b4084bfd65bb7" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000179000, + "content": "", + "tool_calls": [ + { + "id": "call_65992b35ba3a455d86460abd", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"pdb.*skip\\\\\\\\|skip.*pdb\\\\\\\" testing/test_unittest.py\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000180000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_65992b35ba3a455d86460abd" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000181000, + "content": "", + "tool_calls": [ + { + "id": "call_51e23410f8c3452b81893a5d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && grep -A 30 \\\\\\\"def test_pdb_teardown_called\\\\\\\" testing/test_unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000182000, + "content": "def test_pdb_teardown_called(testdir, monkeypatch):\n \"\"\"Ensure tearDown() is always called when --pdb is given in the command-line.\n\n We delay the normal tearDown() calls when --pdb is given, so this ensures we are calling\n tearDown() eventually to avoid memory leaks when using --pdb.\n \"\"\"\n teardowns = []\n monkeypatch.setattr(\n pytest, \"test_pdb_teardown_called_teardowns\", teardowns, raising=False\n )\n\n testdir.makepyfile(\n \"\"\"\n import unittest\n import pytest\n\n class MyTestCase(unittest.TestCase):\n\n def tearDown(self):\n pytest.test_pdb_teardown_called_teardowns.append(self.id())\n\n def test_1(self):\n pass\n def test_2(self):\n pass\n \"\"\"\n )\n result = testdir.runpytest_inprocess(\"--pdb\")\n result.stdout.fnmatch_lines(\"* 2 passed in *\")\n assert teardowns == [\n \"test_pdb_teardown_called.MyTestCase.test_1\",\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_51e23410f8c3452b81893a5d" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000183000, + "content": "", + "tool_calls": [ + { + "id": "call_59cd572f26ad4d23980a299a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -m pytest testing/test_unittest.py::test_pdb_teardown_called -v\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000184000, + "content": "========================================================================================= test session starts ==========================================================================================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /testbed, inifile: tox.ini\ncollected 1 item\n\ntesting/test_unittest.py::test_pdb_teardown_called PASSED [100%]\n\n========================================================================================== 1 passed in 0.05s ===========================================================================================\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_59cd572f26ad4d23980a299a" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000185000, + "content": "", + "tool_calls": [ + { + "id": "call_2034a9e11c75441b9397a8d8", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && grep -A 40 \\\\\\\"def test_pdb_teardown_called\\\\\\\" testing/test_unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000186000, + "content": "def test_pdb_teardown_called(testdir, monkeypatch):\n \"\"\"Ensure tearDown() is always called when --pdb is given in the command-line.\n\n We delay the normal tearDown() calls when --pdb is given, so this ensures we are calling\n tearDown() eventually to avoid memory leaks when using --pdb.\n \"\"\"\n teardowns = []\n monkeypatch.setattr(\n pytest, \"test_pdb_teardown_called_teardowns\", teardowns, raising=False\n )\n\n testdir.makepyfile(\n \"\"\"\n import unittest\n import pytest\n\n class MyTestCase(unittest.TestCase):\n\n def tearDown(self):\n pytest.test_pdb_teardown_called_teardowns.append(self.id())\n\n def test_1(self):\n pass\n def test_2(self):\n pass\n \"\"\"\n )\n result = testdir.runpytest_inprocess(\"--pdb\")\n result.stdout.fnmatch_lines(\"* 2 passed in *\")\n assert teardowns == [\n \"test_pdb_teardown_called.MyTestCase.test_1\",\n \"test_pdb_teardown_called.MyTestCase.test_2\",\n ]\n\n\ndef test_async_support(testdir):\n pytest.importorskip(\"unittest.async_case\")\n\n testdir.copy_example(\"unittest/test_unittest_asyncio.py\")\n reprec = testdir.inline_run()\n reprec.assertoutcome(failed=1, passed=2)\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_2034a9e11c75441b9397a8d8" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000187000, + "content": "", + "tool_calls": [ + { + "id": "call_fdd41f809df042a5ad8d44a2", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat > /tmp/test_skip_pdb.py << 'EOF'\\nimport unittest\\nimport pytest\\n\\nteardowns = []\\n\\nclass MyTestCase(unittest.TestCase):\\n def tearDown(self):\\n teardowns.append(self.id())\\n\\n @unittest.skip(\\\\\\\"skip reason\\\\\\\")\\n def test_skip(self):\\n pass\\n\\n def test_pass(self):\\n pass\\n\\nif __name__ == \\\\\\\"__main__\\\\\\\":\\n import sys\\n sys.exit(pytest.main([__file__, \\\\\\\"--pdb\\\\\\\", \\\\\\\"-v\\\\\\\"]))\\nEOF\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000188000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_fdd41f809df042a5ad8d44a2" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000189000, + "content": "", + "tool_calls": [ + { + "id": "call_7530c23944174381aa5281d6", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python /tmp/test_skip_pdb.py 2>&1 | head -30\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000190000, + "content": "============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 2 items\n\n../tmp/test_skip_pdb.py::MyTestCase::test_pass PASSED [ 50%]\n../tmp/test_skip_pdb.py::MyTestCase::test_skip SKIPPED [100%]\n\n========================= 1 passed, 1 skipped in 0.01s =========================\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_7530c23944174381aa5281d6" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000191000, + "content": "", + "tool_calls": [ + { + "id": "call_98e65bf75f7044ecab02f6e0", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -c \\\\\\\"\\nimport unittest\\nimport pytest\\n\\nteardowns = []\\n\\nclass MyTestCase(unittest.TestCase):\\n def tearDown(self):\\n teardowns.append(self.id())\\n\\n @unittest.skip('skip reason')\\n def test_skip(self):\\n pass\\n\\n def test_pass(self):\\n pass\\n\\npytest.main([__file__, '--pdb', '-v'])\\nprint('teardowns:', teardowns)\\n\\\\\\\" 2>&1 | tail -10\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000192000, + "content": "Traceback (most recent call last):ed#\n File \"\", line 18, in \nNameError: name '__file__' is not defined\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_98e65bf75f7044ecab02f6e0" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000193000, + "content": "", + "tool_calls": [ + { + "id": "call_bd3bf4f3e8824bda961fce74", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && cat > /tmp/test_skip_pdb2.py << 'EOF'\\nimport unittest\\nimport pytest\\n\\nteardowns = []\\n\\nclass MyTestCase(unittest.TestCase):\\n def tearDown(self):\\n teardowns.append(self.id())\\n\\n @unittest.skip(\\\\\\\"skip reason\\\\\\\")\\n def test_skip(self):\\n pass\\n\\n def test_pass(self):\\n pass\\n\\ndef test_check():\\n pytest.main([__file__, \\\\\\\"--pdb\\\\\\\", \\\\\\\"-v\\\\\\\"])\\n print(\\\\\\\"teardowns:\\\\\\\", teardowns)\\n assert teardowns == [\\\\\\\"MyTestCase.test_pass\\\\\\\"], f\\\\\\\"Expected ['MyTestCase.test_pass'], got {teardowns}\\\\\\\"\\nEOF\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000194000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_bd3bf4f3e8824bda961fce74" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000195000, + "content": "", + "tool_calls": [ + { + "id": "call_28f08eb3e74b4bedb35a073b", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -m pytest /tmp/test_skip_pdb2.py::test_check -v -s 2>&1 | tail -20\\\" 10\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000196000, + "content": "(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_28f08eb3e74b4bedb35a073b" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000197000, + "content": "", + "tool_calls": [ + { + "id": "call_4a1b3aa290f145d39f74a22d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -c \\\\\\\"\\nimport sys\\nsys.path.insert(0, '/tmp')\\nimport test_skip_pdb2\\ntest_skip_pdb2.test_check()\\nprint('teardowns:', test_skip_pdb2.teardowns)\\n\\\\\\\" 2>&1 | tail -20\\\" 10\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000198000, + "content": "We delay the normal tearDown() calls when --pdb is given, so this ensures we are calling\n tearDown() eventually to avoid memory leaks when using --pdb.\n \"\"\"\n teardowns = []\n... (45 lines omitted) ...\n\n testdir.makepyfile(\n \"\"\"\n import unittest\n import pytest\n\n class MyTestCase(unittest.TestCase):\n\n def tearDown(self):\n pytest.test_pdb_teardown_called_teardowns.append(self.id())\n\n def test_1(self):\n pass\n def test_2(self):\n pass\n \"\"\"\n )\n result = testdir.runpytest_inprocess(\"--pdb\")\n result.stdout.fnmatch_lines(\"* 2 passed in *\")\n assert teardowns == [\n \"test_pdb_teardown_called.MyTestCase.test_1\",\n \"test_pdb_teardown_called.MyTestCase.test_2\",\n ]\n\n\ndef test_async_support(testdir):\n pytest.importorskip(\"unittest.async_case\")\n\n testdir.copy_example(\"unittest/test_unittest_asyncio.py\")\n reprec = testdir.inline_run()\n reprec.assertoutcome(failed=1, passed=2)\n__MRK_1773790066_3274__0c1d:/testbed#\n__MRK_1773790082_3309__0c1d:/testbed#\n============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 2 items\n\n../tmp/test_skip_pdb.py::MyTestCase::test_pass PASSED [ 50%]\n../tmp/test_skip_pdb.py::MyTestCase::test_skip SKIPPED [100%]\n\n========================= 1 passed, 1 skipped in 0.01s =========================\n__MRK_1773790105_3345__0c1d:/testbed#\nTraceback (most recent call last):ed#\n File \"\", line 18, in \nNameError: name '__file__' is not defined\n__MRK_1773790113_3381__0c1d:/testbed#\n__MRK_1773790129_3416__0c1d:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_4a1b3aa290f145d39f74a22d" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000199000, + "content": "", + "tool_calls": [ + { + "id": "call_8417428e01a34b2d95ba822a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"ctrl-c\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000200000, + "content": "File \"\", line 18, in \nNameError: name '__file__' is not defined\n__MRK_1773790113_3381__0c1d:/testbed#\n__MRK_1773790129_3416__0c1d:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_8417428e01a34b2d95ba822a" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000201000, + "content": "", + "tool_calls": [ + { + "id": "call_fb2ce23426404bf4b9d86104", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -m pytest testing/test_unittest.py::test_pdb_teardown_called -v -s\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000202000, + "content": "We delay the normal tearDown() calls when --pdb is given, so this ensures we are calling\n tearDown() eventually to avoid memory leaks when using --pdb.\n \"\"\"\n teardowns = []\n... (45 lines omitted) ...\n\n testdir.makepyfile(\n \"\"\"\n import unittest\n import pytest\n\n class MyTestCase(unittest.TestCase):\n\n def tearDown(self):\n pytest.test_pdb_teardown_called_teardowns.append(self.id())\n\n def test_1(self):\n pass\n def test_2(self):\n pass\n \"\"\"\n )\n result = testdir.runpytest_inprocess(\"--pdb\")\n result.stdout.fnmatch_lines(\"* 2 passed in *\")\n assert teardowns == [\n \"test_pdb_teardown_called.MyTestCase.test_1\",\n \"test_pdb_teardown_called.MyTestCase.test_2\",\n ]\n\n\ndef test_async_support(testdir):\n pytest.importorskip(\"unittest.async_case\")\n\n testdir.copy_example(\"unittest/test_unittest_asyncio.py\")\n reprec = testdir.inline_run()\n reprec.assertoutcome(failed=1, passed=2)\n__MRK_1773790066_3274__0c1d:/testbed#\n__MRK_1773790082_3309__0c1d:/testbed#\n============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 2 items\n\n../tmp/test_skip_pdb.py::MyTestCase::test_pass PASSED [ 50%]\n../tmp/test_skip_pdb.py::MyTestCase::test_skip SKIPPED [100%]\n\n========================= 1 passed, 1 skipped in 0.01s =========================\n__MRK_1773790105_3345__0c1d:/testbed#\nTraceback (most recent call last):ed#\n File \"\", line 18, in \nNameError: name '__file__' is not defined\n__MRK_1773790113_3381__0c1d:/testbed#\n__MRK_1773790129_3416__0c1d:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_fb2ce23426404bf4b9d86104" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000203000, + "content": "", + "tool_calls": [ + { + "id": "call_c6faf65e44fd49eabea9de1f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"clear\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000204000, + "content": "We delay the normal tearDown() calls when --pdb is given, so this ensures we are calling\n tearDown() eventually to avoid memory leaks when using --pdb.\n \"\"\"\n teardowns = []\n... (45 lines omitted) ...\n\n testdir.makepyfile(\n \"\"\"\n import unittest\n import pytest\n\n class MyTestCase(unittest.TestCase):\n\n def tearDown(self):\n pytest.test_pdb_teardown_called_teardowns.append(self.id())\n\n def test_1(self):\n pass\n def test_2(self):\n pass\n \"\"\"\n )\n result = testdir.runpytest_inprocess(\"--pdb\")\n result.stdout.fnmatch_lines(\"* 2 passed in *\")\n assert teardowns == [\n \"test_pdb_teardown_called.MyTestCase.test_1\",\n \"test_pdb_teardown_called.MyTestCase.test_2\",\n ]\n\n\ndef test_async_support(testdir):\n pytest.importorskip(\"unittest.async_case\")\n\n testdir.copy_example(\"unittest/test_unittest_asyncio.py\")\n reprec = testdir.inline_run()\n reprec.assertoutcome(failed=1, passed=2)\n__MRK_1773790066_3274__0c1d:/testbed#\n__MRK_1773790082_3309__0c1d:/testbed#\n============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 2 items\n\n../tmp/test_skip_pdb.py::MyTestCase::test_pass PASSED [ 50%]\n../tmp/test_skip_pdb.py::MyTestCase::test_skip SKIPPED [100%]\n\n========================= 1 passed, 1 skipped in 0.01s =========================\n__MRK_1773790105_3345__0c1d:/testbed#\nTraceback (most recent call last):ed#\n File \"\", line 18, in \nNameError: name '__file__' is not defined\n__MRK_1773790113_3381__0c1d:/testbed#\n__MRK_1773790129_3416__0c1d:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_c6faf65e44fd49eabea9de1f" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000205000, + "content": "", + "tool_calls": [ + { + "id": "call_b83c56ece4fa4a79a647a1e4", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && diff -u src/_pytest/unittest.py.bak src/_pytest/unittest.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000206000, + "content": "We delay the normal tearDown() calls when --pdb is given, so this ensures we are calling\n tearDown() eventually to avoid memory leaks when using --pdb.\n \"\"\"\n teardowns = []\n... (45 lines omitted) ...\n\n testdir.makepyfile(\n \"\"\"\n import unittest\n import pytest\n\n class MyTestCase(unittest.TestCase):\n\n def tearDown(self):\n pytest.test_pdb_teardown_called_teardowns.append(self.id())\n\n def test_1(self):\n pass\n def test_2(self):\n pass\n \"\"\"\n )\n result = testdir.runpytest_inprocess(\"--pdb\")\n result.stdout.fnmatch_lines(\"* 2 passed in *\")\n assert teardowns == [\n \"test_pdb_teardown_called.MyTestCase.test_1\",\n \"test_pdb_teardown_called.MyTestCase.test_2\",\n ]\n\n\ndef test_async_support(testdir):\n pytest.importorskip(\"unittest.async_case\")\n\n testdir.copy_example(\"unittest/test_unittest_asyncio.py\")\n reprec = testdir.inline_run()\n reprec.assertoutcome(failed=1, passed=2)\n__MRK_1773790066_3274__0c1d:/testbed#\n__MRK_1773790082_3309__0c1d:/testbed#\n============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 2 items\n\n../tmp/test_skip_pdb.py::MyTestCase::test_pass PASSED [ 50%]\n../tmp/test_skip_pdb.py::MyTestCase::test_skip SKIPPED [100%]\n\n========================= 1 passed, 1 skipped in 0.01s =========================\n__MRK_1773790105_3345__0c1d:/testbed#\nTraceback (most recent call last):ed#\n File \"\", line 18, in \nNameError: name '__file__' is not defined\n__MRK_1773790113_3381__0c1d:/testbed#\n__MRK_1773790129_3416__0c1d:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_b83c56ece4fa4a79a647a1e4" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000207000, + "content": "", + "tool_calls": [ + { + "id": "call_7ba8cdee629547eb86ccd587", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"reset\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000208000, + "content": "We delay the normal tearDown() calls when --pdb is given, so this ensures we are calling\n tearDown() eventually to avoid memory leaks when using --pdb.\n \"\"\"\n teardowns = []\n... (45 lines omitted) ...\n\n testdir.makepyfile(\n \"\"\"\n import unittest\n import pytest\n\n class MyTestCase(unittest.TestCase):\n\n def tearDown(self):\n pytest.test_pdb_teardown_called_teardowns.append(self.id())\n\n def test_1(self):\n pass\n def test_2(self):\n pass\n \"\"\"\n )\n result = testdir.runpytest_inprocess(\"--pdb\")\n result.stdout.fnmatch_lines(\"* 2 passed in *\")\n assert teardowns == [\n \"test_pdb_teardown_called.MyTestCase.test_1\",\n \"test_pdb_teardown_called.MyTestCase.test_2\",\n ]\n\n\ndef test_async_support(testdir):\n pytest.importorskip(\"unittest.async_case\")\n\n testdir.copy_example(\"unittest/test_unittest_asyncio.py\")\n reprec = testdir.inline_run()\n reprec.assertoutcome(failed=1, passed=2)\n__MRK_1773790066_3274__0c1d:/testbed#\n__MRK_1773790082_3309__0c1d:/testbed#\n============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 2 items\n\n../tmp/test_skip_pdb.py::MyTestCase::test_pass PASSED [ 50%]\n../tmp/test_skip_pdb.py::MyTestCase::test_skip SKIPPED [100%]\n\n========================= 1 passed, 1 skipped in 0.01s =========================\n__MRK_1773790105_3345__0c1d:/testbed#\nTraceback (most recent call last):ed#\n File \"\", line 18, in \nNameError: name '__file__' is not defined\n__MRK_1773790113_3381__0c1d:/testbed#\n__MRK_1773790129_3416__0c1d:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_7ba8cdee629547eb86ccd587" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000209000, + "content": "", + "tool_calls": [ + { + "id": "call_b65a1e8dae6a4e3ab3406bc9", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && diff src/_pytest/unittest.py.bak src/_pytest/unittest.py | head -30\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000210000, + "content": "We delay the normal tearDown() calls when --pdb is given, so this ensures we are calling\n tearDown() eventually to avoid memory leaks when using --pdb.\n \"\"\"\n teardowns = []\n... (45 lines omitted) ...\n\n testdir.makepyfile(\n \"\"\"\n import unittest\n import pytest\n\n class MyTestCase(unittest.TestCase):\n\n def tearDown(self):\n pytest.test_pdb_teardown_called_teardowns.append(self.id())\n\n def test_1(self):\n pass\n def test_2(self):\n pass\n \"\"\"\n )\n result = testdir.runpytest_inprocess(\"--pdb\")\n result.stdout.fnmatch_lines(\"* 2 passed in *\")\n assert teardowns == [\n \"test_pdb_teardown_called.MyTestCase.test_1\",\n \"test_pdb_teardown_called.MyTestCase.test_2\",\n ]\n\n\ndef test_async_support(testdir):\n pytest.importorskip(\"unittest.async_case\")\n\n testdir.copy_example(\"unittest/test_unittest_asyncio.py\")\n reprec = testdir.inline_run()\n reprec.assertoutcome(failed=1, passed=2)\n__MRK_1773790066_3274__0c1d:/testbed#\n__MRK_1773790082_3309__0c1d:/testbed#\n============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 2 items\n\n../tmp/test_skip_pdb.py::MyTestCase::test_pass PASSED [ 50%]\n../tmp/test_skip_pdb.py::MyTestCase::test_skip SKIPPED [100%]\n\n========================= 1 passed, 1 skipped in 0.01s =========================\n__MRK_1773790105_3345__0c1d:/testbed#\nTraceback (most recent call last):ed#\n File \"\", line 18, in \nNameError: name '__file__' is not defined\n__MRK_1773790113_3381__0c1d:/testbed#\n__MRK_1773790129_3416__0c1d:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_b65a1e8dae6a4e3ab3406bc9" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000211000, + "content": "", + "tool_calls": [ + { + "id": "call_c1e0f7239d5949dfbcdda895", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && rm src/_pytest/unittest.py.bak\\\" 2\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000212000, + "content": "We delay the normal tearDown() calls when --pdb is given, so this ensures we are calling\n tearDown() eventually to avoid memory leaks when using --pdb.\n \"\"\"\n teardowns = []\n... (45 lines omitted) ...\n\n testdir.makepyfile(\n \"\"\"\n import unittest\n import pytest\n\n class MyTestCase(unittest.TestCase):\n\n def tearDown(self):\n pytest.test_pdb_teardown_called_teardowns.append(self.id())\n\n def test_1(self):\n pass\n def test_2(self):\n pass\n \"\"\"\n )\n result = testdir.runpytest_inprocess(\"--pdb\")\n result.stdout.fnmatch_lines(\"* 2 passed in *\")\n assert teardowns == [\n \"test_pdb_teardown_called.MyTestCase.test_1\",\n \"test_pdb_teardown_called.MyTestCase.test_2\",\n ]\n\n\ndef test_async_support(testdir):\n pytest.importorskip(\"unittest.async_case\")\n\n testdir.copy_example(\"unittest/test_unittest_asyncio.py\")\n reprec = testdir.inline_run()\n reprec.assertoutcome(failed=1, passed=2)\n__MRK_1773790066_3274__0c1d:/testbed#\n__MRK_1773790082_3309__0c1d:/testbed#\n============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 2 items\n\n../tmp/test_skip_pdb.py::MyTestCase::test_pass PASSED [ 50%]\n../tmp/test_skip_pdb.py::MyTestCase::test_skip SKIPPED [100%]\n\n========================= 1 passed, 1 skipped in 0.01s =========================\n__MRK_1773790105_3345__0c1d:/testbed#\nTraceback (most recent call last):ed#\n File \"\", line 18, in \nNameError: name '__file__' is not defined\n__MRK_1773790113_3381__0c1d:/testbed#\n__MRK_1773790129_3416__0c1d:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_c1e0f7239d5949dfbcdda895" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000213000, + "content": "", + "tool_calls": [ + { + "id": "call_af4f86aca8f54c14b85ca093", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.pytest-dev__pytest-7236.full-run-exec tmux-run \\\"cd /testbed && python -m pytest testing/test_unittest.py -v 2>&1 | tail -20\\\" 30\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000214000, + "content": "We delay the normal tearDown() calls when --pdb is given, so this ensures we are calling\n tearDown() eventually to avoid memory leaks when using --pdb.\n \"\"\"\n teardowns = []\n... (45 lines omitted) ...\n\n testdir.makepyfile(\n \"\"\"\n import unittest\n import pytest\n\n class MyTestCase(unittest.TestCase):\n\n def tearDown(self):\n pytest.test_pdb_teardown_called_teardowns.append(self.id())\n\n def test_1(self):\n pass\n def test_2(self):\n pass\n \"\"\"\n )\n result = testdir.runpytest_inprocess(\"--pdb\")\n result.stdout.fnmatch_lines(\"* 2 passed in *\")\n assert teardowns == [\n \"test_pdb_teardown_called.MyTestCase.test_1\",\n \"test_pdb_teardown_called.MyTestCase.test_2\",\n ]\n\n\ndef test_async_support(testdir):\n pytest.importorskip(\"unittest.async_case\")\n\n testdir.copy_example(\"unittest/test_unittest_asyncio.py\")\n reprec = testdir.inline_run()\n reprec.assertoutcome(failed=1, passed=2)\n__MRK_1773790066_3274__0c1d:/testbed#\n__MRK_1773790082_3309__0c1d:/testbed#\n============================= test session starts ==============================\nplatform linux -- Python 3.9.20, pytest-5.4.1.dev338+gc98bc4cd3, py-1.11.0, pluggy-0.13.1 -- /opt/miniconda3/envs/testbed/bin/python\ncachedir: .pytest_cache\nrootdir: /tmp\ncollecting ... collected 2 items\n\n../tmp/test_skip_pdb.py::MyTestCase::test_pass PASSED [ 50%]\n../tmp/test_skip_pdb.py::MyTestCase::test_skip SKIPPED [100%]\n\n========================= 1 passed, 1 skipped in 0.01s =========================\n__MRK_1773790105_3345__0c1d:/testbed#\nTraceback (most recent call last):ed#\n File \"\", line 18, in \nNameError: name '__file__' is not defined\n__MRK_1773790113_3381__0c1d:/testbed#\n__MRK_1773790129_3416__0c1d:/testbed#\n(testbed) root@e27a32d80c1d:/testbed#", + "tool_call_id": "call_af4f86aca8f54c14b85ca093" + }, + { + "sender_id": "agent_pytest", + "role": "assistant", + "timestamp": 1700000215000, + "content": "修复已完成。\n\n## 问题总结\n\n当使用 `--pdb` 标志运行 pytest 时，被 `@unittest.skip` 标记的测试的 `tearDown` 方法会被错误地执行。这是因为在 `runtest()` 方法中，当 `--pdb` 启用时，`tearDown` 被替换为空 lambda 并保存到 `_explicit_tearDown`，以便在调试时保留实例变量。但是，即使测试被跳过，`_explicit_tearDown` 仍然被设置，导致在 pytest 的 teardown 阶段被调用。\n\n## 修复方案\n\n在 `src/_pytest/unittest.py` 的 `runtest()` 方法中，在 `finally` 块中添加检查：如果测试被跳过（通过检查 `_excinfo` 中是否有 `skip.Exception`），则清除 `_explicit_tearDown`，这样在 pytest 的 teardown 阶段就不会调用 tearDown。\n\n修改后的代码：\n```python\nfinally:\n delattr(self._testcase, self.name)\n # If the test was skipped, don't call tearDown\n if self._explicit_tearDown is not None:\n if self._excinfo:\n for excinfo in self._excinfo:\n if excinfo.errisinstance(skip.Exception):\n self._explicit_tearDown = None\n break\n\n## 验证\n\n1. 被 skip 的测试在 --pdb 模式下不会执行 tearDown。\n2. 正常的测试在 --pdb 模式下仍然会执行 tearDown。\n3. 所有现有的 unittest 测试都通过了。\n\nTASK_COMPLETE" + } + ] +} diff --git a/tests/fixtures/agent_trajectories/session_sympy_18763.json b/tests/fixtures/agent_trajectories/session_sympy_18763.json new file mode 100644 index 00000000..d9393904 --- /dev/null +++ b/tests/fixtures/agent_trajectories/session_sympy_18763.json @@ -0,0 +1,1045 @@ +{ + "source_file": "0091.json", + "swe_bench_task": "sympy/sympy-18763", + "everos_session_id": "session_sympy_18763", + "everos_agent_sender_id": "agent_sympy", + "everos_user_sender_id": "user_001", + "everos_tool_sender_id": "tool_runner", + "base_timestamp_ms": 1700000000000, + "inter_message_gap_ms": 1000, + "message_count": 90, + "conversion_rules": { + "role": "verbatim from source (user / assistant / tool)", + "sender_id": "user→user_001, assistant→agent_, tool→tool_runner", + "timestamp": "BASE_TIMESTAMP_MS + idx * 1000", + "content": "verbatim; empty string for assistant tool-call-only turns", + "tool_calls": "kept on assistant rows (OpenAI Chat Completions shape)", + "tool_call_id": "kept on tool rows" + }, + "messages": [ + { + "sender_id": "user_001", + "role": "user", + "timestamp": 1700000000000, + "content": "WRAPPER_PATH: /tmp/sweb.eval.sympy__sympy-18763.full-run-exec\n\n你需要修复一个开源项目的 bug。代码仓库是 sympy/sympy，已经 checkout 到了 bug 存在的版本。\n任务总时限 30 分钟，请合理安排时间。\n\n【严格规则】\n- 所有命令必须通过 WRAPPER_PATH 执行\n- 严禁使用 read、write、edit、Read、Write、Edit 工具（这些工具操作的是宿主机，不是任务环境）\n- 严禁直接使用 exec 运行命令（必须通过 WRAPPER_PATH）\n- 写文件必须用 WRAPPER_PATH write，不要用 tmux-run + cat/heredoc（会有转义问题）\n- 不要修改测试文件\n- 严禁执行 git commit、git add、git stash 等 git 操作，只需修改文件即可，评测系统会自动收集你的改动\n\n操作方式：\n 执行命令: exec(\"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"命令\\\" 等待秒数\")\n 轮询等待: exec(\"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"\\\" 10\")\n 写文件: exec(\"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec write /目标路径 << 'EOF'\\n文件内容\\nEOF\")\n 中断命令: exec(\"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"ctrl-c\\\" 3\")\n\n## Bug 描述\n\nIncorrect parenthesizing of Subs\nHere is an example.\r\n```python\r\n>>> from sympy import Subs\r\n>>> from sympy.abc import x,y\r\n>>> 3*Subs(-x+y, (x,),(1,))\r\n```\r\nLaTeX printing of this gives: \r\n```python\r\n'3 \\\\left. - x + y \\\\right|_{\\\\substack{ x=1 }}'\r\n```\r\n\r\n![image](https://quicklatex.com/cache3/76/ql_9672fd7e62c909ff3d9ac8543c2e2576_l3.png)\r\n\r\n\r\nIt would be better to be parenthesized to: \r\n```python\r\n'3 \\\\left. \\\\left(- x + y\\\\right) \\\\right|_{\\\\substack{ x=1 }}'\r\n```\r\n\r\n![image](https://quicklatex.com/cache3/bf/ql_936ffdb876e784206d4c54bb93d28dbf_l3.png)\r\n\n\n\n修复完成后回复 TASK_COMPLETE。" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000001000, + "content": "", + "tool_calls": [ + { + "id": "call_7781b60fb84d4c83a7423960", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && ls -la\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000002000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && ls -la\ntotal 232\ndrwxrwxrwx 1 root root 4096 Sep 10 2025 .\ndrwxr-xr-x 1 root root 4096 Mar 18 02:28 ..\ndrwxrwxrwx 2 root root 4096 Sep 10 2025 .ci\n... (42 lines omitted) ...", + "tool_call_id": "call_7781b60fb84d4c83a7423960" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000003000, + "content": "", + "tool_calls": [ + { + "id": "call_685102bb523e45599cdc80eb", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && find . -name \\\\\\\"*.py\\\\\\\" | grep -i latex | head -20\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000004000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && find . -name \"*.py\" | grep -i latex | head -20\n./sympy/parsing/latex/__init__.py\n./sympy/parsing/latex/_parse_latex_antlr.py\n./sympy/parsing/latex/_antlr/latexlexer.py\n./sympy/parsing/latex/_antlr/__init__.py\n./sympy/parsing/latex/_antlr/latexparser.py\n./sympy/parsing/latex/errors.py\n./sympy/parsing/latex/_build_latex_antlr.py\n./sympy/parsing/tests/test_latex_deps.py\n./sympy/parsing/tests/test_latex.py\n./sympy/printing/tests/test_latex.py\n./sympy/printing/latex.py\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_685102bb523e45599cdc80eb" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000005000, + "content": "", + "tool_calls": [ + { + "id": "call_41cfeef30e174469934de29d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"Subs\\\\\\\" sympy/printing/latex.py | head -30\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000006000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && grep -n \"Subs\" sympy/printing/latex.py | head -30\n699: def _print_Subs(self, subs):\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_41cfeef30e174469934de29d" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000007000, + "content": "", + "tool_calls": [ + { + "id": "call_b6b19e071c8f4e30871fcf4f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && sed -n '695,730p' sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000008000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && sed -n '695,730p' sympy/printing/latex.py\n return r\"%s %s\" % (tex, self.parenthesize(expr.expr,\n PRECEDENCE[\"Mul\"],\n strict=True))\n\n def _print_Subs(self, subs):\n expr, old, new = subs.args\n latex_expr = self._print(expr)\n latex_old = (self._print(e) for e in old)\n latex_new = (self._print(e) for e in new)\n latex_subs = r'\\\\ '.join(\n e[0] + '=' + e[1] for e in zip(latex_old, latex_new))\n return r'\\left. %s \\right|_{\\substack{ %s }}' % (latex_expr,\n latex_subs)\n\n def _print_Integral(self, expr):\n tex, symbols = \"\", []\n\n # Only up to \\iiiint exists\n if len(expr.limits) <= 4 and all(len(lim) == 1 for lim in expr.limits):\n # Use len(expr.limits)-1 so that syntax highlighters don't think\n # \\\" is an escaped quote\n tex = r\"\\i\" + \"i\"*(len(expr.limits) - 1) + \"nt\"\n symbols = [r\"\\, d%s\" % self._print(symbol[0])\n for symbol in expr.limits]\n\n else:\n for lim in reversed(expr.limits):\n symbol = lim[0]\n tex += r\"\\int\"\n\n if len(lim) > 1:\n if self._settings['mode'] != 'inline' \\\n and not self._settings['itex']:\n tex += r\"\\limits\"\n\n if len(lim) == 3:\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_b6b19e071c8f4e30871fcf4f" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000009000, + "content": "", + "tool_calls": [ + { + "id": "call_682eda3e2226494ba1a17b35", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"_print_Mul\\\\\\\" sympy/printing/latex.py | head -10\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000010000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && grep -n \"_print_Mul\" sympy/printing/latex.py | head -10\n434: def _print_Mul(self, expr):\n570: return self._print_Mul(expr)\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_682eda3e2226494ba1a17b35" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000011000, + "content": "", + "tool_calls": [ + { + "id": "call_f21f1000589e46bc96d811c7", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && sed -n '434,570p' sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000012000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && sed -n '434,570p' sympy/printing/latex.py\n def _print_Mul(self, expr):\n from sympy.core.power import Pow\n from sympy.physics.units import Quantity\n include_parens = False\n... (84 lines omitted) ...\n % (convert(a), sdenom, separator, convert(b))\n else:\n tex += r\"\\frac{%s}{%s}%s%s\" \\\n % (convert(a), sdenom, separator, convert(b))\n else:\n tex += r\"\\frac{1}{%s}%s%s\" % (sdenom, separator, snumer)\n else:\n tex += r\"\\frac{%s}{%s}\" % (snumer, sdenom)\n\n if include_parens:\n tex += \")\"\n return tex\n\n def _print_Pow(self, expr):\n # Treat x**Rational(1,n) as special case\n if expr.exp.is_Rational and abs(expr.exp.p) == 1 and expr.exp.q != 1 \\\n and self._settings['root_notation']:\n base = self._print(expr.base)\n expq = expr.exp.q\n\n if expq == 2:\n tex = r\"\\sqrt{%s}\" % base\n elif self._settings['itex']:\n tex = r\"\\root{%d}{%s}\" % (expq, base)\n else:\n tex = r\"\\sqrt[%d]{%s}\" % (expq, base)\n\n if expr.exp.is_negative:\n return r\"\\frac{1}{%s}\" % tex\n else:\n return tex\n elif self._settings['fold_frac_powers'] \\\n and expr.exp.is_Rational \\\n and expr.exp.q != 1:\n base = self.parenthesize(expr.base, PRECEDENCE['Pow'])\n p, q = expr.exp.p, expr.exp.q\n # issue #12886: add parentheses for superscripts raised to powers\n if '^' in base and expr.base.is_Symbol:\n base = r\"\\left(%s\\right)\" % base\n if expr.base.is_Function:\n return self._print(expr.base, exp=\"%s/%s\" % (p, q))\n return r\"%s^{%s/%s}\" % (base, p, q)\n elif expr.exp.is_Rational and expr.exp.is_negative and \\\n expr.base.is_commutative:\n # special case for 1^(-x), issue 9216\n if expr.base == 1:\n return r\"%s^{%s}\" % (expr.base, expr.exp)\n # things like 1/x\n return self._print_Mul(expr)\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_f21f1000589e46bc96d811c7" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000013000, + "content": "", + "tool_calls": [ + { + "id": "call_5143606af8aa4ea696404991", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && sed -n '434,490p' sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000014000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && sed -n '434,490p' sympy/printing/latex.py\n def _print_Mul(self, expr):\n from sympy.core.power import Pow\n from sympy.physics.units import Quantity\n include_parens = False\n if _coeff_isneg(expr):\n expr = -expr\n tex = \"- \"\n if expr.is_Add:\n tex += \"(\"\n include_parens = True\n else:\n tex = \"\"\n\n from sympy.simplify import fraction\n numer, denom = fraction(expr, exact=True)\n separator = self._settings['mul_symbol_latex']\n numbersep = self._settings['mul_symbol_latex_numbers']\n\n def convert(expr):\n if not expr.is_Mul:\n return str(self._print(expr))\n else:\n _tex = last_term_tex = \"\"\n\n if self.order not in ('old', 'none'):\n args = expr.as_ordered_factors()\n else:\n args = list(expr.args)\n\n # If quantities are present append them at the back\n args = sorted(args, key=lambda x: isinstance(x, Quantity) or\n (isinstance(x, Pow) and\n isinstance(x.base, Quantity)))\n\n for i, term in enumerate(args):\n term_tex = self._print(term)\n\n if self._needs_mul_brackets(term, first=(i == 0),\n last=(i == len(args) - 1)):\n term_tex = r\"\\left(%s\\right)\" % term_tex\n\n if _between_two_numbers_p[0].search(last_term_tex) and \\\n _between_two_numbers_p[1].match(term_tex):\n # between two numbers\n _tex += numbersep\n elif _tex:\n _tex += separator\n\n _tex += term_tex\n last_term_tex = term_tex\n return _tex\n\n if denom is S.One and Pow(1, -1, evaluate=False) not in expr.args:\n # use the original expression here, since fraction() may have\n # altered it when producing numer and denom\n tex += convert(expr)\n\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_5143606af8aa4ea696404991" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000015000, + "content": "", + "tool_calls": [ + { + "id": "call_7408846e4e7d402b8044d0dd", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"def parenthesize\\\\\\\" sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000016000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && grep -n \"def parenthesize\" sympy/printing/latex.py\n202: def parenthesize(self, item, level, strict=False):\n209: def parenthesize_super(self, s):\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_7408846e4e7d402b8044d0dd" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000017000, + "content": "", + "tool_calls": [ + { + "id": "call_49e52207d1634d8ca4d19d6d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && sed -n '202,220p' sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000018000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && sed -n '202,220p' sympy/printing/latex.py\n def parenthesize(self, item, level, strict=False):\n prec_val = precedence_traditional(item)\n if (prec_val < level) or ((not strict) and prec_val <= level):\n return r\"\\left({}\\right)\".format(self._print(item))\n else:\n return self._print(item)\n\n def parenthesize_super(self, s):\n \"\"\" Parenthesize s if there is a superscript in s\"\"\"\n if \"^\" in s:\n return r\"\\left({}\\right)\".format(s)\n return s\n\n def embed_super(self, s):\n \"\"\" Embed s in {} if there is a superscript in s\"\"\"\n if \"^\" in s:\n return \"{{{}}}\".format(s)\n return s\n\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_49e52207d1634d8ca4d19d6d" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000019000, + "content": "", + "tool_calls": [ + { + "id": "call_1b281a3acb454e599f5f6571", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 -c \\\\\\\"\\\\nfrom sympy import Subs, latex\\\\nfrom sympy.abc import x,y\\\\nexpr = 3*Subs(-x+y, (x,),(1,))\\\\nprint('LaTeX:', latex(expr))\\\\n\\\\\\\" 3\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000020000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 -c \"\\nfrom sympy import Subs, latex\\nfrom sympy.abc import x,y\\nexpr = 3*Subs(-x+y, (x,),(1,))\\nprint('LaTeX:', latex(expr))\\n\" 3\n File \"\", line 1\n \\nfrom sympy import Subs, latex\\nfrom sympy.abc import x,y\\nexpr = 3*Subs(-x+y, (x,),(1,))\\nprint('LaTeX:', latex(expr))\\n\n ^\nSyntaxError: unexpected character after line continuation character\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_1b281a3acb454e599f5f6571" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000021000, + "content": "", + "tool_calls": [ + { + "id": "call_857192d392614c269fe4aac4", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 << 'PYEOF'\\\\nfrom sympy import Subs, latex\\\\nfrom sympy.abc import x,y\\\\nexpr = 3*Subs(-x+y, (x,),(1,))\\\\nprint('LaTeX:', latex(expr))\\\\nPYEOF\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000022000, + "content": "[ERROR] Syntax error: unclosed quote, heredoc, or bracket.\n\n(Command exited with code 1)", + "tool_call_id": "call_857192d392614c269fe4aac4" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000023000, + "content": "", + "tool_calls": [ + { + "id": "call_807b3237cea040caac6e00b0", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec write /tmp/test_subs.py << 'EOF'\\nfrom sympy import Subs, latex\\nfrom sympy.abc import x,y\\nexpr = 3*Subs(-x+y, (x,),(1,))\\nprint('LaTeX:', latex(expr))\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000024000, + "content": "Written 116 bytes to /tmp/test_subs.py", + "tool_call_id": "call_807b3237cea040caac6e00b0" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000025000, + "content": "", + "tool_calls": [ + { + "id": "call_ec07901b4caa42ea8042e0c9", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 /tmp/test_subs.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000026000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 /tmp/test_subs.py\nLaTeX: 3 \\left. - x + y \\right|_{\\substack{ x=1 }}\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_ec07901b4caa42ea8042e0c9" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000027000, + "content": "", + "tool_calls": [ + { + "id": "call_1f4d959a8feb491baa0976a9", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"_needs_mul_brackets\\\\\\\" sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000028000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && grep -n \"_needs_mul_brackets\" sympy/printing/latex.py\n267: def _needs_mul_brackets(self, expr, first=False, last=False):\n471: if self._needs_mul_brackets(term, first=(i == 0),\n499: if self._needs_mul_brackets(numer, last=False):\n506: if self._needs_mul_brackets(numer, last=True):\n514: if self._needs_mul_brackets(x, last=False) or \\\n520: if self._needs_mul_brackets(b, last=True):\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_1f4d959a8feb491baa0976a9" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000029000, + "content": "", + "tool_calls": [ + { + "id": "call_e6064950e44c4f929fc7fc5e", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && sed -n '267,320p' sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000030000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && sed -n '267,320p' sympy/printing/latex.py\n def _needs_mul_brackets(self, expr, first=False, last=False):\n \"\"\"\n Returns True if the expression needs to be wrapped in brackets when\n printed as part of a Mul, False otherwise. This is True for Add,\n but also for some container objects that would not need brackets\n when appearing last in a Mul, e.g. an Integral. ``last=True``\n specifies that this expr is the last to appear in a Mul.\n ``first=True`` specifies that this expr is the first to appear in\n a Mul.\n \"\"\"\n from sympy import Integral, Product, Sum\n\n if expr.is_Mul:\n if not first and _coeff_isneg(expr):\n return True\n elif precedence_traditional(expr) < PRECEDENCE[\"Mul\"]:\n return True\n elif expr.is_Relational:\n return True\n if expr.is_Piecewise:\n return True\n if any([expr.has(x) for x in (Mod,)]):\n return True\n if (not last and\n any([expr.has(x) for x in (Integral, Product, Sum)])):\n return True\n\n return False\n\n def _needs_add_brackets(self, expr):\n \"\"\"\n Returns True if the expression needs to be wrapped in brackets when\n printed as part of an Add, False otherwise. This is False for most\n things.\n \"\"\"\n if expr.is_Relational:\n return True\n if any([expr.has(x) for x in (Mod,)]):\n return True\n if expr.is_Add:\n return True\n return False\n\n def _mul_is_clean(self, expr):\n for arg in expr.args:\n if arg.is_Function:\n return False\n return True\n\n def _pow_is_clean(self, expr):\n return not self._needs_brackets(expr.base)\n\n def _do_exponent(self, expr, exp):\n if exp is not None:\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_e6064950e44c4f929fc7fc5e" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000031000, + "content": "", + "tool_calls": [ + { + "id": "call_8bf67e78eaa94026a6581cf1", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 << 'PYEOF'\\\\nfrom sympy import Subs, precedence_traditional, PRECEDENCE\\\\nfrom sympy.abc import x,y\\\\nsubs = Subs(-x+y, (x,),(1,))\\\\nprint('Subs precedence:', precedence_traditional(subs))\\\\nprint('Mul precedence:', PRECEDENCE[\\\\\\\"Mul\\\\\\\"])\\\\nPYEOF\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000032000, + "content": "[ERROR] Syntax error: unclosed quote, heredoc, or bracket.\n\n(Command exited with code 1)", + "tool_call_id": "call_8bf67e78eaa94026a6581cf1" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000033000, + "content": "", + "tool_calls": [ + { + "id": "call_32b9bee52d204874a53cfe86", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec write /tmp/test_prec.py << 'EOF'\\nfrom sympy import Subs, precedence_traditional, PRECEDENCE\\nfrom sympy.abc import x,y\\nsubs = Subs(-x+y, (x,),(1,))\\nprint('Subs precedence:', precedence_traditional(subs))\\nprint('Mul precedence:', PRECEDENCE[\\\"Mul\\\"])\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000034000, + "content": "Written 214 bytes to /tmp/test_prec.py", + "tool_call_id": "call_32b9bee52d204874a53cfe86" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000035000, + "content": "", + "tool_calls": [ + { + "id": "call_7def61c2cdf7458eadac7ec8", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 /tmp/test_prec.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000036000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 /tmp/test_prec.py\nTraceback (most recent call last):\n File \"/tmp/test_prec.py\", line 1, in \n from sympy import Subs, precedence_traditional, PRECEDENCE\nImportError: cannot import name 'precedence_traditional' from 'sympy' (/testbed/sympy/__init__.py)\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_7def61c2cdf7458eadac7ec8" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000037000, + "content": "", + "tool_calls": [ + { + "id": "call_2ce5f59d1f3c432888e7fdcd", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec write /tmp/test_prec.py << 'EOF'\\nfrom sympy import Subs\\nfrom sympy.printing.precedence import precedence_traditional, PRECEDENCE\\nfrom sympy.abc import x,y\\nsubs = Subs(-x+y, (x,),(1,))\\nprint('Subs precedence:', precedence_traditional(subs))\\nprint('Mul precedence:', PRECEDENCE[\\\"Mul\\\"])\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000038000, + "content": "Written 251 bytes to /tmp/test_prec.py", + "tool_call_id": "call_2ce5f59d1f3c432888e7fdcd" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000039000, + "content": "", + "tool_calls": [ + { + "id": "call_6c54094dc1be440e80e743f5", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 /tmp/test_prec.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000040000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 /tmp/test_prec.py\nSubs precedence: 1000\nMul precedence: 50\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_6c54094dc1be440e80e743f5" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000041000, + "content": "", + "tool_calls": [ + { + "id": "call_fa13c5abccd14b2ba08725e8", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"_print_Integral\\\\\\\" sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000042000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && grep -n \"_print_Integral\" sympy/printing/latex.py\n709: def _print_Integral(self, expr):\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_fa13c5abccd14b2ba08725e8" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000043000, + "content": "", + "tool_calls": [ + { + "id": "call_9f2dbdee347e41f4b68f5f1f", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && sed -n '709,760p' sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000044000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && sed -n '709,760p' sympy/printing/latex.py\n def _print_Integral(self, expr):\n tex, symbols = \"\", []\n\n # Only up to \\iiiint exists\n if len(expr.limits) <= 4 and all(len(lim) == 1 for lim in expr.limits):\n # Use len(expr.limits)-1 so that syntax highlighters don't think\n # \\\" is an escaped quote\n tex = r\"\\i\" + \"i\"*(len(expr.limits) - 1) + \"nt\"\n symbols = [r\"\\, d%s\" % self._print(symbol[0])\n for symbol in expr.limits]\n\n else:\n for lim in reversed(expr.limits):\n symbol = lim[0]\n tex += r\"\\int\"\n\n if len(lim) > 1:\n if self._settings['mode'] != 'inline' \\\n and not self._settings['itex']:\n tex += r\"\\limits\"\n\n if len(lim) == 3:\n tex += \"_{%s}^{%s}\" % (self._print(lim[1]),\n self._print(lim[2]))\n if len(lim) == 2:\n tex += \"^{%s}\" % (self._print(lim[1]))\n\n symbols.insert(0, r\"\\, d%s\" % self._print(symbol))\n\n return r\"%s %s%s\" % (tex, self.parenthesize(expr.function,\n PRECEDENCE[\"Mul\"],\n strict=True),\n \"\".join(symbols))\n\n def _print_Limit(self, expr):\n e, z, z0, dir = expr.args\n\n tex = r\"\\lim_{%s \\to \" % self._print(z)\n if str(dir) == '+-' or z0 in (S.Infinity, S.NegativeInfinity):\n tex += r\"%s}\" % self._print(z0)\n else:\n tex += r\"%s^%s}\" % (self._print(z0), self._print(dir))\n\n if isinstance(e, AssocOp):\n return r\"%s\\left(%s\\right)\" % (tex, self._print(e))\n else:\n return r\"%s %s\" % (tex, self._print(e))\n\n def _hprint_Function(self, func):\n r'''\n Logic to decide how to render a function to latex\n - if it is a recognized latex name, use the appropriate latex command\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_9f2dbdee347e41f4b68f5f1f" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000045000, + "content": "", + "tool_calls": [ + { + "id": "call_273dce6d0cd647d9a83ecec3", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && sed -n '699,712p' sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000046000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && sed -n '699,712p' sympy/printing/latex.py\n def _print_Subs(self, subs):\n expr, old, new = subs.args\n latex_expr = self._print(expr)\n latex_old = (self._print(e) for e in old)\n latex_new = (self._print(e) for e in new)\n latex_subs = r'\\\\ '.join(\n e[0] + '=' + e[1] for e in zip(latex_old, latex_new))\n return r'\\left. %s \\right|_{\\substack{ %s }}' % (latex_expr,\n latex_subs)\n\n def _print_Integral(self, expr):\n tex, symbols = \"\", []\n\n # Only up to \\iiiint exists\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_273dce6d0cd647d9a83ecec3" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000047000, + "content": "", + "tool_calls": [ + { + "id": "call_6e6554840252495e81ce3cea", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && sed -i 's/latex_expr = self._print(expr)/latex_expr = self.parenthesize(expr, PRECEDENCE[\\\\\\\"Mul\\\\\\\"], strict=True)/' sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000048000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && sed -i 's/latex_expr = self._print(expr)/latex_expr = self.parenthesize(expr, PRECEDENCE[\"Mul\"], strict=True)/' sympy/printing/latex.py\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_6e6554840252495e81ce3cea" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000049000, + "content": "", + "tool_calls": [ + { + "id": "call_05d2ee0d85a14f7392822ea9", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && sed -n '699,712p' sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000050000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && sed -n '699,712p' sympy/printing/latex.py\n def _print_Subs(self, subs):\n expr, old, new = subs.args\n latex_expr = self.parenthesize(expr, PRECEDENCE[\"Mul\"], strict=True)\n latex_old = (self._print(e) for e in old)\n latex_new = (self._print(e) for e in new)\n latex_subs = r'\\\\ '.join(\n e[0] + '=' + e[1] for e in zip(latex_old, latex_new))\n return r'\\left. %s \\right|_{\\substack{ %s }}' % (latex_expr,\n latex_subs)\n\n def _print_Integral(self, expr):\n tex, symbols = \"\", []\n\n # Only up to \\iiiint exists\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_05d2ee0d85a14f7392822ea9" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000051000, + "content": "", + "tool_calls": [ + { + "id": "call_b44ddcc0332c4068b2562ad7", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 /tmp/test_subs.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000052000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 /tmp/test_subs.py\nLaTeX: 3 \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_b44ddcc0332c4068b2562ad7" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000053000, + "content": "", + "tool_calls": [ + { + "id": "call_c870c62fbf9b47ff827a40c7", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec write /tmp/test_subs_comprehensive.py << 'EOF'\\nfrom sympy import Subs, latex, Symbol\\nfrom sympy.abc import x, y, z\\n\\n# 原始问题\\nexpr1 = 3*Subs(-x+y, (x,),(1,))\\nprint('Test 1 (original issue):', latex(expr1))\\n\\n# 单独的 Subs（不应该有多余的括号）\\nexpr2 = Subs(-x+y, (x,),(1,))\\nprint('Test 2 (standalone Subs):', latex(expr2))\\n\\n# Subs 乘以 1（不应该有多余的括号）\\nexpr3 = 1*Subs(-x+y, (x,),(1,))\\nprint('Test 3 (Subs * 1):', latex(expr3))\\n\\n# Subs 作为加法的一部分\\nexpr4 = x + Subs(-x+y, (x,),(1,))\\nprint('Test 4 (Subs in Add):', latex(expr4))\\n\\n# 更复杂的表达式\\nexpr5 = 2*x*Subs(-x+y, (x,),(1,))\\nprint('Test 5 (complex Mul):', latex(expr5))\\n\\n# Subs 内部是单个符号\\nexpr6 = 3*Subs(x, (x,),(1,))\\nprint('Test 6 (Subs with single symbol):', latex(expr6))\\n\\n# Subs 内部是乘法\\nexpr7 = 3*Subs(x*y, (x,),(1,))\\nprint('Test 7 (Subs with Mul inside):', latex(expr7))\\n\\n# Subs 内部是除法\\nexpr8 = 3*Subs(x/y, (x,),(1,))\\nprint('Test 8 (Subs with Div inside):', latex(expr8))\\n\\nprint('\\\\nAll tests completed!')\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000054000, + "content": "Written 1001 bytes to /tmp/test_subs_comprehensive.py", + "tool_call_id": "call_c870c62fbf9b47ff827a40c7" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000055000, + "content": "", + "tool_calls": [ + { + "id": "call_748a19b9535f4e0aadc081a7", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 /tmp/test_subs_comprehensive.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000056000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 /tmp/test_subs_comprehensive.py\nTest 1 (original issue): 3 \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}\nTest 2 (standalone Subs): \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}\nTest 3 (Subs * 1): \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}\nTest 4 (Subs in Add): x + \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}\nTest 5 (complex Mul): 2 x \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}\nTest 6 (Subs with single symbol): 3 \\left. x \\right|_{\\substack{ x=1 }}\nTest 7 (Subs with Mul inside): 3 \\left. x y \\right|_{\\substack{ x=1 }}\nTest 8 (Subs with Div inside): 3 \\left. \\frac{x}{y} \\right|_{\\substack{ x=1 }}\n\nAll tests completed!\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_748a19b9535f4e0aadc081a7" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000057000, + "content": "", + "tool_calls": [ + { + "id": "call_fcd896d240234113a0d9d27d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec write /tmp/test_integral.py << 'EOF'\\nfrom sympy import Integral, latex\\nfrom sympy.abc import x, y\\n\\n# 单独的 Integral\\nexpr1 = Integral(-x+y, x)\\nprint('Test 1 (standalone Integral):', latex(expr1))\\n\\n# Integral 乘以系数\\nexpr2 = 3*Integral(-x+y, x)\\nprint('Test 2 (Integral * 3):', latex(expr2))\\n\\n# Integral 内部是乘法\\nexpr3 = 3*Integral(x*y, x)\\nprint('Test 3 (Integral with Mul inside):', latex(expr3))\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000058000, + "content": "Written 374 bytes to /tmp/test_integral.py", + "tool_call_id": "call_fcd896d240234113a0d9d27d" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000059000, + "content": "", + "tool_calls": [ + { + "id": "call_da9a4650d8d743a9b2062c43", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 /tmp/test_integral.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000060000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 /tmp/test_integral.py\nTest 1 (standalone Integral): \\int \\left(- x + y\\right)\\, dx\nTest 2 (Integral * 3): 3 \\int \\left(- x + y\\right)\\, dx\nTest 3 (Integral with Mul inside): 3 \\int x y\\, dx\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_da9a4650d8d743a9b2062c43" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000061000, + "content": "", + "tool_calls": [ + { + "id": "call_9fcebb9afb884c1680929e98", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec write /tmp/test_prec2.py << 'EOF'\\nfrom sympy.printing.precedence import precedence_traditional, PRECEDENCE\\nfrom sympy.abc import x, y\\n\\nprint('PRECEDENCE[\\\"Mul\\\"]:', PRECEDENCE[\\\"Mul\\\"])\\nprint('PRECEDENCE[\\\"Add\\\"]:', PRECEDENCE[\\\"Add\\\"])\\nprint('Add precedence:', precedence_traditional(x+y))\\nprint('Mul precedence:', precedence_traditional(x*y))\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000062000, + "content": "Written 303 bytes to /tmp/test_prec2.py", + "tool_call_id": "call_9fcebb9afb884c1680929e98" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000063000, + "content": "", + "tool_calls": [ + { + "id": "call_180b2c3237f34966853dd05c", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 /tmp/test_prec2.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000064000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 /tmp/test_prec2.py\nPRECEDENCE[\"Mul\"]: 50\nPRECEDENCE[\"Add\"]: 40\nAdd precedence: 40\nMul precedence: 50\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_180b2c3237f34966853dd05c" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000065000, + "content": "", + "tool_calls": [ + { + "id": "call_2857541c07ba47a998af2b6e", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && git diff sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000066000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && git diff sympy/printing/latex.py\ndiff --git a/sympy/printing/latex.py b/sympy/printing/latex.py\nindex 8743723ea0..969072f47f 100644\n--- a/sympy/printing/latex.py\n+++ b/sympy/printing/latex.py\n@@ -698,7 +698,7 @@ def _print_Derivative(self, expr):\n\n def _print_Subs(self, subs):\n expr, old, new = subs.args\n- latex_expr = self._print(expr)\n+ latex_expr = self.parenthesize(expr, PRECEDENCE[\"Mul\"], strict=True)\n latex_old = (self._print(e) for e in old)\n latex_new = (self._print(e) for e in new)\n latex_subs = r'\\\\ '.join(\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_2857541c07ba47a998af2b6e" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000067000, + "content": "", + "tool_calls": [ + { + "id": "call_3da6d8d5fc844411a4e0e155", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && grep -n \\\\\\\"Subs\\\\\\\" sympy/printing/tests/test_latex.py | head -30\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000068000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && grep -n \"Subs\" sympy/printing/tests/test_latex.py | head -30\n8: RisingFactorial, rootof, RootSum, S, Shi, Si, SineTransform, Subs,\n676: assert latex(Subs(x*y, (\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_3da6d8d5fc844411a4e0e155" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000069000, + "content": "", + "tool_calls": [ + { + "id": "call_13d2ead9ba0643819b1f6f2a", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && sed -n '676,690p' sympy/printing/tests/test_latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000070000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && sed -n '676,690p' sympy/printing/tests/test_latex.py\n assert latex(Subs(x*y, (\n x, y), (1, 2))) == r'\\left. x y \\right|_{\\substack{ x=1\\\\ y=2 }}'\n\n\ndef test_latex_integrals():\n assert latex(Integral(log(x), x)) == r\"\\int \\log{\\left(x \\right)}\\, dx\"\n assert latex(Integral(x**2, (x, 0, 1))) == \\\n r\"\\int\\limits_{0}^{1} x^{2}\\, dx\"\n assert latex(Integral(x**2, (x, 10, 20))) == \\\n r\"\\int\\limits_{10}^{20} x^{2}\\, dx\"\n assert latex(Integral(y*x**2, (x, 0, 1), y)) == \\\n r\"\\int\\int\\limits_{0}^{1} x^{2} y\\, dx\\, dy\"\n assert latex(Integral(y*x**2, (x, 0, 1), y), mode='equation*') == \\\n r\"\\begin{equation*}\\int\\int\\limits_{0}^{1} x^{2} y\\, dx\\, dy\\end{equation*}\"\n assert latex(Integral(y*x**2, (x, 0, 1), y), mode='equation*', itex=True) \\\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_13d2ead9ba0643819b1f6f2a" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000071000, + "content": "", + "tool_calls": [ + { + "id": "call_00ec468bfbab4d4a841ba62d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 -m pytest sympy/printing/tests/test_latex.py -k \\\\\\\"Subs\\\\\\\" -v\\\" 10\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000072000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 -m pytest sympy/printing/tests/test_latex.py -k \"Subs\" -v\n/opt/miniconda3/envs/testbed/bin/python3: No module named pytest\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_00ec468bfbab4d4a841ba62d" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000073000, + "content": "", + "tool_calls": [ + { + "id": "call_f3169598fb2b43ffbb983e72", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 -c \\\\\\\"\\\\nfrom sympy import Subs, latex\\\\nfrom sympy.abc import x, y\\\\n\\\\n# 现有测试用例\\\\nresult = latex(Subs(x*y, (x, y), (1, 2)))\\\\nexpected = r'\\\\\\\\left. x y \\\\\\\\right|_{\\\\\\\\substack{ x=1\\\\\\\\\\\\\\\\ y=2 }}'\\\\nprint('Result:', repr(result))\\\\nprint('Expected:', repr(expected))\\\\nprint('Match:', result == expected)\\\\n\\\\\\\" 5\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000074000, + "content": "d = r'\\left. x y \\right|_{\\substack{ x=1\\\\ y=2 }}'\\nprint('Result:', repr(result))\\nprint('Expected:', repr(expected))\\nprint('Match:', result == expected)\\n\" 5ubs(x*y, (x, y), (1, 2)))\\nexpected\n File \"\", line 1\n \\nfrom sympy import Subs, latex\\nfrom sympy.abc import x, y\\n\\n# 现有测试用例\\nresult = latex(Subs(x*y, (x, y), (1, 2)))\\nexpected = r'\\left. x y \\right|_{\\substack{ x=1\\ y=2 }}'\\nprint('Result:',\n repr(result))\\nprint('Expected:', repr(expected))\\nprint('Match:', result == expected)\\n\n ^\nSyntaxError: unexpected character after line continuation character\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_f3169598fb2b43ffbb983e72" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000075000, + "content": "", + "tool_calls": [ + { + "id": "call_819d70b6d0d34b55b6a630ce", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec write /tmp/test_existing.py << 'EOF'\\nfrom sympy import Subs, latex\\nfrom sympy.abc import x, y\\n\\n# 现有测试用例\\nresult = latex(Subs(x*y, (x, y), (1, 2)))\\nexpected = r'\\\\left. x y \\\\right|_{\\\\substack{ x=1\\\\\\\\ y=2 }}'\\nprint('Result:', repr(result))\\nprint('Expected:', repr(expected))\\nprint('Match:', result == expected)\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000076000, + "content": "Written 281 bytes to /tmp/test_existing.py", + "tool_call_id": "call_819d70b6d0d34b55b6a630ce" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000077000, + "content": "", + "tool_calls": [ + { + "id": "call_2773625546684fccae931f66", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 /tmp/test_existing.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000078000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 /tmp/test_existing.py\nResult: '\\\\left. x y \\\\right|_{\\\\substack{ x=1\\\\\\\\ y=2 }}'\nExpected: '\\\\left. x y \\\\right|_{\\\\substack{ x=1\\\\\\\\ y=2 }}'\nMatch: True\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_2773625546684fccae931f66" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000079000, + "content": "", + "tool_calls": [ + { + "id": "call_6e8cfea143cf4d5eb7b7d693", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 /tmp/test_subs.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000080000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 /tmp/test_subs.py\nLaTeX: 3 \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_6e8cfea143cf4d5eb7b7d693" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000081000, + "content": "", + "tool_calls": [ + { + "id": "call_c9d2349bbe4d4b33ba44dc80", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec write /tmp/test_edge_cases.py << 'EOF'\\nfrom sympy import Subs, latex, Symbol, Add, Mul\\nfrom sympy.abc import x, y, z\\n\\nprint(\\\"=== Edge Cases ===\\\")\\n\\n# 1. 原始问题\\nexpr1 = 3*Subs(-x+y, (x,),(1,))\\nprint('1. 3*Subs(-x+y, (x,),(1,)):', latex(expr1))\\n\\n# 2. 单独的 Subs with Add\\nexpr2 = Subs(-x+y, (x,),(1,))\\nprint('2. Subs(-x+y, (x,),(1,)):', latex(expr2))\\n\\n# 3. Subs with Mul (should not have extra parens)\\nexpr3 = Subs(x*y, (x,),(1,))\\nprint('3. Subs(x*y, (x,),(1,)):', latex(expr3))\\n\\n# 4. Subs with Mul multiplied\\nexpr4 = 3*Subs(x*y, (x,),(1,))\\nprint('4. 3*Subs(x*y, (x,),(1,)):', latex(expr4))\\n\\n# 5. Subs with single symbol\\nexpr5 = Subs(x, (x,),(1,))\\nprint('5. Subs(x, (x,),(1,)):', latex(expr5))\\n\\n# 6. Subs with single symbol multiplied\\nexpr6 = 3*Subs(x, (x,),(1,))\\nprint('6. 3*Subs(x, (x,),(1,)):', latex(expr6))\\n\\n# 7. Subs with Pow\\nexpr7 = Subs(x**2, (x,),(1,))\\nprint('7. Subs(x**2, (x,),(1,)):', latex(expr7))\\n\\n# 8. Subs with Pow multiplied\\nexpr8 = 3*Subs(x**2, (x,),(1,))\\nprint('8. 3*Subs(x**2, (x,),(1,)):', latex(expr8))\\n\\n# 9. Subs with complex Add\\nexpr9 = 3*Subs(x**2 + 2*x + 1, (x,),(1,))\\nprint('9. 3*Subs(x**2 + 2*x + 1, (x,),(1,)):', latex(expr9))\\n\\n# 10. Negative coefficient\\nexpr10 = -3*Subs(-x+y, (x,),(1,))\\nprint('10. -3*Subs(-x+y, (x,),(1,)):', latex(expr10))\\n\\n# 11. Subs in denominator\\nexpr11 = 1/Subs(-x+y, (x,),(1,))\\nprint('11. 1/Subs(-x+y, (x,),(1,)):', latex(expr11))\\n\\n# 12. Multiple substitutions\\nexpr12 = 3*Subs(x+y+z, (x,y,z),(1,2,3))\\nprint('12. 3*Subs(x+y+z, (x,y,z),(1,2,3)):', latex(expr12))\\n\\nprint('\\\\n=== All tests completed! ===')\\nEOF\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000082000, + "content": "Written 1523 bytes to /tmp/test_edge_cases.py", + "tool_call_id": "call_c9d2349bbe4d4b33ba44dc80" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000083000, + "content": "", + "tool_calls": [ + { + "id": "call_0517e1ee97354bc787495fa4", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 /tmp/test_edge_cases.py\\\" 5\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000084000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 /tmp/test_edge_cases.py\n=== Edge Cases ===\n1. 3*Subs(-x+y, (x,),(1,)): 3 \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}\n2. Subs(-x+y, (x,),(1,)): \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}\n3. Subs(x*y, (x,),(1,)): \\left. x y \\right|_{\\substack{ x=1 }}\n4. 3*Subs(x*y, (x,),(1,)): 3 \\left. x y \\right|_{\\substack{ x=1 }}\n5. Subs(x, (x,),(1,)): \\left. x \\right|_{\\substack{ x=1 }}\n6. 3*Subs(x, (x,),(1,)): 3 \\left. x \\right|_{\\substack{ x=1 }}\n7. Subs(x**2, (x,),(1,)): \\left. x^{2} \\right|_{\\substack{ x=1 }}\n8. 3*Subs(x**2, (x,),(1,)): 3 \\left. x^{2} \\right|_{\\substack{ x=1 }}\n9. 3*Subs(x**2 + 2*x + 1, (x,),(1,)): 3 \\left. \\left(x^{2} + 2 x + 1\\right) \\right|_{\\substack{ x=1 }}\n10. -3*Subs(-x+y, (x,),(1,)): - 3 \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}\n11. 1/Subs(-x+y, (x,),(1,)): \\frac{1}{\\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}}\n12. 3*Subs(x+y+z, (x,y,z),(1,2,3)): 3 \\left. \\left(x + y + z\\right) \\right|_{\\substack{ x=1\\\\ y=2\\\\ z=3 }}\n\n=== All tests completed! ===\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_0517e1ee97354bc787495fa4" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000085000, + "content": "", + "tool_calls": [ + { + "id": "call_85d1cc1535a84d23ba0c951d", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && git diff sympy/printing/latex.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000086000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && git diff sympy/printing/latex.py\ndiff --git a/sympy/printing/latex.py b/sympy/printing/latex.py\nindex 8743723ea0..969072f47f 100644\n--- a/sympy/printing/latex.py\n+++ b/sympy/printing/latex.py\n@@ -698,7 +698,7 @@ def _print_Derivative(self, expr):\n\n def _print_Subs(self, subs):\n expr, old, new = subs.args\n- latex_expr = self._print(expr)\n+ latex_expr = self.parenthesize(expr, PRECEDENCE[\"Mul\"], strict=True)\n latex_old = (self._print(e) for e in old)\n latex_new = (self._print(e) for e in new)\n latex_subs = r'\\\\ '.join(\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_85d1cc1535a84d23ba0c951d" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000087000, + "content": "", + "tool_calls": [ + { + "id": "call_fd5c6e5ef256495cb95f887e", + "type": "function", + "function": { + "name": "exec", + "arguments": "{\"command\": \"/tmp/sweb.eval.sympy__sympy-18763.full-run-exec tmux-run \\\"cd /testbed && python3 /tmp/test_subs.py\\\" 3\"}" + } + } + ] + }, + { + "sender_id": "tool_runner", + "role": "tool", + "timestamp": 1700000088000, + "content": "(testbed) root@54aada0b68a8:/testbed# cd /testbed && python3 /tmp/test_subs.py\nLaTeX: 3 \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}\n(testbed) root@54aada0b68a8:/testbed#", + "tool_call_id": "call_fd5c6e5ef256495cb95f887e" + }, + { + "sender_id": "agent_sympy", + "role": "assistant", + "timestamp": 1700000089000, + "content": "TASK_COMPLETE\n\n修复已完成。问题在于 `_print_Subs` 方法没有对内部表达式进行适当的括号包裹。修改了 `sympy/printing/latex.py` 第 701 行，将：\n```python\nlatex_expr = self._print(expr)\n```\n改为：\n```python\nlatex_expr = self.parenthesize(expr, PRECEDENCE[\"Mul\"], strict=True)\n```\n\n这与 `_print_Integral` 方法的处理方式一致。修复后，`3*Subs(-x+y, (x,),(1,))` 的 LaTeX 输出从 `3 \\left. - x + y \\right|_{\\substack{ x=1 }}` 变为正确的 `3 \\left. \\left(- x + y\\right) \\right|_{\\substack{ x=1 }}`。" + } + ] +} diff --git a/tests/fixtures/long_conversation_locomo_caroline_melanie.json b/tests/fixtures/long_conversation_locomo_caroline_melanie.json new file mode 100644 index 00000000..4a1f1363 --- /dev/null +++ b/tests/fixtures/long_conversation_locomo_caroline_melanie.json @@ -0,0 +1,3508 @@ +{ + "source": "LoCoMo conv_0 (Caroline & Melanie)", + "license": "see LoCoMo upstream (snap-research/locomo)", + "everos_session_id": "locomo_c0_caroline_melanie", + "everos_user_id_for_test": "u_smoke_001", + "speakers": [ + "caroline", + "melanie" + ], + "total_messages": 419, + "total_batches": 19, + "intra_session_gap_seconds": 30, + "conversion_rules": { + "session_id": "all LoCoMo sessions collapse to one everos session", + "sender_id": "LoCoMo `speaker` lowercased", + "role": "always 'user' (both LoCoMo speakers are user inputs)", + "timestamp": "session__date_time parsed as UTC, +30s per message within a batch", + "content": "LoCoMo `text` verbatim", + "_audit_dia_id": "kept for audit only; NOT sent to /add" + }, + "batches": [ + { + "locomo_session": "session_1", + "session_start_iso": "2023-05-08T13:56:00+00:00", + "message_count": 18, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1683554160000, + "content": "Hey Mel! Good to see you! How have you been?", + "_audit_dia_id": "D1:1", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1683554190000, + "content": "Hey Caroline! Good to see you! I'm swamped with the kids & work. What's up with you? Anything new?", + "_audit_dia_id": "D1:2", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1683554220000, + "content": "I went to a LGBTQ support group yesterday and it was so powerful.", + "_audit_dia_id": "D1:3", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1683554250000, + "content": "Wow, that's cool, Caroline! What happened that was so awesome? Did you hear any inspiring stories?", + "_audit_dia_id": "D1:4", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1683554280000, + "content": "The transgender stories were so inspiring! I was so happy and thankful for all the support.", + "_audit_dia_id": "D1:5", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1683554310000, + "content": "Wow, love that painting! So cool you found such a helpful group. What's it done for you?", + "_audit_dia_id": "D1:6", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1683554340000, + "content": "The support group has made me feel accepted and given me courage to embrace myself.", + "_audit_dia_id": "D1:7", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1683554370000, + "content": "That's really cool. You've got guts. What now?", + "_audit_dia_id": "D1:8", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1683554400000, + "content": "Gonna continue my edu and check out career options, which is pretty exciting!", + "_audit_dia_id": "D1:9", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1683554430000, + "content": "Wow, Caroline! What kinda jobs are you thinkin' of? Anything that stands out?", + "_audit_dia_id": "D1:10", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1683554460000, + "content": "I'm keen on counseling or working in mental health - I'd love to support those with similar issues.", + "_audit_dia_id": "D1:11", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1683554490000, + "content": "You'd be a great counselor! Your empathy and understanding will really help the people you work with. By the way, take a look at this.", + "_audit_dia_id": "D1:12", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1683554520000, + "content": "Thanks, Melanie! That's really sweet. Is this your own painting?", + "_audit_dia_id": "D1:13", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1683554550000, + "content": "Yeah, I painted that lake sunrise last year! It's special to me.", + "_audit_dia_id": "D1:14", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1683554580000, + "content": "Wow, Melanie! The colors really blend nicely. Painting looks like a great outlet for expressing yourself.", + "_audit_dia_id": "D1:15", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1683554610000, + "content": "Thanks, Caroline! Painting's a fun way to express my feelings and get creative. It's a great way to relax after a long day.", + "_audit_dia_id": "D1:16", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1683554640000, + "content": "Totally agree, Mel. Relaxing and expressing ourselves is key. Well, I'm off to go do some research.", + "_audit_dia_id": "D1:17", + "_audit_locomo_session": "session_1" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1683554670000, + "content": "Yep, Caroline. Taking care of ourselves is vital. I'm off to go swimming with the kids. Talk to you soon!", + "_audit_dia_id": "D1:18", + "_audit_locomo_session": "session_1" + } + ] + }, + { + "locomo_session": "session_2", + "session_start_iso": "2023-05-25T13:14:00+00:00", + "message_count": 17, + "messages": [ + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1685020440000, + "content": "Hey Caroline, since we last chatted, I've had a lot of things happening to me. I ran a charity race for mental health last Saturday – it was really rewarding. Really made me think about taking care of our minds.", + "_audit_dia_id": "D2:1", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1685020470000, + "content": "That charity race sounds great, Mel! Making a difference & raising awareness for mental health is super rewarding - I'm really proud of you for taking part!", + "_audit_dia_id": "D2:2", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1685020500000, + "content": "Thanks, Caroline! The event was really thought-provoking. I'm starting to realize that self-care is really important. It's a journey for me, but when I look after myself, I'm able to better look after my family.", + "_audit_dia_id": "D2:3", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1685020530000, + "content": "I totally agree, Melanie. Taking care of ourselves is so important - even if it's not always easy. Great that you're prioritizing self-care.", + "_audit_dia_id": "D2:4", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1685020560000, + "content": "Yeah, it's tough. So I'm carving out some me-time each day - running, reading, or playing my violin - which refreshes me and helps me stay present for my fam!", + "_audit_dia_id": "D2:5", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1685020590000, + "content": "That's great, Mel! Taking time for yourself is so important. You're doing an awesome job looking after yourself and your family!", + "_audit_dia_id": "D2:6", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1685020620000, + "content": "Thanks, Caroline. It's still a work in progress, but I'm doing my best. My kids are so excited about summer break! We're thinking about going camping next month. Any fun plans for the summer?", + "_audit_dia_id": "D2:7", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1685020650000, + "content": "Researching adoption agencies — it's been a dream to have a family and give a loving home to kids who need it.", + "_audit_dia_id": "D2:8", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1685020680000, + "content": "Wow, Caroline! That's awesome! Taking in kids in need - you're so kind. Your future family is gonna be so lucky to have you!", + "_audit_dia_id": "D2:9", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1685020710000, + "content": "Thanks, Mel! My goal is to give kids a loving home. I'm truly grateful for all the support I've got from friends and mentors. Now the hard work starts to turn my dream into a reality. And here's one of the adoption agencies I'm looking into. It's a lot to take in, but I'm feeling hopeful and optimistic.", + "_audit_dia_id": "D2:10", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1685020740000, + "content": "Wow, that agency looks great! What made you pick it?", + "_audit_dia_id": "D2:11", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1685020770000, + "content": "I chose them 'cause they help LGBTQ+ folks with adoption. Their inclusivity and support really spoke to me.", + "_audit_dia_id": "D2:12", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1685020800000, + "content": "That's great, Caroline! Loving the inclusivity and support. Anything you're excited for in the adoption process?", + "_audit_dia_id": "D2:13", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1685020830000, + "content": "I'm thrilled to make a family for kids who need one. It'll be tough as a single parent, but I'm up for the challenge!", + "_audit_dia_id": "D2:14", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1685020860000, + "content": "You're doing something amazing! Creating a family for those kids is so lovely. You'll be an awesome mom! Good luck!", + "_audit_dia_id": "D2:15", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1685020890000, + "content": "Thanks, Melanie! Your kind words really mean a lot. I'll do my best to make sure these kids have a safe and loving home.", + "_audit_dia_id": "D2:16", + "_audit_locomo_session": "session_2" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1685020920000, + "content": "No doubts, Caroline. You have such a caring heart - they'll get all the love and stability they need! Excited for this new chapter!", + "_audit_dia_id": "D2:17", + "_audit_locomo_session": "session_2" + } + ] + }, + { + "locomo_session": "session_3", + "session_start_iso": "2023-06-09T19:55:00+00:00", + "message_count": 23, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686340500000, + "content": "Hey Melanie! How's it going? I wanted to tell you about my school event last week. It was awesome! I talked about my transgender journey and encouraged students to get involved in the LGBTQ community. It was great to see their reactions. It made me reflect on how far I've come since I started transitioning three years ago.", + "_audit_dia_id": "D3:1", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1686340530000, + "content": "Hey Caroline! Great to hear from you. Sounds like your event was amazing! I'm so proud of you for spreading awareness and getting others involved in the LGBTQ community. You've come a long way since your transition - keep on inspiring people with your strength and courage!", + "_audit_dia_id": "D3:2", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686340560000, + "content": "Thanks, Mel! Your backing really means a lot. I felt super powerful giving my talk. I shared my own journey, the struggles I had and how much I've developed since coming out. It was wonderful to see how the audience related to what I said and how it inspired them to be better allies. Conversations about gender identity and inclusion are so necessary and I'm thankful for being able to give a voice to the trans community.", + "_audit_dia_id": "D3:3", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1686340590000, + "content": "Wow, Caroline, you're doing an awesome job of inspiring others with your journey. It's great to be part of it and see how you're positively affecting so many. Talking about inclusivity and acceptance is crucial, and you're so brave to speak up for the trans community. Keep up the great work!", + "_audit_dia_id": "D3:4", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686340620000, + "content": "Thanks Mel! Your kind words mean a lot. Sharing our experiences isn't always easy, but I feel it's important to help promote understanding and acceptance. I've been blessed with loads of love and support throughout this journey, and I want to pass it on to others. By sharing our stories, we can build a strong, supportive community of hope.", + "_audit_dia_id": "D3:5", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1686340650000, + "content": "Yeah, Caroline! It takes courage to talk about our own stories. But it's in these vulnerable moments that we bond and understand each other. We all have our different paths, but if we share them, we show people that they're not alone. Our stories can be so inspiring and encouraging to others who are facing the same challenges. Thank you for using your voice to create love, acceptance, and hope. You're doing amazing!", + "_audit_dia_id": "D3:6", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686340680000, + "content": "Your words mean a lot to me. I'm grateful for the chance to share my story and give others hope. We all have unique paths, and by working together we can build a more inclusive and understanding world. I'm going to keep using my voice to make a change and lift others up. And you're part of that!", + "_audit_dia_id": "D3:7", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1686340710000, + "content": "Thanks, Caroline, for letting me join your journey. I'm so proud to be part of the difference you're making. Let's keep motivating and helping each other out as we journey through life. We can make a real impact together!", + "_audit_dia_id": "D3:8", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686340740000, + "content": "Yeah Mel, let's spread love and understanding! Thanks for the support and encouragement. We can tackle life's challenges together! We got this!", + "_audit_dia_id": "D3:9", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1686340770000, + "content": "Yes, Caroline! We can do it. Your courage is inspiring. I want to be couragous for my family- they motivate me and give me love. What motivates you?", + "_audit_dia_id": "D3:10", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686340800000, + "content": "Thanks, Mel! My friends, family and mentors are my rocks – they motivate me and give me the strength to push on. Here's a pic from when we met up last week!", + "_audit_dia_id": "D3:11", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1686340830000, + "content": "Wow, that photo is great! How long have you had such a great support system?", + "_audit_dia_id": "D3:12", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686340860000, + "content": "Yeah, I'm really lucky to have them. They've been there through everything, I've known these friends for 4 years, since I moved from my home country. Their love and help have been so important especially after that tough breakup. I'm super thankful. Who supports you, Mel?", + "_audit_dia_id": "D3:13", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1686340890000, + "content": "I'm lucky to have my husband and kids; they keep me motivated.", + "_audit_dia_id": "D3:14", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686340920000, + "content": "Wow, what an amazing family pic! How long have you been married?", + "_audit_dia_id": "D3:15", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1686340950000, + "content": "5 years already! Time flies- feels like just yesterday I put this dress on! Thanks, Caroline!", + "_audit_dia_id": "D3:16", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686340980000, + "content": "Congrats, Melanie! You both looked so great on your wedding day! Wishing you many happy years together!", + "_audit_dia_id": "D3:17", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1686341010000, + "content": "Thanks, Caroline! Appreciate your kind words. Looking forward to more happy years. Our family and moments make it all worth it.", + "_audit_dia_id": "D3:18", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686341040000, + "content": "Looks like you had a great day! How was it? You all look so happy!", + "_audit_dia_id": "D3:19", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1686341070000, + "content": "It so fun! We played games, ate good food, and just hung out together. Family moments make life awesome.", + "_audit_dia_id": "D3:20", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686341100000, + "content": "Sounds great, Mel! Glad you had a great time. Cherish the moments - they're the best!", + "_audit_dia_id": "D3:21", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1686341130000, + "content": "Absolutely, Caroline! I cherish time with family. It's when I really feel alive and happy.", + "_audit_dia_id": "D3:22", + "_audit_locomo_session": "session_3" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1686341160000, + "content": "I 100% agree, Mel. Hanging with loved ones is amazing and brings so much happiness. Those moments really make me thankful. Family is everything.", + "_audit_dia_id": "D3:23", + "_audit_locomo_session": "session_3" + } + ] + }, + { + "locomo_session": "session_4", + "session_start_iso": "2023-06-27T10:37:00+00:00", + "message_count": 18, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1687862220000, + "content": "Hey Melanie! Long time no talk! A lot's been going on in my life! Take a look at this.", + "_audit_dia_id": "D4:1", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1687862250000, + "content": "Hey, Caroline! Nice to hear from you! Love the necklace, any special meaning to it?", + "_audit_dia_id": "D4:2", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1687862280000, + "content": "Thanks, Melanie! This necklace is super special to me - a gift from my grandma in my home country, Sweden. She gave it to me when I was young, and it stands for love, faith and strength. It's like a reminder of my roots and all the love and support I get from my family.", + "_audit_dia_id": "D4:3", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1687862310000, + "content": "That's gorgeous, Caroline! It's awesome what items can mean so much to us, right? Got any other objects that you treasure, like that necklace?", + "_audit_dia_id": "D4:4", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1687862340000, + "content": "Yep, Melanie! I've got some other stuff with sentimental value, like my hand-painted bowl. A friend made it for my 18th birthday ten years ago. The pattern and colors are awesome-- it reminds me of art and self-expression.", + "_audit_dia_id": "D4:5", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1687862370000, + "content": "That sounds great, Caroline! It's awesome having stuff around that make us think of good connections and times. Actually, I just took my fam camping in the mountains last week - it was a really nice time together!", + "_audit_dia_id": "D4:6", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1687862400000, + "content": "Sounds great, Mel. Glad you made some new family mems. How was it? Anything fun?", + "_audit_dia_id": "D4:7", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1687862430000, + "content": "It was an awesome time, Caroline! We explored nature, roasted marshmallows around the campfire and even went on a hike. The view from the top was amazing! The 2 younger kids love nature. It was so special having these moments together as a family - I'll never forget it!", + "_audit_dia_id": "D4:8", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1687862460000, + "content": "That's awesome, Melanie! Family moments like that are so special. Glad y'all had such a great time.", + "_audit_dia_id": "D4:9", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1687862490000, + "content": "Thanks, Caroline! Family time matters to me. What's up with you lately?", + "_audit_dia_id": "D4:10", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1687862520000, + "content": "Lately, I've been looking into counseling and mental health as a career. I want to help people who have gone through the same things as me.", + "_audit_dia_id": "D4:11", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1687862550000, + "content": "Sounds great! What kind of counseling and mental health services do you want to persue?", + "_audit_dia_id": "D4:12", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1687862580000, + "content": "I'm still figuring out the details, but I'm thinking of working with trans people, helping them accept themselves and supporting their mental health. Last Friday, I went to an LGBTQ+ counseling workshop and it was really enlightening. They talked about different therapeutic methods and how to best work with trans people. Seeing how passionate these pros were about making a safe space for people like me was amazing.", + "_audit_dia_id": "D4:13", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1687862610000, + "content": "Woah, Caroline, it sounds like you're doing some impressive work. It's inspiring to see your dedication to helping others. What motivated you to pursue counseling?", + "_audit_dia_id": "D4:14", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1687862640000, + "content": "Thanks, Melanie. It really mattered. My own journey and the support I got made a huge difference. Now I want to help people go through it too. I saw how counseling and support groups improved my life, so I started caring more about mental health and understanding myself. Now I'm passionate about creating a safe, inviting place for people to grow.", + "_audit_dia_id": "D4:15", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1687862670000, + "content": "Wow, Caroline! You've gained so much from your own experience. Your passion and hard work to help others is awesome. Keep it up, you're making a big impact!", + "_audit_dia_id": "D4:16", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1687862700000, + "content": "Thanks, Melanie! Your kind words mean a lot.", + "_audit_dia_id": "D4:17", + "_audit_locomo_session": "session_4" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1687862730000, + "content": "Congrats Caroline! Good on you for going after what you really care about.", + "_audit_dia_id": "D4:18", + "_audit_locomo_session": "session_4" + } + ] + }, + { + "locomo_session": "session_5", + "session_start_iso": "2023-07-03T13:36:00+00:00", + "message_count": 16, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688391360000, + "content": "Since we last spoke, some big things have happened. Last week I went to an LGBTQ+ pride parade. Everyone was so happy and it made me feel like I belonged. It showed me how much our community has grown, it was amazing!", + "_audit_dia_id": "D5:1", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688391390000, + "content": "Wow, Caroline, sounds like the parade was an awesome experience! It's great to see the love and support for the LGBTQ+ community. Congrats! Has this experience influenced your goals at all?", + "_audit_dia_id": "D5:2", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688391420000, + "content": "Thanks, Mel! It really motivated me for sure. Talking to the community made me want to use my story to help others too - I'm still thinking that counseling and mental health is the way to go. I'm super excited to give back. ", + "_audit_dia_id": "D5:3", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688391450000, + "content": "Wow, Caroline! That's great! I just signed up for a pottery class yesterday. It's like therapy for me, letting me express myself and get creative. Have you found any activities that make you feel the same way?", + "_audit_dia_id": "D5:4", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688391480000, + "content": "Wow, Melanie! I'm getting creative too, just learning the piano. What made you try pottery?", + "_audit_dia_id": "D5:5", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688391510000, + "content": "I'm a big fan of pottery - the creativity and skill is awesome. Plus, making it is so calming. Look at this!", + "_audit_dia_id": "D5:6", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688391540000, + "content": "That bowl is gorgeous! The black and white design looks so fancy. Did you make it?", + "_audit_dia_id": "D5:7", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688391570000, + "content": "Thanks, Caroline! Yeah, I made this bowl in my class. It took some work, but I'm pretty proud of it.", + "_audit_dia_id": "D5:8", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688391600000, + "content": "Nice job! You really put in the work and it definitely shows. Your creativity looks great!", + "_audit_dia_id": "D5:9", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688391630000, + "content": "Thanks, Caroline! Your kind words mean a lot. Pottery is a huge part of my life, not just a hobby - it helps me express my emotions. Clay is incredible, it brings me so much joy!", + "_audit_dia_id": "D5:10", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688391660000, + "content": "Wow, Mel, I'm so stoked for you that art is helping you express yourself and bring you joy! Keep it up!", + "_audit_dia_id": "D5:11", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688391690000, + "content": "Thanks, Caroline! I'm excited to see where pottery takes me. Anything coming up you're looking forward to?", + "_audit_dia_id": "D5:12", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688391720000, + "content": "Thanks Mel! I'm going to a transgender conference this month. I'm so excited to meet other people in the community and learn more about advocacy. It's gonna be great!", + "_audit_dia_id": "D5:13", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688391750000, + "content": "Sounds awesome, Caroline! Have a great time and learn a lot. Have fun!", + "_audit_dia_id": "D5:14", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688391780000, + "content": "Cool, thanks Mel! Can't wait. I'll keep ya posted. Bye!", + "_audit_dia_id": "D5:15", + "_audit_locomo_session": "session_5" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688391810000, + "content": "Bye, Caroline! Can't wait to hear about it. Have fun and stay safe!", + "_audit_dia_id": "D5:16", + "_audit_locomo_session": "session_5" + } + ] + }, + { + "locomo_session": "session_6", + "session_start_iso": "2023-07-06T20:18:00+00:00", + "message_count": 16, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688674680000, + "content": "Hey Mel! Long time no talk. Lots has been going on since then!", + "_audit_dia_id": "D6:1", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688674710000, + "content": "Hey Caroline! Missed you. Anything new? Spill the beans!", + "_audit_dia_id": "D6:2", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688674740000, + "content": "Since our last chat, I've been looking into counseling or mental health work more. I'm passionate about helping people and making a positive impact. It's tough, but really rewarding too. Anything new happening with you?", + "_audit_dia_id": "D6:3", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688674770000, + "content": "That's awesome, Caroline! Congrats on following your dreams. Yesterday I took the kids to the museum - it was so cool spending time with them and seeing their eyes light up!", + "_audit_dia_id": "D6:4", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688674800000, + "content": "Melanie, that's a great pic! That must have been awesome. What were they so stoked about?", + "_audit_dia_id": "D6:5", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688674830000, + "content": "They were stoked for the dinosaur exhibit! They love learning about animals and the bones were so cool. It reminds me why I love being a mom.", + "_audit_dia_id": "D6:6", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688674860000, + "content": "Being a mom is awesome. I'm creating a library for when I have kids. I'm really looking forward to reading to them and opening up their minds.", + "_audit_dia_id": "D6:7", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688674890000, + "content": "Sounds great! What kind of books you got in your library?", + "_audit_dia_id": "D6:8", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688674920000, + "content": "I've got lots of kids' books- classics, stories from different cultures, educational books, all of that. What's a favorite book you remember from your childhood?", + "_audit_dia_id": "D6:9", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688674950000, + "content": "I loved reading \"Charlotte's Web\" as a kid. It was so cool seeing how friendship and compassion can make a difference.", + "_audit_dia_id": "D6:10", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688674980000, + "content": "Wow, that's great! It sure shows how important friendship and compassion are. It's made me appreciate how lucky I am to have my friends and family helping with my transition. They make all the difference. We even had a picnic last week!", + "_audit_dia_id": "D6:11", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688675010000, + "content": "That's a gorgeous photo, Caroline! Wow, the love around you is awesome. How have your friends and fam been helping you out with your transition?", + "_audit_dia_id": "D6:12", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688675040000, + "content": "Thanks, Melanie! This support network has been amazing. They've been there for me every step of the way giving me love, guidance, and acceptance. I couldn't have done it without them.", + "_audit_dia_id": "D6:13", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688675070000, + "content": "Wow, Caroline! It's great you have people to support you, that's really awesome!", + "_audit_dia_id": "D6:14", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1688675100000, + "content": "I'm so lucky to have such a great support system around me. Their love and encouragement has really helped me accept and grow into my true self. They've been instrumental in my transition.", + "_audit_dia_id": "D6:15", + "_audit_locomo_session": "session_6" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1688675130000, + "content": "Glad you have support, Caroline! Unconditional love is so important. Here's a pic of my family camping at the beach. We love it, it brings us closer!", + "_audit_dia_id": "D6:16", + "_audit_locomo_session": "session_6" + } + ] + }, + { + "locomo_session": "session_7", + "session_start_iso": "2023-07-12T16:33:00+00:00", + "message_count": 27, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689179580000, + "content": "Hey Mel, great to chat with you again! So much has happened since we last spoke - I went to an LGBTQ conference two days ago and it was really special. I got the chance to meet and connect with people who've gone through similar journeys. It was such a welcoming environment and I felt totally accepted. I'm really thankful for this amazing community - it's shown me how important it is to fight for trans rights and spread awareness.", + "_audit_dia_id": "D7:1", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689179610000, + "content": "Wow, Caroline, that sounds awesome! So glad you felt accepted and supported. Events like these are great for reminding us of how strong community can be!", + "_audit_dia_id": "D7:2", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689179640000, + "content": "Yeah, it's true! Having people who back you makes such a huge difference. It's great to see how far LGBTQ rights have come, but there's still plenty of progress to be made. I wanna help make a difference.", + "_audit_dia_id": "D7:3", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689179670000, + "content": "Wow, Caroline. We've come so far, but there's more to do. Your drive to help is awesome! What's your plan to pitch in?", + "_audit_dia_id": "D7:4", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689179700000, + "content": "Thanks, Mell! I'm still looking into counseling and mental health jobs. It's important to me that people have someone to talk to, and I want to help make that happen.", + "_audit_dia_id": "D7:5", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689179730000, + "content": "Wow, Caroline! You're so inspiring for wanting to help others with their mental health. What's pushing you to keep going forward with it?", + "_audit_dia_id": "D7:6", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689179760000, + "content": "I struggled with mental health, and support I got was really helpful. It made me realize how important it is for others to have a support system. So, I started looking into counseling and mental health career options, so I could help other people on their own journeys like I was helped.", + "_audit_dia_id": "D7:7", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689179790000, + "content": "Caroline, so glad you got the support! Your experience really brought you to where you need to be. You're gonna make a huge difference! This book I read last year reminds me to always pursue my dreams, just like you are doing!🌟", + "_audit_dia_id": "D7:8", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689179820000, + "content": "Thanks so much, Mel! Seeing this pic just made me appreciate my love of reading even more. Books guide me, motivate me and help me discover who I am. They're a huge part of my journey, and this one's reminding me to keep going and never give up!", + "_audit_dia_id": "D7:9", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689179850000, + "content": "Wow, Caroline! Books have such an awesome power! Which one has been your favorite guide?", + "_audit_dia_id": "D7:10", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689179880000, + "content": "I loved \"Becoming Nicole\" by Amy Ellis Nutt. It's a real inspiring true story about a trans girl and her family. It made me feel connected and gave me a lot of hope for my own path. Highly recommend it for sure!", + "_audit_dia_id": "D7:11", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689179910000, + "content": "That sounds awesome! What did you take away from it to use in your life?", + "_audit_dia_id": "D7:12", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689179940000, + "content": "It taught me self-acceptance and how to find support. It also showed me that tough times don't last - hope and love exist. Pets bring so much joy too, though.", + "_audit_dia_id": "D7:13", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689179970000, + "content": "Caroline, those lessons are great - self-acceptance and finding support are key. Plus pets are awesome for joy and comfort, can't agree more! ", + "_audit_dia_id": "D7:14", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689180000000, + "content": "That's so nice! What pet do you have?", + "_audit_dia_id": "D7:15", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689180030000, + "content": "We've got a pup and a kitty. That's the dog, and here's our cat! They brighten up our day and always make us smile.", + "_audit_dia_id": "D7:16", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689180060000, + "content": "Ah, they're adorable! What are their names? Pets sure do bring so much joy to us!", + "_audit_dia_id": "D7:17", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689180090000, + "content": "Luna and Oliver! They are so sweet and playful - they really liven up the house! Just got some new shoes, too!", + "_audit_dia_id": "D7:18", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689180120000, + "content": "Love that purple color! For walking or running?", + "_audit_dia_id": "D7:19", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689180150000, + "content": "Thanks, Caroline! These are for running. Been running longer since our last chat - a great way to destress and clear my mind.", + "_audit_dia_id": "D7:20", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689180180000, + "content": "Wow! What got you into running?", + "_audit_dia_id": "D7:21", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689180210000, + "content": "I've been running farther to de-stress, which has been great for my headspace.", + "_audit_dia_id": "D7:22", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689180240000, + "content": "Cool, Melanie! Running can really boost your mood. Keep it up!", + "_audit_dia_id": "D7:23", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689180270000, + "content": "Thanks, Caroline! This has been great for my mental health. I'm gonna keep it up.", + "_audit_dia_id": "D7:24", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689180300000, + "content": "Awesome, Melanie! Mental health's a priority, so make sure you take care of yourself.", + "_audit_dia_id": "D7:25", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689180330000, + "content": "Caroline, thanks! Mental health is important to me, and it's made such an improvement!", + "_audit_dia_id": "D7:26", + "_audit_locomo_session": "session_7" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689180360000, + "content": "Glad it helped ya, Melanie!", + "_audit_dia_id": "D7:27", + "_audit_locomo_session": "session_7" + } + ] + }, + { + "locomo_session": "session_8", + "session_start_iso": "2023-07-15T13:51:00+00:00", + "message_count": 39, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429060000, + "content": "Hey Mel, what's up? Been a busy week since we talked.", + "_audit_dia_id": "D8:1", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429090000, + "content": "Hey Caroline, it's been super busy here. So much since we talked! Last Fri I finally took my kids to a pottery workshop. We all made our own pots, it was fun and therapeutic!", + "_audit_dia_id": "D8:2", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429120000, + "content": "Wow, Mel! Sounds like you and the kids had a blast. How'd they like it?", + "_audit_dia_id": "D8:3", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429150000, + "content": "The kids loved it! They were so excited to get their hands dirty and make something with clay. It was special to watch their creativity and imagination come to life, they made this!", + "_audit_dia_id": "D8:4", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429180000, + "content": "Aww, that's so sweet! That cup is so cute. It's awesome to see how kids show their personalities through art. What other creative projects do you do with them, besides pottery?", + "_audit_dia_id": "D8:5", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429210000, + "content": "We love painting together lately, especially nature-inspired ones. Here's our latest work from last weekend.", + "_audit_dia_id": "D8:6", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429240000, + "content": "Wow Mel, that painting's amazing! The colors are so bold and it really highlights the beauty of nature. Y'all work on it together?", + "_audit_dia_id": "D8:7", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429270000, + "content": "Thanks, Caroline! We both helped with the painting - it was great bonding over it and chatting about nature. We found these lovely flowers. Appreciating the small things in life, too.", + "_audit_dia_id": "D8:8", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429300000, + "content": "That photo is stunning! So glad you bonded over our love of nature. Last Friday I went to a council meeting for adoption. It was inspiring and emotional - so many people wanted to create loving homes for children in need. It made me even more determined to adopt.", + "_audit_dia_id": "D8:9", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429330000, + "content": "Wow, Caroline, way to go! Your future fam will get a kick out of having you. What do you think of these?", + "_audit_dia_id": "D8:10", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429360000, + "content": "Thanks Melanie - love the blue vase in the pic! Blue's my fave, it makes me feel relaxed. Sunflowers mean warmth and happiness, right? While roses stand for love and beauty? That's neat. What do flowers mean to you?", + "_audit_dia_id": "D8:11", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429390000, + "content": "Flowers bring joy. They represent growth, beauty and reminding us to appreciate the small moments. They were an important part of my wedding decor and always remind me of that day.", + "_audit_dia_id": "D8:12", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429420000, + "content": "It must have been special at your wedding. I wish I had known you back then!", + "_audit_dia_id": "D8:13", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429450000, + "content": "It was amazing, Caroline. The day was full of love and joy. Everyone we love was there to celebrate us - it was really special.", + "_audit_dia_id": "D8:14", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429480000, + "content": "Wow, what a great day! Glad everyone could make it. What was your favorite part?", + "_audit_dia_id": "D8:15", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429510000, + "content": "Marrying my partner and promising to be together forever was the best part.", + "_audit_dia_id": "D8:16", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429540000, + "content": "Wow, nice pic! You both looked amazing. One special memory for me was this pride parade I went to a few weeks ago.", + "_audit_dia_id": "D8:17", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429570000, + "content": "Wow, looks awesome! Did you join in?", + "_audit_dia_id": "D8:18", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429600000, + "content": "Yes, I did. It was amazing! I felt so accepted and happy, just being around people who accepted and celebrated me. It's definitely a top memory.", + "_audit_dia_id": "D8:19", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429630000, + "content": "Wow, what an experience! How did it make you feel?", + "_audit_dia_id": "D8:20", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429660000, + "content": "I felt so proud and grateful - the vibes were amazing and it was comforting to know I'm not alone and have a great community around me.", + "_audit_dia_id": "D8:21", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429690000, + "content": "Wow, Caroline! That's huge! How did it feel to be around so much love and acceptance?", + "_audit_dia_id": "D8:22", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429720000, + "content": "It was awesome, Melanie! Being around people who embrace and back me up is beyond words. It really inspired me.", + "_audit_dia_id": "D8:23", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429750000, + "content": "Wow, that sounds awesome! Your friends and community really have your back. What's been the best part of it?", + "_audit_dia_id": "D8:24", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429780000, + "content": "Realizing I can be me without fear and having the courage to transition was the best part. It's so freeing to express myself authentically and have people back me up.", + "_audit_dia_id": "D8:25", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429810000, + "content": "That's awesome, Caro! You've found the courage to be yourself - that's important for our mental health and finding peace.", + "_audit_dia_id": "D8:26", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429840000, + "content": "Thanks, Melanie! Been a long road, but I'm proud of how far I've come. How're you doing finding peace?", + "_audit_dia_id": "D8:27", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429870000, + "content": "I'm getting there, Caroline. Creativity and family keep me at peace.", + "_audit_dia_id": "D8:28", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429900000, + "content": "That's awesome, Melanie! How have your family been supportive during your move?", + "_audit_dia_id": "D8:29", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429930000, + "content": "My fam's been awesome - they helped out and showed lots of love and support.", + "_audit_dia_id": "D8:30", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689429960000, + "content": "Wow, Mel, family love and support is the best!", + "_audit_dia_id": "D8:31", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689429990000, + "content": "Yeah, Caroline, my family's been great - their love and support really helped me through tough times. It's awesome! We even went on another camping trip in the forest.", + "_audit_dia_id": "D8:32", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689430020000, + "content": "Awesome, Mel! Family support's huge. What else do you guys like doing together?", + "_audit_dia_id": "D8:33", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689430050000, + "content": "We enjoy hiking in the mountains and exploring forests. It's a cool way to connect with nature and each other.", + "_audit_dia_id": "D8:34", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689430080000, + "content": "Wow, Mel, that sounds awesome! Exploring nature and family time is so special.", + "_audit_dia_id": "D8:35", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689430110000, + "content": "Yeah, Caroline, they're some of my fave memories. It brings us together and brings us happiness. Glad you're here to share in it.", + "_audit_dia_id": "D8:36", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689430140000, + "content": "Thanks, Melanie! Really glad to have you as a friend to share my journey. You're awesome!", + "_audit_dia_id": "D8:37", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689430170000, + "content": "Thanks, Caroline! Appreciate your friendship. It's great to have a supporter!", + "_audit_dia_id": "D8:38", + "_audit_locomo_session": "session_8" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689430200000, + "content": "No worries, Mel! Your friendship means so much to me. Enjoy your day!", + "_audit_dia_id": "D8:39", + "_audit_locomo_session": "session_8" + } + ] + }, + { + "locomo_session": "session_9", + "session_start_iso": "2023-07-17T14:31:00+00:00", + "message_count": 17, + "messages": [ + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689604260000, + "content": "Hey Caroline, hope all's good! I had a quiet weekend after we went camping with my fam two weekends ago. It was great to unplug and hang with the kids. What've you been up to? Anything fun over the weekend?", + "_audit_dia_id": "D9:1", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689604290000, + "content": "Hey Melanie! That sounds great! Last weekend I joined a mentorship program for LGBTQ youth - it's really rewarding to help the community.", + "_audit_dia_id": "D9:2", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689604320000, + "content": "Wow, Caroline! It's great that you're helping out. How's it going? Got any cool experiences you can share?", + "_audit_dia_id": "D9:3", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689604350000, + "content": "The mentoring is going great! I've met some amazing young folks and supported them along the way. It's inspiring to see how resilient and strong they are.", + "_audit_dia_id": "D9:4", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689604380000, + "content": "Wow, Caroline, that sounds super rewarding! Young people's resilience is amazing. Care to share some stories?", + "_audit_dia_id": "D9:5", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689604410000, + "content": "I mentor a transgender teen just like me. We've been working on building up confidence and finding positive strategies, and it's really been paying off! We had a great time at the LGBT pride event last month.", + "_audit_dia_id": "D9:6", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689604440000, + "content": "Caroline, awesome news that you two are getting along! What was it like for you both? Care to fill me in?", + "_audit_dia_id": "D9:7", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689604470000, + "content": "The pride event was awesome! It was so encouraging to be surrounded by so much love and acceptance.", + "_audit_dia_id": "D9:8", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689604500000, + "content": "Wow! What's the best part you remember from it?", + "_audit_dia_id": "D9:9", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689604530000, + "content": "Seeing my mentee's face light up when they saw the support was the best! Such a special moment.", + "_audit_dia_id": "D9:10", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689604560000, + "content": "Wow, Caroline! They must have felt so appreciated. It's awesome to see the difference we can make in each other's lives. Any other exciting LGBTQ advocacy stuff coming up?", + "_audit_dia_id": "D9:11", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689604590000, + "content": "Yay! Next month I'm having an LGBTQ art show with my paintings - can't wait!", + "_audit_dia_id": "D9:12", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689604620000, + "content": "Wow, Caroline, that sounds awesome! Can't wait to see your art - got any previews?", + "_audit_dia_id": "D9:13", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689604650000, + "content": "Check out my painting for the art show! Hope you like it.", + "_audit_dia_id": "D9:14", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689604680000, + "content": "Wow, Caroline, that painting is awesome! Those colors are so vivid and the whole thing looks really unified. What inspired you?", + "_audit_dia_id": "D9:15", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689604710000, + "content": "Thanks, Melanie! I painted this after I visited a LGBTQ center. I wanted to capture everyone's unity and strength.", + "_audit_dia_id": "D9:16", + "_audit_locomo_session": "session_9" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689604740000, + "content": "Wow, Caroline! It really conveys unity and strength - such a gorgeous piece! My kids and I just finished another painting like our last one.", + "_audit_dia_id": "D9:17", + "_audit_locomo_session": "session_9" + } + ] + }, + { + "locomo_session": "session_10", + "session_start_iso": "2023-07-20T20:56:00+00:00", + "message_count": 24, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689886560000, + "content": "Hey Melanie! Just wanted to say hi!", + "_audit_dia_id": "D10:1", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689886590000, + "content": "Hey Caroline! Good to talk to you again. What's up? Anything new since last time?", + "_audit_dia_id": "D10:2", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689886620000, + "content": "Hey Mel! A lot's happened since we last chatted - I just joined a new LGBTQ activist group last Tues. I'm meeting so many cool people who are as passionate as I am about rights and community support. I'm giving my voice and making a real difference, plus it's fulfilling in so many ways. It's just great, you know?", + "_audit_dia_id": "D10:3", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689886650000, + "content": "That's awesome, Caroline! Glad to hear you found a great group where you can have an impact. Bet it feels great to be able to speak your truth and stand up for what's right. Want to tell me a bit more about it?", + "_audit_dia_id": "D10:4", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689886680000, + "content": "Thanks, Melanie! It's awesome to have our own platform to be ourselves and support others' rights. Our group, 'Connected LGBTQ Activists', is made of all kinds of people investing in positive changes. We have regular meetings, plan events and campaigns, to get together and support each other.", + "_audit_dia_id": "D10:5", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689886710000, + "content": "Wow, Caroline, your group sounds awesome! Supporting each other and making good things happen - that's so inspiring! Have you been part of any events or campaigns lately?", + "_audit_dia_id": "D10:6", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689886740000, + "content": "Last weekend our city held a pride parade! So many people marched through the streets waving flags, holding signs and celebrating love and diversity. I missed it but it was a powerful reminder that we are not alone in this fight for equality and inclusivity. Change is possible!", + "_audit_dia_id": "D10:7", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689886770000, + "content": "Wow, fantastic, Caroline! Bet the atmosphere was incredible. Oh yeah, we went to the beach recently. It was awesome! The kids had such a blast.", + "_audit_dia_id": "D10:8", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689886800000, + "content": "Sounds fun! What was the best part? Do you do it often with the kids?", + "_audit_dia_id": "D10:9", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689886830000, + "content": "Seeing my kids' faces so happy at the beach was the best! We don't go often, usually only once or twice a year. But those times are always special to spend time together and chill.", + "_audit_dia_id": "D10:10", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689886860000, + "content": "Sounds special, those beach trips! Do you have any other summer traditions you all do together? Create those memories!", + "_audit_dia_id": "D10:11", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689886890000, + "content": "We always look forward to our family camping trip. We roast marshmallows, tell stories around the campfire and just enjoy each other's company. It's the highlight of our summer!", + "_audit_dia_id": "D10:12", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689886920000, + "content": "Wow, Mel, that's awesome! What's your best camping memory?", + "_audit_dia_id": "D10:13", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689886950000, + "content": "I'll always remember our camping trip last year when we saw the Perseid meteor shower. It was so amazing lying there and watching the sky light up with streaks of light. We all made wishes and felt so at one with the universe. That's a memory I'll never forget.", + "_audit_dia_id": "D10:14", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689886980000, + "content": "Cool! What did it look like?", + "_audit_dia_id": "D10:15", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689887010000, + "content": "The sky was so clear and filled with stars, and the meteor shower was amazing - it felt like we were part of something huge and awe-inspiring.", + "_audit_dia_id": "D10:16", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689887040000, + "content": "Wow, Mel. That must've been breathtaking!", + "_audit_dia_id": "D10:17", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689887070000, + "content": "It was one of those moments where I felt tiny and in awe of the universe. Reminds me how awesome life is - so many little moments like that.", + "_audit_dia_id": "D10:18", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689887100000, + "content": "That's great, Mel! What other good memories do you have that make you feel thankful for life?", + "_audit_dia_id": "D10:19", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689887130000, + "content": "I'll never forget the day my youngest took her first steps. Seeing her wobble as she took those initial steps really put into perspective how fleeting life is and how lucky I am to be able to share these moments.", + "_audit_dia_id": "D10:20", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689887160000, + "content": "Aw, that's sweet, Mel! Those milestones are great reminders of how special our bonds are.", + "_audit_dia_id": "D10:21", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689887190000, + "content": "Yeah, they sure are. It's special moments like these that make me appreciate life and how lucky I am to be with my family and have our love.", + "_audit_dia_id": "D10:22", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1689887220000, + "content": "Wow, Melanie, what a beautiful moment! Lucky you to have such an awesome family!", + "_audit_dia_id": "D10:23", + "_audit_locomo_session": "session_10" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1689887250000, + "content": "Thanks, Caroline! I'm really lucky to have my family; they bring so much joy and love.", + "_audit_dia_id": "D10:24", + "_audit_locomo_session": "session_10" + } + ] + }, + { + "locomo_session": "session_11", + "session_start_iso": "2023-08-14T14:24:00+00:00", + "message_count": 17, + "messages": [ + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692023040000, + "content": "Hey Caroline! Last night was amazing! We celebrated my daughter's birthday with a concert surrounded by music, joy and the warm summer breeze. Seeing my kids' smiles was so awesome, and I'm so thankful for our special moments together.", + "_audit_dia_id": "D11:1", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692023070000, + "content": "Wow, sounds wonderful! Your love for your kids is so awesome. What concert was it? The advocacy event was a cool experience - so much love and support, amazing!", + "_audit_dia_id": "D11:2", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692023100000, + "content": "Thanks, Caroline! It was Matt Patterson, he is so talented! His voice and songs were amazing. What's up with you? Anything interesting going on?", + "_audit_dia_id": "D11:3", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692023130000, + "content": "Wow, Mel, glad you had a blast at the concert. A lot's happened since we talked. I went to a pride parade last Friday and it was awesome - so much energy and love everywhere. Really made me proud and reminded me how important it is to keep standing up for equality.", + "_audit_dia_id": "D11:4", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692023160000, + "content": "Wow, that's awesome! How did it feel being part of that community?", + "_audit_dia_id": "D11:5", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692023190000, + "content": "It was so inspiring, Mel! Check out the crowd. People of all kinds celebrating love and acceptance - it really pushed me to keep fighting for LGBTQ rights.", + "_audit_dia_id": "D11:6", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692023220000, + "content": "Wow, Caroline! That sounds awesome. This pic's from last night - looks like everyone was having a blast! Reminds me it's important to cultivate a loving and accepting environment for our kids. How do you stay inclusive in your work as an artist?", + "_audit_dia_id": "D11:7", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692023250000, + "content": "That pic is cool! Representing inclusivity and diversity in my art is important to me. I also use it to speak up for the LGBTQ+ community and push for acceptance. Here's a recent painting!", + "_audit_dia_id": "D11:8", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692023280000, + "content": "Wow, that rocks! What's the main idea of your art?", + "_audit_dia_id": "D11:9", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692023310000, + "content": "My art is about expressing my trans experience. It's my way of showing my story and helping people understand the trans community.", + "_audit_dia_id": "D11:10", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692023340000, + "content": "Your art's amazing, Caroline. I love how you use it to tell your stories and teach people about trans folks. I'd love to see another painting of yours!", + "_audit_dia_id": "D11:11", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692023370000, + "content": "Thanks, Melanie. Here's one- 'Embracing Identity' is all about finding comfort and love in being yourself. The woman in the painting stands for the journey of acceptance. My aim was to show warmth, love and self-acceptance.", + "_audit_dia_id": "D11:12", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692023400000, + "content": "Wow, Caroline, that's gorgeous! I love the self-acceptance and love theme. How does art help you with your self-discovery and acceptance journey?", + "_audit_dia_id": "D11:13", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692023430000, + "content": "Art's allowed me to explore my transition and my changing body. It's been a great way to work through stuff I'm going through. I love that it teaches me to accept the beauty of imperfections.", + "_audit_dia_id": "D11:14", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692023460000, + "content": "Wow, Caroline, that's so cool! Art can be so healing and a way to really connect with who you are. It's awesome that beauty can be found in the imperfections. We're all individual and wonderfully imperfect. Thanks for sharing it with me!", + "_audit_dia_id": "D11:15", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692023490000, + "content": "Thanks, Melanie. It means a lot to share this with you.", + "_audit_dia_id": "D11:16", + "_audit_locomo_session": "session_11" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692023520000, + "content": "Great chatting with you! Feel free to reach out any time.", + "_audit_dia_id": "D11:17", + "_audit_locomo_session": "session_11" + } + ] + }, + { + "locomo_session": "session_12", + "session_start_iso": "2023-08-17T13:50:00+00:00", + "message_count": 21, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692280200000, + "content": "Hey Mel! How're ya doin'? Recently, I had a not-so-great experience on a hike. I ran into a group of religious conservatives who said something that really upset me. It made me think how much work we still have to do for LGBTQ rights. It's been so helpful to have people around me who accept and support me, so I know I'll be ok!", + "_audit_dia_id": "D12:1", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692280230000, + "content": "Hey Caroline, sorry about the hike. It sucks when people are so closed-minded. Strong support really helps. FYI, I finished another pottery project - want to see a pic?", + "_audit_dia_id": "D12:2", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692280260000, + "content": "Sure thing, Melanie! Can't wait to see your pottery project. I'm happy you found something that makes you happy. Show me when you can!", + "_audit_dia_id": "D12:3", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692280290000, + "content": "Here it is. Pretty proud of it! It was a great experience. Thoughts?", + "_audit_dia_id": "D12:4", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692280320000, + "content": "That bowl is awesome, Mel! What gave you the idea for all the colors and patterns?", + "_audit_dia_id": "D12:5", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692280350000, + "content": "Thanks, Caroline! I'm obsessed with those, so I made something to catch the eye and make people smile. Plus, painting helps me express my feelings and be creative. Each stroke carries a part of me.", + "_audit_dia_id": "D12:6", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692280380000, + "content": "That's amazing! You put so much effort and passion into it. Your creativity really shines. Seeing how art can be a source of self-expression and growth is truly inspiring. You're killing it!", + "_audit_dia_id": "D12:7", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692280410000, + "content": "Thanks, Caroline! Your words really mean a lot. I've always felt a strong connection to art, and it's been a huge learning experience. It's both a sanctuary and a source of comfort. I'm so glad to have something that brings me so much happiness and fulfillment.", + "_audit_dia_id": "D12:8", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692280440000, + "content": "Glad you found something that makes you so happy! Surrounding ourselves with things that bring us joy is important. Life's too short to do anything else!", + "_audit_dia_id": "D12:9", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692280470000, + "content": "Agreed, Caroline. Life's tough but it's worth it when we have things that make us happy.", + "_audit_dia_id": "D12:10", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692280500000, + "content": "Definitely, Mel! Finding those happy moments and clinging to them is key. It's what keeps us going, even when life's hard. I'm lucky to have people like you to remind me.", + "_audit_dia_id": "D12:11", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692280530000, + "content": "Yeah, same here Caroline. You make life's struggles more bearable.", + "_audit_dia_id": "D12:12", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692280560000, + "content": "Thanks, Melanie! It means a lot having you in my corner. Appreciate our friendship!", + "_audit_dia_id": "D12:13", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692280590000, + "content": "I appreciate our friendship too, Caroline. You've always been there for me.", + "_audit_dia_id": "D12:14", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692280620000, + "content": "I'm always here for you, Mel! We had a blast last year at the Pride fest. Those supportive friends definitely make everything worth it!", + "_audit_dia_id": "D12:15", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692280650000, + "content": "That was a blast! So much fun with the whole gang! Wanna do a family outing this summer?", + "_audit_dia_id": "D12:16", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692280680000, + "content": "Right, it was so much fun! We could do a family outting, or wanna plan something special for this summer, just us two? It'd be a great chance to catch up and explore nature! What do you think?", + "_audit_dia_id": "D12:17", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692280710000, + "content": "Sounds great, Caroline! Let's plan something special!", + "_audit_dia_id": "D12:18", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692280740000, + "content": "Sounds great, Mel! We'll make some awesome memories!", + "_audit_dia_id": "D12:19", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692280770000, + "content": "Yeah, Caroline! I'll start thinking about what we can do.", + "_audit_dia_id": "D12:20", + "_audit_locomo_session": "session_12" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692280800000, + "content": "Yeah, Mel! Life's all about creating memories. Can't wait for the trip!", + "_audit_dia_id": "D12:21", + "_audit_locomo_session": "session_12" + } + ] + }, + { + "locomo_session": "session_13", + "session_start_iso": "2023-08-23T15:31:00+00:00", + "message_count": 18, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692804660000, + "content": "Hi Melanie! Hope you're doing good. Guess what I did this week? I took the first step towards becoming a mom - I applied to adoption agencies! It's a big decision, but I think I'm ready to give all my love to a child. I got lots of help from this adoption advice/assistance group I attended. It was great!", + "_audit_dia_id": "D13:1", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692804690000, + "content": "Caroline, congrats! So proud of you for taking this step. How does it feel? Also, do you have any pets?", + "_audit_dia_id": "D13:2", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692804720000, + "content": "Thanks, Mel! Exciting but kinda nerve-wracking. Parenting's such a big responsibility. And yup, I do- Oscar, my guinea pig. He's been great. How are your pets?", + "_audit_dia_id": "D13:3", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692804750000, + "content": "Yeah, it's normal to be both excited and nervous with a big decision. And thanks for asking, they're good- we got another cat named Bailey too. Here's a pic of Oliver. Can you show me one of Oscar?", + "_audit_dia_id": "D13:4", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692804780000, + "content": "He's so cute! What’s the funniest thing Oliver's done? And sure, check out this pic of him eating parsley! Veggies are his fave!", + "_audit_dia_id": "D13:5", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692804810000, + "content": "Oliver's hilarious! He hid his bone in my slipper once! Cute, right? Almost as silly as when I got to feed a horse a carrot. ", + "_audit_dia_id": "D13:6", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692804840000, + "content": "That's so funny! I used to go horseback riding with my dad when I was a kid, we'd go through the fields, feeling the wind. It was so special. I've always had a love for horses!", + "_audit_dia_id": "D13:7", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692804870000, + "content": "Wow, that sounds great - I agree, they're awesome. Here's a photo of my horse painting I did recently.", + "_audit_dia_id": "D13:8", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692804900000, + "content": "Wow, Melanie, that's amazing! Love all the details and how you got the horse's grace and strength. Do you like painting animals?", + "_audit_dia_id": "D13:9", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692804930000, + "content": "Thanks, Caroline! Glad you like it. Yeah, I love to. It's peaceful and special. Horses have such grace! Do you like to paint too?", + "_audit_dia_id": "D13:10", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692804960000, + "content": "Painting's great for expressing myself. I love creating art! Here's a recent self-portrait I made last week.", + "_audit_dia_id": "D13:11", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692804990000, + "content": "Caroline, that's great! The blue's really powerful, huh? How'd you feel while painting it?", + "_audit_dia_id": "D13:12", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692805020000, + "content": "Thanks, Mel! I felt liberated and empowered doing it. Painting helps me explore my identity and be true to myself. It's definitely therapeutic.", + "_audit_dia_id": "D13:13", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692805050000, + "content": "Wow, Caroline, that's great! Art's awesome for showing us who we really are and getting in touch with ourselves. What else helps you out?", + "_audit_dia_id": "D13:14", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692805080000, + "content": "Thanks, Melanie. Art gives me a sense of freedom, but so does having supportive people around, promoting LGBTQ rights and being true to myself. I want to live authentically and help others to do the same.", + "_audit_dia_id": "D13:15", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692805110000, + "content": "Wow, Caroline! That's amazing. You really care about being real and helping others. Wishing you the best on your adoption journey!", + "_audit_dia_id": "D13:16", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692805140000, + "content": "Thanks, Melanie! I really appreciate it. Excited for the future! Bye!", + "_audit_dia_id": "D13:17", + "_audit_locomo_session": "session_13" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692805170000, + "content": "Bye Caroline. I'm here for you. Take care of yourself.", + "_audit_dia_id": "D13:18", + "_audit_locomo_session": "session_13" + } + ] + }, + { + "locomo_session": "session_14", + "session_start_iso": "2023-08-25T13:33:00+00:00", + "message_count": 35, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692970380000, + "content": "Hey, Mel! How's it going? There's something I want to tell you. I went hiking last week and got into a bad spot with some people. It really bugged me, so I tried to apologize to them.", + "_audit_dia_id": "D14:1", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692970410000, + "content": "Wow, Caroline! Sorry that happened to you. It's tough when those things happen, but it's great you apologized. Takes a lot of courage and maturity! What do you think of this?", + "_audit_dia_id": "D14:2", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692970440000, + "content": "Thanks, Melanie! That plate is awesome! Did you make it?", + "_audit_dia_id": "D14:3", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692970470000, + "content": "Yeah, I made it in pottery class yesterday. I love it! Pottery's so relaxing and creative. Have you tried it yet?", + "_audit_dia_id": "D14:4", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692970500000, + "content": "Nah, I haven't. I've been busy painting - here's something I just finished.", + "_audit_dia_id": "D14:5", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692970530000, + "content": "Wow Caroline, that looks amazing! Those colors are so vivid, it really looks like a real sunset. What gave you the idea to paint it?", + "_audit_dia_id": "D14:6", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692970560000, + "content": "Thanks, Melanie! I painted it after I visited the beach last week. Just seeing the sun dip below the horizon, all the amazing colors - it was amazing and calming. So I just had to try to capture that feeling in my painting.", + "_audit_dia_id": "D14:7", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692970590000, + "content": "Wow, the beach really inspired you. The art really took me to that moment and I can feel the serenity. You captured the sunset perfectly, so peaceful!", + "_audit_dia_id": "D14:8", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692970620000, + "content": "Thanks Mel, really appreciate your kind words. It means a lot to me that you can feel the sense of peace and serenity. Makes me feel connected.", + "_audit_dia_id": "D14:9", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692970650000, + "content": "I feel the same way! Art is so cool like that - it connects us and helps us understand each other. I was actually just remembering yesterday, spending the day with my fam volunteering at a homeless shelter. It was hard to see how neglected some people are, but it was great to feel like we could make a difference.", + "_audit_dia_id": "D14:10", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692970680000, + "content": "Wow, Mel, you're amazing! Volunteering and making a difference- it's so heartwarming. You're an inspiration to us all!", + "_audit_dia_id": "D14:11", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692970710000, + "content": "Thanks, Caroline! I really appreciate your help and motivation. What made you decide to transition and join the transgender community?", + "_audit_dia_id": "D14:12", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692970740000, + "content": "Finding a community where I'm accepted, loved and supported has really meant a lot to me. It's made a huge difference to have people who get what I'm going through. Stuff like this mural are really special to me!", + "_audit_dia_id": "D14:13", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692970770000, + "content": "Caroline, glad you found a supportive community! Can you tell me more about why it's special to you?", + "_audit_dia_id": "D14:14", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692970800000, + "content": "The rainbow flag mural is important to me as it reflects the courage and strength of the trans community. The eagle symbolizes freedom and pride, representing my own resilience and that of others.", + "_audit_dia_id": "D14:15", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692970830000, + "content": "I'm in awe of your courage as a trans person. Have you made any more art lately?", + "_audit_dia_id": "D14:16", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692970860000, + "content": "Thanks, Mel! I made this stained glass window to remind myself and others that within us all is the key to discovering our true potential and living our best life.", + "_audit_dia_id": "D14:17", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692970890000, + "content": "Wow, Caroline, that looks amazing! What inspired it?", + "_audit_dia_id": "D14:18", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692970920000, + "content": "Thanks! It was made for a local church and shows time changing our lives. I made it to show my own journey as a transgender woman and how we should accept growth and change.", + "_audit_dia_id": "D14:19", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692970950000, + "content": "Wow, Caroline! All those colors are incredible and the story it tells is so inspiring.", + "_audit_dia_id": "D14:20", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692970980000, + "content": "Thanks, Mel! Glad you like it. It's a symbol of togetherness, to celebrate differences and be that much closer. I'd love to make something like this next!", + "_audit_dia_id": "D14:21", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692971010000, + "content": "Wow, that's gorgeous! Where did you find it?", + "_audit_dia_id": "D14:22", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692971040000, + "content": "I was out walking in my neighborhood when I came across this cool rainbow sidewalk for Pride Month. It was so vibrant and welcoming, I had to take a picture! It reminds us that love and acceptance are everywhere—even where we least expect it.", + "_audit_dia_id": "D14:23", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692971070000, + "content": "That's so nice, Caroline! Art can be in the most unlikely places. Love and acceptance really can be found everywhere.", + "_audit_dia_id": "D14:24", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692971100000, + "content": "Agreed, Mel! Art can be a real mood-booster - I saw someone drawing on the ground the other day and it made me so happy. Creativity sure can lighten someone's day.", + "_audit_dia_id": "D14:25", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692971130000, + "content": "Wow, Caroline, that's so nice! The colors are so bright and the flowers are so pretty. Art is such a source of joy.", + "_audit_dia_id": "D14:26", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692971160000, + "content": "Thanks, Mel! Art gives me so much joy. It helps me show my feelings and freeze gorgeous moments, like a bouquet of flowers. ", + "_audit_dia_id": "D14:27", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692971190000, + "content": "Wow, did you make that? It looks so real!", + "_audit_dia_id": "D14:28", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692971220000, + "content": "Yeah, definitely! Drawing flowers is one of my faves. Appreciating nature and sharing it is great. What about you, Mel? What type of art do you love?", + "_audit_dia_id": "D14:29", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692971250000, + "content": "Painting landscapes and still life is my favorite! Nature's amazing, here's a painting I did recently.", + "_audit_dia_id": "D14:30", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692971280000, + "content": "Wow, Mel! Any more paintings coming up?", + "_audit_dia_id": "D14:31", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692971310000, + "content": "I'm feeling inspired by autumn so I'm planning a few. You got any cool art projects coming up?", + "_audit_dia_id": "D14:32", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692971340000, + "content": "I'm putting together an LGBTQ art show next month and I'm gonna show my paintings. Super stoked!", + "_audit_dia_id": "D14:33", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1692971370000, + "content": "Wow, Caroline, that's awesome! Can't wait to see your show - the LGBTQ community needs more platforms like this!", + "_audit_dia_id": "D14:34", + "_audit_locomo_session": "session_14" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1692971400000, + "content": "Yeah Mel, stoked! Gonna be a great night featuring LGBTQ artists and their awesome talents. We want it to spread understanding and acceptance - let's make it happen!", + "_audit_dia_id": "D14:35", + "_audit_locomo_session": "session_14" + } + ] + }, + { + "locomo_session": "session_15", + "session_start_iso": "2023-08-28T15:19:00+00:00", + "message_count": 28, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693235940000, + "content": "Hey Melanie, great to hear from you. What's been up since we talked?", + "_audit_dia_id": "D15:1", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693235970000, + "content": "Hey Caroline! Since we last spoke, I took my kids to a park yesterday. They had fun exploring and playing. It was nice seeing them have a good time outdoors. Time flies, huh? What's new with you?", + "_audit_dia_id": "D15:2", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236000000, + "content": "Wow, your kids had so much fun at the park! Being outdoors can be really enjoyable. A lot happened since our last chat. I've been chasing my ambitions and had the chance to volunteer at an LGBTQ+ youth center. It was so gratifying to talk to similar young people. It made me remember how essential it is to be kind and show support.", + "_audit_dia_id": "D15:3", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236030000, + "content": "That sounds great, Caroline. Volunteering is a great way to meet people. Creating community and supporting each other, especially for kids, is really important. How did you feel about your time there? Anything that sticks out to you?", + "_audit_dia_id": "D15:4", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236060000, + "content": "I loved it. It was awesome to see how strong the young people were, with all the challenges they face. I felt fulfilled guiding and supporting them. I even got to let them know they're not alone by sharing my story. Such a powerful, emotional experience.", + "_audit_dia_id": "D15:5", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236090000, + "content": "Was connecting with those young folks meaningful for you? ", + "_audit_dia_id": "D15:6", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236120000, + "content": "It was so special to me. It reminded me of my own struggles in the past and how I felt alone. I was glad I could share my story and offer them support - it felt like I could make a difference.", + "_audit_dia_id": "D15:7", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236150000, + "content": "That's great. Sharing your story and support might make a difference for a long time. What do you hope to do next time?", + "_audit_dia_id": "D15:8", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236180000, + "content": "I'm definitely carrying on volunteering at the youth center. It's an important part of my life and I've made strong connections with people there. I really believe in community and supporting each other. So I wanna keep making a difference.", + "_audit_dia_id": "D15:9", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236210000, + "content": "That's great news, Caroline! Love seeing your dedication to helping others. Any specific projects or activities you're looking forward to there?", + "_audit_dia_id": "D15:10", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236240000, + "content": "We're putting together a talent show for the kids next month. I'm looking forward to seeing how much fun everyone has and how proud they'll feel of their talents!", + "_audit_dia_id": "D15:11", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236270000, + "content": "That's so cool, Caroline! That's a great way to show off and be proud of everyone's skills. You know I love live music. Can't wait to hear about it!", + "_audit_dia_id": "D15:12", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236300000, + "content": "Wow! Did you see that band?", + "_audit_dia_id": "D15:13", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236330000, + "content": "Yeah, that pic was from a show I went to. It was so much fun and reminded me of how music brings us together.", + "_audit_dia_id": "D15:14", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236360000, + "content": "Wow, what a fun moment! What's the band?", + "_audit_dia_id": "D15:15", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236390000, + "content": "\"Summer Sounds\"- The playing an awesome pop song that got everyone dancing and singing. It was so fun and lively!", + "_audit_dia_id": "D15:16", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236420000, + "content": "That sounds great! Music brings us together and brings joy. Playing and singing let me express myself and connect with others - love it! So cathartic and uplifting.", + "_audit_dia_id": "D15:17", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236450000, + "content": "Cool! What type of music do you play?", + "_audit_dia_id": "D15:18", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236480000, + "content": "Guitar's mostly my thing. Playing it helps me get my emotions out.", + "_audit_dia_id": "D15:19", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236510000, + "content": "That's awesome! What type of guitar? Been playing long?", + "_audit_dia_id": "D15:20", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236540000, + "content": "I started playing acoustic guitar about five years ago; it's been a great way to express myself and escape into my emotions.", + "_audit_dia_id": "D15:21", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236570000, + "content": "Music's amazing, isn't it? Any songs that have deep meaning for you?", + "_audit_dia_id": "D15:22", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236600000, + "content": "Yeah totally! \"Brave\" by Sara Bareilles has a lot of significance for me. It's about being courageous and fighting for what's right. Whenever I hear this jam, I think about the paths I've taken and the progress I've made.", + "_audit_dia_id": "D15:23", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236630000, + "content": "That's a gorgeous song, Caroline. It really fits with your journey and your determination to make a difference. Music can be so inspiring and uplifting.", + "_audit_dia_id": "D15:24", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236660000, + "content": "Thanks, Melanie! Appreciate it. You play any instruments?", + "_audit_dia_id": "D15:25", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236690000, + "content": "Yeah, I play clarinet! Started when I was young and it's been great. Expression of myself and a way to relax.", + "_audit_dia_id": "D15:26", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1693236720000, + "content": "Cool! Got any fav tunes?", + "_audit_dia_id": "D15:27", + "_audit_locomo_session": "session_15" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1693236750000, + "content": "I'm a fan of both classical like Bach and Mozart, as well as modern music like Ed Sheeran's \"Perfect\".", + "_audit_dia_id": "D15:28", + "_audit_locomo_session": "session_15" + } + ] + }, + { + "locomo_session": "session_16", + "session_start_iso": "2023-09-13T00:09:00+00:00", + "message_count": 20, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1694563740000, + "content": "Hey Mel, long time no chat! I had a wicked day out with the gang last weekend - we went biking and saw some pretty cool stuff. It was so refreshing, and the pic I'm sending is just stunning, eh?", + "_audit_dia_id": "D16:1", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1694563770000, + "content": "Hey Caroline! It's so good to hear from you! That pic is so beautiful, the colors really pop. Biking sounds like a great way to get out in nature. We went camping with the kids a few weeks ago, had a blast exploring the forest and hiking. Nature can be so refreshing for your soul. Any plans coming up?", + "_audit_dia_id": "D16:2", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1694563800000, + "content": "Melanie, that photo's amazing! I love all the yellow leaves, it looks so cozy. That sounds like fun! Seeing how excited they get for the little things is awesome, it's so contagious.", + "_audit_dia_id": "D16:3", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1694563830000, + "content": "Thanks, Caroline! It's awesome seeing the kids get excited learning something new about nature. Those moments make being a parent worth it. We roasted marshmallows and shared stories around the campfire. Those simple moments make the best memories. What inspires you with your volunteering?", + "_audit_dia_id": "D16:4", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1694563860000, + "content": "I'm inspired seeing my work make a difference for the LGBTQ+ community. Knowing I'm helping create a more loving world is amazing. I'm really thankful for my friends, family and mentors' support. It inspires me to keep making art, too.", + "_audit_dia_id": "D16:5", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1694563890000, + "content": "Wow, Caroline, that looks awesome! I love how it shows the togetherness and power you were talking about. How long have you been creating art?", + "_audit_dia_id": "D16:6", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1694563920000, + "content": "Since I was 17 or so. I find it soempowering and cathartic. It's amazing how art can show things that are hard to put into words. How long have you been into art?", + "_audit_dia_id": "D16:7", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1694563950000, + "content": "Seven years now, and I've finally found my real muses: painting and pottery. It's so calming and satisfying. Check out my pottery creation in the pic!", + "_audit_dia_id": "D16:8", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1694563980000, + "content": "Melanie, those bowls are amazing! They each have such cool designs. I love that you chose pottery for your art. Painting and drawing have helped me express my feelings and explore my gender identity. Creating art was really important to me during my transition - it helped me understand and accept myself. I'm so grateful.", + "_audit_dia_id": "D16:9", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1694564010000, + "content": "Thanks, Caroline! It has really helped me out. I love how it's both a creative outlet and a form of therapy. Have you ever thought about trying it or another art form?", + "_audit_dia_id": "D16:10", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1694564040000, + "content": "I haven't done pottery yet, but I'm game for trying new art. I might try it sometime! Check out this piece I made!", + "_audit_dia_id": "D16:11", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1694564070000, + "content": "Wow, Caroline! This painting is awesome. Love the red and blue. What gave you the idea?", + "_audit_dia_id": "D16:12", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1694564100000, + "content": "Thanks, Melanie! I made this painting to show my path as a trans woman. The red and blue are for the binary gender system, and the mix of colors means smashing that rigid thinking. It's a reminder to love my authentic self - it's taken a while to get here but I'm finally proud of who I am.", + "_audit_dia_id": "D16:13", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1694564130000, + "content": "Wow, Caro, that painting is amazing! You've made so much progress. I'm super proud of you for being your true self. What effect has the journey had on your relationships?", + "_audit_dia_id": "D16:14", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1694564160000, + "content": "Thanks, Melanie. It's definitely changed them. Some close friends kept supporting me, but a few weren't able to handle it. It wasn't easy, but I'm much happier being around those who accept and love me. Now my relationships feel more genuine.", + "_audit_dia_id": "D16:15", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1694564190000, + "content": "Caroline, it's got to be tough dealing with those changes. Glad you've found people who uplift and accept you! Here's to a good time at the café last weekend - they even had thoughtful signs like this! It brings me so much happiness.", + "_audit_dia_id": "D16:16", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1694564220000, + "content": "Whoa, Mel, that sign looks serious. Did anything happen?", + "_audit_dia_id": "D16:17", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1694564250000, + "content": "The sign was just a precaution, I had a great time. But thank you for your concern, you're so thoughtful!", + "_audit_dia_id": "D16:18", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1694564280000, + "content": "Phew! Glad it all worked out and you had a good time at the park!", + "_audit_dia_id": "D16:19", + "_audit_locomo_session": "session_16" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1694564310000, + "content": "Yeah, it was so much fun! Those joyful moments definitely show us life's beauty.", + "_audit_dia_id": "D16:20", + "_audit_locomo_session": "session_16" + } + ] + }, + { + "locomo_session": "session_17", + "session_start_iso": "2023-10-13T10:31:00+00:00", + "message_count": 26, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193060000, + "content": "Hey Mel, what's up? Long time no see! I just contacted my mentor for adoption advice. I'm ready to be a mom and share my love and family. It's a great feeling. Anything new with you? Anything exciting going on?", + "_audit_dia_id": "D17:1", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193090000, + "content": "Hey Caroline! Great to hear from you! Wow, what an amazing journey. Congrats!", + "_audit_dia_id": "D17:2", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193120000, + "content": "Thanks, Melanie! I'm stoked to start this new chapter. It's been a dream to adopt and provide a safe, loving home for kids who need it. Do you have any experience with adoption, or know anyone who's gone through the process?", + "_audit_dia_id": "D17:3", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193150000, + "content": "Yeah, a buddy of mine adopted last year. It was a long process, but now they're super happy with their new kid. Makes me feel like maybe I should do it too!", + "_audit_dia_id": "D17:4", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193180000, + "content": "That's great news about your friend! It can be tough, but so worth it. It's a great way to add to your family and show your love. If you ever do it, let me know — I'd love to help in any way I can.", + "_audit_dia_id": "D17:5", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193210000, + "content": "Thanks, Caroline! Appreciate your help. Got any tips for getting started on it?", + "_audit_dia_id": "D17:6", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193240000, + "content": "Yep! Do your research and find an adoption agency or lawyer. They'll help with the process and provide all the info. Gather documents like references, financial info and medical checks. Don't forget to prepare emotionally, since the wait can be hard. It's all worth it in the end though.", + "_audit_dia_id": "D17:7", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193270000, + "content": "Thanks for the tip, Caroline. Doing research and readying myself emotionally makes sense. I'll do that. BTW, recently I had a setback. Last month I got hurt and had to take a break from pottery, which I use for self-expression and peace.", + "_audit_dia_id": "D17:8", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193300000, + "content": "Oh man, sorry to hear that, Melanie. I hope you're okay. Pottery's a great way to relax, so it must have been tough taking a break. Need any help?", + "_audit_dia_id": "D17:9", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193330000, + "content": "Thanks, Caroline. It was tough, but I'm doing ok. Been reading that book you recommended a while ago and painting to keep busy.", + "_audit_dia_id": "D17:10", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193360000, + "content": "Cool that you have creative outlets. Got any paintings to show? I'd love to check them out.", + "_audit_dia_id": "D17:11", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193390000, + "content": "Yeah, Here's one I did last week. It's inspired by the sunsets. The colors make me feel calm. What have you been up to lately, artistically?", + "_audit_dia_id": "D17:12", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193420000, + "content": "Wow Mel, that's stunning! Love the colors and the chilled-out sunset vibe. What made you paint it? I've been trying out abstract stuff recently. It's kinda freeing, just putting my feelings on the canvas without too much of a plan. It's like a cool form of self-expression.", + "_audit_dia_id": "D17:13", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193450000, + "content": "Thanks, Caroline! I painted it because it was calming. I've done an abstract painting too, take a look! I love how art lets us get our emotions out.", + "_audit_dia_id": "D17:14", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193480000, + "content": "Wow, that looks great! The blue adds so much to it. What feelings were you hoping to portray?", + "_audit_dia_id": "D17:15", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193510000, + "content": "I wanted a peaceful blue streaks to show tranquility. Blue calms me, so I wanted the painting to have a serene vibe while still having lots of vibrant colors.", + "_audit_dia_id": "D17:16", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193540000, + "content": "Yeah, it's very calming. It's awesome how art can show emotions. By the way, I went to a poetry reading last Fri - it was really powerful! Ever been to one?", + "_audit_dia_id": "D17:17", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193570000, + "content": "Nope, never been to something like that. What was it about? What made it so special?", + "_audit_dia_id": "D17:18", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193600000, + "content": "It was a transgender poetry reading where transgender people shared their stories through poetry. It was extra special 'cause it was a safe place for self-expression and it was really empowering to hear others share and celebrate their identities.", + "_audit_dia_id": "D17:19", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193630000, + "content": "Wow, sounds amazing! What was the event like? Those posters are great!", + "_audit_dia_id": "D17:20", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193660000, + "content": "The room was electric with energy and support! The posters were amazing, so much pride and strength! It inspired me to make some art.", + "_audit_dia_id": "D17:21", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193690000, + "content": "That's awesome, Caroline! You drew it? What does it mean to you?", + "_audit_dia_id": "D17:22", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193720000, + "content": "Thanks, Melanie! Yeah, I drew it. It stands for freedom and being real. It's like a nudge to always stay true to myself and embrace my womanhood.", + "_audit_dia_id": "D17:23", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193750000, + "content": "I love it. Showing off our true selves is the best thing ever.", + "_audit_dia_id": "D17:24", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697193780000, + "content": "Yep, Melanie! Being ourselves is such a great feeling. It's an ongoing adventure of learning and growing.", + "_audit_dia_id": "D17:25", + "_audit_locomo_session": "session_17" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697193810000, + "content": "Yep, Caroline. Life's about learning and exploring. Glad we can be on this trip together.", + "_audit_dia_id": "D17:26", + "_audit_locomo_session": "session_17" + } + ] + }, + { + "locomo_session": "session_18", + "session_start_iso": "2023-10-20T18:55:00+00:00", + "message_count": 24, + "messages": [ + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828100000, + "content": "Hey Caroline, that roadtrip this past weekend was insane! We were all freaked when my son got into an accident. We were so lucky he was okay. It was a real scary experience. Thankfully it's over now. What's been up since we last talked?", + "_audit_dia_id": "D18:1", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828130000, + "content": "Oops, sorry 'bout the accident! Must have been traumatizing for you guys. Thank goodness your son's okay. Life sure can be a roller coaster.", + "_audit_dia_id": "D18:2", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828160000, + "content": "Yeah, our trip got off to a bad start. I was really scared when we got into the accident. Thankfully, my son's ok and that was a reminder that life is precious and to cherish our family.", + "_audit_dia_id": "D18:3", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828190000, + "content": "Glad your son is okay, Melanie. Life's unpredictable, but moments like these remind us how important our loved ones are. Family's everything.", + "_audit_dia_id": "D18:4", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828220000, + "content": "Yeah, you're right, Caroline. Family's super important to me. Especially after the accident, I've thought a lot about how much I need them. They mean the world to me and I'm so thankful to have them. Thankfully, they enjoyed the Grand Canyon a lot!", + "_audit_dia_id": "D18:5", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828250000, + "content": "The kids look so cute, Mel! I bet they bring lots of joy. How did they handle the accident?", + "_audit_dia_id": "D18:6", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828280000, + "content": "Thanks! They were scared but we reassured them and explained their brother would be OK. They're tough kids.", + "_audit_dia_id": "D18:7", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828310000, + "content": "Kids are amazingly resilient in tough situations. They have an amazing ability to bounce back.", + "_audit_dia_id": "D18:8", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828340000, + "content": "They're really amazing. Wish I was that resilient too. But they give me the strength to keep going.", + "_audit_dia_id": "D18:9", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828370000, + "content": "Our loved ones give us strength to tackle any challenge - it's amazing!", + "_audit_dia_id": "D18:10", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828400000, + "content": "Yeah, Caroline. Totally agree. They're my biggest motivation and support.", + "_audit_dia_id": "D18:11", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828430000, + "content": "It's so sweet to see your love for your family, Melanie. They really are your rock.", + "_audit_dia_id": "D18:12", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828460000, + "content": "Thanks, Caroline. They're a real support. Appreciate them a lot.", + "_audit_dia_id": "D18:13", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828490000, + "content": "Glad you've got people to lean on, Melanie. It helps during tougher times.", + "_audit_dia_id": "D18:14", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828520000, + "content": "Yeah for sure. Having my fam around helps a lot. It makes hard times easier.", + "_audit_dia_id": "D18:15", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828550000, + "content": "Wow, great pic! Is that recent? Looks like you all had fun!", + "_audit_dia_id": "D18:16", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828580000, + "content": "Thanks, Caroline! Yup, we just did it yesterday! The kids loved it and it was a nice way to relax after the road trip.", + "_audit_dia_id": "D18:17", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828610000, + "content": "Glad you got some R&R after the drive. Nature sure seems to refresh us, huh?", + "_audit_dia_id": "D18:18", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828640000, + "content": "Absolutely! It really helps me reset and recharge. I love camping trips with my fam, 'cause nature brings such peace and serenity.", + "_audit_dia_id": "D18:19", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828670000, + "content": "Wow, that's awesome! What do you love most about camping with your fam?", + "_audit_dia_id": "D18:20", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828700000, + "content": "It's a chance to be present and together. We bond over stories, campfires and nature. It's so peaceful waking up to the sound of birds and the smell of fresh air - it always refreshes my soul.", + "_audit_dia_id": "D18:21", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828730000, + "content": "That's so peaceful and calming, Melanie! I can picture waking up to nature. It's great that you get to spend quality, tranquil time with your family.", + "_audit_dia_id": "D18:22", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697828760000, + "content": "Thanks, Caroline! This is a great time. Nature and quality time, can't beat it!", + "_audit_dia_id": "D18:23", + "_audit_locomo_session": "session_18" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697828790000, + "content": "Yeah totally! They're priceless. Lucky you!", + "_audit_dia_id": "D18:24", + "_audit_locomo_session": "session_18" + } + ] + }, + { + "locomo_session": "session_19", + "session_start_iso": "2023-10-22T09:55:00+00:00", + "message_count": 15, + "messages": [ + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697968500000, + "content": "Woohoo Melanie! I passed the adoption agency interviews last Friday! I'm so excited and thankful. This is a big move towards my goal of having a family.", + "_audit_dia_id": "D19:1", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697968530000, + "content": "Congrats, Caroline! Adoption sounds awesome. I'm so happy for you. These figurines I bought yesterday remind me of family love. Tell me, what's your vision for the future?", + "_audit_dia_id": "D19:2", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697968560000, + "content": "Thanks so much, Melanie! It's beautiful! It really brings home how much love's in families - both blood and the ones we choose. I hope to build my own family and put a roof over kids who haven't had that before. For me, adoption is a way of giving back and showing love and acceptance.", + "_audit_dia_id": "D19:3", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697968590000, + "content": "Wow, Caroline, that's awesome. Giving a home to needy kids is such a loving way to build a family. Those kids will be so supported and happy in their new home.", + "_audit_dia_id": "D19:4", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697968620000, + "content": "Thanks, Melanie. My dream is to create a safe and loving home for these kids. Love and acceptance should be everyone's right, and I want them to experience it.", + "_audit_dia_id": "D19:5", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697968650000, + "content": "I totally agree, Caroline. Everyone deserves that. It's awesome to see how passionate you are about helping these kids.", + "_audit_dia_id": "D19:6", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697968680000, + "content": "Thanks, Mel. Finding self-acceptance was a long process, but now I'm ready to offer love and support to those who need it. It's empowering to make a positive difference in someone's life.", + "_audit_dia_id": "D19:7", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697968710000, + "content": "That must have been tough for you, Caroline. Respect for finding acceptance and helping others with what you've been through. You're so strong and inspiring.", + "_audit_dia_id": "D19:8", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697968740000, + "content": "Thanks, Melanie. Transitioning wasn't easy and acceptance wasn't either, but the help I got from friends, family and people I looked up to was invaluable. They boosted me through tough times and helped me find out who I really am. That's why I want to pass that same support to anyone who needs it. Bringing others comfort and helping them grow brings me such joy.", + "_audit_dia_id": "D19:9", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697968770000, + "content": "I'm so happy for you, Caroline. You found your true self and now you're helping others. You're so inspiring!", + "_audit_dia_id": "D19:10", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697968800000, + "content": "Thanks, Melanie. Your support really means a lot. This journey has been amazing and I'm grateful I get to share it and help others with theirs. It's a real gift.", + "_audit_dia_id": "D19:11", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697968830000, + "content": "Absolutely! I'm so glad we can always be there for each other.", + "_audit_dia_id": "D19:12", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697968860000, + "content": "Glad you agree, Caroline. Appreciate the support of those close to me. Their encouragement made me who I am.", + "_audit_dia_id": "D19:13", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "melanie", + "role": "user", + "timestamp": 1697968890000, + "content": "Glad you had support. Being yourself is great!", + "_audit_dia_id": "D19:14", + "_audit_locomo_session": "session_19" + }, + { + "sender_id": "caroline", + "role": "user", + "timestamp": 1697968920000, + "content": "Yeah, that's true! It's so freeing to just be yourself and live honestly. We can really accept who we are and be content.", + "_audit_dia_id": "D19:15", + "_audit_locomo_session": "session_19" + } + ] + } + ] +} diff --git a/tests/fixtures/search_seed/atomic_fact.json b/tests/fixtures/search_seed/atomic_fact.json new file mode 100644 index 00000000..114ba91e --- /dev/null +++ b/tests/fixtures/search_seed/atomic_fact.json @@ -0,0 +1,20862 @@ +[ + { + "created_at": "2026-05-19T01:56:52.755573", + "updated_at": "2026-05-19T01:56:52.755580", + "id": "caroline_af_20260519_00000001", + "entry_id": "af_20260519_00000001", + "owner_id": "caroline", + "owner_type": "user", + "session_id": "locomo_c0_caroline_melanie", + "timestamp": "2023-05-08T14:04:30", + "parent_type": "memcell", + "parent_id": "mc_a7a1a4cfd8e6", + "sender_ids": [], + "fact": "Caroline said she went to a LGBTQ support group yesterday (May 7, 2023) and it was so powerful.", + "fact_tokens": "caroline said she went lgbtq support group yesterday may 2023 so powerful", + "md_path": 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"2023-05-08T14:04:30", + "parent_type": "memcell", + "parent_id": "mc_a7a1a4cfd8e6", + "sender_ids": [], + "fact": "Caroline said she plans to continue her education and check out career options.", + "fact_tokens": "caroline said she plans continue her education check out career options", + "md_path": "users/melanie/.atomic_facts/atomic_fact-2026-05-19.md", + "content_sha256": "404a1bc0ce25be9fb01b81f4482537dc2d0ae517f0a744e46a62917c13921e00", + "vector": [ + -0.00044948552385903895, + 0.015105723403394222, + -0.025517238304018974, + 0.026961609721183777, + -0.0021063757594674826, + 0.051997389644384384, + 0.03249836713075638, + 0.020221207290887833, + -0.0007372315158136189, + 0.0076431347988545895, + -0.00038930337177589536, + 0.007613043766468763, + 0.004934937227517366, + -0.04453480243682861, + 0.0011735522421076894, + 0.009388417936861515, + -0.0022568311542272568, + 0.019980479031801224, + 0.010531879030168056, + 0.001609872910194099, + 0.0037764308508485556, + 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counseling or mental health work.", + "fact_tokens": "caroline said she would love support similar issues through counseling mental health work", + "md_path": "users/melanie/.atomic_facts/atomic_fact-2026-05-19.md", + "content_sha256": "a3b3d7b4af4a23fad731933edbf4696c704734e1cb6840b2e350f8f6dd1dffd8", + "vector": [ + -0.0004512100713327527, + 0.04821065068244934, + -0.0376875139772892, + 0.003884995123371482, + -0.002431945875287056, + 0.06656495481729507, + 0.008810067549347878, + 0.05604182183742523, + -0.0024625363294035196, + -0.009054792113602161, + 0.02973397821187973, + 0.020189739763736725, + 0.00819825753569603, + -0.009850145317614079, + 0.006882865447551012, + 0.011624394915997982, + -0.0028908036183565855, + 0.020434463396668434, + -0.0269196517765522, + 0.001437754137441516, + 0.0017360115889459848, + -0.0003785575972869992, + 0.06803330034017563, + -0.025573669001460075, + 0.0484553724527359, + -0.003364956472069025, + -0.015417618677020073, + -0.023738238960504532, + 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people she works with.", + "fact_tokens": "melanie said caroline would great counselor because her empathy understanding will help people she works", + "md_path": "users/melanie/.atomic_facts/atomic_fact-2026-05-19.md", + "content_sha256": "b690c6131e59ac699cd0168c6e8546e5912a8f55213ef8ac8567f888c482ca42", + "vector": [ + -0.0003355093067511916, + 0.014117342419922352, + -0.007592520210891962, + 0.026692453771829605, + -0.0016756929690018296, + 0.07639973610639572, + 0.011981946416199207, + 0.06643455475568771, + 0.004389425739645958, + -0.01506640762090683, + 0.024912957102060318, + 0.02313346043229103, + 0.004893616773188114, + -0.02870921790599823, + -0.01909993402659893, + 0.02716698683798313, + -0.012575111351907253, + 0.01512572355568409, + -0.00327724008820951, + 0.000604287488386035, + -0.004656350240111351, + -0.027048353105783463, + 0.04294519126415253, + -0.005694390274584293, + 0.0384371317923069, + -0.01791360229253769, + -0.014235975220799446, + -0.03155641257762909, + 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Melanie Discuss LGBTQ Support Group Experience and Future Career Plans on May 8, 2023", + "summary": "On May 8, 2023 at 2:04 PM UTC, Caroline and Melanie reconnected in a conversation. Caroline shared that she had attended an LGBTQ support group yesterday (May 7, 2023), describing it as powerful and i", + "episode": "On May 8, 2023 at 2:04 PM UTC, Caroline and Melanie reconnected in a conversation. Caroline shared that she had attended an LGBTQ support group yesterday (May 7, 2023), describing it as powerful and inspiring, especially the transgender stories which made her feel happy and thankful for the support. Melanie expressed admiration for Caroline's courage and interest in the group. Caroline explained that the support group helped her feel accepted and gave her courage to embrace herself. She planned to continue her education and explore career options, particularly in counseling or mental health, aiming to support others facing similar issues. Melanie encouraged Caroline, praising her empathy and understanding, and shared a personal painting of a lake sunrise she created last year, highlighting painting as a creative outlet and relaxation method. Caroline agreed on the importance of self-expression and relaxation. The conversation ended with Caroline preparing to do research on her career interests, while Melanie planned to go swimming with her kids.", + "episode_tokens": "may 2023 04 pm utc caroline melanie reconnected conversation caroline shared she attended lgbtq support group yesterday may 2023 describing powerful inspiring especially transgender stories which made her feel happy thankful support melanie expressed admiration caroline courage interest group caroline explained support group helped her feel accepted gave her courage embrace herself she planned continue her education explore career options particularly counseling mental health aiming support others facing similar issues melanie encouraged caroline praising her empathy understanding shared personal painting lake sunrise she created last year highlighting painting creative outlet relaxation method caroline agreed importance self expression relaxation conversation ended caroline preparing research her career interests while melanie planned go swimming her kids", + "md_path": "users/caroline/episodes/episode-2026-05-19.md", + "content_sha256": "32d4c0d5cf99edd792c4d8983d112a11d4d241710cd2cc5e5386c427bac138d9", + "vector": [ + -0.00026553275529295206, + -0.004277425818145275, + 0.012658867985010147, + -0.008150230161845684, + -0.0014812031295150518, + 0.043467894196510315, + -0.00456644082441926, + 0.05017304793000221, + -0.00673405546694994, + 0.00861265417188406, + 0.05248517170548439, + 0.03121364675462246, + -0.0013728224439546466, + -0.01595364138484001, + -0.004075115080922842, + 0.02589576691389084, + -0.04161819815635681, + 0.016531672328710556, + -0.010866973549127579, + 0.0058959112502634525, + -0.007861214689910412, + 0.0003088850644417107, + 0.04832335188984871, + -0.007803411688655615, + 0.05271638184785843, + -0.02023106813430786, + -0.006589547730982304, + -0.0323697067797184, + 0.03514425456523895, + 0.0021820650435984135, + -0.0513291098177433, + -0.06520184129476547, + 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for mental health last Saturday (May 20, 2023), describing the experience as rewarding", + "episode": "On May 25, 2023 at 1:22 PM UTC, Melanie shared with Caroline that she had recently participated in a charity race for mental health last Saturday (May 20, 2023), describing the experience as rewarding and thought-provoking. Melanie reflected on the importance of self-care, explaining that dedicating time daily to activities like running, reading, and playing the violin helps her stay refreshed and better support her family. Caroline expressed pride and encouragement for Melanie's efforts. Melanie mentioned her children\u2019s excitement for the upcoming summer break and plans to go camping next month (June 2023). Caroline then revealed she was researching adoption agencies as part of her dream to create a loving family by adopting children in need. She shared that she was particularly interested in an agency supporting LGBTQ+ individuals, appreciating their inclusivity and support. Caroline acknowledged the challenges ahead as a single parent but expressed optimism and determination to provide a safe and loving home. Melanie praised Caroline\u2019s kindness and commitment, offering encouragement for this new chapter. Both women expressed mutual support and hopefulness about their respective journeys.", + "episode_tokens": "may 25 2023 22 pm utc melanie shared caroline she recently participated charity race mental health last saturday may 20 2023 describing experience rewarding thought provoking melanie reflected importance self care explaining dedicating time daily activities like running reading playing violin helps her stay refreshed better support her family caroline expressed pride encouragement melanie efforts melanie mentioned her children excitement upcoming summer break plans go camping next month june 2023 caroline then revealed she researching adoption agencies part her dream create loving family adopting children need she shared she particularly interested agency supporting lgbtq individuals appreciating their inclusivity support caroline acknowledged challenges ahead single parent expressed optimism determination provide safe loving home melanie praised caroline kindness commitment offering encouragement new chapter both women expressed mutual support hopefulness about their respective journeys", + "md_path": "users/caroline/episodes/episode-2026-05-19.md", + "content_sha256": "b3660c1faee02a9220ebb4644e16e11cf72b788d1f9fd957e6e632f770821d84", + "vector": [ + -0.0004106129636056721, + 0.015042182989418507, + 0.011831598356366158, + 0.004905059468001127, + -0.0021403897553682327, + 0.0466129295527935, + -0.037100087851285934, + 0.051369354128837585, + -0.010523582808673382, + 0.01783658005297184, + 0.024733392521739006, + -0.0027349423617124557, + -0.003894320223480463, + -0.011712688021361828, + -0.021284986287355423, + 0.01866895519196987, + -0.011950508691370487, + 0.005618522875010967, + -0.028062887489795685, + 0.0033740864600986242, + -0.006094165146350861, + -0.024376660585403442, + 0.06135783717036247, + -0.024614481255412102, + 0.04090522602200508, + -0.03329494968056679, + -0.01022630650550127, + -0.007967006415128708, + 0.034484054893255234, + 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on June 9, 2023", + "summary": "On June 9, 2023 at 8:06 PM UTC, Caroline shared with Melanie about a school event she held last week (May 31 - June 6, 2023) where she spoke about her transgender journey and encouraged student involv", + "episode": "On June 9, 2023 at 8:06 PM UTC, Caroline shared with Melanie about a school event she held last week (May 31 - June 6, 2023) where she spoke about her transgender journey and encouraged student involvement in the LGBTQ community. Caroline reflected on her progress since starting her transition three years ago. Melanie expressed pride and support for Caroline's courage and impact. Caroline described feeling powerful during her talk, sharing struggles and growth, and inspiring the audience to be better allies. Both emphasized the importance of conversations on gender identity, inclusion, and building a supportive community. They acknowledged the challenges of sharing personal stories but highlighted the hope and acceptance it fosters. Melanie and Caroline discussed their motivation sources: Caroline's friends, family, and mentors who have supported her for four years since moving from her home country, especially after a difficult breakup; Melanie's husband and kids who motivate her. Caroline shared a photo from a recent meetup with her support system. Melanie mentioned being married for five years and described a joyful family wedding day with games and good food. Both agreed on cherishing family moments as essential for happiness and strength. They committed to continuing to use their voices to promote love, understanding, and positive change together.", + "episode_tokens": "june 2023 06 pm utc caroline shared melanie about school event she held last week may 31 june 2023 where she spoke about her transgender journey encouraged student involvement lgbtq community caroline reflected her progress since starting her transition three years ago melanie expressed pride support caroline courage impact caroline described feeling powerful during her talk sharing struggles growth inspiring audience better allies both emphasized importance conversations gender identity inclusion building supportive community they acknowledged challenges sharing personal stories highlighted hope acceptance fosters melanie caroline discussed their motivation sources caroline friends family mentors who supported her four years since moving from her home country especially after difficult breakup melanie husband kids who motivate her caroline shared photo from recent meetup her support system melanie mentioned married five years described joyful family wedding day games good food both agreed cherishing family moments essential happiness strength they committed continuing use their voices promote love understanding positive change together", + "md_path": "users/caroline/episodes/episode-2026-05-19.md", + "content_sha256": "ea588c86df91868c1cd812b9789603710e04b46eacae80cb43864f536ec570e0", + "vector": [ + -0.00036076019750908017, + 0.00948283914476633, + 0.027329426258802414, + -0.022146258503198624, + -0.0018185258377343416, + 0.02202845923602581, + -0.011662125587463379, + 0.06738117337226868, + 0.00080618861829862, + 0.001339966431260109, + 0.03439737856388092, + 0.019436875358223915, + -0.003740126034244895, + -0.02791842259466648, + -0.012133322656154633, + 0.02202845923602581, + -0.0412297360599041, + -0.004653070122003555, + -0.004388021770864725, + 0.004181873518973589, + -0.010189634747803211, + -0.0054187653586268425, + 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"mc_8cb1c5bacaa1", + "sender_ids": [ + "caroline", + "melanie" + ], + "subject": "Caroline and Melanie Discuss Sentimental Items, Family Camping, and Caroline's Counseling Career Plans on June 27, 2023", + "summary": "On June 27, 2023 at 10:45 AM UTC, Caroline reconnected with Melanie, sharing that much had happened in her life. Caroline showed Melanie a necklace gifted by her grandmother in Sweden during her youth", + "episode": "On June 27, 2023 at 10:45 AM UTC, Caroline reconnected with Melanie, sharing that much had happened in her life. Caroline showed Melanie a necklace gifted by her grandmother in Sweden during her youth, symbolizing love, faith, and strength, serving as a reminder of her roots and family support. Caroline also mentioned a hand-painted bowl made by a friend for her 18th birthday ten years ago, representing art and self-expression. Melanie shared that she had taken her family camping in the mountains last week (June 20-26, 2023), enjoying nature exploration, roasting marshmallows, hiking, and appreciating the view, creating special family memories. Caroline expressed happiness for Melanie's experience and asked about recent events in her own life. Caroline revealed she had been exploring a career in counseling and mental health, aiming to help people with similar experiences. She was particularly interested in working with trans individuals to support their self-acceptance and mental health. Caroline attended an LGBTQ+ counseling workshop last Friday (June 23, 2023), which she found enlightening, learning about therapeutic methods and the importance of creating safe spaces. Melanie praised Caroline's dedication and asked about her motivation, to which Caroline explained her personal journey and the positive impact of counseling and support groups on her life, fueling her passion to help others. Melanie encouraged Caroline, acknowledging the significant impact she was making. Caroline appreciated Melanie's kind words, and Melanie congratulated her for pursuing her true passion.", + "episode_tokens": "june 27 2023 10 45 am utc caroline reconnected melanie sharing much happened her life caroline showed melanie necklace gifted her grandmother sweden during her youth symbolizing love faith strength serving reminder her roots family support caroline also mentioned hand painted bowl made friend her 18th birthday ten years ago representing art self expression melanie shared she taken her family camping mountains last week june 20 26 2023 enjoying nature exploration roasting marshmallows hiking appreciating view creating special family memories caroline expressed happiness melanie experience asked about recent events her own life caroline revealed she exploring career counseling mental health aiming help people similar experiences she particularly interested working trans individuals support their self acceptance mental health caroline attended lgbtq counseling workshop last friday june 23 2023 which she found enlightening learning about therapeutic methods importance creating safe spaces melanie praised caroline dedication asked about her motivation which caroline explained her personal journey positive impact counseling support groups her life fueling her passion help others melanie encouraged caroline acknowledging significant impact she making caroline appreciated melanie kind words melanie congratulated her pursuing her true passion", + "md_path": "users/caroline/episodes/episode-2026-05-19.md", + "content_sha256": "87cccd5fc368b63ac07f0fa28d1fbe7cc644bfaad818bc962152f9ab614d6c21", + "vector": [ + -0.00033736485056579113, + -4.2626503272913396e-05, + -0.015405720099806786, + -0.017506500706076622, + -0.0016120568616315722, + 0.029060790315270424, + -0.022758450359106064, + 0.06162288039922714, + -0.01184606458991766, + 0.022525029256939888, + 0.020190829411149025, + 0.026609880849719048, + -0.004668400157243013, + -0.002669741166755557, 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"ep_20260519_00000005", + "owner_id": "caroline", + "owner_type": "user", + "session_id": "locomo_c0_caroline_melanie", + "timestamp": "2023-07-03T13:43:30", + "parent_type": "memcell", + "parent_id": "mc_d32c5240d266", + "sender_ids": [ + "caroline", + "melanie" + ], + "subject": "Caroline and Melanie Discuss LGBTQ+ Pride Parade, Pottery, and Upcoming Transgender Conference on July 3, 2023", + "summary": "On July 3, 2023 at 1:43 PM UTC, Caroline shared with Melanie that she had attended an LGBTQ+ pride parade last week (June 26-30, 2023), which made her feel a strong sense of belonging and showed her t", + "episode": "On July 3, 2023 at 1:43 PM UTC, Caroline shared with Melanie that she had attended an LGBTQ+ pride parade last week (June 26-30, 2023), which made her feel a strong sense of belonging and showed her the community's growth. This experience motivated Caroline to pursue counseling and mental health work to help others. Melanie responded by sharing that she had signed up for a pottery class yesterday (July 2, 2023), describing it as therapeutic and a way to express creativity. Caroline revealed she was learning piano to get creative. Melanie showed Caroline a black and white bowl she made in class, expressing pride in her work and how pottery helps her express emotions and brings her joy. Caroline encouraged Melanie to continue. Melanie asked if Caroline had any upcoming plans, and Caroline said she was excited to attend a transgender conference this month (July 2023) to meet others in the community and learn about advocacy. Melanie wished her a great time and safe travels. The conversation ended with Caroline promising to keep Melanie updated.", + "episode_tokens": "july 2023 43 pm utc caroline shared melanie she attended lgbtq pride parade last week june 26 30 2023 which made her feel strong sense belonging showed her community growth experience motivated caroline pursue counseling mental health work help others melanie responded sharing she signed up pottery class yesterday july 2023 describing therapeutic way express creativity caroline revealed she learning piano get creative melanie showed caroline black white bowl she made class expressing pride her work how pottery helps her express emotions brings her joy caroline encouraged melanie continue melanie asked caroline any upcoming plans caroline said she excited attend transgender conference month july 2023 meet others community learn about advocacy melanie wished her great time safe travels conversation ended caroline promising keep melanie updated", + "md_path": 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Caroline shared her recent focus on counseling and mental health work, expressing passion for helping others despite", + "episode": "On July 6, 2023 at 8:25 PM UTC, Caroline and Melanie reconnected after a long time. Caroline shared her recent focus on counseling and mental health work, expressing passion for helping others despite challenges. Melanie responded positively, congratulating Caroline on pursuing her dreams. Melanie recounted taking her kids to a museum yesterday (July 5, 2023), where they enjoyed the dinosaur exhibit, highlighting her joy in motherhood. Caroline shared her plan to create a children's book library for her future kids, including classics, multicultural stories, and educational books. Melanie recalled loving \"Charlotte's Web\" as a childhood favorite, appreciating its themes of friendship and compassion. Caroline reflected on the importance of her friends and family in supporting her transition, mentioning a picnic they had last week (June 29 - July 5, 2023). Melanie admired Caroline's support network and asked how they helped. Caroline explained they provided love, guidance, and acceptance throughout her transition, which was crucial for her growth and self-acceptance. Melanie expressed happiness for Caroline's support and shared a photo of her family camping at the beach, emphasizing how it brings them closer. The conversation highlighted themes of personal growth, family bonding, and the value of supportive relationships.", + "episode_tokens": "july 2023 25 pm utc caroline melanie reconnected after long time caroline shared her recent focus counseling mental health work expressing passion helping others despite challenges melanie responded positively congratulating caroline pursuing her dreams melanie recounted taking her kids museum yesterday july 2023 where they enjoyed dinosaur exhibit highlighting her joy motherhood caroline shared her plan create children book library her future kids including classics multicultural stories educational books melanie recalled loving charlotte web childhood favorite appreciating themes friendship compassion caroline reflected importance her friends family supporting her transition mentioning picnic they last week june 29 july 2023 melanie admired caroline support network asked how they helped caroline explained they provided love guidance acceptance throughout her transition which crucial her growth self acceptance melanie expressed happiness caroline support shared photo her family camping beach emphasizing how brings them closer conversation highlighted themes personal growth family bonding value supportive relationships", + "md_path": "users/caroline/episodes/episode-2026-05-19.md", + "content_sha256": "ab9f795b6b3150c38390d6a482df055876b1d8666e822744f3620b468ee50573", + "vector": [ + -0.00037637873901985586, + 0.014687950722873211, + 0.025968298316001892, + -0.0001220935955643654, + -0.0019388095242902637, + 0.012925396673381329, + -0.022325685247778893, + 0.051936596632003784, + -0.008166500367224216, + -0.0031872852705419064, + 0.01927059143781662, + 0.012337879277765751, + -0.002658519195392728, + 0.006227691192179918, + -0.007285223808139563, + 0.023265713825821877, + -0.04606141522526741, + -0.00033598687150515616, + -0.006374570541083813, + 0.0037894912529736757, + -0.008930274285376072, + -0.03337102383375168, + 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"mc_63627af899f6", + "sender_ids": [ + "caroline", + "melanie" + ], + "subject": "Caroline and Melanie Discuss LGBTQ Conference, Mental Health Careers, and Personal Growth on July 12, 2023", + "summary": "On July 12, 2023 at 4:46 PM UTC, Caroline shared with Melanie her recent experience attending an LGBTQ conference two days ago (July 10, 2023). Caroline described the event as special and welcoming, w", + "episode": "On July 12, 2023 at 4:46 PM UTC, Caroline shared with Melanie her recent experience attending an LGBTQ conference two days ago (July 10, 2023). Caroline described the event as special and welcoming, where she connected with people who had similar journeys and felt fully accepted. She expressed gratitude for the LGBTQ community and emphasized the importance of fighting for trans rights and raising awareness. Melanie responded positively, acknowledging the strength of community support. Caroline reflected on the progress of LGBTQ rights and her desire to contribute by pursuing a career in counseling and mental health to provide support to others. She explained that her own struggles with mental health and the support she received motivated her to help others. Melanie encouraged Caroline, highlighting the impact of her experience and shared inspiration from a book she read. Caroline mentioned her love of reading and recommended \"Becoming Nicole\" by Amy Ellis Nutt, a true story about a trans girl and her family that taught her self-acceptance, finding support, and hope during tough times. The conversation then shifted to pets, with Melanie sharing about her dog Luna and cat Oliver, who bring joy and comfort. Melanie also showed Caroline her new purple running shoes, explaining that she has been running more to de-stress and improve her mental health. Caroline encouraged Melanie to continue, emphasizing the importance of mental health care. Both expressed mutual support and appreciation for mental health awareness and self-care.", + "episode_tokens": "july 12 2023 46 pm utc caroline shared melanie her recent experience attending lgbtq conference two days ago july 10 2023 caroline described event special welcoming where she connected people who similar journeys felt fully accepted she expressed gratitude lgbtq community emphasized importance fighting trans rights raising awareness melanie responded positively acknowledging strength community support caroline reflected progress lgbtq rights her desire contribute pursuing career counseling mental health provide support others she explained her own struggles mental health support she received motivated her help others melanie encouraged caroline highlighting impact her experience shared inspiration from book she read caroline mentioned her love reading recommended becoming nicole amy ellis nutt true story about trans girl her family taught her self acceptance finding support hope during tough times conversation then shifted pets melanie sharing about her dog luna cat oliver who bring joy comfort melanie also showed caroline her new purple running shoes explaining she running more de stress improve her mental health caroline encouraged melanie continue emphasizing importance mental health care both expressed mutual support appreciation mental health awareness self care", + "md_path": "users/caroline/episodes/episode-2026-05-19.md", + "content_sha256": "749c3ee1578dd2463ebcdaf1e5745effd08689767c29bf5b979d1456c09c3a7c", + "vector": [ + -0.00026433588936924934, + 0.03130312263965607, + -0.005552852060645819, + -0.0317634642124176, + -0.001445755478926003, + 0.034065164625644684, + -0.027390234172344208, + 0.05408995598554611, + -0.006617388222366571, + 0.024743277579545975, + 0.0520184263586998, + 0.012716894038021564, + -0.0013306705513969064, + -0.024398023262619972, + -0.024513108655810356, + 0.023592427372932434, + -0.01242918148636818, + 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"locomo_c0_caroline_melanie", + "timestamp": "2023-07-15T14:10:00", + "parent_type": "memcell", + "parent_id": "mc_babf0f72045b", + "sender_ids": [ + "caroline", + "melanie" + ], + "subject": "Caroline and Melanie's July 15, 2023 Conversation on Family Creativity, Adoption, and Personal Growth", + "summary": "On July 15, 2023 at 2:10 PM UTC, Caroline and Melanie engaged in a conversation reflecting on recent personal experiences and emotional growth. Melanie shared that last Friday (July 7, 2023) she took", + "episode": "On July 15, 2023 at 2:10 PM UTC, Caroline and Melanie engaged in a conversation reflecting on recent personal experiences and emotional growth. Melanie shared that last Friday (July 7, 2023) she took her children to a pottery workshop where they made their own pots, an activity the kids loved for its creativity and therapeutic value. She also showed Caroline a painting they created together last weekend (July 8-9, 2023), inspired by nature and flowers, highlighting their bonding and appreciation for small moments. Caroline recounted attending a council meeting for adoption last Friday (July 7, 2023), which inspired her determination to adopt and foster loving homes for children in need. They discussed the symbolism of flowers, with Melanie associating them with joy, growth, and memories of her wedding day, which she described as full of love and joy, with the best moment being marrying her partner. Caroline shared a recent pride parade experience from a few weeks prior, expressing feelings of acceptance, pride, and community support, which strengthened her courage to transition and live authentically. Melanie acknowledged the importance of courage for mental health and peace, noting that creativity and family support help her find peace. Melanie described her family as supportive during her move, including shared activities like camping trips and hiking in forests and mountains, which foster connection and happiness. Caroline expressed gratitude for their friendship and mutual support throughout their journeys.", + "episode_tokens": "july 15 2023 10 pm utc caroline melanie engaged conversation reflecting recent personal experiences emotional growth melanie shared last friday july 2023 she took her children pottery workshop where they made their own pots activity kids loved creativity therapeutic value she also showed caroline painting they created together last weekend july 2023 inspired nature flowers highlighting their bonding appreciation small moments caroline recounted attending council meeting adoption last friday july 2023 which inspired her determination adopt foster loving homes children need they discussed symbolism flowers melanie associating them joy growth memories her wedding day which she described full love joy best moment marrying her partner caroline shared recent pride parade experience from few weeks prior expressing feelings acceptance pride community support which strengthened her courage transition live authentically melanie acknowledged importance courage mental health peace noting creativity family support help her find peace melanie described her family supportive during her move including shared activities like camping trips hiking forests mountains which foster connection happiness caroline expressed gratitude their friendship mutual support throughout their journeys", + "md_path": "users/caroline/episodes/episode-2026-05-19.md", + "content_sha256": "62d158dfae8c07cf4b4a5bbe9f9b783653a4412617eee0a400d1869ab2799cb6", + "vector": [ + -0.0001663293078308925, + -0.028048930689692497, + -0.0021684414241462946, + -0.01644636131823063, + -0.0009856552351266146, + 0.008392149582505226, + -0.015545190311968327, + 0.07164305448532104, + -0.0010630994802340865, + -0.0003344187280163169, + 0.02016368880867958, + 0.004196074791252613, + -0.00266126892529428, + -0.011208307929337025, + -0.012560063041746616, + 0.011602569371461868, + -0.0572243258357048, + 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"2023-07-17T14:39:00", + "parent_type": "memcell", + "parent_id": "mc_a4edd92bb4be", + "sender_ids": [ + "melanie", + "caroline" + ], + "subject": "Caroline's LGBTQ Mentorship and Art Show Plans Discussed with Melanie on July 17, 2023", + "summary": "On July 17, 2023 at 2:39 PM UTC, Melanie and Caroline discussed their recent activities. Melanie shared that she had a quiet weekend after a family camping trip two weekends ago (around July 1-2, 2023", + "episode": "On July 17, 2023 at 2:39 PM UTC, Melanie and Caroline discussed their recent activities. Melanie shared that she had a quiet weekend after a family camping trip two weekends ago (around July 1-2, 2023), enjoying time unplugged with her kids. Caroline described joining a mentorship program for LGBTQ youth last weekend (July 8-9, 2023), finding it rewarding to support resilient young people. She mentors a transgender teen like herself, focusing on building confidence and positive strategies. They attended an LGBTQ pride event last month (June 2023), which Caroline described as encouraging and full of love and acceptance. Caroline highlighted a special moment when her mentee's face lit up seeing the support. Melanie expressed admiration for the impact they make. Caroline also announced an upcoming LGBTQ art show next month (August 2023) featuring her paintings. She shared a painting inspired by a visit to an LGBTQ center, aiming to capture unity and strength. Melanie praised the vivid colors and unified composition, noting the artwork's powerful message. The conversation reflected mutual support, pride in community involvement, and anticipation for Caroline's art show.", + "episode_tokens": "july 17 2023 39 pm utc melanie caroline discussed their recent activities melanie shared she quiet weekend after family camping trip two weekends ago around july 2023 enjoying time unplugged her kids caroline described joining mentorship program lgbtq youth last weekend july 2023 finding rewarding support resilient young people she mentors transgender teen like herself focusing building confidence positive strategies they attended lgbtq pride event last month june 2023 which caroline described encouraging full love acceptance caroline highlighted special moment when her mentee face lit up seeing support melanie expressed admiration impact they make caroline also announced upcoming lgbtq art show next month august 2023 featuring her paintings she shared painting inspired visit lgbtq center aiming capture unity strength melanie praised vivid colors unified composition noting artwork powerful message conversation reflected mutual support pride community involvement anticipation caroline art show", + "md_path": "users/melanie/episodes/episode-2026-05-19.md", + "content_sha256": "87e3fd6dc5f542a85ac1b0374913db7fa0ae55ad5de2e4645bbccdd02ff9b06b", + "vector": [ + -0.00025065953377634287, + -0.03185524418950081, + 0.003437616163864732, + 0.0048986030742526054, + -0.0012747826986014843, + 0.030480196699500084, + -0.03368863835930824, + 0.06371048837900162, + -0.00501319020986557, + 0.003136824816465378, + 0.06325213611125946, + 0.044689010828733444, + -0.0017402932280674577, + -0.01558386068791151, + 0.007448168471455574, + 0.001038446556776762, + -0.05683525651693344, + 0.0017116464441642165, + -0.012260830961167812, + 0.004497548099607229, + -0.0055861263535916805, + -0.007734636776149273, + 0.044230662286281586, + 0.0031081780325621367, + 0.06416884064674377, + -0.03758460283279419, + -0.008250279352068901, + -0.010599317029118538, + 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20, 2023", + "summary": "On July 20, 2023 at 9:07 PM UTC, Caroline and Melanie reconnected in a conversation. Caroline shared that she had joined a new LGBTQ activist group called 'Connected LGBTQ Activists' the previous Tues", + "episode": "On July 20, 2023 at 9:07 PM UTC, Caroline and Melanie reconnected in a conversation. Caroline shared that she had joined a new LGBTQ activist group called 'Connected LGBTQ Activists' the previous Tuesday (July 18, 2023). She described the group as diverse and passionate about rights and community support, holding regular meetings and planning events and campaigns. Caroline expressed fulfillment from making a difference and having a platform to support others. Melanie responded positively, encouraging Caroline to share more. Caroline mentioned missing the recent pride parade held last weekend (July 15-16, 2023) in their city, which celebrated love and diversity and reinforced the fight for equality. Melanie then shifted to family topics, describing a recent beach trip with her kids, which they rarely do, making it special. Caroline asked about other summer traditions, and Melanie highlighted their annual family camping trip, where they roast marshmallows and tell stories. Melanie recalled their best camping memory from last year (2022) watching the Perseid meteor shower, describing the clear starry sky and the awe-inspiring experience of making wishes together. Caroline expressed admiration for this moment. Melanie also shared a deeply emotional memory of her youngest child taking her first steps, emphasizing how such milestones highlight the preciousness of life and family bonds. Caroline acknowledged the sweetness of these moments, and Melanie reflected on the joy and love her family brings her. The conversation ended with mutual appreciation of family and activism, capturing Caroline\u2019s new activist involvement and Melanie\u2019s cherished family experiences.", + "episode_tokens": "july 20 2023 07 pm utc caroline melanie reconnected conversation caroline shared she joined new lgbtq activist group called connected lgbtq activists previous tuesday july 18 2023 she described group diverse passionate about rights community support holding regular meetings planning events campaigns caroline expressed fulfillment from making difference having platform support others melanie responded positively encouraging caroline share more caroline mentioned missing recent pride parade held last weekend july 15 16 2023 their city which celebrated love diversity reinforced fight equality melanie then shifted family topics describing recent beach trip her kids which they rarely making special caroline asked about other summer traditions melanie highlighted their annual family camping trip where they roast marshmallows tell stories melanie recalled their best camping memory from last year 2022 watching perseid meteor shower describing clear starry sky awe inspiring experience making wishes together caroline expressed admiration moment melanie also shared deeply emotional memory her youngest child taking her first steps emphasizing how such milestones highlight preciousness life family bonds caroline acknowledged sweetness moments melanie reflected joy love her family brings her conversation ended mutual appreciation family activism capturing caroline new activist involvement melanie cherished family experiences", + "md_path": "users/melanie/episodes/episode-2026-05-19.md", + "content_sha256": "7b8f8abe2311f270ecd17072a5662118fd3caee48f33dc2ab9eef49d0a5e1b05", + "vector": [ + -0.00029578592511825264, + -0.019503945484757423, + 0.006023277062922716, + -0.01594734378159046, + -0.0012333376798778772, + 0.006769015919417143, + -0.04176138713955879, + 0.04061409458518028, + 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"melanie_ep_20260519_00000012", + "entry_id": "ep_20260519_00000012", + "owner_id": "melanie", + "owner_type": "user", + "session_id": "locomo_c0_caroline_melanie", + "timestamp": "2023-08-17T14:00:00", + "parent_type": "memcell", + "parent_id": "mc_744ffd06aa32", + "sender_ids": [ + "caroline", + "melanie" + ], + "subject": "Caroline and Melanie Discuss LGBTQ Challenges, Pottery, and Summer Plans on August 17, 2023", + "summary": "On August 17, 2023 at 2:00 PM UTC, Caroline shared with Melanie a recent negative experience during a hike where she encountered religious conservatives who upset her, highlighting ongoing struggles f", + "episode": "On August 17, 2023 at 2:00 PM UTC, Caroline shared with Melanie a recent negative experience during a hike where she encountered religious conservatives who upset her, highlighting ongoing struggles for LGBTQ rights. Caroline expressed gratitude for her supportive friends. Melanie empathized with Caroline's experience and shared that she had completed a new pottery project, sending a photo. Caroline admired the colorful bowl and inquired about the inspiration behind it. Melanie explained her passion for vibrant colors and patterns as a form of self-expression and emotional outlet. Both expressed mutual appreciation for their friendship and the importance of surrounding themselves with joy. They reminisced about the previous year's Pride festival and discussed plans for a summer outing, debating between a family event or a special trip just for the two of them. They agreed to plan a special summer activity together to create new memories and strengthen their bond.", + "episode_tokens": "august 17 2023 00 pm utc caroline shared melanie recent negative experience during hike where she encountered religious conservatives who upset her highlighting ongoing struggles lgbtq rights caroline expressed gratitude her supportive friends melanie empathized caroline experience shared she completed new pottery project sending photo caroline admired colorful bowl inquired about inspiration behind melanie explained her passion vibrant colors patterns form self expression emotional outlet both expressed mutual appreciation their friendship importance surrounding themselves joy they reminisced about previous year pride festival discussed plans summer outing debating between family event special trip just two them they agreed plan special summer activity together create new memories strengthen their bond", + "md_path": 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Caroline described the event as special and welcoming, w", + "episode": "On July 12, 2023 at 4:46 PM UTC, Caroline shared with Melanie her recent experience attending an LGBTQ conference two days ago (July 10, 2023). Caroline described the event as special and welcoming, where she connected with people who had similar journeys and felt fully accepted. She expressed gratitude for the LGBTQ community and emphasized the importance of fighting for trans rights and raising awareness. Melanie responded positively, acknowledging the strength of community support. Caroline reflected on the progress of LGBTQ rights and her desire to contribute by pursuing a career in counseling and mental health to provide support to others. She explained that her own struggles with mental health and the support she received motivated her to help others. Melanie encouraged Caroline, highlighting the impact of her experience and shared inspiration from a book she read. Caroline mentioned her love of reading and recommended \"Becoming Nicole\" by Amy Ellis Nutt, a true story about a trans girl and her family that taught her self-acceptance, finding support, and hope during tough times. The conversation then shifted to pets, with Melanie sharing about her dog Luna and cat Oliver, who bring joy and comfort. Melanie also showed Caroline her new purple running shoes, explaining that she has been running more to de-stress and improve her mental health. Caroline encouraged Melanie to continue, emphasizing the importance of mental health care. Both expressed mutual support and appreciation for mental health awareness and self-care.", + "episode_tokens": "july 12 2023 46 pm utc caroline shared melanie her recent experience attending lgbtq conference two days ago july 10 2023 caroline described event special welcoming where she connected people who similar journeys felt fully accepted she expressed gratitude lgbtq community emphasized importance fighting trans rights raising awareness melanie responded positively acknowledging strength community support caroline reflected progress lgbtq rights her desire contribute pursuing career counseling mental health provide support others she explained her own struggles mental health support she received motivated her help others melanie encouraged caroline highlighting impact her experience shared inspiration from book she read caroline mentioned her love reading recommended becoming nicole amy ellis nutt true story about trans girl her family taught her self acceptance finding support hope during tough times conversation then shifted pets melanie sharing about her dog luna cat oliver who bring joy comfort melanie also showed caroline her new purple running shoes explaining she running more de stress improve her mental health caroline encouraged melanie continue emphasizing importance mental health care both expressed mutual support appreciation mental health awareness self care", + "md_path": "users/melanie/episodes/episode-2026-05-19.md", + "content_sha256": "749c3ee1578dd2463ebcdaf1e5745effd08689767c29bf5b979d1456c09c3a7c", + "vector": [ + -0.00026433588936924934, + 0.03130312263965607, + -0.005552852060645819, + -0.0317634642124176, + -0.001445755478926003, + 0.034065164625644684, + -0.027390234172344208, + 0.05408995598554611, + -0.006617388222366571, + 0.024743277579545975, + 0.0520184263586998, + 0.012716894038021564, + -0.0013306705513969064, + -0.024398023262619972, + -0.024513108655810356, + 0.023592427372932434, + -0.01242918148636818, + 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"locomo_c0_caroline_melanie", + "timestamp": "2023-10-22T10:02:00", + "parent_type": "memcell", + "parent_id": "mc_23e27e7b60e7", + "sender_ids": [ + "caroline", + "melanie" + ], + "subject": "Caroline's Adoption Agency Interview Success and Vision for Building a Loving Family on October 22, 2023", + "summary": "On October 22, 2023 at 10:02 AM UTC, Caroline shared with Melanie that she had passed the adoption agency interviews last Friday (October 13, 2023), marking a significant step toward her goal of havin", + "episode": "On October 22, 2023 at 10:02 AM UTC, Caroline shared with Melanie that she had passed the adoption agency interviews last Friday (October 13, 2023), marking a significant step toward her goal of having a family. Melanie congratulated Caroline and mentioned figurines she bought the day before (October 21, 2023) that reminded her of family love. Caroline expressed her vision to build a family by providing a home for children who lacked one, emphasizing adoption as a way to give back and show love and acceptance. Melanie praised Caroline's passion and the loving support she would offer to needy children. Caroline reflected on her journey of self-acceptance, acknowledging it was difficult but empowered her to help others. She credited the support from friends, family, and role models during her transition and expressed a desire to pass on that support to others. Melanie admired Caroline's strength and inspiration. Caroline appreciated Melanie's support and valued sharing her journey to help others. They both agreed on the importance of mutual support and living authentically. The conversation highlighted Caroline's emotional growth, determination to create a safe, loving home for adopted children, and the meaningful support network that sustained her.", + "episode_tokens": "october 22 2023 10 02 am utc caroline shared melanie she passed adoption agency interviews last friday october 13 2023 marking significant step toward her goal having family melanie congratulated caroline mentioned figurines she bought day before october 21 2023 reminded her family love caroline expressed her vision build family providing home children who lacked one emphasizing adoption way give back show love acceptance melanie praised caroline passion loving support she would offer needy children caroline reflected her journey self acceptance acknowledging difficult empowered her help others she credited support from friends family role models during her transition expressed desire pass support others melanie admired caroline strength inspiration caroline appreciated melanie support valued sharing her journey help others they both agreed importance mutual support living authentically conversation highlighted caroline emotional growth determination create safe loving home adopted children meaningful support network sustained her", + "md_path": "users/melanie/episodes/episode-2026-05-19.md", + "content_sha256": "8a6ae2045c0df01e264aeb387f21cca04b792dd164629d5d24a4140362c5aca8", + "vector": [ + -0.00045147552737034857, + 0.03892890363931656, + 0.0394124910235405, + -0.006891141179949045, + -0.002538841450586915, + 0.03989608213305473, + -0.02200329303741455, + 0.04376479238271713, + -0.014386768452823162, + 0.0015792198246344924, + 0.026355592533946037, + 0.007253832649439573, + -0.0043522994965314865, + -0.03082878887653351, + -0.021882396191358566, + 0.01335914246737957, + -0.022124189883470535, + 0.0035513555631041527, + -0.008825496770441532, + 0.0034002340398728848, + -0.008341907523572445, + -0.027201872318983078, + 0.054403744637966156, + 0.008100112900137901, 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Transgender Journey and Support System Discussion with Melanie on June 9, 2023", + "summary": "On June 9, 2023 at 8:06 PM UTC, Caroline shared with Melanie about a school event she held last week (May 31 - June 6, 2023) where she spoke about her transgender journey and encouraged student involv", + "episode": "On June 9, 2023 at 8:06 PM UTC, Caroline shared with Melanie about a school event she held last week (May 31 - June 6, 2023) where she spoke about her transgender journey and encouraged student involvement in the LGBTQ community. Caroline reflected on her progress since starting her transition three years ago. Melanie expressed pride and support for Caroline's courage and impact. Caroline described feeling powerful during her talk, sharing struggles and growth, and inspiring the audience to be better allies. Both emphasized the importance of conversations on gender identity, inclusion, and building a supportive community. They acknowledged the challenges of sharing personal stories but highlighted the hope and acceptance it fosters. Melanie and Caroline discussed their motivation sources: Caroline's friends, family, and mentors who have supported her for four years since moving from her home country, especially after a difficult breakup; Melanie's husband and kids who motivate her. Caroline shared a photo from a recent meetup with her support system. Melanie mentioned being married for five years and described a joyful family wedding day with games and good food. Both agreed on cherishing family moments as essential for happiness and strength. They committed to continuing to use their voices to promote love, understanding, and positive change together.", + "episode_tokens": "june 2023 06 pm utc caroline shared melanie about school event she held last week may 31 june 2023 where she spoke about her transgender journey encouraged student involvement lgbtq community caroline reflected her progress since starting her transition three years ago melanie expressed pride support caroline courage impact caroline described feeling powerful during her talk sharing struggles growth inspiring audience better allies both emphasized importance conversations gender identity inclusion building supportive community they acknowledged challenges sharing personal stories highlighted hope acceptance fosters melanie caroline discussed their motivation sources caroline friends family mentors who supported her four years since moving from her home country especially after difficult breakup melanie husband kids who motivate her caroline shared photo from recent meetup her support system melanie mentioned married five years described joyful family wedding day games good food both agreed cherishing family moments essential happiness strength they committed continuing use their voices promote love understanding positive change together", + "md_path": "users/melanie/episodes/episode-2026-05-19.md", + "content_sha256": "ea588c86df91868c1cd812b9789603710e04b46eacae80cb43864f536ec570e0", + "vector": [ + -0.00032174697844311595, + 0.009418411180377007, + 0.02726074308156967, + -0.021878793835639954, + -0.0016599219525232911, + 0.022814786061644554, + -0.010763898491859436, + 0.06739136576652527, + 0.000789742567576468, + 0.0017330462578684092, + 0.034631673246622086, + 0.019772814586758614, + -0.0038024643436074257, + -0.028196735307574272, + -0.012460382655262947, + 0.022229790687561035, + -0.041885606944561005, + -0.004445958416908979, + -0.00438745878636837, + 0.00424120994284749, + -0.01012040488421917, + -0.005381949245929718, + 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"parent_id": "mc_4c5acf812898", + "sender_ids": [ + "caroline", + "melanie" + ], + "subject": "Caroline and Melanie Reconnect: Mental Health Passion, Family Activities, and Support Networks on July 6, 2023", + "summary": "On July 6, 2023 at 8:25 PM UTC, Caroline and Melanie reconnected after a long time. Caroline shared her recent focus on counseling and mental health work, expressing passion for helping others despite", + "episode": "On July 6, 2023 at 8:25 PM UTC, Caroline and Melanie reconnected after a long time. Caroline shared her recent focus on counseling and mental health work, expressing passion for helping others despite challenges. Melanie responded positively, congratulating Caroline on pursuing her dreams. Melanie recounted taking her kids to a museum yesterday (July 5, 2023), where they enjoyed the dinosaur exhibit, highlighting her joy in motherhood. Caroline shared her plan to create a children's book library for her future kids, including classics, multicultural stories, and educational books. Melanie recalled loving \"Charlotte's Web\" as a childhood favorite, appreciating its themes of friendship and compassion. Caroline reflected on the importance of her friends and family in supporting her transition, mentioning a picnic they had last week (June 29 - July 5, 2023). Melanie admired Caroline's support network and asked how they helped. Caroline explained they provided love, guidance, and acceptance throughout her transition, which was crucial for her growth and self-acceptance. Melanie expressed happiness for Caroline's support and shared a photo of her family camping at the beach, emphasizing how it brings them closer. The conversation highlighted themes of personal growth, family bonding, and the value of supportive relationships.", + "episode_tokens": "july 2023 25 pm utc caroline melanie reconnected after long time caroline shared her recent focus counseling mental health work expressing passion helping others despite challenges melanie responded positively congratulating caroline pursuing her dreams melanie recounted taking her kids museum yesterday july 2023 where they enjoyed dinosaur exhibit highlighting her joy motherhood caroline shared her plan create children book library her future kids including classics multicultural stories educational books melanie recalled loving charlotte web childhood favorite appreciating themes friendship compassion caroline reflected importance her friends family supporting her transition mentioning picnic they last week june 29 july 2023 melanie admired caroline support network asked how they helped caroline explained they provided love guidance acceptance throughout her transition which crucial her growth self acceptance melanie expressed happiness caroline support shared photo her family camping beach emphasizing how brings them closer conversation highlighted themes personal growth family bonding value supportive relationships", + "md_path": "users/melanie/episodes/episode-2026-05-19.md", + "content_sha256": "ab9f795b6b3150c38390d6a482df055876b1d8666e822744f3620b468ee50573", + "vector": [ + -0.0003606714017223567, + 0.014119277708232403, + 0.026246624067425728, + 0.0, + -0.0018747588619589806, + 0.013240484520792961, + -0.022497106343507767, + 0.05225890502333641, + -0.00790913961827755, + -0.0028707245364785194, + 0.01945062354207039, + 0.012713208794593811, + -0.002665672916918993, + 0.006327311508357525, + -0.007235397584736347, + 0.023082969710230827, + -0.0464002825319767, + 0.0002297678147442639, + -0.006034380290657282, + 0.0038081039674580097, + -0.008846518583595753, + -0.033511314541101456, + 0.052493248134851456, + 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Experiences of Family Celebration and LGBTQ+ Advocacy on August 14, 2023", + "summary": "On August 14, 2023 at 2:32 PM UTC, Melanie shared with Caroline the joy of celebrating her daughter's birthday the previous night (August 13, 2023) at a concert featuring Matt Patterson, highlighting", + "episode": "On August 14, 2023 at 2:32 PM UTC, Melanie shared with Caroline the joy of celebrating her daughter's birthday the previous night (August 13, 2023) at a concert featuring Matt Patterson, highlighting the music, warm summer breeze, and her children's smiles. Caroline responded by expressing admiration for Melanie's love for her kids and shared that she had attended a pride parade the previous Friday (August 11, 2023), describing it as an inspiring event full of energy, love, and a strong reminder to continue advocating for LGBTQ equality. Melanie asked about Caroline's feelings being part of that community, to which Caroline replied it was inspiring and motivated her to represent inclusivity and diversity in her art. Caroline then shared a recent painting reflecting her trans experience, aiming to foster understanding and acceptance of the trans community. Melanie praised Caroline's art and requested to see another piece, leading Caroline to describe 'Embracing Identity,' a painting symbolizing the journey of self-acceptance and love. They discussed how art helps Caroline explore her transition and embrace imperfections, with Melanie appreciating the healing and connecting power of art. The conversation ended with mutual gratitude and an open invitation to continue their dialogue.", + "episode_tokens": "august 14 2023 32 pm utc melanie shared caroline joy celebrating her daughter birthday previous night august 13 2023 concert featuring matt patterson highlighting music warm summer breeze her children smiles caroline responded expressing admiration melanie love her kids shared she attended pride parade previous friday august 11 2023 describing inspiring event full energy love strong reminder continue advocating lgbtq equality melanie asked about caroline feelings part community which caroline replied inspiring motivated her represent inclusivity diversity her art caroline then shared recent painting reflecting her trans experience aiming foster understanding acceptance trans community melanie praised caroline art requested see another piece leading caroline describe embracing identity painting symbolizing journey self acceptance love they discussed how art helps caroline explore her transition embrace imperfections melanie appreciating healing connecting power art conversation ended mutual gratitude open invitation continue their dialogue", + "md_path": "users/melanie/episodes/episode-2026-05-19.md", + "content_sha256": "5dac8e686d510b25386e25af6c576d0253166cbbc0b9cb5d753f1471880fbbed", + "vector": [ + -0.0002442522090859711, + -0.027816016227006912, + 0.021724078804254532, + -0.019655119627714157, + -0.00145114550832659, + 0.02091948315501213, + -0.022643616423010826, + 0.03609185665845871, + 0.0014367777621373534, + 0.009942501783370972, + 0.06068949028849602, + 0.05655157193541527, + -0.004971250891685486, + -0.026781536638736725, + -0.008218368515372276, + -0.010172386653721333, + -0.05862053111195564, + -0.0002532320795580745, + -0.00014816770271863788, + 0.0027155098505318165, + -0.013793066143989563, + -0.028275785967707634, + 0.0395401231944561, + -0.011666635051369667, + 0.056781455874443054, + -0.02206890657544136, + 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"created_at": "2026-05-19T01:56:03.515569", + "updated_at": "2026-05-19T01:56:03.515571", + "id": "melanie_fs_20260519_00000002", + "entry_id": "fs_20260519_00000002", + "owner_id": "melanie", + "owner_type": "user", + "session_id": "locomo_c0_caroline_melanie", + "timestamp": "2023-05-08T14:04:30", + "start_time": "2023-05-08T00:00:00", + "end_time": "2023-06-08T00:00:00", + "duration_days": 31, + "parent_type": "memcell", + "parent_id": "mc_a7a1a4cfd8e6", + "sender_ids": [], + "foresight": "melanie might prioritize family leisure activities such as swimming with kids weekly for stress relief", + "foresight_tokens": "melanie might prioritize family leisure activities such swimming kids weekly stress relief", + "evidence": "melanie: I'm off to go swimming with the kids; painting and relaxing are key to self-care", + "evidence_tokens": "melanie off go swimming kids painting relaxing key self care", + "md_path": "users/melanie/.foresights/foresight-2026-05-19.md", + "content_sha256": 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experience", + "evidence": "caroline: I went to a LGBTQ support group and felt accepted and courageous; melanie showed interest", + "evidence_tokens": "caroline went lgbtq support group felt accepted courageous melanie showed interest", + "md_path": "users/melanie/.foresights/foresight-2026-05-19.md", + "content_sha256": "35f0ab76c1324b32b81fc34fb28bbad5709ace01a16fdba9eb6400d81de0961b", + "vector": [ + -0.0004237882967572659, + 0.033455006778240204, + -0.012426144443452358, + 0.005615661386400461, + -0.0021656141616404057, + 0.060457974672317505, + -0.0012993684504181147, + 0.06356450915336609, + 0.006840353831648827, + -0.015891127288341522, + 0.02712245099246502, + 0.02449384331703186, + 0.0060935900546610355, + -0.03560568392276764, + -0.004928639158606529, + 0.007467634975910187, + -0.01947559230029583, + 0.005496179219335318, + -0.009857278317213058, + 0.0007019576733000576, + -0.011350804939866066, + -0.014218376949429512, + 0.054483864456415176, + 0.005376697052270174, + 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b/tests/fixtures/search_seed/user_profile.json new file mode 100644 index 00000000..d710b792 --- /dev/null +++ b/tests/fixtures/search_seed/user_profile.json @@ -0,0 +1,28 @@ +[ + { + "created_at": "2026-05-19T01:58:30.154396", + "updated_at": "2026-05-19T01:58:30.154400", + "id": "caroline", + "owner_id": "caroline", + "owner_type": "user", + "summary": "Caroline recently volunteered at an LGBTQ+ youth center, finding it fulfilling to support and share her story with young people facing challenges. She is involved in organizing community events like a talent show for the youth, showing ongoing social engagement, leadership, and advocacy.", + "explicit_info_json": "[{\"category\": \"Social Engagement\", \"description\": \"Caroline recently volunteered at an LGBTQ+ youth center, finding it fulfilling to support and share her story with young people facing challenges. She is involved in organizing community events like a talent show for the youth, showing ongoing social engagement, leadership, and advocacy.\", \"evidence\": \"Caroline mentioned going to an LGBTQ support group and finding it powerful.\"}, {\"category\": \"Emotional State\", \"description\": \"Caroline feels accepted and courageous after attending the support group and empowered by sharing her story publicly, inspiring others.\", \"evidence\": \"Caroline said the support group made her feel accepted and gave her courage to embrace herself.\"}, {\"category\": \"Career Interests\", \"description\": \"Caroline is interested in pursuing education and a career in counseling or mental health, specifically working with trans people to support their mental health and self-acceptance. She recently attended an LGBTQ+ counseling workshop and a transgender conference to deepen her knowledge and advocacy skills. She is passionate about making a positive impact and creating safe spaces for growth. She is motivated by her own mental health struggles and the support she received, aiming to help others similarly. She is also actively involved in LGBTQ activism groups and mentorship programs for LGBTQ youth, furthering her advocacy and community leadership.\", \"evidence\": \"Caroline expressed excitement about continuing education and exploring career options in counseling or mental health.\"}, {\"category\": \"Values\", \"description\": \"Caroline values empathy and supporting others with similar issues.\", \"evidence\": \"Caroline wants to support those with similar issues through counseling or mental health work.\"}, {\"category\": \"Family Planning\", \"description\": \"Caroline is actively pursuing adoption, having passed adoption agency interviews recently with support from an adoption advice group and her mentor. She is excited and hopeful about becoming a single parent and creating a loving home. She also shares adoption advice and encouragement with friends interested in adoption.\", \"evidence\": \"In May 2023, Caroline mentioned researching adoption agencies that support LGBTQ+ individuals and expressed excitement and hope about adopting children and becoming a single parent.\"}, {\"category\": \"Personal Interests and Hobbies\", \"description\": \"Caroline plays acoustic guitar as a form of emotional expression and self-connection, in addition to piano and painting. Music is a cathartic and uplifting outlet for her.\", \"evidence\": \"In June 2023, Caroline mentioned she is learning the piano to get creative and express herself.\"}, {\"category\": \"Sentimental Items\", \"description\": \"Caroline treasures sentimental objects such as a necklace gifted by her grandmother symbolizing love, faith, and strength, and a hand-painted bowl from a friend that reminds her of art and self-expression. She also values books as important guides and sources of motivation and hope in her life.\", \"evidence\": \"In June and July 2023, Caroline mentioned a special necklace from her grandmother and a hand-painted bowl from a friend as meaningful items.\"}, {\"category\": \"Family and Parenting Aspirations\", \"description\": \"Caroline is creating a children's book library in preparation for adopting children, showing her excitement about reading to them and fostering their growth. She collects a variety of children's books including classics, multicultural stories, and educational materials. She is actively attending council meetings for adoption and is determined to create a loving home as a single parent.\", \"evidence\": \"In July 2023, Caroline shared she is building a library of children's books to prepare for when she has kids.\"}, {\"category\": \"Pets\", \"description\": \"Caroline has a pet guinea pig named Oscar, who she enjoys caring for and shares moments with.\", \"evidence\": \"In August 2023, Caroline mentioned having a guinea pig named Oscar and shared a photo of him eating parsley.\"}, {\"category\": \"Music Interests and Skills\", \"description\": \"Caroline has been playing acoustic guitar for about five years, using it to express emotions and connect with herself. She values music as a source of inspiration and emotional release, with favorite songs like 'Brave' by Sara Bareilles that resonate with her journey.\", \"evidence\": \"In August and September 2023, Caroline mentioned playing acoustic guitar for five years and described the significance of the song 'Brave' by Sara Bareilles.\"}]", + "implicit_traits_json": "[{\"basis\": \"Her interest in counseling and mental health careers combined with her positive response to the support group, public advocacy, recent attendance at LGBTQ+ workshops and conferences, mentorship activities, and community activism, as well as her reflection on her own mental health struggles and support system.\", \"description\": \"Caroline shows a strong desire to help and support others facing challenges similar to her own, especially trans people, inspired by her own journey and counseling experiences. She is passionate about making a positive impact and creating safe spaces for growth, motivated by her personal mental health journey and community involvement. She actively mentors LGBTQ youth and participates in activist groups.\", \"evidence\": \"In June and July 2023, Caroline mentioned attending LGBTQ+ counseling workshops, a transgender conference, joining an LGBTQ activist group, and mentoring LGBTQ youth, expressing passion for helping others and sharing how her own mental health journey motivated her.\", \"trait\": \"Empathetic and Supportive\"}, {\"basis\": \"Her attendance at an LGBTQ support group and her statement about feeling courage to embrace herself.\", \"description\": \"Caroline is open about her identity and courageous in embracing herself publicly.\", \"evidence\": \"Caroline\u2019s participation in the support group and her expressed feelings of acceptance and courage.\", \"trait\": \"Open and Courageous\"}, {\"basis\": \"Her plan to continue education and explore career options after attending the support group.\", \"description\": \"Caroline actively seeks personal growth and self-improvement through education and self-exploration.\", \"evidence\": \"Caroline\u2019s excitement about continuing education and researching career paths.\", \"trait\": \"Reflective and Growth-Oriented\"}, {\"basis\": \"Her expressed goal to adopt children and create a family, despite challenges as a single parent, and recent success in adoption agency interviews.\", \"description\": \"Caroline demonstrates a strong nurturing instinct and a sense of responsibility by planning to adopt children and provide them with a safe and loving home. She has recently passed adoption agency interviews, showing progress and commitment to this goal.\", \"evidence\": \"In Oct 2023, Caroline shared that she passed adoption agency interviews and expressed excitement and hope about adopting children and becoming a single parent.\", \"trait\": \"Nurturing and Responsible\"}, {\"basis\": \"Her public speaking at school events about her transgender journey and encouraging LGBTQ community involvement.\", \"description\": \"Caroline actively advocates for LGBTQ awareness and inclusion, courageously sharing her personal journey to inspire and educate others.\", \"evidence\": \"In June 2023, Caroline gave a talk at a school event about her transgender journey and the importance of LGBTQ community support.\", \"trait\": \"Advocate and Community Leader\"}, {\"basis\": \"Her reflection on overcoming struggles and appreciation for friends, family, and mentors who support her.\", \"description\": \"Caroline demonstrates resilience through her transition journey and expresses gratitude for her support system, which motivates her.\", \"evidence\": \"In June 2023, Caroline shared how her support system has been crucial through her transition and tough times like a breakup.\", \"trait\": \"Resilient and Grateful\"}, {\"basis\": \"Her interest in piano and painting combined with her use of art to express her trans experience and organize community art events.\", \"description\": \"Caroline engages deeply in creative activities like painting and piano to express emotions and explore her identity. She uses art as a therapeutic tool and a platform for LGBTQ advocacy, preparing for an LGBTQ art show.\", \"evidence\": \"In August 2023, Caroline shared multiple paintings, described their meanings, and mentioned preparing for an LGBTQ art show.\", \"trait\": \"Creative and Expressive\"}, {\"basis\": \"Her attachment to a necklace from her grandmother and a hand-painted bowl from a friend, which symbolize love, strength, and creativity.\", \"description\": \"Caroline values meaningful objects that connect her to family, heritage, and self-expression, reflecting on their emotional significance.\", \"evidence\": \"In June and July 2023, Caroline described the sentimental value of these items and their personal meaning.\", \"trait\": \"Sentimental and Reflective\"}, {\"basis\": \"Her expressed goal to adopt children, create a family, and foster their growth through reading and education.\", \"description\": \"Caroline demonstrates a nurturing and responsible nature through her plans to adopt children and prepare a loving environment, including building a children's book library.\", \"evidence\": \"In July 2023, Caroline shared her plans for adoption and creating a children's book library.\", \"trait\": \"Family-Oriented and Nurturing\"}, {\"basis\": \"Her expressed appreciation for books like 'Becoming Nicole' and how reading helps her discover herself and stay motivated.\", \"description\": \"Caroline values books as important sources of guidance, motivation, and hope, using them to support her self-acceptance and personal growth.\", \"evidence\": \"In July 2023, Caroline shared how books guide her, motivate her, and help her discover who she is, highlighting 'Becoming Nicole' as a favorite.\", \"trait\": \"Book-Loving and Reflective\"}, {\"basis\": \"Her active membership in an LGBTQ activist group, participation in pride parades, public speaking, and organizing events.\", \"description\": \"Caroline is deeply involved in LGBTQ activism, participating in activist groups, pride parades, and advocacy events. She uses her voice and leadership to support rights and community support, showing a strong commitment to social justice and community empowerment.\", \"evidence\": \"In July 2023, Caroline mentioned joining an LGBTQ activist group, attending pride parades, and giving talks to encourage community involvement.\", \"trait\": \"Community-Oriented Activist\"}, {\"basis\": \"Her involvement in a mentorship program for LGBTQ youth and her support of a transgender teen mentee.\", \"description\": \"Caroline mentors LGBTQ youth, providing support and confidence-building, serving as a positive role model for others in the community.\", \"evidence\": \"In July 2023, Caroline described mentoring a transgender teen and supporting them at pride events.\", \"trait\": \"Mentor and Role Model\"}, {\"basis\": \"Her reflection on a negative encounter during a hike and her choice to apologize shows maturity and resilience.\", \"description\": \"Caroline demonstrates emotional maturity and resilience by handling difficult social encounters with courage and attempting to apologize to others despite being upset.\", \"evidence\": \"In August 2023, Caroline described a difficult experience with religious conservatives on a hike and her effort to apologize.\", \"trait\": \"Resilient and Mature\"}, {\"basis\": \"Her mention of her guinea pig Oscar, childhood horseback riding memories, and appreciation of horse paintings.\", \"description\": \"Caroline shows affection and care for animals, demonstrated by her pet ownership and fondness for horses and nature.\", \"evidence\": \"In August 2023, Caroline talked about her guinea pig, childhood horseback riding, and discussed horse paintings with a friend.\", \"trait\": \"Animal Lover\"}, {\"basis\": \"Her warm, encouraging conversations with Melanie, expressing gratitude and mutual care.\", \"description\": \"Caroline values close friendships and provides emotional support and encouragement to friends, appreciating mutual support in difficult times.\", \"evidence\": \"In August 2023, Caroline repeatedly expressed appreciation for her friend Melanie and offered support during conversations.\", \"trait\": \"Supportive Friend\"}, {\"basis\": \"Her active volunteering at an LGBTQ+ youth center, organizing a talent show, and sharing her personal story to empower others.\", \"description\": \"Caroline demonstrates a strong commitment to community support and compassion, especially for LGBTQ+ youth, through volunteering and organizing events that foster pride and self-expression.\", \"evidence\": \"In August and September 2023, Caroline described her rewarding experience volunteering at an LGBTQ+ youth center and organizing community events.\", \"trait\": \"Community-Oriented and Compassionate\"}, {\"basis\": \"Her engagement in painting, piano, and guitar playing as means of self-expression and emotional release.\", \"description\": \"Caroline uses multiple artistic outlets including painting, piano, and guitar to explore and express her emotions and identity, finding these activities therapeutic and empowering.\", \"evidence\": \"In August and September 2023, Caroline discussed her painting, piano learning, and guitar playing as important creative and emotional outlets.\", \"trait\": \"Artistic and Emotional Expressive\"}, {\"basis\": \"Caroline offered adoption advice and emotional support to her friend Melanie, encouraging her and sharing tips on preparing for adoption.\", \"description\": \"Caroline provides emotional support and practical advice to friends, showing a nurturing and encouraging nature in helping others through their challenges, especially in adoption preparation.\", \"evidence\": \"In Oct 2023, Caroline shared detailed adoption advice and encouragement with her friend Melanie, showing her supportive and nurturing attitude.\", \"trait\": \"Supportive and Encouraging\"}, {\"basis\": \"Caroline expressed positive reflections on her friend Melanie\u2019s camping experiences with family, emphasizing the peace and bonding nature provides.\", \"description\": \"Caroline values quality time with loved ones and appreciates the calming and refreshing effects of nature, recognizing its role in emotional well-being and family connection.\", \"evidence\": \"In October 2023, Caroline discussed with Melanie the peacefulness and quality time nature camping trips bring to family life.\", \"trait\": \"Appreciative of Nature and Family Bonding\"}, {\"basis\": \"Her excitement and thankfulness after passing adoption interviews and her expressed vision of creating a loving home for children in need.\", \"description\": \"Caroline shows hopefulness and determination in her journey to build a family through adoption, expressing gratitude and a positive outlook despite past challenges.\", \"evidence\": \"In Oct 2023, Caroline expressed excitement and gratitude for passing adoption agency interviews and shared her vision of building a family and providing love and acceptance.\", \"trait\": \"Hopeful and Determined\"}]", + "profile_timestamp_ms": 1697968920000, + "md_path": "users/caroline/user.md", + "content_sha256": "4548ec43f844d2b44c10274da0d669463ad02e9dd5fe811b0d12ef3aa4fe25cb" + }, + { + "created_at": "2026-05-19T01:58:30.155435", + "updated_at": "2026-05-19T01:58:30.155436", + "id": "melanie", + "owner_id": "melanie", + "owner_type": "user", + "summary": "Caroline actively participates in LGBTQ support groups, values community support, engages in public speaking to raise awareness and inspire others about transgender issues and inclusivity, and recently joined an LGBTQ activist group called 'Connected LGBTQ Activists' to further advocacy and community support.", + "explicit_info_json": "[{\"category\": \"Social Engagement\", \"description\": \"Caroline actively participates in LGBTQ support groups, values community support, engages in public speaking to raise awareness and inspire others about transgender issues and inclusivity, and recently joined an LGBTQ activist group called 'Connected LGBTQ Activists' to further advocacy and community support.\", \"evidence\": \"Caroline mentioned attending an LGBTQ support group and feeling inspired and accepted.\"}, {\"category\": \"Career Interests\", \"description\": \"Caroline is interested in pursuing education and a career in counseling or mental health support, specifically focusing on working with transgender people to help them accept themselves and support their mental health. She has recently attended an LGBTQ+ counseling workshop and a transgender conference, is excited about learning therapeutic methods, advocacy, community engagement, mentoring LGBTQ youth, and is actively involved in LGBTQ activism.\", \"evidence\": \"Caroline expressed excitement about continuing education and exploring career options in counseling or mental health.\"}, {\"category\": \"Family Planning\", \"description\": \"Caroline is actively progressing in her adoption journey, having recently passed adoption agency interviews and feeling excited and thankful. She is motivated and hopeful about becoming a loving single parent and creating a safe, loving home for children in need.\", \"evidence\": \"Caroline shared that she is looking into adoption agencies that support LGBTQ+ individuals and is hopeful about becoming a parent.\"}, {\"category\": \"Values\", \"description\": \"Caroline values inclusivity, support, and providing a safe, loving environment for others.\", \"evidence\": \"Caroline chose an adoption agency because of their inclusivity and support for LGBTQ+ people and wants to give kids a loving home.\"}, {\"category\": \"Support System\", \"description\": \"Caroline has a strong and long-term support system consisting of friends, family, and mentors who have supported her through her transition and personal challenges. They provide love, guidance, and acceptance, which have been instrumental in her growth. She frequently expresses gratitude for this support.\", \"evidence\": \"In June 2023, Caroline mentioned having friends she has known for 4 years who have been a crucial support especially after a tough breakup.\"}, {\"category\": \"Sentimental Values\", \"description\": \"Caroline treasures meaningful personal items that symbolize love, faith, strength, and self-expression, such as a necklace from her grandmother and a hand-painted bowl from a friend. She also values creative activities like learning piano and painting as forms of self-expression, and uses her art to express her transgender experience and advocate for LGBTQ+ inclusivity.\", \"evidence\": \"In June 2023, Caroline shared that her necklace from her grandmother symbolizes love, faith, and strength, and she also treasures a hand-painted bowl given by a friend for her 18th birthday.\"}, {\"category\": \"Community Engagement\", \"description\": \"Caroline continues active participation in LGBTQ+ community events including pride parades and is now also volunteering at an LGBTQ+ youth center, mentoring and supporting young people. She is involved in organizing community activities such as a talent show for youth, and finds this work deeply fulfilling and meaningful.\", \"evidence\": \"In July 2023, Caroline attended an LGBTQ+ pride parade and a transgender conference, expressing excitement about community involvement and advocacy.\"}, {\"category\": \"Family Planning\", \"description\": \"Caroline is creating a children's book library in preparation for future parenthood, including classics, multicultural stories, and educational books to foster learning and compassion. She is actively involved in adoption council meetings and is determined to adopt children in need. She recently contacted her adoption mentor for advice and feels ready to become a mom.\", \"evidence\": \"In July 2023, Caroline shared that she is building a library of children's books to read to her future kids, emphasizing classics and diverse cultural stories.\"}, {\"category\": \"Creative Expression\", \"description\": \"Caroline is preparing for an LGBTQ art show featuring her paintings, using art to express her transgender experience and advocate for inclusivity. She also plays acoustic guitar as a form of emotional expression and enjoys painting inspired by nature and personal experiences.\", \"evidence\": \"In July 2023, Caroline mentioned learning piano and preparing for an LGBTQ art show with her paintings, describing her art as conveying unity and strength.\"}, {\"category\": \"Hobbies and Interests\", \"description\": \"Caroline plays acoustic guitar, finding it a therapeutic way to express emotions. She also enjoys painting, often inspired by nature and personal experiences, and uses her art to explore identity and self-acceptance. She has recently experimented with abstract painting as a freeing form of self-expression.\", \"evidence\": \"In August 2023, Caroline mentioned playing acoustic guitar for five years and described painting as a way to explore her identity and emotions.\"}, {\"category\": \"Artistic Expression\", \"description\": \"Caroline actively creates art as a form of self-expression and empowerment, including painting inspired by her transgender journey and abstract art. She also uses music, especially acoustic guitar, to express emotions and find catharsis.\", \"evidence\": \"In September 2023, Caroline shared her recent abstract paintings and described her guitar playing as a therapeutic emotional outlet.\"}, {\"category\": \"Event Participation\", \"description\": \"Caroline attended a transgender poetry reading event where transgender people shared their stories through poetry, finding it empowering and inspiring for self-expression and community connection.\", \"evidence\": \"In October 2023, Caroline described attending a transgender poetry reading that was a safe and empowering space for sharing and celebrating identities.\"}, {\"category\": \"Family Values\", \"description\": \"Caroline deeply values family love and support, seeing family as a source of strength and motivation. She appreciates the importance of cherishing loved ones and finds inspiration in the resilience and support of family members.\", \"evidence\": \"In October 2023, Caroline expressed strong appreciation for family as a source of strength and motivation during conversations about a road trip accident and adoption journey.\"}]", + "implicit_traits_json": "[{\"basis\": \"Her participation in pride parades, transgender conferences, LGBTQ activist group membership, and desire to use her story to help others reflect her advocacy orientation.\", \"description\": \"Caroline is motivated to publicly share her story and engage in advocacy to support and empower the transgender community. She recently joined an LGBTQ activist group and participates actively in community events and campaigns.\", \"evidence\": \"In July and August 2023, Caroline expressed excitement about attending a transgender conference, joining an LGBTQ activist group, and using her story to help others.\", \"trait\": \"Advocate and Activist\"}, {\"basis\": \"She acknowledges the difficulties ahead but remains hopeful and motivated to achieve her dreams.\", \"description\": \"Caroline approaches challenges with hope and determination, especially regarding single parenthood and career goals.\", \"evidence\": \"Caroline expressed feeling hopeful and optimistic about the adoption process despite the challenges of being a single parent.\", \"trait\": \"Resilient and Optimistic\"}, {\"basis\": \"Her active involvement in LGBTQ support groups and preference for inclusive adoption agencies reflect this trait.\", \"description\": \"Caroline values connection and support from communities that share her identity and experiences.\", \"evidence\": \"Caroline's engagement with LGBTQ support groups and choice of an LGBTQ-friendly adoption agency show her community focus.\", \"trait\": \"Community-Oriented\"}, {\"basis\": \"Her active role in giving talks about her transgender journey and encouraging LGBTQ community involvement shows her public speaking and advocacy skills.\", \"description\": \"Caroline is confident and motivated to share her personal journey publicly to educate and inspire others.\", \"evidence\": \"In June 2023, Caroline described giving a school talk about her transgender journey and inspiring students.\", \"trait\": \"Public Speaker\"}, {\"basis\": \"She frequently acknowledges the importance of her support network and cherishes moments with loved ones.\", \"description\": \"Caroline expresses gratitude for the support she receives and values close relationships deeply.\", \"evidence\": \"In June 2023, Caroline expressed thankfulness for her friends, family, and mentors who motivate and support her.\", \"trait\": \"Grateful and Appreciative\"}, {\"basis\": \"Her attachment to a necklace from her grandmother and a hand-painted bowl from a friend shows her sentimental nature.\", \"description\": \"Caroline values personal and meaningful objects that connect her to her roots, loved ones, and self-expression.\", \"evidence\": \"In June 2023, Caroline described the special meaning of her necklace and the significance of the hand-painted bowl.\", \"trait\": \"Sentimental\"}, {\"basis\": \"Her participation in pride parades, transgender conferences, and desire to use her story to help others reflect her advocacy orientation.\", \"description\": \"Caroline is motivated to publicly share her story and engage in advocacy to support and empower the transgender community.\", \"evidence\": \"In July 2023, Caroline expressed excitement about attending a transgender conference and using her story to help others.\", \"trait\": \"Advocate and Activist\"}, {\"basis\": \"Her engagement in learning piano, preparing for an LGBTQ art show, and valuing artistic self-expression indicate a creative personality.\", \"description\": \"Caroline values creative outlets such as painting and piano to express herself and find joy and healing. She uses her art to explore her transgender experience and advocate for inclusivity.\", \"evidence\": \"In July and August 2023, Caroline mentioned learning piano, preparing for an LGBTQ art show with her paintings, and using art to express her trans experience.\", \"trait\": \"Creative and Expressive\"}, {\"basis\": \"She expresses optimism about adoption and career development, showing resilience and forward-looking attitude.\", \"description\": \"Caroline maintains hope and motivation in pursuing her goals despite challenges, especially regarding her career and family planning.\", \"evidence\": \"In July 2023, Caroline expressed feeling hopeful and motivated about adoption and her counseling career despite challenges.\", \"trait\": \"Hopeful and Motivated\"}, {\"basis\": \"Her active involvement in LGBTQ support groups, pride parades, conferences, and preference for inclusive adoption agencies reflect this trait.\", \"description\": \"Caroline values connection and support from communities that share her identity and experiences.\", \"evidence\": \"In June and July 2023, Caroline engaged in LGBTQ+ community events and chose inclusive adoption agencies.\", \"trait\": \"Community-Oriented\"}, {\"basis\": \"Her involvement in a mentorship program for LGBTQ youth and her support for a transgender teen demonstrate her mentoring role.\", \"description\": \"Caroline actively mentors LGBTQ youth, providing support and confidence-building, serving as a positive role model within her community.\", \"evidence\": \"In July 2023, Caroline described mentoring a transgender teen and supporting them at pride events.\", \"trait\": \"Mentor and Role Model\"}, {\"basis\": \"Her engagement in learning piano, preparing for an art show, and valuing artistic self-expression indicate a creative personality.\", \"description\": \"Caroline values and uses creative outlets such as painting and piano to express herself and find joy and healing.\", \"evidence\": \"In July 2023, Caroline mentioned learning piano and preparing for an LGBTQ art show with her paintings.\", \"trait\": \"Creative and Expressive\"}, {\"basis\": \"She continues activism and advocacy despite negative experiences, such as encountering religious conservatives, and remains motivated to fight for equality.\", \"description\": \"Caroline demonstrates resilience in facing challenges related to her identity and advocacy, maintaining hope and determination to create positive change for LGBTQ rights despite encountering opposition.\", \"evidence\": \"In August 2023, Caroline described a negative experience on a hike with religious conservatives but emphasized the importance of support and her continued commitment to LGBTQ rights.\", \"trait\": \"Resilient Advocate\"}, {\"basis\": \"Her involvement in a mentorship program for LGBTQ youth and her support for a transgender teen demonstrate her mentoring role.\", \"description\": \"Caroline actively mentors LGBTQ youth, providing support and confidence-building, serving as a positive role model within her community.\", \"evidence\": \"In July 2023, Caroline described mentoring a transgender teen and supporting them at pride events.\", \"trait\": \"Mentor and Role Model\"}, {\"basis\": \"Her descriptions of art and music as ways to express feelings, work through transition, and find peace indicate a therapeutic use of creativity.\", \"description\": \"Caroline uses creative outlets such as painting and playing guitar as therapeutic tools to process emotions, explore identity, and promote self-acceptance.\", \"evidence\": \"In August 2023, Caroline shared that painting and guitar playing help her explore her identity and emotions, describing these activities as therapeutic.\", \"trait\": \"Therapeutic Creative\"}, {\"basis\": \"Her active volunteering at an LGBTQ+ youth center, mentoring young people, organizing talent shows, and sharing her personal story to empower youth reflect her mentoring role and leadership.\", \"description\": \"Caroline is committed to mentoring LGBTQ youth and supporting community members through volunteering and organizing events, demonstrating leadership and nurturing qualities.\", \"evidence\": \"In August and September 2023, Caroline described volunteering at an LGBTQ+ youth center, mentoring young people, organizing a talent show, and sharing her story to support youth.\", \"trait\": \"Community Mentor\"}, {\"basis\": \"She recounted a negative encounter with religious conservatives but emphasized the importance of support and her ongoing commitment to LGBTQ advocacy.\", \"description\": \"Caroline maintains resilience and optimism in the face of adversity, continuing her advocacy for LGBTQ rights despite negative experiences.\", \"evidence\": \"In August 2023, Caroline described a difficult experience on a hike but remained hopeful and committed to fighting for LGBTQ rights.\", \"trait\": \"Resilient Advocate\"}, {\"basis\": \"Her sharing of personal struggles and stories to help youth feel less alone and more supported shows strong empathy and mentoring skills.\", \"description\": \"Caroline demonstrates empathy and emotional support in her mentoring, drawing from her own experiences to connect deeply with LGBTQ youth and inspire them.\", \"evidence\": \"In August and September 2023, Caroline described mentoring LGBTQ youth by sharing her story and providing emotional support.\", \"trait\": \"Empathetic Mentor\"}, {\"basis\": \"Her engagement in abstract painting, guitar playing, and creating art inspired by her transgender experience indicates a therapeutic use of creativity.\", \"description\": \"Caroline uses art and music as therapeutic tools to process emotions, explore identity, and promote self-acceptance and empowerment.\", \"evidence\": \"In September 2023, Caroline described abstract painting and guitar playing as freeing and cathartic ways to express emotions and identity.\", \"trait\": \"Artistic Therapeutic\"}, {\"basis\": \"Her reflections on the importance of family during a stressful event and her motivation to create a loving family through adoption show a strong family-centered orientation.\", \"description\": \"Caroline places great importance on family bonds, cherishing the support and love from family as a core source of strength and motivation in her life.\", \"evidence\": \"In October 2023, Caroline emphasized the value of family support and love as essential to her resilience and motivation.\", \"trait\": \"Family-Centered\"}]", + "profile_timestamp_ms": 1697968920000, + "md_path": "users/melanie/user.md", + "content_sha256": "c521190d0301953730fee6309afbb6cb08e3329e973f2a20bf68f719503cbf8d" + } +] diff --git a/methods/EverCore/src/core/capability/__init__.py b/tests/integration/__init__.py similarity index 100% rename from methods/EverCore/src/core/capability/__init__.py rename to tests/integration/__init__.py diff --git a/methods/EverCore/src/core/capability/configuration/__init__.py b/tests/integration/search/__init__.py similarity index 100% rename from methods/EverCore/src/core/capability/configuration/__init__.py rename to tests/integration/search/__init__.py diff --git a/tests/integration/search/_helpers.py b/tests/integration/search/_helpers.py new file mode 100644 index 00000000..5629c47d --- /dev/null +++ b/tests/integration/search/_helpers.py @@ -0,0 +1,269 @@ +"""Private helpers shared across the search e2e tests. + +* :func:`pick_query_seeds` — scans the session corpus's + ``.atomic_facts/`` md files and returns a list of + ``(owner_id, fact_text)`` tuples to use as deterministic search + queries. Bootstrapping queries off the corpus's own extraction + output gives us a closed-loop correctness signal — what was + written should be findable. + +* :func:`assert_recall` — the canonical "this search returned at + least one sensible hit for ``owner``" assertion bundle. Used by + the keyword / vector / hybrid recall tests so the assertion logic + is in one place. + +* :func:`flatten_hits` — collapses ``SearchData``'s four arrays into + one ``(owner_id, score, text)`` tuple list for relevance checks. + +The helpers do **not** hardcode topical keywords ("hiking" / "work") +— they are derived from what the pipeline produced. This keeps the +suite stable across LLM-driven boundary-cut variance. +""" + +from __future__ import annotations + +import re +from pathlib import Path +from typing import Any + +import httpx + +# Cap how many fact strings we sample per call — running every test +# against every fact would blow the LLM rerank budget. +_DEFAULT_SEED_LIMIT = 3 + +# Tokenise on word characters; lowercase; drop short tokens that carry +# no signal for the "content overlap" check. +_TOKEN_RE = re.compile(r"\w+", re.UNICODE) +_MIN_TOKEN_LEN = 3 +_STOPWORDS: frozenset[str] = frozenset( + { + "the", + "and", + "for", + "that", + "with", + "this", + "was", + "has", + "have", + "are", + "but", + "from", + "you", + "she", + "her", + "his", + "him", + "they", + "them", + "their", + } +) + + +# ── Query seed extraction ─────────────────────────────────────────────── + + +def pick_query_seeds( + memory_root: Path, + *, + limit: int = _DEFAULT_SEED_LIMIT, +) -> list[tuple[str, str]]: + """Sample ``(owner_id, fact_text)`` tuples from atomic_facts md files. + + Walks ``users//.atomic_facts/atomic_fact-*.md`` and parses + the ``## Fact\\n`` sections inside each daily-log entry. + Returns deterministic seeds (insertion order of ``rglob`` is + sort-stable thanks to the explicit ``sorted`` call) so a flaky + test surfaces a real regression, not query-rotation variance. + + Raises: + AssertionError: if no facts were extracted — that's a fixture + failure, not a test failure, and should fail loudly. + """ + seeds: list[tuple[str, str]] = [] + users_dir = memory_root / "default_app" / "default_project" / "users" + if not users_dir.is_dir(): + raise AssertionError(f"expected {users_dir} to exist after ingest") + + for owner_dir in sorted(users_dir.iterdir()): + if not owner_dir.is_dir(): + continue + facts_dir = owner_dir / ".atomic_facts" + if not facts_dir.is_dir(): + continue + for md in sorted(facts_dir.rglob("*.md")): + for fact in _extract_fact_sections(md): + if fact: + seeds.append((owner_dir.name, fact)) + if len(seeds) >= limit: + return seeds + if not seeds: + raise AssertionError( + f"no atomic_fact md entries under {users_dir} — pipeline did " + "not produce any facts; cannot bootstrap search queries" + ) + return seeds + + +def _extract_fact_sections(md: Path) -> list[str]: + """Return every ``### Fact`` section body in a daily-log md file. + + Daily-log entries are ``## `` blocks; the labelled body + sections inside an entry are h3 (``### Fact``, ``### Foresight``, + …). We scan linearly for ``### Fact`` and collect lines until the + next heading at any level or the end-of-entry marker. + """ + body = md.read_text(encoding="utf-8") + sections: list[str] = [] + in_fact = False + buf: list[str] = [] + for line in body.splitlines(): + stripped = line.lstrip() + if stripped.startswith("### Fact"): + if in_fact: + sections.append("\n".join(buf).strip()) + in_fact = True + buf = [] + continue + # Any subsequent heading or entry-end marker closes the section. + if in_fact and (stripped.startswith("#") or stripped.startswith("`` blocks == N +* ``lance_repo.count_rows(md_path=...) == N`` +* lance ``entry_id`` set == md ``entry_id`` set + +This is the strict counterpart to the loose ``>=`` assertions in +:mod:`test_add_flush_user_pipeline_e2e` (which can't be exact because +LLM output is non-deterministic). + +Skill / profile are single-file (not daily-log) kinds and are covered +by the e2e pipeline tests where the OME drives real LLM emissions. +""" + +from __future__ import annotations + +import asyncio +import dataclasses +import datetime as _dt +from collections.abc import AsyncIterator, Callable, Mapping +from pathlib import Path +from typing import Any + +import pytest +from sqlmodel import SQLModel + +from everos.component.embedding import EmbeddingProvider +from everos.component.tokenizer import build_tokenizer +from everos.core.persistence import MarkdownReader, MemoryRoot +from everos.infra.persistence.lancedb import ( + agent_case_repo, + atomic_fact_repo, + dispose_connection, + ensure_business_indexes, + episode_repo, + foresight_repo, +) +from everos.infra.persistence.lancedb.lancedb_manager import get_table +from everos.infra.persistence.lancedb.tables.agent_case import AgentCase +from everos.infra.persistence.lancedb.tables.atomic_fact import AtomicFact +from everos.infra.persistence.lancedb.tables.episode import Episode +from everos.infra.persistence.lancedb.tables.foresight import Foresight +from everos.infra.persistence.markdown import ( + AgentCaseWriter, + AtomicFactWriter, + EpisodeWriter, + ForesightWriter, +) +from everos.infra.persistence.sqlite import ( + dispose_engine, + get_engine, + md_change_state_repo, +) +from everos.memory.cascade import CascadeConfig, CascadeOrchestrator +from everos.memory.cascade.registry import KIND_REGISTRY +from tests._consistency_assertions import _daily_log_sha_for_entry + + +@pytest.fixture(autouse=True) +def _reset_lancedb_write_locks() -> None: + """ClassVar lock pool reset; see test_repository.py for rationale.""" + from everos.core.persistence.lancedb.repository import LanceRepoBase + + LanceRepoBase._reset_locks_for_tests() + + +class _StubEmbedder(EmbeddingProvider): + dim = 1024 + + async def embed(self, text: str) -> list[float]: + return [0.0] * self.dim + + async def embed_batch(self, texts): # type: ignore[no-untyped-def] + return [[0.0] * self.dim for _ in texts] + + +@pytest.fixture +async def cascade_runtime( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> AsyncIterator[MemoryRoot]: + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + monkeypatch.setenv("EVEROS_EMBEDDING__MODEL", "stub-model") + monkeypatch.setenv("EVEROS_EMBEDDING__BASE_URL", "http://stub.invalid/v1") + monkeypatch.setenv("EVEROS_EMBEDDING__API_KEY", "stub-key") + await dispose_connection() + await dispose_engine() + engine = get_engine() + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + await ensure_business_indexes() + yield MemoryRoot.default() + await dispose_connection() + await dispose_engine() + + +@dataclasses.dataclass(frozen=True) +class _DailyLogKindCase: + """A single registered daily-log kind, packaged for parametrization.""" + + name: str + scope: str # "users" | "agents" + dir_name: str + file_prefix: str + writer_factory: Callable[[MemoryRoot], Any] + repo: Any + table_cls: type + build_item: Callable[[str, int], tuple[Mapping[str, object], Mapping[str, str]]] + + +def _af_item(scope_id: str, j: int): + return ( + { + "owner_id": scope_id, + "session_id": f"s_{j}", + "timestamp": "2026-05-19T07:04:26+00:00", + "parent_id": f"mc_{j}", + "sender_ids": [scope_id], + }, + {"Fact": f"af fact body {j}"}, + ) + + +def _ep_item(scope_id: str, j: int): + return ( + { + "owner_id": scope_id, + "session_id": f"s_{j}", + "timestamp": "2026-05-19T07:04:26+00:00", + "parent_id": f"mc_{j}", + "sender_ids": [scope_id], + }, + {"Subject": f"subj {j}", "Summary": f"sum {j}", "Content": f"content {j}"}, + ) + + +def _fs_item(scope_id: str, j: int): + return ( + { + "owner_id": scope_id, + "session_id": f"s_{j}", + "timestamp": "2026-05-19T07:04:26+00:00", + "parent_id": f"mc_{j}", + "sender_ids": [scope_id], + }, + {"Foresight": f"foresight body {j}"}, + ) + + +def _ac_item(scope_id: str, j: int): + return ( + { + "owner_id": scope_id, + "session_id": f"s_{j}", + "timestamp": "2026-05-19T07:04:26+00:00", + "parent_id": f"mc_{j}", + "quality_score": 0.9, + }, + { + "TaskIntent": f"task intent {j}", + "Approach": f"approach {j}", + "KeyInsight": f"insight {j}", + }, + ) + + +_KIND_CASES: list[_DailyLogKindCase] = [ + _DailyLogKindCase( + name="atomic_fact", + scope="users", + dir_name=".atomic_facts", + file_prefix="atomic_fact", + writer_factory=AtomicFactWriter, + repo=atomic_fact_repo, + table_cls=AtomicFact, + build_item=_af_item, + ), + _DailyLogKindCase( + name="episode", + scope="users", + dir_name="episodes", + file_prefix="episode", + writer_factory=EpisodeWriter, + repo=episode_repo, + table_cls=Episode, + build_item=_ep_item, + ), + _DailyLogKindCase( + name="foresight", + scope="users", + dir_name=".foresights", + file_prefix="foresight", + writer_factory=ForesightWriter, + repo=foresight_repo, + table_cls=Foresight, + build_item=_fs_item, + ), + _DailyLogKindCase( + name="agent_case", + scope="agents", + dir_name=".cases", + file_prefix="agent_case", + writer_factory=AgentCaseWriter, + repo=agent_case_repo, + table_cls=AgentCase, + build_item=_ac_item, + ), +] + + +async def _wait_path_done(md_path: str, *, deadline: float = 15.0) -> None: + async with asyncio.timeout(deadline): + while True: # noqa: ASYNC110 - polling cascade state + row = await md_change_state_repo.get_by_id(md_path) + if row is not None: + break + await asyncio.sleep(0.05) + while True: # noqa: ASYNC110 - polling cascade state + row = await md_change_state_repo.get_by_id(md_path) + if row is not None and row.status in ("done", "failed"): + break + await asyncio.sleep(0.05) + await asyncio.sleep(0.1) + + +@pytest.mark.parametrize("case", _KIND_CASES, ids=lambda c: c.name) +async def test_md_lance_strict_consistency_per_kind( + cascade_runtime: MemoryRoot, + case: _DailyLogKindCase, +) -> None: + """Per-kind strict equality: md entries / frontmatter / lance rows all == N.""" + memory_root = cascade_runtime + orchestrator = CascadeOrchestrator( + memory_root=memory_root, + embedder=_StubEmbedder(), + tokenizer=build_tokenizer(), + config=CascadeConfig( + scan_interval_seconds=60.0, + worker_batch_size=20, + worker_max_retry=1, + worker_poll_interval_seconds=0.05, + worker_retry_backoff_seconds=0.0, + ), + ) + await orchestrator.start() + await asyncio.sleep(0.3) + + try: + writer = case.writer_factory(root=memory_root) + scope_id = f"sid_{case.name}" + bucket = _dt.date(2026, 5, 19) + n = 5 + items = [case.build_item(scope_id, j) for j in range(n)] + eids = await writer.append_entries(scope_id, items, date=bucket) + assert len(eids) == n, f"writer returned {len(eids)} eids, expected {n}" + + md_path = ( + f"default_app/default_project/{case.scope}/{scope_id}/{case.dir_name}/" + f"{case.file_prefix}-{bucket.isoformat()}.md" + ) + absolute = memory_root.root / md_path + await _wait_path_done(md_path) + + # 1) frontmatter.entry_count == N + parsed = await MarkdownReader.read(absolute) + assert parsed.frontmatter.get("entry_count") == n, ( + f"{case.name}: frontmatter.entry_count=" + f"{parsed.frontmatter.get('entry_count')}, expected {n}" + ) + + # 2) md entry blocks == N + assert len(parsed.entries) == n, ( + f"{case.name}: md has {len(parsed.entries)} entry blocks, expected {n}" + ) + + # 3) lance count_rows(md_path) == N (strict equality) + table = await get_table(case.table_cls.TABLE_NAME, case.table_cls) + lance_count = await table.count_rows(filter=f"md_path = '{md_path}'") + assert lance_count == n, ( + f"{case.name}: md={n} lance={lance_count} for {md_path}" + ) + + # 4) lance entry_id set == md entry_id set + lance_rows = await case.repo.find_where(f"md_path = '{md_path}'", limit=100) + lance_eids = {r.entry_id for r in lance_rows} + md_eids = {e.id for e in parsed.entries} + assert lance_eids == md_eids, ( + f"{case.name}: lance eids {lance_eids} != md eids {md_eids}" + ) + + # 4b) lance content_sha256 per entry == md-recomputed content_sha256 + # Catches "id present but content mismatched" — orthogonal to (4). + handler_cls = next( + spec.handler_factory for spec in KIND_REGISTRY if spec.name == case.name + ) + md_sha_by_id = { + e.id: _daily_log_sha_for_entry(handler_cls, e.as_structured()) + for e in parsed.entries + } + lance_sha_by_id = {r.entry_id: r.content_sha256 for r in lance_rows} + assert md_sha_by_id == lance_sha_by_id, ( + f"{case.name}: per-entry content_sha256 mismatch " + f"@ {md_path}: md={md_sha_by_id} lance={lance_sha_by_id}" + ) + + # 5) row state row is terminally done (not failed) + state_row = await md_change_state_repo.get_by_id(md_path) + assert state_row is not None and state_row.status == "done", ( + f"{case.name}: state row status={state_row.status if state_row else 'NONE'}" + ) + finally: + await orchestrator.stop() diff --git a/tests/integration/test_cascade_cli_integration.py b/tests/integration/test_cascade_cli_integration.py new file mode 100644 index 00000000..dcb72fe3 --- /dev/null +++ b/tests/integration/test_cascade_cli_integration.py @@ -0,0 +1,196 @@ +"""Integration test for ``everos cascade`` CLI commands. + +Drives the actual Typer commands against a real sqlite + lancedb under a +tmp memory root. Validates the in-process orchestration that +``test_cascade_command`` (unit) cannot reach: ``_runtime()`` context, +queue summary formatting, fix (no-rows path), and a full +``cascade sync `` round-trip with a stub embedder. + +The CLI commands call ``asyncio.run(_run())`` internally, so this test +is **synchronous** — pytest-asyncio's auto mode would otherwise wrap it +in an event loop, which collides with the CLI's own loop. +""" + +from __future__ import annotations + +import asyncio +import datetime as _dt +import re +from collections.abc import Iterator +from pathlib import Path + +import pytest +from typer.testing import CliRunner + +from everos.component.embedding import EmbeddingProvider +from everos.config import load_settings +from everos.entrypoints.cli.commands import cascade as cascade_mod +from everos.infra.persistence.lancedb import dispose_connection +from everos.infra.persistence.sqlite import dispose_engine + + +class _StubEmbedder(EmbeddingProvider): + dim = 1024 + + async def embed(self, text: str) -> list[float]: + return [0.0] * self.dim + + async def embed_batch(self, texts): # type: ignore[no-untyped-def] + return [[0.0] * self.dim for _ in texts] + + +@pytest.fixture +def cli_runtime(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> Iterator[Path]: + """Tmp memory root + clean singletons; CLI bootstraps the schema itself.""" + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + monkeypatch.setenv("EVEROS_EMBEDDING__MODEL", "stub-model") + monkeypatch.setenv("EVEROS_EMBEDDING__BASE_URL", "http://stub.invalid/v1") + monkeypatch.setenv("EVEROS_EMBEDDING__API_KEY", "stub-key") + load_settings.cache_clear() + + # Strip any singleton state from a neighbouring test. + asyncio.run(_dispose_all()) + yield tmp_path + asyncio.run(_dispose_all()) + + +async def _dispose_all() -> None: + await dispose_connection() + await dispose_engine() + + +def test_status_on_empty_queue(cli_runtime: Path) -> None: + """``cascade status`` boots the runtime + prints zeros for a fresh DB.""" + result = CliRunner().invoke(cascade_mod.app, ["status"]) + assert result.exit_code == 0, result.stdout + assert "queue:" in result.stdout + assert "pending:" in result.stdout + # Fresh DB: every counter is zero. + assert "0" in result.stdout + assert "lsn:" in result.stdout + + +def test_fix_with_no_failed_rows(cli_runtime: Path) -> None: + """``cascade fix`` (no ``--apply``) prints the empty-state message.""" + result = CliRunner().invoke(cascade_mod.app, ["fix"]) + assert result.exit_code == 0, result.stdout + assert "no failed rows" in result.stdout + + +def test_fix_apply_with_no_failed_rows(cli_runtime: Path) -> None: + """``cascade fix --apply`` is a noop when there's nothing to fix.""" + result = CliRunner().invoke(cascade_mod.app, ["fix", "--apply"]) + assert result.exit_code == 0, result.stdout + assert "no failed rows" in result.stdout + + +def test_sync_on_empty_queue_with_stub_embedder( + cli_runtime: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + """``cascade sync`` invokes orchestrator.drain even on empty queue.""" + # CLI builds the embedder via build_embedding_provider() which would + # try to connect; replace the orchestrator builder with one wired to + # the stub embedder. + from everos.component.tokenizer import build_tokenizer + from everos.core.persistence import MemoryRoot + from everos.memory.cascade import CascadeOrchestrator + + def fake_build_orchestrator() -> CascadeOrchestrator: + root = MemoryRoot.default() + root.ensure() + return CascadeOrchestrator( + memory_root=root, + embedder=_StubEmbedder(), + tokenizer=build_tokenizer(), + ) + + monkeypatch.setattr(cascade_mod, "_build_orchestrator", fake_build_orchestrator) + + result = CliRunner().invoke(cascade_mod.app, ["sync"]) + assert result.exit_code == 0, result.stdout + assert "sync complete" in result.stdout + assert "processed 0 row(s)" in result.stdout + + +def test_sync_with_path_outside_root_errors( + cli_runtime: Path, tmp_path_factory: pytest.TempPathFactory +) -> None: + """``cascade sync `` rejects paths outside the memory root.""" + other = tmp_path_factory.mktemp("other") / "x.md" + other.write_text("# unrelated\n") + result = CliRunner().invoke(cascade_mod.app, ["sync", str(other)]) + assert result.exit_code != 0 + # Typer.BadParameter surfaces in stderr / mixed output. The rich + # error box wraps the message at terminal width and pads each line + # with ``│`` (U+2502 box-drawing); so ``not under`` and + # ``memory root`` end up separated by spaces *plus* box characters + # *plus* a newline. ``\s`` doesn't match ``│``, so widen to + # ``[^\w]+`` (anything that isn't an alnum / underscore) — that + # tolerates the rich frame without falsely matching real text + # between the two tokens. + output = result.stdout + (result.stderr or "") + assert re.search(r"not under[^\w]+memory root", output), output + + +def test_sync_with_unmatched_path( + cli_runtime: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + """A path under the root but matching no cascade kind exits 1 with a hint.""" + from everos.component.tokenizer import build_tokenizer + from everos.core.persistence import MemoryRoot + from everos.memory.cascade import CascadeOrchestrator + + def fake_build_orchestrator() -> CascadeOrchestrator: + return CascadeOrchestrator( + memory_root=MemoryRoot.default(), + embedder=_StubEmbedder(), + tokenizer=build_tokenizer(), + ) + + monkeypatch.setattr(cascade_mod, "_build_orchestrator", fake_build_orchestrator) + + # File under the root but in an unregistered subdirectory. + unregistered = cli_runtime / "stuff" / "random.md" + unregistered.parent.mkdir(parents=True, exist_ok=True) + unregistered.write_text("# random\n") + result = CliRunner().invoke(cascade_mod.app, ["sync", str(unregistered)]) + assert result.exit_code == 1 + # stderr in CliRunner is merged into stdout for typer.echo(..., err=True). + output = result.stdout + (result.stderr or "") + assert "does not match any registered cascade kind" in output + + +# Keep a baseline so future regressions show as a hard failure. +def test_status_handles_pending_rows(cli_runtime: Path) -> None: + """Seed one pending row via the repo before invoking status.""" + + async def seed() -> None: + # Bring the runtime up like the CLI does, seed, then dispose. + async with cascade_mod._runtime(): + from everos.infra.persistence.sqlite import md_change_state_repo + + await md_change_state_repo.force_enqueue( + "users/u1/episodes/episode-2026-01-01.md", "episode" + ) + + asyncio.run(seed()) + + result = CliRunner().invoke(cascade_mod.app, ["status"]) + assert result.exit_code == 0, result.stdout + # One row pending; LSN must be ≥ 1. + assert "pending: 1" in result.stdout + + +# Reduce false negatives on date drift. +def test_resolve_relative_via_command_arg(cli_runtime: Path) -> None: + """An absolute path under the root works through ``cascade sync ``.""" + md_file = cli_runtime / "users" / "u1" / "episodes" / "episode-2026-05-25.md" + md_file.parent.mkdir(parents=True, exist_ok=True) + today = _dt.date.today().isoformat() # only used so the var isn't unused + md_file.write_text(f"# {today}\n") + + # We don't need the orchestrator to actually drain anything; pass --help + # against the sync subcommand to verify the path resolution helper + # doesn't barf at construction time. + result = CliRunner().invoke(cascade_mod.app, ["sync", "--help"]) + assert result.exit_code == 0 diff --git a/tests/integration/test_cascade_fsevents_repro.py b/tests/integration/test_cascade_fsevents_repro.py new file mode 100644 index 00000000..f7e36cc9 --- /dev/null +++ b/tests/integration/test_cascade_fsevents_repro.py @@ -0,0 +1,193 @@ +"""Repro: high-frequency atomic-replace bursts vs. cascade drain. + +Drives N successive ``AtomicFactWriter.append_entries`` calls against the +same daily-log md, simulating multiple OME memcells landing in the same +owner+day bucket within a few ms of each other. + +Before the watcher.on_deleted stat-guard, macOS FSEvents emits a paired +(moved, deleted) per ``os.replace`` and the synthetic deletion can +become the final ``change_type`` of the row — driving the worker into +``handle_deleted`` and wiping LanceDB while md is intact. Repeat the +test ~20x to surface the race if it ever resurfaces. + +Scanner interval is held at 60s so the watcher path is the only thing +exercised (a scanner sweep would mask a watcher bug). +""" + +from __future__ import annotations + +import asyncio +import datetime as _dt +from collections.abc import AsyncIterator +from pathlib import Path + +import anyio +import pytest +from sqlmodel import SQLModel + +from everos.component.embedding import EmbeddingProvider +from everos.component.tokenizer import build_tokenizer +from everos.core.persistence import MarkdownReader, MemoryRoot +from everos.infra.persistence.lancedb import ( + dispose_connection, + ensure_business_indexes, +) +from everos.infra.persistence.lancedb.lancedb_manager import get_table +from everos.infra.persistence.lancedb.tables.atomic_fact import AtomicFact +from everos.infra.persistence.markdown import AtomicFactWriter +from everos.infra.persistence.sqlite import ( + dispose_engine, + get_engine, + md_change_state_repo, +) +from everos.memory.cascade import CascadeConfig, CascadeOrchestrator + + +@pytest.fixture(autouse=True) +def _reset_lancedb_write_locks() -> None: + """Drop the per-table write-lock pool between tests; mirrors the + unit-test fixture in test_repository.py. Without this, the second + test in this module hits "Lock bound to a different event loop" + because LanceRepoBase stashes locks in a ClassVar dict.""" + from everos.core.persistence.lancedb.repository import LanceRepoBase + + LanceRepoBase._reset_locks_for_tests() + + +class _StubEmbedder(EmbeddingProvider): + dim = 1024 + + async def embed(self, text: str) -> list[float]: + return [0.0] * self.dim + + async def embed_batch(self, texts): # type: ignore[no-untyped-def] + return [[0.0] * self.dim for _ in texts] + + +@pytest.fixture +async def cascade_runtime( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> AsyncIterator[MemoryRoot]: + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + monkeypatch.setenv("EVEROS_EMBEDDING__MODEL", "stub-model") + monkeypatch.setenv("EVEROS_EMBEDDING__BASE_URL", "http://stub.invalid/v1") + monkeypatch.setenv("EVEROS_EMBEDDING__API_KEY", "stub-key") + + await dispose_connection() + await dispose_engine() + + engine = get_engine() + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + await ensure_business_indexes() + + yield MemoryRoot.default() + + await dispose_connection() + await dispose_engine() + + +async def _wait_drain(deadline: float = 15.0) -> None: + async with asyncio.timeout(deadline): + while True: + summary = await md_change_state_repo.queue_summary() + if summary.pending == 0: + return + await asyncio.sleep(0.05) + + +async def _count_lance_rows(md_path: str) -> int: + table = await get_table(AtomicFact.TABLE_NAME, AtomicFact) + return await table.count_rows(filter=f"md_path = '{md_path}'") + + +async def _count_md_entries(absolute: Path) -> int: + if not await anyio.Path(absolute).is_file(): + return 0 + parsed = await MarkdownReader.read(absolute) + return len(parsed.entries) + + +@pytest.mark.parametrize( + "n_calls,items_per_call,inter_call_sleep_ms", + [ + (20, 1, 0.0), + (20, 1, 1.0), + (20, 3, 0.0), + (10, 3, 5.0), + ], +) +async def test_high_freq_atomic_fact_append_no_loss( + cascade_runtime: MemoryRoot, + n_calls: int, + items_per_call: int, + inter_call_sleep_ms: float, +) -> None: + memory_root = cascade_runtime + orchestrator = CascadeOrchestrator( + memory_root=memory_root, + embedder=_StubEmbedder(), + tokenizer=build_tokenizer(), + config=CascadeConfig( + scan_interval_seconds=60.0, + worker_batch_size=20, + worker_max_retry=1, + worker_poll_interval_seconds=0.05, + worker_retry_backoff_seconds=0.0, + ), + ) + await orchestrator.start() + await asyncio.sleep(0.3) + + try: + writer = AtomicFactWriter(root=memory_root) + bucket = _dt.date(2026, 5, 19) + owner_id = "bob" + total = 0 + for i in range(n_calls): + items = [ + ( + { + "owner_id": owner_id, + "session_id": f"s_{i}_{j}", + "timestamp": "2026-05-19T07:04:26+00:00", + "parent_id": f"mc_{i}", + "sender_ids": [owner_id], + }, + {"Fact": f"fact body call={i} item={j}"}, + ) + for j in range(items_per_call) + ] + await writer.append_entries(owner_id, items, date=bucket) + total += items_per_call + if inter_call_sleep_ms > 0: + await asyncio.sleep(inter_call_sleep_ms / 1000.0) + + await _wait_drain(deadline=15.0) + # FSEvents has ~30-100ms kernel-to-userspace delivery latency, + # so the watcher's `on_*` callbacks for the LAST few + # os.replace() bursts may arrive AFTER sqlite first reads + # `pending == 0`. Absorb that tail: settle 500ms, then drain + # again until truly quiescent. + await asyncio.sleep(0.5) + await _wait_drain(deadline=15.0) + + md_path = ( + f"default_app/default_project/users/{owner_id}/.atomic_facts/" + f"atomic_fact-{bucket.isoformat()}.md" + ) + absolute = memory_root.root / md_path + md_entries = await _count_md_entries(absolute) + lance_rows = await _count_lance_rows(md_path) + state_row = await md_change_state_repo.get_by_id(md_path) + + assert md_entries == total, ( + f"writer self-check failed: total={total} md={md_entries}" + ) + assert lance_rows == md_entries, ( + f"CASCADE LOSS: md={md_entries} lance={lance_rows} " + f"state={state_row.status if state_row else 'NONE'} " + f"lsn={state_row.lsn if state_row else None}" + ) + finally: + await orchestrator.stop() diff --git a/tests/integration/test_cascade_integration.py b/tests/integration/test_cascade_integration.py new file mode 100644 index 00000000..9e077bae --- /dev/null +++ b/tests/integration/test_cascade_integration.py @@ -0,0 +1,242 @@ +"""End-to-end cascade flow. + +Drives the full pipeline once with real components except the embedder +(stubbed so the test never hits an external API): + + EpisodeWriter.append_entry ─▶ md file on disk + watchdog FSEvents thread ─▶ CascadeWatcher._enqueue_async + md_change_state.upsert ─▶ pending row + CascadeWorker.drain_once ─▶ EpisodeHandler.handle_added_or_modified + episode_repo.upsert ─▶ LanceDB row + +Asserts the row landed with the right shape (md_path, content_sha256, +episode tokens, vector dim). Validates that the three loops actually +talk to each other — no unit test covers the cross-loop wiring. +""" + +from __future__ import annotations + +import asyncio +import datetime as _dt +from collections.abc import AsyncIterator +from pathlib import Path + +import pytest +from sqlmodel import SQLModel + +from everos.component.embedding import EmbeddingProvider +from everos.component.tokenizer import build_tokenizer +from everos.core.persistence import MemoryRoot +from everos.infra.persistence.lancedb import ( + dispose_connection, + ensure_business_indexes, + episode_repo, +) +from everos.infra.persistence.markdown import EpisodeWriter +from everos.infra.persistence.sqlite import ( + dispose_engine, + get_engine, + md_change_state_repo, +) +from everos.memory.cascade import CascadeConfig, CascadeOrchestrator + + +class _StubEmbedder(EmbeddingProvider): + """1024-dim deterministic vector; counts calls for the assertion.""" + + dim = 1024 + + def __init__(self) -> None: + self.calls = 0 + + async def embed(self, text: str) -> list[float]: + self.calls += 1 + return [0.0] * self.dim + + async def embed_batch(self, texts): # type: ignore[no-untyped-def] + return [await self.embed(t) for t in texts] + + +@pytest.fixture +async def cascade_runtime( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> AsyncIterator[MemoryRoot]: + """Boot sqlite + lancedb against a tmp memory_root; dispose at teardown. + + Cascade uses module-level singletons; we reset them up-front to + guarantee no state leaks in from neighbouring tests, then dispose + on the way out so the next test sees a clean slate. + """ + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + # Embedding settings are required for the lifespan factory; the + # stub bypasses real network, but the orchestrator still expects + # the env to be valid-looking. + monkeypatch.setenv("EVEROS_EMBEDDING__MODEL", "stub-model") + monkeypatch.setenv("EVEROS_EMBEDDING__BASE_URL", "http://stub.invalid/v1") + monkeypatch.setenv("EVEROS_EMBEDDING__API_KEY", "stub-key") + + await dispose_connection() + await dispose_engine() + + engine = get_engine() + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + await ensure_business_indexes() + + yield MemoryRoot.default() + + await dispose_connection() + await dispose_engine() + + +async def _poll(condition, *, deadline_seconds: float = 10.0, interval: float = 0.05): # type: ignore[no-untyped-def] + """Poll ``condition()`` (async) until truthy, or :class:`TimeoutError`. + + Wraps the loop in :func:`asyncio.timeout` so the test surfaces a + clean ``TimeoutError`` instead of silently spinning. The polling + interval is a low-cost sleep; the deadline is the hard cap. + """ + async with asyncio.timeout(deadline_seconds): + while True: + result = await condition() + if result: + return result + await asyncio.sleep(interval) + + +async def test_append_to_md_propagates_to_lancedb( + cascade_runtime: MemoryRoot, +) -> None: + """Happy path: writer append → watcher → state row → worker → LanceDB.""" + memory_root = cascade_runtime + embedder = _StubEmbedder() + orchestrator = CascadeOrchestrator( + memory_root=memory_root, + embedder=embedder, + tokenizer=build_tokenizer(), + # Tight worker poll so the test wraps in seconds, not minutes. + # Scanner interval kept long so the watcher path is the one + # actually exercised (the scanner would mask a watcher bug). + config=CascadeConfig( + scan_interval_seconds=60.0, + worker_batch_size=10, + worker_max_retry=1, + worker_poll_interval_seconds=0.05, + worker_retry_backoff_seconds=0.0, + ), + ) + await orchestrator.start() + # Give the watchdog Observer thread a beat to actually subscribe; + # this is the watchdog API gap (start() returns before the kqueue + # / FSEvents subscription is live on macOS). + await asyncio.sleep(0.3) + + try: + writer = EpisodeWriter(memory_root) + today = _dt.date(2026, 5, 14) + eid = await writer.append_entry( + "u_integration", + inline={ + "owner_id": "u_integration", + "session_id": "s_int", + "timestamp": "2026-05-14T10:00:00+00:00", + "parent_id": "mc_integration_parent", + "sender_ids": ["u_integration"], + }, + sections={ + "Subject": "Test", + "Summary": "Stub", + "Content": "the user mentioned dark mode preference", + }, + date=today, + ) + md_path = ( + "default_app/default_project/users/u_integration/episodes/" + "episode-2026-05-14.md" + ) + + # 1. Watcher enqueues the path. + async def _state_appeared(): # type: ignore[no-untyped-def] + return await md_change_state_repo.get_by_id(md_path) + + row = await _poll(_state_appeared, deadline_seconds=5.0) + assert row.kind == "episode" + + # 2. Worker drives it to done. + async def _state_done(): # type: ignore[no-untyped-def] + r = await md_change_state_repo.get_by_id(md_path) + return r if (r is not None and r.status == "done") else None + + done_row = await _poll(_state_done, deadline_seconds=10.0) + assert done_row.error is None + + # 3. LanceDB carries the typed episode row. + episode_id = f"u_integration_{eid.format()}" + ep_row = await episode_repo.get_by_id(episode_id) + assert ep_row is not None + assert ep_row.episode == "the user mentioned dark mode preference" + assert ep_row.episode_tokens # tokenizer ran + assert ep_row.md_path == md_path + assert ep_row.parent_id == "mc_integration_parent" + assert ep_row.content_sha256 + assert len(ep_row.vector) == 1024 + assert embedder.calls >= 1 + finally: + await orchestrator.stop() + + +async def test_delete_md_wipes_lancedb_row( + cascade_runtime: MemoryRoot, +) -> None: + """Append + drain, then ``unlink`` the md and watch the row evaporate.""" + memory_root = cascade_runtime + orchestrator = CascadeOrchestrator( + memory_root=memory_root, + embedder=_StubEmbedder(), + tokenizer=build_tokenizer(), + config=CascadeConfig( + scan_interval_seconds=60.0, + worker_batch_size=10, + worker_max_retry=1, + worker_poll_interval_seconds=0.05, + worker_retry_backoff_seconds=0.0, + ), + ) + await orchestrator.start() + await asyncio.sleep(0.3) + + try: + writer = EpisodeWriter(memory_root) + today = _dt.date(2026, 5, 14) + eid = await writer.append_entry( + "u_del", + inline={ + "owner_id": "u_del", + "session_id": "s", + "timestamp": "2026-05-14T10:00:00+00:00", + "parent_id": "mc_del_parent", + "sender_ids": ["u_del"], + }, + sections={"Content": "to be removed"}, + date=today, + ) + md_path = ( + "default_app/default_project/users/u_del/episodes/episode-2026-05-14.md" + ) + absolute = memory_root.root / md_path + + async def _ep_present(): # type: ignore[no-untyped-def] + return await episode_repo.get_by_id(f"u_del_{eid.format()}") + + await _poll(_ep_present, deadline_seconds=10.0) + + # Now remove the file; the watcher's on_deleted should fire. + absolute.unlink() + + async def _ep_gone(): # type: ignore[no-untyped-def] + row = await episode_repo.get_by_id(f"u_del_{eid.format()}") + return row is None + + assert await _poll(_ep_gone, deadline_seconds=10.0) + finally: + await orchestrator.stop() diff --git a/tests/integration/test_cascade_scenarios.py b/tests/integration/test_cascade_scenarios.py new file mode 100644 index 00000000..7ef03d99 --- /dev/null +++ b/tests/integration/test_cascade_scenarios.py @@ -0,0 +1,701 @@ +"""End-to-end cascade scenarios beyond the happy-path append. + +Each test boots the full cascade (writer → watchdog → md_change_state → +worker → LanceDB) against a tmp memory_root and asserts md/LanceDB +convergence after a specific perturbation. Scanner interval is held +at 60s here so the watcher path is the one being exercised — the +scanner-fallback variants live in :mod:`test_cascade_scanner_fallback`. + +Coverage targets +---------------- +* Rename: in-bucket / out-of-glob / cross-owner ``mv`` of a real md + file (not the atomic-replace one — that one's covered by + :mod:`test_cascade_fsevents_repro`). +* Content edits: re-writing an existing entry's body must flip + ``content_sha256`` and trigger LanceDB re-upsert (not skip). +* Isolation: concurrent writes to N different owners must not bleed + across each other's md_paths in LanceDB. +* Lap race: ``writer.append`` calls overlapping a worker's + in-flight handler must all converge once drained, no entries lost. +""" + +from __future__ import annotations + +import asyncio +import datetime as _dt +import shutil +from collections.abc import AsyncIterator +from pathlib import Path + +import anyio +import pytest +from sqlmodel import SQLModel + +from everos.component.embedding import EmbeddingProvider +from everos.component.tokenizer import build_tokenizer +from everos.core.persistence import MarkdownReader, MarkdownWriter, MemoryRoot +from everos.infra.persistence.lancedb import ( + atomic_fact_repo, + dispose_connection, + ensure_business_indexes, +) +from everos.infra.persistence.lancedb.lancedb_manager import get_table +from everos.infra.persistence.lancedb.tables.atomic_fact import AtomicFact +from everos.infra.persistence.markdown import AtomicFactWriter +from everos.infra.persistence.sqlite import ( + dispose_engine, + get_engine, + md_change_state_repo, +) +from everos.memory.cascade import CascadeConfig, CascadeOrchestrator + + +@pytest.fixture(autouse=True) +def _reset_lancedb_write_locks() -> None: + """Drop the per-table write-lock pool between tests. + + ``LanceRepoBase`` stashes ``asyncio.Lock`` objects in a ClassVar dict + keyed by table name; without a reset the lock outlives pytest- + asyncio's function-scoped loop and the next test fails with "Lock + bound to a different event loop". Mirrors the unit-test fixture in + test_repository.py. + """ + from everos.core.persistence.lancedb.repository import LanceRepoBase + + LanceRepoBase._reset_locks_for_tests() + + +class _StubEmbedder(EmbeddingProvider): + dim = 1024 + + async def embed(self, text: str) -> list[float]: + return [0.0] * self.dim + + async def embed_batch(self, texts): # type: ignore[no-untyped-def] + return [[0.0] * self.dim for _ in texts] + + +@pytest.fixture +async def cascade_runtime( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> AsyncIterator[MemoryRoot]: + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + monkeypatch.setenv("EVEROS_EMBEDDING__MODEL", "stub-model") + monkeypatch.setenv("EVEROS_EMBEDDING__BASE_URL", "http://stub.invalid/v1") + monkeypatch.setenv("EVEROS_EMBEDDING__API_KEY", "stub-key") + + await dispose_connection() + await dispose_engine() + + engine = get_engine() + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + await ensure_business_indexes() + + yield MemoryRoot.default() + + await dispose_connection() + await dispose_engine() + + +def _build_orchestrator( + memory_root: MemoryRoot, *, scan_interval: float = 60.0 +) -> CascadeOrchestrator: + return CascadeOrchestrator( + memory_root=memory_root, + embedder=_StubEmbedder(), + tokenizer=build_tokenizer(), + config=CascadeConfig( + scan_interval_seconds=scan_interval, + worker_batch_size=20, + worker_max_retry=1, + worker_poll_interval_seconds=0.05, + worker_retry_backoff_seconds=0.0, + ), + ) + + +async def _wait_path_done(md_path: str, *, deadline: float = 15.0) -> None: + """Wait until ``md_path`` lands in state AND reaches ``status='done'``. + + Bare ``_wait_drain`` returns immediately when the queue is empty, + which is exactly the case right after a single ``append_entries`` + fires once but the watcher hasn't yet enqueued anything. This helper + polls for the row first (i.e. watcher has noticed), then waits for + terminal state, then re-checks after a short settle to absorb any + last-second re-enqueue (e.g. atomic-replace echo). + """ + async with asyncio.timeout(deadline): + while True: # noqa: ASYNC110 - polling cascade state + row = await md_change_state_repo.get_by_id(md_path) + if row is not None: + break + await asyncio.sleep(0.05) + while True: # noqa: ASYNC110 - polling cascade state + row = await md_change_state_repo.get_by_id(md_path) + if row is not None and row.status in ("done", "failed"): + break + await asyncio.sleep(0.05) + await asyncio.sleep(0.1) + row = await md_change_state_repo.get_by_id(md_path) + assert row is not None and row.status in ("done", "failed"), ( + f"path {md_path} flipped back to {row.status if row else 'NONE'} " + f"after reaching done" + ) + + +async def _wait_paths_done(*md_paths: str, deadline: float = 15.0) -> None: + await asyncio.gather(*[_wait_path_done(p, deadline=deadline) for p in md_paths]) + + +async def _wait_drain(deadline: float = 15.0) -> None: + """Wait for the *whole* queue to settle. Use only when you've already + confirmed at least one path is in flight (via _wait_path_done first).""" + async with asyncio.timeout(deadline): + while True: + summary = await md_change_state_repo.queue_summary() + if summary.pending == 0: + return + await asyncio.sleep(0.05) + + +async def _count_lance_rows_md(md_path: str) -> int: + table = await get_table(AtomicFact.TABLE_NAME, AtomicFact) + return await table.count_rows(filter=f"md_path = '{md_path}'") + + +async def _count_md_entries(absolute: Path) -> int: + if not await anyio.Path(absolute).is_file(): + return 0 + parsed = await MarkdownReader.read(absolute) + return len(parsed.entries) + + +def _atomic_fact_md_path(owner_id: str, bucket: _dt.date) -> str: + return ( + f"default_app/default_project/users/{owner_id}/.atomic_facts/" + f"atomic_fact-{bucket.isoformat()}.md" + ) + + +async def _seed_atomic_facts( + writer: AtomicFactWriter, + *, + owner_id: str, + bucket: _dt.date, + n_items: int, + text_prefix: str = "seed fact", +) -> None: + items = [ + ( + { + "owner_id": owner_id, + "session_id": f"s_{j}", + "timestamp": "2026-05-19T07:04:26+00:00", + "parent_id": f"mc_{j}", + "sender_ids": [owner_id], + }, + {"Fact": f"{text_prefix} {j}"}, + ) + for j in range(n_items) + ] + await writer.append_entries(owner_id, items, date=bucket) + + +# ===== A. Rename scenarios ===== + + +async def test_rename_same_owner_kind_in_bucket( + cascade_runtime: MemoryRoot, +) -> None: + """``mv atomic_fact-D1.md atomic_fact-D2.md`` inside the same owner+kind. + + Both paths match the kind glob. Expected: src lancedb rows cleared, + dest md_path becomes the new home for the (entry_id, content) pairs. + """ + memory_root = cascade_runtime + orchestrator = _build_orchestrator(memory_root) + await orchestrator.start() + await asyncio.sleep(0.3) + + try: + writer = AtomicFactWriter(root=memory_root) + owner_id = "u_rename_a" + bucket_src = _dt.date(2026, 5, 18) + bucket_dest = _dt.date(2026, 5, 20) + await _seed_atomic_facts( + writer, owner_id=owner_id, bucket=bucket_src, n_items=5 + ) + src_md_path = _atomic_fact_md_path(owner_id, bucket_src) + dest_md_path = _atomic_fact_md_path(owner_id, bucket_dest) + src_absolute = memory_root.root / src_md_path + dest_absolute = memory_root.root / dest_md_path + + await _wait_path_done(src_md_path) + + # Sanity: cascade has indexed the seed. + assert await _count_lance_rows_md(src_md_path) == 5 + assert await _count_lance_rows_md(dest_md_path) == 0 + + # Real rename — no tmp/atomic-replace involvement. + await anyio.to_thread.run_sync( + shutil.move, str(src_absolute), str(dest_absolute) + ) + await _wait_paths_done(src_md_path, dest_md_path) + + assert await _count_lance_rows_md(src_md_path) == 0, "src not cleared" + assert await _count_lance_rows_md(dest_md_path) == 5, "dest not reindexed" + + # md_change_state should reflect both sides finally settled. + src_row = await md_change_state_repo.get_by_id(src_md_path) + dest_row = await md_change_state_repo.get_by_id(dest_md_path) + assert src_row is not None and src_row.status == "done" + assert dest_row is not None and dest_row.status == "done" + finally: + await orchestrator.stop() + + +async def test_rename_out_of_kind_glob_degrades_to_delete( + cascade_runtime: MemoryRoot, +) -> None: + """``mv`` from inside the kind glob to a path outside it. + + Expected: src lancedb cleared (treated as deletion); dest path is + silently ignored because ``match_kind`` rejects it. + """ + memory_root = cascade_runtime + orchestrator = _build_orchestrator(memory_root) + await orchestrator.start() + await asyncio.sleep(0.3) + + try: + writer = AtomicFactWriter(root=memory_root) + owner_id = "u_rename_oob" + bucket = _dt.date(2026, 5, 18) + await _seed_atomic_facts(writer, owner_id=owner_id, bucket=bucket, n_items=4) + src_md_path = _atomic_fact_md_path(owner_id, bucket) + src_absolute = memory_root.root / src_md_path + # An obviously-out-of-glob target: hide it under a plain dir + # that no kind spec registers. + dest_absolute = memory_root.root / "out_of_scope" / "random.md" + await anyio.Path(dest_absolute.parent).mkdir(parents=True, exist_ok=True) + + await _wait_path_done(src_md_path) + assert await _count_lance_rows_md(src_md_path) == 4 + + await anyio.to_thread.run_sync( + shutil.move, str(src_absolute), str(dest_absolute) + ) + # Wait for the src deletion to settle. The dest path is outside + # the glob so it never enters md_change_state — can't wait on it. + # Re-poll src until row reflects the rename. + await asyncio.sleep(0.5) + await _wait_drain() + + assert await _count_lance_rows_md(src_md_path) == 0 + # No row should appear for the out-of-glob target. + src_row = await md_change_state_repo.get_by_id(src_md_path) + assert src_row is not None and src_row.status == "done" + # The dest path was never registered with any kind spec, so no + # md_change_state row should exist for it. + all_rows = await md_change_state_repo.queue_summary() + # Spot check: pending should be 0; total rows present (done) + # come only from the src side. + assert all_rows.pending == 0 + finally: + await orchestrator.stop() + + +async def test_rename_cross_owner_keeps_frontmatter_owner( + cascade_runtime: MemoryRoot, +) -> None: + """``mv users/u_a/.atomic_facts/X.md users/u_b/.atomic_facts/X.md``. + + Frontmatter ``user_id`` stays as ``u_a`` (rename doesn't rewrite the + file). resolve_owner pulls owner_id from frontmatter, so dest + LanceDB rows carry ``owner_id='u_a'`` even though md_path is under + ``users/u_b/``. This reflects current design (frontmatter is the + truth source) — surface it as a regression anchor. + """ + memory_root = cascade_runtime + orchestrator = _build_orchestrator(memory_root) + await orchestrator.start() + await asyncio.sleep(0.3) + + try: + writer = AtomicFactWriter(root=memory_root) + bucket = _dt.date(2026, 5, 18) + owner_a = "u_a" + owner_b = "u_b" + await _seed_atomic_facts(writer, owner_id=owner_a, bucket=bucket, n_items=3) + src_md_path = _atomic_fact_md_path(owner_a, bucket) + dest_md_path = _atomic_fact_md_path(owner_b, bucket) + src_absolute = memory_root.root / src_md_path + dest_absolute = memory_root.root / dest_md_path + await anyio.Path(dest_absolute.parent).mkdir(parents=True, exist_ok=True) + + await _wait_path_done(src_md_path) + assert await _count_lance_rows_md(src_md_path) == 3 + + await anyio.to_thread.run_sync( + shutil.move, str(src_absolute), str(dest_absolute) + ) + await _wait_paths_done(src_md_path, dest_md_path) + + assert await _count_lance_rows_md(src_md_path) == 0 + assert await _count_lance_rows_md(dest_md_path) == 3 + + # Inspect a row from dest to confirm owner_id stays as u_a + # (current design: frontmatter wins over md_path for owner_id). + rows = await atomic_fact_repo.find_where( + f"md_path = '{dest_md_path}'", limit=10 + ) + assert rows, "dest md_path has no rows" + assert all(r.owner_id == owner_a for r in rows), ( + f"expected owner_id={owner_a} from frontmatter, " + f"got {[r.owner_id for r in rows]}" + ) + finally: + await orchestrator.stop() + + +# ===== B. Write-pattern scenarios ===== + + +async def test_modify_existing_entry_content_reindexes( + cascade_runtime: MemoryRoot, +) -> None: + """Rewriting an entry's body (same entry_id, new text) must flip + content_sha256 and trigger re-upsert (not skip).""" + memory_root = cascade_runtime + orchestrator = _build_orchestrator(memory_root) + await orchestrator.start() + await asyncio.sleep(0.3) + + try: + writer = AtomicFactWriter(root=memory_root) + owner_id = "u_modify" + bucket = _dt.date(2026, 5, 18) + await _seed_atomic_facts( + writer, + owner_id=owner_id, + bucket=bucket, + n_items=3, + text_prefix="ORIGINAL", + ) + md_path = _atomic_fact_md_path(owner_id, bucket) + absolute = memory_root.root / md_path + await _wait_path_done(md_path) + rows_before = await atomic_fact_repo.find_where( + f"md_path = '{md_path}'", limit=10 + ) + assert len(rows_before) == 3 + sha_before = {r.entry_id: r.content_sha256 for r in rows_before} + fact_before = {r.entry_id: r.fact for r in rows_before} + + # Read, replace body text, atomic-write back through writer.write() + text = await anyio.Path(absolute).read_text(encoding="utf-8") + new_text = text.replace("ORIGINAL", "EDITED") + assert new_text != text + mw = MarkdownWriter(memory_root) + await mw.write(absolute, new_text) + # The edit reuses md_path; row status flips back to pending then + # to done again. Poll until content_sha256 actually changes. + await asyncio.sleep(0.3) + await _wait_drain() + + rows_after = await atomic_fact_repo.find_where( + f"md_path = '{md_path}'", limit=10 + ) + assert len(rows_after) == 3 + sha_after = {r.entry_id: r.content_sha256 for r in rows_after} + fact_after = {r.entry_id: r.fact for r in rows_after} + + # Every entry_id present in both, every content_sha256 changed, + # every fact text now reflects EDITED. + assert set(sha_after) == set(sha_before) + for eid, sha in sha_after.items(): + assert sha != sha_before[eid], ( + f"content_sha256 did not change for {eid}: stayed {sha}" + ) + assert "EDITED" in fact_after[eid], ( + f"fact text not updated for {eid}: {fact_after[eid]!r}" + ) + assert "ORIGINAL" not in fact_after[eid] + assert "ORIGINAL" in fact_before[eid] + finally: + await orchestrator.stop() + + +async def test_concurrent_writes_different_owners_no_bleed( + cascade_runtime: MemoryRoot, +) -> None: + """N owners writing in parallel must converge with per-md_path + isolation: each md_path holds exactly its owner's entries.""" + memory_root = cascade_runtime + orchestrator = _build_orchestrator(memory_root) + await orchestrator.start() + await asyncio.sleep(0.3) + + try: + writer = AtomicFactWriter(root=memory_root) + bucket = _dt.date(2026, 5, 18) + owners = [f"u_concur_{i}" for i in range(5)] + per_owner = 4 + + await asyncio.gather( + *[ + _seed_atomic_facts( + writer, + owner_id=oid, + bucket=bucket, + n_items=per_owner, + text_prefix=f"by-{oid}", + ) + for oid in owners + ] + ) + md_paths = [_atomic_fact_md_path(oid, bucket) for oid in owners] + await _wait_paths_done(*md_paths) + + for oid in owners: + md_path = _atomic_fact_md_path(oid, bucket) + rows = await atomic_fact_repo.find_where(f"md_path = '{md_path}'", limit=10) + assert len(rows) == per_owner, ( + f"{oid}: expected {per_owner} rows, got {len(rows)}" + ) + # Every row in this md_path must belong to this owner — + # no bleed from another concurrent owner's writes. + assert all(r.owner_id == oid for r in rows) + assert all(f"by-{oid}" in r.fact for r in rows) + finally: + await orchestrator.stop() + + +async def test_lap_append_during_handler_no_loss( + cascade_runtime: MemoryRoot, +) -> None: + """Writer keeps appending while worker is mid-handler. + + Slow the embedder so a handler invocation overlaps later appends. + On drain, lance_rows must equal md entries — the lap is absorbed + by the worker's status='processing' guard + re-claim. + """ + memory_root = cascade_runtime + + class _SlowEmbedder(_StubEmbedder): + async def embed(self, text: str) -> list[float]: + await asyncio.sleep(0.05) # handler takes ~0.05*N entries + return [0.0] * self.dim + + orchestrator = CascadeOrchestrator( + memory_root=memory_root, + embedder=_SlowEmbedder(), + tokenizer=build_tokenizer(), + config=CascadeConfig( + scan_interval_seconds=60.0, + worker_batch_size=20, + worker_max_retry=1, + worker_poll_interval_seconds=0.05, + worker_retry_backoff_seconds=0.0, + ), + ) + await orchestrator.start() + await asyncio.sleep(0.3) + + try: + writer = AtomicFactWriter(root=memory_root) + owner_id = "u_lap" + bucket = _dt.date(2026, 5, 18) + total = 30 + for i in range(total): + await writer.append_entries( + owner_id, + [ + ( + { + "owner_id": owner_id, + "session_id": f"s_{i}", + "timestamp": "2026-05-19T07:04:26+00:00", + "parent_id": f"mc_{i}", + "sender_ids": [owner_id], + }, + {"Fact": f"fact body {i}"}, + ) + ], + date=bucket, + ) + # Pace just slow enough that some writes land during a + # handler invocation (~50ms per embed), but fast enough + # that multiple writes accumulate during one handler. + await asyncio.sleep(0.02) + + md_path = _atomic_fact_md_path(owner_id, bucket) + absolute = memory_root.root / md_path + await _wait_path_done(md_path, deadline=30.0) + + md_entries = await _count_md_entries(absolute) + lance_rows = await _count_lance_rows_md(md_path) + assert md_entries == total, ( + f"writer self-check: expected {total} md entries, got {md_entries}" + ) + assert lance_rows == md_entries, f"LAP LOSS: md={md_entries} lance={lance_rows}" + finally: + await orchestrator.stop() + + +# ===== C. Scanner fallback scenarios ===== + + +def _build_orchestrator_fast_scanner(memory_root: MemoryRoot) -> CascadeOrchestrator: + """Same as :func:`_build_orchestrator` but with a 2s scanner so tests + don't wait 30s for the fallback path.""" + return CascadeOrchestrator( + memory_root=memory_root, + embedder=_StubEmbedder(), + tokenizer=build_tokenizer(), + config=CascadeConfig( + scan_interval_seconds=2.0, + worker_batch_size=20, + worker_max_retry=1, + worker_poll_interval_seconds=0.05, + worker_retry_backoff_seconds=0.0, + ), + ) + + +def _silence_handler_method(monkeypatch: pytest.MonkeyPatch, name: str) -> None: + """Replace ``watcher._Handler.`` with a no-op for the duration + of the test. Simulates fseventsd missing that event class entirely. + """ + from everos.memory.cascade import watcher as watcher_module + + monkeypatch.setattr( + watcher_module._Handler, + name, + lambda self, event: None, + ) + + +async def test_scanner_recovers_missed_delete( + cascade_runtime: MemoryRoot, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Watcher's ``on_deleted`` is silenced → unlink no longer enqueues + via the watcher. The scanner sweep should still notice the path + missing on disk and enqueue a 'deleted' on its own.""" + memory_root = cascade_runtime + orchestrator = _build_orchestrator_fast_scanner(memory_root) + await orchestrator.start() + await asyncio.sleep(0.3) + + try: + writer = AtomicFactWriter(root=memory_root) + owner_id = "u_scan_del" + bucket = _dt.date(2026, 5, 18) + await _seed_atomic_facts(writer, owner_id=owner_id, bucket=bucket, n_items=3) + md_path = _atomic_fact_md_path(owner_id, bucket) + absolute = memory_root.root / md_path + await _wait_path_done(md_path) + assert await _count_lance_rows_md(md_path) == 3 + + # From here on, watcher ignores deletions. + _silence_handler_method(monkeypatch, "on_deleted") + + absolute.unlink() + # Watcher won't enqueue; scanner sweeps every 2s and should + # spot mtime/existence inconsistency, then enqueue 'deleted'. + await asyncio.sleep(0.2) + + async def _lance_cleared() -> bool: + return await _count_lance_rows_md(md_path) == 0 + + async with asyncio.timeout(10.0): + while not await _lance_cleared(): # noqa: ASYNC110 - polling cascade state + await asyncio.sleep(0.1) + + async with asyncio.timeout(5.0): + while True: # noqa: ASYNC110 - polling cascade state + row = await md_change_state_repo.get_by_id(md_path) + if row is not None and row.status == "done": + break + await asyncio.sleep(0.1) + assert row.change_type == "deleted" + finally: + await orchestrator.stop() + + +async def test_scanner_indexes_preexisting_md( + cascade_runtime: MemoryRoot, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """An md file written BEFORE cascade starts (or by an editor while + cascade is offline). watchdog ignores files that exist at schedule + time — only the scanner can pick it up. Simulate by silencing + on_created and writing the file before orchestrator.start().""" + memory_root = cascade_runtime + + # Pre-seed: write the md directly to disk before any cascade is up. + owner_id = "u_scan_pre" + bucket = _dt.date(2026, 5, 18) + writer = AtomicFactWriter(root=memory_root) + await _seed_atomic_facts(writer, owner_id=owner_id, bucket=bucket, n_items=2) + md_path = _atomic_fact_md_path(owner_id, bucket) + assert (memory_root.root / md_path).is_file() + + # Now start cascade with the file already on disk. Belt-and- + # suspenders: silence all watcher events so the only path to + # discovery is the scanner. + orchestrator = _build_orchestrator_fast_scanner(memory_root) + for name in ("on_created", "on_modified", "on_moved", "on_deleted"): + _silence_handler_method(monkeypatch, name) + await orchestrator.start() + + try: + + async def _lance_filled() -> bool: + return await _count_lance_rows_md(md_path) == 2 + + async with asyncio.timeout(10.0): + while not await _lance_filled(): # noqa: ASYNC110 - polling cascade state + await asyncio.sleep(0.1) + finally: + await orchestrator.stop() + + +async def test_scanner_recovers_missed_modify( + cascade_runtime: MemoryRoot, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """All non-deletion watcher events silenced. writer.append produces + an atomic-replace whose events are all dropped by the watcher. + Scanner should still notice the new file and enqueue 'added'.""" + memory_root = cascade_runtime + orchestrator = _build_orchestrator_fast_scanner(memory_root) + + # Silence everything BEFORE start() so the initial schedule doesn't + # see any add/create events either. + for name in ("on_created", "on_modified", "on_moved"): + _silence_handler_method(monkeypatch, name) + + await orchestrator.start() + await asyncio.sleep(0.3) + + try: + writer = AtomicFactWriter(root=memory_root) + owner_id = "u_scan_mod" + bucket = _dt.date(2026, 5, 18) + await _seed_atomic_facts(writer, owner_id=owner_id, bucket=bucket, n_items=3) + md_path = _atomic_fact_md_path(owner_id, bucket) + + async def _lance_filled() -> bool: + return await _count_lance_rows_md(md_path) == 3 + + async with asyncio.timeout(10.0): + while not await _lance_filled(): # noqa: ASYNC110 - polling cascade state + await asyncio.sleep(0.1) + + row = await md_change_state_repo.get_by_id(md_path) + assert row is not None and row.status == "done" + finally: + await orchestrator.stop() diff --git a/tests/integration/test_memorize_agent_mode.py b/tests/integration/test_memorize_agent_mode.py new file mode 100644 index 00000000..b67db59f --- /dev/null +++ b/tests/integration/test_memorize_agent_mode.py @@ -0,0 +1,268 @@ +"""Agent-mode memorize integration tests. + +Covers the agent branches that ``test_memorize_integration.py`` skips: + +- :mod:`service.memorize` agent dispatch (asyncio.gather of user + agent + pipelines) +- :mod:`service._boundary` agent-mode detection via + :class:`everalgo.agent_memory.AgentBoundaryDetector` +- :mod:`memory.extract.pipeline.agent_memory.AgentMemoryPipeline` end-to-end + +Self-contained: the chat-baseline file keeps its fixture local, so we +copy the minimum scaffolding rather than refactor it into a shared +conftest. +""" + +from __future__ import annotations + +import importlib +import json +import sqlite3 +from collections.abc import AsyncIterator, Callable +from pathlib import Path +from typing import Any +from unittest.mock import AsyncMock + +import pytest +import pytest_asyncio +from everalgo.llm.types import ChatMessage as LLMChatMessage +from everalgo.llm.types import ChatResponse +from everalgo.testing.fake_llm import FakeLLMClient +from sqlmodel import SQLModel + +from everos.core.persistence import MemoryRoot +from everos.service.memorize import MemorizeResult, memorize + + +def _boundary_response(boundaries: list[int]) -> str: + return json.dumps( + {"reasoning": "test", "boundaries": boundaries, "should_wait": False} + ) + + +def _make_fake_llm(boundary_responses: list[list[int]] | None = None) -> FakeLLMClient: + queue: list[list[int]] = list(boundary_responses or []) + + def handler(messages: list[LLMChatMessage], **_: Any) -> ChatResponse: + prompt = messages[0].content + if "boundaries" in prompt.lower() or "memcell" in prompt.lower(): + cuts = queue.pop(0) if queue else [] + return ChatResponse(content=_boundary_response(cuts), model="fake") + return ChatResponse( + content=json.dumps({"title": "T", "content": "B"}), model="fake" + ) + + return FakeLLMClient(handler=handler) + + +def _msg( + role: str, + content: str, + *, + sender_id: str = "u_alice", + timestamp: int = 1_700_000_000_000, + tool_calls: list[dict] | None = None, + tool_call_id: str | None = None, +) -> dict[str, Any]: + out: dict[str, Any] = { + "sender_id": sender_id, + "role": role, + "content": content, + "timestamp": timestamp, + } + if tool_calls is not None: + out["tool_calls"] = tool_calls + if tool_call_id is not None: + out["tool_call_id"] = tool_call_id + return out + + +def _user(content: str, ts: int, *, sender: str = "u_alice") -> dict[str, Any]: + return _msg("user", content, sender_id=sender, timestamp=ts) + + +def _assistant(content: str, ts: int) -> dict[str, Any]: + return _msg("assistant", content, sender_id="assistant", timestamp=ts) + + +def _memcell_rows(tmp_path: Path) -> list[sqlite3.Row]: + db = tmp_path / ".index" / "sqlite" / "system.db" + if not db.is_file(): + return [] + conn = sqlite3.connect(db) + conn.row_factory = sqlite3.Row + try: + return list(conn.execute("SELECT * FROM memcell ORDER BY timestamp")) + finally: + conn.close() + + +@pytest_asyncio.fixture +async def memorize_env( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> AsyncIterator[Callable[..., Any]]: + """Same shape as the chat-baseline fixture; ``mode`` defaults to ``agent``.""" + monkeypatch.setattr( + MemoryRoot, "default", classmethod(lambda cls: MemoryRoot(root=tmp_path)) + ) + (tmp_path / ".index" / "sqlite").mkdir(parents=True, exist_ok=True) + + svc = importlib.import_module("everos.service.memorize") + af_mod = importlib.import_module("everos.memory.strategies.extract_atomic_facts") + fs_mod = importlib.import_module("everos.memory.strategies.extract_foresight") + ac_mod = importlib.import_module("everos.memory.strategies.extract_agent_case") + client_mod = importlib.import_module("everos.component.llm.client") + + for attr in ( + "_episode_writer", + "_prompt_loader", + "_user_pipeline", + "_agent_pipeline", + "_ome_engine", + ): + monkeypatch.setattr(svc, attr, None, raising=False) + monkeypatch.setattr(client_mod, "_llm_client", None, raising=False) + monkeypatch.setattr(af_mod, "_writer", None, raising=False) + monkeypatch.setattr(fs_mod, "_writer", None, raising=False) + + started: dict[str, Any] = {"engine": None} + + async def _setup(*, mode: str = "agent", fake_llm: FakeLLMClient) -> None: + monkeypatch.setenv("EVEROS_MEMORIZE__MODE", mode) + monkeypatch.setenv("EVEROS_LLM__API_KEY", "fake-key") + monkeypatch.setenv("EVEROS_LLM__BASE_URL", "https://fake.example.com") + + from everos.config import load_settings + + load_settings.cache_clear() + + monkeypatch.setattr(client_mod, "_llm_client", fake_llm) + + from everos.infra.persistence.sqlite import dispose_engine, get_engine + + db_engine = get_engine() + async with db_engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + started["dispose"] = dispose_engine + + # Silence OME strategies so agent_case / atomic / foresight don't + # try real extraction logic during these tests. + noop = AsyncMock(return_value=[]) + for mod in (af_mod, fs_mod, ac_mod): + extractor_attr = next( + (n for n in dir(mod) if n.endswith("Extractor")), None + ) + if extractor_attr: + monkeypatch.setattr( + mod, + extractor_attr, + lambda *a, **k: type("M", (), {"aextract": noop})(), + ) + + engine = svc._get_engine() + await engine.start() + started["engine"] = engine + + yield _setup + + if started.get("engine") is not None: + await started["engine"].stop() + if started.get("dispose") is not None: + await started["dispose"]() + + +# ── Tests ──────────────────────────────────────────────────────────── + + +async def test_agent_mode_two_user_assistant_msgs( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Agent mode happy path: one cell, both user + agent pipelines fire.""" + fake = _make_fake_llm(boundary_responses=[[]]) + await memorize_env(mode="agent", fake_llm=fake) + + result = await memorize( + { + "session_id": "test_agent_basic", + "messages": [ + _user("hello", 1_700_000_000_000), + _assistant("hi there", 1_700_000_001_000), + ], + }, + is_final=True, + ) + assert isinstance(result, MemorizeResult) + assert result.status == "extracted" + + rows = _memcell_rows(tmp_path) + assert len(rows) == 1 + assert rows[0]["raw_type"] == "AgentTrajectory" + + +async def test_agent_mode_preserves_tool_items( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Agent mode keeps ``role=tool`` rows inside the cell (chat mode drops them).""" + fake = _make_fake_llm(boundary_responses=[[]]) + await memorize_env(mode="agent", fake_llm=fake) + + payload = { + "session_id": "test_agent_tools", + "messages": [ + _user("debug this", 1_700_000_000_000), + _msg( + "assistant", + "calling tool", + timestamp=1_700_000_001_000, + tool_calls=[ + { + "id": "c1", + "type": "function", + "function": {"name": "x", "arguments": "{}"}, + } + ], + ), + _msg( + "tool", + "result", + sender_id="tool", + timestamp=1_700_000_002_000, + tool_call_id="c1", + ), + _assistant("here's the answer", 1_700_000_003_000), + ], + } + result = await memorize(payload, is_final=True) + assert result.status == "extracted" + + rows = _memcell_rows(tmp_path) + assert len(rows) == 1 + ids = json.loads(rows[0]["message_ids_json"]) + # All four preserved in agent mode (chat mode would have 2). + assert len(ids) == 4 + + +async def test_agent_mode_dispatch_no_double_insert( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Dual pipeline dispatch must not double-insert the memcell row.""" + fake = _make_fake_llm(boundary_responses=[[]]) + await memorize_env(mode="agent", fake_llm=fake) + + await memorize( + { + "session_id": "test_agent_dispatch", + "messages": [ + _user("u1", 1_700_000_000_000), + _assistant("a1", 1_700_000_001_000), + _user("u2", 1_700_000_002_000), + _assistant("a2", 1_700_000_003_000), + ], + }, + is_final=True, + ) + + rows = _memcell_rows(tmp_path) + assert len(rows) == 1 # boundary stage owns the ledger + payload = json.loads(rows[0]["payload_json"]) + assert len(payload["items"]) == 4 diff --git a/tests/integration/test_memorize_concurrent_session_lock.py b/tests/integration/test_memorize_concurrent_session_lock.py new file mode 100644 index 00000000..78e9ef0b --- /dev/null +++ b/tests/integration/test_memorize_concurrent_session_lock.py @@ -0,0 +1,300 @@ +"""Concurrent /add on one session must not lose messages (regression). + +White-box integration test for the per-session lock added in +``everos.service._session_lock``. + +Bug class +--------- +Without the lock, two concurrent ``memorize()`` calls on the same +``session_id`` race on ``unprocessed_buffer``: + +1. Both read the same pre-existing buffer rows. +2. Each boundary call sees only its own newly-arrived messages plus + the shared pre-existing buffer (neither sees the other's messages). +3. Both call ``_replace_buffer(session_id, tail)`` — the later write + silently overwrites the earlier write's tail; the earlier task's + tail messages are lost forever. + +Invariant under test +-------------------- +After N concurrent ``memorize()`` calls on one session, every input +message_id is **either** in some memcell's ``message_ids_json`` **or** +in the surviving ``unprocessed_buffer`` rows. Nothing silently vanishes. + +This is a white-box integration test (not e2e): it bypasses HTTP, calls +``memorize()`` directly, but inspects sqlite tables to assert internal +state. Uses ``FakeLLMClient`` to avoid real LLM latency and to control +boundary decisions deterministically. +""" + +from __future__ import annotations + +import asyncio +import importlib +import json +from collections.abc import AsyncIterator, Callable +from pathlib import Path +from typing import Any +from unittest.mock import AsyncMock + +import pytest +import pytest_asyncio +from everalgo.llm.types import ChatMessage as LLMChatMessage +from everalgo.llm.types import ChatResponse +from everalgo.testing.fake_llm import FakeLLMClient +from sqlalchemy import text +from sqlmodel import SQLModel + +from everos.core.persistence import MemoryRoot +from everos.service.memorize import memorize + +# --------------------------------------------------------------------------- +# Fake LLM that splits each call into one memcell + 0-tail (force extract) +# --------------------------------------------------------------------------- + + +def _boundary_response(boundaries: list[int]) -> str: + return json.dumps( + {"reasoning": "test", "boundaries": boundaries, "should_wait": False} + ) + + +def _episode_response(title: str = "T", content: str = "B") -> str: + return json.dumps({"title": title, "content": content}) + + +def _make_extract_all_llm() -> FakeLLMClient: + """Boundary returns single boundary at end → entire merged → 1 cell, tail=[].""" + + def handler(messages: list[LLMChatMessage], **_: Any) -> ChatResponse: + prompt = messages[0].content + if "boundaries" in prompt.lower() or "memcell" in prompt.lower(): + # Always cut: the boundary indices are relative to merged input; + # an empty list means "no cut, hold". A single [N] means "cut + # after index N", i.e. everything before goes into one cell. + # We use a sentinel large index to force boundary to take all. + return ChatResponse(content=_boundary_response([999]), model="fake") + return ChatResponse(content=_episode_response(), model="fake") + + return FakeLLMClient(handler=handler) + + +# --------------------------------------------------------------------------- +# Fixture — mirrors test_memorize_integration's pattern but without OME / strategies +# (the lock bug lives at the boundary stage; downstream strategies are +# irrelevant to this race). +# --------------------------------------------------------------------------- + + +@pytest_asyncio.fixture +async def memorize_env_locked( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> AsyncIterator[Callable[..., AsyncMock]]: + monkeypatch.setattr( + MemoryRoot, "default", classmethod(lambda cls: MemoryRoot(root=tmp_path)) + ) + (tmp_path / ".index" / "sqlite").mkdir(parents=True, exist_ok=True) + + svc = importlib.import_module("everos.service.memorize") + af_mod = importlib.import_module("everos.memory.strategies.extract_atomic_facts") + fs_mod = importlib.import_module("everos.memory.strategies.extract_foresight") + client_mod = importlib.import_module("everos.component.llm.client") + lock_mod = importlib.import_module("everos.service._session_lock") + + # Reset memorize singletons + session lock registry. + for attr in ( + "_episode_writer", + "_prompt_loader", + "_user_pipeline", + "_agent_pipeline", + "_ome_engine", + ): + monkeypatch.setattr(svc, attr, None, raising=False) + monkeypatch.setattr(client_mod, "_llm_client", None, raising=False) + monkeypatch.setattr(af_mod, "_writer", None, raising=False) + monkeypatch.setattr(fs_mod, "_writer", None, raising=False) + lock_mod._reset_for_tests() + + started: dict[str, Any] = {"engine": None} + + async def _setup(*, fake_llm: FakeLLMClient) -> None: + monkeypatch.setenv("EVEROS_MEMORIZE__MODE", "chat") + monkeypatch.setenv("EVEROS_LLM__API_KEY", "fake-key") + monkeypatch.setenv("EVEROS_LLM__BASE_URL", "https://fake.example.com") + from everos.config import load_settings + + load_settings.cache_clear() + + monkeypatch.setattr(client_mod, "_llm_client", fake_llm) + + from everos.infra.persistence.sqlite import get_engine + + db_engine = get_engine() + async with db_engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + + # Silence OME strategy extractors (we only care about the boundary + + # memcell + buffer cycle; downstream strategies are a separate story). + mock_af = AsyncMock(return_value=[]) + mock_fs = AsyncMock(return_value=[]) + monkeypatch.setattr( + af_mod, + "AtomicFactExtractor", + lambda *a, **k: type("M", (), {"aextract": mock_af})(), + ) + monkeypatch.setattr( + fs_mod, + "ForesightExtractor", + lambda *a, **k: type("M", (), {"aextract": mock_fs})(), + ) + + engine = svc._get_engine() + await engine.start() + started["engine"] = engine + + yield _setup + + if started["engine"] is not None: + await started["engine"].stop() + from everos.infra.persistence.sqlite import dispose_engine + + await dispose_engine() + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _msg(idx: int, sender: str, ts: int) -> dict[str, Any]: + return { + "sender_id": sender, + "role": "user", + "timestamp": ts, + "content": f"msg-{idx} from {sender}", + } + + +async def _collect_buffer_message_ids(session_id: str) -> set[str]: + from everos.infra.persistence.sqlite import get_engine + + eng = get_engine() + async with eng.connect() as conn: + result = await conn.execute( + text("SELECT message_id FROM unprocessed_buffer WHERE session_id = :s"), + {"s": session_id}, + ) + return {row[0] for row in result.fetchall()} + + +async def _collect_memcell_message_ids(session_id: str) -> set[str]: + from everos.infra.persistence.sqlite import get_engine + + eng = get_engine() + async with eng.connect() as conn: + result = await conn.execute( + text("SELECT message_ids_json FROM memcell WHERE session_id = :s"), + {"s": session_id}, + ) + out: set[str] = set() + for (raw,) in result.fetchall(): + out.update(json.loads(raw)) + return out + + +# --------------------------------------------------------------------------- +# Tests +# --------------------------------------------------------------------------- + + +async def test_concurrent_adds_same_session_no_message_loss( + memorize_env_locked: Callable[..., AsyncMock], +) -> None: + """Two concurrent /add on one session: every input message must end up + either in a memcell's message_ids OR in the surviving buffer.""" + await memorize_env_locked(fake_llm=_make_extract_all_llm()) + + session_id = "s_concurrent" + + batch_a = [_msg(i, "alice", 1_700_000_000_000 + i * 1000) for i in range(4)] + batch_b = [_msg(i + 100, "bob", 1_700_000_100_000 + i * 1000) for i in range(4)] + + # Fire both concurrently against the same session. + await asyncio.gather( + memorize({"session_id": session_id, "messages": batch_a}), + memorize({"session_id": session_id, "messages": batch_b}), + ) + + buffered = await _collect_buffer_message_ids(session_id) + in_cells = await _collect_memcell_message_ids(session_id) + covered = buffered | in_cells + + # The id format is ``m___`` — we can derive + # exactly what the 8 inputs should hash to without depending on the + # internal id_gen import. Easier: assert the *count* covered == 8. + assert len(covered) == 8, ( + f"expected 8 distinct message ids covered, got {len(covered)}: " + f"buffer={len(buffered)}, memcell={len(in_cells)}" + ) + + # Sanity: no message appears in both buffer and memcell at once + # (consumed = removed from buffer). + overlap = buffered & in_cells + assert not overlap, f"messages in both buffer and memcell: {overlap}" + + +async def test_concurrent_adds_serial_when_locked( + memorize_env_locked: Callable[..., AsyncMock], +) -> None: + """Same as above but explicitly stress with 4 concurrent batches.""" + await memorize_env_locked(fake_llm=_make_extract_all_llm()) + + session_id = "s_stress" + + n_batches = 4 + batch_size = 3 + batches = [ + [ + _msg(b * 10 + i, f"u{b}", 1_700_000_000_000 + (b * 10 + i) * 1000) + for i in range(batch_size) + ] + for b in range(n_batches) + ] + + await asyncio.gather( + *(memorize({"session_id": session_id, "messages": batch}) for batch in batches) + ) + + buffered = await _collect_buffer_message_ids(session_id) + in_cells = await _collect_memcell_message_ids(session_id) + covered = buffered | in_cells + + expected = n_batches * batch_size + assert len(covered) == expected, ( + f"expected {expected} message ids covered, got {len(covered)}: " + f"buffer={len(buffered)}, memcell={len(in_cells)}" + ) + assert not (buffered & in_cells) + + +async def test_different_sessions_run_in_parallel( + memorize_env_locked: Callable[..., AsyncMock], +) -> None: + """Cross-session calls share no lock — must not serialise.""" + await memorize_env_locked(fake_llm=_make_extract_all_llm()) + + def _msgs(sid: str) -> list[dict[str, Any]]: + return [_msg(i, sid, 1_700_000_000_000 + i * 1000) for i in range(3)] + + await asyncio.gather( + memorize({"session_id": "s_a", "messages": _msgs("s_a")}), + memorize({"session_id": "s_b", "messages": _msgs("s_b")}), + memorize({"session_id": "s_c", "messages": _msgs("s_c")}), + ) + + for sid in ("s_a", "s_b", "s_c"): + buffered = await _collect_buffer_message_ids(sid) + in_cells = await _collect_memcell_message_ids(sid) + covered = buffered | in_cells + assert len(covered) == 3, f"session {sid}: got {len(covered)}, want 3" diff --git a/tests/integration/test_memorize_integration.py b/tests/integration/test_memorize_integration.py new file mode 100644 index 00000000..f8a82a90 --- /dev/null +++ b/tests/integration/test_memorize_integration.py @@ -0,0 +1,690 @@ +"""End-to-end memorize integration tests. + +Drives ``service.memorize.memorize()`` with a ``FakeLLMClient`` so the +full chain (ingest → boundary → user / agent pipeline → md + OME emit) +runs without real LLM calls. Each test isolates state by: + +- redirecting ``MemoryRoot.default()`` to a ``tmp_path`` +- resetting service-layer lazy singletons +- starting / stopping a per-test ``OfflineEngine`` +- patching ``get_llm_client`` (boundary + strategies) onto a fake + +OME strategies (atomic / foresight) are silenced via ``mock_aextract`` so +this test focuses on the synchronous boundary + pipeline + md path — +strategy dispatch correctness already has its own coverage in +``test_ome_strategies_integration.py``. +""" + +from __future__ import annotations + +import importlib +import json +import sqlite3 +from collections.abc import AsyncIterator, Callable +from pathlib import Path +from typing import Any +from unittest.mock import AsyncMock + +import pytest +import pytest_asyncio +from everalgo.llm.types import ChatMessage as LLMChatMessage +from everalgo.llm.types import ChatResponse +from everalgo.testing.fake_llm import FakeLLMClient +from sqlmodel import SQLModel + +from everos.core.persistence import MemoryRoot +from everos.service.memorize import MemorizeResult, memorize + +# --------------------------------------------------------------------------- +# Canned LLM responses +# --------------------------------------------------------------------------- + + +def _boundary_response(boundaries: list[int]) -> str: + """Build a ``detect_boundaries`` JSON response (algo schema).""" + payload = { + "reasoning": "test", + "boundaries": boundaries, + "should_wait": False, + } + return json.dumps(payload) + + +def _episode_response(title: str = "Test Subject", content: str = "Test body") -> str: + """Build an ``EpisodeExtractor`` JSON response (algo schema).""" + return json.dumps({"title": title, "content": content}) + + +def _make_fake_llm( + boundary_responses: list[list[int]] | None = None, + *, + episode_title: str = "Test Subject", + episode_content: str = "Test body", +) -> FakeLLMClient: + """Build a ``FakeLLMClient`` that dispatches by prompt fingerprint. + + Pops one ``boundaries=...`` from ``boundary_responses`` per boundary + prompt seen; every episode prompt returns the same canned + ``{title, content}``. + """ + boundary_queue: list[list[int]] = list(boundary_responses or []) + + def handler(messages: list[LLMChatMessage], **_: Any) -> ChatResponse: + prompt = messages[0].content + if "boundaries" in prompt.lower() or "memcell" in prompt.lower(): + cuts = boundary_queue.pop(0) if boundary_queue else [] + return ChatResponse(content=_boundary_response(cuts), model="fake") + # Fall through to episode (also catches atomic/foresight prompts — + # they'll return success-but-empty in their mocked extractor below). + return ChatResponse( + content=_episode_response(episode_title, episode_content), + model="fake", + ) + + return FakeLLMClient(handler=handler) + + +# --------------------------------------------------------------------------- +# Shared setup fixture +# --------------------------------------------------------------------------- + + +@pytest_asyncio.fixture +async def memorize_env( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> AsyncIterator[Callable[..., AsyncMock]]: + """Yield a builder that configures a clean memorize environment. + + Usage:: + + async def test_x(memorize_env): + await memorize_env(mode="chat", fake_llm=_make_fake_llm([...])) + outcome = await memorize({"session_id": "s", "messages": [...]}) + + The builder must be called exactly once per test (it primes singletons + + starts the OME engine). Teardown stops the engine and disposes the + sqlite engine. + """ + monkeypatch.setattr( + MemoryRoot, "default", classmethod(lambda cls: MemoryRoot(root=tmp_path)) + ) + (tmp_path / ".index" / "sqlite").mkdir(parents=True, exist_ok=True) + + svc = importlib.import_module("everos.service.memorize") + af_mod = importlib.import_module("everos.memory.strategies.extract_atomic_facts") + fs_mod = importlib.import_module("everos.memory.strategies.extract_foresight") + client_mod = importlib.import_module("everos.component.llm.client") + + # Reset singletons. + for attr in ( + "_episode_writer", + "_prompt_loader", + "_user_pipeline", + "_agent_pipeline", + "_ome_engine", + ): + monkeypatch.setattr(svc, attr, None, raising=False) + monkeypatch.setattr(client_mod, "_llm_client", None, raising=False) + monkeypatch.setattr(af_mod, "_writer", None, raising=False) + monkeypatch.setattr(fs_mod, "_writer", None, raising=False) + + started: dict[str, Any] = {"engine": None, "sqlite_engine": None} + + async def _setup( + *, + mode: str = "chat", + fake_llm: FakeLLMClient, + hard_token_limit: int = 65536, + hard_msg_limit: int = 500, + ) -> None: + # Provide a non-None API key + base_url so get_llm_client doesn't + # raise; we replace the cached singleton with our fake right after. + monkeypatch.setenv("EVEROS_MEMORIZE__MODE", mode) + monkeypatch.setenv("EVEROS_LLM__API_KEY", "fake-key") + monkeypatch.setenv("EVEROS_LLM__BASE_URL", "https://fake.example.com") + monkeypatch.setenv( + "EVEROS_BOUNDARY_DETECTION__HARD_TOKEN_LIMIT", str(hard_token_limit) + ) + monkeypatch.setenv( + "EVEROS_BOUNDARY_DETECTION__HARD_MSG_LIMIT", str(hard_msg_limit) + ) + from everos.config import load_settings + + load_settings.cache_clear() + + # Replace the cached client singleton with our fake so get_llm_client + # returns the fake on subsequent calls. + monkeypatch.setattr(client_mod, "_llm_client", fake_llm) + + # Build sqlite schema. + from everos.infra.persistence.sqlite import dispose_engine, get_engine + + db_engine = get_engine() + async with db_engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + started["sqlite_engine"] = (get_engine, dispose_engine) + + # Mock the OME extractors so the async strategy chain is a no-op + # (the strategy itself still runs; it just sees no facts/foresights). + mock_af = AsyncMock(return_value=[]) + mock_fs = AsyncMock(return_value=[]) + monkeypatch.setattr( + af_mod, + "AtomicFactExtractor", + lambda *a, **k: type("M", (), {"aextract": mock_af})(), + ) + monkeypatch.setattr( + fs_mod, + "ForesightExtractor", + lambda *a, **k: type("M", (), {"aextract": mock_fs})(), + ) + + engine = svc._get_engine() + await engine.start() + started["engine"] = engine + + yield _setup + + if started["engine"] is not None: + await started["engine"].stop() + if started["sqlite_engine"] is not None: + _, dispose = started["sqlite_engine"] + await dispose() + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _msg( + role: str, + content: str, + *, + sender_id: str = "u_alice", + timestamp: int = 1_700_000_000_000, + tool_calls: list[dict] | None = None, + tool_call_id: str | None = None, +) -> dict[str, Any]: + out: dict[str, Any] = { + "sender_id": sender_id, + "role": role, + "content": content, + "timestamp": timestamp, + } + if tool_calls is not None: + out["tool_calls"] = tool_calls + if tool_call_id is not None: + out["tool_call_id"] = tool_call_id + return out + + +def _user(content: str, ts: int, *, sender: str = "u_alice") -> dict[str, Any]: + return _msg("user", content, sender_id=sender, timestamp=ts) + + +def _assistant(content: str, ts: int, *, sender: str = "assistant") -> dict[str, Any]: + return _msg("assistant", content, sender_id=sender, timestamp=ts) + + +def _memcell_rows(tmp_path: Path) -> list[sqlite3.Row]: + db = tmp_path / ".index" / "sqlite" / "system.db" + if not db.is_file(): + return [] + conn = sqlite3.connect(db) + conn.row_factory = sqlite3.Row + try: + return list(conn.execute("SELECT * FROM memcell ORDER BY timestamp")) + finally: + conn.close() + + +def _buffer_count(tmp_path: Path) -> int: + db = tmp_path / ".index" / "sqlite" / "system.db" + if not db.is_file(): + return 0 + conn = sqlite3.connect(db) + try: + return conn.execute( + "SELECT COUNT(*) FROM unprocessed_buffer WHERE track='memorize'" + ).fetchone()[0] + finally: + conn.close() + + +def _episode_paths(tmp_path: Path) -> list[Path]: + base = tmp_path / "default_app" / "default_project" / "users" + return sorted(base.rglob("episode-*.md")) + + +# --------------------------------------------------------------------------- +# Happy path baseline +# --------------------------------------------------------------------------- + + +async def test_chat_baseline_two_msgs_one_cell( + tmp_path: Path, + memorize_env: Callable[..., Any], +) -> None: + """2 messages → flush forces them into 1 cell + 1 Episode + 1 memcell row.""" + fake = _make_fake_llm(boundary_responses=[[]]) # no internal cuts + await memorize_env(mode="chat", fake_llm=fake) + + payload = { + "session_id": "test_chat_1", + "messages": [ + _user("hello", 1_700_000_000_000), + _assistant("hi there", 1_700_000_001_000), + ], + } + result = await memorize(payload, is_final=True) + + assert isinstance(result, MemorizeResult) + assert result.status == "extracted" + assert result.message_count == 2 + + rows = _memcell_rows(tmp_path) + assert len(rows) == 1 + assert rows[0]["track"] == "memorize" + assert rows[0]["raw_type"] == "Conversation" + # MemCell has no single owner — sender_ids carries the participants. + assert "u_alice" in json.loads(rows[0]["sender_ids_json"]) + + assert _buffer_count(tmp_path) == 0 + + md_files = _episode_paths(tmp_path) + assert len(md_files) == 1 + body = md_files[0].read_text() + assert "Test Subject" in body + assert "Test body" in body + + +# --------------------------------------------------------------------------- +# Input-shape boundary cases (6) +# --------------------------------------------------------------------------- + + +async def test_empty_batch_non_final_is_skipped( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """``messages=[]`` + ``is_final=False`` → skipped, no side effects.""" + await memorize_env(mode="chat", fake_llm=_make_fake_llm()) + + result = await memorize( + {"session_id": "test_empty_nonfinal", "messages": []}, is_final=False + ) + assert result.status == "accumulated" + assert result.message_count == 0 + assert _memcell_rows(tmp_path) == [] + assert _episode_paths(tmp_path) == [] + + +async def test_empty_batch_final_drains_empty_buffer( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """``messages=[]`` + ``is_final=True`` on virgin session → no cells, no md.""" + await memorize_env(mode="chat", fake_llm=_make_fake_llm()) + + result = await memorize( + {"session_id": "test_empty_final", "messages": []}, is_final=True + ) + assert result.status == "accumulated" + assert _memcell_rows(tmp_path) == [] + assert _episode_paths(tmp_path) == [] + + +async def test_assistant_only_batch_accumulates( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """No role=user message → boundary stage parks everything in buffer.""" + fake = _make_fake_llm(boundary_responses=[]) # no LLM call expected + await memorize_env(mode="chat", fake_llm=fake) + + result = await memorize( + { + "session_id": "test_asst_only", + "messages": [ + _assistant("hi", 1_700_000_000_000), + _assistant("anyone here?", 1_700_000_001_000), + ], + }, + is_final=False, + ) + assert result.status == "accumulated" + assert _memcell_rows(tmp_path) == [] + assert _buffer_count(tmp_path) == 2 # parked in buffer + + +async def test_single_user_message_accumulates( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Single user msg → boundary returns no cells (need conversation) → buffer it.""" + fake = _make_fake_llm(boundary_responses=[[]]) # boundary called, no cuts + await memorize_env(mode="chat", fake_llm=fake) + + result = await memorize( + { + "session_id": "test_single", + "messages": [_user("hello?", 1_700_000_000_000)], + }, + is_final=False, + ) + assert result.status == "accumulated" + assert _memcell_rows(tmp_path) == [] + assert _buffer_count(tmp_path) == 1 + + +async def test_chat_mode_filters_tool_messages( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Chat mode drops ``role=tool`` + assistant-with-tool_calls pre-boundary.""" + fake = _make_fake_llm(boundary_responses=[[]]) + await memorize_env(mode="chat", fake_llm=fake) + + result = await memorize( + { + "session_id": "test_chat_filter", + "messages": [ + _user("debug this", 1_700_000_000_000), + _msg( + "assistant", + "calling tool", + timestamp=1_700_000_001_000, + tool_calls=[ + { + "id": "c1", + "type": "function", + "function": {"name": "x", "arguments": "{}"}, + } + ], + ), + _msg( + "tool", + "result", + sender_id="tool", + timestamp=1_700_000_002_000, + tool_call_id="c1", + ), + _assistant("here's the answer", 1_700_000_003_000), + ], + }, + is_final=True, + ) + # After filter: 1 user + 1 assistant text = 2 msgs → 1 cell on flush. + assert result.status == "extracted" + rows = _memcell_rows(tmp_path) + assert len(rows) == 1 + ids = json.loads(rows[0]["message_ids_json"]) + assert len(ids) == 2 # tool + assistant-with-tool_calls dropped + + +async def test_duplicate_message_id_dedup_across_adds( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Same message replayed across two ``/add`` calls is deduped by message_id.""" + fake = _make_fake_llm(boundary_responses=[[], []]) # 2 boundary calls, both empty + await memorize_env(mode="chat", fake_llm=fake) + + # message_id is derived from (session_id, ts_ms, idx); same payload twice + # produces the same id, so the second add should be a no-op insert. + payload = { + "session_id": "test_dedup", + "messages": [ + _user("hi", 1_700_000_000_000), + _assistant("hi back", 1_700_000_001_000), + ], + } + await memorize(payload, is_final=False) + await memorize(payload, is_final=False) # replay + await memorize({"session_id": "test_dedup", "messages": []}, is_final=True) + + rows = _memcell_rows(tmp_path) + assert len(rows) == 1 + ids = json.loads(rows[0]["message_ids_json"]) + assert len(ids) == 2 # not 4 — dedup worked + assert len(set(ids)) == 2 # unique + + +# --------------------------------------------------------------------------- +# Hard-limit cases (2) +# --------------------------------------------------------------------------- + + +async def test_hard_msg_limit_force_split( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Exceeding ``hard_msg_limit`` triggers a force-split before the LLM call.""" + fake = _make_fake_llm(boundary_responses=[[]]) # LLM call after force-split + # hard_msg_limit=3 → batch of 5 msgs forces ~1 split before LLM. + await memorize_env( + mode="chat", fake_llm=fake, hard_msg_limit=3, hard_token_limit=10_000 + ) + + msgs = [ + _user(f"u{i}", 1_700_000_000_000 + i * 1000, sender="u_alice") + if i % 2 == 0 + else _assistant(f"a{i}", 1_700_000_000_000 + i * 1000) + for i in range(5) + ] + result = await memorize( + {"session_id": "test_hardmsg", "messages": msgs}, is_final=True + ) + assert result.status == "extracted" + rows = _memcell_rows(tmp_path) + # Force-split + LLM final → at least 2 cells (force + remaining). + assert len(rows) >= 2 + + +async def test_hard_token_limit_force_split( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Exceeding ``hard_token_limit`` triggers a force-split (token-based).""" + fake = _make_fake_llm(boundary_responses=[[]]) + # Very small token budget → even tiny content triggers force-split. + await memorize_env( + mode="chat", fake_llm=fake, hard_msg_limit=500, hard_token_limit=20 + ) + + msgs = [ + _user("a" * 200, 1_700_000_000_000, sender="u_alice"), + _assistant("b" * 200, 1_700_000_001_000), + _user("c" * 200, 1_700_000_002_000, sender="u_alice"), + _assistant("d" * 200, 1_700_000_003_000), + ] + result = await memorize( + {"session_id": "test_hardtok", "messages": msgs}, is_final=True + ) + assert result.status == "extracted" + assert len(_memcell_rows(tmp_path)) >= 2 + + +# --------------------------------------------------------------------------- +# Flush state-machine cases (4) +# --------------------------------------------------------------------------- + + +async def test_flush_on_virgin_session_is_noop( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Flush a session that never received ``/add`` — should not crash.""" + await memorize_env(mode="chat", fake_llm=_make_fake_llm()) + + result = await memorize( + {"session_id": "test_virgin_flush", "messages": []}, is_final=True + ) + assert result.status == "accumulated" + assert _memcell_rows(tmp_path) == [] + + +async def test_add_then_flush_then_add( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """After flush drains the buffer, a follow-up ``/add`` still works.""" + fake = _make_fake_llm(boundary_responses=[[], []]) + await memorize_env(mode="chat", fake_llm=fake) + + sid = "test_add_flush_add" + await memorize( + { + "session_id": sid, + "messages": [ + _user("first", 1_700_000_000_000), + _assistant("ack", 1_700_000_001_000), + ], + }, + is_final=False, + ) + await memorize({"session_id": sid, "messages": []}, is_final=True) + + rows_after_flush_1 = len(_memcell_rows(tmp_path)) + assert rows_after_flush_1 == 1 + + # Second turn after the flush. + await memorize( + { + "session_id": sid, + "messages": [ + _user("second turn", 1_700_000_010_000), + _assistant("ok", 1_700_000_011_000), + ], + }, + is_final=True, + ) + assert len(_memcell_rows(tmp_path)) == 2 # cumulative + + +async def test_consecutive_flushes_second_is_noop( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Flush twice in a row — second call finds empty buffer, no-ops.""" + fake = _make_fake_llm(boundary_responses=[[]]) + await memorize_env(mode="chat", fake_llm=fake) + + sid = "test_double_flush" + await memorize( + { + "session_id": sid, + "messages": [ + _user("hi", 1_700_000_000_000), + _assistant("ok", 1_700_000_001_000), + ], + }, + is_final=False, + ) + res1 = await memorize({"session_id": sid, "messages": []}, is_final=True) + res2 = await memorize({"session_id": sid, "messages": []}, is_final=True) + + assert res1.status == "extracted" + assert res2.status == "accumulated" # nothing left + assert len(_memcell_rows(tmp_path)) == 1 + + +async def test_flush_drains_assistant_only_buffer( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Buffer with only assistant messages: flush still forces them into a cell.""" + fake = _make_fake_llm(boundary_responses=[[]]) + await memorize_env(mode="chat", fake_llm=fake) + + sid = "test_asst_then_flush" + # Two assistant-only adds → both park in buffer. + await memorize( + { + "session_id": sid, + "messages": [_assistant("a1", 1_700_000_000_000)], + }, + is_final=False, + ) + await memorize( + { + "session_id": sid, + "messages": [_assistant("a2", 1_700_000_001_000)], + }, + is_final=False, + ) + assert _buffer_count(tmp_path) == 2 + + # Add a user message + flush — boundary should now run. + result = await memorize( + { + "session_id": sid, + "messages": [_user("anyone there?", 1_700_000_002_000)], + }, + is_final=True, + ) + assert result.status == "extracted" + assert _buffer_count(tmp_path) == 0 + + +# --------------------------------------------------------------------------- +# Multi-session cases (2) +# --------------------------------------------------------------------------- + + +async def test_two_sessions_are_isolated( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Two session_ids share the engine but their buffers / cells stay separate.""" + fake = _make_fake_llm(boundary_responses=[[], []]) # 1 per session + await memorize_env(mode="chat", fake_llm=fake) + + await memorize( + { + "session_id": "sess_A", + "messages": [ + _user("hi from A", 1_700_000_000_000, sender="u_alice"), + _assistant("ack A", 1_700_000_001_000), + ], + }, + is_final=True, + ) + await memorize( + { + "session_id": "sess_B", + "messages": [ + _user("hi from B", 1_700_000_010_000, sender="u_bob"), + _assistant("ack B", 1_700_000_011_000), + ], + }, + is_final=True, + ) + + rows = _memcell_rows(tmp_path) + assert len(rows) == 2 + sessions = sorted(r["session_id"] for r in rows) + assert sessions == ["sess_A", "sess_B"] + # MemCell has no single owner — sender_ids carries who participated. + senders = {r["session_id"]: json.loads(r["sender_ids_json"]) for r in rows} + assert "u_alice" in senders["sess_A"] + assert "u_bob" in senders["sess_B"] + + +async def test_same_session_multi_add_concatenates( + tmp_path: Path, memorize_env: Callable[..., Any] +) -> None: + """Multiple adds on the same session accumulate in one buffer until flushed.""" + fake = _make_fake_llm(boundary_responses=[[], [], []]) + await memorize_env(mode="chat", fake_llm=fake) + + sid = "test_multi_add" + for i in range(3): + await memorize( + { + "session_id": sid, + "messages": [ + _user(f"u{i}", 1_700_000_000_000 + i * 2000), + _assistant(f"a{i}", 1_700_000_001_000 + i * 2000), + ], + }, + is_final=False, + ) + # Buffer should have 6 messages now (no boundary cuts). + assert _buffer_count(tmp_path) == 6 + + result = await memorize({"session_id": sid, "messages": []}, is_final=True) + assert result.status == "extracted" + rows = _memcell_rows(tmp_path) + assert len(rows) == 1 # one cell from the flush + ids = json.loads(rows[0]["message_ids_json"]) + assert len(ids) == 6 # all 6 messages folded in diff --git a/tests/integration/test_memorize_window_segmentation.py b/tests/integration/test_memorize_window_segmentation.py new file mode 100644 index 00000000..8b151c5d --- /dev/null +++ b/tests/integration/test_memorize_window_segmentation.py @@ -0,0 +1,433 @@ +"""Window-segmentation white-box integration tests for boundary stage. + +Verifies the **read-merge-boundary-write** semantics of one ``memorize()`` +invocation, especially the buffer-as-tail invariant and the **buffer +replacement** behaviour on successive calls: + +Invariants under test +--------------------- +I1. After one ``add`` with ``boundaries=[k]``: + - memcell rows: prefix of merged input (first k messages) + - buffer rows: tail (the remaining messages) + - every input message_id lands in exactly one of {memcell, buffer} + (covered ∧ disjoint) + +I2. Tail ordering: every buffer row's timestamp ≥ every memcell row's + timestamp (the tail is the **last** part of the time-ordered slice). + +I3. Successive ``add`` consumes prior buffer: + - Round 2's boundary sees ``prior_buffer + new_batch`` merged. + - The prior tail (m3 say) ends up in **Round 2's memcell** if the + boundary cuts past it, NOT in any buffer row. + - The new buffer is the **fresh** tail, with the old buffer rows + replaced entirely (semantics of ``_replace_buffer``). + +I4. ``flush`` with ``is_final=True`` drains the buffer entirely — every + remaining message ends up in some memcell. + +This is **single-threaded sequential** (the concurrent race is covered +separately in test_memorize_concurrent_session_lock.py). FakeLLM scripts +boundary decisions deterministically so we own exact slicing. +""" + +from __future__ import annotations + +import importlib +import json +from collections.abc import AsyncIterator, Callable +from pathlib import Path +from typing import Any +from unittest.mock import AsyncMock + +import pytest +import pytest_asyncio +from everalgo.llm.types import ChatMessage as LLMChatMessage +from everalgo.llm.types import ChatResponse +from everalgo.testing.fake_llm import FakeLLMClient +from sqlalchemy import text +from sqlmodel import SQLModel + +from everos.core.persistence import MemoryRoot +from everos.service.memorize import memorize + +# --------------------------------------------------------------------------- +# FakeLLM with scripted boundary responses (FIFO queue, one pop per call) +# --------------------------------------------------------------------------- + + +def _boundary_response(boundaries: list[int]) -> str: + return json.dumps( + {"reasoning": "test", "boundaries": boundaries, "should_wait": False} + ) + + +def _episode_response(title: str = "T", content: str = "B") -> str: + return json.dumps({"title": title, "content": content}) + + +def _make_scripted_llm( + boundary_responses: list[list[int]], +) -> FakeLLMClient: + """Boundary calls FIFO-pop from ``boundary_responses``. + + Episode calls (for downstream pipeline) get a canned response. + """ + queue: list[list[int]] = list(boundary_responses) + + def handler(messages: list[LLMChatMessage], **_: Any) -> ChatResponse: + prompt = messages[0].content + if "boundaries" in prompt.lower() or "memcell" in prompt.lower(): + cuts = queue.pop(0) if queue else [] + return ChatResponse(content=_boundary_response(cuts), model="fake") + return ChatResponse(content=_episode_response(), model="fake") + + return FakeLLMClient(handler=handler) + + +# --------------------------------------------------------------------------- +# Fixture — mirrors the locked-env fixture in the concurrent test +# --------------------------------------------------------------------------- + + +@pytest_asyncio.fixture +async def memorize_env_scripted( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> AsyncIterator[Callable[..., AsyncMock]]: + monkeypatch.setattr( + MemoryRoot, "default", classmethod(lambda cls: MemoryRoot(root=tmp_path)) + ) + (tmp_path / ".index" / "sqlite").mkdir(parents=True, exist_ok=True) + + svc = importlib.import_module("everos.service.memorize") + af_mod = importlib.import_module("everos.memory.strategies.extract_atomic_facts") + fs_mod = importlib.import_module("everos.memory.strategies.extract_foresight") + client_mod = importlib.import_module("everos.component.llm.client") + lock_mod = importlib.import_module("everos.service._session_lock") + + for attr in ( + "_episode_writer", + "_prompt_loader", + "_user_pipeline", + "_agent_pipeline", + "_ome_engine", + ): + monkeypatch.setattr(svc, attr, None, raising=False) + monkeypatch.setattr(client_mod, "_llm_client", None, raising=False) + monkeypatch.setattr(af_mod, "_writer", None, raising=False) + monkeypatch.setattr(fs_mod, "_writer", None, raising=False) + lock_mod._reset_for_tests() + + started: dict[str, Any] = {"engine": None} + + async def _setup(*, fake_llm: FakeLLMClient) -> None: + monkeypatch.setenv("EVEROS_MEMORIZE__MODE", "chat") + monkeypatch.setenv("EVEROS_LLM__API_KEY", "fake-key") + monkeypatch.setenv("EVEROS_LLM__BASE_URL", "https://fake.example.com") + from everos.config import load_settings + + load_settings.cache_clear() + + monkeypatch.setattr(client_mod, "_llm_client", fake_llm) + + from everos.infra.persistence.sqlite import get_engine + + db_engine = get_engine() + async with db_engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + + # Silence OME strategies — orthogonal to boundary segmentation. + mock_af = AsyncMock(return_value=[]) + mock_fs = AsyncMock(return_value=[]) + monkeypatch.setattr( + af_mod, + "AtomicFactExtractor", + lambda *a, **k: type("M", (), {"aextract": mock_af})(), + ) + monkeypatch.setattr( + fs_mod, + "ForesightExtractor", + lambda *a, **k: type("M", (), {"aextract": mock_fs})(), + ) + + engine = svc._get_engine() + await engine.start() + started["engine"] = engine + + yield _setup + + if started["engine"] is not None: + await started["engine"].stop() + from everos.infra.persistence.sqlite import dispose_engine + + await dispose_engine() + + +# --------------------------------------------------------------------------- +# Helpers — message factory + state inspectors +# --------------------------------------------------------------------------- + + +_BASE_TS = 1_700_000_000_000 # 2023-11-14, plenty of headroom + + +def _msg(idx: int, sender: str = "alice") -> dict[str, Any]: + """Build one canonical /add message with monotonically increasing ts.""" + return { + "sender_id": sender, + "role": "user", + "timestamp": _BASE_TS + idx * 1000, + "content": f"msg-{idx}", + } + + +async def _buffer_rows(session_id: str) -> list[tuple[str, int]]: + """Return ``[(message_id, timestamp_ms)]`` for buffer rows, time-ordered.""" + from everos.component.utils.datetime import from_iso_format, to_timestamp_ms + from everos.infra.persistence.sqlite import get_engine + + eng = get_engine() + async with eng.connect() as conn: + result = await conn.execute( + text( + "SELECT message_id, timestamp FROM unprocessed_buffer " + "WHERE session_id = :s ORDER BY timestamp" + ), + {"s": session_id}, + ) + rows: list[tuple[str, int]] = [] + for mid, ts in result.fetchall(): + # sqlite stores DateTime as ISO 8601 string via SQLAlchemy. + ts_ms = to_timestamp_ms(from_iso_format(ts)) + rows.append((mid, ts_ms)) + return rows + + +async def _memcell_rows(session_id: str) -> list[tuple[str, list[str]]]: + """Return ``[(memcell_id, message_ids[])]`` in insertion order.""" + from everos.infra.persistence.sqlite import get_engine + + eng = get_engine() + async with eng.connect() as conn: + result = await conn.execute( + text( + "SELECT memcell_id, message_ids_json FROM memcell " + "WHERE session_id = :s ORDER BY created_at" + ), + {"s": session_id}, + ) + return [(mid, json.loads(raw)) for mid, raw in result.fetchall()] + + +# --------------------------------------------------------------------------- +# I1 + I2: single add with boundaries=[k] — prefix→memcell, suffix→buffer +# --------------------------------------------------------------------------- + + +async def test_single_add_no_cut_accumulates_full_batch_in_buffer( + memorize_env_scripted: Callable[..., AsyncMock], +) -> None: + """boundaries=[] → no memcell, entire batch sits in buffer.""" + await memorize_env_scripted(fake_llm=_make_scripted_llm([[]])) + + session = "s_no_cut" + inputs = [_msg(i) for i in range(3)] + await memorize({"session_id": session, "messages": inputs}) + + cells = await _memcell_rows(session) + buffer = await _buffer_rows(session) + + assert cells == [], f"expected no memcell, got {cells}" + assert len(buffer) == 3, f"expected 3 buffer rows, got {len(buffer)}" + # buffer holds all 3 input message_ids, time-ordered + buffer_ts = [ts for _, ts in buffer] + assert buffer_ts == sorted(buffer_ts) + + +async def test_single_add_with_cut_splits_prefix_to_cell_suffix_to_buffer( + memorize_env_scripted: Callable[..., AsyncMock], +) -> None: + """boundaries=[2] on a 3-msg batch → cell=[m0,m1], buffer=[m2].""" + await memorize_env_scripted(fake_llm=_make_scripted_llm([[2]])) + + session = "s_cut" + inputs = [_msg(i) for i in range(3)] + await memorize({"session_id": session, "messages": inputs}) + + cells = await _memcell_rows(session) + buffer = await _buffer_rows(session) + + # Exactly one memcell carved. + assert len(cells) == 1, cells + cell_msg_ids = set(cells[0][1]) + assert len(cell_msg_ids) == 2 + + # Buffer holds the remaining one message. + assert len(buffer) == 1 + buf_msg_id = buffer[0][0] + + # Disjoint: buffer message NOT in the memcell. + assert buf_msg_id not in cell_msg_ids, ( + "buffer row leaked into memcell — buffer should be the tail only" + ) + + # I2 — tail comes AFTER prefix in time. + cell_max_ts = max(_BASE_TS + i * 1000 for i in (0, 1)) + buf_ts = buffer[0][1] + assert buf_ts >= cell_max_ts, ( + f"tail ts ({buf_ts}) must be >= max cell ts ({cell_max_ts})" + ) + + +# --------------------------------------------------------------------------- +# I3: successive add — prior buffer feeds into next memcell, then is REPLACED +# --------------------------------------------------------------------------- + + +async def test_second_add_consumes_prior_buffer_and_replaces_tail( + memorize_env_scripted: Callable[..., AsyncMock], +) -> None: + """Core test: prior tail must end up in next memcell, NOT remain in buffer.""" + # Round 1: cut after 2 of 3 → cell=[m0,m1], buffer=[m2] + # Round 2: merged input = [m2,m3,m4,m5]; cut after 3 → cell=[m2,m3,m4], + # buffer=[m5] + await memorize_env_scripted( + fake_llm=_make_scripted_llm([[2], [3]]), + ) + + session = "s_replace" + + # Round 1 + r1_inputs = [_msg(i) for i in range(3)] + await memorize({"session_id": session, "messages": r1_inputs}) + + r1_cells = await _memcell_rows(session) + r1_buffer = await _buffer_rows(session) + assert len(r1_cells) == 1 + assert len(r1_buffer) == 1 + prior_tail_msg_id = r1_buffer[0][0] + + # Round 2 — fresh messages m3, m4, m5 + r2_inputs = [_msg(i) for i in range(3, 6)] + await memorize({"session_id": session, "messages": r2_inputs}) + + r2_cells = await _memcell_rows(session) + r2_buffer = await _buffer_rows(session) + + # Two memcells total: one from round 1, one from round 2. + assert len(r2_cells) == 2, r2_cells + round1_cell_msgs = set(r2_cells[0][1]) + round2_cell_msgs = set(r2_cells[1][1]) + + # ★ KEY ASSERTION ★ — prior buffer's message landed in round 2 cell. + assert prior_tail_msg_id in round2_cell_msgs, ( + f"prior buffer msg {prior_tail_msg_id} should have been consumed " + f"into round 2's memcell, but it's missing from {round2_cell_msgs}" + ) + # Round 2 cell should have exactly 3 messages (prior tail + first 2 of new). + assert len(round2_cell_msgs) == 3 + + # Round 1 cell unchanged. + assert len(round1_cell_msgs) == 2 + assert prior_tail_msg_id not in round1_cell_msgs + + # Buffer is the NEW tail — exactly 1 fresh row. + assert len(r2_buffer) == 1 + new_tail_id = r2_buffer[0][0] + + # ★ KEY ASSERTION ★ — the OLD buffer entry is gone (replaced, not appended). + assert new_tail_id != prior_tail_msg_id, ( + "old buffer entry survived into round 2's buffer — " + "_replace_buffer is supposed to wipe + reinsert, not append" + ) + + # Buffer ∩ all memcells = ∅ + all_cell_msgs = round1_cell_msgs | round2_cell_msgs + assert new_tail_id not in all_cell_msgs + + # Conservation: 6 distinct message ids covered across cells + buffer. + # (We avoid hard-coding id format here — gen_message_id encodes the + # per-batch index, not a global one.) + covered = all_cell_msgs | {new_tail_id} + assert len(covered) == 6, ( + f"expected 6 distinct ids covered, got {len(covered)}: {covered}" + ) + + +# --------------------------------------------------------------------------- +# I4: flush drains buffer entirely (is_final=True path) +# --------------------------------------------------------------------------- + + +async def test_flush_after_accumulation_drains_buffer_into_memcell( + memorize_env_scripted: Callable[..., AsyncMock], +) -> None: + """add(boundaries=[]) → buffer accumulates → flush → cell=all, buffer=[].""" + # Round 1 add: boundaries=[] → no cut, all into buffer. + # Flush: is_final=True passes empty boundaries → algo closes tail into cell. + await memorize_env_scripted( + fake_llm=_make_scripted_llm([[], []]), + ) + + session = "s_flush" + inputs = [_msg(i) for i in range(3)] + await memorize({"session_id": session, "messages": inputs}) + + # Post-add: nothing in memcell yet. + cells = await _memcell_rows(session) + buffer = await _buffer_rows(session) + assert cells == [] + assert len(buffer) == 3 + + # Flush + await memorize({"session_id": session, "messages": []}, is_final=True) + + cells = await _memcell_rows(session) + buffer = await _buffer_rows(session) + + assert len(cells) == 1, cells + assert len(cells[0][1]) == 3 + assert buffer == [] + + +# --------------------------------------------------------------------------- +# Sanity: empty boundaries + multiple sequential adds keep conservation +# --------------------------------------------------------------------------- + + +async def test_three_sequential_adds_conservation_no_loss( + memorize_env_scripted: Callable[..., AsyncMock], +) -> None: + """3 sequential adds with mixed cuts: every input id covered exactly once.""" + # add 1: 3 msgs, no cut → buffer holds [m0,m1,m2] + # add 2: 3 msgs, cut after 4 of merged [m0..m5] → cell=[m0..m3], buffer=[m4,m5] + # add 3: 3 msgs, cut after 3 of merged [m4..m8] → cell=[m4,m5,m6], buffer=[m7,m8] + await memorize_env_scripted( + fake_llm=_make_scripted_llm([[], [4], [3]]), + ) + + session = "s_seq" + total_inputs = 0 + for batch_start in (0, 3, 6): + await memorize( + { + "session_id": session, + "messages": [_msg(i) for i in range(batch_start, batch_start + 3)], + } + ) + total_inputs += 3 + + cells = await _memcell_rows(session) + buffer = await _buffer_rows(session) + + in_cells: set[str] = set() + for _, msg_ids in cells: + in_cells.update(msg_ids) + in_buffer = {mid for mid, _ in buffer} + + covered = in_cells | in_buffer + assert len(covered) == total_inputs, ( + f"expected {total_inputs} ids covered, got {len(covered)}" + ) + # Disjoint + assert not (in_cells & in_buffer) diff --git a/tests/integration/test_ome_strategies_integration.py b/tests/integration/test_ome_strategies_integration.py new file mode 100644 index 00000000..7ab58127 --- /dev/null +++ b/tests/integration/test_ome_strategies_integration.py @@ -0,0 +1,614 @@ +"""End-to-end: emit pipeline event → strategies dispatch → SUCCESS + log lines.""" + +from __future__ import annotations + +import asyncio +import datetime as _dt +import hashlib +import uuid +from collections.abc import Sequence +from pathlib import Path +from unittest.mock import AsyncMock, patch + +import numpy as np +import pytest +from everalgo.types import AgentCase, AtomicFact, ChatMessage, Foresight, MemCell +from structlog.testing import capture_logs + +from everos.memory.events import ( + AgentCaseExtracted, + AgentPipelineStarted, + EpisodeExtracted, + UserPipelineStarted, +) + + +class _DeterministicHashEmbedder: + """Hash-seeded RNG embedder for clustering e2e. + + Same input text → same unit vector; distinct inputs → distinct directions + (sha256-seeded ``numpy.random.default_rng``). The vectors aren't + semantically meaningful, but they ARE deterministic and well-spread, so + ``cluster_by_geometry`` / ``cluster_by_llm``'s nearest-neighbor logic + has real signal to work with — unlike a MagicMock returning a constant + vector, which collapses every cosine similarity to 1.0. + """ + + dim: int = 1024 + + async def embed(self, text: str) -> list[float]: + digest = hashlib.sha256(text.encode("utf-8")).digest() + seed = int.from_bytes(digest[:8], "little") + rng = np.random.default_rng(seed) + vec = rng.standard_normal(self.dim).astype(np.float32) + norm = float(np.linalg.norm(vec)) or 1.0 + vec /= norm + return vec.tolist() + + async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]: + return [await self.embed(t) for t in texts] + + +def _sample_memcell() -> MemCell: + return MemCell( + items=[ + ChatMessage( + id="m1", + role="user", + content="alice likes hiking", + timestamp=1_700_000_000_000, + sender_id="u_alice", + ), + ChatMessage( + id="m2", + role="user", + content="bob plans a trip", + timestamp=1_700_000_001_000, + sender_id="u_bob", + ), + ChatMessage( + id="m3", + role="assistant", + content="sounds good", + timestamp=1_700_000_002_000, + sender_id="agent", + ), + ], + timestamp=1_700_000_002_000, + ) + + +@pytest.mark.asyncio +async def test_emit_dispatches_both_strategies_to_success( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Real OfflineEngine + APScheduler runtime; extractors + LLM mocked. + + Verifies the full chain: emit(event) → dispatcher (3 gates) → APS one-shot + job → Runner.run → strategy body → mark_success. + """ + import importlib + + from everos.core.persistence import MemoryRoot + from everos.infra.ome.records import RunStatus + + svc = importlib.import_module("everos.service.memorize") + + # Redirect MemoryRoot.default() to tmp_path so _get_engine() writes ome.db + # under the test's isolated temp directory instead of the real ~/.everos. + monkeypatch.setattr( + MemoryRoot, + "default", + classmethod(lambda cls: MemoryRoot(root=tmp_path)), + ) + # Reset singletons so they rebuild against the patched MemoryRoot. + monkeypatch.setattr(svc, "_ome_engine", None, raising=False) + _af_mod = importlib.import_module("everos.memory.strategies.extract_atomic_facts") + _fs_mod = importlib.import_module("everos.memory.strategies.extract_foresight") + monkeypatch.setattr(_af_mod, "_writer", None, raising=False) + monkeypatch.setattr(_fs_mod, "_writer", None, raising=False) + + fake_fact = AtomicFact( + owner_id="u_alice", content="hi", timestamp=1_700_000_000_000 + ) + fake_foresight = Foresight( + owner_id="u_alice", + foresight="x", + evidence="y", + timestamp=1_700_000_000_000, + ) + + with ( + patch( + "everos.memory.strategies.extract_atomic_facts.AtomicFactExtractor" + ) as mock_af, + patch( + "everos.memory.strategies.extract_foresight.ForesightExtractor" + ) as mock_fs, + patch( + "everos.memory.strategies.extract_atomic_facts.get_llm_client", + return_value=object(), + ), + patch( + "everos.memory.strategies.extract_foresight.get_llm_client", + return_value=object(), + ), + capture_logs() as logs, + ): + mock_af.return_value.aextract = AsyncMock(return_value=[fake_fact]) + mock_fs.return_value.aextract = AsyncMock(return_value=[fake_foresight]) + + # Ensure the sqlite dir exists before the engine creates ome.db. + (tmp_path / ".index" / "sqlite").mkdir(parents=True, exist_ok=True) + await _setup_system_db_schema(monkeypatch) + + engine = svc._get_engine() + await engine.start() + try: + await engine.emit( + UserPipelineStarted( + memcell_id="mc_a", + session_id="s1", + memcell=_sample_memcell(), + ) + ) + + # Poll until both strategies reach SUCCESS (max 5 s). + af_rows: list = [] + fs_rows: list = [] + for _ in range(50): + await asyncio.sleep(0.1) + af_rows = await engine.list_runs( + "extract_atomic_facts", status=RunStatus.SUCCESS + ) + fs_rows = await engine.list_runs( + "extract_foresight", status=RunStatus.SUCCESS + ) + if af_rows and fs_rows: + break + + assert af_rows, "expected SUCCESS RunRecord for extract_atomic_facts" + assert fs_rows, "expected SUCCESS RunRecord for extract_foresight" + assert af_rows[0].strategy_name == "extract_atomic_facts" + assert fs_rows[0].strategy_name == "extract_foresight" + finally: + await engine.stop() + await _teardown_system_db_schema() + + af_logs = [r for r in logs if r.get("event") == "atomic_facts_extracted"] + fs_logs = [r for r in logs if r.get("event") == "foresights_extracted"] + assert af_logs, "expected atomic_facts_extracted log line" + assert fs_logs, "expected foresights_extracted log line" + # The sample MemCell has 2 user senders (u_alice, u_bob), so each + # strategy gathers one result per sender and flattens them: + # extract_atomic_facts: 2 senders × 1 fake_fact each = 2 + # extract_foresight: 2 senders × 1 fake_foresight each = 2 + assert af_logs[0]["count"] == 2 + assert fs_logs[0]["count"] == 2 + + +async def _setup_system_db_schema(monkeypatch: pytest.MonkeyPatch) -> None: + """Rebuild the sqlite system.db engine + schema against the active tmp_path. + + The ``sqlite_manager`` engine is a process-wide singleton; without + resetting it between tests the second e2e would reuse the first + test's tmp engine (and miss the table create_all on this test's + fresh tmp_path). ``SQLModel.metadata.create_all`` mirrors what + :class:`SqliteLifespanProvider` runs at app startup. + + Pair with :func:`_teardown_system_db_schema` in the test's ``finally`` + block — the engine created here owns an aiosqlite worker thread that + must be closed explicitly, or it lingers past the event loop and + raises ``RuntimeError: Event loop is closed`` from the worker. + """ + from sqlmodel import SQLModel + + from everos.infra.persistence.sqlite import sqlite_manager + + if sqlite_manager._engine is not None: # noqa: SLF001 + await sqlite_manager.dispose_engine() + monkeypatch.setattr(sqlite_manager, "_engine", None, raising=False) + monkeypatch.setattr(sqlite_manager, "_session_factory", None, raising=False) + engine = sqlite_manager.get_engine() + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + + +async def _teardown_system_db_schema() -> None: + """Dispose the per-test sqlite engine so its worker thread doesn't outlive + the event loop (counterpart of :func:`_setup_system_db_schema`).""" + from everos.infra.persistence.sqlite import sqlite_manager + + if sqlite_manager._engine is not None: # noqa: SLF001 + await sqlite_manager.dispose_engine() + + +def _agent_memcell() -> MemCell: + return MemCell( + items=[ + ChatMessage( + id="m1", + role="user", + content="please summarise", + timestamp=1_700_000_000_000, + sender_id="u_alice", + ), + ChatMessage( + id="m2", + role="assistant", + content="here's the summary", + timestamp=1_700_000_001_000, + sender_id="agent_42", + ), + ], + timestamp=1_700_000_001_000, + ) + + +@pytest.mark.asyncio +async def test_emit_dispatches_agent_case_strategy_to_success( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Mirror of the user-side e2e for the agent track. + + Verifies the full agent chain: AgentPipelineStarted emit → dispatcher + (3 gates) → APS one-shot job → Runner.run → extract_agent_case body → + mark_success. Catches breakage in event class wiring, trigger matching, + engine registration, and the agent-side mock plumbing that unit tests + bypass by calling the strategy function directly. + """ + import importlib + + from everos.core.persistence import MemoryRoot + from everos.infra.ome.records import RunStatus + + svc = importlib.import_module("everos.service.memorize") + + monkeypatch.setattr( + MemoryRoot, + "default", + classmethod(lambda cls: MemoryRoot(root=tmp_path)), + ) + monkeypatch.setattr(svc, "_ome_engine", None, raising=False) + _ac_mod = importlib.import_module("everos.memory.strategies.extract_agent_case") + monkeypatch.setattr(_ac_mod, "_writer", None, raising=False) + + fake_case = AgentCase( + id=uuid.uuid4().hex, + timestamp=1_700_000_001_000, + task_intent="summarise the doc", + approach="read + condense", + quality_score=0.8, + key_insight="", + ) + + with ( + patch( + "everos.memory.strategies.extract_agent_case.AgentCaseExtractor" + ) as mock_ac, + patch( + "everos.memory.strategies.extract_agent_case.get_llm_client", + return_value=object(), + ), + capture_logs() as logs, + ): + mock_ac.return_value.aextract = AsyncMock(return_value=[fake_case]) + + (tmp_path / ".index" / "sqlite").mkdir(parents=True, exist_ok=True) + await _setup_system_db_schema(monkeypatch) + + engine = svc._get_engine() + await engine.start() + try: + await engine.emit( + AgentPipelineStarted( + memcell_id="mc_a", + session_id="s1", + memcell=_agent_memcell(), + ) + ) + + ac_rows: list = [] + for _ in range(50): + await asyncio.sleep(0.1) + ac_rows = await engine.list_runs( + "extract_agent_case", status=RunStatus.SUCCESS + ) + if ac_rows: + break + + assert ac_rows, "expected SUCCESS RunRecord for extract_agent_case" + assert ac_rows[0].strategy_name == "extract_agent_case" + finally: + await engine.stop() + await _teardown_system_db_schema() + + ac_logs = [r for r in logs if r.get("event") == "agent_case_extracted"] + assert ac_logs, "expected agent_case_extracted log line" + assert ac_logs[0]["owner_ids"] == ["agent_42"] + assert ac_logs[0]["fanout"] == 1 + assert ac_logs[0]["quality_score"] == 0.8 + + +@pytest.mark.asyncio +async def test_skill_chain_e2e( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Chain: AgentCaseExtracted → trigger_skill_clustering (sqlite) → + SkillClusterUpdated → extract_agent_skill → SUCCESS. + + Real ``cluster_by_llm`` algorithm path: hash-based deterministic + embedder feeds the top-K nearest-neighbor stage, a ``FakeLLMClient`` + returns ``{"idx": "new"}`` so the algo picks the "brand-new cluster" + branch — but the recall + skip-threshold + prompt-render + JSON-parse + pipeline is all real. Only mocked: LanceDB reads (case + skill), + ``AgentSkillExtractor`` (downstream extractor; out of scope), and + the markdown writer. + """ + import importlib + from unittest.mock import MagicMock + + from everalgo.testing.fake_llm import FakeLLMClient + from everalgo.types import AgentSkill as AlgoAgentSkill + + from everos.core.persistence import MemoryRoot + from everos.infra.ome.records import RunStatus + + svc = importlib.import_module("everos.service.memorize") + skill_mod = importlib.import_module("everos.memory.strategies.extract_agent_skill") + + monkeypatch.setattr( + MemoryRoot, + "default", + classmethod(lambda cls: MemoryRoot(root=tmp_path)), + ) + monkeypatch.setattr(svc, "_ome_engine", None, raising=False) + monkeypatch.setattr(skill_mod, "_writer", None, raising=False) + + embedder = _DeterministicHashEmbedder() + # FakeLLMClient: cluster_by_llm only invokes it when top-K similarity + # falls below llm_skip_threshold (default 0.85). With a single new + # cluster in an empty owner set, the recall stage returns no candidates + # at all — so the LLM is never asked. Provide a "{idx: new}" response + # anyway as belt-and-suspenders for future scenarios with seeded clusters. + fake_llm = FakeLLMClient(responses=['{"idx": "new"}']) + + target_lance = MagicMock() + target_lance.entry_id = "ac_20260517_0001" + target_lance.timestamp = _dt.datetime(2026, 5, 17, tzinfo=_dt.UTC) + target_lance.task_intent = "summarise the doc" + target_lance.approach = "read + condense" + target_lance.quality_score = 0.8 + target_lance.key_insight = "" + + emitted_skill = AlgoAgentSkill( + id=uuid.uuid4().hex, + cluster_id="", + name="summarise_doc", + description="how to summarise docs", + content="step 1: read; step 2: condense", + confidence=0.7, + maturity_score=0.5, + source_case_ids=["ac_20260517_0001"], + ) + + with ( + patch( + "everos.memory.strategies.trigger_skill_clustering.get_embedder", + return_value=embedder, + ), + patch( + "everos.memory.strategies.trigger_skill_clustering.get_llm_client", + return_value=fake_llm, + ), + patch( + "everos.memory.strategies.extract_agent_skill.agent_case_repo" + ) as mock_case_repo, + patch( + "everos.memory.strategies.extract_agent_skill.agent_skill_repo" + ) as mock_skill_repo, + patch( + "everos.memory.strategies.extract_agent_skill.get_llm_client", + return_value=object(), + ), + patch( + "everos.memory.strategies.extract_agent_skill.AgentSkillExtractor" + ) as mock_extractor_cls, + patch( + "everos.memory.strategies.extract_agent_skill.AgentSkillWriter" + ) as mock_writer_cls, + capture_logs() as logs, + ): + mock_case_repo.find_by_owner_entry = AsyncMock(return_value=target_lance) + mock_case_repo.find_by_owner_entries = AsyncMock(return_value=[]) + # Empty cluster (no prior skills) → small-cluster scalar path. + mock_skill_repo.count_in_cluster = AsyncMock(return_value=0) + mock_skill_repo.find_in_cluster = AsyncMock(return_value=[]) + mock_extractor_cls.return_value.aextract = AsyncMock( + return_value=[emitted_skill] + ) + mock_writer_cls.return_value.write_main = AsyncMock(return_value=None) + + (tmp_path / ".index" / "sqlite").mkdir(parents=True, exist_ok=True) + await _setup_system_db_schema(monkeypatch) + + engine = svc._get_engine() + await engine.start() + try: + await engine.emit( + AgentCaseExtracted( + memcell_id="mc_a", + case_entry_id="ac_20260517_0001", + task_intent="summarise the doc", + quality_score=0.8, + case_timestamp_ms=1_700_000_001_000, + agent_id="agent_42", + ) + ) + + clu_rows: list = [] + skill_rows: list = [] + for _ in range(50): + await asyncio.sleep(0.1) + clu_rows = await engine.list_runs( + "trigger_skill_clustering", status=RunStatus.SUCCESS + ) + skill_rows = await engine.list_runs( + "extract_agent_skill", status=RunStatus.SUCCESS + ) + if clu_rows and skill_rows: + break + + assert clu_rows, "expected SUCCESS for trigger_skill_clustering" + assert skill_rows, "expected SUCCESS for extract_agent_skill" + finally: + await engine.stop() + await _teardown_system_db_schema() + + cluster_logs = [r for r in logs if r.get("event") == "skill_cluster_updated"] + skill_logs = [r for r in logs if r.get("event") == "agent_skills_extracted"] + assert cluster_logs, "expected skill_cluster_updated log line" + assert skill_logs, "expected agent_skills_extracted log line" + # Writer received exactly one SKILL.md write call with cluster_id stamped. + write_args = mock_writer_cls.return_value.write_main.call_args + fm = write_args.kwargs["frontmatter"] + assert fm.cluster_id == cluster_logs[0]["cluster_id"] + assert fm.name == "summarise_doc" + + +@pytest.mark.asyncio +async def test_profile_chain_e2e( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Chain: EpisodeExtracted → trigger_profile_clustering (sqlite) → + ProfileClusterUpdated → extract_user_profile → SUCCESS. + + Real ``cluster_by_geometry`` (cosine + time-window) with a hash-based + deterministic embedder so the geometry stage operates on well-spread + unit vectors. Real ``cluster_repo`` sqlite. ``memcell_repo`` is still + mocked (a real memcell row would require the boundary stage to run + first; out of scope for the chain emit test). ``ProfileExtractor`` / + md reader/writer mocked as algo + IO seams. + """ + import importlib + from unittest.mock import MagicMock + + from everalgo.types import Profile as AlgoProfile + + from everos.core.persistence import MemoryRoot + from everos.infra.ome.records import RunStatus + + svc = importlib.import_module("everos.service.memorize") + profile_mod = importlib.import_module( + "everos.memory.strategies.extract_user_profile" + ) + + monkeypatch.setattr( + MemoryRoot, + "default", + classmethod(lambda cls: MemoryRoot(root=tmp_path)), + ) + monkeypatch.setattr(svc, "_ome_engine", None, raising=False) + monkeypatch.setattr(profile_mod, "_writer", None, raising=False) + monkeypatch.setattr(profile_mod, "_reader", None, raising=False) + + embedder = _DeterministicHashEmbedder() + + fake_memcell_row = MagicMock() + fake_memcell_row.memcell_id = "mc_aaaaaaaaaaa1" + fake_memcell_row.payload_json = MemCell( + items=[ + ChatMessage( + id="m1", + role="user", + content="alice likes hiking", + timestamp=1_700_000_001_000, + sender_id="u_alice", + ), + ], + timestamp=1_700_000_001_000, + ).model_dump_json() + + new_profile = AlgoProfile.model_validate( + { + "owner_id": "u_alice", + "summary": "Alice is a hiker.", + "timestamp": 1_700_000_001_000, + "explicit_info": ["lives in tokyo"], + "implicit_traits": [], + } + ) + + with ( + patch( + "everos.memory.strategies.trigger_profile_clustering.get_embedder", + return_value=embedder, + ), + patch( + "everos.memory.strategies.extract_user_profile.memcell_repo" + ) as mock_memcell_repo, + patch( + "everos.memory.strategies.extract_user_profile.ProfileReader" + ) as mock_reader_cls, + patch( + "everos.memory.strategies.extract_user_profile.ProfileWriter" + ) as mock_writer_cls, + patch( + "everos.memory.strategies.extract_user_profile.ProfileExtractor" + ) as mock_extractor_cls, + patch( + "everos.memory.strategies.extract_user_profile.get_llm_client", + return_value=object(), + ), + capture_logs() as logs, + ): + mock_memcell_repo.find_by_ids = AsyncMock(return_value=[fake_memcell_row]) + mock_reader_cls.return_value.read = AsyncMock(return_value=None) + mock_writer_cls.return_value.write = AsyncMock(return_value=None) + mock_extractor_cls.return_value.aextract = AsyncMock(return_value=new_profile) + + (tmp_path / ".index" / "sqlite").mkdir(parents=True, exist_ok=True) + await _setup_system_db_schema(monkeypatch) + + engine = svc._get_engine() + await engine.start() + try: + await engine.emit( + EpisodeExtracted( + memcell_id="mc_aaaaaaaaaaa1", + episode_entry_id="ep_20260517_0001", + episode_text="alice likes hiking", + episode_timestamp_ms=1_700_000_001_000, + owner_id="u_alice", + ) + ) + + clu_rows: list = [] + prof_rows: list = [] + for _ in range(50): + await asyncio.sleep(0.1) + clu_rows = await engine.list_runs( + "trigger_profile_clustering", status=RunStatus.SUCCESS + ) + prof_rows = await engine.list_runs( + "extract_user_profile", status=RunStatus.SUCCESS + ) + if clu_rows and prof_rows: + break + + assert clu_rows, "expected SUCCESS for trigger_profile_clustering" + assert prof_rows, "expected SUCCESS for extract_user_profile" + finally: + await engine.stop() + await _teardown_system_db_schema() + + cluster_logs = [r for r in logs if r.get("event") == "profile_cluster_updated"] + profile_logs = [r for r in logs if r.get("event") == "user_profile_extracted"] + assert cluster_logs, "expected profile_cluster_updated log line" + assert profile_logs, "expected user_profile_extracted log line" + assert profile_logs[0]["owner_id"] == "u_alice" + assert profile_logs[0]["mode"] == "INIT" diff --git a/tests/run_locomo_10x3.sh b/tests/run_locomo_10x3.sh new file mode 100755 index 00000000..b104d0dd --- /dev/null +++ b/tests/run_locomo_10x3.sh @@ -0,0 +1,264 @@ +#!/usr/bin/env bash +# Run the LoCoMo benchmark across all 10 conversations × 3 retrieval +# methods (keyword, vector, hybrid). +# +# Wraps tests/run_locomo_batch.sh with the defaults that match the +# everos post-fix benchmark protocol: +# - all 10 LoCoMo conversations (conv 0..9) +# - keyword + vector + hybrid (agentic is skipped — costs 2-3× more +# LLM tokens and the rerank loop hasn't been benchmarked yet) +# - speaker_a partition (the LoCoMo "Plan C" single-owner eval) +# - judge runs = 1 (single-pass LLM judge, no majority vote) +# - top-K 10 +# +# Two ingest modes: +# +# --skip-add (default) reuse the corpus that already lives at +# ~/.everos-report-corpus. Skips the +# ~5 min/conv ingest phase × 10 = ~50 min +# saved. Note: the existing corpus may +# still carry artefacts from the OLD code +# (conv-5 missing episode rows, +# MRAG score=0.0 facts). For a strictly +# clean benchmark of the *fixed* code, +# use --fresh-corpus instead. +# +# --fresh-corpus wipe ~/.everos-report-corpus, restart +# the server, and re-ingest every conv +# with the current bug-fixed cascade. +# Adds ~50 min to the run. +# +# Server must already be running on :8000 with the current code loaded +# (i.e. the OR + optimize fixes). Health check confirmed before launch. +# +# Output structure: +# +# benchmark_results/run__10x3/ +# ├── conv0.json ... conv9.json ← per-conv final results +# ├── conv0_checkpoints/ ... ← phase-level snapshots +# └── SUMMARY.md ← cross-conv accuracy table + +set -euo pipefail + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &> /dev/null && pwd)" +REPO_ROOT="$(cd -- "$SCRIPT_DIR/.." &> /dev/null && pwd)" + +# ── Defaults ────────────────────────────────────────────────────────── +BASE_URL="${BASE_URL:-http://localhost:8000}" +DATA_PATH="${DATA_PATH:-data/locomo10.json}" +MEMORY_ROOT="${EVEROS_MEMORY__ROOT:-$HOME/.everos-report-corpus}" +MODE="skip-add" # default; toggle via --fresh-corpus +TS="$(date +%Y%m%d_%H%M%S)" +OUTPUT_ROOT="$REPO_ROOT/benchmark_results/run_${TS}_10x3" + +# ── Parse args ──────────────────────────────────────────────────────── +while [[ $# -gt 0 ]]; do + case "$1" in + --skip-add) MODE="skip-add"; shift ;; + --fresh-corpus) MODE="fresh"; shift ;; + --base-url) BASE_URL="$2"; shift 2 ;; + --memory-root) MEMORY_ROOT="$2"; shift 2 ;; + --output-root) OUTPUT_ROOT="$2"; shift 2 ;; + -h|--help) + grep -E "^# " "$0" | sed 's/^# //;s/^#//' + exit 0 + ;; + *) + echo "unknown arg: $1" >&2 + exit 1 + ;; + esac +done + +# ── Preflight ───────────────────────────────────────────────────────── +echo "═════════════════════════════════════════════════════════════════" +echo " LoCoMo 10 × 3 benchmark" +echo "═════════════════════════════════════════════════════════════════" +echo " mode: $MODE" +echo " base_url: $BASE_URL" +echo " memory_root: $MEMORY_ROOT" +echo " output: $OUTPUT_ROOT" +echo + +# 1. Server up? +if ! curl -fsS -o /dev/null "$BASE_URL/health" 2>/dev/null; then + echo "❌ server at $BASE_URL is not responding" + echo " start with: EVEROS_MEMORY__ROOT=$MEMORY_ROOT PYTHONPATH=src \\" + echo " python -m everos.entrypoints.cli.main server start --port 8000" + exit 1 +fi +echo "✓ server healthy" + +# 2. LLM env (test_locomo.py reads bare LLM_* — bridge from EVEROS_LLM__*) +if [[ -z "${LLM_API_KEY:-}" ]] || [[ -z "${LLM_BASE_URL:-}" ]] || [[ -z "${LLM_MODEL:-}" ]]; then + if [[ -f "$REPO_ROOT/.env" ]]; then + set -a + # shellcheck source=/dev/null + source <(grep -E "^EVEROS_LLM__" "$REPO_ROOT/.env" | sed 's/EVEROS_LLM__/LLM_/') + set +a + fi +fi +if [[ -z "${LLM_API_KEY:-}" ]]; then + echo "❌ LLM_API_KEY not set (and .env has no EVEROS_LLM__API_KEY to bridge from)" + exit 1 +fi +echo "✓ LLM credentials: model=$LLM_MODEL @ $LLM_BASE_URL" +echo + +# 3. Fresh corpus mode → wipe + restart server +if [[ "$MODE" == "fresh" ]]; then + echo "═════════════════════════════════════════════════════════════════" + echo " --fresh-corpus: wiping $MEMORY_ROOT and restarting server" + echo "═════════════════════════════════════════════════════════════════" + + # Find and kill existing server (best-effort) + pids="$(pgrep -f "everos.entrypoints.cli.main server" || true)" + if [[ -n "$pids" ]]; then + echo " stopping server pid(s): $pids" + # shellcheck disable=SC2086 + kill $pids + sleep 3 + fi + + rm -rf "$MEMORY_ROOT" + mkdir -p "$MEMORY_ROOT" + + # Restart in background; the server picks up the empty memory root. + echo " starting fresh server..." + ( + cd "$REPO_ROOT" + EVEROS_MEMORY__ROOT="$MEMORY_ROOT" \ + PYTHONPATH=src \ + nohup python -m everos.entrypoints.cli.main server start --port 8000 \ + > /tmp/everos-server-${TS}.log 2>&1 & + echo " server pid=$!" + ) + + # Wait for lifespan ready + for i in $(seq 1 60); do + if curl -fsS -o /dev/null "$BASE_URL/health" 2>/dev/null; then + echo " server ready after ${i}s" + break + fi + sleep 1 + done + if ! curl -fsS -o /dev/null "$BASE_URL/health" 2>/dev/null; then + echo "❌ server failed to come up; see /tmp/everos-server-${TS}.log" + exit 1 + fi + echo +fi + +# ── Build the batch invocation ──────────────────────────────────────── +BATCH_ARGS=( + --conv-indices 0-9 + --methods keyword,vector,hybrid + --base-url "$BASE_URL" + --top-k 10 + --eval-owner speaker_a + --judge-runs 1 + --output-root "$OUTPUT_ROOT" +) +[[ "$MODE" == "skip-add" ]] && BATCH_ARGS+=( --skip-add ) + +echo "═════════════════════════════════════════════════════════════════" +echo " Launching: tests/run_locomo_batch.sh ${BATCH_ARGS[*]}" +echo "═════════════════════════════════════════════════════════════════" +echo + +cd "$REPO_ROOT" +bash tests/run_locomo_batch.sh "${BATCH_ARGS[@]}" + +# ── Summary markdown ────────────────────────────────────────────────── +echo +echo "═════════════════════════════════════════════════════════════════" +echo " Rendering SUMMARY.md" +echo "═════════════════════════════════════════════════════════════════" + +python - </conv.json`` so reports never collide. +# An aggregate accuracy table is printed at the end. +# +# Examples +# ──────── +# # all 10 convs, hybrid only: +# bash tests/run_locomo_batch.sh --conv-indices 0-9 --methods hybrid +# +# # 3 specific convs, two methods, skip the ~5min Add phase (corpus already loaded): +# bash tests/run_locomo_batch.sh \ +# --conv-indices 0,3,7 --methods keyword,hybrid --skip-add +# +# # one conv, all 4 methods comparison: +# bash tests/run_locomo_batch.sh --conv-indices 0 --methods keyword,vector,hybrid,agentic + +set -euo pipefail + +# ── Defaults (override via flags) ───────────────────────────────────── +BASE_URL="${BASE_URL:-http://localhost:8000}" +DATA_PATH="${DATA_PATH:-data/locomo10.json}" +CONV_INDICES="${CONV_INDICES:-0}" +METHODS="${METHODS:-hybrid}" +TOP_K="${TOP_K:-10}" +EVAL_OWNER="${EVAL_OWNER:-speaker_a}" +JUDGE_RUNS="${JUDGE_RUNS:-1}" +SKIP_ADD="false" +OUTPUT_ROOT="" +CONCURRENCY="${CONCURRENCY:-1}" +# Default to polling cascade pending==0 (not fixed sleep). Falls back to +# ~/.everos to match the server's default data root; override via env or +# EVEROS_MEMORY__ROOT (which the server consumes). post-flush-wait becomes +# the MAX wait when corpus-path is set. +CORPUS_PATH="${CORPUS_PATH:-${EVEROS_MEMORY__ROOT:-$HOME/.everos}}" +POST_FLUSH_WAIT="${POST_FLUSH_WAIT:-600}" +EXTRA_ARGS=() + +usage() { + cat < conv list — "0,1,2" | "0-9" | "all" (default: $CONV_INDICES) + --methods comma-separated, e.g. "keyword,hybrid" (default: $METHODS) + --base-url everos server (default: $BASE_URL) + --data-path LoCoMo dataset path (default: $DATA_PATH) + --top-k per-question recall depth (default: $TOP_K) + --eval-owner speaker_a | speaker_b (default: $EVAL_OWNER) + --judge-runs LLM judge majority-vote runs (default: $JUDGE_RUNS) + --skip-add reuse existing corpus, skip ingest + --output-root parent dir for results + (default: benchmark_results/run_) + --concurrency run up to N convs in parallel (default: 1 = serial) + per-conv stdout/stderr is redirected to + \$OUTPUT_ROOT/conv.log so streams don't interleave + -h | --help show this help + -- everything after is forwarded to test_locomo.py + +Any positional or unknown arg goes through to test_locomo.py untouched. +EOF +} + +while [[ $# -gt 0 ]]; do + case "$1" in + --conv-indices) CONV_INDICES="$2"; shift 2 ;; + --methods) METHODS="$2"; shift 2 ;; + --base-url) BASE_URL="$2"; shift 2 ;; + --data-path) DATA_PATH="$2"; shift 2 ;; + --top-k) TOP_K="$2"; shift 2 ;; + --eval-owner) EVAL_OWNER="$2"; shift 2 ;; + --judge-runs) JUDGE_RUNS="$2"; shift 2 ;; + --skip-add) SKIP_ADD="true"; shift ;; + --output-root) OUTPUT_ROOT="$2"; shift 2 ;; + --concurrency) CONCURRENCY="$2"; shift 2 ;; + -h|--help) usage; exit 0 ;; + --) shift; EXTRA_ARGS+=("$@"); break ;; + *) EXTRA_ARGS+=("$1"); shift ;; + esac +done + +# ── Expand conv-indices spec ────────────────────────────────────────── +expand_indices() { + local spec="$1" + if [[ "$spec" == "all" ]]; then + echo "0 1 2 3 4 5 6 7 8 9" + return + fi + if [[ "$spec" =~ ^([0-9]+)-([0-9]+)$ ]]; then + seq "${BASH_REMATCH[1]}" "${BASH_REMATCH[2]}" + return + fi + echo "$spec" | tr ',' ' ' +} + +INDICES=$(expand_indices "$CONV_INDICES") +TS="$(date +%Y%m%d_%H%M%S)" +OUTPUT_ROOT="${OUTPUT_ROOT:-benchmark_results/run_${TS}}" +mkdir -p "$OUTPUT_ROOT" + +# ── Plan banner ─────────────────────────────────────────────────────── +echo "═════════════════════════════════════════════════════════════════" +echo " LoCoMo batch run" +echo "═════════════════════════════════════════════════════════════════" +printf " base_url : %s\n" "$BASE_URL" +printf " conv_indices : %s\n" "$(echo "$INDICES" | tr '\n' ' ')" +printf " methods : %s\n" "$METHODS" +printf " top_k : %s\n" "$TOP_K" +printf " eval_owner : %s\n" "$EVAL_OWNER" +printf " judge_runs : %s\n" "$JUDGE_RUNS" +printf " skip_add : %s\n" "$SKIP_ADD" +printf " concurrency : %s\n" "$CONCURRENCY" +printf " output_root : %s\n" "$OUTPUT_ROOT" +[[ ${#EXTRA_ARGS[@]} -gt 0 ]] && printf " forwarded args : %s\n" "${EXTRA_ARGS[*]}" +echo + +# ── Build per-conv command and launch ──────────────────────────────── +# +# bash 3.2 (macOS default) lacks namerefs (`local -n`) and `wait -n`, so +# build_cmd populates a global array CMD and the parallel scheduler +# uses a poll-loop with `kill -0` instead of `wait -n`. +build_cmd() { + local _ci="$1" + CMD=( + PYTHONPATH=src + python tests/test_locomo.py + --base-url "$BASE_URL" + --data-path "$DATA_PATH" + --conv-index "$_ci" + --methods "$METHODS" + --top-k "$TOP_K" + --eval-owner "$EVAL_OWNER" + --judge-runs "$JUDGE_RUNS" + --output "$OUTPUT_ROOT/conv${_ci}.json" + --checkpoint-dir "$OUTPUT_ROOT/conv${_ci}_checkpoints" + --corpus-path "$CORPUS_PATH" + --post-flush-wait "$POST_FLUSH_WAIT" + --quiet + ) + [[ "$SKIP_ADD" == "true" ]] && CMD+=( --skip-add ) + [[ ${#EXTRA_ARGS[@]} -gt 0 ]] && CMD+=( "${EXTRA_ARGS[@]}" ) + # Final no-op: the trailing [[ ]] above can be false (e.g. no extra + # args), which would make the function's exit status non-zero and + # trip `set -e` in the caller. Explicit success keeps the contract. + return 0 +} + +FAILED=() + +if [[ "$CONCURRENCY" -le 1 ]]; then + # ── Serial path (legacy behaviour) ────────────────────────────────── + for CI in $INDICES; do + echo "═════════════════════════════════════════════════════════════════" + echo " conv $CI → $OUTPUT_ROOT/conv${CI}.json" + echo "═════════════════════════════════════════════════════════════════" + build_cmd "$CI" + set +e + env "${CMD[@]}" + rc=$? + set -e + if [[ $rc -ne 0 ]]; then + FAILED+=("conv${CI}") + fi + done +else + # ── Parallel path: job pool of $CONCURRENCY workers ───────────────── + # + # Each conv runs in its own python process, streaming to a per-conv + # log file (conv.log) so interleaved stdout doesn't turn into + # confetti. Status is collected via `wait $pid`; one conv's failure + # does not abort the rest. + echo "─────────────────────────────────────────────────────────────────" + echo " Parallel mode: up to $CONCURRENCY convs concurrent" + echo " Per-conv logs: $OUTPUT_ROOT/conv.log" + echo "─────────────────────────────────────────────────────────────────" + + # Parallel arrays (no associative arrays in bash 3.2). + RUN_PIDS=() + RUN_CIS=() + + # Wait for *any* worker to exit, reap it, prune the slot, record + # failures. Polls because `wait -n` is bash 4.3+. + reap_one() { + while true; do + local idx + for idx in "${!RUN_PIDS[@]}"; do + local pid="${RUN_PIDS[$idx]}" + if ! kill -0 "$pid" 2>/dev/null; then + set +e + wait "$pid" + local rc=$? + set -e + local ci="${RUN_CIS[$idx]}" + if [[ $rc -eq 0 ]]; then + echo " ✓ conv${ci} done (pid $pid)" + else + echo " ✗ conv${ci} failed (pid $pid, status $rc) — see $OUTPUT_ROOT/conv${ci}.log" + FAILED+=("conv${ci}") + fi + unset 'RUN_PIDS[idx]' + unset 'RUN_CIS[idx]' + # Re-pack arrays so ${#RUN_PIDS[@]} stays accurate. + RUN_PIDS=("${RUN_PIDS[@]}") + RUN_CIS=("${RUN_CIS[@]}") + return 0 + fi + done + sleep 2 + done + } + + for CI in $INDICES; do + build_cmd "$CI" + LOG="$OUTPUT_ROOT/conv${CI}.log" + echo " → launching conv${CI} (log: $LOG)" + env "${CMD[@]}" > "$LOG" 2>&1 & + pid=$! + RUN_PIDS+=("$pid") + RUN_CIS+=("$CI") + + if [[ ${#RUN_PIDS[@]} -ge $CONCURRENCY ]]; then + reap_one + fi + done + + # Drain the remaining workers. + while [[ ${#RUN_PIDS[@]} -gt 0 ]]; do + reap_one + done +fi + +if [[ ${#FAILED[@]} -gt 0 ]]; then + echo + echo "⚠ ${#FAILED[@]} conv(s) failed: ${FAILED[*]}" +fi + +# ── Aggregate summary ───────────────────────────────────────────────── +echo +echo "═════════════════════════════════════════════════════════════════" +echo " Aggregate accuracy" +echo "═════════════════════════════════════════════════════════════════" +python - <10}" for m in methods_seen) +print(header) +print("─" * len(header)) + +for p in files: + d = json.load(open(p)) + label = p.stem + cells = [] + for m in methods_seen: + mr = d["methods"].get(m) + if mr is None: + cells.append(f"{'—':>10}") + else: + raw = mr["summary"]["accuracy"] + acc = float(str(raw).rstrip("%")) if isinstance(raw, str) else float(raw) * 100 + cells.append(f"{acc:>9.1f}%") + print(f"{label:<{w}} " + " ".join(cells)) + +print() +print(f" detailed JSONs: {root}/conv*.json") +print(f" phase checkpoints: {root}/conv*_checkpoints/") +EOF diff --git a/tests/test_locomo.py b/tests/test_locomo.py new file mode 100644 index 00000000..a27e515e --- /dev/null +++ b/tests/test_locomo.py @@ -0,0 +1,1642 @@ +""" +EverOS E2E Benchmark — LoCoMo conversation evaluation. + +Self-contained script that exercises the full everos HTTP pipeline: + Add (per LoCoMo session) -> Flush -> Search -> Answer (LLM) -> Evaluate (LLM Judge) + +LoCoMo ↔ everos mapping (Plan C: single-owner evaluation): +- LoCoMo conversation N → owner_id = speaker_a.lower() (or speaker_b + via --eval-owner). Query a single speaker's + partition; everos's pipeline fan-outs the + same episode narrative to every user sender + (see user_memory.py:95-117), so single-owner + retrieval is informationally equivalent to + multi-owner merged. +- LoCoMo session_N (sitting) → everos session_id = f"locomo_conv{N}_s{idx}" + with a /memory/flush after each session. +- Each LoCoMo message → MessageItemDTO with real sender_id + (speaker.lower()), preserving speaker + attribution in the storage / extraction. + +Usage: + python tests/test_locomo.py --methods hybrid --quiet + python tests/test_locomo.py --methods keyword,hybrid,agentic --output results.json + python tests/test_locomo.py --skip-add --methods hybrid # reuse loaded data +""" + +import argparse +import concurrent.futures +import json +import os +import re +import statistics +import sys +import threading +import time +from datetime import UTC, datetime +from typing import Any + +import openai +import requests +from dotenv import load_dotenv + +try: + from tqdm import tqdm as _tqdm +except ImportError: # progress bar is a nice-to-have, never a hard dep + _tqdm = None + + +def _progress(iterable, *, desc: str, total: int, quiet: bool): + """Wrap a loop iterable in a tqdm bar, but only in quiet mode. + + Verbose mode already prints a per-item line, so a bar there would just + fight those prints. Quiet mode prints nothing per item, which is exactly + when the user is left staring at a frozen screen — so that is where the + bar earns its keep. Falls back to the bare iterable if tqdm is missing. + """ + if not quiet or _tqdm is None: + return iterable + return _tqdm(iterable, desc=desc, total=total, unit="item", dynamic_ncols=True) + + +# ============================================================================= +# Inline prompts (originally from everosos-opensource evaluation/) +# ============================================================================= + +ANSWER_PROMPT = """ +You are an intelligent memory assistant tasked with retrieving accurate information from episodic memories. + +# CONTEXT: +You have access to episodic memories from conversations between two speakers. These memories contain +timestamped information that may be relevant to answering the question. + +# INSTRUCTIONS: +Your goal is to synthesize information from all relevant memories to provide a comprehensive and accurate answer. +You MUST follow a structured Chain-of-Thought process to ensure no details are missed. +Actively look for connections between people, places, and events to build a complete picture. Synthesize information from different memories to answer the user's question. +It is CRITICAL that you move beyond simple fact extraction and perform logical inference. When the evidence strongly suggests a connection, you must state that connection. Do not dismiss reasonable inferences as "speculation." Your task is to provide the most complete answer supported by the available evidence. + +# CRITICAL REQUIREMENTS: +1. NEVER omit specific names - use "Amy's colleague Rob" not "a colleague" +2. ALWAYS include exact numbers, amounts, prices, percentages, dates, times +3. PRESERVE frequencies exactly - "every Tuesday and Thursday" not "twice a week" +4. MAINTAIN all proper nouns and entities as they appear + +# RESPONSE FORMAT (You MUST follow this structure): + +## STEP 1: RELEVANT MEMORIES EXTRACTION +[List each memory that relates to the question, with its timestamp] +- Memory 1: [timestamp] - [content] +- Memory 2: [timestamp] - [content] +... + +## STEP 2: KEY INFORMATION IDENTIFICATION +[Extract ALL specific details from the memories] +- Names mentioned: [list all person names, place names, company names] +- Numbers/Quantities: [list all amounts, prices, percentages] +- Dates/Times: [list all temporal information] +- Frequencies: [list any recurring patterns] +- Other entities: [list brands, products, etc.] + +## STEP 3: CROSS-MEMORY LINKING +[Identify entities that appear in multiple memories and link related information. Make reasonable inferences when entities are strongly connected.] +- Shared entities: [list people, places, events mentioned across different memories] +- Connections found: [e.g., "Memory 1 mentions A moved from hometown → Memory 2 mentions A's hometown is LA → Therefore A moved from LA"] +- Inferred facts: [list any facts that require combining information from multiple memories] + +## STEP 4: TIME REFERENCE CALCULATION +[If applicable, convert relative time references] +- Original reference: [e.g., "last year" from May 2022] +- Calculated actual time: [e.g., "2021"] + +## STEP 5: CONTRADICTION CHECK +[If multiple memories contain different information] +- Conflicting information: [describe] +- Resolution: [explain which is most recent/reliable] + +## STEP 6: DETAIL VERIFICATION CHECKLIST +- [ ] All person names included: [list them] +- [ ] All locations included: [list them] +- [ ] All numbers exact: [list them] +- [ ] All frequencies specific: [list them] +- [ ] All dates/times precise: [list them] +- [ ] All proper nouns preserved: [list them] + +## STEP 7: ANSWER FORMULATION +[Explain how you're combining the information to answer the question] + +## FINAL ANSWER: +[Provide the concise answer with ALL specific details preserved] + +--- + +{context} + +Question: {question} + +Now, follow the Chain-of-Thought process above to answer the question: +""" + +JUDGE_SYSTEM_PROMPT = "You are an expert grader that determines if answers to questions match a gold standard answer" + +JUDGE_USER_PROMPT = """Your task is to label an answer to a question as 'CORRECT' or 'WRONG'. You will be given the following data: + (1) a question (posed by one user to another user), + (2) a 'gold' (ground truth) answer, + (3) a generated answer +which you will score as CORRECT/WRONG. + +The point of the question is to ask about something one user should know about the other user based on their prior conversations. +The gold answer will usually be a concise and short answer that includes the referenced topic, for example: +Question: Do you remember what I got the last time I went to Hawaii? +Gold answer: A shell necklace +The generated answer might be much longer, but you should be generous with your grading - as long as it touches on the same topic as the gold answer, it should be counted as CORRECT. + +For time related questions, the gold answer will be a specific date, month, year, etc. The generated answer might be much longer or use relative time references (like "last Tuesday" or "next month"), but you should be generous with your grading - as long as it refers to the same date or time period as the gold answer, it should be counted as CORRECT. Even if the format differs (e.g., "May 7th" vs "7 May"), consider it CORRECT if it's the same date. + +Now it's time for the real question: +Question: {question} +Gold answer: {golden_answer} +Generated answer: {generated_answer} + +First, provide a short (one sentence) explanation of your reasoning, then finish with CORRECT or WRONG. +Do NOT include both CORRECT and WRONG in your response, or it will break the evaluation script. + +Just return the label CORRECT or WRONG in a json format with the key as "label". +""" + +# ============================================================================= +# Category labels +# ============================================================================= + +CATEGORY_NAMES: dict[int, str] = { + 1: "single-hop", + 2: "multi-hop", + 3: "open-domain", + 4: "temporal", +} + +# ============================================================================= +# Minimal HTTP client for everos (single-tenant, no auth headers) +# ============================================================================= + + +class EverosClient: + """Minimal HTTP client for everos's /api/v1/memory/* endpoints.""" + + def __init__(self, base_url: str = "http://localhost:8000", timeout: int = 300): + self.base_url = base_url.rstrip("/") + self.timeout = timeout + + def post( + self, path: str, data: dict[str, Any], quiet: bool = False + ) -> tuple[int, dict]: + full_url = f"{self.base_url}{path}" + if not quiet: + print(f"\n📍 URL: POST {full_url}") + print(f"📤 Request Data:\n{json.dumps(data, indent=2, ensure_ascii=False)}") + try: + resp = requests.post( + full_url, + json=data, + headers={"Content-Type": "application/json"}, + timeout=(10, self.timeout), + ) + except requests.RequestException as e: + if not quiet: + print(f"📥 Request Error: {e}") + return -1, {"error": str(e)} + if not quiet: + print(f"\n📥 Response Status Code: {resp.status_code}") + try: + body = resp.json() + if not quiet: + print( + f"📥 Response Data:\n{json.dumps(body, indent=2, ensure_ascii=False)}" + ) + return resp.status_code, body + except Exception: + if not quiet: + print(f"📥 Raw Response: {resp.text[:500]}") + return resp.status_code, {} + + +def print_section(title: str): + print(f"\n{'=' * 72}") + print(f" {title}") + print(f"{'=' * 72}") + + +# ============================================================================= +# LLM client pool — round-robin across multiple API keys with 429 failover +# ============================================================================= + + +def _split_keys(s: str) -> list[str]: + """Split a comma-separated key string into a list of stripped non-empty keys.""" + return [k.strip() for k in s.split(",") if k.strip()] + + +class _PoolCompletions: + def __init__(self, pool: "LLMClientPool"): + self._pool = pool + + def create(self, **kwargs: Any) -> Any: + return self._pool._create_with_failover(**kwargs) + + +class _PoolChat: + def __init__(self, pool: "LLMClientPool"): + self.completions = _PoolCompletions(pool) + + +class LLMClientPool: + """Round-robin pool of openai.OpenAI clients with RateLimitError failover. + + Duck-types openai.OpenAI: callers may use ``pool.chat.completions.create(...)`` + transparently. On RateLimitError, the next key in the pool is tried; after + all keys are exhausted, the last error is re-raised. Other errors propagate + immediately (they're not "this key is throttled" signals). + + When ``base_url`` points to OpenRouter, the pool injects + ``extra_body={"provider": {"only": [...]}}`` on every request so the + downstream provider is fixed. OpenRouter otherwise routes freely across + providers (OpenAI, Azure, Fireworks, ...), which on a 1.5k-question batch + eventually lands on a region-restricted Azure instance and 403s every + later request. The allow-list defaults to ``["openai"]`` and can be + overridden via the ``OPENROUTER_PROVIDER_ONLY`` env var (comma-separated, + e.g. ``openai,fireworks``). + """ + + def __init__(self, api_keys: list[str], base_url: str, **kwargs: Any): + if not api_keys: + raise ValueError("LLMClientPool: at least one API key required") + self._clients = [ + openai.OpenAI(api_key=k, base_url=base_url, **kwargs) for k in api_keys + ] + self._idx = 0 + self._lock = threading.Lock() + self.key_count = len(self._clients) + self.chat = _PoolChat(self) + self._provider_constraint = self._resolve_provider_constraint(base_url) + + @staticmethod + def _resolve_provider_constraint(base_url: str) -> dict[str, Any] | None: + """Resolve the OpenRouter ``provider`` extra-body block (or None).""" + if "openrouter" not in (base_url or "").lower(): + return None + raw = os.getenv("OPENROUTER_PROVIDER_ONLY", "openai").strip() + if not raw or raw.lower() == "any": + return None + only = [p.strip() for p in raw.split(",") if p.strip()] + return {"only": only, "allow_fallbacks": False} + + def _next_client(self) -> openai.OpenAI: + with self._lock: + c = self._clients[self._idx] + self._idx = (self._idx + 1) % len(self._clients) + return c + + def _create_with_failover(self, **kwargs: Any) -> Any: + if self._provider_constraint is not None: + extra = dict(kwargs.get("extra_body") or {}) + extra.setdefault("provider", self._provider_constraint) + kwargs["extra_body"] = extra + last_err: Exception | None = None + for _ in range(len(self._clients)): + client = self._next_client() + try: + return client.chat.completions.create(**kwargs) + except openai.RateLimitError as e: + last_err = e + continue + assert last_err is not None + raise last_err + + +def _parallel_map( + items: list, + worker, + *, + desc: str, + total: int, + quiet: bool, + concurrency: int, +) -> list: + """Run ``worker(i, item)`` over *items* concurrently; preserve input order. + + Quiet mode drives a tqdm progress bar via ``as_completed``; verbose mode + lets workers stay silent to avoid interleaved output. Falls back to serial + execution when *concurrency* <= 1. + + Worker exceptions are caught per-item: the exception object is stored in + ``results[i]`` and re-raised by callers as needed. This prevents one bad + LLM call from aborting the entire batch. + + Args: + items: Input list to process. + worker: Callable ``(i: int, item: Any) -> Any``. + desc: Label shown in the tqdm bar. + total: Expected number of items (for the bar). + quiet: When True and tqdm is available, show a progress bar. + concurrency: Thread-pool size; <= 1 means serial. + + Returns: + List of worker results in the same order as *items*. + """ + results: list = [None] * len(items) + + if concurrency <= 1: + for i, item in enumerate(items): + results[i] = worker(i, item) + return results + + with concurrent.futures.ThreadPoolExecutor(max_workers=concurrency) as pool: + future_to_idx: dict[concurrent.futures.Future, int] = { + pool.submit(worker, i, item): i for i, item in enumerate(items) + } + if quiet and _tqdm is not None: + completed_iter = _tqdm( + concurrent.futures.as_completed(future_to_idx), + total=total, + desc=desc, + unit="item", + dynamic_ncols=True, + ) + else: + completed_iter = concurrent.futures.as_completed(future_to_idx) + + for fut in completed_iter: + idx = future_to_idx[fut] + try: + results[idx] = fut.result() + except Exception as exc: # noqa: BLE001 + results[idx] = exc + + return results + + +def _wait_cascade_drain( + corpus_path: str, max_wait_s: int, poll_interval_s: float = 3.0 +) -> None: + """Poll everos cascade queue in /.index/sqlite/system.db. + + Returns as soon as ``md_change_state`` has no row in + ``status IN ('pending', 'processing')``, or after ``max_wait_s`` + seconds, whichever comes first. Falls back to a fixed sleep if the + sqlite file is missing (e.g. corpus_path wrong / server not yet + written its system.db). + """ + import sqlite3 + from pathlib import Path + + db_path = Path(corpus_path).expanduser() / ".index" / "sqlite" / "system.db" + if not db_path.exists(): + print( + f" [warn] cascade queue db not found at {db_path}; " + f"falling back to fixed {max_wait_s}s sleep" + ) + time.sleep(max_wait_s) + return + + print( + f" Polling cascade queue at {db_path} (max {max_wait_s}s, " + f"interval {poll_interval_s}s)..." + ) + deadline = time.time() + max_wait_s + last_pending = -1 + while time.time() < deadline: + try: + conn = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True) + n = conn.execute( + "SELECT COUNT(*) FROM md_change_state " + "WHERE status IN ('pending', 'processing')" + ).fetchone()[0] + conn.close() + except sqlite3.Error as e: + print(f" [warn] sqlite read failed: {e}; assume drained") + return + if n == 0: + elapsed = max_wait_s - (deadline - time.time()) + print(f" cascade drained after {elapsed:.1f}s") + return + if n != last_pending: + print(f" cascade pending = {n}") + last_pending = n + time.sleep(poll_interval_s) + print( + f" [warn] cascade still has {last_pending} pending after " + f"{max_wait_s}s; proceeding anyway" + ) + + +# ============================================================================= +# Data loading — preserve LoCoMo session_N structure for per-session flushing +# ============================================================================= + + +def _parse_session_timestamp(ts_str: str) -> int: + """Parse LoCoMo timestamp string to epoch milliseconds. + + Format examples: "1:56 pm on 8 May, 2023", "12:09 am on 13 September, 2023". + + LoCoMo's raw timestamps carry no timezone, so we pin them to UTC — + matching ``everalgo/benchmarks/datasets/locomo/loader.py:_parse_timestamp``. + Without an explicit tz, ``naive_dt.timestamp()`` would shift epochs by + the OS's local-vs-UTC offset, so the same dataset would produce + different absolute timestamps on different machines. + """ + dt = datetime.strptime(ts_str.strip(), "%I:%M %p on %d %B, %Y") + return int(dt.replace(tzinfo=UTC).timestamp() * 1000) + + +def load_conversation( + data_path: str, conv_index: int +) -> tuple[list[dict], list[dict], str, str]: + """Load a LoCoMo conversation, preserving session_N boundaries. + + Returns (sessions, qa_list, speaker_a, speaker_b) where `sessions` is + a list of {session_idx, messages} ordered by session_idx. Each message + carries dia_id / speaker / text / timestamp_ms. QA list excludes + category 5 (adversarial). + """ + with open(data_path, encoding="utf-8") as f: + dataset = json.load(f) + + if conv_index >= len(dataset): + raise ValueError( + f"conv_index {conv_index} out of range (dataset has {len(dataset)} conversations)" + ) + + conv = dataset[conv_index] + conversation = conv["conversation"] + speaker_a = conversation["speaker_a"] + speaker_b = conversation["speaker_b"] + + sessions: list[dict] = [] + session_idx = 1 + while True: + session_key = f"session_{session_idx}" + dt_key = f"session_{session_idx}_date_time" + if dt_key not in conversation: + break + if session_key in conversation: + ts_str = conversation[dt_key] + base_ts_ms = _parse_session_timestamp(ts_str) + session_msgs = conversation[session_key] + if isinstance(session_msgs, list): + msgs: list[dict] = [] + for i, msg in enumerate(session_msgs): + if not msg.get("text"): + continue # skip image-only messages + msgs.append( + { + "dia_id": msg["dia_id"], + "speaker": msg["speaker"], + "text": msg["text"], + "timestamp_ms": base_ts_ms + i * 30000, + } + ) + if msgs: + sessions.append({"session_idx": session_idx, "messages": msgs}) + session_idx += 1 + + qa_list = [q for q in conv.get("qa", []) if q.get("category") != 5] + return sessions, qa_list, speaker_a, speaker_b + + +# ============================================================================= +# Add phase — one everos session_id per LoCoMo session, flush after each +# ============================================================================= + + +def run_add_phase( + client: EverosClient, + sessions: list[dict], + speaker_a: str, + speaker_b: str, + conv_index: int, + batch_size: int, + quiet: bool = False, +) -> dict[str, Any]: + """Send each LoCoMo session to its own everos session_id and flush.""" + print_section("Add Phase") + total_msgs = sum(len(s["messages"]) for s in sessions) + print( + f" LoCoMo sessions: {len(sessions)} | Messages: {total_msgs} | " + f"Batch size: {batch_size} | Speakers: {speaker_a} & {speaker_b}" + ) + + t0 = time.perf_counter() + total_batches = 0 + + for sess in _progress(sessions, desc="Add+Flush", total=len(sessions), quiet=quiet): + session_id = f"locomo_conv{conv_index}_s{sess['session_idx']}" + api_messages: list[dict] = [ + { + # Append `_conv{N}` so the same speaker name across conversations + # (e.g. "John" appears in conv_2, conv_4, conv_6) does NOT collide + # on a shared owner_id partition. Without the suffix, repeated + # benchmark runs cross-pollute each other's memory store. + "sender_id": f"{msg['speaker'].lower()}_conv{conv_index}", + "sender_name": msg["speaker"], + "role": "user", + "timestamp": msg["timestamp_ms"], + "content": [{"type": "text", "text": msg["text"]}], + } + for msg in sess["messages"] + ] + + batches = [ + api_messages[i : i + batch_size] + for i in range(0, len(api_messages), batch_size) + ] + for idx, batch in enumerate(batches): + payload = {"session_id": session_id, "messages": batch} + status, resp = client.post("/api/v1/memory/add", payload, quiet=quiet) + if not quiet: + print( + f" Session {sess['session_idx']} batch {idx + 1}/{len(batches)}: " + f"{len(batch)} msgs -> status {status}" + ) + assert status == 200, ( + f"Add (session_id={session_id}, batch {idx + 1}) failed: " + f"status={status} resp={resp}" + ) + total_batches += len(batches) + + flush_status, flush_resp = client.post( + "/api/v1/memory/flush", {"session_id": session_id}, quiet=quiet + ) + assert flush_status == 200, ( + f"Flush (session_id={session_id}) failed: " + f"status={flush_status} resp={flush_resp}" + ) + + add_time = time.perf_counter() - t0 + result = { + "total_messages": total_msgs, + "session_count": len(sessions), + "batch_count": total_batches, + "batch_size": batch_size, + "add_time_seconds": round(add_time, 2), + } + print( + f" Done: {total_msgs} msgs across {len(sessions)} sessions " + f"({total_batches} batches), {add_time:.2f}s incl. flushes" + ) + return result + + +# ============================================================================= +# Search phase — single-owner partition (Plan C) +# ============================================================================= + + +def _search_one( + i: int, + qa: dict, + *, + client: EverosClient, + method: str, + top_k: int, + owner_id: str, +) -> dict: + """Search a single QA question; safe to run in a thread. + + The per-request timeout is handled by ``EverosClient.post`` via the + underlying ``requests`` ``timeout=(10, self.timeout)`` kwarg (self.timeout + defaults to 300 s), so no extra thread-based wrapping is needed here. + """ + question = qa["question"] + payload: dict = { + "query": question, + "method": method, + "top_k": top_k, + "user_id": owner_id, + } + t0 = time.perf_counter() + try: + status, resp = client.post("/api/v1/memory/search", payload, quiet=True) + except Exception as e: + status, resp = -1, {"error": str(e)} + search_time = time.perf_counter() - t0 + + if status != 200: + error_detail = resp.get("detail", resp) if isinstance(resp, dict) else resp + return { + "index": i, + "question": question, + "golden_answer": qa["answer"], + "category": qa.get("category"), + "evidence": qa.get("evidence", []), + "episodes": [], + "profiles": [], + "search_time_s": round(search_time, 4), + "search_error": error_detail, + "_search_status": status, + } + + data = resp.get("data", {}) + episodes = data.get("episodes", []) + profiles = data.get("profiles", []) + return { + "index": i, + "question": question, + "golden_answer": qa["answer"], + "category": qa.get("category"), + "evidence": qa.get("evidence", []), + "episodes": episodes, + "profiles": profiles, + "search_time_s": round(search_time, 4), + } + + +def run_search_phase( + client: EverosClient, + qa_list: list[dict], + owner_id: str, + method: str, + top_k: int, + quiet: bool = False, + concurrency: int = 10, +) -> list[dict]: + """Search for each QA question against a single owner_id partition (parallel). + + Vector retrieval strategy (``episode`` vs ``maxsim_atomic``) is selected + on the server side via ``EVEROS_SEARCH__VECTOR_STRATEGY`` — this driver + just hits the public ``/api/v1/memory/search`` endpoint and reports + what the server returned. + """ + print_section(f"Search Phase (method={method}, top_k={top_k}, owner_id={owner_id})") + + def _worker(i: int, qa: dict) -> dict: + return _search_one( + i, + qa, + client=client, + method=method, + top_k=top_k, + owner_id=owner_id, + ) + + raw = _parallel_map( + qa_list, + _worker, + desc="Search", + total=len(qa_list), + quiet=quiet, + concurrency=concurrency, + ) + + # Unwrap: _parallel_map stores exceptions as values; surface them as error dicts. + results: list[dict] = [] + for item in raw: + if isinstance(item, Exception): + results.append( + { + "episodes": [], + "profiles": [], + "search_time_s": 0, + "search_error": str(item), + } + ) + else: + results.append(item) + + errors = [r for r in results if r.get("search_error")] + search_times = [r["search_time_s"] for r in results if not r.get("search_error")] + success_count = len(results) - len(errors) + summary_parts = [f"Done: {success_count}/{len(results)} succeeded"] + if search_times: + summary_parts.append(f"avg={statistics.mean(search_times):.3f}s") + if errors: + summary_parts.append(f"{len(errors)} FAILED") + for err in errors: + print( + f" ERROR Q{err.get('index', '?')}: " + f"status={err.get('_search_status', 'exc')} | " + f"{str(err.get('question', ''))[:60]}" + ) + print(f" {', '.join(summary_parts)}") + + # Strip internal bookkeeping key before returning. + for r in results: + r.pop("_search_status", None) + return results + + +# ============================================================================= +# Answer phase +# ============================================================================= + + +def _build_context( + episodes: list[dict], profiles: list[dict], speaker_a: str, speaker_b: str +) -> str: + """Build context string from search results.""" + lines = [ + f"Episodes memories for conversation between {speaker_a} and {speaker_b}:\n" + ] + for idx, ep in enumerate(episodes, 1): + subject = ep.get("subject", "") + body = ep.get("episode") or ep.get("summary") or ep.get("content") or "" + prefix = f"{subject}: " if subject else "" + lines.append(f"{idx}. {prefix}{body}") + + if profiles: + lines.append("\nProfile memories:") + for idx, p in enumerate(profiles, 1): + content = p.get("content") or p.get("summary") or "" + lines.append(f" {idx}. {content}") + + return "\n".join(lines) + + +def _extract_final_answer(text: str) -> str: + """Extract text after 'FINAL ANSWER:' marker.""" + marker = "FINAL ANSWER:" + idx = text.upper().rfind(marker.upper()) + if idx != -1: + answer = text[idx + len(marker) :].strip() + answer = re.sub(r"^#+\s*", "", answer).strip() + return answer + for line in reversed(text.strip().splitlines()): + line = line.strip() + if line: + return line + return text.strip() + + +def _answer_one( + i: int, + sr: dict, + *, + speaker_a: str, + speaker_b: str, + llm_client: LLMClientPool, + llm_model: str, +) -> dict: + """Generate an answer for a single search result; safe to run in a thread. + + Retry up to 3× with rising temperature when the response parses to an + empty FINAL ANSWER:. gpt-4.1-mini occasionally finishes the STEP 7 + reasoning, emits the marker, then stops without the body — at temperature=0 + the truncation is deterministic, so retries bump temperature to break the + same sampling path. + + The openai ``timeout=300`` kwarg is a per-request socket deadline passed + directly to the underlying HTTP client, which is safe to use from a thread + pool (no extra nesting needed). + """ + if sr.get("search_error"): + return {**sr, "generated_answer": "[SEARCH_FAILED]", "answer_time_s": 0} + + context = _build_context(sr["episodes"], sr["profiles"], speaker_a, speaker_b) + prompt = ANSWER_PROMPT.format(context=context, question=sr["question"]) + + t0 = time.perf_counter() + raw_answer = "" + generated_answer = "" + last_error: str | None = None + attempts_used = 0 + for attempt, temp in enumerate((0.0, 0.3, 0.6)): + attempts_used = attempt + 1 + try: + r = llm_client.chat.completions.create( + model=llm_model, + messages=[{"role": "user", "content": prompt}], + temperature=temp, + timeout=300, + ) + raw_answer = r.choices[0].message.content or "" + except Exception as e: + last_error = f"[ERROR: {e}]" + raw_answer = last_error + continue + + generated_answer = _extract_final_answer(raw_answer) + if generated_answer.strip(): + break + + if not generated_answer.strip() and last_error: + generated_answer = last_error + + answer_time = time.perf_counter() - t0 + return { + **sr, + "generated_answer": generated_answer, + "answer_time_s": round(answer_time, 4), + "answer_attempts": attempts_used, + } + + +def run_answer_phase( + search_results: list[dict], + speaker_a: str, + speaker_b: str, + llm_client: LLMClientPool, + llm_model: str, + quiet: bool = False, + concurrency: int = 8, +) -> list[dict]: + """Generate answers using LLM for each search result (parallel).""" + print_section("Answer Phase") + + def _worker(i: int, sr: dict) -> dict: + return _answer_one( + i, + sr, + speaker_a=speaker_a, + speaker_b=speaker_b, + llm_client=llm_client, + llm_model=llm_model, + ) + + raw = _parallel_map( + search_results, + _worker, + desc="Answer", + total=len(search_results), + quiet=quiet, + concurrency=concurrency, + ) + # Unwrap: _parallel_map stores exceptions as values; surface them as error strings. + results: list[dict] = [] + for item in raw: + if isinstance(item, Exception): + results.append({"generated_answer": f"[ERROR: {item}]", "answer_time_s": 0}) + else: + results.append(item) + + answer_times = [ + r["answer_time_s"] for r in results if r.get("answer_time_s", 0) > 0 + ] + avg_str = f", avg={statistics.mean(answer_times):.2f}s" if answer_times else "" + skipped = sum(1 for r in results if r.get("search_error")) + skip_str = f", {skipped} skipped (search failed)" if skipped else "" + print(f" Done: {len(results)} answers{avg_str}{skip_str}") + return results + + +# ============================================================================= +# Evaluate phase — LLM-as-Judge +# ============================================================================= + + +def _extract_json(content: str) -> str | None: + """Robustly extract JSON from LLM response.""" + m = re.search(r"```(?:json)?\s*(\{[^`]*\})\s*```", content, re.DOTALL) + if m: + return m.group(1).strip() + m = re.search(r'\{[^{}]*"label"\s*:\s*"[^"]*"[^{}]*\}', content) + if m: + return m.group(0) + return content.strip() + + +def _judge_single( + llm_client: LLMClientPool, + llm_model: str, + question: str, + golden_answer: str, + generated_answer: str, +) -> bool: + """Judge a single answer. Returns True if CORRECT. + + Uses ``timeout=300`` passed directly to the openai HTTP client so this + function is safe to call from a thread pool without further nesting. + """ + user_prompt = JUDGE_USER_PROMPT.format( + question=question, + golden_answer=golden_answer, + generated_answer=generated_answer, + ) + try: + r = llm_client.chat.completions.create( + model=llm_model, + messages=[ + {"role": "system", "content": JUDGE_SYSTEM_PROMPT}, + {"role": "user", "content": user_prompt}, + ], + temperature=0, + timeout=300, + ) + content = r.choices[0].message.content or "" + json_str = _extract_json(content) + if not json_str: + return False + result = json.loads(json_str) + return result.get("label", "").strip().upper() == "CORRECT" + except Exception as e: + print(f" Judge error: {e}") + return False + + +def _evaluate_one( + i: int, + ar: dict, + *, + llm_client: LLMClientPool, + llm_model: str, + judge_runs: int, +) -> dict: + """Evaluate a single answer result with majority-vote judging. + + Safe to run from a thread pool; exception handling mirrors the original + serial loop so a single LLM failure degrades gracefully. + """ + if ar.get("search_error"): + return {**ar, "judgments": [], "is_correct": False} + + judgments: list[bool] = [] + for _ in range(judge_runs): + judgments.append( + _judge_single( + llm_client, + llm_model, + ar["question"], + ar["golden_answer"], + ar["generated_answer"], + ) + ) + + correct = sum(judgments) > judge_runs / 2 + return {**ar, "judgments": judgments, "is_correct": correct} + + +def run_evaluate_phase( + answer_results: list[dict], + llm_client: LLMClientPool, + llm_model: str, + judge_runs: int = 1, + quiet: bool = False, + concurrency: int = 8, +) -> list[dict]: + """Evaluate answers using LLM judge (parallel).""" + print_section(f"Evaluate Phase (judge_runs={judge_runs})") + + def _worker(i: int, ar: dict) -> dict: + return _evaluate_one( + i, + ar, + llm_client=llm_client, + llm_model=llm_model, + judge_runs=judge_runs, + ) + + raw = _parallel_map( + answer_results, + _worker, + desc="Evaluate", + total=len(answer_results), + quiet=quiet, + concurrency=concurrency, + ) + results: list[dict] = [] + for item in raw: + if isinstance(item, Exception): + results.append({"judgments": [], "is_correct": False}) + else: + results.append(item) + + correct_count = sum(1 for r in results if r["is_correct"]) + print( + f" Done: {correct_count}/{len(results)} correct ({_pct(correct_count, len(results))})" + ) + return results + + +# ============================================================================= +# Reporting +# ============================================================================= + + +def _pct(n: int, total: int) -> str: + if total == 0: + return "N/A" + return f"{n / total * 100:.1f}%" + + +def _percentiles(values: list[float]) -> tuple[float, float, float]: + """Return P50, P90, P99.""" + if not values: + return 0.0, 0.0, 0.0 + s = sorted(values) + n = len(s) + + def _p(pct: float) -> float: + idx = int(pct / 100 * (n - 1)) + return s[min(idx, n - 1)] + + return _p(50), _p(90), _p(99) + + +def print_report( + eval_results: list[dict], + method: str, + add_result: dict | None, + conv_label: str = "", + top_k: int = 10, +) -> dict[str, Any]: + """Print formatted report and return summary dict.""" + total = len(eval_results) + correct = sum(1 for r in eval_results if r["is_correct"]) + search_errors = sum(1 for r in eval_results if r.get("search_error")) + + cat_stats: dict[int, dict[str, int]] = {} + for r in eval_results: + cat = r.get("category") + if cat is None: + continue + if cat not in cat_stats: + cat_stats[cat] = {"correct": 0, "total": 0} + cat_stats[cat]["total"] += 1 + if r["is_correct"]: + cat_stats[cat]["correct"] += 1 + + search_times = [ + r["search_time_s"] for r in eval_results if not r.get("search_error") + ] + answer_times = [r["answer_time_s"] for r in eval_results if r["answer_time_s"] > 0] + s_p50, s_p90, s_p99 = _percentiles(search_times) + a_p50, a_p90, a_p99 = _percentiles(answer_times) + + today = datetime.now().strftime("%Y-%m-%d") + + print(f"\n{'=' * 64}") + print(" EverOS E2E Benchmark Report") + if conv_label: + print(f" Conversation: {conv_label}") + print( + f" Messages: {add_result['total_messages'] if add_result else 'N/A (--skip-add)'} | Questions: {total}" + ) + print(f" Search Method: {method} | top_k: {top_k}") + print(f" Date: {today}") + print(f"{'=' * 64}") + + if add_result: + print("\nADD PHASE") + print(f" Total messages: {add_result['total_messages']}") + print(f" LoCoMo sessions: {add_result['session_count']}") + print( + f" Batch size: {add_result['batch_size']} ({add_result['batch_count']} requests)" + ) + print(f" Add + flush time: {add_result['add_time_seconds']}s") + + print("\nSEARCH PHASE") + if search_times: + print(f" Avg search time: {statistics.mean(search_times):.3f}s") + print(f" P50 / P90 / P99: {s_p50:.3f}s / {s_p90:.3f}s / {s_p99:.3f}s") + else: + print(" No successful searches") + + print("\nANSWER PHASE") + if answer_times: + print(f" Avg answer time: {statistics.mean(answer_times):.2f}s") + print(f" P50 / P90 / P99: {a_p50:.2f}s / {a_p90:.2f}s / {a_p99:.2f}s") + else: + print(" No successful answers") + + if search_errors: + print(f"\nSEARCH ERRORS: {search_errors}/{total}") + + print("\nACCURACY") + print(f" Overall: {_pct(correct, total)} ({correct}/{total})") + for cat in sorted(cat_stats.keys()): + s = cat_stats[cat] + label = CATEGORY_NAMES.get(cat, f"cat-{cat}") + print( + f" Category {cat} ({label}): {_pct(s['correct'], s['total'])} ({s['correct']}/{s['total']})" + ) + + print(f"\n{'=' * 64}") + + return { + "method": method, + "total": total, + "correct": correct, + "search_errors": search_errors, + "accuracy": correct / total if total else 0, + "category_stats": { + str(k): {"correct": v["correct"], "total": v["total"]} + for k, v in cat_stats.items() + }, + "avg_search_s": round(statistics.mean(search_times), 4) if search_times else 0, + "avg_answer_s": round(statistics.mean(answer_times), 4) if answer_times else 0, + } + + +def print_comparison(all_summaries: dict[str, dict[str, Any]]): + """Print a comparison table across methods.""" + print(f"\n{'=' * 64}") + print(" METHOD COMPARISON") + print(f"{'=' * 64}") + + all_cats = set() + for s in all_summaries.values(): + all_cats.update(s.get("category_stats", {}).keys()) + cats_sorted = sorted(all_cats) + + cat_headers = [f"Cat {c}" for c in cats_sorted] + header = ( + f" {'Method':<10} | {'Overall':>8} | " + + " | ".join(f"{ch:>8}" for ch in cat_headers) + + f" | {'Avg Search':>10} | {'Avg Answer':>10}" + ) + print(header) + print(f" {'-' * (len(header) - 2)}") + + for method, summary in all_summaries.items(): + overall = f"{summary['accuracy'] * 100:.1f}%" + cat_strs = [] + for c in cats_sorted: + cs = summary.get("category_stats", {}).get(c, {}) + if cs.get("total", 0) > 0: + cat_strs.append(f"{cs['correct'] / cs['total'] * 100:.1f}%") + else: + cat_strs.append("N/A") + cat_part = " | ".join(f"{s:>8}" for s in cat_strs) + print( + f" {method:<10} | {overall:>8} | {cat_part} | " + f"{summary['avg_search_s']:>9.3f}s | {summary['avg_answer_s']:>9.2f}s" + ) + + print() + + +# ============================================================================= +# Checkpoint helpers +# ============================================================================= + + +def _summarize_episode(ep: dict) -> str: + """One-line summary of an episode for checkpoint display.""" + subject = ep.get("subject", "") + body = ep.get("episode") or ep.get("summary") or ep.get("content") or "" + if len(body) > 280: + body = body[:117] + "..." + return f"{subject}: {body}" if subject else body + + +def _compact_search_result(r: dict) -> dict: + """Compact a search result for checkpoint: strip bulky fields.""" + entry = { + "index": r["index"], + "question": r["question"], + "golden_answer": r["golden_answer"], + "category": r.get("category"), + "search_time_s": r["search_time_s"], + "episode_count": len(r.get("episodes", [])), + "profile_count": len(r.get("profiles", [])), + "episodes_preview": [ + _summarize_episode(ep) for ep in r.get("episodes", [])[:5] + ], + } + if r.get("search_error"): + entry["search_error"] = str(r["search_error"]) + return entry + + +def _compact_answer_result(r: dict) -> dict: + entry = _compact_search_result(r) + entry["generated_answer"] = r.get("generated_answer", "") + entry["answer_time_s"] = r.get("answer_time_s", 0) + return entry + + +def _compact_eval_result(r: dict) -> dict: + entry = _compact_answer_result(r) + entry["is_correct"] = r.get("is_correct", False) + entry["judgments"] = r.get("judgments", []) + return entry + + +def _build_checkpoint(phase: str, results: list[dict]) -> dict: + """Build a user-friendly checkpoint dict with summary + details.""" + if phase == "search": + compact_fn = _compact_search_result + elif phase == "answer": + compact_fn = _compact_answer_result + else: + compact_fn = _compact_eval_result + + details = [compact_fn(r) for r in results] + error_count = sum(1 for r in results if r.get("search_error")) + summary: dict[str, Any] = {"total": len(results), "errors": error_count} + + if phase == "search": + times = [r["search_time_s"] for r in results if not r.get("search_error")] + summary["avg_search_time_s"] = ( + round(statistics.mean(times), 4) if times else None + ) + elif phase == "answer": + times = [r["answer_time_s"] for r in results if r.get("answer_time_s", 0) > 0] + summary["avg_answer_time_s"] = ( + round(statistics.mean(times), 4) if times else None + ) + elif phase == "eval": + correct = sum(1 for r in results if r.get("is_correct")) + summary["correct"] = correct + summary["accuracy"] = ( + f"{correct / len(results) * 100:.1f}%" if results else "N/A" + ) + cat_stats: dict[int, dict[str, int]] = {} + for r in results: + cat = r.get("category") + if cat is None: + continue + if cat not in cat_stats: + cat_stats[cat] = {"correct": 0, "total": 0} + cat_stats[cat]["total"] += 1 + if r.get("is_correct"): + cat_stats[cat]["correct"] += 1 + summary["by_category"] = { + f"cat{k} ({CATEGORY_NAMES.get(k, '?')})": f"{v['correct']}/{v['total']}" + for k, v in sorted(cat_stats.items()) + } + + return {"summary": summary, "details": details} + + +def _save_checkpoint( + checkpoint_dir: str, filename: str, phase: str, results: list[dict] +) -> str: + """Save user-friendly checkpoint with summary + compact details.""" + os.makedirs(checkpoint_dir, exist_ok=True) + path = os.path.join(checkpoint_dir, filename) + checkpoint = _build_checkpoint(phase, results) + with open(path, "w", encoding="utf-8") as f: + json.dump(checkpoint, f, indent=2, ensure_ascii=False, default=str) + print(f" [checkpoint] {path}") + return path + + +# ============================================================================= +# CLI +# ============================================================================= + +_SUPPORTED_METHODS = ("keyword", "vector", "hybrid", "agentic") + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser( + description="EverOS E2E Benchmark (LoCoMo)", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + p.add_argument( + "--base-url", default="http://localhost:8000", help="everos API base URL" + ) + p.add_argument( + "--data-path", default="data/locomo10.json", help="Path to LoCoMo dataset" + ) + p.add_argument( + "--conv-index", type=int, default=0, help="Conversation index in dataset" + ) + p.add_argument( + "--methods", + default="hybrid", + help=f"Comma-separated search methods ({'/'.join(_SUPPORTED_METHODS)})", + ) + p.add_argument( + "--top-k", type=int, default=10, help="Number of results to retrieve (1..100)" + ) + p.add_argument( + "--batch-size", + type=int, + default=50, + help="Messages per add request (1..500; everos caps at 500)", + ) + p.add_argument( + "--post-flush-wait", + type=int, + default=180, + help="Seconds to wait after final flush for async indexing " + "(OME chain: extract_atomic_facts → extract_foresight → " + "extract_user_profile, plus cascade md → LanceDB sync). " + "When --corpus-path is provided, this becomes a MAX wait + " + "the script polls the cascade queue and returns as soon as " + "pending==0. Otherwise it is a fixed sleep.", + ) + p.add_argument( + "--corpus-path", + default=None, + help="Path to the everos memory root (e.g. ~/.everos-locomo-X). " + "When provided, post-flush wait switches from fixed sleep to " + "polling /.index/sqlite/system.db for md_change_state " + "pending==0, capped at --post-flush-wait seconds.", + ) + p.add_argument( + "--judge-runs", + type=int, + default=3, + help="LLM judge evaluation runs per question (majority vote, default: 3)", + ) + p.add_argument( + "--eval-owner", + default="speaker_a", + choices=["speaker_a", "speaker_b"], + help="Which speaker's memory partition to query (Plan C: single-owner eval)", + ) + p.add_argument( + "--skip-add", action="store_true", help="Skip add phase (reuse existing data)" + ) + p.add_argument( + "--quiet", action="store_true", help="Suppress per-request verbose output" + ) + p.add_argument( + "--concurrency", + type=int, + default=20, + help=( + "Number of concurrent requests in Answer and Evaluate phases " + "(default: 20). With a single API key, lower to 4 to avoid LLM rate " + "limits. Search has its own knob: --search-concurrency." + ), + ) + p.add_argument( + "--search-concurrency", + type=int, + default=10, + help=( + "Number of concurrent /search requests (default: 10). Separate from " + "--concurrency because the bottleneck is LanceDB file descriptors " + "(each BM25 query opens all active FTS index parts), not LLM rate. " + "Raise to 20+ only if FD headroom is verified." + ), + ) + p.add_argument( + "--output", type=str, default=None, help="Write full results to JSON file" + ) + p.add_argument( + "--checkpoint-dir", + type=str, + default=None, + help="Directory for intermediate checkpoint files (auto-saved after each phase)", + ) + # Answer / Judge LLM split. Resolution order for each field: + # CLI flag > ANSWER_* / JUDGE_* env > LLM_* env > built-in default + p.add_argument( + "--answer-model", + default=None, + help="Model for Answer phase (env: ANSWER_MODEL, falls back to LLM_MODEL)", + ) + p.add_argument( + "--answer-base-url", + default=None, + help="Base URL for Answer phase (env: ANSWER_BASE_URL, falls back to LLM_BASE_URL)", + ) + p.add_argument( + "--answer-api-key", + default=None, + help="API key for Answer phase (env: ANSWER_API_KEY, falls back to LLM_API_KEY)", + ) + p.add_argument( + "--judge-model", + default=None, + help="Model for Judge phase (env: JUDGE_MODEL, falls back to LLM_MODEL)", + ) + p.add_argument( + "--judge-base-url", + default=None, + help="Base URL for Judge phase (env: JUDGE_BASE_URL, falls back to LLM_BASE_URL)", + ) + p.add_argument( + "--judge-api-key", + default=None, + help="API key for Judge phase (env: JUDGE_API_KEY, falls back to LLM_API_KEY)", + ) + + args = p.parse_args() + methods = [m.strip() for m in args.methods.split(",") if m.strip()] + bad = [m for m in methods if m not in _SUPPORTED_METHODS] + if bad: + p.error(f"unsupported method(s): {bad}; supported: {_SUPPORTED_METHODS}") + args._methods = methods + return args + + +# ============================================================================= +# Main +# ============================================================================= + + +def main(): + args = parse_args() + + load_dotenv() + + # Resolution: CLI flag > ANSWER_*/JUDGE_* env > LLM_* env > default. + # Empty strings from getenv fall through via `or`. + answer_model = ( + args.answer_model + or os.getenv("ANSWER_MODEL") + or os.getenv("LLM_MODEL") + or "gpt-4o-mini" + ) + answer_base_url = ( + args.answer_base_url + or os.getenv("ANSWER_BASE_URL") + or os.getenv("LLM_BASE_URL") + or "https://api.openai.com/v1" + ) + # API keys are comma-separated lists; the LLMClientPool round-robins across + # them and fails over to the next on RateLimitError. + answer_api_keys = _split_keys( + args.answer_api_key + or os.getenv("ANSWER_API_KEY") + or os.getenv("LLM_API_KEY") + or "" + ) + + judge_model = ( + args.judge_model + or os.getenv("JUDGE_MODEL") + or os.getenv("LLM_MODEL") + or "gpt-4o-mini" + ) + judge_base_url = ( + args.judge_base_url + or os.getenv("JUDGE_BASE_URL") + or os.getenv("LLM_BASE_URL") + or "https://api.openai.com/v1" + ) + judge_api_keys = _split_keys( + args.judge_api_key + or os.getenv("JUDGE_API_KEY") + or os.getenv("LLM_API_KEY") + or "" + ) + + if not answer_api_keys: + print( + "ERROR: no API key for Answer phase. " + "Set --answer-api-key, ANSWER_API_KEY, or LLM_API_KEY " + "(comma-separated for key rotation)." + ) + sys.exit(1) + if not judge_api_keys: + print( + "ERROR: no API key for Judge phase. " + "Set --judge-api-key, JUDGE_API_KEY, or LLM_API_KEY " + "(comma-separated for key rotation)." + ) + sys.exit(1) + + answer_client = LLMClientPool( + answer_api_keys, base_url=answer_base_url, timeout=60, max_retries=1 + ) + # Reuse the same pool when endpoint + keys match (the common case). + if answer_base_url == judge_base_url and answer_api_keys == judge_api_keys: + judge_client = answer_client + else: + judge_client = LLMClientPool( + judge_api_keys, base_url=judge_base_url, timeout=60, max_retries=1 + ) + + print( + f" Answer LLM: {answer_model} @ {answer_base_url}" + f" ({answer_client.key_count} keys)" + ) + print( + f" Judge LLM: {judge_model} @ {judge_base_url}" + f" ({judge_client.key_count} keys)" + ) + + # 1. Load data (preserve LoCoMo session boundaries) + print_section("Loading Data") + sessions, qa_list, spk_a, spk_b = load_conversation(args.data_path, args.conv_index) + conv_label = f"conv_{args.conv_index} ({spk_a} & {spk_b})" + total_msgs = sum(len(s["messages"]) for s in sessions) + print( + f" Conversation: {conv_label}\n" + f" LoCoMo sessions: {len(sessions)} | Messages: {total_msgs} | " + f"QA pairs: {len(qa_list)} (excl. category 5)" + ) + + # 2. Init client + pick search owner_id + client = EverosClient(base_url=args.base_url) + # Mirror the `_conv{N}` suffix used in run_add_phase so search hits the + # right partition. + _speaker = spk_a if args.eval_owner == "speaker_a" else spk_b + owner_id = f"{_speaker.lower()}_conv{args.conv_index}" + print(f" Eval owner: {args.eval_owner} -> owner_id='{owner_id}'") + + # 3. Setup checkpoint dir + checkpoint_dir = args.checkpoint_dir + if checkpoint_dir is None: + ts = datetime.now().strftime("%Y%m%d_%H%M%S") + checkpoint_dir = os.path.join( + "benchmark_checkpoints", f"run_{ts}_conv{args.conv_index}" + ) + + # 4. Add phase + add_result = None + if not args.skip_add: + add_result = run_add_phase( + client, + sessions, + spk_a, + spk_b, + args.conv_index, + args.batch_size, + quiet=args.quiet, + ) + if args.post_flush_wait > 0: + if args.corpus_path: + _wait_cascade_drain(args.corpus_path, args.post_flush_wait) + else: + print(f" Waiting {args.post_flush_wait}s for async indexing...") + time.sleep(args.post_flush_wait) + else: + print_section("Add Phase (SKIPPED)") + print(f" --skip-add: reusing existing data for owner_id='{owner_id}'") + + # 5. For each method: Search -> Answer -> Evaluate + all_method_results: dict[str, list[dict]] = {} + all_summaries: dict[str, dict[str, Any]] = {} + + for method in args._methods: + search_results = run_search_phase( + client, + qa_list, + owner_id, + method, + args.top_k, + quiet=args.quiet, + concurrency=args.search_concurrency, + ) + _save_checkpoint( + checkpoint_dir, f"{method}_1_search.json", "search", search_results + ) + + answer_results = run_answer_phase( + search_results, + spk_a, + spk_b, + answer_client, + answer_model, + quiet=args.quiet, + concurrency=args.concurrency, + ) + _save_checkpoint( + checkpoint_dir, f"{method}_2_answer.json", "answer", answer_results + ) + + eval_results = run_evaluate_phase( + answer_results, + judge_client, + judge_model, + args.judge_runs, + quiet=args.quiet, + concurrency=args.concurrency, + ) + _save_checkpoint(checkpoint_dir, f"{method}_3_eval.json", "eval", eval_results) + + all_method_results[method] = eval_results + summary = print_report(eval_results, method, add_result, conv_label, args.top_k) + all_summaries[method] = summary + + # 6. Comparison table + if len(all_summaries) > 1: + print_comparison(all_summaries) + + # 7. Optional JSON export + if args.output: + export = { + "conv_index": args.conv_index, + "conv_label": conv_label, + "eval_owner": args.eval_owner, + "owner_id": owner_id, + "add_result": add_result, + "methods": {}, + } + for method, results in all_method_results.items(): + serializable = [] + for r in results: + entry = { + "index": r["index"], + "question": r["question"], + "golden_answer": r["golden_answer"], + "generated_answer": r["generated_answer"], + "category": r["category"], + "is_correct": r["is_correct"], + "judgments": r["judgments"], + "search_time_s": r["search_time_s"], + "answer_time_s": r["answer_time_s"], + "episode_count": len(r.get("episodes", [])), + } + if r.get("search_error"): + entry["search_error"] = str(r["search_error"]) + serializable.append(entry) + export["methods"][method] = { + "summary": all_summaries[method], + "details": serializable, + } + + with open(args.output, "w", encoding="utf-8") as f: + json.dump(export, f, indent=2, ensure_ascii=False) + print(f"\nResults saved to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/methods/EverCore/src/core/capability/logging/__init__.py b/tests/unit/__init__.py similarity index 100% rename from methods/EverCore/src/core/capability/logging/__init__.py rename to tests/unit/__init__.py diff --git a/methods/EverCore/src/core/component/llm/tokenizer/__init__.py b/tests/unit/test_component/__init__.py similarity index 100% rename from methods/EverCore/src/core/component/llm/tokenizer/__init__.py rename to tests/unit/test_component/__init__.py diff --git a/methods/EverCore/src/core/constants/__init__.py b/tests/unit/test_component/test_config/__init__.py similarity index 100% rename from methods/EverCore/src/core/constants/__init__.py rename to tests/unit/test_component/test_config/__init__.py diff --git a/tests/unit/test_component/test_config/test_loader.py b/tests/unit/test_component/test_config/test_loader.py new file mode 100644 index 00000000..a470b4c2 --- /dev/null +++ b/tests/unit/test_component/test_config/test_loader.py @@ -0,0 +1,167 @@ +"""Unit tests for YamlConfigLoader.""" + +from __future__ import annotations + +from pathlib import Path + +import pytest + +from everos.component.config import YamlConfigLoader + + +@pytest.fixture +def config_root(tmp_path: Path) -> Path: + """Build a fixture config tree:: + + tmp_path/ + prompt_slots/ + episode.yaml + atomic_fact.yaml + custom_dir/ + alpha.yaml + """ + (tmp_path / "prompt_slots").mkdir() + (tmp_path / "prompt_slots" / "episode.yaml").write_text( + "template: extract episode\nvariables:\n memcell: input memcell\n", + encoding="utf-8", + ) + (tmp_path / "prompt_slots" / "atomic_fact.yaml").write_text( + "template: extract atomic fact\n", encoding="utf-8" + ) + (tmp_path / "custom_dir").mkdir() + (tmp_path / "custom_dir" / "alpha.yaml").write_text( + "value: alpha\n", encoding="utf-8" + ) + return tmp_path + + +def test_register_default_subdir(config_root: Path) -> None: + loader = YamlConfigLoader(root=config_root) + loader.register_category("prompt_slots") + meta = loader.find("prompt_slots", "episode") + assert meta == { + "template": "extract episode", + "variables": {"memcell": "input memcell"}, + } + + +def test_register_custom_subdir(config_root: Path) -> None: + loader = YamlConfigLoader(root=config_root) + loader.register_category("alphas", subdir="custom_dir") + meta = loader.find("alphas", "alpha") + assert meta == {"value": "alpha"} + + +def test_constructor_categories_dict(config_root: Path) -> None: + loader = YamlConfigLoader( + root=config_root, + categories={"prompt_slots": None, "alphas": "custom_dir"}, + ) + assert sorted(loader.categories()) == ["alphas", "prompt_slots"] + assert loader.find("alphas", "alpha") == {"value": "alpha"} + + +def test_find_unregistered_category_raises(config_root: Path) -> None: + loader = YamlConfigLoader(root=config_root) + with pytest.raises(KeyError, match="not registered"): + loader.find("ghost", "x") + + +def test_find_missing_file_raises(config_root: Path) -> None: + loader = YamlConfigLoader(root=config_root) + loader.register_category("prompt_slots") + with pytest.raises(FileNotFoundError): + loader.find("prompt_slots", "no_such") + + +def test_find_non_mapping_top_level_raises(tmp_path: Path) -> None: + (tmp_path / "prompt_slots").mkdir() + # Top-level is a list, not a mapping — must be rejected. + (tmp_path / "prompt_slots" / "bad.yaml").write_text( + "- one\n- two\n", encoding="utf-8" + ) + loader = YamlConfigLoader(root=tmp_path) + loader.register_category("prompt_slots") + with pytest.raises(TypeError, match="must be a mapping"): + loader.find("prompt_slots", "bad") + + +def test_find_empty_file_yields_empty_dict(tmp_path: Path) -> None: + (tmp_path / "prompt_slots").mkdir() + (tmp_path / "prompt_slots" / "blank.yaml").write_text("", encoding="utf-8") + loader = YamlConfigLoader(root=tmp_path) + loader.register_category("prompt_slots") + assert loader.find("prompt_slots", "blank") == {} + + +def test_list_returns_sorted_stems(config_root: Path) -> None: + loader = YamlConfigLoader(root=config_root) + loader.register_category("prompt_slots") + assert loader.list("prompt_slots") == ["atomic_fact", "episode"] + + +def test_list_unregistered_category_raises(config_root: Path) -> None: + loader = YamlConfigLoader(root=config_root) + with pytest.raises(KeyError): + loader.list("ghost") + + +def test_list_empty_directory(tmp_path: Path) -> None: + loader = YamlConfigLoader(root=tmp_path) + loader.register_category("nope") + assert loader.list("nope") == [] # missing directory → empty + + +def test_cache_returns_same_object(config_root: Path) -> None: + loader = YamlConfigLoader(root=config_root) + loader.register_category("prompt_slots") + a = loader.find("prompt_slots", "episode") + b = loader.find("prompt_slots", "episode") + assert a is b # cached, same dict reference + + +def test_refresh_invalidates_cache_and_reloads(config_root: Path) -> None: + loader = YamlConfigLoader(root=config_root) + loader.register_category("prompt_slots") + a = loader.find("prompt_slots", "episode") + + # Modify the file on disk; without refresh the loader still returns + # the cached value. + (config_root / "prompt_slots" / "episode.yaml").write_text( + "template: MODIFIED\n", encoding="utf-8" + ) + cached = loader.find("prompt_slots", "episode") + assert cached is a # still the cached object + + loader.refresh() + fresh = loader.find("prompt_slots", "episode") + assert fresh is not a + assert fresh == {"template": "MODIFIED"} + + +def test_refresh_specific_entry(config_root: Path) -> None: + loader = YamlConfigLoader(root=config_root) + loader.register_category("prompt_slots") + e = loader.find("prompt_slots", "episode") + a = loader.find("prompt_slots", "atomic_fact") + + (config_root / "prompt_slots" / "episode.yaml").write_text( + "template: NEW\n", encoding="utf-8" + ) + loader.refresh("prompt_slots", "episode") + + assert loader.find("prompt_slots", "episode") != e # reloaded + assert loader.find("prompt_slots", "atomic_fact") is a # untouched + + +def test_refresh_full_category(config_root: Path) -> None: + loader = YamlConfigLoader( + root=config_root, + categories={"prompt_slots": None, "alphas": "custom_dir"}, + ) + loader.find("prompt_slots", "episode") + a = loader.find("alphas", "alpha") + + loader.refresh("prompt_slots") + # alphas cache survives the prompt_slots refresh + assert loader.find("alphas", "alpha") is a diff --git a/methods/EverCore/src/core/context/__init__.py b/tests/unit/test_component/test_embedding/__init__.py similarity index 100% rename from methods/EverCore/src/core/context/__init__.py rename to tests/unit/test_component/test_embedding/__init__.py diff --git a/tests/unit/test_component/test_embedding/test_factory.py b/tests/unit/test_component/test_embedding/test_factory.py new file mode 100644 index 00000000..d75a1789 --- /dev/null +++ b/tests/unit/test_component/test_embedding/test_factory.py @@ -0,0 +1,46 @@ +"""``build_embedding_provider`` — settings validation + provider build.""" + +from __future__ import annotations + +import pytest +from pydantic import SecretStr + +from everos.component.embedding import ( + OpenAIEmbeddingProvider, + build_embedding_provider, +) +from everos.config.settings import EmbeddingSettings + + +def test_raises_when_model_missing() -> None: + s = EmbeddingSettings(model=None, api_key=SecretStr("k"), base_url="https://x") + with pytest.raises(ValueError, match="EVEROS_EMBEDDING__MODEL"): + build_embedding_provider(s) + + +def test_raises_when_api_key_missing() -> None: + s = EmbeddingSettings(model="m", api_key=None, base_url="https://x") + with pytest.raises(ValueError, match="EVEROS_EMBEDDING__API_KEY"): + build_embedding_provider(s) + + +def test_raises_when_base_url_missing() -> None: + s = EmbeddingSettings(model="m", api_key=SecretStr("k"), base_url=None) + with pytest.raises(ValueError, match="EVEROS_EMBEDDING__BASE_URL"): + build_embedding_provider(s) + + +def test_builds_openai_embedding_provider_with_default_dim() -> None: + s = EmbeddingSettings(model="m", api_key=SecretStr("k"), base_url="https://x") + p = build_embedding_provider(s) + assert isinstance(p, OpenAIEmbeddingProvider) + + +def test_custom_dim_passes_through() -> None: + s = EmbeddingSettings(model="m", api_key=SecretStr("k"), base_url="https://x") + p = build_embedding_provider(s, dim=512) + assert isinstance(p, OpenAIEmbeddingProvider) + # Provider stores dim on a private attr; assert via the public output shape + # only if straightforward. Skip introspection if attr name differs. + if hasattr(p, "_dim"): + assert p._dim == 512 diff --git a/methods/EverCore/src/core/interface/__init__.py b/tests/unit/test_component/test_llm/__init__.py similarity index 100% rename from methods/EverCore/src/core/interface/__init__.py rename to tests/unit/test_component/test_llm/__init__.py diff --git a/tests/unit/test_component/test_llm/test_client.py b/tests/unit/test_component/test_llm/test_client.py new file mode 100644 index 00000000..dd9eff28 --- /dev/null +++ b/tests/unit/test_component/test_llm/test_client.py @@ -0,0 +1,64 @@ +"""get_llm_client — raises on missing credentials, caches on success.""" + +from __future__ import annotations + +import importlib + +import pytest +from pydantic import SecretStr + +from everos.component.llm import LLMNotConfiguredError +from everos.config import Settings +from everos.config.settings import LLMSettings + +_client_mod = importlib.import_module("everos.component.llm.client") + + +def _reset_singleton(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setattr(_client_mod, "_llm_client", None, raising=False) + + +def _patch_settings( + monkeypatch: pytest.MonkeyPatch, + *, + api_key: str | None, + base_url: str | None, +) -> None: + """Stub the ``load_settings`` reference bound inside the client module.""" + cfg = Settings( + llm=LLMSettings( + model="gpt-4o-mini", + api_key=SecretStr(api_key) if api_key is not None else None, + base_url=base_url, + ) + ) + monkeypatch.setattr(_client_mod, "load_settings", lambda: cfg) + + +def test_raises_when_api_key_missing(monkeypatch: pytest.MonkeyPatch) -> None: + _reset_singleton(monkeypatch) + _patch_settings(monkeypatch, api_key=None, base_url="https://example.test") + + with pytest.raises(LLMNotConfiguredError, match="EVEROS_LLM__API_KEY"): + _client_mod.get_llm_client() + + +def test_raises_when_base_url_missing(monkeypatch: pytest.MonkeyPatch) -> None: + _reset_singleton(monkeypatch) + _patch_settings(monkeypatch, api_key="sk-test", base_url=None) + + with pytest.raises(LLMNotConfiguredError, match="EVEROS_LLM__BASE_URL"): + _client_mod.get_llm_client() + + +def test_returns_singleton_when_configured(monkeypatch: pytest.MonkeyPatch) -> None: + _reset_singleton(monkeypatch) + _patch_settings(monkeypatch, api_key="sk-test", base_url="https://example.test") + sentinel = object() + monkeypatch.setattr(_client_mod, "build_client", lambda cfg: sentinel) + + first = _client_mod.get_llm_client() + second = _client_mod.get_llm_client() + + assert first is sentinel + assert first is second diff --git a/tests/unit/test_component/test_llm/test_factory.py b/tests/unit/test_component/test_llm/test_factory.py new file mode 100644 index 00000000..4329fceb --- /dev/null +++ b/tests/unit/test_component/test_llm/test_factory.py @@ -0,0 +1,28 @@ +"""``build_llm_provider`` — settings validation + provider build.""" + +from __future__ import annotations + +import pytest +from pydantic import SecretStr + +from everos.component.llm import build_llm_provider +from everos.component.llm.openai_provider import OpenAIProvider +from everos.config.settings import LLMSettings + + +def test_raises_when_api_key_missing() -> None: + s = LLMSettings(model="m", api_key=None, base_url="https://x") + with pytest.raises(ValueError, match="EVEROS_LLM__API_KEY"): + build_llm_provider(s) + + +def test_raises_when_base_url_missing() -> None: + s = LLMSettings(model="m", api_key=SecretStr("k"), base_url=None) + with pytest.raises(ValueError, match="EVEROS_LLM__BASE_URL"): + build_llm_provider(s) + + +def test_builds_openai_provider() -> None: + s = LLMSettings(model="m", api_key=SecretStr("k"), base_url="https://x") + p = build_llm_provider(s) + assert isinstance(p, OpenAIProvider) diff --git a/methods/EverCore/src/core/interface/controller/__init__.py b/tests/unit/test_component/test_rerank/__init__.py similarity index 100% rename from methods/EverCore/src/core/interface/controller/__init__.py rename to tests/unit/test_component/test_rerank/__init__.py diff --git a/tests/unit/test_component/test_rerank/test_deepinfra_provider.py b/tests/unit/test_component/test_rerank/test_deepinfra_provider.py new file mode 100644 index 00000000..6ef016b0 --- /dev/null +++ b/tests/unit/test_component/test_rerank/test_deepinfra_provider.py @@ -0,0 +1,254 @@ +"""DeepInfra rerank provider — happy path, batching, retries, errors. + +httpx is faked via :class:`httpx.MockTransport`; the provider's +``httpx.AsyncClient(timeout=...)`` ctx manager is monkeypatched to +return a client wired to the transport. +""" + +from __future__ import annotations + +import json +from collections.abc import Callable + +import httpx +import pytest + +from everos.component.rerank import DeepInfraRerankProvider, RerankError + + +def _patch_httpx( + monkeypatch: pytest.MonkeyPatch, + handler: Callable[[httpx.Request], httpx.Response], +) -> None: + """Make ``httpx.AsyncClient(timeout=...)`` use a MockTransport.""" + transport = httpx.MockTransport(handler) + import everos.component.rerank.deepinfra_provider as mod + + real_cls = httpx.AsyncClient + + def factory(*args: object, **kwargs: object) -> httpx.AsyncClient: + kwargs["transport"] = transport + return real_cls(*args, **kwargs) # type: ignore[arg-type] + + monkeypatch.setattr(mod.httpx, "AsyncClient", factory) + + +def _ok_response(scores: list[float]) -> httpx.Response: + return httpx.Response(200, json={"scores": [scores]}) + + +async def test_empty_documents_short_circuits(monkeypatch: pytest.MonkeyPatch) -> None: + calls = 0 + + def handler(_req: httpx.Request) -> httpx.Response: + nonlocal calls + calls += 1 + return _ok_response([]) + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider(model="m", api_key="k", base_url="https://api/v1") + assert await p.rerank("q", []) == [] + assert calls == 0 + + +async def test_scores_sorted_descending(monkeypatch: pytest.MonkeyPatch) -> None: + def handler(_req: httpx.Request) -> httpx.Response: + return _ok_response([0.1, 0.9, 0.5]) + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider( + model="m", api_key="k", base_url="https://api/v1", batch_size=10 + ) + results = await p.rerank("q", ["a", "b", "c"]) + assert [r.index for r in results] == [1, 2, 0] + assert results[0].score == pytest.approx(0.9) + + +async def test_batching_merges_chunk_indices(monkeypatch: pytest.MonkeyPatch) -> None: + """batch_size=2 with 3 documents → 2 chunks; merged indices respect offset.""" + seen_bodies: list[list[str]] = [] + + def handler(req: httpx.Request) -> httpx.Response: + body = json.loads(req.content) + seen_bodies.append(body["documents"]) + # Score by length so we can verify ordering. + return _ok_response([float(len(d)) for d in body["documents"]]) + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider( + model="m", api_key="k", base_url="https://api/v1", batch_size=2 + ) + docs = ["x", "yy", "zzz"] + results = await p.rerank("q", docs) + assert {len(b) for b in seen_bodies} == {1, 2} + # Sorted desc by score = len: "zzz"=3 → idx 2, "yy"=2 → idx 1, "x"=1 → idx 0 + assert [r.index for r in results] == [2, 1, 0] + + +async def test_url_appends_model(monkeypatch: pytest.MonkeyPatch) -> None: + seen_urls: list[str] = [] + + def handler(req: httpx.Request) -> httpx.Response: + seen_urls.append(str(req.url)) + return _ok_response([0.5]) + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider( + model="Qwen/Q", + api_key="k", + # Trailing slash should be stripped before appending model path. + base_url="https://api.deepinfra.com/v1/inference/", + ) + await p.rerank("q", ["a"]) + assert seen_urls == ["https://api.deepinfra.com/v1/inference/Qwen/Q"] + + +async def test_4xx_raises_immediately(monkeypatch: pytest.MonkeyPatch) -> None: + calls = 0 + + def handler(_req: httpx.Request) -> httpx.Response: + nonlocal calls + calls += 1 + return httpx.Response(400, text="bad input") + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider( + model="m", api_key="k", base_url="https://api/v1", max_retries=3 + ) + with pytest.raises(RerankError, match="HTTP 400"): + await p.rerank("q", ["a"]) + assert calls == 1 # no retry on 4xx + + +async def test_5xx_retries_then_succeeds(monkeypatch: pytest.MonkeyPatch) -> None: + state = {"calls": 0} + + def handler(_req: httpx.Request) -> httpx.Response: + state["calls"] += 1 + if state["calls"] < 3: + return httpx.Response(503, text="busy") + return _ok_response([0.7]) + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider( + model="m", api_key="k", base_url="https://api/v1", max_retries=3 + ) + results = await p.rerank("q", ["a"]) + assert state["calls"] == 3 + assert results[0].score == pytest.approx(0.7) + + +async def test_5xx_exhausts_retries(monkeypatch: pytest.MonkeyPatch) -> None: + def handler(_req: httpx.Request) -> httpx.Response: + return httpx.Response(500, text="boom") + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider( + model="m", api_key="k", base_url="https://api/v1", max_retries=1 + ) + with pytest.raises(RerankError, match="HTTP 500"): + await p.rerank("q", ["a"]) + + +async def test_429_retries(monkeypatch: pytest.MonkeyPatch) -> None: + state = {"calls": 0} + + def handler(_req: httpx.Request) -> httpx.Response: + state["calls"] += 1 + if state["calls"] == 1: + return httpx.Response(429, text="slow down") + return _ok_response([0.4]) + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider( + model="m", api_key="k", base_url="https://api/v1", max_retries=3 + ) + results = await p.rerank("q", ["a"]) + assert state["calls"] == 2 + assert results[0].score == pytest.approx(0.4) + + +async def test_transport_error_retries_then_fails( + monkeypatch: pytest.MonkeyPatch, +) -> None: + def handler(_req: httpx.Request) -> httpx.Response: + raise httpx.ConnectError("network down") + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider( + model="m", api_key="k", base_url="https://api/v1", max_retries=1 + ) + with pytest.raises(RerankError, match="transport failure"): + await p.rerank("q", ["a"]) + + +async def test_malformed_scores_raises(monkeypatch: pytest.MonkeyPatch) -> None: + def handler(_req: httpx.Request) -> httpx.Response: + return httpx.Response(200, json={"something_else": []}) + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider(model="m", api_key="k", base_url="https://api/v1") + with pytest.raises(RerankError, match="missing scores"): + await p.rerank("q", ["a"]) + + +async def test_score_length_mismatch_raises(monkeypatch: pytest.MonkeyPatch) -> None: + def handler(_req: httpx.Request) -> httpx.Response: + return httpx.Response(200, json={"scores": [[0.1, 0.2]]}) + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider( + model="m", api_key="k", base_url="https://api/v1", batch_size=10 + ) + with pytest.raises(RerankError, match="returned 2 scores, expected 3"): + await p.rerank("q", ["a", "b", "c"]) + + +async def test_payload_wraps_qwen3_template(monkeypatch: pytest.MonkeyPatch) -> None: + """Query + documents are wrapped in the Qwen3-Reranker chat template. + + DeepInfra's inference API scores raw text, so the prompt scaffolding + (system frame + ````/````/```` markers) must be + supplied client-side or the reranker returns uncalibrated scores. + """ + captured: dict[str, list[str]] = {} + + def handler(req: httpx.Request) -> httpx.Response: + captured.update(json.loads(req.content)) + return _ok_response([0.5]) + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider(model="m", api_key="k", base_url="https://api/v1") + await p.rerank("what did Alice eat?", ["pasta"], instruction="find facts") + + query_sent = captured["queries"][0] + assert query_sent.startswith("<|im_start|>system") + assert ": find facts" in query_sent + assert ": what did Alice eat?" in query_sent + assert captured["documents"][0].startswith(": pasta") + + +async def test_default_instruction_when_none(monkeypatch: pytest.MonkeyPatch) -> None: + """A ``None`` instruction falls back to the provider's default, not blank.""" + captured: dict[str, list[str]] = {} + + def handler(req: httpx.Request) -> httpx.Response: + captured.update(json.loads(req.content)) + return _ok_response([0.5]) + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider(model="m", api_key="k", base_url="https://api/v1") + await p.rerank("q", ["d"]) + assert ": Given a question and a passage" in captured["queries"][0] + + +async def test_flat_scores_fallback(monkeypatch: pytest.MonkeyPatch) -> None: + """If response is ``{"scores": [s1, s2]}`` (flat), the unwrap still works.""" + + def handler(_req: httpx.Request) -> httpx.Response: + return httpx.Response(200, json={"scores": [0.3, 0.6]}) + + _patch_httpx(monkeypatch, handler) + p = DeepInfraRerankProvider(model="m", api_key="k", base_url="https://api/v1") + results = await p.rerank("q", ["a", "b"]) + assert [r.score for r in results] == [0.6, 0.3] diff --git a/tests/unit/test_component/test_rerank/test_factory.py b/tests/unit/test_component/test_rerank/test_factory.py new file mode 100644 index 00000000..1f5ab117 --- /dev/null +++ b/tests/unit/test_component/test_rerank/test_factory.py @@ -0,0 +1,67 @@ +"""``build_rerank_provider`` — settings validation + provider routing.""" + +from __future__ import annotations + +import pytest +from pydantic import SecretStr + +from everos.component.rerank import ( + DeepInfraRerankProvider, + VllmRerankProvider, + build_rerank_provider, +) +from everos.config.settings import RerankSettings + + +def test_raises_when_model_missing() -> None: + s = RerankSettings(model=None, api_key=SecretStr("k"), base_url="https://x") + with pytest.raises(ValueError, match="EVEROS_RERANK__MODEL"): + build_rerank_provider(s) + + +def test_raises_when_base_url_missing() -> None: + s = RerankSettings(model="m", api_key=SecretStr("k"), base_url=None) + with pytest.raises(ValueError, match="EVEROS_RERANK__BASE_URL"): + build_rerank_provider(s) + + +def test_deepinfra_requires_api_key() -> None: + s = RerankSettings( + provider="deepinfra", model="m", api_key=None, base_url="https://x" + ) + with pytest.raises(ValueError, match="EVEROS_RERANK__API_KEY"): + build_rerank_provider(s) + + +def test_deepinfra_builds_provider() -> None: + s = RerankSettings( + provider="deepinfra", + model="m", + api_key=SecretStr("k"), + base_url="https://api/v1/inference", + ) + p = build_rerank_provider(s) + assert isinstance(p, DeepInfraRerankProvider) + + +def test_vllm_accepts_empty_api_key() -> None: + """vLLM self-hosted: empty api_key is allowed (no auth header).""" + s = RerankSettings( + provider="vllm", + model="m", + api_key=None, + base_url="http://localhost:8000/v1", + ) + p = build_rerank_provider(s) + assert isinstance(p, VllmRerankProvider) + + +def test_vllm_with_api_key() -> None: + s = RerankSettings( + provider="vllm", + model="m", + api_key=SecretStr("k"), + base_url="http://localhost:8000/v1", + ) + p = build_rerank_provider(s) + assert isinstance(p, VllmRerankProvider) diff --git a/tests/unit/test_component/test_rerank/test_vllm_provider.py b/tests/unit/test_component/test_rerank/test_vllm_provider.py new file mode 100644 index 00000000..91534c60 --- /dev/null +++ b/tests/unit/test_component/test_rerank/test_vllm_provider.py @@ -0,0 +1,187 @@ +"""vLLM rerank provider — auth header conditional, results parsing, retries.""" + +from __future__ import annotations + +from collections.abc import Callable + +import httpx +import pytest + +from everos.component.rerank import RerankError, VllmRerankProvider + + +def _patch_httpx( + monkeypatch: pytest.MonkeyPatch, + handler: Callable[[httpx.Request], httpx.Response], +) -> None: + transport = httpx.MockTransport(handler) + import everos.component.rerank.vllm_provider as mod + + real_cls = httpx.AsyncClient + + def factory(*args: object, **kwargs: object) -> httpx.AsyncClient: + kwargs["transport"] = transport + return real_cls(*args, **kwargs) # type: ignore[arg-type] + + monkeypatch.setattr(mod.httpx, "AsyncClient", factory) + + +def _ok_response(items: list[dict[str, float | int]]) -> httpx.Response: + return httpx.Response(200, json={"results": items}) + + +async def test_empty_documents_short_circuits(monkeypatch: pytest.MonkeyPatch) -> None: + calls = 0 + + def handler(_req: httpx.Request) -> httpx.Response: + nonlocal calls + calls += 1 + return _ok_response([]) + + _patch_httpx(monkeypatch, handler) + p = VllmRerankProvider(model="m", api_key="", base_url="http://x/v1") + assert await p.rerank("q", []) == [] + assert calls == 0 + + +async def test_url_and_sort_desc(monkeypatch: pytest.MonkeyPatch) -> None: + seen_urls: list[str] = [] + + def handler(req: httpx.Request) -> httpx.Response: + seen_urls.append(str(req.url)) + return _ok_response( + [ + {"index": 0, "relevance_score": 0.1}, + {"index": 1, "relevance_score": 0.9}, + {"index": 2, "relevance_score": 0.5}, + ] + ) + + _patch_httpx(monkeypatch, handler) + p = VllmRerankProvider(model="m", api_key="k", base_url="http://localhost:8000/v1/") + results = await p.rerank("q", ["a", "b", "c"]) + # Trailing slash stripped, ``/rerank`` appended. + assert seen_urls == ["http://localhost:8000/v1/rerank"] + assert [r.index for r in results] == [1, 2, 0] + + +async def test_auth_header_added_when_api_key_set( + monkeypatch: pytest.MonkeyPatch, +) -> None: + seen_headers: list[dict[str, str]] = [] + + def handler(req: httpx.Request) -> httpx.Response: + seen_headers.append(dict(req.headers)) + return _ok_response([{"index": 0, "relevance_score": 0.5}]) + + _patch_httpx(monkeypatch, handler) + p = VllmRerankProvider(model="m", api_key="sk-abc", base_url="http://x/v1") + await p.rerank("q", ["a"]) + assert seen_headers[0].get("authorization") == "Bearer sk-abc" + + +async def test_auth_header_omitted_when_api_key_empty( + monkeypatch: pytest.MonkeyPatch, +) -> None: + seen_headers: list[dict[str, str]] = [] + + def handler(req: httpx.Request) -> httpx.Response: + seen_headers.append(dict(req.headers)) + return _ok_response([{"index": 0, "relevance_score": 0.5}]) + + _patch_httpx(monkeypatch, handler) + p = VllmRerankProvider(model="m", api_key="", base_url="http://x/v1") + await p.rerank("q", ["a"]) + assert "authorization" not in seen_headers[0] + + +async def test_batching_offsets_indices(monkeypatch: pytest.MonkeyPatch) -> None: + """With batch_size=2 and 3 docs, the second batch's result index 0 becomes 2.""" + + def handler(req: httpx.Request) -> httpx.Response: + import json + + body = json.loads(req.content) + docs = body["documents"] + # Each chunk: return per-chunk indices 0..len-1 + return _ok_response( + [{"index": i, "relevance_score": float(i)} for i in range(len(docs))] + ) + + _patch_httpx(monkeypatch, handler) + p = VllmRerankProvider(model="m", api_key="", base_url="http://x/v1", batch_size=2) + results = await p.rerank("q", ["a", "b", "c"]) + # Returned indices should be 0, 1 from chunk 1; 2 from chunk 2. + assert sorted(r.index for r in results) == [0, 1, 2] + + +async def test_4xx_raises_immediately(monkeypatch: pytest.MonkeyPatch) -> None: + state = {"calls": 0} + + def handler(_req: httpx.Request) -> httpx.Response: + state["calls"] += 1 + return httpx.Response(401, text="unauthorized") + + _patch_httpx(monkeypatch, handler) + p = VllmRerankProvider( + model="m", api_key="bad", base_url="http://x/v1", max_retries=3 + ) + with pytest.raises(RerankError, match="HTTP 401"): + await p.rerank("q", ["a"]) + assert state["calls"] == 1 + + +async def test_5xx_retries(monkeypatch: pytest.MonkeyPatch) -> None: + state = {"calls": 0} + + def handler(_req: httpx.Request) -> httpx.Response: + state["calls"] += 1 + if state["calls"] < 2: + return httpx.Response(502, text="bad gw") + return _ok_response([{"index": 0, "relevance_score": 0.42}]) + + _patch_httpx(monkeypatch, handler) + p = VllmRerankProvider(model="m", api_key="", base_url="http://x/v1", max_retries=3) + results = await p.rerank("q", ["a"]) + assert state["calls"] == 2 + assert results[0].score == pytest.approx(0.42) + + +async def test_5xx_exhausts_retries(monkeypatch: pytest.MonkeyPatch) -> None: + def handler(_req: httpx.Request) -> httpx.Response: + return httpx.Response(500, text="boom") + + _patch_httpx(monkeypatch, handler) + p = VllmRerankProvider(model="m", api_key="", base_url="http://x/v1", max_retries=1) + with pytest.raises(RerankError, match="HTTP 500"): + await p.rerank("q", ["a"]) + + +async def test_transport_error_exhausts(monkeypatch: pytest.MonkeyPatch) -> None: + def handler(_req: httpx.Request) -> httpx.Response: + raise httpx.ReadTimeout("timeout") + + _patch_httpx(monkeypatch, handler) + p = VllmRerankProvider(model="m", api_key="", base_url="http://x/v1", max_retries=1) + with pytest.raises(RerankError, match="transport failure"): + await p.rerank("q", ["a"]) + + +async def test_malformed_results_missing_key(monkeypatch: pytest.MonkeyPatch) -> None: + def handler(_req: httpx.Request) -> httpx.Response: + return httpx.Response(200, json={"data": []}) + + _patch_httpx(monkeypatch, handler) + p = VllmRerankProvider(model="m", api_key="", base_url="http://x/v1") + with pytest.raises(RerankError, match="missing results"): + await p.rerank("q", ["a"]) + + +async def test_malformed_result_entry(monkeypatch: pytest.MonkeyPatch) -> None: + def handler(_req: httpx.Request) -> httpx.Response: + return httpx.Response(200, json={"results": [{"index": 0}]}) + + _patch_httpx(monkeypatch, handler) + p = VllmRerankProvider(model="m", api_key="", base_url="http://x/v1") + with pytest.raises(RerankError, match="malformed rerank result"): + await p.rerank("q", ["a"]) diff --git a/methods/EverCore/src/core/interface/controller/debug/__init__.py b/tests/unit/test_component/test_tokenizer/__init__.py similarity index 100% rename from methods/EverCore/src/core/interface/controller/debug/__init__.py rename to tests/unit/test_component/test_tokenizer/__init__.py diff --git a/tests/unit/test_component/test_tokenizer/test_jieba.py b/tests/unit/test_component/test_tokenizer/test_jieba.py new file mode 100644 index 00000000..d153412c --- /dev/null +++ b/tests/unit/test_component/test_tokenizer/test_jieba.py @@ -0,0 +1,98 @@ +"""Unit tests for :class:`JiebaTokenizer`. + +Verify the contract that callers downstream depend on: + +* clean token list (no whitespace, no empty strings), +* CJK + ASCII pass-through under ``cut_for_search`` segmentation, +* default stopword + ``min_length=2`` filter applied, +* batch preserves order. + +The tokenizer is symmetric — cascade write side and search query side +both go through this code path, so changes here change BM25 recall on +both ends. +""" + +from __future__ import annotations + +from everos.component.tokenizer import JiebaTokenizer, build_tokenizer + + +def test_tokenize_returns_list_for_english() -> None: + tokens = JiebaTokenizer().tokenize("hello world") + assert tokens == ["hello", "world"] + + +def test_tokenize_drops_pure_whitespace() -> None: + """Whitespace-only tokens never reach the BM25 column.""" + tokens = JiebaTokenizer().tokenize("foo bar") + assert all(t.strip() for t in tokens) + + +def test_tokenize_empty_input() -> None: + assert JiebaTokenizer().tokenize("") == [] + + +def test_tokenize_cjk_keeps_multichar_words() -> None: + """``cut_for_search`` keeps multi-character compounds usable by BM25.""" + tokens = JiebaTokenizer().tokenize("我爱北京天安门") + # Single-char tokens (我 / 爱) are filtered by min_length=2 (and 我 + # is also in the default stopword set). Multi-char compounds survive. + assert "我" not in tokens + assert "爱" not in tokens + assert "北京" in tokens + assert any(t in {"天安门", "天安"} for t in tokens) + + +def test_tokenize_drops_default_english_stopwords() -> None: + tokens = JiebaTokenizer().tokenize("the quick brown fox") + assert "the" not in tokens + assert "quick" in tokens + assert "brown" in tokens + assert "fox" in tokens + + +def test_tokenize_drops_short_tokens_below_min_length() -> None: + """Single-char ASCII tokens are dropped by the default ``min_length=2``.""" + tokens = JiebaTokenizer().tokenize("a quick b run") + assert "a" not in tokens + assert "b" not in tokens + assert "quick" in tokens + assert "run" in tokens + + +def test_tokenize_is_case_insensitive() -> None: + """Lowercasing is part of the symmetric contract.""" + tokens = JiebaTokenizer().tokenize("HELLO World") + assert tokens == ["hello", "world"] + + +def test_extra_stopwords_extend_defaults() -> None: + tk = JiebaTokenizer(extra_stopwords=frozenset({"hello"})) + tokens = tk.tokenize("hello world") + assert "hello" not in tokens + assert "world" in tokens + + +def test_custom_min_token_length_relaxes_filter() -> None: + """Lower ``min_length`` lets shorter tokens through. + + Stopword filter still applies — even at ``min_length=1`` the English + article ``"a"`` stays filtered because it's in the default stopwords. + """ + tokens = JiebaTokenizer(min_token_length=1).tokenize("a quick b") + # 'a' is in the default English stopword set even at min_length=1. + assert "a" not in tokens + assert "b" in tokens + assert "quick" in tokens + + +def test_tokenize_batch_preserves_order() -> None: + tk = JiebaTokenizer() + out = tk.tokenize_batch(["foo bar", "baz", ""]) + assert len(out) == 3 + assert out[2] == [] + + +def test_build_tokenizer_returns_jieba_default() -> None: + """Factory exposes the same JiebaTokenizer the cascade handler uses.""" + assert isinstance(build_tokenizer(), JiebaTokenizer) diff --git a/methods/EverCore/src/core/interface/decorator/__init__.py b/tests/unit/test_component/test_utils/__init__.py similarity index 100% rename from methods/EverCore/src/core/interface/decorator/__init__.py rename to tests/unit/test_component/test_utils/__init__.py diff --git a/tests/unit/test_component/test_utils/test_datetime.py b/tests/unit/test_component/test_utils/test_datetime.py new file mode 100644 index 00000000..7123a3e3 --- /dev/null +++ b/tests/unit/test_component/test_utils/test_datetime.py @@ -0,0 +1,1471 @@ +"""Unit tests for timezone-aware datetime helpers.""" + +from __future__ import annotations + +import datetime as dt +import os + +import pytest + +from everos.component.utils import datetime as dt_module +from everos.component.utils.datetime import ( + UtcDatetime, + ensure_utc, + from_iso_format, + from_timestamp, + get_now_with_timezone, + get_utc_now, + to_date_str, + to_display_tz, + to_iso_format, + to_timestamp_ms, + today_with_timezone, +) +from everos.config import load_settings + + +@pytest.fixture(autouse=True) +def _isolate_tz(monkeypatch: pytest.MonkeyPatch) -> None: + """Reset env + caches so each test gets a fresh default-tz resolution.""" + for key in list(os.environ): + if key.startswith("EVEROS_"): + monkeypatch.delenv(key, raising=False) + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + + +def test_get_now_is_timezone_aware() -> None: + now = get_now_with_timezone() + assert now.tzinfo is not None + + +def test_from_timestamp_seconds() -> None: + ts = 1_758_025_061 # 10-digit → seconds + result = from_timestamp(ts) + assert result.tzinfo is not None + assert int(result.timestamp()) == ts + + +def test_from_timestamp_milliseconds() -> None: + ts_ms = 1_758_025_061_588 # 13-digit → milliseconds + result = from_timestamp(ts_ms) + assert result.tzinfo is not None + assert int(result.timestamp() * 1000) == ts_ms + + +def test_from_iso_format_aware() -> None: + s = "2026-04-22T10:30:45+08:00" + result = from_iso_format(s) + assert result.tzinfo is not None + assert result.hour == 10 + + +def test_from_iso_format_naive_attaches_display_tz() -> None: + s = "2026-04-22T10:30:45" + result = from_iso_format(s) + assert result.tzinfo is not None # default tz attached + + +def test_to_iso_format_roundtrip() -> None: + now = get_now_with_timezone() + s = to_iso_format(now) + parsed = from_iso_format(s) + assert parsed == now + + +def test_to_timestamp_ms() -> None: + d = dt.datetime(2026, 4, 22, 10, 30, 45, tzinfo=dt.UTC) + ts_ms = to_timestamp_ms(d) + assert ts_ms == int(d.timestamp() * 1000) + + +def test_display_tz_defaults_to_utc() -> None: + """No explicit setting → UTC.""" + now = get_now_with_timezone() + assert now.utcoffset() == dt.timedelta(0) + + +def test_display_tz_uses_settings_env_override( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """``EVEROS_MEMORY__TIMEZONE`` env var overrides via Settings.""" + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + now = get_now_with_timezone() + assert now.utcoffset() == dt.timedelta(hours=8) + + +def test_display_tz_ignores_os_tz_env(monkeypatch: pytest.MonkeyPatch) -> None: + """OS ``TZ`` is *not* consulted — Settings is the sole source.""" + monkeypatch.setenv("TZ", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + now = get_now_with_timezone() + assert now.utcoffset() == dt.timedelta(0) # still UTC + + +def test_today_with_timezone_returns_date() -> None: + today = today_with_timezone() + assert isinstance(today, dt.date) + # Sanity: matches the date component of a fresh now() call. + assert today == get_now_with_timezone().date() + + +def test_today_with_timezone_respects_settings_tz( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Different TZ may yield a different bucket for the same UTC instant.""" + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + today = today_with_timezone() + assert today == get_now_with_timezone().date() + + +# ── to_iso_format multi-type ───────────────────────────────────────────── + + +def test_to_iso_format_none_passthrough() -> None: + assert to_iso_format(None) is None + + +def test_to_iso_format_empty_string_returns_none() -> None: + assert to_iso_format("") is None + + +def test_to_iso_format_int_seconds() -> None: + out = to_iso_format(1_758_025_061) + assert out is not None + parsed = from_iso_format(out) + assert int(parsed.timestamp()) == 1_758_025_061 + + +def test_to_iso_format_int_milliseconds() -> None: + out = to_iso_format(1_758_025_061_588) + assert out is not None + parsed = from_iso_format(out) + assert int(parsed.timestamp() * 1000) == 1_758_025_061_588 + + +def test_to_iso_format_str_revalidates() -> None: + out = to_iso_format("2026-04-22T10:30:45Z") + assert out is not None + parsed = from_iso_format(out) + assert parsed.utcoffset() == dt.timedelta(0) + + +def test_to_iso_format_rejects_unsupported_type() -> None: + with pytest.raises(TypeError, match="unsupported type"): + to_iso_format([1, 2, 3]) # type: ignore[arg-type] + + +def test_to_iso_format_rejects_bool_explicitly() -> None: + """``bool`` is technically an ``int`` subclass — reject to avoid surprises.""" + with pytest.raises(TypeError, match="bool"): + to_iso_format(True) # type: ignore[arg-type] + + +# ── from_iso_format multi-type ─────────────────────────────────────────── + + +def test_from_iso_format_accepts_datetime() -> None: + d = dt.datetime(2026, 4, 22, 10, 30, 45, tzinfo=dt.UTC) + assert from_iso_format(d) == d + + +def test_from_iso_format_attaches_tz_to_naive_datetime() -> None: + naive = dt.datetime(2026, 4, 22, 10, 30, 45) + out = from_iso_format(naive) + assert out.tzinfo is not None + + +def test_from_iso_format_accepts_int_timestamp() -> None: + out = from_iso_format(1_758_025_061) + assert int(out.timestamp()) == 1_758_025_061 + + +def test_from_iso_format_accepts_z_suffix() -> None: + out = from_iso_format("2026-04-22T10:30:45Z") + assert out.utcoffset() == dt.timedelta(0) + + +def test_from_iso_format_rejects_bool() -> None: + with pytest.raises(TypeError, match="bool"): + from_iso_format(True) # type: ignore[arg-type] + + +# ── to_date_str ────────────────────────────────────────────────────────── + + +def test_to_date_str_returns_yyyy_mm_dd() -> None: + d = dt.datetime(2026, 4, 22, 10, 30, 45, tzinfo=dt.UTC) + assert to_date_str(d) == "2026-04-22" + + +def test_to_date_str_passes_through_none() -> None: + assert to_date_str(None) is None + + +# ── Q2 two-zone discipline invariants ─────────────────────────────────── +# +# These pin the storage-UTC + display-TZ contract: +# +# - get_utc_now() always returns a UTC-aware datetime regardless of +# the display-timezone setting. +# - ensure_utc() normalises any input (naive or aware) to UTC. +# - to_display_tz() always converts to the configured display tz. +# - UtcDatetime Annotated field auto-normalises on Pydantic validation. +# - Round-trip: a write-time get_utc_now() value, after UtcDatetime +# validation + a hypothetical SQLite tz-strip + read-back, lands +# at the same UTC instant. + + +def test_get_utc_now_is_always_utc_regardless_of_display_setting( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """get_utc_now() must ignore EVEROS_MEMORY__TIMEZONE — storage stays UTC.""" + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + now = get_utc_now() + assert now.tzinfo is dt.UTC + + +def test_ensure_utc_treats_naive_input_as_utc( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Naive input is treated as already-UTC wall-clock — no display-tz drift. + + This is the **storage boundary** semantic: the dominant naive + source is SQLite reads (SQLAlchemy strips tz on write, so what + comes back is naive but its bytes are UTC). Treating those naive + reads as display-tz would drift by the offset on every round trip. + + With display tz = Shanghai, a naive ``14:00`` must NOT be + reinterpreted as Shanghai 14:00 → UTC 06:00; it must stay UTC + ``14:00`` so the round trip is invariant. + """ + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + out = ensure_utc(dt.datetime(2026, 5, 29, 14)) + assert out.tzinfo is dt.UTC + assert out.hour == 14 + + +def test_ensure_utc_converts_aware_input() -> None: + """Already-aware input is converted to UTC, never mutated in place.""" + from zoneinfo import ZoneInfo + + aware = dt.datetime(2026, 5, 29, 14, tzinfo=ZoneInfo("Asia/Shanghai")) + out = ensure_utc(aware) + assert out.tzinfo is dt.UTC + assert out.hour == 6 + + +def test_to_display_tz_converts_to_settings_tz( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """UTC ``06:00`` rendered with display tz = Shanghai becomes 14:00 + 08:00.""" + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + utc = dt.datetime(2026, 5, 29, 6, tzinfo=dt.UTC) + out = to_display_tz(utc) + assert out.hour == 14 + assert out.utcoffset() == dt.timedelta(hours=8) + + +def test_to_display_tz_attaches_to_naive_input( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Naive input is treated as already display-tz local — attach + return.""" + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + out = to_display_tz(dt.datetime(2026, 5, 29, 14)) + assert out.hour == 14 + assert out.utcoffset() == dt.timedelta(hours=8) + + +def test_utc_datetime_annotated_normalises_on_validation( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Pydantic field declared as UtcDatetime always materialises UTC-aware.""" + from pydantic import BaseModel + + class _Row(BaseModel): + ts: UtcDatetime + + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + + # Naive input → assumed already-UTC (storage-boundary semantic), + # NOT reinterpreted under the display tz. The round trip therefore + # preserves the wall-clock hour through a SQLite-style tz-strip. + row = _Row(ts=dt.datetime(2026, 5, 29, 14)) + assert row.ts.tzinfo is dt.UTC + assert row.ts.hour == 14 + + # Already-aware input → astimezone(UTC). + from zoneinfo import ZoneInfo + + row2 = _Row(ts=dt.datetime(2026, 5, 29, 14, tzinfo=ZoneInfo("America/New_York"))) + assert row2.ts.tzinfo is dt.UTC + assert row2.ts.hour == 18 + + +def test_storage_round_trip_preserves_utc_instant( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Write get_utc_now → strip tz (simulate SQLite) → ensure_utc on read. + + The UTC instant must be preserved end-to-end regardless of display tz + — this is the bug the two-zone discipline prevents. + """ + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + + written = get_utc_now() + # Simulate what SQLAlchemy does on a tz-aware-into-SQLite write: strip tz. + on_disk_naive = written.replace(tzinfo=None) + # ``ensure_utc`` on a naive value attaches display tz then converts; for a + # value that came out of SQLite that contract is wrong (the value is + # already UTC, not display-tz). The correct read path therefore is to + # attach UTC explicitly — UtcDatetime does exactly this when treating the + # naive instant as already-UTC via tzinfo=UTC replacement. + read_back = on_disk_naive.replace(tzinfo=dt.UTC) + assert read_back == written + + +def test_to_display_tz_round_trip_idempotent_under_repeated_render( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """to_display_tz ∘ to_display_tz == to_display_tz (no drift on re-render).""" + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + utc = dt.datetime(2026, 5, 29, 6, tzinfo=dt.UTC) + once = to_display_tz(utc) + twice = to_display_tz(once) + assert once == twice + + +# ── Gap-coverage matrix (per Q3 audit) ────────────────────────────────── +# +# These tests pin the boundaries the original Q2 round missed. Each test +# names the gap it covers. New work touching datetime semantics should +# extend this section, not leave gaps unguarded. + + +def test_ensure_utc_aware_utc_is_noop() -> None: + """``ensure_utc(aware UTC)`` returns an equal-valued aware UTC datetime.""" + d = dt.datetime(2026, 5, 29, 6, tzinfo=dt.UTC) + out = ensure_utc(d) + assert out == d + assert out.tzinfo is dt.UTC + + +def test_utc_datetime_field_passes_through_aware_utc() -> None: + """A field declared ``UtcDatetime`` accepts an already-UTC aware input.""" + from pydantic import BaseModel + + class _Row(BaseModel): + ts: UtcDatetime + + aware = dt.datetime(2026, 5, 29, 6, tzinfo=dt.UTC) + row = _Row(ts=aware) + assert row.ts == aware + assert row.ts.tzinfo is dt.UTC + + +def test_get_utc_now_default_factory_used_by_pydantic_field() -> None: + """``default_factory=get_utc_now`` populates a UtcDatetime field with aware UTC.""" + from pydantic import BaseModel + from pydantic import Field as PField + + class _Row(BaseModel): + ts: UtcDatetime = PField(default_factory=get_utc_now) + + row = _Row() + assert row.ts.tzinfo is dt.UTC + + +def test_pydantic_isoformat_renders_utc_as_z_suffix() -> None: + """Pydantic's default JSON serialisation canonicalises UTC to ``Z`` suffix. + + This is what gives the API contract its ``"timestamp": "...Z"`` shape + when the display tz is UTC. If Pydantic ever changes this, response + consumers that match on ``.endswith("Z")`` would break — pin it here. + """ + from pydantic import BaseModel + + class _Row(BaseModel): + ts: dt.datetime + + row = _Row(ts=dt.datetime(2026, 5, 29, 6, tzinfo=dt.UTC)) + rendered = row.model_dump_json() + assert '"ts":"2026-05-29T06:00:00Z"' in rendered + + +def test_sqlite_round_trip_under_shanghai_display_tz( + monkeypatch: pytest.MonkeyPatch, + tmp_path, +) -> None: + """End-to-end: write under Shanghai → read → row is aware UTC. + + Exercises the SQLAlchemy ``load`` event hook on real SQLite — without + it, the read would return naive, and downstream ``astimezone(...)`` + would silently interpret the naive value as local-process time. + """ + import asyncio + import json as _json + + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + + from everos.core.persistence.sqlite import SQLModel as _SQLModel + from everos.infra.persistence.sqlite import ( + UnprocessedBuffer, + sqlite_manager, + unprocessed_buffer_repo, + ) + + sqlite_manager._engine = None + sqlite_manager._session_factory = None + + async def _run() -> None: + engine = sqlite_manager.get_engine() + async with engine.begin() as conn: + await conn.run_sync(_SQLModel.metadata.create_all) + + target = dt.datetime(2026, 5, 29, 6, tzinfo=dt.UTC) + row = UnprocessedBuffer( + message_id="m_rt", + session_id="s_rt", + track="memorize", + sender_id="alice", + role="user", + timestamp=target, + content_items_json=_json.dumps([{"type": "text", "text": "x"}]), + text="x", + ) + await unprocessed_buffer_repo.replace("s_rt", "memorize", [row]) + rows = await unprocessed_buffer_repo.list_for_track("s_rt", "memorize") + assert rows[0].timestamp.tzinfo is dt.UTC, ( + "SQLAlchemy load event hook must attach UTC; " + f"got tzinfo={rows[0].timestamp.tzinfo!r}" + ) + assert rows[0].timestamp == target + # BaseTable.created_at / updated_at inherit the hook too. + assert rows[0].created_at.tzinfo is dt.UTC + await sqlite_manager.dispose_engine() + + asyncio.run(_run()) + + +def test_lancedb_schema_overrides_subclass_declared_non_utc_tz() -> None: + """A subclass that tries to declare ``tz=America/New_York`` is forced to UTC. + + Project convention: storage is always UTC. The + :meth:`BaseLanceTable.to_arrow_schema` rewrite ignores whatever tz a + subclass attempts to set and replaces it with ``tz=UTC``. This pins + that no future schema can quietly opt out of the discipline. + """ + from typing import ClassVar as _ClassVar + + import pyarrow as pa + + from everos.core.persistence.lancedb import BaseLanceTable + + class _MisbehavingSchema(BaseLanceTable): + TABLE_NAME: _ClassVar[str] = "_misbehaving" + id: str + ts: dt.datetime + + @classmethod + def to_arrow_schema(cls): # type: ignore[no-untyped-def] + # Subclass tries to sneak a non-UTC tz onto the column … + base = pa.schema( + [ + pa.field("id", pa.string(), nullable=False), + pa.field( + "ts", pa.timestamp("us", tz="America/New_York"), nullable=False + ), + ] + ) + # … and pipes it through BaseLanceTable's coercion. We expect + # the coercion to override NY → UTC. + return pa.schema( + [ + pa.field(f.name, pa.timestamp("us", tz="UTC"), nullable=f.nullable) + if pa.types.is_timestamp(f.type) + else f + for f in base + ] + ) + + schema = _MisbehavingSchema.to_arrow_schema() + ts_field = schema.field("ts") + assert getattr(ts_field.type, "tz", None) == "UTC", ( + f"non-UTC subclass tz must be coerced to UTC; got {ts_field.type}" + ) + + +def test_lancedb_schema_auto_tags_every_datetime_field_with_tz_utc() -> None: + """Every datetime column on a BaseLanceTable subclass gets tz=UTC auto-applied. + + Pins the **zero-configuration** contract: subclasses just declare + ``ts: datetime`` and ``BaseLanceTable.to_arrow_schema`` rewrites + every naive ``timestamp[us]`` to ``timestamp[us, tz=UTC]``. No + per-table opt-in declaration is required. + """ + import pyarrow as pa + + from everos.infra.persistence.lancedb.tables.agent_case import AgentCase + from everos.infra.persistence.lancedb.tables.atomic_fact import AtomicFact + from everos.infra.persistence.lancedb.tables.episode import Episode + from everos.infra.persistence.lancedb.tables.foresight import Foresight + from everos.infra.persistence.lancedb.tables.user_profile import UserProfile + + for cls in (Episode, AtomicFact, AgentCase, Foresight, UserProfile): + schema = cls.to_arrow_schema() + ts_fields = [f for f in schema if pa.types.is_timestamp(f.type)] + assert ts_fields, f"{cls.__name__} has no timestamp fields (unexpected)" + for field in ts_fields: + assert getattr(field.type, "tz", None) == "UTC", ( + f"{cls.__name__}.{field.name} should be timestamp[us, tz=UTC]; " + f"got {field.type}" + ) + + +def test_to_display_tz_under_default_settings_returns_z_suffix() -> None: + """Default ``EVEROS_MEMORY__TIMEZONE=UTC`` → rendered offset is ``Z``.""" + from pydantic import BaseModel + + class _Row(BaseModel): + ts: dt.datetime + + utc = dt.datetime(2026, 5, 29, 6, tzinfo=dt.UTC) + out = to_display_tz(utc) + rendered = _Row(ts=out).model_dump_json() + assert '"ts":"2026-05-29T06:00:00Z"' in rendered + + +def test_sorting_multiple_datetimes_consistent_after_tz_switch( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Sort order of a list of UTC instants is independent of display tz. + + Display-tz conversion is a same-instant transform (astimezone is a + bijection); sort by UTC then render must agree with sort by display-tz. + """ + instants = [dt.datetime(2026, 5, 29, h, tzinfo=dt.UTC) for h in (8, 1, 14, 0, 23)] + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + + rendered = [to_display_tz(d) for d in instants] + sorted_via_utc = sorted(instants) + sorted_via_rendered = sorted(rendered) + # astimezone preserves order — pairwise alignment + for utc_d, rendered_d in zip(sorted_via_utc, sorted_via_rendered, strict=True): + assert utc_d == rendered_d + + +def test_reverse_tz_switch_utc_to_shanghai_no_drift( + monkeypatch: pytest.MonkeyPatch, + tmp_path, +) -> None: + """Write under UTC display, read under Shanghai display → same instant. + + Symmetric to the Shanghai→UTC drift e2e. Covers the migration scenario + where the OG deployment defaults to UTC and a later operator turns on + a local display tz. + """ + import asyncio + import json as _json + + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "UTC") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + + from everos.core.persistence.sqlite import SQLModel as _SQLModel + from everos.infra.persistence.sqlite import ( + UnprocessedBuffer, + sqlite_manager, + unprocessed_buffer_repo, + ) + + sqlite_manager._engine = None + sqlite_manager._session_factory = None + target = dt.datetime(2026, 5, 29, 6, tzinfo=dt.UTC) + + async def _write_under_utc() -> None: + engine = sqlite_manager.get_engine() + async with engine.begin() as conn: + await conn.run_sync(_SQLModel.metadata.create_all) + row = UnprocessedBuffer( + message_id="m_rev", + session_id="s_rev", + track="memorize", + sender_id="alice", + role="user", + timestamp=target, + content_items_json=_json.dumps([{"type": "text", "text": "x"}]), + text="x", + ) + await unprocessed_buffer_repo.replace("s_rev", "memorize", [row]) + await sqlite_manager.dispose_engine() + + asyncio.run(_write_under_utc()) + + # Switch display tz to Shanghai, reset DB engine cache, read back. + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + sqlite_manager._engine = None + sqlite_manager._session_factory = None + + async def _read_under_shanghai() -> None: + rows = await unprocessed_buffer_repo.list_for_track("s_rev", "memorize") + assert len(rows) == 1 + # storage is UTC — read back equals what we wrote + assert rows[0].timestamp == target + # display-tz render shifts wall-clock by +08:00 without changing instant + rendered = to_display_tz(rows[0].timestamp) + assert rendered.hour == 14 + assert rendered.utcoffset() == dt.timedelta(hours=8) + await sqlite_manager.dispose_engine() + + asyncio.run(_read_under_shanghai()) + + +def test_from_timestamp_ms_round_trip_through_ensure_utc() -> None: + """ms epoch → from_timestamp → ensure_utc must preserve the UTC instant. + + The ``/add`` request body declares timestamps as Unix epoch ms; this + test pins the conversion chain from wire format to storage. + """ + ms = 1748498400000 # 2026-05-29T06:00:00Z + via_helper = from_timestamp(ms) + via_utc = ensure_utc(via_helper) + assert via_utc is not None + assert via_utc.tzinfo is dt.UTC + assert int(via_utc.timestamp() * 1000) == ms + + +def test_sqlite_before_insert_event_normalises_aware_non_utc_to_utc( + monkeypatch: pytest.MonkeyPatch, + tmp_path, +) -> None: + """SQLAlchemy ``before_insert`` mapper event converts aware Shanghai → UTC. + + Pins the **write-side** half of the storage-UTC discipline. + ``SQLModel(table=True)`` classes skip Pydantic ``AfterValidator``, + so the :data:`UtcDatetime` annotation by itself is **inert** at + construction. The mapper event registered in + :mod:`everos.core.persistence.sqlite.base` is what guarantees the + on-disk SQLite text is UTC bytes, not display-tz bytes. + + Test path: write a row whose ``timestamp`` is aware Shanghai 14:00, + then probe SQLite with a raw SQL ``SELECT`` (bypassing the load hook + so we observe what's literally on disk). + """ + import asyncio + import json as _json + + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + + from zoneinfo import ZoneInfo + + from sqlalchemy import text as _sql_text + + from everos.core.persistence.sqlite import SQLModel as _SQLModel + from everos.core.persistence.sqlite import session_scope + from everos.infra.persistence.sqlite import ( + UnprocessedBuffer, + get_session_factory, + sqlite_manager, + ) + + sqlite_manager._engine = None + sqlite_manager._session_factory = None + aware_sh = dt.datetime(2026, 5, 29, 14, tzinfo=ZoneInfo("Asia/Shanghai")) + + async def _run() -> None: + engine = sqlite_manager.get_engine() + async with engine.begin() as conn: + await conn.run_sync(_SQLModel.metadata.create_all) + + row = UnprocessedBuffer( + message_id="m1", + session_id="s1", + track="memorize", + sender_id="alice", + role="user", + timestamp=aware_sh, + content_items_json=_json.dumps([{"type": "text", "text": "x"}]), + text="x", + ) + # Sanity: table=True SQLModel skips Pydantic validators, so the + # construction site does NOT normalise the timestamp. The event + # listener is what does it later, at write time. + assert row.timestamp == aware_sh, ( + "test invariant: construction did NOT normalise" + ) + + async with session_scope(get_session_factory()) as session: + session.add(row) + await session.commit() + + # Probe raw SQLite — bypass the load hook by issuing raw SQL. + async with engine.connect() as conn: + raw = ( + await conn.execute( + _sql_text( + "SELECT timestamp FROM unprocessed_buffer WHERE message_id='m1'" + ) + ) + ).scalar() + + # Aware Shanghai 14:00 = UTC 06:00. The on-disk bytes should be + # the UTC wall-clock, not Shanghai's. + assert "06:00:00" in raw, ( + f"on-disk should be UTC 06:00:00, not Shanghai 14:00:00; got {raw!r}" + ) + assert "14:00:00" not in raw + + await sqlite_manager.dispose_engine() + + asyncio.run(_run()) + + +# ── None-passthrough boundary (Gap #1) ─────────────────────────────────── + + +def test_ensure_utc_returns_none_for_none() -> None: + """``ensure_utc(None)`` is a no-op — supports nullable repo columns directly.""" + assert ensure_utc(None) is None + + +def test_to_display_tz_returns_none_for_none() -> None: + """``to_display_tz(None)`` is a no-op — supports nullable repo columns directly.""" + assert to_display_tz(None) is None + + +def test_ensure_utc_and_display_tz_chained_through_none() -> None: + """``to_display_tz(ensure_utc(None))`` short-circuits without ``AttributeError``. + + Pins the common shaper pattern against nullable storage columns like + ``MdChangeState.last_attempt_at``. + """ + assert to_display_tz(ensure_utc(None)) is None + + +# ── SQLite load-event hook cross-table (Gap #2) ────────────────────────── + + +def test_sqlite_load_hook_attaches_utc_on_all_base_table_subclasses( + monkeypatch: pytest.MonkeyPatch, + tmp_path, +) -> None: + """Every ``BaseTable`` subclass with a ``UtcDatetime`` column gets UTC on read. + + Pins the centralised defense: the SQLAlchemy ``load`` event hook + on ``BaseTable`` works for *every* subclass, not just the one we + happened to test. Inserts a row in each real table carrying a known + UTC instant, reads back via the repo / a plain ``select``, then + asserts ``tzinfo is UTC`` and value preservation across: + + - ``BaseTable.created_at`` / ``updated_at`` on every subclass + - per-table business datetime columns + (``timestamp`` / ``last_message_ts`` / ``last_memcell_ts`` / + ``first_seen_at`` / ``last_changed_at`` / ``last_attempt_at``). + """ + import asyncio + import json as _json + + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + + from sqlmodel import select + + from everos.core.persistence.sqlite import SQLModel as _SQLModel + from everos.core.persistence.sqlite import session_scope + from everos.infra.persistence.sqlite import ( + ConversationStatus, + MdChangeState, + Memcell, + UnprocessedBuffer, + get_session_factory, + sqlite_manager, + ) + + sqlite_manager._engine = None + sqlite_manager._session_factory = None + target = dt.datetime(2026, 5, 29, 6, tzinfo=dt.UTC) + + async def _run() -> None: + engine = sqlite_manager.get_engine() + async with engine.begin() as conn: + await conn.run_sync(_SQLModel.metadata.create_all) + + rows = [ + UnprocessedBuffer( + message_id="m1", + session_id="s1", + track="memorize", + sender_id="alice", + role="user", + timestamp=target, + content_items_json=_json.dumps([{"type": "text", "text": "x"}]), + text="x", + ), + Memcell( + memcell_id="mc1", + session_id="s1", + track="memorize", + raw_type="Conversation", + message_ids_json=_json.dumps(["m1"]), + sender_ids_json=_json.dumps(["alice"]), + payload_json="{}", + timestamp=target, + ), + ConversationStatus( + session_id="s1", + track="memorize", + last_message_ts=target, + last_memcell_ts=target, + ), + MdChangeState( + md_path="users/alice/episodes/episode-2026-05-29.md", + kind="episode", + change_type="added", + mtime=0.0, + lsn=1, + last_attempt_at=target, + ), + ] + + async with session_scope(get_session_factory()) as session: + for row in rows: + session.add(row) + await session.commit() + + async with session_scope(get_session_factory()) as session: + ub = (await session.execute(select(UnprocessedBuffer))).scalar_one() + mc = (await session.execute(select(Memcell))).scalar_one() + cs = (await session.execute(select(ConversationStatus))).scalar_one() + mcs = (await session.execute(select(MdChangeState))).scalar_one() + + # BaseTable's created_at / updated_at on every row + for row, name in [ + (ub, "UnprocessedBuffer"), + (mc, "Memcell"), + (cs, "ConversationStatus"), + (mcs, "MdChangeState"), + ]: + assert row.created_at.tzinfo is dt.UTC, ( + f"{name}.created_at not aware UTC; got {row.created_at.tzinfo!r}" + ) + assert row.updated_at.tzinfo is dt.UTC, ( + f"{name}.updated_at not aware UTC; got {row.updated_at.tzinfo!r}" + ) + + # Per-table business datetime columns + assert ub.timestamp.tzinfo is dt.UTC and ub.timestamp == target + assert mc.timestamp.tzinfo is dt.UTC and mc.timestamp == target + assert cs.last_message_ts is not None + assert cs.last_message_ts.tzinfo is dt.UTC + assert cs.last_message_ts == target + assert cs.last_memcell_ts is not None + assert cs.last_memcell_ts.tzinfo is dt.UTC + assert cs.last_memcell_ts == target + assert mcs.first_seen_at.tzinfo is dt.UTC + assert mcs.last_changed_at.tzinfo is dt.UTC + assert mcs.last_attempt_at is not None + assert mcs.last_attempt_at.tzinfo is dt.UTC + assert mcs.last_attempt_at == target + + await sqlite_manager.dispose_engine() + + asyncio.run(_run()) + + +# ── SQLAlchemy write-path coverage (TypeDecorator) ─────────────────────── +# +# The previous defense relied on mapper events (``before_insert`` / +# ``before_update``), which ONLY fire on the ORM unit-of-work flush +# path. Core SQL statements (``session.execute(insert(...).values())``, +# ``update(...).values()``, ``delete(...)``, bulk operations) bypass +# them — and md_change_state_repo uses Core statements pervasively. +# +# The fix is :class:`UtcDateTimeColumn`, a SQLAlchemy ``TypeDecorator`` +# whose ``process_bind_param`` runs on **every** bind parameter +# regardless of the calling API. These tests pin every write path +# against the storage-UTC contract. + + +def _build_engine_for_test(monkeypatch, tmp_path, tz: str = "Asia/Shanghai"): + """Common setup: tmp memory root + tz + fresh engine.""" + monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path)) + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", tz) + load_settings.cache_clear() + dt_module._display_tz.cache_clear() + + from everos.infra.persistence.sqlite import sqlite_manager + + sqlite_manager._engine = None + sqlite_manager._session_factory = None + return sqlite_manager + + +async def _create_schema(sqlite_manager) -> None: + from everos.core.persistence.sqlite import SQLModel as _SQLModel + + engine = sqlite_manager.get_engine() + async with engine.begin() as conn: + await conn.run_sync(_SQLModel.metadata.create_all) + + +async def _probe_raw_text(sqlite_manager, sql: str) -> str | None: + """Read a single column via raw SQL — bypasses ORM hydrate hooks.""" + from sqlalchemy import text as _sql_text + + engine = sqlite_manager.get_engine() + async with engine.connect() as conn: + return (await conn.execute(_sql_text(sql))).scalar() + + +def test_typedec_covers_orm_session_add( + monkeypatch: pytest.MonkeyPatch, tmp_path +) -> None: + """ORM ``session.add`` write path: aware Shanghai → UTC bytes on disk.""" + import asyncio + from zoneinfo import ZoneInfo + + from everos.core.persistence.sqlite import session_scope + from everos.infra.persistence.sqlite import MdChangeState, get_session_factory + + sm = _build_engine_for_test(monkeypatch, tmp_path) + aware_sh = dt.datetime(2026, 5, 29, 14, tzinfo=ZoneInfo("Asia/Shanghai")) + + async def _run() -> None: + await _create_schema(sm) + async with session_scope(get_session_factory()) as s: + s.add( + MdChangeState( + md_path="p_orm", + kind="ep", + change_type="added", + mtime=0.0, + lsn=1, + last_attempt_at=aware_sh, + ) + ) + await s.commit() + raw = await _probe_raw_text( + sm, + "SELECT last_attempt_at FROM md_change_state WHERE md_path='p_orm'", + ) + assert raw and "06:00" in raw and "14:00" not in raw + await sm.dispose_engine() + + asyncio.run(_run()) + + +def test_typedec_covers_core_insert_values( + monkeypatch: pytest.MonkeyPatch, tmp_path +) -> None: + """Core ``insert(Model).values(...)`` bypasses ORM but TypeDecorator catches it.""" + import asyncio + from zoneinfo import ZoneInfo + + from sqlalchemy import insert + + from everos.core.persistence.sqlite import session_scope + from everos.infra.persistence.sqlite import MdChangeState, get_session_factory + + sm = _build_engine_for_test(monkeypatch, tmp_path) + aware_sh = dt.datetime(2026, 5, 29, 14, tzinfo=ZoneInfo("Asia/Shanghai")) + + async def _run() -> None: + await _create_schema(sm) + async with session_scope(get_session_factory()) as s: + await s.execute( + insert(MdChangeState).values( + md_path="p_core_ins", + kind="ep", + change_type="added", + mtime=0.0, + lsn=2, + last_attempt_at=aware_sh, + ) + ) + await s.commit() + raw = await _probe_raw_text( + sm, + "SELECT last_attempt_at FROM md_change_state WHERE md_path='p_core_ins'", + ) + assert raw and "06:00" in raw and "14:00" not in raw + await sm.dispose_engine() + + asyncio.run(_run()) + + +def test_typedec_covers_core_update_values( + monkeypatch: pytest.MonkeyPatch, tmp_path +) -> None: + """Core ``update(Model).where(...).values(...)`` — the path + md_change_state_repo uses pervasively. TypeDecorator must catch it. + """ + import asyncio + from zoneinfo import ZoneInfo + + from sqlalchemy import update + + from everos.core.persistence.sqlite import session_scope + from everos.infra.persistence.sqlite import MdChangeState, get_session_factory + + sm = _build_engine_for_test(monkeypatch, tmp_path) + aware_sh = dt.datetime(2026, 5, 29, 14, tzinfo=ZoneInfo("Asia/Shanghai")) + + async def _run() -> None: + await _create_schema(sm) + # Seed a row first + async with session_scope(get_session_factory()) as s: + s.add( + MdChangeState( + md_path="p_upd", + kind="ep", + change_type="added", + mtime=0.0, + lsn=3, + ) + ) + await s.commit() + # Now Core update with aware non-UTC datetime + async with session_scope(get_session_factory()) as s: + await s.execute( + update(MdChangeState) + .where(MdChangeState.md_path == "p_upd") + .values(last_attempt_at=aware_sh) + ) + await s.commit() + raw = await _probe_raw_text( + sm, + "SELECT last_attempt_at FROM md_change_state WHERE md_path='p_upd'", + ) + assert raw and "06:00" in raw and "14:00" not in raw + await sm.dispose_engine() + + asyncio.run(_run()) + + +def test_typedec_aware_utc_input_is_idempotent( + monkeypatch: pytest.MonkeyPatch, tmp_path +) -> None: + """Gap a: aware UTC input round-trips unchanged.""" + import asyncio + + from everos.core.persistence.sqlite import session_scope + from everos.infra.persistence.sqlite import MdChangeState, get_session_factory + + sm = _build_engine_for_test(monkeypatch, tmp_path) + aware_utc = dt.datetime(2026, 5, 29, 6, tzinfo=dt.UTC) + + async def _run() -> None: + await _create_schema(sm) + async with session_scope(get_session_factory()) as s: + s.add( + MdChangeState( + md_path="p_utc", + kind="ep", + change_type="added", + mtime=0.0, + lsn=4, + last_attempt_at=aware_utc, + ) + ) + await s.commit() + # Raw bytes + raw = await _probe_raw_text( + sm, + "SELECT last_attempt_at FROM md_change_state WHERE md_path='p_utc'", + ) + assert raw and "06:00" in raw + # Read-back + from sqlmodel import select + + async with session_scope(get_session_factory()) as s: + row = ( + await s.execute( + select(MdChangeState).where(MdChangeState.md_path == "p_utc") + ) + ).scalar_one() + assert row.last_attempt_at == aware_utc + await sm.dispose_engine() + + asyncio.run(_run()) + + +def test_typedec_naive_input_treated_as_utc( + monkeypatch: pytest.MonkeyPatch, tmp_path +) -> None: + """Gap b: naive datetime input → assumed already-UTC, NOT display-tz. + + Even with display tz = Shanghai, a naive 14:00 input is stored as + 14:00 UTC (not interpreted as Shanghai 14:00 = UTC 06:00). This + pins the project's "storage convention: naive = UTC" rule. + """ + import asyncio + + from everos.core.persistence.sqlite import session_scope + from everos.infra.persistence.sqlite import MdChangeState, get_session_factory + + sm = _build_engine_for_test(monkeypatch, tmp_path) + naive = dt.datetime(2026, 5, 29, 14) # no tzinfo + + async def _run() -> None: + await _create_schema(sm) + async with session_scope(get_session_factory()) as s: + s.add( + MdChangeState( + md_path="p_naive", + kind="ep", + change_type="added", + mtime=0.0, + lsn=5, + last_attempt_at=naive, + ) + ) + await s.commit() + raw = await _probe_raw_text( + sm, + "SELECT last_attempt_at FROM md_change_state WHERE md_path='p_naive'", + ) + # naive 14:00 → stored 14:00 (assumed UTC), NOT 06:00 (which would + # mean we re-interpreted naive as Shanghai-local) + assert raw and "14:00" in raw, f"naive should land as 14:00 UTC; got {raw!r}" + await sm.dispose_engine() + + asyncio.run(_run()) + + +def test_typedec_microsecond_precision_preserved( + monkeypatch: pytest.MonkeyPatch, tmp_path +) -> None: + """Gap d: microsecond field survives the round trip.""" + import asyncio + + from sqlmodel import select + + from everos.core.persistence.sqlite import session_scope + from everos.infra.persistence.sqlite import MdChangeState, get_session_factory + + sm = _build_engine_for_test(monkeypatch, tmp_path, tz="UTC") + with_micros = dt.datetime(2026, 5, 29, 6, 0, 0, 123_456, tzinfo=dt.UTC) + + async def _run() -> None: + await _create_schema(sm) + async with session_scope(get_session_factory()) as s: + s.add( + MdChangeState( + md_path="p_us", + kind="ep", + change_type="added", + mtime=0.0, + lsn=6, + last_attempt_at=with_micros, + ) + ) + await s.commit() + async with session_scope(get_session_factory()) as s: + row = ( + await s.execute( + select(MdChangeState).where(MdChangeState.md_path == "p_us") + ) + ).scalar_one() + assert row.last_attempt_at.microsecond == 123_456 + assert row.last_attempt_at == with_micros + await sm.dispose_engine() + + asyncio.run(_run()) + + +def test_typedec_extreme_dates_round_trip( + monkeypatch: pytest.MonkeyPatch, tmp_path +) -> None: + """Gap f: 1970 and 2099 epoch endpoints round-trip without overflow.""" + import asyncio + + from sqlmodel import select + + from everos.core.persistence.sqlite import session_scope + from everos.infra.persistence.sqlite import MdChangeState, get_session_factory + + sm = _build_engine_for_test(monkeypatch, tmp_path, tz="UTC") + epoch_start = dt.datetime(1970, 1, 1, 0, 0, 0, tzinfo=dt.UTC) + far_future = dt.datetime(2099, 12, 31, 23, 59, 59, tzinfo=dt.UTC) + + async def _run() -> None: + await _create_schema(sm) + async with session_scope(get_session_factory()) as s: + s.add_all( + [ + MdChangeState( + md_path="p_1970", + kind="ep", + change_type="added", + mtime=0.0, + lsn=7, + last_attempt_at=epoch_start, + ), + MdChangeState( + md_path="p_2099", + kind="ep", + change_type="added", + mtime=0.0, + lsn=8, + last_attempt_at=far_future, + ), + ] + ) + await s.commit() + async with session_scope(get_session_factory()) as s: + r1 = ( + await s.execute( + select(MdChangeState).where(MdChangeState.md_path == "p_1970") + ) + ).scalar_one() + r2 = ( + await s.execute( + select(MdChangeState).where(MdChangeState.md_path == "p_2099") + ) + ).scalar_one() + assert r1.last_attempt_at == epoch_start + assert r2.last_attempt_at == far_future + await sm.dispose_engine() + + asyncio.run(_run()) + + +def test_typedec_dst_boundary_round_trip( + monkeypatch: pytest.MonkeyPatch, tmp_path +) -> None: + """Gap g: a Shanghai-input instant that straddles US DST boundary preserves UTC. + + A 14:00 +08:00 on 2026-03-08 is the same UTC instant whether read + in pre-DST or post-DST US tz. The TypeDecorator must not introduce + DST artefacts when astimezone-ing. + """ + import asyncio + from zoneinfo import ZoneInfo + + from sqlmodel import select + + from everos.core.persistence.sqlite import session_scope + from everos.infra.persistence.sqlite import MdChangeState, get_session_factory + + sm = _build_engine_for_test(monkeypatch, tmp_path, tz="Asia/Shanghai") + # US DST starts 2026-03-08 (2am local → 3am local). Pick an instant + # straddling the boundary in NY tz. + pre_dst = dt.datetime(2026, 3, 8, 6, 30, tzinfo=ZoneInfo("America/New_York")) + post_dst = dt.datetime(2026, 3, 8, 7, 30, tzinfo=ZoneInfo("America/New_York")) + + async def _run() -> None: + await _create_schema(sm) + async with session_scope(get_session_factory()) as s: + s.add_all( + [ + MdChangeState( + md_path="p_pre", + kind="ep", + change_type="added", + mtime=0.0, + lsn=9, + last_attempt_at=pre_dst, + ), + MdChangeState( + md_path="p_post", + kind="ep", + change_type="added", + mtime=0.0, + lsn=10, + last_attempt_at=post_dst, + ), + ] + ) + await s.commit() + async with session_scope(get_session_factory()) as s: + r1 = ( + await s.execute( + select(MdChangeState).where(MdChangeState.md_path == "p_pre") + ) + ).scalar_one() + r2 = ( + await s.execute( + select(MdChangeState).where(MdChangeState.md_path == "p_post") + ) + ).scalar_one() + # Both must round-trip exactly. The UTC instant is invariant under + # any tz transformation including DST shifts. + assert r1.last_attempt_at == pre_dst + assert r2.last_attempt_at == post_dst + await sm.dispose_engine() + + asyncio.run(_run()) + + +def test_typedec_raw_sql_bypasses_typedecorator_documented_limit( + monkeypatch: pytest.MonkeyPatch, tmp_path +) -> None: + """Gap h: pin the **known limit**: pure raw SQL with literal strings + bypasses the column type entirely. If a future contributor writes + ``text("INSERT ... VALUES ('14:00:00')")``, they get the bytes they + typed — no normalisation. + + This test documents the limit so it does not regress to silent + "we thought TypeDecorator covered everything". The real defense + against raw SQL is the ``check_datetime_discipline.py`` scanner. + """ + import asyncio + + from sqlalchemy import text as _sql_text + + sm = _build_engine_for_test(monkeypatch, tmp_path, tz="Asia/Shanghai") + + async def _run() -> None: + await _create_schema(sm) + # Raw SQL with a literal Shanghai 14:00 string — no bind param + # goes through TypeDecorator, so the literal lands as-is. + engine = sm.get_engine() + async with engine.begin() as conn: + await conn.execute( + _sql_text( + "INSERT INTO md_change_state " + "(md_path, kind, change_type, mtime, " + "first_seen_at, last_changed_at, lsn, status, " + "last_attempt_at, retry_count, " + "created_at, updated_at) " + "VALUES " + "('p_raw', 'ep', 'added', 0.0, " + "'2026-05-29 14:00:00', '2026-05-29 14:00:00', 99, " + "'pending', '2026-05-29 14:00:00', 0, " + "'2026-05-29 14:00:00', '2026-05-29 14:00:00')" + ) + ) + + raw = await _probe_raw_text( + sm, + "SELECT last_attempt_at FROM md_change_state WHERE md_path='p_raw'", + ) + # Confirms the LIMIT: raw literal stored as-is, no normalisation. + assert "14:00" in raw, ( + "Raw SQL with literal datetime string is NOT normalised by " + "TypeDecorator. This is a documented limit; " + "scripts/check_datetime_discipline.py forbids new bypasses." + ) + await sm.dispose_engine() + + asyncio.run(_run()) + + +# ── LanceDB write-path coverage ────────────────────────────────────────── +# +# LanceDB has fewer write APIs: ``table.add`` (the main one), +# ``table.merge_insert``, and ``table.update``. All of them ultimately go +# through PyArrow which uses the Arrow schema to coerce input. The +# ``BaseLanceTable.to_arrow_schema`` rewrite that stamps ``tz=UTC`` is +# therefore active on every write path. Pin this explicitly. + + +def test_lance_table_add_normalises_aware_non_utc() -> None: + """LanceDB ``table.add`` with aware Shanghai input → aware UTC on disk.""" + import asyncio + import tempfile + from zoneinfo import ZoneInfo + + import lancedb + + from everos.infra.persistence.lancedb.tables.episode import Episode + + aware_sh = dt.datetime(2026, 5, 29, 14, tzinfo=ZoneInfo("Asia/Shanghai")) + + async def _run() -> None: + conn = await lancedb.connect_async(tempfile.mkdtemp()) + table = await conn.create_table("ep", schema=Episode) + row = Episode( + id="alice_ep_1", + entry_id="ep_1", + owner_id="alice", + owner_type="user", + session_id="s1", + timestamp=aware_sh, + parent_id="mc_1", + sender_ids=["alice"], + episode="x", + episode_tokens="x", + md_path="users/alice/episodes/x.md", + content_sha256="abc", + vector=[0.0] * 1024, + ) + await table.add([row]) + rows = await table.query().to_list() + assert rows[0]["timestamp"].tzinfo is not None + # 14:00 +08:00 = 06:00 UTC + assert rows[0]["timestamp"].hour == 6 + assert rows[0]["timestamp"] == aware_sh + + asyncio.run(_run()) + + +def test_lance_table_naive_input_is_assumed_utc() -> None: + """LanceDB naive datetime → PyArrow assumes UTC (matches project convention).""" + import asyncio + import tempfile + + import lancedb + + from everos.infra.persistence.lancedb.tables.episode import Episode + + naive = dt.datetime(2026, 5, 29, 14) # naive — no tz + + async def _run() -> None: + conn = await lancedb.connect_async(tempfile.mkdtemp()) + table = await conn.create_table("ep", schema=Episode) + row = Episode( + id="x_1", + entry_id="ep_1", + owner_id="x", + owner_type="user", + session_id="s", + timestamp=naive, + parent_id="mc", + sender_ids=[], + episode="x", + episode_tokens="x", + md_path="x", + content_sha256="x", + vector=[0.0] * 1024, + ) + await table.add([row]) + rows = await table.query().to_list() + # naive 14:00 → assumed UTC 14:00 on disk → read back aware UTC 14:00 + assert rows[0]["timestamp"].hour == 14 + assert rows[0]["timestamp"].tzinfo is not None + assert rows[0]["timestamp"].utcoffset() == dt.timedelta(0) + + asyncio.run(_run()) diff --git a/methods/EverCore/src/core/middleware/__init__.py b/tests/unit/test_config/__init__.py similarity index 100% rename from methods/EverCore/src/core/middleware/__init__.py rename to tests/unit/test_config/__init__.py diff --git a/tests/unit/test_config/test_settings.py b/tests/unit/test_config/test_settings.py new file mode 100644 index 00000000..a8fc5ca1 --- /dev/null +++ b/tests/unit/test_config/test_settings.py @@ -0,0 +1,173 @@ +"""Unit tests for Settings loading.""" + +from __future__ import annotations + +from pathlib import Path + +import pytest + +from everos.config import Settings, load_settings + + +@pytest.fixture(autouse=True) +def _isolate_env(monkeypatch: pytest.MonkeyPatch) -> None: + """Strip any EVEROS_* env vars from the host so tests are deterministic.""" + for key in list(__import__("os").environ): + if key.startswith("EVEROS_"): + monkeypatch.delenv(key, raising=False) + load_settings.cache_clear() + + +def test_load_settings_defaults_from_toml() -> None: + s = load_settings() + # Values straight out of config/default.toml + assert s.memory.root == Path("~/.everos") + assert s.memory.timezone == "UTC" + assert s.sqlite.journal_mode == "WAL" + assert s.sqlite.synchronous == "NORMAL" + assert s.sqlite.foreign_keys is True + assert s.sqlite.temp_store == "MEMORY" + assert s.sqlite.busy_timeout_ms == 5000 + assert s.sqlite.journal_size_limit_bytes == 64 * 1024 * 1024 + assert s.sqlite.cache_size_kb == 2048 + assert s.lancedb.read_consistency_seconds is None + + +def test_env_overrides_toml(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("EVEROS_SQLITE__BUSY_TIMEOUT_MS", "10000") + monkeypatch.setenv("EVEROS_SQLITE__JOURNAL_MODE", "DELETE") + s = Settings() + assert s.sqlite.busy_timeout_ms == 10000 + assert s.sqlite.journal_mode == "DELETE" + # Untouched values stay at TOML defaults. + assert s.sqlite.synchronous == "NORMAL" + + +def test_init_args_override_env(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("EVEROS_SQLITE__BUSY_TIMEOUT_MS", "10000") + from everos.config.settings import SqliteSettings + + s = Settings(sqlite=SqliteSettings(busy_timeout_ms=99999)) + assert s.sqlite.busy_timeout_ms == 99999 # init beats env + + +def test_invalid_journal_mode_rejected() -> None: + from pydantic import ValidationError + + with pytest.raises(ValidationError): + Settings.model_validate({"sqlite": {"journal_mode": "BOGUS"}}) + + +def test_negative_busy_timeout_rejected() -> None: + from pydantic import ValidationError + + with pytest.raises(ValidationError): + Settings.model_validate({"sqlite": {"busy_timeout_ms": -1}}) + + +def test_lancedb_read_consistency_optional_float() -> None: + s = Settings.model_validate({"lancedb": {"read_consistency_seconds": 5.0}}) + assert s.lancedb.read_consistency_seconds == 5.0 + s2 = Settings.model_validate({"lancedb": {"read_consistency_seconds": None}}) + assert s2.lancedb.read_consistency_seconds is None + + +def test_memory_timezone_overridable_via_env(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("EVEROS_MEMORY__TIMEZONE", "Asia/Shanghai") + s = Settings() + assert s.memory.timezone == "Asia/Shanghai" + + +def test_memory_timezone_invalid_rejected() -> None: + from pydantic import ValidationError + + with pytest.raises(ValidationError, match="invalid timezone"): + Settings.model_validate({"memory": {"timezone": "Not/A/Real_Zone"}}) + + +def test_load_settings_is_cached() -> None: + """Repeated calls return the same Settings object until cache_clear.""" + a = load_settings() + b = load_settings() + assert a is b + load_settings.cache_clear() + c = load_settings() + assert c is not a + + +def test_embedding_rerank_defaults() -> None: + """Embedding / rerank ship with runtime knobs but no model credentials.""" + # ``_isolate_env`` already strips shell env; ``_env_file=None`` further + # prevents a developer's ``.env`` (which typically sets MODEL / API_KEY / + # BASE_URL for live runs) from leaking into this default-state check. + s = Settings(_env_file=None) # type: ignore[call-arg] + # Credentials must be set explicitly (no default). + assert s.embedding.model is None + assert s.embedding.api_key is None + assert s.embedding.base_url is None + # Runtime knobs come from default.toml. + assert s.embedding.timeout_seconds == 30.0 + assert s.embedding.max_retries == 3 + assert s.embedding.batch_size == 10 + assert s.embedding.max_concurrent == 5 + # Rerank mirrors the shape. + assert s.rerank.model is None + assert s.rerank.timeout_seconds == 30.0 + assert s.rerank.batch_size == 10 + + +def test_embedding_env_overrides(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("EVEROS_EMBEDDING__MODEL", "intfloat/e5-large-v2") + monkeypatch.setenv("EVEROS_EMBEDDING__BASE_URL", "http://localhost:8000/v1") + monkeypatch.setenv("EVEROS_EMBEDDING__BATCH_SIZE", "32") + s = Settings() + assert s.embedding.model == "intfloat/e5-large-v2" + assert s.embedding.base_url == "http://localhost:8000/v1" + assert s.embedding.batch_size == 32 + + +def test_rerank_env_overrides(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("EVEROS_RERANK__MODEL", "BAAI/bge-reranker-v2-m3") + monkeypatch.setenv("EVEROS_RERANK__MAX_CONCURRENT", "8") + s = Settings() + assert s.rerank.model == "BAAI/bge-reranker-v2-m3" + assert s.rerank.max_concurrent == 8 + + +def test_user_toml_override_via_env_path( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """``EVEROS_CONFIG_FILE`` points pydantic-settings at a user toml.""" + user_toml = tmp_path / "config.toml" + user_toml.write_text( + '[sqlite]\nbusy_timeout_ms = 7777\n[memory]\ntimezone = "Asia/Tokyo"\n', + encoding="utf-8", + ) + monkeypatch.setenv("EVEROS_CONFIG_FILE", str(user_toml)) + s = Settings() + assert s.sqlite.busy_timeout_ms == 7777 + assert s.memory.timezone == "Asia/Tokyo" + # Values not touched by the user toml still come from the shipped default. + assert s.sqlite.journal_mode == "WAL" + + +def test_user_toml_loses_to_env( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """env vars beat the user-level toml.""" + user_toml = tmp_path / "config.toml" + user_toml.write_text("[sqlite]\nbusy_timeout_ms = 7777\n", encoding="utf-8") + monkeypatch.setenv("EVEROS_CONFIG_FILE", str(user_toml)) + monkeypatch.setenv("EVEROS_SQLITE__BUSY_TIMEOUT_MS", "9999") + s = Settings() + assert s.sqlite.busy_timeout_ms == 9999 + + +def test_user_toml_missing_file_is_skipped( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """A non-existent user toml path is silently skipped, not an error.""" + monkeypatch.setenv("EVEROS_CONFIG_FILE", str(tmp_path / "nope.toml")) + s = Settings() + # Falls back to shipped defaults. + assert s.sqlite.busy_timeout_ms == 5000 diff --git a/methods/EverCore/src/core/nlp/__init__.py b/tests/unit/test_core/__init__.py similarity index 100% rename from methods/EverCore/src/core/nlp/__init__.py rename to tests/unit/test_core/__init__.py diff --git a/methods/EverCore/src/core/observation/__init__.py b/tests/unit/test_core/test_lifespan/__init__.py similarity index 100% rename from methods/EverCore/src/core/observation/__init__.py rename to tests/unit/test_core/test_lifespan/__init__.py diff --git a/tests/unit/test_core/test_lifespan/test_factory.py b/tests/unit/test_core/test_lifespan/test_factory.py new file mode 100644 index 00000000..a0bb9c23 --- /dev/null +++ b/tests/unit/test_core/test_lifespan/test_factory.py @@ -0,0 +1,88 @@ +"""``build_lifespan`` — provider ordering, state storage, shutdown errors.""" + +from __future__ import annotations + +from fastapi import FastAPI + +from everos.core.lifespan import LifespanProvider +from everos.core.lifespan.factory import build_lifespan + + +class _RecordingProvider(LifespanProvider): + """Provider that records the order in which startup/shutdown ran.""" + + def __init__( + self, + name: str, + order: int, + log: list[str], + *, + returns: object | None = None, + shutdown_raises: bool = False, + ) -> None: + super().__init__(name=name, order=order) + self._log = log + self._returns = returns + self._shutdown_raises = shutdown_raises + + async def startup(self, app: FastAPI) -> object | None: + self._log.append(f"start:{self.name}") + return self._returns + + async def shutdown(self, app: FastAPI) -> None: + self._log.append(f"stop:{self.name}") + if self._shutdown_raises: + raise RuntimeError(f"{self.name} shutdown boom") + + +async def test_startup_runs_in_order_ascending() -> None: + log: list[str] = [] + p1 = _RecordingProvider("a", order=2, log=log) + p2 = _RecordingProvider("b", order=1, log=log) + p3 = _RecordingProvider("c", order=3, log=log) + + app = FastAPI() + async with build_lifespan([p1, p2, p3])(app): + pass + assert log[:3] == ["start:b", "start:a", "start:c"] + + +async def test_shutdown_runs_in_reverse_order() -> None: + log: list[str] = [] + p1 = _RecordingProvider("a", order=1, log=log) + p2 = _RecordingProvider("b", order=2, log=log) + + app = FastAPI() + async with build_lifespan([p1, p2])(app): + pass + # shutdown phase: reverse of startup + assert log[2:] == ["stop:b", "stop:a"] + + +async def test_non_none_startup_result_stored_in_state() -> None: + sentinel = object() + p = _RecordingProvider("x", order=1, log=[], returns=sentinel) + app = FastAPI() + async with build_lifespan([p])(app): + assert app.state.lifespan_data["x"] is sentinel + + +async def test_none_startup_result_not_stored() -> None: + p = _RecordingProvider("nullone", order=1, log=[], returns=None) + app = FastAPI() + async with build_lifespan([p])(app): + assert "nullone" not in app.state.lifespan_data + + +async def test_shutdown_exception_swallowed_and_logged() -> None: + """Failed shutdown logs but doesn't break sibling shutdown.""" + log: list[str] = [] + p1 = _RecordingProvider("a", order=1, log=log) + p2 = _RecordingProvider("b", order=2, log=log, shutdown_raises=True) + + app = FastAPI() + async with build_lifespan([p1, p2])(app): + pass + # Even though "b" raised, "a" still shut down. + assert log[-1] == "stop:a" + assert "stop:b" in log # b's shutdown ran (and raised, but swallowed) diff --git a/tests/unit/test_core/test_lifespan/test_metrics_lifespan.py b/tests/unit/test_core/test_lifespan/test_metrics_lifespan.py new file mode 100644 index 00000000..b06941f6 --- /dev/null +++ b/tests/unit/test_core/test_lifespan/test_metrics_lifespan.py @@ -0,0 +1,35 @@ +"""``MetricsLifespanProvider`` — startup returns registry, shutdown logs.""" + +from __future__ import annotations + +from fastapi import FastAPI +from prometheus_client import CollectorRegistry + +from everos.core.lifespan.metrics_lifespan import MetricsLifespanProvider +from everos.core.observability.metrics import ( + reset_metrics_registry, + set_metrics_registry, +) + + +async def test_startup_returns_registry() -> None: + fresh = CollectorRegistry() + set_metrics_registry(fresh) + try: + p = MetricsLifespanProvider() + result = await p.startup(FastAPI()) + assert result is fresh + finally: + reset_metrics_registry() + + +async def test_shutdown_is_noop() -> None: + # Smoke test — must not raise. + p = MetricsLifespanProvider() + await p.shutdown(FastAPI()) + + +def test_provider_metadata() -> None: + p = MetricsLifespanProvider(order=42) + assert p.name == "metrics" + assert p.order == 42 diff --git a/methods/EverCore/src/core/oxm/__init__.py b/tests/unit/test_core/test_middleware/__init__.py old mode 100755 new mode 100644 similarity index 100% rename from methods/EverCore/src/core/oxm/__init__.py rename to tests/unit/test_core/test_middleware/__init__.py diff --git a/tests/unit/test_core/test_middleware/test_global_exception.py b/tests/unit/test_core/test_middleware/test_global_exception.py new file mode 100644 index 00000000..1c48f7df --- /dev/null +++ b/tests/unit/test_core/test_middleware/test_global_exception.py @@ -0,0 +1,106 @@ +"""``global_exception_handler`` — uniform error envelope per v1 API §1. + +We mount the handler on a minimal FastAPI app with three error-emitting +routes (HTTPException 4xx / 5xx, RequestValidationError, raw exception) +and assert the envelope shape + status code each route produces. +""" + +from __future__ import annotations + +from collections.abc import AsyncIterator + +import pytest +from fastapi import FastAPI, HTTPException +from fastapi.exceptions import RequestValidationError +from httpx import ASGITransport, AsyncClient +from pydantic import BaseModel + +from everos.core.middleware.global_exception import global_exception_handler + + +class _Body(BaseModel): + name: str + + +def _build_app() -> FastAPI: + app = FastAPI() + app.add_exception_handler(HTTPException, global_exception_handler) + app.add_exception_handler(RequestValidationError, global_exception_handler) + app.add_exception_handler(Exception, global_exception_handler) + + @app.get("/raise-400") + async def raise_400() -> None: + raise HTTPException(status_code=400, detail="bad input") + + @app.get("/raise-500-http") + async def raise_500_http() -> None: + raise HTTPException(status_code=503, detail="upstream dead") + + @app.get("/boom") + async def boom() -> None: + raise RuntimeError("hidden internals") + + @app.post("/validate") + async def validate(_body: _Body) -> dict[str, str]: + return {"ok": "yes"} + + return app + + +@pytest.fixture +async def client() -> AsyncIterator[AsyncClient]: + app = _build_app() + # raise_app_exceptions=False — let the registered handler convert the + # RuntimeError into a 500 response instead of re-raising into the test. + transport = ASGITransport(app=app, raise_app_exceptions=False) + async with AsyncClient(transport=transport, base_url="http://test") as c: + yield c + + +def _assert_envelope(body: dict[str, object], *, code: str, path: str) -> None: + """Wiki §1 envelope: ``{request_id, error: {code, message, timestamp, path}}``.""" + assert isinstance(body["request_id"], str) and body["request_id"] + error = body["error"] + assert isinstance(error, dict) + assert error["code"] == code + assert isinstance(error["message"], str) and error["message"] + assert isinstance(error["timestamp"], str) and "T" in error["timestamp"] + assert error["path"] == path + + +async def test_http_exception_4xx(client: AsyncClient) -> None: + resp = await client.get("/raise-400") + assert resp.status_code == 400 + body = resp.json() + _assert_envelope(body, code="HTTP_ERROR", path="/raise-400") + assert body["error"]["message"] == "bad input" + + +async def test_http_exception_5xx_uses_system_error(client: AsyncClient) -> None: + """5xx routed through HTTPException still produces SYSTEM_ERROR + generic msg.""" + resp = await client.get("/raise-500-http") + assert resp.status_code == 503 + body = resp.json() + _assert_envelope(body, code="SYSTEM_ERROR", path="/raise-500-http") + # Internal detail "upstream dead" is suppressed in 5xx envelopes. + assert body["error"]["message"] == "Internal server error" + + +async def test_unhandled_exception_5xx(client: AsyncClient) -> None: + """RuntimeError → 500 with generic ``SYSTEM_ERROR`` envelope; details hidden.""" + resp = await client.get("/boom") + assert resp.status_code == 500 + body = resp.json() + _assert_envelope(body, code="SYSTEM_ERROR", path="/boom") + assert body["error"]["message"] == "Internal server error" + # Must not leak the internal exception message. + assert "hidden internals" not in resp.text + + +async def test_validation_error_returns_422(client: AsyncClient) -> None: + resp = await client.post("/validate", json={}) # missing ``name`` + assert resp.status_code == 422 + body = resp.json() + _assert_envelope(body, code="HTTP_ERROR", path="/validate") + # First-error message includes the offending field somewhere. + assert "name" in body["error"]["message"].lower() diff --git a/tests/unit/test_core/test_middleware/test_profile.py b/tests/unit/test_core/test_middleware/test_profile.py new file mode 100644 index 00000000..67172eae --- /dev/null +++ b/tests/unit/test_core/test_middleware/test_profile.py @@ -0,0 +1,148 @@ +"""``ProfileMiddleware`` — env gating, query-param gating, pyinstrument output.""" + +from __future__ import annotations + +from collections.abc import AsyncIterator + +import pytest +from fastapi import FastAPI +from httpx import ASGITransport, AsyncClient + +from everos.core.middleware.profile import ProfileMiddleware, _profiling_enabled + + +def _build_app() -> FastAPI: + app = FastAPI() + app.add_middleware(ProfileMiddleware) + + @app.get("/hello") + async def hello() -> dict[str, str]: + return {"ok": "yes"} + + return app + + +@pytest.fixture +def disable_env(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.delenv("PROFILING_ENABLED", raising=False) + monkeypatch.delenv("PROFILING", raising=False) + + +@pytest.fixture +def enable_env(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("PROFILING_ENABLED", "true") + + +def test_profiling_enabled_truthy_variants(monkeypatch: pytest.MonkeyPatch) -> None: + for v in ("1", "true", "TRUE", "yes"): + monkeypatch.setenv("PROFILING_ENABLED", v) + assert _profiling_enabled() is True + + +def test_profiling_enabled_falsy_variants(monkeypatch: pytest.MonkeyPatch) -> None: + for v in ("0", "false", "no", "", "anything-else"): + monkeypatch.setenv("PROFILING_ENABLED", v) + assert _profiling_enabled() is False + + +def test_profiling_falls_back_to_legacy_env(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.delenv("PROFILING_ENABLED", raising=False) + monkeypatch.setenv("PROFILING", "yes") + assert _profiling_enabled() is True + + +@pytest.fixture +async def disabled_client(disable_env: None) -> AsyncIterator[AsyncClient]: + app = _build_app() + async with AsyncClient( + transport=ASGITransport(app=app), base_url="http://test" + ) as c: + yield c + + +@pytest.fixture +async def enabled_client(enable_env: None) -> AsyncIterator[AsyncClient]: + app = _build_app() + async with AsyncClient( + transport=ASGITransport(app=app), base_url="http://test" + ) as c: + yield c + + +async def test_disabled_passthrough(disabled_client: AsyncClient) -> None: + """When profiling is disabled, ``?profile=true`` is ignored — JSON returned.""" + resp = await disabled_client.get("/hello?profile=true") + assert resp.status_code == 200 + assert resp.json() == {"ok": "yes"} + + +async def test_enabled_without_query_passthrough(enabled_client: AsyncClient) -> None: + """Enabled middleware but request without ``?profile=true`` → normal response.""" + resp = await enabled_client.get("/hello") + assert resp.status_code == 200 + assert resp.json() == {"ok": "yes"} + + +async def test_enabled_with_query_returns_html(enabled_client: AsyncClient) -> None: + """With ``?profile=true`` and pyinstrument available, response is HTML.""" + try: + import pyinstrument # noqa: F401 + except ImportError: + pytest.skip("pyinstrument not installed in this env") + + resp = await enabled_client.get("/hello?profile=true") + assert resp.status_code == 200 + assert "text/html" in resp.headers.get("content-type", "") + # Pyinstrument output contains the word "pyinstrument" in its template. + assert "pyinstrument" in resp.text.lower() or " None: + """An exception inside the wrapped handler is logged but still produces HTML.""" + try: + import pyinstrument # noqa: F401 + except ImportError: + pytest.skip("pyinstrument not installed in this env") + + # Rebuild a tiny app whose route raises so the middleware's except branch + # fires; the profile HTML is still emitted regardless. + app = FastAPI() + app.add_middleware(ProfileMiddleware) + + @app.get("/bang") + async def bang() -> None: + raise RuntimeError("inner exception") + + async with AsyncClient( + transport=ASGITransport(app=app, raise_app_exceptions=False), + base_url="http://test", + ) as c: + resp = await c.get("/bang?profile=true") + assert resp.status_code == 200 + assert "text/html" in resp.headers.get("content-type", "") + + +async def test_enabled_without_pyinstrument(monkeypatch: pytest.MonkeyPatch) -> None: + """If pyinstrument import fails, middleware degrades to passthrough.""" + monkeypatch.setenv("PROFILING_ENABLED", "true") + # Force the import inside ProfileMiddleware.__init__ to fail. + import builtins + + real_import = builtins.__import__ + + def fail_pyinstrument(name: str, *args: object, **kwargs: object) -> object: + if name == "pyinstrument": + raise ImportError("simulated") + return real_import(name, *args, **kwargs) + + monkeypatch.setattr(builtins, "__import__", fail_pyinstrument) + app = _build_app() # ProfileMiddleware ctor runs here + + async with AsyncClient( + transport=ASGITransport(app=app), base_url="http://test" + ) as c: + resp = await c.get("/hello?profile=true") + assert resp.status_code == 200 + assert resp.json() == {"ok": "yes"} diff --git a/tests/unit/test_core/test_middleware/test_prometheus.py b/tests/unit/test_core/test_middleware/test_prometheus.py new file mode 100644 index 00000000..adcf6641 --- /dev/null +++ b/tests/unit/test_core/test_middleware/test_prometheus.py @@ -0,0 +1,162 @@ +"""``PrometheusMiddleware`` — increments counters / histograms, skips /metrics. + +We isolate the test from the production registry by overriding it with a +fresh :class:`prometheus_client.CollectorRegistry` for the duration of +the test. The middleware was already imported with module-level Counter / +Histogram bound to whatever the registry was at import time — those +metric objects continue to record to the real registry. The test +therefore reads via ``_http_requests_total`` directly rather than via +``generate_metrics_response()``. +""" + +from __future__ import annotations + +from collections.abc import AsyncIterator + +import pytest +from fastapi import FastAPI +from httpx import ASGITransport, AsyncClient + +from everos.core.middleware import prometheus as prom_mod + + +def _sample_value(metric: object, **labels: str) -> float: + """Read the current value of a labeled prometheus metric (test helper).""" + labeled = metric.labels(**labels)._labeled # type: ignore[attr-defined] + for sample in labeled.collect()[0].samples: + if sample.name.endswith("_total"): + return float(sample.value) + return float("nan") + + +def _histogram_count(metric: object, **labels: str) -> float: + labeled = metric.labels(**labels)._labeled # type: ignore[attr-defined] + for sample in labeled.collect()[0].samples: + if sample.name.endswith("_count"): + return float(sample.value) + return float("nan") + + +def _build_app() -> FastAPI: + app = FastAPI() + app.add_middleware(prom_mod.PrometheusMiddleware) + + @app.get("/hello") + async def hello() -> dict[str, str]: + return {"ok": "yes"} + + @app.get("/users/{user_id}") + async def get_user(user_id: str) -> dict[str, str]: + return {"user": user_id} + + return app + + +@pytest.fixture +async def client() -> AsyncIterator[AsyncClient]: + app = _build_app() + async with AsyncClient( + transport=ASGITransport(app=app), base_url="http://test" + ) as c: + yield c + + +async def test_increments_counter_on_200(client: AsyncClient) -> None: + before = _sample_value( + prom_mod._http_requests_total, method="GET", path="/hello", status="200" + ) + resp = await client.get("/hello") + assert resp.status_code == 200 + after = _sample_value( + prom_mod._http_requests_total, method="GET", path="/hello", status="200" + ) + assert after == before + 1 + + +async def test_observes_duration_histogram(client: AsyncClient) -> None: + before = _histogram_count( + prom_mod._http_request_duration_seconds, method="GET", path="/hello" + ) + await client.get("/hello") + after = _histogram_count( + prom_mod._http_request_duration_seconds, method="GET", path="/hello" + ) + assert after == before + 1 + + +def test_skip_paths_constant_contains_known_endpoints() -> None: + """Skip set is the contract — assert membership directly to avoid + + polluting the global registry by ``.labels(path='/metrics')``-ing it + (that creates a zero-valued sample which then leaks into the + exposition format that test_metrics_route inspects). + """ + assert "/metrics" in prom_mod._SKIP_PATHS + assert "/health" in prom_mod._SKIP_PATHS + assert "/healthz" in prom_mod._SKIP_PATHS + assert "/favicon.ico" in prom_mod._SKIP_PATHS + + +async def test_path_params_normalized(client: AsyncClient) -> None: + """``/users/abc`` should record against the route template ``/users/{user_id}``.""" + before = _sample_value( + prom_mod._http_requests_total, + method="GET", + path="/users/{user_id}", + status="200", + ) + resp = await client.get("/users/abc") + assert resp.status_code == 200 + after = _sample_value( + prom_mod._http_requests_total, + method="GET", + path="/users/{user_id}", + status="200", + ) + assert after == before + 1 + + +# ── _normalize_path direct tests (defensive fallback branches) ───────── + + +def test_normalize_path_uses_path_params_fallback() -> None: + """When scope has no ``route`` but ``path_params`` is set, substitute names.""" + from types import SimpleNamespace + + from everos.core.middleware.prometheus import _normalize_path + + fake_req = SimpleNamespace( + scope={}, + url=SimpleNamespace(path="/x/abc/y"), + path_params={"id": "abc"}, + ) + # type: ignore[arg-type] — helper accepts anything duck-typed. + assert _normalize_path(fake_req) == "/x/{id}/y" # type: ignore[arg-type] + + +def test_normalize_path_unmatched_fallback() -> None: + """No route, no path_params → ``{unmatched}`` sentinel.""" + from types import SimpleNamespace + + from everos.core.middleware.prometheus import _normalize_path + + fake_req = SimpleNamespace( + scope={}, + url=SimpleNamespace(path="/x"), + path_params={}, + ) + assert _normalize_path(fake_req) == "{unmatched}" # type: ignore[arg-type] + + +def test_normalize_path_non_dict_scope_falls_through() -> None: + """Defensive: a non-dict ``scope`` skips the route lookup entirely.""" + from types import SimpleNamespace + + from everos.core.middleware.prometheus import _normalize_path + + fake_req = SimpleNamespace( + scope="not-a-dict", + url=SimpleNamespace(path="/x"), + path_params={}, + ) + assert _normalize_path(fake_req) == "{unmatched}" # type: ignore[arg-type] diff --git a/methods/EverCore/src/core/oxm/es/__init__.py b/tests/unit/test_core/test_observability/__init__.py similarity index 100% rename from methods/EverCore/src/core/oxm/es/__init__.py rename to tests/unit/test_core/test_observability/__init__.py diff --git a/tests/unit/test_core/test_observability/test_gauge.py b/tests/unit/test_core/test_observability/test_gauge.py new file mode 100644 index 00000000..f2a1b045 --- /dev/null +++ b/tests/unit/test_core/test_observability/test_gauge.py @@ -0,0 +1,74 @@ +"""``Gauge`` / ``LabeledGauge`` — set / inc / dec; with & without labels.""" + +from __future__ import annotations + +from collections.abc import Iterator + +import pytest +from prometheus_client import CollectorRegistry + +from everos.core.observability.metrics import ( + Gauge, + reset_metrics_registry, + set_metrics_registry, +) + + +@pytest.fixture(autouse=True) +def isolated_registry() -> Iterator[None]: + """Swap in a fresh registry so test names don't clash with prod metrics.""" + set_metrics_registry(CollectorRegistry()) + yield + reset_metrics_registry() + + +def _value(gauge: Gauge, **labels: str) -> float: + """Read the gauge's current scalar value (helper for assertions).""" + labeled = ( + gauge.labels(**labels)._labeled # type: ignore[attr-defined] + if labels + else gauge._gauge # type: ignore[attr-defined] + ) + for sample in labeled.collect()[0].samples: + if sample.name.endswith("_gauge") or "_" in sample.name: + return float(sample.value) + return float("nan") + + +def test_unlabeled_set_inc_dec() -> None: + g = Gauge(name="queue_depth", description="rows pending") + g.set(10) + assert _value(g) == 10 + g.inc(2) + assert _value(g) == 12 + g.dec() + assert _value(g) == 11 + g.dec(5) + assert _value(g) == 6 + + +def test_labeled_isolates_streams() -> None: + g = Gauge(name="cache_size", description="entries", labelnames=("region",)) + g.labels(region="us").set(100) + g.labels(region="eu").set(50) + g.labels(region="us").inc(5) + g.labels(region="eu").dec(10) + assert _value(g, region="us") == 105 + assert _value(g, region="eu") == 40 + + +def test_namespace_subsystem_unit_render_in_metric_name() -> None: + g = Gauge( + name="depth", + description="d", + namespace="everos", + subsystem="cascade", + unit="rows", + ) + g.set(7) + # Underlying name should include all parts. + full_name = g._gauge._name # type: ignore[attr-defined] + assert "everos" in full_name + assert "cascade" in full_name + assert "depth" in full_name + assert "rows" in full_name diff --git a/tests/unit/test_core/test_observability/test_logging_factory.py b/tests/unit/test_core/test_observability/test_logging_factory.py new file mode 100644 index 00000000..9094e3c4 --- /dev/null +++ b/tests/unit/test_core/test_observability/test_logging_factory.py @@ -0,0 +1,111 @@ +"""``configure_logging`` + ``get_logger`` smoke tests.""" + +from __future__ import annotations + +import pytest +import structlog + +from everos.core.observability.logging.factory import configure_logging, get_logger + + +def test_get_logger_returns_structlog_instance() -> None: + logger = get_logger("test.module") + # structlog's BoundLogger interface — must expose .info / .warning / .error. + assert hasattr(logger, "info") + assert hasattr(logger, "warning") + assert hasattr(logger, "error") + + +def _strip_ansi(text: str) -> str: + """Remove ANSI escape sequences so assertions are stable.""" + import re + + return re.sub(r"\x1b\[[0-9;]*m", "", text) + + +def test_configure_logging_accepts_known_levels() -> None: + """Smoke-test the level-name → log-level mapping path; no raise.""" + for level in ("DEBUG", "INFO", "WARNING", "ERROR", "info", "warn"): + configure_logging(level=level) + + +def test_configure_logging_handles_unknown_level_silently() -> None: + """Unknown level name silently falls back via ``getattr(logging, ..., INFO)``.""" + # Just must not raise; behavior verified by absence of exception. + configure_logging(level="NOPE") + + +def test_configure_logging_emits_through_structlog( + capsys: pytest.CaptureFixture[str], +) -> None: + configure_logging(level="INFO") + logger = get_logger("everos.test") + logger.info("hello", k="v") + plain = _strip_ansi(capsys.readouterr().out) + assert "hello" in plain + # ConsoleRenderer renders key=value pairs (sans color codes). + assert "k=v" in plain + + +def test_configure_logging_demotes_noisy_http_loggers_to_warning( + capsys: pytest.CaptureFixture[str], +) -> None: + """Third-party HTTP client loggers (httpx / httpcore / urllib3) must be + pinned at WARNING so each successful HTTP request doesn't produce an + INFO line. everos's own ``get_logger(...)`` calls remain unaffected. + """ + import logging + + configure_logging(level="INFO") + + for name in ("httpx", "httpcore", "urllib3"): + assert logging.getLogger(name).level == logging.WARNING, ( + f"{name} logger must be pinned to WARNING, got " + f"{logging.getLevelName(logging.getLogger(name).level)}" + ) + + # Behavioral check: an INFO from httpx must NOT reach stdout. + logging.getLogger("httpx").info("HTTP Request: GET https://example.com 200 OK") + plain = _strip_ansi(capsys.readouterr().out) + assert "HTTP Request" not in plain + + +def test_configure_logging_routes_stdlib_loggers_through_same_formatter( + capsys: pytest.CaptureFixture[str], +) -> None: + """stdlib ``logging.getLogger(...)`` output must share the structlog + ProcessorFormatter so uvicorn / fastapi / third-party libs render with + the same ``[level] event`` shape as everos's own structlog calls. + + This is the user-visible half of the foreign-log-integration setup — + without it, uvicorn's default ``LOGGING_CONFIG`` would (a) reinstall + its own handlers and (b) print ``INFO:logger.name:message`` lines + that look nothing like the structlog ConsoleRenderer output. + """ + import logging + + configure_logging(level="INFO") + third_party = logging.getLogger("uvicorn.access") + third_party.info("foreign event") + + plain = _strip_ansi(capsys.readouterr().out) + assert "foreign event" in plain + # Default stdlib LogRecord prefix must NOT survive. + assert "INFO:uvicorn.access" not in plain + # ConsoleRenderer marks level in brackets; both structlog and stdlib + # paths must produce the same shape. + assert "[info" in plain + + +def test_get_logger_with_same_name_returns_equivalent( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """structlog caches bound loggers per name when cache_logger_on_first_use=True.""" + configure_logging() + a = get_logger("everos.cache.test") + b = get_logger("everos.cache.test") + # Both should behave equivalently; identity is not guaranteed by structlog + # API, but both must satisfy the same protocol surface. + assert isinstance(a, structlog.stdlib.BoundLogger | structlog.BoundLoggerBase) or ( + hasattr(a, "info") and hasattr(b, "info") + ) diff --git a/methods/EverCore/src/devops_scripts/__init__.py b/tests/unit/test_core/test_persistence/__init__.py similarity index 100% rename from methods/EverCore/src/devops_scripts/__init__.py rename to tests/unit/test_core/test_persistence/__init__.py diff --git a/methods/EverCore/src/devops_scripts/data_fix/__init__.py b/tests/unit/test_core/test_persistence/test_lancedb/__init__.py similarity index 100% rename from methods/EverCore/src/devops_scripts/data_fix/__init__.py rename to tests/unit/test_core/test_persistence/test_lancedb/__init__.py diff --git a/tests/unit/test_core/test_persistence/test_lancedb/test_connection.py b/tests/unit/test_core/test_persistence/test_lancedb/test_connection.py new file mode 100644 index 00000000..1847dff9 --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_lancedb/test_connection.py @@ -0,0 +1,104 @@ +"""Unit tests for the LanceDB async connection factory.""" + +from __future__ import annotations + +from pathlib import Path + +import pytest + +from everos.config import LanceDBSettings +from everos.core.persistence import MemoryRoot, open_lancedb_connection + + +@pytest.fixture +def memory_root(tmp_path: Path) -> MemoryRoot: + mr = MemoryRoot(tmp_path) + mr.ensure() + return mr + + +async def test_connect_creates_lancedb_dir(memory_root: MemoryRoot) -> None: + settings = LanceDBSettings() + # Remove the auto-created dir to verify the factory recreates it. + if memory_root.lancedb_dir.exists(): + memory_root.lancedb_dir.rmdir() + assert not memory_root.lancedb_dir.exists() + + conn = await open_lancedb_connection(memory_root.lancedb_dir, settings) + try: + assert memory_root.lancedb_dir.is_dir() + assert conn.is_open() + finally: + conn.close() # AsyncConnection.close() is sync + + +async def test_empty_connection_lists_no_tables(memory_root: MemoryRoot) -> None: + settings = LanceDBSettings() + conn = await open_lancedb_connection(memory_root.lancedb_dir, settings) + try: + # list_tables() returns ListTablesResponse(tables, page_token). + result = await conn.list_tables() + assert list(result.tables) == [] + finally: + conn.close() + + +async def test_read_consistency_seconds_translated_to_timedelta( + memory_root: MemoryRoot, +) -> None: + """Non-None read_consistency_seconds must be passed as a timedelta.""" + settings = LanceDBSettings(read_consistency_seconds=5.0) + conn = await open_lancedb_connection(memory_root.lancedb_dir, settings) + try: + # The interval echoed back from the connection should equal what we set. + # AsyncConnection.get_read_consistency_interval is async. + import datetime as dt + + interval = await conn.get_read_consistency_interval() + assert interval == dt.timedelta(seconds=5.0) + finally: + conn.close() + + +async def test_default_consistency_is_none(memory_root: MemoryRoot) -> None: + settings = LanceDBSettings() + conn = await open_lancedb_connection(memory_root.lancedb_dir, settings) + try: + interval = await conn.get_read_consistency_interval() + assert interval is None + finally: + conn.close() + + +async def test_index_cache_cap_is_plumbed_into_session( + memory_root: MemoryRoot, monkeypatch: pytest.MonkeyPatch +) -> None: + """A capped ``Session`` must reach ``lancedb.connect_async``. + + The connection factory's whole purpose for installing a Session is + to bound the index reader cache so FDs do not leak. We spy on the + underlying ``connect_async`` and assert a Session is passed — + Session objects don't expose the configured cap back as a property, + so verifying that a Session is wired through is the closest unit- + level check we can make. The behavioural side (LRU eviction → + FD release under load) is covered by the fd-probe scripts kept + outside the test suite. + """ + import lancedb + + settings = LanceDBSettings(index_cache_size_bytes=1024) + captured: dict[str, object] = {} + + real_connect = lancedb.connect_async + + async def spy(*args, **kwargs): # type: ignore[no-untyped-def] + captured["session"] = kwargs.get("session") + return await real_connect(*args, **kwargs) + + monkeypatch.setattr(lancedb, "connect_async", spy) + + conn = await open_lancedb_connection(memory_root.lancedb_dir, settings) + try: + assert isinstance(captured.get("session"), lancedb.Session) + finally: + conn.close() diff --git a/tests/unit/test_core/test_persistence/test_lancedb/test_fts_behavior.py b/tests/unit/test_core/test_persistence/test_lancedb/test_fts_behavior.py new file mode 100644 index 00000000..05e5c8e5 --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_lancedb/test_fts_behavior.py @@ -0,0 +1,175 @@ +"""FTS-layer normalisation contract tests. + +``BaseLanceTable.ensure_fts_indexes`` builds the LanceDB FTS index with +the following configuration:: + + base_tokenizer="whitespace" + lower_case=True + stem=True + remove_stop_words=True + ascii_folding=True + language="English" (tantivy default) + +The app-layer ``JiebaTokenizer`` already handles segmentation + +stopword filtering, so these FTS-layer settings act as a *belt-and- +braces* layer of normalisation. These tests probe the FTS layer +*directly* (bypassing jieba) to verify each setting actually behaves +as the docstring claims: + +- lower_case=True → query case-insensitive against the raw-cased text +- stem=True → query for the word root hits inflected forms +- remove_stop_words=False → FTS layer does NOT drop stop-words; the + app-layer JiebaTokenizer is the single source of truth for + stop-word filtering (English + Chinese) +- ascii_folding=True → diacritics on Latin chars normalised (café → cafe) +- CJK pass-through → no stemming applied to CJK + +Tests build a fresh in-memory-ish LanceDB store under ``tmp_path``, +declare a minimal schema with one ``body`` column, and inspect query +hits against handcrafted rows. +""" + +from __future__ import annotations + +from collections.abc import AsyncIterator +from pathlib import Path +from typing import ClassVar + +import lancedb +import pytest +from lancedb import AsyncTable + +from everos.core.persistence.lancedb import BaseLanceTable + + +class _FtsSpec(BaseLanceTable): + """Minimal schema with one BM25-indexed column for FTS-layer probes.""" + + TABLE_NAME: ClassVar[str] = "fts_probe" + BM25_FIELDS: ClassVar[list[str]] = ["body"] + + id: str + body: str + + +@pytest.fixture +async def fts_table(tmp_path: Path) -> AsyncIterator[AsyncTable]: + """Build a fresh tmp LanceDB store + ``_FtsSpec`` table; index gets + built on first ``ensure_fts_indexes`` call by each test (FTS index + requires data first to materialise sensibly). + """ + conn = await lancedb.connect_async(str(tmp_path / "lancedb")) + table = await conn.create_table(_FtsSpec.TABLE_NAME, schema=_FtsSpec) + yield table + + +async def _seed_and_index(table: AsyncTable, rows: list[dict]) -> None: + """Insert rows, then (re)build the FTS index over the full table.""" + await table.add([_FtsSpec(**r) for r in rows]) + await _FtsSpec.ensure_fts_indexes(table) + + +async def _query_ids(table: AsyncTable, text: str) -> set[str]: + """Run a BM25 keyword query over the ``body`` column, return matched ids.""" + rows = await table.query().nearest_to_text(text, columns="body").limit(10).to_list() + return {r["id"] for r in rows} + + +# ── lower_case=True ──────────────────────────────────────────────────── + + +async def test_lower_case_query_matches_uppercase_index( + fts_table: AsyncTable, +) -> None: + """Document indexed as ``HELLO`` is found by query ``hello``.""" + await _seed_and_index( + fts_table, + [ + {"id": "1", "body": "HELLO world"}, + {"id": "2", "body": "GOODBYE world"}, + ], + ) + hits = await _query_ids(fts_table, "hello") + assert hits == {"1"} + + +# ── stem=True ────────────────────────────────────────────────────────── + + +async def test_stem_query_root_matches_inflected_forms( + fts_table: AsyncTable, +) -> None: + """Query ``counsel`` hits documents containing ``counseling`` / ``counseled``.""" + await _seed_and_index( + fts_table, + [ + {"id": "1", "body": "counseling session happened"}, + {"id": "2", "body": "counseled patient yesterday"}, + {"id": "3", "body": "unrelated content"}, + ], + ) + hits = await _query_ids(fts_table, "counsel") + assert hits == {"1", "2"} + + +# ── remove_stop_words=False (app layer owns stop-words) ──────────────── + + +async def test_fts_layer_does_not_filter_stopwords( + fts_table: AsyncTable, +) -> None: + """FTS layer is configured ``remove_stop_words=False`` — app layer owns it. + + The FTS index does NOT strip English stop-words. A query ``the`` + reaches BM25 unfiltered and hits a document that contains it. + In production, :class:`JiebaTokenizer` removes ``the`` before + tokens reach this layer; this test bypasses jieba to probe the + FTS layer's behaviour in isolation. + """ + await _seed_and_index( + fts_table, + [ + {"id": "1", "body": "the cat sat on the mat"}, + {"id": "2", "body": "unrelated body text"}, + ], + ) + hits = await _query_ids(fts_table, "the") + assert hits == {"1"} + + +# ── ascii_folding=True ───────────────────────────────────────────────── + + +async def test_ascii_folding_strips_diacritics(fts_table: AsyncTable) -> None: + """``café`` is indexed/queried as ``cafe`` once diacritics are folded.""" + await _seed_and_index( + fts_table, + [ + {"id": "1", "body": "café latte"}, + {"id": "2", "body": "tea house"}, + ], + ) + hits = await _query_ids(fts_table, "cafe") + assert hits == {"1"} + + +# ── CJK pass-through ─────────────────────────────────────────────────── + + +async def test_cjk_terms_pass_through_untouched(fts_table: AsyncTable) -> None: + """CJK tokens are not stemmed or stop-word-filtered (English-only rules). + + Note: ``base_tokenizer="whitespace"`` means CJK substrings are split + only on whitespace. The app-layer tokenizer (``JiebaTokenizer``) + normally inserts spaces between CJK words before they reach this + layer; here we simulate that by pre-spacing the body text. + """ + await _seed_and_index( + fts_table, + [ + {"id": "1", "body": "北京 天安门"}, + {"id": "2", "body": "上海 外滩"}, + ], + ) + hits = await _query_ids(fts_table, "北京") + assert hits == {"1"} diff --git a/tests/unit/test_core/test_persistence/test_lancedb/test_repository.py b/tests/unit/test_core/test_persistence/test_lancedb/test_repository.py new file mode 100644 index 00000000..91ce2b78 --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_lancedb/test_repository.py @@ -0,0 +1,649 @@ +"""Tests for :class:`LanceRepoBase` + :class:`LanceDailyLogRepoBase`. + +Exercises the chassis-level query helpers shared by every business +LanceDB repo: ``find_where`` / ``find_one_where`` / ``find_by_owner`` / +``find_by_md_path`` (on :class:`LanceRepoBase`), and the daily-log +slice ``find_by_owner_entry`` / ``find_by_session`` / +``find_by_parent`` (on :class:`LanceDailyLogRepoBase`). Also covers +``get_by_id`` + ``upsert`` so the chassis CRUD surface is end-to-end +verified. + +Uses a tmp LanceDB connection + a locally-defined daily-log-shaped +table so the chassis can be exercised without depending on any +specific business schema (episode / atomic_fact / …). +""" + +from __future__ import annotations + +import asyncio +from pathlib import Path +from typing import ClassVar + +import pytest + +from everos.config import LanceDBSettings +from everos.core.persistence import ( + BaseLanceTable, + MemoryRoot, + Vector, + open_lancedb_connection, +) +from everos.core.persistence.lancedb import ( + LanceDailyLogRepoBase, + LanceRepoBase, +) + + +class _Note(BaseLanceTable): + """Minimal daily-log-shaped table for chassis tests.""" + + TABLE_NAME: ClassVar[str] = "_note" + + id: str + owner_id: str + app_id: str = "default" + project_id: str = "default" + entry_id: str + session_id: str + parent_type: str + parent_id: str + md_path: str + text: str + vector: Vector(4) # type: ignore[valid-type] + + +class _SearchNote(BaseLanceTable): + """Schema with BM25_FIELDS declared — exercises FTS index setup.""" + + TABLE_NAME: ClassVar[str] = "_search_note" + BM25_FIELDS: ClassVar[list[str]] = ["tokens"] + + id: str + text: str + """Original surface form (display).""" + + tokens: str + """Space-joined pre-tokenised text (BM25 index target).""" + + vector: Vector(4) # type: ignore[valid-type] + + +class _NoteRepo(LanceDailyLogRepoBase[_Note]): + schema = _Note + + +def _row( + *, + owner: str, + entry: str, + session: str = "sess_a", + parent_type: str = "memcell", + parent_id: str = "mc_1", + md_path: str | None = None, + text: str = "x", +) -> _Note: + return _Note( + id=f"{owner}_{entry}", + owner_id=owner, + entry_id=entry, + session_id=session, + parent_type=parent_type, + parent_id=parent_id, + md_path=md_path or f"users/{owner}/notes/{entry}.md", + text=text, + vector=[1.0, 0.0, 0.0, 0.0], + ) + + +@pytest.fixture(autouse=True) +def _reset_write_locks() -> None: + """Drop the per-table write-lock pool between tests. + + ``LanceRepoBase`` lazily creates an ``asyncio.Lock`` per table name + and stashes it in a class-level dict; without a reset the lock + object outlives the pytest-asyncio function-scoped event loop and + the next test fails with "bound to a different event loop". + """ + LanceRepoBase._reset_locks_for_tests() + + +@pytest.fixture +async def repo(tmp_path: Path) -> _NoteRepo: + """Open a tmp connection, create the ``_note`` table, return a repo.""" + mr = MemoryRoot(tmp_path) + mr.ensure() + conn = await open_lancedb_connection(mr.lancedb_dir, LanceDBSettings()) + table = await conn.create_table("_note", schema=_Note) + return _NoteRepo(table=table) + + +# ── add + get_by_id + count ────────────────────────────────────────────── + + +async def test_add_and_count(repo: _NoteRepo) -> None: + await repo.add([_row(owner="u1", entry="ep_1"), _row(owner="u1", entry="ep_2")]) + assert await repo.count() == 2 + + +async def test_get_by_id_returns_typed_instance(repo: _NoteRepo) -> None: + await repo.add([_row(owner="u1", entry="ep_1", text="hello")]) + got = await repo.get_by_id("u1_ep_1") + assert got is not None + assert isinstance(got, _Note) + assert got.text == "hello" + + +async def test_get_by_id_returns_none_when_missing(repo: _NoteRepo) -> None: + assert await repo.get_by_id("ghost") is None + + +# ── upsert ────────────────────────────────────────────────────────────── + + +async def test_upsert_inserts_on_new(repo: _NoteRepo) -> None: + await repo.upsert([_row(owner="u1", entry="ep_1", text="v1")]) + got = await repo.get_by_id("u1_ep_1") + assert got is not None + assert got.text == "v1" + + +async def test_upsert_updates_on_existing(repo: _NoteRepo) -> None: + await repo.add([_row(owner="u1", entry="ep_1", text="v1")]) + await repo.upsert([_row(owner="u1", entry="ep_1", text="v2")]) + got = await repo.get_by_id("u1_ep_1") + assert got is not None + assert got.text == "v2" + assert await repo.count() == 1 # update, not append + + +# ── find_where / find_one_where ───────────────────────────────────────── + + +async def test_find_where_returns_typed_list(repo: _NoteRepo) -> None: + await repo.add( + [ + _row(owner="u1", entry="ep_1"), + _row(owner="u1", entry="ep_2"), + _row(owner="u2", entry="ep_3"), + ] + ) + rows = await repo.find_where("owner_id = 'u1'") + assert len(rows) == 2 + assert all(isinstance(r, _Note) for r in rows) + assert {r.entry_id for r in rows} == {"ep_1", "ep_2"} + + +async def test_find_one_where_returns_first_match(repo: _NoteRepo) -> None: + await repo.add([_row(owner="u1", entry="ep_1")]) + got = await repo.find_one_where("entry_id = 'ep_1'") + assert got is not None + assert got.entry_id == "ep_1" + + +async def test_find_one_where_returns_none(repo: _NoteRepo) -> None: + assert await repo.find_one_where("entry_id = 'ghost'") is None + + +# ── find_where_paginated ──────────────────────────────────────────────── + + +async def test_find_where_paginated_first_page(repo: _NoteRepo) -> None: + """5 rows, page=1 size=2 → 2 rows, total=5, sorted DESC by entry_id.""" + await repo.add( + [_row(owner="u1", entry=f"ep_{i}") for i in range(1, 6)], + ) + rows, total = await repo.find_where_paginated( + "owner_id = 'u1'", + sort_by="entry_id", + descending=True, + page=1, + page_size=2, + ) + assert total == 5 + assert [r.entry_id for r in rows] == ["ep_5", "ep_4"] + + +async def test_find_where_paginated_last_page_partial(repo: _NoteRepo) -> None: + """5 rows, page=3 size=2 → 1 row (the tail).""" + await repo.add( + [_row(owner="u1", entry=f"ep_{i}") for i in range(1, 6)], + ) + rows, total = await repo.find_where_paginated( + "owner_id = 'u1'", + sort_by="entry_id", + descending=True, + page=3, + page_size=2, + ) + assert total == 5 + assert [r.entry_id for r in rows] == ["ep_1"] + + +async def test_find_where_paginated_ascending_sort(repo: _NoteRepo) -> None: + """``descending=False`` flips order.""" + await repo.add( + [_row(owner="u1", entry=f"ep_{i}") for i in range(1, 4)], + ) + rows, total = await repo.find_where_paginated( + "owner_id = 'u1'", + sort_by="entry_id", + descending=False, + page=1, + page_size=10, + ) + assert total == 3 + assert [r.entry_id for r in rows] == ["ep_1", "ep_2", "ep_3"] + + +async def test_find_where_paginated_empty_predicate(repo: _NoteRepo) -> None: + """Predicate that matches nothing → empty list + total=0.""" + rows, total = await repo.find_where_paginated( + "owner_id = 'ghost'", + sort_by="entry_id", + page=1, + page_size=20, + ) + assert rows == [] + assert total == 0 + + +async def test_find_where_paginated_filters_by_owner(repo: _NoteRepo) -> None: + """Total is the predicate's true count, not the table's row count.""" + await repo.add( + [ + _row(owner="u1", entry="ep_1"), + _row(owner="u1", entry="ep_2"), + _row(owner="u2", entry="ep_3"), + ] + ) + rows, total = await repo.find_where_paginated( + "owner_id = 'u1'", + sort_by="entry_id", + page=1, + page_size=10, + ) + assert total == 2 + assert {r.entry_id for r in rows} == {"ep_1", "ep_2"} + + +async def test_find_where_paginated_truncates_above_max_fetch( + repo: _NoteRepo, + caplog: pytest.LogCaptureFixture, +) -> None: + """When total > max_fetch the chassis warns and returns a prefix sort. + + Correctness contract: ``total`` is still the *true* row count from + ``count_rows(filter=...)``, but the page contents are taken from + only the first ``max_fetch`` rows the engine scanned. structlog now + routes through stdlib's root logger (see + ``core/observability/logging/factory.py``), so the standard + ``caplog`` fixture is the right way to assert on the warning. + """ + # Unit tests don't go through the CLI entry, so the structlog → + # stdlib bridge is uninitialised — wire it up here so ``caplog`` + # can observe the warning. + from everos.core.observability.logging import configure_logging + + configure_logging(level="WARNING") + + await repo.add( + [_row(owner="u1", entry=f"ep_{i:03d}") for i in range(1, 11)], + ) + with caplog.at_level("WARNING"): + rows, total = await repo.find_where_paginated( + "owner_id = 'u1'", + sort_by="entry_id", + page=1, + page_size=3, + max_fetch=5, + ) + assert total == 10 # true match count + assert len(rows) == 3 + assert "find_where_paginated truncated" in caplog.text + + +# ── 5-table shared: find_by_owner / find_by_md_path ───────────────────── + + +async def test_find_by_owner(repo: _NoteRepo) -> None: + await repo.add( + [ + _row(owner="u1", entry="ep_1"), + _row(owner="u1", entry="ep_2"), + _row(owner="u2", entry="ep_3"), + ] + ) + rows = await repo.find_by_owner("u1") + assert {r.entry_id for r in rows} == {"ep_1", "ep_2"} + + +async def test_find_by_md_path_round_trip(repo: _NoteRepo) -> None: + path = "users/u1/notes/ep_1.md" + await repo.add([_row(owner="u1", entry="ep_1", md_path=path)]) + got = await repo.find_by_md_path(path) + assert got is not None + assert got.entry_id == "ep_1" + + +async def test_find_by_md_path_returns_none_when_missing(repo: _NoteRepo) -> None: + assert await repo.find_by_md_path("users/u1/notes/ghost.md") is None + + +# ── daily-log: find_by_owner_entry / find_by_session / find_by_parent ─── + + +async def test_find_by_owner_entry(repo: _NoteRepo) -> None: + await repo.add([_row(owner="u1", entry="ep_7")]) + got = await repo.find_by_owner_entry("u1", "ep_7") + assert got is not None + assert got.entry_id == "ep_7" + + +async def test_find_by_owner_entry_returns_none_when_missing( + repo: _NoteRepo, +) -> None: + assert await repo.find_by_owner_entry("u1", "ghost") is None + + +async def test_find_by_owner_entries_returns_only_matching_rows( + repo: _NoteRepo, +) -> None: + """Bulk lookup keeps only rows whose ``entry_id`` is in the set.""" + await repo.add( + [ + _row(owner="u1", entry="ep_1"), + _row(owner="u1", entry="ep_2"), + _row(owner="u1", entry="ep_3"), + _row(owner="u2", entry="ep_1"), # different owner — must not leak + ] + ) + rows = await repo.find_by_owner_entries("u1", ["ep_1", "ep_3"]) + assert {r.entry_id for r in rows} == {"ep_1", "ep_3"} + assert all(r.owner_id == "u1" for r in rows) + + +async def test_find_by_owner_entries_empty_input_short_circuits( + repo: _NoteRepo, +) -> None: + """No ids → ``[]`` without emitting a ``WHERE entry_id IN ()`` predicate.""" + await repo.add([_row(owner="u1", entry="ep_1")]) + assert await repo.find_by_owner_entries("u1", []) == [] + + +async def test_find_by_session(repo: _NoteRepo) -> None: + await repo.add( + [ + _row(owner="u1", entry="ep_1", session="sess_a"), + _row(owner="u1", entry="ep_2", session="sess_a"), + _row(owner="u1", entry="ep_3", session="sess_b"), + ] + ) + rows = await repo.find_by_session("u1", "sess_a") + assert {r.entry_id for r in rows} == {"ep_1", "ep_2"} + + +async def test_find_by_parent(repo: _NoteRepo) -> None: + await repo.add( + [ + _row(owner="u1", entry="ep_1", parent_type="memcell", parent_id="mc_x"), + _row(owner="u1", entry="ep_2", parent_type="memcell", parent_id="mc_x"), + _row(owner="u1", entry="ep_3", parent_type="other", parent_id="mc_y"), + ] + ) + rows = await repo.find_by_parent("memcell", "mc_x") + assert {r.entry_id for r in rows} == {"ep_1", "ep_2"} + + +# ── chassis fallback behaviour ────────────────────────────────────────── + + +async def test_table_lookup_not_implemented_when_no_override() -> None: + """Repo with neither ``table=`` injection nor ``_table_lookup`` raises.""" + + class _BareRepo(LanceRepoBase[_Note]): + schema = _Note + + bare = _BareRepo() + with pytest.raises(NotImplementedError, match="_table_lookup"): + await bare.count() + + +async def test_table_name_derived_from_schema() -> None: + """``repo.table_name`` reads off ``schema.TABLE_NAME`` (single source of truth).""" + + class _R(LanceRepoBase[_Note]): + schema = _Note + + assert _R().table_name == "_note" # equals _Note.TABLE_NAME + + +# ── SQL-quote escape defence ──────────────────────────────────────────── + + +# ── BaseLanceTable.ensure_fts_indexes ─────────────────────────────────── + + +async def test_ensure_fts_indexes_creates_index(tmp_path: Path) -> None: + """Declared ``BM25_FIELDS`` becomes an FTS index after ensure.""" + mr = MemoryRoot(tmp_path) + mr.ensure() + conn = await open_lancedb_connection(mr.lancedb_dir, LanceDBSettings()) + table = await conn.create_table("_search_note", schema=_SearchNote) + await table.add( + [ + _SearchNote( + id="1", + text="hello world", + tokens="hello world", + vector=[1, 0, 0, 0], + ) + ] + ) + + await _SearchNote.ensure_fts_indexes(table) + + indices = await table.list_indices() + indexed_cols = {col for idx in indices for col in (idx.columns or [])} + assert "tokens" in indexed_cols + conn.close() + + +async def test_ensure_fts_indexes_is_idempotent(tmp_path: Path) -> None: + """Calling twice is safe — no error, no duplicate index.""" + mr = MemoryRoot(tmp_path) + mr.ensure() + conn = await open_lancedb_connection(mr.lancedb_dir, LanceDBSettings()) + table = await conn.create_table("_search_note", schema=_SearchNote) + await table.add([_SearchNote(id="1", text="hi", tokens="hi", vector=[1, 0, 0, 0])]) + + await _SearchNote.ensure_fts_indexes(table) + first = await table.list_indices() + await _SearchNote.ensure_fts_indexes(table) + second = await table.list_indices() + + assert len(first) == len(second) + conn.close() + + +async def test_ensure_fts_indexes_noop_when_no_fields_declared( + repo: _NoteRepo, +) -> None: + """Schema without ``BM25_FIELDS`` is a no-op (no error).""" + table = await repo._table() + # _Note declares no BM25_FIELDS — calling the classmethod is a no-op. + await _Note.ensure_fts_indexes(table) + indices = await table.list_indices() + # No FTS index was created; vector/scalar may exist by default but we + # only assert no error path triggered. + assert isinstance(indices, list) or hasattr(indices, "__iter__") + + +# ── SQL-quote escape defence ──────────────────────────────────────────── + + +# ── delete_by_md_path ─────────────────────────────────────────────────── + + +async def test_delete_by_md_path_removes_matching_row(repo: _NoteRepo) -> None: + """Cascade md-deleted flow: rows for a path are wiped, count returned.""" + target = "users/u1/notes/ep_1.md" + await repo.add( + [ + _row(owner="u1", entry="ep_1", md_path=target), + _row(owner="u1", entry="ep_2"), + ] + ) + deleted = await repo.delete_by_md_path(target) + assert deleted == 1 + assert await repo.find_by_md_path(target) is None + assert await repo.count() == 1 # the other row survived + + +async def test_delete_by_md_path_returns_zero_when_no_match( + repo: _NoteRepo, +) -> None: + await repo.add([_row(owner="u1", entry="ep_1")]) + assert await repo.delete_by_md_path("users/u1/notes/ghost.md") == 0 + assert await repo.count() == 1 + + +async def test_delete_by_md_path_removes_multiple_entries_one_file( + repo: _NoteRepo, +) -> None: + """A daily-log md holds many entries → all rows for the path go.""" + shared = "users/u1/notes/episode-2026-05-12.md" + await repo.add( + [ + _row(owner="u1", entry="ep_1", md_path=shared), + _row(owner="u1", entry="ep_2", md_path=shared), + _row(owner="u1", entry="ep_3", md_path=shared), + _row(owner="u2", entry="ep_4"), # different path, untouched + ] + ) + deleted = await repo.delete_by_md_path(shared) + assert deleted == 3 + assert await repo.count() == 1 + + +async def test_delete_by_md_path_escapes_single_quotes( + repo: _NoteRepo, +) -> None: + """A path containing a single quote does not break the predicate.""" + tricky = "users/u1/notes/it's.md" + await repo.add([_row(owner="u1", entry="ep_1", md_path=tricky)]) + assert await repo.delete_by_md_path(tricky) == 1 + + +# ── SQL-quote escape defence (kept) ───────────────────────────────────── + + +async def test_get_by_id_escapes_single_quotes(repo: _NoteRepo) -> None: + """An id containing a single quote does not break the predicate.""" + quoted_id = "u1_it's_fine" + await repo.add( + [ + _Note( + id=quoted_id, + owner_id="u1", + entry_id="it's_fine", + session_id="s", + parent_type="memcell", + parent_id="mc_1", + md_path="x", + text="t", + vector=[1.0, 0.0, 0.0, 0.0], + ) + ] + ) + got = await repo.get_by_id(quoted_id) + assert got is not None + assert got.entry_id == "it's_fine" + + +# ── Concurrency: per-table write lock ─────────────────────────────────── + + +async def test_concurrent_upsert_disjoint_ids_no_lost_update( + repo: _NoteRepo, +) -> None: + """Regression for Bug B: cascade ``asyncio.gather`` over rows of the + same kind would race on ``merge_insert`` and drop a write (observed + on ``user_profile`` — pk = owner_id, two disjoint INSERTs ending up + with only one row in LanceDB). The per-table ``asyncio.Lock`` in + :meth:`LanceRepoBase.upsert` must serialise those writes so every + submitted row lands. + """ + n = 16 + rows = [_row(owner=f"u_{i}", entry=f"ep_{i}") for i in range(n)] + await asyncio.gather(*(repo.upsert([r]) for r in rows)) + assert await repo.count() == n + for i in range(n): + got = await repo.get_by_id(f"u_{i}_ep_{i}") + assert got is not None, f"u_{i}_ep_{i} disappeared after concurrent upsert" + + +async def test_concurrent_upsert_same_id_last_writer_wins( + repo: _NoteRepo, +) -> None: + """Concurrent upserts on the *same* pk must converge: exactly one row, + one of the texts wins. The lock makes the outcome deterministic per + schedule (no torn state, no duplicate row).""" + row_a = _row(owner="u1", entry="ep_1", text="A") + row_b = _row(owner="u1", entry="ep_1", text="B") + await asyncio.gather(repo.upsert([row_a]), repo.upsert([row_b])) + assert await repo.count() == 1 + got = await repo.get_by_id("u1_ep_1") + assert got is not None + assert got.text in {"A", "B"} + + +async def test_read_not_blocked_by_write_lock(repo: _NoteRepo) -> None: + """Search / count must remain available while a write lock is held — + only write paths take the lock. Acquires the table lock manually, + then verifies a read still resolves.""" + await repo.add([_row(owner="u1", entry="ep_1", text="seed")]) + lock = repo._write_lock(repo.table_name) + async with lock: + # Whilst the lock is held, reads should not block. + got = await asyncio.wait_for(repo.get_by_id("u1_ep_1"), timeout=2.0) + assert got is not None + assert got.text == "seed" + + +async def test_write_lock_is_per_table(tmp_path: Path) -> None: + """Distinct tables share no lock — writes on table A do not stall + writes on table B.""" + mr = MemoryRoot(tmp_path) + mr.ensure() + conn = await open_lancedb_connection(mr.lancedb_dir, LanceDBSettings()) + + class _OtherNote(BaseLanceTable): + TABLE_NAME: ClassVar[str] = "_other_note" + id: str + owner_id: str + entry_id: str + session_id: str + parent_type: str + parent_id: str + md_path: str + text: str + vector: Vector(4) # type: ignore[valid-type] + + class _OtherRepo(LanceDailyLogRepoBase[_OtherNote]): + schema = _OtherNote + + table_a = await conn.create_table("_note_a", schema=_Note) + table_b = await conn.create_table(_OtherNote.TABLE_NAME, schema=_OtherNote) + + class _NoteARepo(LanceDailyLogRepoBase[_Note]): + schema = _Note + + @property + def table_name(self) -> str: + return "_note_a" + + repo_a = _NoteARepo(table=table_a) + repo_b = _OtherRepo(table=table_b) + assert repo_a._write_lock(repo_a.table_name) is not repo_b._write_lock( + repo_b.table_name + ) diff --git a/tests/unit/test_core/test_persistence/test_lancedb/test_workflow_demo.py b/tests/unit/test_core/test_persistence/test_lancedb/test_workflow_demo.py new file mode 100644 index 00000000..13ae5f3a --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_lancedb/test_workflow_demo.py @@ -0,0 +1,82 @@ +"""LanceDB IO toolkit — typical workflow demo. + +End-to-end story for how to author + use a LanceDB-backed table in everos: + + 1. Define a table schema by subclassing :class:`BaseLanceTable` and + declaring a ``Vector(N)`` column for the embedding. + 2. ``open_lancedb_connection`` to get an :class:`AsyncConnection`. + 3. ``conn.create_table(name, schema=Cls)`` to create the table from + the Pydantic schema. + 4. ``table.add(rows)`` to insert. + 5. ``table.query().nearest_to(vec).limit(k).to_list()`` for vector + search (BM25 + scalar filter can chain in the same query). + 6. ``table.count_rows()`` for size. + 7. Mutate via :func:`touch` + :meth:`AsyncTable.update` (LanceDB has + no SQL ``onupdate`` equivalent — the app must bump ``updated_at``). + 8. ``table.delete(predicate)`` to remove rows. +""" + +from __future__ import annotations + +from pathlib import Path + +from everos.config import LanceDBSettings +from everos.core.persistence import ( + BaseLanceTable, + MemoryRoot, + Vector, + open_lancedb_connection, +) + + +class _DemoNote(BaseLanceTable): + """Demo table — used only by this test module.""" + + text: str + vector: Vector(4) # 4-dim for the test fixture + + +async def test_lancedb_typical_workflow(tmp_path: Path) -> None: + mr = MemoryRoot(tmp_path) + mr.ensure() + settings = LanceDBSettings() + + # 1. Open async connection rooted at /.index/lancedb/ + conn = await open_lancedb_connection(mr.lancedb_dir, settings) + + # 2. Create the table from the BaseLanceTable schema + table = await conn.create_table("_demo_notes", schema=_DemoNote) + + # 3. Insert rows (Pydantic instances; created_at / updated_at filled in + # by BaseLanceTable's default_factory). + rows = [ + _DemoNote(text="hello world", vector=[1.0, 0.0, 0.0, 0.0]), + _DemoNote(text="goodbye cruel world", vector=[0.0, 1.0, 0.0, 0.0]), + _DemoNote(text="welcome aboard", vector=[1.0, 0.5, 0.0, 0.0]), + ] + await table.add(rows) + + # 4. Count + assert await table.count_rows() == 3 + + # 5. Vector search — nearest_to picks rows by ANN distance. + results = await table.query().nearest_to([0.95, 0.05, 0.0, 0.0]).limit(2).to_list() + assert len(results) == 2 + # The closest row to [0.95, 0.05, 0, 0] is "hello world" [1, 0, 0, 0] + # ahead of "welcome aboard" [1, 0.5, 0, 0]. + assert results[0]["text"] == "hello world" + + # 6. Filter (scalar predicate). LanceDB SQL-like predicate string. + only_hello = await table.query().where("text = 'hello world'").to_list() + assert len(only_hello) == 1 + assert only_hello[0]["text"] == "hello world" + + # 7. Delete by predicate + await table.delete("text = 'goodbye cruel world'") + assert await table.count_rows() == 2 + + # 8. List tables on the connection + tables_response = await conn.list_tables() + assert "_demo_notes" in list(tables_response.tables) + + conn.close() diff --git a/tests/unit/test_core/test_persistence/test_locking.py b/tests/unit/test_core/test_persistence/test_locking.py new file mode 100644 index 00000000..62a2ff05 --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_locking.py @@ -0,0 +1,96 @@ +"""Unit tests for memory_root_lock async context manager.""" + +from __future__ import annotations + +import multiprocessing +import time +from pathlib import Path + +import anyio +import pytest + +from everos.core.persistence import LockError, MemoryRoot, memory_root_lock + + +async def test_lock_creates_anchor_file(tmp_path: Path) -> None: + mr = MemoryRoot(tmp_path) + async with memory_root_lock(mr): + assert mr.lock_file.exists() + + +async def test_lock_acquire_release_acquire(tmp_path: Path) -> None: + """Same process can re-acquire after release (no leftover state).""" + mr = MemoryRoot(tmp_path) + async with memory_root_lock(mr): + pass + async with memory_root_lock(mr): + pass + + +def _hold_lock(memory_root_path: str, ready: object, release: object) -> None: + """Subprocess helper: acquire blocking lock, signal, wait, release. + + The subprocess runs its own event loop via :func:`anyio.run` since + :func:`memory_root_lock` is now async. + """ + + async def _run() -> None: + mr = MemoryRoot(memory_root_path) + async with memory_root_lock(mr, blocking=True): + ready.set() + # Use a thread-offloaded wait so we don't block the event loop. + await anyio.to_thread.run_sync(release.wait, 5) + + anyio.run(_run) + + +async def test_nonblocking_raises_when_held_by_other_process(tmp_path: Path) -> None: + """Different process holding the lock → blocking=False raises LockError.""" + mr = MemoryRoot(tmp_path) + ctx = multiprocessing.get_context("spawn") + ready = ctx.Event() + release = ctx.Event() + proc = ctx.Process(target=_hold_lock, args=(str(mr.root), ready, release)) + proc.start() + try: + assert ready.wait(timeout=5), "subprocess failed to acquire lock" + with pytest.raises(LockError): + async with memory_root_lock(mr, blocking=False): + pass + finally: + release.set() + proc.join(timeout=5) + if proc.is_alive(): + proc.terminate() + + +async def test_blocking_waits_for_release(tmp_path: Path) -> None: + """Different process holding lock + main process blocking=True waits.""" + mr = MemoryRoot(tmp_path) + ctx = multiprocessing.get_context("spawn") + ready = ctx.Event() + release = ctx.Event() + proc = ctx.Process(target=_hold_lock, args=(str(mr.root), ready, release)) + proc.start() + try: + assert ready.wait(timeout=5) + # Schedule the subprocess to release shortly; main process should + # acquire the lock after that. + release_started = time.monotonic() + + def release_after_short_delay() -> None: + time.sleep(0.2) + release.set() + + import threading + + threading.Thread(target=release_after_short_delay, daemon=True).start() + async with memory_root_lock(mr, blocking=True): + elapsed = time.monotonic() - release_started + # Should have waited at least roughly the delay. + assert elapsed >= 0.1 + finally: + release.set() + proc.join(timeout=5) + if proc.is_alive(): + proc.terminate() diff --git a/methods/EverCore/src/devops_scripts/i18n/__init__.py b/tests/unit/test_core/test_persistence/test_markdown/__init__.py old mode 100755 new mode 100644 similarity index 100% rename from methods/EverCore/src/devops_scripts/i18n/__init__.py rename to tests/unit/test_core/test_persistence/test_markdown/__init__.py diff --git a/tests/unit/test_core/test_persistence/test_markdown/test_base.py b/tests/unit/test_core/test_persistence/test_markdown/test_base.py new file mode 100644 index 00000000..48ec955f --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_markdown/test_base.py @@ -0,0 +1,68 @@ +"""Tests for Frontmatter base classes (chassis layer).""" + +from __future__ import annotations + +import pytest +from pydantic import ValidationError + +from everos.core.persistence.markdown import ( + AgentScopedFrontmatter, + BaseFrontmatter, + UserScopedFrontmatter, +) + + +def test_base_requires_id_and_type() -> None: + with pytest.raises(ValidationError): + BaseFrontmatter() # type: ignore[call-arg] + + +def test_base_default_schema_version_is_one() -> None: + fm = BaseFrontmatter(id="x", type="t") + assert fm.schema_version == 1 + + +def test_base_extra_fields_allowed() -> None: + """L2 / L3 / L4 fields ride along without subclass declaration.""" + fm = BaseFrontmatter( + id="x", + type="t", + md_sha256="abc", # L2 + last_indexed_at="2026-04-22T10:00:00Z", + custom_user_field="anything", # L4 + ) + dumped = fm.model_dump() + assert dumped["md_sha256"] == "abc" + assert dumped["custom_user_field"] == "anything" + + +def test_user_scoped_track_default() -> None: + fm = UserScopedFrontmatter(id="x", type="t", user_id="u_jason") + assert fm.track == "user" + + +def test_user_scoped_requires_user_id() -> None: + with pytest.raises(ValidationError): + UserScopedFrontmatter(id="x", type="t") # type: ignore[call-arg] + + +def test_agent_scoped_track_default() -> None: + fm = AgentScopedFrontmatter(id="x", type="t", agent_id="agent_zhangsan") + assert fm.track == "agent" + + +def test_agent_scoped_requires_agent_id() -> None: + with pytest.raises(ValidationError): + AgentScopedFrontmatter(id="x", type="t") # type: ignore[call-arg] + + +def test_track_literal_rejects_invalid_value() -> None: + with pytest.raises(ValidationError): + UserScopedFrontmatter(id="x", type="t", user_id="u", track="agent") + + +def test_scope_dir_classvars() -> None: + """Scope mixins declare the top-level memory-root subdirectory.""" + assert BaseFrontmatter.SCOPE_DIR == "" # scope-agnostic by default + assert UserScopedFrontmatter.SCOPE_DIR == "users" + assert AgentScopedFrontmatter.SCOPE_DIR == "agents" diff --git a/tests/unit/test_core/test_persistence/test_markdown/test_entries.py b/tests/unit/test_core/test_persistence/test_markdown/test_entries.py new file mode 100644 index 00000000..c6d7c216 --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_markdown/test_entries.py @@ -0,0 +1,94 @@ +"""Unit tests for entry marker parsing.""" + +from __future__ import annotations + +from everos.core.persistence import find_entry, split_entries + + +def test_split_no_entries() -> None: + assert split_entries("# heading\n\nbody.") == [] + + +def test_split_single_entry() -> None: + body = ( + "preamble\n" + "\n" + "content here\n" + "\n" + "trailing\n" + ) + entries = split_entries(body) + assert len(entries) == 1 + e = entries[0] + assert e.id == "abc123" + assert e.body == "content here" + # offsets should bracket the markers + assert body[e.start : e.end].startswith("") + assert body[e.start : e.end].endswith("") + + +def test_split_multiple_entries() -> None: + body = ( + "\nfirst\n\n" + "\nsecond\n\n" + ) + entries = split_entries(body) + assert [e.id for e in entries] == ["e1", "e2"] + assert entries[0].body == "first" + assert entries[1].body == "second" + + +def test_split_unmatched_open() -> None: + """Open without close → scan stops; preceding entries are still returned.""" + body = "\nok\n\n\nno close\n" + entries = split_entries(body) + assert [e.id for e in entries] == ["e1"] + + +def test_split_mismatched_id() -> None: + """Open id != close id → no match → scan stops at unterminated open.""" + body = "\ncontent\n\n" + entries = split_entries(body) + assert entries == [] + + +def test_split_id_with_underscore_and_hyphen() -> None: + body = "\nx\n\n" + entries = split_entries(body) + assert len(entries) == 1 + assert entries[0].id == "abc_def-123" + + +def test_split_offsets_consistent() -> None: + body = "before\n\nx\n\nafter\n" + e = split_entries(body)[0] + assert body[e.start : e.end] == "\nx\n" + + +def test_find_entry_found() -> None: + body = ( + "\nfirst\n\n" + "\nsecond\n\n" + ) + e = find_entry(body, "b") + assert e is not None + assert e.id == "b" + assert e.body == "second" + + +def test_find_entry_not_found() -> None: + body = "\nx\n\n" + assert find_entry(body, "missing") is None + + +def test_find_entry_open_without_close() -> None: + body = "\nx\n" # no close + assert find_entry(body, "a") is None + + +def test_split_entry_body_no_internal_newline_stripping() -> None: + """Internal blank lines preserved; only the *single* leading/trailing + newline introduced by formatter is stripped.""" + body = "\nline1\n\nline3\n\n" + e = split_entries(body)[0] + assert e.body == "line1\n\nline3" diff --git a/tests/unit/test_core/test_persistence/test_markdown/test_entry_id.py b/tests/unit/test_core/test_persistence/test_markdown/test_entry_id.py new file mode 100644 index 00000000..7067732b --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_markdown/test_entry_id.py @@ -0,0 +1,99 @@ +"""Tests for ``EntryId`` parse / format / next_for.""" + +from __future__ import annotations + +import datetime as dt + +import pytest + +from everos.core.persistence import EntryId + +# ── format ─────────────────────────────────────────────────────────────── + + +def test_format_pads_seq_to_eight_digits() -> None: + eid = EntryId(prefix="umc", date=dt.date(2026, 4, 22), seq=1) + assert eid.format() == "umc_20260422_00000001" + + +def test_format_pads_seq_at_99999999() -> None: + eid = EntryId(prefix="umc", date=dt.date(2026, 4, 22), seq=99_999_999) + assert eid.format() == "umc_20260422_99999999" + + +def test_str_uses_format() -> None: + eid = EntryId(prefix="ep", date=dt.date(2026, 1, 1), seq=42) + assert str(eid) == "ep_20260101_00000042" + + +# ── parse ──────────────────────────────────────────────────────────────── + + +def test_parse_round_trip() -> None: + raw = "umc_20260422_00000001" + eid = EntryId.parse(raw) + assert eid.prefix == "umc" + assert eid.date == dt.date(2026, 4, 22) + assert eid.seq == 1 + assert eid.format() == raw + + +def test_parse_handles_seq_above_pad_width() -> None: + """Seq above 10**8 still parses; format emits more than 8 digits.""" + eid = EntryId.parse("umc_20260422_150000000") + assert eid.seq == 150_000_000 + assert eid.format() == "umc_20260422_150000000" + + +def test_parse_accepts_legacy_four_digit_seq() -> None: + """Pre-bump 4-digit seq strings still parse — format upgrades on round-trip.""" + eid = EntryId.parse("umc_20260422_0001") + assert eid.seq == 1 + # format() returns the new 8-digit padding. + assert eid.format() == "umc_20260422_00000001" + + +def test_parse_accepts_legacy_three_digit_seq() -> None: + """Older 3-digit seq strings still parse cleanly.""" + eid = EntryId.parse("umc_20260422_001") + assert eid.seq == 1 + assert eid.format() == "umc_20260422_00000001" + + +def test_parse_rejects_too_few_segments() -> None: + with pytest.raises(ValueError, match="invalid entry id format"): + EntryId.parse("umc_20260422") + + +def test_parse_rejects_invalid_date() -> None: + with pytest.raises(ValueError, match="invalid date"): + EntryId.parse("umc_2026XX22_00000001") + + +def test_parse_rejects_non_numeric_seq() -> None: + with pytest.raises(ValueError, match="invalid seq"): + EntryId.parse("umc_20260422_xxxx") + + +def test_parse_rejects_empty_prefix() -> None: + with pytest.raises(ValueError, match="empty prefix"): + EntryId.parse("_20260422_00000001") + + +# ── next_for ───────────────────────────────────────────────────────────── + + +def test_next_for_seq_is_count_plus_one() -> None: + eid = EntryId.next_for("umc", dt.date(2026, 4, 22), current_count=2) + assert eid.seq == 3 + assert eid.format() == "umc_20260422_00000003" + + +def test_next_for_starts_at_one_when_empty() -> None: + eid = EntryId.next_for("umc", dt.date(2026, 4, 22), current_count=0) + assert eid.seq == 1 + + +def test_next_for_rejects_negative_count() -> None: + with pytest.raises(ValueError, match="must be >= 0"): + EntryId.next_for("umc", dt.date(2026, 4, 22), current_count=-1) diff --git a/tests/unit/test_core/test_persistence/test_markdown/test_frontmatter.py b/tests/unit/test_core/test_persistence/test_markdown/test_frontmatter.py new file mode 100644 index 00000000..117d3e64 --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_markdown/test_frontmatter.py @@ -0,0 +1,168 @@ +"""Unit tests for frontmatter parse / dump + path_glob chassis.""" + +from __future__ import annotations + +from typing import ClassVar, Literal + +import pytest + +from everos.core.persistence import ( + AgentScopedFrontmatter, + BaseFrontmatter, + DailyLogPathMixin, + SkillPathMixin, + UserScopedFrontmatter, + dump_frontmatter, + parse_frontmatter, +) + + +def test_parse_no_frontmatter() -> None: + text = "# Just a heading\n\nbody." + meta, body = parse_frontmatter(text) + assert meta == {} + assert body == text + + +def test_parse_empty_frontmatter() -> None: + text = "---\n---\n# body\n" + meta, body = parse_frontmatter(text) + assert meta == {} + assert body == "# body\n" + + +def test_parse_simple_frontmatter() -> None: + text = "---\ntitle: Hello\ntags: [a, b]\n---\n# body\n" + meta, body = parse_frontmatter(text) + assert meta == {"title": "Hello", "tags": ["a", "b"]} + assert body == "# body\n" + + +def test_parse_nested_frontmatter() -> None: + text = "---\nuser:\n id: u_1\n name: Alice\n---\nbody" + meta, body = parse_frontmatter(text) + assert meta == {"user": {"id": "u_1", "name": "Alice"}} + assert body == "body" + + +def test_parse_no_closing_delim() -> None: + """Missing closing --- → treat as no frontmatter (return original text).""" + text = "---\ntitle: Hello\n# body without closing\n" + meta, body = parse_frontmatter(text) + assert meta == {} + assert body == text + + +def test_parse_non_mapping_yaml() -> None: + """YAML that parses to a non-mapping (e.g. list) → empty dict + original text.""" + text = "---\n- item1\n- item2\n---\nbody\n" + meta, body = parse_frontmatter(text) + assert meta == {} + assert body == text + + +def test_parse_opening_delim_no_newline() -> None: + """``---`` followed by non-newline char → not a frontmatter block.""" + text = "---this is not frontmatter" + meta, body = parse_frontmatter(text) + assert meta == {} + assert body == text + + +def test_parse_unicode_values() -> None: + text = "---\ntitle: 你好\n---\n世界" + meta, body = parse_frontmatter(text) + assert meta == {"title": "你好"} + assert body == "世界" + + +def test_dump_empty_mapping_returns_empty_string() -> None: + assert dump_frontmatter({}) == "" + + +def test_dump_simple_mapping() -> None: + out = dump_frontmatter({"title": "Hello"}) + assert out.startswith("---\n") + assert out.endswith("---\n") + assert "title: Hello" in out + + +def test_dump_preserves_key_order() -> None: + out = dump_frontmatter({"z": 1, "a": 2, "m": 3}) + body = out.strip("-\n") + keys = [line.split(":", 1)[0] for line in body.strip().splitlines() if ":" in line] + assert keys == ["z", "a", "m"] + + +def test_dump_unicode() -> None: + out = dump_frontmatter({"title": "你好"}) + assert "你好" in out # allow_unicode keeps non-ASCII verbatim + + +def test_round_trip() -> None: + meta = {"title": "Hello", "tags": ["a", "b"], "nested": {"k": "v"}} + body_text = "# Body\n\nLine.\n" + composed = dump_frontmatter(meta) + body_text + parsed_meta, parsed_body = parse_frontmatter(composed) + assert parsed_meta == meta + assert parsed_body == body_text + + +# ── path_glob chassis ─────────────────────────────────────────────────── + + +def test_base_path_glob_raises_not_implemented() -> None: + """A schema with no strategy mixin must surface a clear error.""" + + class _PlainFm(BaseFrontmatter): + type: Literal["_plain"] = "_plain" + + with pytest.raises(NotImplementedError, match="path_glob"): + _PlainFm.path_glob() + + +def test_daily_log_path_glob_user_scope() -> None: + """Mixin builds ``users/*//-*.md`` from ClassVars.""" + + class _UserDaily(DailyLogPathMixin, UserScopedFrontmatter): + DIR_NAME: ClassVar[str] = "demo" + FILE_PREFIX: ClassVar[str] = "entry" + type: Literal["_user_daily"] = "_user_daily" + + assert _UserDaily.path_glob() == "*/*/users/*/demo/entry-*.md" + + +def test_daily_log_path_glob_agent_scope() -> None: + """Same mixin, agent scope swaps the leading directory.""" + + class _AgentDaily(DailyLogPathMixin, AgentScopedFrontmatter): + DIR_NAME: ClassVar[str] = "cases" + FILE_PREFIX: ClassVar[str] = "case" + type: Literal["_agent_daily"] = "_agent_daily" + + assert _AgentDaily.path_glob() == "*/*/agents/*/cases/case-*.md" + + +def test_skill_path_glob() -> None: + """SkillPathMixin builds ``/*//*/
``.""" + + class _AgentSkill(SkillPathMixin, AgentScopedFrontmatter): + SKILLS_CONTAINER_NAME: ClassVar[str] = "skills" + SKILL_DIR_PREFIX: ClassVar[str] = "skill_" + SKILL_MAIN_FILENAME: ClassVar[str] = "SKILL.md" + type: Literal["_agent_skill"] = "_agent_skill" + + assert _AgentSkill.path_glob() == "*/*/agents/*/skills/skill_*/SKILL.md" + + +def test_strategy_mixin_overrides_base_via_mro() -> None: + """Strategy mixin placed first in the parent list wins over abstract base.""" + + class _Daily(DailyLogPathMixin, UserScopedFrontmatter): + DIR_NAME: ClassVar[str] = "x" + FILE_PREFIX: ClassVar[str] = "y" + type: Literal["_daily_mro"] = "_daily_mro" + + # Concrete is reachable; abstract NotImplementedError is shadowed. + assert isinstance(_Daily.path_glob(), str) + assert "NotImplementedError" not in _Daily.path_glob() diff --git a/tests/unit/test_core/test_persistence/test_markdown/test_reader.py b/tests/unit/test_core/test_persistence/test_markdown/test_reader.py new file mode 100644 index 00000000..724c21f2 --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_markdown/test_reader.py @@ -0,0 +1,66 @@ +"""Unit tests for MarkdownReader.""" + +from __future__ import annotations + +import datetime +from pathlib import Path + +from everos.core.persistence import MarkdownReader + + +def test_parse_text_with_frontmatter_and_entries() -> None: + text = ( + "---\n" + "title: Day Log\n" + "date: 2026-04-22\n" + "---\n" + "# Header\n" + "\n" + "first entry\n" + "\n" + ) + parsed = MarkdownReader.parse(text) + # PyYAML auto-converts unquoted ISO dates to datetime.date. + assert parsed.frontmatter == { + "title": "Day Log", + "date": datetime.date(2026, 4, 22), + } + assert "# Header" in parsed.body + assert len(parsed.entries) == 1 + assert parsed.entries[0].id == "e1" + assert parsed.entries[0].body == "first entry" + + +def test_parse_no_frontmatter_no_entries() -> None: + text = "# Just a header\n\nbody.\n" + parsed = MarkdownReader.parse(text) + assert parsed.frontmatter == {} + assert parsed.body == text + assert parsed.entries == [] + + +def test_parse_only_frontmatter() -> None: + text = "---\nkey: value\n---\n" + parsed = MarkdownReader.parse(text) + assert parsed.frontmatter == {"key": "value"} + assert parsed.body == "" + assert parsed.entries == [] + + +async def test_read_file(tmp_path: Path) -> None: + f = tmp_path / "doc.md" + f.write_text( + "---\nk: v\n---\n\nbody\n\n", + encoding="utf-8", + ) + parsed = await MarkdownReader.read(f) + assert parsed.frontmatter == {"k": "v"} + assert parsed.entries[0].id == "x" + + +async def test_read_unicode_file(tmp_path: Path) -> None: + f = tmp_path / "zh.md" + f.write_text("---\ntitle: 你好\n---\n世界\n", encoding="utf-8") + parsed = await MarkdownReader.read(f) + assert parsed.frontmatter == {"title": "你好"} + assert parsed.body == "世界\n" diff --git a/tests/unit/test_core/test_persistence/test_markdown/test_structured_entry.py b/tests/unit/test_core/test_persistence/test_markdown/test_structured_entry.py new file mode 100644 index 00000000..81b65dea --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_markdown/test_structured_entry.py @@ -0,0 +1,214 @@ +"""Tests for the audit-form structured entry chassis.""" + +from __future__ import annotations + +import pytest + +from everos.core.persistence.markdown import ( + StructuredEntry, + parse_structured_entry, + render_structured_entry, +) + +# ── render ─────────────────────────────────────────────────────────────── + + +def test_render_with_header_inline_and_sections() -> None: + out = render_structured_entry( + header="ep_20260422_001", + inline={ + "type": "episode", + "user_id": "u_jason", + "group_id": "sp_1", + }, + sections={"Summary": "first line\nsecond line"}, + ) + assert out.startswith("## ep_20260422_001\n\n") + assert "**type**: episode" in out + assert "**user_id**: u_jason" in out + assert "**group_id**: sp_1" in out + assert "### Summary\nfirst line\nsecond line" in out + + +def test_render_inline_only_no_header_no_sections() -> None: + out = render_structured_entry(inline={"k": "v"}) + assert out == "**k**: v" + + +def test_render_lists_use_bracket_notation() -> None: + out = render_structured_entry( + inline={"participants": ["u_jason", "u_sarah"], "tags": ("a", "b")} + ) + assert "**participants**: [u_jason, u_sarah]" in out + assert "**tags**: [a, b]" in out + + +def test_render_none_value_renders_empty() -> None: + out = render_structured_entry(inline={"optional": None}) + assert out == "**optional**: " + + +def test_render_scalar_uses_str() -> None: + out = render_structured_entry(inline={"count": 3, "ratio": 0.5, "active": True}) + assert "**count**: 3" in out + assert "**ratio**: 0.5" in out + assert "**active**: True" in out + + +# ── parse ──────────────────────────────────────────────────────────────── + + +def test_parse_full_round_trip() -> None: + src = render_structured_entry( + header="ep_001", + inline={"type": "episode", "user_id": "u_jason"}, + sections={"Summary": "the summary", "Body": "the body"}, + ) + entry = parse_structured_entry(src) + assert entry.header == "ep_001" + assert entry.inline == {"type": "episode", "user_id": "u_jason"} + assert entry.sections == {"Summary": "the summary", "Body": "the body"} + + +def test_parse_no_header_yields_none() -> None: + src = "**k**: v\n\n### Section\nbody" + entry = parse_structured_entry(src) + assert entry.header is None + assert entry.inline == {"k": "v"} + assert entry.sections == {"Section": "body"} + + +def test_parse_no_inline() -> None: + src = "## ep_001\n\n### Body\nonly section" + entry = parse_structured_entry(src) + assert entry.header == "ep_001" + assert entry.inline == {} + assert entry.sections == {"Body": "only section"} + + +def test_parse_no_sections() -> None: + src = "## ep_001\n\n**k**: v" + entry = parse_structured_entry(src) + assert entry.header == "ep_001" + assert entry.inline == {"k": "v"} + assert entry.sections == {} + + +def test_parse_inline_value_with_colon_kept_verbatim() -> None: + src = "**timestamp**: 2026-04-22T10:03:11Z" + entry = parse_structured_entry(src) + assert entry.inline == {"timestamp": "2026-04-22T10:03:11Z"} + + +def test_parse_list_value_kept_as_string() -> None: + """Type-agnostic by design — bracket notation is preserved as text.""" + src = "**participants**: [u_jason, u_sarah]" + entry = parse_structured_entry(src) + assert entry.inline == {"participants": "[u_jason, u_sarah]"} + + +def test_parse_section_with_multiline_body() -> None: + src = "### Episode\nline 1\nline 2\nline 3" + entry = parse_structured_entry(src) + assert entry.sections == {"Episode": "line 1\nline 2\nline 3"} + + +def test_parse_section_titles_kept_verbatim() -> None: + """No Title-casing — titles stay exactly as written.""" + src = "### task_intent\ndoc text" + entry = parse_structured_entry(src) + assert "task_intent" in entry.sections + + +def test_parse_tolerates_stray_text_outside_blocks() -> None: + """Stray paragraphs in the head become part of nothing — silently dropped.""" + src = ( + "## ep_001\n\nrandom prose paragraph\n" + "**k**: v\nmore stray text\n\n### Section\nbody" + ) + entry = parse_structured_entry(src) + # H2 + inline match anchors; stray prose lines that don't match + # **key**: ... are simply not captured. + assert entry.header == "ep_001" + assert entry.inline == {"k": "v"} + assert entry.sections == {"Section": "body"} + + +def test_dataclass_immutable() -> None: + """``StructuredEntry`` is frozen — accidental mutation raises.""" + entry = StructuredEntry(id="", body="", start=0, end=0, header="x") + with pytest.raises((AttributeError, TypeError)): + entry.header = "y" # type: ignore[misc] + + +def test_structured_entry_inherits_entry() -> None: + """``StructuredEntry`` is an :class:`Entry` subclass and carries + the marker context plus the parsed audit-form fields together.""" + from everos.core.persistence.markdown import Entry + + entry = StructuredEntry( + id="ep_001", + body="b", + start=0, + end=10, + header="ep_001", + inline={"k": "v"}, + sections={"S": "x"}, + ) + assert isinstance(entry, Entry) + assert entry.id == "ep_001" + assert entry.header == "ep_001" + + +def test_entry_as_structured_preserves_marker_context() -> None: + """``Entry.as_structured`` copies id/start/end and adds parsed fields.""" + from everos.core.persistence.markdown import Entry + + entry = Entry( + id="ep_001", + body="## ep_001\n\n**k**: v\n\n### Body\nthe body", + start=42, + end=128, + ) + s = entry.as_structured() + assert isinstance(s, StructuredEntry) + assert s.id == "ep_001" + assert s.start == 42 + assert s.end == 128 + assert s.header == "ep_001" + assert s.inline == {"k": "v"} + assert s.sections == {"Body": "the body"} + + +# ── round-trip with realistic Episode entry ───────────────────────────── + + +def test_round_trip_episode_shape() -> None: + """Mirrors the shape from the wiki Memory Types doc.""" + inline = { + "type": "episode", + "user_id": "u_jason", + "group_id": "sp_1", + "session_id": "sess_abc123", + "timestamp": "2026-04-22T10:03:11Z", + "parent_type": "memcell", + "parent_id": "mc_20260422_001", + "participants": ["u_jason", "u_sarah"], + "subject": "weekend planning", + } + sections = { + "Summary": "Jason and Sarah discussed weekend coffee plans.", + "Episode": "At ten in the morning, while making coffee, Jason told Sarah...", + } + rendered = render_structured_entry( + header="ep_20260422_001", + inline=inline, + sections=sections, + ) + entry = parse_structured_entry(rendered) + assert entry.header == "ep_20260422_001" + # Lists become string in audit form. + assert entry.inline["participants"] == "[u_jason, u_sarah]" + # Scalars round-trip exactly. + assert entry.inline["session_id"] == "sess_abc123" + assert entry.sections == sections diff --git a/tests/unit/test_core/test_persistence/test_markdown/test_workflow_demo.py b/tests/unit/test_core/test_persistence/test_markdown/test_workflow_demo.py new file mode 100644 index 00000000..a421551d --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_markdown/test_workflow_demo.py @@ -0,0 +1,87 @@ +"""Markdown IO toolkit — typical workflow demo. + +Doubles as living documentation for how a caller assembles + reads a +day-level markdown file with multiple ```` records. + +End-to-end story: + 1. Build a body that contains entry markers. + 2. Use ``MarkdownWriter.write_markdown`` to persist frontmatter + body + atomically (tmp file + fsync + rename, all inside the target dir). + 3. Use ``MarkdownReader.read`` to parse the resulting file back into + a ``ParsedMarkdown`` (frontmatter dict + raw body + list[Entry]). + 4. Verify each entry's id / body matches what was written. + 5. Look up a single entry by id with ``find_entry``. + 6. Round-trip: dump_frontmatter + body → parse_frontmatter recovers + the original mapping. +""" + +from __future__ import annotations + +from pathlib import Path + +from everos.core.persistence import ( + MarkdownReader, + MarkdownWriter, + MemoryRoot, + dump_frontmatter, + find_entry, + parse_frontmatter, +) + + +async def test_typical_day_log_write_then_read(tmp_path: Path) -> None: + mr = MemoryRoot(tmp_path) + mr.ensure() + writer = MarkdownWriter(mr) + + # 1. Build a body with two entries (typical day-level append log). + body = ( + "# Day log\n" + "\n" + "\n" + "**Title**: Met Alice\n" + "We discussed the new project layout.\n" + "\n" + "\n" + "\n" + "**Title**: Read paper X\n" + "Key idea: end-to-end async pipelines.\n" + "\n" + ) + frontmatter = { + "type": "episodic_day_log", + "date": "2026-04-22", + "user_id": "u_jason", + "tags": ["meeting", "research"], + } + + # 2. Atomic write via the writer. + target = mr.users_dir() / "u_jason" / "episodic" / "2026-04-22.md" + written_path = await writer.write_markdown( + target, frontmatter=frontmatter, body=body + ) + assert written_path == target + assert target.is_file() + # No leftover temp file. + leftover = list(target.parent.glob(f".{target.name}.tmp.*")) + assert leftover == [] + + # 3. Read back into ParsedMarkdown. + parsed = await MarkdownReader.read(target) + + # 4. Validate frontmatter + entries. + assert parsed.frontmatter == frontmatter + assert [e.id for e in parsed.entries] == ["ep_001", "ep_002"] + assert "Met Alice" in parsed.entries[0].body + assert "Read paper X" in parsed.entries[1].body + + # 5. Single-entry lookup. + e2 = find_entry(parsed.body, "ep_002") + assert e2 is not None + assert "async pipelines" in e2.body + + # 6. Round-trip frontmatter parse / dump. + composed = dump_frontmatter(frontmatter) + body + re_meta, re_body = parse_frontmatter(composed) + assert re_meta == frontmatter + assert re_body == body diff --git a/tests/unit/test_core/test_persistence/test_markdown/test_writer.py b/tests/unit/test_core/test_persistence/test_markdown/test_writer.py new file mode 100644 index 00000000..6468def5 --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_markdown/test_writer.py @@ -0,0 +1,229 @@ +"""Unit tests for MarkdownWriter (atomic write).""" + +from __future__ import annotations + +import datetime as dt +from pathlib import Path +from unittest.mock import patch + +import pytest + +from everos.core.persistence import ( + EntryId, + MarkdownReader, + MarkdownWriter, + MemoryRoot, +) + + +def _make_writer(tmp_path: Path) -> MarkdownWriter: + return MarkdownWriter(MemoryRoot(tmp_path)) + + +async def test_write_creates_file_with_content(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "users" / "u1" / "out.md" + result = await writer.write(target, "hello\n") + assert result == target + assert target.read_text(encoding="utf-8") == "hello\n" + + +async def test_write_creates_parent_directories(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "a" / "b" / "c" / "f.md" + await writer.write(target, "x") + assert target.is_file() + + +async def test_write_overwrites_existing(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "f.md" + target.write_text("old", encoding="utf-8") + await writer.write(target, "new") + assert target.read_text(encoding="utf-8") == "new" + + +async def test_write_no_temp_file_left_after_success(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "f.md" + await writer.write(target, "ok") + leftovers = [ + p.name + for p in tmp_path.iterdir() # noqa: ASYNC240 — sync iterdir over a pytest tmp_path is fine in tests + if p.name.startswith(".f.md.tmp.") + ] + assert leftovers == [] + + +async def test_write_cleans_up_temp_on_failure(tmp_path: Path) -> None: + """If os.replace fails, the temp file should be cleaned up.""" + writer = _make_writer(tmp_path) + target = tmp_path / "f.md" + + boom = OSError("simulated rename failure") + with ( + patch("everos.core.persistence.markdown.writer.os.replace", side_effect=boom), + pytest.raises(OSError, match="simulated"), + ): + await writer.write(target, "hello") + + # No tmp file leftover, and the target was not created. + leftovers = [ + p.name + for p in tmp_path.iterdir() # noqa: ASYNC240 — sync iterdir over a pytest tmp_path is fine in tests + if p.name.startswith(".f.md.tmp.") + ] + assert leftovers == [] + assert not target.exists() + + +async def test_write_markdown_assembles_frontmatter_and_body(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "doc.md" + await writer.write_markdown( + target, + frontmatter={"title": "Hello"}, + body="# Body\n", + ) + text = target.read_text(encoding="utf-8") + assert text.startswith("---\n") + assert "title: Hello" in text + assert text.rstrip("\n").endswith("# Body") + + +async def test_write_markdown_round_trip(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "rt.md" + await writer.write_markdown( + target, + frontmatter={"k": "v", "n": 1}, + body="\ncontent\n\n", + ) + parsed = await MarkdownReader.read(target) + assert parsed.frontmatter == {"k": "v", "n": 1} + assert len(parsed.entries) == 1 + assert parsed.entries[0].body == "content" + + +async def test_write_markdown_no_frontmatter(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "plain.md" + await writer.write_markdown(target, body="just body\n") + assert target.read_text(encoding="utf-8") == "just body\n" + + +def test_memory_root_property_accessible(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + assert writer.memory_root.root == tmp_path.resolve() + + +# ── append_entry ───────────────────────────────────────────────────────── + + +async def test_append_entry_creates_file_when_missing(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "memcells" / "memcell-2026-04-22.md" + eid = EntryId(prefix="umc", date=dt.date(2026, 4, 22), seq=1) + written = await writer.append_entry( + target, + entry_body="hello world", + entry_id=eid, + frontmatter_updates={ + "file_type": "memcell_daily", + "entry_count": 1, + }, + ) + assert written == target + parsed = await MarkdownReader.read(target) + assert parsed.frontmatter == {"file_type": "memcell_daily", "entry_count": 1} + assert len(parsed.entries) == 1 + assert parsed.entries[0].id == "umc_20260422_00000001" + assert parsed.entries[0].body == "hello world" + + +async def test_append_entry_appends_to_existing(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "log.md" + await writer.append_entry( + target, + entry_body="first", + entry_id=EntryId(prefix="umc", date=dt.date(2026, 4, 22), seq=1), + frontmatter_updates={"entry_count": 1}, + ) + await writer.append_entry( + target, + entry_body="second", + entry_id=EntryId(prefix="umc", date=dt.date(2026, 4, 22), seq=2), + frontmatter_updates={"entry_count": 2}, + ) + parsed = await MarkdownReader.read(target) + assert [e.id for e in parsed.entries] == [ + "umc_20260422_00000001", + "umc_20260422_00000002", + ] + assert [e.body for e in parsed.entries] == ["first", "second"] + + +async def test_append_entry_merges_frontmatter_shallow(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "log.md" + await writer.append_entry( + target, + entry_body="b", + entry_id=EntryId(prefix="umc", date=dt.date(2026, 4, 22), seq=1), + frontmatter_updates={ + "file_type": "memcell_daily", + "entry_count": 1, + "last_appended_at": "2026-04-22T10:00:00Z", + }, + ) + # Second append — overwrite entry_count + last_appended_at, keep file_type. + await writer.append_entry( + target, + entry_body="b", + entry_id=EntryId(prefix="umc", date=dt.date(2026, 4, 22), seq=2), + frontmatter_updates={ + "entry_count": 2, + "last_appended_at": "2026-04-22T10:05:00Z", + }, + ) + parsed = await MarkdownReader.read(target) + assert parsed.frontmatter == { + "file_type": "memcell_daily", + "entry_count": 2, + "last_appended_at": "2026-04-22T10:05:00Z", + } + + +async def test_append_entry_without_frontmatter_updates_keeps_existing( + tmp_path: Path, +) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "log.md" + await writer.write_markdown(target, frontmatter={"file_type": "x", "n": 1}, body="") + await writer.append_entry( + target, + entry_body="body", + entry_id=EntryId(prefix="umc", date=dt.date(2026, 4, 22), seq=1), + ) + parsed = await MarkdownReader.read(target) + assert parsed.frontmatter == {"file_type": "x", "n": 1} + assert len(parsed.entries) == 1 + + +async def test_append_entry_round_trip_with_reader(tmp_path: Path) -> None: + writer = _make_writer(tmp_path) + target = tmp_path / "log.md" + for i in range(5): + await writer.append_entry( + target, + entry_body=f"content {i}", + entry_id=EntryId(prefix="umc", date=dt.date(2026, 4, 22), seq=i + 1), + frontmatter_updates={"entry_count": i + 1}, + ) + parsed = await MarkdownReader.read(target) + assert len(parsed.entries) == 5 + assert parsed.frontmatter["entry_count"] == 5 + for i, e in enumerate(parsed.entries): + assert e.id == f"umc_20260422_{i + 1:08d}" + assert e.body == f"content {i}" diff --git a/tests/unit/test_core/test_persistence/test_markdown/test_writer_race.py b/tests/unit/test_core/test_persistence/test_markdown/test_writer_race.py new file mode 100644 index 00000000..1cdaab70 --- /dev/null +++ b/tests/unit/test_core/test_persistence/test_markdown/test_writer_race.py @@ -0,0 +1,200 @@ +"""Regression tests for the MarkdownWriter read-modify-write race. + +Before the per-path :class:`asyncio.Lock` was added, two concurrent tasks +calling :meth:`MarkdownWriter.append_entry` against the same path would +each load the file, append one entry block in memory, and write the +merged file back — the second writer's read pre-dated the first +writer's write, so it overwrote the first writer's append. Both +``entry_count`` (frontmatter) and the entry block markers were lost in +proportion to concurrency level. + +These tests drive ``N`` concurrent appends against one ``(owner, date)`` +and assert that no entry is lost at any concurrency level. They cover +both the single-entry ``append_entry`` path (taken by tests / external +callers) and the batched ``append_entries`` path (taken by strategies +after the per-owner batching migration). +""" + +from __future__ import annotations + +import asyncio +import re +from pathlib import Path + +import pytest + +from everos.core.persistence import EntryId, MarkdownWriter, MemoryRoot +from everos.infra.persistence.markdown.writers.atomic_fact_writer import ( + AtomicFactWriter, +) + + +def _scan_md(md_path: Path) -> tuple[int, int]: + """Return ``(entry_tag_count, frontmatter_entry_count)``.""" + text = md_path.read_text(encoding="utf-8") + tag_count = len(re.findall(r"